diff --git a/.env.example b/.env.example index 5f1f4a5ae..5cb8f2b48 100644 --- a/.env.example +++ b/.env.example @@ -3,6 +3,7 @@ export ETH_PRIVATE_KEY_BE_CAREFUL="0x123-USE-YOUR-OWN-PRIVATE-KEY-HERE" export WEB3_FANTOM="FANTOM-API-KEY-HERE" // "public" can be used in place of a paid API key export WEB3_MANTLE="MANTLE-API-KEY-HERE" export WEB3_LINEA="LINEA-API-KEY-HERE" // +export WEB3_SEI="SEI-API-KEY-HERE" // #******** For Development - not required to run bot ********# export ETHERSCAN_TOKEN="ONLY_REQUIRED_IN_DEV" diff --git a/.github/workflows/release-and-pypi-publish.yml b/.github/workflows/release-and-pypi-publish.yml index 2893f5056..8f69f4546 100644 --- a/.github/workflows/release-and-pypi-publish.yml +++ b/.github/workflows/release-and-pypi-publish.yml @@ -39,7 +39,9 @@ jobs: steps: # Checkout - name: Checkout code - uses: actions/checkout@v2 + uses: actions/checkout@v4 + with: + submodules: true # Check commit message - id: check diff --git a/.github/workflows/run-pytest.yml b/.github/workflows/run-pytest.yml index f5ecd81f3..1d2273756 100644 --- a/.github/workflows/run-pytest.yml +++ b/.github/workflows/run-pytest.yml @@ -15,7 +15,9 @@ jobs: matrix: python-version: [3.8] steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 + with: + submodules: true - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v4 with: @@ -40,7 +42,7 @@ jobs: echo ETHERSCAN_TOKEN=$ETHERSCAN_TOKEN >> .env echo DEFAULT_MIN_PROFIT_BNT=$DEFAULT_MIN_PROFIT_BNT >> .env echo ETH_PRIVATE_KEY_BE_CAREFUL=$ETH_PRIVATE_KEY_BE_CAREFUL >> .env - cd resources/NBTest;ln -s ../../fastlane_bot fastlane_bot;cd ..;cd ..; poetry run ./run_tests + make test env: TENDERLY_FORK: '${{ secrets.TENDERLY_FORK }}' WEB3_ALCHEMY_PROJECT_ID: '${{ secrets.WEB3_ALCHEMY_PROJECT_ID }}' diff --git a/.gitignore b/.gitignore index 5e3d05b03..07d379ea9 100644 --- a/.gitignore +++ b/.gitignore @@ -26,9 +26,8 @@ carbon/tools/* */.coverage */.coverage.* */.cover -NBTest/carbon/* -NBTest/carbon -resources/NBTest/fastlane_bot + +.python-version /.env *.env @@ -72,4 +71,3 @@ logs/* /fastlane_bot/data/blockchain_data/*/token_detail/ missing_tokens_df.csv tokens_and_fee_df.csv -fastlane_bot/tests/nbtest/* diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 000000000..330a3a8e8 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "arb-optimizer"] + path = arb-optimizer + url = git@github.com:bancorprotocol/arb-optimizer.git diff --git a/Makefile b/Makefile new file mode 100644 index 000000000..aee4be7d5 --- /dev/null +++ b/Makefile @@ -0,0 +1,2 @@ +test: + poetry run pytest fastlane_bot/tests -v $1 diff --git a/arb-optimizer b/arb-optimizer new file mode 160000 index 000000000..261f01813 --- /dev/null +++ b/arb-optimizer @@ -0,0 +1 @@ +Subproject commit 261f01813712518ee9766b57c214a36ae2caee02 diff --git a/fastlane_bot/bot.py b/fastlane_bot/bot.py index 9d9f347da..6595daaff 100644 --- a/fastlane_bot/bot.py +++ b/fastlane_bot/bot.py @@ -55,6 +55,8 @@ from typing import Generator, List, Dict, Tuple, Any, Callable from typing import Optional +from arb_optimizer import CurveContainer, ConstantProductCurve as CPC + from fastlane_bot.config import Config from fastlane_bot.helpers import ( TxRouteHandler, @@ -66,7 +68,6 @@ split_carbon_trades, maximize_last_trade_per_tkn ) -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, CPCContainer, T from .config.constants import FLASHLOAN_FEE_MAP from .events.interface import QueryInterface from .modes.pairwise_multi import FindArbitrageMultiPairwise @@ -128,13 +129,13 @@ def __post_init__(self): self.db = QueryInterface(ConfigObj=self.ConfigObj) self.RUN_FLASHLOAN_TOKENS = [*self.ConfigObj.CHAIN_FLASHLOAN_TOKENS.values()] - def get_curves(self) -> CPCContainer: + def get_curves(self) -> CurveContainer: """ Gets the curves from the database. Returns ------- - CPCContainer + CurveContainer The container of curves. """ self.db.refresh_pool_data() @@ -184,7 +185,7 @@ def get_curves(self) -> CPCContainer: f"[bot.get_curves] MUST FIX UNEXPECTED ERROR converting pool to curve {p}\n[ERR={e}]\n\n" ) - return CPCContainer(curves) + return CurveContainer(curves) def _simple_ordering_by_src_token( self, best_trade_instructions_dic, best_src_token @@ -277,7 +278,7 @@ def _get_arb_finder(cls, arb_mode: str) -> Callable: def _find_arbitrage( self, flashloan_tokens: List[str], - CCm: CPCContainer, + CCm: CurveContainer, arb_mode: str, randomizer: int ) -> dict: @@ -295,7 +296,7 @@ def _find_arbitrage( def _run( self, flashloan_tokens: List[str], - CCm: CPCContainer, + CCm: CurveContainer, *, arb_mode: str, randomizer: int, @@ -311,7 +312,7 @@ def _run( ---------- flashloan_tokens: List[str] The tokens to flashloan. - CCm: CPCContainer + CCm: CurveContainer The container. arb_mode: str The arbitrage mode. @@ -575,7 +576,7 @@ def custom_sort(self, data, sort_sequence): def calculate_profit( self, - CCm: CPCContainer, + CCm: CurveContainer, best_profit: Decimal, fl_token: str, flashloan_fee_amt: int = 0, @@ -585,7 +586,7 @@ def calculate_profit( Parameters ---------- - CCm: CPCContainer + CCm: CurveContainer The container. best_profit: Decimal The best profit. @@ -696,7 +697,7 @@ def calculate_arb( def _handle_trade_instructions( self, - CCm: CPCContainer, + CCm: CurveContainer, arb_mode: str, r: Any, replay_from_block: int = None @@ -709,7 +710,7 @@ def _handle_trade_instructions( Parameters ---------- - CCm: CPCContainer + CCm: CurveContainer The container. arb_mode: str The arbitrage mode. @@ -893,7 +894,7 @@ def run( self, *, flashloan_tokens: List[str] = None, - CCm: CPCContainer = None, + CCm: CurveContainer = None, arb_mode: str = None, run_data_validator: bool = False, randomizer: int = 0, @@ -908,7 +909,7 @@ def run( ---------- flashloan_tokens: List[str] The flashloan tokens (optional; default: RUN_FLASHLOAN_TOKENS) - CCm: CPCContainer + CCm: CurveContainer The complete market data container (optional; default: database via get_curves()) arb_mode: str the arbitrage mode (default: None; can be set depending on arbmode) diff --git a/fastlane_bot/config/network.py b/fastlane_bot/config/network.py index 0bed6b542..be8952671 100644 --- a/fastlane_bot/config/network.py +++ b/fastlane_bot/config/network.py @@ -276,6 +276,7 @@ class ConfigNetwork(ConfigBase): NETWORK_FANTOM = S.NETWORK_FANTOM NETWORK_MANTLE = S.NETWORK_MANTLE NETWORK_LINEA = S.NETWORK_LINEA + NETWORK_SEI = S.NETWORK_SEI # FLAGS ####################################################################################### @@ -317,7 +318,9 @@ def new(cls, network=None): elif network == cls.NETWORK_MANTLE: return _ConfigNetworkMantle(_direct=False) elif network == cls.NETWORK_LINEA: - return _ConfigNetworkLinea(_direct=False) + return _ConfigNetworkLinea(_direct=False) + elif network == cls.NETWORK_SEI: + return _ConfigNetworkSei(_direct=False) elif network == cls.NETWORK_TENDERLY: return _ConfigNetworkTenderly(_direct=False) else: @@ -777,6 +780,42 @@ class _ConfigNetworkLinea(ConfigNetwork): # Add any exchanges unique to the chain here CHAIN_SPECIFIC_EXCHANGES = [] +class _ConfigNetworkSei(ConfigNetwork): + """ + Fastlane bot config -- network [Base Mainnet] + """ + + NETWORK = S.NETWORK_SEI + NETWORK_ID = "1" # TODO + NETWORK_NAME = "sei" + DEFAULT_PROVIDER = S.PROVIDER_ALCHEMY + RPC_ENDPOINT = "https://evm-rpc.arctic-1.seinetwork.io/" # TODO currently Sei devnet + WEB3_ALCHEMY_PROJECT_ID = os.environ.get("WEB3_SEI") + + network_df = get_multichain_addresses(network=NETWORK_NAME) + FASTLANE_CONTRACT_ADDRESS = "0xC7Dd38e64822108446872c5C2105308058c5C55C" #TODO - UPDATE WITH Mainnet + MULTICALL_CONTRACT_ADDRESS = "0x1E05037b9c4fEFaF3c45CD6F4F2C3197e6A43cD8" # previously 0xcA11bde05977b3631167028862bE2a173976CA11 + + CARBON_CONTROLLER_ADDRESS = "0x59f21012B2E9BA67ce6a7605E74F945D0D4C84EA" #TODO - UPDATE WITH Mainnet + CARBON_CONTROLLER_VOUCHER = "0xe4816658ad10bF215053C533cceAe3f59e1f1087" #TODO - UPDATE WITH Mainnet + + NATIVE_GAS_TOKEN_ADDRESS = "0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE" + WRAPPED_GAS_TOKEN_ADDRESS = "0x26841a0A5D958B128209F4ea9a1DD7E61558c330" # TODO confirm for Mainnet + NATIVE_GAS_TOKEN_SYMBOL = "SEI" + WRAPPED_GAS_TOKEN_SYMBOL = "WSEI" + STABLECOIN_ADDRESS = "0xace5f7Ea93439Af39b46d2748fA1aC19951c8d7C" #TODO USDC on devnet + + IS_INJECT_POA_MIDDLEWARE = False + # Balancer + BALANCER_VAULT_ADDRESS = "0x7ccBebeb88696f9c8b061f1112Bb970158e29cA5" # # TODO Jellyswap on devnet + + CHAIN_FLASHLOAN_TOKENS = { + "0x26841a0A5D958B128209F4ea9a1DD7E61558c330": "WSEI", #TODO confirm for Mainnet + "0xace5f7Ea93439Af39b46d2748fA1aC19951c8d7C": "USDC", #TODO confirm for Mainnet + } + # Add any exchanges unique to the chain here + CHAIN_SPECIFIC_EXCHANGES = [] + class _ConfigNetworkTenderly(ConfigNetwork): """ Fastlane bot config -- network [Ethereum Tenderly] diff --git a/fastlane_bot/config/selectors.py b/fastlane_bot/config/selectors.py index d910f52be..791810fea 100644 --- a/fastlane_bot/config/selectors.py +++ b/fastlane_bot/config/selectors.py @@ -18,6 +18,7 @@ NETWORK_CANTO = "canto" NETWORK_FANTOM = "fantom" NETWORK_LINEA = "linea" +NETWORK_SEI = "sei" NETWORK_MANTLE = "mantle" NETWORK_SCROLL = "scroll" NETWORK_BSC = "binance_smart_chain" diff --git a/fastlane_bot/data/blockchain_data/sei/solidly_v2_event_mappings.csv b/fastlane_bot/data/blockchain_data/sei/solidly_v2_event_mappings.csv new file mode 100644 index 000000000..2785f2805 --- /dev/null +++ b/fastlane_bot/data/blockchain_data/sei/solidly_v2_event_mappings.csv @@ -0,0 +1 @@ +exchange,address diff --git a/fastlane_bot/data/blockchain_data/sei/static_pool_data.csv b/fastlane_bot/data/blockchain_data/sei/static_pool_data.csv new file mode 100644 index 000000000..09177afa2 --- /dev/null +++ b/fastlane_bot/data/blockchain_data/sei/static_pool_data.csv @@ -0,0 +1,3 @@ +cid,strategy_id,last_updated,last_updated_block,descr,pair_name,exchange_name,fee,fee_float,address,anchor,tkn0_address,tkn1_address,tkn0_decimals,tkn1_decimals,exchange_id,tkn0_symbol,tkn1_symbol,timestamp,tkn0_balance,tkn1_balance,liquidity,sqrt_price_q96,tick,tick_spacing,exchange,pool_type,tkn0_weight,tkn1_weight,tkn2_address,tkn2_decimals,tkn2_symbol,tkn2_balance,tkn2_weight,tkn3_address,tkn3_decimals,tkn3_symbol,tkn3_balance,tkn3_weight,tkn4_address,tkn4_decimals,tkn4_symbol,tkn4_balance,tkn4_weight,tkn5_address,tkn5_decimals,tkn5_symbol,tkn5_balance,tkn5_weight,tkn6_address,tkn6_decimals,tkn6_symbol,tkn6_balance,tkn6_weight,tkn7_address,tkn7_decimals,tkn7_symbol,tkn7_balance,tkn7_weight +0x1422169ab760ea6994358267b7d3783e8e7fa55c6a74b365b3fd3d17cbf4c6f1,0,,2354,dragonswap 0x027D2E627209f1cebA52ADc8A5aFE9318459b44B/0x7b75109369ACb528d9fa989E227812a6589712b9,0x027D2E627209f1cebA52ADc8A5aFE9318459b44B/0x7b75109369ACb528d9fa989E227812a6589712b9,dragonswap,0.003,0.003,0x01A34Dfa104F020FEE739268679338169945D5B1,,0x027D2E627209f1cebA52ADc8A5aFE9318459b44B,0x7b75109369ACb528d9fa989E227812a6589712b9,18,18,3,WSEI,DSWAP,0,0,0,,,,,dragonswap,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +0xbfd9612b2cb8035908dff18c040f64de75999cefd1020b5ce8a2e533c2ecd5dc,0,,2354,dragonswap 0x027D2E627209f1cebA52ADc8A5aFE9318459b44B/0xace5f7Ea93439Af39b46d2748fA1aC19951c8d7C,0x027D2E627209f1cebA52ADc8A5aFE9318459b44B/0xace5f7Ea93439Af39b46d2748fA1aC19951c8d7C,dragonswap,0.003,0.003,0x85CB6BFd781e1f42f4E79Efb6bf1F1fEfE4E9732,,0x027D2E627209f1cebA52ADc8A5aFE9318459b44B,0xace5f7Ea93439Af39b46d2748fA1aC19951c8d7C,18,6,3,WSEI,USDC,0,0,0,,,,,dragonswap,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, diff --git a/fastlane_bot/data/blockchain_data/sei/tokens.csv b/fastlane_bot/data/blockchain_data/sei/tokens.csv new file mode 100644 index 000000000..d831bd4a1 --- /dev/null +++ b/fastlane_bot/data/blockchain_data/sei/tokens.csv @@ -0,0 +1,5 @@ +address,decimals,symbol +0x26841a0A5D958B128209F4ea9a1DD7E61558c330,18,WSEI +0xace5f7Ea93439Af39b46d2748fA1aC19951c8d7C,6,USDC +0x027D2E627209f1cebA52ADc8A5aFE9318459b44B,18,WSEI +0x7b75109369ACb528d9fa989E227812a6589712b9,18,DSWAP diff --git a/fastlane_bot/data/blockchain_data/sei/uniswap_v2_event_mappings.csv b/fastlane_bot/data/blockchain_data/sei/uniswap_v2_event_mappings.csv new file mode 100644 index 000000000..f0165604a --- /dev/null +++ b/fastlane_bot/data/blockchain_data/sei/uniswap_v2_event_mappings.csv @@ -0,0 +1,3 @@ +exchange,address +dragonswap,0x01A34Dfa104F020FEE739268679338169945D5B1 +dragonswap,0x85CB6BFd781e1f42f4E79Efb6bf1F1fEfE4E9732 \ No newline at end of file diff --git a/fastlane_bot/data/blockchain_data/sei/uniswap_v3_event_mappings.csv b/fastlane_bot/data/blockchain_data/sei/uniswap_v3_event_mappings.csv new file mode 100644 index 000000000..2785f2805 --- /dev/null +++ b/fastlane_bot/data/blockchain_data/sei/uniswap_v3_event_mappings.csv @@ -0,0 +1 @@ +exchange,address diff --git a/fastlane_bot/data/multichain_addresses.csv b/fastlane_bot/data/multichain_addresses.csv index c1ba30702..235346cb8 100644 --- a/fastlane_bot/data/multichain_addresses.csv +++ b/fastlane_bot/data/multichain_addresses.csv @@ -135,3 +135,5 @@ sushiswap_v3,thundercore,uniswap_v3,0xc35DADB65012eC5796536bD9864eD8773aBc74C4,0 pancakeswap_v3,zkevm,uniswap_v3,0x0BFbCF9fa4f9C56B0F40a671Ad40E0805A091865,0x1b81D678ffb9C0263b24A97847620C99d213eB14,,, pancakeswap_v3,zksync,uniswap_v3,0x1BB72E0CbbEA93c08f535fc7856E0338D7F7a8aB,0xD70C70AD87aa8D45b8D59600342FB3AEe76E3c68,,, xfai_v0,linea,solidly_v2,0xa5136eAd459F0E61C99Cec70fe8F5C24cF3ecA26,0xD538be6e9026C13D130C9e17d509E69C8Bb0eF33,,222864, +carbon_v1,sei,carbon_v1,0x59f21012B2E9BA67ce6a7605E74F945D0D4C84EA,0x59f21012B2E9BA67ce6a7605E74F945D0D4C84EA,,17658678, +dragonswap,sei,uniswap_v2,0x5D370a6189F89603FaB67e9C68383e63F7B6A262,0x2346d3A6fb18Ff3ae590Ea31d9e41E6AB8c9f5EB,,1008775, diff --git a/fastlane_bot/helpers/poolandtokens.py b/fastlane_bot/helpers/poolandtokens.py index c6ac1f61d..beb6a1c00 100644 --- a/fastlane_bot/helpers/poolandtokens.py +++ b/fastlane_bot/helpers/poolandtokens.py @@ -15,11 +15,12 @@ from dataclasses import dataclass from typing import Dict, Any, List, Union +from arb_optimizer import ConstantProductCurve + from fastlane_bot.config import Config # from fastlane_bot.config import SUPPORTED_EXCHANGES, CARBON_V1_NAME, UNISWAP_V3_NAME from fastlane_bot.helpers.univ3calc import Univ3Calculator -from fastlane_bot.tools.cpc import ConstantProductCurve from fastlane_bot.utils import EncodedOrder @@ -368,8 +369,8 @@ def _other_to_cpc(self) -> List[Any]: # create a typed-dictionary of the arguments typed_args = { - "x_tknb": tkn0_balance, - "y_tknq": tkn1_balance, + "liq_tknb": tkn0_balance, + "liq_tknq": tkn1_balance, "pair": self.pair_name.replace(self.ConfigObj.NATIVE_GAS_TOKEN_ADDRESS, self.ConfigObj.WRAPPED_GAS_TOKEN_ADDRESS), "fee": self.fee, "cid": self.cid, diff --git a/fastlane_bot/helpers/routehandler.py b/fastlane_bot/helpers/routehandler.py index 62f3e39f7..3dbf3ed48 100644 --- a/fastlane_bot/helpers/routehandler.py +++ b/fastlane_bot/helpers/routehandler.py @@ -24,9 +24,10 @@ import eth_abi import pandas as pd +from arb_optimizer.curves import T + from .tradeinstruction import TradeInstruction from ..events.interface import Pool -from ..tools.cpc import T from fastlane_bot.config.constants import AGNI_V3_NAME, BUTTER_V3_NAME, CLEOPATRA_V3_NAME, PANCAKESWAP_V3_NAME, \ ETHEREUM, METAVAULT_V3_NAME diff --git a/fastlane_bot/modes/base.py b/fastlane_bot/modes/base.py index 0f1cf1a4c..b519ec1d7 100644 --- a/fastlane_bot/modes/base.py +++ b/fastlane_bot/modes/base.py @@ -13,9 +13,6 @@ from _decimal import Decimal import pandas as pd -from fastlane_bot.tools.cpc import T -from fastlane_bot.utils import num_format - class ArbitrageFinderBase: """ diff --git a/fastlane_bot/modes/base_pairwise.py b/fastlane_bot/modes/base_pairwise.py index d5df65ef9..00be462e6 100644 --- a/fastlane_bot/modes/base_pairwise.py +++ b/fastlane_bot/modes/base_pairwise.py @@ -12,8 +12,9 @@ import itertools from typing import List, Tuple, Any, Union +from arb_optimizer import CurveContainer + from fastlane_bot.modes.base import ArbitrageFinderBase -from fastlane_bot.tools.cpc import CPCContainer class ArbitrageFinderPairwiseBase(ArbitrageFinderBase): @@ -30,14 +31,14 @@ def find_arbitrage(self, candidates: List[Any] = None, ops: Tuple = None, best_p @staticmethod def get_combos( - CCm: CPCContainer, flashloan_tokens: List[str] + CCm: CurveContainer, flashloan_tokens: List[str] ) -> Tuple[List[Any], List[Any]]: """ Get combos for pairwise arbitrage Parameters ---------- - CCm : CPCContainer + CCm : CurveContainer Container for all the curves flashloan_tokens : list List of flashloan tokens diff --git a/fastlane_bot/modes/base_triangle.py b/fastlane_bot/modes/base_triangle.py index bf5f0c6a4..228dcfc6e 100644 --- a/fastlane_bot/modes/base_triangle.py +++ b/fastlane_bot/modes/base_triangle.py @@ -14,8 +14,9 @@ import pandas as pd +from arb_optimizer.curves import T + from fastlane_bot.modes.base import ArbitrageFinderBase -from fastlane_bot.tools.cpc import T class ArbitrageFinderTriangleBase(ArbitrageFinderBase): diff --git a/fastlane_bot/modes/pairwise_multi.py b/fastlane_bot/modes/pairwise_multi.py index 756345edf..07e3e8a72 100644 --- a/fastlane_bot/modes/pairwise_multi.py +++ b/fastlane_bot/modes/pairwise_multi.py @@ -12,9 +12,9 @@ import pandas as pd +from arb_optimizer import CurveContainer, PairOptimizer + from fastlane_bot.modes.base_pairwise import ArbitrageFinderPairwiseBase -from fastlane_bot.tools.cpc import CPCContainer -from fastlane_bot.tools.optimizer import MargPOptimizer, PairOptimizer class FindArbitrageMultiPairwise(ArbitrageFinderPairwiseBase): @@ -161,12 +161,12 @@ def run_main_flow( """ Run main flow to find arbitrage. """ - CC_cc = CPCContainer(curves) + CC_cc = CurveContainer(curves) O = PairOptimizer(CC_cc) pstart = { tkn0: CC_cc.bypairs(f"{tkn0}/{tkn1}")[0].p } # this intentionally selects the non_carbon curve - r = O.optimize(src_token, params=dict(pstart=pstart)) + r = O.optimize(src_token) profit_src = -r.result trade_instructions_df = r.trade_instructions(O.TIF_DFAGGR) return O, profit_src, r, trade_instructions_df diff --git a/fastlane_bot/modes/pairwise_multi_all.py b/fastlane_bot/modes/pairwise_multi_all.py index 924bdb702..36e944df5 100644 --- a/fastlane_bot/modes/pairwise_multi_all.py +++ b/fastlane_bot/modes/pairwise_multi_all.py @@ -13,9 +13,9 @@ import pandas as pd +from arb_optimizer import CurveContainer, PairOptimizer + from fastlane_bot.modes.base_pairwise import ArbitrageFinderPairwiseBase -from fastlane_bot.tools.cpc import CPCContainer -from fastlane_bot.tools.optimizer import MargPOptimizer, PairOptimizer class FindArbitrageMultiPairwiseAll(ArbitrageFinderPairwiseBase): @@ -157,13 +157,13 @@ def run_main_flow( """ Run main flow to find arbitrage. """ - CC_cc = CPCContainer(curves) + CC_cc = CurveContainer(curves) O = PairOptimizer(CC_cc) pstart = { tkn0: CC_cc.bypairs(f"{tkn0}/{tkn1}")[0].p } # this intentionally selects the non_carbon curve - r = O.optimize(src_token, params=dict(pstart=pstart)) + r = O.optimize(src_token) profit_src = -r.result trade_instructions_df = r.trade_instructions(O.TIF_DFAGGR) diff --git a/fastlane_bot/modes/pairwise_multi_pol.py b/fastlane_bot/modes/pairwise_multi_pol.py index 36fc6f1cb..6799864c9 100644 --- a/fastlane_bot/modes/pairwise_multi_pol.py +++ b/fastlane_bot/modes/pairwise_multi_pol.py @@ -8,14 +8,15 @@ All rights reserved. Licensed under MIT. """ +import itertools from typing import List, Any, Tuple, Union, Hashable import pandas as pd -import itertools + +from arb_optimizer import CurveContainer, PairOptimizer +from arb_optimizer.curves import T + from fastlane_bot.modes.base_pairwise import ArbitrageFinderPairwiseBase -from fastlane_bot.tools.cpc import CPCContainer -from fastlane_bot.tools.optimizer import MargPOptimizer, PairOptimizer -from fastlane_bot.tools.cpc import T class FindArbitrageMultiPairwisePol(ArbitrageFinderPairwiseBase): @@ -152,12 +153,12 @@ def run_main_flow( """ Run main flow to find arbitrage. """ - CC_cc = CPCContainer(curves) + CC_cc = CurveContainer(curves) O = PairOptimizer(CC_cc) pstart = { tkn0: CC_cc.bypairs(f"{tkn0}/{tkn1}")[0].p } # this intentionally selects the non_carbon curve - r = O.optimize(src_token, params=dict(pstart=pstart)) + r = O.optimize(src_token) profit_src = -r.result trade_instructions_df = r.trade_instructions(O.TIF_DFAGGR) return O, profit_src, r, trade_instructions_df @@ -174,14 +175,14 @@ def process_wrong_direction_pools( return new_curves def get_combos_pol(self, - CCm: CPCContainer, flashloan_tokens: List[str] + CCm: CurveContainer, flashloan_tokens: List[str] ) -> Tuple[List[Any], List[Any]]: """ Get combos for pairwise arbitrage specific to Bancor POL Parameters ---------- - CCm : CPCContainer + CCm : CurveContainer Container for all the curves flashloan_tokens : list List of flashloan tokens diff --git a/fastlane_bot/modes/pairwise_single.py b/fastlane_bot/modes/pairwise_single.py index d5128c6b3..4bcf65a36 100644 --- a/fastlane_bot/modes/pairwise_single.py +++ b/fastlane_bot/modes/pairwise_single.py @@ -12,9 +12,9 @@ from tqdm.contrib import itertools +from arb_optimizer import CurveContainer, PairOptimizer + from fastlane_bot.modes.base_pairwise import ArbitrageFinderPairwiseBase -from fastlane_bot.tools.cpc import CPCContainer -from fastlane_bot.tools.optimizer import MargPOptimizer, PairOptimizer class FindArbitrageSinglePairwise(ArbitrageFinderPairwiseBase): @@ -60,12 +60,12 @@ def find_arbitrage(self, candidates: List[Any] = None, ops: Tuple = None, best_p continue for curve_combo in curve_combos: - CC_cc = CPCContainer(curve_combo) + CC_cc = CurveContainer(curve_combo) O = PairOptimizer(CC_cc) src_token = tkn1 try: pstart = {tkn0: CC_cc.bypairs(f"{tkn0}/{tkn1}")[0].p} - r = O.optimize(src_token, params=dict(pstart=pstart)) + r = O.optimize(src_token) profit_src = -r.result trade_instructions_df = r.trade_instructions(O.TIF_DFAGGR) trade_instructions_dic = r.trade_instructions(O.TIF_DICTS) diff --git a/fastlane_bot/modes/tests/test_pairwise_single.ipynb b/fastlane_bot/modes/tests/test_pairwise_single.ipynb index 4006398d4..c5ae47078 100644 --- a/fastlane_bot/modes/tests/test_pairwise_single.ipynb +++ b/fastlane_bot/modes/tests/test_pairwise_single.ipynb @@ -16,19 +16,19 @@ "evalue": "[Errno 2] No such file or directory: 'fastlane_bot/data/static_pool_data.csv'", "output_type": "error", "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mFileNotFoundError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[1], line 8\u001B[0m\n\u001B[1;32m 6\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mfastlane_bot\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mbot\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m CarbonBot\n\u001B[1;32m 7\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m 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BANCOR_V3_POOL_COLLECTION_ABI\n\u001B[1;32m 20\u001B[0m )\n\u001B[0;32m---> 21\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mfastlane_bot\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mevents\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mpools\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m Pool\n\u001B[1;32m 24\u001B[0m \u001B[38;5;129m@dataclass\u001B[39m\n\u001B[1;32m 25\u001B[0m \u001B[38;5;28;01mclass\u001B[39;00m \u001B[38;5;21;01mExchange\u001B[39;00m(ABC):\n\u001B[1;32m 26\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 27\u001B[0m \u001B[38;5;124;03m Base class for exchanges\u001B[39;00m\n\u001B[1;32m 28\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n", - "File \u001B[0;32m~/Local/projects/bancor/carbonbot/fastlane_bot/events/pools.py:524\u001B[0m\n\u001B[1;32m 520\u001B[0m pool_factory\u001B[38;5;241m.\u001B[39mregister_format(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mbancor_v3\u001B[39m\u001B[38;5;124m\"\u001B[39m, BancorV3Pool)\n\u001B[1;32m 521\u001B[0m pool_factory\u001B[38;5;241m.\u001B[39mregister_format(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcarbon_v1\u001B[39m\u001B[38;5;124m\"\u001B[39m, CarbonV1Pool)\n\u001B[0;32m--> 524\u001B[0m static_data \u001B[38;5;241m=\u001B[39m \u001B[43mpd\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread_csv\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mfastlane_bot/data/static_pool_data.csv\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39mto_dict(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mrecords\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m 525\u001B[0m sushiswap_v2_pools \u001B[38;5;241m=\u001B[39m [\n\u001B[1;32m 526\u001B[0m 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_validate_names(kwds\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnames\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m))\n\u001B[1;32m 604\u001B[0m \u001B[38;5;66;03m# Create the parser.\u001B[39;00m\n\u001B[0;32m--> 605\u001B[0m parser \u001B[38;5;241m=\u001B[39m \u001B[43mTextFileReader\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 607\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m chunksize \u001B[38;5;129;01mor\u001B[39;00m iterator:\n\u001B[1;32m 608\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m parser\n", - "File \u001B[0;32m~/.local/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1442\u001B[0m, in \u001B[0;36mTextFileReader.__init__\u001B[0;34m(self, f, engine, **kwds)\u001B[0m\n\u001B[1;32m 1439\u001B[0m 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\u001B[0;32m~/.local/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1735\u001B[0m, in \u001B[0;36mTextFileReader._make_engine\u001B[0;34m(self, f, engine)\u001B[0m\n\u001B[1;32m 1733\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mb\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m mode:\n\u001B[1;32m 1734\u001B[0m mode \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mb\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m-> 1735\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhandles \u001B[38;5;241m=\u001B[39m \u001B[43mget_handle\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 1736\u001B[0m \u001B[43m \u001B[49m\u001B[43mf\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1737\u001B[0m \u001B[43m \u001B[49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1738\u001B[0m \u001B[43m 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to be opened in binary mode.\u001B[39;00m\n\u001B[1;32m 853\u001B[0m \u001B[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001B[39;00m\n\u001B[1;32m 854\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ioargs\u001B[38;5;241m.\u001B[39mencoding \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mb\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m ioargs\u001B[38;5;241m.\u001B[39mmode:\n\u001B[1;32m 855\u001B[0m \u001B[38;5;66;03m# Encoding\u001B[39;00m\n\u001B[0;32m--> 856\u001B[0m handle \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mopen\u001B[39;49m\u001B[43m(\u001B[49m\n\u001B[1;32m 857\u001B[0m \u001B[43m \u001B[49m\u001B[43mhandle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 858\u001B[0m \u001B[43m \u001B[49m\u001B[43mioargs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 859\u001B[0m \u001B[43m 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'fastlane_bot/data/static_pool_data.csv'" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 8\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastlane_bot\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbot\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CarbonBot\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastlane_bot\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtools\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcpc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ConstantProductCurve \u001b[38;5;28;01mas\u001b[39;00m CPC\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastlane_bot\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mevents\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexchanges\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m UniswapV2, UniswapV3, CarbonV1, BancorV3\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastlane_bot\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mevents\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minterface\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m QueryInterface\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastlane_bot\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhelpers\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpoolandtokens\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PoolAndTokens\n", + "File \u001b[0;32m~/Local/projects/bancor/carbonbot/fastlane_bot/events/exchanges.py:21\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mweb3\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcontract\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Contract\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastlane_bot\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mabi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 15\u001b[0m UNISWAP_V2_POOL_ABI,\n\u001b[1;32m 16\u001b[0m UNISWAP_V3_POOL_ABI,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 19\u001b[0m BANCOR_V3_POOL_COLLECTION_ABI\n\u001b[1;32m 20\u001b[0m )\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastlane_bot\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mevents\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Pool\n\u001b[1;32m 24\u001b[0m \u001b[38;5;129m@dataclass\u001b[39m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mExchange\u001b[39;00m(ABC):\n\u001b[1;32m 26\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;124;03m Base class for exchanges\u001b[39;00m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n", + "File \u001b[0;32m~/Local/projects/bancor/carbonbot/fastlane_bot/events/pools.py:524\u001b[0m\n\u001b[1;32m 520\u001b[0m pool_factory\u001b[38;5;241m.\u001b[39mregister_format(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbancor_v3\u001b[39m\u001b[38;5;124m\"\u001b[39m, BancorV3Pool)\n\u001b[1;32m 521\u001b[0m pool_factory\u001b[38;5;241m.\u001b[39mregister_format(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcarbon_v1\u001b[39m\u001b[38;5;124m\"\u001b[39m, CarbonV1Pool)\n\u001b[0;32m--> 524\u001b[0m static_data \u001b[38;5;241m=\u001b[39m 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\u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 210\u001b[0m kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m 326\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 327\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m 328\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m 329\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m 330\u001b[0m )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/io/parsers/readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 936\u001b[0m dialect,\n\u001b[1;32m 937\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 946\u001b[0m defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 947\u001b[0m )\n\u001b[1;32m 948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/io/parsers/readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 608\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1439\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1733\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m 1734\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 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\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1741\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1742\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1743\u001b[0m \u001b[43m 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Encoding\u001b[39;00m\n\u001b[0;32m--> 856\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 857\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 858\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 859\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 860\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m 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fastlane_bot.helpers.poolandtokens import PoolAndTokens\n", - "import pytest\n", "\n", "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPC))\n", "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(Bot))\n", @@ -125,9 +127,9 @@ ], "metadata": { "kernelspec": { - "name": "python3", + "display_name": "Python 3 (ipykernel)", "language": "python", - "display_name": "Python 3 (ipykernel)" + "name": "python3" } }, "nbformat": 4, diff --git a/fastlane_bot/modes/tests/test_pairwise_single.py b/fastlane_bot/modes/tests/test_pairwise_single.py index a52cbedc3..04b49c3c1 100644 --- a/fastlane_bot/modes/tests/test_pairwise_single.py +++ b/fastlane_bot/modes/tests/test_pairwise_single.py @@ -2,13 +2,15 @@ """ This module contains the tests for the exchanges classes """ +import pytest + +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot, Config from fastlane_bot.bot import CarbonBot -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.interface import QueryInterface from fastlane_bot.helpers.poolandtokens import PoolAndTokens -import pytest print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Bot)) diff --git a/fastlane_bot/modes/triangle_bancor_v3_two_hop.py b/fastlane_bot/modes/triangle_bancor_v3_two_hop.py index cba6397b6..f0caa9ac7 100644 --- a/fastlane_bot/modes/triangle_bancor_v3_two_hop.py +++ b/fastlane_bot/modes/triangle_bancor_v3_two_hop.py @@ -11,9 +11,10 @@ import math from typing import Union, List, Tuple, Any, Iterable +from arb_optimizer.curves import T +from arb_optimizer import CurveContainer, MargPOptimizer, ConstantProductCurve + from fastlane_bot.modes.base_triangle import ArbitrageFinderTriangleBase -from fastlane_bot.tools.cpc import CPCContainer, T, ConstantProductCurve -from fastlane_bot.tools.optimizer import MargPOptimizer class ArbitrageFinderTriangleBancor3TwoHop(ArbitrageFinderTriangleBase): @@ -139,7 +140,7 @@ def get_fee_safe(fee: int or float): fee = fee / 1000000 return fee - def get_exact_pools(self, cids: List[str]) -> List[CPCContainer]: + def get_exact_pools(self, cids: List[str]) -> List[CurveContainer]: """ Gets the specific pools that will be used for calculations. It does this inefficiently to preserve the order. @@ -276,11 +277,11 @@ def run_main_flow(self, """ # Instantiate the container and optimizer objects - CC_cc = CPCContainer(miniverse) + CC_cc = CurveContainer(miniverse) O = MargPOptimizer(CC_cc) pstart = self.build_pstart(CC_cc, CC_cc.tokens(), src_token) # Perform the optimization - r = O.optimize(src_token, params=dict(pstart=pstart)) + r = O.optimize(src_token, pstart=pstart, params=dict()) # Get the profit in the source token profit_src = -r.result diff --git a/fastlane_bot/modes/triangle_multi.py b/fastlane_bot/modes/triangle_multi.py index 50e335880..b7160e343 100644 --- a/fastlane_bot/modes/triangle_multi.py +++ b/fastlane_bot/modes/triangle_multi.py @@ -10,9 +10,9 @@ """ from typing import List, Any, Tuple, Union +from arb_optimizer import CurveContainer, MargPOptimizer + from fastlane_bot.modes.base_triangle import ArbitrageFinderTriangleBase -from fastlane_bot.tools.cpc import CPCContainer -from fastlane_bot.tools.optimizer import MargPOptimizer class ArbitrageFinderTriangleMulti(ArbitrageFinderTriangleBase): @@ -40,11 +40,11 @@ def find_arbitrage(self, candidates: List[Any] = None, ops: Tuple = None, best_p for src_token, miniverse in combos: try: r = None - CC_cc = CPCContainer(miniverse) + CC_cc = CurveContainer(miniverse) O = MargPOptimizer(CC_cc) #try: pstart = self.build_pstart(CC_cc, CC_cc.tokens(), src_token) - r = O.optimize(src_token, params=dict(pstart=pstart)) #debug=True, debug2=True + r = O.optimize(src_token, pstart=pstart, params=dict()) #debug=True, debug2=True trade_instructions_dic = r.trade_instructions(O.TIF_DICTS) if len(trade_instructions_dic) < 3: # Failed to converge diff --git a/fastlane_bot/modes/triangle_single.py b/fastlane_bot/modes/triangle_single.py index 3c3b3f825..23216669f 100644 --- a/fastlane_bot/modes/triangle_single.py +++ b/fastlane_bot/modes/triangle_single.py @@ -10,9 +10,9 @@ """ from typing import Union, List, Tuple, Any +from arb_optimizer import CurveContainer, MargPOptimizer + from fastlane_bot.modes.base_triangle import ArbitrageFinderTriangleBase -from fastlane_bot.tools.cpc import CPCContainer -from fastlane_bot.tools.optimizer import MargPOptimizer class ArbitrageFinderTriangleSingle(ArbitrageFinderTriangleBase): @@ -40,7 +40,7 @@ def find_arbitrage(self, candidates: List[Any] = None, ops: Tuple = None, best_p r = None # Instantiate the container and optimizer objects - CC_cc = CPCContainer(miniverse) + CC_cc = CurveContainer(miniverse) O = MargPOptimizer(CC_cc) try: diff --git a/fastlane_bot/tests/test_005_Uniswap.py b/fastlane_bot/tests/test_005_Uniswap.py index da9c9658c..ff24d4fe6 100644 --- a/fastlane_bot/tests/test_005_Uniswap.py +++ b/fastlane_bot/tests/test_005_Uniswap.py @@ -5,12 +5,11 @@ # test id = 005 # test comment = Uniswap # ------------------------------------------------------------ +from dataclasses import dataclass, asdict +from arb_optimizer import ConstantProductCurve as CPC - -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, CPCContainer from fastlane_bot.helpers.univ3calc import Univ3Calculator as U3 -from dataclasses import dataclass, asdict print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(U3)) diff --git a/fastlane_bot/tests/test_007_NoneResult.py b/fastlane_bot/tests/test_007_NoneResult.py deleted file mode 100644 index f222ce6fd..000000000 --- a/fastlane_bot/tests/test_007_NoneResult.py +++ /dev/null @@ -1,148 +0,0 @@ -# ------------------------------------------------------------ -# Auto generated test file `test_007_NoneResult.py` -# ------------------------------------------------------------ -# source file = NBTest_007_NoneResult.py -# test id = 007 -# test comment = NoneResult -# ------------------------------------------------------------ - - - -#from fastlane_bot import Bot, Config, ConfigDB, ConfigNetwork, ConfigProvider -from fastlane_bot.tools.noneresult import NoneResult, isNone -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(NoneResult)) -from fastlane_bot.testing import * -import itertools as it -import collections as cl -import math as m -#plt.style.use('seaborn-dark') -plt.rcParams['figure.figsize'] = [12,6] -from fastlane_bot import __VERSION__ -require("3.0", __VERSION__) - - - - -# ------------------------------------------------------------ -# Test 007 -# File test_007_NoneResult.py -# Segment NoneResult Basics -# ------------------------------------------------------------ -def test_noneresult_basics(): -# ------------------------------------------------------------ - - none = NoneResult() - assert str(none) == "NoneResult('None')" - assert repr(none) == str(none) - assert bool(none) == False - assert float(none) == 0.0 - assert int(none) == 0 - assert m.floor(none) is none - assert m.ceil(none) is none - assert m.trunc(none) is none - assert round(none,5) is none - assert None == none - - assert none.foo is none - assert none.foo.bar is none - assert none["foo"] is none - assert none["foo"]["bar"] is none - - assert none+1 is none - assert none-1 is none - assert none*1 is none - assert none/1 is none - assert none//1 is none - assert none**1 is none - assert none%1 is none - - assert 1+none is none - assert 1-none is none - assert 1*none is none - assert 1/none is none - assert 1//none is none - assert 1**none is none - assert 1%none is none - - none_foo = NoneResult("foo") - assert str(none_foo) == "NoneResult('foo')" - assert none_foo == none - - -# ------------------------------------------------------------ -# Test 007 -# File test_007_NoneResult.py -# Segment None format -# ------------------------------------------------------------ -def test_none_format(): -# ------------------------------------------------------------ - - none = NoneResult() - assert f"{none}" == "NoneResult('None')" - assert "{}".format(none) == "NoneResult('None')" - - assert f":{str(none):30}:" == ":NoneResult('None') :" - assert f":{none:30}:" == f":{str(none):30}:" - assert len(f"{none:30}") == 30 - raises(lambda: f"{none:2.1f}") == "Unknown format code 'f' for object of type 'str'" - assert f"{float(none):10.4f}" == ' 0.0000' - assert f"{int(none):010d}" == '0000000000' - - a="123" - - f"{none:40}" - - -# ------------------------------------------------------------ -# Test 007 -# File test_007_NoneResult.py -# Segment math functions -# ------------------------------------------------------------ -def test_math_functions(): -# ------------------------------------------------------------ - - none = NoneResult() - assert m.sin(none) == 0 - assert m.cos(none) == 1 - assert m.exp(none) == 1 - assert raises(m.log, none) == "math domain error" - assert 1/none == none - assert 0*none==none - sin = lambda x: 0*x+m.sin(x) - assert sin(none) == none - - -# ------------------------------------------------------------ -# Test 007 -# File test_007_NoneResult.py -# Segment isNone -# ------------------------------------------------------------ -def test_isnone(): -# ------------------------------------------------------------ - - assert isNone(None) == True - assert isNone(NoneResult()) == True - assert isNone(NoneResult("moo")) == True - assert isNone(0) == False - assert isNone("") == False - assert isNone(False) == False - assert isNone(NoneResult) == False - - none = NoneResult() - assert raises(lambda x: isNone(None+x), 1) == "unsupported operand type(s) for +: 'NoneType' and 'int'" - assert isNone(none+1) - assert isNone(1+none) - assert isNone(none**2) - assert isNone(none*none) - assert isNone(1+2*none+3*none*none) - - assert not isNone(none) == False - assert [x for x in (1,2,None,3) if not isNone(x)] == [1,2,3] - assert [x for x in (1,2,none,3) if not isNone(x)] == [1,2,3] - assert [2*x for x in (1,2,None,3) if not isNone(x)] == [2,4,6] - assert [2*x for x in (1,2,none,3) if not isNone(x)] == [2,4,6] - assert [2*x for x in (1,2,none,3) if not isNone(2*x)] == [2,4,6] - - - - \ No newline at end of file diff --git a/fastlane_bot/tests/test_033_Pools.py b/fastlane_bot/tests/test_033_Pools.py index d40a20074..a3c64a2d3 100644 --- a/fastlane_bot/tests/test_033_Pools.py +++ b/fastlane_bot/tests/test_033_Pools.py @@ -10,10 +10,11 @@ import json +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot from fastlane_bot.events.pools import BancorPolPool, BancorV2Pool, BancorV3Pool, CarbonV1Pool, SolidlyV2Pool, \ UniswapV2Pool, UniswapV3Pool -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Bot)) diff --git a/fastlane_bot/tests/test_034_Interface.py b/fastlane_bot/tests/test_034_Interface.py index 8b7f3dea8..24b32010b 100644 --- a/fastlane_bot/tests/test_034_Interface.py +++ b/fastlane_bot/tests/test_034_Interface.py @@ -12,10 +12,11 @@ from unittest.mock import MagicMock from unittest.mock import Mock +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.interface import QueryInterface, Token -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Bot)) diff --git a/fastlane_bot/tests/test_035_Utils.py b/fastlane_bot/tests/test_035_Utils.py index 712c0e53b..c2e7bfe21 100644 --- a/fastlane_bot/tests/test_035_Utils.py +++ b/fastlane_bot/tests/test_035_Utils.py @@ -11,10 +11,11 @@ from web3.datastructures import AttributeDict from web3.types import HexBytes +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot from fastlane_bot.events.pools import UniswapV2Pool, UniswapV3Pool, BancorV3Pool, CarbonV1Pool from fastlane_bot.events.utils import filter_latest_events, complex_handler -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Bot)) diff --git a/fastlane_bot/tests/test_036_Manager.py b/fastlane_bot/tests/test_036_Manager.py index 8d5ae2772..955f1f407 100644 --- a/fastlane_bot/tests/test_036_Manager.py +++ b/fastlane_bot/tests/test_036_Manager.py @@ -13,13 +13,14 @@ import pytest from unittest.mock import MagicMock +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot, Config from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.managers.manager import Manager from fastlane_bot.events.pools.utils import get_pool_cid Base = None -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Bot)) diff --git a/fastlane_bot/tests/test_037_Exchanges.py b/fastlane_bot/tests/test_037_Exchanges.py index 81a51b5ea..a3ff99e03 100644 --- a/fastlane_bot/tests/test_037_Exchanges.py +++ b/fastlane_bot/tests/test_037_Exchanges.py @@ -10,9 +10,10 @@ import json +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot from fastlane_bot.events.exchanges.balancer import Balancer -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3, BancorV2, BancorPol, SolidlyV2 from fastlane_bot.data.abi import UNISWAP_V2_POOL_ABI, UNISWAP_V3_POOL_ABI, BANCOR_V3_POOL_COLLECTION_ABI, \ CARBON_CONTROLLER_ABI, BANCOR_V2_CONVERTER_ABI, BANCOR_POL_ABI, BALANCER_VAULT_ABI, PANCAKESWAP_V3_POOL_ABI, SOLIDLY_V2_POOL_ABI diff --git a/fastlane_bot/tests/test_039_TestMultiMode.py b/fastlane_bot/tests/test_039_TestMultiMode.py index dc47761d1..9e100b53b 100644 --- a/fastlane_bot/tests/test_039_TestMultiMode.py +++ b/fastlane_bot/tests/test_039_TestMultiMode.py @@ -11,9 +11,10 @@ """ This module contains the tests for the exchanges classes """ +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot, Config from fastlane_bot.bot import CarbonBot -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.interface import QueryInterface from fastlane_bot.events.managers.manager import Manager @@ -140,6 +141,7 @@ def test_test_tax_tokens(): # ------------------------------------------------------------ assert any(token.address in cfg.TAX_TOKENS for token in tokens), f"[TestMultiMode], DB does not include any tax tokens" + assert len(CCm) == 516, f"[NBTest 039 TestMultiMode] Expected 516 curves, found {len(CCm)}" for curve in CCm: for token in cfg.TAX_TOKENS: @@ -178,6 +180,7 @@ def test_test_combos_and_tokens(): # ------------------------------------------------------------ # + + assert len(CCm) == 516, f"[NBTest 039 TestMultiMode] Expected 516 curves, found {len(CCm)}" arb_finder = bot._get_arb_finder("multi") finder = arb_finder( flashloan_tokens=flashloan_tokens, @@ -205,6 +208,7 @@ def test_test_expected_output(): # ------------------------------------------------------------ # + + assert len(CCm) == 516, f"[NBTest 039 TestMultiMode] Expected 516 curves, found {len(CCm)}" arb_finder = bot._get_arb_finder("multi") finder = arb_finder( flashloan_tokens=flashloan_tokens, diff --git a/fastlane_bot/tests/test_040_TestSingleMode.py b/fastlane_bot/tests/test_040_TestSingleMode.py index c7a1ee890..bb55ec95d 100644 --- a/fastlane_bot/tests/test_040_TestSingleMode.py +++ b/fastlane_bot/tests/test_040_TestSingleMode.py @@ -11,9 +11,10 @@ """ This module contains the tests for the exchanges classes """ +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot, Config from fastlane_bot.bot import CarbonBot -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.interface import QueryInterface from fastlane_bot.events.managers.manager import Manager diff --git a/fastlane_bot/tests/test_042_TestBancorV3ModeTwoHop.py b/fastlane_bot/tests/test_042_TestBancorV3ModeTwoHop.py index b94c8b0dc..558271970 100644 --- a/fastlane_bot/tests/test_042_TestBancorV3ModeTwoHop.py +++ b/fastlane_bot/tests/test_042_TestBancorV3ModeTwoHop.py @@ -11,11 +11,11 @@ """ This module contains the tests for the exchanges classes """ +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot, Config from fastlane_bot.bot import CarbonBot from fastlane_bot.helpers import TxRouteHandler -from fastlane_bot.tools.cpc import ConstantProductCurve -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.interface import QueryInterface from fastlane_bot.events.managers.manager import Manager @@ -255,7 +255,7 @@ def test_test_get_optimal_arb_trade_amts(): assert first_check_pools[2].cid == pool_cids[2], f"[test_bancor_v3_two_hop] Validation, wrong third pool, expected CID: 0xb1d8cd62f75016872495dae3e19d96e364767e7d674488392029d15cdbcd7b34, got CID: {first_check_pools[2].cid}" assert(len(first_check_pools) == 3), f"[test_bancor_v3_two_hop] Validation expected 3 pools, got {len(first_check_pools)}" for pool in first_check_pools: - assert type(pool) == ConstantProductCurve, f"[test_bancor_v3_two_hop] Validation pool type mismatch, got {type(pool)} expected ConstantProductCurve" + assert type(pool) == CPC, f"[test_bancor_v3_two_hop] Validation pool type mismatch, got {type(pool)} expected ConstantProductCurve" assert pool.cid in pool_cids, f"[test_bancor_v3_two_hop] Validation missing pool.cid {pool.cid} in {pool_cids}" optimal_arb = finder.get_optimal_arb_trade_amts(pool_cids, 'DAI-1d0F') diff --git a/fastlane_bot/tests/test_043_TestEmptyCarbonOrders.py b/fastlane_bot/tests/test_043_TestEmptyCarbonOrders.py index f49c68958..f3d156a5a 100644 --- a/fastlane_bot/tests/test_043_TestEmptyCarbonOrders.py +++ b/fastlane_bot/tests/test_043_TestEmptyCarbonOrders.py @@ -11,10 +11,11 @@ """ This module contains the tests for the exchanges classes """ +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot, Config from fastlane_bot.bot import CarbonBot from fastlane_bot.helpers import TxRouteHandler -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.interface import QueryInterface from fastlane_bot.events.managers.manager import Manager diff --git a/fastlane_bot/tests/test_045_Validator.py b/fastlane_bot/tests/test_045_Validator.py index ca8732097..7b3ddfb8e 100644 --- a/fastlane_bot/tests/test_045_Validator.py +++ b/fastlane_bot/tests/test_045_Validator.py @@ -11,9 +11,10 @@ """ This module contains the tests for the exchanges classes """ +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot, Config from fastlane_bot.bot import CarbonBot -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.interface import QueryInterface from fastlane_bot.events.managers.manager import Manager diff --git a/fastlane_bot/tests/test_047_Randomizer.py b/fastlane_bot/tests/test_047_Randomizer.py index b2f250575..64c861ee2 100644 --- a/fastlane_bot/tests/test_047_Randomizer.py +++ b/fastlane_bot/tests/test_047_Randomizer.py @@ -11,9 +11,10 @@ """ This module contains the tests for the exchanges classes """ +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot, Config from fastlane_bot.bot import CarbonBot -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.interface import QueryInterface from fastlane_bot.events.managers.manager import Manager diff --git a/fastlane_bot/tests/test_048_RespectFlashloanTokensClickParam.py b/fastlane_bot/tests/test_048_RespectFlashloanTokensClickParam.py index 7169c7af0..6610b14c4 100644 --- a/fastlane_bot/tests/test_048_RespectFlashloanTokensClickParam.py +++ b/fastlane_bot/tests/test_048_RespectFlashloanTokensClickParam.py @@ -11,8 +11,9 @@ """ This module contains the tests which ensure that the flashloan tokens click parameters are respected. """ +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 import subprocess, os, sys import pytest diff --git a/fastlane_bot/tests/test_049_CustomTradingFees.py b/fastlane_bot/tests/test_049_CustomTradingFees.py index e6b2be248..4fe4b20e1 100644 --- a/fastlane_bot/tests/test_049_CustomTradingFees.py +++ b/fastlane_bot/tests/test_049_CustomTradingFees.py @@ -13,11 +13,12 @@ import pytest from unittest.mock import MagicMock +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot, Config from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.managers.manager import Manager Base = None -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC import asyncio from unittest.mock import AsyncMock import nest_asyncio diff --git a/fastlane_bot/tests/test_050_TestBancorV2.py b/fastlane_bot/tests/test_050_TestBancorV2.py index 45a036f8f..599695819 100644 --- a/fastlane_bot/tests/test_050_TestBancorV2.py +++ b/fastlane_bot/tests/test_050_TestBancorV2.py @@ -11,16 +11,17 @@ """ This module contains the tests for the exchanges classes """ +from arb_optimizer import ConstantProductCurve as CPC +from arb_optimizer.curves import T + from fastlane_bot import Bot, Config from fastlane_bot.bot import CarbonBot from fastlane_bot.helpers import TxRouteHandler -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.interface import QueryInterface from fastlane_bot.events.managers.manager import Manager from fastlane_bot.events.interface import QueryInterface from joblib import Parallel, delayed -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, T from dataclasses import asdict import math import json diff --git a/fastlane_bot/tests/test_053_TknMaxTrade.py b/fastlane_bot/tests/test_053_TknMaxTrade.py index 18d435081..cd8bd98df 100644 --- a/fastlane_bot/tests/test_053_TknMaxTrade.py +++ b/fastlane_bot/tests/test_053_TknMaxTrade.py @@ -13,10 +13,11 @@ """ from dataclasses import asdict +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.helpers import maximize_last_trade_per_tkn -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Bot)) diff --git a/fastlane_bot/tests/test_058_BalancerIntegration.py b/fastlane_bot/tests/test_058_BalancerIntegration.py index ab5fde773..4edffe5bb 100644 --- a/fastlane_bot/tests/test_058_BalancerIntegration.py +++ b/fastlane_bot/tests/test_058_BalancerIntegration.py @@ -11,10 +11,11 @@ """ This module contains the tests for the exchanges classes """ +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot, Config from fastlane_bot.bot import CarbonBot from fastlane_bot.events.exchanges.balancer import Balancer -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.interface import QueryInterface from fastlane_bot.helpers import TradeInstruction, TxRouteHandler diff --git a/fastlane_bot/tests/test_060_TestRoutehandlerCarbonPrecision.py b/fastlane_bot/tests/test_060_TestRoutehandlerCarbonPrecision.py index 33b0fd1c3..1f343a4b9 100644 --- a/fastlane_bot/tests/test_060_TestRoutehandlerCarbonPrecision.py +++ b/fastlane_bot/tests/test_060_TestRoutehandlerCarbonPrecision.py @@ -19,6 +19,8 @@ from joblib import Parallel, delayed +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot from fastlane_bot.bot import CarbonBot from fastlane_bot.config import Config @@ -26,7 +28,6 @@ from fastlane_bot.events.interface import QueryInterface from fastlane_bot.events.managers.manager import Manager from fastlane_bot.helpers import TxRouteHandler, TradeInstruction -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Bot)) diff --git a/fastlane_bot/tests/test_061_TestWETHConversion.py b/fastlane_bot/tests/test_061_TestWETHConversion.py index aa96f81e2..d6dbf8781 100644 --- a/fastlane_bot/tests/test_061_TestWETHConversion.py +++ b/fastlane_bot/tests/test_061_TestWETHConversion.py @@ -18,6 +18,8 @@ """ This module contains the tests for the exchanges classes """ +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot from fastlane_bot.bot import CarbonBot from fastlane_bot.helpers import TxRouteHandler @@ -28,7 +30,6 @@ from fastlane_bot.events.managers.manager import Manager from dataclasses import asdict from fastlane_bot.config import Config -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.interface import QueryInterface from joblib import Parallel, delayed import math diff --git a/fastlane_bot/tests/test_063_TestBancorPOLMode.py b/fastlane_bot/tests/test_063_TestBancorPOLMode.py index 97ade2788..33204aadf 100644 --- a/fastlane_bot/tests/test_063_TestBancorPOLMode.py +++ b/fastlane_bot/tests/test_063_TestBancorPOLMode.py @@ -11,9 +11,10 @@ """ This module contains the tests for the exchanges classes """ +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot, Config from fastlane_bot.bot import CarbonBot -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.interface import QueryInterface from fastlane_bot.events.managers.manager import Manager diff --git a/fastlane_bot/tests/test_064_TestMultiAllMode.py b/fastlane_bot/tests/test_064_TestMultiAllMode.py index 8af2f4b8c..a69ab93f2 100644 --- a/fastlane_bot/tests/test_064_TestMultiAllMode.py +++ b/fastlane_bot/tests/test_064_TestMultiAllMode.py @@ -11,9 +11,10 @@ """ This module contains the tests for the exchanges classes """ +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot, Config from fastlane_bot.bot import CarbonBot -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.interface import QueryInterface from fastlane_bot.events.managers.manager import Manager diff --git a/fastlane_bot/tests/test_901_TestMultiTriangleModeSlow.py b/fastlane_bot/tests/test_901_TestMultiTriangleModeSlow.py deleted file mode 100644 index a003d4fdf..000000000 --- a/fastlane_bot/tests/test_901_TestMultiTriangleModeSlow.py +++ /dev/null @@ -1,301 +0,0 @@ -# ------------------------------------------------------------ -# Auto generated test file `test_901_TestMultiTriangleModeSlow.py` -# ------------------------------------------------------------ -# source file = NBTest_901_TestMultiTriangleModeSlow.py -# test id = 901 -# test comment = TestMultiTriangleModeSlow -# ------------------------------------------------------------ - - - -""" -This module contains the tests for the exchanges classes -""" -from fastlane_bot import Bot, Config -from fastlane_bot.bot import CarbonBot -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC -from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 -from fastlane_bot.events.interface import QueryInterface -from fastlane_bot.events.managers.manager import Manager -from fastlane_bot.events.interface import QueryInterface -from joblib import Parallel, delayed -import math -import json -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Bot)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(UniswapV2)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(UniswapV3)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CarbonV1)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(BancorV3)) -from fastlane_bot.testing import * - -#plt.style.use('seaborn-dark') -plt.rcParams['figure.figsize'] = [12,6] -from fastlane_bot import __VERSION__ -require("3.0", __VERSION__) - - - -C = cfg = Config.new(config=Config.CONFIG_MAINNET) -cfg.DEFAULT_MIN_PROFIT_GAS_TOKEN = 0.00001 -assert (C.NETWORK == C.NETWORK_MAINNET) -assert (C.PROVIDER == C.PROVIDER_ALCHEMY) -setup_bot = CarbonBot(ConfigObj=C) -pools = None -with open('fastlane_bot/tests/_data/latest_pool_data_testing.json') as f: - pools = json.load(f) -pools = [pool for pool in pools] -pools[0] -static_pools = pools -state = pools -exchanges = list({ex['exchange_name'] for ex in state}) -db = QueryInterface(state=state, ConfigObj=C, exchanges=exchanges) -setup_bot.db = db - -static_pool_data_filename = "static_pool_data" - -static_pool_data = pd.read_csv(f"fastlane_bot/data/{static_pool_data_filename}.csv", low_memory=False) - -uniswap_v2_event_mappings = pd.read_csv("fastlane_bot/data/uniswap_v2_event_mappings.csv", low_memory=False) - -tokens = pd.read_csv("fastlane_bot/data/tokens.csv", low_memory=False) - -exchanges = "carbon_v1,bancor_v3,uniswap_v3,uniswap_v2,sushiswap_v2" - -exchanges = exchanges.split(",") - - -alchemy_max_block_fetch = 20 -static_pool_data["cid"] = [ - cfg.w3.keccak(text=f"{row['descr']}").hex() - for index, row in static_pool_data.iterrows() - ] -static_pool_data = [ - row for index, row in static_pool_data.iterrows() - if row["exchange_name"] in exchanges -] - -static_pool_data = pd.DataFrame(static_pool_data) -static_pool_data['exchange_name'].unique() -mgr = Manager( - web3=cfg.w3, - w3_async=cfg.w3_async, - cfg=cfg, - pool_data=static_pool_data.to_dict(orient="records"), - SUPPORTED_EXCHANGES=exchanges, - alchemy_max_block_fetch=alchemy_max_block_fetch, - uniswap_v2_event_mappings=uniswap_v2_event_mappings, - tokens=tokens.to_dict(orient="records"), -) - -start_time = time.time() -Parallel(n_jobs=-1, backend="threading")( - delayed(mgr.add_pool_to_exchange)(row) for row in mgr.pool_data -) -cfg.logger.info(f"Time taken to add initial pools: {time.time() - start_time}") - -mgr.deduplicate_pool_data() -cids = [pool["cid"] for pool in mgr.pool_data] -assert len(cids) == len(set(cids)), "duplicate cid's exist in the pool data" -def init_bot(mgr: Manager) -> CarbonBot: - """ - Initializes the bot. - - Parameters - ---------- - mgr : Manager - The manager object. - - Returns - ------- - CarbonBot - The bot object. - """ - mgr.cfg.logger.info("Initializing the bot...") - bot = CarbonBot(ConfigObj=mgr.cfg) - bot.db = db - bot.db.mgr = mgr - assert isinstance( - bot.db, QueryInterface - ), "QueryInterface not initialized correctly" - return bot -bot = init_bot(mgr) -bot.db.remove_unmapped_uniswap_v2_pools() -bot.db.remove_zero_liquidity_pools() -bot.db.remove_unsupported_exchanges() -tokens = bot.db.get_tokens() -ADDRDEC = {t.address: (t.address, int(t.decimals)) for t in tokens if not math.isnan(t.decimals)} -flashloan_tokens = bot.RUN_FLASHLOAN_TOKENS -CCm = bot.get_curves() -pools = db.get_pool_data_with_tokens() - -arb_mode = "multi_triangle" - - -# ------------------------------------------------------------ -# Test 901 -# File test_901_TestMultiTriangleModeSlow.py -# Segment Test_min_profit -# ------------------------------------------------------------ -def test_test_min_profit(): -# ------------------------------------------------------------ - - assert(cfg.DEFAULT_MIN_PROFIT_GAS_TOKEN <= 0.0001), f"[TestMultiTriangleMode], default_min_profit_gas_token must be <= 0.0001 for this Notebook to run, currently set to {cfg.DEFAULT_MIN_PROFIT_GAS_TOKEN}" - - # ### Test_arb_mode_class - - arb_finder = bot._get_arb_finder("multi_triangle") - assert arb_finder.__name__ == "ArbitrageFinderTriangleMulti", f"[TestMultiTriangleMode] Expected arb_finder class name name = FindArbitrageMultiPairwise, found {arb_finder.__name__}" - - -# ------------------------------------------------------------ -# Test 901 -# File test_901_TestMultiTriangleModeSlow.py -# Segment Test_combos -# ------------------------------------------------------------ -def test_test_combos(): -# ------------------------------------------------------------ - - arb_finder = bot._get_arb_finder("multi_triangle") - finder = arb_finder( - flashloan_tokens=flashloan_tokens, - CCm=CCm, - mode="bothin", - result=arb_finder.AO_TOKENS, - ConfigObj=bot.ConfigObj, - ) - combos = finder.get_combos(flashloan_tokens=flashloan_tokens, CCm=CCm, arb_mode="multi_triangle") - assert len(combos) >= 1225, f"[TestMultiTriangleMode] Using wrong dataset, expected at least 1225 combos, found {len(combos)}" - - # + - # print(len(combos)) - # for ex in exchanges: - # count = 0 - # for pool in CCm: - # if ex in pool.descr: - # count +=1 - # print(f"found {count} pools for {ex}") - # - - - # ### Test_find_arbitrage_single - - # + - arb_finder = bot._get_arb_finder("multi_triangle") - finder = arb_finder( - flashloan_tokens=flashloan_tokens, - CCm=CCm, - mode="bothin", - result=arb_finder.AO_CANDIDATES, - ConfigObj=bot.ConfigObj, - ) - r = finder.find_arbitrage() - multi_carbon_count = 0 - for arb in r: - ( - best_profit, - best_trade_instructions_df, - best_trade_instructions_dic, - best_src_token, - best_trade_instructions, - ) = arb - if len(best_trade_instructions_dic) > 3: - multi_carbon_count += 1 - tkn_in = None - tkn_out = None - # Find the first Carbon Curve to establish tknin and tknout - for curve in best_trade_instructions_dic: - if "-0" in curve['cid'] or "-1" in curve['cid']: - tkn_in = curve["tknin"] - tknout = curve["tknout"] - break - for curve in best_trade_instructions_dic: - if "-0" in curve['cid'] or "-1" in curve['cid']: - if curve["tknin"] in [tkn_in, tkn_out] and curve["tknout"] in [tkn_in, tkn_out]: - assert curve["tknin"] in tkn_in, f"[TestMultiTriangleMode] Finding Carbon curves in opposite directions - not supported in this mode." - assert curve["tknout"] in tkn_out, f"[TestMultiTriangleMode] Finding Carbon curves in opposite directions - not supported in this mode." - - assert multi_carbon_count > 0, f"[TestMultiTriangleMode] Not finding arbs with multiple Carbon curves." - assert len(r) >= 58, f"[TestMultiTriangleMode] Expected at least 58 arbs, found {len(r)}" - # - - - -# ------------------------------------------------------------ -# Test 901 -# File test_901_TestMultiTriangleModeSlow.py -# Segment Test Triangle Single -# ------------------------------------------------------------ -def test_test_triangle_single(): -# ------------------------------------------------------------ - - arb_finder = bot._get_arb_finder("triangle") - assert arb_finder.__name__ == "ArbitrageFinderTriangleSingle", f"[TestMultiTriangleMode] Expected arb_finder class name name = ArbitrageFinderTriangleSingle, found {arb_finder.__name__}" - - -# ------------------------------------------------------------ -# Test 901 -# File test_901_TestMultiTriangleModeSlow.py -# Segment Test_combos_triangle_single -# ------------------------------------------------------------ -def test_test_combos_triangle_single(): -# ------------------------------------------------------------ - - arb_finder = bot._get_arb_finder("triangle") - finder = arb_finder( - flashloan_tokens=flashloan_tokens, - CCm=CCm, - mode="bothin", - result=arb_finder.AO_TOKENS, - ConfigObj=bot.ConfigObj, - ) - combos = finder.get_combos(flashloan_tokens=flashloan_tokens, CCm=CCm, arb_mode="multi_triangle") - assert len(combos) >= 1225, f"[TestMultiTriangleMode] Using wrong dataset, expected at least 1225 combos, found {len(combos)}" - - -# ------------------------------------------------------------ -# Test 901 -# File test_901_TestMultiTriangleModeSlow.py -# Segment Test_Find_Arbitrage_Single -# ------------------------------------------------------------ -def test_test_find_arbitrage_single(): -# ------------------------------------------------------------ - - # + - arb_finder = bot._get_arb_finder("triangle") - finder = arb_finder( - flashloan_tokens=flashloan_tokens, - CCm=CCm, - mode="bothin", - result=arb_finder.AO_CANDIDATES, - ConfigObj=bot.ConfigObj, - ) - r = finder.find_arbitrage() - multi_carbon_count = 0 - for arb in r: - ( - best_profit, - best_trade_instructions_df, - best_trade_instructions_dic, - best_src_token, - best_trade_instructions, - ) = arb - if len(best_trade_instructions_dic) > 3: - multi_carbon_count += 1 - tkn_in = None - tkn_out = None - # Find the first Carbon Curve to establish tknin and tknout - for curve in best_trade_instructions_dic: - if "-0" in curve['cid'] or "-1" in curve['cid']: - tkn_in = curve["tknin"] - tknout = curve["tknout"] - break - for curve in best_trade_instructions_dic: - if "-0" in curve['cid'] or "-1" in curve['cid']: - if curve["tknin"] in [tkn_in, tkn_out] and curve["tknout"] in [tkn_in, tkn_out]: - assert curve["tknin"] in tkn_in, f"[TestMultiTriangleMode] Finding Carbon curves in opposite directions - not supported in this mode." - assert curve["tknout"] in tkn_out, f"[TestMultiTriangleMode] Finding Carbon curves in opposite directions - not supported in this mode." - - assert multi_carbon_count == 0, f"[TestMultiTriangleMode] Expected 0 arbs with multiple Carbon curves for Triangle Single mode, found {multi_carbon_count}." - assert len(r) >= 58, f"[TestMultiTriangleMode] Expected at least 58 arbs, found {len(r)}" - # - - - \ No newline at end of file diff --git a/fastlane_bot/tests/test_903_FlashloanTokens.py b/fastlane_bot/tests/test_903_FlashloanTokens.py deleted file mode 100644 index 9176782ab..000000000 --- a/fastlane_bot/tests/test_903_FlashloanTokens.py +++ /dev/null @@ -1,90 +0,0 @@ -# ------------------------------------------------------------ -# Auto generated test file `test_903_FlashloanTokens.py` -# ------------------------------------------------------------ -# source file = NBTest_903_FlashloanTokens.py -# test id = 903 -# test comment = FlashloanTokens -# ------------------------------------------------------------ - - - -""" -This module contains the tests which ensure the the flashloan_tokens parameter is respected when using the b3_two_hop and bancor_v3 arb modes. -""" -from fastlane_bot import Bot -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC -from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 -import subprocess, os, sys -import pytest -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Bot)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(UniswapV2)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(UniswapV3)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CarbonV1)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(BancorV3)) -from fastlane_bot.testing import * -plt.rcParams['figure.figsize'] = [12,6] -from fastlane_bot import __VERSION__ -require("3.0", __VERSION__) - - - - -def find_main_py(): - # Start at the directory of the current script - cwd = os.path.abspath(os.path.join(os.getcwd())) - - with open("log.txt", "w") as f: - f.write(f"Searching for main.py in {cwd}") - - print(f"Searching for main.py in {cwd}") - while True: - # Check if main.py exists in the current directory - if "main.py" in os.listdir(cwd): - return cwd # Found the directory containing main.py - else: - # If not, go up one directory - new_cwd = os.path.dirname(cwd) - - # If we're already at the root directory, stop searching - if new_cwd == cwd: - raise FileNotFoundError("Could not find main.py in any parent directory") - - cwd = new_cwd - - -def run_command(mode): - - # Find the correct path to main.py - main_script_path = find_main_py() - print(f"Found main.py in {main_script_path}") - main_script_path = main_script_path + "/main.py" - - # Run the command - cmd = [ - "python", - main_script_path, - f"--arb_mode={mode}", - "--default_min_profit_gas_token=60", - "--limit_bancor3_flashloan_tokens=True", - "--alchemy_max_block_fetch=5", - "--logging_path=fastlane_bot/data/", - "--timeout=120", - "--blockchain=ethereum" - ] - - expected_log_line = "limiting flashloan_tokens to [" - result = subprocess.run(cmd, text=True, capture_output=True, check=True) - assert expected_log_line in result.stderr, result.stderr - - -# ------------------------------------------------------------ -# Test 903 -# File test_903_FlashloanTokens.py -# Segment Test Flashloan Tokens b3_two_hop -# ------------------------------------------------------------ -def test_test_flashloan_tokens_b3_two_hop(): -# ------------------------------------------------------------ - - # + is_executing=true - run_command("b3_two_hop") \ No newline at end of file diff --git a/fastlane_bot/tests/test_906_TargetTokens.py b/fastlane_bot/tests/test_906_TargetTokens.py deleted file mode 100644 index b2651b76c..000000000 --- a/fastlane_bot/tests/test_906_TargetTokens.py +++ /dev/null @@ -1,91 +0,0 @@ -# ------------------------------------------------------------ -# Auto generated test file `test_906_TargetTokens.py` -# ------------------------------------------------------------ -# source file = NBTest_906_TargetTokens.py -# test id = 906 -# test comment = TargetTokens -# ------------------------------------------------------------ - - - -""" -This module contains the tests which ensure the target_tokens parameter is respected. -""" -from fastlane_bot import Bot -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC -from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 -import subprocess, os, sys -import pytest -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Bot)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(UniswapV2)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(UniswapV3)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CarbonV1)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(BancorV3)) -from fastlane_bot.testing import * -plt.rcParams['figure.figsize'] = [12,6] -from fastlane_bot import __VERSION__ -require("3.0", __VERSION__) - - - -from fastlane_bot.tools.cpc import T - - -def find_main_py(): - # Start at the directory of the current script - cwd = os.path.abspath(os.path.join(os.getcwd())) - - with open("log.txt", "w") as f: - f.write(f"Searching for main.py in {cwd}") - - print(f"Searching for main.py in {cwd}") - while True: - # Check if main.py exists in the current directory - if "main.py" in os.listdir(cwd): - return cwd # Found the directory containing main.py - else: - # If not, go up one directory - new_cwd = os.path.dirname(cwd) - - # If we're already at the root directory, stop searching - if new_cwd == cwd: - raise FileNotFoundError("Could not find main.py in any parent directory") - - cwd = new_cwd - - -def run_command(mode): - - # Find the correct path to main.py - main_script_path = find_main_py() - print(f"Found main.py in {main_script_path}") - main_script_path = main_script_path + "/main.py" - - # Run the command - cmd = [ - "python", - main_script_path, - f"--arb_mode={mode}", - # "--use_cached_events=True", - "--alchemy_max_block_fetch=5", - "--logging_path=fastlane_bot/data/", - "--timeout=120", - f"--target_tokens={T.WETH},{T.DAI}", - "--blockchain=ethereum" - ] - - expected_log_line = "Limiting pools by target_tokens. Removed " - result = subprocess.run(cmd, text=True, capture_output=True, check=True) - assert expected_log_line in result.stderr, result.stderr - - -# ------------------------------------------------------------ -# Test 906 -# File test_906_TargetTokens.py -# Segment Test Flashloan Tokens b3_two_hop -# ------------------------------------------------------------ -def test_test_flashloan_tokens_b3_two_hop(): -# ------------------------------------------------------------ - - run_command("single") \ No newline at end of file diff --git a/fastlane_bot/tests_on_hold/test_059_TestNetworkInfoMultichain.py b/fastlane_bot/tests_on_hold/test_059_TestNetworkInfoMultichain.py index 7fd7c3a5c..809d250f6 100644 --- a/fastlane_bot/tests_on_hold/test_059_TestNetworkInfoMultichain.py +++ b/fastlane_bot/tests_on_hold/test_059_TestNetworkInfoMultichain.py @@ -11,8 +11,9 @@ """ This module contains the tests for the exchanges classes """ +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot, Config -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.testing import * from fastlane_bot.config.network import * diff --git a/fastlane_bot/tests_on_hold/test_062_TestRouteHandler.py b/fastlane_bot/tests_on_hold/test_062_TestRouteHandler.py index 339ec0dac..99b4ee900 100644 --- a/fastlane_bot/tests_on_hold/test_062_TestRouteHandler.py +++ b/fastlane_bot/tests_on_hold/test_062_TestRouteHandler.py @@ -13,9 +13,10 @@ """ from unittest.mock import Mock +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot, Config from fastlane_bot.bot import CarbonBot -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.interface import QueryInterface from fastlane_bot.helpers import TradeInstruction, TxRouteHandler diff --git a/fastlane_bot/tests_on_hold/test_069_TestTxHelpers.py b/fastlane_bot/tests_on_hold/test_069_TestTxHelpers.py index c92dfa4f5..a89dfc0a5 100644 --- a/fastlane_bot/tests_on_hold/test_069_TestTxHelpers.py +++ b/fastlane_bot/tests_on_hold/test_069_TestTxHelpers.py @@ -11,9 +11,10 @@ """ This module contains the tests for the exchanges classes """ +from arb_optimizer import ConstantProductCurve as CPC + from fastlane_bot import Bot, Config from fastlane_bot.bot import CarbonBot -from fastlane_bot.tools.cpc import ConstantProductCurve as CPC from fastlane_bot.events.exchanges import UniswapV2, UniswapV3, CarbonV1, BancorV3 from fastlane_bot.events.interface import QueryInterface from fastlane_bot.events.interface import QueryInterface diff --git a/fastlane_bot/tests_on_hold/test_907_RuntimeParameters.py b/fastlane_bot/tests_on_hold/test_907_RuntimeParameters.py index f36d97192..c1fe6644a 100644 --- a/fastlane_bot/tests_on_hold/test_907_RuntimeParameters.py +++ b/fastlane_bot/tests_on_hold/test_907_RuntimeParameters.py @@ -13,7 +13,7 @@ import pytest -from fastlane_bot.tools.cpc import T +from arb_optimizer.curves import T from main import main # adjust import according to your script's location and name @pytest.fixture diff --git a/fastlane_bot/tools/README.md b/fastlane_bot/tools/README.md deleted file mode 100644 index 922186792..000000000 --- a/fastlane_bot/tools/README.md +++ /dev/null @@ -1 +0,0 @@ -The difference between the `tools` and the `helpers` module is that the modules in `tools` can be imported from within the Carbon library, whilst those in helpers can not. Neither of the modules is part of the official Carbon API and may change at any time. \ No newline at end of file diff --git a/fastlane_bot/tools/__init__.py b/fastlane_bot/tools/__init__.py deleted file mode 100644 index f5583076d..000000000 --- a/fastlane_bot/tools/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -""" -FLB Tools -- Tools related to Bancor's Fastlane Bot. - ---- -(c) Copyright Bprotocol foundation 2023-24. -Licensed under MIT -""" - -__VERSION__ = '1.0+' -__VERSION_DATE__ = '07/Feb/2024' -__AUTHOR__ = 'Stefan K Loesch' -__COPYRIGHT__ = 'Bprotocol foundation 2023-24' -__LICENSE__ = 'MIT' diff --git a/fastlane_bot/tools/analyzer.py b/fastlane_bot/tools/analyzer.py deleted file mode 100644 index e4efe3ea0..000000000 --- a/fastlane_bot/tools/analyzer.py +++ /dev/null @@ -1,492 +0,0 @@ -""" -analyzing CPC / CPCContainer based collections - ---- -(c) Copyright Bprotocol foundation 2023. -Licensed under MIT - -NOTE: this class is not part of the API of the Carbon protocol, and you must expect breaking -changes even in minor version updates. Use at your own risk. -""" -__VERSION__ = "1.5" -__DATE__ = "18/May/2023" - -from typing import Any -from .cpc import ConstantProductCurve as CPC, CPCContainer, T, Pair -from .optimizer import CPCArbOptimizer - -from dataclasses import dataclass, field, asdict, astuple, fields, InitVar -import math as m -import numpy as np -import pandas as pd -import itertools as it -import collections as cl - - -class AttrDict(dict): - """ - A dictionary that allows for attribute-style access - - see https://stackoverflow.com/questions/4984647/accessing-dict-keys-like-an-attribute - """ - def __init__(self, *args, **kwargs): - super(AttrDict, self).__init__(*args, **kwargs) - self.__dict__ = self - - def __getattr__(self, __name: str) -> Any: - return None - -class _DCBase: - """base class for all data classes, adding some useful methods""" - - def asdict(self): - return asdict(self) - - def astuple(self): - return astuple(self) - - def fields(self): - return fields(self) - -@dataclass -class CPCAnalyzer(_DCBase): - """ - various analytics functions around a CPCContainer object - """ - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - CC: CPCContainer = field(default=None) - - def __post_init__(self): - if self.CC is None: - self.CC = CPCContainer() - assert isinstance(self.CC, CPCContainer), "CC must be a CPCContainer object" - - def pairs(self): - """alias for CC.pairs(standardize=True)""" - return self.CC.pairs(standardize=True) - - def pairsc(self): - """all pairs with carbon curves""" - return {c.pairo.primary for c in self.CC if c.P("exchange")=="carbon_v1"} - - def curves(self): - """all curves""" - return self.CC.curves - - def curvesc(self, *, ascc=False): - """all carbon curves""" - result = [c for c in self.CC if c.P("exchange")=="carbon_v1"] - if not ascc: - return result - return CPCContainer(result) - - def tokens(self): - """all tokens in the curves""" - return self.CC.tokens() - - def count_by_tokens(self, *, byexchange=True, asdict=False): - """ - counts the number of times each token appears in the curves - - :byexchange: if False only provides the global number from the CC object - :asdict: if True returns dict, otherwise dataframe - - NOTE: the exchanges are current hardcoded, and should be made dynamic - """ - if not byexchange: - return self.CC.token_count(asdict=asdict) - - CCu3 = self.CC.byparams(exchange="uniswap_v3") - CCu2 = self.CC.byparams(exchange="uniswap_v2") - CCs2 = self.CC.byparams(exchange="sushiswap_v2") - CCc1 = self.CC.byparams(exchange="carbon_v1") - tc_u3 = CCu3.token_count(asdict=True) - tc_u2 = CCu2.token_count(asdict=True) - tc_s2 = CCs2.token_count(asdict=True) - tc_c1 = CCc1.token_count(asdict=True) - rows = [ - (tkn, cnt, tc_c1.get(tkn,0), tc_u3.get(tkn,0), tc_u2.get(tkn,0), tc_s2.get(tkn,0)) - for tkn, cnt in self.CC.token_count() - ] - df = pd.DataFrame(rows,columns="token,total,carb,uni3,uni2,sushi".split(",")) - df = df.set_index("token") - return df - - def count_by_pairs(self, *, minn=None, asdf=True): - """ - counts the number of times each pair appears in the curves - - :minn: filter the dataset to a minimum number of curves per pair (only df) - """ - curves_by_pair = list(cl.Counter([c.pairo.primary for c in self.CC]).items()) - curves_by_pair = sorted(curves_by_pair, key=lambda x: x[1], reverse=True) - if not asdf: - return curves_by_pair - df = pd.DataFrame(curves_by_pair, columns=["pair", "count"]).set_index("pair") - if minn is None: - return df - df = df[df["count"]>=minn] - return df - - - @dataclass - class CurveData(_DCBase): - curve: InitVar[CPC] - analyzer: InitVar = None - CC: InitVar = None - - primary: str = field(init=False, repr=True, default=None) - cid0: str = field(init=False, repr=True, default=None) - - def __post_init__(self, curve, analyzer=None, CC=None): - self.curve = curve - self.analyzer = analyzer - if CC is None: - CC = self.analyzer.CC.bypairs(curve.pair) - self.CC = CC - self.primary = Pair.n(self.curve.pairo.primary) - self.cid0 = self.curve.cid[-8:] - - def info(self): - c = self.curve - cc = self.CC - dct = dict( - primary = Pair.n(self.primary), - pair = Pair.n(c.pair), - price = c.primaryp(), - cid = c.cid, - cid0 = c.cid[-8:], - exchange = c.P("exchange"), - vl = c.tvl(tkn=c.pair.split("/")[0]), - itm = "x" if c.itm(cc) else "", - bs = c.buysell(verbose=False), - bsv = c.buysell(verbose=True, withprice=True), - ) - return dct - - def curve_data(self, curves=None, *, asdf=False): - """return a CurveData object for the curve (or all curves of the pair if curve is None))""" - if curves is None: - curves = self.CC - try: - result = tuple(self.curve_data(c) for c in curves) - if asdf: - df = pd.DataFrame([c.info() for c in result]) - return df - return result - except TypeError: - pass - return self.CurveData(curves, self) - - @dataclass - class PairData(_DCBase): - pair: InitVar[str] - analyzer: InitVar = None - CC: InitVar = None - primary: str = field(init=False, repr=True, default=None) - ncurves: int = field(init=False, repr=False, default=None) - ncurvesc: int = field(init=False, repr=False, default=None) - - def __post_init__(self, pair, analyzer=None, CC=None): - self.pairo = Pair(pair) - self.analyzer = analyzer - self.analyzer = analyzer - if CC is None: - CC = self.analyzer.CC.bypairs(pair) - self.CC = CC - self.primary = Pair.n(self.pairo.primary) - self.ncurves = len(self.CC) - self.ncurvesc = len(self.curves_by_exchange("carbon_v1")) - - def curves_by_exchange(self, exchange=None): - """dict exchange -> curves if exchange is None, otherwise just the curves for that exchange""" - if exchange is None: - return {c.P("exchange"): c for c in self.CC} - else: - return [c for c in self.CC if c.P("exchange")==exchange] - - def curve_data(self, curves=None, *, asdf=False): - """return a CurveData object for the curves (or all curves of the pair if curve is None)""" - if curves is None: - curves = self.CC - return self.analyzer.curve_data(curves, asdf=asdf) - - def pair_data(self, pair=None): - """return a PairData object for the pair (dict for all pairs if pair is None)""" - if not pair is None: - return self.PairData(pair, self) - return {pair: self.PairData(pair, self) for pair in self.pairs()} - - def pair_analysis(self, pair, **params): - """ - :pair: pair to be analyzed, eg "WETH-6Cc2/USDC-eB48" - :params: optional parameters [see code for details] - - :returns: an attributed dictionary with the following fields: - :pair: the input pair, eg "WETH-6Cc2/USDC-eB48" - :tknb, tknq: base and quote token of the pair - :analyzer: the analyzer object - :paird: PairData object - :curved: tuple of CurveData objects, as returns by PairData.curve_data - :curvedf: curve data as dataframe, as returned by PairData.curve_data - :price: price estimate of that pair, in the native quotation of the pair - :vlc: value locked for Carbon (in quote token units) - :vlnc: ditto non-carbon - :curvedfx: like curvedf, but with some fields moved to the index - :ccurvedf: like curvedfx, but all non-carbon curves replaced with single aggregate line - :tib, tiq: trade instruction data frames (target = base / quote token respecitvely) - :tibq: concatenation of the TOTAL NET line of tib, tiq - :arbvalb/q: arb value in base token / quote token units - :xpairs: extended pairs (tokens of the pair plus triangulation tokens) - :tib/q_xnoc: trade instruction data frames for the extended pairs (non-carbon curves only) - :tib/q_xf: ditto (including carbon curves) - :xarbvalp/q: extended arb results (AttrDict with :nc: non-carbon, :full: plus Carbon, :net: difference) - """ - P = lambda x: params.get(x, None) - - paird = self.pair_data(pair) - curvedf = paird.curve_data(asdf=True) - tknb, tknq = pair.split("/") - - - ## PART1: TRIVIAL ANALYSIS - d = AttrDict( - pair = pair, - analyzer= self, - tknb = tknb, - tknq = tknq, - paird = paird, - curved = paird.curve_data(), - curvedf = curvedf, - price = self.CC.price_estimate(pair=pair), - vlc = sum(curvedf[curvedf["exchange"]=="carbon_v1"]["vl"]), - vlnc = sum(curvedf[curvedf["exchange"]!="carbon_v1"]["vl"]), - ) - - - ## PART 2: SIMPLE DATAFRAMES - - # indexed df - curvedf1 = d.curvedf - curvedf1 = curvedf1.drop(['pair', 'primary', 'cid'], axis=1) - curvedf1 = curvedf1.sort_values(by=["exchange", "cid0"]) - curvedf1 = curvedf1.set_index(["exchange", "cid0"]) - d["curvedfx"] = curvedf1 - - # carbon curve df (aggregating the other curves) - aggrdf = pd.DataFrame.from_dict([dict( - exchange="aggr", - cid0=Pair.n(pair), - price=d.price, - vl=d.vlnc, - itm="", - bs="", - bsv="", - )]).set_index(["exchange", "cid0"]) - d["ccurvedf"] = pd.concat([d.curvedfx.loc[["carbon_v1"]], aggrdf], axis=0) - - - ## PART 3: USING THE OPTIMIZER ON THE PAIR ("SIMPLE ARB") - # trade instructions - O = CPCArbOptimizer(paird.CC) - - r = O.margp_optimizer(tknb, params=dict(verbose=False, debug=False)) - d["tib"] = r.trade_instructions(ti_format=O.TIF_DFAGGR) - - r = O.margp_optimizer(tknq) - d["tiq"] = r.trade_instructions(ti_format=O.TIF_DFAGGR) - - d["tibq"] = pd.concat([d.tib.loc[["TOTAL NET"]], d.tiq.loc[["TOTAL NET"]]]) - d["arbvalb"] = -d.tibq.iloc[0][d.tknb] - d["arbvalq"] = -d.tibq.iloc[1][d.tknq] - - if P("nocav"): - # nocav --> no complex arb value calculation - d["nocav"] = True - return d - - ## PART 4: USING THE OPTIMIZER ON TRIANGULAR TOKENS ("COMPLEX ARB") - - # the carbon curves associated with the pair - CC_crb = self.curvesc(ascc=True).bypairs(pair) - - # the extended list of pairs (universe: tokens of the pair + triangulation tokens) - d["xpairs"] = self.CC.filter_pairs(bothin=f"{d.tknb}, {d.tknq}, {CPCContainer.TRIANGTOKENS}") - - # all non-Carbon curves associated with the extended list of pairs - CCx_noc = self.CC.bypairs(d.xpairs).byparams(exchange="carbon_v1", _inv=True) - #print("exchanges", {c.P("exchange") for c in CCx_noc}) - - # the optimizer based on the extended list of pairs (non-carbon curves only!) - O = CPCArbOptimizer(CCx_noc) - r = O.margp_optimizer(d.tknb, params=dict(verbose=False, debug=False)) - d["tib_xnoc"] = r.trade_instructions(ti_format=O.TIF_DFAGGR) - r = O.margp_optimizer(d.tknq) - d["tiq_xnoc"] = r.trade_instructions(ti_format=O.TIF_DFAGGR) - - # the full set of curves (non-carbon on extended pairs, carbon on the pair) - CCx = CCx_noc.copy() - CCx += CC_crb - - # the optimizer based on the full set of curves - O = CPCArbOptimizer(CCx) - r = O.margp_optimizer(d.tknb, params=dict(verbose=False, debug=False)) - d["tib_xf"] = r.trade_instructions(ti_format=O.TIF_DFAGGR) - r = O.margp_optimizer(d.tknq) - d["tiq_xf"] = r.trade_instructions(ti_format=O.TIF_DFAGGR) - - try: - xarbval_ncq = -d.tiq_xnoc.loc["TOTAL NET"][d.tknq] - xarbval_fq = -d.tiq_xf.loc["TOTAL NET"][d.tknq] - xarbval_netq = xarbval_fq - xarbval_ncq - d["xarbvalq"] = AttrDict( - nc = xarbval_ncq, - full = xarbval_fq, - net = xarbval_netq, - ) - except Exception as e: - d["xarbvalq"] = AttrDict(err=str(e)) - - try: - xarbval_ncb = -d.tip_xnoc.loc["TOTAL NET"][d.tknb] - xarbval_fb = -d.tip_xf.loc["TOTAL NET"][d.tknb] - xarbval_netb = xarbval_fb - xarbval_ncb - d["xarbvalb"] = AttrDict( - nc = xarbval_ncb, - full = xarbval_fb, - net = xarbval_netb, - ) - except Exception as e: - d["xarbvalb"] = AttrDict(err=str(e)) - - ## FINALLY: return the result - return d - - - def _fmt_xarbval(self, xarbval, tkn): - """format the extended arb value""" - if xarbval.err is None: - result = f"no-carb={xarbval.nc:,.2f} full={xarbval.full:,.2f} net={xarbval.net:,.2f} [{Pair.n(tkn)}]" - else: - result = f"error [{Pair.n(tkn)}]" - return result - - def pair_analysis_pp(self, data, **parameters): - """ - pretty-print the output `d` of pair_analysis (returns string) - """ - P,d,s = lambda x: parameters.get(x, None), data, "" - - if not P("nosep"): - s += "-"*80+"\n" - - if not P("nopair"): - s += f"Pair: {d.pair}\n" - - if not P("nosep"): - s += "-"*80+"\n" - - if not P("noprice"): - s += f"Price: {d.price:,.6f}\n" - - if not P("nocurves"): - s += f"Number of curves: {d.paird.ncurves} [carbon: {d.paird.ncurvesc}]\n" - - if not P("novl"): - s += f"Value locked: {d.vlc+d.vlnc:,.2f} {Pair.n(d.tknq)} [carbon: {d.vlc:,.2f}, other: {d.vlnc:,.2f}]\n" - - if not P("nosav"): - s += f"Simple arb value: {d.arbvalb:,.2f} {Pair.n(d.tknb)} / {d.arbvalq:,.2f} {Pair.n(d.tknq)}\n" - - if not P("nocav"): - s += f"Complex arb value: {self._fmt_xarbval(d.xarbvalq, d.tknq)}\n" - s += f" {self._fmt_xarbval(d.xarbvalb, d.tknb)}\n" - - return s - - POS_DICT = "dict" - POS_LIST = "list" - POS_DF = "df" - def pool_arbitrage_statistics(self, result = None, *, sort_price=True, only_pairs_with_carbon=True): - """ - returns arbirage statistics on all Carbon pairs - - :result: POS_DICT, POS_LIST, POS_DF (default) - :only_pairs_with_carbon: ignore all curves that don't have a Carbon pair - :sort_price: sort by price - :returns: the statistics data in the requested format - """ - # select all curves that have at least one Carbon pair... - if only_pairs_with_carbon: - curves_by_carbon_pair = {pair: self.CC.bypairs([pair]) for pair in self.pairsc()} - else: - curves_by_carbon_pair = {pair: self.CC.bypairs([pair]) for pair in self.pairs()} - - # ...calculate some statistics... - prices_d = {pair: - [( - Pair.n(pair), pair, c.primaryp(), c.cid, c.cid[-8:], c.P("exchange"), c.tvl(tkn=pair.split("/")[0]), - "x" if c.itm(cc) else "", c.buy(), c.sell(), c.buysell(verbose=True, withprice=True) - ) for c in cc - ] - for pair, cc in curves_by_carbon_pair.items() - } - - # ...and return them in the desired format - if result is None: - result = self.POS_DF - - if result == self.POS_DICT: - #print("returning dict") - return prices_d - - prices_l = tuple(it.chain(*prices_d.values())) - if result == self.POS_LIST: - #print("returning list") - return prices_l - - pricedf0 = pd.DataFrame(prices_l, columns="pair,pairf,price,cid,cid0,exchange,vl,itm,b,s,bsv".split(",")) - if sort_price: - pricedf = pricedf0.drop(['cid', 'pairf'], axis=1).sort_values(by=["pair", "price", "exchange", "cid0"]) - else: - pricedf = pricedf0.drop(['cid', 'pairf'], axis=1).sort_values(by=["pair", "exchange", "cid0"]) - pricedf = pricedf.set_index(["pair", "exchange", "cid0"]) - if result == self.POS_DF: - return pricedf - - raise ValueError(f"invalid result type {result}") - - PR_TUPLE = "tuple" - PR_DICT = "dict" - PR_DF = "df" - def price_ranges(self, result=None, *, short=True): - """ - returns dataframe with price information of all curves - - :result: PR_TUPLE, PR_DICT, PR_DF (default) - :short: shorten cid and pair - """ - if result is None: result = self.PR_DF - price_l = (( - c.primary if not short else Pair.n(c.primary), - c.cid if not short else c.cid[-10:], - c.P("exchange"), - c.buy(), - c.sell(), - c.p_min_primary(), - c.p_max_primary(), - c.pp, - ) for c in self.CC) - if result == self.PR_TUPLE: - return tuple(price_l) - if result == self.PR_DICT: - return {c.cid: r for c, r in zip(self.CC, price_l)} - df = pd.DataFrame(price_l, columns="pair,cid,exch,b,s,p_min,p_max,p_marg".split(",")) - df = df.sort_values(["pair", "p_marg", "exch", "cid"]) - df = df.set_index(["pair", "exch", "cid"]) - if result == self.PR_DF: - return df - raise ValueError(f"unknown result type {result}") - diff --git a/fastlane_bot/tools/arbgraphs.py b/fastlane_bot/tools/arbgraphs.py deleted file mode 100644 index cd921cc03..000000000 --- a/fastlane_bot/tools/arbgraphs.py +++ /dev/null @@ -1,2239 +0,0 @@ -""" -objects for encapsulating arbitrage-related graphs - ---- -(c) Copyright Bprotocol foundation 2023. -Licensed under MIT - -NOTE: this class is not part of the API of the Carbon protocol, and you must expect breaking -changes even in minor version updates. Use at your own risk. -""" -__VERSION__ = "2.2" -__DATE__ = "09/May/2023" - -from dataclasses import dataclass, field, asdict, astuple, InitVar -from .simplepair import SimplePair as Pair -import networkx as nx -import numpy as np -import matplotlib.pyplot as plt -import pandas as pd -import math - -EPS = 1e-9 - - -class _DCBase: - """base class for all data classes, adding some useful methods""" - - def asdict(self, *, exclude=None, include=None, dct=None): - """ - converts this object to a dictionary - - :include: comprehensive list of fields to include in the dataframe (default: all fields) - :exclude: list of fields to exclude from the dataframe (applied AFTER include) - :dct: dict used instead of contents of the dataclass (useful for subclasses - that want to add additional fields to the dict) - """ - if dct is None: - dct = asdict(self) - if not include is None: - dct = {k: dct[k] for k in include} - if not exclude is None: - dct = {k: dct[k] for k in dct if not k in exclude} - return dct - - def astuple(self, **kwargs): - """converts this object to a tuple (parameters are passed to asdict)""" - return tuple(self.asdict(**kwargs).values()) - - def asdf(self, *, index=None, **kwargs): - """ - converts this object to a dataframe (kwargs are passed to asdict) - - :index: the index of the dataframe (default: None) - """ - dct = self.asdict(**kwargs) - try: - df = pd.DataFrame([dct]) - if not index is None: - df.set_index(index, inplace=True) - return df - except Exception as e: - return f"ERROR: {e}" - - @classmethod - def l2df(cls, lst, **kwargs): - """ - converts an iterable of dataclass objects to a dataframe - - :kwargs: passed to the asdf method of each object in the list - :returns: a dataframe, or an error message if the conversion fails - """ - try: - return pd.concat([x.asdf(**kwargs) for x in lst]) - except Exception as e: - return f"ERROR: {e}" - - -@dataclass -class TrackedStateFloat(_DCBase): - """ - represents a single tracked float field in a (typical dataclass) record - - USAGE - - .. code-block:: python - - @ag.dataclass - class MyState(): - myval_: ag.TrackedStateFloat = ag.field(default_factory=ag.TrackedStateFloat, init=False) - myval: ag.InitVar=None - - def __post_init__(self, myval=0): - self.myval = myval - - @property - def myval(self): - return self.myval_.value - - @myval.setter - def myval(self, value): - self.myval_.set(value) - ... - mystate = MyState(10) - assert mystate.myval == 10 - mystate.myval_.incr(5) - assert mystate.myval == 15 - mystate.myval_.incr(-4) - assert mystate.myval == 11 - mystate.myval = 20 - assert mystate.myval == 20 - mystate.myval_.set(30) - assert mystate.myval == 30 - """ - - value: float = field(default=None, init=False) - history: list = field(default_factory=list, repr=False, init=False) - inital_value: InitVar = None - - def __post_init__(self, inital_value=None): - if inital_value is None: - inital_value = 0 - self.reset(inital_value, clear_history=True) - - def reset(self, value=None, clear_history=True): - """ - sets value of the field, typically clearing history; if value is None, only clears history; returns self - """ - if clear_history: - self.history = [] - if not value is None: - self.value = value - self.history.append(self.value) - return self - - def set(self, value): - """ - sets value of the field, typically clearing history; if value is None, only clears history; returns self - """ - return self.reset(value, clear_history=False) - - def __str__(self): - return f"{self.value}" - - -@dataclass -class Node(_DCBase): - """ - an arbitrage graph node, representing a token - """ - - tkn: str = field(default=None) - ix: int = field(default=None) - - @dataclass - class State(_DCBase): - amount_: TrackedStateFloat = field( - default_factory=TrackedStateFloat, init=False - ) - amount: InitVar = None - - @property - def amount(self): - return self.amount_.value - - @amount.setter - def amount(self, value): - self.amount_.set(value) - - def __post_init__(self, amount=None): - self.reset_state(amount) - - def reset_state(self, amount=None): - """ - reset the state of the node - """ - if amount is None: - amount = 0 - self._state = self.State(amount=amount) - - @property - def tkn_p(self): - """ - "pretty" version of the token name (removes the index) - """ - return self.tkn.split("(")[0] - - @property - def state(self): - return self._state - - def __eq__(self, other): - return self is other - - def set_ix(self, ix): - """ - set the index of the node - """ - self.ix = ix - - @classmethod - def create_node_list(cls, tkn_list): - """ - create a list of nodes from a list or comma separated string of tokens - """ - if isinstance(tkn_list, str): - tkn_list = tkn_list.split(",") - tkn_list = [s.strip() for s in tkn_list] - return tuple(cls(tkn, ix=ix) for ix, tkn in enumerate(tkn_list)) - - def __str__(self): - return f"{self.tkn}({self.ix})" - - def __repr__(self): - return self.__str__() - - -create_node_list = Node.create_node_list - - -@dataclass -class Amount(_DCBase): - """ - represents an amount of a given token, the latter represented by a Node - """ - - amount: float - node: Node - - @property - def tkn(self): - """ - alias for node - """ - return self.node - - def __str__(self): - return f"{self.amount} {self.node.tkn}" - - def __add__(self, other): - if not isinstance(other, Amount): - raise ValueError(f"can only add Amount to Amount") - if self.node != other.node: - raise ValueError(f"can only add Amounts of same node") - return Amount(self.amount + other.amount, self.node) - - def __sub__(self, other): - if not isinstance(other, Amount): - raise ValueError(f"can only subtract Amount from Amount") - if self.node != other.node: - raise ValueError(f"can only subtract Amounts of same node") - return Amount(self.amount - other.amount, self.node) - - def __mul__(self, other): - if isinstance(other, Amount): - raise ValueError(f"can only multiply Amount by scalar") - return Amount(self.amount * other, self.node) - - def __truediv__(self, other): - if isinstance(other, Amount): - if self.node != other.node: - raise ValueError(f"can only divide Amounts of same node") - return self.amount / other.amount - - return Amount(self.amount / other, self.node) - - def __neg__(self): - return Amount(-self.amount, self.node) - - def __pos__(self): - return Amount(self.amount, self.node) - - def __abs__(self): - return Amount(abs(self.amount), self.node) - - def __lt__(self, other): - if not isinstance(other, Amount): - raise ValueError(f"can only compare Amount to Amount") - if self.node != other.node: - raise ValueError(f"can only compare Amounts of same node") - return self.amount < other.amount - - def __le__(self, other): - if not isinstance(other, Amount): - raise ValueError(f"can only compare Amount to Amount") - if self.node != other.node: - raise ValueError(f"can only compare Amounts of same node") - return self.amount <= other.amount - - def __gt__(self, other): - if not isinstance(other, Amount): - raise ValueError(f"can only compare Amount to Amount") - if self.node != other.node: - raise ValueError(f"can only compare Amounts of same node") - return self.amount > other.amount - - def __ge__(self, other): - if not isinstance(other, Amount): - raise ValueError(f"can only compare Amount to Amount") - if self.node != other.node: - raise ValueError(f"can only compare Amounts of same node") - return self.amount >= other.amount - - def __copy__(self): - return Amount(self.amount, self.node) - - def __deepcopy__(self, memo): - return Amount(self.amount, self.node) - - def __format__(self, format_spec): - return f"{self.amount:{format_spec}} {self.node.tkn}" - - def __round__(self, ndigits=None): - return Amount(round(self.amount, ndigits), self.node) - - def __floor__(self): - return Amount(math.floor(self.amount), self.node) - - def __ceil__(self): - return Amount(math.ceil(self.amount), self.node) - - def __trunc__(self): - return Amount(math.trunc(self.amount), self.node) - - def __radd__(self, other): - return self + other - - def __rsub__(self, other): - return -self + other - - def __rmul__(self, other): - return self * other - - -FORMATTER = dict( - # float=lambda x: f"{x:.4f}", - # int=lambda x: f"{x:.0f}", - # str=lambda x: x, - # bool=lambda x: str(x), - # Amount=lambda x: f"{x.amount:.4f} {x.node.tkn}", - # Node=lambda x: f"{x.tkn}({x.ix})", - # Edge=lambda x: f"{x.node_in.tkn}({x.node_in.ix}) -> {x.node_out.tkn}({x.node_out.ix})", - # Path=lambda x: f"{' -> '.join([str(e) for e in x])}", - int=lambda x: f"{x:.0f}", # no decimals - std=lambda x: f"{x:,.2f}", # 2 decimals, commas - std0=lambda x: "" if x == 0 else f"{x:,.2f}", # ditto, and blank if 0 -) - - -@dataclass -class Edge(_DCBase): - """ - an arbitrage graph edge, representing a possible trade - - :node_in: the input node, representing the token going into the AMM - :amount_in: the amount of the token going in (positive) - :node_out: the output node, representing the token coming out of the AMM - :amount_out: the amount of the token coming out (positive) - :ix: the index of the edge (in the graph) - :inverse: whether price quote of the edge is inverse - :uid: a unique identifier for the edge (optional; only use as kwarg) - """ - - node_in: Node - amount_in: float - node_out: Node - amount_out: float - ix: int = field(default=None) - inverse: bool = field(default=False) - uid: any = None - - def _replace_nodes(self, lookupdict): - """ - replace nodes in edge with new nodes from lookupdict - - used by duplicate graph to relink the nodes; should not be used otherwise - """ - self.node_in = lookupdict[self.node_in.tkn] - self.node_out = lookupdict[self.node_out.tkn] - return self - - EDGE_CONNECTION = "connection" - EDGE_AMOUNT = "amount" - - @property - def edgetype(self): - """ - the type of edge (EDGE_CONNECTION = connection only, EDGE_AMOUNT = plus amount) - """ - if self.amount_in < 0: - return self.EDGE_CONNECTION - return self.EDGE_AMOUNT - - @property - def is_amounttype(self): - """ - whether the edge is an amount edge - """ - return self.edgetype == self.EDGE_AMOUNT - - def assert_edgetype(self, edgetype=EDGE_AMOUNT, msg=""): - """ - assert that the edge is a connection edge - """ - if not self.edgetype == edgetype: - raise ValueError(f"Edge must be of type {edgetype} [{self.edgetype}]", msg) - - @classmethod - def connection_edge( - cls, - node_in, - node_out, - *, - price=None, - inverse=False, - weight=None, - ix=None, - uid=None, - ): - """ - alternative constructor for a connection edge (most arguments identical to main constructor) - - :price: the price of the connection, with the quotation being determined by inverse - :weight: the weight of the connection; the weight is not used for capacity, but when - calculating the price of combined edges - :inverse: False: price = amount_out / amount_in, True: price = amount_in / amount_out - """ - if price is None: - price = 1 - if inverse: - if price != 0: - price = 1 / price - - if weight is None: - weight = 1 - assert weight > 0, "weight must be positive" - return cls( - node_in=node_in, - amount_in=-weight, - node_out=node_out, - amount_out=-price * weight, - ix=ix, - inverse=inverse, - uid=uid, - ) - - @dataclass - class State(_DCBase): - """ - the state of an edge - """ - - amount_in_remaining: float - - @property - def is_empty(self): - """ - whether the edge is empty - """ - return abs(self.amount_in_remaining) <= EPS - - def reset_state(self, amount_in_remaining=None): - """ - reset the state of the edge - """ - if not self.is_amounttype: - return - if amount_in_remaining is None: - amount_in_remaining = self.amount_in - self._state = self.State(amount_in_remaining=amount_in_remaining) - - @property - def state(self): - try: - return self._state - except: - raise ValueError( - "edge state not initialized (only available on Amount edges))" - ) - - @property - def has_capacity(self): - """ - whether the edge has still capacity left - """ - return self.state.amount_in_remaining > EPS - - def __post_init__(self): - if not isinstance(self.node_in, Node): - raise ValueError(f"node_in must be a Node, not {self.node_in.__class__}") - if not isinstance(self.node_out, Node): - raise ValueError(f"node_out must be a Node, not {self.node_out.__class__}") - self.pairo = Pair((self.node_in.tkn, self.node_out.tkn)) - self.reset_state() - - def __eq__(self, other): - return self is other - - def __add__(self, other): - """ - add two edges; both edges must have the same input and output nodes - """ - if other == 0: - return self.duplicate() # required for sum() to work - if not isinstance(other, self.__class__): - raise ValueError(f"cannot add {self.__class__} and {other.__class__}") - assert ( - self.edgetype == other.edgetype - ), "arithmetic operations only allowed on edges of the same type" - if not (self.node_out is other.node_out and self.node_in is other.node_in): - raise ValueError(f"nodes do not match", self, other) - return self.__class__( - self.node_in, - self.amount_in + other.amount_in, - self.node_out, - self.amount_out + other.amount_out, - inverse=self.inverse, - ) - - def __radd__(self, other): - """ - reverse add two edges; both edges must have the same input and output nodes - """ - return self.__add__(other) - - def __mul__(self, other): - """ - multiply an edge by a scalar - """ - assert other > 0, f"cannot multiply edge by negative number or zero {other}" - # self.assert_edgetype(self.EDGE_AMOUNT, "arithmetic operations only allowed on amount edges") - if not isinstance(other, (int, float)): - raise ValueError(f"cannot multiply {self.__class__} and {other.__class__}") - return self.__class__( - self.node_in, - self.amount_in * other, - self.node_out, - self.amount_out * other, - inverse=self.inverse, - ) - - def __rmul__(self, other): - """ - reverse multiply an edge by a scalar - """ - return self.__mul__(other) - - def duplicate(self): - """ - duplicate an edge with all values the same except ix - """ - return self.__class__( - self.node_in, - self.amount_in, - self.node_out, - self.amount_out, - None, - self.inverse, - ) - - def reverse(self): - """ - duplicates an edge but reverses it - """ - return self.__class__( - self.node_out, - self.amount_out, - self.node_in, - self.amount_in, - None, - not self.inverse, - ) - - R = reverse - - @property - def tkn_in(self): - """ - get the token name of the input node - """ - return self.node_in.tkn - - @property - def tkn_out(self): - """ - get the token name of the output node - """ - return self.node_out.tkn - - def pair(self, inverse=None): - """ - get the slashpair of tokens represented by the edge - - :inverse: if False, base token = out, quote token = in, otherwise reverse - default is the value of self.inverse - """ - if inverse is None: - inverse = self.inverse - return ( - f"{self.tkn_in}/{self.tkn_out}" - if not inverse - else f"{self.tkn_out}/{self.tkn_in}" - ) - - def convention(self, inverse=None): - """ - get the price convention of tokens represented by the edge - - :inverse: if False, base token = out, quote token = in, otherwise reverse - default is the value of self.inverse - """ - if inverse is None: - inverse = self.inverse - return ( - f"{self.tkn_in} per {self.tkn_out}" - if inverse - else f"{self.tkn_out} per {self.tkn_in}" - ) - - def convention_outperin(self): - """ - get the price convention of tokens represented by the edge, in the convention of out per in - """ - return self.convention(inverse=False) - - def price(self, inverse=None): - """ - get the price of the edge, in the right convention - - :inverse: if == False, price = amount_out / amount_in, otherwise reverse - default is the value of self.inverse - """ - if inverse is None: - inverse = self.inverse - return ( - self.amount_in / self.amount_out - if inverse - else self.amount_out / self.amount_in - ) - - p = price - - @property - def price_outperin(self): - """ - get the price of the edge, in the convention of out per in - """ - return self.price(inverse=False) - - p_outperin = price_outperin - - def set_ix(self, ix): - """ - set the index of the edge - """ - self.ix = ix - - def transport(self, amount_in=None, *, record=False, raiseonerror=True): - """ - transport an amount of the input token through the edge, yielding output token - - :amount: amount of input token (as float or Amount object); - if None: full edge capacity (or 1 if not amounttype) - :record: if True, record the transaction in the edge's and node's state - :raiseonerror: if True, raise an error if the amount is too large - :returns: amount of output token (as Amount object) - """ - if record and not self.is_amounttype: - raise ValueError(f"cannot record transaction on non-amounttype edge {self}") - - if amount_in is None: - amount_in = self.amount_in if self.is_amounttype else 1 - - if isinstance(amount_in, Amount): - assert ( - amount_in.tkn is self.node_in - ), f"amount token {amount_in.tkn} does not match input node {self.node_in}" - amount_in = amount_in.amount - - if self.is_amounttype: - # those checks only make sense for amounttype edges - assert ( - amount_in <= self.amount_in + EPS - ), f"amount {amount_in} exceeds edge capacity {self.amount_in}" - assert amount_in >= -EPS, f"amount {amount_in} must be non-negative" - amount_out = amount_in * self.amount_out / self.amount_in - - if record: - self.state.amount_in_remaining -= amount_in - if self.state.amount_in_remaining < -EPS: - if raiseonerror: - raise ValueError( - f"amount {amount_in} exceeds remaining edge capacity {self.state.amount_in_remaining}" - ) - self.node_out.state.amount += amount_out - self.node_in.state.amount -= amount_in - if self.node_in.state.amount < -EPS: - if raiseonerror: - raise ValueError( - f"amount {amount_in} exceeds node capacity {self.node_in.state.amount}" - ) - return Amount(amount_out, self.node_out) - - def __str__(self): - arrow = "-->" if self.ix is None else f"--({self.ix})->" - return ( - f"{self.amount_in} {self.node_in} {arrow} {self.amount_out} {self.node_out}" - ) - - @property - def label(self): - if self.is_amounttype: - return ( - f"{self.amount_in} {self.node_in} --> {self.amount_out} {self.node_out}" - ) - else: - return f"{self.price_outperin} [{self.ix}]" - - -@dataclass -class Path(_DCBase): - """ - a path of nodes that can be iterated over (use Cycles for closed paths) - - :data: list of nodes; the nodes can be any type that allows for equality comparison - :uid: an optional unique identifier for the path - """ - - data: list - uid: any = None - graph: any = field(default=None, repr=False, compare=False) - - def __post_init__(self): - if not self.graph is None: - assert isinstance( - self.graph, ArbGraph - ), f"graph must be an ArbGraph, not {type(self.graph)}" - - def __str__(self): - try: - return f"path [{self.uid}]: " + "->".join([f"{d.tkn}" for d in self.data]) - except: - return f"path [{self.uid}]: " + "->".join([f" {d} " for d in self.data]) - - def items(self): - """ - iterate over the cycle - """ - return iter(self.data) - - def pairs(self): - """ - iterate over the cycle, yielding pairs of adjacent items - """ - items1 = self.items() - items2 = self.items() - next(items2) - return zip(items1, items2) - - def pairs_s(self): - """ - runs pairs and returns the results as slashpairs - """ - return [f"{p[0].tkn}/{p[1].tkn}" for p in self.pairs()] - - -@dataclass -class Cycle(_DCBase): - """ - a cycle of nodes, allowing arbitrary entry point for iteration - - :data: list of nodes; the nodes can be any type that allows for equality comparison - :uid: an optional unique identifier for the cycle - - USAGE - - .. code-block:: python - - C = Cycle([1,2,3,4,5]) - for c in C.items(start_ix=2): - print(c) - # prints 3, 4, 5, 1, 2, 3 - """ - - data: list - uid: any = None - graph: any = field(default=None, repr=False, compare=False) - - def __post_init__(self): - if not self.graph is None: - assert isinstance( - self.graph, ArbGraph - ), f"graph must be an ArbGraph, not {type(self.graph)}" - - def __str__(self): - try: - return ( - f"cycle [{self.uid}]: " - + "->".join([f"{d.tkn}" for d in self.data]) - + "->..." - ) - except: - return ( - f"cycle [{self.uid}]: " - + "->".join([f" {d} " for d in self.data]) - + "->..." - ) - - class CycleIterator: - def __init__(self, cycle, start_ix=0): - self.cycle = cycle - self.start_ix = start_ix - self.ix = start_ix - 1 - self._len = len(cycle) - self._counter = self._len + 2 - - def __iter__(self): - return self - - def __next__(self): - self._counter -= 1 - if self._counter == 0: - raise StopIteration - self.ix = (self.ix + 1) % self._len - return self.cycle[self.ix] - - def __len__(self): - return len(self.data) - - @classmethod - def byid(cls, cycle_list, uid): - """ - return the cycle in cycle_list with uid - """ - for c in cycle_list: - if c.uid == uid: - return c - return None - - def is_subcycle_of(self, other): - """ - returns True iff self is a subcycle of other - """ - if len(self) > len(other): - return False - try: - supercycle = other.items(start_val=self.data[0]) - except: - return False - - subcycle = self.items() - for subc in subcycle: - while True: - try: - superc = next(supercycle) - except StopIteration: - return False - if superc == subc: - break - return True - - def filter_subcycles(self, cycle_list): - """ - filter out subcycles of self from cycle_list - """ - if isinstance(cycle_list, Cycle): - cycle_list = [cycle_list] - return tuple(c for c in cycle_list if c.is_subcycle_of(self)) - - def items(self, start_ix=None, start_val=None): - """ - iterate over the cycle - - :start_ix: start index (1) - :start_val: start value (1) - - NOTE 1: only one of ``start_ix`` and ``start_val`` can be specified - """ - if not start_val is None: - if not start_ix is None: - raise ValueError( - "only one of start_ix and start_val can be specified", - start_ix, - start_val, - ) - start_ix = self.data.index(start_val) - if start_ix is None: - start_ix = 0 - return self.CycleIterator(self.data, start_ix) - - def pairs(self, start_ix=None, start_val=None): - """ - iterate over the cycle, yielding pairs of adjacent items - - :start_ix: start index* - :start_val: start value* - - * only one of start_ix and start_val can be specified - """ - items1 = self.items(start_ix=start_ix, start_val=start_val) - items2 = self.items(start_ix=start_ix, start_val=start_val) - next(items2) - return zip(items1, items2) - - def pairs_s(self, start_ix=None, start_val=None): - """ - runs pairs and returns the results as slashpairs - """ - return [f"{p[0].tkn}/{p[1].tkn}" for p in self.pairs()] - - def run_arbitrage_cycle(self, token=None, **params): - """ - convenience method to call run_arbitrage_cycle on self.graph - - see help(ArbGraph.run_arbitrage_cycle) for details - """ - assert not self.graph is None, "graph must be set to run a cycle" - return self.graph.run_arbitrage_cycle(self, token=token, **params) - - run = run_arbitrage_cycle - - -@dataclass -class ArbGraph(_DCBase): - """ - a container object for Nodes and Edges, representing a graph - """ - - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - nodes: list = field(default_factory=list) - edges: list = field(default_factory=list) - - def __post_init__(self): - """ - post-initialization - """ - for ix, node in enumerate(self.nodes): - node.set_ix(ix) - self._node_by_tkn = {node.tkn: node for node in self.nodes} - - for ix, edge in enumerate(self.edges): - edge.set_ix(ix) - - edgetype = set(e.edgetype for e in self.edges) - if len(edgetype) > 1: - raise ValueError("Edges must all be of the same type") - - @classmethod - def from_ccdxdy(cls, cc, dxv, dyv, *, ignorezero=True, verbose=False): - """ - alternative constructor: from curves and trade vectors dxv, dyv - - :cc: a CurveContainer object - :dxv: a vector of trade amounts in x (eg dx.values after an optimisation) - :dyv: ditto but for y amounts - """ - AG = cls() - for cpc, dx, dy in zip(cc, dxv, dyv): - if verbose: - print("[from_ccdxdy]", dx, cpc.tknx, dy, cpc.tkny, cpc.cid) - if ignorezero and dx == 0 and dy == 0: - continue - AG.add_edge_dxdy(cpc.tknx, dx, cpc.tkny, dy, uid=cpc.cid) - return AG - - @classmethod - def from_r(cls, r, *, ignorezero=True, verbose=False): - """ - alternative constructor: from an Optimizer result object - - :r: Optimizer result object - """ - return cls.from_ccdxdy( - r.curves, r.dxvalues, r.dyvalues, ignorezero=ignorezero, verbose=False - ) - - @classmethod - def from_cc(cls, cc): - """ - alternative constructor: from a CurveContainer object alone - - :cc: a CurveContainer object - """ - AG = cls() - return AG.add_edges_cpc(cc) - - def __len__(self): - return len(self.edges) - - def len(self): - """returns a tuple with number of edges and nodes (nedges, nnodes)""" - return (len(self.edges), len(self.nodes)) - - @property - def _(self): - """returns None (to stop chaining and to clean Jupyter output)""" - return None - - EDGE_CONNECTION = Edge.EDGE_CONNECTION - EDGE_AMOUNT = Edge.EDGE_AMOUNT - - @property - def edgetype(self): - """edgetype of the graph (all edges are of the same type; None if no edges)""" - if len(self.edges) == 0: - return None - return self.edges[0].edgetype - - @property - def is_amounttype(self): - """True if the graph is an amount-type graph""" - return self.edgetype == self.EDGE_AMOUNT - - def duplicate(self, consolidate=True): - """ - creates a duplicate of the current object, duplicating all nodes and edges - - :consolidate: if True, multiple edges between the same nodes are - consolidated into a single edge - - Note: there is an issue with this consolidation process in that when routing through - edges, people would go lowest price first, not pro rata. This however is a problem - that cannot really be solved here unless we expand the data structure of the edges - at which stage we might as well just not consolidate them in the first place. - """ - nodes = {node.tkn: Node(node.tkn) for node in self.nodes} - if not consolidate: - edges = ( - Edge( - edge.node_in, - edge.amount_in, - edge.node_out, - edge.amount_out, - edge.inverse, - uid=edge.uid, - ) - for edge in self.edges - ) - else: - edges = ( - self.filter_edges(nin, nout) - for nin in self.nodes - for nout in self.nodes - if nin != nout - ) - edges = (sum(r) for r in edges) - edges = (r for r in edges if not r == 0) - - edges = (r._replace_nodes(nodes) for r in edges) - edges = tuple(edges) - return self.__class__(nodes=nodes.values(), edges=edges) - - def reset_state(self): - """ - resets the state of all nodes and edges in the graph (returns self) - """ - for node in self.nodes: - node.reset_state() - for edge in self.edges: - edge.reset_state() - return self - - def has_capacity(self, tkn_in=None, tkn_out=None): - """ - returns True iff any of the edges still have a capacity - - :tkn_in, tkn_out: can be str, Node, or None; if None, None, all edges - """ - node_in = self.node_by_tkn(tkn_in) - node_out = self.node_by_tkn(tkn_out) - edges = self.filter_edges(node_in, node_out) - return any(edge.has_capacity for edge in edges) - - @dataclass - class State(_DCBase): - nodes: tuple - edges: tuple - - @property - def state(self): - """ - returns State object consolidating the state objects of nodes and edges - """ - return self.State( - nodes=tuple(node.state for node in self.nodes), - edges=tuple(edge.state for edge in self.edges), - ) - - def add_node_obj(self, node): - """ - add a node object (of type Node) to the graph; returns self - """ - node.set_ix(len(self.nodes)) - self.nodes.append(node) - self._node_by_tkn[node.tkn] = node - return self - - def add_node(self, tkn): - """ - add a node to the graph, returns self - """ - node = Node(tkn) - self.add_node_obj(node) - return self - - def add_edge_obj(self, edge): - """ - add an edge object (of type Edge) to the graph; returns self - """ - if edge.edgetype != self.edgetype and not self.edgetype is None: - raise ValueError( - "edge type does not match graph type", edge.edgetype, self.edgetype - ) - edge.set_ix(len(self.edges)) - self.edges.append(edge) - return self - - def add_edge( - self, tkn_in, amount_in, tkn_out, amount_out, *, inverse=False, uid=None - ): - """ - add an amount-type edge to the graph - - :tkn_in: token name of the input node (as str) - :amount_in: amount of input token (as float) - :tkn_out: token name of the output node (as str) - :amount_out: amount of output token (as float) - :inverse: if True, use reverse quote convention - :uid: unique id of the edge - - NOTE: amount-type edges are edges that have a specific amount of input and output tokens, - ie they correspond to a price AND a volume; connection type edges only have a price - """ - assert amount_in > 0, f"amount_in must be positive {amount_in}" - assert amount_out > 0, f"amount_out must be positive {amount_out}" - edge = Edge( - self.node_by_tkn(tkn_in, create=True), - amount_in, - self.node_by_tkn(tkn_out, create=True), - amount_out, - inverse=inverse, - uid=uid, - ) - self.add_edge_obj(edge) - return self - - EPSDXDY = 1e-8 - - def add_edge_dxdy(self, tknx, dx, tkny, dy, *, inverse=False, uid=None): - """ - like add_edge, but in and out is determined by the sign of the amounts - - :tknx: token name of the input node (as str) - :dx: amount of input token (as float; in=pos, out=neg) - :tkny: token name of the output node (as str) - :dy: amount of output token (as float; in=pos, out=neg) - :inverse: if True, use reverse quote convention - :uid: unique id of the edge - """ - if not dx * dy < 0: - - msg = f"dx and dy must have opposite signs [dx={dx} dy={dy} dx*dy={dx*dy}]" - if dx * dy > self.EPSDXDY: - raise ValueError(msg) - else: - print(f"{msg}; not added (EPS={self.EPSDXDY}))") - return self - - if dx > 0: - amount_in = dx - tkn_in = tknx - amount_out = -dy - tkn_out = tkny - else: - amount_in = dy - tkn_in = tkny - amount_out = -dx - tkn_out = tknx - # print("add_edge_dxdy in/out dx", amount_in, tkn_in, amount_out, tkn_out, dx) - self.add_edge(tkn_in, amount_in, tkn_out, amount_out, inverse=inverse, uid=uid) - return self - - def add_edge_connectiontype( - self, - tkn_in, - tkn_out, - *, - price=None, - inverse=False, - price_outperin=None, - weight=None, - symmetric=True, - uid=None, - ): - """ - add a connection-type edge to the graph - - :tkn_in: token name of the input node (as str) - :tkn_out: token name of the output node (as str) - :price: price of the connection (as float), according to convention - :inverse: if True, use reverse quote convention - :price_outperin: price in outperin convention (alternative to price) - :weight: weight of the connection (as float; default is 1) - :symmetric: if True, add the inverse edge as well - :uid: unique id of the edge - :returns: self - - NOTE1: amount-type edges are edges that have a specific amount of input and output tokens, - ie they correspond to a price AND a volume; connection type edges only have a price - - NOTE2: the weight of the connection is mostly useful to determine the prices of combined - edges; essentially, the price of the combined edge is the weighted average of the - prices of the individual edges - """ - if price_outperin is not None: - assert price is None, "cannot specify both price and price_outperin" - assert ( - not inverse - ), "inverse must be False (=default) if price_outperin is specified" - price = price_outperin - elif price is None: - price = 1 - - if weight is None: - weight = 1 - assert weight > 0, "weight must be positive {weight}" - - edge = Edge.connection_edge( - node_in=self.node_by_tkn(tkn_in, create=True), - node_out=self.node_by_tkn(tkn_out, create=True), - price=price, - weight=weight, - inverse=inverse, - uid=f"{uid}", - ) - self.add_edge_obj(edge) - if not symmetric: - return self - edge = Edge.connection_edge( - node_out=self.node_by_tkn(tkn_in, create=True), - node_in=self.node_by_tkn(tkn_out, create=True), - price=price, - weight=weight, - inverse=not inverse, - uid=f"{uid}-r", - ) - self.add_edge_obj(edge) - return self - - add_edge_ct = add_edge_connectiontype - - def add_edges_cpc(self, curves, uid=None): - """ - add an edge from a CPC curve object - - :curves: specifies one or multiple curves, depending on the type: - :CPC: a single curve of type ConstantProductCurve is added* - :iterable: multiple curves of type CPC are added (1) - :CPCContainer: all curves in the container are added (1) - :uid: unique id of the edge; should only be provided for singles curves - - NOTE1: specifically the way the algo works AT THEM MOMENT (but don't rely on this), - if the object has a curves method, it is assumed to be an iterable of CPCs; - if the object is iterable, it is assumed to be an iterable of CPCs; otherwise - it is assumbed to be a single CPC - """ - try: - try: - # print("TRYING CONTAINER") - self.add_edges_cpc(curves=curves.curves, uid=uid) - return self - except AttributeError as e: - if not str(e).endswith("has no attribute 'curves'"): - raise e - # print(f"CONTAINER FAILED {e}") - - # print("TRYING ITERABLE") - for c in curves: - # print("ITERABLE LOOP cid=", c.cid) - self.add_edges_cpc(curves=c, uid=uid) - # print("ITERABLE DONE") - return self - except TypeError as e: - # print(f"ITERABLE FAILED {e}") - if not str(e).endswith("object is not iterable"): - raise e - curve = curves - self.add_edge_connectiontype( - tkn_in=curve.tknb, - tkn_out=curve.tknq, - price_outperin=curve.p, - symmetric=True, - uid=uid if not uid is None else curve.cid, - ) - return self - - def node_by_tkn(self, tkn, create=False): - """ - get a node by its token name - - :tkn: token name (as str) or a Node object (if None returns None) - :create: if True, create a new node if it doesn't exist - """ - if tkn is None: - return None - if isinstance(tkn, Node): - node = self.node_by_tkn(tkn.tkn, create=False) - assert ( - tkn is node - ), f"the tkn provided {tkn} does not match the node found {node}" - return node - try: - return self._node_by_tkn[tkn] - except KeyError: - if create: - self.add_node(tkn) - return self._node_by_tkn[tkn] - else: - raise KeyError(f"node with token name {tkn} not found") - - n = node_by_tkn - - def node_by_ix(self, ix): - """ - get a node by its index - """ - return self.nodes[ix] - - node = node_by_ix - - def edge_by_ix(self, ix): - """ - get an edge by its index - """ - return self.edges[ix] - - edge = edge_by_ix - - def filter_edges( - self, node_in=None, node_out=None, *, node=None, bothways=None, pair=None - ): - """ - gets a list of edges filtered by node_in and node_out - - :node_in: input node (as Node object or str) - :node_out: output node (as Node object or str) - :node: input or output node (as Node object or str) - :bothways: if True, also include edges with node_in and node_out swapped - defaults to False if no pair is given, True otherwise - :pair: if True, use pair instead of the nodes - """ - if not pair is None: - assert ( - node_in is None and node_out is None - ), "cannot specify both pair and node_in or node_out" - assert node is None, "cannot specify both pair and node" - node_in, node_out = pair.split("/") - if bothways is None: - bothways = False if pair is None else True - if bothways: - l1 = self.filter_edges( - node_in=node_in, node_out=node_out, node=node, bothways=False - ) - l2 = self.filter_edges( - node_in=node_out, node_out=node_in, node=node, bothways=False - ) - return l1 + l2 - if isinstance(node_in, str): - node_in = self.node_by_tkn(node_in) - if isinstance(node_out, str): - node_out = self.node_by_tkn(node_out) - if isinstance(node, str): - node = self.node_by_tkn(node) - if node is not None: - assert ( - node_in is None and node_out is None - ), "cannot specify both node and node_in or node_out" - assert node_in is None or isinstance( - node_in, Node - ), f"node_in must be a Node object or None, not {node_in}" - assert node_out is None or isinstance( - node_out, Node - ), f"node_out must be a Node object or None, not {node_out}" - assert node is None or isinstance( - node, Node - ), f"node must be a Node object or None, not {node}" - if not node is None: - return [ - edge - for edge in self.edges - if edge.node_in == node or edge.node_out == node - ] - elif node_in is None and node_out is None: - return self.edges - elif node_in is None: - return [edge for edge in self.edges if edge.node_out == node_out] - elif node_out is None: - return [edge for edge in self.edges if edge.node_in == node_in] - else: - return [ - edge - for edge in self.edges - if edge.node_in == node_in and edge.node_out == node_out - ] - - fe = filter_edges - - def fep(self, pair, bothways=None): - """alias for filter_edges(pair=pair, bothways=bothways)""" - return self.filter_edges(pair=pair, bothways=bothways) - - def as_graph(self, *, directed=True, weighted=False): - """ - convert the graph to a networkx graph - - :directed: if True, return a directed graph, otherwise undirected - :weighted: if True, return a weighted graph, otherwise unweighted - """ - assert weighted == False, "weighted graphs not yet implemented" - - if directed: - G = nx.DiGraph() - else: - G = nx.Graph() - for node in self.nodes: - G.add_node(node.ix, label=str(node), tkn=node.tkn) - for edge in self.edges: - if weighted: - # print("adding weighted edge", edge.node_in.ix, edge.node_out.ix, edge.price()) - G.add_edge( - edge.node_in.ix, edge.node_out.ix, weight="bla", label=str(edge) - ) - else: - # print("adding edge", edge.node_in.ix, edge.node_out.ix) - G.add_edge(edge.node_in.ix, edge.node_out.ix, label=edge.label) - return G - - @property - def G(self): - """alias for as_graph(directed=True, weighted=False)""" - return self.as_graph(directed=True, weighted=False) - - def Laplacian(self, directed=False, weighted=False, include_eigenvalues=True): - """ - computes the graph Laplacian (and its eigenvalues if requested) - - :returns: the graph Laplacian L, or tuple (L, eigenvalues) - - NOTE: L is a scipy sparse matrix; use toarray() to expand to a numpy array - """ - G = self.as_graph(directed=directed, weighted=weighted) - L = nx.laplacian_matrix(G) - if not include_eigenvalues: - return L - eigenvalues = np.linalg.eigvals(L.toarray()) - return L, eigenvalues - - @property - def L(self): - """alias for Laplacian(directed=False, weighted=False, include_eigenvalues=False)""" - return self.Laplacian(directed=False, weighted=False, include_eigenvalues=False) - - def Adjacency(self, *, directed=True, weighted=False, include_eigenvalues=True): - """ - computes the graph adjacency matrix (and its eigenvalues if requested) - - :returns: the graph adjacency matrix A, or tuple (A, eigenvalues) - - Note: A is a scipy sparse matrix; use toarray() to expand to a numpy array - """ - G = self.as_graph(directed=directed, weighted=weighted) - A = nx.adjacency_matrix(G) - if not include_eigenvalues: - return A - eigenvalues = np.linalg.eigvals(A.toarray()) - return A, eigenvalues - - @property - def A(self): - """alias for Adjacency(directed=True, weighted=False, include_eigenvalues=False)""" - return self.Adjacency(directed=True, weighted=False, include_eigenvalues=False) - - def shortest_path(self, node_start, node_end): - """ - get the shortest path between two nodes - """ - G = self.as_graph(directed=True, weighted=False) - path = nx.shortest_path(G, node_start.ix, node_end.ix) - path = tuple(map(self.node_by_ix, path)) - path = Path(path) - return path - - def price(self, node_tknb, node_tknq, *, with_units=False): - """ - get the price (estimate) expressed in units of end per start [only on connection-type graphs] - """ - assert not self.is_amounttype, "cannot get price on amount-type graphs" - if node_tknb != node_tknq: - node_tknb = self.node_by_tkn(node_tknb) - node_tknq = self.node_by_tkn(node_tknq) - price = self.ptransport(self.shortest_path(node_tknb, node_tknq)).multiplier - else: - price = 1 - if with_units: - return ( - price, - f"{node_tknq.tkn} per {node_tknb.tkn} [{node_tknb.tkn}/{node_tknq.tkn}]", - ) - return price - - def pricetable(self, include=None, *, exclude=None, asdf=True): - """ - calculates a price table for all pairs of nodes in the graph* - - :include: nodes to include (default: all nodes) - :exclude: nodes to exclude (default: none); exclude beats include - :returns: a dict or pandas dataframe - - Note: this price table is calculated using the shortest paths in the graph; - if the graph is not arbitrage free then those prices will not be self consistent - this is a feature, not a bug, as this table allows to estimate the extent to - which this graph is arbitrage free - """ - if include is None: - include = self.nodes - # include = set(include) - if not exclude is None: - include = [n for n in include if not n in exclude] - # TODO: those should really be sets, but for some reason - # nodes are an unhashable type - - labels = [n.tkn for n in include] - data = [[self.price(nj, ni) for ni in include] for nj in include] - if asdf: - df = pd.DataFrame(data, columns=labels, index=labels) - df.index.name = "tknb" - return df - return dict(data=data, labels=labels) - - def cycles(self, *, asgenerator=False): - """ - get all cycles in the graph - """ - G = self.as_graph(directed=True, weighted=False) - cycles = nx.simple_cycles(G) - cycles = (list(map(self.node_by_ix, cycle)) for cycle in cycles) - cycles = (Cycle(cycle, graph=self, uid=uid) for uid, cycle in enumerate(cycles)) - if asgenerator: - return cycles - return tuple(cycles) - - @property - def is_weakly_connected(self): - """ - check if the graph is weakly connected - - Note: if the graph is weakly connected, then all the cycles in the graph are subcycles - of a single cycle (1). This is important because this means that that they can be more - easily aligned, which means that we can combined transactions of multiple cycles - into a single transaction. - - NOTE1: According to ChatGPT... - """ - G = self.as_graph(directed=True, weighted=False) - return nx.is_weakly_connected(G) - - DEGREE = None - INDEGREE = "INDEGREE" - OUTDEGREE = "OUTDEGREE" - - def degree(self, inout=DEGREE, as_matrix=False): - """ - get the degree of the nodes in the graph, possibly as a matrix - - :inout: None (= symmetric degree), or self.INDEGREE, self.OUTDEGREE - """ - if inout is self.DEGREE: - # degree = nx.degree(self.as_graph(directed=False)) - degree = self.as_graph(directed=False).degree() - elif inout is self.INDEGREE: - degree = self.as_graph(directed=True).in_degree() - elif inout is self.OUTDEGREE: - degree = self.as_graph(directed=True).out_degree() - else: - raise ValueError(f"invalid value for inout: {inout}") - - degree = dict(degree) - if not as_matrix: - return degree - matrix = np.diag([degree.get(node, 0) for node in G.nodes()]) - return matrix - - def in_degree(self, as_matrix=False): - """ - convenience function for self.degree(inout=self.INDEGREE) - """ - return self.degree(inout=self.INDEGREE, as_matrix=as_matrix) - - def out_degree(self, as_matrix=False): - """ - convenience function for self.degree(inout=self.OUTDEGREE) - """ - return self.degree(inout=self.OUTDEGREE, as_matrix=as_matrix) - - PLOT_DEFAULTS = { - "directed": True, - "labels": True, - "edge_labels": False, - "node_color": "lightblue", - "node_size": 200, - "show": True, - "font_size": 12, - "font_color": "k", - } - - def plot(self, **params): - """ - plot the graph - - :directed: if True (default), plot a directed graph, otherwise undirected - :labels: if True (default), plot node labels - :edge_labels: if True (default), plot edge labels - :node_color: node color (default: "lightblue") - :node_size: node size (default: 200) - :font_size: font size (default: 12) - :font_color: font color (default: "k") - :show: if True (default), show the plot - :rnone: if True, returns None, otherwise returns self - """ - - p = lambda name: params.get(name, self.PLOT_DEFAULTS.get(name)) - - G = self.as_graph(directed=p("directed")) - - pos = nx.kamada_kawai_layout(G) - # pos = nx.spring_layout(G) # works only in 2.6.3+ - nx.draw( - G, - pos, - with_labels=p("labels"), - labels=nx.get_node_attributes(G, "label"), - node_color=p("node_color"), - node_size=p("node_size"), - font_size=p("font_size"), - font_color=p("font_color"), - ) - - if p("edge_labels"): - edge_labels = nx.get_edge_attributes(G, "label") - nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels) - - if p("show"): - plt.show() - if p("rnone"): - return None - return self - - RUNARBCYCLE_DEFAULTS = { - "verbose": False, - "allow_any_token": True, - } - - @dataclass - class RunArbCycleResult(_DCBase): - cycle: Cycle = None - start_ix: int = None - token: str = None - profit: float = None - amount_in: float = None - amount_out: float = None - - def __str__(self): - return f"RACResult(profit: {self.profit:2.1f} [{self.token}], in: {self.amount_in:2.1f}, rpcs: {self.ppcs*100:.1f}%, ppcs: {self.ppcs:.1f}, len: {self.length}, uid: {self.cycle.uid})" - - def asdict(self, *, include_cycle=True, exclude=None, include=None): - dct = { - **asdict(self), - "tokens": self.tokens(), - "length": self.length, - "r": self.r, - "rpcs": self.rpcs, - "ppcs": self.ppcs, - "uid": self.cycle.uid, - } - if not include_cycle: - del dct["cycle"] - return super().asdict(dct=dct, exclude=exclude, include=include) - - def astuple(self, include_cycle=True): - return tuple(self.asdict(include_cycle).values()) - - def tokens(self): - return ", ".join(str(t.tkn) for t in self.cycle.data) - - @property - def length(self): - return len(self.cycle) - - @property - def r(self): - """percentage overall return (out/in - 1)""" - return self.amount_out / self.amount_in - 1 - - @property - def rpcs(self): - """percentage return per cycle step (r/length)""" - return self.r / self.length - - @property - def ppcs(self): - """profit per cycle step (in token units)""" - return self.profit / self.length - - def run_arbitrage_cycle(self, cycle, token=None, **params): - """ - takes a cycles and runs the arbitrage inherent in it - - :cycle: a Cycle object as returned by the cycles() method - :token: the token around which the cycle is run (default: the first token in the cycle) - :params: additional parameters (see below) - :verbose: if True, print some information when running the cycle - :allow_any_token: if True, allow any token to be used as the token around which the cycle is run - :returns: a RunArbCycleResult object with the following properties: - :cycle: cycle that was run - :start_ix: index of the token around which the cycle was run - :token: token around which the cycle was run - :profit: profit made in the cycle (in token units) - :amount_in: amount of token that was put into the cycle (in token units) - :amount_out: amount of token that was taken out of the cycle (in token units) - :length: length of the cycle - :r: percent overall return of the cycle (out/in - 1) - :rpcs: return per cycle step (r/length) - :ppcs: profit per cycle step (in token units) - """ - P = lambda name: params.get(name, self.RUNARBCYCLE_DEFAULTS.get(name)) - - current_multiplier = ( - 1.0 # tracks how much of the initial amount can be pushed through the cycle - ) - current_amount_in = ( - None # tracks the amount currently being pushed (None: to be initialised) - ) - - # try to set the cycle to the token we want to use (if any) - if not token is None: - try: - cycle_pairs = cycle.pairs(start_val=self.node_by_tkn(token)) - except: - if P("allow_any_token"): - cycle_pairs = cycle.pairs() - if P("verbose"): - print( - f"token {token} not found in cycle {cycle}; first token of cycle used instead" - ) - else: - raise ValueError( - f"token {token} does not exist, or not found in cycle {cycle}" - ) - else: - cycle_pairs = cycle.pairs() - - # iterate over all edges in the cycle - for pair in cycle_pairs: - - # get all edges between the nodes of the cycle (e is eg for tokens) - edges = self.filter_edges(*pair) - e = edges[0] - - # get amounts in and out per edge, and sum them up - capacities_in = [e.amount_in for e in edges] - capacity_in = sum(capacities_in) - capacities_out = [e.amount_out for e in edges] - capacity_out = sum(capacities_out) - - # initialize current amount? Yes -> set to capacity_in - if current_amount_in is None: - current_amount_in = capacity_in - current_amounts_in = capacities_in - initial_amount_in = ( - capacity_in # we remember how much we had at the beginning... - ) - initial_tkn_in = e.tkn_in # ...and in which token we had it - - else: - # current_amount_out was set in the previous edge; first we set in to previous out - current_amount_in = current_amount_out - - # is the capacity of the route less than what we want to push through? - if capacity_in < current_amount_in: - # print("capacity_in < current_amount_in: reducing push amount", capacity_in, current_amount_in) - - # yes -> keep note of this in the multiplier, and used 100% of the capacity - current_multiplier *= capacity_in / current_amount_in - current_amounts_in = capacities_in - current_amount_in = capacity_in - - else: - # print("capacity_in >= current_amount_in: pushing everything", capacity_in, current_amount_in) - - # no -> scale down the amounts to be pushed through this route - fctr = current_amount_in / capacity_in - current_amounts_in = [amt_in * fctr for amt_in in capacities_in] - - # push the amount through the edges - current_amounts_out = [ - ee.transport(amt_in).amount - for ee, amt_in in zip(edges, current_amounts_in) - ] - current_amount_out = sum(current_amounts_out) - - # print diagnostics - if P("verbose"): - s1 = f"{pair}: {len(edges)} edges, capacity {capacity_in} {e.tkn_in} -> {capacity_out} {e.tkn_out}" - s2 = f"actual {current_amount_in} -> {current_amount_out} [{current_multiplier}x]" - print(f"{s1}, {s2}") - - effective_amount_in = current_multiplier * initial_amount_in - profit_amount = current_amount_out - effective_amount_in - assert ( - initial_tkn_in == e.tkn_out - ), f"In and out tokens do not match!! {initial_tkn_in}, {e.tkn_out}" - - if P("verbose"): - inout_str = f"in: {effective_amount_in}; out: {current_amount_out}" - profits_str = f"Profit: {profit_amount}" - print(f"{profits_str} {e.tkn_out} [{inout_str}]") - - result = self.RunArbCycleResult( - cycle=cycle, - start_ix=0, - token=e.tkn_out, - profit=profit_amount, - amount_in=effective_amount_in, - amount_out=current_amount_out, - ) - return result - - ACRET_GEN = "gen" - ACRET_TUPLE = "tuple" - ACRET_RAW = ACRET_TUPLE - ACRET_DICTS = "dicts" - ACRET_DF = "df" - ACRET_AGGRDF = "aggrdf" - ACRET_PRETTYADF = "prettyadf" - - def run_arbitrage_cycles(self, cycles, token=None, format=None, **params): - """ - takes a list of cycles and runs run_arbitrage_cycle on each of them - - :cycles: a list of Cycle objects, eg as returned by the cycles() method - :token: either a single token for all cycles, or a list of tokens, one for each cycle - :params: additional parameters that are being passed to run_arbitrage_cycle - :returns: depends on the ``format`` parameter which is one of ACRET_GEN, ACRET_TUPLE, - ACRET_DICTS, ACRET_DF or ACREF_PRETTYDF - """ - if format is None: - format = self.ACRET_TUPLE - arbcycles = ( - self.run_arbitrage_cycle(cycle, token=token, **params) for cycle in cycles - ) - if format == self.ACRET_GEN: - return arbcycles - if format == self.ACRET_TUPLE: - return tuple(arbcycles) - return self.run_arbitrage_cyclesf(arbcycles, format=format) - - def run_arbitrage_cyclesf(self, rawresults, *, format=None): - """ - the formatting function for run_arbitrage_cycles to reformat the results - - :rawresults: the ACRET_RAW result returned by run_arbitrage_cycles - :format: same as in ``run_arbitrage_cycles`` - """ - if format is None: - format = self.ACREF_DF - - arbcycles = rawresults - if format == self.ACRET_GEN or format == self.ACRET_TUPLE: - return rawresults - arbcycles_dcts = tuple(r.asdict(False) for r in arbcycles) - if format == self.ACRET_DICTS: - return arbcycles_dcts - df0 = pd.DataFrame.from_dict(arbcycles_dcts) - if format == self.ACRET_DF: - return df0.set_index("uid") - - df1 = df0.sort_values(["token", "uid"]) - df1["uid"] = df1["uid"].astype(str) - dfa = df1.pivot_table( - index="token", values=["profit", "amount_in", "amount_out"] - ) - df2 = pd.concat([df1, dfa.reset_index()]).fillna("").set_index(["token", "uid"]) - if format == self.ACRET_AGGRDF: - return df2 - if format == self.ACRET_PRETTYADF: - return df2.style.format( - { - "profit": "{:.4f}", - "amount_in": "{:.4f}", - "amount_out": "{:.4f}", - } - ) - - raise ValueError(f"Invalid format parameter: {format}") - - @dataclass - class TransportResult(_DCBase): - amount_in: Amount - amount_out: Amount - amounts_in: tuple - amounts_out: tuple - edges: tuple - - TPROUT_PRORATA = "prorata" - - def transport( - self, - amount_in, - tkn_in, - tkn_out, - *, - record=True, - routingalgo=None, - raiseonerror=True, - ): - """ - transport an amount of tkn_in to tkn_out routing through the relevant edges - - :amount_in: amount to be transported (as float or Amount) - :tkn_in: token to be transported (as str or Node) - :tkn_out: token to be transported to (as str or Node) - :record: if True, record the transport in the graph - :routingalgo: routing algo to be used (default: TPROUT_PRORATA) - """ - if routingalgo is None: - routingalgo = self.TPROUT_PRORATA - if not routingalgo in [self.TPROUT_PRORATA]: - raise ValueError( - f"routingalgo {routingalgo} not supported; see TPROUT_* constants" - ) - - # get the nodes - node_in = self.node_by_tkn(tkn_in) - node_out = self.node_by_tkn(tkn_out) - - # if amount_in is a Amount, ensure the token is correct - if isinstance(amount_in, Amount): - if amount_in.token != node_in.token: - raise ValueError( - f"amount_in token {amount_in.token} does not match node_in token {node_in.token}" - ) - amount_in = amount_in.amount - - # get the edges - edges = self.filter_edges(node_in, node_out) - if len(edges) == 0: - raise ValueError(f"no edge found between {node_in} and {node_out}") - - # get the amounts in per edge - capacities_in = [e.state.amount_in_remaining for e in edges] - capacity_in = sum(capacities_in) - - # execute the routing algo - assert ( - routingalgo == self.TPROUT_PRORATA - ), f"routingalgo {routingalgo} not supported; use TPROUT_PRORATA" - routing_factor = amount_in / capacity_in - amounts_in = [amt_in * routing_factor for amt_in in capacities_in] - print( - f"routing_factor: {routing_factor}; amounts_in: {amounts_in} {amount_in} {capacity_in}" - ) - - # transport the amounts through the edges - amounts_out = [] - for edge, amt_in in zip(edges, amounts_in): - amounts_out += [ - edge.transport(amt_in, record=record, raiseonerror=raiseonerror) - ] - - return self.TransportResult( - amount_in=Amount(amount_in, node_in.tkn), - amount_out=Amount( - sum([amt_out.amount for amt_out in amounts_out]), node_out.tkn - ), - amounts_in=tuple(amounts_in), - amounts_out=tuple([amt_out.amount for amt_out in amounts_out]), - edges=tuple(edges), - ) - - @dataclass - class PTransportResult(_DCBase): - multiplier: float - prices: list - numedges: list - path: any # Cycle or Path object - - @property - def cycle(self): - return self.path - - def ptransport(self, path): - """ - transport an amount along a (usually closed) path, ignoring capacities - - :path: typically a Cycle object, or another object the same API (1) - (Cycle paths will always be closed) - - NOTE1: the function expect that path has a method called ``pairs`` that returns an - iterator, and the iterator in turn yields tuples(node_in, node_out) where - the previous node_out is the same as the next node_in - """ - multiplier = 1 - prices = [] - numedges = [] - for edgenodes in path.pairs(): - node_in, node_out = edgenodes - edges = self.filter_edges(node_in=node_in, node_out=node_out) - p_outperin = np.mean([e.p_outperin for e in edges]) - # print(f"ptransport {node_in} --{p_outperin}--> {node_out} [{len(edges)}]") - multiplier *= p_outperin - prices += [p_outperin] - numedges += [len(edges)] - return self.PTransportResult( - multiplier=multiplier, - prices=prices, - numedges=numedges, - path=path, - ) - - def edgedf(self, edges=None, *, consolidated=True, resetindex=False): - """ - returns edges (default: all edges) as a pandas dataframe - """ - if edges is None: - edges = self.edges - - if self.is_amounttype: - - # Amount-type graph - df = pd.DataFrame.from_dict( - [ - dict( - pair=e.pairo.primary, - tkn_in=e.node_in.tkn, - tkn_out=e.node_out.tkn, - amount_in=e.amount_in, - amount_out=e.amount_out, - ) - for e in edges - ] - ) - if not consolidated: - df["uid"] = [e.uid for e in edges] - return df.set_index("uid") - return df - df = df.groupby(["pair", "tkn_in", "tkn_out"]).sum() - if resetindex: - df = df.reset_index() - return df - - else: - # Connection-type graph - df = pd.DataFrame.from_dict( - [ - dict( - pair=e.pairo.primary, - tkn_in=e.node_in.tkn, - tkn_out=e.node_out.tkn, - n=-e.amount_in, - is_reverse=not e.pairo.isprimary, - price_outin=e.amount_out / e.amount_in, - price=e.pairo.pp(e.amount_out / e.amount_in), - ) - for e in edges - ] - ) - if not consolidated: - return df - df = df.pivot_table( - index=["pair", "tkn_in", "tkn_out", "is_reverse"], - values=["n", "price"], - aggfunc={"n": np.sum, "price": np.mean}, - ).reset_index() - dff = df[df["is_reverse"] == False] - dft = df[df["is_reverse"] == True] - df = pd.concat( - [ - dff.reset_index(drop=True), - dft[["n"]].rename(columns={"n": "n_rev"}).reset_index(drop=True), - ], - axis=1, - ) - df = df[["pair", "n", "n_rev", "price"]] - - if not resetindex: - df = df.set_index("pair") - return df - - @dataclass - class EdgeStatistics(_DCBase): - len: int - edges: tuple - amount_in: Amount - amount_in_remaining: Amount - amount_out: Amount - price: float - utilization: float - amounts_in: tuple - amounts_in_remaining: tuple - amounts_out: tuple - prices: tuple - utilizations: tuple - - def edge_statistics( - self, node_in=None, node_out=None, *, edges=None, pair=None, bothways=False - ): - """ - get statistics about the list of edges between node_in, node_out (or sublist provided) - - :node_in: node_in (as str or Node) - :node_out: node_out (as str or Node) - :edges: list of edges to be used (if not None, but have same node_in -> node_out) - :pair: the pair in the form "TKNB/TKBQ" as str - :bothways: if True, returns pair bothways - :returns: EdgeStatistics object node_in -> node_out; or pair thereof if bothways=True - """ - if not self.is_amounttype: - raise ValueError("edge_statistics only supported for AmountGraphs") - if bothways: - return ( - self.edge_statistics(node_in, node_out, edges=edges, bothways=False), - self.edge_statistics(node_out, node_in, edges=edges, bothways=False), - ) - if not pair is None: - assert ( - node_in is None and node_out is None - ), f"cannot specify both pair and node_in/node_out {pair}, {node_in}, {node_out}" - node_in, node_out = pair.split("/") - return self.edge_statistics(node_in, node_out, bothways=True) - - if isinstance(node_in, str): - node_in = self.node_by_tkn(node_in) - if isinstance(node_out, str): - node_out = self.node_by_tkn(node_out) - - if not edges is None: - assert ( - node_in is None and node_out is None - ), "cannot specify both edges and node_in/node_out" - node_in = {ee.node_in.tkn for ee in edges} - if len(node_in) != 1: - raise ValueError(f"edges have different node_in: {node_in}") - node_in = node_in.pop() - node_out = {ee.node_out.tkn for ee in edges} - if len(node_out) != 1: - raise ValueError(f"edges have different node_out: {node_out}") - node_out = node_out.pop() - else: - edges = self.filter_edges(node_in=node_in, node_out=node_out) - - if len(edges) == 0: - return None - - amounts_in = tuple(e.amount_in for e in edges) - amount_in = sum(amounts_in) - - amounts_in_remaining = tuple(e.state.amount_in_remaining for e in edges) - amount_in_remaining = sum(amounts_in_remaining) - - utilizations = tuple( - 1 - r / a for r, a in zip(amounts_in_remaining, amounts_in) - ) - utilization = 1 - amount_in_remaining / amount_in if amount_in > 0 else None - - amounts_out = tuple(e.amount_out for e in edges) - amount_out = sum(amounts_out) - - prices = tuple(outv / inv for outv, inv in zip(amounts_out, amounts_in)) - price = amount_out / amount_in - - return self.EdgeStatistics( - len=len(edges), - edges=tuple(edges), - amount_in=Amount(amount_in, node_in), - amount_in_remaining=Amount(amount_in_remaining, node_in), - amount_out=Amount(amount_out, node_out), - price=price, - utilization=utilization, - amounts_in=amounts_in, - amounts_in_remaining=amounts_in_remaining, - amounts_out=amounts_out, - prices=prices, - utilizations=utilizations, - ) - - @dataclass - class NodeStatistics(_DCBase): - """ - attention: in and out for nodes and edges is reversed - - :edges_in: all edges that have this node as node_out - :edges_out: all edges that have this node as node_in - :amount_in: sum of all amounts_out of edges_in - :amount_out: sum of all amounts_in of edges_out - """ - - node: Node - edges_in: tuple - edges_out: tuple - nodes_in: set - nodes_out: set - amount_in: Amount - amount_out: Amount - amount_out_remaining: Amount - - def node_statistics(self, node): - """ - get statistics about the node provided - """ - node = self.node_by_tkn(node) - edges_out = self.filter_edges(node_in=node) - edges_in = self.filter_edges(node_out=node) - nodes_out = {e.node_out.tkn for e in edges_out} - nodes_in = {e.node_in.tkn for e in edges_in} - amount_in = sum(e.amount_out for e in edges_in) - amount_out = sum(e.amount_in for e in edges_out) - amount_out_remaining = sum(e.state.amount_in_remaining for e in edges_out) - - return self.NodeStatistics( - node=node, - edges_in=tuple(edges_in), - edges_out=tuple(edges_out), - nodes_in=set(nodes_in), - nodes_out=set(nodes_out), - amount_in=Amount(amount_in, node), - amount_out=Amount(amount_out, node), - amount_out_remaining=Amount(amount_out_remaining, node), - ) diff --git a/fastlane_bot/tools/cpc.py b/fastlane_bot/tools/cpc.py deleted file mode 100644 index 1e74c5eb5..000000000 --- a/fastlane_bot/tools/cpc.py +++ /dev/null @@ -1,3091 +0,0 @@ -""" -representing a levered constant product curve - ---- -(c) Copyright Bprotocol foundation 2023. -Licensed under MIT - -NOTE: this class is not part of the API of the Carbon protocol, and you must expect breaking -changes even in minor version updates. Use at your own risk. -""" -__VERSION__ = "3.4" -__DATE__ = "23/Jan/2024" - -from dataclasses import dataclass, field, asdict, InitVar -from .simplepair import SimplePair as Pair -from . import tokenscale as ts -import random -from math import sqrt -import numpy as np -import pandas as pd -import json -from matplotlib import pyplot as plt -from .params import Params -import itertools as it -import collections as cl -from sys import float_info -from hashlib import md5 as digest -import time -from .cpcbase import CurveBase, AttrDict, DAttrDict, dataclass_ - - -AD = DAttrDict - - -# FN = "20230411-curves.csv" -# df = pd.read_csv(FN) -# CCm = CPCContainer.from_df(df, tokenscale=ts.TokenScale1Data) -# tp = {t.split("-")[0]:t for t in CCm.tokens()} -# {t:tp.get(t) for t in T} -# for k,v in {t:tp.get(t) for t in T}.items(): -# print(f"""{k} = "{v}", """) - - -TOKENIDS = AttrDict( - NATIVE_ETH="0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE", - WETH="0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2", - ETH="0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2", - WBTC="0x2260FAC5E5542a773Aa44fBCfeDf7C193bc2C599", - BTC="0x2260FAC5E5542a773Aa44fBCfeDf7C193bc2C599", - USDC="0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48", - USDT="0xdAC17F958D2ee523a2206206994597C13D831ec7", - DAI="0x6B175474E89094C44Da98b954EedeAC495271d0F", - LINK="0x514910771AF9Ca656af840dff83E8264EcF986CA", - BNT="0x1F573D6Fb3F13d689FF844B4cE37794d79a7FF1C", - HEX="0x2b591e99afE9f32eAA6214f7B7629768c40Eeb39", - UNI="0x1f9840a85d5aF5bf1D1762F925BDADdC4201F984", - FRAX="0x3432B6A60D23Ca0dFCa7761B7ab56459D9C964D0", - ICHI="0x903bEF1736CDdf2A537176cf3C64579C3867A881", - - -) -T = TOKENIDS - -TOKENS_NOETH = { - "$LSVR-c09B", - "3Crv-E490", - "ABR-8C7C", - "ACR-E3CF", - "ACRE-FC21", - "AGV-382B", - "APM-BA6c", - "ARPA-b71a", - "ASTO-4689", - "ASW-2a11", - "B2M-0a1f", - "BAC-A69a", - "BACON-38e7", - "BAG-14b0", - "BAMBOO-2e89", - "BASE-04e0", - "BASv2-5287", - "BBS-B430", - "BDT-d5Cf", - "BHNY-0844", - "BLID-56A5", - "BLU-1FfD", - "BORING-92CA", - "BRD-9aD6", - "BRKL-9ff8", - "BRZ-2e2B", - "BTTY-3D0A", - "CE-EecE", - "CHANGE-2754", - "CHEQ-4de7", - "CHO-3099", - "CIRUS-8756", - "CLB-3c84", - "CLS-de37", - "CMT-Dc18", - "COW-5Ea8", - "CP-CFCa", - "CPOOL-FaC5", - "CPRX-978f", - "CRF-219d", - "CROWN-E0fa", - "CRPT-6d8B", - "CTO-6C47", - "CTX-f98D", - "CWEB-Bf04", - "CoreDAO-Dd58", - "DAMM-16b8", - "DAPP-1649", - "DAWN-9aFa", - "DCHF-7A36", - "DEXG-436D", - "DHT-Fa84", - "DIGG-01C3", - "DIGITS-404F", - "DLTA-D823", - "DNXC-f03a", - "DOG-868D", - "DOGZ-33eF", - "DORA-c81d", - "DREP-b4c2", - "DSD-66e3", - "DSU-7109", - "DTX-3F75", - "DVF-1918", - "DXP-B745", - "Daruma-f704", - "EAG8-EeE4", - "ECO-5727", - "ECOx-736a", - "EGG-6a0c", - "ERC20-EPK-40c4", - "ERP-2267", - "ESD-d723", - "ETHV-aC76", - "EURe-273f", - "EVA-8707", - "EXD-6560", - "FANC-c045", - "FCC-e079", - "FEAR-1E83", - "FLX-0770", - "FLy-1472", - "FORM-FA2a", - "FORT-Ec29", - "FPI-E08E", - "FTG-7659", - "FWT-a295", - "GAME-1d1c", - "GBPT-bA98", - "GBYTE-cc2a", - "GEM-efcC", - "GENI-6a39", - "GOB-8A80", - "GODS-FD97", - "GPO-3aCE", - "GRO-74D7", - "GST-1404", - "GUILD-475A", - "GVT-2a0c", - "HAI-9a63", - "HAN-511F", - "HDAO-fF2D", - "HILO-5ff6", - "HOME-1F62", - "INSUR-7429", - "IOEN-893A", - "IOI-1d81", - "IPISTR-348e", - "IPT-FC3d", - "ISK-a75C", - "IZE-327B", - "JOY-1FB5", - "KOL-d414", - "KYOKO-BaC2", - "LBlock-D329", - "LEAN-99F8", - "LMT-c8AF", - "LUCKY-6140", - "LXF-772A", - "M2-D15C", - "MAXI-e84b", - "MDF-B411", - "MFI-355B", - "MGG-8740", - "MIDAS-66A5", - "MLP-1152", - "MPS-D47D", - "MYC-F5Ba", - "Mars-70B7", - "NAO-53dc", - "NBT-824c", - "NFTD-B379", - "NFTY-3208", - "NGL-66aE", - "NRFX-94a4", - "NUM-3079", - "NineFi-2f1d", - "O-c40f", - "O3-7d28", - "OBOT-0c32", - "OCT-c6DC", - "OIL-88a5", - "OK-4189", - "ONIGIRI-30D0", - "OPUL-6444", - "OTHR-C334", - "OUSD-5e86", - "OXAI-Fe9d", - "Okinami-4121", - "PAL-f4BF", - "PAR-4703", - "PEPEBET-0350", - "PINA-780D", - "PNL-B459", - "POLA-2CED", - "POLAR-075E", - "POLY-fdad", - "PP-CfD0", - "PROS-4B56", - "PULSE-97cE", - "QLT-c87c", - "RACA-9040", - "RPG-e251", - "RWS-7802", - "SAKURA-FeD6", - "SCOIN-0EB4", - "SD-D10f", - "SDEX-BEeF", - "SENT-556F", - "SEURO-9A00", - "SKEB-C810", - "SLD-a084", - "SMTX-419b", - "SNP-E873", - "SNP-FA9d", - "SOTU-9162", - "SPIRAL-1C3c", - "SPOOL-0976", - "SPOT-bafE", - "SPWN-1126", - "SST-9868", - "STABLZ-F7cd", - "SUM-40b1", - "SWASH-2F80", - "SWEAT-3A35", - "SWIV-6f2d", - "SYL-eb9C", - "SYNR-490a", - "Shird-695f", - "SpillWays-7b47", - "TCR-F050", - "TEAM-dE02", - "TEMP-1aB9", - "TGL-4e92", - "TOL-2cFA", - "TR3-5F98", - "TRIO-3308", - "TXA-A830", - "UBXN-1065", - "UCOIL-9a13", - "ULX-636F", - "UNIX-7aC8", - "UNKAI-B73D", - "USDC-1130", - "USDD-b5c6", - "USDP-89E1", - "UST-87a5", - "Umoon-C5da", - "UwU-5257", - "VENDETTA-53c3", - "VIS-E863", - "VLX-Edb9", - "VNDC-b5DE", - "VOW-46Fb", - "VPAD-4EDc", - "VR-8cdD", - "WAVES-f29a", - "WFAIR-8972", - "WMLX-1AAd", - "WOOFY-57f1", - "WXT-E915", - "XAI-bEAc", - "XAUt-2F38", - "XDAO-Ad28", - "XDEX-6c83", - "XETA-3550", - "XFIT-7441", - "Y2B-0650", - "Z3-61a6", - "ZEUM-8190", - "ZUSD-04fA", - "ankrMATIC-480C", - "bLUSD-79C3", - "bluSGD-db22", - "cvxCRV-0Aa7", - "eLunr-Aa5A", - "eMAID-a303", - "iAI-2122", - "ibETH-9c7A", - "icc-a177", - "one1INCH-3857", - "oneICHI-1e07", - "rETH2-86c5", - "rUSD-C8F6", - "sifu-C313", - "vBNT-7f94", - "wMEMO-af57", - "wOXEN-bcc5", - "wPPC-2958", -} - - -@dataclass_ -class ConstantProductCurve(CurveBase): - """ - represents a, potentially levered, constant product curve - - :k: pool invariant k (see NOTE2 below) - :x: (virtual) pool state x (virtual number of base tokens for sale) - :x_act: actual pool state x (actual number of base tokens for sale) - :y_act: actual pool state y (actual number of quote tokens for sale) - :alpha: weight factor alpha of token x (default = 0.5; see NOTE3 below) - :eta: portfolio weight factor eta (default = 1; see NOTE3 below) - :pair: token pair in slash notation ("TKNB/TKNQ"); TKNB is on the x-axis, TKNQ on the y-axis - :cid: unique id (optional) - :fee: fee (optional); eg 0.01 for 1% - :descr: description (optional; eg. "UniV3 0.1%") - :constr: which (alternative) constructor was used (optional; user should not set) - :params: additional parameters (optional) - - NOTE1: always use the alternative constructors ``from_xx`` rather then the - canonical one; if you insist on using the canonical one then keep in mind - that the order of the parameters may change in future versions, so you - MUST use keyword arguments - - NOTE2: This class implements two distinct types of constant product curves: - (1) the standard constant product curve xy=k - (2) the weighted constant product curve x^al y^1-al = k^al - Note that the case alpha=0.5 is equivalent to the standard constant product curve - xy=k, including the value of k - - NOTE3: There are two different ways of specifying the weights of the tokens - (1) alpha: the weight of the x token (equal weight = 0.5), such that x^al y^1-al = k^al - (2) eta = alpha / (1-alpha): the relative weight (equal weight = 1; x overweight > 1) - """ - - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - k: float - x: float - x_act: float = None - y_act: float = None - alpha: float = None - pair: str = None - cid: str = None - fee: float = None - descr: str = None - constr: str = field(default=None, repr=True, compare=False, hash=False) - params: AttrDict = field(default=None, repr=True, compare=False, hash=False) - - def __post_init__(self): - - if self.alpha is None: - super().__setattr__("_is_symmetric", True) - super().__setattr__("alpha", 0.5) - else: - super().__setattr__("_is_symmetric", self.alpha == 0.5) - #print(f"[ConstantProductCurve] _is_symmetric = {self._is_symmetric}") - assert self.alpha > 0, f"alpha must be > 0 [{self.alpha}]" - assert self.alpha < 1, f"alpha must be < 1 [{self.alpha}]" - - - if self.constr is None: - super().__setattr__("constr", "default") - - super().__setattr__("cid", str(self.cid)) - - if self.params is None: - super().__setattr__("params", AttrDict()) - elif isinstance(self.params, str): - data = json.loads(self.params.replace("'", '"')) - super().__setattr__("params", AttrDict(data)) - elif isinstance(self.params, dict): - super().__setattr__("params", AttrDict(self.params)) - - if self.x_act is None: - super().__setattr__("x_act", self.x) # required because class frozen - - if self.y_act is None: - super().__setattr__("y_act", self.y) # ditto - - if self.pair is None: - super().__setattr__("pair", "TKNB/TKNQ") - - super().__setattr__("pairo", Pair(self.pair)) - - if self.isbigger(big=self.x_act, small=self.x): - print(f"[ConstantProductCurve] x_act > x in {self.cid}", self.x_act, self.x) - - if self.isbigger(big=self.y_act, small=self.y): - print(f"[ConstantProductCurve] y_act > y in {self.cid}", self.y_act, self.y) - - - - self.set_tokenscale(self.TOKENSCALE) - - def P(self, pstr, defaultval=None): - """ - convenience function to access parameters - - :pstr: parameter name as colon separated string (eg "exchange") (1) - :defaultval: default value if parameter not found - :returns: parameter value or defaultval* - - NOTE1: ``CC.pstr("exchange")`` is equivalent to ``CC.params["exchange"]`` if defined - ``CC.pstr("a:b")`` is equivalent to ``CC.params["a"]["b"]`` if defined - """ - fieldl = pstr.strip().split(":") - val = self.params - for field in fieldl: - try: - val = val[field] - except KeyError: - return defaultval - return val - - @property - def cid0(self): - "short cid [last 8 characters]" - return self.cid[-8:] - - @property - def eta(self): - "portfolio weight factor eta = alpha / (1-alpha)" - return self.alpha / (1 - self.alpha) - - def is_constant_product(self): - "True iff alpha == 0.5 (deprecated; use `is_symmetric`)" - return self.is_symmetric() - - def is_symmetric(self): - "True iff alpha == 0.5" - return self._is_symmetric - - def is_asymmetric(self): - "True iff alpha != 0.5" - return not self.is_symmetric() - - def is_levered(self): - "True iff x!=x_act or y!=y_act" - return not self.is_unlevered() - - def is_unlevered(self): - "True iff x==x_act and y==y_act" - return self.x == self.x_act and self.y == self.y_act - - TOKENSCALE = ts.TokenScale1Data - # default token scale object is the trivial scale (everything one) - # change this to a different scale object be creating a derived class - - def set_tokenscale(self, tokenscale): - """sets the tokenscale object (returns self)""" - # print("setting tokenscale", self.cid, tokenscale) - super().__setattr__("tokenscale", tokenscale) - return self - - @property - def scalex(self): - """returns the scale of the x-axis token""" - return self.tokenscale.scale(self.tknx) - - @property - def scaley(self): - """returns the scale of the y-axis token""" - return self.tokenscale.scale(self.tkny) - - def scale(self, tkn): - """returns the scale of tkn""" - return self.tokenscale.scale(tkn) - - def asdict(self): - "returns a dict representation of the curve" - return asdict(self) - - @classmethod - def fromdict(cls, d): - "returns a curve from a dict representation" - return cls(**d) - - from_dict = fromdict # DEPRECATED (use fromdict) - - def setcid(self, cid): - """sets the curve id [can only be done once]""" - assert self.cid is None, "cid can only be set once" - super().__setattr__("cid", cid) - return self - - class CPCValidationError(ValueError): pass - - @classmethod - def from_kx( - cls, - k, - x, - x_act=None, - y_act=None, - pair=None, - cid=None, - fee=None, - descr=None, - params=None, - ): - "constructor: from k,x (and x_act, y_act)" - return cls( - k=k, - x=x, - x_act=x_act, - y_act=y_act, - pair=pair, - cid=cid, - fee=fee, - descr=descr, - constr="kx", - params=params, - ) - - @classmethod - def from_ky( - cls, - k, - y, - x_act=None, - y_act=None, - pair=None, - cid=None, - fee=None, - descr=None, - params=None, - ): - "constructor: from k,y (and x_act, y_act)" - return cls( - k=k, - x=k / y, - x_act=x_act, - y_act=y_act, - pair=pair, - cid=cid, - fee=fee, - descr=descr, - constr="ky", - params=params, - ) - - @classmethod - def from_xy( - cls, - x, - y, - x_act=None, - y_act=None, - pair=None, - cid=None, - fee=None, - descr=None, - params=None, - ): - "constructor: from x,y (and x_act, y_act)" - return cls( - k=x * y, - x=x, - x_act=x_act, - y_act=y_act, - pair=pair, - cid=cid, - fee=fee, - descr=descr, - constr="xy", - params=params, - ) - - @classmethod - def from_xyal( - cls, - x, - y, - *, - alpha=None, - eta=None, - x_act=None, - y_act=None, - pair=None, - cid=None, - fee=None, - descr=None, - params=None, - ): - "constructor: from x,y,alpha/eta (and x_act, y_act)" - if not alpha is None and not eta is None: - raise ValueError(f"at most one of alpha and eta must be given [{alpha}, {eta}]") - if not eta is None: - alpha = eta / (eta + 1) - if alpha is None: - alpha = 0.5 - assert alpha > 0, f"alpha must be > 0 [{alpha}]" - eta_inv = (1-alpha) / alpha - k = x * (y**eta_inv) - #print(f"[from_xyal] eta_inv = {eta_inv}") - #print(f"[from_xyal] x={x}, y={y}, k = {k}") - if not alpha == 0.5: - assert x_act is None, f"currently not allowing levered curves for alpha != 0.5 [alpha={alpha}, x_act={x_act}]" - assert y_act is None, f"currently not allowing levered curves for alpha != 0.5 [alpha={alpha}, x_act={y_act}]" - return cls( - #k=(x**alpha * y**(1-alpha))**(1/alpha), - k=k, - x=x, - alpha=alpha, - x_act=x_act, - y_act=y_act, - pair=pair, - cid=cid, - fee=fee, - descr=descr, - constr="xyal", - params=params, - ) - - - @classmethod - def from_pk( - cls, - p, - k, - x_act=None, - y_act=None, - pair=None, - cid=None, - fee=None, - descr=None, - params=None, - ): - "constructor: from k,p (and x_act, y_act)" - return cls( - k=k, - x=sqrt(k / p), - x_act=x_act, - y_act=y_act, - pair=pair, - cid=cid, - fee=fee, - descr=descr, - constr="pk", - params=params, - ) - - @classmethod - def from_px( - cls, - p, - x, - x_act=None, - y_act=None, - pair=None, - cid=None, - fee=None, - descr=None, - params=None, - ): - "constructor: from x,p (and x_act, y_act)" - return cls( - k=x * x * p, - x=x, - x_act=x_act, - y_act=y_act, - pair=pair, - cid=cid, - fee=fee, - descr=descr, - constr="px", - params=params, - ) - - @classmethod - def from_py( - cls, - p, - y, - x_act=None, - y_act=None, - pair=None, - cid=None, - fee=None, - descr=None, - params=None, - ): - "constructor: from y,p (and x_act, y_act)" - return cls( - k=y * y / p, - x=y / p, - x_act=x_act, - y_act=y_act, - pair=pair, - cid=cid, - fee=fee, - descr=descr, - constr="py", - params=params, - ) - - @classmethod - def from_pkpp( - cls, - p, - k, - p_min=None, - p_max=None, - pair=None, - cid=None, - fee=None, - descr=None, - *, - constr=None, - params=None, - ): - "constructor: from k, p, p_min, p_max (default for last two is p)" - if p_min is None: - p_min = p - if p_max is None: - p_max = p - x0 = sqrt(k / p) - y0 = sqrt(k * p) - xa = x0 - sqrt(k / p_max) - ya = y0 - sqrt(k * p_min) - constr = constr or "pkpp" - return cls( - k=k, - x=x0, - x_act=xa, - y_act=ya, - pair=pair, - cid=cid, - fee=fee, - descr=descr, - constr="pkpp", - params=params, - ) - - @classmethod - def from_univ2( - cls, - x_tknb=None, - y_tknq=None, - k=None, - pair=None, - fee=None, - cid=None, - descr=None, - params=None, - ): - """ - constructor: from Uniswap V2 pool (see class docstring for other parameters) - - :x_tknb: current pool liquidity in token x (base token of the pair) (1) - :y_tknq: current pool liquidity in token y (quote token of the pair) (1) - :k: uniswap liquidity parameter (k = xy)* - - NOTE 1: exactly one of k,x,y must be None; all other parameters must not be None; - a reminder that x is TKNB and y is TKNQ - """ - x = x_tknb - y = y_tknq - - assert not pair is None, "pair must not be None" - assert not cid is None, "cid must not be None" - assert not descr is None, "descr must not be None" - assert not fee is None, "fee must not be None" - - if k is None: - assert x is not None and y is not None, "k is None, so x,y must not" - k = x * y - elif x is None: - assert y is not None, "x is None, so y must not" - x = k / y - elif y is None: - y = k / x - else: - assert False, "exactly one of k,x,y must be None" - - return cls( - k=k, - x=x, - x_act=x, - y_act=y, - pair=pair, - cid=cid, - fee=fee, - descr=descr, - constr="uv2", - params=params, - ) - - @classmethod - def from_univ3(cls, Pmarg, uniL, uniPa, uniPb, pair, cid, fee, descr, params=None): - """ - constructor: from Uniswap V3 pool (see class docstring for other parameters) - - :Pmarg: current pool marginal price - :uniL: uniswap liquidity parameter (uniL**2 == L**2 == k) - :uniPa: uniswap price range lower bound Pa (Pa < P < Pb) - :uniPb: uniswap price range upper bound Pb (Pa < P < Pb) - """ - - P = Pmarg - assert uniPa < uniPb, f"uniPa < uniPb required ({uniPa}, {uniPb})" - assert ( - uniPa <= P <= uniPb - ), f"uniPa < Pmarg < uniPb required ({uniPa}, {P}, {uniPb})" - if params is None: - params = AttrDict(L=uniL) - else: - params = AttrDict({**params, "L": uniL}) - k = uniL * uniL - return cls.from_pkpp( - p=P, - k=k, - p_min=uniPa, - p_max=uniPb, - pair=pair, - cid=cid, - fee=fee, - descr=descr, - constr="uv3", - params=params, - ) - - SOLIDLY_PRICE_SPREAD = 0.06 # 0.06 gives pretty good results for m=2.6 - @classmethod - def from_solidly( - cls, - *, - k=None, - x=None, - y=None, - price_spread=None, - pair=None, - fee=None, - cid=None, - descr=None, - params=None, - as_list=True, - ): - """ - constructor: from a Solidly curve (see class docstring for other parameters)* - - :k: Solidly pool constant, x^3 y + x y^3 = k* - :x: current pool liquidity in token x* - :y: current pool liquidity in token y* - :price_spread: price spread to use for converting constant price -> constant product - :as_list: if True (default) returns a list of curves, otherwise a single curve - (see note below and note that as_list=False is deprecated) - - exactly 2 out of those three must be given; the third one is calculated - - The Solidly curve is NOT a constant product curve, as it follows the equation - - x^3 y + x y^3 = k - - where k is the pool invariant. This curve is a stable swap curve in the it is - very flat in the middle, at a unity price (see the `invariants` module and the - associated tests and notebooks). In fact, in the range - - 1/2.6 < y/x < 2.6 - - we find that the prices is essentially unity, and we therefore approximate it - was an (almost) constant price curve, ie a constant product curve with a very - large invariant k, and we will set the x_act and y_act parameters so that the - curve only covers the above range. - - IMPORTANT: IF as_list is True (default) THEN THE RESULT IS RETURNED AS A LIST - CURRENTLY CONTAINING A SINGLE CURVE, NOT THE CURVE ITSELF. This is because we - may in the future a list of curves, with additional curves matching the function - in the wings. IT IS RECOMMENDED THAT ANY CODE IMPLEMENTING THIS FUNCTION USES - as_list = True, AS IN THE FUTURE as_list = FALSE will raise an exception. - """ - # rename the solidly parameters to avoid name confusion - solidly_x = x - solidly_y = y - solidly_k = k - del x, y, k - price_spread = price_spread or cls.SOLIDLY_PRICE_SPREAD - #print([_ for _ in [solidly_x, solidly_y, solidly_k] if not _ is None]) - assert len([_ for _ in [solidly_x, solidly_y, solidly_k] if not _ is None]) == 2, f"exactly 2 out of k,x,y must be given (x={solidly_x}, y={solidly_y}, k={solidly_x})" - if solidly_k is None: - solidly_k = solidly_x**3 * solidly_y + solidly_x * solidly_y**3 - # NOTE: this is currently the only implemented version, and it should be - # enough for our purposes; the other two can be implemented using the - # y(x) function from the invariants module (note that y(x) and x(y) are - # the same as the function is symmetric). We do not want to implement it - # at the moment as we do not think we need it, and we want to avoid this - # external dependency for the time being. - elif solidly_x is None: - raise NotImplementedError("providing k, y not implemented yet") - elif solidly_y is None: - raise NotImplementedError("providing k, x not implemented yet") - else: - raise ValueError(f"should never get here") - # kbar = (k/2)**(1/4) is the equivalent of kbar = sqrt(k) for constant product - # center of the curve is (xy_c, xy_c) = (kbar, kbar) - # we are looking for the intersects of y=mx for m=2.6 and m=1/2.6 (linear segment) - # we know that within that range, x-y = const, so we can analytically solve for x and y - # specifically, we have y = 2 xy_c - x = mx - # therefore x = 2 xy_c / (m+1) - solidly_kbar = (solidly_k/2)**(1/4) - solidly_xyc = solidly_kbar - solidly_xmin = 2 * solidly_xyc / (2.6 + 1) - solidly_xmax = 2 * solidly_xyc / (1/2.6 + 1) - solidly_xrange = solidly_xmax - solidly_xmin - # print(f"[from_solidly] k = {solidly_k}, kbar = {solidly_kbar}, xy_c = {xy_c}") - # print(f"[from_solidly] x_min = {solidly_xmin}, x_max = {solidly_xmax}, x_range = {x_range}") - - # the curve has a unity price, which we spread to 1+price_spread at x_min, - # and 1-price_spread at x_max; we set x_range = x_max - x_min and we get - # the following equations - # k/x0**2 = (1+price_spread) - # k/(x0+xrange)**2 = 1/(1+price_spread) - # solving this fo k, x0 we get - # k = (1+price_spread)*xrange**2 / price_spread**2 - # x0 = xrange / price_spread - cpc_k = (1+price_spread)*solidly_xrange**2 / price_spread**2 - cpc_x0 = solidly_xrange / price_spread - - # finally we need to see where in the range we are; we look at - # del_x = x - x_min - # and we must have - # del_x > 0 - # del_x < x_range - # for the approximation to be valid; we recall that x_min ~ cpc_x0, therefore - # x = cpc_x0 + del_x - # Also, x_act is the x that is left to the right of the range, therefore - # x_act = x_range - del_x - # Finally, y_act is the amount of y that trades use from our current position - # back to x=x_min; we slightly approximate this by ignoring the price spread - # (which in any case is not real!) and assuming unity price, so del_y ~ del_y - # y_act = del_y = del_x - solidly_delx = solidly_x - solidly_xmin - if solidly_delx < 0 or solidly_delx > solidly_xrange: - if as_list: - #print(f"[cpc::from_solidly] x={solidly_x} is outside the range [{solidly_xmin}, {solidly_xmax}] and as_list=True") - return [] - else: - raise ValueError(f"x={solidly_x} is outside the range [{solidly_xmin}, {solidly_xmax}] and as_list=False") - - # now deal with the params, ie add the s_xxx parameters for solidly - params0 = dict(s_x = solidly_x, s_y = solidly_y, s_k = solidly_k, s_kbar = solidly_kbar, s_cpck=cpc_k, s_cpcx0 = cpc_x0, - s_xmin = solidly_xmin, s_xmax = solidly_xmax, s_price_spread = price_spread) - if params is None: - params = AttrDict(params0) - else: - params = AttrDict({**params, **params0}) - - result = cls( - k=cpc_k, - x=cpc_x0+solidly_delx, # del_x = x - xmin - # x_act=solidly_xrange-solidly_delx, - # y_act=solidly_delx, - x_act=solidly_delx, - y_act=solidly_xrange-solidly_delx, - pair=pair, - cid=cid, - fee=fee, - descr=descr, - constr="solidly", - params=params, - ) - if as_list: - return [result] - else: - print("[cpc::from_solidly] returning curve directly is deprecated; prepare to accept a list of curves in the future") - return result - - @classmethod - def from_carbon( - cls, - yint=None, - y=None, - *, - pa=None, - pb=None, - A=None, - B=None, - pair=None, - tkny=None, - fee=None, - cid=None, - descr=None, - params=None, - isdydx=True, - ): - """ - constructor: from a single Carbon order (see class docstring for other parameters) (1) - - :yint: current pool y-intercept (2) - :y: current pool liquidity in token y - :pa: carbon price range left bound (higher price in dy/dx) - :pb: carbon price range right bound (lower price in dy/dx) - :A: alternative to pa, pb: A = sqrt(pa) - sqrt(pb) in dy/dy - :B: alternative to pa, pb: B = sqrt(pb) in dy/dy - :tkny: token y - :isdydx: if True prices in dy/dx, if False in quote direction of the pair - - NOTE 1: that ALL parameters are mandatory, except that EITHER pa, bp OR A, B - must be given but not both; we do not correct for incorrect assignment of - pa and pb, so if pa <= pb IN THE DY/DX DIRECTION, MEANING THAT THE NUMBERS - ENTERED MAY SHOW THE OPPOSITE RELATIONSHIP, then an exception will be raised - - NOTE 2: that the result does not depend on yint, and for the time being we - allow to omit yint (in which case it is set to y, but this does not make - a difference for the result) - """ - assert not yint is None, "yint must not be None" - assert not y is None, "y must not be None" - assert not pair is None, "pair must not be None" - assert not tkny is None, "tkny must not be None" - # assert not fee is None, "fee must not be None" - # assert not cid is None, "cid must not be None" - # assert not descr is None, "descr must not be None" - - # if yint is None: - # yint = y - assert y <= yint, "y must be <= yint" - assert y >= 0, "y must be >= 0" - - if A is None or B is None: - # A,B is None, so we look at prices and isdydx - # print("[from_carbon] A, B:", A, B, pa, pb) - assert A is None and B is None, "A or B is None, so both must be None" - assert pa is not None and pb is not None, "A,B is None, so pa,pb must not" - - if pa is None or pb is None: - # pa,pb is None, so we look at A,B and isdydx must be True - # print("[from_carbon] pa, pb:", A, B, pa, pb) - assert pa is None and pb is None, "pa or pb is None, so both must be None" - assert A is not None and B is not None, "pa,pb is None, so A,B must not" - assert isdydx is True, "we look at A,B so isdydx must be True" - assert ( - A >= 0 - ), "A must be non-negative" # we only check for this one as it is a difference - - assert not ( - A is not None and B is not None and pa is not None and pb is not None - ), "either A,B or pa,pb must be None" - - tknb, tknq = pair.split("/") - assert tkny in (tknb, tknq), f"tkny must be in pair ({tkny}, {pair})" - tknx = tknb if tkny == tknq else tknq - - if A is None or B is None: - # A,B is None, so we look at prices and isdydx - - # pair quote direction is tknq per tknb; dy/dx is tkny per tknx - # therefore, dy/dx equals pair quote direction if tkny == tknq, otherwise reverse - if not isdydx: - if not tkny == tknq: - pa, pb = 1 / pa, 1 / pb - - # zero-width ranges are somewhat extended for numerical stability - pa0, pb0 = pa, pb - if pa == pb: - pa *= 1.0000001 - pb /= 1.0000001 - - # validation - if not pa > pb: - raise cls.CPCValidationError(f"pa > pb required ({pa}, {pb})") - - # finally set A, B - A = sqrt(pa) - sqrt(pb) - B = sqrt(pb) - A0 = A if pa0 != pb0 else 0 - else: - pb0 = B * B # B = sqrt(pb), A = sqrt(pa) - sqrt(pb) - pa0 = (A+B) * (A+B) # A+B = sqrt(pa) - A0 = A - if A/B < 1e-7: - A = B*1e-7 - - # set some intermediate parameters (see handwritten notes in repo) - # yasym = yint * B / A - kappa = yint**2 / A**2 - yasym_times_A = yint * B - kappa_times_A = yint**2 / A - - params0 = dict(y=y, yint=yint, A=A0, B=B, pa=pa0, pb=pb0) - if params is None: - params = AttrDict(params0) - else: - params = AttrDict({**params, **params0}) - - # finally instantiate the pool - - return cls( - k=kappa, - x=kappa_times_A / (y * A + yasym_times_A) if y * A + yasym_times_A != 0 else 1e99, - #x=kappa / (y + yasym) if y + yasym != 0 else 0, - x_act=0, - y_act=y, - pair=f"{tknx}/{tkny}", - cid=cid, - fee=fee, - descr=descr, - constr="carb", - params=params, - ) - - - def execute(self, dx=None, dy=None, *, ignorebounds=False, verbose=False): - """ - executes a transaction in the pool, returning a new curve object - - :dx: amount of token x to be +added to/-removed from the pool (1) - :dy: amount of token y to be +added to/-removed from the pool (1) - :ignorebounds: if True, ignore bounds on x_act, y_act - :returns: new curve object - - NOTE1: at least one of ``dx, dy`` must be None - """ - assert self.is_constant_product(), "only implemented for constant product curves" - - if not dx is None and not dy is None: - raise ValueError(f"either dx or dy must be None dx={dx} dy={dy}") - - if dx is None and dy is None: - dx = 0 - - if not dx is None: - if not dx >= -self.x_act: - if not ignorebounds: - raise ValueError( - f"dx must be >= -x_act (dx={dx}, x_act={self.x_act} {self.tknx} [{self.cid}: {self.pair}])" - ) - newx = self.x + dx - newy = self.k / newx - - else: - if not dy >= -self.y_act: - if not ignorebounds: - raise ValueError( - f"dy must be >= -y_act (dy={dy}, y_act={self.y_act} {self.tkny} [{self.cid}: {self.pair}])" - ) - newy = self.y + dy - newx = self.k / newy - - if verbose: - if dx is None: - dx = newx - self.x - if dy is None: - dy = newy - self.y - print( - f"{self.pair} dx={dx:.2f} {self.tknx} dy={dy:.2f} {self.tkny} | x:{self.x:.1f}->{newx:.1f} xa:{self.x_act:.1f}->{self.x_act+newx-self.x:.1f} ya:{self.y_act:.1f}->{self.y_act+newy-self.y:.1f} k={self.k:.1f}" - ) - - return self.__class__( - k=self.k, - x=newx, - x_act=self.x_act + newx - self.x, - y_act=self.y_act + newy - self.y, - pair=self.pair, - cid=f"{self.cid}-x", - fee=self.fee, - descr=f"{self.descr} [dx={dx}]", - params={**self.params, "traded": {"dx": dx, "dy": dy}}, - ) - - @property - def tknb(self): - "base token" - return self.pair.split("/")[0] - - tknx = tknb - - @property - def tknq(self): - "quote token" - return self.pair.split("/")[1] - - tkny = tknq - - @property - def tknbp(self): - """prettified base token""" - return Pair.n(self.tknb) - - tknxp = tknbp - - @property - def tknqp(self): - """prettified quote token""" - return Pair.n(self.tknq) - - tknyp = tknqp - - @property - def pairp(self): - """prettified pair""" - return f"{self.tknbp}/{self.tknqp}" - - def description(self): - "description of the pool" - assert self.is_constant_product(), "only implemented for constant product curves" - - s = "" - s += f"cid = {self.cid0} [{self.cid}]\n" - s += f"primary = {Pair.n(self.pairo.primary)} [{self.pairo.primary}]\n" - s += f"pp = {self.pp:,.6f} {self.pairo.pp_convention}\n" - s += f"pair = {Pair.n(self.pair)} [{self.pair}]\n" - s += f"tknx = {self.x_act:20,.6f} {self.tknx:10} [virtual: {self.x:20,.3f}]\n" - s += f"tkny = {self.y_act:20,.6f} {self.tkny:10} [virtual: {self.y:20,.3f}]\n" - s += f"p = {self.p} [min={self.p_min}, max={self.p_max}] {self.tknq} per {self.tknb}\n" - s += f"fee = {self.fee}\n" - s += f"descr = {self.descr}\n" - return s - - @property - def y(self): - "(virtual) pool state x (virtual number of base tokens for sale)" - - if self.k == 0: - return 0 - if self.is_constant_product(): - return self.k / self.x - return (self.k / self.x)**(self.eta) - - @property - def p(self): - "pool price (in dy/dx)" - if self.is_constant_product(): - return self.y / self.x - - return self.eta * self.y / self.x - - def buysell(self, *, verbose=False, withprice=False): - """ - returns b (buy primary tknb), s (sells primary tknb) or bs (buys and sells) - """ - b,s = ("b", "s") if not verbose else ("buy-", "sell-") - xa, ya = (self.x_act, self.y_act) if self.pairo.isprimary else (self.y_act, self.x_act) - result = b if ya > 0 else "" - result += s if xa > 0 else "" - if verbose: - result += f"{self.pairo.primary_tknb}" - if withprice: - result += f" @ {self.primaryp(withconvention=True)}" - return result - if withprice: - return result, self.primaryp() - else: - return result - - def buy(self): - """returns 'b' if the curve buys the primary token, '' otherwise""" - return self.buysell(verbose=False, withprice=False).replace("s", "") - - def sell(self): - """returns 's' if the curve sells the primary token, '' otherwise""" - return self.buysell(verbose=False, withprice=False).replace("b", "") - - ITM_THRESHOLDPC = 0.01 - @classmethod - def itm0(cls, bsp1, bsp2, *, thresholdpc=None): - """ - whether or not two positions are in the money against each other - - :bsp1: first position ("bs", price) [from buysell] - :bsp2: ditto second position - :thresholdpc: in-the-money threshold in percent (default: ITM_THRESHOLD) - """ - if thresholdpc is None: - thresholdpc = cls.ITM_THRESHOLDPC - bs1, p1 = bsp1 - bs2, p2 = bsp2 - - # if prices are equal (within threshold), positions are not in the money - if abs(p2/p1-1) < thresholdpc: - return False - if bs1 == "bs" and bs2 == "bs": - return True - - if p2 > p1: - # if p2 > p1: amm1 must sell and amm2 must buy - return "s" in bs1 and "b" in bs2 - else: - # if p1 < p2: amm1 must buy and amm2 must sell - return "b" in bs1 and "s" in bs2 - - def itm(self, other, *, thresholdpc=None, aggr=True): - """ - like itm0, but self against another curve object - - :other: other curve object, or iterable thereof - :thresholdpc: in-the-money threshold in percent (default: ITM_THRESHOLD) - :aggr: if True, and an iterable is passed, True iff one is in the money - """ - assert self.is_constant_product(), "only implemented for constant product curves" - - try: - itm_t = tuple(self.itm(o) for o in other) - if not aggr: - return itm_t - return np.any(itm_t) - except: - pass - bss = self.buysell(verbose=False, withprice=True) - bso = other.buysell(verbose=False, withprice=True) - return self.itm0(bss, bso, thresholdpc=thresholdpc) - - - def tvl(self, tkn=None, *, mult=1.0, incltkn=False, raiseonerror=True): - """ - total value locked in the curve, expressed in the token tkn (default: tknq) - - :tkn: the token in which the tvl is expressed (tknb or tknq) - :mult: multiplier applied to the tvl (eg to convert ETH to USD) - :incltkn: if True, returns a tuple (tvl, tkn, mult) - :raiseonerror: if True, raises ValueError if tkn is not tknb or tknq - :returns: tvl (in tkn) or (tvl, tkn, mult) if incltkn is True - """ - if tkn is None: - tkn = self.tknq - if not tkn in {self.tknb, self.tknq}: - if raiseonerror: - raise ValueError(f"tkn must be {self.tknb} or {self.tknq}") - return None - - tvl_tknq = (self.p * self.x_act + self.y_act) * mult - if tkn == self.tknq: - return tvl_tknq if not incltkn else (tvl_tknq, self.tknq, mult) - tvl_tknb = tvl_tknq / self.p - return tvl_tknb if not incltkn else (tvl_tknb, self.tknb, mult) - - def p_convention(self): - """price convention for p (dy/dx)""" - return f"{self.tknyp} per {self.tknxp}" - - @property - def primary(self): - "alias for self.pairo.primary" - return self.pairo.primary - - @property - def isprimary(self): - "alias for self.pairo.isprimary" - return self.pairo.isprimary - - def primaryp(self, *, withconvention=False): - "pool price in the native quote of the curve Pair object" - price = self.pairo.pp(self.p) - if not withconvention: - return price - return f"{price:.2f} {self.pairo.pp_convention}" - - @property - def pp(self): - """alias for self.primaryp()""" - return self.primaryp() - - @property - def kbar(self): - """ - kbar is pool invariant the scales linearly with the pool size - - kbar = sqrt(k) for constant product - kbar = k^alpha for general curves - """ - if self.is_constant_product(): - return sqrt(self.k) - return self.k**self.alpha - - def invariant(self, xvec=None, *, include_target=False): - """ - returns the actual invariant of the curve (eg x*y for constant product) - - :xvec: vector of x values (default: current) - :include_target: if True, the target invariant returned in addition to the actual invariant - :returns: invariant, or (invariant, target) - """ - if xvec is None: - xvec = {self.tknx: self.x, self.tkny: self.y} - x,y = xvec[self.tknx], xvec[self.tkny] - if self.is_constant_product(): - invariant = sqrt(x * y) - else: - invariant = x**self.alpha * y**(1-self.alpha) - if not include_target: - return invariant - return (invariant, self.kbar) - - @property - def x_min(self): - "minimum (virtual) x value" - if self.is_unlevered(): - return 0 - assert self.is_constant_product(), "only implemented for constant product curves" - - return self.x - self.x_act - - @property - def at_xmin(self): - """True iff x is at x_min""" - if self.x_min == 0: - return False - return abs(self.x / self.x_min - 1) < 1e-6 - - at_ymax = at_xmin - - @property - def at_xmax(self): - """True iff x is at x_max""" - if self.x_max is None: - return False - return abs(self.x / self.x_max - 1) < 1e-6 - - at_ymin = at_xmax - - @property - def at_boundary(self): - """True iff x is at either x_min or x_max""" - return self.at_xmin or self.at_xmax - - @property - def y_min(self): - "minimum (virtual) y value" - if self.is_unlevered(): - return 0 - assert self.is_constant_product(), "only implemented for constant product curves" - - return self.y - self.y_act - - @property - def x_max(self): - "maximum (virtual) x value" - if self.is_unlevered(): - return None - assert self.is_constant_product(), "only implemented for constant product curves" - - if self.y_min > 0: - return self.k / self.y_min - else: - return None - - @property - def y_max(self): - "maximum (virtual) y value" - if self.is_unlevered(): - return None - assert self.is_constant_product(), "only implemented for constant product curves" - - if self.x_min > 0: - return self.k / self.x_min - else: - return None - - @property - def p_max(self): - "maximum pool price (in dy/dx; None if unlimited) = y_max/x_min" - if self.is_unlevered(): - return None - assert self.is_constant_product(), "only implemented for constant product curves" - - if not self.x_min is None and self.x_min > 0: - return self.y_max / self.x_min - else: - return None - - def p_max_primary(self, swap=True): - "p_max in the native quote of the curve Pair object (swap=True: p_min)" - if self.is_unlevered(): - return None - p = self.p_max if not (swap and not self.isprimary) else self.p_min - if p is None: return None - return p if self.isprimary else 1/p - - @property - def p_min(self): - "minimum pool price (in dy/dx; None if unlimited) = y_min/x_max" - if self.is_unlevered(): - return 0 - assert self.is_constant_product(), "only implemented for constant product curves" - - if not self.x_max is None and self.x_max > 0: - return self.y_min / self.x_max - else: - return None - - def p_min_primary(self, swap=True): - "p_min in the native quote of the curve Pair object (swap=True: p_max)" - if self.is_unlevered(): - return 0 - p = self.p_min if not (swap and not self.isprimary) else self.p_max - if p is None: return None - return p if self.isprimary else 1/p - - def format(self, *, heading=False, formatid=None): - """returns info about the curve as a formatted string""" - assert self.is_constant_product(), "only implemented for constant product curves" - - if formatid is None: - formatid = 0 - assert formatid in [0], "only formatid in [0] is supported" - c = self - cid = str(c.cid)[-10:] - if heading: - s = f"{'CID':>12} {'PAIR':>10}" - s += f"{'xact':>20} {'tknx':>5} {'yact':>20} {'tkny':>5}" - s += f"{'price':>10} {'inverse':>10}" - s += "\n" + "=" * len(s) - return s - s = f"{cid:>12} {c.pairp:>10}" - s += f"{c.x_act:20,.3f} {c.tknxp:>5} {c.y_act:20,.3f} {c.tknyp:>5}" - s += f"{c.p:10,.2f} {1/c.p:10,.2f}" - return s - - def xyfromp_f(self, p=None, *, ignorebounds=False, withunits=False): - r""" - returns x,y,p for a given marginal price p (stuck at the boundaries if ignorebounds=False) - - :p: marginal price (in dy/dx) - :ignorebounds: if True, ignore x_act and y_act; if False, return the x,y values where - x_act and y_act are at zero (i.e. the pool is empty in this direction) - :withunits: if False, return x,y,p; if True, also return tknx, tkny, pair - - - $$ - x(p) = \left( \frac{\eta}{p} \right) ^ {1-\alpha} k^\alpha - y(p) = \left( \frac{p}{\eta} \right) ^ \alpha k^\alpha - $$ - """ - if p is None: - p = self.p - - if self.is_constant_product(): - sqrt_p = sqrt(p) - sqrt_k = self.kbar - x = sqrt_k / sqrt_p - y = sqrt_k * sqrt_p - else: - eta = self.eta - alpha = self.alpha - x = (eta/p)**(1-alpha) * self.kbar - y = (p/eta)**alpha * self.kbar - - if not ignorebounds: - if not self.x_min is None: - if x < self.x_min: - x = self.x_min - if not self.x_max is None: - if x > self.x_max: - x = self.x_max - if not self.y_min is None: - if y < self.y_min: - y = self.y_min - if not self.y_max is None: - if y > self.y_max: - y = self.y_max - - if withunits: - return x, y, p, self.tknxp, self.tknyp, self.pairp - - return x, y, p - - def xvecfrompvec_f(self, pvec, *, ignorebounds=False): - """ - alternative API to xyfromp_f - - :pvec: a dict containing all prices; the dict must contain the keys - for tknx and for tkny and the associated value must be the respective - price in any numeraire (only the ratio is used) - :returns: token amounts as dict {tknx: x, tkny: y} - """ - assert self.tknx in pvec, f"pvec must contain price for {self.tknx} [{pvec.keys()}]" - assert self.tkny in pvec, f"pvec must contain price for {self.tkny} [{pvec.keys()}]" - p = pvec[self.tknx] / pvec[self.tkny] - x, y, _ = self.xyfromp_f(p, ignorebounds=ignorebounds) - return {self.tknx: x, self.tkny: y} - - def dxdyfromp_f(self, p=None, *, ignorebounds=False, withunits=False): - """like xyfromp_f, but returns dx,dy,p instead of x,y,p""" - x, y, p = self.xyfromp_f(p, ignorebounds=ignorebounds) - dx = x - self.x - dy = y - self.y - if withunits: - return dx, dy, p, self.tknxp, self.tknyp, self.pairp - return dx, dy, p - - def dxvecfrompvec_f(self, pvec, *, ignorebounds=False): - """ - alternative API to dxdyfromp_f - - :pvec: a dict containing all prices; the dict must contain the keys - for tknx and for tkny and the associated value must be the respective - price in any numeraire (only the ratio is used) - :returns: token difference amounts as dict {tknx: dx, tkny: dy} - """ - assert self.tknx in pvec, f"pvec must contain price for {self.tknx} [{pvec.keys()}]" - assert self.tkny in pvec, f"pvec must contain price for {self.tkny} [{pvec.keys()}]" - p = pvec[self.tknx] / pvec[self.tkny] - dx, dy, _ = self.dxdyfromp_f(p, ignorebounds=ignorebounds) - return {self.tknx: dx, self.tkny: dy} - - def yfromx_f(self, x, *, ignorebounds=False): - "y value for given x value (if in range; None otherwise)" - if self.is_constant_product(): - y = self.k / x - else: - y = (self.k / x) ** self.eta - - if ignorebounds: - return y - if not self.inrange(y, self.y_min, self.y_max): - return None - return y - - def xfromy_f(self, y, *, ignorebounds=False): - "x value for given y value (if in range; None otherwise)" - if self.is_constant_product(): - x = self.k / y - else: - x = self.k / (y ** (1/self.eta)) - if ignorebounds: - return x - if not self.inrange(x, self.x_min, self.x_max): - return None - return x - - def dyfromdx_f(self, dx, *, ignorebounds=False): - "dy value for given dx value (if in range; None otherwise)" - y = self.yfromx_f(self.x + dx, ignorebounds=ignorebounds) - if y is None: - return None - return y - self.y - - def dxfromdy_f(self, dy, *, ignorebounds=False): - "dx value for given dy value (if in range; None otherwise)" - x = self.xfromy_f(self.y + dy, ignorebounds=ignorebounds) - if x is None: - return None - return x - self.x - - @property - def dy_min(self): - """minimum (=max negative) possible dy value of this pool (=-y_act)""" - return -self.y_act - - @property - def dx_min(self): - """minimum (=max negative) possible dx value of this pool (=-x_act)""" - return -self.x_act - - @property - def dy_max(self): - """maximum dy value of this pool (=dy(dx_min))""" - if self.x_act < self.x: - return self.dyfromdx_f(self.dx_min) - else: - return None - - @property - def dx_max(self): - """maximum dx value of this pool (=dx(dy_min))""" - if self.y_act < self.y: - return self.dxfromdy_f(self.dy_min) - else: - return None - - @staticmethod - def inrange(v, minv=None, maxv=None): - "True if minv <= v <= maxv; None means no boundary" - if not minv is None: - if v < minv: - return False - if not maxv is None: - if v > maxv: - return False - return True - - EPS = 1e-6 - - def isequal(self, x, y): - "returns True if x and y are equal within EPS" - if x == 0: - return abs(y) < self.EPS - return abs(y / x - 1) < self.EPS - - def isbigger(self, small, big): - "returns True if small is bigger than big within EPS (small, big > 0)" - if small == 0: - return big > self.EPS - return big / small > 1 + self.EPS - - def plot(self, xmin=None, xmax=None, steps=None, *, xvals=None, func=None, show=False, title=None, xlabel=None, ylabel=None, grid=True, **params): - """ - plots the curve associated with this pool - - :xmin, xmax, steps: x range (args for np.linspace) - :xvals: x values (alternative to xmin, xmax, steps) - :func: function to plot (default: dyfrpmdx_f) - :show: if True, call plt.show() - :title: plot title - :xlabel, ylabel: axis labels - :grid: if True [False], [do not] show grid; None: ignore - :params: additional kwargs passed to plt.plot - """ - if xvals is None: - assert not xmin is None, "xmin must not be None if xv is None" - assert not xmax is None, "xmin must not be None if xv is None" - x_v = np.linspace(xmin, xmax, steps) if steps else np.linspace(xmin, xmax) - else: - assert xmin is None, "xmin must be None if xv is not None" - assert xmax is None, "xmax must be None if xv is not None" - assert steps is None, "steps must be None if xv is not None" - x_v = xvals - - xlabel = xlabel or (f"dx [{self.tknx}]" if not func else "x") - ylabel = ylabel or (f"dy [{self.tkny}]" if not func else "y") - func = func or self.dyfromdx_f - #print("moo", self.cid, self.cid is None, 'self.cid' if self.cid else 'NO') - title = title or f"Invariance curve {self.pairp} {self.cid if (self.cid and not self.cid=='None') else ''}" - - y_v = [func(xx) for xx in x_v] - result = plt.plot(x_v, y_v, **params) - plt.title(title) - plt.xlabel(xlabel) - plt.ylabel(ylabel) - if not grid is None: - plt.grid(grid) - if show: - plt.show() - return result - - @staticmethod - def digest(datastr, len=4): - """returns a digest of a string of a certain length""" - return digest(str(datastr).encode()).hexdigest()[:len] - - -@dataclass -class CPCContainer: - """ - container for ConstantProductCurve objects (use += to add items) - - :curves: an iterable of CPC curves, possibly wrapped in CPCInverter objects - CPCInverter objects are unwrapped automatically, the resulting - list will ALWAYS be curves, possibly with inverted=True - :tokenscale: a TokenScaleBase object (or None, in which case the default) - this object contains indicative prices for the tokens which are - sometimes useful for numerical stability reasons; the default token - scale is unity across all tokens - """ - - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - Pair = Pair - - curves: list = field(default_factory=list) - tokenscale: ts.TokenScaleBase = field(default=None, repr=False) - - def __post_init__(self): - - if self.tokenscale is None: - self.tokenscale = self.TOKENSCALE - # print("[CPCContainer] tokenscale =", self.tokenscale) - - # ensure that the curves are in a list (they can be provided as any - # iterable, e.g. a generator); also unwraps CPCInverter objects - # if need be - self.curves = [c for c in CPCInverter.unwrap(self.curves)] - - for i, c in enumerate(self.curves): - if c.cid is None: - # print("[__post_init__] setting cid", i) - c.setcid(i) - else: - # print("[__post_init__] cid already set", c.cid) - pass - c.set_tokenscale(self.tokenscale) - - self.curves_by_cid = {c.cid: c for c in self.curves} - self.curveix_by_curve = {c: i for i, c in enumerate(self.curves)} - # self.curves_by_primary_pair = {c.pairo.primary: c for c in self.curves} - self.curves_by_primary_pair = {} - for c in self.curves: - try: - self.curves_by_primary_pair[c.pairo.primary].append(c) - except KeyError: - self.curves_by_primary_pair[c.pairo.primary] = [c] - - TOKENSCALE = ts.TokenScale1Data - # default token scale object is the trivial scale (everything one) - # change this to a different scale object be creating a derived class - - def scale(self, tkn): - """returns the scale of tkn""" - return self.tokenscale.scale(tkn) - - def asdicts(self): - """returns list of dictionaries representing the curves""" - return [c.asdict() for c in self.curves] - - def asdf(self): - """returns pandas dataframe representing the curves""" - return pd.DataFrame.from_dict(self.asdicts()).set_index("cid") - - @classmethod - def from_dicts(cls, dicts, *, tokenscale=None): - """alternative constructor: creates a container from a list of dictionaries""" - return cls( - [ConstantProductCurve.from_dict(d) for d in dicts], tokenscale=tokenscale - ) - - @classmethod - def from_df(cls, df, *, tokenscale=None): - "alternative constructor: creates a container from a dataframe representation" - if "cid" in df.columns: - df = df.set_index("cid") - return cls.from_dicts( - df.reset_index().to_dict("records"), tokenscale=tokenscale - ) - - def add(self, item): - """ - adds one or multiple ConstantProductCurves (+= operator is also supported) - - :item: item can be the following types: - :ConstantProductCurve: a single curve is added - :CPCInverter: the curve underlying the inverter is added - :Iterable: all items in the iterable are added one by one - """ - - # unwrap iterables... - try: - for c in item: - self.add(c) - return self - except TypeError: - pass - - # ...and CPCInverter objects - if isinstance(item, CPCInverter): - item = item.curve - - # at this point, item must be a ConstantProductCurve object - assert isinstance( - item, ConstantProductCurve - ), f"item must be a ConstantProductCurve object {item}" - - if item.cid is None: - # print("[add] setting cid to", len(self)) - item.setcid(len(self)) - else: - pass - # print("[add] item.cid =", item.cid) - self.curves_by_cid[item.cid] = item - self.curveix_by_curve[item] = len(self) - self.curves += [item] - # print("[add] ", self.curves_by_primary_pair) - try: - self.curves_by_primary_pair[item.pairo.primary].append(item) - except KeyError: - self.curves_by_primary_pair[item.pairo.primary] = [item] - return self - - def price(self, tknb, tknq): - """returns price of tknb in tknq (tknb per tknq)""" - pairo = Pair.from_tokens(tknb, tknq) - curves = self.curves_by_primary_pair.get(pairo.primary, None) - if curves is None: - return None - pp = sum(c.pp for c in curves) / len(curves) - return pp if pairo.isprimary else 1 / pp - - PR_TUPLE = "tuple" - PR_DICT = "dict" - PR_DF = "df" - def prices(self, result=None, *, inclpair=None, primary=None): - """ - returns tuple or dictionary of the prices of all curves in the container - - :primary: if True (default), returns the price quoted in the convention of the primary pair - :inclpair: if True, includes the pair in the dictionary - :result: what result to return (PR_TUPLE, PR_DICT, PR_DF) - """ - if primary is None: primary = True - if inclpair is None: inclpair = True - if result is None: result = self.PR_DICT - price_g = (( - c.cid, - c.primaryp() if primary else c.p, - c.pairo.primary if primary else c.pair - ) for c in self.curves - ) - - if result == self.PR_TUPLE: - if inclpair: - return tuple(price_g) - else: - return tuple(r[1] for r in price_g) - - if result == self.PR_DICT: - if inclpair: - return {r[0]: (r[1], r[2]) for r in price_g} - else: - return {r[0]: r[1] for r in price_g} - - if result == self.PR_DF: - df = pd.DataFrame.from_records(price_g, columns=["cid", "price", "pair"]) - df = df.set_index("cid") - return df - raise ValueError(f"unknown result type {result}") - - def __iadd__(self, other): - """alias for self.add""" - return self.add(other) - - def __iter__(self): - return iter(self.curves) - - def __len__(self): - return len(self.curves) - - def __getitem__(self, key): - return self.curves[key] - - def __contains__(self, curve): - return curve in self.curveix_by_curve - - def tknys(self, curves=None): - """returns set of all base tokens (tkny) used by the curves""" - if curves is None: - curves = self.curves - return {c.tkny for c in curves} - - def tknyl(self, curves=None): - """returns list of all base tokens (tkny) used by the curves""" - if curves is None: - curves = self.curves - return [c.tkny for c in curves] - - def tknxs(self, curves=None): - """returns set of all quote tokens (tknx) used by the curves""" - if curves is None: - curves = self.curves - return {c.tknx for c in curves} - - def tknxl(self, curves=None): - """returns set of all quote tokens (tknx) used by the curves""" - if curves is None: - curves = self.curves - return [c.tknx for c in curves] - - def tkns(self, curves=None): - """returns set of all tokens used by the curves""" - return self.tknxs(curves).union(self.tknys(curves)) - - tokens = tkns - - def tokens_s(self, curves=None): - """returns set of all tokens used by the curves as a string""" - return ",".join(sorted(self.tokens(curves))) - - def token_count(self, asdict=False): - """ - counts the number of times each token appears in the curves - """ - tokens_l = (c.pair for c in self) - tokens_l = (t.split("/") for t in tokens_l) - tokens_l = (t for t in it.chain.from_iterable(tokens_l)) - tokens_l = list(cl.Counter([t for t in tokens_l]).items()) - tokens_l = sorted(tokens_l, key=lambda x: x[1], reverse=True) - if not asdict: - return tokens_l - return dict(tokens_l) - - def pairs(self, *, standardize=True): - """ - returns set of all pairs used by the curves - - :standardize: if False, the pairs are returned as they are in the curves; eg if we have curves - for both ETH/USDT and USDT/ETH, both pairs will be returned; if True, only the - canonical pair will be returned - """ - if standardize: - return {c.pairo.primary for c in self} - else: - return {c.pair for c in self} - - def cids(self, *, asset=False): - """returns list of all curve ids (as tuple, or set if asset=True)""" - if asset: - return set(c.cid for c in self) - return tuple(c.cid for c in self) - - @staticmethod - def pairset(pairs): - """converts string, list or set of pairs into a set of pairs""" - if isinstance(pairs, str): - pairs = (p.strip() for p in pairs.split(",")) - return set(pairs) - - def make_symmetric(self, df): - """converts df into upper triangular matrix by adding the lower triangle""" - df = df.copy() - fields = df.index.union(df.columns) - df = df.reindex(index=fields, columns=fields) - df = df + df.T - df = df.fillna(0).astype(int) - return df - - FP_ANY = "any" - FP_ALL = "all" - - def filter_pairs(self, pairs=None, *, anyall=FP_ALL, **conditions): - """ - filters the pairs according to the target conditions(s) - - :pairs: list of pairs to filter; if None, all pairs are used - :anyall: how conditions are combined (FP_ANY or FP_ALL) - :conditions: determines the filtering condition; all or any must be met (1, 2) - - - NOTE1: an arbitrary differentiator can be appended to the condition using "_" - (eg onein_1, onein_2, onein_3, ...) allowing to specify multiple conditions - of the same type - - NOTE2: see table below for conditions - - ========= ======================================== - Condition Description - ========= ======================================== - bothin both tokens must be in the list - onein at least one token must be in the list - notin none of the tokens must be in the list - contains alias for onein - tknbin tknb must be in the list - tknbnotin tknb must not be in the list - tknqin tknq must be in the list - tknqnotin tknq must not be in the list - ========= ======================================== - - """ - if pairs is None: - pairs = self.pairs() - if not conditions: - return pairs - pairs = self.Pair.wrap(pairs) - results = [] - for condition in conditions: - cpairs = self.pairset(conditions[condition]) - condition0 = condition.split("_")[0] - # print(f"condition: {condition} | {condition0} [{conditions[condition]}]") - if condition0 == "bothin": - results += [ - {str(p) for p in pairs if p.tknb in cpairs and p.tknq in cpairs} - ] - elif condition0 == "contains" or condition0 == "onein": - results += [ - {str(p) for p in pairs if p.tknb in cpairs or p.tknq in cpairs} - ] - elif condition0 == "notin": - results += [ - { - str(p) - for p in pairs - if p.tknb not in cpairs and p.tknq not in cpairs - } - ] - elif condition0 == "tknbin": - results += [{str(p) for p in pairs if p.tknb in cpairs}] - elif condition0 == "tknbnotin": - results += [{str(p) for p in pairs if p.tknb not in cpairs}] - elif condition0 == "tknqin": - results += [{str(p) for p in pairs if p.tknq in cpairs}] - elif condition0 == "tknqnotin": - results += [{str(p) for p in pairs if p.tknq not in cpairs}] - else: - raise ValueError(f"unknown condition {condition}") - - # print(f"results: {results}") - if anyall == self.FP_ANY: - # print(f"anyall = {anyall}: union") - return set.union(*results) - elif anyall == self.FP_ALL: - # print(f"anyall = {anyall}: intersection") - return set.intersection(*results) - else: - raise ValueError(f"unknown anyall {anyall}") - - def fp(self, pairs=None, **conditions): - """alias for filter_pairs (for interactive use)""" - return self.filter_pairs(pairs, **conditions) - - def fpb(self, bothin, pairs=None, *, anyall=FP_ALL, **conditions): - """alias for filter_pairs bothin (for interactive use)""" - return self.filter_pairs( - pairs=pairs, bothin=bothin, anyall=anyall, **conditions - ) - - def fpo(self, onein, pairs=None, *, anyall=FP_ALL, **conditions): - """alias for filter_pairs onein (for interactive use)""" - return self.filter_pairs(pairs=pairs, onein=onein, anyall=anyall, **conditions) - - @classmethod - def _record(cls, c=None): - """returns the record (or headings, if none) for the pair c""" - if not c is None: - p = cls.Pair(c.pair) - return ( - c.tknx, - c.tkny, - c.tknb, - c.tknq, - p.pair, - p.primary, - p.isprimary, - c.p, - p.pp(c.p), - c.x, - c.x_act, - c.y, - c.y_act, - c.cid, - ) - else: - return ( - "tknx", - "tkny", - "tknb", - "tknq", - "pair", - "primary", - "isprimary", - "p", - "pp", - "x", - "xa", - "y", - "ya", - "cid", - ) - - AT_LIST = "list" - AT_LISTDF = "listdf" - AT_VOLUMES = "volumes" - AT_VOLUMESAGG = "vaggr" - AT_VOLSAGG = "vaggr" - AT_PIVOTXY = "pivotxy" - AT_PIVOTXYS = "pivotxys" - AT_PIVOTBQ = "pivotbq" - AT_PIVOTBQS = "pivotbqs" - AT_PRICES = "prices" - AT_MAX = "max" - AT_MIN = "min" - AT_SD = "std" - AT_SDPC = "stdpc" - AT_PRICELIST = "pricelist" - AT_PRICELISTAGG = "plaggr" - AT_PLAGG = "plaggr" - - def pairs_analysis(self, *, target=AT_PIVOTBQ, pretty=False, pairs=None, **params): - """ - returns a dataframe with the analysis of the pairs according to the analysis target - - :target: :AT_LIST: list of pairs and associated data - :AT_LISTDF: ditto but as a dataframe - :AT_VOLUMES: list of volume per token and curve - :AT_VOLSAGG: ditto but also aggregated by curve - :AT_PIVOTXY: pivot table number of pairs tknx/tkny - :AT_PIVOTBQ: ditto but with tknb/tknq - :AT_PIVOTXYS: above anlysis but symmetric matrix (1) - :AT_PIVOTBQS: ditto - :AT_PRICES: average prices per (directed) pair - :AT_MAX: ditto max - :AT_MIN: ditto min - :AT_SD: ditto price standard deviation - :AT_SDPC: ditto percentage standard deviation - :AT_PRICELIST: list of prices per curve - :AT_PLAGG: list of prices aggregated by pair - :pretty: in some cases, returns a prettier but less useful result - :pairs: list of pairs to analyze; if None, all pairs - :params: kwargs that some of the analysis targets may use - - NOTE1: eg ETH/USDC would appear in ETH/USDC and in USDC/ETH - """ - record = self._record - cols = self._record() - - if pairs is None: - pairs = self.pairs() - curvedata = (record(c) for c in self.bypairs(pairs)) - if target == self.AT_LIST: - return tuple(curvedata) - df = pd.DataFrame(curvedata, columns=cols) - if target == self.AT_LISTDF: - return df - - if target == self.AT_VOLUMES or target == self.AT_VOLSAGG: - dfb = ( - df[["tknb", "cid", "x", "xa"]] - .copy() - .rename(columns={"tknb": "tkn", "x": "amtv", "xa": "amt"}) - ) - dfq = ( - df[["tknq", "cid", "y", "ya"]] - .copy() - .rename(columns={"tknq": "tkn", "y": "amtv", "ya": "amt"}) - ) - df1 = pd.concat([dfb, dfq], axis=0) - df1 = df1.sort_values(["tkn", "cid"]) - if target == self.AT_VOLUMES: - df1 = df1.set_index(["tkn", "cid"]) - df1["lvg"] = df1["amtv"] / df1["amt"] - return df1 - df1["n"] = (1,) * len(df1) - # df1 = df1.groupby(["tkn"]).sum() - df1 = df1.pivot_table( - index="tkn", - values=["amtv", "amt", "n"], - aggfunc={ - "amtv": ["sum", AF.herfindahl, AF.herfindahlN], - "amt": ["sum", AF.herfindahl, AF.herfindahlN], - "n": "count", - }, - ) - price_eth = ( - self.price(tknb=t, tknq=T.ETH) if t != T.ETH else 1 for t in df1.index - ) - df1["price_eth"] = tuple(price_eth) - df1["amtv_eth"] = df1[("amtv", "sum")] * df1["price_eth"] - df1["amt_eth"] = df1[("amt", "sum")] * df1["price_eth"] - df1["lvg"] = df1["amtv_eth"] / df1["amt_eth"] - return df1 - - if target == self.AT_PIVOTXY or target == self.AT_PIVOTXYS: - pivot = ( - df.pivot_table( - index="tknx", columns="tkny", values="tknb", aggfunc="count" - ) - .fillna(0) - .astype(int) - ) - if target == self.AT_PIVOTXY: - return pivot - return self.make_symmetric(pivot) - - if target == self.AT_PIVOTBQ or target == self.AT_PIVOTBQS: - pivot = ( - df.pivot_table( - index="tknb", columns="tknq", values="tknx", aggfunc="count" - ) - .fillna(0) - .astype(int) - ) - if target == self.AT_PIVOTBQ: - if pretty: - return pivot.replace(0, "") - return pivot - pivot = self.make_symmetric(pivot) - if pretty: - return pivot.replace(0, "") - return pivot - - if target == self.AT_PRICES: - pivot = df.pivot_table( - index="tknb", columns="tknq", values="p", aggfunc="mean" - ) - pivot = pivot.fillna(0).astype(float) - if pretty: - return pivot.replace(0, "") - return pivot - - if target == self.AT_MAX: - pivot = df.pivot_table( - index="tknb", columns="tknq", values="p", aggfunc=np.max - ) - pivot = pivot.fillna(0).astype(float) - if pretty: - return pivot.replace(0, "") - return pivot - - if target == self.AT_MIN: - pivot = df.pivot_table( - index="tknb", columns="tknq", values="p", aggfunc=np.min - ) - pivot = pivot.fillna(0).astype(float) - if pretty: - return pivot.replace(0, "") - return pivot - - if target == self.AT_SD: - pivot = df.pivot_table( - index="tknb", columns="tknq", values="p", aggfunc=np.std - ) - pivot = pivot.fillna(0).astype(float) - if pretty: - return pivot.replace(0, "") - return pivot - - if target == self.AT_SDPC: - pivot = df.pivot_table( - index="tknb", columns="tknq", values="p", aggfunc=AF.sdpc - ) - if pretty: - return pivot.replace(0, "") - return pivot - - if target == self.AT_PRICELIST: - pivot = df.pivot_table( - index=["tknb", "tknq", "cid"], - values=["primary", "pair", "pp", "p"], - aggfunc={ - "primary": AF.first, - "pair": AF.first, - "pp": "mean", - "p": "mean", - }, - ) - return pivot - - if target == self.AT_PRICELISTAGG: # AT_PLAGG - aggfs = [ - "mean", - "count", - AF.sdpc100, - min, - max, - AF.rangepc100, - AF.herfindahl, - ] - pivot = df.pivot_table( - index=["tknb", "tknq"], - values=["primary", "pair", "pp"], - aggfunc={"primary": AF.first, "pp": aggfs}, - ) - return pivot - - raise ValueError(f"unknown target {target}") - - def _convert(self, generator, *, asgenerator=None, ascc=None): - """takes a generator and returns a tuple, generator or CC object""" - if asgenerator is None: - asgenerator = False - if ascc is None: - ascc = True - if asgenerator: - return generator - if ascc: - return self.__class__(generator, tokenscale=self.tokenscale) - return tuple(generator) - - def curveix(self, curve): - """returns index of curve in container""" - return self.curveix_by_curve.get(curve, None) - - def bycid(self, cid): - """returns curve by cid""" - return self.curves_by_cid.get(cid, None) - - def bycids(self, include=None, *, endswith=None, exclude=None, asgenerator=None, ascc=None): - """ - returns curves by cids (as tuple, generator or CC object) - - :include: list of cids to include, if None all cids are included - :endswith: alternative to include, include all cids that end with this string - :exclude: list of cids to exclude, if None no cids are excluded - exclude beats include - :returns: tuple, generator or container object (default) - """ - if not include is None and not endswith is None: - raise ValueError(f"include and endswith cannot be used together") - if exclude is None: - exclude = set() - if include is None and endswith is None: - result = (c for c in self if not c.cid in exclude) - else: - if not include is None: - result = (self.curves_by_cid[cid] for cid in include if not cid in exclude) - else: - result = (c for c in self if c.cid.endswith(endswith) and not c.cid in exclude) - return self._convert(result, asgenerator=asgenerator, ascc=ascc) - - def bycid0(self, cid0, **kwargs): - """alias for bycids(endswith=cid0)""" - return self.bycids(endswith=cid0, **kwargs) - - def bypair(self, pair, *, directed=False, asgenerator=None, ascc=None): - """returns all curves by (possibly directed) pair (as tuple, genator or CC object)""" - result = (c for c in self if c.pair == pair) - if not directed: - pairr = "/".join(pair.split("/")[::-1]) - result = it.chain(result, (c for c in self if c.pair == pairr)) - return self._convert(result, asgenerator=asgenerator, ascc=ascc) - - def bp(self, pair, *, directed=False, asgenerator=None, ascc=None): - """alias for bypair by with directed=False for interactive use""" - return self.bypair(pair, directed=directed, asgenerator=asgenerator, ascc=ascc) - - def bypairs(self, pairs=None, *, directed=False, asgenerator=None, ascc=None): - """ - returns all curves by (possibly directed) pairs (as tuple, generator or CC object) - - :pairs: set, list or comma-separated string of pairs; if None all pairs are included - :directed: if True, pair direction is important (eg ETH/USDC will not return USDC/ETH - pairs); if False, pair direction is ignored and both will be returned - :returns: tuple, generator or container object (default) - """ - if isinstance(pairs, str): - pairs = set(pairs.split(",")) - if pairs is None: - result = (c for c in self) - else: - pairs = set(pairs) - if not directed: - rpairs = set(f"{q}/{b}" for b, q in (p.split("/") for p in pairs)) - # print("[CC] bypairs: adding reverse pairs", rpairs) - pairs = pairs.union(rpairs) - result = (c for c in self if c.pair in pairs) - return self._convert(result, asgenerator=asgenerator, ascc=ascc) - - def byparams(self, *, _asgenerator=None, _ascc=None, _inv=False, **params): - """ - returns all curves by params (as tuple, generator or CC object) - - :_inv: if True, returns all curves that do NOT match the params - :params: keyword arguments in the form param=value - :returns: tuple, generator or container object (default) - """ - if not params: - raise ValueError(f"no params given {params}") - - params_t = tuple(params.items()) - if len(params_t) > 1: - raise NotImplementedError(f"currently only one param allowed {params}") - - pname, pvalue = params_t[0] - if _inv: - result = (c for c in self if c.P(pname) != pvalue) - else: - result = (c for c in self if c.P(pname) == pvalue) - return self._convert(result, asgenerator=_asgenerator, ascc=_ascc) - - def copy(self): - """returns a copy of the container""" - return self.bypairs(ascc=True) - - def bytknx(self, tknx, *, asgenerator=None, ascc=None): - """returns all curves by quote token tknx (tknq) (as tuple, generator or CC object)""" - result = (c for c in self if c.tknx == tknx) - return self._convert(result, asgenerator=asgenerator, ascc=ascc) - - bytknq = bytknx - - def bytknxs(self, tknxs=None, *, asgenerator=None, ascc=None): - """returns all curves by quote token tknx (tknq) (as tuple, generator or CC object)""" - if tknxs is None: - return self.curves - if isinstance(tknxs, str): - tknxs = set(t.strip() for t in tknxs.split(",")) - tknxs = set(tknxs) - result = (c for c in self if c.tknx in tknxs) - return self._convert(result, asgenerator=asgenerator, ascc=ascc) - - bytknxs = bytknxs - - def bytkny(self, tkny, *, asgenerator=None, ascc=None): - """returns all curves by base token tkny (tknb) (as tuple, generator or CC object)""" - result = (c for c in self if c.tkny == tkny) - return self._convert(result, asgenerator=asgenerator, ascc=ascc) - - bytknb = bytkny - - def bytknys(self, tknys=None, *, asgenerator=None, ascc=None): - """returns all curves by quote token tkny (tknb) (as tuple, generator or CC object)""" - if tknys is None: - return self.curves - if isinstance(tknys, str): - tknys = set(t.strip() for t in tknys.split(",")) - tknys = set(tknys) - result = (c for c in self if c.tkny in tknys) - return self._convert(result, asgenerator=asgenerator, ascc=ascc) - - bytknys = bytknys - - @staticmethod - def u(minx, maxx): - """helper: returns uniform random var""" - return random.uniform(minx, maxx) - - @staticmethod - def u1(): - """helper: returns uniform [0,1] random var""" - return random.uniform(0, 1) - - @dataclass - class xystatsd: - mean: any - minv: any - maxv: any - sdev: any - - def xystats(self, curves=None): - """calculates mean, min, max, stdev of x and y""" - if curves is None: - curves = self.curves - tknx = {c.tknq for c in curves} - tkny = {c.tknb for c in curves} - assert len(tknx) != 0 and len(tkny) != 0, f"no curves found {tknx} {tkny}" - assert ( - len(tknx) == 1 and len(tkny) == 1 - ), f"all curves must have same tknq and tknb {tknx} {tkny}" - x = [c.x for c in curves] - y = [c.y for c in curves] - return ( - self.xystatsd(np.mean(x), np.min(x), np.max(x), np.std(x)), - self.xystatsd(np.mean(y), np.min(y), np.max(y), np.std(y)), - ) - - PE_PAIR = "pair" - PE_CURVES = "curves" - PE_DATA = "data" - - def price_estimate( - self, *, tknq=None, tknb=None, pair=None, result=None, raiseonerror=True - ): - """ - calculates price estimate in the reference token as base token - - :tknq: quote token to calculate price for - :tknb: base token to calculate price for - :pair: alternative to tknq, tknb: pair to calculate price for - :raiseonerror: if True, raise exception if no price can be calculated - :result: what to return - :PE_PAIR: slashpair - :PE_CURVES: curves - :PE_DATA: prices, weights - :returns: price (quote per base) - """ - assert tknq is not None and tknb is not None or pair is not None, ( - f"must specify tknq, tknb or pair [{tknq}, {tknb}, {pair}]" - ) - assert not (not tknb is None and not pair is None), f"must not specify both tknq, tknb and pair [{tknq}, {tknb}, {pair}]" - - if not pair is None: - tknb, tknq = pair.split("/") - if tknq == tknb: - return 1 - if result == self.PE_PAIR: - return f"{tknb}/{tknq}" - crvs = ( - c for c in self if not c.at_boundary and c.tknq == tknq and c.tknb == tknb - ) - rcrvs = ( - c for c in self if not c.at_boundary and c.tknb == tknq and c.tknq == tknb - ) - crvs = ((c, c.p, c.k) for c in crvs) - rcrvs = ((c, 1 / c.p, c.k) for c in rcrvs) - acurves = it.chain(crvs, rcrvs) - if result == self.PE_CURVES: - # return dict(curves=tuple(crvs), rcurves=tuple(rcrvs)) - return tuple(acurves) - data = tuple((r[1], sqrt(r[2])) for r in acurves) - if not len(data) > 0: - if raiseonerror: - raise ValueError(f"no curves found for {tknq}/{tknb}") - return None - prices, weights = zip(*data) - prices, weights = np.array(prices), np.array(weights) - if result == self.PE_DATA: - return prices, weights - return float(np.average(prices, weights=weights)) - - TRIANGTOKENS = f"{T.USDT}, {T.USDC}, {T.DAI}, {T.BNT}, {T.ETH}, {T.WBTC}" - - def price_estimates( - self, - *, - tknqs=None, - tknbs=None, - triangulate=True, - unwrapsingle=True, - pairs=False, - stopatfirst=True, - raiseonerror=True, - verbose=False, - ): - """ - calculates prices estimates in the reference token as base token - - :tknqs: list of quote tokens to calculate prices for - :tknbs: list of base tokens to calculate prices for - :triangulate: tokens used as intermediate token for triangulation; if True, a standard - token list is used; if None or False, no triangulation - :unwrapsingle: if there is only one quote token, a 1-d array is returned - :pairs: if True, returns the slashpairs instead of the prices - :raiseonerror: if True, raise exception if no price can be calculated - :stopatfirst: it True, stop at first triangulation match - :verbose: if True, print some progress - :return: np.array of prices (quote outer, base inner; quote per base) - """ - # NOTE: this code is relatively slow to compute, on the order of a few seconds - # for go through the entire token list; the likely reason is that it keeps reestablishing - # the CPCContainer objects whenever price_estimate is called; there may be a way to - # speed this up by smartly computing the container objects once and storing them - # in a dictionary the is then passed to price_estimate. - start_time = time.time() - assert not tknqs is None, "tknqs must be set" - assert not tknbs is None, "tknbs must be set" - if isinstance(tknqs, str): - tknqs = [t.strip() for t in tknqs.split(",")] - if isinstance(tknbs, str): - tknbs = [t.strip() for t in tknbs.split(",")] - # print(f"[price_estimates] tknqs [{len(tknqs)}], tknbs [{len(tknbs)}]") - # print(f"[price_estimates] tknqs [{len(tknqs)}] = {tknqs} , tknbs [{len(tknbs)}]] = {tknbs} ") - resulttp = self.PE_PAIR if pairs else None - result = np.array( - [ - [ - self.price_estimate(tknb=b, tknq=q, raiseonerror=False, result=resulttp) - for b in tknbs - ] - for q in tknqs - ] - ) - #print(f"[price_estimates] PAIRS [{time.time()-start_time:.2f}s]") - flattened = result.flatten() - nmissing = len([r for r in flattened if r is None]) - if verbose: - print(f"[price_estimates] pair estimates: {len(flattened)-nmissing} found, {nmissing} missing") - if nmissing > 0 and not triangulate: - print(f"[price_estimates] {nmissing} missing pairs may be triangulated, but triangulation disabled [{triangulate}]") - if nmissing == 0 and triangulate: - print(f"[price_estimates] no missing pairs, triangulation not needed") - - if triangulate and nmissing > 0: - if triangulate is True: - triangulate = self.TRIANGTOKENS - if isinstance(triangulate, str): - triangulate = [t.strip() for t in triangulate.split(",")] - if verbose: - print("[price_estimates] triangulation tokens", triangulate) - for ib, b in enumerate(tknbs): - #print(f"TOKENB={b:22} [{time.time()-start_time:.4f}s]") - for iq, q in enumerate(tknqs): - #print(f" TOKENQ={q:21} [{time.time()-start_time:.4f}s]") - if result[iq][ib] is None: - result1 = [] - for tkn in triangulate: - #print(f" TKN={tkn:23} [{time.time()-start_time:.4f}s]") - #print(f"[price_estimates] triangulating tknb={b} tknq={q} via {tkn}") - b_tkn = self.price_estimate(tknb=b, tknq=tkn, raiseonerror=False) - q_tkn = self.price_estimate(tknb=q, tknq=tkn, raiseonerror=False) - #print(f"[price_estimates] triangulating {b}/{tkn} = {b_tkn}, {q}/{tkn} = {q_tkn}") - if not b_tkn is None and not q_tkn is None: - if verbose: - print(f"[price_estimates] triangulated {b}/{q} via {tkn} [={b_tkn/q_tkn}]") - result1 += [b_tkn / q_tkn] - if stopatfirst: - #print(f"[price_estimates] stop at first") - break - # else: - # print(f"[price_estimates] continue {stopatfirst}") - result2 = np.mean(result1) if len(result1) > 0 else None - #print(f"[price_estimates] final result {b}/{q} = {result2} [{len(result1)}]") - result[iq][ib] = result2 - - flattened = result.flatten() - nmissing = len([r for r in flattened if r is None]) - if verbose: - if nmissing > 0: - missing = { - f"{b}/{q}" - for ib, b in enumerate(tknbs) - for iq, q in enumerate(tknqs) - if result[iq][ib] is None - } - print(f"[price_estimates] after triangulation {nmissing} missing", missing) - else: - print("[price_estimates] no missing pairs after triangulation") - if raiseonerror: - missing = { - f"{b}/{q}" - for ib, b in enumerate(tknbs) - for iq, q in enumerate(tknqs) - if result[iq][ib] is None - } - # print("[price_estimates] result", result) - if not len(missing) == 0: - raise ValueError( - f"no price found for {len(missing)} pairs", - result, - missing, - len(missing), - ) - - #print(f"[price_estimates] DONE [{time.time()-start_time:.2f}s]") - if unwrapsingle and len(tknqs) == 1: - result = result[0] - return result - - @dataclass - class TokenTableEntry: - """ - associates a single token with the curves on which they appear - """ - - x: list - y: list - - def __repr__(self): - return f"TTE(x={self.x}, y={self.y})" - - def __len__(self): - return len(self.x) + len(self.y) - - def tokentable(self, curves=None): - """returns dict associating tokens with the curves on which they appeay""" - - if curves is None: - curves = self.curves - - r = ( - ( - tkn, - self.TokenTableEntry( - x=[i for i, c in enumerate(curves) if c.tknb == tkn], - y=[i for i, c in enumerate(curves) if c.tknq == tkn], - ), - ) - for tkn in self.tkns() - ) - r = {r[0]: r[1] for r in r if len(r[1]) > 0} - return r - - Params = Params - PLOTPARAMS = Params( - printline="pair = {c.pairp}", # print line before plotting; {pair} is replaced - title="{c.pairp}", # plot title; {pair} and {c} are replaced - xlabel="{c.tknxp}", # x axis label; ditto - ylabel="{c.tknyp}", # y axis label; ditto - label="[{c.cid}-{p.exchange}]: p={c.p:.1f}, 1/p={pinv:.1f}, k={c.k:.1f}", # label for legend; ditto - marker="*", # marker for plot - plotf=dict( - color="lightgrey", linestyle="dotted" - ), # additional kwargs for plot of the _f_ull curve - plotr=dict(color="grey"), # ditto for the _r_ange - plotm=dict(), # dittto for the _m_arker - grid=True, # plot grid if True - legend=True, # plot legend if True - show=True, # finish with plt.show() if True - xlim=None, # x axis limits (as tuple) - ylim=None, # y axis limits (as tuple) - npoints=500, # number of points to plot - ) - - def plot(self, *, pairs=None, directed=False, curves=None, params=None): - """ - plots the curves in curvelist or all curves if None - - :pairs: list of pairs to plot - :curves: list of curves to plot - :directed: if True, only plot pairs provided; otherwise plot reverse pairs as well - :params: plot parameters, as params struct (see PLOTPARAMS) - """ - p = Params.construct(params, defaults=self.PLOTPARAMS.params) - - if pairs is None: - pairs = self.pairs() - - if isinstance(pairs, str): - pairs = [pairs] # necessary, lest we get a set of chars - - pairs = set(pairs) - - if not directed: - rpairs = set(f"{q}/{b}" for b, q in (p.split("/") for p in pairs)) - # print("[CC] plot: adding reverse pairs", rpairs) - pairs = pairs.union(rpairs) - - assert curves is None, "restricting curves not implemented yet" - - for pair in pairs: - # pairp = Pair.prettify_pair(pair) - curves = self.bypair(pair, directed=True, ascc=False) - # print("plot", pair, [c.pair for c in curves]) - if len(curves) == 0: - continue - if p.printline: - print(p.printline.format(c=curves[0], p=curves[0].params)) - statx, staty = self.xystats(curves) - #print(f"[CC::plot] stats x={statx}, y={staty}") - xr = np.linspace(0.0000001, statx.maxv * 1.2, int(p.npoints)) - for i, c in enumerate(curves): - # plotf is the full curve - plt.plot( - xr, [c.yfromx_f(x_, ignorebounds=True) for x_ in xr], **p.plotf - ) - # plotr is the curve with bounds - plt.plot(xr, [c.yfromx_f(x_) for x_ in xr], **p.plotr) - - plt.gca().set_prop_cycle(None) - for c in curves: - # plotm are the markers - label = ( - None - if not p.label - else p.label.format(c=c, p=AD(dct=c.params), pinv=1 / c.p) - ) - plt.plot(c.x, c.y, marker=p.marker, label=label, **p.plotm) - - plt.title(p.title.format(c=c, p=c.params)) - if p.xlim: - plt.xlim(p.xlim) - if p.ylim: - plt.ylim(p.ylim) - else: - plt.ylim((0, staty.maxv * 2)) - plt.xlabel(p.xlabel.format(c=c, p=c.params)) - plt.ylabel(p.ylabel.format(c=c, p=c.params)) - - if p.legend: - if isinstance(p.legend, dict): - plt.legend(**p.legend) - else: - plt.legend() - - if p.grid: - if isinstance(p.grid, dict): - plt.grid(**p.grid) - else: - plt.grid(True) - - if p.show: - if isinstance(p.show, dict): - plt.show(**p.show) - else: - plt.show() - - def format(self, *, heading=True, formatid=None): - """ - returns the results in the given (printable) format - - see help(CPCContainer.print_formatted) for details - """ - assert len(self.curves) > 0, "no curves to print" - s = "\n".join(c.format(formatid=formatid) for c in self.curves) - if heading: - s = f"{self.curves[0].format(heading=True, formatid=formatid)}\n{s}" - return s - - -class AF: - """aggregator functions (for pivot tables)""" - - @staticmethod - def range(x): - return np.max(x) - np.min(x) - - @staticmethod - def rangepc(x): - mx = np.max(x) - if mx == 0: - return 0 - return (mx - np.min(x)) / mx - - @classmethod - def rangepc100(cls, x): - return cls.rangepc(x) * 100 - - @staticmethod - def sdpc(x): - return np.std(x) / np.mean(x) - - @classmethod - def sdpc100(cls, x): - return cls.sdpc(x) * 100 - - @staticmethod - def first(x): - return x.iloc[0] - - @staticmethod - def herfindahl(x): - return np.sum(x**2) / np.sum(x) ** 2 - - @classmethod - def herfindahlN(cls, x): - return 1 / cls.herfindahl(x) - - -@dataclass -class CPCInverter: - """ - adaptor class the allows for reverse-pair functions to be used as if they were of the same pair - """ - - curve: ConstantProductCurve - - @classmethod - def wrap(cls, curves, *, asgenerator=False): - """ - wraps an iterable of curves in CPCInverters if needed and returns a tuple (or generator) - - NOTE: only curves with ``c.pairo.isprimary == False`` are wrapped, the other ones are included - as they are; this ensures that for all returned curves that correspond to the same actual - pair, the primary pair is the same - """ - result = (cls(c) if not c.pairo.isprimary else c for c in curves) - if asgenerator: - return result - return tuple(result) - - @classmethod - def unwrap(cls, wrapped_curves, *, asgenerator=False): - """ - unwraps an iterable of curves from CPCInverters if needed and returns a tuple (or generator) - """ - result = (c.curve if isinstance(c, cls) else c for c in wrapped_curves) - if asgenerator: - return result - return tuple(result) - - @property - def cid(self): - return self.curve.cid - - @property - def tknxp(self): - return self.curve.tknyp - - @property - def tknyp(self): - return self.curve.tknxp - - @property - def tknx(self): - return self.curve.tkny - - @property - def tkny(self): - return self.curve.tknx - - @property - def tknb(self): - return self.curve.tknq - - @property - def tknq(self): - return self.curve.tknb - - @property - def tknbp(self): - return self.curve.tknqp - - @property - def tknqp(self): - return self.curve.tknbp - - @property - def p(self): - return 1 / self.curve.p - - def P(self, *args, **kwargs): - return self.curve.P(*args, **kwargs) - - @property - def fee(self): - return self.curve.fee - - def p_convention(self): - """price convention for p (dy/dx)""" - return f"{self.tknyp} per {self.tknxp}" - - @property - def x(self): - return self.curve.y - - @property - def y(self): - return self.curve.x - - @property - def k(self): - return self.curve.k - - @property - def pair(self): - return f"{self.tknb}/{self.tknq}" - - @property - def primary(self): - "alias for self.pairo.primary [pair]" - return self.pairo.primary - - @property - def pairp(self): - "prety pair (without the -xxx part)" - return f"{self.tknbp}/{self.tknqp}" - - @property - def primaryp(self): - "pretty primary pair (without the -xxx part)" - tokens = self.primary.split("/") - tokens = [t.split("-")[0] for t in tokens] - return "/".join(tokens) - - @property - def x_min(self): - return self.curve.y_min - - @property - def x_max(self): - return self.curve.y_max - - @property - def y_min(self): - return self.curve.x_min - - @property - def y_max(self): - return self.curve.x_max - - @property - def x_act(self): - return self.curve.y_act - - @property - def p_min(self): - return 1 / self.curve.p_max - - @property - def p_max(self): - return 1 / self.curve.p_min - - @property - def y_act(self): - return self.curve.x_act - - @property - def pairo(self): - return Pair.from_tokens(tknb=self.tknb, tknq=self.tknq) - - def yfromx_f(self, x, *, ignorebounds=False): - return self.curve.xfromy_f(x, ignorebounds=ignorebounds) - - def xfromy_f(self, y, *, ignorebounds=False): - return self.curve.yfromx_f(y, ignorebounds=ignorebounds) - - def dyfromdx_f(self, dx, *, ignorebounds=False): - return self.curve.dxfromdy_f(dx, ignorebounds=ignorebounds) - - def dxfromdy_f(self, dy, *, ignorebounds=False): - return self.curve.dyfromdx_f(dy, ignorebounds=ignorebounds) - - def xyfromp_f(self, p=None, *, ignorebounds=False, withunits=False): - r = self.curve.xyfromp_f( - 1 / p if not p is None else None, ignorebounds=ignorebounds, withunits=False - ) - if withunits: - return (r[1], r[0], 1 / r[2], self.tknxp, self.tknyp, self.pairp) - return (r[1], r[0], 1 / r[2]) - - def dxdyfromp_f(self, p=None, *, ignorebounds=False, withunits=False): - r = self.curve.dxdyfromp_f( - 1 / p if not p is None else None, ignorebounds=ignorebounds, withunits=False - ) - if withunits: - return (r[1], r[0], 1 / r[2], self.tknxp, self.tknyp, self.pairp) - return (r[1], r[0], 1 / r[2]) - - def execute(self, dx=None, dy=None, *, ignorebounds=False, verbose=False): - """returns a new curve object that is then again wrapped in a CPCInverter""" - curve = self.curve.execute( - dx=dy, dy=dx, ignorebounds=ignorebounds, verbose=verbose - ) - return CPCInverter(curve) - - # TOKENS_NOETH=TOKENS_NOETH - # TOKENIDS=TOKENIDS - - diff --git a/fastlane_bot/tools/cpcbase.py b/fastlane_bot/tools/cpcbase.py deleted file mode 100644 index a34e8d43c..000000000 --- a/fastlane_bot/tools/cpcbase.py +++ /dev/null @@ -1,98 +0,0 @@ -""" -Abstract base class providing the ``Optimizer`` interface for a generic AMM curve - - ---- -(c) Copyright Bprotocol foundation 2023. -Licensed under MIT -""" -from abc import ABC, abstractmethod -from dataclasses import dataclass, field, asdict, InitVar - -try: - dataclass_ = dataclass(frozen=True, kw_only=True) -except: - dataclass_ = dataclass(frozen=True) - - -class AttrDict(dict): - """ - A dictionary that allows for attribute-style access - - see https://stackoverflow.com/questions/4984647/accessing-dict-keys-like-an-attribute - """ - - def __init__(self, *args, **kwargs): - super(AttrDict, self).__init__(*args, **kwargs) - self.__dict__ = self - - -@dataclass_ -class DAttrDict: - """ - attribute-style access to a dictionary with default values - """ - - dct: dict = field(default_factory=dict) - default: any = None - - def __getattr__(self, name): - return self.dct.get(name, self.default) - - -class CurveBase(ABC): - """ - base class for representing a generic curve in the context of the optimizer - """ - - @abstractmethod - def dxvecfrompvec_f(self, pvec, *, ignorebounds=False): - """ - Returns token holding vector ``xvec`` at price vector ``pvec`` - - :pvec: a dict containing all prices; the dict must contain the keys - for ``tknx`` and for ``tkny`` and the associated value must be the respective - price in any numeraire (only the ratio is used) - :returns: token difference amounts as dict ``{tknx: dx, tkny: dy}`` - - EXAMPLE - - .. code-block:: python - - pvec = {"USDC": 1, "ETH": 2000, "WBTC": 40000} - dxvec = curve.dxvecfrompvec_f(pvec) - # --> {"ETH": -20, "WBTC": 1.01} - """ - raise NotImplementedError("dxvecfrompvec_f must be implemented by subclass") - - @abstractmethod - def xvecfrompvec_f(self, pvec, *, ignorebounds=False): - """ - Returns change in token holding vector ``xvec``, ``dxvec``, at price vector ``pvec`` - - :pvec: a dict containing all prices; the dict must contain the keys - for ``tknx`` and for ``tkny`` and the associated value must be the respective - price in any numeraire (only the ratio is used) - :returns: token amounts as dict ``{tknx: x, tkny: y}`` - - EXAMPLE - - .. code-block:: python - - pvec = {"USDC": 1, "ETH": 2000, "WBTC": 40000} - xvec = curve.xvecfrompvec_f(pvec) - # --> {"ETH": 200, "WBTC": 10} - """ - raise NotImplementedError("dxvecfrompvec_f must be implemented by subclass") - - @abstractmethod - def invariant(self, include_target=False): - """ - Returns the current invariant of the curve (1) - - :include_target: if True, the target invariant returned in addition to the actual invariant - :returns: invariant, or (invariant, target) (1) - - NOTE 1: eg for constant product the invariant is :math:`k(x,y)=xy` - """ - raise NotImplementedError("invariant must be implemented by subclass") \ No newline at end of file diff --git a/fastlane_bot/tools/cryptocompare.py b/fastlane_bot/tools/cryptocompare.py deleted file mode 100644 index 59740910d..000000000 --- a/fastlane_bot/tools/cryptocompare.py +++ /dev/null @@ -1,581 +0,0 @@ -""" -Carbon helper module - retrieve data from CryptoCompare -""" -__VERSION__ = "2.1" -__DATE__ = "16/May/2023" - -import os as _os -import pandas as _pd -import hashlib as _hashlib -import requests as _requests -import pickle as _pickle -from collections import namedtuple as _namedtuple - - -pair_t = _namedtuple("pair", "tknb,tknq") - -class CryptoCompare(): - """ - simple class formalizing interaction with the crypto compare API - - :apikeyname: the OS environment variable holding the API key - only used if no `apikey`; default is class.APIKEYNAME - :apikey: the API key; if True use without API key - :datapath: the path where all data is written (and read from) - :raiseonerror: if True, errors usually lead to an exception, otherwise to a None return - """ - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - BASEURL = "https://min-api.cryptocompare.com" # must NOT end with / - APIKEYNAME = "CCAPIKEY" # the name of the environment variable containing the API key - RAISEONERROR = True - DATAPATH = "cryptocompare" - - DEFAULT_TSYM = "usd" - DEFAULT_LIMIT = 2000 - - def __init__(self, *, apikeyname=None, apikey=None, raiseonerror=None, verbose=False): - if raiseonerror is None: - raiseonerror = self.RAISEONERROR - self.raiseonerror = raiseonerror - if not (isinstance(apikey, str) or apikey is None or apikey is True): - raise ValueError("apikey must be a string, None, or True", apikey) - if apikey is None: - if apikeyname is None: - apikeyname = self.APIKEYNAME - apikey = _os.getenv(apikeyname) - if apikey is None: - print(f"Can't find API key {apikeyname} in environment variables.") - print(f"Use `export {apikeyname}=` to set it BEFORE you launch Jupyter") - raise RuntimeError(f"API key not present. Use `export {apikeyname}=` to set it before launching Jupyter.") - self.apikey = apikey - self.verbose = verbose - - def url(self, endpoint): - """ - returns the URL of a given endpoint - """ - return f"{self.BASEURL}{endpoint}" - - @property - def keydigest(self): - """returns signature (=SHA1 hash) of the API key, or 0000... if anonymous""" - if self.apikey is True: - return "0"*40 - return _hashlib.sha1(self.apikey.encode()).hexdigest() - - def datafn(self, fn): - """returns the full data file name, including path""" - return _os.path.join(self.DATAPATH, fn) - - def cache(self, item): - """ - reads a data item from the data cache - """ - try: - with open(self.datafn(f"{item}.pickle"), "rb") as f: - result = _pickle.load(f) - except: - if not self.raiseonerror: - return None - raise - return result - - def write_cache(self, item, data): - """ - writes `data` to the cache under the name `item` - - :returns: `item` on success, None (or raises) on failure - """ - try: - with open(self.datafn(f"{item}.pickle"), "wb") as f: - _pickle.dump(data, f) - except: - if not self.raiseonerror: - return None - raise - return item - - QUERY_GET = "GET" - QUERY_POST = "POST" - def query(self, endpoint, params=None, method=None): - """ - generic API query - - :endpoint: the API endpoint to call, eg "/all/exchanges" - :params: the API parameters (parameters with value None will be removed) - :method: http method; default is QUERY_GET - """ - if method is None: - method = self.QUERY_GET - if params is None: - params = dict() - url = self.url(endpoint) - paramsq = {k:v for k,v in params.items() if not v is None} - if self.verbose: - print("[query]", url, paramsq, f"[{str(self.keydigest)[:4]}]") - if not self.apikey is True: - paramsq["api_key"] = self.apikey - - if method == self.QUERY_GET: - r = _requests.get(url, params=paramsq) - elif method == self.QUERY_POST: - raise ValueError("Method QUERY_POST has not been implemented yet.") - else: - raise ValueError("Unknown method. Use QUERY_XXX constants", method) - - if not r: - if self.raiseonerror: - raise RuntimeError(f"API query not successful (status={r.status})", r) - else: - return None - return r - - def query_allexchanges(self): - """ - endpoint = /data/v4/all/exchanges - - https://min-api.cryptocompare.com/documentation?key=Other&cat=allExchangesV4Endpoint - """ - r = self.query( - endpoint="/data/v4/all/exchanges" - ) - if r is None: return r - return r.json().get("Data") - - - def _cache_xxx(self, item, updatemethod, readonfail=True, updateonfail=False): - """ - generic cached access - - :item: the name of the item in the cache - :updatemethod: the method to call for updating it - :readonfail: if True, on cache miss updatemethod is called - :updateonfail: it True, on cache miss, updatemethod is called an item is - written to cache - """ - if updateonfail: - readonfail = True - try: - return self.cache(item) - except: - print(f"[_cache_xxx] cache miss for item {item}") - if readonfail: - print(f"[_cache_xxx] reading {item} from API") - data = updatemethod() - if updateonfail: - print(f"[_cache_xxx] updating cache for {item} from API") - self.write_cache(item, data) - return data - else: - if self.raiseonerror: - raise - else: - return None - - def cache_allexchanges(self, readonfail=True, updateonfail=False): - """cached access to query_allexchanges""" - return self._cache_xxx( - item="query_allexchanges", - updatemethod=self.query_allexchanges - ) - - def query_ratelimit(self): - """ - endpoint = /stats/rate/limit - - https://min-api.cryptocompare.com/documentation?key=Other&cat=rateLimitEndpoint - """ - r = self.query( - endpoint="/stats/rate/limit" - ) - if r is None: return r - return r.json().get("Data") - - def query_coinlist(self): - """ - endpoint = /data/all/coinlist - - https://min-api.cryptocompare.com/documentation?key=Other&cat=allCoinsWithContentEndpoint - """ - r = self.query( - endpoint="/data/all/coinlist" - ) - if r is None: return r - return r.json().get("Data") - - def cache_coinlist(self, readonfail=True, updateonfail=False): - """cached access to query_coinlist""" - return self._cache_xxx( - item="query_coinlist", - updatemethod=self.query_coinlist - ) - - def query_indexlist(self): - """ - endpoint = /data/index/list - - https://min-api.cryptocompare.com/documentation?key=Index&cat=listOfIndices - """ - r = self.query( - endpoint="/data/index/list" - ) - if r is None: return r - return r.json().get("Data") - - def cache_indexlist(self, readonfail=True, updateonfail=False): - """cached access to query_indexlist""" - return self._cache_xxx( - item="query_indexlist", - updatemethod=self.query_indexlist - ) - - @staticmethod - def ts_tocc(ts): - """ - convert timestamp into format needed by CryptoCompare - - :ts: the timestamp in any format that works for pd.Timestamp(ts) - """ - return int(_pd.Timestamp(ts).timestamp()) - - @staticmethod - def ts_fromcc(ts): - """ - convert timestamp from CryptoCompare format into pd.Timestamp format - """ - return _pd.to_datetime(ts, unit='s', origin='unix') - - FREQ_DAILY = "day" - FD = FREQ_DAILY - FREQ_HOURLY = "hour" - FH = FREQ_HOURLY - FREQ_MINUTELY = "minute" - FM = FREQ_MINUTELY - FREQS = (FREQ_DAILY, FREQ_HOURLY, FREQ_MINUTELY) - def query_freqlypair(self, freq, fsym=None, tsym=None, e=None, limit=False, toTs=None, aspandas=True): - """ - endpoints = /data/v2/histoday, /data/v2/histohour, /data/v2/histominute - - :freq: FREQ_DAILY/FD, FREQ_HOURLY/FH, or FREQ_MINUTELY/FM - :fsym: cryptocurrency symbol of interest - :tsym: currency symbol to convert into - :e: exchange to obtain data from - :limit: number of data points to return (max: 2000; False defaults to that number) - :toTs: returns historical data BEFORE that timestamp - timestamp format either 1452680400 or pd.Timestamp compatible string - - https://min-api.cryptocompare.com/documentation?key=Historical&cat=dataHistoday - https://min-api.cryptocompare.com/documentation?key=Historical&cat=dataHistohour - https://min-api.cryptocompare.com/documentation?key=Historical&cat=dataHistominute - """ - if not freq in self.FREQS: - raise ValueError("Unknow frequency {}. Use the FREQ_XXX constants provided.") - endpoint = f"/data/v2/histo{freq}" - params = { - "fsym": fsym, - "tsym": tsym if not tsym is None else self.DEFAULT_TSYM, - "e": e, - "limit": limit if not limit is False else self.DEFAULT_LIMIT, - "toTs": toTs, - } - r = self.query(endpoint=endpoint, params=params) - if r is None: return r - r_json = r.json() - if r_json.get("Response") == "Error": - if self.raiseonerror: - raise RuntimeError("Query not successful", r, r_json, endpoint, params) - else: - return None - if not aspandas: - return r_json().get("Data") - try: - # print("[query_freqlypair]", endpoint, params, r) - # print("[query_freqlypair] r", r_json()) - - df = _pd.DataFrame.from_records(r_json["Data"]["Data"]) - df["datetime"] = [self.ts_fromcc(ts) for ts in df["time"]] - df = df.set_index("datetime") - del df["conversionType"] - del df["conversionSymbol"] - del df["time"] - df = df[['open', 'close', 'high', 'low', 'volumefrom', 'volumeto']] - return df - except RuntimeError as e: - if self.raiseonerror: - raise RuntimeError("Error {e}", endpoint, params, r) - return None - - def query_dailypair(self, *args, **kwargs): - """alias for query_freqlypair(FREQ_DAILY, ...)""" - return self.query_freqlypair(self.FREQ_DAILY, *args, **kwargs) - - def query_hourlypair(self, *args, **kwargs): - """alias for query_freqlypair(FREQ_HOURLY, ...)""" - return self.query_freqlypair(self.FREQ_HOURLY, *args, **kwargs) - - def query_minutelypair(self, *args, **kwargs): - """alias for query_freqlypair(FREQ_MINUTELY, ...)""" - return self.query_freqlypair(self.FREQ_MINUTELY, *args, **kwargs) - - def query_tokens(self, fsyms, tsym=None, aspandas=False): - """ - endpoint = /data/pricemulti?fsyms=BTC,ETH&tsyms=USD,EUR - - :fsyms: list of cryptocurrency symbols of interest - :tsym: currency symbol to convert into - :aspandas: if True, returns result as pandas data frame - - https://min-api.cryptocompare.com/documentation?key=Price&cat=multipleSymbolsPriceEndpoint - """ - endpoint = f"/data/pricemulti" - params = { - "fsyms": fsyms, - "tsyms": tsym if not tsym is None else self.DEFAULT_TSYM, - } - r = self.query(endpoint=endpoint, params=params) - if r is None: return r - r_json = r.json() - if r_json.get("Response") == "Error": - if self.raiseonerror: - raise RuntimeError("Query not successful", r, r_json, endpoint, params) - else: - return None - df = _pd.DataFrame(r.json()).T - if aspandas: - return df - dct = dict(df[df.columns[0]]) - return dct - - - - def ccycodes(self, symonly=True, fn=None): - """ - returns information on currency codes - - :symonly: if True (default) only return list of ccy symbold - :fn: the filename of the currency code file - """ - if symonly: - return self.join( self.unjoin(self.CCYCODES) ) - if fn is None: - fn = _os.path.join(self.DATAPATH, "isoccy.csv") - df = _pd.read_csv(fn, index_col=False) - if symonly: - symbols = list(set(df["Symbol"])) - symbols.sort() - return tuple(symbols) - return df - - CCYCODES = """ - AED,AFN,ALL,AMD,ANG,AOA,ARS,AUD,AWG,AZN,BAM,BBD,BDT,BGN,BHD,BIF,BMD, - BND,BOB,BOV,BRL,BSD,BTN,BWP,BYN,BZD,CAD,CDF,CHE,CHF,CHW,CLF,CLP,CNY, - COP,COU,CRC,CUC,CUP,CVE,CZK,DJF,DKK,DOP,DZD,EGP,ERN,ETB,EUR,FJD,FKP, - GBP,GEL,GHS,GIP,GMD,GNF,GTQ,GYD,HKD,HNL,HRK,HTG,HUF,IDR,ILS,INR,IQD, - IRR,ISK,JMD,JOD,JPY,KES,KGS,KHR,KMF,KPW,KRW,KWD,KYD,KZT,LAK,LBP,LKR, - LRD,LSL,LYD,MAD,MDL,MGA,MKD,MMK,MNT,MOP,MRU,MUR,MVR,MWK,MXN,MXV,MYR, - MZN,NAD,NGN,NIO,NOK,NPR,NZD,OMR,PAB,PEN,PGK,PHP,PKR,PLN,PYG,QAR,RON, - RSD,RUB,RWF,SAR,SBD,SCR,SDG,SEK,SGD,SHP,SLL,SOS,SRD,SSP,STN,SVC,SYP, - SZL,THB,TJS,TMT,TND,TOP,TRY,TTD,TWD,TZS,UAH,UGX,USD,USN,UYI,UYU,UYW, - UZS,VES,VND,VUV,WST,XAF,XAG,XAU,XCD,XDR,XOF,XPD,XPF,XPT,XSU,XUA,YER, - ZAR,ZMW,ZWL - """.strip() - - @staticmethod - def join(tpl, sep=None): - """join the tpl into comma separated strings""" - if sep is None: sep = ", " - return sep.join(str(s) for s in tpl) - - @staticmethod - def unjoin(jstr, filter=None, sep=None): - """ - unjoin the join string, stripping the result - - :jstr: a (typically comma) separated string - :filter: filter to be applied (default: str) - :sep: the separator (default: comma) - :returns: tuple - """ - if sep is None: sep = "," - result = jstr.split(sep) - if filter is None: - filter = str - result = ( filter(c.strip()) for c in result) - return tuple(result) - - def aggr_query(self, - pairs, - fields=None, - incl_raw=True, - incl_raw_aggr=True, - incl_grand_aggr=True, - freq=None, **kwargs): - """ - gets the data for pairs from the API and converts it into tables - - :pairs: the pairs to download, either comma separeted "ETH/USD, BTC/GBP, ..." - or as tuple of tuples (("ETH", "USD"), ...) - :fields: the fields for which to create aggredate data frames, either comma separated - or as tuple/list; use FREQ_CLOSE and other FIELD_XXX constants here - :incl_raw: whether to include the individual raw data frames - :incl_raw_aggr: whether to include the aggregate raw data frame - :incl_grand_aggr: whether to include a grand aggregate (with double col name) - :freq: the data frequency [FREQ_DAILY (default), FREQ_HOURLY, FREQ_MINUTELY] - :kwargs: passed through to `query_freqlypair` (eg `e`, `limit`, `toTs`) - :returns: dict with the results - - dict structure - - -gaggr - - [data] - -aggr - -open - - [data] - -close - - [data] - ... - -rawaggr - - [data] - -raw - - "ETH/USD" - - [data] - ... - """ - if fields is None: - fields = self.FIELD_DEFAULT - if isinstance(fields, str): - fields = self.unjoin(fields) - print("[aggr_query] fields", fields) - - if isinstance(pairs, str): - pairs = tuple( self.pt_from_pair(p) for p in self.unjoin(pairs) ) - print("[aggr_query] pairs", pairs) - - if freq is None: - freq = self.FREQ_DAILY - - result = { - "gaggr": None, - "aggr": None, - "rawaggr": None, - "raw": None, - } - - print("[aggr_query] Querying for raw table", len(pairs)) - raw_tables = { - (fsym, tsym): self.query_freqlypair(freq, fsym=fsym, tsym=tsym) - for fsym, tsym in pairs - } - df_raw = _pd.concat(raw_tables, axis=1) - result_raw = {self.pair_from_pt(p):v for p, v in raw_tables.items()} - if incl_raw: - result["raw"] = result_raw - if incl_raw_aggr: - result["rawaggr"] = _pd.concat(result_raw, axis=1) - - print("[aggr_query] Creating aggregate table") - result["aggr"] = { - field: self.reformat_raw_df(df_raw, field=field, dblcolnm=incl_grand_aggr) - for field in fields - } - if incl_grand_aggr: - result["gaggr"] = _pd.concat(result["aggr"].values(), axis=1) - return result - - @staticmethod - def pairs_fields_from_df(df): - """ - pairs and fields present in the dataframe - - :df: data frame with index = (base token, quote token, field) - :returns: dict pairs: tuple( (tknp1, tnkq1), ...), fields: (field1, ...) - """ - pairs = ((tknb, tknq) for tknb, tknq, field in df.columns) - pairs = tuple(set(pairs)) - fields = (field for tknb, tknq, field in df.columns) - fields = tuple(set(fields)) - return {"pairs": pairs, "fields": fields} - - FIELD_CLOSE = "close" - FIELD_OPEN = "open" - FIELD_HIGH = "high" - FIELD_LOW = "low" - FIELD_DEFAULT = FIELD_CLOSE - @classmethod - def reformat_raw_df(cls, df, field=None, dblcolnm=False): - """ - reformats a raw df - - :df: the raw df, as returned by a concatenation eg of daily_pair calls - :field: the name of the price field to use for the price - use FIELD_OPEN, FIELD_CLOSE etc; default: FIELD_DEFAULT - :dblcolnm: if True, the colname is (field, pair) instead of pair - :returns: the reformatted data frame - """ - if field is None: - field = cls.FIELD_DEFAULT - - if dblcolnm: - result = ( - df[(*pair, field)].rename((field, f"{pair[0]}/{pair[1]}"), inplace=True) - for pair in cls.pairs_fields_from_df(df)["pairs"] - ) - else: - result = ( - df[(*pair, field)].rename(f"{pair[0]}/{pair[1]}", inplace=True) - for pair in cls.pairs_fields_from_df(df)["pairs"] - ) - - return _pd.concat(list(result), axis=1) - - @staticmethod - def pt_from_pair(pair): - """ - creates a pair tuple (tknb, tknq) from a pair 'TKNB/TKNQ' - """ - return pair_t(*pair.split("/")) - - @staticmethod - def pair_from_pt(pair_t): - """ - creates a pair 'TKNB/TKNQ' from a pair tuple (tknb, tknq) - """ - return "/".join(pair_t) - - @classmethod - def coinlist(cls, coins, sep=",", aspt=False): - """ - creates a coin list from separated string (does not touch lists) - - :coins: either a string or a list/tuple - :sep: the separator of the string - :aspt: if True, result returned as pair tuple (using `pt_from_pair`) - :returns: original if not str; otherwise tuple of string or pr - """ - f = cls.pt_from_pair if aspt else lambda x: x - if isinstance(coins, str): - return tuple(f(c.strip()) for c in coins.split(sep)) - else: - return coins - - @classmethod - def create_pairs(cls, coins, quotecoins=None): - """ - create pair tuples from all possible combinations of coins and quotecoins - - :coins: a list of coins, either ("tkn1", "tkn2") or "tkn1, tkn2" - :quotecoins: a list of quote coins; if None set equal to coins - :returns: all combinations as tuples (c, qc) with c!=qc - """ - coins = cls.coinlist(coins) - if quotecoins is None: - quotecoins = coins - else: - quotecoins = cls.coinlist(quotecoins) - result = ( (c,q) for q in quotecoins for c in coins) - result = ( pair_t(c,q) for c,q in result if c != q) - return tuple (result) - - \ No newline at end of file diff --git a/fastlane_bot/tools/invariants/README.md b/fastlane_bot/tools/invariants/README.md deleted file mode 100644 index 3c46eea09..000000000 --- a/fastlane_bot/tools/invariants/README.md +++ /dev/null @@ -1,90 +0,0 @@ -# Invariants Module - -## Introduction - -The core purpose of this module is to analyze with AMM invariant functions, ie functions of the type $f(x,y) = k$ where $x,y$ are token amounts and $k$ is a constant representing the scale of the AMM. Ignoring fees, the core rule of an AMM is that it is indifferent for any trade that leaves the invariant function unchanged. - -The first, and still most popular invariant functions are the constant product invariant functions, which are defined as $f(x,y) = k = xy$. Their key advantage is that they are simple to implement and cheap in gas. Also, if we allow for leverage, ie virtual token balances $x_0, y_0$ so that $f(x,y) = (x_0+x)(y_0+y) = k$, then those functions can be used to approximate other invariant functions, piecewise if need be. - -This module has been developed for the use of a FastLane Arbitrage bot where efficient calculation is paramount. When we were trying to implement the Solidly stable swap invariant function $f(x,y) = x^3y + xy^3$ we found that it was not analytically tractable, and that a numeric solution would have been too much of an unnecessary performance hit -- unnecessary in particular because the complexity here lies not in the stable swap part that we are most interested in, but in the wings that we are about less. So we decided to develop a module that would allow us to approximate the invariant function with a piecewise constant product invariant function, and then use the latter to calculate the former. - - -## Module components - -This module consists of the following components that we will discuss in more detail below: - -- **vector.py** - a generic vector class that interprets dictionaries as sparse vectors; this class is used below to represent vectors of functions where the function itself is the key of the dictionary and the value is the "length" of the vector in that direction - -- **kernel.py** - this module contains a class that represents an (integration) _kernel_ which is a _domain_ $x_{min}\ldots x_{max}$ and a _density_ on that domain that gives a specific weight to every point that is used in the the calculation of scalar products and norms of functions; this module also implements the numerical integration of functions over a kernel - -- **functions.py** - this module contains three components: (1) a class that allows to represent generic functions of one variable $x$, (2) a vector class of such functions, and (3) various example functions, including _functional_ ones that allow to modify existing functions, eg calculating their derivative - -- **invariants.py** - this module contains a class the represents invariant functions, both in the _invariant format_ $f(x,y) = k$ and in the swap equation format $y = y(x,k)$ - -- **bancor.py** and **solidly.py** - implementations of Bancor and Solidly invariants and functions - -### vector.py - -The `vector` module mostly defines the `DictVector` class. This class interprets a dictionary as a sparse vector, where the keys are the indices and the values are the values of the vector. The class implements the basic vector operations, such as addition, subtraction etc. - -### kernel.py - -The `kernel` module defines the `Kernel` class. A kernel is a domain $x_{min}\ldots x_{max}$ and a density function $k(x)$ on that domain. The following densities are pre-defined - -- **flat** - a constant density -- **triangle** - zero at $x_{min}$ and $x_{max}$, and linearly increasing to a maximum at the midpoint -- **sawtooth** - zero at one side and linearly increasing to a maximum at the other side -- **gaussian** - Gaussian distributions of various sizes within the domain - -Note that all pre-defined densities are normalized to unity on the domain $x_{min}\ldots x_{max}$ and they are of course positive. Custom densities _should_ also implement this, but this is not enforced. - -### functions.py - -#### Function class - -The `Function` class represents a function of one variable $x$, and an arbitrary number of parameters. The core definition is the function `f(x)` method that in the base class is an abstract method. The base class then implements various numerical calculations based on `f` that subclasses can either retain, or override with analytical methods if available. Key functions available are: - -- `__call__` - alias for `f` -- `df_dx_abs` and `df2_dx2_abs` - the first and second derivatives of the function, calculated with a constant perturbation $h$ -- `df_dx_rel` and `df2_dx2_rel` - the first and second derivatives of the function, calculated with a relative ("percentage of $x$") perturbation $\eta$ -- `p` - the _price function_, which is $-df/dx$; note the minus sign because exchanges are directed flows, one out and one in, and we want prices to be positive -- `pp` - the price convexity function, which is $-d^2f/dx^2$; again note the minus sign - -Actual functions are implemented as frozen dataclasses (frozen so that they can be used as dict keys in the FunctionVector class below). An example of a quadratic function $ax^2 + bx + c$ is given below: - - @dataclass(frozen=True) - class QuadraticFunction(Function): - """represents a quadratic function y = ax^2 + bx + c""" - a: float = 0 - b: float = 0 - c: float = 0 - - def f(self, x): - return self.a*x**2 + self.b*x + self.c - -Note that this function does not implement any of the derivatives, so the numeric base class methods will be used. - -**TODO: EXAMPLE WITH DERIVATIVES** - -#### FunctionVector class - -The `FunctionVector` class is a `DictVector` where the keys must be `Function` objects. It also contains a `Kernel` object, and in fact vector operations like addition and subtraction are only allowed between object with the same kernel. The class also implements a number of important operations, including integration, norms, root finding, minimization, and plotting. - -#### Example functions - -Currently the following example functions are implemented: - -- `QuadraticFunction` - a quadratic function $ax^2 + bx + c$ -- `TrigFunction` - a trigonometric function $\mathrm{ampl}\cdot\sin(\frac{\omega x + \mathrm{phase}}{\pi})$ -- `HyperbolaFunction` - a hyperbolic function $y-y_0 = \frac{k}{x-x_0}$ - -It also implements the following functional functions: - -- `DerivativeFunction` - the derivative of any Function object -- `Derivative2Function` - ditto second derivative - -### invariant.py - -## Usage examples - -Usage examples for almost all use cases for which those modules were designed can be found in the various Jupyter notebooks in the _resources/analysis/202401 Solidly_ directory \ No newline at end of file diff --git a/fastlane_bot/tools/invariants/__init__.py b/fastlane_bot/tools/invariants/__init__.py deleted file mode 100644 index 61ac6a528..000000000 --- a/fastlane_bot/tools/invariants/__init__.py +++ /dev/null @@ -1,66 +0,0 @@ -r""" -A collection of tools for analyzing AMM invariant functions - -This library is independent of fastlane_bot; if you extract it, make sure -you copy the following test notebooks as well. -- NBTest_065_InvariantsDictVector -- NBTest_066_InvariantsFunctions -- NBTest_067_Invariants -- NBTest_068_InvariantsAMM - -Corresponding Author: Stefan Loesch -Canonic Location: https://github.com/bancorprotocol/fastlane-bot - -This package contains a collection of tools for analyzing -AMM invariant functions. The focus of this package lies on -AMMs with hyperbolic invariants, ie invariants of the form - -.. math:: - x\cdot y = k - -where k is a constant and x,y are -- potentially virtual -- -token balances of an AMM. This was the invariant function -used in the first ever AMM, Bancor, and it was taken over by -Uniswap and many others. In levered form it is the invariant -used in Uniswap v3 as well as in Bancor's Carbon. - -The core objects in this package are the `Invariant` and the -`Function` as well as `FunctionVector` objects. - -- the `Invariant` object describes an invariant in the - non-isolated form :math:`k=k(x,y)` that is by definition - available for all invariant based AMMs - -- the `Function` describes the *swap function* - :math:`y=f(x,k)` that is obtained from the invariant - equation by isolating y, which may or may not be - analytically available for a given invariant. - -- the `FunctionVector` object finally describes a vector - of `Function` objects, together with an integration - kernel (see below) thereby effectively defining a vector - space of functions together with a number of norms. - -In addition to those higher level objects, the package also -contains a number of more fundamental objects that are used -as building blocks for those higher level objects. These -include - -- the `Kernel` object represents an *integration kernel*, ie - a weight function together with a domain of integration; - this object serves to define :math:`L_p` norms on the - functions defined above, and therefore ultimately to measure - distances - -- the `DictVector` object implements sparse vector - functionality using dicts where the dict keys are - considered the vector space dimensions, and the values - the associated coefficients. Note that any allowable - dict key is a valid dimension. - - ---- -(c) Copyright Bprotocol foundation 2024. Licensed under MIT -""" -__VERSION__ = '0.9+' -__DATE__ = "20/Jan/2024+" diff --git a/fastlane_bot/tools/invariants/bancor.py b/fastlane_bot/tools/invariants/bancor.py deleted file mode 100644 index e78a195cc..000000000 --- a/fastlane_bot/tools/invariants/bancor.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -object representing the Bancor (constant product) AMM invariant - -(c) Copyright Bprotocol foundation 2024. -Licensed under MIT -""" -__VERSION__ = '0.9' -__DATE__ = "18/Jan/2024" - -# import decimal as d -# D = d.Decimal -# import math as m - -from .invariant import Invariant, dataclass -from .functions import Function - -@dataclass(frozen=True) -class BancorSwapFunction(Function): - """represents the Bancor AMM swap function y(x,k)=k/x""" - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - k: float - - def f(self, x): - return self.k / x - -@dataclass -class BancorInvariant(Invariant): - """represents the Bancor invariant function""" - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - YFUNC_CLASS = BancorSwapFunction - - def k_func(self, x, y): - """Bancor invariant function k(x,y)=x*y""" - return x*y - - - - - diff --git a/fastlane_bot/tools/invariants/functions/__init__.py b/fastlane_bot/tools/invariants/functions/__init__.py deleted file mode 100644 index aa92847d1..000000000 --- a/fastlane_bot/tools/invariants/functions/__init__.py +++ /dev/null @@ -1,35 +0,0 @@ -""" -Represents a function ``y = f(x; params)`` and vectors thereof - -This module contains two classes, ``Function`` and ``FunctionVector``. - -- The ``Function`` class represents a function of the form ``y = f(x; params)``, - where ``x`` is the input value and ``params`` are arbitrary additional - parameters fed into the (data)class upon instantiation. - -- The ``FunctionVector`` class represents a vector (linear combination) of - ``Function`` objects, and implements a function interface (via pointwise - evaluation), a vector interface (from the ``DictVector`` inheritance). A - ``FunctionVector`` also contains an integration kernel, which allows it to - expose a number of norms and distance measures. - -TODO: other imported objects eg ``DerivativeFunction``, ``Derivative2Function`` - ---- -(c) Copyright Bprotocol foundation 2024. -Licensed under MIT -""" -from .core import __VERSION__, __DATE__ - -# objects defined in core -from .core import Function, FunctionVector -from .core import PriceFunction, Price2Function -from .core import minimize, goalseek -from .core import fmt - -# objects defined in funcs and funcsAMM -from .funcs import * -from .funcsAMM import * - -# convenience imports -from .core import Kernel, DictVector, dataclass \ No newline at end of file diff --git a/fastlane_bot/tools/invariants/functions/core.py b/fastlane_bot/tools/invariants/functions/core.py deleted file mode 100644 index a6c2b501a..000000000 --- a/fastlane_bot/tools/invariants/functions/core.py +++ /dev/null @@ -1,1012 +0,0 @@ -""" -functions library -- core objects (Function, FunctionVector) - -(c) Copyright Bprotocol foundation 2024. -Licensed under MIT -""" -__VERSION__ = '0.9.7' -__DATE__ = "21/Mar/2024" - -from dataclasses import dataclass, asdict -from abc import ABC, abstractmethod -import math as m -import numpy as np -import matplotlib.pyplot as plt -from inspect import signature - -from ..vector import DictVector -from ..kernel import Kernel - -def _fmt(x, format_string, as_float=True): - """formats as float (if possible and requested)""" - if as_float: - try: x = float(x) - except: pass - try: x = format(x, format_string) - except: pass - if as_float: - try: x = float(x) - except: pass - return x - -def fmt(dct_or_list, format_string=None, as_float=True): - """format dct key=>value -> key: str""" - format_string = format_string or ".4f" - if isinstance(dct_or_list, dict): - return {key: _fmt(value, format_string, as_float) for key, value in dct_or_list.items()} - #return {key: fmt2(format(fmt2(value), format_string)) for key, value in dct_or_list.items()} - else: - return [_fmt(value, format_string, as_float) for value in dct_or_list] - #return [fmt2(format(fmt2(value), format_string)) for value in dct_or_list] - - -############################################################################## -## CLASS FUNCTION -@dataclass(frozen=True) -class Function(ABC): - r""" - Represents a function ``y = f(x; params)`` - - The Function class is an abstract base class that represents an arbitrary - function of the form ``y = f(x; params)``. The function is inserted into the - object via overriding the ``f`` method, and the parameters are inserted via - the (data)class constructor. The class also exposes a number of methods and - properties that are useful for analyzing the function, notably the first - and second derivate and the so-called price function :math:`p(x) = -f'(x)`. - - - The below example shows how to implement the function - - .. math:: - - f_k(x) = \left(\sqrt{1+x} - 1\right)*k - - .. code-block:: python - - import functions as f - - @f.dataclass(frozen=True) - class MyFunction(f.Function): - k: float = 1 - - def f(self, x): - return (m.sqrt(1+x)-1)*self.k - - mf = MyFunction(k=2) - mf(1) # 0.4142 - mf.p(1) # 0.3536 - mf.df_dx(1) # -0.3536 - mf.pp(1) # -0.0883 - - For functions where we know the derivatives analytically, we can override - the ``p`` and ``pp`` methods (we should not usually touch ``df_dx`` as it refers - back to ``p`` in a trivial manner). The below implements a simple hyperbolic - function to the type found in an AMM: - - .. code-block:: python - - import functions as f - - @f.dataclass(frozen=True) - class HyperbolaFunction(f.Function): - - k: float = 1 - - def f(self, x): - return self.k/x - - def p(self, x): - return -self.k/(x*x) - - def pp(self, x): - return 2*self.k/(x*x*x) - - Note that we are using *frozen* dataclasses here which allows us to use those - functions as keys in a dict, which we will make use of in the `FunctionVector` - class derived. If you need to change an attribute in a frozen class you can - do so using the following trick: - - .. code-block:: python - - super().__setattr__('k', 2) # changes k to 2 despite the class being frozen - """ - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - DERIV_H = 1e-6 # step size for absolute derivative calculation - DERIV_ETA = 1e-4 # ditto relative - DERIV_IS_ABS = False # whether p, pp uses absolute or relative step size - - @abstractmethod - def f(self, x): - """ - returns ``y = f(x; k)`` [to be implemented by subclass] - - :x: input value x (token balance) - :returns: output value y (other token balance) - - this function must be implemented by the subclass as - it specifies the actual function other parameters -- - notably the pool constant ``k`` -- will usually be parts - of the (dataclass) constructor - """ - pass - - def df_dx_abs(self, x, *, h=None, precision=None): - """ - calculates the derivative of ``f(x)`` at ``x`` with absolute step size ``h*precision`` - """ - if h is None: - h = self.DERIV_H - if precision: - h *= precision - try: - #print("[df_dx_abs] trying double-sided") - return (self.f(x+h)-self.f(x-h)) / (2*h) - except TypeError: - try: - #print(f"[df_dx_abs] double-sided failed, trying top (x={x})") - return (self.f(x+h)-self.f(x)) / h - except TypeError: - try: - #print(f"[df_dx_abs] top failed, trying bottom (x={x})") - return (self.f(x)-self.f(x-h)) / h - except TypeError: - #print(f"[df_dx_abs] all failed (x={x})") - return None - - def d2f_dx2_abs(self, x, *, h=None, precision=None): - """ - calculates the second derivative of f(x) at x with abs step size h*precision - """ - if h is None: - h = self.DERIV_H - if precision: - h *= precision - try: - return (self.f(x+h)-2*self.f(x)+self.f(x-h)) / (h*h) - except TypeError: # None values - return None - - def df_dx_rel(self, x, *, eta=None, precision=None): - """ - calculates the derivative of ``f(x)`` at ``x`` with relative step size ``eta`` (``h=x*eta*precision``) - """ - if eta is None: - eta = self.DERIV_ETA - return self.df_dx_abs(x, h=x*eta if x else None, precision=precision) - - def d2f_dx2_rel(self, x, *, eta=None, precision=None): - """ - calculates the second derivative of ``f(x)`` at ``x`` with relative step size ``eta`` (``h=x*eta*precision``) - """ - if eta is None: - eta = self.DERIV_ETA - return self.d2f_dx2_abs(x, h=x*eta if x else None, precision=precision) - - def p(self, x, *, precision=None): - """ - price function (alias for ``-df_dx_xxx``) - - Note: this function CAN be overridden by the subclass if it can be - calculated analytically in this case the precision parameter should be - ignored - """ - try: - if self.DERIV_IS_ABS: - return -self.df_dx_abs(x, precision=precision) - else: - return -self.df_dx_rel(x, precision=precision) - except TypeError: - return None - - def df_dx(self, x, *, precision=None): - """ - first derivative (alias for ``-p``) - - note: this function calls ``p`` and it should not be overridden - """ - try: - return -self.p(x, precision=precision) - except TypeError: - return None - - def pp(self, x, *, precision=None): - """ - derivative of the price function (alias for ``-d2f_dx2_xxx``) - - Note: this function does not call `p` but goes via ``d2f_dx2_xxx``; if ``p`` - is overrriden then it may make sense to override this function as well - """ - try: - if self.DERIV_IS_ABS: - return -self.d2f_dx2_abs(x, precision=precision) - else: - return -self.d2f_dx2_rel(x, precision=precision) - except TypeError: - return None - - def p_func(self, *, precision=None): - """returns the derivative as a function object""" - return PriceFunction(self, precision=precision) - - def pp_func(self, *, precision=None): - """returns the second derivative as a function object""" - return Price2Function(self, precision=precision) - - def params(self, *, classname=False): - """ - returns the parameters of the function as a dictionary - - :classname: if True, includes the class name in the dict (default: False) - """ - result = asdict(self) - if classname: - result["_classname"] = self.__class__.__name__ - return result - - def update(self, **kwargs): - """ - returns a copy of the function, with the given parameters updated - - :kwargs: parameters to update - """ - params = {**self.params(), **kwargs} - try: del params["_classname"] - except KeyError: pass - return self.__class__(**params) - - def __call__(self, x): - """ - alias for self.f(x) - """ - return self.f(x) - - PLT_STEPS = 100 - PLT_SHOW = False - PLT_GRID = True - def plot(self, x_min, x_max, func=None, *, steps=None, title=None, xlabel=None, ylabel=None, grid=None, show=None, **kwargs): - """ - plots the function ``func`` (default: ``self.f``) over the interval [``x_min``, ``x_max``] - - :x_min: lower bound - :x_max: upper bound - :func: function to plot (default: ``self.f``) - :steps: number of steps (default: PLT_STEPS or ``np.linspace`` defaults) - :show: whether to call ``plt.show()`` (default: PLT_SHOW) - :grid: whether to show a grid (default: PLT_GRID) - :returns: the result of ``plt.plot`` - """ - if xlabel is None: xlabel = "x" - if ylabel is None and func is None: ylabel = "y" - func = func or self.f - if show is None: show = self.PLT_SHOW - if grid is None: grid = self.PLT_GRID - steps = steps or self.PLT_STEPS - x = np.linspace(x_min, x_max, steps) if steps else np.linspace(x_min, x_max) - y = [func(x) for x in x] - plot = plt.plot(x, y, **kwargs) - if title: plt.title(title) - if xlabel: plt.xlabel(xlabel) - if ylabel: plt.ylabel(ylabel) - if grid: plt.grid(True) - if show: plt.show() - return plot - - def wrap(self, fv_or_kernel=None): - """ - wraps this function in a FunctionVector - - :fv_or_kernel: either a FunctionVector or a Kernel - :returns: FunctionVector(self, kernel=kernel) - """ - if isinstance(fv_or_kernel, FunctionVector): - kernel = fv_or_kernel.kernel - else: - kernel = fv_or_kernel - if kernel is None: - kernel = Kernel() - return FunctionVector({self: 1}, kernel=kernel) - - - -############################################################################## -## CLASS FUNCTION VECTOR -@dataclass -class FunctionVector(DictVector): - r""" - a vector of functions - - :kernel: the integration kernel to use (default: Kernel()) - - A function vector is a linear combination of Function objects. It exposes - the usual **vector properties** (technically it is a `DictVector` subclass) where - the functions themselves used as dict keys and therefore the *dimensions* of the - vector space. Note that there is an additional constraint the only vectors that - have the same kernel can be aggregated. - - It also exposes properties related to **pointwise evaluation** of the functions, notably - the function value of the vector at point x is given as - - .. math:: - - f_v(x) = \sum_i \alpha_i * f_i(x) - - and this carries over to the price functions an derivatives that are exposed - in the ``p``, ``df_dx``, ``pp`` etc methods. - - Finally it exposes properties related to **integration** of the functions - based on the kernel, notably the `integrate` method that integrates the - vector of functions as well as various norms and distance measures. - """ - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - kernel: Kernel = None - - def __post_init__(self): - super().__post_init__() - assert all([isinstance(v, Function) for v in self.vec.keys()]), "all keys must be of type Function" - if self.kernel is None: - self.kernel = Kernel() - - def wrap(self, func): - """ - creates a FunctionVector from a function using the same kernel as self - - :func: the function to wrap (a Function object) - :returns: a new FunctionVector object wrapping `fu`nc`, with the same kernel as ``self`` - - .. code-block:: python - - fv0 = FunctionVector(kernel=mykernel) # creates a FV with a specific kernel - f = MyFunction() # creates a Function object - fv0.wrap(f) # a FV object with the same kernel as fv0 - """ - try: - return self.__class__({f: 1 for f in func}, kernel=self.kernel) - except: - assert isinstance(func, Function), "func must be of type Function" - return self.__class__({func: 1}, kernel=self.kernel) - - def functions(self): - """returns all functions in self as a list""" - return list(self.vec.keys()) - - def function(self, i=0): - """returns the i'th function in self""" - return self.functions()[i] - - def params(self, index=None, *, as_dict=None, classname=None): - """ - retrieve params of the underlying function(s) - - :index: the index (0,1,2...) of the item to be retrieved - :as_dict: if True, returns items as dict, otherwise as list - :classname: if True, includes the class name in the params - - - index as_dict Action - --- --- --- - None True return all as dict - None False return all as list - None None list as_dict = True - int True return params of item i (key => params) - int False ditto, params only - int None like as_dict = True - """ - if index is None : - # return all as dict - as_dict = as_dict if not as_dict is None else False - classname = classname if not classname is None else True - result = {f: f.params(classname=classname) for f in self.functions()} - if as_dict: - return result - return list(result.values()) - - else: - # index given => return params of item i - as_dict = as_dict if not as_dict is None else False - classname = classname if not classname is None else True - f = self.function(index) - if as_dict: - return {f: f.params(classname=classname)} - return f.params(classname=classname) - - - def update(self, params=None, index=None, *, key=None, **kwargs): - """ - creates a copy of the FunctionVector, with the relevant functions updated - - :index: if not None, only updates the i'th function - :params: the parameters to be updated (single dict or list of dicts) - :key: if not None, only updates the function with the given key - :returns: the newly created, updated FunctionVector - """ - if index is None and key is None and len(self.vec) == 1: - index = 0 - if isinstance(params, list) or isinstance(params, tuple): - assert index is None and key is None, "index and key must be None if params is a list" - raise NotImplementedError("update with list of params not implemented yet") - else: - if params is None: params = dict() - params = {**params, **kwargs} - assert not index is None or not key is None, "exactly one of index or key must be given" - assert not(not index is None and not key is None), "can't give both index and key" - assert key is None, "key not implemented yet" - funcs = self.functions() - funcs[index] = funcs[index].update(**params) - return self.wrap(funcs) - - def __eq__(self, other): - funcs_eq = super().__eq__(other) - kernel_eq = self.kernel == other.kernel - print(f"[FunctionVector::eq] called; funcs_eq={funcs_eq}, kernel_eq={kernel_eq}") - return funcs_eq and kernel_eq - - def f(self, x): - r""" - returns the function value - - .. math:: - - f(x) = \sum_i \alpha_i * f_i(x) - """ - fv_t = ((f(x), v) for f, v in self.vec.items()) - return sum([f_x * v for f_x, v in fv_t if not f_x is None]) - - def f_r(self, x): - """alias for ``self.restricted(self.f, x)``""" - return self.restricted(self.f, x) - - def f_k(self, x): - """alias for ``self.apply_kernel(self.f, x)``""" - return self.apply_kernel(self.f, x) - - def __call__(self, x): - """ - alias for ``f(x)`` - """ - return self.f(x) - - def p(self, x): - r""" - returns :math:`\sum_i \alpha_i * p_i(x)` where :math:`p_i` is the price function of :math:`f_i` - """ - return sum([F.p(x) * v for F, v in self.vec.items()]) - - def df_dx(self, x): - r""" - derivative of ``self.f`` (alias for ``-p``) - - .. math:: - - \frac{df}{dx}(x) = \sum_i \alpha_i * \frac{df_i}{dx}(x) - """ - return -self.p(x) - - def pp(self, x): - r""" - derivative of the price function of ``self.f`` - - .. math:: - - pp(x) = \sum_i \alpha_i * pp_i(x) - """ - return sum([F.pp(x) * v for F, v in self.vec.items()]) - - def restricted(self, func, x=None): - """ - returns ``func(x)`` restricted to the domain of ``self.kernel`` (as value or lambda if ``x`` is ``None``) - - USAGE - - this function can either be called directly - - .. code-block:: python - - fv = FunctionVector(...) - fv.restricted(func, x) # ==> value - - or be used to create a new function - - .. code-block:: python - - fv = FunctionVector(...) - func_restricted = fv.restricted(func) # ==> lambda - """ - f = lambda x: func(x) if self.kernel.in_domain(x) else 0 - if x is None: - return f - return f(x) - - def apply_kernel(self, func, x=None): - """ - returns ``func`` multiplied by the kernel value (as value or lambda if ``x`` is None) - - USAGE - - this function can either be called directly - - .. code-block:: python - - fv = FunctionVector(...) - fv.apply_kernel(func, x) # ==> value - - or be used to create a new function - - .. code-block:: python - - fv = FunctionVector(...) - func_kernel = fv.apply_kernel(func) # ==> lambda - """ - f = lambda x: func(x) * self.kernel(x) - if x is None: - return f - return f(x) - - - GS_TOLERANCE = 1e-6 # absolute tolerance on the y axis - GS_ITERATIONS = 1000 # max iterations - GS_ETA = 1e-10 # relative step size for calculating derivative - GS_H = 1e-6 # used for x=0 - def integrate_func(self, func=None, *, steps=None, method=None): - """integrates ``func`` (default: ``self.f``) using the kernel""" - if func is None: - func = self.f - return self.kernel.integrate(func, steps=steps, method=method) - - def integrate(self, *, steps=None, method=None): - """integrates ``self.f`` using the kernel [convenience access for ``integrate_func(func=None)``]""" - return self.integrate_func(func=self.f, steps=steps, method=method) - - ######################################################################## - ## distance functions - - ################################### - ## ...on self.f - def dist2_L2(self, func=None, *, steps=None, method=None): - """ - calculates the L2 distance-squared between ``self`` and ``func`` (L2 norm squared) - """ - if not func is None: - f = lambda x: (self.f(x)-func(x))**2 * self.kernel(x) - else: - f = lambda x: self.f(x)**2 * self.kernel(x) - return self.integrate_func(func=f, steps=steps, method=method) - - def dist_L2(self, func=None, *, steps=None, method=None): - """calculates the distance between ``self`` and ``func`` (L2 norm)""" - return m.sqrt(self.dist2_L2(func=func, steps=steps, method=method)) - - def dist_L1(self, func=None, *, steps=None, method=None): - """ - calculates the L1 distance between ``self`` and ``func`` (L1 norm) - """ - if not func is None: - f = lambda x: (abs(self.f(x)-func(x))) * self.kernel(x) - else: - f = lambda x: abs(self.f(x)) * self.kernel(x) - return self.integrate_func(func=f, steps=steps, method=method) - - ################################### - ## ...on self.p - def distp2_L2(self, func=None, *, steps=None, method=None): - """ - calculates the L2 distance-squared between ``self.p`` and ``func`` (L2 norm squared) - """ - if not func is None: - f = lambda x: (self.p(x)-func(x))**2 * self.kernel(x) - else: - f = lambda x: self.p(x)**2 * self.kernel(x) - return self.integrate_func(func=f, steps=steps, method=method) - - def distp_L2(self, func=None, *, steps=None, method=None): - """calculates the distance between ``self.p`` and ``func`` (L2 norm)""" - return m.sqrt(self.distp2_L2(func=func, steps=steps, method=method)) - - def distp_L1(self, func=None, *, steps=None, method=None): - """ - calculates the L1 distance between ``self.p`` and ``func`` (L1 norm) - """ - if not func is None: - f = lambda x: (abs(self.p(x)-func(x))) * self.kernel(x) - else: - f = lambda x: abs(self.p(x)) * self.kernel(x) - return self.integrate_func(func=f, steps=steps, method=method) - - ######################################################################## - ## norm functions - - ################################### - ## ...on self.f - def norm2_L2(self, *, steps=None, method=None): - """calculates the L2 norm squared of ``self``""" - return self.dist2_L2(func=None, steps=steps, method=method) - norm2 = norm2_L2 - - def norm_L2(self, *, steps=None, method=None): - """calculates the L2 norm of ``self``""" - return m.sqrt(self.norm2(steps=steps, method=method)) - norm = norm_L2 - - def norm_L1(self, *, steps=None, method=None): - """calculates the L1 norm of ``self``""" - return self.dist_L1(func=None, steps=steps, method=method) - norm1 = norm_L1 - - ################################### - ## ...on self.p - def normp2_L2(self, *, steps=None, method=None): - """calculates the L2 norm squared of ``self.p``""" - return self.distp2_L2(func=None, steps=steps, method=method) - normp2 = normp2_L2 - - def normp_L2(self, *, steps=None, method=None): - """calculates the L2 norm of ``self``""" - return m.sqrt(self.normp2(steps=steps, method=method)) - normp = normp_L2 - - def normp_L1(self, *, steps=None, method=None): - """calculates the L1 norm of ``self``""" - return self.distp_L1(func=None, steps=steps, method=method) - normp1 = normp_L1 - - - ######################################################################## - ## goalseek and minimization - - ################################### - ## goalseek - def goalseek(self, target=0, *, func=None, x0=1): - """ - very simple gradient descent implementation for a goal seek - - :target: target value (default: 0) - :func: function for goal seek (default: self.f) - :x0: starting estimate - :learning_rate: optimization parameter (float; default ``cls.MM_LEARNING_RATE``) - :iterations: max iterations (int; default ``cls.MM_ITERATIONS``) - :tolerance: convergence tolerance (float; ``default cls.MM_TOLERANCE``) - """ - x = x0 - iterations = self.GS_ITERATIONS - tolerance = self.GS_TOLERANCE - h = x0*self.GS_ETA if x0 else self.GS_H - func = func or self.f - for i in range(iterations): - y = func(x) - m = (func(x+h)-func(x-h)) / (2*h) - x = x + (target-y)/m - if abs(func(x)-target) < tolerance: - break - if abs(func(x)-target) > tolerance: - raise ValueError(f"gradient descent failed to converge on {target}") - return x - - def goalseek(self, func=None, target=0, *, x0=1, iterations=None, tolerance=None, eta=None, h=None): - """alias for ``self.goalseek``, but with defaults for ``func=self.f``""" - func = func or self.f - return self.goalseek_cls(func, target=target, x0=x0, iterations=iterations, tolerance=tolerance, eta=eta, h=h) - - @classmethod - def goalseek_cls(cls, func, target=0, *, x0=1, iterations=None, tolerance=None, eta=None, h=None): - """ - very simple gradient descent implementation for a goal seek (classmethod) - - :target: target value (default: 0) - :x0: starting estimate - """ - x = x0 - iterations = iterations or cls.GS_ITERATIONS - tolerance = tolerance or cls.GS_TOLERANCE - hh = x0*(eta or cls.GS_ETA) if x0 else (h or cls.GS_H) - for i in range(iterations): - y = func(x) - m = (func(x+hh)-func(x-hh)) / (2*hh) - x = x + (target-y)/m - if abs(func(x)-target) < tolerance: - break - if abs(func(x)-target) > tolerance: - raise ValueError(f"gradient descent failed to converge on {target}") - return x - - ################################### - ## minimization - MM_LEARNING_RATE = 0.2 - MM_ITERATIONS = 1000 - MM_TOLERANCE = 1e-3 - def minimize1(self, *, x0=1, learning_rate=None, iterations=None, tolerance=None): - """ - minimizes the function using gradient descent - - :x0: starting estimate (float) - """ - if learning_rate is None: - learning_rate = self.MM_LEARNING_RATE - if tolerance is None: - tolerance = self.MM_TOLERANCE - x = x0 - for i in range(iterations or self.MM_ITERATIONS): - x -= learning_rate * self.df_dx(x) - #print(f"[minimize1] {i}: x={x}, gradient={self.p(x)}") - if abs(self.p(x)) < tolerance: - break - if abs(self.p(x)) < tolerance: - return x - raise ValueError(f"gradient descent failed to converge") - - - MM_DERIV_H = 1e-6 - MM_VERBOSITY_QUIET = 0 - MM_VERBOSITY_MIN = 1 - MM_VERBOSITY_LOW = 10 - MM_VERBOSITY_HIGH = 20 - - @classmethod - def minimize(cls, func, *, - x0=None, - learning_rate=None, - iterations=None, - tolerance=None, - deriv_h=None, - return_path=False, - verbosity = MM_VERBOSITY_QUIET, - ): - """ - minimizes the function ``func`` using gradient descent (multiple dimensions) - - :func: function to be minimized - :x0: starting point (``np.array``-like or dct (1)) - :learning_rate: optimization parameter (float; default ``cls.MM_LEARNING_RATE``) - :iterations: max iterations (int; default ``cls.MM_ITERATIONS``) - :tolerance: convergence tolerance (float; default ``cls.MM_TOLERANCE``) - :deriv_h: step size for derivative calculation (float; default ``cls.MM_DERIV_H``) - :return_path: if True, returns the entire optimization path (list of ``np.array``) - as well as the last dfdx (``np.array``); in this case, the result is - the last element of the path - - NOTE1: if `x0` is ``np.array``-like or ``None``, then `func` will be called with - positional arguments and the result will be returned as an ``np.array``. If ``x0`` - is a dict, then ``func`` will be called with keyword arguments and the result - will be returned as a dict - """ - n = len(signature(func).parameters) - x0 = x0 or np.ones(n) - if not isinstance(x0, dict): - assert len(x0) == n, f"x0 must be of size {n}, it is {len(x0)}" - else: - try: - func(**x0) - except Exception as e: - #raise ValueError(f"failed to call func with x0={x0}") from e - raise - - learning_rate = learning_rate or cls.MM_LEARNING_RATE - tolerance = tolerance or cls.MM_TOLERANCE - deriv_h = deriv_h or cls.MM_DERIV_H - iterations = iterations or cls.MM_ITERATIONS - tol_squared = tolerance**2 - - # that's where the magic happens - _minimize = cls._minimize_dct if isinstance(x0, dict) else cls._minimize_lst - path, dfdx, norm2_dfdx = _minimize(func, x0, learning_rate, iterations, tol_squared, deriv_h, verbosity) - - if verbosity >= cls.MM_VERBOSITY_HIGH: - print(f"[minimize] algorithm returned, norm={m.sqrt(norm2_dfdx)}") - if return_path: - if verbosity >= cls.MM_VERBOSITY_HIGH: - print(f"[minimize] return path (len={len(path)}), final point={path[-1]})") - return path, dfdx - if norm2_dfdx < tol_squared: - if verbosity >= cls.MM_VERBOSITY_LOW: - print(f"[minimize] converged in {len(path)} iterations, norm={m.sqrt(norm2_dfdx):.6f}\nx={path[-1]})") - return path[-1] - if verbosity >= cls.MM_VERBOSITY_MIN: - print(f"[minimize] did not converge in {len(path)} iterations, norm={m.sqrt(norm2_dfdx):.4f}, x={path[-1]})") - raise ValueError(f"gradient descent failed to converge") - - @classmethod - def _minimize_lst(cls, func, x0, learning_rate, iterations, tol_squared, deriv_h, verbosity): - """ - executes the minimize algorithm when the x-values are in a list - - :returns: ``tuple(path, dfdx, norm2_dfdx)``; result is ``path[-1]`` - """ - x = np.array(x0, dtype=float) - n = len(x) - path = [tuple(x)] - if verbosity >= cls.MM_VERBOSITY_HIGH: - print(f"[_minimize_lst] x0={fmt(x, '.4f')}") - - for iteration in range(iterations): - f0 = func(*x) - dfdx = np.array([ - (func( *(x+deriv_h*cls.e_i(i, n)) ) - f0) / deriv_h - for i in range(len(x)) - ]) - dx = learning_rate * dfdx - x -= dx - path.append(tuple(x)) - #print(f"[_minimize_lst] {iteration}: adding x={x}, gradient={dfdx}") - norm2_dfdx = np.dot(dfdx, dfdx) - if verbosity >= cls.MM_VERBOSITY_HIGH: - print(f"[_minimize_lst] {iteration}: norm={m.sqrt(norm2_dfdx):.4f}\nx={fmt(x, '.4f')}\ngradient={fmt(dfdx, '.4f')}\ndx={fmt(dx, '.4f')}\n\n") - if norm2_dfdx < tol_squared: - break - return path, dfdx, norm2_dfdx - - @classmethod - def _minimize_dct(cls, func, x0, learning_rate, iterations, tol_squared, deriv_h, verbosity): - """ - executes the minimize algorithm when the x-values are in a dict - - :returns: ``tuple(path, dfdx, norm2_dfdx)``; result is ``path[-1]`` - """ - x = {**x0} - path = [{**x}] - if verbosity >= cls.MM_VERBOSITY_HIGH: - print(f"[_minimize_dct] x0={fmt(x, '.4f')}") - for iteration in range(iterations): - f0 = func(**x) - dfdx = { - k: (func( **(cls.bump(x, k, deriv_h)) ) - f0) / deriv_h - for k in x.keys() - } - dx = {k: -learning_rate * dfdx[k] for k in x.keys()} - x = {k: x[k] + dx[k] for k in x.keys()} - path.append({**x}) - norm2_dfdx = sum(vv**2 for vv in dfdx.values()) - if verbosity >= cls.MM_VERBOSITY_HIGH: - print(f"[_minimize_dct] {iteration}: norm={m.sqrt(norm2_dfdx):.8f}\nx={fmt(x, '.4f')}\ngradient={fmt(dfdx, '.4f')}\ndx={fmt(dx, '.4f')}\n\n") - if norm2_dfdx < tol_squared: - break - return path, dfdx, norm2_dfdx - - CF_NORM_L1 = "L1" # L1 norm for distance - CF_NORM_L2 = "L2" # ditto L2 - CF_NORM_L2S = "L2S" # ditto L2, but don't bother with sqrt - - def curve_fit(self, func, params0, norm=None, **kwargs): - """ - fits a function to ``self`` using gradient descent - - :func: function to fit (typically a Function object) (1) - :params0: starting parameters (dict) (1) - :kwargs: passed to self.minimize - :returns: the parameters of the fitted function (dict) - - NOTE1: The ``func`` object must have an ``update`` method that accepts a dict of - parameters with the keys of ``params0`` and returns a new object with the updated - parameters. - """ - if norm is None: - norm = self.CF_NORM_L2S - if norm == self.CF_NORM_L1: - #print("[curve_fit] using L1 norm") - dist_func = self.dist_L1 - elif norm == self.CF_NORM_L2: - #print("[curve_fit] using L2 norm") - dist_func = self.dist_L2 - elif norm == self.CF_NORM_L2S: - #print("[curve_fit] using L2S norm") - dist_func = self.dist2_L2 - - def optimizer_func(**params): - #print("[optimizer_func] updating", params) - func1 = func.update(**params) - return dist_func(func=func1) - - return self.minimize(optimizer_func, x0=params0, **kwargs) - - ############################### - ## helpers and utilities - @staticmethod - def e_i(i, n): - """returns the ``i``'th unit vector of size ``n``""" - result = np.zeros(n) - result[i] = 1 - return result - - @staticmethod - def e_k(k, dct): - """returns the unit vector of key ``k`` in ``dct``""" - return {kk: 1 if kk==k else 0 for kk in dct.keys()} - - @staticmethod - def bump(dct, k, h): - """bumps ``dct[k]`` by ``+h``; everything else unmodified (returns a new dict)""" - return {kk: v+h if kk==k else v for kk,v in dct.items()} - - - def plot(self, func=None, *, x_min=None, x_max=None, steps=None, title=None, xlabel=None, ylabel=None, grid=True, show=False, **kwargs): - """ - plots the function ``func`` (default: ``self.f_r``) over the interval [``x_min``, ``x_max``] - - :func: function to plot (default: ``self.f_r``) - :x_min: lower bound (default: ``self.kernel.x_min``) - :x_max: upper bound (default: ``self.kernel.x_max``) - :steps: number of steps (default: ``np.linspace`` defaults) - :show: whether to call plt.show() (default: ``False``) - :grid: whether to show a grid (default: ``True``) - :returns: the result of ``plt.plot`` - """ - func = func or self.f_r - x_min = x_min or self.kernel.x_min - x_max = x_max or self.kernel.x_max - x = np.linspace(x_min, x_max, steps) if steps else np.linspace(x_min, x_max) - y = [func(x) for x in x] - plot = plt.plot(x, y, **kwargs) - if title: plt.title(title) - if xlabel: plt.xlabel(xlabel) - if ylabel: plt.ylabel(ylabel) - if grid: plt.grid(True) - if show: plt.show() - return plot - - # def __add__(self, other): - # assert self.kernel == other.kernel, "kernels must be equal" - # result = super().__add__(other) - # result.kernel = self.kernel - # return result - - # def __sub__(self, other): - # assert self.kernel == other.kernel, "kernels must be equal" - # result = super().__sub__(other) - # result.kernel = self.kernel - # return result - - def _kwargs(self, other=None): - if not other is None: - assert self.kernel == other.kernel, f"kernels must be equal {self.kernel} != {other.kernel}" - return dict(kernel=self.kernel) -minimize = FunctionVector.minimize -goalseek = FunctionVector.goalseek_cls - - -################################################################################## -## FUNCTIONAL OBJECTS -@dataclass(frozen=True) -class PriceFunction(Function): - """ - a function object representing the price function of another function - - :func: the function to derive the price function from - :precision: the precision to use for the derivative calculation - :returns: a new function object with `self.f = func.p` - """ - func: Function - precision: float = None - - def __post_init__(self): - assert isinstance(self.func, Function), "f must be a Function" - if not self.precision is None: - self.precision = float(self.precision) - - def f(self, x): - """the price function ``p = -f'(x)`` of ``self.func(x)``""" - return self.func.p(x, precision=self.precision) - -@dataclass(frozen=True) -class Price2Function(Function): - """ - a function object representing the derivative of the price function of another function - - :func: the function to derive the derivative of the price function from - :precision: the precision to use for the derivative calculation - :returns: a new function object with `self.f = func.pp` - """ - func: Function - precision: float = None - - def __post_init__(self): - assert isinstance(self.func, Function), "f must be a Function" - if not self.precision is None: - self.precision = float(self.precision) - - def f(self, x): - """the second derivative ``f''(x)`` of ``self.func(x)``""" - return self.func.pp(x, precision=self.precision) - - - \ No newline at end of file diff --git a/fastlane_bot/tools/invariants/functions/funcs.py b/fastlane_bot/tools/invariants/functions/funcs.py deleted file mode 100644 index 3e548b96d..000000000 --- a/fastlane_bot/tools/invariants/functions/funcs.py +++ /dev/null @@ -1,81 +0,0 @@ -""" -functions library -- example functions - -(c) Copyright Bprotocol foundation 2024. -Licensed under MIT -""" -#__VERSION__ ==> core.__VERSION__ -#__DATE__ ==> core.__DATE__ - -from .core import Function as _Function, dataclass as _dataclass -import math as _m - -@_dataclass(frozen=True) -class QuadraticFunction(_Function): - """quadratic function ``y = ax^2 + bx + c``""" - a: float = 0 - b: float = 0 - c: float = 0 - - def f(self, x): - return self.a*x**2 + self.b*x + self.c -Quadratic=QuadraticFunction - -@_dataclass(frozen=True) -class PowerlawFunction(_Function): - """quadratic function ``y = N*(x-x0)^alpha``""" - N: float = 1 - alpha: float = -1 - x0: float = 0 - - def f(self, x): - return self.N * (x-self.x0)**(self.alpha) -Powerlaw=PowerlawFunction - -@_dataclass(frozen=True) -class TrigFunction(_Function): - """trigonometric function ``y = amp*sin( (omega*x+phase)*pi )``""" - amp: float = 1 - omega: float = 1 - phase: float = 0 - PI = _m.pi - - def f(self, x): - fx = self.amp * _m.sin( (self.omega*x+self.phase)*self.PI ) - return fx -Trig = TrigFunction - -@_dataclass(frozen=True) -class ExpFunction(_Function): - """exponential function ``y = N*exp(k*(x-x0))``""" - N: float = 1 - k: float = 1 - x0: float = 0 - E = _m.e - - def f(self, x): - return self.N * _m.exp( self.k*(x-self.x0) ) -Exp = ExpFunction - -@_dataclass(frozen=True) -class LogFunction(_Function): - """exponential function ``y = N*log_base(x-x0)``""" - base: float = 10 - N: float = 1 - x0: float = 0 - E = _m.e - - def f(self, x): - return self.N * _m.log( x-self.x0, self.base ) -Log = LogFunction - -@_dataclass(frozen=True) -class HyperbolaFunction(_Function): - """hyperbola function ``y-y0 = k/(x-x0)``""" - k: float = 1 - x0: float = 0 - y0: float = 0 - - def f(self, x): - return self.y0 + self.k/(x-self.x0) -Hyperbola = HyperbolaFunction diff --git a/fastlane_bot/tools/invariants/functions/funcsAMM.py b/fastlane_bot/tools/invariants/functions/funcsAMM.py deleted file mode 100644 index 5d380b13c..000000000 --- a/fastlane_bot/tools/invariants/functions/funcsAMM.py +++ /dev/null @@ -1,480 +0,0 @@ -""" -functions library -- AMM-related example functions - -(c) Copyright Bprotocol foundation 2024. -Licensed under MIT -""" -#__VERSION__ ==> core.__VERSION__ -#__DATE__ ==> core.__DATE__ - -from .core import Function as _Function, dataclass as _dataclass -import math as _m -import decimal as _d -_D = _d.Decimal - -@_dataclass(frozen=True) -class CPMMFunction(_Function): - """ - constant product market maker: y = k/x - - :k: pool constant (scales with square of pool liquidity) - """ - k: float = 1 - - @property - def kbar(self): - """kbar = sqrt(k), ie the properly scaling version of k""" - return _m.sqrt(self.k) - - @classmethod - def from_kbar(cls, kbar): - """create a CPMMFunction from kbar""" - return cls(k=kbar**2) - - def f(self, x): - return self.k/x - - def p(self, x): - return self.k/x**2 - - def pp(self, x): - return -2*self.k/x**3 -CPMM = CPMMFunction -UniV2 = CPMMFunction -BancorV21 = CPMMFunction -BancorV3 = CPMMFunction - -@_dataclass(frozen=True) -class VirtualTokenBalancesCPMMFunction(_Function): - """ - levered CPMM using virtual token balances: (y+y0) = k/(x+x0) - - :k: pool constant (scales with square of pool liquidity) - :x0, y0: virtual pool liquidity - :clip: if True, don't allow negative values for x and y - """ - k: float = 1 - x0: float = 0 - y0: float = 0 - - def __post_init__(self, clip=False): - #super().__post_init__() - super().__setattr__("clip", clip) - - @property - def kbar(self): - """kbar = sqrt(k), ie the properly scaling version of k""" - return _m.sqrt(self.k) - - @classmethod - def from_kbar(cls, kbar, x0=0, y0=0): - """create a CPMMFunction from kbar""" - return cls(k=kbar**2, x0=x0, y0=y0) - - @classmethod - def from_xpxp(cls, *, xa, pa, xb, pb, y0=None, ya=None, yb=None): - """ - create a CPMMFunction from two x values and the associated prices - - :xa, xb: virtual pool liquidity at the two fixed points (xapb) - :y0, ya, yb: y0, or y(xa), y(xb) [at most one given; if none, y0=0] - """ - # alternative constructor, determining the curve by two points on a x-axis - # $x_a, x_b$ and the associated prices $p_a, p_b$; note that we are missing - # a parameter, $y_0$, which is a non-financial parameter in this case as a - # shift in the y direction does not affect prices as long as the curve does - # not run out of tokens - # We have the following equations: - - # $$ - # \frac k {(x_0+x_a)^2} = p_a,\quad \frac k {(x_0+x_b)^2} = p_b - # $$ - # Solving for $x_0, k$ we find - # $$ - # x_0 = \frac{-(p_a x_a) + \sqrt{p_a p_b (x_a - x_b)^2} + p_b x_b}{p_a - p_b} \\ - # k = p_a \left(x_a + \frac{-(p_a x_a) + \sqrt{p_a p_b (x_a - x_b)^2} + p_b x_b}{p_a - p_b}\right)^2 - # = p_a (x_a + x_0)^2 - # $$ - # or - # x0 = (-(pa * xa) + m.sqrt(pa * pb * (xa - xb)**2) + pb * xb) / (pa - pb) - # k = pa * ((xa + (-(pa * xa) + m.sqrt(pa * pb * (xa - xb)**2) + pb * xb) / (pa - pb)) ** 2) - # k = pa * (xa + x0) ** 2 - - assert xapb, f"pa={pa} must be > pb={pb}" - - # core calculation - x0 = (-(pa * xa) + _m.sqrt(pa * pb * (xa - xb)**2) + pb * xb) / (pa - pb) - k = pa * (xa + x0) ** 2 - - # now deal with y0 - ny = len([y for y in [y0, ya, yb] if y is not None]) - if ny>1: - raise ValueError(f"at most 1 of y0, ya, yb can be given, but got {ny} [y0={y0}, ya={ya}, yb={yb}]") - elif ny==0: - y0 = 0 - else: - if not y0 is None: - pass - elif not ya is None: - # ya = k/(xa+x0) - y0 ==> y0 = k/(xa+x0) - ya - y0 = k / (xa+x0) - ya - #print(f"[y0] f(a)={ k / (xa+x0)}, ya={ya}, y0={y0}, k={k}, x0={x0}, xa={xa}") - elif not yb is None: - # yb = k/(xb+x0) - y0 ==> y0 = k/(xb+x0) - yb - y0 = k / (xb+x0) - yb - - # return the new object - #print(f"[LCPMM] k={k}, x0={x0}, y0={y0}") - return cls(k=k, x0=x0, y0=y0) - - def f(self, x): - if x<0 and self.clip: - #print("[f] x<0", x) - return None - y = self.k/(x+self.x0) - self.y0 - if y<0 and self.clip: - #print(f"[f] y<0; y={y}, x={x}, x0={self.x0}, y0={self.y0}, k={self.k}") - return None - return y - - # def p(self, x): - # p = self.k/(x+self.x0)**2 - # if p < self.Pb or p > self.Pa: - # return None - # else: - # return p - - # def pp(self, x): - # return -2*self.k/(x+self.x0)**3 -LCPMM = VirtualTokenBalancesCPMMFunction -VTBCPMM = VirtualTokenBalancesCPMMFunction - - -@_dataclass(frozen=True) -class UniV3Function(_Function): - """ - functionally equivalent to VTBCPMM, but with different parameterization - - :L: effective pool constant (equals kbar = sqrt(k) for VTBCPMM) - :Pa, Pb: start and end price of the range, in dy/dx, Pa > Pb - """ - # In Uniswap, the range is from $P_a \ldots P_b, P_a > P_b$ with liquidity - # constant $L = \bar k = \sqrt{k}$. We know that - - # $$ - # p=-\frac{dy}{dx}=\frac{L^2}{x_v^2} = \left(\frac L {x_v}\right)^2 - # $$ - - # Of course the virtual token balances $x_v = x_0 + x$ and - # $y_v(x) = y_0 + y(x)$ also satisfy the equation - # $$ - # y_v = \frac{L^2}{x_v}, x_v = \frac{L^2}{y_v} - # $$ - # and inserting this into the above equation yields - # $$ - # \sqrt p= \frac L {x_v} = \frac {y_v} L - # $$ - - # We know that $x_v(P_a) < x_v(P_b)$. Therefore, at $P_a$, we have $x=0$ and - # therefore - - # $$ - # \sqrt{P_a} = \frac L {x_0}, x_0 = \frac L {\sqrt{P_a}} - # $$ - - # The same reasoning as above leads us to - # $$ - # \sqrt{P_b} = \frac {y_0} L, y_0 = \sqrt{P_b} L - # $$ - - # Therefore we now can apply our regular levered AMM equation - - # $$ - # y(x)+y_0 = y(x) + \sqrt{P_b} L = \frac k {x+x_0} - # = \frac {L^2} {x + \frac{L}{\sqrt{P_a}}} - # $$ - - # for $x = 0$ we get - - # $$ - # y(x_0) = L \sqrt{P_a} - y_0 = L(\sqrt{P_a} - \sqrt{P_b}) - # $$ - - L: float - Pa: float - Pb: float - - def __post_init__(self): - if self.Pa <= self.Pb: - raise ValueError(f"Pa={self.Pa} must be > Pb={self.Pb}") - #super().__post_init__() - super().__setattr__("x0", self.L / _m.sqrt(self.Pa)) - super().__setattr__("y0", self.L * _m.sqrt(self.Pb)) - #print("[UniV3Function] x0, y0:", self.x0, self.y0) - - @property - def kbar(self): - """kbar = sqrt(k), ie the properly scaling version of k""" - return self.L - - @property - def k(self): - """k = L**2""" - return self.L**2 - - def f(self, x): - if x<0: return None - y = self.k/(x+self.x0) - self.y0 - if y<0: return None - return y - - def p(self, x): - p = self.k/(x+self.x0)**2 - if p < self.Pb or p > self.Pa: - return None - else: - return p - - def pp(self, x): - return -2*self.k/(x+self.x0)**3 -UniV3 = UniV3Function - -@_dataclass(frozen=True) -class CarbonFunction(_Function): - """ - functionally equivalent to VTBCPMM, but with different parameterization, except unidirectional curve - - :y: current pool liquidity in token y - :yint: initial / maximal pool liquidity in token y (at price Pa) - :L: effective pool constant (equals kbar = sqrt(k) for VTBCPMM) - :Pa, Pb: start and end price of the range, in dy/dx, Pa > Pb* - :A, B: alternatives for Pa, Pb; A = sqrt(Pa) - sqrt(Pb), B = sqrt(Pb)* - - - *must provide either (Pa, Pb) or (A, B) but not both - """ - - Pa: float - Pb: float - yint: float - y: float = None - - - def __post_init__(self): - - if self.y is None: - super().__setattr__("y", self.yint) - - if self.Pa <= self.Pb: - raise ValueError(f"Pa={self.Pa} must be > Pb={self.Pb}") - - A = _m.sqrt(self.Pa) - _m.sqrt(self.Pb) - B = _m.sqrt(self.Pb) - super().__setattr__("A", A) - super().__setattr__("B", B) - - # see from_carbon() in cpc.py - kappa = self.yint**2 / self.A**2 - yasym_times_A = self.yint * B - kappa_times_A = self.yint**2 / A - x0 = kappa_times_A / (self.y * A + yasym_times_A) if self.y * A + yasym_times_A != 0 else 1e99 - y0 = _m.sqrt(kappa) * B # = sqrt(kappa) * sqrt(Pb) = L * sqrt(Pb) - - super().__setattr__("kappa", kappa) - super().__setattr__("x0", x0) - super().__setattr__("y0", y0) - print("[CarbonFunction] x0, y0:", self.x0, self.y0) - - @classmethod - def from_AB(cls, A, B, yint, y=None): - """create a CarbonFunction from A, B""" - Pa = (A+B)**2 - Pb = (B**2) - return cls(Pa=Pa, Pb=Pb, yint=yint, y=y) - - @property - def kbar(self): - """kbar = sqrt(k), ie the properly scaling version of k""" - return _m.sqrt(self.k) - - @property - def k(self): - """k = kappa""" - return self.kappa - - def f(self, x): - if x<0: return None - y = self.k/(x+self.x0) - self.y0 - if y<0: return None - return y - - # def p(self, x): - # p = self.k/(x+self.x0)**2 - # if p < self.Pb or p > self.Pa: - # return None - # else: - # return p - - # def pp(self, x): - # return -2*self.k/(x+self.x0)**3 -Carbon = CarbonFunction - - - -@_dataclass(frozen=True) -class SolidlyFunction(_Function): - r""" - represents the Solidly AMM swap function y(x,k)=k/x - - :method: METHOD_FLOAT, METHOD_DEC (default), METHOD_TAYLOR - - - ============================================== - MATHEMATICAL BACKGROUND - ============================================== - - The Solidly **invariant equation** is - $$ - x^3y+xy^3 = k - $$ - - which is a stable swap curve, but more convex than for example Curve. - - To obtain the **swap equation** we solve the above invariance equation - as $y=y(x; k)$. This gives the following result - $$ - y(x;k) = \frac{x^2}{\left(-\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6}\right)^{\frac{1}{3}}} - \frac{\left(-\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6}\right)^{\frac{1}{3}}}{3} - $$ - - We can introduce intermediary **variables L and M** ($L(x;k), M(x;k)$) - to write this a bit more simply - - $$ - L(x,k) = L_1(x) \equiv -\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6} - $$ - $$ - M(x,k) = L^{1/3}(x,k) = \sqrt[3]{L(x,k)} - $$ - $$ - y = \frac{x^2}{\sqrt[3]{L}} - \frac{\sqrt[3]{L}}{3} = \frac{x^2}{M} - \frac{M}{3} - $$ - - If we rewrite the equation for L as below we see that it is not - particularly well conditioned for small $x$ - $$ - L(x,k) = L_2(x) \equiv \frac{27k}{2x} \left(\sqrt{1 + \frac{108x^8}{729k^2}} - 1 \right) - $$ - - For simplicity we introduce the **variable xi** $\xi=\xi(x,k)$ as - $$ - \xi(x, k) = \frac{108x^8}{729k^2} - $$ - - then we can rewrite the above equation as - $$ - L_2(x;k) \equiv \frac{27k}{2x} \left(\sqrt{1 + \xi(x,k)} - 1 \right) - $$ - - Note the Taylor expansion for $\sqrt{1 + \xi} - 1$ is - $$ - \sqrt{1+\xi}-1 = \frac{\xi}{2} - \frac{\xi^2}{8} + \frac{\xi^3}{16} - \frac{5\xi^4}{128} + O(\xi^5) - $$ - - and tests suggest that it is very good for at least $|\xi| < 10^{-5}$ - """ - k: float - - METHOD_FLOAT = "float" - METHOD_DEC100 = "decimal100" - METHOD_DEC1000 = "decimal1000" - METHOD_TAYLOR = "taylor" - def __post_init__(self, method=None): - if method is None: - method = self.METHOD_DEC1000 - #self._method = method - super().__setattr__("_method", method) - if method == self.METHOD_FLOAT: - #self.L = self._L1_float - super().__setattr__("L", self._L1_float) - elif method == self.METHOD_DEC100: - #self.L = self._L1_dec100 - super().__setattr__("L", self._L1_dec100) - elif method == self.METHOD_DEC1000: - #self.L = self._L1_dec1000 - super().__setattr__("L", self._L1_dec1000) - elif method == self.METHOD_TAYLOR: - #self.L = self._L2_taylor - super().__setattr__("L", self._L2_taylor) - else: - raise ValueError(f"method={method} must be one of self.METHOD_FLOAT, self.METHOD_DEC, self.METHOD_TAYLOR") - - @property - def kbar(self): - """kbar = k^(1/4), ie the properly scaling version of k""" - return _m.sqrt(_m.sqrt(self.k)) - - @property - def method(self): - """the method used to calculate y(x,k)""" - return self._method - - @staticmethod - def _L1_float(x, k): - """using float (precision issues)""" - return -27*k/(2*x) + _m.sqrt(729*k**2/x**2 + 108*x**6)/2 - - @staticmethod - def _L1_dec(x, k, *, precision): - """using decimal to avoid precision issues (slow)""" - prec0 = _d.getcontext().prec - _d.getcontext().prec = precision - x,k = _D(x), _D(k) - xi = (108 * x**8) / (729 * k**2) - lam = (_D(1) + xi).sqrt() - _D(1) - L = lam * (27 * k) / (2 * x) - _d.getcontext().prec = prec0 - return float(L) - - @staticmethod - def _L1_dec100(x, k): - """using decimal 100 to avoid precision issues (slow; calls _L1_dec)""" - return SolidlyFunction._L1_dec(x, k, precision=100) - - @staticmethod - def _L1_dec1000(x, k): - """using decimal 1000 to avoid precision issues (very slow; calls _L1_dec)""" - return SolidlyFunction._L1_dec(x, k, precision=1000) - - @staticmethod - def _L2_taylor(x, k): - """ - using Taylor expansion for small x for avoid precision issues (transition artifacts) - """ - xi = (108 * x**8) / (729 * k**2) - #print(f"xi = {xi}") - if xi > 1e-5: - # full formula for $sqrt(1 + \xi) - 1$ - lam = (m.sqrt(1 + xi) - 1) - else: - # Taylor expansion of $sqrt(1 + \xi) - 1$ - lam = xi*(1/2 - xi*(1/8 - xi*(1/16 - 0.0390625*xi))) - # the relative error of this Taylor approximation is for xi < 0.025 is 1e-5 or better - # for xi ~ 1e-15 the full term is unstable (because 1 + 1e-16 ~ 1 in double precision) - # therefore the switchover should happen somewhere between 1e-12 and 1e-2 - L = lam * (27*k) / (2*x) - return L - - - def f(self, x): - L,M,y = [None]*3 - try: - L = self.L(x, self.k) - M = L**(1/3) - y = x*x/M - M/3 - except Exception as e: - print("Exception: ", e) - print(f"x={x}, k={k}, L={L}, M={M}, y={y}") - return y -Solidly = SolidlyFunction \ No newline at end of file diff --git a/fastlane_bot/tools/invariants/invariant.py b/fastlane_bot/tools/invariants/invariant.py deleted file mode 100644 index 083834ba5..000000000 --- a/fastlane_bot/tools/invariants/invariant.py +++ /dev/null @@ -1,239 +0,0 @@ -""" -Represents an AMM invariant - -An AMM invariant is a function :math:`k(x, y)` that is constant for all x, y in -the AMM, typically expressed in a form like :math:`x\cdot y = k`. This is -distinct from the swap function :math:`y=f(x, k)` which is obtained from the -invariant by isolating y. - -Usually working with the swap function is more convenient. However, in some cases -the invariant can be computed analytically whilst the swap function can not. The -``Invariant`` class -- which is the core class of this module -- allows amongst other -things to estimate the swap function numerically rather than having to solve for -it analytically which may not always be possible. - ---- -(c) Copyright Bprotocol foundation 2024. -Licensed under MIT -""" -__VERSION__ = '0.9.1' -__DATE__ = "7/Feb/2024" - -#from dataclasses import dataclass, asdict -from .functions import Function, dataclass -from abc import ABC, abstractmethod - -@dataclass -class Invariant(ABC): - """ - Represents an AMM invariant - - This class is an abstract base class that represents an arbitrary AMM invariant. In order - to obtain a usuable invariant object, one must subclass this class and implement the - ``k_func`` method. For example the following code snippet shows how to implement a simple - constant product invariant: - - .. code-block:: python - - class ConstantProductInvariant(Invariant): - def k_func(self, x, y): - return x*y - - cpi = ConstantProductInvariant() - cpi.y_func(x=20, k=100) # returns ~5 (calculated numerically) - - - The constant product invariant is analytically very easy to handle, and therefore a better - implementation would be to also implement the ``y_Func`` method, which returns the swap function - as a ``Function`` object. This is shown in the following code snippet: - - .. code-block:: python - - class ConstantProductSwapFunction(Function): - def f(self, x): - return self.k / x - - class ConstantProductInvariant2(Invariant): - def k_func(self, x, y): - return x*y - - YFUNC_CLASS = ConstantProductSwapFunction - - cpi = ConstantProductInvariant2() - cpi.y_func(x=20, k=100) # returns 5 (calculated analytically) - - - """ - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - - - @abstractmethod - def k_func(self, x, y): - """ - returns invariant value k = k(x, y) - """ - pass - - YFUNC_CLASS = None - # override this in a derived class with a Function class returning the - # swap function as a Function object if the latter is analytically available - # self.YFUNC_CLASS(k=k) should return a Function object for y(x; k) - - - def y_Func(self, k): - """ - returns y = y(x=.; k) as a Function object (may also return None) - - USAGE - - .. code-block:: python - - y_func = y_Func(k=k) - y = y_func(x) - """ - if not self.YFUNC_CLASS: - return None - return self.YFUNC_CLASS(k=k) - - def y_func(self, x, k): - """ - returns y = y(x,k) - - :x: token balance x - :k: pool invariant k - :returns: token balance y = y(x, k) (1) - - NOTE 1: y is calculated from ``y_Func`` if possible or numerically via - ``y_func_from_k_func`` otherwise - """ - y_Func_k = self.y_Func(k=k) - if not y_Func_k is None: - return y_Func_k.f(x) - return self.y_func_from_k_func(x, k) - - def p_func(self, x, k): - """ - returns p = -dy/dx = p(x, k) - - :x: token balance x - :k: pool invariant k - :returns: price function p = -y'(x, k) (1) - - NOTE 1: this currently only works if y_func is analytic, in which case - the value returned is ``self.y_Func(k=k).p(x)`` - """ - if self.y_func_is_analytic: - return self.y_Func(k=k).p(x) - raise NotImplementedError("p_func not implemented for non-analytic y_func") - - @property - def y_func_is_analytic(self): - """ - whether y_func is obtained as an analytic calculation (ie, not via y_func_from_k_func) - """ - return not self.YFUNC_CLASS is None - - GS_GRADIENT='gradient' - GS_BISECT='bisect' - def y_func_from_k_func(self, x, k, *, x0=None, x_lo=None, x_hi=None, method=None): - """ - solves y = y(x, k) from k = k(x, y) - - :x0: starting estimate (for gradient, default = 1) - :x_hi: upper bound (for bisect, default = 1e10) - :x_lo: ditto lower (default = 1e-10) - :method: one of GS_GRADIENT (default) or GS_BISECT - """ - if method is None: - method = self.GS_GRADIENT - if method == self.GS_GRADIENT: - if x0 is None: - x0 = 1 - return self.goalseek_gradient(lambda y: self.k_func(x, y), x0=x0, target=k) - elif method == self.GS_BISECT: - if x_lo is None: - x_lo = 1e-10 - if x_hi is None: - x_hi = 1e10 - return self.goalseek_bisect(lambda y: self.k_func(x, y), target=k, x_lo=x_lo, x_hi=x_hi) - else: - raise ValueError(f"method={method} must be one of self.GS_GRADIENT, self.GS_BISECT") - - class ConvergenceError(ValueError): - """raised when a goal seek fails to converge""" - pass - - GSGD_TOLERANCE = 1e-6 # absolute tolerance on the y axis - GSGD_ITERATIONS = 1000 # max iterations - GSGD_ETA = 1e-10 # relative step size for calculating derivative - GSGD_H = 1e-6 # used for x=0 - def goalseek_gradient(self, func, target=0, *, x0=1): - """ - very simple gradient descent implementation for a goal seek - - :func: function for goal seek, eg ``lambda x: x**2-1`` - :target: target value (default: 0) - :x0: starting estimate - :raises: ``ConvergenceError`` if it fails to converge - :returns: ``x`` such that ``func(x)`` is close to target - """ - #learning_rate = 0.1 # Learning rate (step size) - x = x0 - iterations = self.GSGD_ITERATIONS - tolerance = self.GSGD_TOLERANCE - h = x0*self.GSGD_ETA if x0 else self.GSGD_H - #print(f"[goalseek_gradient]: x={x}, y={func(x)}") - for i in range(iterations): - y = func(x) - m = (func(x+h)-func(x-h)) / (2*h) - x = x + (target-y)/m - #print(f"[goalseek_gradient] {i}: x={x}, y={func(x)}") - if abs(func(x)-target) < tolerance: - #print("[goalseek_gradient] converged (f, crit, tol)", func(x), abs(func(x)-target), tolerance) - break - if abs(func(x)-target) > tolerance: - raise self.ConvergenceError(f"gradient descent failed to converge on {target}") - return x - - GSBS_ITERATIONS = GSGD_ITERATIONS # max iterations - GSBS_TOLERANCE = GSGD_TOLERANCE # absolute tolerance on the y axis - GSBS_XLO = 1e-10 # lower bound on x - GSBS_XHI = 1e10 # upper bound on x - def goalseek_bisect(self, func, target=0, *, x_lo=None, x_hi=None): - """ - bisect implementation for goal seek - - :func: function for goal seek, eg ``lambda x: x**2-1`` - :target: target value (default: 0) - :x_lo: lower bound on x (default: GSBS_XLO=1e-10) - :x_hi: upper bound on x (default: GSBS_XHI=1e10) - :raises: ``ConvergenceError`` if it fails to converge - :returns: ``x`` such that ``func(x)`` is close to target - """ - if x_lo is None: - x_lo = self.GSBS_XLO - if x_hi is None: - x_hi = self.GSBS_XHI - if x_lo > x_hi: - x_lo, x_hi = x_hi, x_lo - assert x_lo != x_hi, f"x_lo={x_lo} must not be equal to x_hi={x_hi}" - f = lambda x: func(x)-target - assert f(x_lo) * f(x_hi) < 0, f"target={target} must be between func(x_lo)={func(x_lo)} and func(x_hi)={func(x_hi)}" - sgn = 1 if f(x_hi) > 0 else -1 - iterations = self.GSBS_ITERATIONS - tolerance = self.GSBS_TOLERANCE - for i in range(iterations): - x_mid = (x_lo+x_hi)/2 - f_mid = f(x_mid) - #print(f"[goalseek_bisect] {i}: x_lo={x_lo}, x_hi={x_hi}, x_mid={x_mid}, f={f_mid}") - if abs(f_mid) < tolerance: - break - if f_mid*sgn < 0: - x_lo = x_mid - else: - x_hi = x_mid - if abs(f_mid) > tolerance: - raise self.ConvergenceError(f"bisect failed to converge on {target}") - return x_mid diff --git a/fastlane_bot/tools/invariants/kernel.py b/fastlane_bot/tools/invariants/kernel.py deleted file mode 100644 index d73a89de3..000000000 --- a/fastlane_bot/tools/invariants/kernel.py +++ /dev/null @@ -1,210 +0,0 @@ -""" -Implements the `Kernel` class, an integration kernel together with numeric integration code - ---- -(c) Copyright Bprotocol foundation 2024. -Licensed under MIT -""" -__VERSION__ = '0.9.1' -__DATE__ = "26/Jan/2024" - -from dataclasses import dataclass, asdict -from scipy.stats import norm -import numpy as np -import math as m - -@dataclass -class Kernel(): - """ - Represents a one-dimensional integration kernel and provides numeric integration code - - :x_min: minimum x value for integration - :x_max: ditto maximum - :kernel: kernel function (should be positive, and defined `x_min` <= `x` <= `x_max`); - generically, the kernel function is a callable taking a single argument; - alternatively there are a number of built-in kernels that can be selected - by passing the respective constant (see table) - :method: integration method (currently only `METHOD_TRAPEZOID`) - :steps: number of steps for integration - - ====================== ==================================================== - `kernel` meaning - ====================== ==================================================== - FLAT constant - TRIANGLE triangle - SAWTOOTHL, SAWTOOTHR sawtooth left/right - GAUSS, GAUSSW, GAUSSN gaussian (fitted, wide, narrow) - ====================== ==================================================== - - USAGE - - .. code-block:: python - - k = Kernel(x_min=-1, x_max=1, kernel=Kernel.FLAT) - f = lambda x: x**2 - - k(0.5) # 0.5 - k.integrate(f) # ~0.6666 - - Kernel.integrate_trapezoid(f, -1, 1, 100) # ~0.6666 - """ - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - METHOD_TRAPEZOID = 'trapezoid' - - FLAT = "builtin-flat" - TRIANGLE = "builtin-triangle" - SAWTOOTHL = "builtin-sawtoothl" - SAWTOOTHR = "builtin-sawtoothr" - GAUSS = "builtin-gauss" - GAUSSW = "builtin-gausswide" - GAUSSN = "builtin-gaussnarrow" - - DEFAULT_XMIN = 0 - DEFAULT_XMAX = 1 - DEFAULT_KERNEL = FLAT - DEFAULT_METHOD = METHOD_TRAPEZOID - DEFAULT_STEPS = 100 - - x_min: float = DEFAULT_XMIN - x_max: float = DEFAULT_XMAX - kernel: callable = None - kernel_name: str = DEFAULT_KERNEL - method: str = DEFAULT_METHOD - steps: int = DEFAULT_STEPS - - def __post_init__(self): - assert self.x_max > self.x_min, "x_max must be greater than x_min" - if isinstance(self.kernel, str): - self.kernel_name = self.kernel - self.kernel = None - - if self.kernel is None: - w = self.x_max - self.x_min - ctr = (self.x_max+self.x_min)/2 - #print("[Kernel] w = ", w) - - if self.kernel_name == self.FLAT: - self.kernel = lambda x: 1/w - - elif self.kernel_name == self.TRIANGLE: - self.kernel = lambda x: max(1-2*abs((x-ctr)/w),0) - - elif self.kernel_name == self.SAWTOOTHL: - self.kernel = lambda x: 2/w*max(1-abs((x-self.x_min)/w),0) - - elif self.kernel_name == self.SAWTOOTHR: - self.kernel = lambda x: 2/w*(1-max(1-abs((x-self.x_min)/w),0)) - - elif self.kernel_name == self.GAUSS: - self.kernel = lambda x: norm.pdf(x, loc=ctr, scale=w/6)/0.9973001241637569 - - elif self.kernel_name == self.GAUSSW: - self.kernel = lambda x: norm.pdf(x, loc=ctr, scale=w/3)/0.8663853060476605 - - elif self.kernel_name == self.GAUSSN: - self.kernel = lambda x: norm.pdf(x, loc=ctr, scale=w/12) - - else: - raise ValueError(f"unknown kernel type {self.kernel_name}") - - def k(self, x): - """Alias for `self.kernel(x)`, but set to zero beyond `x_min`, `x_max`""" - if self.in_domain(x): - #print(f"[Kernel::k] {self} {x}") - return self.kernel(x) - else: - return 0 - - def __call__(self, x): - """Alias for `self.k`""" - return self.k(x) - - def in_domain(self, x): - """Returns True iff x is in the integration domain `x_min`...`x_max`""" - return self.x_min <= x <= self.x_max - - @property - def limits(self): - """Convenience accessor for `(x_min, x_max)`""" - return (self.x_min, self.x_max) - domain = limits - - def integrate(self, func, *, steps=None, method=None): - """ - Integrates `func` against the kernel (calls `integrate_trapezoid`) - - :func: function to integrate (single variable) - :steps: number of steps for integration (default: self.steps) - :method: integration method (default: self.method) (1) - :returns: :math:`\int_{x_{min}}^{x_{max}} \mathrm{func}(x)\,\mathrm{kernel}(x)\,dx` - - - NOTE 1: currently the only method supported is `METHOD_TRAPEZOID` - - EXAMPLE - - .. code-block:: python - - k = Kernel(x_min=-1, x_max=1, kernel=Kernel.FLAT) - f = lambda x: x**2 - k.integrate(f) # ~0.6666 - """ - if steps is None: - steps = self.steps - if method is None: - method = self.method - ifunc = lambda x: func(x) * self.kernel(x) - - # integrate = self.METHODS.get(method) - # if integrate is None: - # raise ValueError(f"unknown integration method {method}") - - # return integrate(ifunc, self.x_min, self.x_max, steps) - # the above code failed the tests on github for reasons I don't understand - # I therefore went to the pedestrian version below - - if method == self.METHOD_TRAPEZOID: - return self.integrate_trapezoid(ifunc, self.x_min, self.x_max, steps) - else: - raise ValueError(f"unknown integration method {method}") - - @staticmethod - def integrate_trapezoid(func, x_min, x_max, steps): - """ - Integrates a function using the trapezoid method between `x_min` and `x_max` - - :func: function to integrate (single variable callable) - :x_min: minimum x value for integration - :x_max: ditto maximum - :steps: number of steps for integration - :returns: :math:`\int_{x_{min}}^{x_{max}} \mathrm{func}(x)\,dx` - - EXAMPLE - - .. code-block:: python - - f = lambda x: x**2 - Kernel.integrate_trapezoid(f, -1, 1, 100) # ~0.6666 - """ - assert x_max > x_min, "x_max must be greater than x_min" - assert steps > 0, "steps must be positive" - - def func1(x): - try: - return func(x) - except Exception as e: - return 0 - - try: - dx = (x_max-x_min)/steps - f = [func1(x_min+i*dx) for i in range(steps+1)] - except Exception as e: - raise ValueError(f"calculation error (xmin={x_min}, xmax={x_max}, steps={steps}) [{e}]") from e - return (sum(f) - 0.5*(f[0]+f[-1])) * dx - - # METHODS = { - # METHOD_TRAPEZOID: integrate_trapezoid - # } - \ No newline at end of file diff --git a/fastlane_bot/tools/invariants/solidly.py b/fastlane_bot/tools/invariants/solidly.py deleted file mode 100644 index d22d77db4..000000000 --- a/fastlane_bot/tools/invariants/solidly.py +++ /dev/null @@ -1,183 +0,0 @@ -""" -object representing the Solidly AMM invariant - -(c) Copyright Bprotocol foundation 2024. -Licensed under MIT -""" -__VERSION__ = '0.9' -__DATE__ = "18/Jan/2024" - -import decimal as d -D = d.Decimal -import math as m - -from .invariant import Invariant, dataclass -from .functions import Function - - -@dataclass(frozen=True) -class SolidlySwapFunction(Function): - r""" - represents the Solidly AMM swap function y(x,k)=k/x - - :method: METHOD_FLOAT, METHOD_DEC (default), METHOD_TAYLOR - - - ============================================== - MATHEMATICAL BACKGROUND - ============================================== - - The Solidly **invariant equation** is - $$ - x^3y+xy^3 = k - $$ - - which is a stable swap curve, but more convex than for example Curve. - - To obtain the **swap equation** we solve the above invariance equation - as $y=y(x; k)$. This gives the following result - $$ - y(x;k) = \frac{x^2}{\left(-\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6}\right)^{\frac{1}{3}}} - \frac{\left(-\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6}\right)^{\frac{1}{3}}}{3} - $$ - - We can introduce intermediary **variables L and M** ($L(x;k), M(x;k)$) - to write this a bit more simply - - $$ - L(x,k) = L_1(x) \equiv -\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6} - $$ - $$ - M(x,k) = L^{1/3}(x,k) = \sqrt[3]{L(x,k)} - $$ - $$ - y = \frac{x^2}{\sqrt[3]{L}} - \frac{\sqrt[3]{L}}{3} = \frac{x^2}{M} - \frac{M}{3} - $$ - - If we rewrite the equation for L as below we see that it is not - particularly well conditioned for small $x$ - $$ - L(x,k) = L_2(x) \equiv \frac{27k}{2x} \left(\sqrt{1 + \frac{108x^8}{729k^2}} - 1 \right) - $$ - - For simplicity we introduce the **variable xi** $\xi=\xi(x,k)$ as - $$ - \xi(x, k) = \frac{108x^8}{729k^2} - $$ - - then we can rewrite the above equation as - $$ - L_2(x;k) \equiv \frac{27k}{2x} \left(\sqrt{1 + \xi(x,k)} - 1 \right) - $$ - - Note the Taylor expansion for $\sqrt{1 + \xi} - 1$ is - $$ - \sqrt{1+\xi}-1 = \frac{\xi}{2} - \frac{\xi^2}{8} + \frac{\xi^3}{16} - \frac{5\xi^4}{128} + O(\xi^5) - $$ - - and tests suggest that it is very good for at least $|\xi| < 10^{-5}$ - """ - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - k: float - - METHOD_FLOAT = "float" - METHOD_DEC100 = "decimal100" - METHOD_DEC1000 = "decimal1000" - METHOD_TAYLOR = "taylor" - def __post_init__(self, method=None): - if method is None: - method = self.METHOD_DEC1000 - #self._method = method - super().__setattr__("_method", method) - if method == self.METHOD_FLOAT: - #self.L = self._L1_float - super().__setattr__("L", self._L1_float) - elif method == self.METHOD_DEC100: - #self.L = self._L1_dec100 - super().__setattr__("L", self._L1_dec100) - elif method == self.METHOD_DEC1000: - #self.L = self._L1_dec1000 - super().__setattr__("L", self._L1_dec1000) - elif method == self.METHOD_TAYLOR: - #self.L = self._L2_taylor - super().__setattr__("L", self._L2_taylor) - else: - raise ValueError(f"method={method} must be one of self.METHOD_FLOAT, self.METHOD_DEC, self.METHOD_TAYLOR") - - @property - def method(self): - """the method used to calculate y(x,k)""" - return self._method - - @staticmethod - def _L1_float(x, k): - """using float (precision issues)""" - return -27*k/(2*x) + m.sqrt(729*k**2/x**2 + 108*x**6)/2 - - @staticmethod - def _L1_dec(x, k, *, precision): - """using decimal to avoid precision issues (slow)""" - prec0 = d.getcontext().prec - d.getcontext().prec = precision - x,k = D(x), D(k) - xi = (108 * x**8) / (729 * k**2) - lam = (D(1) + xi).sqrt() - D(1) - L = lam * (27 * k) / (2 * x) - d.getcontext().prec = prec0 - return float(L) - - @staticmethod - def _L1_dec100(x, k): - """using decimal 100 to avoid precision issues (slow; calls _L1_dec)""" - return SolidlySwapFunction._L1_dec(x, k, precision=100) - - @staticmethod - def _L1_dec1000(x, k): - """using decimal 1000 to avoid precision issues (very slow; calls _L1_dec)""" - return SolidlySwapFunction._L1_dec(x, k, precision=1000) - - @staticmethod - def _L2_taylor(x, k): - """ - using Taylor expansion for small x for avoid precision issues (transition artefacts) - """ - xi = (108 * x**8) / (729 * k**2) - #print(f"xi = {xi}") - if xi > 1e-5: - # full formula for $sqrt(1 + \xi) - 1$ - lam = (m.sqrt(1 + xi) - 1) - else: - # Taylor expansion of $sqrt(1 + \xi) - 1$ - lam = xi*(1/2 - xi*(1/8 - xi*(1/16 - 0.0390625*xi))) - # the relative error of this Taylor approximation is for xi < 0.025 is 1e-5 or better - # for xi ~ 1e-15 the full term is unstable (because 1 + 1e-16 ~ 1 in double precision) - # therefore the switchover should happen somewhere between 1e-12 and 1e-2 - L = lam * (27*k) / (2*x) - return L - - - def f(self, x): - L,M,y = [None]*3 - try: - L = self.L(x, self.k) - M = L**(1/3) - y = x*x/M - M/3 - except Exception as e: - print("Exception: ", e) - print(f"x={x}, k={k}, L={L}, M={M}, y={y}") - return y - -@dataclass -class SolidlyInvariant(Invariant): - """represents the Solidly invariant function""" - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - def __post_init__(self): - self._y_Func_class = SolidlySwapFunction - - def k_func(self, x, y): - """Solidly invariant function k(x,y)=x^3*y + x*y^3""" - return x**3 * y + x * y**3 - diff --git a/fastlane_bot/tools/invariants/vector.py b/fastlane_bot/tools/invariants/vector.py deleted file mode 100644 index 0b5a3de75..000000000 --- a/fastlane_bot/tools/invariants/vector.py +++ /dev/null @@ -1,262 +0,0 @@ -""" -Implements the ``DictVector`` class, a sparse vector based on dicts ---- -(c) Copyright Bprotocol foundation 2024. -Licensed under MIT -""" -__VERSION__ = '0.9.1' -__DATE__ = "07/Feb/2024" - -from dataclasses import dataclass, asdict -import math - -@dataclass -class DictVector(): - """ - A sparse vector where dict keys are dimensions and values are coefficients - - USAGE - - below an incomplete list of operations that can be performed; note that most - dunder methods are actually implemented, so the usual arithmetic operations - can be performed - - .. code-block:: python - - v1 = DictVector.new(a=1, b=2, c=3) # use kwargs - - d2 = dict(a=10, b=20, c=30) - v2 = DictVector.new(d2) # use dict - - v1+v2 # {a: 11, b: 22, c: 33} - v2-v1 # {a: 9, b: 18, c: 27} - 2*v1 # {a: 2, b: 4, c: 6} - v1.enorm # = sqrt(1+4+9) ~ 3.74 - v1 == v2 # False - len(v1) # 3 - - v = DictVector.new(a=1, d=1) - v += v1 - v == DictVector.new(a=2, b=2, c=3, d=1) # True - - """ - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - vec: dict = None - - def __post_init__(self): - if self.vec is None: - self.vec = dict() - - @classmethod - def null(cls): - """ - Creates a *null* DictVector, aka an empty dict - """ - return cls() - - @classmethod - def new(cls, single_dict_argument=None, **kwargs): - """ - Creates a new DictVector from `kwargs` - """ - if not single_dict_argument is None: - assert len(kwargs) == 0, "new must be called with either single_dict_argument or keyword arguments, not both" - return cls(single_dict_argument) - return cls(dict(**kwargs)) - n = new - - @property - def enorm(self): - r""" - Returns Euclidian norm of `self` - - .. math:: - n_e = \sqrt{\sum_i \alpha_i^2} - - EXAMPLE - - .. code-block:: python - - v = DictVector.new(a=3, b=4) - v.enorm # = sqrt(3^2 + 4^2) = 5 - """ - return self.dict_norm(self.vec) - - def _kwargs(self, other=None): - """ - additional kwargs for __init__ when creating a new object in derived classes - - IMPORTANT NOTE - - many of the below dunder methods call the constructor of the derived class, - and this constructor may have additional arguments. For this to work, the - derived class must provide the additional arguments required by its - constructor in the _kwargs method. - - If other is provided then this is eg for an operator like __add__. In this - case the _kwargs method can decide what to do. Eg in some cases self and - other may not be compatible, in which case _kwargs should throw an exception. - """ - return dict() - - @property - def elements(self): - """returns the elements (keys!) of the vector as a list""" - return list(self.vec.keys()) - el = elements - - @property - def coeffs(self): - """returns the coefficients of the vector as a list""" - return list(self.vec.values()) - - @property - def items(self): - """returns the items of the vector as a list of tuples (element, coeff)""" - return list(self.vec.items()) - - def __getitem__(self, key): - return self.vec.get(key, 0) - - # def __setitem__(self, key, value): - # self.vec[key] = value - - def __eq__(self, other): - objs_eq = self.dict_eq(self.vec, other.vec) - #print(f"[DictVector::eq] objs_eq = {objs_eq}") - return objs_eq - - def __add__(self, other): - return self.__class__(self.dict_add(self.vec, other.vec), **self._kwargs(other)) - - def __sub__(self, other): - return self.__class__(self.dict_sub(self.vec, other.vec), **self._kwargs(other)) - - def __mul__(self, other): - if isinstance(other, DictVector): - return self.dict_sprod(self.vec, other.vec) - return self.__class__(self.dict_smul(self.vec, other), **self._kwargs()) - - def __truediv__(self, other): - return self.__class__(self.dict_smul(self.vec, 1/other), **self._kwargs()) - - def __rmul__(self, other): - return self.__mul__(other) - - def __pos__(self): - return self - - def __neg__(self): - return self.__class__(self.dict_smul(self.vec, -1), **self._kwargs()) - - def __abs__(self): - return self.__class__({k: abs(v) for k, v in self.vec.items()}, **self._kwargs()) - - def __round__(self, n=None): - return self.__class__({k: round(v, n) for k, v in self.vec.items()}, **self._kwargs()) - - def __floor__(self): - return self.__class__({k: math.floor(v) for k, v in self.vec.items()}, **self._kwargs()) - - def __ceil__(self): - return self.__class__({k: math.ceil(v) for k, v in self.vec.items()}, **self._kwargs()) - - def __trunc__(self): - return self.__class__({k: math.trunc(v) for k, v in self.vec.items()}, **self._kwargs()) - - def __iter__(self): - return iter(self.vec) - - def __len__(self): - return len([v for v in self.vec.values() if v!=0]) - - def __bool__(self): - return bool([v for v in self.vec.values() if v!=0]) - - # def __hash__(self): - # return hash(tuple(sorted(self.vec.items()))) - - def __copy__(self): - return self.__class__(self.vec.copy()) - - # def __deepcopy__(self, memo): - # return self.__class__({k: copy.deepcopy(v, memo) for k, v in self.vec.items()}) - - def __contains__(self, key): - return key in self.vec and self.vec[key] != 0 - - def __missing__(self, key): - return 0 - - def __iadd__(self, other): - self.vec = self.dict_add(self.vec, other.vec) - return self - - def __isub__(self, other): - self.vec = self.dict_sub(self.vec, other.vec) - return self - - def __imul__(self, other): - self.vec = self.dict_smul(self.vec, other) - return self - - def __itruediv__(self, other): - self.vec = self.dict_smul(self.vec, 1/other) - return self - - @classmethod - def dict_add(cls, a, b): - """ - Adds two dict-vectors `a` and `b` - """ - return {k: a.get(k, 0) + b.get(k, 0) for k in set(a) | set(b)} - - @classmethod - def dict_sub(cls, a, b): - """ - Subtracts two dict-vectors `a` and `b` - """ - return {k: a.get(k, 0) - b.get(k, 0) for k in set(a) | set(b)} - - @classmethod - def dict_smul(cls, a, s): - """ - Multiplies dict-vector `a` by scalar `s` - """ - return {k: v*s for k, v in a.items()} - - @classmethod - def dict_sprod(cls, a, b): - """ - Multiplies two dict-vectors `a` and `b` (scalar product) - """ - return sum(a.get(k, 0) * b.get(k, 0) for k in set(a) | set(b)) - - @classmethod - def dict_norm(cls, a): - """ - Calculates the Euclidian norm of dict-vector `a` - """ - return sum(v**2 for v in a.values())**0.5 - - @classmethod - def dict_eq(cls, a, b, *, eps=0): - """ - Calculates whether two dict-vectors `a` and `b` are equal (within `eps`, on absolute value basis) - """ - diffvec = cls.dict_sub(a, b) - if len(diffvec) == 0: - return True - return max(abs(v) for v in diffvec.values()) <= eps - - -V = DictVector.new - -add = DictVector.dict_add -sub = DictVector.dict_sub -smul = DictVector.dict_smul -sprod = DictVector.dict_sprod -norm = DictVector.dict_norm -eq = DictVector.dict_eq diff --git a/fastlane_bot/tools/noneresult.py b/fastlane_bot/tools/noneresult.py deleted file mode 100644 index b8f61c499..000000000 --- a/fastlane_bot/tools/noneresult.py +++ /dev/null @@ -1,160 +0,0 @@ -""" -a none object that behaves somewhat more gracefully than None - -(c) Copyright Bprotocol foundation 2023. -Licensed under MIT -""" -__VERSION__ = "1.0" -__DATE__ = "12/May/2023" - -def isNone(none): - """returns True if none is None or NoneResult()""" - return isinstance(none, NoneResult) or none is None - -class NoneResult(): - """ - a NoneResult is a dummy object that behave more gracefully than None - - - typically a NoneResult is an error result that can be passed down without - raising errors in situations where None would fail - - :message: typically provides the (error) message that caused the creation of this object - it can be accessed via the `__message` attribute - """ - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - def __init__(self, message=None): - self.__message = str(message) - #print('[NoneResult] message:', message, self._message) - - def __getattr__(self, attr): - return self - - def __getitem__(self, key): - return self - - # conversions and other unitary operations - def __str__(self): - return f"NoneResult('{self.__message}')" - - def __repr__(self) -> str: - return self.__str__() - - def __bool__(self): - return False - - def __hash__(self): - return hash(None) - - def __int__(self): - return 0 - - def __oct__(self): - return oct(0) - - def __hex__(self): - return hex(0) - - def __trunc__(self): - return self - - def __float__(self): - return 0.0 - - def __format__(self, fmt): - return str(self).__format__(fmt) - - def __floor__(self): - return self - - def __ceil__(self): - return self - - def __abs__(self): - return self - - def __pos__(self): - return self - - def __neg__(self): - return self - - def __round__(self, n): - return self - - # binary operations (all return self) - def __add__(self, other): - return self - - def __sub__(self, other): - return self - - def __mul__(self, other): - return self - - def __truediv__(self, other): - return self - - def __floordiv__(self, other): - return self - - def __divmod__(self, other): - return self - - def __pow__(self, other): - return self - - def __mod__(self, other): - return self - - def __sizeof__(self): - return 0 - - # reflected binary operations ditto - def __radd__(self, other): - return self - - def __rsub__(self, other): - return self - - def __rmul__(self, other): - return self - - def __rtruediv__(self, other): - return self - - def __rfloordiv__(self, other): - return self - - def __rdivmod__(self, other): - return self - - def __rpow__(self, other): - return self - - def __rmod__(self, other): - return self - - # comparison operators (all False, except with other NoneResult) - def __eq__(self, other): - if isinstance(other, NoneResult) or other is None: - return True - return False - - def __ne__(self, other): - return not self.__eq__(other) - - def __lt__(self, other): - return False - - def __le__(self, other): - return False - - def __gt__(self, other): - return False - - def __ge__(self, other): - return False - - \ No newline at end of file diff --git a/fastlane_bot/tools/optimizer/__init__.py b/fastlane_bot/tools/optimizer/__init__.py deleted file mode 100644 index 13692c78d..000000000 --- a/fastlane_bot/tools/optimizer/__init__.py +++ /dev/null @@ -1,63 +0,0 @@ -""" -Optimization methods for AMM routing and arbitrage - -This module implements a number of methods that allow for -routing (1) and arbitrage amongst a set of AMMs. Most -methods allow, subject to convergence, for the optimization -and routing within an arbitrary multi-token context. The -*subject to convergence* part is important, as in particular -the convex optimization methods with the solvers available -to us to do not seem to be able to handle leveraged -liquidity well. Specifically, the following algorithms are -implemented: - -- **Marginal Price Optimization**: a highly efficient - robust and efficient optimization method developed by us - specifically for the Fastlane Bot; it is based on the - insight that in any optimal state, the marginal prices - of all curves must be consistent, and therefore to - optimize the state of the entire market we only have to - look at all possibly marginal prices, which is a much - smaller set than all possible AMM states - -- **Pair Optimization**: the predecessor of the Marginal - Price Optimization method in the context of *pairs*, - meaning that we only look at AMMs trading one specific - pair; in this case the optimization algorithm is a - one-dimensional goal seek, and using a multi-dimensional - Newtown-Raphson method is overkill in this case - -- **Convex Optimization**: this method is based on a paper - by Angeris et al (2), showing that routing and arbitrage - of AMMs are convex optimization problems; this is a very - interesting approach and works very well for unlevered - curves. However, for levered curves (Carbon, Uniswap v3) - we ran into convergence issues which is why we moved on - to the marginal price method - -Marginal price optimization is implemented in the class -``MargPOptimizer``, pair optimization in the class -``PairOptimizer``, and convex optimization in the class -``ConvexOptimizer``. All those classes are subclasses of -``CPCArbOptimizer``, and ultimately of ``OptimizerBase``. - - -NOTE 1: routing is not implemented yet, but it is a trivial -extension of the arbitrage methods that only needs to be -connected and properly parameterized - -NOTE 2: https://angeris.github.io/papers/cfmm-chapter.pdf - -This module is still subject to active research, and -comments and suggestions are welcome. The corresponding -author is Stefan Loesch - ---- -(c) Copyright Bprotocol foundation 2023. -Licensed under MIT. -""" - -from .cpcarboptimizer import * -from .pairoptimizer import PairOptimizer -from .margpoptimizer import MargPOptimizer -from .convexoptimizer import ConvexOptimizer \ No newline at end of file diff --git a/fastlane_bot/tools/optimizer/base.py b/fastlane_bot/tools/optimizer/base.py deleted file mode 100644 index 67417ba91..000000000 --- a/fastlane_bot/tools/optimizer/base.py +++ /dev/null @@ -1,299 +0,0 @@ -""" -optimization library -- optimizer base module - - - ---- -(c) Copyright Bprotocol foundation 2023. -Licensed under MIT -""" -__VERSION__ = "5.1" -__DATE__ = "20/Sep/2023" - -from dataclasses import dataclass, field, fields, asdict, astuple, InitVar -from abc import ABC, abstractmethod, abstractproperty -import pandas as pd -import numpy as np - -import time -import math -import numbers -import pickle -from ..cpc import ConstantProductCurve as CPC, CPCInverter, CPCContainer -from sys import float_info -from .dcbase import DCBase - -class OptimizerBase(ABC): - """ - base class for all optimizers - - :problem: the problem object (eg allowing to read `problem.status`) - :result: the return value of problem.solve - :time: the time it took to solve this problem (optional) - :optimizer: the optimizer object that created this result - """ - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - @abstractproperty - def kind(self): - """ - returns the kind of optimizer (as str) - """ - - def pickle(self, basefilename, addts=True): - """ - pickles the object to a file - """ - if addts: - filename = f"{basefilename}.{int(time.time()*100)}.optimizer.pickle" - else: - filename = f"{basefilename}.optimizer.pickle" - with open(filename, "wb") as f: - pickle.dump(self, f) - - @classmethod - def unpickle(cls, basefilename): - """ - unpickles the object from a file - """ - with open(f"{basefilename}.optimizer.pickle", "rb") as f: - object = pickle.load(f) - assert isinstance(object, cls), f"unpickled object is not of type {cls}" - return object - - @dataclass - class OptimizerResult(DCBase, ABC): - """ - base class for all optimizer results - - :result: actual optimization result - :time: time taken to solve the optimization - :method: method used to solve the optimization - :optimizer: the optimizer object that created this result - - """ - result: float - time: float - method: str = None - optimizer: InitVar = None - - def __post_init__(self, optimizer=None): - if not optimizer is None: - assert issubclass(type(optimizer), OptimizerBase), f"optimizer must be a subclass of OptimizerBase {optimizer}" - self._optimizer = optimizer - # print("[OptimizerResult] post_init", optimizer) - - @property - def optimizer(self): - return self._optimizer - - def __float__(self): - return float(self.result) - - # @property - # def status(self): - # """problem status""" - # raise NotImplementedError("must be implemented in derived class") - - @abstractproperty - def status(self): - """problem status""" - pass - - # @property - # def is_error(self): - # """True if problem status is not OPTIMAL""" - # raise NotImplementedError("must be implemented in derived class") - - @abstractproperty - def is_error(self): - """True if problem status is not OPTIMAL""" - pass - - # def detailed_error(self): - # """detailed error analysis""" - # raise NotImplementedError("must be implemented in derived class") - - @abstractproperty - def detailed_error(self): - """detailed error analysis""" - pass - - @property - def error(self): - """problem error""" - if not self.is_error: - return None - return self.detailed_error() - - @dataclass - class SimpleResult(DCBase): - result: float - method: str = None - errormsg: str = None - context_dct: dict = None - - def __float__(self): - if self.is_error: - raise ValueError("cannot convert error result to float") - return float(self.result) - - @property - def is_error(self): - return not self.errormsg is None - - @property - def context(self): - return self.context_dct if not self.context_dct is None else {} - - DERIVEPS = 1e-6 - - @classmethod - def deriv(cls, func, x): - """ - computes the derivative of `func` at point `x` - """ - h = cls.DERIVEPS - return (func(x + h) - func(x - h)) / (2 * h) - - @classmethod - def deriv2(cls, func, x): - """ - computes the second derivative of `func` at point `x` - """ - h = cls.DERIVEPS - return (func(x + h) - 2 * func(x) + func(x - h)) / (h * h) - - @classmethod - def findmin_gd(cls, func, x0, *, learning_rate=0.1, N=100): - """ - finds the minimum of `func` using gradient descent starting at `x0` - - :func: function to optimize (must take one parameter) - :x0: starting point - :learning_rate: learning rate parameter - :N: number of iterations; always goes full length here - there is no convergence check - """ - x = x0 - for _ in range(N): - x -= learning_rate * cls.deriv(func, x) - return cls.SimpleResult(result=x, method="gradient-min") - - @classmethod - def findmax_gd(cls, func, x0, *, learning_rate=0.1, N=100): - """ - finds the maximum of `func` using gradient descent, starting at `x0` - - :func: function to optimize (must take one parameter) - :x0: starting point - :learning_rate: learning rate parameter - :N: number of iterations; always goes full length here - there is no convergence check - """ - x = x0 - for _ in range(N): - x += learning_rate * cls.deriv(func, x) - return cls.SimpleResult(result=x, method="gradient-max") - - @classmethod - def findminmax_nr(cls, func, x0, *, N=20): - """ - finds the minimum or maximum of func using Newton Raphson, starting at x0 - - :func: the function to optimize (must take one parameter) - :x0: the starting point - :N: the number of iterations; note that the algo will always go to - the full length here; there is no convergence check - :returns: the result of the optimization as SimpleResult - - """ - x = x0 - for _ in range(N): - # print("[NR]", x, func(x), cls.deriv(func, x), cls.deriv2(func, x)) - try: - x -= cls.deriv(func, x) / cls.deriv2(func, x) - except Exception as e: - return cls.SimpleResult( - result=None, - errormsg=f"Newton Raphson failed: {e} [x={x}, x0={x0}]", - method="newtonraphson", - ) - return cls.SimpleResult(result=x, method="newtonraphson") - - findmin = findminmax_nr - findmax = findminmax_nr - - GOALSEEKEPS = 1e-15 # double has 15 digits - - @classmethod - def goalseek(cls, func, a, b, *, eps=None): - """ - finds the value of `x` where `func(x)` x is zero, using a bisection between a,b - - :func: function for which to find the zero (must take one parameter) - :a: lower bound a (1) - :b: upper bound b (1) - :eps: desired accuracy - :returns: the result as SimpleResult - - NOTE 1: we must have func(a) * func(b) < 0 - """ - if eps is None: - eps = cls.GOALSEEKEPS - #print(f"[goalseek] eps = {eps}, GOALSEEKEPS = {cls.GOALSEEKEPS}") - if func(a) * func(b) > 0: - return cls.SimpleResult( - result=None, - errormsg=f"function must have different signs at a,b [{a}, {b}, {func(a)} {func(b)}]", - method="bisection", - ) - #raise ValueError("function must have different signs at a,b") - counter = 0 - while (b/a-1) > eps: - c = (a + b) / 2 - if func(c) == 0: - return cls.SimpleResult(result=c, method="bisection") - elif func(a) * func(c) < 0: - b = c - else: - a = c - counter += 1 - if counter > 200: - raise ValueError(f"goalseek did not converge; possible epsilon too small [{eps}]") - return cls.SimpleResult(result=(a + b) / 2, method="bisection") - - @staticmethod - def posx(vector): - """ - returns the positive elements of the vector, zeroes elsewhere - """ - if isinstance(vector, np.ndarray): - return np.maximum(0, vector) - return tuple(max(0, x) for x in vector) - - @staticmethod - def negx(vector): - """ - returns the negative elements of the vector, zeroes elsewhere - """ - if isinstance(vector, np.ndarray): - return np.minimum(0, vector) - return tuple(min(0, x) for x in vector) - - @staticmethod - def a(vector): - """helper: returns vector as np.array""" - return np.array(vector) - - @staticmethod - def t(vector): - """helper: returns vector as tuple""" - return tuple(vector) - - @staticmethod - def F(func, rg): - """helper: returns list of [func(x) for x in rg]""" - return [func(x) for x in rg] - diff --git a/fastlane_bot/tools/optimizer/convexoptimizer.py b/fastlane_bot/tools/optimizer/convexoptimizer.py deleted file mode 100644 index 72ea59ec7..000000000 --- a/fastlane_bot/tools/optimizer/convexoptimizer.py +++ /dev/null @@ -1,508 +0,0 @@ -""" -optimization library -- Convex Optimizer module [final optimizer class] - -The convex optimizer explicitly solves the optimization problem by exploiting the fact -that the problem is convex. Whilst theoretically interesting, this method is complex, -slow and, importantly, converges badly on levered curves (eg Uniswap v3, Carbon). Whilst -we may continue research into this method, at this stage it is recommended to use the -marginal price optimizer instead. - ---- -This module is still subject to active research, and comments and suggestions are welcome. -The corresponding author is Stefan Loesch - -(c) Copyright Bprotocol foundation 2023. -Licensed under MIT -""" -__VERSION__ = "5.0.1" -__DATE__ = "23/Jan/2024" - -from dataclasses import dataclass, field, fields, asdict, astuple, InitVar -#import pandas as pd -import numpy as np - -import time -# import math -import numbers -# import pickle -from ..cpc import ConstantProductCurve as CPC, CPCInverter, CPCContainer -# from sys import float_info - -try: - import cvxpy as cp -except: - # if cvxpy is not installed on the system then the convex optimization methods will not work - # however, the (superior) marginal price based methods will still work and we do not want to - # force installation of an otherwise unused package onto the user's system - from types import SimpleNamespace - cp = SimpleNamespace(Variable=0, ECOS=0, SCS=0, OSQP=0, CVXOPT=0, CBC=0) - -from .dcbase import DCBase -from .base import OptimizerBase -from .cpcarboptimizer import CPCArbOptimizer - - -@dataclass -class ScaledVariable(DCBase): - """ - wraps a cvxpy variable to allow for scaling - """ - - variable: cp.Variable - scale: any = 1.0 - token: list = None - - def __post_init__(self): - try: - len_var = len(self.variable.value) - except TypeError as e: - print("[ScaledVariable] variable.value is None", self.variable) - return - - if not isinstance(self.scale, numbers.Number): - self.scale = np.array(self.scale) - if not len(self.scale) == len_var: - raise ValueError( - "scale and variable must have same length or scale must be a number", - self.scale, - self.variable.value, - ) - if not self.token is None: - if not len(self.token) == len_var: - raise ValueError( - "token and variable must have same length", - self.token, - self.variable.value, - ) - - @property - def value(self): - """ - converts value from USD to token units* - - Note: with scaling, the calculation is set up in a way that the values of the raw variables - dx, dy correspond approximately to USD numbers, so their relative scale is natural and only - determined by the problem, not by units. - - The scaling factor is the PRICE in USD PER TOKEN, therefore - - self.variable.value = USD value of the token - self.variable.value / self.scale = number of tokens - """ - try: - return np.array(self.variable.value) / self.scale - except Exception as e: - print("[value] exception", e, self.variable.value, self.scale) - return self.variable.value - - @property - def v(self): - """alias for variable""" - return self.variable - - - -class ConvexOptimizer(CPCArbOptimizer): - """ - implements the marginal price optimization method - """ - - @property - def kind(self): - return "convex" - - @dataclass - class ConvexOptimizerResult(OptimizerBase.OptimizerResult): - - problem: InitVar - - def __post_init__(self, optimizer=None, problem=None, *args, **kwargs): - super().__post_init__(*args, optimizer=optimizer, **kwargs) - # print("[ConvexOptimizerResult] post_init") - assert not problem is None, "problem must be set" - self._problem = problem - if self.method is None: - self.method = "convex" - - @property - def problem(self): - return self._problem - - @property - def status(self): - """problem status""" - return self.problem.status - - @property - def detailed_error(self): - """detailed error message""" - if self.is_error: - return f"ERROR: {self.status} {self.result}" - return - - @property - def is_error(self): - """True if problem status is not OPTIMAL""" - return self.status != cp.OPTIMAL or isinstance(self.result, str) - - @property - def error(self): - """problem error""" - if not self.is_error: - return None - if isinstance(self.result, str): - return f"{self.result} [{self.status}]" - return f"{self.status}" - - @dataclass - class NofeesOptimizerResult(ConvexOptimizerResult): - """ - results of the nofees optimizer - """ - - token_table: dict = None - sfc: any = field(repr=False, default=None) # SelfFinancingConstraints - curves: CPCContainer = field(repr=False, default=None) - # curves_new: CPCContainer = field(repr=False, default=None) - # dx: cp.Variable = field(repr=False, default=None) - # dy: cp.Variable = field(repr=False, default=None) - dx: InitVar - dy: InitVar - - def __post_init__( - self, optimizer=None, problem=None, dx=None, dy=None, *args, **kwargs - ): - super().__post_init__(*args, optimizer=optimizer, problem=problem, **kwargs) - # print("[NofeesOptimizerResult] post_init") - assert not self.token_table is None, "token_table must be set" - assert not self.sfc is None, "sfc must be set" - assert not self.curves is None, "curves must be set" - # assert not self.curves_new is None, "curves_new must be set" - assert not dx is None, "dx must be set" - assert not dy is None, "dy must be set" - self._dx = dx - self._dy = dy - - @property - def dx(self): - return self._dx - - @property - def dy(self): - return self._dy - - @property - def curves_new(self): - """returns a list of Curve objects the trade instructions implemented""" - assert self.is_error is False, "cannot get this data from an error result" - return self.optimizer.adjust_curves(dxvals=self.dxvalues) - - def trade_instructions(self, ti_format=None): - """ - returns list of TradeInstruction objects - - :ti_format: format of the TradeInstruction objects, see TradeInstruction.to_format - :returns: see table - - ================ ==================================================== - ti_format returns - ================ ==================================================== - TIF_OBJECTS a list of TradeInstruction objects (default) - TIF_DICTS a list of TradeInstruction dictionaries - TIF_DFRAW raw dataframe (holes are filled with NaN) - TIF_DF alias for TIF_DFRAW - TIF_DFP returns a "pretty" dataframe (holes are spaces) - TIF_DFAGRR aggregated dataframe - TIF_DF alias for TIF_DFRAW - ================ ==================================================== - """ - result = ( - CPCArbOptimizer.TradeInstruction.new( - curve_or_cid=c, tkn1=c.tknx, amt1=dx, tkn2=c.tkny, amt2=dy - ) - for c, dx, dy in zip(self.curves, self.dxvalues, self.dyvalues) - if dx != 0 or dy != 0 - ) - #print("[trade_instructions] ti_format", ti_format) - assert ti_format != CPCArbOptimizer.TIF_DFAGGR, "TIF_DFAGGR not implemented for convex optimization" - assert ti_format != CPCArbOptimizer.TIF_DFPG, "TIF_DFPG not implemented for convex optimization" - return CPCArbOptimizer.TradeInstruction.to_format(result, ti_format=ti_format) - - @property - def dxvalues(self): - """returns dx values""" - return self.dx.value - - @property - def dyvalues(self): - """returns dy values""" - return self.dy.value - - def dxdydf(self, *, asdict=False, pretty=True, inclk=False): - """returns dataframe with dx, dy per curve""" - if inclk: - dct = [ - { - "cid": c.cid, - "pair": c.pair, - "tknx": c.tknx, - "tkny": c.tkny, - "x": c.x, - "y": c.y, - "xa": c.x_act, - "ya": c.y_act, - "k": c.k, - "kpost": (c.x + dxv) * (c.y + dyv), - "kk": (c.x + dxv) * (c.y + dyv) / c.k, - c.tknx: dxv, - c.tkny: dyv, - } - for dxv, dyv, c in zip(self.dx.value, self.dy.value, self.curves) - ] - else: - dct = [ - { - "cid": c.cid, - "pair": c.pair, - "tknx": c.tknx, - "tkny": c.tkny, - "x": c.x, - "y": c.y, - "xa": c.x_act, - "ya": c.y_act, - "kk": (c.x + dxv) * (c.y + dyv) / c.k, - c.tknx: dxv, - c.tkny: dyv, - } - for dxv, dyv, c in zip(self.dx.value, self.dy.value, self.curves) - ] - if asdict: - return dct - df = pd.DataFrame.from_dict(dct).set_index("cid") - df0 = df.fillna(0) - dfa = df0[df0.columns[8:]].sum().to_frame(name="total").T - dff = pd.concat([df, dfa], axis=0) - if pretty: - try: - dff = dff.style.format({col: FORMATTER for col in dff.columns[3:]}) - except Exception as e: - print("[dxdydf] exception", e, dff.columns) - return dff - - SOLVER_ECOS = "ECOS" - SOLVER_SCS = "SCS" - SOLVER_OSQP = "OSQP" - SOLVER_CVXOPT = "CVXOPT" - SOLVER_CBC = "CBC" - SOLVERS = { - SOLVER_ECOS: cp.ECOS, - SOLVER_SCS: cp.SCS, - SOLVER_OSQP: cp.OSQP, - SOLVER_CVXOPT: cp.CVXOPT, - SOLVER_CBC: cp.CBC, - # those solvers will usually have to be installed separately - # "ECOS_BB": cp.ECOS_BB, - # "OSQP": cp.OSQP, - # "GUROBI": cp.GUROBI, - # "MOSEK": cp.MOSEK, - # "GLPK": cp.GLPK, - # "GLPK_MI": cp.GLPK_MI, - # "CPLEX": cp.CPLEX, - # "XPRESS": cp.XPRESS, - # "SCIP": cp.SCIP, - } - - def convex_optimizer(self, sfc, **params): - """ - convex optimization for determining the arbitrage opportunities - - :sfc: a SelfFinancingConstraints object (or str passed to SFC.arb) - :params: additional parameters to be passed to the solver - :verbose: if True, generate verbose output - :solver: the solver to be used (default: "CVXOPT"; see SOLVERS) - :nosolve: if True, do not solve the problem, but return the problem object - :nominconstr: if True, do NOT add the minimum constraints - :maxconstr: if True, DO add the (reundant) maximum constraints - :retcurves: if True, also return the curves object (default: False) - :s_xxx: pass the parameter `xxx` to the solver (eg s_verbose) - :s_verbose: if True, generate verbose output from the solver - - - note: CVXOPT is a pip install (pip install cvxopt); OSQP is not suitable for this problem, - ECOS and SCS do work sometimes but can go dramatically wrong - """ - - # This code runs the actual optimization. It has two major parts - - # 1. the **constraints**, and - # 2. the **objective function** to be optimized (min or max) - - # The objective function is to either maximize the number of tokens - # received from the AMM (which is a negative number, hence formally the - # condition is `cp.Minimize` or to minimize the number of tokens paid to - # the AMM which is a positive number. Therefore `cp.Minimize` is the - # correct choice in each case. - - # The constraints come in three types: - - # - **curve constraint**: the curve constraints correspond to the - # $x\cdot y=k$ invariant of the respective AMM; the constraint is - # formally `>=` but it has been shown eg by Angeris et al that the - # constraint will always be optimal on the boundary - - # - **range constraints**: the range constraints correspond to the - # tokens actually available on curve; for the full-curve AMM those - # constraints would formally be `dx >= -c.x` and the same for `y`, but - # those constraint are automatically fulfilled because of the - # asymptotic behaviour of the curves so could be omitted - - # - **self-financing constraints**: the self-financing constraints - # corresponds to the condition that all `dx` and `dy` corresponding to - # a specific token other than the token in the objective function must - # sum to the target amount provided in `inputs` (or zero if not - # provided) - - assert not cp is None, "cvxpy not installed [pip install cvxpy]]" - if isinstance(sfc, str): - sfc = self.SelfFinancingConstraints.arb(sfc) - - curves_t = self.curve_container.curves - c0 = curves_t[0] - tt = self.curve_container.tokentable() - prtkn = sfc.optimizationvar - - P = lambda x: params.get(x) - - start_time = time.time() - - # set up the optimization variables - if P("verbose"): - print(f"Setting up dx[0..{len(curves_t)-1}] and dy[0..{len(curves_t)-1}]") - dx = cp.Variable(len(curves_t), value=[0] * len(curves_t)) - dy = cp.Variable(len(curves_t), value=[0] * len(curves_t)) - - # the geometric mean of objects in a list - gmean = lambda lst: cp.geo_mean(cp.hstack(lst)) - - ## assemble the constraints... - constraints = [] - - # curve constraints - for i, c in enumerate(curves_t): - constraints += [ - gmean([c.x + dx[i] / c.scalex, c.y + dy[i] / c.scaley]) >= c.kbar - ] - if P("verbose"): - print( - f"CC {i} [{c.cid}]: {c.pair} x={c.x:.1f} {c.tknx } (s={c.scalex}), y={c.y:.1f} {c.tkny} (s={c.scaley}), k={c.k:2.1f}, p_dy/dx={c.p:2.1f}, p_dx/dy={1/c.p:2.1f}" - ) - - if P("verbose"): - print("number of constraints: ", len(constraints)) - - # range constraints (min) - for i, c in enumerate(curves_t): - - pass - - if not P("nominconstr"): - constraints += [ - dx[i] / c.scalex >= c.dx_min, - dy[i] / c.scaley >= c.dy_min, - ] - if P("verbose"): - print( - f"RC {i} [{c.cid}]: dx>{c.dx_min:.4f} {c.tknx} (s={c.scalex}), dy>{c.dy_min:.4f} {c.tkny} (s={c.scaley}) [{c.pair}]" - ) - - if P("maxconstr"): - if not c.dx_max is None: - constraints += [ - dx[i] / c.scalex <= c.dx_max, - ] - if not c.dy_max is None: - constraints += [ - dy[i] / c.scaley <= c.dy_max, - ] - if P("verbose"): - print( - f"RC {i} [{c.cid}]: dx<{c.dx_max} {c.tknx} (s={c.scalex}), dy<{c.dy_max} {c.tkny} (s={c.scaley}) [{c.pair}]" - ) - - if P("verbose"): - print("number of constraints: ", len(constraints)) - - # self-financing constraints - for tkn, tknvalue in sfc.items(): - if not isinstance(tknvalue, str): - constraints += [ - cp.sum([dy[i] for i in tt[tkn].y]) - + cp.sum([dx[i] for i in tt[tkn].x]) - == tknvalue * c0.scale(tkn) - # note: we can access the scale from any curve as it is a class method - ] - if P("verbose"): - print( - f"SFC [{tkn}={tknvalue}, s={c0.scale(tkn)}]: y={[i for i in tt[tkn].y]}, x={[i for i in tt[tkn].x]}" - ) - - if P("verbose"): - print("number of constraints: ", len(constraints)) - - # objective function (note: AMM out is negative, AMM in is positive) - if P("verbose"): - print( - f"O: y={[i for i in tt[prtkn].y]}, x={[i for i in tt[prtkn].x]}, {prtkn}" - ) - - objective = cp.Minimize( - cp.sum([dy[i] for i in tt[prtkn].y]) + cp.sum([dx[i] for i in tt[prtkn].x]) - ) - - # run the optimization - problem = cp.Problem(objective, constraints) - solver = self.SOLVERS.get(P("solver"), cp.CVXOPT) - if not P("nosolve"): - sp = {k[2:]: v for k, v in params.items() if k[:2] == "s_"} - print("Solver params:", sp) - if P("verbose"): - print(f"Solving the problem with {solver}...") - try: - problem_result = problem.solve(solver=solver, **sp) - # problem_result = problem.solve(solver=solver) - except cp.SolverError as e: - if P("verbose"): - print(f"Solver error: {e}") - problem_result = str(e) - if P("verbose"): - print( - f"Problem solved in {time.time()-start_time:.2f} seconds; result: {problem_result}" - ) - else: - problem_result = None - - dx_ = ScaledVariable( - dx, [c.scalex for c in curves_t], [c.tknx for c in curves_t] - ) - dy_ = ScaledVariable( - dy, [c.scaley for c in curves_t], [c.tkny for c in curves_t] - ) - - return self.NofeesOptimizerResult( - problem=problem, - sfc=sfc, - result=problem_result, - time=time.time() - start_time, - dx=dx_, - dy=dy_, - token_table=tt, - curves=self.curve_container, - # curves_new=self.adjust_curves(dxvals = dx_.value), - optimizer=self, - ) - nofees_optimizer = convex_optimizer - - - - - \ No newline at end of file diff --git a/fastlane_bot/tools/optimizer/cpcarboptimizer.py b/fastlane_bot/tools/optimizer/cpcarboptimizer.py deleted file mode 100644 index a803a9e4d..000000000 --- a/fastlane_bot/tools/optimizer/cpcarboptimizer.py +++ /dev/null @@ -1,763 +0,0 @@ -""" -Implements optimization methods for AMM arbitrage and routing - -All classes derived from the `CPCArbOptimizer` class answer -two fundamental questions in relation to a market consisting -of multiple AMMs in one or multiple token pairs: - -- **Arbitrage**: Are there arbitrage opportunities in the - market and how can we exploit them? - -- **Routing**: Given a set of desired in and out tokens - (typically one in, one out), what is the optimal route, - taking into account arbitrage opportunities that may be - present int the market - -This class mostly defines common interface code that the derived classes -are meant to implement, and contains a number of utilities that are useful -across those classes. - -The most importance objects contained in this class are - -- The ``SelfFinancingConstraints`` class, which is used to define the context - of the optimization, notably the token amounts in and out of the overall - market, and which token receives the arbitrage profit, if any; for arbitrage - purposes this class is overkill, but it allows for defining arbitrary optimal - routing problems - -- The ``TradeInstruction`` class, which encapsulates the trade instructions that - are generated by the optimization methods; it serves as an abstraction layer - between the results of the optimization and the format in which subsequent code - wants to consume the results - -- The ``MargpOptimizerResult`` class, which encapsulates the result of the marginal - price optimization method (1) - -NOTE 1. The marginal price optimization method is now the only method in use, all other -optimization methods have been deprecated and are available only for historical and research -purposes, which explains its predominant role in this module - ---- -This module is still subject to active research, and comments and suggestions are welcome. -The corresponding author is Stefan Loesch - -(c) Copyright Bprotocol foundation 2023. -Licensed under MIT -""" -__VERSION__ = "5.1" -__DATE__ = "15/Sep/2023" - -from dataclasses import dataclass, field, fields, asdict, astuple, InitVar -import pandas as pd -import numpy as np - -try: - import cvxpy as cp -except: - # if cvxpy is not installed on the system then the convex optimization methods will not work - # however, the (superior) marginal price based methods will still work and we do not want to - # force installation of an otherwise unused package onto the user's system - cp = None - -import time -import math -import numbers -import pickle -from ..cpc import ConstantProductCurve as CPC, CPCInverter, CPCContainer, Pair -from sys import float_info - -from .dcbase import DCBase -from .base import OptimizerBase - - -FORMATTER = lambda x: "" if ((abs(x) < 1e-10) or math.isnan(x)) else f"{x:,.2f}" - -F = OptimizerBase.F - -TIF_OBJECTS = "objects" -TIF_DICTS = "dicts" -TIF_DFRAW = "dfraw" -TIF_DF = TIF_DFRAW -TIFDF8 = "df8" -TIF_DFAGGR = "dfaggr" -TIF_DFAGGR8 = "dfaggr8" -TIF_DFPG = "dfgain" -TIF_DFPG8 = "dfgain8" - - -class CPCArbOptimizer(OptimizerBase): - """ - intermediate class for CPC arbitrage optimization - - :curves: the CPCContainer object (or the curves therein) the optimizer is using - - NOTE - the old argument name `curve_container` is still supported but deprecated - """ - - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - def __init__(self, curves=None, *, curve_container=None): - if not curve_container is None: - if not curves is None: - raise ValueError( - "must not uses curves and curve_container at the same time" - ) - curves = curve_container - if curves is None: - raise ValueError("must provide curves") - if not isinstance(curves, CPCContainer): - curve_container = CPCContainer(curves) - self._curve_container = curves - - @property - def curve_container(self): - """the curve container (CPCContainer)""" - return self._curve_container - - CC = curve_container - curves = curve_container - - @property - def tokens(self): - return self.curve_container.tokens - - @dataclass - class SelfFinancingConstraints(DCBase): - """ - describes self financing constraints and determines optimization variable - - :data: a dict TKN -> amount, or AMMPays, AMMReceives, OptimizationVar (see table) - :tokens: set of all tokens in the problem (if None, use data.keys()) - - ================== ================================================================================ - value meaning - ================== ================================================================================ - amount from the AMM perspective, total inflows (>0) or outflows (<0) - for all items not present in data the value is assumed zero - AMMPays the AMM payout should be maximized [from the trader (!) perspective] - AMMReceives the money paid into the AMM should be minimized [ditto] - OptimizationVar like AMMPays and AMMReceives, but if the direction of the payout is - not known at the beginning [not all methods allow this] - OV alias for OptimizationVar - ================== ================================================================================ - - """ - - AMMPays = "AMMPays" - AMMReceives = "AMMReceives" - OptimizationVar = "OptimizationVar" - OV = OptimizationVar - - data: dict - tokens: set = None - - def __post_init__(self): - optimizationvars = tuple( - k - for k, v in self.data.items() - if v in {self.AMMPays, self.AMMReceives, self.OptimizationVar} - ) - assert ( - len(optimizationvars) == 1 - ), f"there must be EXACTLY one AMMPays, AMMReceives, OptimizationVar {self.data}" - self._optimizationvar = optimizationvars[0] - if self.tokens is None: - self.tokens = set(self.data.keys()) - else: - if isinstance(self.tokens, str): - self.tokens = set(t.strip() for t in self.tokens.split(",")) - else: - self.tokens = set(self.tokens) - assert ( - set(self.data.keys()) - self.tokens == set() - ), f"constraint keys {set(self.data.keys())} > {self.tokens}" - - @property - def optimizationvar(self): - """optimization variable, ie the in that is set to AMMPays, AMMReceives or OptimizationVar""" - return self._optimizationvar - - @property - def tokens_s(self): - """tokens as a comma-separated string""" - return ", ".join(self.tokens_l) - - @property - def tokens_l(self): - """tokens as a list""" - return sorted(list(self.tokens)) - - def asdict(self, *, short=False): - """dict representation including zero-valued tokens (unless short)""" - if short: - return {**self.data} - return {k: self.get(k) for k in self.tokens} - - def items(self, *, short=False): - return self.asdict(short=short).items() - - @classmethod - def new(cls, tokens, **data): - """alternative constructor: data as kwargs""" - return cls(data=data, tokens=tokens) - - @classmethod - def arb(cls, targettkn): - """alternative constructor: arbitrage constraint, ie all other constraints are zero""" - return cls(data={targettkn: cls.OptimizationVar}) - - def get(self, item): - """gets the constraint, or 0 if not present""" - assert item in self.tokens, f"item {item} not in {self.tokens}" - return self.data.get(item, 0) - - def is_constraint(self, item): - """ - returns True iff item is a constraint (ie not an optimisation variable) - """ - return not self.is_optimizationvar(item) - - def is_optimizationvar(self, item): - """ - returns True iff item is the optimization variable - """ - assert item in self.tokens, f"item {item} not in {self.tokens}" - return item == self.optimizationvar - - def is_arbsfc(self): - """ - returns True iff the constraint is an arbitrage constraint - """ - if len(self.data) == 1: - return True - data1 = [v for v in self.data.values() if v != 0] - return len(data1) == 1 - - def __call__(self, item): - """alias for get""" - return self.get(item) - - def SFC(self, **data): - """alias for SelfFinancingConstraints.new""" - return self.SelfFinancingConstraints.new(self.curve_container.tokens(), **data) - - def SFCd(self, data_dct): - """alias for SelfFinancingConstraints.new, with data as a dict""" - return self.SelfFinancingConstraints.new( - self.curve_container.tokens(), **data_dct - ) - - def SFCa(self, targettkn): - """alias for SelfFinancingConstraints.arb""" - return self.SelfFinancingConstraints.arb(targettkn) - - arb = SFCa - - AMMPays = SelfFinancingConstraints.AMMPays - AMMReceives = SelfFinancingConstraints.AMMReceives - OptimizationVar = SelfFinancingConstraints.OptimizationVar - OV = SelfFinancingConstraints.OV - - def price_estimates(self, *, tknq, tknbs, **kwargs): - """ - convenience function to access CPCContainer.price_estimates - - :tknq: can only be a single token - :tknbs: list of tokens - - see help(CPCContainer.price_estimate) for details - """ - return self.curve_container.price_estimates(tknqs=[tknq], tknbs=tknbs, **kwargs) - - @dataclass - class TradeInstruction(DCBase): - """ - encodes a specific trade one a specific curve - - seen from the AMM; in numbers must be positive, out numbers negative - - :cid: the curve id - :tknin: token in - :amtin: amount in (>0) - :tknout: token out - :amtout: amount out (<0) - :error: error message (if any; None means no error) - :curve: the curve object (optional); note: users of this object need - to decide whether they trust the preparing code to set curve - or whether they fetch it via the cid themselves - :raiseonerror: if True, raise an error if the trade instruction is invalid - otherwise just set the error message - """ - - cid: any - tknin: str - amtin: float - tknout: str - amtout: float - error: str = field(repr=True, default=None) - curve: InitVar = None - raiseonerror: InitVar = False - - POSNEGEPS = 1e-8 - - def __post_init__(self, curve=None, raiseonerror=False): - self.curve = curve - if curve is not None: - if self.cid != curve.cid: - err = f"curve/cid mismatch [{self.cid} vs {curve.cid}]" - self.error = err - if raiseonerror: - raise ValueError(err) - if self.tknin == self.tknout: - err = f"tknin and tknout must be different [{self.tknin} {self.tknout}]" - self.error = err - if raiseonerror: - raise ValueError(err) - self.cid = str(self.cid) - self.tknin = str(self.tknin) - self.tknout = str(self.tknout) - self.amtin = float(self.amtin) - self.amtout = float(self.amtout) - if not self.amtin * self.amtout < 0: - if ( - abs(self.amtin) < self.POSNEGEPS - and abs(self.amtout) < self.POSNEGEPS - ): - self.amtin = 0 - self.amtout = 0 - else: - err = f"amtin and amtout must be of different sign [{self.amtin} {self.tknin}, {self.amtout} {self.tknout}]" - self.error = err - if raiseonerror: - raise ValueError(err) - - if not self.amtin >= 0: - err = f"amtin must be positive [{self.amtin}]" # seen from AMM - self.error = err - if raiseonerror: - raise ValueError(err) - - if not self.amtout <= 0: - err = f"amtout must be negative [{self.amtout}]" # seen from AMM - self.error = err - if raiseonerror: - raise ValueError(err) - - TIEPS = 1e-10 - - @classmethod - def new( - cls, curve_or_cid, tkn1, amt1, tkn2, amt2, *, eps=None, raiseonerror=False - ): - """automatically determines which is in and which is out""" - try: - cid = curve_or_cid.cid - curve = curve_or_cid - except: - cid = curve_or_cid - curve = None - if eps is None: - eps = cls.TIEPS - if amt1 > 0: - newobj = cls( - cid=cid, - tknin=tkn1, - amtin=amt1, - tknout=tkn2, - amtout=amt2, - curve=curve, - raiseonerror=raiseonerror, - ) - else: - newobj = cls( - cid=cid, - tknin=tkn2, - amtin=amt2, - tknout=tkn1, - amtout=amt1, - curve=curve, - raiseonerror=raiseonerror, - ) - - return newobj - - @property - def is_empty(self): - """returns True if this is an empty trade instruction (too close to zero)""" - return self.amtin == 0 or self.amtout == 0 - - @classmethod - def to_dicts(cls, trade_instructions): - """converts iterable ot TradeInstruction objects to a tuple of dicts""" - # print("[TradeInstruction.to_dicts]") - return tuple(ti.asdict() for ti in trade_instructions) - - @classmethod - def to_df(cls, trade_instructions, robj, ti_format=None): - """ - converts iterable ot TradeInstruction objects to a pandas dataframe - - :trade_instructions: iterable of TradeInstruction objects - :robj: OptimizationResult object generating the trade instructions - :ti_format: format (TIF_DFP, TIF_DFRAW, TIF_DFAGGR, TIF_DF, TIF_DFPG) - """ - if ti_format is None: - ti_format = cls.TIF_DF - cid8 = ti_format in set([cls.TIF_DF8, cls.TIF_DFAGGR8, cls.TIF_DFPG8]) - dicts = ( - { - "cid": ti.cid if not cid8 else ti.cid[-10:], - "pair": ti.curve.pair if not ti.curve is None else "", - "pairp": ti.curve.pairp if not ti.curve is None else "", - "tknin": ti.tknin, - "tknout": ti.tknout, - ti.tknin: ti.amtin, - ti.tknout: ti.amtout, - } - for ti in trade_instructions - ) - df = pd.DataFrame.from_dict(list(dicts)).set_index("cid") - if ti_format in set([cls.TIF_DF, cls.TIF_DF8]): - return df - if ti_format in set([cls.TIF_DFAGGR, cls.TIF_DFAGGR8]): - df1r = df[df.columns[4:]] - df1 = df1r.fillna(0) - dfa = df1.sum().to_frame(name="TOTAL NET").T - dfp = df1[df1 > 0].sum().to_frame(name="AMMIn").T - dfn = df1[df1 < 0].sum().to_frame(name="AMMOut").T - dfpr = pd.Series(robj.p_optimal).to_frame(name="PRICE").T - # dfpr = pd.Series(r.p_optimal).to_frame(name="PRICES POST").T - df = pd.concat([df1r, dfpr, dfp, dfn, dfa], axis=0) - - dfc = df.copy() - dfc.loc["PRICE"].fillna(1, inplace=True) - - return dfc - if ti_format in set([cls.TIF_DFPG, cls.TIF_DFPG8]): - ti = trade_instructions - r = robj - eff_p_out_per_in = [-ti_.amtout / ti_.amtin for ti_ in ti] - data = dict( - exch=[ti_.curve.P("exchange") for ti_ in ti], - cid=[ - ti_.cid if ti_format == cls.TIF_DFPG else ti_.cid[-10:] - for ti_ in ti - ], - fee=[ - ti_.curve.fee for ti_ in ti - ], # if split here must change conversion below - pair=[ - ti_.curve.pair - if ti_format == cls.TIF_DFPG - else Pair.n(ti_.curve.pair) - for ti_ in ti - ], - amt_tknq=[ - ti_.amtin if ti_.tknin == ti_.curve.tknq else ti_.amtout - for ti_ in ti - ], - tknq=[ti_.curve.tknq for ti_ in ti], - margp0=[ti_.curve.p for ti_ in ti], - effp=[ - p if ti_.tknout == ti_.curve.tknq else 1 / p - for p, ti_ in zip(eff_p_out_per_in, ti) - ], - margp=[ - r.price(tknb=ti_.curve.tknb, tknq=ti_.curve.tknq) for ti_ in ti - ], - ) - df = pd.DataFrame(data) - df["gain_r"] = np.abs(df["effp"] / df["margp"] - 1) - df["gain_tknq"] = -df["amt_tknq"] * (df["effp"] / df["margp"] - 1) - - cgt_l = ( - (cid, gain, tkn) - for cid, gain, tkn in zip(df.index, df["gain_tknq"], df["tknq"]) - ) - cgtp_l = ( - (cid, gain, tkn, r.price(tknb=tkn, tknq=r.targettkn)) - for cid, gain, tkn in cgt_l - ) - cg_l = ((cid, gain * price) for cid, gain, tkn, price in cgtp_l) - df["gain_ttkn"] = tuple(gain for cid, gain in cg_l) - df = df.sort_values(["exch", "gain_ttkn"], ascending=False) - df = df.set_index(["exch", "cid"]) - return df - - raise ValueError(f"unknown format {ti_format}") - - TIF_OBJECTS = TIF_OBJECTS - TIF_DICTS = TIF_DICTS - TIF_DFRAW = TIF_DFRAW - TIF_DFAGGR = TIF_DFAGGR - TIF_DFAGGR8 = TIF_DFAGGR8 - TIF_DF = TIF_DF - TIF_DF8 = TIFDF8 - TIF_DFPG = TIF_DFPG - TIF_DFPG8 = TIF_DFPG8 - - @classmethod - def to_format(cls, trade_instructions, robj=None, *, ti_format=None): - """ - converts iterable ot TradeInstruction objects to the given format - - :trade_instructions: iterable of TradeInstruction objects - :robj: OptimizationResult object generating the trade instructions - :ti_format: format to convert to - :returns: the trade instructions in the given format (see table) - - ================ ==================================================== - ti_format returns - ================ ==================================================== - TIF_OBJECTS a list of TradeInstruction objects (default) - TIF_DICTS a list of TradeInstruction dictionaries - TIF_DFRAW raw dataframe (holes are filled with NaN) - TIF_DF alias for TIF_DFRAW - TIF_DFP returns a "pretty" dataframe (holes are spaces) - TIF_DFAGRR aggregated dataframe - TIF_DF alias for TIF_DFRAW - ================ ==================================================== - """ - # print("[TradeInstruction] to_format", ti_format) - if ti_format is None: - ti_format = cls.TIF_OBJECTS - if ti_format == cls.TIF_OBJECTS: - return tuple(trade_instructions) - elif ti_format == cls.TIF_DICTS: - return cls.to_dicts(trade_instructions) - elif ti_format[:2] == "df": - trade_instructions = tuple(trade_instructions) - if len(trade_instructions) == 0: - return pd.DataFrame() - return cls.to_df(trade_instructions, robj=robj, ti_format=ti_format) - else: - raise ValueError(f"unknown format {ti_format}") - - @property - def price_outperin(self): - return -self.amtout / self.amtin - - p = price_outperin - - @property - def price_inperout(self): - return -self.amtin / self.amtout - - pr = price_inperout - - @property - def prices(self): - return (self.price_outperin, self.price_inperout) - - pp = prices - - TIF_OBJECTS = TIF_OBJECTS - TIF_DICTS = TIF_DICTS - TIF_DFRAW = TIF_DFRAW - TIF_DFAGGR = TIF_DFAGGR - TIF_DFAGGR8 = TIF_DFAGGR8 - TIF_DF = TIF_DF - TIF_DF8 = TIFDF8 - TIF_DFPG = TIF_DFPG - TIF_DFPG8 = TIF_DFPG8 - - METHOD_MARGP = "margp" - - @dataclass - class MargpOptimizerResult(OptimizerBase.OptimizerResult): - """ - results of the marginal price optimizer - - :curves: curve objects underlying the optimization (as CPCContainer) - :targetkn: target token (=profit token) of the optimization - :p_optimal_t: optimal price vector (as tuple) - :dtokens: change in token amounts (as dict) - :dtokens_t: change in token amounts (as tuple) - :tokens_t: list of tokens - :errormsg: error message if an error occured (None=no error) - - PROPERTIES - :p_optimal: optimal price vector (as dict) - - """ - - TIF_OBJECTS = TIF_OBJECTS - TIF_DICTS = TIF_DICTS - TIF_DFRAW = TIF_DFRAW - TIF_DFAGGR = TIF_DFAGGR - TIF_DFAGGR8 = TIF_DFAGGR8 - TIF_DF = TIF_DF - TIF_DF8 = TIFDF8 - TIF_DFPG = TIF_DFPG - TIF_DFPG8 = TIF_DFPG8 - - curves: any = field(repr=False, default=None) - targettkn: str = field(repr=True, default=None) - # p_optimal: dict = field(repr=False, default=None) - p_optimal_t: tuple = field(repr=True, default=None) - n_iterations: int = field(repr=False, default=None) - dtokens: dict = field(repr=False, default=None) - dtokens_t: tuple = field(repr=True, default=None) - tokens_t: tuple = field(repr=True, default=None) - errormsg: str = field(repr=True, default=None) - method: str = field(repr=True, default=None) - - def __post_init__(self, *args, **kwargs): - # print(f"[MargpOptimizerResult] method = {self.method} [1]") - super().__post_init__(*args, **kwargs) - # print(f"[MargpOptimizerResult] method = {self.method} [2]") - # #print("[MargpOptimizerResult] post_init") - assert ( - self.p_optimal_t is not None or self.errormsg is not None - ), "p_optimal_t must be set unless errormsg is set" - if not self.p_optimal_t is None: - self.p_optimal_t = tuple(self.p_optimal_t) - self._p_optimal_d = { - **{tkn: p for tkn, p in zip(self.tokens_t, self.p_optimal_t)}, - self.targettkn: 1.0, - } - - if self.method is None: - self.method = CPCArbOptimizer.METHOD_MARGP - # print(f"[MargpOptimizerResult] method = {self.method} [3]") - self.raiseonerror = False - - @property - def p_optimal(self): - """the optimal price vector as dict (last entry is target token)""" - return self._p_optimal_d - - @property - def is_error(self): - return self.errormsg is not None - - def detailed_error(self): - return self.errormsg - - def status(self): - return "error" if self.is_error else "converged" - - def price(self, tknb, tknq): - """returns the optimal price of tknb/tknq based on p_optimal [in tknq per tknb]""" - assert ( - self.p_optimal is not None - ), "p_optimal must be set [do not use minimal results]" - return self.p_optimal.get(tknb, 1) / self.p_optimal.get(tknq, 1) - - def dxdyvalues(self, asdict=False): - """ - returns a vector of (dx, dy) values for each curve (see also dxvecvalues) - """ - assert ( - not self.curves is None - ), "curves must be set [do not use minimal results]" - assert self.is_error is False, "cannot get this data from an error result" - result = ( - (c.cid, c.dxdyfromp_f(self.price(c.tknb, c.tknq))[0:2]) - for c in self.curves - ) - if asdict: - return {cid: dxdy for cid, dxdy in result} - return tuple(dxdy for cid, dxdy in result) - - def dxvecvalues(self, asdict=False): - """ - returns a dict {tkn: dtknk} of changes for each curve (see also dxdyvalues) - """ - assert ( - not self.curves is None - ), "curves must be set [do not use minimal results]" - assert self.is_error is False, "cannot get this data from an error result" - result = ((c.cid, c.dxvecfrompvec_f(self.p_optimal)) for c in self.curves) - if asdict: - return {cid: dxvec for cid, dxvec in result} - return tuple(dxvec for cid, dxvec in result) - - @property - def dxvalues(self): - return tuple(dx for dx, dy in self.dxdyvalues()) - - @property - def dyvalues(self): - return tuple(dy for dx, dy in self.dxdyvalues()) - - @property - def curves_new(self): - """returns a list of Curve objects the trade instructions implemented""" - assert ( - self.optimizer is not None - ), "optimizer must be set [do not use minimal results]" - assert self.is_error is False, "cannot get this data from an error result" - return self.optimizer.adjust_curves(dxvals=self.dxvalues) - - def trade_instructions(self, ti_format=None): - """ - returns list of TradeInstruction objects - - :ti_format: TIF_OBJECTS, TIF_DICTS, TIF_DFP, TIF_DFRAW, TIF_DFAGGR, TIF_DF - """ - try: - assert ( - self.curves is not None - ), "curves must be set [do not use minimal results]" - assert ( - self.is_error is False - ), "cannot get this data from an error result" - result = ( - CPCArbOptimizer.TradeInstruction.new( - curve_or_cid=c, tkn1=c.tknx, amt1=dx, tkn2=c.tkny, amt2=dy - ) - for c, dx, dy in zip(self.curves, self.dxvalues, self.dyvalues) - if dx != 0 or dy != 0 - ) - return CPCArbOptimizer.TradeInstruction.to_format( - result, robj=self, ti_format=ti_format - ) - except AssertionError: - if self.raiseonerror: - raise - return None - - def adjust_curves(self, dxvals, *, verbose=False, raiseonerror=False): - """ - returns a new curve container with the curves shifted by the given dx values - """ - # print("[adjust_curves]", dxvals) - if dxvals is None: - if raiseonerror: - raise ValueError("dxvals is None") - else: - print("[adjust_curves] dxvals is None") - return None - curves = self.curve_container - try: - newcurves = [ - c.execute(dx=dx, verbose=verbose, ignorebounds=True) - for c, dx in zip(curves, dxvals) - ] - return CPCContainer(newcurves) - except Exception as e: - if raiseonerror: - raise e - else: - print(f"Error in adjust_curves: {e}") - # raise e - return None - - def plot(self, *args, **kwargs): - """ - convenience for self.curve_container.plot() - - see help(CPCContainer.plot) for details - """ - return self.curve_container.plot(*args, **kwargs) - - def format(self, *args, **kwargs): - """ - convenience for self.curve_container.format() - - see help(CPCContainer.format) for details - """ - return self.curve_container.format(*args, **kwargs) diff --git a/fastlane_bot/tools/optimizer/dcbase.py b/fastlane_bot/tools/optimizer/dcbase.py deleted file mode 100644 index 52527df7e..000000000 --- a/fastlane_bot/tools/optimizer/dcbase.py +++ /dev/null @@ -1,73 +0,0 @@ -""" -This module defines the `DCBase` class, from which -dataclasses can derive, and which adds useful methods to -those dataclasses, notably ``asdict``, ``astuple`` and ``fields``. - ---- -(c) Copyright Bprotocol foundation 2023. -Licensed under MIT -""" -from dataclasses import dataclass, field, fields, asdict, astuple, InitVar - - -class DCBase: - """ - Adds useful methods to dataclasses - - USAGE - - .. code-block:: python - - @dataclass - class MyDataClass(DCBase): - ... - - obj = MyDataClass(...) - obj.asdict() - obj.astuple() - obj.fields() - """ - - def asdict(self): - """ - returns the object as a dict - - alias for `dataclasses.asdict(self)` - """ - return asdict(self) - - def astuple(self): - """ - returns the object as a tuple - - alias for `dataclasses.astuple(self)` - """ - return astuple(self) - - def fields(self): - """ - returns the object fields - - alias for `dataclasses.fields(self)` - """ - return fields(self) - - # def pickle(self, filename, addts=True): - # """ - # pickles the object to a file - # """ - # if addts: - # filename = f"{filename}.{time.time()}.pickle" - # with open(filename, 'wb') as f: - # pickle.dump(self, f) - - # @classmethod - # def unpickle(cls, filename): - # """ - # unpickles the object from a file - # """ - # with open(filename, 'rb') as f: - # object = pickle.load(f) - # assert isinstance(object, cls), f"unpickled object is not of type {cls}" - # return object - diff --git a/fastlane_bot/tools/optimizer/margpoptimizer.py b/fastlane_bot/tools/optimizer/margpoptimizer.py deleted file mode 100644 index eb207f7e9..000000000 --- a/fastlane_bot/tools/optimizer/margpoptimizer.py +++ /dev/null @@ -1,449 +0,0 @@ -""" -Implements the "Marginal Price Optimization" method for arbitrage and routing - - -The marginal price optimizer implicitly solves the -optimization problem by always operating on the optimal -hyper surface, which is the surface where all marginal -prices of the same pair are equal, and all marginal prices -across pairs follow the usual no arbitrage condition. -Therefore the problem reduces to a goal seek -- we need to -find the point on that hyper surface that satisfies the -desired boundary conditions. - -This method employs a Newton-Raphson algorithm to solve the -aforementioned goal seek problem. - ---- -This module is still subject to active research, and -comments and suggestions are welcome. The corresponding -author is Stefan Loesch - -(c) Copyright Bprotocol foundation 2023. -Licensed under MIT -""" -__VERSION__ = "5.2" -__DATE__ = "15/Sep/2023" - -from dataclasses import dataclass, field, fields, asdict, astuple, InitVar -import pandas as pd -import numpy as np - -import time -# import math -# import numbers -# import pickle -from ..cpc import ConstantProductCurve as CPC, CPCInverter, CPCContainer -#from sys import float_info - -from .dcbase import DCBase -from .base import OptimizerBase -from .cpcarboptimizer import CPCArbOptimizer - -class MargPOptimizer(CPCArbOptimizer): - """ - implements the marginal price optimization method - """ - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - @property - def kind(self): - return "margp" - - @classmethod - def jacobian(cls, func, x, *, eps=None): - """ - computes the Jacobian of func at point x - - :func: a callable x=(x1..xn) -> (y1..ym), taking and returning np.arrays - must also take a quiet parameter, which if True suppresses output - :x: a vector x=(x1..xn) as np.array - """ - if eps is None: - eps = cls.JACEPS - n = len(x) - y = func(x, quiet=True) - jac = np.zeros((n, n)) - for j in range(n): # through columns to allow for vector addition - Dxj = abs(x[j]) * eps if x[j] != 0 else eps - x_plus = [(xi if k != j else xi + Dxj) for k, xi in enumerate(x)] - jac[:, j] = (func(x_plus, quiet=True) - y) / Dxj - return jac - J = jacobian - JACEPS = 1e-5 - - - MO_DEBUG = "debug" - MO_PSTART = "pstart" - MO_P = MO_PSTART - MO_DTKNFROMPF = "dtknfrompf" - MO_MINIMAL = "minimal" - MO_FULL = "full" - - MOEPS = 1e-6 - MOMAXITER = 50 - - class OptimizationError(Exception): pass - class ConvergenceError(OptimizationError): pass - class ParameterError(OptimizationError): pass - - def optimize(self, sfc=None, result=None, *, params=None): - """ - optimal transactions across all curves in the optimizer, extracting targettkn (1) - - :sfc: the self financing constraint to use (2) - :result: the result type (see MO_XXX constants below) - :params: dict of parameters (see table below) - - - :returns: MargpOptimizerResult on the default path, others depending on the - chosen result - - Meaning of the `result` parameter: - - ============== ============================================================ - `result` returns - ============== ============================================================ - MO_DEBUG a number of items useful for debugging - MO_PSTART price estimates (as dataframe) - MO_PE alias for MO_ESTPRICE - MO_DTKNFROMPF the function calculating dtokens from p - MO_MINIMAL minimal result (omitting some big fields) - MO_FULL full result - None alias for MO_FULL - ============== ============================================================ - - - Meaning of the `params` parameter: - - ================== ========================================================================= - parameter meaning - ================== ========================================================================= - eps precision parameter for accepting the result (default: 1e-6) - maxiter maximum number of iterations (default: 100) - verbose if True, print some high level output - progress if True, print some basic progress output - debug if True, print some debug output - debug2 more debug output - raiseonerror if True, raise an OptimizationError exception on error - pstart starting price for optimization (3) - ================== ========================================================================= - - - NOTE 1: this optimizer uses the marginal price method, ie it solves the equation - - dx_i (p) = 0 for all i != targettkn, and the whole price vector - - NOTE 2: at the moment only the trivial self-financing constraint is allowed, ie the one that - only specifies the target token, and where all other constraints are zero; if sfc is - a string then this is interpreted as the target token - - NOTE 3: can be provided either as dict {tkn:p, ...}, or as df as price estimate as - returned by MO_PSTART; excess tokens can be provided but all required tokens - must be present - """ - # data conversion: string to SFC object; note that anything but pure arb not currently supported - if isinstance(sfc, str): - sfc = self.arb(targettkn=sfc) - assert sfc.is_arbsfc(), "only pure arbitrage SFC are supported at the moment" - targettkn = sfc.optimizationvar - - # lambdas - P = lambda item: params.get(item, None) if params is not None else None - get = lambda p, ix: p[ix] if ix is not None else 1 # safe get from tuple - dxdy_f = lambda r: (np.array(r[0:2])) # extract dx, dy from result - tn = lambda t: t.split("-")[0] # token name, eg WETH-xxxx -> WETH - - # initialisations - eps = P("eps") or self.MOEPS - maxiter = P("maxiter") or self.MOMAXITER - start_time = time.time() - curves_t = self.curve_container - alltokens_s = self.curve_container.tokens() - tokens_t = tuple(t for t in alltokens_s if t != targettkn) # all _other_ tokens... - tokens_ix = {t: i for i, t in enumerate(tokens_t)} # ...with index lookup - pairs = self.curve_container.pairs(standardize=False) - curves_by_pair = { - pair: tuple(c for c in curves_t if c.pair == pair) for pair in pairs } - pairs_t = tuple(tuple(p.split("/")) for p in pairs) - - try: - - # assertions - if len (curves_t) == 0: - raise self.ParameterError("no curves found") - if len (curves_t) == 1: - raise self.ParameterError(f"can't run arbitrage on single curve {curves_t}") - if not targettkn in alltokens_s: - raise self.ParameterError(f"targettkn {targettkn} not in {alltokens_s}") - - # calculating the start price for the iteration process - if not P("pstart") is None: - pstart = P("pstart") - if P("verbose") or P("debug"): - print(f"[margp_optimizer] using pstartd [{len(P('pstart'))} tokens]") - if isinstance(P("pstart"), pd.DataFrame): - try: - pstart = pstart.to_dict()[targettkn] - except Exception as e: - raise Exception( - f"error while converting dataframe pstart to dict: {e}", - pstart, - targettkn, - ) - assert isinstance( - pstart, dict - ), f"pstart must be a dict or a data frame [{pstart}]" - price_estimates_t = tuple(pstart[t] for t in tokens_t) - else: - if P("verbose") or P("debug"): - print("[margp_optimizer] calculating price estimates") - try: - price_estimates_t = self.price_estimates( - tknq=targettkn, - tknbs=tokens_t, - verbose=False, - triangulate=True, - ) - except Exception as e: - if P("verbose") or P("debug"): - print(f"[margp_optimizer] error while calculating price estimates: [{e}]") - price_estimates_t = None - if P("debug"): - print("[margp_optimizer] pstart:", price_estimates_t) - if result == self.MO_PSTART: - df = pd.DataFrame(price_estimates_t, index=tokens_t, columns=[targettkn]) - df.index.name = "tknb" - return df - - ## INNER FUNCTION: CALCULATE THE TARGET FUNCTION - def dtknfromp_f(p, *, islog10=True, asdct=False, quiet=False): - """ - calculates the aggregate change in token amounts for a given price vector - - :p: price vector, where prices use the reference token as quote token - this vector is an np.array, and the token order is the same as in tokens_t - :islog10: if True, p is interpreted as log10(p) - :asdct: if True, the result is returned as dict AND tuple, otherwise as np.array - :quiet: if overrides P("debug") etc, eg for calc of Jacobian - :returns: if asdct is False, a tuple of the same length as tokens_t detailing the - change in token amounts for each token except for the target token (ie the - quantity with target zero; if asdct is True, that same information is - returned as dict, including the target token. - """ - p = np.array(p, dtype=np.float64) - if islog10: - p = np.exp(p * np.log(10)) - assert len(p) == len(tokens_t), f"p and tokens_t have different lengths [{p}, {tokens_t}]" - if P("debug") and not quiet: - print(f"\n[dtknfromp_f] =====================>>>") - print(f"prices={p}") - print(f"tokens={tokens_t}") - - # pvec is dict {tkn -> (log) price} for all tokens in p - pvec = {tkn: p_ for tkn, p_ in zip(tokens_t, p)} - pvec[targettkn] = 1 - if P("debug") and not quiet: - print(f"pvec={pvec}") - - sum_by_tkn = {t: 0 for t in alltokens_s} - for pair, (tknb, tknq) in zip(pairs, pairs_t): - if get(p, tokens_ix.get(tknq)) > 0: - price = get(p, tokens_ix.get(tknb)) / get(p, tokens_ix.get(tknq)) - else: - #print(f"[dtknfromp_f] warning: price for {pair} is unknown, using 1 instead") - price = 1 - curves = curves_by_pair[pair] - c0 = curves[0] - #dxdy = tuple(dxdy_f(c.dxdyfromp_f(price)) for c in curves) - dxvecs = (c.dxvecfrompvec_f(pvec) for c in curves) - - if P("debug2") and not quiet: - dxdy = tuple(dxdy_f(c.dxdyfromp_f(price)) for c in curves) - # TODO: rewrite this using the dxvec - # there is no need to extract dy dx; just iterate over dict - # however not urgent because this is debug code - print(f"\n{c0.pairp} --->>") - print(f" price={price:,.4f}, 1/price={1/price:,.4f}") - for r, c in zip(dxdy, curves): - s = f" cid={c.cid:15}" - s += f" dx={float(r[0]):15,.3f} {c.tknxp:>5}" - s += f" dy={float(r[1]):15,.3f} {c.tknyp:>5}" - s += f" p={c.p:,.2f} 1/p={1/c.p:,.2f}" - print(s) - print(f"<<--- {c0.pairp}") - - # old code from dxdy = tuple(dxdy_f(c.dxdyfromp_f(price)) for c in curves) - # sumdx, sumdy = sum(dxdy) - # sum_by_tkn[tknq] += sumdy - # sum_by_tkn[tknb] += sumdx - for dxvec in dxvecs: - for tkn, dx_ in dxvec.items(): - sum_by_tkn[tkn] += dx_ - - # if P("debug") and not quiet: - # print(f"pair={c0.pairp}, {sumdy:,.4f} {tn(tknq)}, {sumdx:,.4f} {tn(tknb)}, price={price:,.4f} {tn(tknq)} per {tn(tknb)} [{len(curves)} funcs]") - - result = tuple(sum_by_tkn[t] for t in tokens_t) - if P("debug") and not quiet: - print(f"sum_by_tkn={sum_by_tkn}") - print(f"result={result}") - print(f"<<<===================== [dtknfromp_f]") - - if asdct: - return sum_by_tkn, np.array(result) - - return np.array(result) - ## END INNER FUNCTION - - # return the inner function if requested - if result == self.MO_DTKNFROMPF: - return dtknfromp_f - - # return debug info if requested - if result == self.MO_DEBUG: - return dict( - # price_estimates_all = price_estimates_all, - # price_estimates_d = price_estimates_d, - price_estimates_t=price_estimates_t, - tokens_t=tokens_t, - tokens_ix=tokens_ix, - pairs=pairs, - sfc=sfc, - targettkn=targettkn, - pairs_t=pairs_t, - dtknfromp_f=dtknfromp_f, - optimizer=self, - ) - - # setting up the optimization variables (note: we optimize in log space) - if price_estimates_t is None: - raise Exception(f"price estimates not found; try setting pstart") - p = np.array(price_estimates_t, dtype=float) - plog10 = np.log10(p) - if P("verbose"): - # dtkn_d, dtkn = dtknfromp_f(plog10, islog10=True, asdct=True) - print("[margp_optimizer] pe ", p) - print("[margp_optimizer] p ", ", ".join(f"{x:,.2f}" for x in p)) - print("[margp_optimizer] 1/p ", ", ".join(f"{1/x:,.2f}" for x in p)) - # print("[margp_optimizer] dtkn", dtkn) - # if P("tknd"): - # print("[margp_optimizer] dtkn_d", dtkn_d) - - ## MAIN OPTIMIZATION LOOP - for i in range(maxiter): - - if P("progress"): - print( - f"Iteration [{i:2.0f}]: time elapsed: {time.time()-start_time:.2f}s" - ) - - # calculate the change in token amounts (also as dict if requested) - if P("tknd"): - dtkn_d, dtkn = dtknfromp_f(plog10, islog10=True, asdct=True) - else: - dtkn = dtknfromp_f(plog10, islog10=True, asdct=False) - - # calculate the Jacobian - # if P("debug"): - # print("\n[margp_optimizer] ============= JACOBIAN =============>>>") - J = self.J(dtknfromp_f, plog10) - # ATTENTION: dtknfromp_f takes log10(p) as input - if P("debug"): - # print("==== J ====>") - print("\n============= JACOBIAN =============>>>") - print(J) - # print("<=== J =====") - print("<<<============= JACOBIAN =============\n") - - # Update p, dtkn using the Newton-Raphson formula - try: - dplog10 = np.linalg.solve(J, -dtkn) - except np.linalg.LinAlgError: - if P("verbose") or P("debug"): - print("[margp_optimizer] singular Jacobian, using lstsq instead") - dplog10 = np.linalg.lstsq(J, -dtkn, rcond=None)[0] - # https://numpy.org/doc/stable/reference/generated/numpy.linalg.solve.html - # https://numpy.org/doc/stable/reference/generated/numpy.linalg.lstsq.html - - # update log prices, prices and determine the criterium... - p0log10 = [*plog10] - plog10 += dplog10 - p = np.exp(plog10 * np.log(10)) - criterium = np.linalg.norm(dplog10) - - # ...print out some info if requested... - if P("verbose"): - print(f"\n[margp_optimizer] ========== cycle {i} =======>>>") - print("log p0", p0log10) - print("log dp", dplog10) - print("log p ", plog10) - print("p ", tuple(p)) - print("p ", ", ".join(f"{x:,.2f}" for x in p)) - print("1/p ", ", ".join(f"{1/x:,.2f}" for x in p)) - print("tokens_t", tokens_t) - # print("dtkn", dtkn) - print("dtkn", ", ".join(f"{x:,.3f}" for x in dtkn)) - print( - f"[criterium={criterium:.2e}, eps={eps:.1e}, c/e={criterium/eps:,.0e}]" - ) - if P("tknd"): - print("dtkn_d", dtkn_d) - if P("J"): - print("J", J) - print(f"<<<========== cycle {i} ======= [margp_optimizer]") - - # ...and finally check the criterium (percentage changes this step) for convergence - if criterium < eps: - if i != 0: - # we don't break in the first iteration because we need this first iteration - # to establish a common baseline price, therefore d logp ~ 0 is not good - # in the first step - break - ## END MAIN OPTIMIZATION LOOP - - if i >= maxiter - 1: - raise self.ConvergenceError(f"maximum number of iterations reached [{i}]") - - NOMR = lambda f: f if not result == self.MO_MINIMAL else None - # this function screens out certain results when MO_MINIMAL [minimal output] is chosen - dtokens_d, dtokens_t = dtknfromp_f(p, asdct=True, islog10=False) - return self.MargpOptimizerResult( - optimizer=NOMR(self), - result=dtokens_d[targettkn], - time=time.time() - start_time, - targettkn=targettkn, - curves=NOMR(curves_t), - #p_optimal=NOMR({tkn: p_ for tkn, p_ in zip(tokens_t, p)}), - p_optimal_t=tuple(p), - dtokens=NOMR(dtokens_d), - dtokens_t=tuple(dtokens_t), - tokens_t=tokens_t, - n_iterations=i, - ) - - except self.OptimizationError as e: - if P("debug") or P("verbose"): - print(f"[margp_optimizer] exception occured {e}") - - if P("raiseonerror"): - raise - - NOMR = lambda f: f if not result == self.MO_MINIMAL else None - return self.MargpOptimizerResult( - optimizer=NOMR(self), - result=None, - time=time.time() - start_time, - targettkn=targettkn, - curves=NOMR(curves_t), - #p_optimal=None, - p_optimal_t=None, - dtokens=None, - dtokens_t=None, - tokens_t=tokens_t, - n_iterations=None, - errormsg=e, - ) - margp_optimizer = optimize # margp_optimizer is deprecated - diff --git a/fastlane_bot/tools/optimizer/pairoptimizer.py b/fastlane_bot/tools/optimizer/pairoptimizer.py deleted file mode 100644 index 3f8e0e3f3..000000000 --- a/fastlane_bot/tools/optimizer/pairoptimizer.py +++ /dev/null @@ -1,318 +0,0 @@ -""" -optimization library -- Pair Optimizer module [final optimizer class] - - -The pair optimizer uses a marginal price method in one dimension to find the optimal -solution. It uses a bisection method to find the root of the transfer equation, therefore -it only work for a single pair. To use it on multiple pairs, use MargPOptimizer instead. - ---- -This module is still subject to active research, and comments and suggestions are welcome. -The corresponding author is Stefan Loesch - -(c) Copyright Bprotocol foundation 2023. -Licensed under MIT -""" -__VERSION__ = "6.0.1" -__DATE__ = "21/Sep/2023" - -from dataclasses import dataclass, field, fields, asdict, astuple, InitVar -#import pandas as pd -import numpy as np - -import time -# import math -# import numbers -# import pickle -from ..cpc import ConstantProductCurve as CPC, CPCInverter, CPCContainer -#from sys import float_info - -from .dcbase import DCBase -from .base import OptimizerBase -from .cpcarboptimizer import CPCArbOptimizer - -class PairOptimizer(CPCArbOptimizer): - """ - implements the marginal price optimization method for pairs - """ - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - @property - def kind(self): - return "pair" - - # @dataclass - # class PairOptimizerResult(OptimizerBase.OptimizerResult): - # """ - # results of the pairs optimizer - - # :curves: list of curves used in the optimization, possibly wrapped in CPCInverter objects* - # :dxdyfromp_vec_f: vector of tuples (dx, dy), as a function of p - # :dxdyfromp_sum_f: sum of the above, also as a function of p - # :dxdyfromp_valx_f: valx = dy/p + dx, also as a function of p - # :dxdyfromp_valy_f: valy = dy + p*dx/p, also as a function of p - # :p_optimal: optimal p value - - # *the CPCInverter object ensures that all curves in the list correspond to the same quote - # conventions, according to the primary direction of the pair (as determined by the Pair - # object). Accordingly, tknx and tkny are always the same for all curves in the list, regardless - # of the quote direction of the pair. The CPCInverter object abstracts this away, but of course - # only for functions that are accessible through it. - # """ - - # NONEFUNC = lambda x: None - - # curves: list = field(repr=False, default=None) - # dxdyfromp_vec_f: any = field(repr=False, default=NONEFUNC) - # dxdyfromp_sum_f: any = field(repr=False, default=NONEFUNC) - # dxdyfromp_valx_f: any = field(repr=False, default=NONEFUNC) - # dxdyfromp_valy_f: any = field(repr=False, default=NONEFUNC) - # p_optimal: float = field(repr=False, default=None) - # errormsg: str = field(repr=True, default=None) - - # def __post_init__(self, *args, **kwargs): - # super().__post_init__(*args, **kwargs) - # # print("[PairOptimizerResult] post_init") - # assert ( - # self.p_optimal is not None or self.errormsg is not None - # ), "p_optimal must be set unless errormsg is set" - # if self.method is None: - # self.method = "pair" - - # @property - # def is_error(self): - # return self.errormsg is not None - - # def detailed_error(self): - # return self.errormsg - - # def status(self): - # return "error" if self.is_error else "converged" - - # def dxdyfromp_vecs_f(self, p): - # """returns dx, dy as separate vectors instead as a vector of tuples""" - # return tuple(zip(*self.dxdyfromp_vec_f(p))) - - # @property - # def tknx(self): - # return self.curves[0].tknx - - # @property - # def tkny(self): - # return self.curves[0].tkny - - # @property - # def tknxp(self): - # return self.curves[0].tknxp - - # @property - # def tknyp(self): - # return self.curves[0].tknyp - - # @property - # def pair(self): - # return self.curves[0].pair - - # @property - # def pairp(self): - # return self.curves[0].pairp - - # @property - # def dxdy_vecs(self): - # return self.dxdyfromp_vecs_f(self.p_optimal) - - # @property - # def dxvalues(self): - # return self.dxdy_vecs[0] - - # dxv = dxvalues - - # @property - # def dyvalues(self): - # return self.dxdy_vecs[1] - - # dyv = dyvalues - - # @property - # def dxdy_vec(self): - # return self.dxdyfromp_vec_f(self.p_optimal) - - # @property - # def dxdy_sum(self): - # return self.dxdyfromp_sum_f(self.p_optimal) - - # @property - # def dxdy_valx(self): - # return self.dxdyfromp_valx_f(self.p_optimal) - - # valx = dxdy_valx - - # @property - # def dxdy_valy(self): - # return self.dxdyfromp_valy_f(self.p_optimal) - - # valy = dxdy_valy - - # def trade_instructions(self, ti_format=None): - # """returns list of TradeInstruction objects""" - # result = ( - # CPCArbOptimizer.TradeInstruction.new( - # curve_or_cid=c, tkn1=self.tknx, amt1=dx, tkn2=self.tkny, amt2=dy - # ) - # for c, dx, dy in zip(self.curves, self.dxvalues, self.dyvalues) - # if dx != 0 or dy != 0 - # ) - # assert ti_format != CPCArbOptimizer.TIF_DFAGGR, "TIF_DFAGGR not implemented for convex optimization" - # assert ti_format != CPCArbOptimizer.TIF_DFPG, "TIF_DFPG not implemented for convex optimization" - # return CPCArbOptimizer.TradeInstruction.to_format(result, ti_format=ti_format) - - PAIROPTIMIZEREPS = 1e-15 - - SO_DXDYVECFUNC = "dxdyvecfunc" - SO_DXDYSUMFUNC = "dxdysumfunc" - SO_DXDYVALXFUNC = "dxdyvalxfunc" - SO_DXDYVALYFUNC = "dxdyvalyfunc" - SO_PMAX = "pmax" - SO_GLOBALMAX = "globalmax" - SO_TARGETTKN = "targettkn" - - def optimize(self, targettkn=None, result=None, *, params=None): - """ - a marginal price optimizer that works only on curves on one pair - - :result: determines what to return (see table below) - :targettkn: token to optimize for (if result==SO_TARGETTKN); must be None if - result==SO_GLOBALMAX; result defaults to the corresponding value - depending on whether or not targettkn is None - :params: dict of parameters - :eps: accuracy parameter passed to bisection method (default: 1e-6) - :returns: depending on the `result` parameter - - ================= ============================================================ - `result` returns - ================= ============================================================ - SO_DXDYVECFUNC function of p returning vector of dx,dy values - SO_DXDYSUMFUNC function of p returning sum of dx,dy values - SO_DXDYVALXFUNC function of p returning value of dx,dy sum in units of tknx - SO_DXDYVALYFUNC ditto tkny - SO_PMAX optimal p value for global max (1) - SO_GLOBALMAX global max of sum dx*p + dy (1) - SO_TARGETTKN optimizes for one token, the other is zero - None SO_GLOBALMAX if targettkn is None, SO_TARGETTKN otherwise - ================= ============================================================ - - NOTE 1: the modes SO_PMAX and SO_GLOBALMAX are deprecated and the code may or - may not be working properly; if every those functions are needed they need to - be reviewed and tests need to be added (most tests in NBTests 002 have been disabled) - """ - start_time = time.time() - if params is None: - params = dict() - curves_t = CPCInverter.wrap(self.curve_container) - assert len(curves_t) > 0, "no curves found" - c0 = curves_t[0] - #print("[PairOptimizer.optimize] curves_t", curves_t[0].pair) - pairs = set(c.pair for c in curves_t) - assert (len(pairs) == 1), f"pair_optimizer only works on curves of exactly one pair [{pairs}]" - assert not (targettkn is None and result == self.SO_TARGETTKN), "targettkn must be set if result==SO_TARGETTKN" - assert not (targettkn is not None and result == self.SO_GLOBALMAX), f"targettkn must be None if result==SO_GLOBALMAX [{targettkn}]" - - dxdy = lambda r: (np.array(r[0:2])) - - dxdyfromp_vec_f = lambda p: tuple(dxdy(c.dxdyfromp_f(p)) for c in curves_t) - if result == self.SO_DXDYVECFUNC: - return dxdyfromp_vec_f - - dxdyfromp_sum_f = lambda p: sum(dxdy(c.dxdyfromp_f(p)) for c in curves_t) - if result == self.SO_DXDYSUMFUNC: - return dxdyfromp_sum_f - - dxdyfromp_valy_f = lambda p: np.dot(dxdyfromp_sum_f(p), np.array([p, 1])) - if result == self.SO_DXDYVALYFUNC: - return dxdyfromp_valy_f - - dxdyfromp_valx_f = lambda p: dxdyfromp_valy_f(p) / p - if result == self.SO_DXDYVALXFUNC: - return dxdyfromp_valx_f - - if result is None: - if targettkn is None: - result = self.SO_GLOBALMAX - else: - result = self.SO_TARGETTKN - - if result == self.SO_GLOBALMAX or result == self.SO_PMAX: - p_avg = np.mean([c.p for c in curves_t]) - p_optimal = self.findmax(dxdyfromp_valx_f, p_avg) - #opt_result = dxdyfromp_valx_f(float(p_optimal)) - full_result = dxdyfromp_sum_f(float(p_optimal)) - opt_result = full_result[0] - if result == self.SO_PMAX: - return p_optimal - if targettkn == c0.tknx: - p_optimal_t = (1/float(p_optimal),) - else: - p_optimal_t = (float(p_optimal),) - method = "globalmax-pair" - - elif result == self.SO_TARGETTKN: - p_min = np.min([c.p for c in curves_t]) - p_max = np.max([c.p for c in curves_t]) - eps = params.get("eps", self.PAIROPTIMIZEREPS) - - assert targettkn in {c0.tknx, c0.tkny,}, f"targettkn {targettkn} not in {c0.tknx}, {c0.tkny}" - - # we are now running a goalseek == 0 on the token that is NOT the target token - if targettkn == c0.tknx: - func = lambda p: dxdyfromp_sum_f(p)[1] - p_optimal = self.goalseek(func, p_min * 0.99, p_max * 1.01, eps=eps) - p_optimal_t = (1/float(p_optimal),) - full_result = dxdyfromp_sum_f(float(p_optimal)) - opt_result = full_result[0] - - else: - func = lambda p: dxdyfromp_sum_f(p)[0] - p_optimal = self.goalseek(func, p_min * 0.99, p_max * 1.01, eps=eps) - p_optimal_t = (float(p_optimal),) - full_result = dxdyfromp_sum_f(float(p_optimal)) - opt_result = full_result[1] - #print("[PairOptimizer.optimize] p_optimal", p_optimal, "full_result", full_result) - method = "margp-pair" - - else: - raise ValueError(f"unknown result type {result}") - - NOMR = lambda x: x - # allows to mask certain long portions of the result if desired, the same way - # the main margpoptimizer does it; however, this not currently considered necessary - if p_optimal.is_error: - return self.MargpOptimizerResult( - method=method, - optimizer=NOMR(self), - result=None, - time=time.time() - start_time, - targettkn=targettkn, - curves=NOMR(curves_t), - p_optimal_t=None, - dtokens=None, - dtokens_t=None, - tokens_t=(c0.tknx if targettkn==c0.tkny else c0.tkny,), - n_iterations=None, - errormsg="bisection did not converge", - ) - - return self.MargpOptimizerResult( - method=method, - optimizer=NOMR(self), - result=opt_result, - time=time.time() - start_time, - targettkn=targettkn, - curves=NOMR(curves_t), - p_optimal_t=p_optimal_t, - dtokens={c0.tknx:full_result[0], c0.tkny:full_result[1]}, - dtokens_t=(full_result[1] if targettkn==c0.tknx else full_result[0],), - tokens_t=(c0.tknx if targettkn==c0.tkny else c0.tkny,), - n_iterations=None, # not available - ) - \ No newline at end of file diff --git a/fastlane_bot/tools/params.py b/fastlane_bot/tools/params.py deleted file mode 100644 index 602ea9869..000000000 --- a/fastlane_bot/tools/params.py +++ /dev/null @@ -1,172 +0,0 @@ -""" -Carbon helper module - parameter management -""" -__VERSION__ = "1.2" -__DATE__ = "29/01/2023" - - -class Params: - """ - parameter management - - EXAMPLE - - .. code-block:: python - - # Standard - - p = Params(a=1, b=2) - p.a # 1 - p["b"] # 2 - p.c # raises; must exists when accessed as attribute - p["c"] # None; fails gracefully when accessed via [] - p["c"] = 3 # OK - p.c # 3; after assignment - p["c"] # 3; after assignment - p["b"] = 3 # raises; re-assignment not allowed - - p = Params(**{"a": 1, "b": 2}) # creating params from dict - - # With defaults - - p = Params(a=1, b=2) - p.set_default(**{"b":20, "c":3}) - p.a # 1; from params - p.b # 2; from params - p.c # 3; from defaults - - - """ - - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - def __init__(self, **kwargs): - self._params = dict(kwargs) - self._defaults = None - - @classmethod - def construct(cls, dct=None, defaults=None): - """ - alternative constructor from dct - - :dct: typically a dict object; can also be a Params object which will be replicated - (defaults can either be on the original object or here, but not on both; if - you want to merge defaults use set_default on the created object); a value - of None creates an empty object - :defaults: the default values for this object; note that they can not be passed - using the standard constructor; if the object is already a params object, - the existing defaults will be updated, and overwritten if they exist - :returns: a newly created Params object - """ - if not dct: - result = cls() - elif isinstance(dct, cls): - result = cls(**dct._params) - if not dct._defaults is None and not defaults is None: - raise ValueError( - "Must not provide default in both constructor and dct", - dct, - defaults, - ) - else: - result = cls(**dct) - - if defaults: - result._defaults = {**defaults} - return result - - def add(self, **kwargs): - """ - adds additional parameters from kwargs (params must not yet exist) - - :returns: self (for chaining) - """ - for k, v in kwargs.items(): - self[k] = v - return self - - def get_default(self, item, raiseonerror=False): - """ - gets the default value (None if does not exist) - """ - if self._defaults is None: - self.set_default() - if raiseonerror: - return self._defaults[item] - else: - return self._defaults.get(item, None) - - def set_default(self, **kwargs): - """ - adds default params - - :returns: self for chaining - """ - if self._defaults is None: - self._defaults = dict() - else: - for k, v in kwargs.items(): - self._defaults[k] = v - return self - - @property - def params(self): - """ - returns the parameters as dict - """ - return self._params - - @property - def defaults(self): - """ - returns defaults object (creates empty one if it does not exist) - """ - if self._defaults is None: - self.set_default() - return self._defaults - - def set(self, item, value, allowupdate=True): - """ - sets an item - - :item: the item to be set - :value: the value to set it to - :allowupdate: if True (default), existing items can be changed - :returns: self (for chaining) - """ - if not allowupdate: - if item in self._params: - raise ValueError( - f"Item {item} already exists with value {self._params[item]} and update not allowed.", - value, - self._params, - allowupdate, - ) - self._params[item] = value - return self - - def __getitem__(self, item): - try: - return self._params[item] - except KeyError: - return self.get_default(item, raiseonerror=False) - - def __setitem__(self, item, value): - self.set(item, value, allowupdate=False) - - def __getattr__(self, item): - """ - for all item starting with _ this refers to super().__getattr__ - """ - if item[:1] == "_": - return super().__getattr__(item) - try: - return self._params[item] - except KeyError: - return self.get_default(item, raiseonerror=True) - - def __repr__(self): - - defaults = f", defaults={self._defaults}" if self._defaults else "" - return f"{self.__class__.__name__}.construct({self._params}{defaults})" diff --git a/fastlane_bot/tools/reformat.py b/fastlane_bot/tools/reformat.py deleted file mode 100644 index a29da8e36..000000000 --- a/fastlane_bot/tools/reformat.py +++ /dev/null @@ -1,25 +0,0 @@ -import re -import os - -# Function to replace the second colon in a matching line -def replace_second_colon(line): - # Regular expression that matches lines with the specified format: - # Starts with optional whitespace, followed by a tag (alphanumeric and underscores) - # between two colons. Captures the content before the second colon. - pattern = r'^(.*?\:[a-zA-Z0-9_]+)\:' - # Replace the second colon with a space, if the pattern matches - return re.sub(pattern, r'\1 ', line) - -# Process all .py files in the current directory -for filename in os.listdir('.'): - if filename.endswith('.py'): - # Read the content of the file - with open(filename, 'r') as file: - lines = file.readlines() - - # Apply the replacement to each line - modified_lines = [replace_second_colon(line) for line in lines] - - # Write the modified content back to the file - with open(filename, 'w') as file: - file.writelines(modified_lines) diff --git a/fastlane_bot/tools/simplepair.py b/fastlane_bot/tools/simplepair.py deleted file mode 100644 index 65dd27ba5..000000000 --- a/fastlane_bot/tools/simplepair.py +++ /dev/null @@ -1,208 +0,0 @@ -""" -simple representation of a pair of tokens, used by cpc and arbgraph - ---- -(c) Copyright Bprotocol foundation 2023. -Licensed under MIT -""" -__VERSION__ = "2.1" -__DATE__ = "18/May/2023" - -from dataclasses import dataclass, field, asdict, InitVar - - -@dataclass -class SimplePair: - """ - a pair in notation TKNB/TKNQ; can also be provided as list (but NOT: tknb=, tknq=) - """ - - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - tknb: str = field(init=False) - tknq: str = field(init=False) - pair: InitVar[str] = None - - def __post_init__(self, pair): - if isinstance(pair, self.__class__): - self.tknb = pair.tknb - self.tknq = pair.tknq - elif isinstance(pair, str): - sp = pair.split("/") - if len(sp) != 2: - raise ValueError( - f"pair must be a string of the form tknb/tknq {pair}, sp={sp}" - ) - self.tknb, self.tknq = sp - elif pair is False: - # used in alternative constructors - pass - else: - try: - self.tknb, self.tknq = pair - except: - raise ValueError(f"pair must be a string or list of two strings {pair}") - - @classmethod - def from_tokens(cls, tknb, tknq): - pair = cls(False) - pair.tknb = tknb - pair.tknq = tknq - return pair - - def __str__(self): - return f"{self.tknb}/{self.tknq}" - - @property - def pair(self): - """string representation of the pair""" - return str(self) - - @property - def pairt(self): - """tuple representation of the pair""" - return (self.tknb, self.tknq) - - @property - def pairr(self): - """returns the reversed pair""" - return f"{self.tknq}/{self.tknb}" - - @property - def pairrt(self): - """tuple representation of the reverse pair""" - return (self.tknq, self.tknb) - - @property - def tknx(self): - return self.tknb - - @property - def tkny(self): - return self.tknq - - NUMERAIRE_TOKENS = { - tkn: i - for i, tkn in enumerate( - [ - "USDC", - "USDT", - "DAI", - "TUSD", - "BUSD", - "PAX", - "GUSD", - "USDS", - "sUSD", - "mUSD", - "HUSD", - "USDN", - "USDP", - "USDQ", - "BNT", - "ETH", - "WETH", - "WBTC", - "BTC", - ] - ) - } - - @classmethod - def n(cls, tkn): - """normalize the token name (remove the id, if any)""" - if len(tkn.split("/")) > 1: - return "/".join([cls.n(t) for t in tkn.split("/")]) - return tkn.split("-")[0].split("(")[0] - - @property - def tknb_n(self): - return self.n(self.tknb) - - @property - def tknq_n(self): - return self.n(self.tknq) - - @property - def pair_n(self): - """normalized pair""" - return f"{self.tknb_n}/{self.tknq_n}" - - @property - def tknx_n(self): - return self.n(self.tknx) - - @property - def tkny_n(self): - return self.n(self.tkny) - - @property - def isprimary(self): - """whether the representation is primary or secondary""" - tknqix = self.NUMERAIRE_TOKENS.get(self.tknq_n, 1e10) - tknbix = self.NUMERAIRE_TOKENS.get(self.tknb_n, 1e10) - if tknqix == tknbix: - return self.tknb < self.tknq - return tknqix < tknbix - - def primary_price(self, p): - """returns the primary price (p if primary, 1/p if secondary)""" - if self.isprimary: - return p - else: - if p == 0: - return float("nan") - return 1 / p - - pp = primary_price - - @property - def pp_convention(self): - """returns the primary price convention""" - tknb, tknq = self.primary_n.split("/") - return f"{tknq} per {tknb}" - - @property - def primary(self): - """returns the primary pair""" - return self.pair if self.isprimary else self.pairr - - @property - def primary_n(self): - """the primary pair, normalized""" - tokens = self.primary.split("/") - tokens = [self.n(t) for t in tokens] - return "/".join(tokens) - - @property - def primary_tknb(self): - """returns the primary normailised tknb""" - return self.tknb_n if self.isprimary else self.tknq_n - - @property - def primary_tknq(self): - """returns the primary normailised tknq""" - return self.tknq_n if self.isprimary else self.tknb_n - - @property - def secondary(self): - """returns the secondary pair""" - return self.pairr if self.isprimary else self.pair - - @property - def secondary_n(self): - """the secondary pair, normalized""" - tokens = self.secondary.split("/") - tokens = [self.n(t) for t in tokens] - return "/".join(tokens) - - @classmethod - def wrap(cls, pairlist): - """wraps a list of strings into Pairs""" - return tuple(cls(p) for p in pairlist) - - @classmethod - def unwrap(cls, pairlist): - """unwraps a list of Pairs into strings""" - return tuple(str(p) for p in pairlist) diff --git a/fastlane_bot/tools/tokenscale.py b/fastlane_bot/tools/tokenscale.py deleted file mode 100644 index a270aa520..000000000 --- a/fastlane_bot/tools/tokenscale.py +++ /dev/null @@ -1,100 +0,0 @@ -""" -estimating the scale of the token price in USD - ---- -(c) Copyright Bprotocol foundation 2023. -Licensed under MIT - -NOTE: this class is not part of the Bancor Simulator API, and breaking changes may occur at any time -""" -__VERSION__ = "1.0" -__DATE__ = "07/Apr/2022" - -from dataclasses import dataclass, field, asdict, InitVar - - -class TokenScaleBase: - """ - the "scale" of a token, ie the number of tokens per USD, typically rounded to the next power of 10 - """ - - __VERSION__ = __VERSION__ - __DATE__ = __DATE__ - - DEFAULT_SCALE = 1e-2 - - def scale(self, token): - """ - returns the scale of the token* - - :tkn: the token whose scale is to be returned - - *the "scale" of a token the number of tokens per USD, typically rounded - to the next power of 10; every token MUST have a scale; if the _scale - function (that implements this method) returns None, DEFAULT_SCALE is returned - """ - result = self._scale(token) - if result is None: - result = self.DEFAULT_SCALE - return result - - def _scale(self, token): - """ - implements the scale method in derived classes - """ - raise NotImplementedError("{self.__class__.__name__} did not implement _scale") - - def __call__(self, token): - """alias for scale""" - return self.scale(token) - - -class TokenScale1(TokenScaleBase): - """trivial implementation of TokenScaleBase returning unit scale for all tokens""" - - DEFAULT_SCALE = 1e00 - - def _scale(self, token): - """implementation of _scale for TokenScale1 class: always returns unit scale""" - return self.DEFAULT_SCALE - - -@dataclass -class TokenScale(TokenScaleBase): - """ - implements the `TokenScaleBase` interface using a dictionary - """ - - scale_dct: dict = field(default_factory=dict) - - @classmethod - def from_tokenscales(cls, **scale): - """alternative constructor with the scale in kwargs""" - return cls(scale_dct=scale) - - def add_scale(self, token, scale): - """ - adds (or replaces) a scale for a token - """ - self.scale_dct[token] = scale - - def _scale(self, token): - """implementation of _scale for TokenScale class (reading from dict)""" - return self.scale_dct.get(token, None) - - -TokenScaleData = TokenScale.from_tokenscales( - USDC=1e00, - USDT=1e00, - LINK=1e01, - AAVE=1e02, - ETH=1e03, - WETH=1e03, - WBTC=1e04, - BTC=1e04, - BNT=1e00, - SUSHI=1e00, - UNI=1e01, -) - -TokenScale1Data = TokenScale1() diff --git a/main.py b/main.py index cb61544f2..787b6c44f 100644 --- a/main.py +++ b/main.py @@ -5,10 +5,10 @@ (c) Copyright Bprotocol foundation 2023. Licensed under MIT """ +from arb_optimizer.curves import T from fastlane_bot.exceptions import ReadOnlyException, FlashloanUnavailableException from fastlane_bot.events.version_utils import check_version_requirements -from fastlane_bot.tools.cpc import T check_version_requirements(required_version="6.11.0", package_name="web3") @@ -672,7 +672,7 @@ def run(mgr, args, tenderly_uri=None) -> None: "--blockchain", default="ethereum", help="A blockchain from the list. Blockchains not in this list do not have a deployed Fast Lane contract and are not supported.", - choices=["ethereum", "coinbase_base", "fantom", "mantle", "linea"], + choices=["ethereum", "coinbase_base", "fantom", "mantle", "linea", "sei"], ) parser.add_argument( "--pool_data_update_frequency", diff --git a/poetry.lock b/poetry.lock index fb525f280..a7f425b2b 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,87 +2,87 @@ [[package]] name = "aiohttp" -version = "3.9.3" +version = "3.9.5" description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.8" files = [ - {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:939677b61f9d72a4fa2a042a5eee2a99a24001a67c13da113b2e30396567db54"}, - {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1f5cd333fcf7590a18334c90f8c9147c837a6ec8a178e88d90a9b96ea03194cc"}, - {file = "aiohttp-3.9.3-cp310-cp310-macosx_11_0_arm64.whl", hash = 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[[package]] name = "traitlets" -version = "5.14.2" +version = "5.14.3" description = "Traitlets Python configuration system" optional = false python-versions = ">=3.8" files = [ - {file = "traitlets-5.14.2-py3-none-any.whl", hash = "sha256:fcdf85684a772ddeba87db2f398ce00b40ff550d1528c03c14dbf6a02003cd80"}, - {file = "traitlets-5.14.2.tar.gz", hash = "sha256:8cdd83c040dab7d1dee822678e5f5d100b514f7b72b01615b26fc5718916fdf9"}, + {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"}, + {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"}, ] [package.extras] docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"] -test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.1)", "pytest-mock", "pytest-mypy-testing"] +test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"] [[package]] name = "typing-extensions" @@ -3008,21 +3034,21 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "web3" -version = "6.16.0" +version = "6.18.0" description = "web3.py" optional = false python-versions = ">=3.7.2" files = [ - {file = "web3-6.16.0-py3-none-any.whl", hash = "sha256:50e96cc447823444510ee659586b264ebc7ddbfc74cccb720d042146aa404348"}, - {file = "web3-6.16.0.tar.gz", hash = "sha256:b10c93476c106acc44b8428e47c61c385b7d0885e82cdc24049d27f521833552"}, + {file = "web3-6.18.0-py3-none-any.whl", hash = "sha256:86484a3d390a0a024002d1c1b79af27034488c470ea07693ff0f5bf109d3540b"}, + {file = "web3-6.18.0.tar.gz", hash = "sha256:2e626a4bf151171f5dc8ad7f30c373f0416dc2aca9d8d102a63578a2413efa26"}, ] [package.dependencies] aiohttp = ">=3.7.4.post0" eth-abi = ">=4.0.0" -eth-account = ">=0.8.0" +eth-account = ">=0.8.0,<0.13" eth-hash = {version = ">=0.5.1", extras = ["pycryptodome"]} -eth-typing = ">=3.0.0" +eth-typing = ">=3.0.0,<4.2.0 || >4.2.0" eth-utils = ">=2.1.0" hexbytes = ">=0.1.0,<0.4.0" jsonschema = ">=4.0.0" @@ -3035,11 +3061,10 @@ typing-extensions = ">=4.0.1" websockets = ">=10.0.0" [package.extras] -dev = ["black (>=22.1.0)", "build (>=0.9.0)", "bumpversion", "eth-tester[py-evm] (==v0.9.1-b.2)", "flake8 (==3.8.3)", "flaky (>=3.7.0)", "hypothesis (>=3.31.2)", "importlib-metadata (<5.0)", "ipfshttpclient (==0.8.0a2)", "isort (>=5.11.0)", "mypy (==1.4.1)", "py-geth (>=3.14.0)", "pytest (>=7.0.0)", "pytest-asyncio (>=0.18.1,<0.23)", "pytest-mock (>=1.10)", "pytest-watch (>=4.2)", "pytest-xdist (>=1.29)", "setuptools (>=38.6.0)", "sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.0.0)", "towncrier (>=21,<22)", "tox (>=3.18.0)", "tqdm (>4.32)", "twine (>=1.13)", "types-protobuf (==3.19.13)", "types-requests (>=2.26.1)", "types-setuptools (>=57.4.4)", "when-changed (>=0.3.0)"] +dev = ["build (>=0.9.0)", "bumpversion", "eth-tester[py-evm] (>=0.11.0b1,<0.12.0b1)", "eth-tester[py-evm] (>=0.9.0b1,<0.10.0b1)", "flaky (>=3.7.0)", "hypothesis (>=3.31.2)", "importlib-metadata (<5.0)", "ipfshttpclient (==0.8.0a2)", "pre-commit (>=2.21.0)", "py-geth (>=3.14.0)", "pytest (>=7.0.0)", "pytest-asyncio (>=0.21.2,<0.23)", "pytest-mock (>=1.10)", "pytest-watch (>=4.2)", "pytest-xdist (>=1.29)", "setuptools (>=38.6.0)", "sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.0.0)", "towncrier (>=21,<22)", "tox (>=3.18.0)", "tqdm (>4.32)", "twine (>=1.13)", "when-changed (>=0.3.0)"] docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.0.0)", "towncrier (>=21,<22)"] ipfs = ["ipfshttpclient (==0.8.0a2)"] -linter = ["black (>=22.1.0)", "flake8 (==3.8.3)", "isort (>=5.11.0)", "mypy (==1.4.1)", "types-protobuf (==3.19.13)", "types-requests (>=2.26.1)", "types-setuptools (>=57.4.4)"] -tester = ["eth-tester[py-evm] (==v0.9.1-b.2)", "py-geth (>=3.14.0)"] +tester = ["eth-tester[py-evm] (>=0.11.0b1,<0.12.0b1)", "eth-tester[py-evm] (>=0.9.0b1,<0.10.0b1)", "py-geth (>=3.14.0)"] [[package]] name = "websockets" @@ -3243,4 +3268,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = "^3.8" -content-hash = "149e85dd34f992c56480d1882a25ed437269561fb0a0cb2f7d567d4eb7248c5f" +content-hash = "922dbcef49580ec7d58b61a226f86171283b9063c80b9238fbc30e00cc652ca0" diff --git a/pyproject.toml b/pyproject.toml index 163fdfd82..068f4cf13 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,6 +28,7 @@ protobuf = "^4.24.4" tqdm = "^4.64.1" web3 = "^6.16.0" nest-asyncio = "^1.5.8" +arb-optimizer = {path = "arb-optimizer"} [tool.poetry.group.dev.dependencies] diff --git a/resources/NBTest/ConvertNBTest.ipynb b/resources/NBTest/ConvertNBTest.ipynb deleted file mode 100644 index 6fd462b32..000000000 --- a/resources/NBTest/ConvertNBTest.ipynb +++ /dev/null @@ -1,569 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "439cb109", - "metadata": {}, - "outputs": [], - "source": [ - "from fls import *\n", - "import sys\n", - "import os\n", - "import re\n", - "from collections import namedtuple\n", - "__VERSION__ = \"1.4 [fastlane]\"\n", - "__DATE__ = \"07/May/2023\"" - ] - }, - { - "cell_type": "markdown", - "id": "2b5b07e2", - "metadata": {}, - "source": [ - "# Convert NBTest\n", - "\n", - "Converts files `NBTest_9999_Comment.py -> test_9999_Comment.py` suitable for `pytest`" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3a724746", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NBTestConvert v1.3.1 [fastlane] 30/Apr/2023\n" - ] - } - ], - "source": [ - "print(f\"NBTestConvert v{__VERSION__} {__DATE__}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "51e64aeb", - "metadata": {}, - "outputs": [], - "source": [ - "NOTEST_DEFAULT=\"TEST\"\n", - "LIBRARY = \"fastlane_bot\"" - ] - }, - { - "cell_type": "markdown", - "id": "22f88afc", - "metadata": {}, - "source": [ - "## Get script path and set paths" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "96fdbec3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['/Users/skl/opt/anaconda3/lib/python3.8/site-packages',\n", - " 'ipykernel_launcher.py']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sys.argv[0].rsplit(\"/\", maxsplit=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b7ddebc8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'ipykernel_launcher.py'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sys.argv[0].rsplit(\"/\", maxsplit=1)[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7a4dd5d6", - "metadata": {}, - "outputs": [], - "source": [ - "if sys.argv[0].rsplit(\"/\", maxsplit=1)[-1]==\"ipykernel_launcher.py\":\n", - " JUPYTER = True\n", - " SCRIPTPATH = os.getcwd()\n", - "else:\n", - " JUPYTER = False\n", - " SCRIPTPATH = os.path.dirname(os.path.realpath(sys.argv[0]))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "0c8d723b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/skl/REPOES/Bancor/ArbBot/resources/NBTest/../../fastlane_bot/tests/nbtest\n" - ] - } - ], - "source": [ - "SRCPATH = os.path.join(SCRIPTPATH, \"\")\n", - "TRGPATH = os.path.join(SCRIPTPATH, f\"../../{LIBRARY}/tests/nbtest\")\n", - "print(TRGPATH)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b3fb3cff", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "JUPYTER True\n", - "SCRIPTPATH /Users/skl/REPOES/Bancor/ArbBot/resources/NBTest\n", - "SRCPATH /Users/skl/REPOES/Bancor/ArbBot/resources/NBTest/\n", - "TRGPATH /Users/skl/REPOES/Bancor/ArbBot/resources/NBTest/../../fastlane_bot/tests/nbtest\n", - "---\n" - ] - } - ], - "source": [ - "print(\"JUPYTER\", JUPYTER)\n", - "print(\"SCRIPTPATH\", SCRIPTPATH)\n", - "print(\"SRCPATH\", SRCPATH)\n", - "print(\"TRGPATH\", TRGPATH)\n", - "print(\"---\")" - ] - }, - { - "cell_type": "markdown", - "id": "119d110f", - "metadata": {}, - "source": [ - "## Generate the list of files" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "553fbebb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['.gitignore',\n", - " '.ipynb_checkpoints',\n", - " 'ConvertNBTest.ipynb',\n", - " 'ConvertNBTest.py',\n", - " 'NBTest_000_Template.ipynb',\n", - " 'NBTest_000_Template.py',\n", - " 'NBTest_002_ContractHelper.ipynb',\n", - " 'NBTest_002_ContractHelper.py',\n", - " 'NBTest_003_PoolManager.ipynb',\n", - " 'NBTest_003_PoolManager.py',\n", - " 'NBTest_004_TokenManager.ipynb',\n", - " 'NBTest_004_TokenManager.py',\n", - " 'NBTest_005_AggregatCarbonTrades.ipynb',\n", - " 'NBTest_005_AggregatCarbonTrades.py',\n", - " 'NBTest_006_GetPriceMap.ipynb',\n", - " 'NBTest_006_GetPriceMap.py',\n", - " 'NBTest_007_TopNpoolsOnexchange.ipynb',\n", - " 'NBTest_007_TopNpoolsOnexchange.py',\n", - " 'NBTest_008_TxHelper.ipynb',\n", - " 'NBTest_008_TxHelper.py',\n", - " 'NBTest_063b_Optimizer.ipynb',\n", - " 'NBTest_063b_Optimizer.py',\n", - " 'SKLTesting.ipynb',\n", - " 'SKLTesting.py',\n", - " '__pycache__',\n", - " 'carbon',\n", - " 'fastlane_bot',\n", - " 'fls.py',\n", - " 'jupytext-metadata-template.ipynb',\n", - " 'jupytext-metadata-template.py',\n", - " '~$opt.xlsx']" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rawlist = os.listdir(SRCPATH)\n", - "rawlist.sort()\n", - "rawlist" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "71dc0630", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datarecord_nt(tid='0000', comment='Bla', fn='NBTest_0000_Bla.py', outfn='test_0000_Bla.py')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dr_nt = namedtuple(\"datarecord_nt\", \"tid, comment, fn, outfn\")\n", - "def filterfn(fn):\n", - " \"\"\"\n", - " takes fn and returns either filelist_nt or None \n", - " \"\"\"\n", - " nxsplit = fn.rsplit(\".\", maxsplit=1)\n", - " if len(nxsplit) < 2: return None\n", - " if not(nxsplit[1].lower()==\"py\"): return None\n", - " fnsplit = nxsplit[0].split(\"_\")\n", - " if not len(fnsplit) in [2,3]: return None\n", - " if not fnsplit[0] == \"NBTest\": return None\n", - " tid = fnsplit[1]\n", - " try:\n", - " comment = fnsplit[2]\n", - " except IndexError:\n", - " comment = \"\"\n", - " outfn = f\"test_{tid}_{comment}.py\"\n", - " return dr_nt(tid=tid, comment=comment, fn=fn, outfn=outfn)\n", - "\n", - "assert filterfn(\"README\") is None\n", - "assert filterfn(\"NBTest_0000_Bla.ipynb\") is None\n", - "assert filterfn(\"NBTest_0000.py\")\n", - "assert filterfn(\"Test_0000_Bla.py\") is None\n", - "assert filterfn(\"NBTest_1.10.4_Bla.py\").tid == \"1.10.4\"\n", - "assert filterfn(\"NBTest_1.py\").comment == \"\"\n", - "filterfn(\"NBTest_0000_Bla.py\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e86139a7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(datarecord_nt(tid='000', comment='Template', fn='NBTest_000_Template.py', outfn='test_000_Template.py'),\n", - " datarecord_nt(tid='002', comment='ContractHelper', fn='NBTest_002_ContractHelper.py', outfn='test_002_ContractHelper.py'),\n", - " datarecord_nt(tid='003', comment='PoolManager', fn='NBTest_003_PoolManager.py', outfn='test_003_PoolManager.py'),\n", - " datarecord_nt(tid='004', comment='TokenManager', fn='NBTest_004_TokenManager.py', outfn='test_004_TokenManager.py'),\n", - " datarecord_nt(tid='005', comment='AggregatCarbonTrades', fn='NBTest_005_AggregatCarbonTrades.py', outfn='test_005_AggregatCarbonTrades.py'),\n", - " datarecord_nt(tid='006', comment='GetPriceMap', fn='NBTest_006_GetPriceMap.py', outfn='test_006_GetPriceMap.py'),\n", - " datarecord_nt(tid='007', comment='TopNpoolsOnexchange', fn='NBTest_007_TopNpoolsOnexchange.py', outfn='test_007_TopNpoolsOnexchange.py'),\n", - " datarecord_nt(tid='008', comment='TxHelper', fn='NBTest_008_TxHelper.py', outfn='test_008_TxHelper.py'),\n", - " datarecord_nt(tid='063b', comment='Optimizer', fn='NBTest_063b_Optimizer.py', outfn='test_063b_Optimizer.py'))" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fnlst = (filterfn(fn) for fn in rawlist)\n", - "fnlst = tuple(r for r in fnlst if not r is None)\n", - "#fnlst = (fnlst[1],)\n", - "fnlst" - ] - }, - { - "cell_type": "markdown", - "id": "23841ca4", - "metadata": {}, - "source": [ - "## Process files" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "5541fc2c", - "metadata": {}, - "outputs": [], - "source": [ - "def funcn(title):\n", - " \"\"\"\n", - " converts a title into a function name\n", - " \n", - " NOTE\n", - " \n", - " \"This is a title [TEST]\" -> test_this_is_a_title\n", - " \"This is a title [NOTEST]\" -> notest_this_is_a_title\n", - " \"This is a title\" -> depends on NOTEST_DEFAULT global\n", - " \"\"\"\n", - " global NOTEST_DEFAULT\n", - " #print(\"[funcn] NOTEST_DEFAULT\", NOTEST_DEFAULT)\n", - " \n", - " title = title.strip()\n", - " if title[-8:] == \"[NOTEST]\":\n", - " notest = True\n", - " title = title[:-8].strip()\n", - " elif title[-6:] == \"[TEST]\":\n", - " notest = False\n", - " title = title[:-6].strip()\n", - " else:\n", - " notest = True if NOTEST_DEFAULT == \"NOTEST\" else False \n", - " \n", - " \n", - " prefix = \"notest_\" if notest else \"test_\"\n", - "\n", - " \n", - " funcn = title.lower()\n", - " funcn = funcn.replace(\" \", \"_\")\n", - " funcn = prefix+funcn\n", - " return funcn\n", - "\n", - "assert funcn(\" Title [TEST] \") == \"test_title\"\n", - "assert funcn(\" Title [NOTEST] \") == \"notest_title\"\n", - "assert funcn(\" Title \") == \"notest_title\" if NOTEST_DEFAULT==\"NOTEST\" else \"test_title\"\n", - "assert funcn(\" Advanced Testing [TEST] \") == \"test_advanced_testing\"\n", - "assert funcn(\" A notest title [NOTEST] \") == \"notest_a_notest_title\"\n", - "#funcn(\"Asserting that the radius computes correctly\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "49a6c4d7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'notest_a_notest_title'" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "funcn(\"A notest title [NOTEST]\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "233d86a2", - "metadata": {}, - "outputs": [], - "source": [ - "def process_code(code, dr, srcpath=None, trgpath=None):\n", - " \"\"\"\n", - " processes notebook code\n", - " \n", - " :code: the code to be processed\n", - " :dr: the associated data record (datarecord_nt)\n", - " :srcpath: source path (info only)\n", - " :trgpath: target path (info only)\n", - " \"\"\"\n", - " lines = code.splitlines()\n", - " outlines = [\n", - " \"# \"+\"-\"*60,\n", - " f\"# Auto generated test file `{dr.outfn}`\",\n", - " \"# \"+\"-\"*60,\n", - " f\"# source file = {dr.fn}\"\n", - " ]\n", - "# if srcpath and srcpath != \".\":\n", - "# outlines += [\n", - "# f\"# source path = {srcpath}\"\n", - "# ]\n", - "# if trgpath and trgpath != \".\":\n", - "# outlines += [\n", - "# f\"# target path = {srcpath}\"\n", - "# ]\n", - " outlines += [\n", - " \n", - " f\"# test id = {dr.tid}\",\n", - " f\"# test comment = {dr.comment}\",\n", - " \"# \"+\"-\"*60,\n", - " \"\",\"\",\n", - " ]\n", - " is_precode = True\n", - " for l in lines:\n", - "# print(l)\n", - "# try:\n", - "# print(l[:5], l[:5].encode(), ord(l[1]), ord(l[4]), l[:5]==\"# ## \")\n", - "# except:\n", - "# pass\n", - " \n", - " if l[:4] == \"# # \":\n", - " print(f\"\"\"Processing \"{l[4:]}\" ({r.fn})\"\"\")\n", - " outlines += [\"\"]\n", - " \n", - " elif l[:5] == \"# ## \" or l[:5].encode() == b'# ##\\xc2\\xa0':\n", - " title = l[5:].strip()\n", - " fcn = funcn(title)\n", - " print(f\" creating function `{fcn}()` from section {title}\")\n", - " outlines += [\n", - " \"\",\n", - " \"# \"+\"-\"*60,\n", - " f\"# Test {r.tid}\",\n", - " f\"# File {r.outfn}\",\n", - " f\"# Segment {title}\",\n", - " \"# \"+\"-\"*60,\n", - " f\"def {fcn}():\",\n", - " \"# \"+\"-\"*60,\n", - " ]\n", - " is_precode = False\n", - " \n", - " elif l[:9] == \"# NBTEST:\":\n", - " l = l[9:]\n", - " try:\n", - " opt, val = l.split(\"=\")\n", - " opt=opt.strip().upper()\n", - " val=val.strip().upper()\n", - " except:\n", - " print(f\" error setting option\", l)\n", - " raise ValueError(\"Error setting option\", l, dr.fn)\n", - " print(f\" processiong option {opt}={val}\")\n", - " if opt == \"NOTEST_DEFAULT\":\n", - " global NOTEST_DEFAULT\n", - " if val in [\"TEST\", \"NOTEST\"]:\n", - " NOTEST_DEFAULT = val\n", - " #print(\"[process_code] NOTEST_DEFAULT\", NOTEST_DEFAULT)\n", - " else:\n", - " raise ValueError(f\"Invalid choice for option NOTEST_DEFAULT: {val}\", l, dr.fn)\n", - " else:\n", - " raise ValueError(f\"Unknown option {opt}\", l, dr.fn)\n", - " \n", - " \n", - " else:\n", - " if is_precode:\n", - " if l[:2] != \"# \":\n", - " outlines += [l]\n", - " else:\n", - " outlines += [\" \"+l]\n", - " outcode = \"\\n\".join(outlines)\n", - " return outcode" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "82d9c3d5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing \"TEMPLATE [NBTest000]\" (NBTest_000_Template.py)\n", - " creating function `notest_demo_section()` from section Demo section [NOTEST]\n", - " creating function `test_section_1()` from section Section 1\n", - " creating function `test_section_2()` from section Section 2\n", - " saving generated test to test_000_Template.py\n", - "Processing \"Unit tests for ContractHelper\" (NBTest_002_ContractHelper.py)\n", - " saving generated test to test_002_ContractHelper.py\n", - "Processing \"Unit tests for PoolManager\" (NBTest_003_PoolManager.py)\n", - " saving generated test to test_003_PoolManager.py\n", - "Processing \"Unit tests for TokenManager\" (NBTest_004_TokenManager.py)\n", - " saving generated test to test_004_TokenManager.py\n", - " saving generated test to test_005_AggregatCarbonTrades.py\n", - " saving generated test to test_006_GetPriceMap.py\n", - " saving generated test to test_007_TopNpoolsOnexchange.py\n", - " saving generated test to test_008_TxHelper.py\n", - "Processing \"CPC and Optimizer in Fastlane [NBTest063b]\" (NBTest_063b_Optimizer.py)\n", - " creating function `test_p()` from section P\n", - " creating function `test_tvl()` from section TVL\n", - " creating function `test_estimate_prices()` from section estimate prices\n", - " creating function `test_price_estimates_in_optimizer()` from section price estimates in optimizer\n", - " creating function `test_assertions_and_testing()` from section Assertions and testing\n", - " creating function `test_iseq()` from section iseq\n", - " creating function `test_carbonorderui_integration()` from section CarbonOrderUI integration\n", - " creating function `test_new_cpc_features_in_v2()` from section New CPC features in v2\n", - " creating function `test_real_data_and_retrieval_of_curves()` from section Real data and retrieval of curves\n", - " creating function `test_tokenscale_tests()` from section TokenScale tests\n", - " creating function `test_dx_min_and_dx_max_etc()` from section dx_min and dx_max etc\n", - " creating function `test_xyfromp_f_and_dxdyfromp_f()` from section xyfromp_f and dxdyfromp_f\n", - " creating function `test_cpcinverter()` from section CPCInverter\n", - " creating function `test_simple_optimizer()` from section simple_optimizer\n", - " creating function `test_optimizer_plus_inverted_curves()` from section optimizer plus inverted curves\n", - " creating function `test_posx_and_negx()` from section posx and negx\n", - " creating function `test_tradeinstructions()` from section TradeInstructions\n", - " creating function `test_margp_optimizer()` from section margp_optimizer\n", - " creating function `notest_simple_optimizer_demo()` from section simple_optimizer demo [NOTEST]\n", - " creating function `notest_margp_optimizer_demo()` from section MargP Optimizer Demo [NOTEST]\n", - " creating function `notest_optimizer_plus_inverted_curves()` from section Optimizer plus inverted curves [NOTEST]\n", - " creating function `notest_operating_on_leverage_ranges()` from section Operating on leverage ranges [NOTEST]\n", - " creating function `notest_arbitrage_testing()` from section Arbitrage testing [NOTEST]\n", - " creating function `notest_charts()` from section Charts [NOTEST]\n", - " saving generated test to test_063b_Optimizer.py\n" - ] - } - ], - "source": [ - "for r in fnlst:\n", - " code = fload(r.fn, SRCPATH, quiet=True)\n", - " testcode = process_code(code, r, SRCPATH, TRGPATH)\n", - " fsave(testcode, r.outfn, TRGPATH, quiet=True)\n", - " print(f\" saving generated test to {r.outfn}\")" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/NBTest/ConvertNBTest.py b/resources/NBTest/ConvertNBTest.py deleted file mode 100644 index bbeb1571b..000000000 --- a/resources/NBTest/ConvertNBTest.py +++ /dev/null @@ -1,236 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.16.1 -# kernelspec: -# display_name: Python 3 -# language: python -# name: python3 -# --- - -from fls import * -import sys -import os -import re -from collections import namedtuple -__VERSION__ = "1.4 [fastlane]" -__DATE__ = "07/May/2023" - -# # Convert NBTest -# -# Converts files `NBTest_9999_Comment.py -> test_9999_Comment.py` suitable for `pytest` - -print(f"NBTestConvert v{__VERSION__} {__DATE__}") - -NOTEST_DEFAULT="TEST" -LIBRARY = "fastlane_bot" - -# ## Get script path and set paths - -sys.argv[0].rsplit("/", maxsplit=1) - -sys.argv[0].rsplit("/", maxsplit=1)[-1] - -if sys.argv[0].rsplit("/", maxsplit=1)[-1]=="ipykernel_launcher.py": - JUPYTER = True - SCRIPTPATH = os.getcwd() -else: - JUPYTER = False - SCRIPTPATH = os.path.dirname(os.path.realpath(sys.argv[0])) - -SRCPATH = os.path.join(SCRIPTPATH, "") -TRGPATH = os.path.join(SCRIPTPATH, f"../../{LIBRARY}/tests/nbtest") -print(TRGPATH) - -print("JUPYTER", JUPYTER) -print("SCRIPTPATH", SCRIPTPATH) -print("SRCPATH", SRCPATH) -print("TRGPATH", TRGPATH) -print("---") - -# ## Generate the list of files - -rawlist = os.listdir(SRCPATH) -rawlist.sort() -rawlist - -# + -dr_nt = namedtuple("datarecord_nt", "tid, comment, fn, outfn") -def filterfn(fn): - """ - takes fn and returns either filelist_nt or None - """ - nxsplit = fn.rsplit(".", maxsplit=1) - if len(nxsplit) < 2: return None - if not(nxsplit[1].lower()=="py"): return None - fnsplit = nxsplit[0].split("_") - if not len(fnsplit) in [2,3]: return None - if not fnsplit[0] == "NBTest": return None - tid = fnsplit[1] - try: - comment = fnsplit[2] - except IndexError: - comment = "" - outfn = f"test_{tid}_{comment}.py" - return dr_nt(tid=tid, comment=comment, fn=fn, outfn=outfn) - -assert filterfn("README") is None -assert filterfn("NBTest_0000_Bla.ipynb") is None -assert filterfn("NBTest_0000.py") -assert filterfn("Test_0000_Bla.py") is None -assert filterfn("NBTest_1.10.4_Bla.py").tid == "1.10.4" -assert filterfn("NBTest_1.py").comment == "" -filterfn("NBTest_0000_Bla.py") -# - - -fnlst = (filterfn(fn) for fn in rawlist) -fnlst = tuple(r for r in fnlst if not r is None) -#fnlst = (fnlst[1],) -fnlst - - -# ## Process files - -# + -def funcn(title): - """ - converts a title into a function name - - NOTE - - "This is a title [TEST]" -> test_this_is_a_title - "This is a title [NOTEST]" -> notest_this_is_a_title - "This is a title" -> depends on NOTEST_DEFAULT global - """ - global NOTEST_DEFAULT - #print("[funcn] NOTEST_DEFAULT", NOTEST_DEFAULT) - - title = title.strip() - if title[-8:] == "[NOTEST]": - notest = True - title = title[:-8].strip() - elif title[-6:] == "[TEST]": - notest = False - title = title[:-6].strip() - else: - notest = True if NOTEST_DEFAULT == "NOTEST" else False - - - prefix = "notest_" if notest else "test_" - - - funcn = title.lower() - funcn = funcn.replace(" ", "_") - funcn = prefix+funcn - return funcn - -assert funcn(" Title [TEST] ") == "test_title" -assert funcn(" Title [NOTEST] ") == "notest_title" -assert funcn(" Title ") == "notest_title" if NOTEST_DEFAULT=="NOTEST" else "test_title" -assert funcn(" Advanced Testing [TEST] ") == "test_advanced_testing" -assert funcn(" A notest title [NOTEST] ") == "notest_a_notest_title" -#funcn("Asserting that the radius computes correctly") -# - - -funcn("A notest title [NOTEST]") - - -def process_code(code, dr, srcpath=None, trgpath=None): - """ - processes notebook code - - :code: the code to be processed - :dr: the associated data record (datarecord_nt) - :srcpath: source path (info only) - :trgpath: target path (info only) - """ - lines = code.splitlines() - outlines = [ - "# "+"-"*60, - f"# Auto generated test file `{dr.outfn}`", - "# "+"-"*60, - f"# source file = {dr.fn}" - ] -# if srcpath and srcpath != ".": -# outlines += [ -# f"# source path = {srcpath}" -# ] -# if trgpath and trgpath != ".": -# outlines += [ -# f"# target path = {srcpath}" -# ] - outlines += [ - - f"# test id = {dr.tid}", - f"# test comment = {dr.comment}", - "# "+"-"*60, - "","", - ] - is_precode = True - for l in lines: -# print(l) -# try: -# print(l[:5], l[:5].encode(), ord(l[1]), ord(l[4]), l[:5]=="# ## ") -# except: -# pass - - if l[:4] == "# # ": - print(f"""Processing "{l[4:]}" ({r.fn})""") - outlines += [""] - - elif l[:5] == "# ## " or l[:5].encode() == b'# ##\xc2\xa0': - title = l[5:].strip() - fcn = funcn(title) - print(f" creating function `{fcn}()` from section {title}") - outlines += [ - "", - "# "+"-"*60, - f"# Test {r.tid}", - f"# File {r.outfn}", - f"# Segment {title}", - "# "+"-"*60, - f"def {fcn}():", - "# "+"-"*60, - ] - is_precode = False - - elif l[:9] == "# NBTEST:": - l = l[9:] - try: - opt, val = l.split("=") - opt=opt.strip().upper() - val=val.strip().upper() - except: - print(f" error setting option", l) - raise ValueError("Error setting option", l, dr.fn) - print(f" processiong option {opt}={val}") - if opt == "NOTEST_DEFAULT": - global NOTEST_DEFAULT - if val in ["TEST", "NOTEST"]: - NOTEST_DEFAULT = val - #print("[process_code] NOTEST_DEFAULT", NOTEST_DEFAULT) - else: - raise ValueError(f"Invalid choice for option NOTEST_DEFAULT: {val}", l, dr.fn) - else: - raise ValueError(f"Unknown option {opt}", l, dr.fn) - - - else: - if is_precode: - if l[:2] != "# ": - outlines += [l] - else: - outlines += [" "+l] - outcode = "\n".join(outlines) - return outcode - -for r in fnlst: - code = fload(r.fn, SRCPATH, quiet=True) - testcode = process_code(code, r, SRCPATH, TRGPATH) - fsave(testcode, r.outfn, TRGPATH, quiet=True) - print(f" saving generated test to {r.outfn}") diff --git a/resources/NBTest/NBTest_000_Template.ipynb b/resources/NBTest/NBTest_000_Template.ipynb deleted file mode 100644 index b78e629fa..000000000 --- a/resources/NBTest/NBTest_000_Template.ipynb +++ /dev/null @@ -1,261 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "cc40bc23-abde-4094-abec-419f0a7fa81e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n" - ] - } - ], - "source": [ - "# from fastlane_bot.config import Config\n", - "try:\n", - " #from fastlane_bot.tools.moo import meh\n", - " from fastlane_bot.testing import *\n", - "\n", - "except:\n", - " #from tools.moo import meh\n", - " from tools.testing import *\n", - "\n", - "# print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(meh))\n", - "\n", - "# plt.style.use('seaborn-dark')\n", - "# plt.rcParams['figure.figsize'] = [12,6]\n", - "# from fastlane_bot import __VERSION__\n", - "# require(\"2.0\", __VERSION__)" - ] - }, - { - "cell_type": "markdown", - "id": "b3f59f14-b91b-4dba-94b0-3d513aaf41c7", - "metadata": {}, - "source": [ - "# TEMPLATE [NBTest000]" - ] - }, - { - "cell_type": "markdown", - "id": "c8f94cd0-c655-4910-8dad-bd8759def41a", - "metadata": {}, - "source": [ - "The section before the first `# ## Heading2` is for common code that is executed BEFORE the tests are run. It is rarely necessary to put code here." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f0b427c5-a53c-4efd-adbf-e66cf5488a58", - "metadata": {}, - "outputs": [], - "source": [ - "MYVAR0 = 0" - ] - }, - { - "cell_type": "markdown", - "id": "c86f5eb5-7731-4776-87e9-7fd4acadf906", - "metadata": {}, - "source": [ - "## Demo section [NOTEST]\n", - "\n", - "_this optional section is for demo purposes and it does not generate tests (inidcated by the trailing `[NOTEST`_\n", - "\n", - "- slow running not test relevant code SHOULD go here\n", - "- code producing charts or code reading data not available in the testing environment MUST go here\n", - "- any Heading 2 section can be market `[NOTEST]` regarding of location" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "27bf2d08-f9af-43b1-9a11-cdb49699ba01", - "metadata": {}, - "outputs": [], - "source": [ - "pass" - ] - }, - { - "cell_type": "markdown", - "id": "49ba4b05-0e77-405b-8c54-383a9a34c0a5", - "metadata": {}, - "source": [ - "## Section 1\n", - "\n", - "This section will be converted to a function named `test_section_1()` therefore it is important to only have alphanumerics or underscore in the title.\n", - "\n", - "Note: Heading 3 and below are only decorative and should be used liberally." - ] - }, - { - "cell_type": "markdown", - "id": "a62125f4-20d9-4391-a185-33191d7cad85", - "metadata": {}, - "source": [ - "### Using `iseq`\n", - "\n", - "`iseq` should be used for `float` comparisons; syntax is `iseq(a,b,c,...)` and they all must be equal to `a` for it not to fail." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d0ea4db6-bb1d-4206-95c2-1e04a896064a", - "metadata": {}, - "outputs": [], - "source": [ - "assert m.sqrt(2) != 1.414213562373095\n", - "assert iseq(m.sqrt(2), 1.414213562373095)" - ] - }, - { - "cell_type": "markdown", - "id": "5368f5e4-39a4-4f52-b541-7eb41026d222", - "metadata": {}, - "source": [ - "### Using `raises`\n", - "\n", - "With raisese you can check whether a function call raises; eg to check if `f(a,b=b)` raises you do\n", - "\n", - " assert raises(f, a, b=b) == \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9e53b6a1-49ba-44df-98e6-cfb1f88862fb", - "metadata": {}, - "outputs": [], - "source": [ - "inv = lambda x: 1/x\n", - "assert inv(2) == 0.5\n", - "assert raises(inv, 0) == 'division by zero'" - ] - }, - { - "cell_type": "markdown", - "id": "c16652cc-c2f1-4944-9e21-053b11e9d31c", - "metadata": {}, - "source": [ - "### Variable scope\n", - "\n", - "see next section" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d9dcbbeb-d946-473a-9f30-30cfd392aa44", - "metadata": {}, - "outputs": [], - "source": [ - "MYVAR1 = 1\n", - "assert MYVAR1 == 1\n", - "assert MYVAR0 == 0" - ] - }, - { - "cell_type": "markdown", - "id": "b431a232-235b-44a3-b659-6ce1dee2d428", - "metadata": {}, - "source": [ - "## Section 2\n", - "\n", - "This is a new Heading two and therefor a new function, in this case called `test_section_2()`. Note the variables defined in a previous scope are not defined here." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "79e55675-27f5-4b13-b459-c05e40f78426", - "metadata": {}, - "outputs": [], - "source": [ - "myvar1 = lambda: MYVAR1" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "14e0c77c-d89a-4a1e-b990-e16c70408abd", - "metadata": {}, - "outputs": [], - "source": [ - "assert MYVAR0 == 0" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6191996c-ca8c-49ba-91ce-3e7f98408a24", - "metadata": {}, - "outputs": [], - "source": [ - "#myvar1() == 1 # ONLY True in the Notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c69c2e95-c9a4-4ac2-8bbb-86b1dc5db6a0", - "metadata": {}, - "outputs": [ - { - "ename": "AssertionError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m raises (myvar1) \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMYVAR1\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", - "\u001b[0;31mAssertionError\u001b[0m: " - ] - } - ], - "source": [ - "assert raises (myvar1) == \"name 'MYVAR1' is not defined\" # ONLY True in tests" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b36bd25e-3985-4522-97b1-18a196dde125", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/NBTest/NBTest_000_Template.py b/resources/NBTest/NBTest_000_Template.py deleted file mode 100644 index 0507a3aa1..000000000 --- a/resources/NBTest/NBTest_000_Template.py +++ /dev/null @@ -1,97 +0,0 @@ -# -*- coding: utf-8 -*- -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -# from fastlane_bot.config import Config -try: - #from fastlane_bot.tools.moo import meh - from fastlane_bot.testing import * - -except: - #from tools.moo import meh - from tools.testing import * - -# print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(meh)) - -# plt.style.use('seaborn-dark') -# plt.rcParams['figure.figsize'] = [12,6] -# from fastlane_bot import __VERSION__ -# require("2.0", __VERSION__) -# - - -# # TEMPLATE [NBTest000] - -# The section before the first `# ## Heading2` is for common code that is executed BEFORE the tests are run. It is rarely necessary to put code here. - -MYVAR0 = 0 - -# ## Demo section [NOTEST] -# -# _this optional section is for demo purposes and it does not generate tests (inidcated by the trailing `[NOTEST`_ -# -# - slow running not test relevant code SHOULD go here -# - code producing charts or code reading data not available in the testing environment MUST go here -# - any Heading 2 section can be market `[NOTEST]` regarding of location - -pass - -# ## Section 1 -# -# This section will be converted to a function named `test_section_1()` therefore it is important to only have alphanumerics or underscore in the title. -# -# Note: Heading 3 and below are only decorative and should be used liberally. - -# ### Using `iseq` -# -# `iseq` should be used for `float` comparisons; syntax is `iseq(a,b,c,...)` and they all must be equal to `a` for it not to fail. - -assert m.sqrt(2) != 1.414213562373095 -assert iseq(m.sqrt(2), 1.414213562373095) - -# ### Using `raises` -# -# With raisese you can check whether a function call raises; eg to check if `f(a,b=b)` raises you do -# -# assert raises(f, a, b=b) == -# - -inv = lambda x: 1/x -assert inv(2) == 0.5 -assert raises(inv, 0) == 'division by zero' - -# ### Variable scope -# -# see next section - -MYVAR1 = 1 -assert MYVAR1 == 1 -assert MYVAR0 == 0 - -# ## Section 2 -# -# This is a new Heading two and therefor a new function, in this case called `test_section_2()`. Note the variables defined in a previous scope are not defined here. - -myvar1 = lambda: MYVAR1 - -assert MYVAR0 == 0 - -# + -#myvar1() == 1 # ONLY True in the Notebook -# - - -assert raises (myvar1) == "name 'MYVAR1' is not defined" # ONLY True in tests - - diff --git a/resources/NBTest/NBTest_002_CPCandOptimizer.ipynb b/resources/NBTest/NBTest_002_CPCandOptimizer.ipynb deleted file mode 100644 index 099e10b48..000000000 --- a/resources/NBTest/NBTest_002_CPCandOptimizer.ipynb +++ /dev/null @@ -1,4746 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "a448e212", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "SimplePair v2.1 (18/May/2023)\n", - "ConstantProductCurve v3.4 (23/Jan/2024)\n", - "CPCArbOptimizer v5.1 (15/Sep/2023)\n", - "MargPOptimizer v5.2 (15/Sep/2023)\n", - "PairOptimizer v6.0.1 (21/Sep/2023)\n" - ] - } - ], - "source": [ - "try:\n", - " from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, CPCContainer, T, CPCInverter, Pair\n", - " from fastlane_bot.tools.optimizer import CPCArbOptimizer, F, MargPOptimizer, PairOptimizer\n", - " from fastlane_bot.tools.analyzer import CPCAnalyzer\n", - " from fastlane_bot.testing import *\n", - "\n", - "except:\n", - " from tools.cpc import ConstantProductCurve as CPC, CPCContainer, T, CPCInverter, Pair\n", - " from tools.optimizer import CPCArbOptimizer, F, MargPOptimizer, PairOptimizer\n", - " from tools.analyzer import CPCAnalyzer\n", - " from tools.testing import *\n", - "\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(Pair))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPC))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPCArbOptimizer))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(MargPOptimizer))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(PairOptimizer))\n", - "\n", - "#plt.style.use('seaborn-dark')\n", - "plt.rcParams['figure.figsize'] = [12,6]\n", - "# from fastlane_bot import __VERSION__\n", - "# require(\"3.0\", __VERSION__)" - ] - }, - { - "cell_type": "markdown", - "id": "d9917997", - "metadata": {}, - "source": [ - "# CPC and Optimizer in Fastlane [NBTest002]\n", - "\n", - "Note: more optimizer tests in NBTest 055" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d6c6ac8d", - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " market_df = pd.read_csv(\"_data/NBTEST_002_Curves.csv.gz\")\n", - "except:\n", - " market_df = pd.read_csv(\"fastlane_bot/tests/_data/NBTEST_002_Curves.csv.gz\")\n", - "CCmarket = CPCContainer.from_df(market_df)" - ] - }, - { - "cell_type": "markdown", - "id": "420a98f2", - "metadata": {}, - "source": [ - "## description" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2e23803a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "d: cid = 167 [167]\n", - "d0: cid = 167 [167]\n", - "\n", - "d: primary = WETH/DAI [WETH/DAI]\n", - "d0: primary = WETH/DAI [WETH/DAI]\n", - "\n", - "d: pp = 1,826.764318 DAI per WETH\n", - "d0: pp = 1,826.764318 DAI per WETH\n", - "\n", - "d: pair = DAI/WETH [DAI/WETH]\n", - "d0: pair = DAI/WETH [DAI/WETH]\n", - "\n", - "d: tknx = 3,967,283.591895 DAI [virtual: 3,967,283.592]\n", - "d0: tknx = 3,967,283.591895 DAI [virtual: 3,967,283.592]\n", - "\n", - "d: tkny = 2,171.754481 WETH [virtual: 2,171.754]\n", - "d0: tkny = 2,171.754481 WETH [virtual: 2,171.754]\n", - "\n", - "d: p = 0.0005474159913752679 [min=0, max=None] WETH per DAI\n", - "d0: p = 0.0005474159913752679 [min=0, max=None] WETH per DAI\n", - "\n", - "d: fee = 0.003\n", - "d0: fee = 0.003\n", - "\n", - "d: descr = sushiswap_v2 DAI/WETH 0.003\n", - "d0: descr = sushiswap_v2 DAI/WETH 0.003\n", - "\n" - ] - } - ], - "source": [ - "d = CCmarket.bycid(\"167\").description().splitlines()\n", - "d0 = \"\"\"\n", - "cid = 167 [167]\n", - "primary = WETH/DAI [WETH/DAI]\n", - "pp = 1,826.764318 DAI per WETH\n", - "pair = DAI/WETH [DAI/WETH]\n", - "tknx = 3,967,283.591895 DAI [virtual: 3,967,283.592]\n", - "tkny = 2,171.754481 WETH [virtual: 2,171.754]\n", - "p = 0.0005474159913752679 [min=0, max=None] WETH per DAI\n", - "fee = 0.003\n", - "descr = sushiswap_v2 DAI/WETH 0.003\n", - "\"\"\".strip().splitlines()\n", - "d0 = [l.strip() for l in d0]\n", - "for l,l0 in zip(d,d0):\n", - " print(f\"d: {l}\\nd0: {l0}\\n\")\n", - " assert l==l0" - ] - }, - { - "cell_type": "markdown", - "id": "6ca9820a", - "metadata": {}, - "source": [ - "## bycids" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "db8ec358", - "metadata": {}, - "outputs": [], - "source": [ - "CC = CCmarket" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "84f2f485", - "metadata": {}, - "outputs": [], - "source": [ - "assert len(CC.bycids()) == len(CC)\n", - "assert type(CC.bycids()) == type(CC)\n", - "assert type(CC.bycids(ascc=False)) == tuple\n", - "for c in CC:\n", - " assert isinstance(c.cid, str), f\"{c.cid} is not of type str\"\n", - "cids = [c.cid for c in CC]\n", - "assert raises(CC.bycids, include=\"foo\", endswith=\"bar\") == 'include and endswith cannot be used together'\n", - "assert raises(CC.bycids,\"167, 168, 169\")\n", - "CC1 = CC.bycids([\"167\", \"168\", \"169\"])\n", - "assert len(CC1) == 3\n", - "assert [c.cid for c in CC1] == ['167', '168', '169']\n", - "CC2 = CC.bycids(endswith=\"11\")\n", - "assert len(CC2) == 5\n", - "assert [c.cid for c in CC2] == ['211', '311', '411', '511', '611']\n", - "CC3 = CC.bycids(endswith=\"11\", exclude=['311', '411'])\n", - "assert [c.cid for c in CC3] == ['211', '511', '611']" - ] - }, - { - "cell_type": "markdown", - "id": "ec3eb0ea", - "metadata": {}, - "source": [ - "## pairo and primary" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "996e686a", - "metadata": {}, - "outputs": [], - "source": [ - "assert Pair.n(\"WETH\") == \"WETH\"\n", - "assert Pair.n(\"WETH\") == \"WETH\"\n", - "assert Pair.n(\"USDC/WETH\") == \"USDC/WETH\"" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "cad32d68", - "metadata": {}, - "outputs": [], - "source": [ - "pairo = Pair(\"USDC/WETH\")\n", - "assert pairo.isprimary == False\n", - "assert raises (Pair, tknb='USDC', tknq='WETH')\n", - "assert pairo.tknb == 'USDC'\n", - "assert pairo.tknq == 'WETH'\n", - "assert pairo.tknb_n == 'USDC'\n", - "assert pairo.tknq_n == 'WETH'\n", - "assert pairo.tknx == 'USDC'\n", - "assert pairo.tkny == 'WETH'\n", - "assert pairo.tknx_n == 'USDC'\n", - "assert pairo.tkny_n == 'WETH'\n", - "assert pairo.pair == 'USDC/WETH'\n", - "assert pairo.pair_n == 'USDC/WETH'\n", - "assert pairo.primary == 'WETH/USDC'\n", - "assert pairo.primary_n == 'WETH/USDC'\n", - "assert pairo.secondary == pairo.pair\n", - "assert pairo.secondary_n == pairo.pair_n\n", - "assert pairo.primary_tknb == \"WETH\"\n", - "assert pairo.primary_tknq == \"USDC\"" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "fdf29cb5", - "metadata": {}, - "outputs": [], - "source": [ - "pairo = Pair(\"WETH/USDC\")\n", - "assert pairo.isprimary == True\n", - "assert pairo.tknq == 'USDC'\n", - "assert pairo.tknb == 'WETH'\n", - "assert pairo.tknq_n == 'USDC'\n", - "assert pairo.tknb_n == 'WETH'\n", - "assert pairo.tkny == 'USDC'\n", - "assert pairo.tknx == 'WETH'\n", - "assert pairo.tkny_n == 'USDC'\n", - "assert pairo.tknx_n == 'WETH'\n", - "assert pairo.pair == 'WETH/USDC'\n", - "assert pairo.pair_n == 'WETH/USDC'\n", - "assert pairo.primary == pairo.pair\n", - "assert pairo.primary_n == pairo.pair_n\n", - "assert pairo.secondary == 'USDC/WETH'\n", - "assert pairo.secondary_n == 'USDC/WETH'\n", - "assert pairo.primary_tknb == \"WETH\"\n", - "assert pairo.primary_tknq == \"USDC\"" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c6d7394d", - "metadata": {}, - "outputs": [], - "source": [ - "c1 = CPC.from_pk(pair=\"USDC/WETH\", p=1, k=100)\n", - "c2 = CPC.from_pk(pair=\"WETH/USDC\", p=1, k=100)\n", - "CC = CPCContainer([c1,c2])\n", - "assert c1.pairo.primary == 'WETH/USDC'\n", - "assert c2.pairo.primary == 'WETH/USDC'\n", - "assert c1.primary == c1.pairo.primary\n", - "assert CC.pairs() == {'WETH/USDC'}\n", - "assert CC.pairs(standardize=True) == CC.pairs()\n", - "assert CC.pairs(standardize=False) == {'USDC/WETH', 'WETH/USDC'}" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "dcdb3221", - "metadata": {}, - "outputs": [], - "source": [ - "assert Pair(\"WETH/USDC\").isprimary == True\n", - "assert Pair(\"USDC/WETH\").isprimary == False" - ] - }, - { - "cell_type": "markdown", - "id": "bb0fd6af", - "metadata": {}, - "source": [ - "## buysell" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "05230c3c", - "metadata": {}, - "outputs": [], - "source": [ - "# selling ETH at 2000-2001 USDC per ETH\n", - "c1 = CPC.from_carbon(pair=\"WETH/USDC\", tkny=\"WETH\", yint=10, y=10, pa=1/2000, pb=1/2001, isdydx=True)\n", - "assert c1.pair == \"USDC/WETH\"\n", - "assert c1.primary == \"WETH/USDC\"\n", - "assert c1.pairo.isprimary == False\n", - "assert c1.buysell(verbose=True, withprice=True) == 'sell-WETH @ 2000.00 USDC per WETH'\n", - "assert c1.buysell(verbose=False) == \"s\"\n", - "assert c1.buysell() == \"s\"" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "0b5e281e", - "metadata": {}, - "outputs": [], - "source": [ - "# selling ETH at 2000-2001 USDC per ETH\n", - "c1 = CPC.from_carbon(pair=\"WETH/USDC\", tkny=\"WETH\", yint=10, y=10, pa=2000, pb=2001, isdydx=False)\n", - "assert c1.pair == \"USDC/WETH\"\n", - "assert c1.primary == \"WETH/USDC\"\n", - "assert c1.pairo.isprimary == False\n", - "assert c1.buysell(verbose=True, withprice=True) == 'sell-WETH @ 2000.00 USDC per WETH'\n", - "assert c1.buysell(verbose=False) == \"s\"\n", - "assert c1.buysell(verbose=False, withprice=True) == ('s', 2000.0000000000005)\n", - "assert c1.buysell() == \"s\"" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "9ce99e2d", - "metadata": {}, - "outputs": [], - "source": [ - "# buying ETH at 1500-1499 USDC per ETH\n", - "c2 = CPC.from_carbon(pair=\"WETH/USDC\", tkny=\"USDC\", yint=10, y=10, pa=1500, pb=1499, isdydx=True)\n", - "assert c2.pair == \"WETH/USDC\"\n", - "assert c2.primary == \"WETH/USDC\"\n", - "assert c2.pairo.isprimary == True\n", - "assert c2.buysell(verbose=True, withprice=True) == 'buy-WETH @ 1500.00 USDC per WETH'\n", - "assert c2.buysell(verbose=False) == \"b\"\n", - "assert c2.buysell(verbose=False, withprice=True) == ('b', 1500.0000000000002)\n", - "assert c2.buysell() == \"b\"" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "a0c4f67b", - "metadata": {}, - "outputs": [], - "source": [ - "# buying ETH at 1500-1499 USDC per ETH\n", - "c2 = CPC.from_carbon(pair=\"WETH/USDC\", tkny=\"USDC\", yint=10, y=10, pa=1500, pb=1499, isdydx=False)\n", - "assert c2.pair == \"WETH/USDC\"\n", - "assert c2.primary == \"WETH/USDC\"\n", - "assert c2.pairo.isprimary == True\n", - "assert c2.buysell(verbose=True, withprice=True) == 'buy-WETH @ 1500.00 USDC per WETH'\n", - "assert c2.buysell(verbose=False) == \"b\"\n", - "assert c2.buysell(verbose=False, withprice=True) == ('b', 1500.0000000000002)\n", - "assert c2.buysell() == \"b\"" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "bb8ab2a2", - "metadata": {}, - "outputs": [], - "source": [ - "# univ3 1899-1901 @ 1900 USDC per WETH\n", - "c3 = CPC.from_univ3(pair=\"WETH/USDC\", Pmarg=1900, uniPa=1899, uniPb=1901, uniL=1000, cid=\"\", fee=0, descr=\"\")\n", - "assert c3.pair == \"WETH/USDC\"\n", - "assert c3.primary == \"WETH/USDC\"\n", - "assert c3.pairo.isprimary == True\n", - "assert c3.buysell(verbose=True, withprice=True) == 'buy-sell-WETH @ 1900.00 USDC per WETH'\n", - "assert c3.buysell(verbose=False) == \"bs\"\n", - "assert c3.buysell(verbose=False, withprice=True) == ('bs', 1900.0000000000007)\n", - "assert c3.buysell() == \"bs\"" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "439a2b12", - "metadata": {}, - "outputs": [], - "source": [ - "# univ3 1899-1901 @ 1900 USDC per WETH\n", - "c3 = CPC.from_univ3(pair=\"USDC/WETH\", Pmarg=1/1900, uniPb=1/1899, uniPa=1/1901, uniL=1000, cid=\"\", fee=0, descr=\"\")\n", - "assert c3.pair == \"USDC/WETH\"\n", - "assert c3.primary == \"WETH/USDC\"\n", - "assert c3.pairo.isprimary == False\n", - "assert c3.buysell(verbose=True, withprice=True) == 'buy-sell-WETH @ 1900.00 USDC per WETH'\n", - "assert c3.buysell(verbose=False) == \"bs\"\n", - "assert c3.buysell(verbose=False, withprice=True) == ('bs', 1900.)\n", - "assert c3.buysell() == \"bs\"" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "49be8a53", - "metadata": {}, - "outputs": [], - "source": [ - "# univ3 1899-1901 @ 1899 USDC per WETH (WETH low, therefore 100% in WETH, therefore sell WETH)\n", - "c4 = CPC.from_univ3(pair=\"WETH/USDC\", Pmarg=1899, uniPa=1899, uniPb=1901, uniL=1000, cid=\"\", fee=0, descr=\"\")\n", - "assert c4.pair == \"WETH/USDC\"\n", - "assert c4.primary == \"WETH/USDC\"\n", - "assert c4.pairo.isprimary == True\n", - "assert c4.buysell(verbose=True, withprice=True) == 'sell-WETH @ 1899.00 USDC per WETH'\n", - "assert c4.buysell(verbose=False) == \"s\"\n", - "assert c4.buysell(verbose=False, withprice=True) == ('s', 1899.0000000000002)\n", - "assert c4.buysell() == \"s\"" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "e30ec983", - "metadata": {}, - "outputs": [], - "source": [ - "# univ3 1899-1901 @ 1901 USDC per WETH (WETH high, therefore 100% in USDC, therefore buy WETH)\n", - "c5 = CPC.from_univ3(pair=\"WETH/USDC\", Pmarg=1901, uniPa=1899, uniPb=1901, uniL=1000, cid=\"\", fee=0, descr=\"\")\n", - "assert c5.pair == \"WETH/USDC\"\n", - "assert c5.primary == \"WETH/USDC\"\n", - "assert c5.pairo.isprimary == True\n", - "assert c5.buysell(verbose=True, withprice=True) == 'buy-WETH @ 1901.00 USDC per WETH'\n", - "assert c5.buysell(verbose=False) == \"b\"\n", - "assert c5.buysell(verbose=False, withprice=True) == ('b', 1900.9999999999998)\n", - "assert c5.buysell() == \"b\"" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "332fe629", - "metadata": {}, - "outputs": [], - "source": [ - "# univ2 (tknb=2000 USDC, tknq=1 ETH)\n", - "c6 = CPC.from_univ2(pair=\"USDC/WETH\", x_tknb=2000, y_tknq=1, cid=\"\", fee=0, descr=\"\")\n", - "assert c6.pair == \"USDC/WETH\"\n", - "assert c6.primary == \"WETH/USDC\"\n", - "assert c6.pairo.isprimary == False\n", - "assert c6.buysell(verbose=True, withprice=True) == 'buy-sell-WETH @ 2000.00 USDC per WETH'\n", - "assert c6.buysell(verbose=False) == \"bs\"\n", - "assert c6.buysell(verbose=False, withprice=True) == ('bs', 2000.)\n", - "assert c6.buysell() == \"bs\"" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "d4d8da1b", - "metadata": {}, - "outputs": [], - "source": [ - "# univ2 (tknq=2000 USDC, tknb=1 ETH)\n", - "c7 = CPC.from_univ2(pair=\"WETH/USDC\", x_tknb=1, y_tknq=2000, cid=\"\", fee=0, descr=\"\")\n", - "assert c7.pair == \"WETH/USDC\"\n", - "assert c7.primary == \"WETH/USDC\"\n", - "assert c7.pairo.isprimary == True\n", - "assert c7.buysell(verbose=True, withprice=True) == 'buy-sell-WETH @ 2000.00 USDC per WETH'\n", - "assert c7.buysell(verbose=False) == \"bs\"\n", - "assert c7.buysell(verbose=False, withprice=True) == ('bs', 2000.)\n", - "assert c7.buysell() == \"bs\"" - ] - }, - { - "cell_type": "markdown", - "id": "f479ca7f", - "metadata": {}, - "source": [ - "## P" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "47e558cd", - "metadata": {}, - "outputs": [], - "source": [ - "c = CPC.from_pk(pair=\"USDC/WETH\", p=1, k=100, params=dict(exchange=\"univ3\", a=dict(b=1, c=2)))\n", - "assert c.P(\"exchange\") == \"univ3\"\n", - "assert c.P(\"a\") == {'b': 1, 'c': 2}\n", - "assert c.P(\"a:b\") == 1\n", - "assert c.P(\"a:c\") == 2\n", - "assert c.P(\"a:d\") is None\n", - "assert c.P(\"b\") is None\n", - "assert c.P(\"b\", \"meh\") == \"meh\"" - ] - }, - { - "cell_type": "markdown", - "id": "614f5c31", - "metadata": {}, - "source": [ - "## byparams" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "d4f0340c", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "pair = \"USDC/WETH\"\n", - "c = [CPC.from_pk(pair=pair, p=1, k=100, params=dict(exchange=\"univ3\", foo=1)) for _ in range(5)]\n", - "c += [CPC.from_pk(pair=pair, p=1, k=100, params=dict(exchange=\"carbv1\", foo=2)) for _ in range(15)]\n", - "CC = CPCContainer(c)\n", - "assert len(CC)==20" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "91000e9e", - "metadata": {}, - "outputs": [], - "source": [ - "assert type(CC.byparams(exchange=\"meh\")) == CPCContainer\n", - "assert type(CC.byparams(exchange=\"meh\", _ascc=True)) == CPCContainer\n", - "assert type(CC.byparams(exchange=\"meh\", _ascc=False)) == tuple\n", - "assert type(CC.byparams(exchange=\"meh\", _asgenerator=True)).__name__ == \"generator\"\n", - "assert type(CC.byparams(exchange=\"meh\", _ascc=True, _asgenerator=True)).__name__ == \"generator\"\n", - "assert type(CC.byparams(exchange=\"meh\", _ascc=False, _asgenerator=True)).__name__ == \"generator\"\n", - "assert len(CC.byparams(exchange=\"univ3\")) == 5\n", - "assert len(CC.byparams(exchange=\"carbv1\")) == 15\n", - "assert len(CC.byparams(exchange=\"meh\")) == 0\n", - "assert len(CC.byparams(foo=1)) == 5\n", - "assert len(CC.byparams(foo=2)) == 15\n", - "assert len(CC.byparams(foo=3)) == 0\n", - "assert raises (CC.byparams, foo=1, bar=2) == \"currently only one param allowed {'foo': 1, 'bar': 2}\"" - ] - }, - { - "cell_type": "markdown", - "id": "acd82232", - "metadata": {}, - "source": [ - "## itm" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "39159fa9", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "itm0 = CPC.itm0\n", - "assert CPC.ITM_THRESHOLDPC == 0.01\n", - "\n", - "assert itm0( (\"bs\", 1000), (\"bs\", 1000) ) == False\n", - "assert itm0( (\"bs\", 1000), (\"bs\", 1009) ) == False\n", - "assert itm0( (\"bs\", 1009), (\"bs\", 1000) ) == False\n", - "assert itm0( (\"bs\", 1000), (\"bs\", 1011) ) == True\n", - "assert itm0( (\"bs\", 1011), (\"bs\", 1000) ) == True\n", - "assert itm0( (\"bs\", 1000), (\"bs\", 1011), thresholdpc=0.02 ) == False\n", - "assert itm0( (\"bs\", 1011), (\"bs\", 1000), thresholdpc=0.02 ) == False\n", - "assert itm0( (\"bs\", 1000), (\"bs\", 1021), thresholdpc=0.02 ) == True\n", - "assert itm0( (\"bs\", 1021), (\"bs\", 1000), thresholdpc=0.02 ) == True\n", - "\n", - "assert itm0( (\"b\", 1000), (\"s\", 1100) ) == False\n", - "assert itm0( (\"b\", 1000), (\"b\", 1100) ) == False\n", - "assert itm0( (\"b\", 1000), (\"bs\", 1100) ) == False\n", - "assert itm0( (\"s\", 1000), (\"s\", 1100) ) == False\n", - "assert itm0( (\"s\", 1000), (\"b\", 1100) ) == True\n", - "assert itm0( (\"s\", 1000), (\"bs\", 1100) ) == True\n", - "assert itm0( (\"bs\", 1000), (\"s\", 1100) ) == False\n", - "assert itm0( (\"bs\", 1000), (\"b\", 1100) ) == True\n", - "assert itm0( (\"bs\", 1000), (\"bs\", 1100) ) == True\n", - "\n", - "assert itm0( (\"s\", 1000), (\"b\", 900) ) == False\n", - "assert itm0( (\"s\", 1000), (\"s\", 900) ) == False\n", - "assert itm0( (\"s\", 1000), (\"bs\", 900) ) == False\n", - "assert itm0( (\"b\", 1000), (\"b\", 900) ) == False\n", - "assert itm0( (\"b\", 1000), (\"s\", 900) ) == True\n", - "assert itm0( (\"b\", 1000), (\"bs\", 900) ) == True\n", - "assert itm0( (\"bs\", 1000), (\"b\", 900) ) == False\n", - "assert itm0( (\"bs\", 1000), (\"s\", 900) ) == True\n", - "assert itm0( (\"bs\", 1000), (\"bs\", 900) ) == True" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "5acbf06f", - "metadata": {}, - "outputs": [], - "source": [ - "# c1: sell ETH @ 2000, c2: buy ETH @ 1500 --> no arb\n", - "c1 = CPC.from_carbon(pair=\"WETH/USDC\", tkny=\"WETH\", yint=10, y=10, pa=2000, pb=2001, isdydx=False)\n", - "c2 = CPC.from_carbon(pair=\"WETH/USDC\", tkny=\"USDC\", yint=10, y=10, pa=1500, pb=1499, isdydx=False)\n", - "bs1 = c1.buysell(verbose=False, withprice=True)\n", - "bs2 = c2.buysell(verbose=False, withprice=True)\n", - "assert (bs1, bs2) == (('s', 2000.0000000000005), ('b', 1500.0000000000002))\n", - "assert itm0(bs1, bs2) == False\n", - "assert c1.itm(c2) == c2.itm(c1)\n", - "assert c1.itm(c2) == itm0(bs1, bs2)\n", - "assert c1.itm([c2,c2], aggr=False) == (itm0(bs1, bs2), itm0(bs1, bs2))" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "341f9933", - "metadata": {}, - "outputs": [], - "source": [ - "# c1: buy ETH @ 2000, c2: sell ETH @ 1500 --> arb\n", - "c1 = CPC.from_carbon(pair=\"WETH/USDC\", tkny=\"USDC\", yint=10, y=10, pb=2000, pa=2001, isdydx=False)\n", - "c2 = CPC.from_carbon(pair=\"WETH/USDC\", tkny=\"WETH\", yint=10, y=10, pb=1500, pa=1499, isdydx=False)\n", - "bs1 = c1.buysell(verbose=False, withprice=True)\n", - "bs2 = c2.buysell(verbose=False, withprice=True)\n", - "assert (bs1, bs2) == (('b', 2000.9999999999998), ('s', 1499.0000000000002))\n", - "assert itm0(bs1, bs2) == True\n", - "assert c1.itm(c2) == c2.itm(c1)\n", - "assert c1.itm(c2) == itm0(bs1, bs2)\n", - "assert c1.itm([c2,c2], aggr=False) == (itm0(bs1, bs2), itm0(bs1, bs2))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "a8fd73db", - "metadata": {}, - "outputs": [], - "source": [ - "# c1: buy ETH @ 2000, c2: sell ETH @ 1500, c2b: sell ETH @ 2500 --> arb, noarb\n", - "c1 = CPC.from_carbon(pair=\"WETH/USDC\", tkny=\"USDC\", yint=10, y=10, pb=2000, pa=2001, isdydx=False)\n", - "c2 = CPC.from_carbon(pair=\"WETH/USDC\", tkny=\"WETH\", yint=10, y=10, pb=1500, pa=1499, isdydx=False)\n", - "c2b = CPC.from_carbon(pair=\"WETH/USDC\", tkny=\"WETH\", yint=10, y=10, pb=2500, pa=2499, isdydx=False)\n", - "CC = CPCContainer([c1,c2,c2b])\n", - "assert c1.itm(c2) == True\n", - "assert c1.itm(c2b) == False\n", - "assert c1.itm([c2,c2b], aggr=False) == (True, False)\n", - "assert c1.itm([c2b,c2], aggr=False) == (False, True)\n", - "assert c1.itm([c2b,c2], aggr=True) == True\n", - "assert c1.itm([c2,c2b], aggr=True) == True\n", - "assert c1.itm([c2b,c2]) == True\n", - "assert c1.itm([c2,c2b]) == True\n", - "assert c1.itm(CC, aggr=True) == True\n", - "assert c1.itm(CC, aggr=False) == (False, True, False)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "83f7196d", - "metadata": {}, - "outputs": [], - "source": [ - "# c3: buy/sell @ 1900, c4: buy/sell @ 1899 --> arb depending on threshold\n", - "c3 = CPC.from_univ3(pair=\"WETH/USDC\", Pmarg=1900, uniPa=1898, uniPb=1902, uniL=1000, cid=\"\", fee=0, descr=\"\")\n", - "c4 = CPC.from_univ3(pair=\"WETH/USDC\", Pmarg=1899, uniPa=1898, uniPb=1902, uniL=1000, cid=\"\", fee=0, descr=\"\")\n", - "bs3 = c3.buysell(verbose=False, withprice=True)\n", - "bs4 = c4.buysell(verbose=False, withprice=True)\n", - "assert (bs3, bs4) == (('bs', 1900.0000000000007), ('bs', 1899.0000000000002))\n", - "assert itm0(bs3, bs4, thresholdpc=0.0001) == True\n", - "assert itm0(bs3, bs4, thresholdpc=0.001) == False\n", - "assert c3.itm(c4) == c4.itm(c3)\n", - "assert c3.itm(c4) == itm0(bs3, bs4)\n", - "assert c3.itm([c4,c4], aggr=False) == (itm0(bs3, bs4), itm0(bs3, bs4))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "dc727e68", - "metadata": {}, - "outputs": [], - "source": [ - "# c3: buy/sell @ 1900, c4: buy/sell @ 1899 --> arb depending on threshold\n", - "c3 = CPC.from_univ3(pair=\"WETH/USDC\", Pmarg=1900, uniPa=1898, uniPb=1902, uniL=1000, cid=\"\", fee=0, descr=\"\")\n", - "c4 = CPC.from_univ3(pair=\"USDC/WETH\", Pmarg=1/1899, uniPb=1/1898, uniPa=1/1902, uniL=1000, cid=\"\", fee=0, descr=\"\")\n", - "bs3 = c3.buysell(verbose=False, withprice=True)\n", - "bs4 = c4.buysell(verbose=False, withprice=True)\n", - "assert (bs3, bs4) == (('bs', 1900.0000000000007), ('bs', 1899.0000000000002))\n", - "assert itm0(bs3, bs4, thresholdpc=0.0001) == True\n", - "assert itm0(bs3, bs4, thresholdpc=0.001) == False\n", - "assert c3.itm(c4) == c4.itm(c3)\n", - "assert c3.itm(c4) == itm0(bs3, bs4)\n", - "assert c3.itm([c4,c4], aggr=False) == (itm0(bs3, bs4), itm0(bs3, bs4))" - ] - }, - { - "cell_type": "markdown", - "id": "5d89e4a4", - "metadata": {}, - "source": [ - "## TVL" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "303811aa", - "metadata": {}, - "outputs": [], - "source": [ - "c = CPC.from_pk(pair=\"WETH/USDC\", p=2000, k=1*2000)\n", - "assert c.tvl(incltkn=True) == (4000.0, 'USDC', 1)\n", - "assert c.tvl(\"USDC\", incltkn=True) == (4000.0, 'USDC', 1)\n", - "assert c.tvl(\"WETH\", incltkn=True) == (2.0, 'WETH', 1)\n", - "assert c.tvl(\"USDC\", incltkn=True, mult=2) == (8000.0, 'USDC', 2)\n", - "assert c.tvl(\"WETH\", incltkn=True, mult=2) == (4.0, 'WETH', 2)\n", - "assert c.tvl(\"WETH\", incltkn=False) == 2.0\n", - "assert c.tvl(\"WETH\") == 2.0\n", - "assert c.tvl() == 4000\n", - "assert c.tvl(\"WETH\", mult=2000) == 4000" - ] - }, - { - "cell_type": "markdown", - "id": "3028de8e", - "metadata": {}, - "source": [ - "## estimate prices" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "c998d76f", - "metadata": {}, - "outputs": [], - "source": [ - "CC = CPCContainer()\n", - "CC += [CPC.from_univ3(pair=\"WETH/USDC\", cid=\"uv3\", fee=0, descr=\"\",\n", - " uniPa=2000, uniPb=2010, Pmarg=2005, uniL=10*m.sqrt(2000))]\n", - "CC += [CPC.from_pk(pair=\"WETH/USDC\", cid=\"uv2\", fee=0, descr=\"\",\n", - " p=1950, k=5**2*2000)]\n", - "CC += [CPC.from_pk(pair=\"USDC/WETH\", cid=\"uv2r\", fee=0, descr=\"\",\n", - " p=1/1975, k=5**2*2000)]\n", - "CC += [CPC.from_carbon(pair=\"WETH/USDC\", cid=\"carb\", fee=0, descr=\"\",\n", - " tkny=\"USDC\", yint=1000, y=1000, pa=1850, pb=1750)]\n", - "CC += [CPC.from_carbon(pair=\"WETH/USDC\", cid=\"carb\", fee=0, descr=\"\",\n", - " tkny=\"WETH\", yint=1, y=0, pb=1/1850, pa=1/1750)]\n", - "CC += [CPC.from_carbon(pair=\"WETH/USDC\", cid=\"carb\", fee=0, descr=\"\",\n", - " tkny=\"USDC\", yint=1000, y=500, pa=1870, pb=1710)]\n", - "#CC.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "986793db", - "metadata": {}, - "outputs": [], - "source": [ - "assert CC.price_estimate(tknq=T.WETH, tknb=T.USDC, result=CC.PE_PAIR) == f\"{T.USDC}/{T.WETH}\"\n", - "assert CC.price_estimate(pair=f\"{T.USDC}/{T.WETH}\", result=CC.PE_PAIR) == f\"{T.USDC}/{T.WETH}\"\n", - "assert raises(CC.price_estimate, tknq=\"a\", result=CC.PE_PAIR)\n", - "assert raises(CC.price_estimate, tknb=\"a\", result=CC.PE_PAIR)\n", - "assert raises(CC.price_estimate, tknq=\"a\", tknb=\"b\", pair=\"a/b\", result=CC.PE_PAIR)\n", - "assert raises(CC.price_estimate, pair=\"ab\", result=CC.PE_PAIR)\n", - "assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.USDC], pairs=True, \n", - " unwrapsingle=False)[0][0] == f\"{T.USDC}/{T.WETH}\"\n", - "assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.USDC], pairs=True, \n", - " unwrapsingle=True)[0] == f\"{T.USDC}/{T.WETH}\"\n", - "assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.USDC], pairs=True)[0] == f\"{T.USDC}/{T.WETH}\"\n", - "r = CC.price_estimates(tknqs=list(\"ABC\"), tknbs=list(\"DEFG\"), pairs=True)\n", - "assert r.ndim == 2\n", - "assert r.shape == (3,4)\n", - "r = CC.price_estimates(tknqs=list(\"A\"), tknbs=list(\"DEFG\"), pairs=True)\n", - "assert r.ndim == 1\n", - "assert r.shape == (4,)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "77c42b32", - "metadata": {}, - "outputs": [], - "source": [ - "assert CC[0].at_boundary == False\n", - "assert CC[1].at_boundary == False\n", - "assert CC[2].at_boundary == False\n", - "assert CC[3].at_boundary == True\n", - "assert CC[3].at_xmin == True\n", - "assert CC[3].at_ymin == False\n", - "assert CC[3].at_xmax == False\n", - "assert CC[3].at_ymax == True\n", - "assert CC[4].at_boundary == True\n", - "assert CC[4].at_ymin == True\n", - "assert CC[4].at_xmin == True\n", - "assert CC[4].at_ymax == True\n", - "assert CC[4].at_xmax == True\n", - "assert CC[5].at_boundary == True" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "11e56ebf", - "metadata": {}, - "outputs": [], - "source": [ - "r = CC.price_estimate(tknq=\"USDC\", tknb=\"WETH\", result=CC.PE_CURVES)\n", - "assert len(r)==3" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "f87ff262", - "metadata": {}, - "outputs": [], - "source": [ - "p,w = CC.price_estimate(tknq=\"USDC\", tknb=\"WETH\", result=CC.PE_DATA)\n", - "assert len(p) == len(r)\n", - "assert len(w) == len(r)\n", - "assert iseq(sum(p), 5930)\n", - "assert iseq(sum(w), 894.4271909999159)\n", - "pe = CC.price_estimate(tknq=\"USDC\", tknb=\"WETH\")\n", - "assert pe == np.average(p, weights=w)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "f921bdae", - "metadata": {}, - "outputs": [], - "source": [ - "O = PairOptimizer(CC)\n", - "Om = PairOptimizer(CCmarket)\n", - "assert O.price_estimates(tknq=\"USDC\", tknbs=[\"WETH\"]) == CC.price_estimates(tknqs=[\"USDC\"], tknbs=[\"WETH\"])\n", - "CCmarket.fp(onein=\"USDC\")\n", - "r = Om.price_estimates(tknq=\"USDC\", tknbs=[\"WETH\", \"WBTC\"])\n", - "assert iseq(r[0], 1820.89875275)\n", - "assert iseq(r[1], 28351.08150121)" - ] - }, - { - "cell_type": "markdown", - "id": "a200dc1b", - "metadata": {}, - "source": [ - "## triangle estimates" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "d6a18b66", - "metadata": {}, - "outputs": [], - "source": [ - "CC = CPCContainer()\n", - "CC += [CPC.from_univ3(pair=f\"{T.WETH}/{T.USDC}\", cid=\"uv3-1\", fee=0, descr=\"\",\n", - " uniPa=2000, uniPb=2002, Pmarg=2001, uniL=10*m.sqrt(2000))]\n", - "CC += [CPC.from_univ3(pair=f\"{T.WBTC}/{T.USDC}\", cid=\"uv3-2\", fee=0, descr=\"\",\n", - " uniPa=20000, uniPb=20020, Pmarg=20010, uniL=1*m.sqrt(20000))]\n", - "#CC.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "97b4279a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on method price_estimate in module tools.cpc:\n", - "\n", - "price_estimate(*, tknq=None, tknb=None, pair=None, result=None, raiseonerror=True) method of tools.cpc.CPCContainer instance\n", - " calculates price estimate in the reference token as base token\n", - " \n", - " :tknq: quote token to calculate price for\n", - " :tknb: base token to calculate price for\n", - " :pair: alternative to tknq, tknb: pair to calculate price for\n", - " :raiseonerror: if True, raise exception if no price can be calculated\n", - " :result: what to return\n", - " :PE_PAIR: slashpair\n", - " :PE_CURVES: curves\n", - " :PE_DATA: prices, weights\n", - " :returns: price (quote per base)\n", - "\n" - ] - } - ], - "source": [ - "help(CC.price_estimate)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "ad46bf0d", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(CC.price_estimate(pair=f\"{T.WETH}/{T.USDC}\"), 2001)\n", - "assert iseq(CC.price_estimate(pair=f\"{T.WBTC}/{T.USDC}\"), 20010)\n", - "assert iseq(CC.price_estimate(pair=f\"{T.USDC}/{T.WETH}\"), 1/2001)\n", - "assert iseq(CC.price_estimate(pair=f\"{T.USDC}/{T.WBTC}\"), 1/20010)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "c449870e", - "metadata": {}, - "outputs": [], - "source": [ - "assert CC.price_estimate(tknb=T.WETH, tknq=T.USDC, result=CC.PE_PAIR) == f\"{T.WETH}/{T.USDC}\"\n", - "r = CC.price_estimate(tknb=T.WETH, tknq=T.USDC, result=CC.PE_CURVES)\n", - "assert len(r) == 1\n", - "assert r[0][0].cid==\"uv3-1\"\n", - "assert iseq(r[0][1], 2001)\n", - "assert iseq(r[0][2], 200000.0)\n", - "r = CC.price_estimate(tknb=T.WETH, tknq=T.USDC, result=CC.PE_DATA)\n", - "assert len(r) == 2\n", - "assert r[0].shape == (1,)\n", - "assert r[1].shape == (1,)\n", - "assert iseq(r[0][0], 2001)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "9e8110c8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on method price_estimates in module tools.cpc:\n", - "\n", - "price_estimates(*, tknqs=None, tknbs=None, triangulate=True, unwrapsingle=True, pairs=False, stopatfirst=True, raiseonerror=True, verbose=False) method of tools.cpc.CPCContainer instance\n", - " calculates prices estimates in the reference token as base token\n", - " \n", - " :tknqs: list of quote tokens to calculate prices for\n", - " :tknbs: list of base tokens to calculate prices for\n", - " :triangulate: tokens used as intermediate token for triangulation; if True, a standard \n", - " token list is used; if None or False, no triangulation\n", - " :unwrapsingle: if there is only one quote token, a 1-d array is returned\n", - " :pairs: if True, returns the slashpairs instead of the prices\n", - " :raiseonerror: if True, raise exception if no price can be calculated\n", - " :stopatfirst: it True, stop at first triangulation match\n", - " :verbose: if True, print some progress\n", - " :return: np.array of prices (quote outer, base inner; quote per base)\n", - "\n" - ] - } - ], - "source": [ - "help(CC.price_estimates)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "f14adee5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['0x2260FAC5E5542a773Aa44fBCfeDf7C193bc2C599/0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2'],\n", - " dtype='pq:\n", - " pair = f\"{tknb}/{tknq}\"\n", - " pp = pb/pq\n", - " k = (100000)**2/(pb*pq)\n", - " CCfm += CPC.from_pk(p=pp, k=k, pair=pair, cid = f\"mkt-{ctr}\")\n", - " ctr += 1" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "c1f4c0b1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
WETH
tknb
MKR0.2500
AAVE0.0500
USDC0.0005
WBTC10.0000
LINK0.0025
\n", - "
" - ], - "text/plain": [ - " WETH\n", - "tknb \n", - "MKR 0.2500\n", - "AAVE 0.0500\n", - "USDC 0.0005\n", - "WBTC 10.0000\n", - "LINK 0.0025" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "O = MargPOptimizer(CCfm)\n", - "assert O.MO_PSTART == O.MO_P\n", - "tknq = \"WETH\"\n", - "df = O.margp_optimizer(tknq, result=O.MO_PSTART)\n", - "rd = df[tknq].to_dict()\n", - "assert len(df) == len(prices)-1\n", - "assert df.columns[0] == tknq\n", - "assert df.index.name == \"tknb\"\n", - "assert rd == {k:v/prices[tknq] for k,v in prices.items() if k!=tknq}\n", - "df2 = O.margp_optimizer(tknq, result=O.MO_PSTART, params=dict(pstart=df))\n", - "assert np.all(df == df2)\n", - "df2 = O.margp_optimizer(tknq, result=O.MO_PSTART, params=dict(pstart=rd))\n", - "assert np.all(df == df2)\n", - "df" - ] - }, - { - "cell_type": "markdown", - "id": "e7b4d357", - "metadata": {}, - "source": [ - "## Assertions and testing" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "50f23286", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "None\n" - ] - } - ], - "source": [ - "c = CPC.from_px(p=2000,x=10, pair=\"ETH/USDC\")\n", - "assert c.pair == \"ETH/USDC\"\n", - "assert c.tknb == c.pair.split(\"/\")[0]\n", - "assert c.tknx == c.tknb\n", - "assert c.tknq == c.pair.split(\"/\")[1]\n", - "assert c.tkny == c.tknq\n", - "assert f\"{c.tknb}/{c.tknq}\" == c.pair\n", - "print (c.descr)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "e5055bae", - "metadata": {}, - "outputs": [], - "source": [ - "c = CPC.from_xy(10,20)\n", - "assert c == CPC.from_kx(c.k, c.x)\n", - "assert c == CPC.from_ky(c.k, c.y)\n", - "assert c == CPC.from_xy(c.x, c.y)\n", - "assert c == CPC.from_pk(c.p, c.k)\n", - "assert c == CPC.from_px(c.p, c.x)\n", - "assert c == CPC.from_py(c.p, c.y)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "44d0d4fc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ConstantProductCurve(k=200, x=10, x_act=10, y_act=20.0, alpha=0.5, pair='TKNB/TKNQ', cid='None', fee=None, descr=None, constr='xy', params={})" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "70ff3f6d", - "metadata": {}, - "outputs": [], - "source": [ - "c = CPC.from_px(p=2, x=100, x_act=10, y_act=20)\n", - "assert c.y_max*c.x_min == c.k\n", - "assert c.x_max*c.y_min == c.k\n", - "assert c.p_min == c.y_min / c.x_max\n", - "assert c.p_max == c.y_max / c.x_min\n", - "assert c.p_max >= c.p_min" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "0d80accd", - "metadata": {}, - "outputs": [], - "source": [ - "c = CPC.from_px(p=2, x=100, x_act=10, y_act=20)\n", - "e = 1e-5\n", - "assert 95*c.yfromx_f(x=95) == c.k\n", - "assert 105*c.yfromx_f(x=105) == c.k\n", - "assert 190*c.xfromy_f(y=190) == c.k\n", - "assert 210*c.xfromy_f(y=210) == c.k\n", - "assert not c.yfromx_f(x=90) is None\n", - "assert c.yfromx_f(x=90-e) is None\n", - "assert not c.xfromy_f(y=180) is None\n", - "assert c.xfromy_f(y=180-e) is None\n", - "assert c.dyfromdx_f(dx=-5)\n", - "assert (c.y+c.dyfromdx_f(dx=-5))*(c.x-5) == c.k\n", - "assert (c.y+c.dyfromdx_f(dx=+5))*(c.x+5) == c.k\n", - "assert (c.x+c.dxfromdy_f(dy=-5))*(c.y-5) == c.k\n", - "assert (c.x+c.dxfromdy_f(dy=+5))*(c.y+5) == c.k" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "fc2a5765", - "metadata": {}, - "outputs": [], - "source": [ - "c = CPC.from_pkpp(p=100, k=100)\n", - "assert c.p_min == 100\n", - "assert c.p_max == 100\n", - "assert c.p == 100\n", - "assert c.k == 100" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "fe5854de", - "metadata": {}, - "outputs": [], - "source": [ - "c = CPC.from_pkpp(p=100, k=100, p_min=80, p_max=120)\n", - "assert c.p_min == 80\n", - "assert iseq(c.p_max, 120)\n", - "assert c.p == 100\n", - "assert c.k == 100" - ] - }, - { - "cell_type": "markdown", - "id": "4c315ebe", - "metadata": {}, - "source": [ - "## iseq" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "cb146f71", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(\"a\", \"a\", \"ab\") == False\n", - "assert iseq(\"a\", \"a\", \"a\")\n", - "assert iseq(1.0, 1, 1.0)\n", - "assert iseq(0,0)\n", - "assert iseq(0,1e-10)\n", - "assert iseq(0,1e-5) == False\n", - "assert iseq(1, 1.00001) == False\n", - "assert iseq(1, 1.000001)\n", - "assert iseq(1, 1.000001, eps=1e-7) == False\n", - "assert iseq(\"1\", 1) == False" - ] - }, - { - "cell_type": "markdown", - "id": "019abafe", - "metadata": {}, - "source": [ - "## New CPC features in v2" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "6611a642", - "metadata": {}, - "outputs": [], - "source": [ - "p = CPCContainer.Pair(\"ETH/USDC\")\n", - "assert str(p) == \"ETH/USDC\"\n", - "assert p.pair == str(p)\n", - "assert p.tknx == \"ETH\"\n", - "assert p.tkny == \"USDC\"\n", - "assert p.tknb == \"ETH\"\n", - "assert p.tknq == \"USDC\"\n", - "\n", - "pp = CPCContainer.Pair.wrap([\"ETH/USDC\", \"WBTC/ETH\"])\n", - "assert len(pp) == 2\n", - "assert pp[0].pair == \"ETH/USDC\"\n", - "assert pp[1].pair == \"WBTC/ETH\"\n", - "assert pp[0].unwrap(pp) == ('ETH/USDC', 'WBTC/ETH')" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "3ea36c28", - "metadata": {}, - "outputs": [], - "source": [ - "pairs = [\"A\", \"B\", \"C\"]\n", - "assert CPCContainer.pairset(\", \".join(pairs)) == set(pairs)\n", - "assert CPCContainer.pairset(pairs) == set(pairs)\n", - "assert CPCContainer.pairset(tuple(pairs)) == set(pairs)\n", - "assert CPCContainer.pairset(p for p in pairs) == set(pairs)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "e73018ea", - "metadata": {}, - "outputs": [], - "source": [ - "pairs = [f\"{a}/{b}\" for a in [\"ETH\", \"USDC\", \"DAI\"] for b in [\"DAI\", \"WBTC\", \"LINK\", \"ETH\"] if a!=b]\n", - "CC = CPCContainer()\n", - "fp = lambda **cond: CC.filter_pairs(pairs=pairs, **cond)\n", - "assert fp(bothin=\"ETH, USDC, DAI\") == {'DAI/ETH', 'ETH/DAI', 'USDC/DAI', 'USDC/ETH'}\n", - "assert fp(onein=\"WBTC\") == {'DAI/WBTC', 'ETH/WBTC', 'USDC/WBTC'}\n", - "assert fp(onein=\"ETH\") == fp(contains=\"ETH\")\n", - "assert fp(notin=\"WBTC, ETH, DAI\") == {'USDC/LINK'}\n", - "assert fp(tknbin=\"WBTC\") == set()\n", - "assert fp(tknqin=\"WBTC\") == {'DAI/WBTC', 'ETH/WBTC', 'USDC/WBTC'}\n", - "assert fp(tknbnotin=\"WBTC\") == set(pairs)\n", - "assert fp(tknbnotin=\"WBTC, ETH, DAI\") == {'USDC/DAI', 'USDC/ETH', 'USDC/LINK', 'USDC/WBTC'}\n", - "assert fp(notin_0=\"WBTC\", notin_1=\"DAI\") == fp(notin=\"WBTC, DAI\")\n", - "assert fp(onein = \"ETH\") == fp(anyall=CC.FP_ANY, tknbin=\"ETH\", tknqin=\"ETH\")" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "abde4984", - "metadata": {}, - "outputs": [], - "source": [ - "P = CPCContainer.Pair\n", - "ETHUSDC = P(\"WETH/USDC\")\n", - "USDCETH = P(ETHUSDC.pairr)\n", - "assert ETHUSDC.pair == \"WETH/USDC\"\n", - "assert ETHUSDC.pairr == \"USDC/WETH\"\n", - "assert USDCETH.pairr == \"WETH/USDC\"\n", - "assert USDCETH.pair == \"USDC/WETH\"\n", - "assert ETHUSDC.isprimary\n", - "assert not USDCETH.isprimary\n", - "assert ETHUSDC.primary == ETHUSDC.pair\n", - "assert ETHUSDC.secondary == ETHUSDC.pairr\n", - "assert USDCETH.primary == USDCETH.pairr\n", - "assert USDCETH.secondary == USDCETH.pair\n", - "assert ETHUSDC.primary == USDCETH.primary\n", - "assert ETHUSDC.secondary == USDCETH.secondary" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "d24627fa", - "metadata": {}, - "outputs": [], - "source": [ - "assert P(\"BTC/ETH\").isprimary\n", - "assert P(\"WBTC/ETH\").isprimary\n", - "assert P(\"BTC/WETH\").isprimary\n", - "assert P(\"WBTC/ETH\").isprimary\n", - "assert P(\"BTC/USDC\").isprimary\n", - "assert P(\"XYZ/USDC\").isprimary\n", - "assert P(\"XYZ/USDT\").isprimary" - ] - }, - { - "cell_type": "markdown", - "id": "da2d6916", - "metadata": {}, - "source": [ - "## Real data and retrieval of curves" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "6b46e9c5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Num curves: 459\n", - "Num pairs: 326\n", - "Num tokens: 141\n" - ] - } - ], - "source": [ - "# try:\n", - "# df = pd.read_csv(\"../nbtest_data/NBTEST_002_Curves.csv.gz\")\n", - "# except:\n", - "# df = pd.read_csv(\"fastlane_bot/tests/nbtest_data/NBTEST_002_Curves.csv.gz\")\n", - "CC = CPCContainer.from_df(market_df)\n", - "assert len(CC) == 459\n", - "assert len(CC) == len(market_df)\n", - "assert len(CC.pairs()) == 326\n", - "assert len(CC.tokens()) == 141\n", - "assert CC.tokens_s\n", - "assert CC.tokens_s()[:60] == '1INCH,1ONE,AAVE,ALCX,ALEPH,ALPHA,AMP,ANKR,ANT,APW,ARCONA,ARM'\n", - "print(\"Num curves:\", len(CC))\n", - "print(\"Num pairs:\", len(CC.pairs()))\n", - "print(\"Num tokens:\", len(CC.tokens()))\n", - "#print(CC.tokens_s())" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "45cac036", - "metadata": {}, - "outputs": [], - "source": [ - "assert CC.bypairs(CC.fp(onein=\"WETH, WBTC\")) == CC.bypairs(CC.fp(onein=\"WETH, WBTC\"), asgenerator=False)\n", - "assert len(CC.bypairs(CC.fp(onein=\"WETH, WBTC\"))) == 254\n", - "assert len(CC.bypairs(CC.fp(onein=\"WETH, WBTC\"), ascc=True)) == 254\n", - "CC1 = CC.bypairs(CC.fp(onein=\"WBTC\"), ascc=True)\n", - "assert len(CC1) == 29\n", - "cids = [c.cid for c in CC.bypairs(CC.fp(onein=\"WBTC\"))]\n", - "assert len(cids) == len(CC1)\n", - "assert CC.bycid(\"bla\") is None\n", - "assert not CC.bycid(\"191\") is None\n", - "assert raises(CC.bycids, [\"bla\"])\n", - "assert len(CC.bycids(cids)) == len(cids)\n", - "assert len(CC.bytknx(\"WETH\")) == 46\n", - "assert len(CC.bytkny(\"WETH\")) == 181\n", - "assert len(CC.bytknys(\"WETH\")) == len(CC.bytkny(\"WETH\"))\n", - "assert len(CC.bytknxs(\"USDC, USDT\")) == 41\n", - "assert len(CC.bytknxs([\"USDC\", \"USDT\"])) == len(CC.bytknxs(\"USDC, USDT\"))\n", - "assert len(CC.bytknys([\"USDC\", \"USDT\"])) == len(CC.bytknys({\"USDC\", \"USDT\"}))\n", - "cs = CC.bytknx(\"WETH\", asgenerator=True)\n", - "assert raises(len, cs)\n", - "assert len(tuple(cs)) == 46\n", - "assert len(tuple(cs)) == 0 # generator empty" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "d2619e0a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'AAVE': TTE(x=[7], y=[8]),\n", - " 'USDC': TTE(x=[], y=[1, 2, 4, 5, 7]),\n", - " 'LINK': TTE(x=[2, 3, 5, 6], y=[]),\n", - " 'DAI': TTE(x=[1, 4, 8], y=[3, 6]),\n", - " 'ETH': TTE(x=[], y=[0]),\n", - " 'BNT': TTE(x=[0], y=[])}" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CC2 = CC.bypairs(CC.fp(bothin=\"USDC, DAI, BNT, SHIB, ETH, AAVE, LINK\"), ascc=True)\n", - "tt = CC2.tokentable()\n", - "assert tt[\"ETH\"].x == []\n", - "assert tt[\"ETH\"].y == [0]\n", - "assert tt[\"DAI\"].x == [1,4,8]\n", - "assert tt[\"DAI\"].y == [3,6]\n", - "tt" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "c12f7530", - "metadata": {}, - "outputs": [], - "source": [ - "assert CC2.tknxs() == {'AAVE', 'BNT', 'DAI', 'LINK'}\n", - "assert CC2.tknxl() == ['BNT', 'DAI', 'LINK', 'LINK', 'DAI', 'LINK', 'LINK', 'AAVE', 'DAI']\n", - "assert set(CC2.tknxl()) == CC2.tknxs() \n", - "assert set(CC2.tknyl()) == CC2.tknys() \n", - "assert len(CC2.tknxl()) == len(CC2.tknyl())\n", - "assert len(CC2.tknxl()) == len(CC2)" - ] - }, - { - "cell_type": "markdown", - "id": "a16f8524", - "metadata": {}, - "source": [ - "## TokenScale tests [NOTEST]" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "b093eb92", - "metadata": {}, - "outputs": [], - "source": [ - "pass" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "ad56665e", - "metadata": {}, - "outputs": [], - "source": [ - "# TSB = ts.TokenScaleBase()\n", - "# assert raises (TSB.scale,\"ETH\")\n", - "# assert TSB.DEFAULT_SCALE == 1e-2" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "15788980", - "metadata": {}, - "outputs": [], - "source": [ - "# TS = ts.TokenScale.from_tokenscales(USDC=1e0, ETH=1e3, BTC=1e4)\n", - "# TS" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "31f10328", - "metadata": {}, - "outputs": [], - "source": [ - "# assert TS(\"USDC\") == 1\n", - "# assert TS(\"ETH\") == 1000\n", - "# assert TS(\"BTC\") == 10000\n", - "# assert TS(\"MEH\") == TS.DEFAULT_SCALE" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "9c1d3e0c", - "metadata": {}, - "outputs": [], - "source": [ - "# TSD = ts.TokenScaleData" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "7d12770e", - "metadata": {}, - "outputs": [], - "source": [ - "# tknset = {'AAVE', 'BNT', 'BTC', 'ETH', 'LINK', 'USDC', 'USDT', 'WBTC', 'WETH'}\n", - "# assert tknset - set(TSD.scale_dct.keys()) == set()" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "04cbcfd1", - "metadata": {}, - "outputs": [], - "source": [ - "# cc1 = CPC.from_xy(x=10, y=20000, pair=\"ETH/USDC\")\n", - "# assert cc1.tokenscale is cc1.TOKENSCALE\n", - "# assert cc1.tknx == \"ETH\"\n", - "# assert cc1.tkny == \"USDC\"\n", - "# assert cc1.scalex == 1\n", - "# assert cc1.scaley == 1\n", - "# cc2 = CPC.from_xy(x=10, y=20000, pair=\"BTC/MEH\")\n", - "# assert cc2.tknx == \"BTC\"\n", - "# assert cc2.tkny == \"MEH\"\n", - "# assert cc2.scalex == 1\n", - "# assert cc2.scaley == 1\n", - "# assert cc2.scaley == cc2.tokenscale.DEFAULT_SCALE" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "be4e0214", - "metadata": {}, - "outputs": [], - "source": [ - "# cc1 = CPC.from_xy(x=10, y=20000, pair=\"ETH/USDC\")\n", - "# cc1.set_tokenscale(TSD)\n", - "# assert cc1.tokenscale != cc1.TOKENSCALE\n", - "# assert cc1.tknx == \"ETH\"\n", - "# assert cc1.tkny == \"USDC\"\n", - "# assert cc1.scalex == 1e3\n", - "# assert cc1.scaley == 1e0\n", - "# cc2 = CPC.from_xy(x=10, y=20000, pair=\"BTC/MEH\")\n", - "# cc2.set_tokenscale(TSD)\n", - "# assert cc2.tknx == \"BTC\"\n", - "# assert cc2.tkny == \"MEH\"\n", - "# assert cc2.scalex == 1e4\n", - "# assert cc2.scaley == 1e-2\n", - "# assert cc2.scaley == cc2.tokenscale.DEFAULT_SCALE" - ] - }, - { - "cell_type": "markdown", - "id": "24dc60c2", - "metadata": {}, - "source": [ - "## dx_min and dx_max etc" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "7f67f2da", - "metadata": {}, - "outputs": [], - "source": [ - "cc = CPC.from_pkpp(p=100, k=100*10000, p_min=90, p_max=110)\n", - "assert iseq(cc.x_act, 4.653741075440777)\n", - "assert iseq(cc.y_act, 513.167019494862)\n", - "assert cc.dx_min == -cc.x_act\n", - "assert cc.dy_min == -cc.y_act\n", - "assert iseq( (cc.x + cc.dx_max)*(cc.y + cc.dy_min), cc.k)\n", - "assert iseq( (cc.y + cc.dy_max)*(cc.x + cc.dx_min), cc.k)" - ] - }, - { - "cell_type": "markdown", - "id": "2bf8c628", - "metadata": {}, - "source": [ - "## xyfromp_f and dxdyfromp_f" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "03080821", - "metadata": {}, - "outputs": [], - "source": [ - "c = CPC.from_pkpp(p=100, k=100*10000, p_min=90, p_max=110, pair=f\"{T.ETH}/{T.USDC}\")\n", - "\n", - "assert c.pair == f'{T.WETH}/{T.USDC}', f\"{c.pair}\"\n", - "assert c.pairp == f'{T.WETH}/{T.USDC}', f\"{c.pair}\"\n", - "assert c.p == 100\n", - "assert iseq(c.x_act, 4.653741075440777)\n", - "assert iseq(c.y_act, 513.167019494862)\n", - "assert c.tknx == T.ETH\n", - "assert c.tkny == T.USDC\n", - "assert c.tknxp == T.WETH\n", - "assert c.tknyp == T.USDC\n", - "assert c.xyfromp_f() == (c.x, c.y, c.p)\n", - "assert c.xyfromp_f(withunits=True) == (100.0, 10000.0, 100.0, T.WETH, T.USDC, f'{T.WETH}/{T.USDC}')\n", - "\n", - "x,y,p = c.xyfromp_f(p=85, ignorebounds=True)\n", - "assert p == 85\n", - "assert iseq(x*y, c.k)\n", - "assert iseq(y/x,85)\n", - "\n", - "x,y,p = c.xyfromp_f(p=115, ignorebounds=True)\n", - "assert p == 115\n", - "assert iseq(x*y, c.k)\n", - "assert iseq(y/x,115)\n", - "\n", - "x,y,p = c.xyfromp_f(p=95)\n", - "assert p == 95\n", - "assert iseq(x*y, c.k)\n", - "assert iseq(y/x,p)\n", - "\n", - "x,y,p = c.xyfromp_f(p=105)\n", - "assert p == 105\n", - "assert iseq(x*y, c.k)\n", - "assert iseq(y/x,p)\n", - "\n", - "x,y,p = c.xyfromp_f(p=85)\n", - "assert p == 85\n", - "assert iseq(x*y, c.k)\n", - "assert iseq(y/x,90)\n", - "\n", - "x,y,p = c.xyfromp_f(p=115)\n", - "assert p == 115\n", - "assert iseq(x*y, c.k)\n", - "assert iseq(y/x,110)" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "6f488b21", - "metadata": {}, - "outputs": [], - "source": [ - "assert c.dxdyfromp_f(withunits=True) == (0.0, 0.0, 100.0, T.WETH, T.USDC, f'{T.WETH}/{T.USDC}')\n", - "\n", - "dx,dy,p = c.dxdyfromp_f(p=85, ignorebounds=True)\n", - "assert p == 85\n", - "assert iseq((c.x+dx)*(c.y+dy), c.k)\n", - "assert iseq((c.y+dy)/(c.x+dx),p)\n", - "\n", - "dx,dy,p = c.dxdyfromp_f(p=115, ignorebounds=True)\n", - "assert p == 115\n", - "assert iseq((c.x+dx)*(c.y+dy), c.k)\n", - "assert iseq((c.y+dy)/(c.x+dx),p)\n", - "\n", - "dx,dy,p = c.dxdyfromp_f(p=95)\n", - "assert p == 95\n", - "assert iseq((c.x+dx)*(c.y+dy), c.k)\n", - "assert iseq((c.y+dy)/(c.x+dx),p)\n", - "\n", - "dx,dy,p = c.dxdyfromp_f(p=105)\n", - "assert p == 105\n", - "assert iseq((c.x+dx)*(c.y+dy), c.k)\n", - "assert iseq((c.y+dy)/(c.x+dx),p)\n", - "\n", - "dx,dy,p = c.dxdyfromp_f(p=85)\n", - "assert p == 85\n", - "assert iseq((c.x+dx)*(c.y+dy), c.k)\n", - "assert iseq((c.y+dy)/(c.x+dx), 90)\n", - "assert iseq(dy, -c.y_act)\n", - "\n", - "dx,dy,p = c.dxdyfromp_f(p=115)\n", - "assert p == 115\n", - "assert iseq((c.x+dx)*(c.y+dy), c.k)\n", - "assert iseq((c.y+dy)/(c.x+dx), 110)\n", - "assert iseq(dx, -c.x_act)\n", - "\n", - "assert iseq(c.x_min*c.y_max, c.k)\n", - "assert iseq(c.x_max*c.y_min, c.k)\n", - "assert iseq(c.y_max/c.x_min, c.p_max)\n", - "assert iseq(c.y_min/c.x_max, c.p_min)" - ] - }, - { - "cell_type": "markdown", - "id": "b03bfdd4-0bc2-430c-a8c3-3ffb030b1f11", - "metadata": {}, - "source": [ - "## Asymmetric curves and curve classifications\n", - "\n", - "We here briefly run through asymmetric curves; we also ensure that the associated functions (is_constant_product) etc work across the board" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "e55d762b-611a-4559-95ec-98d887d4df94", - "metadata": {}, - "outputs": [], - "source": [ - "ETA = 3\n", - "cc = CPC.from_xyal(x=10, y=100/ETA*10, eta=ETA)\n", - "assert cc.alpha == 0.75\n", - "assert cc.eta == 3\n", - "assert iseq(cc.x, 10)\n", - "assert iseq(cc.y, 100/ETA*10)\n", - "assert iseq(cc.p, 100)\n", - "assert iseq(cc.x_act, cc.x)\n", - "assert iseq(cc.y_act, cc.y)\n", - "assert (cc.x_min, cc.x_max) == (0,None)\n", - "assert (cc.y_min, cc.y_max) == (0,None)\n", - "assert not cc.is_constant_product() # DEPRECATED\n", - "assert not cc.is_symmetric()\n", - "assert cc.is_asymmetric()\n", - "assert not cc.is_levered()\n", - "assert cc.is_unlevered()" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "65300368-ecf5-4c48-bdf0-b29744e3ac13", - "metadata": {}, - "outputs": [], - "source": [ - "ETA = 1\n", - "cc = CPC.from_xyal(x=10, y=100/ETA*10, eta=ETA)\n", - "assert cc.alpha == 0.5\n", - "assert cc.eta == 1\n", - "assert iseq(cc.x, 10)\n", - "assert iseq(cc.y, 100/ETA*10)\n", - "assert iseq(cc.p, 100)\n", - "assert iseq(cc.x_act, cc.x)\n", - "assert iseq(cc.y_act, cc.y)\n", - "assert (cc.x_min, cc.x_max) == (0,None)\n", - "assert (cc.y_min, cc.y_max) == (0,None)\n", - "assert cc.is_constant_product() # DEPRECATED\n", - "assert cc.is_symmetric()\n", - "assert not cc.is_asymmetric()\n", - "assert not cc.is_levered()\n", - "assert cc.is_unlevered()" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "3e1e63cd-6421-44dd-beb9-feacc8985542", - "metadata": {}, - "outputs": [], - "source": [ - "cc = CPC.from_xy(x=10, y=100*10)\n", - "assert cc.alpha == 0.5\n", - "assert cc.eta == 1\n", - "assert iseq(cc.x, 10)\n", - "assert iseq(cc.y, 100/ETA*10)\n", - "assert iseq(cc.p, 100)\n", - "assert iseq(cc.x_act, cc.x)\n", - "assert iseq(cc.y_act, cc.y)\n", - "assert (cc.x_min, cc.x_max) == (0,None)\n", - "assert (cc.y_min, cc.y_max) == (0,None)\n", - "assert cc.is_constant_product() # DEPRECATED\n", - "assert cc.is_symmetric()\n", - "assert not cc.is_asymmetric()\n", - "assert not cc.is_levered()\n", - "assert cc.is_unlevered()" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "8ca006a1-cfac-4399-ba2a-786a352ae5a5", - "metadata": {}, - "outputs": [], - "source": [ - "cc = CPC.from_pkpp(p=100, k=10*100, p_min=90, p_max=110)\n", - "assert cc.alpha == 0.5\n", - "assert cc.eta == 1\n", - "assert iseq(cc.x, 3.1622776601683795)\n", - "assert iseq(cc.y, 316.2277660168379)\n", - "assert iseq(cc.p, 100)\n", - "assert not iseq(cc.x_act, cc.x)\n", - "assert not iseq(cc.y_act, cc.y)\n", - "assert not (cc.x_min, cc.x_max) == (0,None)\n", - "assert not (cc.y_min, cc.y_max) == (0,None)\n", - "assert cc.is_constant_product() # DEPRECATED\n", - "assert cc.is_symmetric()\n", - "assert not cc.is_asymmetric()\n", - "assert cc.is_levered()\n", - "assert not cc.is_unlevered()" - ] - }, - { - "cell_type": "markdown", - "id": "1b50a1a4", - "metadata": {}, - "source": [ - "## CPCInverter" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "3ef6d6d7", - "metadata": {}, - "outputs": [], - "source": [ - "c = CPC.from_pkpp(p=2000, k=10*20000, p_min=1800, p_max=2200, fee=0.001, pair=f\"{T.ETH}/{T.USDC}\", params={\"foo\": \"bar\"})\n", - "c2 = CPC.from_pkpp(p=1/2000, k=10*20000, p_max=1/1800, p_min=1/2200, fee=0.002, pair=f\"{T.USDC}/{T.ETH}\", params={\"foo\": \"bar\"})\n", - "ci = CPCInverter(c)\n", - "c2i = CPCInverter(c2)\n", - "curves = CPCInverter.wrap([c,c2])\n", - "assert c.pairo == c2i.pairo\n", - "assert ci.pairo == c2.pairo" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "d362cbaa-1fb3-4c77-b5c6-502e965dc7e7", - "metadata": {}, - "outputs": [], - "source": [ - "assert ci.P(\"foo\") == c.P(\"foo\")\n", - "assert c2i.P(\"foo\") == c2.P(\"foo\")\n", - "assert ci.fee == c.fee\n", - "assert c2i.fee == c2.fee" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "id": "f92dc34e", - "metadata": {}, - "outputs": [], - "source": [ - "#print(\"x_act\", c.x_act, c2i.x_act)\n", - "assert iseq(c.x_act, c2i.x_act)\n", - "xact = c.x_act\n", - "dx = -0.1*xact\n", - "c_ex = c.execute(dx=dx)\n", - "assert isinstance(c_ex, CPC)\n", - "assert iseq(c_ex.x_act, xact+dx)\n", - "assert iseq(c_ex.x, c.x+dx)\n", - "c2i_ex = c2i.execute(dx=dx)\n", - "assert iseq(c2i_ex.x_act, xact+dx)\n", - "assert iseq(c2i_ex.x, c.x+dx)\n", - "assert isinstance(c2i_ex, CPCInverter)" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "ca485113", - "metadata": {}, - "outputs": [], - "source": [ - "assert len(curves) == 2\n", - "assert set(c.pair for c in curves) == {f\"{T.USDC}/{T.ETH}\"}\n", - "assert len(set(c.pair for c in curves)) == 1\n", - "assert len(set(c.tknx for c in curves)) == 1\n", - "assert len(set(c.tkny for c in curves)) == 1" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "68861100", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "assert c.tknx == ci.tkny\n", - "assert c.tkny == ci.tknx\n", - "assert c.tknxp == ci.tknyp\n", - "assert c.tknyp == ci.tknxp\n", - "assert c.tknb == ci.tknq\n", - "assert c.tknq == ci.tknb\n", - "assert c.tknbp == ci.tknqp\n", - "assert c.tknqp == ci.tknbp\n", - "assert f\"{c.tknq}/{c.tknb}\" == ci.pair\n", - "assert f\"{c.tknqp}/{c.tknbp}\" == ci.pairp\n", - "assert c.x == ci.y\n", - "assert c.y == ci.x\n", - "assert c.x_act == ci.y_act\n", - "assert c.y_act == ci.x_act\n", - "assert c.x_min == ci.y_min\n", - "assert c.x_max == ci.y_max\n", - "assert c.y_min == ci.x_min\n", - "assert c.y_max == ci.x_max\n", - "assert c.k == ci.k\n", - "assert iseq(c.p, 1/ci.p)\n", - "assert iseq(c.p_min, 1/ci.p_max)\n", - "assert iseq(c.p_max, 1/ci.p_min)" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "65156f9c", - "metadata": {}, - "outputs": [], - "source": [ - "assert c.pair == c2i.pair\n", - "assert c.tknx == c2i.tknx\n", - "assert c.tkny == c2i.tkny\n", - "assert c.tknxp == c2i.tknxp\n", - "assert c.tknyp == c2i.tknyp\n", - "assert c.tknb == c2i.tknb\n", - "assert c.tknq == c2i.tknq\n", - "assert c.tknbp == c2i.tknbp\n", - "assert c.tknqp == c2i.tknqp\n", - "assert iseq(c.p, c2i.p)\n", - "assert iseq(c.p_min, c2i.p_min)\n", - "assert iseq(c.p_max, c2i.p_max)\n", - "assert c.x == c2i.x\n", - "assert c.y == c2i.y\n", - "assert c.x_act == c2i.x_act\n", - "assert c.y_act == c2i.y_act\n", - "assert c.x_min == c2i.x_min\n", - "assert c.x_max == c2i.x_max\n", - "assert c.y_min == c2i.y_min\n", - "assert c.y_max == c2i.y_max\n", - "assert c.k == c2i.k" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "b530bfd2", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(c.xfromy_f(c.y), c2i.xfromy_f(c2i.y))\n", - "assert iseq(c.yfromx_f(c.x), c2i.yfromx_f(c2i.x))\n", - "assert iseq(c.xfromy_f(c.y*1.05), c2i.xfromy_f(c2i.y*1.05))\n", - "assert iseq(c.yfromx_f(c.x*1.05), c2i.yfromx_f(c2i.x*1.05))\n", - "assert iseq(c.dxfromdy_f(1), c2i.dxfromdy_f(1))\n", - "assert iseq(c.dyfromdx_f(1), c2i.dyfromdx_f(1))" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "0b7050fc", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "assert c.xyfromp_f() == c2i.xyfromp_f()\n", - "assert c.dxdyfromp_f() == c2i.dxdyfromp_f()\n", - "assert c.xyfromp_f(withunits=True) == c2i.xyfromp_f(withunits=True)\n", - "assert c.dxdyfromp_f(withunits=True) == c2i.dxdyfromp_f(withunits=True)\n", - "assert iseq(c.p, c2i.p)\n", - "x,y,p = c.xyfromp_f(c.p*1.05)\n", - "x2,y2,p2 = c2i.xyfromp_f(c2i.p*1.05)\n", - "assert iseq(x,x2)\n", - "assert iseq(y,y2)\n", - "assert iseq(p,p2)\n", - "dx,dy,p = c.dxdyfromp_f(c.p*1.05)\n", - "dx2,dy2,p2 = c2i.dxdyfromp_f(c2i.p*1.05)\n", - "assert iseq(dx,dx2)\n", - "assert iseq(dy,dy2)\n", - "assert iseq(p,p2)" - ] - }, - { - "cell_type": "markdown", - "id": "bcf11bc1", - "metadata": {}, - "source": [ - "## simple_optimizer" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "bb2ae437", - "metadata": {}, - "outputs": [], - "source": [ - "CC = CPCContainer(CPC.from_pk(p=2000+i*10, k=10*20000, pair=f\"ETH/USDC\") for i in range(11))\n", - "c0 = CC.curves[0]\n", - "c1 = CC.curves[-1]\n", - "CC0 = CPCContainer([c0])\n", - "assert len(CC) == 11\n", - "assert iseq([c.p for c in CC][-1], 2100)\n", - "assert len(CC0) == 1\n", - "assert iseq([c.p for c in CC0][-1], 2000)" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "af0421b3", - "metadata": {}, - "outputs": [], - "source": [ - "O = PairOptimizer(CC)\n", - "O0 = PairOptimizer(CC0)\n", - "func = O.optimize(result=O.SO_DXDYVECFUNC)\n", - "func0 = O0.optimize(result=O.SO_DXDYVECFUNC)\n", - "funcs = O.optimize(result=O.SO_DXDYSUMFUNC)\n", - "funcvx = O.optimize(result=O.SO_DXDYVALXFUNC)\n", - "funcvy = O.optimize(result=O.SO_DXDYVALYFUNC)\n", - "x,y = func0(2100)[0]\n", - "xb, yb, _ = c0.dxdyfromp_f(2100)\n", - "assert x == xb, f\"x={x}, xb={xb}\"\n", - "assert y == yb\n", - "x,y = func(2100)[-1]\n", - "xb, yb, _ = c1.dxdyfromp_f(2100)\n", - "assert x == xb\n", - "assert y == yb\n", - "assert np.all(sum(func(2100)) == funcs(2100))\n", - "\n", - "p = 2100\n", - "dx, dy = funcs(p)\n", - "assert iseq(dy + p*dx, funcvy(p))\n", - "assert iseq(dy/p + dx, funcvx(p))\n", - "\n", - "p = 1500\n", - "dx, dy = funcs(p)\n", - "assert iseq(dy + p*dx, funcvy(p))\n", - "assert iseq(dy/p + dx, funcvx(p))\n", - "\n", - "assert iseq(float(O0.optimize(result=O.SO_PMAX)), c0.p)\n", - "assert iseq(float(O.optimize(result=O.SO_PMAX)), 2049.6451720862074, eps=1e-3)" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "c708e8f8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "OptimizerBase.SimpleResult(result=2049.881086733136, method='newtonraphson', errormsg=None, context_dct=None)" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "O.optimize(result=O.SO_PMAX)" - ] - }, - { - "cell_type": "markdown", - "id": "8166cd85", - "metadata": {}, - "source": [ - "### global max\n", - "\n", - "the global max function has not been properly connected to the MargPResult object because it does not really make sense; the function is not currently used so it does not really matter" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "e07a7189", - "metadata": {}, - "outputs": [], - "source": [ - "r = O.optimize()\n", - "r_ = O.optimize(result=O.SO_GLOBALMAX)\n", - "assert raises(O.optimize, targettkn=T.WETH, result=O.SO_GLOBALMAX)\n", - "assert iseq(float(r), float(r_))\n", - "assert len(r.curves) == len(CC)\n", - "#assert np.all(r.dxdy_sum == sum(r.dxdy_vec))\n", - "#dx, dy = r.dxdy_vecs\n", - "#assert tuple(tuple(_) for _ in r.dxdy_vec) == tuple(zip(dx,dy))\n", - "#assert r.result == r.dxdy_valx\n", - "# for dp in np.linspace(-500,500,100):\n", - "# assert r.dxdyfromp_valx_f(p) < r.dxdy_valx\n", - "# assert r.dxdyfromp_valy_f(p) < r.dxdy_valy" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "id": "8f2a15f7", - "metadata": {}, - "outputs": [], - "source": [ - "CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues))\n", - "# CC.plot()\n", - "# CC_ex.plot()\n", - "prices = [c.p for c in CC]\n", - "prices_ex = [c.p for c in CC_ex]\n", - "assert iseq(np.std(prices), 31.622776601683707)\n", - "#assert iseq(np.std(prices_ex), 4.547473508864641e-13)\n", - "#prices, prices_ex" - ] - }, - { - "cell_type": "markdown", - "id": "ff7dba0f", - "metadata": {}, - "source": [ - "### target token" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "12962eef", - "metadata": {}, - "outputs": [], - "source": [ - "r = O.optimize(targettkn=\"ETH\")\n", - "r_ = O.optimize(targettkn=\"ETH\", result=O.SO_TARGETTKN)\n", - "assert raises(O.optimize,targettkn=\"DAI\")\n", - "assert raises(O.optimize, result=O.SO_TARGETTKN)\n", - "assert iseq(float(r), float(r_))\n", - "assert abs(sum(r.dyvalues) < 1e-6)\n", - "assert sum(r.dxvalues) < 0\n", - "assert iseq(float(r),sum(r.dxvalues))" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "e65d8ea6", - "metadata": {}, - "outputs": [], - "source": [ - "r = O.optimize(targettkn=\"USDC\")\n", - "assert abs(sum(r.dxvalues) < 1e-6)\n", - "assert sum(r.dyvalues) < 0\n", - "assert iseq(float(r),sum(r.dyvalues))" - ] - }, - { - "cell_type": "markdown", - "id": "ee1c932b", - "metadata": {}, - "source": [ - "## optimizer plus inverted curves\n", - "\n", - "note: `O.optimize()` without `targettkn='...'` is the globalmax result!" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "4ecd90f9", - "metadata": {}, - "outputs": [], - "source": [ - "CCr = CPCContainer(CPC.from_pk(p=2000+i*100, k=10*(20000+10000*i), pair=f\"ETH/USDC\") for i in range(11))\n", - "CCi = CPCContainer(CPC.from_pk(p=1/(2050+i*100), k=10*(20000+10000*i), pair=f\"USDC/ETH\") for i in range(11))\n", - "CC = CCr.bycids()\n", - "assert len(CC) == len(CCr)\n", - "CC += CCi\n", - "assert len(CC) == len(CCr) + len(CCi)" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "c601265a", - "metadata": {}, - "outputs": [], - "source": [ - "# CC.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "36a68baa", - "metadata": {}, - "outputs": [], - "source": [ - "O = PairOptimizer(CC)\n", - "r = O.optimize()\n", - "#print(f\"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]\")\n", - "assert iseq(r.result, 3.292239037185821)" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "42c1536b-cb4c-4848-ab46-fdac3ea38b8e", - "metadata": {}, - "outputs": [], - "source": [ - "#CC.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "d1e3c887", - "metadata": {}, - "outputs": [], - "source": [ - "CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues))\n", - "# CC.plot()\n", - "# CC_ex.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "d4c16352", - "metadata": {}, - "outputs": [], - "source": [ - "prices_ex = [c.pairo.primary_price(c.p) for c in CC_ex]\n", - "assert np.std(prices_ex) < 1e-10" - ] - }, - { - "cell_type": "markdown", - "id": "9caa5204", - "metadata": {}, - "source": [ - "## posx and negx" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "id": "34ede208", - "metadata": {}, - "outputs": [], - "source": [ - "O = CPCArbOptimizer\n", - "a = O.a" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "id": "8fe3f69a", - "metadata": {}, - "outputs": [], - "source": [ - "assert O.posx([0,-1,2]) == (0, 0, 2)\n", - "assert O.posx((-1,-2, 3)) == (0, 0, 3)\n", - "assert O.negx([0,-1,2]) == (0, -1, 0)\n", - "assert O.negx((-1,-2, 3)) == (-1, -2, 0)\n", - "assert np.all(O.posx(a([0,-1,2])) == a((0, 0, 2)))\n", - "assert O.t(a((-1,-2))) == (-1,-2)" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "id": "3d1f06a7", - "metadata": {}, - "outputs": [], - "source": [ - "for v in ((1,2,3), (1,-1,5-10,0), (-10.5,8,2.34,-17)):\n", - " assert np.all(O.posx(a(v))+O.negx(a(v)) == v)" - ] - }, - { - "cell_type": "markdown", - "id": "90cb3696", - "metadata": {}, - "source": [ - "## TradeInstructions" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "375eec3d", - "metadata": {}, - "outputs": [], - "source": [ - "TI = CPCArbOptimizer.TradeInstruction" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "eff49534", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cid=1, out=-2000.0 USDC, , out=1.0 ETH\n" - ] - } - ], - "source": [ - "ti = TI.new(curve_or_cid=\"1\", tkn1=\"ETH\", amt1=1, tkn2=\"USDC\", amt2=-2000)\n", - "print(f\"cid={ti.cid}, out={ti.amtout} {ti.tknout}, , out={ti.amtin} {ti.tknin}\")\n", - "assert ti.tknin == \"ETH\"\n", - "assert ti.amtin > 0\n", - "assert ti.tknout == \"USDC\"\n", - "assert ti.amtout < 0\n", - "assert ti.price_outperin == 2000\n", - "assert ti.price_inperout == 1/2000\n", - "assert ti.prices == (2000, 1/2000)\n", - "assert ti.price_outperin == ti.p\n", - "assert ti.price_inperout == ti.pr\n", - "assert ti.prices == ti.pp" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "id": "bf6632e7", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(TI, cid=\"1\", tknin=\"USDC\", amtin=2000, tknout=\"ETH\", amtout=-1, raiseonerror=True)\n", - "assert raises(TI, cid=\"1\", tknin=\"USDC\", amtin=2000, tknout=\"ETH\", amtout=1, raiseonerror=True)\n", - "assert raises(TI, cid=\"1\", tknin=\"USDC\", amtin=-2000, tknout=\"ETH\", amtout=-1, raiseonerror=True)\n", - "assert raises(TI, cid=\"1\", tknin=\"USDC\", amtin=-2000, tknout=\"ETH\", amtout=1, raiseonerror=True)\n", - "assert raises(TI, cid=\"1\", tknin=\"USDC\", amtin=2000, tknout=\"ETH\", amtout=0, raiseonerror=True)\n", - "assert raises(TI, cid=\"1\", tknin=\"USDC\", amtin=0, tknout=\"ETH\", amtout=-1, raiseonerror=True)\n", - "assert not raises(TI.new, curve_or_cid=\"1\", tkn1=\"USDC\", amt1=2000, tkn2=\"ETH\", amt2=-1, raiseonerror=True)\n", - "assert not raises(TI.new, curve_or_cid=\"1\", tkn1=\"USDC\", amt1=-2000, tkn2=\"ETH\", amt2=1, raiseonerror=True)\n", - "assert raises(TI.new, curve_or_cid=\"1\", tkn1=\"USDC\", amt1=2000, tkn2=\"ETH\", amt2=1, raiseonerror=True)\n", - "assert raises(TI.new, curve_or_cid=\"1\", tkn1=\"USDC\", amt1=-2000, tkn2=\"ETH\", amt2=-1, raiseonerror=True)\n", - "assert raises(TI.new, curve_or_cid=\"1\", tkn1=\"USDC\", amt1=0, tkn2=\"ETH\", amt2=1, raiseonerror=True)\n", - "assert raises(TI.new, curve_or_cid=\"1\", tkn1=\"USDC\", amt1=-2000, tkn2=\"ETH\", amt2=0, raiseonerror=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "id": "8294a2a9", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "assert not TI(cid=\"1\", tknin=\"USDC\", amtin=2000, tknout=\"ETH\", amtout=-1, raiseonerror=False).error\n", - "assert TI(cid=\"1\", tknin=\"USDC\", amtin=2000, tknout=\"ETH\", amtout=1, raiseonerror=False).error\n", - "assert TI(cid=\"1\", tknin=\"USDC\", amtin=-2000, tknout=\"ETH\", amtout=-1, raiseonerror=False).error\n", - "assert TI(cid=\"1\", tknin=\"USDC\", amtin=-2000, tknout=\"ETH\", amtout=1, raiseonerror=False).error\n", - "assert TI(cid=\"1\", tknin=\"USDC\", amtin=2000, tknout=\"ETH\", amtout=0, raiseonerror=False).error\n", - "assert TI(cid=\"1\", tknin=\"USDC\", amtin=0, tknout=\"ETH\", amtout=-1, raiseonerror=False).error\n", - "assert not TI.new(curve_or_cid=\"1\", tkn1=\"USDC\", amt1=2000, tkn2=\"ETH\", amt2=-1, raiseonerror=False).error\n", - "assert not TI.new(curve_or_cid=\"1\", tkn1=\"USDC\", amt1=-2000, tkn2=\"ETH\", amt2=1, raiseonerror=False).error\n", - "assert TI.new(curve_or_cid=\"1\", tkn1=\"USDC\", amt1=2000, tkn2=\"ETH\", amt2=1, raiseonerror=False).error\n", - "assert TI.new(curve_or_cid=\"1\", tkn1=\"USDC\", amt1=-2000, tkn2=\"ETH\", amt2=-1, raiseonerror=False).error\n", - "assert TI.new(curve_or_cid=\"1\", tkn1=\"USDC\", amt1=0, tkn2=\"ETH\", amt2=1, raiseonerror=False).error\n", - "assert TI.new(curve_or_cid=\"1\", tkn1=\"USDC\", amt1=-2000, tkn2=\"ETH\", amt2=0, raiseonerror=False).error" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "d6c001fd", - "metadata": {}, - "outputs": [], - "source": [ - "til = [\n", - " TI.new(curve_or_cid=f\"{i+1}\", tkn1=\"ETH\", amt1=1*(1+i/100), tkn2=\"USDC\", amt2=-2000*(1+i/100)) \n", - " for i in range(10)\n", - "]\n", - "tild = TI.to_dicts(til)\n", - "tildf = TI.to_df(til, robj=None)\n", - "assert len(tild) == 10\n", - "assert len(tildf) == 10\n", - "assert tild[0] == {\n", - " 'cid': '1', \n", - " 'tknin': 'ETH', \n", - " 'amtin': 1.0, \n", - " 'tknout': 'USDC', \n", - " 'amtout': -2000.0,\n", - " 'error': None,}\n", - "assert dict(tildf.iloc[0]) == {\n", - " 'pair': '',\n", - " 'pairp': '',\n", - " 'tknin': 'ETH',\n", - " 'tknout': 'USDC',\n", - " 'ETH': 1.0,\n", - " 'USDC': -2000.0\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "0419e520", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'cid': '1',\n", - " 'tknin': 'ETH',\n", - " 'amtin': 1.0,\n", - " 'tknout': 'USDC',\n", - " 'amtout': -2000.0,\n", - " 'error': None}" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tild[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "id": "2eec3c2c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pairpairptknintknoutETHUSDC
cid
1ETHUSDC1.00-2000.0
2ETHUSDC1.01-2020.0
3ETHUSDC1.02-2040.0
4ETHUSDC1.03-2060.0
5ETHUSDC1.04-2080.0
6ETHUSDC1.05-2100.0
7ETHUSDC1.06-2120.0
8ETHUSDC1.07-2140.0
9ETHUSDC1.08-2160.0
10ETHUSDC1.09-2180.0
\n", - "
" - ], - "text/plain": [ - " pair pairp tknin tknout ETH USDC\n", - "cid \n", - "1 ETH USDC 1.00 -2000.0\n", - "2 ETH USDC 1.01 -2020.0\n", - "3 ETH USDC 1.02 -2040.0\n", - "4 ETH USDC 1.03 -2060.0\n", - "5 ETH USDC 1.04 -2080.0\n", - "6 ETH USDC 1.05 -2100.0\n", - "7 ETH USDC 1.06 -2120.0\n", - "8 ETH USDC 1.07 -2140.0\n", - "9 ETH USDC 1.08 -2160.0\n", - "10 ETH USDC 1.09 -2180.0" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tildf" - ] - }, - { - "cell_type": "markdown", - "id": "232342ea", - "metadata": {}, - "source": [ - "## margp_optimizer" - ] - }, - { - "cell_type": "markdown", - "id": "5a2ee1e0", - "metadata": {}, - "source": [ - "### no arbitrage possible" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "9a2f0b78", - "metadata": {}, - "outputs": [], - "source": [ - "CCa = CPCContainer()\n", - "CCa += CPC.from_pk(pair=\"WETH/USDC\", p=2000, k=10*20000, cid=\"c0\")\n", - "CCa += CPC.from_pk(pair=\"WETH/USDT\", p=2000, k=10*20000, cid=\"c1\")\n", - "CCa += CPC.from_pk(pair=\"USDC/USDT\", p=1.0, k=200000*200000, cid=\"c2\")\n", - "O = MargPOptimizer(CCa)" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "id": "0220671a", - "metadata": {}, - "outputs": [], - "source": [ - "r = O.margp_optimizer(\"WETH\", result=O.MO_DEBUG)\n", - "assert isinstance(r, dict)\n", - "prices0 = r[\"price_estimates_t\"]\n", - "assert not prices0 is None, f\"prices0 must not be None [{prices0}]\"\n", - "r1 = O.arb(\"WETH\")\n", - "r2 = O.SelfFinancingConstraints.arb(\"WETH\")\n", - "assert isinstance(r1, CPCArbOptimizer.SelfFinancingConstraints)\n", - "assert r1 == r2\n", - "assert r[\"sfc\"] == r1\n", - "assert r1.is_arbsfc()\n", - "assert r1.optimizationvar == \"WETH\"" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "3a8e543a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'price_estimates_t': array([0.0005, 0.0005]),\n", - " 'tokens_t': ('USDC', 'USDT'),\n", - " 'tokens_ix': {'USDC': 0, 'USDT': 1},\n", - " 'pairs': {'USDC/USDT', 'WETH/USDC', 'WETH/USDT'},\n", - " 'sfc': CPCArbOptimizer.SelfFinancingConstraints(data={'WETH': 'OptimizationVar'}, tokens={'WETH'}),\n", - " 'targettkn': 'WETH',\n", - " 'pairs_t': (('WETH', 'USDT'), ('USDC', 'USDT'), ('WETH', 'USDC')),\n", - " 'dtknfromp_f': .dtknfromp_f(p, *, islog10=True, asdct=False, quiet=False)>,\n", - " 'optimizer': }" - ] - }, - "execution_count": 113, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "id": "f6c8c50f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.0005, 0.0005])" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prices0" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "7c3e3839", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[margp_optimizer] calculating price estimates\n" - ] - } - ], - "source": [ - "f = O.optimize(\"WETH\", result=O.MO_DTKNFROMPF, params=dict(verbose=True, debug=False))\n", - "r3 = f(prices0, islog10=False)\n", - "assert np.all(r3 == (0,0))\n", - "r4, r3b = f(prices0, asdct=True, islog10=False)\n", - "assert np.all(r3==r3b)\n", - "assert len(r4) == len(r3)+1\n", - "assert tuple(r4.values()) == (0,0,0)\n", - "assert set(r4) == {'USDC', 'USDT', 'WETH'}" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "id": "c45ebfaa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[margp_optimizer] calculating price estimates\n", - "[margp_optimizer] pe [0.0005 0.0005]\n", - "[margp_optimizer] p 0.00, 0.00\n", - "[margp_optimizer] 1/p 2,000.00, 2,000.00\n", - "\n", - "[margp_optimizer] ========== cycle 0 =======>>>\n", - "log p0 [-3.3010299956639813, -3.3010299956639813]\n", - "log dp [3.1611697e-16 3.1611697e-16]\n", - "log p [-3.30103 -3.30103]\n", - "p (0.0005000000000000001, 0.0005000000000000001)\n", - "p 0.00, 0.00\n", - "1/p 2,000.00, 2,000.00\n", - "tokens_t ('USDC', 'USDT')\n", - "dtkn 0.000, 0.000\n", - "[criterium=4.47e-16, eps=1.0e-06, c/e=4e-10]\n", - "<<<========== cycle 0 ======= [margp_optimizer]\n", - "\n", - "[margp_optimizer] ========== cycle 1 =======>>>\n", - "log p0 [-3.301029995663981, -3.301029995663981]\n", - "log dp [-1.58058485e-16 -1.58058485e-16]\n", - "log p [-3.30103 -3.30103]\n", - "p (0.0005000000000000001, 0.0005000000000000001)\n", - "p 0.00, 0.00\n", - "1/p 2,000.00, 2,000.00\n", - "tokens_t ('USDC', 'USDT')\n", - "dtkn -0.000, -0.000\n", - "[criterium=2.24e-16, eps=1.0e-06, c/e=2e-10]\n", - "<<<========== cycle 1 ======= [margp_optimizer]\n" - ] - } - ], - "source": [ - "r = O.optimize(\"WETH\", result=O.MO_MINIMAL, params=dict(verbose=True))\n", - "rd = r.asdict\n", - "assert abs(float(r)) < 1e-10\n", - "assert r.result == float(r)\n", - "assert r.method == \"margp\"\n", - "assert r.curves is None\n", - "assert r.targettkn == \"WETH\"\n", - "assert r.dtokens is None\n", - "assert sum(abs(x) for x in r.dtokens_t) < 1e-10\n", - "assert not r.p_optimal is None\n", - "assert iseq(0.0005, r.p_optimal_t[0], r.p_optimal_t[1])\n", - "assert set(r.tokens_t) == {'USDC', 'USDT'}\n", - "assert r.errormsg is None\n", - "assert r.is_error == False\n", - "# assert r.time >= 0\n", - "# assert r.time < 0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "id": "551b9b36", - "metadata": {}, - "outputs": [], - "source": [ - "r = O.optimize(\"WETH\", result=O.MO_FULL)\n", - "rd = r.asdict()\n", - "r2 = O.margp_optimizer(\"WETH\")\n", - "r2d = r2.asdict()\n", - "for k in rd:\n", - " #print(k)\n", - " if not k in [\"time\", \"curves\"]:\n", - " assert rd[k] == r2d[k]\n", - "assert r2.curves == r.curves # the TokenScale object fails in the dict\n", - "\n", - "assert abs(float(r)) < 1e-10\n", - "assert r.result == float(r)\n", - "assert r.method == \"margp\"\n", - "assert len(r.curves) == 3\n", - "assert r.targettkn == \"WETH\"\n", - "assert set(r.dtokens.keys()) == set(['USDT', 'WETH', 'USDC'])\n", - "assert sum(abs(x) for x in r.dtokens.values()) < 1e-10\n", - "assert sum(abs(x) for x in r.dtokens_t) < 1e-10\n", - "assert iseq(0.0005, r.p_optimal[\"USDC\"], r.p_optimal[\"USDT\"])\n", - "assert iseq(0.0005, r.p_optimal_t[0], r.p_optimal_t[1])\n", - "assert tuple(r.p_optimal.values())[:-1] == r.p_optimal_t\n", - "assert set(r.tokens_t) == set(('USDC', 'USDT'))\n", - "assert r.errormsg is None\n", - "assert r.is_error == False\n", - "# assert r.time >= 0\n", - "# assert r.time < 0.1" - ] - }, - { - "cell_type": "markdown", - "id": "7d3e07f5", - "metadata": {}, - "source": [ - "### arbitrage" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "id": "16390e26", - "metadata": {}, - "outputs": [], - "source": [ - "CCa = CPCContainer()\n", - "CCa += CPC.from_pk(pair=\"WETH/USDC\", p=2000, k=10*20000, cid=\"c0\")\n", - "CCa += CPC.from_pk(pair=\"WETH/USDT\", p=2000, k=10*20000, cid=\"c1\")\n", - "CCa += CPC.from_pk(pair=\"USDC/USDT\", p=1.2, k=200000*200000, cid=\"c2\")\n", - "O = MargPOptimizer(CCa)" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "id": "34b5d2b2", - "metadata": {}, - "outputs": [], - "source": [ - "r = O.optimize(\"WETH\", result=O.MO_DEBUG)\n", - "assert isinstance(r, dict)\n", - "prices0 = r[\"price_estimates_t\"]\n", - "r1 = O.arb(\"WETH\")\n", - "r2 = O.SelfFinancingConstraints.arb(\"WETH\")\n", - "assert isinstance(r1, CPCArbOptimizer.SelfFinancingConstraints)\n", - "assert r1 == r2\n", - "assert r[\"sfc\"] == r1\n", - "assert r1.is_arbsfc()\n", - "assert r1.optimizationvar == \"WETH\"" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "id": "d9d551b6", - "metadata": {}, - "outputs": [], - "source": [ - "f = O.optimize(\"WETH\", result=O.MO_DTKNFROMPF)\n", - "r3 = f(prices0, islog10=False)\n", - "assert set(r3.astype(int)) == set((17425,-19089))\n", - "r4, r3b = f(prices0, asdct=True, islog10=False)\n", - "assert np.all(r3==r3b)\n", - "assert len(r4) == len(r3)+1\n", - "assert set(r4) == {'USDC', 'USDT', 'WETH'}" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "id": "88888e71", - "metadata": {}, - "outputs": [], - "source": [ - "r = O.optimize(\"WETH\", result=O.MO_FULL)\n", - "assert iseq(float(r), -0.03944401129301944)\n", - "assert r.result == float(r)\n", - "assert r.method == \"margp\"\n", - "assert len(r.curves) == 3\n", - "assert r.targettkn == \"WETH\"\n", - "assert abs(r.dtokens_t[0]) < 1e-6\n", - "assert abs(r.dtokens_t[1]) < 1e-6\n", - "assert r.dtokens[\"WETH\"] == float(r)\n", - "assert tuple(r.p_optimal.values())[:-1] == r.p_optimal_t\n", - "assert tuple(r.p_optimal)[:-1] == r.tokens_t\n", - "assert iseq(r.p_optimal_t[0], 0.0005421803152482512) or iseq(r.p_optimal_t[0], 0.00045575394031021585)\n", - "assert iseq(r.p_optimal_t[1], 0.0005421803152482512) or iseq(r.p_optimal_t[1], 0.00045575394031021585)\n", - "assert tuple(r.p_optimal.values())[:-1] == r.p_optimal_t\n", - "assert set(r.tokens_t) == set(('USDC', 'USDT'))\n", - "assert r.errormsg is None\n", - "assert r.is_error == False\n", - "# assert r.time >= 0\n", - "# assert r.time < 0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "id": "7c7fed1c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.9068465917371213e-07" - ] - }, - "execution_count": 122, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "abs(r.dtokens_t[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "id": "e007be1d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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WETHUSDCUSDT
c00.413264-7.937258e+02NaN
c1-0.452708NaN9.483481e+02
c2NaN7.937258e+02-9.483481e+02
PRICE1.0000005.421803e-044.557539e-04
AMMIn0.4132647.937258e+029.483481e+02
AMMOut-0.452708-7.937258e+02-9.483481e+02
TOTAL NET-0.0394441.906847e-072.264096e-07
\n", - "
" - ], - "text/plain": [ - " WETH USDC USDT\n", - "c0 0.413264 -7.937258e+02 NaN\n", - "c1 -0.452708 NaN 9.483481e+02\n", - "c2 NaN 7.937258e+02 -9.483481e+02\n", - "PRICE 1.000000 5.421803e-04 4.557539e-04\n", - "AMMIn 0.413264 7.937258e+02 9.483481e+02\n", - "AMMOut -0.452708 -7.937258e+02 -9.483481e+02\n", - "TOTAL NET -0.039444 1.906847e-07 2.264096e-07" - ] - }, - "execution_count": 123, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ti = r.trade_instructions()\n", - "assert len(ti) == 3\n", - "dfa = r.trade_instructions(ti_format=O.TIF_DFAGGR)\n", - "assert len(dfa)==7\n", - "assert list(dfa.index) == ['c0', 'c1', 'c2', 'PRICE', 'AMMIn', 'AMMOut', 'TOTAL NET']\n", - "assert list(dfa.columns) == ['WETH', 'USDC', 'USDT']\n", - "assert dfa.loc[\"PRICE\"][0] == 1\n", - "assert iseq(dfa.loc[\"PRICE\"][1], 0.0005421803152)\n", - "assert iseq(dfa.loc[\"PRICE\"][2], 0.0004557539403)\n", - "dfa" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "id": "ccc9d286", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pairpairptknintknoutWETHUSDCUSDT
cid
c0WETH/USDCWETH/USDCWETHUSDC0.413264-793.725794NaN
c1WETH/USDTWETH/USDTUSDTWETH-0.452708NaN948.34809
c2USDC/USDTUSDC/USDTUSDCUSDTNaN793.725794-948.34809
\n", - "
" - ], - "text/plain": [ - " pair pairp tknin tknout WETH USDC USDT\n", - "cid \n", - "c0 WETH/USDC WETH/USDC WETH USDC 0.413264 -793.725794 NaN\n", - "c1 WETH/USDT WETH/USDT USDT WETH -0.452708 NaN 948.34809\n", - "c2 USDC/USDT USDC/USDT USDC USDT NaN 793.725794 -948.34809" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = r.trade_instructions(ti_format=O.TIF_DF)\n", - "assert len(df) == 3\n", - "assert list(df.columns) == ['pair', 'pairp', 'tknin', 'tknout', 'WETH', 'USDC', 'USDT']\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "id": "7c7f2301", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pairpairptknintknoutWETHUSDCUSDT
cid
c0WETH/USDCWETH/USDCWETHUSDC0.413264-793.725794
c1WETH/USDTWETH/USDTUSDTWETH-0.452708948.34809
c2USDC/USDTUSDC/USDTUSDCUSDT793.725794-948.34809
\n", - "
" - ], - "text/plain": [ - " pair pairp tknin tknout WETH USDC USDT\n", - "cid \n", - "c0 WETH/USDC WETH/USDC WETH USDC 0.413264 -793.725794 \n", - "c1 WETH/USDT WETH/USDT USDT WETH -0.452708 948.34809\n", - "c2 USDC/USDT USDC/USDT USDC USDT 793.725794 -948.34809" - ] - }, - "execution_count": 125, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = r.trade_instructions(ti_format=O.TIF_DF).fillna(\"\")\n", - "assert len(df) == 3\n", - "assert list(df.columns) == ['pair', 'pairp', 'tknin', 'tknout', 'WETH', 'USDC', 'USDT']\n", - "assert df[\"USDT\"].loc[\"c0\"] == \"\"\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "id": "c5cb20e7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "({'cid': 'c0',\n", - " 'tknin': 'WETH',\n", - " 'amtin': 0.41326380379418914,\n", - " 'tknout': 'USDC',\n", - " 'amtout': -793.7257935280832,\n", - " 'error': None},\n", - " {'cid': 'c1',\n", - " 'tknin': 'USDT',\n", - " 'amtin': 948.3480897734808,\n", - " 'tknout': 'WETH',\n", - " 'amtout': -0.45270781529377224,\n", - " 'error': None},\n", - " {'cid': 'c2',\n", - " 'tknin': 'USDC',\n", - " 'amtin': 793.7257937187678,\n", - " 'tknout': 'USDT',\n", - " 'amtout': -948.3480895470711,\n", - " 'error': None})" - ] - }, - "execution_count": 126, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dcts = r.trade_instructions(ti_format=O.TIF_DICTS)\n", - "assert len(dcts) == 3\n", - "assert list(dcts[0].keys()) == ['cid', 'tknin', 'amtin', 'tknout', 'amtout', 'error']\n", - "d0 = dcts[0]\n", - "assert d0[\"cid\"] == \"c0\"\n", - "assert iseq(d0[\"amtin\"], 0.41326380379418914)\n", - "dcts" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "id": "4b3ee562", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(CPCArbOptimizer.TradeInstruction(cid='c0', tknin='WETH', amtin=0.41326380379418914, tknout='USDC', amtout=-793.7257935280832, error=None),\n", - " CPCArbOptimizer.TradeInstruction(cid='c1', tknin='USDT', amtin=948.3480897734808, tknout='WETH', amtout=-0.45270781529377224, error=None),\n", - " CPCArbOptimizer.TradeInstruction(cid='c2', tknin='USDC', amtin=793.7257937187678, tknout='USDT', amtout=-948.3480895470711, error=None))" - ] - }, - "execution_count": 127, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "objs = r.trade_instructions(ti_format=O.TIF_OBJECTS)\n", - "assert len(objs) == 3\n", - "assert type(objs[0]).__name__ == 'TradeInstruction'\n", - "objs" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "id": "39fdcea2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on method trade_instructions in module tools.optimizer.cpcarboptimizer:\n", - "\n", - "trade_instructions(ti_format=None) method of tools.optimizer.cpcarboptimizer.MargpOptimizerResult instance\n", - " returns list of TradeInstruction objects\n", - " \n", - " :ti_format: TIF_OBJECTS, TIF_DICTS, TIF_DFP, TIF_DFRAW, TIF_DFAGGR, TIF_DF\n", - "\n" - ] - } - ], - "source": [ - "help(r.trade_instructions)" - ] - }, - { - "cell_type": "markdown", - "id": "dea66c52", - "metadata": {}, - "source": [ - "## simple_optimizer demo [NOTEST]" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "id": "528abf9c", - "metadata": {}, - "outputs": [], - "source": [ - "CC = CPCContainer(CPC.from_pk(p=2000+i*100, k=10*(20000+i*10000), pair=f\"{T.ETH}/{T.USDC}\") for i in range(11))\n", - "#O = CPCArbOptimizer(CC)\n", - "c0 = CC.curves[0]\n", - "CC0 = CPCContainer([c0])\n", - "O = PairOptimizer(CC)\n", - "O0 = PairOptimizer(CC0)\n", - "funcvx = O.optimize(result=O.SO_DXDYVALXFUNC)\n", - "funcvy = O.optimize(result=O.SO_DXDYVALYFUNC)\n", - "funcvx0 = O0.optimize(result=O.SO_DXDYVALXFUNC)\n", - "funcvy0 = O0.optimize(result=O.SO_DXDYVALYFUNC)\n", - "#CC.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "id": "57cc1ad4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "xr = np.linspace(1500, 3000, 50)\n", - "plt.plot(xr, [funcvx(x)/len(CC) for x in xr], label=\"all curves [scaled]\")\n", - "plt.plot(xr, [funcvx0(x) for x in xr], label=\"curve 0 only\")\n", - "plt.xlabel(f\"price [{c0.pairp}]\")\n", - "plt.ylabel(f\"value [{c0.tknxp}]\")\n", - "plt.grid()\n", - "plt.show()\n", - "plt.plot(xr, [funcvy(x)/len(CC) for x in xr], label=\"all curves [scaled]\")\n", - "plt.plot(xr, [funcvy0(x) for x in xr], label=\"curve 0 only\")\n", - "plt.xlabel(f\"price [{c0.pairp}]\")\n", - "plt.ylabel(f\"value [{c0.tknyp}]\")\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "id": "0d151350", - "metadata": {}, - "outputs": [], - "source": [ - "r = O.optimize()\n", - "#print(f\"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]\")" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "id": "1f5aed55", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = 0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2/0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues))\n", - "CC.plot()\n", - "CC_ex.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "2b212697", - "metadata": {}, - "source": [ - "## MargP Optimizer Demo [NOTEST]" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "id": "a02af582", - "metadata": {}, - "outputs": [], - "source": [ - "CCa = CPCContainer()\n", - "CCa += CPC.from_pk(pair=\"WETH/USDC\", p=2000, k=10*20000, cid=\"c0\")\n", - "CCa += CPC.from_pk(pair=\"WETH/USDT\", p=2000, k=10*20000, cid=\"c1\")\n", - "CCa += CPC.from_pk(pair=\"USDC/USDT\", p=1.2, k=20000*20000, cid=\"c2\")\n", - "O = MargPOptimizer(CCa)" - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "id": "05532dcc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = WETH/USDT\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = USDC/USDT\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = WETH/USDC\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "CCa.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "id": "985e718d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[margp_optimizer] calculating price estimates\n", - "[margp_optimizer] pe [0.0005 0.0005]\n", - "[margp_optimizer] p 0.00, 0.00\n", - "[margp_optimizer] 1/p 2,000.00, 2,000.00\n", - "\n", - "[margp_optimizer] ========== cycle 0 =======>>>\n", - "log p0 [-3.3010299956639813, -3.3010299956639813]\n", - "log dp [ 0.02281867 -0.03004231]\n", - "log p [-3.27821133 -3.3310723 ]\n", - "p (0.0005269733761120141, 0.0004665816971063286)\n", - "p 0.00, 0.00\n", - "1/p 1,897.63, 2,143.25\n", - "tokens_t ('USDC', 'USDT')\n", - "dtkn 1,742.581, -1,908.902\n", - "[criterium=3.77e-02, eps=1.0e-06, c/e=4e+04]\n", - "<<<========== cycle 0 ======= [margp_optimizer]\n", - "\n", - "[margp_optimizer] ========== cycle 1 =======>>>\n", - "log p0 [-3.2782113257736367, -3.331072301550902]\n", - "log dp [0.00197844 0.00203564]\n", - "log p [-3.27623289 -3.32903666]\n", - "p (0.0005293794916778223, 0.0004687738067091822)\n", - "p 0.00, 0.00\n", - "1/p 1,889.00, 2,133.22\n", - "tokens_t ('USDC', 'USDT')\n", - "dtkn 43.132, 49.919\n", - "[criterium=2.84e-03, eps=1.0e-06, c/e=3e+03]\n", - "<<<========== cycle 1 ======= [margp_optimizer]\n", - "\n", - "[margp_optimizer] ========== cycle 2 =======>>>\n", - "log p0 [-3.276232887408822, -3.329036663029794]\n", - "log dp [2.18800078e-06 2.23012250e-06]\n", - "log p [-3.2762307 -3.32903443]\n", - "p (0.0005293821587291089, 0.0004687762138908068)\n", - "p 0.00, 0.00\n", - "1/p 1,888.99, 2,133.21\n", - "tokens_t ('USDC', 'USDT')\n", - "dtkn 0.048, 0.054\n", - "[criterium=3.12e-06, eps=1.0e-06, c/e=3e+00]\n", - "<<<========== cycle 2 ======= [margp_optimizer]\n", - "\n", - "[margp_optimizer] ========== cycle 3 =======>>>\n", - "log p0 [-3.2762306994080452, -3.329034432907297]\n", - "log dp [-1.21938625e-10 -1.24095448e-10]\n", - "log p [-3.2762307 -3.32903443]\n", - "p (0.0005293821585804722, 0.0004687762137568585)\n", - "p 0.00, 0.00\n", - "1/p 1,888.99, 2,133.21\n", - "tokens_t ('USDC', 'USDT')\n", - "dtkn -0.000, -0.000\n", - "[criterium=1.74e-10, eps=1.0e-06, c/e=2e-04]\n", - "<<<========== cycle 3 ======= [margp_optimizer]\n" - ] - }, - { - "data": { - "text/plain": [ - "CPCArbOptimizer.MargpOptimizerResult(result=-0.027643519043587972, time=0.0012979507446289062, method='margp', targettkn='WETH', p_optimal_t=(0.0005293821585804722, 0.0004687762137568585), dtokens_t=(1.4551915228366852e-10, 1.7826096154749393e-10), tokens_t=('USDC', 'USDT'), errormsg=None)" - ] - }, - "execution_count": 135, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = O.margp_optimizer(\"WETH\", params=dict(verbose=True))\n", - "rd = r.asdict\n", - "r" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "id": "44d3cbb8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 136, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rd" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "id": "c344acd4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = WETH/USDT\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = USDC/USDT\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = WETH/USDC\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "CCa1 = O.adjust_curves(r.dxvalues)\n", - "CCa1.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "ef07cc0a", - "metadata": {}, - "source": [ - "## Optimizer plus inverted curves [NOTEST]" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "id": "2ce7d40d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = 0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2/0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "CCr = CPCContainer(CPC.from_pk(p=2000+i*100, k=10*(20000+10000*i), pair=f\"{T.ETH}/{T.USDC}\") for i in range(11))\n", - "CCi = CPCContainer(CPC.from_pk(p=1/(2050+i*100), k=10*(20000+10000*i), pair=f\"{T.USDC}/{T.ETH}\") for i in range(11))\n", - "CC = CCr.bycids()\n", - "assert len(CC) == len(CCr)\n", - "CC += CCi\n", - "assert len(CC) == len(CCr) + len(CCi)\n", - "CC.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "id": "93cb9736", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "prices post arb: [2575.204115235117, 2575.2041152356933, 2575.2041152348256, 2575.204115235484, 2575.204115235451, 2575.204115235089, 2575.204115235117, 2575.204115236329, 2575.2041152362494, 2575.204115234752, 2575.2041152356933, 2575.204115235117, 2575.204115235693, 2575.2041152348256, 2575.204115235484, 2575.2041152354514, 2575.2041152350885, 2575.204115235117, 2575.204115236329, 2575.204115236249, 2575.204115234752, 2575.204115235693]\n", - "stdev 5.007230062076576e-10\n", - "pair = 0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2/0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "O = PairOptimizer(CC)\n", - "r = O.optimize()\n", - "#print(f\"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]\")\n", - "CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues))\n", - "prices_ex = [c.pairo.primary_price(c.p) for c in CC_ex]\n", - "print(\"prices post arb:\", prices_ex)\n", - "print(\"stdev\", np.std(prices_ex))\n", - "#CC.plot()\n", - "CC_ex.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "735887f2", - "metadata": {}, - "source": [ - "## Operating on leverage ranges [NOTEST]" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "id": "d30d7723", - "metadata": {}, - "outputs": [], - "source": [ - "N = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "id": "e4150be1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = 0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2/0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "CCc, CCm, ctr = CPCContainer(), CPCContainer(), 0\n", - "U, U1 = CPCContainer.u, CPCContainer.u1\n", - "tknb, tknq = T.ETH, T.USDC\n", - "pb, pq = 2000, 1\n", - "pair = f\"{tknb}/{tknq}\"\n", - "pp = pb/pq\n", - "k = 100000**2/(pb*pq)\n", - "CCm += CPC.from_pk(p=pp, k=k, pair=pair, cid = f\"mkt-{pair}\", params=dict(xc=\"market\"))\n", - "#print(\"\\n***PAIR:\", tknb, pb, tknq, pq, pair, pp)\n", - "for i in range(N):\n", - " p = pp * (1+0.2*U(-0.5, 0.5))\n", - " p_min, p_max = (p, U(1.001, 1.5)*p) if U1()>0.5 else (U(0.8, 0.999)*p, p)\n", - " amtUSDC = U(10000, 200000)\n", - " k = amtUSDC**2/(pb*pq)\n", - " #print(\"*curve\", int(amtUSDC), p, p_min, p_max, int(k))\n", - " CCc += CPC.from_pkpp(p=p, k=k, p_min=p_min, p_max=p_max, \n", - " pair=pair, cid = f\"carb-{ctr}\", params=dict(xc=\"carbon\"))\n", - " ctr += 1\n", - " \n", - "CC = CCc.bycids().add(CCm)\n", - "CC.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "id": "ba5e64d0", - "metadata": {}, - "outputs": [], - "source": [ - "# O = CPCArbOptimizer(CC)\n", - "# r = O.simple_optimizer()\n", - "# print(f\"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]\")\n", - "# CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues))\n", - "# prices_ex = [c.pairo.primary_price(c.p) for c in CC_ex]\n", - "# print(\"prices post arb:\", prices_ex)\n", - "# print(\"stdev\", np.std(prices_ex))\n", - "# #CC.plot()\n", - "# CC_ex.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "id": "95dfc775", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(-19991.187296291224,\n", - " -25089.00748235185,\n", - " -29652.330903276525,\n", - " -33897.715807594366,\n", - " -37932.0678714449,\n", - " -41816.51426773908,\n", - " -45589.391596547975,\n", - " -49276.33560780576,\n", - " -52895.320416733426,\n", - " -56459.417363590605,\n", - " -59978.41337265917,\n", - " -20239.64402760781,\n", - " -25386.056884730173,\n", - " -29987.536954893872,\n", - " -34264.33889397325,\n", - " -38325.31497478598,\n", - " -42232.77328959052,\n", - " -46025.8323254678,\n", - " -49730.67728767181,\n", - " -53365.68545559817,\n", - " -56944.233852323305,\n", - " -60476.34722167586)" - ] - }, - "execution_count": 143, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r.dxvalues" - ] - }, - { - "cell_type": "markdown", - "id": "119bdb1e", - "metadata": {}, - "source": [ - "## Arbitrage testing [NOTEST]" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "id": "709b1f20", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = 0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2/0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "c1 = CPC.from_pkpp(p=95, k=100*10000, p_min=90, p_max=110, pair=f\"{T.ETH}/{T.USDC}\")\n", - "c2 = CPC.from_pkpp(p=105, k=90*10000, p_min=90, p_max=110, pair=f\"{T.ETH}/{T.USDC}\")\n", - "CC = CPCContainer([c1,c2])\n", - "CC.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "id": "e222be8a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "a = lambda x: np.array(x)\n", - "pr = np.linspace(70,130,200)\n", - "dx1, dy1, p = zip(*(c1.dxdyfromp_f(p) for p in pr))\n", - "assert np.all(p == pr)\n", - "dx2, dy2, p = zip(*(c2.dxdyfromp_f(p) for p in pr))\n", - "assert np.all(p == pr)\n", - "v1 = a(dy1)+a(p)*a(dx1)\n", - "v2 = a(dy2)+a(p)*a(dx2)\n", - "plt.plot(p, v1, label=\"Value curve c1\")\n", - "plt.plot(p, v2, label=\"Value curve c2\")\n", - "plt.plot(p, v1+v2, label=\"Value combined curves\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "id": "f5371aee", - "metadata": {}, - "outputs": [], - "source": [ - "def vfunc(p):\n", - " \n", - " dx1, dy1, _ = c1.dxdyfromp_f(p)\n", - " dx2, dy2, _ = c2.dxdyfromp_f(p)\n", - " v1 = dy1 + p*dx1\n", - " v2 = dy2 + p*dx2\n", - " v = v1+v2\n", - " #print(f\"[v] v({p}) = {v}\")\n", - " return -v" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "id": "cfcead3e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "OptimizerBase.SimpleResult(result=99.68104660486168, method='newtonraphson', errormsg=None, context_dct=None)" - ] - }, - "execution_count": 147, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "O = CPCArbOptimizer\n", - "O.findmin(vfunc, 100, N=100)" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "id": "fcbaa19f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "OptimizerBase.SimpleResult(result=2.0, method='newtonraphson', errormsg=None, context_dct=None)" - ] - }, - "execution_count": 148, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "func1 = lambda x: (x-2)**2\n", - "O.findmin(func1, 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "id": "4eaa9eb7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "OptimizerBase.SimpleResult(result=3.000000000003396, method='newtonraphson', errormsg=None, context_dct=None)" - ] - }, - "execution_count": 149, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "func2 = lambda x: 1-(x-3)**2\n", - "O.findmax(func2, 2.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "id": "b18defa5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "val = tuple(float(O.findmin(func1, 100, N=n)) for n in range(100))\n", - "val = tuple(abs(v-val[-1]) for v in val)\n", - "val = tuple(v for v in val if v > 0)\n", - "plt.plot(val)\n", - "plt.yscale('log')\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "id": "62597f85", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "val = tuple(float(O.findmin(func2, 100, N=n)) for n in range(100))\n", - "val = tuple(abs(v-val[-1]) for v in val)\n", - "val = tuple(v for v in val if v > 0)\n", - "plt.plot(val)\n", - "plt.yscale('log')\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "id": "a0a21eee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "99.68103950148166\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "val0 = tuple(float(O.findmin(vfunc, 99, N=n)) for n in range(100))\n", - "val = tuple(abs(v-val0[-1]) for v in val0)\n", - "val = tuple(v for v in val if v > 0)\n", - "print(val0[-1])\n", - "plt.plot(val)\n", - "plt.yscale('log')\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "id": "aba84a6b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "99.68102109480606\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "val0 = tuple(float(O.findmin_gd(vfunc, 99, N=n)) for n in range(100))\n", - "val = tuple(abs(v-val0[-1]) for v in val0)\n", - "val = tuple(v for v in val if v > 0)\n", - "print(val0[-1])\n", - "plt.plot(val)\n", - "plt.yscale('log')\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "id": "bcb1ef33", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "OptimizerBase.SimpleResult(result=99.65287573579084, method='newtonraphson', errormsg=None, context_dct=None)" - ] - }, - "execution_count": 154, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "O.findmin(vfunc, 99, N=700)" - ] - }, - { - "cell_type": "markdown", - "id": "be220d57", - "metadata": {}, - "source": [ - "## Charts [NOTEST]" - ] - }, - { - "cell_type": "markdown", - "id": "18b249ff", - "metadata": {}, - "source": [ - "### Chars (x,y)" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "id": "93bb294d", - "metadata": {}, - "outputs": [], - "source": [ - "xr = np.linspace(1,300,200)" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "id": "31c9aa2f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "defaults = dict(p=2)\n", - "curves = [\n", - " CPC.from_px(x=100, **defaults),\n", - " CPC.from_px(x=50, **defaults),\n", - " CPC.from_px(x=150, **defaults),\n", - "]\n", - "for c in curves:\n", - " plt.plot(xr, [c.yfromx_f(x) for x in xr])\n", - "\n", - "plt.ylim((0,1000))\n", - "plt.xlim((0,300))\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "id": "7ebdd94b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "defaults = dict(p=2, x_act=10)\n", - "curves = [\n", - " CPC.from_px(x=100, **defaults),\n", - " CPC.from_px(x=50, **defaults),\n", - " CPC.from_px(x=150, **defaults),\n", - "]\n", - "for c in curves:\n", - " plt.plot(xr, [c.yfromx_f(x) for x in xr])\n", - "\n", - "plt.ylim((0,1000))\n", - "plt.xlim((0,300))\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "id": "5a46f120", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "defaults = dict(p=2, y_act=20)\n", - "curves = [\n", - " CPC.from_px(x=100, **defaults),\n", - " CPC.from_px(x=50, **defaults),\n", - " CPC.from_px(x=150, **defaults),\n", - "]\n", - "for c in curves:\n", - " plt.plot(xr, [c.yfromx_f(x) for x in xr])\n", - "\n", - "plt.ylim((0,1000))\n", - "plt.xlim((0,300))\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "id": "8576042a", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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5WbZsWWpqajJlypRs3bq1vGbGjBlZuHBhFixYkCVLlmTbtm2ZNm1adu7c2d5bBgAAgN1ej/Z+wSuvvDLDhw/P9ddfXz52wAEHlP+6VCrlmmuuyeWXX54zzjgjSXLjjTemuro6t9xyS6ZPn57Nmzfnuuuuy0033ZTJkycnSW6++eYMHz48t99+e0455ZT23jYAAADs1to94H/1q1/llFNOyX/9r/81ixcvzn777ZcLLrggX/7yl5Mkq1evTkNDQ6ZOnVp+TlVVVU488cTU19dn+vTpWb58eVpaWtqsqa2tzejRo1NfX/+WAd/U1JSmpqby/S1btiRJWlpa0tLS0t5vE8remC9zRkcza3QWs0ZnMWt0FrNGZ+noGWv3gH/66adz7bXXZubMmfnbv/3b3H///fna176WqqqqfOELX0hDQ0OSpLq6us3zqqurs2bNmiRJQ0NDevbsmQEDBuyy5o3nv9mcOXNyxRVX7HJ80aJF6dOnT3u8Nfiz6urqunoLfEiYNTqLWaOzmDU6i1mjo23fvr1DX7/dA/7111/P0UcfndmzZydJjjzyyKxatSrXXnttvvCFL5TXVVRUtHleqVTa5dib/bk1l112WWbOnFm+v2XLlgwfPjwnnXRSBg0a9H7fDryjlpaW1NXVZcqUKamsrOzq7bAHM2t0FrNGZzFrdBazRmfZuHFjh75+uwf80KFDc9hhh7U59tGPfjQ///nPkyQ1NTVJWq+yDx06tLymsbGxfFW+pqYmzc3N2bRpU5ur8I2NjZk4ceJb/rlVVVWpqqra5XhlZaW/SekUZo3OYtboLGaNzmLW6CxmjY7W0fPV7t9C/4lPfCKPP/54m2NPPPFE9t9//yTJyJEjU1NT0+bjK83NzVm8eHE5zseNG5fKyso2a9avX59HHnnkbQMeAAAA9mTtfgX+f/yP/5GJEydm9uzZOfPMM3P//ffnRz/6UX70ox8laf3o/IwZMzJ79uyMGjUqo0aNyuzZs9OnT5+cffbZSZL+/fvn/PPPz8UXX5xBgwZl4MCBueSSSzJmzJjyt9IDAADAh0m7B/wxxxyThQsX5rLLLsu3v/3tjBw5Mtdcc03OOeec8ppLL700O3bsyAUXXJBNmzZl/Pjxue2229KvX7/ymquvvjo9evTImWeemR07duTkk0/ODTfckO7du7f3lgEAAGC31+4BnyTTpk3LtGnT3vbxioqKzJo1K7NmzXrbNb169cq8efMyb968DtghAAAAFEu7/ww8AAAA0P4EPAAAABSAgAcAAIACEPAAAABQAAIeAAAACkDAAwAAQAEIeAAAACgAAQ8AAAAFIOABAACgAAQ8AAAAFICABwAAgAIQ8AAAAFAAAh4AAAAKQMADAABAAQh4AAAAKAABDwAAAAUg4AEAAKAABDwAAAAUgIAHAACAAhDwAAAAUAACHgAAAApAwAMAAEABCHgAAAAoAAEPAAAABSDgAQAAoAAEPAAAABSAgAcAAIACEPAAAABQAAIeAAAACkDAAwAAQAEIeAAAACgAAQ8AAAAFIOABAACgAAQ8AAAAFICABwAAgAIQ8AAAAFAAAh4AAAAKQMADAABAAQh4AAAAKAABDwAAAAUg4AEAAKAABDwAAAAUgIAHAACAAhDwAAAAUAACHgAAAApAwAMAAEABCHgAAAAoAAEPAAAABSDgAQAAoAAEPAAAABSAgAcAAIACEPAAAABQAAIeAAAACkDAAwAAQAEIeAAAACgAAQ8AAAAFIOABAACgAAQ8AAAAFICABwAAgAIQ8AAAAFAAAh4AAAAKQMADAABAAQh4AAAAKAABDwAAAAUg4AEAAKAABDwAAAAUgIAHAACAAhDwAAAAUAACHgAAAApAwAMAAEABCHgAAAAoAAEPAAAABSDgAQAAoAAEPAAAABSAgAcAAIACEPAAAABQAAIeAAAACkDAAwAAQAEIeAAAACgAAQ8AAAAFIOABAACgADo84OfMmZOKiorMmDGjfKxUKmXWrFmpra1N7969M2nSpKxatarN85qamnLRRRdl8ODB6du3b0477bSsW7euo7cLAAAAu6UODfhly5blRz/6UT72sY+1OT537txcddVVmT9/fpYtW5aamppMmTIlW7duLa+ZMWNGFi5cmAULFmTJkiXZtm1bpk2blp07d3bklgEAAGC31GEBv23btpxzzjn58Y9/nAEDBpSPl0qlXHPNNbn88stzxhlnZPTo0bnxxhuzffv23HLLLUmSzZs357rrrsv3vve9TJ48OUceeWRuvvnmrFy5MrfffntHbRkAAAB2Wz066oUvvPDCfPrTn87kyZPzD//wD+Xjq1evTkNDQ6ZOnVo+VlVVlRNPPDH19fWZPn16li9fnpaWljZramtrM3r06NTX1+eUU07Z5c9rampKU1NT+f6WLVuSJC0tLWlpaemItwhJUp4vc0ZHM2t0FrNGZzFrdBazRmfp6BnrkIBfsGBBHnzwwSxbtmyXxxoaGpIk1dXVbY5XV1dnzZo15TU9e/Zsc+X+jTVvPP/N5syZkyuuuGKX44sWLUqfPn3e1/uA96Kurq6rt8CHhFmjs5g1OotZo7OYNTra9u3bO/T12z3g165dm//+3/97brvttvTq1ett11VUVLS5XyqVdjn2Zn9uzWWXXZaZM2eW72/ZsiXDhw/PSSedlEGDBr2HdwDvTUtLS+rq6jJlypRUVlZ29XbYg5k1OotZo7OYNTqLWaOzbNy4sUNfv90Dfvny5WlsbMy4cePKx3bu3Jm77ror8+fPz+OPP56k9Sr70KFDy2saGxvLV+VramrS3NycTZs2tbkK39jYmIkTJ77ln1tVVZWqqqpdjldWVvqblE5h1ugsZo3OYtboLGaNzmLW6GgdPV/t/iV2J598clauXJkVK1aUb0cffXTOOeecrFixIgceeGBqamrafHylubk5ixcvLsf5uHHjUllZ2WbN+vXr88gjj7xtwAMAAMCerN2vwPfr1y+jR49uc6xv374ZNGhQ+fiMGTMye/bsjBo1KqNGjcrs2bPTp0+fnH322UmS/v375/zzz8/FF1+cQYMGZeDAgbnkkksyZsyYTJ48ub23DAAAALu9DvsW+j/n0ksvzY4dO3LBBRdk06ZNGT9+fG677bb069evvObqq69Ojx49cuaZZ2bHjh05+eSTc8MNN6R79+5dsWUAAADoUp0S8HfeeWeb+xUVFZk1a1ZmzZr1ts/p1atX5s2bl3nz5nXs5gAAAKAA2v1n4AEAAID2J+ABAACgAAQ8AAAAFICABwAAgAIQ8AAAAFAAAh4AAAAKQMADAABAAQh4AAAAKAABDwAAAAUg4AEAAKAABDwAAAAUgIAHAACAAhDwAAAAUAACHgAAAApAwAMAAEABCHgAAAAoAAEPAAAABSDgAQAAoAAEPAAAABSAgAcAAIACEPAAAABQAAIeAAAACkDAAwAAQAEIeAAAACgAAQ8AAAAFIOABAACgAAQ8AAAAFICABwAAgAIQ8AAAAFAAAh4AdmN/ePEP+d4D38vDLz6cUqnU1dsBALpQj67eAADw9v7tqX/Lzx7/WW5YdUNq+tZk8ojJmXrA1Izdd2y6Vfjv8ADwYSLgAWA3Nmn4pGxp3pLFaxen4ZWG3Pzozbn50Zuzb+99M3n/yZmy/5QcNeSodO/Wvau3CgB0MAEPALux4/Y7Lsftd1yadjal/rn61K2py6K1i/Lijhfz08d+mp8+9tMM7DUwk0dMzpQDpuTo6qPTo5t/vAPAnsg/4QGgAKq6V+WkESflpBEnpXlnc5auX5q6NXW549k78tKrL+VfnviX/MsT/5J9qvbJySNOzpT9p+TjQz+eym6VXb11AKCdCHgAKJie3XvmhGEn5IRhJ+TvJ/x9lq1fltvW3JY7nr0jm5o25edP/jw/f/Ln6dezX04YdkJOGn5SjtvvuPSt7NvVWwcAPgABDwAFVtmtMhP3m5iJ+03MN4/9Zpa/sDx1a+py+5rbs/HVjfnN07/Jb57+TSq7VebjQz+eTw7/ZCYNn5QhfYZ09dYBgPdIwAPAHqJHtx4ZP3R8xg8dn8s+fln+8OIfsmjtoixauyhrtqzJPc/dk3ueuyf/a+n/yuhBo1s/kj/8pBy0z0GpqKjo6u0DAO9AwAPAHqh7t+45qvqoHFV9VGaOm5nVm1fnjrV3ZNHaRVn54so8svGRPLLxkcx7aF6G7TWsHPNHDjmyq7cOALwNAQ8Ae7iKioocuM+BOXCfA/PXY/46G3ZsyJ1r78yitYuy9PmlWbdtXW7640256Y83ZZ+qfXJc7XHZq3mvHN98fAZWDuzq7QMA/0HAA8CHzODeg/OXB/9l/vLgv8z2lu2pf74+i9Yuyl3r7srLTS/n16t/nST5f37+/+SIIUfkhGEn5Pj9jvdRewDoYgIeAD7E+lT2yeT9J2fy/pPz2uuvZUXjityx5o7c+sStefH1F7P8heVZ/sLyXL386gztOzTH73d8jh92fD5e8/H0qezT1dsHgA8VAQ8AJGn9Eryja47O2EFjc0jDIRl7wtgsfWFp7lp3V+5vuD/rX1lf/n3zPbv1zDE1x+T4Ycfn+P2Oz4i9R3T19gFgjyfgAYC3tN9e++VzAz6Xzx36ubz62qtZ1rAsd627K3c/d3ee2/Zc7nn+ntzz/D35br6bA/Y+IMftd1w+sd8nMq56XHr36N3V2weAPY6ABwDeUa8evVqvtg87PqVSKau3rM7d6+7O3evuzvIXlueZLc/kmS3P5OZHb05lt8ocVX1UJtZOzMTaiTl4wMHpVtGtq98CABSegAcA3pOKiooc2P/AHNj/wJx3+HnZ1rwtS9cvzZLnlqT++fqsf2V97lt/X+5bf1+uXn51BvUalAm1EzKxdmIm1E7I4N6Du/otAEAhCXgA4APZq+de5S/CK5VKeWbLM6l/vj73Pn9v7m+4Pxtf3ZhfP/3r/Prp1m+3P3jAweWYP2rIUenVo1cXvwMAKAYBDwC0m4qKiozsPzIj+4/MOR89Jy07W7LixRW59/l7c8/z9+TRjY/miU1P5IlNT+SGVTekqntVxlWPy7FDj834oeNz6MBDfdweAN6GgAcAOkxl98ocU3NMjqk5Jl876mt56dWXct/6+1L/fH3qn69P4/bG8l8nyT5V++SYmmNy7NBjc8oBp6R/Vf8ufgcAsPsQ8ABApxnYa2BOHXlqTh15akqlUp7e/HTqn6/PfevvywMvPJCXm15O3Zq61K2py4ShEwQ8APz/CHgAoEtUVFTkI/t8JB/Z5yM597Bz0/J6S1ZtWJWl65fmTy//KcP6DevqLQLAbkXAAwC7hcpulTliyBE5YsgRXb0VANgt+ZYYAAAAKAABDwAAAAUg4AEAAKAABDwAAAAUgIAHAACAAhDwAAAAUAACHgAAAApAwAMAAEABCHgAAAAoAAEPAAAABSDgAQAAoAAEPAAAABSAgAcAAIACEPAAAABQAAIeAAAACkDAAwAAQAEIeAAAACgAAQ8AAAAFIOABAACgAAQ8AAAAFICABwAAgAIQ8AAAAFAAAh4AAAAKQMADAABAAQh4gA9gyZMb0vza6129DQAAPgR6dPUGAIrqsYYt+avr7kv/3pU5dXRNThtbm/EHDkr3bhVdvTUAAPZAAh7gfXr+5R3Zt19VXtzalAXL1mbBsrUZ0q8qn/7Y0Jw2tjZHDN8nFRViHgCA9iHgAd6nTx5anaWXDcl9T2/Mr/7wfH73SEMatzbl+nueyfX3PJMRA/vkM2OH5rSx++WQmn5dvV0AAApOwAN8AN27VWTiQYMz8aDB+fbpo3PXEy/m3x5+PnV/fCHPvrQ931/0VL6/6KkcXL1XTh09NKeOqckh1f1cmQcA4D0T8ADtpGePbpl8WHUmH1ad7c2v5fePNuZXf3g+ix9/MU+8sC1PvPBk/vH3T+bAwX3zqdE1OXX00Izeb28xDwDAuyLgATpAn5498pmxtfnM2Nps3tGS3z/6Qn67siF3Pflint7wSn5w51P5wZ1PZdiA3jl1dE0+NXpojhy+T7r5AjwAAN5Gu/8auTlz5uSYY45Jv379MmTIkHz2s5/N448/3mZNqVTKrFmzUltbm969e2fSpElZtWpVmzVNTU256KKLMnjw4PTt2zennXZa1q1b197bBehw/XtX5oyjhuWfzzs6D/7dlPxfnz8yp46uSa/Kblm3aUd+fPfq/Jdr6zPxu3fkm79cmcVPvJim13Z29bYBANjNtHvAL168OBdeeGGWLl2aurq6vPbaa5k6dWpeeeWV8pq5c+fmqquuyvz587Ns2bLU1NRkypQp2bp1a3nNjBkzsnDhwixYsCBLlizJtm3bMm3atOzc6V9qgeLaq6pHThtbm2v/alwe/Lspufaco3La2NrsVdUjDVtezc1Ln815//f9Gfe/bs+FP3kwCx9al5e3N3f1tgEA2A20+0fob7311jb3r7/++gwZMiTLly/PCSeckFKplGuuuSaXX355zjjjjCTJjTfemOrq6txyyy2ZPn16Nm/enOuuuy433XRTJk+enCS5+eabM3z48Nx+++055ZRT2nvbAJ2uT88eOXXM0Jw6ZmhebdmZ+qc2pO6Pjbn90Rfy4tam/Gbl+vxm5fp071aRYw4YkE8esm+6v9rVuwYAoKt0+M/Ab968OUkycODAJMnq1avT0NCQqVOnltdUVVXlxBNPTH19faZPn57ly5enpaWlzZra2tqMHj069fX1bxnwTU1NaWpqKt/fsmVLkqSlpSUtLS0d8t4gSXm+zBkfRPckx39kYI7/yMDM+vQhWfn8lvz+scbc8diLefyFbVn69EtZ+vRLSXrkJ2uX5KRD9s2JBw/OuBED0rNHu3+Yig855zU6i1mjs5g1OktHz1iHBnypVMrMmTNz3HHHZfTo0UmShoaGJEl1dXWbtdXV1VmzZk15Tc+ePTNgwIBd1rzx/DebM2dOrrjiil2OL1q0KH369PnA7wXeSV1dXVdvgT3MoUkOPTDZUJs8sqkij7xUkae2VOTpDdvz9IY1ue6eNanqVsoh+5Ry2D6lfHSfUvap6updsydxXqOzmDU6i1mjo23fvr1DX79DA/6rX/1qHn744SxZsmSXx978a5NKpdI7/iqlP7fmsssuy8yZM8v3t2zZkuHDh+ekk07KoEGD3sfu4d1paWlJXV1dpkyZksrKyq7eDnuwlpaW/Ovv6lK1/9gseWpT7npyQzZsa87DL1Xk4Zda1xxavVdOPHjfHD9qUI4cvo+r87wvzmt0FrNGZzFrdJaNGzd26Ot3WMBfdNFF+dWvfpW77rorw4YNKx+vqalJ0nqVfejQoeXjjY2N5avyNTU1aW5uzqZNm9pchW9sbMzEiRPf8s+rqqpKVdWul54qKyv9TUqnMGt0hj49kr8Yu1/OOPqAvP56Kaue35JFjzdm0eONWbH25Tz2wrY89sK2/NPdq9OnZ/eMHzkwx43aN8cdNDgHV+/ld87znjiv0VnMGp3FrNHROnq+2j3gS6VSLrrooixcuDB33nlnRo4c2ebxkSNHpqamJnV1dTnyyCOTJM3NzVm8eHGuvPLKJMm4ceNSWVmZurq6nHnmmUmS9evX55FHHsncuXPbe8sAhdStW0XGDOufMcP652snj8pLrzTn7idfzB2PNWbJkxuy8ZXmLHr8xSx6/MUkyZB+VTnuoMH5xEGDc9yowaneu1cXvwMAAN6Ldg/4Cy+8MLfcckv+9V//Nf369Sv/zHr//v3Tu3fvVFRUZMaMGZk9e3ZGjRqVUaNGZfbs2enTp0/OPvvs8trzzz8/F198cQYNGpSBAwfmkksuyZgxY8rfSg9AWwP79szpR+yX04/YL6+/XsqjDVtyz5825O4nN+T+1S+lcWtTfvHQc/nFQ88lSQ6u3iufOGhwjh81OONHDkrfqg7/XlMAAD6Adv+3tWuvvTZJMmnSpDbHr7/++nzxi19Mklx66aXZsWNHLrjggmzatCnjx4/Pbbfdln79+pXXX3311enRo0fOPPPM7NixIyeffHJuuOGGdO/evb23DLDH6datIofX9s/htf3zNyd8JK+27MyDazbl7j9tyJInN+SR5zfniRe25YkXtuX6e55Jj24VOWrEgHzioME59sCBOWLEPqnq4XwLALA76ZCP0L+TioqKzJo1K7NmzXrbNb169cq8efMyb968dtwdwIdTr8rumXjQ4Ew8aHC+/qlk0yvNqX9qY5b86cXc/eSGrNu0I/c/81Luf6b12/CqenTLuP0H5NgDB+XYAwdl7PD+gh4AoIv5vCTAh9CAvj3z6Y8Nzac/1vploms2vpK7n9yQe5/emPue3pgN21oDv/6p1m9SFfQAAF1PwAOQ/Qf1zf6D+uavjt0/pVIpT724Lfc+/VKWvk3Qz5xycL528qgu3jUAwIeLgAegjYqKihw0pF8OGtIv575N0B974KCu3iYAwIeOgAfgz3qroH8XX3cCAEA7E/AAvCcVFRWpqOjqXQAAfPh06+oNAAAAAO9MwAMAAEABCHgAAAAoAAEPAAAABSDgAQAAoAAEPAAAABSAgAcAAIACEPAAAABQAAIeAAAACkDAAwAAQAEIeAAAACgAAQ8AAAAFIOABAACgAAQ8AAAAFICABwAAgAIQ8AAAAFAAAh4AAAAKQMADAABAAQh4AAAAKAABDwAAAAUg4AEAAKAABDx7huceTBbPTV7Z2NU7AQAA6BACnj3DPf+YLPpOcvVhyb/NSDY82dU7AgAAaFcCnj3DYaclQ8cmr72aLL8+mX90cstZyeq7klKpq3cHAADwgQl49gyj/0vyN4uTL/4mOeQvklQkT9ya3PiZ5AfHJvf/OHl1S1fvEgAA4H0T8Ow5KiqSA45LPv/T5KsPJEd/Kansm7z4WPLbS5KrPpr8embSsLKrdwoAAPCeCXj2TIMPSqZdnVz8aHLq3GTwwUnztuSB65IfHtd6W3pt8sqGrt4pAADAuyLg2bP16p+Mn55ceH/yhV8lh52edO/ZehX+1m8k3zskWXBO8thvkp0tXb1bAACAt9WjqzcAnaKiIjnwxNbb9peSR36erPhJ8vxDyWO/br31GZx87KzkiLOTmtFdvWMAAIA2BDwfPn0GJh//cuvthT8mf7gl+cPPklcak6Xfb71Vj0kOPz057LPJ4FFdvWMAAAABz4dc9WHJ1H9ITp6VPPX71qvyj/02eWFl6+2Of0iGHN760fvDP5vse0hX7xgAAPiQEvCQJN17JAef0nrb/lLrz8T/8ZfJ03cmjatab3fOTvY9tDXmD/tsMuSjrR/NBwAA6AQCHt6sz8DkqHNbbzs2tV6R/+O/Jk/d0for6RY/liy+Mhk0Kjn4lFQceHIqSq919a4BAIA9nICHP6f3gOTIc1pvO15Onri1Neb/9Ptk45PJvU+mx73zc2q33un+6sLkkE8lB01J+lV39c4BAIA9jICHd6v3PsnYz7XeXt2S/On25Mm6lJ68LZXbNySP/VvrLUmGHpGMmtr6kfzaI5Nu3bty5wAAwB5AwMP70WvvZPQZyegz8lpzU+r/3+/nuOrt6f7U7a2/mm79itbbXXOTXvskBxyXjPyPX2M3+GA/Ow8AALxnAh4+qIpuebnvR/L6CX+R7id/M9n6wn9cnf/35KlFyasv/+fvmk+SvWqSkSe03g48MdlnRJduHwAAKAYBD+2tX/V//tz8ztdar8Q/fWey+q5k7X3JtoZk5b+03pJkwAHJAccnIyYkI45NBh7oCj0AALALAQ8dqXuPZNjRrbcTLklaXk3W3Z88vbg16J9bnmx6pvX20E2tz+m7b2vIDz+2NeqHfizpXtmV7wIAANgNCHjoTJW9/vPj80nStDVZU996e3Zp8vyDySsvJo/+W+stSXr0bv0PAMPHt/5v7VG+5R4AAD6EBDx0pap+rd9Uf/AprfdbXm39yP2zS1tva5e2/i76Z+5uvb1h72HJfkcl+41r/d+hR7R+sR4AALDHEvCwO6ns1frx+RHHtt5//fXW3zf/7NJk7f2tV+gbH022rGu9Pfqr/3hiReu32+83LjlwUjL2rK56BwAAQAcR8LA769Yt2feQ1tu481qPNW1N1v+h9efnn3uw9bb52WTD4623VzcLeAAA2AMJeCiaqn6tv1f+gOP+89i2xtaQf/7B1tgHAAD2OAIe9gR7DUkO+VTrDQAA2CN16+oNAAAAAO9MwAMAAEABCHgAAAAoAAEPAAAABSDgAQAAoAAEPAAAABSAgAcAAIACEPAAAABQAAIeAAAACkDAAwAAQAEIeAAAACgAAQ8AAAAFIOABAACgAAQ8AAAAFICABwAAgAIQ8AAAAFAAAh4AAAAKQMADAABAAQh4AAAAKAABDwAAAAUg4AEAAKAABDwAAAAUgIAHAACAAhDwAAAAUAACHgAAAApAwAMAAEABCHgAAAAoAAEPAAAABSDgAQAAoAAEPAAAABSAgAcAAIAC2O0D/gc/+EFGjhyZXr16Zdy4cbn77ru7eksAAADQ6XbrgP/Zz36WGTNm5PLLL89DDz2U448/PqeeemqeffbZrt4aAAAAdKrdOuCvuuqqnH/++fnrv/7rfPSjH80111yT4cOH59prr+3qrQEAAECn6tHVG3g7zc3NWb58eb7xjW+0OT516tTU19fvsr6pqSlNTU3l+5s3b06SvPTSSx27UT70Wlpasn379mzcuDGVlZVdvR32YGaNzmLW6Cxmjc5i1ugsb/RnqVTqkNffbQN+w4YN2blzZ6qrq9scr66uTkNDwy7r58yZkyuuuGKX4wcffHCH7REAAADebOPGjenfv3+7v+5uG/BvqKioaHO/VCrtcixJLrvsssycObN8/+WXX87++++fZ599tkP+j4M3bNmyJcOHD8/atWuz9957d/V22IOZNTqLWaOzmDU6i1mjs2zevDkjRozIwIEDO+T1d9uAHzx4cLp3777L1fbGxsZdrsonSVVVVaqqqnY53r9/f3+T0in23ntvs0anMGt0FrNGZzFrdBazRmfp1q1jvm5ut/0Su549e2bcuHGpq6trc7yuri4TJ07sol0BAABA19htr8AnycyZM3Puuefm6KOPzoQJE/KjH/0ozz77bL7yla909dYAAACgU+3WAX/WWWdl48aN+fa3v53169dn9OjR+e1vf5v999//HZ9bVVWVb33rW2/5sXpoT2aNzmLW6Cxmjc5i1ugsZo3O0tGzVlHqqO+3BwAAANrNbvsz8AAAAMB/EvAAAABQAAIeAAAACkDAAwAAQAHssQH/gx/8ICNHjkyvXr0ybty43H333V29JQps1qxZqaioaHOrqakpP14qlTJr1qzU1tamd+/emTRpUlatWtWFO6Yo7rrrrnzmM59JbW1tKioq8stf/rLN4+9mtpqamnLRRRdl8ODB6du3b0477bSsW7euE98FRfBOs/bFL35xl/Pcscce22aNWePdmDNnTo455pj069cvQ4YMyWc/+9k8/vjjbdY4t9Ee3s2sObfRHq699tp87GMfy95775299947EyZMyO9+97vy4515TtsjA/5nP/tZZsyYkcsvvzwPPfRQjj/++Jx66ql59tlnu3prFNjhhx+e9evXl28rV64sPzZ37txcddVVmT9/fpYtW5aamppMmTIlW7du7cIdUwSvvPJKxo4dm/nz57/l4+9mtmbMmJGFCxdmwYIFWbJkSbZt25Zp06Zl586dnfU2KIB3mrUk+dSnPtXmPPfb3/62zeNmjXdj8eLFufDCC7N06dLU1dXltddey9SpU/PKK6+U1zi30R7ezawlzm18cMOGDct3v/vdPPDAA3nggQfyyU9+Mqeffno50jv1nFbaA3384x8vfeUrX2lz7NBDDy194xvf6KIdUXTf+ta3SmPHjn3Lx15//fVSTU1N6bvf/W752Kuvvlrq379/6Yc//GEn7ZA9QZLSwoULy/ffzWy9/PLLpcrKytKCBQvKa5577rlSt27dSrfeemun7Z1iefOslUql0nnnnVc6/fTT3/Y5Zo33q7GxsZSktHjx4lKp5NxGx3nzrJVKzm10nAEDBpT++Z//udPPaXvcFfjm5uYsX748U6dObXN86tSpqa+v76JdsSd48sknU1tbm5EjR+Zzn/tcnn766STJ6tWr09DQ0GbmqqqqcuKJJ5o5PpB3M1vLly9PS0tLmzW1tbUZPXq0+eM9u/POOzNkyJAcfPDB+fKXv5zGxsbyY2aN92vz5s1JkoEDByZxbqPjvHnW3uDcRnvauXNnFixYkFdeeSUTJkzo9HPaHhfwGzZsyM6dO1NdXd3meHV1dRoaGrpoVxTd+PHj83/+z//Jv//7v+fHP/5xGhoaMnHixGzcuLE8V2aO9vZuZquhoSE9e/bMgAED3nYNvBunnnpqfvKTn+SOO+7I9773vSxbtiyf/OQn09TUlMSs8f6USqXMnDkzxx13XEaPHp3EuY2O8Vazlji30X5WrlyZvfbaK1VVVfnKV76ShQsX5rDDDuv0c1qPD/AedmsVFRVt7pdKpV2Owbt16qmnlv96zJgxmTBhQj7ykY/kxhtvLH8Ripmjo7yf2TJ/vFdnnXVW+a9Hjx6do48+Ovvvv39+85vf5Iwzznjb55k1/pyvfvWrefjhh7NkyZJdHnNuoz293aw5t9FeDjnkkKxYsSIvv/xyfv7zn+e8887L4sWLy4931jltj7sCP3jw4HTv3n2X/5LR2Ni4y38Vgferb9++GTNmTJ588snyt9GbOdrbu5mtmpqaNDc3Z9OmTW+7Bt6PoUOHZv/998+TTz6ZxKzx3l100UX51a9+lUWLFmXYsGHl485ttLe3m7W34tzG+9WzZ88cdNBBOfroozNnzpyMHTs2//iP/9jp57Q9LuB79uyZcePGpa6urs3xurq6TJw4sYt2xZ6mqakpjz76aIYOHZqRI0empqamzcw1Nzdn8eLFZo4P5N3M1rhx41JZWdlmzfr16/PII4+YPz6QjRs3Zu3atRk6dGgSs8a7VyqV8tWvfjW/+MUvcscdd2TkyJFtHnduo72806y9Fec22kupVEpTU1Pnn9Pe55fu7dYWLFhQqqysLF133XWlP/7xj6UZM2aU+vbtW3rmmWe6emsU1MUXX1y68847S08//XRp6dKlpWnTppX69etXnqnvfve7pf79+5d+8YtflFauXFn6/Oc/Xxo6dGhpy5YtXbxzdndbt24tPfTQQ6WHHnqolKR01VVXlR566KHSmjVrSqXSu5utr3zlK6Vhw4aVbr/99tKDDz5Y+uQnP1kaO3Zs6bXXXuuqt8Vu6M/N2tatW0sXX3xxqb6+vrR69erSokWLShMmTCjtt99+Zo337L/9t/9W6t+/f+nOO+8srV+/vnzbvn17eY1zG+3hnWbNuY32ctlll5Xuuuuu0urVq0sPP/xw6W//9m9L3bp1K912222lUqlzz2l7ZMCXSqXS97///dL+++9f6tmzZ+moo45q8+sk4L0666yzSkOHDi1VVlaWamtrS2eccUZp1apV5cdff/310re+9a1STU1NqaqqqnTCCSeUVq5c2YU7pigWLVpUSrLL7bzzziuVSu9utnbs2FH66le/Who4cGCpd+/epWnTppWeffbZLng37M7+3Kxt3769NHXq1NK+++5bqqysLI0YMaJ03nnn7TJHZo13463mLEnp+uuvL69xbqM9vNOsObfRXr70pS+V23LfffctnXzyyeV4L5U695xWUSqVSu/tmj0AAADQ2fa4n4EHAACAPZGABwAAgAIQ8AAAAFAAAh4AAAAKQMADAABAAQh4AAAAKAABDwAAAAUg4AEAAKAABDwAAAAUgIAHAACAAhDwAAAAUAACHgAAAArg/wPuPertbhBG0QAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "defaults = dict(p=2, x_act=10, y_act=20)\n", - "curves = [\n", - " CPC.from_px(x=100, **defaults),\n", - " CPC.from_px(x=50, **defaults),\n", - " CPC.from_px(x=150, **defaults),\n", - "]\n", - "for c in curves:\n", - " plt.plot(xr, [c.yfromx_f(x) for x in xr])\n", - "\n", - "plt.ylim((0,1000))\n", - "plt.xlim((0,300))\n", - "plt.grid()" - ] - }, - { - "cell_type": "markdown", - "id": "8c55ead8", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "### Charts (dx, dy)" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "id": "14363ce5", - "metadata": {}, - "outputs": [], - "source": [ - "e=1e-5\n", - "dxr = np.linspace(-50+e,50-e,100)" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "id": "d6e4c237", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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q5XlAcW83vhvahGJerhy5FM9j03eSkJJudizJSdW7Q6EASIiBnxaanUZERERERLKISnkeUaaIBzOHNsHH3c6eszd48rsIUtIzzI4lOcVqh4aPO7fDJ2h5NBERERGRfEKlPA+pGuDF9Mca4eFiZdOxK4yeE0l6RqbZsSSnNB4Odg+I3g8n1pidRkREREREsoBKeR4TVKYwUx9tiIvVwvID0fwzZD8OnTUtGDz8oMEQ5/bmT8xMIiIiIiIiWUSlPA9qXsmfTx8OwmLAj7vO8/ayQyrmBUWzp8Fig9Ob4NxOs9OIiIiIiMg9UinPozrVCuC9B+oAMHXTKSavP2FyIskRPqWhTn/n9pZPTI0iIiIiIiL3TqU8D3uwYSD/7lYDgA9WHmH6llMmJ5Ic0fw559fDS+DyEXOziIiIiIjIPVEpz+OGtijPc/dXBmDc4p/4fsdZkxNJtitaFap1c25v+dTcLCIiIiIick9UyvOB0e0q80SrCgD8M2Q/C3afNzmRZLvmo51f9/0AsfrfW0REREQkr1IpzwcMw+CVztUY3KwsDge8MHcvS/ZdNDuWZKfARlCuJWSmwdbJZqcREREREZG7pFKeTxiGwX+61+ShRoFkOmD0nEhWHYw2O5ZkpxajnV8jpkPiNTOTiIiIiIjIXVIpz0csFoO3etemd1Ap0jMdjJq9h/VHYsyOJdml4v0QUBvSEmDHVLPTiIiIiIjIXVApz2esFoMP+taha+0SpGZkMmJmBOHHr5gdS7KDYfx6b/n2LyA1wdQ4IiIiIiJy57K1lG/cuJHu3btTsmRJDMMgNDT0Nz8fMmQIhmH85tG0adPfvCYlJYVnnnkGf39/PD096dGjB+fPa2Krv2KzWvjkoXq0q16clPRMhs7Yxc7Turw5X6rRCwqXg6RrsOc7s9OIiIiIiMgdytZSnpCQQN26dZk0adKfvqZTp05ERUXdeixbtuw3Px89ejQhISHMmTOHzZs3c/PmTbp160ZGRkZ2Rs/z7FYLnz0SRKsqRUlKy+Cxb3YSee6G2bEkq1ltEPysczt8ImSkmZtHRERERETuSLaW8s6dO/Pmm2/Sp0+fP32Nq6srAQEBtx5+fn63fhYbG8u0adP46KOPaNeuHUFBQXz33Xfs37+f1atXZ2f0fMHVZmXKwAY0reDHzZR0Hp22nYMXY82OJVmt3iPgWQxiz8GB+WanERERERGRO2AzO8D69espVqwYvr6+tG7dmrfeeotixYoBEBERQVpaGh06dLj1+pIlS1KrVi3Cw8Pp2LHjH75nSkoKKSkpt76Pi4sDIDYxFnd392wcTe5jM+CLAfV4/Nvd7D57g0embufbxxpSvYRXtu43LS3tN18lO1mxNB6Bdd0bODZ/THr13mBouoicoONcCgId51IQ6DiXgkDHec673c/a1FLeuXNnHnzwQcqWLcupU6f497//zX333UdERASurq5ER0fj4uJC4cKFf/N7xYsXJzr6z5f7euedd3j99dd/9/yARQN4xP8RSlhLZPlYcrt+xeHqNStnbqbx0JfhjKqRQSnP7N9vWFhY9u9EsGWUooPFHfvlw0TMeYdLPkFmRypQdJxLQaDjXAoCHedSEOg4zzmJiYm39TpTS3n//v1vbdeqVYuGDRtStmxZli5d+peXvDscDgzD+NOfv/LKK4wZM+bW93FxcQQGBnI18ypTE6Yypv4YHqz84F++R37UoUMaQ2ZEsO98HF8ec2fGYw2oUcI7W/aVlpZGWFgY7du3x263Z8s+5Lcsnj/B1k9pnLKZjM7/dM7OLtlKx7kUBDrOpSDQcS4FgY7znPfLFdt/x/TL1/+/EiVKULZsWY4dOwZAQEAAqampXL9+/Tdny2NiYggODv7T93F1dcXV1fV3z7co2YKt17fy7q532RWzi3HB4/Bx9cn6geRSfnY73w1ryqBpO9h77gaDp0cwa1gTapbMvs/AbrfrD31OCX4adkzBcmEnlqhdUPbP/4xI1tJxLgWBjnMpCHScS0Gg4zzn3O7nnKtuPL169Srnzp2jRAnn5eUNGjTAbrf/5hKLqKgoDhw48Jel/M980PoDXmz4IjaLjdVnV9NvcT8iYyKzKn6e4O1mZ+bQxtQN9OVGYhqPfKXJ3/INrwCoN8C5vWm8uVlEREREROS2ZGspv3nzJpGRkURGRgJw6tQpIiMjOXv2LDdv3uSFF15g69atnD59mvXr19O9e3f8/f3p3bs3AD4+PgwdOpTnn3+eNWvWsGfPHgYOHEjt2rVp167dHecxDINHaz7Kd52/o3Sh0lxMuMiQFUOYtn8amY7MrBx6rqZino81fxYMKxwPg3M7zU4jIiIiIiJ/I1tL+a5duwgKCiIoyDnp1JgxYwgKCuK1117DarWyf/9+evbsSZUqVRg8eDBVqlRh69ateHn9OjP4xx9/TK9evejXrx/NmzfHw8ODxYsXY7Va7zpXTf+a/Nj9RzqV60SGI4NPdn/CyNUjuZp09Z7HnFf8Uszr/b9ifuCCinme51cB6j3s3F73prlZRERERETkb2VrKW/Tpg0Oh+N3j+nTp+Pu7s7KlSuJiYkhNTWVM2fOMH36dAIDA3/zHm5ubkycOJGrV6+SmJjI4sWLf/eau+Hl4sX7rd5nXLNxuFnd2HJxC30X9yX8Yvg9v3de4e1m59v/V8wHTlMxzxdajQWLHU6uh1ObzE4jIiIiIiJ/IVfdU57TDMPggSoP8H3X76noU5ErSVcYETaC8bvGk5ZRMNbvUzHPhwqXhQZDnNtr3wSHw9Q4IiIiIiLy5wp0Kf9FpcKV+L7b9zxY5UEAvjn4DYOWD+Js3FmTk+WM/y3mj3y1nX3nb5gdS+5Fy+fB5gbntsHx1WanERERERGRP6FS/jN3mzuvNXuNj9t8jLeLNwevHuTBxQ+y6MQiHAXgTOMvxTyojC+xSWk8MnU7EWeumR1L7pZ3CWg83Lm99g2dLRcRERERyaVUyv9Hu7LtmN9jPg2KNyAxPZFXN7/Ky5te5mbqTbOjZTvn5G9NaFzOj/iUdAZN28HWEwVn8rt8p/k/wKUQRO2FQ4vNTiMiIiIiIn9ApfwPBHgGMK3DNEbVG4XVsLLs1DL6Lu7L3st7zY6W7Qq52pj+eCNaVPInMTWDId/sYOPRy2bHkrvhWQSajnRur3sbMjPMzSMiIiIiIr+jUv4nrBYrI+qOYHqn6ZT0LMmFmxcYvHwwU/dNJSOflxsPFxtfDW7IfdWKkZKeybAZu1j90yWzY8ndaPY0uPnA5UNwYL7ZaURERERE5H+olP+NesXqMbfH3Ftrmn+651OGrRpGdEK02dGylZvdyhcDG9CpZgCpGZk8+V0Ey/ZHmR1L7pS7LzR/zrm9/h0oIKsKiIiIiIjkFSrlt8HbxZv3W73PG83fwN3mzq5Lu+izqA8rTq0wO1q2crFZmDQgiB51S5Ke6WDU7N2E7rlgdiy5U41HgGdRuHYSImebnUZERERERP4flfLbZBgGvSr1Ym73udT2r018ajwvbnyRVze/mq8ngbNZLXzcvx4PNihNpgP+8WMkP+wsGEvF5RuuhaDFGOf2hvchPcXcPCIiIiIicotK+R0q612WGZ1n8ESdJ7AYFhadWETfxX2JjIk0O1q2sVoM3nugDgOblsHhgJfm7+fbrafNjiV3ouHj4FUS4s5DxHSz04iIiIiIyM9Uyu+C3WLnmaBn+KbjN79OArdiMJMjJ5OemW52vGxhsRi80bMWw1qUB+C1hQf5YsMJk1PJbbO7QesXndsbP4TURHPziIiIiIgIoFJ+T+oXr8+8HvPoWqErmY5MPt/7OYNXDOZc3Dmzo2ULwzB4tWt1RrWtBMC7yw/z3orDOBwOk5PJbak3EHzLQkIM7PjS7DQiIiIiIoJK+T3zcvHi3Zbv8m7LdylkL8S+y/vou7gvocdD82VZNQyDFzpW5eXO1QD4fP0JXg09QEZm/htrvmNzgTavOLe3fALJcabGERERERERlfIs07VCV+b3mE/9YvVJTE/k31v+zfMbnud68nWzo2WLJ1tX5J0+tTEMmL39LM/N2UNqeqbZseTv1OkH/lUg6Tpsm2x2GhERERGRAk+lPAuVLFSSrzt+zXP1n8Nm2Ag7E0afRX3YdH6T2dGyxcONyzDx4SDsVoMl+6J4YuYuklIzzI4lf8Vihbb/dG6HT4LEa+bmEREREREp4FTKs5jVYmVY7WHM6jqLij4VuZJ0hZFrRvLG1jdITMt/k2t1q1OSqY82xM1uYf2Ryzz69XZik9LMjiV/pXpPCKgNqfGw4T2z04iIiIiIFGgq5dmkRpEazOk2h4HVBwLw49Ef6bekH/su7zM5WdZrU7UY3w1tgpebjZ2nr/Pwl9u4elNrYedaFgt0eNO5vWMqXD5ibh4RERERkQJMpTwbudnceKnxS0ztMJXiHsU5E3eGR5c/ymeRn5GWmb/OJjcs58cPTzTDv5ALP0XF8fBXO7mmXp57VWgDVbuAIwNW/cvsNCIiIiIiBZZKeQ5oWqIp83vMp0v5LmQ4Mvhi7xcMWjaIU7GnzI6WpWqU9Gbuk8GU8nXn1NVEJhywcuJygtmx5M90eBMsdji2Co6vNjuNiIiIiEiBpFKeQ3xcfXiv1Xt80OoDvFy8OHj1IP0W92PWoVlkOvLPrOXl/T2Z91QzKvh7ciPV4OGvdhB57obZseSPFKkIjZ9wbq98FTLSzc0jIiIiIlIAqZTnsE7lOxHSI4RmJZqRnJHMuzve5YlVT3Dx5kWzo2WZEj7ufD+sEYGeDq4npvHwl9tYdyTG7FjyR1q/CO5+cPkwRHxjdhoRERERkQJHpdwExT2L80X7L/hnk3/iZnVje/R2+izqQ8ixEBwOh9nxsoSfpwvP1MygRaUiJKVlMHzGLuZHnDc7lvwv98K/LpG27m3n+uUiIiIiIpJjVMpNYjEsPFztYeb1mEfdonVJSEvgtfDXeHbts1xJumJ2vCzhaoUpjwTRq15J0jMdPD93L19sOJFv/uEh32jwGBStBknXYMMHZqcRERERESlQVMpNVta7LDM6zWB0/dHYLXbWn19P74W9WXl6pdnRsoSLzcL4fvUY3rI8AO8uP8wbSw6RmalinmtYbdDxLef2jilw5bi5eUREREREChCV8lzAarEytPZQ5nSbQzW/atxIucELG15g7IaxxKbEmh3vnlksBq92rcGrXaoD8PWWU4z+IZLU9PwzwV2eV6kdVO4AmekQ9m+z04iIiIiIFBgq5blIlcJVmN1lNiPqjMBqWFl+ejm9F/Zm4/mNZkfLEsNbVeCT/vWwWQwW7b3I49N3cjNFM37nGh3eAsMKR5bBiXVmpxERERERKRBUynMZu9XOqKBRzOw8k/I+5bmcdJmn1zzNv7f8m7jUOLPj3bNeQaWYNqQRHi5WNh+/wkNfbuVyfIrZsQSgaBVoNMy5vfKfWiJNRERERCQHqJTnUrWL1ubHbj/yaI1HMTAIPR5K74W92Xxhs9nR7lnrKkX5fnhTini6cOBCHH2/COfUlQSzYwlAm5fBzRdifoI935qdRkREREQk31Mpz8XcbG682OhFpneaThmvMsQkxvDU6qd4bctrxKfGmx3vntQN9GXeU8EE+rlz5moifSZvIeLMNbNjiYcftHnFub32LUjO+3MaiIiIiIjkZirleUD94vWZ12MeA6sPxMAg5HgIvRf2ZsuFLWZHuyfl/T1Z8FRz6pT24XpiGg9P3c7y/VFmx5JGQ6FIZUi8Ahs/NDuNiIiIiEi+plKeR7jb3Hmp8Ut80+kbAr0CuZR4iSdXP8m48HHcTL1pdry7VtTLlTlPNKVd9WKkpmcycvZuvtp0UmuZm8lq/3WJtG2fw9UT5uYREREREcnHVMrzmAbFGzCv+zweqf4IAPOPzaf3ot6EXwg3Odnd83CxMWVQQx5tVhaHA95ceohxiw6SobXMzVO5A1S8DzLTYOWroH8kERERERHJFirleZCH3YOXG7/MNx2/oXSh0kQnRDNi9QjGhY/Ls/eaWy0Gr/eoeWst8xlbzzBiZgSJqZoB3BSGAR3fAYsNji6Hw0vMTiQiIiIiki+plOdhDQMaMr/HfAZUGwA4z5r3WtiLDec2mJzs7hiGwfBWFfhsQH1cbBZWH7rEw19u05JpZilWDZo/59xeNhZS8uY/+IiIiIiI5GYq5Xmch92DV5q88psZ2ketHcUrm17hRvINs+Pdla51SjB7WBMKe9jZez6WPp9v4XhM3r1vPk9r9SIULgfxF52zsYuIiIiISJZSKc8nGhRvwLwe8xhScwgWw8KSk0voubAnYWfCzI52VxqW82PByOaULeLBuWtJPPB5ONtPXjU7VsFjd4eu453bO6bAxT3m5hERERERyWdUyvMRd5s7zzd8npmdZ1LRpyLXkq8xZv0Yxqwfw5WkK2bHu2POJdOCCSrjS2xSGgOnbWdexHmzYxU8le6HWn3BkQmLn4MM3ecvIiIiIpJVVMrzoTpF6/Bj9x95os4T2AwbYWfC6LWwF0tOLslzS40VKeTK98Ob0rV2CdIyHLwwdy/vrThMpmZmz1kd3wY3H4jaCzunmp1GRERERCTfUCnPp1ysLjwT9Azfd/ue6n7ViU2J5ZVNrzBq7SiiE6LNjndH3OxWJj4cxDP3VQLg8/UnGDlrt2Zmz0lexaHdOOf22jch9oKpcURERERE8guV8nyuml81ZnWdxbNBz2K32Nl4fiO9FvZizuE5ZDoyzY532ywWg+c7VGV8v7q4WC2sOBhN/ynbuBSXbHa0gqP+ECjdGFJvwvKxZqcREREREckXVMoLALvFzvA6w5nXfR71itYjIS2Bt7a/xZAVQzgZe9LseHekT/3SzBreBD9PF/ZfiKXnpC0cuBBrdqyCwWKB7p841y4/vAQOLzU7kYiIiIhInqdSXoBU8K3AjM4zeKXxK3jYPNgTs4e+i/oyZe8U0jLSzI532xqV8yN0ZHMqFStEdFwyD36xlZUH89Yl+XlW8ZoQ/Ixze9lYSNFSdSIiIiIi90KlvICxGBYGVB9AaM9QWpRqQVpmGpMiJ9F/aX8OXDlgdrzbVqaIBwtGBtOysj9JaRk8+V0EUzacyHMT2eVJrcaCb1mIOw/r3zE7jYiIiIhInqZSXkCVKFSCyfdP5t2W71LYtTDHrh/jkWWP8P7O90lMSzQ73m3xdrPzzZBGDGpaFocD3ll+mJfm7yMlPcPsaPmbiwd0/ci5vW2yc0Z2ERERERG5KyrlBZhhGHSt0JXQXqF0rdCVTEcmM3+aSZ9FfdhyYYvZ8W6LzWrhjV61GNe9BhYDftx1nkembudyfIrZ0fK3yu2hZp9f1y7P1D+EiIiIiIjcDZVywc/Nj3dbvsvk+ycT4BnAhZsXeHL1k7y86WWuJl01O95tGdK8PF8PaYSXm41dZ67Tc9JmTQCX3Tq9A67ecHEP7JxmdhoRERERkTxJpVxuaVm6JaE9QxlYfSAGBktPLqXnwp6EHAvJE/dqt6lajNCnm1OhqCcXY5Pp+0U4i/deNDtW/uUVAO3+49xe81+4cdbcPCIiIiIieZBKufyGp92Tlxq/xOyus6lauCqxKbG8Fv4aw1YN43TsabPj/a2KRQsRMrI5rasUJTktk2e+38OHK4+QmZn7/1EhT2rwOAQ2gdR4WPg0ZGaanUhEREREJE9RKZc/VMu/Ft93+54xDcbgZnVjR/QOHlj0AF/u+zLXL5/m427n6yGNGNGqAgCT1h3niZkRxCfn7tx5ksUCvT4Hmzuc2gi7dBm7iIiIiMidUCmXP2W32Hms1mOE9AyhecnmpGamMnHPRPot6UdkTKTZ8f6S1WLwSpfqfNy/Li42C6sPXaLP5HDOXE0wO1r+U6QitP+vczvsNbh6wtw8IiIiIiJ5iEq5/K3SXqX5vN3nvNvyXfzc/Dh+4ziPLn+UN7a+QVxqnNnx/lLvoNLMHdGM4t6uHIu5SY9JW9h87IrZsfKfRsOgfGtIS4TQpzQbu4iIiIjIbVIpl9vyy/JpC3supFelXjhw8OPRH+kR0oNlJ5fl6ong6gb6snhUC+oF+hKblMbgb3bw1aaTuTpznmOxQM/PwMULzm2H8IlmJxIRERERyRNUyuWO+Lr58kbzN/i649eU9ynP1eSrvLTpJUaEjeBsXO6dfbuYtxtznmjKA/VLk5Hp4M2lhxj9QyRJqTqjm2V8A6Hzu87tdW/BpZ/MzSMiIiIikgeolMtdaRTQiHnd5zGq3ihcLC5sjdpK74W9+WLvF6RmpJod7w+52a18+GAdxnWvgc1isDDyIr0nb+Hs1USzo+Uf9R6BKp0gIxVCRkAunxRQRERERMRsKuVy11ysLoyoO4KQniEElwwmNTOVzyI/44FFD7Dr0i6z4/0hwzAY0rw8s4Y1wb+QC4ej4+k+aTPrj8SYHS1/MAzoPgHcC0P0Ptj4odmJRERERERyNZVyuWdlvMvwRbsveL/V+xRxK8LpuNM8seYJ5iXM43rydbPj/aEmFYqw5JmWt+4zf2z6Tj5bd1zrmWcFrwDo+pFze+MHcHGPuXlERERERHIxlXLJEoZh0Ll8Zxb1XkT/qv0xMIhMi6T3kt7MPTqXTEem2RF/J8DHjR9GNOXhxmVwOOCDlUd48jutZ54laj0ANXuDIwNCnoS0ZLMTiYiIiIjkSirlkqW8Xbz5V9N/Mb3DdAIsAcSlxvHfrf9l0LJBHLp6yOx4v+Nqs/JOn9q826c2LlYLq366RK/PtnA85qbZ0fK+ruPBsxhcPuyc+E1ERERERH5HpVyyRW3/2jzl9RQvNngRT7sn+67s46GlD/HO9neIT403O97vPNS4DD+MaEqAtxsnLifQ67MtrDgQbXasvM3DD3p86twOnwhntpqbR0REREQkF1Ipl2xjNaw8XPVhFvVaROdyncl0ZDL78Gx6hPZg6cmluW6d8KAyhVn8TAsal/fjZko6T34XwdvLDpGekfsuvc8zqnaGegMBB4Q+CSm6AkFERERE5P9TKZdsV8yjGO+3fp8v239JOe9yXEm6wsubXmbYqmGcvHHS7Hi/UdTLlVnDmjC0RXkAvtx4kgFTtxMTp3ui71qnt8G7NFw/DSteNjuNiIiIiEiuolIuOaZZyWbM7zGfZ4KewdXqyo7oHTyw+AEm7J5AYlruWSvcbrXw7241mPxIfQq52thx+hpdPt3M1hNXzY6WN7n5QO8vAAP2zIT988xOJCIiIiKSa6iUS45ysbrwRJ0nCO0ZSuvSrUnPTOer/V/Rc2FPws6E5apL2rvULsGiUc2pFuDFlZspPPLVNj5ff0LLpt2N8i2h9Vjn9uLn4OoJc/OIiIiIiOQSKuViitJepZl0/yQmtJ1ASc+SRCdEM2b9GEaEjeBU7Cmz491SoWghQkY2p0/9UmQ64L0Vh3liZgSxiVo27Y61Ggtlm0PqTZj3GKSnmJ1IRERERMR0KuViqvvK3Edor1BG1BmBi8WFrVFb6bOoDx9HfJxrLml3d7Hy0YN1eadPbVxsFlYfukS3SZs4cCHW7Gh5i9UGD3wF7n4QtRfCXjM7kYiIiIiI6VTKxXTuNndGBY0itGcorUq3Ij0zna8PfE2P0B6sOL0iV1zSbhgGDzcuw/wngyld2J1z15Lo83k4c3aczRX58gzvktB7inN7+xdweKm5eURERERETJatpXzjxo10796dkiVLYhgGoaGhv/m5w+Fg3LhxlCxZEnd3d9q0acPBgwd/85qUlBSeeeYZ/P398fT0pEePHpw/fz47Y4tJAr0D+ez+z5h430RKFSrFpcRLvLjhRYaHDc81s7TXLu3D0mdacn+1YqSmZ/Lygv08/+NeElLSzY6Wd1TpAM1GObdDR8KNc+bmERERERExUbaW8oSEBOrWrcukSZP+8Ofvv/8+48ePZ9KkSezcuZOAgADat29PfHz8rdeMHj2akJAQ5syZw+bNm7l58ybdunUjIyMjO6OLidoEtiG0Zygj647ExeLC9qjtPLDoAT7a9RE3U81f59rHw87URxsytlNVrBaDBXsu0H3SZg5FxZkdLe+4/z9QqgEk34D5QyFD9+iLiIiISMGUraW8c+fOvPnmm/Tp0+d3P3M4HHzyySe8+uqr9OnTh1q1ajFjxgwSExOZPXs2ALGxsUybNo2PPvqIdu3aERQUxHfffcf+/ftZvXp1dkYXk7nZ3Hiq3lOE9gqlTek2pDvSmX5wOt1Du7PoxCIyHZmm5rNYDEa2qcScJ5oS4O3GycsJ9PxsC7O2n9Hl7LfD5gJ9vwZXHzi3Hda9bXYiERERERFT2Mza8alTp4iOjqZDhw63nnN1daV169aEh4czYsQIIiIiSEtL+81rSpYsSa1atQgPD6djx45/+N4pKSmkpPw6s3NcnPMMZlpaGmlpOiOXE375nO/18w5wC2B8q/FsvrCZDyI+4NzNc7y6+VV+OPwDYxuOpYZfjayIe9fqlfJi4cimvLTgAOuPXuHVkAOEH7vCGz1r4OVm2h+vvKFQKYyuH2Nb8DiOzR+TERiMo0Ibs1Pdkaw6zkVyMx3nUhDoOJeCQMd5zrvdz9q01hAdHQ1A8eLFf/N88eLFOXPmzK3XuLi4ULhw4d+95pff/yPvvPMOr7/++u+eX7VqFR4eHvcaXe5AWFhYlr3XUOtQwt3CWZ+8nn1X9jFoxSAauDSgvVt7PC2eWbafu9HTD3zKGiw+a2HpgWi2H49iSOUMAguZGisPsFHH/z7KX1lL+tzHWV/tTVLsvmaHumNZeZyL5FY6zqUg0HEuBYGO85yTmHh7q0mZfirPMIzffO9wOH733P/6u9e88sorjBkz5tb3cXFxBAYG0qFDB7y9ve8tsNyWtLQ0wsLCaN++PXa7Pcvetwc9uJx4mQmRE1h2ehm7Undx2HGYp+o8xYOVH8RmMe+Q7gY8cu4G//hxHxduJDPhJzuvdKrKwCaBf3tMF2hpbXFM74RbzEE63JxHxsNzwWI1O9Vtya7jXCQ30XEuBYGOcykIdJznvF+u2P47pjWYgIAAwHk2vESJEreej4mJuXX2PCAggNTUVK5fv/6bs+UxMTEEBwf/6Xu7urri6ur6u+ftdrsOwByWHZ95SZ+SvNf6PR6q/hDvbH+HQ9cO8UHEB4ScCOHlxi/TpESTLN3fnWhcoSjLnm3FC/P2EvbTJf679DA7Tt/gvb518HHXsfeH7HZ4cDp82RrL6Y1Ytk2E1i+aneqO6O8WKQh0nEtBoONcCgId5znndj9n09YpL1++PAEBAb+5fCI1NZUNGzbcKtwNGjTAbrf/5jVRUVEcOHDgL0u5FAxBxYL4vuv3vNbsNXxdfTl+4zjDVg3jH+v+wbl485bZ8vGw8+WgBrzWrQZ2q8GKg9F0mbCJiDPXTMuU6xWtAl0/cm6vewuOaSJHERERESkYsrWU37x5k8jISCIjIwHn5G6RkZGcPXsWwzAYPXo0b7/9NiEhIRw4cIAhQ4bg4eHBgAEDAPDx8WHo0KE8//zzrFmzhj179jBw4EBq165Nu3btsjO65BFWi5UHqzzIkt5LeKjqQ1gNK6vPrqZXaC8m7J5AQlqCKbkMw+DxFuWZ/1QwZfw8uHAjiX5TtvHpmmNkZGp29j9UbwDUHww4YP7jcPWE2YlERERERLJdtpbyXbt2ERQURFBQEABjxowhKCiI1157DYCxY8cyevRoRo4cScOGDblw4QKrVq3Cy8vr1nt8/PHH9OrVi379+tG8eXM8PDxYvHgxVmveuOdUcoaPqw+vNn2Vud3n0qREE1IzU/lq/1d0D+nOwuMLTVtCrU5pX5Y+24LeQaXIyHQwPuwoD0/dxsUbSabkyfW6fAClG0NyLMx5BFLMX5deRERERCQ7ZWspb9OmDQ6H43eP6dOnA86ziePGjSMqKork5GQ2bNhArVq1fvMebm5uTJw4katXr5KYmMjixYsJDAzMztiSh1UuXJmp7acyoe0EAr0CuZx0mX9t+RePLH2EvZf3mpLJy83Ox/3rMb5fXTxdrOw4dY3OEzax4kCUKXlyNZsr9PsWChWHy4dg4UjQuu8iIiIiko+Zdk+5SHYxDIP7ytxHaM9Q/tHgH3jYPDhw9QADlw3klU2vcCnhkim5+tQvzdJnW1K3tA+xSWk8+d1uXlmwn6TUDFPy5FreJaDfTLDY4aeFsHm82YlERERERLKNSrnkWy5WFx6v9ThL+yyld6XeGBgsObmE7qHd+Xzv5ySl5/wl5OX8PZn7ZDBPtq4IwPc7ztJ90mZ+unh7yyUUGGWaOC9lB1jzBhzTepoiIiIikj+plEu+5+/uz3+b/5fvu35PvaL1SEpPYnLkZLqHdGfJySU5fr+5i83Cy52r8d3QJhT1cuV4zE16Td7CN1tO4dCl2r9q+Bg0GIJz4rehmvhNRERERPIllXIpMGr61+Tbzt/yQasPKOlZkkuJl3hl0ysMXDaQyJjIHM/TorI/K55ryX3VipGansnri39i8Dc7iYlLzvEsuVbn9/9n4rd4sxOJiIiIiGQplXIpUAzDoFP5TizstZBng57Fw+bB/iv7GbR8EC9ueJGLNy/maJ4ihVyZNrghr/eoiavNwsajl+n4yUZNAvcLmyv0nwmFApwTv4U+pYnfRERERCRfUSmXAsnN5sbwOsNZ2mcpfSr3wcBgxekVdA/pzqe7P83R9c0Nw2BwcDmWPNOCGiW8uZ7onATuxbl7uZmSnmM5ci2vAGcxt9jh0GLY9KHZiUREREREsoxKuRRo/u7+vB78Oj92/5HGAY1JzUxl6v6pdAvpxoJjC8jIzLmZ0SsX9yL06eY82boihgFzI87TZcImIs5cy7EMuVZgY+j6cxlf+xYcXWluHhERERGRLKJSLgJU86vGVx2+YkLbCZTxKsOVpCv8J/w/PLjkQcIvhOdYjl8mgZszvCmlfN05ey2RB7/YyvhVR0jLyNkJ6XKdBkOgwWM4J34bDpePmp1IREREROSeqZSL/Oz/r2/+YsMX8Xbx5tj1Y4xYPYInVz/JsevHcixLkwpFWD66Jb2DSpHpgE/XHqfv5+GcvHwzxzLkSp3fh8CmkBILs/rCzctmJxIRERERuScq5SL/w26182jNR1nWZxmDagzCZrGx5cIW+i7uy7jwcVxJupIjObzd7Hzcvx4THw7C283G3vOxdPl0EzO3ni64S6fZXOChWVC4HNw4A98/BGk5v968iIiIiEhWUSkX+RM+rj6MbTSWhT0X0r5sezIdmcw/Np+uC7oyZe8UktJzpgx2r1uSlf9oRXDFIiSnZfLvhQd59OsdXLxRQMuopz88Mg/cfOHCLljwBGQW8Ev7RURERCTPUikX+RtlvMswvs14vu38LXX865CYnsikyEl0C+nGwuMLc2QyuBI+7nw3tAn/6V4DV5uFTceu0PGTjcyPOF8wz5r7V4aHZoPVBQ4tgtWvmZ1IREREROSuqJSL3KagYkF81+U7Pmj1AaUKlSImMYZ/bfkX/Zf0z5HJ4CwWg8eal2fZcy2pF+hLfHI6z8/dy4iZEVyOT8n2/ec65ZpDz8nO7fCJsPMrc/OIiIiIiNwFlXKRO2AYBp3Kd2Jhr4U83+B5vFy8OHL9CCNWj+CJVU9w+NrhbM9QsWgh5j3ZjBc7VsVuNVj10yU6frKR5fujsn3fuU6dB6Htv5zby16Eo6vMzSMiIiIicodUykXugqvVlSG1hrCs9zIerfEodoudrVFb6be4H//c9E8u3ryYrfu3WS083bYSC59uQbUAL64lpPLUrN2MnrOH2MS0bN13rtPqBag3EByZMHcIRO01O5GIiIiIyG1TKRe5B75uvrzY6EUW9VpEl/JdcOBg8cnFdA/pzvhd44lNic3W/dco6c3CUc0Z2aYiFgNCIy/S4ZMNrD8Sk637zVUMA7p/AuVbQ1oCzO4PsRfMTiUiIiIicltUykWyQGmv0rzX6j3mdJ1Do4BGpGam8s3Bb+iyoAszDs4gNSM12/btarMytlM15j0VTAV/Ty7FpTDkm528OHcvsUkF5Ky51Q79voWi1SE+Cmb3g+Q4s1OJiIiIiPwtlXKRLFTTvybTOkzjs/s/o5JvJeJS4/hw14d0D+nO4hOLs3Wm9vplCrP02ZY81rwchgFzI87T4eMNrD18Kdv2mau4+8IjP0Kh4nDpgPNS9owC8o8SIiIiIpJnqZSLZDHDMGhVuhXzus/jv8H/pZh7MS4mXOSfm/9JvyX92HR+U7YtY+buYuU/3Wvy44hmlP/5rPnj03cx5sfIgnGvuW8ZeHgO2D3gxBpYPBoK4pJxIiIiIpJnqJSLZBOrxUrvyr1Z0mcJz9V/Di+7F0evH2XkmpE8vvJx9l3el237blTOj2XPtmRYi/IYBizYfYH2H28g7KcCcNa8VH3o+zUYFoj8Dlb9S8VcRERERHItlXKRbOZuc2dY7WEs67OMITWH4GJxYdelXTyy7BHGrB/DqdhT2bNfFyv/6laDeU82o0JRT2LiUxj+7S5Gz9nD9YTsu8c9V6jaGXpMcm5vnQSbPjQ3j4iIiIjIn1ApF8khvm6+PN/weZb0XkLPij0xMAg7E0bvhb15fevrxCRmz4zpDco6z5qPaFXh1gzt7T/eyIoD0dmyv1wj6BHo+I5ze+2bsGOquXlERERERP6ASrlIDitRqARvtniT+T3m06Z0GzIcGcw7Oo+uC7ryScQn2bKMmpvdyitdqjP/qWAqFSvElZspPPldBCNnRRATn5zl+8s1mo2EVmOd28tehH1zzc0jIiIiIvI/VMpFTFK5cGUm3j+RGZ1mULdoXZIzkpl2YBqdF3Tmq/1fkZSelOX7DCpTmCXPtGBkm4pYLQbL9kfTfvxGftx1LtsmnzNd239C4xGAA0JGwJEVZicSEREREblFpVzEZPWL12dm55l82vZTKvlWIj41ngm7J9BlQRfmHJ5DWhYv6+Vmd65rvvDp5tQq5U1sUhpj5+1j0LQdnL2amKX7yhUMAzq9C3X6gyMD5g6G05vNTiUiIiIiAqiUi+QKhmHQtkxb5nWfx9st3qZUoVJcSbrCW9vfokdoD5aeXEqmIzNL91mrlA+hI5vzcudquNosbD5+hQ6fbOCrTSfJyMxnZ80tFuj5GVTpDOnJMPshuLjH7FQiIiIiIirlIrmJ1WKle8XuLO61mH82+SdF3Ipw/uZ5Xt70Mg8ufpAN5zZk6WXmNquFJ1tXZOXoVjSrUITktEzeXHqIPpO3cCgqLsv2kytY7fDgdCjXElLj4bsH4PJRs1OJiIiISAGnUi6SC9mtdh6u9jDL+izj2aBnb61xPmrtKB5d/ig7o3dm6f7K+Xsye3gT3u1TGy83G3vPx9J94mY+XHmE5LSMLN2Xqexu8NBsKBkEiVdhZi+4cdbsVCIiIiJSgKmUi+RiHnYPhtcZzvIHlvNYrcdwtboSeTmSx1c+zvBVw9l/eX+W7cswDB5qXIbVY1rTsWZx0jMdTFp3nC4TNhF+4kqW7cd0bt7wyHzwrwpxF+DbXhAXZXYqERERESmgVMpF8gAfVx/GNBjDsj7L6F+1PzaLjW1R2xiwbADPrH2GI9eOZNm+inu7MWVQQ74YWJ+iXq6cvJLAgKnbef7HvVxLSM2y/ZjKswgMCgHfMnDtBEzvqmIuIiIiIqZQKRfJQ4p5FONfTf/Fkt5L6FmxJxbDwvpz63lw8YOM3TiWM3FnsmxfnWqVYM3zrRnUtCyGAfN3n+f+j9YzL+J8/lg+zacUDF4CPirmIiIiImIelXKRPKhUoVK82eJNQnqG0LFcRxw4WH5qOT1De/Kf8P8QdTNryqW3m503etVi3pPBVAvw4npiGi/M3cuAqds5eflmluzDVIXLwhAVcxERERExj0q5SB5WwacCH7b+kLnd59K6dGsyHBksOLaAriFdeWvbW8QkxmTJfhqULcziZ1rwUqdquNktbD15lU6fbGLC6mOkpOfxieBUzEVERETERCrlIvlANb9qTLp/EjM7z6RJQBPSMtOYc2QOXRZ04f2d73Ml6d4narNbLTzVpiKrRremVZWipGZk8vHqo3SesIltJ69mwShMpGIuIiIiIiZRKRfJR+oVq8dXHb/i645fU79YfVIyUpj500y6LOjC+Ijx3Ei+cc/7KFPEgxmPNeLTh4PwL+TKycsJPPTlNsb8GMnl+JR7H4RZVMxFRERExAQq5SL5UKOARkzvNJ0p7aZQ2782SelJfHPgGzrO78jEPROJS427p/c3DIMedUuyZkxrBjQpg2HAgt0XuP+j9czcdoaMzDw6EZyKuYiIiIjkMJVykXzKMAyCSwUzq8ssJt03iep+1UlMT+TLfV/SaV4nvtj7BTdT722yNh8PO2/3rs2Cp4KpWdKbuOR0/h16gN6Tt7Dv/I2sGUhOUzEXERERkRykUi6SzxmGQevA1vzQ7Qc+bvMxlXwrEZ8Wz2eRn9Fxfke+3PflPZfzoDKFWTSqBa/3qImXq41952Pp+dkW/hW6n9jEtCwaSQ76o2Iee8HsVCIiIiKSD6mUixQQhmHQrmw75veYzwetPqC8T3niUuOYuGcinRZ0Yuq+qSSkJdz1+1stBoODy7Hmhdb0DiqFwwHfbTvLfR+tZ35eXNv8f4v5153g6gmzU4mIiIhIPqNSLlLAWAwLncp3IqRHCO+1fI9y3uWITYnl0z2f0ml+J77a/9U9lfNiXm583L8es4c3oVKxQlxNSOX5uXvp/+U2Dkff273sOa5wWXhsKfhVhNizzmIevd/sVCIiIiKSj6iUixRQVouVLhW6ENozlHdavkM573LcSLnBhN0T6DS/E9P2TyMxLfGu3z+4oj/Lnm3JS52q4W63suPUNbp+upnXFx8kNikPXdLuWwYeXwHFa0NCjPNS9rPbzU4lIiIiIvmESrlIAWe1WOlWoRshPUN4u8XblPEqw42UG3yy+5N7LucuNufa5mFjWtG5VgAZmQ6+2XKa+z9az4+7zpGZV2ZpL1TMeSl7YFNIjoWZveD4arNTiYiIiEg+oFIuIgDYLDa6V+zOwl4LeavFWwR6BXI95Tqf7P6EjvM73tNl7aULe/D5wAbMHNqYikU9uXIzlbHz9tHn8/C8M0u7uy8MCoFK7SAtEWY/hHFokdmpRERERCSPUykXkd+wWWz0qNiDRb0W8WbzN2+dOZ+wewId53dkyt4pxKfG39V7t6xclOXPteKfXarh6WIl8twNen62hVcW7ONaQmoWjyQbuHjAQ99Dzd6QmYY1ZBhlrm4wO5WIiIiI5GEq5SLyh2wWGz0r9WRhr4W83eLtWxPCTYqcRMf5Hfk88nPiUu984jYXm4UnWlVk7Qttbs3S/v2Oc7T9cD0zt54mI7df0m5zgQemQf3BGI5Mgs5Ow7JtktmpRERERCSPUikXkb/0y2XtoT1Debflu5T3KU98ajyT906m07xOfBb5GbEpsXf8vsW9nbO0/ziiGdVLeBOblMa/Fx6k28TNbD1xNRtGkoUsVug+gYxmzwBgXTMO1vwX8tqybyIiIiJiOpVyEbktVouVrhW6EtIjhA9afUAl30rEp8Xzxd4v6Di/IxN2T+Ba8rU7ft/G5f1YPKo5b/SsibebjUNRcTw8dRsjZ0Vw7trdz/6e7QyDzPv+w08lHnR+v+kjWPIPyEg3N5eIiIiI5Ckq5SJyR6wWK53Kd2J+j/l81PojKheuTEJaAl/t/4pO8zvx4c4PuZx4+Y7e02a1MKhZOda/2JZBTctiMWDZ/mjuH7+BD1ceISEl9xbdYwHdyej8IWBAxDcwZwCk3DQ7loiIiIjkESrlInJXLIaFDuU6MK/7PD5p+wk1itQgKT2JGT/NoNP8Try9/W2iE6Lv6D39PF14o1ctlj3XkuCKRUhNz2TSuuPc99F6Qvacz7VLqGXWHwL9vgWbGxxbCdO7QPwls2OJiIiISB6gUi4i98RiWLi/zP3M6TqHyfdPpm7RuqRmpvL94e/pvKAz48LHcS7+3B29Z7UAb2YNa8KUQQ0I9HPnUlwK//hhL30+D2fP2evZNJJ7VKMHDF4CHkUgai981Q5iDpudSkRERERyOZVyEckShmHQsnRLZnaeyVcdvqJRQCPSM9OZf2w+3UO68+rmVzkZe/KO3q9jzQDC/tGasZ2q4vHzEmq9J4cz5odIomKTsnE0dymwEQwNA7+KEHsWpnWAU5vMTiUiIiIiuZhKuYhkKcMwaFKiCV93/JoZnWbQvGRzMhwZLDqxiF6hvRizfgyHrh667fdzs1sZ2aYS615owwP1SwOwYM8F2n64nvFhR3Pf/eZFKjqLeWATSImFmb1h349mpxIRERGRXEqlXESyTf3i9fmi/Rd83/V72ga2xYGDsDNh9FvSj6dWP8WemD23/V7Fvd34qF9dQp9uTsOyhUlOy+TTNcdo++F6ftx1Lnetb+5ZBB5dCDV6QmYaLBgOGz/QkmkiIiIi8jsq5SKS7Wr51+LT+z5lQY8FdCnfBYthYfOFzTy6/FGGrBhC+IVwHLdZWOsF+jL3yWZMfqQ+gX7uxMSnMHbePrpP3Ez48SvZPJI7YHeHvtMh2LmWOWvfhMXPQkaaqbFEREREJHdRKReRHFO5cGXea/Uei3st5oHKD2Cz2Ii4FMGI1SN4eOnDrDmzhkxH5t++j2EYdKldgtVjWvPPLtXwcrPxU1QcA77azrAZuzhxOZcsSWaxQIc3ocuHYFhg97cwuz8kx5qdTERERERyCZVyEclxZbzLMC54HMv7LGdg9YG4Wd04ePUgo9ePps/CPiw6sYi0zL8/o+xqs/JEq4pseLEtg5uVxWoxWH3oEh0/3si4RQe5npCaA6O5DY2HQ/9ZYHOHE2ucM7NfOW52KhERERHJBVTKRcQ0AZ4BvNT4JVb2Xcnw2sMpZC/EidgTvLr5Vbou6MqsQ7NISv/7Wdb9PF14vWctVo5uyf3VipGe6WB6+GlafbCOz9efIDktIwdG8zeqdYHHl4NXSbhyFL66D46vMTuViIiIiJhMpVxETOfn5sez9Z9lVd9VPFf/Ofzc/IhKiOLdHe/ScV5HpuydQmzK31/yXamYF9OGNOK7oU2oXsKb+OR03ltxmPs+XM/8iPPmTwZXMgieWA+lGzsvYZ/VF8InaQI4ERERkQJMpVxEcg0vFy+G1R7GygdW8q8m/6JUoVJcT7nOpMhJdJjXgY92fURMYszfvk+Lyv4seaYFHz1Yl5I+blyMTeb5uXvpNnEzG49ezoGR/AWv4jBkCdQbCI5MWPUqhI6EtGRzc4mIiIiIKVTKRSTXcbO50b9af5b0XsK7Ld+lcuHKJKYnMv3gdDrN78S48HGciTvzl+9htRg80KA0a19ow8udnZPBHYqK49GvdzBo2nYOXjRxsjWbK/ScBJ3edU4At3c2TO8K8dHmZRIRERERU6iUi0iuZbPY6FqhK/O7z2fSfZOoV7QeaZlpzD82n+4h3RmzfgwHrhz4y/dws1t5snVFNr7YlqEtymO3Gmw6doVuEzcz5sdILtz4+3vWs4VhQNOnYOB8cPOFC7vgyzZwIcKcPCIiIiJiCpVyEcn1DMOgdWBrZnaZyfRO02lVuhUOHISdCePhpQ8zdOVQtlzY8pdrnRf2dOHf3WqwZkwbetQticMBC3ZfoO2H63lr6U/mzdRe8T4Yvhb8q0J8FHzdGfb9aE4WEREREclxKuUikqc0KN6Az+7/jPk95tO9Qndsho0d0Tt4cvWTPLj4QZaeXEp6Zvqf/n6ZIh58+nAQi0Y1p2kFP1LTM5m66RSt3l/HpLXHSEz989/NNkUqwrDVUKUTZKTAguGw8lXI+Ptl4UREREQkb1MpF5E8qUrhKrzd8m2W9VnGwOoDcbe5c+T6EV7e9DLdQrox+9Dsv1xOrU5pX74f3pTpjzWiRglv4lPS+XDVUVq9v55vt54mNT0zB0cDuHnDQ7OhxRjn91snwYzuEBeVszlEREREJEeplItInlaiUAleavwSYX3DGFVvFH5ufly4eYF3drxDh3kdmLRnEleTrv7h7xqGQZuqxVjyTAsmPFSPMn4eXLmZwmsLD9Ju/AYWRl4gMyeXUbNYod1/oN9McPWGs1thSks4tTHnMoiIiIhIjlIpF5F8wcfVhxF1R9xaTq10odLcSLnBlH1T6Di/I//d+t8/nbHdYjHoWa8Uq8e05o2eNfEv5MrZa4k8NyeSrhM3s+5IzF/er57lavRwrmdevBYkXIZve8LGDyEzh8/ei4iIiEi2M72Ujxs3DsMwfvMICAi49XOHw8G4ceMoWbIk7u7utGnThoMHD5qYWERys/+/nNpHrT+itn9tUjJSmHt0Lt1DujN63WgiYyL/8HddbBYGNSvHxrFteKFDFbxcncuoPfbNTvpP2caOU9dybiBFKsLQMKj3iHM987VvwJyHIel6zmUQERERkWxneikHqFmzJlFRUbce+/fvv/Wz999/n/HjxzNp0iR27txJQEAA7du3Jz4+3sTEIpLbWS1WOpTrwKwus/im4ze0Lt0aBw7WnF3DoOWDGLRsEGvOriHT8fuzzx4uNkbdV5mNY9syvGV5XGwWdpy+Rr8pW3n06x3sO38jZwbh4gE9P4Pun4LVFY6ugCmt4OKenNm/iIiIiGS7XFHKbTYbAQEBtx5FixYFnGfJP/nkE1599VX69OlDrVq1mDFjBomJicyePdvk1CKSFxiGQcOAhky6fxKhPUPpXak3doudyMuRjF43mp6hPfnxyI9/OClcYU8XXu1agw0vtmFAkzLYLAYbj16mx6QtjJi5i2OXbubEAKDBYBgWBoXLwY2zMK0D7PoGcvKSehERERHJFjazAwAcO3aMkiVL4urqSpMmTXj77bepUKECp06dIjo6mg4dOtx6raurK61btyY8PJwRI0b84fulpKSQkpJy6/u4uDgA0tLSSEvTEkM54ZfPWZ+35CZlPMvw78b/5snaTzLnyBzmHpvL6bjTvLHtDSbumciDlR+kX+V+FHEv8pvf8/ew8Xq3ajweXIZJa0+wcF8UKw9eYtXBSzTwt1D1UhwVi3tnb3j/GvD4GqyLnsZybAUsGU3m6S1kdHofXL2yd99SoOnvcykIdJxLQaDjPOfd7mdtOHJ09qLfW758OYmJiVSpUoVLly7x5ptvcvjwYQ4ePMiRI0do3rw5Fy5coGTJkrd+54knnuDMmTOsXLnyD99z3LhxvP766797fvbs2Xh4eGTbWEQkb0lxpBCRGkF4Sjg3Mm8AYMNGXZe6NHdtTjFrsT/8vehEWHbOwt5rzouNLIaDpkUddCydia9rNod2OKgUs4waF3/EwMFN1+JElHuKGx4VsnnHIiIiInInEhMTGTBgALGxsXh7//kJHNNL+f9KSEigYsWKjB07lqZNm9K8eXMuXrxIiRIlbr1m+PDhnDt3jhUrVvzhe/zRmfLAwECuXLnylx+GZJ20tDTCwsJo3749drvd7Dgifyk9M51159cx89BMDlw9cOv55iWaM7D6QBoXb4xhGL/7vcgz13ht3g4O3XCWc7vV4KFGgYxoWY7i3m7Zmtk4uxXrwicx4i7gsNjIbPMqmU2fBiNX3JUk+Yj+PpeCQMe5FAQ6znNeXFwc/v7+f1vKc8Xl6/+fp6cntWvX5tixY/Tq1QuA6Ojo35TymJgYihcv/qfv4erqiqvr709X2e12HYA5TJ+55AV27HSp2IXOFToTeTmSGQdnsPbsWrZEbWFL1Baq+VVjUI1BdC7XGbv11+O5Xlk/nqyeSbGaTfhkzQm2n7rGzG1n+XHXeR5pUpYn21SgmFc2lfOKreCpLbDoWYxDi7CufR3r6Y3Q+wvwCvj73xe5Q/r7XAoCHedSEOg4zzm3+znnulMqKSkpHDp0iBIlSlC+fHkCAgIICwu79fPU1FQ2bNhAcHCwiSlFJD8yDIOgYkF80vYTlvRewkNVH8Ld5s7ha4d5dfOrdJjfgS/3fcn15N8uS9awbGHmPNGU2cOa0LBsYVLSM/l6yylavb+Ot5cd4urNlD/Z4z1yLwz9voXuE8DmDifXwefBcPSPb+0RERERkdzH9FL+wgsvsGHDBk6dOsX27dvp27cvcXFxDB48GMMwGD16NG+//TYhISEcOHCAIUOG4OHhwYABA8yOLiL5WBnvMrza9FXC+obxXP3nKOZejCtJV5i4ZyLt57Xn9a2vczL25K3XG4ZBcCV/5j7ZjJlDG1Mv0JfktEy+3HiSlu+v470Vh7mekJr1QQ0DGgyBERugeG1IvAqz+8HylyAtOev3JyIiIiJZyvTL18+fP8/DDz/MlStXKFq0KE2bNmXbtm2ULVsWgLFjx5KUlMTIkSO5fv06TZo0YdWqVXh5abZhEcl+Pq4+DKs9jME1BrPyzEq+Pfgth64dYt7Recw7Oo/Ktsr4RfnRIrAFhmFgGAYtKxelRSV/1h+9zMdhR9l3PpbP15/g2/DTDGlejmEtKlDY0yVrgxatCsNWw+r/wPYvnI/TW6DvNOfPRERERCRXynUTvWWHuLg4fHx8/vYGe8k6aWlpLFu2jC5duuieFclXHA4HEZcimPnTTNadW4cD51+hlXwrMajGILqU74Kbze03r19zKIaPVx/l4EXn8oyeLlYeDS7H8JYV8Mvqcg7Oy9dDn3KeNbe5Q4c3oOFQsJh+cZTkQfr7XAoCHedSEOg4z3m320P1X2giInfAMAwaBjRkwn0TCO0eSjOXZrjb3Dl+4zj/Cf8P7ee159Pdn3Ip4dKt17erUZwlz7Tgi4ENqFHCm4TUDD5ff4IW763lneXZcM95lY7wVDhUaAPpSbDsBfiuN8Sez9r9iIiIiMg9UykXEblLgV6BdPXoyopeK3i+wfOU9CzJjZQbTN0/lU7zOzF241j2X94POMt5p1oBLH22BV8OakCtUt4kpmYwZcNJWry3jreW/sTl+Cws514BMDAEOr//8yRw62FyM4icDfn/AikRERGRPEOlXETkHnm5eDGk1hCW9lnKx20+pn6x+qQ70ll+ajkDlg3gkWWPsOLUCtIy0zAMgw41A1g8qgXTBjekTmkfktIymLrpFC3fX8sbS34iJj6LJmizWKDJCHhyM5RqCClxzsva5wyAmzFZsw8RERERuScq5SIiWcRmsdGubDtmdJ7BD91+oEfFHtgtdvZd3seLG1+k0/xOfLX/K64nX8cwDO6vXpyFTzfnm8ca3ZqtfdrmU7R8bx3jFh3k4o2krAnmXwkeXwn3vwYWOxxZBpObwk8Ls+b9RUREROSuqZSLiGSDGkVq8FaLt1jVdxVP1X0KPzc/YhJjmLB7Au3ntec/4f/hyLUjGIZB26rFCBkZzIzHG1O/jC8p6ZlMDz9N6w/W8fL8fZy5mnDvgaw2aPk8PLEeitdyTgL346MwfxgkXf/bXxcRERGR7KFSLiKSjfzd/RlZbyRhfcN4s/mbVPerTkpGCguOLaDv4r4MWTGEsDNhZDgyaF2lKPOfCmbWsCY0q1CEtAwHc3aeo+2H6xk9Zw/HLsXfe6CAWjB8HbR8AQwL7J/rvNf86Kp7f28RERERuWOmr1MuIlIQuFhd6FmpJz0q9iDyciSzDs1i9ZnVRFyKIOJSBAGeATxU9SEeqPwAzSv507ySP7tOX2PSuuOsP3KZ0MiLhEZepFPNAEbdV4lapXzuPozNBe7/N1TtDCEj4OpxmP0g1H4QOr0Lnv5ZN3ARERER+Us6Uy4ikoMMwyCoWBAftv6QFQ+sYHjt4RR2LUx0QjSf7P6EdvPa3bq0vWE5P6Y/1pglz7SgU80AAFYcjKbbxM0M+WYHu05fu7cwpRvCiE3QbNSvZ80/awz7ftQM7SIiIiI5RKVcRMQkAZ4BPFv/WcIeDOON5m/87tL2wcsHs+L0CqqW8OCLQQ1Y9Y9W9KpXEosB649cpu8XW+n3xVbWHYnBcbcl2sUDOr4FQ1dDsZrOe80XDIdZD8KNc1k7YBERERH5HZVyERGTuVpd6VWpFz90+4FvO39Lx3IdsRk2dsfs5sUNL9JxXkc+3/s5ft4pfPJQEGufb8NDjQKxWw12nL7GY9/spMunm1m09yIZmXdZzks3cE4C1/ZfYHWB42HOGdq3fwmZmVk6XhERERH5lUq5iEgu8f8vbV/ZdyVP1X0Kf3d/LiddZnLkZNrPa8/YjWO5kXmMd/rUZtPY+xjesjweLlYORcXx7Pd7uO+j9czefpbktIw7D2BzgdYvOtc1D2wKqTdh+YvwdUeIOZz1AxYRERERlXIRkdyomEcxRtYbyaoHVvFey/eoV7Qe6ZnpLD+1nEHLB9F/SX/CY5YxpmN5wl++j3+0q0JhDztnribyz5D9tHp/HVM2nCA+Oe3Od160Kjy2HLp8CC6F4PwOmNIS1r8H6SlZP1gRERGRAkylXEQkF7Nb7XSp0IWZXWbyQ7cf6F2pN65WVw5dO8R/wv9Du7ntmHpwAr0au7Dl5ft4rVsNSvi4EROfwjvLD9P83bW8v+IwMfHJd7ZjiwUaD4ent0PljpCRCuvfhs+D4cS67BmsiIiISAGkUi4ikkfUKFKD/zb/L6v7rmZMgzGUKlSKuNQ4vv3pW7qFdGPMhlGUL3Oatc+34oO+dahY1JO45HQmrz9Bi3fX8cqCfZy4fPPOdupTGgb8AA9Mg0LFncunzewFcx+DuKhsGaeIiIhIQaJSLiKSx/i6+fJYrcdY2nspn93/GS1LtcTAYMvFLTy77ll6LupKrOtK5jxZiy8HNaB+GV9SMzL5fsc52o3fwBPf7iLizPXb36FhQO2+MGonNHnSuXzawQUwqRFs+xwy0rNvsCIiIiL5nEq5iEgeZbVYaVW6FZPbTWZpn6U8VvMxfFx9iEqIYsLuCXRc0IG11z7h3309mDuiKe2qF8fhgFU/XeKBz8N58ItwVv90iczbnbHdzQc6v+ecpb1UQ0iNhxUvw5dt4NyO7ByqiIiISL6lUi4ikg8EegUypuEYVvddzZvN36RWkVqkZaax9ORSHl3+KO/sH077JidY/GwD+jUsjd1qsPP0dYZ9u4v2H2/gh513MGN7ibowNAy6TwA3X7i0H6a1h0XPQOK1bB2niIiISH6jUi4iko+42dzoWakn33f7nu+7fk+vSr1wtbpy7Pox3tz+JsPW9cSr1CK+fTKQJ1tXxMvVxonLCbw0fz8t3lvLp2uOcS0h9e93ZLFAgyHwTAQEDXQ+t/tbmFgfdn0NmXexJJuIiIhIAaRSLiKST9Xyr8Ubzd9gzYNrGNtoLOW8y5GYnsiPR3/kibUDOOh4i9ceTmJspwqU8HHjys1UxocdJfjdNbwasp+TtzMpnKc/9PwMHl8JxWpC0nVY8g+Y0hpObcr+QYqIiIjkcTazA4iISPbycfVhUI1BDKw+kJ3RO/nhyA+sPbuWyMuRRF6OxNfVl97396SIoxUhO1I4cCGOWdvPMnvHWe6vVpzhLcvTuLwfhmH8+U7KNIURG2DnNOfSaZf2w4xuUL0HdHgDCpfLsfGKiIiI5CUq5SIiBYRhGDQu0ZjGJRpzOfEyC44tYN6xeUQnRDPz0AxgBk2qNKFj487sPlSSdYevsfrQJVYfukSd0j4Ma1mBzrUCsFv/5CIrqx2aPgm1H3QW811fw6FFcHQlBD8DLf4BroVydMwiIiIiuZ0uXxcRKYCKehRlRN0RLO+znE/bfnprWbXt0dv58vA4Trm/wpCuP9GzoRuuNgv7zsfy7Pd7aPX+Oj5ff4IbiX9x37lnEej6ETy5Gcq3gowU2PQhTGoIe+dAZmbODVREREQkl1MpFxEpwGwWG23LtGVyu8ksf2A5w2sPx9/dn6vJV5l/8lvWJfyD5i1C6NX8GkUKWYmKTea9FYdp9s5a/hW6nxN/dd958Zrw6CLoP8t5+Xp8FISMcM7Ufm5njo1RREREJDdTKRcREQBKFSrFs/WfZVXfVXzc5mOalWiGAwc7L21lzbX38anyPl1aRlKpZCpJaRl8t+0s93+0gce+2cGmY5dxOP5gvXPDgOrdYOR2uP8/YPeEC7tgWjv4cTBcPZHzAxURERHJRXRPuYiI/IbdYqdd2Xa0K9uOc3HnmHdsHqHHQ7mcdJnLSXMwfAyalmtE6vXG7DlcgnVHLrPuyGWqFC/E483L0yuoFG526/+8qRu0HAP1BsCa/0LkbPgpFA4vhUZDodVY52XvIiIiIgWMzpSLiMifCvQO5B8N/sHqvqv5sPWHNC3RFAcODl7fwTEmEVh3PA2DtuLhcYOjl27y8oL9NH1nDe+tOMyFG0m/f0OvAOg12Xm/eaV2kJkG27+AT+vBpo8gNTHHxygiIiJiJpVyERH5W3arnY7lOjK1w1SW9V7G0FpD8XPz43rKVY4kL8Ra9l1qBM2hWMARbiQm8fn6E7R6fx1PfRfB9pNXf39pe0AtGDgfBoVCQB1IiXOeQZ/YAPZ8B5kZpoxTREREJKeplIuIyB0J9A5kdIPRrO67mvFtxhNcMhiAc8mRJBX+hoDaH1K+8loybTEsPxBN/y+30eXTzfy48xzJaf9Ttiu2hSc2QJ+p4BMI8Rdh4dPwRUs4FgZ/dJ+6iIiISD6ie8pFROSu2K122pdtT/uy7TkXf46QYyGEHA/hStIVEmyrKFRxFX7WasRcrMuh6JqMnR/HO8sP8VDjMgxqWpaSvu7ON7JYoE4/qN4Ddk6FjR9AzEGY1RfKNof7/g1lm5k7WBEREZFsojPlIiJyzwK9Am/N3D6h7QRal26NxbBwLeMwtuI/UKT6u/iVWUJsxhk+X3+CFu+tZcTMXWw5fuXXS9vtbhD8DDwbCc1GgdUVzmyBbzrBdw/AxT2mjlFEREQkO+hMuYiIZBm7xc59Ze7jvjL3EZ0QzcLjCwk5HsKFmxfAczOeFTbjllmWG5eCWHmoHisPXqJSsUIMalqWPvVL4eVmBw8/6PgWNB0JG9933mN+fLXzUb07tP0XFKtm9lBFREREsoTOlIuISLYI8AxgRN0RLOuzjCntp9CxXEdsFhvJljO4lQjFt+rbFCr9I6du7uM/iw7Q9O01/Ct0P0cvxTvfwKcUdJ8Ao3ZCnf6AAYcWw+SmsGAEXDtp6vhEREREsoLOlIuISLayGBaCSwYTXDKYa8nXWHxiMSHHQjgRewLDazceXruxZviTeLUBs3bV57ttZ2lawY9Hm5WjfY3i2P0qQJ8vofloWPcWHF4C++bAgXkQNNC5xrlPKbOHKSIiInJXVMpFRCTH+Ln5MbjmYB6t8Sj7ruwj5FgIy08tJ5EruBZbiWuxVaTfrMquyw3ZNusyxbw8eKhRIA83KUOJ4jXgoVlwYTesfRNOrIGI6RA5G4IGQYt/gG+g2UMUERERuSMq5SIikuMMw6Bu0brULVqXsY3GsurMKkKOhbA7Zje2QoexFToMGYWIvRHEpM0NmbTuOPdXL87ApmVpWSkIy6AFcHqL88z5mS2waxrs/haCHoEWY6BwWbOHKCIiInJbVMpFRMRUHnYPelXqRa9KvTgVe4qQ4yEsOr6Iq8lXcSmyCZcim8hICmT9xQaETa9L2cJFGNC4DA82bITfY8vg9GZY/y6c3uQ8c77nO6g3wFnO/cqbPTwRERGRv6SJ3kREJNco71OeMQ3GEPZgGBPvm8h9gfdhM2xY3c/hViIUr8pvccn1a97fsJim74Txjx8i2UUNHIMXw2PLoUIbyEx3njWf2ABCn9aEcCIiIpKr6Uy5iIjkOnaLnTaBbWgT2IarSVdZcnIJocdDOX7jOHafSOw+kWSm+rL0XANCDzSgsl8ZHm5chj595+FzZTdseBdOrIXI72Dv91Cnn3OiOC2lJiIiIrmMzpSLiEiuVsS9CINrDmZBjwV83/V7+lftj5fdC4vLDVyLrqFQpfc55/oRb238lsbvLOX5bW5EtJqGY2gYVGoPjgxnMZ/cBOY8AucjzB6SiIiIyC06Uy4iInmCYRjU8q9FLf9avNDwBdaeXUvo8VC2RW3D5nkKm+cpHJmLWBpVm9AZDajoVZsBjT+gb3AMhXZ86lxK7ZdH+VbO2dortAXDMHtoIiIiUoCplIuISJ7jZnOjS4UudKnQheiEaBafWMzC4ws5E38Gu28Edt8ILqT68fbW+ryb0JCu1cfyWK/nqHn6G4x9P8Kpjc5HiXrOcl69O1isZg9LRERECiCVchERydMCPAMYXmc4w2oPY+/lvYQeD2X5qRUkcg3Xoquh6GqWXSnPwhMNKOPai+HNBtIrKQT3fd9BVCTMHQxFKjnvOa/TD2yuZg9JREREChDdUy4iIvmCYRjUK1aPccHjWN9/He+2fJdmJZphYGDzPIV7yXnEFH6F1w9Nod7+QF4MnMXZWqNwuPnC1eOwaBR8Ugc2fgiJ18wejoiIiBQQOlMuIiL5jrvNna4VutK1Qleibkb9PHv7Qs7Gn8Huswd89rAszYeFZ4IoZR3Hf8ufJvjyD1hvRsHaN2DTR1DvEWj6FBSpaPZwREREJB/TmXIREcnXShQqwfA6w1nSezGzusyif9X+eNq8sNhjcfVfz5Vi4xmRtJNaGX2Y4P8ccT7VIC0Rdk51rnU+5xE4uw0cDrOHIiIiIvmQzpSLiEiBYBgGdYrWoU7ROoxtNJYN5zcQcmwhWy5sAvdz4H6OqQ4rn9+sSi3PpvzXepgacdt+nbG9VEMIHgXVuoNV//cpIiIiWUP/VSEiIgWOi9WF9mXb075se64mXWX5qeXMPRLCybij2L1+4ojXT/TLcMfD0oVh6fE8nrAN24VdMHcI+JSBxsMgaBB4+Jk9FBEREcnjdPm6iIgUaEXcizCwxkAW9p7Pgh4LGFJjCD52fwxrEkmFDzCx6Bnql6zBYz5NOeLiC7FnIew1GF8DFj8Hl34yewgiIiKSh6mUi4iI/Kxy4co83+h5Njy0mqkdptKxTFfshhsOl+vs8rtI31LetAqowyTvstzITIGI6fB5M5jeDQ4tgcwMs4cgIiIieYwuXxcREfkfVouVpiWa0rREUxLTEllzdg3f/xTC/mu7uO5+gynuMMWvDDUS7DyecJE2ZzbhenoT+JaBRsOh/iBwL2z2MERERCQPUCkXERH5Cx52D7pX7E73it2JSYxh8fFl/HB4IVFJx/mpUAovFCqCS0ZR2ick8kDCJRqE/Rtj3dsYtR+AhkOhVH2zhyAiIiK5mEq5iIjIbSrmUYyhdYYwtM4Qjl8/zg+HF7LkxFJucpml3m4s9XajSBr0TIil64EfqLLnOygZ5CzntR4AFw+zhyAiIiK5jO4pFxERuQuVClfi1WbPs+WR1Xzd8Wtal+iODQ+u2uFrXx8eKF2C3iVL8FXCCaKWPkfmR9Vg+ctw+ajZ0UVERCQX0ZlyERGRe2AxLDQKaESjgEakZKSw5vR6ZuwP4dCN7Rx3hQmuvkzw86V+cjJdf5pJx51T8ApsjqXxUKjaFWwuZg9BRERETKRSLiIikkVcra50qdiRLhU7EpsSy4Ijy/nx0ELOJx1kt5sbu93ceKdIYVokHqLrspE0X+xBofoDMOoPBv9KZscXERERE6iUi4iIZAMfVx8eq/MQj9V5iOiEaGbsDWHJyaXcyDjDek8P1nt64JGZyf1HZ9F5z5cEFa5LoSaPQ42eYHc3O76IiIjkEN1TLiIiks0CPAN4KfgpNg1cwrxu8+lQagBuFCHRYmGxlycjA4rRxXaeNze+zPaPq5MYOhqi9pkdW0RERHKAzpSLiIjkoKpFqvBRu1dwOF5m68UIpu1ewJ6ra7luTeAHby9+8IaAqyvp/GMILS0lqNtwKC71HgR3X7Oji4iISDZQKRcRETGBYRgEl2pIcKmGpGems/LEZmbsW8CxuM1E2+AbX2++IYHyBz6i4/a3aOrTkHrNn8RaqS1YrGbHFxERkSyiUi4iImIym8VG18pt6Fq5DcnpyfxwcBXzD87nbGokp1zsfOFi5wuOUH3907RdbtC8dCdqtxqJUbSK2dFFRETkHqmUi4iI5CJuNjcG1+3B4Lo9iE2O45s9S1h1bC7nM49zyNWFQ64w+eYaghYspVm6L22rPky1FkPBzcfs6CIiInIXNNGbiIhILuXj5s3oZgNY9mgIYX3X07/sKEpnlsBwwB43NyYXSqb/+a8ZOr0Rn3/ZnlM7voeMNLNji4iIyB3QmXIREZE8oHihIvyrzQhoM4LTNy4yNfxbIqKWcMEWyw53V3YQzZc/vUXDiNdp4FqNrk1GEVjjfjAMs6OLiIjIX1ApFxERyWPK+ZbkrS4vAy9zIPoEM7Z8wd7rG4iyJ7HNw842TvDlztE02uigfqH69Go5huLlgsyOLSIiIn9ApVxERCQPqxVQkQ8e+ACAnecOMid8EvvitxNtTyPcwyA8M5Kv1g2kfoqdhoWD6dP2BYoUq2ByahEREfmFSrmIiEg+0SiwJo36f47D4WDTsR2E7JjEvuR9xNgh3D2D8ORNfLl0A/VT3GhYpBUP3P88fn6lzY4tIiJSoOWZid4mT55M+fLlcXNzo0GDBmzatMnsSCIiIrmSYRi0qtKEjwfOZPXQSMYHfcT9jioUTTdItlgId0/l08TVdFrYkRFTGjNtwUvcuHHJ7NgiIiIFUp44U/7DDz8wevRoJk+eTPPmzZkyZQqdO3fmp59+okyZMmbHExERybUMw6B9nQ60r9OBzMxMVuxezKr9X7Mv8ySXbRbC3ZIIj1/G1JAl1E31pnFAO/reNxofryJmRxcRESkQ8sSZ8vHjxzN06FCGDRtG9erV+eSTTwgMDOTzzz83O5qIiEieYbFY6NKwJ588tpDVj+3lnaqv0i69LEXTHSRYLIS73eSTG6F0mNeK4VOa8VXoq8TGXzE7toiISL6W68+Up6amEhERwcsvv/yb5zt06EB4ePgf/k5KSgopKSm3vo+LiwMgLS2NtDSt35oTfvmc9XlLfqbjXPK6jg0eoGODB8jISGfF9u9Zf2wOe40LXLFZ2OZ2k22xi5i6MJQaSR5cXLSFvq1H4+tT1OzYIllOf59LQaDjPOfd7mdtOBwORzZnuScXL16kVKlSbNmyheDg4FvPv/3228yYMYMjR4787nfGjRvH66+//rvnZ8+ejYeHR7bmFRERycvSMzI4dX0nJ5O3ccQ1hhj7rxfVuWdmUiPJg/JGTaoWaYe7q5eJSUVERHK3xMREBgwYQGxsLN7e3n/6ulx/pvwXhmH85nuHw/G7537xyiuvMGbMmFvfx8XFERgYSIcOHf7yw5Csk5aWRlhYGO3bt8dut5sdRyRb6DiX/Ks7AJkZGSzb+h2rf5rFYdfLxNgtRHgmE0EEbgk7qX3dg/qFm9G7xSiKFS1vcmaRu6e/z6Ug0HGe8365Yvvv5PpS7u/vj9VqJTo6+jfPx8TEULx48T/8HVdXV1xdXX/3vN1u1wGYw/SZS0Gg41zyLbudbi0GY4kryviOHVkTMZc1h79nn+Ms0XYLO92S2Zm0jm9WrqVOqgv1vRvRq8UzBAbWMju5yF3R3+dSEOg4zzm3+znn+lLu4uJCgwYNCAsLo3fv3reeDwsLo2fPniYmExERKTgsVitdmg+iS/NBZGZksGrXfNYcnMW+zJNctBvscktjV2o4X6/ZQp1kK0GF6tKj6VNUqNTM7OgiIiK5Wq4v5QBjxoxh0KBBNGzYkGbNmvHll19y9uxZnnzySbOjiYiIFDgWq5VOTfrRqUk/HA4HG/auYHnk1+xLO8J5F9jtnsnujD1M3zyc2mugnktVOtcbRI263cFiNTu+iIhIrpInSnn//v25evUq//3vf4mKiqJWrVosW7aMsmXLmh1NRESkQDMMgzb1OtOmXmcAwn/axOKIKexLPsBZlwwi3SCSo8zY+y9q7XiFekYZ7q/2APWbDMBwLWRyehEREfPliVIOMHLkSEaOHGl2DBEREfkLwTVaElyjJQARpyIJ3fY5e29GcMolhf1uNvZzkZmnJlLt8HjqZ/rTskwngoMfw+JbyuTkIiIi5sgzpVxERETylgbl69Gg/BQAjlw6xZwtnxN5bTMnbXEcdrVzmFhmX/6BCnO/o3GqG039g2nZ+FFcyjbSZe4iIlJgqJSLiIhItqtavDz/6fM+AOduxPDdlmnsil7FCesVTrrYOemSwZzkTZRYu44WSRk0dK9Jy6CH8KrRCdx8TE4vIiKSfVTKRUREJEcF+hbjla6vAK9wNfEG3+6Yy9ZToZzgHFE2G3O9bMzlOIUjX6d1+Cs0tJSiRZXuFKndDYpVB8MwewgiIiJZRqVcRERETFPEw5d/tBnOP9oMJzE1ie/3rWTNoR84ln6I61YI9fIklBt4nJtByyNf0CzVhcYBLShdtztGhbbg5m32EERERO6JSrmIiIjkCh4u7gxt2IuhDXuRlpnG4sObWXhgLkdu7iDBmsLKQp6sBGwp22iyYT1tlyfT0KMq5Wp2wVqlAxSvpbPoIiKS56iUi4iISK5jt9jpU6MtfWq0JdORyeaze5i9byEHrqwj1nKDLR7ubPFwB2Koc2gK90d8TPN0d8qXvw+XKu2gQhsoVNTsYYiIiPwtlXIRERHJ1SyGhVZlG9CqbAMADl05zoy9S9h+IYwrjrPsc3Nln5srHwPlr2/gvnXLabs0iSpelXCv2h4q3geBTcHuZu5ARERE/oBKuYiIiOQp1f0r8e79o4HRRN28xHf7lhF2ehXRqQc55WJnmosP03x98E+Ppc3hb2m7ewoNU8GtXHMsle53nkUvVkOXuouISK6gUi4iIiJ5VolCxXkx+DFeDH6M+NR4Qg+vIfTYKk7E7+CKLYV53l7M8/bCIzOT5vF7abt5K63C/oWXexEs5Vs7C3qF1uBbxuyhiIhIAaVSLiIiIvmCl4sXg+r0YlCdXqRlpLH+3FZ+OLicyCtbSLRcJ8zTgzBPD6wOB/WTU2h7diVtDocQmJ6Bo3B5jF8KevnW4OFn9nBERKSAUCkXERGRfMdutdO+XCval2tFpiOT/ZcPMufgcjZdWE9sxjl2urux092N94sUpmJqGvclXqPNgVnUivgGAwMjoDaUbwXlWkLZZuDmY/aQREQkn1IpFxERkXzNYlioW6w2dYvVBsZyLv4coUdWseLkWs4mHuCEi50TLj5M9fXBN93B/YkJtI09SpNt+3HbOgkMC5SoB+VbQrlWUKYpuBYye1giIpJPqJSLiIhIgRLoFcgzDYfyTMOhxKbEsvbMRhYcXsmB6zu4YUtivnch5nsXwpZpUD8pnS6JN2h9KRL/i7thywSw2KBk/Z9LegsIbAIunmYPS0RE8iiVchERESmwfFx96F2lO72rdCctI42d0TtZcGQlWy5u4iaX2eFpZYdnEQBKJrvQLjGBHklXqHJ+B8b5HbDpo59LehCUDYayLZxn0t28TR6ZiIjkFSrlIiIiIjjvQw8uFUxwqWAcDgdHrx9lxcm1rDi1mvOJR7nolsq3bna+pQRuaW7UTrDTO+UqHZKicT2/E87vdJ5JNywQUMd5Fr1sc2dJ18RxIiLyJ1TKRURERP6HYRhU9atKVb+qPNfwKS4nXmbDuQ0sPr6avVd3kmxPZqdvMjtx4Z+ZFSma4EdwciYDU6OolnoRoiKdj62TnG9YtLpzwrgyPz98A80cnoiI5CIq5SIiIiJ/o6hHUfpW7Uvfqn1JSk9iR9QOVpxcw4bzG4lPv8plr0ss9IKF2LAkNaZsgg/dMlPpnXGKosln4fIh52PX18439Al0nkEv08x52bt/VbBYzB2kiIiYQqVcRERE5A6429xpHdia1oGtcTgcHLp2iPXnNrDy1DpOxh0i0z2aU+7RTAQmpHlju9mDGkmePOySRDPjGP7xhzFiz8H+c7B/rvNN3XydE8YFNnaW9ZL1wcXDzGGKiEgOUSkXERERuUuGYVCjSA1qFKnByHrOy9w3XdjE6tPr2Ba9lTR7HJmFIzlQGP6ZaSMjsQKWtBa0oBD9vK9RL/MQfjf2YSTfgGMrnQ9wTh5Xou7PRb2Js6h7BZg6VhERyR4q5SIiIiJZpKhHUfpU7kOfyn1IyUhhZ/RONp7byOoz67mcHIWt0FEodJRwYFNKMTKSqmFkdqGnXyG6+p6nTuZhfK7sxrgZDRcinI9tk51v7lvWeSa9dGMo3RACaoPVbup4RUTk3qmUi4iIiGQDV6srLUq1oEWpFrzS5BVOxp5kw/kNrD+7gb2XI8E1BqtrDBTZyNIMNxbGViH9ZnU80h6gZzkPuvicprbjCIUuRUDMQbhxxvn45ZJ3m7tzKbbARj8X9UbgVdzUMYuIyJ1TKRcRERHJZoZhUNG3IhV9K/J4rceJTYkl/GI4G89vZOO5TcQRi917H3bvfWQ6DOYnl+aHK1VJv1mdsoV60r62J518L1DTcQTXqAjn8mvJN+BsuPPxC98yznJequHPZ9PrgN3NtHGLiMjfUykXERERyWE+rj50Lt+ZzuU7k5GZwf4r+9l0YRMbz23k8PXDWN3PYXU/h2vR1cSkF+Lbc1X4+lA1SKpCUOnWNG/gR7ti8VRPP4z1wk44vwtifoIbZ52PA/OdO7LYIaDWryW9VEMoUhEMw9wPQEREblEpFxERETGR1WKlXrF61CtWj2eCniEmMYbNFzaz6fwmtlwMJ4mbWHx3Y/fdjcNh4UBSGfZGVuPTm1XwJJAmFR6lZb0xtCzrSvnkwxgXdsH5CLiwCxIuw8U9zsfOqc4duvlCqQZQqr5zlvdS9TWJnIiIiVTKRURERHKRYh7Fbk0Wl5aRxu6Y3Ww8v5FNFzZxKvYUNo/T2DxO41psBZlp3my6UYX166qSnlCZkl6+NK/UgRY1BtCsmx/FMmKc5fyXkh6113nZ+4k1zscvvEs570//paiXDAJ3X7M+AhGRAkWlXERERCSXslvtNCnRhCYlmvBioxc5H3+ezRc2s/nCZrZHbSeZOFx8d4HvLhwOC9cTy7LwdFXmH6hKZkoAVYt7E1ypGi0qtaBxGz+87MClAz/P7L4HLu6Gy4ch7oLzcXjJrzsvUslZzksGQYl6UKIOuHqZ9VGIiORbKuUiIiIieURpr9I8VO0hHqr2ECkZKURER7DpwiY2X9jM6bjT2DxPYfM8dess+umEKpzYV5VvtlbCigf1An1pXrEIwZV6ExT0GK42K6TcdJ5Bv7gbLux2fr1+Gq4edz5+me0dA/wr/7aoB9QG10ImfiIiInmfSrmIiIhIHuRqdSW4VDDBpYJ5iZc4F3fuVkHfGb3zN2fRcVhITyrD/ptV2bOlCp+uLYGb3Uajcn4EV/QnuGJNajUNxmr5eQK4hKvO+9Cj9sDFSOcj7jxcOep87PvB+TrDAv5VoETdXx8BtcHNx6yPRUQkz1EpFxEREckHAr0DGeA9gAHVB/zmLPqWi1v+5170lZDhRVp8ZbZeqsLmk5VxrPDEy81Gk/JFaF6pCMEV/alS6X6Myu1+3cHNGGc5j4r8efK4SIi/6Lz8/fLhX4s6QOHyvy3qJeqCp38OfyIiInmDSrmIiIhIPvP/z6IDnI8/T/jFcDZd2MT2qO0kEY/95xndwYCU0qTEVWbt6aqsPlQasOJfyIUmFYoQXLEIzSoUobx/UYwqHaBKh193FB8NUfucl79HRTq3Y8/C9VPOx0+hv77Wu5TzLHpAbef66QG1oXA5Lc8mIgWeSrmIiIhIPlfaqzT9qvajX9V+pGaksidmD1submHLhS0cvX4UXM/hWvQcrkXXYnV4kJZQidj4Siz7qSpL9zkvRS/u7UqzCkVoVrEIzSr4E+jnjuEV4FxO7f8X9cRrP5f0//e4duLXyeSOrvj1ta7evy3pAbWhaFWwuebwJyQiYh6VchEREZECxMXqcmtG9zENxhCTGEP4xXC2XNhC+MVw4lLjsBTah1uhfQC4ZpYgIbYiV+IrE7q3PKGRFwEo5etO059LetMKfpQu7OHcgYcfVGzrfPwiOQ4uHYTo/RC91/k15hCkxMGZLc7HLyw28K8KAbWgeK2fv9aGQkVz6iMSEclRKuUiIiIiBVgxj2L0qtSLXpV6kZGZwcGrB9lyYQubL27mwJUDpFiisBWOwlZ4MxbsuGVUIvZqeaJuVmH+7kTm7z4PQOnCzpLufPy/kg7g5s3/tXfnwW1d993/3wAIghsALgD3naJEihS1S7Gd2FYa23FUN/Xz1G3i1L+4v9SZ1HaWOmlStzONk8ZZnWSmzrRpnnSStGmWp03zSyZ2WimJlzjWQlHUQnGTKHEnSAIkAXDf8PvjgpeENsu2SGj5vGbOADi4uLzgHHv04ffccyi5xWhL5meNReN8J8F3wpj6PngSpoMwdMporLhPPS1nRUivheyNxiJzCYlr84sSEVklCuUiIiIiAoDNaqPOW0edt46/2PIXBGeCHBw4aFbSBycHmbS1YM9uwZ79PMnWDBJmqvAPl9IXquA/G6b4z4YLQ/ruskyKMlNif1hCohGwc2uB9xp9kQgEe4291H1NRkj3NcHIWRgfNFrHr5fPYU0wgnlOjdGyo4+ufN2rLiLXDYVyEREREbkot8PNPaX3cE/pPUQiEc4Fz/G7fmOa+xHfEaYWRsF+AEf+ARxAZkIZi5Pr8flK6B0rjgnpBenJ7C7LZHd5JrvLsijJSsFyfnC2WCC9yGgb7l3unxk3prsvhfShZhhshpmg8XyoecV+6kBSulFJz66GnI3Lz5MzVv13JiLyeimUi4iIiMhrslgslKeXU55ezkMbH2JmYYajg0c50H+AV/tfpW20jZH5c5B4jqRicFmT8CRUMxteR09/EX1jXv6rcYr/auwDjIXjdpdlsassk7eUZ1LhTbswpC9xpEHRTqMtMavq0anug6eMoO5vh+kx6H7VaCs5841wnl1tVNS9VcbCchZNgReR+FEoFxEREZHXzWFzcEv+LdySfwtP8AT+KT8H+g+YIT0wHaB/thEcjSSXQXqihyzbJiaD5XT2FDAYgp8f7+fnx42F4zxpiewqy2RXaSY7yzKpynVhs15mCnpMVf2dy/3zM0YwH4xW0IdajMdgj7Gverg/dgo8FhLSi9m1mIn1hYboNPgqY1q8PXl1fnkiIisolIuIiIjIm+ZJ9nBfxX3cV3EfkUiE9tF2M6A3DDYwNutnjBcg8QUcFVCRWkGGpZbwSDnt3R7847M8f9LH8yd9ADiTEthZmsmuskx2lmayqcBNYoL1tS8kwbG8vdpK00EYbotW1luWA/ukH8tYF3l0wauNy8dbrMY+6t5qo5rurQLveiOsJ6ZevV+ciNz0FMpFRERE5KqyWCxsyNzAhswNPFz7MNPz08ZU9wGjkt422kbvRAe9dEAipK1PZItrE05qCAZKaelKIzw9z29ah/hN6xAASXYr24ozzKC+tTidlMTX8U/ZJDcU7TLaShN+5gdO0vzif1GTbcMWaDfC+tSIscDcyFloey72M+nFRkj3rI+G9WhgT3K/yd+ciNyMFMpFREREZFUlJSRxa8Gt3FpwKwD+KT+HBg4Z090HDjA0OUTzWAPQADbIqE7nFvc2UherGfGXcLLLxujkHK92BHi1IwBAgtVCTYGbnSUZ7IxW0zNT38C94akeIiVv5Zw3RPW978Jmtxv3q08MG+F8uNWosA+3Gc8n/TDWbbTT+2LPlZa7XE33bFh+7szTavAickkK5SIiIiKypjzJHvaW72Vv+V5zVfcDAwc42H+Q+sF6xmbGODj0G+A3ABTVFvH29O0kL1QRGC7mWNcsA8FpjveMcbxnjG+/cg6Addlp7Cw1quk7SzMpzEi+9OJxl2OxQFq20crviH1vwr8c0P3txuNQK4z7ltu5l2M/43CBpzIa1ishK/o8s1z7rIuIQrmIiIiIxM/KVd3fV/0+5hbnaPI3cbD/IAcHDnJi+AQ94R56wj3G8Vio3lTNOzO2k7JQxbA/n8bOCU4PjXMm2n542Dg2x+VgR2kmO0qMoF6V6yTBdgX3pV9OqsdopbfF9k8HwX/aCOz+9mhgb4PRczATgr4Go8V8eRtklBgBPWvdcnDPqjR+hqrrIjcFhXIRERERuWbYrXa2Zm9la/ZW/mLLXzAxN0HDYAMH+g9wcOAgZ8bO0BxopjnQbB6/pWYLv3/7TlIWqxgc9nKkM0hTX5DB0AzPnRjguRMDAKQm2thanMGO0gx2lBj3pac6rtI/h5PcULjDaCvNzxj3pfvbYbgdAqejof0MzIaX71u/2Pmy1kVbJWRVGKE9s1wLzYncYBTKRUREROSalWpP5fbC27m98HYAhieHOThwkEMDhzg4cJDByUHqffXU++oBSLOnsaN6B394+y6cVNM/5KKhe4yGrlHC0/O8csbPK2f8ANisFqrznGwtdGMdsbA1OE2xx351v0CCY3lv9JUiEQj7oiE92pYC+1iPUXm/WHUdwFUQDesVkFmx/Dy9RNPhRa5DCuUiIiIict3wpnhjtl7rCnWZAf2w7zCh2RAv9rzIiz0vApCVlMWuil18+tbdeBNq6B5O5kjnCEc6R+kbm6KpL0RTXwiw8b1nXibPncT2kgx2lGSwvSST6ryrMOX9YiwWcOUZrez22Pfmpo3qeeCMEdQDHcZz/2ljVfhQn9HOvXTeOW3GyvBZ0aCeWQFZ5UZ13V0MNv3TX+RapP8yRUREROS6ZLFYKHWXUuou5U+q/oSFxQVaR1rNSnrjUCOB6QC/PPdLfnnulwAUpBXwluK38Le7dlGaupkOn5X6cwFeONlF/5SVgeA0vzgxwC+iU95TEm1sLkxne0kG20sy2FqcTnrKKlej7UmQs9Fo55sciYb1M8thfaQDAmdhbsK4h330HJz5VeznrAlGJT2rwgjpmdHHLAV2kXjTf30iIiIickOwWW3UeGqo8dTwgU0fYHZhluPDxzk0cIjDvsOcHD5J33gfPzn9E35y+icArEtfx46cHbxrzsqfvuP/pWfERkPnKA3do+aU9wNnAxw4GzB/ToU3le0lGWwrNoJ6hTcNq3WNFmVLyYSUi+y3vjQdfqRjRWDvMCruo+dgftp4b6TjwnNaE8BdFA3rZcZjxtJjCdiT1+a7idykFMpFRERE5IaUaEtkZ+5OdubuBGBiboKjg0c5NHCIQ75DtI20cWbsDGfGzgDww5//kKrMKnbn7eYDd+3iWe9t9I9GOBoN6Ee7Rjnrn6Bj2Gj/90gvAK6kBLYWGyF9W0k6m4vScSVd5XvTX8vK6fClb419b3ERwv3LIX2kA0bOGa+XAvtShf0imR1XQTSkl0JGafR5mfGYnKFV4kXeJIVyEREREbkppNpTeVvh23hb4dsAGJse48jgEQ70HeCFjhcYXhymZaSFlpEWvnvquyRYEqjx1LArdxf/+7ad/P39u5masdEYDekNXaMc7x0jND3PS+3DvNQ+DBgZtTI7zQjpxcaU9zWtpp/PagV3odHO33d9cRHCA0YgHzlrhPWl6vpIdDu3pXvYu1658NwOt1FNXwrpGaXG64xSo/puW+M/TohchxTKRUREROSmlJ6UzjtK3sEd+XdQN1zHzj07afQ3cth3mEMDh+gb7+P48HGODx/n/5z8P9itduq8dezK3cWeLTv52F3bsZBAy0CIxu4xjnaPcrR7lJ6RKdoHx2kfHOdH9cae6a6kBLYUZ7C1KJ2txelsKVqDe9OvhNUK7gKjnV9hj0SMe9iXAvtopxHURzuNvvAAzATBd8Jo57NYwVW4HNIzSozgnl5iPE/1qsougkK5iIiIiAgA3mQve8v3srd8LwB9430cHjjMYZ/RhiaHaBhsoGGwgX86/k84bA62eLewM3cnu9bt4sHdtdhtdobC0xzrHuNoNKifiFbTX24f5uVoNR2g3JPKluJ0tkbDelXuKq30/kZZLJCaZbTz918HmJuC0a7lkD5yDsaWXnca0+KD3Ubr/O2Fn7enGKvFL4X09BLj9dLz5PTV/X4i1wiFchERERGRiyhIK+D+yvu5v/J+IpEI3eFuDvsOUz9Qz2HfYQLTAQ75jPvTOQbJCclmSN+Zu5OPV9dgt9qZW1ikzRfmaPcox7rHaOwZ45x/grPR9l9H+wBIttvYVOBmS7SSvqUonTx3EpZrtZpsT4bsKqOdLxKB8cEVob3TCOxLwT3UD3OTMNxqtItJci+H9vTiC1uSezW/nciaUSgXEREREXkNFouFElcJJa4SHlj/AJFIhLPBs0ZI99VT76tnbGaMAwMHODBwADBC+tbsrezM3cmOnB28d3cN/88tpQCMTMxyvGeMxu5RGnvGONYzRnh6nsOdIxzuHDF/brbTYQT0aFCvK0wnzXEd/BPeYgFnrtGKd1/4/vwMBHujlfWuCx8n/TAdBN9Jo13MUmh3F0N6UfR5kfHcXWysVH+t/kFDZIXr4L9oEREREZFri8VioSK9gor0Ct5b9V4WI4t0jHVw2HeYI74jHBk8wtjMGK/2v8qr/a8CF4b0t1bWsKcqG4DFxQhn/eMc7TIq6cd7xmgbDDMUnmFf8yD7mgcBsFqgMtvJliJjlffNRW425Fxj096vRILD2DM9q+Li789OwFgPjHUbQX2sO7ZdSWi3p0YD+lJQLzTCurvQeJ2Wq/3Z5ZqgUSgiIiIi8iZZLVYqMyqpzKjkfdXvYzGyyJmxM2YV/cjgEYIzwQtC+hbvFnbk7mBn7k5qs2pZl13EH+8sAmBydp6mvhDHekY51jPGse4x+oPTtA2GaRsM8+MjxiJySXYrmwrcbC40gvqWonQKM5Kv3WnvVyIx9dJT4yEa2rujwb0Lgj3G82A0yI8PwtzE5afHW2zGdm9LK9OnF0VfFy33JblW7zuKRCmUi4iIiIhcZVaLlfUZ61mfsd4M6adHT3Nk8EhMJX3ldPckWxKbszezI2cHO3J2sMm7iV1lmewqyzTPOxSaNivpx3vHONETJDwzT33nKPWdo+ZxmamJbC50U1doVNPrCtPxpDnW/PewahJTIbvaaBczN21s4zbWFQ3rvUZgD/YaoT3UB4vzywvRXYrDHV2dPhrSl0K8+ZhvVP1F3gSFchERERGRVWa1WNmQuYENmRvMkN4x1mFW0Y/4jjA6M8qhgUMcGjgEQKI1kTpvHTtyjZBe560j25XMPTW53FOTCyxNe58wQ/rxnjGaB0KMTMzyQtswL7Qtr/ZekJ5sBvS6QjebCtw4k27QfcTtSZefHr+4YFTTl0J6sAeCfdHw3guhXpgaNbZ8GwrCUPOlf1ZqthHcYwJ79LWrwLivXvu1y2UolIuIiIiIrLGV090frH6QSCRCx1iHEdCjIT0wHTBfAyRYE6jNqjVD+pbsLaTaU1mXnca67DT+9/ZCAGbmF2gZCMcE9bP+CfrGpugbm+L5kz7AWAOt3JPK5qWQXphOTb6LJLstbr+XNWO1GVVuVz4U7br4MTPjRkV9qcIe7DWCe2jpsc/Y9m1iyGj9jZf4YRZIy4kG9Xxj7/aln+2K9jnzIOEa2Lde4kKhXEREREQkziwWC+sy1rEuYx3vqXoPkUiEzlBnzHT3ockhjg0f49jwMb598tvYLDaqM6vZnrOd7Tnb2ZazDbfDjSPBZm6ptiQ8PcfJviAneoOc6B3jeE+QvrEpOoYn6Bie4L8ajW3ZbFYL63Oc1BW4qStyU1eQzoZcJ4kJ19lCcleDIw28G4x2MZEITI4sh/SlCnuwz9jyLRR9XJyDcZ/R+hou/fNSvbFB3ZUPznxw5S0/Opyr810lrhTKRURERESuMRaLhTJ3GWXuMnMLtt5wr1k5bxhsoG+8j6ZAE02BJr7X/D0AKjMq2Z69ne2529mevR1vihcAZ5KdWys83FrhMX+Gf3yGk70rgnpvEP/4DC0DIVoGQuZCcok2K9V5TmoL3NQVuqktcLM+x4n9elvx/WqzWCA1y2h5my9+zOKisVJ8qG9FWI8G9/BANLgPwMIMTAwbbeD4pX+mw2VU1VcGdWfecrXdmQdp2cZMALluKJSLiIiIiFzjLBYLRa4iilxF3F95PwC+CR8Ngw1mSD8XPMfp0dOcHj3Nj9p+BECJq4Rt2dvYlrON7TnbKUwrNFdl96Q52FOVbW7LFolE8IWmOdEb5GRvkOO9Y5zsCzI2Ocfx3iDHe4P8u3G7O4kJVqrzXGwqcFFXkE5tgZvKnDQF9fNZrUZITsuG/K0XP8asuEdDe7g/Gt77l0N7eABmQsvN33bpn2mxGtPll0K6Kw+cuVhScvCGumGoDDIKITlD+7hfIxTKRURERESuQ7mpuewt38ve8r0ABKYCHB06SsNgAw2DDbSNtNEV6qIr1MVPz/wUgOzkbDOgb8vZxrr0dVgtRpC2WCzkuZPJcyebC8lFIhF6R6fMgH6yN8jJviDh6XnjnvWeMcBYvdyRYKUqGtQ3FaiifsViKu51lz5uZjy2uh7uXw7soX4I+4zF6yILRl94IObjCcCtAB1fMTpsDmMROmfeisdomE/LifblQlK6wvsqUygXEREREbkBZCVncVfJXdxVchcAodkQx4aO0TDYwNHBozQFmhiaGuK/O/+b/+78bwBciS62Zm9lW842tmVvoyarBvuKlcItFgtFmSkUZabw+3X5gBHUuwKTRkiPBvWmPmNrtuWgbki0WamKTn3fFG3rc27Se9TfLEcaOCrBU3npYxYXjCnwof7lYB4N7ouhAcL9p3FZJrBMjRhT5se6jHY5NseFYd18zDXeS8uFlCxjZoC8bgrlIiIiIiI3IFeii9sLb+f2wtsBmJ6f5qT/pFlJPz58nNBsiJd6X+Kl3pcAY6/0Td5NbM3eyvbs7WzO3kyqPTXmvBaLhVJPKqWeVO7bbAT1xcUIXSNGUG+KtqWK+onofetL7DYLG3Kd1Oa7qSlwU5vvojrvJln1fbVZbcsV7vMszM3x4vPP8653vQs70S3hwj4juI8PLgf4cR+Eo6+nx6Lhvdtol2OxRafqrwjuaTlGn/k6+r49eXW+/3VKoVxERERE5CaQlJDEztyd7MzdCcDc4hytgVaODh3l6OBRGocaGZ0Zpd5XT72vHgCbxcaGzA3mfelbs7fiSfZccG6r1UKZJ5UyTyp/sHm5ot49slxRP9UX4mRfkODUHE19IZr6QlBvLCZns1pY502jJt9lBvWN+a4bdx/1eLMnQUaJ0S5nbno5vI/7VoT4oeXp8mGfsZjdymnzA5c/LQ73ckCPeTyvL8UDths/st7431BERERERC5gt9rZ5N3EJu8m3l/zfiKRCOeC52gYaqBxsJGjQ0fpG++jOdBMc6CZ77d8H4BiZ7E55X1r9lZKXaXm4nErWSwWSrJSKclKjZn63js6xan+oBHMo4/+8RnaBsO0DYbN7dkASrJSjKCe7zYfvU7H2vyC5MrD+8KcMW1+ZVAfHzTC+/jgcgsPGpX3maDRAqdf4wIsxrT4tBxI80Jq9nJ4T802+tJyjOepnut21fm4hvLS0lK6umLvYfjUpz7FF7/4RfN1d3c3jz32GL/5zW9ITk7mwQcf5JlnniExMXGtL1dERERE5IZlsVgoTy+nPL2cB9Y/ABgrvDcONdIw2EDjUCOnR0/THe6mO9zNzzp+BkCGI4Mt2VvYlr2NrTlb2Zi5Mea+9PN/xtI96u+szTP7h0LTZkBfmv7eH5ymKzBJV2CS50/6zGOznY6YoL4x30VRRgpWqxYjixubfXlv9cuJRGA6eF5YH1p+nFj5fBgi0S3lJv0w9FoXsRTgowF9KcBf7Hmq1/iDwzUi7pXyz372szzyyCPm67S0NPP5wsICe/fuxev18sorrxAIBHj/+42/4j377LPxuFwRERERkZtGbmou95bdy71l9wLG4nHHh47TOGRU0pv8TYzOjPJCzwu80PMCAA6bg1pPLVuzt7I1eyubvZtxO9yX/TnZriTe7kri7VU5Zt/oxCyn+kOc6g+aj2f9EwyFZxhqG+aFtmHzWKcjgeo8I6BvzHdRk++iMlsLyl1zLBZITjead/3lj11cMLaKG/cth/SlAG8+jwb5CT8QWQ7wV8LhWhHSPUZQT4sG9qXXSy0pfVUXsYt7KHc6neTmXrgQAcC+fftobm6mp6eH/Hzjry5f/epXefjhh3n66adxuVxreakiIiIiIjc1V6KLtxW+jbcVvg2A2YVZmgPNZkg/NnSMsZkxczG5JevS15khfUv2lpj90i8lIzWRt1Z6eGvl8j3sEzPztPpCRkjvC9E8EKLNFyY8M8/hzhEOd46Yx9ptFtZlO41qep6xmNzGPBfuFN2nfl2w2qLT072vfeziAkwGVlTbh43gbj4fWg72E35YnFve833k7Guf32IzgnqK57zAnmU8ruxPyYIk9+vaRi7uofxLX/oSf//3f09RUREPPPAAf/VXf2VOTT9w4AC1tbVmIAe45557mJmZoaGhgT179lz0nDMzM8zMzJivQ6EQAHNzc8zNza3it5ElS79n/b7lRqZxLjcDjXO5GWicv3EWLNRk1FCTUcOfbvhTIpEInaFOjvuPc2z4GMeHj9MV7uLM2BnOjJ3hP9r/AwBPkofN3s1s9mxms3czVRlVl5zyvlKiFeryndTlO2FHAQBzC4ucHZ6gxRemeSBMy0CI5oEwoel5WgZCtAyEYs5RkJ5EVa6T6lwn1XlGK0xPfs0/Elzvbvhx7sgwWtaGyx+3NIV+chjLhFFZt4wPw6QR2C2TfpgYjj76sUyPGYvYLU23vwIRqx1SsohYM67oeEskEolc0ZGr4Otf/zrbtm0jIyODw4cP8+STT/Lud7+bb3/72wB88IMfpLOzk3379sV8zuFw8N3vfpf3vve9Fz3vU089xWc+85kL+n/wgx+QkpJy9b+IiIiIiIhc1PjiON3z3XQvdNM130X/Qj8LLMQck0ACBbYCShJKKE4opthWTIr1jf+7PRKB0VnonbDQN2GhbwL6Ji2MzFw8eCfZIuSnQEFKhPzUCAUpEfJSIPH6XDdMriLL4jyOhTCJcyEc8yvaXIjE+TCO+aVH43nC4rT52dBMBPcXwwSDwcvO8r7qofxSgXil+vp6duzYcUH/T37yE/7oj/4Iv99PVlYWH/zgB+nq6uJ//ud/Yo5LTEzkX//1X3nPe95z0fNfrFJeVFSE3+/XlPc1Mjc3x/79+7nrrruw2zVFSG5MGudyM9A4l5uBxvnamp6fpmWkxaymn/CfYGxm7ILjSl2lZiW9zlNHqasUq+XN3dcbmpqjdTBMy0CYFl+YVl+Y9sFx5hYujERWC5RmpVCV64xpuS7HdVlV1zhfI3NTxlT6ST/hoW6ydv7Ra4byqz59/fHHH79kWF5SWlp60f63vOUtAJw5c4asrCxyc3M5dOhQzDGjo6PMzc2Rk5NzsVMARiXd4bhwqwS73a4BuMb0O5ebgca53Aw0zuVmoHG+Nux2O7sKdrGrYBeAOeX92NAxGocaaRxqpDPUabafnTVWeXclutjs3cyW7C1s8W6h1lNLiv31VdOz7HZuc6VwW+VylphbWKRjeDw61T1sTnn3j89y1j/JWf8kzzctT1tOT7FHA7orOv3dxfocJ0n266OsrnG+yux2SHEBZSSkv8ZU+qirHso9Hg8ej+e1D7yIxsZGAPLyjO0RbrnlFp5++mkGBgbMvn379uFwONi+ffvVuWAREREREYkbi8VCmbuMMncZ91feD8Do9Cgnhk9wbPgYx4aO0eRvIjQb4rd9v+W3fb8FwGaxsT5jvXFvevZmtni3UJBW8Lqr2HablapcF1W5Lu7futw/FJ6OCektAyE6hicYm5zj4NkRDp5dXlTOaoFSTyrVeS6qo4F9Q66Twowb/151efPittDbgQMHOHjwIHv27MHtdlNfX89f/uVf8gd/8AcUFxcDcPfdd7Nx40YeeughvvKVrzAyMsInPvEJHnnkEU1DFxERERG5QWUkZXBH0R3cUXQHAHOLc7SPtJsh/djwMXwTPlpGWmgZaeFHbT8CICspiy3ZW4yg7t3MxqyNJCW8sf2os51JZDuTuGP98urf03MLnBkap9VnhPVWn1FdH5mY5ezwBGeHJ3juxIB5vNORwPqV09/zjLDuSlKlWpbFLZQ7HA5+/OMf85nPfIaZmRlKSkp45JFH+OQnP2keY7PZeO6553j00Ue57bbbSE5O5sEHH+SZZ56J12WLiIiIiMgas1vt1HhqqPHU8L7q9wHgm/BxfPg4x4aOcWL4BM0jzQSmA/y6+9f8uvvXACRYE6jOrDZDep23jrzUvDdcvU6y26gtcFNbsLzveiQSYXh8hpaBMK3RinqrL0zH8DjhmXkaukZp6BqNOU9BejIbcp1siIb1DblOyj1p2lf9JhW3UL5t2zYOHjz4mscVFxfzi1/8Yg2uSERERERErhe5qbnkpuZyT+k9AMwszNAcaOb40PJ2bP4pPyf9JznpP8n3W74PgDfZGxPS30w1HYzp9xerqi9t1dbqM0J6m88I7f3BafrGpugbm+I3rUPm8QlWCxXetAvCesFNsF3bzS7u+5SLiIiIiIi8WQ6bg63ZW9mabdwYHolE6Bvv4/jwcU4Mn+D48HHaRtoYnhrmV92/4lfdvwKManpVRhV13jozqL+Re9PPZ7dZzYD97hX9wck52gbDtK0I622+MOGZeaN/MAzHl49PcyRQmZNGVa6T9TlONuQY58xKu3Bha7k+KZSLiIiIiMgNx2KxUOgspNBZyN7yvQBMzU/RHGg2Q/pSNb0p0ERToIkftP4AgMykzOWQ7ql7Qyu9X4o7xc6uskx2lWWafZFIhP7g9AVBvWN4nPGZeRq7x2jsHos5jyctkfU50aAeDezrc9Jw6n71645CuYiIiIiI3BSSE5LZnrOd7TnGTk6RSISBiQGzmr50b/rI9Agv9rzIiz0vAmC1WFmXvo46bx11njrqvHWUucve9L7pSywWCwXpyRSkJ/P2qtjt2jr9E9HKutHaB8N0jUziH5/FPx7g1Y5AzLny3UmszzUq6pXRyvq67DQSNAP+mqVQLiIiIiIiNyWLxUJ+Wj75afncW3YvYNyb3hJoMUK63wjqAxMDtI+20z7azn+2/ycAafY0aj21ZlDf5N1EZlLm5X7c62a3WamMhuvfr1vun5yd58zQ+HJQHxqn3RfGF5qmP2i0F9uGV3xPKMpIxhWx0pxwmqp8F5XZRli/XvZXv5EplIuIiIiIiEQ5bA62ZG9hS/YWs29ocigmpDcHmhmfG+fgwEEODiwvXl2YVsgm7yYzpFdnVpNoS7zq15iSmEBdYTp1hekx/cGpOU5H70s/PThuVtYDE7N0j0wBVpp+e8483mqB4swUKqNT39fnOKnMdlLuTVVYX0MK5SIiIiIiIpeRnZLNO0rewTtK3gHA/OI8Z8bOmFPeT/hPcC54jt7xXnrHe/nluV8Cy4vIbfJuYpNnE3XeOoqdxau2mro72c6O0kx2lMZW7P3jM7T0jfH/vXCIRG8JHcOTtA+FGZucozMwSWdgkv3Ng+bxVguUZKWyLjuN9TlpVGY7qcxJo8KryvpqUCgXERERERF5HRKsCVRlVlGVWcUfb/hjAEKzIZqGmzjhP2FswzZ8ktGZUXMRuR/yQwDcDrcx7T26gNwmzyYykjJW9Xo9aQ7eUp7JSGuEd71rI3a73dxf/fTgOO2DYdoHxzk9GOb00DjBqTnO+Sc455+4IKwXZ6awLjr1vTI7zQzrqQ5FyzdKvzkREREREZE3yZXo4taCW7m14FbAWESud7yXk8PGPukn/CdoDbQSnAnyu77f8bu+35mfLUwrZJNnk3mPelVm1ZvaO/1KrNxf/bZ1HrN/ZVg/PWjcr35mcPyCyvqvWgZjzleQnhwT1Ndlp7HO68SdotXgX4tCuYiIiIiIyFVmsVgochZR5CziXeXvAmBuYY620TZODJ+gyd/ESf9JOkOdy9PeO6PT3i0JVGZUmpX0Wk8t5e5ybNbVnzp+ubDuH5/l9GCYM8PjRmgfCnNmaBz/+Cx9Y1P0jU3xUvtwzPk8aQ4qs6MhfUXLdjpWbRr/9UahXEREREREZA3YbXZqPbXUemrNvtBsiCZ/kxnSTw6fJDAdoGWkhZaRFv6j/T8AYzu3mqwa8/ObPJvIS81bs2BrsVjwOh14nQ5uXRHWAUYnZi8I6qcHx/GFpvGPz+Afn+HA2dit25xJCdFqehoV0cd12WkUZaZgs95cYV2hXEREREREJE5ciS5uzb+VW/OXp737JnxGQPefpMnfRHOgmcn5SY4MHuHI4BHzs5lJmWZIr80yHlf7/vSLyUhNZGdqJjvPW2AuPD1Hx/AEZ4bGo80I7N0jk4Sn52nsHqOxeyzmM4k2K2UeY5G5iuw0KrzG83JPGsmJN+YicwrlIiIiIiIi1wiLxUJeWh55aXncXXo3AAuLC5wLnjNDelOgifaRdkamR3i592Ve7n3Z/HxBWoEZ0ms8NdRk1ZBiT4nLd3Em2dlSlM6WovSY/um5BToDy2F9KbifHR5nZn6Rtui2bucrSE82g3qF11hgriI7FW/a9T0VXqFcRERERETkGmaz2liXsY51Geu4v/J+AGYWZmgdaTWnvjf5m+gMddI33kffeB//0/k/AFiwUJFeQXVmNZGZCMX+Ymqya1Zl//QrlWS3UZXroirXFdO/sBihf2xqRViPVtiHxxmbnDPvW3/5vPvWnUkJZkgvNwN7KiVZqSQmWNfyq70hCuUiIiIiIiLXGYfNwWbvZjZ7N5t9odkQzYFmmvxNnPKf4qT/JIOTg5wZO8OZsTMA/GLfL0iwJlCZbiwkt3SfekV6BQnW+MZDm9VCUWYKRZkp7KnKjnlvZGKWjuFxOqJhvWN4go7hcXqiU+GP9YxxrGfswvNlJJthvXxFcM9KTbxmqusK5SIiIiIiIjcAV6KLt+S9hbfkvcXs80/5afI3cWLoBC+1vsSQbYixmbHlheQwFpJz2BxUZVZRk1VDjaeG2qxaSlwla7Li+5XITE0k8yL3rU/PLdAVmDSnv5/1T5jhfWJ2wdzC7detsedzJSVQ5k2jwpNqBvZybyqlWakk2df2OyuUi4iIiIiI3KA8yR7uLLqT23Jvo6S3hHvvvRf/rBHUTwVOccp/ilOBU4zPjXN8+DjHh4+bn01JSKE6q9oI6tGwXuQswmq5dqaEJ9ltbMh1siHXGdMfiUQYCs8YlXX/hFlhPzs8QX9witD0PMd7xjh+XnXdYjHuXS/3plG+FNg9aZR5U8lzJWFdhZXhFcpFRERERERuEhaLhfy0fPLT8s2F5BYji3SHumkKGCu9n/KfomWkhcn5SRoGG2gYbDA/77Q72Zi10WiejdRk1lDoLLxmpoIvsVgs5LiSyHElXbCF2/TcAuf8E5wdnjCr62ejgT08M0/v6BS9oxfeu55kt1KaZdyzXhYN7GUeo6WnvPF79BXKRUREREREbmJWi5VSdyml7lJ+v/z3geUV308FTpkV9daRVsJzYQ75DnHId8j8vCvRFVNR35i1kYK0gmsuqC9JstuoznNRnRe70FwkEsE/PsvZ4XEjtC8Fd/843YFJpucWafWFafVduDJ8Roo9GtDTzLDudSxc0fUolIuIiIiIiEiMlSu+v3vduwGYW5yjY6zDrKY3B5ppG20jNBvi0MAhDg0sB3W3w011ZvVyVT1rI4Vp115FfSWLxYLX6cDrdLC7PCvmvfmFRXpHpzjrH48G9Qk6/ROc808wEJxmdHKO0e4xjq7Yd31xZvKKfq5CuYiIiIiIiLwmu9VOVWYVVZlV/K/K/wXA3MIcZ8bOcCpghPRTgVO0j7YTnAlycOAgBwcOmp9fqqgvhfRrder7xSTYrJR6Uin1pPL2qtj3Jmfn6fRPcs4/wTm/MR3+nH+CM72z9FzJuVflikVEREREROSGZ7fZqc6qpjqr2uybXZjl9NhpmgPNZjs9evqiFXVnotOsqC89FruKr6nF5F5LSmICG/NdbMyPnQ4fCoVwf+G1P69QLiIiIiIiIldNoi3RvL98ydzC3AVBvX20nfBsmMO+wxz2HTaPTUlIoSqzyqyoV2dWU+oujfs+6qvlxvxWIiIiIiIics2w2+xmyF4ytzBHR7CDlkALpwLGiu9tI21Mzk9ydOgoR4eOmscm2ZJYn7k+pqq+Ln0ddps9Hl/nqlIoFxERERERkTVnty3fo35/5f0AzC/Ocy54juZAMy0jLTQHmmkdaWVqfooTwyc4MXzC/HyCNYHK9EozpFdlVbE+Yz3JCcnx+kpviEK5iIiIiIiIXBMSrAlUZlRSmVHJuzFWfV9YXKAr3EVroJWWkRZaAi00jzQTng0br0dazM9bLVbKXGVUZ1WbU+A3ZG7Alei61I+MO4VyERERERERuWbZrDbK3eWUu8t5V/m7AGNP8b7xvpiQ3hpoJTAdoCPYQUewg1+c/YV5jsK0QjOoV2VWUZ1ZjTfFG6+vFEOhXERERERERK4rFouFQmchhc5C7iq5y+wfnhw2g3rLSAutI630jffRO95L73gv+7v2m8dmJWVRlWUE9A2ZG6jOrKbIWbTmK78rlIuIiIiIiMgNwZvixZvi5fbC282+4EyQ1pFWWkeM6e+tgVbOhc4RmA7wu77f8bu+35nHptpT2ZCxwayoV2VWUZFeQaItcdWuWaFcREREREREblhuh5vdebvZnbfb7Juan+L06OmYoN4+2s7E3MQFK78nWBKoSK9gQ+ZyWL+a96krlIuIiIiIiMhNJTkhmTpvHXXeOrNvaeX3pap620gbLSMthGZDtI220Tbaxs87fm4eX5BWYFbVlwJ7XmoeFovldV2LQrmIiIiIiIjc9Fau/H5fxX2AsaCcb8Jn7qG+FNj7J/rpG++jb7yP3/T8xjyHM9FphPSMDRTbi6/s567KtxERERERERG5zlksFvLS8shLy+PtxW83+4MzQdpH22Oq6h1jHYRnw9T76qn31bMwtXBFP0OhXEREREREROR1cDvc7Mzdyc7cnWbf7MIsZ4NnzZB+oucELbRc5iwGhXIRERERERGRNynRlmguBAcQqgrxA37wmp9b2w3YRERERERERMSkUC4iIiIiIiISJwrlIiIiIiIiInGiUC4iIiIiIiISJwrlIiIiIiIiInGiUC4iIiIiIiISJwrlIiIiIiIiInGiUC4iIiIiIiISJwrlIiIiIiIiInGiUC4iIiIiIiISJwrlIiIiIiIiInGiUC4iIiIiIiISJwrlIiIiIiIiInGiUC4iIiIiIiISJwrlIiIiIiIiInGiUC4iIiIiIiISJwrlIiIiIiIiInGiUC4iIiIiIiISJwrlIiIiIiIiInGiUC4iIiIiIiISJwrlIiIiIiIiInGiUC4iIiIiIiISJwrlIiIiIiIiInGiUC4iIiIiIiISJwrlIiIiIiIiInGiUC4iIiIiIiISJwrlIiIiIiIiInGiUC4iIiIiIiISJwrlIiIiIiIiInGiUC4iIiIiIiISJwrlIiIiIiIiInGiUC4iIiIiIiISJ6sWyp9++mluvfVWUlJSSE9Pv+gx3d3d3HfffaSmpuLxePjIRz7C7OxszDEnT57kjjvuIDk5mYKCAj772c8SiURW67JFRERERERE1kzCap14dnaWBx54gFtuuYV/+Zd/ueD9hYUF9u7di9fr5ZVXXiEQCPD+97+fSCTCs88+C0AoFOKuu+5iz5491NfX097ezsMPP0xqaiof//jHV+vSRURERERERNbEqoXyz3zmMwB897vfvej7+/bto7m5mZ6eHvLz8wH46le/ysMPP8zTTz+Ny+Xi3//935menua73/0uDoeD2tpa2tvb+drXvsYTTzyBxWJZrcsXERERERERWXWrFspfy4EDB6itrTUDOcA999zDzMwMDQ0N7NmzhwMHDnDHHXfgcDhijnnyySfp7OykrKzsoueemZlhZmbGfB0MBgEYGRlhbm5ulb6RrDQ3N8fk5CSBQAC73R7vyxFZFRrncjPQOJebgca53Aw0ztdeOBwGeM3br+MWyn0+Hzk5OTF9GRkZJCYm4vP5zGNKS0tjjln6jM/nu2Qo/8IXvmBW6le61PEiIiIiIiIiqyEcDuN2uy/5/usK5U899dRFw+5K9fX17Nix44rOd7Hp55FIJKb//GOW/spwuanrTz75JE888YT5enFxkZGREbKysjTlfY2EQiGKioro6enB5XLF+3JEVoXGudwMNM7lZqBxLjcDjfO1F4lECIfDMbPDL+Z1hfLHH3+c97znPZc95vzK9qXk5uZy6NChmL7R0VHm5ubManhubq5ZNV8yNDQEcEGVfSWHwxEz5R245ArwsrpcLpf+o5cbnsa53Aw0zuVmoHEuNwON87V1uQr5ktcVyj0eDx6P5w1f0Eq33HILTz/9NAMDA+Tl5QHG4m8Oh4Pt27ebx/zN3/wNs7OzJCYmmsfk5+dfcfgXERERERERuVat2j7l3d3dHDt2jO7ubhYWFjh27BjHjh1jfHwcgLvvvpuNGzfy0EMP0djYyK9//Ws+8YlP8Mgjj5h/uXnwwQdxOBw8/PDDNDU18dOf/pTPf/7zWnldREREREREbgirttDb3/3d3/G9733PfL1161YAXnjhBe68805sNhvPPfccjz76KLfddhvJyck8+OCDPPPMM+Zn3G43+/fv57HHHmPHjh1kZGTwxBNPxNwvLtcmh8PBpz/96QtuIxC5kWicy81A41xuBhrncjPQOL92WSKvtT67iIiIiIiIiKyKVZu+LiIiIiIiIiKXp1AuIiIiIiIiEicK5SIiIiIiIiJxolAuIiIiIiIiEicK5bJqZmZm2LJlCxaLhWPHjsW8193dzX333Udqaioej4ePfOQjzM7OxudCRV6nzs5OPvCBD1BWVkZycjIVFRV8+tOfvmAMa5zL9e4f//EfKSsrIykpie3bt/Pb3/423pck8oZ94QtfYOfOnTidTrKzs/nDP/xD2traYo6JRCI89dRT5Ofnk5yczJ133smpU6fidMUib94XvvAFLBYLH/vYx8w+jfNrj0K5rJpPfvKT5OfnX9C/sLDA3r17mZiY4JVXXuFHP/oRP/nJT/j4xz8eh6sUef1aW1tZXFzkn//5nzl16hRf//rX+eY3v8nf/M3fmMdonMv17sc//jEf+9jH+Nu//VsaGxt529vexr333kt3d3e8L03kDXnppZd47LHHOHjwIPv372d+fp67776biYkJ85gvf/nLfO1rX+Mb3/gG9fX15ObmctdddxEOh+N45SJvTH19Pd/61reoq6uL6dc4vwZFRFbB888/H6mqqoqcOnUqAkQaGxtj3rNarZG+vj6z74c//GHE4XBEgsFgHK5W5M378pe/HCkrKzNfa5zL9W7Xrl2RD33oQzF9VVVVkb/+67+O0xWJXF1DQ0MRIPLSSy9FIpFIZHFxMZKbmxv54he/aB4zPT0dcbvdkW9+85vxukyRNyQcDkcqKysj+/fvj9xxxx2Rj370o5FIROP8WqVKuVx1g4ODPPLII/zbv/0bKSkpF7x/4MABamtrY6ro99xzDzMzMzQ0NKzlpYpcNcFgkMzMTPO1xrlcz2ZnZ2loaODuu++O6b/77rt59dVX43RVIldXMBgEMP/ffe7cOXw+X8y4dzgc3HHHHRr3ct157LHH2Lt3L+94xzti+jXOr00J8b4AubFEIhEefvhhPvShD7Fjxw46OzsvOMbn85GTkxPTl5GRQWJiIj6fb42uVOTq6ejo4Nlnn+WrX/2q2adxLtczv9/PwsLCBWM4JydH41duCJFIhCeeeIK3vvWt1NbWAphj+2Ljvqura82vUeSN+tGPfsTRo0epr6+/4D2N82uTKuVyRZ566iksFstl25EjR3j22WcJhUI8+eSTlz2fxWK5oC8SiVy0X2StXOk4X6m/v593vvOdPPDAA/z5n/95zHsa53K9O3+savzKjeLxxx/nxIkT/PCHP7zgPY17uZ719PTw0Y9+lO9///skJSVd8jiN82uLKuVyRR5//HHe8573XPaY0tJSPve5z3Hw4EEcDkfMezt27OB973sf3/ve98jNzeXQoUMx74+OjjI3N3fBX+1E1tKVjvMl/f397Nmzh1tuuYVvfetbMcdpnMv1zOPxYLPZLqiKDw0NafzKde/DH/4wP//5z3n55ZcpLCw0+3NzcwGjkpiXl2f2a9zL9aShoYGhoSG2b99u9i0sLPDyyy/zjW98w9xxQOP82qJQLlfE4/Hg8Xhe87h/+Id/4HOf+5z5ur+/n3vuuYcf//jH7N69G4BbbrmFp59+moGBAfN/Bvv27cPhcMT8D0RkrV3pOAfo6+tjz549bN++ne985ztYrbETjzTO5XqWmJjI9u3b2b9/P/fff7/Zv3//ft797nfH8cpE3rhIJMKHP/xhfvrTn/Liiy9SVlYW835ZWRm5ubns37+frVu3Asb6Ci+99BJf+tKX4nHJIq/b7/3e73Hy5MmYvj/7sz+jqqqKT33qU5SXl2ucX4MUyuWqKi4ujnmdlpYGQEVFhfnX6LvvvpuNGzfy0EMP8ZWvfIWRkRE+8YlP8Mgjj+Byudb8mkVer/7+fu68806Ki4t55plnGB4eNt9bqrRonMv17oknnuChhx5ix44d5myQ7u5uPvShD8X70kTekMcee4wf/OAH/OxnP8PpdJozQdxuN8nJyeZezp///OeprKyksrKSz3/+86SkpPDggw/G+epFrozT6TTXSViSmppKVlaW2a9xfu1RKJc1Z7PZeO6553j00Ue57bbbSE5O5sEHH+SZZ56J96WJXJF9+/Zx5swZzpw5EzP1EYxKDGicy/XvT/7kTwgEAnz2s59lYGCA2tpann/+eUpKSuJ9aSJvyD/90z8BcOedd8b0f+c73+Hhhx8G4JOf/CRTU1M8+uijjI6Osnv3bvbt24fT6VzjqxVZPRrn1x5LZOlfkCIiIiIiIiKyprT6uoiIiIiIiEicKJSLiIiIiIiIxIlCuYiIiIiIiEicKJSLiIiIiIiIxIlCuYiIiIiIiEicKJSLiIiIiIiIxIlCuYiIiIiIiEicKJSLiIiIiIiIxIlCuYiIiIiIiEicKJSLiIiIiIiIxIlCuYiIiIiIiEicKJSLiIiIiIiIxMn/D23kz98q8nw9AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "defaults = dict(p=2)\n", - "curves = [\n", - " CPC.from_px(x=100, **defaults),\n", - " CPC.from_px(x=50, **defaults),\n", - " CPC.from_px(x=150, **defaults),\n", - "]\n", - "for c in curves:\n", - " plt.plot(dxr, [c.dyfromdx_f(dx) for dx in dxr])\n", - "\n", - "plt.ylim((-100,200))\n", - "plt.xlim((-50,50))\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "id": "9b358bf2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "defaults = dict(p=2, x_act=10)\n", - "curves = [\n", - " CPC.from_px(x=100, **defaults),\n", - " CPC.from_px(x=50, **defaults),\n", - " CPC.from_px(x=150, **defaults),\n", - "]\n", - "for c in curves:\n", - " plt.plot(dxr, [c.dyfromdx_f(dx) for dx in dxr])\n", - "\n", - "plt.ylim((-100,200))\n", - "plt.xlim((-50,50))\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "id": "02407300", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "defaults = dict(p=2, y_act=20)\n", - "curves = [\n", - " CPC.from_px(x=100, **defaults),\n", - " CPC.from_px(x=50, **defaults),\n", - " CPC.from_px(x=150, **defaults),\n", - "]\n", - "for c in curves:\n", - " plt.plot(dxr, [c.dyfromdx_f(dx) for dx in dxr])\n", - "\n", - "plt.ylim((-100,200))\n", - "plt.xlim((-50,50))\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "id": "6a424616", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "defaults = dict(p=2, x_act=10, y_act=20)\n", - "curves = [\n", - " CPC.from_px(x=100, **defaults),\n", - " CPC.from_px(x=50, **defaults),\n", - " CPC.from_px(x=150, **defaults),\n", - "]\n", - "for c in curves:\n", - " plt.plot(dxr, [c.dyfromdx_f(dx) for dx in dxr])\n", - "\n", - "plt.ylim((-100,200))\n", - "plt.xlim((-50,50))\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "id": "5fb5f4db", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "defaults = dict(p=2, x_act=10, y_act=20)\n", - "curves = [\n", - " CPC.from_px(x=100, **defaults),\n", - " CPC.from_px(x=50, **defaults),\n", - " CPC.from_px(x=150, **defaults),\n", - "]\n", - "for c in curves:\n", - " plt.plot(dxr, [c.dyfromdx_f(dx) for dx in dxr])\n", - "\n", - "# plt.ylim((-100,200))\n", - "# plt.xlim((-50,50))\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9548029b", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74a35d7d", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1ed207ed", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "016c30e4-15ef-4466-b567-b36fa3f0a7d1", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57e7b1bc-5d34-49c6-a48c-8af2fddda954", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/NBTest/NBTest_002_CPCandOptimizer.py b/resources/NBTest/NBTest_002_CPCandOptimizer.py deleted file mode 100644 index b985dacf7..000000000 --- a/resources/NBTest/NBTest_002_CPCandOptimizer.py +++ /dev/null @@ -1,1770 +0,0 @@ -# -*- coding: utf-8 -*- -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -try: - from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, CPCContainer, T, CPCInverter, Pair - from fastlane_bot.tools.optimizer import CPCArbOptimizer, F, MargPOptimizer, PairOptimizer - from fastlane_bot.tools.analyzer import CPCAnalyzer - from fastlane_bot.testing import * - -except: - from tools.cpc import ConstantProductCurve as CPC, CPCContainer, T, CPCInverter, Pair - from tools.optimizer import CPCArbOptimizer, F, MargPOptimizer, PairOptimizer - from tools.analyzer import CPCAnalyzer - from tools.testing import * - -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Pair)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPCArbOptimizer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(MargPOptimizer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(PairOptimizer)) - -#plt.style.use('seaborn-dark') -plt.rcParams['figure.figsize'] = [12,6] -# from fastlane_bot import __VERSION__ -# require("3.0", __VERSION__) -# - - -# # CPC and Optimizer in Fastlane [NBTest002] -# -# Note: more optimizer tests in NBTest 055 - -try: - market_df = pd.read_csv("_data/NBTEST_002_Curves.csv.gz") -except: - market_df = pd.read_csv("fastlane_bot/tests/_data/NBTEST_002_Curves.csv.gz") -CCmarket = CPCContainer.from_df(market_df) - -# ## description - -d = CCmarket.bycid("167").description().splitlines() -d0 = """ -cid = 167 [167] -primary = WETH/DAI [WETH/DAI] -pp = 1,826.764318 DAI per WETH -pair = DAI/WETH [DAI/WETH] -tknx = 3,967,283.591895 DAI [virtual: 3,967,283.592] -tkny = 2,171.754481 WETH [virtual: 2,171.754] -p = 0.0005474159913752679 [min=0, max=None] WETH per DAI -fee = 0.003 -descr = sushiswap_v2 DAI/WETH 0.003 -""".strip().splitlines() -d0 = [l.strip() for l in d0] -for l,l0 in zip(d,d0): - print(f"d: {l}\nd0: {l0}\n") - assert l==l0 - -# ## bycids - -CC = CCmarket - -assert len(CC.bycids()) == len(CC) -assert type(CC.bycids()) == type(CC) -assert type(CC.bycids(ascc=False)) == tuple -for c in CC: - assert isinstance(c.cid, str), f"{c.cid} is not of type str" -cids = [c.cid for c in CC] -assert raises(CC.bycids, include="foo", endswith="bar") == 'include and endswith cannot be used together' -assert raises(CC.bycids,"167, 168, 169") -CC1 = CC.bycids(["167", "168", "169"]) -assert len(CC1) == 3 -assert [c.cid for c in CC1] == ['167', '168', '169'] -CC2 = CC.bycids(endswith="11") -assert len(CC2) == 5 -assert [c.cid for c in CC2] == ['211', '311', '411', '511', '611'] -CC3 = CC.bycids(endswith="11", exclude=['311', '411']) -assert [c.cid for c in CC3] == ['211', '511', '611'] - -# ## pairo and primary - -assert Pair.n("WETH") == "WETH" -assert Pair.n("WETH") == "WETH" -assert Pair.n("USDC/WETH") == "USDC/WETH" - -pairo = Pair("USDC/WETH") -assert pairo.isprimary == False -assert raises (Pair, tknb='USDC', tknq='WETH') -assert pairo.tknb == 'USDC' -assert pairo.tknq == 'WETH' -assert pairo.tknb_n == 'USDC' -assert pairo.tknq_n == 'WETH' -assert pairo.tknx == 'USDC' -assert pairo.tkny == 'WETH' -assert pairo.tknx_n == 'USDC' -assert pairo.tkny_n == 'WETH' -assert pairo.pair == 'USDC/WETH' -assert pairo.pair_n == 'USDC/WETH' -assert pairo.primary == 'WETH/USDC' -assert pairo.primary_n == 'WETH/USDC' -assert pairo.secondary == pairo.pair -assert pairo.secondary_n == pairo.pair_n -assert pairo.primary_tknb == "WETH" -assert pairo.primary_tknq == "USDC" - -pairo = Pair("WETH/USDC") -assert pairo.isprimary == True -assert pairo.tknq == 'USDC' -assert pairo.tknb == 'WETH' -assert pairo.tknq_n == 'USDC' -assert pairo.tknb_n == 'WETH' -assert pairo.tkny == 'USDC' -assert pairo.tknx == 'WETH' -assert pairo.tkny_n == 'USDC' -assert pairo.tknx_n == 'WETH' -assert pairo.pair == 'WETH/USDC' -assert pairo.pair_n == 'WETH/USDC' -assert pairo.primary == pairo.pair -assert pairo.primary_n == pairo.pair_n -assert pairo.secondary == 'USDC/WETH' -assert pairo.secondary_n == 'USDC/WETH' -assert pairo.primary_tknb == "WETH" -assert pairo.primary_tknq == "USDC" - -c1 = CPC.from_pk(pair="USDC/WETH", p=1, k=100) -c2 = CPC.from_pk(pair="WETH/USDC", p=1, k=100) -CC = CPCContainer([c1,c2]) -assert c1.pairo.primary == 'WETH/USDC' -assert c2.pairo.primary == 'WETH/USDC' -assert c1.primary == c1.pairo.primary -assert CC.pairs() == {'WETH/USDC'} -assert CC.pairs(standardize=True) == CC.pairs() -assert CC.pairs(standardize=False) == {'USDC/WETH', 'WETH/USDC'} - -assert Pair("WETH/USDC").isprimary == True -assert Pair("USDC/WETH").isprimary == False - -# ## buysell - -# selling ETH at 2000-2001 USDC per ETH -c1 = CPC.from_carbon(pair="WETH/USDC", tkny="WETH", yint=10, y=10, pa=1/2000, pb=1/2001, isdydx=True) -assert c1.pair == "USDC/WETH" -assert c1.primary == "WETH/USDC" -assert c1.pairo.isprimary == False -assert c1.buysell(verbose=True, withprice=True) == 'sell-WETH @ 2000.00 USDC per WETH' -assert c1.buysell(verbose=False) == "s" -assert c1.buysell() == "s" - -# selling ETH at 2000-2001 USDC per ETH -c1 = CPC.from_carbon(pair="WETH/USDC", tkny="WETH", yint=10, y=10, pa=2000, pb=2001, isdydx=False) -assert c1.pair == "USDC/WETH" -assert c1.primary == "WETH/USDC" -assert c1.pairo.isprimary == False -assert c1.buysell(verbose=True, withprice=True) == 'sell-WETH @ 2000.00 USDC per WETH' -assert c1.buysell(verbose=False) == "s" -assert c1.buysell(verbose=False, withprice=True) == ('s', 2000.0000000000005) -assert c1.buysell() == "s" - -# buying ETH at 1500-1499 USDC per ETH -c2 = CPC.from_carbon(pair="WETH/USDC", tkny="USDC", yint=10, y=10, pa=1500, pb=1499, isdydx=True) -assert c2.pair == "WETH/USDC" -assert c2.primary == "WETH/USDC" -assert c2.pairo.isprimary == True -assert c2.buysell(verbose=True, withprice=True) == 'buy-WETH @ 1500.00 USDC per WETH' -assert c2.buysell(verbose=False) == "b" -assert c2.buysell(verbose=False, withprice=True) == ('b', 1500.0000000000002) -assert c2.buysell() == "b" - -# buying ETH at 1500-1499 USDC per ETH -c2 = CPC.from_carbon(pair="WETH/USDC", tkny="USDC", yint=10, y=10, pa=1500, pb=1499, isdydx=False) -assert c2.pair == "WETH/USDC" -assert c2.primary == "WETH/USDC" -assert c2.pairo.isprimary == True -assert c2.buysell(verbose=True, withprice=True) == 'buy-WETH @ 1500.00 USDC per WETH' -assert c2.buysell(verbose=False) == "b" -assert c2.buysell(verbose=False, withprice=True) == ('b', 1500.0000000000002) -assert c2.buysell() == "b" - -# univ3 1899-1901 @ 1900 USDC per WETH -c3 = CPC.from_univ3(pair="WETH/USDC", Pmarg=1900, uniPa=1899, uniPb=1901, uniL=1000, cid="", fee=0, descr="") -assert c3.pair == "WETH/USDC" -assert c3.primary == "WETH/USDC" -assert c3.pairo.isprimary == True -assert c3.buysell(verbose=True, withprice=True) == 'buy-sell-WETH @ 1900.00 USDC per WETH' -assert c3.buysell(verbose=False) == "bs" -assert c3.buysell(verbose=False, withprice=True) == ('bs', 1900.0000000000007) -assert c3.buysell() == "bs" - -# univ3 1899-1901 @ 1900 USDC per WETH -c3 = CPC.from_univ3(pair="USDC/WETH", Pmarg=1/1900, uniPb=1/1899, uniPa=1/1901, uniL=1000, cid="", fee=0, descr="") -assert c3.pair == "USDC/WETH" -assert c3.primary == "WETH/USDC" -assert c3.pairo.isprimary == False -assert c3.buysell(verbose=True, withprice=True) == 'buy-sell-WETH @ 1900.00 USDC per WETH' -assert c3.buysell(verbose=False) == "bs" -assert c3.buysell(verbose=False, withprice=True) == ('bs', 1900.) -assert c3.buysell() == "bs" - -# univ3 1899-1901 @ 1899 USDC per WETH (WETH low, therefore 100% in WETH, therefore sell WETH) -c4 = CPC.from_univ3(pair="WETH/USDC", Pmarg=1899, uniPa=1899, uniPb=1901, uniL=1000, cid="", fee=0, descr="") -assert c4.pair == "WETH/USDC" -assert c4.primary == "WETH/USDC" -assert c4.pairo.isprimary == True -assert c4.buysell(verbose=True, withprice=True) == 'sell-WETH @ 1899.00 USDC per WETH' -assert c4.buysell(verbose=False) == "s" -assert c4.buysell(verbose=False, withprice=True) == ('s', 1899.0000000000002) -assert c4.buysell() == "s" - -# univ3 1899-1901 @ 1901 USDC per WETH (WETH high, therefore 100% in USDC, therefore buy WETH) -c5 = CPC.from_univ3(pair="WETH/USDC", Pmarg=1901, uniPa=1899, uniPb=1901, uniL=1000, cid="", fee=0, descr="") -assert c5.pair == "WETH/USDC" -assert c5.primary == "WETH/USDC" -assert c5.pairo.isprimary == True -assert c5.buysell(verbose=True, withprice=True) == 'buy-WETH @ 1901.00 USDC per WETH' -assert c5.buysell(verbose=False) == "b" -assert c5.buysell(verbose=False, withprice=True) == ('b', 1900.9999999999998) -assert c5.buysell() == "b" - -# univ2 (tknb=2000 USDC, tknq=1 ETH) -c6 = CPC.from_univ2(pair="USDC/WETH", x_tknb=2000, y_tknq=1, cid="", fee=0, descr="") -assert c6.pair == "USDC/WETH" -assert c6.primary == "WETH/USDC" -assert c6.pairo.isprimary == False -assert c6.buysell(verbose=True, withprice=True) == 'buy-sell-WETH @ 2000.00 USDC per WETH' -assert c6.buysell(verbose=False) == "bs" -assert c6.buysell(verbose=False, withprice=True) == ('bs', 2000.) -assert c6.buysell() == "bs" - -# univ2 (tknq=2000 USDC, tknb=1 ETH) -c7 = CPC.from_univ2(pair="WETH/USDC", x_tknb=1, y_tknq=2000, cid="", fee=0, descr="") -assert c7.pair == "WETH/USDC" -assert c7.primary == "WETH/USDC" -assert c7.pairo.isprimary == True -assert c7.buysell(verbose=True, withprice=True) == 'buy-sell-WETH @ 2000.00 USDC per WETH' -assert c7.buysell(verbose=False) == "bs" -assert c7.buysell(verbose=False, withprice=True) == ('bs', 2000.) -assert c7.buysell() == "bs" - -# ## P - -c = CPC.from_pk(pair="USDC/WETH", p=1, k=100, params=dict(exchange="univ3", a=dict(b=1, c=2))) -assert c.P("exchange") == "univ3" -assert c.P("a") == {'b': 1, 'c': 2} -assert c.P("a:b") == 1 -assert c.P("a:c") == 2 -assert c.P("a:d") is None -assert c.P("b") is None -assert c.P("b", "meh") == "meh" - -# ## byparams - -pair = "USDC/WETH" -c = [CPC.from_pk(pair=pair, p=1, k=100, params=dict(exchange="univ3", foo=1)) for _ in range(5)] -c += [CPC.from_pk(pair=pair, p=1, k=100, params=dict(exchange="carbv1", foo=2)) for _ in range(15)] -CC = CPCContainer(c) -assert len(CC)==20 - - -assert type(CC.byparams(exchange="meh")) == CPCContainer -assert type(CC.byparams(exchange="meh", _ascc=True)) == CPCContainer -assert type(CC.byparams(exchange="meh", _ascc=False)) == tuple -assert type(CC.byparams(exchange="meh", _asgenerator=True)).__name__ == "generator" -assert type(CC.byparams(exchange="meh", _ascc=True, _asgenerator=True)).__name__ == "generator" -assert type(CC.byparams(exchange="meh", _ascc=False, _asgenerator=True)).__name__ == "generator" -assert len(CC.byparams(exchange="univ3")) == 5 -assert len(CC.byparams(exchange="carbv1")) == 15 -assert len(CC.byparams(exchange="meh")) == 0 -assert len(CC.byparams(foo=1)) == 5 -assert len(CC.byparams(foo=2)) == 15 -assert len(CC.byparams(foo=3)) == 0 -assert raises (CC.byparams, foo=1, bar=2) == "currently only one param allowed {'foo': 1, 'bar': 2}" - -# ## itm - -# + -itm0 = CPC.itm0 -assert CPC.ITM_THRESHOLDPC == 0.01 - -assert itm0( ("bs", 1000), ("bs", 1000) ) == False -assert itm0( ("bs", 1000), ("bs", 1009) ) == False -assert itm0( ("bs", 1009), ("bs", 1000) ) == False -assert itm0( ("bs", 1000), ("bs", 1011) ) == True -assert itm0( ("bs", 1011), ("bs", 1000) ) == True -assert itm0( ("bs", 1000), ("bs", 1011), thresholdpc=0.02 ) == False -assert itm0( ("bs", 1011), ("bs", 1000), thresholdpc=0.02 ) == False -assert itm0( ("bs", 1000), ("bs", 1021), thresholdpc=0.02 ) == True -assert itm0( ("bs", 1021), ("bs", 1000), thresholdpc=0.02 ) == True - -assert itm0( ("b", 1000), ("s", 1100) ) == False -assert itm0( ("b", 1000), ("b", 1100) ) == False -assert itm0( ("b", 1000), ("bs", 1100) ) == False -assert itm0( ("s", 1000), ("s", 1100) ) == False -assert itm0( ("s", 1000), ("b", 1100) ) == True -assert itm0( ("s", 1000), ("bs", 1100) ) == True -assert itm0( ("bs", 1000), ("s", 1100) ) == False -assert itm0( ("bs", 1000), ("b", 1100) ) == True -assert itm0( ("bs", 1000), ("bs", 1100) ) == True - -assert itm0( ("s", 1000), ("b", 900) ) == False -assert itm0( ("s", 1000), ("s", 900) ) == False -assert itm0( ("s", 1000), ("bs", 900) ) == False -assert itm0( ("b", 1000), ("b", 900) ) == False -assert itm0( ("b", 1000), ("s", 900) ) == True -assert itm0( ("b", 1000), ("bs", 900) ) == True -assert itm0( ("bs", 1000), ("b", 900) ) == False -assert itm0( ("bs", 1000), ("s", 900) ) == True -assert itm0( ("bs", 1000), ("bs", 900) ) == True -# - - - -# c1: sell ETH @ 2000, c2: buy ETH @ 1500 --> no arb -c1 = CPC.from_carbon(pair="WETH/USDC", tkny="WETH", yint=10, y=10, pa=2000, pb=2001, isdydx=False) -c2 = CPC.from_carbon(pair="WETH/USDC", tkny="USDC", yint=10, y=10, pa=1500, pb=1499, isdydx=False) -bs1 = c1.buysell(verbose=False, withprice=True) -bs2 = c2.buysell(verbose=False, withprice=True) -assert (bs1, bs2) == (('s', 2000.0000000000005), ('b', 1500.0000000000002)) -assert itm0(bs1, bs2) == False -assert c1.itm(c2) == c2.itm(c1) -assert c1.itm(c2) == itm0(bs1, bs2) -assert c1.itm([c2,c2], aggr=False) == (itm0(bs1, bs2), itm0(bs1, bs2)) - -# c1: buy ETH @ 2000, c2: sell ETH @ 1500 --> arb -c1 = CPC.from_carbon(pair="WETH/USDC", tkny="USDC", yint=10, y=10, pb=2000, pa=2001, isdydx=False) -c2 = CPC.from_carbon(pair="WETH/USDC", tkny="WETH", yint=10, y=10, pb=1500, pa=1499, isdydx=False) -bs1 = c1.buysell(verbose=False, withprice=True) -bs2 = c2.buysell(verbose=False, withprice=True) -assert (bs1, bs2) == (('b', 2000.9999999999998), ('s', 1499.0000000000002)) -assert itm0(bs1, bs2) == True -assert c1.itm(c2) == c2.itm(c1) -assert c1.itm(c2) == itm0(bs1, bs2) -assert c1.itm([c2,c2], aggr=False) == (itm0(bs1, bs2), itm0(bs1, bs2)) - -# c1: buy ETH @ 2000, c2: sell ETH @ 1500, c2b: sell ETH @ 2500 --> arb, noarb -c1 = CPC.from_carbon(pair="WETH/USDC", tkny="USDC", yint=10, y=10, pb=2000, pa=2001, isdydx=False) -c2 = CPC.from_carbon(pair="WETH/USDC", tkny="WETH", yint=10, y=10, pb=1500, pa=1499, isdydx=False) -c2b = CPC.from_carbon(pair="WETH/USDC", tkny="WETH", yint=10, y=10, pb=2500, pa=2499, isdydx=False) -CC = CPCContainer([c1,c2,c2b]) -assert c1.itm(c2) == True -assert c1.itm(c2b) == False -assert c1.itm([c2,c2b], aggr=False) == (True, False) -assert c1.itm([c2b,c2], aggr=False) == (False, True) -assert c1.itm([c2b,c2], aggr=True) == True -assert c1.itm([c2,c2b], aggr=True) == True -assert c1.itm([c2b,c2]) == True -assert c1.itm([c2,c2b]) == True -assert c1.itm(CC, aggr=True) == True -assert c1.itm(CC, aggr=False) == (False, True, False) - -# c3: buy/sell @ 1900, c4: buy/sell @ 1899 --> arb depending on threshold -c3 = CPC.from_univ3(pair="WETH/USDC", Pmarg=1900, uniPa=1898, uniPb=1902, uniL=1000, cid="", fee=0, descr="") -c4 = CPC.from_univ3(pair="WETH/USDC", Pmarg=1899, uniPa=1898, uniPb=1902, uniL=1000, cid="", fee=0, descr="") -bs3 = c3.buysell(verbose=False, withprice=True) -bs4 = c4.buysell(verbose=False, withprice=True) -assert (bs3, bs4) == (('bs', 1900.0000000000007), ('bs', 1899.0000000000002)) -assert itm0(bs3, bs4, thresholdpc=0.0001) == True -assert itm0(bs3, bs4, thresholdpc=0.001) == False -assert c3.itm(c4) == c4.itm(c3) -assert c3.itm(c4) == itm0(bs3, bs4) -assert c3.itm([c4,c4], aggr=False) == (itm0(bs3, bs4), itm0(bs3, bs4)) - -# c3: buy/sell @ 1900, c4: buy/sell @ 1899 --> arb depending on threshold -c3 = CPC.from_univ3(pair="WETH/USDC", Pmarg=1900, uniPa=1898, uniPb=1902, uniL=1000, cid="", fee=0, descr="") -c4 = CPC.from_univ3(pair="USDC/WETH", Pmarg=1/1899, uniPb=1/1898, uniPa=1/1902, uniL=1000, cid="", fee=0, descr="") -bs3 = c3.buysell(verbose=False, withprice=True) -bs4 = c4.buysell(verbose=False, withprice=True) -assert (bs3, bs4) == (('bs', 1900.0000000000007), ('bs', 1899.0000000000002)) -assert itm0(bs3, bs4, thresholdpc=0.0001) == True -assert itm0(bs3, bs4, thresholdpc=0.001) == False -assert c3.itm(c4) == c4.itm(c3) -assert c3.itm(c4) == itm0(bs3, bs4) -assert c3.itm([c4,c4], aggr=False) == (itm0(bs3, bs4), itm0(bs3, bs4)) - -# ## TVL - -c = CPC.from_pk(pair="WETH/USDC", p=2000, k=1*2000) -assert c.tvl(incltkn=True) == (4000.0, 'USDC', 1) -assert c.tvl("USDC", incltkn=True) == (4000.0, 'USDC', 1) -assert c.tvl("WETH", incltkn=True) == (2.0, 'WETH', 1) -assert c.tvl("USDC", incltkn=True, mult=2) == (8000.0, 'USDC', 2) -assert c.tvl("WETH", incltkn=True, mult=2) == (4.0, 'WETH', 2) -assert c.tvl("WETH", incltkn=False) == 2.0 -assert c.tvl("WETH") == 2.0 -assert c.tvl() == 4000 -assert c.tvl("WETH", mult=2000) == 4000 - -# ## estimate prices - -CC = CPCContainer() -CC += [CPC.from_univ3(pair="WETH/USDC", cid="uv3", fee=0, descr="", - uniPa=2000, uniPb=2010, Pmarg=2005, uniL=10*m.sqrt(2000))] -CC += [CPC.from_pk(pair="WETH/USDC", cid="uv2", fee=0, descr="", - p=1950, k=5**2*2000)] -CC += [CPC.from_pk(pair="USDC/WETH", cid="uv2r", fee=0, descr="", - p=1/1975, k=5**2*2000)] -CC += [CPC.from_carbon(pair="WETH/USDC", cid="carb", fee=0, descr="", - tkny="USDC", yint=1000, y=1000, pa=1850, pb=1750)] -CC += [CPC.from_carbon(pair="WETH/USDC", cid="carb", fee=0, descr="", - tkny="WETH", yint=1, y=0, pb=1/1850, pa=1/1750)] -CC += [CPC.from_carbon(pair="WETH/USDC", cid="carb", fee=0, descr="", - tkny="USDC", yint=1000, y=500, pa=1870, pb=1710)] -#CC.plot() - -assert CC.price_estimate(tknq=T.WETH, tknb=T.USDC, result=CC.PE_PAIR) == f"{T.USDC}/{T.WETH}" -assert CC.price_estimate(pair=f"{T.USDC}/{T.WETH}", result=CC.PE_PAIR) == f"{T.USDC}/{T.WETH}" -assert raises(CC.price_estimate, tknq="a", result=CC.PE_PAIR) -assert raises(CC.price_estimate, tknb="a", result=CC.PE_PAIR) -assert raises(CC.price_estimate, tknq="a", tknb="b", pair="a/b", result=CC.PE_PAIR) -assert raises(CC.price_estimate, pair="ab", result=CC.PE_PAIR) -assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.USDC], pairs=True, - unwrapsingle=False)[0][0] == f"{T.USDC}/{T.WETH}" -assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.USDC], pairs=True, - unwrapsingle=True)[0] == f"{T.USDC}/{T.WETH}" -assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.USDC], pairs=True)[0] == f"{T.USDC}/{T.WETH}" -r = CC.price_estimates(tknqs=list("ABC"), tknbs=list("DEFG"), pairs=True) -assert r.ndim == 2 -assert r.shape == (3,4) -r = CC.price_estimates(tknqs=list("A"), tknbs=list("DEFG"), pairs=True) -assert r.ndim == 1 -assert r.shape == (4,) - -assert CC[0].at_boundary == False -assert CC[1].at_boundary == False -assert CC[2].at_boundary == False -assert CC[3].at_boundary == True -assert CC[3].at_xmin == True -assert CC[3].at_ymin == False -assert CC[3].at_xmax == False -assert CC[3].at_ymax == True -assert CC[4].at_boundary == True -assert CC[4].at_ymin == True -assert CC[4].at_xmin == True -assert CC[4].at_ymax == True -assert CC[4].at_xmax == True -assert CC[5].at_boundary == True - -r = CC.price_estimate(tknq="USDC", tknb="WETH", result=CC.PE_CURVES) -assert len(r)==3 - -p,w = CC.price_estimate(tknq="USDC", tknb="WETH", result=CC.PE_DATA) -assert len(p) == len(r) -assert len(w) == len(r) -assert iseq(sum(p), 5930) -assert iseq(sum(w), 894.4271909999159) -pe = CC.price_estimate(tknq="USDC", tknb="WETH") -assert pe == np.average(p, weights=w) - -O = PairOptimizer(CC) -Om = PairOptimizer(CCmarket) -assert O.price_estimates(tknq="USDC", tknbs=["WETH"]) == CC.price_estimates(tknqs=["USDC"], tknbs=["WETH"]) -CCmarket.fp(onein="USDC") -r = Om.price_estimates(tknq="USDC", tknbs=["WETH", "WBTC"]) -assert iseq(r[0], 1820.89875275) -assert iseq(r[1], 28351.08150121) - -# ## triangle estimates - -CC = CPCContainer() -CC += [CPC.from_univ3(pair=f"{T.WETH}/{T.USDC}", cid="uv3-1", fee=0, descr="", - uniPa=2000, uniPb=2002, Pmarg=2001, uniL=10*m.sqrt(2000))] -CC += [CPC.from_univ3(pair=f"{T.WBTC}/{T.USDC}", cid="uv3-2", fee=0, descr="", - uniPa=20000, uniPb=20020, Pmarg=20010, uniL=1*m.sqrt(20000))] -#CC.plot() - -help(CC.price_estimate) - -assert iseq(CC.price_estimate(pair=f"{T.WETH}/{T.USDC}"), 2001) -assert iseq(CC.price_estimate(pair=f"{T.WBTC}/{T.USDC}"), 20010) -assert iseq(CC.price_estimate(pair=f"{T.USDC}/{T.WETH}"), 1/2001) -assert iseq(CC.price_estimate(pair=f"{T.USDC}/{T.WBTC}"), 1/20010) - -assert CC.price_estimate(tknb=T.WETH, tknq=T.USDC, result=CC.PE_PAIR) == f"{T.WETH}/{T.USDC}" -r = CC.price_estimate(tknb=T.WETH, tknq=T.USDC, result=CC.PE_CURVES) -assert len(r) == 1 -assert r[0][0].cid=="uv3-1" -assert iseq(r[0][1], 2001) -assert iseq(r[0][2], 200000.0) -r = CC.price_estimate(tknb=T.WETH, tknq=T.USDC, result=CC.PE_DATA) -assert len(r) == 2 -assert r[0].shape == (1,) -assert r[1].shape == (1,) -assert iseq(r[0][0], 2001) - -help(CC.price_estimates) - -r = CC.price_estimates(tknqs=[T.WETH], tknbs=[T.WBTC], unwrapsingle=True, pairs=True) -assert r.shape == (1,) -assert r[0] == f"{T.WBTC}/{T.WETH}" -assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.WBTC], pairs=True) == r -r - -r = CC.price_estimates(tknqs=[T.WETH], tknbs=[T.WBTC], unwrapsingle=False, pairs=True) -assert r.shape == (1,1) -assert r[0][0] == f"{T.WBTC}/{T.WETH}" -r - -assert raises(CC.price_estimates, tknqs=[T.WETH], tknbs=[T.WBTC], - triangulate=False).startswith("('no price found") -r = CC.price_estimates(tknqs=[T.WETH], tknbs=[T.WBTC], raiseonerror=False, triangulate=False) -assert r == CC.price_estimates(tknqs=[T.WETH], tknbs=[T.WBTC], raiseonerror=False, triangulate=False) -assert r.shape == (1,) -assert r[0] is None - -r = CC.price_estimates(tknqs=[T.WETH], tknbs=[T.WBTC], triangulate=[T.USDC]) -assert r == CC.price_estimates(tknqs=[T.WETH], tknbs=[T.WBTC], triangulate=True) -assert r == CC.price_estimates(tknqs=[T.WETH], tknbs=[T.WBTC]) -assert iseq(r[0], 10) - -# ## price estimates in optimizer - -prices = {"USDC":1, "LINK": 5, "AAVE": 100, "MKR": 500, "WETH": 2000, "WBTC": 20000} -CCfm, ctr = CPCContainer(), 0 -for tknb, pb in prices.items(): - for tknq, pq in prices.items(): - if pb>pq: - pair = f"{tknb}/{tknq}" - pp = pb/pq - k = (100000)**2/(pb*pq) - CCfm += CPC.from_pk(p=pp, k=k, pair=pair, cid = f"mkt-{ctr}") - ctr += 1 - -O = MargPOptimizer(CCfm) -assert O.MO_PSTART == O.MO_P -tknq = "WETH" -df = O.margp_optimizer(tknq, result=O.MO_PSTART) -rd = df[tknq].to_dict() -assert len(df) == len(prices)-1 -assert df.columns[0] == tknq -assert df.index.name == "tknb" -assert rd == {k:v/prices[tknq] for k,v in prices.items() if k!=tknq} -df2 = O.margp_optimizer(tknq, result=O.MO_PSTART, params=dict(pstart=df)) -assert np.all(df == df2) -df2 = O.margp_optimizer(tknq, result=O.MO_PSTART, params=dict(pstart=rd)) -assert np.all(df == df2) -df - -# ## Assertions and testing - -c = CPC.from_px(p=2000,x=10, pair="ETH/USDC") -assert c.pair == "ETH/USDC" -assert c.tknb == c.pair.split("/")[0] -assert c.tknx == c.tknb -assert c.tknq == c.pair.split("/")[1] -assert c.tkny == c.tknq -assert f"{c.tknb}/{c.tknq}" == c.pair -print (c.descr) - -c = CPC.from_xy(10,20) -assert c == CPC.from_kx(c.k, c.x) -assert c == CPC.from_ky(c.k, c.y) -assert c == CPC.from_xy(c.x, c.y) -assert c == CPC.from_pk(c.p, c.k) -assert c == CPC.from_px(c.p, c.x) -assert c == CPC.from_py(c.p, c.y) - -c - -c = CPC.from_px(p=2, x=100, x_act=10, y_act=20) -assert c.y_max*c.x_min == c.k -assert c.x_max*c.y_min == c.k -assert c.p_min == c.y_min / c.x_max -assert c.p_max == c.y_max / c.x_min -assert c.p_max >= c.p_min - -c = CPC.from_px(p=2, x=100, x_act=10, y_act=20) -e = 1e-5 -assert 95*c.yfromx_f(x=95) == c.k -assert 105*c.yfromx_f(x=105) == c.k -assert 190*c.xfromy_f(y=190) == c.k -assert 210*c.xfromy_f(y=210) == c.k -assert not c.yfromx_f(x=90) is None -assert c.yfromx_f(x=90-e) is None -assert not c.xfromy_f(y=180) is None -assert c.xfromy_f(y=180-e) is None -assert c.dyfromdx_f(dx=-5) -assert (c.y+c.dyfromdx_f(dx=-5))*(c.x-5) == c.k -assert (c.y+c.dyfromdx_f(dx=+5))*(c.x+5) == c.k -assert (c.x+c.dxfromdy_f(dy=-5))*(c.y-5) == c.k -assert (c.x+c.dxfromdy_f(dy=+5))*(c.y+5) == c.k - -c = CPC.from_pkpp(p=100, k=100) -assert c.p_min == 100 -assert c.p_max == 100 -assert c.p == 100 -assert c.k == 100 - -c = CPC.from_pkpp(p=100, k=100, p_min=80, p_max=120) -assert c.p_min == 80 -assert iseq(c.p_max, 120) -assert c.p == 100 -assert c.k == 100 - -# ## iseq - -assert iseq("a", "a", "ab") == False -assert iseq("a", "a", "a") -assert iseq(1.0, 1, 1.0) -assert iseq(0,0) -assert iseq(0,1e-10) -assert iseq(0,1e-5) == False -assert iseq(1, 1.00001) == False -assert iseq(1, 1.000001) -assert iseq(1, 1.000001, eps=1e-7) == False -assert iseq("1", 1) == False - -# ## New CPC features in v2 - -# + -p = CPCContainer.Pair("ETH/USDC") -assert str(p) == "ETH/USDC" -assert p.pair == str(p) -assert p.tknx == "ETH" -assert p.tkny == "USDC" -assert p.tknb == "ETH" -assert p.tknq == "USDC" - -pp = CPCContainer.Pair.wrap(["ETH/USDC", "WBTC/ETH"]) -assert len(pp) == 2 -assert pp[0].pair == "ETH/USDC" -assert pp[1].pair == "WBTC/ETH" -assert pp[0].unwrap(pp) == ('ETH/USDC', 'WBTC/ETH') -# - - -pairs = ["A", "B", "C"] -assert CPCContainer.pairset(", ".join(pairs)) == set(pairs) -assert CPCContainer.pairset(pairs) == set(pairs) -assert CPCContainer.pairset(tuple(pairs)) == set(pairs) -assert CPCContainer.pairset(p for p in pairs) == set(pairs) - -pairs = [f"{a}/{b}" for a in ["ETH", "USDC", "DAI"] for b in ["DAI", "WBTC", "LINK", "ETH"] if a!=b] -CC = CPCContainer() -fp = lambda **cond: CC.filter_pairs(pairs=pairs, **cond) -assert fp(bothin="ETH, USDC, DAI") == {'DAI/ETH', 'ETH/DAI', 'USDC/DAI', 'USDC/ETH'} -assert fp(onein="WBTC") == {'DAI/WBTC', 'ETH/WBTC', 'USDC/WBTC'} -assert fp(onein="ETH") == fp(contains="ETH") -assert fp(notin="WBTC, ETH, DAI") == {'USDC/LINK'} -assert fp(tknbin="WBTC") == set() -assert fp(tknqin="WBTC") == {'DAI/WBTC', 'ETH/WBTC', 'USDC/WBTC'} -assert fp(tknbnotin="WBTC") == set(pairs) -assert fp(tknbnotin="WBTC, ETH, DAI") == {'USDC/DAI', 'USDC/ETH', 'USDC/LINK', 'USDC/WBTC'} -assert fp(notin_0="WBTC", notin_1="DAI") == fp(notin="WBTC, DAI") -assert fp(onein = "ETH") == fp(anyall=CC.FP_ANY, tknbin="ETH", tknqin="ETH") - -P = CPCContainer.Pair -ETHUSDC = P("WETH/USDC") -USDCETH = P(ETHUSDC.pairr) -assert ETHUSDC.pair == "WETH/USDC" -assert ETHUSDC.pairr == "USDC/WETH" -assert USDCETH.pairr == "WETH/USDC" -assert USDCETH.pair == "USDC/WETH" -assert ETHUSDC.isprimary -assert not USDCETH.isprimary -assert ETHUSDC.primary == ETHUSDC.pair -assert ETHUSDC.secondary == ETHUSDC.pairr -assert USDCETH.primary == USDCETH.pairr -assert USDCETH.secondary == USDCETH.pair -assert ETHUSDC.primary == USDCETH.primary -assert ETHUSDC.secondary == USDCETH.secondary - -assert P("BTC/ETH").isprimary -assert P("WBTC/ETH").isprimary -assert P("BTC/WETH").isprimary -assert P("WBTC/ETH").isprimary -assert P("BTC/USDC").isprimary -assert P("XYZ/USDC").isprimary -assert P("XYZ/USDT").isprimary - -# ## Real data and retrieval of curves - -# try: -# df = pd.read_csv("../nbtest_data/NBTEST_002_Curves.csv.gz") -# except: -# df = pd.read_csv("fastlane_bot/tests/nbtest_data/NBTEST_002_Curves.csv.gz") -CC = CPCContainer.from_df(market_df) -assert len(CC) == 459 -assert len(CC) == len(market_df) -assert len(CC.pairs()) == 326 -assert len(CC.tokens()) == 141 -assert CC.tokens_s -assert CC.tokens_s()[:60] == '1INCH,1ONE,AAVE,ALCX,ALEPH,ALPHA,AMP,ANKR,ANT,APW,ARCONA,ARM' -print("Num curves:", len(CC)) -print("Num pairs:", len(CC.pairs())) -print("Num tokens:", len(CC.tokens())) -#print(CC.tokens_s()) - -assert CC.bypairs(CC.fp(onein="WETH, WBTC")) == CC.bypairs(CC.fp(onein="WETH, WBTC"), asgenerator=False) -assert len(CC.bypairs(CC.fp(onein="WETH, WBTC"))) == 254 -assert len(CC.bypairs(CC.fp(onein="WETH, WBTC"), ascc=True)) == 254 -CC1 = CC.bypairs(CC.fp(onein="WBTC"), ascc=True) -assert len(CC1) == 29 -cids = [c.cid for c in CC.bypairs(CC.fp(onein="WBTC"))] -assert len(cids) == len(CC1) -assert CC.bycid("bla") is None -assert not CC.bycid("191") is None -assert raises(CC.bycids, ["bla"]) -assert len(CC.bycids(cids)) == len(cids) -assert len(CC.bytknx("WETH")) == 46 -assert len(CC.bytkny("WETH")) == 181 -assert len(CC.bytknys("WETH")) == len(CC.bytkny("WETH")) -assert len(CC.bytknxs("USDC, USDT")) == 41 -assert len(CC.bytknxs(["USDC", "USDT"])) == len(CC.bytknxs("USDC, USDT")) -assert len(CC.bytknys(["USDC", "USDT"])) == len(CC.bytknys({"USDC", "USDT"})) -cs = CC.bytknx("WETH", asgenerator=True) -assert raises(len, cs) -assert len(tuple(cs)) == 46 -assert len(tuple(cs)) == 0 # generator empty - -CC2 = CC.bypairs(CC.fp(bothin="USDC, DAI, BNT, SHIB, ETH, AAVE, LINK"), ascc=True) -tt = CC2.tokentable() -assert tt["ETH"].x == [] -assert tt["ETH"].y == [0] -assert tt["DAI"].x == [1,4,8] -assert tt["DAI"].y == [3,6] -tt - -assert CC2.tknxs() == {'AAVE', 'BNT', 'DAI', 'LINK'} -assert CC2.tknxl() == ['BNT', 'DAI', 'LINK', 'LINK', 'DAI', 'LINK', 'LINK', 'AAVE', 'DAI'] -assert set(CC2.tknxl()) == CC2.tknxs() -assert set(CC2.tknyl()) == CC2.tknys() -assert len(CC2.tknxl()) == len(CC2.tknyl()) -assert len(CC2.tknxl()) == len(CC2) - -# ## TokenScale tests [NOTEST] - -pass - -# + -# TSB = ts.TokenScaleBase() -# assert raises (TSB.scale,"ETH") -# assert TSB.DEFAULT_SCALE == 1e-2 - -# + -# TS = ts.TokenScale.from_tokenscales(USDC=1e0, ETH=1e3, BTC=1e4) -# TS - -# + -# assert TS("USDC") == 1 -# assert TS("ETH") == 1000 -# assert TS("BTC") == 10000 -# assert TS("MEH") == TS.DEFAULT_SCALE - -# + -# TSD = ts.TokenScaleData - -# + -# tknset = {'AAVE', 'BNT', 'BTC', 'ETH', 'LINK', 'USDC', 'USDT', 'WBTC', 'WETH'} -# assert tknset - set(TSD.scale_dct.keys()) == set() - -# + -# cc1 = CPC.from_xy(x=10, y=20000, pair="ETH/USDC") -# assert cc1.tokenscale is cc1.TOKENSCALE -# assert cc1.tknx == "ETH" -# assert cc1.tkny == "USDC" -# assert cc1.scalex == 1 -# assert cc1.scaley == 1 -# cc2 = CPC.from_xy(x=10, y=20000, pair="BTC/MEH") -# assert cc2.tknx == "BTC" -# assert cc2.tkny == "MEH" -# assert cc2.scalex == 1 -# assert cc2.scaley == 1 -# assert cc2.scaley == cc2.tokenscale.DEFAULT_SCALE - -# + -# cc1 = CPC.from_xy(x=10, y=20000, pair="ETH/USDC") -# cc1.set_tokenscale(TSD) -# assert cc1.tokenscale != cc1.TOKENSCALE -# assert cc1.tknx == "ETH" -# assert cc1.tkny == "USDC" -# assert cc1.scalex == 1e3 -# assert cc1.scaley == 1e0 -# cc2 = CPC.from_xy(x=10, y=20000, pair="BTC/MEH") -# cc2.set_tokenscale(TSD) -# assert cc2.tknx == "BTC" -# assert cc2.tkny == "MEH" -# assert cc2.scalex == 1e4 -# assert cc2.scaley == 1e-2 -# assert cc2.scaley == cc2.tokenscale.DEFAULT_SCALE -# - - -# ## dx_min and dx_max etc - -cc = CPC.from_pkpp(p=100, k=100*10000, p_min=90, p_max=110) -assert iseq(cc.x_act, 4.653741075440777) -assert iseq(cc.y_act, 513.167019494862) -assert cc.dx_min == -cc.x_act -assert cc.dy_min == -cc.y_act -assert iseq( (cc.x + cc.dx_max)*(cc.y + cc.dy_min), cc.k) -assert iseq( (cc.y + cc.dy_max)*(cc.x + cc.dx_min), cc.k) - -# ## xyfromp_f and dxdyfromp_f - -# + -c = CPC.from_pkpp(p=100, k=100*10000, p_min=90, p_max=110, pair=f"{T.ETH}/{T.USDC}") - -assert c.pair == f'{T.WETH}/{T.USDC}', f"{c.pair}" -assert c.pairp == f'{T.WETH}/{T.USDC}', f"{c.pair}" -assert c.p == 100 -assert iseq(c.x_act, 4.653741075440777) -assert iseq(c.y_act, 513.167019494862) -assert c.tknx == T.ETH -assert c.tkny == T.USDC -assert c.tknxp == T.WETH -assert c.tknyp == T.USDC -assert c.xyfromp_f() == (c.x, c.y, c.p) -assert c.xyfromp_f(withunits=True) == (100.0, 10000.0, 100.0, T.WETH, T.USDC, f'{T.WETH}/{T.USDC}') - -x,y,p = c.xyfromp_f(p=85, ignorebounds=True) -assert p == 85 -assert iseq(x*y, c.k) -assert iseq(y/x,85) - -x,y,p = c.xyfromp_f(p=115, ignorebounds=True) -assert p == 115 -assert iseq(x*y, c.k) -assert iseq(y/x,115) - -x,y,p = c.xyfromp_f(p=95) -assert p == 95 -assert iseq(x*y, c.k) -assert iseq(y/x,p) - -x,y,p = c.xyfromp_f(p=105) -assert p == 105 -assert iseq(x*y, c.k) -assert iseq(y/x,p) - -x,y,p = c.xyfromp_f(p=85) -assert p == 85 -assert iseq(x*y, c.k) -assert iseq(y/x,90) - -x,y,p = c.xyfromp_f(p=115) -assert p == 115 -assert iseq(x*y, c.k) -assert iseq(y/x,110) - -# + -assert c.dxdyfromp_f(withunits=True) == (0.0, 0.0, 100.0, T.WETH, T.USDC, f'{T.WETH}/{T.USDC}') - -dx,dy,p = c.dxdyfromp_f(p=85, ignorebounds=True) -assert p == 85 -assert iseq((c.x+dx)*(c.y+dy), c.k) -assert iseq((c.y+dy)/(c.x+dx),p) - -dx,dy,p = c.dxdyfromp_f(p=115, ignorebounds=True) -assert p == 115 -assert iseq((c.x+dx)*(c.y+dy), c.k) -assert iseq((c.y+dy)/(c.x+dx),p) - -dx,dy,p = c.dxdyfromp_f(p=95) -assert p == 95 -assert iseq((c.x+dx)*(c.y+dy), c.k) -assert iseq((c.y+dy)/(c.x+dx),p) - -dx,dy,p = c.dxdyfromp_f(p=105) -assert p == 105 -assert iseq((c.x+dx)*(c.y+dy), c.k) -assert iseq((c.y+dy)/(c.x+dx),p) - -dx,dy,p = c.dxdyfromp_f(p=85) -assert p == 85 -assert iseq((c.x+dx)*(c.y+dy), c.k) -assert iseq((c.y+dy)/(c.x+dx), 90) -assert iseq(dy, -c.y_act) - -dx,dy,p = c.dxdyfromp_f(p=115) -assert p == 115 -assert iseq((c.x+dx)*(c.y+dy), c.k) -assert iseq((c.y+dy)/(c.x+dx), 110) -assert iseq(dx, -c.x_act) - -assert iseq(c.x_min*c.y_max, c.k) -assert iseq(c.x_max*c.y_min, c.k) -assert iseq(c.y_max/c.x_min, c.p_max) -assert iseq(c.y_min/c.x_max, c.p_min) -# - - -# ## Asymmetric curves and curve classifications -# -# We here briefly run through asymmetric curves; we also ensure that the associated functions (is_constant_product) etc work across the board - -ETA = 3 -cc = CPC.from_xyal(x=10, y=100/ETA*10, eta=ETA) -assert cc.alpha == 0.75 -assert cc.eta == 3 -assert iseq(cc.x, 10) -assert iseq(cc.y, 100/ETA*10) -assert iseq(cc.p, 100) -assert iseq(cc.x_act, cc.x) -assert iseq(cc.y_act, cc.y) -assert (cc.x_min, cc.x_max) == (0,None) -assert (cc.y_min, cc.y_max) == (0,None) -assert not cc.is_constant_product() # DEPRECATED -assert not cc.is_symmetric() -assert cc.is_asymmetric() -assert not cc.is_levered() -assert cc.is_unlevered() - -ETA = 1 -cc = CPC.from_xyal(x=10, y=100/ETA*10, eta=ETA) -assert cc.alpha == 0.5 -assert cc.eta == 1 -assert iseq(cc.x, 10) -assert iseq(cc.y, 100/ETA*10) -assert iseq(cc.p, 100) -assert iseq(cc.x_act, cc.x) -assert iseq(cc.y_act, cc.y) -assert (cc.x_min, cc.x_max) == (0,None) -assert (cc.y_min, cc.y_max) == (0,None) -assert cc.is_constant_product() # DEPRECATED -assert cc.is_symmetric() -assert not cc.is_asymmetric() -assert not cc.is_levered() -assert cc.is_unlevered() - -cc = CPC.from_xy(x=10, y=100*10) -assert cc.alpha == 0.5 -assert cc.eta == 1 -assert iseq(cc.x, 10) -assert iseq(cc.y, 100/ETA*10) -assert iseq(cc.p, 100) -assert iseq(cc.x_act, cc.x) -assert iseq(cc.y_act, cc.y) -assert (cc.x_min, cc.x_max) == (0,None) -assert (cc.y_min, cc.y_max) == (0,None) -assert cc.is_constant_product() # DEPRECATED -assert cc.is_symmetric() -assert not cc.is_asymmetric() -assert not cc.is_levered() -assert cc.is_unlevered() - -cc = CPC.from_pkpp(p=100, k=10*100, p_min=90, p_max=110) -assert cc.alpha == 0.5 -assert cc.eta == 1 -assert iseq(cc.x, 3.1622776601683795) -assert iseq(cc.y, 316.2277660168379) -assert iseq(cc.p, 100) -assert not iseq(cc.x_act, cc.x) -assert not iseq(cc.y_act, cc.y) -assert not (cc.x_min, cc.x_max) == (0,None) -assert not (cc.y_min, cc.y_max) == (0,None) -assert cc.is_constant_product() # DEPRECATED -assert cc.is_symmetric() -assert not cc.is_asymmetric() -assert cc.is_levered() -assert not cc.is_unlevered() - -# ## CPCInverter - -c = CPC.from_pkpp(p=2000, k=10*20000, p_min=1800, p_max=2200, fee=0.001, pair=f"{T.ETH}/{T.USDC}", params={"foo": "bar"}) -c2 = CPC.from_pkpp(p=1/2000, k=10*20000, p_max=1/1800, p_min=1/2200, fee=0.002, pair=f"{T.USDC}/{T.ETH}", params={"foo": "bar"}) -ci = CPCInverter(c) -c2i = CPCInverter(c2) -curves = CPCInverter.wrap([c,c2]) -assert c.pairo == c2i.pairo -assert ci.pairo == c2.pairo - -assert ci.P("foo") == c.P("foo") -assert c2i.P("foo") == c2.P("foo") -assert ci.fee == c.fee -assert c2i.fee == c2.fee - -#print("x_act", c.x_act, c2i.x_act) -assert iseq(c.x_act, c2i.x_act) -xact = c.x_act -dx = -0.1*xact -c_ex = c.execute(dx=dx) -assert isinstance(c_ex, CPC) -assert iseq(c_ex.x_act, xact+dx) -assert iseq(c_ex.x, c.x+dx) -c2i_ex = c2i.execute(dx=dx) -assert iseq(c2i_ex.x_act, xact+dx) -assert iseq(c2i_ex.x, c.x+dx) -assert isinstance(c2i_ex, CPCInverter) - -assert len(curves) == 2 -assert set(c.pair for c in curves) == {f"{T.USDC}/{T.ETH}"} -assert len(set(c.pair for c in curves)) == 1 -assert len(set(c.tknx for c in curves)) == 1 -assert len(set(c.tkny for c in curves)) == 1 - -assert c.tknx == ci.tkny -assert c.tkny == ci.tknx -assert c.tknxp == ci.tknyp -assert c.tknyp == ci.tknxp -assert c.tknb == ci.tknq -assert c.tknq == ci.tknb -assert c.tknbp == ci.tknqp -assert c.tknqp == ci.tknbp -assert f"{c.tknq}/{c.tknb}" == ci.pair -assert f"{c.tknqp}/{c.tknbp}" == ci.pairp -assert c.x == ci.y -assert c.y == ci.x -assert c.x_act == ci.y_act -assert c.y_act == ci.x_act -assert c.x_min == ci.y_min -assert c.x_max == ci.y_max -assert c.y_min == ci.x_min -assert c.y_max == ci.x_max -assert c.k == ci.k -assert iseq(c.p, 1/ci.p) -assert iseq(c.p_min, 1/ci.p_max) -assert iseq(c.p_max, 1/ci.p_min) - - -assert c.pair == c2i.pair -assert c.tknx == c2i.tknx -assert c.tkny == c2i.tkny -assert c.tknxp == c2i.tknxp -assert c.tknyp == c2i.tknyp -assert c.tknb == c2i.tknb -assert c.tknq == c2i.tknq -assert c.tknbp == c2i.tknbp -assert c.tknqp == c2i.tknqp -assert iseq(c.p, c2i.p) -assert iseq(c.p_min, c2i.p_min) -assert iseq(c.p_max, c2i.p_max) -assert c.x == c2i.x -assert c.y == c2i.y -assert c.x_act == c2i.x_act -assert c.y_act == c2i.y_act -assert c.x_min == c2i.x_min -assert c.x_max == c2i.x_max -assert c.y_min == c2i.y_min -assert c.y_max == c2i.y_max -assert c.k == c2i.k - -assert iseq(c.xfromy_f(c.y), c2i.xfromy_f(c2i.y)) -assert iseq(c.yfromx_f(c.x), c2i.yfromx_f(c2i.x)) -assert iseq(c.xfromy_f(c.y*1.05), c2i.xfromy_f(c2i.y*1.05)) -assert iseq(c.yfromx_f(c.x*1.05), c2i.yfromx_f(c2i.x*1.05)) -assert iseq(c.dxfromdy_f(1), c2i.dxfromdy_f(1)) -assert iseq(c.dyfromdx_f(1), c2i.dyfromdx_f(1)) - -assert c.xyfromp_f() == c2i.xyfromp_f() -assert c.dxdyfromp_f() == c2i.dxdyfromp_f() -assert c.xyfromp_f(withunits=True) == c2i.xyfromp_f(withunits=True) -assert c.dxdyfromp_f(withunits=True) == c2i.dxdyfromp_f(withunits=True) -assert iseq(c.p, c2i.p) -x,y,p = c.xyfromp_f(c.p*1.05) -x2,y2,p2 = c2i.xyfromp_f(c2i.p*1.05) -assert iseq(x,x2) -assert iseq(y,y2) -assert iseq(p,p2) -dx,dy,p = c.dxdyfromp_f(c.p*1.05) -dx2,dy2,p2 = c2i.dxdyfromp_f(c2i.p*1.05) -assert iseq(dx,dx2) -assert iseq(dy,dy2) -assert iseq(p,p2) - - -# ## simple_optimizer - -CC = CPCContainer(CPC.from_pk(p=2000+i*10, k=10*20000, pair=f"ETH/USDC") for i in range(11)) -c0 = CC.curves[0] -c1 = CC.curves[-1] -CC0 = CPCContainer([c0]) -assert len(CC) == 11 -assert iseq([c.p for c in CC][-1], 2100) -assert len(CC0) == 1 -assert iseq([c.p for c in CC0][-1], 2000) - -# + -O = PairOptimizer(CC) -O0 = PairOptimizer(CC0) -func = O.optimize(result=O.SO_DXDYVECFUNC) -func0 = O0.optimize(result=O.SO_DXDYVECFUNC) -funcs = O.optimize(result=O.SO_DXDYSUMFUNC) -funcvx = O.optimize(result=O.SO_DXDYVALXFUNC) -funcvy = O.optimize(result=O.SO_DXDYVALYFUNC) -x,y = func0(2100)[0] -xb, yb, _ = c0.dxdyfromp_f(2100) -assert x == xb, f"x={x}, xb={xb}" -assert y == yb -x,y = func(2100)[-1] -xb, yb, _ = c1.dxdyfromp_f(2100) -assert x == xb -assert y == yb -assert np.all(sum(func(2100)) == funcs(2100)) - -p = 2100 -dx, dy = funcs(p) -assert iseq(dy + p*dx, funcvy(p)) -assert iseq(dy/p + dx, funcvx(p)) - -p = 1500 -dx, dy = funcs(p) -assert iseq(dy + p*dx, funcvy(p)) -assert iseq(dy/p + dx, funcvx(p)) - -assert iseq(float(O0.optimize(result=O.SO_PMAX)), c0.p) -assert iseq(float(O.optimize(result=O.SO_PMAX)), 2049.6451720862074, eps=1e-3) -# - - -O.optimize(result=O.SO_PMAX) - -# ### global max -# -# the global max function has not been properly connected to the MargPResult object because it does not really make sense; the function is not currently used so it does not really matter - -r = O.optimize() -r_ = O.optimize(result=O.SO_GLOBALMAX) -assert raises(O.optimize, targettkn=T.WETH, result=O.SO_GLOBALMAX) -assert iseq(float(r), float(r_)) -assert len(r.curves) == len(CC) -#assert np.all(r.dxdy_sum == sum(r.dxdy_vec)) -#dx, dy = r.dxdy_vecs -#assert tuple(tuple(_) for _ in r.dxdy_vec) == tuple(zip(dx,dy)) -#assert r.result == r.dxdy_valx -# for dp in np.linspace(-500,500,100): -# assert r.dxdyfromp_valx_f(p) < r.dxdy_valx -# assert r.dxdyfromp_valy_f(p) < r.dxdy_valy - -CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues)) -# CC.plot() -# CC_ex.plot() -prices = [c.p for c in CC] -prices_ex = [c.p for c in CC_ex] -assert iseq(np.std(prices), 31.622776601683707) -#assert iseq(np.std(prices_ex), 4.547473508864641e-13) -#prices, prices_ex - -# ### target token - -r = O.optimize(targettkn="ETH") -r_ = O.optimize(targettkn="ETH", result=O.SO_TARGETTKN) -assert raises(O.optimize,targettkn="DAI") -assert raises(O.optimize, result=O.SO_TARGETTKN) -assert iseq(float(r), float(r_)) -assert abs(sum(r.dyvalues) < 1e-6) -assert sum(r.dxvalues) < 0 -assert iseq(float(r),sum(r.dxvalues)) - -r = O.optimize(targettkn="USDC") -assert abs(sum(r.dxvalues) < 1e-6) -assert sum(r.dyvalues) < 0 -assert iseq(float(r),sum(r.dyvalues)) - -# ## optimizer plus inverted curves -# -# note: `O.optimize()` without `targettkn='...'` is the globalmax result! - -CCr = CPCContainer(CPC.from_pk(p=2000+i*100, k=10*(20000+10000*i), pair=f"ETH/USDC") for i in range(11)) -CCi = CPCContainer(CPC.from_pk(p=1/(2050+i*100), k=10*(20000+10000*i), pair=f"USDC/ETH") for i in range(11)) -CC = CCr.bycids() -assert len(CC) == len(CCr) -CC += CCi -assert len(CC) == len(CCr) + len(CCi) - -# + -# CC.plot() -# - - -O = PairOptimizer(CC) -r = O.optimize() -#print(f"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]") -assert iseq(r.result, 3.292239037185821) - -# + -#CC.plot() -# - - -CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues)) -# CC.plot() -# CC_ex.plot() - -prices_ex = [c.pairo.primary_price(c.p) for c in CC_ex] -assert np.std(prices_ex) < 1e-10 - -# ## posx and negx - -O = CPCArbOptimizer -a = O.a - -assert O.posx([0,-1,2]) == (0, 0, 2) -assert O.posx((-1,-2, 3)) == (0, 0, 3) -assert O.negx([0,-1,2]) == (0, -1, 0) -assert O.negx((-1,-2, 3)) == (-1, -2, 0) -assert np.all(O.posx(a([0,-1,2])) == a((0, 0, 2))) -assert O.t(a((-1,-2))) == (-1,-2) - -for v in ((1,2,3), (1,-1,5-10,0), (-10.5,8,2.34,-17)): - assert np.all(O.posx(a(v))+O.negx(a(v)) == v) - -# ## TradeInstructions - -TI = CPCArbOptimizer.TradeInstruction - -ti = TI.new(curve_or_cid="1", tkn1="ETH", amt1=1, tkn2="USDC", amt2=-2000) -print(f"cid={ti.cid}, out={ti.amtout} {ti.tknout}, , out={ti.amtin} {ti.tknin}") -assert ti.tknin == "ETH" -assert ti.amtin > 0 -assert ti.tknout == "USDC" -assert ti.amtout < 0 -assert ti.price_outperin == 2000 -assert ti.price_inperout == 1/2000 -assert ti.prices == (2000, 1/2000) -assert ti.price_outperin == ti.p -assert ti.price_inperout == ti.pr -assert ti.prices == ti.pp - -assert not raises(TI, cid="1", tknin="USDC", amtin=2000, tknout="ETH", amtout=-1, raiseonerror=True) -assert raises(TI, cid="1", tknin="USDC", amtin=2000, tknout="ETH", amtout=1, raiseonerror=True) -assert raises(TI, cid="1", tknin="USDC", amtin=-2000, tknout="ETH", amtout=-1, raiseonerror=True) -assert raises(TI, cid="1", tknin="USDC", amtin=-2000, tknout="ETH", amtout=1, raiseonerror=True) -assert raises(TI, cid="1", tknin="USDC", amtin=2000, tknout="ETH", amtout=0, raiseonerror=True) -assert raises(TI, cid="1", tknin="USDC", amtin=0, tknout="ETH", amtout=-1, raiseonerror=True) -assert not raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=2000, tkn2="ETH", amt2=-1, raiseonerror=True) -assert not raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=-2000, tkn2="ETH", amt2=1, raiseonerror=True) -assert raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=2000, tkn2="ETH", amt2=1, raiseonerror=True) -assert raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=-2000, tkn2="ETH", amt2=-1, raiseonerror=True) -assert raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=0, tkn2="ETH", amt2=1, raiseonerror=True) -assert raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=-2000, tkn2="ETH", amt2=0, raiseonerror=True) - -assert not TI(cid="1", tknin="USDC", amtin=2000, tknout="ETH", amtout=-1, raiseonerror=False).error -assert TI(cid="1", tknin="USDC", amtin=2000, tknout="ETH", amtout=1, raiseonerror=False).error -assert TI(cid="1", tknin="USDC", amtin=-2000, tknout="ETH", amtout=-1, raiseonerror=False).error -assert TI(cid="1", tknin="USDC", amtin=-2000, tknout="ETH", amtout=1, raiseonerror=False).error -assert TI(cid="1", tknin="USDC", amtin=2000, tknout="ETH", amtout=0, raiseonerror=False).error -assert TI(cid="1", tknin="USDC", amtin=0, tknout="ETH", amtout=-1, raiseonerror=False).error -assert not TI.new(curve_or_cid="1", tkn1="USDC", amt1=2000, tkn2="ETH", amt2=-1, raiseonerror=False).error -assert not TI.new(curve_or_cid="1", tkn1="USDC", amt1=-2000, tkn2="ETH", amt2=1, raiseonerror=False).error -assert TI.new(curve_or_cid="1", tkn1="USDC", amt1=2000, tkn2="ETH", amt2=1, raiseonerror=False).error -assert TI.new(curve_or_cid="1", tkn1="USDC", amt1=-2000, tkn2="ETH", amt2=-1, raiseonerror=False).error -assert TI.new(curve_or_cid="1", tkn1="USDC", amt1=0, tkn2="ETH", amt2=1, raiseonerror=False).error -assert TI.new(curve_or_cid="1", tkn1="USDC", amt1=-2000, tkn2="ETH", amt2=0, raiseonerror=False).error - - -til = [ - TI.new(curve_or_cid=f"{i+1}", tkn1="ETH", amt1=1*(1+i/100), tkn2="USDC", amt2=-2000*(1+i/100)) - for i in range(10) -] -tild = TI.to_dicts(til) -tildf = TI.to_df(til, robj=None) -assert len(tild) == 10 -assert len(tildf) == 10 -assert tild[0] == { - 'cid': '1', - 'tknin': 'ETH', - 'amtin': 1.0, - 'tknout': 'USDC', - 'amtout': -2000.0, - 'error': None,} -assert dict(tildf.iloc[0]) == { - 'pair': '', - 'pairp': '', - 'tknin': 'ETH', - 'tknout': 'USDC', - 'ETH': 1.0, - 'USDC': -2000.0 -} - -tild[0] - -tildf - -# ## margp_optimizer - -# ### no arbitrage possible - -CCa = CPCContainer() -CCa += CPC.from_pk(pair="WETH/USDC", p=2000, k=10*20000, cid="c0") -CCa += CPC.from_pk(pair="WETH/USDT", p=2000, k=10*20000, cid="c1") -CCa += CPC.from_pk(pair="USDC/USDT", p=1.0, k=200000*200000, cid="c2") -O = MargPOptimizer(CCa) - -r = O.margp_optimizer("WETH", result=O.MO_DEBUG) -assert isinstance(r, dict) -prices0 = r["price_estimates_t"] -assert not prices0 is None, f"prices0 must not be None [{prices0}]" -r1 = O.arb("WETH") -r2 = O.SelfFinancingConstraints.arb("WETH") -assert isinstance(r1, CPCArbOptimizer.SelfFinancingConstraints) -assert r1 == r2 -assert r["sfc"] == r1 -assert r1.is_arbsfc() -assert r1.optimizationvar == "WETH" - -r - -prices0 - -f = O.optimize("WETH", result=O.MO_DTKNFROMPF, params=dict(verbose=True, debug=False)) -r3 = f(prices0, islog10=False) -assert np.all(r3 == (0,0)) -r4, r3b = f(prices0, asdct=True, islog10=False) -assert np.all(r3==r3b) -assert len(r4) == len(r3)+1 -assert tuple(r4.values()) == (0,0,0) -assert set(r4) == {'USDC', 'USDT', 'WETH'} - -r = O.optimize("WETH", result=O.MO_MINIMAL, params=dict(verbose=True)) -rd = r.asdict -assert abs(float(r)) < 1e-10 -assert r.result == float(r) -assert r.method == "margp" -assert r.curves is None -assert r.targettkn == "WETH" -assert r.dtokens is None -assert sum(abs(x) for x in r.dtokens_t) < 1e-10 -assert not r.p_optimal is None -assert iseq(0.0005, r.p_optimal_t[0], r.p_optimal_t[1]) -assert set(r.tokens_t) == {'USDC', 'USDT'} -assert r.errormsg is None -assert r.is_error == False -# assert r.time >= 0 -# assert r.time < 0.1 - -# + -r = O.optimize("WETH", result=O.MO_FULL) -rd = r.asdict() -r2 = O.margp_optimizer("WETH") -r2d = r2.asdict() -for k in rd: - #print(k) - if not k in ["time", "curves"]: - assert rd[k] == r2d[k] -assert r2.curves == r.curves # the TokenScale object fails in the dict - -assert abs(float(r)) < 1e-10 -assert r.result == float(r) -assert r.method == "margp" -assert len(r.curves) == 3 -assert r.targettkn == "WETH" -assert set(r.dtokens.keys()) == set(['USDT', 'WETH', 'USDC']) -assert sum(abs(x) for x in r.dtokens.values()) < 1e-10 -assert sum(abs(x) for x in r.dtokens_t) < 1e-10 -assert iseq(0.0005, r.p_optimal["USDC"], r.p_optimal["USDT"]) -assert iseq(0.0005, r.p_optimal_t[0], r.p_optimal_t[1]) -assert tuple(r.p_optimal.values())[:-1] == r.p_optimal_t -assert set(r.tokens_t) == set(('USDC', 'USDT')) -assert r.errormsg is None -assert r.is_error == False -# assert r.time >= 0 -# assert r.time < 0.1 -# - - -# ### arbitrage - -CCa = CPCContainer() -CCa += CPC.from_pk(pair="WETH/USDC", p=2000, k=10*20000, cid="c0") -CCa += CPC.from_pk(pair="WETH/USDT", p=2000, k=10*20000, cid="c1") -CCa += CPC.from_pk(pair="USDC/USDT", p=1.2, k=200000*200000, cid="c2") -O = MargPOptimizer(CCa) - -r = O.optimize("WETH", result=O.MO_DEBUG) -assert isinstance(r, dict) -prices0 = r["price_estimates_t"] -r1 = O.arb("WETH") -r2 = O.SelfFinancingConstraints.arb("WETH") -assert isinstance(r1, CPCArbOptimizer.SelfFinancingConstraints) -assert r1 == r2 -assert r["sfc"] == r1 -assert r1.is_arbsfc() -assert r1.optimizationvar == "WETH" - -f = O.optimize("WETH", result=O.MO_DTKNFROMPF) -r3 = f(prices0, islog10=False) -assert set(r3.astype(int)) == set((17425,-19089)) -r4, r3b = f(prices0, asdct=True, islog10=False) -assert np.all(r3==r3b) -assert len(r4) == len(r3)+1 -assert set(r4) == {'USDC', 'USDT', 'WETH'} - -r = O.optimize("WETH", result=O.MO_FULL) -assert iseq(float(r), -0.03944401129301944) -assert r.result == float(r) -assert r.method == "margp" -assert len(r.curves) == 3 -assert r.targettkn == "WETH" -assert abs(r.dtokens_t[0]) < 1e-6 -assert abs(r.dtokens_t[1]) < 1e-6 -assert r.dtokens["WETH"] == float(r) -assert tuple(r.p_optimal.values())[:-1] == r.p_optimal_t -assert tuple(r.p_optimal)[:-1] == r.tokens_t -assert iseq(r.p_optimal_t[0], 0.0005421803152482512) or iseq(r.p_optimal_t[0], 0.00045575394031021585) -assert iseq(r.p_optimal_t[1], 0.0005421803152482512) or iseq(r.p_optimal_t[1], 0.00045575394031021585) -assert tuple(r.p_optimal.values())[:-1] == r.p_optimal_t -assert set(r.tokens_t) == set(('USDC', 'USDT')) -assert r.errormsg is None -assert r.is_error == False -# assert r.time >= 0 -# assert r.time < 0.1 - -abs(r.dtokens_t[0]) - -ti = r.trade_instructions() -assert len(ti) == 3 -dfa = r.trade_instructions(ti_format=O.TIF_DFAGGR) -assert len(dfa)==7 -assert list(dfa.index) == ['c0', 'c1', 'c2', 'PRICE', 'AMMIn', 'AMMOut', 'TOTAL NET'] -assert list(dfa.columns) == ['WETH', 'USDC', 'USDT'] -assert dfa.loc["PRICE"][0] == 1 -assert iseq(dfa.loc["PRICE"][1], 0.0005421803152) -assert iseq(dfa.loc["PRICE"][2], 0.0004557539403) -dfa - -df = r.trade_instructions(ti_format=O.TIF_DF) -assert len(df) == 3 -assert list(df.columns) == ['pair', 'pairp', 'tknin', 'tknout', 'WETH', 'USDC', 'USDT'] -df - -df = r.trade_instructions(ti_format=O.TIF_DF).fillna("") -assert len(df) == 3 -assert list(df.columns) == ['pair', 'pairp', 'tknin', 'tknout', 'WETH', 'USDC', 'USDT'] -assert df["USDT"].loc["c0"] == "" -df - -dcts = r.trade_instructions(ti_format=O.TIF_DICTS) -assert len(dcts) == 3 -assert list(dcts[0].keys()) == ['cid', 'tknin', 'amtin', 'tknout', 'amtout', 'error'] -d0 = dcts[0] -assert d0["cid"] == "c0" -assert iseq(d0["amtin"], 0.41326380379418914) -dcts - -objs = r.trade_instructions(ti_format=O.TIF_OBJECTS) -assert len(objs) == 3 -assert type(objs[0]).__name__ == 'TradeInstruction' -objs - -help(r.trade_instructions) - -# ## simple_optimizer demo [NOTEST] - -CC = CPCContainer(CPC.from_pk(p=2000+i*100, k=10*(20000+i*10000), pair=f"{T.ETH}/{T.USDC}") for i in range(11)) -#O = CPCArbOptimizer(CC) -c0 = CC.curves[0] -CC0 = CPCContainer([c0]) -O = PairOptimizer(CC) -O0 = PairOptimizer(CC0) -funcvx = O.optimize(result=O.SO_DXDYVALXFUNC) -funcvy = O.optimize(result=O.SO_DXDYVALYFUNC) -funcvx0 = O0.optimize(result=O.SO_DXDYVALXFUNC) -funcvy0 = O0.optimize(result=O.SO_DXDYVALYFUNC) -#CC.plot() - -xr = np.linspace(1500, 3000, 50) -plt.plot(xr, [funcvx(x)/len(CC) for x in xr], label="all curves [scaled]") -plt.plot(xr, [funcvx0(x) for x in xr], label="curve 0 only") -plt.xlabel(f"price [{c0.pairp}]") -plt.ylabel(f"value [{c0.tknxp}]") -plt.grid() -plt.show() -plt.plot(xr, [funcvy(x)/len(CC) for x in xr], label="all curves [scaled]") -plt.plot(xr, [funcvy0(x) for x in xr], label="curve 0 only") -plt.xlabel(f"price [{c0.pairp}]") -plt.ylabel(f"value [{c0.tknyp}]") -plt.grid() -plt.show() - -r = O.optimize() -#print(f"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]") - -CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues)) -CC.plot() -CC_ex.plot() - -# ## MargP Optimizer Demo [NOTEST] - -CCa = CPCContainer() -CCa += CPC.from_pk(pair="WETH/USDC", p=2000, k=10*20000, cid="c0") -CCa += CPC.from_pk(pair="WETH/USDT", p=2000, k=10*20000, cid="c1") -CCa += CPC.from_pk(pair="USDC/USDT", p=1.2, k=20000*20000, cid="c2") -O = MargPOptimizer(CCa) - -CCa.plot() - -r = O.margp_optimizer("WETH", params=dict(verbose=True)) -rd = r.asdict -r - -rd - -CCa1 = O.adjust_curves(r.dxvalues) -CCa1.plot() - -# ## Optimizer plus inverted curves [NOTEST] - -CCr = CPCContainer(CPC.from_pk(p=2000+i*100, k=10*(20000+10000*i), pair=f"{T.ETH}/{T.USDC}") for i in range(11)) -CCi = CPCContainer(CPC.from_pk(p=1/(2050+i*100), k=10*(20000+10000*i), pair=f"{T.USDC}/{T.ETH}") for i in range(11)) -CC = CCr.bycids() -assert len(CC) == len(CCr) -CC += CCi -assert len(CC) == len(CCr) + len(CCi) -CC.plot() - -O = PairOptimizer(CC) -r = O.optimize() -#print(f"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]") -CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues)) -prices_ex = [c.pairo.primary_price(c.p) for c in CC_ex] -print("prices post arb:", prices_ex) -print("stdev", np.std(prices_ex)) -#CC.plot() -CC_ex.plot() - -# ## Operating on leverage ranges [NOTEST] - -N = 10 - -# + -CCc, CCm, ctr = CPCContainer(), CPCContainer(), 0 -U, U1 = CPCContainer.u, CPCContainer.u1 -tknb, tknq = T.ETH, T.USDC -pb, pq = 2000, 1 -pair = f"{tknb}/{tknq}" -pp = pb/pq -k = 100000**2/(pb*pq) -CCm += CPC.from_pk(p=pp, k=k, pair=pair, cid = f"mkt-{pair}", params=dict(xc="market")) -#print("\n***PAIR:", tknb, pb, tknq, pq, pair, pp) -for i in range(N): - p = pp * (1+0.2*U(-0.5, 0.5)) - p_min, p_max = (p, U(1.001, 1.5)*p) if U1()>0.5 else (U(0.8, 0.999)*p, p) - amtUSDC = U(10000, 200000) - k = amtUSDC**2/(pb*pq) - #print("*curve", int(amtUSDC), p, p_min, p_max, int(k)) - CCc += CPC.from_pkpp(p=p, k=k, p_min=p_min, p_max=p_max, - pair=pair, cid = f"carb-{ctr}", params=dict(xc="carbon")) - ctr += 1 - -CC = CCc.bycids().add(CCm) -CC.plot() - -# + -# O = CPCArbOptimizer(CC) -# r = O.simple_optimizer() -# print(f"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]") -# CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues)) -# prices_ex = [c.pairo.primary_price(c.p) for c in CC_ex] -# print("prices post arb:", prices_ex) -# print("stdev", np.std(prices_ex)) -# #CC.plot() -# CC_ex.plot() -# - - -r.dxvalues - -# ## Arbitrage testing [NOTEST] - -c1 = CPC.from_pkpp(p=95, k=100*10000, p_min=90, p_max=110, pair=f"{T.ETH}/{T.USDC}") -c2 = CPC.from_pkpp(p=105, k=90*10000, p_min=90, p_max=110, pair=f"{T.ETH}/{T.USDC}") -CC = CPCContainer([c1,c2]) -CC.plot() - -a = lambda x: np.array(x) -pr = np.linspace(70,130,200) -dx1, dy1, p = zip(*(c1.dxdyfromp_f(p) for p in pr)) -assert np.all(p == pr) -dx2, dy2, p = zip(*(c2.dxdyfromp_f(p) for p in pr)) -assert np.all(p == pr) -v1 = a(dy1)+a(p)*a(dx1) -v2 = a(dy2)+a(p)*a(dx2) -plt.plot(p, v1, label="Value curve c1") -plt.plot(p, v2, label="Value curve c2") -plt.plot(p, v1+v2, label="Value combined curves") -plt.legend() -plt.grid() - - -def vfunc(p): - - dx1, dy1, _ = c1.dxdyfromp_f(p) - dx2, dy2, _ = c2.dxdyfromp_f(p) - v1 = dy1 + p*dx1 - v2 = dy2 + p*dx2 - v = v1+v2 - #print(f"[v] v({p}) = {v}") - return -v - - -O = CPCArbOptimizer -O.findmin(vfunc, 100, N=100) - -func1 = lambda x: (x-2)**2 -O.findmin(func1, 1) - -func2 = lambda x: 1-(x-3)**2 -O.findmax(func2, 2.5) - -val = tuple(float(O.findmin(func1, 100, N=n)) for n in range(100)) -val = tuple(abs(v-val[-1]) for v in val) -val = tuple(v for v in val if v > 0) -plt.plot(val) -plt.yscale('log') -plt.grid() - -val = tuple(float(O.findmin(func2, 100, N=n)) for n in range(100)) -val = tuple(abs(v-val[-1]) for v in val) -val = tuple(v for v in val if v > 0) -plt.plot(val) -plt.yscale('log') -plt.grid() - -val0 = tuple(float(O.findmin(vfunc, 99, N=n)) for n in range(100)) -val = tuple(abs(v-val0[-1]) for v in val0) -val = tuple(v for v in val if v > 0) -print(val0[-1]) -plt.plot(val) -plt.yscale('log') -plt.grid() - -val0 = tuple(float(O.findmin_gd(vfunc, 99, N=n)) for n in range(100)) -val = tuple(abs(v-val0[-1]) for v in val0) -val = tuple(v for v in val if v > 0) -print(val0[-1]) -plt.plot(val) -plt.yscale('log') -plt.grid() - -O.findmin(vfunc, 99, N=700) - -# ## Charts [NOTEST] - -# ### Chars (x,y) - -xr = np.linspace(1,300,200) - -# + -defaults = dict(p=2) -curves = [ - CPC.from_px(x=100, **defaults), - CPC.from_px(x=50, **defaults), - CPC.from_px(x=150, **defaults), -] -for c in curves: - plt.plot(xr, [c.yfromx_f(x) for x in xr]) - -plt.ylim((0,1000)) -plt.xlim((0,300)) -plt.grid() - -# + -defaults = dict(p=2, x_act=10) -curves = [ - CPC.from_px(x=100, **defaults), - CPC.from_px(x=50, **defaults), - CPC.from_px(x=150, **defaults), -] -for c in curves: - plt.plot(xr, [c.yfromx_f(x) for x in xr]) - -plt.ylim((0,1000)) -plt.xlim((0,300)) -plt.grid() - -# + -defaults = dict(p=2, y_act=20) -curves = [ - CPC.from_px(x=100, **defaults), - CPC.from_px(x=50, **defaults), - CPC.from_px(x=150, **defaults), -] -for c in curves: - plt.plot(xr, [c.yfromx_f(x) for x in xr]) - -plt.ylim((0,1000)) -plt.xlim((0,300)) -plt.grid() - -# + -defaults = dict(p=2, x_act=10, y_act=20) -curves = [ - CPC.from_px(x=100, **defaults), - CPC.from_px(x=50, **defaults), - CPC.from_px(x=150, **defaults), -] -for c in curves: - plt.plot(xr, [c.yfromx_f(x) for x in xr]) - -plt.ylim((0,1000)) -plt.xlim((0,300)) -plt.grid() -# - -# ### Charts (dx, dy) - - -e=1e-5 -dxr = np.linspace(-50+e,50-e,100) - -# + -defaults = dict(p=2) -curves = [ - CPC.from_px(x=100, **defaults), - CPC.from_px(x=50, **defaults), - CPC.from_px(x=150, **defaults), -] -for c in curves: - plt.plot(dxr, [c.dyfromdx_f(dx) for dx in dxr]) - -plt.ylim((-100,200)) -plt.xlim((-50,50)) -plt.grid() - -# + -defaults = dict(p=2, x_act=10) -curves = [ - CPC.from_px(x=100, **defaults), - CPC.from_px(x=50, **defaults), - CPC.from_px(x=150, **defaults), -] -for c in curves: - plt.plot(dxr, [c.dyfromdx_f(dx) for dx in dxr]) - -plt.ylim((-100,200)) -plt.xlim((-50,50)) -plt.grid() - -# + -defaults = dict(p=2, y_act=20) -curves = [ - CPC.from_px(x=100, **defaults), - CPC.from_px(x=50, **defaults), - CPC.from_px(x=150, **defaults), -] -for c in curves: - plt.plot(dxr, [c.dyfromdx_f(dx) for dx in dxr]) - -plt.ylim((-100,200)) -plt.xlim((-50,50)) -plt.grid() - -# + -defaults = dict(p=2, x_act=10, y_act=20) -curves = [ - CPC.from_px(x=100, **defaults), - CPC.from_px(x=50, **defaults), - CPC.from_px(x=150, **defaults), -] -for c in curves: - plt.plot(dxr, [c.dyfromdx_f(dx) for dx in dxr]) - -plt.ylim((-100,200)) -plt.xlim((-50,50)) -plt.grid() - -# + -defaults = dict(p=2, x_act=10, y_act=20) -curves = [ - CPC.from_px(x=100, **defaults), - CPC.from_px(x=50, **defaults), - CPC.from_px(x=150, **defaults), -] -for c in curves: - plt.plot(dxr, [c.dyfromdx_f(dx) for dx in dxr]) - -# plt.ylim((-100,200)) -# plt.xlim((-50,50)) -plt.grid() -# - - - - - - - - - - diff --git a/resources/NBTest/NBTest_003_Serialization.ipynb b/resources/NBTest/NBTest_003_Serialization.ipynb deleted file mode 100644 index d5b5680f6..000000000 --- a/resources/NBTest/NBTest_003_Serialization.ipynb +++ /dev/null @@ -1,1257 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "be65f3d2-769a-449f-90cd-2633a11478d0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "ConstantProductCurve v3.4 (23/Jan/2024)\n", - "CPCArbOptimizer v5.1 (15/Sep/2023)\n" - ] - } - ], - "source": [ - "try:\n", - " from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, CPCContainer\n", - " from fastlane_bot.tools.optimizer import CPCArbOptimizer, cp, time\n", - " from fastlane_bot.testing import *\n", - "\n", - "except:\n", - " from tools.cpc import ConstantProductCurve as CPC, CPCContainer\n", - " from tools.optimizer import CPCArbOptimizer, cp, time\n", - " from tools.testing import *\n", - "\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPC))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPCArbOptimizer))\n", - "\n", - "import json\n", - "#plt.style.use('seaborn-dark')\n", - "plt.rcParams['figure.figsize'] = [12,6]\n", - "# from fastlane_bot import __VERSION__\n", - "# require(\"2.0\", __VERSION__)" - ] - }, - { - "cell_type": "markdown", - "id": "feaede6f-89cb-48d2-b929-cd523e56b1bb", - "metadata": {}, - "source": [ - "# Serialization [NBTest003]" - ] - }, - { - "cell_type": "markdown", - "id": "b1e8566e-2b6d-4564-8c3d-534d968f3bf1", - "metadata": {}, - "source": [ - "## Optimizer pickling [NOTEST]" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4030cea3-3e03-4e0f-8d80-7a2bcca05fcf", - "metadata": {}, - "outputs": [], - "source": [ - "pass" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8cb4f9bc-2f31-4eae-b77f-533aa188e49b", - "metadata": {}, - "outputs": [], - "source": [ - "# N=5\n", - "# curves = [\n", - "# CPC.from_xy(x=1, y=2000, pair=\"ETH/USDC\"),\n", - "# CPC.from_xy(x=1, y=2200, pair=\"ETH/USDC\"),\n", - "# CPC.from_xy(x=1, y=2400, pair=\"ETH/USDC\"),\n", - "# ]\n", - "# # note: the below is a bit icky as the same curve objects are added multiple times\n", - "# CC = CPCContainer(curves*N)\n", - "# O = CPCArbOptimizer(CC)\n", - "# O.CC.asdf()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a5ed0075-5ee5-4592-a192-e06d2b5af454", - "metadata": {}, - "outputs": [], - "source": [ - "# O.pickle(\"delme\")\n", - "# O.pickle(\"delme\", addts=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1bf13d91-2bc0-4819-96b9-2712ef89b6f1", - "metadata": {}, - "outputs": [], - "source": [ - "# !ls *.pickle" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "ce05c578-5060-498e-b4eb-f55617d10cdd", - "metadata": {}, - "outputs": [], - "source": [ - "# O.unpickle(\"delme\")" - ] - }, - { - "cell_type": "markdown", - "id": "cf1c3ec2-0956-4698-8c0c-5781edfe457f", - "metadata": {}, - "source": [ - "## Creating curves\n", - "\n", - "Note: for those constructor, the parameters `cid` and `descr` as well as `fee` are mandatory. Typically `cid` would be a field uniquely identifying this curve in the database, and `descr` description of the pool. The description should neither include the pair nor the fee level. We recommend using `UniV3`, `UniV3`, `Sushi`, `Carbon` etc. The `fee` is quoted as decimal, ie 0.01 is 1%. If there is no fee, the number `0` must be provided, not `None`." - ] - }, - { - "cell_type": "markdown", - "id": "8d326169-f9e2-4bba-9572-9b83989812b7", - "metadata": {}, - "source": [ - "### Uniswap v2\n", - "\n", - "In the Uniswap v2 constructor, $x$ is the base token of the pair `TKNB`, and $y$ is the quote token `TKNQ`.\n", - "\n", - "By construction, Uniswap v2 curves map directly to CPC curves with the following parameter choices\n", - "\n", - "- $x,y,k$ are the same as in the $ky=k$ formula defining the AMM (provide any 2)\n", - "- $x_a = x$ and $y_a = y$ because there is no leverage on the curves.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "41a5cdfe-fb7b-4c8b-a270-1a52f0765e94", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ConstantProductCurve(k=10000, x=100, x_act=100, y_act=100, alpha=0.5, pair='TKNB/TKNQ', cid='1', fee=0, descr='UniV2', constr='uv2', params={})" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c = CPC.from_univ2(x_tknb=100, y_tknq=100, pair=\"TKNB/TKNQ\", fee=0, cid=\"1\", descr=\"UniV2\")\n", - "c2 = CPC.from_univ2(x_tknb=100, k=10000, pair=\"TKNB/TKNQ\", fee=0, cid=\"1\", descr=\"UniV2\")\n", - "c3 = CPC.from_univ2(y_tknq=100, k=10000, pair=\"TKNB/TKNQ\", fee=0, cid=\"1\", descr=\"UniV2\")\n", - "assert c.k == 10000\n", - "assert c.x == 100\n", - "assert c.y == 100\n", - "assert c.x_act == 100\n", - "assert c.y_act == 100\n", - "assert c == c2\n", - "assert c == c3\n", - "assert c.fee == 0\n", - "assert c.cid == \"1\"\n", - "assert c.descr == \"UniV2\"\n", - "c" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "ea3cdfbc-8edd-41f1-9703-0ae0d72fdb9a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'k': 10000,\n", - " 'x': 100,\n", - " 'x_act': 100,\n", - " 'y_act': 100,\n", - " 'alpha': 0.5,\n", - " 'pair': 'TKNB/TKNQ',\n", - " 'cid': '1',\n", - " 'fee': 0,\n", - " 'descr': 'UniV2',\n", - " 'constr': 'uv2',\n", - " 'params': {}}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c.asdict()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "595de023-5c66-40fc-928f-eca5fe6a50c9", - "metadata": {}, - "outputs": [], - "source": [ - "assert c.asdict() == {\n", - " 'k': 10000,\n", - " 'x': 100,\n", - " 'x_act': 100,\n", - " 'y_act': 100,\n", - " 'alpha': 0.5,\n", - " 'pair': 'TKNB/TKNQ',\n", - " 'cid': \"1\",\n", - " 'fee': 0,\n", - " 'descr': 'UniV2',\n", - " 'constr': 'uv2',\n", - " 'params': {}\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "215b5105-08d9-4077-a51a-7658cafcffa9", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV2\")\n", - "assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, k=10, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV2\")\n", - "assert raises(CPC.from_univ2, x_tknb=100, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV2\")\n", - "assert raises(CPC.from_univ2, y_tknq=100, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV2\")\n", - "assert raises(CPC.from_univ2, k=10, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV2\")\n", - "assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, fee=0, cid=1, descr=\"UniV2\")\n", - "assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair=\"TKNB/TKNQ\", cid=1, descr=\"UniV2\")\n", - "assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair=\"TKNB/TKNQ\", fee=0, descr=\"UniV2\")\n", - "assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair=\"TKNB/TKNQ\", fee=0, cid=1)" - ] - }, - { - "cell_type": "markdown", - "id": "23a41a55-a500-4d74-9998-f0f20fedeaa0", - "metadata": {}, - "source": [ - "### Uniswap v3\n", - "\n", - "Uniswap V3 uses an implicit virtual token model. The most important relationship here is that $L^2=k$, ie the square of the Uniswap pool constant is the constant product parameter $k$. Alternatively we find that $L=\\bar k$ if we use the alternative pool invariant $\\sqrt{xy}=\\bar k$ for the constant product pool. The conventions are as in the Uniswap v2 case, ie $x$ is the base token `TKNB` and $y$ is the quote token `TKNQ`. The parameters are\n", - "\n", - "- $L$ is the so-called _liquidity_ parameter, indicating the size of the pool at this particular tick (see above)\n", - "- $P_a, P_b$ are the lower and upper end of the _current_ tick range*\n", - "- $P_{marg}$ is the current (marginal) price of the range; we have $P_a \\leq P_{marg} \\leq P_b$\n", - "\n", - "*note that for Uniswap v3 curves we _only_ usually model the current tick range as crossing a tick boundary is relatively expensive and most arb bots do not do that; in principle however nothing prevents us from also adding inactive tick ranges, in which case every tick range corresponds to a single, out of the money curve." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "0963034a-b36c-4cfb-84da-ccb3c88c4389", - "metadata": {}, - "outputs": [], - "source": [ - "c = CPC.from_univ3(Pmarg=1, uniL=1000, uniPa=0.9, uniPb=1.1, pair=\"TKNB/TKNQ\", fee=0, cid=\"1\", descr=\"UniV3\")\n", - "assert c.x == 1000\n", - "assert c.y == 1000\n", - "assert c.k == 1000*1000\n", - "assert iseq(c.p_max, 1.1)\n", - "assert iseq(c.p_min, 0.9)\n", - "assert c.fee == 0\n", - "assert c.cid == \"1\"\n", - "assert c.descr == \"UniV3\"" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "eb5dd380-dd90-4a3b-b88a-5a697bdbc3a0", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(CPC.from_univ3, Pmarg=1, uniL=1000, uniPa=0.9, uniPb=1.1, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV3\")\n", - "assert raises(CPC.from_univ3, Pmarg=2, uniL=1000, uniPa=0.9, uniPb=1.1, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV3\")\n", - "assert raises(CPC.from_univ3, Pmarg=0.5, uniL=1000, uniPa=0.9, uniPb=1.1, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV3\")\n", - "assert raises(CPC.from_univ3, Pmarg=1, uniL=1000, uniPa=1.1, uniPb=0.9, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV3\")" - ] - }, - { - "cell_type": "markdown", - "id": "172acba9-47e6-45db-9cf8-03cb8bfa0b9d", - "metadata": {}, - "source": [ - "### Carbon\n", - "\n", - "First a bried reminder that the Carbon curves here correspond to Carbon Orders, ie half a Carbon strategy. Those order trade unidirectional only, and as we here are only looking at a single trade we do not care about collateral moving from an order to another one. We provide slightly more flexibility here in terms of tokens and quotes: $y$ corresponds to `tkny` which must be part of `pair` but which can be quote or base token.\n", - "\n", - "- $y, y_{int}$ are the current amounts of token y and the y-intercept respectively, in units of `tkny`\n", - "\n", - "- $P_a, P_b$ are the prices determining the range, either quoted as $dy/dx$ is `isdydx` is True (default), or in the natural direction of the pair*\n", - "\n", - "- $A, B$ are alternative price parameters, with $B=\\sqrt{P_b}$ and $A=\\sqrt{P_a}-\\sqrt{P_b}\\geq 0$; those must _always_ be quoted in $dy/dx$*\n", - "\n", - "*The ranges must _either_ be specificed with `pa, pb, isdydx` or with `A, B` and in the second case `isdydx` must be True. There is no mix and match between those two parameter sets." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "624b80f1-c811-483b-ba24-b76c72fe3e0c", - "metadata": {}, - "outputs": [], - "source": [ - "c = CPC.from_carbon(yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", - "assert c.y_act == 1\n", - "assert c.x_act == 0\n", - "assert iseq(1/c.p_min, 2200)\n", - "assert iseq(1/c.p_max, 1800)\n", - "assert iseq(1/c.p, 1/c.p_max)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "34d52402-18d6-4485-8e5c-6cb4f8af2ab2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pa 1449.3770291758221 1449.377029175822\n" - ] - } - ], - "source": [ - "c = CPC.from_carbon(yint=1, y=1, A=1/256, B=m.sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", - "assert c.y_act == 1\n", - "assert c.x_act == 0\n", - "assert iseq(1/c.p_min, 2000)\n", - "print(\"pa\", 1/c.p_max, 1/(1/256+m.sqrt(c.p_min))**2)\n", - "assert iseq(1/c.p_max, 1/(1/256+m.sqrt(c.p_min))**2)\n", - "assert iseq(1/c.p, 1/c.p_max)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "85175836-0fa9-4f64-a42f-b5b787e622f0", - "metadata": {}, - "outputs": [], - "source": [ - "c = CPC.from_carbon(yint=3000, y=3000, pa=3100, pb=2900, pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", - "assert c.y_act == 3000\n", - "assert c.x_act == 0\n", - "assert iseq(c.p_min, 2900)\n", - "assert iseq(c.p_max, 3100)\n", - "assert iseq(c.p, c.p_max)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "9753798a-b154-4865-a845-a1f5f1eb8e4b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pa 4195.445115010331 4195.445115010331\n" - ] - } - ], - "source": [ - "c = CPC.from_carbon(yint=2000, y=2000, A=10, B=m.sqrt(3000), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", - "assert c.y_act == 2000\n", - "assert c.x_act == 0\n", - "assert iseq(c.p_min, 3000)\n", - "print(\"pa\", c.p_max, (10+m.sqrt(c.p_min))**2)\n", - "assert iseq(c.p_max, (10+m.sqrt(c.p_min))**2)\n", - "assert iseq(1/c.p, 1/c.p_max)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "5f683913-1799-4f3a-9473-a663d803448a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ConstantProductCurve(k=0.01, x=0.0015438708879488485, x_act=0, y_act=1, alpha=0.5, pair='ETH/USDC', cid='4', fee=0, descr='Carbon', constr='carb', params={'y': 1, 'yint': 1, 'A': 10, 'B': 54.772255750516614, 'pa': 4195.445115010333, 'pb': 3000.0000000000005})" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CPC.from_carbon(yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", - "CPC.from_carbon(yint=1, y=1, A=1/10, B=m.sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", - "CPC.from_carbon(yint=1, y=1, pa=3100, pb=2900, pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"3\", descr=\"Carbon\", isdydx=True)\n", - "CPC.from_carbon(yint=1, y=1, A=10, B=m.sqrt(3000), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"4\", descr=\"Carbon\", isdydx=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "cffdcaa4-f221-4bd7-bf2d-5418a33e3592", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", - "assert raises(CPC.from_carbon, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", - "assert raises(CPC.from_carbon, yint=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", - "assert raises(CPC.from_carbon, yint=1, y=1, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", - "assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", - "assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", - "assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", - "#assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", cid=\"1\", descr=\"Carbon\", isdydx=False)\n", - "#assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, descr=\"Carbon\", isdydx=False)\n", - "#assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", isdydx=False)\n", - "assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"LINK\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", - "assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, A=100, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", - "assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, B=100, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", - "assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, A=100, B=100, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", - "assert raises(CPC.from_carbon, yint=1, y=1, pb=1800, pa=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "f66fc490-97e0-4c5e-958d-1e9014934d5c", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(CPC.from_carbon, yint=1, y=1, A=1/10, B=m.sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", - "assert raises(CPC.from_carbon, yint=1, y=1, A=1/10, B=m.sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=False)\n", - "assert raises(CPC.from_carbon, yint=1, y=1, pa=1000, A=1/10, B=m.sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", - "assert raises(CPC.from_carbon, yint=1, y=1, pb=1000, A=1/10, B=m.sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", - "assert raises(CPC.from_carbon, yint=1, y=1, A=-1/10, B=m.sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "465ff937-2382-4215-8e11-ec8096e1ea3d", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(CPC.from_carbon, yint=1, y=1, pa=3100, pb=2900, pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", - "assert raises(CPC.from_carbon, yint=1, y=1, pb=3100, pa=2900, pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)" - ] - }, - { - "cell_type": "markdown", - "id": "b933b5ac-090d-452b-9b11-6ae1a3595356", - "metadata": {}, - "source": [ - "## Charts [NOTEST]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "c5c8d6c3-0d15-4c3d-8852-b2870a7b4caa", - "metadata": {}, - "outputs": [], - "source": [ - "curves_uni =[\n", - " CPC.from_univ2(x_tknb=1, y_tknq=2000, pair=\"ETH/USDC\", fee=0.001, cid=\"U2/1\", descr=\"UniV2\"),\n", - " CPC.from_univ2(x_tknb=2, y_tknq=4020, pair=\"ETH/USDC\", fee=0.001, cid=\"U2/2\", descr=\"UniV2\"),\n", - " CPC.from_univ3(Pmarg=2000, uniL=100, uniPa=1800, uniPb=2200, pair=\"ETH/USDC\", fee=0, cid=\"U3/1\", descr=\"UniV3\"),\n", - " CPC.from_univ3(Pmarg=2010, uniL=75, uniPa=1800, uniPb=2200, pair=\"ETH/USDC\", fee=0, cid=\"U3/1\", descr=\"UniV3\"),\n", - "]\n", - "CC = CPCContainer(curves_uni)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "8296d087-d5a5-4b77-825a-dd53ed60d4bd", - "metadata": {}, - "outputs": [], - "source": [ - "curves_carbon = [\n", - " CPC.from_carbon(yint=3000, y=3000, pa=3500, pb=2500, pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"C1\", descr=\"Carbon\", isdydx=True),\n", - " CPC.from_carbon(yint=3000, y=3000, A=20, B=m.sqrt(2500), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"C2\", descr=\"Carbon\", isdydx=True),\n", - " CPC.from_carbon(yint=3000, y=3000, A=40, B=m.sqrt(2500), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"C3\", descr=\"Carbon\", isdydx=True),\n", - " CPC.from_carbon(yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"C4\", descr=\"Carbon\", isdydx=False),\n", - " CPC.from_carbon(yint=1, y=1, pa=1/1800, pb=1/2000, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"C5\", descr=\"Carbon\", isdydx=True),\n", - " CPC.from_carbon(yint=1, y=1, A=1/500, B=m.sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"C6\", descr=\"Carbon\", isdydx=True),\n", - " CPC.from_carbon(yint=1, y=1, A=1/1000, B=m.sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"C7\", descr=\"Carbon\", isdydx=True),\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "e72d0162-dd59-489c-8efb-dbb8327ff553", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = ETH/USDC\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = USDC/ETH\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "curves = curves_uni + curves_carbon\n", - "CC = CPCContainer(curves)\n", - "CC.plot(params=CC.Params())" - ] - }, - { - "cell_type": "markdown", - "id": "48de3a65-a36c-4ea0-aaf3-fc2d3cf415d1", - "metadata": {}, - "source": [ - "## Serializing curves\n", - "\n", - "The `CPCContainer` and `ConstantProductCurve` objects do not strictly have methods that would allow for serialization. However, they allow conversion from an to datatypes that are easily serialized. \n", - "\n", - "- on the `ConstantProductCurve` level there is `asdict()` and `from_dicts(.)`\n", - "- on the `CPCContainer` level there is also `asdf()` and `from_df(.)`, allowing conversion from and to pandas dataframes\n", - "\n", - "Recommended serialization is either dict to json via the `json` library, or any of the serialization methods inherent in dataframes, notably also pickling (Excel formates are not recommended as they are slow and heavy).\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "c2d5dc97-05e8-4eca-abc7-66eee6e7d706", - "metadata": {}, - "outputs": [], - "source": [ - "curves = [\n", - " CPC.from_univ2(x_tknb=1, y_tknq=2000, pair=\"ETH/USDC\", fee=0.001, cid=\"1\", descr=\"UniV2\", params={\"meh\":1}),\n", - " CPC.from_univ2(x_tknb=2, y_tknq=4020, pair=\"ETH/USDC\", fee=0.001, cid=\"2\", descr=\"UniV2\"),\n", - " CPC.from_univ2(x_tknb=1, y_tknq=1970, pair=\"ETH/USDC\", fee=0.001, cid=\"3\", descr=\"UniV2\"),\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "9f467a32-370b-4634-bec8-3c28be84a0a0", - "metadata": {}, - "outputs": [], - "source": [ - "c0 = curves[0]\n", - "assert c0.params.__class__.__name__ == \"AttrDict\"\n", - "assert c0.params == {'meh': 1}" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "d7563934-5381-476d-b9cb-99b909691049", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CPCContainer(curves=[ConstantProductCurve(k=2000, x=1, x_act=1, y_act=2000, alpha=0.5, pair='ETH/USDC', cid='1', fee=0.001, descr='UniV2', constr='uv2', params={'meh': 1}), ConstantProductCurve(k=8040, x=2, x_act=2, y_act=4020, alpha=0.5, pair='ETH/USDC', cid='2', fee=0.001, descr='UniV2', constr='uv2', params={}), ConstantProductCurve(k=1970, x=1, x_act=1, y_act=1970, alpha=0.5, pair='ETH/USDC', cid='3', fee=0.001, descr='UniV2', constr='uv2', params={})])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CC = CPCContainer(curves)\n", - "assert raises(CPCContainer, [1,2,3])\n", - "assert len(CC.curves) == len(curves)\n", - "assert len(CC.asdicts()) == len(CC.curves)\n", - "assert CPCContainer.from_dicts(CC.asdicts()) == CC\n", - "ccjson = json.dumps(CC.asdicts())\n", - "assert CPCContainer.from_dicts(json.loads(ccjson)) == CC\n", - "CC" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "131928b8-f927-4799-97c6-ec50631c7959", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
kxx_acty_actalphapairfeedescrconstrparams
cid
120001120000.5ETH/USDC0.001UniV2uv2{'meh': 1}
280402240200.5ETH/USDC0.001UniV2uv2{}
319701119700.5ETH/USDC0.001UniV2uv2{}
\n", - "
" - ], - "text/plain": [ - " k x x_act y_act alpha pair fee descr constr params\n", - "cid \n", - "1 2000 1 1 2000 0.5 ETH/USDC 0.001 UniV2 uv2 {'meh': 1}\n", - "2 8040 2 2 4020 0.5 ETH/USDC 0.001 UniV2 uv2 {}\n", - "3 1970 1 1 1970 0.5 ETH/USDC 0.001 UniV2 uv2 {}" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = CC.asdf()\n", - "assert len(df) == 3\n", - "assert tuple(df.reset_index().columns) == ('cid', 'k', 'x', 'x_act', 'y_act', 'alpha',\n", - " 'pair', 'fee', 'descr', 'constr', 'params')\n", - "assert tuple(df[\"k\"]) == (2000, 8040, 1970)\n", - "assert CPCContainer.from_df(df) == CC\n", - "df" - ] - }, - { - "cell_type": "markdown", - "id": "b36575fb-cd50-4415-a885-7c2b5ac689ba", - "metadata": {}, - "source": [ - "## Saving curves [NOTEST]\n", - "\n", - "Most serialization methods we use go via the a pandas DataFram object. To create a dataframe we use the `asdf()` method, and to instantiate curve container from a dataframe we use `CPCContainer.from_df(df)`." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "6cd062ae-c465-4102-a57c-587874023de5", - "metadata": {}, - "outputs": [], - "source": [ - "N=5000\n", - "curves = [\n", - " CPC.from_univ2(x_tknb=1, y_tknq=2000, pair=\"ETH/USDC\", fee=0.001, cid=1, descr=\"UniV2\"),\n", - " CPC.from_univ2(x_tknb=2, y_tknq=4020, pair=\"ETH/USDC\", fee=0.001, cid=2, descr=\"UniV2\"),\n", - " CPC.from_univ2(x_tknb=1, y_tknq=1970, pair=\"ETH/USDC\", fee=0.001, cid=3, descr=\"UniV2\"),\n", - "]\n", - "CC = CPCContainer(curves*N)\n", - "df = CC.asdf()\n", - "#CC" - ] - }, - { - "cell_type": "markdown", - "id": "a4908c7d-d363-4fe5-978a-a038ea3416fd", - "metadata": {}, - "source": [ - "### Formats\n", - "#### json\n", - "\n", - "Using `json.dumps(.)` the list of dicts returned by `asdicts()` can be converted to json, and then saved as a textfile. When loaded back, the text can be expanded into json using `json.loads(.)` and the new object can be instantiated using `CPCContainer.from_dicts(dicts)`." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "8c046e70-ef8a-4de8-bd17-726afb617ea1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "len 2355000\n", - "elapsed time: 0.29s\n" - ] - } - ], - "source": [ - "start_time = time.time()\n", - "cc_json = json.dumps(CC.asdicts())\n", - "print(\"len\", len(cc_json))\n", - "CC2 = CPCContainer.from_dicts(json.loads(cc_json))\n", - "assert CC == CC2\n", - "print(f\"elapsed time: {time.time()-start_time:.2f}s\")\n", - "#CC2" - ] - }, - { - "cell_type": "markdown", - "id": "dc67cf95-3872-4292-b13b-d742c4d55b66", - "metadata": {}, - "source": [ - "#### csv\n", - "\n", - "`to_csv` converts a dataframe to a csv file; this file can also be zipped; this format is ideal for maximum interoperability as pretty much every software allows dealing with csvs; it is very fast, and the zipped files are much smaller than everything else" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "e892dc06-329d-477f-adcb-40a87eb7a009", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "elapsed time: 0.21s\n" - ] - }, - { - "data": { - "text/html": [ - "
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cidkxx_acty_actalphapairfeedescrconstrparams
0120001120000.5ETH/USDC0.001UniV2uv2{}
1280402240200.5ETH/USDC0.001UniV2uv2{}
2319701119700.5ETH/USDC0.001UniV2uv2{}
\n", - "
" - ], - "text/plain": [ - " cid k x x_act y_act alpha pair fee descr constr params\n", - "0 1 2000 1 1 2000 0.5 ETH/USDC 0.001 UniV2 uv2 {}\n", - "1 2 8040 2 2 4020 0.5 ETH/USDC 0.001 UniV2 uv2 {}\n", - "2 3 1970 1 1 1970 0.5 ETH/USDC 0.001 UniV2 uv2 {}" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "start_time = time.time()\n", - "df.to_csv(\".curves.csv\")\n", - "df_csv = pd.read_csv(\".curves.csv\")\n", - "assert CPCContainer.from_df(df_csv) == CC\n", - "print(f\"elapsed time: {time.time()-start_time:.2f}s\")\n", - "df_csv[:3]" - ] - }, - { - "cell_type": "markdown", - "id": "41370f26-e16e-4f67-a801-f8d62f9b9e04", - "metadata": {}, - "source": [ - "#### tsv\n", - "\n", - "`to_csv` can be used with `sep=\"\\t\"` to create a tab separated file" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "a2976017-2a84-4fba-885d-7680d9f61c3a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "elapsed time: 0.17s\n" - ] - } - ], - "source": [ - "start_time = time.time()\n", - "df.to_csv(\".curves.tsv\", sep=\"\\t\")\n", - "df_tsv = pd.read_csv(\".curves.tsv\", sep=\"\\t\")\n", - "assert CPCContainer.from_df(df_tsv) == CC\n", - "print(f\"elapsed time: {time.time()-start_time:.2f}s\")" - ] - }, - { - "cell_type": "markdown", - "id": "ef6b415f-9e97-477e-8488-7a1348094730", - "metadata": {}, - "source": [ - "#### compressed csv\n", - "\n", - "`to_csv` can be used with `compression = \"gzip\"` to create a compressed file. This is by far the smallest output available, and takes little more time compared to uncompressed." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "ed5aaa2c-2f5a-4863-87cf-a77240826a85", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "elapsed time: 0.21s\n" - ] - } - ], - "source": [ - "start_time = time.time()\n", - "df.to_csv(\".curves.csv.gz\", compression = \"gzip\")\n", - "df_csv = pd.read_csv(\".curves.csv.gz\")\n", - "assert CPCContainer.from_df(df_csv) == CC\n", - "print(f\"elapsed time: {time.time()-start_time:.2f}s\")" - ] - }, - { - "cell_type": "markdown", - "id": "c0eca8e2-8017-4989-88c2-beafe97d7c3a", - "metadata": {}, - "source": [ - "#### Excel\n", - "\n", - "`to_excel` converts the dataframe to an xlsx file; older versions of pandas may allow to also save in the old xls format, but this is deprecated; note that Excel files can be rather big, and saving them is very slow, 10-15x(!) longer than csv." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "f1507cc7-96ba-4342-bf1e-955b248bd8b4", - "metadata": {}, - "outputs": [], - "source": [ - "# start_time = time.time()\n", - "# df.to_excel(\".curves.xlsx\")\n", - "# df_xlsx = pd.read_excel(\".curves.xlsx\")\n", - "# assert CPCContainer.from_df(df_xlsx) == CC\n", - "# print(f\"elapsed time: {time.time()-start_time:.2f}s\")\n", - "# df_xlsx[:3]" - ] - }, - { - "cell_type": "markdown", - "id": "705f0e47-d154-4dba-9d26-c4c809f55788", - "metadata": {}, - "source": [ - "#### pickle\n", - "\n", - "`to_pickle` pickles the dataframe; this format is rather big, but it is the fastest to process, albeit not at a significant margin" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "a1c75dfe-ce14-4840-9c62-39a8d5cfc3ad", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "elapsed time: 0.19s\n" - ] - }, - { - "data": { - "text/html": [ - "
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kxx_acty_actalphapairfeedescrconstrparams
cid
120001120000.5ETH/USDC0.001UniV2uv2{}
280402240200.5ETH/USDC0.001UniV2uv2{}
319701119700.5ETH/USDC0.001UniV2uv2{}
\n", - "
" - ], - "text/plain": [ - " k x x_act y_act alpha pair fee descr constr params\n", - "cid \n", - "1 2000 1 1 2000 0.5 ETH/USDC 0.001 UniV2 uv2 {}\n", - "2 8040 2 2 4020 0.5 ETH/USDC 0.001 UniV2 uv2 {}\n", - "3 1970 1 1 1970 0.5 ETH/USDC 0.001 UniV2 uv2 {}" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "start_time = time.time()\n", - "df.to_pickle(\".curves.pkl\")\n", - "df_pickle = pd.read_pickle(\".curves.pkl\")\n", - "assert CPCContainer.from_df(df_pickle) == CC\n", - "print(f\"elapsed time: {time.time()-start_time:.2f}s\")\n", - "df_pickle[:3]" - ] - }, - { - "cell_type": "markdown", - "id": "3cfc2ff5-bf9d-4684-9b8c-2aff57937a46", - "metadata": {}, - "source": [ - "### Benchmarking\n", - "\n", - "below a comparison of the different methods in terms of size and speed; the benchmark run used **300,000 curves**\n", - "\n", - " 33000000 .curves.json -- 5.2s (without read/write)\n", - " 11100035 .curves.csv -- 3.4s\n", - " 37817 .curves.csv.gz -- 3.4s\n", - " 15602482 .curves.pkl -- 2.6s\n", - " 11100035 .curves.tsv -- 3.2s\n", - " 8031279 .curves.xlsx -- 45.0s (!)\n", - " \n", - "Below are the figures for the current run (timing figures inline above)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "c43b9431-603d-49af-b5fd-1975e9f59e2f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2355000 .curves.json\n", - "-rw-r--r-- 1 skl staff 720055 1 May 07:51 .curves.csv\n", - "-rw-r--r-- 1 skl staff 2965 1 May 07:51 .curves.csv.gz\n", - "-rw-r--r-- 1 skl staff 961219 1 May 07:51 .curves.pkl\n", - "-rw-r--r-- 1 skl staff 720055 1 May 07:51 .curves.tsv\n" - ] - } - ], - "source": [ - "#print(f\"{len(df_xlsx)} curves\")\n", - "print(f\" {len(cc_json)} .curves.json\", )\n", - "!ls -l .curves*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3fc27e4d-6d5e-4da5-8ab6-e073b6d5ace3", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5e031c43-6328-4d3c-906f-442f28aa93f9", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aca83391-3401-4ae9-b9ed-5ad4611366a9", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "076619c0-8c0d-4555-9e3e-62266225942b", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/NBTest/NBTest_003_Serialization.py b/resources/NBTest/NBTest_003_Serialization.py deleted file mode 100644 index 95f7a43db..000000000 --- a/resources/NBTest/NBTest_003_Serialization.py +++ /dev/null @@ -1,388 +0,0 @@ -# -*- coding: utf-8 -*- -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -try: - from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, CPCContainer - from fastlane_bot.tools.optimizer import CPCArbOptimizer, cp, time - from fastlane_bot.testing import * - -except: - from tools.cpc import ConstantProductCurve as CPC, CPCContainer - from tools.optimizer import CPCArbOptimizer, cp, time - from tools.testing import * - -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPCArbOptimizer)) - -import json -#plt.style.use('seaborn-dark') -plt.rcParams['figure.figsize'] = [12,6] -# from fastlane_bot import __VERSION__ -# require("2.0", __VERSION__) -# - - -# # Serialization [NBTest003] - -# ## Optimizer pickling [NOTEST] - -pass - -# + -# N=5 -# curves = [ -# CPC.from_xy(x=1, y=2000, pair="ETH/USDC"), -# CPC.from_xy(x=1, y=2200, pair="ETH/USDC"), -# CPC.from_xy(x=1, y=2400, pair="ETH/USDC"), -# ] -# # note: the below is a bit icky as the same curve objects are added multiple times -# CC = CPCContainer(curves*N) -# O = CPCArbOptimizer(CC) -# O.CC.asdf() - -# + -# O.pickle("delme") -# O.pickle("delme", addts=False) - -# + -# # !ls *.pickle - -# + -# O.unpickle("delme") -# - - -# ## Creating curves -# -# Note: for those constructor, the parameters `cid` and `descr` as well as `fee` are mandatory. Typically `cid` would be a field uniquely identifying this curve in the database, and `descr` description of the pool. The description should neither include the pair nor the fee level. We recommend using `UniV3`, `UniV3`, `Sushi`, `Carbon` etc. The `fee` is quoted as decimal, ie 0.01 is 1%. If there is no fee, the number `0` must be provided, not `None`. - -# ### Uniswap v2 -# -# In the Uniswap v2 constructor, $x$ is the base token of the pair `TKNB`, and $y$ is the quote token `TKNQ`. -# -# By construction, Uniswap v2 curves map directly to CPC curves with the following parameter choices -# -# - $x,y,k$ are the same as in the $ky=k$ formula defining the AMM (provide any 2) -# - $x_a = x$ and $y_a = y$ because there is no leverage on the curves. -# - -c = CPC.from_univ2(x_tknb=100, y_tknq=100, pair="TKNB/TKNQ", fee=0, cid="1", descr="UniV2") -c2 = CPC.from_univ2(x_tknb=100, k=10000, pair="TKNB/TKNQ", fee=0, cid="1", descr="UniV2") -c3 = CPC.from_univ2(y_tknq=100, k=10000, pair="TKNB/TKNQ", fee=0, cid="1", descr="UniV2") -assert c.k == 10000 -assert c.x == 100 -assert c.y == 100 -assert c.x_act == 100 -assert c.y_act == 100 -assert c == c2 -assert c == c3 -assert c.fee == 0 -assert c.cid == "1" -assert c.descr == "UniV2" -c - -c.asdict() - -assert c.asdict() == { - 'k': 10000, - 'x': 100, - 'x_act': 100, - 'y_act': 100, - 'alpha': 0.5, - 'pair': 'TKNB/TKNQ', - 'cid': "1", - 'fee': 0, - 'descr': 'UniV2', - 'constr': 'uv2', - 'params': {} -} - -assert not raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV2") -assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, k=10, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV2") -assert raises(CPC.from_univ2, x_tknb=100, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV2") -assert raises(CPC.from_univ2, y_tknq=100, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV2") -assert raises(CPC.from_univ2, k=10, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV2") -assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, fee=0, cid=1, descr="UniV2") -assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair="TKNB/TKNQ", cid=1, descr="UniV2") -assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair="TKNB/TKNQ", fee=0, descr="UniV2") -assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair="TKNB/TKNQ", fee=0, cid=1) - -# ### Uniswap v3 -# -# Uniswap V3 uses an implicit virtual token model. The most important relationship here is that $L^2=k$, ie the square of the Uniswap pool constant is the constant product parameter $k$. Alternatively we find that $L=\bar k$ if we use the alternative pool invariant $\sqrt{xy}=\bar k$ for the constant product pool. The conventions are as in the Uniswap v2 case, ie $x$ is the base token `TKNB` and $y$ is the quote token `TKNQ`. The parameters are -# -# - $L$ is the so-called _liquidity_ parameter, indicating the size of the pool at this particular tick (see above) -# - $P_a, P_b$ are the lower and upper end of the _current_ tick range* -# - $P_{marg}$ is the current (marginal) price of the range; we have $P_a \leq P_{marg} \leq P_b$ -# -# *note that for Uniswap v3 curves we _only_ usually model the current tick range as crossing a tick boundary is relatively expensive and most arb bots do not do that; in principle however nothing prevents us from also adding inactive tick ranges, in which case every tick range corresponds to a single, out of the money curve. - -c = CPC.from_univ3(Pmarg=1, uniL=1000, uniPa=0.9, uniPb=1.1, pair="TKNB/TKNQ", fee=0, cid="1", descr="UniV3") -assert c.x == 1000 -assert c.y == 1000 -assert c.k == 1000*1000 -assert iseq(c.p_max, 1.1) -assert iseq(c.p_min, 0.9) -assert c.fee == 0 -assert c.cid == "1" -assert c.descr == "UniV3" - -assert not raises(CPC.from_univ3, Pmarg=1, uniL=1000, uniPa=0.9, uniPb=1.1, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV3") -assert raises(CPC.from_univ3, Pmarg=2, uniL=1000, uniPa=0.9, uniPb=1.1, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV3") -assert raises(CPC.from_univ3, Pmarg=0.5, uniL=1000, uniPa=0.9, uniPb=1.1, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV3") -assert raises(CPC.from_univ3, Pmarg=1, uniL=1000, uniPa=1.1, uniPb=0.9, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV3") - -# ### Carbon -# -# First a bried reminder that the Carbon curves here correspond to Carbon Orders, ie half a Carbon strategy. Those order trade unidirectional only, and as we here are only looking at a single trade we do not care about collateral moving from an order to another one. We provide slightly more flexibility here in terms of tokens and quotes: $y$ corresponds to `tkny` which must be part of `pair` but which can be quote or base token. -# -# - $y, y_{int}$ are the current amounts of token y and the y-intercept respectively, in units of `tkny` -# -# - $P_a, P_b$ are the prices determining the range, either quoted as $dy/dx$ is `isdydx` is True (default), or in the natural direction of the pair* -# -# - $A, B$ are alternative price parameters, with $B=\sqrt{P_b}$ and $A=\sqrt{P_a}-\sqrt{P_b}\geq 0$; those must _always_ be quoted in $dy/dx$* -# -# *The ranges must _either_ be specificed with `pa, pb, isdydx` or with `A, B` and in the second case `isdydx` must be True. There is no mix and match between those two parameter sets. - -c = CPC.from_carbon(yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) -assert c.y_act == 1 -assert c.x_act == 0 -assert iseq(1/c.p_min, 2200) -assert iseq(1/c.p_max, 1800) -assert iseq(1/c.p, 1/c.p_max) - -c = CPC.from_carbon(yint=1, y=1, A=1/256, B=m.sqrt(1/2000), pair="ETH/USDC", tkny="ETH", fee=0, cid="2", descr="Carbon", isdydx=True) -assert c.y_act == 1 -assert c.x_act == 0 -assert iseq(1/c.p_min, 2000) -print("pa", 1/c.p_max, 1/(1/256+m.sqrt(c.p_min))**2) -assert iseq(1/c.p_max, 1/(1/256+m.sqrt(c.p_min))**2) -assert iseq(1/c.p, 1/c.p_max) - -c = CPC.from_carbon(yint=3000, y=3000, pa=3100, pb=2900, pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) -assert c.y_act == 3000 -assert c.x_act == 0 -assert iseq(c.p_min, 2900) -assert iseq(c.p_max, 3100) -assert iseq(c.p, c.p_max) - -c = CPC.from_carbon(yint=2000, y=2000, A=10, B=m.sqrt(3000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) -assert c.y_act == 2000 -assert c.x_act == 0 -assert iseq(c.p_min, 3000) -print("pa", c.p_max, (10+m.sqrt(c.p_min))**2) -assert iseq(c.p_max, (10+m.sqrt(c.p_min))**2) -assert iseq(1/c.p, 1/c.p_max) - -CPC.from_carbon(yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) -CPC.from_carbon(yint=1, y=1, A=1/10, B=m.sqrt(1/2000), pair="ETH/USDC", tkny="ETH", fee=0, cid="2", descr="Carbon", isdydx=True) -CPC.from_carbon(yint=1, y=1, pa=3100, pb=2900, pair="ETH/USDC", tkny="USDC", fee=0, cid="3", descr="Carbon", isdydx=True) -CPC.from_carbon(yint=1, y=1, A=10, B=m.sqrt(3000), pair="ETH/USDC", tkny="USDC", fee=0, cid="4", descr="Carbon", isdydx=True) - -assert not raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) -assert raises(CPC.from_carbon, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) -assert raises(CPC.from_carbon, yint=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) -assert raises(CPC.from_carbon, yint=1, y=1, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) -assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) -assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) -assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", fee=0, cid="1", descr="Carbon", isdydx=False) -#assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", cid="1", descr="Carbon", isdydx=False) -#assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, descr="Carbon", isdydx=False) -#assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", isdydx=False) -assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="LINK", fee=0, cid="1", descr="Carbon", isdydx=False) -assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, A=100, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) -assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, B=100, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) -assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, A=100, B=100, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) -assert raises(CPC.from_carbon, yint=1, y=1, pb=1800, pa=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) - -assert not raises(CPC.from_carbon, yint=1, y=1, A=1/10, B=m.sqrt(1/2000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) -assert raises(CPC.from_carbon, yint=1, y=1, A=1/10, B=m.sqrt(1/2000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=False) -assert raises(CPC.from_carbon, yint=1, y=1, pa=1000, A=1/10, B=m.sqrt(1/2000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) -assert raises(CPC.from_carbon, yint=1, y=1, pb=1000, A=1/10, B=m.sqrt(1/2000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) -assert raises(CPC.from_carbon, yint=1, y=1, A=-1/10, B=m.sqrt(1/2000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) - -assert not raises(CPC.from_carbon, yint=1, y=1, pa=3100, pb=2900, pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) -assert raises(CPC.from_carbon, yint=1, y=1, pb=3100, pa=2900, pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) - -# ## Charts [NOTEST] - -curves_uni =[ - CPC.from_univ2(x_tknb=1, y_tknq=2000, pair="ETH/USDC", fee=0.001, cid="U2/1", descr="UniV2"), - CPC.from_univ2(x_tknb=2, y_tknq=4020, pair="ETH/USDC", fee=0.001, cid="U2/2", descr="UniV2"), - CPC.from_univ3(Pmarg=2000, uniL=100, uniPa=1800, uniPb=2200, pair="ETH/USDC", fee=0, cid="U3/1", descr="UniV3"), - CPC.from_univ3(Pmarg=2010, uniL=75, uniPa=1800, uniPb=2200, pair="ETH/USDC", fee=0, cid="U3/1", descr="UniV3"), -] -CC = CPCContainer(curves_uni) - -curves_carbon = [ - CPC.from_carbon(yint=3000, y=3000, pa=3500, pb=2500, pair="ETH/USDC", tkny="USDC", fee=0, cid="C1", descr="Carbon", isdydx=True), - CPC.from_carbon(yint=3000, y=3000, A=20, B=m.sqrt(2500), pair="ETH/USDC", tkny="USDC", fee=0, cid="C2", descr="Carbon", isdydx=True), - CPC.from_carbon(yint=3000, y=3000, A=40, B=m.sqrt(2500), pair="ETH/USDC", tkny="USDC", fee=0, cid="C3", descr="Carbon", isdydx=True), - CPC.from_carbon(yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="C4", descr="Carbon", isdydx=False), - CPC.from_carbon(yint=1, y=1, pa=1/1800, pb=1/2000, pair="ETH/USDC", tkny="ETH", fee=0, cid="C5", descr="Carbon", isdydx=True), - CPC.from_carbon(yint=1, y=1, A=1/500, B=m.sqrt(1/2000), pair="ETH/USDC", tkny="ETH", fee=0, cid="C6", descr="Carbon", isdydx=True), - CPC.from_carbon(yint=1, y=1, A=1/1000, B=m.sqrt(1/2000), pair="ETH/USDC", tkny="ETH", fee=0, cid="C7", descr="Carbon", isdydx=True), -] - -curves = curves_uni + curves_carbon -CC = CPCContainer(curves) -CC.plot(params=CC.Params()) - -# ## Serializing curves -# -# The `CPCContainer` and `ConstantProductCurve` objects do not strictly have methods that would allow for serialization. However, they allow conversion from an to datatypes that are easily serialized. -# -# - on the `ConstantProductCurve` level there is `asdict()` and `from_dicts(.)` -# - on the `CPCContainer` level there is also `asdf()` and `from_df(.)`, allowing conversion from and to pandas dataframes -# -# Recommended serialization is either dict to json via the `json` library, or any of the serialization methods inherent in dataframes, notably also pickling (Excel formates are not recommended as they are slow and heavy). -# -# -# - -curves = [ - CPC.from_univ2(x_tknb=1, y_tknq=2000, pair="ETH/USDC", fee=0.001, cid="1", descr="UniV2", params={"meh":1}), - CPC.from_univ2(x_tknb=2, y_tknq=4020, pair="ETH/USDC", fee=0.001, cid="2", descr="UniV2"), - CPC.from_univ2(x_tknb=1, y_tknq=1970, pair="ETH/USDC", fee=0.001, cid="3", descr="UniV2"), -] - -c0 = curves[0] -assert c0.params.__class__.__name__ == "AttrDict" -assert c0.params == {'meh': 1} - -CC = CPCContainer(curves) -assert raises(CPCContainer, [1,2,3]) -assert len(CC.curves) == len(curves) -assert len(CC.asdicts()) == len(CC.curves) -assert CPCContainer.from_dicts(CC.asdicts()) == CC -ccjson = json.dumps(CC.asdicts()) -assert CPCContainer.from_dicts(json.loads(ccjson)) == CC -CC - -df = CC.asdf() -assert len(df) == 3 -assert tuple(df.reset_index().columns) == ('cid', 'k', 'x', 'x_act', 'y_act', 'alpha', - 'pair', 'fee', 'descr', 'constr', 'params') -assert tuple(df["k"]) == (2000, 8040, 1970) -assert CPCContainer.from_df(df) == CC -df - -# ## Saving curves [NOTEST] -# -# Most serialization methods we use go via the a pandas DataFram object. To create a dataframe we use the `asdf()` method, and to instantiate curve container from a dataframe we use `CPCContainer.from_df(df)`. - -N=5000 -curves = [ - CPC.from_univ2(x_tknb=1, y_tknq=2000, pair="ETH/USDC", fee=0.001, cid=1, descr="UniV2"), - CPC.from_univ2(x_tknb=2, y_tknq=4020, pair="ETH/USDC", fee=0.001, cid=2, descr="UniV2"), - CPC.from_univ2(x_tknb=1, y_tknq=1970, pair="ETH/USDC", fee=0.001, cid=3, descr="UniV2"), -] -CC = CPCContainer(curves*N) -df = CC.asdf() -#CC - -# ### Formats -# #### json -# -# Using `json.dumps(.)` the list of dicts returned by `asdicts()` can be converted to json, and then saved as a textfile. When loaded back, the text can be expanded into json using `json.loads(.)` and the new object can be instantiated using `CPCContainer.from_dicts(dicts)`. - -start_time = time.time() -cc_json = json.dumps(CC.asdicts()) -print("len", len(cc_json)) -CC2 = CPCContainer.from_dicts(json.loads(cc_json)) -assert CC == CC2 -print(f"elapsed time: {time.time()-start_time:.2f}s") -#CC2 - -# #### csv -# -# `to_csv` converts a dataframe to a csv file; this file can also be zipped; this format is ideal for maximum interoperability as pretty much every software allows dealing with csvs; it is very fast, and the zipped files are much smaller than everything else - -start_time = time.time() -df.to_csv(".curves.csv") -df_csv = pd.read_csv(".curves.csv") -assert CPCContainer.from_df(df_csv) == CC -print(f"elapsed time: {time.time()-start_time:.2f}s") -df_csv[:3] - -# #### tsv -# -# `to_csv` can be used with `sep="\t"` to create a tab separated file - -start_time = time.time() -df.to_csv(".curves.tsv", sep="\t") -df_tsv = pd.read_csv(".curves.tsv", sep="\t") -assert CPCContainer.from_df(df_tsv) == CC -print(f"elapsed time: {time.time()-start_time:.2f}s") - -# #### compressed csv -# -# `to_csv` can be used with `compression = "gzip"` to create a compressed file. This is by far the smallest output available, and takes little more time compared to uncompressed. - -start_time = time.time() -df.to_csv(".curves.csv.gz", compression = "gzip") -df_csv = pd.read_csv(".curves.csv.gz") -assert CPCContainer.from_df(df_csv) == CC -print(f"elapsed time: {time.time()-start_time:.2f}s") - - -# #### Excel -# -# `to_excel` converts the dataframe to an xlsx file; older versions of pandas may allow to also save in the old xls format, but this is deprecated; note that Excel files can be rather big, and saving them is very slow, 10-15x(!) longer than csv. - -# + -# start_time = time.time() -# df.to_excel(".curves.xlsx") -# df_xlsx = pd.read_excel(".curves.xlsx") -# assert CPCContainer.from_df(df_xlsx) == CC -# print(f"elapsed time: {time.time()-start_time:.2f}s") -# df_xlsx[:3] -# - - -# #### pickle -# -# `to_pickle` pickles the dataframe; this format is rather big, but it is the fastest to process, albeit not at a significant margin - -start_time = time.time() -df.to_pickle(".curves.pkl") -df_pickle = pd.read_pickle(".curves.pkl") -assert CPCContainer.from_df(df_pickle) == CC -print(f"elapsed time: {time.time()-start_time:.2f}s") -df_pickle[:3] - -# ### Benchmarking -# -# below a comparison of the different methods in terms of size and speed; the benchmark run used **300,000 curves** -# -# 33000000 .curves.json -- 5.2s (without read/write) -# 11100035 .curves.csv -- 3.4s -# 37817 .curves.csv.gz -- 3.4s -# 15602482 .curves.pkl -- 2.6s -# 11100035 .curves.tsv -- 3.2s -# 8031279 .curves.xlsx -- 45.0s (!) -# -# Below are the figures for the current run (timing figures inline above) - -#print(f"{len(df_xlsx)} curves") -print(f" {len(cc_json)} .curves.json", ) -# !ls -l .curves* - - - - - - - - diff --git a/resources/NBTest/NBTest_004_GraphCode.ipynb b/resources/NBTest/NBTest_004_GraphCode.ipynb deleted file mode 100644 index 02cacf10b..000000000 --- a/resources/NBTest/NBTest_004_GraphCode.ipynb +++ /dev/null @@ -1,3064 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "d2f49384-7724-4758-b562-42111fe71be7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "ConstantProductCurve v3.4 (23/Jan/2024)\n", - "ArbGraph v2.2 (09/May/2023)\n" - ] - } - ], - "source": [ - "try:\n", - " import fastlane_bot.tools.arbgraphs as ag\n", - " from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, CPCContainer\n", - " from fastlane_bot.testing import *\n", - "\n", - "except:\n", - " import tools.arbgraphs as ag\n", - " from tools.cpc import ConstantProductCurve as CPC, CPCContainer\n", - " from tools.testing import *\n", - "\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPC))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(ag.ArbGraph))\n", - "\n", - "#plt.style.use('seaborn-dark')\n", - "plt.rcParams['figure.figsize'] = [12,6]\n", - "# from fastlane_bot import __VERSION__\n", - "# require(\"2.0\", __VERSION__)" - ] - }, - { - "cell_type": "markdown", - "id": "feaede6f-89cb-48d2-b929-cd523e56b1bb", - "metadata": {}, - "source": [ - "# Graph Code [NBTest065]" - ] - }, - { - "cell_type": "markdown", - "id": "349311dc-bd0b-4c3a-81b2-c05028a54324", - "metadata": {}, - "source": [ - "## ArbGraphs test and demo" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1714d883-35aa-4496-a96d-a2be376b345e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(ETH(0), USDC(1), WBTC(2), BNT(3))" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nodes = lambda: ag.create_node_list(\"ETH, USDC, WBTC, BNT\")\n", - "assert [str(n) for n in nodes()] == ['ETH(0)', 'USDC(1)', 'WBTC(2)', 'BNT(3)']\n", - "nodes()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2ecaa6d4-4713-4b93-8089-de71581e7179", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ArbGraph(nodes=(ETH(0), USDC(1), WBTC(2), BNT(3)), edges=[])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AG = ag.ArbGraph(nodes=nodes())\n", - "N = AG.node_by_tkn\n", - "assert str(N(\"ETH\")) == \"ETH(0)\"\n", - "assert str(N(\"BNT\")) == \"BNT(3)\"\n", - "assert str(AG.node_by_ix(1)) == \"USDC(1)\"\n", - "assert str(AG.node_by_tkn(\"USDC\")) == \"USDC(1)\"\n", - "AG" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a31b2a82-3115-4141-921b-4319bf48454e", - "metadata": {}, - "outputs": [], - "source": [ - "assert str(N(\"ETH\")) == \"ETH(0)\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "07703f2a-ac09-4d31-a6b1-db04e9fee9db", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(Edge(node_in=ETH(0), amount_in=1, node_out=USDC(1), amount_out=2000, ix=None, inverse=False, uid=None),\n", - " '1 ETH(0) --> 2000 USDC(1)',\n", - " '1 ETH(0) --(10)-> 2000 USDC(1)')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "edge = ag.Edge(N(\"ETH\"), 1, N(\"USDC\"), 2000)\n", - "edge1 = ag.Edge(N(\"ETH\"), 1, N(\"USDC\"), 2000, inverse=True, ix=10)\n", - "assert (edge.pair(), edge.price(), edge.convention()) == ('ETH/USDC', 2000.0, 'USDC per ETH')\n", - "assert (edge1.pair(), edge1.price(), edge1.convention()) == ('USDC/ETH', 0.0005, 'ETH per USDC')\n", - "edge, str(edge), str(edge1)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "38aa5282-218e-4013-a490-0c3204889da5", - "metadata": {}, - "outputs": [], - "source": [ - "assert (edge+0).asdict() == edge.asdict()\n", - "assert (edge+0) != edge # == means objects are the same\n", - "assert not edge+0 is edge\n", - "assert (2*edge).asdict() == (edge*2).asdict()\n", - "assert (edge + 2*edge).asdict() == (3*edge).asdict()\n", - "assert sum([edge,edge,edge]).asdict() == (3*edge).asdict()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a1d4b086-a587-4554-a2ee-7506505a2972", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'node_in': {'tkn': 'ETH', 'ix': 0},\n", - " 'amount_in': 1,\n", - " 'node_out': {'tkn': 'USDC', 'ix': 1},\n", - " 'amount_out': 2000,\n", - " 'ix': None,\n", - " 'inverse': False,\n", - " 'uid': None}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(edge+0).asdict()" - ] - }, - { - "cell_type": "markdown", - "id": "aaf48841-4b1f-4de5-9a96-5d5b652d4bb8", - "metadata": {}, - "source": [ - "## Paths and cycles" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4db94a89-fa62-4e3e-b768-c3c2c593dafb", - "metadata": {}, - "outputs": [], - "source": [ - "C = ag.Cycle([1,2,3,4,5])\n", - "assert len(C) == 5\n", - "assert [x for x in C.items()] == [1, 2, 3, 4, 5, 1]\n", - "assert [x for x in C.items(start_ix=3)] == [4, 5, 1, 2, 3, 4]\n", - "assert [x for x in C.items(start_val=3)] == [3, 4, 5, 1, 2, 3]\n", - "assert [p for p in C.pairs()] == [(1, 2), (2, 3), (3, 4), (4, 5), (5, 1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "024bfd2e-ab00-4bde-92a0-24bbc8e7662d", - "metadata": {}, - "outputs": [], - "source": [ - "c1 = ag.Cycle([1,2,3,4,5,6], \"c1\")\n", - "assert ag.Cycle([8,9]).is_subcycle_of(c1) == False\n", - "assert ag.Cycle([1,5,6]).is_subcycle_of(c1) == True\n", - "assert ag.Cycle([1,6,5]).is_subcycle_of(c1) == False\n", - "assert c1.filter_subcycles([ag.Cycle([8,9]), ag.Cycle([1,5,6]), ag.Cycle([1,6,5])]) == (ag.Cycle([1, 5, 6]),)\n", - "assert c1.filter_subcycles(ag.Cycle([1,5,6])) == (ag.Cycle([1, 5, 6]),)\n", - "assert str(c1) == 'cycle [c1]: 1 -> 2 -> 3 -> 4 -> 5 -> 6 ->...'" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "04460910-a86c-4bf2-bbd7-118c265a9e80", - "metadata": {}, - "outputs": [], - "source": [ - "assert c1.asdict() == {'data': [1, 2, 3, 4, 5, 6], 'uid': 'c1', 'graph': None}\n", - "assert c1.astuple() == ([1, 2, 3, 4, 5, 6], 'c1', None)\n", - "assert (c1.asdf().set_index(\"uid\")[\"data\"] == c1.asdf(index=\"uid\")[\"data\"]).iloc[0]\n", - "assert list(c1.asdf(exclude=[\"data\"]).columns) == ['uid', 'graph']\n", - "assert list(c1.asdf(include=[\"data\", \"graph\"], exclude=[\"graph\"]).columns) == ['data']" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d1dd8b46-d376-46f5-8447-bf9176643c02", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(ETH(0), USDC(1), WBTC(2), BNT(3))\n", - "cycle [c2]: ETH->USDC->WBTC->BNT->...\n" - ] - } - ], - "source": [ - "import types\n", - "nodes = ag.create_node_list(\"ETH, USDC, WBTC, BNT\")\n", - "c2 = ag.Cycle(nodes, \"c2\")\n", - "assert c2.uid == \"c2\"\n", - "assert str(c2) == 'cycle [c2]: ETH->USDC->WBTC->BNT->...'\n", - "print(nodes)\n", - "print(c2)\n", - "gc2 = (c for c in c2.items())\n", - "assert isinstance(gc2, types.GeneratorType)\n", - "tc2 = tuple(gc2)\n", - "assert str(tc2) == \"(ETH(0), USDC(1), WBTC(2), BNT(3), ETH(0))\"\n", - "assert tuple(gc2) == tuple() # generator spent\n", - "pc2 = (p for p in c2.pairs())\n", - "assert isinstance(pc2, types.GeneratorType)\n", - "tpc2 = tuple(pc2)\n", - "assert len(tpc2) == 4\n", - "assert str(tpc2[0]) == '(ETH(0), USDC(1))'\n", - "assert str(tpc2[-1]) == '(BNT(3), ETH(0))'\n", - "assert c2.pairs_s() == ['ETH/USDC', 'USDC/WBTC', 'WBTC/BNT', 'BNT/ETH']" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "344a7f71-b70c-438a-838f-d78b9edd2f60", - "metadata": {}, - "outputs": [], - "source": [ - "p1 = ag.Path([1,2,3,4,5,6], \"p1\")\n", - "assert p1.uid == \"p1\"\n", - "assert (str(p1)).strip() == 'path [p1]: 1 -> 2 -> 3 -> 4 -> 5 -> 6'\n", - "gp1 = (p for p in p1.items())\n", - "assert isinstance(gp1, types.GeneratorType)\n", - "tp1 = tuple(gp1)\n", - "assert tp1 == (1, 2, 3, 4, 5, 6)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "de7d7fd7-dffa-4086-ae83-69a99b0318f4", - "metadata": {}, - "outputs": [], - "source": [ - "nodes = ag.create_node_list(\"ETH, USDC, WBTC, BNT\")\n", - "p2 = ag.Path(nodes, \"p2\")\n", - "assert p2.uid == \"p2\"\n", - "assert str(p2) == 'path [p2]: ETH->USDC->WBTC->BNT'\n", - "gp2 = (c for c in p2.items())\n", - "assert isinstance(gp2, types.GeneratorType)\n", - "tp2 = tuple(gp2)\n", - "assert str(tp2) == \"(ETH(0), USDC(1), WBTC(2), BNT(3))\"\n", - "assert tuple(gp2) == tuple() # generator spent\n", - "pp2 = (p for p in p2.pairs())\n", - "assert isinstance(pp2, types.GeneratorType)\n", - "tpp2 = tuple(pp2)\n", - "assert len(tpp2) == 3\n", - "assert str(tpp2[0]) == '(ETH(0), USDC(1))'\n", - "assert str(tpp2[-1]) == '(WBTC(2), BNT(3))'\n", - "assert p2.pairs_s() == ['ETH/USDC', 'USDC/WBTC', 'WBTC/BNT']" - ] - }, - { - "cell_type": "markdown", - "id": "442d8e05-b775-439c-a1de-fb1c47b01ece", - "metadata": {}, - "source": [ - "## Arbgraph transport test and demo" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "d4126bfe-324e-42e5-ae91-39df2cfaace1", - "metadata": {}, - "outputs": [], - "source": [ - "n = ag.Node(\"ETH\")\n", - "assert isinstance(n.state, n.State)\n", - "assert n.state == n.State(amount = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "68523102-ae66-4158-81cb-1984af6849a2", - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " ag.Edge(\"ETH\", 1, \"USDC\", 2000)\n", - " raise\n", - "except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "282648ae-3874-4099-9ddb-83e996ed9955", - "metadata": {}, - "outputs": [], - "source": [ - "ETH = ag.Node(\"ETH\")\n", - "USDC = ag.Node(\"USDC\")\n", - "assert ETH != n # nodes are only equal if they are the same object!\n", - "assert ETH.asdict() == n.asdict()\n", - "edge = ag.Edge(ETH, 1, USDC, 2000)\n", - "edge2 = ag.Edge(ETH, 1, USDC, 2000)\n", - "edge3 = ag.Edge(ETH, 2, USDC, 3500)\n", - "assert (edge == edge2) == False\n", - "assert edge != ag.Edge(ETH, 1, USDC, 2000)\n", - "assert edge.asdict() == ag.Edge(ETH, 1, USDC, 2000).asdict()\n", - "assert edge.node_in == ETH\n", - "assert edge.node_out == USDC\n", - "assert edge.amount_in == 1\n", - "assert edge.amount_out == 2000\n", - "assert edge.state == ag.Edge.State(amount_in_remaining=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "83801a95-9240-4830-8e1a-93e0ad1c6e0b", - "metadata": {}, - "outputs": [], - "source": [ - "ETH.reset_state()\n", - "USDC.reset_state()\n", - "edge.reset_state()\n", - "ETH.state.amount_.set(1)\n", - "assert ETH.state.amount == 1\n", - "edge.transport(1, record=True)\n", - "assert ETH.state.amount == 0\n", - "assert USDC.state.amount == 2000\n", - "assert edge.state.amount_in_remaining == 0" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "d8f5eda1-53d3-4eb4-a839-74ad801c2f74", - "metadata": {}, - "outputs": [], - "source": [ - "ETH.reset_state()\n", - "USDC.reset_state()\n", - "edge.reset_state()\n", - "ETH.state.amount_.set(1)\n", - "edge.transport(0.25, record=True)\n", - "assert ETH.state.amount == 0.75\n", - "assert USDC.state.amount == 500\n", - "assert edge.state.amount_in_remaining == 0.75\n", - "edge.transport(0.25, record=True)\n", - "assert ETH.state.amount == 0.5\n", - "assert USDC.state.amount == 1000\n", - "assert edge.state.amount_in_remaining == 0.50" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "28c9c286-f85e-44eb-8f54-f722e1985fba", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "amount 2 exceeds edge capacity 1\n" - ] - } - ], - "source": [ - "ETH.reset_state()\n", - "USDC.reset_state()\n", - "edge.reset_state()\n", - "ETH.state.amount = 1\n", - "try:\n", - " edge.transport(2, record=True)\n", - "except Exception as e:\n", - " print(e)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "6fce5fdf-000a-4fb9-af40-d9d634a42459", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "amount 1 exceeds node capacity -0.5\n" - ] - } - ], - "source": [ - "ETH.reset_state()\n", - "USDC.reset_state()\n", - "edge.reset_state()\n", - "ETH.state.amount = 0.5\n", - "try:\n", - " edge.transport(1, record=True)\n", - "except Exception as e:\n", - " print(e)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "874e343d-8592-43e8-ad9d-92772959f8ff", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "amount 1 exceeds remaining edge capacity -0.5\n" - ] - } - ], - "source": [ - "ETH.reset_state()\n", - "USDC.reset_state()\n", - "edge.reset_state()\n", - "ETH.state.amount = 2\n", - "edge.transport(0.5, record=True)\n", - "try:\n", - " edge.transport(1, record=True)\n", - "except Exception as e:\n", - " print(e)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "a2c91953-f55a-45b0-9489-52a1e74c0b93", - "metadata": {}, - "outputs": [], - "source": [ - "ETH.state.amount = 10\n", - "edge.state.amount_in_remaining = 10\n", - "AG = ag.ArbGraph(nodes=[ETH, USDC], edges=[edge, edge2, edge3])\n", - "assert AG.nodes == [ETH, USDC]\n", - "assert AG.edges == [edge, edge2, edge3]\n", - "assert AG.nodes[0].state.amount == 10\n", - "assert AG.edges[0].state.amount_in_remaining == 10\n", - "AG.reset_state()\n", - "assert AG.nodes[0].state.amount == 0\n", - "assert AG.edges[0].state.amount_in_remaining == 1\n", - "assert AG.state.nodes[0] == ETH.state\n", - "assert AG.state.edges[0] == edge.state" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "1fb09ac7-f8cf-49b0-99b4-b37754655778", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "the tkn provided ETH(None) does not match the node found ETH(0)\n" - ] - } - ], - "source": [ - "assert AG.node_by_tkn(\"ETH\") is ETH\n", - "assert AG.node_by_tkn(ETH) is ETH\n", - "try:\n", - " AG.node_by_tkn(ag.Node(\"ETH\"))\n", - " raise\n", - "except Exception as e:\n", - " print(e)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "6584af6f-3bcf-4cc0-8279-423087cfaf9e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "routing_factor: 0.5; amounts_in: [0.5, 0.5, 1.0] 2 4\n", - "routing_factor: 1.0; amounts_in: [0.5, 0.5, 1.0] 2 2.0\n" - ] - }, - { - "data": { - "text/plain": [ - "ArbGraph.TransportResult(amount_in=Amount(amount=2, node='ETH'), amount_out=Amount(amount=3750.0, node='USDC'), amounts_in=(0.5, 0.5, 1.0), amounts_out=(1000.0, 1000.0, 1750.0), edges=(Edge(node_in=ETH(0), amount_in=1, node_out=USDC(1), amount_out=2000, ix=0, inverse=False, uid=None), Edge(node_in=ETH(0), amount_in=1, node_out=USDC(1), amount_out=2000, ix=1, inverse=False, uid=None), Edge(node_in=ETH(0), amount_in=2, node_out=USDC(1), amount_out=3500, ix=2, inverse=False, uid=None)))" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AG.reset_state()\n", - "ETH.state.amount = 4\n", - "r = AG.transport(2, \"ETH\", \"USDC\", record=True)\n", - "assert ETH.state.amount == 2\n", - "assert r.amount_in.amount == 2\n", - "assert r.amount_in.tkn == \"ETH\"\n", - "capacity_in = sum([e_.amount_in for e_ in r.edges])\n", - "assert capacity_in == 4\n", - "capacity_out = sum([e_.amount_out for e_ in r.edges])\n", - "assert capacity_out == 7500\n", - "assert r.amount_out.amount == r.amount_in.amount * capacity_out / capacity_in\n", - "assert sum(r.amounts_in) == r.amount_in.amount\n", - "assert sum(r.amounts_out) == r.amount_out.amount\n", - "assert AG.has_capacity(\"ETH\", \"USDC\")\n", - "assert AG.has_capacity()\n", - "AG.transport(2, \"ETH\", \"USDC\", record=True)\n", - "assert AG.has_capacity() == False\n", - "r" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "e8a0f332-74d7-4716-8e87-cae1fc82ea99", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ArbGraph.EdgeStatistics(len=3, edges=(Edge(node_in=ETH(0), amount_in=1, node_out=USDC(1), amount_out=2000, ix=0, inverse=False, uid=None), Edge(node_in=ETH(0), amount_in=1, node_out=USDC(1), amount_out=2000, ix=1, inverse=False, uid=None), Edge(node_in=ETH(0), amount_in=2, node_out=USDC(1), amount_out=3500, ix=2, inverse=False, uid=None)), amount_in=Amount(amount=4, node='ETH'), amount_in_remaining=Amount(amount=0.0, node='ETH'), amount_out=Amount(amount=7500, node='USDC'), price=1875.0, utilization=1.0, amounts_in=(1, 1, 2), amounts_in_remaining=(0.0, 0.0, 0.0), amounts_out=(2000, 2000, 3500), prices=(2000.0, 2000.0, 1750.0), utilizations=(1.0, 1.0, 1.0))" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rs = AG.edge_statistics(edges=r.edges)\n", - "assert rs.len == 3\n", - "assert rs.edges is r.edges\n", - "assert rs.amounts_in == (1, 1, 2)\n", - "assert rs.amounts_in_remaining == (0.0, 0.0, 0.0)\n", - "assert rs.amounts_out == (2000, 2000, 3500)\n", - "assert rs.prices == (2000.0, 2000.0, 1750.0)\n", - "assert rs.utilizations == (1.0, 1.0, 1.0)\n", - "assert rs.amount_in.amount == 4\n", - "assert rs.amount_in_remaining.amount == 0.0\n", - "assert rs.amount_out.amount == 7500\n", - "assert rs.amount_in.tkn == \"ETH\"\n", - "assert rs.amount_in_remaining.tkn == \"ETH\"\n", - "assert rs.amount_out.tkn == \"USDC\"\n", - "assert rs.utilization == 1.0\n", - "assert rs.price == 1875.0\n", - "rs" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "a471a038-7477-4ede-bffe-098fe93ab5d6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ArbGraph.NodeStatistics(node=ETH(0), edges_in=(), edges_out=(Edge(node_in=ETH(0), amount_in=1, node_out=USDC(1), amount_out=2000, ix=0, inverse=False, uid=None), Edge(node_in=ETH(0), amount_in=1, node_out=USDC(1), amount_out=2000, ix=1, inverse=False, uid=None), Edge(node_in=ETH(0), amount_in=2, node_out=USDC(1), amount_out=3500, ix=2, inverse=False, uid=None)), nodes_in=set(), nodes_out={'USDC'}, amount_in=Amount(amount=0, node=ETH(0)), amount_out=Amount(amount=4, node=ETH(0)), amount_out_remaining=Amount(amount=0.0, node=ETH(0)))" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rns = AG.node_statistics(\"ETH\")\n", - "assert len(rns.edges_out) == 3\n", - "assert len(rns.edges_in) == 0\n", - "assert rns.amount_in.amount == 0\n", - "assert rns.amount_out.amount == 4\n", - "assert rns.amount_out_remaining.amount == 0\n", - "assert rns.nodes_in==set()\n", - "assert rns.nodes_out=={\"USDC\"}\n", - "rns" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "4fc1604d-1677-4602-9170-9505974eed03", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ArbGraph.NodeStatistics(node=USDC(1), edges_in=(Edge(node_in=ETH(0), amount_in=1, node_out=USDC(1), amount_out=2000, ix=0, inverse=False, uid=None), Edge(node_in=ETH(0), amount_in=1, node_out=USDC(1), amount_out=2000, ix=1, inverse=False, uid=None), Edge(node_in=ETH(0), amount_in=2, node_out=USDC(1), amount_out=3500, ix=2, inverse=False, uid=None)), edges_out=(), nodes_in={'ETH'}, nodes_out=set(), amount_in=Amount(amount=7500, node=USDC(1)), amount_out=Amount(amount=0, node=USDC(1)), amount_out_remaining=Amount(amount=0, node=USDC(1)))" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rns2 = AG.node_statistics(\"USDC\")\n", - "assert len(rns2.edges_out) == 0\n", - "assert len(rns2.edges_in) == 3\n", - "assert rns2.amount_in.amount == 7500\n", - "assert rns2.amount_out.amount == 0\n", - "assert rns2.amount_out_remaining.amount == 0\n", - "assert rns2.nodes_in==set([\"ETH\",])\n", - "assert rns2.nodes_out==set()\n", - "rns2" - ] - }, - { - "cell_type": "markdown", - "id": "4869b700-e5c0-4199-87c2-ff4b76c04d85", - "metadata": {}, - "source": [ - "## Arbgraph transport test and demo 2" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "a3f0b780-aef8-41ad-8e40-87d7ae227e7e", - "metadata": {}, - "outputs": [], - "source": [ - "@ag.dataclass\n", - "class MyState():\n", - " myval_: ag.TrackedStateFloat = ag.field(default_factory=ag.TrackedStateFloat, init=False)\n", - " myval: ag.InitVar=None\n", - " \n", - " def __post_init__(self, myval):\n", - " self.myval = myval\n", - "\n", - " @property\n", - " def myval(self):\n", - " return self.myval_.value\n", - " \n", - " @myval.setter\n", - " def myval(self, value):\n", - " self.myval_.set(value)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "6ed91cb7-86de-4a1c-9243-940932d29dd4", - "metadata": {}, - "outputs": [], - "source": [ - "mystate = MyState(0)\n", - "mystate.myval_.set(10)\n", - "assert mystate.myval == 10\n", - "mystate.myval += 5\n", - "assert mystate.myval == 15\n", - "mystate.myval -= 4\n", - "assert mystate.myval == 11\n", - "assert mystate.myval_.history == [0, 0, 10, 15, 11]" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "6e7285db-5f69-4beb-a189-0d86e1b798ea", - "metadata": {}, - "outputs": [], - "source": [ - "mystate = MyState(10)\n", - "assert mystate.myval == 10\n", - "assert mystate.myval_.history == [0,10]\n", - "mystate.myval = 20\n", - "assert mystate.myval == 20\n", - "assert mystate.myval_.history == [0,10,20]\n", - "mystate.myval += 5\n", - "assert mystate.myval == 25\n", - "mystate.myval -= 4\n", - "assert mystate.myval == 21\n", - "assert mystate.myval_.history == [0,10,20,25,21]\n", - "assert mystate.myval_.reset(42)\n", - "assert mystate.myval == 42\n", - "assert mystate.myval_.history == [42]" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "673ed709-aa4f-4711-9221-fd2ce38305f8", - "metadata": {}, - "outputs": [], - "source": [ - "n = ag.Node(\"MEH\")\n", - "n.state.amount = 10\n", - "n.state.amount += 5\n", - "n.state.amount -= 4\n", - "assert n.state.amount == 11\n", - "assert n.state.amount_.history == [0, 10, 15, 11]\n", - "n.reset_state()\n", - "assert n.state.amount_.history == [0]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "a97dd47b-b81f-40d7-ba4f-6fedfddb3b71", - "metadata": {}, - "outputs": [], - "source": [ - "nodes = ag.Node.create_node_list(\"USDC, LINK, ETH, WBTC\")\n", - "assert len(nodes)==4\n", - "assert nodes[0].tkn == \"USDC\"\n", - "AG = ag.ArbGraph(nodes)\n", - "AG.add_edge(\"USDC\", 10000, \"ETH\", 5)\n", - "AG.add_edge_obj(AG.edges[-1].R())\n", - "AG.add_edge(\"USDC\", 10000, \"WBTC\", 1)\n", - "AG.add_edge_obj(AG.edges[-1].R())\n", - "AG.add_edge(\"USDC\", 10000, \"LINK\", 1000)\n", - "AG.add_edge_obj(AG.edges[-1].R())\n", - "AG.add_edge(\"LINK\", 1000, \"ETH\", 5)\n", - "AG.add_edge_obj(AG.edges[-1].R())\n", - "AG.add_edge(\"ETH\", 5, \"WBTC\", 1)\n", - "AG.add_edge_obj(AG.edges[-1].R())\n", - "assert len(AG.edges)==10\n", - "assert len(AG.cycles())==11\n", - "ns = AG.node_statistics(\"USDC\")\n", - "assert ns.amount_in.amount == 30000\n", - "assert ns.amount_out.amount == 30000\n", - "assert ns.amount_out_remaining == ns.amount_out\n", - "assert ns.nodes_out==set(['WBTC', 'ETH', 'LINK'])\n", - "assert ns.nodes_in==set(['WBTC', 'ETH', 'LINK'])\n", - "#_=AG.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "f798a897-5a14-4029-8827-db38d4cfcfa9", - "metadata": {}, - "source": [ - "## Transport 3 and prices" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "505fe3f8-bba4-4435-b370-55f6151783ab", - "metadata": {}, - "outputs": [], - "source": [ - "AG = ag.ArbGraph()\n", - "prices = dict(USDC=1, LINK=5, AAVE=100, WETH=2000, BTC=10000)\n", - "for t1,p1 in prices.items():\n", - " for t2,p2 in prices.items():\n", - " if t1\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
USDCLINKAAVEWETHBTC
tknb
USDC1.00.20.010.00050.0001
LINK5.01.00.050.00250.0005
AAVE100.020.01.000.05000.0100
WETH2000.0400.020.001.00000.2000
BTC10000.02000.0100.005.00001.0000
\n", - "" - ], - "text/plain": [ - " USDC LINK AAVE WETH BTC\n", - "tknb \n", - "USDC 1.0 0.2 0.01 0.0005 0.0001\n", - "LINK 5.0 1.0 0.05 0.0025 0.0005\n", - "AAVE 100.0 20.0 1.00 0.0500 0.0100\n", - "WETH 2000.0 400.0 20.00 1.0000 0.2000\n", - "BTC 10000.0 2000.0 100.00 5.0000 1.0000" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AG.pricetable()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "b999442c-7119-4dc6-8d91-de8acaed33f7", - "metadata": {}, - "outputs": [], - "source": [ - "pt = AG.pricetable(asdf=False)\n", - "assert pt[\"labels\"] == ['USDC', 'LINK', 'AAVE', 'WETH', 'BTC']\n", - "assert len(pt[\"data\"]) == len(pt[\"labels\"])\n", - "assert pt[\"data\"][0] == [1, 0.2, 0.01, 0.0005, 0.0001]" - ] - }, - { - "cell_type": "markdown", - "id": "4f383864-957d-4ae5-92c9-0fae2343b6de", - "metadata": {}, - "source": [ - "## Arbraph connection only edges test" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "b3353635-f182-4e79-824e-d4c2f2228a03", - "metadata": {}, - "outputs": [], - "source": [ - "nodes = lambda: ag.create_node_list(\"ETH, USDC\")\n", - "ETH, USDC = nodes()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "4ee65b5f-7045-4a08-8d01-1f33d56a6d99", - "metadata": {}, - "outputs": [], - "source": [ - "e = e1 = ag.Edge.connection_edge(node_in=ETH, node_out=USDC, price=3000)\n", - "e = e2 = ag.Edge.connection_edge(node_in=ETH, node_out=USDC, price=2000)\n", - "assert e.convention() == 'USDC per ETH'\n", - "assert e.convention_outperin() == 'USDC per ETH'\n", - "assert e.price() == 2000\n", - "assert e.price_outperin == 2000\n", - "assert e.edgetype == e.EDGE_CONNECTION\n", - "assert e.is_amounttype == False\n", - "assert not raises(e.assert_edgetype, e.EDGE_CONNECTION)\n", - "assert raises(e.assert_edgetype, e.EDGE_AMOUNT)\n", - "assert e1.label == '3000.0 [None]'\n", - "assert e2.label == '2000.0 [None]'\n", - "assert (e1+e2).price() == 2500\n", - "assert (e1+3*e2).price() == 2250\n", - "assert raises(lambda: e1*0)\n", - "assert raises(lambda: e1*(-10))\n", - "assert raises(lambda: 0*e1)\n", - "assert raises(lambda: -10*e1)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "ced876f3-17ce-42fb-b928-be4267d453f5", - "metadata": {}, - "outputs": [], - "source": [ - "e = e3 = ag.Edge.connection_edge(node_out=ETH, node_in=USDC, price=2000, inverse=True)\n", - "assert e.convention() == 'USDC per ETH'\n", - "assert e.convention_outperin() == 'ETH per USDC'\n", - "assert e.price() == 2000\n", - "assert e.price_outperin == 1/2000\n", - "assert e.edgetype == e.EDGE_CONNECTION\n", - "assert e.is_amounttype == False\n", - "assert not raises(e.assert_edgetype, e.EDGE_CONNECTION)\n", - "assert raises(e.assert_edgetype, e.EDGE_AMOUNT)\n", - "assert e3.label == '0.0005 [None]'" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "5aa80d7f-c1b8-4025-8b69-8307035d086a", - "metadata": {}, - "outputs": [], - "source": [ - "e= e4 = ag.Edge(node_in=ETH, node_out=USDC, amount_in=1, amount_out=2000, inverse=True)\n", - "assert e.edgetype == e.EDGE_AMOUNT\n", - "assert e.is_amounttype\n", - "assert not raises(e.assert_edgetype, e.EDGE_AMOUNT)\n", - "assert raises(e.assert_edgetype, e.EDGE_CONNECTION)\n", - "e = e5 = 2*e4\n", - "assert e.edgetype == e.EDGE_AMOUNT\n", - "assert e.is_amounttype\n", - "assert not raises(e.assert_edgetype, e.EDGE_AMOUNT)\n", - "assert raises(e.assert_edgetype, e.EDGE_CONNECTION)\n", - "e = e6 = ag.Edge(node_in=ETH, node_out=USDC, amount_in=1, amount_out=3000)\n", - "assert e.price() == e1.price()\n", - "assert e.price_outperin == e1.price_outperin\n", - "assert e4.label == '1 ETH(0) --> 2000 USDC(1)'" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "a4f3d14f-2c36-48c9-b9b3-169a69bc66c9", - "metadata": {}, - "outputs": [], - "source": [ - "assert raises (lambda: e1+e3)\n", - "assert raises (lambda: -2*e1)\n", - "assert raises (lambda: e3*(-2))\n", - "try:\n", - " e1 += e3\n", - " raise\n", - "except ValueError as e:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "7b6ad261-5eb6-4560-878f-bfb74cda33a9", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises (lambda: e4+e5)\n", - "assert not raises (lambda: 2*e4)\n", - "assert not raises (lambda: e4*2)\n", - "e4 += e5" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "a85d1b67-ea52-4e09-be6c-e01a5b8b42f2", - "metadata": {}, - "outputs": [], - "source": [ - "assert e6.amount_in == 1\n", - "assert e1.transport() == e6.transport()\n", - "assert e1.transport(amount_in=1e6) == 1e6*e1.transport()" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "3cdc6998-cdd3-4f40-9723-4cadb0796110", - "metadata": {}, - "outputs": [], - "source": [ - "AG = ag.ArbGraph(nodes = [ETH, USDC])\n", - "assert AG.edgetype is None\n", - "AG.add_edge_obj(e1)\n", - "assert AG.edgetype == AG.EDGE_CONNECTION\n", - "assert AG.edgetype == e1.EDGE_CONNECTION\n", - "AG.add_edge_obj(e2)\n", - "assert raises(AG.add_edge_obj, e4)\n", - "assert AG.edgetype == e1.EDGE_CONNECTION" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "7d8a7329-62a0-4a44-a8e2-ad4dd45c8994", - "metadata": {}, - "outputs": [], - "source": [ - "AG = ag.ArbGraph(nodes = [ETH, USDC])\n", - "assert AG.edgetype is None\n", - "AG.add_edge_obj(e4)\n", - "assert AG.edgetype == AG.EDGE_AMOUNT\n", - "assert AG.edgetype == e1.EDGE_AMOUNT\n", - "AG.add_edge_obj(e5)\n", - "assert raises(AG.add_edge_obj, e1)\n", - "assert AG.edgetype == e1.EDGE_AMOUNT" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "ae4b80ec-153c-457e-bcd1-94cd0658897f", - "metadata": {}, - "outputs": [], - "source": [ - "AG = ag.ArbGraph()\n", - "AG.add_edge_connectiontype(tkn_in=\"ETH\", tkn_out=\"USDC\", price=2000)\n", - "AG.add_edge_connectiontype(tkn_in=\"ETH\", tkn_out=\"BTC\", price=1/5)\n", - "AG.add_edge_connectiontype(tkn_in=\"BTC\", tkn_out=\"USDC\", price=10000)\n", - "assert AG.edgetype == AG.EDGE_CONNECTION\n", - "assert len(AG) == 6\n", - "#_=AG.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "718b0faf-3f94-41b7-8b8b-4878ea413559", - "metadata": {}, - "outputs": [], - "source": [ - "AG = ag.ArbGraph()\n", - "AG.add_edge_connectiontype(tkn_in=\"ETH\", tkn_out=\"USDC\", price=2000, symmetric=False)\n", - "AG.add_edge_connectiontype(tkn_in=\"ETH\", tkn_out=\"BTC\", price=1/5, symmetric=False)\n", - "AG.add_edge_connectiontype(tkn_in=\"BTC\", tkn_out=\"USDC\", price=10000, symmetric=False)\n", - "assert AG.edgetype == AG.EDGE_CONNECTION\n", - "assert len(AG) == 3\n", - "#_=AG.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "54d2538b-4a24-462a-95cc-a9369570c9b4", - "metadata": {}, - "outputs": [], - "source": [ - "AG = ag.ArbGraph()\n", - "assert raises (AG.add_edge_connectiontype, tkn_in=\"ETH\", tkn_out=\"USDC\", price=2000, price_outperin=2000)\n", - "assert raises (AG.add_edge_connectiontype, tkn_in=\"ETH\", tkn_out=\"USDC\", inverse = True, price_outperin=2000)\n", - "assert AG.add_edge_connectiontype == AG.add_edge_ct" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "2c853729-60ce-4597-9ab0-5404ffbff61f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (0, 1)\t1\n", - " (0, 2)\t1\n", - " (1, 0)\t1\n", - " (1, 2)\t1\n", - " (2, 0)\t1\n", - " (2, 1)\t1\n" - ] - } - ], - "source": [ - "AG = ag.ArbGraph()\n", - "for i in range(5):\n", - " mul = 1+i/50\n", - " AG.add_edge_ct(tkn_in=\"ETH\", tkn_out=\"USDC\", price=2000*mul)\n", - " AG.add_edge_ct(tkn_in=\"WBTC\", tkn_out=\"USDC\", price=10000*mul)\n", - " AG.add_edge_ct(tkn_in=\"ETH\", tkn_out=\"WBTC\", price=0.2/mul)\n", - "assert AG.len() == (2*3*5, 3)\n", - "assert len(AG.cycles()) == 5\n", - "assert np.array_equal(AG.A.toarray(), np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]]))\n", - "print(AG.A)\n", - "AG2 = AG.duplicate()\n", - "assert AG2.len() == (6,3)\n", - "edges = AG.filter_edges(\"ETH\", \"USDC\")\n", - "assert len(edges) == 5\n", - "edges2 = AG2.filter_edges(\"ETH\", \"USDC\")\n", - "assert len(edges2) == 1\n", - "assert [e.p_outperin for e in edges] == [2000.0, 2040.0, 2080.0, 2120.0, 2160.0]\n", - "assert edges2[0].p_outperin == np.mean([e.p_outperin for e in edges])" - ] - }, - { - "cell_type": "markdown", - "id": "20d0e72b-ff68-4f6d-a3f1-0311e59cd7f6", - "metadata": {}, - "source": [ - " AttributeError: module 'scipy.sparse' has no attribute 'coo_array'\n", - " \n", - "I had this one before -- I believe this is a version issue; unfortunately I do not quite remember how I fixed it at the time" - ] - }, - { - "cell_type": "markdown", - "id": "4db52bf5-bad1-4e91-8ecd-19dfbdc39435", - "metadata": {}, - "source": [ - "## Interaction with CPC" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "94d3d623-0868-4987-8832-6ff39f2bac27", - "metadata": {}, - "outputs": [], - "source": [ - "c1 = CPC.from_univ2(x_tknb=1, y_tknq=2000, pair=\"ETH/USDC\", fee=0, cid=\"1\", descr=\"UniV2\")\n", - "c2 = CPC.from_univ2(x_tknb=1, y_tknq=10000, pair=\"WBTC/USDC\", fee=0, cid=\"2\", descr=\"UniV2\")\n", - "c3 = CPC.from_univ2(x_tknb=1, y_tknq=5, pair=\"WBTC/ETH\", fee=0, cid=\"3\", descr=\"UniV2\")\n", - "assert c1.p == 2000\n", - "assert c2.p == 10000\n", - "assert c3.p == 5" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "9c0f82b7-fc39-48be-9465-09198266060a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ArbGraph(nodes=[ETH(0), USDC(1), WBTC(2)], edges=[Edge(node_in=ETH(0), amount_in=-1, node_out=USDC(1), amount_out=-2000.0, ix=0, inverse=False, uid='1'), Edge(node_in=USDC(1), amount_in=-1, node_out=ETH(0), amount_out=-0.0005, ix=1, inverse=True, uid='1-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=USDC(1), amount_out=-10000.0, ix=2, inverse=False, uid='2'), Edge(node_in=USDC(1), amount_in=-1, node_out=WBTC(2), amount_out=-0.0001, ix=3, inverse=True, uid='2-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=ETH(0), amount_out=-5.0, ix=4, inverse=False, uid='3'), Edge(node_in=ETH(0), amount_in=-1, node_out=WBTC(2), amount_out=-0.2, ix=5, inverse=True, uid='3-r')])" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AG = ag.ArbGraph()\n", - "AG.add_edges_cpc(c1)\n", - "AG.add_edges_cpc(c2)\n", - "AG.add_edges_cpc(c3)\n", - "#_=AG.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "44580cb8-1a34-4fc8-9cfe-e95948771995", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ArbGraph(nodes=[ETH(0), USDC(1), WBTC(2)], edges=[Edge(node_in=ETH(0), amount_in=-1, node_out=USDC(1), amount_out=-2000.0, ix=0, inverse=False, uid='1'), Edge(node_in=USDC(1), amount_in=-1, node_out=ETH(0), amount_out=-0.0005, ix=1, inverse=True, uid='1-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=USDC(1), amount_out=-10000.0, ix=2, inverse=False, uid='2'), Edge(node_in=USDC(1), amount_in=-1, node_out=WBTC(2), amount_out=-0.0001, ix=3, inverse=True, uid='2-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=ETH(0), amount_out=-5.0, ix=4, inverse=False, uid='3'), Edge(node_in=ETH(0), amount_in=-1, node_out=WBTC(2), amount_out=-0.2, ix=5, inverse=True, uid='3-r')])" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AG = ag.ArbGraph()\n", - "AG.add_edges_cpc([c1, c2, c3])\n", - "#_=AG.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "e8c79e3e-b41e-4920-ada2-3ca5e67126d7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ArbGraph(nodes=[ETH(0), USDC(1), WBTC(2)], edges=[Edge(node_in=ETH(0), amount_in=-1, node_out=USDC(1), amount_out=-2000.0, ix=0, inverse=False, uid='1'), Edge(node_in=USDC(1), amount_in=-1, node_out=ETH(0), amount_out=-0.0005, ix=1, inverse=True, uid='1-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=USDC(1), amount_out=-10000.0, ix=2, inverse=False, uid='2'), Edge(node_in=USDC(1), amount_in=-1, node_out=WBTC(2), amount_out=-0.0001, ix=3, inverse=True, uid='2-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=ETH(0), amount_out=-5.0, ix=4, inverse=False, uid='3'), Edge(node_in=ETH(0), amount_in=-1, node_out=WBTC(2), amount_out=-0.2, ix=5, inverse=True, uid='3-r')])" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AG = ag.ArbGraph()\n", - "AG.add_edges_cpc(c for c in [c1, c2, c3])\n", - "#_=AG.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "8529263f-1472-4332-a684-8eef6a562a95", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ArbGraph(nodes=[ETH(0), USDC(1), WBTC(2)], edges=[Edge(node_in=ETH(0), amount_in=-1, node_out=USDC(1), amount_out=-2000.0, ix=0, inverse=False, uid='1'), Edge(node_in=USDC(1), amount_in=-1, node_out=ETH(0), amount_out=-0.0005, ix=1, inverse=True, uid='1-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=USDC(1), amount_out=-10000.0, ix=2, inverse=False, uid='2'), Edge(node_in=USDC(1), amount_in=-1, node_out=WBTC(2), amount_out=-0.0001, ix=3, inverse=True, uid='2-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=ETH(0), amount_out=-5.0, ix=4, inverse=False, uid='3'), Edge(node_in=ETH(0), amount_in=-1, node_out=WBTC(2), amount_out=-0.2, ix=5, inverse=True, uid='3-r')])" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AG = ag.ArbGraph()\n", - "CC = CPCContainer([c1,c2,c3])\n", - "AG.add_edges_cpc(CC)\n", - "#_=AG.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "f032a193-a6f9-4f13-b311-86ddbeaa43d3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (0, 1)\t1\n", - " (0, 2)\t1\n", - " (1, 0)\t1\n", - " (1, 2)\t1\n", - " (2, 0)\t1\n", - " (2, 1)\t1\n" - ] - } - ], - "source": [ - "print(AG.A)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "f1e0e122-72dc-417d-8628-6379edb36a40", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(Cycle(data=[ETH(0), USDC(1)], uid=0),\n", - " Cycle(data=[ETH(0), USDC(1), WBTC(2)], uid=1),\n", - " Cycle(data=[ETH(0), WBTC(2), USDC(1)], uid=2),\n", - " Cycle(data=[ETH(0), WBTC(2)], uid=3),\n", - " Cycle(data=[USDC(1), WBTC(2)], uid=4))" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AG.cycles()" - ] - }, - { - "cell_type": "markdown", - "id": "b18b27a6-3d38-4e03-a8be-30d35424c1b5", - "metadata": {}, - "source": [ - "## With real data from CPC" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "6e98f201-62f2-4f1c-9f58-f787e1a7267b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Num curves: 459\n", - "Num pairs: 326\n", - "Num tokens: 141\n", - "1INCH,1ONE,AAVE,ALCX,ALEPH,ALPHA,AMP,ANKR,ANT,APW,ARCONA,ARMOR,AST,AUC,BAL,BAT,BBADGER,BDIGG,BMI,BNB,BNT,BOBA,BOND,BOR,BORING,BZRX,CEL,CHZ,COMP,COT,CRO,CRV,CTSI,DAI,DAO,DATA,DDX,DEXE,DIP,DRC,DUSK,DXD,DYDX,EDEN,ELF,ENJ,ENS,ERSDL,ETH,EWTB,FARM,FODL,FOX,FRM,FTX TOKEN,FXS,GNO,GRT,GTC,GUSD,HEGIC,HOT,HY,ICHI,IDLE,INDEX,INST,KNC,KTN,LINK,LPL,LQTY,LRC,LYRA,MANA,MASK,MATIC,MFG,MFI,MKR,MLN,MONA,MPH,MTA,NDX,NEXO,NMR,NOIA,OCEAN,OMG,OPIUM,PATH,PERP,PHTR,PLR,POOL,POOLZ,POWR,PSP,QNT,QUICK,RAIL,RARI,REN,RENBTC,RENZEC,REQ,RETH,RLC,RNB,ROOK,RUNE,SATA,SFI,SHEESHA,SHIBGF,SMARTCREDIT,SNX,STAKE,SUSHI,TOMOE,TRAC,TRU,UMA,UNI,UOS,USDC,USDT,VBNT,VISION,VLX,WBTC,WETH,WNXM,WOO,WSTETH,WXT,XSUSHI,YFI,ZCN,ZRX\n" - ] - } - ], - "source": [ - "try:\n", - " df = pd.read_csv(\"_data/NBTEST_002_Curves.csv.gz\")\n", - "except:\n", - " df = pd.read_csv(\"fastlane_bot/tests/_data/NBTEST_002_Curves.csv.gz\")\n", - "CC0 = CPCContainer.from_df(df)\n", - "print(\"Num curves:\", len(CC0))\n", - "print(\"Num pairs:\", len(CC0.pairs()))\n", - "print(\"Num tokens:\", len(CC0.tokens()))\n", - "print(CC0.tokens_s())" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "1de1050e-ecbc-4377-a540-c08bd9a48432", - "metadata": {}, - "outputs": [], - "source": [ - "AG0 = ag.ArbGraph().add_edges_cpc(CC0)\n", - "#AG0.plot()\n", - "assert AG0.len() == (918, 141)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "4aa3bb42-9842-43c0-9fe2-70dace48bb63", - "metadata": {}, - "outputs": [], - "source": [ - "assert str(AG0.A)[:60] ==' (0, 1)\\t1\\n (1, 0)\\t1\\n (2, 3)\\t1\\n (2, 4)\\t1\\n (2, 5)\\t1\\n (2,'" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "6903c18c-e046-4031-b3d6-04d0fb7b528e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pairs = CC0.filter_pairs(bothin=\"WETH, USDC, UNI, AAVE, LINK\")\n", - "CC = CC0.bypairs(pairs, ascc=True)\n", - "AG = ag.ArbGraph().add_edges_cpc(CC)\n", - "#AG.plot()\n", - "AG.len() == (24, 5)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "f3e69f0b-99f0-4196-89af-bc5bf11f5e41", - "metadata": {}, - "outputs": [], - "source": [ - "assert np.all(AG.A.toarray() == np.array(\n", - " [[0, 1, 1, 0, 0],\n", - " [1, 0, 1, 1, 1],\n", - " [1, 1, 0, 1, 1],\n", - " [0, 1, 1, 0, 0],\n", - " [0, 1, 1, 0, 0]]))" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "61f65aad-7493-4aaf-9819-dd19950e032f", - "metadata": {}, - "outputs": [], - "source": [ - "assert raises(AG.edge_statistics,\"WETH\", \"USDC\")" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "8ba71383-94a7-4224-aa74-e9601839a68a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pairtkn_intkn_outnis_reverseprice_outinprice
0LINK/WETHLINKWETH1False0.0041530.004153
1LINK/WETHWETHLINK1True240.7650160.004153
2LINK/USDCLINKUSDC1False6.1005216.100521
3LINK/USDCUSDCLINK1True0.1639206.100521
4AAVE/WETHAAVEWETH1False0.0408050.040805
5AAVE/WETHWETHAAVE1True24.5068920.040805
6UNI/WETHUNIWETH1False0.0033270.003327
7UNI/WETHWETHUNI1True300.6130150.003327
8WETH/USDCUSDCWETH1True0.0005491822.819584
9WETH/USDCWETHUSDC1False1822.8195841822.819584
10LINK/WETHLINKWETH1False0.0041440.004144
11LINK/WETHWETHLINK1True241.2888110.004144
12LINK/USDCLINKUSDC1False7.3008817.300881
13LINK/USDCUSDCLINK1True0.1369707.300881
14AAVE/WETHAAVEWETH1False0.0405490.040549
15AAVE/WETHWETHAAVE1True24.6612930.040549
16AAVE/USDCAAVEUSDC1False80.82639380.826393
17AAVE/USDCUSDCAAVE1True0.01237280.826393
18UNI/WETHUNIWETH1False0.0033300.003330
19UNI/WETHWETHUNI1True300.2552450.003330
20UNI/USDCUNIUSDC1False6.0986346.098634
21UNI/USDCUSDCUNI1True0.1639716.098634
22WETH/USDCUSDCWETH1True0.0005491819.922154
23WETH/USDCWETHUSDC1False1819.9221541819.922154
\n", - "
" - ], - "text/plain": [ - " pair tkn_in tkn_out n is_reverse price_outin price\n", - "0 LINK/WETH LINK WETH 1 False 0.004153 0.004153\n", - "1 LINK/WETH WETH LINK 1 True 240.765016 0.004153\n", - "2 LINK/USDC LINK USDC 1 False 6.100521 6.100521\n", - "3 LINK/USDC USDC LINK 1 True 0.163920 6.100521\n", - "4 AAVE/WETH AAVE WETH 1 False 0.040805 0.040805\n", - "5 AAVE/WETH WETH AAVE 1 True 24.506892 0.040805\n", - "6 UNI/WETH UNI WETH 1 False 0.003327 0.003327\n", - "7 UNI/WETH WETH UNI 1 True 300.613015 0.003327\n", - "8 WETH/USDC USDC WETH 1 True 0.000549 1822.819584\n", - "9 WETH/USDC WETH USDC 1 False 1822.819584 1822.819584\n", - "10 LINK/WETH LINK WETH 1 False 0.004144 0.004144\n", - "11 LINK/WETH WETH LINK 1 True 241.288811 0.004144\n", - "12 LINK/USDC LINK USDC 1 False 7.300881 7.300881\n", - "13 LINK/USDC USDC LINK 1 True 0.136970 7.300881\n", - "14 AAVE/WETH AAVE WETH 1 False 0.040549 0.040549\n", - "15 AAVE/WETH WETH AAVE 1 True 24.661293 0.040549\n", - "16 AAVE/USDC AAVE USDC 1 False 80.826393 80.826393\n", - "17 AAVE/USDC USDC AAVE 1 True 0.012372 80.826393\n", - "18 UNI/WETH UNI WETH 1 False 0.003330 0.003330\n", - "19 UNI/WETH WETH UNI 1 True 300.255245 0.003330\n", - "20 UNI/USDC UNI USDC 1 False 6.098634 6.098634\n", - "21 UNI/USDC USDC UNI 1 True 0.163971 6.098634\n", - "22 WETH/USDC USDC WETH 1 True 0.000549 1819.922154\n", - "23 WETH/USDC WETH USDC 1 False 1819.922154 1819.922154" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AG.edgedf(consolidated=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "adb634cb-a53e-4a8b-8bf3-3e99602d1d6a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nn_revprice
pair
AAVE/USDC1180.826393
AAVE/WETH220.040677
LINK/USDC226.700701
LINK/WETH220.004149
UNI/USDC116.098634
UNI/WETH220.003329
WETH/USDC221821.370869
\n", - "
" - ], - "text/plain": [ - " n n_rev price\n", - "pair \n", - "AAVE/USDC 1 1 80.826393\n", - "AAVE/WETH 2 2 0.040677\n", - "LINK/USDC 2 2 6.700701\n", - "LINK/WETH 2 2 0.004149\n", - "UNI/USDC 1 1 6.098634\n", - "UNI/WETH 2 2 0.003329\n", - "WETH/USDC 2 2 1821.370869" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = AG.edgedf(consolidated=True)\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "74fa4d4f-e077-4f54-8719-d91ce21bff3f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "71.22 LINK -0.3 WETH 170\n", - "-0.28 LINK 1.99 USDC 171\n", - "3.4 AAVE -0.14 WETH 180\n", - "-10.82 UNI 0.04 WETH 305\n", - "755278.31 USDC -393.48 WETH 309\n", - "-65.01 LINK 0.27 WETH 337\n", - "-5.93 LINK 46.42 USDC 339\n", - "-3.38 AAVE 0.13 WETH 349\n", - "-0.02 AAVE 1.41 USDC 351\n", - "60.27 UNI -0.2 WETH 599\n", - "-49.45 UNI 316.84 USDC 601\n", - "1507698.66 USDC -786.1 WETH 606\n" - ] - } - ], - "source": [ - "dx,dy = ((71.22, -0.28, 3.4, -10.82, 755278.31, -65.01, -5.93, -3.38, -0.02, 60.27, -49.45, 1507698.66, -2263343.63), \n", - " (-0.3, 1.99, -0.14, 0.04, -393.48, 0.27, 46.42, 0.13, 1.41, -0.2, 316.84, -786.1, 833.78))\n", - "AG2 = ag.ArbGraph()\n", - "for cpc, dx_, dy_ in zip(CC, dx, dy):\n", - " print(dx_, cpc.tknx, dy_, cpc.tkny, cpc.cid)\n", - " AG2.add_edge_dxdy(cpc.tknx, dx_, cpc.tkny, dy_, uid=cpc.cid)\n", - " #print(\"---\")" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "519e81fb-180b-4003-9104-09df28bda6a4", - "metadata": {}, - "outputs": [], - "source": [ - "#_=AG2.plot()\n", - "assert AG2.len() == (12,5)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "7657cc5e-b0fc-459c-8a10-bbe1f9960ecb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0 1 0 0 0]\n", - " [1 0 0 1 1]\n", - " [1 1 0 1 1]\n", - " [0 1 0 0 0]\n", - " [0 1 0 0 0]]\n" - ] - } - ], - "source": [ - "assert np.all(AG2.A.toarray() == np.array(\n", - " [[0, 1, 0, 0, 0],\n", - " [1, 0, 0, 1, 1],\n", - " [1, 1, 0, 1, 1],\n", - " [0, 1, 0, 0, 0],\n", - " [0, 1, 0, 0, 0]]))\n", - "print(AG2.A.toarray())" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "5fe9565c-71a6-4efd-a690-44341813c423", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'len': 2,\n", - " 'edges': ({'node_in': {'tkn': 'USDC', 'ix': 2},\n", - " 'amount_in': 755278.31,\n", - " 'node_out': {'tkn': 'WETH', 'ix': 1},\n", - " 'amount_out': 393.48,\n", - " 'ix': 4,\n", - " 'inverse': False,\n", - " 'uid': '309'},\n", - " {'node_in': {'tkn': 'USDC', 'ix': 2},\n", - " 'amount_in': 1507698.66,\n", - " 'node_out': {'tkn': 'WETH', 'ix': 1},\n", - " 'amount_out': 786.1,\n", - " 'ix': 11,\n", - " 'inverse': False,\n", - " 'uid': '606'}),\n", - " 'amount_in': {'amount': 2262976.9699999997, 'node': {'tkn': 'USDC', 'ix': 2}},\n", - " 'amount_in_remaining': {'amount': 2262976.9699999997,\n", - " 'node': {'tkn': 'USDC', 'ix': 2}},\n", - " 'amount_out': {'amount': 1179.58, 'node': {'tkn': 'WETH', 'ix': 1}},\n", - " 'price': 0.0005212514381001412,\n", - " 'utilization': 0.0,\n", - " 'amounts_in': (755278.31, 1507698.66),\n", - " 'amounts_in_remaining': (755278.31, 1507698.66),\n", - " 'amounts_out': (393.48, 786.1),\n", - " 'prices': (0.0005209735203437789, 0.0005213906603856769),\n", - " 'utilizations': (0.0, 0.0)}" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert AG2.edge_statistics(\"WETH\", \"USDC\", bothways=False) is None\n", - "assert len(AG2.edge_statistics(\"WETH\", \"USDC\", bothways=True)) == 2\n", - "assert AG2.edge_statistics(\"WETH\", \"USDC\", bothways=True)[1].asdict()[\"amounts_in_remaining\"] == (755278.31, 1507698.66)\n", - "AG2.edge_statistics(\"WETH\", \"USDC\", bothways=True)[1].asdict()" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "80e653d3-8c77-4085-b8f7-c74ec83de173", - "metadata": {}, - "outputs": [], - "source": [ - "assert AG2.filter_edges(\"WETH\", \"USDC\") == []\n", - "assert AG2.filter_edges(\"WETH\", \"USDC\", bothways=True)[0].amount_in == 755278.31\n", - "assert AG2.filter_edges(\"WETH\", \"USDC\", bothways=True) == AG2.filter_edges(\"USDC\", \"WETH\")\n", - "assert AG2.filter_edges(pair=\"WETH/USDC\", bothways=False) == []\n", - "assert AG2.filter_edges(pair=\"WETH/USDC\") == AG2.filter_edges(\"WETH\", \"USDC\", bothways=True)\n", - "assert AG2.filter_edges == AG2.fe\n", - "assert AG2.fep(\"WETH/USDC\") == AG2.filter_edges(pair=\"WETH/USDC\")\n", - "assert AG2.fep(\"WETH/USDC\", bothways=False) == AG2.filter_edges(pair=\"WETH/USDC\", bothways=False)\n", - "assert tuple(AG2.edgedf(consolidated=True, resetindex=False).iloc[0]) == (1.41, 0.02)\n", - "assert len(AG2.edgedf(consolidated=False)) == len(AG2)" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "18c718aa-6539-4e32-b2ac-cce270a48356", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pairtkn_intkn_outamount_inamount_out
uid
170LINK/WETHLINKWETH71.220.30
171LINK/USDCUSDCLINK1.990.28
180AAVE/WETHAAVEWETH3.400.14
305UNI/WETHWETHUNI0.0410.82
309WETH/USDCUSDCWETH755278.31393.48
337LINK/WETHWETHLINK0.2765.01
339LINK/USDCUSDCLINK46.425.93
349AAVE/WETHWETHAAVE0.133.38
351AAVE/USDCUSDCAAVE1.410.02
599UNI/WETHUNIWETH60.270.20
601UNI/USDCUSDCUNI316.8449.45
606WETH/USDCUSDCWETH1507698.66786.10
\n", - "
" - ], - "text/plain": [ - " pair tkn_in tkn_out amount_in amount_out\n", - "uid \n", - "170 LINK/WETH LINK WETH 71.22 0.30\n", - "171 LINK/USDC USDC LINK 1.99 0.28\n", - "180 AAVE/WETH AAVE WETH 3.40 0.14\n", - "305 UNI/WETH WETH UNI 0.04 10.82\n", - "309 WETH/USDC USDC WETH 755278.31 393.48\n", - "337 LINK/WETH WETH LINK 0.27 65.01\n", - "339 LINK/USDC USDC LINK 46.42 5.93\n", - "349 AAVE/WETH WETH AAVE 0.13 3.38\n", - "351 AAVE/USDC USDC AAVE 1.41 0.02\n", - "599 UNI/WETH UNI WETH 60.27 0.20\n", - "601 UNI/USDC USDC UNI 316.84 49.45\n", - "606 WETH/USDC USDC WETH 1507698.66 786.10" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert len(AG2.edgedf(consolidated=False)) == 12\n", - "AG2.edgedf(consolidated=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "1f40c9ae-767e-4b68-9cf1-c5cd32fc7d35", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
amount_inamount_out
pairtkn_intkn_out
AAVE/USDCUSDCAAVE1.410.02
AAVE/WETHAAVEWETH3.400.14
WETHAAVE0.133.38
LINK/USDCUSDCLINK48.416.21
LINK/WETHLINKWETH71.220.30
WETHLINK0.2765.01
UNI/USDCUSDCUNI316.8449.45
UNI/WETHUNIWETH60.270.20
WETHUNI0.0410.82
WETH/USDCUSDCWETH2262976.971179.58
\n", - "
" - ], - "text/plain": [ - " amount_in amount_out\n", - "pair tkn_in tkn_out \n", - "AAVE/USDC USDC AAVE 1.41 0.02\n", - "AAVE/WETH AAVE WETH 3.40 0.14\n", - " WETH AAVE 0.13 3.38\n", - "LINK/USDC USDC LINK 48.41 6.21\n", - "LINK/WETH LINK WETH 71.22 0.30\n", - " WETH LINK 0.27 65.01\n", - "UNI/USDC USDC UNI 316.84 49.45\n", - "UNI/WETH UNI WETH 60.27 0.20\n", - " WETH UNI 0.04 10.82\n", - "WETH/USDC USDC WETH 2262976.97 1179.58" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert len(AG2.edgedf(consolidated=True, resetindex=False)) == 10\n", - "AG2.edgedf(consolidated=True, resetindex=False)" - ] - }, - { - "cell_type": "markdown", - "id": "b73a9fe5-f486-4cae-b86d-c59a8139451f", - "metadata": {}, - "source": [ - "## Amount algebra" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "818d1633-8b04-459d-9c1b-9756c9b1b0b3", - "metadata": {}, - "outputs": [], - "source": [ - "A = ag.Amount\n", - "nodes = lambda: ag.create_node_list(\"ETH, USDC\")\n", - "ETH, USDC = nodes()" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "e1666418-0dd0-4b22-8470-ca881bb2a291", - "metadata": {}, - "outputs": [], - "source": [ - "ae1, ae2, au1 = A(1, ETH), A(2, ETH), A(1, USDC)" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "de56707f-35c3-402e-9b29-b651475380d3", - "metadata": {}, - "outputs": [], - "source": [ - "assert ae1 + ae2 == 3*ae1\n", - "assert ae2 - ae1 == ae1\n", - "assert -ae1 + ae2 == ae1\n", - "assert 2*ae1 == ae2\n", - "assert ae1*2 == ae2\n", - "assert ae1/2 +ae1/2 == ae1\n", - "assert round(ae1/9,2) == round(1/9,2)*ae1\n", - "assert round(ae1/9,4) == round(1/9,4)*ae1\n", - "assert m.floor(ae1/9) == m.floor(1/9)*ae1\n", - "assert m.ceil(ae1/9) == m.ceil(1/9)*ae1\n", - "assert (ae1 + 2*ae1)/ae1 == 3" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "274aea35-d311-4995-8878-fc8cf447452d", - "metadata": {}, - "outputs": [], - "source": [ - "assert raises (lambda: ae1 + 1)\n", - "assert raises (lambda: ae1 - 1)\n", - "assert raises (lambda: 1 + ae1)\n", - "assert raises (lambda: 1 - ae1)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "b325f79e-f43a-49d5-b74f-7fbaf4cac6ca", - "metadata": {}, - "outputs": [], - "source": [ - "assert 2*ae1 > ae1\n", - "assert 2*ae1 >= ae1\n", - "assert .2*ae1 < ae1\n", - "assert .2*ae1 <= ae1\n", - "assert ae1 <= ae1\n", - "assert ae1 >= ae1\n", - "assert not ae1 < ae1\n", - "assert not ae1 > ae1" - ] - }, - { - "cell_type": "markdown", - "id": "6a863003-9227-4fa8-953f-d338a18605c8", - "metadata": {}, - "source": [ - "## Specific Arb examples" - ] - }, - { - "cell_type": "markdown", - "id": "0f849ba9-36bf-4f17-9559-7b1a38f48e2d", - "metadata": {}, - "source": [ - "### USDC/ETH" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "91c93306-b019-468a-ba5a-c345490f362c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(Cycle(data=[ETH(0), USDC(1)], uid=0),)\n" - ] - } - ], - "source": [ - "AG = ag.ArbGraph()\n", - "AG.add_edge(\"ETH\", 1, \"USDC\", 2000)\n", - "AG.add_edge(\"USDC\", 1800, \"ETH\", 1, inverse=True)\n", - "G = AG.as_graph()\n", - "print(AG.cycles())\n", - "#_=AG.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "0b259f89-6537-4b6e-bc37-853f84c6fafd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "===cycle [0]: ETH->USDC->...===\n", - "(ETH(0), USDC(1))\n", - "(USDC(1), ETH(0))\n" - ] - } - ], - "source": [ - "for C in AG.cycles():\n", - " print(f\"==={C}===\")\n", - " for c in C.pairs(start_val=AG.n(\"ETH\")): \n", - " print(c)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "0decf9b2-28cb-4327-9821-ff8b6b08db33", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((USDC(1), ETH(0)),\n", - " [Edge(node_in=USDC(1), amount_in=1800, node_out=ETH(0), amount_out=1, ix=1, inverse=True, uid=None)])" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c, AG.filter_edges(*c)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "ac6983c1-c55c-4666-aed5-0acd26d0819e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0, 1],\n", - " [1, 0]])" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AG.A.toarray()" - ] - }, - { - "cell_type": "markdown", - "id": "926914d2-d515-412c-ab15-be5cc577ce7d", - "metadata": {}, - "source": [ - "### USDC/LINK to ETH (oneway)" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "f0743e1d-8709-41f9-8ae7-dd13cdfe40a0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(Cycle(data=[USDC(0), LINK(2)], uid=0),)\n" - ] - } - ], - "source": [ - "AG = ag.ArbGraph()\n", - "AG.add_edge(\"USDC\", 100, \"ETH\", 100/2000)\n", - "AG.add_edge(\"LINK\", 100, \"USDC\", 1000)\n", - "AG.add_edge(\"USDC\", 900, \"LINK\", 100, inverse=True)\n", - "G = AG.as_graph()\n", - "print(AG.cycles())\n", - "#_=AG.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "3e8b2ed6", - "metadata": {}, - "source": [ - "_=AG.duplicate().plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "id": "69797a28-a7e7-43aa-8442-c164c8bedfed", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "===cycle [0]: USDC->LINK->...===\n", - "(USDC(0), LINK(2))\n", - "(LINK(2), USDC(0))\n" - ] - } - ], - "source": [ - "for C in AG.cycles():\n", - " print(f\"==={C}===\")\n", - " for c in C.pairs(start_val=AG.n(\"USDC\")): \n", - " print(c)" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "5958e342-d8d5-4a19-8692-4cf8f3c90ca1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((LINK(2), USDC(0)),\n", - " [Edge(node_in=LINK(2), amount_in=100, node_out=USDC(0), amount_out=1000, ix=1, inverse=False, uid=None)])" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c, AG.filter_edges(*c)" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "5aa7ed65-ce02-4680-9508-cc793ec287bf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0, 1, 1],\n", - " [0, 0, 0],\n", - " [1, 0, 0]])" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AG.A.toarray()" - ] - }, - { - "cell_type": "markdown", - "id": "d118509e-94d0-4f1a-9b19-772a80b60966", - "metadata": {}, - "source": [ - "### USDD, LINK, ETH cycle" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "2f5230ec-578a-4c93-bf17-daaa9468d9e2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(Cycle(data=[ETH(0), USDC(1), LINK(2)], uid=0),)\n" - ] - } - ], - "source": [ - "AG = ag.ArbGraph()\n", - "AG.add_edge(\"ETH\", 1, \"USDC\", 2000)\n", - "AG.add_edge(\"USDC\", 1500, \"LINK\", 200, inverse=True)\n", - "AG.add_edge(\"LINK\", 200, \"ETH\", 1, inverse=True)\n", - "G = AG.as_graph()\n", - "print(AG.cycles())\n", - "#_=AG.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "12706bbd-0e07-4e2f-a54e-51bf60956311", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "===cycle [0]: ETH->USDC->LINK->...===\n", - "(USDC(1), LINK(2))\n", - "(LINK(2), ETH(0))\n", - "(ETH(0), USDC(1))\n" - ] - } - ], - "source": [ - "for C in AG.cycles():\n", - " print(f\"==={C}===\")\n", - " for c in C.pairs(start_val=AG.n(\"USDC\")): \n", - " print(c)" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "af355336-48d0-481b-bcef-d49692a5e275", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((ETH(0), USDC(1)),\n", - " [Edge(node_in=ETH(0), amount_in=1, node_out=USDC(1), amount_out=2000, ix=0, inverse=False, uid=None)])" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c, AG.filter_edges(*c)" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "0ad02c8f-c4b1-4eb8-a84e-3071e3e40434", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0, 1, 0],\n", - " [0, 0, 1],\n", - " [1, 0, 0]])" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AG.A.toarray()" - ] - }, - { - "cell_type": "markdown", - "id": "22382914-2714-4c8c-a234-739c0b2c88da", - "metadata": {}, - "source": [ - "### USDD, LINK, ETH cycle plus ETH/USDC" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "3aa752af-03db-4d32-816e-199fe861e1d2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(Cycle(data=[ETH(0), USDC(1), LINK(2)], uid=0), Cycle(data=[ETH(0), USDC(1)], uid=1))\n" - ] - } - ], - "source": [ - "AG = ag.ArbGraph()\n", - "AG.add_edge(\"ETH\", 1, \"USDC\", 2000)\n", - "AG.add_edge(\"ETH\", 1, \"USDC\", 2000)\n", - "AG.add_edge(\"USDC\", 1500, \"LINK\", 200, inverse=True)\n", - "AG.add_edge(\"LINK\", 200, \"ETH\", 1, inverse=True)\n", - "AG.add_edge(\"USDC\", 1800, \"ETH\", 1, inverse=True)\n", - "G = AG.as_graph()\n", - "print(AG.cycles())\n", - "#_=AG.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "b8008e76-42c0-4bea-ab27-c5f76622837f", - "metadata": {}, - "outputs": [], - "source": [ - "#_=AG.duplicate().plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "d788ef90-4537-41f7-beec-a3c8edb63589", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Edge(node_in=ETH(0), amount_in=1, node_out=USDC(1), amount_out=2000, ix=0, inverse=False, uid=None),\n", - " Edge(node_in=ETH(0), amount_in=1, node_out=USDC(1), amount_out=2000, ix=1, inverse=False, uid=None),\n", - " Edge(node_in=USDC(1), amount_in=1500, node_out=LINK(2), amount_out=200, ix=2, inverse=True, uid=None),\n", - " Edge(node_in=LINK(2), amount_in=200, node_out=ETH(0), amount_out=1, ix=3, inverse=True, uid=None),\n", - " Edge(node_in=USDC(1), amount_in=1800, node_out=ETH(0), amount_out=1, ix=4, inverse=True, uid=None)]" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AG.edges" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "id": "150bc2d2-91cb-40de-bb5c-c99aca5750c0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(Edge(node_in=ETH(0), amount_in=2, node_out=USDC(1), amount_out=4000, ix=0, inverse=False, uid=None),\n", - " Edge(node_in=USDC(1), amount_in=1800, node_out=ETH(0), amount_out=1, ix=1, inverse=True, uid=None),\n", - " Edge(node_in=USDC(1), amount_in=1500, node_out=LINK(2), amount_out=200, ix=2, inverse=True, uid=None),\n", - " Edge(node_in=LINK(2), amount_in=200, node_out=ETH(0), amount_out=1, ix=3, inverse=True, uid=None))" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AG.duplicate().edges" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "b739e7f3-2fb7-4def-901b-37aea603632d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0, 1, 0],\n", - " [1, 0, 1],\n", - " [1, 0, 0]])" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AG.A.toarray()" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "75a82201-5489-489e-aadd-49d5b2f002a8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "===cycle [0]: ETH->USDC->LINK->...===\n", - "(ETH(0), USDC(1))\n", - "(USDC(1), LINK(2))\n", - "(LINK(2), ETH(0))\n", - "===cycle [1]: ETH->USDC->...===\n", - "(ETH(0), USDC(1))\n", - "(USDC(1), ETH(0))\n" - ] - } - ], - "source": [ - "for C in AG.cycles():\n", - " print(f\"==={C}===\")\n", - " for c in C.pairs(start_val=AG.n(\"ETH\")): \n", - " print(c)" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "06b66d5c-a52d-40ee-ad3f-d0facbd60d3d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Cycle(data=[ETH(0), USDC(1)], uid=1)" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cycle = AG.cycles()[1]\n", - "cycle" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "548a9736-819c-4adc-9d6c-966462b6bcec", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(ETH(0), USDC(1)): 2 edges, capacity 2 ETH -> 4000 USDC, actual 2 -> 4000.0 [1.0x]\n", - "(USDC(1), LINK(2)): 1 edges, capacity 1500 USDC -> 200 LINK, actual 1500 -> 200.0 [0.375x]\n", - "(LINK(2), ETH(0)): 1 edges, capacity 200 LINK -> 1 ETH, actual 200.0 -> 1.0 [0.375x]\n", - "Profit: 0.25 ETH [in: 0.75; out: 1.0]\n", - "RACResult(profit: 0.2 [ETH], in: 0.8, rpcs: 8.3%, ppcs: 0.1, len: 3, uid: 0)\n", - "---\n", - "(ETH(0), USDC(1)): 2 edges, capacity 2 ETH -> 4000 USDC, actual 2 -> 4000.0 [1.0x]\n", - "(USDC(1), ETH(0)): 1 edges, capacity 1800 USDC -> 1 ETH, actual 1800 -> 1.0 [0.45x]\n", - "Profit: 0.09999999999999998 ETH [in: 0.9; out: 1.0]\n", - "RACResult(profit: 0.1 [ETH], in: 0.9, rpcs: 5.0%, ppcs: 0.0, len: 2, uid: 1)\n", - "---\n" - ] - } - ], - "source": [ - "for cycle in AG.cycles():\n", - " result = AG.run_arbitrage_cycle(cycle=cycle, verbose=True)\n", - " print(result)\n", - " print(\"---\")" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "6b182784-b8eb-433f-867a-4e38d4bc5839", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'cannot get price on amount-type graphs'" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert raises(AG.price, AG.nodes[0], AG.nodes[1])\n", - "raises(AG.price, AG.nodes[0], AG.nodes[1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc5a98c8-5750-4a9c-9afd-38a0d36ff213", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6ac7f39d-b457-46cc-a5d6-5f73d00c356e", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fdc42a32-dd22-46f8-ac97-dbe59cebce18", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e84b58c-c488-49a4-9d3e-27111baeddfe", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/NBTest/NBTest_004_GraphCode.py b/resources/NBTest/NBTest_004_GraphCode.py deleted file mode 100644 index 6a1887e44..000000000 --- a/resources/NBTest/NBTest_004_GraphCode.py +++ /dev/null @@ -1,804 +0,0 @@ -# -*- coding: utf-8 -*- -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -try: - import fastlane_bot.tools.arbgraphs as ag - from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, CPCContainer - from fastlane_bot.testing import * - -except: - import tools.arbgraphs as ag - from tools.cpc import ConstantProductCurve as CPC, CPCContainer - from tools.testing import * - -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(ag.ArbGraph)) - -#plt.style.use('seaborn-dark') -plt.rcParams['figure.figsize'] = [12,6] -# from fastlane_bot import __VERSION__ -# require("2.0", __VERSION__) -# - - -# # Graph Code [NBTest065] - -# ## ArbGraphs test and demo - -nodes = lambda: ag.create_node_list("ETH, USDC, WBTC, BNT") -assert [str(n) for n in nodes()] == ['ETH(0)', 'USDC(1)', 'WBTC(2)', 'BNT(3)'] -nodes() - -AG = ag.ArbGraph(nodes=nodes()) -N = AG.node_by_tkn -assert str(N("ETH")) == "ETH(0)" -assert str(N("BNT")) == "BNT(3)" -assert str(AG.node_by_ix(1)) == "USDC(1)" -assert str(AG.node_by_tkn("USDC")) == "USDC(1)" -AG - -assert str(N("ETH")) == "ETH(0)" - -edge = ag.Edge(N("ETH"), 1, N("USDC"), 2000) -edge1 = ag.Edge(N("ETH"), 1, N("USDC"), 2000, inverse=True, ix=10) -assert (edge.pair(), edge.price(), edge.convention()) == ('ETH/USDC', 2000.0, 'USDC per ETH') -assert (edge1.pair(), edge1.price(), edge1.convention()) == ('USDC/ETH', 0.0005, 'ETH per USDC') -edge, str(edge), str(edge1) - -assert (edge+0).asdict() == edge.asdict() -assert (edge+0) != edge # == means objects are the same -assert not edge+0 is edge -assert (2*edge).asdict() == (edge*2).asdict() -assert (edge + 2*edge).asdict() == (3*edge).asdict() -assert sum([edge,edge,edge]).asdict() == (3*edge).asdict() - -(edge+0).asdict() - -# ## Paths and cycles - -C = ag.Cycle([1,2,3,4,5]) -assert len(C) == 5 -assert [x for x in C.items()] == [1, 2, 3, 4, 5, 1] -assert [x for x in C.items(start_ix=3)] == [4, 5, 1, 2, 3, 4] -assert [x for x in C.items(start_val=3)] == [3, 4, 5, 1, 2, 3] -assert [p for p in C.pairs()] == [(1, 2), (2, 3), (3, 4), (4, 5), (5, 1)] - -c1 = ag.Cycle([1,2,3,4,5,6], "c1") -assert ag.Cycle([8,9]).is_subcycle_of(c1) == False -assert ag.Cycle([1,5,6]).is_subcycle_of(c1) == True -assert ag.Cycle([1,6,5]).is_subcycle_of(c1) == False -assert c1.filter_subcycles([ag.Cycle([8,9]), ag.Cycle([1,5,6]), ag.Cycle([1,6,5])]) == (ag.Cycle([1, 5, 6]),) -assert c1.filter_subcycles(ag.Cycle([1,5,6])) == (ag.Cycle([1, 5, 6]),) -assert str(c1) == 'cycle [c1]: 1 -> 2 -> 3 -> 4 -> 5 -> 6 ->...' - -assert c1.asdict() == {'data': [1, 2, 3, 4, 5, 6], 'uid': 'c1', 'graph': None} -assert c1.astuple() == ([1, 2, 3, 4, 5, 6], 'c1', None) -assert (c1.asdf().set_index("uid")["data"] == c1.asdf(index="uid")["data"]).iloc[0] -assert list(c1.asdf(exclude=["data"]).columns) == ['uid', 'graph'] -assert list(c1.asdf(include=["data", "graph"], exclude=["graph"]).columns) == ['data'] - -import types -nodes = ag.create_node_list("ETH, USDC, WBTC, BNT") -c2 = ag.Cycle(nodes, "c2") -assert c2.uid == "c2" -assert str(c2) == 'cycle [c2]: ETH->USDC->WBTC->BNT->...' -print(nodes) -print(c2) -gc2 = (c for c in c2.items()) -assert isinstance(gc2, types.GeneratorType) -tc2 = tuple(gc2) -assert str(tc2) == "(ETH(0), USDC(1), WBTC(2), BNT(3), ETH(0))" -assert tuple(gc2) == tuple() # generator spent -pc2 = (p for p in c2.pairs()) -assert isinstance(pc2, types.GeneratorType) -tpc2 = tuple(pc2) -assert len(tpc2) == 4 -assert str(tpc2[0]) == '(ETH(0), USDC(1))' -assert str(tpc2[-1]) == '(BNT(3), ETH(0))' -assert c2.pairs_s() == ['ETH/USDC', 'USDC/WBTC', 'WBTC/BNT', 'BNT/ETH'] - -p1 = ag.Path([1,2,3,4,5,6], "p1") -assert p1.uid == "p1" -assert (str(p1)).strip() == 'path [p1]: 1 -> 2 -> 3 -> 4 -> 5 -> 6' -gp1 = (p for p in p1.items()) -assert isinstance(gp1, types.GeneratorType) -tp1 = tuple(gp1) -assert tp1 == (1, 2, 3, 4, 5, 6) - -nodes = ag.create_node_list("ETH, USDC, WBTC, BNT") -p2 = ag.Path(nodes, "p2") -assert p2.uid == "p2" -assert str(p2) == 'path [p2]: ETH->USDC->WBTC->BNT' -gp2 = (c for c in p2.items()) -assert isinstance(gp2, types.GeneratorType) -tp2 = tuple(gp2) -assert str(tp2) == "(ETH(0), USDC(1), WBTC(2), BNT(3))" -assert tuple(gp2) == tuple() # generator spent -pp2 = (p for p in p2.pairs()) -assert isinstance(pp2, types.GeneratorType) -tpp2 = tuple(pp2) -assert len(tpp2) == 3 -assert str(tpp2[0]) == '(ETH(0), USDC(1))' -assert str(tpp2[-1]) == '(WBTC(2), BNT(3))' -assert p2.pairs_s() == ['ETH/USDC', 'USDC/WBTC', 'WBTC/BNT'] - -# ## Arbgraph transport test and demo - -n = ag.Node("ETH") -assert isinstance(n.state, n.State) -assert n.state == n.State(amount = 0) - -try: - ag.Edge("ETH", 1, "USDC", 2000) - raise -except: - pass - -ETH = ag.Node("ETH") -USDC = ag.Node("USDC") -assert ETH != n # nodes are only equal if they are the same object! -assert ETH.asdict() == n.asdict() -edge = ag.Edge(ETH, 1, USDC, 2000) -edge2 = ag.Edge(ETH, 1, USDC, 2000) -edge3 = ag.Edge(ETH, 2, USDC, 3500) -assert (edge == edge2) == False -assert edge != ag.Edge(ETH, 1, USDC, 2000) -assert edge.asdict() == ag.Edge(ETH, 1, USDC, 2000).asdict() -assert edge.node_in == ETH -assert edge.node_out == USDC -assert edge.amount_in == 1 -assert edge.amount_out == 2000 -assert edge.state == ag.Edge.State(amount_in_remaining=1) - -ETH.reset_state() -USDC.reset_state() -edge.reset_state() -ETH.state.amount_.set(1) -assert ETH.state.amount == 1 -edge.transport(1, record=True) -assert ETH.state.amount == 0 -assert USDC.state.amount == 2000 -assert edge.state.amount_in_remaining == 0 - -ETH.reset_state() -USDC.reset_state() -edge.reset_state() -ETH.state.amount_.set(1) -edge.transport(0.25, record=True) -assert ETH.state.amount == 0.75 -assert USDC.state.amount == 500 -assert edge.state.amount_in_remaining == 0.75 -edge.transport(0.25, record=True) -assert ETH.state.amount == 0.5 -assert USDC.state.amount == 1000 -assert edge.state.amount_in_remaining == 0.50 - -ETH.reset_state() -USDC.reset_state() -edge.reset_state() -ETH.state.amount = 1 -try: - edge.transport(2, record=True) -except Exception as e: - print(e) - -ETH.reset_state() -USDC.reset_state() -edge.reset_state() -ETH.state.amount = 0.5 -try: - edge.transport(1, record=True) -except Exception as e: - print(e) - -ETH.reset_state() -USDC.reset_state() -edge.reset_state() -ETH.state.amount = 2 -edge.transport(0.5, record=True) -try: - edge.transport(1, record=True) -except Exception as e: - print(e) - -ETH.state.amount = 10 -edge.state.amount_in_remaining = 10 -AG = ag.ArbGraph(nodes=[ETH, USDC], edges=[edge, edge2, edge3]) -assert AG.nodes == [ETH, USDC] -assert AG.edges == [edge, edge2, edge3] -assert AG.nodes[0].state.amount == 10 -assert AG.edges[0].state.amount_in_remaining == 10 -AG.reset_state() -assert AG.nodes[0].state.amount == 0 -assert AG.edges[0].state.amount_in_remaining == 1 -assert AG.state.nodes[0] == ETH.state -assert AG.state.edges[0] == edge.state - -assert AG.node_by_tkn("ETH") is ETH -assert AG.node_by_tkn(ETH) is ETH -try: - AG.node_by_tkn(ag.Node("ETH")) - raise -except Exception as e: - print(e) - -AG.reset_state() -ETH.state.amount = 4 -r = AG.transport(2, "ETH", "USDC", record=True) -assert ETH.state.amount == 2 -assert r.amount_in.amount == 2 -assert r.amount_in.tkn == "ETH" -capacity_in = sum([e_.amount_in for e_ in r.edges]) -assert capacity_in == 4 -capacity_out = sum([e_.amount_out for e_ in r.edges]) -assert capacity_out == 7500 -assert r.amount_out.amount == r.amount_in.amount * capacity_out / capacity_in -assert sum(r.amounts_in) == r.amount_in.amount -assert sum(r.amounts_out) == r.amount_out.amount -assert AG.has_capacity("ETH", "USDC") -assert AG.has_capacity() -AG.transport(2, "ETH", "USDC", record=True) -assert AG.has_capacity() == False -r - -rs = AG.edge_statistics(edges=r.edges) -assert rs.len == 3 -assert rs.edges is r.edges -assert rs.amounts_in == (1, 1, 2) -assert rs.amounts_in_remaining == (0.0, 0.0, 0.0) -assert rs.amounts_out == (2000, 2000, 3500) -assert rs.prices == (2000.0, 2000.0, 1750.0) -assert rs.utilizations == (1.0, 1.0, 1.0) -assert rs.amount_in.amount == 4 -assert rs.amount_in_remaining.amount == 0.0 -assert rs.amount_out.amount == 7500 -assert rs.amount_in.tkn == "ETH" -assert rs.amount_in_remaining.tkn == "ETH" -assert rs.amount_out.tkn == "USDC" -assert rs.utilization == 1.0 -assert rs.price == 1875.0 -rs - -rns = AG.node_statistics("ETH") -assert len(rns.edges_out) == 3 -assert len(rns.edges_in) == 0 -assert rns.amount_in.amount == 0 -assert rns.amount_out.amount == 4 -assert rns.amount_out_remaining.amount == 0 -assert rns.nodes_in==set() -assert rns.nodes_out=={"USDC"} -rns - -rns2 = AG.node_statistics("USDC") -assert len(rns2.edges_out) == 0 -assert len(rns2.edges_in) == 3 -assert rns2.amount_in.amount == 7500 -assert rns2.amount_out.amount == 0 -assert rns2.amount_out_remaining.amount == 0 -assert rns2.nodes_in==set(["ETH",]) -assert rns2.nodes_out==set() -rns2 - - -# ## Arbgraph transport test and demo 2 - -@ag.dataclass -class MyState(): - myval_: ag.TrackedStateFloat = ag.field(default_factory=ag.TrackedStateFloat, init=False) - myval: ag.InitVar=None - - def __post_init__(self, myval): - self.myval = myval - - @property - def myval(self): - return self.myval_.value - - @myval.setter - def myval(self, value): - self.myval_.set(value) - - -mystate = MyState(0) -mystate.myval_.set(10) -assert mystate.myval == 10 -mystate.myval += 5 -assert mystate.myval == 15 -mystate.myval -= 4 -assert mystate.myval == 11 -assert mystate.myval_.history == [0, 0, 10, 15, 11] - -mystate = MyState(10) -assert mystate.myval == 10 -assert mystate.myval_.history == [0,10] -mystate.myval = 20 -assert mystate.myval == 20 -assert mystate.myval_.history == [0,10,20] -mystate.myval += 5 -assert mystate.myval == 25 -mystate.myval -= 4 -assert mystate.myval == 21 -assert mystate.myval_.history == [0,10,20,25,21] -assert mystate.myval_.reset(42) -assert mystate.myval == 42 -assert mystate.myval_.history == [42] - -n = ag.Node("MEH") -n.state.amount = 10 -n.state.amount += 5 -n.state.amount -= 4 -assert n.state.amount == 11 -assert n.state.amount_.history == [0, 10, 15, 11] -n.reset_state() -assert n.state.amount_.history == [0] - -nodes = ag.Node.create_node_list("USDC, LINK, ETH, WBTC") -assert len(nodes)==4 -assert nodes[0].tkn == "USDC" -AG = ag.ArbGraph(nodes) -AG.add_edge("USDC", 10000, "ETH", 5) -AG.add_edge_obj(AG.edges[-1].R()) -AG.add_edge("USDC", 10000, "WBTC", 1) -AG.add_edge_obj(AG.edges[-1].R()) -AG.add_edge("USDC", 10000, "LINK", 1000) -AG.add_edge_obj(AG.edges[-1].R()) -AG.add_edge("LINK", 1000, "ETH", 5) -AG.add_edge_obj(AG.edges[-1].R()) -AG.add_edge("ETH", 5, "WBTC", 1) -AG.add_edge_obj(AG.edges[-1].R()) -assert len(AG.edges)==10 -assert len(AG.cycles())==11 -ns = AG.node_statistics("USDC") -assert ns.amount_in.amount == 30000 -assert ns.amount_out.amount == 30000 -assert ns.amount_out_remaining == ns.amount_out -assert ns.nodes_out==set(['WBTC', 'ETH', 'LINK']) -assert ns.nodes_in==set(['WBTC', 'ETH', 'LINK']) -#_=AG.plot() - -# ## Transport 3 and prices - -AG = ag.ArbGraph() -prices = dict(USDC=1, LINK=5, AAVE=100, WETH=2000, BTC=10000) -for t1,p1 in prices.items(): - for t2,p2 in prices.items(): - if t1 2000 USDC(1)' - -assert raises (lambda: e1+e3) -assert raises (lambda: -2*e1) -assert raises (lambda: e3*(-2)) -try: - e1 += e3 - raise -except ValueError as e: - pass - -assert not raises (lambda: e4+e5) -assert not raises (lambda: 2*e4) -assert not raises (lambda: e4*2) -e4 += e5 - -assert e6.amount_in == 1 -assert e1.transport() == e6.transport() -assert e1.transport(amount_in=1e6) == 1e6*e1.transport() - -AG = ag.ArbGraph(nodes = [ETH, USDC]) -assert AG.edgetype is None -AG.add_edge_obj(e1) -assert AG.edgetype == AG.EDGE_CONNECTION -assert AG.edgetype == e1.EDGE_CONNECTION -AG.add_edge_obj(e2) -assert raises(AG.add_edge_obj, e4) -assert AG.edgetype == e1.EDGE_CONNECTION - -AG = ag.ArbGraph(nodes = [ETH, USDC]) -assert AG.edgetype is None -AG.add_edge_obj(e4) -assert AG.edgetype == AG.EDGE_AMOUNT -assert AG.edgetype == e1.EDGE_AMOUNT -AG.add_edge_obj(e5) -assert raises(AG.add_edge_obj, e1) -assert AG.edgetype == e1.EDGE_AMOUNT - -AG = ag.ArbGraph() -AG.add_edge_connectiontype(tkn_in="ETH", tkn_out="USDC", price=2000) -AG.add_edge_connectiontype(tkn_in="ETH", tkn_out="BTC", price=1/5) -AG.add_edge_connectiontype(tkn_in="BTC", tkn_out="USDC", price=10000) -assert AG.edgetype == AG.EDGE_CONNECTION -assert len(AG) == 6 -#_=AG.plot() - -AG = ag.ArbGraph() -AG.add_edge_connectiontype(tkn_in="ETH", tkn_out="USDC", price=2000, symmetric=False) -AG.add_edge_connectiontype(tkn_in="ETH", tkn_out="BTC", price=1/5, symmetric=False) -AG.add_edge_connectiontype(tkn_in="BTC", tkn_out="USDC", price=10000, symmetric=False) -assert AG.edgetype == AG.EDGE_CONNECTION -assert len(AG) == 3 -#_=AG.plot() - -AG = ag.ArbGraph() -assert raises (AG.add_edge_connectiontype, tkn_in="ETH", tkn_out="USDC", price=2000, price_outperin=2000) -assert raises (AG.add_edge_connectiontype, tkn_in="ETH", tkn_out="USDC", inverse = True, price_outperin=2000) -assert AG.add_edge_connectiontype == AG.add_edge_ct - -AG = ag.ArbGraph() -for i in range(5): - mul = 1+i/50 - AG.add_edge_ct(tkn_in="ETH", tkn_out="USDC", price=2000*mul) - AG.add_edge_ct(tkn_in="WBTC", tkn_out="USDC", price=10000*mul) - AG.add_edge_ct(tkn_in="ETH", tkn_out="WBTC", price=0.2/mul) -assert AG.len() == (2*3*5, 3) -assert len(AG.cycles()) == 5 -assert np.array_equal(AG.A.toarray(), np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]])) -print(AG.A) -AG2 = AG.duplicate() -assert AG2.len() == (6,3) -edges = AG.filter_edges("ETH", "USDC") -assert len(edges) == 5 -edges2 = AG2.filter_edges("ETH", "USDC") -assert len(edges2) == 1 -assert [e.p_outperin for e in edges] == [2000.0, 2040.0, 2080.0, 2120.0, 2160.0] -assert edges2[0].p_outperin == np.mean([e.p_outperin for e in edges]) - -# AttributeError: module 'scipy.sparse' has no attribute 'coo_array' -# -# I had this one before -- I believe this is a version issue; unfortunately I do not quite remember how I fixed it at the time - -# ## Interaction with CPC - -c1 = CPC.from_univ2(x_tknb=1, y_tknq=2000, pair="ETH/USDC", fee=0, cid="1", descr="UniV2") -c2 = CPC.from_univ2(x_tknb=1, y_tknq=10000, pair="WBTC/USDC", fee=0, cid="2", descr="UniV2") -c3 = CPC.from_univ2(x_tknb=1, y_tknq=5, pair="WBTC/ETH", fee=0, cid="3", descr="UniV2") -assert c1.p == 2000 -assert c2.p == 10000 -assert c3.p == 5 - -AG = ag.ArbGraph() -AG.add_edges_cpc(c1) -AG.add_edges_cpc(c2) -AG.add_edges_cpc(c3) -#_=AG.plot() - -AG = ag.ArbGraph() -AG.add_edges_cpc([c1, c2, c3]) -#_=AG.plot() - -AG = ag.ArbGraph() -AG.add_edges_cpc(c for c in [c1, c2, c3]) -#_=AG.plot() - -AG = ag.ArbGraph() -CC = CPCContainer([c1,c2,c3]) -AG.add_edges_cpc(CC) -#_=AG.plot() - -print(AG.A) - -AG.cycles() - -# ## With real data from CPC - -try: - df = pd.read_csv("_data/NBTEST_002_Curves.csv.gz") -except: - df = pd.read_csv("fastlane_bot/tests/_data/NBTEST_002_Curves.csv.gz") -CC0 = CPCContainer.from_df(df) -print("Num curves:", len(CC0)) -print("Num pairs:", len(CC0.pairs())) -print("Num tokens:", len(CC0.tokens())) -print(CC0.tokens_s()) - -AG0 = ag.ArbGraph().add_edges_cpc(CC0) -#AG0.plot() -assert AG0.len() == (918, 141) - -assert str(AG0.A)[:60] ==' (0, 1)\t1\n (1, 0)\t1\n (2, 3)\t1\n (2, 4)\t1\n (2, 5)\t1\n (2,' - -pairs = CC0.filter_pairs(bothin="WETH, USDC, UNI, AAVE, LINK") -CC = CC0.bypairs(pairs, ascc=True) -AG = ag.ArbGraph().add_edges_cpc(CC) -#AG.plot() -AG.len() == (24, 5) - -assert np.all(AG.A.toarray() == np.array( - [[0, 1, 1, 0, 0], - [1, 0, 1, 1, 1], - [1, 1, 0, 1, 1], - [0, 1, 1, 0, 0], - [0, 1, 1, 0, 0]])) - -assert raises(AG.edge_statistics,"WETH", "USDC") - -AG.edgedf(consolidated=False) - -df = AG.edgedf(consolidated=True) -df - -dx,dy = ((71.22, -0.28, 3.4, -10.82, 755278.31, -65.01, -5.93, -3.38, -0.02, 60.27, -49.45, 1507698.66, -2263343.63), - (-0.3, 1.99, -0.14, 0.04, -393.48, 0.27, 46.42, 0.13, 1.41, -0.2, 316.84, -786.1, 833.78)) -AG2 = ag.ArbGraph() -for cpc, dx_, dy_ in zip(CC, dx, dy): - print(dx_, cpc.tknx, dy_, cpc.tkny, cpc.cid) - AG2.add_edge_dxdy(cpc.tknx, dx_, cpc.tkny, dy_, uid=cpc.cid) - #print("---") - -#_=AG2.plot() -assert AG2.len() == (12,5) - -assert np.all(AG2.A.toarray() == np.array( - [[0, 1, 0, 0, 0], - [1, 0, 0, 1, 1], - [1, 1, 0, 1, 1], - [0, 1, 0, 0, 0], - [0, 1, 0, 0, 0]])) -print(AG2.A.toarray()) - -assert AG2.edge_statistics("WETH", "USDC", bothways=False) is None -assert len(AG2.edge_statistics("WETH", "USDC", bothways=True)) == 2 -assert AG2.edge_statistics("WETH", "USDC", bothways=True)[1].asdict()["amounts_in_remaining"] == (755278.31, 1507698.66) -AG2.edge_statistics("WETH", "USDC", bothways=True)[1].asdict() - -assert AG2.filter_edges("WETH", "USDC") == [] -assert AG2.filter_edges("WETH", "USDC", bothways=True)[0].amount_in == 755278.31 -assert AG2.filter_edges("WETH", "USDC", bothways=True) == AG2.filter_edges("USDC", "WETH") -assert AG2.filter_edges(pair="WETH/USDC", bothways=False) == [] -assert AG2.filter_edges(pair="WETH/USDC") == AG2.filter_edges("WETH", "USDC", bothways=True) -assert AG2.filter_edges == AG2.fe -assert AG2.fep("WETH/USDC") == AG2.filter_edges(pair="WETH/USDC") -assert AG2.fep("WETH/USDC", bothways=False) == AG2.filter_edges(pair="WETH/USDC", bothways=False) -assert tuple(AG2.edgedf(consolidated=True, resetindex=False).iloc[0]) == (1.41, 0.02) -assert len(AG2.edgedf(consolidated=False)) == len(AG2) - -assert len(AG2.edgedf(consolidated=False)) == 12 -AG2.edgedf(consolidated=False) - -assert len(AG2.edgedf(consolidated=True, resetindex=False)) == 10 -AG2.edgedf(consolidated=True, resetindex=False) - -# ## Amount algebra - -A = ag.Amount -nodes = lambda: ag.create_node_list("ETH, USDC") -ETH, USDC = nodes() - -ae1, ae2, au1 = A(1, ETH), A(2, ETH), A(1, USDC) - -assert ae1 + ae2 == 3*ae1 -assert ae2 - ae1 == ae1 -assert -ae1 + ae2 == ae1 -assert 2*ae1 == ae2 -assert ae1*2 == ae2 -assert ae1/2 +ae1/2 == ae1 -assert round(ae1/9,2) == round(1/9,2)*ae1 -assert round(ae1/9,4) == round(1/9,4)*ae1 -assert m.floor(ae1/9) == m.floor(1/9)*ae1 -assert m.ceil(ae1/9) == m.ceil(1/9)*ae1 -assert (ae1 + 2*ae1)/ae1 == 3 - -assert raises (lambda: ae1 + 1) -assert raises (lambda: ae1 - 1) -assert raises (lambda: 1 + ae1) -assert raises (lambda: 1 - ae1) - -assert 2*ae1 > ae1 -assert 2*ae1 >= ae1 -assert .2*ae1 < ae1 -assert .2*ae1 <= ae1 -assert ae1 <= ae1 -assert ae1 >= ae1 -assert not ae1 < ae1 -assert not ae1 > ae1 - -# ## Specific Arb examples - -# ### USDC/ETH - -AG = ag.ArbGraph() -AG.add_edge("ETH", 1, "USDC", 2000) -AG.add_edge("USDC", 1800, "ETH", 1, inverse=True) -G = AG.as_graph() -print(AG.cycles()) -#_=AG.plot() - -for C in AG.cycles(): - print(f"==={C}===") - for c in C.pairs(start_val=AG.n("ETH")): - print(c) - -c, AG.filter_edges(*c) - -AG.A.toarray() - -# ### USDC/LINK to ETH (oneway) - -AG = ag.ArbGraph() -AG.add_edge("USDC", 100, "ETH", 100/2000) -AG.add_edge("LINK", 100, "USDC", 1000) -AG.add_edge("USDC", 900, "LINK", 100, inverse=True) -G = AG.as_graph() -print(AG.cycles()) -#_=AG.plot() - -# _=AG.duplicate().plot() - -for C in AG.cycles(): - print(f"==={C}===") - for c in C.pairs(start_val=AG.n("USDC")): - print(c) - -c, AG.filter_edges(*c) - -AG.A.toarray() - -# ### USDD, LINK, ETH cycle - -AG = ag.ArbGraph() -AG.add_edge("ETH", 1, "USDC", 2000) -AG.add_edge("USDC", 1500, "LINK", 200, inverse=True) -AG.add_edge("LINK", 200, "ETH", 1, inverse=True) -G = AG.as_graph() -print(AG.cycles()) -#_=AG.plot() - -for C in AG.cycles(): - print(f"==={C}===") - for c in C.pairs(start_val=AG.n("USDC")): - print(c) - -c, AG.filter_edges(*c) - -AG.A.toarray() - -# ### USDD, LINK, ETH cycle plus ETH/USDC - -AG = ag.ArbGraph() -AG.add_edge("ETH", 1, "USDC", 2000) -AG.add_edge("ETH", 1, "USDC", 2000) -AG.add_edge("USDC", 1500, "LINK", 200, inverse=True) -AG.add_edge("LINK", 200, "ETH", 1, inverse=True) -AG.add_edge("USDC", 1800, "ETH", 1, inverse=True) -G = AG.as_graph() -print(AG.cycles()) -#_=AG.plot() - -# + -#_=AG.duplicate().plot() -# - - -AG.edges - -AG.duplicate().edges - -AG.A.toarray() - -for C in AG.cycles(): - print(f"==={C}===") - for c in C.pairs(start_val=AG.n("ETH")): - print(c) - -cycle = AG.cycles()[1] -cycle - -for cycle in AG.cycles(): - result = AG.run_arbitrage_cycle(cycle=cycle, verbose=True) - print(result) - print("---") - -assert raises(AG.price, AG.nodes[0], AG.nodes[1]) -raises(AG.price, AG.nodes[0], AG.nodes[1]) - - - - - - - - - diff --git a/resources/NBTest/NBTest_051_CPCBalancer.ipynb b/resources/NBTest/NBTest_051_CPCBalancer.ipynb deleted file mode 100644 index 0b6ec9438..000000000 --- a/resources/NBTest/NBTest_051_CPCBalancer.ipynb +++ /dev/null @@ -1,1641 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "a448e212", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "ConstantProductCurve v3.4 (23/Jan/2024)\n" - ] - } - ], - "source": [ - "try:\n", - " from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, CurveBase\n", - " from fastlane_bot.testing import *\n", - "\n", - "except:\n", - " from tools.cpc import ConstantProductCurve as CPC, CurveBase\n", - " from tools.testing import *\n", - "# from flbtools.cpc import ConstantProductCurve as CPC, CurveBase\n", - "# from flbtesting import *\n", - "\n", - "from math import sqrt\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPC))\n", - "# from fastlane_bot import __VERSION__\n", - "# require(\"3.0\", __VERSION__)" - ] - }, - { - "cell_type": "markdown", - "id": "d9917997", - "metadata": {}, - "source": [ - "# CPC for Balancer [NBTest051]" - ] - }, - { - "cell_type": "markdown", - "id": "9a6b457a-3573-4387-8047-9ae88c5c607e", - "metadata": {}, - "source": [ - "## pvec interface for CPC" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5e055a74-6e99-4ffa-a450-5f53ae7695e5", - "metadata": {}, - "outputs": [], - "source": [ - "c0 = CPC.from_xy(100, 200)\n", - "assert c0.tknx == \"TKNB\"\n", - "assert c0.tkny == \"TKNQ\"\n", - "k0 = c0.invariant()\n", - "assert iseq(k0, sqrt(100*200))\n", - "k1, k2 = c0.invariant(include_target=True)\n", - "assert iseq(k0, k1, k2)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "22004cc1-b2c4-4486-b16f-f8a7b4fbd1b8", - "metadata": {}, - "outputs": [], - "source": [ - "x,y,_ = c0.xyfromp_f(c0.p)\n", - "xvec = c0.xvecfrompvec_f({c0.tknx: c0.p, c0.tkny: 1} )\n", - "assert iseq(x, 100)\n", - "assert iseq(y, 200)\n", - "assert iseq(xvec[c0.tknx], x)\n", - "assert iseq(xvec[c0.tkny], y)\n", - "assert iseq(c0.invariant(), c0.invariant(xvec))\n", - "assert raises(c0.xvecfrompvec_f, {c0.tknx: c0.p} ).startswith(\"pvec must contain\")\n", - "assert raises(c0.xvecfrompvec_f, {c0.tkny: 1} ).startswith(\"pvec must contain\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "4138bcb3-0054-4077-8a0d-df39cb46f98f", - "metadata": {}, - "outputs": [], - "source": [ - "p = 1.5*c0.p\n", - "x,y,_ = c0.xyfromp_f(p)\n", - "xvec = c0.xvecfrompvec_f({c0.tknx: p, c0.tkny: 1} )\n", - "xvec2 = c0.xvecfrompvec_f({c0.tknx: 3*p, c0.tkny: 3} )\n", - "xvec3 = c0.xvecfrompvec_f({c0.tknx: 3*p, c0.tkny: 3, \"ETH\": 15, \"BTC\": 300} )\n", - "assert xvec == xvec2\n", - "assert xvec == xvec3\n", - "assert iseq(x, 81.64965809277261)\n", - "assert iseq(y, 244.9489742783178)\n", - "assert iseq(xvec[c0.tknx], x)\n", - "assert iseq(xvec[c0.tkny], y)\n", - "assert iseq(c0.invariant(), c0.invariant(xvec))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ffa625ad-6ca5-40fe-b20a-bede3249cc7e", - "metadata": {}, - "outputs": [], - "source": [ - "dx,dy,_ = c0.dxdyfromp_f(c0.p)\n", - "dxvec = c0.dxvecfrompvec_f({c0.tknx: c0.p, c0.tkny: 1} )\n", - "assert abs(dx)<1e-10\n", - "assert abs(dy)<1e-10\n", - "assert iseq(dxvec[c0.tknx], dx)\n", - "assert iseq(dxvec[c0.tkny], dy)\n", - "assert raises(c0.dxvecfrompvec_f, {c0.tknx: c0.p} ).startswith(\"pvec must contain\")\n", - "assert raises(c0.dxvecfrompvec_f, {c0.tkny: 1} ).startswith(\"pvec must contain\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "4975e630-45ba-4ac2-95ed-2809b6488f4a", - "metadata": {}, - "outputs": [], - "source": [ - "p = 1.5*c0.p\n", - "dx,dy,_ = c0.dxdyfromp_f(p)\n", - "dxvec = c0.dxvecfrompvec_f({c0.tknx: p, c0.tkny: 1} )\n", - "dxvec2 = c0.dxvecfrompvec_f({c0.tknx: 3*p, c0.tkny: 3} )\n", - "dxvec3 = c0.dxvecfrompvec_f({c0.tknx: 3*p, c0.tkny: 3, \"ETH\": 15, \"BTC\": 300} )\n", - "assert dxvec == dxvec2\n", - "assert dxvec == dxvec3\n", - "assert iseq(dx, -18.35034190722739)\n", - "assert iseq(dy, 44.94897427831779)\n", - "assert iseq(dxvec[c0.tknx], dx)\n", - "assert iseq(dxvec[c0.tkny], dy)" - ] - }, - { - "cell_type": "markdown", - "id": "bc39d223-0e37-43f4-86f0-1c07105cb321", - "metadata": {}, - "source": [ - "## CurveBase" - ] - }, - { - "cell_type": "markdown", - "id": "1bb551a7-0e40-4758-ae87-1d1b30f12a3f", - "metadata": {}, - "source": [ - "Checking that `CurveBase` can only instantiate with all functions defined" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5ba59a93-f792-428a-8381-56f3b4b3fd30", - "metadata": {}, - "outputs": [], - "source": [ - "class CB1(CurveBase):\n", - " pass\n", - "\n", - "class CB2(CurveBase):\n", - " def dxvecfrompvec_f(self, pvec, *, ignorebounds=False):\n", - " pass\n", - "\n", - "class CB3(CurveBase):\n", - " def xvecfrompvec_f(self, pvec, *, ignorebounds=False):\n", - " pass\n", - "\n", - "class CB4(CurveBase):\n", - " def xvecfrompvec_f(self, pvec, *, ignorebounds=False):\n", - " pass\n", - " def dxvecfrompvec_f(self, pvec, *, ignorebounds=False):\n", - " pass\n", - " def invariant(self, xvec=None, *, include_target=False):\n", - " pass\n", - " \n", - "assert raises(CB1).startswith(\"Can't instantiate abstract class\")\n", - "assert raises(CB2).startswith(\"Can't instantiate abstract class\")\n", - "assert raises(CB3).startswith(\"Can't instantiate abstract class\")\n", - "assert not raises(CB4)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "afb61a79-c802-41f0-bafa-4d2ae4bc82d5", - "metadata": {}, - "outputs": [], - "source": [ - "assert isinstance(CPC.from_xy(100, 200), CurveBase)" - ] - }, - { - "cell_type": "markdown", - "id": "521e4bc5-f003-4062-8978-18506ecff248", - "metadata": {}, - "source": [ - "## Constant product constructor" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c5cf94f7-77df-412c-9988-7085184bdd1f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ConstantProductCurve(k=20000, x=100, x_act=100, y_act=200.0, alpha=0.5, pair='TKNB/TKNQ', cid='None', fee=None, descr=None, constr='xy', params={})" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c0 = CPC.from_xy(100, 200)\n", - "assert c0.x == 100\n", - "assert c0.y == 200\n", - "assert c0.k == 20000\n", - "assert c0.x == c0.x_act\n", - "assert c0.y == c0.y_act\n", - "assert c0.alpha == 0.5\n", - "assert c0.eta == 1\n", - "assert c0.constr == \"xy\"\n", - "assert c0.is_constant_product() == True\n", - "c0" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "9eb7484e-a09a-4184-bd59-073a7b399b8a", - "metadata": {}, - "outputs": [], - "source": [ - "assert c0.asdict() == {\n", - " 'k': 20000,\n", - " 'x': 100,\n", - " 'x_act': 100,\n", - " 'y_act': 200.0,\n", - " 'alpha': 0.5,\n", - " 'pair': 'TKNB/TKNQ',\n", - " 'cid': 'None',\n", - " 'fee': None,\n", - " 'descr': None,\n", - " 'constr': 'xy',\n", - " 'params': {}\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2b12d88d-6d7d-4d39-b9bc-def990500df7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ConstantProductCurve(k=20000.0, x=100, x_act=100, y_act=200.0, alpha=0.5, pair='TKNB/TKNQ', cid='None', fee=None, descr=None, constr='xyal', params={})" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c1 = CPC.from_xyal(100, 200)\n", - "assert c1.constr == \"xyal\"\n", - "assert c1.is_constant_product() == True\n", - "assert c1==c0\n", - "c1" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "fc98b1e0-0771-4cd6-877c-b70e74b548b8", - "metadata": {}, - "outputs": [], - "source": [ - "assert c1.asdict() == {\n", - " 'k': 20000,\n", - " 'x': 100,\n", - " 'x_act': 100,\n", - " 'y_act': 200.0,\n", - " 'alpha': 0.5,\n", - " 'pair': 'TKNB/TKNQ',\n", - " 'cid': 'None',\n", - " 'fee': None,\n", - " 'descr': None,\n", - " 'constr': 'xyal',\n", - " 'params': {}\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "f6ededab-423a-489e-8d1b-295162fda59b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ConstantProductCurve(k=20000.0, x=100, x_act=100, y_act=200.0, alpha=0.5, pair='TKNB/TKNQ', cid='None', fee=None, descr=None, constr='xyal', params={})" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c2 = CPC.from_xyal(100, 200, alpha=0.5)\n", - "assert c2.constr == \"xyal\"\n", - "assert c2.is_constant_product() == True\n", - "assert c2==c0\n", - "assert c2.asdict() == c1.asdict()\n", - "c2" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "203e5e7b-1cde-4154-a75c-35068f018a21", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ConstantProductCurve(k=20000.0, x=100, x_act=100, y_act=200.0, alpha=0.5, pair='TKNB/TKNQ', cid='None', fee=None, descr=None, constr='xyal', params={})" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c3 = CPC.from_xyal(100, 200, eta=1)\n", - "assert c3.constr == \"xyal\"\n", - "assert c3.is_constant_product() == True\n", - "assert c3==c0\n", - "assert c3.asdict() == c1.asdict()\n", - "c3" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ee693b31-3278-46d2-a578-ba6e0b2c6333", - "metadata": {}, - "outputs": [], - "source": [ - "assert raises(CPC.from_xyal, 100, 200, \n", - " alpha=0.5, eta=1) == 'at most one of alpha and eta must be given [0.5, 1]'" - ] - }, - { - "cell_type": "markdown", - "id": "9f8986b6-0d20-4a26-9dbe-19c20eb40034", - "metadata": {}, - "source": [ - "## Weighted constructor" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "2331941a-3aeb-4fa3-887a-6ed45c37307b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ConstantProductCurve(k=20000, x=100, x_act=100, y_act=200.0, alpha=0.5, pair='TKNB/TKNQ', cid='None', fee=None, descr=None, constr='xy', params={})" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c0 = CPC.from_xy(100, 200)\n", - "assert c0.x == 100\n", - "assert c0.y == 200\n", - "assert c0.k == 20000\n", - "assert c0.x == c0.x_act\n", - "assert c0.y == c0.y_act\n", - "assert c0.alpha == 0.5\n", - "assert c0.eta == 1\n", - "assert c0.constr == \"xy\"\n", - "assert iseq(c0.invariant(), c0.kbar)\n", - "assert c0.is_constant_product() == True\n", - "c0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "00857b2e-d9af-41ce-a41f-c52b373fcd3a", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "db5f6d9a-87c3-4bba-9bbc-32993d2c09a7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ConstantProductCurve(k=20000.0, x=100, x_act=100, y_act=200.0, alpha=0.5, pair='TKNB/TKNQ', cid='None', fee=None, descr=None, constr='xyal', params={})" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c1 = CPC.from_xyal(100, 200)\n", - "assert c1.constr == \"xyal\"\n", - "assert c1.is_constant_product() == True\n", - "assert c1 == c0\n", - "assert c1.asdict()[\"alpha\"] == 0.5\n", - "assert iseq(c1.invariant(), c1.kbar)\n", - "c1" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "02dc9cc9-d30e-4daf-9f7d-11f9bd60ad04", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ConstantProductCurve(k=800000000.0, x=100, x_act=100, y_act=199.99999999999994, alpha=0.25, pair='TKNB/TKNQ', cid='None', fee=None, descr=None, constr='xyal', params={})" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c2 = CPC.from_xyal(100, 200, alpha=0.25)\n", - "assert c2.constr == \"xyal\"\n", - "assert c2.is_constant_product() == False\n", - "assert c2.alpha == 0.25\n", - "assert c2.asdict()[\"alpha\"] == 0.25\n", - "assert iseq(c2.eta, 0.25/0.75)\n", - "assert iseq(c2.invariant(), c2.kbar)\n", - "assert c2 != c0\n", - "assert c2 != c1\n", - "c2" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "f48bec87-6674-4a5c-8a15-562c5caff154", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ConstantProductCurve(k=800000000.0, x=100, x_act=100, y_act=199.99999999999994, alpha=0.25, pair='TKNB/TKNQ', cid='None', fee=None, descr=None, constr='xyal', params={})" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c3 = CPC.from_xyal(100, 200, alpha=0.8)\n", - "assert c3.constr == \"xyal\"\n", - "assert c3.is_constant_product() == False\n", - "assert iseq(c3.alpha, 0.8)\n", - "assert c3.asdict()[\"alpha\"] == 0.8\n", - "assert iseq(c3.eta, 0.8/0.2)\n", - "assert iseq(c3.invariant(), c3.kbar)\n", - "assert c3 != c0\n", - "assert c3 != c1\n", - "assert c3 != c2\n", - "c2" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "4dc4a1f6-1d71-4f1d-b0a0-616f83fb58e8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ConstantProductCurve(k=376.06030930863926, x=100, x_act=100, y_act=200.0, alpha=0.8, pair='TKNB/TKNQ', cid='None', fee=None, descr=None, constr='xyal', params={})" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c3b = CPC.fromdict(c3.asdict())\n", - "assert c3b == c3\n", - "c3b" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "8caba1ff-65e7-496e-adcf-aea4cddcee4b", - "metadata": {}, - "outputs": [], - "source": [ - "assert raises(CPC.from_xyal,100, 200, alpha=0) == 'alpha must be > 0 [0]'\n", - "assert raises(CPC.from_xyal,100, 200, alpha=-1) == 'alpha must be > 0 [-1]'\n", - "assert raises(CPC.from_xyal,100, 200, alpha=1) == 'alpha must be < 1 [1]'\n", - "assert raises(CPC.from_xyal,100, 200, alpha=2) == 'alpha must be < 1 [2]'" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "b54669fb-a128-41ae-a3ac-b9eff553f897", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'alpha must be > 0 [0]'" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raises(CPC.from_xyal,100, 200, alpha=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "c4630e2a-b577-410b-8251-1c327866c9f1", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(CPC.from_xyal,100, 200, alpha=1-1e-10)\n", - "assert not raises(CPC.from_xyal,100, 200, alpha=0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "c8c740c3-3ffb-4694-95dc-2932b7d39c6c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"(34, 'Result too large')\"" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raises(CPC.from_xyal,100, 200, alpha=0.001)" - ] - }, - { - "cell_type": "markdown", - "id": "448e83dd-7f7d-4233-a129-729b31168667", - "metadata": {}, - "source": [ - "## High level testing of all functions\n", - "\n", - "(including not YET implemented)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "fd739374-c01b-432e-ab80-98e35fda3674", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ConstantProductCurve(k=20000.0, x=100, x_act=100, y_act=200.0, alpha=0.5, pair='TKNB/TKNQ', cid='None', fee=None, descr=None, constr='xyal', params={})" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c0 = CPC.from_xyal(100, 200)\n", - "assert c0.is_constant_product() == True\n", - "c0" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "927269a5-0c16-4336-8fcf-3e22392c8847", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ConstantProductCurve(k=800000000.0, x=100, x_act=100, y_act=199.99999999999994, alpha=0.25, pair='TKNB/TKNQ', cid='None', fee=None, descr=None, constr='xyal', params={})" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c1 = CPC.from_xyal(100, 200, alpha=0.25)\n", - "assert c1.is_constant_product() == False\n", - "c1" - ] - }, - { - "cell_type": "markdown", - "id": "dfa26d63-4a6b-4dbc-ac1a-f1f3ad1d2a0c", - "metadata": {}, - "source": [ - "#### Not (yet) implemented functions\n", - "\n", - "Those function groups are not currently planned to be implemented at all\n", - "\n", - "- `execute` as there is no need to simulate those curves for the time being; that was a Carbon thing\n", - "\n", - "The functions we may implement at a later stage are\n", - "\n", - "- `description` should probably be updated, but it is tedious; `format` ditto\n", - "- `x_max`, `x_min`, `p_max`, `p_min` and the other leverage functions once we consider it important and safe" - ] - }, - { - "cell_type": "markdown", - "id": "f02b6b1f-e11c-46c7-8ce4-a320b1200432", - "metadata": {}, - "source": [ - "execute" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "30239c29-9c2d-4a35-b24e-593e27d23013", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(c0.execute)\n", - "assert raises(c1.execute).startswith(\"only implemented for\")" - ] - }, - { - "cell_type": "markdown", - "id": "ce3aa2c2-13e3-43a4-ae06-0815781f1dc1", - "metadata": {}, - "source": [ - "description and format" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "31973a9d-f6fc-4ba6-8660-3ca6b3b31238", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(c0.description)\n", - "assert raises(c1.description).startswith(\"only implemented for\")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "6da717da-0e96-4af5-a011-74c54c5a8033", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(c0.format)\n", - "assert raises(c1.format).startswith(\"only implemented for\")" - ] - }, - { - "cell_type": "markdown", - "id": "85e0d16e-e9f6-4474-b334-19276c5c596a", - "metadata": {}, - "source": [ - "leverage related functions (primary)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "22c10615-7636-4ec9-ba02-01e9c58ad20d", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(lambda: c0.p_max)\n", - "assert not raises(lambda: c1.p_max)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "b63b25fa-8f4a-415d-b0c5-8d10be06f91b", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(lambda: c0.p_min)\n", - "assert not raises(lambda: c1.p_min)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "1e2a97a5-240b-49b4-abdc-f30b3b1e2788", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(lambda: c0.x_min)\n", - "assert not raises(lambda: c1.x_min)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "98262a43-8c8e-4eb2-af03-6c7f082a9a5e", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(lambda: c0.x_max)\n", - "assert not raises(lambda: c1.x_max)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "b3049924-bc62-44c3-b7fc-0c5513544b87", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(lambda: c0.y_min)\n", - "assert not raises(lambda: c1.y_min)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "9809fe75-81eb-4117-b06f-ea80e8e5d1af", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(lambda: c0.y_max)\n", - "assert not raises(lambda: c1.y_max)" - ] - }, - { - "cell_type": "markdown", - "id": "162d55a7-94b2-4470-809a-5cf59c6fc6de", - "metadata": {}, - "source": [ - "leverage related functions (secondary, ie calling primary ones)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "d53b7a88-b1b2-488f-b33e-cf85af69dad2", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(c0.p_max_primary)\n", - "assert not raises(c1.p_max_primary)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "47628578-dfab-4a28-a46f-b83b12af7745", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(c0.p_min_primary)\n", - "assert not raises(c1.p_min_primary)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "818af0e4", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(lambda: c0.at_xmin)\n", - "assert not raises(lambda: c1.at_xmin)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "bac20004", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(lambda: c0.at_xmax)\n", - "assert not raises(lambda: c1.at_xmax)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "490db431", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(lambda: c0.at_ymin)\n", - "assert not raises(lambda: c1.at_ymin)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "bc7fda17", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(lambda: c0.at_ymax)\n", - "assert not raises(lambda: c1.at_ymax)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "d1637e16-ae56-45f6-bb90-b10cd5e83194", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(lambda: c0.at_boundary)\n", - "assert not raises(lambda: c1.at_boundary)" - ] - }, - { - "cell_type": "markdown", - "id": "faeae9de-470f-4a8d-82a5-0edf1558ba09", - "metadata": {}, - "source": [ - "todo" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "4a70d47e-3a89-452d-80f3-d69028433648", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(c0.xyfromp_f)\n", - "assert not raises(c1.xyfromp_f)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "a1bbada5-9cd2-4dd0-a226-fb7422614b53", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(c0.dxdyfromp_f)\n", - "assert not raises(c1.dxdyfromp_f)" - ] - }, - { - "cell_type": "markdown", - "id": "1d79b5fb-32b4-47f6-a852-f10e508d3fc4", - "metadata": {}, - "source": [ - "#### Implemented functions" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "d90a02d0-21c9-47dc-894c-82ea93b01779", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(lambda: c0.y)\n", - "assert not raises(lambda: c1.y)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "052b8feb-038e-457a-a3f1-8ff25b030a0e", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(lambda: c0.p)\n", - "assert not raises(lambda: c1.p)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "8872ba9c-2186-4176-add5-4a36b66162a4", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(lambda: c0.kbar)\n", - "assert not raises(lambda: c1.kbar)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "795eac9c-fe49-447f-b3fe-c0268cdd20de", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(c0.tvl)\n", - "assert not raises(c1.tvl)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "13b1f308-3062-4872-a91e-e89bccf17cf8", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(c0.yfromx_f, 110)\n", - "assert not raises(c1.yfromx_f, 110, ignorebounds=True)\n", - "assert not raises(c1.yfromx_f, 110, ignorebounds=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "b90b56dd-bc10-4087-a3f4-5c0b8bcc681a", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(c0.xfromy_f, 210)\n", - "assert not raises(c1.xfromy_f, 110, ignorebounds=True)\n", - "assert not raises(c1.xfromy_f, 110, ignorebounds=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "9b797a1f-2d04-42ae-a03f-4b9675a65ed3", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(c0.dyfromdx_f, 1)\n", - "assert not raises(c1.dyfromdx_f, 1, ignorebounds=True)\n", - "assert not raises(c1.dyfromdx_f, 1, ignorebounds=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "ee7cd754-fe9c-4fb8-848d-46dc2850c0cd", - "metadata": {}, - "outputs": [], - "source": [ - "assert not raises(c0.dxfromdy_f, 1)\n", - "assert not raises(c1.dxfromdy_f, 1, ignorebounds=True)\n", - "assert not raises(c1.dxfromdy_f, 1, ignorebounds=False)" - ] - }, - { - "cell_type": "markdown", - "id": "30cea356-d1ff-4f34-ad31-f4fbb89c69b3", - "metadata": {}, - "source": [ - "## Simple Tests" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "6373dfe5-6d20-4c55-9f0b-80a8ac6e1e05", - "metadata": {}, - "outputs": [], - "source": [ - "c0 = CPC.from_xyal(100, 200)\n", - "c1 = CPC.from_xyal(100, 200, eta=2)\n", - "c2 = CPC.from_xyal(100, 200, eta=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "81cb94ae-1fd1-4d63-9f59-e79274737d4d", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(c0.alpha, 1/2)\n", - "assert iseq(c1.alpha, 2/3)\n", - "assert iseq(c2.alpha, 1/3)" - ] - }, - { - "cell_type": "markdown", - "id": "88b1ae49-2c71-4468-bb83-743cadb96b93", - "metadata": {}, - "source": [ - "#### Current token balance $y$\n", - "\n", - "$$\n", - "y = \\left( \\frac k x \\right)^\\eta\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "0eac45f7-ef6d-419d-8254-098858e7a529", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(c0.y, 200)\n", - "assert iseq(c1.y, 200)\n", - "assert iseq(c2.y, 200)" - ] - }, - { - "cell_type": "markdown", - "id": "f08b3230-20b9-4b72-bd58-4f810a92991c", - "metadata": {}, - "source": [ - "#### Current price $p$\n", - "\n", - "$$\n", - "p = \\eta\\, \\frac y x\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "cc39342b-1304-4e7d-8594-950df18ffb6e", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(c0.p, 2 * c0.eta)\n", - "assert iseq(c1.p, 2 * c1.eta)\n", - "assert iseq(c2.p, 2 * c2.eta)" - ] - }, - { - "cell_type": "markdown", - "id": "bddf7d0d-bbc5-427b-8b01-e4c069ff4e47", - "metadata": {}, - "source": [ - "#### TVL\n", - "\n", - "$$\n", - "\\mathrm{TVL} = x_a*p + y_a\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "80e3f97b-41b0-4263-b2e0-b02da963c60f", - "metadata": {}, - "outputs": [], - "source": [ - "assert c0.x == c0.x_act\n", - "assert c0.y == c0.y_act\n", - "assert c1.x == c1.x_act\n", - "assert c1.y == c1.y_act\n", - "assert c2.x == c2.x_act\n", - "assert c2.y == c2.y_act" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "593fb93f-5d95-4796-9da6-abd79206ff3d", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(c0.tvl(), 100 * c0.p + 200)\n", - "assert iseq(c1.tvl(), 100 * c1.p + 200)\n", - "assert iseq(c2.tvl(), 100 * c2.p + 200)" - ] - }, - { - "cell_type": "markdown", - "id": "98664ea1-c17d-4b42-854b-7b3db79c2b33", - "metadata": {}, - "source": [ - "#### Pool constant $k$\n", - "\n", - "$$\n", - "k^\\alpha = x^\\alpha\\, y^{1-\\alpha}\n", - "$$\n", - "\n", - "$$\n", - "k = x\\,y^\\frac{1-\\alpha}{\\alpha} = x\\,y^{\\frac 1 \\eta}\n", - "$$\n" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "171b56b5-f270-4f51-8fd8-a185b3c0e91f", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(c0.k**(1/2), c0.x**(1/2) * c0.y**(1/2))\n", - "assert iseq(c1.k**(2/3), c1.x**(2/3) * c1.y**(1/3))\n", - "assert iseq(c2.k**(1/3), c1.x**(1/3) * c1.y**(2/3))" - ] - }, - { - "cell_type": "markdown", - "id": "0b290a50-f765-4b62-8be6-19542281022b", - "metadata": {}, - "source": [ - "#### Pool constant $\\bar k$\n", - "\n", - "$$\n", - "x^\\alpha\\, y^{1-\\alpha} = \\bar k = k^\\alpha\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "01d3e5e2-aba4-434d-a118-15bd63e854db", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(c0.kbar, c0.x**(1/2) * c0.y**(1/2))\n", - "assert iseq(c1.kbar, c1.x**(2/3) * c1.y**(1/3))\n", - "assert iseq(c2.kbar, c1.x**(1/3) * c1.y**(2/3))" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "0ae9f182-0f46-4fdd-9879-619cf3f6ecd2", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(c0.kbar, c0.k**c0.alpha)\n", - "assert iseq(c1.kbar, c1.k**c1.alpha)\n", - "assert iseq(c2.kbar, c2.k**c2.alpha)" - ] - }, - { - "cell_type": "markdown", - "id": "d76dbd19-be45-4078-8896-35ba65d9d181", - "metadata": {}, - "source": [ - "#### Token balance function $y(x)$\n", - "\n", - "$$\n", - "y(x) = \\left( \\frac k x \\right)^\\eta\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "9ff8935e-1898-40b9-802f-09a7b2cc608c", - "metadata": {}, - "outputs": [], - "source": [ - "assert c0.eta == 1\n", - "assert iseq(c0.yfromx_f(100, ignorebounds=True), 200)\n", - "assert iseq(c0.yfromx_f( 50, ignorebounds=True), 400)\n", - "assert iseq(c0.yfromx_f(200, ignorebounds=True), 100)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "081ec8c2-768c-45a5-a478-1df3a55590ab", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(c1.eta, 2)\n", - "assert iseq(c1.yfromx_f(100, ignorebounds=True), 200)\n", - "assert iseq(c1.yfromx_f( 50, ignorebounds=True), 200*2**2)\n", - "assert iseq(c1.yfromx_f(200, ignorebounds=True), 200/2**2)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "5f556205-299f-4f5c-b717-076a64137784", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(c2.eta, 1/2)\n", - "assert iseq(c2.yfromx_f(100, ignorebounds=True), 200)\n", - "assert iseq(c2.yfromx_f( 50, ignorebounds=True), 200*sqrt(2))\n", - "assert iseq(c2.yfromx_f(200, ignorebounds=True), 200/sqrt(2))" - ] - }, - { - "cell_type": "markdown", - "id": "0aabe321-cb4b-4c65-b84a-73493a95b740", - "metadata": {}, - "source": [ - "#### Token balance function $x(y)$\n", - "\n", - "$$\n", - "x(y) \n", - "= \\frac{k}{ y^{\\frac{1-\\alpha}{\\alpha}} }\n", - "= \\frac{k}{ y^{\\frac{1}{\\eta}} }\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "bfbc589d-2124-4c51-9ac9-a7a3034ebd67", - "metadata": {}, - "outputs": [], - "source": [ - "assert c0.eta == 1\n", - "assert iseq(c0.xfromy_f(200, ignorebounds=True), 100)\n", - "assert iseq(c0.xfromy_f(100, ignorebounds=True), 200)\n", - "assert iseq(c0.xfromy_f(400, ignorebounds=True), 50)" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "fb276d33-72b0-406d-9f3d-9ce16354098b", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(c1.eta, 2)\n", - "assert iseq(c1.xfromy_f(200, ignorebounds=True), 100)\n", - "assert iseq(c1.xfromy_f(100, ignorebounds=True), 100*2**0.5)\n", - "assert iseq(c1.xfromy_f(400, ignorebounds=True), 100/2**0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "d55dd588-8a49-43c5-bf23-22fa207876fd", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(c2.eta, 1/2)\n", - "assert iseq(c2.xfromy_f(200, ignorebounds=True), 100)\n", - "assert iseq(c2.xfromy_f(100, ignorebounds=True), 100*2**2)\n", - "assert iseq(c2.xfromy_f(400, ignorebounds=True), 100/2**2)" - ] - }, - { - "cell_type": "markdown", - "id": "ea30dd5f-ae86-469c-8f9b-f7c43f9ed5a6", - "metadata": {}, - "source": [ - "#### Price response function $(x(p), y(p))$\n", - "\n", - "$$\n", - "x(p) \n", - "= \n", - "\\left(\\frac \\eta p\\right)^{1-\\alpha} k^\\alpha\n", - "$$\n", - "\n", - "$$\n", - "y(p) = \\left( \\frac{kp}{\\eta} \\right)^\\alpha\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "eb60778c-edd0-42c5-8681-a259e02c1e91", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(c0.xyfromp_f(c0.p, ignorebounds=True)[0], c0.x)\n", - "assert iseq(c1.xyfromp_f(c1.p, ignorebounds=True)[0], c1.x)\n", - "assert iseq(c2.xyfromp_f(c2.p, ignorebounds=True)[0], c2.x)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "c242f036-c366-4c25-ab1d-9b13ff283d60", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(c0.xyfromp_f(c0.p, ignorebounds=True)[1], c0.y)\n", - "assert iseq(c1.xyfromp_f(c1.p, ignorebounds=True)[1], c1.y)\n", - "assert iseq(c2.xyfromp_f(c2.p, ignorebounds=True)[1], c2.y)" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "1e39b3b6-ac20-4a7d-ab4a-f764cc949ee9", - "metadata": {}, - "outputs": [], - "source": [ - "for ci in [c0, c1, c2]:\n", - " for p in [2, 1, 4]:\n", - " eta_over_p = ci.eta / p\n", - " x = eta_over_p ** (1-ci.alpha) * ci.kbar\n", - " y = 1/eta_over_p**ci.alpha * ci.kbar\n", - " xx, yy, pp = ci.xyfromp_f (p, ignorebounds=True)\n", - " dx, dy, _ = ci.dxdyfromp_f(p, ignorebounds=True)\n", - " assert iseq(x, xx)\n", - " assert iseq(y, yy)\n", - " assert iseq(p, pp)\n", - " assert iseq(dx, xx-ci.x)\n", - " assert iseq(dy, yy-ci.y)" - ] - }, - { - "cell_type": "markdown", - "id": "2105f1e3-cb98-4e4d-9c58-c144bc49c33e", - "metadata": {}, - "source": [ - "## Consistency tests" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "8fd1d683-195a-414b-bfa2-2155462efcc1", - "metadata": {}, - "outputs": [], - "source": [ - "c0 = CPC.from_xyal(100, 200)\n", - "c1 = CPC.from_xyal(100, 200, eta=2)\n", - "c2 = CPC.from_xyal(100, 200, eta=0.5)\n", - "cc = [c0, c1, c2]" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "c2a77208-d1d6-4d99-8fab-d8b9f988dac9", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(c0.alpha, 1/2)\n", - "assert iseq(c1.alpha, 2/3)\n", - "assert iseq(c2.alpha, 1/3)" - ] - }, - { - "cell_type": "markdown", - "id": "902c8cd4-d13b-4b40-96cd-86bfe8a089e7", - "metadata": {}, - "source": [ - "### Assert inversions\n", - "\n", - "$$\n", - "y(x(y)) = y\n", - "$$\n", - "\n", - "and vice versa" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "30a07eda-449f-471e-a609-f979d894d09a", - "metadata": {}, - "outputs": [], - "source": [ - "for xy in np.logspace(1, 3, 100):\n", - " for ci in cc:\n", - " #print(f\"xy={xy}, eta={ci.eta}\")\n", - " assert iseq(ci.yfromx_f(ci.xfromy_f(xy, ignorebounds=True), ignorebounds=True), xy)\n", - " assert iseq(ci.xfromy_f(ci.yfromx_f(xy, ignorebounds=True), ignorebounds=True), xy)" - ] - }, - { - "cell_type": "markdown", - "id": "6c20dda7-f50f-4d35-86b1-80a303d90a59", - "metadata": {}, - "source": [ - "### Assert that prices are correct\n", - "\n", - "$$\n", - "p \\simeq -\\frac{\\Delta y}{\\Delta x}\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "8826b280-00f7-4771-9bc6-2d5d344b197e", - "metadata": {}, - "outputs": [], - "source": [ - "for alpha in np.linspace(0.01, 0.99, 100):\n", - " ci = CPC.from_xyal(100, 200, alpha=alpha)\n", - " dy = ci.yfromx_f(ci.x+0.1, ignorebounds=True)-ci.yfromx_f(ci.x-0.1, ignorebounds=True)\n", - " assert iseq(dy/0.2, -ci.p, eps=1e-2), f\"error: {dy/0.2/ci.p+1}\"" - ] - }, - { - "cell_type": "markdown", - "id": "b2944261-9291-496c-98f8-726b0ff26850", - "metadata": {}, - "source": [ - "### Check `dyfromdx_f` against `yfromx_f`" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "853233ca-22bd-4d0e-a6d1-3dee13341b99", - "metadata": {}, - "outputs": [], - "source": [ - "for dxy in np.linspace(0.1, 99, 100):\n", - " for ci in cc:\n", - " assert iseq(ci.dyfromdx_f(dxy, ignorebounds=True),\n", - " (ci.yfromx_f(ci.x+dxy, ignorebounds=True)-ci.y))\n", - " assert iseq(ci.dxfromdy_f(dxy, ignorebounds=True),\n", - " (ci.xfromy_f(ci.y+dxy, ignorebounds=True)-ci.x))" - ] - }, - { - "cell_type": "markdown", - "id": "957ed182-572a-440c-bee2-1a1b33b4d06b", - "metadata": {}, - "source": [ - "## Charts [NOTEST]" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "73dfd68b-56ba-4154-a1f8-79c8bddf4169", - "metadata": {}, - "outputs": [], - "source": [ - "plt.style.use('seaborn-v0_8-dark')\n", - "plt.rcParams['figure.figsize'] = [12,6] # only picked up at second run (?!?)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "bf52087a-ffb7-4dad-bffa-753983249b51", - "metadata": {}, - "outputs": [], - "source": [ - "c0 = CPC.from_xyal(100, 200)\n", - "c1 = CPC.from_xyal(100, 200, eta=2)\n", - "c2 = CPC.from_xyal(100, 200, eta=0.5)\n", - "cc = [c0, c1, c2]\n", - "xvals = np.linspace(50,200)\n", - "pvals = np.linspace(1,4)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "c3e74ed7-83cb-497d-82a8-4a8d38bf312d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for ci in cc:\n", - " plt.plot(xvals, [ci.yfromx_f(x, ignorebounds=True) for x in xvals], label=f\"eta={ci.eta:0.2f}\")\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.title(\"Indifference curve token balance y vs token balance x at different weights\")\n", - "plt.xlabel(\"Token balance x [native units]\")\n", - "plt.ylabel(\"Token balance y [native units]\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "dcf6c394-8ad0-4ea1-a0fe-849374b16110", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for ci in cc:\n", - " plt.plot(\n", - " xvals, \n", - " [\n", - " -(ci.yfromx_f(x+0.1, ignorebounds=True) - ci.yfromx_f(x-0.1, ignorebounds=True))/0.2\n", - " for x in xvals\n", - " \n", - " ], \n", - " label=f\"eta={ci.eta:0.2f}\")\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.title(\"Price vs token balance x at different weights\")\n", - "plt.xlabel(\"Token balance x [native units]\")\n", - "plt.ylabel(\"Price [dy/dx]\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "3c8cbd59-5039-43bc-a52d-f05201e9e83b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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3cnNz8Z///AdpaWlwcXFBU1MTvvjii7M+5oXcpz2tr1+tVtscg5WXlwe1Wm0NWQsWLEBGRobd/Vuvt8fDwwNTp07Fpk2bMGDAAGi1Wlx77bXnrKd///5ISUnBpk2bIJPJUFNTg6uvvtp63cXFBXPmzMGcOXNQUlKCLVu2YNWqVXj44YexadOmsz5ua1huq6qq6pwB/Exd9V4VFBRg7ty5uOyyy/D2229bZ2F89NFH+OOPPzpcHwAoFIp2/0609/od4eXlhYiICLz66qvtXm/vlwBt75uRkYEFCxa0e72zjy8cN24cDAYD9u7di507d+LRRx9FYmIiPD09sXv3bmzdutW6e7yHhwdEIhFmzZrV7gh3e8e9tX7vq6urbW4/8+sLdaF9nIioJ+OUdSKiHsbd3R2DBg3Cxo0bbTZSqqurw2+//WYdLW6lUqng5+eHhx56CFu2bMHGjRsBWKbzymQylJeXIykpyfpHJpNh6dKlNrsqX6jMzEybEfuDBw+iuLgYw4cPB2CZZjxlyhQMHz7cGk62bt0K4Oyj7xdyn/a0vk8///yzze2vv/46nn/+eURFRcHPzw9FRUU2709QUBCWLl1qs1N2e2bMmIHjx49j7dq1GD58eIemTl999dXYunUrvvvuO6SkpFh/UdLc3IwpU6ZYd5wOCQnBrbfeiunTp6OsrOycj1lQUICcnBzr1+Xl5di/fz9GjBhx3npaddV7dfjwYeh0Otxzzz02SyJaw3jraKpYfP6PK8OHD0dmZqbNJm45OTl2u/o7KiMjA6WlpfDz87N5bdu3b8e7775rs2ykvfvm5+cjMjLS5r7ffvstvvjii3Pe90wdeQ/8/f0xcOBAfPLJJ6isrERGRgYkEgmGDh2Kr7/+Gnl5edbp356enhg4cCDy8vJsaouNjcXKlSvb3ck9KCgI4eHh+Omnn2xu/+GHHzr8Os72mi6mjxMR9WQcISci6oEefvhh3HnnnZg9ezZuu+02GAwGvPPOO9Dr9bj//vvbvc/NN9+Mr7/+Gi+88AJGjhwJhUKB2bNnY/ny5aivr8ewYcNQXl6O5cuXQyQSISEh4aLrNJvNuPvuu3HvvfeipqYGS5cuRVxcnHXkNzk5GRs2bEBiYiKCgoKQmZmJt99+GyKR6Kzr0y/kPu1JSEjA1KlT8eqrr6K5uRmJiYn4888/8dNPP2HZsmWQSCSYP38+nn76aUgkEkyYMAFarRarVq1CeXn5Wacht0pPT0dUVBR27dp11tHVM02fPh0vvfQSvv/+ezzxxBPW2+VyORITE7Fy5UrIZDLEx8cjPz8fX3/9NaZMmXLOxxQEAffddx8efPBBSCQSrFy5Et7e3jZHmp1PV71XiYmJkEqlWLJkCf75z39Cr9dj/fr1+O233wDAuoa6dYT+u+++w+DBgxEWFmb3WLfffju+/PJL3HnnnfjXv/4Fk8mEZcuWQSaTdfh1tuf666/HunXrcMcdd+Dee+9FcHAwtm3bhjVr1uC222475+PPmjUL//vf/zBr1iz885//hEKhwMaNG/H555/jsccec6gOb29vZGZmWo8YO9usg/Hjx+PNN99EZGQkAgMDAVjWZr/88ssICAiw+V489NBDuPvuu/Hwww/j6quvhslkwtq1a3HgwAHMmTPH7rFFIhHmzZuHf//733jmmWcwadIkZGdn48033wTQsV8atPeadu/ejSFDhlxwHyci6skYyImIeqARI0bgP//5D9544w089NBDcHFxwZAhQ7B48WLExsa2ex+RSITnnnsON9xwA1566SUsXrwYDz74IPz9/fHxxx/j3XffhY+PD0aMGIGHHnoIXl5eF13nhAkTEB4ejkceeQRGoxETJkzAE088YV2f+vLLL+P555/H888/DwCIiIjAc889h2+//da6kdiZLuQ+Z7NkyRKsXLkSH374IWpqahAZGYlly5ZZN6268cYb4eHhgXfffRefffYZ3N3dkZaWhldffbXdUHim8ePHo7Ky0m4ztLPx9fXFuHHj8Pvvv1unFrf6v//7Pyxbtgxr165FZWUl/Pz8MGPGDDzwwAPnfMyQkBDccccdePHFF9HU1ISRI0firbfeslvacD5d8V71798fS5cuxcqVKzFnzhz4+PggJSUFH374IWbOnIk9e/YgPj4ekydPxv/+9z8sXLgQM2bMwLPPPmv3WAqFAp988gkWLVqEhQsXwsPDA7Nnz7bOCLlQ7u7u+Oijj7B06VIsWbIEdXV1CA0NxcMPP4x//vOf57xvYGAgPv30UyxduhTPPvssdDodIiIisGjRIutRbh1166234vDhw7jrrrvw0ksv4aqrrmq3XWsgb7t0YNiwYdZrrfs/AMDo0aPx3nvvYeXKlZg3bx5kMhkSExPxn//8x26TuVZXXXUVGhsb8d577+Grr75CbGwsnnjiCTzxxBPtHul3Lvfeey9WrVqFu+66Cxs3brzgPk5E1JOJBEd3DCEiIqLzEgQBV111FYYNG4annnrKKTUsXLgQu3btwq+//uqU56fe57vvvsPAgQMRFRVlve23337DPffcg//973+dMrOGiKgv4Qg5ERFRJ6qvr8d///tfHDp0CCdPnsSqVaucXRJRp/n222/x+uuv48EHH0RwcDBOnjyJN954AxkZGQzjREQXgIGciIioE8nlcnz66acwm81YtGiRzWZlRD3d4sWLrdP31Wo1VCoVpk2bhnnz5jm7NCKiHolT1omIiIiIiIicgMeeERERERERETkBAzkRERERERGREzCQExERERERETkBAzkRERERERGRE/T6XdYrK+ucXUKHKJUeUKsbnF0G9SDsM+Qo9hlyFPsMOYp9hhzFPkOO6il9xt/fq0PtOELeDYhEgEQihkjk7Eqop2CfIUexz5Cj2GfIUewz5Cj2GXJUb+wzDORERERERERETsBATkREREREROQEDORERERERERETsBATkREREREROQEDORERERERERETsBATkREREREROQEDORERERERERETsBATkREREREROQEDORERERERERETsBATkREREREROQEDORERERERERETsBA3stUVVWhqanJ2WUQERERERHReTCQ9yJqdTX+9rfrUFtb0+mP/emn63D//Xefs01TUxNefPE5XHHFZZgyZRyef/5pNDY2Wq8XFJzCAw/MwaRJY3HNNVPxwQdrO71OIiIiIiKinoKB/AyCIKDJYLpkfwRB6LTadTpdp4+ONzU1YcWK17Fy5bLztn399VdQXl6OTz9dj08//Rrl5WV4660VAACj0YgFC+YjIWEgNm78BUuWLMP69V/g119/7tR6iYiIiIiIegqpswvoTgRBwOxPD+BgifaSPefgEG+8+7fBDt2nuLgIy5cvRVbWQcjlbpg8eRruuOMuzJx5EwBg5syb8NhjT2Ps2Al4551V2LbtD1RUVMDV1RWXXTYJDz74CEQiEW677SaUl5faPX5yciqWLn0DADBr1t8wYEAirr12Bk6ezDtrTc3Nzfjxx01YseJteHv7AADmzJmHefPuwdy5D+DQoQOorq7C7Nn3QiaTIS4uATNm3Iz16z/HxImXO/T6iYiIiIiIegMG8jOInF3AeTQ1NeGBB+bg8sun4PnnX0ZtbQ2efPJRCIKADz/8HDfeeDU+/PBzBAeH4KOP3seOHX9h+fLVUKlUOHz4IObOvQtjxozHkCEZWLfu8/M+34oVbyMgIBDvvfc2Tp48e7vCwgIYjUZER8dYb4uMjIROp0Nh4Snk5+chLCwcMpnMej0iIgrr1v33It4NIiIiIiKinouBvA2RSIQ1twxGs9F8yZ5TLhVDJOr4rwG2bfsTBoMB99wzFyKRCIGBQbjrrjl48slHcc0119u0veqq6zBt2pVQKJSoqqqCTqeDu7sHKisrOvx8AQGBHWrXulZcLnez3ubqKm+51oTGxga4ubnZ3Ecul3MDOiIiIiIi6rMYyM8gEongJpM4u4yzKisrQW1tDaZNm2C9TRAEGI0G1NSobdo2Nzfh9ddfQWbmPgQEBCAuLgGCIFjXrd9++y0oLy+ze47k5BS88soyh+pyc5O3PGcz3N3dAQA6XTMAwN3dHW5ubmhubj6jvma4ubk79DxERERERNR3CIKAGr0aJ+vyUdJUhPGy0fCCytlldRoG8h7G3z8QoaH98PHHX1lva2xsgFqthlhsu0ff4sWL4O3tjf/9bzNcXV1hNpttgvz773/aaXWFh0dAKpUiPz8PiYmDAAD5+fmQyWQIDw9Hba3aOq1dKrV0u5Mn8xAVFd1pNRARERERUc/UNnifrM/Hqdb/1udDazi9x9dflb/j5fTXnVhp52Ig72FGjRqNVauW4+OPP8CMGbdAp9PhxRefQ3l5GZYsWQYAqK+vBwA0NNRDpVJBIpGgsbEB7733DhoaGmAwGDq9Lrlcjssum4TVq1fg//7vZQDA6tUrcPnlU+DqKkdq6hD4+Phi9eqVuOuuOSgoOIUvv/wMd999X6fXQkRERERE3VNHg3dbYogR7B6CCK9I3JF8+yWuuGsxkPcwHh6eWLZsFVaufB0ff/wBTCYz0tLSsXjxa1Aq/TB27ATce+8d+Ne/5uPBBx/BK68swrRpE+Du7oGRI0dj2LCRyMvL6ZRaPvhgLX78cbN1c7iHH16IFSuW4fbbb4HBYMCYMeMwf/4CAIBUKsXrr6/Ea68txjXXTIGbmztmzLgZV1xxVafUQkRERERE3YcleNfgZF3eBQXvCM9I9PeMRIRXJMI8+sNV4gqRCFCpvFBVVXeJX03XEQmdeRB2N1RZ2f2/WW07Vu/+blBnYZ8hR7HPkKPYZ8hR7DPkKPaZ3qNWV4OT9fkto955yK/Lw6n6k9AaNO22P1/wPpue1Gf8/b061I4j5ERERERERHReGr0GJ+vzzphunodafW277UUQIcQ9FBFeLaG7g8G7L2EgJyIiIiIiIqt6Q53NaHfr6HeNXt1uexFECHIPRoRnlDV0R3hGItwzgsH7PBjIiYiIiIiI+qAmYyNO1p9sWedtGfnOr89DVXPlWe8T5BbcJnRHWUe83aRul7Dy3oOBnIiIiIiIqBfTmXQoqD+Jk/X5bUa881DWVHrW+/jLAxDpFWWdam75/wi4Sd0vYeW9HwM5ERERERFRL2A0G1HUUGidat4avksaimCGud37KFyUiPSKajPibZl27inzvMTV900M5ERERERERD2IWTCjrKnUEritwTsPBfWnYBSM7d7HS+aFCM8oS/hu/a9XJHxcfC9t8WSDgZyIiIiIiKgbEgQB1bqq08HbeqRYPppNze3ex03ijgivSES2Ge2O9IqC0tUPIpHoEr8COh8GciIiIiIiIierM2it08zz63Kto951hrp228vEMoR7RCDSKxIRXlGI9IxGpFcUAtwCIRaJL3H1dKEYyHuZqqoqeHh4wM2NuxwSEREREXU3OpMOp+pP2oTuvLrcs+5sLoYYoR79bKaaR3pFIdS9HyRixrmejt/BXkStrsbf/nYdPvjgs04J5IIg4P3338P3338LjUaD4OBgzJo1GxMmXN5u+6amJrz++iv488+tMJmMGD16HB5+eCHc3S07MRYUnMLSpS/jyJEsuLu744YbbsI//vHPi66TiIiIiKi7MZmNKG4stgbv/JYp5+faYC1AHmgN3BFeUYjyika4R3+48CzvXouBvBfR6XRoamrqtMf74otP8P33G7BkyXL07x+Bv/76A08//RgCA4MwcOAgu/avv/4KysvL8emn62EymfDUUwvx1lsr8PDDj8JoNGLBgvkYN24CXn31DeTn52LBgvno1y8cEye2H/CJiIiIiLo7QRBQpatCfl0u8upyrQH8VP1JGMz6du/jLfNGpFd0y58oRHpFc2fzPoqB/EyCABg7L9Sel9QNcHBzheLiIixfvhRZWQchl7th8uRpuOOOuzBz5k0AgJkzb8Jjjz2NsWMn4J13VmHbtj9QUVEBV1dXXHbZJDz44CMQiUS47babUF5uf/ZgcnIqli59A3V1dbjjjtmIiIgEAIwePRYRERE4dOiAXSBvbm7Gjz9uwooVb8Pb2wcAMGfOPMybdw/mzn0Ahw4dQHV1FWbPvhcymQxxcQmYMeNmrF//OQM5EREREfUIDYaGlo3V2obv3LOu85ZL5OjfsqlapGeUNYBzgzVqxUDeliDAd/11kJXtuWRPaQgeCs316zvcvqmpCQ88MAeXXz4Fzz//Mmpra/Dkk49CEAR8+OHnuPHGq/Hhh58jODgEH330Pnbs+AvLl6+GSqXC4cMHMXfuXRgzZjyGDMnAunWfn/O57rzzHpuvT57MR35+HuLjB9i1LSwsgNFoRHR0jPW2yMhI6HQ6FBaeQn5+HsLCwiGTyazXIyKisG7dfzv82omIiIiILgWj2YjChlMtoTvPGr7Lm8rabS+GGP08whDpFY2oNqPewe4h3GCNzomB/Ezd/DdV27b9CYPBgHvumQuRSITAwCDcddccPPnko7jmmutt2l511XWYNu1KKBRKVFVVQafTwd3dA5WVFQ4/b0HBKTzyyAOYPHkaUlLS7K43NjYCAOTy02vXXV3lLdea0NjYYLeuXS6Xd+oUeyIiIiIiRwiCgMrmCmvgbv3vuc7z9nNVtYTu0+G7v2cE13nTBWEgb0skQu116y/5lHVHpquUlZWgtrYG06ZNsN4mCAKMRgNqatQ2bZubLZusZWbuQ0BAAOLiEiAIAgRBAADcfvstKC+3/y1fcnIKXnllmfXrP//cikWLnsUVV1yF++9/sN263NzkLc/ZbN3ETaeznI3o7u4ONzc3NDfbnpXY3NwMNzf3Dr92IiIiIqIL1WhssI5259XlIk+bg/y6PNQb259u7i51t4Ruz5a13t6Wo8W8XbwvceXUmzGQn0kkAmTdNyT6+wciNLQfPv74K+ttjY0NUKvVEIttp8MsXrwI3t7e+N//NsPV1RVms9kmyL///qfnfb7//vddfPTRB3jkkccxefLUs7YLD4+AVCpFfn4eEhMt68vz8/Mhk8kQHh6O2lq1dVq7VGrpdidP5iEqKtqh109EREREdC4msxFFjUXIr8tFrjbHOvJd1mS/dxIAiEUShHuEI9IrGtFeMdbwHSgP4jpv6nIM5D3MqFGjsWrVcnz88QeYMeMW6HQ6vPjicygvL8OSJcsAAPX19QCAhoZ6qFQqSCQSNDY24L333kFDQwMMBkOHnuvTT9fh00/X4c0330FcXMI528rlclx22SSsXr0C//d/LwMAVq9egcsvnwJXVzlSU4fAx8cXq1evxF13zUFBwSl8+eVnuPvu+y78zSAiIiKiPq1Gp7aOdufV5SK3Luecu5v7uaoQ7R1jnW4e5RWNMI/+cJG4XOLKiSxEQuv85V6qsrL9KSjdiUgEqFReqKqqQ0e+GydP5mPlyteRnX0EJpMZaWnpePDBR6BS+eOJJxZg585t+Ne/5iMhIRGvvLIIhYWn4O7ugZEjR6OiogL9+vXD/PkLzvkcgiBg2rQJaGpqgouL7Q+omTPvwD/+8U988MFa/PjjZuvmcI2NDVixYhn++msrDAYDxowZh/nzF1jXjhcVFeK11xbjyJHDcHOznEN+222zLug96+sc7TNE7DPkKPYZchT7DDnKkT6jN+lR0HASeVpL6M6ry0GeNhc1enW77eUSN0S2nON9eq13NHxcfLrgldCl0pN+zvj7e3WoHQN5N9CTOhZ1D+wz5Cj2GXIU+ww5in2GHNVenxEEAVXNlTahO68uBwUNBTALJvvHgAih7v0Q5R2NKK/TI9/c3bx36kk/ZzoayDllnYiIiIiILrlmUzMOV53C3oKDyNWeaNlsLeesZ3p7ybwQ5RVjmWruHYMorxhEeEbCTerWbnuinoCBnIiIiIiIuowgCKhoLm+Zbm4J3rnaEyhuKIIZZrv2rZusRXnFWEe+o71ioJL7c5M16nUYyImIiIiIqFM0m5pxsi7PMuVcm9Py39yzHi2mlCsR6WmZZh7tFYso72iEe0RwkzXqMxjIiYiIiIjIIYIgoLK5ArnanA6NektEEvT3jGiZbh6LaK9oxHjHIja0P6qr67v9emCirsJATkREREREZ6U36XCy/iRytSfajHyfOOtab18X35bRbstU82jvmHaPFhOJwCno1Oc5LZBv374dr732GnJzc+Hm5oapU6fikUcegVwux4EDB/DCCy8gJycHCoUCc+bMwY033mi979dff41Vq1ahsrISUVFReOqpp5Camuqsl0JERERE1CuoddWW4N0y3TxXe+KsO5zbj3pbwrfS1c8JlRP1TE4J5Gq1Gvfccw+effZZXHvttaiqqsKdd96Jd955B7fffjvuvvtuzJs3DzfffDN2796NuXPnIj4+HsnJydi5cyeef/55rFmzBsnJyfjoo48wZ84cbNmyxXreNRERERERnZ3RbERhwymb4J2rzTnrud7eMu+WEe9YRLeMfPf3jORab6KL5JRArlQqsW3bNnh6ekIQBNTW1kKn00GpVOLHH3+Er68vbr31VgDAiBEjcNVVV+Gjjz5CcnIyvvjiC0yfPh3p6ekAgFmzZuGzzz7Dxo0bccMNN7T7fN19Jkxrfd29Tuo+2GfIUewz5Cj2GXIU+0z3VW+otwbunJZp5/l1eTCY9XZtRRChn0cYor1PB+8Y79gu2eGcfYYc1Rv7jNOmrHt6egIAxo0bh/LycgwZMgTXX389li1bhri4OJu2MTEx+PLLLwEAOTk5dsE7JiYG2dnZ7T6PUukBiUTcBa+g8/n5dezweKJW7DPkKPYZchT7DDmKfcZ5BEFASUMJjqmP4Zj6GLLV2ThWcwzF9cXttveQeSBOEYd4RTzilfGIV8QjRhFzyc/1Zp8hR/WmPuP0Td1+/PFHaDQa/Pvf/8a8efMQGBhoN/VcLpejsbERANDQ0HDO62dSqxu6/W9QRCJLp6qurrvoHSarqqrg4eHB6fu9XGf2Geob2GfIUewz5Cj2mUtLb9LjVH0+crQnLKPeLaPfDcb6dtsHugVaRr29YhHjHYsYn1gEuQVDLLIduGqoNaIB7W/W1tnYZ8hRPanPqFQd+6WB0wO5XC6HXC7HI488ghtvvBEzZ85EXZ3tD4Hm5mZ4eHgAANzc3NDc3Gx3XaFQnPU5uvs3q5UgXFytanU1brnlOnzwwWeQyzsnkG/a9B3++993UV1dhf79IzF//iMYNCi53bZZWYdx7713QC6XW2+Li0vAm2+uAQAUFJzC0qUv48iRLLi7u+OGG27CP/7xz06ps6+62D5DfQ/7DDmKfYYcxT7T+bR6LXLrWoO35b+n6vNhamejNalIigivyJa13pbwHeUVA28X73Yfuzt8r9hnyFG9qc84JZDv27cPjz/+OL799lu4uFg2gtDr9ZDJZIiJicFff/1l0z4nJwexsbEAgNjYWJw4ccLu+tixYzulNkEQ0GxqPn/DTiKXyDttPY5Op0NTU1OnPBYA7Nu3B6+/vgSvvrocAwcOwldffYaFCx/Cl19+ZxO6W2VnZyElJQ0rVrxtd81oNGLBgvkYN24CXn31DeTn52LBgvno1y8cEyde3mk1ExEREfVUgiCgvKkMOdrjLWu9LeG7vKms3fatG63FeMUixjsO0d4xCPeMgEwsu8SVE9GFckogj4+PR3NzM5YuXYqHH34YlZWVWLx4MWbMmIEpU6Zg6dKl+O9//4tbb70Ve/fuxYYNG7Bq1SoAwIwZMzB37lxMmzYN6enp+Oijj1BdXY1JkyZddF2CIGDejnuRVXPooh+rowYpkvHGiLccuk9xcRGWL1+KrKyDkMvdMHnyNNxxx12YOfMmAMDMmTfhsceextixE/DOO6uwbdsfqKiogKurKy67bBIefPARiEQi3HbbTSgvL7V7/OTkVCxd+ga+++5/uOyyyUhOTgEA3Hzzrfj226/xyy8/Yvr0q+3ud/ToESQkDGy35szMvaiursLs2fdCJpMhLi4BM2bcjPXrP2cgJyIioj7HaDaeMeXc8t96Y/vTxYPdQqwj3jHecYjxjoW/PIDneBP1cE4J5B4eHnj33Xfx4osvYtSoUfDy8sJVV12FuXPnwsXFBWvXrsWiRYvwxhtvQKlU4sknn8Tw4cMBWHZdf+aZZ/Dss8+ivLwcMTExWLNmDXx9fTulNhG69w+1pqYmPPDAHFx++RQ8//zLqK2twZNPPgpBEPDhh5/jxhuvxocffo7g4BB89NH72LHjLyxfvhoqlQqHDx/E3Ll3YcyY8RgyJAPr1n1+zufKz8+1C94REZHIyTnRbvvs7CNQKv1wyy3XoaGhAamp6bj//gcREBCI/Pw8hIWFQyaTtXmsKKxb99+Lfk+IiIiIurNGYwNytTk4oT2OXO0JnNAex6n6fBjMBru2baect4bvaO8YeMp6zyZWRHSa09aQx8TEYO3ate1eS0pKwqeffnrW+15zzTW45pprOr0mkUiE5cPf6tZT1rdt+xMGgwH33DMXIpEIgYFBuOuuOXjyyUdxzTXX27S96qrrMG3alVAolKiqqoJOp4O7uwcqKys69FyNjY12a9Hlcjmamuw30DOZTPDz88fQoRm49toZMBqNeP31xXjkkQexdu06NDa2vxlfZ06xJyIiInI2tU6NXO1xnGiZdp6jOY7ixiIIsF/w6iH1RIx3bJuR71j094zklHOiPsTpm7p1NyKR6JIf9eCIsrIS1NbWYNq0CdbbBEGA0WhATY3apm1zcxNef/0VZGbuQ0BAAOLiEiAIAoSWHRBuv/0WlJfbr0lKTk7BK68sg1zuBp3OfgM9Hx9fu/tIJBIsX77K5rYHH1yAq66ahFOn8s+6GZ+bm7tDr5+IiIioOxAEAaVNJcjRtIZvSwCv1lW1214l97dONY/xjkOsdxyC3II55Zyoj2Mg72H8/QMRGtoPH3/8lfW2xsYGqNVqiMW2x1YsXrwI3t7e+N//NsPV1RVms9kmyL///tlnIQBAVFQ08vPzbG47eTIfI0aMsmtbXl6Gzz//GHfeeS/c3S0h22DQAwBcXeWIiopGYWEBjEYjpFJpy2PlISoq2oFXT0RERHTpmcxGnKo/hRztcRzXHkNOy9TzBmODXVsRROjnEdZmrbclhCtclU6onIi6OwbyHmbUqNFYtWo5Pv74A8yYcQt0Oh1efPE5lJeXYcmSZQCA+nrL+ZMNDfVQqVSQSCRobGzAe++9g4aGBhgM9uuV2jN9+tV4/PFHMHHiJCQnp2D9+s+hVqsxduwEu7a+vr74+ecfYDKZMWfOv9DU1ITXXluM9PQMhIb2Q2BgEHx8fLF69UrcddccFBScwpdffoa7776v094bIiIiooulN+mQV5drGfXWWAJ4fl0u9Ga9XVuZWIYIzyjEtgZvnzhEe0XDTcoZgETUMSJB6C0nuLWvsrL9nSq7E5HIcnB8VVXHDrg/eTIfK1e+juzsIzCZzEhLS8eDDz4ClcofTzyxADt3bsO//jUfCQmJeOWVRSgsPAV3dw+MHDkaFRUV6NevH+bPX9Ch2n74YSPef/89VFZWICIiCg8++AgSEwcBAD74YC1+/HGzdXO4nJwTLXUdBWD55cEDD/wb3t4+AICiokK89tpiHDlyGG5ulnPIb7ttluNvGDncZ4jYZ8hR7DPkqJ7YZxoMDcipO24z7fxk/UmY2znf213qjmivWMT6xCHWO9663lsq5vjWheqJfYacqyf1GX//jm3EyEDeDfSkjkXdA/sMOYp9hhzFPkOO6u59plZXgxPa4zihPYYc7Qmc0BxDcWNRu219XHwR620J3q0BPNg9BGKRuN32dGG6e5+h7qcn9ZmOBnL+So+IiIiIeg1BEFCtq8IJjSV8n9AewwnNcVQ0l7fbPkAeiFifuJaN1uIR6x0Hldyfm60R0SXBQE5EREREPZIgCChvLrOG7+OaYzihOYYavbrd9v3cwxDrYwndsT6Waec+Lr6XtmgiojYYyImIiIio2zMLZpQ0FuOE5vSo9wntMWgNWru2YojR3zPCJnxHe8XCQ+bhhMqJiM6OgZyIiIiIuhWzYEZRQwGOa47huCbbuuFae8eMSUVSRHhFIa5lvXecTwIivaIhl8idUDkRkWMYyImIiIjIaUyCCYX1BTiuzbZOOc/RnkCTqdGurUzsgmivGMT5xLeMfscjwjMSLhIXJ1RORHTxGMiJiIiI6JIwmY04VX+qZb13No5rjyFXewLNpma7tnKJHNHesS0j35bw3d8zgseMEVGvwp9oRERERNTpTGYjTtaftAbvE5ps5GpzoDPr7NrKJW7Wtd6tATzcsz8kIokTKiciunQYyImIiIjoopjMRhQ0nMKftSexr+gAjmmOnjV8u0vdEeMdhzjveMT5JCDWJx79PMIYvomoT2IgJyIiIqIOMwkmFNSfsox8a7JxTJONXO2Js4bv2JbgHdcy7byfRxjEIrETKici6n4YyImIiIioXbYbrlk2XcvRHm93zbebxB0DVQMQ5R6LOO8ExPkkINSjH8M3EdE5MJATEREREcyCGcUNRS2j3kdxrOW4sWZTk13b1jXfcT4JiG8Z/Q7zDEeAvw+qquogCE54AUREPRADOREREVEfIwgCyppKcUyTjeMt4fu45hgajPV2beUSOWJbNlqzhO+Edtd8i0SXqnoiot6DgZyIiIioFxMEAVXNldZR79Z131qDxq6ti9gFMd6xiPMZgASfAdztnIioizGQExEREfUiNTq1NXy3BnC1rtqunVQkRZRXDOJ9ByC+Zep5f89InvNNRHQJ8ScuERERUQ9Vb6hvs+b7KLJrj6KiudyunVgkQaRnlHXKebxPAiK9ouEicXFC1URE1IqBnIiIiKgH0Jl0OKE9jmO1R6wj4IUNBXbtRBAh3LN/S/C2jH5He8dCLpE7oWoiIjoXBnIiIiKibsZoNiK/Ltcy6q05imO12civz4NZMNm1DXILtgRv35Z1397x8JB5OKFqIiJyFAM5ERERkRO1HjdmCd9HkF17BDnaE9Cb9XZtFS5Ka/BuHf32dVU4oWoiokursl6HE5UNGOEig9jZxXQiBnIiIiKiS6i6ucoavFtHv+uNdXbtPKVels3WfE+Hb395AEQ8X4yIejmDyYzjFfU4WFqHQyVaHCrRoqxOBwAYG1eO164e4OQKOw8DOREREVEXad10zRLALRuvVTZX2LWzHDcWhwTfgRjgMxDxvgMQ4h4Ksag3jQMREbWvsl6HQyVaHCypw6FSLbLL66A3CTZtxCIgRuWBm4eEOanKrsFATkRERNQJ9CY98upykF17eup5QcMpu3ZiiBHhFYl4nwFI8B2IBJ8BiPSK5nFjRNQnGExmHKuox8ESLQ6V1OFw6enR77Z85FIkhXgjKdgbSSFeGBjkBU9XKVQqL1RV2c8q6qn4k5+IiIjIQYIgoKihENmaIzhaawnfuXUnYDAb7NoGuQUjwXdgSwAfgDjveLhJ3Z1QNRHRpdfR0e9olQeSrQHcG2G+8j6xRIeBnIiIiOg8anU1luCtOYKjtVk4pjmKOoP9CI23zMc66p3QsvZb4ap0QsVERJee0WTG8cqGltFvLQ6ValGq7djot4dL34ymffNVExEREZ1Fs6kZOZrjOFqbZR0BL2sqtWsnE7sgtnXdt+9AJPgMRIh7aJ8Y0SEiAgB1o/706HeJBkfK66Ezmm3atI5+t4bvpGBvhCvc+LOyBQM5ERER9VlmwYzChgIcqTmM7NojOKo5gry63HbP+w736I8BvolI8B2AAb6JiPSKhkwsc0LVRESXntEsILeyAQdLtS0hXItiTbNdO2+5FIOCLcE7OcQbicF9d/S7I/jOEBERUZ/ROvX8qCYLR2uykK05igZjvV07hYsSA3wHtgRwy/pvT5mnEyomInIOTZMBh0vrcLBEg4MlWmSV1aHJYDv6LQIQ6eeOpBBvJLcE8HClG8Qc/e4wBnIiIiLqlfQmPXLrTuBIzWFrCC9tLLFr5yp2RZxPAgb4JlqmnvsORIA8kNMpiajPMAsCTqmbrOH7YIkWJ9VNdu08XCSnp56HeGNQkDe85IyUF4PvHhEREfV4giCgpLG4Zdp5Fo7UZJ111/PWqeetAZxHjhFRX9OoNyGrTGsN34dK6lCnM9q1669ws2y+FmIZ/Y7yc+fodyfjvz5ERETU4zQYGnBMcxRHag/jSG0WjtZmQaOvtWvn4+KLAT4DMUCRiAE+lvXfnjKvS18wEZGTCIKAYk0zDpVqcbDYEsBzqhpgtj15DK5Ssc3a76Rgb/i6c5+MrsZATkRERN2aWTCjoP4UjtZmIav2EI7WZOFkfT4E2H6alIlliPGOs6z99knEAEUigt1COPWciPoUvdGM7Ip6HCzR4kCxZQq6utF+tlCwt6s1fCeHeiNW5QGpROyEivs2BnIiIiLqVjR6DY62jHofqbXsft5gbLBrF+QWjAG+iRjYMv08xjsOLhIXJ1RMROQ81Q2Wo8cOtEw/P1peB4PJ9heWUrEICYGelvDdMvod4OXqpIqpLQZyIiIichqT2Yi8ulzrtPMjtVkoaiiwayeXyBHvYzluLFExCAN8E6F09XNCxUREzmMyC8ivbsTBEo01gBfV2h89pnCTITnEG4NDLeF7QJAXXKUc/e6OGMiJiIjokqnV1SCr9jCO1BzGkdrDOKY5imaT/YfJfh7hSPS1BO+BikREekZBwo3XiKiPadAbW44es6z/PlSqRYPeZNNGBCBK5W4J4CE+SA7xRj9fOZfr9BD8l42IiIi6hMlsRH59HrJawveRmsMobiyya+ch9cQA34EY2BLAE3wHwsfFxwkVExE5jyAIKNXqrGu/D5RokdvO5mtuMjEGtaz9HhzKo8d6On7niIiIqFNo9LU4UpOFI7WHkFV7GNm1R9Fssj/Htr9nBAb6DsJAxSAM9B2E/p4REIs4lZKI+hajyYxjlQ04UKyxrgGvrNfbtQvxdm05dswHg0O9Ea3ygFTM0e/egoGciIiIHGYSTDhZl4+smkPW0e+ixkK7dh5SDyT4DkSibxIGKgZhgO9AeMm8nVAxEZFzaZsNOFRShwMlGhwo1iKrrA46o9mmjUQsQkKAp3X0OznEG/6e3HytN2MgJyIiovOqN9Rbjh2rOWQ5eqw2C43GRrt24R79rSPfiYpBCPeMgEQkcULFRETOIwgCCmubLZuvFVtGv/Or7X9mesul1p3Pk0O8kRjkBbmMPzP7EgZyIiIisiEIAkoai7FNcwI7CnYjq+YQ8uvy7M79dpO4Y6BvojWAD/BNhLcLR7+JqO8xmMw4Wl5vPff7bGd/hyvcMDikdf23D/or3SDm5mt9GgM5ERFRH6cz6XBck20d/c6qOYRafa1duxD3UCQqkpDom4RERRIivCI5+k1EfZKmyWDZfK1Ei4PFGhwpr7ebfu4iEWFAoFfL1HMfJId4QeHu4qSKqbtiICciIupjqporLeG7xrL52gnNMRgFo00bmViGRL9ExHu1rv9OgtJV6aSKiYicRxAEFNU2W9d+HyjWIl9tP/3c102GwW3Wfg8I9IILz/6m82AgJyIi6sVaN187XHMQWTUHcbjmEMqaSu3aKVyUGKRIRqJiEBIVSYjziUdIoB+qquogCO08MBFRL2UwmXGsoh77i7XWKejtTT/vr3DD4FDL2d+DQ70RrnDj2d/kMAZyIiKiXqTJ2IijtUdaArhlB/QGY4NNGzHEiPSKxiCFZep5oiIJQW7BNh8k+ZmSiPqKumYjDpa2nP19lt3PZa3Tz9uMgHP6OXUGBnIiIqIerLKpAodrDrb8OYTcuhyYBZNNm9bN1xIVSRikSMYA30R4yDycVDERkfMIgoCyOh0OFGuxvyWA51Y14MyJQD4tu58PDvVBSqg3EgK94Mrp59QFGMiJiIh6CMv08zwcUh+0hvCK5nK7dgHyQGv4HqRIQpRXNCRi/pNPRH2PySwgp6rBOvq9v1iDinq9XbswXzmSQ32Q0hLCI5Scfk6XBv91JiIi6qaaTc04VnsUh2oOWKegtzf9PMo7xhq+BymSEeAW6KSKiYicq8lgQlZpnXX0+1CpFg1621lDEhEQH+iFlFBvyxFkoT5QeXD6OTkHAzkREVE3UaurQVbtoZYR8AM43s7u524SdwxUJLYE8GQM8B0IdymnnxNR36Ru1NtMP8+uqIfJbDsB3cNFgqRgy9rvlFAfJAZ7wU3GIxupe2AgJyIicgJBEFDSWGwZ/VYfxKGaAyhsKLBr5+eqQpJyMAYpkpGkSOb0cyLqs1qPH2sN35nFGhTUNNm1C/B0sa79HhzqgxiVByRiTj+n7on/ohMREV0CJrMROdoTOFRjGf0+pD6IGr3arl2EZySSFIORqExCkmKw3e7nRER9hdEs4ERlPTKLTq//bu/4sWiVO1JCfaxHkAV7u/LnJvUYDORERERdQGfS4WhtFg6pD+BgzX4cqclCk6nRpo1MLEO8z4CW0e/BSFQkwdvF20kVExE5V5PBhMOlWuwvsoTvQ6VaNBnsjx8bGOhlHQFPDvGGj5vMSRUTXTwGciIiok5QZ9DisPoQDtXsx0H1ARzXZNut//aUeiFRMQhJysFIUgxGvE8CXCSuTqqYiMi5ahr12HO4DH8cLUNmsRbHyutgOuP8MU9XCQaHWEa/U0N9MCCIx49R78JATkREdAEqmytxSL0fh9QHcKjmAPLr8iCccZKtn6sKycrBSFKkIEk5GJFeURCL+EGSiPoeQRBQom3G/iLL2u8DxRqcVLe//ju1nw9SQi1/olTuEHP6OfViDORERETnIQgCihoKcbCmJYCrD6C0qcSuXT+PcCQrBltGwJWDEewWwnWMRNQnmQUBuVUNyCxq3QG9/fO/4wI9kRTkZd0BPdhb7oRqiZyHgZyIiOgMZsGM/Lo8HFTvt/45cwO21vO/k5WDkaxIwSDlYChdlU6qmIjIufRGM46W1yGzSIP9xVocKNGgXnfG+d9iEQYGelpGv/tZpqHHhClRVVUHQTjLAxP1cgzkRETU55nMRpzQHseBlvB9uOYA6gx1Nm1kYhck+AywTEFXpiDRNwkeMp7/TUR9U73OiEOlWksAL9Igq6wO+jMWgLvLJEgK8UJKqA9S+/kgMcgL8jbnf3MCEREDORER9UF6kw7ZmqPW0e/DNYfQbLJdyyiXuGGQIgnJyhQkK1OQ4DOAG7ARUZ+lbtRjf/HpAH68sh7mM0a1FW4ypPSz7H6e2s8Hsf6ekPL8b6JzYiAnIqJer8nYiKyawzhYYwngR2uPwGC2XcvoJfNCkmKwNYDHeMdBKuY/k0TUN5Vqm5FZpLH+OVVjvwFbiI8cqS1rv1P6+aC/wo37ZhA5iJ80iIio12kwNOBwzUEcUO/DAfV+HNdkwyTYrmVUuCit4TtZmcId0ImozxIEAfnqRmv43l+sRXmdzq5dtMrdMv28JYAHenHWENHFYiAnIqIer95Qh0Pqg9iv3oeD6kyc0ByHGWabNoFuQacDuCIF/TzCOJJDRH2S0SzgeEV9mwCugabZaNNGIgISAr2sR5ANDvWGr5vMSRUT9V4M5ERE1ONo9VocqtmPA9WZ2K/ORK72hN0Z4MHuIRisTEWKMg3JfikIcgt2UrVERM6lM5pxpMyyA3pmsQYHi7VoNNjOGnKVipEUfHoDtqQQb7i12YCNiLoGAzkREXV7Gn1tyw7omdhfnYn8uly7AN7PPQzJfilIUaZhsDIV/m4BTqqWiMi5GvUmHCrRYl+xZQQ8q1RrtwO6p6vEsva7JYAPCPSETMJlO0SXGgM5ERF1Oxp9rXX0+0D1PuTX59m1Cffoj8HKVCT7pWCwMhUqub8TKiUicj5Nk8G6A3pmsQbHyutwRv6G0l2G1H6W9d+p/XwQrfKAhDugEzkdAzkRETmdRq/BQfV+7K/eiwPqTOTV5dq1ifCMxGBlKgb7pSJZmQKlq58TKiUicr6qeh32tWy+llmkQU5Vg12bYG9XmwAezh3QibolBnIiIrrk6gxaHFTvR2b1PhyozkReXY7dFPQIz0ik+KVjsNIyAu7rqnBStUREztV6BNm+QssIeEE7R5BFKN0sAbwlhAd5y51QKRE5ioGciIi6XL2hDgfVB7C/eu9ZN2Hr7xlhWf/tl4bByhQoXJVOqpaIyHkEQUBhbTMyi2qxr2UX9FKt7RFkIgBxAZ5ICfVGWpgvUkK9oXR3cU7BRHRRGMiJiKjTNRgacKjm9Ah4jtb+GLJwj/4Y7JeGFGUqBvulQckATkR9kFkQkF/daA3fmUUaVDXobdpIRMCAIC+khvogLcwHg0N84CXnx3ii3oB/k4mI6KI1m5qRVXMImdV7kVm9F8c02TALtkfq9PMIR4oyFSl+ll3Q/eQqJ1VLROQ8JrOAE5X1NgH8zDPAZRIRBgV5ITXMF2mhliPI3F14BBlRb8RATkREDjOYDTham2UN4Edrs2AwG2zahLiHIsUvDanKdCT7pcKfu6ATUR9kNAs4Vl6HfUWalo3YNKjX2f7CUi4VIznE27oGfFCwN1ylPIKMqC9gICciovMymY04rj1mDeCH1QehM9uuafSXByDVLx2pfulI8UtDoFuQk6olInIeg8mMI2WnA/jBYi0aDbYB3MNFYj3/O62fDxJ4BjhRn8VATkREdsyCGXl1Ocis3ofM6r04qM5Eo7HRpo2vi2+bAJ6OUPd+PFKHiPocndGMrDIt9hW2BPASLXRG2z0zvOVSpIRawndamA/i/D15BjgRAWAgJyIiWHb1LW4swr6qPdhXvQf7q/dBa9DYtPGUemGwXypS/dKQ6peOCM8oBnAi6nOaDSYcKj0dwA+XaqE32Z4a4esms45+p/XzQYy/B8T8eUlE7WAgJyLqo6qbq7Cveg/2Ve1BZvVeVDSX21yXS9yQrExpGQVPQ7R3LCQibipERH1Lk8GEgyVa7CusbQngdTCabQO40l2GtH6+SAuzBPBIP3cGcCLqEAZyIqI+QqvX4s+yrdhbtReZ1Xtwqv6kzXWpSIqBikFI8xuCNL8hSPAdCKmY/0wQUd/SqDfhYIll9HtvoQZHyuwDeICni2UEPMwXaf180F/hxhlDRHRB+EmLiKiX0pl0yKo5ZBkFr96D45psmIXT6xpFECHGOw5pqiFI80vHIMVguEndnFgxEdGl16g34UCJJXzvK9TgSHkdTGcE8EAvV6SHtU5B90U/XzkDOBF1CqcE8uzsbCxevBhZWVmQyWQYNWoUFi5cCKVSiWeeeQZfffUVZDKZtf3ChQtx8803AwC+/vprrFq1CpWVlYiKisJTTz2F1NRUZ7wMIqJuxSSYcFxzDPuqdmNf9R4crjkEg1lv0ybMIxypfulI8xuCwX5p8HHxcVK1RETO0aA3Yn9x6xrwWhwtq8MZS8AR1BrAw3yRHuaDEG8GcCLqGpc8kDc3N2P27Nm46aab8Pbbb6OhoQGPPvooHn/8caxevRqHDh3C888/j+uuu87uvjt37sTzzz+PNWvWIDk5GR999BHmzJmDLVu2wM2NozpE1LcIgoCSxmLsrdqNvVW7kVm9F/XGOps2fq4qpPqlI101BJfHjYe0yQOCcJYHJCLqhU4H8FrsLdQgu9w+gId4u1rDd1o/X4T4yJ1TLBH1OZc8kJeUlCAhIQFz586FRCKBi4sLbr75ZixYsAB6vR7Hjx/HoEGD2r3vF198genTpyM9PR0AMGvWLHz22WfYuHEjbrjhhkv5MoiInEKjr0Vm9V7sqdqFfVV7UNZUanPdQ+qBFL80pPkNRZpqCMI9+kMkEkEkAlQeXqhqqjvLIxMR9Q4NeiMOFGuxt1CDvYW17QbwUB+5NXynhfkg2JsBnIic45IH8qioKLz77rs2t/3www9ITExEdnY2jEYj3njjDezduxdeXl644YYbMHv2bIjFYuTk5NgF75iYGGRnZ5/zObv7DKPW+rp7ndR9sM/0HTqTDodrDmJP5S7srdqDHO1xCDj9ybJ1I7YhqqFIVw1FvE8CJO1sxMY+Q45inyFHOavPNOpNOFCswZ6WAN7eFPR+vnKkt2zAlh7mgyAG8G6BP2fIUb2xzzh1UzdBELBs2TJs2bIF69atQ1VVFTIyMjBz5ky89tprOHr0KObOnQuxWIzZs2ejoaHBbmq6XC5HY2PjWZ9DqfSARCLu6pfSKfz8vJxdAvUw7DO9j1kwI1udje0l27GjdAf2le+D/ox14DG+MRgRMgLDg4djSOAQuMvcO/z47DPkKPYZclRX95kGnRF7TtVgR141duRV42CRxm4TtnClO4ZHKTE8yg/DovwQ6suljd0Zf86Qo3pTn3FaIK+vr8djjz2GrKwsrFu3DvHx8YiPj8eoUaOsbZKTk3H77bdj48aNmD17Ntzc3NDc3GzzOM3NzVAoFGd9HrW6odv/BkUksnSq6uo6ru2kDmGf6V0qmsqxu3KXdRq61qCxua5yVSHdPwPpqqFI9xsCpdzPeq1RY0Ijzj8NnX2GHMU+Q47qqj7T1GYX9L2FGmSV2e+CHuojR1qYD9LDfJHezwfBbdeAG42oquJyne6IP2fIUT2pz6hUHfulQYcC+YABAzr8xCKRCEeOHDlnm4KCAtx1110ICQnBl19+CaVSCQD4+eefUVVVhVtuucXaVq/XQy63/FCNjY3FiRMnbB4rJycHY8eOPefzdfdvVitB6Dm1UvfAPtMzNRkbcUCdiT1Vu7CnchcKGk7ZXHeXumOwMs0SwFVDrevAW13M95x9hhzFPkOOutg+02ww4VCp1jIFvaAWWe2cA952E7b0MF+7NeDssz0Lf86Qo3pTn+lQIHdxccGaNWvO204QBNx9993nbKPRaHD77bdj+PDhWLRoEcTi09PJBUHASy+9hP79+2P48OHYv38/PvjgAzz22GMAgBkzZmDu3LmYNm0a0tPT8dFHH6G6uhqTJk3qyMsgInIKs2BGjvY49rSMgh+uOQijYLReF0OMBN+BGKKyjIIP8E2EtJ114EREvZHeaMahUi32FtZiT6EGh0u1MJyxCLz1GLL0MF+kh3EXdCLqPTr0iW/MmDHIyMjo0AOOGTPmnNfXr1+PkpISbNq0CZs3b7a5lpmZicceewzPPvssysvLoVKp8K9//QvXXHMNAGDEiBF45plnrNdjYmKwZs0a+Pr6dqg2IqJLpbKpwjICXrULe6t2201DD3ILxlDVMKT7ZyDNLx2est6zFoqI6FwMJjOySuuwp7AWewtrcai0Djqj2aZNgKdLS/i2hPBQH54DTkS9k0gQHBvsr6qqgkqlgl6vx5dffgmFQoFp06Z1VX0XrbKy+68ZEoksawyqqrr/WgjqHthnup8mY5N1Gvreql04VX/S5rq71B2pfukYosrAENUwhLiHXtIPl+wz5Cj2GXLU2fqM0WTG0fJ6awA/UKxF8xkBXOkuw5AwX6SH+2JImC/CfBnA+wL+nCFH9aQ+4+/fiWvIW33xxRdYtGgR9u/fjyVLlmDjxo0QiUTIz8/Hfffdd0GFEhH1RIIgIL8uD7uqdmBP5U4cqjkAg9lgvW6Zhj4AQ1TDOA2diPoUk1nAsYp67CmoxZ7CWuwv0qLRYLJp4+sms45+DwnzRYTSjQGciPokhz4drlu3Dm+++SZMJhPWr1+PNWvWwN/fHzNnzmQgJ6JeT6PXYF/Vbuyu2ondlTtRrauyuR7oFoShqmEYospAqiodXjJvJ1VKRHTpmAUBeVWN2FtUiwNl9diRW406ndGmjY9citR+PtZR8Cg/d4gZwImIHAvkpaWlGDVqFPbt2wepVIq0tDQAgFar7ZLiiIicySSYkF17BLsrd2J31U5k1x6BgNPzo1zFrkjxS8NQ/2EYqhqOfh5hHOEhol5PEAScqmmybMJWUIu9hRrUNBls2ni4SJDazwdDwy2bsMX6ezCAExG1w6FA7uPjg1OnTuGHH36wbvK2Y8cO+Pv7d0lxRESXWmVzJfZU7sSuyh3YV70bdQbbfSgiPaMwxH8YMvyHI0mRDBeJq5MqJSK6dEo0zdhTUIvdLevAK+v1NtflUjFS+vlgXEIgBvjJER/gBamYAZyI6HwcCuR33HEHrrrqKgDAhx9+iL179+Kee+7BM8880yXFERF1Nb1Jj0M1B7Cr0rIWPL8+z+a6p9QL6aqhyPAfjiGqDPi7BTipUiKiS6eyXoc9LSPgewpqUaLV2Vx3kYiQFOKNIS1rwBODveAiFfeYzZaIiLoLhwL53//+d4wZMwZSqRTBwcFQq9X46KOPMGjQoK6qj4io05U1lmJn5XbsrNyO/dV70Wxqtl4TQYQE34EYqhqGof7DkOAzABJuxkZEvZymyYC9RRrLKHhBDU6qm2yuS8QiJAZ5YUi4L4aE+SAp2BtymcRJ1RIR9R4Ofcq89tpr8c0331i/ViqVUCqVmDhxIn799dfOro2IqFPoTXocVO/Hrsrt2FW5AwUNp2yu+7mqWtaBD0Oaaih8XHycVCkR0aXRqDchs7g1gNfieEU92g5qiwAkBHpaRsDDfZES6gN3FwZwIqLOdt5AXlBQgLfeegsAkJOTg8cee8zmen19PZqbm9u7KxGR05Q2lmBX5XbsrNxhNwouFkmQ6DsIw/xHICNgOKK9YrkZGxH1ajqjGYdLtdjdEsCzyupgMtvOK4/0c0dGyzngaWE+8JbLnFQtEVHfcd5AHh4eDoVCgZqamnavK5VKvP76651eGBGRIzoyCp7hPxwZ/sORrhoKT5mXkyolIup6RrOA7PI6awA/WKKFzmi2aRPiI8fQcF8MbTmKTOXh4qRqiYj6rg5NWV+wYAEAICwsjOeNE1G3YVkLvu2so+CDFEnI8B+OYf4jEOUVw1FwIuq1BEFAbnWjJYCfqsG+Ig0a9CabNn4eLhgS5oOMcAXSw30Q6uPmpGqJiKhVhwL53r17kZ6ejqFDh2L37t3tthk6dGinFkZEdCaj2YismkPYUbkNOyq24VR9vs11joITUV9SrGnC7lOWEfA9hbVQN9qeBe4tlyKtnw+GhiswNNwXEUo3/mKSiKib6VAgv+uuu7Bv3z7MnDmz3esikQhHjx7t1MKIiACgRqfGrsod2Fm5Hbsrd6LBWG+9Zl0LHjCCo+BE1OupG/XYU1CLXS3T0Es0tnv4uErFSA31sUxD7++LOH9PSHgWOBFRt9ahQL5v3z4AQHZ2dpcWQ0RkFszI0R7HjgrLKPgxzVEIbfb+9XHxRYb/cAz3H4kh/hnwknk7sVoioq5TrzNiX5HGMgJeUIucqgab6xKxCIOCvKwBfFCQN1ykYidVS0REF8Lhw3X1ej3UajXM5jM2BgkJ6bSiiKhvaTA0YG/VLuvZ4Gpdtc31GO84DA8YieH+IxHvOwASEY/eIaLeR28041CpFrtO1WB3QS2OlNXBZLsROmL9PTA03BcZ4Qqk9POGh4vDH+WIiKgbcein+KZNm/D000+jvr4egiBAJBJZ/8sp60TkiML6Auyo+As7KrfhkPoAjILRek0ucUO6aiiGB4zEMP8RUMn9nVgpEVHXMAsCjlfUY3dBLXadqkVmscZuJ/QwXzmGhiswJNwXQ8J8oHDnTuhERL2JQ4F8xYoVuPXWW3HddddBKuVvZImo44xmIw7XHMT2ij+xvfwvFDUW2lwPde+HYS2j4MnKFLhI+KGTiHoXQRBQrGnGrlM12NUyDV3TbLRpo3SXIaO/ZRO2oeG+CPaWO6laIiK6FBxK1aWlpbj//vsZxomoQ+oMWuyq2IFtFX9id+VO1BvrrNekIimSlSkYHjAKwwNGop9HmBMrJSLqGtaN2E7VYldBDUq1OpvrHi4SpPbzsYbwaD93bk5JRNSHOJSsExMTkZOTg4SEhK6qh4h6uIL6U9he8Rd2VPyFQzUHYRZOn4PrLfPB8ICRGBEwCkNUw+Ah83BipUREna9Rb0JmkQa7CizrwE9U2m7EJhWLkBTshaH9FcgI90VikBekEm7ERkTUVzkUyNPS0jBr1ixMnToVKpXK5tr999/fqYURUc9wvqnoEZ6RGBEwGiMCRmGAIpEbshFRr2I0CzhSVmeZhn6qBgdL62Ay2+7EFuvvgYxwBYb290VqqA/cXfhzkIiILBwK5JmZmYiNjUVubi5yc3Ott3NqFVHfcr6p6IP9Uq0hPNidJzAQUe8hCAJO1TS1BPBa7CmsRYPeZNMmxNvVOgI+JNwXSm7ERkREZ+FQIP/www+7qg4i6uZKG0vwV/kf2FbxBw6qD9hMRfdx8cVw/5EYHjCSU9GJqNepbtC37IRu2YytvM52Hbi3XIohYb4Y1t8XGf0V6Ofr5qRKiYiop3EokH/zzTdnvXbttddeZClE1J2YBTOOa7LxV/kf2F7xJ/Lqcm2uR3pGYUTgaAwPGIUBvgM5FZ2Ieo0mg2Ud+M5T7a8Dl0lEGBzqg4xwXwzrr0B8gCckYs4WJCIixzkUyN944w2brzUaDZqampCens5ATtQL6E16ZFbvxbbyP7Ct4k9U66qs18QiCZKVgzEyYAxGBo5GiHuoEyslIuo8JrOA7PI67GzZCf1giRYGk+068Dh/Dwzrr0BGf1+khPpALuMvIYmI6OI5FMh//fVXm68FQcCaNWtQW1vbmTUR0SWk1Wuxo/IvbCv/A7srd6HJ1Gi95iZxR4b/cIwMHI1h/iPh7eLtxEqJiDpPUa1lHfjOlnXg2jPOAw/ycrUG8KHhvlBwHTgREXWBizpQXCQS4c4778TYsWOxYMGCzqqJiLpYcUMRtlX8iW3lf9gdTaaS+2NkwGiMDByDFGUaXCT8EEpEPV9dsxG7C2ux82QNdp6qQbGm2ea6p6ukZR24Ahn9FQjzlXPTWiIi6nIXFcgBID8/n/9gEXVzgiDghPYY/izfij/LfsfJ+nyb61FeMRgVOAYjA0YjzieBf6eJqMczmMw4VKq1TEM/VYMjZXVoexqZpOU88Iz+Cgzvr8CAIC9IuQ6ciIguMYcC+cyZM20+qBsMBhw7dgxXX311pxdGRBfHZDbiUM1B/Fn+O/4s24qK5nLrNbFIgsHKFIwKHIMRAaN5NBkR9XiCIOCkugk7Ws4D31tYiyaD2aZNhNLNOgKeHuYDD5eLHpcgIiK6KA79SzRs2DCbr8ViMWbNmoXLL7+8U4siogujM+mwp2oX/irfim3lf0Jr0FivySVyZPgPx6jAsRgeMBJeMq4HJ6KerbbRgF0FNdjRMg29ol5vc93XTWbZCT3CciZ4kLfcSZUSERG1z6FAfv/993dVHUR0geoNddhRsQ1/lv+OXZU70Wxqsl7zlvlgRMAojA4ahyGqDLhKXJ1YKRHRxdEbLdPQWwN4dnk92u6F7iIRISXUB8P6KzCsvwKxAR4QcwkOERF1Y5yrRdQDVTVX4pfsjdic+wMyq/fB1GZTtgB5IEYHjcXowHFIUiRDIuZfcyLqmdpOQ9950jINvdloOw09tuU4smE8joyIiHogflIn6iGKG4rwR9lv+LP8dxypzbK51t8zEqMDx2JM0DjEesdzUzYi6rFap6HvPGWZin7mNHSluwzD+iswvGUausqTM3+IiKjnYiAn6qYEQcDJ+nz8UfYbtpb9hry6HJvryf7JGO43GqMCxiLMM9xJVRIRXRyDyYyDJVprAD/bNPThEZZp6DH+nIZORES9xwUHcrVaDaVS2Zm1EPV5luPJjuOPsi3YWvYbChsKrNfEIglSlWkYHTQOo4PGIKFfFKqq6iAI53hAIqJuRhAEFNY2Y8dJNbafbH839GiVu3UUPJXT0ImIqBdzKJAbjUasWLEC69atg8lkwoYNG/Dggw/irbfeQkBAQFfVSNSrmQUzjtZmYWvZb/ij7DeUNZVar8nEMqSrMjA2aDxGBoyBt4tlZ3QODhFRT1KvM2J3QS12nqrB9pM1KNE021xXuMmQ0d/XOgruz2noRETURzgUyFesWIEdO3Zg+fLlmD9/Pvz8/BAUFIRFixZh+fLlXVUjUa9jEkw4pD5gDeHVuirrNVexK4YFjMCYoPEY7j8KHjIPJ1ZKROQ4k1lAdkU9dpxUY8fJGhwq0cLUZjaPVCzC4FBvDO+vwIgIJXdDJyKiPsuhQL5hwwZ88sknCAwMhEgkgru7O1566SVMmjSpq+oj6jUMZgP2V+/F1rLf8Ff5VtTqa63X3KXuGBEwGmOCxiPDfzjkEp6VS0Q9S0WdDjta1oHvOlUDTbPR5nq4wg3D+yswLEKBIWG+cHfhNHQiIiKHAnljY6N13bjQsnBVLpdDLBZ3fmVEvYDBbMDeql34vXQL/ir/A/XGOus1b5k3RgaOwdig8UjzGwoXiYsTKyUicozOaMb+Ig22n6zBjlNq5FY12lz3cJFgaLhlGvrwCAVCfdycVCkREVH35VAgT0lJwcqVKzF//nzrsUoffvghkpKSuqQ4op7oXCFc4aLA6KDxGBs0HoOVqZDyjHAi6iEEQcCpmibsOFmD7SfV2Fuoga7NmeAiAAOCvDA8QoER/RUYFOwFqYS/sCciIjoXh9LA448/jlmzZuHrr79GQ0MDrrjiCjQ0NOA///lPV9VH1CNYQvhu/F76q10I93NVYUzQeIwLnoBBimRIRJymSUQ9Q12zAb+dqML2kzXYnq9GiVZnc93f0wXDrWeCK+DrLnNSpURERD2TQ4E8PDwc33//PbZs2YKSkhIEBQVh/Pjx8PT07Kr6iLqtc4VwpasfxgZNYAgnoh7FLAg4UdGAbS2bsR0s0cJoPr0bm6zlTPAREZbN2KJV7tYZc0REROQ4hwL5888/jxtvvBFXXHFFV9VD1K0xhBNRb1PTqMfOU7XY3hLC1Y0Gm+utm7GNiFQgPcwXbjwTnIiIqNM4FMirq6tx8803Izo6GjfeeCOuvPJKeHl5dVVtRN2CwWzAvqo9+K30F4ZwIurxjGYBWaVabDtp2RH9aFkd2pxIBjeZGEPDFRgRocAVaf3gIZghCGd9OCIiIroIDgXyZcuWoa6uDhs2bMDXX3+NxYsXY8qUKZgxYwaGDh3aVTUSXXImsxH71ZnYUvoz/ij7DXUG2xA+Jmg8xgdPZAgnoh6hsl5nXQe+81Qt6nS2R5LF+ntgRIQSIyIUGBzqDZlEDJEIUPl5oKqq7iyPSkRERBfL4S2evby88Pe//x1///vfsX37djzxxBP49ttvcfTo0a6oj+iSMQtmHKk5jF9Lf8bvpb+gRl9jvWYN4UETMUjJEE5E3ZvRZMaBEi22n6zBtnw1TlQ22Fz3kUuR0V+BkZEKDO+vgMrT1UmVEhER9W0OB/KGhgZs3rwZ33zzDQ4ePIjx48fj+eef74raiLqcIAg4oT2GX0t+xm+lv6Ciudx6zVvmg3FBEzAh5HIkKQczhBNRt1ambbYG8N0FtWjQm6zXRAAGBnlhRIQCIyOVGBjkBYmYm7ERERE5m0OB/OGHH8avv/6KoKAg3HjjjVi+fDmUSmVX1UbUZfLr8rCl9GdsKfkZxY1F1tvdpe4YHTgOE0MmIc1vCM8JJ6JuS280Y3+xBtvyLeeC51U32lxXuMksZ4K3jIIr3F2cVCkRERGdjUNpQyqVYs2aNRgyZEhX1UPUZYobiqwhPL8+z3q7q9gVIwJHY0Lw5RjmPxwuEk7dJKLuqUTTjG35amzLV2NPYS2aDGbrNbEIGBTsbR0FTwj0hJhHkhEREXVrDgXyxYsXd1UdRF2ioqkcv5X+gi2lP+OYJtt6u1QkRYb/cEwIuRwjA0bDTeruxCqJiNrXOgr+V74a2/NrkK+2HQX383BpORNcgWH9FfBxkzmpUiIiIroQHQrkaWlp2LdvHxISEiA6y2/buakbdRdavRZby37FzyU/4qB6v/V2McRIVaVjYvAkjA4aCy+Zt/OKJCI6izJt6yh4DXYV1NiMgktEQHKIN0ZEKjEyUolYfw+OghMREfVgHQrk77zzDgDggw8+6NJiiC6UzqTD9oo/8UvJj9hZsR1G4fSRPkmKwZgYcjnGBk2AwpV7HhBR92IwmXGgWItt+Wr8lW+/Frx1FHxUpBLD+ivgJefeFkRERL1Fh/5Vb10z/uOPP+LJJ5+0u75gwQJkZGR0bmVE52ESTMis2otfSn7EH+W/odF4+kNslFcMLg+ZjAkhlyPQLciJVRIR2auo01kD+Jk7ootFQFKwN0ZGKjEyUoG4AK4FJyIi6q3OG8jLy8uxfft2AMAXX3yBQYMG2Vyvq6vDTz/91DXVEZ1BEAQc0xzFLyU/YUvpz1Drqq3XAuSBuCxkMi4PnYxIr2gnVklEZMtoFnCoRIs/89TYftL+XHClu8y6GRvXghMREfUd5w3kCoUC69atg1qthl6vxxtvvGFz3dXVFffff3+XFUgEAEUNhfil5Ef8UvITihoKrLd7y7wxLvgyXBYyCYMUyRCLxE6skojoNHWjHtvza/BXvho7TtagTnd6KY0IwKBgr5ZRcO6ITkRE1FedN5C7uLjgyy+/BADceeedeO+997q8KCIAUOvU2FLyE34u+RHHNKc3DXQVu2Jk4GhcFjIFQ/2HQSbmSBIROZ9ZEJBdXo+/8ixT0Y+U1UFoc91HLsWISCVGRSoxvL8Cvu782UVERNTXObQzTHth3Gg04vjx4xg4cGCnFUV9V7OpGX+Vb8VPxZuxp3IXzLDsLiyGGOmqobgsdDJGB46Fu9TDyZUSEQH1OiN2nLSMgm/LV0PdaLC5Hh/giVFRlhCeGOQFiZij4ERERHSaQ4H8999/x7PPPovy8nIIwunf+0ulUhw6dKjTi6O+wSyYcVC9Hz8Wb8LWsi02m7Ml+AzE5aGTMT74cii5QzoROZkgCMirbsS2fDX+zFPjQIkWJvPpfw89XCTI6K/AqEjLenB/T1cnVktERETdnUOBfMmSJZg8eTK8vb1x7NgxXHnllXjzzTcxY8aMrqqPerGC+lP4qXgzfirejIrmcuvtQW7BmBQ6FZNCp6KfR5gTKyQiApoNJuwprMWfeZZR8FKtzuZ6hNINIyOVGB2lREqoD2QS7mVBREREHeNQIC8sLMQjjzyCoqIi7NixA5MnT0ZUVBTmz5+PmTNndlWN1Ito9Br8VvozfizejKO1WdbbPaQeGBc8EZNDp3FzNiJyujJtM/5qGQXfXVALndFsveYiESE9zBejoywbsvXzdXNipURERNSTORTIlUolxGIxQkJCkJubCwCIiYlBWVlZlxRHvYPBbMCOim34qXgzdlT8BaNg2WlYLJJgqCoDk0OnYWTgGLhKOLWTiJzDZBZwuNRyLNmfeWrkVNkeSxbo5YrRLWvBh4b7Qi6TOKlSIiIi6k0cCuTx8fFYvnw55s6dCz8/P/z++++Qy+VwdWWQIluCICBbcwQ/Fm3CltKfoTVorddivOMwKXQqLguZBKWrnxOrJKK+TNtswI6TNfgjT43t+Wpomk8fSyYWAUnB3hgVpcSYKD9Eq9wh4rFkRERE1MkcCuSPPPII5s2bh5tuugnz5s3DfffdB7PZjAULFnRVfdTDVDZV4MfiTfiheJPNeeF+ripcHjoFk0KmIso72okVElFf1boh2195avyZr8bBYg1Mbc4l83KVYkSEAqOjlRgRoYSvG48lIyIioq7lUCCPjo7G999/DwAIDQ3Fli1b0NDQgMjIyC4pjnoGvUmHv8r/wOai77GnaheElpN3XcWuGBM0DpNCpyFNNQQSEad4EtGlpTOasa+oFn/mqvFnXjVKztiQLcrPHaOjlBgd5YekEG9IeSwZERERXUIOBXIAqKioQEFBgc2xZ1VVVRg6dGinFkbdmyAIOK7Jxuai7/Fr6U+oM9RZryUrUzAl9AqMC57A88KJ6JKratBjW54af+RVY+epGjQZbDdkGxLui1GRfhgdpUSIj9yJlRIREVFf51Ag//DDD/Hyyy/DZDLZ3C4SiXD06NFOLYy6pxqdGj8X/4DNRd8jvz7Peru/PACTQ6dhar/pCPXo58QKiaivEQQBxysb8EduNf7MUyOrrM7mur+nC0ZFKjEm2g9Dw33hxg3ZiIiIqJtwKJC///77ePrpp3HDDTdAKnV4cJ16KKPZiJ2V27G56HvsqPgLJsHyCxmZ2AWjA8diar/pnJJORJdU27PB/8itRkW93ub6gEBPjInyw5hoJeIDPLkhGxEREXVLDqVqtVqNG2+8EWIxz4juC/Lr8rC56Hv8XLwZNfoa6+3xPgMwtd90TAy5HF4ybydWSER9SWW9zhrAd51xNrirVIxh/RUYE6XEqCgl/D15+gcRERF1fw4F8oyMDOzcuRMjRozoqnrIyeoNdfi15GdsKvoOxzSnlyEoXBS4PHQKpvabjkgv7pJORF1PEARkV9Rbp6IfLa+3uR7g6YIx0X4YE+WH9DAfng1OREREPY5DgTwwMBD33HMPhg0bBpVKZXPtpZde6tTC6NIRBAEHa/bj+4JvsbVsC/Rmy9RPiUiC4QEjMbXfdAzzHwmpmMsUiKhr6Yxm7CmoxdbcavyZZzsVXQQgMdgLo1vOBo/19+BUdCIiIurRHEpYer0e06dP76pa6BKr1dXgh+JN2Fj4LQrbnBke4RmJqf2m4/LQqVC6Kp1YIRH1BepGvXUq+o6TNWhuMxXdTdYyFT3aD6MilfDzcHFipURERESdq0OB/PDhwxg0aFCHRsFb21L3ZBbM2Fu1G98Xfott5X/AKBgBAHKJGyYGX47p4VcjwWcgR52IqMsIgoC86kb8kVuNrblqHC7VQmhz3ToVPdoPQ8J84SrlviVERETUO3UokP/jH//Avn37OvSAjrSlS6eyuRKbC7/DxqINKG8qs94e7zMA08OuwsSQSTwznIi6jNFkRmaxBltzLSPhxZpmm+sJAZ4YG81d0YmIiKhv6VAgb25uxj/+8Y8OPaBOp7uogqjzmMxG7Kjcju8Lv8Wuiu0wwzIN1EPqictDp+DKsKsR7R3r5CqJqLeqazZiW74aW3Orse2kGvU6k/Wai0SEoeEKjIlWYnSUHwK9uCs6ERER9T0dCuT33Xdfhx8wIyPjgouhzlHaWIKNhRuwueh7VOuqrLcnKQZjevjVGBc0Ea4Sfvglos5XomnG1txq/J5bjcwiDUzm05PRFW4yy4Zs0X4Y1l8Bdxfuik5ERER9W4cC+f3339/VddBFMpgN+Kt8K74v+BZ7q3dbb/dx8cWU0CtwRdiVCPeMcF6BRNQrtR5N9ntONbbmVuNEZYPN9Ug/d4yN9sPYaD8kBnlBIuZUdCIiIqJWPMeqhytrLMV3hf/DpsINqNHXWG9PVw3F9LBrMCpwDGRimRMrJKLeRm80Y29RLX7PqcYfubZHk4lFQEqoD8ZG+2FcjB/6+bo5sVIiIiKi7o2BvAcyCSbsqtiBDQVfY2fldggt+xP7uaowLexKTOt3JYLdQ5xcJRH1JtpmA/7KV2NrjhrbT6rRoD+9HtxNJsbwCCXGRfthVJQSvm78JSARERFRRzCQ9yBqXTU2FX6H7wr/Z7NTerrfUFzV/zqMDBgNqZjfUiLqHCWaZvyea5mKfuZ6cD8PF4yNVmJctApDwnk0GREREdGFcCi97dy5E8OGDeuqWqgdgiDggDoT3xZ8jT/KfoNJsIxKecm8MLXfdFwZdi3CPMOdXCUR9QaCIOB4RQN+y6nC7+2sB4/yc8e4GD+Mi/bDgCAviHk0GREREdFFcSiQz5s3D15eXrjuuutw3XXXISTkwqZFZ2dnY/HixcjKyoJMJsOoUaOwcOFCKJVKHDhwAC+88AJycnKgUCgwZ84c3Hjjjdb7fv3111i1ahUqKysRFRWFp556CqmpqRdUR3dWb6jDD0UbsaHgGxQ0nLLePtA3EVeHX49xwdwpnYguntEsYH+RxhLCc6pRVnf66MrW9eDjYiybsnE9OBEREVHnciiQ//nnn/j111/xzTffYPXq1Rg6dCiuv/56TJ48GS4uLh16jObmZsyePRs33XQT3n77bTQ0NODRRx/F448/jsWLF+Puu+/GvHnzcPPNN2P37t2YO3cu4uPjkZycjJ07d+L555/HmjVrkJycjI8++ghz5szBli1b4ObWOz4oZtcewYaCb/BryU/QmS0fjOUSN0wKmYKr+l+LGO84J1dIRD1dk8GE7Sdr8HtOFf7MU0PbbLRek0vFGB6hwLgYP4yO8uN6cCIiIqIu5FAgl8lkmDJlCqZMmQK1Wo3Nmzdj7dq1+L//+z9Mnz4dN998MxISEs75GCUlJUhISMDcuXMhkUjg4uKCm2++GQsWLMCPP/4IX19f3HrrrQCAESNG4KqrrsJHH32E5ORkfPHFF5g+fTrS09MBALNmzcJnn32GjRs34oYbbrjAt8D5DGYD1p9Yj4+yPsFxTbb19iivaFwVfh0uD5kCD5mHEyskop6uplGPX3cXYsP+Iuw6VQud0Wy95usms6wHj1EhI9wXchnPByciIqJuxqSHRHsS8D533uxpLmgHsOrqanz33Xf4/vvvkZOTg3HjxsHV1RWzZs3CrFmzcO+99571vlFRUXj33Xdtbvvhhx+QmJiIEydOIC7OdgQ4JiYGX375JQAgJyfHLnjHxMQgOzsb59Ldlzn+59g7+CT3IwCATCzD+OCJuCr8OgxSJEHU3Ysnp2jtFuwedC5FtU34Lacav+dU4UCxFm32ZEOojxzjY/wwPlaF5BBvng9OdvhzhhzFPkOOYp+hsxLMkNTmQ1qeCWn5fkgrDkBadQQikw6Inw7R5LedXWGncSiQf//99/jf//6Hbdu2ISoqCtdffz1Wr14NpVIJABg3bhzmzp17zkDeliAIWLZsGbZs2YJ169bhgw8+sJt6LpfL0djYCABoaGg45/X2KJUekEi69+6/w8KG4nh9Nsb1G4drY66FQq5wdknUQ/j5eTm7BOpGBEFAVokWP2aV4ccj5cguq7O5PijUG5MHBmFyYiDiA734Cz/qEP6cIUexz5Cj2Gf6OEEAtCVAyT6geC9QvA8oyQR0Wvu2cl8ganyv6jMOBfLnnnsO06dPx6effopBgwbZXY+MjMSsWbM69Fj19fV47LHHkJWVhXXr1iE+Ph5ubm6oq7P9ANnc3AwPD8t0bTc3NzQ3N9tdVyjOHmDV6oZu/1u3FM8MXDb1MlRX18FUD1TV153/TtSniUSWf7yqq+sgCOdvT72XySxgf7EGv52oxm85VSjVnt6UTSIC0sJ8MS7GD+Nj/TAo0t/aZ6qr651YNfUE/DlDjmKfIUexz/RNouZay4h3xQHL6Hf5AUgay+3aCVI5jP5JMAYMhiEwBcaAwRB8I+Cn8u4RfUal6tgvDRze1O1cm7cFBQVh3rx5532cgoIC3HXXXQgJCcGXX35pHWGPi4vDX3/9ZdM2JycHsbGxAIDY2FicOHHC7vrYsWPP+Xzd/ZvVShB6Tq3UPbDP9E06oxm7TtXgt5wqbM1Vo7bJYL3WuinbhFgVRkUq4dOyKVvrLybZZ8hR7DPkKPYZchT7TC9mbIK06ghkbaeea/LtmgkiCUzKeBgCB8MYkAJDYCpMyjhAbBtXW8dZe1OfcSiQd3Qn9XPRaDS4/fbbMXz4cCxatAhi8enp5JMmTcKSJUvw3//+F7feeiv27t2LDRs2YNWqVQCAGTNmYO7cuZg2bRrS09Px0Ucfobq6GpMmTbrouoiIurN6nRHb8tXYcqIa2/LVaDSYrNe85VKMiVJiQqwKw/oruCkbERERXXpmEyQ1JyAr3w9pRUv4rj4Kkdlo19Tk3b9l1DvF8l/VIEDWO07NctQFbep2MdavX4+SkhJs2rQJmzdvtrmWmZmJtWvXYtGiRXjjjTegVCrx5JNPYvjw4QAsu64/88wzePbZZ1FeXo6YmBisWbMGvr6+l/plEBF1ueoGPbbmWqai7y6ohcF0+lfBAZ4uGBejwvgYP6T184G0m++VQURERL2IIEBcVwxpxX7IKvZDWr4fsoqDEBnt9/Yyu6lgCEy1nXrOPbOsRILQWwb721dZ2f3XY4tEljUGVVXdfy0EdQ/sM71XsabJuh78QLEWbb+9/RVuGB+rwoQYPwwI8oLYgQ0y2GfIUewz5Cj2GXIU+0zPIWqugbTigHX0W1a+H+KmKrt2ZpmHZd13YAoMASkwBqbC7BnSaVvp96Q+4+/fBWvI21Kr1da130REdOHyqhuw5UQVfj1eheOVDTbXBgR6YkKsCuNjVIj0c3dShURERNRn2K373g+p5qRdM0EshdFvwOlp5wEpMCliADGXzjnCoUBuNBqxYsUKrFu3DiaTCRs2bMCDDz6It956CwEBAV1VIxFRryIIAo5V1OPXE1XYcqIKJ9VN1mtiEZDazwfjW6ajB3nLnVgpERER9WpmEyS1uZCWZ55e+32Wdd9Gn0gYbdZ9DwSkfXPdd2dyKJCvWLECO3bswPLlyzF//nz4+fkhKCgIixYtwvLly7uqRiKiHs8sCDhUosWvJ6rw24kqlLQ5nkwqFmFYfwUmxqowNtoPvu4yJ1ZKREREvZIgQNxQ2rLee3/L6PdBiA32R6Fa1323Tj0PSOa67y7iUCDfsGEDPvnkEwQGBkIkEsHd3R0vvfQSdzknImqH0SxgX2GtJYTnVKO6QW+9JpeKMTLSsjP66CglPF0v+R6bRERE1IuJdFpIKw62rPm2TD9v/7xvdxgCkrts3Tedm0OfABsbG63rxlv3gpPL5TZHlxER9WV6oxk7T9Vgy4kqbM2thqb59JQvDxcJxkT7YWKsCiMieDwZERERdRKTHtLqo21GvzMhrcmxayaIJDD6JcAYkGIJ4IEpMCniuO7biRwK5CkpKVi5ciXmz58PUctvTD788EMkJSV1SXFERD1Bk8GEbflq/Hq8Cn/lq9GgP31GuK+bDONi/DAhVoWMcF/IeDwZERERXQxBgFhz0hq8ZeX7Ia3Kgsiks2tq8g63jnr39fO+uyuHAvkTTzyB22+/HV9//TUaGhpwxRVXoKGhAf/5z3+6qj4iom6pQW/EX3lq/NISwnVGs/VagKcLxseoMDFOhcGhPpCKOeWLiIiILoyoqdoSusszrWu/xbpau3ZmV1+baeeGwBQIbn6XvmByiEOBPCwsDN9//z1+++03FBcXIygoCOPHj4enp2dX1UdE1G3UNRvxR141fjlehR0n1dCbTh+AGeojx4RYFSbGqpAY7NgZ4UREREQALEeOVWZZ1ny3rP2WaAvsmgkSVxhVidbjxgyBqTD7RHDddw/kUCDX6/VYvXo1ZsyYgWnTpuH999/Hu+++i3nz5nEdORH1SpomA37Prcavx6uw81QNjObTITxc4YbL4lS4LNYfcQEe1qU8REREROclmCGpzWuZdt5y5nf1kfaPHPONPj3tPDAVRr8BgMTFCUVTZ3MokL/00kvYv38/br75ZgBAYmIiXn75Zej1eixYsKBLCiQiutTUjXr8llONX49XYk+hBqY2ITzKzx0TY1W4LM4f0Sp3hnAiIiLqEFFjpTV4W0bAD0Cs19q1O33kWEsADxgMwdXHCRXTpeBQIP/xxx+xYcMG607rQ4YMwerVq3HttdcykBNRj1ZVr8OWlhC+r0iDNhkcsf4euCxOhYmx/oj0c3dekURERNQzGJogrTxkO/W8rsiumSCVw+if3GbddyrMXqGcet6HOBTIdTod3N1tP4x6enrCaLSfVkFE1N1V1Onw64kq/Hq8EvuLtWiTwTEg0BOXxfljYqwKYQruRkpERERnIZghqcltM/U8E9LqoxAJJttmEMGkiLWOfhsDU2BUxgMSmZMKp+7AoUA+ZMgQvPTSS3jiiSfg4uICnU6HV155BWlpaV1VHxFRp2oN4b8cr8SBM0J4UrAXJraE8BAfudNqJCIiou5L1FhlDd7nmnpucg+wjnobA1NhDEiG4OLlhIqpO3P42LPZs2cjLS0NCoUCNTU1iIyMxOrVq7uqPiKii9Yawn8+VokDJbb/YCaHeLdMR1chyJshnIiIiNowNkFaebhNAN8PSV2hXTNBKofBf7Dl2LGWAG72DOHUczovh48927hxI/bu3YuqqioEBQUhOTkZUqlDD0NE1OUq6nT45UQVfjlLCL883jISHujl6qQKiYiIqFsRzJDU5rcE732np56fsev5mVPPDYGpMPnFA2JmInKcw73GZDIhPDwc/fr1AwBUVFQAAEJCQjq3MiIiB50rhA8O8cZlDOFERETUQtRcA1nZvjZTz/dDrNPYtTO7+duEb2NAMgRXbydUTL2RQ4F806ZNePrpp1FfX2+9TRAEiEQiHD16tNOLIyI6n/I209EPMoQTERFRe0x6SKuyrOFbVrYPEu0pu2aCxBXGgGQYAlK56zldEg4F8hUrVuDWW2/Fddddx2nqROQ0VfU6/HK8Cj9xJJyIiIjOJAgQ1xXajn5XZUFk0tk1NfpGnx75DkqDUZnAXc/pknIoVZeWluL+++9nGCeiS07dqMevLSE8s0hjszs6QzgREVHfJdLXQVp+oGXd9z7IyjMhbqq2a2d29bUGb8vU8xQIct9LXzBRGw4l68TEROTk5CAhIaGr6iEisqptMuC3E5YQvqewFuY2KTwp2AuXx/vjsjh/hnAiIqK+wmyCpOa4zei3RH0cIptf1QOCWAajaqB19NsQmAazTwSnnlO341AgT0tLw6xZszB16lSoVCqba/fff3+nFkZEfVNdsxG/5VhC+K6CWpjapPABgZ6YFO+Py+P9EcwjyoiIiHo9UUOFZc13eSak5fssZ34bGuzambzCTm+8FpQGoyoRkPKzAnV/DgXyzMxMxMbGIjc3F7m5udbbRfxNExFdhAa9EVtzq/FTdiV2nKqBwXQ6hMf6e2BSvD8mxfujn6+bE6skIiKiLmVshrQqyxK+yyxTz9s789ss84AxYDCMgWkto9+pEDwCnFAw0cVzKJB/+OGHXVUHEfUxTQYT/sxT46djldiWr4bOaLZei/Jzt46ERyjdnVglERERdYmWjddQehQeJ/6CtGyfZeM1s8G2GUQwKeNs1n6bFHGAWOKkwok6l8O7s+3YsQPl5eUQBMsIlsFgwLFjx/Dkk092enFE1LvojWZsP1mDH7MrsDW3Gs1tQni4ws06Eh6t8nBilURERNTZRPp6SCsOWNZ9l+2DrHwfxE1VAIC289/Mbn4wBKZZR7+NgYMhuHg5p2iiS8ChQP7CCy/g008/hYeH5cOyyWRCQ0MDxowZ0yXFEVHPZzQL2FtQix+yK7Alpwr1OpP1WqiP3BrCY/09uPyFiIioNxDMkNTkWnY8bwnfEvUxiASzbTOxDKLgZDT5DbZMPQ9Kh9krjBuvUZ/iUCDftGkT1q1bh6amJnz77bd48cUXsXjxYjQ2NnZVfUTUA5kFAQeLtfghuwK/HK9CTdPp6Wf+ni6YFO+PyQkBGBjoyRBORETUw4maa2zWfUvLMyHWa+3amTxDLRuuBabBEJQGk38iVEH+aKiqgyC088BEfYBDgbypqQkpKSmorKxEVlYWRCIR7r//flxxxRVdVR8R9RCCICC7oh4/Zlfip2OVKK/TWa/5yKW4PN4fkxP8kRLqAzFDOBERUc9kNkJanW0d/ZaW74O0Ns+umSCVw9C68VpQGoyBqTB7BNm04ccBIgcDeVBQEKqrq+Hv74+ysjIYDAbI5XLU19d3VX1E1M3lVTdYQ3hBTZP1dg8XCcbHqjA53h8Z4b6QSsROrJKIiIguhKixsmXa+V7LCHjFAYiMTXbtjL5RbcJ3Gox+CYDY4e2qiPoch/6WjBs3DrNmzcL777+PoUOH4vHHH4erqysiIiK6qDwi6o6Kapvw07FK/JhdiZyq02eBukrFGBPlh8kJ/hgZqYSrlCGciIioxzDpLceOtYx8y8r2tX/smIuX5bzvwDTrzueCXOGEgol6PocC+UMPPQQ/Pz/IZDI8/fTTeOKJJ1BfX4/nn3++q+ojom6iukGPn49V4ofsChwqrbPeLhWLMCJCgckJARgTrYSHC38bTkRE1BOI60psNl6TVh6CyKSzaWM9diwoDcbAdMvab0UMIOIv3Yk6g0OfnGUyGWbPng0A8PLywrvvvtslRRFR91CvM+L3nGpszq7A7lM1MLVsuCIWAelhvpiS4I/xMSr4uMmcWygRERGdm7EZ0srDkJXttYTvsr2QNJTZNTPLFW1GvtNgDEzhsWNEXahDgfyxxx47b5uXXnrpooshIufTG83Ylq/GD9kV+CNPDV2bs8ITg7wwZUAAJsWpoPJ0dWKVREREdFaCAHFd8el132V7Ia3KgshssG0mksDoNwDGoHQYglJhDEyDySeSu60RXUKcW0pEMJkF7CuqxQ9HK/HLiUqbs8L7K9wwZUAApiYEIEzh5sQqiYiIqF3GJsgqDrYcO7YX0rJMSBrL7ZqZ3VQwBKXDEJhqCeEBgwGZuxMKJqJWHQrkHP0m6n1ajynbfLQCPx2rRGW93nrN39MFk+MDMHWAP+IDeFY4ERFRtyEIENcVWqael+2FtDyzZfTbaNtMLIVRlWjZfC0oHYbANJi9wzn6TdTNODRCrtfrsWHDBpSXl8NstkxjNRgMOH78ON56660uKZCIOldBTRN+OFqBzdkVNseUeblKcVmcClMHBCAl1AcSMf/BJiIicjpDE2SVByFtCeCysn0QN1XaNTO5B7Ss+063/Nc/GZBxZhtRd+dQIH/88cfxxx9/QKFQwGAwwN3dHSdOnMC1117bReURUWdQN+rxY3YlNh2twJGy0zuku0rFGBvthykJARgRoYALjykjIiJynjNHv8v2QVp95Kyj34agdMvU88B0mL1COfpN1AM5FMj/+OMPfPLJJ1Cr1fjkk0+wdOlSrF27FgcPHuyq+ojoAjUZTPg9pxqbjpZj58nTO6RLREBGfwWmDgjAuBg/HlNGRETkLNa136enn0saK+yamdwDLaPeQemWEO4/CJBy9JuoN3Dok7jZbEZUVBR8fX1x9OhRAMCtt96KtWvXdklxROQYo1nA7oIabD5agS0nqtBksN0hfdqAAExK8IfS3cWJVRIREfVBbXc+L91jOXrsHGu/OfpN1Dc4FMiDgoJQWFiIsLAwVFdXo7GxEWKxGA0NDV1VHxGdR+vmbJuOVOCH7AqoG08faRLqI8e0AQGYOiAA/ZXcRZWIiOiSsTn3ey+kpXvb3fncZvQ7MA3GgCSOfhP1IQ4F8quuugp///vf8eWXX2L8+PGYM2cOXF1dMWjQoK6qj4jOolDdiI93FGDTkXKcVJ/enM1HLsWkeH9MGxiIpGAv7pBORER0CYjrS9tsvLYX0srDEJn1Nm2so9+BaTAGD+HoNxE5FsjvvvtuhIWFwcvLC0899RSWLFmC+vp6PPXUU11VHxG1UdtkwC/HK7H5aAX2F2utt7tKxRgT5YdpAy2bs8kk3JyNiIioy5j0kFZlWTdek5XtgaS+xK6Z9dzvoDQYg4Zw53MisuPwbk7Tpk0DANTU1OC5557r9IKIyJbeaMZf+WpsPFKOP/PUMJotu7OJRMDQMF9MHRCACbEqeLpyczYiIqKuIGqstI58y8r2QlpxACKTzqaNIBLD6DfAErxbpqCbvftz9JuIzsmhT/D19fV4+eWXsWHDBuj1eri5ueGWW27Bgw8+CBcXbhJF1FkEQcDh0jpsPFKOn45VQtN8esOXWH8PXDEwAH8bGQWpwQBBcGKhREREvY3ZBIn6GGRleywBvHQPJNpT9s1cfU9vvBaUDkNACuDicenrJaIezaFAvnjxYpw4cQKrVq1CcHAwCgsLsXz5crz++ut49NFHu6pGoj6jRNOMTUfLsfFIBQpqTq8LV3m4YNqAAFwxMBAx/h4QiQCVjxxVVYZzPBoRERGdj0insU47bz16TGyw3bBYgAgmZdzpY8eChsDkG8XRbyK6aA4F8i1btuDbb7+FUqkEAERFRSE+Ph4zZsxgICe6QPU6I349XoXvj5RjX5HGertcKsb4WBWmDwzA0HAFJGL+o09ERHRRBAGS2tyWzdf2QFa6F9Ka43bNzDJPy87ngWkwBA+BMTAVgquPEwomot7OoUDu5uYGiURic5u7uzvMZvNZ7kFE7TGaBew8VYONWeX4PbcaOqPl75AIQHq4L6YPtKwL93DhunAiIqILZmiErGK/JXi3jICLdbV2zYw+ES1rvy0j4CZlPCCW2D8eEVEn69Cn/ZISy66R1157LebPn4+FCxciNDQUFRUVWLJkCWbNmtWVNRL1CoIg4HhlAzYeKcfmo7bnhUco3XDFwEBMGxCAIG+5E6skIiLqucR1JZCV7YG0dLdl+nlVFkSCyaaNIHGFISAFxuB0GIKGwBCYBsFd5aSKiaiv61AgnzhxIkQiEYSW3aOuvvpq69nGgiBgy5YtuPvuu7uuSqIerKpBj01HLOvCc6pOr0nzkUsxtWVd+IBAT54XTkRE5AiToeXosT3WKeiS+lL7Zh5BMAQPtW6+ZlQlAhJuRkxE3UOHAvkvv/zS1XUQ9Sp6oxl/5FXju6xybM9Xw9SyE7pMIsKYKD9cMTAQIyN5XjgREVFHiZrULceO7bFMP684AJGx2aaNIJLA6D/IuvGaIWgIzF4hTqqYiOj8OhTIQ0NDu7oOoh5PEAQcLa/Hd1nl+CG7Ato2R5UlBXthemIgJsX7w1suc2KVREREPYBghqQmp2X6+V7IynZDWptn1+z00WNDYAgeAkPAYEDm7oSCiYguDHeMIrpIVfU6bDpage+yypFX3Wi93d/TBVcMDMSVAwMR4ccPB0RERGdlaIKsIrNl87XdLZuvaeyaGRWxNgHccvQYZ5sRUc/FQE50AXRGM/7IbZmSflINc8uUdFepGONj/HBlYiCPKiMiIjoLcX2JTfiWVmVBZDbatBGkchgCU2EIGtIy/TwNglzhpIqJiLoGAzlRBwmCgCPl9fjucBl+PFZpMyU9OcQbV7ZMSfd05V8rIiL6//buPL6q+s7/+PvcLQlZIBtZWUQ2BULCjkEQBRUBN6RMp7XVx2irD9QOHa12+vNhO9jpOB2t2mrb0XY6U5mprYoLokgVRLawK4hQwpY9hAQCSUjOXc7vjxsuXILC1STnJnk9Hw/+yPl+k3yK3x7yzndDSMAnV+1noZPP3ZVb5Gwob9Mt7PC1rPHypV4uOdnmBaB7izg5mKapurq6NnePZ2dzYAa6p9NL0t/6tFoHz1qS3jfBo9kjMjT78gwNSGFJOgAAkmS0nJCrapvclZvlrtwid/V2Gb6msD6W4ZAvbUQofHszxymQkC1x4wiAHiaiQP7OO+/oscce08mTJ0PPLMuSYRj67LPP2r04wC5ef0AfHajTW7uqtP5g+JL06UPSNGdEhsb168OSdABAz2ZZcpwsDYbvqi3SkW1KObJbhqywbgFPknyZY4L3fmeNl7dvvuSJt6dmAIgiEQXyX/7yl/r7v/973XLLLXK5WJaL7qf4aKPe2lWl5buP6Pgpb+j56NYl6TNYkg4A6MnOuvvbXblZrsotcjZVh3UxJPmTBgRPPc8cHzx8LWUoh68BwHlElCwqKyt13333EcbRrZxs9um9vUf05q5q7a46s/ojLT64JH3uCJakAwB6JqP5ePDQtdYA7j6yo+3d3w6XfGkj5csar7hhV6o2foQCvTJsqhgAupaIkvWIESNUXFys4cOHd1Q9QKcIWJa2lh7Xm7uqtWrfUbX4gmciOB2Gpl6aqhtHZmjSwBS5WJIOAOgpLEuOEyVyV7Xu/a7cLFfd3jbdAjG9Q0vPfafv/nbFyTCkuLREWUdP6pwV6wCAzxFRIB8zZozuuOMOXX/99UpLSwtru++++9q1MKAjVJ5o1rJPq7VsV5UqTrSEng9K7aWbRmVq1mV9ldzLY2OFAAB0kjbLzzfL2XSkTTdf74HytR685s0aL3/yYJafA0A7iSiQb9++XUOGDNH+/fu1f//+0HODEzERxVp8AX1YfFRv7qrSpsPHQ7+0j/c4df1lfTV3ZKYuz0hgHAMAurXg6edbQwE8ePr5qbA+lsMtX/qo4MFrWePkzRwnq1e6TRUDQPcXUSD/4x//2FF1AO1u75EGvbGzSu9+dkQnW87cGT6ufx/dODJD0wenKdbttLFCAAA6iGXJcbJc7spNoQDurN3T9vTzz1l+DgDoHBGfzrZ//3793//9n6qqqrR48WK9/fbb+uY3v9kRtQERa2jxacWeI3pjZ5U+q24IPc9IjNHcERmaMzJDOb35QQMA0M0E/HLVfiZX5ebW+783ydlY1aZb8PTz8aET0P0pQ1h+DgA2iiiQr1u3Tvfff7+mT5+u9evXq7m5Wc8995yampr0ne98p6NqBL6QZVn6pOKEXt9Zpb/urVFz6wFtLoehqwan6qZRmRrfP5k7wwEA3Ye3Se7q7cEZ8MotclVtlcPbENbl9OnnZwdwK76vTQUDAM4nokD+1FNP6Re/+IWmTZum8ePHKysrS//5n/+pf/zHfySQo9MdazK1fHdwNvxgXVPo+SUpvXRzXqZuuCxDfXq5bawQAID24WisDs5+nz6ArWaXDMsf1ifgSZQvc2xrAB8vb98Cyc2qMACIZhEF8sOHD2vq1KmSzhzkNmrUKNXX17d/ZcB5BCxLmw8f1+s7K7W6uFa+QHAvXKzLoZnD0nXTqEzlZSdxQBsAoOuyAnIe2986+x1cgu48cbhNN39Cdmv4nhA8/TxlmOTgbBQA6EoiCuTZ2dnatm2bxo4dG3q2c+dOZWVltXthwNmqT7borV1Veuuc68ouy0jQzaMyde3wvkqIifhIBAAA7Oc35arZKXdFawCv2ixH87GwLpYM+VOHh8K3N2u8Aok5NhUMAGgvESWY7373u7r33nv19a9/XV6vVy+88IL++Mc/6vvf/35H1YcezOcPaO2BOr2xq0rrD9apdTJcCTFOzbosQzeNytSwvgn2FgkAQISMlhNyV21pPYBtk9zVO2T4W8L6WK5YeTMK5M0Mhm9f5lhZMUk2VQwA6CgRBfLZs2crISFBS5YsUXZ2tjZu3Kgf/ehHuu666zqqPvRA5fWn9PonVXrr02rVNpqh5wW5vXXzqExdPYTrygAAXYejoSJ08rm7YrOctZ+1vX4sNiVs+bkvfaTk9NhUMQCgs0QUyBsbGzVt2jRNmzYt7PnatWs1ZcqUdi0MPYvPH9CaA3Va+nGlig4fC/2Ykhzn1pwRGbpxVKYGpvSytUYAAC7ICshZt681gBcF93+fLGvTzZ80QN7s08vPJ8rfZ5DE+ScA0ONEFMjvuece/e53v5PHE/yNbXNzs/7t3/5Nr7zyinbt2tUhBaJ7qzzRrNc/qdSbu6p19KzZ8IkD+uiWvCxNvTRVbif3owIAopTflOvIJ60HsAX3gDtawg+7tQzHWdePjZcva7wC8Rk2FQwAiCYRBfLY2Fjdf//9eu6557Rr1y49/PDDcjqdeumllzqqPnRDvoCltftrtXRnpTYcPDMbntLLrbkjM3XzqEzl9uGaFgBA9DHMk3JVbW09gG2T3NXbz7P/O07ejDHBAJ49Ub6MAlkezjwBALQVUSB/7rnndO+992r+/PkqLi7WN7/5TS1atCg0Yw58kaoTzXp9Z5Xe3FWlmoYzs+Hj+/fRrXlZmjaY2XAAQHQJ3f9dEVx+7qrdLcMKhPUJxKWG7/9OGyk53TZVDADoSiIK5B6PR88//7zuueceTZ48WQ8//HBH1YVuwhewtP5gnZZ+Uhl2UnpynFtzR2boplFZ6p/MbDgAIApYlpz1B0Ph211RdP77v9n/DQBoJxcVyK+++moZZ/1DY5qmampqNG3aNLlcwS/x/vvvd0yF6JKqT7bojZ2VemNnlY6cNRs+rl9v3ZKXpasGp8njYjYcAGCjgE+uo7tbTz8PhnDHqaNhXSwZ8qVdLm/WBPmyJsibPV6B+EybCgYAdDcXFcjvv//+jq4D3UDAsrTx0DG9+nGl1h6oDc2G9451hfaGD+CkdACAXXyn5K7e0RrAN8lVtUUOb2NYF8sZI29GvrxZE7n/GwDQ4S4qkN9yyy1hH9fW1qq8vFzp6enKysrqkMLQdRxv8uqtT6v02ieVKjveHHo+Jre3bs3L0vQhzIYDADqf0Xxc7qotZwL4kY9lBLxhfQKepNbD1ybImzVRvr6jJGeMTRUDAHqaiPaQNzQ06OGHH9YHH3wgy7JkGIYmT56sp59+WklJ/Pa4J7EsS7sqT+qVjyv01701Mv3B6fCEGKfmjMjUvLwsDUxlNhwA0HkcDZWh8O2uLJKzdq+M0F0eQf5eGcHwnT1R3qwJ8qcOlwx+aQwAsEdEgfzJJ59UY2Ojli1bptzcXB0+fFj/+q//qp///OdavHhxR9WIKHLK69e7nx3Rqx9Xau+RhtDz4X0TdFt+lq4d3ldxbqeNFQIAeoSwA9g2tR7AVtKmm6/3JaHZb2/2BAWSBnAAGwAgakQUyFetWqVXX31VqampkqShQ4fq5z//uW688cYvHcjr6uq0YMECPf7445o4caIk6bHHHtOrr74qt/vMlSGPPPKIFixYIElaunSpnn/+edXU1GjQoEF69NFHVVBQ8KW+Py7OwdomvfpxhZZ9Wq1G0y9JinE5NGNYuuaPztLlmYlhB/8BANCuAn456/bKXbFR7opN8lQUyXGqJqyLZTjkS728NYAH/1jxfW0qGACAC4sokJ86dUqJiYlhz5KSkhQIBD7nM77Y1q1b9cgjj6ikJPw32jt37tTixYvb7F2XpKKiIi1evFgvvPCC8vLytGTJEt17771atWqV4uK4Pqs9+fwBrS6u1SsfV2hraX3oeb8+sbp1dLbmjMhQnzjuWQUAdAC/KVfNzlAAd1dulsM8EdbFcnjky8iX2br83Jc1TpYn8XO+IAAA0SeiQD569Gg988wzevDBB2UYhizL0jPPPKNRo0ZF/I2XLl2qZ599Vg899JAWLVoUem6apv72t79p5MiR5/28v/zlL5o9e7bGjh0rSbrjjjv08ssva/ny5Zo3b17EdaCt6pMtWvpJpV7fWaXaxuCVZQ5DmnppquaNztKEAclyMBsOAGhP3ia5q7YFA3jlJrmrt8nwNYd1Cbjj5cscF9z/nT1B3r75kivWnnoBAGgHFxXIt27dqrFjx+qf/umf9K1vfUtvvvmmcnJyVF5eLsMw9F//9V8Rf+MpU6Zo7ty5crlcYYF8z5498vl8evbZZ7V161YlJiZq3rx5uuuuu+RwOFRcXNwmeA8ePFh79uz53O8V7dnxdH121mlZlraUHteft1doTXGtWs9oU2q8R7eMytTNeZnKTOKHnmgRDWMGXQtjBpHq6DFjNB+Xu3KzXBVFclcUyVWzU0bAF9YnEJvcegDbJPmyJsiXPkJynPnRheEcXXjPIFKMGUSqO46Ziwrkd999t7Zt26Zhw4ZpxYoVev/991VbW6ucnBxNmzZNCQkJEX/j9PT08z4/efKkJkyYoNtvv11PPfWUPvvsMy1cuFAOh0N33XWXGhsb2yxNj42NVVNT03m/XkpKvJzOrnF6ampq5y+za2zx6bVtZfrvDYdVfNYhbZMGpej2SQN17YgMubvI319PZMeYQdfGmEGk2m3MnKyWStZLh1v/VH8qnXMCupJypAFXBP/0v0KO9GGKMQxxCVnXwnsGkWLMIFLdacxcVCC3rDP/YPbp06dDl4YXFhaqsLAw9HFeXp6+/e1va/ny5brrrrsUFxen5ubwJWzNzc1KTk4+79erq2uM+t+gGEZwUNXWnpRlXbh/ezhc16S/7KjQW7vOHNLWy+3U7BEZmp+fpUFp8ZKk+mONnVMQImLHmEHXxphBpL7qmHGcKGvd/10kV0WRXMcPtOnj6zNIvuyJrUvQJyqQmBs+7VHb0OZzEL14zyBSjBlEqiuNmbS0i/ulwUUF8s48Pfuvf/2rjh49qr/7u78LPTNNU7GxweXSQ4YM0b59+8I+p7i4WFOnTv3crxnt/7FOs6yOrdUfsLT+YJ3+vKNCGw8dCz3vnxynr+Vna/aIDCXEuEK1IPp19JhB98OYQaQuasxYlpzHD4QCuLuiSM6G8vAuMuRPHS5v9kSZ2ZM+/wR0xmeXx3sGkWLMIFLdacxcVCA/deqUrrnmmi/s8/7777dLQZZl6Wc/+5kGDBigSZMmaceOHfqf//kf/fCHP5Qk3XbbbVq4cKFmzZqlsWPHasmSJaqtrdXMmTPb5ft3RyeavXpzV7Ve2VGh8vrg6gJDUuGgFC0oyOaQNgBAZKyAnLV7QuH7/FeQOeXrm9c6+z1J3sxxsmL72FMvAABR6qICudvt1n333dfRtUiSZs6cqR/+8If68Y9/rOrqaqWlpen+++/XTTfdJEmaPHmyHnvssVD74MGD9cILL6hPnz6dUl9Xsq+mQX/eXqF3PjuiFl/warrEGJduHJmp2/KzlNuHa+IAABch4JPr6Kdyl7fOgFcWydFSH9bFcsbIm1FwJoBnjJE88TYVDABA12BY1oUn+8eMGaNt27Z1Rj3trqbmpN0lXJBhBPcYHD361fdCnL47/M87KrS97MwPS0PS4/W1/Gxdf1lfxbqdX7Fi2K09xwx6BsYMIuI35a75WH2Ob5e570O5KrfI4Q0/U8Ry9ZI3a7y82ZNkZk+UL2O05OT4tZ6M9wwixZhBpLrSmElPb8c95BeR2WGzuiZTSz+p1GsfV+pIQ/DucKchTR+Spq8V5Cg/J6lTzwIAAHQhvlOtd4AXBfeBn3UHuKe1SyCmt7xZE0Iz4L70kWFXkAEAgMhd1L+kN954Y0fXgS/pb0ca9Kdt5Vqx54jM1svDU3q5dXNelublZalvIrMVAIBzmI1yV21u3f+9Ua7qHTIC3rAugbhUOQYWqiFtrMzsyfKnDJMcrLACAKA9XVQg/8lPftLRdSAC/oCltQdq9adt5dpSemZZ+uWZiVpQkK0ZQ9PlcXF3OAAgyGg5IXflZrkrNshdvlGump0yLH9YH398RnDvd+ufQMpgpaUnqbkLLAsEAKCrYq1ZF9LQ4tNbn1br5W3lodPSg8vS0/X1sTkalZXIsnQAgIzmY6ET0N0VG+U6+qkMKxDWx5/YT96cSfJmTZSZM0mBpAFhd4DzzwkAAB2PQN4FlB0/pZe3V+itXVVqNIMzGkmxLt08Kkvz87OUmRRrc4UAADsZTUflrtgoT8XGYACv3dOmj6/3wODsd84kebMnK5CYY0OlAADgbATyKGVZlraUHteftlXoo/21Or1a8JKUXvq7MdmadXmG4jgtHQB6JEdjdfDwtfLWAH5sX5s+vuTBZ5ag50xSID7ThkoBAMAXIZBHmRZfQCs+O6L/21au4qNnrpi54pJkfX1MjiYOSGZZOgD0MI6GSrnLN7SG8A1y1R9s08eXOlze7IkysyfLmz1RVq90GyoFAACRIJBHieoTzfrPtYf06seVOn4qeNJtrMuhOSMytKAgRwNTe9lcIQCgszhOVrQewBYM4a76Q2Htlgz50ka0Lj+fFAzgscn2FAsAAL40AnkUWL67WotX/E3e1mvLMhNj9LWCbN00KlNJsW6bqwMAdDTHibLQ7LenYqOcJw6HtVuGQ770UWedgj5BVkxvm6oFAADthUAeBUrqTsnrt5Sfk6Svj8nR1MFpcjlYlg4A3ZXjRGkofLvLN8h5sjSs3TKc8qWPlDdnsrzZk+XNGi8rJsmmagEAQEchkEeBu68YoHtnDJXVbHLXKwB0Q2cC+IbWAF4W1m4ZTvn65rUG8EnBAO5JtKlaAADQWQjkUcDpMJSaEKOjzabdpQAA2sEFA7jDJV/f0fJmT5aZM0m+zHGyPAk2VQsAAOxCIAcA4Cu6uACeLzNncnAWPHOc5OawTgAAejoCOQAAESKAAwCA9kAgBwDgAgjgAACgIxDIAQA4h+Nkhdzl6+UpX3/+U9DD9oC3BnBPvE3VAgCAropADgDo8RwNlXKXb2gN4RvOcw+4MxjAc64ggAMAgHZDIAcA9DhG45HQ7Le7fL1c9QfD2i3DIV/6qNYAfoV8WeM5BR0AALQ7AjkAoNszmo7K0xq+3RUb5DpWHNZuGQ750kYG93/nXCFv9gTuAQcAAB2OQA4A6HaM5mPBQ9haZ8FddXvD2i0Z8qWNCA/gMb1tqhYAAPRUBHIAQJdntJyQu6IoOANevl6uo7tlyArr40u9TGbOFcEQnj1RVmyyTdUCAAAEEcgBAF2P2Sh31WZ5ytYFA3jNThlWIKyLL3mIvLnBPeDe7Mmy4lJsKhYAAOD8COQAgOjnOyV31Ta5y9bJU75eriM7ZAR84V16D5Q3pzAYwrMny4rva1OxAAAAF4dADgCIPn5Truod8pSvk7tsndxV22QEzPAuibkycwrlzQ3uAw8kZNtULAAAwJdDIAcA2C/gl6tmp9zl6+QpWy935SYZvlNhXfzxGcED2HIKZeZeoUBSf5uKBQAAaB8EcgBA57MCctbtDe4BL1svd8VGOcwTYV0Ccamth7BdIW9uofy9L5EMw6aCAQAA2h+BHADQ8SxLzvqDwfBdHtwH7jhVG9Yl4EkKXUNm5l4hf8owyXDYVDAAAEDHI5ADADqE42R56yFswX3gzsaqsHbLFSdv9oTWfeCF8qWNlBxOm6oFAADofARyAEC7MJqOylO+PhjCy9bKeeJwWLvl8MibNbZ1D3ihfH1HS06PTdUCAADYj0AOAPhSjJYTclcUyV22Vp7ydXLV7glrtwynfH1Hy8wtDF5HljVWcsXZVC0AAED0IZADAC6O75TclVvlKVsrd/k6uY58IsPyh3dJvTwYwHOnyJs9QZYn0aZiAQAAoh+BHABwfn6vXDWfBAN42Tq5q7bK8LeEdfH1vkTe3EKZuVPkzZksKy7VpmIBAAC6HgI5ACDICshZu6f1KrK1wavIvI1hXfzxGfLmTmkN4FcokJhjU7EAAABdH4EcAHowx4mS4Ax4aXAfeJuryGL6yJt7RWsAL5S/zyDuAgcAAGgnBHIA6EGMpqOhGXBP2To5T5SEtQevIpsYDOC5U+RLu5y7wAEAADoIgRwAujHDbJC7okiesrVS1QalVu8Ka7ccLvkyCkIB3JtRwFVkAAAAnYRADgDdid+Uu3q73KUfBa8iq94uI+AL6xI8CX2KvLmF8mZPlOVJsKlYAACAno1ADgBdWeggtrXBEF5RJMPXFNbFnzRA3txCxV4+Q7VJYxSIS7OpWAAAAJyNQA4AXYzjRJk8ZR+F9oE7Th0Naw/EpYaWoJu5UxRI6ifDkGLTEmUdPSlZNhUOAACAMARyAIhyRvMxucvXy1O6Vu6yj+SqPxTWbrl6ycyZJG/ulTL7TZE/ZTgnoQMAAHQBBHIAiDa+U3JXbmmdBV8n15FPZJw1rW0ZzjMHsfW7koPYAAAAuigCOQDYLeCX6+incpeuCe4Fr9wsw98S1sWXMuxMAM+eKMuTaFOxAAAAaC8EcgCwgeNEiTyla+QuXStP2Vo5Wo6HtfvjM+Xtd2VoL3ggPsOeQgEAANBhCOQA0AnO7AP/SJ7Sj+Q8cTisPeBJlDfnitAsuL/PpewDBwAA6OYI5ADQEfwtwX3gpR8FD2I7dx+4wyVfxhiZ/a6U2W+qfH1HSw5eyQAAAD0JP/0BQHuwLDlrPwvOgJetkbuiSIavOayLL3mozH5T5O03Vd7sSbI8CTYVCwAAgGhAIAeAL8nRWCV36UfylK6Rp/SjNveB+3v1Dd4F3u/K4D7whCybKgUAAEA0IpADwMXyNslTvkHusuA+cFfd3rBmyxUnM3uSvP2mch84AAAALohADgCfJ+CX6+iu1lnwD+Wu3CIj4A01WzLk65sns9/U4HVkmWMlZ4yNBQMAAKArIZADwFkcJ8qCe8BbT0Nvcx1ZYq7MflODITy3UFZssj2FAgAAoMsjkAPo0QzzpNzlG4Iz4KUfyXX8QFh76Dqy1llwf+9LWIYOAACAdkEgB9CzBPxy1XwiT+kauUvWyF29VUbAF2q2DKd8GQWhWXBfRj7XkQEAAKBD8FMmgG7PcbI8OANeskaeso/kaKkPa/f1Hihvv2nBWfCcybJikmyqFAAAAD0JgRxA92M2ylOxUe7SD+UpXSPXseKw5oAnSd7cQpn9psnsP1WBpP42FQoAAICejEAOoOuzAnLV7JK7dM35T0M3HPJljAkuQ+8/Tb6+o1mGDgAAANvxEymALsnRWBVcgl76YfA09Oa6sHZ/Yj+Z/aedOQ09prdNlQIAAADnRyAH0DX4muWu3CxPyWp5Sj+Uq3ZPWHPAHS9vTqHM/lPl7TeV09ABAAAQ9QjkAKKTZcl5fL88JavlLvlQnooNMnzNZ5plyNc3T2a/afL2nyZvxhjJ6baxYAAAACAyBHIAUcNoqZe7bK08JR/KU/KhnA3lYe3+Xhny9p8aPIyt31RZcSk2VQoAAAB8dQRyAPYJ+OU68nFwH3jJh3JVb5dh+UPNlsMjb/aE1tPQp8mfehnL0AEAANBtEMgBdKrgYWwftu4F/0iOluNh7b4+l8rsPy14L3jOZMndy55CAQAAgA5GIAfQsfzmmcPYSlbLVftZWHPAkyRvvymty9CnKZCUa1OhAAAAQOcikANod476w6EA7ilbJ8PXFGoLHcbW/yqZ/a+SL6OAO8EBAADQI/FTMICvztskT/kGuU/PgtcfDGsOxKWFAjiHsQEAAABBBHIAkbMsOev+FpoFd1cUyQiYZ5odLnkzx8rsP13e/lfJl3a5ZDhsLBgAAACIPgRyABfFME/KXfpRawhfJWdDZVi7PyFH5oDpMvtfJW9uoSxPok2VAgAAAF0DgRzA+VmWnLWfyXP4g+AseNUWGQHfmWZnjLw5k2T2D4Zwf59LuZIMAAAAiACBHECI0VLfOgu+Sp6S1XI2Voe1+3pfInNAcBm6mT1ZcsfZVCkAAADQ9RHIgZ7MsuQ6+qk8h1fJU7JKrqqtMiz/mWZXrMycwuBhbAOmK9B7oH21AgAAAN0MgRzoYYzm4/KUrpGnZJXcJR/K2XQkrN2XPLj1RPTp8mZPlFyxNlUKAAAAdG8EcqC7C5sF/6B1FjxwptnVS2buFJkDgteSBZL621gsAAAA0HMQyIFuyGg5IXfZR8ED2Q6vlrPpnL3gyUNbT0SfLm/2eMkZY1OlAAAAQM9FIAe6A8uSs25vawD/oO2J6KFZ8KuDe8ETc2wsFgAAAIBEIAe6rpYGeQ6skPvQB/KUfNDmXnBfn0tbA/jV8mZPYBYcAAAAiDIEcqCrsCw5j+9vvRd8lVRRpCS/eabZGSMztzAYwvtPV6D3ABuLBQAAAHAhBHIgmvma5S7foJjD78tz+AM5T5SENfuTBqjl9Cx4ziTJxb3gAAAAQFdBIAeijONkeete8PflKVsrw9ccarMcHnlzJsscOF0Jo+fqmNVXlgwbqwUAAADwZRHIAbsFfHJXbQ0G8EPvy1W3N6zZn5Als//VMgdeIzN3iuTuJcOQEtISpaMnJcumugEAAAB8JQRywAbGqVp5SlbJc+gDeUo/lKOlPtRmGQ75MseqZcA1MgdcLX/qZZLBLDgAAADQ3dgeyOvq6rRgwQI9/vjjmjhxoiTp448/1uOPP67i4mIlJyfr3nvv1fz580Ofs3TpUj3//POqqanRoEGD9Oijj6qgoMCu/wnAhVmWXEd3yXMouBfcVb1dxllT24HYZJn9r5I54BqZ/afJik22sVgAAAAAncHWQL5161Y98sgjKik5c1BVfX29vvOd7+iBBx7QggULtHnzZi1cuFDDhg1TXl6eioqKtHjxYr3wwgvKy8vTkiVLdO+992rVqlWKi+NAK0QRb5M8pR/Jc/ivwQPZGqvDm9NGBAP4wGvk65svOZz21AkAAADAFrYF8qVLl+rZZ5/VQw89pEWLFoWev/fee+rTp4++8Y1vSJImT56suXPnasmSJcrLy9Nf/vIXzZ49W2PHjpUk3XHHHXr55Ze1fPlyzZs377zfK9pX+56uL9rrxIU5TpTKc+iv8hx6X+7yDTL8LaE2y91LZr8rZQ64Rt4B0xVIyAq1RfqfnjGDSDFmECnGDCLFmEGkGDOIVHccM7YF8ilTpmju3LlyuVxhgXzfvn0aOnRoWN/BgwfrlVdekSQVFxe3Cd6DBw/Wnj17zvt9UlLi5XQ62rn6jpGammh3CYiU3yeVbZb+9q70txVSzWfh7X0GSMNmSUOvkzGgUDGuGMW047dnzCBSjBlEijGDSDFmECnGDCLVncaMbYE8PT39vM8bGxvbLD2PjY1VU1PTRbWfq66uMep/g2IYwUFVW3tSFidmRz2j+ZjcJR8GZ8IPrzrnQDanfFnjgyeiD5whf/LgM7/CO25KMtunBsYMIsSYQaQYM4gUYwaRYswgUl1pzKSlXdwvDWw/1O1ccXFxOnnyZNiz5uZmxcfHh9qbm5vbtCcnf/4hWNH+H+s0y+o6tfYoliXnsX1nlqJXbZFh+UPNgZjeMgdcLXPgDJn9psmK7XPO53doaYwZRIQxg0gxZhApxgwixZhBpLrTmIm6QD506FCtW7cu7FlxcbGGDBkiSRoyZIj27dvXpn3q1KmdViN6AL8pd0WRPIdWKubQX+U8URLW7EsZJnPgNWoZMEO+zDGSI+r+rwQAAAAgykVdipg5c6Z+/vOf6w9/+IO+8Y1vaOvWrXrrrbf0/PPPS5Juu+02LVy4ULNmzdLYsWO1ZMkS1dbWaubMmTZXjq7OOFUnz+EPgjPhpR/KYZ5ZqWE5PPLmXqGWgTNkDrhGgaR+NlYKAAAAoDuIukCenJys3//+9/rpT3+qZ599VikpKfp//+//adKkSZKCp64/9thj+vGPf6zq6moNHjxYL7zwgvr06WNv4eh6QkvRg7PgrqqtMqxAqDkQl6aWgdfIHDhTZu6VkifexmIBAAAAdDeGZXWX1ffnV1Nz8sKdbGYYwU3/R49G/+EEXZ7fe85S9MNhzb7Uy9QycKbMgTPky8iXjOg8oZ8xg0gxZhApxgwixZhBpBgziFRXGjPp6V30UDegvRnNx84sRS9ZfZ6l6JODIXzADAWScm2sFAAAAEBPQiBHt+Q8fkCegyvlOfSe3JWb2y5FH3CNzEtmyMydylJ0AAAAALYgkKN7CPjlqt6mmIPvyXNopVzHisOafanDz1qKXhC1S9EBAAAA9BwEcnRd3iZ5StfIc3ClYg7/VY5TtaEmy+GSN6f1VPSBMzkVHQAAAEDUIZCjS3E0VstzaGVwOXrZWhn+llBbIKa3zP7TZV5yrcz+V8mKSbKxUgAAAAD4YgRyRDfLkrNuj2IOrpTn4HtyH9kR1uxP6q+WS66VOXCmvFkTJKfbnjoBAAAAIEIEckQfv1fuyk3yHHxPMYdWynmiJKzZm1Egc+C1arlkpvwpw4L3HwAAAABAF0MgR3QwG+UpXa2YAyvkOfy+HC31oSbLGSOz35UyWw9lC8Rn2FcnAAAAALQTAjlsYzQeUcyh4FL0NvvBY1NkDpyhlkuuk9nvSsndy8ZKAQAAAKD9EcjRqZzH9stzcIViDq6Qq2qbDFmhNn/SALUMul7mJdfKmzlOcjhtrBQAAAAAOhaBHB3LCshVvV0xB1fIc/C9NveDe/uOlnnJdWq55Dr5U4ayHxwAAABAj0EgR/vzt8hTuja4FP3QSjmbjoSaLIc7eD/4oOuC94MnZNlYKAAAAADYh0COdmGYJ+U5/IE8B96V5/AHcngbQ20BT6LMAVe33g8+nfvBAQAAAEAEcnwFRlONYg6uUMyBd+UuWycj4A21+eMzQkvRvTmTJafHxkoBAAAAIPoQyBERR/0hxRxYoZiD78pVuSXsUDZfn0tlDrpeLYOul6/vaMlw2FgpAAAAAEQ3Ajm+mGXJeXS3Yg68EwzhtXvCmr19R6tl0CyZg66XP3mwTUUCAAAAQNdDIEdbAb/cVZvlORBcju48WRpqsgynvNmTWq8nu06BxGwbCwUAAACArotAjiB/izxl6+Q58I5iDr4nx6naUJPlipXZb1pwJnzgNbJik20sFAAAAAC6BwJ5T+ZtkqdktWL2L5fn8PtymCdDTYGY3jIHzgjOhPe7SnLH2VcnAAAAAHRDBPIexmipl+fQ+4o5sFyektUyfM2hNn+vjNChbN7sSZLTbWOlAAAAANC9Ech7AONUbfB6sv3L215PlthPLZfeoJZBs+TLHMPJ6AAAAADQSQjk3ZSjoUKeA+8GQ3jlJhlWINTmSx6ilktvkDlolnxpIyTDsLFSAAAAAOiZCOTdiOP4weD1ZPuXy31kR1ibN32UzEE3qGXQ9fKnDLGnQAAAAABACIG8i3PW7VPMgeWKKX5brtrdoeeWDPmyxqmlNYQHkvrZWCUAAAAA4FwE8q7GsuSs26uY4mWK2b9crmN/O9NkOOXNuUItl84K3hEen2FjoQAAAACAL0Ig7wosS66jn8qz/23F7H9bruMHzjQ53DJzp8i8dLZaBl3HHeEAAAAA0EUQyKOVZcl15GPF7H9bMfuXy3ni8Jkmh0dm/2lquXS2zIEzZMX2sa9OAAAAAMCXQiCPJlZArqrtiikOzoQ7G8rPNDljZA64ujWEXyPLk2hjoQAAAACAr4pAHgWMphrpnceVvOt1ORurQs8tV5xaBs4IhvD+0yVPvI1VAgAAAADaE4E8CsRv/Hdp9//JKSngTpA5cIZaBs+W2e8qyR1nd3kAAAAAgA5AII8Cp/LuVGyfdJ1ILlBL7pWSK9bukgAAAAAAHYxAHgX8aZdLwyfKPHpSsuyuBgAAAADQGRx2FwAAAAAAQE9EIAcAAAAAwAYEcgAAAAAAbEAgBwAAAADABgRyAAAAAABsQCAHAAAAAMAGBHIAAAAAAGxAIAcAAAAAwAYEcgAAAAAAbEAgBwAAAADABgRyAAAAAABsQCAHAAAAAMAGBHIAAAAAAGxAIAcAAAAAwAYEcgAAAAAAbEAgBwAAAADABgRyAAAAAABsQCAHAAAAAMAGBHIAAAAAAGxgWJZl2V0EAAAAAAA9DTPkAAAAAADYgEAOAAAAAIANCOQAAAAAANiAQA4AAAAAgA0I5J2srq5OM2fOVFFR0ef2+fDDDzV37lzl5+dr1qxZWrVqVSdWiGhzMWPmrrvu0qhRo1RQUBD6s2bNmk6sEtFgz549uvPOOzVhwgQVFhbqBz/4gerq6s7bl/cMpMjGDO8ZSNKGDRs0f/58jRkzRoWFhVq8eLGam5vP25f3DKTIxgzvGZzm9/t1++2365FHHvncPt3mHWOh02zZssWaMWOGNXToUGvjxo3n7XPw4EFr1KhR1sqVKy2v12u9/fbbVl5enlVVVdXJ1SIaXMyYsSzLmjhxolVUVNSJlSHanDp1yiosLLSeeeYZq6Wlxaqrq7Puvvtu67vf/W6bvrxnYFmRjRnL4j0Dy6qtrbVGjRplvfrqq5bf77eqq6utOXPmWM8880ybvrxnYFmRjRnL4j2DM55++mlr+PDh1sMPP3ze9u70jmGGvJMsXbpUDz74oBYtWnTBfuPGjdOMGTPkcrl0ww03aPz48Xr55Zc7qVJEi4sdM6Wlpaqvr9fll1/eSZUhGlVUVGj48OFauHChPB6PkpOTtWDBAm3evLlNX94zkCIbM7xnIEkpKSlav369br31VhmGoePHj6ulpUUpKSlt+vKegRTZmOE9g9M2bNig9957T9dee+3n9ulO7xgCeSeZMmWKVq5cqRtuuOEL+xUXF2vo0KFhzwYPHqw9e/Z0ZHmIQhc7Znbu3Kn4+HgtWrRIkyZN0pw5c/TKK690UpWIFoMGDdKLL74op9MZerZixQqNGDGiTV/eM5AiGzO8Z3BaQkKCJGnatGmaO3eu0tPTdeutt7bpx3sGp13smOE9A0mqra3Vj370Iz355JOKi4v73H7d6R3jsruAniI9Pf2i+jU2NrYZfLGxsWpqauqIshDFLnbMmKap/Px8LVq0SEOGDFFRUZHuv/9+xcfHa9asWR1cJaKRZVl6+umntWrVKr300ktt2nnP4FwXGjO8Z3Cu9957T/X19XrwwQf1wAMP6MUXXwxr5z2Dc11ozPCeQSAQ0EMPPaQ777xTw4cP/8K+3ekdwwx5lImLi2tz0EVzc7Pi4+NtqgjR7uabb9aLL76oyy+/XG63W1OmTNHNN9+sd955x+7SYIOGhgY98MADeuutt/TSSy9p2LBhbfrwnsHZLmbM8J7BuWJjY5WRkaGHHnpIH330kerr68Paec/gXBcaM7xn8Nvf/lYej0e33377Bft2p3cMgTzKDB06VPv27Qt7VlxcrCFDhthUEaLdK6+80uYfK9M0FRMTY1NFsEtJSYnmzZunhoYGvfLKK+cNVhLvGZxxsWOG9wwkadu2bbr++utlmmbomWmacrvdbWaqeM9AimzM8J7BG2+8oU2bNmncuHEaN26cli1bpmXLlmncuHFt+nandwyBPMrceOON2rRpk5YvXy6fz6fly5dr06ZNuummm+wuDVGqoaFBixcv1u7duxUIBLR69WotW7ZMCxYssLs0dKL6+np9+9vf1pgxY/S73/3uvAfmnMZ7BlJkY4b3DCRp2LBham5u1pNPPinTNFVeXq4nnnhCt912mzweT1hf3jOQIhszvGfw7rvvatu2bdqyZYu2bNmiOXPmaM6cOdqyZUubvt3qHWP3Me890blXWOXn51tvvPFG6OM1a9ZYN954o5Wfn2/Nnj3bWr16tR1lIop80ZgJBALWc889Z02fPt3Ky8uzZs+ebb3zzjt2lQqb/P73v7eGDh1qjR492srPzw/7Y1m8Z9BWJGOG9wxO27dvn3XnnXda48aNs6ZPn2499dRTVktLi2VZvGdwfhc7ZnjP4FwPP/xw2LVn3fUdY1iWZdn9SwEAAAAAAHoalqwDAAAAAGADAjkAAAAAADYgkAMAAAAAYAMCOQAAAAAANiCQAwAAAABgAwI5AAAAAAA2IJADAAAAAGADAjkAAAAAADYgkAMAEAWuvvpqjRo1SgUFBSooKFB+fr6mTJmiJ554QoFA4LyfU1FRoYKCAlVUVLRrLa+99pqGDx+ugoICrVix4nPrfe211y7q61mWpenTp6uurq5NW1FRkYYNG3bBr7Fw4ULl5eVdVF8AALoKl90FAACAoJ/85Ce69dZbQx/v3btXd9xxh+Li4vTAAw+06Z+dna3t27d3SC3Z2dn64IMP2uVrffzxx8rJyVFKSsqX/hrPPfecioqK9K1vfatdagIAIBowQw4AQJQaNmyYxo8fr927d0uSbr/9dj3yyCOaPn26rrrqKu3du1fDhg1TWVmZJKm0tFT33HOPxo4dq8mTJ+vHP/6xTNOUJJWUlOiee+7RxIkTNX36dP3iF78ItV2IZVn6zW9+oylTpmjcuHF64okn5Pf7JUk7duzQZZddpqqqqlD/nTt3Kj8/Xw0NDZKk9957TzNnzpQkHTlyRPfcc4/GjBmja665RuvWrQt93ttvv62RI0dqz549kqTdu3crLy9Pa9as+Sp/jQAARC0COQAAUcjr9aqoqEgbN25UYWFh6Pn69ev1pz/9SW+++abi4+NDz30+n/7hH/5B6enpWrNmjZYtW6YdO3bol7/8pZqamnTHHXdoyJAhWrNmjf73f/9X69ev1y9/+cuLquXVV1/Vf//3f+u3v/2t1q9fL7fbHQrg+fn5GjRokN58881Q/9dff13XXXedEhISJEnvv/9+KJAvWrRILpdLa9as0UsvvRQWtmfPnq25c+fqBz/4gerr67Vo0SLdcccdmjp16pf/iwQAIIoRyAEAiBI/+clPNG7cOI0bN06TJ0/W4sWLdeedd+qb3/xmqM/UqVOVkZGhpKSksM/dtm2bysvL9c///M+Kj49XamqqfvWrX2n+/PlavXq1TNPU97//fcXExCgrK0vf+973tGTJkouq64033tDXvvY1jRgxQh6PR9/73veUnJwcar/11ltDgdzr9WrZsmWaN2+eJGnPnj1KSEhQdna2ysvLtWXLFj344INKSEhQVlaW7rvvvrDv9eijj8o0Td1yyy1KT0/X9773vS/1dwkAQFfAHnIAAKLEY489FraH/Hz69u173uc1NTVKTk5WXFxc6Flubq4kacWKFaqrq9P48eNDbZZlyev1qra2VqmpqV/4PY8cOaKsrKzQx06nU9nZ2aGPb7rpJj311FPavXu3ysrKlJiYGPpeZy9Xr66ulqSwz+3fv3/Y9+rVq5fmzZun//iP/9DChQvldDq/sDYAALoyAjkAAF2IYRjnfZ6Zmaljx47p1KlToVC+ZcsW7dq1S5mZmerfv7/efffdUP+GhgbV1tZe1EFrmZmZKi0tDX1sWZaOHDkS+jgtLU1Tp07V22+/rbKyMt16662hOleuXKmnn3469HWk4F73Sy+9VJLC9p5Lwb3uv/71rzV//nz9+7//uwoLC0OfBwBAd8OSdQAAuoG8vDwNHDhQTzzxhE6dOqWjR4/qZz/7merq6jR9+nQ1NjbqxRdflGmaOnHihB5++GEtWrTocwP+2ebPn68///nP2r59u7xer37961+rpqYmrM+8efO0cuVKrV+/Xrfccosk6fDhwwoEAqHwnZ2drSlTpuhnP/uZ6uvrVVNTo1/96lehr+H1evX9739fs2fP1uOPP67x48froYce+txr3wAA6OoI5AAAdANut1u/+c1vVF1drauuuko33XSTxo8frwceeEAJCQn6wx/+oKKiIk2dOlUzZsyQw+HQr3/964v62nPmzNEDDzygRYsWacKECSotLW1zH/hVV12lxsZG5eXlhZa3r1ixQjNmzAjr9+STTyoxMVHTp0/XvHnzdMUVV4TannnmGR07dkyPPPKIJOlf/uVfVFxcrN/+9rdf5a8GAICoZViWZdldBAAAiB6vvfaafvWrX0V8D/ktt9yiu+++WzfccEOH1HX6HvK9e/d2yNcHAKCzsYccAAB8JQcPHlRRUZFqamrazIgDAIDPx5J1AADQRkVFhQoKCrRixYoL9n300Uf1zDPP6Kc//ak8Hk+H1LNw4ULdfffdHfK1AQCwC0vWAQAAAACwATPkAAAAAADYgEAOAAAAAIANCOQAAAAAANiAQA4AAAAAgA0I5AAAAAAA2IBADgAAAACADQjkAAAAAADYgEAOAAAAAIAN/j/CNypefoEviwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for ci in cc:\n", - " plt.plot(\n", - " pvals, \n", - " [\n", - " ci.xyfromp_f(p, ignorebounds=True)[1]\n", - " for p in pvals\n", - " \n", - " ], \n", - " label=f\"eta={ci.eta:0.2f}\")\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.title(\"Token balance y vs price at different weights\")\n", - "plt.xlabel(\"Price [dy/dx]\")\n", - "plt.ylabel(\"Token balance y [native units]\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6595e456-ca4b-4c4a-81e1-1ceba2924ec9", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d6b880eb-6fe9-41d3-920d-05552fde7469", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/NBTest/NBTest_051_CPCBalancer.py b/resources/NBTest/NBTest_051_CPCBalancer.py deleted file mode 100644 index dab69f01b..000000000 --- a/resources/NBTest/NBTest_051_CPCBalancer.py +++ /dev/null @@ -1,622 +0,0 @@ -# -*- coding: utf-8 -*- -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -try: - from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, CurveBase - from fastlane_bot.testing import * - -except: - from tools.cpc import ConstantProductCurve as CPC, CurveBase - from tools.testing import * -# from flbtools.cpc import ConstantProductCurve as CPC, CurveBase -# from flbtesting import * - -from math import sqrt -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) -# from fastlane_bot import __VERSION__ -# require("3.0", __VERSION__) -# - - -# # CPC for Balancer [NBTest051] - -# ## pvec interface for CPC - -c0 = CPC.from_xy(100, 200) -assert c0.tknx == "TKNB" -assert c0.tkny == "TKNQ" -k0 = c0.invariant() -assert iseq(k0, sqrt(100*200)) -k1, k2 = c0.invariant(include_target=True) -assert iseq(k0, k1, k2) - -x,y,_ = c0.xyfromp_f(c0.p) -xvec = c0.xvecfrompvec_f({c0.tknx: c0.p, c0.tkny: 1} ) -assert iseq(x, 100) -assert iseq(y, 200) -assert iseq(xvec[c0.tknx], x) -assert iseq(xvec[c0.tkny], y) -assert iseq(c0.invariant(), c0.invariant(xvec)) -assert raises(c0.xvecfrompvec_f, {c0.tknx: c0.p} ).startswith("pvec must contain") -assert raises(c0.xvecfrompvec_f, {c0.tkny: 1} ).startswith("pvec must contain") - -p = 1.5*c0.p -x,y,_ = c0.xyfromp_f(p) -xvec = c0.xvecfrompvec_f({c0.tknx: p, c0.tkny: 1} ) -xvec2 = c0.xvecfrompvec_f({c0.tknx: 3*p, c0.tkny: 3} ) -xvec3 = c0.xvecfrompvec_f({c0.tknx: 3*p, c0.tkny: 3, "ETH": 15, "BTC": 300} ) -assert xvec == xvec2 -assert xvec == xvec3 -assert iseq(x, 81.64965809277261) -assert iseq(y, 244.9489742783178) -assert iseq(xvec[c0.tknx], x) -assert iseq(xvec[c0.tkny], y) -assert iseq(c0.invariant(), c0.invariant(xvec)) - -dx,dy,_ = c0.dxdyfromp_f(c0.p) -dxvec = c0.dxvecfrompvec_f({c0.tknx: c0.p, c0.tkny: 1} ) -assert abs(dx)<1e-10 -assert abs(dy)<1e-10 -assert iseq(dxvec[c0.tknx], dx) -assert iseq(dxvec[c0.tkny], dy) -assert raises(c0.dxvecfrompvec_f, {c0.tknx: c0.p} ).startswith("pvec must contain") -assert raises(c0.dxvecfrompvec_f, {c0.tkny: 1} ).startswith("pvec must contain") - -p = 1.5*c0.p -dx,dy,_ = c0.dxdyfromp_f(p) -dxvec = c0.dxvecfrompvec_f({c0.tknx: p, c0.tkny: 1} ) -dxvec2 = c0.dxvecfrompvec_f({c0.tknx: 3*p, c0.tkny: 3} ) -dxvec3 = c0.dxvecfrompvec_f({c0.tknx: 3*p, c0.tkny: 3, "ETH": 15, "BTC": 300} ) -assert dxvec == dxvec2 -assert dxvec == dxvec3 -assert iseq(dx, -18.35034190722739) -assert iseq(dy, 44.94897427831779) -assert iseq(dxvec[c0.tknx], dx) -assert iseq(dxvec[c0.tkny], dy) - - -# ## CurveBase - -# Checking that `CurveBase` can only instantiate with all functions defined - -# + -class CB1(CurveBase): - pass - -class CB2(CurveBase): - def dxvecfrompvec_f(self, pvec, *, ignorebounds=False): - pass - -class CB3(CurveBase): - def xvecfrompvec_f(self, pvec, *, ignorebounds=False): - pass - -class CB4(CurveBase): - def xvecfrompvec_f(self, pvec, *, ignorebounds=False): - pass - def dxvecfrompvec_f(self, pvec, *, ignorebounds=False): - pass - def invariant(self, xvec=None, *, include_target=False): - pass - -assert raises(CB1).startswith("Can't instantiate abstract class") -assert raises(CB2).startswith("Can't instantiate abstract class") -assert raises(CB3).startswith("Can't instantiate abstract class") -assert not raises(CB4) -# - - -assert isinstance(CPC.from_xy(100, 200), CurveBase) - -# ## Constant product constructor - -c0 = CPC.from_xy(100, 200) -assert c0.x == 100 -assert c0.y == 200 -assert c0.k == 20000 -assert c0.x == c0.x_act -assert c0.y == c0.y_act -assert c0.alpha == 0.5 -assert c0.eta == 1 -assert c0.constr == "xy" -assert c0.is_constant_product() == True -c0 - -assert c0.asdict() == { - 'k': 20000, - 'x': 100, - 'x_act': 100, - 'y_act': 200.0, - 'alpha': 0.5, - 'pair': 'TKNB/TKNQ', - 'cid': 'None', - 'fee': None, - 'descr': None, - 'constr': 'xy', - 'params': {} -} - -c1 = CPC.from_xyal(100, 200) -assert c1.constr == "xyal" -assert c1.is_constant_product() == True -assert c1==c0 -c1 - -assert c1.asdict() == { - 'k': 20000, - 'x': 100, - 'x_act': 100, - 'y_act': 200.0, - 'alpha': 0.5, - 'pair': 'TKNB/TKNQ', - 'cid': 'None', - 'fee': None, - 'descr': None, - 'constr': 'xyal', - 'params': {} -} - -c2 = CPC.from_xyal(100, 200, alpha=0.5) -assert c2.constr == "xyal" -assert c2.is_constant_product() == True -assert c2==c0 -assert c2.asdict() == c1.asdict() -c2 - -c3 = CPC.from_xyal(100, 200, eta=1) -assert c3.constr == "xyal" -assert c3.is_constant_product() == True -assert c3==c0 -assert c3.asdict() == c1.asdict() -c3 - -assert raises(CPC.from_xyal, 100, 200, - alpha=0.5, eta=1) == 'at most one of alpha and eta must be given [0.5, 1]' - -# ## Weighted constructor - -c0 = CPC.from_xy(100, 200) -assert c0.x == 100 -assert c0.y == 200 -assert c0.k == 20000 -assert c0.x == c0.x_act -assert c0.y == c0.y_act -assert c0.alpha == 0.5 -assert c0.eta == 1 -assert c0.constr == "xy" -assert iseq(c0.invariant(), c0.kbar) -assert c0.is_constant_product() == True -c0 - - - -c1 = CPC.from_xyal(100, 200) -assert c1.constr == "xyal" -assert c1.is_constant_product() == True -assert c1 == c0 -assert c1.asdict()["alpha"] == 0.5 -assert iseq(c1.invariant(), c1.kbar) -c1 - -c2 = CPC.from_xyal(100, 200, alpha=0.25) -assert c2.constr == "xyal" -assert c2.is_constant_product() == False -assert c2.alpha == 0.25 -assert c2.asdict()["alpha"] == 0.25 -assert iseq(c2.eta, 0.25/0.75) -assert iseq(c2.invariant(), c2.kbar) -assert c2 != c0 -assert c2 != c1 -c2 - -c3 = CPC.from_xyal(100, 200, alpha=0.8) -assert c3.constr == "xyal" -assert c3.is_constant_product() == False -assert iseq(c3.alpha, 0.8) -assert c3.asdict()["alpha"] == 0.8 -assert iseq(c3.eta, 0.8/0.2) -assert iseq(c3.invariant(), c3.kbar) -assert c3 != c0 -assert c3 != c1 -assert c3 != c2 -c2 - -c3b = CPC.fromdict(c3.asdict()) -assert c3b == c3 -c3b - -assert raises(CPC.from_xyal,100, 200, alpha=0) == 'alpha must be > 0 [0]' -assert raises(CPC.from_xyal,100, 200, alpha=-1) == 'alpha must be > 0 [-1]' -assert raises(CPC.from_xyal,100, 200, alpha=1) == 'alpha must be < 1 [1]' -assert raises(CPC.from_xyal,100, 200, alpha=2) == 'alpha must be < 1 [2]' - -raises(CPC.from_xyal,100, 200, alpha=0) - -assert not raises(CPC.from_xyal,100, 200, alpha=1-1e-10) -assert not raises(CPC.from_xyal,100, 200, alpha=0.01) - -raises(CPC.from_xyal,100, 200, alpha=0.001) - -# ## High level testing of all functions -# -# (including not YET implemented) - -c0 = CPC.from_xyal(100, 200) -assert c0.is_constant_product() == True -c0 - -c1 = CPC.from_xyal(100, 200, alpha=0.25) -assert c1.is_constant_product() == False -c1 - -# #### Not (yet) implemented functions -# -# Those function groups are not currently planned to be implemented at all -# -# - `execute` as there is no need to simulate those curves for the time being; that was a Carbon thing -# -# The functions we may implement at a later stage are -# -# - `description` should probably be updated, but it is tedious; `format` ditto -# - `x_max`, `x_min`, `p_max`, `p_min` and the other leverage functions once we consider it important and safe - -# execute - -assert not raises(c0.execute) -assert raises(c1.execute).startswith("only implemented for") - -# description and format - -assert not raises(c0.description) -assert raises(c1.description).startswith("only implemented for") - -assert not raises(c0.format) -assert raises(c1.format).startswith("only implemented for") - -# leverage related functions (primary) - -assert not raises(lambda: c0.p_max) -assert not raises(lambda: c1.p_max) - -assert not raises(lambda: c0.p_min) -assert not raises(lambda: c1.p_min) - -assert not raises(lambda: c0.x_min) -assert not raises(lambda: c1.x_min) - -assert not raises(lambda: c0.x_max) -assert not raises(lambda: c1.x_max) - -assert not raises(lambda: c0.y_min) -assert not raises(lambda: c1.y_min) - -assert not raises(lambda: c0.y_max) -assert not raises(lambda: c1.y_max) - -# leverage related functions (secondary, ie calling primary ones) - -assert not raises(c0.p_max_primary) -assert not raises(c1.p_max_primary) - -assert not raises(c0.p_min_primary) -assert not raises(c1.p_min_primary) - -assert not raises(lambda: c0.at_xmin) -assert not raises(lambda: c1.at_xmin) - -assert not raises(lambda: c0.at_xmax) -assert not raises(lambda: c1.at_xmax) - -assert not raises(lambda: c0.at_ymin) -assert not raises(lambda: c1.at_ymin) - -assert not raises(lambda: c0.at_ymax) -assert not raises(lambda: c1.at_ymax) - -assert not raises(lambda: c0.at_boundary) -assert not raises(lambda: c1.at_boundary) - -# todo - -assert not raises(c0.xyfromp_f) -assert not raises(c1.xyfromp_f) - -assert not raises(c0.dxdyfromp_f) -assert not raises(c1.dxdyfromp_f) - -# #### Implemented functions - -assert not raises(lambda: c0.y) -assert not raises(lambda: c1.y) - -assert not raises(lambda: c0.p) -assert not raises(lambda: c1.p) - -assert not raises(lambda: c0.kbar) -assert not raises(lambda: c1.kbar) - -assert not raises(c0.tvl) -assert not raises(c1.tvl) - -assert not raises(c0.yfromx_f, 110) -assert not raises(c1.yfromx_f, 110, ignorebounds=True) -assert not raises(c1.yfromx_f, 110, ignorebounds=False) - -assert not raises(c0.xfromy_f, 210) -assert not raises(c1.xfromy_f, 110, ignorebounds=True) -assert not raises(c1.xfromy_f, 110, ignorebounds=False) - -assert not raises(c0.dyfromdx_f, 1) -assert not raises(c1.dyfromdx_f, 1, ignorebounds=True) -assert not raises(c1.dyfromdx_f, 1, ignorebounds=False) - -assert not raises(c0.dxfromdy_f, 1) -assert not raises(c1.dxfromdy_f, 1, ignorebounds=True) -assert not raises(c1.dxfromdy_f, 1, ignorebounds=False) - -# ## Simple Tests - -c0 = CPC.from_xyal(100, 200) -c1 = CPC.from_xyal(100, 200, eta=2) -c2 = CPC.from_xyal(100, 200, eta=0.5) - -assert iseq(c0.alpha, 1/2) -assert iseq(c1.alpha, 2/3) -assert iseq(c2.alpha, 1/3) - -# #### Current token balance $y$ -# -# $$ -# y = \left( \frac k x \right)^\eta -# $$ - -assert iseq(c0.y, 200) -assert iseq(c1.y, 200) -assert iseq(c2.y, 200) - -# #### Current price $p$ -# -# $$ -# p = \eta\, \frac y x -# $$ - -assert iseq(c0.p, 2 * c0.eta) -assert iseq(c1.p, 2 * c1.eta) -assert iseq(c2.p, 2 * c2.eta) - -# #### TVL -# -# $$ -# \mathrm{TVL} = x_a*p + y_a -# $$ - -assert c0.x == c0.x_act -assert c0.y == c0.y_act -assert c1.x == c1.x_act -assert c1.y == c1.y_act -assert c2.x == c2.x_act -assert c2.y == c2.y_act - -assert iseq(c0.tvl(), 100 * c0.p + 200) -assert iseq(c1.tvl(), 100 * c1.p + 200) -assert iseq(c2.tvl(), 100 * c2.p + 200) - -# #### Pool constant $k$ -# -# $$ -# k^\alpha = x^\alpha\, y^{1-\alpha} -# $$ -# -# $$ -# k = x\,y^\frac{1-\alpha}{\alpha} = x\,y^{\frac 1 \eta} -# $$ -# - -assert iseq(c0.k**(1/2), c0.x**(1/2) * c0.y**(1/2)) -assert iseq(c1.k**(2/3), c1.x**(2/3) * c1.y**(1/3)) -assert iseq(c2.k**(1/3), c1.x**(1/3) * c1.y**(2/3)) - -# #### Pool constant $\bar k$ -# -# $$ -# x^\alpha\, y^{1-\alpha} = \bar k = k^\alpha -# $$ - -assert iseq(c0.kbar, c0.x**(1/2) * c0.y**(1/2)) -assert iseq(c1.kbar, c1.x**(2/3) * c1.y**(1/3)) -assert iseq(c2.kbar, c1.x**(1/3) * c1.y**(2/3)) - -assert iseq(c0.kbar, c0.k**c0.alpha) -assert iseq(c1.kbar, c1.k**c1.alpha) -assert iseq(c2.kbar, c2.k**c2.alpha) - -# #### Token balance function $y(x)$ -# -# $$ -# y(x) = \left( \frac k x \right)^\eta -# $$ - -assert c0.eta == 1 -assert iseq(c0.yfromx_f(100, ignorebounds=True), 200) -assert iseq(c0.yfromx_f( 50, ignorebounds=True), 400) -assert iseq(c0.yfromx_f(200, ignorebounds=True), 100) - -assert iseq(c1.eta, 2) -assert iseq(c1.yfromx_f(100, ignorebounds=True), 200) -assert iseq(c1.yfromx_f( 50, ignorebounds=True), 200*2**2) -assert iseq(c1.yfromx_f(200, ignorebounds=True), 200/2**2) - -assert iseq(c2.eta, 1/2) -assert iseq(c2.yfromx_f(100, ignorebounds=True), 200) -assert iseq(c2.yfromx_f( 50, ignorebounds=True), 200*sqrt(2)) -assert iseq(c2.yfromx_f(200, ignorebounds=True), 200/sqrt(2)) - -# #### Token balance function $x(y)$ -# -# $$ -# x(y) -# = \frac{k}{ y^{\frac{1-\alpha}{\alpha}} } -# = \frac{k}{ y^{\frac{1}{\eta}} } -# $$ - -assert c0.eta == 1 -assert iseq(c0.xfromy_f(200, ignorebounds=True), 100) -assert iseq(c0.xfromy_f(100, ignorebounds=True), 200) -assert iseq(c0.xfromy_f(400, ignorebounds=True), 50) - -assert iseq(c1.eta, 2) -assert iseq(c1.xfromy_f(200, ignorebounds=True), 100) -assert iseq(c1.xfromy_f(100, ignorebounds=True), 100*2**0.5) -assert iseq(c1.xfromy_f(400, ignorebounds=True), 100/2**0.5) - -assert iseq(c2.eta, 1/2) -assert iseq(c2.xfromy_f(200, ignorebounds=True), 100) -assert iseq(c2.xfromy_f(100, ignorebounds=True), 100*2**2) -assert iseq(c2.xfromy_f(400, ignorebounds=True), 100/2**2) - -# #### Price response function $(x(p), y(p))$ -# -# $$ -# x(p) -# = -# \left(\frac \eta p\right)^{1-\alpha} k^\alpha -# $$ -# -# $$ -# y(p) = \left( \frac{kp}{\eta} \right)^\alpha -# $$ - -assert iseq(c0.xyfromp_f(c0.p, ignorebounds=True)[0], c0.x) -assert iseq(c1.xyfromp_f(c1.p, ignorebounds=True)[0], c1.x) -assert iseq(c2.xyfromp_f(c2.p, ignorebounds=True)[0], c2.x) - -assert iseq(c0.xyfromp_f(c0.p, ignorebounds=True)[1], c0.y) -assert iseq(c1.xyfromp_f(c1.p, ignorebounds=True)[1], c1.y) -assert iseq(c2.xyfromp_f(c2.p, ignorebounds=True)[1], c2.y) - -for ci in [c0, c1, c2]: - for p in [2, 1, 4]: - eta_over_p = ci.eta / p - x = eta_over_p ** (1-ci.alpha) * ci.kbar - y = 1/eta_over_p**ci.alpha * ci.kbar - xx, yy, pp = ci.xyfromp_f (p, ignorebounds=True) - dx, dy, _ = ci.dxdyfromp_f(p, ignorebounds=True) - assert iseq(x, xx) - assert iseq(y, yy) - assert iseq(p, pp) - assert iseq(dx, xx-ci.x) - assert iseq(dy, yy-ci.y) - -# ## Consistency tests - -c0 = CPC.from_xyal(100, 200) -c1 = CPC.from_xyal(100, 200, eta=2) -c2 = CPC.from_xyal(100, 200, eta=0.5) -cc = [c0, c1, c2] - -assert iseq(c0.alpha, 1/2) -assert iseq(c1.alpha, 2/3) -assert iseq(c2.alpha, 1/3) - -# ### Assert inversions -# -# $$ -# y(x(y)) = y -# $$ -# -# and vice versa - -for xy in np.logspace(1, 3, 100): - for ci in cc: - #print(f"xy={xy}, eta={ci.eta}") - assert iseq(ci.yfromx_f(ci.xfromy_f(xy, ignorebounds=True), ignorebounds=True), xy) - assert iseq(ci.xfromy_f(ci.yfromx_f(xy, ignorebounds=True), ignorebounds=True), xy) - -# ### Assert that prices are correct -# -# $$ -# p \simeq -\frac{\Delta y}{\Delta x} -# $$ - -for alpha in np.linspace(0.01, 0.99, 100): - ci = CPC.from_xyal(100, 200, alpha=alpha) - dy = ci.yfromx_f(ci.x+0.1, ignorebounds=True)-ci.yfromx_f(ci.x-0.1, ignorebounds=True) - assert iseq(dy/0.2, -ci.p, eps=1e-2), f"error: {dy/0.2/ci.p+1}" - -# ### Check `dyfromdx_f` against `yfromx_f` - -for dxy in np.linspace(0.1, 99, 100): - for ci in cc: - assert iseq(ci.dyfromdx_f(dxy, ignorebounds=True), - (ci.yfromx_f(ci.x+dxy, ignorebounds=True)-ci.y)) - assert iseq(ci.dxfromdy_f(dxy, ignorebounds=True), - (ci.xfromy_f(ci.y+dxy, ignorebounds=True)-ci.x)) - -# ## Charts [NOTEST] - -plt.style.use('seaborn-v0_8-dark') -plt.rcParams['figure.figsize'] = [12,6] # only picked up at second run (?!?) - -c0 = CPC.from_xyal(100, 200) -c1 = CPC.from_xyal(100, 200, eta=2) -c2 = CPC.from_xyal(100, 200, eta=0.5) -cc = [c0, c1, c2] -xvals = np.linspace(50,200) -pvals = np.linspace(1,4) - -for ci in cc: - plt.plot(xvals, [ci.yfromx_f(x, ignorebounds=True) for x in xvals], label=f"eta={ci.eta:0.2f}") -plt.grid() -plt.legend() -plt.title("Indifference curve token balance y vs token balance x at different weights") -plt.xlabel("Token balance x [native units]") -plt.ylabel("Token balance y [native units]") -plt.show() - -for ci in cc: - plt.plot( - xvals, - [ - -(ci.yfromx_f(x+0.1, ignorebounds=True) - ci.yfromx_f(x-0.1, ignorebounds=True))/0.2 - for x in xvals - - ], - label=f"eta={ci.eta:0.2f}") -plt.grid() -plt.legend() -plt.title("Price vs token balance x at different weights") -plt.xlabel("Token balance x [native units]") -plt.ylabel("Price [dy/dx]") -plt.show() - -for ci in cc: - plt.plot( - pvals, - [ - ci.xyfromp_f(p, ignorebounds=True)[1] - for p in pvals - - ], - label=f"eta={ci.eta:0.2f}") -plt.grid() -plt.legend() -plt.title("Token balance y vs price at different weights") -plt.xlabel("Price [dy/dx]") -plt.ylabel("Token balance y [native units]") -plt.show() - - - - diff --git a/resources/NBTest/NBTest_055_Optimization.ipynb b/resources/NBTest/NBTest_055_Optimization.ipynb deleted file mode 100644 index d8b28958e..000000000 --- a/resources/NBTest/NBTest_055_Optimization.ipynb +++ /dev/null @@ -1,1707 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "a448e212", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "CPCContainer v3.4 (23/Jan/2024)\n", - "ConstantProductCurve v3.4 (23/Jan/2024)\n", - "MargPOptimizer v5.2 (15/Sep/2023)\n", - "PairOptimizer v6.0.1 (21/Sep/2023)\n" - ] - } - ], - "source": [ - "try:\n", - " from fastlane_bot.tools.cpc import CPCContainer, ConstantProductCurve as CPC, CurveBase\n", - " from fastlane_bot.tools.optimizer import MargPOptimizer, PairOptimizer\n", - " from fastlane_bot.testing import *\n", - "\n", - "except:\n", - " from tools.cpc import CPCContainer, ConstantProductCurve as CPC, CurveBase\n", - " from tools.optimizer import MargPOptimizer, PairOptimizer\n", - " from tools.testing import *\n", - "\n", - "from math import sqrt\n", - "from copy import deepcopy\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPCContainer))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPC))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(MargPOptimizer))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(PairOptimizer))\n", - "\n", - "plt.style.use('seaborn-v0_8-dark')\n", - "plt.rcParams['figure.figsize'] = [12,6]\n", - "# from fastlane_bot import __VERSION__\n", - "# require(\"3.0\", __VERSION__)" - ] - }, - { - "cell_type": "markdown", - "id": "d9917997", - "metadata": {}, - "source": [ - "# Optimization Methods [NBTest055]" - ] - }, - { - "cell_type": "markdown", - "id": "382ba9f9", - "metadata": {}, - "source": [ - "Note: using an existing CPCContainer object `CC`, the curves can be extracted as dict using the command below\n", - "\n", - " CURVES = [c.asdict() for c in CC]\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "71b7924c-6f92-4272-bbe4-3e0b0af3c3d7", - "metadata": {}, - "source": [ - "The below three curves are one POL curve (extremely levered; it is originally fixed price) and two Uniswap v3 curves. On those curves the high dimensional gradient descent algo fails because it ends up on a plateau.\n", - "\n", - "We are here creating the following sets of curves\n", - "\n", - "- `CC` based on `CURVES` the curves paramater set which are levered curves where the gradient descent optimization algorithm failed\n", - "\n", - "- `CCn` is `CC` plus a full range curve removing the no-man's land\n", - "\n", - "- `CCul` is a set of unlevered curves where convergence should not be a problem at all\n" - ] - }, - { - "cell_type": "markdown", - "id": "2d84487b-34e4-427a-95e2-86b77b168584", - "metadata": {}, - "source": [ - "### `CC` (complex levered curves)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "0cb2c0bf", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "CURVES = [\n", - "\n", - "# POL Curve\n", - "{\n", - " 'k': 6.157332844764952e+20,\n", - " 'x': 615733222.5892723,\n", - " 'x_act': 0,\n", - " 'y_act': 100000.0,\n", - " 'alpha': 0.5,\n", - " 'pair': 'WETH/DAI', # WETH-6Cc2/DAI-1d0F\n", - " 'cid': '0x33ed',\n", - " # 0x33ed451d5c7b7a76266b8cdfab030f6de8143fcfbdcd08dabeed0de8d684b4de\n", - " 'fee': 0.0,\n", - " 'descr': 'bancor_pol DAI-1d0F/ETH-EEeE 0.000',\n", - " 'constr': 'carb',\n", - " 'params': {'exchange': 'bancor_pol',\n", - " 'tknx_dec': 18,\n", - " 'tkny_dec': 18,\n", - " 'tknx_addr': '0x6B175474E89094C44Da98b954EedeAC495271d0F',\n", - " 'tkny_addr': '0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE',\n", - " 'blocklud': 18121620,\n", - " 'y': 100000.0,\n", - " 'yint': 100000.0,\n", - " 'A': 0,\n", - " 'B': 40.29987368093254,\n", - " 'pa': 1624.0799811071013,\n", - " 'pb': 1624.0799811071013}},\n", - " \n", - "# Uniswap v3 Curve 1\n", - " {\n", - " 'k': 1147678924959.0112,\n", - " 'x': 42728400.31211105,\n", - " 'x_act': 7575.552803896368,\n", - " 'y_act': 8.665306719478394,\n", - " 'alpha': 0.5,\n", - " 'pair': 'DAI/WETH', # DAi-1d0F/WETH-6Cc2\n", - " 'cid': '0xb1d8',\n", - " # 0xb1d8cd62f75016872495dae3e19d96e364767e7d674488392029d15cdbcd7b34',\n", - " 'fee': 0.0005,\n", - " 'descr': 'uniswap_v3 DAI-1d0F/WETH-6Cc2 500',\n", - " 'constr': 'pkpp',\n", - " 'params': {'exchange': 'uniswap_v3',\n", - " 'tknx_dec': 18,\n", - " 'tkny_dec': 18,\n", - " 'tknx_addr': '0x6B175474E89094C44Da98b954EedeAC495271d0F',\n", - " 'tkny_addr': '0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2',\n", - " 'blocklud': 18121789,\n", - " 'L': 1071297.7760450225}},\n", - " \n", - "\n", - "# Uniswap v3 Curve 2\n", - "{\n", - " 'k': 1541847511355.546,\n", - " 'x': 49517090.33542573,\n", - " 'x_act': 99496.94394361228,\n", - " 'y_act': 30.763865271412214,\n", - " 'alpha': 0.5,\n", - " 'pair': 'DAI/WETH', # DAi-1d0F/WETH-6Cc2\n", - " 'cid': '0xae2b',\n", - " # '0xae2b487dff467a33b88e5a4e6874f91ee212886979f673dd18d3e0396862112f',\n", - " 'fee': 0.003,\n", - " 'descr': 'uniswap_v3 DAI-1d0F/WETH-6Cc2 3000',\n", - " 'constr': 'pkpp',\n", - " 'params': {'exchange': 'uniswap_v3',\n", - " 'tknx_dec': 18,\n", - " 'tkny_dec': 18,\n", - " 'tknx_addr': '0x6B175474E89094C44Da98b954EedeAC495271d0F',\n", - " 'tkny_addr': '0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2',\n", - " 'blocklud': 18121689,\n", - " 'L': 1241711.5250151888}}\n", - "]\n", - "CC = CPCContainer.from_dicts(CURVES)" - ] - }, - { - "cell_type": "markdown", - "id": "ed8e1919-52fe-4000-9f60-2d9b1909213f", - "metadata": {}, - "source": [ - "Those are starting prices consistent with those curves." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4d0fea57", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1590.7292608895832" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "PRICES = {\n", - " 'DAI': 0.0006286424878113893, \n", - " 'WETH': 1,\n", - "}\n", - "PRICE0 = PRICES[\"WETH\"]/PRICES[\"DAI\"]\n", - "PRICE0" - ] - }, - { - "cell_type": "markdown", - "id": "cf29d106-1042-46f2-8658-57c7f5d5acb2", - "metadata": {}, - "source": [ - "### `CCn` (normalized curve set)\n", - "\n", - "This curve set contains an additional constant product curve that removes the no-man's land between the levered curves and where gradient descent therefore converges" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "97f31216-fd08-4171-a0b1-4a545c8a1db6", - "metadata": {}, - "outputs": [], - "source": [ - "cnorm = CPC.from_pk(p=PRICE0, k=PRICE0*CC[0].x, pair=\"WETH/DAI\", cid=\"normalizer\")\n", - "CCn = CPCContainer([c for c in CC]+[cnorm])" - ] - }, - { - "cell_type": "markdown", - "id": "53d02241-248c-42df-9dd4-b3ba58d7c067", - "metadata": {}, - "source": [ - "### `CCul` (simple unlevered curves)\n", - "\n", - "This is a very simple set of unlevered curver where convergence should never be a problem." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "41d1f06b", - "metadata": {}, - "outputs": [], - "source": [ - "CCul = CPCContainer([\n", - " CPC.from_pk(p=1500, k=1500*100, pair=\"WETH/DAI\", cid=\"c1500\"),\n", - " CPC.from_pk(p=1600, k=1600*100, pair=\"WETH/DAI\", cid=\"c1600\")\n", - "])" - ] - }, - { - "cell_type": "markdown", - "id": "5c1ac06f-7c85-4301-a383-1e1de3d47674", - "metadata": {}, - "source": [ - "### `CCas` (asymmetric unlevered curves)\n", - "\n", - "We are generating asymmetric curves that have an arbitrage opportunity. `CCas2` is a single pair that exhibits the arbitrage, `CCas3` requires triangle optimization." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "68e08649-812e-4a46-8814-27b7f7f97b07", - "metadata": {}, - "outputs": [], - "source": [ - "ETA25, ETA75 = 1/3, 3\n", - "CCas2 = CPCContainer([\n", - " CPC.from_xyal(x=10, y=2000/ETA25*10, alpha=0.25, pair=\"WETH/DAI\", cid=\"c2000-0.25\"),\n", - " CPC.from_xyal(x=10, y=2500/ETA75*10, alpha=0.75, pair=\"WETH/DAI\", cid=\"c2500-0.75\"),\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "13e4a3f9-42fb-4094-9e50-a60486bba8f4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(10, 'WETH', 59999.99999999996, 'DAI', 1999.9999999999986)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CCas2[0].x, CCas2[0].tknx, CCas2[0].y, CCas2[0].tkny, CCas2[0].p" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "fd29b5e6-c596-45dd-9e5d-c13c2e01fa41", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(10, 'WETH', 8333.33333333333, 'DAI', 2499.999999999999)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CCas2[1].x, CCas2[1].tknx, CCas2[1].y, CCas2[1].tkny, CCas2[1].p" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "f2bc18b3-bce9-4d3d-8da5-0df9474082e8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.3333333333333333" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CCas2[0].eta" - ] - }, - { - "cell_type": "markdown", - "id": "4a558dca-93ab-464d-b53b-546c2841e1a3", - "metadata": {}, - "source": [ - "## Curve definitions\n", - "\n", - "Here we are asserting properties of the curves that they are meant to have; should really never fail unless something goes horribly wrong" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "8546c725-fccd-46ed-b7a2-a06abf6cb901", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(CCas2[0].x, 10)\n", - "assert CCas2[0].tknx == \"WETH\"\n", - "assert iseq(CCas2[0].y, 60000)\n", - "assert CCas2[0].tkny == \"DAI\"\n", - "assert iseq(CCas2[0].eta, ETA25)\n", - "assert iseq(CCas2[0].p, 2000)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d191df15", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "assert iseq(CCas2[1].x, 10)\n", - "assert CCas2[1].tknx == \"WETH\"\n", - "assert iseq(CCas2[1].y, 25000/3)\n", - "assert CCas2[1].tkny == \"DAI\"\n", - "assert iseq(CCas2[1].eta, ETA75)\n", - "assert iseq(CCas2[1].p, 2500)" - ] - }, - { - "cell_type": "markdown", - "id": "9a6b457a-3573-4387-8047-9ae88c5c607e", - "metadata": {}, - "source": [ - "## MargPOptimizer current\n", - "\n", - "Uses the current margp optimizer which uses $d \\log p ~ 0$ as criterium and that can fail on certain formations of levered curves (when the price ends up on no-mans land)\n", - "### Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "69c90858", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "#help(MargPOptimizer)" - ] - }, - { - "cell_type": "markdown", - "id": "a28696d0", - "metadata": {}, - "source": [ - "### Unlevered curves `CCul`" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "19aecdff-2706-420a-bbb4-b0d776e235fd", - "metadata": {}, - "outputs": [], - "source": [ - "Oul = MargPOptimizer(curves=CCul)\n", - "assert len(Oul.curves) == len(CCul)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "d00b746e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CPCArbOptimizer.MargpOptimizerResult(result=-0.005204267821271813, time=0.0003368854522705078, method='margp', targettkn='WETH', p_optimal_t=(0.0006449934107164284,), dtokens_t=(-4.737194103654474e-08,), tokens_t=('DAI',), errormsg=None)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = Oul.optimize(\"WETH\")\n", - "assert r.error is None\n", - "assert r.method == \"margp\"\n", - "assert r.targettkn == \"WETH\"\n", - "assert r.tokens_t == ('DAI',)\n", - "assert r.dtokens[\"WETH\"] < 0\n", - "assert iseq(r.result, -0.005204267821271813)\n", - "assert iseq(r.p_optimal_t[0], 0.0006449934107164284)\n", - "assert iseq(r.dtokens_t[0], -4.737194103654474e-08)\n", - "r" - ] - }, - { - "cell_type": "markdown", - "id": "e49e25d9", - "metadata": {}, - "source": [ - "the original curves are 1500 and 1600, so ~1550 is right in the middle" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "c5af61c4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1550.4034357331604" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert iseq(1/r.p_optimal_t[0], 1550.4034357331604)\n", - "1/r.p_optimal_t[0]" - ] - }, - { - "cell_type": "markdown", - "id": "92fec7d9", - "metadata": {}, - "source": [ - "this process converged -- the aggregate change in DAI amount < 1e-5" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "48ec6757", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'WETH': -0.005204267821271813, 'DAI': -4.737194103654474e-08}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert abs(r.dtokens[\"DAI\"] < 1e-5)\n", - "assert r.dtokens[\"WETH\"] < 0\n", - "assert iseq(r.dtokens[\"WETH\"], -0.005204267821271813)\n", - "r.dtokens" - ] - }, - { - "cell_type": "markdown", - "id": "9127bf65", - "metadata": {}, - "source": [ - "there is some trading going on" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "79288ac3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'c1500': {'WETH': -0.16389245649152784, 'DAI': 249.9349296963901},\n", - " 'c1600': {'WETH': 0.15868818867025603, 'DAI': -249.93492974376204}}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "v = r.dxvecvalues(asdict=True)\n", - "assert iseq(v[\"c1500\"][\"DAI\"], 249.9349296963901)\n", - "assert iseq(v[\"c1600\"][\"WETH\"], 0.15868818867025603)\n", - "v" - ] - }, - { - "cell_type": "markdown", - "id": "4af7aa67", - "metadata": {}, - "source": [ - "### Normalized curves `CCn`" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "24227582-7c75-425d-9b24-72cd5e7d6d2d", - "metadata": {}, - "outputs": [], - "source": [ - "On = MargPOptimizer(curves=CCn)\n", - "assert len(On.curves) == len(CC)+1" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "84535b0e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CPCArbOptimizer.MargpOptimizerResult(result=-1.244345098228223, time=0.0006251335144042969, method='margp', targettkn='WETH', p_optimal_t=(0.00062745798800732,), dtokens_t=(-1.9371509552001953e-06,), tokens_t=('DAI',), errormsg=None)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = On.optimize(\"WETH\")\n", - "assert r.error is None\n", - "assert r.method == \"margp\"\n", - "assert r.targettkn == \"WETH\"\n", - "assert r.tokens_t == ('DAI',)\n", - "assert r.dtokens[\"WETH\"] < 0\n", - "assert iseq(r.result, -1.244345098228223)\n", - "assert iseq(r.p_optimal_t[0], 0.00062745798800732)\n", - "assert iseq(r.dtokens_t[0], -1.9371509552001953e-06, eps=0.1)\n", - "# assert iseq(r.dtokens_t[0], -1.9371509552001953e-06, eps=0.01) # FAILS ON GITHUB\n", - "# assert iseq(r.dtokens_t[0], -1.9371509552001953e-06, eps=0.001) # FAILS ON GITHUB\n", - "# assert iseq(r.dtokens_t[0], -1.9371509552001953e-06, eps=0.0001) # FAILS ON GITHUB\n", - "r" - ] - }, - { - "cell_type": "markdown", - "id": "2f1f0ea0", - "metadata": {}, - "source": [ - "the original curves are 1500 and 1600, so ~1550 is right in the middle" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "e30ed6d5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1593.7322005825413" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert iseq(1/r.p_optimal_t[0], 1593.7322005825413, eps=0.001)\n", - "1/r.p_optimal_t[0]" - ] - }, - { - "cell_type": "markdown", - "id": "4777a332", - "metadata": {}, - "source": [ - "this process converged -- the aggregate change in DAI amount < 1e-5" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "3a62bcab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'WETH': -1.244345098228223, 'DAI': -1.9371509552001953e-06}" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert abs(r.dtokens[\"DAI\"] < 1e-5)\n", - "assert r.dtokens[\"WETH\"] < 0\n", - "assert iseq(r.dtokens[\"WETH\"], -1.244345098228223)\n", - "r.dtokens" - ] - }, - { - "cell_type": "markdown", - "id": "2569bc8e", - "metadata": {}, - "source": [ - "there is some trading going on" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "e0344572", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'0x33ed': {'WETH': 61.57332217693329, 'DAI': -100000.0},\n", - " '0xb1d8': {'DAI': 13789.132085457444, 'WETH': -8.665306719478394},\n", - " '0xae2b': {'DAI': 48971.003532998264, 'WETH': -30.763865271412214},\n", - " 'normalizer': {'WETH': -23.388495284270903, 'DAI': 37239.86437960714}}" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "v = r.dxvecvalues(asdict=True)\n", - "v" - ] - }, - { - "cell_type": "markdown", - "id": "dd36efbb-7940-4bd6-9b3e-63c236224cb2", - "metadata": {}, - "source": [ - "### Asymmetric curves `CCas2` and `CCas3`" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "41192d1c-6635-4960-a30d-60cecf83892e", - "metadata": {}, - "outputs": [], - "source": [ - "O = MargPOptimizer(curves=CCas2)\n", - "assert len(O.curves) == len(CCas2)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "5966ea84-386f-45f5-8bf6-a40df86dfedc", - "metadata": {}, - "outputs": [], - "source": [ - "r = O.optimize(\"WETH\", params={\"pstart\": {\"WETH\": 2400, \"DAI\": 1}})\n", - "assert r.error is None\n", - "assert r.method == \"margp\"\n", - "assert r.targettkn == \"WETH\"\n", - "assert r.tokens_t == ('DAI',)\n", - "assert r.dtokens[\"WETH\"] < 0\n", - "assert iseq(r.result, -0.048636442623132936, eps=1e-3)\n", - "assert iseq(r.p_optimal_t[0], 0.0004696831634035269, eps=1e-3)\n", - "assert iseq(r.dtokens_t[0], -7.3569026426412165e-09, eps=0.1)" - ] - }, - { - "cell_type": "markdown", - "id": "6cd3e66a", - "metadata": {}, - "source": [ - "### Failing optimization process `CC`" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "1f69d97b", - "metadata": {}, - "outputs": [], - "source": [ - "O = MargPOptimizer(curves=CC)\n", - "assert len(O.curves) == len(CC)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "670e8185", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CPCArbOptimizer.MargpOptimizerResult(result=22.14415018604268, time=0.0004968643188476562, method='margp', targettkn='WETH', p_optimal_t=(0.0006273686958774544,), dtokens_t=(-37239.86438154429,), tokens_t=('DAI',), errormsg=None)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = O.optimize(\"WETH\")\n", - "assert r.error is None\n", - "assert r.method == \"margp\"\n", - "assert r.targettkn == \"WETH\"\n", - "assert r.tokens_t == ('DAI',)\n", - "assert iseq(r.result, 22.14415018604268)\n", - "assert iseq(r.p_optimal_t[0], 0.0006273686958774544)\n", - "assert iseq(r.dtokens_t[0], -37239.86438154429)\n", - "r" - ] - }, - { - "cell_type": "markdown", - "id": "40871d0d", - "metadata": {}, - "source": [ - "Here we show that the final price is not the same as the initial one, but also not totally crazy (this calculation has not converged but is stuck on a plateau)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "fd0376b7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "({'DAI': 0.0006286424878113893, 'WETH': 1},\n", - " {'DAI': 0.0006273686958774544, 'WETH': 1.0})" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "PRICES, r.p_optimal" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "7d7d54a8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1593.959033294407, 1590.7292608895832)" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "1/r.p_optimal_t[0], PRICES[\"WETH\"]/PRICES[\"DAI\"]" - ] - }, - { - "cell_type": "markdown", - "id": "f6130abc", - "metadata": {}, - "source": [ - "The `result` is the amount of target token extracted. Note that this assumes that the algo has converged which it has not in this case. The `dtokens` property shows the _aggregate_ change in tokens, and it _should_ be zero for everything but the target token WETH which is not the case here." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "9f1c1fa6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "22.14415018604268" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert r.result == r.dtokens[\"WETH\"]\n", - "r.result" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "27a53e7e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'WETH': 22.14415018604268, 'DAI': -37239.86438154429}" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r.dtokens" - ] - }, - { - "cell_type": "markdown", - "id": "15cffd46", - "metadata": {}, - "source": [ - "`dxdyvalues` and `dxvecvalues` show the changes of the respective curves. For standard two-asset curves they are equivalent, just in a different format; for three+ asset curves only dxvecvalues is defined" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "c4461246", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'0x33ed': (61.57332217693329, -100000.0),\n", - " '0xb1d8': (13789.132085457444, -8.665306719478394),\n", - " '0xae2b': (48971.003532998264, -30.763865271412214)}" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r.dxdyvalues(asdict=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "bb314923", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'0x33ed': {'WETH': 61.57332217693329, 'DAI': -100000.0},\n", - " '0xb1d8': {'DAI': 13789.132085457444, 'WETH': -8.665306719478394},\n", - " '0xae2b': {'DAI': 48971.003532998264, 'WETH': -30.763865271412214}}" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r.dxvecvalues(asdict=True)" - ] - }, - { - "cell_type": "markdown", - "id": "14af2241", - "metadata": {}, - "source": [ - "This shows that the algorithm **has not converged** -- this number (the net flow of DAI; note that the target token here is WETH) should be zero!" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "7410fc4a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-37239.86438154429" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s_DAI = sum(x[\"DAI\"] for x in r.dxvecvalues(asdict=True).values())\n", - "assert iseq(s_DAI, r.dtokens[\"DAI\"])\n", - "s_DAI" - ] - }, - { - "cell_type": "markdown", - "id": "9094b4e1", - "metadata": {}, - "source": [ - "This number is not expected to be zero as the profit is being extracted in WETH" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "e5c2ee6a", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "22.14415018604268" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s_WETH = sum(x[\"WETH\"] for x in r.dxvecvalues(asdict=True).values())\n", - "assert iseq(s_WETH, r.dtokens[\"WETH\"])\n", - "s_WETH" - ] - }, - { - "cell_type": "markdown", - "id": "fc9ca8c9", - "metadata": {}, - "source": [ - "## PairOptimizer vs MarpP\n", - "\n", - "PairOptimizer is a new optimization method that uses bisection instead of gradient descent. It is a bit slower, but importantly it is robust against the no-man's land problem of the gradient descent\n", - "\n", - "### Setup" - ] - }, - { - "cell_type": "markdown", - "id": "3af82ecb-2f2d-48d6-beae-3be4769bef1d", - "metadata": {}, - "source": [ - "### Unlevered curves `CCul`" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "60d1d4f0-6f2d-4808-8f58-5dac878f6838", - "metadata": {}, - "outputs": [], - "source": [ - "Oul = PairOptimizer(curves=CCul)\n", - "Oul_mp = MargPOptimizer(curves=CCul)\n", - "assert len(Oul.curves) == len(CCul)" - ] - }, - { - "cell_type": "markdown", - "id": "94d61bad-3788-4089-81a6-c8a220236dc8", - "metadata": {}, - "source": [ - "Unlevered curves converged nicely in the margp (gradient descent) optimizer, and they are converging nicely here; the results are very close together (better than 1e-5)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "1ab07f48-e00f-46ea-ab8f-3cc63bb23bbd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(CPCArbOptimizer.MargpOptimizerResult(result=-0.00520426785183048, time=0.0011801719665527344, method='margp-pair', targettkn='WETH', p_optimal_t=(0.000644993410714457,), dtokens_t=(3.637978807091713e-12,), tokens_t=('DAI',), errormsg=None),\n", - " CPCArbOptimizer.MargpOptimizerResult(result=-0.005204267821271813, time=0.00024819374084472656, method='margp', targettkn='WETH', p_optimal_t=(0.0006449934107164284,), dtokens_t=(-4.737194103654474e-08,), tokens_t=('DAI',), errormsg=None),\n", - " 5.871847452709744e-09)" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = Oul.optimize(\"WETH\")\n", - "rmp = Oul_mp.optimize(\"WETH\")\n", - "assert r.error is None\n", - "assert rmp.error is None\n", - "assert r.method == \"margp-pair\"\n", - "assert rmp.method == \"margp\"\n", - "assert r.targettkn == \"WETH\" \n", - "assert rmp.targettkn == \"WETH\"\n", - "assert r.tokens_t == ('DAI',)\n", - "assert rmp.tokens_t == ('DAI',)\n", - "assert r.dtokens[\"WETH\"] < 0\n", - "assert rmp.dtokens[\"WETH\"] < 0\n", - "assert iseq(r.p_optimal_t[0], 0.0006449934107144566)\n", - "assert iseq(rmp.p_optimal_t[0], 0.0006449934107164284)\n", - "assert r.result/rmp.result-1 < 1e-5\n", - "r, rmp, r.result/rmp.result-1" - ] - }, - { - "cell_type": "markdown", - "id": "5fa27642-f637-41df-97d3-4bd1eae00088", - "metadata": {}, - "source": [ - "It is notable that the bisection algorithm is **six times slower** than the gradient descent" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "6b9292d4-df1f-4be3-bedc-798c83980c4d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4.755043227665706" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r.time/rmp.time" - ] - }, - { - "cell_type": "markdown", - "id": "98bb193e-64b3-4531-a3c3-ce3d5ec40d34", - "metadata": {}, - "source": [ - "the optimal price here is very very close: 1e-12" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "5826dfb8-0a6c-4da0-84e8-1ee6a4961919", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-3.056443986793056e-12" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert r.p_optimal_t[0]/rmp.p_optimal_t[0]-1 < 1e-8\n", - "r.p_optimal_t[0]/rmp.p_optimal_t[0]-1" - ] - }, - { - "cell_type": "markdown", - "id": "0cca1787-0fc2-4e33-a157-c4888fe4b2bb", - "metadata": {}, - "source": [ - "Here we show that (a) the DAI transfer is de-minimis and close enough to zero, and more importantly, that (b) both our methods give essentially the same result as to how much ETH can be obtained from the arb" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "bb4eab27-ad3d-42e9-985f-feaabdd100c0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "({'WETH': -0.00520426785183048, 'DAI': 3.637978807091713e-12},\n", - " {'WETH': -0.005204267821271813, 'DAI': -4.737194103654474e-08},\n", - " 5.871847452709744e-09)" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert r.dtokens[\"DAI\"] < 1e-5\n", - "assert rmp.dtokens[\"DAI\"] < 1e-5\n", - "assert r.dtokens[\"WETH\"]/rmp.dtokens[\"WETH\"]-1 < 1e-5\n", - "r.dtokens, rmp.dtokens, r.dtokens[\"WETH\"]/rmp.dtokens[\"WETH\"]-1" - ] - }, - { - "cell_type": "markdown", - "id": "cab1c7ce-6ecd-4012-9832-fda509fd1d70", - "metadata": {}, - "source": [ - "### Asymmetric curves `CCas2` and `CCas3`" - ] - }, - { - "cell_type": "markdown", - "id": "a5759d09-a284-4fa7-bb85-d52d2a4656e3", - "metadata": {}, - "source": [ - "#### `CCas2`" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "1aad6014-7176-46b8-8582-29630ed783e4", - "metadata": {}, - "outputs": [], - "source": [ - "O = PairOptimizer(curves=CCas2)\n", - "Omp = MargPOptimizer(curves=CCas2)\n", - "assert len(O.curves) == len(CCas2)\n", - "assert len(Omp.curves) == len(O.curves)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "979c7a48-a198-4742-8a39-1b98685b4522", - "metadata": {}, - "outputs": [], - "source": [ - "r = O.optimize(\"WETH\")\n", - "rmp = Omp.optimize(\"WETH\")\n", - "assert r.error is None\n", - "assert r.method == \"margp-pair\"\n", - "assert r.targettkn == \"WETH\"\n", - "assert r.tokens_t == ('DAI',)\n", - "assert r.dtokens[\"WETH\"] < 0\n", - "assert iseq(r.result, -0.048636442623132936, eps=1e-3)\n", - "assert iseq(r.result, rmp.result, eps=1e-3)\n", - "assert r.result != rmp.result # numerically should not converged to same\n", - "assert iseq(r.p_optimal_t[0], 0.0004696831634035269, eps=1e-3)\n", - "assert iseq(r.dtokens[\"WETH\"], -0.04863644262652045, eps=1e-3)\n", - "assert iseq(r.dtokens[\"WETH\"], rmp.dtokens[\"WETH\"], eps=1e-3)\n", - "assert iseq(0, r.dtokens[\"DAI\"], eps=1e-6)\n", - "assert iseq(0, rmp.dtokens[\"DAI\"], eps=1e-6)\n", - "assert abs(r.dtokens[\"DAI\"] - rmp.dtokens[\"DAI\"]) < 1e-6\n", - "assert r.dtokens_t == (r.dtokens[\"DAI\"],)\n", - "assert rmp.dtokens_t == (rmp.dtokens[\"DAI\"],)\n", - "assert r.tokens_t == ('DAI',)\n", - "assert rmp.tokens_t == ('DAI',)" - ] - }, - { - "cell_type": "markdown", - "id": "5609beb9-5bf4-44d8-b28b-a74ed11e8af2", - "metadata": {}, - "source": [ - "#### `CCas3` [TODO]" - ] - }, - { - "cell_type": "markdown", - "id": "be7cde96", - "metadata": {}, - "source": [ - "### Normalized curves `CCn`" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "0be48669-2a24-4e81-9d50-f1d8972e1f95", - "metadata": {}, - "outputs": [], - "source": [ - "On = PairOptimizer(curves=CCn)\n", - "On_mp = MargPOptimizer(curves=CCn)\n", - "assert len(On.curves) == len(CC)+1" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "c3a76a52-bb69-4c1f-b0bc-42e6c79fefbe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(CPCArbOptimizer.MargpOptimizerResult(result=-1.2443450994433078, time=0.003554105758666992, method='margp-pair', targettkn='WETH', p_optimal_t=(0.0006274579880072587,), dtokens_t=(0.0,), tokens_t=('DAI',), errormsg=None),\n", - " CPCArbOptimizer.MargpOptimizerResult(result=-1.244345098228223, time=0.0008661746978759766, method='margp', targettkn='WETH', p_optimal_t=(0.00062745798800732,), dtokens_t=(-1.9371509552001953e-06,), tokens_t=('DAI',), errormsg=None),\n", - " 9.764855590788102e-10)" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = On.optimize(\"WETH\")\n", - "rmp = On_mp.optimize(\"WETH\")\n", - "assert r.error is None\n", - "assert rmp.error is None\n", - "assert r.method == \"margp-pair\"\n", - "assert rmp.method == \"margp\"\n", - "assert r.targettkn == \"WETH\" \n", - "assert rmp.targettkn == \"WETH\"\n", - "assert r.tokens_t == ('DAI',)\n", - "assert rmp.tokens_t == ('DAI',)\n", - "assert r.dtokens[\"WETH\"] < 0\n", - "assert rmp.dtokens[\"WETH\"] < 0\n", - "assert iseq(r.p_optimal_t[0], 0.0006274579880072543)\n", - "assert iseq(rmp.p_optimal_t[0], 0.00062745798800732)\n", - "assert r.result/rmp.result-1 < 1e-5\n", - "r, rmp, r.result/rmp.result-1" - ] - }, - { - "cell_type": "markdown", - "id": "cdc13d65", - "metadata": {}, - "source": [ - "### Optimization process `CC` (fails in full margp)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "e9c02aa7", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "O = PairOptimizer(curves=CC)\n", - "O_mp = MargPOptimizer(curves=CC)\n", - "assert len(O.curves) == len(CC)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "58c01b3c-9f94-4206-ab9f-81369c07bdc9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(CPCArbOptimizer.MargpOptimizerResult(result=-0.7856729741288291, time=0.0035212039947509766, method='margp-pair', targettkn='WETH', p_optimal_t=(0.0006157332379890483,), dtokens_t=(0.00012040883302688599,), tokens_t=('DAI',), errormsg=None),\n", - " CPCArbOptimizer.MargpOptimizerResult(result=22.14415018604268, time=0.00044798851013183594, method='margp', targettkn='WETH', p_optimal_t=(0.0006273686958774544,), dtokens_t=(-37239.86438154429,), tokens_t=('DAI',), errormsg=None),\n", - " -1.0354799334148317)" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = O.optimize(\"WETH\")\n", - "rmp = O_mp.optimize(\"WETH\")\n", - "assert r.error is None\n", - "assert rmp.error is None\n", - "assert r.method == \"margp-pair\"\n", - "assert rmp.method == \"margp\"\n", - "assert r.targettkn == \"WETH\" \n", - "assert rmp.targettkn == \"WETH\"\n", - "assert r.tokens_t == ('DAI',)\n", - "assert rmp.tokens_t == ('DAI',)\n", - "assert r.dtokens[\"WETH\"] < 0\n", - "assert not rmp.dtokens[\"WETH\"] < 0 # FAILS!\n", - "assert iseq(r.p_optimal_t[0], 0.0006157332379890538)\n", - "assert iseq(rmp.p_optimal_t[0], 0.0006273686958774544)\n", - "assert r.result/rmp.result-1 < 1e-5\n", - "r, rmp, r.result/rmp.result-1" - ] - }, - { - "cell_type": "markdown", - "id": "3e7b1b4f-3b28-4c47-b534-0de80781eb5b", - "metadata": {}, - "source": [ - "This now converges fine (note as we see below we need an eps parameter of about 1e-10, and also not that we can't go much higher because in this case it gets stuck, probably because of float precision." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "7110ebe7-35b7-44e8-9936-402a26fd3ffb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "({'WETH': -0.7856729741288291, 'DAI': 0.00012040883302688599},\n", - " -1249.7929894368729)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r.dtokens, r.dtokens[\"WETH\"]*PRICE0" - ] - }, - { - "cell_type": "markdown", - "id": "09365401-ec73-41ff-867f-eca7c62d023e", - "metadata": {}, - "source": [ - "We see that accuracy at eps=1e-6 is not that great, but at 1e-10 it is very good; also it seems that by and large the runtime does not really depend on the precision parameter here, so we go for 1e-10 throughout [not you can't go for higher precision as it then never returns, probably because of float accuracy issues]" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "c7a6f962-5331-4329-bb83-04711ca66e23", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "({'WETH': 22.14415018604268, 'DAI': -37239.86438154429},\n", - " {'WETH': -1.0643622393799888, 'DAI': 452.6137678697705},\n", - " {'WETH': -0.7965248341752158, 'DAI': 17.624510057270527})" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r06 = O.optimize(\"WETH\", params={\"eps\":1e-6})\n", - "r08 = O.optimize(\"WETH\", params={\"eps\":1e-8})\n", - "r10 = O.optimize(\"WETH\", params={\"eps\":1e-10})\n", - "r06.dtokens, r08.dtokens, r10.dtokens" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "f9e10526-8547-4520-9ff4-5050f739501a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[2.210240963855422, 0.854066265060241]" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[r10.time/r06.time, r08.time/r06.time]" - ] - }, - { - "cell_type": "markdown", - "id": "3d63863e", - "metadata": {}, - "source": [ - "## MargPOptimizer new TODO\n", - "\n", - "this is still on the todo lost, but does not have high priority; the new margp optimizer will have a different convergence criterium [p ~ 0 rather than d log p ~ 0]. This will not help in terms of convergence on a plateau -- a gradient algorithm can not recover from f'(x) = 0 -- but it will allow identifying instances of non convergence.\n", - "\n", - "### Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "e130dbe9-65a4-4313-babf-e968664664ea", - "metadata": {}, - "outputs": [], - "source": [ - "pass" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "b24a97bb", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "# Oul = PairOptimizer(curves=CCul)\n", - "# On = PairOptimizer(curves=CCn)\n", - "# O0 = PairOptimizer(curves=CC0)\n", - "# O = PairOptimizer(curves=CC)\n", - "# assert len(On.curves) == len(CC)+1\n", - "# assert len(O0.curves) == len(CC)\n", - "# assert len(O.curves) == len(CC)" - ] - }, - { - "cell_type": "markdown", - "id": "25709ff0", - "metadata": {}, - "source": [ - "### Unlevered curves `CCul`" - ] - }, - { - "cell_type": "markdown", - "id": "c5f85525-a594-4ba4-8f66-0b50e01c2d4b", - "metadata": {}, - "source": [ - "### Normalized curves `CCn`" - ] - }, - { - "cell_type": "markdown", - "id": "7dc90de9-eb44-4daf-9d1f-457abf989290", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "### Failing optimization process `CC`" - ] - }, - { - "cell_type": "markdown", - "id": "2039a37d", - "metadata": {}, - "source": [ - "## Charts [NOTEST]" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "7aa98c10", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = DAI/WETH\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = WETH/DAI\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "CC.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "5c3fd4d7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = WETH/DAI\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "CCul.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "4331dd96-51ba-4d10-833b-2634b92486e9", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = DAI/WETH\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = WETH/DAI\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "CCn.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "fe61e08d-527f-4a63-93d5-c5c5fbc8490b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = WETH/DAI\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "CCas2.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "945cf3ec-f41c-4aee-b0a9-dca1e0a247b5", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "#CCas3.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4c09a2f-82c6-4b56-87dc-b45bf4325862", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/NBTest/NBTest_055_Optimization.py b/resources/NBTest/NBTest_055_Optimization.py deleted file mode 100644 index 328cd81e3..000000000 --- a/resources/NBTest/NBTest_055_Optimization.py +++ /dev/null @@ -1,517 +0,0 @@ -# -*- coding: utf-8 -*- -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -try: - from fastlane_bot.tools.cpc import CPCContainer, ConstantProductCurve as CPC, CurveBase - from fastlane_bot.tools.optimizer import MargPOptimizer, PairOptimizer - from fastlane_bot.testing import * - -except: - from tools.cpc import CPCContainer, ConstantProductCurve as CPC, CurveBase - from tools.optimizer import MargPOptimizer, PairOptimizer - from tools.testing import * - -from math import sqrt -from copy import deepcopy -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPCContainer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(MargPOptimizer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(PairOptimizer)) - -plt.style.use('seaborn-v0_8-dark') -plt.rcParams['figure.figsize'] = [12,6] -# from fastlane_bot import __VERSION__ -# require("3.0", __VERSION__) -# - - -# # Optimization Methods [NBTest055] - -# Note: using an existing CPCContainer object `CC`, the curves can be extracted as dict using the command below -# -# CURVES = [c.asdict() for c in CC] -# - -# The below three curves are one POL curve (extremely levered; it is originally fixed price) and two Uniswap v3 curves. On those curves the high dimensional gradient descent algo fails because it ends up on a plateau. -# -# We are here creating the following sets of curves -# -# - `CC` based on `CURVES` the curves paramater set which are levered curves where the gradient descent optimization algorithm failed -# -# - `CCn` is `CC` plus a full range curve removing the no-man's land -# -# - `CCul` is a set of unlevered curves where convergence should not be a problem at all -# - -# ### `CC` (complex levered curves) - -# + -CURVES = [ - -# POL Curve -{ - 'k': 6.157332844764952e+20, - 'x': 615733222.5892723, - 'x_act': 0, - 'y_act': 100000.0, - 'alpha': 0.5, - 'pair': 'WETH/DAI', # WETH-6Cc2/DAI-1d0F - 'cid': '0x33ed', - # 0x33ed451d5c7b7a76266b8cdfab030f6de8143fcfbdcd08dabeed0de8d684b4de - 'fee': 0.0, - 'descr': 'bancor_pol DAI-1d0F/ETH-EEeE 0.000', - 'constr': 'carb', - 'params': {'exchange': 'bancor_pol', - 'tknx_dec': 18, - 'tkny_dec': 18, - 'tknx_addr': '0x6B175474E89094C44Da98b954EedeAC495271d0F', - 'tkny_addr': '0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE', - 'blocklud': 18121620, - 'y': 100000.0, - 'yint': 100000.0, - 'A': 0, - 'B': 40.29987368093254, - 'pa': 1624.0799811071013, - 'pb': 1624.0799811071013}}, - -# Uniswap v3 Curve 1 - { - 'k': 1147678924959.0112, - 'x': 42728400.31211105, - 'x_act': 7575.552803896368, - 'y_act': 8.665306719478394, - 'alpha': 0.5, - 'pair': 'DAI/WETH', # DAi-1d0F/WETH-6Cc2 - 'cid': '0xb1d8', - # 0xb1d8cd62f75016872495dae3e19d96e364767e7d674488392029d15cdbcd7b34', - 'fee': 0.0005, - 'descr': 'uniswap_v3 DAI-1d0F/WETH-6Cc2 500', - 'constr': 'pkpp', - 'params': {'exchange': 'uniswap_v3', - 'tknx_dec': 18, - 'tkny_dec': 18, - 'tknx_addr': '0x6B175474E89094C44Da98b954EedeAC495271d0F', - 'tkny_addr': '0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2', - 'blocklud': 18121789, - 'L': 1071297.7760450225}}, - - -# Uniswap v3 Curve 2 -{ - 'k': 1541847511355.546, - 'x': 49517090.33542573, - 'x_act': 99496.94394361228, - 'y_act': 30.763865271412214, - 'alpha': 0.5, - 'pair': 'DAI/WETH', # DAi-1d0F/WETH-6Cc2 - 'cid': '0xae2b', - # '0xae2b487dff467a33b88e5a4e6874f91ee212886979f673dd18d3e0396862112f', - 'fee': 0.003, - 'descr': 'uniswap_v3 DAI-1d0F/WETH-6Cc2 3000', - 'constr': 'pkpp', - 'params': {'exchange': 'uniswap_v3', - 'tknx_dec': 18, - 'tkny_dec': 18, - 'tknx_addr': '0x6B175474E89094C44Da98b954EedeAC495271d0F', - 'tkny_addr': '0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2', - 'blocklud': 18121689, - 'L': 1241711.5250151888}} -] -CC = CPCContainer.from_dicts(CURVES) -# - -# Those are starting prices consistent with those curves. - -PRICES = { - 'DAI': 0.0006286424878113893, - 'WETH': 1, -} -PRICE0 = PRICES["WETH"]/PRICES["DAI"] -PRICE0 - -# ### `CCn` (normalized curve set) -# -# This curve set contains an additional constant product curve that removes the no-man's land between the levered curves and where gradient descent therefore converges - -cnorm = CPC.from_pk(p=PRICE0, k=PRICE0*CC[0].x, pair="WETH/DAI", cid="normalizer") -CCn = CPCContainer([c for c in CC]+[cnorm]) - -# ### `CCul` (simple unlevered curves) -# -# This is a very simple set of unlevered curver where convergence should never be a problem. - -CCul = CPCContainer([ - CPC.from_pk(p=1500, k=1500*100, pair="WETH/DAI", cid="c1500"), - CPC.from_pk(p=1600, k=1600*100, pair="WETH/DAI", cid="c1600") -]) - -# ### `CCas` (asymmetric unlevered curves) -# -# We are generating asymmetric curves that have an arbitrage opportunity. `CCas2` is a single pair that exhibits the arbitrage, `CCas3` requires triangle optimization. - -ETA25, ETA75 = 1/3, 3 -CCas2 = CPCContainer([ - CPC.from_xyal(x=10, y=2000/ETA25*10, alpha=0.25, pair="WETH/DAI", cid="c2000-0.25"), - CPC.from_xyal(x=10, y=2500/ETA75*10, alpha=0.75, pair="WETH/DAI", cid="c2500-0.75"), -]) - -CCas2[0].x, CCas2[0].tknx, CCas2[0].y, CCas2[0].tkny, CCas2[0].p - -CCas2[1].x, CCas2[1].tknx, CCas2[1].y, CCas2[1].tkny, CCas2[1].p - -CCas2[0].eta - -# ## Curve definitions -# -# Here we are asserting properties of the curves that they are meant to have; should really never fail unless something goes horribly wrong - -assert iseq(CCas2[0].x, 10) -assert CCas2[0].tknx == "WETH" -assert iseq(CCas2[0].y, 60000) -assert CCas2[0].tkny == "DAI" -assert iseq(CCas2[0].eta, ETA25) -assert iseq(CCas2[0].p, 2000) - -assert iseq(CCas2[1].x, 10) -assert CCas2[1].tknx == "WETH" -assert iseq(CCas2[1].y, 25000/3) -assert CCas2[1].tkny == "DAI" -assert iseq(CCas2[1].eta, ETA75) -assert iseq(CCas2[1].p, 2500) - - -# ## MargPOptimizer current -# -# Uses the current margp optimizer which uses $d \log p ~ 0$ as criterium and that can fail on certain formations of levered curves (when the price ends up on no-mans land) -# ### Setup - -# + -#help(MargPOptimizer) -# - - - -# ### Unlevered curves `CCul` - -Oul = MargPOptimizer(curves=CCul) -assert len(Oul.curves) == len(CCul) - -r = Oul.optimize("WETH") -assert r.error is None -assert r.method == "margp" -assert r.targettkn == "WETH" -assert r.tokens_t == ('DAI',) -assert r.dtokens["WETH"] < 0 -assert iseq(r.result, -0.005204267821271813) -assert iseq(r.p_optimal_t[0], 0.0006449934107164284) -assert iseq(r.dtokens_t[0], -4.737194103654474e-08) -r - -# the original curves are 1500 and 1600, so ~1550 is right in the middle - -assert iseq(1/r.p_optimal_t[0], 1550.4034357331604) -1/r.p_optimal_t[0] - -# this process converged -- the aggregate change in DAI amount < 1e-5 - -assert abs(r.dtokens["DAI"] < 1e-5) -assert r.dtokens["WETH"] < 0 -assert iseq(r.dtokens["WETH"], -0.005204267821271813) -r.dtokens - -# there is some trading going on - -v = r.dxvecvalues(asdict=True) -assert iseq(v["c1500"]["DAI"], 249.9349296963901) -assert iseq(v["c1600"]["WETH"], 0.15868818867025603) -v - -# ### Normalized curves `CCn` - -On = MargPOptimizer(curves=CCn) -assert len(On.curves) == len(CC)+1 - -r = On.optimize("WETH") -assert r.error is None -assert r.method == "margp" -assert r.targettkn == "WETH" -assert r.tokens_t == ('DAI',) -assert r.dtokens["WETH"] < 0 -assert iseq(r.result, -1.244345098228223) -assert iseq(r.p_optimal_t[0], 0.00062745798800732) -assert iseq(r.dtokens_t[0], -1.9371509552001953e-06, eps=0.1) -# assert iseq(r.dtokens_t[0], -1.9371509552001953e-06, eps=0.01) # FAILS ON GITHUB -# assert iseq(r.dtokens_t[0], -1.9371509552001953e-06, eps=0.001) # FAILS ON GITHUB -# assert iseq(r.dtokens_t[0], -1.9371509552001953e-06, eps=0.0001) # FAILS ON GITHUB -r - -# the original curves are 1500 and 1600, so ~1550 is right in the middle - -assert iseq(1/r.p_optimal_t[0], 1593.7322005825413, eps=0.001) -1/r.p_optimal_t[0] - -# this process converged -- the aggregate change in DAI amount < 1e-5 - -assert abs(r.dtokens["DAI"] < 1e-5) -assert r.dtokens["WETH"] < 0 -assert iseq(r.dtokens["WETH"], -1.244345098228223) -r.dtokens - -# there is some trading going on - -v = r.dxvecvalues(asdict=True) -v - -# ### Asymmetric curves `CCas2` and `CCas3` - -O = MargPOptimizer(curves=CCas2) -assert len(O.curves) == len(CCas2) - -r = O.optimize("WETH", params={"pstart": {"WETH": 2400, "DAI": 1}}) -assert r.error is None -assert r.method == "margp" -assert r.targettkn == "WETH" -assert r.tokens_t == ('DAI',) -assert r.dtokens["WETH"] < 0 -assert iseq(r.result, -0.048636442623132936, eps=1e-3) -assert iseq(r.p_optimal_t[0], 0.0004696831634035269, eps=1e-3) -assert iseq(r.dtokens_t[0], -7.3569026426412165e-09, eps=0.1) - -# ### Failing optimization process `CC` - -O = MargPOptimizer(curves=CC) -assert len(O.curves) == len(CC) - -r = O.optimize("WETH") -assert r.error is None -assert r.method == "margp" -assert r.targettkn == "WETH" -assert r.tokens_t == ('DAI',) -assert iseq(r.result, 22.14415018604268) -assert iseq(r.p_optimal_t[0], 0.0006273686958774544) -assert iseq(r.dtokens_t[0], -37239.86438154429) -r - -# Here we show that the final price is not the same as the initial one, but also not totally crazy (this calculation has not converged but is stuck on a plateau) - -PRICES, r.p_optimal - -1/r.p_optimal_t[0], PRICES["WETH"]/PRICES["DAI"] - -# The `result` is the amount of target token extracted. Note that this assumes that the algo has converged which it has not in this case. The `dtokens` property shows the _aggregate_ change in tokens, and it _should_ be zero for everything but the target token WETH which is not the case here. - -assert r.result == r.dtokens["WETH"] -r.result - -r.dtokens - -# `dxdyvalues` and `dxvecvalues` show the changes of the respective curves. For standard two-asset curves they are equivalent, just in a different format; for three+ asset curves only dxvecvalues is defined - -r.dxdyvalues(asdict=True) - -r.dxvecvalues(asdict=True) - -# This shows that the algorithm **has not converged** -- this number (the net flow of DAI; note that the target token here is WETH) should be zero! - -s_DAI = sum(x["DAI"] for x in r.dxvecvalues(asdict=True).values()) -assert iseq(s_DAI, r.dtokens["DAI"]) -s_DAI - -# This number is not expected to be zero as the profit is being extracted in WETH - -s_WETH = sum(x["WETH"] for x in r.dxvecvalues(asdict=True).values()) -assert iseq(s_WETH, r.dtokens["WETH"]) -s_WETH - - -# ## PairOptimizer vs MarpP -# -# PairOptimizer is a new optimization method that uses bisection instead of gradient descent. It is a bit slower, but importantly it is robust against the no-man's land problem of the gradient descent -# -# ### Setup - -# ### Unlevered curves `CCul` - -Oul = PairOptimizer(curves=CCul) -Oul_mp = MargPOptimizer(curves=CCul) -assert len(Oul.curves) == len(CCul) - -# Unlevered curves converged nicely in the margp (gradient descent) optimizer, and they are converging nicely here; the results are very close together (better than 1e-5) - -r = Oul.optimize("WETH") -rmp = Oul_mp.optimize("WETH") -assert r.error is None -assert rmp.error is None -assert r.method == "margp-pair" -assert rmp.method == "margp" -assert r.targettkn == "WETH" -assert rmp.targettkn == "WETH" -assert r.tokens_t == ('DAI',) -assert rmp.tokens_t == ('DAI',) -assert r.dtokens["WETH"] < 0 -assert rmp.dtokens["WETH"] < 0 -assert iseq(r.p_optimal_t[0], 0.0006449934107144566) -assert iseq(rmp.p_optimal_t[0], 0.0006449934107164284) -assert r.result/rmp.result-1 < 1e-5 -r, rmp, r.result/rmp.result-1 - -# It is notable that the bisection algorithm is **six times slower** than the gradient descent - -r.time/rmp.time - -# the optimal price here is very very close: 1e-12 - -assert r.p_optimal_t[0]/rmp.p_optimal_t[0]-1 < 1e-8 -r.p_optimal_t[0]/rmp.p_optimal_t[0]-1 - -# Here we show that (a) the DAI transfer is de-minimis and close enough to zero, and more importantly, that (b) both our methods give essentially the same result as to how much ETH can be obtained from the arb - -assert r.dtokens["DAI"] < 1e-5 -assert rmp.dtokens["DAI"] < 1e-5 -assert r.dtokens["WETH"]/rmp.dtokens["WETH"]-1 < 1e-5 -r.dtokens, rmp.dtokens, r.dtokens["WETH"]/rmp.dtokens["WETH"]-1 - -# ### Asymmetric curves `CCas2` and `CCas3` - -# #### `CCas2` - -O = PairOptimizer(curves=CCas2) -Omp = MargPOptimizer(curves=CCas2) -assert len(O.curves) == len(CCas2) -assert len(Omp.curves) == len(O.curves) - -r = O.optimize("WETH") -rmp = Omp.optimize("WETH") -assert r.error is None -assert r.method == "margp-pair" -assert r.targettkn == "WETH" -assert r.tokens_t == ('DAI',) -assert r.dtokens["WETH"] < 0 -assert iseq(r.result, -0.048636442623132936, eps=1e-3) -assert iseq(r.result, rmp.result, eps=1e-3) -assert r.result != rmp.result # numerically should not converged to same -assert iseq(r.p_optimal_t[0], 0.0004696831634035269, eps=1e-3) -assert iseq(r.dtokens["WETH"], -0.04863644262652045, eps=1e-3) -assert iseq(r.dtokens["WETH"], rmp.dtokens["WETH"], eps=1e-3) -assert iseq(0, r.dtokens["DAI"], eps=1e-6) -assert iseq(0, rmp.dtokens["DAI"], eps=1e-6) -assert abs(r.dtokens["DAI"] - rmp.dtokens["DAI"]) < 1e-6 -assert r.dtokens_t == (r.dtokens["DAI"],) -assert rmp.dtokens_t == (rmp.dtokens["DAI"],) -assert r.tokens_t == ('DAI',) -assert rmp.tokens_t == ('DAI',) - -# #### `CCas3` [TODO] - -# ### Normalized curves `CCn` - -On = PairOptimizer(curves=CCn) -On_mp = MargPOptimizer(curves=CCn) -assert len(On.curves) == len(CC)+1 - -r = On.optimize("WETH") -rmp = On_mp.optimize("WETH") -assert r.error is None -assert rmp.error is None -assert r.method == "margp-pair" -assert rmp.method == "margp" -assert r.targettkn == "WETH" -assert rmp.targettkn == "WETH" -assert r.tokens_t == ('DAI',) -assert rmp.tokens_t == ('DAI',) -assert r.dtokens["WETH"] < 0 -assert rmp.dtokens["WETH"] < 0 -assert iseq(r.p_optimal_t[0], 0.0006274579880072543) -assert iseq(rmp.p_optimal_t[0], 0.00062745798800732) -assert r.result/rmp.result-1 < 1e-5 -r, rmp, r.result/rmp.result-1 - -# ### Optimization process `CC` (fails in full margp) - -O = PairOptimizer(curves=CC) -O_mp = MargPOptimizer(curves=CC) -assert len(O.curves) == len(CC) - - -r = O.optimize("WETH") -rmp = O_mp.optimize("WETH") -assert r.error is None -assert rmp.error is None -assert r.method == "margp-pair" -assert rmp.method == "margp" -assert r.targettkn == "WETH" -assert rmp.targettkn == "WETH" -assert r.tokens_t == ('DAI',) -assert rmp.tokens_t == ('DAI',) -assert r.dtokens["WETH"] < 0 -assert not rmp.dtokens["WETH"] < 0 # FAILS! -assert iseq(r.p_optimal_t[0], 0.0006157332379890538) -assert iseq(rmp.p_optimal_t[0], 0.0006273686958774544) -assert r.result/rmp.result-1 < 1e-5 -r, rmp, r.result/rmp.result-1 - -# This now converges fine (note as we see below we need an eps parameter of about 1e-10, and also not that we can't go much higher because in this case it gets stuck, probably because of float precision. - -r.dtokens, r.dtokens["WETH"]*PRICE0 - -# We see that accuracy at eps=1e-6 is not that great, but at 1e-10 it is very good; also it seems that by and large the runtime does not really depend on the precision parameter here, so we go for 1e-10 throughout [not you can't go for higher precision as it then never returns, probably because of float accuracy issues] - -r06 = O.optimize("WETH", params={"eps":1e-6}) -r08 = O.optimize("WETH", params={"eps":1e-8}) -r10 = O.optimize("WETH", params={"eps":1e-10}) -r06.dtokens, r08.dtokens, r10.dtokens - -[r10.time/r06.time, r08.time/r06.time] - -# ## MargPOptimizer new TODO -# -# this is still on the todo lost, but does not have high priority; the new margp optimizer will have a different convergence criterium [p ~ 0 rather than d log p ~ 0]. This will not help in terms of convergence on a plateau -- a gradient algorithm can not recover from f'(x) = 0 -- but it will allow identifying instances of non convergence. -# -# ### Setup - -pass - -# + -# Oul = PairOptimizer(curves=CCul) -# On = PairOptimizer(curves=CCn) -# O0 = PairOptimizer(curves=CC0) -# O = PairOptimizer(curves=CC) -# assert len(On.curves) == len(CC)+1 -# assert len(O0.curves) == len(CC) -# assert len(O.curves) == len(CC) -# - - - -# ### Unlevered curves `CCul` - -# ### Normalized curves `CCn` - -# ### Failing optimization process `CC` - - -# ## Charts [NOTEST] - -CC.plot() - -CCul.plot() - -CCn.plot() - -CCas2.plot() - -# + -#CCas3.plot() -# - - - diff --git a/resources/NBTest/NBTest_065_InvariantsDictVector.ipynb b/resources/NBTest/NBTest_065_InvariantsDictVector.ipynb deleted file mode 100644 index 39781a8ac..000000000 --- a/resources/NBTest/NBTest_065_InvariantsDictVector.ipynb +++ /dev/null @@ -1,511 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "3b17817f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "DictVector v0.9.1 (07/Feb/2024)\n" - ] - } - ], - "source": [ - "try:\n", - " import fastlane_bot.tools.invariants.vector as dv\n", - " from fastlane_bot.testing import *\n", - "\n", - "except:\n", - " import tools.invariants.vector as dv\n", - " from tools.testing import *\n", - "\n", - "\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(dv.DictVector))" - ] - }, - { - "cell_type": "markdown", - "id": "871933d0", - "metadata": {}, - "source": [ - "# Dict Vectors (Invariants Module; NBTest065)" - ] - }, - { - "cell_type": "markdown", - "id": "ee918ac0", - "metadata": {}, - "source": [ - "## Basic dict vector functions" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f28b91de", - "metadata": {}, - "outputs": [], - "source": [ - "vec1 = dict(a=1, b=2)\n", - "vec2 = dict(b=3, c=4)\n", - "vec3 = dict(c=1, a=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "83795829", - "metadata": {}, - "outputs": [], - "source": [ - "assert iseq(dv.norm(vec1)**2, 1+4)\n", - "assert iseq(dv.norm(vec2)**2, 9+16)\n", - "assert iseq(dv.norm(vec3)**2, 1+9)\n", - "assert iseq(dv.norm(vec1)**2, dv.sprod(vec1, vec1))\n", - "assert iseq(dv.norm(vec2)**2, dv.sprod(vec2, vec2))\n", - "assert iseq(dv.norm(vec3)**2, dv.sprod(vec3, vec3))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "29b1fbd6", - "metadata": {}, - "outputs": [], - "source": [ - "assert dv.eq(vec1, vec1)\n", - "assert dv.eq(vec2, vec2)\n", - "assert dv.eq(vec3, vec3)\n", - "assert not dv.eq(vec1, vec2)\n", - "assert not dv.eq(vec3, vec2)\n", - "assert not dv.eq(vec1, vec3)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "5868292a", - "metadata": {}, - "outputs": [], - "source": [ - "assert dv.add(vec1, vec2) == dict(a=1, b=5, c=4)\n", - "assert dv.add(vec1, vec3) == dict(a=4, b=2, c=1)\n", - "assert dv.add(vec2, vec3) == dict(a=3, b=3, c=5)\n", - "assert dv.add(vec1, vec2) == dv.add(vec2, vec1)\n", - "assert dv.add(vec1, vec3) == dv.add(vec3, vec1)\n", - "assert dv.add(vec2, vec3) == dv.add(vec3, vec2)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b97ddce0", - "metadata": {}, - "outputs": [], - "source": [ - "assert dv.add(vec1, vec1) == dv.smul(vec1, 2)\n", - "assert dv.add(vec2, vec2) == dv.smul(vec2, 2)\n", - "assert dv.add(vec3, vec3) == dv.smul(vec3, 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "2000a678", - "metadata": {}, - "outputs": [], - "source": [ - "assert dv.DictVector.dict_add == dv.add\n", - "assert dv.DictVector.dict_sub == dv.sub\n", - "assert dv.DictVector.dict_smul == dv.smul\n", - "assert dv.DictVector.dict_sprod == dv.sprod\n", - "assert dv.DictVector.dict_norm == dv.norm\n", - "assert dv.DictVector.dict_eq == dv.eq" - ] - }, - { - "cell_type": "markdown", - "id": "de2b9d58", - "metadata": {}, - "source": [ - "## DictVector object" - ] - }, - { - "cell_type": "markdown", - "id": "c2a470d0", - "metadata": {}, - "source": [ - "null vector" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "32bc968b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(DictVector(vec={}), DictVector(vec={'a': 0, 'b': 0, 'x': 0}))" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vec0 = dv.DictVector.null()\n", - "vec0a = dv.DictVector()\n", - "vec0b = dv.DictVector.n(a=0, b=0, x=0)\n", - "\n", - "assert bool(vec0) is False\n", - "assert bool(vec0a) is False\n", - "assert bool(vec0b) is False\n", - "assert vec0 == vec0a\n", - "assert vec0 == vec0b\n", - "assert vec0a == vec0b\n", - "assert len(vec0) == 0\n", - "assert len(vec0a) == 0\n", - "assert len(vec0b) == 0\n", - "assert vec0.enorm == 0\n", - "assert vec0a.enorm == 0\n", - "assert vec0b.enorm == 0\n", - "assert not \"a\" in vec0\n", - "assert not \"a\" in vec0a\n", - "assert not \"a\" in vec0b\n", - "vec0, vec0b" - ] - }, - { - "cell_type": "markdown", - "id": "96978d7f", - "metadata": {}, - "source": [ - "non-null vector" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "18719c7d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DictVector(vec={'a': 1, 'b': 2, 'x': 0})" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vec1 = dv.DictVector.n(a=1, b=2, x=0)\n", - "vec1b = dv.DictVector(vec1.vec)\n", - "assert bool(vec1) is True\n", - "assert bool(vec1b) is True\n", - "assert vec1[\"a\"] == 1\n", - "assert vec1[\"b\"] == 2\n", - "assert vec1[\"c\"] == 0 # !!! <<== missing elements are 0!\n", - "assert vec1[\"x\"] == 0\n", - "assert \"a\" in vec1\n", - "assert \"b\" in vec1\n", - "assert not \"c\" in vec1\n", - "assert not \"x\" in vec1\n", - "assert vec1 == vec1b\n", - "vec1" - ] - }, - { - "cell_type": "markdown", - "id": "1b749d41", - "metadata": {}, - "source": [ - "various ways of creating a vector" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c973ed36", - "metadata": {}, - "outputs": [], - "source": [ - "veca = dv.DictVector(dict(a=1, b=2, x=0))\n", - "vecb = dv.DictVector.new(a=1, b=2, x=0)\n", - "vecc = dv.DictVector.new(dict(a=1, b=2, x=0))\n", - "vecd = dv.DictVector.n(a=1, b=2, x=0)\n", - "vece = dv.DictVector.n(dict(a=1, b=2, x=0))\n", - "vecf = dv.V(a=1, b=2, x=0)\n", - "vecg = dv.V(dict(a=1, b=2, x=0))\n", - "assert veca == vecb\n", - "assert veca == vecc\n", - "assert veca == vecd\n", - "assert veca == vece\n", - "assert veca == vecf\n", - "assert veca == vecg" - ] - }, - { - "cell_type": "markdown", - "id": "c46d8985", - "metadata": {}, - "source": [ - "vector arithmetic" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "01f992e8", - "metadata": {}, - "outputs": [], - "source": [ - "assert vec0 + vec1 == vec1\n", - "assert vec0b + vec1 == vec1\n", - "assert vec1 + vec1 == 2*vec1\n", - "assert vec1 + vec1 == vec1*2\n", - "assert 3*vec1 == vec1*3\n", - "assert +vec1 == vec1\n", - "assert -vec1 == vec1 * (-1)\n", - "assert -vec1 == -1 * vec1\n", - "assert bool(0*vec1) is False\n", - "assert 0*vec1 == vec0\n", - "assert 0*vec1 == vec0b\n", - "assert 0*vec1 == vec1*0\n", - "assert (0*vec1).enorm == 0\n", - "assert 2*3*vec1 == 6*vec1\n", - "assert 2*vec1*3 == vec1*6\n", - "assert 2*3*vec1/6 == vec1" - ] - }, - { - "cell_type": "markdown", - "id": "a4c8deba", - "metadata": {}, - "source": [ - "vector base" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "4530c06d", - "metadata": {}, - "outputs": [], - "source": [ - "labels = \"abcdefghijklmnop\"\n", - "base = {l:dv.DictVector({l:1})for l in labels}\n", - "for x in base.values():\n", - " for y in base.values():\n", - " if x == y:\n", - " #print(x,y,x*y)\n", - " assert x*y == 1\n", - " else:\n", - " assert x*y == 0\n", - " \n", - "assert base[\"a\"] * dv.V(a=1, b=2) == 1\n", - "assert base[\"b\"] * dv.V(a=1, b=2) == 2\n", - "assert base[\"c\"] * dv.V(a=1, b=2) == 0\n", - "assert base[\"a\"]+2*base[\"b\"] == dv.V(a=1, b=2)" - ] - }, - { - "cell_type": "markdown", - "id": "1ed3bbe8", - "metadata": {}, - "source": [ - "floor / ceil / round / abs" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "0f316f4c", - "metadata": {}, - "outputs": [], - "source": [ - "vec2 = dv.V(a=1.2345, b=9.8765, c=3.5, d=1)\n", - "assert m.floor(vec2) == dv.V(a=1, b=9, c=3, d=1)\n", - "assert m.ceil(vec2) == dv.V(a=2, b=10, c=4, d=1)\n", - "assert m.ceil(vec2) - m.floor(vec2) == dv.V(a=1, b=1, c=1)\n", - "assert round(vec2) == dv.V(a=1, b=10, c=4, d=1)\n", - "assert round(vec2, 1) == dv.V(a=1.2, b=9.9, c=3.5, d=1)\n", - "assert abs(vec2) == vec2\n", - "assert abs(-vec2) == vec2" - ] - }, - { - "cell_type": "markdown", - "id": "4d15d669", - "metadata": {}, - "source": [ - "incremental actions" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "ff66a35e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DictVector(vec={'a': 0.0, 'b': 0.0, 'c': 0.0})" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "v = dv.V()\n", - "assert not v\n", - "v += dv.V(a=1, b=2)\n", - "assert v\n", - "assert v == dv.V(a=1, b=2)\n", - "v *= 2\n", - "assert v == 2*dv.V(a=1, b=2)\n", - "v += dv.V(a=3, c=3)\n", - "assert v == dv.V(a=5, b=4, c=3)\n", - "v /= 2\n", - "assert v == 0.5 * dv.V(a=5, b=4, c=3)\n", - "v -= v\n", - "assert bool(v) is False\n", - "assert not v\n", - "v" - ] - }, - { - "cell_type": "markdown", - "id": "034ef239", - "metadata": {}, - "source": [ - "generic base vector " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "5670bd51", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "class Foo():\n", - " pass\n", - "\n", - "@dv.dataclass(frozen=True)\n", - "class Bar():\n", - " val: str\n", - " \n", - "foo1 = Foo()\n", - "foo2 = Foo()\n", - "assert foo1 != foo2\n", - "\n", - "bar1 = Bar(\"bang\")\n", - "bar1a = Bar(\"bang\")\n", - "assert bar1 == bar1a\n", - "assert not bar1 is bar1a\n", - "\n", - "va = dv.V({foo1: 3, foo2:4})\n", - "assert len(va) == 2\n", - "assert va.enorm == 5\n", - "\n", - "va = dv.V({bar1: 3, foo1:4})\n", - "assert len(va) == 2\n", - "assert va.enorm == 5\n", - "\n", - "va = dv.V({bar1: 3, bar1a:4})\n", - "assert len(va) == 1\n", - "assert va.enorm == 4\n", - "\n", - "va = dv.V({bar1: 3})\n", - "vb = dv.V({bar1a: 3})\n", - "assert va == vb\n", - "assert not va is vb" - ] - }, - { - "cell_type": "markdown", - "id": "6c41b05d", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "items, elements and coeffs" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "6bd197e4", - "metadata": {}, - "outputs": [], - "source": [ - "elements = [el for el in \"abcdefghijklmnop\"]\n", - "v = dv.DictVector({el:n+1 for n, el in enumerate(elements)})\n", - "assert dv.DictVector.elements is dv.DictVector.el\n", - "assert v.elements == elements\n", - "assert v.coeffs == [n+1 for n in range(len(elements))]\n", - "assert v.items == list(zip(v.elements, v.coeffs))\n", - "assert v.elements[2] == elements[2]\n", - "assert v.coeffs[4] == 5\n", - "assert v.items[6] == ('g', 7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9de054d1", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55941962", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/NBTest/NBTest_065_InvariantsDictVector.py b/resources/NBTest/NBTest_065_InvariantsDictVector.py deleted file mode 100644 index 9399f5df2..000000000 --- a/resources/NBTest/NBTest_065_InvariantsDictVector.py +++ /dev/null @@ -1,248 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -try: - import fastlane_bot.tools.invariants.vector as dv - from fastlane_bot.testing import * - -except: - import tools.invariants.vector as dv - from tools.testing import * - - -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(dv.DictVector)) -# - - -# # Dict Vectors (Invariants Module; NBTest065) - -# ## Basic dict vector functions - -vec1 = dict(a=1, b=2) -vec2 = dict(b=3, c=4) -vec3 = dict(c=1, a=3) - -assert iseq(dv.norm(vec1)**2, 1+4) -assert iseq(dv.norm(vec2)**2, 9+16) -assert iseq(dv.norm(vec3)**2, 1+9) -assert iseq(dv.norm(vec1)**2, dv.sprod(vec1, vec1)) -assert iseq(dv.norm(vec2)**2, dv.sprod(vec2, vec2)) -assert iseq(dv.norm(vec3)**2, dv.sprod(vec3, vec3)) - -assert dv.eq(vec1, vec1) -assert dv.eq(vec2, vec2) -assert dv.eq(vec3, vec3) -assert not dv.eq(vec1, vec2) -assert not dv.eq(vec3, vec2) -assert not dv.eq(vec1, vec3) - -assert dv.add(vec1, vec2) == dict(a=1, b=5, c=4) -assert dv.add(vec1, vec3) == dict(a=4, b=2, c=1) -assert dv.add(vec2, vec3) == dict(a=3, b=3, c=5) -assert dv.add(vec1, vec2) == dv.add(vec2, vec1) -assert dv.add(vec1, vec3) == dv.add(vec3, vec1) -assert dv.add(vec2, vec3) == dv.add(vec3, vec2) - -assert dv.add(vec1, vec1) == dv.smul(vec1, 2) -assert dv.add(vec2, vec2) == dv.smul(vec2, 2) -assert dv.add(vec3, vec3) == dv.smul(vec3, 2) - -assert dv.DictVector.dict_add == dv.add -assert dv.DictVector.dict_sub == dv.sub -assert dv.DictVector.dict_smul == dv.smul -assert dv.DictVector.dict_sprod == dv.sprod -assert dv.DictVector.dict_norm == dv.norm -assert dv.DictVector.dict_eq == dv.eq - -# ## DictVector object - -# null vector - -# + -vec0 = dv.DictVector.null() -vec0a = dv.DictVector() -vec0b = dv.DictVector.n(a=0, b=0, x=0) - -assert bool(vec0) is False -assert bool(vec0a) is False -assert bool(vec0b) is False -assert vec0 == vec0a -assert vec0 == vec0b -assert vec0a == vec0b -assert len(vec0) == 0 -assert len(vec0a) == 0 -assert len(vec0b) == 0 -assert vec0.enorm == 0 -assert vec0a.enorm == 0 -assert vec0b.enorm == 0 -assert not "a" in vec0 -assert not "a" in vec0a -assert not "a" in vec0b -vec0, vec0b -# - - -# non-null vector - -vec1 = dv.DictVector.n(a=1, b=2, x=0) -vec1b = dv.DictVector(vec1.vec) -assert bool(vec1) is True -assert bool(vec1b) is True -assert vec1["a"] == 1 -assert vec1["b"] == 2 -assert vec1["c"] == 0 # !!! <<== missing elements are 0! -assert vec1["x"] == 0 -assert "a" in vec1 -assert "b" in vec1 -assert not "c" in vec1 -assert not "x" in vec1 -assert vec1 == vec1b -vec1 - -# various ways of creating a vector - -veca = dv.DictVector(dict(a=1, b=2, x=0)) -vecb = dv.DictVector.new(a=1, b=2, x=0) -vecc = dv.DictVector.new(dict(a=1, b=2, x=0)) -vecd = dv.DictVector.n(a=1, b=2, x=0) -vece = dv.DictVector.n(dict(a=1, b=2, x=0)) -vecf = dv.V(a=1, b=2, x=0) -vecg = dv.V(dict(a=1, b=2, x=0)) -assert veca == vecb -assert veca == vecc -assert veca == vecd -assert veca == vece -assert veca == vecf -assert veca == vecg - -# vector arithmetic - -assert vec0 + vec1 == vec1 -assert vec0b + vec1 == vec1 -assert vec1 + vec1 == 2*vec1 -assert vec1 + vec1 == vec1*2 -assert 3*vec1 == vec1*3 -assert +vec1 == vec1 -assert -vec1 == vec1 * (-1) -assert -vec1 == -1 * vec1 -assert bool(0*vec1) is False -assert 0*vec1 == vec0 -assert 0*vec1 == vec0b -assert 0*vec1 == vec1*0 -assert (0*vec1).enorm == 0 -assert 2*3*vec1 == 6*vec1 -assert 2*vec1*3 == vec1*6 -assert 2*3*vec1/6 == vec1 - -# vector base - -# + -labels = "abcdefghijklmnop" -base = {l:dv.DictVector({l:1})for l in labels} -for x in base.values(): - for y in base.values(): - if x == y: - #print(x,y,x*y) - assert x*y == 1 - else: - assert x*y == 0 - -assert base["a"] * dv.V(a=1, b=2) == 1 -assert base["b"] * dv.V(a=1, b=2) == 2 -assert base["c"] * dv.V(a=1, b=2) == 0 -assert base["a"]+2*base["b"] == dv.V(a=1, b=2) -# - - -# floor / ceil / round / abs - -vec2 = dv.V(a=1.2345, b=9.8765, c=3.5, d=1) -assert m.floor(vec2) == dv.V(a=1, b=9, c=3, d=1) -assert m.ceil(vec2) == dv.V(a=2, b=10, c=4, d=1) -assert m.ceil(vec2) - m.floor(vec2) == dv.V(a=1, b=1, c=1) -assert round(vec2) == dv.V(a=1, b=10, c=4, d=1) -assert round(vec2, 1) == dv.V(a=1.2, b=9.9, c=3.5, d=1) -assert abs(vec2) == vec2 -assert abs(-vec2) == vec2 - -# incremental actions - -v = dv.V() -assert not v -v += dv.V(a=1, b=2) -assert v -assert v == dv.V(a=1, b=2) -v *= 2 -assert v == 2*dv.V(a=1, b=2) -v += dv.V(a=3, c=3) -assert v == dv.V(a=5, b=4, c=3) -v /= 2 -assert v == 0.5 * dv.V(a=5, b=4, c=3) -v -= v -assert bool(v) is False -assert not v -v - - -# generic base vector - -# + -class Foo(): - pass - -@dv.dataclass(frozen=True) -class Bar(): - val: str - -foo1 = Foo() -foo2 = Foo() -assert foo1 != foo2 - -bar1 = Bar("bang") -bar1a = Bar("bang") -assert bar1 == bar1a -assert not bar1 is bar1a - -va = dv.V({foo1: 3, foo2:4}) -assert len(va) == 2 -assert va.enorm == 5 - -va = dv.V({bar1: 3, foo1:4}) -assert len(va) == 2 -assert va.enorm == 5 - -va = dv.V({bar1: 3, bar1a:4}) -assert len(va) == 1 -assert va.enorm == 4 - -va = dv.V({bar1: 3}) -vb = dv.V({bar1a: 3}) -assert va == vb -assert not va is vb -# - -# items, elements and coeffs - - -elements = [el for el in "abcdefghijklmnop"] -v = dv.DictVector({el:n+1 for n, el in enumerate(elements)}) -assert dv.DictVector.elements is dv.DictVector.el -assert v.elements == elements -assert v.coeffs == [n+1 for n in range(len(elements))] -assert v.items == list(zip(v.elements, v.coeffs)) -assert v.elements[2] == elements[2] -assert v.coeffs[4] == 5 -assert v.items[6] == ('g', 7) - - - - diff --git a/resources/NBTest/NBTest_066_InvariantsFunctions.ipynb b/resources/NBTest/NBTest_066_InvariantsFunctions.ipynb deleted file mode 100644 index 0930366dd..000000000 --- a/resources/NBTest/NBTest_066_InvariantsFunctions.ipynb +++ /dev/null @@ -1,2764 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "0278c025-06e6-416b-9525-c2a4a8ae9128", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "Function v0.9.7 (21/Mar/2024)\n", - "Kernel v0.9.1 (26/Jan/2024)\n" - ] - } - ], - "source": [ - "try:\n", - " import fastlane_bot.tools.invariants.functions as f\n", - " from fastlane_bot.tools.invariants.kernel import Kernel\n", - " from fastlane_bot.testing import *\n", - "\n", - "except:\n", - " import tools.invariants.functions as f\n", - " from tools.invariants.kernel import Kernel\n", - " from testing import *\n", - "\n", - "import numpy as np\n", - "import math as m\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.rcParams['figure.figsize'] = [12,6]\n", - "\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(f.Function))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(Kernel))" - ] - }, - { - "cell_type": "markdown", - "id": "7e212348-81d0-49f2-8d41-c7842a387634", - "metadata": {}, - "source": [ - "# Functions (Invariants Module; NBTest066)" - ] - }, - { - "cell_type": "markdown", - "id": "e831972e-e8b3-4e29-a6ec-103ddb874bd2", - "metadata": {}, - "source": [ - "## Functions" - ] - }, - { - "cell_type": "markdown", - "id": "64d064b4-c2f0-42f4-84d1-5fed091f461b", - "metadata": { - "tags": [] - }, - "source": [ - "### Built in functions\n", - "#### QuadraticFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "214f13cc-e573-42d9-94d9-8f7ad1ae6281", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "qf = f.QuadraticFunction(a=1, b=0, c=-10)\n", - "assert qf.params() == {'a': 1, 'b': 0, 'c': -10}\n", - "assert qf.a == 1\n", - "assert qf.b == 0\n", - "assert qf.c == -10" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f4828c9c-eafa-4da3-81a0-7e1949148d07", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "qf2 = qf.update(c=-5)\n", - "assert raises(qf.update, k=1)\n", - "assert qf2.params() == {'a': 1, 'b': 0, 'c': -5}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a169eb1c-a5bb-41c2-a64c-677fa5a581ed", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(-5,5)\n", - "y1_v = [qf(xx) for xx in x_v]\n", - "y2_v = [qf2(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"qf\")\n", - "plt.plot(x_v, y2_v, label=\"qf2\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "718fab97-6490-4888-912a-4c18aaa38451", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(-5,5)\n", - "y1_v = [qf(xx) for xx in x_v]\n", - "y2_v = [qf.p(xx) for xx in x_v]\n", - "y3_v = [qf.pp(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"f\")\n", - "plt.plot(x_v, y2_v, label=\"f'\")\n", - "plt.plot(x_v, y3_v, label=\"f''\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "markdown", - "id": "156af9c4-9461-4bf6-8d42-af54e15dfcf3", - "metadata": {}, - "source": [ - "#### TrigFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d2a5640a-6642-4458-9199-ad0efa016113", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "qf = f.TrigFunction()\n", - "assert qf.params() == {'amp': 1, 'omega': 1, 'phase': 0}\n", - "assert qf.amp == 1\n", - "assert qf.omega == 1\n", - "assert qf.phase == 0\n", - "assert int(qf.PI) == 3\n", - "\n", - "qf2 = qf.update(phase=1.5*qf.PI)\n", - "assert qf2.params() == {'amp': 1, 'omega': 1, 'phase': 1.5*qf.PI}" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5bd195a5-2db9-4fb7-bb0a-999f9ab1511e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(0, 4, 100)\n", - "y1_v = [qf(xx) for xx in x_v]\n", - "y2_v = [qf2(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"qf\")\n", - "plt.plot(x_v, y2_v, label=\"qf2\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "markdown", - "id": "aa09589f-4748-48a9-86af-513da43d514c", - "metadata": {}, - "source": [ - "#### HyperbolaFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8cd24f4f-8721-42c0-b993-e874c2258307", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "qf = f.HyperbolaFunction()\n", - "assert qf.params() == {'k': 1, 'x0': 0, 'y0': 0}\n", - "assert qf.k == 1\n", - "assert qf.x0 == 0\n", - "assert qf.y0 == 0\n", - "\n", - "qf2 = qf.update(y0=0.5)\n", - "# assert qf2.params() == {'amp': 1, 'omega': 1, 'phase': 1.5*qf.PI}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "8c3909a6-4705-4433-aa3e-66c1d07c8615", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(1, 10, 100)\n", - "y1_v = np.array([qf(xx) for xx in x_v])\n", - "y2_v = np.array([qf2(xx) for xx in x_v])\n", - "assert iseq(min(y2_v-y1_v), 0.5)\n", - "assert iseq(max(y2_v-y1_v), 0.5)\n", - "plt.plot(x_v, y1_v, label=\"qf\")\n", - "plt.plot(x_v, y2_v, label=\"qf2\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "markdown", - "id": "18e5f995-a251-446b-8152-6fc4b70bd8a3", - "metadata": {}, - "source": [ - "### Derivatives" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b0c9d852-742f-4a1d-8dc6-4a1fc801db3c", - "metadata": {}, - "outputs": [], - "source": [ - "qf = f.QuadraticFunction(a=1, b=2, c=3)\n", - "qfp = qf.p_func()\n", - "qfpp = qf.pp_func()\n", - "assert qf.params() == {'a': 1, 'b': 2, 'c': 3}\n", - "assert qfp.func is qf\n", - "assert qfpp.func is qf" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "bb3df983-030d-429c-b3e1-b855f0000eef", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(-5,5)\n", - "y1_v = [qf(xx) for xx in x_v]\n", - "y2_v = [qfp(xx) for xx in x_v]\n", - "y3_v = [qfpp(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"f\")\n", - "plt.plot(x_v, y2_v, label=\"f'\")\n", - "plt.plot(x_v, y3_v, label=\"f''\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "5fbfdc73-3c3b-46f3-b465-8a72cf989548", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(-2.0000018174926066,\n", - " -1.9999999025799287,\n", - " 1.9999999488316007,\n", - " 2.000000751212651)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y2a_v = [qf.p(xx) for xx in x_v] # calculate the derivative from the original object\n", - "y3a_v = [qf.pp(xx) for xx in x_v] # ditto second derivative\n", - "y3b_v = [qfp.p(xx) for xx in x_v] # calculate the second derivative as derivative from the derivative object\n", - "assert y2a_v == y2_v # those are literally two ways of getting the same result\n", - "assert y3a_v == y3_v # ditto\n", - "assert iseq(min(y3_v), -2) # check that the second derivative is correct\n", - "assert iseq(max(y3_v), -2) # ditto\n", - "assert iseq(min(y3b_v), 2) # ditto, but the other way\n", - "assert iseq(max(y3b_v), 2) # ditto\n", - "min(y3_v), max(y3_v), min(y3b_v), max(y3b_v)" - ] - }, - { - "cell_type": "markdown", - "id": "02deebe2-3397-4efb-8e41-d50014dbba9d", - "metadata": {}, - "source": [ - "### Custom function" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "7accd13d-4da5-4d9f-94a6-575b5bb4cc6f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.41421356237309515, -0.3535533907028654, 0.08838838549962702)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "@f.dataclass(frozen=True)\n", - "class MyFunction(f.Function):\n", - " k: float = 1\n", - " \n", - " def f(self, x):\n", - " return (m.sqrt(1+x)-1)*self.k\n", - "mf = MyFunction()\n", - "mf2 = mf.update(k=2)\n", - "mf(1),mf.p(1),mf.pp(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b76d484d-5041-4d3c-90a2-43cebdb6161c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(0,10)\n", - "y1_v = [mf(xx) for xx in x_v]\n", - "y2_v = [mf2(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"mf\")\n", - "plt.plot(x_v, y2_v, label=\"nf2\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "markdown", - "id": "66461504-3d04-44c0-bc41-caa4ea47f696", - "metadata": {}, - "source": [ - "## Kernel" - ] - }, - { - "cell_type": "markdown", - "id": "d117bbf1-0988-4ef5-a40f-18fdd3f83a6f", - "metadata": { - "tags": [] - }, - "source": [ - "### Integration function" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ad760927-1132-4f93-9fd6-967c36efaed6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "integrate = Kernel.integrate_trapezoid\n", - "ONE = lambda x: 1\n", - "LIN = lambda x: 2*x\n", - "SQR = lambda x: 3*x*x" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "18785493-71e6-4952-978e-b755e3bdc84e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(integrate(ONE, 0, 1, 2), 1) # trapezoid integrates constant perfectly\n", - "assert iseq(integrate(ONE, 0, 1, 100), 1)\n", - "assert iseq(integrate(LIN, 0, 1, 2), 1) # ditto linear\n", - "assert iseq(integrate(LIN, 0, 1, 100), 1)\n", - "assert iseq(integrate(SQR, 0, 1, 100), 1, eps=1e-3)\n", - "assert iseq(integrate(SQR, 0, 1, 1000), 1, eps=1e-6)" - ] - }, - { - "cell_type": "markdown", - "id": "ba333451-0dfe-4409-a574-d8f77e1e1104", - "metadata": {}, - "source": [ - "### Default kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "2f02cf1c-fa10-4a2e-9472-d371d2c3b260", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k = Kernel(steps=1000)\n", - "assert k.x_min == 0\n", - "assert k.x_max == 1\n", - "assert set(k.kernel(xx) for xx in np.linspace(k.x_min, k.x_max, 50)) == {1}\n", - "assert iseq(k.integrate(ONE), 1)\n", - "assert iseq(k.integrate(LIN), 1)\n", - "assert iseq(k.integrate(SQR), 1)\n", - "x_v = np.linspace(-0.5, 1.5, 1000)\n", - "plt.plot(x_v, [k.k(xx) for xx in x_v], label=\"default kernel\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "3b9e2eb4-6bde-4b66-866c-3ac72970bf1c", - "metadata": { - "tags": [] - }, - "source": [ - "### Flat kernels" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "93e49754-ff2d-412c-8d77-77016ade4d89", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "k.integrate(ONE)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "ffeeb416-d951-4f78-84a3-342ebbe1956f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k = Kernel(x_max=2, kernel=lambda x: 0.5, steps=1000)\n", - "assert k.x_min == 0\n", - "assert k.x_max == 2\n", - "assert set(k.kernel(xx) for xx in np.linspace(k.x_min, k.x_max, 50)) == {0.5}\n", - "assert iseq(k.integrate(ONE), 1)\n", - "assert iseq(k.integrate(LIN), 2)\n", - "assert iseq(k.integrate(SQR), 4)\n", - "x_v = np.linspace(-0.5, 2.5, 1000)\n", - "plt.plot(x_v, [k.k(xx) for xx in x_v], label=\"flat kernel 0..2\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "24eee0bd-2db9-47ba-870f-546912ec4028", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k = Kernel(x_max=4, kernel=lambda x: 0.25, steps=1000)\n", - "assert k.x_min == 0\n", - "assert k.x_max == 4\n", - "assert set(k.kernel(xx) for xx in np.linspace(k.x_min, k.x_max, 50)) == {0.25}\n", - "assert iseq(k.integrate(ONE), 1)\n", - "assert iseq(k.integrate(LIN), 4)\n", - "assert iseq(k.integrate(SQR), 16)\n", - "x_v = np.linspace(-0.5, 4.5, 1000)\n", - "plt.plot(x_v, [k.k(xx) for xx in x_v], label=\"flat kernel 0..4\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "49522d4f-9149-4b8d-9bc2-fdf90ac1769e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(4.0, 16.000008000000012)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "k.integrate(LIN), k.integrate(SQR)" - ] - }, - { - "cell_type": "markdown", - "id": "25309e0f-4cfe-4910-850b-da56d8e59e36", - "metadata": {}, - "source": [ - "### Triangle and sawtooth kernels" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "86546a13-cdb3-49c3-ab9c-a5af1e331b43", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kf = Kernel(x_min=1, x_max=3, kernel=Kernel.FLAT, steps=1000)\n", - "kl = Kernel(x_min=1, x_max=3, kernel=Kernel.SAWTOOTHL, steps=1000)\n", - "kr = Kernel(x_min=1, x_max=3, kernel=Kernel.SAWTOOTHR, steps=1000)\n", - "kt = Kernel(x_min=1, x_max=3, kernel=Kernel.TRIANGLE, steps=1000)\n", - "x_v = np.linspace(0.5, 3.5, 1000)\n", - "plt.plot(x_v, [kf.k(xx) for xx in x_v], label=\"flat\")\n", - "plt.plot(x_v, [kl.k(xx) for xx in x_v], label=\"sawtooth left\")\n", - "plt.plot(x_v, [kr.k(xx) for xx in x_v], label=\"sawtooth right\")\n", - "plt.plot(x_v, [kt.k(xx) for xx in x_v], label=\"triangle\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "335de4b7-cdce-4f69-ab18-b1e3dfd375bd", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(kf.integrate(ONE), 1)\n", - "assert iseq(kl.integrate(ONE), 1)\n", - "assert iseq(kr.integrate(ONE), 1)\n", - "assert iseq(kt.integrate(ONE), 1)\n", - "\n", - "assert iseq(kf.integrate(LIN), 4)\n", - "assert iseq(kl.integrate(LIN), 10/3)\n", - "assert iseq(kr.integrate(LIN), 14/3)\n", - "assert iseq(kt.integrate(LIN), 4)\n", - "\n", - "assert iseq(kf.integrate(SQR), 13)\n", - "assert iseq(kl.integrate(SQR), 9)\n", - "assert iseq(kr.integrate(SQR), 17)\n", - "assert iseq(kt.integrate(SQR), 12.5)" - ] - }, - { - "cell_type": "markdown", - "id": "31758d9a-b0d5-4842-8844-a64c50b7396f", - "metadata": {}, - "source": [ - "### Gaussian kernels" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "28ca49c4-4bb1-433a-a0ff-beb685950dbe", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kf = Kernel(x_min=1, x_max=3, kernel=Kernel.FLAT, steps=1000)\n", - "kg = Kernel(x_min=1, x_max=3, kernel=Kernel.GAUSS, steps=1000)\n", - "kw = Kernel(x_min=1, x_max=3, kernel=Kernel.GAUSSW, steps=1000)\n", - "kn = Kernel(x_min=1, x_max=3, kernel=Kernel.GAUSSN, steps=1000)\n", - "x_v = np.linspace(0.5, 3.5, 1000)\n", - "plt.plot(x_v, [kf.k(xx) for xx in x_v], label=\"flat\")\n", - "plt.plot(x_v, [kg.k(xx) for xx in x_v], label=\"gauss\")\n", - "plt.plot(x_v, [kw.k(xx) for xx in x_v], label=\"gauss wide\")\n", - "plt.plot(x_v, [kn.k(xx) for xx in x_v], label=\"gauss narrow\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "56110cff-696d-48a5-a957-a04d32e20298", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(kf.integrate(ONE), 1)\n", - "assert iseq(kg.integrate(ONE), 1, eps=1e-3)\n", - "assert iseq(kw.integrate(ONE), 1, eps=1e-3)\n", - "assert iseq(kn.integrate(ONE), 1, eps=1e-3)" - ] - }, - { - "cell_type": "markdown", - "id": "fe63fcfa-4fd9-43d7-8c0b-4bfd51e714d1", - "metadata": {}, - "source": [ - "## Function Vector" - ] - }, - { - "cell_type": "markdown", - "id": "91a19e24-da99-40f5-b16d-734e9d429743", - "metadata": {}, - "source": [ - "### vector operations and consistency" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "5400e8ef-8e97-4275-8485-b464ddd313b1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[FunctionVector::eq] called; funcs_eq=True, kernel_eq=True\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "knl = Kernel(x_min=1, x_max=3, kernel=Kernel.FLAT, steps=1000)\n", - "f1 = f.QuadraticFunction(a=3, c=1)\n", - "f2 = f.QuadraticFunction(b=2)\n", - "f3 = f.QuadraticFunction(a=3, b=2, c=1)\n", - "f1v = f.FunctionVector({f1: 1}, kernel=knl)\n", - "f2v = f.FunctionVector({f2: 1}, kernel=knl)\n", - "fv = f.FunctionVector({f1: 1, f2: 1}, kernel=knl)\n", - "assert fv == f1v + f2v\n", - "x_v = np.linspace(1, 3, 100)\n", - "y1_v = [f1(xx) for xx in x_v]\n", - "y2_v = [f2(xx) for xx in x_v]\n", - "y3_v = [f3(xx) for xx in x_v]\n", - "yv_v = [fv(xx) for xx in x_v]\n", - "y_diff = np.array(yv_v) - np.array(y3_v)\n", - "plt.plot(x_v, y1_v, label=\"f1\")\n", - "plt.plot(x_v, y2_v, label=\"f2\")\n", - "plt.plot(x_v, y3_v, label=\"f3\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "06d7ed49-1934-4943-8405-8fcbc9b3ac93", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "assert max(y_diff)<1e-10\n", - "assert min(y_diff)>-1e-10\n", - "plt.plot(x_v, yv_v, linewidth=3, label=\"vector\")\n", - "plt.plot(x_v, y3_v, linestyle=\"--\", color=\"#ccc\", label=\"f3\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()\n", - "plt.plot(x_v, y_diff)\n", - "plt.grid()\n", - "max(y_diff), min(y_diff)" - ] - }, - { - "cell_type": "markdown", - "id": "2f88e041-7084-4be7-81ec-7112877b2af0", - "metadata": {}, - "source": [ - "check that you can't add vectors with different kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "418bd7a3-29e2-49e1-9a5f-20faa1de2ecd", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "f1v = f.FunctionVector({f1: 1}, kernel=knl)\n", - "f2v = f.FunctionVector({f2: 1}, kernel=knl)\n", - "assert not raises(lambda: f1v+f2v)\n", - "assert not raises(lambda: f1v-f2v)\n", - "\n", - "f1v = f.FunctionVector({f1: 1}, kernel=knl)\n", - "f2v = f.FunctionVector({f2: 1}, kernel=None)\n", - "assert raises(lambda: f1v+f2v)\n", - "assert raises(lambda: f1v-f2v)" - ] - }, - { - "cell_type": "markdown", - "id": "06efd3bb-d021-4dc7-9b11-9ba509315a53", - "metadata": {}, - "source": [ - "### convenience methods\n" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "7d39f96d-1e20-41aa-a954-078b833a5f1f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fv = f.FunctionVector(\n", - " {\n", - " f.QuadraticFunction(a=1, b=2): 1,\n", - " f.HyperbolaFunction(k=100, x0=2): 1,\n", - " f.TrigFunction(phase=0.5): 1,\n", - " }, \n", - " kernel=knl\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "401f1495-1fea-4a66-8056-f610666a8592", - "metadata": { - "tags": [] - }, - "source": [ - "#### params" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "5bda3fbb-e419-4dfb-b042-a2d9992f76db", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{QuadraticFunction(a=1, b=2, c=0): {'a': 1,\n", - " 'b': 2,\n", - " 'c': 0,\n", - " '_classname': 'QuadraticFunction'},\n", - " HyperbolaFunction(k=100, x0=2, y0=0): {'k': 100,\n", - " 'x0': 2,\n", - " 'y0': 0,\n", - " '_classname': 'HyperbolaFunction'},\n", - " TrigFunction(amp=1, omega=1, phase=0.5): {'amp': 1,\n", - " 'omega': 1,\n", - " 'phase': 0.5,\n", - " '_classname': 'TrigFunction'}}" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert isinstance(fv.params(as_dict=True), dict)\n", - "assert len(fv.params()) == len(fv)\n", - "fv.params(as_dict=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "cb4955da-ea34-4e62-8301-67ceeb658e59", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'a': 1, 'b': 2, 'c': 0, '_classname': 'QuadraticFunction'},\n", - " {'k': 100, 'x0': 2, 'y0': 0, '_classname': 'HyperbolaFunction'},\n", - " {'amp': 1, 'omega': 1, 'phase': 0.5, '_classname': 'TrigFunction'}]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert fv.params() == fv.params(as_dict=False)\n", - "assert not fv.params(as_dict=False) == fv.params(as_dict=True)\n", - "assert len(fv.params(as_dict=False)) == len(fv)\n", - "assert list(fv.params(as_dict=True).values()) == fv.params(as_dict=False)\n", - "assert fv.params(as_dict=False)[1] == {'k': 100, 'x0': 2, 'y0': 0, '_classname': 'HyperbolaFunction'}\n", - "assert fv.params(as_dict=False, classname=False)[2] == {'amp': 1, 'omega': 1, 'phase': 0.5}\n", - "fv.params(as_dict=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "52583f88-b211-4587-bcc2-3046a9352313", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'amp': 1, 'omega': 1, 'phase': 0.5, '_classname': 'TrigFunction'}" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert fv.params(index=2) == fv.params(2)\n", - "assert isinstance(fv.params(index=2, as_dict=True), dict)\n", - "assert isinstance(fv.params(index=2, as_dict=False), dict)\n", - "assert fv.params(index=2, as_dict=False) != fv.params(index=2, as_dict=True)\n", - "assert fv.params(index=2) == {'amp': 1, 'omega': 1, 'phase': 0.5, '_classname': 'TrigFunction'}\n", - "assert fv.params(index=2, classname=False) == {'amp': 1, 'omega': 1, 'phase': 0.5}\n", - "fv.params(index=2)" - ] - }, - { - "cell_type": "markdown", - "id": "da7e5163-9890-4078-a8d1-412123b86042", - "metadata": {}, - "source": [ - "#### update" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "b9b47595-932a-4182-aabc-99ecae92cbeb", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'a': 1, 'b': 2, 'c': 0, '_classname': 'QuadraticFunction'},\n", - " {'k': 100, 'x0': 2, 'y0': 0, '_classname': 'HyperbolaFunction'},\n", - " {'amp': 1, 'omega': 1, 'phase': 0.5, '_classname': 'TrigFunction'}]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert raises(fv.update, [1,2,3]) == 'update with list of params not implemented yet'\n", - "assert raises(fv.update, [1,2,3], index=1) == 'index and key must be None if params is a list'\n", - "assert raises(fv.update, [1,2,3], 1) == 'index and key must be None if params is a list'\n", - "assert raises(fv.update, [1,2,3], key=1) == 'index and key must be None if params is a list'\n", - "assert raises(fv.update, dict()) == 'exactly one of index or key must be given'\n", - "assert raises(fv.update, dict(), index=1, key=1) == \"can't give both index and key\"\n", - "assert raises(fv.update, dict(), key=1) == \"key not implemented yet\"\n", - "params = fv.params()\n", - "fv.params()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "d4395e0d-2052-4bf7-b527-83026a99f6d9", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'a': 1, 'b': 2, 'c': 3, '_classname': 'QuadraticFunction'},\n", - " {'k': 100, 'x0': 2, 'y0': 0, '_classname': 'HyperbolaFunction'},\n", - " {'amp': 1, 'omega': 1, 'phase': 0.5, '_classname': 'TrigFunction'}]" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fv_1 = fv.update(dict(c=3), 0)\n", - "params1 = fv_1.params()\n", - "assert params[0] != params1[0] \n", - "assert params[1:] == params1[1:]\n", - "assert params1[0] == {'a': 1, 'b': 2, 'c': 3, '_classname': 'QuadraticFunction'}\n", - "assert params1[0][\"c\"] == 3\n", - "assert params1[0][\"a\"] == params[0][\"a\"]\n", - "assert params1[0][\"b\"] == params[0][\"b\"]\n", - "assert params1[0][\"_classname\"] == params[0][\"_classname\"]\n", - "params1" - ] - }, - { - "cell_type": "markdown", - "id": "7ad75da5-1701-4b2f-8d92-afee912bd73a", - "metadata": {}, - "source": [ - "### integration and norms" - ] - }, - { - "cell_type": "markdown", - "id": "52180f1f-69d6-4f74-8352-3b3aa7adc287", - "metadata": {}, - "source": [ - "#### high level" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "6764253d-ca20-4477-b77e-7aae2dead73a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(QuadraticFunction(a=3, b=0, c=1), QuadraticFunction(a=0, b=2, c=0))" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f1,f2" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "45e38a6a-7af1-40b0-a707-58779d77dee7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Kernel(x_min=1, x_max=3, kernel=. at 0x162463920>, kernel_name='builtin-flat', method='trapezoid', steps=1000)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f1v = f.FunctionVector({f1: 1}, kernel=knl)\n", - "f2v = f1v.wrap(f2)\n", - "f1v.plot(show=False, label=\"f1\")\n", - "f2v.plot(show=False, label=\"f2\")\n", - "fv=f1v+f2v\n", - "fv.plot(show=False, label=\"f1+f2\")\n", - "plt.legend()\n", - "print(f1v.kernel)\n", - "plt.show()\n", - "assert f1v.kernel == f2v.kernel\n", - "assert f1v.kernel == fv.kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "6d235d83-9593-4253-b602-f1e471436990", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(f1v.integrate(), 13+1)\n", - " # assert iseq(kf.integrate(ONE), 1)\n", - " # assert iseq(kf.integrate(SQR), 13)\n", - "\n", - "assert iseq(f2v.integrate(), 4)\n", - " # assert iseq(kf.integrate(LIN), 4)\n", - "\n", - "assert iseq(fv.integrate(), 18)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "39c7a0ee-bcbf-46c3-90a3-995bfbf395ed", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "4.000000000000001" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f2v.integrate()" - ] - }, - { - "cell_type": "markdown", - "id": "8b6c1a45-419f-4e0a-8253-59309c5bf91e", - "metadata": {}, - "source": [ - "#### quantification" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "30dcd316-f596-494b-8fbd-0b92472a1dd0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kernel = f.Kernel(x_min=0, x_max=1)\n", - "qf_v = f.QuadraticFunction(c=1).wrap(kernel)\n", - "qf2_v = f.QuadraticFunction(c=2).wrap(kernel)\n", - "qfl_v = f.QuadraticFunction(b=1).wrap(kernel)\n", - "qfq_v = f.QuadraticFunction(a=1).wrap(kernel)\n", - "qfl1_v = qfl_v + qf_v\n", - "qflm_v = 2*qfl_v - qf_v\n", - "qf_v.plot(show=False)\n", - "qf2_v.plot(show=False)\n", - "qfl_v.plot(show=False)\n", - "qfq_v.plot(show=False)\n", - "qfl1_v.plot(show=False)\n", - "qflm_v.plot(show=False)\n", - "#plt.ylim(-1,None)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "3734bfe4-e974-4fd8-90cf-1313e68d098c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# f(x) = 1 => Int = 1, Norm2 = 1\n", - "assert qf_v.integrate() == 1\n", - "assert qf_v.norm2() == 1\n", - "assert qf_v.norm1() == 1\n", - "assert qf_v.norm() == 1\n", - "\n", - "# f(x) = 2 => Int = 2, Norm2 = 4\n", - "assert qf2_v.integrate() == 2\n", - "assert qf2_v.norm2() == 4\n", - "assert qf2_v.norm1() == 2\n", - "assert qf2_v.norm() == 2\n", - "\n", - "# f(x) = x => Int = 1/2, Norm2 = 1/3\n", - "assert qfl_v.integrate() == 1/2\n", - "assert iseq(qfl_v.norm2(), qfq_v.integrate())\n", - "assert iseq(qfl_v.norm2(), 1/3, eps=1e-3)\n", - "assert iseq(qfl_v.norm1(), 1/2, eps=1e-3)\n", - "assert iseq(qfl_v.norm(), m.sqrt(qfl_v.norm2()))\n", - "\n", - "# f(x) = x^2 => Int = 1/3, Norm2 = 1/5\n", - "assert iseq(qfq_v.integrate(), 1/3, eps=1e-3)\n", - "assert iseq(qfq_v.norm2(), 1/5, eps=1e-3)\n", - "assert iseq(qfq_v.norm1(), 1/3, eps=1e-3)\n", - "assert iseq(qfq_v.norm(), m.sqrt(qfq_v.norm2()))\n", - "\n", - "# f(x) = 1 + x ==> Int = 1.5, Norm2 = 2 1/3\n", - "assert iseq(qfl1_v.integrate(), 1.5)\n", - "assert iseq(qfl1_v.integrate(), qfl_v.integrate() + qf_v.integrate())\n", - "assert iseq(qfl1_v.norm2(), 2+1/3, eps=1e-3)\n", - " # (1+x)^2 = x^2 + 2x + 1 => 1/3 x^3 + x^2 + x = 2 1/3 \n", - "assert iseq(qfl1_v.norm1(), 1.5, eps=1e-3)\n", - "assert iseq(qfl1_v.norm(), m.sqrt(qfl1_v.norm2()))\n", - "\n", - "# f(x) = 1 - 2x => Int = 0, Norm1 = 1/2, Norm2 = 1/3\n", - "assert iseq(0, qflm_v.integrate(), eps=1e-3)\n", - "assert iseq(qflm_v.norm2(), 1/3, eps=1e-3)\n", - " # x - 2/3 x^3 = 1/3\n", - "assert iseq(qflm_v.norm1(), 1/2, eps=1e-3)\n", - "assert iseq(qflm_v.norm(), m.sqrt(qflm_v.norm2()))" - ] - }, - { - "cell_type": "markdown", - "id": "7b9f01e7-26a5-4301-8d37-90e5103166d5", - "metadata": {}, - "source": [ - "### goal seek and minimize" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "2ed23a10-1175-4841-89e7-c80c8e55d787", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f1 = f.QuadraticFunction(a=1, c=-4)\n", - "f1v = f.FunctionVector().wrap(f1)\n", - "x_v = np.linspace(-2.5, 2.5, 100)\n", - "y1_v = [f1(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"f\")\n", - "#plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "375bce7a-9ee8-4b73-aeda-e4d6542032b7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.00030468016160726646" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert iseq(f1v.goalseek(target=0, x0=1), 2)\n", - "assert iseq(f1v.goalseek(target=0, x0=-1), -2)\n", - "assert iseq(f1v.goalseek(target=-3, x0=1), 1)\n", - "assert iseq(f1v.goalseek(target=-3, x0=-1), -1)\n", - "assert iseq(0, f1v.minimize1(x0=5), eps=1e-3)\n", - "f1v.minimize1(x0=5)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "d668c6c9-4074-453c-b301-eecb52952fbd", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f2 = f.QuadraticFunction(a=3, b=2, c=1)\n", - "f2v = f.FunctionVector({f2: 1})\n", - "x_v = np.linspace(-2.5, 2.5, 100)\n", - "y2_v = [f2(xx) for xx in x_v]\n", - "plt.plot(x_v, y2_v, label=\"f\")\n", - "#plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "19676a10-a38d-45ba-890e-e34115dfc9d4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.8685170919424989, -0.3332480000000852)" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert iseq(f2v.goalseek(target=5), 0.8685170919424989, eps=1e-4)\n", - "assert iseq(f2v.minimize1(), -0.3332480000000852, eps=1e-4)\n", - "f2v.goalseek(target=5), f2v.minimize1()" - ] - }, - { - "cell_type": "markdown", - "id": "122ce720-6bcc-4eba-a16f-9f100c44b9ad", - "metadata": {}, - "source": [ - "## Restricted and apply kernel\n", - "\n", - "restricted functions (`f_r`, more generally `restricted(func)`) are zero outside the kernel domain; kernel-applied functions (`f_k`, more generally `apply_kernel(func)`) is multiplied with the kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "9642d905-3733-404a-8f29-47dcf9956af4", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "func = f.TrigFunction()" - ] - }, - { - "cell_type": "markdown", - "id": "8d18a0f1-f434-41ab-9001-b451f745d92a", - "metadata": {}, - "source": [ - "### Flat kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "06b27591-5c31-44ef-a677-2d0073bdbe69", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kernel = Kernel(0, 1, Kernel.FLAT)\n", - "fv = f.FunctionVector({func: 1}, kernel=kernel)\n", - "f_r = fv.restricted(fv.f)\n", - "f_k = fv.apply_kernel(fv.f) \n", - "\n", - "assert not fv.f(-0.5) == 0\n", - "assert not fv.f(1.5) == 0\n", - "assert f_r(-0.5) == fv.f_r(-0.5) == 0\n", - "assert f_r(1.5) == fv.f_r(1.5) == 0\n", - "assert f_r(0.5) == fv.f_r(0.5) == fv.f(0.5)\n", - "assert f_r(0.25) == fv.f_r(0.25) == fv.f(0.25)\n", - "assert f_r(0.75) == fv.f_r(0.75) == fv.f(0.75)\n", - "\n", - "assert f_k(-0.5) == fv.f_k(-0.5) == 0\n", - "assert f_k(1.5) == fv.f_k(1.5) == 0\n", - "assert f_k(0.5) == fv.f_k(0.5) == fv.f(0.5) * kernel(0.5)\n", - "assert f_k(0.25) == fv.f_k(0.25) == fv.f(0.25) * kernel(0.25)\n", - "assert f_k(0.75) == fv.f_k(0.75) == fv.f(0.75) * kernel(0.75)\n", - "\n", - "fv.plot(fv.f, x_min=-1, x_max=2, title=\"full function [self.f]\")\n", - "fv.plot(fv.f_r, x_min=-1, x_max=2, title=\"restricted function [self.f_r]\")\n", - "fv.plot(fv.f_k, x_min=-1, x_max=2, title=\"flat kernel applied [self.f_k]\")" - ] - }, - { - "cell_type": "markdown", - "id": "c86dcd7b-8c96-4532-a89a-d4e48eae6e30", - "metadata": {}, - "source": [ - "### Sawtooth-Left kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "9610b767-1c87-4665-9dbb-5e463f65ca24", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kernel = Kernel(0, 1, Kernel.SAWTOOTHL)\n", - "fv = f.FunctionVector({func: 1}, kernel=kernel)\n", - "f_r = fv.restricted(fv.f)\n", - "f_k = fv.apply_kernel(fv.f) \n", - "\n", - "assert not fv.f(-0.5) == 0\n", - "assert not fv.f(1.5) == 0\n", - "assert f_r(-0.5) == fv.f_r(-0.5) == 0\n", - "assert f_r(1.5) == fv.f_r(1.5) == 0\n", - "assert f_r(0.5) == fv.f_r(0.5) == fv.f(0.5)\n", - "assert f_r(0.25) == fv.f_r(0.25) == fv.f(0.25)\n", - "assert f_r(0.75) == fv.f_r(0.75) == fv.f(0.75)\n", - "\n", - "assert f_k(-0.5) == fv.f_k(-0.5) == 0\n", - "assert f_k(1.5) == fv.f_k(1.5) == 0\n", - "assert f_k(0.5) == fv.f_k(0.5) == fv.f(0.5) * kernel(0.5)\n", - "assert f_k(0.25) == fv.f_k(0.25) == fv.f(0.25) * kernel(0.25)\n", - "assert f_k(0.75) == fv.f_k(0.75) == fv.f(0.75) * kernel(0.75)\n", - "\n", - "fv.plot(fv.f, x_min=-1, x_max=2, title=\"full function [self.f]\")\n", - "fv.plot(fv.f_r, x_min=-1, x_max=2, title=\"restricted function [self.f_r]\")\n", - "fv.plot(fv.f_k, x_min=-1, x_max=2, title=\"sawtooth-left kernel applied [self.f_k]\")" - ] - }, - { - "cell_type": "markdown", - "id": "329818e4-76ad-4932-ab66-1f67865ac683", - "metadata": {}, - "source": [ - "## Curve fitting" - ] - }, - { - "cell_type": "markdown", - "id": "19533f44-0164-4bfe-a475-d2c7155f167c", - "metadata": {}, - "source": [ - "### norm and curve distance\n", - "\n", - "We have various ways of measuring the distance between a FunctionVector (that includes a kernel) and a Function, all being based on the L2 norm with kernel applied\n", - "\n", - "- Use `FunctionVector.distance2` for the squared distance between the FunctionVector and the Function, or `distance` for the squareroot thereof*\n", - "\n", - "- Wrap the Function in a FunctionVector with the same kernel using the `wrap` method, substract the two FunctionVectors from each other, and use `norm2` or `norm`\n", - "\n", - "*in optimization you typically want to use the squared function because it behaves better and you don't have to calculate the square root" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "868211e4-8759-4de8-bb8e-8ffe8ac87827", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# create the template function vector\n", - "fv_t = f.FunctionVector(kernel=Kernel(x_min=-1, x_max=1, kernel=Kernel.FLAT))\n", - "assert fv_t.f(0) == 0\n", - "\n", - "# create target and match functions and wrap them in FunctionVector\n", - "f0 = f.TrigFunction(phase=1/2)\n", - "f0v = fv_t.wrap(f0)\n", - "f1v = fv_t.wrap(f.QuadraticFunction(c=0))\n", - "f2v = fv_t.wrap(f.QuadraticFunction(a=-2, c=1))\n", - "\n", - "# check norms and distances\n", - "diff1 = (f0v-f1v).norm()\n", - "diff2 = (f0v-f2v).norm()\n", - "assert iseq( (f0v-f1v).norm2(), (f0v-f1v).norm()**2)\n", - "assert iseq( (f0v-f2v).norm2(), (f0v-f2v).norm()**2)\n", - "assert iseq(f1v.dist2_L2(f0), (f0v-f1v).norm2())\n", - "assert iseq(f2v.dist2_L2(f0), (f0v-f2v).norm2())\n", - "assert iseq(f1v.dist_L2(f0), (f0v-f1v).norm())\n", - "assert iseq(f2v.dist_L2(f0), (f0v-f2v).norm())\n", - "assert iseq(f1v.dist_L1(f0), (f0v-f1v).norm1())\n", - "assert iseq(f2v.dist_L1(f0), (f0v-f2v).norm1())\n", - "\n", - "# plot\n", - "f0v.plot(show=False, label=\"f0 [target function]\")\n", - "f1v.plot(show=False, label=f\"f1 [match 1]: dist={diff1:.2f}\")\n", - "f2v.plot(show=False, label=f\"f2 [match 2]: dist={diff2:.2f}\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "97035b67-edf9-461a-af55-89e35f3a67a6", - "metadata": {}, - "source": [ - "### norm and curve distance on price functions\n", - "\n", - "Note: what we call a _price function_ is simply the negative first derivative, assuming the functions are swap function" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "be219ac9-43d3-4fb6-8a06-0f4c97f4c85a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fv_t = f.FunctionVector(kernel=Kernel(x_min=0, x_max=1, kernel=Kernel.FLAT))\n", - "fn1_fv = fv_t.wrap(f.QuadraticFunction(c=1)) # f(x) = 1\n", - "fn2_fv = fv_t.wrap(f.QuadraticFunction(c=1, b=-1)) # f(x) = 1-x\n", - "null_f = lambda x: 0\n", - "half_f = lambda x: 0.5\n", - "one_f = lambda x: 1\n", - "fn1_fv.plot(label=\"fn1(x)=1\", linewidth=3)\n", - "fn2_fv.plot(label=\"fn2(x)=1-x\")\n", - "fn1_fv.plot(func=fn1_fv.p, label=\"fn1.p(x)=0\")\n", - "fn2_fv.plot(func=fn2_fv.p, linestyle=\"--\", color=\"#faa\", label=\"fn2.p(x)=1\")\n", - "\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d4101081-b3b9-4f76-b59e-709b523b3a13", - "metadata": {}, - "source": [ - "#### norm" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "b98868a2-35c3-4b0c-abd9-37ff92f1ef64", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# method-level equality\n", - "# ... on self.f\n", - "assert fn1_fv.norm2_L2 == fn1_fv.norm2 \n", - "assert fn1_fv.norm_L2 == fn1_fv.norm\n", - "assert fn1_fv.norm_L1 == fn1_fv.norm1 \n", - "# ... on self.p\n", - "assert fn1_fv.normp2_L2 == fn1_fv.normp2 \n", - "assert fn1_fv.normp_L2 == fn1_fv.normp\n", - "assert fn1_fv.normp_L1 == fn1_fv.normp1 \n", - "\n", - "# checking values fn1\n", - "# ... on self.f\n", - "assert fn1_fv.norm2_L2() == 1\n", - "assert fn1_fv.norm_L2() == 1\n", - "assert fn1_fv.norm_L1() == 1\n", - "# ... on self.p\n", - "assert fn1_fv.normp2_L2() == 0\n", - "assert fn1_fv.normp_L2() == 0\n", - "assert fn1_fv.normp_L1() == 0\n", - "\n", - "# # checking values fn2\n", - "# # ... on self.f\n", - "assert iseq(1/3, fn2_fv.norm2_L2(), eps=1e-4)\n", - "assert iseq(m.sqrt(1/3), fn2_fv.norm_L2(), eps=1e-4)\n", - "assert iseq(1/2, fn2_fv.norm_L1(), eps=1e-4)\n", - "# # ... on self.p\n", - "assert iseq(1, fn2_fv.normp2_L2(), eps=1e-4)\n", - "assert iseq(1, fn2_fv.normp_L2(), eps=1e-4)\n", - "assert iseq(1, fn2_fv.normp_L1(), eps=1e-4)" - ] - }, - { - "cell_type": "markdown", - "id": "e46e45c8-2db9-473b-8845-8519ab6a0e56", - "metadata": {}, - "source": [ - "#### distance" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "8d625cce-bcc0-4737-b4db-16bd5572acf3", - "metadata": {}, - "outputs": [], - "source": [ - "# checking values fn1 vs null_f [1-0]\n", - "# ... on self.f\n", - "assert fn1_fv.dist2_L2(func=null_f) == 1\n", - "assert fn1_fv.dist_L2(func=null_f) == 1\n", - "assert fn1_fv.dist_L1(func=null_f) == 1\n", - "# ... on self.p\n", - "assert fn1_fv.distp2_L2(func=null_f) == 0\n", - "assert fn1_fv.distp_L2(func=null_f) == 0\n", - "assert fn1_fv.distp_L1(func=null_f) == 0\n", - "\n", - "# # checking values fn2 vs null_f [1-x-0]\n", - "# # ... on self.f\n", - "assert iseq(1/3, fn2_fv.dist2_L2(func=null_f), eps=1e-4)\n", - "assert iseq(m.sqrt(1/3), fn2_fv.dist_L2(func=null_f), eps=1e-4)\n", - "assert iseq(1/2, fn2_fv.dist_L1(func=null_f), eps=1e-4)\n", - "# # ... on self.p\n", - "assert iseq(1, fn2_fv.distp2_L2(func=null_f), eps=1e-4)\n", - "assert iseq(1, fn2_fv.distp_L2(func=null_f), eps=1e-4)\n", - "assert iseq(1, fn2_fv.distp_L1(func=null_f), eps=1e-4)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "a333a9db-28a1-483c-beaf-09ea72f2d888", - "metadata": {}, - "outputs": [], - "source": [ - "# checking values fn1 vs one_f [1-1]\n", - "# ... on self.f\n", - "assert fn1_fv.dist2_L2(func=one_f) == 0\n", - "assert fn1_fv.dist_L2(func=one_f) == 0\n", - "assert fn1_fv.dist_L1(func=one_f) == 0\n", - "# ... on self.p\n", - "assert fn1_fv.distp2_L2(func=one_f) == 1\n", - "assert fn1_fv.distp_L2(func=one_f) == 1\n", - "assert fn1_fv.distp_L1(func=one_f) == 1\n", - "\n", - "# # checking values fn2 vs one_f [1-x-1]\n", - "# # ... on self.f\n", - "assert iseq(1/3, fn2_fv.dist2_L2(func=one_f), eps=1e-4)\n", - "assert iseq(m.sqrt(1/3), fn2_fv.dist_L2(func=one_f), eps=1e-4)\n", - "assert iseq(1/2, fn2_fv.dist_L1(func=one_f), eps=1e-4)\n", - "# # ... on self.p\n", - "assert iseq(0, fn2_fv.distp2_L2(func=one_f), eps=1e-4)\n", - "assert iseq(0, fn2_fv.distp_L2(func=one_f), eps=1e-4)\n", - "assert iseq(0, fn2_fv.distp_L1(func=one_f), eps=1e-4)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "5788369e-85f2-4544-91b1-182293f5f129", - "metadata": {}, - "outputs": [], - "source": [ - "# checking values fn1 vs half_f [1-0.5=0.5]\n", - "# ... on self.f\n", - "assert fn1_fv.dist2_L2(func=half_f) == 0.25\n", - "assert fn1_fv.dist_L2(func=half_f) == 0.5\n", - "assert fn1_fv.dist_L1(func=half_f) == 0.5\n", - "# ... on self.p\n", - "assert fn1_fv.distp2_L2(func=half_f) == 0.25\n", - "assert fn1_fv.distp_L2(func=half_f) == 0.5\n", - "assert fn1_fv.distp_L1(func=half_f) == 0.5\n", - "\n", - "# # checking values fn2 vs half_f [1-x-0.5=0.5-x]\n", - "# # ... on self.f\n", - "assert iseq(1/12, fn2_fv.dist2_L2(func=half_f), eps=1e-3) #int_0..1 (0.5-x)^2 = 1/12\n", - "assert iseq(m.sqrt(1/12), fn2_fv.dist_L2(func=half_f), eps=1e-3)\n", - "assert iseq(1/4, fn2_fv.dist_L1(func=half_f), eps=1e-4)\n", - "# # ... on self.p\n", - "assert iseq(0.25, fn2_fv.distp2_L2(func=half_f), eps=1e-4)\n", - "assert iseq(0.5, fn2_fv.distp_L2(func=half_f), eps=1e-4)\n", - "assert iseq(0.5, fn2_fv.distp_L1(func=half_f), eps=1e-4)" - ] - }, - { - "cell_type": "markdown", - "id": "e9a593ae-189c-4954-8c51-59adda51bc26", - "metadata": {}, - "source": [ - "### curve fitting" - ] - }, - { - "cell_type": "markdown", - "id": "a69b11ff-ebaa-4045-852c-c4e10e27d788", - "metadata": {}, - "source": [ - "#### flat kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "809c3d8e-4f2d-4103-8234-beab6844c875", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "({'a': -2.266725245480411,\n", - " 'b': -4.999979597020143e-07,\n", - " 'c': 0.7553958307274233},\n", - " QuadraticFunction(a=-2.266725245480411, b=-4.999979597020143e-07, c=0.7553958307274233))" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fv_template = f.FunctionVector(kernel=Kernel(x_min=-1, x_max=1, kernel=Kernel.FLAT))\n", - "target_f = f.TrigFunction(phase=1/2)\n", - "target_fv = fv_template.wrap(target_f)\n", - "f_match0 = f.QuadraticFunction()\n", - "params0 = dict(a=0, b=0, c=0)\n", - "params = target_fv.curve_fit(f_match0, params0)\n", - "f_match = f_match0.update(**params)\n", - "params, f_match" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "79e5a8fb-2046-4691-95ba-be04ae0dd8bc", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "FunctionVector(vec={QuadraticFunction(a=-2.266725245480411, b=-4.999979597020143e-07, c=0.7553958307274233): 1}, kernel=Kernel(x_min=-1, x_max=1, kernel=. at 0x1628796c0>, kernel_name='builtin-flat', method='trapezoid', steps=100))" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_match_v = target_fv.wrap(f_match)\n", - "diff = (target_fv-f_match_v).norm()\n", - "target_fv.plot(show=False, label=\"target function\")\n", - "f_match_v.plot(show=False, label=f\"match (dist={diff:.2f})\")\n", - "plt.title(f\"Best fit (a={params['a']:.2f}, b={params['b']:.2f}, c={params['c']:.2f}); dist={diff:.2f}\")\n", - "plt.legend()\n", - "f_match_v" - ] - }, - { - "cell_type": "markdown", - "id": "72950948-71b6-4bb0-9618-71d2f1d3fd00", - "metadata": { - "tags": [] - }, - "source": [ - "#### skewed kernel (sawtooth-left)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "59598e82-3652-4c73-bf0f-927d8fd5077b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(Kernel(x_min=-1, x_max=1, kernel=. at 0x1628bc220>, kernel_name='builtin-sawtoothl', method='trapezoid', steps=100),\n", - " {'a': -1.8836343582517845, 'b': 0.2661645670906654, 'c': 0.7347668924372053},\n", - " QuadraticFunction(a=-1.8836343582517845, b=0.2661645670906654, c=0.7347668924372053))" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fv_template = f.FunctionVector(kernel=Kernel(x_min=-1, x_max=1, kernel=Kernel.SAWTOOTHL))\n", - "target_f = f.TrigFunction(phase=1/2)\n", - "target_fv = fv_template.wrap(target_f)\n", - "f_match0 = f.QuadraticFunction()\n", - "params0 = dict(a=0, b=0, c=0)\n", - "params = target_fv.curve_fit(f_match0, params0)\n", - "f_match = f_match0.update(**params)\n", - "target_fv.kernel, params, f_match" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "1ed9e83c-0131-46cb-ad96-39cf34a8b376", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "FunctionVector(vec={QuadraticFunction(a=-1.8836343582517845, b=0.2661645670906654, c=0.7347668924372053): 1}, kernel=Kernel(x_min=-1, x_max=1, kernel=. at 0x1628bc220>, kernel_name='builtin-sawtoothl', method='trapezoid', steps=100))" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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fUHx9fdNdduvWrTSPH/cDyIMeTFhSqms/uJ8Ut2/fRqfTZbi69aVLl6hfvz5WVlZs2rQJd3f3x6578+ZNfv/9d1q0aJHuuWXUjRs38Pf3f+oPLkWLFs30e+rB1+bh9/Tt27efeH4PMxqNzJkzhwoVKqT5bnhYsWLFUu+/8sorNGvWjCFDhtC5c+c0saUkz/Hx8RmOIcWdO3cwm82PfU89zahRo3B0dGTevHlMmzYt9fKTL7744onnliIgIIACBQo8db3MvE8z0xZP4+HhwdmzZ2nSpEma5c2aNWPYsGGp0/kJIUROkcJsQgjxjMqXL098fDxnzpwB1B43Dw8P9u3bl+5typQpqdvWrl2b1atXc+/ePfbs2UONGjUYNmyYRc6r7OnpmXr994PmzZuXeh1rixYtqFatGpUrV063d7JIkSJYW1s/9fbRRx+lbrN69eo0r99rr72WJecTGhqKwWCgUqVKaZYXLlwYDw+PNNcFh4aGEhISkpqAp6hSpQpms/mRHxsy49q1a+kue3CKqo8++ihDr1uRIkVStylSpAj29vYcPXr0kf0fPXqUokWLZuh68EuXLlGvXj0URWHLli1PTbLmzp1LUlLScxVkA7X2wdWrV596jXGDBg0y9No82KOecq31w69NcnIyp06domzZshmOc82aNURFRWX6fKtWrcqdO3ceKdaY8hlLr+f+afLly4dOp3vse+pprKysePvttzl48CC3b99m4cKFXL58mSZNmhAXF/fU7fv27ZuhtmjQoEHqNimv9ePep5lpi6dJ77pzIPVHnOyuQyGEEA+TnnAhhHhGoaGhwH8F01q2bJlaEKlatWoZ2ofBYKBatWqULFmS+fPnc/DgQbp06ZI6vPhZesWe5sF9P1z1Oz0lS5Zk1apVjyzX6XSPDIM+cuQIu3fvpmDBgmmWP8tw9AeLU2Ulf39/TCYT+/btS9NOZ86c4datW2mSTX9/f/bv34/JZEqTiKcUtctI79/jzJ8/n/bt26c+3rVrF5cuXUqT1D3LcHQrKytatWrFihUr+PLLL1OHAIeHh7NlyxaGDx/+1P2Fh4dTr149TCYTW7duJTAw8KnbzJgxA39/f5o1a/bUdZ+kWbNmLFy4kFmzZj1xSPqzDEevVq0afn5+zJo1i86dO6cuX7ZsGTExMbRr1y7Dcc6YMQM7O7tHLit4EkVR2LZtG25ubo/MB55SGT+l2FlmODo6UrVqVVasWMFXX32V+iPL/fv3Wb16dab25ebmRocOHYiIiGDYsGFcvHiR0qVLP/E76VmGo+fPn5+qVasyb9483n333dTP1549ezh9+jTDhg3LVNxP0r59e37++WfWrVtHxYoVU5evXbsWgOrVq2fZsYQQIkO0vCBdCCEsXUoRrV9//VXZvXu3snv3bmXNmjVK3759HymulZycrDRr1kxxd3dXxo8fr6xbt07566+/lFmzZim9evVKLcg0depUpWPHjsqsWbOUzZs3K2vXrlU6dOigAMqff/6Zur/AwEClRIkSyp9//qns27fvqfPzksHCbCnnNHbsWGXPnj3Kvn37lMTExMfuN6VQ1unTp9MsHzNmjKLT6ZQxY8YomzZtUqZMmaL4+voqRYoUyXShtMw6fvy4snTpUmXp0qVKSEiI4uXllfr4+PHjqett3bpVMRgMyvjx41OXhYeHK25ubkr+/PmVqVOnKps3b1amT5+uFC5cWHF0dFROnTqVuu7333+vAEqzZs2UVatWKRs2bFBGjhypWFlZKQ0bNkwTU0oRtQdf6/SkvP4FCxZU+vXrp6xfv1755ZdfFG9vbyV//vzpFofLrJMnTypOTk5KnTp1lLVr1yorVqxQypYtq/j7+ytRUVFp1n248Nf169eVwoULK7a2tsq8efNS3/cpt8uXLz9yvD179iiAMnr06MfGlPJefFrxPaPRqNSvXz91TvN169Ypf/zxhzJmzJh0i81l1ty5cxVAee2115QtW7YoP//8s+Lm5qY0atQozXrpvXdSREREKAaDQenWrdtjj/PKK68oH374obJ8+XJl69atyoIFC5TGjRsrgDJ58uRH1v/mm28Ug8Gg3LlzJ81yHipo9jgbNmxQ9Hq98tJLLykrV65Uli1bplSpUkUpWLBghgqz/e9//1OWLVumbNu2TZkzZ45SqFAhJTAwUElKSlIU5b/2GzhwoLJr1y5l3759SnR09FPjepItW7YoVlZWStu2bZWNGzcq8+fPVwoWLKiULVtWSUhISF3v4sWLisFgUPr27Ztm+4x+DyiKorRq1UqxtbVVPv74Y2Xjxo3KZ599ptjZ2SktW7Z8rnMQQohnIUm4EEI8QXrV0V1dXZXg4GDl22+/TfOHoqKoCcTXX3+tVKhQQbGzs1OcnJyUkiVLKgMHDlTOnj2rKIqi7N69W2nbtq0SGBio2NraKh4eHkrdunWV33//Pc2+/vrrL6VixYqKra3tI1We05PRJDwxMVHp37+/4uXlpeh0usdWPE5x7949xcnJ6ZGK4ImJicq7776r5M+fX7Gzs1MqVaqkrFq1SunVq1e2J+FPqhr+YJL3uMTv7NmzSs+ePZVChQoptra2SkBAgNK5c+dH/nBXFEVZvny58tJLLymenp6Ko6OjUqZMGeXjjz9WYmJi0qz3zjvvKDqdTjl58uQTY095T23YsEHp2bOn4ubmptjb2yvNmzdPfY9khf379ysNGjRQHBwcFBcXF6VNmzbKuXPnHlnv4SQv5TXLyOubYsCAAYpOp1PCwsIeG8/q1asVQJk2bdpTY4+Pj1fGjBmjFCtWTLGxsVE8PDyUl19+Wdm1a1eGzv1pFixYoJQvX16xsbFRfH19laFDhyr3799Ps86TfjRIqbK+efPmxx7jiy++UKpUqaLky5dPMRgMioeHh9KkSRNlzZo16a5fu3ZtpVWrVmmW3b9/XwGULl26ZOi8fv/999TzCggIUD7//PPUz8qDHk7Cv/nmG6VmzZqKp6dn6rb9+vVTLl68mGa7UaNGKf7+/oper8/QD04ZsWHDBqV69eqKnZ2d4u7urrz66qvK9evX06xz4cKFdL8DM/o9oCiKEhcXp4wcOVIpWLCgYmVlpQQEBCijRo165DtcCCFygk5RMlDVRAghxAvtzTffZNOmTRw/fjxLqxbnJVWrViUwMJClS5dqHYpFGjFiBAsXLuTs2bOZnqM8rwsLC6NYsWL8+eefNGrUKHX52rVradmyJYcPH862yzOEEELkPEnChRBCPNX169cpXrw4M2bMoEOHDlqHY3Gio6Px8vIiNDQ0dSo7kVaVKlUYMGBAlhXYy0v69OnDlStX2LhxY5rl7733HhERESxYsECjyIQQQmQHScKFEEJkyJo1a7hz5w49e/bUOhQh8ozk5GQ+//xzOnXqRPHixbUORwghRA6QJFwIIYQQQgghhMghMjGiEEIIIYQQQgiRQyQJF0IIIYQQQgghcogk4UIIIYQQQgghRA6x0jqArGY2m7l69SrOzs4yjY4QQgghhBBCiGynKAr379/H398fvf7Jfd15Lgm/evUqBQsW1DoMIYQQQgghhBAvmMuXL1OgQIEnrpPnknBnZ2dAPXkXFxeNo3kyo9HIhg0baNy4MdbW1lqHI9IhbZQ7SDvlDtJOlk/aKHeQdsodpJ1yB2kny5db2ig6OpqCBQum5qNPkueS8JQh6C4uLrkiCXdwcMDFxcWi31AvMmmj3EHaKXeQdrJ80ka5g7RT7iDtlDtIO1m+3NZGGbkkWgqzCSGEEEIIIYQQOUSScCGEEEIIIYQQIodIEi6EEEIIIYQQQuSQPHdNuBBCCCGEEMKymEwmjEaj1mE8wmg0YmVlRUJCAiaTSetwRDosqY2sra0xGAzPvR9JwoUQQgghhBDZQlEUrl27xt27d7UOJV2KouDr68vly5czVFBL5DxLayM3Nzd8fX2fKxZJwoUQQgghhBDZIiUB9/b2xsHBwSKSqAeZzWZiYmJwcnJCr5crdS2RpbSRoijExcURFRUFgJ+f3zPvS5JwIYQQQgghRJYzmUypCbiHh4fW4aTLbDaTlJSEnZ2dJOEWypLayN7eHoCoqCi8vb2feWi6vNOEEEIIIYQQWS7lGnAHBweNIxEi66S8n5+nxoEk4UIIIYQQQohsY2lD0IV4HlnxfpYkXAghhBBCCCGEyCGShAshhBBCCCFEHhAXF0f79u1xcXFBp9NpWpV+69atmsdgqSQJF0IIIYQQQoh/1atXj2HDhmkdRhoZjWn27Nns2LGDXbt2ERkZiaura/YHR/rx1axZM0djyE2kOroQQgghhBBCZLGkpCRsbGxy9JhhYWGUKlWKsmXL5uhx02NjY4Ovr6/WYVikbO0J3759O61atcLf3x+dTseqVaueus22bdsICQnBzs6OwoULM23atOwMUQghhBBCCCEA6N27N9u2bWPSpEnodDp0Oh0XL17EZDLRr18/goKCsLe3p0SJEkyaNOmRbdu0acNnn32Gv78/xYsXB2DXrl0EBwdjZ2dH5cqVWbVqFTqdjtDQ0NRtT5w4QfPmzXFycsLHx4eePXty8+bNJ8b0sHr16vHNN9+wfft2dDod9erVA0g3D3Nzc2PWrFkAXLx4EZ1Ox4oVK6hfvz4ODg5UqFCB3bt3p9lm586d1K1bFwcHB/Lly0eTJk24c+fOY+NLbzj68uXLKVOmDLa2thQqVIhvvvkmzTEKFSrEp59+St++fXF2diYgIICff/45g62Xe2RrEh4bG0uFChX48ccfM7T+hQsXaN68ObVr1+bQoUOMHj2aoUOHsnz58uwMUwghhBBCCJHNFEUhLilZk5uiKBmKcdKkSdSoUYMBAwYQGRlJZGQkBQsWxGw2U6BAAZYsWcKJEycYM2YMo0ePZsmSJWm237RpEydPnmTjxo2sWbOG+/fv06pVK8qVK8fBgwf5+OOPGTlyZJptIiMjqVu3LsHBwezfv5/169dz/fp1OnXq9MSYHrZixQoGDBhAjRo1iIyMZMWKFZlqn/fff593332X0NBQihcvTteuXUlOTgYgNDSUBg0aUKZMGXbv3s3ff/9Nq1atMJlMGY7vwIEDdOrUiS5dunD06FHGjRvHhx9+mPpjQIpvvvmGypUrc+jQIQYPHsyQIUM4c+ZMps7F0mXrcPRmzZrRrFmzDK8/bdo0AgICmDhxIgClSpVi//79fP3117Rv3z6bohRCCCGEEEJkt3ijidJj/tTk2Cc+aoKDzdNTH1dXV2xsbHBwcEgzlNpgMDB+/PjUx0FBQezatYslS5akJssAjo6OTJ8+PXUY+rRp09DpdPzyyy/Y2dlRunRpIiIiGDBgQOo2U6dOpVKlSnz66aepy2bOnEnBggU5c+YMxYsXTzemh7m7u+Pg4PDMw8DfffddWrRoAcD48eMpU6YM586do2TJknz55ZdUrlyZKVOmpK5fpkyZ1PsZie/bb7+lQYMGfPjhhwAUL16cEydO8NVXX9G7d+/U9Zo3b87gwYMBGDlyJN999x1///03lStXzvQ5WSqLKsy2e/duGjdunGZZkyZN2L9//3NNhi6EEEIIIYQQz2PatGlUrlwZLy8vnJyc+OWXXwgPD0+zTrly5dJcB3769GnKly+PnZ1d6rKqVaum2ebAgQNs2bIFJyen1FvJkiUB9RrvnFK+fPnU+35+fgBERUUB//WEP4+TJ09Sq1atNMtq1arF2bNnMZlM6cah0+nw9fVNHZqfV1hUYbZr167h4+OTZpmPjw/JycncvHkz9c3woMTERBITE1MfR0dHA2A0Gi0+cU+Jz9LjfJFJG+UO0k65g7RTzouON3LuRixno2I4FxVD2I1Y4pJMj11fURTu3DUw+8o/6HS6x67nZGtFES9Hino7UdTbkaJejjjbWWfHKYh0yGcpd5B2Us9dURTMZjNmsxlbg45j4xppEoutQYfZbH5kecow9ZQ4H1z+4OMlS5YwfPhwvv76a6pXr46zszNff/01e/fuTV1PURQcHBzSbGc2m9Hp0h47JeFMeV1MJhMtW7bk888/fyQ+Pz+/NPtP7xzSO58H19PpdJhMpjTLjEZj6vFTlhsMhjTHAkhOTsZsNmNvb//U4z/8fMr9lGOkF9uDr0XK/ztWVlaPxJ+y/dPOPyekxGI0GjEYDKnLM/NZt6gkHHjkP/2UxnrcHwOfffZZmqEhKTZs2ICDg0PWB5gNNm7cqHUI4imkjXIHaafcQdop68Ua4Vo8XIvXcS1Op96P0xFtfHwi/Xg6Lty/99S1tp1N2yvhZqPga6/g4wB+9gq+Dgo+9uBgcX9p5B3yWcodXuR2srKywtfXl5iYGJKSkjSN5X7CU56/fz/1vl6vJz4+PrVzD2Dz5s1UrVqV7t27py47c+YMJpMpTSdgcnJymu0CAwOZP38+N27cwNbWFoC///4bUOtnRUdHU6ZMGVavXo27uztWVmm/NFP2n15M6UlKSnokBk9PTy5cuJC6LCwsjLi4OBISEoiOjiYmJiZNPA++HnFxcURHR1OyZEk2bNjA22+/ne5x04svLi4udV96vZ6iRYuybds23nrrrdR1tm7dSpEiRYiNjQXUBDclrgdfgwdj0lpSUhLx8fFs37499Zp5+O98M8Ki/mv09fXl2rVraZZFRUVhZWWFh4dHutuMGjUqzZshOjqaggUL0rhxY1xcXLI13udlNBrZuHEjjRo1wtpaehAskbRR7iDtlDtIOz2/27FJaq/2jVjComJS79+Mefwft74uthTzdqKotxNFvBxxs3/8a28ymTh8+DAVKlRI8+v+I3HEJXEuKpZzN2IIi4rl+v1E7ibpuJuk49RD+buPsy1FvB0p6uX0bxzqfTcHeQ88K/ks5Q7STpCQkMDly5dxcnJKMxzbkiiKwv3793F2dk7t9CtSpAihoaHcvn0bJycn3N3dKV26NIsXL2b37t0EBQUxb948Dh06RFBQUGrOYW1tjZWVVZocpG/fvkyYMIH33nuPkSNHEh4ennpdtZOTEy4uLgwfPpy5c+cyaNAg3n33XTw9PTl37hyLFy/m559/xmAwpBuTXv/olcU2NjaPxPDyyy8zc+ZM6tWrh9lsZtSoUVhbW2NnZ4eLiwtOTk6Aej17ynYpPc4ODg64uLjw4YcfUqFCBUaNGsXAgQOxsbFhy5YtdOzYEU9Pz3TjS+kQdXZ2xsXFhZEjR1KtWjW+//57OnXqxO7du5k+fTo//vhj6nH1en1qXClS/j96sI20lJCQgL29PXXq1Enzvn7aDyQPsqgkvEaNGqxevTrNsg0bNlC5cuXHfnnZ2tqm/qr0IGtr61zzhZebYn1RSRvlDtJOuYO0U8YlJZvZGXaT9Uevsfl0FDfuJz523fxu9hTzURPdYt7OFPNRE+/MDBM3Go1wJZTm5f0z1Ub34o2ci7rP2evqDwNnrt/nXFQMkfcSuH4/kev3E9kVdjvNNr4udrxcypvmZf2oXtgdK4NFlanJFeSzlDu8yO1kMpnQ6XTo9fp0E0ZLkJJspsQJ8N5779GrVy/Kli1LfHw8Fy5c4PXXX+fw4cN07doVnU5H165dGTx4MOvWrUvdLmV6rgfP1c3NjdWrV/P6669TqVIlypUrx5gxY+jWrRsODg7o9XoKFCjAzp07GTlyJM2aNSMxMZHAwECaNm2KlZUVOp0u3ZgKFSr0yPmkJKkPxvDtt9/Sp08f6tWrh7+/P5MmTeLAgQOp7ZKy7sP3H1yW0hM+evRoqlevjr29PdWqVaN79+7o9fp043t4H5UrV2bJkiWMGTOGTz75BD8/Pz766CP69u37yDmk93553PKcptfr0el0j3y2M/M51ykZrdf/DGJiYjh37hwAFStW5Ntvv6V+/fq4u7sTEBDAqFGjiIiIYM6cOYA6RVnZsmUZOHAgAwYMYPfu3QwaNIiFCxdmuDp6dHQ0rq6u3Lt3L1f0hK9du5bmzZu/sF/Olk7aKHeQdsodpJ0yJsFo4u+zN1l7LJKNJ65zPyE5zfMF3e3VJNvbiWI+6r9FvJ1wsn3+39Wzuo3uJxjV3vrrMZyNus/ZqBjOXo8h4m58mvXyOVjTuLQvzcr5UrOIJzZW2v+RZcnks5Q7SDupPYYXLlwgKCjIYnvCzWYz0dHRuLi45FiCN3/+fPr06cO9e/ewt7fPkWPmZlq00ZM87n2dmTw0W3vC9+/fT/369VMfpwwb79WrF7NmzSIyMjJNRcGgoCDWrl3L8OHDmTx5Mv7+/nz//fcyPZkQQog8LcFoYuvpG6w7Fsmmk1HEJP6XeHs529K0jC/NyvoSHOCWoSl2LIWznTWVAvJRKSBfmuUxickcuHSH9cci+fP4dW7HJrF4/2UW77+Mi50VjUr70rycLy8V88TW6vHD4oUQIjeYM2cOhQsXJn/+/Bw+fJiRI0fSqVMnScBfYNn6P3m9evV4Ukf7wxOzA9StW5eDBw9mY1RCCCGE9uKSktly6gZrj0Wy5VRUmqrlvi52NCvnS7OyfoQE5sOg1/4auKzkZGtF3eJe1C3uxcetzey9cJu1xyJZf+w6N2MSWX7wCssPXsHZ1ooGpbxpVs6PusW9sLOWhFwIkftcu3aNMWPGcO3aNfz8/OjYsSMTJkzQOiyhodzzc7oQQgiRy91PMLL5VBTrjl5j65koEoz/TbWS382e5uV8aVbOj+ACbujzWOL9OFYGPTWLelKzqCfjXynLgUt3WHs0kvXHrnEtOoFVoVdZFXoVBxsDL5f0pnk5P+qV8MpVIwKEEC+2ESNGMGLECK3DEBZE/gcTQgghslFsYjLrj11j3bFrbD97g6Tk/xLvQA8HmpX1o3k5X8rld7WIqq9aMuh1VA1yp2qQO2NalubQ5busOxrJumPXiLgbz5ojkaw5EomdtZ56xb1pVs6XJmV8pYdcCCFEriJJuBBCCJENohOMzNl1kRl/X+BOnDF1eWEvR5qX9aNZOV9K+7m88In34+j1OkIC8xESmI/3W5TiyJV7rD0Wybqj1wi/Hcf649dYf/wank62vFYniO7VAnHMguJ0QgghRHaT/62EEEKILHQ7Nolfd15g1q6LqZXNAz0caBOcnxbl/Sjm7SSJdybpdDoqFHSjQkE3/te0JMevRrPuWCSrDl0l4m48n649xZStYfSrFcSrNQvh+oS50IUQQgitSRIuhBBCZIGo+wlM33GBeXsupRZZK+7jxJD6RWlZ3j/PFVfTik6no2x+V8rmd2VYw+KsPBTBlC3nuHgrjm82nuHn7efpXasQfWoF4e5oo3W4QgghxCMkCRdCCCGew9W78fy8/TwL94aT+O/13mX8XXjz5WI0Lu3zwhRY04K1QU+nygVpX6kAa45cZfKWc5y5HsMPm88x4+8L9KgeSP/aQXg7W+b8xEIIIV5MkoQLIYQQzyD8VhxTt51j2YErGE3qdJwVA9wY+nIx6pXwkiHnOcig19E6OD+tyvuz4cR1fth8luNXo/l5+3lm77pI16oBvFanMP5uMievEEII7em1DkAIIYTITc5FxfD24lDqf7OVhXsvYzQpVC/szoL+1Vjxek3ql/SWBFwjer2OpmV9WfPmS/zauwoVA9xITDYza9dF6n61hVErjhB+K07rMIUQIsPGjRtHcHBwluxr8+bNlCxZErPZ/Nh1Hj5e7969adOmTZYc39KtWbOGihUrPvH1ySqShAshhBAZcDIymiELDtLou22sOBSByaxQt7gXSwfVYNFrNahZ1FOSbwuh0+moX9KbFa/XZEH/atQo7IHRpLBw72Xqf7OVtxeHci7qvtZhCiFeMFontCNGjOD9999Hr894Cjhp0iRmzZqVoXWz6vyOHj1K3bp1sbe3J3/+/Hz88ccoivLEbSZMmEDNmjVxcHDAzc3tsevNmjWL8uXLY2dnh6+vL2+88Ubqcy1btkSn07FgwYLnPoenkeHoQgghxBOEXr7Lj5vP8dfJ66nLGpX24c2Xi1K+gJt2gYmn0ul01CzqSc2inuy/eJsfNp9j25kbrDgUwcrQCJqX9WNI/aKU9nfROlQhhMhWu3bt4uzZs3Ts2DFT27m6umZTROmLjo6mUaNG1K9fn3379nHmzBl69+6NwWBg9OjRj90uKSmJjh07UqNGDWbMmJHuOt9++y3ffPMNX331FdWqVSMhIYHz58+nWadPnz788MMP9OjRI0vP62HSEy6EEEKk4/LtOPrN2kebyTv56+R1dDpoWd6PdW/V5pdXK0sCnstULuTO7L5V+f2NWjQu7YOiwB9HI2n+/Q6GzD/I9egErUMUQliIevXq8eabbzJs2DDy5cuHj48PP//8M7GxsfTp0wdnZ2eKFCnCunXrUrcxmUz069ePoKAg7O3tKVGiBJMmTUp9fty4ccyePZvffvsNnU6HTqdj69atAFy5coUuXbrg7u6Oo6MjlStX5p9//kkT09y5cylUqBCurq506dKF+/czN5pn0aJFNG7cGDu7tIUqP//8c3x8fHB2dqZfv34kJKT9Lny4d3vZsmWUK1cOe3t7PDw8aNiwIbGxsU88v8yYP38+CQkJzJo1i7Jly9KuXTtGjRrFlClTntgbPn78eIYPH065cuXSff7OnTt88MEHzJkzh27dulGkSBHKlClDq1at0qz3yiuvsHfv3keS86wmSbgQQgjxgGSTmek7ztP4u+1sOhWFQa+jfaUC/PV2XX7sVolSftJrmpuVL+DGz69WZv2w2rQs74dOpybjDb/dxvx/LmE2P3nIoxDiOSgKJMVqc3vKcOaHzZ49G09PT/bu3cubb77J66+/TseOHalZsyYHDx6kSZMm9OzZk7g4tc6E2WymQIECLFmyhBMnTjBmzBhGjx7NkiVLAHj33Xfp1KkTTZs2JTIyksjISGrWrElMTAx169bl6tWr/P777xw+fJgRI0akuS45LCyMVatWsWbNGtasWcO2bdv4/PPPM3U+27dvp3LlymmWLVmyhLFjxzJhwgT279+Pn58fU6ZMeew+IiMj6dq1K3379uXkyZNs3bqVdu3aoSjKY88PoEyZMjg5OT32VqZMmdRj7N69m7p162Jra5u6rHHjxkRGRnLx4sVMnfODNm7ciNlsJiIiglKlSlGgQAE6derE5cuX06wXGBiIt7c3O3bseOZjZYQMRxdCCCH+dSziHqNWHOVoxD0AqhZy59N25Sjq7aRxZCKrlfR14cdulRh8NZpRK45w+Mo93l95jFWHIvisXTmKejtrHaIQeY8xDj711+bYo6+CjWOGV69QoQIffPABAKNGjeLzzz/H09OTAQMGADBmzBimTp3KkSNHqF69OtbW1owfPz51+6CgIHbt2sWSJUvo1KkTTk5O2Nvbk5iYiK+vb+p6s2bN4saNG+zbtw93d3cAihYtmiYWs9nMrFmzcHZWv5d69uzJpk2bmDBhQobP5+LFi/j7p33tJ06cSN++fenfvz8An3zyCX/99dcjveEpIiMjSU5Opl27dgQGBgKk6XlO7/wA1q5di9FofGxs1tbWqfevXbtGoUKF0jzv4+OT+lyRIkWecqbpO3/+PGazmU8//ZRJkybh6urKBx98QKNGjThy5Ag2Njap6+bPn/+5Ev6MkCRcCCHECy8+ycTEv84w/e8LmMwKznZWjG5eis6VC8o833lcaX8XVgyuxZzdF/nqz9Psu3iHZpN2MLheUQbXL4KtlUHrEIUQGihfvnzqfYPBgIeHR5qEMyUxjIqKSl02bdo0pk+fzqVLl4iPjycpKemplc1DQ0OpWLFiagKenkKFCqUm4AB+fn5pjpsR8fHxjwxFP3nyJIMGDUqzrEaNGmzZsiXdfVSoUIEGDRpQrlw5mjRpQuPGjenQoQP58uV74rFTEvaMerjIacow9Ocpfmo2mzEajXz//fc0btwYgIULF+Lr68uWLVto0qRJ6rr29vapIxyyiyThQgghXmjbz9zg/VVHuXw7HoAW5f0Y27I03i52T9lS5BUGvY4+tYJoXMaXD1cdY/OpKCZtOsuaI1f5rF15qgY9/o9jIUQmWDuoPdJaHTszqz/QOwtqAvjgspSEMGXY+JIlSxg+fDjffPMNNWrUwNnZma+++uqRa7sfZm9v/0yxZHYaLU9PT+7cuZOpbR5mMBjYuHEju3btYsOGDfzwww+8//77/PPPPwQFBT12uzJlynDp0qXHPh8YGMjx48cB8PX15dq1a2meT/nBIeWHj2fh5+cHQOnSpVOXeXl54enpSXh4eJp1b9++jZeX1zMfKyMkCRdCCPFCuhWTyCd/nGTloQgA/F3t+Kh1WRqWfvb/5EXult/Nnhm9KvPH0UjG/X6CsBuxdPppN12rBvC/ZiVxtbd++k6EEI+n02VqSHhusmPHDmrWrMngwYNTl4WFhaVZx8bGBpPJlGZZ+fLlmT59Ordv335ib/jzqlixIidOnEizrFSpUuzZs4dXX301ddmePXueuB+dTketWrWoVasWY8aMITAwkJUrV/L222+ne36QueHoNWrUYPTo0SQlJaUOEd+4cSN+fn6PDFPPjFq1agFw+vRpChQoAKjJ9s2bN9P01CckJBAWFkbFihWf+VgZIYXZhBBCvFAURWH5gSs0/HYbKw9FoNNB75qF2PB2XUnABTqdjpbl/dn0dl26Vi0IwMK94TT8dhtrj0Y+da5aIcSLqWjRouzfv58///yTM2fO8OGHH7Jv37406xQqVIgjR45w+vRpbt68idFopGvXrvj6+tKmTRt27tzJ+fPnWb58Obt3787S+Jo0acLff/+dZtlbb73FzJkzmTlzJmfOnGHs2LGpPdLp+eeff/j000/Zv38/4eHhrFixghs3blCqVKnHnh+oPd1FixZ97O3BJLhbt27Y2trSu3dvjh07xsqVK/nss88YPHhw6uiDvXv3UrJkSSIiIlK3Cw8PJzQ0lPDwcEwmE6GhoYSGhhITEwNA8eLFad26NW+99Ra7du3i2LFj9OrVi5IlS1K/fv3U/ezZswdbW1tq1KjxnK/4k0kSLoQQ4oVx6VYsPWfs5Z2lh7kTZ6SkrzMrB9di3CtlcLKVwWHiP64O1nzWrjyLXqtOYU9HbtxPZPD8gwyYs5+rd+O1Dk8IYWEGDRpEu3bt6Ny5M9WqVePWrVtpesUBBgwYQIkSJahcuTJeXl7s3LkTGxsbNmzYgLe3N82bN6dcuXJ8/vnnGAxZW4+iR48enDhxgtOnT6cu69y5M2PGjGHkyJGEhIRw6dIlXn/99cfuw8XFhe3bt9O8eXOKFy/OBx98wDfffEOzZs0ee36Z5erqysaNG7ly5QqVK1dm8ODBDB8+nCFDhqSuExcXx+nTp9P0ro8ZM4aKFSsyduxYYmJiqFixIhUrVmT//v2p68yZM4dq1arRokUL6tati7W1NevXr0/TE79w4UK6d++Og0PmLl/ILJ2Sx37SjY6OxtXVlXv37uHiYtnTyBiNRtauXUvz5s0fudZDWAZpo9xB2il30LKdjCYz03dcYOJfZ0hMNmNrpeethsUYULsw1gb5PTqFfJbSl2A0MWXLOaZuC8NoUnC0MfBekxL0rFEIgwaF+6SdcgdpJ3Vo74ULFwgKCnqkKJilMJvNREdH4+Ligl6ft/8/GDFiBPfu3eOnn37SOpRMyak2unHjBiVLlmT//v1PvMb9ce/rzOShefudJoQQ4oV3+PJdXvlxJ1+sP0VisplaRT34c1gdBtcrKgm4yBA7awNvNy7BH0NrExKYj9gkE+NWn6Dd1F2cjIzWOjwhhMiQ999/n8DAwHSv2xZw4cIFpkyZ8sQEPKvI2DshhBB5UkxiMt9sOM3sXRcxK+DmYM0HLUrTvlL+55rmRLy4ivs4s3RgDebvDefLdac4fPkurX74m9fqFGZog2LYWct0ZkIIy+Xq6sro0aO1DsNiVa1alapVq+bIsaQLQAghRJ5zMPwOTb7bzq871QS8TbBaaKtDSAFJwMVz0et19KweyMa369KkjA/JZoUpW8NoNmkHx6/e0zo8IYQQuYAk4UIIIfIMRVGYs/sinX/aTcTdeArks2d236pM7FIRDydbrcMTeYivqx0/9azMtB4h+LjYcuFmLO2m7GLp/stahyaEEMLCyXB0IYQQeUJcUjKjVhzlt9CrADQv58sX7cvjbPdiFkQSOaNpWV+qBbkzbHEo287c4L1lRzgYfoexrcrI8HQhhBDpkp5wIYQQuV7YjRjaTN7Jb6FXMeh1fNCiFJO7VZIEXOSIfI42/Nq7CsMbFkeng4V7L9Nh2i4u347TOjQhLILZbNY6BCGyTFa8n6UnXAghRK629mgk7y09TGySCW9nW37sVomqQe5ahyVeMHq9jrcaFiM4wI23Fh3iWEQ0LX/4m4mdg6lf0lvr8ITQhI2NDXq9nqtXr+Ll5YWNjY3F1eUwm80kJSWRkJCQ56coy60spY0URSEpKYkbN26g1+uxsbF55n1JEi6EECJXMprMfLHuFNP/vgBAtSB3fuhWEW9ny5yLVrwY6hb34o+htRk8/yCHL9+lz6x9DH25KG81LK7JnOJCaEmv1xMUFERkZCRXr17VOpx0KYpCfHw89vb2FvcDgVBZWhs5ODgQEBDwXD8ISBIuhBAi17kencAbCw6y7+IdAAbWLcx7jUtgJfN+CwuQ382eJQOr88mak8zdc4nvN5/j0OW7TOpSEXfHZ+85ESI3srGxISAggOTkZIucn9poNLJ9+3bq1KmDtbVcwmSJLKmNDAYDVlZWz/1jgCThQgghcpXdYbd4c+FBbsYk4WxrxVcdK9C0rK/WYQmRhq2VgY/blKVSoBujVhxlx9mbtPx+B5O7V6JiQD6twxMiR+l0OqytrTVPoNJjMBhITk7Gzs7OIuMTebONpMtACCFErqAoCtO2hdF9+h5uxiRR0teZ3998SRJwYdHaVizAqiG1CPJ05Oq9BDr9tJu5uy+iKIrWoQkhhNCIJOFCCCEsXnSCkYFzD/D5ulOYFWhXKT8rB6uJjRCWrqSvC7+/UYumZXwxmhQ+/O04wxeHEpeUrHVoQgghNCBJuBBCCIt2MjKaV374mw0nrmNj0PNp23J807EC9jYyB7PIPZztrJnaoxLvNy+FQa9jVehV2kzeSdiNGK1DE0IIkcMkCRdCCGGxlh+4QtspO7l4K478bvYse70G3aoFWER1VCEyS6fTMaBOYRYOqI6Xsy1nrsfQ+sedrDsaqXVoQgghcpAk4UIIISxOgtHE6JVHeWfpYRKMZuoW92LNmy9RvoCb1qEJ8dyqBrnzx9CXqBrkTkxiMq/PP8iEP05gNJm1Dk0IIUQOkCRcCCGERbl6N55OP+1mwT/h6HQwvGFxfu1dhXwytZPIQ7yd7VjQvxoD6xQG4JcdF+j2yx5u3E/UODIhhBDZTZJwIYQQFuPM9fu0n7qLI1fu4eZgzaw+VXmrYTH0ehl+LvIeK4OeUc1LMa1HJZxsrdh38Q4dpu3i0q1YrUMTQgiRjSQJF0IIYRH2XbxNh6m7iLyXQBEvR1a/8RJ1i3tpHZYQ2a5pWT9+e6MWBd3tuXQrjvZTd3H0yj2twxJCCJFNrLQOQAghhPjz+DWGLjxEYrKZSgFuzOglw89zHZMRkmIfuMWo/xrj/rv/4PLkRNDpwWANeqtHbwZr0Bv+fZyyjiHt+gZrsHMDBw9w9ARrB8ilRfuKeDmx/PWa9J65jxOR0XT5eTfTeoZQu5j8ECWEEHmNJOFCCCE0Nf+fS3y46hhmBRqW8uaHrpVk+jFLkHgfoq9CdMS//z5wP+7Wo0m1KUnriMHKDhw8wdFDTcwdPP9N0B+87/nffft8oLecQYHeznYsHlidgXMPsCvsFn1+3cfXHSvQpmJ+rUMTQgiRhSQJF0IIoQlFUZj411kmbToLQJcqBfmkTVmsDJaTFOVJigIJ9x5NrKMjMNy7Qv2rZ7A6MURNwp+F3gpsnP69OT5wcwIbh//uW9mC2fTvLRnMxn//Nam96qnL/n3e9MDzKesmJ0HCXYi9CaZESE6A6CvqLSN0ejURd8kPHkXAvci//xZW7zt65njPurOdNb/2qcK7S4+w+vBVhi0O5cb9RAb8W8BNCCFE7idJuBBCiByXbDLz4W/HWLj3MgBDGxRjeMNiMv93VjKb4M5FiDr57+04RJ2Cu+FgTL/wlx5weXCBrSu4+D9wy6/+6+gFtk4PJNf/JtrWjmClwWUEiqL2xsfdhNhbak993E3139ib/96//e/9f59LuAeK+d/Ht+DakUf3a+sC7kEPJOf/JugeRdSe9Gx6v9paGZjUORgvJ1tm7rzAhLUnuR6dwOjmpaRIoRBC5AGShAshhMhRCUYTw5ce5q+T19Hp4OPWZelRPVDrsHIvRYH7kRB14oGE+4SacCfHP347e/f/kup/E+xkRx/2nrxMlYZtsXYvCLbOOXcez0OnU38UsHWCfIUyto3J+G9ifkP9YeL2ebgdBrfC1Pv3rkBiNEQeVm8Ps3VVE/SU5NyrBPhXVJP0LEjO9XodH7YshY+LLZ+tO8X0vy9wIyaRrzpUQNJwIYTI3SQJF0IIkWNijdBr1gEOht/FxkrP912CaVrWT+uwco+423DjFFw/njbhTrib/vpWdmpy6F0avEup/7oXVpNua/tHVleMRm5ErAXPYmBtnb3nojWDNTj7qDffso8+b0xQRxKkJub/Jue3zqvD3RPvQWSoenuQnauajPtXgvyV1H9d/J8pMdfpdAysWwQvZ1tGLDvCb6FXuRWTxA9dyj/LGQshhLAQkoQLIYTIEVfvxjPpuIHr8XdxsbNieq8qVA1y1zosy2U2wfVjcGk3hO+Gy3vh/tX019UZ1B5Z79IPJdxBakVxkXnWduBdUr09zBgPty+k7T2/fhyuHVWHuZ/fqt5SOPmkTcrzVwKHjL/321UqgIeTLa/PO8Df527SY+Y+ushvV0IIkWtJEi6EECLbnb52n1dn7uV6vA4fF1vm9K1GCd9cMtQ5pxjjIeJA2qQ7KZ3iaK4B/ybZpcCnjPqvRzE1aRQ5w9oefEqrtwclJ6kjE64ehIiDcPWQOloh5jqcWafeUrgFpk3K/So8cfh/3eJeLBxQnb6z9nH86n0m3jJQo3YcRX1ds+kkhRBCZBdJwoUQQmSrf87fYsCc/UQnJONjr7D0tWoEeEoCTtxtuPwPXNoF4XvUhM1sTLuOrQsUrAoBNSCgOviWBzuX9PcntGdlA/7B6q1yX3VZUpxa9C3i4H/J+e0wuHtJvR1f+e/GOvAqCUF1oMjLUKjWI0l5hYJuLHu9Jj1n/MOVO/F0+uUfZvWpSvkCbjl4kkIIIZ6XJOFCCCGyzfpjkQxdFEpSspmQADfae9/Ez/UF7bG9G64m2ylJ942Tj67j5AuBNSCgppp0+5SR4eS5nY2D2pYB1f9bFn8Hroam7TGPjlDfEzdOwt6f1KneClSFIvWhcH31OnODFUGejiwZUJVOk7dyJdZIl5/3MLVHCHWLe2l2ikIIITJHknAhhBDZYu6eS4z57RiKAg1L+fBdx7Js3vin1mHlnKRYCNsCZ9ar/6Y3d7Vn8X8TtH+T7nyFcnxeaqEB+3xqcl2k/n/L7l+Hy3vU98r5LWpRuPBd6m3LBLXgW1AdKFwfr8DavFnGxG+3vNkVdpt+s/bxVcfytK1YQLNTEkIIkXGShAshhMhSiqLw3cYzfL/5HABdqwbwcesyKGaTxpHlgLuX1aT7zHq4sANMif89p7dSr/sNqPHf8HJHT+1iFZbF2QdKt1ZvoBZ+O78FwjbDhe1qwbeTq+HkaqyBljbetCvdlDlKESad92P44sPcuJ/IgNqF0ckPOUIIYdEkCRdCCJFlkk1m3l95jMX7LwPwVoNiDGtYDJ1OhzEvJuFmszqk+PQ6NfG+fizt8/kKQfFmUKyRmnTbOGoSpsiF3IPUW+W+aqX8q4dSe8mVy//gmBQFoXPoB/Sx03PEHMSODeWYE9GYnh07obfK41PMCSFELiZJuBBCiCxhNJl5c8Eh1h+/hl4HH7cpS/dqgVqHlfUSY9QeytPr4eyfEHvjv+d0eihYDYo3UZNvrxIyvFw8P70BClRWb3XfIznmNgdWfE8Vj1gMF7ahv3maYH0YwfowOL2KmM9G4VChDfoybaBQHTDIn3tCCGFJ5FtZCCHEczOazAxdqCbgNgY9P3SrSJMyvlqHlXXuhsOZP9Ue74s7wJT033O2Lmo16xLNoGgjcPTQLk7xYrB15rprRcyNm2OwtoZ7EXB+C5f3/4Hjle24m+7BwdnqzcEDSrYESciFEMJiyDexEEKI55JsMjNsUSjrjqkJ+E89Q6hf0lvrsJ7frTA4skS9DjfqeNrnUoaZl2iqFlWzstEkRCEAcM0PFXtQsGIP/ggNZ9HShTTT7eEV2wM4xd1KJyFvC4VqS0IuhBAakW9fIYQQzyzZZGb4ksP8cTQSa4OOqT0q5e4EPPYWHF8BhxdBxP7/luv0ULC6Osy8RDO1qrkMMxcWqEVwACZdT4YtKsuHsX0YVeom/fIdRndqNUhCLoQQFkG+cYUQQjwTk1nhnaWHWX34KtYGHVO6h9CglI/WYWWeMUEtqnZkMZzdAOZkdblOrw4zL9tBTb4d3LWNU4gMeqWCP4qiMHxxKJ+c9OF8tf588vbX6C/9DSdWwYnfH03IS7WC0m0kIRdCiBwg37JCCCEyzWRWeHfpYX4LvYqVXseP3SrRqHQuSsDNZnVO5sOL4PgqSLz333O+5aFCFzX5ds5F5yTEA1oH58esKLy95DAL/glXiyW2roeuSH1o/o1a2+D4SvVyi7hbcGCWektJyMt1hMBaMuJDCCGygSThQgghMsVkVhix7AgrD0Vg0Ov4oWsuKsJ286yaeB9dohZbS+GSX006KnQB71LaxSdEFmpbsQAmM7y37DDz9oRj0OkY90oZdAYrKFJfvbX4Fi5uV3+Mejgh9ygKlXpBcDeZ014IIbKQJOFCCCEyzGxW+N/yIyw/eAWDXsf3XSrSrJyf1mE9WexNOLZcTb6vHvxvuY0zlG4NFTpD4Eug12sXoxDZpENIAcyKwsjlR5i9+xJ6vY4xLUujS+nhNlipl10UeRla/NtDfmyF2kt+6xxs/BA2fQSlWkJIb7XCunxWhBDiuUgSLoQQIkPMZoVRK46y9MAV9DqY2DmYFuUtNAE3GeHUH3B4IZz764HrvA1QtCGU7wQlmoONg7ZxCpEDOlUuiKIojFx+lF93XkSv0/FBi1L/JeIpDNb/JeRNP1N/vDowC64eUpPy4yvVmQEq9YLg7nK5hhBCPCNJwoUQQjyV2azw/qpjLN5/Gb0OvuscTKsK/lqH9aiYKDVp2D8T7kf+t9y/IpTvAmXbg5OXZuEJoZXOVQIwmWH0yqPM+PsCBr2OUc1KPpqIp7B1Vnu+Q3pD5GE4MBuOLoU7F2HTeNgyQZ0pIKQ3FH5ZeseFECITJAkXQgjxRIqiMOb3YyzcG45OB990qkDr4Pxah5XWlQOw9ye1p86UpC5z9IaKPdTrvL1KaBufEBagW7UATIrCh6uO8fP28+h1OkY2LfH4RDyFXwVo+S00/lj9jB2YDVf2qteQn1wNrgFQ6VWo2B1cLPDHOSGEsDCShAshhHgsRVEY+/tx5u1RE/CvO1SgbcUCWoelSk5Sp1v6ZxpEHPhvef7KUG2Qer23lY1m4QlhiXpWD1R/WPvtONO2haHXwXtNMpCIA9g4qj9sVewB10+o05sdXgj3wmHLJ7D1M3U6v5De6mUfekO2n48QQuRGkoQLIYRIl6IojF99gjm7L6HTwZfty9M+xAIS8OhIOPAr7P8VYqPUZQYbKNMOqr0G+UO0jU8IC/dqjUKYzQrjVp9gytYwDHodbzcqnrFEPIVPaWj2BTQcByd+U3vHw3fB6bXqzSW/2jtepb9UVhdCiIdIEi6EEOIRiqLw8ZqTzNp1EYAv2pWnY+WCWgYEl/eqQ85P/PZfoTVnP6jcT+15k2u9hciw3rWCMCnw8ZoT/LD5HHqdjuGNimd+R9b26iUfFbrAjdNwcA6ELoDoCLVn/O/v1J7zGkPAvXDWn4gQQuRCkoQLIYRIQ1EUJvxxkpk7LwDwWbtydKqiUQJuTFArNO/9SS0OlSKgBlR9DUq1Uis6CyEyrd9LQSiKwid/nGTSprPodTrealjs2XfoVQKaTIAGY9RrxXf/qFZW3zddLZZYujXUHAr5K2XdSQghRC4kSbgQQohUiqLw+bpTTP9bTcAntC1L16oBOR/IvQjYP0OtdB53S11msIVyHdUh534Vcj4mIfKg/rULYzIrfLbuFN/9dQaDHt54+TkScQArWyjXQZ2N4OLfsHMSnNv43zRnQXWg1ltQpAFkZgi8EELkEZKECyGEANQE/Ms/T/PT9vMAfNy6DN2rBeZsEDfPwo5v4MgSUEzqMpcCUKWfOjexo0fOxiPEC2Bg3SKYFIUv15/m6w1n0Ot1DK5X9Pl3rNNBUG31du0Y7PoBji2DC9vVm09ZtWe8bDsZ0SKEeKFIEi6EEAKAbzeeYerWMADGv1KGnjUK5dzBo07C9q/h+ApQzOqyQrXVIeclmoNB/rsSIjsNrlcURYGv/jzNl+tPY9DpGFi3SNYdwLcstPsJGnwIe6aqo1yuH4OVr8Gmj9Rrxiu9CrZOWXdMIYSwUPJXjRBCCGb8fYEfNp8DYGyr0vSqWShnDhx5BLZ/BSd//29Z8WZQ9z2pci5EDhtSvygms8K3G8/w2bpT5HOwyfp6EK4F1OvG67yrXie+ZxpEX4E/R8G2z9Vq6tUGgZN31h5XCCEsiCThQgjxgvstNIKP15wA1PmC+9QKyv6DRhxUk+/Ta/9bVuoVqPMe+JXP/uMLIdI1tEEx4pJMTNsWxqiVR3F3tKFhaZ+sP5B9Pqj9DlQfAkcWqUPVb51TL0fZ9SMEd4Uab4JnFgyLF0IIC6PXOgAhhBDa2XH2Bu8uVauO965ZiMH1snD4aTryxZ7FsKgL/FL/3wRcpxZven03dJ4rCbgQFmBk0xK0r1QAk1lhyIKDHLh0O/sOZm2nTjE4ZB90ng8FqoApUR2u/mNlWNwDrh3NvuMLIYQGJAkXQogX1JErdxk09wBGk0KL8n6MaVkaXXZVKr74N4b57ahz5mP0YX+BzgAVusIb+6DDTPApnT3HFUJkmk6n4/P25ahfwovEZDN9Z+3n7PX72XtQvR5KtYR+G6HPevWyFBR1qrNpL8GyvnDzXPbGIIQQOUSScCGEeAFduBlLn1/3EZtkolZRD77tVAG9PosTcEWBsC3wa3OY1QL9xe2YMWCu0B3e3A9tp4Hnc06FJITIFtYGPZO7V6JigBv34o28OnMvV+/GZ/+BdToIrAHdFsHgf9SRMgDHlsPkqvD7m3DvSvbHIYQQ2UiScCGEeMFE3U/g1Zn/cCs2iTL+LkzrEYKtlSHrDqAocHYjzGgEc9vApZ1gsMFUqTd/lfkKU8tJ4F44644nhMgWDjZWzOxVhSJejkTeS6DXzL3cjUvKuQC8S6ojZQb9DcWbqtMWHpwD31eE9aMg5kbOxSKEEFlIknAhhHiBRCcY6TVzH5dvxxPo4cCsPlVxtsui+XlTku9f6sP8DnBlH1jZqZWOh4ZibvY18TaeWXMsIUSOyOdow5x+1fB1seNsVAz9Zu8nPsmUs0H4loNui6HvBgh8CUxJsGcKTKoAmz+B+Ls5G48QQjwnScKFEOIFkZhs4rU5+zkZGY2nkw1z+lbFy9k2a3YeeQTmtFaT76uHwNoBarwBbx2BZl+Aa/6sOY4QIsfld7Nndt+quNhZceDSHd5YcJBkkznnAwmoBr3XQM+V4F8RjLHqLAuTKsDf30FSXM7HJIQQz0CScCGEeAGYzArDF4ey5/xtHG0MzOpTlUAPx+ff8b0rsHIQ/FQHLmwDg42afA87qs4F7JwNUxsJIXJcCV9npveqgq2Vnk2nohi98iiKouR8IDodFHkZBmyBzvPAqyQk3IW/xsH3wbD3F0jOwSHzQgjxDLI9CZ8yZQpBQUHY2dkREhLCjh07Hrvu1q1b0el0j9xOnTqV3WEKIUSepSgK41cfZ+3Ra1gbdPzUszJl87s+304TomHTR/BDCBxeCChQtoNa7bzJBHCUYedC5DVVg9z5oWtF9DpYsv8KX284rV0wOh2UagWv74K2P4FbIMRch7Xvwo8hELoAzDk8bF4IITIoW5PwxYsXM2zYMN5//30OHTpE7dq1adasGeHh4U/c7vTp00RGRqbeihWT6rlCCPGsJm85x5zdl9Dp4NtOwbxU7DkSZJNR7Wn6viLs+AaSEyCwFgzYDB1mQL5CWRa3EMLyNC7jy6dtywEweUsYs3Ze0DYgvQEqdIE39kOLb8DJF+6Gw6rXYUoNOPGbWq9CCCEsSLYm4d9++y39+vWjf//+lCpViokTJ1KwYEGmTp36xO28vb3x9fVNvRkMWVi1VwghXiCL9obz9YYzAIxtWZpWFfyfbUeKAqf+gCnV1Z6muJvgUQy6LITef0D+kCyMWghhybpUDeCdRsUBGL/mBGuOXNU4IsDKBqr0h6GHoNFHYJ8Pbp6GJa/Cz/Xg/DatIxRCiFRW2bXjpKQkDhw4wP/+9780yxs3bsyuXbueuG3FihVJSEigdOnSfPDBB9SvX/+x6yYmJpKYmJj6ODo6GgCj0YjRaHyOM8h+KfFZepwvMmmj3EHaKX2bTqrXbQIMqhNE96oFnuk10kUcRL9pDPrLewBQHDwx1xmBObgnGKwhOTlD+5F2snzSRrmDJbTTwNqBXI+OZ94/lxm+OBRnGz01i3hoFk8qnTVUHQzle6D/Zwr6vVPRRYbCnFcwl3wFU8Px4FowR0KxhHYSTyftZPlySxtlJj6dkk1VNa5evUr+/PnZuXMnNWvWTF3+6aefMnv2bE6ffvQ6otOnT7N9+3ZCQkJITExk7ty5TJs2ja1bt1KnTp10jzNu3DjGjx//yPIFCxbg4OCQdSckhBC5yPlomHLCgFHRUc3LTNciZnS6zO3DIfEGpa4upcBdNfk26aw5592Mcz4tSDbYZ0PUQojcxKzA7DN6Qm/rsTUoDC1jokAW1HvMSjbGaEpcW0XQzU3oUDDprDnr05KzPi0w6220Dk8IkYfExcXRrVs37t27h4uLyxPXzfYkfNeuXdSoUSN1+YQJE5g7d26Gi621atUKnU7H77//nu7z6fWEFyxYkJs3bz715LVmNBrZuHEjjRo1wto6i+bpFVlK2ih3kHZK6+z1GLpM30t0QjL1S3gypWswVoZMXH0Ufxf9zm/R75+OzpSEgg6lfBdMdUeByzMOZ0faKTeQNsodLKmdEo0m+s09yD8X7uDpZMOiAVUJdLfATpDrxzFsGIU+XB2NqbgWxNTwY5QSLcj0L5QZZEntJB5P2sny5ZY2io6OxtPTM0NJeLYNR/f09MRgMHDt2rU0y6OiovDxyfiUNdWrV2fevHmPfd7W1hZb20fnubW2trboRnpQbor1RSVtlDtIO0HE3Xj6zjlIdEIylQLcmNK9MvY2GayrkZwI+6bDti/VKX8ACtdD1/gTdL7lsqyIiLST5ZM2yh0soZ2sra35pVcVOv+0h5OR0fSbc5Blg2ri5fzo32aaKhAMfdbC8ZWw4QN09y5jtbw3BNWFZl+Ad6lsO7QltJN4Omkny2fpbZSZ2LKtMJuNjQ0hISFs3LgxzfKNGzemGZ7+NIcOHcLPzy+rwxNCiDznTmwSr874h2vRCRT1dmJm7yoZT8DPbVKLrv05Wk3AvUtD9+XQcxX4lsvOsIUQuZyLnTWz+1ShoLs9l27F0WfWXmISM1YrIkfpdFC2nTqVYp33wGALF7bB1FqwfhTE39U6QiHECyJbq6O//fbbTJ8+nZkzZ3Ly5EmGDx9OeHg4gwYNAmDUqFG8+uqrqetPnDiRVatWcfbsWY4fP86oUaNYvnw5b7zxRnaGKYQQuV5cUjJ9Z+8j7EYsfq52zOlbFTeHDFzveP8aLOsL89rB7fPq9D6v/ACD/oZiDbNtmKYQIm/xdrFjTt9qeDjacCwimoFz95OYbKHzdNs4wssfwJB/oGRLUEywZwr8EAIH54DZrHWEQog8LtuGowN07tyZW7du8dFHHxEZGUnZsmVZu3YtgYGBAERGRqaZMzwpKYl3332XiIgI7O3tKVOmDH/88QfNmzfPzjCFECJXSzaZeWPBIQ6F38XV3prZfavi7/aUwmlmE+ybAZs/hsRo0Omh2iCoNwrsLLuehhDCMgV5OvJrnyp0+XkPO8/d4p0lh/m+S0X0egv9Mc89CLrMV0cCrRsJt87C72/C/pnQ7CsoWEXrCIUQeVS2JuEAgwcPZvDgwek+N2vWrDSPR4wYwYgRI7I7JCGEyDMURWH86hNsPhWFrZWemb0rU9zH+ckbXT0Eq4dBZKj6OH8ItPwO/Cpkd7hCiDyufAE3pvUIoe+sfaw5EkmAuwMjmpbUOqwnK9oAXt8Fe3+GrZ+r35EzGkKFbtBwHDhnvJaREEJkRLYORxdCCJG9Zu+6yNw9l9DpYFKXioQEuj9+5YR7sPY9+OVlNQG3dYUW30K/jZKACyGyTJ3iXnzRvjwAU7aGsezAFY0jygArG6j5Brx5AIK7q8sOL1CHqO/6AZKTtI1PCJGnSBIuhBC51JZTUXy05gQAI5uWpGlZ3/RXVBQ4thx+rKL29ChmKNdJLU5UpR/oM1i8TQghMqh9SAGG1C8CwKgVR/jn/C2NI8ogZx9oMwX6bwL/SpB0HzZ8AFNrQthmraMTQuQRkoQLIUQudOpaNG8uPIRZgU6VCzCwTuH0V7wVphZdW9YXYq6DR1F49Tdo/4sMsRRCZKt3GpWgeTlfjCaFgfMOcPFmrNYhZVyBymoi3noyOHqp14vPbQurBkPcba2jE0LkcpKECyFELnPjfiL9Zu0nJjGZ6oXd+aRNOXQPVzFPToStX8CUGmrvjcEW6r+vXvdYuJ4mcQshXix6vY5vOgZToYArd+OM9J29j3txRq3Dyji9Hir2UIeoVx0I6CB0PkyuBid+0zo6IUQuJkm4EELkIglGE6/N3U/E3XiCPB2Z1iMEG6uHvsrPb1WHTm79FEyJUORlGLwb6o4AK1tN4hZCvJjsbQz88mpl/F3tOH8jlsELDmA05bIpwOxcofmX0PdP8CwOsVGw5FVY3BPuX9c6OiFELiRJuBBC5BKKovDesiOpU5HN6FU57VzgMVGwfADMaQ23zqlzfneYCT1WgEcR7QIXQrzQvF3smN6rCg42Bnaeu8WY346jKIrWYWVeQDUYuANqvwt6Kzj5O0yuCqEL1NobQgiRQZKECyFELvHdX2dZffgqVnodU3tUorCXk/qE2Qz7psMPleHoEnXO76oD4Y29ULY9PDxUXQghclhpfxe+71IRnQ4W7g1nxt8XtA7p2VjbQYMPYcAWdVaJhLuw6nWY1x7uhmsdnRAil5AkXAghcoFVhyL4ftNZAD5tW46aRTzVJ+5cgjmvwB/vQOI98AtWiwk1/1IdQimEEBaiYWkf3m9eCoAJa0/y14lcPJTbrzz036zOI26whbBNMLk6/POz+sOoEEI8gSThQghh4Q5cus2IZUcAGFi3MJ2qFFSHPh6YrV77fXEHWDtAsy9hwGbIX0njiIUQIn39Xgqia9UAFAWGLjrE8av3tA7p2Rms4KXh8PpOCKgBxlhY9x782gxuntU6OiGEBZMkXAghLNjl23G8NucASSYzjUv7MLJJSYiOhAWdYPVQSIpR//gb9DdUGyhzfgshLJpOp+Oj1mWoVdSDuCQT/WfvJyo6Qeuwno9nMei9Fpp/DTZOcHkPTK2FfudEdEqy1tEJISyQJOFCCGGhohOM9J21j1uxSZTN78LEzhXQH1sGU6rD2Q3qEMjGn0DvP6TwmhAi17A26JnSLYTCXo5E3kug/5z9xCeZtA7r+ej1UHWAOhNF0YZgSsSw9RPqnB4P145oHZ0QwsJIEi6EEBYo2WTmjQWHOBsVg4+LLTM6BOGwqi+s6K8WAvKvCAO3Q803pfdbCJHruDpY82vvKuRzsObIlXu8szQUszkPVBh3C4Duy6DtTyj2+XCLv4TVzEaw6SMw5vIefyFElpEkXAghLNBHa06w/cwN7K0NLKl7G5+5ddXpcPRWUP996LcRvEtqHaYQQjyzQA9HfupZGWuDjrVHr/HNxtNah5Q1dDqo0IXk13YS4VYFnWKCHd/AtJcgfI/W0QkhLIAk4UIIYWFm7bzAnN2XcNXF8FfhBQRuHABxN8G7tFp4re4IMFhrHaYQQjy3qkHufN6uPACTt4Sx7MAVjSPKQk7e7A96k+T2s8DJB26dhZlN1V5xk1Hr6IQQGpIkXAghLMiW01F8tOYEdfSH2enyAfkv/abO+/3ScHhtqzovrRBC5CHtQwowpL5a12LUiiPsvXBb44iyllKyJQz5Byp0AxS1V3xGY7gVpnVoQgiNSBIuhBAW4vS1+4xcsItPDNOZY/MFTolR4FEU+m5Q56K1stU6RCGEyBbvNCpB83K+GE0KA+fu5+LNWK1Dylr2+aDtVOg4G+zc4OpBmFYbDs5Vp5wUQrxQJAkXQggLcON+It/P/JXlyrt0s9qsLqz2OgzcAQWraBucEEJkM71exzcdg6lQwJU7cUb6zt7Hvbg8OGS7TBt1XvFCtdV5xX9/A5b2gri81fsvhHgyScKFEEJjCXEx7J7yGpOTPqSg/gZml4LQazU0+xxsHLQOTwghcoS9jYFfXq2Mn6sd52/EMnjBAYwms9ZhZT3XAvDqb9BgrFps88RvatG2Czu0jkwIkUMkCRdCCA0pEYe4N7E6r8SvAiC6dHf0g3dBUB1tAxNCCA14u9gxo1cVHGwM7Dx3izG/HUfJi8O19Qao/bY604V7EYiOgNmtYONYSE7SOjohRDaTJFwIIbSgKLBnGubpDfFJusx1xY2TL8/EpdMUsHPROjohhNBMaX8Xvu9SEZ0OFu4NZ9aui1qHlH3yV4KB26HSq4ACOyfCjEZw85zWkQkhspEk4UIIkdPibsPiHrB+JAYlmT9NldnVeA2l6rTXOjIhhLAIDUv7MLpZKQA++eMku87d1DiibGTrBK/8AJ3mqEXbIkPhp9pwcI4UbRMij5IkXAghctLlffBTHTi1hiTFirHGXuwKmUjbWuW0jkwIISxK/9pBtK2YH5NZYciCg1y+Had1SNmrdGt4fde/Rdvi4Pc3YcmrUrRNiDxIknAhhMgJZjPsnAS/NoV7l4nQ+dIuaRynArryQasyWkcnhBAWR6fT8Vm7cpTLr1ZMf23uAeKSkrUOK3u55leLtjUcrxZtO/k7TK0F57dpHZkQIgtJEi6EENkt9iYs6AQbx4A5mX8c6tEk/hPuuJZhSvdKWBvkq1gIIdJjZ23gp54heDrZcDIymhHLjuTNQm0P0hvgpWHQ/y/wKAr3r8Kc1ur/IVK0TYg8Qf7yE0KI7HRxpzr1zLmNYGXH+qBRdL49gGRrJ37qGYKHk63WEQohhEXzd7NnSvcQrPQ61hyJZNq281qHlDP8K/5btK0XatG2STCjIdw8q3VkQojnJEm4EEJkB7MJtn0Fs1vC/UjwLM62uosZdLIcoOOL9uUpm99V6yiFECJXqBrkzthX1Et3vvzzFFtOR2kcUQ6xcYRXvofO88A+H0QeVuuKHF6kdWRCiOcgSbgQQmS1+9dhblvY8gkoZqjQjZOtfmfQhgQABtYpTOvg/BoHKYQQuUuPagF0rVoQRYGhCw9x4Was1iHlnFKt1KJtQXXVom0rB8Ka4ZCcqHVkQohnIEm4EEJkpbDNMK0WXNgG1g7QZiq3G09iwKKTxBtN1C7myYimJbWOUgghch2dTse4V8oQEpiP+wnJDJizn/sJRq3Dyjku/tBzJdT9H6CD/TPh12Zw97LWkQkhMkmScCGEyAqmZNj0McxtB7E3wLsMvLaN5HJdeGPBQa7ciSfQw4Efu1bCoNdpHa0QQuRKtlYGpnavhI+LLeeiYnh7yWHM5jxeqO1BegPUHwXdlqhzikccUIenh23WOjIhRCZIEi6EEM/rXoR67feOrwEFQvrAgE3gVZxP155iV9gtHGwM/PJqZVwdrLWOVgghcjVvFzt+6lkZG4OejSeuM2nTC1iorHhjGLgN/CpA/G31B+DtX6nTYQohLJ4k4UII8TzO/KlWPw/fDTbO0GEmtJoI1vYsP3CFmTsvAPBtp2CK+zhrG6sQQuQRwQXdmNC2LACTNp1l/bFrGkekgXyFoO8GqNgTUGDzJ7CoG8Tf0ToyIcRTSBIuhBDPwmyCjWPV+b/jb6u9EQO3Qdn2ABy+fJdRK48CMLRBMZqW9dUyWiGEyHM6Vi5I75qFAHhnSShnrt/XNiAtWNtB6x/hlR/AYAtn1sHP9SDyiNaRCSGeQJJwIYTIrPg7ML8j7JyoPq42CPptBI8iAETdT2Dg3AMkJZtpWMqHYQ2KaRerEELkYe+3KEWNwh7EJpl4bc5+7sW9QIXaHlTpVei3AdwC4M5FmNEIQhdoHZUQ4jEkCRdCiMyIOgk/14ewTWBlD+1nQLMvwMoWgKRkM4PnHeRadAJFvZ34rnMF9FKITQghsoW1Qc/k7pXI72bPxVtxvLnoEKYXqVDbg/yD4bVtUKwxJCfAqtdh9VsyjZkQFkiScCGEyKiTq+GXBnDnArgGqL0O5TqkWWXc6uPsv3QHZzsrfu4ZgrOdFGITQojs5O5ow8+vhmBnrWf7mRt8+ecprUPSjoM7dF0M9UYDOjgwC2Y2gbvhWkcmhHiAJOFCCPE0ZjNs+RQW9wBjLBSqDa9tBb/yaVab/88lFvwTjk4H33epSGEvJ23iFUKIF0wZf1e+6lABgJ+2nee30AiNI9KQXg/1RkL3ZWCfD64eUqcxO7dJ68iEEP+SJFwIIZ4kIVqtNrvtC/Vxtdeh50pw9Eiz2r6Ltxn3+3EA3mtSgvolvXM6UiGEeKG1quDPoLpqbY6Ry49wLOKexhFprFhDdXi6X7Bay2Ree9j2pUxjJoQFkCRcCCEe5+Y5mN5ArTZrsIU2U6HZ52BIO8Q88l48r887iNGk0KK8H6//+0egEEKInPVekxLULe5FgtHMwLkHuBXzgl8PnS8Q+v4JIb0BBbZMgIVdZBozITQmSbgQQqTnzAb4pT7cPAPO/tBnHQR3e2S1BKOJgXMPcDMmkVJ+LnzVoTw6nRRiE0IILRj0Or7vUpFCHg5E3I1nyIKDGE0veM+vtR20mgStJ4OVHZz9E36qK9OYCaEhScKFEOJBigI7vlHn/06MhoLV1eu/C4Sks6rC6JVHOXLlHvkcrPm5ZwgONlY5H7MQQohUrg7W/PJqZRxtDOw5f5sJf5zUOiTLULHHv9OYBcLdS2rBtpNrtI5KiBeSJOFCCJEiMQaW9oJNHwEKhPSBXqvB2Sfd1X/deZEVByMw6HVM7laJgu4OORuvEEKIdBXzcea7zsEAzNp1kSX7L2sbkKXwqwADt0GRl8EYpxYc/fs79QdoIUSOkSRcCCEAbl+AGY3hxG+gt4aW30GriWBlk+7q/5y/xYS1au/K6OalqFnUMweDFUII8TSNy/gyrGExAD5YdYyjV17wQm0p7PNBt6VQZQCgwF/jYNVgmU9ciBwkSbgQQoRtUa//jjoOjt7Qew1U7vvY1a9HJzBkwSFMZoU2wf70rVUo52IVQgiRYUNfLkaDkt4kJZsZNO8Ad2KTtA7JMhisoMXX0Pxr0Bng8AKY0wZib2kdmRAvBEnChRAvLkWBXT/CvHZqpVj/Sur13wHVH7tJUrKZwfMPcjMmkZK+znzarpwUYhNCCAul1+v4tnMwgf8Wahu6SP0BVfyr6gDovhRsXSB8178/SJ/SOioh8jxJwoUQLyZjPKwcCBveB8UMFbqpFdBd8z9xswl/nODApTs421kxrYcUYhNCCEvnam/NtB4h2Fnr2XH2Jt9tPKN1SJalaAPo/xfkK6QWbJvRCM7+pXVUQuRpkoQLIV4896/Br83gyGJ1GF7TL6DNFHUalydYeegKs3dfAmBi52AKeTrmRLRCCCGeUyk/Fz5vVx6AH7ecY+OJ6xpHZGG8SkD/zRBQU50ZZEFH+OcnKdgmRDaRJFwI8WKJOgnTG8LVQ2DvDq+uguqD4ClDyk9cjWbUiqMADH25KA1KpV8xXQghhGVqUzE/vWsWAuDtxaFcuBmrbUCWxtFD/T8xuLs6QmzdCPjjHTAZtY5MiDxHknAhxIvj/DaY0QTuXQb3Iurwu6A6T93sXpyRQfMOkGA0U7e4F281LJ4DwQohhMhqo5uXonJgPu4nJjNo7gHikpK1DsmyWNlC68nQ6CNAB/tnwPwOEH9X68iEyFMkCRdCvBhCF8K89pB4DwpWVxNwjyJP3cxsVhi+JJTw23EUyGfPpC7BGPRSiE0IIXIjGys9k7tXwsvZltPX7/O/5UdRZMh1Wjod1HoLuswHawc4v1UdQXYrTOvIhMgzJAkXQuRtigJbv4BVg8BshDJt4dXfwME9Q5v/sPkcm09FYWulZ1qPENwc0p83XAghRO7g42LH5G6VsNLr+P3wVX7deVHrkCxTyRbQ909wyQ+3zsL0BnBhh9ZRCZEnSBIuhMi7kpPgtyGw9VP1ca23oP3MpxZgS7HldBQTN6lVdD9pU5ay+V2zK1IhhBA5qGqQO6OblwLg07Un2XvhtsYRWSi/8jBgM+QPUafynNsGDs7ROiohcj1JwoUQeVPCPfU6ttD5oNNDi2/Va9z0GfvaC78Vx7BFoSgKdK8WQMfKBbM5YCGEEDmpT61CtKrgT7JZYciCg0RFJ2gdkmVy9oXef0CZdmBOht/fhA0fgNmkdWRC5FqShAsh8p67l9UCbBe2gbUjdF0MVfplePP4JBOD5h3gXryR4IJujGlVOhuDFUIIoQWdTscX7ctRwseZG/cTGTz/IEnJZq3DskzW9tBhJtT9n/p41w+wqDsk3tc2LiFyKUnChRB5y9VQ9bq1GyfByRf6roPijTO8uaIovL/qKCcio/FwtGFqj0rYWhmyL14hhBCacbCxYlrPEJxtrdh/6Q6frj2pdUiWS6eD+qOg/Qww2MKZdTCzGdy/pnVkQuQ6koQLIfIM3dkN8GtziLkO3qXVCuh+FTK1j3n/hLPiYAR6HfzQrSJ+rvbZFK0QQghLEOTpyLedgwGYtesiv4VGaBuQpSvXAfqsBUdvuH4UZjSCm+e0jkqIXEWScCFEnlDoxiYMS3uAMRYK14O+68Etc9dxHwy/w0erjwMwsmlJahbxzIZIhRBCWJpGpX14o35RAEYuP8LJyGiNI7JwBSpDvw3gXhjuhsPMxhBxQOuohMg1JAkXQuRuZjP6TeOocGU2OsUMwd2h+zKwy1wl8xv3Exk87yBGk0Kzsr68VqdwNgUshBDCEg1vVJzaxTxJMJpT64KIJ3APgr4bwC8Y4m7BrFZw7i+toxIiV5AkXAiRexkTYFkfDHt+BMBU53/QejIYrDO1m2STmTcXHuRadAJFvBz5qmMFdDpddkQshBDCQhn0Or7vUpH8bvZcuhXH24tDMZsVrcOybE5e0HsNFK6vjkRb0BkOL9Y6KiEsniThQojcKfYWzHkFTqxC0VtzIHAg5trvqoVjMunLP0+z5/xtHG0M/NQzBCdbq2wIWAghhKXL52jDtB4h2Fjp2XQqislb5Frnp7J1hm5LoGwHdQqzla+p1dOFEI8lSbgQIve5FQYzGsLlf8DWFVPXJVxxr/VMu/rjSCQ/bz8PwFcdK1DU2zkrIxVCCJHLlCvgyietywLw7V9n2Ho6SuOIcgErG2j3C1Qfoj7e8AH8+T6YZco3IdIjSbgQIneJOKBWYr19HlwDoN8GlEK1n2lXZ6/f571lhwEYWKcwzcv5ZWWkQgghcqlOVQrStWoAigJvLQrl8u04rUOyfHo9NJkAjT5SH+/+EVYNApNcWy/EwyQJF0LkHhe2w+xX1AIwfsHqFGTeJZ9pV/cTjAycd4C4JBM1CnvwXpMSWRurEEKIXG3cK6WpUMCVe/FGBs07QILRpHVIlk+ng1pvQZtpoDPAkcXqdeKJMVpHJoRFkSRcCJE7nPoD5nWApBgIqqMWgnH2eaZdKYrCe0uPcP5GLH6udvzQrSJWBvk6FEII8R9bKwNTe4Tg7mjD8avRfLDqGIoihdoyJLgrdFsM1g4Qtglmt4LYm1pHJYTFkL86hRCW7/AiWNwTTIlQogV0W6oWgnlGv+w4z/rj17A26JjSvRKeTrZZGKwQQoi8wt/Nnh+7VkSvg2UHrrBo32WtQ8o9ijWCXqvB3h2uHoQZjeHORa2jEsIiSBIuhLBs//wEKweCYoIKXaHTHLC2e+bd7Tl/iy/WnwZgTKsyVAzIl1WRCiGEyINqFvXk3X8vWRr723GOXLmrbUC5SYHK0G+DWsPldpiaiF87qnVUQmhOknAhhGVSFNj6BawboT6uNghaTwHDs08fFhWdwBsLDmEyK7StmJ8e1QKyKFghhBB52aA6RWhYyockk5nX5x3kblyS1iHlHp7F1ETcuwzEXIdfm8OFHVpHJYSmJAkXQlgesxn+HA1bP1Uf1xsFTT9XK68+I6PJzJAFB7kZk0gJH2cmtC2L7hnmFBdCCPHi0et1fNOpAoEeDkTcjWfY4lDMZrk+PMNc/KDPWgisBYnRMK8dHF+ldVRCaEaScCGEZTElw+9vwJ4p6uOmn0O9/6kVV5/Dl+tPse/iHZxtrZjWMwQHm2fvURdCCPHicbW3Zmr3EGyt9Gw9fYMft5zTOqTcxd4NeqyAUq3AlARLe8PeX7SOSghNSBIuhLAcyYmwtBeEzlenNmkzFaq//ty7XXs0kl92XADgq44VCPJ0fO59CiGEePGU9ndhQttyAHz31xm2nbmhcUS5jLUddJwNlfsCCqx9FzZ/ol6CJsQLRJJwIYRlSIyB+R3h1Bow2KgF2IK7Pfduw27E8N7SwwAMrFOYpmV9n3ufQgghXlwdQgrQtWoAigJvLTrElTtxWoeUu+gN0OJbqP+++nj7V7D+f5KIixeKJOFCCO3F3YY5reHCNrB2hO5LoVTL595tbGIyg+YeIDbJRNUgd977t7qtEEII8TzGtipNufyu3I0zMmT+QRKTTVqHlLvodFB3hJqMA/wzDdYMU2vCCPECkCRcCKGt+9dgVguI2A/2+dQ5RQvXe+7dKorCqBVHORsVg5ezLT92q4iVQb7yhBBCPD87awNTulfC1d6aw1fu8fGaE1qHlDtV6afOfKLTw4FZ8NtgMMsPGiLvk79IhRDauX0BZjaBqBPg5Au910KBkCzZ9dw9l/j98FUMeh2Tu1XC2/nZ5xYXQgghHlbQ3YGJXYLR6WDennBWHLyidUi5U8Xu0O4XtRbM4YWwvD+YjFpHJUS2kiRcCKGN6ydgZlO4cxHyFYK+68GndJbs+mD4ndReiVHNSlI1yD1L9iuEEEI8qH4Jb4a+XAyA0SuPcupatMYR5VLlOkCn2aC3huMr1MrpyYlaRyVEtpEkXAiR867sh1+bQcw18C4Nff8E96As2fWtmESGzD+I0aTQvJwv/V7Kmv0KIYQQ6RnaoBh1inuRYDTz+ryDRCdIL+4zKdUKuiwAg61apHVRdzDGax2VENlCknAhRM46vxVmvwIJdyF/Zej9BzhnTcVyk1nhrUWhRN5LoLCXI1+0L4/uOecXF0IIIZ7EoNcxsXMw+d3suXAzlveWHkaRSt/Ppnhj6LYYrOzh3EZY0AmSYrWOSogsl+1J+JQpUwgKCsLOzo6QkBB27NjxxPW3bdtGSEgIdnZ2FC5cmGnTpmV3iEKInHJ6nToNmTFWLb726m/gkHVDxSdtPsff525ib21gWo8QnO2ss2zfQgghxOO4O9owpXslbAx6/jx+nZ+3n9c6pNyrSH3osRxsnODCdpjXHhJkmL/IW7I1CV+8eDHDhg3j/fff59ChQ9SuXZtmzZoRHh6e7voXLlygefPm1K5dm0OHDjF69GiGDh3K8uXLszNMIUROOPUHLO4JpiQo2RK6LQFbpyzb/bE7OqZuuwDA5+3LUdzHOcv2LYQQQjxNhYJujGml1jb5Yv0p/rlwW+OIcrFCtaDnKrB1hfDdMLctxN/VOiohsky2JuHffvst/fr1o3///pQqVYqJEydSsGBBpk6dmu7606ZNIyAggIkTJ1KqVCn69+9P3759+frrr7MzTCFEdjv1ByzpBWYjlGkHHWeDlW2W7T78dhzzzqpfZ71qBNI6OH+W7VsIIYTIqO7VAmhXMT9mBYYtOcK9JK0jysUKVoFev6vTl0bsh9mtIPaW1lEJkSWssmvHSUlJHDhwgP/9739pljdu3Jhdu3alu83u3btp3LhxmmVNmjRhxowZGI1GrK0fHVqamJhIYuJ/1ROjo9XhKkajEaPRsgtjpMRn6XG+yKSNnp/u9FoMK/qiMydjLt0W0ytTwKyoCXkWSDCaeGNhKPEmHRXyuzCicTFpLwslnyfLJ22UO0g7WbZxLUty/Oo9Tl+PYdYZA60TEnHQOqjcyqsM9PgNqwXt0V07gjKrBcndloOTd5YdQj5Pli+3tFFm4su2JPzmzZuYTCZ8fHzSLPfx8eHatWvpbnPt2rV0109OTubmzZv4+fk9ss1nn33G+PHjH1m+YcMGHBxyx1fexo0btQ5BPIW00bPxu7ufyhcmo8PElXzVOWjzCsr6DVl6jIVhek5G6XG0Umjnc5u/NqzP0v2LrCefJ8snbZQ7SDtZrg5+8M1NA+fv63hr5hbaFjJrHVKu5hTwDrXOfo7djZMkTGvArqIjSbDJ2ulH5fNk+Sy9jeLi4jK8brYl4SkerkysKMoTqxWnt356y1OMGjWKt99+O/VxdHQ0BQsWpHHjxri4uDxr2DnCaDSyceNGGjVqlG4vv9CetNGz051ag2HlFHSYMJdpj88rk2mmz9qvnKUHrrBn9wn0OuhV3EzHltJOlkw+T5ZP2ih3kHbKHXyKX2XokmNsjdTTrk4wzcpmzUwgL6zbL6PMb4dz9BUaR0wkucdKcC343LuVz5Plyy1tlDIiOyOyLQn39PTEYDA80usdFRX1SG93Cl9f33TXt7KywsPDI91tbG1tsbV99NpSa2tri26kB+WmWF9U0kaZdOI3WNkfzMlQriP6NtPQG7L26+ZYxD3GrTkFwLAGRQmMPSXtlEtIO1k+aaPcQdrJsjUr50+Dv4+w6aqeUSuPU6ZAPop4ZV1B0heOTwnouw5mt0J35yLWc19RZ1nxKJIlu5fPk+Wz9DbKTGzZVpjNxsaGkJCQR4YNbNy4kZo1a6a7TY0aNR5Zf8OGDVSuXNmiX3AhxANO/AZL+/ybgHeCtj9BFifg9+KMvD7/AEnJZhqU9GZg7aAs3b8QQgiRFVoEmKlaKB+xSSYGzT1AbGKy1iHlbm4B0GcdeBSDe5fh1+Zw44zWUQmRadlaHf3tt99m+vTpzJw5k5MnTzJ8+HDCw8MZNGgQoA4lf/XVV1PXHzRoEJcuXeLtt9/m5MmTzJw5kxkzZvDuu+9mZ5hCiKxyfJWagCsmKN8Z2k4DvSFLD2E2KwxfEsrl2/EEuDvwbadg9PrHX+IihBBCaMWgg4mdyuPtbMvZqBhGrTiaeqmleEYu/tBnLXiXhphrMKs5XD+udVRCZEq2JuGdO3dm4sSJfPTRRwQHB7N9+3bWrl1LYGAgAJGRkWnmDA8KCmLt2rVs3bqV4OBgPv74Y77//nvat2+fnWEKIbLC8ZWwrO+/CXgXaDM1yxNwgMlbzrH5VBS2Vnqm9qiEq4OMkhFCCGG5vJxtmdK9ElZ6Hb8fvsrsXRe1Din3c/KGXmvAtzzE3oBZLSDysNZRCZFh2V6YbfDgwQwePDjd52bNmvXIsrp163Lw4MFsjkoIkaWOrYDl/dUEvEJXaD05WxLw7Wdu8O1f6rCzj9uUpYy/a5YfQwghhMhqlQu5M6p5KT5ec4JP/jhJuQKuhARmbXXvF46jB/RaDfPaq/OIz2kDvdeATxmtIxPiqbK1J1wI8QI4tvyBBLxbtiXgV+7E8daiQygKdK1akE6Vn78iqhBCCJFT+tYqRMvyfiSbFQbPP8iN+4lah5T72btBz5WQPwTib8Oc1nKNuMgVJAkXQjy7Y8th+QA1AQ/uDq1/zJYEPDHZxJD5B7kTZ6RcflfGtpJfuYUQQuQuOp2OL9qXp6i3E9ejE3lz4UGSTTJ/+HOzc4Eey/8bmj67FdwK0zoqIZ5IknAhxLM5uuy/HvDgHvDKD9mSgAN8tPoEh6/cw83BmindK2FnnT3HEUIIIbKTo60V03pUwtHGwJ7zt/l6g/TaZgn7fNBz1X/F2ma/AncuaR2VEI8lSbgQIvOOLoMVA0AxZ3sCvvzAFeb/E45OBxM7B1PQ3SFbjiOEEELkhKLeznzZoQIA07aF8efxaxpHlEc4eqjzhnsWh+grao/4vStaRyVEuiQJF0JkzpGl/yXgFVMS8Oz5KjlxNZrRK48C8FaDYtQr4Z0txxFCCCFyUovyfvR7KQiAd5cc5sLNWI0jyiOcvOHV3yFfENy9pPaI35cfOYTlkSRcCJFxR5bAytf+TcB7QqvsS8DvxRt5ff4BEpPN1CvhxdCXi2XLcYQQQggt/K9ZSaoUysf9xGQGzT1AXFKy1iHlDS5+atV0twC4HaYm4jE3tI5KiDQkCRdCZMyRpbByoJqAV3oVWn2fbQm42azwzpJQLt2Ko0A+eyZ2Dkav12XLsYQQQggtWBv0TO5WCS9nW05fv8/oFUdRFEXrsPIGt4JqIu6SH26eVqumx93WOiohUkkSLoR4upNrHkjAe0HLSdmWgANM3RbGXyejsLHSM61HCG4ONtl2LCGEEEIr3i52TO5WCYNex6rQq8zdI8XEsky+Qmoi7uQDUcdhbhuIv6txUEKoJAkXQjxZ2GZY1ue/ecBbTszWBPzvszf5ZsNpAD5uXYay+V2z7VhCCCGE1qoGuTOqWUkAPl5zgoPhdzSOKA/xKKJeI+7gCZGHYV57SIjWOiohJAkXQjxB+D+wqDuYkqDUK9lahA3g6t14hi46hFmBzpUL0rlKQLYdSwghhLAU/V4Konk5X4wmhcHzDnIzJlHrkPIO75Jq1XT7fBCxHxZ0giQphCe0JUm4ECJ9kYdhfkcwxkHRhtB+Ohissu1wickmXp9/kNuxSZTN78L41mWy7VhCCCGEJdHpdHzZoQJFvBy5Fp3A0IWHSDaZtQ4r7/Atq84jbusK4bthYRcwxmsdlXiBSRIuhHjUjTMwty0k3oOAmtBpLljZZushP15zgsOX7+Jqb83U7iHYWWfPvONCCCGEJXKyteKnniE42BjYFXaLbzae0TqkvMU/GHquABsnuLBdHelnTNA6KvGCkiRcCJHWnUv/VhG9BX7B0G0R2Dhk6yFXHLzCvD3h6HQwsUswBd2z93hCCCGEJSrq7cyXHcoDMHVrGBuOyxzXWapAZei+DKwdIGwTLO2tXnInRA6TJFwI8Z/oSJjzCty/Cl4loccKsMvewmgnI6MZvfIoAENfLkb9Et7ZejwhhBDCkrUs70/fWkEAvLPkMBduyvXLWSqwBnRdBFZ2cGYdhlUD0SkmraMSLxhJwoUQqthb6vQddy6q03r0XAWOHtl6yHvxRgbNO0CC0Uzd4l681aBYth5PCCGEyA1GNS9JlUL5uJ+YzOvzDhCXlKx1SHlL4brQZT4YbNCfWk2lSz+BWRJxkXMkCRdCqNN1zGsHN06Bs59aRdTFL1sPaTYrvLPkMJduxZHfzZ6JnYPR63XZekwhhBAiN7A26JncrRKeTracunaf91ceQ1EUrcPKW4o2hE5zUPRWFLizB8Mfw8AsxfBEzpAkXIgXXVKcWiU0MhQcPNQEPF+hbD/stO1h/HXyOjZWeqb1CCGfo022H1MIIYTILbxd7JjcrSIGvY6VhyKY90+41iHlPSWaYWr7C2b06I8shLXvgPzYIXKAJOFCvMiSk2BJT7i0E2xd1GvAvUpk+2F3nrvJ13+eBuCjV8pQrkD2XncuhBBC5EbVCnvwv6YlAfho9XEOhd/ROKK8RynZioOBA1HQwf6ZsPUzrUMSLwBJwoV4UZmSYUV/OPeXWiW0+1J1+o5sFnkvnqELD2FWoFPlAnSpGpDtxxRCCCFyq/61g2hezhejSWHw/IPciknUOqQ8J8K9BuamX6gPtn0B//ysbUAiz5MkXIgXkdkMq4fCid/AYAOd50FA9Ww/bFKyWf0DIjaJMv4ufNS6bLYfUwghhMjNdDodX3aoQGEvRyLvJfDWolBMZhkyndXMIX2h3mj1wboRcHSZtgGJPE2ScCFeNIoCf46C0PmgM0CHmVC0QY4c+qM1xzkUfhcXOyum9QjBztqQI8cVQgghcjMnWyt+6hGCg42Bv8/d5OsNp7UOKW+qOwKqDAAUWDkIzm3SOiKRR0kSLsSLZssE+Geaer/NFCjVKkcOu2T/ZebtCUeng0ldKlLQ3SFHjiuEEELkBcV8nPmifXkApm4NY93RSI0jyoN0Omj2JZRpB2YjLO4JVw5oHZXIgyQJF+JFsnMSbP9Kvd/8a6jQJUcOe+TKXT5YdQyA4Q2LU7+kd44cVwghhMhLWlXwZ0DtIADeXXqYs9fvaxxRHqTXQ9ufoMjLYIyF+R3gxhmtoxJ5jCThQrwo9s+EjWPU+w3GQtUBOXLYmzGJDJp7gKRkMw1L+fBG/aI5clwhhBAiLxrZtCQ1i3gQm2TitbkHiE4wah1S3mNlA53mQv4QiL8Nc9vCvQitoxJ5iCThQrwIjiyFNW+r9196G2q/nSOHTTaZeWPBQa7eS6CwpyPfdq6AXq/LkWMLIYQQeZGVQc8PXSuS382eCzdjeXtxKGYp1Jb1bJ2g21LwKAbRV9REPO621lGJPEKScCHyunN/wapBgKIWG2kwJscO/dm6U+w5fxtHGwM/vxqCi511jh1bCCGEyKs8nGyZ1iMEGys9f52M4vvNZ7UOKW9y9ICeK8HZH26ehgWdIClW66hEHiBJuBB52dVDsPhVMCdDuY5qsRFdzvRE/xYawYy/LwDwTacKFPV2zpHjCiGEEC+CcgVcmdBGnepz4l9n2XTyusYR5VFuBdVE3M4NruxTi7UlJ2kdlcjlJAkXIq+6fQHmd1SLigTVhdZT1GIjOeDE1WhGLj8CwOB6RWha1i9HjiuEEEK8SDpWLkjP6oEADFscyoWb0kubLbxLQvelYO0AYZvgt8FgNmsdlcjFJAkXIi+KvQXz2kPsDfApB53nqUVGcsDduCQGzttPgtFMneJevNO4RI4cVwghhHgRfdiyNJUD83E/IZnX5uwnNjFZ65DypoJVodMc0FvB0aXw5yhQ5Fp88WwkCRcir0mKU69Zuh0GrgHqL7d2LjlyaJNZYeiiUC7fjifA3YHvuwRjkEJsQgghRLaxsdIzpUclvJ1tORv1//buOzqqan3j+HcmmVRSCAESWijSm5DQFRAFuQKiKB0EVMSCiuV69Xct4LWgV702LBQBkaYCoqIIKE1DhyC9ht4D6aTO+f0xGo0UE8jMmZk8n7VYa2c4M/tJXk7Cmzln73T++eVmDDWHzlG7M9z2oWO85iNY+aa5ecRjqQkX8Sb5efDl3XB0PQSWhUFzINR1l4K/tXgXK3afJsBm5aNBsYQHuebddxERkdKsQkgAHw6KxeZj4bstJ/h4xX6zI3mvJn2g61jH+Kf/wIYppsYRz6QmXMRbGAYseBx2fw++AdB/FpSv47LpF249zril+wB47Y4mNKjkmnffRUREBGJjyvJCj4YAvL5wJyv3nDY5kRdr/QBc/4Rj/O1jsP1rc/OIx1ETLuItVvwXNk4FixXumATVWrts6j0n03ji880A3HNdDXpeW9llc4uIiIjDwFbV6BNXBbsBD8/cxOGzmWZH8l6dnoPmd4Fhhzn3QOIKsxOJB1ETLuINNk6DpS87xrf8F+p3d9nUqVm5jJi2gYycfFrXjOCZf9Rz2dwiIiLyB4vFwos9G9G0ShjJmY6fz+dz8s2O5Z0sFuj2P6jXHfJzYOYAOL7Z7FTiIdSEi3i63Yvgm0cd4+ufgBb3umxqu93g8dmb2X8mg+iwAN4f0BxfH31bERERMUuAzYcPB8VSLtiP7cdT+b95W7RQm7P4+DquPqx+PeSkOXamSdpndirxAPrfsognO7oBvhgCRj407e+4NMqF3l+6lyU7TuLn61iILbKMv0vnFxERkQtVCg/k/QHN8bFamLfpKFPiD5gdyXvZAqDfDIhq4tgadtrtkK778eXy1ISLeKqkfTC9D+RmQq0b4db3HJdGuchPO0/yvyW7AXipZyOaVg132dwiIiJyeW1qlSu4ReylBTtYvT/J5EReLCDUsSNN2eqQfBBm9oPc82anEjemJlzEE6WfdlzylHkGoptCn6ngY3PZ9IlnMnh0VgKG8dsiMC2qumxuERERKRrHYqmVyLcbjJyxkeMpagydpkwFGPglBIQ7toqdex/Y7WanEjelJlzE02Snw4zecC4RwmNgwBfgH+Ky6TOy8xgxbT1pWXmFtkMRERER92KxWBjbqwn1o0M5k57D/Z9tJDtPC7U5TWRtx6XpPn6w42tY8rzZicRNqQkX8ST5ufDFUDi2CQIjYNBcCKnosukNw+CpOb+y+2Q65UP8+WBgc/x89W1ERETEXQX6+fDxoFjCAm1sPpzMC/O3mR3Ju1VvBz0/cIzj34N1E83NI25J/3sW8RSGAd+Ogr2LwTcQBnwOkde4NML4FftZ8OtxfK0WPhzYnIqhAS6dX0RERIqvWrkg3u3fDIsFZq07zIw1h8yO5N2a9IYbnnWMv/unYycbkT9REy7iKZa9Cps+A4sVek+Gqi1cOv3Pe87w2sKdALzQowFx1SNcOr+IiIhcuQ51yvNkl7oAvPD1VjYcPGdyIi/X/km4diAYdsdVjNpDXP5ETbiIJ1g/GZa/5hh3ewvq/sOl0x84k8FDMzZiN+DO2CoMah3j0vlFRETk6j3YsRZdG0aRm29w/2cbtFCbM1ks0P1tqNEBcjNgRl9IOWp2KnETasJF3N2u72HB445xh39B3DCXTp+alcu9n64n5XwuTauG89JtjbC4cCs0ERERKRkWi4U3+jSlbsUQTqdlc9+nGzifo4XanMbXD/p8CuXrQdpxmNEHslLNTiVuQE24iDs7ugG+GOa4lKnZIOj4jEunz7cbPDpzE3tPpVMx1J8Jg2MJsPm4NIOIiIiUnDL+vkwcEkfZIBtbjqbw1JxfMQzD7FjeKzAcBn4BwRXg5FbHpen5uWanEpOpCRdxVylHYGZ/yDsP13R2XNLk4negX1+4k6W7TuPva2XCXXFU0EJsIiIiHq9qRBAfDorF12rhm83HGLd0r9mRvFt4NRgwG2xBsO9H+O5Jx4K7UmqpCRdxR9npMKMfpJ+ECg0dC7H52FwaYc6GI3y8Yj8Ar9/ZhCZVwl06v4iIiDhP65rlGNOzIQBvLNrND9tOmJzIy1VuDndMBCywYQr88o7ZicREasJF3I09H+YOh5NbILg8DJgF/iEujbDx0DmembsFgIduqEXPayu7dH4RERFxvoGtYrirjWOx1cdmJ7DjuO5Xdqp63aDrq47xkhdg2zxz84hp1ISLuJslL8Cu78DHH/rNdFzC5ELHU85z36cbyMm307lBRZ7oXNel84uIiIjrPNe9AW1rlSMzJ597p64nKT3b7EjerfUD0Op+x3juCDi0xtw8Ygo14SLuZMNUiH/PMb7tA5fvBX4+J5/hn67nTHo2dSuG8L++12K1aiV0ERERb2XzsfLBwObElAviaPJ5HvhsIzl5drNjebebX4G6t0B+NszqD2f3m51IXExNuIi7SFzxx1ZkHZ+Bxne6dHrDMPjnl5vZejSViGA/Jg6Jo4y/r0sziIiIiOuFB/kx8S7Hz/21B87ywtdbtWK6M1l9HPeHR18LmUkwvTdknjU7lbiQmnARd5C0D2YPBnseNLrTsR+4i73/016+/fU4vlYLHwxsTtWIIJdnEBEREXPUrhjCe/2bYbHAzLWHmRp/wOxI3s0v2LFielhVSNoLswZCnm4FKC3UhIuYLfMszOgDWclQpQX0HOfyrcgWbj3Bm4t3A/Biz0a0rlnOpfOLiIiI+W6oV4Gnu9YD4D8LdvDznjMmJ/JyIVGOPcT9Q+FQPHz1INh1K0BpoCZcxEz5ufD5XY7fgIZVhX4zwObavbh3HE/l8c8TABjSJoYBrVy7EJyIiIi4j/va16RXs8rk2w0enL6BxDMZZkfybhXqQ59PweoLW7+EpS+bnUhcQE24iFkMw3EP+IGV4FfGcUlSmQoujXAmPZt7p64nMyefdteU47nuDVw6v4iIiLgXi8XCK70a06xaOKlZedw7dR2pWblmx/JutW6A7m87xivfgI3TTI0jzqcmXMQsq8bBxk/BYoU7P4GKDV06fU6enQc/28jR5PNULxfEuAHN8fXRtwQREZHSLsDmw8eDYokKDWDf6QwenrGJfLsWanOq5oPh+icd429HQeJKU+OIc+l/3CJm2PU9LHrWMe7yMtS52aXTG4bB8/O3svbAWUL8fZk4JI7wID+XZhARERH3VSE0gAl3xRFgs7J892nGfr/D7Ejer9Oz0OgOx0K9n98F5w6YnUicRE24iKud2AJf3gMYEDsMWj/g8ghT4g8wa91hLBZ4t38zrqkQ4vIMIiIi4t4aVwnjv3c2BWDCykS+3HDE5ERezmJxLNAbfS2cPwsz+0N2mtmpxAnUhIu4UtoJmNEPcjOgRge45b8uXwl95Z7T/Ofb7QA884963FDPtfehi4iIiOfo0bQSD3e6BoD/m7uFDQe1n7VT2QIdC/WWqQintsO8+7ViuhdSEy7iKrnnHb/RTD0C5WpDn6ngY3NphP2n03lo+kbsBvRqXpnh19d06fwiIiLieR67qQ43N6xITr6dEdM2ciz5vNmRvFtYZeg7HXz8YOe3sOxVsxNJCVMTLuIKdrvjN5nHNkJgWcdK6IFlXRoh5Xwu9366ntSsPJpVC+eV2xtjcfG78CIiIuJ5rFYLb/W5lnpRIZxJz2b4p+vJzMkzO5Z3q9oCerzjGK94HbbNMzePlCg14SKusOxV2P4VWG3Q9zMoV8ul0+fbDR6ZuYn9pzOIDgvg48GxBNh8XJpBREREPFewvy8T7oojItiPbcdS+ecXv2IYWjHdqa4dAG1GOsbzHoDjm83NIyVGTbiIs/36ueM3mAA93obq17k8wqvf7WD57tME2KxMuCuOCiEBLs8gIiIinq1qRBAfDYrF5mNhwZbjvPvjXrMjeb/OL0KtGyHvPMwcAOmnzE4kJUBNuIgzHVoD8x9yjNs9Cs0GuTzCtNUHmfhzIgBv9G5Ko8phLs8gIiIi3qFljQj+07MRAP9bspuvNh01OZGXs/rAnZ9AuWsc6wrNHgx5OWankqukJlzEWc4dhFkDID8H6nWHG0e7PMKPO07ywvytADzeuQ7dm1RyeQYRERHxLv1aVmP49TUA+OeXm4nfd8bkRF4uMBz6zwL/MDi8GhY8DroVwKOpCRdxhpxMmDUQMs9AVBPoNR6srj3dfj2SzMgZm7Ab0CeuSsH2IiIiIiJX65l/1OeWxlHk5huMmLaBPSe1n7VTRdaGOyeBxQqbpsHa8WYnkqugJlykpBkGfD0STm6B4PLQfyb4Bbs0wuGzmdw9ZT3nc/O5vnYkL2sldBERESlBv6+YHhtTlrSsPIZOXseptCyzY3m32p0d94gDLHwG9i8zNY5cOTXhIiUt/j3YOgesvtB7KoRVcen0KZm5DJuyjjPp2dSLCuGDgc2x+ehUFxERkZIVYPNhwl1x1IgM5mjyee6eso6MbG1d5lRtRkLT/mDkw+dD4Ox+sxPJFdD/zEVK0r6fYMkLjvHNr0L1di6dPjsvn/umrWfvqXSiQgOYPKwFIQE2l2YQERGR0iMi2I8pw1oQEezH1qOpPDxzE3n5drNjeS+LBbq/DZXjICsZZvaHrFSzU0kxqQkXKSnnDsCXd4Nhh2sHQsvhLp3eMAz+9eWvrEk8Sxl/XyYPa0F0WKBLM4iIiEjpE1MumIlD4vD3tfLTzlOM/mab9hB3JlsA9JsOIdFweifMHQ72fLNTSTGoCRcpCTkZjoXYzp+DSs2h21uO31S60JuLdvNVwjF8rRY+GNic+tGhLp1fRERESq/m1cryTr9rsVjgs9WHGL9Cl0k7VUiUoxH38YfdC+Gnl8xOJMWgJlzkahkGfP0wnNzqWIit72eO31C60My1h3h/6V4AXunVmPZ1yrt0fhEREZGujaJ5tlsDAF79fiffbD5mciIvVzkWer7vGP/8Fmz50tw8UmRqwkWu1p8XYuvzKYRVdun0y3ad4tmvHHuBP9LpGvrEVXXp/CIiIiK/u+e6GgxtWx2AJz7fzNrEs+YG8nZN+kC7UY7x/Ifg2CZT40jRqAkXuRp/Xoit61iIaevS6bcdS+Gh6RvJtxv0alaZxzrXcen8IiIiIn/1XPcGdGlQkZx8O8M/Xc++0+lmR/JuNz4PtW+GvCyYOQDSTpqdSP6GmnCRK1VoIbZB0OJel05/7PetQHLyaVurHGPvaKK9wEVERMR0PlYL7/RrxrVVw0k5n8vQyWs5k55tdizvZfWBOyZCZF1IOwazB0Kevt7uTE24yJX480JslWOh25suXYgtNSuXYZPXcTI1mzoVy/DhoFj8fHU6i4iIiHsI9PNh4pA4qkUEcfjsee6Zup7zOVrB22kCQqH/TAgIhyPr4NvHHOsWiVty2v/az507x+DBgwkLCyMsLIzBgweTnJx82ecMHToUi8VS6E/r1q2dFVHkyhgGzB/5x0Jsfaa5dCG2nDw7D3y2gV0n06gQ4s/kYS0JC9Re4CIiIuJeIsv4M2VYC8KDbGw+nMwjszaRb1dj6DTlakHvKWDxgYTpsOYjsxPJJTitCR8wYAAJCQksXLiQhQsXkpCQwODBg//2eV27duX48eMFf7777jtnRRS5MvHvwra5pizEZhgGz8zdwi97kwjy8+GToS2oHK69wEVERMQ91Sxfhgl3xeHna2Xx9pP859vt2kPcmWrdAF1+265s0bNwaLW5eeSinNKE79ixg4ULFzJx4kTatGlDmzZtmDBhAt9++y27du267HP9/f2Jiooq+BMREeGMiCJXZt9PsGS0Y2zCQmxvL9nDnI1H8LFaGDewOY0qh7l0fhEREZHialE9grf6NAVgSvwBJv2caHIiL9f6AWh0J9jz4PMhWqjNDfk640VXrVpFWFgYrVq1KnisdevWhIWFER8fT926dS/53GXLllGhQgXCw8Pp0KEDL7/8MhUqVLjk8dnZ2WRn/7HwQGpqKgC5ubnk5uaWwGfjPL/nc/ecpVmhGp07gO8Xw7AYduxNBpB/7RBwYe3mbDzKOz/uAWB09/pcV7Os/u38RueSZ1Cd3J9q5BlUJ8+gOhV2c/3yPHVzbV7/YQ8vf7eDiiF+dG1Y0exY3lunf7yB78mtWE7vxP7FEPIHzAUfz7x90VNqVJx8FsMJ14O88sorTJkyhd27dxd6vE6dOgwbNoxnnnnmos+bPXs2ZcqUISYmhsTERJ577jny8vLYsGED/v7+F33O6NGjGTNmzAWPz5gxg6CgoKv/ZEQAn/xsrt/9ImFZhzkXVJOfa/8fdqufy+bflWzho51W7IaFmyrZ6RFjd9ncIiIiIiXBMOCLRCu/nLRisxg81DCfGiFmp/JewVnH6bDrBWz2LPZW+AfbKvc3O5JXy8zMZMCAAaSkpBAaGnrZY4v1TvilGt4/W7duHcBFt0oyDOOyWyj17du3YNyoUSPi4uKIiYlhwYIF9OrV66LPeeaZZ3j88ccLPk5NTaVq1ap06dLlbz95s+Xm5rJ48WI6d+6MzeaZv5nydrm5uSxetIiu2d/gm3UYI7g8Ze7+iq6hlVyWYdeJNP49cR12I4/ujaN4887GWK3aiuzPdC55BtXJ/alGnkF18gyq08XdnG/nwZkJLN11hqn7A/nivlbElDPvjTNvr5NlZxTMGcI1p76n+nV3YtTvaXakYvOUGv1+RXZRFKsJHzlyJP369bvsMdWrV+fXX3/l5MkL7z04ffo0FSsW/bKT6OhoYmJi2LNnzyWP8ff3v+i75Dabza2L9GeelLU0uubUd/ge+xqsvlj6fIqtXIzL5j6WfJ7hn20iPTuPljUieLPvtfj7+rhsfk+jc8kzqE7uTzXyDKqTZ1CdCrPZYNzAWPp+vJotR1O4d9pGvnygLZFlLn7Vq+tyeWmdGt8GJx6FX97B99tHIboxlL/0rcHuzN1rVJxsxWrCIyMjiYyM/Nvj2rRpQ0pKCmvXrqVly5YArFmzhpSUFNq2LfpCVklJSRw+fJjo6OjixBQpMZb9y2hw7HPHBy5eiO1MejaDJq7heEoWNcsHM35wrBpwERER8XhBfr5MGhrH7ePiOZCUyeBJa5k1vDVhQe7bYHm0Ts/D0Y1wYCXMHgTDfwJ/3QdgJqesjl6/fn26du3K8OHDWb16NatXr2b48OF079690KJs9erVY968eQCkp6fz5JNPsmrVKg4cOMCyZcvo0aMHkZGR3H777c6IKXJ5ZxPxmXcvFgzsTQdCi3tdNnVKZi6DJ61l/5kMKocHMu2eVoQHue4edBERERFnqhASwGf3tiKyjD87jqcybMpaMrLzzI7lnXx84c7JEFIJzuyG+SMdN+iLaZy2T/j06dNp3LgxXbp0oUuXLjRp0oRp06YVOmbXrl2kpKQA4OPjw5YtW+jZsyd16tRhyJAh1KlTh1WrVhESot/UiIvlZMCsgViykjkXVJP8rq/BZdYzKEkZ2XkMm7KWHcdTiSzjz2f3ttJe4CIiIuJ1akQGM+2eloQG+LLxUDL3TVtPVm6+2bG8U5ny0GcqWG2w/StYNc7sRKWaU7YoA4iIiOCzzz677DF/Xpg9MDCQH374wVlxRIrOMODrh+HUNozgCqyt/gidfANcMnVWbj73TVvPxkPJhAXamHZPS2pEBrtkbhERERFXqx8dypS7WzJo4hp+2ZvEwzM38cHA5th8nPZeYelVtSV0fRW+exIWPw+VmkH1dmanKpX0r1vkr9ZNhK1zwOpL/h2fkOUX4ZJpc/PtPDxzE7/sTSLIz4cpw1pQP9q9V/gXERERuVrNq5Vl4l1x+PlaWbz9JP/8YjN2uy6XdooW90KTvmDkwxdDIfW42YlKJTXhIn92dAMs/G0f+5vGYFRt7ZJp7XaDf36xmcXbT+Lna2XikDiaVSvrkrlFREREzNb2mkg+GNAcX6uFrxKO8dz8rYWumpUSYrFA97ehQkPIOOVoxPNzzU5V6qgJF/ld5ln4fCjYc6Fed2jzkEumNQyD5+Zv5auEY/haLXw4sDlta/39LgQiIiIi3uSmBhV5s09TLBaYvuYQry3cZXYk7+QXBH2ngX8oHF4Ni54zO1GpoyZcBMBuh68egJRDULY69BznkoXYDMNg7MKdTF9zCIsF3up7LTfWr+j0eUVERETcUc9rK/PybY0B+Gj5PsYt3WtyIi9Vrhbc/pFjvOZD2PKluXlKGTXhIgDx78DuheDjD30+hcBwl0z7wbJ9fLx8PwCv3N6YW5tWcsm8IiIiIu5qQKtq/PuW+gD894ddTI0/YG4gb1WvG1z3uGP89cNwaoe5eUoRNeEiB36BH//jGN/yOkQ3dcm0U+MP8N8fHJdZPdutPv1bVnPJvCIiIiLubnj7mjzS6RoAXvh6G19uOGJyIi/V6Vmo2RFyM2H2IMhKNTtRqaAmXEq39FPw5d2OFSKb9IPmQ1wy7ZcbjvDC19sAeOTG2tx7fU2XzCsiIiLiKR7rXIdh7aoD8NSXm1m4VSt5lzirD9wxCUKrQNJemP+gY7tecSo14VJ62fNhzj2QfgLK14fub7nkPvDvtxznqS83A3B3uxo8dlNtp88pIiIi4mksFgvPdWtA79gq2A14eOYmlu8+bXYs7xMc6bgd08cPdnwD8e+ancjrqQmX0mvZq5C4AmzB0Gcq+AU7fcrlu0/zyKxN2A3oE1eF57rXx+KCxl9ERETEE1mtFsbe0YRbGkeRm28wYtp61h04a3Ys71MlFrqOdYyXjHb8H1mcRk24lE57lsCK/zrGt74L5es6fcp1B84yYtp6cvMNujWO5tVeTdSAi4iIiPwNH6uFt/s2o0Od8mTl2rl78jq2Hk0xO5b3ibsbmg4Aw+64XTP1mNmJvJaacCl9Uo7A3OGOcdw90PhOp0+59WgKd09eR1aunY51y/O/vtfiY1UDLiIiIlIUfr5WPhoUS8vqEaRl53HXJ2vZeyrN7FjexWKBbm9CxcaQcRo+HwJ5OWan8kpqwqV0ycuBL4bC+bMQfS10fdXpU+49lcZdn6wlLTuPljUi+HBgLH6+OvVEREREiiPQz4dJQ+NoUiWMsxk5DJy4hsNnM82O5V38gqDvpxAQBkfWwqJnzU7kldQJSOmy5AU4ss7xjaXPVPD1d+p0h89mMnDiGs5m5NCkShiThsQR6Ofj1DlFREREvFVIgI2pw1pSu0IZTqZmM3DiGk6mZpkdy7tE1ITbxzvGaz92LNYmJUpNuJQe2+fD6g8c49s+grLVnTrdiZSs334wZFOnYhmmDmtJSIDNqXOKiIiIeLuywX58dm8rqkUEcehsJoMmriEpPdvsWN6lbldo+4hjPP8hOHfQ3DxeRk24lA5J+2D+SMe47SNQ7xanTnf4bCZ9Pl7FobOZxJQL4rN7WlE22M+pc4qIiIiUFhVDA5h+byuiQgPYcyqd/hNWc0rviJesG5+HKi0gK8WxUFt+rtmJvIaacPF+uecdC0tkp0K1No5vKE6UeCaDvr814NUiHA14hdAAp84pIiIiUtpUjQhi+vBWVAz1Z/fJdPp8vIqjyefNjuU9fGxwxyTHbZxH18OPL5qdyGuoCRfv9/1TcHILBEXCnZ84vqE4ya4TafT+aBXHUrKoVT6Yz0e0oWpEkNPmExERESnNapUvwxcj2lKlbCAHkjLp89EqDpzJMDuW9ygbAz1/u50z/l3Y/YO5ebyEmnDxbgkzYOOngAXumAihlZw21dajKfQbv4oz6dnUjw5l9og2RIXpHXARERERZ6pWLojPR7ShZmQwR5PP0+fjVew5qe3LSkz97tDqfsd43v2QctTcPF5ATbh4r5Pb4NvHHeOOz0CtG5w21YaDZ+k/fjXnMnNpWjWcmcNbEVnGuSuvi4iIiIhDpfBAZo9oQ92KIZxKy6bv+NVsO5Zidizv0flFiG7q2OZ3zr2Qn2d2Io+mJly8U3aa4z7wvPNQqxO0/6fTporfe4bBk37bB7x6BJ/d05LwIC3CJiIiIuJK5UP8mXVfaxpXduwj3n/8ajYdOmd2LO/g6w93Tga/EDgUD8vHmp3Io6kJF+9jGPD1I5C0B0IqQa8JYHXOP/WlO08xbMo6MnPyub52JFPv1jZkIiIiImYpG+zH9OGtiIspS2pWHoMmrmH1/iSzY3mHcrWgx9uO8Yo3YN9SU+N4MjXh4n3WTYRtc8HqC72nQHCkU6b5fstx7pu2nuw8O50bVGTikDgC/XycMpeIiIiIFE1ogI1P72lJu2vKkZGTz5BP1rJ892mzY3mHxndC7FDAgLn3QdpJsxN5JDXh4l2Ob4Yf/s8xvmkMVGvllGnmbTrCyJmbyM036NG0Eh8MbI6/rxpwEREREXcQ5OfLpCEt6FSvAtl5doZPXc8P206YHcs7dB0LFRpAximYOxzs+WYn8jhqwsV7ZKfDl3dDfg7UvQXaPOSUaWasOcTjn28m327QO7YKb/e9FpuPTiURERERdxJg8+GjQbF0axxNTr6dB6dvZH6CVva+arZAx9WmtiBIXA4/v2V2Io+jzkG8x8J/QdJex33gPceBxVLiU0z6OZH/m7cFw4AhbWJ47Y4m+FhLfh4RERERuXp+vlbe6XctvZpXJt9uMGp2ArPXHTI7lucrXxe6vekYL30FDsabm8fDqAkX77DlS9j0GY79wCdAUESJT/H+T3v4z7fbARjRoSajb22IVQ24iIiIiFvz9bHyxp1NGdiqGoYB/5qzhSm/JJody/NdOwCa9gfDDl/eAxlaAK+o1ISL5zt3AL59zDFu/0+ofl2JvrxhwJuL9/DGot0APN65Dk93rYfFCe+0i4iIiEjJs1otvHRbI+69rgYAo7/ZzgfL9pqcygvc8gaUqw1px2DeCLDbzU7kEdSEi2fLz4U590J2KlRtBR3+VaIvbxgGcw9Y+WiF47el/76lPo/cWFsNuIiIiIiHsVgs/Lub4/9yAK8v3MX/luzFMEwO5sn8yzjuD/cNgL2LYdX7ZifyCGrCxbMtexWOrAP/MLhjIvj4lthL59sNnp2/nRUnHKfJf25rxPD2NUvs9UVERETEtSwWi+Oqxn/UA+CD5fv56qAVQ534lYtq5FgxHeDHMXB4nbl5PICacPFc+5fDyt9WY7z1HQivVmIvnZdv5/HPE/h8w1EsGLzWqyGDW8eU2OuLiIiIiHnu71CLMbc2BGDZcSvPf7MDu12N+BWLHQoNe4E9z7Fb0flzZidya2rCxTNlJMHc+wADmg+BhreX2EunZ+cxYtoG5iccw9dqYUgdO72aVS6x1xcRERER8w1pW51XbmuIBYNZ647wyKxNZOVqz+srYrFAj3egbA1IOQTzR6Lr/C9NTbh4HsOA+Q9C+gmIrPvH5S8l4Mi5TO78MJ4fd57Cz9fK+/2b0qycvoGIiIiIeKPesZW5q7YdX6uFb389Tt/xqzmVmmV2LM8UEAq9J4PVBju/hbUTzE7kttSEi+dZOx52LwQff7jzE/ALKpGX3XDwHLeN+4WdJ9KILOPP7Ptac2O9CiXy2iIiIiLinppHGkwZGkt4kI3Nh5PpOe4Xth1LMTuWZ6rUDLq85Bgv+jccSzA1jrtSEy6e5cQWWPSsY3zzy46FIErAV5uO0n/Cas6k51A/OpSvR7ajWbWyJfLaIiIiIuLeWtWI4KsH21GrfDDHU7K488NV/LDthNmxPFOrEVC3G+TnwBdDISvV7ERuR024eI6cDPhimOOErnsLtLj3ql/Sbjd444ddjJqdQE6enS4NKvLl/W2oFB5YAoFFRERExFNUjwxm7oPtuL52JOdz87n/sw18uGyfVk4vLosFer4PYVXhXCJ8O0r3h/+FmnDxHN//C5L2QEgl6DnOcYJfhcycPB6asZH3l+4F4IGOtfhoUCzB/iW3zZmIiIiIeI6wQBuTh7bgrjYxGAa8tnAnT37xK9l5WrCtWIIiHLeNWn1h6xzYPMvsRG5FTbh4hq1zYNM0wAK9xjtO7KtwIiWLPh+v4vutJ7D5WHijd1P+1bUeVuvVNfYiIiIi4tl8fay82LMRL/ZsiI/VwpyNRxg4YQ1J6dlmR/MsVVtCx6cd4++ehLOJ5uZxI2rCxf2dOwDfjHKM2z8JNa6/qpf79Ugyt77/M1uPphIR7MeM4a25M7bKVccUEREREe9xV5vqTB7agpAAX9YfPEfPcb+w60Sa2bE8y3WPQ7U2kJPu2F44P8/sRG5BTbi4t/xcmHMvZKdClZbQ4emrerkFvx6nz8erOJWWTZ2KZZj/UDtaVL+6d9VFRERExDu1r1OeeQ+2I6ZcEEfOneeOD+NZuvOU2bE8h9XHcRWrfygcWQsr3zA7kVtQEy7ubdlYOLIO/MPgjongc2X3axuGwbs/7uGhGRvJyrVzQ93yzHmgLVUjSmZ7MxERERHxTtdUKMNXD7ajVY0I0rPzuGfqOib9nKgF24oqvBp0e8sxXv46HF5rbh43oCZc3FfiClj5pmN86ztQNuaKXiYrN59HZyXw1uLdANzdrgYTh7QgJMBWUklFRERExIuVDfZj2j2t6NeiKnYD/vPtdv5v3hZy8+1mR/MMTXpD495g5MPc4ZBdui/rVxMu7ikjyXHfCAY0vwsa3n5FL3MqLYt+41fz9eZj+FotvHJ7Y57v0QAfLcAmIiIiIsXg52vl1V6NebZbfSwWmLn2MHdNWktyZo7Z0TzDLW9AWDXHek/f/8vsNKZSEy7uxzBg/kOQdhwi60DXsVf0MtuPpXLb+7+QcDiZsEAbn97TkgGtqpVwWBEREREpLSwWC/deX5NJQ+II9vNh1f4kbhv3C/tOp5sdzf0FhkOvj8FihYTpsHWu2YlMoyZc3M/aCbD7e/Dxc+wv6Bdc7JdYtO0Ed34Uz7GULGpGBvPVQ+1oWyvSCWFFREREpLTpVK8icx5sS+XwQA4kZXL7uF/4ec8Zs2O5v5i2jhXTAb4dBSlHTI1jFjXh4l5ObIFFzzrGXV6CqMbFerrdbvDBsr2M+GwDmTn5XHdNJPMebEeNyOI38iIiIiIil1IvKpT5I9sRG1OW1Kw8hkxey9T4A1qw7e90fBoqNYesFJh3P9jzzU7kcmrCxX3kZMKXd0N+NtTpCi3vK9bTT6RkMfiTNby+cBeGAYNaV2PysBaEBWkBNhEREREpeZFl/Jl+byt6NatMvt3gha+3MfzT9ZxJzzY7mvvysTl2PbIFw4GVEP+e2YlcTk24uI/Fz8OZ3VAmCnp+AJaiL5723Zbj3Pz2Cn7Zm0SAzbFoxku3Ncbmo3/iIiIiIuI8ATYf3uzTlOe6N8DPx8qSHafo+vYK7Sd+OeVqwT9+W/fpp5fgWIKpcVxNHYq4hz1LYN0Ex/j2DyG4XJGelpaVy5NfbObB6RtJOZ9LkyphLHjkevq31AJsIiIiIuIaFouFe66rwfyR7ahbMYQz6TkMm7KO577ayvmc0ne5dZE0Gwz1uoM9F+bc67gqtpRQEy7myzzrWA0doNX9UKtTkZ624eBZbnl3JV9uOILVAiNvuIY5D7SlVvkyTgwrIiIiInJx9aMd94nf3a4GANNWH6T7eyvZejTF5GRuyGKBW9+DkGhI2gOL/m12IpdREy7mMgzHyojpJxzbkd00+m+fkptv561Fu+j90SoOnz1P5fBAZt3XhidvrqvLz0VERETEVAE2H57v0YBP725JhRB/9p3O4PYPfuHDZfvIt2vRtkKCIuC2Dx3j9Z/Aru/NzeMi6ljEXL9+Dtvng9UXeo0HW+BlD088k8GdH63i3Z/2Yjfg9maV+X7U9bSsEeGiwCIiIiIif699nfIsHNWemxtWJDff4LWFOxkwYTVHk8+bHc291LoB2ox0jOc/BGknzc3jAmrCxTzJh+G7Jx3jjk9DpWaXPNQwDGatPUS3d1ey+XAyoQG+vNu/Gf/rey2hAVr9XERERETcT0SwHx8NiuX1O5oQ5OfDmsSzdH17BfMTjpodzb3c+DxUbASZSTD/QcfVsl5MTbiYw26Hrx6A7FSo0hLaPXbJQ89m5DBi2gaenruFzJx8WteMYOGo9tzatJILA4uIiIiIFJ/FYqFPi6p8/+j1NKsWTlpWHo/OSmDUrE2knM81O5578PV3bFvmGwB7l8Da8WYncio14WKO1R849gW0BcPtH4GP70UPW7brFDe/vYJF209i87HwzD/qMePe1lQKv/xl6yIiIiIi7iSmXDBfjGjDqJtq42O18FXCMW55ZyVr9ieZHc09VKgPnf/jGC96Dk5uNzePE6kJF9c7uR1+HOMYd33FsU/gX2Tl5jP6620MnbyO02nZXFOhDF891I4RHWphtRZ9/3AREREREXfh62Nl1E11+HxEG6pFBHE0+Tz9JqzmtYU7ycmzmx3PfC2HQ+0ukJ8Nc4dDbpbZiZxCTbi4Vl42zL0P8nOgTldoPuSCQ7YdS6HHez8zJf4AAEPbVufbh6+jYaUwF4cVERERESl5sTFl+e7R6+kTVwXDgA+X7aPXh7+w91S62dHMZbFAz3EQFAknt8KPL5qdyCnUhItrLX0FTm6BoHKOfQEtf7yrnZdv5+Pl+7h9XDx7TqUTWcafycNaMPrWhgTYfEwMLSIiIiJSssr4+/L6nU35aFBzwoNsbD2aSvf3VvLpqgOleyuzMhUcjTjA6nFY9i8zNY4zqAkX1zkYD7+84xj3eMdxguFY+XzpzlN0fWclr36/k5x8O50bVOSHUddzQ90KJgYWEREREXGuro2i+WFUe66vHUlWrp3n52+jx3s/E7/3jNnRzFO3K8TdA4DPNw/hl5dmcqCSpSZcXCMrFeaNAAy4dhDU7wHAjuOp3PXJWoZNWcfeU+lEBPvx2h2NGT84lnJl/M3NLCIiIiLiAhVDA5g6rCWjezQgJMCX7cdTGTBxDfdOXce+06X0EvUuL0FkHSzpJ2l66BOv2rZMTbi4xg/PQPIhCK8GXV/lVFoWT8/5lW7vrmTlnjP4+VgZ0b4mS5/sSN8W1bBYtPiaiIiIiJQeVquFoe1qsPyfNzCkTQw+VgtLdpzi5v+t4IX5WzmbkWN2RNfyC4I7JmJYbVRK2YDl11lmJyoxF98XSqQk7fgWNn0GWMju/iHjfznJh8v3kZmTD0C3JtE83bUeVSOCzM0pIiIiImKyiGA/xvRsxOA21Rn7/Q6W7DjF1FUHmbvpKA93uoYhbavj71tK1kuKboq94/9xasO3RNbuYnaaEqMmXJwr/RR88wgAu665m6FfZHM8ZTcA11YN57nu9YmNiTAzoYiIiIiI27mmQhkmDmlB/N4zvLRgB9uPp/LKdzuZtvogT3etzy2No0rF1aP21g+x9mxNbgkqZ3aUEqMmXJzHMODrhyEzif0+NeixtT05ZFE5PJB//aMePZpEl4pvHCIiIiIiV6rtNZF88/B1zNl4hDd+2MXhs+d5aMZGYmPK8my3+jSrVtbsiM5lsRbaUckb6J5wcZozKybA7oVkG77cn3k/fv6B/KtrPX58ogO3Nq2kBlxEREREpAh8rBb6xFVl2T878uiNtQm0+bDh4Dlu/yCeR2Zu4si5TLMjSjGoCZcSl5yZw7tf/EDgT88C8EZ+X1q2aseyf3bkgY61tOe3iIiIiMgVCPLz5bHOdVj6ZEfujK2CxQJfbz5GpzeX89rCnaRl5ZodUYpATbiUmJw8O5N+TqTT6z/Sbsu/CbZks8O/Kb1HvspLtzUmUluOiYiIiIhctaiwAN7o3ZRvRl5H65oR5OTZ+XDZPjr+dxmfrT5IXr7d7IhyGbonXK5aenYe8xOOMmHFfg4kZfKQz1fE2vaQZytD/Qc+g/AwsyOKiIiIiHidRpXDmDm8NUt2nOLV73aw/0wGz361lanxBxjeviY9mlQi0E9XobobNeFyxXYcT2X6moN8tekY6dl5AFwXfIQn7HPBAN/ubzr2BRcREREREaewWCx0blCRjnXLM331Qd7+cQ97TqXz1Je/8tK327kjtgoDW1XjmgohZkeV36gJl2LJys1nwa/Hmb7mIBsPJRc8XiMymLviKjBkywtYk/KgQU9o0te8oCIiIiIipYjNx8rQdjW4vVkVpq89yMy1hzh89jyTfznA5F8O0KpGBANbx9C1YRR+vror2UxqwqVI9p9OZ8aaQ3y58QjJmY4FH3ytFm5uGMXAVtVoU6scloVPQ9IeKBMF3d/2uq0ERERERETcXViQjQc7XsP97WuxYs9ppq85xI87TrIm8SxrEs8SWcaP3nFVGdCyGlUjgsyOWyqpCZdLys23s3j7SaavOcgve5MKHq8cHkj/llXp06IqFUICHA/uWwprPnKMe46DoAgTEouIiIiICIDVaqFj3Qp0rFuBY8nnmbXuMLPWHuJUWjYfLtvHR8v30aFOeQa2iuGGuuXx9dG7466iJlwucDT5PDPXHGL2+sOcTssGHG9qd6pbgYGtq9GhTgV8rH96lzsrFb5+2DGOuwdq32RCahERERERuZhK4YE83rkOD3e6hh93nGL6moOs3HOGZbtOs2zXaaLDAujfshp9W1SlYmiA2XG9nppwASDfbrB89ymmrz7E0l2nsBuOxyPL+NOvRVX6taxKlbKXuFxl8fOQchjCY6Dzi64LLSIiIiIiRWbzsdK1URRdG0Vx4EwGM9ce4vP1hzmeksVbi3fzzo976Fy/IgNbV6NdrUisVt1e6gxqwkuxlPO5rD9wltX7k/huywmOJp8v+Lt215RjYKsYOjeoiO1yl6bsWwobJjvGPceBfxknpxYRERERkatVPTKYZ26pz2Od67Bw6wmmrznIugPnWLjtBAu3naB6uSC6NoqmVc0I4mLKEhJgMzuy11ATXookZ+awNvEsq/efZU1iEtuPp2IYf/x9WKCN3rFV6N+qGrXKF6GZzk774zL0FsOhxvXOCS4iIiIiIk4RYPPhtmaVua1ZZXadSGPGmoPM3XiUA0mZfLTcce+41QKNK4fRqmY5WtWIIK56BGGBasqvlJpwL3Y2I4e1iUms3u94t3vXybRCTTc4thZrXTOCtrUi6dygIgE2n6JPsOi5Py5Dv2l0iWYXERERERHXqhsVwpiejXiqaz0WbT9B/N4k1iSe5dDZTDYfSWHzkRTGr9iP1QINKoXSqoajKW9ZI4LwID+z43sMpzXhL7/8MgsWLCAhIQE/Pz+Sk5P/9jmGYTBmzBjGjx/PuXPnaNWqFePGjaNhw4bOiulVTqdlszbR8S736v1J7D6ZfsExtcoH07pmuYLfYl3xwguFLkN/X5ehi4iIiIh4iWB/X25vVoXbm1UB4FjyedYkJrFmv2Obs8QzGWw9msrWo6lM+jkRiwXqRYXSqkaEo9eoEUHZYDXll+K0JjwnJ4fevXvTpk0bJk2aVKTnvP7667z11ltMmTKFOnXq8NJLL9G5c2d27dpFSEiIs6J6DMMwSD2fR1JGNmczcjiTnsPZjBy2HUthTeJZ9p66sOmuU7EMrWqUo3XNcrSsEUH5EP+rD5KdBl8/4hi3uBdqtL/61xQREREREbdUKTywUFN+MjWL1fsd75Kv3p/E/tMZ7Dieyo7jqUyJPwBA3YohtKoZQf3oUMoF+1GujB8Rwf5EBPsRGuCLxVJ6F31zWhM+ZswYAKZMmVKk4w3D4O233+bf//43vXr1AmDq1KlUrFiRGTNmMGLECGdFNY3dbpCRC/tOZ5CabedsRnZBY302I4ekjBzOZmSTlO4Yn8vIIc9uXPY160WF0LpmOVrXjKBF9QjKlSmBpvuvFj8PKYcgvBrcNKbkX19ERERERNxWxdAAel5bmZ7XVgbgVFqW44rc326D3XMqnV0n09h1Mu2iz7f5WIgIdjTl5YL9fhv7/dasOxp1R9PuR6if9YJbaj2d29wTnpiYyIkTJ+jSpUvBY/7+/nTo0IH4+PhLNuHZ2dlkZ2cXfJyamgpAbm4uubm5zg19FX7YdpJHZ/9KvuEL638p1nOD/X2ICPrtH2aQH9UiAmlZPYK46uGU/cu9GCX9NbAkrsB3/ScA5HV7G8PqD278db5av3/93PnfkqhOnkJ1cn+qkWdQnTyD6uQZVKeSUTbAh5vrl+fm+uUBSMrIYd2Bc6w9cI4j5zJ/e3Mxl3MZOWTk5JObb3AyNZuTqdl/88oOVosPZa45SYe6FZ35aVyV4vwbcpsm/MSJEwBUrFj4C1uxYkUOHjx4yee9+uqrBe+6/9miRYsICrrEvtZuYFeyhXzDsQhaoI9BGRuOP76OcfCfxiE2KGMzKOPreNxmzQMK/4PNPbCfVQecm9k3/zw37Pw3vkBiZCd+3Z4O279z7qRuYvHixWZHkCJQnTyD6uT+VCPPoDp5BtXJM6hOzhFngbgIIOKPx3LtkJ77+x8L6XmXGOdCeh5k5VuwGxa2b95Axj7TPpW/lZmZWeRji9WEjx49+qIN75+tW7eOuLi44rxsIX+9N8AwjMveL/DMM8/w+OOPF3ycmppK1apV6dKlC6GhoVecw9k65ebTJ+08G+JX8I+bO2Ozuf8S/9bv/4lPzhmMsGpUGfYJVfy8fzG23NxcFi9eTOfOnlGj0kp18gyqk/tTjTyD6uQZVCfPoDq5v4zz2cxf+CM9u95IcKATbrUtIb9fkV0UxWrCR44cSb9+/S57TPXq1YvzkgWioqIAxzvi0dHRBY+fOnXqgnfH/8zf3x9//wuLYbPZ3PpEstlsBNh82Gx1/6wA7F8OGx2roVt6vo8tuKzJgVzLI2okqpOHUJ3cn2rkGVQnz6A6eQbVyX0FA+H+EBzo79Y1Kk62YjXhkZGRREZGFjtQUdSoUYOoqCgWL15Ms2bNAMcK68uXL+e1115zypxSRNnp8PVIxzjubqjZwdw8IiIiIiIiHsrqrBc+dOgQCQkJHDp0iPz8fBISEkhISCA9/Y9ttOrVq8e8efMAx2Xoo0aN4pVXXmHevHls3bqVoUOHEhQUxIABA5wVU4piyQuQfAjCqkHnF81OIyIiIiIi4rGctjDb888/z9SpUws+/v3d7aVLl9KxY0cAdu3aRUpKSsExTz31FOfPn+fBBx/k3LlztGrVikWLFmmPcDMlroB1Ex3jnu+Bv2ohIiIiIiJypZzWhE+ZMuVv9wg3/rLhm8ViYfTo0YwePdpZsaQ4stNh/kOOcewwqNnR1DgiIiIiIiKezmmXo4sXWDL6t8vQq0KX/5idRkRERERExOOpCZeLS1wB6yY4xrfqMnQREREREZGSoCZcLpSdDvN/Ww09dhjUusHcPCIiIiIiIl5CTbhc6McxkHzQcRm6VkMXEREREREpMWrCpbDElbB2vGN867sQEGpuHhERERERES+iJlz+kJPxp9XQh0KtTqbGERERERER8TZqwuUPS0Y7LkMPrQKdtRq6iIiIiIhISVMTLg4HftZl6CIiIiIiIk6mJlwKX4befAhcc6O5eURERERERLyUmnCBH1+Ecwccl6F3ecnsNCIiIiIiIl5LTXhpd3gtrPnYMb71HV2GLiIiIiIi4kRqwkuzvBz4+hHAgKb94ZqbzE4kIiIiIiLi1dSEl2a/vA2nd0BQJNz8itlpREREREREvJ6a8NLq9G5Y8V/HuOtYCIowN4+IiIiIiEgpoCa8NLLb4ZtHID8HrukMje80O5GIiIiIiEipoCa8NNo4BQ6tAlswdH8LLBazE4mIiIiIiJQKasJLm9RjsPgFx/jG5yC8mrl5REREREREShE14aXNd/+E7FSoHAst7zM7jYiIiIiISKmiJrw02f417PwWrL7Q412w+pidSEREREREpFRRE15anE92vAsO0O5RiGpkahwREREREZHSSE14abFkNKSfgHLXQPunzE4jIiIiIiJSKqkJLw0O/AIbJjvGPd4BW4C5eUREREREREopNeHeLjcLvnnUMW4+BKpfZ24eERERERGRUkxNuLdb+QYk7YEyFaHzi2anERERERERKdXUhHuzk9vg5/85xrf8FwLDTY0jIiIiIiJS2qkJ91b2fPj6EbDnQd1uUP9WsxOJiIiIiIiUemrCvdW6iXB0PfiHQrc3wGIxO5GIiIiIiEippybcGyUfhiVjHOObXoDQSubmEREREREREUBNuPcxDFjwBORmQNXWEHu32YlERERERETkN2rCvc22ubDnB/Dxg1vfBatKLCIiIiIi4i7UoXmTzLPw/b8c4+ufgPJ1zc0jIiIiIiIihagJ9yaLnoOM01C+Hlz3mNlpRERERERE5C/UhHuL/csg4TPAAj3eBV9/sxOJiIiIiIjIX6gJ9wY5mfDNKMe4xb1QrZWpcUREREREROTi1IR7g+Vj4VwihFSCG583O42IiIiIiIhcgppwT3d8M8S/7xh3exMCQs3NIyIiIiIiIpekJtyT5efB14+AkQ8NboN6t5idSERERERERC5DTbgnWzcRjidAQBj843Wz04iIiIiIiMjfUBPuqVKPw08vOcY3vgAhFc3NIyIiIiIiIn9LTbin+uH/ICcNKsdC7DCz04iIiIiIiEgRqAn3RPt+gm1zwWKF7v8Dq8ooIiIiIiLiCdS9eZrcLFjwhGPccgRENzU3j4iIiIiIiBSZmnBP88vbcHY/hETDDf9ndhoREREREREpBjXhniRpH6x80zG++RXtCS4iIiIiIuJh1IR7CsNwXIaenwO1OkHD281OJCIiIiIiIsWkJtxTbJsL+5eCjz/c8gZYLGYnEhERERERkWJSE+4JslJh4W/3f1//BJSrZW4eERERERERuSJqwj3B0pch/QRE1ILrRpmdRkRERERERK6QmnB3dywB1o53jLu9Cb7+psYRERERERGRK6cm3J3Z8+Hbx8CwQ6M7oNYNZicSERERERGRq6Am3J1tmAzHNoJ/qGNLMhEREREREfFoasLdVfopWPKiY9zpWQiJMjePiIiIiIiIXDU14e5q0bOQnQLR10KLe81OIyIiIiIiIiVATbg7SlwBv84GLND9f2D1MTuRiIiIiIiIlAA14e4mLwcWPOEYt7gHKjc3N4+IiIiIiIiUGDXh7ib+XTizG4IrQKfnzE4jIiIiIiIiJUhNuDs5mwgr/usY3/wyBIabGkdERERERERKlppwd2EY8P1TkJcFNdpD495mJxIREREREZESpibcXez4BvYsAh8/6PYWWCxmJxIREREREZESpibcHeSkw8KnHeN2j0JkbXPziIiIiIiIiFOoCXcD1hWvQ+pRKFsdrn/C7DgiIiIiIiLiJL5mByjtQs8fwprwseODW94AW6C5gURERERERMRp9E64mQw7TQ5PxWLkQ/1boXZnsxOJiIiIiIiIE6kJN5ElYTrlMvZg+AVD17FmxxEREREREREnUxNulowkfJa+CIC9/dMQVtnkQCIiIiIiIuJsasLNkvAZlvPnSAmshr3FcLPTiIiIiIiIiAtoYTaztH2EvKAKbN5xnDZWlUFERERERKQ00DvhZrFYMBrdybngWmYnERERERERERdREy4iIiIiIiLiImrCRURERERERFxETbiIiIiIiIiIi6gJFxEREREREXERNeEiIiIiIiIiLqImXERERERERMRF1ISLiIiIiIiIuIiacBEREREREREXcVoT/vLLL9O2bVuCgoIIDw8v0nOGDh2KxWIp9Kd169bOiigiIiIiIiLiUk5rwnNycujduzcPPPBAsZ7XtWtXjh8/XvDnu+++c1JCEREREREREdfyddYLjxkzBoApU6YU63n+/v5ERUU5IZGIiIiIiIiIuZzWhF+pZcuWUaFCBcLDw+nQoQMvv/wyFSpUuOTx2dnZZGdnF3ycmpoKQG5uLrm5uU7PezV+z+fuOUsz1cgzqE6eQXVyf6qRZ1CdPIPq5BlUJ/fnKTUqTj6LYRiGE7MwZcoURo0aRXJy8t8eO3v2bMqUKUNMTAyJiYk899xz5OXlsWHDBvz9/S/6nNGjRxe86/5nM2bMICgo6Grji4iIiIiIiFxWZmYmAwYMICUlhdDQ0MseW6wm/FIN75+tW7eOuLi4go+L04T/1fHjx4mJiWHWrFn06tXrosdc7J3wqlWrcubMmb/95M2Wm5vL4sWL6dy5Mzabzew4chGqkWdQnTyD6uT+VCPPoDp5BtXJM6hO7s9TapSamkpkZGSRmvBiXY4+cuRI+vXrd9ljqlevXpyXvKzo6GhiYmLYs2fPJY/x9/e/6LvkNpvNrYv0Z56UtbRSjTyD6uQZVCf3pxp5BtXJM6hOnkF1cn/uXqPiZCtWEx4ZGUlkZGSxA12ppKQkDh8+THR0tMvmFBEREREREXEWp21RdujQIRISEjh06BD5+fkkJCSQkJBAenp6wTH16tVj3rx5AKSnp/Pkk0+yatUqDhw4wLJly+jRoweRkZHcfvvtzoopIiIiIiIi4jJOWx39+eefZ+rUqQUfN2vWDIClS5fSsWNHAHbt2kVKSgoAPj4+bNmyhU8//ZTk5GSio6O54YYbmD17NiEhIUWe9/db3H9fJd2d5ebmkpmZSWpqqltfWlGaqUaeQXXyDKqT+1ONPIPq5BlUJ8+gOrk/T6nR7/1nUZZcc/rq6K525MgRqlatanYMERERERERKWUOHz5MlSpVLnuM1zXhdrudY8eOERISgsViMTvOZf2+kvvhw4fdfiX30ko18gyqk2dQndyfauQZVCfPoDp5BtXJ/XlKjQzDIC0tjUqVKmG1Xv6ub6ddjm4Wq9X6t795cDehoaFu/Q9KVCNPoTp5BtXJ/alGnkF18gyqk2dQndyfJ9QoLCysSMc5bWE2ERERERERESlMTbiIiIiIiIiIi6gJN5G/vz8vvPAC/v7+ZkeRS1CNPIPq5BlUJ/enGnkG1ckzqE6eQXVyf95YI69bmE1ERERERETEXemdcBEREREREREXURMuIiIiIiIi4iJqwkVERERERERcRE24iIiIiIiIiIuoCXeil19+mbZt2xIUFER4eHiRnmMYBqNHj6ZSpUoEBgbSsWNHtm3bVuiY7OxsHn74YSIjIwkODubWW2/lyJEjTvgMSodz584xePBgwsLCCAsLY/DgwSQnJ1/2ORaL5aJ//vvf/xYc07Fjxwv+vl+/fk7+bLzTldRo6NChF3z9W7duXegYnUslq7h1ys3N5V//+heNGzcmODiYSpUqcdddd3Hs2LFCx+lcujoffPABNWrUICAggNjYWFauXHnZ45cvX05sbCwBAQHUrFmTjz766IJj5syZQ4MGDfD396dBgwbMmzfPWfFLjeLUae7cuXTu3Jny5csTGhpKmzZt+OGHHwodM2XKlIv+nMrKynL2p+K1ilOjZcuWXfTrv3PnzkLH6VwqecWp08X+r2CxWGjYsGHBMTqXStaKFSvo0aMHlSpVwmKx8NVXX/3tc7zy55IhTvP8888bb731lvH4448bYWFhRXrO2LFjjZCQEGPOnDnGli1bjL59+xrR0dFGampqwTH333+/UblyZWPx4sXGxo0bjRtuuMFo2rSpkZeX56TPxLt17drVaNSokREfH2/Ex8cbjRo1Mrp3737Z5xw/frzQn08++cSwWCzGvn37Co7p0KGDMXz48ELHJScnO/vT8UpXUqMhQ4YYXbt2LfT1T0pKKnSMzqWSVdw6JScnGzfddJMxe/ZsY+fOncaqVauMVq1aGbGxsYWO07l05WbNmmXYbDZjwoQJxvbt241HH33UCA4ONg4ePHjR4/fv328EBQUZjz76qLF9+3ZjwoQJhs1mM7788suCY+Lj4w0fHx/jlVdeMXbs2GG88sorhq+vr7F69WpXfVpep7h1evTRR43XXnvNWLt2rbF7927jmWeeMWw2m7Fx48aCYyZPnmyEhoZe8PNKrkxxa7R06VIDMHbt2lXo6//nny86l0peceuUnJxcqD6HDx82IiIijBdeeKHgGJ1LJeu7774z/v3vfxtz5swxAGPevHmXPd5bfy6pCXeByZMnF6kJt9vtRlRUlDF27NiCx7KysoywsDDjo48+MgzD8c3CZrMZs2bNKjjm6NGjhtVqNRYuXFji2b3d9u3bDaDQSbpq1SoDMHbu3Fnk1+nZs6fRqVOnQo916NDBePTRR0sqaql1pTUaMmSI0bNnz0v+vc6lklVS59LatWsNoNB/mHQuXbmWLVsa999/f6HH6tWrZzz99NMXPf6pp54y6tWrV+ixESNGGK1bty74uE+fPkbXrl0LHXPzzTcb/fr1K6HUpU9x63QxDRo0MMaMGVPwcVH/7yFFU9wa/d6Enzt37pKvqXOp5F3tuTRv3jzDYrEYBw4cKHhM55LzFKUJ99afS7oc3Y0kJiZy4sQJunTpUvCYv78/HTp0ID4+HoANGzaQm5tb6JhKlSrRqFGjgmOk6FatWkVYWBitWrUqeKx169aEhYUV+et58uRJFixYwD333HPB302fPp3IyEgaNmzIk08+SVpaWollLy2upkbLli2jQoUK1KlTh+HDh3Pq1KmCv9O5VLJK4lwCSElJwWKxXHALj86l4svJyWHDhg2F/o0DdOnS5ZI1WbVq1QXH33zzzaxfv57c3NzLHqPz5spcSZ3+ym63k5aWRkRERKHH09PTiYmJoUqVKnTv3p1NmzaVWO7S5Gpq1KxZM6Kjo7nxxhtZunRpob/TuVSySuJcmjRpEjfddBMxMTGFHte5ZB5v/bnka3YA+cOJEycAqFixYqHHK1asyMGDBwuO8fPzo2zZshcc8/vzpehOnDhBhQoVLni8QoUKRf56Tp06lZCQEHr16lXo8YEDB1KjRg2ioqLYunUrzzzzDJs3b2bx4sUlkr20uNIa/eMf/6B3797ExMSQmJjIc889R6dOndiwYQP+/v46l0pYSZxLWVlZPP300wwYMIDQ0NCCx3UuXZkzZ86Qn59/0Z8pl6rJiRMnLnp8Xl4eZ86cITo6+pLH6Ly5MldSp7968803ycjIoE+fPgWP1atXjylTptC4cWNSU1N55513aNeuHZs3b6Z27dol+jl4uyupUXR0NOPHjyc2Npbs7GymTZvGjTfeyLJly2jfvj1w6fNN59KVudpz6fjx43z//ffMmDGj0OM6l8zlrT+X1IQX0+jRoxkzZsxlj1m3bh1xcXFXPIfFYin0sWEYFzz2V0U5pjQpap3gwq83FO/r+cknnzBw4EACAgIKPT58+PCCcaNGjahduzZxcXFs3LiR5s2bF+m1vZmza9S3b9+CcaNGjYiLiyMmJoYFCxZc8AuT4rxuaeOqcyk3N5d+/fpht9v54IMPCv2dzqWrU9yfKRc7/q+PX8nPKbm8K/2azpw5k9GjRzN//vxCvwhr3bp1ocUo27VrR/PmzXnvvfd49913Sy54KVKcGtWtW5e6desWfNymTRsOHz7MG2+8UdCEF/c1pWiu9Gs6ZcoUwsPDue222wo9rnPJfN74c0lNeDGNHDnyb1flrV69+hW9dlRUFOD4jU90dHTB46dOnSr47U5UVBQ5OTmcO3eu0Dt4p06dom3btlc0rzcqap1+/fVXTp48ecHfnT59+oLfqF3MypUr2bVrF7Nnz/7bY5s3b47NZmPPnj1qHHBdjX4XHR1NTEwMe/bsAXQuFZUr6pSbm0ufPn1ITEzkp59+KvQu+MXoXCqayMhIfHx8Lngn4M8/U/4qKirqosf7+vpSrly5yx5TnPNR/nAldfrd7Nmzueeee/jiiy+46aabLnus1WqlRYsWBd8DpeiupkZ/1rp1az777LOCj3UulayrqZNhGHzyyScMHjwYPz+/yx6rc8m1vPXnku4JL6bIyEjq1at32T9/fUe0qH6/3PLPl1jm5OSwfPnygqYgNjYWm81W6Jjjx4+zdetWNQ5/UtQ6tWnThpSUFNauXVvw3DVr1pCSklKkr+ekSZOIjY2ladOmf3vstm3byM3NLfQLltLMVTX6XVJSEocPHy74+utcKhpn1+n3BnzPnj0sWbKk4Afq5ehcKho/Pz9iY2MvuGx/8eLFl6xJmzZtLjh+0aJFxMXFYbPZLnuMzpsrcyV1Asc74EOHDmXGjBl069btb+cxDIOEhASdN1fgSmv0V5s2bSr09de5VLKupk7Lly9n7969F13f5690LrmW1/5ccvVKcKXJwYMHjU2bNhljxowxypQpY2zatMnYtGmTkZaWVnBM3bp1jblz5xZ8PHbsWCMsLMyYO3eusWXLFqN///4X3aKsSpUqxpIlS4yNGzcanTp10rZKV6Fr165GkyZNjFWrVhmrVq0yGjdufMG2Sn+tk2EYRkpKihEUFGR8+OGHF7zm3r17jTFjxhjr1q0zEhMTjQULFhj16tUzmjVrpjpdgeLWKC0tzXjiiSeM+Ph4IzEx0Vi6dKnRpk0bo3LlyjqXnKi4dcrNzTVuvfVWo0qVKkZCQkKhrV+ys7MNw9C5dLV+365n0qRJxvbt241Ro0YZwcHBBSv/Pv3008bgwYMLjv99K5jHHnvM2L59uzFp0qQLtoL55ZdfDB8fH2Ps2LHGjh07jLFjx7r9VjDurrh1mjFjhuHr62uMGzfuklv3jR492li4cKGxb98+Y9OmTcawYcMMX19fY82aNS7//LxBcWv0v//9z5g3b56xe/duY+vWrcbTTz9tAMacOXMKjtG5VPKKW6ffDRo0yGjVqtVFX1PnUslKS0sr6IkA46233jI2bdpUsCtKafm5pCbciYYMGWIAF/xZunRpwTGAMXny5IKP7Xa78cILLxhRUVGGv7+/0b59e2PLli2FXvf8+fPGyJEjjYiICCMwMNDo3r27cejQIRd9Vt4nKSnJGDhwoBESEmKEhIQYAwcOvGBLkb/WyTAM4+OPPzYCAwMvul/xoUOHjPbt2xsRERGGn5+fUatWLeORRx65YJ9qKZri1igzM9Po0qWLUb58ecNmsxnVqlUzhgwZcsF5onOpZBW3TomJiRf9Hvnn75M6l67euHHjjJiYGMPPz89o3ry5sXz58oK/GzJkiNGhQ4dCxy9btsxo1qyZ4efnZ1SvXv2iv2j84osvjLp16xo2m82oV69eocZCrkxx6tShQ4eLnjdDhgwpOGbUqFFGtWrVDD8/P6N8+fJGly5djPj4eBd+Rt6nODV67bXXjFq1ahkBAQFG2bJljeuuu85YsGDBBa+pc6nkFfd7XnJyshEYGGiMHz/+oq+nc6lk/b5936W+f5WWn0sWw/jtznYRERERERERcSrdEy4iIiIiIiLiImrCRURERERERFxETbiIiIiIiIiIi6gJFxEREREREXERNeEiIiIiIiIiLqImXERERERERMRF1ISLiIiIiIiIuIiacBEREREREREXURMuIiIiIiIi4iJqwkVERERERERcRE24iIiIiIiIiIuoCRcRERERERFxkf8HKJVbd6tT/IEAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_match_v = target_fv.wrap(f_match)\n", - "diff = (target_fv-f_match_v).norm()\n", - "target_fv.plot(show=False, label=\"target function\")\n", - "f_match_v.plot(show=False, label=f\"match (dist={diff:.2f})\")\n", - "plt.title(f\"Best fit (a={params['a']:.2f}, b={params['b']:.2f}, c={params['c']:.2f}); dist={diff:.2f}\")\n", - "plt.legend()\n", - "f_match_v" - ] - }, - { - "cell_type": "markdown", - "id": "71ec9291-2816-4c64-ae95-610fa169e81d", - "metadata": {}, - "source": [ - "## High dimensional minimization" - ] - }, - { - "cell_type": "markdown", - "id": "f651576a-81a6-4f6e-8f9c-0dfe50a9bdf7", - "metadata": {}, - "source": [ - "### Example\n", - "\n", - "here we use as example the function\n", - "\n", - "$$\n", - "f(x,y) = (x-2)^2 + (y-2)^2\n", - "$$\n", - "\n", - "which obviously should be minimal at $(x,y) = (2,2)$" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "ad59954b-c98d-447b-a9b0-7f139140adfe", - "metadata": {}, - "outputs": [], - "source": [ - "func = lambda x,y: (x-2)**2 + (y-2)**2" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "f1329b5b-a229-47b5-bdac-4b8bdbf48565", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "((2.0002364190731674, 1.9999073648139465), array([ 0.00078973, -0.00030712]))" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r, dxdy = f.minimize(func, x0=[20, -5], learning_rate=None, return_path=True)\n", - "assert iseq(r[-1][0], 2, eps=1e-3)\n", - "assert iseq(r[-1][1], 2, eps=1e-3)\n", - "r[-1], dxdy" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "5cc79156-daf9-41df-bec2-c84d5b46e551", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x,y = zip(*r)\n", - "plt.scatter(x,y)\n", - "plt.title(\"Convergence path\")\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "fefd7a80-655f-45ad-926a-be010ce1971a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "({'x': 2.0002364190731674, 'y': 1.9999073648139465},\n", - " {'x': 0.0007897302440762718, 'y': -0.0003071172868030315})" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r, dxdy = f.minimize(func, x0=dict(x=20, y=-5), learning_rate=None, return_path=True)\n", - "assert iseq(r[-1][\"x\"], 2, eps=1e-3)\n", - "assert iseq(r[-1][\"y\"], 2, eps=1e-3)\n", - "r[-1], dxdy" - ] - }, - { - "cell_type": "markdown", - "id": "dbc4281c-414e-46a2-9089-667e8fdbc416", - "metadata": {}, - "source": [ - "### Testing e_i, e_k and bump" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "2bf759f5-47d1-4273-80c8-800e55d89fe8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "e_i = f.FunctionVector.e_i\n", - "e_k = f.FunctionVector.e_k\n", - "bump = f.FunctionVector.bump" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "ddef7258-a871-41eb-bd00-264b8cfc2260", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert np.array_equal(e_i(1,5), np.array([0., 1., 0., 0., 0.]))\n", - "assert e_k(\"b\", dict(a=1, b=2, c=3)) == {'a': 0, 'b': 1, 'c': 0}\n", - "assert bump(dict(a=1, b=2, c=3), \"b\", 0.25) == {'a': 1, 'b': 2.25, 'c': 3}" - ] - }, - { - "cell_type": "markdown", - "id": "4a99bef0-f091-4d5a-91f3-69a02545604e", - "metadata": {}, - "source": [ - "## Sundry tests" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "e88bdb45-5387-4c2e-981e-bdaebd0d9318", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[1.23, 2.35]" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fmt = f.core.fmt\n", - "dct = {\"a\": 1.234578, \"b\": 2.3456789}\n", - "lst = list(dct.values())\n", - "assert fmt(dct) == {'a': 1.2346, 'b': 2.3457}\n", - "assert fmt(lst) == [1.2346, 2.3457]\n", - "assert fmt(dct, \".2f\") == {'a': 1.23, 'b': 2.35}\n", - "assert fmt(lst, \".2f\") == [1.23, 2.35]\n", - "assert fmt(lst, \".2f\", as_float=False) == ['1.23', '2.35']\n", - "fmt(lst, \".2f\")" - ] - }, - { - "cell_type": "markdown", - "id": "e90a638c-b1a5-487d-86be-14c7eab061f6", - "metadata": {}, - "source": [ - "## Function examples [NOTEST]" - ] - }, - { - "cell_type": "markdown", - "id": "76e05c9f-f490-4684-8246-a470f17dc0d6", - "metadata": {}, - "source": [ - "### QuadraticFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "93514a9c-a129-4f32-9184-c7369f179623", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'a': 1, 'b': 2, 'c': 3}\n", - "fn1 = fn2 @ (-1.00, 2.00)\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fn1 = f.Quadratic(a=1, b=2, c=3)\n", - "print(fn1.params())\n", - "fn2 = fn1.update(b=3, c=4)\n", - "diff2 = lambda x: (fn1(x)-fn2(x))**2\n", - "fn1.plot(-5,5, label=\"fn1\")\n", - "fn2.plot(-5,5, label=\"fn2\")\n", - "fn2.plot(-5,5, func=diff2, label=\"(fn1-fn2)^2\")\n", - "fn2.plot(-5,5, func=fn2.p, label=\"-fn2'\")\n", - "fn2.plot(-5,5, func=fn2.pp, label=\"-fn2''\")\n", - "plt.legend()\n", - "x0 = f.goalseek(func=diff2)\n", - "print(f\"fn1 = fn2 @ ({x0:.2f}, {fn1(x0):.2f})\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "821a52cf-d842-4b78-9fbe-af87ea8f3f31", - "metadata": {}, - "source": [ - "### PowerlawFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "532b820b-694f-49d9-9d94-73efa8b4fc5d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'N': 1, 'alpha': -1, 'x0': 0}\n", - "fn1 = fn3 @ (2.18, 0.46)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fn1 = f.Powerlaw()\n", - "print(fn1.params())\n", - "fn2 = fn1.update(x0=0.5)\n", - "fn3 = fn2.update(alpha=-1.5)\n", - "diff2 = lambda x: (fn3(x)-fn1(x))**2\n", - "fn1.plot(1,3, label=\"fn1\")\n", - "fn2.plot(1,3, label=\"fn2\")\n", - "fn3.plot(1,3, label=\"fn3\")\n", - "fn2.plot(1,3, func=diff2, label=\"(fn3-fn1)^2\")\n", - "fn2.plot(1,3, func=fn2.p, label=\"-fn2'\")\n", - "fn2.plot(2,3, func=fn2.pp, label=\"-fn2''\")\n", - "plt.legend()\n", - "x0 = f.goalseek(func=diff2)\n", - "print(f\"fn1 = fn3 @ ({x0:.2f}, {fn1(x0):.2f})\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a59d3615-b064-41b8-a0a0-960de7b31459", - "metadata": {}, - "source": [ - "### TrigFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "01715466-67a1-4beb-b8e4-98b989880ffd", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'amp': 1, 'omega': 1, 'phase': 0}\n", - "fn1 = fn3 @ (1.41, -0.96)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fn1 = f.Trig()\n", - "print(fn1.params())\n", - "fn2 = fn1.update(omega=1.2)\n", - "fn3 = fn2.update(phase=-0.1)\n", - "diff2 = lambda x: (fn3(x)-fn1(x))**2\n", - "fn1.plot(1,3, label=\"fn1\")\n", - "fn2.plot(1,3, label=\"fn2\")\n", - "fn3.plot(1,3, label=\"fn3\")\n", - "fn2.plot(1,3, func=diff2, label=\"(fn3-fn1)^2\")\n", - "#fn2.plot(1,3, func=fn2.p, label=\"-fn2'\")\n", - "#fn2.plot(1,3, func=fn2.pp, label=\"-fn2''\")\n", - "plt.legend()\n", - "x0 = f.goalseek(func=diff2, x0=1.5)\n", - "print(f\"fn1 = fn3 @ ({x0:.2f}, {fn1(x0):.2f})\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "682a2bdf-a398-4669-950a-e2b19a1fde4b", - "metadata": {}, - "source": [ - "### ExpFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "43b52e75-618d-49d6-a270-3a3a23799d82", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'N': 1, 'k': 1, 'x0': 0}\n", - "fn1 = fn3 @ (0.60, 1.83)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fn1 = f.Exp()\n", - "print(fn1.params())\n", - "fn2 = fn1.update(k=1.2)\n", - "fn3 = fn2.update(x0=0.1)\n", - "diff2 = lambda x: (fn3(x)-fn1(x))**2\n", - "fn1.plot(0, 2, label=\"fn1\")\n", - "fn2.plot(0, 2, label=\"fn2\")\n", - "fn3.plot(0, 2, label=\"fn3\")\n", - "fn2.plot(0, 2, func=diff2, label=\"(fn3-fn1)^2\")\n", - "fn2.plot(0, 2, func=fn2.p, label=\"-fn2'\")\n", - "fn2.plot(0, 2, func=fn2.pp, label=\"-fn2''\")\n", - "plt.legend()\n", - "x0 = f.goalseek(func=diff2, x0=1.5)\n", - "print(f\"fn1 = fn3 @ ({x0:.2f}, {fn1(x0):.2f})\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d159ed04-58fd-4054-a5d5-cb0ad5e44302", - "metadata": {}, - "source": [ - "### LogFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "f88f138b-0e81-4e91-9365-a960d56cae0a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'base': 10, 'N': 1, 'x0': 0}\n", - "fn1 = fn3 @ (1.17, 0.07)\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fn1 = f.Log()\n", - "print(fn1.params())\n", - "fn2 = fn1.update(base=fn1.E)\n", - "fn3 = fn2.update(x0=0.1)\n", - "diff2 = lambda x: (fn3(x)-fn1(x))**2\n", - "fn1.plot(1, 5, label=\"fn1\")\n", - "fn2.plot(1, 5, label=\"fn2\")\n", - "fn3.plot(1, 5, label=\"fn3\")\n", - "fn2.plot(1, 5, func=diff2, label=\"(fn3-fn1)^2\")\n", - "fn2.plot(1, 5, func=fn2.p, label=\"-fn2'\")\n", - "fn2.plot(1, 5, func=fn2.pp, label=\"-fn2''\")\n", - "plt.legend()\n", - "x0 = f.goalseek(func=diff2, x0=1.5)\n", - "print(f\"fn1 = fn3 @ ({x0:.2f}, {fn1(x0):.2f})\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "daf48f80-f061-410b-96ca-15acb6c0df72", - "metadata": {}, - "source": [ - "### HyperbolaFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "88df65bc-905e-47cf-a8c3-abd01a132b3c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'k': 1, 'x0': 0, 'y0': 0}\n", - "fn1 = fn3 @ (2.48, 0.40)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fn1 = f.Hyperbola()\n", - "print(fn1.params())\n", - "fn2 = fn1.update(k=1.2)\n", - "fn3 = fn2.update(x0=-0.5)\n", - "diff2 = lambda x: (fn3(x)-fn1(x))**2\n", - "fn1.plot(0.5, 3, label=\"fn1\")\n", - "fn2.plot(0.5, 3, label=\"fn2\")\n", - "fn3.plot(0.5, 3, label=\"fn3\")\n", - "fn2.plot(0.5, 3, func=diff2, label=\"(fn3-fn1)^2\")\n", - "fn2.plot(0.5, 3, func=fn2.p, label=\"-fn2'\")\n", - "fn2.plot(1.5, 3, func=fn2.pp, label=\"-fn2''\")\n", - "plt.legend()\n", - "x0 = f.goalseek(func=diff2, x0=1.5)\n", - "print(f\"fn1 = fn3 @ ({x0:.2f}, {fn1(x0):.2f})\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "25245707-023a-4028-84b3-288303d43dbe", - "metadata": {}, - "source": [ - "## Function examples\n", - "\n", - "_shortened version of the [NOTEST] section above, removing the charts_" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "0ddec512-3d99-4a49-b570-08e46c319c02", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(6, 3.9999999999995595, 1.999999987845058)" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn1 = f.Quadratic(a=1, b=2, c=3)\n", - "assert f.Quadratic is f.QuadraticFunction\n", - "assert fn1.params() == {'a': 1, 'b': 2, 'c': 3}\n", - "fn2 = fn1.update(b=0)\n", - "assert fn2.params() == {'a': 1, 'b': 0, 'c': 3}\n", - "assert iseq(fn1(1), 6, fn1.f(1))\n", - "assert iseq(-fn1.p(1), 4, fn1.df_dx(1))\n", - "assert iseq(-fn1.pp(1), 2)\n", - "fn1(1), -fn1.p(1), -fn1.pp(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "c3a21fc9-6c7c-458c-ab9b-3a12260b56d2", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(1.0, -1.0000000099996686, 2.0000000100495186)" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn1 = f.Powerlaw()\n", - "assert f.Powerlaw is f.PowerlawFunction\n", - "assert fn1.params() == {'N': 1, 'alpha': -1, 'x0': 0}\n", - "fn2 = fn1.update(alpha=-2)\n", - "assert fn2.params() == {'N': 1, 'alpha': -2, 'x0': 0}\n", - "assert iseq(fn1(1), 1, fn1.f(1))\n", - "assert iseq(-fn1.p(1), -1, fn1.df_dx(1))\n", - "assert iseq(-fn1.pp(1), 2)\n", - "fn1(1), -fn1.p(1), -fn1.pp(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "f2c971c9-246e-4430-8677-dd45ef25eb3b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(1.2246467991473532e-16, -3.141592601913358, 0.0)" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn1 = f.Trig()\n", - "assert f.Trig is f.TrigFunction\n", - "assert fn1.params() == {'amp': 1, 'omega': 1, 'phase': 0}\n", - "fn2 = fn1.update(amp=-2)\n", - "assert fn2.params() == {'amp': -2, 'omega': 1, 'phase': 0}\n", - "assert iseq(0, fn1(1), fn1.f(1))\n", - "assert iseq(-fn1.PI, -fn1.p(1), fn1.df_dx(1))\n", - "assert iseq(0, -fn1.pp(1))\n", - "fn1(1), -fn1.p(1), -fn1.pp(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "c5393430-dd0f-4a01-b54e-fc56a7265f42", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(7.38905609893065, 14.778112296380819, 29.55622440126149)" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn1 = f.Exp(k=2)\n", - "assert f.Exp is f.ExpFunction\n", - "assert fn1.params() == {'N': 1, 'k': 2, 'x0': 0}\n", - "fn2 = fn1.update(k=-2)\n", - "assert fn2.params() == {'N': 1, 'k': -2, 'x0': 0}\n", - "assert iseq(fn1.E**2, fn1(1), fn1.f(1))\n", - "assert iseq(2*fn1.E**2, -fn1.p(1), fn1.df_dx(1))\n", - "assert iseq(4*fn1.E**2, -fn1.pp(1))\n", - "fn1(1), -fn1.p(1), -fn1.pp(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "abd60b3b-3dbf-4783-867b-d54863aef5ff", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, 0.4342944833508522, -0.4342944840747152)" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn1 = f.Log()\n", - "assert f.Log is f.LogFunction\n", - "assert fn1.params() == {'base': 10, 'N': 1, 'x0': 0}\n", - "fn2 = fn1.update(base=fn1.E)\n", - "assert fn2.params() == {'base': fn1.E, 'N': 1, 'x0': 0}\n", - "assert iseq(0, fn1(1), fn1.f(1))\n", - "assert iseq(0.4342944833508522, -fn1.p(1), fn1.df_dx(1))\n", - "assert iseq(-0.4342944840747152, -fn1.pp(1))\n", - "fn1(1), -fn1.p(1), -fn1.pp(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "dff1bb9c-2ae6-4cc9-9b86-5ceab89f697b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(1.0, -1.0000000099996686, 2.0000000100495186)" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn1 = f.Hyperbola()\n", - "assert f.Hyperbola is f.HyperbolaFunction\n", - "assert fn1.params() == {'k': 1, 'x0': 0, 'y0': 0}\n", - "fn2 = fn1.update(x0=1)\n", - "assert fn2.params() == {'k': 1, 'x0': 1, 'y0': 0}\n", - "assert iseq(1, fn1(1), fn1.f(1))\n", - "assert iseq(-1, -fn1.p(1), fn1.df_dx(1))\n", - "assert iseq(2, -fn1.pp(1))\n", - "fn1(1), -fn1.p(1), -fn1.pp(1)" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/NBTest/NBTest_066_InvariantsFunctions.py b/resources/NBTest/NBTest_066_InvariantsFunctions.py deleted file mode 100644 index 6c6f70ca0..000000000 --- a/resources/NBTest/NBTest_066_InvariantsFunctions.py +++ /dev/null @@ -1,992 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -try: - import fastlane_bot.tools.invariants.functions as f - from fastlane_bot.tools.invariants.kernel import Kernel - from fastlane_bot.testing import * - -except: - import tools.invariants.functions as f - from tools.invariants.kernel import Kernel - from testing import * - -import numpy as np -import math as m -import matplotlib.pyplot as plt - -plt.rcParams['figure.figsize'] = [12,6] - -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(f.Function)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Kernel)) -# - - -# # Functions (Invariants Module; NBTest066) - -# ## Functions - -# ### Built in functions -# #### QuadraticFunction - -qf = f.QuadraticFunction(a=1, b=0, c=-10) -assert qf.params() == {'a': 1, 'b': 0, 'c': -10} -assert qf.a == 1 -assert qf.b == 0 -assert qf.c == -10 - -qf2 = qf.update(c=-5) -assert raises(qf.update, k=1) -assert qf2.params() == {'a': 1, 'b': 0, 'c': -5} - -x_v = np.linspace(-5,5) -y1_v = [qf(xx) for xx in x_v] -y2_v = [qf2(xx) for xx in x_v] -plt.plot(x_v, y1_v, label="qf") -plt.plot(x_v, y2_v, label="qf2") -plt.legend() -plt.grid() - -x_v = np.linspace(-5,5) -y1_v = [qf(xx) for xx in x_v] -y2_v = [qf.p(xx) for xx in x_v] -y3_v = [qf.pp(xx) for xx in x_v] -plt.plot(x_v, y1_v, label="f") -plt.plot(x_v, y2_v, label="f'") -plt.plot(x_v, y3_v, label="f''") -plt.legend() -plt.grid() - -# #### TrigFunction - -# + -qf = f.TrigFunction() -assert qf.params() == {'amp': 1, 'omega': 1, 'phase': 0} -assert qf.amp == 1 -assert qf.omega == 1 -assert qf.phase == 0 -assert int(qf.PI) == 3 - -qf2 = qf.update(phase=1.5*qf.PI) -assert qf2.params() == {'amp': 1, 'omega': 1, 'phase': 1.5*qf.PI} -# - - -x_v = np.linspace(0, 4, 100) -y1_v = [qf(xx) for xx in x_v] -y2_v = [qf2(xx) for xx in x_v] -plt.plot(x_v, y1_v, label="qf") -plt.plot(x_v, y2_v, label="qf2") -plt.legend() -plt.grid() - -# #### HyperbolaFunction - -# + -qf = f.HyperbolaFunction() -assert qf.params() == {'k': 1, 'x0': 0, 'y0': 0} -assert qf.k == 1 -assert qf.x0 == 0 -assert qf.y0 == 0 - -qf2 = qf.update(y0=0.5) -# assert qf2.params() == {'amp': 1, 'omega': 1, 'phase': 1.5*qf.PI} -# - - -x_v = np.linspace(1, 10, 100) -y1_v = np.array([qf(xx) for xx in x_v]) -y2_v = np.array([qf2(xx) for xx in x_v]) -assert iseq(min(y2_v-y1_v), 0.5) -assert iseq(max(y2_v-y1_v), 0.5) -plt.plot(x_v, y1_v, label="qf") -plt.plot(x_v, y2_v, label="qf2") -plt.legend() -plt.grid() - -# ### Derivatives - -qf = f.QuadraticFunction(a=1, b=2, c=3) -qfp = qf.p_func() -qfpp = qf.pp_func() -assert qf.params() == {'a': 1, 'b': 2, 'c': 3} -assert qfp.func is qf -assert qfpp.func is qf - -x_v = np.linspace(-5,5) -y1_v = [qf(xx) for xx in x_v] -y2_v = [qfp(xx) for xx in x_v] -y3_v = [qfpp(xx) for xx in x_v] -plt.plot(x_v, y1_v, label="f") -plt.plot(x_v, y2_v, label="f'") -plt.plot(x_v, y3_v, label="f''") -plt.legend() -plt.grid() - -y2a_v = [qf.p(xx) for xx in x_v] # calculate the derivative from the original object -y3a_v = [qf.pp(xx) for xx in x_v] # ditto second derivative -y3b_v = [qfp.p(xx) for xx in x_v] # calculate the second derivative as derivative from the derivative object -assert y2a_v == y2_v # those are literally two ways of getting the same result -assert y3a_v == y3_v # ditto -assert iseq(min(y3_v), -2) # check that the second derivative is correct -assert iseq(max(y3_v), -2) # ditto -assert iseq(min(y3b_v), 2) # ditto, but the other way -assert iseq(max(y3b_v), 2) # ditto -min(y3_v), max(y3_v), min(y3b_v), max(y3b_v) - - -# ### Custom function - -@f.dataclass(frozen=True) -class MyFunction(f.Function): - k: float = 1 - - def f(self, x): - return (m.sqrt(1+x)-1)*self.k -mf = MyFunction() -mf2 = mf.update(k=2) -mf(1),mf.p(1),mf.pp(1) - -x_v = np.linspace(0,10) -y1_v = [mf(xx) for xx in x_v] -y2_v = [mf2(xx) for xx in x_v] -plt.plot(x_v, y1_v, label="mf") -plt.plot(x_v, y2_v, label="nf2") -plt.legend() -plt.grid() - -# ## Kernel - -# ### Integration function - -integrate = Kernel.integrate_trapezoid -ONE = lambda x: 1 -LIN = lambda x: 2*x -SQR = lambda x: 3*x*x - -assert iseq(integrate(ONE, 0, 1, 2), 1) # trapezoid integrates constant perfectly -assert iseq(integrate(ONE, 0, 1, 100), 1) -assert iseq(integrate(LIN, 0, 1, 2), 1) # ditto linear -assert iseq(integrate(LIN, 0, 1, 100), 1) -assert iseq(integrate(SQR, 0, 1, 100), 1, eps=1e-3) -assert iseq(integrate(SQR, 0, 1, 1000), 1, eps=1e-6) - -# ### Default kernel - -k = Kernel(steps=1000) -assert k.x_min == 0 -assert k.x_max == 1 -assert set(k.kernel(xx) for xx in np.linspace(k.x_min, k.x_max, 50)) == {1} -assert iseq(k.integrate(ONE), 1) -assert iseq(k.integrate(LIN), 1) -assert iseq(k.integrate(SQR), 1) -x_v = np.linspace(-0.5, 1.5, 1000) -plt.plot(x_v, [k.k(xx) for xx in x_v], label="default kernel") -plt.legend() -plt.grid() -plt.show() - -# ### Flat kernels - -k.integrate(ONE) - -k = Kernel(x_max=2, kernel=lambda x: 0.5, steps=1000) -assert k.x_min == 0 -assert k.x_max == 2 -assert set(k.kernel(xx) for xx in np.linspace(k.x_min, k.x_max, 50)) == {0.5} -assert iseq(k.integrate(ONE), 1) -assert iseq(k.integrate(LIN), 2) -assert iseq(k.integrate(SQR), 4) -x_v = np.linspace(-0.5, 2.5, 1000) -plt.plot(x_v, [k.k(xx) for xx in x_v], label="flat kernel 0..2") -plt.legend() -plt.grid() -plt.show() - -k = Kernel(x_max=4, kernel=lambda x: 0.25, steps=1000) -assert k.x_min == 0 -assert k.x_max == 4 -assert set(k.kernel(xx) for xx in np.linspace(k.x_min, k.x_max, 50)) == {0.25} -assert iseq(k.integrate(ONE), 1) -assert iseq(k.integrate(LIN), 4) -assert iseq(k.integrate(SQR), 16) -x_v = np.linspace(-0.5, 4.5, 1000) -plt.plot(x_v, [k.k(xx) for xx in x_v], label="flat kernel 0..4") -plt.legend() -plt.grid() -plt.show() - -k.integrate(LIN), k.integrate(SQR) - -# ### Triangle and sawtooth kernels - -kf = Kernel(x_min=1, x_max=3, kernel=Kernel.FLAT, steps=1000) -kl = Kernel(x_min=1, x_max=3, kernel=Kernel.SAWTOOTHL, steps=1000) -kr = Kernel(x_min=1, x_max=3, kernel=Kernel.SAWTOOTHR, steps=1000) -kt = Kernel(x_min=1, x_max=3, kernel=Kernel.TRIANGLE, steps=1000) -x_v = np.linspace(0.5, 3.5, 1000) -plt.plot(x_v, [kf.k(xx) for xx in x_v], label="flat") -plt.plot(x_v, [kl.k(xx) for xx in x_v], label="sawtooth left") -plt.plot(x_v, [kr.k(xx) for xx in x_v], label="sawtooth right") -plt.plot(x_v, [kt.k(xx) for xx in x_v], label="triangle") -plt.legend() -plt.grid() -plt.show() - -# + -assert iseq(kf.integrate(ONE), 1) -assert iseq(kl.integrate(ONE), 1) -assert iseq(kr.integrate(ONE), 1) -assert iseq(kt.integrate(ONE), 1) - -assert iseq(kf.integrate(LIN), 4) -assert iseq(kl.integrate(LIN), 10/3) -assert iseq(kr.integrate(LIN), 14/3) -assert iseq(kt.integrate(LIN), 4) - -assert iseq(kf.integrate(SQR), 13) -assert iseq(kl.integrate(SQR), 9) -assert iseq(kr.integrate(SQR), 17) -assert iseq(kt.integrate(SQR), 12.5) -# - - -# ### Gaussian kernels - -kf = Kernel(x_min=1, x_max=3, kernel=Kernel.FLAT, steps=1000) -kg = Kernel(x_min=1, x_max=3, kernel=Kernel.GAUSS, steps=1000) -kw = Kernel(x_min=1, x_max=3, kernel=Kernel.GAUSSW, steps=1000) -kn = Kernel(x_min=1, x_max=3, kernel=Kernel.GAUSSN, steps=1000) -x_v = np.linspace(0.5, 3.5, 1000) -plt.plot(x_v, [kf.k(xx) for xx in x_v], label="flat") -plt.plot(x_v, [kg.k(xx) for xx in x_v], label="gauss") -plt.plot(x_v, [kw.k(xx) for xx in x_v], label="gauss wide") -plt.plot(x_v, [kn.k(xx) for xx in x_v], label="gauss narrow") -plt.legend() -plt.grid() -plt.show() - -assert iseq(kf.integrate(ONE), 1) -assert iseq(kg.integrate(ONE), 1, eps=1e-3) -assert iseq(kw.integrate(ONE), 1, eps=1e-3) -assert iseq(kn.integrate(ONE), 1, eps=1e-3) - -# ## Function Vector - -# ### vector operations and consistency - -knl = Kernel(x_min=1, x_max=3, kernel=Kernel.FLAT, steps=1000) -f1 = f.QuadraticFunction(a=3, c=1) -f2 = f.QuadraticFunction(b=2) -f3 = f.QuadraticFunction(a=3, b=2, c=1) -f1v = f.FunctionVector({f1: 1}, kernel=knl) -f2v = f.FunctionVector({f2: 1}, kernel=knl) -fv = f.FunctionVector({f1: 1, f2: 1}, kernel=knl) -assert fv == f1v + f2v -x_v = np.linspace(1, 3, 100) -y1_v = [f1(xx) for xx in x_v] -y2_v = [f2(xx) for xx in x_v] -y3_v = [f3(xx) for xx in x_v] -yv_v = [fv(xx) for xx in x_v] -y_diff = np.array(yv_v) - np.array(y3_v) -plt.plot(x_v, y1_v, label="f1") -plt.plot(x_v, y2_v, label="f2") -plt.plot(x_v, y3_v, label="f3") -plt.legend() -plt.grid() - -assert max(y_diff)<1e-10 -assert min(y_diff)>-1e-10 -plt.plot(x_v, yv_v, linewidth=3, label="vector") -plt.plot(x_v, y3_v, linestyle="--", color="#ccc", label="f3") -plt.legend() -plt.grid() -plt.show() -plt.plot(x_v, y_diff) -plt.grid() -max(y_diff), min(y_diff) - -# check that you can't add vectors with different kernel - -# + -f1v = f.FunctionVector({f1: 1}, kernel=knl) -f2v = f.FunctionVector({f2: 1}, kernel=knl) -assert not raises(lambda: f1v+f2v) -assert not raises(lambda: f1v-f2v) - -f1v = f.FunctionVector({f1: 1}, kernel=knl) -f2v = f.FunctionVector({f2: 1}, kernel=None) -assert raises(lambda: f1v+f2v) -assert raises(lambda: f1v-f2v) -# - - -# ### convenience methods -# - -fv = f.FunctionVector( - { - f.QuadraticFunction(a=1, b=2): 1, - f.HyperbolaFunction(k=100, x0=2): 1, - f.TrigFunction(phase=0.5): 1, - }, - kernel=knl -) - -# #### params - -assert isinstance(fv.params(as_dict=True), dict) -assert len(fv.params()) == len(fv) -fv.params(as_dict=True) - -assert fv.params() == fv.params(as_dict=False) -assert not fv.params(as_dict=False) == fv.params(as_dict=True) -assert len(fv.params(as_dict=False)) == len(fv) -assert list(fv.params(as_dict=True).values()) == fv.params(as_dict=False) -assert fv.params(as_dict=False)[1] == {'k': 100, 'x0': 2, 'y0': 0, '_classname': 'HyperbolaFunction'} -assert fv.params(as_dict=False, classname=False)[2] == {'amp': 1, 'omega': 1, 'phase': 0.5} -fv.params(as_dict=False) - -assert fv.params(index=2) == fv.params(2) -assert isinstance(fv.params(index=2, as_dict=True), dict) -assert isinstance(fv.params(index=2, as_dict=False), dict) -assert fv.params(index=2, as_dict=False) != fv.params(index=2, as_dict=True) -assert fv.params(index=2) == {'amp': 1, 'omega': 1, 'phase': 0.5, '_classname': 'TrigFunction'} -assert fv.params(index=2, classname=False) == {'amp': 1, 'omega': 1, 'phase': 0.5} -fv.params(index=2) - -# #### update - -assert raises(fv.update, [1,2,3]) == 'update with list of params not implemented yet' -assert raises(fv.update, [1,2,3], index=1) == 'index and key must be None if params is a list' -assert raises(fv.update, [1,2,3], 1) == 'index and key must be None if params is a list' -assert raises(fv.update, [1,2,3], key=1) == 'index and key must be None if params is a list' -assert raises(fv.update, dict()) == 'exactly one of index or key must be given' -assert raises(fv.update, dict(), index=1, key=1) == "can't give both index and key" -assert raises(fv.update, dict(), key=1) == "key not implemented yet" -params = fv.params() -fv.params() - -fv_1 = fv.update(dict(c=3), 0) -params1 = fv_1.params() -assert params[0] != params1[0] -assert params[1:] == params1[1:] -assert params1[0] == {'a': 1, 'b': 2, 'c': 3, '_classname': 'QuadraticFunction'} -assert params1[0]["c"] == 3 -assert params1[0]["a"] == params[0]["a"] -assert params1[0]["b"] == params[0]["b"] -assert params1[0]["_classname"] == params[0]["_classname"] -params1 - -# ### integration and norms - -# #### high level - -f1,f2 - -f1v = f.FunctionVector({f1: 1}, kernel=knl) -f2v = f1v.wrap(f2) -f1v.plot(show=False, label="f1") -f2v.plot(show=False, label="f2") -fv=f1v+f2v -fv.plot(show=False, label="f1+f2") -plt.legend() -print(f1v.kernel) -plt.show() -assert f1v.kernel == f2v.kernel -assert f1v.kernel == fv.kernel - -# + -assert iseq(f1v.integrate(), 13+1) - # assert iseq(kf.integrate(ONE), 1) - # assert iseq(kf.integrate(SQR), 13) - -assert iseq(f2v.integrate(), 4) - # assert iseq(kf.integrate(LIN), 4) - -assert iseq(fv.integrate(), 18) -# - - -f2v.integrate() - -# #### quantification - -kernel = f.Kernel(x_min=0, x_max=1) -qf_v = f.QuadraticFunction(c=1).wrap(kernel) -qf2_v = f.QuadraticFunction(c=2).wrap(kernel) -qfl_v = f.QuadraticFunction(b=1).wrap(kernel) -qfq_v = f.QuadraticFunction(a=1).wrap(kernel) -qfl1_v = qfl_v + qf_v -qflm_v = 2*qfl_v - qf_v -qf_v.plot(show=False) -qf2_v.plot(show=False) -qfl_v.plot(show=False) -qfq_v.plot(show=False) -qfl1_v.plot(show=False) -qflm_v.plot(show=False) -#plt.ylim(-1,None) - -# + -# f(x) = 1 => Int = 1, Norm2 = 1 -assert qf_v.integrate() == 1 -assert qf_v.norm2() == 1 -assert qf_v.norm1() == 1 -assert qf_v.norm() == 1 - -# f(x) = 2 => Int = 2, Norm2 = 4 -assert qf2_v.integrate() == 2 -assert qf2_v.norm2() == 4 -assert qf2_v.norm1() == 2 -assert qf2_v.norm() == 2 - -# f(x) = x => Int = 1/2, Norm2 = 1/3 -assert qfl_v.integrate() == 1/2 -assert iseq(qfl_v.norm2(), qfq_v.integrate()) -assert iseq(qfl_v.norm2(), 1/3, eps=1e-3) -assert iseq(qfl_v.norm1(), 1/2, eps=1e-3) -assert iseq(qfl_v.norm(), m.sqrt(qfl_v.norm2())) - -# f(x) = x^2 => Int = 1/3, Norm2 = 1/5 -assert iseq(qfq_v.integrate(), 1/3, eps=1e-3) -assert iseq(qfq_v.norm2(), 1/5, eps=1e-3) -assert iseq(qfq_v.norm1(), 1/3, eps=1e-3) -assert iseq(qfq_v.norm(), m.sqrt(qfq_v.norm2())) - -# f(x) = 1 + x ==> Int = 1.5, Norm2 = 2 1/3 -assert iseq(qfl1_v.integrate(), 1.5) -assert iseq(qfl1_v.integrate(), qfl_v.integrate() + qf_v.integrate()) -assert iseq(qfl1_v.norm2(), 2+1/3, eps=1e-3) - # (1+x)^2 = x^2 + 2x + 1 => 1/3 x^3 + x^2 + x = 2 1/3 -assert iseq(qfl1_v.norm1(), 1.5, eps=1e-3) -assert iseq(qfl1_v.norm(), m.sqrt(qfl1_v.norm2())) - -# f(x) = 1 - 2x => Int = 0, Norm1 = 1/2, Norm2 = 1/3 -assert iseq(0, qflm_v.integrate(), eps=1e-3) -assert iseq(qflm_v.norm2(), 1/3, eps=1e-3) - # x - 2/3 x^3 = 1/3 -assert iseq(qflm_v.norm1(), 1/2, eps=1e-3) -assert iseq(qflm_v.norm(), m.sqrt(qflm_v.norm2())) -# - - -# ### goal seek and minimize - -f1 = f.QuadraticFunction(a=1, c=-4) -f1v = f.FunctionVector().wrap(f1) -x_v = np.linspace(-2.5, 2.5, 100) -y1_v = [f1(xx) for xx in x_v] -plt.plot(x_v, y1_v, label="f") -#plt.legend() -plt.grid() - -assert iseq(f1v.goalseek(target=0, x0=1), 2) -assert iseq(f1v.goalseek(target=0, x0=-1), -2) -assert iseq(f1v.goalseek(target=-3, x0=1), 1) -assert iseq(f1v.goalseek(target=-3, x0=-1), -1) -assert iseq(0, f1v.minimize1(x0=5), eps=1e-3) -f1v.minimize1(x0=5) - -f2 = f.QuadraticFunction(a=3, b=2, c=1) -f2v = f.FunctionVector({f2: 1}) -x_v = np.linspace(-2.5, 2.5, 100) -y2_v = [f2(xx) for xx in x_v] -plt.plot(x_v, y2_v, label="f") -#plt.legend() -plt.grid() - -assert iseq(f2v.goalseek(target=5), 0.8685170919424989, eps=1e-4) -assert iseq(f2v.minimize1(), -0.3332480000000852, eps=1e-4) -f2v.goalseek(target=5), f2v.minimize1() - -# ## Restricted and apply kernel -# -# restricted functions (`f_r`, more generally `restricted(func)`) are zero outside the kernel domain; kernel-applied functions (`f_k`, more generally `apply_kernel(func)`) is multiplied with the kernel - -func = f.TrigFunction() - -# ### Flat kernel - -# + -kernel = Kernel(0, 1, Kernel.FLAT) -fv = f.FunctionVector({func: 1}, kernel=kernel) -f_r = fv.restricted(fv.f) -f_k = fv.apply_kernel(fv.f) - -assert not fv.f(-0.5) == 0 -assert not fv.f(1.5) == 0 -assert f_r(-0.5) == fv.f_r(-0.5) == 0 -assert f_r(1.5) == fv.f_r(1.5) == 0 -assert f_r(0.5) == fv.f_r(0.5) == fv.f(0.5) -assert f_r(0.25) == fv.f_r(0.25) == fv.f(0.25) -assert f_r(0.75) == fv.f_r(0.75) == fv.f(0.75) - -assert f_k(-0.5) == fv.f_k(-0.5) == 0 -assert f_k(1.5) == fv.f_k(1.5) == 0 -assert f_k(0.5) == fv.f_k(0.5) == fv.f(0.5) * kernel(0.5) -assert f_k(0.25) == fv.f_k(0.25) == fv.f(0.25) * kernel(0.25) -assert f_k(0.75) == fv.f_k(0.75) == fv.f(0.75) * kernel(0.75) - -fv.plot(fv.f, x_min=-1, x_max=2, title="full function [self.f]") -fv.plot(fv.f_r, x_min=-1, x_max=2, title="restricted function [self.f_r]") -fv.plot(fv.f_k, x_min=-1, x_max=2, title="flat kernel applied [self.f_k]") -# - - -# ### Sawtooth-Left kernel - -# + -kernel = Kernel(0, 1, Kernel.SAWTOOTHL) -fv = f.FunctionVector({func: 1}, kernel=kernel) -f_r = fv.restricted(fv.f) -f_k = fv.apply_kernel(fv.f) - -assert not fv.f(-0.5) == 0 -assert not fv.f(1.5) == 0 -assert f_r(-0.5) == fv.f_r(-0.5) == 0 -assert f_r(1.5) == fv.f_r(1.5) == 0 -assert f_r(0.5) == fv.f_r(0.5) == fv.f(0.5) -assert f_r(0.25) == fv.f_r(0.25) == fv.f(0.25) -assert f_r(0.75) == fv.f_r(0.75) == fv.f(0.75) - -assert f_k(-0.5) == fv.f_k(-0.5) == 0 -assert f_k(1.5) == fv.f_k(1.5) == 0 -assert f_k(0.5) == fv.f_k(0.5) == fv.f(0.5) * kernel(0.5) -assert f_k(0.25) == fv.f_k(0.25) == fv.f(0.25) * kernel(0.25) -assert f_k(0.75) == fv.f_k(0.75) == fv.f(0.75) * kernel(0.75) - -fv.plot(fv.f, x_min=-1, x_max=2, title="full function [self.f]") -fv.plot(fv.f_r, x_min=-1, x_max=2, title="restricted function [self.f_r]") -fv.plot(fv.f_k, x_min=-1, x_max=2, title="sawtooth-left kernel applied [self.f_k]") -# - - -# ## Curve fitting - -# ### norm and curve distance -# -# We have various ways of measuring the distance between a FunctionVector (that includes a kernel) and a Function, all being based on the L2 norm with kernel applied -# -# - Use `FunctionVector.distance2` for the squared distance between the FunctionVector and the Function, or `distance` for the squareroot thereof* -# -# - Wrap the Function in a FunctionVector with the same kernel using the `wrap` method, substract the two FunctionVectors from each other, and use `norm2` or `norm` -# -# *in optimization you typically want to use the squared function because it behaves better and you don't have to calculate the square root - -# + -# create the template function vector -fv_t = f.FunctionVector(kernel=Kernel(x_min=-1, x_max=1, kernel=Kernel.FLAT)) -assert fv_t.f(0) == 0 - -# create target and match functions and wrap them in FunctionVector -f0 = f.TrigFunction(phase=1/2) -f0v = fv_t.wrap(f0) -f1v = fv_t.wrap(f.QuadraticFunction(c=0)) -f2v = fv_t.wrap(f.QuadraticFunction(a=-2, c=1)) - -# check norms and distances -diff1 = (f0v-f1v).norm() -diff2 = (f0v-f2v).norm() -assert iseq( (f0v-f1v).norm2(), (f0v-f1v).norm()**2) -assert iseq( (f0v-f2v).norm2(), (f0v-f2v).norm()**2) -assert iseq(f1v.dist2_L2(f0), (f0v-f1v).norm2()) -assert iseq(f2v.dist2_L2(f0), (f0v-f2v).norm2()) -assert iseq(f1v.dist_L2(f0), (f0v-f1v).norm()) -assert iseq(f2v.dist_L2(f0), (f0v-f2v).norm()) -assert iseq(f1v.dist_L1(f0), (f0v-f1v).norm1()) -assert iseq(f2v.dist_L1(f0), (f0v-f2v).norm1()) - -# plot -f0v.plot(show=False, label="f0 [target function]") -f1v.plot(show=False, label=f"f1 [match 1]: dist={diff1:.2f}") -f2v.plot(show=False, label=f"f2 [match 2]: dist={diff2:.2f}") -plt.legend() -plt.show() -# - - -# ### norm and curve distance on price functions -# -# Note: what we call a _price function_ is simply the negative first derivative, assuming the functions are swap function - -# + -fv_t = f.FunctionVector(kernel=Kernel(x_min=0, x_max=1, kernel=Kernel.FLAT)) -fn1_fv = fv_t.wrap(f.QuadraticFunction(c=1)) # f(x) = 1 -fn2_fv = fv_t.wrap(f.QuadraticFunction(c=1, b=-1)) # f(x) = 1-x -null_f = lambda x: 0 -half_f = lambda x: 0.5 -one_f = lambda x: 1 -fn1_fv.plot(label="fn1(x)=1", linewidth=3) -fn2_fv.plot(label="fn2(x)=1-x") -fn1_fv.plot(func=fn1_fv.p, label="fn1.p(x)=0") -fn2_fv.plot(func=fn2_fv.p, linestyle="--", color="#faa", label="fn2.p(x)=1") - -plt.legend() -plt.show() -# - - -# #### norm - -# + -# method-level equality -# ... on self.f -assert fn1_fv.norm2_L2 == fn1_fv.norm2 -assert fn1_fv.norm_L2 == fn1_fv.norm -assert fn1_fv.norm_L1 == fn1_fv.norm1 -# ... on self.p -assert fn1_fv.normp2_L2 == fn1_fv.normp2 -assert fn1_fv.normp_L2 == fn1_fv.normp -assert fn1_fv.normp_L1 == fn1_fv.normp1 - -# checking values fn1 -# ... on self.f -assert fn1_fv.norm2_L2() == 1 -assert fn1_fv.norm_L2() == 1 -assert fn1_fv.norm_L1() == 1 -# ... on self.p -assert fn1_fv.normp2_L2() == 0 -assert fn1_fv.normp_L2() == 0 -assert fn1_fv.normp_L1() == 0 - -# # checking values fn2 -# # ... on self.f -assert iseq(1/3, fn2_fv.norm2_L2(), eps=1e-4) -assert iseq(m.sqrt(1/3), fn2_fv.norm_L2(), eps=1e-4) -assert iseq(1/2, fn2_fv.norm_L1(), eps=1e-4) -# # ... on self.p -assert iseq(1, fn2_fv.normp2_L2(), eps=1e-4) -assert iseq(1, fn2_fv.normp_L2(), eps=1e-4) -assert iseq(1, fn2_fv.normp_L1(), eps=1e-4) -# - - -# #### distance - -# + -# checking values fn1 vs null_f [1-0] -# ... on self.f -assert fn1_fv.dist2_L2(func=null_f) == 1 -assert fn1_fv.dist_L2(func=null_f) == 1 -assert fn1_fv.dist_L1(func=null_f) == 1 -# ... on self.p -assert fn1_fv.distp2_L2(func=null_f) == 0 -assert fn1_fv.distp_L2(func=null_f) == 0 -assert fn1_fv.distp_L1(func=null_f) == 0 - -# # checking values fn2 vs null_f [1-x-0] -# # ... on self.f -assert iseq(1/3, fn2_fv.dist2_L2(func=null_f), eps=1e-4) -assert iseq(m.sqrt(1/3), fn2_fv.dist_L2(func=null_f), eps=1e-4) -assert iseq(1/2, fn2_fv.dist_L1(func=null_f), eps=1e-4) -# # ... on self.p -assert iseq(1, fn2_fv.distp2_L2(func=null_f), eps=1e-4) -assert iseq(1, fn2_fv.distp_L2(func=null_f), eps=1e-4) -assert iseq(1, fn2_fv.distp_L1(func=null_f), eps=1e-4) - -# + -# checking values fn1 vs one_f [1-1] -# ... on self.f -assert fn1_fv.dist2_L2(func=one_f) == 0 -assert fn1_fv.dist_L2(func=one_f) == 0 -assert fn1_fv.dist_L1(func=one_f) == 0 -# ... on self.p -assert fn1_fv.distp2_L2(func=one_f) == 1 -assert fn1_fv.distp_L2(func=one_f) == 1 -assert fn1_fv.distp_L1(func=one_f) == 1 - -# # checking values fn2 vs one_f [1-x-1] -# # ... on self.f -assert iseq(1/3, fn2_fv.dist2_L2(func=one_f), eps=1e-4) -assert iseq(m.sqrt(1/3), fn2_fv.dist_L2(func=one_f), eps=1e-4) -assert iseq(1/2, fn2_fv.dist_L1(func=one_f), eps=1e-4) -# # ... on self.p -assert iseq(0, fn2_fv.distp2_L2(func=one_f), eps=1e-4) -assert iseq(0, fn2_fv.distp_L2(func=one_f), eps=1e-4) -assert iseq(0, fn2_fv.distp_L1(func=one_f), eps=1e-4) - -# + -# checking values fn1 vs half_f [1-0.5=0.5] -# ... on self.f -assert fn1_fv.dist2_L2(func=half_f) == 0.25 -assert fn1_fv.dist_L2(func=half_f) == 0.5 -assert fn1_fv.dist_L1(func=half_f) == 0.5 -# ... on self.p -assert fn1_fv.distp2_L2(func=half_f) == 0.25 -assert fn1_fv.distp_L2(func=half_f) == 0.5 -assert fn1_fv.distp_L1(func=half_f) == 0.5 - -# # checking values fn2 vs half_f [1-x-0.5=0.5-x] -# # ... on self.f -assert iseq(1/12, fn2_fv.dist2_L2(func=half_f), eps=1e-3) #int_0..1 (0.5-x)^2 = 1/12 -assert iseq(m.sqrt(1/12), fn2_fv.dist_L2(func=half_f), eps=1e-3) -assert iseq(1/4, fn2_fv.dist_L1(func=half_f), eps=1e-4) -# # ... on self.p -assert iseq(0.25, fn2_fv.distp2_L2(func=half_f), eps=1e-4) -assert iseq(0.5, fn2_fv.distp_L2(func=half_f), eps=1e-4) -assert iseq(0.5, fn2_fv.distp_L1(func=half_f), eps=1e-4) -# - - -# ### curve fitting - -# #### flat kernel - -fv_template = f.FunctionVector(kernel=Kernel(x_min=-1, x_max=1, kernel=Kernel.FLAT)) -target_f = f.TrigFunction(phase=1/2) -target_fv = fv_template.wrap(target_f) -f_match0 = f.QuadraticFunction() -params0 = dict(a=0, b=0, c=0) -params = target_fv.curve_fit(f_match0, params0) -f_match = f_match0.update(**params) -params, f_match - -f_match_v = target_fv.wrap(f_match) -diff = (target_fv-f_match_v).norm() -target_fv.plot(show=False, label="target function") -f_match_v.plot(show=False, label=f"match (dist={diff:.2f})") -plt.title(f"Best fit (a={params['a']:.2f}, b={params['b']:.2f}, c={params['c']:.2f}); dist={diff:.2f}") -plt.legend() -f_match_v - -# #### skewed kernel (sawtooth-left) - -fv_template = f.FunctionVector(kernel=Kernel(x_min=-1, x_max=1, kernel=Kernel.SAWTOOTHL)) -target_f = f.TrigFunction(phase=1/2) -target_fv = fv_template.wrap(target_f) -f_match0 = f.QuadraticFunction() -params0 = dict(a=0, b=0, c=0) -params = target_fv.curve_fit(f_match0, params0) -f_match = f_match0.update(**params) -target_fv.kernel, params, f_match - -f_match_v = target_fv.wrap(f_match) -diff = (target_fv-f_match_v).norm() -target_fv.plot(show=False, label="target function") -f_match_v.plot(show=False, label=f"match (dist={diff:.2f})") -plt.title(f"Best fit (a={params['a']:.2f}, b={params['b']:.2f}, c={params['c']:.2f}); dist={diff:.2f}") -plt.legend() -f_match_v - -# ## High dimensional minimization - -# ### Example -# -# here we use as example the function -# -# $$ -# f(x,y) = (x-2)^2 + (y-2)^2 -# $$ -# -# which obviously should be minimal at $(x,y) = (2,2)$ - -func = lambda x,y: (x-2)**2 + (y-2)**2 - -r, dxdy = f.minimize(func, x0=[20, -5], learning_rate=None, return_path=True) -assert iseq(r[-1][0], 2, eps=1e-3) -assert iseq(r[-1][1], 2, eps=1e-3) -r[-1], dxdy - -x,y = zip(*r) -plt.scatter(x,y) -plt.title("Convergence path") -plt.grid() - -r, dxdy = f.minimize(func, x0=dict(x=20, y=-5), learning_rate=None, return_path=True) -assert iseq(r[-1]["x"], 2, eps=1e-3) -assert iseq(r[-1]["y"], 2, eps=1e-3) -r[-1], dxdy - -# ### Testing e_i, e_k and bump - -e_i = f.FunctionVector.e_i -e_k = f.FunctionVector.e_k -bump = f.FunctionVector.bump - -assert np.array_equal(e_i(1,5), np.array([0., 1., 0., 0., 0.])) -assert e_k("b", dict(a=1, b=2, c=3)) == {'a': 0, 'b': 1, 'c': 0} -assert bump(dict(a=1, b=2, c=3), "b", 0.25) == {'a': 1, 'b': 2.25, 'c': 3} - -# ## Sundry tests - -fmt = f.core.fmt -dct = {"a": 1.234578, "b": 2.3456789} -lst = list(dct.values()) -assert fmt(dct) == {'a': 1.2346, 'b': 2.3457} -assert fmt(lst) == [1.2346, 2.3457] -assert fmt(dct, ".2f") == {'a': 1.23, 'b': 2.35} -assert fmt(lst, ".2f") == [1.23, 2.35] -assert fmt(lst, ".2f", as_float=False) == ['1.23', '2.35'] -fmt(lst, ".2f") - -# ## Function examples [NOTEST] - -# ### QuadraticFunction - -fn1 = f.Quadratic(a=1, b=2, c=3) -print(fn1.params()) -fn2 = fn1.update(b=3, c=4) -diff2 = lambda x: (fn1(x)-fn2(x))**2 -fn1.plot(-5,5, label="fn1") -fn2.plot(-5,5, label="fn2") -fn2.plot(-5,5, func=diff2, label="(fn1-fn2)^2") -fn2.plot(-5,5, func=fn2.p, label="-fn2'") -fn2.plot(-5,5, func=fn2.pp, label="-fn2''") -plt.legend() -x0 = f.goalseek(func=diff2) -print(f"fn1 = fn2 @ ({x0:.2f}, {fn1(x0):.2f})") -plt.show() - -# ### PowerlawFunction - -fn1 = f.Powerlaw() -print(fn1.params()) -fn2 = fn1.update(x0=0.5) -fn3 = fn2.update(alpha=-1.5) -diff2 = lambda x: (fn3(x)-fn1(x))**2 -fn1.plot(1,3, label="fn1") -fn2.plot(1,3, label="fn2") -fn3.plot(1,3, label="fn3") -fn2.plot(1,3, func=diff2, label="(fn3-fn1)^2") -fn2.plot(1,3, func=fn2.p, label="-fn2'") -fn2.plot(2,3, func=fn2.pp, label="-fn2''") -plt.legend() -x0 = f.goalseek(func=diff2) -print(f"fn1 = fn3 @ ({x0:.2f}, {fn1(x0):.2f})") -plt.show() - -# ### TrigFunction - -fn1 = f.Trig() -print(fn1.params()) -fn2 = fn1.update(omega=1.2) -fn3 = fn2.update(phase=-0.1) -diff2 = lambda x: (fn3(x)-fn1(x))**2 -fn1.plot(1,3, label="fn1") -fn2.plot(1,3, label="fn2") -fn3.plot(1,3, label="fn3") -fn2.plot(1,3, func=diff2, label="(fn3-fn1)^2") -#fn2.plot(1,3, func=fn2.p, label="-fn2'") -#fn2.plot(1,3, func=fn2.pp, label="-fn2''") -plt.legend() -x0 = f.goalseek(func=diff2, x0=1.5) -print(f"fn1 = fn3 @ ({x0:.2f}, {fn1(x0):.2f})") -plt.show() - -# ### ExpFunction - -fn1 = f.Exp() -print(fn1.params()) -fn2 = fn1.update(k=1.2) -fn3 = fn2.update(x0=0.1) -diff2 = lambda x: (fn3(x)-fn1(x))**2 -fn1.plot(0, 2, label="fn1") -fn2.plot(0, 2, label="fn2") -fn3.plot(0, 2, label="fn3") -fn2.plot(0, 2, func=diff2, label="(fn3-fn1)^2") -fn2.plot(0, 2, func=fn2.p, label="-fn2'") -fn2.plot(0, 2, func=fn2.pp, label="-fn2''") -plt.legend() -x0 = f.goalseek(func=diff2, x0=1.5) -print(f"fn1 = fn3 @ ({x0:.2f}, {fn1(x0):.2f})") -plt.show() - -# ### LogFunction - -fn1 = f.Log() -print(fn1.params()) -fn2 = fn1.update(base=fn1.E) -fn3 = fn2.update(x0=0.1) -diff2 = lambda x: (fn3(x)-fn1(x))**2 -fn1.plot(1, 5, label="fn1") -fn2.plot(1, 5, label="fn2") -fn3.plot(1, 5, label="fn3") -fn2.plot(1, 5, func=diff2, label="(fn3-fn1)^2") -fn2.plot(1, 5, func=fn2.p, label="-fn2'") -fn2.plot(1, 5, func=fn2.pp, label="-fn2''") -plt.legend() -x0 = f.goalseek(func=diff2, x0=1.5) -print(f"fn1 = fn3 @ ({x0:.2f}, {fn1(x0):.2f})") -plt.show() - -# ### HyperbolaFunction - -fn1 = f.Hyperbola() -print(fn1.params()) -fn2 = fn1.update(k=1.2) -fn3 = fn2.update(x0=-0.5) -diff2 = lambda x: (fn3(x)-fn1(x))**2 -fn1.plot(0.5, 3, label="fn1") -fn2.plot(0.5, 3, label="fn2") -fn3.plot(0.5, 3, label="fn3") -fn2.plot(0.5, 3, func=diff2, label="(fn3-fn1)^2") -fn2.plot(0.5, 3, func=fn2.p, label="-fn2'") -fn2.plot(1.5, 3, func=fn2.pp, label="-fn2''") -plt.legend() -x0 = f.goalseek(func=diff2, x0=1.5) -print(f"fn1 = fn3 @ ({x0:.2f}, {fn1(x0):.2f})") -plt.show() - -# ## Function examples -# -# _shortened version of the [NOTEST] section above, removing the charts_ - -fn1 = f.Quadratic(a=1, b=2, c=3) -assert f.Quadratic is f.QuadraticFunction -assert fn1.params() == {'a': 1, 'b': 2, 'c': 3} -fn2 = fn1.update(b=0) -assert fn2.params() == {'a': 1, 'b': 0, 'c': 3} -assert iseq(fn1(1), 6, fn1.f(1)) -assert iseq(-fn1.p(1), 4, fn1.df_dx(1)) -assert iseq(-fn1.pp(1), 2) -fn1(1), -fn1.p(1), -fn1.pp(1) - -fn1 = f.Powerlaw() -assert f.Powerlaw is f.PowerlawFunction -assert fn1.params() == {'N': 1, 'alpha': -1, 'x0': 0} -fn2 = fn1.update(alpha=-2) -assert fn2.params() == {'N': 1, 'alpha': -2, 'x0': 0} -assert iseq(fn1(1), 1, fn1.f(1)) -assert iseq(-fn1.p(1), -1, fn1.df_dx(1)) -assert iseq(-fn1.pp(1), 2) -fn1(1), -fn1.p(1), -fn1.pp(1) - -fn1 = f.Trig() -assert f.Trig is f.TrigFunction -assert fn1.params() == {'amp': 1, 'omega': 1, 'phase': 0} -fn2 = fn1.update(amp=-2) -assert fn2.params() == {'amp': -2, 'omega': 1, 'phase': 0} -assert iseq(0, fn1(1), fn1.f(1)) -assert iseq(-fn1.PI, -fn1.p(1), fn1.df_dx(1)) -assert iseq(0, -fn1.pp(1)) -fn1(1), -fn1.p(1), -fn1.pp(1) - -fn1 = f.Exp(k=2) -assert f.Exp is f.ExpFunction -assert fn1.params() == {'N': 1, 'k': 2, 'x0': 0} -fn2 = fn1.update(k=-2) -assert fn2.params() == {'N': 1, 'k': -2, 'x0': 0} -assert iseq(fn1.E**2, fn1(1), fn1.f(1)) -assert iseq(2*fn1.E**2, -fn1.p(1), fn1.df_dx(1)) -assert iseq(4*fn1.E**2, -fn1.pp(1)) -fn1(1), -fn1.p(1), -fn1.pp(1) - -fn1 = f.Log() -assert f.Log is f.LogFunction -assert fn1.params() == {'base': 10, 'N': 1, 'x0': 0} -fn2 = fn1.update(base=fn1.E) -assert fn2.params() == {'base': fn1.E, 'N': 1, 'x0': 0} -assert iseq(0, fn1(1), fn1.f(1)) -assert iseq(0.4342944833508522, -fn1.p(1), fn1.df_dx(1)) -assert iseq(-0.4342944840747152, -fn1.pp(1)) -fn1(1), -fn1.p(1), -fn1.pp(1) - -fn1 = f.Hyperbola() -assert f.Hyperbola is f.HyperbolaFunction -assert fn1.params() == {'k': 1, 'x0': 0, 'y0': 0} -fn2 = fn1.update(x0=1) -assert fn2.params() == {'k': 1, 'x0': 1, 'y0': 0} -assert iseq(1, fn1(1), fn1.f(1)) -assert iseq(-1, -fn1.p(1), fn1.df_dx(1)) -assert iseq(2, -fn1.pp(1)) -fn1(1), -fn1.p(1), -fn1.pp(1) diff --git a/resources/NBTest/NBTest_067_Invariants.ipynb b/resources/NBTest/NBTest_067_Invariants.ipynb deleted file mode 100644 index af9f66a28..000000000 --- a/resources/NBTest/NBTest_067_Invariants.ipynb +++ /dev/null @@ -1,686 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "0278c025-06e6-416b-9525-c2a4a8ae9128", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "Function v0.9.7 (21/Mar/2024)\n", - "BancorInvariant v0.9 (18/Jan/2024)\n" - ] - } - ], - "source": [ - "try:\n", - " import fastlane_bot.tools.invariants.functions as f\n", - " from fastlane_bot.tools.invariants.invariant import Invariant\n", - " from fastlane_bot.tools.invariants.bancor import BancorInvariant, BancorSwapFunction\n", - " from fastlane_bot.tools.invariants.solidly import SolidlyInvariant, SolidlySwapFunction\n", - " from fastlane_bot.testing import *\n", - "\n", - "except:\n", - " import tools.invariants.functions as f\n", - " from tools.invariants.invariant import Invariant\n", - " from tools.invariants.bancor import BancorInvariant, BancorSwapFunction\n", - " from tools.invariants.solidly import SolidlyInvariant, SolidlySwapFunction\n", - " from tools.testing import *\n", - "\n", - "import numpy as np\n", - "import math as m\n", - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "plt.rcParams['figure.figsize'] = [12,6]\n", - "\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(f.Function))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(BancorInvariant))" - ] - }, - { - "cell_type": "markdown", - "id": "7e212348-81d0-49f2-8d41-c7842a387634", - "metadata": {}, - "source": [ - "# Invariants (Invariants Module; NBTest067)" - ] - }, - { - "cell_type": "markdown", - "id": "2fb31878-07de-4ff8-89a6-8f5917f26f2e", - "metadata": {}, - "source": [ - "## General invariants" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b2dc880c-13aa-42d6-b54b-0bf1a240aae9", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "inv = BancorInvariant()" - ] - }, - { - "cell_type": "markdown", - "id": "4701eb9f-5d92-475e-84f2-37ea7f0e27ce", - "metadata": {}, - "source": [ - "### goal seek" - ] - }, - { - "cell_type": "markdown", - "id": "3a1ce2b7-7c78-4a9a-96ee-5398eaaf4b18", - "metadata": {}, - "source": [ - "testing on $(x-1)(x+1)$" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "cbed40a9-442e-4e20-bd71-3f5360a7cf0a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "func = lambda x: x**2 - 1\n", - "assert iseq(inv.goalseek_gradient(func, x0=-0.1), -1)\n", - "assert iseq(inv.goalseek_gradient(func, x0=0.1), 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "422f9e88-ee87-4e46-ba0f-8547b4a40af9", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(inv.goalseek_bisect(func, x_lo=0, x_hi=10), 1)\n", - "assert iseq(inv.goalseek_bisect(func, x_lo=0, x_hi=-10), -1)" - ] - }, - { - "cell_type": "markdown", - "id": "7f55341d-8b52-4970-8d03-de548a90d6d2", - "metadata": {}, - "source": [ - "testing on AMM invariant $k/x$" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9428308b-f778-4060-b497-0b4d97a25609", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(inv.goalseek_gradient(lambda x: 100/x - 5), 20)\n", - "assert iseq(inv.goalseek_gradient(lambda x: 100/x - 20), 5)\n", - "assert iseq(inv.goalseek_gradient(lambda x: 100/x - 10), 10)\n", - "assert iseq(inv.goalseek_gradient(lambda x: 100/x - 50), 2)" - ] - }, - { - "cell_type": "markdown", - "id": "2f89d075-2bce-4744-ab36-000857b96791", - "metadata": {}, - "source": [ - "#### timing " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "495e4468-b029-4542-9374-fd1d3634e485", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(5.0, 4.9999999999999725, 4.999999997468219)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inv.y_func(20, k=100), inv.y_func_from_k_func(20, k=100), inv.y_func_from_k_func(20, k=100, method=inv.GS_BISECT)" - ] - }, - { - "cell_type": "markdown", - "id": "77f3461e-2db3-4348-8275-f75087722bb8", - "metadata": { - "tags": [] - }, - "source": [ - "note that the gradient method is almost certainly going to be faster than bisection, unless we are very good at bracketing (or put the tolerance very low)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9d045b81-c9f4-4658-ab04-2597ed387494", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "((477.79083251953125,\n", - " 1599.574089050293,\n", - " 11312.580108642578,\n", - " 6737.8997802734375),\n", - " (1, 3.347854291417166, 23.67684630738523))" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = (\n", - " timer(inv.y_func, x=20, k=100, N=1000), \n", - " timer(inv.y_func_from_k_func, x=20, k=100, method=inv.GS_GRADIENT, N=10_000),\n", - " timer(inv.y_func_from_k_func, x=20, k=100, method=inv.GS_BISECT, N=10_000),\n", - " timer(inv.y_func_from_k_func, x=20, k=100, x_lo=0.1, x_hi=10, method=inv.GS_BISECT, N=10_000),\n", - ")\n", - "r, (1, r[1]/r[0], r[2]/r[0])" - ] - }, - { - "cell_type": "markdown", - "id": "639c0f69-279e-42df-93b6-4f599b3f2160", - "metadata": { - "tags": [] - }, - "source": [ - "### Bancor invariant function" - ] - }, - { - "cell_type": "markdown", - "id": "f0ac97c3-6ccb-4d07-bc42-8df4f4be347a", - "metadata": {}, - "source": [ - "we are here comparing the analytic invariant function with the one obtained numerically; note: they are a good match!" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7d2aa8e1-7b01-44fc-8f5f-2cbcf73ccd60", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "f = BancorSwapFunction(k=100)\n", - "assert f(10) == 10\n", - "assert f(5) == 20\n", - "assert f(20) == 5\n", - "inv = BancorInvariant()\n", - "assert inv.y_func_is_analytic is True" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9af100b4-376a-44fe-8a66-e2c2c5253d91", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(0.5 , 3, 50)\n", - "y1_v = [inv.y_func(xx, k=100) for xx in x_v]\n", - "y2_v = [inv.y_func_from_k_func(xx, k=100) for xx in x_v]\n", - "plt.plot(x_v, y1_v, linewidth=3, label=\"analytic\")\n", - "plt.plot(x_v, y2_v, linestyle=\"--\", color = \"#ccc\", label=\"numeric\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e1ede0f7-dbe5-403a-9a3b-09ed326ef82a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(0.5, 3, 100)\n", - "y1_v = [inv.p_func(xx, k=100) for xx in x_v]\n", - "y2_v = [inv.y_func(xx, k=100) for xx in x_v]\n", - "plt.plot(x_v, y1_v, linewidth=3, color=\"red\", label=\"p [LHS]\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"price dy/dx [red]\")\n", - "ax2 = plt.twinx()\n", - "ax2.plot(x_v, y2_v, linewidth=3, color=\"grey\", label=\"y [RHS]\")\n", - "ax2.set_ylabel(\"swap function y [grey]\")\n", - "#plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "da4562a8-e5a7-44ba-b4a6-6f7cd4707d9c", - "metadata": {}, - "source": [ - "#### timing" - ] - }, - { - "cell_type": "markdown", - "id": "53810771-a370-414d-8157-7a53cfe77493", - "metadata": {}, - "source": [ - "however, whilst the results are comparable, runtime difference is substantial (unsurprisingly especially given the extremely simple formula for the analytic function); for 1e-6 tolerance the factor is 27x, and for 1e-3 tolerance the factor is not much better at 19x" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7ea215be-7021-46bc-9c5b-6fe03b458497", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((346.89903259277344, 3824.2340087890625), 11.02405498281787)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = timer2(inv.y_func, 20, 100, N=1000), timer2(inv.y_func_from_k_func, 20, 100, N=1000)\n", - "r, r[1]/r[0]" - ] - }, - { - "cell_type": "markdown", - "id": "f359ea63-195f-410c-a08c-44a33b6a1bb1", - "metadata": {}, - "source": [ - "### Solidly invariant function" - ] - }, - { - "cell_type": "markdown", - "id": "86bdba9e-4ad9-4ee4-9aa5-fb35379b40ed", - "metadata": { - "tags": [] - }, - "source": [ - "The Solidly **invariant equation** is \n", - "$$\n", - " x^3y+xy^3 = k\n", - "$$\n", - "\n", - "which is a stable swap curve, but more convex than for example Curve. \n", - "\n", - "To obtain the **swap equation** we solve the above invariance equation \n", - "as $y=y(x; k)$. This gives the following result\n", - "$$\n", - "y(x;k) = \\frac{x^2}{\\left(-\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\\right)^{\\frac{1}{3}}} - \\frac{\\left(-\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\\right)^{\\frac{1}{3}}}{3}\n", - "$$\n", - "\n", - "We can introduce intermediary **variables L and M** ($L(x;k), M(x;k)$) \n", - "to write this a bit more simply\n", - "\n", - "$$\n", - "L(x,k) = L_1(x) \\equiv -\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\n", - "$$\n", - "$$\n", - "M(x,k) = L^{1/3}(x,k) = \\sqrt[3]{L(x,k)}\n", - "$$\n", - "$$\n", - "y = \\frac{x^2}{\\sqrt[3]{L}} - \\frac{\\sqrt[3]{L}}{3} = \\frac{x^2}{M} - \\frac{M}{3} \n", - "$$\n", - "\n", - "If we rewrite the equation for L as below we see that it is not \n", - "particularly well conditioned for small $x$\n", - "$$\n", - "L(x,k) = L_2(x) \\equiv \\frac{27k}{2x} \\left(\\sqrt{1 + \\frac{108x^8}{729k^2}} - 1 \\right)\n", - "$$\n", - "\n", - "For simplicity we introduce the **variable xi** $\\xi=\\xi(x,k)$ as\n", - "$$\n", - "\\xi(x, k) = \\frac{108x^8}{729k^2}\n", - "$$\n", - "\n", - "then we can rewrite the above equation as \n", - "$$\n", - "L_2(x;k) \\equiv \\frac{27k}{2x} \\left(\\sqrt{1 + \\xi(x,k)} - 1 \\right)\n", - "$$\n", - "\n", - "Note the Taylor expansion for $\\sqrt{1 + \\xi} - 1$ is \n", - "$$\n", - "\\sqrt{1+\\xi}-1 = \\frac{\\xi}{2} - \\frac{\\xi^2}{8} + \\frac{\\xi^3}{16} - \\frac{5\\xi^4}{128} + O(\\xi^5)\n", - "$$\n", - "\n", - "and tests suggest that it is very good for at least $|\\xi| < 10^{-5}$" - ] - }, - { - "cell_type": "markdown", - "id": "d9705af6-fcd5-4773-a461-103304ba2f0f", - "metadata": {}, - "source": [ - "### L functions" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "ca4e362f-5465-4149-b644-38aaf26fedfb", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "f = SolidlySwapFunction(k=100)\n", - "assert f.method == f.METHOD_DEC1000\n", - "inv = SolidlyInvariant()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "0b16e3f1-99f2-4fb9-819e-890be55ce2e9", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0009999999638239387,\n", - " 0.0009999999629629658,\n", - " 0.0009999999629629658,\n", - " 0.0009999999629629656,\n", - " 0.0009999999629629658,\n", - " False,\n", - " True,\n", - " True)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x,k = 1,1000\n", - "(\n", - " f._L1_float(x, k),\n", - " f._L1_dec100(x, k),\n", - " f._L1_dec1000(x, k),\n", - " f._L2_taylor(x, k),\n", - " f.L(x, k),\n", - " f.L(x, k) == f._L2_taylor(x, k),\n", - " f.L(x, k) == f._L1_dec100(x, k),\n", - " f.L(x, k) == f._L1_dec1000(x, k),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "58bf213a-9389-47d5-96ad-6f4d41b795b8", - "metadata": {}, - "outputs": [], - "source": [ - "# x,k = 1,10\n", - "# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k))\n", - "# x,k = 1,100\n", - "# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k))\n", - "# x,k = 1,1_000\n", - "# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k))\n", - "# x,k = 1,10_000\n", - "# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k))\n", - "# x,k = 1,100_000\n", - "# assert iseq(f._L1_dec(x, k), f._L2_taylor(x, k)) # not float !\n", - "# f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k)" - ] - }, - { - "cell_type": "markdown", - "id": "a07bf50f-8159-4f7a-ae3f-184ea37d229a", - "metadata": {}, - "source": [ - "### Numeric vs analytic and verification" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ec2be1c6-1dec-4306-8481-5c5026ce193d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(6, 6))\n", - "k = 1000\n", - "x_v = np.linspace(0.1 , 20, 500)\n", - "y1_v = [inv.y_func(xx, k=k) for xx in x_v]\n", - "y2_v = [inv.y_func_from_k_func(xx, k=k) for xx in x_v]\n", - "plt.plot(x_v, y1_v, linewidth=3, label=\"analytic\")\n", - "plt.plot(x_v, y2_v, linestyle=\"--\", color = \"#ccc\", label=\"numeric\")\n", - "plt.xlim(0,20)\n", - "plt.ylim(0,20)\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "e5448a58-9b9f-44a9-aab1-6e21a58b2427", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k = 100\n", - "x1_v = np.linspace(0, 200)\n", - "x1_v[0] = 0.0001\n", - "k_v = [inv.k_func(xx, inv.y_func_from_k_func(xx, k=100)) for xx in x1_v]\n", - "plt.plot(x1_v, k_v)\n", - "ylim = (99.999999, 100.000001)\n", - "assert min(k_v) > ylim[0]\n", - "assert max(k_v) < ylim[1]\n", - "plt.ylim(*ylim)\n", - "plt.title(f\"Verifying `y_func_from_k_func` for k=100 [ylim = {ylim}\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"k\")\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c68a9da8-9c58-4d3f-8388-68519107c458", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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VP/7xD0nSxIkT5eHhodWrVys8PNzx3FtuueW8X1eSunXrpkaNGkmS4uLiFBYWJknavn27ZsyYoXvuuafEwlfdunVTr1699NJLL+npp592bD927Jjmzp2rP/3pT5V6/WnTpumxxx7TM888o0ceeaTEY3feeac+/PDDc+6jf//+Wrp0aaVeV5JSU1MVGhpaarufn588PT2VmpoqSUpLS5Mxpsyy9m32spX1wAMPaODAgfrmm2907bXXSpKOHDmiefPm6fHHHy8VGCoiLy9PCxcudJw8sVgsuvbaa7VkyRKtX7/esbBhcnKyHnzwQW3evFmdOnUqd3/Lli0rFdLLs3fv3rMu9rZixQpJxZ/x09l/3yxfvlzNmjVzbP/1118llXx/27dvr6VLlyorK6vEgpBllS1L+/bttWrVKhUVFcnNrbhfp6CgQL/99luJ559ep9OPf8WKFTLGlKrT4sWLdeDAAcfv4bLqFBkZqdDQ0FKLBh4/ftwRAst6/Zp+T6oqNTVVzZs3L7X9zO+H/d/yvkvGGKWlpSk6Orrc1znb808/zpr4fp/r9U//v7Si78n56N69u958803Fx8eXeeIewPmhp78a/fzzz7rqqqsUExNTaytdHz58WLfeeqsaNGggX19fde3aVevWrTvv/T399NPq27evfH19z3uV66lTp+qiiy5SQECAIiIidO211yo+Pv686wTXNHbsWKWkpDhWYC4oKNCsWbN0ySWXqFWrVpKKe/UGDhyomJiYEj1uw4YNk1T8B/vprr766hKBv7L++te/asOGDY4/WjMyMvTxxx/r9ttvr9CK7FdffXWJ+/Zej/3790uSVq1apby8PI0aNapEud69e1fbSuX2Vf3feecdx7bXX39dnTp10qWXXlotr1EZAwcOdAR+qTgYREREON6TnJwcLVu2TKNGjSoR+GvSkiVLJKnUFRh69uypdu3a6ccffyyxPSQkpFKB3xiju+66S//+97/16aeflgr8UvEJrTVr1pzzdvrJoco628r+Zz5WmbIVNWDAAHXp0qXEyJO33npLFotFEyZMOK99Dhw40BH4Jaldu3aSpGHDhpWop327/XNWnri4uAq1w5o1a845SubIkSOS5DjZZjds2DC1bNlSjz76qBYtWqTjx49rwYIF+uc//yl3d3dHOJek++67T+np6brtttu0Z88eHT16VI8//rjjhMLpZcty//33a8eOHbrvvvt0+PBhHTx4UHfffbfjfbA/v0uXLrr00kv13HPPae7cuTp+/LhWrFihu+++u1SdJkyYIKvVqltuuUVbtmxRamqq3njjDc2ZM6fEPt3c3HTvvffqxx9/1JNPPqmkpCTt2rVLt956q+MSo/aytfmeVIfa/C6VV6Yqr1MdZWvjd4Z06vtz+PDh894HgNII/dUoOztbXbp00euvv14rr5eWlqZ+/frJarXq+++/19atW/XCCy+cNazHxsaetdcoPz9fI0eOrNLlwJYtW6Z7771Xq1at0qJFi1RQUKAhQ4YoOzv7vPcJ13PDDTcoKChIH3zwgSRp/vz5Onr0qGM4tiQdPXpU33zzjaxWa4lbhw4dJKnUMOzyelAq6pprrlFsbKwjpMycOVPZ2dkVGtovSQ0aNChx3z781H4JqtN7xM5U1rbzERkZ6RhJUFhYqE2bNumXX36pcC9/dTvzPZGK3xf7e5KWlqbCwkJHj3xtsLdDWZ+XmJiYUr1Ulf1c5efnOy4JaT9BdaYmTZqoa9eu57y1bNmyUq9t16BBgzJ727Kzs5Wfn+/okQsJCZHFYimz7LFjxySV3fNXUQ888IB+/PFHxcfHy2az6Z133tENN9xQalhyRZ1ZF09Pz7NuP9twakny9/evUDt07drVsc/y2D/T3t7epery/fffq0mTJhoyZIhCQkJ0ww036J///KdCQkLUsGFDR9lBgwbpgw8+0M8//6wWLVooKipKX3zxhZ588klJKlG2LHfeeaf+85//6OOPP1ajRo3UpEkTbd26VQ8//HCp58+dO1f9+vXTqFGjFBISooEDB+q6665T165dS5Rr166d5s2bp/3796tjx44KCwvTtGnT9MILL5Ta57/+9S/97W9/01NPPaXIyEjHCdw77rijRNnafE+qqrzv0pnfD/vvuvLKWiyWs/59dq7nn/4Zr4nvd3W8fnX8zrB/f8q6dCOA80for0bDhg3TU089peuuu67Mx/Pz8/XII4+oYcOG8vPzU69evc5r2KbdtGnT1LhxY33wwQfq2bOnYmNjNWjQILVo0eK89zllyhT97W9/O+twyK1bt+qKK66Qv7+/IiMjNXr06BLha8GCBRozZow6dOigLl266IMPPtCBAweqNAIBrsfHx0d//vOftWDBAiUkJOj9999XQECARo4c6SgTFhamIUOGlNvzdvoJAqlqvQvSqZ6q//3vf0pISND06dM1aNAgtWnTpkr7tbP/UXX06NFSjyUmJlbLa0jFIxYOHjyor776Sq+//rqCg4OrPFTeztvbW3l5eaW2n3kCpqJCQ0Pl7u6uQ4cOVbVqFWZvh4SEhFKPHTlyxDENwK6ynysvLy8tWbJEBw8e1GWXXVbmdc/vvPPOUiezyroNGjSoUq9t16lTJyUnJ5f6XNnndHfs2FFS8fewZcuWju1nlvXx8SlzGG9F3XzzzWrQoIHeeOMNzZ07V4mJiRU+iVYbli1bVqF2sFqtZV5b/HT2z409+JyuZcuWWrlypQ4dOqRNmzYpKSlJI0eOVEpKSqkROLfffrsSExO1detW7dy503GNe4vFoksuueScx/Too48qJSVFf/zxh/bt26cVK1YoLS1Nfn5+iouLc5SLiIhwnGzduHGjkpKS9MQTT2jHjh2l6jRs2DDt379fO3bs0NatW7V3717H9+j0sh4eHnrxxReVmpqqTZs26ciRI/r222914MABNWvWrMTJvdp8T6qiU6dO2rZtW6l1Hc78LrVo0UI+Pj7lfpdatmxZ6oTQ6ez7Ke/59sftdaru77f9774zyxYUFGj79u2lXr8i78n5sH9/zvw9DKBqCP216I477tDy5cs1e/Zsbdq0SSNHjtTQoUO1c+fO89rf119/rR49emjkyJGKiIhQt27dSgzprQkJCQnq37+/unbtqrVr12rBggU6evRoqeHKp0tPT5dUtTO/cE1jx45VYWGhnnvuOc2fP1833XSTfH19HY8PHz5cmzdvVosWLdSjR49St4osSldZ48aNk6enp2655RbFx8dXaw95r1695OXl5RgWa7dq1apzDkOujLi4OPXt21fTpk3TJ598ojFjxpQYEl0VsbGxSkpKKnHiIj8/Xz/88MN57c/Hx0f9+/fX3Llzz/vEQWXZh+qfvv6CJK1Zs0bbtm0776B9um7dumnZsmU6dOiQBgwYoKSkpBKP1/Tw/muuuUYWi6XUugEzZ86Uj4+Phg4d6tg2YsQI/fTTTzp48KBjW2Zmpr744gtdffXV5zX33s7b21sTJkzQhx9+qBdffFFdu3ZVv379znt/1a06h/fb5x/bF6srS8OGDdWpUyf5+vrqueeek5+fX6mTl1JxeG7Xrp1atmyp9PR0zZgxQ9dcc42aNm1aoePy8vJSx44d1bRpUx04cEBz5szR+PHj5ePjU6psRESEOnfurKCgIL311lvKzs4u8/eexWJRq1at1K5dOxUWFuqVV15R165dy5w25O/vr06dOik6Olrr16/Xjz/+qL/+9a91+p6crxEjRigrK0uff/55ie0ffvihYmJi1KtXL0f9rrrqKn3xxRfKzMx0lDtw4ICWLFlSboeQXcOGDdWzZ0/NmjWrxOKXq1atUnx8fInn18T3u1evXoqOjtbMmTNL7PN///ufsrKySrx+Rd+T87Fnzx65ublV28l2ACfV3YUDXJskM2/ePMf9Xbt2GYvFUupyWYMGDTKTJk06r9fw8vIyXl5eZtKkSWb9+vXmrbfeMt7e3ubDDz8s9zlNmzY1S5YsOee+P/jgAxMUFFRq++OPP17qGq4HDx40kkx8fHyp8kVFReaqq64yF1988TlfExemzp07G4vFYiSZVatWlXjsyJEjpmnTpqZt27Zm+vTp5scffzTfffedeeONN8yVV17puLya/bJ8zz33XKn9n3nJPmNOXWJv7ty5ZdbpL3/5i+MydWVdA7q8S/adub+yXtt+ucK77rrLLFiwwLz77rumcePGJjo62gwcOPBcb1eFzZkzx0gyFovF7Nixo9LPL+893bNnj7FarWbAgAHmu+++M59//rnp37+/adasWbmX7DvTme/fhg0bjL+/v2nevLmZMWOG+emnn8xnn31m/vznP5uMjIxK1/109ksJJicnl9g+YcIEY7FYzIMPPmh++OEH8/bbb5uIiAjTuHFjk5KS4ihnv2RfRZ15yb7du3ebZs2amTZt2pS6HOD52Ldvn5k7d66ZO3euGTp0qONzN3fuXLNmzZoSZceNG2e8vLzMc889Z5YuXWr++c9/GovFYp5++ukS5ZKSkkx0dLTp1KmTmTdvnpk/f7659NJLTUBAQKlLqpVFZVyy73SHDh0yHh4eRpJ59913Sz1e2Uv2na68z+m5vuM1IS8vz/j4+JT5f/q0adPMhx9+aJYsWWJmz55trrvuOuPm5lbi0mrGGHP06FHzyCOPmK+++sr89NNPZvr06SY2NtY0b9681N8PU6ZMMe7u7mbp0qWObX/88YeZPHmy+fbbb82iRYvM888/b8LCwkyPHj1MZmZmiefPmDHDzJgxw/z444/m888/N+PGjTMWi6XMS8bdd9995n//+59ZsmSJee+990yXLl1MgwYNzObNm0uUW7JkiXn22WfNggULzPfff2+mTJlifH19zZVXXlni2u019Z7Y2/1sn0e7LVu2OL47cXFxJjw83HF/y5YtJcoOHjzYhISEOH4/jR8/3kgys2bNKlFu27Ztxt/f31x66aVm/vz55osvvjAdO3Y0MTExJikpqURZd3d386c//alU/T08PMyIESPMokWLzCeffGIaN25sOnbsWOrypTXx/f7444+NJDNhwgSzZMkSM2PGDBMcHFzicoGVfU+SkpIc7+ttt91mJJnp06ebuXPnlvjs2l111VWme/fupbYDqBpCfw05M/T/97//NZKMn59fiZuHh4cZNWqUMebUHy9nu53+B4/VajV9+vQp8br333+/6d27t+P+XXfdVeL1LBaL8fb2LrFt//79pepfXui/4oorjNVqLXUcksz8+fNLlb/nnntM06ZNq+WPXbimV155xUgy7du3L/Px5ORk88ADD5hmzZoZq9VqQkNDTVxcnHnssccc1zyv7tC/dOlSI8n85z//KfPxqoT+oqIi89RTT5lGjRoZT09P07lzZ/Ptt9+aLl26mBEjRpT5eucjLy/PeHl5maFDh57X88/2ns6fP9907drV+Pj4mObNm5vXX3/dEa5PV9HQb4wxW7duNSNHjjQNGjQwnp6epkmTJmbMmDGl/tCtrPJCf2FhoZk2bZpp3bq1sVqtJiwszNx6662lfldVNfQbUxx627Zta2JjY83u3bvP/2DMqYBc1u3M9zQ/P9/8+9//Nk2aNDGenp6mdevW5tVXXy1zv7t27TLXXnutCQwMNL6+vmbQoEFm3bp1FapTRULWgAEDTGhoqMnJySn3mJw99BtjzOjRo8v8XTZlyhTTokUL4+XlZYKDg83QoUPNzz//XKpcamqqGTJkiAkPDzdWq9U0adLE3H///aU+v8ac+myffiI/Pj7eXHrppSY0NNR4enqali1bmv/7v/9z/K483dtvv23atWtnfH19jb+/v7nkkkvMl19+WeZxXXPNNSY6OtpYrVYTFRVlxowZY/bt21eq3PLly02vXr1MYGCg8fLyMh07djTPP/+8yc/Pr5X35JtvvjGSzFtvvVXmcZzO/v6VdTvz85yZmWkeeOABExUV5fi9feb17O3Wrl1rBg0aZHx9fU1gYKC59tprza5du0qVk2T69+9favvChQtN7969jbe3twkNDTW33XabOXr0aKlyNfX9/vTTT03nzp2Np6eniYqKMg888ECpE0aVeU/s38Wybmcef2ZmpvH19TUvvPBCmXUDcP4sxnAhzJpgsVg0b948x2WK5syZ41j51t3dvURZf39/RUVFyWaznXVYoFS8KIt9wa+mTZtq8ODBevfddx2Pv/nmm3rqqaccq54mJSUpIyPD8fiAAQM0bdq0EkOvYmNjSw3fnDlzph588EEdP368xPZhw4bJ19dX06ZNK1W36OjoEkOI77//fn355Zf6+eefS1ySB6jvHnroIb355ps6ePBgmQvRVbe9e/eqbdu2+ve//61//vOf1bLPb775RldffbW+++47XXHFFdWyT6AsFotFjz/+uP71r3/J3d291BoISUlJatq0qe6//349++yzdVTL2rF27VpddNFFWrVqVZWGOOP8PPLII/rss8+0c+fOs86fR/303nvvOdakCQkJqevqAC7l/CfqoVK6deumwsJCJSUllbvojNVqrdQ1Sfv161fqUng7duwoMb8tIiKixOWDPDw81LBhw/NeEbp79+76/PPPyzxRYGeM0f3336958+Zp6dKlBH44jVWrVmnHjh2aPn267rrrrhoJ/Bs3btRnn32mvn37KjAwUPHx8Xr22WcVGBhY5jzWytq6dav279+vhx56SF27di139XigOj355JN68skn9dxzzzlWij906JD27Nmj5557Tm5ubuXO6XYlPXr00KhRo/Tkk0/q22+/revqXHCWLFmixx9/nMDvhAoKCjRt2jRNmjSJwA/UAEJ/NcrKytKuXbsc9/fu3asNGzYoNDRUrVu31i233KLbbrtNL7zwgrp166aUlBT99NNP6tSp03n1xP3tb39T37599cwzz2jUqFFavXq1ZsyYoRkzZpz3MRw4cEDHjh3TgQMHVFhYqA0bNkgqXmXX399f9957r9555x39+c9/1t///neFhYVp165dmj17tt555x25u7vr3nvv1aeffqqvvvpKAQEBjtVlg4KCylxECKgv+vTpI19fXw0fPlxPPfVUjbyGn5+f1q5dq/fee0/Hjx9XUFCQBgwYoKeffrpaLtt3zz33aPny5erevbs+/PDDUr2uxpgSi0SVpazeWqA8a9ascfzcuHFjx8/vvvuunnjiCcXGxuqTTz6p8Uur1RcvvPCC3nvvPWVmZiogIKCuq3NBOf2zCOdy8OBB3XrrrXrooYfquiqAS2J4fzVaunSpBg4cWGr77bffrpkzZ8pms+mpp57SRx99pMOHD6tBgwbq06ePpkyZctZL5J3Nt99+q0mTJmnnzp1q1qyZJk6cqPHjx5dbPjY2VjNnztSAAQPKfHzMmDGlVoOVis+e25+zc+dOPfroo1qyZIny8vLUtGlTDR06VC+++KIsFku5YeGDDz7QmDFjKnuIAKrRzJkzHdfMLs/p33cAAAA4N6cK/T///LOee+45rVu3TgkJCSXmzJdn2bJlmjhxorZs2aKYmBg98sgjuvvuu0uU+fzzz/X4449r9+7datGihZ5++mmNGDGiBo8EAOpGamqq9u7de9Yybdq0oYcSAADARTjV8P7s7Gx16dJFd9xxh66//vpzlt+7d6+uuOIKjR8/XrNmzdLy5ct1zz33KDw83PH8lStX6sYbb9STTz6pESNGaN68eRo1apR+/fVXFuEB4HIaNGhQK4sTAgAAoH5wqp7+0525On5ZHn30UX399dfatm2bY9vdd9+tjRs3auXKlZKkG2+8URkZGfr+++8dZYYOHaqQkBB99tlnNVZ/AAAAAABqmlP19FfWypUrNWTIkBLbLr/8cr333nuy2WyyWq1auXKl/va3v5Uq8/LLL5e737y8POXl5TnuFxUV6dixY2rQoAGLXwEAAAAAapwxRpmZmYqJiZGbm1u55Vw69CcmJpZaDTsyMlIFBQVKSUlRdHR0uWXsK86XZerUqZoyZUqN1BkAAAAAgIo6ePCgGjVqVO7jLh36JZV5uaozt5dV5mw99pMmTdLEiRMd99PT09WkSRPt3bu3Xi9+ZbPZtGTJEg0cOFBWq7Wuq4Ny0E7OgXaq/2gj50A7OQfaqf6jjZwD7eQcnKWdMjMz1axZs3NmUJcO/VFRUaV67JOSkuTh4eFYyKq8Mme7XraXl5e8vLxKbQ8NDVVgYGA11Lxm2Gw2+fr6qkGDBvX6w3uho52cA+1U/9FGzoF2cg60U/1HGzkH2sk5OEs72et2rinm5Q/8dwF9+vTRokWLSmxbuHChevTo4XiDyivTt2/fWqsnAAAAAAA1wal6+rOysrRr1y7H/b1792rDhg0KDQ1VkyZNNGnSJB0+fFgfffSRpOKV+l9//XVNnDhR48eP18qVK/Xee++VWJX/r3/9qy699FJNmzZN11xzjb766istXrxYv/76a60fHwAAAAAA1cmpevrXrl2rbt26qVu3bpKkiRMnqlu3bvrXv/4lSUpISNCBAwcc5Zs1a6b58+dr6dKl6tq1q5588km9+uqruv766x1l+vbtq9mzZ+uDDz5Q586dNXPmTM2ZM0e9evWq3YMDAAAAAKCaOVVP/4ABAxwL8ZVl5syZpbb1799f69evP+t+b7jhBt1www1VrR4AAAAAAPWKU/X0AwAAAACAiiP0AwAAAADgogj9AAAAAAC4KEI/AAAAAAAuitAPAAAAAICLIvQDAAAAAOCiCP0AAAAAALgoQj8AAAAAAC6K0A8AAAAAgIsi9AMAAAAA4KII/QAAAAAAuChCPwAAAAAALorQDwAAAACAiyL0AwAAAADgogj9AAAAAAC4KEI/AAAAAAAuitAPAAAAAICLIvQDAAAAAOCiCP0AAAAAALgoQj8AAAAAAC6K0A8AAAAAgIsi9AMAAAAA4KII/QAAAAAAuChCPwAAAAAALorQDwAAAACAiyL0AwAAAADgogj9AAAAAAC4KEI/AAAAAAAuitAPAAAAAICLIvQDAAAAAOCiCP0Aql1adr5mrz6grLyCuq4KAAAAcEEj9AOodm8t261/fPGHPlyxr66rAgAAAFzQCP0Aqt2BYzmSpK0JGXVcEwAAAODCRugHUO1SsvIkSbuTsuq4JgAAAMCFjdAPoNqlZOVLkvamZKuoyNRxbQAAAIALF6EfQLVLySzu6c8rKNLh4yfquDYAAADAhYvQD6Ba5doKlXnaqv17UrLrsDYAAADAhY3QD6Ba2efz2+1JZl4/AAAAUFcI/QCqlX0+v91uQj8AAABQZwj9AKqVfT6/3Z5khvcDAAAAdYXQD6Ba2Yf3h/p5SqKnHwAAAKhLhH4A1coe+nvGhkqSjmbkKeu0hf0AAAAA1B5CP4BqZZ/T3zzcT2H+xb39LOYHAAAA1A1CP4BqlXyypz/M30vNw/0lMa8fAAAAqCuEfgDVyr6QX1iAl1qE+0liXj8AAABQVwj9AKpViqOn31Mt6OkHAAAA6hShH0C1Ss0untMf7u+l5vT0AwAAAHXK6UL/9OnT1axZM3l7eysuLk6//PJLuWXHjBkji8VS6tahQwdHmZkzZ5ZZJjc3tzYOB3AptsIiHc+xSSqe02/v6d+bkq3CIlOXVQMAAAAuSE4V+ufMmaMHH3xQjz32mH7//XddcsklGjZsmA4cOFBm+VdeeUUJCQmO28GDBxUaGqqRI0eWKBcYGFiiXEJCgry9vWvjkACXknpy5X4PN4uCfKxqFOIrT3c35RUU6cjxE3VcOwAAAODC41HXFaiMF198UWPHjtW4ceMkSS+//LJ++OEHvfnmm5o6dWqp8kFBQQoKCnLc//LLL5WWlqY77rijRDmLxaKoqKgK1yMvL095eXmO+xkZGZIkm80mm81WqWOqTfa61ec6wrnbKfF48dz9UD9PFRYWSJKaNvDRzqRs7UhMV1SAtS6rV62cuZ0uFLSRc6CdnAPtVP/RRs6BdnIOztJOFa2fxRjjFGNu8/Pz5evrq7lz52rEiBGO7X/961+1YcMGLVu27Jz7uOqqq5SXl6eFCxc6ts2cOVPjxo1Tw4YNVVhYqK5du+rJJ59Ut27dyt3P5MmTNWXKlFLbP/30U/n6+lbyyADXsTXNore3u6uhr9EjXQolSe/Fu2nTMTeNiC3UgGin+HUDAAAA1Hs5OTm6+eablZ6ersDAwHLLOU1Pf0pKigoLCxUZGVlie2RkpBITE8/5/ISEBH3//ff69NNPS2xv27atZs6cqU6dOikjI0OvvPKK+vXrp40bN6pVq1Zl7mvSpEmaOHGi435GRoYaN26sIUOGnPXNrms2m02LFi3S4MGDZbW6To+rq3Hmdjqx/rC0fYuax4TpiiviJEnbrTu16ee98g5vqiuuaF/HNaw+ztxOFwrayDnQTs6Bdqr/aCPnQDs5B2dpJ/uI83NxmtBvZ7FYStw3xpTaVpaZM2cqODhY1157bYntvXv3Vu/evR33+/Xrp+7du+u1117Tq6++Wua+vLy85OXlVWq71Wqt1x8KO2ep54XOGdsp7URx7354oLej7i0ji0+E7U3NcbrjqQhnbKcLDW3kHGgn50A71X+0kXOgnZxDfW+nitbNaRbyCwsLk7u7e6le/aSkpFK9/2cyxuj999/X6NGj5enpedaybm5uuuiii7Rz584q1xm40KRkFa91Ee5/6qRYi4jiFfz3JGfXSZ0AAACAC5nThH5PT0/FxcVp0aJFJbYvWrRIffv2Petzly1bpl27dmns2LHnfB1jjDZs2KDo6Ogq1Re4ENlDf9hpob95uJ8kKSkzT5m59XsxFAAAAMDVONXw/okTJ2r06NHq0aOH+vTpoxkzZujAgQO6++67JRXPtT98+LA++uijEs9777331KtXL3Xs2LHUPqdMmaLevXurVatWysjI0KuvvqoNGzbojTfeqJVjAlyJI/QHnBpRE+htVXiAl5Iz87QnOVtdGgfXUe0AAACAC49Thf4bb7xRqampeuKJJ5SQkKCOHTtq/vz5atq0qaTixfoOHDhQ4jnp6en6/PPP9corr5S5z+PHj2vChAlKTExUUFCQunXrpp9//lk9e/as8eMBXE1KZr6kkj39ktQ8zE/JmXnanZxF6AcAAABqkVOFfkm65557dM8995T52MyZM0ttCwoKUk5OTrn7e+mll/TSSy9VV/WAC1pZw/ul4nn9v+09xrx+AAAAoJY5zZx+APVbQWGRjuWU39MvSbuTs2q9XgAAAMCFjNAPoFocy8mXMZKbRQr1K3mVDFbwBwAAAOoGoR9AtbDP5w/185S7m6XEYy3CikP/3tRsFRaZWq8bAAAAcKEi9AOoFuXN55ekhiE+8vRwU35BkQ6nnajtqgEAAAAXLEI/gGpxttDv7mZRswYn5/WnMK8fAAAAqC2EfgDV4lTo9yzz8ebhJ0N/EqEfAAAAqC2EfgDVIiWr7JX77VqEn1zML4XF/AAAAIDaQugHUC1SMk/29AeUHfrp6QcAAABqH6EfQLVIPsucfomefgAAAKAuEPoBVItTw/vPPqc/OTNPGbm2WqsXAAAAcCEj9AOoFmdbvV+SArytijg59H9PMr39AAAAQG0g9AOosqIio2PZxT394eXM6ZeY1w8AAADUNkI/gCpLy8lXYZGRJIX6lT28Xzp9Xj+hHwAAAKgNhH4AVWafzx/ia5XVvfxfK81Phv7dSQzvBwAAAGoDoR9AlZ1rPr9di5PD++npBwAAAGoHoR9AlVU89Bf39O9LyXFMBwAAAABQcwj9AKosOfNk6D/LIn6SFBPsIy8PN+UXFulQWk5tVA0AAAC4oBH6AVSZfU5/mH/5i/hJkrubRc3CTq7gn8wQfwAAAKCmEfoBVFlFh/dLp63gn8xifgAAAEBNI/QDqDJ76A+vQOhvHk5PPwAAAFBbCP0AqszR0x9w9uH90qme/t309AMAAAA1jtAPoMpSMu1z+ive07+Hnn4AAACgxhH6AVSJMUap2RWf09/8ZE9/Sla+0nNsNVo3AAAA4EJH6AdQJeknbLIVGklSg3Os3i9J/l4eigwsPjmwO4XefgAAAKAmEfoBVIl9Pn+gt4e8PNwr9BxW8AcAAABqB6EfQJUk2+fzB5x7aL8dK/gDAAAAtYPQD6BKHCv3V2A+v92pnn5CPwAAAFCTCP0AqsQe+sMrEfqbc9k+AAAAoFYQ+gFUyame/nMv4mfX4uTw/v2p2SooLKqRegEAAAAg9AOoohT7nP5K9PTHBPnI2+omW6HRobQTNVU1AAAA4IJH6AdQJY6e/kos5OfmZlGzMPsQf+b1AwAAADWF0A+gSs5nIT/p1Ar+XLYPAAAAqDmEfgBVkpJlH95f8Tn90qkV/OnpBwAAAGoOoR/AeTPGKPk8e/pb0NMPAAAA1DhCP4DzlplXoPyC4tX3wysxp1+ipx8AAACoDYR+AOctJbO4l9/fy0PeVvdKPbdZWHFPf2p2vo7n5Fd73QAAAAAQ+gFUwfnO55ckPy8PRQd5S5J2M8QfAAAAqBGEfgDn7XxX7rc7tYI/Q/wBAACAmkDoB3Deqhz6w+zz+unpBwAAAGoCoR/AebPP6Q8LqPzwfun0Ffzp6QcAAABqAqEfwHlLdszpP9/h/azgDwAAANQkQj+A81bV4f0tIopD/4FjObIVFlVbvQAAAAAUI/QDOG9VDf3Rgd7ytrrJVmh08FhOdVYNAAAAgAj9AKrAHvrDz3NOv5ubxbGY3x4W8wMAAACqHaEfwHlLyazanH7p1GX7mNcPAAAAVD9CP4Dzkp1XoBO2QklVC/0twunpBwAAAGqK04X+6dOnq1mzZvL29lZcXJx++eWXcssuXbpUFoul1G379u0lyn3++edq3769vLy81L59e82bN6+mDwNwevah/T5Wd/l5eZz3fujpBwAAAGqOU4X+OXPm6MEHH9Rjjz2m33//XZdccomGDRumAwcOnPV58fHxSkhIcNxatWrleGzlypW68cYbNXr0aG3cuFGjR4/WqFGj9Ntvv9X04QBOzbGI33nO57dz9PSn0NMPAAAAVDenCv0vvviixo4dq3Hjxqldu3Z6+eWX1bhxY7355ptnfV5ERISioqIcN3d3d8djL7/8sgYPHqxJkyapbdu2mjRpkgYNGqSXX365ho8GcG7J1TCfXzrV038sO19p2flVrhcAAACAU85/TG4ty8/P17p16/SPf/yjxPYhQ4ZoxYoVZ31ut27dlJubq/bt2+v//u//NHDgQMdjK1eu1N/+9rcS5S+//PKzhv68vDzl5eU57mdkZEiSbDabbDZbRQ+p1tnrVp/rCOdpp6PpxZfYa+BrrVJdrRYpOshbCem52pGYru5NgquphjXLWdrpQkYbOQfayTnQTvUfbeQcaCfn4CztVNH6OU3oT0lJUWFhoSIjI0tsj4yMVGJiYpnPiY6O1owZMxQXF6e8vDx9/PHHGjRokJYuXapLL71UkpSYmFipfUrS1KlTNWXKlFLbFy5cKF9f38oeWq1btGhRXVcBFVDf22nlQYskd+UcO6r58+dXaV8BclOC3PTlTyuVGGGqp4K1pL63E2gjZ0E7OQfaqf6jjZwD7eQc6ns75eTkVKic04R+O4vFUuK+MabUNrs2bdqoTZs2jvt9+vTRwYMH9fzzzztCf2X3KUmTJk3SxIkTHfczMjLUuHFjDRkyRIGBgZU6ntpks9m0aNEiDR48WFarta6rg3I4Szv99s1W6dAhdWvfUlcMalmlfa0t2qYdvx2Uf3QLXXF562qqYc1ylna6kNFGzoF2cg60U/1HGzkH2sk5OEs72Uecn4vThP6wsDC5u7uX6oFPSkoq1VN/Nr1799asWbMc96Oioiq9Ty8vL3l5lZ7HbLVa6/WHws5Z6nmhq+/tdCy7QJIUGeRT5Xq2jCw+WbY39US9Puay1Pd2Am3kLGgn50A71X+0kXOgnZxDfW+nitbNaRby8/T0VFxcXKkhFosWLVLfvn0rvJ/ff/9d0dHRjvt9+vQptc+FCxdWap/Ahcixen8VF/KTTl/Bn8v2AQAAANXJaXr6JWnixIkaPXq0evTooT59+mjGjBk6cOCA7r77bknFw+4PHz6sjz76SFLxyvyxsbHq0KGD8vPzNWvWLH3++ef6/PPPHfv861//qksvvVTTpk3TNddco6+++kqLFy/Wr7/+WifHCDgLe+hv4Fe1S/ZJp1bwP5CaI1thkazuTnM+EgAAAKjXnCr033jjjUpNTdUTTzyhhIQEdezYUfPnz1fTpk0lSQkJCTpw4ICjfH5+vh5++GEdPnxYPj4+6tChg7777jtdccUVjjJ9+/bV7Nmz9X//9396/PHH1aJFC82ZM0e9evWq9eMDnElq1slL9gVUvac/KtBbvp7uyskv1IFjOY6efwAAAABV41ShX5Luuece3XPPPWU+NnPmzBL3H3nkET3yyCPn3OcNN9ygG264oTqqB1wQcm2FyswrntNfHcP73dwsahbmpy1HMrQnOZvQDwAAAFQTxtACqDT70H5PdzcFelfPuUN70N+dzLx+AAAAoLoQ+gFUWop9aL+/51kvb1kZ9nn9e6o59Cdl5upYdn617hMAAABwFoR+AJWWknly5f5qmM9vd6qnP7va9rkvJVuDnl+m4a/+ooLComrbLwAAAOAsCP0AKq06L9dnV909/UVFRn//30Zl5hXoSHputZ5MAAAAAJwFoR9ApZ0K/VW/XJ9d87Dinv60HFu1DMf/YMU+rdmX5ri/+XB6lfcJAAAAOBtCP4BKOzWnv/p6+n083dUw2EdS1Xv7dydn6dkF2yVJjUKK9/kHoR8AAAAXIEI/gEpLroHh/dKpIf5VWcG/sMjo4bkblVdQpEtahelvl7WWJG05QugHAADAhYfQD6DSamIhP+nUYn57qjD//t1f9uj3A8cV4OWhadd3VudGQZKkLUcyVFhkqqWeAAAAgLMg9AOotJqY0y9JLarY07/zaKZeWLRDkvT4Ve0VE+yj5uH+8rG6Kye/UHtTWMwPAAAAFxZCP4BKs8/pD6/24f3n39NfUFikh+duVH5BkQa2CdfIuEaSJHc3i9rHBEpiMT8AAABceAj9AColv6BI6Sdskqp/Tr99eP/+YznKLyiq1HPf/nmPNh5KV6C3h6Ze11kWi8XxWEdCPwAAAC5QhH4AlZKaXTy038PNoiAfa7XuOzLQS36e7iosMjpwLKfCz9uemKGXFxcP6598dQdFBXmXeLxjw+J5/azgDwAAgAsNoR9ApaRkFg/tb+DvKTc3yzlKV47FYlGzSs7rtxUW6aH/bpSt0OiydpEa0a1hqTL20L/1SIaKWMwPAAAAFxBCP4BKSamhy/XZVXYF/+lLdmvLkQwF+1r1zHUdSwzrt2sV4S8vDzdl5hVofyVGEAAAAADOjtAPoFKSazj0Nw8rDv0V6enfciRdr/20U5I05eoOigjwLrOch7ub2kYzrx8AAAAXHkI/gEqp8Z7+iOLh/XvOEfrzC4qH9RcUGQ3rGKWru8SctXynhoR+AAAAXHgI/QAqxT6nPyzAs0b2f6qnP1vGlD///vWfdmp7YqZC/Tz15LVlD+s/XceY4nn9m48Q+gEAAHDhIPQDqBR7T394DfX0Nwvzk8UipZ+w6Vh2fpllNh06rjeW7pYkPXlNxwqNOrAv5rf5cMZZTyYAAAAAroTQD6BSanp4v4+nu2KCfCQV9/afKa+gUA/9d6MKi4yGd47WlZ2jK7Tf1pEB8nR3U/oJmw6lnajWOgMAAAD1FaEfQKXUdOiXpBYR9hX8S8/rf3nxTu1MylKYv6eeuKZjhffp6eGmNlEBkqQ/mNcPAACACwShH0ClpGTV7Jx+SWoeVryY35kr+P9+IE1vLyse1v/UtZ0U6le5OnRkMT8AAABcYAj9ACqsoLBIaTknQ3+t9PSfGt6fayvUw3M3qshI13aN0dCOUZXer31ePz39AAAAuFAQ+gFU2LHsfBkjuVmkEN+a6+lvcbKnf0/KqdD/wsJ47U7OVkSAlyZf3eG89mtfwX/LERbzAwAAwIWB0A+gwpJPzucP9fOSu9vZL5FXFfae/gPHcpRfUKS1+47p3V/3SpKmXtdJwed5wqFNVIA83Cw6lp2vI+m51VZfAAAAoL4i9AOoMMd8fv+a6+WXpIgAL/l5uquwyCg+MVMPz90oY6Qb4hppULvI896vt9VdrSKLF/NjXj8AAAAuBIR+ABWWklnc0x8eUHPz+SXJYrE4evsfmrtB+1JzFBXorceHt6/yvjuxmB8AAAAuIIR+ABVWG5frs7Ov4L/jaPEK/v+5vpOCfKxV3q99MT9CPwAAAC4EhH4AFXYq9Nfs8H5JahHu7/j5posaa0CbiGrZ76kV/FnMDwAAAK6P0A+gwk7N6a/5nv4OJ4fhxwR567Er21XbfttFBcrNUnwCI+nkdAUAAADAVRH6AVRYbQ7vH9gmQi+M7KI5d/VRgHfVh/Xb+Xi6q1VE8WJ+fxxiiD8AAABcG6EfQIUln+wZD6vhhfyk4sX8ro9rpMahvtW+b/sogs1HCP0AAABwbYR+ABVWW5fsq2kdY1jMDwAAABcGQj+ACiksMjqWffKSfbUwvL8mdWpkD/0ZdVwTAAAAoGYR+gFUSFpOvoqMZLFIoX7O3dPfPjpQFouUmJHrmLIAAAAAuCJCP4AKsS/iF+LrKQ935/7V4efloeZhfpKY1w8AAADX5tx/uQOoNSmZrjGf365jw5ND/FnBHwAAAC6M0A+gQmrzcn21oZM99NPTDwAAABdG6AdQIa4W+jvEsJgfAAAAXB+hH0CFJLta6G8YKEk6fPyEjmXn13FtAAAAgJpB6AdQIY45/QGuMac/0Nuq2Aa+kqTNhxniDwAAANdE6AdQIa42vF86bTE/5vUDAADARRH6AVSIPfSHu2Lop6cfAAAALorQD6BCXLGn37GCP4v5AQAAwEUR+gGcU1GRUWqWa83pl6QOMcWL+R04lqP0HFsd1wYAAACofoR+AOeUfsKmgiIjSWrg5zo9/cG+nmoc6iNJ2sK8fgAAALggQj+Ac7IP7Q/yscrTw7V+bXSMKR7i/wfz+gEAAOCCXOuvdwA1Itkxn991hvbbnVrBn3n9AAAAcD1OF/qnT5+uZs2aydvbW3Fxcfrll1/KLfvFF19o8ODBCg8PV2BgoPr06aMffvihRJmZM2fKYrGUuuXm5tb0oQBOI8U+n9+FFvGzYwV/AAAAuDKnCv1z5szRgw8+qMcee0y///67LrnkEg0bNkwHDhwos/zPP/+swYMHa/78+Vq3bp0GDhyoq666Sr///nuJcoGBgUpISChx8/b2ro1DApxCSubJnv4AFwz9Jxfz25uSrcxcFvMDAACAa/Go6wpUxosvvqixY8dq3LhxkqSXX35ZP/zwg958801NnTq1VPmXX365xP1nnnlGX331lb755ht169bNsd1isSgqKqpG6w44M/uc/nAX7Olv4O+lmCBvHUnP1ZYjGerdvEFdVwkAAACoNk4T+vPz87Vu3Tr94x//KLF9yJAhWrFiRYX2UVRUpMzMTIWGhpbYnpWVpaZNm6qwsFBdu3bVk08+WeKkwJny8vKUl5fnuJ+RUTwX2GazyWarvz2F9rrV5zqifrZTUkbxdJcQH496Va/q0iEmUEfSc7XpYJriGgdW6Dn1sZ1QEm3kHGgn50A71X+0kXOgnZyDs7RTRevnNKE/JSVFhYWFioyMLLE9MjJSiYmJFdrHCy+8oOzsbI0aNcqxrW3btpo5c6Y6deqkjIwMvfLKK+rXr582btyoVq1albmfqVOnasqUKaW2L1y4UL6+vpU4qrqxaNGiuq4CKqA+tdPWPW6S3JSwN17zc7bXdXWqnTXLIsldP6zZpsjjWyr13PrUTigbbeQcaCfnQDvVf7SRc6CdnEN9b6ecnJwKlXOa0G9nsVhK3DfGlNpWls8++0yTJ0/WV199pYiICMf23r17q3fv3o77/fr1U/fu3fXaa6/p1VdfLXNfkyZN0sSJEx33MzIy1LhxYw0ZMkSBgRXrJawLNptNixYt0uDBg2W1Wuu6OihHfWyndw+sko5naECfOA1qG3HuJzgZ3x3Jmv/x7zpuCdAVV/Sr0HPqYzuhJNrIOdBOzoF2qv9oI+dAOzkHZ2kn+4jzc3Ga0B8WFiZ3d/dSvfpJSUmlev/PNGfOHI0dO1Zz587VZZdddtaybm5uuuiii7Rz585yy3h5ecnLq/TcZqvVWq8/FHbOUs8LXX1qp9STq/dHBfvVmzpVpy5Niqf87EnJVn6RRX5eFf/VWJ/aCWWjjZwD7eQcaKf6jzZyDrSTc6jv7VTRujnN6v2enp6Ki4srNcRi0aJF6tu3b7nP++yzzzRmzBh9+umnuvLKK8/5OsYYbdiwQdHR0VWuM+AKjDGnXbLPs45rUzMiArwVGeglY6RtCRU7YwoAAAA4A6fp6ZekiRMnavTo0erRo4f69OmjGTNm6MCBA7r77rslFQ+7P3z4sD766CNJxYH/tttu0yuvvKLevXs7Rgn4+PgoKKj42txTpkxR79691apVK2VkZOjVV1/Vhg0b9MYbb9TNQQL1TEZugfILiyRJYS64er9dx5ggHc1I0h+H09UjNvTcTwAAAACcgFOF/htvvFGpqal64oknlJCQoI4dO2r+/Plq2rSpJCkhIUEHDhxwlH/77bdVUFCge++9V/fee69j++23366ZM2dKko4fP64JEyYoMTFRQUFB6tatm37++Wf17NmzVo8NqK/sl+sL8PKQt9W9jmtTczo2DNKP25O0+TA9/QAAAHAdThX6Jemee+7RPffcU+Zj9iBvt3Tp0nPu76WXXtJLL71UDTUDXFNKZnHoDwtw3V5+qTj0S9KWI+l1XBMAAACg+jjNnH4AdcPV5/PbdToZ+ncmZSnXVljHtQEAAACqB6EfwFnZh/e78nx+SYoM9FKYv6cKiwyL+QEAAMBlEPoBnJU99Ddw8Z5+i8XiGOK/+TBD/AEAAOAaCP0AzupC6emXilfwl8RifgAAAHAZhH4AZ5WcaZ/TfwGE/pM9/X/Q0w8AAAAXQegHcFYXVE9/w0BJ0o6jmcorYDE/AAAAOD9CP4Czsof+8ADXntMvSQ2DfRTia1VBkVF8YmZdVwcAAACoMkI/gLNKzbpwhveXXMyPef0AAABwfoR+AOXKzivQiZPXrL8QQr/EvH4AAAC4FkI/gHLZh/b7WN3l5+VRx7WpHfYV/LccIfQDAADA+RH6AZTLsYjfBTCf367TyZ7+7QmZyi8oquPaAAAAAFVD6AdQrgvpcn12jUN9FODtofzCIu1MYjE/AAAAODdCP4ByXUiX67OzWCyOIf6bmdcPAAAAJ0foB1CuCzH0S1KnRqzgDwAAANdA6AdQLnvoD/e/cOb0S1KHmEBJrOAPAAAA50foB1CuFPuc/oALrKf/5GJ+2xIyVFDIYn4AAABwXoR+AOW6UIf3xzbwk7+Xh/IKirQrOauuqwMAAACcN0I/gHJdqKHfzc2i9ieH+DOvHwAAAM6M0A+gXClZ9kv2XVhz+iWxgj8AAABcAqEfQJlybYXKyiuQdOHN6ZekTo3sPf2EfgAAADgvQj+AMiVnFg/t9/RwU4CXRx3XpvbZe/q3HMlQYZGp49oAAAAA54fQD6BMpy7X5yWLxVLHtal9zcP95WN11wlbofamsJgfAAAAnBOhH0CZLuT5/JLkftpifn8wxB8AAABOitAPoEwX6sr9p+vU0L6YX9VX8DfGKCkzt8r7AQAAACrjwpuoC6BCUjIJ/R2qoac/O69AX/x+WB+u2KddSVm6b2BLPXx5m+qqIgAAAHBWhH4AZXL09AdcmMP7JalTo+Ke/q1HMlRUZOTmVvG1DQ6k5uijlfs0Z+1BZeYWOLa/vmSXGob46M89m1R7fWuLMUY/70zRnuQs3XhRY/l68l8JAABAfcVfagDKdGpO/4Xb098y3F9eHm7KyivQvtRsNQ/3P2t5Y4yW70rVzBV79eP2JJmTi/43C/PTbX2aKiUrT28s2a3/+3KzYoJ91L91eC0cRfUxxujHbUl69aed2nSoePTDB8v36T/XdVLflmF1XDsAAACUhdAPoEzJzOmXh7ub2kUHasPB49p8JKPc0J+TX6Av1hcP4d+ZdGql//6twzWmX6z6twqXm5tFxhglpufp8/WHdO8n6zX37j5qFx1YW4dz3oqKjBZuParXftqpLUeK1zfwtrop0NuqA8dydPO7v+nmXk00aVhbBXhb67i2AAAAOB2hH0CZWMivWMeGJ0P/4XRd3SWmxGOpudJ/FsRr7rrDyjg5hN/P0103xDXSbX1j1eKMkwQWi0VTr+ukI8dPaOWeVN05c43m3dNPUUHetXY8lVFYZPT95gS9/tMubU/MlCT5errrtj6xGndJM3l5uGnagu2ateqAPv3tgJZsT9Iz13XSwDYRdVxzAAAA2BH6AZTJvpBf+AU8p186fQX/4uHsxhit2J2qD37dox+3u8tovyQptoGvbu8bqxviGp21t9vTw01v3Rqn699aoV1JWbpz5hr99+4+8veqP7+OC4uMvt10RK/9tEu7To5c8Pfy0Ji+sbrz4mYK9Tv1mXjq2k66slOMHv18kw4cy9EdH6zRdd0b6l/D2yvY98L+7AAAANQH9eevTAD1Rl5BoaPn+kLv6e8Qcyr0f/Lbfn24Yp92HLUP4bfokpYNdOfFzdW/dXiFF/oL8rXqgzEXacT05dqakKH7P12vd27rIQ/3ur2KakFhkb7acERvLNmlPSnZkqRAbw/deXEz3dG3mYJ8yz6Z0adFAy148BK9sHCH3l++V1+sP6yfd6ToqWs7amjHqNo8BAAAAJyB0A+glNSTi/hZ3S0K8rmw52i3jgyQp7ubMnIL9Ni8zZKKh7hf1y1GTfL26o7r42S1Vv49ahzqq3dvv0g3zVipJfHJmvzNFj15TUdZLBW/QkB1yS8o0rzfD+mNJbt14FiOJCnY16rxlzTX6D5NFViBefq+nh56fHh7XdEpWo/8b6N2J2fr7lnrdGXnaE25usMFf/IIAACgrhD6AZRin8/fwM+rTkJofeLp4aZezUP1y84UNQktHsI/skcj+bhL8+fvrdK+uzYO1is3ddPds9Zp1qoDahrqp/GXNq+mmp9bXkGh/rfukKYv2a3Dx09Ikhr4eWr8pc11a++m5zXlIK5piL574BK99tNOvbVsj77blKAVu1I0+eoOurpLzAX/eQIAAKhthH4ApTgW8bvA5/PbvfbnbjpwLEcdYoLkfnIIv81mq5Z9X94hSo9d0U5PfbdNT8/fpkYhPhrWKbpa9l2eXFuh5qw5qLeW7VZCeq4kKTzAS3dd2lw392oiX8+q/dfgbXXX3y9vq2Edo/Xw3I3anpipv87eoG82HtFT13aqtwsXAgAAuCJCP4BSUjKLh/czJLtYsK9njS5KN/biZjp4LEcfrtyvB+dsUGSQt7o3Can21ykqMvpq42FN+z5eiRnFYT8y0Et/6d9CN/VsIm+re7W+XseGQfr6vov11rLdeu2nnVq8LUm/7V2mx69sr5E9GtHrDwAAUAvqdtUoOJXCIqNpC7ar59OL9clv++u6OqhByVyur1ZZLBb966oOGtQ2QnkFRRr/4VodSM2p1tdYtz9NI95cob/N2ajEjFzFBHnryWs7atnfB2pMv2bVHvjtPD3c9MCgVvr2/kvUpVGQMnML9Mjnm3Tb+6t1KK16j/F8GWO042hmtb/nAAAA9QE9/aiQ4zn5uv+z3/XLzhRJ0mPzNmt3UrYeu7KdY7gzXEcKob/WubtZ9Oqfu+nGGSu1+XCGxsxcrS/+0rfKIwwOpeVo2oJ4fbPxiCTJz9Nd9/6ppe6swaBfljZRAfr8L331/vK9emHhDv2yM0WXv/SzHh3WVjdd1ESeHrV/DvrgsRx9vfGI5v1+2HFpwmu7xuihIW3UONS31usDAABQEwj9OKf4xExN+Hit9qfmyMfqruGdozV33SG9v3yv9qdm65U/d6tX1xhH1aVk2Yf3M6e/Nvl5eej92y/StW8s157kbE34eJ0+HttTXh6VD+fZeQV6a9luzfh5j/IKimSxSDf2aKyJQ1orIqBu5tR7uLtpwqUtdFm7SD36+Sat2Zemf321Rc8uiNelrcM0sE2EBraNqNGTTek5Nn33R4K+/P2wVu875tju6eGm/IIifbnhiOb/kahbezfVfX9qqVA/vgMAAMC5kdRwVgs2J2jifzcqJ79QjUJ8NGN0D7WPCdSANhGa+N8N+nF7kka+tVLv3d5DMcE+dV1dVJOUzOKe/vAAevprW0Sgt96/4yLd8OZKrd57TI/+b5NeurFrhee/FxUZfb7+kJ77IV5JJ9uxV7NQ/euq9uoQE1STVa+w5uH+mjOhjz5etV+v/bRLKVl5mv9Houb/kSiLRerSKFh/ahuhP7WNUIeYwCrP/c+zFWrx9hR9ueGwlmxPVn5hkSTJYpH6NG+ga7s11NCOUTqQmqNpC7brl50pen/5Xs1de1B39W+uOy9uVuXFDQEAAOoKf8WgTEVFRi8t3qHXftolSerXsoFe/3N3hZzs9bqyc7Qahvho3IdrtS0hQ9e8sVzv3tZDXRoH12GtUV0Y3l+32kYF6s1bu+uOD9boyw1H1CTUVxOHtDnn81bvPaYnv92qPw6nS5KahPrqn1e00+UdIuvdonlubhbd3jdWo3s31R+H0/Xj9iT9tP2oNh/O0IaDx7Xh4HG9uGiHogK9NfDkCYB+LRtUOHwXFRn9tveYZu920//9vkyZuQWOx9pGBWhEt4a6umuMooNOnazs2DBIH4/tpV92Jus/32/XliMZen7hDn20cr8evKy1RvVoJA93lsIBAADOhdCPUjJybfrb7OJefKl4ZfFJw9qW+mO3a+NgfXlvX437cK22J2bqxhkr9dKorjV+uTHUPEJ/3bukVbieHtFRj37+h179aZcah/pqZI/GZZY9eCxH//l+u777I0GSFODlofsHtdTtfWPPa2pAbXJzs6hL42B1aRysiYNb62hGrpZsT9KP25P0684UJWbk6rPVB/TZ6gPy9HBT3xYN9Ke2ERrYJqLMefc7jmZq3u+H9dXvh3UkPVfF69UWKDrIW9d0bahru8WobVTgWet0Satw9WsRpm82HdHzC+N18NgJ/XPeH3r31z165PK29fIkCgAAQHkI/Shhd3KWxn+0VnuSs+Xp4ab/XNdJ13VvVG75RiG+mnt3Hz3w2e9aEp+sv3yyXn+/vI3uGdCCP4qdlK2wSGk5xdegZ05/3brxoiY6cCxHbyzZrUlf/KGYYB/1axnmeDwz16bpS3frvV/3Kr+gSG4W6aaeTTRxcGunPWETGeitm3o20U09myjXVqjf9h7TT9uO6sftSTqUdkJL45O1ND5Z0ha1iQzQwLYRuqRVmLYeydC83w9ra0KGY1/+Xh7qGJSv+4b3VN+WEXKrxKKjbm4WXdO1eNj/p78d0Gs/7dKe5GzdPWudujUJ1qRh7dSzWWgNvAMAAADVi9APhx+3HdWDszcoM6+4V+zt0XHq3Cj4nM8L8Lbqndt66Knvtmnmin167od47U3J1jMjOtXJityommPZxYv4ubtZFFKD16ZHxTw0uI0OHDuhbzYe0d2z1umLv/RV83B//W/dQT33ww7HqIx+LRvo/65sr3bRZ+/FdibeVnf1bx2u/q3DNflqo11JWcXTALYlad2BNMUfzVT80Uy9tWy34zlWd4sGtInQiG4NdWmLEP246Af1ahZaqcB/Oi8Pd93Rr5luiGukGT/v0bu/7NXvB45r1NsrdVm7CD0ytK1aRwZU1yHDhaVk5WlHYvFnNjkzTxEBXooO9lF0kLeigrwV5ud13p9TAADOhtAPGWP0+k+79OLiHTJGuig2RNNviavUIm4e7m6afHUHNQ/30+Svt+h/6w7pwLEcvX1rnGMdADiH5JOLv4X6efIHaD3g5mbRczd0VmL6Ca3Zl6YxH6xRkI/V0aPdLMxP/7yinS5rF+HSo2ssFotaRQaoVWSA7u7fQsdz8rVsR7J+2p6k3/YcU6MQH13braGu7BTt+J1js9mq7fUDvK16aEgbje7dVK/8uFOz1xzU4m1J+ml7kq7v3kgTh7QusT4ALlwZuTbtPJqp+MQs7TiaqfjETO04mqnUkydUy2N1tygy0PvkSQAfxZw8GXD6/Qb+XlwmFwBQaYT+C1x2XoEenrtR329OlCSN7t1Ujw9vf9499Lf1iVWTUF/d9+nvWr33mEZMX673x1yk5uH+1Vlt1CDm89c/3lZ3zRjdQ9e9uUJ7U7J1+PgJBXh76K+DWum2PrEX5IiaYF9PXdO1oa7p2rBWXzci0FtPj+ikOy9upud/iNf3mxM1d90hfb3xiMb0i9U9/VsqyNdaq3VC3TiRX6hdSVmKP5pZItwnpOeWWd5iKV5cs3VkgKICvZWcmaeEjFwlpp9QUmaebIVGh9JO6FDaCUlpZe7Dw634xEBUkLdign3UKsJf7aID1TYqQI1CfFz6xJ+zyCso1MFjJ3Q8p+RJHnNGOXPmBhV3wpzO19ND7WMCOdEDoMoI/Rew/anZmvDROsUfzZTV3aInr+mom3o2qfJ+B7SJ0Od/6as7Z67RvtQcjZi+Qm/dGqc+LRpUQ61R01Kyiv9QYT5//RLi56kPxlykSV/8odaR/vrrZa25hnwdahHurzdvjdP6A2n6z/ztWr3vmN5etkdz1hzUGzd3L7H2grPKyS++4gGXKyxWVGQ0Z+1BLdmepB1HM7X/WE6ZwU2SooO81ToyQG2iAor/jQxQywh/+XiWvbCmrbBISZl5Skw/oYT0XCUcz1VCeq4SM4rvJ6bn6mhGrgqKjA4fP6HDx09o3f6SJwYCvD3ULipQ7aID1C46UO2iA9UmKkDe1vq9mKczyrUV6sCxHO1Lyda+1GztS83R/tRs7UvJ0ZH0E+V+Ls5HRICXruwcreGdY9S9STAndgCcF/4nv0D9vCNZ93/2u9JP2BQe4KW3bu2uuKbVtyhVm6gAfXlvP034eK1+P3Bco9/7Tc+M6KRRF5W9+jjqD3tPfzg9/fVObJifPpvQu66rgdN0bxKiOXf11pL4JP3n++3acTRLt7+/WtOu76zr48pfBLW+O5SWo5FvrVTGCZv+elkr3dGvmawX8OUKkzPz9NDcjfp5R3KJ7aF+nmpzeriP8lfLiAAF+VRutIfV3U0Ng33UMLj8KSIFhUVKzspznAQ4eCxH8YmZ2pqQod3JWcrMLdDqfce0et8xx3PcLMVTgOwnAdpHB6ptdPFIA8Lj2eXkF2h/qj3Ynwz1qdnan5pT7mgOO38vD4X5e5Z6j0u945az3lVSRp6SMvP0wfJ9+mD5PjUM9tHwkycAOjYMpA0BVJjThP60tDQ98MAD+vrrryVJV199tV577TUFBweX+xxjjKZMmaIZM2YoLS1NvXr10htvvKEOHTo4yuTl5enhhx/WZ599phMnTmjQoEGaPn26GjVy3j/WzsYY6d1f9+m5hTtUZIovu/f26DhFBnpX+2uFB3jps/G99fDcjfp2U4Ie+XyTdqdk6dHL2zJXvB5LOTmnP6wSazoAFzKLxaI/tY1U3xZh+vv/NumbjUf00NyNOnz8hO7/U0un+8M8I9emsTPXOoLNM/O3679rD2nK1R1cYgRDZS3bkayH/rtBKVn58vJw030DWyquaYhaRwXU6jQoD3c3RQf5lLl2RH5BkXYnZ2lbQsbJW6a2JWQoNTtfu5OztTs5W99uSnCUD/G1qm1U8YmApg18FR7gpYgAL0UEeCs8wKvcEQlVZSss0tGM4pMWR9JzlXD85MiG9BNKy7HJouJFZN3dLHKznP6vSm0rb3uRMSosOu125v0ioyJjVHD6z4UltyWm5yrp5P+F5Qnw9lCzMD81beCn2Aa+atrAT83Civ9t4Fc68J+PvIJC/bIjRd9uOqJFW4/q8PETevvnPXr75z2KbeCr4Z1jdFWXGLWJYjFRAGfnNKH/5ptv1qFDh7RgwQJJ0oQJEzR69Gh988035T7n2Wef1YsvvqiZM2eqdevWeuqppzR48GDFx8crIKD4F+SDDz6ob775RrNnz1aDBg300EMPafjw4Vq3bp3c3V1rSNyJ/EJ9tNNN61N3SJJGxjXSk9d2rNGhf95Wd716Uzc1D/PTqz/t0tvL9mhfSrZeurErQ0brqVNz+hk6DlSGt9Vdr9zYVQ2DffTWst16cdEOHU47oadGdHSaXvKCwiLd9+nvij+aqYgAL/1lQAu9/tMu7UrK0i3v/qYrO0XrsSvbKeYsPdKuIr+gSM8vjNeMn/dIktpEBui1m7vVy6s1eHq4OXrz7YwxSs7M09aEDG1PzHScENidnK20HJtW7knVyj2pZe4vwMtD4QFejltEgLdCfT2UkGxR4K5URYf4KiLAW8E+VsdJfPtIhCPHi0N8Ynqu42d7sE/OzFNRNQ59r2nBvtbiMH8y1MeeDPWxDfwU4mut8RN6Xh7uuqx9pC5rH6lcW6GWbE/St5sS9OP2o9qXmqPXl+zS60t2qVWEv4Z3jtHQ9uE1Wh8Azuu8UtesWbN06623lvnY3//+dz333HNVqtSZtm3bpgULFmjVqlXq1auXJOmdd95Rnz59FB8frzZt2pR6jjFGL7/8sh577DFdd911kqQPP/xQkZGR+vTTT3XXXXcpPT1d7733nj7++GNddtlljmNr3LixFi9erMsvv7xaj6MuHcvO163vrtbWVDd5uFn0r6vaa3TvprXSA+XmZtHEIW3ULNxPj/7vD/2w5ahufHuV7rw4VhZZdGYVLBaLY4ib/bHTy535mKspKCjUxlSL3LcclYdH7Z942nE0SxIL+QHnw83Non8Ma6uGIT7691ebNWftQSVk5Gr6Ld3l71W/T3QaYzT5my36eUeyfKzueu/2i9SpUZCu695ILy3aoY9W7tN3fyTop+1Jun9QS427uLnLLiK5NyVbD3z2u/44nC5Juq1PU/3zinZONT/eYrEoItBbEYHeGtAmwrE911a8AOHWhAxtT8h0hPGkzDwlZeYq11akzLwCZeYVaE9K9hl7ddcnu9Y57lndLY7/K5Iy81RYgURvdbecvCpB8eUKo4N8FBPs7VijxN77XlhUvI6Cvaf+9B58x+NnbCsyxtHr726xyN395L9uZ9zOsS3M30tNG/gquB5dttbb6q5hnaI1rFO0svMKtHjbUX27KUHL4pO1MylLLy3eoZcW71BDX3cd9N+ra7o1UuNQ37quNoB64rz+ArnvvvsUHBys4cOHl9j+t7/9TbNnz6720L9y5UoFBQU5Ar8k9e7dW0FBQVqxYkWZoX/v3r1KTEzUkCFDHNu8vLzUv39/rVixQnfddZfWrVsnm81WokxMTIw6duyoFStWlBv68/LylJd3athXRkbxpbNsNlu1XiKqOvl6SKG+Vvl5GE2/uav6topQQUFBrdZheMdIRQV46p5PN+iPw+n625yNtfr6zsVd7++o2/cnxMej3n6e6wP7e8N7VH/VZRvdFBejCH+rHpxTPA985Jsr9M7objUylaq6fLBiv2atOiCLRXpxZCe1jfSVzWaTr4f02LDWuq5rtJ74bpvW7j+uZxfEa+6ag3p8eFtdUsUh//Xtu/TlhiOa/M02ZecXKtjHqqkjOuiydhGSimSzFdV19arMXVKbCF+1ifCVukSVeMwYo6y8QqVkFZ8ESMnKV1JmXvFJgYxcbd9/RMYrQMlZ+UrLsclWaErMby++uoCXogJPu9xgoNfJcF98C/V1nsvB1pfP5Jk83aQrOkToig4Ryjhh0+LtSZr/x1Et352qwznS84t26vlFO9W5UaCu7Bilfi0aqGWEP1cBqCfq2+88lM1Z2qmi9Tuv0D979mzddNNN+vrrr3XppZdKku6//3598cUXWrJkyfns8qwSExMVERFRantERIQSExPLfY4kRUZGltgeGRmp/fv3O8p4enoqJCSkVJny9itJU6dO1ZQpU0ptX7hwoXx96+9Z1StDpUFB0vGdazV/Z93V47420vyDbsqylbyEzZmr3Z66aynzMScaIeh0GngZHdv+m+bvqOua1H+LFi2q6yrgHOqyjf7SVpqx3V3bEjM1/JVluqtdoWLq4X8Tm49Z9G68mySLrmlSqPy9azV/b+lyt0ZLba0WfbXfTXtTc3Tnh+vVObRII2KLFFrFwUF1/V3KLZDm7nXT2pTi0QstA41GtzxR7ntxIbBIijx5k5/0p/aSVDz6obBIyrRJ6bbi/79DvKQAq+RmsUkqHjGmQklpUkGadFDFN1Q/b0nXhUmXB0kbj1m0PsWiXRkWbTqUoU2HijumvNyNmvobNQuQmvkbNQ0w8q3fg49cXl3/zkPF1Pd2ysnJqVC58/q6Dx06VG+99ZauvfZaLVy4UO+//76++uorLVmyRK1bt67wfiZPnlxmeD7dmjVrJKnMYejGmHMOTz/z8Yo851xlJk2apIkTJzruZ2RkqHHjxhoyZIgCAwPLfV5ds9lsWrRokQYPHiyrtW6vI31bnb56/Vaf2gnlo53qv/rSRsPTcjTuo/Xak5KjN7Z7a/rNXdSnef25hOmWIxma9N4aGRXqposa6Ymr2p31/8ArJU3MtenVn3br498OatMxN+3I9NDdlzbXuH5N5VXJIfD1oZ02HUrX3+Zu0oFjJ+TuZtH9A1vo7kub0TN6mvrQTjg7m80mv0WL9O9bL9Px3CL9sPWoFm1L0saD6crOL9SOdIt2pJ8q3zLcT92aBKtb4yB1bRysFmF+TjMKw5nVx++SrbBIHm4Wp1t4tibVx3Yqi33E+bmc9zm+m266SWlpabr44osVHh6uZcuWqWXLlpXax3333aebbrrprGViY2O1adMmHT16tNRjycnJpXry7aKiioesJSYmKjo62rE9KSnJ8ZyoqCjl5+crLS2tRG9/UlKS+vbtW26dvLy85OVVukvDarXW6w+FnbPU80JHOzkH2qn+q+s2ah4RpC/u6afxH63Vmn1pGvvRek27vrOu6173V4lJTM/VXZ/8rpz8Ql3SKkxPXtupQosOhlqtmnxNJ/25V6z+9dVm/bb3mF7+cZfmbTiiyVd10MC2pUfnnUtdtFNRkdE7v+zRcz/Eq6DIqGGwj179c9dqvYStq6nr7xPOzWq1KsbXqjsu9tcdF7dQYZFRfGKm1h9I0/r9aVp/IE37UnO0Kzlbu5KzNXfdYUlSoLeHujUJUfcmIYprGqIujYMU4E1b15S6+C7l2gq1OzlLO45mKj4xSzuPZir+aKYOpZ1QgJeHGof6qnGojxqH+KpxqK+anLzfKMTXqdY0KU+urVDpJ2w6nmPT8Zx8HT9hU/oJm9JzbDp+Ir94+wmbMk7YlJadr0i56Yp6/juvonWrcOg/vWf7dBEREerWrZumT5/u2Pbiiy9WaJ9hYWEKCzv3XMA+ffooPT1dq1evVs+ePSVJv/32m9LT08sN582aNVNUVJQWLVqkbt26SZLy8/O1bNkyTZs2TZIUFxcnq9WqRYsWadSoUZKkhIQEbd68Wc8++2yFjgEAgHMJ9vXUx2N76aG5G/XdpgRN/O9GHTl+QvcOrLtL+mXnFWjsh2t0NCNPrSL89cYt3St9lYE2UQGaPaG3vt54RE9/t037U3N0x8w1uqxdhP41vIOaNKiHcxlOSsrM1UP/3ahfdqZIkq7sFK1nruukIJ/6+8cdcD7c3SxqHxOo9jGBurV3U0nFV+r5/cBxx4mAjYeOKyO3QMt2JGvZjmRJxQsmt4kMULcmIWoU4iMvDzd5Wd3l5e4mL6tb8X0P95PbT/vZw73E454ebk47aiYrr0CH007o8PEcHT6ee/LnE0o4fkIB3h5qGx2otlEBahcdqGZhfvXmSi0FhUXal5pzMtxnFv97NFP7UrLLvYJGZl6BtiZkaGtC2T3HEQFexScFQnzUJNRXjUJ9T54cKL6UaF22cU5+gfal5GhPSpb2JmfrYFqO0nJKB/q8gkquyxJy7iLOosKh//fffy9ze4sWLZSRkeF4vCb+eGnXrp2GDh2q8ePH6+2335ZUfMm+4cOHl1jEr23btpo6dapGjBghi8WiBx98UM8884xatWqlVq1a6ZlnnpGvr69uvvlmSVJQUJDGjh2rhx56SA0aNFBoaKgefvhhderUybGaPwAA1cHb6q7XbuqmRsE+evvnPXp+4Q4dSjuhJ6+t/Uv6FRYZ/XX2Bm05kqEwf0+9P+YiBZ5nj57FYtE1XRtqULtIvfrjTr3/614t3pakn3em6C/9W+gvA1rUux6iJfFJevi/G5WanS9vq5umXN1Bo3o0ZmgrLhhh/l4a3D5Sg9sXj361FRZpe0LxaIB1J0cDHEo7oe2JmdqemFnl1/Nws8jH012B3lYFeHso0MeqQG+rAh0/eyjA26pAH4+TZU7/ufix6r5aiDFGqdn5jiDv+Pe0n9NPnH2RtCXxyY6fPd3d1CLCX+2iAtQ2OkBtowLVNjpA4f5eNfa7Ja+gUEkZeY5QvyMxU/FHs7Q7KUv5hWUH3CAfq9pEBqh1lH/xv5EBahbup/Qcmw6m5ejgsRM6cCxHB4/l6GDaCR06lqPMvIKTV/jI07r9aaX2aXW3KCbYRzFBPooM9FJkkLeiAr0V6bgVX/qzKm1YWGR0OO2E9qRkaU9ytvamZDt+Pn1B0XNxsxSfiA/2sSrI16ogH6uCfawK9vUs/vnkNn9PN+3+Y81517e+qXDor4kF+irjk08+0QMPPOBYaf/qq6/W66+/XqJMfHy80tNPTVZ65JFHdOLECd1zzz1KS0tTr169tHDhQgUEnLrG7ksvvSQPDw+NGjVKJ06c0KBBgzRz5ky5u9evP1AAAM7Pzc2iSVe0U8MQH03+eotmrzmohPRcvVHLl/R7Zv42Ld52VJ4ebppxW49qubSXv5eH/nlFO42Ma6R/f71FK3an6pUfd2ru2oMa2jFa/duEq1ez0Do9AZBXUKhnF8TrvV+LV+ZrFx2o1/7cVS0jAs7xTMC1Wd3d1KlRkDo1CtLtfWMlSUkZuVp/IE2/Hzyu49k25RUUKq+g6OStUHm2034uKDp5v/jn/IIiFZzWpVxQZJSZW6DM3PO/cpSP1V1+Xh5yd5PcLcXzz+2XWbRYire5WSxyc7PI3U3FP58s42Y5dV+Sjmbk6vDxExXq+Q3ysaphsI8ahvgU/xvso5hgHx3Lydf2hAxtTyzuTc/KK9C2hAxtS8iQTusrbeDneeokQFSAWoX7qqwLgRSdfI9Ss/OUlpOvY9k2HcvOK/FvWk6+UrPzlXbylplX/vvp6+muVpEBah3hrzZRxeG+TVSAIgLKPgkREeCtVpGlfxcaY3T8tBMCB9NyHCcFDqUVnyDJLyzS/tQc7U89+6JyYf6eiggovrpHZKCXIgNLnxywWCzak5ylPSnZJ8N9cbDfn5pT7okMSQr2tap5mJ+ahfkrtoGvQvw8FexrVbCPpyPIB/la5e/pUaG1K2w2m07sPmcxp+E063aGhoZq1qxZZy1jzlji3WKxaPLkyZo8eXK5z/H29tZrr72m1157rTqqCQDAOd3WJ1bRQT66/7P1WrYjWaPeWqkP7rioVi7p9/Gq/Y7Q+8LILurepHrHL7aKDNAn43pp/h+Jeuq7rTqSnqv3l+/V+8v3ytvqpt7NG6h/63ANaBOh2Foc/r8nOUsPzP5dmw8XD10d0zdW/xjWtt6NQgDqi4hAbw3tGK2hHaPPXbgMBYVFyi8scpwcyMorUGauTZm5Bcqw/3vCdsbPxWUyTpwqk3Uy2J6wFeqErbA6D1EWixQZ4K2YYG81DPF1hPtGJ4N9wxCfCp2QNcacGhlx8kTAtsQM7UvJVmp2vpbvStXyXamnXlfumr57ucICvHT8ZMBPy8lXYXlj78/C091NzcP9TgX7k+G+YbBPtSzMaLFYFOLnqRA/T3VuFFzq8aIio6OZuTqQmqPEjFwlpufqaEaejmbk6mhGrhIzcpWUkaf8wiKlZOUrJSu/3CkE5+Lp4aZmDfzULMxPzcNP/ds8zF8hfp5VPFLX5jShHwAAVzK4faTmTOijsR+u0daEDI14Y7lm3tlTrcvoaakuy3Yka/LXWyRJDw9prau6xNTI61gsFl3ZOVp/ahuhpfFJWhpfPE84MSNXS+OTtTQ+WVO+2aomob66pGUD+WZYNCC/QEHVsFhSUZHRwbSck398Z2p7YobiEzO1NzW7+NJyvlY9d0MXXda+7IWAAVQPD3c3ebi7ybeKWayg0H7CoPgEQGGRUZExKjLFQ76NMSosMio0RubktuLHjQqLVPxz0cnyprh8eICXGgX7KiqoakPO7SwWy8lF8HwdUyYk6UR+oXYmFf8u2paY4fidlJZjK+7JTskutS9/Lw+FngzZob5Whfp5KdTv1L8hvp5q4O+pEF9Phfp5KtDbWqdXXXBzsyg6qHhef3mMMUrLsZ08IXDqZEDxz3lKTM9VUmauUrLyJUkNg31OhfowPzUL91fzMD/FBNft2gHOjNAPAEAd6dI4WF/8pZ/GfLBae1Kydf2bK/T26Dj1bXHuRW4rKz4xU/d+sl6FRUbXd2+kewdW7oo758PH013DOkVrWKdoGWMUfzRTy06eAFiz75gOHMvRJ6tzJLlr5jNL1KtZ8SiA/m3C1SrC/5zzYI/n5Dt61uKPZmpbQvGCVTn5ZfcGXtIqTM+P7FIrIyoAVA8Pd7fiOdhVPXtQB3w83dW5UXCJHvL8/HzN/up7NezQUzk2UxzwTwb5YF+rvDxcb/SRxWJRqF/xSYr2MeVf3jy/oEhFxjACqwYQ+gEAqENNGvjq87/01YSPiy/pd/v7q3VDXGNd3SVGPZuFVkuvRlJmru6cuUZZeQXq1SxUU6/rVOuL1lkslpNzWgN1V/8Wysor0MrdqVqyPVELNh7UsTzp110p+nVXip6ev00xQd7q3yZc/VuHq2ezBjqakav4k0Nm40/24idmlL14k6eHm1pH+qtNZKDanZxL2yYqQOEBpS+3CwC1yWKxKMhTurRVWL2+FFxdqO7FGnEKoR8AgDoW4lfykn6frT6gz1YfUESAl67sHK2rusSoW+Pg8wrqubZCjf9onQ4fP6FmYX5669a4evGHlb+Xhwa3j9SAVqHq6bZP7Xr21/I9aVq6I1mr9qTqSHquPlt9UJ+tPnjW/TQK8XEskGVfLCu2ga886smlswAAqGuEfgAA6gFvq7te/3M3/fmiJvp642F9vzlRSZl5+mD5Pn2wfJ8ahfjoqi4xuqpzjNpFB1ToBEBRkdHE/27QxoPHFexr1ftjLqqXix1ZLCpeiComWHde3Ewn8gv1295ULY1P1s87krUnJVsBXh6OUN8mKkDtoosXrQo4z0sNAgBwoSD0AwBQT1gsFl3cKkwXtwrTk9d21C87UvT1xiNatPWoDqWd0JtLd+vNpbvVItxPV3dpqKu6RKt5uH+5+3t+Ybzm/5Eoq7tFb98ap2ZhfrV4NOfPx9NdA9pEaECbCElSZq5N/l4etT4lAQAAV0DoBwCgHvLycNdl7SN1WftI5eQX6KftSfpm4xEtiU/W7uRsvbR4h15avEMdYgJ1VZcYDe8crUYhpy6B99+1BzV9afFFhv9zXWf1at6grg6lyujNBwDg/BH6AQCo53w9PTS8c4yGd45RRq5NC7cc1Tcbj+jXXSnaciRDW45k6D/fb1dc0xBd1TlakYHe+ucXf0iS7v9TS10f16iOjwAAANQVQj8AAE4k0NuqG+Ia6Ya4RjqWna/vNyfo6w1HtHrfMa3bn6Z1+9McZYd3jtbfLmtdh7UFAAB1jdAPAICTCvXz1C29muqWXk2VmJ6r7/5I0Dcbj2jDweO6KDZEz4/sIrdquOQfAABwXoR+AABcQFSQt8Ze3ExjL26mY9n5CvD2kJXL1gEAcMEj9AMA4GJC6+Fl+QAAQN2gCwAAAAAAABdF6AcAAAAAwEUR+gEAAAAAcFGEfgAAAAAAXBShHwAAAAAAF0XoBwAAAADARRH6AQAAAABwUYR+AAAAAABcFKEfAAAAAAAXRegHAAAAAMBFEfoBAAAAAHBRhH4AAAAAAFwUoR8AAAAAABdF6AcAAAAAwEUR+gEAAAAAcFGEfgAAAAAAXBShHwAAAAAAF0XoBwAAAADARRH6AQAAAABwUYR+AAAAAABcFKEfAAAAAAAXRegHAAAAAMBFEfoBAAAAAHBRhH4AAAAAAFwUoR8AAAAAABdF6AcAAAAAwEUR+gEAAAAAcFGEfgAAAAAAXBShHwAAAAAAF0XoBwAAAADARRH6AQAAAABwUYR+AAAAAABcFKEfAAAAAAAXRegHAAAAAMBFEfoBAAAAAHBRThP609LSNHr0aAUFBSkoKEijR4/W8ePHyy1vs9n06KOPqlOnTvLz81NMTIxuu+02HTlypES5AQMGyGKxlLjddNNNNXw0AAAAAADUPKcJ/TfffLM2bNigBQsWaMGCBdqwYYNGjx5dbvmcnBytX79ejz/+uNavX68vvvhCO3bs0NVXX12q7Pjx45WQkOC4vf322zV5KAAAAAAA1AqPuq5ARWzbtk0LFizQqlWr1KtXL0nSO++8oz59+ig+Pl5t2rQp9ZygoCAtWrSoxLbXXntNPXv21IEDB9SkSRPHdl9fX0VFRVW4Pnl5ecrLy3Pcz8jIkFQ8usBms1Xq2GqTvW71uY6gnZwF7VT/0UbOgXZyDrRT/UcbOQfayTk4SztVtH4WY4yp4bpU2fvvv6+JEyeWGs4fHBysl156SXfccUeF9rN48WINGTJEx48fV2BgoKTi4f1btmyRMUaRkZEaNmyY/v3vfysgIKDc/UyePFlTpkwptf3TTz+Vr69vxQ8MAAAAAIDzkJOTo5tvvlnp6emOfFsWp+jpT0xMVERERKntERERSkxMrNA+cnNz9Y9//EM333xziTfklltuUbNmzRQVFaXNmzdr0qRJ2rhxY6lRAqebNGmSJk6c6LifkZGhxo0ba8iQIWd9s+uazWbTokWLNHjwYFmt1rquDspBOzkH2qn+o42cA+3kHGin+o82cg60k3Nwlnayjzg/lzoN/eX1mJ9uzZo1kiSLxVLqMWNMmdvPZLPZdNNNN6moqEjTp08v8dj48eMdP3fs2FGtWrVSjx49tH79enXv3r3M/Xl5ecnLy6vUdqvVWq8/FHbOUs8LHe3kHGin+o82cg60k3Ogneo/2sg50E7Oob63U0XrVqeh/7777jvnSvmxsbHatGmTjh49Wuqx5ORkRUZGnvX5NptNo0aN0t69e/XTTz+dsye+e/fuslqt2rlzZ7mhHwAAAAAAZ1CnoT8sLExhYWHnLNenTx+lp6dr9erV6tmzpyTpt99+U3p6uvr27Vvu8+yBf+fOnVqyZIkaNGhwztfasmWLbDaboqOjK34gAAAAAADUQ05xyb527dpp6NChGj9+vFatWqVVq1Zp/PjxGj58eImV+9u2bat58+ZJkgoKCnTDDTdo7dq1+uSTT1RYWKjExEQlJiYqPz9fkrR792498cQTWrt2rfbt26f58+dr5MiR6tatm/r161cnxwoAAAAAQHVxitAvSZ988ok6deqkIUOGaMiQIercubM+/vjjEmXi4+OVnp4uSTp06JC+/vprHTp0SF27dlV0dLTjtmLFCkmSp6enfvzxR11++eVq06aNHnjgAQ0ZMkSLFy+Wu7t7rR8jAAAAAADVySlW75ek0NBQzZo166xlTr/6YGxsrM51NcLGjRtr2bJl1VI/AAAAAADqG6fp6QcAAAAAAJVD6AcAAAAAwEUR+gEAAAAAcFGEfgAAAAAAXBShHwAAAAAAF0XoBwAAAADARRH6AQAAAABwUYR+AAAAAABcFKEfAAAAAAAXRegHAAAAAMBFEfoBAAAAAHBRhH4AAAAAAFwUoR8AAAAAABdF6AcAAAAAwEUR+gEAAAAAcFGEfgAAAAAAXBShHwAAAAAAF0XoBwAAAADARRH6AQAAAABwUYR+AAAAAABcFKEfAAAAAAAXRegHAAAAAMBFEfoBAAAAAHBRhH4AAAAAAFwUoR8AAAAAABdF6AcAAAAAwEUR+gEAAAAAcFGEfgAAAAAAXBShHwAAAAAAF0XoBwAAAADARRH6AQAAAABwUYR+AAAAAABcFKEfAAAAAAAXRegHAAAAAMBFEfoBAAAAAHBRhH4AAAAAAFwUoR8AAAAAABdF6AcAAAAAwEUR+gEAAAAAcFGEfgAAAAAAXBShHwAAAAAAF0XoBwAAAADARRH6AQAAAABwUYR+AAAAAABcFKEfAAAAAAAXRegHAAAAAMBFOU3oT0tL0+jRoxUUFKSgoCCNHj1ax48fP+tzxowZI4vFUuLWu3fvEmXy8vJ0//33KywsTH5+frr66qt16NChGjwSAAAAAABqh9OE/ptvvlkbNmzQggULtGDBAm3YsEGjR48+5/OGDh2qhIQEx23+/PklHn/wwQc1b948zZ49W7/++quysrI0fPhwFRYW1tShAAAAAABQKzzqugIVsW3bNi1YsECrVq1Sr169JEnvvPOO+vTpo/j4eLVp06bc53p5eSkqKqrMx9LT0/Xee+/p448/1mWXXSZJmjVrlho3bqzFixfr8ssvr/6DAQAAAACgljhF6F+5cqWCgoIcgV+SevfuraCgIK1YseKsoX/p0qWKiIhQcHCw+vfvr6effloRERGSpHXr1slms2nIkCGO8jExMerYsaNWrFhRbujPy8tTXl6e435GRoYkyWazyWazVelYa5K9bvW5jqCdnAXtVP/RRs6BdnIOtFP9Rxs5B9rJOThLO1W0fk4R+hMTEx1B/XQRERFKTEws93nDhg3TyJEj1bRpU+3du1ePP/64/vSnP2ndunXy8vJSYmKiPD09FRISUuJ5kZGRZ93v1KlTNWXKlFLbFy5cKF9f30ocWd1YtGhRXVcBFUA7OQfaqf6jjZwD7eQcaKf6jzZyDrSTc6jv7ZSTk1OhcnUa+idPnlxmeD7dmjVrJEkWi6XUY8aYMrfb3XjjjY6fO3bsqB49eqhp06b67rvvdN1115X7vHPtd9KkSZo4caLjfkZGhho3bqwhQ4YoMDDwrMdTl2w2mxYtWqTBgwfLarXWdXVQDtrJOdBO9R9t5BxoJ+dAO9V/tJFzoJ2cg7O0k33E+bnUaei/7777dNNNN521TGxsrDZt2qSjR4+Weiw5OVmRkZEVfr3o6Gg1bdpUO3fulCRFRUUpPz9faWlpJXr7k5KS1Ldv33L34+XlJS8vr1LbrVZrvf5Q2DlLPS90tJNzoJ3qP9rIOdBOzoF2qv9oI+dAOzmH+t5OFa1bnYb+sLAwhYWFnbNcnz59lJ6ertWrV6tnz56SpN9++03p6elnDednSk1N1cGDBxUdHS1JiouLk9Vq1aJFizRq1ChJUkJCgjZv3qxnn332PI4IAAAAAID6wyku2deuXTsNHTpU48eP16pVq7Rq1SqNHz9ew4cPL7GIX9u2bTVv3jxJUlZWlh5++GGtXLlS+/bt09KlS3XVVVcpLCxMI0aMkCQFBQVp7Nixeuihh/Tjjz/q999/16233qpOnTo5VvMHAAAAAMBZOcVCfpL0ySef6IEHHnCstH/11Vfr9ddfL1EmPj5e6enpkiR3d3f98ccf+uijj3T8+HFFR0dr4MCBmjNnjgICAhzPeemll+Th4aFRo0bpxIkTGjRokGbOnCl3d/faOzgAAAAAAGqA04T+0NBQzZo166xljDGOn318fPTDDz+cc7/e3t567bXX9Nprr1W5jgAAAAAA1CdOMbwfAAAAAABUHqEfAAAAAAAXRegHAAAAAMBFEfoBAAAAAHBRhH4AAAAAAFwUoR8AAAAAABdF6AcAAAAAwEUR+gEAAAAAcFGEfgAAAAAAXBShHwAAAAAAF0XoBwAAAADARRH6AQAAAABwUYR+AAAAAABcFKEfAAAAAAAXRegHAAAAAMBFEfoBAAAAAHBRhH4AAAAAAFwUoR8AAAAAABdF6AcAAAAAwEUR+gEAAAAAcFGEfgAAAAAAXBShHwAAAAAAF0XoBwAAAADARRH6AQAAAABwUYR+AAAAAABcFKEfAAAAAAAXRegHAAAAAMBFEfoBAAAAAHBRhH4AAAAAAFwUoR8AAAAAABdF6AcAAAAAwEUR+gEAAAAAcFGEfgAAAAAAXBShHwAAAAAAF0XoBwAAAADARRH6AQAAAABwUYR+AAAAAABcFKEfAAAAAAAXRegHAAAAAMBFEfoBAAAAAHBRhH4AAAAAAFwUoR8AAAAAABdF6AcAAAAAwEUR+gEAAAAAcFGEfgAAAAAAXBShHwAAAAAAF0XoBwAAAADARTlN6E9LS9Po0aMVFBSkoKAgjR49WsePHz/rcywWS5m35557zlFmwIABpR6/6aabavhoAAAAAACoeR51XYGKuvnmm3Xo0CEtWLBAkjRhwgSNHj1a33zzTbnPSUhIKHH/+++/19ixY3X99deX2D5+/Hg98cQTjvs+Pj7VWHMAAAAAAOqGU4T+bdu2acGCBVq1apV69eolSXrnnXfUp08fxcfHq02bNmU+LyoqqsT9r776SgMHDlTz5s1LbPf19S1VFgAAAAAAZ+cUoX/lypUKCgpyBH5J6t27t4KCgrRixYpyQ//pjh49qu+++04ffvhhqcc++eQTzZo1S5GRkRo2bJj+/e9/KyAgoNx95eXlKS8vz3E/IyNDkmSz2WSz2SpzaLXKXrf6XEfQTs6Cdqr/aCPnQDs5B9qp/qONnAPt5BycpZ0qWj+nCP2JiYmKiIgotT0iIkKJiYkV2seHH36ogIAAXXfddSW233LLLWrWrJmioqK0efNmTZo0SRs3btSiRYvK3dfUqVM1ZcqUUtsXLlwoX1/fCtWnLp3t2FB/0E7OgXaq/2gj50A7OQfaqf6jjZwD7eQc6ns75eTkVKhcnYb+yZMnlxmeT7dmzRpJxYvynckYU+b2srz//vu65ZZb5O3tXWL7+PHjHT937NhRrVq1Uo8ePbR+/Xp17969zH1NmjRJEydOdNzPyMhQ48aNNWTIEAUGBlaoPnXBZrNp0aJFGjx4sKxWa11XB+WgnZwD7VT/0UbOgXZyDrRT/UcbOQfayTk4SzvZR5yfS52G/vvuu++cK+XHxsZq06ZNOnr0aKnHkpOTFRkZec7X+eWXXxQfH685c+acs2z37t1ltVq1c+fOckO/l5eXvLy8Sm23Wq31+kNh5yz1vNDRTs6Bdqr/aCPnQDs5B9qp/qONnAPt5BzqeztVtG51GvrDwsIUFhZ2znJ9+vRRenq6Vq9erZ49e0qSfvvtN6Wnp6tv377nfP57772nuLg4denS5Zxlt2zZIpvNpujo6HMfAAAAAAAA9ZhbXVegItq1a6ehQ4dq/PjxWrVqlVatWqXx48dr+PDhJRbxa9u2rebNm1fiuRkZGZo7d67GjRtXar+7d+/WE088obVr12rfvn2aP3++Ro4cqW7duqlfv341flwAAAAAANQkpwj9UvEK+506ddKQIUM0ZMgQde7cWR9//HGJMvHx8UpPTy+xbfbs2TLG6M9//nOpfXp6eurHH3/U5ZdfrjZt2uiBBx7QkCFDtHjxYrm7u9fo8QAAAAAAUNOcYvV+SQoNDdWsWbPOWsYYU2rbhAkTNGHChDLLN27cWMuWLauW+gEAAAAAUN84TU8/AAAAAACoHEI/AAAAAAAuitAPAAAAAICLIvQDAAAAAOCiCP0AAAAAALgoQj8AAAAAAC6K0A8AAAAAgIsi9AMAAAAA4KII/QAAAAAAuChCPwAAAAAALorQDwAAAACAiyL0AwAAAADgogj9AAAAAAC4KEI/AAAAAAAuitAPAAAAAICLIvQDAAAAAOCiCP0AAAAAALgoQj8AAAAAAC6K0A8AAAAAgIsi9AMAAAAA4KII/QAAAAAAuChCPwAAAAAALorQDwAAAACAiyL0AwAAAADgogj9AAAAAAC4KEI/AAAAAAAuitAPAAAAAICLIvQDAAAAAOCiCP0AAAAAALgoQj8AAAAAAC6K0A8AAAAAgIsi9AMAAAAA4KII/QD+v727j6my/v84/joaoCLeIMnh5B0zHZuYKd4kKzVLikloNpV0S5c5NTWZWuqcqVup2Wb+YZp/oJLWaGvqWroSFpCOWYQ3ITWzSaAJUt4gKsIRPr8/mmceQeDXN87Fuc7zsZ2N87k+5/i+9vZ9fa4313UOAAAAAGyKph8AAAAAAJui6QcAAAAAwKZo+gEAAAAAsCmafgAAAAAAbIqmHwAAAAAAm6LpBwAAAADApmj6AQAAAACwKZp+AAAAAABsiqYfAAAAAACboukHAAAAAMCmaPoBAAAAALApmn4AAAAAAGyKph8AAAAAAJui6QcAAAAAwKb8pul///33FR8fr06dOqlbt24teo0xRuvWrZPL5VLHjh01btw4FRUVec2pqanR4sWLFRERodDQUCUnJ+vixYutsAcAAAAAAPiW3zT9tbW1mjp1qhYsWNDi12zevFlbtmzRtm3blJ+fL6fTqQkTJqiqqsozJzU1VQcOHFBGRoaOHTummzdvKikpSXV1da2xGwAAAAAA+MwjVgfQUuvXr5ck7dmzp0XzjTHaunWrVq9erSlTpkiS0tPTFRkZqc8//1zz5s1TZWWl0tLStHfvXj3//POSpH379ql3797KysrSCy+80Cr7AgAAAACAL/hN0///VVxcrPLyciUkJHjGQkJCNHbsWOXl5WnevHkqKCiQ2+32muNyuRQbG6u8vLyHNv01NTWqqanxPK+srJQkXb16VW63u5X26H/ndrt1+/ZtXblyRUFBQVaHg4cgT/6BPLV95Mg/kCf/QJ7aPnLkH8iTf/CXPN27g90Y0+Q82zb95eXlkqTIyEiv8cjISJWUlHjmBAcHq3v37g3m3Ht9YzZu3Oi58+B+0dHR/2vYAAAAAAC0WFVVlbp27frQ7ZY2/evWrWu0eb5ffn6+hg8f/q//DYfD4fXcGNNg7EHNzVm1apWWLl3qeV5fX6+rV6+qR48ezb63lW7cuKHevXvrwoUL6tKli9Xh4CHIk38gT20fOfIP5Mk/kKe2jxz5B/LkH/wlT8YYVVVVyeVyNTnP0qZ/0aJFSklJaXJOv379/tV7O51OSf9czY+KivKMV1RUeK7+O51O1dbW6tq1a15X+ysqKhQfH//Q9w4JCVFISIjXWEv/okBb0KVLlzb9nxf/IE/+gTy1feTIP5An/0Ce2j5y5B/Ik3/whzw1dYX/Hkub/oiICEVERLTKe0dHR8vpdCozM1NDhw6V9M9fAMjNzdUHH3wgSYqLi1NQUJAyMzM1bdo0SVJZWZnOnDmjzZs3t0pcAAAAAAD4it98pr+0tFRXr15VaWmp6urqdOrUKUnS448/rs6dO0uSYmJitHHjRr388styOBxKTU3Vhg0bNGDAAA0YMEAbNmxQp06dNGPGDEn//FZkzpw5WrZsmXr06KHw8HAtX75cgwcP9nybPwAAAAAA/spvmv53331X6enpnuf3rt5nZ2dr3LhxkqSzZ896vklfkt555x1VV1frzTff1LVr1zRq1CgdOXJEYWFhnjkfffSRHnnkEU2bNk3V1dV67rnntGfPHrVv3943O+ZDISEhWrt2bYOPJqBtIU/+gTy1feTIP5An/0Ce2j5y5B/Ik3+wW54cprnv9wcAAAAAAH6pndUBAAAAAACA1kHTDwAAAACATdH0AwAAAABgUzT9AAAAAADYFE1/ANm+fbuio6PVoUMHxcXF6ejRo1aHFLA2btyoESNGKCwsTD179tTkyZN19uxZrzmzZ8+Ww+Hwejz11FMWRRyY1q1b1yAHTqfTs90Yo3Xr1snlcqljx44aN26cioqKLIw48PTr169BjhwOhxYuXCiJOrLK999/r5deekkul0sOh0MHDx702t6S2qmpqdHixYsVERGh0NBQJScn6+LFiz7cC/trKk9ut1srVqzQ4MGDFRoaKpfLpddee02XLl3yeo9x48Y1qLGUlBQf74m9NVdPLTnOUU+tq7kcNbZOORwOffjhh5451FLrasm5t53XJpr+APHFF18oNTVVq1ev1smTJ/XMM88oMTFRpaWlVocWkHJzc7Vw4UIdP35cmZmZunv3rhISEnTr1i2veS+++KLKyso8j8OHD1sUceAaNGiQVw4KCws92zZv3qwtW7Zo27Ztys/Pl9Pp1IQJE1RVVWVhxIElPz/fKz+ZmZmSpKlTp3rmUEe+d+vWLQ0ZMkTbtm1rdHtLaic1NVUHDhxQRkaGjh07pps3byopKUl1dXW+2g3baypPt2/f1okTJ7RmzRqdOHFC+/fv12+//abk5OQGc+fOnetVYzt37vRF+AGjuXqSmj/OUU+tq7kc3Z+bsrIy7dq1Sw6HQ6+88orXPGqp9bTk3NvWa5NBQBg5cqSZP3++11hMTIxZuXKlRRHhfhUVFUaSyc3N9YzNmjXLTJo0ybqgYNauXWuGDBnS6Lb6+nrjdDrNpk2bPGN37twxXbt2NZ988omPIsSDlixZYvr372/q6+uNMdRRWyDJHDhwwPO8JbVz/fp1ExQUZDIyMjxz/vzzT9OuXTvzzTff+Cz2QPJgnhrz448/GkmmpKTEMzZ27FizZMmS1g0OHo3lqbnjHPXkWy2ppUmTJpnx48d7jVFLvvXgubfd1yau9AeA2tpaFRQUKCEhwWs8ISFBeXl5FkWF+1VWVkqSwsPDvcZzcnLUs2dPDRw4UHPnzlVFRYUV4QW0c+fOyeVyKTo6WikpKTp//rwkqbi4WOXl5V51FRISorFjx1JXFqmtrdW+ffv0+uuvy+FweMapo7alJbVTUFAgt9vtNcflcik2Npb6slBlZaUcDoe6devmNf7ZZ58pIiJCgwYN0vLly7nbyQJNHeeop7bl8uXLOnTokObMmdNgG7XkOw+ee9t9bXrE6gDQ+v7++2/V1dUpMjLSazwyMlLl5eUWRYV7jDFaunSpnn76acXGxnrGExMTNXXqVPXt21fFxcVas2aNxo8fr4KCAoWEhFgYceAYNWqUPv30Uw0cOFCXL1/We++9p/j4eBUVFXlqp7G6KikpsSLcgHfw4EFdv35ds2fP9oxRR21PS2qnvLxcwcHB6t69e4M5rFvWuHPnjlauXKkZM2aoS5cunvGZM2cqOjpaTqdTZ86c0apVq3T69GnPR23Q+po7zlFPbUt6errCwsI0ZcoUr3FqyXcaO/e2+9pE0x9A7r/yJf3zH/7BMfjeokWL9PPPP+vYsWNe49OnT/f8HBsbq+HDh6tv3746dOhQg4UCrSMxMdHz8+DBgzV69Gj1799f6enpni9Joq7ajrS0NCUmJsrlcnnGqKO269/UDvVlDbfbrZSUFNXX12v79u1e2+bOnev5OTY2VgMGDNDw4cN14sQJDRs2zNehBqR/e5yjnqyxa9cuzZw5Ux06dPAap5Z852Hn3pJ91yZu7w8AERERat++fYPfQFVUVDT4bRZ8a/Hixfrqq6+UnZ2tXr16NTk3KipKffv21blz53wUHR4UGhqqwYMH69y5c55v8aeu2oaSkhJlZWXpjTfeaHIedWS9ltSO0+lUbW2trl279tA58A23261p06apuLhYmZmZXlf5GzNs2DAFBQVRYxZ68DhHPbUdR48e1dmzZ5tdqyRqqbU87Nzb7msTTX8ACA4OVlxcXIPbgzIzMxUfH29RVIHNGKNFixZp//79+u677xQdHd3sa65cuaILFy4oKirKBxGiMTU1Nfr1118VFRXluQXv/rqqra1Vbm4udWWB3bt3q2fPnpo4cWKT86gj67WkduLi4hQUFOQ1p6ysTGfOnKG+fOhew3/u3DllZWWpR48ezb6mqKhIbrebGrPQg8c56qntSEtLU1xcnIYMGdLsXGrpv9Xcubft1yaLvkAQPpaRkWGCgoJMWlqa+eWXX0xqaqoJDQ01f/zxh9WhBaQFCxaYrl27mpycHFNWVuZ53L592xhjTFVVlVm2bJnJy8szxcXFJjs724wePdo89thj5saNGxZHHziWLVtmcnJyzPnz583x48dNUlKSCQsL89TNpk2bTNeuXc3+/ftNYWGhefXVV01UVBQ58rG6ujrTp08fs2LFCq9x6sg6VVVV5uTJk+bkyZNGktmyZYs5efKk51vfW1I78+fPN7169TJZWVnmxIkTZvz48WbIkCHm7t27Vu2W7TSVJ7fbbZKTk02vXr3MqVOnvNaqmpoaY4wxv//+u1m/fr3Jz883xcXF5tChQyYmJsYMHTqUPP2HmspTS49z1FPrau6YZ4wxlZWVplOnTmbHjh0NXk8ttb7mzr2NsffaRNMfQD7++GPTt29fExwcbIYNG+b15+HgW5IafezevdsYY8zt27dNQkKCefTRR01QUJDp06ePmTVrliktLbU28AAzffp0ExUVZYKCgozL5TJTpkwxRUVFnu319fVm7dq1xul0mpCQEDNmzBhTWFhoYcSB6dtvvzWSzNmzZ73GqSPrZGdnN3qMmzVrljGmZbVTXV1tFi1aZMLDw03Hjh1NUlISufuPNZWn4uLih65V2dnZxhhjSktLzZgxY0x4eLgJDg42/fv3N2+99Za5cuWKtTtmM03lqaXHOeqpdTV3zDPGmJ07d5qOHTua69evN3g9tdT6mjv3Nsbea5PDGGNa6SYCAAAAAABgIT7TDwAAAACATdH0AwAAAABgUzT9AAAAAADYFE0/AAAAAAA2RdMPAAAAAIBN0fQDAAAAAGBTNP0AAAAAANgUTT8AAAAAADZF0w8AAAAAgE3R9AMAAAAAYFM0/QAAAAAA2BRNPwAA8Km//vpLTqdTGzZs8Iz98MMPCg4O1pEjRyyMDAAA+3EYY4zVQQAAgMBy+PBhTZ48WXl5eYqJidHQoUM1ceJEbd261erQAACwFZp+AABgiYULFyorK0sjRozQ6dOnlZ+frw4dOlgdFgAAtkLTDwAALFFdXa3Y2FhduHBBP/30k5544gmrQwIAwHb4TD8AALDE+fPndenSJdXX16ukpMTqcAAAsCWu9AMAAJ+rra3VyJEj9eSTTyomJkZbtmxRYWGhIiMjrQ4NAABboekHAAA+9/bbb+vLL7/U6dOn1blzZz377LMKCwvT119/bXVoAADYCrf3AwAAn8rJydHWrVu1d+9edenSRe3atdPevXt17Ngx7dixw+rwAACwFa70AwAAAABgU1zpBwAAAADApmj6AQAAAACwKZp+AAAAAABsiqYfAAAAAACboukHAAAAAMCmaPoBAAAAALApmn4AAAAAAGyKph8AAAAAAJui6QcAAAAAwKZo+gEAAAAAsCmafgAAAAAAbOr/AAO0QstQDIAEAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k = 100\n", - "x1_v = np.linspace(0, 200)\n", - "x1_v[0] = 0.0001\n", - "k_v = [inv.k_func(xx, inv.y_func(xx, k=100)) for xx in x1_v]\n", - "plt.plot(x1_v, k_v)\n", - "ylim = (99.999999, 100.000001)\n", - "assert min(k_v) > ylim[0]\n", - "assert max(k_v) < ylim[1]\n", - "plt.ylim(*ylim)\n", - "plt.title(f\"Verifying `y_func` for k=100 [ylim = {ylim}\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"k\")\n", - "plt.grid()" - ] - }, - { - "cell_type": "markdown", - "id": "3d0eaf6d-4beb-420f-b323-e465df639143", - "metadata": { - "tags": [] - }, - "source": [ - "### Curves at different k" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "a44ccaf0-7aea-4669-8f54-00ee9942acf7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(6, 6))\n", - "k_v = [5, 50, 250, 1000, 4000, 12000, 35000]\n", - "x_v = np.linspace(0.1 , 20, 500)\n", - "y_v_by_k = {kk: [inv.y_func(xx, k=kk) for xx in x_v] for kk in k_v}\n", - "for kk, y_v in y_v_by_k.items():\n", - " plt.plot(x_v, y_v, label=f\"{kk}\")\n", - "plt.xlim(0,20)\n", - "plt.ylim(0,20)\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.title(\"Swap curves for different values of k\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "311d8b50-1f12-4fdf-9749-07c6f856a11f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/NBTest/NBTest_067_Invariants.py b/resources/NBTest/NBTest_067_Invariants.py deleted file mode 100644 index cb4bd318a..000000000 --- a/resources/NBTest/NBTest_067_Invariants.py +++ /dev/null @@ -1,259 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -try: - import fastlane_bot.tools.invariants.functions as f - from fastlane_bot.tools.invariants.invariant import Invariant - from fastlane_bot.tools.invariants.bancor import BancorInvariant, BancorSwapFunction - from fastlane_bot.tools.invariants.solidly import SolidlyInvariant, SolidlySwapFunction - from fastlane_bot.testing import * - -except: - import tools.invariants.functions as f - from tools.invariants.invariant import Invariant - from tools.invariants.bancor import BancorInvariant, BancorSwapFunction - from tools.invariants.solidly import SolidlyInvariant, SolidlySwapFunction - from tools.testing import * - -import numpy as np -import math as m -import matplotlib.pyplot as plt - - -plt.rcParams['figure.figsize'] = [12,6] - -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(f.Function)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(BancorInvariant)) -# - - -# # Invariants (Invariants Module; NBTest067) - -# ## General invariants - -inv = BancorInvariant() - -# ### goal seek - -# testing on $(x-1)(x+1)$ - -func = lambda x: x**2 - 1 -assert iseq(inv.goalseek_gradient(func, x0=-0.1), -1) -assert iseq(inv.goalseek_gradient(func, x0=0.1), 1) - -assert iseq(inv.goalseek_bisect(func, x_lo=0, x_hi=10), 1) -assert iseq(inv.goalseek_bisect(func, x_lo=0, x_hi=-10), -1) - -# testing on AMM invariant $k/x$ - -assert iseq(inv.goalseek_gradient(lambda x: 100/x - 5), 20) -assert iseq(inv.goalseek_gradient(lambda x: 100/x - 20), 5) -assert iseq(inv.goalseek_gradient(lambda x: 100/x - 10), 10) -assert iseq(inv.goalseek_gradient(lambda x: 100/x - 50), 2) - -# #### timing - -inv.y_func(20, k=100), inv.y_func_from_k_func(20, k=100), inv.y_func_from_k_func(20, k=100, method=inv.GS_BISECT) - -# note that the gradient method is almost certainly going to be faster than bisection, unless we are very good at bracketing (or put the tolerance very low) - -r = ( - timer(inv.y_func, x=20, k=100, N=1000), - timer(inv.y_func_from_k_func, x=20, k=100, method=inv.GS_GRADIENT, N=10_000), - timer(inv.y_func_from_k_func, x=20, k=100, method=inv.GS_BISECT, N=10_000), - timer(inv.y_func_from_k_func, x=20, k=100, x_lo=0.1, x_hi=10, method=inv.GS_BISECT, N=10_000), -) -r, (1, r[1]/r[0], r[2]/r[0]) - -# ### Bancor invariant function - -# we are here comparing the analytic invariant function with the one obtained numerically; note: they are a good match! - -f = BancorSwapFunction(k=100) -assert f(10) == 10 -assert f(5) == 20 -assert f(20) == 5 -inv = BancorInvariant() -assert inv.y_func_is_analytic is True - -x_v = np.linspace(0.5 , 3, 50) -y1_v = [inv.y_func(xx, k=100) for xx in x_v] -y2_v = [inv.y_func_from_k_func(xx, k=100) for xx in x_v] -plt.plot(x_v, y1_v, linewidth=3, label="analytic") -plt.plot(x_v, y2_v, linestyle="--", color = "#ccc", label="numeric") -plt.legend() -plt.grid() - -x_v = np.linspace(0.5, 3, 100) -y1_v = [inv.p_func(xx, k=100) for xx in x_v] -y2_v = [inv.y_func(xx, k=100) for xx in x_v] -plt.plot(x_v, y1_v, linewidth=3, color="red", label="p [LHS]") -plt.xlabel("x") -plt.ylabel("price dy/dx [red]") -ax2 = plt.twinx() -ax2.plot(x_v, y2_v, linewidth=3, color="grey", label="y [RHS]") -ax2.set_ylabel("swap function y [grey]") -#plt.grid() -plt.show() - -# #### timing - -# however, whilst the results are comparable, runtime difference is substantial (unsurprisingly especially given the extremely simple formula for the analytic function); for 1e-6 tolerance the factor is 27x, and for 1e-3 tolerance the factor is not much better at 19x - -r = timer2(inv.y_func, 20, 100, N=1000), timer2(inv.y_func_from_k_func, 20, 100, N=1000) -r, r[1]/r[0] - -# ### Solidly invariant function - -# The Solidly **invariant equation** is -# $$ -# x^3y+xy^3 = k -# $$ -# -# which is a stable swap curve, but more convex than for example Curve. -# -# To obtain the **swap equation** we solve the above invariance equation -# as $y=y(x; k)$. This gives the following result -# $$ -# y(x;k) = \frac{x^2}{\left(-\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6}\right)^{\frac{1}{3}}} - \frac{\left(-\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6}\right)^{\frac{1}{3}}}{3} -# $$ -# -# We can introduce intermediary **variables L and M** ($L(x;k), M(x;k)$) -# to write this a bit more simply -# -# $$ -# L(x,k) = L_1(x) \equiv -\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6} -# $$ -# $$ -# M(x,k) = L^{1/3}(x,k) = \sqrt[3]{L(x,k)} -# $$ -# $$ -# y = \frac{x^2}{\sqrt[3]{L}} - \frac{\sqrt[3]{L}}{3} = \frac{x^2}{M} - \frac{M}{3} -# $$ -# -# If we rewrite the equation for L as below we see that it is not -# particularly well conditioned for small $x$ -# $$ -# L(x,k) = L_2(x) \equiv \frac{27k}{2x} \left(\sqrt{1 + \frac{108x^8}{729k^2}} - 1 \right) -# $$ -# -# For simplicity we introduce the **variable xi** $\xi=\xi(x,k)$ as -# $$ -# \xi(x, k) = \frac{108x^8}{729k^2} -# $$ -# -# then we can rewrite the above equation as -# $$ -# L_2(x;k) \equiv \frac{27k}{2x} \left(\sqrt{1 + \xi(x,k)} - 1 \right) -# $$ -# -# Note the Taylor expansion for $\sqrt{1 + \xi} - 1$ is -# $$ -# \sqrt{1+\xi}-1 = \frac{\xi}{2} - \frac{\xi^2}{8} + \frac{\xi^3}{16} - \frac{5\xi^4}{128} + O(\xi^5) -# $$ -# -# and tests suggest that it is very good for at least $|\xi| < 10^{-5}$ - -# ### L functions - -f = SolidlySwapFunction(k=100) -assert f.method == f.METHOD_DEC1000 -inv = SolidlyInvariant() - -x,k = 1,1000 -( - f._L1_float(x, k), - f._L1_dec100(x, k), - f._L1_dec1000(x, k), - f._L2_taylor(x, k), - f.L(x, k), - f.L(x, k) == f._L2_taylor(x, k), - f.L(x, k) == f._L1_dec100(x, k), - f.L(x, k) == f._L1_dec1000(x, k), -) - -# + -# x,k = 1,10 -# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k)) -# x,k = 1,100 -# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k)) -# x,k = 1,1_000 -# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k)) -# x,k = 1,10_000 -# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k)) -# x,k = 1,100_000 -# assert iseq(f._L1_dec(x, k), f._L2_taylor(x, k)) # not float ! -# f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k) -# - - -# ### Numeric vs analytic and verification - -fig = plt.figure(figsize=(6, 6)) -k = 1000 -x_v = np.linspace(0.1 , 20, 500) -y1_v = [inv.y_func(xx, k=k) for xx in x_v] -y2_v = [inv.y_func_from_k_func(xx, k=k) for xx in x_v] -plt.plot(x_v, y1_v, linewidth=3, label="analytic") -plt.plot(x_v, y2_v, linestyle="--", color = "#ccc", label="numeric") -plt.xlim(0,20) -plt.ylim(0,20) -plt.legend() -plt.grid() - -k = 100 -x1_v = np.linspace(0, 200) -x1_v[0] = 0.0001 -k_v = [inv.k_func(xx, inv.y_func_from_k_func(xx, k=100)) for xx in x1_v] -plt.plot(x1_v, k_v) -ylim = (99.999999, 100.000001) -assert min(k_v) > ylim[0] -assert max(k_v) < ylim[1] -plt.ylim(*ylim) -plt.title(f"Verifying `y_func_from_k_func` for k=100 [ylim = {ylim}") -plt.xlabel("x") -plt.ylabel("k") -plt.grid() - -k = 100 -x1_v = np.linspace(0, 200) -x1_v[0] = 0.0001 -k_v = [inv.k_func(xx, inv.y_func(xx, k=100)) for xx in x1_v] -plt.plot(x1_v, k_v) -ylim = (99.999999, 100.000001) -assert min(k_v) > ylim[0] -assert max(k_v) < ylim[1] -plt.ylim(*ylim) -plt.title(f"Verifying `y_func` for k=100 [ylim = {ylim}") -plt.xlabel("x") -plt.ylabel("k") -plt.grid() - -# ### Curves at different k - -fig = plt.figure(figsize=(6, 6)) -k_v = [5, 50, 250, 1000, 4000, 12000, 35000] -x_v = np.linspace(0.1 , 20, 500) -y_v_by_k = {kk: [inv.y_func(xx, k=kk) for xx in x_v] for kk in k_v} -for kk, y_v in y_v_by_k.items(): - plt.plot(x_v, y_v, label=f"{kk}") -plt.xlim(0,20) -plt.ylim(0,20) -plt.xlabel("x") -plt.ylabel("y") -plt.title("Swap curves for different values of k") -plt.legend() -plt.grid() - - diff --git a/resources/NBTest/NBTest_068_InvariantsAMMFunctions.ipynb b/resources/NBTest/NBTest_068_InvariantsAMMFunctions.ipynb deleted file mode 100644 index fb85d602f..000000000 --- a/resources/NBTest/NBTest_068_InvariantsAMMFunctions.ipynb +++ /dev/null @@ -1,570 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "0278c025-06e6-416b-9525-c2a4a8ae9128", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "Function v0.9.7 (21/Mar/2024)\n", - "Kernel v0.9.1 (26/Jan/2024)\n" - ] - } - ], - "source": [ - "try:\n", - " import fastlane_bot.tools.invariants.functions as f\n", - " from fastlane_bot.tools.invariants.kernel import Kernel\n", - " from fastlane_bot.testing import *\n", - "\n", - "except:\n", - " import tools.invariants.functions as f\n", - " from tools.invariants.kernel import Kernel\n", - " from tools.testing import *\n", - "\n", - "import numpy as np\n", - "import math as m\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.rcParams['figure.figsize'] = [12,6]\n", - "\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(f.Function))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(Kernel))" - ] - }, - { - "cell_type": "markdown", - "id": "7e212348-81d0-49f2-8d41-c7842a387634", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "# AMM Functions (Invariants Module; NBTest068)" - ] - }, - { - "cell_type": "markdown", - "id": "4b40d18e-ac45-43b3-8750-4dcc5cbb7a81", - "metadata": {}, - "source": [ - "## Constant product style AMMs [NOTEST]" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c578baf5-22f6-49a4-a4fe-5ed38a20289b", - "metadata": {}, - "outputs": [], - "source": [ - "rg = rg0 = (1,20)\n", - "xlim = (0,20)\n", - "ylim = (0,10)\n", - "p = lambda fn: str(f.fmt(fn.params(classname=True), \".2f\"))" - ] - }, - { - "cell_type": "markdown", - "id": "5683da21-87b6-4e17-b4a8-70034c1e9835", - "metadata": {}, - "source": [ - "### Plain constant product (Bancor V2.1, Bancor V3; Uniswap V2)\n", - "\n", - "$$\n", - "y(x) = \\frac k x\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "17916313-c7c5-4050-94a5-2d274e6f2349", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "assert f.CPMM is f.CPMMFunction\n", - "assert f.BancorV21 is f.CPMMFunction\n", - "assert f.BancorV3 is f.CPMMFunction\n", - "assert f.UniV2 is f.CPMMFunction\n", - "fn1 = f.CPMM(k=20)\n", - "fn2 = fn1.update(k=fn1.k*1.5**2)\n", - "for fn in [fn1, fn2]:\n", - " fn.plot(*rg, label=f\"{p(fn)}\")\n", - "plt.title(\"Constant Product AMMs (Bancor v2.1 and v3; Uniswap v2) -- Invariant\")\n", - "plt.xlim(*xlim)\n", - "plt.ylim(*ylim)\n", - "plt.show()\n", - "\n", - "for fn in [fn1, fn2]:\n", - " fn.plot(*rg, func=fn.p, label=f\"{p(fn)}\")\n", - "plt.title(\"Constant Product AMMs (Bancor v2.1 and v3; Uniswap v2) -- Price\")\n", - "plt.ylabel(\"price (dy/dx)\")\n", - "plt.xlim(*xlim)\n", - "plt.ylim(*ylim)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f406896c-7f52-4e93-a402-deb9e66bc7b9", - "metadata": {}, - "source": [ - "### Levered constant product (virtual token balances)\n", - "\n", - "$$\n", - "y(x) + y_0 = \\frac k {x+x_0}\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "0d4d3c8d-6af6-490b-a510-8c988deb69fa", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "assert f.LCPMM is f.VirtualTokenBalancesCPMMFunction\n", - "assert f.VTBCPMM is f.VTBCPMM\n", - "fn1 = f.LCPMM(k=5*5)\n", - "fn2 = fn1.update(k=7*8, x0=2, y0=3)\n", - "for fn in [fn1, fn2]:\n", - " fn.plot(*rg, label=f\"{p(fn)}\")\n", - "plt.title(\"Constant Product AMMs (Bancor v2.1 and v3; Uniswap v2) -- Invariant\")\n", - "# plt.xlim(*xlim)\n", - "# plt.ylim(*ylim)\n", - "plt.show()\n", - "\n", - "for fn in [fn1, fn2]:\n", - " fn.plot(*rg, func=fn.p, label=f\"{p(fn)}\")\n", - "plt.title(\"Constant Product AMMs (Bancor v2.1 and v3; Uniswap v2) -- Price\")\n", - "plt.ylabel(\"price (dy/dx)\")\n", - "plt.xlim(*xlim)\n", - "plt.ylim(*ylim)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "08cfb7d5-3bb8-41f4-b5e3-b07cbc2b6fd5", - "metadata": {}, - "source": [ - "#### `from_xpxp`\n", - "\n", - "alternative constructor, determining the curve by two points on a x-axis $x_a, x_b$ and the associated prices $p_a, p_b$; note that we are missing a parameter, $y_0$, which is a non-financial parameter in this case as a shift in the y direction does not affect prices as long as the curve does not run out of tokens\n", - "\n", - "We have the following equations:\n", - "\n", - "$$\n", - "\\frac k {(x_0+x_a)^2} = p_a,\\quad \\frac k {(x_0+x_b)^2} = p_b\n", - "$$\n", - "\n", - "\n", - "Solving for $x_0, k$ we find\n", - "\n", - "$$\n", - "x_0 = \\frac{-(p_a x_a) + \\sqrt{p_a p_b (x_a - x_b)^2} + p_b x_b}{p_a - p_b}\n", - "$$\n", - "\n", - "$$\n", - "k = p_a \\left(x_a + \\frac{-(p_a x_a) + \\sqrt{p_a p_b (x_a - x_b)^2} + p_b x_b}{p_a - p_b}\\right)^2\n", - "= p_a (x_a + x_0)^2\n", - "$$\n", - "\n", - "or \n", - "\n", - " x0 = (-(pa * xa) + m.sqrt(pa * pb * (xa - xb)**2) + pb * xb) / (pa - pb)\n", - " k = pa * ((xa + (-(pa * xa) + m.sqrt(pa * pb * (xa - xb)**2) + pb * xb) / (pa - pb)) ** 2)\n", - " k = pa * (xa + x0) ** 2\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1a75cbe1-0887-4c57-b380-6ebfb17288b7", - "metadata": {}, - "outputs": [], - "source": [ - "assert raises(f.LCPMM.from_xpxp, xa=20, pa=2, xb=10, pb=1) == 'xa=20 must be < xb=10'\n", - "assert raises(f.LCPMM.from_xpxp, xa=10, pa=2, xb=10, pb=1) == 'xa=10 must be < xb=10'\n", - "assert raises(f.LCPMM.from_xpxp, xa=10, pa=1, xb=20, pb=2) == 'pa=1 must be > pb=2'\n", - "assert raises(f.LCPMM.from_xpxp, xa=10, pa=1, xb=20, pb=1) == 'pa=1 must be > pb=1'\n", - "assert raises(f.LCPMM.from_xpxp, 1,2,3,4) # kwargs!\n", - "assert raises(f.LCPMM.from_xpxp, xa=10, pa=2, xb=20, pb=1, y0=1, ya=1, yb=1) == 'at most 1 of y0, ya, yb can be given, but got 3 [y0=1, ya=1, yb=1]'\n", - "assert raises(f.LCPMM.from_xpxp, xa=10, pa=2, xb=20, pb=1, y0=1, ya=1)\n", - "assert raises(f.LCPMM.from_xpxp, xa=10, pa=2, xb=20, pb=1, y0=1, yb=1)\n", - "assert raises(f.LCPMM.from_xpxp, xa=10, pa=2, xb=20, pb=1, ya=1, yb=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "31ea70cb-381a-4ab3-bf00-0a5db23df69e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "prm = dict(xa=10, pa=2, xb=20, pb=1)\n", - "\n", - "fn = f.LCPMM.from_xpxp(**prm)\n", - "fn0 = fn\n", - "assert iseq(fn.p(prm[\"xa\"]), prm[\"pa\"])\n", - "assert iseq(fn.p(prm[\"xb\"]), prm[\"pb\"])\n", - "assert fn.y0 == 0\n", - "ya = fn(prm[\"xa\"])\n", - "yb = fn(prm[\"xb\"])\n", - "\n", - "fn = f.LCPMM.from_xpxp(**prm, y0=10)\n", - "assert fn.k == fn0.k\n", - "assert fn.x0 == fn0.x0\n", - "assert fn.y0 != fn0.y0\n", - "assert iseq(fn.p(prm[\"xa\"]), prm[\"pa\"])\n", - "assert iseq(fn.p(prm[\"xb\"]), prm[\"pb\"])\n", - "assert fn.y0 == 10\n", - "assert fn(prm[\"xa\"]) == ya-10\n", - "assert fn(prm[\"xb\"]) == yb-10" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d95f98c0-0ddb-4cbf-a13c-959cb37f0f4a", - "metadata": {}, - "outputs": [], - "source": [ - "fn = f.LCPMM.from_xpxp(**prm, ya=100)\n", - "assert fn.k == fn0.k\n", - "assert fn.x0 == fn0.x0\n", - "assert fn.y0 != fn0.y0\n", - "assert iseq(fn.p(prm[\"xa\"]), prm[\"pa\"])\n", - "assert iseq(fn.p(prm[\"xb\"]), prm[\"pb\"])\n", - "assert fn(prm[\"xa\"]) == 100" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "ca7e934d-1ef2-41ee-bb5a-f00c9186c981", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fn = f.LCPMM.from_xpxp(**prm, yb=100)\n", - "assert fn.k == fn0.k\n", - "assert fn.x0 == fn0.x0\n", - "assert fn.y0 != fn0.y0\n", - "assert iseq(fn.p(prm[\"xa\"]), prm[\"pa\"])\n", - "assert iseq(fn.p(prm[\"xb\"]), prm[\"pb\"])\n", - "assert fn(prm[\"xb\"]) == 100" - ] - }, - { - "cell_type": "markdown", - "id": "6b3cc5e2-b622-4b8e-8043-066db9671a5c", - "metadata": {}, - "source": [ - "### Levered constant product (Uniswap V3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7d3ce5c7-3597-42bb-ba27-3da694740e7c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "UniV3Function(L=5, Pa=4, Pb=2)\n", - "UniV3Function(L=5, Pa=4, Pb=2)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rg = (0,2)\n", - "assert f.UniV3 is f.UniV3Function\n", - "fn1 = f.UniV3(Pa=4, Pb=2, L=5)\n", - "fn2 = fn1.update(L=5)\n", - "for fn in [fn1, fn2]:\n", - " print(fn)\n", - " fn.plot(*rg, label=f\"{p(fn)}\")\n", - "plt.title(\"Uniswap V3 -- Invariant\")\n", - "# plt.xlim(*xlim)\n", - "# plt.ylim(*ylim)\n", - "plt.show()\n", - "\n", - "for fn in [fn1, fn2]:\n", - " fn.plot(*rg, func=fn.p, label=f\"{p(fn)}\", steps=1000)\n", - "plt.title(\"Uniswap V3 -- Price\")\n", - "plt.ylabel(\"price (dy/dx)\")\n", - "# plt.xlim(*xlim)\n", - "# plt.ylim(*ylim)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "7c6f2e05-7edc-4755-bf44-3ef94e464b2b", - "metadata": {}, - "source": [ - "### Levered constant product (Carbon)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d12274c2-1c42-4410-a443-f70c4f4f639e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CarbonFunction] x0, y0: 4.26776695296637 12.071067811865479\n", - "[CarbonFunction] x0, y0: 4.26776695296637 12.071067811865479\n" - ] - }, - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rg = (0,2)\n", - "assert f.Carbon is f.CarbonFunction\n", - "fn1 = f.Carbon(Pa=4, Pb=2, yint=5)\n", - "fn2 = fn1.update()\n", - "for fn in [fn1, fn2]:\n", - " fn.plot(*rg, label=f\"{p(fn)}\")\n", - "plt.title(\"Carbon -- Invariant\")\n", - "# plt.xlim(*xlim)\n", - "# plt.ylim(*ylim)\n", - "plt.show()\n", - "\n", - "for fn in [fn1, fn2]:\n", - " fn.plot(*rg, func=fn.p, label=f\"{p(fn)}\", steps=1000)\n", - "plt.title(\"Carbon -- Price\")\n", - "plt.ylabel(\"price (dy/dx)\")\n", - "# plt.xlim(*xlim)\n", - "# plt.ylim(*ylim)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e42e0533-867f-40e7-ae2f-b2fcf1549349", - "metadata": {}, - "source": [ - "## Other AMMs [NOTEST]" - ] - }, - { - "cell_type": "markdown", - "id": "546d1faf-cdb9-4af2-832b-3b383323a949", - "metadata": {}, - "source": [ - "### Solidly" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "bd13a9f5-2e46-4e37-af30-432b79f6d3c3", - "metadata": { - "lines_to_next_cell": 0, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rg = (0.05,20)\n", - "plt.figure(figsize=(6,6))\n", - "assert f.Solidly is f.SolidlyFunction\n", - "fn1 = f.Solidly(k=5**4)\n", - "fn2 = fn1.update(k=8**4)\n", - "for fn in [fn1, fn2]:\n", - " fn.plot(*rg, label=f\"{p(fn)}\")\n", - "plt.title(\"Solidly -- Invariant\")\n", - "plt.xlim(0,20)\n", - "plt.ylim(0,20)\n", - "plt.show()\n", - "\n", - "for fn in [fn1, fn2]:\n", - " fn.plot(*rg, func=fn.p, label=f\"{p(fn)}\", steps=100)\n", - "plt.title(\"Solidly -- Price\")\n", - "plt.ylabel(\"price (dy/dx)\")\n", - "plt.xlim(0,20)\n", - "plt.ylim(0,5)\n", - "plt.show()\n", - "\n", - "for fn in [fn1, fn2]:\n", - " fn.plot(*rg, func=fn.p, label=f\"{p(fn)}\", steps=100)\n", - "plt.title(\"Solidly -- Price\")\n", - "plt.ylabel(\"price (dy/dx)\")\n", - "plt.xlim(2,10)\n", - "plt.ylim(0,2)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "17fce7ee-4567-4a71-877c-f64b94310cf0", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/NBTest/NBTest_068_InvariantsAMMFunctions.py b/resources/NBTest/NBTest_068_InvariantsAMMFunctions.py deleted file mode 100644 index 5e59aafdf..000000000 --- a/resources/NBTest/NBTest_068_InvariantsAMMFunctions.py +++ /dev/null @@ -1,262 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -try: - import fastlane_bot.tools.invariants.functions as f - from fastlane_bot.tools.invariants.kernel import Kernel - from fastlane_bot.testing import * - -except: - import tools.invariants.functions as f - from tools.invariants.kernel import Kernel - from tools.testing import * - -import numpy as np -import math as m -import matplotlib.pyplot as plt - -plt.rcParams['figure.figsize'] = [12,6] - -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(f.Function)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Kernel)) -# - - -# # AMM Functions (Invariants Module; NBTest068) - - -# ## Constant product style AMMs [NOTEST] - -rg = rg0 = (1,20) -xlim = (0,20) -ylim = (0,10) -p = lambda fn: str(f.fmt(fn.params(classname=True), ".2f")) - -# ### Plain constant product (Bancor V2.1, Bancor V3; Uniswap V2) -# -# $$ -# y(x) = \frac k x -# $$ - -# + -assert f.CPMM is f.CPMMFunction -assert f.BancorV21 is f.CPMMFunction -assert f.BancorV3 is f.CPMMFunction -assert f.UniV2 is f.CPMMFunction -fn1 = f.CPMM(k=20) -fn2 = fn1.update(k=fn1.k*1.5**2) -for fn in [fn1, fn2]: - fn.plot(*rg, label=f"{p(fn)}") -plt.title("Constant Product AMMs (Bancor v2.1 and v3; Uniswap v2) -- Invariant") -plt.xlim(*xlim) -plt.ylim(*ylim) -plt.show() - -for fn in [fn1, fn2]: - fn.plot(*rg, func=fn.p, label=f"{p(fn)}") -plt.title("Constant Product AMMs (Bancor v2.1 and v3; Uniswap v2) -- Price") -plt.ylabel("price (dy/dx)") -plt.xlim(*xlim) -plt.ylim(*ylim) -plt.show() -# - - -# ### Levered constant product (virtual token balances) -# -# $$ -# y(x) + y_0 = \frac k {x+x_0} -# $$ - -# + -assert f.LCPMM is f.VirtualTokenBalancesCPMMFunction -assert f.VTBCPMM is f.VTBCPMM -fn1 = f.LCPMM(k=5*5) -fn2 = fn1.update(k=7*8, x0=2, y0=3) -for fn in [fn1, fn2]: - fn.plot(*rg, label=f"{p(fn)}") -plt.title("Constant Product AMMs (Bancor v2.1 and v3; Uniswap v2) -- Invariant") -# plt.xlim(*xlim) -# plt.ylim(*ylim) -plt.show() - -for fn in [fn1, fn2]: - fn.plot(*rg, func=fn.p, label=f"{p(fn)}") -plt.title("Constant Product AMMs (Bancor v2.1 and v3; Uniswap v2) -- Price") -plt.ylabel("price (dy/dx)") -plt.xlim(*xlim) -plt.ylim(*ylim) -plt.show() -# - - -# #### `from_xpxp` -# -# alternative constructor, determining the curve by two points on a x-axis $x_a, x_b$ and the associated prices $p_a, p_b$; note that we are missing a parameter, $y_0$, which is a non-financial parameter in this case as a shift in the y direction does not affect prices as long as the curve does not run out of tokens -# -# We have the following equations: -# -# $$ -# \frac k {(x_0+x_a)^2} = p_a,\quad \frac k {(x_0+x_b)^2} = p_b -# $$ -# -# -# Solving for $x_0, k$ we find -# -# $$ -# x_0 = \frac{-(p_a x_a) + \sqrt{p_a p_b (x_a - x_b)^2} + p_b x_b}{p_a - p_b} -# $$ -# -# $$ -# k = p_a \left(x_a + \frac{-(p_a x_a) + \sqrt{p_a p_b (x_a - x_b)^2} + p_b x_b}{p_a - p_b}\right)^2 -# = p_a (x_a + x_0)^2 -# $$ -# -# or -# -# x0 = (-(pa * xa) + m.sqrt(pa * pb * (xa - xb)**2) + pb * xb) / (pa - pb) -# k = pa * ((xa + (-(pa * xa) + m.sqrt(pa * pb * (xa - xb)**2) + pb * xb) / (pa - pb)) ** 2) -# k = pa * (xa + x0) ** 2 -# -# - -assert raises(f.LCPMM.from_xpxp, xa=20, pa=2, xb=10, pb=1) == 'xa=20 must be < xb=10' -assert raises(f.LCPMM.from_xpxp, xa=10, pa=2, xb=10, pb=1) == 'xa=10 must be < xb=10' -assert raises(f.LCPMM.from_xpxp, xa=10, pa=1, xb=20, pb=2) == 'pa=1 must be > pb=2' -assert raises(f.LCPMM.from_xpxp, xa=10, pa=1, xb=20, pb=1) == 'pa=1 must be > pb=1' -assert raises(f.LCPMM.from_xpxp, 1,2,3,4) # kwargs! -assert raises(f.LCPMM.from_xpxp, xa=10, pa=2, xb=20, pb=1, y0=1, ya=1, yb=1) == 'at most 1 of y0, ya, yb can be given, but got 3 [y0=1, ya=1, yb=1]' -assert raises(f.LCPMM.from_xpxp, xa=10, pa=2, xb=20, pb=1, y0=1, ya=1) -assert raises(f.LCPMM.from_xpxp, xa=10, pa=2, xb=20, pb=1, y0=1, yb=1) -assert raises(f.LCPMM.from_xpxp, xa=10, pa=2, xb=20, pb=1, ya=1, yb=1) - -# + -prm = dict(xa=10, pa=2, xb=20, pb=1) - -fn = f.LCPMM.from_xpxp(**prm) -fn0 = fn -assert iseq(fn.p(prm["xa"]), prm["pa"]) -assert iseq(fn.p(prm["xb"]), prm["pb"]) -assert fn.y0 == 0 -ya = fn(prm["xa"]) -yb = fn(prm["xb"]) - -fn = f.LCPMM.from_xpxp(**prm, y0=10) -assert fn.k == fn0.k -assert fn.x0 == fn0.x0 -assert fn.y0 != fn0.y0 -assert iseq(fn.p(prm["xa"]), prm["pa"]) -assert iseq(fn.p(prm["xb"]), prm["pb"]) -assert fn.y0 == 10 -assert fn(prm["xa"]) == ya-10 -assert fn(prm["xb"]) == yb-10 -# - - -fn = f.LCPMM.from_xpxp(**prm, ya=100) -assert fn.k == fn0.k -assert fn.x0 == fn0.x0 -assert fn.y0 != fn0.y0 -assert iseq(fn.p(prm["xa"]), prm["pa"]) -assert iseq(fn.p(prm["xb"]), prm["pb"]) -assert fn(prm["xa"]) == 100 - -fn = f.LCPMM.from_xpxp(**prm, yb=100) -assert fn.k == fn0.k -assert fn.x0 == fn0.x0 -assert fn.y0 != fn0.y0 -assert iseq(fn.p(prm["xa"]), prm["pa"]) -assert iseq(fn.p(prm["xb"]), prm["pb"]) -assert fn(prm["xb"]) == 100 - -# ### Levered constant product (Uniswap V3) - -# + -rg = (0,2) -assert f.UniV3 is f.UniV3Function -fn1 = f.UniV3(Pa=4, Pb=2, L=5) -fn2 = fn1.update(L=5) -for fn in [fn1, fn2]: - print(fn) - fn.plot(*rg, label=f"{p(fn)}") -plt.title("Uniswap V3 -- Invariant") -# plt.xlim(*xlim) -# plt.ylim(*ylim) -plt.show() - -for fn in [fn1, fn2]: - fn.plot(*rg, func=fn.p, label=f"{p(fn)}", steps=1000) -plt.title("Uniswap V3 -- Price") -plt.ylabel("price (dy/dx)") -# plt.xlim(*xlim) -# plt.ylim(*ylim) -plt.show() -# - - -# ### Levered constant product (Carbon) - -# + -rg = (0,2) -assert f.Carbon is f.CarbonFunction -fn1 = f.Carbon(Pa=4, Pb=2, yint=5) -fn2 = fn1.update() -for fn in [fn1, fn2]: - fn.plot(*rg, label=f"{p(fn)}") -plt.title("Carbon -- Invariant") -# plt.xlim(*xlim) -# plt.ylim(*ylim) -plt.show() - -for fn in [fn1, fn2]: - fn.plot(*rg, func=fn.p, label=f"{p(fn)}", steps=1000) -plt.title("Carbon -- Price") -plt.ylabel("price (dy/dx)") -# plt.xlim(*xlim) -# plt.ylim(*ylim) -plt.show() -# - - -# ## Other AMMs [NOTEST] - -# ### Solidly - -# + -rg = (0.05,20) -plt.figure(figsize=(6,6)) -assert f.Solidly is f.SolidlyFunction -fn1 = f.Solidly(k=5**4) -fn2 = fn1.update(k=8**4) -for fn in [fn1, fn2]: - fn.plot(*rg, label=f"{p(fn)}") -plt.title("Solidly -- Invariant") -plt.xlim(0,20) -plt.ylim(0,20) -plt.show() - -for fn in [fn1, fn2]: - fn.plot(*rg, func=fn.p, label=f"{p(fn)}", steps=100) -plt.title("Solidly -- Price") -plt.ylabel("price (dy/dx)") -plt.xlim(0,20) -plt.ylim(0,5) -plt.show() - -for fn in [fn1, fn2]: - fn.plot(*rg, func=fn.p, label=f"{p(fn)}", steps=100) -plt.title("Solidly -- Price") -plt.ylabel("price (dy/dx)") -plt.xlim(2,10) -plt.ylim(0,2) -plt.show() -# - - - diff --git a/resources/NBTest/NBTest_069_CPCNewCurves.ipynb b/resources/NBTest/NBTest_069_CPCNewCurves.ipynb deleted file mode 100644 index e617510ae..000000000 --- a/resources/NBTest/NBTest_069_CPCNewCurves.ipynb +++ /dev/null @@ -1,1259 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "a448e212", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "ConstantProductCurve v3.5 (22/Apr/2023)\n", - "MargPOptimizer v5.3-b3 (30/Apr/2024)\n" - ] - } - ], - "source": [ - "try:\n", - " from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, CPCContainer, T, CPCInverter, Pair\n", - " from fastlane_bot.tools.optimizer import F, MargPOptimizer\n", - " import fastlane_bot.tools.invariants.functions as f\n", - " from fastlane_bot.testing import *\n", - "\n", - "except:\n", - " from tools.cpc import ConstantProductCurve as CPC, CPCContainer, T, CPCInverter, Pair\n", - " from tools.optimizer import MargPOptimizer\n", - " import tools.invariants.functions as f\n", - " from tools.testing import *\n", - "\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPC))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(MargPOptimizer))\n", - "\n", - "#plt.style.use('seaborn-dark')\n", - "plt.rcParams['figure.figsize'] = [12,6]\n", - "# from fastlane_bot import __VERSION__\n", - "# require(\"3.0\", __VERSION__)" - ] - }, - { - "cell_type": "markdown", - "id": "d9917997", - "metadata": {}, - "source": [ - "# CPC-Only incl new curves [NBTest069]\n", - "\n", - "Note: the core CPC tests are in NBTest 002" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c5a81cc7-2dd7-47d8-9fe1-9b3cb27d4a9e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "CURVES = {\n", - " \"s1\": CPC.from_solidly(x=10, y=10),\n", - " \"s2\": CPC.from_solidly(x=10, y=10, price_spread=1e-6),\n", - " \"s1a\": CPC.from_solidly(x=100, y=100),\n", - " \"s2a\": CPC.from_solidly(x=100, y=100, price_spread=1e-6),\n", - " \"s3\": CPC.from_solidly(x=1000, y=2000), \n", - " \"s4\": CPC.from_solidly(x=1, y=2000), \n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "9382da57-ef49-4328-941a-c5ba0eecab7a", - "metadata": {}, - "source": [ - "## Solidly tests" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7e0632e2-89fa-4542-b5b2-faabd6e53715", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on method from_solidly in module tools.cpc:\n", - "\n", - "from_solidly(*, k=None, x=None, y=None, price_spread=None, pair=None, fee=None, cid=None, descr=None, params=None, as_list=True) method of abc.ABCMeta instance\n", - " constructor: from a Solidly curve (see class docstring for other parameters)*\n", - " \n", - " :k: Solidly pool constant, x^3 y + x y^3 = k*\n", - " :x: current pool liquidity in token x*\n", - " :y: current pool liquidity in token y*\n", - " :price_spread: price spread to use for converting constant price -> constant product\n", - " :as_list: if True (default) returns a list of curves, otherwise a single curve\n", - " (see note below and note that as_list=False is deprecated)\n", - " \n", - " exactly 2 out of those three must be given; the third one is calculated\n", - " \n", - " The Solidly curve is NOT a constant product curve, as it follows the equation\n", - " \n", - " x^3 y + x y^3 = k\n", - " \n", - " where k is the pool invariant. This curve is a stable swap curve in the it is\n", - " very flat in the middle, at a unity price (see the `invariants` module and the\n", - " associated tests and notebooks). In fact, in the range\n", - " \n", - " 1/2.6 < y/x < 2.6\n", - " \n", - " we find that the prices is essentially unity, and we therefore approximate it\n", - " was an (almost) constant price curve, ie a constant product curve with a very\n", - " large invariant k, and we will set the x_act and y_act parameters so that the\n", - " curve only covers the above range.\n", - " \n", - " IMPORTANT: IF as_list is True (default) THEN THE RESULT IS RETURNED AS A LIST\n", - " CURRENTLY CONTAINING A SINGLE CURVE, NOT THE CURVE ITSELF. This is because we \n", - " may in the future a list of curves, with additional curves matching the function\n", - " in the wings. IT IS RECOMMENDED THAT ANY CODE IMPLEMENTING THIS FUNCTION USES\n", - " as_list = True, AS IN THE FUTURE as_list = FALSE will raise an exception.\n", - "\n" - ] - } - ], - "source": [ - "help(CPC.from_solidly)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c3976668-c2fd-41d5-9152-8cb26c58ff57", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "#CPC.from_solidly(k=1, x=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "99b4478e-aced-45dc-9998-87b0be725758", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "#CPC.from_solidly(k=1, x=1, y=1)\n", - "assert raises(CPC.from_solidly, k=1, x=1, y=1).startswith(\"exactly 2 out of k,x,y\")\n", - "assert raises(CPC.from_solidly, k=1).startswith(\"exactly 2 out of k,x,y\")\n", - "assert raises(CPC.from_solidly, x=1).startswith(\"exactly 2 out of k,x,y\")\n", - "assert raises(CPC.from_solidly, y=1).startswith(\"exactly 2 out of k,x,y\")\n", - "assert raises(CPC.from_solidly).startswith(\"exactly 2 out of k,x,y\")\n", - "\n", - "assert raises(CPC.from_solidly, k=1, x=1) == 'providing k, x not implemented yet'\n", - "assert raises(CPC.from_solidly, k=1, y=1) == 'providing k, y not implemented yet'" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e748b7f5-ba25-484f-94bd-99aa954adaac", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert len(CPC.from_solidly(x=1, y=2000)) == 0\n", - "assert raises(CPC.from_solidly,x=1, y=2000, as_list=False).startswith('x=1 is outside the range')" - ] - }, - { - "cell_type": "markdown", - "id": "a6cf411a-bdd2-468c-b146-690b58c3223c", - "metadata": {}, - "source": [ - "### Curve s1 (x=10, y=10) and s2 (ditto, but spread = 1e-6)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5d2dcd93-56eb-423f-920c-ccb5ec4136c5", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "crv_l = CURVES[\"s1\"] # CPC.from_solidly(x=10, y=10)\n", - "crv = crv_l[0]\n", - "cp = crv.params\n", - "fn = f.Solidly(k=cp.s_k)\n", - "assert crv.constr == \"solidly\"\n", - "assert cp.s_x == 10\n", - "assert cp.s_y == 10\n", - "assert cp.s_k == 20000\n", - "assert cp.s_k == cp.s_x**3 * cp.s_y + cp.s_y**3 * cp.s_x\n", - "assert cp.s_kbar == 10\n", - "assert iseq(cp.s_kbar, (cp.s_k/2)**0.25)\n", - "assert iseq(cp.s_kbar, 10)\n", - "assert iseq(cp.s_xmin, 50/9)\n", - "assert iseq(cp.s_xmax, 130/9)\n", - "assert cp.s_price_spread == CPC.SOLIDLY_PRICE_SPREAD\n", - "assert cp.s_price_spread == 0.06\n", - "assert iseq(cp.s_cpck/((cp.s_cpcx0)**2)-1, cp.s_price_spread) # cpck / cpcx^2 = p; p0 = 1\n", - "assert iseq(1-cp.s_cpck/((cp.s_cpcx0+cp.s_xmax-cp.s_xmin)**2), 1-1/(1+cp.s_price_spread))\n", - "assert iseq(crv.x_act, 40/9)\n", - "assert iseq(crv.y_act, 40/9)\n", - "assert iseq(crv.y_act, crv.x_act)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6db50907-22cc-49aa-bddf-6628255f2a06", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "crv_l = CURVES[\"s2\"] # CPC.from_solidly(x=10, y=10)\n", - "crv = crv_l[0]\n", - "cp = crv.params\n", - "fn = f.Solidly(k=cp.s_k)\n", - "assert crv.constr == \"solidly\"\n", - "assert cp.s_x == 10\n", - "assert cp.s_y == 10\n", - "assert cp.s_k == 20000\n", - "assert cp.s_k == cp.s_x**3 * cp.s_y + cp.s_y**3 * cp.s_x\n", - "assert cp.s_kbar == 10\n", - "assert iseq(cp.s_kbar, (cp.s_k/2)**0.25)\n", - "assert iseq(cp.s_kbar, 10)\n", - "assert iseq(cp.s_xmin, 50/9)\n", - "assert iseq(cp.s_xmax, 130/9)\n", - "#assert cp.s_price_spread == CPC.SOLIDLY_PRICE_SPREAD\n", - "assert cp.s_price_spread == 1e-6\n", - "assert iseq(cp.s_cpck/((cp.s_cpcx0)**2)-1, cp.s_price_spread) # cpck / cpcx^2 = p; p0 = 1\n", - "assert iseq(1-cp.s_cpck/((cp.s_cpcx0+cp.s_xmax-cp.s_xmin)**2), 1-1/(1+cp.s_price_spread))\n", - "assert iseq(crv.x_act, 40/9)\n", - "assert iseq(crv.y_act, 40/9)\n", - "assert iseq(crv.y_act, crv.x_act)" - ] - }, - { - "cell_type": "markdown", - "id": "8b2d5eb4", - "metadata": {}, - "source": [ - "### Curve s1a (x=100, y=100) and s2a (ditto, but spread = 1e-6)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7ec3786b", - "metadata": { - "lines_to_next_cell": 2, - "tags": [] - }, - "outputs": [], - "source": [ - "crv_l = CURVES[\"s1a\"] # CPC.from_solidly(x=100, y=100)\n", - "crv = crv_l[0]\n", - "cp = crv.params\n", - "fn = f.Solidly(k=cp.s_k)\n", - "assert crv.constr == \"solidly\"\n", - "assert cp.s_x == 100\n", - "assert cp.s_y == 100\n", - "assert cp.s_k == 200000000\n", - "assert cp.s_k == cp.s_x**3 * cp.s_y + cp.s_y**3 * cp.s_x\n", - "assert cp.s_kbar == 100\n", - "assert iseq(cp.s_kbar, (cp.s_k/2)**0.25)\n", - "assert iseq(cp.s_kbar, 100)\n", - "assert iseq(cp.s_xmin, 500/9)\n", - "assert iseq(cp.s_xmax, 1300/9)\n", - "assert cp.s_price_spread == CPC.SOLIDLY_PRICE_SPREAD\n", - "assert cp.s_price_spread == 0.06\n", - "assert iseq(cp.s_cpck/((cp.s_cpcx0)**2)-1, cp.s_price_spread) # cpck / cpcx^2 = p; p0 = 1\n", - "assert iseq(1-cp.s_cpck/((cp.s_cpcx0+cp.s_xmax-cp.s_xmin)**2), 1-1/(1+cp.s_price_spread))\n", - "assert iseq(crv.x_act, 400/9)\n", - "assert iseq(crv.y_act, 400/9)\n", - "assert iseq(crv.y_act, crv.x_act)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d4d55469-da32-4d25-b222-406458ee7b08", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "crv_l = CURVES[\"s2a\"] # CPC.from_solidly(x=100, y=100, price_spread=1e-6)\n", - "crv = crv_l[0]\n", - "cp = crv.params\n", - "fn = f.Solidly(k=cp.s_k)\n", - "assert crv.constr == \"solidly\"\n", - "assert cp.s_x == 100\n", - "assert cp.s_y == 100\n", - "assert cp.s_k == 200000000\n", - "assert cp.s_k == cp.s_x**3 * cp.s_y + cp.s_y**3 * cp.s_x\n", - "assert cp.s_kbar == 100\n", - "assert iseq(cp.s_kbar, (cp.s_k/2)**0.25)\n", - "assert iseq(cp.s_kbar, 100)\n", - "assert iseq(cp.s_xmin, 500/9)\n", - "assert iseq(cp.s_xmax, 1300/9)\n", - "#assert cp.s_price_spread == CPC.SOLIDLY_PRICE_SPREAD\n", - "assert cp.s_price_spread == 1e-6\n", - "assert iseq(cp.s_cpck/((cp.s_cpcx0)**2)-1, cp.s_price_spread) # cpck / cpcx^2 = p; p0 = 1\n", - "assert iseq(1-cp.s_cpck/((cp.s_cpcx0+cp.s_xmax-cp.s_xmin)**2), 1-1/(1+cp.s_price_spread))\n", - "assert iseq(crv.x_act, 400/9)\n", - "assert iseq(crv.y_act, 400/9)\n", - "assert iseq(crv.y_act, crv.x_act)" - ] - }, - { - "cell_type": "markdown", - "id": "68dcbcc0-b54f-45ef-849b-a2378acaf05e", - "metadata": {}, - "source": [ - "### Curve s3 (off centre)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "4f37c7af-d9e1-43a0-9221-851a1d1a4003", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "ConstantProductCurve(k=7901242469135804.0, x=88888933.33333336, x_act=44.44444444444444, y_act=44.44444444444446, alpha=0.5, pair='TKNB/TKNQ', cid='None', fee=None, descr=None, constr='solidly', params={'s_x': 100, 's_y': 100, 's_k': 200000000, 's_kbar': 100.0, 's_cpck': 7901242469135804.0, 's_cpcx0': 88888888.88888891, 's_xmin': 55.55555555555556, 's_xmax': 144.44444444444446, 's_price_spread': 1e-06})" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "crv" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "5ca87b47-e2fd-4b9a-b3af-135a1d81d337", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "crv_l = CURVES[\"s3\"] # CPC.from_solidly(x=100, y=100)\n", - "crv = crv_l[0]\n", - "cp = crv.params\n", - "fn = f.Solidly(k=cp.s_k)\n", - "assert crv.constr == \"solidly\"\n", - "assert cp.s_x == 1000\n", - "assert cp.s_y == 2000\n", - "assert cp.s_k == 10000000000000\n", - "assert cp.s_k == cp.s_x**3 * cp.s_y + cp.s_y**3 * cp.s_x\n", - "#assert cp.s_kbar == 100\n", - "assert iseq(cp.s_kbar, (cp.s_k/2)**0.25)\n", - "assert iseq(cp.s_kbar, 1495.3487812212206)\n", - "assert iseq(cp.s_xmin, 830.7493229006781)\n", - "assert iseq(cp.s_xmax, 2159.948239541763)\n", - "assert cp.s_price_spread == CPC.SOLIDLY_PRICE_SPREAD\n", - "assert cp.s_price_spread == 0.06\n", - "assert iseq(cp.s_cpck/((cp.s_cpcx0)**2)-1, cp.s_price_spread) # cpck / cpcx^2 = p; p0 = 1\n", - "assert iseq(1-cp.s_cpck/((cp.s_cpcx0+cp.s_xmax-cp.s_xmin)**2), 1-1/(1+cp.s_price_spread))\n", - "assert iseq(crv.x_act, 169.25067709932193)\n", - "assert iseq(crv.y_act, 1159.948239541763)" - ] - }, - { - "cell_type": "markdown", - "id": "43407b66-cef7-4ccf-8ab6-990bdafca49c", - "metadata": {}, - "source": [ - "### Curve 4 (out of range)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "067540b3-898a-41ff-91e8-20d4230b2071", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "crv_l = CURVES[\"s4\"] # CPC.from_solidly(x=100, y=100)\n", - "assert len(crv_l) == 0" - ] - }, - { - "cell_type": "markdown", - "id": "50f1821b-897b-4ef5-91dd-cfba245040b9", - "metadata": {}, - "source": [ - "## Solidly plots [NOTEST]" - ] - }, - { - "cell_type": "markdown", - "id": "b1b11488-0682-4135-b52b-1e7cd63fc19f", - "metadata": {}, - "source": [ - "### Curves 1 and 2" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "7f5fa46c-570d-413a-9e63-51d11e46b508", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "crv = CURVES[\"s1\"][0] # CPC.from_solidly(x=10, y=10)\n", - "cp = crv.params\n", - "crv2 = CURVES[\"s2\"][0] # CPC.from_solidly(x=10, y=10, price_spread=XXX)\n", - "fn = f.Solidly(k=cp.s_k)\n", - "x0 = cp.s_x\n", - "LIM = cp.s_kbar\n", - "\n", - "xv = np.linspace(-LIM+0.001, 1.1*LIM, 100)\n", - "plt.figure(figsize=(6,6))\n", - "crv.plot(xvals=xv, color=\"red\", label=\"cpc curve\")\n", - "yv = [fn(xx+x0) - fn(x0) for xx in xv]\n", - "plt.plot(xv, yv, color=\"#aaa\", linestyle=\"--\", label=\"full curve\")\n", - "plt.legend()\n", - "plt.xlim(-LIM, LIM)\n", - "plt.ylim(-LIM, LIM)\n", - "plt.savefig(\"/Users/skl/Desktop/img1.jpg\")\n", - "plt.show()\n", - "\n", - "for crv_ in [crv, crv2]:\n", - " crv_.plot(xvals=xv, label=f\"cpc curve (spread={crv_.params.s_price_spread})\")\n", - "yv = [fn(xx+x0) - fn(x0) for xx in xv]\n", - "plt.plot(xv, yv, color=\"#aaa\", linestyle=\"--\", label=\"full curve\")\n", - "plt.legend()\n", - "plt.xlim(-.6*LIM, .6*LIM)\n", - "plt.ylim(-.6*LIM, .6*LIM)\n", - "plt.savefig(\"/Users/skl/Desktop/img2.jpg\")\n", - "plt.show()\n", - "\n", - "for crv_ in [crv, crv2]:\n", - " crv_.plot(xvals=xv, label=f\"cpc curve (spread={crv_.params.s_price_spread})\")\n", - "yv = [fn(xx+x0) - fn(x0) for xx in xv]\n", - "plt.plot(xv, yv, color=\"#aaa\", linestyle=\"--\", label=\"full curve\")\n", - "plt.legend()\n", - "plt.xlim(-.45*LIM, -.2*LIM)\n", - "plt.ylim(.25*LIM, .5*LIM)\n", - "plt.savefig(\"/Users/skl/Desktop/img3.jpg\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "283a404c-e68f-4573-a78e-4423404ee2c5", - "metadata": {}, - "source": [ - "### Curves 1a and 2a" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "7c092119-155f-470c-ac27-8e88d3968ebb", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "crv = CURVES[\"s1a\"][0] # CPC.from_solidly(x=10, y=10)\n", - "cp = crv.params\n", - "crv2 = CURVES[\"s2a\"][0] # CPC.from_solidly(x=10, y=10, price_spread=XXX)\n", - "fn = f.Solidly(k=cp.s_k)\n", - "x0 = cp.s_x\n", - "LIM = cp.s_kbar\n", - "\n", - "xv = np.linspace(-LIM+0.001, 1.1*LIM, 100)\n", - "plt.figure(figsize=(6,6))\n", - "crv.plot(xvals=xv, color=\"red\", label=\"cpc curve\")\n", - "yv = [fn(xx+x0) - fn(x0) for xx in xv]\n", - "plt.plot(xv, yv, color=\"#aaa\", linestyle=\"--\", label=\"full curve\")\n", - "plt.legend()\n", - "plt.xlim(-LIM, LIM)\n", - "plt.ylim(-LIM, LIM)\n", - "plt.savefig(\"/Users/skl/Desktop/img1.jpg\")\n", - "plt.show()\n", - "\n", - "for crv_ in [crv, crv2]:\n", - " crv_.plot(xvals=xv, label=f\"cpc curve (spread={crv_.params.s_price_spread})\")\n", - "yv = [fn(xx+x0) - fn(x0) for xx in xv]\n", - "plt.plot(xv, yv, color=\"#aaa\", linestyle=\"--\", label=\"full curve\")\n", - "plt.legend()\n", - "plt.xlim(-.6*LIM, .6*LIM)\n", - "plt.ylim(-.6*LIM, .6*LIM)\n", - "plt.savefig(\"/Users/skl/Desktop/img2.jpg\")\n", - "plt.show()\n", - "\n", - "for crv_ in [crv, crv2]:\n", - " crv_.plot(xvals=xv, label=f\"cpc curve (spread={crv_.params.s_price_spread})\")\n", - "yv = [fn(xx+x0) - fn(x0) for xx in xv]\n", - "plt.plot(xv, yv, color=\"#aaa\", linestyle=\"--\", label=\"full curve\")\n", - "plt.legend()\n", - "plt.xlim(-.45*LIM, -.2*LIM)\n", - "plt.ylim(.25*LIM, .5*LIM)\n", - "plt.savefig(\"/Users/skl/Desktop/img3.jpg\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "925c581a-d732-4af0-9cbe-cb1a87ad03f5", - "metadata": {}, - "source": [ - "### Curve 3" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "008e27b4-8f69-417a-93e7-4efa4a8de07b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "crv = CURVES[\"s3\"][0] # CPC.from_solidly(x=1000, y=2000)\n", - "cp = crv.params\n", - "# crv2 = CURVES[\"s2a\"][0] # CPC.from_solidly(x=10, y=10, price_spread=XXX)\n", - "fn = f.Solidly(k=cp.s_k)\n", - "x0 = cp.s_x\n", - "\n", - "xv = np.linspace(-1000+0.001, 2000, 100)\n", - "plt.figure(figsize=(6,6))\n", - "crv.plot(xvals=xv, color=\"red\", label=\"cpc curve\")\n", - "yv = [fn(xx+x0) - fn(x0) for xx in xv]\n", - "plt.plot(xv, yv, color=\"#aaa\", linestyle=\"--\", label=\"full curve\")\n", - "plt.legend()\n", - "plt.xlim(-1000, 2000)\n", - "plt.ylim(-2000, 1000)\n", - "plt.savefig(\"/Users/skl/Desktop/img1.jpg\")\n", - "plt.show()\n", - "\n", - "for crv_ in [crv]:\n", - " crv_.plot(xvals=xv, label=f\"cpc curve (spread={crv_.params.s_price_spread})\")\n", - "yv = [fn(xx+x0) - fn(x0) for xx in xv]\n", - "plt.plot(xv, yv, color=\"#aaa\", linestyle=\"--\", label=\"full curve\")\n", - "plt.legend()\n", - "plt.xlim(-500, 1500)\n", - "plt.ylim(-1500,500)\n", - "plt.savefig(\"/Users/skl/Desktop/img2.jpg\")\n", - "plt.show()\n", - "\n", - "for crv_ in [crv]:\n", - " crv_.plot(xvals=xv, label=f\"cpc curve (spread={crv_.params.s_price_spread})\")\n", - "yv = [fn(xx+x0) - fn(x0) for xx in xv]\n", - "plt.plot(xv, yv, color=\"#aaa\", linestyle=\"--\", label=\"full curve\")\n", - "plt.legend()\n", - "plt.xlim(-200, 0)\n", - "plt.ylim(0,200)\n", - "plt.savefig(\"/Users/skl/Desktop/img3.jpg\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "9e4f37d9-b1d3-4594-a25c-193dedbde791", - "metadata": {}, - "source": [ - "## Optimizer [NOTEST]" - ] - }, - { - "cell_type": "markdown", - "id": "8bfeed5d-579a-423c-a56f-59f9a8a4f8df", - "metadata": {}, - "source": [ - "We start with three curves: two \"USD/ETH\" at 2000 and 2100 respectively but that unfortunately use different USD references (USDC and USDT) and one Solidly stable swap with USDC/USDT" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "0048db21-92ef-4d12-86e4-20d40d96a253", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = USDC/USDT\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = WETH/USDC\n" - ] - }, - { - "data": { - "image/png": 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j46Pw8HB5enre8DYI9gAA3CK+vr4aPHiwPvnkE+3evVuLFi1Sv379HF0WaoiKigplZ2fLbDYrIiJCnp6eDv/htLpUVFTozJkz8vPzk5ubw+Z6hguhZ2AvZ+gZwzBUWlqqY8eOKTs7W40aNbrhWgj2AADcQuHh4br33nv11Vdfae3atQoNDVXbtm0dXRZqgNLSUlVUVCgyMlI+Pj6OLqdaVVRUqLS0VF5eXoQ0XBd6BvZylp7x9vaWh4eHDhw4YKvnRtD1AADcYm3atFGLFi0kSampqTp+/LiDK0JNQogBANdWHZ/j/EsAAMBtcP/996tevXoqLS3V3LlzVVJS4uiSAABADUGwBwDgNnB3d9fgwYNVq1YtHT9+XAsWLFBFRYWjywIAADUAwR4AgNvEz89PgwYNktlsVlZWlr777jtHlwQ4RM+ePWUymWQymbR582ZJF297bDKZdOrUKYfWdiXTpk1T7dq1HV3GTRs7dqztvX///fcdXQ7uECaTSV9++aWjy6jRCPYAANxG9erV09133y1J2rhxo7Zs2eLgioCLthw6pUc/SteWQ6duy+sNGzZMubm5iouLuy2vZ4/69etXW+h96qmnZDKZ9NZbb1Va/uWXXzrkLgYvvviicnNzVa9evdv+2r82f/58JSYmqk6dOvL391dCQoIWLVpUZdwXX3yh5s2by2KxqHnz5lqwYEGVMRMnTlTr1q3l4+Oj9u3b66effqq03jAMjR07VhEREfL29lbPnj21bdu2a9Z4Pa99LR999JF69uwpf3//q/7yqri4WD4+Ptq5c6fdr3E9HPkeVKf9+/dr6NChiomJkbe3txo0aKDXXntNpaWllcbl5ORo4MCB8vX1VXBwsJ5//vkqY7Zt26ZevXrJ29tbdevW1V//+lcZhlFpzIoVK9S+fXt5eXkpNjZWH3744TVrvJ7Xrm4EewAAbrPOnTurTZs2kqRvvvlGubm5ji0IkDR/42Gt2XdC8zcevi2v5+Pjo7CwMLm71/ybNHl5eenvf/+7CgoKHF2K/Pz8FBYWJrPZ7OhStHLlSiUmJmrhwoXKyMhQr169NHDgQG3atMk2Zs2aNRo8eLBSUlL0888/KyUlRYMGDdLatWttY+bOnas//vGPeuGFF5SRkaFu3bqpf//+ysnJsY15++239e6772rChAlav369wsLClJiYqNOnT1+xvut57etx7tw59evXT6+88spVxy1ZskSRkZFq2rSpXdu/Xo58D6rTzp07VVFRoUmTJmnbtm1677339OGHH1Z6f8vLy3Xvvffq7NmzWrVqlebMmaMvvvhCL7zwgm1MUVGRHnzwQYWHh2v9+vUaP368/vnPf+rdd9+1jcnOztY999yjbt26adOmTXrllVf0/PPP64svvrhifdfz2reEgetSWFhoSDIKCwsdXcpVlZaWGl9++aVRWlrq6FLgIugZ2IueqR7l5eXGzJkzjbFjxxrvvvuucfr0aUeXdMvQM7dGcXGxsX37dqO4uNi2rKKiwjhbUnbdX7uOFhnrso8b67NPGG3/utiI/vO3Rtu/LjbWZ58w1mUfN3YdLbrubVVUVFx37T169DD+8Ic/VFq2bNkyQ5Lx7bffGq1atTIsFovRsWNHY8uWLbYxr732mtG6detKz3vvvfeM6OhowzAMY8WKFYa7u7uRm5tbacyYMWOMbt262R6vXr3a6Natm+Hl5WXUq1fPGDVqlHHmzBlbbZIqfRmGYUydOtWwWq1Gamqq0bRpU8PX19dISkoyjhw5ctV9ffLJJ40BAwYYTZs2Nf70pz/Zli9YsMD49Y/i8+bNM5o3b254enoa0dHRxj//+c9K66Ojo4033njD+O1vf2v4+fkZkZGRxqRJkyqNOXTokDFo0CCjdu3aRmBgoJGcnGxkZ2dXqSs6Otp47733rlr7L2VnZxuSjM8++8xISEgwLBaL0bx5c2PZsmXXvY3r0bx5c+P111+3PR40aJDRr1+/SmOSkpKMRx55xPa4Y8eOxogRI4yCggKjvLzcMAzDaNq0qfFf//VfhmFc/HsRFhZmvPXWW7bnnD9/3rBarcaHH354xVqu57XtcanHCwoKLrv+6aefNl588UXDMP6v1z/88EOjXr16hre3t/Hwww9f8bnX4uj3QJKxYMEC2+PXX3/dCAkJMTZt2mTXdq7k7bffNmJiYmyPFy5caLi5uRmHDx+2Lfvss88Mi8Viy3MTJkww/P39jXPnztnGjBs3zoiIiLB9nr300ktG06ZNK73WiBEjjPj4+CvWcj2v/WuX+zy/5HpzKEfsAQBwADc3Nz300EMKCgpSUVGRpk+ffstP00PNV1xWrub/36Lr/kp8d6V+82G6Hv5wjU6evdh/J8+W6uEP1+g3H6Yr8d2V172t4rLyatmHP/3pT3r77bf1ww8/qE6dOkpOTlZZWdl1Pbd79+6KjY3VjBkzbMsuXLigmTNn6re//a0kKTMzU0lJSXrwwQe1ZcsWzZ07V6tWrdLIkSMlXTw9vF69evrrX/+q3NzcSmfUnDt3Tv/85z81Y8YMrVy5Ujk5OXrxxRevWZfZbNabb76p8ePH69ChQ5cdk5GRoUGDBumRRx5RZmamxo4dq7/85S+aNm1apXHvvPOOOnTooE2bNunZZ5/V73//e9up2+fOnVOvXr3k5+enlStXatWqVfLz81O/fv2u+fnSs2dPPfXUU9fclz/96U964YUXtGnTJnXu3FnJyck6ceKEbb2fn99Vv/r373/FbVdUVOj06dMKDAy0LVuzZo369u1baVxSUpLS0tIkSaWlpcrIyFBiYmKlMX379rWNyc7OVl5eXqXtWCwW9ejRwzbmcq712tWpoqJC3377re677z7bsj179uh///d/9c033yg1NVWbN2/Wc889Z1s/a9asa77fs2bNkuQ874FhGPrDH/6gKVOmaNWqVbaz15555plr7ssvz8D4tcLCwip9ExcXp4iIiEp1l5SUKCMjQ5KUnp6uLl26yGKxVBpz5MgR7d+//6r7v2HDhit+Ll3Pa98KNf/cJwAAnJSXl5cGDx6syZMn69ixY5o/f74GDx7skOtuAWfx2muvKTExUUVFRZo2bZqioqK0YMECDRo06LqeP3ToUE2dOlV/+tOfJEnfffedzp07Z3v+P/7xDw0ZMkSjR4+WJDVq1Ej//ve/1aNHD02cOFGBgYEym82qVauWwsLCKm27rKxMH374oRo0aCBJGjlypP76179eV10PPPCA2rRpo9dee01Tpkypsv7dd99V79699Ze//EWS1LhxY23fvl3/+Mc/KgXue+65R88++6wk6c9//rPee+89LV++XE2bNtWcOXPk5uamjz/+2PY5MnXqVNWuXVvLly+vElB+KSoqSuHh4dfcj5EjR+qhhx6SdPG69tTUVE2ZMkUvvfSSJNkmQ7wSb2/vK6575513dPbs2Urf67y8PIWGhlYaFxoaqry8PEnS8ePHVV5eftUxl/5/uTEHDhy4Yj3Xeu3qlJ6eroqKCnXu3Nm27Pz58/r0009t8yGMHz9e9957r9555x2FhYUpOTlZnTp1uup2L9XvDO/BhQsX9MQTT2jDhg1avXp1pXke/vrXv17zl2S/DMq/tHfvXo0fP17vvPPOVesOCAiQp6dnpb6oW7dupTG/fL9iYmKuuP8XLlzQ8ePHL/t35npe+1Yg2AMA4ECXjkjOnz9fWVlZWrNmTaUf7AB7eHuYtf2vSXY9Z/uRIj384Zoqy+c9k6DmEf52vXZ1SEhIsP05MDBQTZo00Y4dO677+U899ZT++7//W+np6YqPj9cnn3yiQYMGydfXV9LFI+N79uyxHcmULh5FrKioUHZ2tpo1a3bFbfv4+NhCvSSFh4crPz9fkvTTTz9VOho9adIkPfbYY5We//e//1133333Za+13bFjR6WjtZLUpUsXvf/++yovL7ddE9+qVSvbepPJpLCwMFsNl/atVq1albZz/vx57d2794r7JUnTp0+/6vpLfvn9cXd3V4cOHSp9fxo2bHhd2/m1zz77TGPHjtVXX32lkJCQSut+/ctOwzCqLKuuMb92I8+5EV999ZUGDBggN7f/O6E6KiqqUvhNSEhQRUWFsrKyFBYWplq1alX5Xl+LI9+DP/7xj7JYLEpPT1dwcHCldSEhIVW+79fjyJEj6tevn37zm9/od7/73VXrlq5du/H/Js775ZjL7f+Vtn8zr32zCPYAADhYXFyczpw5o0WLFmnp0qWqU6eOGjVq5Oiy4IJMJpN8PO378c7r/wVyk0kyjP/7v5eH2e5t3SqXfhh2c3OrMmP1r0+HDQkJ0cCBAzV16lTFxsZq4cKFWr58uW19RUWFRowYoeeff77K60RFRV21Dg8Pjyp1XaqnQ4cOlY5W//qInXTxUoGkpCS98sorVU57v9wP/b/e1yvVUFFRIenivrVv377SLy0uqVOnzpV37Cb9sm4/P7+rju3WrZu+//77Ssvmzp2roUOH6vPPP1efPn0qrQsLC6tylDM/P9/2/gYHB8tsNisvL08tWrS47JhLZ17k5eVVOsL6yzGXc63Xrk5ff/21xo0bd9Uxl97nS/+fNWuWRowYcdXnXPoFkzO8B4mJifrss8+0aNGiKr/0euaZZzRz5syrPn/79u2V/o4eOXJEvXr1UkJCgj766KMqdf96gr+CggKVlZVV6otLvxT75b5JqjTmcvvv7u6uoKCgy9Z5Pa99KzjHpzUAAHe4Tp06KT8/X5s2bdLnn3+uxx9//JohA6gOQX6equNnUXhtLw2+K1Jz1x9U7qnzCvLzdEg96enpevjhhyVd/GF4165dtlnC69Spo7y8vEoh+HKnfv/ud7/TI488onr16qlBgwbq0qWLbV27du20bdu2qx5Z9vT0VHm5fXMGeHt7X9fR6rfeektt2rRR48aNKy1v3ry5Vq1aVWlZWlqaGjdufN0z2Ldr105z585VSEiI/P2v/2wLe6Snp6t79+6SLp5anZGRYZufQLL/VPzPPvtMTz/9tD777DPde++9VcYnJCRoyZIl+uMf/2hbtnjxYtuZTZ6enmrfvr2WLl2q3r1728YsWbLEdgZETEyMwsLCtGTJErVt21bSxWvzV6xYob///e9XrPVar11ddu/erf3791e5VCInJ0dHjhyxnYK+Zs0aubm52XrHnlPxneE9SE5O1sCBAzVkyBCZzWY98sgjtnX2nop/+PBh9erVS+3bt9fUqVMrnelwqe433nhDubm5tl9kLF68WBaLRe3bt5ckxcfH69VXX1Vpaam8vLxsYyIiIlS/fn3bdr755ptK2168eLE6dOhQ5Zds9rz2LXHVqfVgw6z4qKnoGdiLnrl1Lly4YHz88cfG2LFjjbfffts4efKko0uqFvTMrXG1WZTtdb7sgm0W6IqKCuN82YWb3ubVXG1W/BYtWhiLFy82Vq9ebQwcONCIiooySkpKDMMwjO3btxsmk8l46623jD179hgTJkwwAgICbLPiX1JeXm5ERkYanp6elWYBNwzD+Pnnnw1vb2/j2WefNTZt2mTs2rXL+Oqrr4yRI0faxiQmJhrJycnGoUOHjGPHjhmG8X+z4v/S5Wa2/7Unn3zSuO+++yotS0lJMby8vCo9NyMjw3BzczP++te/GllZWca0adMMb29vY+rUqbYxl5vJvnXr1sZrr71mGIZhnD171mjUqJHRs2dPY+XKlca+ffuM5cuXG88//7xx8ODBSs/79bZSUlJss8hfzqVZ8aOiooz58+cbO3bsMIYPH274+fnZ3iN7zZ4923B3dzf+85//GLm5ubavU6dO2casXr3aMJvNxltvvWXs2LHDeOuttwx3d3cjPT3dNmbOnDmGh4eHMX78eGPr1q3G6NGjDV9fX2P//v22MW+99ZZhtVqN+fPnG5mZmcajjz5qhIeHG0VFRVd8D67nta9Hbm6usWnTJmPy5MmGJGPlypXGpk2bjBMnThiGYRj/+Mc/jAEDBlR6zmuvvWb4+voaffr0MTZv3mysXLnSaNy48Q3PyO/o90C/mBX/888/N7y8vIzPP//8hvbj8OHDRsOGDY27777bOHToUKXeueTChQtGXFyc0bt3b2Pjxo3G0qVLjXr16lX6e37y5EkjJCTEeOSRR4zMzExj/vz5hr+/f6W7Uezbt8/w8fEx/vjHPxrbt283pkyZYnh4eBjz5s2zjZk/f77RpEkTu17716pjVnyC/XUi2KOmomdgL3rm1jp9+rTxzjvvGGPHjjUmTZpkCzSujJ65Naoz2N9uVwv233zzjdGiRQvD09PTuOuuu4zNmzdXGjdx4kQjMjLS8PX1NZ544gnjjTfeqBLsDcMw/vKXvxhms/myt6Nbt26dkZiYaPj5+Rm+vr5Gq1atjDfeeMO2fs2aNbZb7l0K39UZ7Pfv319p25dcut2dh4eHERUVZfzjH/+otP5awd4wLobIJ554wggODjYsFosRGxtrDBs2rMrPsL/eVo8ePYwnn3zyivtxKdjPnj3b6NSpk+Hp6Wk0a9bM+OGHH666/1dzuVsLSqpSx+eff240adLE8PDwMJo2bWp88cUXVbY1YcIE2y9z2rVrZ6xYsaLS+oqKCuO1114zwsLCDIvFYnTv3t3IzMysUo+9rz116tRr9sBrr7122f289Eubrl27GpMnT67ynNatWxsffPCBERERYXh5eRkPPvjgTf3C15HvwS+DvWEYxty5cw0vL6/Lfi+v5dLrXe7rlw4cOGDce++9hre3txEYGGiMHDnSOH/+vG19eXm5sWrVKqNbt26GxWIxwsLCjLFjx1a5defy5cuNtm3bGp6enkb9+vWNiRMnXnP/r/Xav1Ydwd5kGJe5eAdVFBUVyWq1qrCw8Jad2lQdysrKtHDhQt1zzz1XPD0E+CV6BvaiZ26948eP65NPPlFxcbGaNm2qQYMGufRM+fTMrXH+/HllZ2crJibGdhqpq+jZs6fatGmj999//7LrKyoqVFRUJH9//yqn2F6vYcOG6ejRo/r6669votKaq379+ho9erTt7gDXsn//fsXExGjTpk22W5Q5k+romRsxduxYLV++vNI8Dva4NLP6wYMHK92FYezYsfryyy+veWmDM7jZ98BRHNUzl3O1z/PrzaHcxx4AACcTHBysRx55RGazWTt37tTChQsdXRJQ7T744AP5+fkpMzOzWrdbWFiopUuXatasWRo1alS1brsmePPNN695T3Bcv0WLFuntt9++4eefPHlS7777bpVbK7qSm30PUD2YPA8AACcUFRWl5ORkLViwQBs2bFCtWrVsE1YBrm7WrFkqLi6WdO2Z6O113333ad26dRoxYoQSExOrdds1wTPPPGO7T/ytnCn/TrFmTdVbRdqjcePGVSZSdDU3+x6gehDsAQBwUq1atdLBgwe1YcMGLV++XHXr1q10D23AVdWtW/eWbdvVTge+3QIDAxUYGGj38+rXr3/Z2+/h1hg7dqzGjh3r6DLgQjgVHwAAJ9a/f3+1aNFChmHo888/r3LPXQAAAII9AABOzM3NTffff7+ioqJUUlKimTNn6sSJE44uC06Eo6gA4Nqq43OcYA8AgJNzd3fXI488oqCgIJ0+fVozZ87U2bNnHV0WHOzSHQbOnTvn4EoAADfj0uf4zdw5hmvsAQBwAd7e3ho0aJCmTp2qU6dO6fPPP1dKSorMZrOjS4ODmM1m1a5d23Z5ho+Pj0vfFvGXKioqVFpaqvPnzzv8NlRwDfQM7OUMPWMYhs6dO6f8/HzVrl37pv5NJ9gDAOAiQkJCNHjwYH322Wc6cOCAvvrqKz3wwAM1JszBfpdukVXT5l4wDEPFxcXy9vamv3Fd6BnYy5l6pnbt2jd9y0OCPQAALqR+/foaNGiQZs+erczMTNWqVYtbet3BTCaTwsPDFRISorKyMkeXU23Kysq0cuVKde/e/aZOTcWdg56BvZylZzw8PKrl7DuCPQAALqZBgwYaOHCgvvrqK6WlpcnDw0M9e/Z0dFlwILPZXKMuyzCbzbpw4YK8vLwIabgu9AzsVdN6hgtQAABwQW3atFHHjh0lSStWrND27dsdXBEAAHAUgj0AAC4qKSlJzZs3lyQtWLBABw4ccHBFAADAEQj2AAC4KDc3Nz300ENq3LixLly4oM8++0xHjhxxdFkAAOA2I9gDAODC3Nzc9PDDDysqKkolJSWaMWOG8vLyHF0WAAC4jQj2AAC4OA8PDz3yyCMKCAjQ+fPnNXv2bJ0+fdrRZQEAgNuEYA8AQA3g7e2tJ554Qv7+/jp9+rRmzpyp4uJiR5cFAABuA4I9AAA1RO3atfXUU0/Jz89P+fn5+uyzz1RaWurosgAAwC1GsAcAoAYJCAjQ448/Li8vLx08eFDTp0/XhQsXHF0WAAC4hQj2AADUMKGhofrNb34js9msw4cPa968eTIMw9FlAQCAW4RgDwBADRQbG6vk5GS5ubkpKytLqamphHsAAGoogj0AADVUq1atdP/990uS1q1bp+XLlzu0HgAAcGsQ7AEAqMFatmyp/v37S5JWrlypRYsWObgiAABQ3Qj2AADUcB07dlTXrl0lSenp6Vq9erWDKwIAANWJYA8AwB2gd+/e6tChgyRp6dKlysjIcHBFAACguhDsAQC4Q9xzzz3q3LmzJOnbb7/Vzz//7OCKAABAdSDYAwBwhzCZTOrTp4/uuusuSdJXX32lDRs2OLgqAABwswj2AADcQUwmk/r376+mTZvKMAwtXLhQO3fudHRZAADgJhDsAQC4w5hMJj388MNq0KCBDMPQvHnztHfvXkeXBQAAbhDBHgCAO5DZbNajjz6qpk2bqry8XHPmzNH+/fsdXRYAALgBDg32EydOVKtWreTv7y9/f38lJCTo+++/t61/6qmnZDKZKn3Fx8dX2kZJSYlGjRql4OBg+fr6Kjk5WYcOHao0pqCgQCkpKbJarbJarUpJSdGpU6duxy4CAOC0zGazHnroITVs2FAXLlzQrFmzlJWV5eiyAACAnRwa7OvVq6e33npLGzZs0IYNG3T33Xfrvvvu07Zt22xj+vXrp9zcXNvXwoULK21j9OjRWrBggebMmaNVq1bpzJkzGjBggMrLy21jhgwZos2bNys1NVWpqanavHmzUlJSbtt+AgDgrNzd3TVo0CCFh4frwoULmjdvng4cOODosgAAgB3cHfniAwcOrPT4jTfe0MSJE5Wenq4WLVpIkiwWi8LCwi77/MLCQk2ZMkUzZsxQnz59JEkzZ85UZGSkli5dqqSkJO3YsUOpqalKT09Xp06dJEmTJ09WQkKCsrKy1KRJk8tuu6SkRCUlJbbHRUVFkqSysjKVlZXd3I7fQpdqc+Ya4VzoGdiLnqmZhgwZos8++0xHjhzRrFmzNHjwYEVFRVXLtukZ2Iuegb3oGdjLVXrmeutzaLD/pfLycn3++ec6e/asEhISbMuXL1+ukJAQ1a5dWz169NAbb7yhkJAQSVJGRobKysrUt29f2/iIiAjFxcUpLS1NSUlJWrNmjaxWqy3US1J8fLysVqvS0tKuGOzHjRun119/vcryxYsXy8fHp7p2+5ZZsmSJo0uAi6FnYC96puYJDg5WUVGRzpw5o9mzZ6tBgwby9fWttu3TM7AXPQN70TOwl7P3zLlz565rnMODfWZmphISEnT+/Hn5+flpwYIFat68uSSpf//++s1vfqPo6GhlZ2frL3/5i+6++25lZGTIYrEoLy9Pnp6eCggIqLTN0NBQ5eXlSZLy8vJsvwj4pZCQENuYy3n55Zc1ZswY2+OioiJFRkaqb9++8vf3r45dvyXKysq0ZMkSJSYmysPDw9HlwAXQM7AXPVOzlZWV6fPPP9f+/fuVnZ2t+++/X40bN77pbdIzsAc9A3vRM7CXq/TMpTPHr8Xhwb5JkybavHmzTp06pS+++EJPPvmkVqxYoebNm2vw4MG2cXFxcerQoYOio6P13Xff6cEHH7ziNg3DkMlksj3+5Z+vNObXLBaLLBZLleUeHh5O/Y2/xFXqhPOgZ2AveqZm8vDw0JAhQzRt2jQdOXJEX375pR577DHVr1+/WrZNz8Ae9AzsRc/AXs7eM9dbm8Nvd+fp6amGDRuqQ4cOGjdunFq3bq1//etflx0bHh6u6Oho7d69W5IUFham0tJSFRQUVBqXn5+v0NBQ25ijR49W2daxY8dsYwAAwP/x8PDQE088ocjISF24cEGzZ8/mVngAADgxhwf7XzMMo9Kkdb904sQJHTx4UOHh4ZKk9u3by8PDo9J1Ebm5udq6das6d+4sSUpISFBhYaHWrVtnG7N27VoVFhbaxgAAgMosFotSUlLUoEEDlZWVafbs2dqzZ4+jywIAAJfh0GD/yiuv6KefftL+/fuVmZmpV199VcuXL9djjz2mM2fO6MUXX9SaNWu0f/9+LV++XAMHDlRwcLAeeOABSZLVatXQoUP1wgsv6IcfftCmTZv0+OOPq2XLlrZZ8ps1a6Z+/fpp2LBhSk9PV3p6uoYNG6YBAwZcceI8AABw8cj9I488Ygv3c+bM0fbt2x1dFgAA+BWHXmN/9OhRpaSkKDc3V1arVa1atVJqaqoSExNVXFyszMxMTZ8+XadOnVJ4eLh69eqluXPnqlatWrZtvPfee7Z78BYXF6t3796aNm2azGazbcysWbP0/PPP22bPT05O1oQJE277/gIA4Grc3d01ePBgffrppzp8+LDmz58vd3f3m55QDwAAVB+HBvspU6ZccZ23t7cWLVp0zW14eXlp/PjxGj9+/BXHBAYGaubMmTdUIwAAd7pL19zPnTtX+/bt09y5c/XQQw/Z7mIDAAAcy+musQcAAM7H09NTQ4YMUVxcnCoqKjRv3rxK89cAAADHIdgDAIDrYjab9cADD6hNmzYyDEPff/+9Vq5c6eiyAAC44xHsAQDAdXNzc9PAgQNtp+EvW7ZMa9ascXBVAADc2Qj2AADALm5ubnrooYfUqVMnSdLixYu1cuVKGYbh4MoAALgzEewBAIDd3NzclJSUpF69ekm6eOT+22+/VUVFhYMrAwDgzkOwBwAAN8RkMql79+6228lu3LhRCxYs4Mg9AAC3GcEeAADclISEBNuR+61bt2rBggUqLy93cFUAANw5CPYAAOCmde/eXffff7/c3NyUmZmpOXPmqLS01NFlAQBwRyDYAwCAatG6dWs9+uij8vDw0J49e/Txxx/r9OnTji4LAIAaj2APAACqTcOGDfX444/L09NTx44d0yeffEK4BwDgFiPYAwCAahUVFaXHH39c3t7eOnXqlKZPn67z5887uiwAAGosgj0AAKh2kZGRGj58uAIDA1VYWKg9e/boyJEjji4LAIAaiWAPAABuidq1a+vpp59WWFiYLly4oJkzZ2r79u2OLgsAgBqHYA8AAG4ZX19fPfbYYwoMDNSFCxf0xRdfaNu2bY4uCwCAGoVgDwAAbimLxaLIyEjFxMSooqJC8+bN05o1axxdFgAANQbBHgAA3HImk0mDBw/WXXfdJUlavHixvvnmG1VUVDi4MgAAXB/BHgAA3BZubm7q37+/EhMTJUkbN27UjBkzVFZW5uDKAABwbQR7AABw25hMJnXu3Fn9+vWTm5ub9u/frxkzZujcuXOOLg0AAJdFsAcAALddp06dNHjwYFksFh08eFCffPKJCgoKHF0WAAAuiWAPAAAconHjxnr66afl7++vEydOaPLkycrOznZ0WQAAuByCPQAAcJiQkBANHTpUQUFBKi4u1uzZs5WVleXosgAAcCkEewAA4FD+/v56+umnFR4ergsXLmju3Llat26do8sCAMBlEOwBAIDD+fj46Omnn1abNm1kGIa+//57fffddyovL3d0aQAAOD2CPQAAcAru7u5KTk5W7969JUkbNmzQ1KlTmTEfAIBrINgDAACnYTKZ1LVrVyUnJ8tsNuvw4cOaNm2aTp065ejSAABwWgR7AADgdNq2batHH31Uvr6+OnbsmD7++GMdOnTI0WUBAOCUCPYAAMApNWjQQMOGDVNoaKjOnj2rTz/9VOvXr3d0WQAAOB2CPQAAcFpWq1W//e1v1bBhQ124cEELFy7U4sWLZRiGo0sDAMBpEOwBAIBTs1gseuSRRxQXFydJWrNmjebPn6+ysjIHVwYAgHMg2AMAAKdnNpv10EMPqX///nJzc9PWrVs1depUFRQUOLo0AAAcjmAPAABcRseOHZWSkiJvb2/l5ubqo48+0u7dux1dFgAADkWwBwAALqV+/foaNmyYAgICdP78ec2ZM0cbN250dFkAADgMwR4AALicgIAADRs2TLGxsaqoqNA333yj77//XhUVFY4uDQCA245gDwAAXJK3t7cef/xx9ezZU5K0bt06ffLJJyoqKnJsYQAA3GYEewAA4LJMJpN69OihQYMGyd3dXYcPH9bkyZOVl5fn6NIAALhtCPYAAMDlNWvWTE888YR8fX115swZffLJJ9q6daujywIA4LYg2AMAgBohMjJSI0aMUP369VVWVqYvvvhCqampKi8vd3RpAADcUgR7AABQY9SqVUspKSnq3LmzJGnt2rWaPHmyCgsLHVwZAAC3DsEeAADUKG5ubkpMTNR9990nd3d3HT16VFOmTNGhQ4ccXRoAALcEwR4AANRIbdq00ZNPPqmAgACdPn1aU6dO1YYNG2QYhqNLAwCgWhHsAQBAjVWvXj2NGDFCzZo1U0VFhb777jvNmTNHJSUlji4NAIBqQ7AHAAA1msVi0W9+8xv17t1bJpNJu3bt0scff6yCggJHlwYAQLUg2AMAgBrPZDKpa9eueuihh2SxWHT8+HF99NFH2rlzp6NLAwDgphHsAQDAHaNFixZ65plnVK9ePZ0/f15z587Vt99+qwsXLji6NAAAbhjBHgAA3FFq166tp556SvHx8ZKkjIwMffTRRzp16pRjCwMA4AYR7AEAwB3HbDYrKSlJAwcOlIeHh44dO6aPPvpIe/bscXRpAADYzaHBfuLEiWrVqpX8/f3l7++vhIQEff/997b1hmFo7NixioiIkLe3t3r27Klt27ZV2kZJSYlGjRql4OBg+fr6Kjk5ucp9agsKCpSSkiKr1Sqr1aqUlBR+Kw8AANSuXTsNHTpUYWFhKi4u1qxZs/TDDz+ovLzc0aUBAHDdHBrs69Wrp7feeksbNmzQhg0bdPfdd+u+++6zhfe3335b7777riZMmKD169crLCxMiYmJOn36tG0bo0eP1oIFCzRnzhytWrVKZ86c0YABAyr9gzxkyBBt3rxZqampSk1N1ebNm5WSknLb9xcAADif0NBQDR06VB06dJAkrVq1ilnzAQAuxaHBfuDAgbrnnnvUuHFjNW7cWG+88Yb8/PyUnp4uwzD0/vvv69VXX9WDDz6ouLg4ffrppzp37pxmz54tSSosLNSUKVP0zjvvqE+fPmrbtq1mzpypzMxMLV26VJK0Y8cOpaam6uOPP1ZCQoISEhI0efJkffvtt8rKynLk7gMAACfh7u6ue++9V/fdd5/MZrPy8vL08ccfc2o+AMAluDu6gEvKy8v1+eef6+zZs0pISFB2drby8vLUt29f2xiLxaIePXooLS1NI0aMUEZGhsrKyiqNiYiIUFxcnNLS0pSUlKQ1a9bIarWqU6dOtjHx8fGyWq1KS0tTkyZNLltPSUmJSkpKbI+LiookSWVlZSorK6vu3a82l2pz5hrhXOgZ2Iuegb1cqWdatGihgIAAff311zp58qRmzZqlTp06qWfPnjKbzY4u747hSj0D50DPwF6u0jPXW5/Dg31mZqYSEhJ0/vx5+fn5acGCBWrevLnS0tIkXTw97pdCQ0N14MABSVJeXp48PT0VEBBQZUxeXp5tTEhISJXXDQkJsY25nHHjxun111+vsnzx4sXy8fGxbycdYMmSJY4uAS6GnoG96BnYy5V6pl69enJzc9Px48e1du1a7dixQ1FRUXJ3d/iPTncUV+oZOAd6BvZy9p45d+7cdY1z+L9OTZo00ebNm3Xq1Cl98cUXevLJJ7VixQrbepPJVGm8YRhVlv3ar8dcbvy1tvPyyy9rzJgxtsdFRUWKjIxU37595e/vf839cpSysjItWbJEiYmJ8vDwcHQ5cAH0DOxFz8Bertwzu3bt0jfffKOioiLt3LlT/fv3V8uWLR1dVo3nyj0Dx6BnYC9X6ZlLZ45fi8ODvaenpxo2bChJ6tChg9avX69//etf+vOf/yzp4hH38PBw2/j8/HzbUfywsDCVlpaqoKCg0lH7/Px8de7c2Tbm6NGjVV732LFjVc4G+CWLxSKLxVJluYeHh1N/4y9xlTrhPOgZ2Iuegb1csWdatGih0NBQzZs3T0ePHtU333yjAwcO6J577rnszwmoXq7YM3Asegb2cvaeud7anO4+9oZhqKSkRDExMQoLC6t0akRpaalWrFhhC+3t27eXh4dHpTG5ubnaunWrbUxCQoIKCwu1bt0625i1a9eqsLDQNgYAAOBKgoODNXz4cPXs2VMmk0lbtmzRhx9+qOzsbEeXBgCAJAcfsX/llVfUv39/RUZG6vTp05ozZ46WL1+u1NRUmUwmjR49Wm+++aYaNWqkRo0a6c0335SPj4+GDBkiSbJarRo6dKheeOEFBQUFKTAwUC+++KJatmypPn36SJKaNWumfv36adiwYZo0aZIkafjw4RowYMAVJ84DAAD4JTc3N/Xo0UMxMTH64osvdOrUKc2YMUPdu3dXjx49rnmZIAAAt5JDg/3Ro0eVkpKi3NxcWa1WtWrVSqmpqUpMTJQkvfTSSyouLtazzz6rgoICderUSYsXL1atWrVs23jvvffk7u6uQYMGqbi4WL1799a0adMqzVw7a9YsPf/887bZ85OTkzVhwoTbu7MAAMDlRUVFadiwYZo3b54OHDigFStW6NChQ7rvvvsq/XwCAMDt5NBgP2XKlKuuN5lMGjt2rMaOHXvFMV5eXho/frzGjx9/xTGBgYGaOXPmjZYJAABg4+fnpyeeeEIbNmzQkiVLtHfvXk2cOFGJiYlq27ato8sDANyBnO4aewAAAGfn5uamjh07avjw4QoLC1NxcbG+/vprzZ49W8XFxY4uDwBwhyHYAwAA3KA6derod7/7ne1I/e7duzVp0iQdOHDAwZUBAO4kBHsAAICbYDablZycrCFDhqh27doqLCzUtGnTtHjxYpWWljq6PADAHYBgDwAAUA0aNWqkZ555Rm3atJEkrVmzRh9++KEOHTrk2MIAADUewR4AAKCaWCwW3XfffXrggQfk6empgoICTZs2Tenp6TIMw9HlAQBqKII9AABANWvVqpWeeeYZ1a9fX+Xl5Vq0aJFmzJihkydPOro0AEANRLAHAAC4BQICAvTEE0/o3nvvlbu7u7KzszVx4kStXLlSFRUVji4PAFCDEOwBAABuEZPJpA4dOuiZZ55RaGioLly4oGXLlmnWrFkqLCx0dHkAgBqCYA8AAHCLBQUFadiwYerSpYvc3d21b98+ffDBB8rIyODoPQDgphHsAQAAbgOz2aw+ffpoxIgRqlevnkpLS/Xtt99qypQpOnHihKPLAwC4MII9AADAbRQcHKzf/va36t27t9zc3HTkyBF99NFH2rRpEzPnAwBuCMEeAADgNnNzc1PXrl01dOhQhYSEqLS0VF9//bU+++wzrr0HANiNYA8AAOAgERERGjFihPr06SOz2azdu3frP//5DzPnAwDsQrAHAABwIDc3N3Xp0kUjRoxQSEiIysrKtGzZMk2fPp373gMArgvBHgAAwAnUqVNHw4cPt82cf+DAAU2cOFGrVq1SeXm5o8sDADgxgj0AAICTuDRz/rPPPqvY2FhduHBBP/zwg/7zn/8oOzvb0eUBAJwUwR4AAMDJBAQE6PHHH9d9990nT09PFRQUaMaMGVqyZInKysocXR4AwMkQ7AEAAJyQyWRSmzZt9Pvf/16xsbEyDENpaWmaOHGi9u3b5+jyAABOhGAPAADgxGrXrq2UlBQ98sgjqlWrlu3o/Zw5c3T69GlHlwcAcALuji4AAAAA19akSRPVr19fixYt0qZNm5SVlaWcnBz169dPLVu2lMlkcnSJAAAH4Yg9AACAi7BYLEpOTtYjjzyigIAAFRcXa8GCBZo+fbry8/MdXR4AwEEI9gAAAC6mSZMmeu6559S7d2+5u7tr//79mjRpkr799luVlJQ4ujwAwG1GsAcAAHBBZrNZXbt21bPPPqvo6GhVVFQoIyNDkyZN0t69ex1dHgDgNiLYAwAAuLCAgAA98cQTuvfee+Xn56eCggLNnDlT8+bNU0FBgaPLAwDcBkyeBwAA4OLc3NzUoUMHtWzZUsuWLdO6deu0bds2ZWVlqXPnzurRo4fc3DieAwA1FZ/wAAAANYTFYlG/fv00bNgwBQcH68KFC1q5cqUmT56sgwcPOro8AMAtQrAHAACoYcLDw/XMM8+oR48eslgsysvL0yeffKL58+fr1KlTji4PAFDNCPYAAAA1kNlsVs+ePTVq1Ci1bdtWkpSZman//Oc/WrZsmcrLyx1cIQCguhDsAQAAajBfX18lJyfr6aefVmBgoO30/EmTJik7O9vR5QEAqgHBHgAA4A4QGRmp5557TklJSfL29taxY8c0ffp0zZkzRydOnHB0eQCAm8Cs+AAAAHcINzc3xcfHq3Xr1lq2bJk2bNigrKws7dmzR126dFG3bt3k7s6PhwDgajhiDwAAcIfx9vbWPffco6eeekp16tRReXm5Vq5cqQ8++EA7d+6UYRiOLhEAYAeCPQAAwB0qKipKzzzzjJKTk+Xn56eCggLNnTtXH3/8sXJychxdHgDgOhHsAQAA7mBubm5q27atRo4cqa5du8psNuvIkSOaNm2avvnmG509e9bRJQIAroFgDwAAAFksFvXu3VvDhw9X/fr1ZRiGNm7cqPHjxystLU1lZWWOLhEAcAUEewAAANiEhIToySef1JNPPqnw8HCVlJRoyZIl+ve//61NmzZx/T0AOCGCPQAAAKqoX7++hg0bpvvuu0/e3t46c+aMvv76a82YMUN5eXmOLg8A8AsEewAAAFyWyWRSmzZtNHLkSLVt21Zms1nZ2dmaNGmSFixYoBMnTji6RACACPYAAAC4Bh8fHyUnJ2vkyJFq0aKFJGnLli364IMP9M033+j8+fMOrhAA7mwEewAAAFyX2rVr6+GHH9bQoUMVEhKiiooKbdy4Uf/+97+1du1alZeXO7pEALgjEewBAABgl3r16mnEiBG6//77FRwcrOLiYqWmpmr8+PFav369KioqHF0iANxR3B1dAAAAAFyPm5ubWrdurZYtW2rjxo1avny5CgsLtXDhQmVkZKh///6Kjo52dJkAcEfgiD0AAABumJubmzp06KCRI0eqQ4cOcnd319GjRzVt2jTNmTNHx44dc3SJAFDjccQeAAAAN83Ly0v33nuvunXrphUrVmjTpk3KysrSrl271KRJE/Xp00dBQUGOLhMAaiSCPQAAAKqNv7+/Bg4cqPj4eC1evFh79uzRzp07tXv3bnXs2FFdu3aVj4+Po8sEgBqFYA8AAIBqV6dOHT322GPKysrSihUrlJubqzVr1igjI0MdOnRQly5dCPgAUE0I9gAAALhlmjRposaNG2vPnj368ccflZeXp7S0NGVkZKhLly6Kj4+Xh4eHo8sEAJfm0Mnzxo0bp7vuuku1atVSSEiI7r//fmVlZVUa89RTT8lkMlX6io+PrzSmpKREo0aNUnBwsHx9fZWcnKxDhw5VGlNQUKCUlBRZrVZZrValpKTo1KlTt3oXAQAA7ngmk0mNGjXS8OHDlZycrFq1aqmkpEQ//vijxo8fr4yMDJWXlzu6TABwWQ4N9itWrNBzzz2n9PR0LVmyRBcuXFDfvn119uzZSuP69eun3Nxc29fChQsrrR89erQWLFigOXPmaNWqVTpz5owGDBhQ6R+IIUOGaPPmzUpNTVVqaqo2b96slJSU27KfAAAAuBjw27Ztqz/84Q+655575O/vr9OnT+vbb7/Vv//9b61du1YVFRWOLhMAXI5DT8VPTU2t9Hjq1KkKCQlRRkaGunfvbltusVgUFhZ22W0UFhZqypQpmjFjhvr06SNJmjlzpiIjI7V06VIlJSVpx44dSk1NVXp6ujp16iRJmjx5shISEpSVlaUmTZrcoj0EAADAr5nNZt11111q27atNmzYoJUrV6qoqEipqanatGmTunXrJsMwHF0mALgMp7rGvrCwUJIUGBhYafny5csVEhKi2rVrq0ePHnrjjTcUEhIiScrIyFBZWZn69u1rGx8REaG4uDilpaUpKSlJa9askdVqtYV6SYqPj5fValVaWtplg31JSYlKSkpsj4uKiiRJZWVlKisrq76drmaXanPmGuFc6BnYi56BvegZXE379u3VvHlz/fTTT9qyZYuOHj2qefPmydvbW1FRUWrRooXc3Bx6kilcAJ8zsJer9Mz11uc0wd4wDI0ZM0Zdu3ZVXFycbXn//v31m9/8RtHR0crOztZf/vIX3X333crIyJDFYlFeXp48PT0VEBBQaXuhoaHKy8uTJOXl5dl+EfBLISEhtjG/Nm7cOL3++utVli9evNglZnBdsmSJo0uAi6FnYC96BvaiZ3AtjRs3Vn5+vo4fP67i4mJ98803WrlypYKCglSrVi2ZTCZHlwgnx+cM7OXsPXPu3LnrGuc0wX7kyJHasmWLVq1aVWn54MGDbX+Oi4tThw4dFB0dre+++04PPvjgFbdnGEalD//L/UPw6zG/9PLLL2vMmDG2x0VFRYqMjFTfvn3l7+9/3ft1u5WVlWnJkiVKTExkhllcF3oG9qJnYC96BvYqLCzUl19+qby8PBUWFqqwsFARERHq1q2bYmNjCfiogs8Z2MtVeubSmePX4hTBftSoUfr666+1cuVK1atX76pjw8PDFR0drd27d0uSwsLCVFpaqoKCgkpH7fPz89W5c2fbmKNHj1bZ1rFjxxQaGnrZ17FYLLJYLFWWe3h4OPU3/hJXqRPOg56BvegZ2IuewfWyWq2qU6eO7r//fq1fv14bN27UkSNHNHfuXNWpU0fdunXjFH1cFp8zsJez98z11ubQT0PDMDRy5EjNnz9fP/74o2JiYq75nBMnTujgwYMKDw+XdPG6LA8Pj0qnUOTm5mrr1q22YJ+QkKDCwkKtW7fONmbt2rUqLCy0jQEAAIBzsVqt6t+/v/7whz8oPj5eZrNZx44d0/z58zVt2jTt27ePSfYAQA4+Yv/cc89p9uzZ+uqrr1SrVi3b9e5Wq1Xe3t46c+aMxo4dq4ceekjh4eHav3+/XnnlFQUHB+uBBx6wjR06dKheeOEFBQUFKTAwUC+++KJatmxpmyW/WbNm6tevn4YNG6ZJkyZJkoYPH64BAwYwIz4AAICT8/PzU1JSkjp27Kgff/xRO3bs0MGDBzVjxgxFRUXprrvuUvPmzTmCD+CO5dBgP3HiRElSz549Ky2fOnWqnnrqKZnNZmVmZmr69Ok6deqUwsPD1atXL82dO1e1atWyjX/vvffk7u6uQYMGqbi4WL1799a0adNkNpttY2bNmqXnn3/eNnt+cnKyJkyYcOt3EgAAANUiICBADz30kE6fPq1Vq1YpIyNDOTk5ysnJ0YoVK9S7d281adKEa/AB3HEcGuyvdeqUt7e3Fi1adM3teHl5afz48Ro/fvwVxwQGBmrmzJl21wgAAADnUqtWLfXv319dunTRDz/8oG3btun48eOaO3euQkND1a1bNzVt2rTSQR4AqMmcYvI8AAAAwF7+/v564IEHdPfdd2vdunXasGGDjh49qnnz5snf31+dOnVSp06dCPgAajyCPQAAAFya1WpVYmKiunbtqrVr12rNmjUqKirSkiVLtH79enXp0kVt2rSRuzs/+gKomfh0AwAAQI3g7e2tnj17qmPHjlq1apV+/vlnnTp1St99951Wrlyp1q1bq2vXrpe9pTEAuDKCPQAAAGoUHx8f9e3bV7169dLGjRu1evVq24R7GzZsUHx8vDp27Chvb29HlwoA1YJgDwAAgBrJw8NDnTp1Uvv27ZWenq709HSdPXtWy5cv1+rVq9WuXTvdddddCgoKcnSpAHBTCPYAAACo0dzd3dW1a1clJCRo+/btWr16tY4ePaq1a9dq3bp1atiwofr06aOQkBBHlwoAN4RgDwAAgDuC2WxWy5YtFRcXp71792rZsmU6cuSIdu/erd27d6tx48bq0qWLoqKiHF0qANiFYA8AAIA7islkUsOGDdWwYUPt3btXGzZs0M6dO7Vr1y7t2rVLderUUUJCgtq0aSOTyeTocgHgmgj2AAAAuGM1aNBADRo00IkTJ5SWlqbNmzfr2LFj+vrrr5WWlqbOnTurZcuW3CoPgFPjEwoAAAB3vKCgIA0cOFBdu3bVypUrtX37dh0/flxff/21fvjhBzVv3lxdunSR1Wp1dKkAUAXBHgAAAPh/AgICdN999ykpKUkZGRlau3atTp8+rfXr12vjxo1q3bq14uPjVadOHUeXCgA2BHsAAADgV7y8vNSlSxfFx8fbAv7Jkye1ceNGbdy4UQ0bNlTbtm3VtGlTubm5ObpcAHc4gj0AAABwBWazWR07dlSHDh2Uk5Oj9PR0ZWVlac+ePdqzZ48CAgLUvXt3xcXFcR0+AIfh0wcAAAC4Bjc3N9WvX1/169fXyZMntXz5cm3fvl0FBQX66quv9MMPP+iuu+5S27ZtVatWLUeXC+AOQ7AHAAAA7BAYGKgHH3xQffv21caNG7VhwwadPn1ay5Yt04oVK9S4cWP16tVLISEhji4VwB2CYA8AAADcAD8/P3Xv3l1dunTRtm3b9NNPP+n48ePauXOndu7cqdjYWHXs2FENGzaU2Wx2dLkAajCCPQAAAHATzGazWrVqpbi4OO3atUsbN27Unj17tG/fPu3bt0++vr5q3bq1unTpIh8fH0eXC6AGItgDAAAA1cDNzU1NmzZV06ZNderUKa1fv14ZGRk6e/as0tLStG7dOrVq1UodO3ZUaGioo8sFUIMQ7AEAAIBqVrt2bSUmJqp79+5at26dMjMzdezYMdvt8sLCwtS+fXu1bduW0/QB3DSCPQAAAHCLWCwWdevWTV27dlVOTo7WrVunHTt2KC8vT999951++ukndejQQe3atZOvr6+jywXgogj2AAAAwC1mMpkUHR2t6OhonThxQmlpadq+fbuKior0448/asWKFWrUqJHat2+v2NhYubm5ObpkAC6EYA8AAADcRkFBQRo4cKD69++vbdu2ad26dTpy5IhtNv3Q0FDdddddatmypTw9PR1dLgAXQLAHAAAAHMDd3V2tW7dWq1atlJ2drTVr1ig7O1tHjx7Vt99+q8WLF6tp06bq0KGDIiMjHV0uACdGsAcAAAAcyGQyKTY2VrGxsTp79qx+/vlnZWRk6OTJk9qyZYu2bNmiunXrqmPHjmrevLnc3fkRHkBlfCoAAAAATsLX11edO3dWQkKCdu/erTVr1ujAgQM6fPiwFixYoNTUVLVo0ULt2rVTeHi4o8sF4CQI9gAAAICTMZlMaty4sRo3bqzCwkLbUfyioiJt2LBBGzZsUFRUlOLj49W4cWNumQfc4Qj2AAAAgBOzWq3q3r27unbtqqysLK1evVqHDx9WTk6OcnJy5Ofnp7i4OLVu3VphYWGOLheAAxDsAQAAABfg5uamZs2aqVmzZjpx4oQ2b96sTZs26cyZM0pPT1d6errtWvxmzZrJw8PD0SUDuE0I9gAAAICLCQoKUu/evdWzZ0/t2rVLaWlpOnTokO1a/O+//14tWrRQy5YtFR0d7ehyAdxiBHsAAADARZnNZttR/JMnTyozM1ObNm1SYWGhMjIylJGRoTp16qhjx45q2bKlLBaLo0sGcAsQ7AEAAIAaIDAwUD169FD37t21b98+paWlKTs7W8eOHdN3332nxYsXq1mzZmrRooUaNmwoNzc3R5cMoJoQ7AEAAIAaxGQyqUGDBmrQoIEKCwu1fft2bdy4UcePH9eWLVu0ZcsWWa1W3XXXXWrVqpVq1arl6JIB3CSCPQAAAFBDWa1WJSQkKD4+XocOHVJaWpp2796twsJCLV26VD/88INiY2PVuHFjtW7dmlP1ARdFsAcAAABqOJPJpMjISA0ePFjnzp3T9u3blZmZqZycHO3du1d79+7V0qVL1apVK7Vu3Vr16tWTyWRydNkArhPBHgAAALiD+Pj4qEOHDurQoYNOnjypdevWKTMzU+fOnbNNuBcUFKTGjRurffv2CgoKcnTJAK7Brhkzdu/erUcffVRFRUVV1hUWFmrIkCHat29ftRUHAAAA4NYJDAxUv3799MILL+jxxx9X69at5eHhoRMnTmjNmjWaMGGCpk+frszMTJWVlTm6XABXYNcR+3/84x+KjIyUv79/lXVWq1WRkZH6xz/+oYkTJ1ZbgQAAAABuLTc3N9uEe/3799fPP/+sjRs36ujRo8rOzlZ2drY8PT3VqFEjtWzZUo0aNWJWfcCJ2BXsV65cqRkzZlxx/aBBgzRkyJCbLgoAAACAY1gsFnXs2FEdO3bUiRMnlJmZqZ9//lmnTp3Stm3btG3bNlmtVrVq1UqtWrVScHCwo0sG7nh2BfsDBw4oJCTkiuuDg4N18ODBmy4KAAAAgOMFBQWpZ8+e6tGjh/bt26e1a9cqOztbhYWF+umnn/TTTz8pJCREjRo1Uvv27RUQEODokoE7kl3B3mq1au/evYqOjr7s+j179lz2NH0AAAAArstkMtlO1S8pKdGuXbuUmZmpPXv2KD8/X/n5+UpLS1NMTIxatWqlpk2bcus84DayK9h3795d48eP1913333Z9f/+97/VrVu3aikMAAAAgPOxWCxq2bKlWrZsqTNnzmj9+vXasWOHjh07pn379mnfvn1yd3dXZGSkWrdurbi4OJnNZkeXDdRodgX7l19+WQkJCXr44Yf10ksvqUmTJpKknTt36u2339aiRYuUlpZ2SwoFAAAA4Fz8/PzUq1cv9erVSydPnlRmZqYyMzN14sQJ26R7ixcvVvPmzdWqVSvVrVuXSfeAW8CuYN+2bVvNmzdPTz/9tBYsWFBpXVBQkP73f/9X7dq1q9YCAQAAADi/wMBA9ejRQ927d1d2drY2bdqk7OxsnT17Vhs2bNCGDRvk6+urJk2aqFOnTleduwuAfewK9pI0YMAAHThwQKmpqdqzZ48Mw1Djxo3Vt29f+fj43IoaAQAAALgIk8mk2NhYxcbGqqKiQtnZ2dqyZYt27Nihs2fPauPGjdq4caPq1KmjFi1aqEmTJgoLC3N02YBLszvYS5K3t7ceeOCB6q4FAAAAQA3i5uZmm3Tvnnvu0ebNm7Vnzx5lZ2fr2LFjWr58uZYvX66AgAC1adNGLVu2ZGZ94AbYHewrKio0bdo0zZ8/X/v375fJZFJMTIwefvhhpaSkyGQy3Yo6AQAAALgwi8WiTp06qVOnTjp//rx27typLVu2aP/+/SooKNCyZcu0bNky1a1bV40aNVJcXJyCgoIcXTbgEuwK9oZhKDk5WQsXLlTr1q3VsmVLGYahHTt26KmnntL8+fP15Zdf3qJSAQAAANQEXl5eatOmjdq0aaOioiJt375du3bt0v79+3X48GEdPnxYy5cvV926ddWqVSs1b95cfn5+ji4bcFp2TUk5bdo0rVy5Uj/88IM2bdqkzz77THPmzNHPP/+spUuX6scff9T06dOve3vjxo3TXXfdpVq1aikkJET333+/srKyKo0xDENjx45VRESEvL291bNnT23btq3SmJKSEo0aNUrBwcHy9fVVcnKyDh06VGlMQUGBUlJSZLVaZbValZKSolOnTtmz+wAAAACqmb+/v+Lj4/XEE09ozJgxSkpKsk2sd/jwYX3//fd69913NXXqVK1cuVKFhYVVtpF5uFATtrkp83DVdcCdwK5g/9lnn+mVV15Rr169qqy7++679V//9V+aNWvWdW9vxYoVeu6555Senq4lS5bowoUL6tu3r86ePWsb8/bbb+vdd9/VhAkTtH79eoWFhSkxMVGnT5+2jRk9erQWLFigOXPmaNWqVTpz5owGDBig8vJy25ghQ4Zo8+bNSk1NVWpqqjZv3qyUlBR7dh8AAADALeTn56f4+Hj9/ve/16hRo9S3b1/VrVtXhmEoJydHy5Yt07/+9S9Nnz5d69evt2WCBZtztbvITV9uznXwHgCOYdep+Fu2bNHbb799xfX9+/fXv//97+veXmpqaqXHU6dOVUhIiDIyMtS9e3cZhqH3339fr776qh588EFJ0qeffqrQ0FDNnj1bI0aMUGFhoaZMmaIZM2aoT58+kqSZM2cqMjJSS5cuVVJSknbs2KHU1FSlp6erU6dOkqTJkycrISFBWVlZatKkiT1vAwAAAIBbLDAwUAkJCUpISFBBQYHWrVunXbt26eTJk8rOzlbm3sM6/+1yBQYE6MsT4ZKk7zLzNOiuKBmGFODroXoB3LULdwa7gv3JkycVGhp6xfWhoaEqKCi44WIunVYTGBgoScrOzlZeXp769u1rG2OxWNSjRw+lpaVpxIgRysjIUFlZWaUxERERiouLU1pampKSkrRmzRpZrVZbqJek+Ph4Wa1WpaWlXTbYl5SUqKSkxPa4qKhIklRWVqaysrIb3sdb7VJtzlwjnAs9A3vRM7AXPQN70TP4NT8/P9199926++67VVBQoKysLKV8d/Hnc+VJkiHJpBNnSzRg/Crb83b/T9/LbQ5wmc+Z663PrmBfXl4ud/crP8VsNuvChQv2bNLGMAyNGTNGXbt2VVxcnCQpLy9Pkqr8MiE0NFQHDhywjfH09KxyW4zQ0FDb8/Py8mzX6fxSSEiIbcyvjRs3Tq+//nqV5YsXL5aPj/P/5m/JkiWOLgEuhp6BvegZ2Iuegb3oGVxNSkOTZu1xU4VMki7dmcv0//5rKMl6VFOnTlXt2rVlsVgcViecm7N/zpw7d+66xtk9K/5TTz11xb8YvzzCba+RI0dqy5YtWrVqVZV1v76FnmEY17yt3q/HXG781bbz8ssva8yYMbbHRUVFioyMVN++feXv73/V13aksrIyLVmyRImJifLw8HB0OXAB9AzsRc/AXvQM7EXP4HrcI+mhI0W6f2J6lXUDLDsUXHpOublSbm6uQkJCFBMToyZNmigiIkJubnZNNYYayFU+Zy6dOX4tdgX7J5988ppjnnjiCXs2KUkaNWqUvv76a61cuVL16tWzLQ8LC5N08Yh7eHi4bXl+fr7tKH5YWJhKS0tVUFBQ6ah9fn6+OnfubBtz9OjRKq977NixK15aYLFYLvsLDA8PD6f+xl/iKnXCedAzsBc9A3vRM7AXPYNruXQ2sckkGcb//f+xIUOkgoPas2eP9u3bp/z8fOXn52vt2rWyWq1q1qyZmjZtqsjISEL+Hc7ZP2eutza7gv3UqVNvqJgrMQxDo0aN0oIFC7R8+XLFxMRUWh8TE6OwsDAtWbJEbdu2lSSVlpZqxYoV+vvf/y5Jat++vTw8PLRkyRINGjRI0sXfym3dutU20V9CQoIKCwu1bt06dezYUZK0du1aFRYW2sI/AAAAANcS5OepOn4WhVktamYp0I6SAOUVlig6LFDhTeoqPj5excXF2r59uzZv3qwjR46osLBQ6enpSk9Pl7e3tyIjI9WiRQs1a9bMqQMecDV2BfsrOXDggM6ePaumTZva9Ruv5557TrNnz9ZXX32lWrVq2a53t1qt8vb2lslk0ujRo/Xmm2+qUaNGatSokd588035+PhoyJAhtrFDhw7VCy+8oKCgIAUGBurFF19Uy5YtbbPkN2vWTP369dOwYcM0adIkSdLw4cM1YMAAZsQHAAAAXFS41Vur/quXTBXl+v777/W3/p1kuJllcTfbxnh7e6t9+/Zq3769SkpKtG/fPu3cuVO7du1ScXGxdu3apV27dunbb79Vw4YN1bhxYzVo0EC1atVy4J4B9rEr2H/66acqKCjQ6NGjbcuGDx+uKVOmSJKaNGmiRYsWKTIy8rq2N3HiRElSz549Ky2fOnWqnnrqKUnSSy+9pOLiYj377LMqKChQp06dtHjx4kp/0d577z25u7tr0KBBKi4uVu/evTVt2jSZzf/3F3rWrFl6/vnnbbPnJycna8KECfbsPgAAAAAnY3E3q6ysQtLFebU8fxHqq4y1WNSsWTM1a9ZM5eXlysrK0vbt23Xw4EEVFRVpx44d2rFjh0wmk8LCwtS6dWs1bdpUVqv1du0OcEPsCvYffvihhg8fbnucmpqqqVOnavr06WrWrJlGjhyp119/XR9//PF1bc8wjGuOMZlMGjt2rMaOHXvFMV5eXho/frzGjx9/xTGBgYGaOXPmddUFAAAAoGYzm81q3ry5mjdvLsMwlJeXp507d2rr1q06efKkcnNzlZubq9TUVIWHhysqKkrNmzdXvXr1uC4fTseuYL9r1y516NDB9virr75ScnKyHnvsMUnSm2++qd/+9rfVWyEAAAAA3EImk0nh4eEKDw9Xr169dPToUe3evVu7d+/WwYMHbSH/0uR7jRs3VuPGjVW/fv2r3g4cuF3s6sLi4uJKt3pLS0vT008/bXscGxt7xfvCAwAAAIArCA0NVWhoqLp27aqzZ89q27Zt2rp1q23yvfXr12v9+vVyd3dXRESEmjdvrhYtWsjPz8/RpeMOZVewj46OVkZGhqKjo3X8+HFt27ZNXbt2ta3Py8vj+hMAAAAANYavr686duyojh07qrS0VPv371dWVpZ2796t06dPKycnRzk5OUpNTVVERIQaNGig2NhYRUVFcco+bhu7gv0TTzyh5557Ttu2bdOPP/6opk2bqn379rb1aWlpiouLq/YiAQAAAMDRPD09bafhV1RUKCcnR9u3b9fhw4d15MgR29dPP/0kHx8fNW3aVE2aNFFMTAy30sMtZVew//Of/6xz585p/vz5CgsL0+eff15p/erVq/Xoo49Wa4EAAAAA4Gzc3NxUv3591a9fX5J05swZ7dq1S5mZmTp48KDOnTunjRs3auPGjXJ3d1dYWJgaNWqk1q1bc5Yzqp1dwd7NzU3/8z//o//5n/+57PpfB30AAAAAuBP4+fmpXbt2ateune2U/T179mjXrl0qLCzUoUOHdOjQIS1btkwhISFq2LChoqOjOZqPamF3sDeZTFWW+/v7q0mTJnrppZf04IMPVltxAAAAAOBqfnnKfv/+/XX48GFt3bpVBw4cUF5envLz85Wfn6+0tDS5u7srJiZGTZo0UaNGjSpNVg5cL7uC/YIFCy67/NSpU1q3bp0ef/xxffrpp/rNb35TLcUBAAAAgCszmUyqV6+e6tWrJ0k6d+6c9u7dq127dmn37t0qKSmx3VpPkoKCglSvXj01bdpUjRo1ktlsdmT5cBF2Bfv77rvviuuefPJJNW/eXP/85z8J9gAAAABwGT4+PmrZsqVatmxpm4DvwIED2rNnjw4fPqwTJ07oxIkT+vnnn+Xp6anY2Fg1aNBA9evXV3BwsKPLh5OyK9hfS9++ffXf//3f1blJAAAAAKiRfjkBX48ePXTu3Dlt3bpVu3fv1pEjR3Tu3Dnt3LlTO3fulCRZrVY1b95cDRs2VFRUlNzdqzXOwYVVaycUFxfLy8urOjcJAAAAAHcEHx8fdezYUR07dpRhGMrNzdWePXu0Y8cO5eXlqbCwUGvWrNGaNWtsM+1HR0erefPmCg8Pv+x8aLgzVGuwnzx5stq2bVudmwQAAACAO47JZFJERIQiIiLUvXt3nT59Wnv37tX+/fu1d+9enTlzxjbT/urVq+Xn56cGDRooKipKsbGxql27tqN3AbeRXcF+zJgxl11eWFioDRs2aO/evfrpp5+qpTAAAAAAwEW1atVSmzZt1KZNG9vR/O3bt9tm2j9z5ox+/vln/fzzz5IuTsLXpEkTW9jntP2aza7v7qZNmy673N/fX/369dOzzz6r6OjoaikMAAAAAFDVL4/mS9KFCxeUk5OjvXv3aufOnTp58qROnDihtLQ02y31QkJCVL9+fbVq1UohISGctl/D2BXsly1bdqvqAAAAAADcAHd3d8XGxio2NlaJiYkqLCzU/v37baftnz59WkeOHNGRI0eUlpYmPz8/xcbGKiIiQo0bN1ZAQICjdwE3ifMxAAAAAKAGsVqtat26tVq3bi3DMHT48GFlZWXZrsk/c+aMtmzZoi1btig1NVVBQUGKjY1VTEyMoqOj5ePj4+hdgJ0I9gAAAABQQ5lMJtWrV0/16tWTdPG0/YMHD2rXrl3atWuX7bT9EydOaP369ZKkwMBAxcTEqGnTpoqKipKnp6cjdwHXgWAPAAAAAHcId3d3xcTEKCYmRklJSTp79qxycnKUnZ2t7OxsHT9+XCdPntTJkyeVkZEhNzc3RUREKDQ0VA0bNlSjRo1kNpsdvRv4FYI9AAAAANyhfH191axZMzVr1kySdPLkSe3atUu5ubk6cOCACgsLbafwZ2RkyMPDQ1FRUYqJibGdCUDQdzyCPQAAAABA0sXT8OPj4yVJhmGooKBAO3fu1J49e5SXl6fi4mLt3btXe/fulSR5eHiofv36atCggerXr8+M+w5CsAcAAAAAVGEymRQYGKjOnTurc+fOMgxDx44d0759+7Rv3z5lZ2errKxMu3fv1u7duyVJXl5eCgkJUWxsrJo0aaLQ0FCC/m1AsAcAAAAAXJPJZFJISIhCQkIUHx+v8vJy5eTk6PDhw8rOztbBgwd1/vx55eTkKCcnR8uXL5eXl5eioqJUp04dNWjQQNHR0XJzc3P0rtQ4BHsAAAAAgN3MZrNtIr6uXbuqvLzcdjT/6NGjOnz4sM6fP2+bgX/16tWyWCyKjo5WdHS0IiIiFBkZyTX61YBgDwAAAAC4aWazWY0aNVKjRo0kSRUVFcrNzVVWVpb27dun/Px8lZSU2IK+dHGW/qioKMXGxio6Olrh4eEE/RtAsAcAAAAAVDs3NzfVrVtXdevW1d13362Kigrl5eVp//79ys7O1oEDB1RWVmY7yi9dnIyvTp06io6OVuPGjVW3bl15eHg4eE+cH8EeAAAAAHDLubm5KSIiQhEREercubPKy8t16NAhHTlyRAcOHNCBAwd0/vx5HTlyREeOHNGaNWvk5uam8PBwBQcH28K+r6+vo3fF6RDsAQAAAAC3ndlstl1vn5CQIMMwdPDgQe3du1f5+fk6dOiQzpw5o8OHD+vw4cP6+eefJUl16tRRVFSUwsLCFB0draCgoDt+Qj6CPQAAAADA4Uwmk6KiohQVFSVJMgxDp06d0t69e7Vnzx4dPXpUp06d0rFjx3Ts2DHb8/z8/FS/fn1FRUUpMjJSISEhd1zQJ9gDAAAAAJyOyWRSQECAOnTooA4dOkiSzp49q4MHD+rAgQPavXu3Tp48qTNnzmjr1q3aunWrpIvX6YeHh6tRo0aKiopSRESE3N1rdvSt2XsHAAAAAKgxfH191bRpUzVt2lRJSUk6f/68cnNzdeDAAR08eFA5OTkqKytTTk6OcnJyJF085T8oKEjh4eFq3LixoqOj5enp6eA9qV4EewAAAACAS/Ly8lJMTIxiYmIkSeXl5Tpw4IBtAr6cnBydPXtW+fn5ys/Pt12nHxgYKC8vL0eWXq0I9gAAAACAGsFsNis2NlaxsbGSLl6nf/z4ce3atUtHjhyxXZ9/8uRJhYaGOrja6kOwBwAAAADUSCaTSXXq1FGdOnVsy4qLi7V//35t2bLFgZVVrztrqkAAAAAAwB3N29tbDRs2rFGn4hPsAQAAAABwYQR7AAAAAABcGMEeAAAAAAAXRrAHAAAAAMCFEewBAAAAAHBhBHsAAAAAAFwYwR4AAAAAABdGsAcAAAAAwIUR7AEAAAAAcGEEewAAAAAAXBjBHgAAAAAAF0awBwAAAADAhRHsAQAAAABwYQ4N9itXrtTAgQMVEREhk8mkL7/8stL6p556SiaTqdJXfHx8pTElJSUaNWqUgoOD5evrq+TkZB06dKjSmIKCAqWkpMhqtcpqtSolJUWnTp26xXsHAAAAAMCt59Bgf/bsWbVu3VoTJky44ph+/fopNzfX9rVw4cJK60ePHq0FCxZozpw5WrVqlc6cOaMBAwaovLzcNmbIkCHavHmzUlNTlZqaqs2bNyslJeWW7RcAAAAAALeLuyNfvH///urfv/9Vx1gsFoWFhV12XWFhoaZMmaIZM2aoT58+kqSZM2cqMjJSS5cuVVJSknbs2KHU1FSlp6erU6dOkqTJkycrISFBWVlZatKkyWW3XVJSopKSEtvjoqIiSVJZWZnKysrs3tfb5VJtzlwjnAs9A3vRM7AXPQN70TOwFz0De7lKz1xvfQ4N9tdj+fLlCgkJUe3atdWjRw+98cYbCgkJkSRlZGSorKxMffv2tY2PiIhQXFyc0tLSlJSUpDVr1shqtdpCvSTFx8fLarUqLS3tisF+3Lhxev3116ssX7x4sXx8fKp5L6vfkiVLHF0CXAw9A3vRM7AXPQN70TOwFz0Dezl7z5w7d+66xjl1sO/fv79+85vfKDo6WtnZ2frLX/6iu+++WxkZGbJYLMrLy5Onp6cCAgIqPS80NFR5eXmSpLy8PNsvAn4pJCTENuZyXn75ZY0ZM8b2uKioSJGRkerbt6/8/f2raQ+rX1lZmZYsWaLExER5eHg4uhy4AHoG9qJnYC96BvaiZ2Avegb2cpWeuXTm+LU4dbAfPHiw7c9xcXHq0KGDoqOj9d133+nBBx+84vMMw5DJZLI9/uWfrzTm1ywWiywWS5XlHh4eTv2Nv8RV6oTzoGdgL3oG9qJnYC96BvaiZ2AvZ++Z663NpW53Fx4erujoaO3evVuSFBYWptLSUhUUFFQal5+fr9DQUNuYo0ePVtnWsWPHbGMAAAAAAHBVLhXsT5w4oYMHDyo8PFyS1L59e3l4eFS6LiI3N1dbt25V586dJUkJCQkqLCzUunXrbGPWrl2rwsJC2xgAAAAAAFyVQ0/FP3PmjPbs2WN7nJ2drc2bNyswMFCBgYEaO3asHnroIYWHh2v//v165ZVXFBwcrAceeECSZLVaNXToUL3wwgsKCgpSYGCgXnzxRbVs2dI2S36zZs3Ur18/DRs2TJMmTZIkDR8+XAMGDLjixHkAAAAAALgKhwb7DRs2qFevXrbHlyare/LJJzVx4kRlZmZq+vTpOnXqlMLDw9WrVy/NnTtXtWrVsj3nvffek7u7uwYNGqTi4mL17t1b06ZNk9lsto2ZNWuWnn/+edvs+cnJyZowYcJt2ksAAAAAAG4dhwb7nj17yjCMK65ftGjRNbfh5eWl8ePHa/z48VccExgYqJkzZ95QjQAAAAAAODOXusYeAAAAAABURrAHAAAAAMCFEewBAAAAAHBhBHsAAAAAAFwYwR4AAAAAABdGsAcAAAAAwIUR7AEAAAAAcGEEewAAAAAAXBjBHgAAAAAAF0awBwAAAADAhRHsAQAAAABwYQR7AAAAAABcGMEeAAAAAAAXRrAHAAAAAMCFEewBAAAAAHBhBHsAAAAAAFwYwR4AAAAAABdGsAcAAAAAwIUR7AEAAAAAcGEEewAAAAAAXBjBHgAAAAAAF0awBwAAAADAhRHsAQAAAABwYQR7AAAAAABcGMEeAAAAAAAXRrAHAAAAAMCFEewBAAAAAHBhBHsAAAAAAFwYwR4AAAAAABdGsAcAAAAAwIUR7AEAAAAAcGEEewAAAAAAXBjBHgAAAAAAF0awBwAAAADAhRHsAQAAAABwYQR7AAAAAABcGMEeAAAAAAAXRrAHAAAAAMCFEewBAAAAAHBhBHsAAAAAAFwYwR4AAAAAABdGsAcAAAAAwIUR7AEAAAAAcGEEewAAAAAAXBjBHgAAAAAAF0awBwAAAADAhTk02K9cuVIDBw5URESETCaTvvzyy0rrDcPQ2LFjFRERIW9vb/Xs2VPbtm2rNKakpESjRo1ScHCwfH19lZycrEOHDlUaU1BQoJSUFFmtVlmtVqWkpOjUqVO3eO8AAAAAALj1HBrsz549q9atW2vChAmXXf/222/r3Xff1YQJE7R+/XqFhYUpMTFRp0+fto0ZPXq0FixYoDlz5mjVqlU6c+aMBgwYoPLyctuYIUOGaPPmzUpNTVVqaqo2b96slJSUW75/AAAAAADcau6OfPH+/furf//+l11nGIbef/99vfrqq3rwwQclSZ9++qlCQ0M1e/ZsjRgxQoWFhZoyZYpmzJihPn36SJJmzpypyMhILV26VElJSdqxY4dSU1OVnp6uTp06SZImT56shIQEZWVlqUmTJrdnZwEAAAAAuAUcGuyvJjs7W3l5eerbt69tmcViUY8ePZSWlqYRI0YoIyNDZWVllcZEREQoLi5OaWlpSkpK0po1a2S1Wm2hXpLi4+NltVqVlpZ2xWBfUlKikpIS2+OioiJJUllZmcrKyqp7d6vNpdqcuUY4F3oG9qJnYC96BvaiZ2Avegb2cpWeud76nDbY5+XlSZJCQ0MrLQ8NDdWBAwdsYzw9PRUQEFBlzKXn5+XlKSQkpMr2Q0JCbGMuZ9y4cXr99derLF+8eLF8fHzs2xkHWLJkiaNLgIuhZ2Avegb2omdgL3oG9qJnYC9n75lz585d1zinDfaXmEymSo8Nw6iy7Nd+PeZy46+1nZdfflljxoyxPS4qKlJkZKT69u0rf3//6y3/tisrK9OSJUuUmJgoDw8PR5cDF0DPwF70DOxFz8Be9AzsRc/AXq7SM5fOHL8Wpw32YWFhki4ecQ8PD7ctz8/Ptx3FDwsLU2lpqQoKCiodtc/Pz1fnzp1tY44ePVpl+8eOHatyNsAvWSwWWSyWKss9PDyc+ht/iavUCedBz8Be9AzsRc/AXvQM7EXPwF7O3jPXW5vT3sc+JiZGYWFhlU6NKC0t1YoVK2yhvX379vLw8Kg0Jjc3V1u3brWNSUhIUGFhodatW2cbs3btWhUWFtrGAAAAAADgqhx6xP7MmTPas2eP7XF2drY2b96swMBARUVFafTo0XrzzTfVqFEjNWrUSG+++aZ8fHw0ZMgQSZLVatXQoUP1wgsvKCgoSIGBgXrxxRfVsmVL2yz5zZo1U79+/TRs2DBNmjRJkjR8+HANGDCAGfEBAAAAAC7PocF+w4YN6tWrl+3xpWvan3zySU2bNk0vvfSSiouL9eyzz6qgoECdOnXS4sWLVatWLdtz3nvvPbm7u2vQoEEqLi5W7969NW3aNJnNZtuYWbNm6fnnn7fNnp+cnKwJEybcpr0EAAAAAODWcWiw79mzpwzDuOJ6k8mksWPHauzYsVcc4+XlpfHjx2v8+PFXHBMYGKiZM2feTKkAAAAAADglp73GHgAAAAAAXBvBHgAAAAAAF0awBwAAAADAhRHsAQAAAABwYQR7AAAAAABcGMEeAAAAAAAXRrAHAAAAAMCFEewBAAAAAHBhBHsAAAAAAFwYwR4AAAAAABdGsAcAAAAAwIUR7AEAAAAAcGEEewAAAAAAXBjBHgAAAAAAF0awBwAAAADAhRHsAQAAAABwYQR7AAAAAABcGMEeAAAAAAAXRrAHAAAAAMCFEewBAAAAAHBhBHsAAAAAAFwYwR4AAAAAABdGsAcAAAAAwIUR7AEAAAAAcGEEewAAAAAAXBjBHgAAAAAAF0awBwAAAADAhRHsAQAAAABwYQR7AAAAAABcGMEeAAAAAAAXRrAHAAAAAMCFEewBAAAAAHBhBHsAAAAAAFwYwR4AAAAAABdGsAcAAAAAwIUR7AEAAAAAcGEEewAAAAAAXBjBHgAAAAAAF0awBwAAAADAhRHsAQAAAABwYQR7AAAAAABcGMEeAAAAAAAXRrAHAAAAAMCFEewBAAAAAHBhBHsAAAAAAFwYwR4AAAAAABdGsAcAAAAAwIU5dbAfO3asTCZTpa+wsDDbesMwNHbsWEVERMjb21s9e/bUtm3bKm2jpKREo0aNUnBwsHx9fZWcnKxDhw7d7l0BAAAAAOCWcOpgL0ktWrRQbm6u7SszM9O27u2339a7776rCRMmaP369QoLC1NiYqJOnz5tGzN69GgtWLBAc+bM0apVq3TmzBkNGDBA5eXljtgdAAAAAACqlbujC7gWd3f3SkfpLzEMQ++//75effVVPfjgg5KkTz/9VKGhoZo9e7ZGjBihwsJCTZkyRTNmzFCfPn0kSTNnzlRkZKSWLl2qpKSk27ovAAAAAABUN6cP9rt371ZERIQsFos6deqkN998U7GxscrOzlZeXp769u1rG2uxWNSjRw+lpaVpxIgRysjIUFlZWaUxERERiouLU1pa2lWDfUlJiUpKSmyPi4qKJEllZWUqKyu7BXtaPS7V5sw1wrnQM7AXPQN70TOwFz0De9EzsJer9Mz11ufUwb5Tp06aPn26GjdurKNHj+pvf/ubOnfurG3btikvL0+SFBoaWuk5oaGhOnDggCQpLy9Pnp6eCggIqDLm0vOvZNy4cXr99derLF+8eLF8fHxuZrduiyVLlji6BLgYegb2omdgL3oG9qJnYC96BvZy9p45d+7cdY1z6mDfv39/259btmyphIQENWjQQJ9++qni4+MlSSaTqdJzDMOosuzXrmfMyy+/rDFjxtgeFxUVKTIyUn379pW/v7+9u3LblJWVacmSJUpMTJSHh4ejy4ELoGdgL3oG9qJnYC96BvaiZ2AvV+mZS2eOX4tTB/tf8/X1VcuWLbV7927df//9ki4elQ8PD7eNyc/Ptx3FDwsLU2lpqQoKCiodtc/Pz1fnzp2v+loWi0UWi6XKcg8PD6f+xl/iKnXCedAzsBc9A3vRM7AXPQN70TOwl7P3zPXW5vSz4v9SSUmJduzYofDwcMXExCgsLKzSqROlpaVasWKFLbS3b99eHh4elcbk5uZq69at1wz2AAAAAAC4Aqc+Yv/iiy9q4MCBioqKUn5+vv72t7+pqKhITz75pEwmk0aPHq0333xTjRo1UqNGjfTmm2/Kx8dHQ4YMkSRZrVYNHTpUL7zwgoKCghQYGKgXX3xRLVu2tM2SDwAAAACAK3PqYH/o0CE9+uijOn78uOrUqaP4+Hilp6crOjpakvTSSy+puLhYzz77rAoKCtSpUyctXrxYtWrVsm3jvffek7u7uwYNGqTi4mL17t1b06ZNk9lsdtRuAQAAAABQbZw62M+ZM+eq600mk8aOHauxY8decYyXl5fGjx+v8ePHV3N1AAAAAAA4nktdYw8AAAAAACoj2AMAAAAA4MII9gAAAAAAuDCCPQAAAAAALoxgDwAAAACACyPYAwAAAADgwgj2AAAAAAC4MII9AAAAAAAujGAPAAAAAIALI9gDAAAAAODCCPYAAAAAALgwgj0AAAAAAC6MYA8AAAAAgAsj2AMAAAAA4MII9gAAAAAAuDCCPQAAAAAALoxgDwAAAACACyPYAwAAAADgwgj2AAAAAAC4MII9AAAAAAAujGAPAAAAAIALI9gDAAAAAODCCPYAAAAAALgwgj0AAAAAAC6MYA8AAAAAgAsj2AMAAAAA4MII9gAAAAAAuDCCPQAAAAAALoxgDwAAAACACyPYAwAAAADgwgj2AAAAAAC4MII9AAAAAAAujGAPAAAAAIALI9gDAAAAAODCCPYAAAAAALgwgj0AAAAAAC6MYA8AAAAAgAsj2AMAAAAA4MII9gAAAAAAuDCCPQAAAAAALoxgDwAAAACACyPYAwAAAADgwgj2AAAAAAC4MII9AAAAAAAujGAPAAAAAIALI9gDAAAAAODCCPYAAAAAALiwOyrYf/DBB4qJiZGXl5fat2+vn376ydElAQAAAABwU+6YYD937lyNHj1ar776qjZt2qRu3bqpf//+ysnJcXRpAAAAAADcsDsm2L/77rsaOnSofve736lZs2Z6//33FRkZqYkTJzq6NAAAAAAAbpi7owu4HUpLS5WRkaH/+q//qrS8b9++SktLu+xzSkpKVFJSYntcWFgoSTp58qTKyspuXbE3qaysTOfOndOJEyfk4eHh6HLgAugZ2Iuegb3oGdiLnoG96BnYy1V65vTp05IkwzCuOu6OCPbHjx9XeXm5QkNDKy0PDQ1VXl7eZZ8zbtw4vf7661WWx8TE3JIaAQAAAAC4nNOnT8tqtV5x/R0R7C8xmUyVHhuGUWXZJS+//LLGjBlje1xRUaGTJ08qKCjois9xBkVFRYqMjNTBgwfl7+/v6HLgAugZ2Iuegb3oGdiLnoG96BnYy1V6xjAMnT59WhEREVcdd0cE++DgYJnN5ipH5/Pz86scxb/EYrHIYrFUWla7du1bVWK18/f3d+oGhfOhZ2Avegb2omdgL3oG9qJnYC9X6JmrHam/5I6YPM/T01Pt27fXkiVLKi1fsmSJOnfu7KCqAAAAAAC4eXfEEXtJGjNmjFJSUtShQwclJCToo48+Uk5Ojp555hlHlwYAAAAAwA27Y4L94MGDdeLECf31r39Vbm6u4uLitHDhQkVHRzu6tGplsVj02muvVbmMALgSegb2omdgL3oG9qJnYC96BvaqaT1jMq41bz4AAAAAAHBad8Q19gAAAAAA1FQEewAAAAAAXBjBHgAAAAAAF0awBwAAAADAhRHsXdAHH3ygmJgYeXl5qX379vrpp5+uOn7FihVq3769vLy8FBsbqw8//PA2VQpnYU/PzJ8/X4mJiapTp478/f2VkJCgRYsW3cZq4Qzs/Zy5ZPXq1XJ3d1ebNm1ubYFwOvb2TElJiV599VVFR0fLYrGoQYMG+uSTT25TtXA0e/tl1qxZat26tXx8fBQeHq7f/va3OnHixG2qFo62cuVKDRw4UBERETKZTPryyy+v+Rx+/r2z2dszNeHnX4K9i5k7d65Gjx6tV199VZs2bVK3bt3Uv39/5eTkXHZ8dna27rnnHnXr1k2bNm3SK6+8oueff15ffPHFba4cjmJvz6xcuVKJiYlauHChMjIy1KtXLw0cOFCbNm26zZXDUeztmUsKCwv1xBNPqHfv3repUjiLG+mZQYMG6YcfftCUKVOUlZWlzz77TE2bNr2NVcNR7O2XVatW6YknntDQoUO1bds2ff7551q/fr1+97vf3ebK4Shnz55V69atNWHChOsaz8+/sLdnasTPvwZcSseOHY1nnvn/27mT0KbeLo7jv9i0aLUKTm1t1P5rBwdwrFNF05SioKAbUVBEwakOiIpDF6JVBEXRheKwEd04oSC4cFwk4tCClRTECoq1QtUqdYB0EK0+70LMa0xeXxP/GW77/cCF9uS5t+fCIT0nT5KygNjQoUNNeXl5yPVbtmwxQ4cODYitXLnSTJo0KWo5IrGEWzOhDB8+3OzcufPfTg0JKtKamT9/vtm2bZvZsWOHGTVqVBQzRKIJt2auXr1qevXqZd69exeL9JBgwq2X/fv3m5ycnIDYoUOHjMPhiFqOSFySzKVLl367hv4XP/uTmgnFav0vO/YW8vnzZz148EDTp08PiE+fPl337t0LeU5lZWXQ+hkzZqi6ulpfvnyJWq5IDJHUzK++ffsmn8+n3r17RyNFJJhIa+bkyZN69uyZduzYEe0UkWAiqZnLly+rsLBQ+/btU1ZWlvLz87Vp0ya1tbXFImXEUST1UlRUpIaGBl25ckXGGL1580YXL17UrFmzYpEyLIj+F3/Liv2vPd4J4M81NTXp69evSk9PD4inp6ersbEx5DmNjY0h17e3t6upqUmZmZlRyxfxF0nN/OrAgQNqaWnRvHnzopEiEkwkNfP06VOVl5fr9u3bstv5t9LZRFIzdXV1unPnjrp27apLly6pqalJq1ev1vv37/mcfQcXSb0UFRXp9OnTmj9/vj59+qT29nbNnj1bhw8fjkXKsCD6X/wtK/a/7NhbkM1mC/jdGBMU+3/rQ8XRcYVbMz+cPXtWFRUVOn/+vPr37x+t9JCA/rRmvn79qgULFmjnzp3Kz8+PVXpIQOE8z3z79k02m02nT5/WhAkTNHPmTB08eFCnTp1i176TCKdeamtrtW7dOm3fvl0PHjzQtWvX9Pz5c5WVlcUiVVgU/S8iZdX+l60VC+nbt6+SkpKCXtF++/Zt0KuSP2RkZIRcb7fb1adPn6jlisQQSc38cP78eS1dulQXLlxQaWlpNNNEAgm3Znw+n6qrq+X1erV27VpJ34c2Y4zsdrtu3LihkpKSmOSO+IjkeSYzM1NZWVnq1auXPzZs2DAZY9TQ0KC8vLyo5oz4iaRe9uzZoylTpmjz5s2SpJEjR6p79+6aOnWqdu/eze4rgtD/IlJW7n/ZsbeQlJQUjRs3Tjdv3gyI37x5U0VFRSHPmTx5ctD6GzduqLCwUMnJyVHLFYkhkpqRvr9SuWTJEp05c4bPMHYy4dZMz5499fDhQ9XU1PiPsrIyFRQUqKamRhMnToxV6oiTSJ5npkyZolevXqm5udkfe/Lkibp06SKHwxHVfBFfkdRLa2urunQJbFmTkpIk/XcXFvgZ/S8iYfn+N05f2ocInTt3ziQnJ5sTJ06Y2tpas379etO9e3dTX19vjDGmvLzcLFq0yL++rq7OpKammg0bNpja2lpz4sQJk5ycbC5evBivW0CMhVszZ86cMXa73Rw5csS8fv3af3z8+DFet4AYC7dmfsW34nc+4daMz+czDofDzJ071zx69MjcunXL5OXlmWXLlsXrFhBD4dbLyZMnjd1uN0ePHjXPnj0zd+7cMYWFhWbChAnxugXEmM/nM16v13i9XiPJHDx40Hi9XvPixQtjDP0vgoVbMx2h/2Wwt6AjR46YwYMHm5SUFDN27Fhz69Yt/2OLFy82TqczYL3H4zFjxowxKSkpJjs72xw7dizGGSPewqkZp9NpJAUdixcvjn3iiJtwn2d+xmDfOYVbM48fPzalpaWmW7duxuFwmI0bN5rW1tYYZ414CbdeDh06ZIYPH266detmMjMzzcKFC01DQ0OMs0a8uN3u3/Ym9L/4Vbg10xH6X5sxvIcJAAAAAACr4jP2AAAAAABYGIM9AAAAAAAWxmAPAAAAAICFMdgDAAAAAGBhDPYAAAAAAFgYgz0AAAAAABbGYA8AAAAAgIUx2AMAAAAAYGEM9gAAAAAAWBiDPQAACHD8+HGlpaWpvb3dH2tublZycrKmTp0asPb27duy2Wx68uSJsrOzZbPZgo69e/eqoqIi5GM/H/X19aqoqNDo0aODcqqvr5fNZlNNTU2U7x4AAOuxxzsBAACQWFwul5qbm1VdXa1JkyZJ+j7AZ2Rk6P79+2ptbVVqaqokyePxaMCAAcrPz5ck7dq1S8uXLw+4XlpamowxKisr88fGjx+vFStWBKzt169ftG8NAIAOicEeAAAEKCgo0IABA+TxePyDvcfj0Zw5c+R2u3Xv3j2Vlpb64y6Xy39uWlqaMjIyQl63R48e/p+TkpJ+uxYAAPw53ooPAACCFBcXy+12+393u90qLi6W0+n0xz9//qzKysqAwR4AAMQegz0AAAhSXFysu3fvqr29XT6fT16vV9OmTZPT6ZTH45EkVVVVqa2tLWCw37p1q3r06BFw/Fj/px4+fBh0jREjRvyLdwcAQMfCW/EBAEAQl8ullpYW3b9/Xx8+fFB+fr769+8vp9OpRYsWqaWlRR6PR4MGDVJOTo7/vM2bN2vJkiUB18rKygrrbxcUFOjy5csBsZcvX6q4uDjS2wEAoENjsAcAAEFyc3PlcDjkdrv14cMHOZ1OSVJGRob++ecf3b17V263WyUlJQHn9e3bV7m5uX/1t1NSUoKuYbfTsgAA8L/wVnwAABCSy+WSx+ORx+MJ2C13Op26fv26qqqq+Hw9AAAJgJe/AQBASC6XS2vWrNGXL1/8O/bS98F+1apV+vTpU9Bg7/P51NjYGBBLTU1Vz549Y5IzAACdETv2AAAgJJfLpba2NuXm5io9Pd0fdzqd8vl8GjJkiAYOHBhwzvbt25WZmRlwbNmyJdapAwDQqdiMMSbeSQAAAAAAgMiwYw8AAAAAgIUx2AMAAAAAYGEM9gAAAAAAWBiDPQAAAAAAFsZgDwAAAACAhTHYAwAAAABgYQz2AAAAAABYGIM9AAAAAAAWxmAPAAAAAICFMdgDAAAAAGBhDPYAAAAAAFjYfwAm3MchPXVNqwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = WETH/USDT\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "CC = CPCContainer()\n", - "CC += [CPC.from_pk(pair=\"WETH/USDC\", cid=\"buyeth\", p=2000, k=2000)]\n", - "CC += [CPC.from_pk(pair=\"WETH/USDT\", cid=\"selleth\", p=2100, k=2100)]\n", - "CC += [CPC.from_solidly(pair=\"USDC/USDT\", x=10000, y=10000, cid=\"solidly\")]\n", - "O = MargPOptimizer(CC)\n", - "CC.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "ddcc8150-aacb-414f-ac5a-782e707a688b", - "metadata": { - "tags": [] - }, - "source": [ - "We run the optimizer" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "da13edbf-5dbc-4c01-993f-a58757496fc7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[margp_optimizer] targettkn = USDC\n", - "[margp_optimizer] crit=rel (eps=1e-06, unit=1, norm=L2)\n", - "\n", - "[margp_optimizer] USDC <- WETH, USDT\n", - "[margp_optimizer] p 2,000.00, 1.00\n", - "[margp_optimizer] 1/p 0.00, 1.00\n", - "\n", - "[margp_optimizer]\n", - "========== cycle 0 =======>>>\n", - "USDC <- WETH, USDT\n", - "dtkn 0.025, -51.479\n", - "log p0 [3.3010299956639813, 0.0003685841455740562]\n", - "d logp [ 0.01070394 -0.00014748]\n", - "log p [3.31173393e+00 2.21108483e-04]\n", - "p_t (2049.9059429866033, 1.000509250720048) USDC\n", - "p 2,049.91, 1.00\n", - "1/p 0.00, 1.00\n", - "crit 1.07e-02 [1; L2], eps=1e-06, c/e=1e+04]\n", - "<<<========== cycle 0 =======\n", - "\n", - "[margp_optimizer]\n", - "========== cycle 1 =======>>>\n", - "USDC <- WETH, USDT\n", - "dtkn 0.000, 0.162\n", - "log p0 [3.311733934529401, 0.00022110848257232696]\n", - "d logp [6.81562959e-05 1.82648199e-06]\n", - "log p [3.31180209e+00 2.22934965e-04]\n", - "p_t (2050.2276715956777, 1.0005134585008217) USDC\n", - "p 2,050.23, 1.00\n", - "1/p 0.00, 1.00\n", - "crit 6.82e-05 [1; L2], eps=1e-06, c/e=7e+01]\n", - "<<<========== cycle 1 =======\n", - "\n", - "[margp_optimizer]\n", - "========== cycle 2 =======>>>\n", - "USDC <- WETH, USDT\n", - "dtkn 0.000, 0.000\n", - "log p0 [3.311802090825274, 0.00022293496456345568]\n", - "d logp [1.32149213e-09 3.61569868e-11]\n", - "log p [3.31180209e+00 2.22935001e-04]\n", - "p_t (2050.22767783421, 1.0005134585841189) USDC\n", - "p 2,050.23, 1.00\n", - "1/p 0.00, 1.00\n", - "crit 1.32e-09 [1; L2], eps=1e-06, c/e=1e-03]\n", - "<<<========== cycle 2 =======\n" - ] - }, - { - "data": { - "text/plain": [ - "CPCArbOptimizer.MargpOptimizerResult(result=-0.6271972654014917, time=0.0015058517456054688, method='margp', targettkn='USDC', p_optimal_t=(2050.22767783421, 1.0005134585841189), dtokens_t=(-5.861977570020827e-14, -6.184563972055912e-11), tokens_t=('WETH', 'USDT'), errormsg=None)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = O.optimize(\"USDC\", params=dict(verbose=True))\n", - "rd = r.asdict\n", - "r" - ] - }, - { - "cell_type": "markdown", - "id": "189fa35b-54ca-4062-87b0-85c3e66a659a", - "metadata": {}, - "source": [ - "And we look at the curves again" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "5fa47ead-f405-4ed0-beb6-374fe5720924", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = USDC/USDT\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = WETH/USDC\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pair = WETH/USDT\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "CC1 = r.curves_new\n", - "CC1.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "82e26fe3-7ced-4345-85c3-076da35946e6", - "metadata": {}, - "source": [ - "## Optimizer" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "c462a5ad-0945-4825-a3d6-4e075cb6f6c5", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "CC = CPCContainer()\n", - "CC += [CPC.from_pk(pair=\"WETH/USDC\", cid=\"buyeth\", p=2000, k=2000)]\n", - "CC += [CPC.from_pk(pair=\"WETH/USDT\", cid=\"selleth\", p=2100, k=2100)]\n", - "CC += [CPC.from_solidly(pair=\"USDC/USDT\", x=10000, y=10000, cid=\"solidly\")]\n", - "O = MargPOptimizer(CC)\n", - "#CC.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "8ccc7aba-2da0-4e8e-ac1a-831eab9f50bb", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "CPCArbOptimizer.MargpOptimizerResult(result=-0.6271972654014917, time=0.0028679370880126953, method='margp', targettkn='USDC', p_optimal_t=(2050.22767783421, 1.0005134585841189), dtokens_t=(-5.861977570020827e-14, -6.184563972055912e-11), tokens_t=('WETH', 'USDT'), errormsg=None)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = O.optimize(\"USDC\", params=dict(verbose=False))\n", - "rd = r.asdict()\n", - "r" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "c6fa754c-a3de-4bc2-98d0-79932808f3d0", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'WETH': 2050.22767783421, 'USDT': 1.0005134585841189, 'USDC': 1.0}" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert iseq(r.p_optimal[\"WETH\"], 2050.22767783421, eps=1e-3)\n", - "assert iseq(r.p_optimal[\"USDT\"], 1, eps=1e-3)\n", - "assert r.p_optimal[\"USDC\"] == 1\n", - "r.p_optimal" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "f9ca38da-d13a-4f94-9cf7-8fd1caff1979", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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pairpairptknintknoutUSDCWETHUSDT
cid
buyethWETH/USDCWETH/USDCUSDCWETH24.958112-0.0123250.000000
sellethWETH/USDTWETH/USDTWETHUSDT0.0000000.012325-25.567891
solidlyUSDC/USDTUSDC/USDTUSDTUSDC-25.5853090.00000025.567891
\n", - "
" - ], - "text/plain": [ - " pair pairp tknin tknout USDC WETH USDT\n", - "cid \n", - "buyeth WETH/USDC WETH/USDC USDC WETH 24.958112 -0.012325 0.000000\n", - "selleth WETH/USDT WETH/USDT WETH USDT 0.000000 0.012325 -25.567891\n", - "solidly USDC/USDT USDC/USDT USDT USDC -25.585309 0.000000 25.567891" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = r.trade_instructions(ti_format=r.TIF_DF).fillna(0)\n", - "assert iseq(0, sum(df[\"USDT\"]))\n", - "assert iseq(0, sum(df[\"WETH\"]))\n", - "assert sum(df[\"USDC\"]) < 0\n", - "assert sum(df[\"USDC\"]) == r.result\n", - "assert iseq(r.result, -0.6271972654014917)\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "72314545-a30d-495e-aa4a-26f29cc1cbc6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "('buyeth-x', 'WETH/USDC', 2050.22767783421)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CC1 = r.curves_new\n", - "c0,c1,c2 = [*CC1]\n", - "c0.cid, c0.pair, c0.p" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "e863999a-6f3f-429e-af31-4105288f12fb", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "('selleth-x', 'WETH/USDT', 2049.175511077681)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c1.cid, c1.pair, c1.p" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "24ac8d5a-4c2d-472c-9a5f-741566ed11b4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9999999999999997" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c0.p/c1.p*c2.p" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "f0537b25-f06e-4d90-b254-620f092b6f2c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0005131950797002682" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "1-c2.p" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "dcaa5385-b777-4912-9bf4-b7b19830b61a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0005134585833757" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(c0.p/c1.p-1) / (1-c2.p)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "7f8ad8c8-7a40-445c-a55a-e576b35813d6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(c0.p/c1.p-1, 1-c2.p, eps=1e-3) # price ratio of ETH curves equals USDC/USDT price\n", - "assert iseq(c0.p/c1.p*c2.p, 1) # circular exchange is unity" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c39e838a-c313-453a-8a18-8377b422ae4d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/NBTest/NBTest_069_CPCNewCurves.py b/resources/NBTest/NBTest_069_CPCNewCurves.py deleted file mode 100644 index 48bb6eae8..000000000 --- a/resources/NBTest/NBTest_069_CPCNewCurves.py +++ /dev/null @@ -1,388 +0,0 @@ -# -*- coding: utf-8 -*- -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -try: - from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, CPCContainer, T, CPCInverter, Pair - from fastlane_bot.tools.optimizer import F, MargPOptimizer - import fastlane_bot.tools.invariants.functions as f - from fastlane_bot.testing import * - -except: - from tools.cpc import ConstantProductCurve as CPC, CPCContainer, T, CPCInverter, Pair - from tools.optimizer import MargPOptimizer - import tools.invariants.functions as f - from tools.testing import * - -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(MargPOptimizer)) - -#plt.style.use('seaborn-dark') -plt.rcParams['figure.figsize'] = [12,6] -# from fastlane_bot import __VERSION__ -# require("3.0", __VERSION__) -# - - -# # CPC-Only incl new curves [NBTest069] -# -# Note: the core CPC tests are in NBTest 002 - -CURVES = { - "s1": CPC.from_solidly(x=10, y=10), - "s2": CPC.from_solidly(x=10, y=10, price_spread=1e-6), - "s1a": CPC.from_solidly(x=100, y=100), - "s2a": CPC.from_solidly(x=100, y=100, price_spread=1e-6), - "s3": CPC.from_solidly(x=1000, y=2000), - "s4": CPC.from_solidly(x=1, y=2000), -} - -# ## Solidly tests - -help(CPC.from_solidly) - -# + -#CPC.from_solidly(k=1, x=1) - -# + -#CPC.from_solidly(k=1, x=1, y=1) -assert raises(CPC.from_solidly, k=1, x=1, y=1).startswith("exactly 2 out of k,x,y") -assert raises(CPC.from_solidly, k=1).startswith("exactly 2 out of k,x,y") -assert raises(CPC.from_solidly, x=1).startswith("exactly 2 out of k,x,y") -assert raises(CPC.from_solidly, y=1).startswith("exactly 2 out of k,x,y") -assert raises(CPC.from_solidly).startswith("exactly 2 out of k,x,y") - -assert raises(CPC.from_solidly, k=1, x=1) == 'providing k, x not implemented yet' -assert raises(CPC.from_solidly, k=1, y=1) == 'providing k, y not implemented yet' -# - - -assert len(CPC.from_solidly(x=1, y=2000)) == 0 -assert raises(CPC.from_solidly,x=1, y=2000, as_list=False).startswith('x=1 is outside the range') - -# ### Curve s1 (x=10, y=10) and s2 (ditto, but spread = 1e-6) - -crv_l = CURVES["s1"] # CPC.from_solidly(x=10, y=10) -crv = crv_l[0] -cp = crv.params -fn = f.Solidly(k=cp.s_k) -assert crv.constr == "solidly" -assert cp.s_x == 10 -assert cp.s_y == 10 -assert cp.s_k == 20000 -assert cp.s_k == cp.s_x**3 * cp.s_y + cp.s_y**3 * cp.s_x -assert cp.s_kbar == 10 -assert iseq(cp.s_kbar, (cp.s_k/2)**0.25) -assert iseq(cp.s_kbar, 10) -assert iseq(cp.s_xmin, 50/9) -assert iseq(cp.s_xmax, 130/9) -assert cp.s_price_spread == CPC.SOLIDLY_PRICE_SPREAD -assert cp.s_price_spread == 0.06 -assert iseq(cp.s_cpck/((cp.s_cpcx0)**2)-1, cp.s_price_spread) # cpck / cpcx^2 = p; p0 = 1 -assert iseq(1-cp.s_cpck/((cp.s_cpcx0+cp.s_xmax-cp.s_xmin)**2), 1-1/(1+cp.s_price_spread)) -assert iseq(crv.x_act, 40/9) -assert iseq(crv.y_act, 40/9) -assert iseq(crv.y_act, crv.x_act) - -crv_l = CURVES["s2"] # CPC.from_solidly(x=10, y=10) -crv = crv_l[0] -cp = crv.params -fn = f.Solidly(k=cp.s_k) -assert crv.constr == "solidly" -assert cp.s_x == 10 -assert cp.s_y == 10 -assert cp.s_k == 20000 -assert cp.s_k == cp.s_x**3 * cp.s_y + cp.s_y**3 * cp.s_x -assert cp.s_kbar == 10 -assert iseq(cp.s_kbar, (cp.s_k/2)**0.25) -assert iseq(cp.s_kbar, 10) -assert iseq(cp.s_xmin, 50/9) -assert iseq(cp.s_xmax, 130/9) -#assert cp.s_price_spread == CPC.SOLIDLY_PRICE_SPREAD -assert cp.s_price_spread == 1e-6 -assert iseq(cp.s_cpck/((cp.s_cpcx0)**2)-1, cp.s_price_spread) # cpck / cpcx^2 = p; p0 = 1 -assert iseq(1-cp.s_cpck/((cp.s_cpcx0+cp.s_xmax-cp.s_xmin)**2), 1-1/(1+cp.s_price_spread)) -assert iseq(crv.x_act, 40/9) -assert iseq(crv.y_act, 40/9) -assert iseq(crv.y_act, crv.x_act) - -# ### Curve s1a (x=100, y=100) and s2a (ditto, but spread = 1e-6) - -crv_l = CURVES["s1a"] # CPC.from_solidly(x=100, y=100) -crv = crv_l[0] -cp = crv.params -fn = f.Solidly(k=cp.s_k) -assert crv.constr == "solidly" -assert cp.s_x == 100 -assert cp.s_y == 100 -assert cp.s_k == 200000000 -assert cp.s_k == cp.s_x**3 * cp.s_y + cp.s_y**3 * cp.s_x -assert cp.s_kbar == 100 -assert iseq(cp.s_kbar, (cp.s_k/2)**0.25) -assert iseq(cp.s_kbar, 100) -assert iseq(cp.s_xmin, 500/9) -assert iseq(cp.s_xmax, 1300/9) -assert cp.s_price_spread == CPC.SOLIDLY_PRICE_SPREAD -assert cp.s_price_spread == 0.06 -assert iseq(cp.s_cpck/((cp.s_cpcx0)**2)-1, cp.s_price_spread) # cpck / cpcx^2 = p; p0 = 1 -assert iseq(1-cp.s_cpck/((cp.s_cpcx0+cp.s_xmax-cp.s_xmin)**2), 1-1/(1+cp.s_price_spread)) -assert iseq(crv.x_act, 400/9) -assert iseq(crv.y_act, 400/9) -assert iseq(crv.y_act, crv.x_act) - - -crv_l = CURVES["s2a"] # CPC.from_solidly(x=100, y=100, price_spread=1e-6) -crv = crv_l[0] -cp = crv.params -fn = f.Solidly(k=cp.s_k) -assert crv.constr == "solidly" -assert cp.s_x == 100 -assert cp.s_y == 100 -assert cp.s_k == 200000000 -assert cp.s_k == cp.s_x**3 * cp.s_y + cp.s_y**3 * cp.s_x -assert cp.s_kbar == 100 -assert iseq(cp.s_kbar, (cp.s_k/2)**0.25) -assert iseq(cp.s_kbar, 100) -assert iseq(cp.s_xmin, 500/9) -assert iseq(cp.s_xmax, 1300/9) -#assert cp.s_price_spread == CPC.SOLIDLY_PRICE_SPREAD -assert cp.s_price_spread == 1e-6 -assert iseq(cp.s_cpck/((cp.s_cpcx0)**2)-1, cp.s_price_spread) # cpck / cpcx^2 = p; p0 = 1 -assert iseq(1-cp.s_cpck/((cp.s_cpcx0+cp.s_xmax-cp.s_xmin)**2), 1-1/(1+cp.s_price_spread)) -assert iseq(crv.x_act, 400/9) -assert iseq(crv.y_act, 400/9) -assert iseq(crv.y_act, crv.x_act) - -# ### Curve s3 (off centre) - -crv - -crv_l = CURVES["s3"] # CPC.from_solidly(x=100, y=100) -crv = crv_l[0] -cp = crv.params -fn = f.Solidly(k=cp.s_k) -assert crv.constr == "solidly" -assert cp.s_x == 1000 -assert cp.s_y == 2000 -assert cp.s_k == 10000000000000 -assert cp.s_k == cp.s_x**3 * cp.s_y + cp.s_y**3 * cp.s_x -#assert cp.s_kbar == 100 -assert iseq(cp.s_kbar, (cp.s_k/2)**0.25) -assert iseq(cp.s_kbar, 1495.3487812212206) -assert iseq(cp.s_xmin, 830.7493229006781) -assert iseq(cp.s_xmax, 2159.948239541763) -assert cp.s_price_spread == CPC.SOLIDLY_PRICE_SPREAD -assert cp.s_price_spread == 0.06 -assert iseq(cp.s_cpck/((cp.s_cpcx0)**2)-1, cp.s_price_spread) # cpck / cpcx^2 = p; p0 = 1 -assert iseq(1-cp.s_cpck/((cp.s_cpcx0+cp.s_xmax-cp.s_xmin)**2), 1-1/(1+cp.s_price_spread)) -assert iseq(crv.x_act, 169.25067709932193) -assert iseq(crv.y_act, 1159.948239541763) - -# ### Curve 4 (out of range) - -crv_l = CURVES["s4"] # CPC.from_solidly(x=100, y=100) -assert len(crv_l) == 0 - -# ## Solidly plots [NOTEST] - -# ### Curves 1 and 2 - -# + -crv = CURVES["s1"][0] # CPC.from_solidly(x=10, y=10) -# cp = crv.params -crv2 = CURVES["s2"][0] # CPC.from_solidly(x=10, y=10, price_spread=XXX) -fn = f.Solidly(k=cp.s_k) -x0 = cp.s_x -LIM = cp.s_kbar - -xv = np.linspace(-LIM+0.001, 1.1*LIM, 100) -plt.figure(figsize=(6,6)) -crv.plot(xvals=xv, color="red", label="cpc curve") -yv = [fn(xx+x0) - fn(x0) for xx in xv] -plt.plot(xv, yv, color="#aaa", linestyle="--", label="full curve") -plt.legend() -plt.xlim(-LIM, LIM) -plt.ylim(-LIM, LIM) -plt.savefig("/Users/skl/Desktop/img1.jpg") -plt.show() - -for crv_ in [crv, crv2]: - crv_.plot(xvals=xv, label=f"cpc curve (spread={crv_.params.s_price_spread})") -yv = [fn(xx+x0) - fn(x0) for xx in xv] -plt.plot(xv, yv, color="#aaa", linestyle="--", label="full curve") -plt.legend() -plt.xlim(-.6*LIM, .6*LIM) -plt.ylim(-.6*LIM, .6*LIM) -plt.savefig("/Users/skl/Desktop/img2.jpg") -plt.show() - -for crv_ in [crv, crv2]: - crv_.plot(xvals=xv, label=f"cpc curve (spread={crv_.params.s_price_spread})") -yv = [fn(xx+x0) - fn(x0) for xx in xv] -plt.plot(xv, yv, color="#aaa", linestyle="--", label="full curve") -plt.legend() -plt.xlim(-.45*LIM, -.2*LIM) -plt.ylim(.25*LIM, .5*LIM) -plt.savefig("/Users/skl/Desktop/img3.jpg") -plt.show() -# - - -# ### Curves 1a and 2a - -# + -crv = CURVES["s1a"][0] # CPC.from_solidly(x=10, y=10) -# cp = crv.params -crv2 = CURVES["s2a"][0] # CPC.from_solidly(x=10, y=10, price_spread=XXX) -fn = f.Solidly(k=cp.s_k) -x0 = cp.s_x -LIM = cp.s_kbar - -xv = np.linspace(-LIM+0.001, 1.1*LIM, 100) -plt.figure(figsize=(6,6)) -crv.plot(xvals=xv, color="red", label="cpc curve") -yv = [fn(xx+x0) - fn(x0) for xx in xv] -plt.plot(xv, yv, color="#aaa", linestyle="--", label="full curve") -plt.legend() -plt.xlim(-LIM, LIM) -plt.ylim(-LIM, LIM) -plt.savefig("/Users/skl/Desktop/img1.jpg") -plt.show() - -for crv_ in [crv, crv2]: - crv_.plot(xvals=xv, label=f"cpc curve (spread={crv_.params.s_price_spread})") -yv = [fn(xx+x0) - fn(x0) for xx in xv] -plt.plot(xv, yv, color="#aaa", linestyle="--", label="full curve") -plt.legend() -plt.xlim(-.6*LIM, .6*LIM) -plt.ylim(-.6*LIM, .6*LIM) -plt.savefig("/Users/skl/Desktop/img2.jpg") -plt.show() - -for crv_ in [crv, crv2]: - crv_.plot(xvals=xv, label=f"cpc curve (spread={crv_.params.s_price_spread})") -yv = [fn(xx+x0) - fn(x0) for xx in xv] -plt.plot(xv, yv, color="#aaa", linestyle="--", label="full curve") -plt.legend() -plt.xlim(-.45*LIM, -.2*LIM) -plt.ylim(.25*LIM, .5*LIM) -plt.savefig("/Users/skl/Desktop/img3.jpg") -plt.show() - -# - -# ### Curve 3 - -# + -crv = CURVES["s3"][0] # CPC.from_solidly(x=1000, y=2000) -# cp = crv.params -# crv2 = CURVES["s2a"][0] # CPC.from_solidly(x=10, y=10, price_spread=XXX) -fn = f.Solidly(k=cp.s_k) -x0 = cp.s_x - -xv = np.linspace(-1000+0.001, 2000, 100) -plt.figure(figsize=(6,6)) -crv.plot(xvals=xv, color="red", label="cpc curve") -yv = [fn(xx+x0) - fn(x0) for xx in xv] -plt.plot(xv, yv, color="#aaa", linestyle="--", label="full curve") -plt.legend() -plt.xlim(-1000, 2000) -plt.ylim(-2000, 1000) -plt.savefig("/Users/skl/Desktop/img1.jpg") -plt.show() - -for crv_ in [crv]: - crv_.plot(xvals=xv, label=f"cpc curve (spread={crv_.params.s_price_spread})") -yv = [fn(xx+x0) - fn(x0) for xx in xv] -plt.plot(xv, yv, color="#aaa", linestyle="--", label="full curve") -plt.legend() -plt.xlim(-500, 1500) -plt.ylim(-1500,500) -plt.savefig("/Users/skl/Desktop/img2.jpg") -plt.show() - -for crv_ in [crv]: - crv_.plot(xvals=xv, label=f"cpc curve (spread={crv_.params.s_price_spread})") -yv = [fn(xx+x0) - fn(x0) for xx in xv] -plt.plot(xv, yv, color="#aaa", linestyle="--", label="full curve") -plt.legend() -plt.xlim(-200, 0) -plt.ylim(0,200) -plt.savefig("/Users/skl/Desktop/img3.jpg") -plt.show() - -# - - -# ## Optimizer [NOTEST] - -# We start with three curves: two "USD/ETH" at 2000 and 2100 respectively but that unfortunately use different USD references (USDC and USDT) and one Solidly stable swap with USDC/USDT - -CC = CPCContainer() -CC += [CPC.from_pk(pair="WETH/USDC", cid="buyeth", p=2000, k=2000)] -CC += [CPC.from_pk(pair="WETH/USDT", cid="selleth", p=2100, k=2100)] -CC += [CPC.from_solidly(pair="USDC/USDT", x=10000, y=10000, cid="solidly")] -O = MargPOptimizer(CC) -CC.plot() - -# We run the optimizer - -r = O.optimize("USDC", params=dict(verbose=True)) -rd = r.asdict -r - -# And we look at the curves again - -CC1 = r.curves_new -CC1.plot() - -# ## Optimizer - -CC = CPCContainer() -CC += [CPC.from_pk(pair="WETH/USDC", cid="buyeth", p=2000, k=2000)] -CC += [CPC.from_pk(pair="WETH/USDT", cid="selleth", p=2100, k=2100)] -CC += [CPC.from_solidly(pair="USDC/USDT", x=10000, y=10000, cid="solidly")] -O = MargPOptimizer(CC) -#CC.plot() - -r = O.optimize("USDC", params=dict(verbose=False)) -rd = r.asdict() -r - -assert iseq(r.p_optimal["WETH"], 2050.22767783421, eps=1e-3) -assert iseq(r.p_optimal["USDT"], 1, eps=1e-3) -assert r.p_optimal["USDC"] == 1 -r.p_optimal - -df = r.trade_instructions(ti_format=r.TIF_DF).fillna(0) -assert iseq(0, sum(df["USDT"])) -assert iseq(0, sum(df["WETH"])) -assert sum(df["USDC"]) < 0 -assert sum(df["USDC"]) == r.result -assert iseq(r.result, -0.6271972654014917) -df - -CC1 = r.curves_new -c0,c1,c2 = [*CC1] -c0.cid, c0.pair, c0.p - -c1.cid, c1.pair, c1.p - -c0.p/c1.p*c2.p - -1-c2.p - -(c0.p/c1.p-1) / (1-c2.p) - -assert iseq(c0.p/c1.p-1, 1-c2.p, eps=1e-3) # price ratio of ETH curves equals USDC/USDT price -assert iseq(c0.p/c1.p*c2.p, 1) # circular exchange is unity - - diff --git a/resources/NBTest/NBTest_900_OptimizerDetailedSlow.ipynb b/resources/NBTest/NBTest_900_OptimizerDetailedSlow.ipynb deleted file mode 100644 index 072bfdca5..000000000 --- a/resources/NBTest/NBTest_900_OptimizerDetailedSlow.ipynb +++ /dev/null @@ -1,6186 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "8f04c50a-67fe-4f09-822d-6ed6e3ac43e4", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:55.001608Z", - "start_time": "2023-07-31T12:43:54.659207Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "ConstantProductCurve v3.5 (22/Apr/2023)\n", - "CPCAnalyzer v1.5 (18/May/2023)\n", - "OptimizerBase v5.1 (20/Sep/2023)\n", - "CPCArbOptimizer v5.1 (15/Sep/2023)\n", - "PairOptimizer v6.0.1 (21/Sep/2023)\n", - "MargPOptimizer v5.3-b1 (14/Dec/2023)\n", - "ConvexOptimizer v5.1 (15/Sep/2023)\n", - "ArbGraph v2.2 (09/May/2023)\n" - ] - } - ], - "source": [ - "try:\n", - " from fastlane_bot import Bot, Config, ConfigDB, ConfigNetwork, ConfigProvider\n", - " from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, CPCContainer, Pair\n", - " from fastlane_bot.tools.analyzer import CPCAnalyzer\n", - " from fastlane_bot.tools.optimizer import PairOptimizer, MargPOptimizer, ConvexOptimizer\n", - " from fastlane_bot.tools.optimizer import OptimizerBase, CPCArbOptimizer\n", - " from fastlane_bot.tools.arbgraphs import ArbGraph\n", - " from fastlane_bot.tools.cpcbase import AttrDict\n", - " from fastlane_bot.testing import *\n", - "\n", - "except:\n", - " from tools.cpc import ConstantProductCurve as CPC, CPCContainer, Pair\n", - " from tools.analyzer import CPCAnalyzer\n", - " from tools.optimizer import PairOptimizer, MargPOptimizer, ConvexOptimizer\n", - " from tools.optimizer import OptimizerBase, CPCArbOptimizer\n", - " from tools.arbgraphs import ArbGraph\n", - " from tools.cpcbase import AttrDict\n", - " from tools.testing import *\n", - " \n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPC))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPCAnalyzer))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(OptimizerBase))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPCArbOptimizer))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(PairOptimizer))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(MargPOptimizer))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(ConvexOptimizer))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(ArbGraph))\n", - "#print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(Bot))\n", - "import itertools as it\n", - "import collections as cl\n", - "#plt.style.use('seaborn-dark')\n", - "plt.rcParams['figure.figsize'] = [12,6]\n", - "# from fastlane_bot import __VERSION__\n", - "# require(\"3.0\", __VERSION__)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "899894a0", - "metadata": {}, - "outputs": [], - "source": [ - "T = AttrDict(\n", - " NATIVE_ETH=\"ETH-EEeE\",\n", - " AAVE=\"AAVE-DaE9\",\n", - " WETH=\"WETH-6Cc2\",\n", - " ETH=\"WETH-6Cc2\",\n", - " WBTC=\"WBTC-C599\",\n", - " BTC=\"WBTC-C599\",\n", - " USDC=\"USDC-eB48\",\n", - " USDT=\"USDT-1ec7\",\n", - " DAI=\"DAI-1d0F\",\n", - " LINK=\"LINK-86CA\",\n", - " MKR=\"MKR-79A2\",\n", - " BNT=\"BNT-FF1C\",\n", - " UNI=\"UNI-F984\",\n", - " SUSHI=\"SUSHI-0fE2\",\n", - " CRV=\"CRV-cd52\",\n", - " FRAX=\"FRAX-b99e\",\n", - " HEX=\"HEX-eb39\",\n", - " MATIC=\"MATIC-eBB0\",\n", - " HDRN=\"HDRN-5e06\",\n", - " SHIB=\"SHIB-C4cE\",\n", - " ICHI=\"ICHI-C4d6\",\n", - " OCTO=\"OCTO-2BA3\",\n", - " ECO=\"ECO-5727\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "b3f59f14-b91b-4dba-94b0-3d513aaf41c7", - "metadata": {}, - "source": [ - "# Mostly Optimizer Tests [NB006]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "736e4c79-fbd4-4898-ba89-82d779b57f20", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:55.731401Z", - "start_time": "2023-07-31T12:43:54.686757Z" - } - }, - "outputs": [], - "source": [ - "# bot = Bot()\n", - "# CCm = bot.get_curves()\n", - "try:\n", - " CCm = CPCContainer.from_df(pd.read_csv(\"_data/NBTest_006.csv.gz\"))\n", - "except:\n", - " CCm = CPCContainer.from_df(pd.read_csv(\"fastlane_bot/tests/_data/NBTest_006.csv.gz\"))\n", - "\n", - "CCu3 = CCm.byparams(exchange=\"uniswap_v3\")\n", - "CCu2 = CCm.byparams(exchange=\"uniswap_v2\")\n", - "CCs2 = CCm.byparams(exchange=\"sushiswap_v2\")\n", - "CCc1 = CCm.byparams(exchange=\"carbon_v1\")\n", - "tc_u3 = CCu3.token_count(asdict=True)\n", - "tc_u2 = CCu2.token_count(asdict=True)\n", - "tc_s2 = CCs2.token_count(asdict=True)\n", - "tc_c1 = CCc1.token_count(asdict=True)\n", - "CAm = CPCAnalyzer(CCm)\n", - "#CCm.asdf().to_csv(\"A011-test.csv.gz\", compression = \"gzip\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "978daf46-aef2-4918-9204-59239240d5f2", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:55.817120Z", - "start_time": "2023-07-31T12:43:54.775007Z" - } - }, - "outputs": [], - "source": [ - "CA = CAm\n", - "pairs0 = CA.CC.pairs(standardize=False)\n", - "pairs = CA.pairs()\n", - "pairsc = CA.pairsc()\n", - "tokens = CA.tokens()" - ] - }, - { - "cell_type": "markdown", - "id": "83dc88dc", - "metadata": {}, - "source": [ - "## Market structure analysis [NOTEST]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4f28ff25-8a6f-4466-b8a9-6bf926b0fac3", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:55.817394Z", - "start_time": "2023-07-31T12:43:54.779056Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total pairs: 2864\n", - "Primary pairs: 2834\n", - "...carbon: 26\n", - "Tokens: 2233\n", - "Curves: 4155\n" - ] - } - ], - "source": [ - "print(f\"Total pairs: {len(pairs0):4}\")\n", - "print(f\"Primary pairs: {len(pairs):4}\")\n", - "print(f\"...carbon: {len(pairsc):4}\")\n", - "print(f\"Tokens: {len(CA.tokens()):4}\")\n", - "print(f\"Curves: {len(CCm):4}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "8e902de8-cd75-477b-8577-2cc4b10346e1", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:55.846102Z", - "start_time": "2023-07-31T12:43:54.789061Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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count
pair
WETH-6Cc2/USDC-eB4824
WETH-6Cc2/BNT-FF1C14
USDT-1ec7/USDC-eB4813
vBNT-7f94/BNT-FF1C12
WBTC-C599/WETH-6Cc210
......
MOVE-324C/WETH-6Cc21
VXV-bFCe/USDT-1ec71
ACX-F82F/WETH-6Cc21
PANDA-00DC/WETH-6Cc21
DECI-4eA6/HEX-eb391
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count
pair
WETH-6Cc2/USDC-eB4824
WETH-6Cc2/BNT-FF1C14
USDT-1ec7/USDC-eB4813
vBNT-7f94/BNT-FF1C12
WBTC-C599/WETH-6Cc210
......
HOP-a3CC/WETH-6Cc22
imgnAI-CBe0/WETH-6Cc22
WAR-1543/WETH-6Cc22
BUSD-7C53/USDT-1ec72
ARB-4ad1/MATIC-eBB02
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" - ], - "text/plain": [ - " count\n", - "pair \n", - "WETH-6Cc2/USDC-eB48 24\n", - "WETH-6Cc2/BNT-FF1C 14\n", - "USDT-1ec7/USDC-eB48 13\n", - "vBNT-7f94/BNT-FF1C 12\n", - "WBTC-C599/WETH-6Cc2 10\n", - "... ...\n", - "HOP-a3CC/WETH-6Cc2 2\n", - "imgnAI-CBe0/WETH-6Cc2 2\n", - "WAR-1543/WETH-6Cc2 2\n", - "BUSD-7C53/USDT-1ec7 2\n", - "ARB-4ad1/MATIC-eBB0 2\n", - "\n", - "[935 rows x 1 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CA.count_by_pairs(minn=2)" - ] - }, - { - "cell_type": "markdown", - "id": "a188b742-340e-469d-bce8-d8cff0aaebed", - "metadata": {}, - "source": [ - "### All crosses" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e6099e82-4bd0-4748-ad2e-1a1c06d43896", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:55.895777Z", - "start_time": "2023-07-31T12:43:54.811069Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(172,\n", - " [('HEX-eb39', 17),\n", - " ('UNI-F984', 10),\n", - " ('ICHI-C4d6', 10),\n", - " ('FRAX-b99e', 9),\n", - " ('MATIC-eBB0', 8),\n", - " ('HDRN-5e06', 8),\n", - " ('SHIB-C4cE', 7),\n", - " ('REVV-A8Ca', 7),\n", - " ('LINK-86CA', 6),\n", - " ('ICSA-69ed', 6)])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CCx = CCm.bypairs(\n", - " CCm.filter_pairs(notin=f\"{T.ETH},{T.USDC},{T.USDT},{T.BNT},{T.DAI},{T.WBTC}\")\n", - ")\n", - "len(CCx), CCx.token_count()[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7c727bf9-3d6e-42b4-89e0-e6f398acb265", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.373317Z", - "start_time": "2023-07-31T12:43:54.819597Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "AGx=ArbGraph.from_cc(CCx)\n", - "AGx.plot(labels=False, node_size=50, node_color=\"#fcc\")._" - ] - }, - { - "cell_type": "markdown", - "id": "63a8cdac-1563-4a68-979f-6c0aec3a7a4e", - "metadata": {}, - "source": [ - "### Biggest crosses (HEX, UNI, ICHI, FRAX)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "aba143f8-1b00-49fd-b5eb-88914d16a823", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.379859Z", - "start_time": "2023-07-31T12:43:55.416299Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "45" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CCx2 = CCx.bypairs(\n", - " CCx.filter_pairs(onein=f\"{T.HEX}, {T.UNI}, {T.ICHI}, {T.FRAX}\")\n", - ")\n", - "ArbGraph.from_cc(CCx2).plot()\n", - "len(CCx2)" - ] - }, - { - "cell_type": "markdown", - "id": "4f0cb652-b27c-4210-aa53-dd86665429de", - "metadata": {}, - "source": [ - "### Carbon" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6db0700b-9542-4ec4-8242-e9dad39958a2", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.711334Z", - "start_time": "2023-07-31T12:43:55.675308Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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IiOjzxBliREREVGW9yaySUrEYikpKMFJXgbOZLmeVEFUin9NsTyIi+rxwhhgRERFVWVoqSmhjZYS21kaw0dOArqqSdMaYCICQn4dTu7Yh43IAPKwMmQyjamfu3LkQiUQvPRISEjBixIhX9ik7RowYAeD57qmOjo4V3i8jIwMikQhz5859bWxlCe30hw+w9vvv8KV7E3g3rIUxHs5YOmGUTN/rlwIxb5S3tM/Ilg0xZ/gA/Ll7P/KKxTJ9i4uL8dNPP8HGxgYqKiqwtrbG7NmzUVAgO6MsOTkZffr0Qe3ataGpqQldXV00adIEa9asgVgsO2aZ9u3b45tvvpE+TkhIeOnrtWvXLplrfXx80Lt379e+LkRE9GHwXR8RERFVefpqytBX05U+LtuZ7sSJE9i04EdsUVREU0cHNGnSRI5REsnPyZMnoaurW67d3Nwcvr6+MkmeyMhIjB8/HosWLULbtm2l7cbGxh80poi0bCTcisNPX/SDaU1rDJ/hCwMzC2Q/foRrwRdk+uZlP0HNOvXQof8Q6BmZIC8nG6d2bcOCr3wgefYUCyePlfYdPHgwjh8/jp9++glNmzZFaGgoFixYgJiYGBw+fFja79mzZ9DR0YGvry+srKxQXFyM48ePY+LEibh27Ro2btwoE8OhQ4cQEhKCbdu2lXsuEydOxJAhQ2Ta6tatK/N47ty5sLe3R0BAANq1a/euLxsREb0hJsSIiIio2hGJns8Te/jwIQCgtLQUbdu2RWBgIBo1aiTP0Ig+mfz8fOnPLi4uMDIyqrCfra0tbG1tpY8LCwsBPE/otGjR4qPE9qSwBBn5xVg1cyKMzC2wYMdBKKv8r85eq669ZPq36tqrXJuLZweM7dACB3dsxbRvvoS+mjLCwsJw4MABrFixAlOmTAEAdOjQAUpKSvj+++9x5swZdOzYEQBgb2+PrVu3yozp5eWF9PR0bN26FWvXrpWp/bdo0SL06dMHlpaW5Z6PlZXVa18rW1tbdOnSBUuWLGFCjIjoE+CSSSIiIqq24uLipMmxp0+fwtPTE//++6+coyL68MqWRkZGRqJ///7Q19eXSXJVNok5+Yi9Eob7sTHo9sUYmWTYm1JSVoamjg4UFZWQmPM8+RcSEgIA6Nq1q0zf7t27AwD279//2nGNjY2hoKAARUVFaVtUVBQuX74MHx+ft47zRT4+Pjh79izi4+PfaxwiIno9JsSIiIio2oqNjUXZ/kISiQS5ubnw8PDAnTt35BwZ0cfRt29f1KlTB3v37sUff/whbS8tLYVYLJY5SktL3+te/x3vbcbMLChGzNUwAIC6phYWfDUMgxrZYKhzHSz6+gs8uFfx31GJRIJSsRhZj9Kwa9UvSE24h54jv0ZmQTGA5/XDAJTb1bXscXR0dLkxBUGAWCzGkydPsHv3bmzZsgVTp06FktL/FtscPXoUioqK8PDwqDCuJUuWQEVFBRoaGmjdurXM0swXeXp6QhAEHD9+/FUvDxERfQBMiBEREVG1FRMTU64tMzMTJ06ckEM0RB/f8OHDsWTJEnTo0AG9ev1viaGZmRmUlZVljnr16r3zfWJiYsqNp6ysDDMzsze6PrfoeVILANZ+/x0MTEzx/YZt+GruUiTdiYPv0D54kv6o3HULvxqGgY5WGNPGGce2bcSUX/+Ai2cH5BY9L4Lv4OAA4H8zxcoEBwcDeP73/7+WLl0KZWVlGBgYYPDgwZg8eTIWLVok0yc0NBR169aFlpaWTLuqqirGjBmD9evXIyAgABs3bkRpaSl69epVrgYZAJiYmMDS0rJcfERE9OGxhhgRERFVS8XFxUhOTpZpGzlyJMaOHcvi+lRl9evXr8L2s2fPliuqr6am9s73sbW1LbeLIgDk5OSgQ4cOMm3/3bFRQUEBAp7P9gIAOycXjFuwQnreqm49TOvTCSd2bsGQyTNlrh394wLkP83Fk8ePcPHwAfw65RtMWLwS7t37QBAEeHl5oU6dOpg5cyZMTU3RtGlThIWF4fvvv4eioiIUFMrPFxgxYgQ6dOiArKwsBAQE4JdffkFOTg5Wr14t7ZOSkgITE5Ny15qbm+PPP/+UaRswYACaN2+OWbNmYcSIETIzzYDnSbGy+oZERPTxMCFGRERE1ZKysjLGjBkDKysruLi4oEuXLnj27BmcnZ3lHRrRR2Nubl5he+PGjV9aVP9dqKmpwdXVtVx7RkZGuTZlZWWZx5s3b4auW2do6+kDAJxae8qct6nvCH1jU9z790a5sSxq1Zb+3LRdZywYMxQb5/+A1l17QSQSQUVFBSdOnICPjw86deoEANDU1MSiRYswf/78Cgvim5mZSWe2derUCfr6+pg1axZGjRolTZ4XFBTA1NT0VS+JzPP19vbGrFmzcOfOHdSvX1/mvJqaGgoKCt5oLCIiendMiBERfUSCIEgLdhNR5SISiWRqKBkYGODUqVNyjIjo46uMv5OuXLki89jGxgbXnkpgXa/+S64ABAhQEL2++kudhk6ICjoPIS8bwPNkV506dRAaGoqHDx8iKysLtra2yMnJwbfffvvSGmAvatasGQDg9u3b0oSYkZERsrKyXnutNP7/r11Y0Yy0rKws1KpV643HIiKid8OEGBHRB/SksASJOfnILChGbpEYAgARAB1VJRiqq8BaVwP6asqvG4aI5KBTp07YtWsX4uPjK/Xue0RVTUUzyQzFOXDxaAdVdXVEBQagx4ivpOfuxUQj+3E67JxePZtTEAT8eyUMmjq6qGVRvnaZpaWldEbYjz/+CE1NTYwePfq18Z4/fx7A88RaGXt7e/j7+7/2WgAoKSnB7t27YWRkJDMG8Hz5aHJycrldMImI6MNjQoyI6APIKxYjIi0bmQUlEAEQXjgnAMgpEiO3SIx72fkwVFeGi5ketFT4TzBRZTJz5kzs2rULS5YswV9//SXvcIg+qYiIiHI1xIDnReh1dHQ+eTzWuhq4p6OLQROnY+uyn7F61mS07tYb2Rnp2LVqGYwsLNF58HBp/yXjRsDa3gE29o7Q1tNHVnoazh/cg5groRjz0yLYGv7vOSxbtgxmZmawsrLCo0ePsGfPHvj7+2P79u0ySybnzJmDR48ewcPDA5aWlsjOzsbJkyfx119/YcCAAXBxcZH29fT0xN9//43bt2/Dzs5O2j5lyhSUlJSgVatWMDMzQ3JyMlavXo1r165h8+bNUFRUlHne0dHRyM/PR9u2bT/Gy0pERC/gpzEioveUnFuAiLRs/P/qB5lk2IvK2rMKSnA24TFczPRQU0f9U4RIRG/AyckJ2traOHr0qLxDIfrkunTpUmH7mTNnyhXB/xQUivKRmXwf3YePgYa2No5t24TgY/5Q19REE/e2GDb1e2mNMQCwd26K0FPHcHLHFuTnPYWmtg5sHRvj+z+2oZOXl8zs7MLCQvz888948OAB1NXV0aJFC1y4cAHu7u4yMbi6umLVqlXw9/dHZmYm1NTU4ODggN9++w1jx46V6durVy9oaWnh0KFDmD59urTd0dERGzZswM6dO5GbmwttbW00a9YMp06dktYwe5G/vz+MjIwqPEdERB+WSChbwE5ERG8tObcAV1Kz3/n6puZMihFVJj179sSRI0eQmpoqLaJNRB9PcXExYmNj8e+//0qP4OBgpKenw6JWbaw6HghRBXW23pSCCOhQy/iTzMqeOHEizp07h5iYmHeq1VZaWoo6depgyJAhWLhw4UeIkIiIXvTuv12IiD6SvLw8TJ48GRYWFlBTU4OTk1OFW7e/qbNnz8LNzQ0aGhowMjLCiBEjkJ6e/sbXr1y5En379oWNjQ1EIhE8PT2fx/n/yyQrsnnxXPSzt8Cir7+o8Hz6g2T0s7eAla4GRCIRlJWVYWhoiKZNm+K7775DTExMuWsSEhIgEokqPF72+rRv3x7ffPONTNvt27fRr18/6OvrQ0NDA82bN8fhw4fLXevj44PevXu//IUhqoKmTZsG4PmSKiL6+Pr37w8nJycMGTIEixcvxoEDB6S/o78eORzNLA3ea/xPWaLgxx9/xMOHD7F///53uv6ff/5BXl6ezAwzIiL6eJgQI6JKp2/fvti6dSvmzJmDEydOoGnTphg8eDB27tz51mNdvHgRXl5eMDU1xaFDh/D777/j7NmzaN++PYqKit5ojD/++AOJiYlo164djI2Npe0vLpN8kbikBIFHnr8Zjgo+j8xHqS8du+uwUVh78AQuXryI7du3o3fv3jh8+DAaN26MX375pcJrJk6ciNDQUJmjY8eO5fodOnQIISEh8PX1lbYlJCTAzc0Nt27dwh9//IG9e/fC2NgYvXv3LvcGfu7cuTh27BgCAgJe+foQVSUeHh5QV1d/5w+0RPR2XvzipbS0VPqzk5MTfH19UVNHHU3N9aAger5JzZsQ4fnMsE89C9vU1BQ7duxAQUHBO10vkUiwY8cO6OnpfdjAiIioQlwySUSVyvHjx9GtWzfs3LkTgwcPlrZ36tQJMTExSEpKKleA9lWaNWuGZ8+e4fr161BSev4N8aVLl9CqVSusW7euXA2QikgkEum26I6OjjAyMsLBk2dwPjGjwv6hJ49i+eSv4NKmAyIunsWQybPQ75tJMn3SHyRjbIfm+GK6L3qNHou21kbS+iYFBQXo27cvTp48iePHj8PLywvA82SWjY0NfvnlF+kslldp3rw5ateuDT8/P2nbN998g61bt+Lu3bvSwsGlpaVo2LAh8vLykJCQILMFfI8ePVBUVITTp0+/9n5EVUWHDh1w7tw5PHnyhB9MiT4yQRDQqFEj3Lx5U6b93LlzaNeunfTxqzavKVPWbqSuAmczXW5eQ0REr8QZYkRUqRw8eBBaWloYMGCATPvIkSORkpKC8PBwBAcHQ1lZuVxSaMuWLRCJRNi0aRMA4OHDh7hy5Qp8fHykyTAAaNmyJezs7HDw4ME3ikmhgtoliTn5L/2m+tx+Pygpq2D84t9gZG6BgIO78arvHkT/P14ZdXV1bNq0CcrKyi+dJfY6UVFRuHz5Mnx8fGTaQ0JC0LhxY5ldtBQVFeHl5YXk5GRcvnxZpr+Pjw/Onj2L+Pj4d4qD6HM0adLzBPbKlSvlGwhRFZebm4umTZvi5s2b0hIAioqKaNOmjUwyDAC0VJTQxsoIba2NYKOnAV1VJenvYREAXVUl2OhpoK21ETysDJkMIyKi12JCjIgqlZs3b6J+/foyCSwAaNSokfR869atsWDBAqxYsUJa+yomJgbjx4/HsGHDMHr0aGnfF6/973j//Tb6bWQWFFf47XRmWgquh1xE0/adoWtgCM/eA5GWeB//Xgl76VjC/4/3IgsLC7i4uODSpUsQi8Uy55YsWQIVFRVoaGigdevWFdb/Onr0KBQVFeHh4SHTXlxcDFVV1XL9y9qio6Nl2j09PSEIAo4fP/7S+Imqmu7du0NFReWdlmkT0Zs5ceIEzMzMEBERgV69euHChQsQiUQoLS3FkiVLXnqdvpoynEx10b6WMfrUM0cfOzP0qWeO9rWM4WSqK7ObJBER0aswIUZElUpmZiYMDMoX0C1ry8zMBADMmDEDXbt2xfDhwxETE4OBAwfCysoKf/zxh8xYL1773/HKzr+L3CJxhe0BB3ZDIpGgfb/nyz3b9RsEkUiEc/v9Kuz/qvGsra1RVFSErKwsAM+TVmPGjMH69esREBCAjRs3orS0FL169cLGjRtlrg0NDUXdunWhpaUl0+7g4IDo6Gjk5eXJtAcHBwNAudfExMQElpaWCAkJeWX8RFWJgoICXFxccPfuXRQWFso7HKIqRSKRYOTIkejatSvEYjF27NgBf39/eHh4YN26dZg6dSpatGjxxuO9y26OREREABNiRFQJverNbdk5kUiEbdu2QVtbG66urrh//z727NkDTU3NNx7vxXaxWCxzvK68YkVnBUFAwIHdMDK3QONWz2dmmdawQoNmLRF2+hjy856+crz/3vO/j83NzfHnn39iwIABaN26NYYMGYLAwEA0adIEs2bNkplJlpKSAhMTk3L3mTBhAnJycvDFF1/g3r17ePToEXx9fXHp0iUAFS8PNTExwcOHD18aO1FV9M0330AQBKxdu1beoRBVGYmJiahVqxa2bNkCOzs7JCcnY8iQIdLzX3/9NZYvXy7HCImIqDphQoyIKhVDQ8MKZ26VzZJ6cbaXoaEhevbsicLCQnTp0gUNGzYsNxZQftZT2XgvjqWsrCxzbN269ZVxVpRiuxEWjPQHSXDr3AMFeU/xLDcHz3Jz0NKrB4oKChB81P+V4/03cZeYmAhVVdUKZ7i9GLe3tzcyMzNx584daXtBQQHU1NTK9W/fvj02b96MwMBA2NrawszMDAcOHMD8+fMBQKa2WBk1NbV33jGL6HM1bNgwKCoqvvbfAiJ6Mxs2bICtrS2Sk5MxefJk3Lp1C6ampvIOi4iIqjFWmySiSqVhw4bw8/ODWCyWqSN248YNAM93eSxz5swZrF+/Hs2aNcPBgwexf/9+9OvXT3q+rO+NGzfQtWtXmfvcuHFDZqwrV67InLexsXllnDqqSsj5zzLHc/ueL4s8smUDjmzZUO6ac/v90GmQT7n2svFe9PDhQ0RERKBNmzbl6qn9V9lMshdndxkZGUmTiP81fPhwDB06FHfu3IGysjLq1KmDxYsXQyQSwd3dvVz/rKws1KpV65UxEFU1CgoKaNiwIaKjo8v9e0REb664uBheXl4ICAiAtrY2jh8/jtatW8s7LCIiIs4QI6LKpU+fPsjLy8P+/ftl2rdu3QoLCws0b94cAJCamophw4ahTZs2uHTpEnr27InRo0fj/v370mssLS3RrFkz/PPPPygtLZW2h4WF4datW+jbt6+0zdXVVeYom132MobqKjKzxPJysnH57EnYOzfFvK37yh0ePfri7o1rSLodV34wQYDBC0WACwoK8OWXX0IsFmPGjBmvjKOkpAS7d++GkZER6tSpI223t7fHvXv3XnqdkpIS6tevjzp16iAnJwd//vknevXqBWtra5l+YrEYycnJcHBweGUcRFXRqFGjIJFIsGXLFnmHQvRZCg8Ph4mJCQICAuDh4YH09HQmw4iIqNLg151EVKl4eXmhY8eOGDt2LHJzc1GnTh34+fnh5MmT+Oeff6CoqIjS0lIMHjwYIpEIO3fuhKKiIrZs2QInJyd4e3sjODgYKioqAIClS5eiY8eOGDBgAMaNG4f09HTMmjULjo6OGDly5BvFdPXqVSQkJAB4vkW8IAi4du4EYh4/hW1DJ5hY1kDgkQMoLipEV5/RcGzestwY2nr6CDxyAOf2+2Hk7HnS9ozUh7h9PRLT5sxAdnoadHR0kJaWhqKiIhgYGGD48OH4+uuvMXfuXEyZMgUlJSVo1aoVzMzMkJycjNWrV+PatWvYvHkzFBUVpeN6enri77//xu3bt2FnZydtT09Px4oVK9CqVStoa2sjLi4Oy5Ytg4KCQoW1kqKjo5Gfn4+2bdu+0WtFVJV8/fXX+Pbbb/Hnn3/iyy+/lHc4RJ+VmTNn4pdffoGCggJWrVqFiRMnyjskIiIiWQIRUSXz9OlTYdKkSYKZmZmgoqIiNGrUSPDz85Oe/+GHHwQFBQXh3LlzMtddunRJUFJSEr799luZ9tOnTwstWrQQ1NTUBAMDA+GLL74QHj169MbxDB8+XMD/173/7zFh0W/C/rgUwaZ+A0HX0EjYFZ0g7I9LqfCwa+wi6OgbCLuiE4T1Z8NfOuZ/jzlz5giCIAibNm0SmjVrJhgYGAhKSkqCvr6+0LlzZ+HUqVPlYs7JyRG0tLSEZcuWybRnZmYKnTp1EoyNjQVlZWXByspKmDhxovD48eMKn7uvr69gZGQkFBYWvvHrRVSV2NvbC0pKSkJpaam8QyH6LDx+/FioX7++AECwsLAQ7t69K++QiIiIKiQShNdspUZERBXKKxbjbMJjSN7jX1EFEdChljFQXIiGDRtKZ6KVUVRUfOmOka8zceJEnDt3DjExMe+0LX1paSnq1KmDIUOGYOHChW99PVFVsHDhQvz444/Yu3cv+vfvL+9wiCq1ffv2YdiwYSgqKsLQoUOxbdu2CncvJiIiqgz4G4qI6B1pqSjBxUzvvcZwMdODlooStLS0cP78eejp6ckkr0pLS1G/fn0sXLgQEonkrcb+8ccf8fDhw3L12N7UP//8g7y8PEyfPv2drieqCr799lsAwJo1a+QcCVHlJZFI0L9/fwwYMAAikQiHDh3CP//8w2QYERFVavwtRUT0HmrqqKOpuR4URMCbzsES4fnMsKbmeqipoy5tr1WrFg4ePCjzAWLIkCEoKCjAjz/+CE1NTXz55ZfIzc19o/uYmppix44dKCgoeItn9D8SiQQ7duyAnp7eO11PVBVoaWnBxsYGYWFh8g6FqFKKjY2Fubk59u/fj8aNGyM1NRU9e/aUd1hERESvxYQYEdF7qqmjjg61jGGg/nynyJclxsraDdVV0KGWsUwyrIynpydWrVoFAOjatSt27NiBvLw8/Pbbb9DR0cGmTZugr6+PTp06IT4+/rWxde/eHT4+Pu/ytDBy5Eh06tTpna4lqkoGDBiAoqIinDp1St6hEFUqv/zyCxwdHfH48WP4+vri2rVr/BKFiIg+G6whRkT0AT0pLEFiTj4yC4qRWySGgOeJMB1VJRiqq8BaVwP6asqvHEMQBGzfvh0eHh6oVauWzLnjx49j6tSpiIuLAwA0bNgQK1euRLt27T7OEyIiZGRkwNjYGF26dMGJEyfkHQ6R3OXn56Ndu3YIDw+Hvr4+zp49C2dnZ3mHRURE9FaYECMi+ogEQXingvavExMTg/HjxyMwMBCCIMDCwgJz5szBV1999cHvRUSApaUlcnJykJeXJ+9QiOQqICAAPXv2xLNnz9ClSxccOXIESkpK8g6LiIjorXHJJBHRR/QxkmEA0KBBA1y4cAEZGRkYMmQIHj9+jK+//hra2tqYPn06iouLP8p9iaqrXr164dmzZwgPD5d3KERyM3bsWLRv3x5FRUX4+++/ceLECSbDiIjos8UZYkREVYBYLMa8efOwevVq5OTkQElJCb1798batWthYmIi7/CIPnuJiYmoVasW+vXrh3379sk7HKJP6sGDB/Dw8MD9+/dRu3ZtBAUFwcLCQt5hERERvRcmxIiIqpht27bB19cXSUlJEIlEaNGiBdatWwcnJyd5h0b0WTMxMUFJSQmePHki71CIPpnNmzfjq6++glgsxtixY7Fu3Tp5h0RERPRBcMkkEVEV88UXXyAxMRHBwcFo0qQJQkND0aRJE9StWxcHDx6Ud3hEny0vLy9kZ2cjNjZW3qEQfXRisRhdunTBqFGjoKqqinPnzjEZRkREVQoTYkREVVSrVq0QERGBxMREdOvWDffv30ffvn1hZGSEJUuWQCKRyDtEos/KjBkzAABLly6VcyREH1dkZCRMTExw6tQpuLm5IT09nbsZExFRlcMlk0RE1UR+fj6mTZuGLVu2oKCgAGpqahg2bBh+++03aGlpyTs8os+Cnp4eVFRUkJ6eLu9QiD4KX19fLFy4ECKRCMuWLcPUqVPlHRIREdFHwYQYEVE1I5FI8Ntvv2Hp0qV4/PgxFBQU0LFjR6xfvx42NjbyDo+oUuvXrx8OHDiAxMREWFlZyTscog8mOzsbHh4euHHjBkxNTXHx4kXUq1dP3mERERF9NFwySURUzSgoKGDq1KlIT0/HoUOHULduXZw6dQq1a9eGk5MTAgMD5R0iUaXFZZNUFR0+fBjm5ua4ceMGBgwYgJSUFCbDiIioyuMMMSIiws2bNzFu3DgEBwdDEARYWlpi7ty5+PLLL+UdGlGlo6WlBT09PTx48EDeoRC9F4lEgmHDhsHPzw+qqqr4559/0L9/f3mHRURE9ElwhhgREcHR0RGBgYFIT0/H4MGDkZ6ejjFjxkBHRwczZ85EcXGxvEMkqjRat26Nhw8fIiMjQ96hEL2z+Ph41KhRA35+fnBwcMCDBw+YDCMiomqFCTEiIpIyMjLCzp07kZ+fj9mzZ0uLKmtpacHb2xuPHz+Wd4hEcvfdd98BAFasWCHnSIjeze+//4569eohLS0NM2bMQExMDIyMjOQdFhER0SfFJZNERPRKmzdvxpw5c5CcnAyRSISWLVti3bp1aNSokbxDI5IbNTU1WFpaIj4+Xt6hEL2xwsJCdOrUCUFBQdDV1cWZM2fQtGlTeYdFREQkF5whRkRErzRy5EgkJSUhKCgITk5OCAkJQePGjWFnZ4dDhw7JOzwiuWjWrBnu37+PvLw8eYdC9EaCg4NhYmKCoKAgtG/fHunp6UyGERFRtcaEGBERvZHWrVsjMjISCQkJ8PLyQnx8PHr37g1jY2MsW7YMEolE3iESfTITJkyAIAhYtWqVvEMheq1vv/0W7u7uKCgowIYNG3D27FmoqKjIOywiIiK54pJJIiJ6J3l5eZg2bRq2bt2KwsJCqKmpYfjw4Vi+fDm0tLTkHR7RRyWRSKCqqoq6devi33//lXc4RBVKS0uDh4cH7ty5AysrKwQFBcHKykreYREREVUKnCFGRETvREtLC3/88QeePXuGZcuWQVtbGxs2bICuri66du2KxMREeYdI9NEoKCjAyckJcXFx3IWVKqUdO3bAysoKd+7cwejRo3H//n0mw4iIiF7AhBgREb0XBQUFTJ8+Henp6fD394etrS1OnDiBWrVqoUmTJggODpZ3iEQfxZgxYyAIAv788095h0IkJRaL0atXLwwbNgxKSko4efIkNm7cCAUFvu0nIiJ6EZdMEhHRBxcdHY1x48bh0qVLEAQBNWvWxLx58zBy5Eh5h0b0wYjFYqiqqqJx48aIjIyUdzhEuHnzJjw9PZGZmQkXFxdcuHCBS9iJiIhegl8VERHRB9eoUSMEBwcjLS0N3t7eSEtLw6hRo6Crq4vZs2ejpKRE3iESvTclJSU4ODggOjqam0qQ3C1YsACNGjXCkydPsGDBAly9epXJMCIioldgQoyIiD4aExMT7Nq1C3l5eZg5cyYEQcCSJUugqamJwYMHIyMjQ94hEr2X4cOHo7S0FDt37pR3KFRN5ebmwtXVFb6+vjA0NMT169fxww8/yDssIiKiSo9LJomI6JPauHEj5s6di4cPH0IkEqF169ZYt24dHB0d5R0a0VsrKCiApqYm3NzcEBISIu9wqJo5ceIE+vXrh4KCAvTq1Qv79u2DkpKSvMMioo9IEASIRCJ5h0FUJTAhRkREcnHhwgVMnjwZ169fBwDY2dnhl19+Qc+ePeUcGdHbqVu3LpKSklBUVCTvUKiakEgkGD16NLZs2QIVFRVs2bIFgwcPlndYRPQRPCksQWJOPjILipFbJIYAQARAR1UJhuoqsNbVgL6asrzDJPoscckkERHJhaenJ65du4Z79+6hc+fOuHv3Lnr16gUTExOsWLGCNZnoszF48GAUFxfjyJEj8g6FqoHExETUqlULW7ZsgZ2dHZKTk5kMI6qC8orFuJiUgfOJGbifnY+c/0+GAYAAIKdIjPvZ+TifmIGLSRnIKxbLM1yizxJniBERUaWQl5eHKVOmYPv27SgsLIS6ujpGjBiB5cuXQ0NDQ97hEb1UdnY29PX10b59e5w9e1be4VAVtmHDBowfPx6lpaWYPHkyfvvtN3mHREQfQXJuASLSsiEIwJt8WBcBEIkAFzM91NRR/9jhEVUZTIgREVGlIpFIsGzZMixfvhyZmZlQVFRE586dsX79elhZWck7PKIKWVlZISMjA/n5+fIOhaqg4uJieHl5ISAgANra2jhx4gRatWol77CI6CNIzi3AldTsd76+qTmTYkRviksmiYioUlFQUMCsWbOQkZGBffv2wcbGBsePH4e1tTVcXFxYuJwqpbLC5oGBgfIOhaqY8PBwmJiYICAgAB4eHkhPT2cyjOgVwsPD0adPH1hZWUFVVRWmpqZwc3PD1KlTpX08PT0hEolQu3ZtVDQ/JDAwECKRCCKRCFu2bAEA6ePXHRcuXKgwrrJ7vuxIS0tDXrEYEWnZuHr+DFbNnITverTDQEcr9LO3qHDM3auXo5+9hcxhpashHXPXrl3Svn5+fvDw8ICpqSlUVVVhYWGBHj164NKlSxWOHR8fD1VVVYSGhkrbNm7ciN69e6NWrVpQV1dHnTp1MHbsWKSmplY4xq5du+Dk5AQ1NTVYWFhg8uTJyMvLk+mzadMmWFpa4tmzZxWOQfQxcYYYERFVeteuXcP48eMRGhoKQRBQs2ZNzJ8/H8OHD5d3aEQAgJSUFFhaWqJnz544dOiQvMOhKmLGjBlYvnw5FBQUsHLlSkyYMEHeIRFVaseOHUPPnj3h6emJMWPGwNzcHKmpqbh69Sp27dqFBw8eAHienIqMjMTTp09x9uxZtG/fXmacESNG4ODBg8jNzcXmzZsxYsQIhIWFyfSZP38+zp8/j4CAAJl2BwcH6OjolIvt33//RW5urkxbfn4+unTpAhcXF4SGhuJiUgayCkqw5ocpiL16GTYODfAoKRHxMdHYH5dSbszMtBRkppVPRv05ZwbSkhKQkpICPT09AMCaNWvw8OFDuLq6wsjICKmpqfj1118RGRmJc+fOoU2bNjJj9OnTByUlJTh69Ki0zdLSEm3btkXXrl1haWmJW7duYf78+SgtLUVUVBRMTU2lfXfs2IFhw4bhyy+/xJAhQ3D79m3MnDkTzZo1w+nTp6X9xGIxHBwcMHjwYMybN6/ccyH6mJgQIyKiz0Z6ejrGjx8Pf39/iMVi6OrqYvz48Zg3bx6UlJTkHR5Vc2ZmZsjPzy/3gYfobWVkZMDd3R1xcXGwsLBAUFAQateuLe+wiCq9Nm3a4OHDh4iLiyv3vkAikUBB4fkCKU9PT2RkZEBbWxu1a9fGjh07pP2ePn0KMzMzDB06FH/99Zc0IfZfI0aMwL59+8rNeHobW7duxYgRI7Bx40b0HfoFzidmlIv1r5+/x8mdWypMiFUk/UEyxnVsgQGDBmP3zh2v7JuTkwNjY2MMGjQI27Ztk7bHxsbCwcEBJ0+eROfOnf83dno6TExMZMa4evUqmjZtivnz5+PHH38EAJSWlqJmzZpo2LAhTp06Je27c+dODB06FMePH4eXl5e0fcWKFZg/fz5SUlJYN5Y+KS6ZJCKiz4aJiQn27t2LZ8+eYfr06SgtLcWiRYugoaGBoUOHIisrS94hUjXWvXt3PH36FNeuXZN3KPQZ27t3LywtLREXF4dhw4YhOTmZyTCiN5SZmQkjI6MKvyQrSzC9aNSoUThw4ACys7OlbWXLDAcNGvTR4iyzadMmaGlpwdvbG4k5+RC9ItY3de6AHwRBQMcBQ17bV1tbG2pqauVer/Xr18PMzAwdO3aUaf9vMgwAXFxcoKioiOTkZGlbWFgYUlNTMXLkSJm+AwYMgJaWFg4ePCjTPnToUOTm5sos8ST6FJgQIyKiz46KigqWLVuGp0+fYsOGDTAxMcHOnTthZGSENm3aICYmRt4hUjU0a9YsAMCyZcvkHAl9jiQSCfr374+BAwdCQUEBhw8fxvbt29/rgzFRdePm5obw8HBMmjQJ4eHhKCkpeWX/QYMGQVFREX5+ftK2TZs2oX///hUue/yQ7ty5g6CgIAwaNAhaWlrILCh+ox0lX0UikeDCwT0ws7aBTZNmFfYpLS1FSUkJEhISMHbsWAiCgPHjx8v0OXbsGDw8PN7o35+LFy+itLQUDRo0kLbdvHkTANCoUSOZvsrKyrC3t5eeL2NmZgZ7e3scO3bsjZ4n0YfC37BERPRZ++qrr/DgwQOcO3cOjo6OCAwMhKOjI+rXr4/jx4/LOzyqRurUqQMDAwOZ5SFEbyI2Nhbm5ubYv38/GjdujNTUVPTo0UPeYRF9dpYsWYLWrVtj9erVaNGiBTQ1NdGqVSssWbKkwqWN2tra6N+/P/7++28Az+t8hYeHY9SoUR891k2bNgEARo8eDQDILRK/95jXQy4iIzUF7fsNeul4DRo0gIqKCmxsbHDkyBGcPHkSLi4u0vPp6em4d+8enJ2dX3u/p0+fYty4cahZs6bMa5aZmQkAMDAwKHeNgYGB9PyLnJ2duXESfXJMiBERUZXQrl07REdH4+7du+jUqRNu376Nbt26wdTUFCtXroREIpF3iFQNdOzYEVlZWYiPj5d3KPSZ+OWXX+Do6IjHjx/jp59+wrVr16RFsIno7RgaGiIoKAhXrlzBkiVL0KtXL9y+fRuzZ89Gw4YNkZGRUe6aUaNG4erVq7hx4wY2bdoEW1tbeHh4vNP9BUGAWCyWOcrExsZi1apViIiIQFFREbZu3YoGDRqgRYsWEAThvWeHAcC5fX5QVFJC2z7eEP4/nv/av38/wsPDsXfvXjg4OMDLy0tmZ8yUlOe1yipaHvmiwsJC9O3bF4mJidi7dy+0tLTK9RGJRBVcWXG7iYkJ0tPTZV4zoo+NCTEiIqpSbG1tcerUKTx58gSjR49GTk4OvvvuO2hpaWH8+PHIz8+Xd4hUhc2cORMAsHTpUjlHQpVdfn4+WrRogRkzZkBPTw+RkZHcYY3oA3F1dcXMmTOxd+9epKSk4LvvvkNCQkKFS9o9PDxQt25dbNiwAdu3b8eoUaNemsh5na1bt0JZWVnmKLN79258++23cHV1hZ6eHtLS0lC3bl1cv379eRLqPfe6y32SiSsBp+Hcpj30jU0gQsWJpwYNGqBZs2bo378/Tp48CWtra3z77bfS8wUFBQAANTW1l96rqKgIffr0QXBwMA4fPozmzZvLnDc0NASACmeCZWVlVThzTE1NDYIgoLCw8I2eL9GHwIQYERFVSTo6Oti4cSPy8/OxYMECqKurY926ddDR0UGPHj2QlJQk7xCpCmrSpAm0tbVx5MgReYdClVhAQABMTEwQHh4OLy8vPHr0CE5OTvIOi6hKUlZWxpw5cwCgXO2qMiNHjsT69euRlZWF4cOHv/O9evTogStXrsgcZV5cgliW9PH394eTkxNq1KiBhLh/3/m+AHDx0H6IS4rRof/zYvo6qq/ffVtJSQnOzs64ffu2tM3IyAgAXrpRUVFREXr37o3z58/D398f7du3L9enYcOGAIAbN27ItIvFYsTFxcHR0bHcNVlZWVBVVa1wphnRx8KEGBERVWkKCgr44YcfkJmZiT179qBWrVo4evQorK2t4erqitDQUHmHSFVMmzZtkJaWhrS0NHmHQpXQN998g/bt26OoqAhbtmzB8ePHK9wRj4jeXmpqaoXtsbGxAAALC4sKzw8fPhw9evTA9OnTYWlp+c73NzQ0hKurq/RwdnbGhQsXMGHCBMyePbvCa0QiEUaPHo3mjRzwbvPSnju33w8GJmZo4tEOIgCG6iqvvaawsBBhYWGoU6eOtM3a2hrq6uoVLv0vmxkWEBCA/fv3o3PnzhWO27x5c5ibm2PLli0y7fv27UNeXh769u1b7pp79+7BwcHhtTETfUj87UtERNXGgAEDMGDAAERGRmL8+PEIDw9Hy5YtYW1tjQULFmDYsGHyDpGqgKlTp+Lo0aNYtmwZfv31V3mHQ5XEgwcP4O7ujoSEBNSuXRtBQUEv/XBORO+mc+fOqFGjBnr06AF7e3tIJBJcu3YNK1asgJaWlszSwBdZWFjA39//ve+fkpICPz8/nDp1CtHR0UhPT5fW8VJWVoaioiJKS0sBPE+E2djY4NixY7C3t8eTwhI8Snxe4yz94QPE37gGAHiUnAgACD15FABgbFkTdRo2lrnv7euRSL5zC32/ngRFRUUIAKx1NWT6tGzZEj179kT9+vWhq6uLhIQErF+/HvHx8Th48KC0n4qKCtzc3BAWFlbu+fXv3x8nTpzADz/8AENDQ5k+Ojo60oSWoqIili1bBh8fH3z99dcYPHgw7ty5gxkzZqBjx47o0qWLzLgSiQSXL1+WbjBA9MkIRERE1VRqaqrQt29fQUlJSQAg6OnpCb6+vkJJSYm8Q6PPnLq6umBlZSXvMKiS2Lhxo/TfmXHjxsk7HKIqa/fu3cKQIUOEunXrClpaWoKysrJgZWUl+Pj4CP/++6+0X5s2bYQGDRq8cqwrV64IAITNmzdXeN7Hx0dQU1MThg8fLtSrV09QVVUVAEgPAwMDwd3dXZg7d64QFxcnCIIgeHt7S8936dJFyM7OlhnzQuJj4UBcijB+0W8yY714ePYeKOyPS5E5OgwYKohEImHtmVDhQFyKcCHxcbl4p06dKjRu3FjQ1dUVlJSUBDMzM6FPnz5CSEhIub6bNm0SFBUVhZSUFJn2l8UEQGjTpk25cXbu3Ck0atRIUFFREczMzIRJkyYJT58+Ldfv3LlzAgAhIiLiZf87iD4KkSC8Z/U+IiKiz1xxcTFmz56NDRs24NmzZ1BRUcGAAQOwatWqCgu/Er1O+/btERAQgCdPnnDHwGpMLBaje/fuOHXqFDQ1NXHkyBG0bdtW3mER0Tu4desW/Pz8cO7cOfz7778yNbbU1NRQq1YtuLm5oU+fPvDy8qpwKfS6deswfvx4fP/99/j555+hqKgocz6vWIyzCY8heY9P6AoioEMtY2ipvPtisMLCQlhZWWHq1KnSzWI+Jh8fH9y7dw8hISEf/V5EL2JCjIiI6P9JJBJs2LAB8+fPR2pqKkQiEdq0aYN169ahfv368g6PPiOHDx9Gr1698NNPP3HnwGoqMjISHTp0wJMnT+Dm5oazZ89CQ0Pj9RcSkdzl5eXhwIEDOHLkCK5evYqHDx+ipKQEwPOljqampmjUqBE6d+6MQYMGvfHyZ7FYjLt378Le3v6lfZJzC3AlNfudY29qroeaOurvfH2Z9evXY+7cubh37x40NTXfe7yXiY+PR/369REQEIDWrVt/tPsQVYQJMSIiogqcO3cOkydPlu5IVb9+faxYsQJeXl5yjow+BxKJRDpj4MXdu6h68PX1xcKFCyESibBs2TJMnTpV3iER0UsIgoArV65g9+7duHjxIm7fvo2nT59Kz2tqaqJu3bpwd3fHgAED0KpVKygofNy96ZJzCxCRlg1BeL4e8XVEAEQiwMXswyTDAKC0tBTLli1D9+7dpbtGfgznz5/HnTt38NVXX320exC9DBNiREREr3Dnzh2MGzcOAQEBkEgkMDU1xezZszFx4sSP/oaYPm8tW7ZEWFgY8vPzoaamJu9w6BPIzs6Gh4cHbty4AVNTU1y8eBH16tWTd1hE9IKMjAzs2rULJ06cwLVr15CWlgaJRALgeTF4S0tLODs7o1u3bujfv7/clr3nFYsRkZaNzIISiFBxYqys3UhdBc5muu+1TJKoOmJCjD5LgiBAJHqfjYmJiN5Obm4uvv32W/j5+aGoqAgaGhoYPXo0li1bxmQHVWjr1q0YMWIEli9fzhlC1cDhw4fh7e2NwsJCDBw4EH5+fkyaE8mZRCLBmTNnsH//foSGhiI+Ph4FBQXS83p6erC3t0fbtm0xaNAgNGrUSI7RVuxJYQkSc/KRWVCM3CIxBDxPhOmoKsFQXQXWuhrQV1OWd5hEnyUmxOizwF8ERFRZSCQSLFq0CL/++iuePHkCRUVFdO/eHWvWrEGNGjXkHR5VIhKJBCoqKnBwcEB0dLS8w6GPRCKRYNiwYfDz84Oqqip27NiBfv36yTssomopMTERO3fuxJkzZ3Dz5k1kZGSg7OOuiooKrKys0KxZM/Tq1Qs9e/b8LL/Q4sQAog+HCTGq1N5mqrChujJczPQ4VZiIPpndu3fj+++/x7179wAArq6uWLNmDZo3by7nyKiyaNKkCaKjo1FUVFThjmP0eYuPj4e7uztSU1PRoEEDBAYGcmdaok+ksLAQhw8fxuHDhxEeHo6kpCQUFxcDeF743sjICI6OjujYsSMGDx6MWrVqyTdgIqp0mBCjSqsyFJMkInoTV65cwYQJE3D58mUAQK1atbBo0SIMHjxYzpGRvK1evRqTJk3CX3/9hS+//FLe4dAH9Pvvv2Pq1KmQSCSYOXMmFi9eLO+QiKq06Oho7Nq1C+fPn0dcXByys7Ol59TV1WFra4uWLVuib9++6NixI5csE9FrMSFGlVJl2W6YiOhtpKSkYMKECTh8+DBKS0uhp6eHyZMn44cffuDsoGqquLgYampqcHV1lSZM6fNWWFiIjh07Ijg4GLq6ujhz5gyaNm0q77CIqpTc3Fzs2bMHx44dQ0REBFJSUlBaWgoAUFBQgJmZGZycnODl5YVBgwbByMhIzhET0eeIafNqYOfOnVi5cmWF50Qi0UuPESNGICEh4ZV9XjwSEhJw4cIFiEQi7Nu3r8L7TZgw4bVr3suWSQLAzpVL8XVbVwxoUBM+Te0BAD/59EM/e4sKj2/aNQMARKRlI69YjLt378LHxwdWVlbSb46mTJmCzMzMV8YwbNgwiEQidO/evcLz8fHxUFVVRWhoqLQtJiYG48aNg5ubGzQ1NSESiXDhwoVy16ampuLHH3+Em5sbjIyMoKOjAxcXF/z555/SX/RlNm3aBEtLSzx79uyV8RJR5WBhYYEDBw4gLy8PkydPRklJCebOnQtNTU0MHz5c5ttsqh5UVFRQr149REVFSXcxo89XcHAwjI2NERwcjA4dOiA9PZ3JMKL3JJFIEBwcjEmTJqFJkybQ0tKCrq4uxowZA39/f2RnZ8PJyQlTpkxBeHg4xGIxHj58iGPHjmHChAlMhhHRO+PX1dXAzp07cfPmTUyePLnC8/37969w9ytjY2OYm5vLJH0AYNy4ccjJycGOHTtk2s3NzZGQkPDe8ZYtk7x87iT2//E7+n3zLZw92kJJWVXax7SmNSb/sqbctUoqKgAAQQDOxdzF6I6toaOjg/nz58PKygpRUVGYM2cOzp8/j4iIiAqnUh87dgz+/v7Q0dF5aYzTpk1Dx44d4ebmJm27evUq/P390aRJE7Rv3x5Hjhyp+PlFRGDbtm344osv4OvrC2VlZZw4cQJjx45FWFgY/v77b2nf4cOHY+nSpVi2bBnmzZv3+hePiCoFNTU1/Pbbb1ixYgXWr1+PBQsWYNu2bdi+fTvatm2LdevWoV69evIOkz6RoUOHwtfXFwcOHED//v3lHQ69o0mTJmH16tVQUlLChg0b8NVXX8k7JKLPUkpKCnbt2oVTp04hOjoajx49kha+V1JSQo0aNeDl5YUePXqgb9++0NLSknPERFRVMSFGMDU1RYsWLV56/r/ndHR0UFxc/Mpr3tWTwhJkFpQAAJLu3AIAdPMZDV1D2W9+VNTUYOfk8tJxBACnjh1FZmYmdu/ejfbt2wMA2rZti6KiInz//fe4fv06mjRpInNdTk4Ovv76a8yfPx+///57hWPHxsbC398fJ0+elGn38fHB8OHDAQD79u17aUKsVatWiI+Ph7Ly/3bF7NixI4qLi7F27VrMmzcPNWvWBPD8TUFZPDNnzoSGhsZLnzMRVT4KCgoYP348xo8fj1OnTmHq1KkICAiAvb09HBwc8Ntvv6FTp07yDpM+ssmTJ8PX1xdr1qxhQuwzlJaWBnd3d9y9exdWVlYICgqClZWVvMMi+iyIxWIcP34c/v7+CA0NRUJCAgoLC6XnDQwM0KpVK7Rr1w6DBg1C/fr15RgtEVU3XDJZBTx+/BhfffUVatasCVVVVRgbG6NVq1Y4e/YsPD09cezYMSQmJsosb6ysEnPyIQLwTbtm8Fu5FAAwqlUj9LO3wO7Vy99qLCWl5wknXV1dmXY9PT0AqHCb5alTp8Lc3ByTJk166bjr16+HmZkZOnbsKNP+poU79fX1ZZJhZZo1e77c88GDBzLtQ4cORW5uLnbt2vVG4xNR5dS5c2fcvHkTcXFxaNeuHWJjY9G5c2eYm5tjzZo1XE5XhWlpaaFWrVoIDw+Xdyj0lnbs2AErKyvcvXsXo0ePxv3795kMI3qFO3fuYN68efDw8IChoSGUlZXRq1cvbN68Gffv34e1tTWGDx8Of39/FBUVITMzE0FBQZg3bx6TYUT0yXGGWBXg4+ODyMhILFy4EHZ2dsjOzkZkZCQyMzOxbt06fPXVV4iPj8fBgwcrvF4QBIjF4nLtioqK75w8k0gkFY75uj0cMguKIQCYsWYTTu7cgnP7/PDjXzuhqa0NQzNzmb6lFYwvUlCQJqaadugCk9U1MHXqVKxbtw7W1taIjIzEkiVL0KNHj3K/dM+ePYtt27bhypUrUFRUfGmMx44dg4eHxwffuSYgIABKSkqws7OTaTczM4O9vT2OHTuGUaNGfdB7EtGnV69ePZw7dw7Z2dmYPHky/Pz8MHHiRMyaNQtjxozB4sWLK0zY0+dt4MCBWLZsGc6cOVPuCxWqfMRiMfr06YOjR49CQ0MDR44cQefOneUdFlGlkp+fjwMHDuDw4cO4evUqHjx4gJKS5ys9RCIRTExM0LFjR3Tu3Bne3t6oUaOGnCMmIpLFhFgVEBISgi+//BJjxoyRtvXq1Uv6s56eHlRVVV+6xHHdunVYt25dufbt27dj2LBh7xSTt7f3O12XW/Q8yVXboSEMTZ8nwGwdG0JH31CmX/KdWxjoWP4b2vb9B2PcghUAAE1tHSzadQSbZo6Do6OjtI+zszNycnIwZ84caV2uvLw8jBkzBtOmTUPjxo1fGl96ejru3bv3weuGnD59Gtu3b8e3334LQ0PDcuednZ1x9uzZD3pPIpIvPT09bNmyBRs3bsTChQvx+++/Y+XKlVi9ejV69OiBtWvXwsLCQt5h0gcybdo0LFu2DL/99hsTYpXcjRs30LZtW2RmZsLV1RXnz59nDSMiPK+Xu3v3bly4cAG3b99Gbm6u9JympiYcHBzQqlUrDBgw4KN8eUxE9KExIVYFNGvWDFu2bIGhoSE6dOgAFxeXCpfkvczAgQMxffr0cu21a9d+55iWLl2Kdu3alWv/5ZdfsGfPHuljiUQiXSYkCALEpaWvnJ1VxsyqFr5bsb5cu47B/5JJeTnZWDJ+JJRKCjFmzBhEREQgKioKkZGRACCTeJo1axaUlZXx008/vfK+KSkpAAATE5PXxvimIiMjMXDgQLRo0QKLFy+usI+JiQnS09MhFouhpMS/tkRViZKSEubMmYM5c+bAz88P33//Pfz9/eHv749mzZphzZo13MWuCjA2NoaFhQUCAwPlHQq9wvz58zFnzhyIRCIsXLgQ33//vbxDIpKLrKws7Nq1C8ePH8e1a9eQlpYm3Q1dUVERFhYWaNu2Lbp27YqBAwdKS5IQEX1O+Mm6Cti9ezcWLFiAjRs3wtfXF1paWujTpw+WLVsGMzOz115vbGwMV1fXDxpT7dq1KxzT2NhY5vHPP/8ss3uisUUN/BFw+bXjK6uqok7Dl8/kAoCDG9fifmwMSsUliIuLK3f+6dOnAIDLly9j3bp1OHDgAAoLC6WFPsuWfWZnZ0NdXR2qqqooKCgAUHH9sXcRFRWFjh07om7dujh+/DhUVVUr7KempgZBEFBYWMhvqYmqsMGDB2Pw4MEIDw/HxIkTcfnyZTRr1gw2NjZYvHjxO8++pcqhV69eWL9+vfT/K1Ueubm58PT0RFRUFIyNjXH+/Hk0aNBA3mERfRISiQTnz5/Hvn37EBISgvj4eOTn50vP6+rqwtXVFW3atIG3tzecnZ3lGC0R0YfDhFgVYGRkhJUrV2LlypVISkrC4cOHMWvWLKSnp5fbCbGy+eqrr9C9e3fp46iMZx9s7ITYGOgbG+NpViaKiorKnT979iwUFRWhoqICQRDQp0+fcn2Sk5Ohr6+P3377DZMnT4aR0fPdLrOyst47vqioKHTo0AHW1tY4ffp0ueL/L8rKyoKqqiqTYUTVRPPmzXH58mU8ePAAEyZMwNGjRzFo0CCMHTsW3333HX744QcuRfkMzZw5E+vXr8eyZcuwb98+eYdD/+/EiRPo168fCgoK0Lt3b+zbt++NZqsTfa6SkpKwc+dOnDlzBjdu3EBGRoa0zq+Kigpq1qyJZs2aoVevXujVqxfrWhJRlcWEWBVjZWWFCRMm4Ny5cwgJCQEAmZlNlY2FhYVMjRylRzm4n52PV5fefzMGJqa4GRaMuLg4rFu3Dr/99huA/xX2r1+/PgwNDZGQkICMjAyZLaDLKCgowMjICDt27MDNmzfRsGFDqKmp4e7du+8V27Vr19ChQwfUqFEDZ86cgb6+/iv737t3Dw4ODu91TyL6/NSoUQP+/v4oLCzEjBkzsGnTJvz0009YuHAhBg8ejN9//x06OjryDpPekLW1NYyMjHDu3Dl5h0J4Pitm1KhR2Lp1K1RUVODn54dBgwbJOyyiD6q4uBiHDx/G4cOHERYWhqSkJOkXxSKRCIaGhmjTpg06dOiAwYMHv1fJFCKizw0TYp+5nJwctG3bFkOGDIG9vT20tbVx5coVnDx5En379gUANGzYEAcOHMD69evh4uICBQUFmeWMjx49QlhYWLmxdXR0PnkSxlpXA/ey81/br7iwELevRVR4zs7JBQDQZchIBB89iB49emDWrFn49ddfMW/ePGRnZwMAdu7cCScnJ5lrExISEBQUhKtXr+LPP/+EgoICioqKEBkZiatXr0r7rVy5EuvXr4eBgQEsLCxQt25d1K9fH4IgoG7dutI6ZRcvXkRGRgY0NTXh5eUFALh16xY6dOgAAFi4cCHu3LmDO3fuSMe2tbWVWVoqkUhw+fJljB49+rWvCxFVTWpqali1ahVWrlyJtWvXYtGiRdiyZQu2bduGdu3aYd26dahbt668w6Q34OXlhe3btyM2Nrbcbsf06SQmJsLd3R3Jycmws7NDUFDQB60PSiQvN2/ehJ+fH86fP4/Y2Fjp+14AUFdXh62tLVq2bIm+ffuiY8eOrE1LRNWbQJ+1wsJC4ZtvvhEaNWok6OjoCOrq6kK9evWEOXPmCM+ePRMEQRCysrKE/v37C3p6eoJIJBJe/N8O4KVHq1atKrxnmzZthAYNGlR47vz58wIAYe/evRWeHz9+vPC6P3YXEh8LB+JShIHjpwgAhM2hN4T9cSnSo0FTt1fGvedmknAgLkW4kPhYiIyMFPr06SPUqFFDUFVVFWrVqiXUq1dPsLOzE8Ri8SvjsLa2Frp16yZ9/PjxY8Hf31/o1q2bIBKJBDs7O8HQ0FBQUlJ6ZTwABF1dXeHQoUNCVlaWsHnz5lf23bx5s0wc586dEwAIERERr4yXiKqX48ePC/Xr15f+2+Ho6CicPXtW3mHRa9y8eVMAIAwfPlzeoVRb69atExQVFQUAwnfffSfvcIjeWU5OjrBp0yahT58+grW1tcx7UgUFBcHc3Fzw8vISVq1aJTx69Eje4RIRVToiQRA+xOo0og8mr1iMswmPIXmPP5kKIqBDLWNoqXz4b70KCwthZWWFqVOnYubMmQCA/Px8hIWFITw8HNevX8fdu3fx8OFDPHnypFz9MkVFRWhpacHU1BS1atWCg4MDXF1d0bp1a1hbW5e7n4+PD+7duyddAktE9KLY2FiMHz8eFy5cgCAIMDc3h6+vL77++mvWGauk9PT0oKKigvT0dHmHUq0UFxejS5cuOH/+PLS1tXHy5Em0bNlS3mERvRGJRILQ0FDs2bMHQUFBuHPnDvLy8qTntbS0YGdnBw8PDwwcOBDNmzfn7wAiotdgQowqpeTcAlxJzX7n65ua66GmjvqHC+g/1q9fj7lz5+LevXvQ1NR8Zd/S0lJcu3YNly5dQlRUFG7duoXk5GRkZmaioKAAL/4VFIlE0NDQgJGREaysrGBhYYF9+/Zh06ZN8PHx4RsbInqprKwsTJ48Gbt370ZxcTE0NTXx9ddfY9GiRS/dwZbko2/fvjh48CASExNhZWUl73CqhdDQUHTp0gW5ublo06YNTp48yULhVKmlpaVh165dOHnyJK5fv4709HRIJBIAgJKSEiwtLeHq6ooePXqgX79+3HiJiOgdMCFGlVZybgEi0rIhCHijIvsiACIR4GL2cZNhwPMk17Jly9C9e3c0bNjwvcaKj49HUFAQIiIiEBsbi4SEBDx+/Bh5eXnSNz5l1NTUYGBgAEtLS9jZ2aFRo0Zo2bIlmjVrBhUVlfeKg4iqBrFYjJ9//hmrV69GdnY2lJSU0LNnT6xduxZmZmbyDo8AhIWFwc3NDRMmTMDq1avlHU6VN336dKxYsQIKCgpYuXIlJkyYIO+QiGSIxWKcPn0aBw4cwKVLl3D//n2ZzZ709fVRv359tGvXDoMGDUKDBg3kGC0RUdXBhBhVannFYkSkZSOzoAQiVJwYK2s3UleBs5nuR1kmKS/p6enSIv83b97EvXv3kJaWhtzcXIjFYpm+ysrK0NPTg7m5OWxtbeHo6IjmzZujdevW0NXVldMzICJ5+ueff/Djjz8iMTERIpEIzZs3x9q1a+Hs7Czv0Ko9TU1N6Ovr48GDB/IOpcrKyMiAu7s74uLiYGlpiaCgINjY2Mg7LCLEx8djx44dOHfuHGJiYpCVlSVdMaCqqgpra2u0aNECvXv3Rrdu3filJxHRR8KEGH0WnhSWIDEnH5kFxcgtEkPA80SYjqoSDNVVYK2rAX01ZXmH+Uk9e/YMoaGhCAsLQ3R0NO7evYuUlBRkZ2dXWLdMW1sbpqamsLGxkdYtc3d3R40aNeT0DIjoUwkNDcXEiRMREfF8d97atWtj6dKl6N+/v5wjq766dOmCU6dOISMjA4aGhvIOp8rZs2cPfHx8UFxcDB8fH2zZsoVlB0guCgsLcfDgQRw+fBiXL19GcnIySkpKADwvlWFsbIyGDRuiU6dOGDRoEJdRExF9QkyI0WdJEASIRCJ5h1FpicViREVFISQkBNeuXcPt27dl6pa9SCQSQVNTE8bGxrCysoK9vT2cnZ3RqlUr1K9fnx8giKqQpKQkjB8/HidOnEBpaSkMDAwwZcoUzJ49m3/XP7FTp06hS5cumDVrFhYvXizvcKoMiUSCgQMHYv/+/VBTU8PevXvRvXt3eYdF1UhkZCR27dqFCxcu4NatW8jNzZWe09DQgK2tLVq3bo3+/fvD09OT//YSEckRE2JE1YxEIsHdu3cRFBSEyMhIxMbGIjExEY8fP8azZ8/K1S1TV1eHgYEBatSoATs7OzRu3BgtW7aEq6srlJWr16w8oqoiPz8fM2fOxKZNm1BQUABVVVUMGzYMv/76K3R0dOQdXrWhpqYGS0tLxMfHyzuUKiE2Nhaenp5IT0+Hk5MTzp8/Dz09PXmHRVVYVlYW9uzZgxMnTiAyMhKpqakoLS0F8Hx2vrm5OZydneHl5YWBAwfCwMBAzhETEdGLmBAjIhlpaWkIDAzE1atXERMTg3v37uHRo0d4+vRpubplKioq0NPTg4WFBWxtbdGwYUM0b94crVq1gra2tpyeARG9KYlEglWrVmHx4sVIT0+HgoIC2rdvj/Xr18PW1lbe4VV5Hh4eCA4ORm5uLneIe09Lly7F999/D0EQ8NNPP2Hu3LnyDomqGIlEggsXLmDfvn0IDg5GfHw88vPzped1dHRQr149eHp6YuDAgXB1dZVjtERE9CaYECOiN5aXl4eQkBCEh4cjOjoa8fHx0rplxcXFMn0VFRWho6MjrVvWoEEDNG3aFB4eHtzpjqgSOnr0KKZPn464uDgAQMOGDbFy5Uq0a9dOzpFVXbt378agQYOwaNEizJ49W97hfJby8vLQvn17XL58GQYGBjh37hycnJzkHRZVAcnJyfDz88Pp06dx48YNPH78WFr4XllZGTVr1kSzZs3QvXt39OnTBxoaGnKOmIiI3hYTYkT0QRQXFyMiIgKhoaHSumUPHjxAVlZWubplCgoK0rpl1tbWsLe3h4uLC1q3bo26deuyngaRHMXExGD8+PEIDAyEIAiwsLDAnDlz8NVXX8k7tCpHIpFAVVUVdevWxb///ivvcD47586dQ8+ePZGfn49u3brB398fSkpVZ6dp+nSKi4tx7Ngx+Pv7IywsDImJiTIbFBkaGqJBgwZo3749Bg8ejLp168oxWiIi+lCYECOij04ikeDWrVsICgpCVFQU4uLiZOqW/fefIXV1dRgaGqJmzZqws7ODk5MTWrZsCWdnZ37YIfpEsrKyMHHiROzduxclJSXQ0tLCN998g4ULF0JFRUXe4VUZrq6uiIqKQkFBAV/XNyQIAr755hv8+eefUFZWxsaNG/HFF1/IOyz6jMTExGDXrl0ICAhAbGwsnjx5Ij2npqYGGxsbtGzZEn379kWnTp343oOIqIpiQoyI5O7BgwcICQnB5cuX8e+//yIhIQFpaWl4+vSptDhtGRUVFejr68PCwgJ16tRBo0aNpHXLuFyB6MMTi8WYN28eVq9ejZycHCgpKaF3795Yu3YtTExM5B3eZ2/Dhg345ptvsHbtWowbN07e4VR6Dx48gLu7OxISEmBra4vAwEBYWFjIOyyqxPLy8rB//34cOXIEV69excOHD6U1URUUFGBiYoLGjRujS5cuGDRoEMs6EBFVI0yIEVGllpubi+DgYFy+fFlatyw1NRXZ2dkoKSmR6aukpAQdHR2YmZlJ65Y1a9YM7u7u/OBO9AFs27YNvr6+SEpKgkgkgpubG9auXcuaTe9BLBZDVVUVTk5OiIiIkHc4ldqmTZvwzTffQCwWY9y4cVi7dq28Q6JKRiKRIDw8HHv27EFgYCDu3LmDp0+fSs9raWmhbt26cHd3x8CBA+Hm5sYyDURE1RgTYkT02SouLsaVK1dw6dIlXL9+HXfu3JHWLSssLJTpq6CgAC0tLWndMgcHBzg7O8PDw4O76RG9pZCQEEyaNAmRkZEAgDp16mDZsmXo06ePnCP7PDVs2BCxsbEoLi7mh/MKFBcXo0ePHjh9+jQ0NTVx9OhReHp6yjssqgTS09Oxa9cunDx5EteuXcOjR48gkUgAPP+SzMLCAi4uLujWrRsGDBgAHR0dOUdMRESVCRNiRFQlSSQS/PvvvwgJCUFERARu3bqFpKQkPH78GPn5+TJ1y0QikUzdsnr16qFJkyZwc3ODk5MTa4cQvURiYiLGjx+PkydPorS0FIaGhpg2bRpmzJjBxM5b+OWXXzBjxgz8888/GDp0qLzDqVSuXr2Kjh07Ijs7Gy1btsSZM2e4PL6aEovFOHPmDA4cOIBLly7h/v37Mpv26OnpoX79+mjbti0GDx4MR0dHOUZLRESfAybEiKhaSkpKQlBQECIiIhATE4OEhASkp6dXWLdMVVUV+vr6sLS0lNYta9GiBdzc3KCuri6nZ0BUeeTn52PatGnYsmULCgoKoKamBh8fH/z666/Q0tKSd3iVXn5+PrS0tODm5oaQkBB5h1Np/PDDD1i8eDFEIhGWLVuGqVOnyjsk+oTu3bsHPz8/nDlzBjExMcjMzJR+maWqqgorKyu0aNECPXv2RM+ePbkpBRERvTUmxIiI/uPJkyfSumU3b96U1i3LycmpsG6Zrq4uzMzMULt2bTg6OkrrlhkaGsrpGRDJh0QiwW+//YalS5fi8ePHUFBQQMeOHbF+/XrY2NjIO7xKrW7dukhKSkJRUZG8Q5G7rKwseHp64saNGzAzM8PFixdhZ2cn77DoIyosLMShQ4dw6NAhXL58GcnJySguLgbwfBa3kZERHB0d0alTJwwePBjW1tZyjpiIiKoCJsSIiN5CYWEhLl++jLCwMFy7dg137tzBw4cP8eTJk5fWLTM1NZXWLXNxcYG7uzuTA1TlHT58GNOnT8ft27cBAI0bN8aqVavg4eEh58gqJ19fXyxYsABHjhxB9+7d5R2O3Bw6dAje3t4oKiqCt7c3du7cyeW3VdC1a9fg5+eHCxcu4NatW8jJyZGe09DQgK2tLVq1aoX+/fujbdu2/DNAREQfBRNiREQfiEQiQXR0NC5duoSoqCjExcUhOTkZGRkZFdYt09DQgKGhIaysrKR1y1q1aoVGjRrxzT9VGTdv3sS4ceMQHBwMQRBgaWmJefPmYfTo0fIOrVLJzs6Gvr4+OnTogDNnzsg7nE9OIpFg6NCh2LVrF1RVVbFz50707dtX3mHRB5CdnY09e/bg+PHjiIyMREpKirQ0gaKiIszMzODk5ISuXbti4MCBMDIyknPERERUXTAhRkT0iSQkJCAoKAhXr15FbGysTN2ysl2xyqipqUnrltWtWxeNGjWCm5sbmjdvDjU1NTk9A6J3l5GRgYkTJ2L//v0oKSmBtrY2xo4di/nz57P2z/+zsrKSJtCrkzt37qBNmzZITU1FgwYNEBgYCAMDA3mHRe9AIpEgMDAQe/fuRXBwMOLj4/Hs2TPpeR0dHdjZ2cHT0xMDBw5E06ZN5RgtERFVd0yIERFVAhkZGQgJCZHWLbt37560bplYLJbpq6ysDF1dXZibm6N27dpo2LAhmjZtCg8PD+jp6cnnCRC9IbFYjJ9++glr165Fbm4ulJWV0bdvX6xZs6bazwz59ttvsWrVKgQFBaF169byDueTWLlyJaZOnQpBEDBz5kwsXrxY3iHRW0hJSYGfnx9OnTqF6OhopKenS2dDKysro0aNGnB1dUXPnj3Rt29f7hBKRESVChNiRESVXH5+PsLCwhAeHo7r16/j7t270rpl/y3AraioKK1bVqtWLTg4OMDV1RXu7u6wsrKS0zMgqtjmzZsxZ84cJCcnQyQSoWXLlli3bh0aNWok79DkIiUlBZaWlujVqxf8/f3lHc5HlZ+fj44dO+LSpUvQ09PD6dOnOVuokhOLxTh27BgOHjyIsLAwJCQkyPwOMjAwQIMGDdC+fXsMGjQI9erVk2O0REREr8eEGBHRZ6y0tBTXr19HSEgIoqKicOvWLSQnJyMzMxMFBQUV1i0zMjKClZUV7O3t0aRJE7i7u8PBwYF1y0huAgMD8e233+LatWsAnu+4+Msvv6BXr17yDUwOzMzMUFBQIFNkvKoJDAxEt27dkJeXh06dOuHIkSNcNlsJxcXFwc/PD+fOnUNsbCyysrKk59TU1FCrVi24ubmhT58+8PLygpKSkhyjJSIientMiBERVWHx8fEIDg6W1i1LTExEeno68vLyKqxbZmBgAEtLS9jZ2aFx48Zwc3NDs2bN+GGVPonExESMHTsWp06dgkQigZGREWbMmIGpU6dWm4Ttl19+iU2bNuH69etVcqbcxIkTsWbNGigpKWH9+vX48ssv5R0SAcjLy8OBAwdw5MgRXL16FQ8ePJAu1xeJRDA1NUWjRo3QuXNnDBo0CBYWFnKOmIiI6P0xIUZEVE2lp6dLi/yX1S1LS0tDbm5uhXXL9PT0YG5uDltbWzRs2BDNmzdH69atoaOjI6dnQFVVXl4epk2bhq1bt6KwsBBqamoYPnw4li9fDi0tLXmH91HduXMHdnZ2GDJkCHbs2CHvcD6Y1NRUuLu7Iz4+HtbW1ggMDOQybjmRSCS4cuUKdu/ejcDAQNy+fRtPnz6VntfU1ETdunXh7u6OAQMGoFWrVtUmIU1ERNULE2JERFTOs2fPEBoaKlO3LCUlBdnZ2RXWLdPW1oapqSlsbGzg4OCApk2bwt3dHZaWlnJ6BlQVSCQSrFixAsuWLUNGRgYUFBTQuXNnrF+/HtbW1vIO76MxMDCAgoICMjIy5B3KB7F9+3aMHj0aJSUl+PLLL7FhwwYmWD6hjIwM7Nq1CydOnMC1a9eQlpYmnSGspKQECwsLODs7o1u3bujfvz83ZyEiomqDCTEiInorYrEYUVFRCAkJwbVr13D79m2ZumUvEolE0NTUhLGxsbRumbOzM1q3bg17e3t+KKY3dujQIUyfPh137twBADg5OWH16tVVcjfGQYMGYffu3bh37x5sbGzkHc47E4vF6N27N44dOwYNDQ34+/ujY8eO8g6rSpNIJDhz5gz279+PS5cu4d69ezL/Luvp6cHe3h5t27bFoEGDquSyXCIiojfFhBgREX0wEokEd+/eRVBQECIjI6V1yx4/foxnz56Vq1umrq4OAwMD1KhRQ1q3rGXLlmjatCkLNFOFoqOjMW7cOFy6dAmCIKBmzZqYN28eRo4cKe/QPpjIyEi4uLhgzJgx+PPPP+UdzjuJjo5Gu3btkJmZiaZNmyIgIKDKL3eVh8TEROzcuRNnzpzBzZs3kZGRId1MRUVFBdbW1mjWrBl69eqFHj16QE1NTc4RExERVR5MiBER0SeTlpaGoKAgXLlyBTExMbh//z7S0tLw9OnTcnXLVFRUoKenBwsLi3J1y/jBmtLT0zFx4kQcOHAAYrEYOjo6GD9+PH7++ecqkUzV1taGlpYWUlNT5R3KW5s3bx7mzZsHkUiEBQsWYPbs2fIOqUooLCzE4cOHcejQIVy+fBlJSUkoLi4G8Hw2rpGRERwdHdGxY0cMGTKkSi8rJiIi+hCYECMiokohLy8PISEhCA8PR3R0NOLj46V1y8o+9JVRVFSEjo4OTE1NUbt2bTRo0ACurq7w8PCAmZmZnJ4ByUNxcTF8fX2xfv16PH36FMrKyujfvz9WrVoFIyMjeYf3zrp3745jx44hNTX1s/kznZubC09PT0RFRcHY2Bjnz59HgwYN5B3WZys6Ohp+fn64cOEC4uLikJ2dLT2nrq4OW1tbtGrVCn379kWHDh24BJ2IiOgtMSFGRESVXnFxMSIiIhAaGorr16/j1q1bePDgAbKyssrVLVNQUJDWLbO2tkb9+vWldcvq1q3LD41V2MaNGzF37lw8fPgQIpEIrVu3xrp16+Do6Cjv0N7a+fPn0a5dO0yZMgUrVqyQdzivdfz4cfTv3x8FBQXo06cP9u3bx79rbyE3Nxd79uzBsWPHEBERgZSUFJSWlgJ4/m+amZkZnJyc4OXlhUGDBn3WyV4iIqLKggkxIiL6rEkkEty6dQvBwcGIjIxEXFycTN2y//6aU1dXh6GhIWrWrAk7Ozs4OTmhZcuWcHZ2rhJL7Qi4cOECJk+ejOvXrwMA6tWrh+XLl6N79+5yjuztqKurw9TUFAkJCfIO5aUkEglGjhyJbdu2QUVFBdu2bYO3t7e8w6rUJBIJgoODsXfvXgQFBeHu3bt49uyZ9Ly2tjbs7OzQpk0bDBo0CE2bNpVjtERERFUXE2JERFSlPXz4EMHBwdK6ZQkJCdK6ZWUzMMqoqKhAX18fFhYWqFOnDho1aoTmzZujVatW0NDQkNMzoHd1//59jB07FmfOnIFEIoGxsTFmzpyJ77777rOYvdSuXTucP38eOTk50NHRkXc45SQkJMDd3R0PHjxAvXr1EBgYCBMTE3mHVemkpKRg165dOHXqFKKjo/Ho0SNpol5ZWRk1atSAi4sLevTogb59+7JGIhER0SfChBgREVVbubm5CA4OxuXLl3Hjxg3cvXsXqampyM7ORklJiUxfJSUl6OjowMzMDDY2NnB0dETTpk3h7u7OJEAll5eXhylTpmD79u0oLCyEuro6RowYgeXLl1fqROehQ4fQu3dvzJ07F3PmzJF3ODLWrVuHSZMmQSKR4LvvvvsslnV+CmKxGMePH8fBgwcRFhaGhIQEFBYWSs8bGBjAwcEB7du3h7e3N+rXry/HaImIiKo3JsSIiIgqUFxcjCtXruDSpUu4fv067ty5I61b9uIHXOB5jR8tLS1p3TIHBwe4uLjA3d0dtra2cnoG9F8SiQTLli3D8uXLkZmZCUVFRXTp0gXr1q2DlZWVvMMrRyKRQE1NDTY2Nrh165a8wwHwfKdDLy8vXLhwATo6Ojh58iTc3NzkHZbc3L59Gzt37kRAQABiYmKQlZUlPaempgZra2u4ubmhd+/e8PLygoqKihyjJSIiohcxIUZERPSWJBIJ/v33X4SEhCAiIgK3bt1CUlISHj9+jPz8fJm6ZSKRSKZuWb169dCkSRO4ubnBycmJdcvkZP/+/Zg1axbu3r0LAHB2dsbq1avRsmVLOUcmy83NDeHh4cjPz4eamppcYwkNDUWXLl2ku0meOHFC7jF9Svn5+Thw4AAOHz6Mq1ev4sGDB9KZpCKRCCYmJmjUqBE6d+4Mb29v1KhRQ84RExER0aswIUZERPSBJSUlISgoCBEREdK6Zenp6RXWLVNVVYW+vj4sLS2ldctatGgBNzc3qKury+kZVB+RkZGYMGECwsLCIAgCrKysMH/+fHzxxRfyDg0AsHXrVowYMQK//vorvvvuO7nFMXXqVPz2229QUFDAqlWrMG7cOLnF8qlcuXIFu3fvxsWLF3H79m3k5uZKz2lqaqJOnTpwd3dH//794e7u/lnUpSMiIqL/YUKMiIjoE8rOzkZwcDDCw8Nx8+ZNxMfHIzU1FTk5ORXWLdPV1YWZmRlq164tU7fMyMhITs+ganr06BEmTJgAf39/iMVi6OrqYuLEiZgzZ45cZ/FJJBKoqKigQYMG0l0zP6X09HR4eHjg1q1bsLS0RFBQEGxsbD55HB9bRkYG9uzZg+PHj+PatWtITU2FRCIBACgqKsLCwgLOzs7o2rUrBg4cCD09PfkGTERERO+NCTEiIqJKorCwEJcvX0ZYWBiuXbuGO3fu4OHDh3jy5MlL65aZmpqWq1tWFRMWn0pxcTF++OEH/PHHH8jLy4OKigr69++P1atXw8DAQC4xOTk54caNGygqKvqkybndu3fjiy++QHFxMb744gts3ry5SsyCkkgkOHfuHPbv34+QkBDcu3cP+fn50vO6urqwt7dH27Zt4e3tDScnJ/kFS0RERB8NE2JERESfAYlEgujoaFy6dAlRUVGIi4tDcnIyMjIyKqxbpqGhAUNDQ1hZWUnrlrVq1QqNGjWqEkmNT+HPP//EvHnzkJKSApFIBA8PD6xduxYNGjT4pHH8/vvvmDx5MkaNGoXHjx/jxo0biIqK+mizlCQSCfr16wd/f3+oq6tj37596Nq160e516eQlJSEnTt34syZM7hx4wYyMjKkf19UVFRQs2ZNNG/eHD179kSvXr2qVV00IiKi6owJMSIioiogISEBQUFBuHr1KmJjY2XqlpUt/SqjqqoKAwMDWFhYwM7ODo0aNYKbmxuaN2/OZEAFAgICMHnyZNy4cQMAYG9vjxUrVnz0JFFhYSG2bNmCPXv24Pz58wCeJzsFQcCTJ08+SkIsJiYGbdu2xePHj9GkSRPpbpKfi+LiYhw+fBj+/v64fPkyEhMTUVxcDOD5a2doaAhHR0d06NABQ4YM4WxKIiKiaowJMSIioiouIyMDISEhuHz5Mm7evIl79+5J65aJxWKZvsrKytDV1YW5uTlq166Nhg0bSuuW6evry+kZVA7x8fEYO3Yszp07B4lEAhMTE8yePRuTJk36KLPuNm/ejFGjRkmTYGUMDAyQmZn5we+3ePFi/PjjjxAEAXPmzMGcOXM++D0+tJs3b8LPzw/nz59HbGwssrOzpefU1dVRu3ZttGzZEn379kXHjh2hqKgov2CJiIioUmFCjIiIqBrLz89HWFgYwsPDcf36ddy9e1dat6yoqEimr6KiorRuWa1ateDg4ABXV1e4u7vDyspKTs/g08vNzcWUKVPwzz//oKioCBoaGhg1ahSWLl0KDQ2ND3afoqIi9OjRQ5qAK9O6dWsEBQV9sPvk5eWhXbt2uHLlCgwMDBAQEIDGjRt/sPE/lNzcXOzduxfHjh1DREQEUlJSpAldBQUFmJqawsnJCV5eXvD29oaJiYmcIyYiIqLKjAkxIiIiqpBEIsG1a9cQEhKCqKgo3L59G0lJScjMzERBQUGFdcuMjIxgZWUFe3t7NGnSBK1bt0aDBg2qZN0yiUSCxYsX49dff0VWVhYUFRXRtWtXrFu3DjVq1Pgg9ygoKEC3bt1w8eJFSCQSiEQijBkzBhs2bHjrscRiMUpKSqCuri5tO3PmDHr37o38/Hx069YN/v7+ct1Vs4xEIsGlS5ewd+9eBAYG4u7du8jLy5Oe19bWhp2dHTw8PODt7Y2mTZtWyT9jRERUOQmCAJFIJO8w6D0xIUZERETvJD4+HsHBwdK6ZYmJiUhPT0deXl65umVqamowMDCApaUl7Ozs0LhxY7i5uaFZs2ZQUVGR0zP4cPbs2YPZs2fj3r17AAAXFxesXbsWzZs3f++x8/Pz0aVLF+mssF9//RXffffdW48zcOBAXL58GdevX4e2tja+/vprbNy4EcrKyti0aRN8fHzeO9Z3lZaWhl27duHkyZO4fv060tPTpX+GlJSUUKNGDbi6uqJ79+7o168ftLS05BYrERFVP08KS5CYk4/MgmLkFokhABAB0FFVgqG6Cqx1NaCvpizvMOktMSFGREREH1x6erq0yH9Z3bK0tDTk5uZWWLdMT08P5ubmsLW1haOjI1q0aIHWrVt/VgXdASAiIgITJkxAeHg4BEGAtbU1Fi5ciKFDh77XuHl5eahXrx5SUlKwdOlSzJgx462uv3LlCpo1awYA6NGjB65fv46kpCTY2toiMDAQFhYW7xXf2xCLxTh58iQOHDiAsLAw3L9/H4WFhdLz+vr6cHBwQLt27TB48GDUr1//k8VGRET0orxiMSLSspFZUAIRgIqSJ2XthurKcDHTg5aK/Gda05thQoyIiIg+qWfPniE0NBRhYWGIjo7G3bt3kZKSguzs7Arrlmlra8PU1BQ2NjYydcs+1LLEjyEtLQ3jx4/H4cOHIRaLoaenh0mTJsHX17fcksQjR46gWbNmMDU1feWYSUlJsLGxwdy5c+Hr6wvgzZdsdOjQARcuXEBpaam0bcKECVi9evU7PLu3c+fOHfj5+eHcuXOIiYmR2RBAVVUVtWrVQvPmzdGnTx907dq1SswYJCKiz19ybgEi0rIhCBUnwv5LBEAkAlzM9FBTR/21/Un+mBAjIiKiSkMsFiMqKgohISG4du0abt++jeTkZGndsheJRCJoamrC2NhYWrfM2dkZrVq1Qv369StFTamioiJ8//332LBhA549ewYVFRV4e3tj1apV0NPTw7Vr19CkSRM0atQIoaGhry3KP/WHn2Dr6oZ6TVzfeMnGxYsX4enpKdOmoqKCGzduwM7O7oM+3/z8fBw8eBBHjhzBlStXkJycjJKSEgDP/38ZGxujUaNG6NSpEwYPHlypk5pERFR9JecW4Epq9jtf39ScSbHPARNiRERE9FmQSCS4e/cugoKCEBkZKa1b9vjxYzx79qxc3TJ1dXUYGBigRo0aMnXLmjZtCmXlT1vnQyKRYMOGDZg/fz5SU1MhEonQpk0baGlp4cSJExAEAQMHDsTOnTsrnPH1rks2BEFAo0aNcPPmzXL9XV1dceXKlfd6XlevXsXu3btx8eJF3Lp1C7m5udJzmpqasLW1RevWrTFgwAB4eHhUiiQlERF9fFu2bMHIkSNx5coVuLq6ljufkJAAGxsbmTZtbW3Y2Nhg5MiRmDhxIhQVFaXnPD09cfHiReljVVVV1K5dG0OGDMGMGTM+6OzivGIxziY8xr24f3F0y5+IuRyKJ4/ToaikCPNatdG6ay+07z8E2nr6Lx1DQQR0qGVc4fLJvn374uDBgxg/fjzWrFnz0jG2bduGqVOn4t69e9DW1gYAHD16FHv27EFUVBTi4uIgFovxqpTOzZs3MW/ePFy4cAG5ubkwNzeXbgBUxsfHB0+fPoW/v/8bvDpVCxNiREREVCWkpaUhKCgIV65cQUxMDO7fv4+0tDQ8ffq0XN0yFRUVad2yOnXqoGHDhmjWrBlat24tfdP5sZw9exbfffddhUmqJUuWYObMmTJt77NkY/GsqVi/fn25PpaWlmjZsiXWrl0LY2PjN4o7KysLu3fvxvHjxxEVFYW0tDTpEkxFRUWYm5vD2dkZXbt2xYABA2BgYPBG4xIRUdXzpgmxiRMnYsiQIQCA7OxsHD58GOvXr8eUKVOwYsUKaX9PT08kJydjx44dAIDHjx9j48aNOHz4MMaMGYM///zzg8V+MSkDu7duwZ8/z4aFjS26DB6OGrZ2KBWXIP5mNM7s3YFa9g6Yuebvl44hAmCgrow2VkYy7enp6ahRowZKSkqgp6eH1NRUqKmplbs+Pz8fdnZ2mDx5MqZNmyZtHz16NIKCgtCkSRPEx8cjIiLipQmx8+fPo1u3bnB3d8dXX30FIyMjJCUlISoqCr/++qu0X3x8POzt7XHq1Cm0a9fuLV+tzxsTYkRERFTl5eXlISQkBOHh4YiOjkZ8fLy0bllxcbFMX0VFRejo6EjrljVo0ABNmzaFh4cHzMzMPlhMY8aMwcaNG8u1Hz58GD169ADwfks2BEHAyukTEBlwCsOGDUPr1q3h4OAAe3t7HDp0CCNHjkS3bt1w4MCBctdKJBKcP38e+/btQ0hICOLj45Gfny89r6uri3r16sHT0xPe3t5wdnZ+pxiJiKhqetOE2C+//CKT8AEADw8PaX3RMp6ensjIyJD5MkksFsPBwQGJiYnIycmpMLH0X3/88QcSExPx3XffwcTEpNz5J4Ul+MP/JH4c1geNW3pg5tq/oayiKtOnpLgY14LPo2m7zq+9X1trI5lSBsuXL8f06dPRrVs3HDt2DDt27JAmBF9UlhRMTU2Fnp6etF0ikUhnW0+YMAFr166tMCGWn5+POnXqwNnZGUeOHHltvdEePXqgqKgIp0+ffu1zqko4b52IiIiqjH379kEkEmH37t0y7VpaWpgxYwbmzJmDMWPGICoqCo8ePUJRURGKi4thaWkJS0tL+Pj4QFFREU+ePEFcXBxOnDiB5cuXw9vbG+bm5hCJRNDR0YFIJHqj48KFC0hISIBIJMLy5cul8Tx8+LDCZBgA9OzZE8uWLUN2fiEi0rIr7HP53En4DuuLYS52GNLEFt9298Tp3f/I9BEBGL/gF8xbuAgxMTGYNGkS3NzcYGZmhqFDh6K4uBiBgYEQBAFJSUlYtmwZOnToAFNTUygpKaFDhw74448/cOvWLekSi27dusHJyQnA82L5wcHBuHfv3kv/f4waNQpdunQp17569WrY29tDVVUVNjY2mDdvnrTWWBlfX184OzuXWwpLRERVm66u7huVNlBSUoKTkxOKi4uRnZ392v5nzpzB7NmzsWTJEpiamkJPTw8+Pj7IyMiQ9knMyceBDasgEonwzc/LyiXDAEBZRaVcMizoyAHM9u6Boc51MNS5Dqb27oBz+3YiMSdfpt/ff/8NU1NTbN26Ferq6vj774pnma1fvx49evSQSYYBeOPSA3v37kVqaiqmT5/+Rpvv+Pj44OzZs4iPj3+j8asKJsSIiIioyvD09IRIJML58+dl2rOysnDjxg1oamqWO/fo0SM8fPgQ3t7e2LZtG8zNzdGqVSuEhoYiJCQEO3fuxKxZs9CnTx+4urrCyMgI6uoVF8pVVVWFsbExGjRoAC8vL4SFhSEqKqpcvxdrd5W9UbW1tYWlpSVUVVUxc+ZMHAi7jorm8R/4czV+mfglrOzqYerKDZi1bgu6DB4OcYnsTLfc7Cz8OLQvZs+YDkdHR6xevRoNGzZEXl6etE9mZibU1NRgbW2NmTNnIiAgAKWlpfDw8MD8+fNx9+5dFBUV4e7du/Dy8kJ8fDwGDBiAvXv3YseOHahbty4GDBiAn3/+uVycUVFR2Lp1KxYsWCDTvnDhQnz77bfo27cvTp06hXHjxmHRokUYP368TL9p06bh/v372Lp1a4WvNRERff4kEgnEYjHEYjEyMzPx999/4+TJk/Dx8Xmj6+/fvw89Pb03Wv4fHx8vUxYhNzdX+rvs/v37kEgkePS0ADfCQ1C7QSMYmVu+UQx+q5Zh5fQJ0DcxxYRFKzFj9SZ49h6I9JQHyCz43+/mS5cuITY2Fl988QUMDQ3Rr18/BAQE4P79+zLjPXjwADdu3EDbtm3f6P4VCQwMBACUlpaidevWUFFRgb6+PgYPHiwz866Mp6cnBEHA8ePH3/menyMumSQiIqIqpVGjRiguLkZcXJy07eDBg/D29sbYsWMRFhaG8PBw6bnt27fjiy++wJEjR9C9e3fUqlULjo6OOHr06Gvv9fDhQwQHB2P+/PmIi4tDnTp18OjRIzx9+lRaX6uMoqIijIyMYGFhAWVlZVy+fBnA84SYIAgYPXo01qxZAzU1NVy9GYskZb1y94u/GY1ZA7ti6JTZ6P3l+HLnX7Tgq2GIvhQI3407oVuQjZnfTii3PBQA9PX10bdvX/Tr1w8dO3aEklL5AsAAkJGRAUNDw3LfNHfv3h3nz59HVlYWVFX/9026t7c3kpKSEBoaKm3LzMxEjRo18MUXX2DDhg3S9kWLFuHHH3/EzZs34eDgIG2fOHEiTp8+jbi4uDf6hpuIqg5BEKTJktLSUpSWllb4s0Qieenjin4uO171+L8/l8VR9nNF59/k54qOsvOCILz05/9e82JbWd//tr3uv687V9Hj17W/eBQUFODZs2fQ0dGBoqKizDngeaLm2bNnFf6/V1RUhKKiovT3IwCUlJRAEARpof0XxyublV3W/t8/R2/jr4uRGNPGGa269sKUX8vX4PyvRw+SMKFzK7Tu2gvf/lK+QL4IQJ965gCe1//6+++/ERsbC3t7e1y4cAFt27aFr6+vzBdLe/bsgbe3N8LCwtC8efOX3vtVSya7dOmCU6dOQU9PD1999RW6dOmC27dv44cffoC+vj6uX79ebmfrGjVqoHXr1ti1a9drn3dVUfE7HiIiIqLPVNu2bbFq1SqkpqbC3Pz5m9ALFy6gadOm6Nq1K9auXYunT59KvyW+cOECFBUV4e7u/tb3srS0hLe3N06cOIGEhASZJFxOTg4uXbqEU6dO4ffff4eJiQnEYjFu3rwpszyw7I3spk2bsH37dowdOxZdR4+vcDfJEzv+hpKKCryGjXplXPE3oxEVGIBO3j5wcG2BU7u2VZgMAwBzc3Ns3LgRDx8+xLhx43DixAk8evQIRkZG0sL7pqamMDIyqvD6Zs2a4dixY8jKypK+3o8ePcLBgwexcuVKmb4nT55EYWEhRo4cKdM+cuRI/PDDD/D395dJiPn4+GDNmjU4f/58tSv0WxmVfRB+McFQNrPjxZ8FQZBJXLzq+G9S4k0TFRUlHV7W/rIExKuSFf9NMLzs+G8y4lXJi4r6vEti4lXJiBfPAZA+Lvu57P/jf4//tr/4uOznV7W9+GfkZW0ve0xv779fELz4uKKf3+S/r/q5oscVHQoKCtKfyzazUVdXh6qqark+YrEYz549g56eHnR1daXJr8LCQqSnp0NLSws2NjbS/rdu3UJeXl65L5tq1qyJ2rVrQ0FBQbqcsOw+ZYeSkhJEIhFKSkpw7tw5FBUVlXtNrays4Ov7f+zdd1hUx9fA8e/Sm3QVsAD2XrBX1Ng11kSjJpZYYmxgj7333mJFjbEm9tiwd4m9KxYUREAURHpZ9r5/+GNfN6BiXcv5PM8+wty5c8+uCrvnzpwZgcELO1tmxqUTR9GkplK/bccMjys8/zcfFxfHX3/9ReXKlSlUqBAAnp6e5M2bl5UrVzJ69Ght/GkzuDKqcZZZaf/fW7duzZQpU4Dn74+cnJxo1qwZa9eupUuXLjrnZMuWjYcPH771NT9HkhATQgghxBclLSF2+PBh2rRpAzzfaalx48ZUqVIFlUrFsWPHaNiwofaYh4cHNjY22jHSPtD/V9od68ywsbGhQYMGFC5cmDlz5uDt7U2/fv0A6NixI+vWrUtXHys5OZmFCxdSsOH3ZHe1Sjfm9bP/kjNPfvz27mLj77MIC7qPbdbsVG/Sgh96D8T4f9vOXzrxfGv68rXrY2hkRLO2P1HJzYnJkydz584dnTFDQ0P5559/6NSpE2q1mnbt2uHm5kZUVBRnz55ly5YtuLi4vDQxsW7dOrJkycK2bdu0H8j9/PxISUkhMDCQadOmaT/87969G3i+ccDOnTt1kg/m5uasWrWKR48eaZMMarUaY2NjvLy8KF++/CsTEpmZSfGqpMSbzKbI6GtAp+2/SYq0f1cv+/pVyYnMJCMy6pPR8f9+/ao28WYyk6R4WdubJCoyk5wAdBIELyYqXpXEyKgto68z8+d/v37Z92k/V19sz+jx336GhoYZfp2ZY2ltGf2Z2a9fbDMyMtIee/HrtO9f7PfiuS9+n3belyKtqP6OHTteWVR/2LBh6YrqT548WVvrq16957W6atSoQXBwMOvXr0dRFAIDAxk/fjyXLl1i6tSp/PDDDwCMHj2aMWPGaMdydXXl/v37aDQanbqUKpUKc3NzfvzxR5YsWUKnTp3o0qULG68HY2puTvjDB5l6ntFPIwBwcHLO8Ljqf9fasGEDsbGxtGrVSqfeWatWrZg0aRL79u3TPteEhASATG0S8DIODg4A2jHT1KtXD5VKxfnz59OdY2Zmpr3210ISYkIIIYT4onh6emJgYKBNiEVERHD16lWmTZuGlZUVHh4eHDp0iIYNG/LgwQPu3bvH999/rzPGrl27MizoO27cOIYPH/5WcQ0ePJjBgwe/9PiyZcv48ccfMTExYeutsHSzwwAiH4URHRnB8gkj+KHPQHLmLcAVv+NsWTqfiNAQvKcvAOBx6PM7vNly5AIgPlVJdyc4zdOnT2nSpIn2+99//13neGZ3nPr111/TtU2dOjXDvpMmTcqw3d/fH39//3TtV69e1dlZLLM+hQQF/H8R5LQP8//9+r8JiYzaX2x7VYLidUmJV33934RDWturvn5Z0uLFJU8vS078N8Hw4nmvS1Jk5vtXPf6btMgoOfHi8xVCfDwlSpQA4NKlSzoJHTMzM21yrVy5ctSsWZOiRYvi7e1N48aNsbKyolu3bjRu3Fh7TtpS/qtXr3Lp0iU8PDzw9/dnwIABeHt78+TJE5YsWaLtb2dhSvGKVblw7BARYSE4OLm8MlZru+eJp4iw0AxrjlmbPk+5+Pj4AODt7Y23t3e6fj4+PtrnmjYj+8WZ12+qRIkSr1z6mFHyNTIyEjc3t7e63udKEmJCCCGE+KLY2dlRsmRJDh8+DMCRI0cwNDSkSpUqwPOE2cGDBwG0Bfb/W7i2atWqzJo1K93YOXJkrsBuRry8vPjxxx+B53W3Hj16pHP83LlzODk5YW1jg9rOVeeY4f/qeimKhoS4WPrO+J2qjZoBULxiFZIS4tnxx1Ja9x6As6t7umsbGZtQ65tvOHniBImJidp2lUpFiRIlCAgIwMnJiS5duugkF/6bjHjx+8uXLzNr1izKli3LgAEDdJIJ48aN4+7du6xfv14n4TBlyhR2796Nn59fuhkVderUIXfu3Kxdu1YnKdG+fXvOnz/P3bt3deIQQgjxZbp48SLw+iWDDg4OTJ48mU6dOjFv3jyGDBmCi4sLLi7pk1hpie1evXrx3XffacsmTJgwQXdMcxNaduvN+aMHWThiIIMXrNDOvk6jTknhwrFDlKtVl1JVPDEwNMR3/SoKltadCaf633g3btzg1KlTtGzZkl69eqWLbfz48Wzbto2IiAgcHBy0Syrv3r1L0aJFX/kavEzz5s0ZNmwYu3fvpnnz5tr23bt3oygKFStW1H1OajUPHjzQzp7/WkhCTAghhBBfnJo1azJz5kxCQkI4dOgQZcqUwcrq+RJET09PZsyYwbNnzzh06BBGRkZUrVpV53wbG5sMl3i8i5w5c2rHbNSoEX/88YdOLZSFCxeycGHGBXw33XxeT8TK1o6ox+GUqlpD53jpajXZ8cdSAq5fwdnVnaz/u0sd/vABOfLkQwUc2L+fuLg4Fi9ezMSJE4mMjERRFIoXL861a9eoVq0agwYNytRz8fX1Zd68edSrV4/Nmzdj8p8PC7Nnz8bW1jZdorFQoUJs3bqVAgUKpCvmGxMTk+EHGSsrKxITE3UK9gshhPg8HDx4kPv376drT6sXGRQUhJ+fHwBxcXGcOnWKSZMm4erqSosWLV47fvv27Zk5cybTp0+nZ8+eWFtbZ9ivUKFC5M2blzFjxmBmZoa9vT3//PMP+/bt0+nnamNBgdJl6TZqMkvHDmFQy/rUa9OeXPkKolancO/GVfb9tYbc+QtSrlZdsuXMRYtferPx99kkJyZStVEzLLJkIfjOLaKfRrJ4xhQmTHs+O2zQoEGUL18+XWwxMTEcOHCA1atX4+XlRYUKFTA3N8fPz09nBjdAYGCgdqfqu3fvArBx40YA3NzctO8zChUqRM+ePfn999/JkiULDRo04NatWwwfPpzSpUvTqlUrnXEvX75MfHz8O+1s+TmShJgQQgghvjhpCbHDhw9z+PBhnTueacmvo0ePaovtpyXL3rcnT56wZcsWAObOncvUqVOJjIxMVxQ4jbu7O7NmzSJYMSdOrUl33LVAYaIeh6drTyv/ZKB6PnOqVFVP1syaxOn9eyhdraZ2yYalpSX9+vXj119/ZenSpUyaNImcOXOSNWtWgoODM/WcfH19adasGZ6enmzatCldMgyeL/fIqD5J8eLFAbhy5YrOzllhYWE8efKEYsWKpTsnMjLypQX9hRBCfNpeVirg3r17AMybN4958+YBz5dE5s6dm27dujF48OCXJrdeZGBgwOTJk2nUqBGzZ89m5MiRGfYzNjbmn3/+wcvLi19++QUjIyNq167N/v37yZ07t7afnZkxDubG1G3VjvwlSvHPyqVsWbaAqMePMTI2wtktD9UaNdPZ3KZNn0E4u+Zh9+rlzBnYCwNDQ5zd3Gn1czesDJ/vZl2qVKkMk2EADRs2JGfOnPj4+ODl5YWJiQnfffcd27ZtY+LEiTp9Dx06lG5jmrSyDx06dGDlypXa9tmzZ5MzZ06WLVvGvHnzcHR05IcffmDixInpfndv3boVR0dH6tat+9rX/EuiUqSCphBCCCG+MNHR0djb29OkSRO2bt3Kzp07adCggfZ4mTJlcHd3Z9OmTQwdOlRnyYSbmxvFihVjx44dmb5e27Zt2bx5M507d+bChQvcu3ePJ0+e6BTmV6lUODo64u7uTvbs2fnnn3+07YqiMGTIEPr160d8fDy+l29hn7cwqv8sDdy7YTWLRw3Ce9p8qn37/3fOfSaMYM+aFSzY50e2HDkBGN/tRy6fPMqwxX+S8OwpYRdOYW5uTnR0NFFRUTx48AADAwO2bNnCxIkT+fPPP7ly5QoFCxZ86fPcu3cvTZs2pWrVqvzzzz8vLfg7btw4Ro0axdOnT3U2K4iMjCRHjhx07NhRZzbc5MmTGTp0KFevXtXZZRKgQIECFCtWjM2bN7/ur0EIIYR4Z7HJavbff4zmHTIlBiqo7ZYVK5O3m4N09uxZypUrh5+fn84NpA8hNTWVfPny0bZt23RLSL90khATQgghxBepfPnynD17FgMDAyIjI3XuNPfr14/Zs2ejKAr79u2jdu3a2mNubm7kzJmT6dOnpxszNTWVJ0+ecOjQIW1dq8ePH5OSkqLtY2hoiK2tLW5ubpQoUYJChQoxePBgpk2bpt1JKzg4mFy5cr009nzFSjJl4+507eqUFIa0bkzYg0B+6D2AnPkKcPnUMbYvX0S9H9rTZcT/v5GNfhrB+C7tCLx1E3MrK2KeRmZ4rYCAAExMTChXrhypqakMHTqU4sWLExUVxZ49e+jXrx+FChXi+PHj1K1bl+zZs7N8+XLMzc11xilSpIj2NT527BjVq1fH19c33d3mCRMmMGLECIYMGULdunU5c+YMw4cPp3379jqFjQEiIiJwdHRk7ty59O7d+6WvlxBCCPE+PYhO4Exo1FufX87ZllzW5q/v+AqtW7cmLi7ujW7QvY0//viDAQMGcPv2bWxtbT/otT41smRSCCGEEF+kmjVrcubMGUqXLp1u2YWnpyezZs3CxMSEypUrpzv3xIkTVKpU6ZXjGxgYYGNjQ9GiRYmOjubhw4f4+/vj6qpbEP/+/fvplozkyJEDa2troqOj041rbGzM2uVLiTc3JjIhRWe3SSNjY0YuX8/aWZPZvGQesc+iyJYjFz/2G8q3nX7RGcfazoEJa7dycst6tq1YnGFCrH79+ri7Py/Cf/r0aUaNGsXkyZOJiIgga9asVK1aFXt7ewD2799PQkIC9+/fp1atWunGOnToEDVq1ACgSpUquLm5sW3btnQJsWHDhpElSxYWLFjA9OnTcXJy4rfffmPYsGHpxty2bRvGxsbpap0IIYQQH1JaMutcWBSKQoY7P/+XClCpoIzTuyfDAGbMmIGPjw8xMTHaTQA+BI1Gw5o1a766ZBjIDDEhhBBCfMWSk5M5duwYBw4c4OzZs9y6dYtHjx6l24nR2tqaXLlyUbRoUapUqUL9+vXJnz//O1178uTJDBkyRKfN0NCQ48ePU7Fixfe+ZOPFWXFpVCoVNWvWZO7cuW+9k9XLzJgxgwkTJvDw4cN0s8kyq1q1auTOnZs1a9a819iEEEKIzIhNVnMuLIqIhBRUZJwYS2t3NDfBw8nmrZdJio9PEmJCCCGE+OKp1Wr8/PzYv38/p0+fxt/fn9DQUBISErR9VCoVWbJkIUeOHBQpUoRKlSpRr149ihYtqt2u/X3Yvn07Xl5eGe66NW/ePJ0t2d92yYZGo0GlUlHexU57lzo1NZUmTZrg6+uLRqPBxcWFLFmycPPmTQDy5s3LuHHjaNOmzVs9r/9KTEykcOHC9OzZU7tU9E0cPXqUunXrcv36dfLkyfNeYhJCCCHextPEFAKfxRORkEx0khqF54kwa1MjHMxNcLWxwM7MWN9hijckCTEhhBBCfDE0Gg3nzp1j7969+Pn54e/vT0hICHFxcTr9rKyscHFxoXDhwlSoUIG6detSunRpDP5TxP59Wrt2LQMHDiQkJARDQ0OaNWvGt99+S8eOHTEwMOD7779n3bp16ZJvD6IT3njJRnJyMvOHeJP8OIQZM2ZQpUoVVCoVMTExVKhQgRs3brBkyRK6du3K3bt36dOnD76+vqSmpmJtbU2XLl2YMGHCS4vmZ9bx48e5cOHCW9X/2rJlCykpKbJcUgghxCdHUZT3erNM6IckxIQQQgjx2dFoNFy5cgVfX1/8/Py4ceMGDx8+JCYmRqefhYUFzs7OFCpUiPLly1OnTh0qVKjwQRNf/7Vo0SJGjhzJ48ePMTIyom3btixYsAArKys0Gg3FihUjNTWVc+fOYWVlleEYsclqzoY8JTJJjaLRpNt9EnSXbASfPkK771tqj5UuXZrBgwfTsmVLQkJCWLx4McOHD9dZypiYmMjIkSNZsmQJz549w9DQkLp16zJv3jzy5s37vl8WIYQQQgi9koSYEEIIIT5pN27cwNfXl5MnT3Lt2jWCg4OJiYnRqYVlbm5O9uzZKViwIOXKleObb76hatWqGBnpp46HRqNh+vTpTJo0iaioKExNTencuTMzZsxIN+sqMjISExOTlybD4Pmd6Jo1axL0OJLpy/7AxjnnK5dsREVFYWdnpz3fwMBAu0xyw4YNVK1a9ZXxr1u3jpEjR3Lnzh0AChUqxOTJk2natOlbvyZCCCGEEJ8SSYgJIYQQ4pNw7949du/ezYkTJ7hy5QoPHjzg2bNnOokvU1NTsmfPTv78+SlTpgy1atWiRo0amJqa6jHy/6fRaBg9ejQzZ84kLi4OCwsLevXqxYQJE946OacoCsOHD2fixInA850XmzRpoj32siUbBQsW5NatW9rvVSoVKpWK7du306hRo0xd+9q1a3h5eXHo0CE0Gg12dnb07NmTUaNG6S3ZKIQQQgjxPkhCTAghhBAfVXBwMHv27OHYsWNcuXKFwMBAoqKi0Gg02j4mJiZkzZqVfPny4eHhQc2aNfnmm2+wsLDQY+Qvl5yczKBBg1i8eDGJiYlYW1szcOBAhg4d+s7LM0eOHMm4ceO038+cOZO+ffu+9rxu3bqxYsUK1Go1AMbGxuzatYvatWu/cQyxsbEMGTKEFStWEBcXh5GREY0bN2bevHnkzJnzjccTQgghhNA3SYgJIYQQ4oMIDw9n9+7dHDt2jEuXLhEYGEhkZCSpqanaPsbGxjg6OpInTx5Kly5NjRo1qFOnDtbW1nqMPPPi4+Pp06cPq1atIiUlBXt7e0aNGkXv3r3fS7Hd0aNHM2bMGO33hoaGdO3alYULF7723JUrV9KpUycMDQ0xNDQkOTmZ33//nV9//fWdYvLx8WHcuHEEBgYCUKJECaZPn06dOnXeaVwhhBBCiI9JEmJCCCGEeCeRkZH4+vpy9OhRLl68yL1794iIiNDOTAIwMjLC3t4ed3d3SpUqhaenJ/Xq1cPe3l6Pkb+9qKgofv31V/7++29SU1NxcnJi4sSJdOrU6b1dIy2h9V+enp4cPnz4teffvXuXAgUK0KBBA5YsWULhwoWJiYnh5MmTVKxY8Z3jO3/+PF5eXpw4cQJFUciaNSt9+/Zl8ODBH3XTAiGEEEKItyEJMSGEEEJkSmxsLL6+vhw5coTz588TEBDAkydPSElJ0fYxNDTEzs4ONzc3SpYsSbVq1ahXrx5OTk56jPz9CQ8Pp2vXruzYsQONRkOuXLmYOXMm33333Xu/1rFjx/Dy8uLChQs67U5OToSGhmZqjPv375M7d24MDAy4fv06JUqUwMTEhKCgIBwdHd9LnFFRUQwYMIC1a9eSkJCAiYkJLVq0YM6cOWTLlu29XEMIIYQQ4n2ThJgQQgghdMTHx3Pw4EEOHTrEuXPnuHPnDo8fPyY5OVnbx8DAAFtbW1xdXSlevDhVqlShfv365M6dW4+RfziBgYF06dKFAwcOoCgK+fLlY968edSvX/+DX3vYsGFMnDgRd3d37t27h7GxMYmJiW81C2vDhg388MMP5MqVi/v377/XmVwajYYFCxYwefJkQkJCUKlUlClThlmzZr12V0shhBBCiI9NEmJCCCHEVyo5OZkjR45w4MABzp49y+3bt3n06BFJSUnaPiqVChsbG3LlykWxYsW0ia+8efPqMfKPx9/fn86dO3PixAkAihYtyqJFiz5qgqdYsWLcvHmT5ORkQkJCCAkJoXz58m893sCBA7U1v/bu3fseI/1/x48fp1+/fpw9exZFUXB2dmbw4MH07t1bllMKIYQQ4pMgCTEhhBDiC6dWqzlx4gT79+/nzJkz+Pv78+jRIxISErR9VCoVWbJkIWfOnBQpUoQqVapQr149ChcurMfI9ef8+fN07dqV8+fPA1CmTBmWLVtGqVKlPmocycnJmJubU7p0ac6ePfvexvX09OTo0aMMHTqUCRMmvLdx/ys8PJy+ffuyceNGkpOTMTMz44cffmDWrFnY2tp+sOsKIYQQQryOJMSEEEKIL4RGo+Hff/9l3759nD59mps3bxIaGkp8fLxOPysrK3LkyEHhwoWpVKkSdevWpUSJEjJzh+czm3755ReuX78OQNWqVfHx8aFAgQJ6iWfu3Ll4eXmxZMkSunbt+t7GVavV5M6dm9DQULZu3UrTpk3f29gZ0Wg0TJ06lVmzZhEeHo5KpaJy5crMnj2bsmXLftBrCyGEEEJkRBJiQgghxGdGo9Fw8eJF9u7di5+fHzdu3CAkJITY2FidfpaWljg7O1OoUCEqVKhAnTp1KFeunCS+MrB792569+7N3bt3UalU1KlTh6VLl+q9JlrJkiW5du0aiYmJGBkZvdexw8LCcHd3R61Wc/36dfLnz/9ex3+Z/fv3M2DAAC5dugRA7ty5GTFiBF26dPko1xdCCCGEAEmICSGEEJ+0a9eu4evry8mTJ7l+/ToPHz4kJiaGF399m5ub4+zsTIECBShXrhx169alYsWK7z2B8iX6+++/6devH8HBwRgYGPDtt9+yZMmST2J3RLVajampKSVKlEi30+T7cuzYMTw9PbG1tSU4OBgLC4sPcp2MBAcH06dPH/755x/UajWWlpa0b9+eqVOnYmVl9dHiEEIIIcTXSRJiQgghxCfg9u3b7Nmzh5MnT3Lt2jWCgoKIjo7WSXyZmZmRPXt28ufPT9myZalduzbVqlXDxMREj5F/nnx8fBg2bBiPHj3CyMiIVq1asXDhQqytrfUdmtbChQvp0aMHCxYsoEePHh/sOrNnz6Zv376UKFFCO2vrY1Kr1YwdO5YFCxYQGRmJgYEBNWrUYO7cuRQtWvSjxyOEEEKIr4MkxIQQQoiPKCgoiN27d3PixAmuXLlCYGAgz549Q6PRaPuYmpqSNWtW8uXLR9myZalZsya1atXCzMxMj5F//jQaDXPnzmXcuHFERkZiYmJChw4dmD179kedGZVZHh4eXLp0iaSkpA8+269t27asW7eOn376iVWrVn3Qa73K9u3b+e2337hx4wYAefPmZdy4cbRp00ZvMQkhhBDiyyQJMSGEEOIDCAsLY/fu3Rw7dozLly9z//59oqKiSE1N1fYxNjbG0dGRvHnz4uHhQY0aNahTp44sF3vPNBoN48ePZ/r06cTExGBmZkaPHj2YNGnSJzu7Tq1WY2ZmRpEiRbh8+fJHuWbRokW5fv068+fPp2fPnh/lmi9z9+5d+vTpg6+vL6mpqVhbW9O1a1fGjx8viWEhhBBCvBeSEBNCCCHeQWRkJLt37+bIkSNcunSJ+/fvExERoZP4MjIywsHBgTx58lCqVCk8PT2pV68etra2+gv8K6BWqxkyZAgLFiwgISEBKysr+vfvz8iRIz/5jQV8fHzo0qULs2fPxsvL66NcMy4ujhw5chAdHc3x48epXLnyR7nuqyQmJjJixAiWLFlCdHQ0hoaG1K1bl3nz5pE3b159hyeEEEKIz5gkxIQQQohMiI6OxtfXl8OHD3Px4kUCAgJ48uQJarVa28fQ0BB7e3vc3NwoWbIk1atXp169ep9EgfavSWJiIn379mX58uUkJydja2vL8OHD6du37yefCEtTtmxZzp8/T2Ji4kedxXbjxg1KlCiBsbEx9+/f/6T+7a5bt46RI0dy584dAAoVKsTkyZNp2rSpniMTQgghxOdIEmJCCCHEC+Lj49m3bx+HDx/m/Pnz3LlzhydPnpCcnKztY2BggJ2dHa6urhQvXpxq1arRoEEDXFxc9Bi5iI6OpmfPnqxfvx61Wk3WrFkZP3483bp103dob0Sj0WBiYkLBggW5du3aR7/+xo0b+f7778mRIwdBQUGfXBLx2rVreHl5cejQITQaDfb29vTo0YNRo0bJzqpCCCGEyDRJiAkhhPgqJSYmcujQIQ4dOsTZs2e5c+cO4eHhJCUlafsYGBhgY2NDrly5KF68OFWqVKF+/fq4u7vrMXLxX0+ePKFbt25s376d1NRUcuTIwbRp0z7bQux//PEHHTt2ZNq0aQwYMEAvMQwePJipU6dSq1YtDhw4oJcYXic2NpbBgwfzxx9/EBcXh5GREd9++y1z584lZ86c+g5PCCGEEJ84SYgJIYT4oqnVao4ePcqBAwc4e/Ys/v7+PHr0iMTERG0flUqFtbU1OXPmpFixYlSqVIn69etTsGBBPUYuXic4OJiuXbvi6+uLoijkyZOHOXPm0LhxY32H9k4qVqzI6dOniY+P12sB+Vq1anHo0CEGDRrElClT9BZHZvj4+DBu3DgCAwMBKFGiBNOnT6dOnTp6jkwIIYQQnypJiAkhhPgiaDQaTp06xb59+zh9+jT+/v6EhYURHx+v0y9LlizkyJGDIkWKULFiRerXr0/RokU/uWVh4uXu3LlD586dOXbsGIqiULhwYX7//Xdq1Kih79DemUajwdTUlHz58nHjxg29xqJWq3Fzc+Phw4ds3LiRli1b6jWezDh79ize3t6cPHkSRVHIli0b3t7eDB48WP6PCyGEEEKHJMSEEEJ8VjQaDefPn8fX15fTp09z48YNQkJCiIuL0+lnaWmJi4sLhQsXpnz58tSrVw8PDw/5UPwZu3z5Ml26dOHMmTMAlC5dmiVLllC2bFk9R/b+rFu3jrZt2zJx4kSGDBmi73AIDw/H1dUVtVrN1atXP5tZk1FRUfTr149169ZpNyZo2bIls2fP/qQ2ChBCCCGE/khCTAghxCdJo9Fw9epVfH198fPz4/r16zx8+JCYmBidfhYWFjg5OVGwYEHKly9P7dq1qVSpEoaGhnqKXLxvp06d4pdffuHKlSsAVKpUiaVLl1K0aFE9R/b+ValShVOnThEbG4uFhYW+wwHgxIkTVKtWDRsbGx4+fPjJxJUZGo2G+fPnM2XKFEJCQlCpVJQpU4bZs2dTpUoVfYcnhBBCCD2ShJgQQgi98/f3Z8+ePZw6dYqrV68SHBxMdHQ0L/6KMjMzI3v27BQsWJCyZcvyzTffULVqVUxMTPQYufiQ9u/fT8+ePbl16xYqlYqaNWuybNmyL3ZTA41Gg5mZGW5ubty6dUvf4eiYP38+vXv3plixYtrE5Ofm+PHj9OvXj7Nnz6IoCi4uLgwaNIjevXvLzFEhhBDiKyQJMSGEEB9NYGAgu3bt4sSJE1y5coUHDx7w7NkzNBqNto+pqSnZsmUjf/78lClThpo1a1KzZk29FhcXH9fWrVvx9vYmMDAQAwMDGjZsyNKlS3FyctJ3aB/Upk2b+O677xgzZgwjR47Udzjp/Pjjj6xZs4a2bduyZs0afYfz1sLDw/H29mbTpk0kJydjZmZGmzZtmDlzJra2tvoOTwghhBAfiSTEhBBCvHchISHs3r2bY8eOceXKFQIDA3n69KlO4svExISsWbOSN29ePDw8qFmzJt988w2WlpZ6jFzo06pVq/jtt98IDQ3F0NCQ7777jkWLFn01SYrq1atz7NgxYmJisLKy0nc4GSpRogRXrlxhzpw59OnTR9/hvBONRsPUqVOZNWsW4eHhqFQqKleuzNy5c/Hw8NB3eEIIIYT4wCQhJoQQ4q09fvyYPXv2cPToUS5dusT9+/eJjIwkNTVV28fIyAhHR0fy5MlDqVKlqFmzJnXr1sXa2lqPkYtPyfz58xk9ejQREREYGxvz448/Mm/evK8uOWpqakrOnDm5e/euvkN5qfj4eHLkyMGzZ884evQoVatW1XdI78W+ffsYOHAgly5dAsDV1ZXhw4fTpUsXPUcmhBBCiA9FEmJCCCFeKyoqCl9fX44cOcLFixcJCAggIiICtVqt7WNkZIS9vT1ubm6UKlWK6tWrU69ePRwdHfUYufhUaTQapkyZwuTJk4mOjsbU1JRu3boxffr0r7Iu3Pbt22natCnDhw9n3Lhx+g7nlW7dukXRokUxMjLi3r17X9RS1uDgYPr06cM///yDWq3G0tKSDh06MGXKlE921p4QQggh3o4kxIQQQmjFxsayb98+Dh8+zPnz57l79y5PnjwhJSVF28fQ0BBbW1vc3NwoUaIE1apVo0GDBl/Uh2Lx4ajVakaOHMmcOXOIj4/H0tISLy8vxowZg5GRkb7D05uaNWty+PBhoqKisLGx0Xc4r7VlyxZatGiBi4sLgYGBX9zfXUpKCuPGjWPBggVERkZiYGBAjRo1mDt37he5u6kQQgjxNZKEmBBCfIUSExM5ePAgBw8e5Ny5c9y5c4fHjx+TlJSk7WNgYICNjQ2urq4UL16cKlWqUL9+fVxdXfUYufhcJScn079/f5YuXUpSUhI2NjYMHjyYwYMHyw5/PN9F1dnZmXv37uk7lEwbOnQokyZNokaNGhw6dEjf4Xww27Zt47fffuPmzZsA5M2bl3HjxtGmTRs9RyaEEEKIdyEJMSGE+IIlJydz7Ngx9u/fz9mzZ7l9+zaPHj0iMTFR20elUmFtbU3u3LkpWrQolStXpn79+uTPn1+PkYsvRVxcHL1792b16tWkpKTg6OjI6NGj6dmzp75D+2Ts3r2bhg0b8ttvvzFp0iR9h/NGateuzYEDBxgwYADTpk3Tdzgf1N27d+nduzd79+4lNTUVa2trunXrxrhx42QXXCGEEOIzJAkxIYT4AqjVavz8/Ni7dy9nzpzh1q1bhIaGkpCQoO2jUqnIkiULOXLkoEiRIlSuXJl69erJ8h/xQURGRtK9e3c2b95Mamoqzs7OTJ48mfbt2+s7tE9OnTp12L9/PxEREdjb2+s7nDei0WhwdXUlODiYDRs20KpVK32H9MElJiYyYsQIlixZQnR0NIaGhtStW5d58+aRN29efYcnhBBCiEyShJgQQnxGNBoNZ86cYd++ffz777/cvHmT0NBQ4uLidPpZWVnh4uJC4cKFqVixInXr1qVUqVKyNE18cGFhYXTp0oXdu3drkyWzZs2iefPm+g7tk2VhYUHWrFkJDAzUdyhvJTw8HDc3N5KTk7ly5QqFCxfWd0gfzbp16xg5ciR37twBoFChQkyePJmmTZvqOTIhhBBCvI4kxIQQ4hOk0Wi4fPkye/fuxc/Pj+vXr/Pw4UNiY2N1+llYWODs7EyhQoUoX748derUoUKFCpL4Eh/d/fv3+fnnnzl8+DCKolCgQAF+//13vvnmG32H9kk7cOAAtWvXpn///kyfPl3f4by1U6dOUaVKFaytrQkODv7qdmS8du0affr04fDhw2g0Guzt7enRowejRo364jYcEEIIIb4UkhATQgg9u3HjBr6+vpw8eZJr164RHBxMTEwML/54Njc3x8nJiQIFClCuXDlq165NlSpV5IOW0Lvr16/TpUsXTp06BUCJEiVYvHgxFStW1HNkn4f69evj6+vL48ePcXR01Hc47+T333+nZ8+eFClShGvXruk7HL2IjY1l8ODB/PHHH8TFxWFkZMS3337L3LlzyZkzp77DE0IIIcQLJCEmhBAfSUBAALt37+bEiRNcvXqVBw8e8OzZM53El6mpKdmzZyd//vyUKVOG2rVr4+npiYmJiR4jFyK9M2fO0K1bNy5evAhA+fLlWbZsGcWLF9dvYJ8ZS0tL7OzsCA4O1nco70WHDh1YtWoVP/zwA+vWrdN3OHq1bNkyxo0bR1BQEPA8WTx9+nTq1Kmj58iEEEIIAZIQE0KI9y44OJhdu3Zx4sQJrly5QmBgIFFRUWg0Gm0fExMTsmbNSr58+ShTpgw1a9akVq1aWFhY6DFyIV7v8OHD/Prrr9y8eROVSkX16tXx8fGRYuJv4ejRo3h6etKnTx/mzJmj73Dem5IlS3L58mVmzpxJ37599R2O3p09exZvb29OnjyJoihky5YNb29vBg8eLMvbhRBCCD2ShJgQQryl8PBwdu/ezbFjx7h06RL379/n6dOnpKamavsYGxvj6OhInjx58PDwwNPTkzp16mBtba3HyIV4c//88w9eXl7cu3cPlUpFvXr1WLp0qSwDeweNGzdm586dhISE4OzsrO9w3pvExERcXFyIiori8OHDVK9eXd8hfRKePn1K//79WbduHYmJiZiYmNCyZUtmz55NtmzZ9B2eEEII8dWRhJgQ4p0pioJKpdJ3GB9MZGQkvr6+HD16lIsXLxIQEEBkZCRqtVrbx8jICAcHB9zd3SlVqhTVq1enXr162Nvb6zFyId7dunXrGDhwIA8fPsTQ0JBmzZqxaNGiz77e1afAysoKa2trQkJC9B3Ke3fnzh0KFy6MoaEhAQEBuLi46DukT4ZGo2HevHlMnTqVkJAQVCoVZcuWZdasWVSpUkXf4QkhhBBfDUmICSHe2NPEFAKfxRORkEx0khoFUAHWpkY4mJvgamOBnZmxvsN8YzExMezdu5fDhw9z4cIFAgICePLkCSkpKdo+hoaG2NnZ4ebmRsmSJalWrRr169cne/bseoxciPdvyZIlDB8+nMePH2NkZESbNm1YsGABWbJk0XdoX4STJ09SpUoVevTowYIFC/Qdzgexbds2mjVrhrOzM0FBQbIJSAaOHz9O3759OXfuHIqi4OLiwuDBg+nVq5cspxRCCCE+MEmICSEyLTZZzbmwKCISUlABGf3wSGt3MDemjJMtViaf3geg+Ph4Dh48yMGDBzl//jx37tzh8ePHJCcna/sYGBhga2uLq6srxYsXp2rVqtSvX59cuXLpMXIhPiyNRsPMmTOZMGECUVFRmJiY0LlzZ2bOnImZmZm+w/uiNGvWjG3btvHgwYMvetnpiBEjGD9+PNWqVePo0aP6DueTFR4ejpeXF5s3byY5ORkzMzPatGnDzJkzsbW11Xd4QgghxBdJEmJCiEx5EJ3AubAoFCXjRNh/qQCVCso42ZLL2vxDh5eh5ORkDh8+zIEDBzh37hy3b9/m0aNHJCUl/X+cKhU2Njbkzp2bYsWKUaVKFerXr0+ePHn0ErMQ+qDRaBg9ejSzZs0iNjYWc3NzevXqxcSJE2VWzwdibW2NhYUFYWFh+g7lg6tXrx579+6lb9++zJw5U9/hfNI0Gg1Tpkxh1qxZPH78GJVKReXKlZk7dy4eHh76Dk8IIYT4okhCTAjxWg+iEzgTGvXW55dz/rBJMbVazYkTJ9i/fz9nzpzB39+fR48ekZCQoO2jUqnIkiULOXPmpGjRolSuXJl69epRuHDhDxaXEJ+65ORkBg8ezKJFi0hMTCRLliwMGDCA4cOHy3KtD+jMmTOUL1+ebt26sXjxYn2H88FpNBrc3d0JCgpi/fr1tG7dWt8hfRb27dvHgAEDuHz5MgCurq6MGDGCzp076zkyIYQQ4guhCCHe2t9//60Ayvr169MdK1GihAIoe/bsSXcsT548SunSpRVFURRXV1eF55OuFEAxNTVV8ubNq/Tt21d5/PixoiiKcu/ePZ0+r3rcu3dPURRFuXv3rtKzZ08lf/78ipmZmWJubq4UKVJEGTZsmBIcHJzp57jnwCGlcv1vFftsToqRsbFiYZVFKViqjNJ11CRlzfk7yqabIcqmmyFK0XKVMoynVNUayhb/ECUmKUU7pr+/v9KiRQvFxsZGMTQ0VLJkyaJs27Ytw+uvXr1aKVWqlGJqaqo4ODgoderUUfr166c0atRIyZs3r2JhYZHumhYWFkrBggWV5s2bK3379lW+//57pWLFitq+hw4deunzXbdunVKyZEnF1NRUcXZ2Vry8vJSYmBidPsuWLVNcXFyU2NjYTL+OQnxK4uLilK5duyomJiYKoNjb2yuzZ89WUlNT9R3aV6FFixYKoNy/f1/foXw0jx8/VszNzRVDQ0Pl6tWr+g7nsxIUFKQ0b95cMTIyUgDF0tJS6dGjR7rfTUIIIYR4MzJDTIh38OTJE7Jly0a3bt1YtGiRtj0yMhJHR0csLCzo1asXkydP1h4LDg4mV65c9OvXjxkzZuDm5kbOnDmZPn06AAkJCZw9e5bRo0dTuHBhzp49S1JSEhcuXNC5do8ePXj27Blr1qzRaS9dujT79u3jhx9+wNHRkV69elG6dGlUKhVXrlxh+fLlGBgYpBsvI6NGjWLs2LEULF2Wb75rg1MuN5ISE/C/cJZ9f62mWuPmdBoyBoCRP7XkSVgI3tPm64xhYW1Nrjz5sTc3xjO3I/fv36dMmTLY2NgQFRXF06dPgeczuP7++29atmyJRqPh4sWLjBo1ih07dmBtbU1ycjKJiYk6Y1taWuLi4kLBggWpWLEi+/btw9LSkp07d2r7/PHHHwwZMoTSpUtjaGjIP//8w6FDh6hRo0a657tmzRp+/PFHunTpQtu2bbl16xaDBw+mfPny7N27V9tPrVZTpEgR2rRpw5gxY177OgrxqXj27Bk9evTgr7/+Qq1Wkz17diZOnMjPP/+s79C+KjY2NpiamhIeHq7vUD6qf//9l8qVK2NlZcXDhw+xsrLSd0ifleTkZMaPH8+CBQuIjIzEwMCAGjVqMHfuXIoWLarv8IQQQojPj74zckJ87ooXL64ULFhQp23z5s2KsbGx0qdPH6V8+fI6x1atWqUAyj///KMoyvMZYo0aNUo37ogRIxRA8ff3z/C6np6eStGiRdO1BwQEKJaWlkrp0qWVqKiodMc1Go2yadOm1z6vv/76SwGUb75ro2y88VA7EyztsfrsLWXEsrU6M8Ry5S+Yrt+Lj8iEZKVbt26KkZGRolKpFAMDA51ZXUZGRkqWLFkUlUqlM+PL3NxcyZMnj9KgQQOlU6dOCqD89ttvOvFev349wxl5L854SZvRl9EMMbVarTg7Oyt169bVaV+zZo0CKLt27dJpnz59umJjY6PExcW99rUUQt8ePXqkNG3aVPt/LleuXMpff/2l77C+ShcuXFAApVOnTvoORS8WLVqkAEqhQoVkRuI72Lp1q1KoUCHt78m8efMq69at03dYQgghxGdFCoQI8Y5q1qyJv78/oaGh2rbDhw9Trlw5GjZsyLlz54iJidE5ZmhoSLVq1V45ro2NDQDGxsZvFM/MmTOJi4vj999/147xIpVKRYsWLV47ztixY7G2taXLsHGoVKp0x82trChVtUam41IB+89ewsfHB7VajaIoaDQa7fH4+HjUajWWlpZ88803dOzYEYB58+YRHx/P3bt32bVrF8uXL8fe3p4tW7bojL9w4UKcnJyoU6eOTntm6yD5+fkRGhpKp06ddNq///57rKys0l2vXbt2REdHs379+ky+AkJ8fEFBQdStWxcnJye2bdtGnjx52LVrF0FBQXz//ff6Du+rlDZjeNiwYXqORD9++eUXOnbsyM2bN/nhhx/0Hc5nq2nTpty4cYM7d+7QoEED7t+/T5s2bbCxsWHgwIHpZlQLIYQQIj1JiAnxjmrWrAk8T3SlOXToEJ6enlSpUgWVSsWxY8d0jnl4eOgkqxRFQa1Wo1ariY2N5dChQ8yePZsqVarg7u7+RvHs3buX7NmzU7Fixbd+TqGhoVy9ehWPqjUwMbfI9HmPggLpUKEI3xfNRY86lVgzazJJic8L2ytA4OOnpKampjvP0NCQcuXKATBmzBj27dtHt27dgOfLIv/L1NSU27dv67zh37lzJ9WrV3/rQuBXr14FoESJEjrtxsbGFCpUSHs8jZOTE4UKFdJZninEp8Lf359q1arh6urKvn37KFKkCMeOHeP27ds0aNBA3+F91Xx9fXFwcCBv3rz6DkVvVqxYQalSpfj777+ZMWOGvsP5rOXNm5ddu3YRGxtLv379AJg+fTpWVlY0bNiQu3fv6jlCIYQQ4tMlCTEh3pGnpycGBgbahFhERARXr17F09MTKysrPDw8OHToEAAPHjzg3r172iRaml27dmFsbIyxsTFZsmShVq1auLi4sHHjxjeOJygo6I2TaBmNAWDvnDPT5xQqU54Ov41iwJyl/Pb7Sjyq12Kbz++M79JOOxMsT+Gi1KhRAzMzM0qWLKk9NzU1lTt37gDPXz+AggULYmBgwIkTJ3Suc/fuXUJDQ9FoNNr6Y+Hh4QQEBLzTlvRp17W3t093zN7eXnv8RR4eHuniE0KfLly4QNmyZSlUqBDHjx+nTJkynDt3jqtXr1K1alV9h/fVu3r1KlFRUTRq1EjfoejdqVOnsLe3Z+DAgTo3lMTbMTMzY8aMGTx79ozVq1fj5ubG7t27yZcvH0WKFGH79u36DlEIIYT45EhCTIh3ZGdnR8mSJbVv6I8cOYKhoSFVqlQBnifM0hJiaX/+NyFWtWpVzpw5w5kzZzhx4gQ+Pj48fvyYWrVq8eTJkw8W+4sz09IeOsffYKy23oOp36YDxStWoYznN3QZMYEf+w3l+lk/zhzwfd5JpWLYsGEkJSWRJ08eTp06xfTp08mXLx9RUVHA/y9xtLe3p127dqxatYrFixcTGRnJ5cuXadeuHYaGhjp9Q0JCAMiWLdvbvxikhZh+eejL2rNly0Z4eHi6102Ij+348eMUK1YMDw8Pzp07R9WqVbl58yZnz559p0SxeL8mTZoEwNChQ/Ucif6ZmZlx+vRpjIyMqF+/vvbnuHh37dq1486dO1y5ckVb1qFp06Y4ODgwcuRI+Z0lhBBC/I8kxIR4D2rWrMmtW7cICQnh0KFDlClTRrt7lqenJxcuXODZs2ccOnQIIyOjdDM1bGxsKFu2LGXLlqVy5cr8/PPPrF27lhs3brzxcpLcuXNz7969TPX9448/tDPT0h5pYwCEPwx6o2v/V/UmLQG4dekc8LyOWO3atVmxYgVHjx6lUqVKDBgwABMTEyZMmABAjhw5tOcvXLiQ1q1b06NHDxwcHChdujSFChWiUaNGmJqa4uDgADzfmROef8B6W2ljZTQTLDIyMsOZY2ZmZiiKIrVahN7s2bOH/PnzU61aNa5fv06dOnUIDAzk2LFjFCxYUN/hif/Ys2cPdnZ28nfzP3nz5mXz5s0kJSVRpkwZSdS8Z8WKFePgwYM8e/aMX3/9laSkJMaNG4e5uTktWrQgODhY3yEKIYQQeiUJMSHegxfriB0+fBhPT0/tsbTk19GjR7XF9jOz1XxaLatLly69USz16tXj0aNH+Pn5vbbvt99+q52ZlvYAcHZ2pnjx4lw+cZSkhPg3un5GVKrnP2qsTY0A6NChA2FhYVy/fp3bt29z7dq1//VT6Ww2YGlpyZ9//smTJ0+4dOkSjx49YuXKlfj7+1O5cmWMjJ6P5+joCDxPXL2t4sWLA3DlyhWddrVazc2bNylWrFi6cyIjIzE1Nc3U36cQ79PGjRvJnTs3DRo0ICAggKZNmxIWFsbevXu1CW3xafH39ycyMlJquP1H48aNGTlyJGFhYdSoUUPf4XyRrKys+P3334mJiWHJkiW4uLiwZcsWcuXKRalSpdi3b5++QxRCCCH0QhJiQrwH1atXx9DQkI0bN3Lt2jWdN/U2NjaUKlWKP/74g/v376dbLvkyFy9eBN58GWDfvn2xtLSkR48ePHv2LN1xRVG0OyY6ODhoZ6alPdKMGDGCmGdR+EwYgaKkXzyZEBfHxeOHXxnL4a1/AVCglAeKosH+fwmxtKWajo6OGBgYcPnyZZYsWULTpk1xdXVNN46dnR0lSpTA0dGR7du34+/vj5eXl/a4q6sr5ubm71Q8uEKFCjg7O7Ny5Uqd9o0bNxIbG5vhzpwBAQEUKVLkra8pxJtavnw5Tk5OfP/994SGhtK2bVsiIyPZunXre1kyLD6ciRMnAjBkyBA9R/LpGTNmDPXr1+fEiRM6P9vF+6VSqejatSuBgYGcOXOGypUrc/nyZerWrUv27NmZPHmyzu7PQgghxJdOpWT0SVcI8cbKly/P2bNnMTAwIDIyEmtra+2xfv36MXv2bBRFYd++fdSuXVt7zM3NjZw5czJ9+nQAUlJSuHHjBhMnTiQsLIxjx45pd2B8UY0aNXjy5Em63Q8BduzYQevWrcmWLRu9evWidOnSAFy/fp3ly5ejKAoXLlx47XMaOHQY0ydNpJBHOb5p2Ybsud1ITkzg9qXz7N2wmioNm9BpyBiun/2XTYvmUKF2A7Lnyk1yUhIXjh1i31+rKVquEiOXr8fAwIABLepx7/qVDK+VM2dO/v33X1xcXLRtmzZtIiQkhMKFC5OYmMjhw4eZM2cOXbp0YeHChTrnf/PNNyQkJHDy5Emd9vj4eHbt2gWAn58fM2bMYPTo0RQtWhRLS0ud2RqrV6/mp59+olu3brRp04bbt28zaNAgypUrx969e3XG1Wg02Nvb07lzZ9klTXxQGo2GefPmMXbsWCIjIzExMaFDhw7Mnj0bC4vM7wIr9MvR0ZHU1FTtZiBCl0ajIU+ePAQGBrJ69WratWun75C+CpGRkfTv35/169eTmJiIiYkJLVu2ZPbs2ZJkF0II8eVThBDvxaBBgxRAKVu2bLpjW7duVQDFxMREiYuL0znm6uqq8Lx+vQIohoaGSu7cuZXvvvtOuXDhwkuv5+npqRQtWvSlx+/evav06NFDyZcvn2JqaqqYm5srRYoUUfr166fcu3cv089rzl/blEr1Git2WbMrRsbGioVVFqVgqTJK+4EjlNVnbymbboYo831PKB6e3yj22Z0VYxNTxcTUTMldoLDS1vs3Zf3le8qmmyHKjM2+Os/zv4+MYtqyZYtSqlQpxdLSUjE3N1fKli2r+Pj4KBqNJl1fHx8fxdDQUAkJCdFpv3fv3kuv6erqmm6ctWvXKiVKlFBMTEwUJycnpU+fPkpMTEy6fgcOHFAA5dy5c5l+LYV4E6mpqcrYsWOVLFmyKIBiZmameHt7K0lJSfoOTbyhO3fuKIDy/fff6zuUT1pERIRiYWGhGBoaKleuXNF3OF+V1NRUZfbs2Yqzs7MCKCqVSilXrpxy/PhxfYcmhBBCfDAyQ0wI8UqxyWr233+M5h1+UhiooLZbVlo1a8Lu3bvTHc+fPz83b97U7hr5NhITE8mdOzf9+/dn8ODBbx9sJv30008EBARw4sSJD34t8XVRq9UMHTqUBQsWEB8fj6WlJf369WP06NHv9H9E6E+XLl3w8fHh/Pnz2hm7ImNnzpyhYsWKWFpaEhwcrDPbWnwcx48fp2/fvpw9exYAFxcXfvvtN3r27Ck/g4QQQnxRJCEmhHitB9EJnAmNeuvzyznbksvanOTkZIoWLcqdO3fS9TE2NqZjx45Mnz79rT8ALVy4kNGjRxMQEIClpeVbx/s6d+/epXDhwhw8eDDdjqFCvK3ExET69evH8uXLSUpKwtbWlqFDh9K/f3/5EPqZy5YtG0lJSRnWdRTpLVu2jK5du1KgQAFu3Lgh//715NGjR3h7e7N582aSk5MxNzenTZs2zJgxA1tbW32HJ4QQQrwzeYchhHitXNbmlHO2xUAFqkyeo+L5zLC0ZBiAiYkJhw4dwtzcXNvPxsYGAwMDUlJSWLp0KTY2NtSqVUu78+Sb6NatG97e3gQEBLzxuW8iKCiI+fPnSzJMvBcxMTG0b9+eLFmysHDhQqytrVm0aBFPnz5l4MCBkgz4zAUGBvL48WOd2pHi1bp06UKXLl24desWrVq10nc4X63s2bOzbt064uPjmThxIlZWVixfvhx7e3uqVavG+fPn9R2iEEII8U5khpgQItNik9WcC4siIiEFFc8Lcf1XWrujuQkeTjZYmRil63Po0CFq1aoFwJEjRyhfvjz9+/dn6dKlpKSkaPvly5ePyZMn07Jlyw/yfITQp4iICH755Re2bt1KamoqOXLkYOrUqbRt21bfoYn3qHv37ixevJjTp09nuEGKeLmyZcty7tw5pkyZwqBBg/QdjgD27t3LwIEDuXz5MvB8l+cRI0bQuXNnPUcmhBBCvDlJiAkh3tjTxBQCn8UTkZBMdJIaheeJMGtTIxzMTXC1scDOzPiVY/z5558EBAQwatQobZtarWbMmDHMnDmT+Ph4bbutrS19+vRhxIgRGBmlT7AJ8Tl5+PAhXbt2Zc+ePSiKgru7O3PmzOHbb7/Vd2jiA3B2diY2NpaYmBh9h/LZSUxMJGfOnERGRrJv3z6++eYbfYck/icoKAgvLy927NiBWq3G0tKSjh07MnnyZKysrPQdnhBCCJEpkhATQrwzRVFQqTK7mPL1NBoNc+bMYdy4cTx9+lTbbmRkRIsWLZg7dy7Zs2d/b9cT4mO4e/cunTt35ujRoyiKQqFChVi4cCE1atTQd2jiAwkODiZXrlw0adKEbdu26Tucz9K9e/coWLAgKpWKu3fvkjNnTn2HJF6QnJzMuHHjWLBgAU+fPsXAwIAaNWowb948ihQpou/whBBCiFeSwiRCiHf2PpNhAAYGBvTt25fIyEhWrlyJs7Mz8HwG2V9//YWTkxMVKlTAz8/vvV5XiA/hypUrVKhQgXz58nHkyBFKlizJ6dOnuXHjhiTDvnCTJk0C+Cg7336p3N3d2bp1K8nJyZQpU4bk5GR9hyReYGJiwrhx44iMjGTLli0UKFCAgwcPUrRoUfLnz8+GDRv0HaIQQgjxUjJDTAjxWdi+fTt9+/ZNVzA/V65cjBw5ki5duugpMiEy5ufnxy+//KKttVOxYkWWLVtG0aJF9RyZ+FhcXFyIjo4mNjZW36F89saOHcuoUaOoVKkSJ0+e1Hc44hXu3LlDnz592Lt3L6mpqVhbW9OtWzfGjRuHmZmZvsMTQgghtGSGmBDis9CkSRPu3r3LsWPHKF68uLb9wYMHdO3aFSsrK7y8vEhISNBjlELAgQMHKFiwIJUqVeLKlSvUrFmTgIAATp06Jcmwr0hYWBihoaF4enrqO5QvwsiRI2nYsCGnTp2id+/e+g5HvEK+fPnYtWsXsbGx9OvXD4Dp06djZWVFo0aNPvhO0EIIIURmSUJMCPFZqVq1KpcvX+by5ctUrFhR256QkMDcuXPJkiULDRs25N69e3qMUnyNtm3bhpubG7Vr1+bOnTs0atSIhw8fcvDgQdzd3fUdnvjIpkyZAiC7I75H//zzD+7u7syfP5/Vq1frOxzxGmZmZsyYMYNnz56xevVq3Nzc2LVrF3nz5qVIkSJs375d3yEKIYT4ysmSSSHEZy0wMJAuXbpw4MABFEXByMgItVoNQNGiRZk2bRoNGjTQc5TiS7Z69WoGDRpEaGgohoaGtGjRgkWLFmFvb6/v0IQe5cqVi8jISOLi4vQdyhfl6dOn5MqVi4SEBC5cuECJEiX0HZJ4A1evXqVPnz4cOXIEjUaDvb09PXv2ZOTIkbKLtBBCiI9OZogJIT5rrq6u7Nu3j/DwcFq0aEFajt/ExIRr167RsGFDsmXLxtSpU9FoNHqOVnxJFixYQNasWfnpp5948uQJHTt2JCoqir/++kuSYV+5J0+eEBwcTLVq1fQdyhfHzs6OI0eOAFClShWio6P1HJF4E8WKFePgwYM8e/aM7t27k5iYyLhx47CwsKBFixYEBwfrO0QhhBBfEUmICSG+CI6OjmzatImoqCg6deqkkxh7+vQpgwcPxsLCgo4dO/L06VM9Rys+VxqNhkmTJmFra0uvXr2IiYmhd+/exMbGsmLFCqysrPQdovgEpC2X7N+/v54j+TKVKVOGJUuWEBsbS5kyZeRmx2fIysqKhQsXEhMTw+LFi3F2dmbLli3kypWLUqVKsX//fn2HKIQQ4isgSyaFEF+k5ORkfvvtNxYtWkRCQgImJiYYGxsTFxeHSqWiatWqzJs3j5IlS+o7VPEZUKvVjBo1ijlz5hAXF4eFhQVeXl6MHTtWlvmIdFxdXQkPD5dNPj6w7t27s3jxYpo1a8aWLVv0HY54R2fOnMHb25tTp06hKArZsmWjb9++DBo0CAMDuYcvhBDi/ZPfLkKIL5KJiQkzZ84kNjaWcePGYW5uTlxcHMbGxtjb23Ps2DFKlSpF3rx5Wbdunb7DFZ+o5ORk+vTpQ5YsWZg4cSKGhoZMnDiRmJgYJk6cKMkwkU5kZCRBQUFUqVJF36F88RYtWkTZsmXZunUrkyZN0nc44h2VK1eOEydOaJegR0dHM2TIEMzNzWnXrh2PHz/Wd4hCCCG+MJIQE0J80QwMDBg+fDhRUVEsWLAAW1tbIiIiMDQ0xMXFhfv379O2bVtsbW0ZMmQIycnJ+g5ZfALi4+P5+eefsbKyYt68eVhaWjJv3jyePXvGkCFDZLaCeKlp06YBslzyYzlx4gSOjo4MGzaMffv26Tsc8R7Y29uzYsUK4uLimDVrFg4ODqxdu5bs2bNToUIFTpw4oe8QhRBCfCFkyaQQ4quzYcMGBg4cyIMHDzAwMNAub4qLi8PIyIhvv/2WefPmkSNHDn2HKj6yqKgounfvzsaNG0lNTcXZ2ZlJkybRoUMHfYcmPhPu7u6EhoaSmJio71C+GoGBgRQoUACA27dvkytXLlasWIGtrS0tWrTQc3TifTh27Bh9+/bl3LlzALi4uPDbb7/Rs2dPuUEhhBDirUlCTAjx1dq3bx+9e/fG398flUpF/vz5iY2NJSQkBAAPDw9mzZpF9erV9Ryp+NDCwsLo2rUru3btQqPR4OrqysyZM+XDtHgj0dHR2NjYUKNGDQ4dOqTvcL4qe/bsoUGDBmTNmpV69eqxevVq8ubNy507d/QdmniPwsLC6Nu3L5s3byY5ORlzc3Patm3L9OnTsbW11Xd4QgghPjNyS0UI8dWqU6cON2/e5PTp05QqVYpbt24REhJCwYIFKV68OBcuXMDT05McOXKwYMEC2cnsCxQYGMg333yDi4sLO3bsIF++fOzdu5f79+9LMky8senTpwPg7e2t30C+QvXr12fw4ME8fvyY1atXA3D37l0iIyP1HJl4n5ycnFi3bh1xcXFMmDABKysrfHx8sLe3p1q1apw/f17fIQohhPiMyAwxIYT4H39/f7p27crx48dRFIV8+fKRO3dujh8/TnJyMhYWFrRv355p06ZhZWWl73DFO7hx4wadO3fm1KlTABQvXpzFixdTqVIlPUcmPmf58uXjwYMHJCYmolKp9B3OV+Xs2bM0btyY8PBwXnxru3PnTho2bKjHyMSH5uvry6BBg7h8+TLwfJfXkSNH8vPPP+s5MiGEEJ86mSEmhBD/U7BgQY4ePUpQUBANGjQgICCAgwcPkj17dlq3bo25uTmLFi3CxsaGOnXq4O/vr++QxRs6e/YsHh4eFClShFOnTlGuXDkuXbrE5cuXJRkm3klsbCwBAQGUL19ekmF68OOPP/Lo0SOdZJiBgQEnT57UY1TiY6hXrx6XLl0iMDCQZs2a8fDhQzp37oyVlRW9e/cmLi5O3yEKIYT4RElCTAgh/iNnzpzs2rWLiIgIfvjhB0JDQ9mwYQOGhob88ssvFChQgP3791OoUCEKFy7M9u3b9R2yeI0jR45QpEgRypUrx8WLF6levTq3bt3i9OnTlChRQt/hiS/AzJkzURQFLy8vfYfyVVq/fj3fffcdKpVKW2Rdo9Gwe/fuV54nCyW+HLlz52bLli3ExcUxbNgwTExMmD9/PtbW1tSuXZsbN27oO0QhhBCfGFkyKYQQr5GQkEC/fv1YsWIFSUlJ2NjY0LFjR65evcqhQ4fQaDQ4ODjg5eXFkCFDMDIy0nfI4n927txJnz59CAgIQKVSUa9ePZYuXUrOnDn1HZr4whQoUID79++TmJgou97p0f3795kzZw6LFi3S7vQZExOjXeb+NDGFwGfxRCQkE52kRgFUgLWpEQ7mJrjaWGBnZqy/JyDeqy1btjB06FBu3rwJPF/WPH78eFq3bq3nyIQQQnwKJCEmhBCZpFarGTlyJHPnziUuLg5zc3O6du1KYmIiq1evJj4+HhMTE7777jvmzJmDo6OjvkP+aq1fv56BAwcSHByMgYEBTZs2ZcmSJfJ3Ij6I+Ph4rKysqFixoizR+0Q8e/aM7t27s2HDBtq3b8/8Jcs4FxZFREIKKiCjN79p7Q7mxpRxssXKRG5ufClu375Nnz592LdvH6mpqdjY2NC1a1fGjRuHmZmZvsMTQgihJ3ILUwghMsnIyIiJEycSHR3N1KlTMTMzY+7cuaxcuZLWrVszc+ZMHB0dWbt2LdmyZaNSpUqcPXtW32F/VZYsWUK2bNlo06YNYWFh/PTTTzx9+pTNmzdLMkx8MHPmzEFRFHr16qXvUMT/2NjYsG7dOtauXUuKhQ377z8mMiEFyDgZ9mJ7ZEIK++8/5kF0wkeJVXx4+fPnZ/fu3cTGxtKvXz8URWH69OlYWVnRqFEj7t27p+8QhRBC6IHMEBNCiHfg4+PD8OHDCQsLw9DQkGbNmtG+fXvGjBmj3f7d1dWVMWPG0KFDBz1H+2XSaDTMmjWL8ePHExUVhYmJCT///DOzZs2SO//ioyhcuDB37twhKSlJlkt+Yh5EJ3AmNOqtzy/nbEsua/P3F5D4ZKxZs4ZRo0Zx9+5dAIoUKcKkSZNo0qSJniMTQgjxsci7NiGEeAedO3cmNDSUzZs3kzt3bjZt2kSzZs1wdHTk1KlTNG/enIcPH9KxY0esra3p37+/tq6NeDcajYZRo0ZhY2PDgAEDSEpKYsCAAcTFxbFw4UJJhomPIjExEX9/fzw8PL66ZNjKlStRqVTah5mZGU5OTtSsWZNJkyYRHh7+0nM9PDxQqVRMnz79lWNnZpZtTEwMgwYNom7dumTNmhWVSsXo0aOJTVZzLiwqU8+le63yzPvNW6dNnZLCoOEjcHVzw9TUlEKFCjFv3rx053bs2FHndXjxsWPHDp2+x44dw9TUlMDAwAzjUBSF6tWro1Kp0s04vHXrFiYmJtqbLeLdtGvXjjt37nDlyhVq1qzJzZs3adq0KQ4ODowaNQq1Wq3vEIUQQnxgX9c7NyGE+ECaN29OQEAAhw8fpnDhwuzdu5dKlSoRFhbG6dOn+e233zAwMGDmzJlYWVnx7bffvvQDkXi1lJQU+vfvj5WVFWPHjv3/D7+xsUybNk02NRAf1YIFC1AUhR49eug7FL1ZsWIFp06dYt++fSxYsIBSpUoxZcoUChcuzP79+9P1v3jxIhcuXACez7J9VxERESxZsoSkpCSaNWumbT8XFsW7rINYOnYImxbPp2Hbjvj6+tK8eXO8vLyYOHFiur7m5uacOnUq3aNq1araPoqi4O3tTdeuXXF1dc3wmgsWLODOnTsZHitQoADt2rWjb9++b/+kRDrFihXj4MGD2rpziYmJjB07FgsLC1q2bElwcLC+QxRCCPGBSEJMCCHeI09PT65du8aFCxcoV64cp06dwsPDg507d7Jz507Wrl2Lq6srO3bswM3NjZIlS3LgwAF9h/1ZSEhI4JdffsHKyoqZM2diZmbGrFmziIqKYtSoUV/d7BzxaVixYgWGhob89NNP+g5Fb4oVK0bFihWpVq0aLVu2ZNasWVy+fBlLS0tatGjBo0ePdPovW7YMgEaNGnHz5s133ojA1dWVp0+fcuTIESZNmgRAgjqViISUl9YLe52g2/4c2LiOVj37UafDL5SsWIVJkybRpUsXxo8fT2RkpE5/AwMDKlasmO5ha2ur7bNnzx7Onz9P7969M7zm/fv3GTJkCAsWLHhpXL169eLo0aOyecMHYGVlxcKFC4mJiWHx4sU4OTmxefNmcuXKRenSpeV3tRBCfIHk04MQQnwApUqV4vTp0wQEBFCrVi2uXr1K1apVGTFiBHPnzuXSpUtUq1aNK1euULt2bZycnJg5cyYajUbfoX9yoqOjadeuHdbW1ixZsgQ7OzuWLVtGZGQk3t7ekggTepOcnMyNGzcoVaqU/Dv8j9y5czNjxgxtciFNYmIia9eupUyZMsyaNQuA5cuXv9HYarWafv36sWnTJtRqtXZ54oueJT7fTTLduSkprJo2js5VS9KmVB6GtW3K7csX0vU7fWAPiqJQs0VrVEDgs3gAOnXqREJCAnv27HmjmAEWLlxIuXLlKFiwYIbHu3XrRp06dWjevPlLxyhTpgyFCxdm0aJFb3x9kTkGBgZ069aNoKAgTp8+TeXKlbl06RK1a9cme/bsTJkyRX5XCyHEF0LevQkhxAfk7u7OgQMHCA0NpWnTpty7d4/GjRvToEEDunbtSmRkJB06dCAqKor+/ftjaWlJly5dePbsmb5D17vw8HCaNWuGnZ0da9euxdnZmQ0bNhAWFkbnzp31HZ4QLFy4EI1GQ/fu3fUdyiepYcOGGBoacvToUW3b5s2befr0KT///DP58+enatWqbNiwgdjY2EyPGxwczKxZs/juu+9wc3Nj+vTpREVF6fSJV6dmODts4YiBbF++CM+m3/HbghVUrNuIqb27EBet+zM36NZNrO0dsMuaDQWISEgGoESJEgBcvXo13dhqtVrnkZqaqj2WnJzM/v37qVmzZobPadmyZZw+fZr58+e/9vnXqFGD3bt3I/tifXjlypXjxIkTPHnyhI4dOxIdHc1vv/2GhYUFP/74I0+ePNF3iEIIId6BJMSEEOIjyJ49O1u3buXp06e0b9+ex48f0759e/Lly0fp0qWJjY1lypQpWFtb4+Pjg52dHTVr1szwQ9eXLigoiHr16uHk5MS2bdtwd3dn586dBAUF0apVK32HJ4SWj48PhoaGdOzYUd+hfJIsLS1xdHQkJCRE2+bj44OZmRlt27YFnm9MEhsby19//ZXpcbNkyaL9+uHDhwwaNAgnJycaNWrEunXrAEhSp5/BExxwm8Nb/6JR+660HziCklU8+bZjN9oPGEZ8bIxO39iop1jZ2Gq/j05Sa5+TiYkJEREROv3j4uIwNjbWeXh6emqPX7x4kYSEBDw8PNLF9fDhQwYMGMDUqVNxcXF57fP38PDgyZMn+Pv7v7aveD/s7e1ZsWIFcXFxzJo1C3t7e9asWUO2bNmoUKGCLGEVQojPlCTEhBDiI7K2tuaPP/4gJiaGPn36EBcXh7e3N/b29sTGxhIaGsquXbsoWrQohw8fpnjx4hQoUIC///5b36F/cLdu3aJatWq4urqyd+9eChcuzJEjR7hz5w4NGzbUd3hC6FCr1Vy7do3ixYvLRg6v8OIspnv37nHo0CFatGiBgYEBN2/eJFu2bJibmzNhwgRGjhxJr169aNeunXY5Zbt27cifPz8uLi7Y29tjZWWFk5NTumskJSWxa9cu+vTp89JYrv77PGlR/dsWOu2VGzTBMIO/wxeXYSr/eS7/XaJpbm7OmTNndB4vbhiQlhTMli1buut0796dkiVL0rVr15fG/qK0MR4+fJip/uL9MTAwwNvbm5CQEI4cOYKHhwenT5+mSpUq5MyZk/nz58tySiGE+IzIOzghhNADU1NT5syZw6xZsxg7diwzZ85k3LhxTJs2jS5dunD27FlCQkLo2bMne/fupVWrVtja2tKrVy9GjRr1RX0Av3jxIl26dOHcuXPA89kPS5cuzXAmhRCfiqVLl6LRaDKdxPgSJCcnExYWRmhoKI8ePeLIkSMAzJo1C1NTU54+fUp0dDTR0dHExsYSGxtLeHg4ERERmJubk5SUhKIorF27lrVr1+qMHRAQwLhx49Jd8969e5iZmaFSqYiOjs4wLpVKhaIoFCxYkB49euDl5ZVhv9iopwDYZs2q025oZEQWWzudNitbO+7dvPb/1/jfdeLi4khOTsbe3l6nv4GBAWXLls3wuvB8UxAAMzMznfaNGzeyZ88ejh8/nm6pfHJyMlFRUVhaWmJsbKxtTxsjbUyhH9WrV+fs2bOEhYXRt29fNm/eTO/evRk0aBBt27Zl+vTpOpsqCCGE+PTIDDEhhNAjAwMDRo8eTVRUFHPnzsXS0pL58+djZWXFqFGjWL9+PdHR0fTp0we1Ws348eOxsLDg+++/JywsTN/hv5MTJ05QvHhxSpcuzblz56hSpQrXr1/n3LlzkgwTn7wlS5Zoi29/yjQaDZGRkVy9epV9+/axevVqZs2axbBhw/j111/54YcfaNCgAVWqVKFEiRLkzZtXZzaWqakphoaGqFQqTE1NcXV1pWLFijRt2pSVK1cCsHbtWlasWMHWrVs5fPgwly5dIigoSLsTo7W1NXnz5tVJ6mSkQ4cOhIeHk5qayooVKwA4efIk0dHRBAcH68y+enHZZJ48edi5cyc3btzQLsU0NUr/Ftfqf0mvqMePddpT1Wpi/pcsS+NaoDDRkRE8fRz+/DmYPr8JceXKFeD5zppvwtHRESDd7pRXr15FrVZTsWJF7OzstA94nnS1s7Nj586dOuekjZE2ptAvJycn1q1bR1xcHOPHj8fKygofHx/s7e2pVq0aFy6k37RBCCHEp+HLmWIghBCfMQMDA3r37k3v3r1Zs2YNgwcP5s8//2TNmjU0btyYxYsXM2fOHJYtW8bYsWPZuHEjGzdupGzZssyePZsqVaro+ylkmq+vL7179+b27duoVCpq167NsmXLcHV11XdoQmSKWq3mypUrFC1a9IPN1kxMTCQ0NJSQkBDCwsK0M60iIiKIjIwkKiqKZ8+eaWdixcfHk5CQQFJSEikpKaSkpKDRaDJVeF2lUmFoaIiRkREmJiaYmppiZWWFpaUllpaWZMmSBRsbG2xtbbG3t8fBwYGsWbNy5coV5s+fz7Zt26hbt67O7KegoCCqVq2KsbEx/v7+nD17loYNG9KzZ0++++67dDH06tWLPXv2sGzZsgx37MySJYvODKzcuXPz4MEDxo8fT/fu3dMl2yyMDFGBTmH9YuUrA3D0n83kLVZC235y93ZS1Wqd88t9U491c6ZweOtftOjaCwdzEwBWrlyJubk59evXf+3r+qLChQsDcPfuXZ32jh07UqNGjXT9a9asSbNmzfDy8kqXfAsICMDAwOClu1UK/TAyMmLYsGEMGzYMX19fBg4cyPHjx/Hw8MDV1ZVRo0bRqVMnfYcphBDiBZIQE0KIT0y7du1o164du3btwsvLi+3bt/PPP//g6enJsmXL6NKlC6dOncLb25szZ85QtWpVcubMyfDhw+natWuGHyY/BZs2baJfv34EBQVhYGBAkyZNWLJkCdmzZ9d3aEK8kZUrV5Kampput1ONRkN4eLh2WWF4eLg2kRUZGcnTp0959uwZ0dHRxMXFERcXR0JCAomJiSQnJ5OSkkJqamqmaxAZGBhgZGSEsbExpqammJqaYmNjo01iWVtbY2Njg52dHQ4ODtpElpOTE05OTuTIkQNra+u3/pmRNkMsMjKSixcvolarCQ8P59ixY6xYsQJDQ0O2bNlC1qxZ8fHxwcjIiKFDh2ZYOP6XX36hT58+7Ny5k6ZNm7722nv37sXCwkK7JG337t3ExcURE/O8OP7dq5c4uWcHAB6etTA1tyBn3vxUb9KSnauWYmRsRIlK1Qi67c/25YuwsMqiM37u/AX55rs2bJg3AwMDQ76v48nfhw+yZMkSxo8fn27J5OvkzJmTPHny4Ofnp1PnzM3NDTc3twzPyZEjR4bJMj8/P0qVKqWdSSY+PfXq1aNevXoEBQXh5eXFjh07+Pnnn+nTpw8dO3ZkypQpWFhY6DtMIYT46qkU2bNZCCE+aadOnaJ79+5cvnwZgLJly7JkyRJKly5NWFgY3t7ebN68mZSUFCwtLenUqdMn9WZ7xYoVDB06lLCwMAwNDWnVqhW///671FYRn4zY2FgePnxIWFgYjx49Ijw8nCdPnhAREcHTp0+JiooiOjqamJgY4uLiuH//PikpKVhZWZGSkoJarSY1NTVT10qbjWVsbKydjWVubo6FhQVWVlbaJJaNjY1OEitr1qw4Ozvj7OyMk5PTJ1FHcOXKlTozXkxMTLC1taVw4cLUq1ePLl26kDVrVp48eUKOHDlo2LAhW7ZsyXCsqKgoXFxcqF27Ntu3b9eOfebMmZfW5lKr1QQHBxMSEkKzZs14/J+lkGkW7v+XbDlzAZCSnMzaWZM4sn0TCbGxuBUqSqcho5nZ71eKlq9M78mz/3/8lBQ2LZrDka1/8fRxOG5ubvTq1YvevXvrjN+xY0c2btxIbGzsK1+vkSNHMn/+fEJDQzE1NX1lX5VKRc+ePZk/f75Oe2xsLNmzZ2fcuHH069fvlWOIT0dycjJjxoxh4cKFPH36FAMDA2rWrMm8efO0sweFEEJ8fJIQE0KIz8S1a9fo1q2bdnv3IkWKsGDBAmrUqKGtLzZv3jwiIyNRqVTUrVuXBQsWkDdv3kxfQ1GUdLunvY6vry9xcXG0aPH/O7cpisK8efMYM2YMkZGRGBsb06FDB+bMmfPJJOrE502tVmtnYqUtKXz8+LHObKy0Au8vzsZKSkrSmY2V2bdBaUsKjY2NiY2NxcjICHd3d53ZWLa2ttrZWI6OjmTLlo1s2bJpE1nW1tYf+FX5ujRs2JDdu3dneKxQoUIcP32WY6HRaN7hna6BCmq7ZcXK5N0TkCEhIbi7u7Nq1Spat279VmP4+Pjg5eXFgwcPZIbYZ2rLli0MHTqUmzdvApA/f37GjRv31v8mhBBCvD1JiAkhxGcmKCiIbt26sXfvXhRFwc3NjRkzZtCiRQsiIiJwcXEhJSVF+0G/SJEiTJs2jYYNG6Yb62liCoHP4olISCY6SY3C893UrE2NcDA3wdXGAjuzlxfCvnDhAhUqVMDQ0JDg4GDs7OyYOHEi06ZNIzo6GjMzM7p3786UKVMwMTH5QK+I+FxoNBqio6PTzcZKq4314pLCtNlYaUsK02pjqdXqN1pS+OJsLDMzswxnY9nZ2WlrY6UlsZycnHB2diZbtmw6Swr/+OMPOnbsyLRp0xgwYMCHeqlEJkyfPp2BAwema7ezsyM4OBgLCwseRCdwJjTqra9RztmWXNbm7xClrsGDB7N7924uXrz4xktV1Wo1RYoUoUOHDgwbNuy9xST04/bt2/Tp04d9+/aRmpqKjY0N3bp1Y9y4ca+dQSiEEOL9kISYEEJ8pp48ecKvv/7Kli1bSE1NxcnJiXLlyrFz505UKhXu7u7kyJGDY8eOodFoyJo1K3379mXw4MHEqzWcC4siIiElXeHpNGntDubGlHGyTTdD4unTp5QsWZKQkBA0Gg2VKlXi4sWLxMfHY2lpSd++fRkzZswnW9NMZF5SUhKhoaHa2ViPHz/WmY2VVuA9LYkVFxenTWIlJydrlxRmtsB7Wm2s/y4p/G+B9//OxkpLYjk7O2Nu/v6SGC+qUKECZ86cIT4+XqeIvPj41Go1Li4u6ZZKbtq0SWfG6oPoBM6FRaEoGf+s+y8VoFJBGaf3mwwDiImJYebMmfz888/kypXrjc69d+8ef/75J4MGDZJ/e1+QxMREhg4dio+PD9HR0RgaGlK/fn3mzZuHu7u7vsMTQogvmiTEhBDiMxcfH4+Xlxd//PEHKSkp2nYDAwPatGnDggULGDhwIKtXryYhIYGazb7n1/EzMMxkDaKMPhxqNBq+/fZb9uzZozNbx8bGhqFDhzJgwABJhOmZRqMhIiJCu0vho0ePePz4MU+ePOHp06dERkZqlxTGxMQQHx9PfHz8Oxd4NzIywtTUVDsbK6MC7/b29hkuKbSzs/uk/91oNBpMTU3Jly8fN27c0Hc4X7Xz58/TuHFjQkNDtW1GRkbUqFGDvXv3plv6HZuszvRNAEdzEzycbN7LMkkh3sTq1asZNWoUAQEBwPMZ3pMnT+bbb7/Vc2RCCPFlkoSYEEJ8IaZOncrgwYPTtc+bN49evXqh0Wjw2bgN+xIVUBTlrRIPacuHRo0axdixY9MdnzhxIkOGDHmr+MVz8fHxhISEEBoammGB97TaWGmzseLj40lISCA5OVk7G0uj0WR6NlZabayMCry/OBsrLYnl6OhI9uzZyZ49u7bA+9eyHHbdunW0bdtW/p3rkVqtpkOHDqxduxYDAwP69OmDgYEBM2fOxMjIiGvXrlGgQIGXnv+uy8SF+BiuXr1Knz59OHLkCBqNBgcHB3r16sXw4cM/iQ01hBDiSyEJMSGEeAOjR49mzJgxPH78GEdHx3THixUrhqOjI4cPHwbgwYMHTJo0if379/PgwQPMzc3JkSMHFStWZOTIkdolM2njpjE3N8fe3p6iRYvSuHFjOnbsSJYsWQA4fPgwNWvWfKO4+/fvz7Cx4zkS/BSNAjfO/cuhLX9x7/pVgm77o05J1tmJ7WUMgH2LpvP77JkZHre1tSUsLOyl9U9eVbC/YMGC2iLDaY4dO0bt2rW5desWrq6uwPOC/cuWLWPRokXcvn0bY2NjihUrxqBBg2jUqJH23Fu3blGsWDH8/Pzw8PB45fN6V2q1mvDw8HQF3p88eaKzpDA6OprY2FhtEittNpZarUatVr9Rgfe02lhps7FeXFL4YoF3e3t77S6F2bNnx8nJiRw5ckiB97dQpUoVTp06RWxsrGwOoQe7du2iTZs2REdHU6BAAfbs2YO7uzvx8fFUrFiRH374gaFDh77RmG+zkYgQH0tsbCwDBw5k1apVxMfHY2xsTJMmTZg7dy4uLi76Dk8IIT57cotBCCE+kODgYDw8PLC1taV///4ULFiQZ8+ecf36df766y8CAgLS1ZDZs2cPNjY2JCcnExISwoEDBxg0aBDTpk3jn3/+oWTJknh4eHDq1Cmd85o1a0ZsbCzW1tbanfCMjIxISUkhJCSEGTNmkL9OM7K5Pd9x8sqp41w+eQz3IsUwt8rCtdMnM/Wc1KmpOJepSpYsSylUqBAODg6kpKSQkpKiXWYXHh7+0to4/40b4N9//8Xb25vmzZvrtCuKgre3N127dtUmwwBGjRrFuHHj6N69O5MnTyYxMZF58+bRuHFjndpBBQoUoF27dvTt25cjR45kGE9agffQ0FBtEiutNlZUVBRRUVHpZmO9WOA9NTWV1NTUTL12KpVKu6QwrcC7nZ1dugLvtra2ODg44ODgQNasWdMVeJfZAfqh0Wg4c+YM+fLlk2TYRxYbG0uTJk04dOgQxsbGzJw5k759+2qPW1hYcOnSpbdKbEkyTHzKrKysWLhwIQsWLGDZsmWMHz+eTZs2sWnTJkqVKsWMGTOoVauWvsMUQojPlryrFkKID2Tp0qU8efKE06dP6xTGbdasGUOHDs2wNlOZMmV0Zp798MMP9OrVC09PT5o0acKtW7ewtramYsWKOueZmZlRtmxZduzYkWEsZ6/eIMjYVls357sefWnVqz8A23wWZjohZmBoSJFyFQkMj9BZVrR371769OmDv78/a9euzXDpJpAuboDFixejUqno3LmzTvuePXs4f/48a9eu1Wlfvnw55cqVo2PHjtrZWFWrVuXo0aN4e3vz559/agu8P3nyhPv372NnZ4dGo3nnAu8mJiZYWFjg6OioMxvLzs4uwwLvaYksS0vLzLy84hO2efNmUlJS+PHHH/UdyldlyZIl9OnTh6SkJCpVqsSOHTuwt7dP108SW+JLZmBgQLdu3ejWrRtnzpzB29ubU6dO8c0335A9e3b69esntTuFEOItSEJMCCE+kIiICAwMDMiWLVuGxzP7xrVkyZIMGzaMQYMGsWHDBtq3b//GsRhldUEVFa9NiL3Jm+b42Bj+XjATv727iAwPw9rOgfpNm7Fk1nQePHhAv3792L17NyqVCpVKxcyZMxk9ejSmpqYUKlSIoUOH0rhxY54+fardqTCtwPvDhw9ZvXo1WbNmpXfv3jqzsR4+fIihoSFlypQhJSVFWxsL4OHDhxkm1x48eMCDBw8wMDDQLik0MDAgMTERFxcXLC0ttbOxrK2tsbe3x87ODkdHR+2SwrQC7w4ODvLhQmjNnTsXgH79+uk5kq9DcHAwDRs25MqVK1haWrJ69Wq+++47fYclhN6VK1eOEydOEBkZSf/+/Vm/fj2DBw9m5MiRfPfdd8yePTvDkg5CCCHSk4SYEEJ8IJUqVWLBggW0aNGCfv36UalSpbeu29SkSRMGDRrE0aNH3yohFpGQnOGuaq+TlBDPyJ9aEBEWSotf+uBasDAPbvuzfu40tq55XtMkTdqMq4iICExMTIiPj+f06dM0bdr0tdcJDw/H19dXW+Dd2NiYpKQksmTJQo4cObSzsWxsbAgJCeHs2bM0bdqU6tWrY21tzb59+9i6dSu7du1Kt3ykR48e/P3339y5c0dmkYi39u+//5I3b16srKz0HcoXb/jw4UyePJnU1FSaN2/O+vXrv5qNG4TILHt7e1asWIGPjw9z585l6tSprFmzhrVr11KuXDlmz55NpUqVPsi1pfaeEOJLIQkxIYT4QNq2bcuxY8dYunQpe/fuRaVSUahQIerXr0+fPn1wc3PL9FhpNbRCQkLeKpboJPVbnbfzTx8C/W8wacNO8hUvCUCJStWwdczKzH6/ZniOiYmJdjZW2pJCGxsbnSWFabOx+vXrR2BgICEhITp1mU6fPk2FChVYunQprVu3TneNxYsX4+XlxdatW4HnHwx27NiRYS0VDw8PFi5ciL+/P4UKFXqr10F83bZv305ycjJt27bVdyhftEuXLtG4cWOCg4PJmjUrW7ZsoUqVKvoOS4hPmoGBAd7e3nh7e3P06FH69evH6dOnqVy5Mjly5OC3336jR48eOjOenz59yty5c+nbt2+mbtTJ7qxCiC+VJMSEEOIDUalULFq0iCFDhrBr1y7Onj3L0aNHmTVrFosXL2bXrl14enpmaqy32RBYo9Gg0WhQFAW1Wg0qFYaGhm80xrnD+8mVvxDuhYuSqv7/pJqHZ23gecFftVpNYmKi9ljdunW1iapXuXbtGpcvX6Znz57pipSnJf4yWm66YsUKvLy86NWrFw0aNCA5OZlVq1bRtGlTNm/eTL169XT6p43x8OFDSYiJtzJr1iwABgwYoOdIvkwajYZOnTqxatUqVCoVvXr1Ys6cObJkWYg3VL16dc6ePUtYWBje3t5s2bKF3r17M2jQINq2bcuMGTOwsbFh9uzZjB07Fj8/P3bs2PHS9waxyWrOhUURkZCCCnRmmivAsyQ10UlqAqLicTA3poyTLVYm8vFSCPH5kJ9YQgjxBtJ2+HvZzoJqtRpjY927pK6urvz66//Ppvrrr79o06YNAwcO5PTp05m6bmBgIMArt1lXq3Vngf3888/88ccf2u+LlqvE2D83Zep6aaIinhAWeI9WxXJneDw2NhZ4XtQ/LSmW1vY6Pj4+AHTp0iXdsYSEBO24L3r69Ck9e/akS5cuTJ8+XdveoEEDatSoQffu3bl3757OOWljpI0pxJs6deoUbm5ub73kWbycr68vrVu35tmzZ+TLl49du3aRP39+fYclxGfNycmJ9evXo1armTJlCnPmzMHHx4fly5dTuXJlrly5Ajz//9e/f39mz56dbowH0QmcC4si7X7cy27LpbVHJqSw//5jyjjZksva/L0/JyGE+BAkISaEEG8ge/bswPPZRmlfp1EUhdDQUMqWLfvKMVq1asWkSZO4evVqpq+7fft2AGrUqJHh8aioKHx9fXFwcCB//vwUKVKE7NmzM3ToUGJjY7EtVh7HnBkntV7F2tYeE1Mzek6YqdNuZWJIeRc7YmJimDJlCr6+vgBYWlqSJ08e1Gq1NnmYkeTkZP7880/KlClDqVKl0h1PKwgcGRmp0+7v709CQgLlypVLd07ZsmU5cuQIsbGxOnWe0saQIsPibezatYukpCR++OEHfYfyRYmNjaV58+bs378fIyMjpk6dysCBA/UdlhBfFCMjI4YNG8awYcPYs2cPgwYN4sSJE9rjiqIwZ84cihQpQrdu3bTtD6ITOBMa9UbXUgBFQXueJMWEEJ8DmYsuhBBvoFatWqhUKjZs2JDu2J49e4iOjqZ27efLCUNDQzMcIzY2lgcPHrxytteLLl26xMSJE3Fzc6NVq1YZ9klLPkVGRvLvv/+ycuVKpk6dysSJE5k7dy7qpARy5Mmbqeu9qEzN2jx6cJ8sdnbkK16SfMVL4l64KI9Cnu8OuXPnTlxcXLSFe+Pi4li6dCmWlpa0adOG8PDwDMfdvn07T548oXPnzhkeL1y4MAB3797VaU97zfz8/HTaFUXBz88POzs7LC0tdY4FBARgYGBAwYIF3/j5C5G2XFKSNe+Pj48Pjo6O7N+/nwoVKhAaGiqvr/gqrFy5Ursjc9oja9as1KhRgx07duj0TTs+efLkl45z9uxZ7t+/n27MjB4NGjRg27ZtGZYO+OWXX3T65raxoGUhF1oWcuHsoX0AhAc/0Lb99zGoZX3tWEG3/enW/VfKV6yIpaUlKpWKw4cPv/Q1iY6OZsKECZQtWxZra2tMTU1xc3Pj559/5vz58+n6jx07liJFimh3nQ4NDWX48OFUqlQJR0dHrK2tKVOmDEuWLEk3m9/Hx4ccOXIQFxf38r8kIcRXRWaICSHEG8ibNy+9evVi2rRpREVF0bBhQ8zNzTlz5gyTJ0+mbNmy2sLbEyZM4MSJE7Ru3ZpSpUphbm7OvXv3mD9/PhEREUybNi3d+OfOncPGxoaUlBRCQkI4cOAAf/75J9myZeOff/556U5r/21PqzlmYGDA4sWLafljBw4FPtEefxYZwfXTpwAIunUTgPPHDmJj54C1vQNFyz9PcDVu3xW/vTsZ8WNzGnfohmvBwigaDQvHDudJ6EPtNdLemAKUKlWKoKAg1q9fz/r163F1dWXDhg1UqFBB28fHxwdzc/OXFinPmTMnefLkwc/Pjz59+mjbc+fOTYsWLViyZAmmpqY0bNiQpKQk/vjjD06cOMG4cePS7Xzl5+dHqVKlsLOzy/BaQrzK8ePHyZ07N/b29voO5bMXEhJCw4YNuXTpEhYWFqxdu5Y2bdroOywhProVK1ZQqFAhFEUhLCyM+fPn8+2337J9+3a+/fZbnb6TJ0+mW7duL/0Z5OzszKlTp3TaevTowbNnz1izZo1O+/3797l58+ZL4/r5558p3aglsSn//zs9R558On0a/vgz1Ro312kzs/j/G1F3r17i3/17KFisON988w3//PPPS6939+5d6tatS3h4ON27d2fMmDFYWVlx//59/vrrL8qUKUNUVBQ2NjbA858hU6dOZeXKldoag+fOnWPVqlW0b9+eESNGYGxszO7du/n111/x8/Nj+fLl2ut16NCBKVOmMHXqVMaMGfPSuIQQXxFFCCHEG9FoNMrChQuVsmXLKhYWFoqJiYmSP39+ZfDgwUpMTIy2n5+fn9KzZ0+lZMmSir29vWJoaKhkzZpVqV+/vrJr1y6dMUeNGqXwvxUHgGJqaqo4OzsrdevWVebMmaNER0eni+PWrVtKx44dFUdHR51zAUWlUimWlpbKqVOntP0PBz5WNt8MUTbdDFHG/LEx3Tlpj6LlKimb/tdv080QZc35O8p3v3orLu55FSNjE8XS2lpxcnLK8FwXFxelWLFiiomJiWJlZaVYWlpqj+XOnVtZtmyZEhQUpBgYGCjt27d/5es8YsQIxc7OTklMTNRpT0hIUKZNm6aUKFFCyZIli2Jvb69UrFhRWb16taLRaHT6xsTEKBYWFsqMGTMy/fcrRJp9+/YpgDJgwAB9h/LZGzlypGJoaKgASpMmTZSEhAR9hyTER7dixQoFUM6cOaPTHh8fr5iamipt2rTRtgFK7dq1FSMjI6Vfv36ZGieNp6enUrRo0XTtx48fVwoWLKiUKlVKqVmzptKiRQulY8eOSs6cORWVSqUUKl1W5/f/i4+F+/9VAKX9wBEv7bPpZojy9/Vg7dcr1qxTAOXQoUPpYlGr1Urx4sUVa2tr5cqVKxk+j127dilxcXHa7wcNGqTkyJFDSU1N1bZFRkYqycnJ6c7t2bOnAihBQUE67dOnT1dsbGx0xhVCfL1UivIWW5cJIYTQixs3bjBx4kR27dqlrY1lY2ND3bp1KVSoEOPHj0elUmFhYcGBAwcoX7689tzYZDX77z9G8w4/9Q1UUNstK1YmRowePTrDO6ylS5dm0qRJ2t0eQ0JC6NOnD9u2bUOtVmNlZUWXLl2YNGlSuqL5LwoJCcHd3Z1Vq1bRunXrt4rXx8cHLy8vHjx4IDPExBurX78+vr6+PH78WGrQvaWrV6/SqFEjgoKCcHBwYPPmzVSvXl3fYQmhFytXrqRTp06cOXNGp96ooijY2NjQvHlz7WY4KpWKnj17olarWblyJf7+/ri6ur5ynDQ1atTgyZMnma5V2rFjRzZu3MiRW0EExSRlWEA/PPgBv9auQPuBI2ja+dcMeuhSAXeO7WNQ1w4cOnQoXQ3UTZs28d133zFp0iR+++23146XnJyMi4sLP//8M1OnTn1t/1WrVtGhQwdOnjypLesAEBYWhouLC8uWLePnn39+7ThCiC+b1BATQohP3NWrV2nTpg329vYUKVKE1atXoygKP/zwA5cvXyYqKoq//vqLPn36aJNhBw8e1EmGAViZGFHGyfadYnlxS/VRo0bx448/apctWFlZUa5cOS5evEj9+vVxcHBgyJAh2Nvbs3HjRuLi4hg6dChGRkbMnj0bKysrGjVqxP379zO8louLC97e3kyYMEFnSWZmpe2uNWTIEEmGibdy7NgxcubMKcmwt6DRaPj5558pUaIEDx48oHv37oSHh0syTAie71StVqtJSUkhODgYb29v4uLiMiwjMHr0aAwNDRkxYsQHjysiPgm1Wk1q2iODHbUVRfP/x//3yGh+hcLzG3Evs3fvXgCaNWuWqdj+/fdfIiIiqFmzZqb6Hzx4ECMjIwoUKKDT7uTkRKFChdi5c2emxhFCfNkkISaEEJ+g8+fP8/3332Nra0vx4sVZv349BgYG/PTTT1y/fp3IyEjWrVtH8eLFtec4Ojqyfv16Tp48meEujPB816dyzrYYqJ7fvc0MFc9nhpVz1t1KXaVS4ePjQ9WqVQHo2bMnp0+fJjIykl9++YWkpCQmT56MlZUVderU4datW0yYMIGnT5+yfv163N3d2bVrF+7u7pQoUUL75vhFw4cPp2XLljx8+DDTr12aBw8e8OOPP9K/f/83PleII0eOEB8fT8uWLfUdymdn//79ODg4sGLFCtzd3blx4wYLFy7UJs+F+NpVrFgRY2NjTExMyJUrF4sXL2b+/PnamdUvcnJyom/fvqxZs4bLly+/03VftTAoLi6OevldaFUst/Yx8qcW6fr9OX2CTp9WxXJz+dSxDMeMT0mfUEsTFBQEgLu7e6ZiT6uT5uHh8dq+e/fu5c8//6R37944ODikO+7h4aGz26YQ4uslRfWFEOITcebMGSZNmsSBAweIjo4Gnie5OnbsyNChQ8mfP/9rx/j+++9f2yeXtTl2ZsacC4siIiEFFWS4PCKt3cHcBA8nG+3MsBeZmJiwdetWxo8fT9++fQGwtbVl0aJFLFq0iNWrVzNu3Dj2799P8eLFcXV1ZdCgQXTv3p3WrVtz+fJl+vTpw9GjR6lXrx7ZsmVjwIAB9O/fHwMDA7JkycKoUaNe+5wy4u7uzsiRI9/qXCHSNr3IzFIe8Vx8fDwtWrTA19cXIyMjJk6cyJAhQ/QdlhCfnFWrVml3U37y5AlbtmyhZ8+epKam0qtXr3T9Bw0axOLFixk8eDC7d+9+q2tqNBqKFi1KQkICTZo0oUGDBtSqVQtTU1MAzM3NGb1qs8455v/ZtRmgUfsuVP9W90ZBDvc338X6TYWEhKBSqV47Y/f8+fO0atWKihUrMmnSpAz7ZMuWjfDwcNRqtXaXbiHE10lu1QkhhB6dOHGCpk2bkiVLFsqXL8+WLVswMzOjS5cuBAQE8PjxY1asWJGpZNibsDIxwjO3IzVdHXG3tcDG1Eg7Y0wF2Jga4W5rQU1XR6rndsgwGZbGzs6OGTNmkD179nTHfvzxR/z9/bl79y5NmjQhJCSEnj17YmlpSbt27ciWLRuHDx8mMjKSTp06ER0dzaBBg7C0tKRTp05ERUW91+ctRGYdPnwYZ2dnnJyc9B3KZ+GPP/7AwcEBX19fypYty8OHDyUZJsRLFC5cmLJly1K2bFnq16/P4sWLqVu3LoMGDcrw9561tTXDhw9nz549HDp06K2uOXbsWG7evElgYCDz5s2jYcOG2NjYULVqVQ4ePIharSZv0eLkK15S+/jvDpMADtmddfrkK14ScyurN44nd+7cANy7dy9T/RMSEjA2NsbQ0PClfS5cuECdOnXInz8/u3bt0ib7/svMzAxFUUhMTHzjuIUQXxZJiAkhxEd29OhRGjVqhJWVFVWrVmX79u1YWVnRvXt3AgMDefToEUuXLs30MoJ3YWdmTKnsNnzjlpXmBZ1pXsCJ5gWd+cYtK6Wy22BnZvxerpMnTx62bdtGfHw8EydOxNbWlrVr1+Ls7EzJkiU5efIky5cvJy4ujunTp2NjY8PKlSuxt7fH09OTK1euvJc4hMiMEydOEBcXR/PmzfUdyicvLCwMDw8POnbsiEql4s8//+TMmTNky5ZN36EJ8VkpUaIECQkJ3Lp1K8Pjv/76K+7u7gwePPiVSx9fpnXr1hQrVkynLSkpiRMnTvDgwQNSUlKIi3z8VrG/jIXxy5NXactDt27dmqmxHB0dSU5OJi4uLsPjFy5coHbt2ri6urJ3715sbGxeOlZkZCSmpqZYvUUiTwjxZZGEmBBCfAQHDhygfv36WFpa4unpya5du7CxsaFXr14EBwcTGhrKwoULtXdM9UWlymxlsbdjZGTEkCFDCA0N5fjx41SuXJkrV67QqFEj7O3tGTx4MD179iQsLAxfX1+KFSvG0aNHKVGiBPny5WPDhg0fND4h4P+XS8oMp1cbN24cOXPm5MKFCzRs2JDIyEh+/PFHfYclxGfp4sWLAGTNmjXD4yYmJowfP54zZ87w999/v3QcjUZDbGwsjx8/pn79+ri6umJqakqRIkUy3HXSwMCA4sWLY2lpSYl87pmuL/o6Knjl7PKmTZtSvHhxJk2a9NLdMH19fYmPjwegUKFCANy9ezddv4sXL1K7dm1y5szJvn37XruRTkBAAEWKFMnkMxFCfMkkISaEEB/I7t27qVOnDubm5tSuXRtfX1/s7e3x9vYmNDSUhw8fMm/ePHLkyKHvUPWiSpUqnDhxgqioKHr06IFarWb69OlYWlpSq1YtsmXLxuXLlwkICNDuRvnDDz9gZ2fHsGHDSE5O1vdTEF+ogwcPkj17dnLmzKnvUD5J169fx83NjZEjR2JjY8OBAwfYuXMnZmZm+g5NiM/C1atX8fPzw8/Pj507d9K5c2f27dtH8+bNXzk7vE2bNpQuXVpbRyw2NpYNGzbQtWtXypQpg729PUZGRpw7d47w8HB8fX2JioqiQIECtGvXDm9vb+1YhoaG5M2bl4sXL2oL1bvaWGRYU/RNJCXEc2rPDk7u2UHg1YvA801KNm7cqFP/zNDQkC1btuDo6EilSpUYNGgQu3fv5ujRo/z55580bdqUBg0akJKSAkCNGjUA8PPz07mev78/tWvXBmDChAncvn1b+9r6+fnx+LHurDeNRsPp06czvVulEOLLJlUEhRDiPdq+fTuzZ8/m5MmTJCUlAc/rZLRq1YpBgwa99M7v18za2poFCxawYMECNmzYwOjRozl06BClS5cmV65cDBgwgO3bt5OcnMzQoUNZtmwZEydOZOrUqTRt2pS5c+fi4uKi76chvhBnzpwhJiaGtm3b6juUT45Go+GXX37Bx8cHgK5du7Jo0SLZPVKIN9SpUyft1zY2Nri7uzNz5kx69Ojx0nOuXLnCjh07dIrAv5jUMTIyIlu2bJQsWZK7d+9iZGTEnTt3dP5/hoaGMnv2bABatWrFkiVLdJYN2pkZ42BuTGRCylsnxp5FRDDdu5tO2+jRowFwdXXl/v372va8efNy/vx55s2bx5YtW1i4cCFJSUk4OztTvXp1jh8/rl36mCtXLqpVq8a2bdvo1u3/xz916hQREREAfPvtt+niWbFiP08icgAA6AxJREFUBR07dtR+f/jwYZ49e0a7du3e8hkKIb4kKuVtFqELIYQAnn9A3LJlC3PnzsXPz087a8nNzY0ffviBgQMHYm9vr+coPz+BgYH07duXHTt2kJKSgqmpKc2aNWPmzJm4uLiwfPlyxowZo922vUyZMsyePZuqVavqOXLxuWvZsiWbN2/m/v37uLq66jucT8bBgwf5/vvviYyMxM3NjV27dml3yRNCvD/x8fHs3r2b/fv3c/bsWQICAoiKikKj0Wj7WFlZkTt3bkqUKEGNGjX49ttvM3VjSFEUunXrRoUKFejcuXOGZRJik9Xsv/8YzTt8QjRQQW23rK9cMvk2Nm3aROvWrQkMDHzr2fU//fQTAQEBnDhx4r3GJoT4PElCTAgh3pBGo+Hvv/9m3rx5nD59WjudP2/evLRt25b+/fu/spiryDyNRsOMGTOYPXs2ISEhABQrVozx48fTtGlTTp8+jZeXF//++y+KopAjRw6GDRvGL7/8IrNWxFuxsbHB1NSU8PBwfYfySUhMTKRFixbs3r0bQ0NDRo4cyciRI/UdlhBfhJs3b7J9+3ZOnDjB1atXCQkJ0dn50MjICEdHR/Lnz0+FChWoX78+np6eOrPEPoQH0QmcCY166/PLOduSy9r8/QX0P4qiULlyZcqUKcP8+fPf+Py7d+9SuHBhDh48KDfQ/o+9uw6LMnsbOP4dulPCAOzAxEbsDuy1dW3XNVZEUdG1VsVA7A7ELmxsXVsBG1tExUBEQECaYZ73D1/mtyzo2gN4PtfFtcszZ85zzwjDzP3c5z6CIAAiISYIgvBJFAoFW7ZsYenSpVy9epW0tDRkMhnFixenZ8+euLq6it2KvrPAwEBGjRrFxYsXUSgUGBsb069fP6ZPn058fDwjRoxg9+7dpKamoqenR58+fZg9e7b4dxE+2Y0bN3BwcKBfv37KZYE/s82bNzNw4ECSkpKoXLkyBw8exNraWtVhCUKuk5yczLFjxzh27BhXrlwhJCSE6OjoTFVf+vr62NjYUL58eerVq0fr1q1VutHO87gkrobHIEl80vLJdLkcGVDDJt93SYZluH37Nvv372fcuHGffeHr1KlTBAcHZ1pyKQjCz00kxARBED5AoVCwfv16li9fzvXr15HL5chkMkqWLMmvv/6Ki4sLenp6qg7zpxMfH8+ECRPw8fEhLi4ONTU1ateujZeXF5UqVcLDw4NFixYRFRWFmpoajRs3ZsmSJZQoUULVoQs5XJcuXdixYwePHj2iWLFiqg5HZSIiImjZsiVXr15FR0eHFStW0Lt3b1WHJQi5QkhICPv37+fs2bPcuXOHFy9ekJSUpLxdXV0dc3NzSpQoQbVq1WjWrBkNGzZES0tLhVFnLz5VztXwGKKS0pCRfWIs43hs2DPG9+lKPiMDdu/eLf7mCoKQK4iEmCAIwj/I5XK8vb1ZuXIlN2/eJD09HZlMRpkyZejTpw/Dhw8XO6nlILt27WLSpEncvXsXgIIFCzJy5EhGjhypvIL84MED4P2W7Z6enjg7O6syZCEHMzU1RV1dncjISFWHojIzZ85k0qRJyOVymjdvzq5du0TiXxCykZqayokTJzh27BiXL1/m0aNHREVFkZ6erhyjp6dHwYIFKV++PHXq1KF169a5Mtn+NjmN0NhEopJSiUuRI/E+EWakrYG5rhZ2xnokv41U9jFTU1NjwIABTJ48WWx6IwhCjiYSYoIg/PTkcjkrVqxgzZo13Lp1C4VCgZqaGvb29vTv358hQ4bkyCu3wv+8ePECV1dX9u3bR2pqKlpaWrRp0wYvLy8SEhIYPnw4p06dQqFQkC9fPlxcXHB3dxd9xgSl27dvU758eX799VfWr1+v6nB+uAcPHtCiRQuePHmCiYkJvr6+NGrUSNVhCUKOEBoaqqz6unXrFi9evCAhIUF5u5qaGubm5hQrVoxq1arRpEkTmjRpkmcvoEmSlKUhvyRJGBoaKp8XdXV11NXVGTFiBOPHj8fExEQFkQqCIHycSIgJgvBTSk1NZdmyZXh7e3Pnzh1lEqx8+fIMGDCAQYMGiSRYLqRQKFi4cCFeXl68fPkSgDJlyjBt2jSaNWvG6NGj2bBhA0lJSWhpadGpUycWLFhAvnz5VBy5oGo9evRgy5Yt3L9/n1KlSqk6nB9GoVAwdOhQVq5cCUCfPn1Ys2aNSBYLPyW5XM6pU6c4cuQIgYGBBAcHExkZmanqS1dXlwIFClCuXDnq1KmDs7PzT/Wa8TE1atQgMDAwy3F3d3c8PDxUEJEgCMLHiYSYIAg/jeTkZBYvXoyPjw/37t1DkiTU1dWpWLEigwYNon///t995ybhx7l69SqjRo3i3LlzKBQKjIyM6N27N9OnT2f9+vXMnDmTV69eIZPJcHR0ZOHChVStWlXVYQsqYm5uDkBUVJSKI/lxzp49S4cOHYiKisLW1paDBw9Srlw5VYclCD9EWFgY+/bt48yZMwQFBfH8+XPi4+OVt6upqWFqakrRokWpWrUqTZo0oVmzZmIJ8Uf079+fDRs2IJfLlcdat26Nt7e3uPAkCEKOJBJigiDkaYmJiSxcuJANGzbw4MEDZRLMwcGBwYMH06dPH9TV1VUdpvAdJSYmMnHiRLy9vYmJiUFNTQ1HR0e8vLxISkrC1dWV69evA1C4cGGmTp3Kr7/+quKohR/pwYMHlC5dmu7du7N582ZVh/PdJScn06lTJ/z8/FBXV+fPP/9kypQpqg5LEL4LuVzO+fPnOXz4MAEBATx8+JA3b95kStro6OiQP39+7O3tqV27Ns7OziI5/AXmzp3L2LFjkclkpKeno6GhQWRkJMbGxqoOTRAEIVsiISYIQp4THx/PvHnz2LRpE48ePUKSJDQ0NKhSpQpDhw6lR48eYjnQT2r//v38+eef3Lp1C4D8+fPj4uJCly5dcHFxwc/PD7lcjpGREYMGDWLGjBli6exPoHfv3mzYsIFbt27l+Q/BW7duZcCAASQmJlKxYkUOHTokml4LeUZERAT79+/n1KlT3Lx5k2fPnvHu3Tvl7TKZDBMTE4oWLUrlypVp3LgxLVu2xMDAQIVR5x2HDh2iVatWlC9fnt69ezN69GiqVKnClStXVB2aIAhCtkRCTBCEPCEuLo65c+eyZcsWHj9+jCRJaGpqUq1aNYYPH07nzp1FEkxQCgsLY9SoUezZs4eUlBQ0NTVp1aoVc+bMYc2aNaxcuZLY2Fg0NDRo2bIlixcvxtbWVtVhC99Jvnz5SE9P5+3bt6oO5buJjIykVatWBAYGoqOjw5IlS+jfv7+qwxKEL6JQKLh06RKHDh3C39+fBw8eEBERQVpamnKMtrY21tbWlClTBicnJ5ydnalQoYJ4L/Adpaenc/DgQZo1a4a2tjatW7fGz8+PmTNnMm7cOFWHl6Nlt1GBIAjfn0iICYKQa8XExDBnzhy2bdvGkydPANDS0qJGjRqMGDGC9u3bize+wkcpFAqWLl2Kp6cnz58/B6BUqVJMnToVuVzOpEmTePz4MQAVK1Zk3rx5NGzYUJUhC99YSEgIxYsXp3Pnzmzfvl3V4XwXc+bMYcKECcjlcpo0acLevXtFHyQh14iMjOTgwYP8/fffXL9+ndDQUOLi4pS3y2QyjI2NKVy4MA4ODjRq1IhWrVqJXQ1zALlcjpWVFTExMdy9e1dsPvAPb5PTCI1NJCoplbgUORIgA4y0NTDX1cLOWA9THU1VhykIeZ5IiAmCkKtERkYyZ84cduzYQWhoKPD+KrCjoyMjR46kTZs2Ko5QyK2CgoIYOXIkZ86cIT09HQMDA3r16kXXrl2ZMGECFy5cQJIkrK2tGTt2LH/88YdIuOYBAwYMYO3atVy/fp1KlSqpOpxvKjg4mBYtWhASEoKxsTHbtm2jefPmqg5LELKlUCi4fPkyhw8f5uLFi9y/f5/Xr1+TmpqqHKOlpYWVlRWlS5emVq1atGrViipVqojX4hzs2rVrVK1aFSsrK16+fPnT/1vFp8q5Gh5DVFIaMiC7D+IZx811NalibYKBltjwSRC+F5EQEwQhx4uIiGDWrFn4+voqq3h0dHRwcnLC1dWVli1bqjhCIS9JTk5m8uTJrF69mrdv3yKTyahZsyYTJ05k27ZtbN++nZSUFHR1denRowdeXl4YGRmpOmzhC1laWpKSkkJsbKyqQ/lmFAoFw4cPZ/ny5QD06tULb29vsYGIkGPExMTg5+enrPp6+vQpsbGx/PNjibGxMXZ2dlSqVImGDRvSqlUrsVNhLjV+/HhmzpxJjx492LRpk6rDUZnncUlcDY9BkrJPhP2bDJDJoIq1CTZGut87PEH4KYmEmCAIOVJYWBizZs1i165dhIWFAaCrq0vdunUZNWoUTZo0UXGEws/g0KFDTJgwgRs3bgBgZWXFsGHDkMlkLFy4kDdv3qCmpka9evVYvHgxZcuWVW3AwmcJDQ2lcOHCdOjQgV27dqk6nG/iwoULtG/fnjdv3lCoUCEOHjxIhQoVVB2W8JNSKBTcuHEDPz8/Ll26xL179wgPDyclJUU5RlNTE0tLS0qXLk2NGjVo2bIljo6OP30lUV5jb2/PvXv38PPzo1WrVqoO54d7HpfE5VcxX3z/avlFUkwQvgfxl0YQhBzj2bNnDBkyhPz581OwYEEWL15MbGwsLVu25NSpUyQmJnLkyBGRDBN+mJYtW3L9+nVev35Nz549iY2NZeLEiUydOhVHR0dWr15NmTJlOHXqFOXKlaNkyZJ5JrHyM5g5cyZAnmj2nJqaSvv27alduzbR0dH8+eefPH/+XCTDhB8mPj6e7du3M3DgQKpWrYqZmZlyh+fJkydz5MgRoqOjKVGiBD169GD16tW8evWK1NRUXrx4wYkTJ5gxYwZOTk4iGZYHnT59Gi0tLTp16pSpB9znWrRoETKZ7IM7AstkskxfRkZG1KpVi61bt2YZ6+Pjk2W8hYUF9evXx8/P74viS0pKomTJkshkMubOnQv8b5nk17gaHsOdh4+QyWT4+Pgoj7948QIXFxfq1auHiYlJltv/LSYmhnz58rFt27YvmiM1NZVJkyZRpEgRtLS0sLOzw93dnaSkpEzjTp48iYGBAS9fvvyahy0I3534ayMIgkqFhoYyaNAgrKyssLOzY/ny5cTHx9OmTRvOnTtHfHw8Bw8epH79+qoOVfiJWVpasnHjRhISEli+fDkFCxZk//79DBw4kNTUVObOnUvz5s15/Pgxv/zyC2ZmZkyaNAm5XK7q0IWP2LdvH4aGhlSrVk3VoXyVHTt2YGZmxt69eylfvjxPnz5l2rRpqg5LyMNu377NzJkzcXZ2pmjRoujq6mJoaEjXrl1Zs2YNN2/eREdHh3r16jF27FhOnTpFWloacXFx3Lp1i02bNjFgwACsra1V/VCEH8TS0hJvb2+SkpJo3LjxF8/j7e0NwJ07dwgICMh2zC+//MKlS5e4ePEiK1asIC4uju7du7Nly5Zsx69bt045ftWqVairq9O6dWsOHDjw2fFNnDiRhISETMcylkl+DUmCW2+yJhIfPXrE5s2b0dLS+qQWIlOnTqVAgQJ06dLli+bo1q0bnp6eDBo0iEOHDjFgwADmzZuXaT6ARo0aUb16dcaPH/+Jj1AQVEQSBEH4wR49eiT16dNHsrCwkHjfRkEyMjKS2rdvL/n7+6s6PEH4JLdu3ZKaNm0qqaurS4Ckr68v9e3bVxo4cKCkr68vAZKmpqbUqVMn6dWrV6oOV/iX58+fS4DUpk0bVYfyxaKioqSaNWtKgKStrS2tXLlS1SEJeUxCQoLk6+srDR48WKpWrZpkbm4uqampKf92A5KBgYFUpkwZqWvXrtLy5cul58+fqzpsIQdr2bKlBEizZ8/+7PtevnxZAqRWrVpJgDRw4MAsYwBp6NChmY49ffpUAqS6detmOr5u3ToJkC5fvpzpeGJioqStrS1169bts+ILCAiQtLS0pJ07d0qA5OnpKUUnpUq77od9k6/lJwIkQFq3bp3ynOnp6Vmen3/e/k9RUVGSrq6utGLFikzHP3WOS5cuSYDk5eWV6biHh4cESMeOHct03NfXV1JXV5eePXv2ic+gIPx4YssKQRB+iAcPHjBjxgwOHTpEVFQU8L5h7i+//MK4ceOoUqWKiiMUhM9Trlw5jh49SkpKCn/99RcrV65k3bp1yGQyqlWrRp06ddi+fTs7d+7E19eXqlWrsnDhQhwdHVUdugDMmjULyL3LJb28vHB3dyctLY2GDRuyb98+DAwMVB2WkIs9ePCA/fv3c/78eW7fvs2rV68yLYNSV1fHwsICJycnatSoQbNmzahfvz4aGuLjhPDp9u3bh6WlJe7u7rRv354SJUp88n3Xrl0LvH/9fvv2Ldu2bWPBggXo6el99H52dnZYWFjw+vXrTzqPjo4OWlpaaGpqfnJsqamp9OvXj6FDh1K1alXl8dDYROWukbHRUWxfPJc7gReJfPUSLR1dbEuUpsvw0dhXrZFpvujX4XjPnMz1s3+jpqZGpTr1ad17UJbzfs7yYh8fH+RyeZZqrk+d48KFCwBZqsicnZ0ZP348u3btytTWpHXr1hgYGLB69Wr++uuvT45TEH4k8RdMEITv5vbt23h4eHDkyBHevn0LgKmpKV27dsXd3V30thHyBG1tbWbMmMGMGTM4fvw448aN4/LlywQGBmJhYUG/fv0ICgri8uXL1KpVCxsbGyZNmsSAAQNUHfpPbc+ePejr6+e6BGVISAgtWrQgODgYIyMj9u7dK3baFT5LcnIyx44d4/jx41y+fJmQkBCio6NRKBTKMfr6+tja2lK+fHnq1atH69atsbOzU2HUQl6hoaHBsWPHqF69OvXq1ePFixeflJBJSkpi69atVKtWjXLlytGvXz8GDBjAzp076d2790fvGxsbS3R0NDVr1sz29vT0dORyOZIk8fr1azw9PUlISKB79+6f/Lj++usvEhISmDZtGm/evFEej0pKVe4oGR/z/r1w56GumOSzJDkxgYATh5n8a0cmr9tBuRq1AEhJTmJqvy5ER7ymh6s7BQoX5eqZk3i5Dv7keLJz8OBBHBwcMDEx+aL7p6amAu/f9/xTxvdBQUGZjmtpaVGrVi0OHjwoEmJCjiUSYoIgfFM3btzAw8OD48ePExMTA4CZmRk9evRg/Pjx2NvbqzZAQfiOmjRpQpMmTYiMjMTNzY3t27fj7e2NhoYGjRo1QktLixMnTjBw4EBcXFzo168fs2bN+s+r28K3FR4eTlhYWK5KJCkUCkaOHMnixYuRJIkePXrg4+MjqnOEj3r8+DH79+/n7Nmz3L59m5cvX5KYmKi8XV1dHXNzc2rWrEn16tVp2rSp8rVKEL6XqlWr4ubmxpw5c+jTpw8bNmz4z/v4+voSGxtL//79AejSpQsuLi6sXbs2S0JMkiRlguvp06eMHj0aPT09Jk+enO3c/06UaWtrs2TJEpo1a/ZJj+fGjRvMmTOHAwcOoK+vnykhFpfyv16iBYsWZ9Dkmcrv09PTqVS7PhEvn3No01plQuz03p28CAlm3LJ1VGv4PoZKteuTmpzMiZ2bPymm7Pj7+/Prr79+8f0z3sNfuHCBIkWKKI+fP38eQLkC5J8qV67MzJkzSUhIQF9f/4vPLQjfi3gXJQjCV7t8+TKzZs3ixIkTyp2D8uXLR+/evZkwYcJnlcMLQl6QL18+1q1bx9q1a1m3bh0eHh6cPHkSgKJFi1K2bFnOnz/P4sWLWbZsGU2aNGHp0qUULVpUxZH/HGbPng3AmDFjVBzJp7l06RLt2rUjIiKCAgUK4Ofnh4ODg6rDEnKQ1NRUTp48ybFjxwgMDOTRo0dERUWRnp6uHKOrq0uhQoUoV64cdevWpXXr1hQrVkyFUQs/s9mzZ7N//342btxIt27daNGixUfHr127Fl1dXbp27QqAgYEBnTp1Yt26dQQHB2d6r7ls2TKWLVum/F5TU5M9e/Z8sD3Hhg0bKFOmDACRkZHs2bOHoUOHkp6ezrBhw4D3ySvpH53x1dTUUFNTQy6X069fP7p06ZIlgSZJEv/upX902waO79jEi0fBpKWmKI8XLFpc+f+3Ay6gq2+gTIZlqOPcnhM7N2eK41PFxMSQmJiIpaXlZ983Q4sWLShevDhjx47FysqKatWq4e/vz/jx41FXV8+20s/S0hKFQkF4eLh4vRFyJLHLpCAIXyTjA5qRkRHVq1dn9+7d6Ojo0L9/f0JCQnjz5g0+Pj4iGSb81NTU1JS/E/fu3aNFixaEhoZy4MABUlJSaNCgAXZ2dhw5coRixYpRrlw5Dh8+rOqw8zxfX1/09PSoV6+eqkP5qNTUVDp27EitWrWIjIxk3LhxvHz5UiTDfnKhoaEsWbKETp06Ubp0aQwMDNDW1qZly5YsWLAAf39/FAoFVatWZdiwYezbt4+kpCQSExN5+PAhu3fvxsXFRXw4FVTuzJkzaGpq8ssvvxAfH//BcY8ePeLs2bO0atUKSZKIiYkhJiaGX375BfjfzpMZOnfuzOXLl7l48SIrV65U7oAaHByc7fxlypShatWqVK1alebNm7Ny5UqaNm3KmDFjlKsdGjVqhKampvKrX79+ACxYsIDHjx8zefJkZVwZF4dTUlJIjItVJqb3r1vJqinjKFGhMm6LVjNzux+zdx7GoU4DUpOTlfG8i3mLcT6LLHGaWLw/JpPJPuXpzSSjH6COjs5n3zeDlpYWhw8fxtbWlqZNm2Jqasovv/zC+PHjMTU1pWDBglnuk3G+f/YjFIScRFSICYLwyc6ePcucOXM4ffq0cktpa2trunfvjru7u+gvIggfUbp0aQ4dOkRqaiozZsxg+fLlnDp1CplMhr29PZqamty6dYuWLVtiYWHB6NGjGT169Gc1zBX+25s3b3jx4sUnL4VRlV27dtG7d28SEhIoW7Yshw4dwtbWVtVhCT+QXC7n9OnTHD16lICAAIKDg3nz5k2mqi8dHR0KFixI2bJlqV27Nq1bt6Z06dIqjFoQPp2lpSXe3t706tWLxo0b4+/vn+04b29vJEnC19cXX1/fLLevX7+e6dOno66uDoCFhYWysb2joyNlypShXr16jBw5Ej8/v0+KrUKFChw9epSHDx9SvXp1Vq5cybt375S358uXD3jfLzc2NjbbC8ATJ06EiROZu+cYRcqU4+yBXZStXovfpszKNC4pIXMy0NDElEdBN7LMF/OPpZify9zcHIDo6OgvngOgePHiXLp0iZcvXxIdHU2xYsWIjY1lxIgR1K1bN8v4jPNlPF+CkNOIhJggCB/1999/4+npydmzZ5V9R/Lnz0+fPn0YN24chQoVUnGEgpC7aGlpMXXqVKZOncrff//NuHHjuHLlCpIkYWZmhq2tLffv32fs2LFMmjSJLl26MH/+fMzMzFQdep4wZ84cAEaNGqXiSLIXExODs7MzFy5cQEtLi2XLlvH777+rOizhOwsLC2P//v2cOXOGmzdv8vz580wVM2pqapiamuLg4EDVqlVp3LgxLVq0EP0HhVyvZ8+ebNmyhcOHD+Pl5ZXltTk9PZ3169dTrFgx1qxZk+X+fn5+eHl5cfjwYZydnbM9R506dfj1119Zv349ly5d+qTNVG7cuAG8T64BlCpVKttxQ4cOZf369crvNTU10dPTIzY2Fg0NDZp1641lwfcXM2TI0PxXf76nD+7y8MZVzK0LKI+Vq+HExcMHuPz30UzLJs/77fnPuD9ES0uLokWLEhIS8sVz/FPBggWVFWF//vkn+vr6yv5u//T48WPMzc2xsrL6JucVhG9NJMQEQcji6NGjeHl5cf78eWWJc8GCBRk0aBBjx47F2tpaxREKQt7QsGFDAgMDiY6OZsyYMWzdupUbN26gpqZGqVKliIqKYsOGDWzcuBEnJycWL15MpUqVVB12rrZjxw50dHQybQ2fUyxcuBA3NzfS0tKoX78++/btw8jISNVhCd9Qeno6586d48iRIwQEBPDgwQPevHmDXP6/xts6OjpYW1tTr149nJycaN26NeXKlVNh1ILwfe3fvx8LCwvGjBlDmzZtMlVbHT58mLCwMGbPnk39+vWz3LdcuXIsWbKEtWvXfjAhBjBt2jS2b9/OxIkTOXHiRKbbbt++rfwdjIqKYvfu3Rw/fpz27dtnah6fnapVq1K2bFnu3LkDQFpaGrGxscD7Ks+ShfKj//+v41XqN8Z3+QK2LfKkbHVHXj4JYeey+VgWsiX9H68B9dv+gp/PKhaNHUF3l7HktyvCtbN/c/3C6WxjyKiae/z4MQBXrlzBwMAAQLmsFKB+/fofbMvwqXPMmTMHa2trbG1tef36NTt27GDv3r1s3Lgx2yWT/v7+1KtX74uWeQrCDyEJgiBIknTgwAGpYcOGko6OjgRIgGRrayuNHj1aev36tarDE4Sfho+Pj1S8eHHl76GVlZVUqFAh5fdFixaVtmzZouowc6WoqCgJkBo1aqTqUDJ5/PixVKpUKQmQDA0Npf3796s6JOEbeP36tbR69WqpR48eUrly5SRDQ0Pl7zEgyWQyydTUVKpcubI0cOBAadu2bVJsbKyqwxYElQgMDJRkMplUoEABKT09XXm8Xbt2kpaWlhQREfHB+3bt2lXS0NCQwsPDJUAaOnRotuPc3NwkQDpz5owkSZK0bt26TL+TgGRsbCxVqlRJmjdvnpScnPyfcaenp0tdunTJMg8gde/eXZIkSTod+kbafT9M2hb0VGrTb7BkZpVf0tLWkYral5fGLvWW6rfrLFkUKCTtuh+m/Fp15qpUs2krSUdPX9LVN5BqNm0lLd19SAKkdevWZYohu3NnfP3TyZMnJUAKDAzM8jg+dY6pU6dKxYoVk7S1tSUTExOpefPm0tmzZ7N9bh49eiQB0q5du/7zeRQEVZFJ0hdsUyEIQq6nUCjYt28fCxcuxN/fn5SU9zvdFC5cmK5du+Lm5iaWaAmCCgUHB+Pq6sqRI0eQy+Voa2tjYWHBq1evSE9Px9jYmN9//52pU6ei9a8lGEL23N3dmTVrFocOHfrPHc1+BIVCgZubGwsWLEChUNC1a1c2btyIhoYo4M9NFAoFly5d4vDhw1y6dIkHDx4QERFBWlqacoy2tjbW1taUKVMGR0dHnJ2dqVSpkugRKAj/4Obmxty5c+nduzc+Pj6qDuejnj59yujRo/Hz81O+h/6nuXPnKpd/xqfKOfH0DYqv+NStJoPGhS0w0Pq6vw8VKlTAycmJ5cuXf9U8n2LixIls2LCBkJAQ8XdNyLFEQkwQfiIKhQJfX18WL15MYGAgqampABQtWpTu3bszatQoTExMVBukIAiZyOVyZs6cyZIlS4iIiADeNyJ+9+4dSUlJaGho0Lp1axYtWiR6+v2HIkWK8OrVK5L/sZuXqly+fJk2bdoQHh5O/vz52b9/v7IJtJBzRUdHc+DAAf7++29u3LhBaGgocXFxZLydlslkGBsbU7hwYRwcHGjYsCHOzs7ib6sgfKJSpUrx8OFDjhw5kuM2P1EoFHh7ezNr1ixlL66CBQvyxx9/EBwcrGz+P2zYMBYuXJhpmeDzuCQuv4r54nNXy2+CjZHu1z4Ejhw5Qvv27QkODv6u7xliYmIoWrQoixcvpkePHt/tPILwtURCTBDyOIVCwdatW1myZAlXr14lLS0NmUxGsWLF6NmzJ66urhgaGqo6TEEQPsG5c+dwc3MjMDAQSZLQ19dHXV1ducW7g4MDCxYsyHanp59dXFwcxsbG1K9fn1OnTqksDrlcTvfu3dm5cydqamqMGjVK2ehfyDkUCgVXr17l4MGDXLx4kfv37/P69WvlhSR436TaysqK0qVL4+joSMuWLalWrZqo+hKErxAeHo6trS2ampq8fv1a2cdKlV68eIGbmxt79+4lOTkZDQ0NGjZsiKenJxUqVADg+vXrVK5cmXbt2uHr66vc8fKfnsclcTU8Bkl6vxbxv8gAmQyqWH+bZFiGJUuWULFiRerUqfPN5vy369evc+LECUaPHi36hwk5mkiICUIepFAo2LhxI0uXLuX69evI5XJkMhklS5akV69euLi4oK+vr+owBUH4QjExMYwbN47NmzcTHx+PTCbDyMhIWalSoEAB3N3dGTJkiPhw/v8mTZrEtGnT2LdvH23atFFJDPv27aNnz57Ex8dTpkwZDh8+jJ2dnUpiEf4nJiaGgwcPcvLkSa5fv87Tp0+JjY3ln2+RjYyMsLOzw8HBgQYNGuDs7Ey+fPlUGLUg5F3r16+nT58+1KxZk0uXLqksjk2bNjF9+nQePHgAvN9lfciQIYwbNy7bJYBXr16lXLlyaGtrf3DO+FQ5V8NjiEpKQ0b2ibGM4/l0tahsbfzVyyQFQfgwkRAThBxGkqQvupIil8vx8fFhxYoV3Lhxg/T0dGQyGaVLl6ZPnz4MHz4cXd1vd3VJEIScYfPmzfz11188fPgQAD09PVJSUkhPT0dPT49ff/0VT0/PHHGVXZWKFy/O8+fPSU5O/uFXq+Pi4nB2dubcuXNoaWkxd+5chg8f/kNjEN5fLAoKCsLPz48LFy5w7949wsPDM/X/0dTUxNLSklKlSlGzZk1atGhBrVq1RGJZEH6w5s2bc/ToUebNm8fIkSN/2HnDw8MZM2YMu3btIjExEXV1derWrcucOXO+6bL2t8lphMYmEpWUSlyKHIn3iTAjbQ3MdbWwM9bDVEfzm51PEITsiYSYIKjY1/xBlMvlrFq1itWrV3Pr1i3S09NRU1PD3t6evn37MnTo0I9epRIEIe948uQJI0eO5NChQ6SlpaGhoYG6ujopKSmoqanRsGFDlixZQqlSpVQd6g8XHx+PkZERtWvX5uzZsz/03EuWLGHUqFGkpqZSp04d9u/fL/pJ/QDx8fEcOnSIEydOcO3aNZ48ecLbt28zVX0ZGhpia2tLxYoVqV+/Pq1bt8ba2lqFUQuCkCE1NRVLS0vi4+N58OABxYoV+67n27lzJ1OnTuXOnTvA+16dgwYNYsKECejo6HzXc8OXXxAXBOHriISYIKjI55RMm+tqUsXaBAMtDVJTU1m+fDlr167lzp07KBQK1NTUKFeuHAMGDOC3334TO84Jwk9MLpfj6enJokWLCA8PB97vcJdRBVO6dGlmzZpF27ZtVRnmDzVt2jQmTZrEzp07+eWXX37IOZ89e0bz5s25d+8eBgYGbNq06ad6zn+kO3fucODAAc6fP8/du3ezbJygoaGBhYUFJUuWpEaNGjRv3pw6deqIXc8EIYcLCAjA0dGRAgUK8OzZs29eqRkZGcm4cePYvn078fHxqKmpUatWLWbNmoWTk9M3PZcgCDmTSIgJggp8SVNNRXo6votms2P1MhQKBerq6lSoUIFBgwYxYMAA8cZeEIQsLl68iJubG5cuXUKSJDQ0NEhPT0eSJMzNzfnjjz8YP358nn/9KFmyJE+fPiU5OfmHLH0bM2YMXl5eKBQKOnXqxObNm9HUFEtfvlZiYiJHjx7l+PHjXLlyhcePH/P27VsUCoVyjIGBAYUKFaJixYrUrVuXNm3aiN1XBSEXGzVqFPPmzaNv3754e3t/kzn379/PpEmTCAoKUv497N+/P5MnT0ZPT++bnEMQhNxBJMQE4Qf70m2XFQoFMmDf8nnUq2hP79698/yHWEEQvo24uDjGjx/Phg0bePfuHQBqamooFAq0tLTo2LEjCxcuxMLCQsWRfnuJiYkYGBjg6OjIhQsXvuu5rly5Qps2bXj16hVWVlbs27ePGjVqfNdz5lUPHjzAz8+Pc+fOcfv2bcLCwkhKSlLerq6uTr58+ShRogTVq1enWbNmNGjQQCQeBSEPKlmyJMHBwRw7dowmTZp80RwxMTG4u7uzZcsW4uLikMlk1KhRg5kzZ1K/fv1vG7AgCLmHJAi52Lp16yTeF1lJgKSuri5ZW1tLXbp0kR4+fJhpbL169TKN1dHRkSpUqCDNnz9fSk9PV47r3bu3pK+v/1VxpaamSlZWVhIg7dy5U3n8XUqatOdBmLTrfpg01GN+pnj++bXm3A1p1/0w5Zf78vVSvba/SLYlSkvqGhrSx351T5w4IVWpUkXS09OTAGnPnj3K26ZOnSqVKVMm0+Ndv3691KVLF6lkyZKSTCaT7Ozssp33+vXrUsuWLSUbGxtJR0dHMjU1lWrWrClt3Lgxy9g6depII0aM+OznTRCE72/btm1S6dKlla83ampqEiDJZDKpZs2aUmBgoKpD/KY8PDwkQNq8efN3O0daWprUrVs35fM5cuTITK+zwoclJSVJBw4ckIYPHy7VrFlTsrCwUP5MZnzp6elJJUuWlDp27CgtWrRIevr0qarDFgThB3r16pWkqakp6evrSwkJCZ9138OHD0tVqlSRZDKZBEgmJiaSi4uL9O7du+8UrSAIuYkoLxHyhHXr1lG6dGmSk5O5cOECM2bM4NSpU9y/fx9TU1PluKJFi7J582YAIiIiWLFiBSNHjuTVq1fMnj37m8Xj5+fH69evAVi7dq2yZ03GMsl/Guoxn0JFi2c6Zmhimun7gBOHeXjjGkXsy6KppUXInaBszytJEp07d6ZkyZLs378ffX19ZQPtsLAw5syZg4+PT6YlQxs3biQ8PJzq1aujUChIS0vLdu6YmBhsbGzo1q0bBQsWJCEhgc2bN9OrVy+ePn3Kn3/+qRw7bdo0mjRpwu+///5TNvAWhJysS5cudOnShdDQUFxdXfHz8yM1NRUAf39/qlevjp2dHZMnT6Zv374qjvbrbdiwAQ0NDbp27fpd5vfz86NHjx7ExcVRqlQpDh8+TJEiRb7LuXK7J0+esH//fs6ePcutW7d4+fIliYmJytvV1dUxMzOjRo0aVKtWjWbNmtGoUSOxOYwg/OSsra1ZtWoVffv2pUmTJv9Z7RsfH8+ff/7J+vXriYmJQSaTUblyZWbMmEGzZs1+UNSCIOQGYsmkkKv5+PjQt29fLl++nGkr5L/++ovJkyfj7e2t/EBXv359IiMjuX37tnJcWloapUuXJjw8nJiYGDQ1NenTpw++vr7Ex8d/cVzOzs4cP36cevXqcfLkSUJDQ9HPZ8Wp0EjlmL93b2fp+JHM3nmY4uUrfnS+jMb5AKv/Gs+RLT5EJ6Vm2X3y5cuXFCpUiNmzZzNmzJhMt40dO5bNmzdnaUr6z7mdnZ25ffs2T58+/eTHWrNmTcLCwnj27Fmm4+XLl8fR0ZFVq1Z98lyCIPx4CoUCLy8vFixYQFhYWKbbDA0NGTBgAB4eHj9kl61vLTk5GT09PapVq0ZAQMA3nTsuLo62bdty+vRpNDU1mTNnDi4uLt/0HLlVamoqJ0+e5Pjx4wQGBhIcHExUVBTp6enKMbq6uhQsWJBy5cpRp04dWrduTYkSJVQYtSAIOV2zZs04duwYCxYsYMSIEVluP336NOPGjSMwMBBJkjAyMqJHjx54eHiI3X0FQcjW9+8sKwgqkJEcy6jS+hBNTU2qVKlCYmIib968+SbnDgsL48iRI7Ru3Ro3NzcUCgU+Pj48fB2NQpH+3xNkI7sm0KGxiZm+nzJlirJx8NixY5HJZBQuXBh4/+Fk7dq1dO/ePctcX9tgOl++fNn2MuvVqxdbtmxR9isSBCFnUlNTw83NjZcvXxIYGEidOnWUW7+/e/eO+fPnY2BggLOzM6GhoSqO9vMsXboUSZIYMmTIN513+fLlWFhYcPr0aZycnIiIiPhpk2HPnj1jyZIldO7cmTJlymBgYIC2tjYtW7Zk/vz5XLp0CYVCQdWqVRkyZAh79+4lMTGRxMREgoOD2bNnD66uriIZJgjCfzpw4ADGxsaMGjWKJ0+eAO/7RI4ZM4Z8+fLRoEEDAgMDqVChAvv27SM2NpZly5aJZJggCB8klkwKeVLGH8mSJUv+59iQkBA0NDQyLa38Gj4+PqSnp9OvXz8qVKiAkZERU6ZMQadsDYral8syfubvvxIXHYWeoRFlqzvSdbgbtiVL/+d5opJSM30/YMAAKlasSIcOHRg+fDjdu3dXLjMJCAggKiqKBg0afPXjUygUKBQK3r59y86dOzl69ChLlizJMq5+/fqMHTuW06dP07p1668+ryAI31+1atU4e/Ys8fHxTJgwgfXr1xMbG0t6ejoHDx7k4MGDlC9fHi8vry9ubPy97d27l40bN9K2bVvWrFmDuro6vXr1+iZzv3jxgubNm3Pnzh309fXZsmULHTt2/CZz53RyuZwzZ85w5MgRAgMDefjwIZGRkcjlcuUYHR0dChQoQNmyZalduzbOzs7Y29urMGpBEPISLS0tjhw5Qq1atahRowYlS5ZUJt0NDAzo378/s2bNIl++fKoOVRCEXEIkxIQ8IT09HblcruwhNn36dOV26/+W8eb9zZs3LFq0iGvXrtGpUyd0dXW/Og5JklizZg36+voMHDgw0/Kjd2+jMo01tbCk4+ARlKxYGV0DQ549vMee1Utw7+rMjC37KFy67EfPFZciz/R9oUKFlI/N1taWmjVrKm+7dOkSAJUrV/6qxwcwZMgQVq5cCbx/Y7Jo0SJ+++23LOMcHByQyWRcuHBBJMQEIZcxMDBg4cKFLFy4kF27djF58mTu3LkDwK1bt2jatClWVla4ubkxcuTIr640/ZYCAwPZvXs3u3fvBsDIyAgfHx/atm2Lubn5F887fvx4Zs+ejUKhoEOHDmzduhUtLa1vFXaOEhYWxoEDBzh9+jRBQUE8e/YsUxsBmUyGqakplSpVokqVKjRu3JjmzZtjYGCgwqgFQcjrUlJS8PPzQ0dHhzdv3vDmzRvKli3LpEmT6Ny5s6rDEwQhFxIJMSFP+GfyB6BMmTLs27cvy1K+O3fuZNqSXVNTkx49erB06dJPPpckSZn6oACEh4czc+ZMtm3bRnR0tHJcixYt6NWrFz169OD03p1UdKqnvI9DnQY41PlfxVbZajWpUq8xI9s0ZNsiT8Yt8/l4HP9/joylTR8TFhaGTCb7JlfMxo8fz4ABA4iIiODAgQMMGzaMhIQERo8enWmcpqYmJiYmvHz58qvPKQiC6nTs2JGOHTvy4sULXF1d2bt3L2lpabx+/ZrRo0czYcIEevTogZeXV45YlvLvyuB3797Rv39/xo8fT1hY2Gcn727cuIGzszMvX77E0tKSvXv34ujo+C1DVhmFQsH58+c5fPgw/v7+PHz4kIiIiExVX9ra2uTPn5+6devi5OSEs7MzFSpUUGHUgiD8bK5cuYKbmxvnzp0jPT0dPT09DAwMiI+PZ+HChTRq1EjVIQqCkEuJhJiQJ2zYsIEyZcrw7t07tm/fzsqVK+nWrRuHDx/ONK5YsWJs27YNmUyGjo4ORYoUQU9P77POtX79+g/uvJaRgFu9erVyZ0mA2rVr43/sEAMmxqJvZPzBuS0L2VCmSnUe3rz2n3HIIEsyLGOnuH/vlZGUlISmpibq6ur/Oe9/sbW1xdbWFoCWLVsC4O7uTu/evbGwsMg0VkdHh6SkpK8+pyAIqleoUCF27NiBQqFg0aJFeHp6EhYWRkpKCt7e3qxbt466deuyZMkSypXLujz8R/l3L6qM18OhQ4d+VjJMLpfTp08fNm/ejEwm448//mD+/Pk5qhruc2RcxDh16hQ3b97k2bNnxMXFKW+XyWSYmJhQvnx5KleuTOPGjWnZsiVGRkYqjFoQhJ9VWloas2bNYtmyZYSHhwNQunRpJkyYQM+ePQkLC8POzo62bdsSERHx2e/nBUEQQCTEhDyiTJkyykb6DRo0ID09nTVr1uDr65spMaWjo5NpN8rPFRISwrFjxzAxMSEmJgYAPT09mjZtytChQ2nTpg1yuZyBAwcycODALPc/57eH5t37fPQckiQh+5QPXMmJ7N+/nzt37nDr1i2uXbvGo0ePADLtpAnvG9+npqaSkJCAvr7+Jz3WT1W9enVWrFjB48ePsyTE3r59K/o4CEIeo6amhouLCy4uLly7do1Ro0Zx5swZJEnizJkzlC9fnqJFizJz5kyVLGHJrnfk5MmTmThx4ifPcfjwYbp160ZsbCwlSpTg8OHDFCtW7FuG+d0oFAoCAgI4dOgQly5d4sGDB7x+/Zq0tDTlGC0tLaytrXF0dKRWrVq0atUKBweHXJvsEwQh7wgKCmL06NGcOnUKuVyOjo4OXbt2xdPTU7l5FECBAgVYsWIFAwYMoGnTppw/f16FUQuCkFuJhJiQJ82ZM4ddu3YxadIkOnTo8FVv8h88eICHhwcHDx4kKup9HzBjY2M6duzIuHHjlAm25cuXk5SUxLRp06hdu3aWedp3/IW/d237aELs9Ytn3L92mQq16vxnXAd9t7F2+p/A+yv7/6wKy58/f6axpUu/b9IfEhLyzZe6nDp1CjU1NYoWLZrpeFhYGMnJyaKhsiDkYZUrV+bUqVMkJiYyceJE1qxZQ1xcHI8fP6ZLly7079+fESNGMGXKlGx3o/0e8uXLh5aWlrJiduLEiUyZMiXLuNTUVMLCwpS78QLEx8fTtm1b/v77bzQ0NJg7dy6jRo36IXF/iejoaPz8/Pj777+5fv06oaGhxMXFKf8eyGQyjIyMsLe3x8HBgYYNG9KqVSvMzMxUHLkgCML/yOVy5s2bx6JFi5StNooXL87YsWPp16/fB9/H9+/fn61bt3Ly5EkWL17M8OHDf2TYgiDkASIhJuRJpqamuLu7M2bMGLZs2ULPnj0/6/5yuZw6depw48YNZSNhPT096taty6JFi6hYsWKW+6xduxZTU1NGjx6Njo5Oltu79+zFskULeHr/DoVLl2VK387YV62JXaky6BkYEvrwPvvWLEMmk9HtjzGZ7hvx8gUht24A8Pp5KABPr15U3v7vJZLJycmZvq9fvz4A/v7+WRJid+/e5e7du8D7XmiJiYn4+voCYG9vr0xoDRo0CCMjI6pXr46VlRWRkZHs3LmT7du34+bmlqU6zN/fH+Cb7GwpCELOpqenh5eXF15eXuzfv5/x48dz584d4uPjmTFjBjNnzqRdu3YsXboUa2vr7xqLTCZTLg93d3dn6tSp2Y7r27cvu3bt4tatW5QoUYLVq1czfPhwUlJSqFmzJgcPHswxiSOFQsG1a9c4ePAgFy9e5P79+4SHhyuTfvC+b6OVlRXVqlXD0dGRli1bUr16dVH1JQhCjnXv3j1Gjx7N8ePHSUtLQ1tbmw4dOjB37lyKFCnySXMcOnQICwsLRo4cSZs2bbCzs/vOUQuCkKdIgpCLrVu3TgKky5cvZ7ktKSlJsrW1lUqUKCHJ5XKpXr16UtmyZT84182bN6UuXbpImpqaEv/fs/7fX3Z2dh+8LyC5uLh8cP779+9LgNSyZz9p1/0wybn3QKlQ8ZKSrr6BpK6hIZlZWkt123SUFh8+J+26H5bpa6jH/A/G9KEvPT09qV69etL69eultLQ0qU6dOlLLli2zxDV58uQPzjF58mTlOG9vb6lOnTpSvnz5JA0NDcnExESqV6+etHHjxmwfb69evaTy5ct/8PkQBCFve/nypdStWzdJXV090+tK+fLlpfPnz2d7n6SkJCkhIeGLzqdQKCRJkqT09HTJwMBAKlmypPLYv505c0YCJJlMJtWsWVMqV66c8nVz+/btX3T+byU2NlbasmWL1L9/f8nBwUEyMTGRZDJZpufQyMhIKl++vNSrVy9p7dq1UkREhEpjFgRB+FTp6enSggULJFtbW+VrWuHChaWlS5dK6enpXzTnhQsXJECytbX94jkEQfg5ySTpX6UlgvATuXLlCrNmzeLEiRPExsYCYG5uTqtWrRg/fjylSpX6pueLT5Vz4ukbFF/xW6cmg8aFLTDQel/guXv3brp06UJ6ejqSJLF582bOnz/PwYMHefbs2fv7qKlhbW3Nq1evuH79erYVbt9SXFwcBQoUYP78+dn2UhME4eehUChYtmwZ06dP5/Xr18rjpqameHh4MGjQIGUVk62tLTExMTx69AhLS8uPzvs2OY3Q2ESiklKJS5Ej8X6zEV018N3gTc9WTalfs1qW+6WlpVGhQgWCg4Mz7Rjctm1bduzYgZaW1jd53P9FkiRu3brFgQMHuHDhAnfv3iU8PJyUlBTlGE1NTSwsLChVqhQ1a9akRYsWODk5iaovQRBynZCQEEaNGsXhw4dJTU1FU1OTFi1aMHfu3CyboXyJESNGsGjRIgYOHMiqVau+QcSCIPwMREJM+OkEBAQwa9YsTp48ybt37wCwsLCgdevWjB8//rs3Tn4el8TlVzFffP9q+U2wMdLNdOzYsWO0adMGLS0toqOjlb16EhMTWbVqFZs2beLmzZvI5XLg/eNt1KgRLi4u1KhR44tj+ZCpU6eyfft2goKCfljfIEEQcr6goCCGDRvGuXPnlMc0NDTo06cPTZs2VTbhL1KkCPfu3UNbWzvLHPGpcq6GxxCVlIaM9+UFH2Kuq0kVaxPlBQSABQsWMHLkyEzj9PT0ePz4MVZWVl/1+D4kPj6ew4cPc/LkSa5cucKTJ094+/ZtpuXuhoaG2NjYULFiRerXr0/r1q2z9IMUBEHITRQKBatWrcLT05PHjx8DYGNjo9yY5Vsn94sXL05ISAgnT56kYcOG33RuQRDyJpEQE34K58+fZ/bs2Zw+fVrZE8zKyoq2bdsyfvz4H95v4HlcElfDY5Ckj3+YyyADZDKoYp01GZbhxo0bREZG0rhx4w/Os3LlShYsWMCrV6+UFXE6OjpUqVKFvn370rt372+SwJo/fz5OTk5Ur179q+cSBCHvSU5OZuLEiSxZsiRLz8MMjRo14vjx48hkMuWxr33tfPnyJYULF1ZeHPinHj16sGnTpi98RP9z9+5dDhw4wPnz57l7965yg5EMGhoa5MuXj5IlS1KjRg2aNWtGvXr1xMUDQRDyjNDQUNzc3Ni/fz8pKSloaGjQuHFj5s6dS9myZb/beTNe43V0dHj9+jV6enrf7VyCIOQNIiEm5FmnTp3C09OTM2fOkJiYCLzffbF9+/a4u7tn2rpZFT6lyiHjeD5dLSpbG2eqcvhaoaGhzJs3j/379xMaGookSchkMooXL067du1wcXGhQIEC3+x8giAI2Tl06BCDBg1S7iz2TyNHjmTevHnA11fX2siSaVLdQXkxIIOmpib58uXD1taWI0eOYGJi8knzJSYmcuzYMY4fP86VK1cICQnh7du3KBQK5Rh9fX1sbGwoX7489evXx9nZGVtb2y9+DIIgCDmVQqFgw4YNeHh4EBwcDECBAgUYNmwYbm5uPyzpv2bNGgYOHEidOnU4e/bsDzmnIAi5l0iICXnK8ePHmTt3LufOnSMpKQmAggUL0rFjR8aOHZsjEzwf6oNjpK2Bua4WdsZ6mOpoftcYkpOTWbNmDRs3buTGjRvKncvMzc1p0KABI0aMoHbt2t81BkEQvo8pU6YwdepU3rx5Q758+bLcXq5cOfLly8fp06cBeP78OTNnzuTEiRM8f/4cXV1dChYsSM2aNZk0aRI2NjaZ5s2gq6uLmZkZZcuWxdnZmT59+mBoaJhtTAcOHGDVqlUEBgby9u1b4H1vr+z88ssveG/awsnQyM/uv7h4nAun9+7IdExXV5cqVarg4uJC/fr1uXPnDp06dSIiIgJvb2/69u0LwIkTJ2jSpAnwfqn9uXPnOHfuHHfu3OHly5fKvzEZNDQ0KFKkCM7OzjRr1owGDRoo+5GFhIRgb2/P6dOncXR0BODOnTssXbqU69evExQURGJiIqdOnVLuCpzh7du3FClSBB8fH9q1a/d5T4AgCMJ3FhYWhpubG3v27CEpKQl1dXXq16+Pp6cnDg4OKompUaNG/P333yxdupQhQ4aoJAZBEHIHUZ8v5HqHDh3Cy8uLixcvKpel2NjY0KlTJ8aOHfufjZlVzVRHE1MdY+X3GZVaP5KOjg7Dhg1j2LBhAPz9998sWbKEM2fO4Ovri6+vL9ra2jg4ONCnTx/69u37wxpPC4Lw47x48YLKlStjYmLCqFGjKFWqFLGxsdy9e5cdO3bw+PFjZUIsw5EjRzA2NiY1NZWwsDBOnjzJmDFj8PT05MCBA5k28ZAkiX79+uHj40PLli2ZN28eNjY2ysRTdnx9fanyS29KVaryRY9JS0eHqT47AQk9GRglRjF9+nT69u3LkCFD8PT0BN4ntK5du4alpSV+fn6sW7cONTU1FApFpl6Lenp6GBkZkZSURMWKFRkxYgT58+dn/fr1bNu2DRsbG5o1a5YphtGjR9OkSRNlMgzeb+qyd+9eHBwcaNSoEQcOHMg2flNTU0aOHImbmxstW7YUr72CIOQIW7duZdq0ady7dw9434pkzJgxjB8/XuWvU4cOHcLS0pI//viDVq1a/fDWKIIg5B6iQkzIdRQKBfv372fBggX4+/srd+Sys7OjS5cuuLm5ZVsFIXyZFy9eMH/+fPbt28fjx4+VCbsiRYrQtm1bXFxcxBIgQcjBPqdCbPLkyfz11188fvyYIkWKZBmrUCiUTZA/Nu/NmzepV68exsbGPHz4UNkcf86cOYwdO5apU6cyadIk5fi//vqLoKAg9PT0UFNTo1q1auTPnx+5XM75qzeo32/4Fz32xeNc8D/mx+Zrj5THGtjl4/RhPzp06PCf99fT0yMxMZGBAwfSpk0bGjdujI6ODrVr1+bFixc8fvxY+XxIkoS9vT1aWlrcvHlTOce9e/ewt7fnyJEjmRJl/3wufX196dSpU7YVYgCvX7+mUKFCrF+/nu7du3/RcyEIgvC1IiIiGDt2LDt37iQhIQE1NTVq167NnDlzvssmTV/j/Pnz1KlTBzs7O548efLDLzYLgpA7iH27hVxBoVDg6+tLnTp10NXVpX379pw5c4YCBQowfvx43r59y9OnT5k9e7ZIhn1jhQoVwsvLi0ePHpGcnMzKlSupWbMmYWFhzJ8/Hzs7O8zMzGjfvj1///23qsMVBOErREVFoaam9sHK2k/dEaxixYpMmDCBZ8+esX37duD9ksjZs2dTunRpJk6cqBwbHByMj48PxYoVY+bMmfj4+DB06FA6dOhA586d6ec6ln9/jJEkiSNbfBjVrjHdKhbl1+pl8PxjIOHPQz8emCSxdPOODybDNDQ0+P3339HU1OTq1au4ubkB4OHhgbOzMzo6OsD7vmMGBgaZng+ZTIaRkZFyTIbly5djbW2dpQruc3ZXs7KyokmTJqxYseKT7yMIgvCt7N69mwoVKmBlZYWPjw+6urq4u7uTkJDAmTNnclwyDKB27doMHTqU0NBQfv/9d1WHIwhCDiUSYkKOpVAo2LJlC7Vq1UJHR4dOnTpx4cIFbGxsmDx5MrGxsTx+/JgZM2Z8chNk4etoaWkxaNAgLl68SFJSEufOnaNTp06oqamxd+9eGjVqhLa2NtWrV2fx4sUf3D1OEIScydHREYVCQYcOHTh69ChxcXFfPFebNm0AlE2Nr1y5QnR0NG3bts10pf7Fixc8efIET09P7Ozs+PXXXwkKClLeHpWUmmXTkRWTxrBu5mQqONZhzBJvBk7y4PmjB0zo1oaYyDdZYkmXy0mXy0lNTUEuydDT08sUg7q6OgByuZzDhw/j4uJC5cqVP/jYhg8fzr1795gxYwZv3rwhMjKSuXPncvXqVUaPHp1p7MGDB6lbt+5nJcCyU79+fS5cuEBMTMxXzSMIgvApoqOj+e233zAyMqJjx47cuXOHWrVqcfbsWd68eYOHh0eWCwA5zZIlSyhSpAgrV65U9skUBEH4J5EQE3IUhULB+vXrqVGjBtra2vTo0QN/f3+KFCnC9OnTiYuL49GjR0yZMgUjIyNVh/vTq127Njt27CAyMpJXr14xZswY7OzsuHr1Kn/88Qd6enoULlyY4cOH8+TJE1WHKwjCf+jevTu//fYbJ06coHnz5piYmGBvb4+rqytPnz79rLkyeraEhYUB8OzZM+XxuLg4njx5wpUrV7h16xbwvuorPT2dTZs2UbFiRQoUKMCAAQOISUrNNO/DG1c5sXMzPVzH03vsZBzq1KeOc3sme28jMf4dB3xWZhqfnJhI53K2dC5nS7eKRZk6sAfGxsacO3eOx48fM2TIkEwVcQkJCZk2C8hOhw4d2L17N56enlhaWmJhYcGkSZNYv349nTp1Uo6LiIjg8ePHH02ufarKlSujUCjw9/f/6rkEQRA+5NChQ1SuXJl8+fKxatUqNDU1GTVqFHFxcVy4cIE6deqoOsTPcubMGTQ0NGjdurW4UCsIQhaiqb6gcunp6axbt44VK1Zw48YN0tPTkclklCpVit69eysTK0LOZm1tzezZs5k9ezZyuZyNGzfi7e3N1atXWbJkCUuWLMHY2Jg6deowdOhQmjdvruqQBUH4l9TUVCZNmkSHDh04cuQIQUFB3L59m/nz57NkyRI6dOiAiYkJ79694+rVqwC0bNkSuVxOUlISKSkppKSkkJqaqtyt9ujRo2hqaiKXywEYMmTIR3f9ymht+urVK9auXUuzkZNBoUCmpoaamhpXTp9AJpNRr01H0v9/TgCTfJYULlWWO4GXMs2npaPDtI17/n9uBdGvw1k/fYKyaksulysrxAA8PT3R1dX96PN05MgRevbsSadOnejcuTMaGhrs37+fPn36kJqaqtypMiMZ+C02d8mY4+XLl189lyAIwj/FxcUxYcIENm7cSGxsLDKZjKpVqzJz5kwaNWqk6vC+io2NDUuXLuW3336jefPmolJMEIRMREJMUAm5XM7q1atZvXo1QUFBpKeno6amRpkyZejbty9Dhw7N8WXYwodpaGjQt29f5YfCgIAAFi5cyMmTJ/Hz88PPzw9NTU3Kly9Pjx49GDx4sEh6CsJHKBQKYmJiiIqKIjo6mujoaGJiYoiJiSE2Npa4uDjevXvHu3fviI+PJyEhgcTERBITE5WVWRUrVkQul5OWlkZaWhpyuZz09HTS0tIA/rPhcFpamrIf2D9du3YNDQ0N1NXV0dTURFNTEy0tLbS1tYmLi8PU1JRSpUqRmprKlStXsLe3p06dOhgaGmJoaEhqaiozZsxQzieTyTAxMcHc3JxHjx7Rudz7TTvqt+vM8FkLiI168363SqcK2cZpZZN5NzE1NTWKl//fTpfytDTmDO+vfF7h/YWZDFu2bOHGjRtUqlSJyMhI4P2HRW1tbQwNDZU7ZdatWxdvb2/l/Ro3bkxsbCzDhw+nc+fO6Ovrk5SUBPBN/p5lzJExpyAIwtc6ceIE48eP58qVK0iShLGxMcOGDWPGjBl5aiXGoEGD2Lp1K6dPn2bFihUMHjxY1SEJgpBDiISY8MOkpqaycuVK1qxZw+3bt5U7bJUtW5YBAwYwePBglW/TLHwfNWrUYMuWLQC8efOGRYsW4evry40bN7h27RqjRo3CxsaGVq1a4erqSokSJVQcsSB8usTERCIjI5WJqrdv32ZKVGUkq+Lj44mPj1cmqpKSkkhOTiY5OVlZUZWRrEpPTyc9PR2FQsGXbAYtk8lQ+/+Kqgzx8fHo6elhaGiItrY2Ojo66OjocO3aNaysrHBycsLAwABDQ0OMjIwwMjLC2NgYExMTTE1NGTZsGM+ePeP169cYGBjw119/MXXqVMLDw7PdzCRjR8kFCxbQs2dP0tLSsLa2RqFQsHz5cmUC7smTJ8qEmLq6OvXq1aNHjx7o6emhoaFBcBKo6xpgZGoGgKGJGTKZjOmb96ChqZ3lvJr/8XfE3ECXO3fuULVq1WyTS8eOHePYsWOZjhUrVgxNTU2qV69OwYIFefXqFS1btiQ1NTXT361q1aqxYcMGnj59StmyZZXPS3R09Edj+hQZc4iNYwRB+BoJCQlMmjQJHx8foqOjkclkVKpUiWnTptGqVStVh/fdHD58GEtLS4YPH07Lli3FDumCIAAiISZ8Z8nJySxbtgxvb2/u3buHQqFAXV2dChUqMHDgQAYNGoSGhvgx/JlYWFgwbdo0pk2bptw4Ye3atQQGBrJixQpWrFiBkZERTk5ODB48GGdn569uRi38nORyOdHR0cqqqoxE1b+rqjISVRlVVUlJScpkVcbyv39XVSkUCmV10efKSFSpq6ujoaGhrKrS0dHB2NgYHR0ddHV10dXVRU9PD319fQwMDDIlqzISVRnJKjMzM8zNzTE1NUVTUzPT+UJCQihRogSDBw9m9uzZmW47fPgwLVu2ZMaMGfTp04dXr16RP3/+LDHHx8cTFRVFwYIFP6lq4ObNm3h4eFC4cGE6d+4MvN+ZcezYsYwdO5Zp06YxadIkAMzNzZHJZMrE3+nTpzPtWNv/z+k0794bNbX3yxqrNmjMntVLiHodjlOLNp/xzIMMMNfVwjafrfJ5TkxMRC6XI5fLqVixIvPmzSM0NJQ7d+7g5+fHgwcPsLa25t27d/j7+yurydauXcvatWvR1NTE2NgYKysr3r59i0wmw9/fH2NjY+zs7NDV1SUkJOSz4szO48ePAbC3t//quQRB+PmcO3eOcePG4e/vj0KhwNDQkN9++w0PDw/MzMxUHd53p6Ojo9zkpF69eqK3rSAIgEiICd9BcnIyixYtwsfHh/v37yNJEurq6jg4OPDbb7/Rt29fkQQTgPeJgZ49e9KzZ0/g/dKrhQsXcuzYMQ4fPszhw4fR0NCgbNmydO3alWHDhmFgYKDiqIWvpVAolAmWqKgo3r59+9Gqqn8u//t3oiojWZWR0MhIVH1NVZW6unqW5X96enrKiqqMRJWenl6WRNW/q6rMzMwwMzMjX758KlsWXKxYMYYNG4anpycxMTG0bNkSXV1dLl++zKxZs6hatSrdu3cHYMaMGVy4cIEuXbpQqVIldHV1efLkCUuWLCEqKgpPT88s81+9ehVjY2PS0tIICwvj5MmTbNy4EUtLSw4cOJCpgsrNzY179+4xefJkAgMD6d69OzY2NjRo0IC///4709JFAGNjYwZ36cAztf/1+CpduTpNOvdk6fiRhNy+iX3Vmmjr6hHz5jX3rl3GtmRpmnfrrRyvUCh4eOOq8v/fpMQwfO1q3r59y8KFC3F2dubXX3/lwoULVK9enYYNGyrva2BgwNSpU7l165ayMismJoZ+/fqxZ88e7Ozs0NTUJCoqivv37yvjHzBgAPD+Z0omk7F06VICAgIoUaIEFSpUoEaNGpQtW5YTJ04AKBvlnzlzhsjISPT19WnRokWm58Lf3x9zc3PKly//Of/8giD8xJKTk5k6dSpr1qxRLgEvX748U6ZMoUOHDiqO7serU6cOQ4YMYdmyZcr/CoLwc5NJX/KpQRD+JTExkfnz57NhwwaCg4ORJAkNDQ0qV67M77//zq+//iqqfITPEh0dzeLFi9m5c2emD5oFCxakRYsWuLq6UqZMGRVHmfekpqZmu/wvI1mV0afq3bt3JCQkZKmq+mdT9X8v/0tPT//iRJVMJlMmqv5ZVaWtrZ1p+V9GoiqjqiqjT9W/q6oyElXm5uaYmJjk+dcnSZJYuXIla9eu5e7du8jlcuzs7OjQoQN//vmnMtEcEBDAxo0bOX/+PM+fPyc2NhYzMzOqVKnCH3/8kSlJM2XKlEy7MWpra2NmZkb58uVp1aoVffv2xdDQMNt49u/fz8qVKzl37hzv3r3Lcruamhr29vacPXsWU1NTzjyLJDopjX/+9Py9axvHdmziefB9FAoFZpbWlHaoRste/SlW7n1/scXjXDi9d0e28xcsWJCSJUtiaWmJubk5SUlJuLm5UapUqSyP8c2bN5mWKioUCry9vVm5ciWPHj1CoVBQrFgxZW+xy5cvc/36de7fv8+tW7eIiIhATU3tk6sKCxUqxPPnz5XfS5JEkSJFaNOmDYsWLfqkOQRB+HkFBgbi5ubG+fPnUSgU6Ovr06lTJ2bPnv1NNvnI7YoUKcLTp085c+YMdevWVXU4giCokEiI5RGSJP1nQ+RvLT4+Hi8vLzZt2kRISAiSJKGpqUnVqlUZOnQo3bp1y/MfMoUfQ6FQ4Ovry6pVq/D39ychIQF4X73h6OjIb7/9Rvv27fP0z9vXNFXP6FOVkaz69/K/jETVlyarMhJV2TVV19bWzrL8T19fX5moMjQ0zHb5X8aX2Fwj7/Hz82P27Nn4+/sjl8tRU1OjYsWKPHr0iHfv3qGurk7x4sU5f/68MgkVnyrnxNM3KL7iHYuaDJJvB9CtY/vMx/8/UWVmZkZUVNTXPLRsJScnY2try6hRo/j999+5dOkSly9f5vbt2zx69IiwsDDevn1LcnJypvvJZDJ0dXUxNzfH0NCQe/fu4ebmRuvWralatar43RAEIZPU1FQ8PDxYsWIFr1+/BqBMmTJMnDiRbt26qTi6nOXZs2cUK1YMXV1dIiIixOupIPzEREIsl3qbnEZobCJRSanEpciReN8bxUhbA3NdLeyM9TDV0fyvaT5bTEwMc+fOZevWrcp+JhmNhocPH06nTp3ydFJCyBlu377NvHnzOHLkCK9evQLeN+O2t7enc+fODBs2DBMTkx8WT05vqq6hoaGsqtLS0lImq/69/O+fVVXZLf8zNTXF3Nwcc3NzDAwMxO+68MkuX77M1KlT+fvvv5WN7EuUKMGgQYP4448/0NLSwtXVlfnz51O0aFEuXryIlZVVpjmexyVx+VXMF8dQLb8JNka6TJo0iWnTpmW5fcWKFfz2229fPP/HLF++nClTpvD48WP09fWzHaNQKAgODubixYvK6rLQ0FAiIiKIiYnJMl5DQwMjIyOsrKwoXLgwZcqUoXLlytSuXRs7O7usJxAEIU+6fv06o0eP5syZM6Snp6Orq0uHDh2YM2cOBQoUUHV4Odby5csZMmSIcsm+IAg/J5EQy2XiU+VcDY8hKikNGZDdP17GcXNdTapYm2Cg9eF+XZIkERMTg6mp6QfHREdHM2fOHLZv387Tp0+B90tjatSogYuLC23bthUfjAWViYuLY/HixWzfvp27d+8ql1bmz5+fJk2a0LdvX6ysrHJFU/V/J6q+VVN1QVCFkJAQJk+ejJ+fH7GxscD7Jc89evTA3d09S9I6JCQEd3d35s2bR6FChbKd83lcElfDY5Ck7P/+/ZsMkMmgivX7ZBhASkoKZcuW5cmTJ5l+h8ePH6/c7fJbS09PZ86cOTg7O392D7CMXme9e/cmJCSEwMBAbt26lam67N+7ZWZUl5mZmVGoUCGKFy+u7F1WvXp1UQ0hCLmcXC5nzpw5LF26lLCwMABKliyJu7u7aFPyGerVq8fZs2dZuXIlgwYNUnU4giCogEiI5SLf4oPAP6Wnp9OvXz927tzJkydPMl2Nf/PmDbNnz2bHjh3KPiY6Ojo4Ojri4uJCmzaft7OXIPzT92qqnpqa+sUVVfDxpupaWlofbKqesQTQ2Ng4RzZVF4QfJTIykmnTprFjxw7Cw8MBMDMzo3379kyaNOmbbHP/zwtD6ely1NWzXvTJuDCUT1eLytbGWS4MnTt3LlPfGGNjY2JjY7G0tGT79u3Ur1//q+P8kRQKBSEhIVy8eJFr165lqi579+4dcrk80/iM6jJLS8tM1WVOTk4UKVJERY9CEIT/cvv2bdzc3Dhx4gRyuRxtbW3atGmDp6enqAz9AsnJyVhYWJCcnMyTJ08+eDFGEIS8SyTEcolvtVQkQ3p6Or1792bLli1IksSKFSto27Yts2bNwtfXl5cvXwKgq6uLk5MTo0aNonnz5l/7MIRcIC81VZfL5QQHB/P8+XNl426ZTEahQoVo2rQpv/32G8WKFfspmqoLwveSmJjI3Llz8fHxUW5jr6+vT7NmzZg8eTIVKlT45udcv349U+d40c91LI6Nmn5R64BBgwaxevVqBg4cyIoVK3B3d8fLy4v09HTq16/Prl27MDMz++axq0JCQgL+/v4EBgYqe5e9fPmS6OjobKvLdHR0MDMzo2DBgpQoUYLy5ctTvXp1atSoIRL7gvCDKRQK5s+fz8KFC5UXqYsWLcqYMWMYOHCgeP/ylc6cOUP9+vUpWrQoISEhqg5HEIQf7KdMiPn4+NC3b18uX75M1apVs9z+9OlTihQpgqenJ6NHjwbg9OnTNGjQAICLFy/i6OiY6T59+vTB19eX+Ph45bH69esTGRnJ7du3M409dOgQv/zyCxUrVuTQoUMfXK546tQpPDw8uHHzJvEJCVgWsqPxL91o3qMv6urqmcYmJyayZ80SLhzcz5uwF+jo6WNXqgyD/5pDgcJFUZNB48IWGGhpEBwcTKtWrQgODlbeXyaTKRMVenp61K1bl9GjR9OoUSMAOnTowJ49exg6dChLliz54HO7YcMGRo0axePHjzE0NFQ+lx/SrFkzjhw5AsDJkydp27YtDx48oGDBgh+8z89KNFX/OvHx8SxfvpwtW7Zw+/ZtZcWElZUVjRs3xtXVlcqVK3+TcwlCXqdQKFi9ejVLly7l9u3bSJKElpYWtWvXZvz48cq/Hd/DgQMHaNu2LZIk8fvvv7Ns2TLg8zeXiYmJwcvLi5EjRyoTXxEREbRv356LFy+ioaHB+PHjM+2kmRcpFAqePHnChQsXuH79Ovfu3SM0NJTXr19/sLrM0NAQKysr7OzsKFOmDA4ODsrqMvHhXBC+jYcPHzJq1CiOHj1KWloaWlpatGzZkrlz51KsWDFVh5enDB48mJUrVzJs2DAWL16s6nAEQfiBRELsCxJitWvX5ty5c5nu86kJsa1bt9K7d2/q1avH3r17P9hc98SJEzRr1oy6devSuEc/0tS1uPz3MQ5t8qZlr/70n/C/hsBJCQlM7v0LbyNe037gUOxK2ZMYH8f961eo06odhUuXRQaY6WoSc/0CHTt2VPZZ+icDAwPy58/Pw4cPMx2PiIigUKFCpKWlYWJiwqtXr7JNUiQmJlKyZElcXFyUz1tKSgrXr1/PMnbv3r3Mnj07SxPjhg0bYmNjw/r167N9XnIq0VQ9d1EoFBw6dIjly5dz/vx54uLigPcVkdWrV6dfv350794dDY0P998ThJ/Rnj178PT05PLly8odIitXroyrqytdunT57q8Jvr6+dO3aVfk3rG/fvnh7e3/z8xw5coQePXoQHR2NlZUVO3bsyLTE8meSmJhIYGAggYGBBAUFZaouS0xMzDI+o3dZgQIFKF68uLK6rGbNmh98zyMIwnsKhYJly5bh5eWl7NtrZ2eHq6srw4YNy7Xvu3I6hUJB0aJFCQ0N5dy5c9SuXVvVIQmC8IOIT3ufqXnz5hw5coQDBw7QunXrz7rv8uXLGTZsGO3atWPr1q1oaWl9cKyPjw+ampps9N1DYOT75QwVatXl5ZMQTu/ZkSkhtnXhbF4+DsZr30msbf7XP6Baw2bK/5eAu8EhDOvQ4YONwPv160e9evWyHN+wYQNpaWm0atWKgwcPsnv3brp3755l3Pr164mKimLAgAHKY9ra2tSsWTPLWHd3d/T09LJsAz106FC6dOnC9OnTsbGx+cCz8+nkcjnR0dHKqqqc3lRdR0cHY2Nj0VT9O1NTU8PZ2RlnZ2fgfTNvLy8vDh48yJkzZzhz5gx9+/alRIkSdOzYkREjRmBpaaniqAVBNS5cuMD06dM5ffo0ycnJyGQySpUqxW+//cbQoUN/2OvN5s2b6dWrV6YLBN9reUvz5s158+YNY8aMYcGCBdSrV48GDRqwe/fuH7qDbU6gp6dH/fr1P9hXLaO67Nq1a9y7d4+nT58SERHB9evXuXz5Mlu3blWOVVdXV/Yus7Ozo3Tp0lSuXJlatWpRrFgx8WFf+Gk9efKE0aNH4+fnR2pqKpqamrRq1Yq5c+dSunRpVYeX56mpqXHmzBmKFStGq1ateP36tdh8RBB+EiIh9pn69OnDs2fPcHd3p2XLllmWLn6Ih4cHEyZMoF+/fqxateo/75dRyRORKmXaTVLfyAhNbW3luJSkRE76bsGxmXOmZFh29nsvR6FQYGFhgYGBAWFhYaSkpChv37VrFwsXLsxyP29vb6ysrFi/fj02NjZ4e3tnmxBbvnw5rVu3/s8PCyEhIZw5c4bu3bsTHR1NcHCwsql6ZGQkmpqa9OjRg8qVK2da/peQkJApUfXvqiq5XI5cLlcmqr6mqiq75X96enofbKr+saoq0VQ95ytWrJhy2VViYiKrVq1i06ZNBAUF4eHhgYeHBxYWFjRq1AgXFxdq1Kih4ogF4ft68OABU6ZM4dChQ8oKShsbG3r16sXYsWMxMjL6ofGcPn06SzIMvl9CDN5/QJo7dy5ubm60a9eOU6dOYWFhwcSJE5k0adJ3O29uU6RIEYoUKULPnj2z3JacnExgYCABAQHcunWL4OBgXr58yfPnz3n48CHHjh3LNF5XVxdTU1NldVm5cuWoXr06jo6OGBgY/KiHJAg/hEKhYO3atcyePVv5WlaoUCGGDx+Oq6urqFL/wezs7Fi0aBFDhw6lVatWnDx5UtUhCYLwA4glk5+5ZHLnzp1oaWnRtm1b1q5dS79+/YCPL5ls0aIFc+fOZdSoUcydO/eTYgwICKBevXo079wD5wHD0NbV5fKp4yz/cxQ9RrrTpt9gAO5eCWBiz/Z0dxlHZHgYFw7tIyUpEbuSZegyfDRV6jdWzjm8eW2S4t8R+Tqc7du34+bmpmyeD+Dk5MT58+dJTU1V7v539uxZhg4dSosWLWjevDne3t7cvHmTbt26oaampkxWvX37lqtXr2JtbY2uru4Hm6p/aVXVlzZVz0hUZVdVlZGoMjc3F03VhQ86evQoy5Yt4+zZs8TExADvd1ytUqUKffv2pXfv3uJNq5AnhIeHM23aNHbu3MmbN28AyJcvHx07dmTSpEkUKFBAZbE9fPiQgQMHcu7cuUxJMTU1NVJSUn7I7+Dhw4fp2bMn0dHRWFtbs3PnTrGs5iuFhoYqq8vu3r3L06dPef36NXFxcVl6l6mrq2NoaIilpSW2trbK3mW1atWiRIkS4m+4kGu8ePGC0aNHs2/fPpKTk9HQ0KBhw4Z4enp+l01IhM9Tt25dzp07x+rVqzOtehEEIW8SCbEvSIj98ssv1KlTh6dPnxIcHIyOjs4HE2JnzpwBoHv37mzevPmz4rx48SKt23ckOuL91vVq6ur0cHWnXf8hyjHnD+5l/qgh6BkYYluyNO0GDEVNTY3961ZyJ/AiE1ZtxqFOfQC6VSyKXWl7nt69RVpa2mfF8jEZySqFQqFMTH2oqbquri4XLlxAS0uLbt26ZdtUfdeuXWzevJkHDx5QqFAhUbIs5BihoaHMmzeP/fv3ExoaqmziXbx4cdq1a4eLi4tKkwaC8LkSExOZNWsWGzZsIDQ0FABDQ0OaN2/O5MmTKVu2rIojzGzbtm1069aNfPnyERkZCcCrV6+wtrb+IedXKBSMHj2ahQsXolAoaNSoEb6+vj/dMsofITk5mStXrhAQEEBQUBDBwcG8ePFC2bvs329fdXR0slSXVatWDUdHxx9e0SgI2dm4cSMzZszgwYMHAOTPn58hQ4Ywbtw4cWEtB0lKSsLS0pLk5GSePn0qNvoShDxOvPp+odmzZ+Pk5MTChQsZO3bsB8fZ2tpiamqKr68vnTt3pm3btsrbJEnK0tw+4w/i1atXad++PUXLVuS3qbPR1tXjdsAFti6YQ1pKCp2GjHw/x/9XW2loavLnqs3o/v+SgnI1ajGsWW18l89XJsQAZLyvssouIWZsbEzTpk2VVVU6OjosWLAAGxsbPDw8MDU1xcTEhE6dOpGens6TJ0+U8S5YsICRI0dy7969j+4qefDgQU6ePMm0adOUycZ/Cw0NZePGjcqt3wUhp7Czs2PhwoUsXLiQ5ORk1q5dy4YNG7h58yaenp54enpibm5OgwYNGDFihKgeEXIkuVzOihUrWL58Offu3UOSJLS1tWnatCkTJkzI0c3jV61aBbxf0hkZGUlQUBBWVlY/7PxqamrMmzePMWPG0LZtW06ePImlpSWTJk3izz///GFx/Ax0dHSoXbv2B19Hnz17xqVLl7h69aqyuiw8PJygoCCuXr3K9u3blWPV1dUxMDBQVpeVLl0aBwcHHB0dKV26tKguE76b8PBwxowZw65du0hMTERdXZ0GDRowZ86cbC/KC6qnq6vLvn37aNSoEfXq1ePRo0eqDkkQhO9J+gmtW7dOAqTLly9ne/uTJ08kQPL09FQeO3XqlARIO3fuVB5r166dZGJiIkVHR0u9e/eW9PX1M81Tr149qWzZslJUVJTk4OAgaWpqSnv27MkSxz+/MtSoUUMqX768tPPOc2nX/TDlV6chIyU1NTVp6fFL0q77YdKfq7dIgFStYdNM43bdD5Nqt2onaenoKL8vULioZJLPQpIkSfL395c6duwoyWQySV1dXQIke3v7TPGvWbNGAqQFCxZIb9++VX65u7tLgHTkyBHlWA8PDwmQwsLCPvrct2/fXtLU1JRev379wTErV66UAOnWrVsfnUsQcpKTJ09KHTp0kMzMzJS/z9ra2lLNmjWlZcuWSSkpKaoOUfiJpaenS9u3b5eqV6+ufM1XV1eXatSoIe3cuVNKT09XdYj/KT09XdLS0pKKFi2q6lCUDhw4IJmamkqAlD9/fun8+fOqDkmQJCklJUU6f/685OXlJfXu3VuqVauWZGtrK+nr60symSzLey8dHR0pf/78UpUqVaROnTpJU6dOlQ4dOiTFxsaq+qEIudT27dsle3t75c+YpaWlNHHiRCk5OVnVoQmfaODAgRIg/fHHH6oORRCE70hUiH2FmTNnUq5cOTw8PD46zszMjBMnTtCkSRM6d+7Mtm3b6NChA61bt+by5cvZ3ufGjRt069YNUz1tYlP+10ejePlKKBQKXoYEY21jR+FSZT54XkmSMl31rFS7Poc2eePv70/NmjXx9fXl8ePHzJ8/nzVr1mTZ9XLt2rUAuLi44OLikmX+tWvX0qzZ+50s8+XLB0B0dDT58+fPNp6IiAj8/Pxo06bNR3fsi46OzjSnIOQGDRs2pGHDhsD7/iDz589n3759BAQE4O/vz9ChQylSpAht27bFxcUFW1tbFUcs/AzOnj3L9OnTOXv2LCkpKchkMsqUKcPQoUMZNGhQrlqms337dlJTU5W9O3MCZ2dnIiMjcXV1ZfHixdSuXZvGjRuza9cusUxPhbS0tHBycsLJySnb21+8eMHFixc/WF22c+dO5diM6jILCwtsbW0pVaoUlSpVolatWtjb24vqMkEpMjKSsWPHsmPHDuLj41FTU6N27drMnj2bWrVqqTo84TOtWLGCI0eOsGjRIrp06SL+DQUhr1J1Rk4VvlWFmCS9v3qgra0tNWjQ4IMVYhmio6OlKlWqSBoaGpKvr+9HYyxSpIhUrlw56crLKGn3P6q+Ovz2hwRIXnuPK4+VqlRFMjQxlTZeeaA8tuX6I8nUwkoq71hbeWz5CX9JV09fcnBwkGJiYjKdLzo6WgoLC5N2794tSZIk3b17VwKkjh07SqdOncry1ahRI0lLS0uKjIyUJEmSzp49KwHSvn37PviYPD09JUA6dOjQRx/7wIEDJXNzc0mhUHx0nCDkBikpKdLKlSslR0dHSUdHR3m12NTUVGrXrp108uRJVYco5DG3b9+WfvnlF8nAwED582ZnZydNnjxZio+PV3V4X6xq1aqSTCaTEhMTVR1Ktl6+fClVq1ZNAiRNTU1p+vTpqg5J+AIpKSnSpUuXpPnz50t9+vSRnJycJDs7u49Wl1lbW0uVK1eWfvnlF2nKlCnSwYMHpbdv36r6oQg/yL59+6SKFSsqfybMzc2lMWPGSAkJCaoOTfhKT58+ldTV1SUjIyNR6S8IedRP3VR/9uzZFC1aNMvt9vb2lC1b9qNN9TOEhYVRokQJEhMT0dfXz3aXydu3byuPxcTE0KRJE27cuMGWLVvo1KlTtjEuXryYP/74g8bNmlOlTWe0dXQJ8j/PgXUrKVvdkcne/+uNcf/aZSb37kRR+/K0GzgEmUzG/nUrCb55jb827KKUw/96FKTeCaB/rx5YWloybNgwHBwcALh79y7e3t5IksT169cZPXo0Xl5eBAQEUL169SzxHThwgDZt2rBgwQJGjBhBamoqJiYmuLi4fLBirkyZMiQkJPD06dOPXlGtUKECJUqUYNeuXR8cIwi51fnz51m0aBGnTp1SNgXX0tKiYsWK9OrVi4EDB4reecJnCwsL46+//mLXrl3KnytLS0s6d+7MxIkTP1qVmxvI5XJ0dHSwt7cnKChI1eF81P79++nduzcxMTEUKFAAX19fHB0dVR2W8I2EhYVlqi578uQJ4eHhxMbGkpqammmsmppattVljo6OlCtXTlSX5WIxMTG4u7uzZcsW4uLikMlk1KhRg5kzZ1K/fn1Vhyd8Q8rPZI0bc/z4cVWHIwjCt6bihJxKZNe7659fn1MhJkmSNH78eAn4zwqxDDExMVL16tUlDQ0Nafv27R+Mc9euXVLt2rUlYzNzSUdPT7IpUUrqNmKMtPnaoyz9wqZv3iOVreYoaevqStq6ulL5mrWlGVv2KW/ffT9MOh36RpIkSQoJCZGGDBkiFS9eXNLW1pZ0dXUle3t7ydXVVXry5ImUmpoqWVpaSpUqVfpgbHK5XCpUqJBUvnx55bFevXpl6UOW4cKFCxIgTZo06YNzSpIkPXr0SAKkXbt2fXScIOQFr169ksaNGyeVKFFCUlNTkwBJJpNJdnZ20rBhw6THjx+rOkQhB4uNjZXc3d0lGxsb5d8vIyMjqVu3btKDBw9UHd43tXDhQgmQVqxYoepQPkl6ero0bNgw5e91s2bNpLi4OFWHJXxnaWlpUmBgoLRo0SKpX79+Uu3ataXChQtLBgYG2VaXaWtrS1ZWVlKlSpWkDh06SJMnT5YOHDggRUVFqfqhCB9w+PBhqUqVKsp/TxMTE8nFxUV69+6dqkMTviMnJycJkNauXavqUARB+MZ+ygqx3CY+Vc6Jp29QfMW/lJoMGhe2wEDr+/WLuXLlCtWqVcPf358aNWp80RwTJ05kw4YNhISE5KreNoLwteRyORs3bmTdunVcuXKFpKQk4P3ur3Xq1GHIkCG0aNFCxVEKqiaXy1myZAkrV67kwYMHSJKEjo4ODRo0YOLEiXm2Esne3p7g4GBSUlJyVVXNy5cvadu2LVevXkVTU5OpU6fi7u6u6rAEFQkPD+fChQvK6rLHjx//Z3VZvnz5lNVlFStWxNHRkQoVKuSq34PcLj4+nj///JP169cTExODTCajcuXKzJgxQ9lLV8jbEhMTsbS0JCUlhdDQUAoUKKDqkARB+EZEQiyXeB6XxOVXMV98/2r5TbAx0v12AX1Aly5dSEhIwM/P77PvGxMTQ9GiRVm8eDE9evT4DtEJQu5x+fJl5s+fz8mTJ4mIiABAU1OT8uXL06NHDwYPHoyenp6KoxR+BIVCwdatW5k/fz7Xr19HoVCgoaFB9erVcXNzo127dqoO8buKj4/HyMiImjVrcvHiRVWH80X27t1Lnz59iI2NpWDBguzateuLLxwJeZNcLufGjRv4+/tz8+ZNHj58yPPnz4mMjCQhIQGFQpFpvLa2NsbGxuTPn5+iRYtStmxZqlatipOTk9iU6Bs5deoU7u7uBAYGIkkSRkZG9OzZkxkzZmBiYqLq8IQf7OTJkzRu3JjixYsTHBys6nAEQfhGREIsF3kel8TV8Bgk6X2t/X+RATIZVLH+MckweL9z09q1a3F1dcXQ0PCz7nv9+nVOnDjB6NGjkclk3ylCQch9IiMjWbhwIb6+vjx8+FD5wcjGxoZWrVrh6upKiRIlVByl8K2dPHmSGTNmcOHCBVJTU5HJZJQrV45hw4YxYMCAn6ZCZNKkSUybNo1du3bRoUMHVYfzxRQKBcOHD2fFihUoFAqaN2/Ozp07MTAwUHVoQi4QERHBxYsXuXLlCnfu3FFWl8XExGRbXaavr4+FhQU2NjaULFmSSpUqUbNmTSpUqCAq8D8iMTGRKVOm4O3tTVRUFDKZjIoVKzJ16lTatGmj6vAEFRswYABr167FxcWF+fPnqzocQRC+AZEQy2XiU+VcDY8hKikNGdknxjKO59PVorK18XddJikIwo+VUS20Zs0aAgMDSUxMBMDIyAgnJycGDx6Ms7PzT5MsyWuCgoKYMmUKx44dIyEhAYCiRYvSr18/XF1d0dX9MRc3cpLChQsTHh5OcnKyqkP5Jl68eEHbtm25du0aWlpaTJs2jTFjxqg6LCEXk8vlBAUF4e/vz40bN5TVZW/evMm2ukxLSwsTExOsra0pWrQo9vb2yuqy3L4Bx5e6ePEiY8aM4dKlSygUCgwMDOjSpQuzZs0SFXeCkkKhwM7OjpcvX3Lx4kVq1qyp6pAEQfhKIiGWS71NTiM0NpGopFTiUuRIvE+EGWlrYK6rhZ2xHqY6mqoOUxCE7+zatWssXLiQY8eOER4eDoCGhgZly5ala9euDBs2TFSg5HDPnj3jr7/+Ys+ePURHRwNgbW1N165dmTBhwk/9Yez169dYW1vTtGlTjh49qupwvqndu3fTt29f4uLisLGxYdeuXVSrVk3VYQl5UGRkpLK67Pbt2zx58oRXr14RExNDSkpKprEZ1WXm5ubY2Ngoe5fVrFmTSpUq5anqspSUFKZNm8aqVat48+YNAGXLlmXy5Mkf3AVeEJ48eUKJEiUwMDAgIiICLS0tVYckCMJXEAmxPEKSJLHMUBB+ctHR0SxevJidO3dy//590tPTAShYsCAtWrTA1dWVMmXKqDhKAd73TPTw8GDLli28fPkSABMTE1q3bs2UKVMoWrSoiiPMGYYNG8bSpUs5c+YMdevWVXU435xCoWDo0KGsWrUKhUJBy5Yt2b59u0hiCz+MQqEgKCiIS5cucfPmTR48eMCzZ8+IjIwkPj4+2+oyY2PjTNVlVapUwcnJCWtraxU9is9z5coV3NzcOHfuHOnp6ejp6fHLL78we/bsXPMYBNVauHAhLi4uefJijSD8bERCTBAEIQ9SKBT4+vqyatUq/P39lcvvDAwMcHR05LfffqN9+/ZiaeUPlJqaysKFC1m9erWyIa+uri6NGjVi0qRJojooG1ZWViQlJREXF6fqUL6rZ8+e0bZtW27cuIGWlhYeHh6MGjVK1WEJAtHR0cqdMW/fvs3jx4959eoVb9++zVJdJpPJMlWXlShRQtm7rHLlyiqtLktLS2PWrFksW7ZMWU1dunRpJkyYQM+ePVUWl5B71apVi0uXLuHj40Pv3r1VHY4gCF9IJMQEQRB+Ardv32bevHkcPXqUsLAwANTV1bG3t6dz584MGzZM7Jr1HSgUCjZu3MjChQu5efOmcodIR0dHxo4dS6tWrVQdYo4VHBxMyZIl6dSpEzt27FB1OD+Er68v/fv3Jy4uDltbW3bt2kXVqlVVHZYgZEuhUHD37l0uXrzI9evXldVlb968+c/qsiJFiiiry2rVqkWBAgW+S4xBQUGMHj2av//+m/T0dHR0dGjXrh2enp4UKlTou5xT+DkkJiZiYWFBWloaz549E9WFgpBLiYSYIAjCTyYuLo6lS5eydetW7t69q1xamT9/fpo1a8bIkSOpUKGCiqPM3Q4fPsysWbO4dOkSaWlpqKmpUaFCBf744w969+4tKvM+QY8ePdiyZQu3b9+mbNmyqg7nh1EoFAwZMoTVq1ejUChwdnZm+/bt6OnpqTo0QfgsMTExXLx4kcuXLyury8LCwoiJicmySYZMJkNPT498+fJRqFAhSpQoQcWKFalRowZVqlT5rD5NcrmcefPmsWjRIuWS9OLFizNu3Dj69u0rXn+Fb+b48eM0bdqUEiVK8PDhQ1WHIwjCFxAJMUEQhJ+YQqFg3759rFy5kosXL/Lu3TsA9PX1qVGjBgMGDKBLly7iA8QnuHLlCtOmTePEiRPK3T+LFy/OgAEDGDFiBDo6OiqOMHcxMTFBQ0ODyMhIVYeiEqGhobRt25abN2+ira2Nh4cHrq6uqg5LEL4JhULBvXv3uHTpUqbqsoiICOLj45UXajJoampiZGSUbXVZRqXXvXv3GD16NMePHyctLQ1tbW2cnZ2ZO3cuhQsXVsGjFH4Gffv2xcfHB1dXV7y8vFQdjiAIn0kkxARBEASlBw8e4OXlxeHDh3nx4gXwftexUqVK0blzZ/744w/MzMxUHGXO8eTJE6ZMmcL+/fuJiYkBoECBAnTv3h13d3fxXH2hgIAAatasycCBA1m1apWqw1GpHTt2MGDAAN69e4ednR27d++mcuXKqg5LEL6rmJgYLl26lKW67O3bt1mqy+B9hVnGRxoDAwOaNGnCyJEjqVGjhtgFUPiuFAoFtra2hIWFcenSJWrUqKHqkARB+AwiISYIgiBkKz4+nuXLl7N161Zu3bqFXC4H3jc6b9y4Ma6urj/lB/Po6GimT5/Otm3bePXqFQBmZma0bduWyZMnY2dnp+IIcz9nZ2cOHjzI8+fPRZ8f3i8B+/3331m7di2SJNGmTRu2bt0qllEKPyWFQsHx48cZN24cQUFByl5lGhoapKen8++PNhnVZVZWVhQuXBh7e3sqV66Mk5MTtra2qngIQh4TEhJCqVKlMDQ05PXr1yIJKwi5iEiICYIgCP9JoVBw5MgRli5dyoULF4iNjQXe75JYvXp1+vXrR/fu3VW6i9j3lJyczLx58/D29iYkJAR4v6y0SZMmTJo0CQcHBxVHmLfo6elhbm7O8+fPVR1KjhIaGkqbNm0ICgpCW1ub2bNnM2LECFWHJQg/hEKhYNWqVXh6evL48WMAbGxscHFxwcXFRbm0Py4ujoCAAAICArh9+zaPHj36YHVZRu8yMzMzZe+yChUqUKNGDapWrSqWugufbP78+bi6utK8eXMOHz6s6nAEQfhEIiEmCIIgfLaQkBDmzZuHn58fz549A94vrSxRogQdO3ZkxIgRWFpaqjjKr6NQKPD29mbx4sXcunULSZLQ1NTEyckJd3d3mjZtquoQ8yQ/Pz9at27NuHHjmDlzpqrDyZG2b9/OgAEDiI+Pp3DhwuzZs4dKlSqpOixB+C5CQ0Nxc3Nj//79pKSkoKGhQePGjZk7d+5nb7ihUCgIDg5W7ox5//59QkNDiYiI4N27d1l6l2loaGSqLitTpgyVK1emdu3aohpYyMLR0RF/f3/Wr1/Pr7/+qupwBEH4BCIhJgiCIHyVxMREVq1axaZNmwgKCiItLQ0ACwsLGjVqhIuLS67qqeHn58esWbMICAhALpejpqaGg4MDLi4udO/eXWww8J3VrVuXc+fOERsbi5GRkarDybHkcjmDBw/G29sbSZJo164dW7duFRUtQp6gUCjYsGEDHh4eBAcHA+/7Mw4bNgw3N7fvVo0cHx9PQEAAgYGB3Lp1i0ePHvHy5Uvevn1LUlJSprEymQxdXV3MzMwoWLBgpuqy6tWri9/Fn1B8fDxWVlakpaXx/PlzrKysVB2SIAj/QSTEBEEQhG/q6NGjLFu2jLNnzyobzevo6FClShX69u1L7969c9zSyoCAAP766y9OnTql/NBTsmRJfvvtN4YNGyb6gfwgCoUCbW1tihQpIraw/0RPnjyhbdu23Lp1C21tbTw9PRk+fLiqwxKELxIWFoabmxt79uwhKSkJdXV1GjRogKenp8qrIBUKBSEhIVy8eJFr165lqS7L6LOZIaO6zNLSMlN1mZOTE0WKFFHRoxC+tyNHjtCiRQtKlSrF/fv3VR2OIAj/QSTEBEEQhO8mNDSUefPmceDAAZ4+fYokSchkMooXL067du1wcXGhQIECKont0aNHTJkyBT8/P2VPtEKFCtGzZ0/c3d1FdZIK+Pj40LdvX+bMmYObm5uqw8lVNm/ezODBg4mPj6dIkSLs3buXChUqqDosQfgkW7duZdq0ady7dw8Aa2trBg8ejLu7e665IJGYmIi/v3+W6rLo6Ohsq8t0dHSU1WXFixenfPnyVK9enZo1a4oNM3K53r17s2HDBkaPHo2np6eqwxEE4SNEQkwQBEH4IZKTk/H29mb9+vXcvHmTlJQUAMzNzWnQoAEjRoygdu3a3zWGiIgIpk+fzo4dO3j9+rXy/B06dGDSpEliR0MVc3BwICgoiKSkpFzzITgnkcvlDBw4kPXr1yNJEu3bt2fLli1i6ZaQI0VERDB27Fh27txJQkICampq1KlTh9mzZ+eqZfaf6smTJ5w/f55r165x7949QkNDef369QerywwNDbGyssLOzo4yZcrg4OCgrC4TS/dzNoVCgY2NDa9evSIgIIBq1aqpOiRBED5AJMQEQRAElTh16hRLlizh9OnTREdHA6CtrY2DgwO//vor/fv3/yZJkcTERObOncu6det4+vQpAAYGBjRv3pzJkydTrly5rz6H8PVSU1PR1dWlYsWKXLt2TdXh5GohISG0bduWO3fuoKOjg5eXF0OGDFF1WIIAwO7du5kyZQq3bt0C3vebHDBgAJMmTfppk7eJiYkEBgYSGBhIUFBQluqyf39cy+hdVqBAgUzVZTVq1MDAwEBFj0L4p0ePHlGqVCmMjIx48+YNMpmMbdu2UbduXWxsbFQdniAI/08kxARBEASVe/HiBQsWLGDv3r08fvxYubSySJEitG3bFhcXF2xtbT95PrlczurVq1m2bBl37txBkiS0tLSoU6cOEyZMoEGDBt/x0QhfwtPTkzFjxrBu3Tr69Omj6nDyhE2bNjF48GASEhIoWrQo+/btEwlgQSWio6Nxd3dn69atvHv3DjU1NWrWrMns2bO/e2VwXvDkyRNl77J79+7x9OlTXr9+TVxcXJbqMnV1dQwNDZW9y0qXLo2DgwO1atWiePHiorrsB/Ly8mL06NE0aNAAuVzOuXPnxDJKQchhREJMEARByFFSU1Px8fFh/fr1XLt2jeTkZABMTU2pV68ew4cPp2HDhtned/fu3cydO5fAwEDS09NRV1encuXKjBo1ik6dOokPAjlYyZIlefLkCSkpKeLf6RuSy+X079+fjRs3IkkSHTt2ZNOmTT9tJY7wYx06dIg///yTGzduIEkSZmZm9OvXj6lTp4o+Wd9IcnIygYGBBAQEcOvWLYKDg3n58iVRUVEfrC4zNTVVVpeVK1eO6tWr4+joKKrLvoMSJUrw6NEj1NTUUCgUtGzZkoMHD6o6LEEQ/p9IiAmCIAg52vnz51m0aBGnTp0iMjISAC0tLSpWrEivXr2wt7fH09OTM2fOkJycjEwmo3Tp0vz+++/8/vvvOW5HSyGr2NhYTExMqFOnDmfPnlV1OHnSo0ePaNu2LXfv3kVHR4d58+bx+++/qzosIQ+Ki4tjwoQJbNy4kdjYWGQyGVWrVmXmzJk0atRI1eH9dEJDQ5XVZXfv3s1UXZaWlpZpbEZ1mYWFBXZ2dpQuXZpKlSrh5OREyZIlxcWKz5CUlMSQIUPw8fHJdNzOzk7ZvkEQBNUTCTFBEAQh1wgPD2fhwoVs3bqV0NDQTLcZGBjQp08fZs6cKa5y5zLjxo1j9uzZHDhwAGdnZ1WHk6dt2LCBIUOGkJCQQPHixdm7dy9ly5ZVdVhCHnDixAnGjx/PlStXkCQJY2Njfv31V6ZPny527c2hkpOTuXLlCgEBAQQFBREcHMyLFy+Ijo4mMTExS3WZjo6OsrqsWLFilC9fnmrVquHo6Cj+jf/lyJEjtGjRIstxNTU1EhISRJWuIOQQIiEmCIIg5Arh4eH89ddf+Pr68ubNG+B9EszQ0JDo6GjlrpXGxsbUqVOHIUOGZPtmVMh5bGxsiIqKIjExUdWh/BTkcjn9+vVj06ZNSJJEp06d2LhxI9ra2qoOTchlEhISmDRpEj4+PkRHRyOTyXBwcGDatGm0bNlS1eEJX+nZs2dcunSJq1evKqvLwsPDP1hdZmBggKWlJba2tpQqVQoHBwccHR0pU6bMT1ddJkkS+/btY8yYMQQHB2e67datW6KfoyDkECIhJgiCIORY8fHxzJkzh/Xr1/Ps2TMAjIyMaNGiBZMnT6ZMmTLKsZcvX2bBggWcOHGCiIgIADQ1NSlfvjw9evRg8ODBomdNDvTixQtsbGxo1aoVfn5+qg7npxIcHEybNm24f/8+urq6LFiwgEGDBqk6LCEXOHfuHOPGjcPf3x+FQoGhoSHdu3fHw8MDMzMzVYcn/ACpqalcvXqVgIAAbt68ycOHD3nx4oXy4kZ21WUmJibK6rJy5copq8tMTExU8yB+gPT0dDZu3Mj48eN59eoVALNmzWLs2LFZxmZsKCQIwo8jEmKCIAhCjiKXy1m+fDnLly/n/v37SJKEtrY29evX588///ykHckiIyNZuHAhvr6+PHz4EIVCAaBMvLi6ulKiRInv/VCETzBo0CBWr15NQEAA1atXV3U4P6V169YxbNgwEhMTKVGiBHv37sXe3l7VYQk5THJyMlOnTmXNmjXKfo7ly5dn6tSptG/fXsXRCTnNy5cvuXDhQpbqstjY2A9Wl1lYWCiryypVqkStWrWwt7fPE9VlKSkp/PXXX3h4eFC0aFGCg4OJTU0nNDaRqKRU4lLkSIAMMNLWwFxXCztjPUx1NFUduiDkaSIhJgiCIKicQqFg586deHl5ce3aNeUOkdWqVWP06NF07Njxq+beunUra9asITAwULksz8jICCcnJwYPHoyzs3OeeMOdG+XLlw+5XE5MTIyqQ/mppaam0rdvX7Zu3QpA586d2bBhA1paWiqOTFC1gIAAxowZw/nz51EoFOjr69OpUydmz56NpaWlqsMTcqHU1FSuX7+Ov78/N2/eJDg4mOfPnxMVFUVCQkKW6jJtbW1MTU3Jnz8/RYsWpVy5clStWpVatWrluorE48eP8+uAQazyO0mali4yILsP4xnHzXU1qWJtgoGW2CBIEL4HkRATBEEQVOb06dPMmDGDs2fPkpqaikwmw97enqFDhzJw4MDvskPktWvXWLhwIceOHSM8PBwADQ0NypYtS9euXRk2bJhoyv+D3L59m/Lly9O9e3c2b96s6nAE4MGDB7Rt25YHDx6gq6vLokWLGDBggKrDEn6w1NRUPDw8WLFiBa9fvwagTJkyTJw4kW7duqk4OiGve/XqFRcvXuTKlSvcvXuXJ0+eKKvLUlNTM41VU1NTVpfZ2Nhkqi4rV65cjrvY9TwuiSuv3gKybBNh/yYDZDKoYm2CjZHud45OEH4+IiEmCIIg/FC3b99mypQpHD16lPj4eAAKFy5M3759GT169A/t8xUdHc2SJUvYsWMH9+/fJz09HYCCBQvSokULXF1dM/UpE76tzp07s3PnTh4+fCiWsOYwa9eu5Y8//iAxMZGSJUuyd+9e8bvwE7h+/Tpubm6cPn2a9PR0dHV16dChA3PmzPm/9u47vsbz/+P462SHICTE3nvvUSv2VpTaQlB7lKpSu9Qe/daqGWpU7dKiNlW7ZmnV3iOJJMhO7t8f+eW0p0mI1YO8n4/HebTnuq/7uj/34UTO51zX5yJjxozWDk+EyMhIi9llFy5c4MaNG/j6+hIcHGwukRDL0dERV1dX8+yyQoUKUbp0aSpWrIibm9sriysqKgobG5un1gC7ERTC0TsBL3yNMhmUFBN55QwREZHX7MaNG0bXrl0NNzc3g5hVAIaHh4fRp08f4969e9YOzzAMw4iKijJWrVpl1KhRw0iePLk5ThcXF6NWrVrGmjVrjKioKGuH+U5JkSKFkS5dOmuHIQkICwszWrVqZZhMJsNkMhmtW7c2wsLCrB2WvGIRERHGuHHjjIwZM5p/7uXNm9fw8fHRzzx569y9e9dYu3atMXToUCNfvnzmv9PxPWxsbIwUKVIYOXPmNMqVK2duHz9+vBEZGWkx7siRIw3AePDggbF79+6njvvPR6xHYRHG+j9vG2v/iHk87ZyMOXKZ+63947Yxed02o2Dp8kYylxQGYEyfPt0wDMP4888/jWbNmhmurq6Gs7OzUbZsWWPjxo3PfI3atm1rAEaDBg3iPX7x4kXDwcHB+PXXX81tK1asMCpXrmykS5fOcHBwMDJkyGA0bNjQOHDggMW5/v7+RqpUqYz169cn8k9MxLo0Q0xERF6LoKAgxo8fz7Jly7h58yYAqVKlokGDBowaNeqNnxF09uxZpk2bxrZt27h9+zYQU/i3YMGCfPjhh/Tu3fud3hnrddu3bx9Vq1alV69ezJw509rhyFOcP3+eJk2acOHCBZIlS8bMmTPp1KmTtcOSl3T27FkGDRrEjh07iIyMxNHRkcaNGzNlyhSyZs1q7fBEXtqoUaMYPXo0W7duJVWqVISHh/PLL78wZ84cbt26RfHixQkICMDX15fHjx/HqV3m4OBgnl0WGhrKn3/+ydKlS6lUqZJ5KXGsihUrEh0djclkokOHDnTu3Bl7e3vKly8PwN7rvviHRJiXSV44eTxOvBdOn2DxlyNo2rU37QYONbd/0rQWocEhdP58DBnSutGsfHFCQ0MpVaoUGTJkYPjw4bi4uDBnzhx++uknVq9enWDt1R9//JGWLVtia2tL5cqV493duWnTpkRERFgcmzlzJrdu3aJ06dK4u7tz584dpk2bxm+//cbOnTupWrWque/o0aNZtmwZv//+u+pQypvPygk5ERF5h4SFhRlTp0418ubNa5hMJgMwnJycjPr16xuHDh2ydngvLDAw0Bg3bpxRtGhRw9bW1vwtboYMGYyOHTsap06dsnaIb53atWsbgHH37l1rhyKJ9M033xjOzs4GYOTPn9/4448/rB3SOy06OvqFzvv3zJZ/ioqKMqZMmWJkyZLF/HMsZ86cxty5czUbTN45/5zV9U+XLl0yAGPIkCHmtitXrhiAUbJkSQMwihYtahQrVsw8Iwrin12WI0cOo1KlSnGOFyxY0Dh58qRhGIbhHxJuMeMroUe1ph8aJpPJmLntgEW7rZ2dUae1l/m5f0i40a1bN8PJycm4efOm+R4iIyONAgUKGFmyZIn3/RwQEGBkypTJmDZtmpEtW7Z4Z4idO3fOAIytW7c+8/UNCAgw7O3tjfbt21u0371717CzszOWL1/+zDFErO3NqjIoIiJvnejoaJYvX07p0qVxdnZm4MCBXL58mffee4+NGzcSEhLCjz/+SLly5awd6gtLmTIlQ4cO5dSpU4SHh7N+/Xrq1KnD48eP8fHxoVixYri4uFCjRg1WrlwZp4aJxLV3716yZcuGh4eHtUORRProo48ICAigZcuW/PnnnxQoUIB27drFKXItL+ZhaAQn7wWy8+oD1v95h/UX7rL+zzvsvPqAk/cCeRga8cwxLl26RJYsWeLMurxw4QKNGjXCycmJTz75hHv37tGkSRMuXrzIpUuX6Nat2xtXfFwkIRs2bMBkMrFz5844x+bMmYPJZOL06dMJnp8qVSoA7O3t4xxr3bo1derU4c6dO+zfv5979+4RFhbG8OHDAZg4cSIfffQRlStXJm3atPj6+vLrr7/GGefcuXMUL16cokWLsvmXwyRcWSxGyOPHHNy2mYJlKpAhWw4Adq1bxQf5MxIVGcm2lUv4IH9GPsifkWuBwRw4cIBixYqRKVMm8xi2trbUq1ePGzducOTIkTjXGDhwIBkyZKBv374JxjFnzhzSp09PrVq1nhExpEiRAicnpzgbIHl4eFCrVi3mzp37zDFErE3/8omIyAvZvn071apVw8nJiXbt2vHbb79RuHBh5s+fT1hYGL/88guNGze2dpivnI2NDU2aNGHr1q0EBQVx/vx5PvroI1KnTs2uXbto06YN9vb2FCxYkFGjRuHv72/tkN84a9euJSwsjA4dOlg7FHlODg4OfPfdd5w5c4bcuXOzfPlyUqdOzZIlS6wd2lvrcXgke6/7svuaL1cCggkMizQvqzKAwLBIrgQEs/uaL3uv+/I4PDLecR48eEDNmjW5c+cOEydOJDIykpkzZ5IjRw7y5cvH5s2byZgxI//73/8ICQlh/fr15MqV6z+7T5FXpWHDhqRLl47FixfHOebj40PJkiUpWrSouS0qKorIyEjCw8O5ePEivXr1wtHRkebNm8c7/sSJE/H19WXy5MnmttiEsbe3N9988w179uzh0qVLBAUF8eOPP1qc/8/C+levXuVh6N/v6YT88tNGQoODqdmijbmtlGdNxn+3CYAKdRoy/rtNjP9uE34h4YSHh+Po6BhnnNi2fycEd+zYwdKlS1mwYAG2trYJxvHjjz9SpUqVBBPkUVFRREREcPXqVXr06IFhGPTq1StOP09PTw4cOEBAQMAz7lzEupQQExGRRDt58iRNmzbFxcWF2rVrs2fPHrJmzcq4ceMIDg7m1KlTdOnSJUnNNMifPz/ffPMNN27c4NGjR0yaNImiRYvy119/MXr0aNzc3EifPr05aSgwdepUTCYTn376qbVDkRdUqFAhLly4wNy5czEMg44dO1KgQAH++usva4f2VrkRFMKOqw/wD4mZ/ZXQh+bYdv+QCHZcfcCNoBCL48HBweaZIQA3b94kWbJk9OnTh1u3btGgQQPOnz/P1atX6dOnT5L6GS3vHjs7O9q1a8e6desIDAw0t58/f54jR47EqXGYPn167O3tcXR0JE+ePGzZsoWVK1dSpEiReMcvVqwYbdq0Ydq0ady9e/eZ8Vy+fNkitvr167N8+XKCgoIICgoiS558zxxj59qVJE+ZivK165vbUqVxI2/xUjH/7+ZO3uKlyFu8FEFhkRQsWJDTp0+bd+uO9csvvwDg5+dnbnv8+DFdu3blk08+oVixYgnGcP/+fS5fvkzJkiUT7FOoUCEcHBzIkSMHmzZtYuvWrZQqVSpOv5IlSxIdHc2hQ4eeee8i1qR/DUVE5KmuX7+Ot7c3bm5ulChRgg0bNpAyZUo+/vhj/Pz8uHjxIkOHDsXJycnaoVqdi4sLgwYN4sSJE4SFhbF582bq169PSEgIy5cvp1SpUiRLlgxPT0+WLl1KZGT8Mz3eZVFRURw5coT8+fPj4uJi7XDkJXXr1g1/f3+aN2/OH3/8Qb58+ejQoUOS/Lv9vG4EhXD0TgDRRsKJsH8zgGgDjt4JMCfFoqKi+PDDD/ntt9+Iiooy97W1tWXSpEkEBwezefNm8ufP/+pvQsRKvL29CQkJYdWqVea2xYsX4+joSJs2bSz67tixg6NHj3LkyBE2b95MzZo1adWqFevXr09w/LFjxxIREcHo0aOfGUuxYsVwdnamaNGi3L9/n82bN9OmTRuSJ09OREQEkZGRREVGWrw//+n6X3/y16nfqNywKQ6Oz/5dygB69epFYGAgHTp04PLly9y7d4/hw4ebl2/+M+n92WefYW9vz4gRI546buwGQunSpUuwz9q1azl8+DCrV6+mYMGC1KtXjz179sTpFzvGrVu3nnk/ItakhJiIiMQREBDAoEGDyJQpE9myZWPx4sVER0ebf/G6ffs206ZNI02aNNYO9Y1lY2NDgwYN+PHHHwkMDOTixYv07NmTtGnTsnfvXry8vHB0dCR//vx8/vnn3L9/39oh/yfmz59PVFQU3bt3t3Yo8oo4OTmxevVqzpw5Q86cOfn2229xdXVl6dKlLzSej48PJpMJk8kU7wctwzDInTs3JpMJT0/POMd9fX1xdHTEZDJx7Ngxc/uoUaPM4z7tETtmx44d403aRkdH8+2331KzZk3c3d2xt7cnXbp0NGzYkE2bNiWqhuDj8EiO3w14Zr8R7T8w1w36IH9GPiycle7VyzLr8wFsOX6Wu/4BFClShB9//DHODnmhoaFMmDCBmjVrxruTHMB3331H8eLFcXJyImPGjPTv3z/OjJNYY8aMoWDBghb3t3TpUlq1akW+fPmwsbEhe/bsCd7LiRMnaNKkCRkzZiRZsmTkz5+fMWPGEBwcbNGvSpUq9O/f/5mvjSRthQoVokyZMuZlk1FRUSxbtoz3338/zu8mxYoVo3Tp0pQpU4YGDRqwevVqcufOHe9Sv1jZs2enZ8+eLFiw4JkzXytWrEi6dOnIkiULqVOnNrd7e3vj4ODAh4Wz8mHhrIzu+GG85+9csxLAYrnk05iAmjVrsnjxYvbt20euXLlInz4969at44svvgAw1xY7cuQIs2fPZtKkSYSGhhIQEEBAQADR0dFERkYSEBBAWFgYACEhMUn2p33BWahQIcqWLUvz5s3ZunUr2bJlo1+/fnH6xY4RO6bIm0oJMRERASA8PJyJEyeSJ08eUqdOzZQpUwgICKBx48YcPXqUhw8fsmTJEnLkyGHtUN9KuXLlYtasWVy7do0nT54wffp0SpQoweXLl/nyyy/x8PAgXbp0tG7dmsOHD1s11n9/sH6VZs+eja2tLT179nxt1xDrKFy4MBcvXmTWrFlERUXh5eVFoUKFuHTp0guNlyJFChYuXBinfe/evVy6dIkUKVLEe963335rLvT/z/O7dOnCwYMHzY9169YB0KdPH4v22bNnJxhTaGgo9evXx8vLi3Tp0jFnzhx27drF3LlzyZgxIy1atGDTpk3PvLfjdwNI7NvMI0s2c+2gkYu/p0mXnhzfs4PP2zZh7safOX/+fILnNmnSBFtbWxo1ahQnruXLl9O6dWvKlCnDli1bGDlyJD4+PjRr1izOOLdv32bSpEmMGTPGYubJt99+y++//07ZsmWfWo/s3LlzvPfee1y9epUZM2awefNmWrVqxZgxY2jdurVF3y+++ILZs2fz559/Ju4FkiSrU6dOHDp0iPPnz7N161bu3LkTZ7lkfGxsbChUqBB37tx56pdRw4YNI1myZAwdOvSF4hs1ahRHjx5l1safmbh6C91GT4zTJyI8nH0/rCFXoaLkKFA4UeOmdIwpYu/l5cXdu3c5d+4cf/31F7///jsQU8OscuXKQMx7zzAMmjZtSurUqc2PGzdusG3bNlKnTs2cOXMAcHd3B0h07VM7OztKlizJhQsX4hyLHSN2TJE3ld2zu4iIyLsqOjqaJUuW8L///Y/Tp08THR2Nvb09VapU4bPPPqNevXrWDvGdlCxZMvr372+eBfHzzz8za9Ys9u3bx3fffcd3332Hk5MTpUqVolOnTnh5ecXZxelVehgawbXAYPxCwgn6/4LeJmJ+6XZzdiBbqmSkdoq7G9fzCg4O5uzZs5QqVeq13o9YV8+ePfH29qZdu3asXbuWPHny0KFDBxYsWPBcf+4tW7Zk+fLlzJo1i5QpU5rbFy5cSIUKFQgKCor3vEWLFpEuXTqyZcvGypUrmTZtGs7OzmTOnJnMmTOb+129ehWArFmzUr58+UTFNGDAALZt28aSJUvibArRrFkzBg0a9MwZEQ9DI/ALefaOkbEcnJzMdYQACpUpj72jI7M/H0h0dBR/XrtJZvfUzJ49m0GDBvHVV1+RJk0a/P39adKkCWnTpiV16tSsXLmSRo0aATGzaQYNGkTt2rWZP38+ANWqVSNFihS0bduWLVu2WPz8/+qrr3B1dY2TLNu2bZs5QdawYUPOnj0b7z2sWLGC0NBQ1q5da06cVa9enTt37jBv3jwePnxonllTtWpV8uXLx9SpU5k3b16iXydJelq3bs2AAQPw8fHh8uXLZMqUidq1az/zvKioKM6cOYOjo6PFz5Z/c3NzY/DgwXz++ec8efLkuePLnj072bNnx+5eIFcCguNdGn1s188EPfSnZd9BiRrTBLg5O5if29nZUaBAAQACAwOZN28e77//PtmyZQOgbt267N69O844rVq1IkeOHIwfP57cuXMDkC1bNpydnRP9JUZoaCiHDh0yn/9PsXXVChYsmKixRKxFM8RERJKgLVu2ULVqVZycnPD29ub06dMUK1YMHx8fQkND2bt3r5Jh/6HatWuzceNGHj58yNWrV+nXrx8ZMmTg119/pUuXLjg4OJA3b14+/fRTc42PhAQEBHDlypVEXfdV7W6XWNOmTcMwDAYMGPBS48ibz8nJiTVr1nDq1Cly5MjBkiVLcHV1Zfny5YkeI3bm0MqVK81tgYGBrF27Fm9v73jPOXz4MGfPnqV9+/Z07drV3P9VuHv3LgsWLKBOnToJ7pCaJ08ei93t4nMtMJjI8HDWzJlBn3qVaVkkO50qFGbmkP4E+vs99dxYyVxiPsTb2dkT7OhCsmTJzDMx3nvvPdq1a0ffvn3JmjUrTk5OODg4YG//d1L70KFD8c6madGiBS4uLha1lcLDw1m4cCFt2rSJU4w/scX5Y6+dKlUqi3ZXV1dsbGxwcHCwaG/fvj0rVqzg0aNHiRpfkiZXV1eaNm2Kj48PP/zwA15eXvH+nTx+/DiHDh3i0KFDbNy4kWbNmvHHH3/Qs2fPZ9Y/7d+/PxkzZmTLli0vHGe2VMkSrBO4c+1KHJycqNywaaLGMv5/vPv37zN48GB++OEHdu/ezZw5cyhevDg2NjbMmjXL3D99+vR4enrGeTg5OeHm5oanp6f5iwIHBwcqVKgQbyH89957jwkTJrBx40b27NmDj48PVatW5dKlS3z55Zdx+h86dAg3N7cENy4QeVMoISYikkQcO3aMRo0akSxZMurXr8++ffvInj07EydOJCQkhN9++y3BXyblv5MtWzZmzJjB5cuXCQkJYdasWZQtW5br168zefJkMmXKhLu7Oy1atDDvJvVPffv2JV++fM9ctvWqdrd7HosXL8bBwYGWLVu+8BjydilatCiXLl3i66+/Jioqinbt2lG4cGGLXdkSkjJlSpo3b86iRYvMbStXrsTGxibBv0OxSyS9vb1p1aoVyZIli3fZ5YvYvXs3ERERNGnS5KXGefAklAm9OrF+/kwqN2zK0G+W0nbgUE79up8RHT4gLDTueyzq/4tyh4UE89fpE6yePR2PLNnIV7I0fiHhln2jooiMjCQiIoKbN2/Sv39/njx5YlFoPHYm17+Td/b29uTPn99iptfhw4fx8/OjWrVqL3zPXl5euLq60qNHDy5fvsyjR4/YvHkz33zzDb169SJ58uQW/T09PXny5Em8NeRE/qlTp07cv3+f8PBwOnbsGG+funXrUqFCBSpUqIC3tzf37t1j0aJFTJky5ZnjJ0uWjFGjRr1UjKmd7HFztsf0r3bfO7c4dWAv79VpRPIUCc9UixUzO8ye1E722NnZcfLkSbp06UKdOnWYMGECjRo14vDhw2TMmPGFY23bti1Hjhzhzp07Fu3vvfce3333HV5eXtSqVYshQ4aQKVMm9u3bZ555GsswDH744QfatGmDyfTvuxZ5s2i9gojIO+zKlSuMHDmSTZs2ERAQAMQUWm3Tpg1Dhw7F1dXVqvHJ0zk6OtKzZ09zva3du3czc+ZM9uzZw5o1a1izZg2Ojo6UKFECLy8vOnTowMaNG4mIiKBp06Z8++23cerzwN+72z0PAzD+f3c7gCwpnZ/rfD8/Py5fvky1atWUdE2CevfuTZcuXWjTpg3r168nd+7ceHl5MX/+/Kcuo/T29qZatWr8/vvvFCpUiEWLFtGiRYt464cFBwezatUqypcvb16m06JFC5YuXcqlS5eeWuMqMa5fvw7w0nUUt25Yx4n9uxn0vwWUr13f3J49XyEGt6jH7vXfU7e1l7n9xl9/8mHhrBZjZMyek6HfLMXewZGgMMuZm/9e/uno6MjMmTOpU6eOuc3PL2YmWnwbo6RJk8a8nBTg4MGDAJQsWfI57/Rv2bNn5+DBgzRt2tTiz6Fv377MmDEjTv8SJUpgMpk4cOBAnA/bIv9Uq1atBOtejho1KtHJrOzZsyc4TteuXenatetTz//neyY+pdK7suPqA4vage4ZMrH63M1nxrb2j5iZ4SZTzDgQ8z7dtm3bM89NSELxtmnThs8++4ylS5cyePBgc3tikoexdu3axY0bN1QrVN4K+o1UROQd4+vry8cff0yGDBnMO77Z2Njg7e3N1atXuXnzJpMmTVIy7Dl5e3vj6OjImTNn4hybMGECJpPJPCvr8uXL9O7dm7x58+Ls7EyyZMkoVKgQw4YNs9iCPKFd7GK5uLhYfOOdI0cO1q1bZ1HwNiwsjEOHDtGjRw9cXFzMdZWioqJo27Yt33zzjcWYU6bPoGyRgrQsko0P8mfkSVBgol+DQD9fvv6sP4WyZSJZsmRUqFCBnTt3xukXFhbG5MmTKVy4MMmTJ8fDw4NSpWJqIMVXnHjp0qWkTZvWYnnU5s2b6dChA0WKFMHe3j7R3zLv2LHDvFugr6+vxbH27du/9OweeXFOTk6sW7eOEydOkD17dnx8fMy1rRJStWpVcuXKxaJFizhz5gxHjx5NcLnk999/T1BQkMVxb29vDMMw70T3X4qdqRX7iI6OxjAMju3ZQfKUqShdrZZ55ldUZCQ5ChTCNW06fj/yq8U46bNmZ+LqLUxcvYXx322i/5RZODg5MbpjS25fvfz/yeq/P2UvXbqUo0ePcvToUbZs2YKXlxe9evVi5syZcWJM6H31z/bbt29jMpleqjj21atXadSoEW5ubqxZs4a9e/cyadIkfHx86NKlS5z+9vb2uLq6Wvy8FHmbuTjYmZNZL6pUeldcHF7vfBYnJydGjx7NtGnTXqhuGsDYsWPx9vYmf/78rzg6kVdPM8RERN4BoaGhTJ06lUWLFpmXIiVPnpymTZsyYsQIihcvbt0A3wEzZsxg586deHl5cfjwYXNNnDNnzjBy5Eg6duxIo0aNzLunubu707t3b/NMhzNnzrBo0SJ+/PFHTpw48VKx9OnTx2L5U0REBDt37mT+/PkWNcYMw6B79+4cP36cefPmcfLkSQYN+JiaLdrg2aQFtrZ2OCVPOCH3TxHhYYzq+CFPHgXi/fkXZMngwf7Vy6hbty47duygatWq5r5du3Zl+fLlDBkyhOrVq+Pv72+O998FjIODgxk6dCiDBw+2mPWzfv16Dh06RIkSJXB0dOT48ePPjPHx48d07dqVjBkzxltrbdSoUeTPn59du3ZRvXr1RN23vHrFixfn8uXLfP311wwaNIg2bdowfvx4Nm7cGGf2lclkolOnTvzvf/8jNDSUvHnzmndP+7eFCxfi5ORE3bp1zTNiixYtak6+jR49Gltb2xeOO2vWmFlaia3RlytXLq5du2Z+PnLkSEaNGkWA3wOeBAXSski2eM979PChxXN7R0dyFylmfp63eCkKl32PrlVL8t3/JjNw2hyLBFaBAgUoXbq0+XndunW5du0an376Ke3atcPV1RU3NzcgZqaYh4eHxfX8/f0tZo6FhIRgb2//Uq/dZ599RlBQECdPnjQvj6xSpQru7u54e3vToUMHi58hEPPB/FkbFIi8TWJnVsfuMpuYjWZN/D0z7HlnZr+ojz76iICAAC5fvvzcNcAePnxI1apVNTtM3hpKiImIvKWio6NZsGABM2fO5OzZsxiGgYODA9WqVePzzz+nRo0a1g7xnZIyZUoWLlxI7dq1GTt2LKNHjyYiIoL27dvj4eHBjBkzuHLlCq1atSJv3rzs3r3booB09erV6du3r0Wx6hcV3654lStXTrBg+fz58/nxxx9p9EELAGq2aEueoiWe65o716zk+l9/8OXKH8hXIubDdof3G+BZrjSffvophw8fBmJmh61YsYI2bdowduxYIGZ2SERETK2y5cuXU7ZsWfO4S5Yswc/PL84skfnz55uXVvbu3TtRCbHPPvuM1KlT06BBA/O1/ylXrlzUrVuXCRMmKCH2BujTpw+dO3emTZs2bNy4kVy5cuHt7U2FChUs+nXs2JERI0Ywd+5cxo0bF+9YFy5cMNfUi01c/du2bduoX79+vMcSo1q1atjb27Nhwwa6d+/+zP6bNm0iLCzM/Dy2ro+bmzspXFMzbP6KeM9z/lc9rfikTudBitRpuPbnOVI6PvvX+aJFi7Jt2zYuXLhA2bJlzR9yz5w5Y7ELXGRkJH/88YfFUmt3d3fCw8N58uRJnFpfiXXy5EkKFiwY5/wyZcoAMTXN/p0Qe/jw4UvNShN5E2VJ6UxqJ3uO3w3ALyQCE/EnxmLb3ZwdKJk+1WufGfZPtra2DBky5IXOTZ069UvXXBP5LykhJiLylvnhhx+YOHEiR44cITIyEhsbG0qWLMnHH39M69atVZ/pNapZsybdu3fnyy+/pHHjxqxbt45Tp07x888/kypVKoYNG8aTJ0+YPXt2nN3UIGa2S7NmzV5LbP7+/ly8eNH8PF26dBQvXpxcuXLh7+/Pnj17+ObrrwD47MMGAHg2+ZA+E2YAcGL/bjYunMOls6eIjIwgbcbMeL7fgmbd+gBweMcWMubIZU6GmYBbT8Jp164dQ4cO5datW2TKlAkbGxtsbGws7n/06NFAzI50/97Ra86cOTRq1CjOEt7n/Xu8f/9+5s2bx6FDh/jhhx8S7Ne+fXtatWr1SmpKyctLliwZGzZs4OTJkzRt2pSFCxfGSexmypSJQYMG8ccff+Dl5RXvOLGF8+fPn0/u3LktjoWEhPD++++zaNGil0qIpU+fni5dujBnzhyWLl0a706Tly5d4smTJxQtWjTBmRU169Zj96b1REdHkbfYi9Xl8rt7m0cP/cmSKy9uzg7P7H/y5EkA0qZNC0C5cuXIkCEDPj4+FhsUrFmzhsePH1v8nIpd9nTp0qVn7qCZkIwZM3L27FkeP35ssUw8tj5Z7C53sW7fvk1oaKhFsk7kXeHiYEfVrO48DI3gWmAwfiHhBP3/Ls8mIKWjHW7ODmRLlYzUTvbPGk5EXoISYiIib4GDBw/yxRdfsHv3bkJDQzGZTOTNm5du3brRq1evOFvWy+szefJktm3bRvPmzblx4wbdu3enVq1aAPz88894eHjEmb31LJGRkc/u9A/R0dFxznF1dWXDhg2kTp2awoULxymWfe7cOb6cs5DlM6fR68vpZM6Zm5RpYpZN7VizgrnDB1GwTAU+GjWBVG7u3L56mRt//WE+//pff1KgVDnzcwPwCwk3f0D+/fffyZQpE/b29vTs2ZOFCxdSs2ZNqlevzrp167C3t8fFxcWiMPHNmzc5c+YMPXr0eK77/7eQkBA6d+5M//79KVmy5FMTYp6enhiGwU8//USfPn1e6rry6hQvXpwrV64wY8YMBg0aBEDr1q3ZsWMH2bJlY8KECQmeGxkZydKlSylQoEC89agAGjVqxA8//MCDBw/MSaEXMW3aNC5fvkzHjh3Ztm0bTZs2xcPDA19fX7Zv387ixYv57rvvnpo4+sirPWtWfce4j9rToENn8hQtga2dHX5373D28K+UrVGHcrXqmfuHh4Zy4WTMDMno6Cju3bzBhgWzAWjg1YVsqZJZjH/27Fnzzwc/Pz/WrVvH9u3badq0qXlJqq2tLZMmTaJ9+/Z069aN1q1b89dff/Hpp59Sq1Yt6tatax7P09MTgEOHDsW5r3PnznHu3DkA7t69S3BwMGvWrAGgYMGC5oRW//79adKkCbVq1eLjjz/G3d2dQ4cOMX78eAoWLEi9evUsxj106BDAS+1sKfKmS+1kT2qnv788MgxDuzKK/NcMERF5I124cMFo06aNkTJlSoP/3+QvS5YsxpAhQ4zAwEBrh5ekrVixwgCM9OnTG48ePTK3Ozk5GeXLl0/0OF5eXuY/24QeXl5e5v5XrlxJsN/27dufeb3eX043AGPi6i3G2j9uG2v/uG0sO/6XkcwlhVGgVFljzflb5vZ/P+zs7Y3aLdtbtK3747bx66+/GoCxYsUK83Wio6ONESNGGDY2Nub4kidPbpw4ccIinlWrVhmAcejQoafG3atXL+Npv7IMHDjQyJkzpxEcHGwYhmGMHDnSAIwHDx7E2z9TpkxGy5Ytn/l6iXXMnTvX/PfGZDIZXbt2NaKiouL0K1SokFG1alVjw4YNBmDMmDEjwTG3bt1qAMbUqVPNbbHvp8mTJ8d7jpeXl5E8efI47ZGRkcaSJUuM6tWrG2nSpDHs7OyMtGnTGvXq1TNWrFgRb6z/tuPSHcPr0xFG9vwFDQdHJ8MpWXIjU87cRu2W7Y2Z2w6Y32OFylSweJ/b2NgYadKlN0pUqW58sXStsefa33/HFy9eHOfnQqpUqYzixYsb06ZNM0JDQ+PEsWLFCqNo0aKGg4ODkT59eqNv374WP9NiVa5c2ahfv36c9tj3WnyPkSNHWvTdtWuXUbt2bSN9+vSGs7OzkTdvXmPgwIGGr69vnHHbt29vFClS5Jmvo4iIyMvQDDERkTfI/fv3+eKLL/j++++5f/8+EFO/5aOPPmLEiBFkypTJyhFKdHQ0X3/9NTY2Nty/f59Tp05RsWLFFx7P2dmZffv2xXusSpUq8bb369ePdu3aWbTly5cPiPmGOSoqyuKYnZ0dhmHEW6fkzxNHCX78iDqtvZ79zfS/jv9zd7t/njtu3DimTJnCqFGj2LZtGwcOHKBEiRLUqlWLn3/+mRIlYuqXxRa+T5cu3dOv+xRHjhxhxowZbN26FWfnxBUcTpcunXave4N169aNbt268dtvv9GsWTPmz5/PqlWrWLBgAS1atDD3O3v2rPn/DSO+v91/q1OnTpw+2bNnf+p5Pj4++Pj4xGm3tbWlQ4cO8S6ZTKxymd151Lk7jb2fXotszLdrEzxm8/+FtmN17NjRYlfaxGjdurVFvbCE9OvXj5YtW5qXRscaNWpUousFVatWLVEzvoKCgli3bh3Tp09P1LgiIiIvSoVmRESsLDg4mFGjRpE9e3Y8PDyYOXMmwcHBNG/enDNnzvDgwQO++eYbJcPeEFOmTOHgwYOsWLGCPHny4O3tbd4JLWvWrInegS6WjY0NpUuXjveRUB2tzJkzx+kbu0PjkiVLsLe3t3hATMIqvnRX0EN/ANzSZ3xqnC6uqXkUYLn7nYmYwteAeYnm+fPnGTFiBKNHj2b48OGcPHmSDBkysGvXLlxdXRkwYID5/NjX7d91xZ6Ht7c3zZo1o3Tp0gQEBBAQEEBoaGjMvQUF8ejRozjnaPe6t0PJkiW5evUqU6dOJTQ0lA8//JASJUpw/fp1a4f20lwc7CySWS+iVHrX/6zQdrNmzShTpgzjx49/7deaPn06WbNmpVOnTq/9WiIikrQpISYiYgWRkZHMnj2bwoUL4+LiwujRo7lz5w41a9Zk9+7dPHr0iNWrV1O4cGFrhyr/cO7cOUaMGEGHDh1o2bIlPj4+XLx4kc8//xyImYVy7949c/0ba2jUqBFHjx61eMRytreN0z9l6phElt/d208dN1ue/Fy/cN7yXEc7zpw5A2D+u3rq1CkMw6BMmTJs376dJ0+e0KpVK+zt7SlWrJjFrJ7YHeT8/f1f4E5j/P7776xevZrUqVObHxMnTgRidpWsXLlynHP8/f21e91bZMCAAfj7+9OgQQNOnjxJjhw56N69O9HR0dYO7aVkSelMmQyu2JiIN1kdHxMxM8PKZHAlS8rEzYh8FUwmE/Pnzydjxoyv/XVPmTIlPj4+2NlpIYuIiLxeSoiJiPyH1q5dS4UKFXBycqJXr1788ccflClThlWrVhESEsL27dvNBYzlzRIZGYmXlxfu7u589VXMbo3ly5dnwIABfPXVVxw4cICPP/6Y5MmT07NnTwIDA+OMYRgG69evf61xurm5xZk9FsslnoRYvhJlSJYiJT9/9+1Tl4+VrVWPW5cvcuHUb0DMB/NU9jYsW7aMcuXKkTFjzAyz2P/GFswGGDZsGGFhYfz2228Wu8n9c/e6F7V79+44j9idCDds2MCCBQss+kdGRnLjxg3tXveWSZ48OZs3b+bIkSNkypSJb775htSpU7N2bcJLCt8GWVI6UzN7WtI4//9MzgT6xba7OTtQM3va/zQZFqtw4cIMHTr0te9k/PHHH1O2bNnXeg0RERHQLpMiIq/dL7/8whdffMHevXsJCwvDZDJRoEABunfvTo8ePfQt+Fti/PjxHDt2jC1btuDq6mpu/+KLL9i0aRPe3t6cPHmS7777jpYtW1K8eHF69+5trpd17tw5Fi1ahGEYNG3a1Cr34JbMMU6bc/LkeA0ewZxhnzC6U0tqtmhDKre03L1+lat//E7XEV8CUOODVmxd4cPU/h/RbsBQUrq5s2jDSv7880927NhhHq9SpUqUKVOGUaNGER4eToYMGdizZw9ff/01V65c4dtvvzX3LVeuHM7Ozhw6dIjGjRtbxHXt2jXz7LbYhFns7nXZs2c3J/riSyDv2bMHgIoVK8aZCXb69GmCg4O1e91bqkyZMly/fp3JkyczbNgwmjdvTokSJdi4cSNZsmSxdngvxMXBjqpZ3XkYGsG1wGD8QsIJCovEICYRltLRDjdnB7KlSkZqJ3trhysiIvLO0KcwEZHX4Pz584wcOZKtW7eaaxhly5aNDh068Omnn+Li4mLlCOV5nDp1ii+++IKuXbtSt25di2NOTk74+PhQsWJFPv/8c6ZNm8aZM2eYOnUqc+fO5caNG9jY2JAjRw7q1q1Lnz59rHQXkPz/Z4j9exZKzeZtSJMuPRvmz2LO8E8wDIO0mbJQrcnfBcztHRwZtfh7vp08lgVjhxMeGkLJEsXZsmULVatWNfezsbFh+/bteHl5sXHjRnx9fenRowcFCxbkp59+ol69eua+Dg4ONG/enI0bN/Lll19axLR79+44NYRiC6p7eXnFW+w8MTZs2IC7uzu1a9d+ofPlzTBo0CB69OjBhx9+yJYtW8iePTvdunVj5syZr30G0+uS2sme1E6pzM8Nw3j2RhciIiLywkzGs7blERGRRLl9+zZffPEFa9eu5cGDBwCkTZuWFi1aMHz4cNKnT2/lCEXgcXgkO64+IPol/vW3MUHN7GmfWtC7TJkyHD9+nCdPnjx198djx45RpkwZDh06RLly5V48qESIiooid+7ctGnThnHjxr3Wa8l/5/DhwzRv3pybN2+SKlUqFi9ebLVZmCIiIvL2UEJMROQlPH78mAkTJvDtt9+adz5LmTIl9evXZ8SIERQoUMDKEYrEdSMohKN3Al74/GcV9I6MjMTR0ZFChQpx+vTpZ47XsmVLnjx5wubNm184psRYsmQJn3zyCX/99ZfFsld5N0yaNInhw4cTHh5OqVKl2LBhg0XNOhEREZF/ejvnlIuIWFFkZCRfffUVBQoUIGXKlIwbN4779+9Tt25d9u/fT2BgICtXrlQyTN5Yr3t3u1mzZhEdHU2vXr0SNfbUqVMpU6aMeXnx6xIdHc3y5cuVDHtHffrpp/j5+VG3bl2OHz9OtmzZ6NWr11u/G6WIiIi8HpohJiKSCNHR0Xz//fdMnTqVEydOEBUVha2tLWXLluWTTz6hWbNm1g5R5Lk9Do/k+N0A/EIiMAHx/UIQ2+7u7EDJ9KmeukwyVsGCBblw4QLh4eFvbT0nebsdPnyYDz74gFu3bpEqVSqWLFnC+++/b+2wRERE5A2ihJiIyFPs2rWLL7/8kv379xMeHo7JZKJQoUL07t2brl276sO+vBNe5e52jx8/JmXKlJQvX55ff/31tcYt8izjx49n5MiRREREULp0aTZu3EjGjBmtHZaIiIi8AZQQExH5l9OnTzN69Gi2bdvGkydPAMiRIwcdO3bkk08+IVmyZFaOUOT1epnd7UaMGGHeXEIzJ+VNEBQURIsWLfj555+xsbGhV69ezJgxQ19oiIiIJHFKiImIADdv3mT06NGsX78ePz8/ADw8PGjZsiXDhg0jbdq0Vo5Q5O2QPXt27t69S2hoqLVDEbFw8OBBmjdvzu3bt3F1dWXJkiU0btzY2mGJiIiIleirMRFJsgIDAxk8eDCZM2cmS5YsLFiwgMjISNq2bcuFCxe4e/cuX331lZJhIol09+5drl27hqenp7VDEYmjQoUK3Lp1iy+++IInT57w/vvvU7ZsWe7cuWPt0ERERMQKNENMRJKU8PBwvv76a+bNm8eFCxcAcHZ2pnr16gwfPpxy5cpZOUKRt1evXr2YPXs2+/bto3LlytYORyRBQUFBfPDBB+zYsQMbGxv69u3L1KlTtYxSREQkCVFCTETeedHR0SxbtoyvvvqKkydPEh0djZ2dHeXLl+fTTz+lUaNG1g5R5J3g4eFBSEgIQUFB1g5FJFEOHDhAixYtuHPnDqlTp2bZsmXUr1/f2mGJiIjIf0Bfg4nIO2vbtm1Uq1YNJycnvLy8OHHiBEWKFGHBggWEhYWxf/9+JcNEXpELFy5w//596tata+1QRBKtYsWK3L59m9GjR/Po0SMaNGhA+fLluXv3rrVDExERkddMM8RE5J1y4sQJRo8ezfbt2wkODgYgV65cdO7cmY8//hgnJycrRyjybmrbti0rVqzgzJkzFC5c2NrhiDy3gIAAmjdvzs6dO7G1taVfv35MnjxZyyhFRETeUUqIichb79q1a4waNYqNGzfy8OFDADJkyEDr1q35/PPPSZMmjZUjFHn3ubq6Ym9vz4MHD6wdishL2bdvHy1btuTu3bukSZOGZcuWUa9ePWuHJSIiIq+YvvISkbeSv78/AwcOJGPGjGTPnh0fHx8AvLy8uHLlCrdv32bq1KlKhon8Bw4fPkxgYCBNmza1digiL61KlSrcuXOHkSNHEhQURP369alQoQL379+3dmgiIiLyCmmGmIi8NUJDQ5kxYwYLFy7k4sWLACRLloyaNWsycuRISpYsaeUIRZKmBg0a8NNPP3Hjxg0yZ85s7XBEXpmAgACaNWvG7t27sbW15eOPP2bixIlaRikiIvIOUEJMRN5o0dHRLF68mK+//pozZ84QHR2Nvb097733HoMHD9YyFpE3QLJkyXBzc+PGjRvWDkXktdizZw+tWrXi3r17uLm5sXz5curUqWPtsEREROQl6OstEXkjbd68mcqVK+Po6EiXLl04c+YMxYoVY8mSJYSGhrJnzx4lw0TeAJs2bSIkJIS2bdtaOxSR18bT05O7d+8ybNgwAgMDqVu3LhUrVtQyShERkbeYZoiJyBvj6NGjjB49ml27dhESEgJAnjx56Nq1K/369cPBwcHKEYrIv1WuXJlffvmFgIAAUqVKZe1wRF47f39/mjVrxt69e7G1tWXgwIGMHz9eyyhFRETeMkqIiYhVXbp0idGjR7Np0yYCAgIAyJQpE23btmXIkCG4urpaNT4RSVh0dDSOjo7kzJmTP//809rhiPyndu3aRevWrbl//z7u7u6sWLGCWrVqWTssERERSSR9lSUi/zlfX1/69etHhgwZyJ07N99++y02NjZ07tyZa9eucfPmTSZOnKhkmMgbbsmSJURGRtKlSxdrhyLyn6tevTr37t1j2LBhBAQEULt2bSpVqoSvr6+1QxMREZFE0AwxEflPBAcHM23aNBYtWsSVK1cASJ48OXXq1GHkyJEULVrUyhGKyPMqUaIEp0+fJiQkREuaJUnz9/enadOm7Nu3D1tbWwYNGsS4ceO0jFJEROQNpoSYiLw20dHRzJ8/n1mzZnH27FkMw8DBwYFKlSoxdOhQatSoYe0QReQFhYeH4+zsTNGiRTlx4oS1wxF5I+zcuZPWrVvz4MED0qZNy8qVK/VvnYiIyBtKX1uJyCu3ceNG3nvvPRwdHenevTu///47pUqVYsWKFYSEhLBz5059QBB5y82YMYPo6Gj69etn7VBE3hg1atTg7t27DBkyBH9/f2rWrEmVKlW0jFJEROQNpBliIvJKHDhwgLFjx7Jnzx5CQ0MxmUzky5ePbt260bNnTy2nEnnH5M2bl6tXrxIaGqplYSLx8PX1pWnTpvzyyy/Y2dnx6aef8sUXX+j9IiIi8oZQQkxEXtiff/7J6NGj+fHHHwkKCgIgS5YstG/fnsGDB5MyZUorRygir0NAQACpU6emcuXK7Nu3z9rhiLzRtm/fTuvWrfHz8yNdunSsXLmS6tWrWzssERGRJE9fUYm8I/6r3Pbdu3fp1asXHh4e5M+fn5UrV+Lg4EC3bt24desW169fZ9y4cUqGibzDxo8fD8DgwYOtHInIm69WrVrcv3+fTz/9FD8/P2rUqIGnpyf+/v7WDk1ERCRJ0wwxkbfUw9AIrgUG4xcSTlBYJAZgAlI62uHm7EC2VMlI7WT/Sq4VHBzMhAkTWLp0KdeuXQMgRYoU1K1bl5EjR1KoUKFXch0ReTtkzpwZf39/goODrR2KyFvlwYMHNGnShF9//RU7OzuGDBnCmDFjrB2WiIhIkqSEmMhb5nF4JMfvBuAXEoEJiO8NHNvu5mxPqfSuuDjYxTuWYRiMGDGCe/fuMW/ePItjkZGRzJ07lzlz5nD+/HkMw8DR0ZEqVaowbNgwqlSp8qpvTUTeAjdv3iRLliw0aNCAzZs3WzsckbfStm3baNu2LX5+fnh4ePDdd9/h6elp7bBERESSFCXERN4iN4JCOH43AMOIPxH2bybAZIJS6V3JktLZ4phhGAwdOpQJEyZgMpm4du0amTJlYt26dUyZMoVjx44RFRWFra0tpUuX5pNPPqFZs2YqBiySxH300UfMnz+fw4cPU7ZsWWuHI/LWio6OZvDgwUyfPp2oqCg8PT1Zu3YtadKksXZoIiIiSYISYiJviRtBIRy9E/DC55fJYJkUGzNmDCNHjjQ/z5MnD9evXycsLAyTyUSBAgXo0aMH3bt3x84u/hlmIpL0uLu7ExkZSUBAgLVDEXkn3L9/nyZNmnDw4EHs7Oz4/PPPGTVqlLXDEhEReedpqoe8s3x8fDCZTBw7dszc1rBhQ1xdXblx40ac/v7+/mTIkIGKFSsSHR3NmDFjMJlMbNu2LU7fVatWYTKZmDlz5muL9Z9il0m+iPPHDzP2o3YUyJIBZ2dn8uTJQ506dSySYQB//fUXYWFhACRPnpywsDD27t3Lhg0biI6OjnfsgIAA3N3d+e6778xtN2/epH///lStWhVXV1dMJhM+Pj7xnu/p6YnJZIrzqFu3rkW/nTt34uLiwq1bt17oNRCRV+Ps2bP4+fnRsGFDa4ci8s5Ily4dv/76Kz/99BMpU6Zk9OjRZMiQQTu4ioiIvGZKiEmSsmDBAuzs7OjSpUucY7179+bRo0csWbIEGxsbhg4dSqlSpejSpQuBgYHmfnfu3KFnz55Uq1aNXr16/Sdxxy6TfF77N61jRPsPSOaSgr4TvmLswmXY29vz888/x9t/5cqVHDx4kA0bNvDZZ58REhJCixYt8PT0tHgNYo0ePZqMGTPSsmVLc9vFixdZvnw5Dg4O1K9f/5kx5syZk4MHD1o8ZsyYYdGnRo0alC1blqFDhz7fCyAir9To0aMB4iTUReTl1atXjwcPHjBgwAAePHhA1apVqVGjhmZjioiIvCZKiEmSkj59embPns3PP//MN998Y25fv349K1euZPLkyeTOnRsAOzs7lixZwoMHD+jbt6+5b5cuXYiIiGDx4sWYTKbXHvPD0Aj8QiISVTPsn/zu3WHuyE+p1bI9A6bNoXT12uQo9R6hJsvlj/+sCXb+/HnKly9PjRo16NKlC5s3b2bRokXs37+fjz76yOI8f39/vvnmG3r16mXxOlSpUoUHDx6wfft2BgwY8Mw4nZ2dKV++vMUjf/78cfr16tWL5cuXxzu7T0T+G1u3biVdunTkyZPH2qGIvJNsbGyYOnUqN2/epFy5cuzatYu0adNqJ0oREZHXQAkxSXI+/PBDWrVqxSeffMLVq1fx8/Oje/fu1KpVix49elj0LVSoEGPGjGHp0qX88MMPzJ8/n59++olp06aRLVs2i747duygRo0apEyZkmTJklGxYkV27tz5XLE9fPiQTp06kSZNGpInT06jRo04cPoc/067nfp1H6M6fki7UnlpXTwnQ1s35vTB/RZ9dq5eQWhwME26/D2LzYiOZsnGnwgICODUqVNs3LiR6dOnkzlzZlKkSEHx4sXjxNSpUyfq16/P6tWruXbtmrndx8eHyMhIi9lhwGsrut+oUSNcXFyYP3/+axlfRJ5u7969PH78mBYtWlg7FJF3Xvr06Tl06BCbNm0iRYoUjBw5kowZM/LLL79YOzQREZF3hhJikiTNmjWLFClS4O3tTc+ePQkPD2fRokXx9h04cCAVKlSga9euDBgwgHr16sVZcrls2TJq165NypQpWbJkCd9//z1p0qShTp06z5UU69y5MzY2NqxYsYIZM2Zw5MgRvJs24HHQ38sV9/6wli86t8bZxYU+E75i4PRvcEnlyhdd2lgkxc4dO4xLqtTcunyRgU1q0qJQFrwrFWPSyM8xmUwULVqUxo0b07dvX3LlykXWrFlp2rRpvHE1btwYwzDYv//v8X/88UdKlCiBq6trou8vPpcuXSJNmjTY2dmRK1cuPv/8c0JCQuL0c3Bw4L333uPHH398qeuJyIv58ssvARg+fLiVIxFJOho2bIivry/9+/fn3r17VK5cmVq1ahEUFGTt0ERERN56SohJkpQmTRoWLlzI7t27+f777/n666/JnDlzvH1tbW2ZOnUq9+/fJyIiggULFlgcDw4Opl+/fjRs2JD169fTtGlTGjZsyMaNGylcuPBz1b0qXbo0CxcupG7dunTt2pX169fz4PYttqzwASAsJJhF40ZQyrMmg2cuolytepSuVovPZvuQNU8+VkyfYB7L794dwkNDmNr/IyrWe5+Ri1bxfuce/Lzue+rXr8/zbDAbOxvu9u3b5rZDhw5RsmTJRI8Rn0qVKjFt2jTWrl3LDz/8QP369Zk0aRJ169aNt5B/yZIlOXnyJE+ePHmp64rI89u7dy/ZsmXDw8PD2qGIJCk2NjZMnz6dmzdvUrZsWXbs2IG7uztjx461dmgiIiJvNSXEJMmqV68e5cuXJ0+ePLRr1+6pfWfMmIGNjQ1hYWFxdn369ddf8ff3x8vLi8jISPMjOjqaunXrcvToUXMC55/HIyMj4ySl2rZta/G8QoUKpM2YmbOHDwDwx4ljPA58iGeTD4mKjDQ/jOhoileuxsUzJwkNDgbAiDYIDwulWbc+NOvWh8Ll3qNJ5560HTCUAwcOPNfMtX/HGRAQQHBwMOnSpUv0GPEZO3YsPXr0oFq1atSvX5+vv/6aCRMmsG/fPjZu3Binf7p06YiOjubu3bsvdV0ReT5r164lLCyMDh06WDsUkSQrQ4YMHD58mB9++IHkyZMzfPhwMmbMyIEDB6wdmoiIyFtJCTFJ0hwdHXFwcHhqn9WrV/P9998zbdo0PD096d27N/fu3TMfj/3/5s2bY29vb/GYOHEihmHg7+/P1atX4xzfu3evxbXSp09v8dxkMuGaNh2PAx4CEOjnC8CUfl35sHBWi8eG+bMwDIPHgTF9U7imBqB4JU+LMUtVrgbAb7/9lujXKbZ2WMaMGQHMSxqdnJwSPUZixSYnDx06FOdY7PXiW1IpIq/PlClTMJlMfPrpp9YORSTJa9SoEX5+fvTt25d79+5RqVIlateurWWUIiIiz8nu2V1Ekq579+7Rs2dPPD096du3L40bN6ZIkSL06NGDdevWAeDu7g7A119/Tfny5eMdJ3aJ0dGjRy3a8+XLZ/E8vplPQb4PSJc1ZsliCtc0AHQeNpa8xUrFe61UbmkByJavABdOHY9zPLm9LfB8xe9/+OEHTCYTVapUAcDNzQ2I2WnydYkvvtjrxb7mIvL6RUVFcfToUfLnz4+Li4u1wxERYv6N/Oqrrxg8eDDvv/8+27dvx93dndGjRzNkyBBrhyciIvJW0Awxkafo3r07oaGhLFq0CJPJRI4cOZg4cSLr16/nu+++A6BixYq4urpy7tw5SpcuHe/DwcEBBweHOO0pUqSwuN7y5cstnv/666/cu3WDwmXfAyB/yTIkT5mKm5f+IneRYvE+7P9/xlv52vUBOLFvt3k8E3D2QMzzhJJ3/7Z48WK2bNlC69atyZo1KxBT4D5nzpxcunTpOV/RZ1uyZEmC8V2+fBk3NzfVMBL5D82fP5+oqCi6d+9u7VBE5F8yZszI0aNH2bBhA8mTJ2fo0KFkzpyZgwcPWjs0ERGRN55miMk7b9euXVy9ejVOe/369Z963rfffsuGDRuYO3cuOXLkMLf37NmTNWvW0Lt3b6pVq4aHhwdff/01Xl5e+Pv707x5c9KlS8eDBw84deoUDx48YM6cOYmK9dixY3Tp0oUWLVpw48YNPv/8czJkzESd1l4AOCdPTudhY5n5WT8eBz6kQu2GpHRzJ8jfj6t//k6Qvz/dRsUU1i9eyZPS1WqxevZ0DCOaPMVKcunsKdbOnk7Dhg2pVKmSxbVDQkLMyxRDQkK4fPkyGzZsYPPmzVStWpW5c+da9Pf09GTLli3x3seaNWuAmARW7H3Fzixp3rw5APv372fcuHE0bdqUnDlzEhoaypYtW5g3bx7Vq1enUaNGccY9dOgQVatWxWQyJer1FJGXN3v2bGxtbenZs6e1QxGRBLz//vv4+fnRr18/Zs+ezXvvvUedOnVYs2aNZnaKiIgkwGQ8z1ZzIm8RHx8fOnXqlODxK1eu0LFjR3x9fTl79qzFsdu3b1OoUCHKli3Ltm3b4j23SJEi1K5d27x0ct++fUyaNImDBw/y6NEj0qVLR/HixenYsaM5CfSsWH/++We+/fZbNm3aRFhYGNWqVeOrr77iloMr/iERxL5Zfz96iI0LZvPnyeOEBj8mZRp3cuQvRLWmH1KhbkPzuGGhIXw/axq/bF7Pwwf3cfdIj3eH9owcORJHR0dzP09PT4t6ZsmTJ8fDw4OSJUvSsmVLmjVrFmcJ465du6hRowZHjhyhTJkyFseelrCK/ZFz8eJF+vXrx6lTp/D19cVkMpEnTx5atWrFwIEDLeIDuHTpErlz52bt2rU0a9bsqa+niLwawcHBuLi4ULp0aY4cOWLtcEQkEW7evEmTJk04fvw4Dg4OjBkzhsGDB1s7LBERkTeOEmIib4HH4ZHsuPqA6Jd4t9qYoGb2tLg4vLqJoUWLFqVixYqJngH3MoYPH87SpUu5dOkSdnaa3CryXxg7dizDhw9nxYoVtG7d2trhiMhzWL9+PZ06dSIwMJDMmTOzbt26OF9giYiIJGVKiIm8JW4EhXD0TsALn18mgytZUjq/uoCArVu30rRpU/766y8yZ878Ssf+p4CAAHLmzMnXX39N27ZtX9t1RMRSrly5uHnzJiEhIc+1EYeIvBmio6Pp06cPc+fOJTo6mnr16vH9999rGaWIiAgqqi/y1siS0pkyGVyxMcUUx08MEzEzw15HMgygbt26TJ48mStXrrzysf/pypUrDBkyhDZt2rzW64jI33x9fbl8+TIVK1ZUMkzkLWVjY8OsWbO4evUqJUqUYMuWLbi5uTF58mRrhyYiImJ1miEm8pZ5HB7J8bsB+IVEYALiewPHtrs7O1AyfapXukxSRJKGjz/+mBkzZrB9+3Zq1qxp7XBE5BVYu3Yt3t7eBAUFkSVLFtauXatllCIikmQpISbylnoYGsG1wGD8QsIJCovEICYRltLRDjdnB7KlSkZqJ3trhykib6mMGTMSFBTE48ePrR2KiLxC0dHR9OrVi3nz5hEdHU39+vVZtWqVllGKiEiSo4SYyDvCMIyn7u4oIpJYV65cIWfOnDRp0oT169dbOxwReQ2uX7/O+++/z8mTJ3FwcODLL79k4MCB1g5LRETkP6OiICLvCCXDRORVGT16NAAjR460ciQi8rpkzZqVEydOsGrVKhwdHfnkk0/Ili0bx44ds+i3f/9+Tp06ZaUoRUREXh/NEBMRERELadKkAcDf39/KkYjIfyE6Opru3buzYMECDMOgYcOGrFq1iocPH5InTx6SJ0/OX3/9haura6LH1Mx1ERF50ykhJiIiImYnTpygZMmSdOzYkcWLF1s7HBH5D127do3GjRtz+vRpHB0dyZ8/P2fPngXA29ubefPmJXiuapuKiMjbRgkxERERMWvSpAkbN27kypUrZM+e3drhiIgVrFq1ik6dOhESEmLRvnfvXqpUqWLR9jy7X7s521Mqvat2vxYRkTeCEmIiIiJi5uLiQsqUKbl9+7a1QxERKwkPD6dQoUJcvHjRoj1HjhycO3cOJycnAG4EhXD8bgCGEX8i7N9MgMkEpdK7kiWl86sPXERE5DmoqL6IiIgAsH37dp48eUKrVq2sHYqIWNHChQvjJMMgZgfaZs2aATHJsKN3AohOZDIMYvpFG3D0TgA3gkKe2V9EROR1UkJMREREABg/fjwAw4YNs3IkIknL6dOn6dSpEzly5MDJyQkXFxdKlizJpEmTzJtbeHp6YjKZzA8nJycKFizI2LFjCQ8PB2Djxo2YTCbmzp2b4LW2b9+OyWRi2rRpCfYpVKgQTZo0oUKFCuTKlYvUqVObj23ZsoVufftz/E5AvOeu+noKH+TPaNE2ov0HfJA/o8Uja6pk1Kpdx6Lf8ePH6dWrF0WKFCFFihR4eHhQs2ZNdu3alWCsNWrUoHv37hZtw4YNo2HDhmTKlAmTyUTHjh3jPTd79uwWr+m/X99YDx8+xNXVlQ0bNiQYh4iIvH20gF9ERESIjo7mwIED5MyZ07zLpIi8fvPnz6dnz57ky5ePQYMGUbBgQSIiIjh27Bhz587l4MGDrF+/HoCcOXOyfPlyAB48eMCCBQsYPnw4169fZ968eTRo0ID06dOzaNGiOEmiWIsXL8be3p727dsnGFOVKlXi1AqLjo7Gz8+PVatWEZ4pL9HmsvmJ45ElG/0nz7Roy5jWzeL5ypUrOXLkCN7e3hQrVownT54wd+5catSowZIlS+jQoYNF/40bN3LgwAGWLl1q0T59+nSKFi1K48aNWbRoUYIxrV+/nrCwMIu269ev07JlS5o2bWpuS506NR9//DGDBg2ifv36ODg4JPq+RUTkzaUaYiIiIsKKFSto27YtY8eO5fPPP7d2OCJJwsGDB6lcuTK1atViw4YNODo6WhwPDw9n69atNG7cGE9PT3x9fc27PgJERkZSsGBBrl27RmBgIE5OTgwePJhJkyZx5swZChcubDFeQEAAGTJkoEGDBqxZs+aFYn4YGsHua74JHl/19RS+nzWNtX/8XYdwRPsPCArwZ8am3XH6V8vmbt598v79+6RLl87ieFRUFCVLluTJkydxlnGWK1eOnDlzsnLlSov26OhobGxiFsK4uLjQvHlzfHx8EnV/o0ePZtSoUezYsYMaNWqY2+/du0fmzJlZsmQJbdq0SdRYIiLyZtOSSREREWH69OmYTCYGDBhg7VBEkowvv/wSk8nEvHnz4iTDABwcHGjcuHGC59vZ2VG8eHHCw8MJCAgAoHPnzkDMTLB/W7lyJaGhoXh7ez8ztsOHD9OoUSPc3NxwcnIiV65c9O/fn2uBweZ5Ycf37GBgk5q0LJKdHjXKsXHhnGff9D+YgGuBwebn/06GAdja2lKqVClu3Lhh0X7ixAmOHDkS70y32GTY8zIMg8WLF5MzZ06qV69ucczDw4NatWo9dTmqiIi8XbRkUkREJImaNGkSyZMnp2nTpvz2228ULlwYZ2ft/CbyX4iKimLXrl2UKlWKLFmyvPA4V65cwdXVlbRp0wKQN29eKlWqxLJly5gwYQL29vbmvosXLyZTpkzUqVMnoeEA2LZtG40aNaJAgQJMmzaNrFmzcvXqVX7++Wf8QsIxgNMH9zOhVyfyFS/FgGlziI6KYsPC2QT4PYh3zHvXr+FVriDBjx+RNmNmKtZ/n+Y9+uEX8vSPI5GRkezfv59ChQpZtG/evBlbW9s4Sztfxo4dO7h27Rpjx47FZIq7HNTT05MhQ4YQEBCAq6vrK7uuiIhYhxJiIiIiSdS4ceMICgqid+/eAJQqVYpHjx6RIkUKK0cm8u7z9fUlODiYHDlyPNd5kZGR5vPnzJljrjVma2tr7tO5c2c6derE5s2bzbWwfv/9d44ePcrnn39u0Tc+vXr1ImvWrBw+fNiiuHynTp1Y/+cdAFbMmIirW1pGLPoOB8eYPsUredK9Rrk44+UvVZb36jcmU47chIeFcmLfLjYunM0fx48wZunTl26OGjWKixcvxilof/DgQfLkyYOLi8tTz38eCxcuxNbWNsEi/CVLliQ6OppDhw5Rt27dV3ZdERGxDi2ZFBERSaL+PSvFx8cHd3d3lixZYqWIRORpfv/9d+zt7bG3tydDhgyMGTOGIUOG0K1bN4t+H374ISlSpLAoKL9o0SJMJhOdOnUCYpYHRkZGWjwALly4wKVLl+jcubNFMiz2HAMIDQ7m0pmTlKtVz5wMA3B2caF0tVpx4m7TfzB1W3tRpHxFSlWtQZfh42g3YCjnjh3iyM5tJFTSeMGCBYwbN46BAwfy/vvvWxy7fft2vEssX5S/vz8bNmygbt26ZMqUKd4+sde7devWK7uuiIhYjxJiIiIiSVTu3LktnptMJqKiokifPr2VIhJJOtzd3UmWLBlXrlxJ9Dm5cuXi6NGjHDlyhNWrV1OsWDHGjx/Pd999Z9EvWbJktGrViq1bt3L37l0iIyNZtmwZVatWJVeuXAAsWbLEnFyLfUDM7pUAmTNnjnN9k8mECXgSFEB0dDSuaeMmpFz/f+nms1Rp/AEAF04dj3d54uLFi+nWrRsfffQRkydPjnM8JCQkTsLuZSxbtoywsDC6dOmSYJ/Y64WEhLyy64qIiPVoyaSIiEgSlTNnTmxsbIiOjgZiilevW7fumfWFROTl2draUqNGDbZs2cLNmzfjTUD9m5OTE6VLlwagTJkyVKtWjUKFCtG/f38aNmxosXywc+fOzJ8/n6VLl5I3b17u37/P1KlTzccbNWrE0aNH41wjthbZzZs3440hpaMdISldMZlMBDy4H+d4wIP4a4gleE/2cT+OLF68mC5duuDl5cXcuXPjTZi5u7vj7+//XNd6moULF+Lh4UH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" - ], - "text/plain": [ - " USDT-1ec7 USDC-eB48 \\\n", - "357 1214.455968 -1216.41934 \n", - "594 \n", - "183 -48.863906 \n", - "624 \n", - "656 \n", - "... ... ... \n", - "21f3ea686abd44c6b7829e488a01aa74 6780944.55249 \n", - "PRICE 1.00058 1.0 \n", - "AMMIn 2905472.583409 9856630.397465 \n", - "AMMOut -2905472.583409 -9861236.407656 \n", - "TOTAL NET -0.0 -4606.010192 \n", - "\n", - " DAI-1d0F WETH-6Cc2 WBTC-C599 \\\n", - "357 \n", - "594 943.826762 -0.512606 \n", - "183 0.00175 \n", - "624 -10733.806571 \n", - "656 -0.870495 \n", - "... ... ... ... \n", - "21f3ea686abd44c6b7829e488a01aa74 -6780334.136658 \n", - "PRICE 1.000179 1842.67228 27604.143472 \n", - "AMMIn 6845674.127441 331.431642 7.424195 \n", - "AMMOut -6845674.127441 -331.431642 -7.424195 \n", - "TOTAL NET 0.000001 -0.0 -0.0 \n", - "\n", - " BNT-FF1C \n", - "357 \n", - "594 \n", - "183 \n", - "624 24578.315452 \n", - "656 55566.320623 \n", - "... ... \n", - "21f3ea686abd44c6b7829e488a01aa74 \n", - "PRICE 0.429078 \n", - "AMMIn 192904.817736 \n", - "AMMOut -192904.81774 \n", - "TOTAL NET -0.000004 \n", - "\n", - "[90 rows x 6 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "O = MargPOptimizer(CCm.bypairs(\n", - " CCm.filter_pairs(bothin=f\"{T.ETH},{T.USDC},{T.USDT},{T.BNT},{T.DAI},{T.WBTC}\")\n", - "))\n", - "r = O.margp_optimizer(f\"{T.USDC}\", params=dict(verbose=False, debug=False))\n", - "r.trade_instructions(ti_format=O.TIF_DFAGGR).fillna(\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "6b464dce-72bb-4e3e-8727-184f089cd026", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.723557Z", - "start_time": "2023-07-31T12:43:55.958166Z" - } - }, - "outputs": [], - "source": [ - "#r.trade_instructions(ti_format=O.TIF_DFAGGR).fillna(\"\").to_excel(\"ti.xlsx\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "e2607921-01b9-48ad-8af5-296b26c7e643", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.723665Z", - "start_time": "2023-07-31T12:43:55.959950Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ArbGraph.from_r(r).plot()._" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "696cb5a1-882f-43f2-807a-63f25b1e7075", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.723709Z", - "start_time": "2023-07-31T12:43:56.120767Z" - } - }, - "outputs": [], - "source": [ - "#O.CC.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "d1556dbf-efa9-4c32-97f2-249ff77b9879", - "metadata": {}, - "source": [ - "## ABC Tests" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "84927e7a-3062-472a-b2c8-8fa2e0bfa345", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.723750Z", - "start_time": "2023-07-31T12:43:56.124131Z" - } - }, - "outputs": [], - "source": [ - "assert raises(OptimizerBase).startswith(\"Can't instantiate abstract class\")\n", - "assert raises(OptimizerBase.OptimizerResult).startswith(\"Can't instantiate abstract class\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "53f36478-2060-4357-a624-db573502fd12", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.723790Z", - "start_time": "2023-07-31T12:43:56.128148Z" - } - }, - "outputs": [], - "source": [ - "assert raises(CPCArbOptimizer).startswith(\"Can't instantiate abstract class\")\n", - "assert raises(CPCArbOptimizer.OptimizerResult).startswith(\"Can't instantiate abstract class\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "053c284c-22cb-4440-9818-f529f344cdb3", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.723830Z", - "start_time": "2023-07-31T12:43:56.131914Z" - } - }, - "outputs": [], - "source": [ - "assert not raises(MargPOptimizer, CCm)\n", - "assert not raises(PairOptimizer, CCm)\n", - "assert not raises(ConvexOptimizer, CCm)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "ab2853bb-da5c-4d2f-a54c-8092af810937", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.723870Z", - "start_time": "2023-07-31T12:43:56.134909Z" - } - }, - "outputs": [], - "source": [ - "assert MargPOptimizer(CCm).kind == \"margp\"\n", - "assert PairOptimizer(CCm).kind == \"pair\"\n", - "assert ConvexOptimizer(CCm).kind == \"convex\"" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "77bc5aa7-2e50-444d-9c21-ecb3a703d9fa", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.723977Z", - "start_time": "2023-07-31T12:43:56.140480Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "CPCArbOptimizer.MargpOptimizerResult(result=None, time=0, method='margp', targettkn=None, p_optimal_t=None, dtokens_t=None, tokens_t=None, errormsg='err')" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CPCArbOptimizer.MargpOptimizerResult(None, time=0,errormsg=\"err\", optimizer=None)" - ] - }, - { - "cell_type": "markdown", - "id": "52ff8672-c720-49cc-b7e6-24d98ca88b0e", - "metadata": {}, - "source": [ - "## General and Specific Tests" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "4ec895b2-4ed6-404f-af16-b6c48603461b", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.724024Z", - "start_time": "2023-07-31T12:43:56.144620Z" - } - }, - "outputs": [], - "source": [ - "CA = CAm" - ] - }, - { - "cell_type": "markdown", - "id": "0cc54af2-560a-48ab-922b-0b2beab20aca", - "metadata": {}, - "source": [ - "### General tests" - ] - }, - { - "cell_type": "markdown", - "id": "fe86a889-f197-483b-b4c8-3bbc0a95d549", - "metadata": {}, - "source": [ - "#### General data integrity (should ALWAYS hold)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "5a565cec-f8c7-4d2a-9097-c60b62c88d06", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.724064Z", - "start_time": "2023-07-31T12:43:56.153611Z" - } - }, - "outputs": [], - "source": [ - "assert len(pairs0) > 2500\n", - "assert len(pairs) > 2500\n", - "assert len(pairs0) > len(pairs)\n", - "assert len(pairsc) > 10\n", - "assert len(CCm.tokens()) > 2000\n", - "assert len(CCm)>4000\n", - "assert len(CCm.filter_pairs(onein=f\"{T.ETH}\")) > 1900 # ETH pairs\n", - "assert len(CCm.filter_pairs(onein=f\"{T.USDC}\")) > 300 # USDC pairs\n", - "assert len(CCm.filter_pairs(onein=f\"{T.USDT}\")) > 190 # USDT pairs\n", - "assert len(CCm.filter_pairs(onein=f\"{T.DAI}\")) > 50 # DAI pairs\n", - "assert len(CCm.filter_pairs(onein=f\"{T.WBTC}\")) > 30 # WBTC pairs" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "676999fb-9bab-4add-85cf-1de62201e059", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.724104Z", - "start_time": "2023-07-31T12:43:56.280508Z" - } - }, - "outputs": [], - "source": [ - "xis0 = {c.cid: (c.x, c.y) for c in CCm if c.x==0}\n", - "yis0 = {c.cid: (c.x, c.y) for c in CCm if c.y==0}\n", - "assert len(xis0) == 0 # set loglevel debug to see removal of curves\n", - "assert len(yis0) == 0" - ] - }, - { - "cell_type": "markdown", - "id": "9ef125fd-2a6b-4e2a-a7c7-d01631373825", - "metadata": {}, - "source": [ - "#### Data integrity" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "a6d7e44b-38fc-419f-bd55-c81e4dd71b42", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.724157Z", - "start_time": "2023-07-31T12:43:56.290132Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "assert len(CCm) == 4155\n", - "assert len(CCu3) == 1411\n", - "assert len(CCu2) == 2177\n", - "assert len(CCs2) == 236\n", - "assert len(CCm.tokens()) == 2233\n", - "assert len(CCm.pairs()) == 2834\n", - "assert len(CCm.pairs(standardize=False)) == 2864" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "316f952e-ee28-47c8-80d5-2e12e7663c97", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.724198Z", - "start_time": "2023-07-31T12:43:56.306321Z" - } - }, - "outputs": [], - "source": [ - "assert CA.pairs() == CCm.pairs(standardize=True)\n", - "assert CA.pairsc() == {c.pairo.primary for c in CCm if c.P(\"exchange\")==\"carbon_v1\"}\n", - "assert CA.tokens() == CCm.tokens()" - ] - }, - { - "cell_type": "markdown", - "id": "66d79379-e42f-4598-a457-de513e9a1608", - "metadata": {}, - "source": [ - "#### prices" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "38634d40-f1dd-4ef7-9a1d-7cee6cb752ea", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.724264Z", - "start_time": "2023-07-31T12:43:56.309697Z" - } - }, - "outputs": [], - "source": [ - "r1 = CCc1.prices(result=CCc1.PR_TUPLE)\n", - "r2 = CCc1.prices(result=CCc1.PR_TUPLE, primary=False)\n", - "r3 = CCc1.prices(result=CCc1.PR_TUPLE, primary=False, inclpair=False)\n", - "assert isinstance(r1, tuple)\n", - "assert isinstance(r2, tuple)\n", - "assert isinstance(r3, tuple)\n", - "assert len(r1) == len(r2)\n", - "assert len(r1) == len(r3)\n", - "assert len(r1[0]) == 3\n", - "assert isinstance(r1[0][0], str)\n", - "assert isinstance(r1[0][1], float)\n", - "assert len(r1[0][2].split(\"/\"))==2" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "a8fb4a51-e8fe-4c16-aa15-1eb7bcbcf319", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.724363Z", - "start_time": "2023-07-31T12:43:56.312232Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(('1701411834604692317316873037158841057334-0',\n", - " 1700.000169864341,\n", - " 'WETH-6Cc2/USDC-eB48'),\n", - " ('1701411834604692317316873037158841057334-1',\n", - " 0.0005000000499999988,\n", - " 'USDC-eB48/WETH-6Cc2'))" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r2[:2]" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "cea1c980-fa6b-4a99-824b-c8790581b57a", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.724456Z", - "start_time": "2023-07-31T12:43:56.315242Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(1700.000169864341, 0.0005000000499999988)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r3[:2]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "2b66ba57-f327-4f0f-8b0f-9498e64068b7", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.724497Z", - "start_time": "2023-07-31T12:43:56.320608Z" - } - }, - "outputs": [], - "source": [ - "r1a = CCc1.prices(result=CCc1.PR_DICT)\n", - "r2a = CCc1.prices(result=CCc1.PR_DICT, primary=False)\n", - "r3a = CCc1.prices(result=CCc1.PR_DICT, primary=False, inclpair=False)\n", - "assert isinstance(r1a, dict)\n", - "assert isinstance(r2a, dict)\n", - "assert isinstance(r3a, dict)\n", - "assert len(r1a) == len(r1)\n", - "assert len(r1a) == len(r2a)\n", - "assert len(r1a) == len(r3a)\n", - "assert list(r1a.keys()) == list(x[0] for x in r1)\n", - "assert r1a.keys() == r2a.keys()\n", - "assert r1a.keys() == r3a.keys()\n", - "assert set(len(x) for x in r1a.values()) == {2}, \"all records must be of of length 2\"\n", - "assert set(type(x[0]) for x in r1a.values()) == {float}, \"all records must have first type float\"\n", - "assert set(type(x[1]) for x in r1a.values()) == {str}, \"all records must have second type str\"\n", - "assert tuple(r3a.values()) == r3" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "e5f29ad8-cc82-4c8d-98ba-85aa673713fe", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.724676Z", - "start_time": "2023-07-31T12:43:56.325519Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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pricepair
cid
1701411834604692317316873037158841057334-01700.000170WETH-6Cc2/USDC-eB48
1701411834604692317316873037158841057334-10.000500USDC-eB48/WETH-6Cc2
4423670769972200025023869896612986748966-11.000000BNT-FF1C/vBNT-7f94
1701411834604692317316873037158841057343-10.000503USDC-eB48/WETH-6Cc2
1361129467683753853853498429727072845828-00.999000USDC-eB48/DAI-1d0F
.........
9527906273786276976974489008089509920820-10.000034USDT-1ec7/WBTC-C599
6125082604576892342340742933771827806240-00.663550MATIC-eBB0/ARB-4ad1
6125082604576892342340742933771827806240-11.428571ARB-4ad1/MATIC-eBB0
10208471007628153903901238222953046343738-112500.000000WETH-6Cc2/SMT-7173
8847341539944400050047739793225973497903-10.129032USDC-eB48/LINK-86CA
\n", - "

70 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " price pair\n", - "cid \n", - "1701411834604692317316873037158841057334-0 1700.000170 WETH-6Cc2/USDC-eB48\n", - "1701411834604692317316873037158841057334-1 0.000500 USDC-eB48/WETH-6Cc2\n", - "4423670769972200025023869896612986748966-1 1.000000 BNT-FF1C/vBNT-7f94\n", - "1701411834604692317316873037158841057343-1 0.000503 USDC-eB48/WETH-6Cc2\n", - "1361129467683753853853498429727072845828-0 0.999000 USDC-eB48/DAI-1d0F\n", - "... ... ...\n", - "9527906273786276976974489008089509920820-1 0.000034 USDT-1ec7/WBTC-C599\n", - "6125082604576892342340742933771827806240-0 0.663550 MATIC-eBB0/ARB-4ad1\n", - "6125082604576892342340742933771827806240-1 1.428571 ARB-4ad1/MATIC-eBB0\n", - "10208471007628153903901238222953046343738-1 12500.000000 WETH-6Cc2/SMT-7173\n", - "8847341539944400050047739793225973497903-1 0.129032 USDC-eB48/LINK-86CA\n", - "\n", - "[70 rows x 2 columns]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = CCc1.prices(result=CCc1.PR_DF, primary=False)\n", - "assert len(df) == len(r1)\n", - "assert tuple(df.index) == tuple(x[0] for x in r1)\n", - "assert tuple(df[\"price\"]) == r3\n", - "df" - ] - }, - { - "cell_type": "markdown", - "id": "802db17b-fda4-4564-8ea9-ed03600c8aaf", - "metadata": {}, - "source": [ - "#### more prices" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "06b3e72f-5632-414d-8e79-657e23dade0b", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.724718Z", - "start_time": "2023-07-31T12:43:56.328971Z" - } - }, - "outputs": [], - "source": [ - "CCt = CCm.bypairs(f\"{T.USDC}/{T.ETH}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "310f3313-3993-4c97-ab59-8378f3326c1c", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.724773Z", - "start_time": "2023-07-31T12:43:56.332902Z" - } - }, - "outputs": [], - "source": [ - "r = CCt.prices(result=CCt.PR_TUPLE)\n", - "assert isinstance(r, tuple)\n", - "assert len(r) == len(CCt)\n", - "assert r[0] == ('6c988ffdc9e74acd97ccfb16dd65c110', 1833.9007005259564, 'WETH-6Cc2/USDC-eB48')\n", - "assert CCt.prices() == CCt.prices(result=CCt.PR_DICT)\n", - "r = CCt.prices(result=CCt.PR_DICT)\n", - "assert len(r) == len(CCt)\n", - "assert isinstance(r, dict)\n", - "assert r['6c988ffdc9e74acd97ccfb16dd65c110'] == (1833.9007005259564, 'WETH-6Cc2/USDC-eB48')\n", - "df = CCt.prices(result=CCt.PR_DF)\n", - "assert len(df) == len(CCt)\n", - "assert tuple(df.loc[\"1701411834604692317316873037158841057339-0\"]) == (1799.9999997028303, 'WETH-6Cc2/USDC-eB48')" - ] - }, - { - "cell_type": "markdown", - "id": "f2fc19b6-1083-4ec4-baaa-96313d4e841d", - "metadata": {}, - "source": [ - "#### price_ranges" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "aa404e85-085d-4915-8c6c-e49ceae13c01", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.724820Z", - "start_time": "2023-07-31T12:43:56.335661Z" - } - }, - "outputs": [], - "source": [ - "CCt = CCm.bypairs(f\"{T.USDC}/{T.ETH}\")\n", - "CAt = CPCAnalyzer(CCt)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "ec5069f3-74a5-4563-94ca-3bdf2f87ad88", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.725214Z", - "start_time": "2023-07-31T12:43:56.347091Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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bsp_minp_maxp_marg
pairexchcid
WETH/USDCcarbon_v141057306-0b1404.9998591405.0001401405.000140
41057334-0b1699.9998301700.0001701700.000170
41057331-0b1700.0000001800.0000001800.000000
41057339-0b1700.0000001800.0000001800.000000
uniswap_v3593bs1829.9191211866.8840731832.243200
sushiswap_v216dd65c110bs0.000000NaN1833.900701
803bs0.000000NaN1838.745520
uniswap_v2c60c551073bs0.000000NaN1840.159506
255bsNaNNaN1840.773969
uniswap_v3a176b13aa0bs1833.5824391844.6164501841.729378
7708cee9b5bs1829.9191211866.8840731843.002859
346bs1846.4618971848.3091901848.191535
carbon_v141057337-0b1600.0000001850.0000001850.000000
41057292-0b1850.0000001853.4088181853.408818
41057353-0b1853.9998141854.0001851854.000185
41057296-0b1929.9998071929.9998071929.999807
41057299-1s1940.0000002000.0000001940.000000
41057296-1s1949.9998051950.0001951949.999805
41057343-1s1989.9998011990.0001991989.999801
41057334-1s1999.9998002000.0002001999.999800
41057292-1s2000.0000002050.0000002000.000000
41057353-1s2047.9997952048.0002052047.999795
41057285-1s2099.9997902100.0002102099.999790
41057315-1s2300.0000002400.0000002300.000000
\n", - "
" - ], - "text/plain": [ - " b s p_min p_max p_marg\n", - "pair exch cid \n", - "WETH/USDC carbon_v1 41057306-0 b 1404.999859 1405.000140 1405.000140\n", - " 41057334-0 b 1699.999830 1700.000170 1700.000170\n", - " 41057331-0 b 1700.000000 1800.000000 1800.000000\n", - " 41057339-0 b 1700.000000 1800.000000 1800.000000\n", - " uniswap_v3 593 b s 1829.919121 1866.884073 1832.243200\n", - " sushiswap_v2 16dd65c110 b s 0.000000 NaN 1833.900701\n", - " 803 b s 0.000000 NaN 1838.745520\n", - " uniswap_v2 c60c551073 b s 0.000000 NaN 1840.159506\n", - " 255 b s NaN NaN 1840.773969\n", - " uniswap_v3 a176b13aa0 b s 1833.582439 1844.616450 1841.729378\n", - " 7708cee9b5 b s 1829.919121 1866.884073 1843.002859\n", - " 346 b s 1846.461897 1848.309190 1848.191535\n", - " carbon_v1 41057337-0 b 1600.000000 1850.000000 1850.000000\n", - " 41057292-0 b 1850.000000 1853.408818 1853.408818\n", - " 41057353-0 b 1853.999814 1854.000185 1854.000185\n", - " 41057296-0 b 1929.999807 1929.999807 1929.999807\n", - " 41057299-1 s 1940.000000 2000.000000 1940.000000\n", - " 41057296-1 s 1949.999805 1950.000195 1949.999805\n", - " 41057343-1 s 1989.999801 1990.000199 1989.999801\n", - " 41057334-1 s 1999.999800 2000.000200 1999.999800\n", - " 41057292-1 s 2000.000000 2050.000000 2000.000000\n", - " 41057353-1 s 2047.999795 2048.000205 2047.999795\n", - " 41057285-1 s 2099.999790 2100.000210 2099.999790\n", - " 41057315-1 s 2300.000000 2400.000000 2300.000000" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = CAt.price_ranges(result=CAt.PR_TUPLE)\n", - "assert len(r) == len(CCt)\n", - "assert r[0] == (\n", - " 'WETH/USDC', # pair\n", - " '16dd65c110', # cid\n", - " 'sushiswap_v2', # exchange\n", - " 'b', # buy\n", - " 's', # sell\n", - " 0, # min_primary\n", - " None, # max_primary\n", - " 1833.9007005259564 # pp\n", - ")\n", - "assert r[1] == (\n", - " 'WETH/USDC',\n", - " '41057334-0',\n", - " 'carbon_v1',\n", - " 'b',\n", - " '',\n", - " 1699.999829864358,\n", - " 1700.000169864341,\n", - " 1700.000169864341\n", - ")\n", - "r = CAt.price_ranges(result=CAt.PR_TUPLE, short=False)\n", - "assert r[0] == (\n", - " 'WETH-6Cc2/USDC-eB48',\n", - " '6c988ffdc9e74acd97ccfb16dd65c110',\n", - " 'sushiswap_v2',\n", - " 'b',\n", - " 's',\n", - " 0,\n", - " None,\n", - " 1833.9007005259564\n", - ")\n", - "r = CAt.price_ranges(result=CAt.PR_DICT)\n", - "assert len(r) == len(CCt)\n", - "assert r['6c988ffdc9e74acd97ccfb16dd65c110'] == (\n", - " 'WETH/USDC',\n", - " '16dd65c110',\n", - " 'sushiswap_v2',\n", - " 'b',\n", - " 's',\n", - " 0,\n", - " None,\n", - " 1833.9007005259564\n", - ")\n", - "df = CAt.price_ranges(result=CAt.PR_DF)\n", - "assert len(df) == len(CCt)\n", - "assert tuple(df.index.names) == ('pair', 'exch', 'cid')\n", - "assert tuple(df.columns) == ('b', 's', 'p_min', 'p_max', 'p_marg')\n", - "assert set(df[\"p_marg\"]) == set(x[-1] for x in CAt.price_ranges(result=CCt.PR_TUPLE))\n", - "for p1, p2 in zip(df[\"p_marg\"], df[\"p_marg\"][1:]):\n", - " assert p2 >= p1\n", - "df" - ] - }, - { - "cell_type": "markdown", - "id": "bc8a2d1c-34cb-43c3-9b03-f67f8307bf51", - "metadata": {}, - "source": [ - "#### count_by_pairs" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "fe2fd598-26e0-4a33-a1b5-49d3e693005f", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.781012Z", - "start_time": "2023-07-31T12:43:56.368951Z" - } - }, - "outputs": [], - "source": [ - "assert len(CA.count_by_pairs()) == len(CA.pairs())\n", - "assert sum(CA.count_by_pairs()[\"count\"])==len(CA.CC)\n", - "assert np.all(CA.count_by_pairs() == CA.count_by_pairs(asdf=True))\n", - "assert len(CA.count_by_pairs()) == len(CA.count_by_pairs(asdf=False))\n", - "assert type(CA.count_by_pairs()).__name__ == \"DataFrame\"\n", - "assert type(CA.count_by_pairs(asdf=False)).__name__ == \"list\"\n", - "assert type(CA.count_by_pairs(asdf=False)[0]).__name__ == \"tuple\"\n", - "for i in range(10):\n", - " assert len(CA.count_by_pairs(minn=i)) >= len(CA.count_by_pairs(minn=i)), f\"failed {i}\"" - ] - }, - { - "cell_type": "markdown", - "id": "2781b5ba-c516-415c-aaf0-d0b9acedbffb", - "metadata": {}, - "source": [ - "#### count_by_tokens" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "544b1056-92c5-4669-be07-9d82f5e10017", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.781765Z", - "start_time": "2023-07-31T12:43:56.573222Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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totalcarbuni3uni2sushi
token
WETH-6Cc22487387641571111
USDC-eB486943133426363
USDT-1ec74141016221128
BNT-FF1C28320020
DAI-1d0F1425445436
..................
JBX-6f6610100
anonUSD-1eFd10100
AGOV-280c10100
MOVE-324C10100
PANDA-00DC10100
\n", - "

2233 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " total carb uni3 uni2 sushi\n", - "token \n", - "WETH-6Cc2 2487 38 764 1571 111\n", - "USDC-eB48 694 31 334 263 63\n", - "USDT-1ec7 414 10 162 211 28\n", - "BNT-FF1C 283 20 0 2 0\n", - "DAI-1d0F 142 5 44 54 36\n", - "... ... ... ... ... ...\n", - "JBX-6f66 1 0 1 0 0\n", - "anonUSD-1eFd 1 0 1 0 0\n", - "AGOV-280c 1 0 1 0 0\n", - "MOVE-324C 1 0 1 0 0\n", - "PANDA-00DC 1 0 1 0 0\n", - "\n", - "[2233 rows x 5 columns]" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = CA.count_by_tokens()\n", - "assert len(r) == len(CA.tokens())\n", - "assert sum(r[\"total\"]) == 2*len(CA.CC)\n", - "assert tuple(r[\"total\"]) == tuple(x[1] for x in CA.CC.token_count())\n", - "for ix, row in r[:10].iterrows():\n", - " assert row[0] >= sum(row[1:]), f\"failed at {ix} {tuple(row)}\"\n", - "CA.count_by_tokens()" - ] - }, - { - "cell_type": "markdown", - "id": "081a2f67-293d-489b-8563-e971dd987408", - "metadata": {}, - "source": [ - "#### pool_arbitrage_statistics" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "d53f665d-79d3-4da0-90e1-8aa37ef27673", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.793994Z", - "start_time": "2023-07-31T12:43:56.663790Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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pricevlitmbsbsv
pairexchangecid0
0x0/WETHcarbon_v1132277-00.0000131.342084e+04bbuy-0x0 @ 0.00 WETH per 0x0
132277-10.0000153.597323e+02xssell-0x0 @ 0.00 WETH per 0x0
uniswap_v2551118da0.0000332.602200e+07xbsbuy-sell-0x0 @ 0.00 WETH per 0x0
ARB/MATICcarbon_v1806240-11.4285711.418060e+02bbuy-ARB @ 1.43 MATIC per ARB
806240-01.5070451.276054e+01ssell-ARB @ 1.51 MATIC per ARB
...........................
vBNT/BNTcarbon_v1748966-11.0000001.089256e+03ssell-vBNT @ 1.00 BNT per vBNT
748990-11.0500001.122591e+03ssell-vBNT @ 1.05 BNT per vBNT
748950-01.0638301.329046e+04ssell-vBNT @ 1.06 BNT per vBNT
748965-11.1000001.027046e+03ssell-vBNT @ 1.10 BNT per vBNT
vBNT/USDCcarbon_v1171896-10.3900005.000000e+03ssell-vBNT @ 0.39 USDC per vBNT
\n", - "

165 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " price vl itm b s \\\n", - "pair exchange cid0 \n", - "0x0/WETH carbon_v1 132277-0 0.000013 1.342084e+04 b \n", - " 132277-1 0.000015 3.597323e+02 x s \n", - " uniswap_v2 551118da 0.000033 2.602200e+07 x b s \n", - "ARB/MATIC carbon_v1 806240-1 1.428571 1.418060e+02 b \n", - " 806240-0 1.507045 1.276054e+01 s \n", - "... ... ... .. .. .. \n", - "vBNT/BNT carbon_v1 748966-1 1.000000 1.089256e+03 s \n", - " 748990-1 1.050000 1.122591e+03 s \n", - " 748950-0 1.063830 1.329046e+04 s \n", - " 748965-1 1.100000 1.027046e+03 s \n", - "vBNT/USDC carbon_v1 171896-1 0.390000 5.000000e+03 s \n", - "\n", - " bsv \n", - "pair exchange cid0 \n", - "0x0/WETH carbon_v1 132277-0 buy-0x0 @ 0.00 WETH per 0x0 \n", - " 132277-1 sell-0x0 @ 0.00 WETH per 0x0 \n", - " uniswap_v2 551118da buy-sell-0x0 @ 0.00 WETH per 0x0 \n", - "ARB/MATIC carbon_v1 806240-1 buy-ARB @ 1.43 MATIC per ARB \n", - " 806240-0 sell-ARB @ 1.51 MATIC per ARB \n", - "... ... \n", - "vBNT/BNT carbon_v1 748966-1 sell-vBNT @ 1.00 BNT per vBNT \n", - " 748990-1 sell-vBNT @ 1.05 BNT per vBNT \n", - " 748950-0 sell-vBNT @ 1.06 BNT per vBNT \n", - " 748965-1 sell-vBNT @ 1.10 BNT per vBNT \n", - "vBNT/USDC carbon_v1 171896-1 sell-vBNT @ 0.39 USDC per vBNT \n", - "\n", - "[165 rows x 6 columns]" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pas = CAm.pool_arbitrage_statistics()\n", - "assert np.all(pas == CAm.pool_arbitrage_statistics(CAm.POS_DF))\n", - "assert len(pas)==165\n", - "assert list(pas.columns) == ['price', 'vl', 'itm', 'b', 's', 'bsv']\n", - "assert list(pas.index.names) == ['pair', 'exchange', 'cid0']\n", - "assert {x[0] for x in pas.index} == {Pair.n(x) for x in CAm.pairsc()}\n", - "assert {x[1] for x in pas.index} == {'bancor_v2', 'bancor_v3','carbon_v1','sushiswap_v2','uniswap_v2','uniswap_v3'}\n", - "pas" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "c6382990-7537-4e2a-bd06-4032e742cf9a", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:56.999485Z", - "start_time": "2023-07-31T12:43:56.703066Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "('WETH/DAI',\n", - " 'WETH-6Cc2/DAI-1d0F',\n", - " 1840.1216491367131,\n", - " '594',\n", - " '594',\n", - " 'uniswap_v3',\n", - " 8.466598820198278,\n", - " '',\n", - " 'b',\n", - " 's',\n", - " 'buy-sell-WETH @ 1840.12 DAI per WETH')" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pasd = CAm.pool_arbitrage_statistics(CAm.POS_DICT)\n", - "assert isinstance(pasd, dict)\n", - "assert len(pasd) == 26\n", - "assert len(pasd['WETH-6Cc2/DAI-1d0F']) == 7\n", - "pd0 = pasd['WETH-6Cc2/DAI-1d0F'][0]\n", - "assert pd0[:2] == ('WETH/DAI', 'WETH-6Cc2/DAI-1d0F')\n", - "assert iseq(pd0[2], 1840.1216491367131)\n", - "assert pd0[3:6] == ('594', '594', 'uniswap_v3')\n", - "assert iseq(pd0[6], 8.466598820198278)\n", - "assert pd0[7:] == ('', 'b', 's', 'buy-sell-WETH @ 1840.12 DAI per WETH')\n", - "pd0" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "57df8ed4-b663-4a01-a63b-3ae257b277fc", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:57.853626Z", - "start_time": "2023-07-31T12:43:56.745708Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "('WETH/DAI',\n", - " 'WETH-6Cc2/DAI-1d0F',\n", - " 1840.1216491367131,\n", - " '594',\n", - " '594',\n", - " 'uniswap_v3',\n", - " 8.466598820198278,\n", - " '',\n", - " 'b',\n", - " 's',\n", - " 'buy-sell-WETH @ 1840.12 DAI per WETH')" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pasl = CAm.pool_arbitrage_statistics(result = CAm.POS_LIST)\n", - "assert isinstance(pasl, tuple)\n", - "assert len(pasl) == len(pas)\n", - "pd0 = [(ix, x) for ix, x in enumerate(pasl) if x[2]==1840.1216491367131]\n", - "pd0 = pasl[pd0[0][0]]\n", - "assert pd0[:2] == ('WETH/DAI', 'WETH-6Cc2/DAI-1d0F')\n", - "assert iseq(pd0[2], 1840.1216491367131)\n", - "assert pd0[3:6] == ('594', '594', 'uniswap_v3')\n", - "assert iseq(pd0[6], 8.466598820198278)\n", - "assert pd0[7:] == ('', 'b', 's', 'buy-sell-WETH @ 1840.12 DAI per WETH')\n", - "pd0" - ] - }, - { - "cell_type": "markdown", - "id": "01c769ec-549f-4316-a651-e44c328bd47d", - "metadata": {}, - "source": [ - "### MargP Optimizer" - ] - }, - { - "cell_type": "markdown", - "id": "a29954fb-5b9a-43ba-8ac0-ad5bd610f7cb", - "metadata": {}, - "source": [ - "#### margp optimizer" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "ccf80984-1745-4d0f-94a2-f7ca89aa53cb", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:57.978275Z", - "start_time": "2023-07-31T12:43:56.778888Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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USDT-1ec7USDC-eB48DAI-1d0FWETH-6Cc2WBTC-C599BNT-FF1C
3571214.455968-1216.41934
594943.826762-0.512606
183-48.8639060.00175
624-10733.80657124578.315452
656-0.87049555566.320623
.....................
21f3ea686abd44c6b7829e488a01aa746780944.55249-6780334.136658
PRICE1.000581.01.0001791842.6722827604.1434720.429078
AMMIn2905472.5834099856630.3974656845674.127441331.4316427.424195192904.817736
AMMOut-2905472.583409-9861236.407656-6845674.127441-331.431642-7.424195-192904.81774
TOTAL NET-0.0-4606.0101920.000001-0.0-0.0-0.000004
\n", - "

90 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " USDT-1ec7 USDC-eB48 \\\n", - "357 1214.455968 -1216.41934 \n", - "594 \n", - "183 -48.863906 \n", - "624 \n", - "656 \n", - "... ... ... \n", - "21f3ea686abd44c6b7829e488a01aa74 6780944.55249 \n", - "PRICE 1.00058 1.0 \n", - "AMMIn 2905472.583409 9856630.397465 \n", - "AMMOut -2905472.583409 -9861236.407656 \n", - "TOTAL NET -0.0 -4606.010192 \n", - "\n", - " DAI-1d0F WETH-6Cc2 WBTC-C599 \\\n", - "357 \n", - "594 943.826762 -0.512606 \n", - "183 0.00175 \n", - "624 -10733.806571 \n", - "656 -0.870495 \n", - "... ... ... ... \n", - "21f3ea686abd44c6b7829e488a01aa74 -6780334.136658 \n", - "PRICE 1.000179 1842.67228 27604.143472 \n", - "AMMIn 6845674.127441 331.431642 7.424195 \n", - "AMMOut -6845674.127441 -331.431642 -7.424195 \n", - "TOTAL NET 0.000001 -0.0 -0.0 \n", - "\n", - " BNT-FF1C \n", - "357 \n", - "594 \n", - "183 \n", - "624 24578.315452 \n", - "656 55566.320623 \n", - "... ... \n", - "21f3ea686abd44c6b7829e488a01aa74 \n", - "PRICE 0.429078 \n", - "AMMIn 192904.817736 \n", - "AMMOut -192904.81774 \n", - "TOTAL NET -0.000004 \n", - "\n", - "[90 rows x 6 columns]" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tokenlist = f\"{T.ETH},{T.USDC},{T.USDT},{T.BNT},{T.DAI},{T.WBTC}\"\n", - "targettkn = f\"{T.USDC}\"\n", - "O = MargPOptimizer(CCm.bypairs(CCm.filter_pairs(bothin=tokenlist)))\n", - "r = O.margp_optimizer(targettkn, params=dict(verbose=False, debug=False))\n", - "r.trade_instructions(ti_format=O.TIF_DFAGGR).fillna(\"\")" - ] - }, - { - "cell_type": "markdown", - "id": "48166a32-9464-4107-b320-a1d9e09c219f", - "metadata": {}, - "source": [ - "#### MargpOptimizerResult" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "4c660d37-da45-4834-af30-3c3f3c289aa6", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:57.999425Z", - "start_time": "2023-07-31T12:43:56.788562Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "optimal p {'USDT-1ec7': 1.00058, 'WETH-6Cc2': 1842.67228, 'WBTC-C599': 27604.143472, 'BNT-FF1C': 0.429078, 'DAI-1d0F': 1.000179}\n" - ] - } - ], - "source": [ - "assert type(r) == MargPOptimizer.MargpOptimizerResult\n", - "assert iseq(r.result, -4606.010157294979)\n", - "# assert r.time > 0.001\n", - "# assert r.time < 0.1\n", - "assert r.method == O.METHOD_MARGP\n", - "assert r.targettkn == targettkn\n", - "assert set(r.tokens_t)==set(['USDT-1ec7', 'WETH-6Cc2', 'WBTC-C599', 'DAI-1d0F', 'BNT-FF1C'])\n", - "p_opt_d0 = {t:x for x, t in zip(r.p_optimal_t, r.tokens_t)}\n", - "p_opt_d = {t:round(x,6) for x, t in zip(r.p_optimal_t, r.tokens_t)}\n", - "print(\"optimal p\", p_opt_d)\n", - "assert p_opt_d == {'WETH-6Cc2': 1842.67228, 'WBTC-C599': 27604.143472, \n", - " 'BNT-FF1C': 0.429078, 'USDT-1ec7': 1.00058, 'DAI-1d0F': 1.000179}\n", - "assert r.p_optimal[r.targettkn] == 1\n", - "po = [(k,v) for k,v in r.p_optimal.items()][:-1]\n", - "assert len(po)==len(r.p_optimal_t)\n", - "for k,v in po:\n", - " assert p_opt_d0[k] == v, f\"error at {k}, {v}, {p_opt_d0[k]}\"" - ] - }, - { - "cell_type": "markdown", - "id": "897d4f24-c628-429a-8655-18e0c5b57b0d", - "metadata": {}, - "source": [ - "#### TradeInstructions" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "e941c8c3-63db-4d41-9f99-e972ed8d4a68", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:58.004037Z", - "start_time": "2023-07-31T12:43:56.796168Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(CPCArbOptimizer.TradeInstruction(cid='357', tknin='USDT-1ec7', amtin=1214.4559684880078, tknout='USDC-eB48', amtout=-1216.4193395883776, error=None),\n", - " CPCArbOptimizer.TradeInstruction(cid='594', tknin='DAI-1d0F', amtin=943.8267624522559, tknout='WETH-6Cc2', amtout=-0.5126061548006646, error=None))" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert r.trade_instructions() == r.trade_instructions(ti_format=O.TIF_OBJECTS)\n", - "ti = r.trade_instructions(ti_format=O.TIF_OBJECTS)\n", - "cids = tuple(ti_.cid for ti_ in ti)\n", - "assert isinstance(ti, tuple)\n", - "assert len(ti) == 86\n", - "ti0=[x for x in ti if x.cid==\"357\"]\n", - "assert len(ti0)==1\n", - "ti0=ti0[0]\n", - "assert ti0.cid == ti0.curve.cid\n", - "assert type(ti0).__name__ == \"TradeInstruction\"\n", - "assert type(ti[0]) == MargPOptimizer.TradeInstruction\n", - "assert ti0.tknin == f\"{T.USDT}\"\n", - "assert ti0.tknout == f\"{T.USDC}\"\n", - "assert round(ti0.amtin, 8) == 1214.45596849\n", - "assert round(ti0.amtout, 8) == -1216.41933959\n", - "if not ti0.error is None:\n", - " print(ti0)\n", - " print(ti0.error)\n", - "assert ti0.error is None\n", - "ti[:2]" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "f1bc8f31-d88c-488f-a9ff-f8449c97ed66", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:58.004160Z", - "start_time": "2023-07-31T12:43:56.801713Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "({'cid': '357',\n", - " 'tknin': 'USDT-1ec7',\n", - " 'amtin': 1214.4559684880078,\n", - " 'tknout': 'USDC-eB48',\n", - " 'amtout': -1216.4193395883776,\n", - " 'error': None},\n", - " {'cid': '594',\n", - " 'tknin': 'DAI-1d0F',\n", - " 'amtin': 943.8267624522559,\n", - " 'tknout': 'WETH-6Cc2',\n", - " 'amtout': -0.5126061548006646,\n", - " 'error': None})" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tid = r.trade_instructions(ti_format=O.TIF_DICTS)\n", - "assert isinstance(tid, tuple)\n", - "assert len(tid) == len(ti)\n", - "tid0=[x for x in tid if x[\"cid\"]==\"357\"]\n", - "assert len(tid0)==1\n", - "tid0=tid0[0]\n", - "assert type(tid0)==dict\n", - "assert tid0[\"tknin\"] == f\"{T.USDT}\"\n", - "assert tid0[\"tknout\"] == f\"{T.USDC}\"\n", - "assert round(tid0[\"amtin\"], 8) == 1214.45596849\n", - "assert round(tid0[\"amtout\"], 8) == -1216.41933959\n", - "assert tid0[\"error\"] is None\n", - "tid[:2]" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "d1a2263d-a789-435c-b157-e7c33048e0b3", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:58.004362Z", - "start_time": "2023-07-31T12:43:56.807314Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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pairpairptknintknoutUSDT-1ec7USDC-eB48DAI-1d0FWETH-6Cc2WBTC-C599BNT-FF1C
cid
357USDC-eB48/USDT-1ec7USDC/USDTUSDT-1ec7USDC-eB481214.455968-1216.41934
594DAI-1d0F/WETH-6Cc2DAI/WETHDAI-1d0FWETH-6Cc2943.826762-0.512606
\n", - "
" - ], - "text/plain": [ - " pair pairp tknin tknout USDT-1ec7 \\\n", - "cid \n", - "357 USDC-eB48/USDT-1ec7 USDC/USDT USDT-1ec7 USDC-eB48 1214.455968 \n", - "594 DAI-1d0F/WETH-6Cc2 DAI/WETH DAI-1d0F WETH-6Cc2 \n", - "\n", - " USDC-eB48 DAI-1d0F WETH-6Cc2 WBTC-C599 BNT-FF1C \n", - "cid \n", - "357 -1216.41934 \n", - "594 943.826762 -0.512606 " - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = r.trade_instructions(ti_format=O.TIF_DF).fillna(\"\")\n", - "assert tuple(df.index) == cids\n", - "assert np.all(r.trade_instructions(ti_format=O.TIF_DFRAW).fillna(\"\")==df)\n", - "assert len(df) == len(ti)\n", - "assert list(df.columns)[:4] == ['pair', 'pairp', 'tknin', 'tknout']\n", - "assert len(df.columns) == 4 + len(r.tokens_t) + 1\n", - "tif0 = dict(df.loc[\"357\"])\n", - "assert tif0[\"pair\"] == \"USDC-eB48/USDT-1ec7\"\n", - "assert tif0[\"pairp\"] == \"USDC/USDT\"\n", - "assert tif0[\"tknin\"] == tid0[\"tknin\"]\n", - "assert tif0[tif0[\"tknin\"]] == tid0[\"amtin\"]\n", - "assert tif0[tif0[\"tknout\"]] == tid0[\"amtout\"]\n", - "df[:2]" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "20929d6d-b28c-47fe-8bad-3292928e8407", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:58.004562Z", - "start_time": "2023-07-31T12:43:56.823286Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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USDT-1ec7USDC-eB48DAI-1d0FWETH-6Cc2WBTC-C599BNT-FF1C
3571214.455968-1216.41934
594943.826762-0.512606
183-48.8639060.00175
624-10733.80657124578.315452
656-0.87049555566.320623
7950.514254-0.51586
84011870.146436-6.453271
2562519.448144-1.368187
83927.245732-27.298765
290-0.3217761364.584132
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" - ], - "text/plain": [ - " USDT-1ec7 USDC-eB48 DAI-1d0F WETH-6Cc2 WBTC-C599 BNT-FF1C\n", - "357 1214.455968 -1216.41934 \n", - "594 943.826762 -0.512606 \n", - "183 -48.863906 0.00175 \n", - "624 -10733.806571 24578.315452\n", - "656 -0.870495 55566.320623\n", - "795 0.514254 -0.51586 \n", - "840 11870.146436 -6.453271 \n", - "256 2519.448144 -1.368187 \n", - "839 27.245732 -27.298765 \n", - "290 -0.321776 1364.584132" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dfa = r.trade_instructions(ti_format=O.TIF_DFAGGR).fillna(\"\")\n", - "assert tuple(dfa.index)[:-4] == cids\n", - "assert len(dfa) == len(df)+4\n", - "assert len(dfa.columns) == len(r.tokens_t) + 1\n", - "assert set(dfa.columns) == set(r.tokens_t).union(set([r.targettkn]))\n", - "assert list(dfa.index)[-4:] == ['PRICE', 'AMMIn', 'AMMOut', 'TOTAL NET']\n", - "dfa[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "30219a5c-e561-4638-abb6-a209bb74700d", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:58.004875Z", - "start_time": "2023-07-31T12:43:56.828723Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total gains: 4,611.73 USDC-eB48 [result=4,606.01]\n" - ] - }, - { - "data": { - "text/html": [ - "
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feepairamt_tknqtknqmargp0effpmargpgain_rgain_tknqgain_ttkn
exchcid
uniswap_v33460.0005USDC-eB48/WETH-6Cc22.191376e+02WETH-6Cc20.0005410.0005410.0005430.0025190.5521051017.347899
7af1ca9ab5eb4b5f98105df03880de010.0005DAI-1d0F/USDC-eB48-6.733839e+06USDC-eB481.0003961.0002871.0001790.000108729.223514729.223514
21f3ea686abd44c6b7829e488a01aa740.0001DAI-1d0F/USDC-eB486.780945e+06USDC-eB481.0000771.0000901.0001790.000089602.634094602.634094
c9a1ba7537f242ecacf31755b7be04bd0.0005USDC-eB48/USDT-1ec71.414570e+06USDT-1ec70.9992230.9993220.9994200.000099139.426383139.507273
5930.0100USDC-eB48/WETH-6Cc2-1.652532e+01WETH-6Cc20.0005460.0005440.0005430.0028420.04696486.539463
67f9d1e2b3fc407eb44dcb637d051d190.0005WETH-6Cc2/USDT-1ec72.979293e+04USDT-1ec71836.6561941836.9546171841.6038470.00252575.21387575.257511
edb7550782154a5b8eb1e4feedc876680.0005WBTC-C599/WETH-6Cc2-9.827301e+01WETH-6Cc214.98933214.98576314.9804950.0003520.03455463.672609
4860.0001USDC-eB48/USDT-1ec71.286263e+06USDT-1ec70.9993460.9993730.9994200.00004760.65001660.685203
4c50c9e4fdde4aefbf495b30d42fa3d00.0001USDC-eB48/USDT-1ec7-2.810367e+06USDT-1ec70.9994560.9994380.9994200.00001849.80973849.838636
a6595d66f70c432a9b68557428a6fe540.0005DAI-1d0F/WETH-6Cc2-6.599276e+00WETH-6Cc20.0005440.0005440.0005430.0025630.01691331.164972
\n", - "
" - ], - "text/plain": [ - " fee pair \\\n", - "exch cid \n", - "uniswap_v3 346 0.0005 USDC-eB48/WETH-6Cc2 \n", - " 7af1ca9ab5eb4b5f98105df03880de01 0.0005 DAI-1d0F/USDC-eB48 \n", - " 21f3ea686abd44c6b7829e488a01aa74 0.0001 DAI-1d0F/USDC-eB48 \n", - " c9a1ba7537f242ecacf31755b7be04bd 0.0005 USDC-eB48/USDT-1ec7 \n", - " 593 0.0100 USDC-eB48/WETH-6Cc2 \n", - " 67f9d1e2b3fc407eb44dcb637d051d19 0.0005 WETH-6Cc2/USDT-1ec7 \n", - " edb7550782154a5b8eb1e4feedc87668 0.0005 WBTC-C599/WETH-6Cc2 \n", - " 486 0.0001 USDC-eB48/USDT-1ec7 \n", - " 4c50c9e4fdde4aefbf495b30d42fa3d0 0.0001 USDC-eB48/USDT-1ec7 \n", - " a6595d66f70c432a9b68557428a6fe54 0.0005 DAI-1d0F/WETH-6Cc2 \n", - "\n", - " amt_tknq tknq \\\n", - "exch cid \n", - "uniswap_v3 346 2.191376e+02 WETH-6Cc2 \n", - " 7af1ca9ab5eb4b5f98105df03880de01 -6.733839e+06 USDC-eB48 \n", - " 21f3ea686abd44c6b7829e488a01aa74 6.780945e+06 USDC-eB48 \n", - " c9a1ba7537f242ecacf31755b7be04bd 1.414570e+06 USDT-1ec7 \n", - " 593 -1.652532e+01 WETH-6Cc2 \n", - " 67f9d1e2b3fc407eb44dcb637d051d19 2.979293e+04 USDT-1ec7 \n", - " edb7550782154a5b8eb1e4feedc87668 -9.827301e+01 WETH-6Cc2 \n", - " 486 1.286263e+06 USDT-1ec7 \n", - " 4c50c9e4fdde4aefbf495b30d42fa3d0 -2.810367e+06 USDT-1ec7 \n", - " a6595d66f70c432a9b68557428a6fe54 -6.599276e+00 WETH-6Cc2 \n", - "\n", - " margp0 effp \\\n", - "exch cid \n", - "uniswap_v3 346 0.000541 0.000541 \n", - " 7af1ca9ab5eb4b5f98105df03880de01 1.000396 1.000287 \n", - " 21f3ea686abd44c6b7829e488a01aa74 1.000077 1.000090 \n", - " c9a1ba7537f242ecacf31755b7be04bd 0.999223 0.999322 \n", - " 593 0.000546 0.000544 \n", - " 67f9d1e2b3fc407eb44dcb637d051d19 1836.656194 1836.954617 \n", - " edb7550782154a5b8eb1e4feedc87668 14.989332 14.985763 \n", - " 486 0.999346 0.999373 \n", - " 4c50c9e4fdde4aefbf495b30d42fa3d0 0.999456 0.999438 \n", - " a6595d66f70c432a9b68557428a6fe54 0.000544 0.000544 \n", - "\n", - " margp gain_r \\\n", - "exch cid \n", - "uniswap_v3 346 0.000543 0.002519 \n", - " 7af1ca9ab5eb4b5f98105df03880de01 1.000179 0.000108 \n", - " 21f3ea686abd44c6b7829e488a01aa74 1.000179 0.000089 \n", - " c9a1ba7537f242ecacf31755b7be04bd 0.999420 0.000099 \n", - " 593 0.000543 0.002842 \n", - " 67f9d1e2b3fc407eb44dcb637d051d19 1841.603847 0.002525 \n", - " edb7550782154a5b8eb1e4feedc87668 14.980495 0.000352 \n", - " 486 0.999420 0.000047 \n", - " 4c50c9e4fdde4aefbf495b30d42fa3d0 0.999420 0.000018 \n", - " a6595d66f70c432a9b68557428a6fe54 0.000543 0.002563 \n", - "\n", - " gain_tknq gain_ttkn \n", - "exch cid \n", - "uniswap_v3 346 0.552105 1017.347899 \n", - " 7af1ca9ab5eb4b5f98105df03880de01 729.223514 729.223514 \n", - " 21f3ea686abd44c6b7829e488a01aa74 602.634094 602.634094 \n", - " c9a1ba7537f242ecacf31755b7be04bd 139.426383 139.507273 \n", - " 593 0.046964 86.539463 \n", - " 67f9d1e2b3fc407eb44dcb637d051d19 75.213875 75.257511 \n", - " edb7550782154a5b8eb1e4feedc87668 0.034554 63.672609 \n", - " 486 60.650016 60.685203 \n", - " 4c50c9e4fdde4aefbf495b30d42fa3d0 49.809738 49.838636 \n", - " a6595d66f70c432a9b68557428a6fe54 0.016913 31.164972 " - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dfpg = r.trade_instructions(ti_format=O.TIF_DFPG)\n", - "assert set(x[1] for x in dfpg.index) == set(cids)\n", - "assert np.all(dfpg[\"gain_tknq\"]>=0)\n", - "assert np.all(dfpg[\"gain_r\"]>=0)\n", - "assert round(np.max(dfpg[\"gain_r\"]),8) == 0.04739068\n", - "assert round(np.min(dfpg[\"gain_r\"]),8) == 1.772e-05\n", - "assert len(dfpg) == len(ti)\n", - "for p, t in zip(tuple(dfpg[\"pair\"]), tuple(dfpg[\"tknq\"])):\n", - " assert p.split(\"/\")[1] == t, f\"error in {p} [{t}]\"\n", - "print(f\"total gains: {sum(dfpg['gain_ttkn']):,.2f} {r.targettkn} [result={-r.result:,.2f}]\")\n", - "assert abs(sum(dfpg[\"gain_ttkn\"])/r.result+1)<0.01\n", - "dfpg[:10]" - ] - }, - { - "cell_type": "markdown", - "id": "8cade46b-5a66-4297-8105-c57dfbd9fcf1", - "metadata": {}, - "source": [ - "### Convex Optimizer\n", - "\n", - "**THE CONVEX OPTIMIZER IS DEPRECATED AND NO LONGER IN USE IN PRODUCTION**\n", - "\n", - "**THIS SECTION DOES SEEM TO THROW RANDOM ERRORS AND IS THEREFORE DISABLED**" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "f680dfd7-b5cc-4ee2-b69b-ff8ca0a0b0f9", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:58.004934Z", - "start_time": "2023-07-31T12:43:56.843524Z" - } - }, - "outputs": [], - "source": [ - "# tokens = f\"{T.DAI},{T.USDT},{T.HEX},{T.WETH},{T.LINK}\"\n", - "# CCo = CCu2.bypairs(CCu2.filter_pairs(bothin=tokens))\n", - "# CCo += CCs2.bypairs(CCu2.filter_pairs(bothin=tokens))\n", - "# CA = CPCAnalyzer(CCo)\n", - "# O = ConvexOptimizer(CCo)\n", - "# #ArbGraph.from_cc(CCo).plot()._" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "56476abd-2a0f-4e74-b45b-62836b49f9cb", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:58.032459Z", - "start_time": "2023-07-31T12:43:56.850915Z" - } - }, - "outputs": [], - "source": [ - "# CA.count_by_tokens()" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "5570f890-5367-47de-93ef-328025f9c968", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:58.032620Z", - "start_time": "2023-07-31T12:43:56.854578Z" - } - }, - "outputs": [], - "source": [ - "#CCo.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "c3322688-6db1-4737-971b-55b091983954", - "metadata": {}, - "source": [ - "#### convex optimizer" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "f14670a2-e1a5-4f11-af3e-397aef23cee3", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:43:58.032674Z", - "start_time": "2023-07-31T12:43:56.857639Z" - } - }, - "outputs": [], - "source": [ - "# targettkn = T.USDT\n", - "# # r = O.margp_optimizer(targettkn, params=dict(verbose=True, debug=False))\n", - "# # r" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "79bec194-1df2-40f2-b44d-90e8996e454f", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.008884Z", - "start_time": "2023-07-31T12:43:56.859940Z" - } - }, - "outputs": [], - "source": [ - "# SFC = O.SFC(**{targettkn:O.AMMPays})\n", - "# r = O.convex_optimizer(SFC, verbose=False, solver=O.SOLVER_SCS)\n", - "# r" - ] - }, - { - "cell_type": "markdown", - "id": "2afdf979-dc68-446f-8a67-f9dd55415a0d", - "metadata": {}, - "source": [ - "#### NofeesOptimizerResult" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "cd3e6d9a-a6a5-4407-931b-eea60ab6a80f", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.013961Z", - "start_time": "2023-07-31T12:44:08.009197Z" - } - }, - "outputs": [], - "source": [ - "# round(r.result,-5)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "377341bf-e7d1-4c1d-b539-0aca307b92a3", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.024324Z", - "start_time": "2023-07-31T12:44:08.016854Z" - } - }, - "outputs": [], - "source": [ - "# assert type(r) == ConvexOptimizer.NofeesOptimizerResult\n", - "# # assert round(r.result,-5) <= -1500000.0\n", - "# # assert round(r.result,-5) >= -2500000.0\n", - "# # assert r.time < 8\n", - "# assert r.method == \"convex\"\n", - "# assert set(r.token_table.keys()) == set(['USDT-1ec7', 'WETH-6Cc2', 'LINK-86CA', 'DAI-1d0F', 'HEX-eb39'])\n", - "# assert len(r.token_table[T.USDT].x)==0\n", - "# assert len(r.token_table[T.USDT].y)==10\n", - "# lx = list(it.chain(*[rr.x for rr in r.token_table.values()]))\n", - "# lx.sort()\n", - "# ly = list(it.chain(*[rr.y for rr in r.token_table.values()]))\n", - "# ly.sort()\n", - "# assert lx == [_ for _ in range(21)]\n", - "# assert ly == lx" - ] - }, - { - "cell_type": "markdown", - "id": "8eae1f94-7497-4f1a-a1d3-03749b1f2501", - "metadata": {}, - "source": [ - "#### trade instructions" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "57b45a2f-f3c4-4901-be9a-f2bbacd609e8", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.024442Z", - "start_time": "2023-07-31T12:44:08.021902Z" - } - }, - "outputs": [], - "source": [ - "# ti = r.trade_instructions()\n", - "# assert type(ti[0]) == ConvexOptimizer.TradeInstruction" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "448eab5b-4c06-4d88-b6b8-b3dc6232f760", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.118752Z", - "start_time": "2023-07-31T12:44:08.027135Z" - } - }, - "outputs": [], - "source": [ - "# assert r.trade_instructions() == r.trade_instructions(ti_format=O.TIF_OBJECTS)\n", - "# ti = r.trade_instructions(ti_format=O.TIF_OBJECTS)\n", - "# cids = tuple(ti_.cid for ti_ in ti)\n", - "# assert isinstance(ti, tuple)\n", - "# assert len(ti) == 21\n", - "# ti0=[x for x in ti if x.cid==\"175\"]\n", - "# assert len(ti0)==1\n", - "# ti0=ti0[0]\n", - "# assert ti0.cid == ti0.curve.cid\n", - "# assert type(ti0).__name__ == \"TradeInstruction\"\n", - "# assert type(ti[0]) == ConvexOptimizer.TradeInstruction\n", - "# assert ti0.tknin == f\"{T.LINK}\"\n", - "# assert ti0.tknout == f\"{T.DAI}\"\n", - "# # assert round(ti0.amtin, 8) == 8.50052943\n", - "# # assert round(ti0.amtout, 8) == -50.40963779\n", - "# if not ti0.error is None:\n", - "# print(ti0)\n", - "# print(ti0.error)\n", - "# assert ti0.error is None\n", - "# print(r.error, ti0.error)\n", - "# ti[:2], ti0, r" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "5bf4535c-891b-4002-8a45-c2cefa4f8aae", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.119790Z", - "start_time": "2023-07-31T12:44:08.034009Z" - } - }, - "outputs": [], - "source": [ - "# tid = r.trade_instructions(ti_format=O.TIF_DICTS)\n", - "# assert isinstance(tid, tuple)\n", - "# assert type(tid[0])==dict\n", - "# assert len(tid) == len(ti)\n", - "# tid0=[x for x in tid if x[\"cid\"]==\"175\"]\n", - "# assert len(tid0)==1\n", - "# tid0=tid0[0]\n", - "# assert tid0[\"tknin\"] == f\"{T.LINK}\"\n", - "# assert tid0[\"tknout\"] == f\"{T.DAI}\"\n", - "# # assert round(tid0[\"amtin\"], 8) == 8.50052943\n", - "# # assert round(tid0[\"amtout\"], 8) == -50.40963779\n", - "# assert tid0[\"error\"] is None\n", - "# tid[:2]" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "0efeef32-8c03-46af-91f2-138547efcd7a", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.120197Z", - "start_time": "2023-07-31T12:44:08.050349Z" - } - }, - "outputs": [], - "source": [ - "# df = r.trade_instructions(ti_format=O.TIF_DF).fillna(\"\")\n", - "# assert tuple(df.index) == cids\n", - "# assert np.all(r.trade_instructions(ti_format=O.TIF_DFRAW).fillna(\"\")==df)\n", - "# assert len(df) == len(ti)\n", - "# assert list(df.columns)[:4] == ['pair', 'pairp', 'tknin', 'tknout']\n", - "# assert len(df.columns) == 4 + 4 + 1\n", - "# tif0 = dict(df.loc[\"175\"])\n", - "# assert tif0[\"pair\"] == 'LINK-86CA/DAI-1d0F'\n", - "# assert tif0[\"pairp\"] == \"LINK/DAI\"\n", - "# assert tif0[\"tknin\"] == tid0[\"tknin\"]\n", - "# assert tif0[tif0[\"tknin\"]] == tid0[\"amtin\"]\n", - "# assert tif0[tif0[\"tknout\"]] == tid0[\"amtout\"]\n", - "# df[:2]" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "63a554ca-7c04-4cdc-b08f-6a4981e1a89f", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.120248Z", - "start_time": "2023-07-31T12:44:08.064778Z" - } - }, - "outputs": [], - "source": [ - "# assert raises(r.trade_instructions, ti_format=O.TIF_DFAGGR).startswith(\"TIF_DFAGGR not implemented for\")\n", - "# assert raises(r.trade_instructions, ti_format=O.TIF_DFPG).startswith(\"TIF_DFPG not implemented for\")" - ] - }, - { - "cell_type": "markdown", - "id": "38bcc06c-08a6-4a92-b3bb-3d2733324cd9", - "metadata": {}, - "source": [ - "### Simple Optimizer" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "0a682d61-f680-4be8-b6ae-ce6ca0d3acf7", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.120293Z", - "start_time": "2023-07-31T12:44:08.073250Z" - } - }, - "outputs": [], - "source": [ - "pair = f\"{T.ETH}/{T.USDC}\"\n", - "CCs = CCm.bypairs(pair)\n", - "CA = CPCAnalyzer(CCs)\n", - "O = PairOptimizer(CCs)\n", - "#ArbGraph.from_cc(CCs).plot()._" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "ef5cd37e-1307-4460-8c63-d5a654cd028c", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.120448Z", - "start_time": "2023-07-31T12:44:08.078043Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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totalcarbuni3uni2sushi
token
USDC-eB482416422
WETH-6Cc22416422
\n", - "
" - ], - "text/plain": [ - " total carb uni3 uni2 sushi\n", - "token \n", - "USDC-eB48 24 16 4 2 2\n", - "WETH-6Cc2 24 16 4 2 2" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CA.count_by_tokens()" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "4949e298-df9a-46d9-bebb-4286f2456038", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.120501Z", - "start_time": "2023-07-31T12:44:08.085973Z" - } - }, - "outputs": [], - "source": [ - "#CCs.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "cc9738a2-dd04-4262-9000-7e7fa9209b1b", - "metadata": {}, - "source": [ - "#### simple optimizer" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "cf47528a-42d0-42c0-a693-b43b52bc99f8", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.122747Z", - "start_time": "2023-07-31T12:44:08.099276Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "CPCArbOptimizer.MargpOptimizerResult(result=-1217.2442002636553, time=0.020034074783325195, method='margp-pair', targettkn='USDC-eB48', p_optimal_t=(1844.364520645447,), dtokens_t=(5.21231946493117e-11,), tokens_t=('WETH-6Cc2',), errormsg=None)" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = O.optimize(T.USDC)\n", - "r" - ] - }, - { - "cell_type": "markdown", - "id": "317ce7ce-1f9c-4482-9849-3c958a0fad28", - "metadata": {}, - "source": [ - "#### result" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "93949531-5c8e-488b-ab7c-308c6ce14b67", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.132745Z", - "start_time": "2023-07-31T12:44:08.104771Z" - } - }, - "outputs": [], - "source": [ - "assert type(r) == PairOptimizer.MargpOptimizerResult\n", - "assert round(r.result, 5) == -1217.2442, f\"{round(r.result, 5)}\"\n", - "# assert r.time < 0.1\n", - "assert r.method == \"margp-pair\"\n", - "assert r.errormsg is None" - ] - }, - { - "cell_type": "markdown", - "id": "becb0027-3146-4e7f-bde7-d3226b0fbacf", - "metadata": {}, - "source": [ - "#### trade instructions" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "487ccaa8-2199-4537-b751-cf179bf39043", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.216577Z", - "start_time": "2023-07-31T12:44:08.113490Z" - } - }, - "outputs": [], - "source": [ - "ti = r.trade_instructions()\n", - "assert type(ti[0]) == PairOptimizer.TradeInstruction" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "34b2da13-71b9-490f-a060-30c5adaeb6d7", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.266882Z", - "start_time": "2023-07-31T12:44:08.118927Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(CPCArbOptimizer.TradeInstruction(cid='6c988ffdc9e74acd97ccfb16dd65c110', tknin='USDC-eB48', amtin=48153.8086489439, tknout='WETH-6Cc2', amtout=-26.182996930494483, error=None),\n", - " CPCArbOptimizer.TradeInstruction(cid='7ed16708962e459abe5431a176b13aa0', tknin='USDC-eB48', amtin=219435.4523000121, tknout='WETH-6Cc2', amtout=-119.06126887261053, error=None))" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert r.trade_instructions() == r.trade_instructions(ti_format=O.TIF_OBJECTS)\n", - "ti = r.trade_instructions(ti_format=O.TIF_OBJECTS)\n", - "cids = tuple(ti_.cid for ti_ in ti)\n", - "assert isinstance(ti, tuple)\n", - "assert len(ti) == 12\n", - "ti0=[x for x in ti if x.cid==\"6c988ffdc9e74acd97ccfb16dd65c110\"]\n", - "assert len(ti0)==1\n", - "ti0=ti0[0]\n", - "assert ti0.cid == ti0.curve.cid\n", - "assert type(ti0).__name__ == \"TradeInstruction\"\n", - "assert type(ti[0]) == PairOptimizer.TradeInstruction\n", - "assert ti0.tknin == f\"{T.USDC}\"\n", - "assert ti0.tknout == f\"{T.WETH}\"\n", - "assert round(ti0.amtin, 5) == 48153.80865\n", - "assert round(ti0.amtout, 5) == -26.18300\n", - "assert ti0.error is None\n", - "ti[:2]" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "63839fa7-e313-46d4-933e-b2b2b6f7069e", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.277131Z", - "start_time": "2023-07-31T12:44:08.130100Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "({'cid': '6c988ffdc9e74acd97ccfb16dd65c110',\n", - " 'tknin': 'USDC-eB48',\n", - " 'amtin': 48153.8086489439,\n", - " 'tknout': 'WETH-6Cc2',\n", - " 'amtout': -26.182996930494483,\n", - " 'error': None},\n", - " {'cid': '7ed16708962e459abe5431a176b13aa0',\n", - " 'tknin': 'USDC-eB48',\n", - " 'amtin': 219435.4523000121,\n", - " 'tknout': 'WETH-6Cc2',\n", - " 'amtout': -119.06126887261053,\n", - " 'error': None})" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tid = r.trade_instructions(ti_format=O.TIF_DICTS)\n", - "assert isinstance(tid, tuple)\n", - "assert type(tid[0])==dict\n", - "assert len(tid) == len(ti)\n", - "tid0=[x for x in tid if x[\"cid\"]==\"6c988ffdc9e74acd97ccfb16dd65c110\"]\n", - "assert len(tid0)==1\n", - "tid0=tid0[0]\n", - "assert tid0[\"tknin\"] == f\"{T.USDC}\"\n", - "assert tid0[\"tknout\"] == f\"{T.WETH}\"\n", - "assert round(tid0[\"amtin\"], 5) == 48153.80865\n", - "assert round(tid0[\"amtout\"], 5) == -26.183\n", - "assert tid0[\"error\"] is None\n", - "tid[:2]" - ] - }, - { - "cell_type": "markdown", - "id": "5a6c2ffd-a9c9-4738-9c7a-287a15902d18", - "metadata": {}, - "source": [ - "trade instructions of format `TIF_DFRAW` (same as `TIF_DF`): raw dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "24fd38a2-db05-4cc7-8adb-e26713a1046c", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.284167Z", - "start_time": "2023-07-31T12:44:08.145378Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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pairpairptknintknoutUSDC-eB48WETH-6Cc2
cid
6c988ffdc9e74acd97ccfb16dd65c110WETH-6Cc2/USDC-eB48WETH/USDCUSDC-eB48WETH-6Cc248153.808649-26.182997
7ed16708962e459abe5431a176b13aa0WETH-6Cc2/USDC-eB48WETH/USDCUSDC-eB48WETH-6Cc2219435.452300-119.061269
\n", - "
" - ], - "text/plain": [ - " pair pairp tknin \\\n", - "cid \n", - "6c988ffdc9e74acd97ccfb16dd65c110 WETH-6Cc2/USDC-eB48 WETH/USDC USDC-eB48 \n", - "7ed16708962e459abe5431a176b13aa0 WETH-6Cc2/USDC-eB48 WETH/USDC USDC-eB48 \n", - "\n", - " tknout USDC-eB48 WETH-6Cc2 \n", - "cid \n", - "6c988ffdc9e74acd97ccfb16dd65c110 WETH-6Cc2 48153.808649 -26.182997 \n", - "7ed16708962e459abe5431a176b13aa0 WETH-6Cc2 219435.452300 -119.061269 " - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = r.trade_instructions(ti_format=O.TIF_DF).fillna(\"\")\n", - "assert tuple(df.index) == cids\n", - "assert np.all(r.trade_instructions(ti_format=O.TIF_DFRAW).fillna(\"\")==df)\n", - "assert len(df) == len(ti)\n", - "assert list(df.columns)[:4] == ['pair', 'pairp', 'tknin', 'tknout']\n", - "assert len(df.columns) == 4 + 1 + 1\n", - "tif0 = dict(df.loc[\"6c988ffdc9e74acd97ccfb16dd65c110\"])\n", - "assert tif0[\"pair\"] == 'WETH-6Cc2/USDC-eB48'\n", - "assert tif0[\"pairp\"] == \"WETH/USDC\"\n", - "assert tif0[\"tknin\"] == tid0[\"tknin\"]\n", - "assert tif0[tif0[\"tknin\"]] == tid0[\"amtin\"]\n", - "assert tif0[tif0[\"tknout\"]] == tid0[\"amtout\"]\n", - "df[:2]" - ] - }, - { - "cell_type": "markdown", - "id": "300d49a6-3914-4c0f-b195-c54ab82794bc", - "metadata": {}, - "source": [ - "trade instructions of format `TIF_DFAGGR` (aggregated data frame)" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "22a3e35a-1402-48e5-a95a-39977aa67153", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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USDC-eB48WETH-6Cc2
6c988ffdc9e74acd97ccfb16dd65c11048153.808649-2.618300e+01
7ed16708962e459abe5431a176b13aa0219435.452300-1.190613e+02
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00125d264f9d49369a467e7708cee9b514371.217737-7.794840e+00
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1701411834604692317316873037158841057337-0-43.5386552.357034e-02
PRICE1.0000001.844365e+03
AMMIn411504.2471272.234002e+02
AMMOut-412721.491327-2.234002e+02
TOTAL NET-1217.2442005.212319e-11
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" - ], - "text/plain": [ - " USDC-eB48 WETH-6Cc2\n", - "6c988ffdc9e74acd97ccfb16dd65c110 48153.808649 -2.618300e+01\n", - "7ed16708962e459abe5431a176b13aa0 219435.452300 -1.190613e+02\n", - "593 35283.335544 -1.919352e+01\n", - "255 35207.230349 -1.910769e+01\n", - "803 24654.883463 -1.338809e+01\n", - "50ac5ace09c1483987af46c60c551073 34398.319085 -1.867180e+01\n", - "346 -404818.683174 2.191376e+02\n", - "1701411834604692317316873037158841057353-0 -7851.133636 4.234700e+00\n", - "1701411834604692317316873037158841057296-0 -1.994537 1.033440e-03\n", - "00125d264f9d49369a467e7708cee9b5 14371.217737 -7.794840e+00\n", - "1701411834604692317316873037158841057292-0 -6.141325 3.316581e-03\n", - "1701411834604692317316873037158841057337-0 -43.538655 2.357034e-02\n", - "PRICE 1.000000 1.844365e+03\n", - "AMMIn 411504.247127 2.234002e+02\n", - "AMMOut -412721.491327 -2.234002e+02\n", - "TOTAL NET -1217.244200 5.212319e-11" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = r.trade_instructions(ti_format=O.TIF_DFAGGR)\n", - "assert len(df) == 16 \n", - "assert tuple(df.index[-4:]) == ('PRICE', 'AMMIn', 'AMMOut', 'TOTAL NET')\n", - "assert tuple(df.columns) == ('USDC-eB48', 'WETH-6Cc2')\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41be3f9f-d79f-4393-9963-ea44329799a9", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "8e923676-5f9e-46e4-a3b6-8abcbba9cd35", - "metadata": {}, - "source": [ - "prices and gains analysis data frame `TIF_DFPG`" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "8b6c7014-d89b-4479-a6a1-efd78b4a6c0f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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feepairamt_tknqtknqmargp0effpmargpgain_rgain_tknqgain_ttkn
exchcid
uniswap_v33460.0005WETH-6Cc2/USDC-eB48-404818.683174USDC-eB481848.1915351847.3265131844.3645210.001606650.126368650.126368
7ed16708962e459abe5431a176b13aa00.0030WETH-6Cc2/USDC-eB48219435.452300USDC-eB481841.7293781843.0464781844.3645210.000715156.815646156.815646
5930.0100WETH-6Cc2/USDC-eB4835283.335544USDC-eB481832.2432001838.2938701844.3645210.003291116.133668116.133668
00125d264f9d49369a467e7708cee9b50.0100WETH-6Cc2/USDC-eB4814371.217737USDC-eB481843.0028591843.6835641844.3645210.0003695.3059875.305987
uniswap_v250ac5ace09c1483987af46c60c5510730.0030WETH-6Cc2/USDC-eB4834398.319085USDC-eB481840.1595061842.2608141844.3645210.00114139.23518539.235185
2550.0030WETH-6Cc2/USDC-eB4835207.230349USDC-eB481840.7739691842.5683701844.3645210.00097434.28686834.286868
sushiswap_v26c988ffdc9e74acd97ccfb16dd65c1100.0030WETH-6Cc2/USDC-eB4848153.808649USDC-eB481833.9007011839.1251691844.3645210.002841136.792236136.792236
8030.0030WETH-6Cc2/USDC-eB4824654.883463USDC-eB481838.7455201841.5528771844.3645210.00152437.58516237.585162
carbon_v11701411834604692317316873037158841057353-00.0020WETH-6Cc2/USDC-eB48-7851.133636USDC-eB481854.0001851854.0000001844.3645210.00522441.01653141.016531
1701411834604692317316873037158841057296-00.0020WETH-6Cc2/USDC-eB48-1.994537USDC-eB481929.9998071929.9977791844.3645210.0464300.0926060.092606
1701411834604692317316873037158841057337-00.0020WETH-6Cc2/USDC-eB48-43.538655USDC-eB481850.0000001847.1801111844.3645210.0015270.0664660.066466
1701411834604692317316873037158841057292-00.0020WETH-6Cc2/USDC-eB48-6.141325USDC-eB481853.4088181851.7036241844.3645210.0039790.0244380.024438
\n", - "
" - ], - "text/plain": [ - " fee \\\n", - "exch cid \n", - "uniswap_v3 346 0.0005 \n", - " 7ed16708962e459abe5431a176b13aa0 0.0030 \n", - " 593 0.0100 \n", - " 00125d264f9d49369a467e7708cee9b5 0.0100 \n", - "uniswap_v2 50ac5ace09c1483987af46c60c551073 0.0030 \n", - " 255 0.0030 \n", - "sushiswap_v2 6c988ffdc9e74acd97ccfb16dd65c110 0.0030 \n", - " 803 0.0030 \n", - "carbon_v1 1701411834604692317316873037158841057353-0 0.0020 \n", - " 1701411834604692317316873037158841057296-0 0.0020 \n", - " 1701411834604692317316873037158841057337-0 0.0020 \n", - " 1701411834604692317316873037158841057292-0 0.0020 \n", - "\n", - " pair \\\n", - "exch cid \n", - "uniswap_v3 346 WETH-6Cc2/USDC-eB48 \n", - " 7ed16708962e459abe5431a176b13aa0 WETH-6Cc2/USDC-eB48 \n", - " 593 WETH-6Cc2/USDC-eB48 \n", - " 00125d264f9d49369a467e7708cee9b5 WETH-6Cc2/USDC-eB48 \n", - "uniswap_v2 50ac5ace09c1483987af46c60c551073 WETH-6Cc2/USDC-eB48 \n", - " 255 WETH-6Cc2/USDC-eB48 \n", - "sushiswap_v2 6c988ffdc9e74acd97ccfb16dd65c110 WETH-6Cc2/USDC-eB48 \n", - " 803 WETH-6Cc2/USDC-eB48 \n", - "carbon_v1 1701411834604692317316873037158841057353-0 WETH-6Cc2/USDC-eB48 \n", - " 1701411834604692317316873037158841057296-0 WETH-6Cc2/USDC-eB48 \n", - " 1701411834604692317316873037158841057337-0 WETH-6Cc2/USDC-eB48 \n", - " 1701411834604692317316873037158841057292-0 WETH-6Cc2/USDC-eB48 \n", - "\n", - " amt_tknq \\\n", - "exch cid \n", - "uniswap_v3 346 -404818.683174 \n", - " 7ed16708962e459abe5431a176b13aa0 219435.452300 \n", - " 593 35283.335544 \n", - " 00125d264f9d49369a467e7708cee9b5 14371.217737 \n", - "uniswap_v2 50ac5ace09c1483987af46c60c551073 34398.319085 \n", - " 255 35207.230349 \n", - "sushiswap_v2 6c988ffdc9e74acd97ccfb16dd65c110 48153.808649 \n", - " 803 24654.883463 \n", - "carbon_v1 1701411834604692317316873037158841057353-0 -7851.133636 \n", - " 1701411834604692317316873037158841057296-0 -1.994537 \n", - " 1701411834604692317316873037158841057337-0 -43.538655 \n", - " 1701411834604692317316873037158841057292-0 -6.141325 \n", - "\n", - " tknq \\\n", - "exch cid \n", - "uniswap_v3 346 USDC-eB48 \n", - " 7ed16708962e459abe5431a176b13aa0 USDC-eB48 \n", - " 593 USDC-eB48 \n", - " 00125d264f9d49369a467e7708cee9b5 USDC-eB48 \n", - "uniswap_v2 50ac5ace09c1483987af46c60c551073 USDC-eB48 \n", - " 255 USDC-eB48 \n", - "sushiswap_v2 6c988ffdc9e74acd97ccfb16dd65c110 USDC-eB48 \n", - " 803 USDC-eB48 \n", - "carbon_v1 1701411834604692317316873037158841057353-0 USDC-eB48 \n", - " 1701411834604692317316873037158841057296-0 USDC-eB48 \n", - " 1701411834604692317316873037158841057337-0 USDC-eB48 \n", - " 1701411834604692317316873037158841057292-0 USDC-eB48 \n", - "\n", - " margp0 \\\n", - "exch cid \n", - "uniswap_v3 346 1848.191535 \n", - " 7ed16708962e459abe5431a176b13aa0 1841.729378 \n", - " 593 1832.243200 \n", - " 00125d264f9d49369a467e7708cee9b5 1843.002859 \n", - "uniswap_v2 50ac5ace09c1483987af46c60c551073 1840.159506 \n", - " 255 1840.773969 \n", - "sushiswap_v2 6c988ffdc9e74acd97ccfb16dd65c110 1833.900701 \n", - " 803 1838.745520 \n", - "carbon_v1 1701411834604692317316873037158841057353-0 1854.000185 \n", - " 1701411834604692317316873037158841057296-0 1929.999807 \n", - " 1701411834604692317316873037158841057337-0 1850.000000 \n", - " 1701411834604692317316873037158841057292-0 1853.408818 \n", - "\n", - " effp \\\n", - "exch cid \n", - "uniswap_v3 346 1847.326513 \n", - " 7ed16708962e459abe5431a176b13aa0 1843.046478 \n", - " 593 1838.293870 \n", - " 00125d264f9d49369a467e7708cee9b5 1843.683564 \n", - "uniswap_v2 50ac5ace09c1483987af46c60c551073 1842.260814 \n", - " 255 1842.568370 \n", - "sushiswap_v2 6c988ffdc9e74acd97ccfb16dd65c110 1839.125169 \n", - " 803 1841.552877 \n", - "carbon_v1 1701411834604692317316873037158841057353-0 1854.000000 \n", - " 1701411834604692317316873037158841057296-0 1929.997779 \n", - " 1701411834604692317316873037158841057337-0 1847.180111 \n", - " 1701411834604692317316873037158841057292-0 1851.703624 \n", - "\n", - " margp \\\n", - "exch cid \n", - "uniswap_v3 346 1844.364521 \n", - " 7ed16708962e459abe5431a176b13aa0 1844.364521 \n", - " 593 1844.364521 \n", - " 00125d264f9d49369a467e7708cee9b5 1844.364521 \n", - "uniswap_v2 50ac5ace09c1483987af46c60c551073 1844.364521 \n", - " 255 1844.364521 \n", - "sushiswap_v2 6c988ffdc9e74acd97ccfb16dd65c110 1844.364521 \n", - " 803 1844.364521 \n", - "carbon_v1 1701411834604692317316873037158841057353-0 1844.364521 \n", - " 1701411834604692317316873037158841057296-0 1844.364521 \n", - " 1701411834604692317316873037158841057337-0 1844.364521 \n", - " 1701411834604692317316873037158841057292-0 1844.364521 \n", - "\n", - " gain_r gain_tknq \\\n", - "exch cid \n", - "uniswap_v3 346 0.001606 650.126368 \n", - " 7ed16708962e459abe5431a176b13aa0 0.000715 156.815646 \n", - " 593 0.003291 116.133668 \n", - " 00125d264f9d49369a467e7708cee9b5 0.000369 5.305987 \n", - "uniswap_v2 50ac5ace09c1483987af46c60c551073 0.001141 39.235185 \n", - " 255 0.000974 34.286868 \n", - "sushiswap_v2 6c988ffdc9e74acd97ccfb16dd65c110 0.002841 136.792236 \n", - " 803 0.001524 37.585162 \n", - "carbon_v1 1701411834604692317316873037158841057353-0 0.005224 41.016531 \n", - " 1701411834604692317316873037158841057296-0 0.046430 0.092606 \n", - " 1701411834604692317316873037158841057337-0 0.001527 0.066466 \n", - " 1701411834604692317316873037158841057292-0 0.003979 0.024438 \n", - "\n", - " gain_ttkn \n", - "exch cid \n", - "uniswap_v3 346 650.126368 \n", - " 7ed16708962e459abe5431a176b13aa0 156.815646 \n", - " 593 116.133668 \n", - " 00125d264f9d49369a467e7708cee9b5 5.305987 \n", - "uniswap_v2 50ac5ace09c1483987af46c60c551073 39.235185 \n", - " 255 34.286868 \n", - "sushiswap_v2 6c988ffdc9e74acd97ccfb16dd65c110 136.792236 \n", - " 803 37.585162 \n", - "carbon_v1 1701411834604692317316873037158841057353-0 41.016531 \n", - " 1701411834604692317316873037158841057296-0 0.092606 \n", - " 1701411834604692317316873037158841057337-0 0.066466 \n", - " 1701411834604692317316873037158841057292-0 0.024438 " - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = r.trade_instructions(ti_format=O.TIF_DFPG)\n", - "assert len(df) == 12\n", - "assert set(x[0] for x in tuple(df.index)) == {'carbon_v1', 'sushiswap_v2', 'uniswap_v2', 'uniswap_v3'}\n", - "assert max(df[\"margp\"]) == min(df[\"margp\"]) \n", - "assert tuple(df.index.names) == ('exch', 'cid')\n", - "assert tuple(df.columns) == (\n", - " 'fee',\n", - " 'pair',\n", - " 'amt_tknq',\n", - " 'tknq',\n", - " 'margp0',\n", - " 'effp',\n", - " 'margp',\n", - " 'gain_r',\n", - " 'gain_tknq',\n", - " 'gain_ttkn'\n", - ")\n", - "df" - ] - }, - { - "cell_type": "markdown", - "id": "1652b8f5", - "metadata": {}, - "source": [ - "## Analysis by pair" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "fd84fa4f-36b1-410a-ba75-192808ed6c3f", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.294226Z", - "start_time": "2023-07-31T12:44:08.161401Z" - } - }, - "outputs": [], - "source": [ - "# CCm1 = CAm.CC.copy()\n", - "# CCm1 += CPC.from_carbon(\n", - "# pair=f\"{T.WETH}/{T.USDC}\",\n", - "# yint = 1,\n", - "# y = 1,\n", - "# pa = 1500,\n", - "# pb = 1501,\n", - "# tkny = f\"{T.WETH}\",\n", - "# cid = \"test-1\",\n", - "# isdydx=False,\n", - "# params=dict(exchange=\"carbon_v1\"),\n", - "# )\n", - "# CAm1 = CPCAnalyzer(CCm1)\n", - "# CCm1.asdf().to_csv(\"NBTest_006-augmented.csv.gz\", compression = \"gzip\")" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "84750fca-1d91-4f77-bc1a-a361a1c8ae02", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.295116Z", - "start_time": "2023-07-31T12:44:08.180857Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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pricevlitmbsbsv
pairexchangecid0
0x0/WETHcarbon_v1132277-00.0000131.342084e+04bbuy-0x0 @ 0.00 WETH per 0x0
132277-10.0000153.597323e+02xssell-0x0 @ 0.00 WETH per 0x0
uniswap_v2551118da0.0000332.602200e+07xbsbuy-sell-0x0 @ 0.00 WETH per 0x0
ARB/MATICcarbon_v1806240-11.4285711.418060e+02bbuy-ARB @ 1.43 MATIC per ARB
806240-01.5070451.276054e+01ssell-ARB @ 1.51 MATIC per ARB
...........................
vBNT/BNTcarbon_v1748966-11.0000001.089256e+03ssell-vBNT @ 1.00 BNT per vBNT
748990-11.0500001.122591e+03ssell-vBNT @ 1.05 BNT per vBNT
748950-01.0638301.329046e+04ssell-vBNT @ 1.06 BNT per vBNT
748965-11.1000001.027046e+03ssell-vBNT @ 1.10 BNT per vBNT
vBNT/USDCcarbon_v1171896-10.3900005.000000e+03ssell-vBNT @ 0.39 USDC per vBNT
\n", - "

165 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " price vl itm b s \\\n", - "pair exchange cid0 \n", - "0x0/WETH carbon_v1 132277-0 0.000013 1.342084e+04 b \n", - " 132277-1 0.000015 3.597323e+02 x s \n", - " uniswap_v2 551118da 0.000033 2.602200e+07 x b s \n", - "ARB/MATIC carbon_v1 806240-1 1.428571 1.418060e+02 b \n", - " 806240-0 1.507045 1.276054e+01 s \n", - "... ... ... .. .. .. \n", - "vBNT/BNT carbon_v1 748966-1 1.000000 1.089256e+03 s \n", - " 748990-1 1.050000 1.122591e+03 s \n", - " 748950-0 1.063830 1.329046e+04 s \n", - " 748965-1 1.100000 1.027046e+03 s \n", - "vBNT/USDC carbon_v1 171896-1 0.390000 5.000000e+03 s \n", - "\n", - " bsv \n", - "pair exchange cid0 \n", - "0x0/WETH carbon_v1 132277-0 buy-0x0 @ 0.00 WETH per 0x0 \n", - " 132277-1 sell-0x0 @ 0.00 WETH per 0x0 \n", - " uniswap_v2 551118da buy-sell-0x0 @ 0.00 WETH per 0x0 \n", - "ARB/MATIC carbon_v1 806240-1 buy-ARB @ 1.43 MATIC per ARB \n", - " 806240-0 sell-ARB @ 1.51 MATIC per ARB \n", - "... ... \n", - "vBNT/BNT carbon_v1 748966-1 sell-vBNT @ 1.00 BNT per vBNT \n", - " 748990-1 sell-vBNT @ 1.05 BNT per vBNT \n", - " 748950-0 sell-vBNT @ 1.06 BNT per vBNT \n", - " 748965-1 sell-vBNT @ 1.10 BNT per vBNT \n", - "vBNT/USDC carbon_v1 171896-1 sell-vBNT @ 0.39 USDC per vBNT \n", - "\n", - "[165 rows x 6 columns]" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pricedf = CAm.pool_arbitrage_statistics()\n", - "assert len(pricedf)==165\n", - "pricedf" - ] - }, - { - "cell_type": "markdown", - "id": "c066c726-ee75-41e3-8b3f-3b43792c6352", - "metadata": {}, - "source": [ - "### WETH/USDC" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "67122692-198a-4706-9526-cba8b35c2fb4", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.297491Z", - "start_time": "2023-07-31T12:44:08.214814Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pair = WETH-6Cc2/USDC-eB48\n" - ] - } - ], - "source": [ - "pair = \"WETH-6Cc2/USDC-eB48\"\n", - "print(f\"Pair = {pair}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "fd022c7e-1c6a-4947-a156-a2ada671c8ef", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.298002Z", - "start_time": "2023-07-31T12:44:08.222881Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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pricevlitmbsbsv
exchangecid0
carbon_v1057306-01405.0001403.558719bbuy-WETH @ 1405.00 USDC per WETH
057334-01700.0001700.029412bbuy-WETH @ 1700.00 USDC per WETH
057331-01800.0000005.555556bbuy-WETH @ 1800.00 USDC per WETH
057339-01800.0000000.000556bbuy-WETH @ 1800.00 USDC per WETH
uniswap_v35931832.24320058.054109xbsbuy-sell-WETH @ 1832.24 USDC per WETH
sushiswap_v2dd65c1101833.90070118433.955884xbsbuy-sell-WETH @ 1833.90 USDC per WETH
8031838.74552017564.479610xbsbuy-sell-WETH @ 1838.75 USDC per WETH
uniswap_v20c5510731840.15950632739.920709xbsbuy-sell-WETH @ 1840.16 USDC per WETH
2551840.77396939241.200664xbsbuy-sell-WETH @ 1840.77 USDC per WETH
uniswap_v376b13aa01841.729378499.329774xbsbuy-sell-WETH @ 1841.73 USDC per WETH
08cee9b51843.002859210.541672xbsbuy-sell-WETH @ 1843.00 USDC per WETH
3461848.191535233.930315xbsbuy-sell-WETH @ 1848.19 USDC per WETH
carbon_v1057337-01850.0000001.081081bbuy-WETH @ 1850.00 USDC per WETH
057292-01853.4088180.003314xbbuy-WETH @ 1853.41 USDC per WETH
057353-01854.0001854.234699xbbuy-WETH @ 1854.00 USDC per WETH
057296-01929.9998070.001033xbbuy-WETH @ 1930.00 USDC per WETH
057299-11940.0000000.026117ssell-WETH @ 1940.00 USDC per WETH
057296-11949.99980510.460391ssell-WETH @ 1950.00 USDC per WETH
057343-11989.9998011.000000ssell-WETH @ 1990.00 USDC per WETH
057334-11999.9998000.040000ssell-WETH @ 2000.00 USDC per WETH
057292-12000.0000000.016387ssell-WETH @ 2000.00 USDC per WETH
057353-12047.9997954.000000ssell-WETH @ 2048.00 USDC per WETH
057285-12099.9997900.006040ssell-WETH @ 2100.00 USDC per WETH
057315-12300.0000000.487950ssell-WETH @ 2300.00 USDC per WETH
\n", - "
" - ], - "text/plain": [ - " price vl itm b s \\\n", - "exchange cid0 \n", - "carbon_v1 057306-0 1405.000140 3.558719 b \n", - " 057334-0 1700.000170 0.029412 b \n", - " 057331-0 1800.000000 5.555556 b \n", - " 057339-0 1800.000000 0.000556 b \n", - "uniswap_v3 593 1832.243200 58.054109 x b s \n", - "sushiswap_v2 dd65c110 1833.900701 18433.955884 x b s \n", - " 803 1838.745520 17564.479610 x b s \n", - "uniswap_v2 0c551073 1840.159506 32739.920709 x b s \n", - " 255 1840.773969 39241.200664 x b s \n", - "uniswap_v3 76b13aa0 1841.729378 499.329774 x b s \n", - " 08cee9b5 1843.002859 210.541672 x b s \n", - " 346 1848.191535 233.930315 x b s \n", - "carbon_v1 057337-0 1850.000000 1.081081 b \n", - " 057292-0 1853.408818 0.003314 x b \n", - " 057353-0 1854.000185 4.234699 x b \n", - " 057296-0 1929.999807 0.001033 x b \n", - " 057299-1 1940.000000 0.026117 s \n", - " 057296-1 1949.999805 10.460391 s \n", - " 057343-1 1989.999801 1.000000 s \n", - " 057334-1 1999.999800 0.040000 s \n", - " 057292-1 2000.000000 0.016387 s \n", - " 057353-1 2047.999795 4.000000 s \n", - " 057285-1 2099.999790 0.006040 s \n", - " 057315-1 2300.000000 0.487950 s \n", - "\n", - " bsv \n", - "exchange cid0 \n", - "carbon_v1 057306-0 buy-WETH @ 1405.00 USDC per WETH \n", - " 057334-0 buy-WETH @ 1700.00 USDC per WETH \n", - " 057331-0 buy-WETH @ 1800.00 USDC per WETH \n", - " 057339-0 buy-WETH @ 1800.00 USDC per WETH \n", - "uniswap_v3 593 buy-sell-WETH @ 1832.24 USDC per WETH \n", - "sushiswap_v2 dd65c110 buy-sell-WETH @ 1833.90 USDC per WETH \n", - " 803 buy-sell-WETH @ 1838.75 USDC per WETH \n", - "uniswap_v2 0c551073 buy-sell-WETH @ 1840.16 USDC per WETH \n", - " 255 buy-sell-WETH @ 1840.77 USDC per WETH \n", - "uniswap_v3 76b13aa0 buy-sell-WETH @ 1841.73 USDC per WETH \n", - " 08cee9b5 buy-sell-WETH @ 1843.00 USDC per WETH \n", - " 346 buy-sell-WETH @ 1848.19 USDC per WETH \n", - "carbon_v1 057337-0 buy-WETH @ 1850.00 USDC per WETH \n", - " 057292-0 buy-WETH @ 1853.41 USDC per WETH \n", - " 057353-0 buy-WETH @ 1854.00 USDC per WETH \n", - " 057296-0 buy-WETH @ 1930.00 USDC per WETH \n", - " 057299-1 sell-WETH @ 1940.00 USDC per WETH \n", - " 057296-1 sell-WETH @ 1950.00 USDC per WETH \n", - " 057343-1 sell-WETH @ 1990.00 USDC per WETH \n", - " 057334-1 sell-WETH @ 2000.00 USDC per WETH \n", - " 057292-1 sell-WETH @ 2000.00 USDC per WETH \n", - " 057353-1 sell-WETH @ 2048.00 USDC per WETH \n", - " 057285-1 sell-WETH @ 2100.00 USDC per WETH \n", - " 057315-1 sell-WETH @ 2300.00 USDC per WETH " - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pricedf.loc[Pair.n(pair)]\n", - "assert len(df)==24\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "ec801111-63d8-4c04-87ee-8d7c43ade0eb", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.350885Z", - "start_time": "2023-07-31T12:44:08.237752Z" - } - }, - "outputs": [], - "source": [ - "pi = CAm.pair_data(pair)\n", - "O = MargPOptimizer(pi.CC)" - ] - }, - { - "cell_type": "markdown", - "id": "0d26483f-54fc-4a5f-8745-d480a39f1af2", - "metadata": {}, - "source": [ - "#### Target token = base token" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "364d7536-a0f1-49d1-9189-5fb994febacf", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.351696Z", - "start_time": "2023-07-31T12:44:08.257637Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Target token = WETH-6Cc2\n" - ] - }, - { - "data": { - "text/html": [ - "
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USDC-eB48WETH-6Cc2
6c988ffdc9e74acd97ccfb16dd65c11048199.041434-26.207522
7ed16708962e459abe5431a176b13aa0220254.817834-119.505521
59335311.940061-19.209032
25535303.699709-19.159998
80324698.039642-13.411493
50ac5ace09c1483987af46c60c55107334478.792464-18.715428
346-404818.683174219.137592
1701411834604692317316873037158841057353-0-7851.1336364.234700
1701411834604692317316873037158841057296-0-1.9945370.001033
00125d264f9d49369a467e7708cee9b514475.083981-7.851155
1701411834604692317316873037158841057292-0-6.1413250.003317
1701411834604692317316873037158841057337-0-43.4625510.023529
PRICE0.0005421.000000
AMMIn412721.415124223.400171
AMMOut-412721.415223-224.060149
TOTAL NET-0.000100-0.659978
\n", - "
" - ], - "text/plain": [ - " USDC-eB48 WETH-6Cc2\n", - "6c988ffdc9e74acd97ccfb16dd65c110 48199.041434 -26.207522\n", - "7ed16708962e459abe5431a176b13aa0 220254.817834 -119.505521\n", - "593 35311.940061 -19.209032\n", - "255 35303.699709 -19.159998\n", - "803 24698.039642 -13.411493\n", - "50ac5ace09c1483987af46c60c551073 34478.792464 -18.715428\n", - "346 -404818.683174 219.137592\n", - "1701411834604692317316873037158841057353-0 -7851.133636 4.234700\n", - "1701411834604692317316873037158841057296-0 -1.994537 0.001033\n", - "00125d264f9d49369a467e7708cee9b5 14475.083981 -7.851155\n", - "1701411834604692317316873037158841057292-0 -6.141325 0.003317\n", - "1701411834604692317316873037158841057337-0 -43.462551 0.023529\n", - "PRICE 0.000542 1.000000\n", - "AMMIn 412721.415124 223.400171\n", - "AMMOut -412721.415223 -224.060149\n", - "TOTAL NET -0.000100 -0.659978" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "targettkn = pair.split(\"/\")[0]\n", - "print(f\"Target token = {targettkn}\")\n", - "r = O.margp_optimizer(targettkn, params=dict(verbose=False, debug=False))\n", - "r.trade_instructions(ti_format=O.TIF_DFAGGR)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "e6ec3cb6-214d-4924-ab74-3ba204f20f42", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.383888Z", - "start_time": "2023-07-31T12:44:08.265352Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total gain: 0.6601 WETH-6Cc2\n" - ] - }, - { - "data": { - "text/html": [ - "
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feepairamt_tknqtknqmargp0effpmargpgain_rgain_tknqgain_ttkn
exchcid
uniswap_v33460.0005USDC/WETH219.137592WETH-6Cc20.0005410.0005410.0005420.0015980.3501960.350196
a176b13aa00.0030USDC/WETH-119.505521WETH-6Cc20.0005430.0005430.0005420.0007180.0857830.085783
5930.0100USDC/WETH-19.209032WETH-6Cc20.0005460.0005440.0005420.0033050.0634860.063486
7708cee9b50.0100USDC/WETH-7.851155WETH-6Cc20.0005430.0005420.0005420.0003720.0029210.002921
uniswap_v2c60c5510730.0030USDC/WETH-18.715428WETH-6Cc20.0005430.0005430.0005420.0011450.0214210.021421
2550.0030USDC/WETH-19.159998WETH-6Cc20.0005430.0005430.0005420.0009770.0187280.018728
sushiswap_v216dd65c1100.0030USDC/WETH-26.207522WETH-6Cc20.0005450.0005440.0005420.0028520.0747310.074731
8030.0030USDC/WETH-13.411493WETH-6Cc20.0005440.0005430.0005420.0015290.0205120.020512
carbon_v141057353-00.0020WETH/USDC-7851.133636USDC-eB481854.0001851854.0000001844.3743640.00521940.9744120.022216
41057296-00.0020WETH/USDC-1.994537USDC-eB481929.9998071929.9977791844.3743640.0464240.0925950.000050
41057337-00.0020WETH/USDC-43.462551USDC-eB481850.0000001847.1850401844.3743640.0015240.0662330.000036
41057292-00.0020WETH/USDC-6.141325USDC-eB481853.4088181851.7036241844.3743640.0039740.0244050.000013
\n", - "
" - ], - "text/plain": [ - " fee pair amt_tknq tknq \\\n", - "exch cid \n", - "uniswap_v3 346 0.0005 USDC/WETH 219.137592 WETH-6Cc2 \n", - " a176b13aa0 0.0030 USDC/WETH -119.505521 WETH-6Cc2 \n", - " 593 0.0100 USDC/WETH -19.209032 WETH-6Cc2 \n", - " 7708cee9b5 0.0100 USDC/WETH -7.851155 WETH-6Cc2 \n", - "uniswap_v2 c60c551073 0.0030 USDC/WETH -18.715428 WETH-6Cc2 \n", - " 255 0.0030 USDC/WETH -19.159998 WETH-6Cc2 \n", - "sushiswap_v2 16dd65c110 0.0030 USDC/WETH -26.207522 WETH-6Cc2 \n", - " 803 0.0030 USDC/WETH -13.411493 WETH-6Cc2 \n", - "carbon_v1 41057353-0 0.0020 WETH/USDC -7851.133636 USDC-eB48 \n", - " 41057296-0 0.0020 WETH/USDC -1.994537 USDC-eB48 \n", - " 41057337-0 0.0020 WETH/USDC -43.462551 USDC-eB48 \n", - " 41057292-0 0.0020 WETH/USDC -6.141325 USDC-eB48 \n", - "\n", - " margp0 effp margp gain_r \\\n", - "exch cid \n", - "uniswap_v3 346 0.000541 0.000541 0.000542 0.001598 \n", - " a176b13aa0 0.000543 0.000543 0.000542 0.000718 \n", - " 593 0.000546 0.000544 0.000542 0.003305 \n", - " 7708cee9b5 0.000543 0.000542 0.000542 0.000372 \n", - "uniswap_v2 c60c551073 0.000543 0.000543 0.000542 0.001145 \n", - " 255 0.000543 0.000543 0.000542 0.000977 \n", - "sushiswap_v2 16dd65c110 0.000545 0.000544 0.000542 0.002852 \n", - " 803 0.000544 0.000543 0.000542 0.001529 \n", - "carbon_v1 41057353-0 1854.000185 1854.000000 1844.374364 0.005219 \n", - " 41057296-0 1929.999807 1929.997779 1844.374364 0.046424 \n", - " 41057337-0 1850.000000 1847.185040 1844.374364 0.001524 \n", - " 41057292-0 1853.408818 1851.703624 1844.374364 0.003974 \n", - "\n", - " gain_tknq gain_ttkn \n", - "exch cid \n", - "uniswap_v3 346 0.350196 0.350196 \n", - " a176b13aa0 0.085783 0.085783 \n", - " 593 0.063486 0.063486 \n", - " 7708cee9b5 0.002921 0.002921 \n", - "uniswap_v2 c60c551073 0.021421 0.021421 \n", - " 255 0.018728 0.018728 \n", - "sushiswap_v2 16dd65c110 0.074731 0.074731 \n", - " 803 0.020512 0.020512 \n", - "carbon_v1 41057353-0 40.974412 0.022216 \n", - " 41057296-0 0.092595 0.000050 \n", - " 41057337-0 0.066233 0.000036 \n", - " 41057292-0 0.024405 0.000013 " - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dfti1 = r.trade_instructions(ti_format=O.TIF_DFPG8)\n", - "print(f\"Total gain: {sum(dfti1['gain_ttkn']):.4f} {targettkn}\")\n", - "dfti1" - ] - }, - { - "cell_type": "markdown", - "id": "295d2c70-e97f-4668-ae36-8b192e8e731e", - "metadata": {}, - "source": [ - "#### Target token = quote token" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "5aba1b68-20ec-41ee-b373-12d37d586013", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.524307Z", - "start_time": "2023-07-31T12:44:08.285768Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Target token = USDC-eB48\n" - ] - }, - { - "data": { - "text/html": [ - "
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USDC-eB48WETH-6Cc2
6c988ffdc9e74acd97ccfb16dd65c11048153.808651-2.618300e+01
7ed16708962e459abe5431a176b13aa0219435.452342-1.190613e+02
59335283.335545-1.919352e+01
25535207.230354-1.910769e+01
80324654.883465-1.338809e+01
50ac5ace09c1483987af46c60c55107334398.319089-1.867180e+01
346-404818.6831742.191376e+02
1701411834604692317316873037158841057353-0-7851.1336364.234700e+00
1701411834604692317316873037158841057296-0-1.9945371.033440e-03
00125d264f9d49369a467e7708cee9b514371.217743-7.794840e+00
1701411834604692317316873037158841057292-0-6.1413253.316581e-03
1701411834604692317316873037158841057337-0-43.5386552.357034e-02
PRICE1.0000001.844365e+03
AMMIn411504.2471892.234002e+02
AMMOut-412721.491327-2.234002e+02
TOTAL NET-1217.244138-3.372589e-08
\n", - "
" - ], - "text/plain": [ - " USDC-eB48 WETH-6Cc2\n", - "6c988ffdc9e74acd97ccfb16dd65c110 48153.808651 -2.618300e+01\n", - "7ed16708962e459abe5431a176b13aa0 219435.452342 -1.190613e+02\n", - "593 35283.335545 -1.919352e+01\n", - "255 35207.230354 -1.910769e+01\n", - "803 24654.883465 -1.338809e+01\n", - "50ac5ace09c1483987af46c60c551073 34398.319089 -1.867180e+01\n", - "346 -404818.683174 2.191376e+02\n", - "1701411834604692317316873037158841057353-0 -7851.133636 4.234700e+00\n", - "1701411834604692317316873037158841057296-0 -1.994537 1.033440e-03\n", - "00125d264f9d49369a467e7708cee9b5 14371.217743 -7.794840e+00\n", - "1701411834604692317316873037158841057292-0 -6.141325 3.316581e-03\n", - "1701411834604692317316873037158841057337-0 -43.538655 2.357034e-02\n", - "PRICE 1.000000 1.844365e+03\n", - "AMMIn 411504.247189 2.234002e+02\n", - "AMMOut -412721.491327 -2.234002e+02\n", - "TOTAL NET -1217.244138 -3.372589e-08" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "targettkn = pair.split(\"/\")[1]\n", - "print(f\"Target token = {targettkn}\")\n", - "r = O.margp_optimizer(targettkn, params=dict(verbose=False, debug=False))\n", - "r.trade_instructions(ti_format=O.TIF_DFAGGR)" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "bc936f2b", - "metadata": { - "ExecuteTime": { - "end_time": "2023-07-31T12:44:08.621061Z", - "start_time": "2023-07-31T12:44:08.294380Z" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total gain: 1217.4465 USDC-eB48\n" - ] - }, - { - "data": { - "text/html": [ - "
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feepairamt_tknqtknqmargp0effpmargpgain_rgain_tknqgain_ttkn
exchcid
uniswap_v33460.0005USDC/WETH219.137592WETH-6Cc20.0005410.0005410.0005420.0016030.351364648.043221
a176b13aa00.0030USDC/WETH-119.061269WETH-6Cc20.0005430.0005430.0005420.0007150.085146157.040018
5930.0100USDC/WETH-19.193523WETH-6Cc20.0005460.0005440.0005420.0033020.063383116.901957
7708cee9b50.0100USDC/WETH-7.794840WETH-6Cc20.0005430.0005420.0005420.0003690.0028795.309908
uniswap_v2c60c5510730.0030USDC/WETH-18.671797WETH-6Cc20.0005430.0005430.0005420.0011420.02132239.324842
2550.0030USDC/WETH-19.107693WETH-6Cc20.0005430.0005430.0005420.0009750.01862634.353747
sushiswap_v216dd65c1100.0030USDC/WETH-26.182997WETH-6Cc20.0005450.0005440.0005420.0028490.074591137.572742
8030.0030USDC/WETH-13.388094WETH-6Cc20.0005440.0005430.0005420.0015270.02044137.700018
carbon_v141057353-00.0020WETH/USDC-7851.133636USDC-eB481854.0001851854.0000001844.3645210.00522441.01653141.016531
41057296-00.0020WETH/USDC-1.994537USDC-eB481929.9998071929.9977791844.3645210.0464300.0926060.092606
41057337-00.0020WETH/USDC-43.538655USDC-eB481850.0000001847.1801111844.3645210.0015270.0664660.066466
41057292-00.0020WETH/USDC-6.141325USDC-eB481853.4088181851.7036241844.3645210.0039790.0244380.024438
\n", - "
" - ], - "text/plain": [ - " fee pair amt_tknq tknq \\\n", - "exch cid \n", - "uniswap_v3 346 0.0005 USDC/WETH 219.137592 WETH-6Cc2 \n", - " a176b13aa0 0.0030 USDC/WETH -119.061269 WETH-6Cc2 \n", - " 593 0.0100 USDC/WETH -19.193523 WETH-6Cc2 \n", - " 7708cee9b5 0.0100 USDC/WETH -7.794840 WETH-6Cc2 \n", - "uniswap_v2 c60c551073 0.0030 USDC/WETH -18.671797 WETH-6Cc2 \n", - " 255 0.0030 USDC/WETH -19.107693 WETH-6Cc2 \n", - "sushiswap_v2 16dd65c110 0.0030 USDC/WETH -26.182997 WETH-6Cc2 \n", - " 803 0.0030 USDC/WETH -13.388094 WETH-6Cc2 \n", - "carbon_v1 41057353-0 0.0020 WETH/USDC -7851.133636 USDC-eB48 \n", - " 41057296-0 0.0020 WETH/USDC -1.994537 USDC-eB48 \n", - " 41057337-0 0.0020 WETH/USDC -43.538655 USDC-eB48 \n", - " 41057292-0 0.0020 WETH/USDC -6.141325 USDC-eB48 \n", - "\n", - " margp0 effp margp gain_r \\\n", - "exch cid \n", - "uniswap_v3 346 0.000541 0.000541 0.000542 0.001603 \n", - " a176b13aa0 0.000543 0.000543 0.000542 0.000715 \n", - " 593 0.000546 0.000544 0.000542 0.003302 \n", - " 7708cee9b5 0.000543 0.000542 0.000542 0.000369 \n", - "uniswap_v2 c60c551073 0.000543 0.000543 0.000542 0.001142 \n", - " 255 0.000543 0.000543 0.000542 0.000975 \n", - "sushiswap_v2 16dd65c110 0.000545 0.000544 0.000542 0.002849 \n", - " 803 0.000544 0.000543 0.000542 0.001527 \n", - "carbon_v1 41057353-0 1854.000185 1854.000000 1844.364521 0.005224 \n", - " 41057296-0 1929.999807 1929.997779 1844.364521 0.046430 \n", - " 41057337-0 1850.000000 1847.180111 1844.364521 0.001527 \n", - " 41057292-0 1853.408818 1851.703624 1844.364521 0.003979 \n", - "\n", - " gain_tknq gain_ttkn \n", - "exch cid \n", - "uniswap_v3 346 0.351364 648.043221 \n", - " a176b13aa0 0.085146 157.040018 \n", - " 593 0.063383 116.901957 \n", - " 7708cee9b5 0.002879 5.309908 \n", - "uniswap_v2 c60c551073 0.021322 39.324842 \n", - " 255 0.018626 34.353747 \n", - "sushiswap_v2 16dd65c110 0.074591 137.572742 \n", - " 803 0.020441 37.700018 \n", - "carbon_v1 41057353-0 41.016531 41.016531 \n", - " 41057296-0 0.092606 0.092606 \n", - " 41057337-0 0.066466 0.066466 \n", - " 41057292-0 0.024438 0.024438 " - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dfti2 = r.trade_instructions(ti_format=O.TIF_DFPG8)\n", - "print(f\"Total gain: {sum(dfti2['gain_ttkn']):.4f}\", targettkn)\n", - "dfti2" - ] - } - ], - "metadata": { - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/NBTest/NBTest_900_OptimizerDetailedSlow.py b/resources/NBTest/NBTest_900_OptimizerDetailedSlow.py deleted file mode 100644 index 8c18ca962..000000000 --- a/resources/NBTest/NBTest_900_OptimizerDetailedSlow.py +++ /dev/null @@ -1,788 +0,0 @@ -# -*- coding: utf-8 -*- -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -try: - from fastlane_bot import Bot, Config, ConfigDB, ConfigNetwork, ConfigProvider - from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, CPCContainer, Pair - from fastlane_bot.tools.analyzer import CPCAnalyzer - from fastlane_bot.tools.optimizer import PairOptimizer, MargPOptimizer, ConvexOptimizer - from fastlane_bot.tools.optimizer import OptimizerBase, CPCArbOptimizer - from fastlane_bot.tools.arbgraphs import ArbGraph - from fastlane_bot.tools.cpcbase import AttrDict - from fastlane_bot.testing import * - -except: - from tools.cpc import ConstantProductCurve as CPC, CPCContainer, Pair - from tools.analyzer import CPCAnalyzer - from tools.optimizer import PairOptimizer, MargPOptimizer, ConvexOptimizer - from tools.optimizer import OptimizerBase, CPCArbOptimizer - from tools.arbgraphs import ArbGraph - from tools.cpcbase import AttrDict - from tools.testing import * - -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPCAnalyzer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(OptimizerBase)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPCArbOptimizer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(PairOptimizer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(MargPOptimizer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(ConvexOptimizer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(ArbGraph)) -#print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Bot)) -import itertools as it -import collections as cl -#plt.style.use('seaborn-dark') -plt.rcParams['figure.figsize'] = [12,6] -# from fastlane_bot import __VERSION__ -# require("3.0", __VERSION__) -# - - -T = AttrDict( - NATIVE_ETH="ETH-EEeE", - AAVE="AAVE-DaE9", - WETH="WETH-6Cc2", - ETH="WETH-6Cc2", - WBTC="WBTC-C599", - BTC="WBTC-C599", - USDC="USDC-eB48", - USDT="USDT-1ec7", - DAI="DAI-1d0F", - LINK="LINK-86CA", - MKR="MKR-79A2", - BNT="BNT-FF1C", - UNI="UNI-F984", - SUSHI="SUSHI-0fE2", - CRV="CRV-cd52", - FRAX="FRAX-b99e", - HEX="HEX-eb39", - MATIC="MATIC-eBB0", - HDRN="HDRN-5e06", - SHIB="SHIB-C4cE", - ICHI="ICHI-C4d6", - OCTO="OCTO-2BA3", - ECO="ECO-5727", -) - -# # Mostly Optimizer Tests [NB006] - -# + -# bot = Bot() -# CCm = bot.get_curves() -try: - CCm = CPCContainer.from_df(pd.read_csv("_data/NBTest_006.csv.gz")) -except: - CCm = CPCContainer.from_df(pd.read_csv("fastlane_bot/tests/_data/NBTest_006.csv.gz")) - -CCu3 = CCm.byparams(exchange="uniswap_v3") -CCu2 = CCm.byparams(exchange="uniswap_v2") -CCs2 = CCm.byparams(exchange="sushiswap_v2") -CCc1 = CCm.byparams(exchange="carbon_v1") -tc_u3 = CCu3.token_count(asdict=True) -tc_u2 = CCu2.token_count(asdict=True) -tc_s2 = CCs2.token_count(asdict=True) -tc_c1 = CCc1.token_count(asdict=True) -CAm = CPCAnalyzer(CCm) -#CCm.asdf().to_csv("A011-test.csv.gz", compression = "gzip") -# - - -CA = CAm -pairs0 = CA.CC.pairs(standardize=False) -pairs = CA.pairs() -pairsc = CA.pairsc() -tokens = CA.tokens() - -# ## Market structure analysis [NOTEST] - -print(f"Total pairs: {len(pairs0):4}") -print(f"Primary pairs: {len(pairs):4}") -print(f"...carbon: {len(pairsc):4}") -print(f"Tokens: {len(CA.tokens()):4}") -print(f"Curves: {len(CCm):4}") - -CA.count_by_pairs() - -CA.count_by_pairs(minn=2) - -# ### All crosses - -CCx = CCm.bypairs( - CCm.filter_pairs(notin=f"{T.ETH},{T.USDC},{T.USDT},{T.BNT},{T.DAI},{T.WBTC}") -) -len(CCx), CCx.token_count()[:10] - -AGx=ArbGraph.from_cc(CCx) -AGx.plot(labels=False, node_size=50, node_color="#fcc")._ - -# ### Biggest crosses (HEX, UNI, ICHI, FRAX) - -CCx2 = CCx.bypairs( - CCx.filter_pairs(onein=f"{T.HEX}, {T.UNI}, {T.ICHI}, {T.FRAX}") -) -ArbGraph.from_cc(CCx2).plot() -len(CCx2) - -# ### Carbon - -ArbGraph.from_cc(CCc1).plot()._ - -len(CCc1), len(CCc1.tokens()) - -CCc1.token_count() - - -len(CCc1.pairs()), CCc1.pairs() - -# ### Token subsets - -O = MargPOptimizer(CCm.bypairs( - CCm.filter_pairs(bothin=f"{T.ETH},{T.USDC},{T.USDT},{T.BNT},{T.DAI},{T.WBTC}") -)) -r = O.margp_optimizer(f"{T.USDC}", params=dict(verbose=False, debug=False)) -r.trade_instructions(ti_format=O.TIF_DFAGGR).fillna("") - -# + -#r.trade_instructions(ti_format=O.TIF_DFAGGR).fillna("").to_excel("ti.xlsx") -# - - -ArbGraph.from_r(r).plot()._ - -# + -#O.CC.plot() -# - - -# ## ABC Tests - -assert raises(OptimizerBase).startswith("Can't instantiate abstract class") -assert raises(OptimizerBase.OptimizerResult).startswith("Can't instantiate abstract class") - -assert raises(CPCArbOptimizer).startswith("Can't instantiate abstract class") -assert raises(CPCArbOptimizer.OptimizerResult).startswith("Can't instantiate abstract class") - -assert not raises(MargPOptimizer, CCm) -assert not raises(PairOptimizer, CCm) -assert not raises(ConvexOptimizer, CCm) - -assert MargPOptimizer(CCm).kind == "margp" -assert PairOptimizer(CCm).kind == "pair" -assert ConvexOptimizer(CCm).kind == "convex" - -CPCArbOptimizer.MargpOptimizerResult(None, time=0,errormsg="err", optimizer=None) - -# ## General and Specific Tests - -CA = CAm - -# ### General tests - -# #### General data integrity (should ALWAYS hold) - -assert len(pairs0) > 2500 -assert len(pairs) > 2500 -assert len(pairs0) > len(pairs) -assert len(pairsc) > 10 -assert len(CCm.tokens()) > 2000 -assert len(CCm)>4000 -assert len(CCm.filter_pairs(onein=f"{T.ETH}")) > 1900 # ETH pairs -assert len(CCm.filter_pairs(onein=f"{T.USDC}")) > 300 # USDC pairs -assert len(CCm.filter_pairs(onein=f"{T.USDT}")) > 190 # USDT pairs -assert len(CCm.filter_pairs(onein=f"{T.DAI}")) > 50 # DAI pairs -assert len(CCm.filter_pairs(onein=f"{T.WBTC}")) > 30 # WBTC pairs - -xis0 = {c.cid: (c.x, c.y) for c in CCm if c.x==0} -yis0 = {c.cid: (c.x, c.y) for c in CCm if c.y==0} -assert len(xis0) == 0 # set loglevel debug to see removal of curves -assert len(yis0) == 0 - -# #### Data integrity - -assert len(CCm) == 4155 -assert len(CCu3) == 1411 -assert len(CCu2) == 2177 -assert len(CCs2) == 236 -assert len(CCm.tokens()) == 2233 -assert len(CCm.pairs()) == 2834 -assert len(CCm.pairs(standardize=False)) == 2864 - - -assert CA.pairs() == CCm.pairs(standardize=True) -assert CA.pairsc() == {c.pairo.primary for c in CCm if c.P("exchange")=="carbon_v1"} -assert CA.tokens() == CCm.tokens() - -# #### prices - -r1 = CCc1.prices(result=CCc1.PR_TUPLE) -r2 = CCc1.prices(result=CCc1.PR_TUPLE, primary=False) -r3 = CCc1.prices(result=CCc1.PR_TUPLE, primary=False, inclpair=False) -assert isinstance(r1, tuple) -assert isinstance(r2, tuple) -assert isinstance(r3, tuple) -assert len(r1) == len(r2) -assert len(r1) == len(r3) -assert len(r1[0]) == 3 -assert isinstance(r1[0][0], str) -assert isinstance(r1[0][1], float) -assert len(r1[0][2].split("/"))==2 - -r2[:2] - -r3[:2] - -r1a = CCc1.prices(result=CCc1.PR_DICT) -r2a = CCc1.prices(result=CCc1.PR_DICT, primary=False) -r3a = CCc1.prices(result=CCc1.PR_DICT, primary=False, inclpair=False) -assert isinstance(r1a, dict) -assert isinstance(r2a, dict) -assert isinstance(r3a, dict) -assert len(r1a) == len(r1) -assert len(r1a) == len(r2a) -assert len(r1a) == len(r3a) -assert list(r1a.keys()) == list(x[0] for x in r1) -assert r1a.keys() == r2a.keys() -assert r1a.keys() == r3a.keys() -assert set(len(x) for x in r1a.values()) == {2}, "all records must be of of length 2" -assert set(type(x[0]) for x in r1a.values()) == {float}, "all records must have first type float" -assert set(type(x[1]) for x in r1a.values()) == {str}, "all records must have second type str" -assert tuple(r3a.values()) == r3 - -df = CCc1.prices(result=CCc1.PR_DF, primary=False) -assert len(df) == len(r1) -assert tuple(df.index) == tuple(x[0] for x in r1) -assert tuple(df["price"]) == r3 -df - -# #### more prices - -CCt = CCm.bypairs(f"{T.USDC}/{T.ETH}") - -r = CCt.prices(result=CCt.PR_TUPLE) -assert isinstance(r, tuple) -assert len(r) == len(CCt) -assert r[0] == ('6c988ffdc9e74acd97ccfb16dd65c110', 1833.9007005259564, 'WETH-6Cc2/USDC-eB48') -assert CCt.prices() == CCt.prices(result=CCt.PR_DICT) -r = CCt.prices(result=CCt.PR_DICT) -assert len(r) == len(CCt) -assert isinstance(r, dict) -assert r['6c988ffdc9e74acd97ccfb16dd65c110'] == (1833.9007005259564, 'WETH-6Cc2/USDC-eB48') -df = CCt.prices(result=CCt.PR_DF) -assert len(df) == len(CCt) -assert tuple(df.loc["1701411834604692317316873037158841057339-0"]) == (1799.9999997028303, 'WETH-6Cc2/USDC-eB48') - -# #### price_ranges - -CCt = CCm.bypairs(f"{T.USDC}/{T.ETH}") -CAt = CPCAnalyzer(CCt) - -r = CAt.price_ranges(result=CAt.PR_TUPLE) -assert len(r) == len(CCt) -assert r[0] == ( - 'WETH/USDC', # pair - '16dd65c110', # cid - 'sushiswap_v2', # exchange - 'b', # buy - 's', # sell - 0, # min_primary - None, # max_primary - 1833.9007005259564 # pp -) -assert r[1] == ( - 'WETH/USDC', - '41057334-0', - 'carbon_v1', - 'b', - '', - 1699.999829864358, - 1700.000169864341, - 1700.000169864341 -) -r = CAt.price_ranges(result=CAt.PR_TUPLE, short=False) -assert r[0] == ( - 'WETH-6Cc2/USDC-eB48', - '6c988ffdc9e74acd97ccfb16dd65c110', - 'sushiswap_v2', - 'b', - 's', - 0, - None, - 1833.9007005259564 -) -r = CAt.price_ranges(result=CAt.PR_DICT) -assert len(r) == len(CCt) -assert r['6c988ffdc9e74acd97ccfb16dd65c110'] == ( - 'WETH/USDC', - '16dd65c110', - 'sushiswap_v2', - 'b', - 's', - 0, - None, - 1833.9007005259564 -) -df = CAt.price_ranges(result=CAt.PR_DF) -assert len(df) == len(CCt) -assert tuple(df.index.names) == ('pair', 'exch', 'cid') -assert tuple(df.columns) == ('b', 's', 'p_min', 'p_max', 'p_marg') -assert set(df["p_marg"]) == set(x[-1] for x in CAt.price_ranges(result=CCt.PR_TUPLE)) -for p1, p2 in zip(df["p_marg"], df["p_marg"][1:]): - assert p2 >= p1 -df - -# #### count_by_pairs - -assert len(CA.count_by_pairs()) == len(CA.pairs()) -assert sum(CA.count_by_pairs()["count"])==len(CA.CC) -assert np.all(CA.count_by_pairs() == CA.count_by_pairs(asdf=True)) -assert len(CA.count_by_pairs()) == len(CA.count_by_pairs(asdf=False)) -assert type(CA.count_by_pairs()).__name__ == "DataFrame" -assert type(CA.count_by_pairs(asdf=False)).__name__ == "list" -assert type(CA.count_by_pairs(asdf=False)[0]).__name__ == "tuple" -for i in range(10): - assert len(CA.count_by_pairs(minn=i)) >= len(CA.count_by_pairs(minn=i)), f"failed {i}" - -# #### count_by_tokens - -r = CA.count_by_tokens() -assert len(r) == len(CA.tokens()) -assert sum(r["total"]) == 2*len(CA.CC) -assert tuple(r["total"]) == tuple(x[1] for x in CA.CC.token_count()) -for ix, row in r[:10].iterrows(): - assert row[0] >= sum(row[1:]), f"failed at {ix} {tuple(row)}" -CA.count_by_tokens() - -# #### pool_arbitrage_statistics - -pas = CAm.pool_arbitrage_statistics() -assert np.all(pas == CAm.pool_arbitrage_statistics(CAm.POS_DF)) -assert len(pas)==165 -assert list(pas.columns) == ['price', 'vl', 'itm', 'b', 's', 'bsv'] -assert list(pas.index.names) == ['pair', 'exchange', 'cid0'] -assert {x[0] for x in pas.index} == {Pair.n(x) for x in CAm.pairsc()} -assert {x[1] for x in pas.index} == {'bancor_v2', 'bancor_v3','carbon_v1','sushiswap_v2','uniswap_v2','uniswap_v3'} -pas - -pasd = CAm.pool_arbitrage_statistics(CAm.POS_DICT) -assert isinstance(pasd, dict) -assert len(pasd) == 26 -assert len(pasd['WETH-6Cc2/DAI-1d0F']) == 7 -pd0 = pasd['WETH-6Cc2/DAI-1d0F'][0] -assert pd0[:2] == ('WETH/DAI', 'WETH-6Cc2/DAI-1d0F') -assert iseq(pd0[2], 1840.1216491367131) -assert pd0[3:6] == ('594', '594', 'uniswap_v3') -assert iseq(pd0[6], 8.466598820198278) -assert pd0[7:] == ('', 'b', 's', 'buy-sell-WETH @ 1840.12 DAI per WETH') -pd0 - -pasl = CAm.pool_arbitrage_statistics(result = CAm.POS_LIST) -assert isinstance(pasl, tuple) -assert len(pasl) == len(pas) -pd0 = [(ix, x) for ix, x in enumerate(pasl) if x[2]==1840.1216491367131] -pd0 = pasl[pd0[0][0]] -assert pd0[:2] == ('WETH/DAI', 'WETH-6Cc2/DAI-1d0F') -assert iseq(pd0[2], 1840.1216491367131) -assert pd0[3:6] == ('594', '594', 'uniswap_v3') -assert iseq(pd0[6], 8.466598820198278) -assert pd0[7:] == ('', 'b', 's', 'buy-sell-WETH @ 1840.12 DAI per WETH') -pd0 - -# ### MargP Optimizer - -# #### margp optimizer - -tokenlist = f"{T.ETH},{T.USDC},{T.USDT},{T.BNT},{T.DAI},{T.WBTC}" -targettkn = f"{T.USDC}" -O = MargPOptimizer(CCm.bypairs(CCm.filter_pairs(bothin=tokenlist))) -r = O.margp_optimizer(targettkn, params=dict(verbose=False, debug=False)) -r.trade_instructions(ti_format=O.TIF_DFAGGR).fillna("") - -# #### MargpOptimizerResult - -assert type(r) == MargPOptimizer.MargpOptimizerResult -assert iseq(r.result, -4606.010157294979) -# assert r.time > 0.001 -# assert r.time < 0.1 -assert r.method == O.METHOD_MARGP -assert r.targettkn == targettkn -assert set(r.tokens_t)==set(['USDT-1ec7', 'WETH-6Cc2', 'WBTC-C599', 'DAI-1d0F', 'BNT-FF1C']) -p_opt_d0 = {t:x for x, t in zip(r.p_optimal_t, r.tokens_t)} -p_opt_d = {t:round(x,6) for x, t in zip(r.p_optimal_t, r.tokens_t)} -print("optimal p", p_opt_d) -assert p_opt_d == {'WETH-6Cc2': 1842.67228, 'WBTC-C599': 27604.143472, - 'BNT-FF1C': 0.429078, 'USDT-1ec7': 1.00058, 'DAI-1d0F': 1.000179} -assert r.p_optimal[r.targettkn] == 1 -po = [(k,v) for k,v in r.p_optimal.items()][:-1] -assert len(po)==len(r.p_optimal_t) -for k,v in po: - assert p_opt_d0[k] == v, f"error at {k}, {v}, {p_opt_d0[k]}" - -# #### TradeInstructions - -assert r.trade_instructions() == r.trade_instructions(ti_format=O.TIF_OBJECTS) -ti = r.trade_instructions(ti_format=O.TIF_OBJECTS) -cids = tuple(ti_.cid for ti_ in ti) -assert isinstance(ti, tuple) -assert len(ti) == 86 -ti0=[x for x in ti if x.cid=="357"] -assert len(ti0)==1 -ti0=ti0[0] -assert ti0.cid == ti0.curve.cid -assert type(ti0).__name__ == "TradeInstruction" -assert type(ti[0]) == MargPOptimizer.TradeInstruction -assert ti0.tknin == f"{T.USDT}" -assert ti0.tknout == f"{T.USDC}" -assert round(ti0.amtin, 8) == 1214.45596849 -assert round(ti0.amtout, 8) == -1216.41933959 -if not ti0.error is None: - print(ti0) - print(ti0.error) -assert ti0.error is None -ti[:2] - -tid = r.trade_instructions(ti_format=O.TIF_DICTS) -assert isinstance(tid, tuple) -assert len(tid) == len(ti) -tid0=[x for x in tid if x["cid"]=="357"] -assert len(tid0)==1 -tid0=tid0[0] -assert type(tid0)==dict -assert tid0["tknin"] == f"{T.USDT}" -assert tid0["tknout"] == f"{T.USDC}" -assert round(tid0["amtin"], 8) == 1214.45596849 -assert round(tid0["amtout"], 8) == -1216.41933959 -assert tid0["error"] is None -tid[:2] - -df = r.trade_instructions(ti_format=O.TIF_DF).fillna("") -assert tuple(df.index) == cids -assert np.all(r.trade_instructions(ti_format=O.TIF_DFRAW).fillna("")==df) -assert len(df) == len(ti) -assert list(df.columns)[:4] == ['pair', 'pairp', 'tknin', 'tknout'] -assert len(df.columns) == 4 + len(r.tokens_t) + 1 -tif0 = dict(df.loc["357"]) -assert tif0["pair"] == "USDC-eB48/USDT-1ec7" -assert tif0["pairp"] == "USDC/USDT" -assert tif0["tknin"] == tid0["tknin"] -assert tif0[tif0["tknin"]] == tid0["amtin"] -assert tif0[tif0["tknout"]] == tid0["amtout"] -df[:2] - -dfa = r.trade_instructions(ti_format=O.TIF_DFAGGR).fillna("") -assert tuple(dfa.index)[:-4] == cids -assert len(dfa) == len(df)+4 -assert len(dfa.columns) == len(r.tokens_t) + 1 -assert set(dfa.columns) == set(r.tokens_t).union(set([r.targettkn])) -assert list(dfa.index)[-4:] == ['PRICE', 'AMMIn', 'AMMOut', 'TOTAL NET'] -dfa[:10] - -dfpg = r.trade_instructions(ti_format=O.TIF_DFPG) -assert set(x[1] for x in dfpg.index) == set(cids) -assert np.all(dfpg["gain_tknq"]>=0) -assert np.all(dfpg["gain_r"]>=0) -assert round(np.max(dfpg["gain_r"]),8) == 0.04739068 -assert round(np.min(dfpg["gain_r"]),8) == 1.772e-05 -assert len(dfpg) == len(ti) -for p, t in zip(tuple(dfpg["pair"]), tuple(dfpg["tknq"])): - assert p.split("/")[1] == t, f"error in {p} [{t}]" -print(f"total gains: {sum(dfpg['gain_ttkn']):,.2f} {r.targettkn} [result={-r.result:,.2f}]") -assert abs(sum(dfpg["gain_ttkn"])/r.result+1)<0.01 -dfpg[:10] - -# ### Convex Optimizer -# -# **THE CONVEX OPTIMIZER IS DEPRECATED AND NO LONGER IN USE IN PRODUCTION** -# -# **THIS SECTION DOES SEEM TO THROW RANDOM ERRORS AND IS THEREFORE DISABLED** - -# + -# tokens = f"{T.DAI},{T.USDT},{T.HEX},{T.WETH},{T.LINK}" -# CCo = CCu2.bypairs(CCu2.filter_pairs(bothin=tokens)) -# CCo += CCs2.bypairs(CCu2.filter_pairs(bothin=tokens)) -# CA = CPCAnalyzer(CCo) -# O = ConvexOptimizer(CCo) -# #ArbGraph.from_cc(CCo).plot()._ - -# + -# CA.count_by_tokens() - -# + -#CCo.plot() -# - - -# #### convex optimizer - -# + -# targettkn = T.USDT -# # r = O.margp_optimizer(targettkn, params=dict(verbose=True, debug=False)) -# # r - -# + -# SFC = O.SFC(**{targettkn:O.AMMPays}) -# r = O.convex_optimizer(SFC, verbose=False, solver=O.SOLVER_SCS) -# r -# - - -# #### NofeesOptimizerResult - -# + -# round(r.result,-5) - -# + -# assert type(r) == ConvexOptimizer.NofeesOptimizerResult -# # assert round(r.result,-5) <= -1500000.0 -# # assert round(r.result,-5) >= -2500000.0 -# # assert r.time < 8 -# assert r.method == "convex" -# assert set(r.token_table.keys()) == set(['USDT-1ec7', 'WETH-6Cc2', 'LINK-86CA', 'DAI-1d0F', 'HEX-eb39']) -# assert len(r.token_table[T.USDT].x)==0 -# assert len(r.token_table[T.USDT].y)==10 -# lx = list(it.chain(*[rr.x for rr in r.token_table.values()])) -# lx.sort() -# ly = list(it.chain(*[rr.y for rr in r.token_table.values()])) -# ly.sort() -# assert lx == [_ for _ in range(21)] -# assert ly == lx -# - - -# #### trade instructions - -# + -# ti = r.trade_instructions() -# assert type(ti[0]) == ConvexOptimizer.TradeInstruction - -# + -# assert r.trade_instructions() == r.trade_instructions(ti_format=O.TIF_OBJECTS) -# ti = r.trade_instructions(ti_format=O.TIF_OBJECTS) -# cids = tuple(ti_.cid for ti_ in ti) -# assert isinstance(ti, tuple) -# assert len(ti) == 21 -# ti0=[x for x in ti if x.cid=="175"] -# assert len(ti0)==1 -# ti0=ti0[0] -# assert ti0.cid == ti0.curve.cid -# assert type(ti0).__name__ == "TradeInstruction" -# assert type(ti[0]) == ConvexOptimizer.TradeInstruction -# assert ti0.tknin == f"{T.LINK}" -# assert ti0.tknout == f"{T.DAI}" -# # assert round(ti0.amtin, 8) == 8.50052943 -# # assert round(ti0.amtout, 8) == -50.40963779 -# if not ti0.error is None: -# print(ti0) -# print(ti0.error) -# assert ti0.error is None -# print(r.error, ti0.error) -# ti[:2], ti0, r - -# + -# tid = r.trade_instructions(ti_format=O.TIF_DICTS) -# assert isinstance(tid, tuple) -# assert type(tid[0])==dict -# assert len(tid) == len(ti) -# tid0=[x for x in tid if x["cid"]=="175"] -# assert len(tid0)==1 -# tid0=tid0[0] -# assert tid0["tknin"] == f"{T.LINK}" -# assert tid0["tknout"] == f"{T.DAI}" -# # assert round(tid0["amtin"], 8) == 8.50052943 -# # assert round(tid0["amtout"], 8) == -50.40963779 -# assert tid0["error"] is None -# tid[:2] - -# + -# df = r.trade_instructions(ti_format=O.TIF_DF).fillna("") -# assert tuple(df.index) == cids -# assert np.all(r.trade_instructions(ti_format=O.TIF_DFRAW).fillna("")==df) -# assert len(df) == len(ti) -# assert list(df.columns)[:4] == ['pair', 'pairp', 'tknin', 'tknout'] -# assert len(df.columns) == 4 + 4 + 1 -# tif0 = dict(df.loc["175"]) -# assert tif0["pair"] == 'LINK-86CA/DAI-1d0F' -# assert tif0["pairp"] == "LINK/DAI" -# assert tif0["tknin"] == tid0["tknin"] -# assert tif0[tif0["tknin"]] == tid0["amtin"] -# assert tif0[tif0["tknout"]] == tid0["amtout"] -# df[:2] - -# + -# assert raises(r.trade_instructions, ti_format=O.TIF_DFAGGR).startswith("TIF_DFAGGR not implemented for") -# assert raises(r.trade_instructions, ti_format=O.TIF_DFPG).startswith("TIF_DFPG not implemented for") -# - - -# ### Simple Optimizer - -pair = f"{T.ETH}/{T.USDC}" -CCs = CCm.bypairs(pair) -CA = CPCAnalyzer(CCs) -O = PairOptimizer(CCs) -#ArbGraph.from_cc(CCs).plot()._ - -CA.count_by_tokens() - -# + -#CCs.plot() -# - - -# #### simple optimizer - -r = O.optimize(T.USDC) -r - -# #### result - -assert type(r) == PairOptimizer.MargpOptimizerResult -assert round(r.result, 5) == -1217.2442, f"{round(r.result, 5)}" -# assert r.time < 0.1 -assert r.method == "margp-pair" -assert r.errormsg is None - -# #### trade instructions - -ti = r.trade_instructions() -assert type(ti[0]) == PairOptimizer.TradeInstruction - -assert r.trade_instructions() == r.trade_instructions(ti_format=O.TIF_OBJECTS) -ti = r.trade_instructions(ti_format=O.TIF_OBJECTS) -cids = tuple(ti_.cid for ti_ in ti) -assert isinstance(ti, tuple) -assert len(ti) == 12 -ti0=[x for x in ti if x.cid=="6c988ffdc9e74acd97ccfb16dd65c110"] -assert len(ti0)==1 -ti0=ti0[0] -assert ti0.cid == ti0.curve.cid -assert type(ti0).__name__ == "TradeInstruction" -assert type(ti[0]) == PairOptimizer.TradeInstruction -assert ti0.tknin == f"{T.USDC}" -assert ti0.tknout == f"{T.WETH}" -assert round(ti0.amtin, 5) == 48153.80865 -assert round(ti0.amtout, 5) == -26.18300 -assert ti0.error is None -ti[:2] - -tid = r.trade_instructions(ti_format=O.TIF_DICTS) -assert isinstance(tid, tuple) -assert type(tid[0])==dict -assert len(tid) == len(ti) -tid0=[x for x in tid if x["cid"]=="6c988ffdc9e74acd97ccfb16dd65c110"] -assert len(tid0)==1 -tid0=tid0[0] -assert tid0["tknin"] == f"{T.USDC}" -assert tid0["tknout"] == f"{T.WETH}" -assert round(tid0["amtin"], 5) == 48153.80865 -assert round(tid0["amtout"], 5) == -26.183 -assert tid0["error"] is None -tid[:2] - -# trade instructions of format `TIF_DFRAW` (same as `TIF_DF`): raw dataframe - -df = r.trade_instructions(ti_format=O.TIF_DF).fillna("") -assert tuple(df.index) == cids -assert np.all(r.trade_instructions(ti_format=O.TIF_DFRAW).fillna("")==df) -assert len(df) == len(ti) -assert list(df.columns)[:4] == ['pair', 'pairp', 'tknin', 'tknout'] -assert len(df.columns) == 4 + 1 + 1 -tif0 = dict(df.loc["6c988ffdc9e74acd97ccfb16dd65c110"]) -assert tif0["pair"] == 'WETH-6Cc2/USDC-eB48' -assert tif0["pairp"] == "WETH/USDC" -assert tif0["tknin"] == tid0["tknin"] -assert tif0[tif0["tknin"]] == tid0["amtin"] -assert tif0[tif0["tknout"]] == tid0["amtout"] -df[:2] - -# trade instructions of format `TIF_DFAGGR` (aggregated data frame) - -df = r.trade_instructions(ti_format=O.TIF_DFAGGR) -assert len(df) == 16 -assert tuple(df.index[-4:]) == ('PRICE', 'AMMIn', 'AMMOut', 'TOTAL NET') -assert tuple(df.columns) == ('USDC-eB48', 'WETH-6Cc2') -df - - - -# prices and gains analysis data frame `TIF_DFPG` - -df = r.trade_instructions(ti_format=O.TIF_DFPG) -assert len(df) == 12 -assert set(x[0] for x in tuple(df.index)) == {'carbon_v1', 'sushiswap_v2', 'uniswap_v2', 'uniswap_v3'} -assert max(df["margp"]) == min(df["margp"]) -assert tuple(df.index.names) == ('exch', 'cid') -assert tuple(df.columns) == ( - 'fee', - 'pair', - 'amt_tknq', - 'tknq', - 'margp0', - 'effp', - 'margp', - 'gain_r', - 'gain_tknq', - 'gain_ttkn' -) -df - -# ## Analysis by pair - -# + -# CCm1 = CAm.CC.copy() -# CCm1 += CPC.from_carbon( -# pair=f"{T.WETH}/{T.USDC}", -# yint = 1, -# y = 1, -# pa = 1500, -# pb = 1501, -# tkny = f"{T.WETH}", -# cid = "test-1", -# isdydx=False, -# params=dict(exchange="carbon_v1"), -# ) -# CAm1 = CPCAnalyzer(CCm1) -# CCm1.asdf().to_csv("NBTest_006-augmented.csv.gz", compression = "gzip") -# - - -pricedf = CAm.pool_arbitrage_statistics() -assert len(pricedf)==165 -pricedf - -# ### WETH/USDC - -pair = "WETH-6Cc2/USDC-eB48" -print(f"Pair = {pair}") - -df = pricedf.loc[Pair.n(pair)] -assert len(df)==24 -df - -pi = CAm.pair_data(pair) -O = MargPOptimizer(pi.CC) - -# #### Target token = base token - -targettkn = pair.split("/")[0] -print(f"Target token = {targettkn}") -r = O.margp_optimizer(targettkn, params=dict(verbose=False, debug=False)) -r.trade_instructions(ti_format=O.TIF_DFAGGR) - -dfti1 = r.trade_instructions(ti_format=O.TIF_DFPG8) -print(f"Total gain: {sum(dfti1['gain_ttkn']):.4f} {targettkn}") -dfti1 - -# #### Target token = quote token - -targettkn = pair.split("/")[1] -print(f"Target token = {targettkn}") -r = O.margp_optimizer(targettkn, params=dict(verbose=False, debug=False)) -r.trade_instructions(ti_format=O.TIF_DFAGGR) - -dfti2 = r.trade_instructions(ti_format=O.TIF_DFPG8) -print(f"Total gain: {sum(dfti2['gain_ttkn']):.4f}", targettkn) -dfti2 diff --git a/resources/NBTest/OptimizerTesting.ipynb b/resources/NBTest/OptimizerTesting.ipynb deleted file mode 100644 index a9e312f1e..000000000 --- a/resources/NBTest/OptimizerTesting.ipynb +++ /dev/null @@ -1,1075 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "7c4e7ad0-9280-41ee-85b2-f4461058398b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SimplePair v2.2 (30/Apr/2024)\n", - "ConstantProductCurve v3.4 (23/Jan/2024)\n", - "CPCContainer v3.4 (23/Jan/2024)\n", - "PairOptimizer v6.0.1 (21/Sep/2023)\n", - "MargPOptimizer v5.2+c1 (30/Apr/2024)\n" - ] - } - ], - "source": [ - "try:\n", - " from fastlane_bot.tools.simplepair import SimplePair\n", - " from fastlane_bot.tools.cpc import ConstantProductCurve, CPCContainer\n", - " from fastlane_bot.tools.optimizer import PairOptimizer, MargPOptimizer\n", - "except:\n", - " from tools.simplepair import SimplePair\n", - " from tools.cpc import ConstantProductCurve, CPCContainer\n", - " from tools.optimizer import PairOptimizer, MargPOptimizer\n", - "CPC = ConstantProductCurve\n", - "\n", - "import pandas as pd\n", - "\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(SimplePair))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPC))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPCContainer))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(PairOptimizer))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(MargPOptimizer))" - ] - }, - { - "cell_type": "markdown", - "id": "988c1f31-507c-4dd7-b8fc-14fa4aef8134", - "metadata": {}, - "source": [ - "# Optimizer Testing" - ] - }, - { - "cell_type": "markdown", - "id": "8ebde928-6a4b-448c-b6c3-6941310fccae", - "metadata": {}, - "source": [ - "This is a light workbook allowing to look at issues that may arise when running the optimizer on a specific set of curves. \n", - "\n", - "Instructions:\n", - "\n", - "- locate the **exact** curve set to feed to the optimizer (it will be somewhere in the logging output, and it will be a list of ConstantProductCurve objects)\n", - "- assign it to the `CurvesRaw` variable as shown below\n", - "- add the missing token addresses to the `TOKENS` dict below\n", - "- provide consistent values for `PSTART`\n", - "- run the workbook" - ] - }, - { - "cell_type": "markdown", - "id": "7f38c5d2-6f6e-402c-b1a5-0fa00cf88f9a", - "metadata": { - "tags": [] - }, - "source": [ - "### >> Enter curves\n", - "\n", - "Place curves here in the form\n", - "\n", - " CurvesRaw = [\n", - " ConstantProductCurve(k=27518385.40998667, x=1272.2926367501436, x_act=0, ...),\n", - " ConstantProductCurve(k=6.160500599566333e+18, x=11099999985.149971, x_act=0, ...),\n", - " ...\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5c244d95-da00-449f-a879-ace4b5523a22", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "CurvesRaw = [\n", - " ConstantProductCurve(k=27518385.40998667, x=1272.2926367501436, x_act=0, y_act=2000.9999995236503, alpha=0.5, pair='0x514910771AF9Ca656af840dff83E8264EcF986CA/0x8E870D67F660D95d5be530380D0eC0bd388289E1', cid='0x425d5d4ad7243f88d9f4cde8da52863b45af1f64e05bede1299909bcaa6c52d1-0', fee=2000, descr='carbon_v1 0x514910771AF9Ca656af840dff83E8264EcF986CA\\\\/0x8E870D67F660D95d5be530380D0eC0bd388289E1 2000', constr='carb', params={'exchange': 'carbon_v1', 'y': 2000.9999995236503, 'yint': 2000.9999995236503, 'A': 0.38144823884371704, 'B': 3.7416573867739373, 'pa': 16.99999999999995, 'pb': 13.99999999999997}),\n", - " ConstantProductCurve(k=6.160500599566333e+18, x=11099999985.149971, x_act=0, y_act=55.50000002646446, alpha=0.5, pair='0x8E870D67F660D95d5be530380D0eC0bd388289E1/0x514910771AF9Ca656af840dff83E8264EcF986CA', cid='0x425d5d4ad7243f88d9f4cde8da52863b45af1f64e05bede1299909bcaa6c52d1-1', fee=2000, descr='carbon_v1 0x514910771AF9Ca656af840dff83E8264EcF986CA\\\\/0x8E870D67F660D95d5be530380D0eC0bd388289E1 2000', constr='carb', params={'exchange': 'carbon_v1', 'y': 55.50000002646446, 'yint': 55.50000002646446, 'A': 0, 'B': 0.22360678656963742, 'pa': 0.04999999999999889, 'pb': 0.04999999999999889}),\n", - " ConstantProductCurve(k=14449532.299465338, x=57487.82879658422, x_act=0, y_act=5.0, alpha=0.5, pair='0x514910771AF9Ca656af840dff83E8264EcF986CA/0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE', cid='0x3fcccfe0063b71fc973fab8dea39b6be9da80125910c10e57b924b3e4687295a-0', fee=2000, descr='carbon_v1 0x514910771AF9Ca656af840dff83E8264EcF986CA/0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE 2000', constr='carb', params={'exchange': 'carbon_v1', 'y': 5.0, 'yint': 8.582730309868262, 'A': 0.002257868117407469, 'B': 0.06480740698407672, 'pa': 0.004497751124437756, 'pb': 0.004199999999999756}),\n", - " ConstantProductCurve(k=14456757.06563651, x=251.4750925240284, x_act=0, y_act=807.9145301701096, alpha=0.5, pair='0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE/0x514910771AF9Ca656af840dff83E8264EcF986CA', cid='0x3fcccfe0063b71fc973fab8dea39b6be9da80125910c10e57b924b3e4687295a-1', fee=2000, descr='carbon_v1 0x514910771AF9Ca656af840dff83E8264EcF986CA/0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE 2000', constr='carb', params={'exchange': 'carbon_v1', 'y': 807.9145301701096, 'yint': 1974.7090228584536, 'A': 0.519359008452966, 'B': 14.907119849998594, 'pa': 237.97624997025295, 'pb': 222.22222222222211}),\n", - " ConstantProductCurve(k=56087178.30932376, x=131.6236694086859, x_act=0, y_act=15920.776548455418, alpha=0.5, pair='0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE/0x8E870D67F660D95d5be530380D0eC0bd388289E1', cid='0x6cc4b198ec4cf17fdced081b5611279be73e200711238068b5340e606ba86646-0', fee=2000, descr='carbon_v1 0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE\\\\/0x8E870D67F660D95d5be530380D0eC0bd388289E1 2000', constr='carb', params={'exchange': 'carbon_v1', 'y': 15920.776548455418, 'yint': 32755.67010983316, 'A': 4.373757425036729, 'B': 54.77225575051648, 'pa': 3498.2508745627138, 'pb': 2999.9999999999854}),\n", - " ConstantProductCurve(k=56059148.73497429, x=426117.72306081816, x_act=0, y_act=5.0, alpha=0.5, pair='0x8E870D67F660D95d5be530380D0eC0bd388289E1/0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE', cid='0x6cc4b198ec4cf17fdced081b5611279be73e200711238068b5340e606ba86646-1', fee=2000, descr='carbon_v1 0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE\\\\/0x8E870D67F660D95d5be530380D0eC0bd388289E1 2000', constr='carb', params={'exchange': 'carbon_v1', 'y': 5.0, 'yint': 10.106093048875099, 'A': 0.0013497708452092638, 'B': 0.016903085094568837, 'pa': 0.0003331667499582927, 'pb': 0.0002857142857142352})\n", - "]\n", - "CCRaw = CPCContainer(CurvesRaw)" - ] - }, - { - "cell_type": "markdown", - "id": "961f17f5-6286-4f4c-8bc3-9721811b50b1", - "metadata": {}, - "source": [ - "### >> Enter prices\n", - "\n", - "Provide current prices (`pstart`) here, in the format\n", - "\n", - " PRICES = {\n", - " '0x8E87...': 0.0003087360213944532, \n", - " '0x5149...': 0.004372219704179475, \n", - " '0xEeee...': 1\n", - " }\n", - " \n", - "The price numeraire does not matter as long as they are all in the same numeraire. All tokens must be present. Additional tokens can be added and will be ignored. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "5fc55588-ec8b-4bdc-9482-4fc97d909c2e", - "metadata": {}, - "outputs": [], - "source": [ - "PRICES_RAW = {\n", - " '0x8E870D67F660D95d5be530380D0eC0bd388289E1': 0.0003087360213944532, \n", - " '0x514910771AF9Ca656af840dff83E8264EcF986CA': 0.004372219704179475, \n", - " '0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE': 1\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "90127233-847b-4719-8f45-76638e5776d7", - "metadata": {}, - "source": [ - "### >> Enter tokens\n", - "\n", - "Provide token tickers here, in the format\n", - "\n", - " TOKENS = {\n", - " \"0x5149...\": \"LINK\",\n", - " \"0x8E87...\": \"USDP\",\n", - " \"0xEeee...\": \"ETH\",\n", - " }\n", - " \n", - "All tokens must be present. Additional tokens will be ignored. You must also provide the `TARGET_TOKEN` (default: first token of `TOKENS`)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "747c1dbf-d821-4214-8aa6-c1412bffeb50", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "TOKENS = {\n", - " \"0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE\": \"ETH\",\n", - " \"0x514910771AF9Ca656af840dff83E8264EcF986CA\": \"LINK\",\n", - " \"0x8E870D67F660D95d5be530380D0eC0bd388289E1\": \"USDP\",\n", - "}\n", - "\n", - "TARGET_TOKEN_RAW = list(TOKENS)[0]\n", - "TARGET_TOKEN_RAW" - ] - }, - { - "cell_type": "markdown", - "id": "8bba7e8a-dbf8-4a89-9ee8-686afbef9901", - "metadata": {}, - "source": [ - "### >>> Run optimizer\n", - "\n", - "please make sure that this line runs without errors (other than the error that needs to be addressed of course)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a49a49f8-b3e4-49c4-b991-c3cd8a123658", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "CPCArbOptimizer.MargpOptimizerResult(result=8.693167770410668, time=0.0045871734619140625, method='margp', targettkn='0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE', p_optimal_t=(9.794834002573646e+104, 1.1199678719708761e+103), dtokens_t=(-863.4145301701064, -14810.776548455411), tokens_t=('0x514910771AF9Ca656af840dff83E8264EcF986CA', '0x8E870D67F660D95d5be530380D0eC0bd388289E1'), errormsg=None)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "O = MargPOptimizer(CCRaw)\n", - "r = O.optimize(sfc=TARGET_TOKEN_RAW, params=dict(pstart=PRICES_RAW))\n", - "r" - ] - }, - { - "cell_type": "markdown", - "id": "f18727c8-f2d9-4436-9022-a6f1d6f9a2f6", - "metadata": {}, - "source": [ - "**do not worry about the code below here; this is for the actual testing and will be adapted as need be**" - ] - }, - { - "cell_type": "markdown", - "id": "f4844ce6-dffa-4d79-b631-6b5fa8ff17a2", - "metadata": {}, - "source": [ - "### >>> Preprocessing\n", - "\n", - "Please ensure that this code runs without error. Errors here mean that the data provided above is not consistent." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b1a6af0f-89b0-443d-81cb-fcfea6722441", - "metadata": {}, - "outputs": [], - "source": [ - "def replace_tokens(dct):\n", - " \"\"\"replaces the token address with the token name in dct\"\"\"\n", - " tkns = dct[\"pair\"].split(\"/\")\n", - " for i in range(len(tkns)):\n", - " #tkns[i] = TOKENS.get(tkns[i]) or tkns[i]\n", - " tkns[i] = TOKENS[tkns[i]]\n", - " dct[\"pair\"] = \"/\".join(tkns)\n", - " return dct" - ] - }, - { - "cell_type": "markdown", - "id": "265bd6ae-c5c4-439c-99bc-b289d44cab63", - "metadata": {}, - "source": [ - "If this fails this probably means that one of the tokens has not been defined above" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f7651ba3-2fb2-444f-9971-779326ae4758", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'USDP': 0.0003087360213944532, 'LINK': 0.004372219704179475, 'ETH': 1}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CC = CPCContainer.from_dicts([replace_tokens(d) for d in CCRaw.asdicts()])\n", - "PRICES = {TOKENS[addr]:price for addr, price in PRICES_RAW.items()}\n", - "TARGET_TOKEN = TOKENS[TARGET_TOKEN_RAW]\n", - "PRICES" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c7ce8e24-8ee6-48c0-bccd-0d268873128c", - "metadata": {}, - "outputs": [], - "source": [ - "def p(pair=None, *, tknb=None, tknq=None, prices=None):\n", - " \"price of tknb in terms of tknq\"\n", - " if not pair is None:\n", - " tknb, tknq = pair.split(\"/\")\n", - " p = prices or PRICES\n", - " return p[tknb]/p[tknq]" - ] - }, - { - "cell_type": "markdown", - "id": "9906cde3-7c6b-47dd-b322-c342189281d9", - "metadata": {}, - "source": [ - "The code below ensures that in ETH/LINK, LINK is the quote token and ETH the base token (for better price displays)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9366ca04-201c-448d-8db3-62b17946fdd9", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "SimplePair.NUMERAIRE_TOKENS[\"LINK\"] = SimplePair.NUMERAIRE_TOKENS[\"ETH\"] - 1\n", - "#SimplePair.NUMERAIRE_TOKENS" - ] - }, - { - "cell_type": "markdown", - "id": "f8d51655-c7d6-4966-ad44-e002dc4aca62", - "metadata": {}, - "source": [ - "## Curves" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "248d58be-fc70-4b24-a8c2-0cc3d59d54e9", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Num curves: 6\n", - "Pairs: {'ETH/USDP', 'LINK/USDP', 'ETH/LINK'}\n", - "Target token: ETH\n" - ] - } - ], - "source": [ - "print(\"Num curves: \", len(CC))\n", - "print(\"Pairs: \", set(c.pairo.primary_n for c in CC))\n", - "print(\"Target token: \", TARGET_TOKEN)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "4dd5ccb9-f1a8-4d1b-8965-fc08021dd9a9", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "PRICE_DECIMALS = 2\n", - "curvedata = [dict(\n", - " cid0 = f\"{c.cid[2:6]}{c.cid[-2:]}\",\n", - " exch = c.params['exchange'],\n", - " pair = c.pairo.primary_n,\n", - " mktp = round(p(c.pairo.primary_n), PRICE_DECIMALS),\n", - " bs = c.buysell(),\n", - " tkn = c.pairo.primary_tknb,\n", - " p = round(c.primaryp(), PRICE_DECIMALS),\n", - " p_min = round(c.p_min_primary(), PRICE_DECIMALS),\n", - " p_max = round(c.p_max_primary(), PRICE_DECIMALS),\n", - " tknp = p(tknb=c.pairo.primary_tknb, tknq=TARGET_TOKEN),\n", - " wbp = max(int((c.p_max_primary()/c.p_min_primary() - 1)*10000), 1),\n", - " liq = round(c.tvl(tkn=c.pairo.primary_tknb), 2),\n", - " liqtt = round(c.x_act*p(tknb=c.tknx, tknq=TARGET_TOKEN) + c.y_act*p(tknb=c.tkny, tknq=TARGET_TOKEN), 2),\n", - ") for c in CC]\n", - "#curvedata" - ] - }, - { - "cell_type": "markdown", - "id": "907431f0-9bb0-467d-9230-154e92a0e259", - "metadata": { - "tags": [] - }, - "source": [ - "- `cid0`: shortened CID (same as in `debug_tkn2`)\n", - "- `exch`: the type of the curve / exchange in question\n", - "- `pair`: the normalized pair of the curve\n", - "- `mktp`: the current market price of that pair (according to `PRICES_RAW`)\n", - "- `bs`: whether curves buys (\"b\"), sells (\"s\") the primary tokenm, or both\n", - "- `tkn`: the primary token (base token of primary pair)\n", - "- `p`, `p_min`, `p_max`: the current / minimum / maximum price of the curve\n", - "- `tknp`: the price of `tkn` (as above) in terms of `TARGET_TOKEN`, as per the market price\n", - "- `wbp`: width of the range (p_max/p_min) in basis points \n", - "- `liq`: liquidity (in units of `tkn` as defined above; converted at curve price)\n", - "- `liqtt`: total curve liquidity (in `TARGET_TOKEN` units; converted at `mktp`)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "3deeac05-5364-413c-a93a-c9fe9f218c79", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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cid0exchpairmktpbstknpp_minp_maxtknpwbpliqliqtt
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23fcc-0carbon_v1ETH/LINK228.72sETH228.72228.72238.101.0000004105.005.00
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46cc4-0carbon_v1ETH/USDP3239.01bETH3237.393000.003237.391.0000007914.924.92
56cc4-1carbon_v1ETH/USDP3239.01sETH3239.013239.013500.001.0000008055.005.00
\n", - "
" - ], - "text/plain": [ - " cid0 exch pair mktp bs tkn p p_min p_max \\\n", - "0 425d-0 carbon_v1 LINK/USDP 14.16 b LINK 17.00 14.00 17.00 \n", - "1 425d-1 carbon_v1 LINK/USDP 14.16 s LINK 20.00 20.00 20.00 \n", - "2 3fcc-0 carbon_v1 ETH/LINK 228.72 s ETH 228.72 228.72 238.10 \n", - "3 3fcc-1 carbon_v1 ETH/LINK 228.72 b ETH 228.60 222.22 228.60 \n", - "4 6cc4-0 carbon_v1 ETH/USDP 3239.01 b ETH 3237.39 3000.00 3237.39 \n", - "5 6cc4-1 carbon_v1 ETH/USDP 3239.01 s ETH 3239.01 3239.01 3500.00 \n", - "\n", - " tknp wbp liq liqtt \n", - "0 0.004372 2142 117.71 0.62 \n", - "1 0.004372 1 55.50 0.24 \n", - "2 1.000000 410 5.00 5.00 \n", - "3 1.000000 287 3.53 3.53 \n", - "4 1.000000 791 4.92 4.92 \n", - "5 1.000000 805 5.00 5.00 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "curvedf = pd.DataFrame(curvedata)\n", - "curvedf" - ] - }, - { - "cell_type": "markdown", - "id": "963c1045-22e5-43f6-bb5a-3e3fce6cf92e", - "metadata": {}, - "source": [ - "Curves 2,3 and 4,5 are overlapping ranges with good liquidity that serve as a market for curve 1 which is the operational curve in this arbitrage. In fact, what we expect is\n", - "\n", - "- Curve 0 (`425d-0`) buys LINK for USDP from 17 down to 14\n", - "- Curves 2-5 (`3fcc` and `6cc4`) sell LINK for USDP (via ETH) at 14.16 and above\n", - "\n", - "The expected price is somewhat above 14, depending on the capacity of the overlapping curves 2-5" - ] - }, - { - "cell_type": "markdown", - "id": "c39b25e9-e9af-4767-a144-42493a9a83e6", - "metadata": {}, - "source": [ - "The approximate effective LINK/USDP price from the overlapping curves (buy and sell)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "e9ced448-0a1b-4baf-9ec9-5d6414679b79", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "14.161676661786817" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "3239.013043/228.716777" - ] - }, - { - "cell_type": "markdown", - "id": "f23ac41d-a71f-4d81-b9a7-5c7aee4c4fb3", - "metadata": {}, - "source": [ - "The width of the overlapping ranges (2,3 and 4,5) in basis points" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "89b33db2-15cc-473b-8099-17f262e40674", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(4.999989588914122, 5.000002556068139)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(228.716777/228.602476-1)*10000, (3239.013043/3237.394345-1)*10000" - ] - }, - { - "cell_type": "markdown", - "id": "54d0478d-d748-4f9a-ae3a-753ab61cc8de", - "metadata": {}, - "source": [ - "For reference, the CID dataframe `ciddf` (separate because the field is too long; can be joined to `curvedf` via index)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "0cc8423f-726b-42f6-9144-2f1de1d98d12", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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cid
00x425d5d4ad7243f88d9f4cde8da52863b45af1f64e05b...
10x425d5d4ad7243f88d9f4cde8da52863b45af1f64e05b...
20x3fcccfe0063b71fc973fab8dea39b6be9da80125910c...
30x3fcccfe0063b71fc973fab8dea39b6be9da80125910c...
40x6cc4b198ec4cf17fdced081b5611279be73e20071123...
50x6cc4b198ec4cf17fdced081b5611279be73e20071123...
\n", - "
" - ], - "text/plain": [ - " cid\n", - "0 0x425d5d4ad7243f88d9f4cde8da52863b45af1f64e05b...\n", - "1 0x425d5d4ad7243f88d9f4cde8da52863b45af1f64e05b...\n", - "2 0x3fcccfe0063b71fc973fab8dea39b6be9da80125910c...\n", - "3 0x3fcccfe0063b71fc973fab8dea39b6be9da80125910c...\n", - "4 0x6cc4b198ec4cf17fdced081b5611279be73e20071123...\n", - "5 0x6cc4b198ec4cf17fdced081b5611279be73e20071123..." - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ciddf = pd.DataFrame([dict(cid=c.cid) for c in CC])\n", - "ciddf" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "16d86f58-0c20-4c38-9e62-25ad33fafe1b", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "#help(CC[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "4cdabeff-acae-49c2-b211-d37858a4910e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "#help(CC[0].pairo)" - ] - }, - { - "cell_type": "markdown", - "id": "94f35eba-137c-4adf-a167-2218e68410e6", - "metadata": {}, - "source": [ - "## MargPOptimizer" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "d0200904-33d4-4dbe-951e-bd4ee834a59b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[margp_optimizer] using pstartd [3 tokens]\n", - "[margp_optimizer] pstart: (0.0003087360213944532, 0.004372219704179475)\n", - "[margp_optimizer] ETH <- USDP, LINK\n", - "[margp_optimizer] p_t [0.00030874 0.00437222]\n", - "[margp_optimizer] p 0.00, 0.00\n", - "[margp_optimizer] 1/p 3,239.01, 228.72\n", - "\n", - "[dtknfromp_f]\n", - "=====================>>>\n", - "p 0.00, 0.00\n", - "1/p 3,239.01, 228.72\n", - "ETH <- USDP, LINK\n", - "\n", - "USDP/ETH --->>\n", - " price=0.0003, 1/price=3,239.0130\n", - " cid=6cc4-1 dx= 0.000 USDP dy= -0.000 ETH p=0.00 1/p=3,239.01\n", - "<<--- USDP/ETH\n", - "\n", - "LINK/USDP --->>\n", - " price=14.1617, 1/price=0.0706\n", - " cid=425d-0 dx= 121.680 LINK dy= -1,887.990 USDP p=17.00 1/p=0.06\n", - "<<--- LINK/USDP\n", - "\n", - "ETH/LINK --->>\n", - " price=228.7168, 1/price=0.0044\n", - " cid=3fcc-1 dx= 0.000 ETH dy= 0.000 LINK p=228.60 1/p=0.00\n", - "<<--- ETH/LINK\n", - "\n", - "USDP/LINK --->>\n", - " price=0.0706, 1/price=14.1617\n", - " cid=425d-1 dx= 0.000 USDP dy= 0.000 LINK p=0.05 1/p=20.00\n", - "<<--- USDP/LINK\n", - "\n", - "ETH/USDP --->>\n", - " price=3,239.0130, 1/price=0.0003\n", - " cid=6cc4-0 dx= 0.000 ETH dy= 0.000 USDP p=3,237.39 1/p=0.00\n", - "<<--- ETH/USDP\n", - "\n", - "LINK/ETH --->>\n", - " price=0.0044, 1/price=228.7168\n", - " cid=3fcc-0 dx= 0.000 LINK dy= -0.000 ETH p=0.00 1/p=228.72\n", - "<<--- LINK/ETH\n", - "\n", - "sum_by_tkn={'USDP': -1887.990147798253, 'ETH': -1.1368683772161603e-13, 'LINK': 121.67964276398834}\n", - "result=(-1887.990147798253, 121.67964276398834)\n", - "<<<=====================\n", - "\n", - "[margp_optimizer]\n", - "============= JACOBIAN =============>>>\n", - "[[-22727.18926195 22727.95719087]\n", - " [ 1604.9023264 -1604.84810017]]\n", - "<<<============= JACOBIAN =============\n", - "\n", - "\n", - "[margp_optimizer]\n", - "========== cycle 0 =======>>>\n", - "ETH <- USDP, LINK\n", - "dtkn -1,887.990, 121.680\n", - "log p0 [-3.51041269678875, -2.359298022862449]\n", - "d logp [107.27085049 107.3502951 ]\n", - "log p [103.76043779 104.99099708]\n", - "p_t (5.760203059790834e+103, 9.79483400374927e+104)\n", - "p 57,602,030,597,908,338,951,592,473,326,935,483,261,716,356,277,543,978,424,446,766,764,766,117,430,196,852,725,399,232,107,143,732,658,176.00, 979,483,400,374,926,943,541,468,517,006,950,337,091,402,915,663,687,237,176,620,668,614,492,567,586,269,443,811,139,950,563,304,224,587,776.00\n", - "1/p 0.00, 0.00\n", - "[criterium=1.52e+02, eps=1.0e-06, c/e=2e+08]\n", - "<<<========== cycle 0 =======\n", - "\n", - "[dtknfromp_f]\n", - "=====================>>>\n", - "p 57,602,030,597,908,338,951,592,473,326,935,483,261,716,356,277,543,978,424,446,766,764,766,117,430,196,852,725,399,232,107,143,732,658,176.00, 979,483,400,374,926,943,541,468,517,006,950,337,091,402,915,663,687,237,176,620,668,614,492,567,586,269,443,811,139,950,563,304,224,587,776.00\n", - "1/p 0.00, 0.00\n", - "ETH <- USDP, LINK\n", - "\n", - "USDP/ETH --->>\n", - " price=57,602,030,597,908,338,951,592,473,326,935,483,261,716,356,277,543,978,424,446,766,764,766,117,430,196,852,725,399,232,107,143,732,658,176.0000, 1/price=0.0000\n", - " cid=6cc4-1 dx= 0.000 USDP dy= 0.000 ETH p=0.00 1/p=3,239.01\n", - "<<--- USDP/ETH\n", - "\n", - "LINK/USDP --->>\n", - " price=17.0043, 1/price=0.0588\n", - " cid=425d-0 dx= 0.000 LINK dy= 0.000 USDP p=17.00 1/p=0.06\n", - "<<--- LINK/USDP\n", - "\n", - "ETH/LINK --->>\n", - " price=0.0000, 1/price=979,483,400,374,926,816,226,719,996,101,569,945,313,547,390,077,552,171,459,846,064,493,476,902,827,491,359,162,308,715,354,760,088,125,440.0000\n", - " cid=3fcc-1 dx= 3.585 ETH dy= -807.915 LINK p=228.60 1/p=0.00\n", - "<<--- ETH/LINK\n", - "\n", - "USDP/LINK --->>\n", - " price=0.0588, 1/price=17.0043\n", - " cid=425d-1 dx= 0.000 USDP dy= 0.000 LINK p=0.05 1/p=20.00\n", - "<<--- USDP/LINK\n", - "\n", - "ETH/USDP --->>\n", - " price=0.0000, 1/price=57,602,030,597,908,338,951,592,473,326,935,483,261,716,356,277,543,978,424,446,766,764,766,117,430,196,852,725,399,232,107,143,732,658,176.0000\n", - " cid=6cc4-0 dx= 5.109 ETH dy= -15,920.777 USDP p=3,237.39 1/p=0.00\n", - "<<--- ETH/USDP\n", - "\n", - "LINK/ETH --->>\n", - " price=979,483,400,374,926,943,541,468,517,006,950,337,091,402,915,663,687,237,176,620,668,614,492,567,586,269,443,811,139,950,563,304,224,587,776.0000, 1/price=0.0000\n", - " cid=3fcc-0 dx= 0.000 LINK dy= 0.000 ETH p=0.00 1/p=228.72\n", - "<<--- LINK/ETH\n", - "\n", - "sum_by_tkn={'USDP': -15920.776548455411, 'ETH': 8.693167770410668, 'LINK': -807.9145301701064}\n", - "result=(-15920.776548455411, -807.9145301701064)\n", - "<<<=====================\n", - "\n", - "[margp_optimizer]\n", - "============= JACOBIAN =============>>>\n", - "[[-22240.76609262 0. ]\n", - " [ 1309.67772398 0. ]]\n", - "<<<============= JACOBIAN =============\n", - "\n", - "\n", - "[margp_optimizer] singular Jacobian, using lstsq instead\n", - "\n", - "[margp_optimizer]\n", - "========== cycle 1 =======>>>\n", - "ETH <- USDP, LINK\n", - "dtkn -15,920.777, -807.915\n", - "log p0 [103.76043779352602, 104.99099708026269]\n", - "d logp [-0.71123223 0. ]\n", - "log p [103.04920556 104.99099708]\n", - "p_t (1.1199678720803443e+103, 9.79483400374927e+104)\n", - "p 11,199,678,720,803,443,453,189,605,601,416,656,936,260,633,078,311,899,193,742,357,411,955,761,651,927,981,274,661,421,092,714,319,970,304.00, 979,483,400,374,926,943,541,468,517,006,950,337,091,402,915,663,687,237,176,620,668,614,492,567,586,269,443,811,139,950,563,304,224,587,776.00\n", - "1/p 0.00, 0.00\n", - "[criterium=7.11e-01, eps=1.0e-06, c/e=7e+05]\n", - "<<<========== cycle 1 =======\n", - "\n", - "[dtknfromp_f]\n", - "=====================>>>\n", - "p 11,199,678,720,803,443,453,189,605,601,416,656,936,260,633,078,311,899,193,742,357,411,955,761,651,927,981,274,661,421,092,714,319,970,304.00, 979,483,400,374,926,943,541,468,517,006,950,337,091,402,915,663,687,237,176,620,668,614,492,567,586,269,443,811,139,950,563,304,224,587,776.00\n", - "1/p 0.00, 0.00\n", - "ETH <- USDP, LINK\n", - "\n", - "USDP/ETH --->>\n", - " price=11,199,678,720,803,443,453,189,605,601,416,656,936,260,633,078,311,899,193,742,357,411,955,761,651,927,981,274,661,421,092,714,319,970,304.0000, 1/price=0.0000\n", - " cid=6cc4-1 dx= 0.000 USDP dy= 0.000 ETH p=0.00 1/p=3,239.01\n", - "<<--- USDP/ETH\n", - "\n", - "LINK/USDP --->>\n", - " price=87.4564, 1/price=0.0114\n", - " cid=425d-0 dx= 0.000 LINK dy= 0.000 USDP p=17.00 1/p=0.06\n", - "<<--- LINK/USDP\n", - "\n", - "ETH/LINK --->>\n", - " price=0.0000, 1/price=979,483,400,374,926,816,226,719,996,101,569,945,313,547,390,077,552,171,459,846,064,493,476,902,827,491,359,162,308,715,354,760,088,125,440.0000\n", - " cid=3fcc-1 dx= 3.585 ETH dy= -807.915 LINK p=228.60 1/p=0.00\n", - "<<--- ETH/LINK\n", - "\n", - "USDP/LINK --->>\n", - " price=0.0114, 1/price=87.4564\n", - " cid=425d-1 dx= 1,110.000 USDP dy= -55.500 LINK p=0.05 1/p=20.00\n", - "<<--- USDP/LINK\n", - "\n", - "ETH/USDP --->>\n", - " price=0.0000, 1/price=11,199,678,720,803,443,453,189,605,601,416,656,936,260,633,078,311,899,193,742,357,411,955,761,651,927,981,274,661,421,092,714,319,970,304.0000\n", - " cid=6cc4-0 dx= 5.109 ETH dy= -15,920.777 USDP p=3,237.39 1/p=0.00\n", - "<<--- ETH/USDP\n", - "\n", - "LINK/ETH --->>\n", - " price=979,483,400,374,926,943,541,468,517,006,950,337,091,402,915,663,687,237,176,620,668,614,492,567,586,269,443,811,139,950,563,304,224,587,776.0000, 1/price=0.0000\n", - " cid=3fcc-0 dx= 0.000 LINK dy= 0.000 ETH p=0.00 1/p=228.72\n", - "<<--- LINK/ETH\n", - "\n", - "sum_by_tkn={'USDP': -14810.776548455411, 'ETH': 8.693167770410668, 'LINK': -863.4145301701064}\n", - "result=(-14810.776548455411, -863.4145301701064)\n", - "<<<=====================\n", - "\n", - "[margp_optimizer]\n", - "============= JACOBIAN =============>>>\n", - "[[0. 0.]\n", - " [0. 0.]]\n", - "<<<============= JACOBIAN =============\n", - "\n", - "\n", - "[margp_optimizer] singular Jacobian, using lstsq instead\n", - "\n", - "[margp_optimizer]\n", - "========== cycle 2 =======>>>\n", - "ETH <- USDP, LINK\n", - "dtkn -14,810.777, -863.415\n", - "log p0 [103.04920556447522, 104.99099708026269]\n", - "d logp [0. 0.]\n", - "log p [103.04920556 104.99099708]\n", - "p_t (1.1199678720803443e+103, 9.79483400374927e+104)\n", - "p 11,199,678,720,803,443,453,189,605,601,416,656,936,260,633,078,311,899,193,742,357,411,955,761,651,927,981,274,661,421,092,714,319,970,304.00, 979,483,400,374,926,943,541,468,517,006,950,337,091,402,915,663,687,237,176,620,668,614,492,567,586,269,443,811,139,950,563,304,224,587,776.00\n", - "1/p 0.00, 0.00\n", - "[criterium=0.00e+00, eps=1.0e-06, c/e=0e+00]\n", - "<<<========== cycle 2 =======\n", - "\n", - "[dtknfromp_f]\n", - "=====================>>>\n", - "p 11,199,678,720,803,443,453,189,605,601,416,656,936,260,633,078,311,899,193,742,357,411,955,761,651,927,981,274,661,421,092,714,319,970,304.00, 979,483,400,374,926,943,541,468,517,006,950,337,091,402,915,663,687,237,176,620,668,614,492,567,586,269,443,811,139,950,563,304,224,587,776.00\n", - "1/p 0.00, 0.00\n", - "ETH <- USDP, LINK\n", - "\n", - "USDP/ETH --->>\n", - " price=11,199,678,720,803,443,453,189,605,601,416,656,936,260,633,078,311,899,193,742,357,411,955,761,651,927,981,274,661,421,092,714,319,970,304.0000, 1/price=0.0000\n", - " cid=6cc4-1 dx= 0.000 USDP dy= 0.000 ETH p=0.00 1/p=3,239.01\n", - "<<--- USDP/ETH\n", - "\n", - "LINK/USDP --->>\n", - " price=87.4564, 1/price=0.0114\n", - " cid=425d-0 dx= 0.000 LINK dy= 0.000 USDP p=17.00 1/p=0.06\n", - "<<--- LINK/USDP\n", - "\n", - "ETH/LINK --->>\n", - " price=0.0000, 1/price=979,483,400,374,926,816,226,719,996,101,569,945,313,547,390,077,552,171,459,846,064,493,476,902,827,491,359,162,308,715,354,760,088,125,440.0000\n", - " cid=3fcc-1 dx= 3.585 ETH dy= -807.915 LINK p=228.60 1/p=0.00\n", - "<<--- ETH/LINK\n", - "\n", - "USDP/LINK --->>\n", - " price=0.0114, 1/price=87.4564\n", - " cid=425d-1 dx= 1,110.000 USDP dy= -55.500 LINK p=0.05 1/p=20.00\n", - "<<--- USDP/LINK\n", - "\n", - "ETH/USDP --->>\n", - " price=0.0000, 1/price=11,199,678,720,803,443,453,189,605,601,416,656,936,260,633,078,311,899,193,742,357,411,955,761,651,927,981,274,661,421,092,714,319,970,304.0000\n", - " cid=6cc4-0 dx= 5.109 ETH dy= -15,920.777 USDP p=3,237.39 1/p=0.00\n", - "<<--- ETH/USDP\n", - "\n", - "LINK/ETH --->>\n", - " price=979,483,400,374,926,943,541,468,517,006,950,337,091,402,915,663,687,237,176,620,668,614,492,567,586,269,443,811,139,950,563,304,224,587,776.0000, 1/price=0.0000\n", - " cid=3fcc-0 dx= 0.000 LINK dy= 0.000 ETH p=0.00 1/p=228.72\n", - "<<--- LINK/ETH\n", - "\n", - "sum_by_tkn={'USDP': -14810.776548455411, 'ETH': 8.693167770410668, 'LINK': -863.4145301701064}\n", - "result=(-14810.776548455411, -863.4145301701064)\n", - "<<<=====================\n" - ] - }, - { - "data": { - "text/plain": [ - "CPCArbOptimizer.MargpOptimizerResult(result=8.693167770410668, time=0.003350973129272461, method='margp', targettkn='ETH', p_optimal_t=(1.1199678720803443e+103, 9.79483400374927e+104), dtokens_t=(-14810.776548455411, -863.4145301701064), tokens_t=('USDP', 'LINK'), errormsg=None)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "O = MargPOptimizer(CC)\n", - "r = O.optimize(sfc=\"ETH\", params=dict(\n", - " pstart=PRICES,\n", - " verbose=True,\n", - " debug=True,\n", - " debug_j=True,\n", - " debug_dtkn=True,\n", - " debug_dtkn2=True,\n", - "))\n", - "r" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5282276-20fc-49e5-a69f-762bb6b6da2f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,auto:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/NBTest/OptimizerTesting.py b/resources/NBTest/OptimizerTesting.py deleted file mode 100644 index b3d1d4579..000000000 --- a/resources/NBTest/OptimizerTesting.py +++ /dev/null @@ -1,236 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -try: - from fastlane_bot.tools.simplepair import SimplePair - from fastlane_bot.tools.cpc import ConstantProductCurve, CPCContainer - from fastlane_bot.tools.optimizer import PairOptimizer, MargPOptimizer -except: - from tools.simplepair import SimplePair - from tools.cpc import ConstantProductCurve, CPCContainer - from tools.optimizer import PairOptimizer, MargPOptimizer -CPC = ConstantProductCurve - -import pandas as pd - -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(SimplePair)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPCContainer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(PairOptimizer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(MargPOptimizer)) -# - - -# # Optimizer Testing - -# This is a light workbook allowing to look at issues that may arise when running the optimizer on a specific set of curves. -# -# Instructions: -# -# - locate the **exact** curve set to feed to the optimizer (it will be somewhere in the logging output, and it will be a list of ConstantProductCurve objects) -# - assign it to the `CurvesRaw` variable as shown below -# - add the missing token addresses to the `TOKENS` dict below -# - provide consistent values for `PSTART` -# - run the workbook - -# ### >> Enter curves -# -# Place curves here in the form -# -# CurvesRaw = [ -# ConstantProductCurve(k=27518385.40998667, x=1272.2926367501436, x_act=0, ...), -# ConstantProductCurve(k=6.160500599566333e+18, x=11099999985.149971, x_act=0, ...), -# ... -# ] - -CurvesRaw = [ - ConstantProductCurve(k=27518385.40998667, x=1272.2926367501436, x_act=0, y_act=2000.9999995236503, alpha=0.5, pair='0x514910771AF9Ca656af840dff83E8264EcF986CA/0x8E870D67F660D95d5be530380D0eC0bd388289E1', cid='0x425d5d4ad7243f88d9f4cde8da52863b45af1f64e05bede1299909bcaa6c52d1-0', fee=2000, descr='carbon_v1 0x514910771AF9Ca656af840dff83E8264EcF986CA\\/0x8E870D67F660D95d5be530380D0eC0bd388289E1 2000', constr='carb', params={'exchange': 'carbon_v1', 'y': 2000.9999995236503, 'yint': 2000.9999995236503, 'A': 0.38144823884371704, 'B': 3.7416573867739373, 'pa': 16.99999999999995, 'pb': 13.99999999999997}), - ConstantProductCurve(k=6.160500599566333e+18, x=11099999985.149971, x_act=0, y_act=55.50000002646446, alpha=0.5, pair='0x8E870D67F660D95d5be530380D0eC0bd388289E1/0x514910771AF9Ca656af840dff83E8264EcF986CA', cid='0x425d5d4ad7243f88d9f4cde8da52863b45af1f64e05bede1299909bcaa6c52d1-1', fee=2000, descr='carbon_v1 0x514910771AF9Ca656af840dff83E8264EcF986CA\\/0x8E870D67F660D95d5be530380D0eC0bd388289E1 2000', constr='carb', params={'exchange': 'carbon_v1', 'y': 55.50000002646446, 'yint': 55.50000002646446, 'A': 0, 'B': 0.22360678656963742, 'pa': 0.04999999999999889, 'pb': 0.04999999999999889}), - ConstantProductCurve(k=14449532.299465338, x=57487.82879658422, x_act=0, y_act=5.0, alpha=0.5, pair='0x514910771AF9Ca656af840dff83E8264EcF986CA/0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE', cid='0x3fcccfe0063b71fc973fab8dea39b6be9da80125910c10e57b924b3e4687295a-0', fee=2000, descr='carbon_v1 0x514910771AF9Ca656af840dff83E8264EcF986CA/0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE 2000', constr='carb', params={'exchange': 'carbon_v1', 'y': 5.0, 'yint': 8.582730309868262, 'A': 0.002257868117407469, 'B': 0.06480740698407672, 'pa': 0.004497751124437756, 'pb': 0.004199999999999756}), - ConstantProductCurve(k=14456757.06563651, x=251.4750925240284, x_act=0, y_act=807.9145301701096, alpha=0.5, pair='0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE/0x514910771AF9Ca656af840dff83E8264EcF986CA', cid='0x3fcccfe0063b71fc973fab8dea39b6be9da80125910c10e57b924b3e4687295a-1', fee=2000, descr='carbon_v1 0x514910771AF9Ca656af840dff83E8264EcF986CA/0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE 2000', constr='carb', params={'exchange': 'carbon_v1', 'y': 807.9145301701096, 'yint': 1974.7090228584536, 'A': 0.519359008452966, 'B': 14.907119849998594, 'pa': 237.97624997025295, 'pb': 222.22222222222211}), - ConstantProductCurve(k=56087178.30932376, x=131.6236694086859, x_act=0, y_act=15920.776548455418, alpha=0.5, pair='0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE/0x8E870D67F660D95d5be530380D0eC0bd388289E1', cid='0x6cc4b198ec4cf17fdced081b5611279be73e200711238068b5340e606ba86646-0', fee=2000, descr='carbon_v1 0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE\\/0x8E870D67F660D95d5be530380D0eC0bd388289E1 2000', constr='carb', params={'exchange': 'carbon_v1', 'y': 15920.776548455418, 'yint': 32755.67010983316, 'A': 4.373757425036729, 'B': 54.77225575051648, 'pa': 3498.2508745627138, 'pb': 2999.9999999999854}), - ConstantProductCurve(k=56059148.73497429, x=426117.72306081816, x_act=0, y_act=5.0, alpha=0.5, pair='0x8E870D67F660D95d5be530380D0eC0bd388289E1/0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE', cid='0x6cc4b198ec4cf17fdced081b5611279be73e200711238068b5340e606ba86646-1', fee=2000, descr='carbon_v1 0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE\\/0x8E870D67F660D95d5be530380D0eC0bd388289E1 2000', constr='carb', params={'exchange': 'carbon_v1', 'y': 5.0, 'yint': 10.106093048875099, 'A': 0.0013497708452092638, 'B': 0.016903085094568837, 'pa': 0.0003331667499582927, 'pb': 0.0002857142857142352}) -] -CCRaw = CPCContainer(CurvesRaw) - -# ### >> Enter prices -# -# Provide current prices (`pstart`) here, in the format -# -# PRICES = { -# '0x8E87...': 0.0003087360213944532, -# '0x5149...': 0.004372219704179475, -# '0xEeee...': 1 -# } -# -# The price numeraire does not matter as long as they are all in the same numeraire. All tokens must be present. Additional tokens can be added and will be ignored. - -PRICES_RAW = { - '0x8E870D67F660D95d5be530380D0eC0bd388289E1': 0.0003087360213944532, - '0x514910771AF9Ca656af840dff83E8264EcF986CA': 0.004372219704179475, - '0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE': 1 -} - -# ### >> Enter tokens -# -# Provide token tickers here, in the format -# -# TOKENS = { -# "0x5149...": "LINK", -# "0x8E87...": "USDP", -# "0xEeee...": "ETH", -# } -# -# All tokens must be present. Additional tokens will be ignored. You must also provide the `TARGET_TOKEN` (default: first token of `TOKENS`) -# - -# + -TOKENS = { - "0xEeeeeEeeeEeEeeEeEeEeeEEEeeeeEeeeeeeeEEeE": "ETH", - "0x514910771AF9Ca656af840dff83E8264EcF986CA": "LINK", - "0x8E870D67F660D95d5be530380D0eC0bd388289E1": "USDP", -} - -TARGET_TOKEN_RAW = list(TOKENS)[0] -TARGET_TOKEN_RAW -# - - -# ### >>> Run optimizer -# -# please make sure that this line runs without errors (other than the error that needs to be addressed of course) - -O = MargPOptimizer(CCRaw) -r = O.optimize(sfc=TARGET_TOKEN_RAW, params=dict(pstart=PRICES_RAW)) -r - - -# **do not worry about the code below here; this is for the actual testing and will be adapted as need be** - -# ### >>> Preprocessing -# -# Please ensure that this code runs without error. Errors here mean that the data provided above is not consistent. - -def replace_tokens(dct): - """replaces the token address with the token name in dct""" - tkns = dct["pair"].split("/") - for i in range(len(tkns)): - #tkns[i] = TOKENS.get(tkns[i]) or tkns[i] - tkns[i] = TOKENS[tkns[i]] - dct["pair"] = "/".join(tkns) - return dct - - -# If this fails this probably means that one of the tokens has not been defined above - -CC = CPCContainer.from_dicts([replace_tokens(d) for d in CCRaw.asdicts()]) -PRICES = {TOKENS[addr]:price for addr, price in PRICES_RAW.items()} -TARGET_TOKEN = TOKENS[TARGET_TOKEN_RAW] -PRICES - - -def p(pair=None, *, tknb=None, tknq=None, prices=None): - "price of tknb in terms of tknq" - if not pair is None: - tknb, tknq = pair.split("/") - p = prices or PRICES - return p[tknb]/p[tknq] - - -# The code below ensures that in ETH/LINK, LINK is the quote token and ETH the base token (for better price displays) - -SimplePair.NUMERAIRE_TOKENS["LINK"] = SimplePair.NUMERAIRE_TOKENS["ETH"] - 1 -#SimplePair.NUMERAIRE_TOKENS - -# ## Curves - -print("Num curves: ", len(CC)) -print("Pairs: ", set(c.pairo.primary_n for c in CC)) -print("Target token: ", TARGET_TOKEN) - -PRICE_DECIMALS = 2 -curvedata = [dict( - cid0 = f"{c.cid[2:6]}{c.cid[-2:]}", - exch = c.params['exchange'], - pair = c.pairo.primary_n, - mktp = round(p(c.pairo.primary_n), PRICE_DECIMALS), - bs = c.buysell(), - tkn = c.pairo.primary_tknb, - p = round(c.primaryp(), PRICE_DECIMALS), - p_min = round(c.p_min_primary(), PRICE_DECIMALS), - p_max = round(c.p_max_primary(), PRICE_DECIMALS), - tknp = p(tknb=c.pairo.primary_tknb, tknq=TARGET_TOKEN), - wbp = max(int((c.p_max_primary()/c.p_min_primary() - 1)*10000), 1), - liq = round(c.tvl(tkn=c.pairo.primary_tknb), 2), - liqtt = round(c.x_act*p(tknb=c.tknx, tknq=TARGET_TOKEN) + c.y_act*p(tknb=c.tkny, tknq=TARGET_TOKEN), 2), -) for c in CC] -#curvedata - -# - `cid0`: shortened CID (same as in `debug_tkn2`) -# - `exch`: the type of the curve / exchange in question -# - `pair`: the normalized pair of the curve -# - `mktp`: the current market price of that pair (according to `PRICES_RAW`) -# - `bs`: whether curves buys ("b"), sells ("s") the primary tokenm, or both -# - `tkn`: the primary token (base token of primary pair) -# - `p`, `p_min`, `p_max`: the current / minimum / maximum price of the curve -# - `tknp`: the price of `tkn` (as above) in terms of `TARGET_TOKEN`, as per the market price -# - `wbp`: width of the range (p_max/p_min) in basis points -# - `liq`: liquidity (in units of `tkn` as defined above; converted at curve price) -# - `liqtt`: total curve liquidity (in `TARGET_TOKEN` units; converted at `mktp`) -# - -curvedf = pd.DataFrame(curvedata) -curvedf - -# Curves 2,3 and 4,5 are overlapping ranges with good liquidity that serve as a market for curve 1 which is the operational curve in this arbitrage. In fact, what we expect is -# -# - Curve 0 (`425d-0`) buys LINK for USDP from 17 down to 14 -# - Curves 2-5 (`3fcc` and `6cc4`) sell LINK for USDP (via ETH) at 14.16 and above -# -# The expected price is somewhat above 14, depending on the capacity of the overlapping curves 2-5 - -# The approximate effective LINK/USDP price from the overlapping curves (buy and sell) - -3239.013043/228.716777 - -# The width of the overlapping ranges (2,3 and 4,5) in basis points - -(228.716777/228.602476-1)*10000, (3239.013043/3237.394345-1)*10000 - -# For reference, the CID dataframe `ciddf` (separate because the field is too long; can be joined to `curvedf` via index) - -ciddf = pd.DataFrame([dict(cid=c.cid) for c in CC]) -ciddf - -# + -#help(CC[0]) - -# + -#help(CC[0].pairo) -# - - -# ## MargPOptimizer - -O = MargPOptimizer(CC) -r = O.optimize(sfc="ETH", params=dict( - pstart=PRICES, - verbose=True, - debug=True, - debug_j=True, - debug_dtkn=True, - debug_dtkn2=True, -)) -r - - diff --git a/resources/NBTest/fls.py b/resources/NBTest/fls.py deleted file mode 100644 index 9ded0101c..000000000 --- a/resources/NBTest/fls.py +++ /dev/null @@ -1,119 +0,0 @@ -""" -FLS - File Load Save (simple wrappers for loading and saving data) - -:fsave: save data into a file -:fload: load data from a file -:join: convenience wrapper for os.path.join - -:VERSION HISTORY: - -- v1.0: fload, fsave, join -- v1.0.1: minor change in output -- v1.1: json and yaml -- v1.2: copyright notice, license & canonic URL - -:copyright: (c) Copyright Stefan LOESCH / topaze.blue 2022; ALL RIGHTS RESERVED -:license: [MIT](https://opensource.org/licenses/MIT) -:canonicurl: https://github.com/topazeblue/TopazePublishing/blob/main/code/fls.py -""" -__VERSION__ = "1.2-noyaml" -__DATE__ = "06/Jan/2023" - -import os as _os -import gzip as _gzip -import json as _json -#import yaml as _yaml - -######################################################### -# FSAVE -def fsave(data, fn, path=None, binary=False, json=False, yaml=False, wrapper=None, quiet=True, compressed=False): - """ - saves data to the file fn - - :data: the data to be saved - :fn: the filename - :path: the file path (default is "") - :binary: if True, the data is to be written as binary data* - :json: if True, the data is to be written as json data* - :yaml: if True, the data is to be written as yaml data* - :wrapper: a wrapper string where `{}` is replaced with the data - :quiet: if True, do not print info message - :compressed: use gz compression (implies binary*) - - *binary, json, yaml == True are mutually exclusive; behaviour is undefined otherwise - """ - assert yaml is False - if path is None: - path = "." - ffn = _os.path.join(path, fn) - - if not quiet: print (f"[fsave] Writing {fn} to {path}") - - if compressed: binary = True - ftype = "wb" if binary else "w" - - if json: - data = _json.dumps(data) - - if yaml: - data = _yaml.safe_dump(data) - - if not wrapper is None: - data = wrapper.format(data) - - if compressed: - data = _gzip.compress(data.encode()) - - with open (ffn, ftype) as f: - f.write(data) - -######################################################### -# FLOAD -def fload(fn, path=None, binary=False, json=False, yaml=False, wrapper=None, quiet=True, compressed=False): - """ - loads data from the file fn - - :fn: the filename - :path: the file path (default is "") - :binary: if the data is to be written as binary data - :json: if True, the data is to be written as json data* - :yaml: if True, the data is to be written as yaml data* - :wrapper: a wrapper string where `{}` is replaced with the data - :quiet: if True, do not print info message - :compressed: use gz compression (implies binary) - - *binary, json, yaml == True are mutually exclusive; behaviour is undefined otherwise - """ - assert yaml is False - - if path is None: - path = "." - ffn = _os.path.join(path, fn) - - if not quiet: print (f"[fload] Reading {fn} from {path}") - - if compressed: binary = True - ftype = "rb" if binary else "r" - with open (ffn, ftype) as f: - data = f.read() - - if compressed: - data = _gzip.decompress(data) - - if json: - data = _json.loads(data) - - if yaml: - data = _yaml.safe_load(data) - - if not wrapper is None: - data = wrapper.format(data) - return data - -CSSWRAPPER = "\n\n" - -######################################################### -# JOIN -def join(*args): - "convenience wrapper for os.path.join" - return _os.path.join(*args) \ No newline at end of file diff --git a/resources/NBTest/log.txt b/resources/NBTest/log.txt deleted file mode 100644 index bd1fe2f90..000000000 --- a/resources/NBTest/log.txt +++ /dev/null @@ -1 +0,0 @@ -Searching for main.py in /Users/mikewcasale/Documents/GitHub/bancorprotocol/fastlane-bot/resources/NBTest \ No newline at end of file diff --git a/resources/NBTest/requirements.txt b/resources/NBTest/requirements.txt deleted file mode 100644 index eb5ad1259..000000000 --- a/resources/NBTest/requirements.txt +++ /dev/null @@ -1,21 +0,0 @@ -psutil~=5.9.6 -packaging==21.3 -requests~=2.31.0 -python-dateutil~=2.8.2 -typing-extensions~=4.7.1 -python-dotenv~=0.16.0 -joblib~=1.2.0 -pandas~=1.5.2 -alchemy-sdk~=0.1.1 -pyarrow~=11.0.0 -networkx~=3.0 -cvxpy~=1.3.1 -matplotlib~=3.7.1 -dataclass_wizard~=0.22.2 -hexbytes~=0.3.1 -click~=8.1.3 -setuptools~=67.6.1 -protobuf~=4.24.4 -tqdm~=4.64.1 -web3~=6.11.2 -nest-asyncio~=1.5.8 diff --git a/resources/NBTest/test_900_OptimizerDetailedSlow.py b/resources/NBTest/test_900_OptimizerDetailedSlow.py deleted file mode 100644 index 8e4ea94f1..000000000 --- a/resources/NBTest/test_900_OptimizerDetailedSlow.py +++ /dev/null @@ -1,793 +0,0 @@ -# ------------------------------------------------------------ -# Auto generated test file `test_900_OptimizerDetailedSlow.py` -# ------------------------------------------------------------ -# source file = NBTest_900_OptimizerDetailedSlow.py -# test id = 900 -# test comment = OptimizerDetailedSlow -# ------------------------------------------------------------ - - - -try: - from fastlane_bot import Bot, Config, ConfigDB, ConfigNetwork, ConfigProvider - from fastlane_bot.tools.cpc import ConstantProductCurve as CPC, CPCContainer, Pair - from fastlane_bot.tools.analyzer import CPCAnalyzer - from fastlane_bot.tools.optimizer import PairOptimizer, MargPOptimizer, ConvexOptimizer - from fastlane_bot.tools.optimizer import OptimizerBase, CPCArbOptimizer - from fastlane_bot.tools.arbgraphs import ArbGraph - from fastlane_bot.tools.cpcbase import AttrDict - from fastlane_bot.testing import * - -except: - from tools.cpc import ConstantProductCurve as CPC, CPCContainer, Pair - from tools.analyzer import CPCAnalyzer - from tools.optimizer import PairOptimizer, MargPOptimizer, ConvexOptimizer - from tools.optimizer import OptimizerBase, CPCArbOptimizer - from tools.arbgraphs import ArbGraph - from tools.cpcbase import AttrDict - from tools.testing import * - -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPCAnalyzer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(OptimizerBase)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPCArbOptimizer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(PairOptimizer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(MargPOptimizer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(ConvexOptimizer)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(ArbGraph)) -#print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Bot)) -import itertools as it -import collections as cl -#plt.style.use('seaborn-dark') -plt.rcParams['figure.figsize'] = [12,6] - -T = AttrDict( - NATIVE_ETH="ETH-EEeE", - AAVE="AAVE-DaE9", - WETH="WETH-6Cc2", - ETH="WETH-6Cc2", - WBTC="WBTC-C599", - BTC="WBTC-C599", - USDC="USDC-eB48", - USDT="USDT-1ec7", - DAI="DAI-1d0F", - LINK="LINK-86CA", - MKR="MKR-79A2", - BNT="BNT-FF1C", - UNI="UNI-F984", - SUSHI="SUSHI-0fE2", - CRV="CRV-cd52", - FRAX="FRAX-b99e", - HEX="HEX-eb39", - MATIC="MATIC-eBB0", - HDRN="HDRN-5e06", - SHIB="SHIB-C4cE", - ICHI="ICHI-C4d6", - OCTO="OCTO-2BA3", - ECO="ECO-5727", -) - - - -try: - CCm = CPCContainer.from_df(pd.read_csv("_data/NBTest_006.csv.gz")) -except: - CCm = CPCContainer.from_df(pd.read_csv("fastlane_bot/tests/_data/NBTest_006.csv.gz")) - -CCu3 = CCm.byparams(exchange="uniswap_v3") -CCu2 = CCm.byparams(exchange="uniswap_v2") -CCs2 = CCm.byparams(exchange="sushiswap_v2") -CCc1 = CCm.byparams(exchange="carbon_v1") -tc_u3 = CCu3.token_count(asdict=True) -tc_u2 = CCu2.token_count(asdict=True) -tc_s2 = CCs2.token_count(asdict=True) -tc_c1 = CCc1.token_count(asdict=True) -CAm = CPCAnalyzer(CCm) -#CCm.asdf().to_csv("A011-test.csv.gz", compression = "gzip") - -CA = CAm -pairs0 = CA.CC.pairs(standardize=False) -pairs = CA.pairs() -pairsc = CA.pairsc() -tokens = CA.tokens() - - -# ------------------------------------------------------------ -# Test 900 -# File test_900_OptimizerDetailedSlow.py -# Segment Market structure analysis [NOTEST] -# ------------------------------------------------------------ -def notest_market_structure_analysis(): -# ------------------------------------------------------------ - - print(f"Total pairs: {len(pairs0):4}") - print(f"Primary pairs: {len(pairs):4}") - print(f"...carbon: {len(pairsc):4}") - print(f"Tokens: {len(CA.tokens()):4}") - print(f"Curves: {len(CCm):4}") - - CA.count_by_pairs() - - CA.count_by_pairs(minn=2) - - # ### All crosses - - CCx = CCm.bypairs( - CCm.filter_pairs(notin=f"{T.ETH},{T.USDC},{T.USDT},{T.BNT},{T.DAI},{T.WBTC}") - ) - len(CCx), CCx.token_count()[:10] - - AGx=ArbGraph.from_cc(CCx) - AGx.plot(labels=False, node_size=50, node_color="#fcc")._ - - # ### Biggest crosses (HEX, UNI, ICHI, FRAX) - - CCx2 = CCx.bypairs( - CCx.filter_pairs(onein=f"{T.HEX}, {T.UNI}, {T.ICHI}, {T.FRAX}") - ) - ArbGraph.from_cc(CCx2).plot() - len(CCx2) - - # ### Carbon - - ArbGraph.from_cc(CCc1).plot()._ - - len(CCc1), len(CCc1.tokens()) - - CCc1.token_count() - - - len(CCc1.pairs()), CCc1.pairs() - - # ### Token subsets - - O = MargPOptimizer(CCm.bypairs( - CCm.filter_pairs(bothin=f"{T.ETH},{T.USDC},{T.USDT},{T.BNT},{T.DAI},{T.WBTC}") - )) - r = O.margp_optimizer(f"{T.USDC}", params=dict(verbose=False, debug=False)) - r.trade_instructions(ti_format=O.TIF_DFAGGR).fillna("") - - # + - #r.trade_instructions(ti_format=O.TIF_DFAGGR).fillna("").to_excel("ti.xlsx") - # - - - ArbGraph.from_r(r).plot()._ - - # + - #O.CC.plot() - # - - - -# ------------------------------------------------------------ -# Test 900 -# File test_900_OptimizerDetailedSlow.py -# Segment ABC Tests -# ------------------------------------------------------------ -def test_abc_tests(): -# ------------------------------------------------------------ - - assert raises(OptimizerBase).startswith("Can't instantiate abstract class") - assert raises(OptimizerBase.OptimizerResult).startswith("Can't instantiate abstract class") - - assert raises(CPCArbOptimizer).startswith("Can't instantiate abstract class") - assert raises(CPCArbOptimizer.OptimizerResult).startswith("Can't instantiate abstract class") - - assert not raises(MargPOptimizer, CCm) - assert not raises(PairOptimizer, CCm) - assert not raises(ConvexOptimizer, CCm) - - assert MargPOptimizer(CCm).kind == "margp" - assert PairOptimizer(CCm).kind == "pair" - assert ConvexOptimizer(CCm).kind == "convex" - - CPCArbOptimizer.MargpOptimizerResult(None, time=0,errormsg="err", optimizer=None) - - -# ------------------------------------------------------------ -# Test 900 -# File test_900_OptimizerDetailedSlow.py -# Segment General and Specific Tests -# ------------------------------------------------------------ -def test_general_and_specific_tests(): -# ------------------------------------------------------------ - - CA = CAm - - # ### General tests - - # #### General data integrity (should ALWAYS hold) - - assert len(pairs0) > 2500 - assert len(pairs) > 2500 - assert len(pairs0) > len(pairs) - assert len(pairsc) > 10 - assert len(CCm.tokens()) > 2000 - assert len(CCm)>4000 - assert len(CCm.filter_pairs(onein=f"{T.ETH}")) > 1900 # ETH pairs - assert len(CCm.filter_pairs(onein=f"{T.USDC}")) > 300 # USDC pairs - assert len(CCm.filter_pairs(onein=f"{T.USDT}")) > 190 # USDT pairs - assert len(CCm.filter_pairs(onein=f"{T.DAI}")) > 50 # DAI pairs - assert len(CCm.filter_pairs(onein=f"{T.WBTC}")) > 30 # WBTC pairs - - xis0 = {c.cid: (c.x, c.y) for c in CCm if c.x==0} - yis0 = {c.cid: (c.x, c.y) for c in CCm if c.y==0} - assert len(xis0) == 0 # set loglevel debug to see removal of curves - assert len(yis0) == 0 - - # #### Data integrity - - assert len(CCm) == 4155 - assert len(CCu3) == 1411 - assert len(CCu2) == 2177 - assert len(CCs2) == 236 - assert len(CCm.tokens()) == 2233 - assert len(CCm.pairs()) == 2834 - assert len(CCm.pairs(standardize=False)) == 2864 - - - assert CA.pairs() == CCm.pairs(standardize=True) - assert CA.pairsc() == {c.pairo.primary for c in CCm if c.P("exchange")=="carbon_v1"} - assert CA.tokens() == CCm.tokens() - - # #### prices - - r1 = CCc1.prices(result=CCc1.PR_TUPLE) - r2 = CCc1.prices(result=CCc1.PR_TUPLE, primary=False) - r3 = CCc1.prices(result=CCc1.PR_TUPLE, primary=False, inclpair=False) - assert isinstance(r1, tuple) - assert isinstance(r2, tuple) - assert isinstance(r3, tuple) - assert len(r1) == len(r2) - assert len(r1) == len(r3) - assert len(r1[0]) == 3 - assert isinstance(r1[0][0], str) - assert isinstance(r1[0][1], float) - assert len(r1[0][2].split("/"))==2 - - r2[:2] - - r3[:2] - - r1a = CCc1.prices(result=CCc1.PR_DICT) - r2a = CCc1.prices(result=CCc1.PR_DICT, primary=False) - r3a = CCc1.prices(result=CCc1.PR_DICT, primary=False, inclpair=False) - assert isinstance(r1a, dict) - assert isinstance(r2a, dict) - assert isinstance(r3a, dict) - assert len(r1a) == len(r1) - assert len(r1a) == len(r2a) - assert len(r1a) == len(r3a) - assert list(r1a.keys()) == list(x[0] for x in r1) - assert r1a.keys() == r2a.keys() - assert r1a.keys() == r3a.keys() - assert set(len(x) for x in r1a.values()) == {2}, "all records must be of of length 2" - assert set(type(x[0]) for x in r1a.values()) == {float}, "all records must have first type float" - assert set(type(x[1]) for x in r1a.values()) == {str}, "all records must have second type str" - assert tuple(r3a.values()) == r3 - - df = CCc1.prices(result=CCc1.PR_DF, primary=False) - assert len(df) == len(r1) - assert tuple(df.index) == tuple(x[0] for x in r1) - assert tuple(df["price"]) == r3 - df - - # #### more prices - - CCt = CCm.bypairs(f"{T.USDC}/{T.ETH}") - - r = CCt.prices(result=CCt.PR_TUPLE) - assert isinstance(r, tuple) - assert len(r) == len(CCt) - assert r[0] == ('6c988ffdc9e74acd97ccfb16dd65c110', 1833.9007005259564, 'WETH-6Cc2/USDC-eB48') - assert CCt.prices() == CCt.prices(result=CCt.PR_DICT) - r = CCt.prices(result=CCt.PR_DICT) - assert len(r) == len(CCt) - assert isinstance(r, dict) - assert r['6c988ffdc9e74acd97ccfb16dd65c110'] == (1833.9007005259564, 'WETH-6Cc2/USDC-eB48') - df = CCt.prices(result=CCt.PR_DF) - assert len(df) == len(CCt) - assert tuple(df.loc["1701411834604692317316873037158841057339-0"]) == (1799.9999997028303, 'WETH-6Cc2/USDC-eB48') - - # #### price_ranges - - CCt = CCm.bypairs(f"{T.USDC}/{T.ETH}") - CAt = CPCAnalyzer(CCt) - - r = CAt.price_ranges(result=CAt.PR_TUPLE) - assert len(r) == len(CCt) - assert r[0] == ( - 'WETH/USDC', # pair - '16dd65c110', # cid - 'sushiswap_v2', # exchange - 'b', # buy - 's', # sell - 0, # min_primary - None, # max_primary - 1833.9007005259564 # pp - ) - assert r[1] == ( - 'WETH/USDC', - '41057334-0', - 'carbon_v1', - 'b', - '', - 1699.999829864358, - 1700.000169864341, - 1700.000169864341 - ) - r = CAt.price_ranges(result=CAt.PR_TUPLE, short=False) - assert r[0] == ( - 'WETH-6Cc2/USDC-eB48', - '6c988ffdc9e74acd97ccfb16dd65c110', - 'sushiswap_v2', - 'b', - 's', - 0, - None, - 1833.9007005259564 - ) - r = CAt.price_ranges(result=CAt.PR_DICT) - assert len(r) == len(CCt) - assert r['6c988ffdc9e74acd97ccfb16dd65c110'] == ( - 'WETH/USDC', - '16dd65c110', - 'sushiswap_v2', - 'b', - 's', - 0, - None, - 1833.9007005259564 - ) - df = CAt.price_ranges(result=CAt.PR_DF) - assert len(df) == len(CCt) - assert tuple(df.index.names) == ('pair', 'exch', 'cid') - assert tuple(df.columns) == ('b', 's', 'p_min', 'p_max', 'p_marg') - assert set(df["p_marg"]) == set(x[-1] for x in CAt.price_ranges(result=CCt.PR_TUPLE)) - for p1, p2 in zip(df["p_marg"], df["p_marg"][1:]): - assert p2 >= p1 - df - - # #### count_by_pairs - - assert len(CA.count_by_pairs()) == len(CA.pairs()) - assert sum(CA.count_by_pairs()["count"])==len(CA.CC) - assert np.all(CA.count_by_pairs() == CA.count_by_pairs(asdf=True)) - assert len(CA.count_by_pairs()) == len(CA.count_by_pairs(asdf=False)) - assert type(CA.count_by_pairs()).__name__ == "DataFrame" - assert type(CA.count_by_pairs(asdf=False)).__name__ == "list" - assert type(CA.count_by_pairs(asdf=False)[0]).__name__ == "tuple" - for i in range(10): - assert len(CA.count_by_pairs(minn=i)) >= len(CA.count_by_pairs(minn=i)), f"failed {i}" - - # #### count_by_tokens - - r = CA.count_by_tokens() - assert len(r) == len(CA.tokens()) - assert sum(r["total"]) == 2*len(CA.CC) - assert tuple(r["total"]) == tuple(x[1] for x in CA.CC.token_count()) - for ix, row in r[:10].iterrows(): - assert row[0] >= sum(row[1:]), f"failed at {ix} {tuple(row)}" - CA.count_by_tokens() - - # #### pool_arbitrage_statistics - - pas = CAm.pool_arbitrage_statistics() - assert np.all(pas == CAm.pool_arbitrage_statistics(CAm.POS_DF)) - assert len(pas)==165 - assert list(pas.columns) == ['price', 'vl', 'itm', 'b', 's', 'bsv'] - assert list(pas.index.names) == ['pair', 'exchange', 'cid0'] - assert {x[0] for x in pas.index} == {Pair.n(x) for x in CAm.pairsc()} - assert {x[1] for x in pas.index} == {'bancor_v2', 'bancor_v3','carbon_v1','sushiswap_v2','uniswap_v2','uniswap_v3'} - pas - - pasd = CAm.pool_arbitrage_statistics(CAm.POS_DICT) - assert isinstance(pasd, dict) - assert len(pasd) == 26 - assert len(pasd['WETH-6Cc2/DAI-1d0F']) == 7 - pd0 = pasd['WETH-6Cc2/DAI-1d0F'][0] - assert pd0[:2] == ('WETH/DAI', 'WETH-6Cc2/DAI-1d0F') - assert iseq(pd0[2], 1840.1216491367131) - assert pd0[3:6] == ('594', '594', 'uniswap_v3') - assert iseq(pd0[6], 8.466598820198278) - assert pd0[7:] == ('', 'b', 's', 'buy-sell-WETH @ 1840.12 DAI per WETH') - pd0 - - pasl = CAm.pool_arbitrage_statistics(result = CAm.POS_LIST) - assert isinstance(pasl, tuple) - assert len(pasl) == len(pas) - pd0 = [(ix, x) for ix, x in enumerate(pasl) if x[2]==1840.1216491367131] - pd0 = pasl[pd0[0][0]] - assert pd0[:2] == ('WETH/DAI', 'WETH-6Cc2/DAI-1d0F') - assert iseq(pd0[2], 1840.1216491367131) - assert pd0[3:6] == ('594', '594', 'uniswap_v3') - assert iseq(pd0[6], 8.466598820198278) - assert pd0[7:] == ('', 'b', 's', 'buy-sell-WETH @ 1840.12 DAI per WETH') - pd0 - - # ### MargP Optimizer - - # #### margp optimizer - - tokenlist = f"{T.ETH},{T.USDC},{T.USDT},{T.BNT},{T.DAI},{T.WBTC}" - targettkn = f"{T.USDC}" - O = MargPOptimizer(CCm.bypairs(CCm.filter_pairs(bothin=tokenlist))) - r = O.margp_optimizer(targettkn, params=dict(verbose=False, debug=False)) - r.trade_instructions(ti_format=O.TIF_DFAGGR).fillna("") - - # #### MargpOptimizerResult - - assert type(r) == MargPOptimizer.MargpOptimizerResult - assert iseq(r.result, -4606.010157294979) - assert r.time > 0.001 - assert r.time < 0.1 - assert r.method == O.METHOD_MARGP - assert r.targettkn == targettkn - assert set(r.tokens_t)==set(['USDT-1ec7', 'WETH-6Cc2', 'WBTC-C599', 'DAI-1d0F', 'BNT-FF1C']) - p_opt_d0 = {t:x for x, t in zip(r.p_optimal_t, r.tokens_t)} - p_opt_d = {t:round(x,6) for x, t in zip(r.p_optimal_t, r.tokens_t)} - print("optimal p", p_opt_d) - assert p_opt_d == {'WETH-6Cc2': 1842.67228, 'WBTC-C599': 27604.143472, - 'BNT-FF1C': 0.429078, 'USDT-1ec7': 1.00058, 'DAI-1d0F': 1.000179} - assert r.p_optimal[r.targettkn] == 1 - po = [(k,v) for k,v in r.p_optimal.items()][:-1] - assert len(po)==len(r.p_optimal_t) - for k,v in po: - assert p_opt_d0[k] == v, f"error at {k}, {v}, {p_opt_d0[k]}" - - # #### TradeInstructions - - assert r.trade_instructions() == r.trade_instructions(ti_format=O.TIF_OBJECTS) - ti = r.trade_instructions(ti_format=O.TIF_OBJECTS) - cids = tuple(ti_.cid for ti_ in ti) - assert isinstance(ti, tuple) - assert len(ti) == 86 - ti0=[x for x in ti if x.cid=="357"] - assert len(ti0)==1 - ti0=ti0[0] - assert ti0.cid == ti0.curve.cid - assert type(ti0).__name__ == "TradeInstruction" - assert type(ti[0]) == MargPOptimizer.TradeInstruction - assert ti0.tknin == f"{T.USDT}" - assert ti0.tknout == f"{T.USDC}" - assert round(ti0.amtin, 8) == 1214.45596849 - assert round(ti0.amtout, 8) == -1216.41933959 - if not ti0.error is None: - print(ti0) - print(ti0.error) - assert ti0.error is None - ti[:2] - - tid = r.trade_instructions(ti_format=O.TIF_DICTS) - assert isinstance(tid, tuple) - assert len(tid) == len(ti) - tid0=[x for x in tid if x["cid"]=="357"] - assert len(tid0)==1 - tid0=tid0[0] - assert type(tid0)==dict - assert tid0["tknin"] == f"{T.USDT}" - assert tid0["tknout"] == f"{T.USDC}" - assert round(tid0["amtin"], 8) == 1214.45596849 - assert round(tid0["amtout"], 8) == -1216.41933959 - assert tid0["error"] is None - tid[:2] - - df = r.trade_instructions(ti_format=O.TIF_DF).fillna("") - assert tuple(df.index) == cids - assert np.all(r.trade_instructions(ti_format=O.TIF_DFRAW).fillna("")==df) - assert len(df) == len(ti) - assert list(df.columns)[:4] == ['pair', 'pairp', 'tknin', 'tknout'] - assert len(df.columns) == 4 + len(r.tokens_t) + 1 - tif0 = dict(df.loc["357"]) - assert tif0["pair"] == "USDC-eB48/USDT-1ec7" - assert tif0["pairp"] == "USDC/USDT" - assert tif0["tknin"] == tid0["tknin"] - assert tif0[tif0["tknin"]] == tid0["amtin"] - assert tif0[tif0["tknout"]] == tid0["amtout"] - df[:2] - - dfa = r.trade_instructions(ti_format=O.TIF_DFAGGR).fillna("") - assert tuple(dfa.index)[:-4] == cids - assert len(dfa) == len(df)+4 - assert len(dfa.columns) == len(r.tokens_t) + 1 - assert set(dfa.columns) == set(r.tokens_t).union(set([r.targettkn])) - assert list(dfa.index)[-4:] == ['PRICE', 'AMMIn', 'AMMOut', 'TOTAL NET'] - dfa[:10] - - dfpg = r.trade_instructions(ti_format=O.TIF_DFPG) - assert set(x[1] for x in dfpg.index) == set(cids) - assert np.all(dfpg["gain_tknq"]>=0) - assert np.all(dfpg["gain_r"]>=0) - assert round(np.max(dfpg["gain_r"]),8) == 0.04739068 - assert round(np.min(dfpg["gain_r"]),8) == 1.772e-05 - assert len(dfpg) == len(ti) - for p, t in zip(tuple(dfpg["pair"]), tuple(dfpg["tknq"])): - assert p.split("/")[1] == t, f"error in {p} [{t}]" - print(f"total gains: {sum(dfpg['gain_ttkn']):,.2f} {r.targettkn} [result={-r.result:,.2f}]") - assert abs(sum(dfpg["gain_ttkn"])/r.result+1)<0.01 - dfpg[:10] - - # ### Convex Optimizer - - tokens = f"{T.DAI},{T.USDT},{T.HEX},{T.WETH},{T.LINK}" - CCo = CCu2.bypairs(CCu2.filter_pairs(bothin=tokens)) - CCo += CCs2.bypairs(CCu2.filter_pairs(bothin=tokens)) - CA = CPCAnalyzer(CCo) - O = ConvexOptimizer(CCo) - #ArbGraph.from_cc(CCo).plot()._ - - CA.count_by_tokens() - - # + - #CCo.plot() - # - - - # #### convex optimizer - - targettkn = T.USDT - # r = O.margp_optimizer(targettkn, params=dict(verbose=True, debug=False)) - # r - - SFC = O.SFC(**{targettkn:O.AMMPays}) - r = O.convex_optimizer(SFC, verbose=False, solver=O.SOLVER_SCS) - r - - # #### NofeesOptimizerResult - - round(r.result,-5) - - assert type(r) == ConvexOptimizer.NofeesOptimizerResult - # assert round(r.result,-5) <= -1500000.0 - # assert round(r.result,-5) >= -2500000.0 - # assert r.time < 8 - assert r.method == "convex" - assert set(r.token_table.keys()) == set(['USDT-1ec7', 'WETH-6Cc2', 'LINK-86CA', 'DAI-1d0F', 'HEX-eb39']) - assert len(r.token_table[T.USDT].x)==0 - assert len(r.token_table[T.USDT].y)==10 - lx = list(it.chain(*[rr.x for rr in r.token_table.values()])) - lx.sort() - ly = list(it.chain(*[rr.y for rr in r.token_table.values()])) - ly.sort() - assert lx == [_ for _ in range(21)] - assert ly == lx - - # #### trade instructions - - ti = r.trade_instructions() - assert type(ti[0]) == ConvexOptimizer.TradeInstruction - - assert r.trade_instructions() == r.trade_instructions(ti_format=O.TIF_OBJECTS) - ti = r.trade_instructions(ti_format=O.TIF_OBJECTS) - cids = tuple(ti_.cid for ti_ in ti) - assert isinstance(ti, tuple) - assert len(ti) == 21 - ti0=[x for x in ti if x.cid=="175"] - assert len(ti0)==1 - ti0=ti0[0] - assert ti0.cid == ti0.curve.cid - assert type(ti0).__name__ == "TradeInstruction" - assert type(ti[0]) == ConvexOptimizer.TradeInstruction - assert ti0.tknin == f"{T.LINK}" - assert ti0.tknout == f"{T.DAI}" - # assert round(ti0.amtin, 8) == 8.50052943 - # assert round(ti0.amtout, 8) == -50.40963779 - if not ti0.error is None: - print(ti0) - print(ti0.error) - assert ti0.error is None - print(r.error, ti0.error) - ti[:2], ti0, r - - tid = r.trade_instructions(ti_format=O.TIF_DICTS) - assert isinstance(tid, tuple) - assert type(tid[0])==dict - assert len(tid) == len(ti) - tid0=[x for x in tid if x["cid"]=="175"] - assert len(tid0)==1 - tid0=tid0[0] - assert tid0["tknin"] == f"{T.LINK}" - assert tid0["tknout"] == f"{T.DAI}" - # assert round(tid0["amtin"], 8) == 8.50052943 - # assert round(tid0["amtout"], 8) == -50.40963779 - assert tid0["error"] is None - tid[:2] - - df = r.trade_instructions(ti_format=O.TIF_DF).fillna("") - assert tuple(df.index) == cids - assert np.all(r.trade_instructions(ti_format=O.TIF_DFRAW).fillna("")==df) - assert len(df) == len(ti) - assert list(df.columns)[:4] == ['pair', 'pairp', 'tknin', 'tknout'] - assert len(df.columns) == 4 + 4 + 1 - tif0 = dict(df.loc["175"]) - assert tif0["pair"] == 'LINK-86CA/DAI-1d0F' - assert tif0["pairp"] == "LINK/DAI" - assert tif0["tknin"] == tid0["tknin"] - assert tif0[tif0["tknin"]] == tid0["amtin"] - assert tif0[tif0["tknout"]] == tid0["amtout"] - df[:2] - - assert raises(r.trade_instructions, ti_format=O.TIF_DFAGGR).startswith("TIF_DFAGGR not implemented for") - assert raises(r.trade_instructions, ti_format=O.TIF_DFPG).startswith("TIF_DFPG not implemented for") - - # ### Simple Optimizer - - pair = f"{T.ETH}/{T.USDC}" - CCs = CCm.bypairs(pair) - CA = CPCAnalyzer(CCs) - O = PairOptimizer(CCs) - #ArbGraph.from_cc(CCs).plot()._ - - CA.count_by_tokens() - - # + - #CCs.plot() - # - - - # #### simple optimizer - - r = O.optimize(T.USDC) - r - - # #### result - - assert type(r) == PairOptimizer.MargpOptimizerResult - assert round(r.result, 5) == -1217.2442, f"{round(r.result, 5)}" - assert r.time < 0.1 - assert r.method == "margp-pair" - assert r.errormsg is None - - # #### trade instructions - - ti = r.trade_instructions() - assert type(ti[0]) == PairOptimizer.TradeInstruction - - assert r.trade_instructions() == r.trade_instructions(ti_format=O.TIF_OBJECTS) - ti = r.trade_instructions(ti_format=O.TIF_OBJECTS) - cids = tuple(ti_.cid for ti_ in ti) - assert isinstance(ti, tuple) - assert len(ti) == 12 - ti0=[x for x in ti if x.cid=="6c988ffdc9e74acd97ccfb16dd65c110"] - assert len(ti0)==1 - ti0=ti0[0] - assert ti0.cid == ti0.curve.cid - assert type(ti0).__name__ == "TradeInstruction" - assert type(ti[0]) == PairOptimizer.TradeInstruction - assert ti0.tknin == f"{T.USDC}" - assert ti0.tknout == f"{T.WETH}" - assert round(ti0.amtin, 5) == 48153.80865 - assert round(ti0.amtout, 5) == -26.18300 - assert ti0.error is None - ti[:2] - - tid = r.trade_instructions(ti_format=O.TIF_DICTS) - assert isinstance(tid, tuple) - assert type(tid[0])==dict - assert len(tid) == len(ti) - tid0=[x for x in tid if x["cid"]=="6c988ffdc9e74acd97ccfb16dd65c110"] - assert len(tid0)==1 - tid0=tid0[0] - assert tid0["tknin"] == f"{T.USDC}" - assert tid0["tknout"] == f"{T.WETH}" - assert round(tid0["amtin"], 5) == 48153.80865 - assert round(tid0["amtout"], 5) == -26.183 - assert tid0["error"] is None - tid[:2] - - # trade instructions of format `TIF_DFRAW` (same as `TIF_DF`): raw dataframe - - df = r.trade_instructions(ti_format=O.TIF_DF).fillna("") - assert tuple(df.index) == cids - assert np.all(r.trade_instructions(ti_format=O.TIF_DFRAW).fillna("")==df) - assert len(df) == len(ti) - assert list(df.columns)[:4] == ['pair', 'pairp', 'tknin', 'tknout'] - assert len(df.columns) == 4 + 1 + 1 - tif0 = dict(df.loc["6c988ffdc9e74acd97ccfb16dd65c110"]) - assert tif0["pair"] == 'WETH-6Cc2/USDC-eB48' - assert tif0["pairp"] == "WETH/USDC" - assert tif0["tknin"] == tid0["tknin"] - assert tif0[tif0["tknin"]] == tid0["amtin"] - assert tif0[tif0["tknout"]] == tid0["amtout"] - df[:2] - - # trade instructions of format `TIF_DFAGGR` (aggregated data frame) - - df = r.trade_instructions(ti_format=O.TIF_DFAGGR) - assert len(df) == 16 - assert tuple(df.index[-4:]) == ('PRICE', 'AMMIn', 'AMMOut', 'TOTAL NET') - assert tuple(df.columns) == ('USDC-eB48', 'WETH-6Cc2') - df - - - - # prices and gains analysis data frame `TIF_DFPG` - - df = r.trade_instructions(ti_format=O.TIF_DFPG) - assert len(df) == 12 - assert set(x[0] for x in tuple(df.index)) == {'carbon_v1', 'sushiswap_v2', 'uniswap_v2', 'uniswap_v3'} - assert max(df["margp"]) == min(df["margp"]) - assert tuple(df.index.names) == ('exch', 'cid') - assert tuple(df.columns) == ( - 'fee', - 'pair', - 'amt_tknq', - 'tknq', - 'margp0', - 'effp', - 'margp', - 'gain_r', - 'gain_tknq', - 'gain_ttkn' - ) - df - - -# ------------------------------------------------------------ -# Test 900 -# File test_900_OptimizerDetailedSlow.py -# Segment Analysis by pair -# ------------------------------------------------------------ -def test_analysis_by_pair(): -# ------------------------------------------------------------ - - # + - # CCm1 = CAm.CC.copy() - # CCm1 += CPC.from_carbon( - # pair=f"{T.WETH}/{T.USDC}", - # yint = 1, - # y = 1, - # pa = 1500, - # pb = 1501, - # tkny = f"{T.WETH}", - # cid = "test-1", - # isdydx=False, - # params=dict(exchange="carbon_v1"), - # ) - # CAm1 = CPCAnalyzer(CCm1) - # CCm1.asdf().to_csv("NBTest_006-augmented.csv.gz", compression = "gzip") - # - - - pricedf = CAm.pool_arbitrage_statistics() - assert len(pricedf)==165 - pricedf - - # ### WETH/USDC - - pair = "WETH-6Cc2/USDC-eB48" - print(f"Pair = {pair}") - - df = pricedf.loc[Pair.n(pair)] - assert len(df)==24 - df - - pi = CAm.pair_data(pair) - O = MargPOptimizer(pi.CC) - - # #### Target token = base token - - targettkn = pair.split("/")[0] - print(f"Target token = {targettkn}") - r = O.margp_optimizer(targettkn, params=dict(verbose=False, debug=False)) - r.trade_instructions(ti_format=O.TIF_DFAGGR) - - dfti1 = r.trade_instructions(ti_format=O.TIF_DFPG8) - print(f"Total gain: {sum(dfti1['gain_ttkn']):.4f} {targettkn}") - dfti1 - - # #### Target token = quote token - - targettkn = pair.split("/")[1] - print(f"Target token = {targettkn}") - r = O.margp_optimizer(targettkn, params=dict(verbose=False, debug=False)) - r.trade_instructions(ti_format=O.TIF_DFAGGR) - - dfti2 = r.trade_instructions(ti_format=O.TIF_DFPG8) - print(f"Total gain: {sum(dfti2['gain_ttkn']):.4f}", targettkn) - dfti2 - -for i in range(1000): - print("=="*40) - print(f"Test {i}") - print("=="*40) - test_abc_tests() - test_general_and_specific_tests() - test_abc_tests() - print() \ No newline at end of file diff --git a/resources/analysis/202401 Solidly/202401 Solidly-2.ipynb b/resources/analysis/202401 Solidly/202401 Solidly-2.ipynb deleted file mode 100644 index e10bb7f95..000000000 --- a/resources/analysis/202401 Solidly/202401 Solidly-2.ipynb +++ /dev/null @@ -1,1277 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "96348e86-5892-417a-9e2d-2fda430683d0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "---\n", - "Function v0.9.6 (26/Jan/2024)\n", - "SolidlyInvariant v0.9 (18/Jan/2024)\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import math as m\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "from sympy import symbols, sqrt, Eq\n", - "#import decimal as d\n", - "\n", - "import invariants.functions as f\n", - "from invariants.solidly import SolidlyInvariant, SolidlySwapFunction\n", - "\n", - "from testing import *\n", - "#D = d.Decimal\n", - "plt.rcParams['figure.figsize'] = [6,6]\n", - "\n", - "print(\"---\")\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(f.Function))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(SolidlyInvariant))" - ] - }, - { - "cell_type": "markdown", - "id": "a14a57f8-e21f-4652-9d68-0cff0c4afead", - "metadata": {}, - "source": [ - "# Solidly Analysis -- Notebook 2" - ] - }, - { - "cell_type": "markdown", - "id": "9bcaf580-1389-41dc-b329-c68a80c75d56", - "metadata": {}, - "source": [ - "## Introduction" - ] - }, - { - "cell_type": "markdown", - "id": "114aac35-1cbf-4fe2-857f-05640f9f7c2a", - "metadata": {}, - "source": [ - "### Invariant function\n", - "\n", - "The Solidly invariant function is a stable swap curve\n", - "\n", - "$$\n", - " f(x,y) = x^3y+xy^3 = k\n", - "$$\n", - "\n", - "### Swap equation\n", - "\n", - "Solving the invariance equation as $y=y(x; k)$ gives the following result for what we want to call the **swap equation**\n", - "\n", - "$$\n", - "y(x; k) = \\frac{x^2}{\\sqrt[3]{L(x; k)}} - \\frac{\\sqrt[3]{L(x;k)}}{3}\n", - "$$\n", - "\n", - "$$\n", - "L(x;k) = -\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\n", - "$$\n", - "\n", - "Using the function $y(x;k)$ we can easily derive the **actual swap equation** at point $(x; k)$ as\n", - "\n", - "$$\n", - "\\Delta y = y(x+\\Delta x; k) - y(x; k)\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "1ac5dc18-0a49-4d37-a49b-0f57ef5ebdc4", - "metadata": {}, - "source": [ - "#### Precision issues and L\n", - "\n", - "The above form of L -- that we want to denote $L_1$ -- is numerically not well conditioned for small $x$. In order to improve conditioning we rewrite $L$ into the format $L_2$ below\n", - "\n", - "$$\n", - "L_2(x;k) = \\frac{27k}{2x} \\left(\\sqrt{1 + \\frac{108x^8}{729k^2}} - 1 \\right)\n", - "$$\n", - "\n", - "We note that for small $x$ the Taylor development below gives better results than finite precision numerics\n", - "\n", - "$$\n", - "\\sqrt{1+\\xi}-1 = \\frac{\\xi}{2} - \\frac{\\xi^2}{8} + \\frac{\\xi^3}{16} - \\frac{5\\xi^4}{128} + O(\\xi^5)\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "4c115505-7076-47b4-9c3e-fd0dd826683c", - "metadata": {}, - "source": [ - "### Price equation\n", - "\n", - "The derivative $p=dy/dx$ -- the **price equation** -- can be determined analytically but its complexity is such that perturbative calculation is preferrable. Importantly, we do not have how to invert it (ie write $x=x(p)$, which creates complications for our preferred method of optimization, the _marginal price optimization_.\n" - ] - }, - { - "cell_type": "markdown", - "id": "4b7faea6-a1ac-420e-b428-9cb579b55d4b", - "metadata": {}, - "source": [ - "## Analysing the invariance curve" - ] - }, - { - "cell_type": "markdown", - "id": "79cafbe1-7a31-45e5-a729-1045a267dcc2", - "metadata": {}, - "source": [ - "### Overall shape in real space\n", - "\n", - "Here we draw the invariance curves for difference values of $k$ (or $\\sqrt[4]{k}$ which is the quantity that scales linearly with currency amounts; see the notes in the first notebook regarding the scaling properties of the equation and its implications). More specifically we draw\n", - "\n", - "- the **invariance curves** for various values of $\\sqrt[4]{k}$ \n", - "\n", - "- their **central tangents**, showing the curves are very flat in the core region, and finally\n", - "\n", - "- the **boundary rays** of the different regimes of the equation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3e026df5-1fa0-401d-b081-de3e2a517de7", - "metadata": {}, - "outputs": [], - "source": [ - "k_sqrt4_v = [2, 4, 6, 8]\n", - "k_v = [kk**4 for kk in k_sqrt4_v]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2a783f42-7083-4cf1-97bb-cd6b8bcb61a2", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(0, m.sqrt(10), 50)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "\n", - "# draw the invariance curves\n", - "for kk in k_v: \n", - " y_f = SolidlySwapFunction(k=kk)\n", - " yy_v = [y_f(xx) for xx in x_v]\n", - " #yy_v = [y_f(xx, kk) for xx in x_v]\n", - " plt.plot(x_v, yy_v, marker=None, linestyle='-', label=f\"k={kk**0.25:.0f}^4\")\n", - "\n", - "# draw the central tangents\n", - "C = 0.5**(0.25)\n", - "label=\"tangents\"\n", - "for kk in k_sqrt4_v:\n", - " yy_v = [C*kk - (xx-C*kk) for xx in x_v]\n", - " plt.plot(x_v, yy_v, marker=None, linestyle='--', color=\"#aaa\", label=label)\n", - " label = \"\"\n", - "\n", - "# draw the rays\n", - "for mm in [2.6, 6]:\n", - " yy_v = [mm*xx for xx in x_v]\n", - " plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color=\"#aaa\", label=f\"ray (m={mm})\")\n", - " yy_v = [1/mm*xx for xx in x_v]\n", - " plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color=\"#aaa\")\n", - "\n", - "plt.grid(True)\n", - "plt.legend()\n", - "plt.xlim(0, max(x_v))\n", - "plt.ylim(0, max(x_v))\n", - "plt.title(\"Invariance curves for different values of $\\sqrt[4]{k}$\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.savefig(\"/Users/skl/Desktop/image.jpg\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "88c83e02-4a1e-4e19-ab27-c69fc093f2ea", - "metadata": {}, - "source": [ - "### In log/log space" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c2a56fdd-1c9f-48d8-ad3e-2cf8c061013a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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D8tGjR2stnzNnjqzX6+WtW7c2eJsdnSTLstwiVZYgCIIgCEIrIO72EgRBEAShQxHFjyAIgiAIHYoofm5hypQpuLi4MG3aNKWjCIIgCILQBETxcwtPP/00//nPf5SOIQiCIAhCExHFzy0kJCTc9HZPQRAEQRDalnZd/GzdupUJEybg6+uLJEn88ssv1z3no48+IiQkBL1eT+/evdm2bVvLBxUEQRAEocW06+KnrKyMnj178uGHH9b5/UWLFvHss8/y2muvcejQIQYPHsyYMWNISUlp4aSCIAiCILSUdj3C85gxY246Udy//vUvHnroIR5++GEA5s6dy7p16/j4448bNWdKVVVVrfloLBYL+fn5uLm53fYooIIgCILQkciyTElJCb6+vtfNBHC72nXxczPV1dUcOHCAV155pdbykSNHsnPnzkat86233uKvf/1rU8QTBEEQBAFITU297emNfqvDFj+5ubmYzWbrpHlXeXl51ZrNedSoURw8eJCysjL8/f1ZunQpffv2rXOdr776Ks8//7z166KiIgIDAzl79iyurq7NsyPNzGQxcSDrABtTN7L1ylbKTGXW7wXYBzA8YDiJQYkEOgQqmLJxysrKsLW1bTWtckajkc2bN5OQkIBWq1U6TpvV0sfRYrGQl5dHeno6mZmZGI1GAPr372+djNZisSCXlFC8ZAnFP/2M+b9zhqns7XGYOAHH6dPR/uazKDMzk+rqagID635vtcXzpTVmVjJTS267ubfVHOvPz8+nS5cuzXLTUYctfq767S8+WZZrLWvIzNg2NjbXzWIM4OrqipubW+NDKmysx1jGdh9LlbmK7WnbWZ28mqS0JNLN6Xx76Vu+vfQt3Vy7MSZkDGOCx+Bj76N05DolJyfj4+Njnbm7tb0mRqMRW1tb3NzcWs0vhrZIiePo4eFBt27dsFgsZGdnk56eTlhYmLWp/ujRo+Tk5BA8ZQqRjz1Gxdq15H09n+oLFzAv+pGCxUtwGDMG5/tmYf/fSUZvdX62xfOlNWZWMlNLbru5t9Wc62+OP1A7bPHj7u6OWq2u1coDkJ2dfV1rkFDDRm3D8KDhDA8aTpmxjE0pm1iTvIZd6bs4nX+a0/mnee/Ae8R4xjAmZAwjg0biZmgdBcb58+c5dOgQjo6ODB8+HI2mw576QjNSqVR4e3vj7e1tXSbLMikpKVRUVJCfn89hlQr/gACCvvwCz9Onyf96PmV793JGlinfuJGIf7+P132zsBs8uNW0SgpCe9NhfwPodDp69+7Nhg0bmDJlinX5hg0bmDRpkoLJ2gY7rR0TQicwIXQCBZUFbLi8gbWX1rI/cz+Hsg9xKPsQ/9j7D2J9YhkTMobhgcNx0Ck3XpK3tzd6vZ6AgABR+AgtSpIkRowYweXLl7l06RLFxcWkpKSQkpKCra0tYa/9Ae/ycs6cPIlJoyF3yxaq5zyKrnMobg8+iOOECah0OqV3QxDalXb9W6C0tJTz589bv05OTubw4cO4uroSGBjI888/z6xZs+jTpw8DBgzgs88+IyUlhccee0zB1G2Pi96Fu7rexV1d7yKrLIt1l9axJnkNx/OOszN9JzvTd/K3XX8j3j+esSFjifePR6/Rt2hGe3t7Ro0ahU78EhEUoNfr6dq1K126dKGgoIDk5GRSUlIoLy+nrKyMLjExJAQHk3/pEvqBgyhJTaP6/AUyXvsj2XPn4nrvvThPn47GxUXpXRGEdqFdFz/79+8nISHB+vXVzsj3338/8+fPZ/r06eTl5fG3v/2NjIwMunfvzurVqwkKClIqcpvnZefFfZH3cV/kfaQUp7AmeQ1rktdwoegCG1M2sjFlI3ZaO4YHDmdcyDj6+fRDo2qe0/DMmTN4eXnh7OwMIAofQXGSJOHq6oqrqys9e/bk0qVLeHp6AuDs7Ey1ry87u4ThP+9D3E6fwTh/PubMTHLm/pvcTz/D5a67cH1oNtr//owgCI3TroufoUOHIsvyTZ/zxBNP8MQTT7RQoo4l0DGQR3s+ypyoOZwtOMua5DWsTl5NRlkGyy8sZ/mF5bjqXRkdPJqxncYS5R7VZH0ckpOTOXr0KDqdjtGjR9fZEV0QlCLLMocPHyYjI6NWH8MrV65gNBpJTk0l2c4Wx1dexruiAv2PP2E5dIj8b76h4IcfcJ42DccHH1BuBwShjWvXxY/QOkiSRFfXrnR17crTvZ7mSM4RVl1cxfpL68mvzGfB6QUsOL0Af3t/xoSMYVyncYQ6h97WNv39/bl48SJ+fn6i8BFaHZPJRH5+PpWVlRQVFVlv5Y2OjsbX15dLly6RlpZGcUkJxYA09Q487rkb3yVLMe3eTcGCBRT8+CNeMTEYe0Sh7RSi7A4JQhsjih+hRakkFTGeMcR4xvByv5fZnb6b1cmr2ZiykbTSND4/9jmfH/ucri5dGdtpbKNvnddqtSQkJDT5qKCC0BS0Wi1DhgwhLy8PX19f63JJkvDy8sLLy4uYmBhSU1O5dOkS+fn5lGu1hH71JRX79pP70UeU7dmD0759XJ4wAafx43F79FFsRBEkCPUiih9BMVqVlsH+gxnsP5hyYzlb07ayKnkV269s50zBGc4cOMN7B96jl2cvxnUaR2JQIi76ujt8yrLMyZMncXJyso4EKgofoTWRZZmioiJrHzQbG5tahc9v6XQ6QkNDCQ0NpaioiOrqalQqFXax/dD36c26FSvQHDqE95q1FC1bRtGKFTiOHo3bY4+i79KlhfZKENomUfwIrYKt1pbRIaMZHTKaoqoiNlzewKqLqziQdYCD2Qc5mH2Qt/a8xUC/gYwNGUtCQAK2Wlvrz1+5coWTJ08iSRKjR4/G3t5ewb0RhNpkWWbfvn2kpqYyaNCgWuMA1YeTk1Otr69cuUKZ0Qjdu1PcvTuuWdk4LVuGvHo1xatX45CYiPvjj6GPiGjK3RCEdkMUP0Kr42TjxLQu05jWZRqZZZmsTV7L6uTVnMo/xda0rWxN24pBY6i5Y6zTOPr79MfPz4/AwEBcXFxE4SO0OrIsYzQakWUZk8l02+sLCAiwFlSyLJPn5UnenEewLynBee06LJs2UbJhA/ZDh+L++GMYevZsgr0QhPZDFD9Cq+Zt580D3R/gge4PcLHoImuS17Dq4ipSS1JZeXElKy+uxFXvau0oHeYepnRkQbiOSqViwIAB5OXl4eHhcdvrkyQJX19ftFotgwYNso4bVOrgQOmd04ju2gXT4iWUbtlC6ZYt2A0ciPsTj2Pbp08T7I0gtH2i+BHajE5OnXgy+kme6PkER3KOsOfgHs4VnWNd5Tq+P/U935/6niDHIMZ1Gsf4kPEEOAYoHVnowCwWC1lZWfj41HTYV6lUTVL4/JaTkxN9+/YlKiqKCxcuUFpaStidd1I1ezZ5n31OcloqlRcuUDZzFrZ9+9YUQf37i6kzhA5NFD9CmyNJEn6SH64lrsSqYhkTO4Z12evYnLKZy8WX+ejwR3x0+COi3KMY12kco0NG46p3VTq20IHIsszevXtJTU0lOjqasLDmb5G0sbEh4po+PjYhIbj8+U/sXLUKWZaxS07GbecuymY/hG3Pnng8+wx2/fs3ey5BaI1E8SO0SR4eHkRFRaHRaAgNDWV4t/9Ntrrq4ip2ZeziaO5RjuYe5e19bzPQdyDjO41naMDQWh2lBaE5SJKEnZ0dkiRha6vc+WaxWPDz86vpIB0SQllICLr8Alx376Z0zqM49u2L5++fFx2jhQ5HFD9CmyHLMrIsW29h79q1a63vXzvZam5FLmuT17Ly4kpO5J1g25VtbLuyDYPGwIjAEYzrNI5Yn9hmm1pDELp3705QUBCOjo6KZbCzs2PAgAGUl5dz/vx5Ll68SLWrC5ljx5AzJJ7AHxZSdsdUHMeNw+OZp9EFBiqWVRBakvjkbwGHDh3Cy8sLg8GAwWDA1tYWW1tbMfJwA8iyzJEjRygvL6d///63HMPH3eDOzIiZzIyYSXJRMqsurmLVxVWklaax4uIKVlxcgZvejTEhYxjfaTwRbhGiD4RwWywWC8nJyXTq1AlJkpAkSdHC51q2trZERUURERHB5cuXOXv2LBUqFe4xMVRcvkzxqlUUr1uHy1134f7E42jc3ZWOLAjNShQ/LeDKlSsUFBTUWubt7c3gwYOtX+/fvx8bGxtsbW2xs7OzFkgajXiJAIqLi7lw4QIWi4WcnJxa8yHdSohTCE/FPMWT0U9ap9ZYe2kteZV5fHfqO7479R2dnDoxIXQC4zuNx9uuYWOwCMK1fXwKCwvp3bu30pHqdPUycUhICEVFRbhMnUrlfbPI/td7nPLzJSclhbxx4/G8dwaus2ejFsNGCO2U+M3aArp06YJOp6OiooLKykoqKirQ6/UYjUaAmokMk5Pr/FmdToefnx89rxmnIzMzE71ej62tLVqttkO0WNja2tKvXz8qKytxdXW1HruGinSJJLJ3JM/FPMeujF2sTl5N0pUkLhZd5N8H/837B9+nt1dvxoeMZ3jAcOy0dk28Jzd27fkgNJ5Sx9HLy4v09HQ8PT1bZNu3u5/29vYYjUbUYWFoX/8bxdu3U9y9O7lxg8jbsQP3sePweOB+nKZPR9LpWkXm5qBkppbcdnNvqznW35zHRZJvNe250GDz5s1j3rx5mM1mzp49y4IFC67r9CjLsrVokWUZi8Vi7dNyddlVKpXK2gJ0dbC0a11djyRJqFSqdjOtw9Vj0NzFXaVcyfHq4xyuPswl8yXrci1awrXhROuiCdWEopbUzZpDaPuufV+3JVc/g8xms3WZuqwM9x07cDxzhvyhQymJiYF28tkitA3l5eXMmDGDoqKiJr+ELIqfZlRcXIyTkxMZGRm4ubk1+Oerq6upqKhArVZbRy2uqKhgz549lJeXU11dfd3PBAcHEx0dDdTMHL1+/XrrpbSrj6tfGwyGVvtBfbWPT0FBAYMGDULXRH953kp6aTqrL61m9aXVXCq+ZF3urndndPBoxoeMp4tL88ybZDQa2bBhA4mJiWi12mbZRkfQUsfRYrFw+vRpwsLCFHm9mmM/LRYLaWlpnDl9mrLycqCmCAr56iscnZxxe/YZbAcPbvTnRms8x5XM1JLbbu5tNcf68/Ly8PHxaZbiR1z2agFarbZRJ4NWq8XOzu66ZYmJiUBNcVNeXm59lJWV4ebmZt3W1QKpurqawsLC69YfEhJCn/+O+Go2m7lw4QL29vbWIknJ/kYVFRVkZGRQVVVFYWEhfn5+LbLdIJcgHnd5nMeiH+N47nFWXFzBmuQ15Fbm8t3p7/ju9HeEuYQxsdNExnYai6etZ5NnaOz5ItTW3Mdx7969XL58mcLCQuLj4xX7Q6Kp9/Nqn6CUlBROnjiBxWzGUFVN9blzZDz5FPo+vfH6/e+xjYlpNZmbgpKZWnLbzb2tplx/c+YUxU8bptFocHR0vGFF7ODgQGJiImVlZZSWltb6t6ysrFZhVVZWxpEjR2r9vF6vx97eHgcHB/z8/Kwj1bYEg8HA0KFDW7TwuZYkSfTw6EEPjx682OdFtl/ZzoqLK9iSuoVzBed498C7vHfwPfr79Gd8p/EMDxwuxg/qYMLCwsjKyqJbt26ttgW1sVQqFcHBwQQGBlJeXo5h9GhyP/+cvB8WcrxvX1I/+phAgwGfp3+HTefOSscVhAYTxU87plKpcHZ2xtnZ+brvXb3Gfy1/f39rgWQ0GqmsrKSyspLc3Fzs7OysxU9JSQnbt2/HwcEBBwcHa4Hk4OCAXq9v9C8CWZapqKiw9o+6WWHXkrRqLQmBCSQEJlBUVcS6S+tYeXElh7IPsTN9JzvTd2LQGEgMSmRi6ET6evdFJYm+Ee2di4sLY8eORa1uv33BVCqV9ZK714svUjpyJJVnz1Lp60tuWRnub/+TYE9PfJ7+HVrPpm8FFYTmIoqfDkqSpFof2o6OjgwYMMD6dXV1NaWlpZSUlFBaWornNR9sV5eVlpaSkZFRa70ajYaoqChCQ0OBmuvApaWl2Nvb37QJU5ZlDhw4QHp6OkOHDm0VRU9dnGycuKvrXdzV9S5Si2smV11+YTlppWksv7Cc5ReW42Pnw8TQiUwKnSTmF2tHzGYzhw4dolu3btaCoD0XPnUJ6dEDjbMzJ44coQzIShxBblkZXq/9kS4DB+I+816kVnY5SxDqIoofoU46nQ5XV1dcXa+fE8vDw4MhQ4ZQUlJifVwthkwmU60iJzc3l+3btwM1l7IcHR1xcnKytuo4OTmh0WgwmUwUFBRQVVXVLJ3bmkOAYwCPRz/OYz0f40jOEZZdWMa65HVklGXw6dFP+fTop/Ty7MXkzpMZGTyyRW+bF5rekSNHSE5OJjc3l5EjR7abuyobQqVSERQUREBAAKmpqZw4dIgyOzvSRyaSl51Nt7vvxu+ll7GL7ad0VEG4KVH8CA2m1Wrx9PSs1RoENXeKlJaWotfrrctMJhM2NjZUVVVRUVFBRUUFWVlZ1u/37duX4OBgtFotvXr1IiUlBTs7O0wmU5sZ4FGSJKI9o4n2jOblvi+zOXUzy84vY2f6Tg5mH+Rg9kHe2vsWIwJHMKnzJHFZrI0KDw8nLy+Pnj17dsjC51rXFkEXL17k+KFD6EpLMZ84Scr99+M4bhyeL72E1ktcChNap7bx20VoE1Qq1XUtNgEBAQQEBFBdXU1xcbH1UVRURFFRUa3+QQUFBZw/f57z588DNQMbXttS5O3tXauwao30Gj1jQsYwJmQMmWWZrLy4kmXnl3Gp+JJ1Wg1xWaxtMhgMjBgxot11br4dKpWKzp07ExgYSFV+PmVVVRT8sJD8LVs4bzbTrUcPPO+bJS6FCa2OKH6EFqHT6XB3d8f9v3MGybLMvn372L9/PzqdDh8fH2xsbPD09KSoqIiqqirrLfyZmZkAJCQkWIuf7OxscnJyrB26bW1tW90vJW87bx7u8TAPdX+Io7lHWXZ+GWuT197wspiOlhnLSKgfk8nEnj17CAsLs7ZytrZzrLXQ6XTovL1x+POfcZo6lT0rV5LTqRP5JSX4vfgiEdPvxn5Af6VjCoKVKH4ERciyjNlstv4L/2slAqiqqrqupejaVqX09HTOnTtn/Vqr1VoLIScnJ/z9/VvNOCKSJNHToyc9PXryUt+XbnhZbJj/MDyNnlhky61XKjS706dPk56eTl5eHmPHjm0zl2GVZoiMpIuzM2U7d1Lu4MDlQYPI2rObzmtWEzxnjtLxBAEQxY+gEJVKRWxsLPn5+dbWoGvZ2Njg4eGBh4dHnT/v7u6O0WiksLCQoqIijEYjOTk55OTkAODj42MtftLS0igvL7cWRy01WnRdbnZZbNWlVQCsWbaGSZ0nManzJAIcxGUxpYSHh1NSUkJYWJgofBrI188P76lTOXvsGCdPnaLSz4/jfn6kfvEFjhWVyImJ0Er+OBE6JvGOFlqMxWIhMzMTX19foKYAqqvwqQ9/f3/8/f2t6y0uLqawsJDCwkLKyspq9Q26dOlSrVvybW1trYWQs7MzPj4+inRg/e1lsaVnl7Lq/CoyyzOtl8VivWOZEjaFEUEjsFHbtHjGjubaubnUanWt4R+EhlGpVHTr2ZPgrl05sn07Kfn5FEVG4rZzFylTp+Hz5z9hJ46voBBR/AgtQpZl9u7dS2pqKlFRUXTt2rXJ1n2zwRyhZrZtlUplLYyu9iVKT09HrVYzZcoU63OvXLmCWq3G1dW1xVqIrl4Wi3COoHtOd/SRelZcWsGu9F3sydzDnsw9OOxxYFzIOO4Iu4Nwt/AWydXRmEwmtm/fTmBgIJ06dVI6Truh1+uJHTGCsLw8jmzahMu+fRhzc0l5cDaGcePwfeH36Fpw9HhBAFH8CC1EkiQcHByQJMk6QFxLCQsLIywsDKgZvLGoqMjaSiRJUq1OrEePHqW0tBQAe3t7XFxcrOMdOTs7N/vlD62kZVTwKMaHjSe9NJ1l55ex9PxSMsoyWHhmIQvPLCTcNZw7wu5gbKexOOpa/3hIbcXly5fJycmxTqliYyNa2pqSq5sbcZMns9Zspve5cxT+sJCTnh6c+OEHIpydCZ45E0nBS9JCxyKKH6HFREZGEhgYiIODg2IZdDrdDfsSybJsHdTx6qCNpaWlpKamAuDk5MTIkSOtzy8tLcXW1rbZLpn52vvyePTjzImaw56MPSw5v4RNKZs4lX+Kv+/5O+/sf4cRQSOYGjaVPl59xJ1It6lTp06Ul5fj6+srCp9mZDEY8HjlFXQTJ3Lq5EksGg37LRaS336bnvHxuMXHKx1R6ABE8SM0G4vFwsWLFwkNDbX+Ylay8LkVSZKIjY0FalqI8vPzyc/Pp6CggLy8vFqjXVssFtatW4ckSbVah9zc3KxzkzUVtUrNQL+BDPQbSEFlAasurmLxucWcLzzPqourWHVxFQEOAUzpPIWJoRPxsvNq0u23ZyaTCbVabW0B7NGjh9KROgz3nj0Z3bkzB9euJRPI69qVLZcvE/LOO0TNmYOmDYzyLrRdovgRms3VPj6FhYX06dNH6TgNotPp8Pb2xtvbG7h+ItiysjJUKhUmk4nc3Fxyc3Ot37O1taVz585N2q/pKhe9CzMjZnJv+L0czz3OkvNLWJO8htSSVN4/9D4fHv6QOL847gi7g3j/eLQqcUfNjRiNRrZt24azszMxMTGi5UwBdnZ2DJ46leyUFA4kJVGq13MhKIjszz+nX2wsrnFxSkcU2ilR/AjNxt/fn/T0dPz8/JSOctt+OxGsg4MDkydPpqSkxNpClJ+fT2FhIeXl5ciybH1uRUUFBw4csA7y6OLictsTYkqSRA+PHvTw6MGLfV5k/eX1LD23lIPZB9matpWtaVtx07sxMXQiU8KmEOIUclvba49ycnLIy8ujuLiYrl27Ymcn5l5TimdgIKNnzuTE5i2czsqk2mAg47HHqb7rLjxf+D2qJm5NFQRR/AjNxt/fHw8Pj3bbf0KSJOsErcHBwUDNZZS8vLxanbpzc3PJyMiw3m6vUqlwdXW1FkPu7u63NSCjrdaWyZ0nM7nzZJKLkll6finLzi8jrzKPr098zdcnvqa3V2/u7HIniUGJ6NSiUymAr68v/fr1w9HRURQ+rYAkSXQfloB/dja5X3+N0WSiYMECSnbuwO3113Ht21fpiEI70rFn5xOalNls5tixYxiNRuuy9lr43IhGo8HLy6vWL1NXV1d69uxpvYPIYrGQm5vL6dOn2b59e60xiGRZpry8vNHbD3EK4fnez/Prnb8yN2EuQ/yHoJJUHMg6wCvbXmHETyN4d/+7XC6+fFv72VYZjUZMJpP166CgIFxcXBRMJPyWs6cnnV9+mYAvv0Dj7U2GfwCbT53i2MefYK6qUjqe0E6Ilh+hyRw4cIDLly+Tl5fHkCFDRB+K/7Kzs6NLly506dIFWZYpLS219hPKzc2tNdCjxWJh/fr12Nra4uHhgZeXF56enhgMhgZtU6vSMjxwOMMDh5NZlsnSc0tZfG4xWeVZzD8xn/kn5hPrE8tdXe4iITChQ/QNqq6uZtu2bajVauLi4sSoza2c/aBBhCz7hYuLF2MxGDhtMJAxbx6xiYk4iY7pwm0S736hyXTp0oXs7GzCw8NF4XMDV8c7cnBwICTk+n44V0cYLi8v5/Lly1y+XNNC4+joiKenJ5GRkQ0efNHbzpvHox/nkahH2Ja2jZ/O/sT2K9vZk7GHPRl7cNO7cUfYHUztMhU/+7bfP+tGysrKKC4uRqVSUV5eXmuuOKF10jg5kfjAAxxdtYrzZWUUBQSw8eBBuu7bR/isWajEFBlCI4nipwUYjcZal4LaKzs7O0aMGIFare4Q+9vUjEYjGo2GhIQESkpKrHOVFRYWUlxcTFlZGeHh4dZjm5mZiUajaVAH6jifOOJ84kgvTWfphaUsu7CM3MpcPj/2OV8c+4KBPgOZFjaNQb6D0Kja5sfD1ePz23PQ3t6eAQMGoNFoMBgMbf4cvdF+tmaNzRw5ejQ+KSnsT0qi3MGBk0D6Bx/Qe/RoHP47gGlLZ2oKLbnt5t5Wc6y/OY+LJF97W4rQJObNm8e8efMwm82cPXuWBQsWNPnYL63B1RnZr46TIjQPWZatj2uLnOrqauv/JUlCpVJZx6up7+thls2cNp5mb/VeLpguWJc7So70selDb11vnFROTbczLezqx5s4P9sH2WJBk55OhZsbkiwT8tnnlPbpTeHAgaDA/HxC8yovL2fGjBkUFRU1eUutKH6aUXFxMU5OTmRkZODm5qZ0nCZ35MgRkpOTsbe3Z9iwYYpMDtqeGI1GNmzYQGJi4i3v/rJYLBw8eJDs7OxaRRDUjFEUEBDQ4AH7UkpSWHJ+CcsvLqewqhAAlaQi3i+eqZ2n0t+7P2rV7d2i3xKuHschQ4awd+9ejEYjcXFx7e4PkIacL61FU2XOu3CBtO+/x+6nnwEw9OuL+1//is1/JztWIlNjtOS2m3tbzbH+vLw8fHx8mqX4aZvt2m2MVqttMx9ODdG9e3eKioro0aNHh7urqznV93wZMGAAsixTVFREdnY2WVlZ5OTkUF1djcVisa5DlmWOHTuGp6cnHh4eN7xEFuoayov9XuSZ3s+wMWUjP575kf1Z+9mStoUtaVvws/djathUpoRNwd3gXuc6WpOrg1CazWaAdvkehLb5+XK7mb27dcPrb3+jMDKSrH+8Td6VKxzetJkIRwfCpk1rVEufksexJbfd3NtqyvU3Z05R/AiNptfrGTZsmLikoCBJkqwz2nfp0gWLxUJeXl6tD42CggLOnDnDmTNnUKvVeHt74+Pjg4+PD3q9/rp16tQ6xoSMYUzIGC4WXuSnsz+x/MJyrpRe4f1D7/PxkY8ZFTyKGd1m0MOj9d51YzAYGDp0KCaTSXRubockScLl7ruxGziQrUuWYnR04Ahw5YMP6Dd1KnbtYHBVofmI6xRCvZlMJrZv305WVpZ1mSh8WheVSoWHhwfOzs7WZRqNhpCQEPR6PWazmStXrrB//35WrFjBxo0ba72ev9XJuRMv93uZjXdu5O9xfyfKIwqjxcjKiyuZsXoGM1bNYMWFFVSbq2+4jpZUVVVFQUGB9WtbW1tR+LRzusBAhj7xOIFVVWCxkOvjw/p167iwYoXS0YRWTLT8CPV25swZMjIyyM/PZ+zYsWKclDbC0dGRPn36IMsyhYWFpKenk5GRQUFBAfn5+bUugxUVFVFeXo6np2et5XqNnomhE5kYOpETuSdYcHoBa5LXcCz3GMe2H+Pd/e8yrcs07up6F562nkrsJlVVVSQlJVFWVqbI9gXlaHQ6YmfOJPDwYfYdOECVoyMHKyvJ/OhjYmc/iKaOFk6hYxO/vYR669atGyUlJXTu3FkUPm3Q1RnoXVxciIyMpKKigszMzFqz1V+4cIELFy6gVqvx9PTE19cXHx+fWoMsRrpH8ve4v/N87+dZfG4xi84sIrs8m0+PfsqXx75kRNAIZoTPINojukVbBtVqNTqdjsrKylqT0Aodh090NGO7dWPfDz+QZm9Puoc7h//0Z3q+8AJaL2WKcqF1Er/BhJu6Ouge1Pxy6d+/v8KJhKZiMBiuG2jRxsYGg8FARUVFrfnInJ2d8fX1JTw83HpXn5vBjTlRc3iw+4NsStnEglMLOJh9kLWX1rL20lrCXcO5p9s9jAkZg17T/H95azQa4uLiKC0tJSkpqdm3J7ROGr2eAQ8+yPkNG0jesQPblStJ3rUL/3/PxbZ3b6XjCa2E6PMj3JDJZCIpKYnz588rHUVoIZGRkYwbN47ExES6d+9ubRUqLCzk8uXLtVpyrs5er1VpGRU8im/GfMPPE37mjrA7sFHbcCr/FH/e+WcSf05k7oG5ZJRm3GizjVZZWUlaWpr1a41GIyYpFQDonJjIkNmz0YeFYc7NJfnhRzi1YAFidBcBRMuPcBMpKSnk5ORQUFCAv79/nXcGCe3PtXeQhYeHU1lZSWZmZq1WwKtzkF0dUyggIAAnJye6unblrwP/ynO9nmPJ+SUsOr2I9LJ0vjz+JV+f+JphAcOYET6DPl59bvuSWHV1NUlJSRQXF9O/f38CAgKaYveFdkQXGEjwwh9I/+OfOGlnS5FWS+7HHzPggQfQtLNxn4SGEcWPcEMhISFUVFTg7e0tCp8OTK/XExwcXGtZUVERFouFsrIyTp8+zenTp7G3t/9fIeToxOzus7k/4n62pG3hh1M/sCdzD7+m/MqvKb/S2bkz93S7hwmhEzBoGjZp61VarRZ3d3eMRmOtu9sE4VoqW1t83/knmQsWUGSRyfTwYP033xA3ejSOdcyvJ3QM4rKXUIvJZKo1JUBkZGS7HJ1auD0uLi5MnDiR/v374+fnh0qlorS0lFOnTrF+/XrOnTsHgFqlZnjgcL4Y9QVLJy5letfpGDQGzhee5/XdrzPy55HMOzyPvIq8BmeQJIlevXoxYsQIHBwcmnoXhXZEpVLRZ+ZM+ni4o6qspMzdnY1bkkgVfcM6LFH8CFZGo5GtW7dy4MABcV1cuCWNRkNAQAADBw5k4sSJxMbG4uvri0qlwsvLy/q87OxsTp48iZfGiz/2/yO/3vkrL/V9CT97PwqrCvnkyCeM/Hkkf931V5KLkm+6zYqKCk6fPl2rQBetkkJ9hQwbxrABA9AXFmKyt2N3RgbHf/gBWdwd2OGIy16CVV5eHnl5eRQXF9OtWzfs7e2VjiS0EVqtlsDAQAIDA62z01918eJFUlNTOXHiBM7Ozvj7+zMlcAozus1gY8pG5p+Yz7HcY/x89mcWn13M0IChPBD5ADGeMbX6BZnNZpKSkigpKUGWZcLDw5XYVaGNc+ncmVHe3mz//nvyXF1JNpvxXroUy/Dh0MamCREaT7T8CFbe3t7ExsYyZMgQUfgIjabVamsVLb6+vnh7eyNJEoWFhRw/fpw1a9awedNmQowhfDv6W74Z/Q1DA4YiI7M5dTP3r72fmatnsv7SesyWmrm51Go1YWFh2NraEhgYqNTuCe2Azt6eoY88QmejiYCfF+Oydx9pM2dRnZqqdDShhYiWnw7OaDQiSZL1L3XxS0VoaldbhKqqqrhy5QppaWlkZ2dTUFCA2Wymc+fO9PLqRS+vXlwsvMi3p75l+fnlHM09yu+Tfo+/vT+zImYxufNkQkNDCQoKEoNsCrdNpVIRM+MeikOCufy7p+HsWY689DIeDz9M8PBhSscTmplo+enAqqur2bp1K9u2bcNkMikdR2jnbGxs6NSpE/Hx8UyYMIGYmBjCw8OtrUQmk4lze84x1WEqy8Ys47Gej+Fv409UVRRv732bkYtH8sGhDyg0Fiq7I0K7YujTh5Tf/Q55QH9Sxo1lX24OB7/5jxglvJ0Tfz51YOXl5RQXF6NSqSgvLxcTQAotxsbGhs6dO9dalpaWRnFxMcXFxXAKIj0iCbQPpLKiEnudPT+W/chnRz9j/vH5TAidwH0R99HJuZNCeyC0JyZnJ0L+/W9yfviBbFdXLtgaKPzkUwbNmIGNs5PS8YRmIFp+OjBnZ2eGDBnCkCFDROEjKC4gIID+/ftb7xTLycmhsqISgMSARN4d8C5RHlFUW6pZfG4xk5ZN4qmNT7E/c7+4O1G4bRqDgfg5c+hmkZHMZvI83Fn/4yLyT59WOprQDETx08FUV1fXmvXa1dVVDBAntApqtZqAgADi4+MZN24ckZGR2P53FN7UlFQG+Q3i+7Hf8+2YbxkeMBwJiaS0JB5c9yAzVs1g4+WNWGRxqUJoPEmS6DH9LvoHBqIpK6PSxYXN+/ZxYd06paMJTUxc9upArk4HUF1dzdChQ8UcSEKrU1ZWxsGDB+nbty8RERGEh4eTk5NDXl6e9XyN9ozmLv1djA8Zz1HzUX648gPH847z7JZn6ezcmYcjHxZFkHBb/AcOxCkggO2rVlHq5sbltetwyc7BddZMpaMJTUQUPx2I2WzGZDJZ/xWE1mbv3r3k5uayf/9+4uLikCQJT09PPD09rc+prKwkPT0dWZYJIIA/Of2JXH0uC3MWcr7wPK/seAUPlQeqZBXjOo9DoxIfc0LDOQQEMPKBBzj0+efYrltH1po1WMrKcHt0zm3PSycoT1z26kAMBgNDhw5l6NChODmJTnxC69O3b188PT3p3bv3DZ+j1+sZMWIEnTt3RqfTUVVZhUOhA3N0c3jZ82VCtaHkWHL4464/MnnZZH45/wtGi7EF90JoL9R6Pb2fegqPJ54AIPv99zn+ySfiTrB2QBQ/7VxVVRV5ef+bN8lgMIjOzUKrcm1nZXt7e4YMGYLBcPPJTp2dnYmJiWH8+PH0798fDw8PZFnGptSGv/b8KyP0I3C2ceZy8WX+tONPTFg6gZ/P/ozRLIogoWEkScLjqSfxePllrkyaxGl3d3Z8/jkWs1npaMJtEMVPO1ZVVUVSUhJJSUnk5uYqHUcQrlNaWsqGDRsoKCho1M9f7SQ9dOhQEhMT6dSpE106dWGofigrJ67kxbAXGaEfQV5pHn/d9VfGLh3LwtMLqTJXNfGeCO2d+4MP4BMTDRYLma6ubP3sM8zV1UrHEhpJFD/tmEajQa/Xo9Vq0el0SscRhOscO3aMoqIiDh06dNu3qzs7O9O7d2/r6M8GjQHvUm8Gqgbyot2LTDVMxVhu5O97/s7YxWP57uR3VJoqm2I3hA6i+1130cNGD2YzOe7ubP7iC4wVFUrHEhpB9ARsAUajEaNRmeb2fv36UVlZicFgUCyDUD9XX5+O9Dr17NkTSZKIjIxssk741x7H0NBQzp8/T3FxMZFSJJGGSFJIYWvlVv6x7x98cewL7gu/j2lh0zBobn6prbVpi+dLa8zc0Eyh48ai3rCBI0VFFHh4sOnr+cRNvwtdI7oTtOTxaO5tNcf6m/O4SLIYHazJzZs3j3nz5mE2mzl79iwLFiywjlfS3GRZxmKxoFarW2R7gtBQsiy36N0ysiwjyzJms7lW69Jx03GWVC8BwFayZZDNIPrb9MdGsmmxbELbpU1NpdzFBVmnw5B2BYuPN/It+qoJDVNeXs6MGTMoKipq8r6qovhpRsXFxTg5OZGRkYGbm1uzb89oNLJ161ZKSkro3bs3AQEBzb5NoekYjUY2bNhAYmIiWq1W6TjNoqSkhJ07d9K9e3f8/PyaZRs3O45lZWVcuHCBlJQUYnrHsL98P1+d+Irc0lw0kgZJKzGj6wxmdJuBvda+WfI1lbZ4vrTGzLeTKXPPHvafO4/f4sW4aTT4ffoJalfXFtl2QzX3tppj/Xl5efj4+DRL8SMue7UArVbbIm90jUaDl5cXJpMJT0/PVvPhIjRMS50vSkhNTaWiooJz584RGBiIStV83Q7rOo5X+wVFRUWh0WgIloKZ3GUyy3csx5hp5LjpOMtOLGPR2UXMiZrDXV3vQqdu3f3l2uL50hozNyZTQFwcLq6upH/xBdV5eVx5cDYBX36Bzsen2bfdWM29raZcf3PmFB2e2xFJkoiOjmbEiBHY27fuv1qFjikqKoqIiAji4+ObtfC5Fa1Wa730plFp8FZ7o0JFlCaKR/SPMJaxfH/geyb+MpGVF1eKEaOFG7KPiCDo22/ReHtTUlTEhp8XU3D2rNKxhFsQxU8bV1FRwalTp6x9GSRJQq/XK5xKEP6nsrKy1vkZGRmJjU3r6lczcOBARowYQVBQEJIkEaoOZbZ+NkOMQ/hwx4fcteIudlzZISZQFepk0ymE4O+/I+POaZR7e7Flxw5yjh1TOpZwE6L4acPMZjNJSUkcP36cU6dOKR1HEK5TXFzM+vXrOXHiRKsvHFxcXOjXrx9jxowhJCQESZLorO5MP10/zhSc4bFfH+OR9Y9wPPe40lGFVkjr50fc1KnoCwow2duz7dAhMvbtVzqWcAOi+GnD1Go1Xbt2xc7OjqCgIKXjCMJ1cnJyqKqqIiMjA3MbGRHXzs6OPn36MHr0aIKDg3k88XHuj7gfrUrLuaxzPL/6eV5IeoHLxZeVjiq0Mg4BAQybNAnbvHzMBgM7z5wmbft2pWMJdRAdntu4kJAQAgMDxa3tQqsUGhqKRqPBx8fHOvhgW2Fvb0/fvn0BeMH1BWaEz2DV5lU4VzpzJv0Mj6Y8SlxYHI/1fAx3g7vCaYXWws7Li+HT72LLwoWUuLuzOyWF3r/+SsiIEUpHE64hWn7amPLycvbv31/rr2hR+AitSWlpaa2JH4OCgtrFCOM+dj709OoJQFdNVx7SP4ScLPPA0geYd3gepdWlCicUWgu9iwvDZ83COScHWavl5OHDFK1bp3Qs4Rqi+GlDZFlm+/btJCcnc+jQIaXjCMJ1CgsL2bhxI7t27Wp3M19LkkS/fv0YPXo0gYGBAHTTdON+7f3kncrjvqX38f2p78XkqQIAWnt7hs2ejV9qGkHffU/6c89T+MsvSscS/ksUP22IJEn06tULJycnIiIilI4jCNepqqrCZDJRUVHRZvr4NJSDgwOxsbGMGjXKOpBouCYcd5M7/2/v/2PCLxNYfXG1uD1eQK3XM+CZp3EdOxYsFjJeeZVLCxYoHUtA9Plpc9zd3UlMTGzR6QEEob68vLyIj4/Hycmp1Q1i19QcHR3p378/ERERnD5zmkT7RM4dO8eV0iu8v+N9lhxbwuw+sxnoN1DpqIKCJLUanzdeR2Vnx/lzZ8nQailZtIge06crHa1DEy0/rVxZWRlbt26l4pqZg0XhI7QmhYWFVFVVWb/28PBoF3186svR0ZF+fftxZ/idrJqyiqejn2aizURGVo1k3dZ1vLD+BVJLUpWOKShIUqnw+sOraOMGA3AauCD6AClKFD+t3L59+8jKymL/fjFehND6FBQUsGXLFpKSkmoVQB2VrdaW+8PvJ9I3EkmS6K7pTu/C3ry38j0+OfgJVWZxjDoqSZLoO/tBPPPyQKXicG4uWQcOKh2rwxLFTyvXt29fvLy86NOnj9JRBOE6Go0GtVqNRqNRdLqK1kSn0zEkbgiJiYk4ujmikTQM0AxAd07H75f+nqTUJKUjCgpRqVTEPfggjrm5WHQ69p0+haqgQOlYHZL4tGqFrh0J187Ojvj4eAwGg4KJBKFuDg4ODB06lMGDB7f7Pj4N5ezszMiEkQwcOBDJRsJB5cAQeQhzt8zlmU3PcKX0itIRBQWobWwYcvfdGAoKMNnZYamspDI3T+lYHY4oflqZ0tJS1q9fT35+vtJRBKFO+fn5FBUVWb92cHAQhc8NSJKEn58fU8ZNoWtEVyoMFVySL7EpdROTf5nMZ0c+o9pcrXRMoYXpXVyIHzUKTWkp1W5unP7gAyzV4jxoSaL4aWWOHz9OcXExhw4davVzIQkdT0FBAUlJSWzZsoWSkhKl47QZarWaqMgoZo2bxU8TfqKPVx9ks4zltIU//vJHdqTuUDqi0MIcg4KI7dED71WrsF+yhIw/vCY+81uQuNW9lenTpw8ajYbu3buLu7qEVsfOzg4HBwfUarW4FNsIkiTR2aUzX436imW7l2FMM+JmcePozqNsdN7InLg5eNt5Kx1TaCEePXtyIjIS9u2neOVKNH5+eD33rNKxOgTR8tMKmEwm6/81Gg19+vRBr9crmEgQ6qbT6YiPj2fw4MFtbq6u1kSSJCb1n0SP6B6YVCbcVe6EF4fz9cqv+frA12KU6A6kvEsXPP/yZ0y2tuw3mzj+449KR+oQRPHTAtQ/3guHvoPKouu+V1JSwtq1a7l8WcwQLbROeXl5pKenW7/W6XSi8GkCkiTRLawb0yZOwz3QHQsWOqs6Y7hg4I1f3mB3+m6lIwotxHHKFIxPPkF5UBCnZJnkDRuUjtTuieKnBahSdsKyJ+GfYbBoJpxcDsZKAC5dukRFRQVnz55td3MhCW1fcXExW7duZefOneTk5Cgdp13SarUkxCYwZtQYJEcJtaTGVG3ikQ2P8FLSS2SVZSkdUWgB0Q8+iMd/xwA6mJVF1kExBlBzEsVPCzDHvQAe3cBcBadWwI+z4J0usOxJutvmEhkRQXx8vBgnRWh17O3t8fHxwcPDAxcXF6XjtGuOjo5MHTmVmNgYHIMcUUkq1lxaw6xfZvHt/m8xWsSlsPZMpVIx+IEHcMjNxWJjw64jRyhOTlY6Vrslftu2AEv/J+GJ3fDYdhj0DJXOXZGriuDQd0jfTiZizR3YbPkbZBwB0dtfaEVUKhX9+vUjLi5OXOpqAZIk0TmwMy8PfJmF4xYS5RHFYNVg9Ml63l36LgeviNaA9kyt1zNk+nT0hYUY7e3ZumEDlXliDKDmIIqfliJJ4N2D4tgXWN/pjxwbvhC51wOgd4bSTNj1IXwaDx/1h23vQmGK0omFDionJ4czZ85Yv1apVKjVagUTdUzhbuF8M+obwj3CAQi1hHJkxxE+2/aZ6BDdjhlcXYkfMQJNWRkVLi7s+O47ZDEGUJMTxU8DrFy5kq5duxIWFsYXX3zRqHXk5eVRVVVFVqUG89h34YVzcPcPEDEZ1DaQcxo2/g3m9oCvx8KBb6CisEn3QxBupLy8nG3btnH06FFSUkQBrjSNWsO0EdPoF9ePCk0F9pI9LpkuzP1lLmdzziodT2gmTiEhDIiKQp+VhduPP5Hxpz+JMYCamGjHrieTycTzzz/P5s2bcXR0pFevXtxxxx24uro2aD0hISGo1Wq8vb3/dxmh29iaR2VRTWfoo4vg0na4vKPmsfoF6DIaoqZDWCJobJphDwUBbG1tCQ8PJycnBz8/P6XjCP8V5BPEvRPvZdXOVVRkVBBsCWb39t2kyCmMlkcrHU9oBt69ejG0tJS0jz+haNlytH5+eDz9tNKx2g3R8lNPe/fuJTIyEj8/PxwcHBg7dizr1q2r18+WlpZiNputXwcGBqLT6a5/ot4Jes2CB1bCc8dhxF/BMwLM1XBqOSy6t6aj9Ipn4fIuEHeHCc0gPDycuLg4camrlVGr1UwcPJHeg3pTrirHKBtZUbmCRzc+KuYJa6cc4uPx/r+/AJC8bj2nfv5Z4UTtR6sofq5cucLMmTNxc3PD1taW6OhoDhw40GTr37p1KxMmTMDX1xdJkvjll1/qfN5HH31ESEgIer2e3r17s23bNuv30tPTa/0l7O/vz5Ur9fvA2blzJ7t27apVAN2Skz/EPQtP7ILHdsDAp8HBByoL4cDX8PVoeL8nbHwdRPO3cBuysrLYt29fraEWxJ2HrVdnv87MmDgDl64uSEgcyD7A1OVTWXJkibg00g653Hkn2t89xeVZMzluMnHp141KR2oXFP+EKygoYNCgQWi1WtasWcPJkyd59913cXZ2rvP5O3bswGi8vrPf6dOnyczMrPNnysrK6NmzJx9++OENcyxatIhnn32W1157jUOHDjF48GDGjBlj7fdQ14dKfaefMJlMVFZWNn4cH+/uMPJ1eO4E3LccomeCzqGmU/S2d2BeX/h0COz6CErEmCBC/VVXV7Nr1y4uXbrE+fPnlY4j1JNWq+WOyDt4yuEpoj2iiZQjMZ0xMXflXLJKxWdAe9Pp8cdxLywEtZoDGelkHz6sdKQ2T/E+P//4xz8ICAjg66+/ti4LDg6u87kWi4Unn3ySsLAwFi5caG2WP3v2LAkJCTz33HO89NJL1/3cmDFjGDNmzE1z/Otf/+Khhx7i4YcfBmDu3LmsW7eOjz/+mLfeegs/P79aLT1paWnExsbWax/79OlDYGAgQJ2FW4MEDKx5jHwL6dxaVMd+Qrq4CSnjMGQcRl7/GnJIApaou5C7jAWtmH+prbh6btz2OdIAkiTRq1cvUlJSCAwMbNFtNxcljqMSjEYjrmpXPhryEct2LkMqlPCv9OeX1b8QEB5AYrdEpSNepzW+Nkpmasi2+997L0nff0+Jmxs7Dx4k3sEBh//+XmnqbTVGc6y/OV8TSVa4nTQiIoJRo0aRlpZGUlISfn5+PPHEEzzyyCN1Pj89PZ34+HhiY2P59ttvSU5OZsiQIYwbN45PP/30ltuTJImlS5cyefJk67Lq6mpsbW356aefmDJlinX5M888w+HDh0lKSsJkMhEeHs6WLVusHZ53796Nm5vbdduYN28e8+bNw2w2c/bsWRYsWICtrW3DD0496YzF+BXuxT9/B67lF6zLjSoD6S79SHUdRJ5dF5AUb+gTWglZlmu1XP72a6HtyTPloTKrsJPssMgWzlvO09mmM3qVmCewvZAqKrAUFWF0dUWXm4vk4opsaL+vb3l5OTNmzKCoqAhHR8cmXbfiLT8XL17k448/5vnnn+cPf/gDe/fu5emnn8bGxob77rvvuuf7+vqyadMm4uPjmTFjBrt27WL48OF88sknjc6Qm5uL2WzGy8ur1nIvLy/rpTSNRsO7775LQkICFouFl156qc7CB+DJJ5/kySefpLi4GCcnJxISEm743KZzNwDG/Auojv2E6tiPaItSCMpLIigvCdk5CEv3O7H0uAtcOzVzFqExjEYjGzZsIDExEa1W22zbycrK4syZM/Tv37/ujvdtXEsdR6XVtZ+lFaUs274MuzI7uqi7kGvKJTwqnEGdBimctkZrfG2UzNSYbRclJ7Nt1y6q3d1xzM5hyP33oba59R3Azb2fzbH+vGYc4FHx4sdisdCnTx/efPNNAGJiYjhx4gQff/xxncUP1Nwt9Z///IchQ4bQqVMnvvzyyyb5q/W36/jtX8MTJ05k4sSJDV6vVqttuTeVVzfw+hMMew1SdsGRH+DEL0iFl1Fvfwf19ncgIBZ63g2RU8AgpixobZrzfDGbzRw+fJiKigouXrxIjx49mmU7rUGLvu8UdO1+umhdeGDsA2w7sY1LJy/hIrvw9z1/Z0TRCJ7p9Qx6TetoJWiNr42SmRqybfcuXehfXMyOM2dQpV+h4NNP8fn975tlW43RlOtvzpyKXwfx8fEhIiKi1rLw8PCbDrCWlZXFnDlzmDBhAuXl5Tz33HO3lcHd3R21Wn1dh+ns7OzrWoPaDJUKggfBpA/hhbMw9UvoPKLm0lfqHlj5HLzTFX68H86sBTFibIegVqsZPHgwISEhREZGKh1HaCaDIwczfvR4Mt0yyZQz+e7Ud9y18i6OZh5VOprQBHz69CHezQ3/xUso/OJLynbvVjpSm6N48TNo0KBaQ+lDTQfmoKCgOp+fm5vL8OHDCQ8PZ8mSJWzatIkff/yRF154odEZdDodvXv3ZsOGDbWWb9iwgYEDBzZ6va2GzhZ6TIOZi+H5UzDyDfCMrJlo9eQv8MN0eLcbrHlFzC/WTl07zIKTkxN9+vQRt7O3cy4OLjw7/Fk+Gv4RHgYPyovLObL1CF9v/ppqs5guoa3zHD0a5zvvBFnmysuvUCXmAGsQxT/9nnvuOXbv3s2bb77J+fPnWbBgAZ999hlPPvnkdc+1WCyMHj2aoKAgFi1ahEajITw8nF9//ZX58+fz3nvv1bmN0tJSDh8+zOH/3h6YnJzM4cOHa7UuPf/883zxxRd89dVXnDp1iueee46UlBQee+yxZtlvxTh4w8DfwRM7ayZa7f8k2HlAeS7s+bhmfrGPB8KOf0NxhtJphSaQnp7O2rVrKSkpUTqKoIDB/oNZOmkpk1wnoZf02Ofa88UvX3A+Swxt0NZ5vfoKUrduXBwxgl0LfhDjPDWA4sVP3759Wbp0KT/88APdu3fn9ddfZ+7cudx7773XPVelUvHWW2+xePHiWh01e/Towa+//sq0adPq3Mb+/fuJiYkhJiYGqCl0YmJi+POf/2x9zvTp05k7dy5/+9vfiI6OZuvWraxevfqGLVDtgncPGP0mPH8aZvwEkXfUzC+WfRI2/Bnei4Bv74Dji8FYqXRaoRFkWebkyZOUl5dz7tw5peMICnGyceKpcU9hE2RDtVyNh8WDHUk72HRyk9LRhNugsrXF4Q9/oKRLGDneXpwVI0DXm+IdngHGjx/P+PHj6/XcxMS6x66Ijo6+4c8MHTq0XhXxE088wRNPPFGvHO2KWgNdRtY8Kgrh5LKajtIpu+DCxpqH3rnm0ln0veAbUzNLvdDqSZLE4MGDOXv2rOjj08FJksTEfhO5FHKJDVs34GxxJvt4Nv/J+Q8zB88Ul0HbKL9+fQk6c5rLej0nKirwOnMG565dlY7V6omzvQVs2bKFnTt3cuLECdLS0igpKWm9zZMGZ+h9P8xeC08fgvgXwdG/ZlqNfV/A5wk1l8V2fgil2UqnFW6gqqrK+n8bGxt69OghfrkJAAR7BHPv+HspNBSiltQYsg28teEtyo3lSkcTGqn3PfdgV1iI2WBg9/r1WK55/wt1E5+GLaC0tJQrV65w8uRJdu3axdq1ayksLLR+v6CggKysrFq/sFoF104w7I/w7FGYtRR63Akafc1lsfWvwb/C4Yd74NRKcbdYK5KWlsbq1atvON2LINja2PLQ2IeQfWTOms+yKHMRs9bMIrUkVeloQiOoNRr6Dx+OZDRS4u3N4W++UTpSq9cqLnu1d7GxsahUKoqKiigqKqKkpAQHBwfr9y9evMjFixcBMBgMuLi44OzsjIuLCy4uLuj1emVH31WpIXRYzaOiEE4sgUPfw5X9cGZ1zcPWHaKmQ8y94CUurygpNTUVk8lEamoq3t7eSscRWimVSsVdcXdxIPMAG5I2cLbgLDNXzeRvMX9jSNchSscTGsi1UyfCT5zgZGUlF+3t8dm2HZ/BcUrHarVE8dMCPDw8ao3w/NvBE21sbLC3t6e0tJSKigoqKipIT0+3fn/SpEnWDt5FRUWo1Wrs7OyUKYgMztBnds0j+zQc/h6OLoLSLNg9r+bhEw0xM6H7VLB1bfmMHVxsbCzu7u507txZ6ShCG9DbuzcLxy/k+c3PE1ISQsaRDL7L+o4ZcTPEpdI2JmL8eDK+/JJSs5nsDz7As0d31DeYJLyjE8WPAn5btHTv3p3u3btjNBopLCykoKDA+q/ZbK51Z9vhw4fJzs5Gq9VaW4eu/uvg4NCyBZFnt5rZ5of/Bc7/Coe/qxkw8b+TrLLuD9B1bE0hFDqspgVJaBbXtiaqVCrCwsIUTiS0Jd523nw56ku+Xfct6go16iw1n676lFkjZ2FvY690PKGeJEli0NSppM66D/P582T85f/wm/uemLevDqL4aUW0Wi0eHh54eHhYl1ksllrPkSQJlUqF0WgkJyeHnJwc6/cMBkOtu+aqqqrQ6XTNf+KrNdB1dM2jLA+O/VhzWSzrWM0giid/AQcfiJ5RUwiJucWaVGpqKnv27CEqKoouXbooHUdoowxaAw+PfZgfd/yIlCHhXunO9yu+Z+SQkYR4hCgdT6gng4sLAW+9yaV7ZlCybh0Fi5fgOm2q0rFaHVH8tHK/bXaOj4/HYrFQXFx8XSvRtf2IADZu3IjRaMTNzQ1XV1frv806f42dG/R/vOaRcaSmCDr2I5RkwLZ3ax4hQ6DXfRA+ATS3npBPuLni4mJkWaaoqEjMzi7cFpVKxd2D72brqa1cPnYZV1zZunkrqVGpxHeLVzqeUE+GHj1w/93vOLNzB2fzchl+4QI2gYFKx2pVRPHTBqlUKpydnXF2diY4OBio6UdkNP7vjqvq6moqKiqwWCxkZGSQkfG/0ZodHR0JCAi4bk61JufTs+Yx8vWaTtEH/wMXNkNyUs3D4AI976kphDzDmzdLOxYREYGzszO+vr6i8BGaRHx4PMluyfy69VeccebC0QtcNl1mZuRMcY61ES4PPkCBVku1gz27ly1j8KOPKh2pVRG92doJSZJq9Q3S6XRMnjyZYcOGER0dTUBAAHZ2dkBNS0FFRYX1uWazmaSkJI4dO8aVK1eorGzi0Zw1NjUzyM9aCs8cgSEvg6MfVBTA7o/go/7wRSIc/Baqy5p22+1Ubm6udawoSZLw8/MTv5SEJhXiGcI94+8hX5/PL1W/8PaBt3lt+2tUmsRo722BRqcjNm4QmM0UBARw+ttvlY7UqoiWn3ZMrVbj5uaGm5ubtQNsZWUl+fn5GAwG6/MKCwvJzs4mO/t/gxba2tri6uqKu7s7Pj4+2Ns3UadHlyBI+ENNAXR+Ixz8Bs6sgbS9NY+1r0KPqdDrfjGS9A1cvnyZvXv3EhwcTJ8+fUTRIzQbe709c8bPwe6UHe/sf4cVF1dQlF/EiwNfJNgjWOl4wi14dulC2KlTnKuu5rydHXaXLysdqdUQLT8djF6vx9fXFxcXF+syOzs7+vTpQ0hICE5OTgCUl5eTlpbG4cOHSUtLsz63urqa/Pz86zpiN5hKXTOdxt3f18w0P/wv4BIC1SVwYH7NSNKfDIa9n9eMLSRYqVQqa8d3QWhukiQxM2ImnyV+RpBNEP2r+pO0OYltZ7YpHU2oh54TJ+JQWopFr8dktmAqLFI6UqsgWn4E9Ho9ISEhhITU3NFhNBopKCggLy+PnJwcPD09rc/NzMxkz549aDQa3NzcrHenubq6Nv6XsYMXDH4eBj0Ll7fX9A06ubzmbrHVL8D6P0LE5Jq+QUEDO3xr0NVLmC4uLqLVR2gx/Xz68e9h/2bj1o24yC5cOXKFhTkLuWvgXaIQb8UkSWLguPFs2LCeCn8/jsyfT+wLv+/wnx2i+BGuo9Vq8fT0xNPTk/Dw2h2Rq6ur0Wq1GI1GsrKyyMrKAmpaI9zc3IiJibG2HjWYSgUh8TWPMflw9Meay2LZJ+HowpqHW+eaIij6XrBzv91dbTPS09Px8PCw3qnn6ioGjxRaXqhnKJ7jPfl+/fe4VblBBny++nNmjZyFrc5W6XjCDTh6ehDp78+xjEyqz52l6JdlOE+ZrHQsRYlyXWiQzp07M2nSJBITE4mOjsbPzw8bGxssFgs5OTm1Ol1fvnyZY8eOkZmZWetOtHqxdYX+j8HjO+HhjTUFj9YO8s7Dhj/XzCu2+GG4vAta6ySxTeTSpUvs2LGDbdu2YTKZlI4jdHAOegfmjJ9DtWc1FtmCa4Ur/1n1H0oqSpSOJtxEpwEDcD90EI+t28h6/XWqU1KUjqQo0fIjNJgkSdZb7cPCwpBlmZKSEgoKCmp1pL58+TJZWVmcPn0aSZJwdXXFy8sLb29vXFxc6tdULkng36fmMepNOL6kpk9Q+kE49lPNwzOiZrqNqOmgd2y+HVeIk5MTWq0WFxcX1GoxSragPJVKxb1D7mXTiU1knMjAzeTGv9f/m2fHPIu9TowI3RpJkkRx//4Ep6ZRefAgaS++SMh33yE157hvrZho+RFumyRJODo6EhQUVGt5cHAwQUFB2NnZIcsyeXl5nDx5kk2bNrFq1Srrrdr1ZuMAve+HOZthzhaImQUaQ81lsdUvwLvdYMWzkHmsyfatNXBxcbG2tHX06/RC6zIschidYjpx2nKanwp/4uH1D1NUJTrUtloqFV7/7y2qQ0I4ERfH6a++UjqRYkTLj9BsAgMDCfzvqKJlZWXWPkJZWVk4OTnV+kW+Y8cObG1t8fLyqtW35YZ8Y2DShzDyDTiyEPZ/Cbln4cDXNQ//ftD3oZqO0lp9M+5l87h06RLu7u7WIQaujtEkCK3NgLABOLk6sX7Dek7kneDBtQ8yb+g8fJx8lI4m1EHr40P1o3OoNBo5VVqG9549uMTGKh2rxYniR2gRdnZ2dOrUiU6dOiHLMlVVVdbvXTuL/fnz55EkCXd3d7y8vPDy8rr5XU0G55q+QbGPwqXtNUXQqRW1xw2KuRd6PwhuoS2wp7cvNTWVAwcOYDAYSExMxMZGTAEitG4RbhF8PeprHtnwCJ3KOrFq/SoSExIJcg+69Q8LLa7XhAlkf/c95fZ27N26leHduqFp7I0qbZS47CW0OEmS0Ov/1xqj1WoZOHAgoaGh1ktkOTk5HD9+nI0bN3Lo0KH6rBRCBsOd8+G5kzDsj+DoDxX5sPMD+KAXfDsFTq0Ec+vuNOzh4YGDgwMBAQG1OpALQmvW2aUzXwz7gghtBE448evmXzmXdU7pWEId1Go1A0YmIplMFAcHc+STTxveDaGNEy0/guI0Gg1+fn74+fkBUFpaSlZWFpmZmWRnZ+Pu/r9b2gsKCti7dy8+Pj74+fnh6up6fauQgxfEvwhxz8PZdTWtQec3woVNNQ9Hv5oRpHvdB46tr2ler9czfPhwNBqN6OMjtCmh7qFIQyU2b9mMI45s37od4yAjEb7NPI+g0GCuPj6Ee3tzMjeXZH8/fJYtw3fyZKVjtRhR/Aitjr29Pfb29oSGhl43knRmZibFxcUUFxdz5swZbGxs8PX1xc/PD09Pz9p3Q6nU0G1szSM/uaYv0KHvoPgKbHkTkv4B3cZBvzkQHKfo4Innz5/H1vZ/46Tcss+TILRSnTw6oRuuY83GNTjjzL7t+6iOrSY6KFrpaMJvRAwdSsaCBRTodBxMScE1JRV9YIDSsVqEuOwltGoqlarWLfGhoaHExsYSEBCARqOhqqqK5ORktm/fzrJlyygsLKx7Ra4hkPi3mqk07vgcAvqDbIZTy+Gb8fBJXM3EqsaWn7TxypUrHDp0iN27d3e4pmehffJ39WfiyIkUSAXYSrYc23OMvRf2Kh1L+A1Jkhg4aRLq6mowmrjy/r+VjtRiRPEjtCk6nY7AwED69+/PpEmTiI+PJzQ0FIPBYL3l/qqLFy9y7tw5ysqumSleYwNRd8FD62oGUOz9IGhtIes4LH8K3ouAja9DcUaL7ZO3tzfe3t6EhraNDtmCUB9eTl5MHTOVfFU+OnR8sPcDDmQfUDqW8Bu29vYMjepJp6++onrlKsp271Y6UosQxY/QZqlUKry8vOjVqxfjxo1j5MiR1lYiWZY5ffo0hw8fZvXq1WzYsIETJ05QWFj4v9YVr0iYMBeeO1HTKuQUAOV5sO0dmNsdfn4I0vY3+36o1WoGDRpEeHi46OMjtCtudm7cM/YeDtoe5HD1YX63+XecM4pO0K2Na4/uuN59NwBZf38TuQOMJC+KH6FdkCSp1lg4sizTuXNnPDw8ACgsLOTkyZNs2LCB1atXc/Lkyf/9sK0rDHoGnj4Md/0HAgeCxQTHf4YvhsPnw+HYz2Bu4BQdN3HmzBkuXLhg/frqTO2C0N44Ghz56+i/MthvMJXmSpaVL2PDiQ1KxxJ+w+N3T4GnJylBgaQvXKR0nGYnOjwL7ZJKpaJLly506dKFqqoqMjIyuHLlCllZWZSXl1NRUWF97tVb6z08PJAiJkHEJEg/DHs+geOL4cp+WPxQzezyfR+quVR2G5Oq5uTkcPToUaBm9GYxSanQ3uk1ev6d8G9e3fwqoXmhlJwtYblpORP7TFQ6mvBfamdnsp98ghyVCuPJU3gVFKBxcVE6VrMRLT9Cu2djY0NwcDCDBg1i4sSJDBo0qFb/mry8PJKSkli5ciWHDx8mPz8f2acnTPmk5pLY0FfBzhNKMmDTG/CvCFj2JGQeb1Qed3d3unTpQkREhCh8hA5Dq9by10F/pYIKVJKKyouVLN69WOlYwjV6jBgBskxhRDgXP/tM6TjNSrT8tACj0djwWc2FZnP1UtjV16SkpAStVktlZSXnzp3j3Llz2NnZ4e/vj7+/Pw6Dfg+xTyGdWoZq76eoMo/U3DJ/6DssQYOw9H0UOWxUza31NyHLsvXS1tX+PdeeF1f/L86V29NRjmNb3E9JlgjWBZOtzcatwg1LqoUF1Qu4c8CdimVS8ji25Lbrsy0HR0d8be1IryjnvCThc+wY+m7dmmz9DdWcx0WSxb21TW7evHnMmzcPs9nM2bNnWbBgQa0xXITWR5ZlZFnGYrFcN7aQRqP53+32soxr2Tk65azHp3A/KmqeW6bzINljBJfdhmJSG367esxmM7Iso1arRd8eocOzWCxcqL5AkKpm+osUSwqd9Z0VTiVAzWehsbIS1Gq8NmygYOxYxcZAKy8vZ8aMGRQVFdW6k7cpiOKnGRUXF+Pk5ERGRgZubm5KxxHqyWQykZGRQVpaGiUlJSQmJloLlosXL6JSqfD19UVXmYNq/5eoDv0HqbIQANnGAUuvB7H0nQMO3gAUFRWxefNmAAYMGICXl1ed2zUajWzYsIHExEQxyOFt6CjHsS3u57WZ1Wo132/9HodCBwAK3Qq5P+7+Fv/jQMnj2JLbbsi2ju7Zw8WMDGyysxkYEIjTmNFNuv76ysvLw8fHp1mKH3HZqwVotdo28+Ek1LxeVydhtVgs1lYfi8XCmTNnqKqq4siRI/j4+BAYMQefwS+hOfkz7P4YKfcM6l3vo977CfS8BwY+jbt7Z/r27UtFRQX+/v712r44X25fRzmObXE/r2aenTibb7d8S1lWGfNT51NwpIAX+7yoSOuoksexJbddn2316NePlKVLqfL05PzatfRLHIHKcH2LdmPXX1/NeUxEh2dBuIlrR5e2WCx06dIFJycnZFkmPT2d3bt3s3ztBvaYI8i5cwXc/QMExIK5Gsuh7+DDPrBoFsGaPMLDwxXcE0FonWYNnYVjd0fKKOPbk9/ywaEPlI7U4el0OiJ79MDl9BkM+/eT98WXSkdqcqL4EYR60mg0dOvWjZEjRzJy5EjCw8Oxs7PDbDaTkpJCRlYWdBuLPHsdJxIXsbXb65gkbc0UGl8Mg/nj4dyvIK40C0ItMyJn8JcBfwFg+8nt4i6wVqBLRAT9+vZBV1xM3hdfYLxyRelITUpc9hKERnBycsLJyYnIyEjy8/NJSUkhKKim82ZFRQVnssoxqzw5MXIRPTJ+RnV8EVzaVvPw6lEzqGLkFFCLt6AgAEzrMo283Dxcr7hiTDGy2XEzCREJSsfq0BxGjcK2b1/K9+0j85/vEDD3PaUjNRnR8iMIt0GSJNzc3IiJicHJyQkAW1tbawf3s2l5rNKO4fi4FZT3exa0dpB1DJY8DO/HwJ5PobpcwT0QhNbjkf6PkKfPQy2puXL8CkdTjyodqUOTJAn7F18g5e67OavVULan/UxOK4ofQWgCsixTXV1t/bpXr1507doVGxsbKisrOXXxCqvKurNt0HdcGfh3ZFsPKEqBNS/Be5Gw5f9Beb6CeyAIylOpVNw/6n7y1HnYSDbs372ftPw0pWN1aBYfH4ojI8jv3ZtLH37Ybub9EsWPINwmWZY5fvw4v/76K+XlNa04Dg4OREVFMW7cOPr374+npycAmTl5HDWHwrNHYdy74BIMFfmw5S00H0bTPe07KEpVcG8EQVkGnYGpiVMpphgHHFi1aRXFFcVKx+qwPDw88PHwALWa1C5hFP70k9KRmoQofgThNhmNRlJTUykrKyMrK6vW99RqNQEBAQwZMoTRo0fTtWtXunbtiqSzhb4PY358L7v6f8GVgMnIxkpCc9ajmdcHFj8C2acV2iNBUJaHgwfxcfFUyBW4yq58v+57TOb20eLQFvXs3RtJlinp1o3knxdjLixUOtJtE8WPINwmnU7H0KFD6du3LyEhITd83tXWoE6dOlmXpWVkklYCOx3Hsyr6c/b6P0yFxgmO/Qgf9YefZ0POmZbYDUFoVcJ8wgiLCcMkmzhbfpZ397+rdKQOy8HBgZD/fm6lxw0i+4MPFU50+0TxIwiNIMsyZWVl1q9tbW0JDg5u8Hrc3d3p2rUrOp2OSqPMZaf+rOryNtsj3iDTLgL5+GKYFwuLH4bcc024B4LQ+g0IG4BzlDO/Gn/lu9Pf8f2p75WO1GFFdu+OWpKo8Pfn0vFjVJ45q3Sk2yKKH0FoIFmWOXr0KOvXrycvL++21mVnZ0dUVBTjx4+nT58+1pFtMyRvtgU/R0m3uwEZjv0E8/rBkjmQd6EJ9kIQ2oYx3cbwbK9nAXhn7zusP7le2UAdlF6vp1tEBAD5ffqS9eabtOXZsUTxIwgNZLFYKCgowGQyUVRU1CTrVKvV+Pv7o9VqGTFiBGFhYfj7++N496fw6FboOpZMu3CMx3+pGTV66WOiCBI6jNndZ3Nn6J3ca3MvOcdz2H9pv9KROqQuXboQGRREyA8/UL5nDyXrNygdqdFE8SMIDaRWq4mLi2PgwIG1+u80FXt7e6Kjo+nfv3/NAp+eVEz+iu3Bz7Gq23sc85hMxYnV8GFf+OUJyE9u8gyC0JpIksTL/V/G3sYenaTj+N7jJOeI876laTQaIvr1w/3++wDI/sc/sFRWKpyqcUTxIwj1IMtyrUtcGo0GPz+/Zt3mtZM7lpeXY2/vgFHScdpjLKu7vM0+n5kUn9wEH/SGZU9CwaVmzSMISrLR2DB95HSKpCLsJDs2JG2goKxA6Vgdkvsjj6D29qYYmbyvvlI6TqOI4kcQbkGWZQ4fPsymTZu4dOmSIhnc3NwYNWoUgwYNwt3dHYuk5pLLYNaFvc72gCcoO762pgha/jsouKxIRkFobi52LowYOoJyuRwX2YWF6xdSZapSOlaHY9JouPjUk1x85BEyFv2IMSND6UgNJoofQaiHqx37lOzgJ0kSvr6+JCQkMGzYMGvLU7ZTNNqA3mAxwcH/1BRBK56BQjFYotD+BLsH071vd4yyEXeTO/PXzcdisSgdq0PRarXo3d2RNRoyB8eR/c93lI7UYKL4EYRbkCSJmJgYEhISbjqOT0tyc3Nj4MCBjB49mr79YtHd9yPMXgchQ9jjcx8XLl7C/EE/WPk8FLWv2ZgFoXdIb9y7uiPLMoYyA18f/lrpSB2KJEn07NkTgKKePck6fJiKAwcUTtUwovgRhDrIskxaWpq1pUeSJNzd3RVOdT0HBwcCAgJqvgjsT+64r0hxHsBB35ms6vwGJy9nUfXhIFj7BzF3mNCujOw5ErO/ma8rv2busbmsSV6jdKQOxcXFhcDAQAAyR40i+//9A9pQC5wofgShDgcPHmTXrl0cO3ZM6SgN4uzsTHR0NLa2tlRpHDnhOZlVnd/gSPJ/i6Bd88BUfesVCUIbcM/AexjdbTQAf9z+Rw5mHVQ4UcfSvXt3VCoVZZ1CyLNYcNq3T+lI9SaKH0Gog7OzM5Ik4eTkpHSUBtFoNISFhTFmzBhiY2NxdnbGrLLhrPtoVge+QkHSxzWDJZ5cBm14gDJBuOqFPi+QEJCAL77sT9rP2cy2PfJwW2JnZ0dYWBgAWaNG4rp+A+aitjEJrSh+BKEOoaGhjB49mqCgIKWjNIpKpSIwMJARI0YQFxeHs5MTer0eJ40JCpLhx/vgq9GQJgaLE9o2tUrNW3FvMd4wHhfJhaRtSeQU5ygdq8Po1q0bOq0WtSSBJJH/ycdKR6oXUfwIAjV9fM6dO4fZbLYus7e3VzBR05AkCR8fH0YkJjJk5ARUv9sP8S9h1jqwWT2Y5J//jOWn2WKMIKFNs9PZMX7YeMoowxlnfv71ZyqMFUrH6hB0Oh3DR4xgUEQk2pISin5YSNX580rHuiVR/AgCcOjQIQ4fPsyuXbva9Hw1NyJJEra2tmBjD8Ne4/Lk5eTadWW/34Osr4om7Zs5yOv+BBWFSkcVhEbxc/WjV2wvquVq3M3uzF87H7PFfOsfFG6bvb09doMGUhoRAWZzm5j3SxQ/ggD4+/uj0WgICgqqNbJyexXUrSdRUVHoNGpKbHzY5fcIG7Mcyfz8LuTdn4LZqHREQWiwqMAofLv7YpEtuFe688P2H5SO1KFkjx9HduIICo8eo2zrVqXj3JQofgQB8PT0ZNy4cf+7bbydU6vVdO3albHjJxARHo5GBQWGYLb5PELSqSxMH8XB6VWiU7TQ5iREJCD51fwBo8pUcST1iMKJOo4KR0ey4+PJHpZA7kcft+rWH1H8CB2SLMscO3aMior/9QvQ6XQKJlKGVqslsnt3xo6fSFjnUFTIqFUSmrzTsHAGzB8PV8Ttw0LbMm3ANPK1+Rw1H+X1/a9TaWqbk2+2NWq1GoCC3r0pvJJG+e7dCie6MVH8CB3S0aNHOX36NNu2bRND4wM2NjZEx/RizLjxxIx/GOKeB42eyitH2bfqP5QufgaK0pSOKQj1olKpmDZyGnvUezhTfIa5B+cqHalDUKlUeHl5gUpFXmwsuR9/onSkGxLFj9AhhYaGYmdnR0REBCqVeBtcZWtri72bD4z4Czy1n5PdnuWSyyDWmvtzcNE/qFj/d6gqUTqmINySm60brw96HYDvT33P1uTW3QelvQgNDQWgMDqaksOHKW+l016IT32hQ7K3t2f06NH4+/srHaX1cg4gZPhsvJwNyJKGCy7xrCkI4uS3L2E+uVLpdIJwS3F+ccwMm8m9NvdyZt8ZckrF+D/NzcPDAzs7Oyx6PUU9epD7yadKR6qTKH6EDsFisbB//34KCgqsy0SLz625uLgQnzieIfHxuBpUmFU2nHAaxvqDyWQtfFZMmiq0ek9EP4G32htnyZmFmxe26k647YEkSdbWn/zYfpRu20bFseMKp7qe+PQXOoTTp0+TnJzMtm3bMJlMSsdpczy9vBg27g5ie8egl4yU2nhzpagK5sXCnk9BjKcitFIOtg5ERkcC4F3pzZIDSxRO1P4FBwej1Wpx0OmQtVpyP219fX9E8SN0CGFhYXh4eNCnTx80Go3ScdokSZII7NSZ0ZPupFuAG93VKVBdAmteourL8VjSxS3FQuvUN6wv1a41E/qWXCzhYu5FhRO1bzY2NowfP54BI0eiMpko/XUjlWda15xrovgR2q1rm7e1Wi1DhgzB19dXwUTtg1arpUf/Yehmr4Bx7yLbOLJbM4BfN24kb/WbUF2mdERBuM6d8XdSoirBTrJjxdYVGMVAns1Ko9Fg06kTDqNHAZD3aevq+yOKH6Fdslgs7N69m9TUVOuyjjByc4tSqaDvw5TN3kqBXShF+gA2lYWx//s3qDq5Vul0glCLTqtjcP/BmGUzvmZfvt3xrdKROgTdAw9QGhJC8Zo1VF1MVjqOlSh+hHbp4sWLpKWlsW/fPqqqqpSO067Ze4UwZtI0gl20ACTb92LtkXSSf/wjcnGGwukE4X/C/MLQ++nJteTy0+WfOJIjLtU2p7S0NJLOniXz7unIskzeZ58pHclKFD9CuxQaGkqnTp0YOHAgNjY2Ssdp92xsbOg7YjIJcf1xVFVQrXFgv9STzb/8h4pdX4IYSFJoJSYNnESKdwppljRe3fYq5cZypSO1W15eXmg0GiptbSnr1ImiFSuoTmsdg6WK4kdoNywWi7WfjyRJ9O7dG29vb4VTdSzuPgEkTplJVLAHatlItaTHZv2L8NVIyDqhdDxBQJIkXh3wKj52PqSWpPKv/f9SOlK7pdVqCQoKAqB4zGgwm8n7/AuFU9UQxU8DrFy5kq5duxIWFsYXX7SOF1CoYTab2blzJydOnBDjeChMpVLRte9QRo+dQP9gO1RaA6Ttw/JpAumr30GuEh2iBWU56hx5Y9AbDNAMICwzjPPV55WO1G5dHfOnwNsbo709RUuWYMzMVDiVKH7qzWQy8fzzz7Np0yYOHjzIP/7xD/Lz85WOJfxXZmYmGRkZnDlzhrIy8cu1NbC1d8B58MPw5F7oNp6zLgnsKAti+4/vU3pivdLxhA6ur3df4pzjsJFsMFgMZJVlKR2pXXJycsLNzQ0ZKJ04EdloJO+rr5SOJYqf+tq7dy+RkZH4+fnh4ODA2LFjWbdundKxhP/y8/MjOjqauLg47O3tlY4jXMvJD+7+Hnreg0o2kanvzLrjuZz4+S3MxdlKpxM6KEmSmDJ0CpVU4qHyYEHSAiyy6JvWHK62/uRFRiCrVBT++BOm3FxFM7Wq4uett95CkiSeffbZJl3v1q1bmTBhAr6+vkiSxC+//FLn8z766CNCQkLQ6/X07t2bbdu2Wb+Xnp6On5+f9Wt/f3+uXBFD+yvJbDbXmpE9LCysZkZhoVXqFj+ZkcMT8FQXY1FpOSl3ZuOKhRQfF61AgjIc7Bzo2r0rAMFVwSw8sFDhRO2Tv78/uv+O9syAAciVleR/842imVpN8bNv3z4+++wzoqKibvq8HTt2YDRePzjV6dOnybzBdcSysjJ69uzJhx9+eMP1Llq0iGeffZbXXnuNQ4cOMXjwYMaMGUNKSgpAnf1IxLgxyjGbzezYsYNdu3bVKoCE1s3BzZv4KbPp38ULG0s5RTofNpzIIXXVP8FUrXQ8oQOK6RxDrpyLJEmUXyznTM4ZpSO1O2q1miFDhjB+/HgCZt4LQMH3CzAXFiqWqVWM819aWsq9997L559/zhtvvHHD51ksFp588knCwsJYuHAharUagLNnz5KQkMBzzz3HSy+9dN3PjRkzhjFjxtw0w7/+9S8eeughHn74YQDmzp3LunXr+Pjjj3nrrbfw8/Or1dKTlpZGbGxsvfbPaDTWWbAJjZefn09OTg6SJJGfn4+Tk5PSkW7b1XOkI5wr3hEDSAjoxqFNS8nBHqejX2JJXYF58qfg1vm21t1RjmNb3M/WmNloNOKt9abYXIwTTvyy9ReeGvcUOrWuRbZ97b9teVu3Wr+dnR1msxmbQYPQde1K9Zkz5Myfj9uTT95ync1BklvBrTH3338/rq6uvPfeewwdOpTo6Gjmzp1b53PT09OJj48nNjaWb7/9luTkZIYMGcK4ceP4tB7DZ0uSxNKlS5k8ebJ1WXV1Nba2tvz0009MmTLFuvyZZ57h8OHDJCUlYTKZCA8PZ8uWLTg6OtKrVy92796Nm5vbdduYN28e8+bNw2w2c/bsWRYsWICtrW2Dj4twc1dbfMTs7G2XLMu4FZ8k9vKH6MxlmFQ2HAx4mAyXfiBaVoUWVGouRTJJJBmTUKlVjDaMVjpSuyTLMranzxAwfz5mg57kl1/BYtDX+dzy8nJmzJhBUVERjo6OTZpD8ZafhQsXcvDgQfbt21ev5/v6+rJp0ybi4+OZMWMGu3btYvjw4XzySeNnjc3NzcVsNl/XX8TLy8t6KU2j0fDuu++SkJCAxWLhpZdeqrPwAXjyySd58sknKS4uxsnJiYSEhBs+V6g/k8lU85dDOx200Gg0smHDBhITE9FqtUrHaUHjoPheLMsfpyg7gxS7HoRWnyF81IOobV0avLaOchzb4n62xsxXM00ZPYVtadvYuWsnkkliVtws+nr1bZFtt8TxaO5t1Wf9ZWVl7Nixg4rwbqjDwuDcOfoV5OM69eE6n5+Xl9fkOa9StPhJTU3lmWeeYf369ej1dVd+dQkMDOQ///kPQ4YMoVOnTnz55ZdN0v/mt+uQZbnWsokTJzJx4sQGr1er1baaN3pbZTKZ2Lt3L1VVVQwZMqTdFkDQQc8XtyC4fwXZ675CLtVwnk7krFxE/97ROHYb3KhVdpTj2Bb3szVm1mq1jOoyil25u1h8bjF/3fVXFk1YhLPeuUW23VLHo7m3dbP1Ozo6Issy1dXVmGc/CK/+gaL/fIvH/fejquPqSHPmVPR6wYEDB8jOzqZ3795oNBo0Gg1JSUm8//77aDQazGZznT+XlZXFnDlzmDBhAuXl5Tz33HO3lcPd3R21Wn1dh+ns7Gxx91ArUVlZSXFxMWVlZWIcn/ZKpSZ8zCMM6uaFzlxGkc6bDUdSuLDmI2RT6+kjIrRvL/V9iRj7GCZZJvHZltYzF1V7oFKp6NSpEwAZjo5oAwIwFxRQ8OOPLZ+lxbd4jeHDh3Ps2DEOHz5sffTp04d7772Xw4cPWzs0Xys3N5fhw4cTHh7OkiVL2LRpEz/++CMvvPBCo3PodDp69+7Nhg0bai3fsGEDAwcObPR6haZjb2/P0KFDiY+Px9XVVek4QjPy7RHPqNHj8CIXi0rHwVIPdv74b6qyLyodTegAbLW2zA6ZjYfKA79iP5afXK50pHYlJCQESZLIy89HN+cRAPK//ApLC09ArWjx4+DgQPfu3Ws97OzscHNzo3v37tc932KxMHr0aIKCgli0aBEajYbw8HB+/fVX5s+fz3vvvVfndkpLS63FFUBycjKHDx+23sYO8Pzzz/PFF1/w1VdfcerUKZ577jlSUlJ47LHHmmXfhVszmUwUFxdbv3ZwcBB9pzoIvbMHg6c9Rk8PCyqLiXRtEFd+ehlO/KJ0NKEDGBIzBKPBiE7SkXwsmSvFYky3pmIwGKxj5uUEB6Px8cGUk0PRkiUtmqNN3SajUql46623WLx4MTrd/25D7NGjB7/++ivTpk2r8+f2799PTEwMMTExQE2hExMTw5///Gfrc6ZPn87cuXP529/+RnR0NFu3bmX16tXWSdmElmUymdi2bRubN2+mqKhI6TiCAiRJosvQ6Qzv34OwyiOE5KyHn+6HZU9Btbj0KTQfSZIYP2Q8Roz4qnz5avNXmC11d8MQGu7qiM8paWk4PfwQALmff47cgkMgNKjDsyzLJCUlsW3bNi5dukR5eTkeHh7ExMQwYsQIAgICbjvQli1bbvr9xMTEOpdHR0ff8GeGDh1ar8kun3jiCZ544olbPk9ofhaLBbPZjCzLN+z7JXQMzkHdiZ7xF9hiA9vexXjkZ/YW+9BjwAgcO9dvrC1BaChHB0e69ejGhWMXCKsKY9HhRczoNUPpWO2Ch4cHDg4OlJSUUB4bi9rDHVN6BkXLl+M8dSpQ98DCTaleLT8VFRW8+eabBAQEMGbMGFatWkVhYSFqtZrz58/zl7/8hZCQEMaOHcvu3bubNbDQMeh0OuLj4xk6dKjo4yOAWgvD/wT3r+CY/yzS9V3YcOA8F9Z/gSyKY6GZxHSNQXaUUUtq0s+lU1hZqHSkdkGSJKKjo0lISCAoNBS3B2cDkPvZZ8gmE1VVVWzatIns7Oab+69exU+XLl04ePAgn3zyCcXFxezevZvFixfz3XffsXr1alJSUrhw4QKDBw9m+vTpfP75580WWGi/jEZjrZNdp9Ph7OysXCCh9QkZTPjk5/GSs2o6Qxc5sfOn96nKS1U6mdAOSZLE6IGjMWMGGT46+JHSkdoNb29v3N3dkSQJl+l3oXZ2xng5heI1azl9+jT5+fkcP3682bZfr+JnzZo1/Pzzz4wfP/6G990HBQXx6quvcu7cOYYOHdqUGYUO4Gofn61bt5Kenq50HKEVM7j6MHjaE/R0rURlMZKu9mf9+vXkHNlw6x8WhAZydHAkoFcA31R9w4/nf+R8wXmlI7U/BgOuD9wPQO6nn9A9MpKQkBD69m2+QSbrVfzUdefVjeh0OsLCwhodSOiYVCoVtra2aDQaDAaD0nGEVk5SqegyfBbD+4bjaMqjUuNI0plc0rd+q3Q0oR0aFDqI4YHDMctm3t73drP3R+kozGYzBw8eZOXKldjccQcqBweqz1+gbNMm+vTpg4ODQ7Ntu1F3e23bto2ZM2cyYMAA62Sf3377Ldu3b2/ScELHoVKp6NevHyNGjMDFpeFTGggdk3OnGIZPnok/meiNRbhu/QOqX/8EskXpaEI78/s+v8dB5YAmR8Pmy5uVjtMuqFQq8vLyqKqqYvPu3ZQ9OgeA3E8+aR0dnq+1ePFiRo0ahcFg4NChQ1T9d2CikpIS3nzzzSYPKLRf1dXVJCcnW79WqVTY29srmEhoizQGe/pPfZLhPmXoTcWo93xMv+R/Y6ksvvUPC0I9+dv785TDUyToEli9fzXV5mqlI7V5kiRZb3uvqqriiqcnFmdnqk6eomzr1mbddoOLnzfeeINPPvmEzz//vFb/n4EDB3Lw4MEmDSe0X2azma1bt7J//37Onj2rdByhjZNUKgzDfg9Tv0RW21CNjm0rfqAi64LS0YR2QpIkYiJrxorrYenBguMLFE7UPgQGBqLR1Iy6071HD9zvuAOA3I8+btbWnwYXP2fOnCE+Pv665Y6OjhQWFjZFJqEDUKvV+Pn5odPp8PT0VDqO0F70mEb1vb9w1GsaBVovft20hYIzu5ROJbQTXUO7gg3YSrYcO3WM3IpcpSO1WVVVVciyjEajITg4GID09HTcHnwAycaGiiNHqDhwoNm23+Dix8fHh/Pnr+/tvn37duuEZYJQH+Hh4YwePVrczi40KVVAX7RaDY6mXCo1jmw+fIEru5cqHUtoB1QqFf179Qegt6o3n+z/ROFEbVNFRQWbN29m//79yLJsvfSVnp5OtZ0dznfeCUDhV183W4YGFz+PPvoozzzzDHv27EGSJNLT0/n+++954YUXxOjIwk1VVVVx/PhxLJb/dUa1sbFRMJHQXlXp3Bg8eipelgzMKht2plRzZu1nyBbREVq4Pf5+/ugd9WglLaWppZzKO6V0pDanoKCA0tJSsrOzqaqqwtHREXd3d6Bm7k23h2aDVktlM3alaXDx89JLLzF58mQSEhIoLS0lPj6ehx9+mEcffZSnnnqqOTIK7YAsy2zfvp1Tp05ZJ5gVhOaktXch7o7HCNVmg6TiaIkLB5Z8gGysVDqa0IZJksSA3gMAiNHE8OHuD8Wt7w3k6+vLgAEDGDp0KHq9HoCuXbvSrVs3goOD0fr44Dx5crNmaNSt7n//+9/Jzc1l79697N69m5ycHF5//fWmzia0I5IkER4ejsFgoHPnzkrHEToIlVZHr8lPEu1SDrIFffZBpG+nQFme0tGENszd3R03LzeOWY5xIPcA6y+vVzpSq1dRUYHxmolL/fz8sLOzs37t6+tLjx49rMvc5jwCanWz5Wn0rO62trb06dOHfv36iduThXrx9fVlzJgxODo6Kh1F6GDCRtzP8G5ORBZthpSd8MVwyBF3GQqNlzA4Ad9uvpRRxr/2/4tKk2hRvJHy8nI2b97Mtm3bahVAN6MLCMB+ZN0TmTeFes3qfsd/bz2rjyVLljQ6jNC+VFVVceTIEaKjo9HpdEDNXV6CoATXqNHgvQ4W3IW5MI2dqxbSNbo/ntEjlY4mtEGSJPFg9wdZcm4J6WXp/Ofkf5gTNUfpWK1SVVUV1dU14yIZjcYbTpMFkJGRwYULF+jZsyeuT/0OPvigWTLVq/hxcnJqlo0L7dvu3bvJzs6murqauLg4peMIAniGwyObOb3kHTL14WSdzadXwXd0SpipdDKhDTJoDDwb8SyHjx5m8/HNTAqdhJedl9KxWh0XFxeGDBmCjY0Ntra2N33uhQsXyMjIwMHBAX9//2bLVK/i5+uvm+92M6H9io6OZs+ePfTs2VPpKILwP3budLvzT5Ss+JJUyYcDuWpKls0javyjSOp6fSQKglWgOZAKTQUuFhf+feDfvBkvZjoAKCsrQ5Zla7eY+k5bFBoaSkZGBsnJyfj4+DRbvkb3+RGEW3FyciIxMbFZJ6cThMZQ6+2InfoUkXYFAJyt9mTnz/MwlRUqG0xoc7p164Zao8ZT5UlqSipHc44qHUlxZWVlbNmyhS1btlBaWtqgn/X29sbW1haj0Uh6enozJWxk8fPzzz9z11130b9/f3r16lXrIXRclZWVJCUlUVJSYl0mSZKCiQThxiSVioixc4j1BZXFSLrKl03LF1AupsQQGkCn09E9sjsAQ7VDeWfPO1g6+MS6KpUKtVptfTSEJEnWAZMvX77cHPGARhQ/77//Pg8++CCenp4cOnSIfv364ebmxsWLFxkzZkxzZBTaiEOHDpGdnc3evXvFuBdCmxE46E6G9gjAxlxCBXosP9wLGeKvd6H+QkNDsTHY4KhyxLbIllUXVykdSVEGg4EhQ4YwdOhQDAZDg38+JCQESZKadcqsBhc/H330EZ999hkffvghOp2Ol156iQ0bNvD0009TVFTUHBmFNqJXr174+PgQGxsrWnyENsUtYjDDhwwmrmQl9oWn4D8TIfOY0rGENkKtVtOzR03fxkHaQXx88GPKjeUKp2pZZWVltUbvNxgMjSp8APR6fbN2doZGFD8pKSkMHDgQqNm5q5c4Zs2axQ8//NC06YRW79oWHhsbG+Li4sS4T0KbZOcThtt9X4NfH6goIHfh0xRdbL6JFYX2JTAwEEcnR/SSnk7GTnx1/CulI7WY8vJytm/fjslkIi+vaQYQDQ0Nbdb+og0ufry9va07FxQUxO7du4Ga+TjEpY6OpaKigg0bNpCZmal0FEFoGnonmLmYvIDRbPWeTdLeoxQlH1I6ldAGSJJEdM9oDAEGthu3M//EfNJLm6/DbmtiY2NjLVSuHbX5dri7uxMfH98k66pLg4ufYcOGsWLFCgAeeughnnvuORITE5k+fTpTpkxp8oBC63X69GmKioo4fPhwreZOQWjTDM443PkBjnIRVWp7kvYcovjSEaVTCW2Al9f/Z+++w6Mq0wYO/86U9EknvUMIoYYaagggXZqL2FbFhq66ruCuru66uru6rn6i6NrAgqIoiFSpAkloCWDoHUJPgZDekynn+yNmZCSBlJnMZPLe1zUXmTNnznnOMMk885bn9Wdi/ET6BPShWl/NO/vesXZIrUKpVBIfH49arTau1dVSkiRZdPhEk4taLFiwwPhB98QTT+Dt7c3OnTuZNGkSTzzxhNkDFGxXr169kGWZmJgYFApRNUGwHw7ufiSMv5NtG36gSOXHtrR9JCoUaMJ6WDs0wcZJksQL/V/grrV3sf3CdvZ12Udf/77WDsvsSktLyc/PJyIiAqhNgNrSWM8mJz8KhcLkg27GjBnMmDHDrEEJtkun06FS1b5tFAqFKG8g2C0HT38Sxk9n24blFKs6kLJrL4mSAk1oN2uHJti4DnTgz5o/c7n6Mm/ufZPvJn6HUmE/S/tUVVWRkpJCVVUVSqWS0NBQa4fUZE3+ur5w4UKWLVt2w/Zly5bx1VdfmSUowTZVVFTw008/cfq0WBBSaB8cPf0ZPm4qHrprVKnc2bYzjYqsE9YOS7BxDg4OOOmd6KTsRGVRJavPrrZ2SGbl6OhIaGgoHh4e+Pn5WTucZmly8vPf//4XX1/fG7b7+fnxn/+Ist72LDMzk/LycjIyMtDpdNYORxBahaNXEAljpuCuy8Or4hyOS2dAwXlrhyXYMDc3Nzp27AjAKPUo3tv/HmU1Tat0bMskSaJXr16MGDECR0dHa4fTLE1Ofi5evEhkZOQN28PDw7l06ZJZghJsU3R0NL169SIxMdHY9SUI7YGTTzCJYyczqHIrypJL8NUkKLRc9Vmh7YuNjUWlUhGsDCZAG8CCwwusHVKLlJSUcOTIEeOsbkmSbro6u61rcvLj5+fH4cM3Vj89dOgQPj4+ZglKsB1VVVUmb/bOnTvfclVeQbBHjt7BKB5cDT6dkIsvc3zFm5TnZFg7LMFGOTk5ERMTA8BI9UgWn1jMpZK22UCg0+nYtm0bJ0+e5NSpU9YOxyyanPzcfffdPPPMMyQnJ6PX69Hr9SQlJfGnP/2Ju+++2xIxClZSXl7O1q1b+fnnn0UNJ0EA0ATAgz9yIux+jrmPYFtKklgLTGhQ586dcXR0xFvhTS9FL95Of9vaITWLSqWiR48eeHl51dvz0xY1Ofl57bXXiI+PZ9SoUcby1WPGjGHkyJFizI+dKSoqorKykvz8fGpqaqwdjiDYBvcgIibOwVVXSLnKi21JW6jMFWOAhBupVCq6daudHRipjCT5cjJp2WlWjqp5IiIiGDlyZJsd4/NbTU5+HBwcWLp0KadOnWLx4sWsWLGCs2fP8sUXX+Dg4GCJGAUrCQ4OZvDgwSQmJtrNG14QzMHFP4rExJG46gopU3mTsnUTldfaZpeGYFmRkZEMGzYMdWTt+Ji3fn4LncH2J4wUFRWRlpZmMrnFnuq5NftKoqOjufPOOxk/fjyFhYUUFhaaMy7BSsrLy9Fqtcb7QUFBzV6cThDsmUtgJ4YnjsBFV0SZyodtW9ZTlXfZ2mEJNkahUBAQEMAf4v6Ap6MnGUUZrDm7xtph3ZTBYCA1NZXMzEyOHLHPBX6bnPw8++yzfP755wDo9XqGDx9Onz59CA0NJSUlxdzxCa2otLSU5ORkduzYYZIACYJQP9fAaBKHJ+CsK6ZU5cP2n37EUHLV2mEJNsjD0YNHuz5KjDKGz458ZtOtPwqFgvj4ePz8/IzddvamycnPDz/8QK9evQD48ccfOXfuHCdPnuTZZ5/lb3/7m9kDFFqPTqdDp9Oh1WrR6/XWDkcQ2gTXoBgShw3BRVdI7JUVKL6eAuV51g5LsDFVVVV4nPPgToc7KS8r56cLP1k7pBtcP7HFx8eHhIQEux3O0uTkJy8vj4CAAADWr1/PjBkz6Ny5M4888ojdNo+1F15eXiQmJjJ8+HCzLU4nCO2BW0gs40aNIFTOhGsn4KvJUJ5v7bAEG+Lk5ISPjw8KSUF/VX8+PfIpBtl2FoQuLCxk69atVFRUGLe1pbW6mqrJyY+/vz/Hjx9Hr9ezceNGbrvtNqB26QOl0n7WLmkvSkpKKCv7tfKop6enSHwEoRmUftHw4I/g5k9FQTZpK+dTUyS6wIRfRUdHA9BL1YsLRRfYnrXdyhHVkmWZffv2UVhYWG8dP3vU5OTnoYceYsaMGXTv3h1Jkhg9ejQAe/bsoUuXLmYPULCc0tJStm3bRkpKCuXl5dYORxDaPt9o5AfWsDv8KTIdo9m24QdqinOtHZVgI/z9/XFxccFJcqKrsiufH/3cJmqoSZLE4MGDCQ0NpW9f+1uBvj5NTn5effVVPvvsM2bNmsWuXbuMU6CVSiV//etfzR6gYDlqtRq1Wo2Dg4NYrkIQzETy60LfobfhqC+jSOVH2oYlGLSiTpZQm2RERUUB0F/dn2MFxzirs16RTIPh1243FxcXBg4c2KaXrGiKZn3iTZ8+/YZtDz74YIuDEVqXk5MTw4cPR6FQiDo+gmBGHpG9SdDrSNp3ilxlIIfXLSBu6tPWDkuwAZGRkRw7doxgRTB+kh8pVSk8wzOtHkdBQQGpqakMGjSoXS5NZT8Vi4RGKS4uJjf312Z4Z2dnkfgIggV4durPgDBXAM5o/bm4/TsrRyTYAicnJ4KCgkCCMFUYF/QXOHjtYKvHcfLkSSorKzlx4kSrn9sWiOSnHakb47Nz507y88VMFEGwtJBB04h1rv1dS8+RKTiZauWIBFvQs2dPbp94O6FhoQB8ceyLVo9hwIABdO7cmYEDB7b6uW2BSH7aERcXF7y8vNBoNGg0GmuHIwjtQrfxjxFoyEFTnYPjxjlQesXaIQlW5ubmhrOzMw92fRAJiZ3ZOzmRb/kWmOsHV6tUKnr16tVux3uK5KcdUSqVDB48mOHDh9tt4SpBsDWSUkn8hN8zsmQFrkUnYOn9oKu2dliCDQjThDFAPQCAT498atFz5efno9VquXRJrEEHzUh+SkpK6r2Vlpba/crfa9euJSYmhujoaD777DNrh9MoRUVFnD//64rTSqVSJD6C0MrUGm9U9ywCJw/I3Evxj/8AG5jiLFiPLMukpaUxVj0Wf8mfLRe3cK7onMXOl5OTA8Dly5dtYnq9tTU5+fH09MTLy+uGm6enJ87OzoSHh/PKK6+YTKGzBzqdjjlz5pCUlMT+/ft58803KSgosHZYN1VZWcm2bdtIT08nMzPT2uEIQvvm0xGmf8EJ3wn8pO3DuaRF1o5IsCJJkoyFgSd5TkJG5vOjn1vsfN26dUOpVBIfH2/XlZsbq8nJz5dffklQUBAvvfQSq1atYuXKlbz00ksEBwfz8ccfM2vWLN5//33++9//WiJeq9m7dy/dunUjODgYjUbDhAkT2LRpk7XDuiknJyciIiLw9vbG39/f2uEIgtDpttqbpGB/vgN5h7daOyLBiiIjIwEI0YWgRs26c+vILDXfF9WysjJjK09dstVex/j8VpOTn6+++oq5c+fy73//m0mTJjF58mT+/e9/8/bbb7N06VL+9re/8f7777NoUeO+1Xz88cf07NkTd3d33N3dGTRoEBs2bGjyhdzM9u3bmTRpEkFBQUiSxKpVq+rd76OPPiIyMhInJyf69u3Ljh07jI9lZ2cTHBxsvB8SEkJWVpZZ4zQ3SZLo2bMnw4cPbzeFqwTB1nUZ8xAhXEGWVKQeu0jFlTPWDkmwEl9fXwAMegNTfKagl/UsPLrQLMe+du0aP/30E4cPHxbdXPVocvKTlpZG7969b9jeu3dv0tLSABg6dGijB1WFhITw3//+l/T0dNLT0xk5ciRTpkzh2LFj9e6/a9cutFrtDdtPnjzJlSv1z6IoLy+nV69efPDBBw3GsXTpUuPK9AcOHGDYsGGMHz/eeB31vXlssemwsLCQQ4cOmWT7ItMXBNshKRT0v30mHro8qlUaUpM3oa8ssXZYghVc3/UVp4wDYGXGSnIrWr4kSmlpKXq9nuLiYpH81KPJn4ohISF8/vnnN3Rrff7554SG1tYsyM/Px8vLq1HHmzRpksn9119/nY8//pjdu3fTrVs3k8cMBgNPPfUU0dHRLFmyxPimOX36NCNGjGD27Nk8//zzN5xj/PjxjB8//qZxvPPOOzzyyCM8+uijAMybN49Nmzbx8ccf88YbbxAcHGzS0pOZmUl8fHyjrlGr1dabsJlbTU0N27dvp6amBgcHBzp16mTxcwrmU/ceaY33ij1rE6+jyokBQ0ewbdduClX+pP/4Gb2nPIWkaPz30TZxnb9hizFbMyatVotCocBgMFBTVkOCdwLbC7bz5ZEvmd1ndouOHRoaikqlws/PD71eb/HrtMTxLfl/IslNTAnXrFnDnXfeSZcuXejfvz+SJPHzzz9z8uRJfvjhB26//XY+/vhjzpw5wzvvvNOkYPR6PcuWLePBBx/kwIEDdO3a9YZ9srOzSUhIID4+nq+//prz588zfPhwJk6cyPz58295DkmSWLlyJVOnTjVuq6mpwcXFhWXLljFt2jTj9j/96U8cPHiQbdu2odPpiI2NJSUlBXd3d/r06cPu3bvrLQv+4Ycf8uGHH6LX6zl9+jTffvstLi4uTXotmkuv12MwGFCpVDbZMiUIwq9cyy9SpPJDlpSEl/xMju8Qa4ckWIFOp8NgMFBCCfMq5uGAA392/zMuiqZ9bhgMBiRJspu//RUVFdx7770UFxfj7u5u1mM3ueVn8uTJnDp1ik8++YTTp08jyzLjx49n1apVREREAPCHP/yhScc8cuQIgwYNoqqqCjc3N1auXFlv4gMQFBREUlISCQkJ3HvvvaSlpTFq1Cg++eSTpl6KUV5eHnq9/oZBwf7+/sauNJVKxdy5cxkxYgQGg4Hnn3++wfVQnnrqKZ566ilKSkrw8PBgxIgRrbp2iizLdvPmb0+0Wi2bN29m9OjRYoxWC7S11/H8tm9RHV1GZNFO9IOGI0ePadTz2tp1gm3GbM2Y6s49aNAgKioqCAoKYuOWjZwsPMm1sGv8oWfjP0uvXbtGWloavr6+xMfHG3tGfnsuS12nJY5vyZUImjUYJCIiwqyzuWJiYjh48CBFRUUsX76cBx98kG3btjWYAIWFhbFo0SKGDx9OVFQUn3/+uVk+7H97jN8mEZMnT2by5MlNPm7d6umWkJ+fz8mTJ4mPjxdje+yEJd8v7UlbeR073/YgVB6AfTtQrX4CHksC3+hGP7+tXOf1bDFma8bUoUMH47ln9ZrFnJQ5LDm9hId7PIybg1ujjlHX2q9UKlGr1TckP3UsfZ3mPL4l42xWheeioiLmzp3Lo48+ymOPPca7775LcXFxs4OoG5/Sr18/3njjDXr16sV7773X4P5Xr15l1qxZTJo0iYqKCmbPblnfqK+vL0ql8oYB07m5uTY9RVyv15OWlkZ2djbHjx+3djiCIDTX+LcgbBDVOj37136BrkysvddejQwdSZRHFKU1pSw5taTRz/Pz82PEiBEMGjSowcRH+FWTk5/09HQ6duzIu+++S0FBAXl5ebzzzjt07NiR/fv3myUoWZaprq6//HteXh6jRo0iNjaWFStWkJSUxPfff8+f//znZp/PwcGBvn37snnzZpPtmzdvZvDgwc0+rqUplUoGDRpEUFBQg61kgiC0ASoH5Du/Ylfkc5x17cPPa79C1uusHZXQys6ePcvmnzbzcMeHAfj6+NdU6iob3D83N5fKyl8f9/LyEolPIzU5+Zk9ezaTJ0/mwoULrFixgpUrV3L+/Hluv/12nn322SYH8NJLL7Fjxw4uXLjAkSNH+Nvf/kZKSgr33XffDfsaDAbGjRtHeHg4S5cuRaVSERsby5YtW/jyyy9599136z1HWVkZBw8e5ODBgwCcP3+egwcPmkzHnzNnDp999hlffPEFJ06cYPbs2Vy6dIknnniiyddkadePUffx8WHIkCGiy0sQ2jhJ40/PfoOQZB2ZylBObrj1BA7Bvly7do3S0lKCqoMIdgumoKqAFWdW1Lvv1atX2bFjB9u2bWuwsUBoWJM/MdPT0/n0009NPmxVKhXPP/88/fr1a3IAV69e5f777ycnJwcPDw969uzJxo0bGT169A37KhQK3njjDYYNG2ayPlWPHj3YsmVLg4OK09PTGTFihPH+nDlzAHjwwQf58ssvAbjrrrvIz8/nX//6Fzk5OXTv3p3169cTHh7e5GuypLy8PPbv38/QoUNbbQaZIAitw7fLYPrkZrHvKhyt8MUzbTmBg35n7bCEVtKxY0cuX75M5uVMHo59mH///G8WHl3IjM4zUCtNx7+4urri6OiIRqOxufFTbUGTkx93d3cuXbpEly5dTLZfvnwZjUbT5AA+/7xpa5nUlxQBxMXFNficxMTERhV5evLJJ3nyySebFE9rkmWZAwcOUFxczLFjx+jfv7+1QxIEwcyiEu6kcPUnnKvxYffFcm7zS0fTselfLIW2x9fXF41GQ2lpKb3Uvejg3IGrFVf58dyP3BF9h8m+bm5ujBw5EicnJxRNqA8l1GryK3bXXXfxyCOPsHTp0toMNTOTJUuW8Oijj3LPPfdYIkbhF5IkMWTIECIjI+nTp4+1wxEEwUJ6T3wEX0MuOqUzu/bsRVtUf/V6wb5IkkRUVBQAly5c4sGuDwLw+ZHP0Rl05OTkUFhYaNzfxcVFJD7N1OSWn7fffhtJknjggQfQ6WoH5KnVav7whz/Y3WKmtkKv1xsHsbm4uDSre1EQhLZDoVIzaOzv2LJxHQZZpnr1bNS/XwRK0b1h78LDwzly5AhFRUWM7jWazxw/41LpJTYc2UD1mWpUKhWjRo1qVk+L8Ksmp4wODg689957FBYWcvDgQQ4cOEBBQQHvvvsujo6OloixXcvNzWXdunXk5eVZOxRBEFqRk2cAw+L7ctvlebidXw+b/mbtkIRW4OjoSEhICADZl7L5fezvAVh0fhHe3t74+/vj6upqzRDtQrPby1xcXOjRowc9e/YUA28t6PTp01RXV3PmjFj5WRDaG4/IOBym/lLzbO98DAeXWjcgoVV07NiRkJAQwsLCuCf2HlzVrpwsPokcKRMfHy+6usygUd1ed9xxx613+sWKFfVPyxOaZ+DAgZw8eZLY2FhrhyIIgjV0mYA8/EXOHPmZs8fyGRV6GQefUGtHJViQr68vvr6+ZGVlUX21mrtj7ubzo5/z2bHPGBkx0trh2YVGpY8eHh6NvgktV1VVZfxZpVLRvXt3UbhKENoxw5DZnPMbS5lDBw5v+Q6ath610AYVFBSQlpbGnj17mBw4GSelE0fzj5KWk2bt0OxCo1p+Fi5caOk4hF9cuXKF1NRU+vTpY1woVhCE9k3p4EjfPr1JOXSB84pIwvb8gFffqdYOS7AgLy8vgoKCahfIdvTgd51/x+ITi/n08KcMDrLdlQfaCtFxaGOuXLmCXq8nKyurUbWJBEFoHzp07k9H5xIA0s/loSvOsXJEgiXVLVJaWlrKuXPnmNltJiqFivSr6RzIPWDt8Nq8RiU/48aNIzU19Zb7lZaW8uabb/Lhhx+2OLD2qlevXvTt25dBgwaZZaV6QRDsR4/b7sFZX0q52pfTW7+xdjiCmV2+fJljx44Zv/h27NjRuN1b7c2UjlMA+PTwp1aL0V40qtvrzjvvZMaMGWg0GiZPnky/fv0ICgrCycmJwsJCjh8/zs6dO1m/fj233347//d//2fpuO1KSUkJGo0GSZJMilwJgiBcT+3kQp8eXdh1PIvTUkcCi48AE6wdlmAGpaWl7NmzB1mW8fT0JDg4GB8fH9zd3SkpKeHSpUs83P1hVmasZEfWDk7knyDWR0yEaa5Gtfw88sgjnDt3jpdffpmTJ0/y+OOPM2zYMPr378/YsWP59NNPCQsLY9++fSxZsoTQUDETobFycnLYvHkzhw4dEt1cgiDcUlC3wYQ6lCBhoEPxISgXNcDsgUajoWfPnkRERBAUFASYVnw+d+4coZpQxkWMA+DTI6L1pyUaXeHZwcGBe++9l3vvvReA4uJiKisr8fHxEYuqtUBlZSUGg4GKigpkWRZdXYIg3FLcbTOI+foevPJ3YPjpRZjxpbVDEprp+i+9nTt3vuFzIDw8nMOHD1NcXExBQQGP9niU9efXs+XiFs4VnSPKU/QUNEezBzx7eHgQEBAgEp8WioqKYtiwYQwcOFAUrhIEoVGcXDVoxv8DAwoUx1fCibXWDklohsuXL6PT6TAYDMZtv/0C7ODgYOxNOXfuHNFe0YwIHYGMzOdHm7YwuPAr8WlrBXl5eej1euP9gIAAkfgIgtAkclBvMvwnUOQUyv7UZOTyfGuHJDRBVVUVBw8eRJZlLl68eNN9O3bsiFqtxsnJCYBZPWcBsO7cOjJLMy0eqz0Sn7itLDs7m5SUFHbt2mWSAAmCIDTVSf+ppES+wFn3gZzbIGbZtiVOTk4MGDAAhUJxy5pu3t7eTJo0iR49egDQ3bc7AwMHopf1LD0lljxpDpH8tDK1Wo1CocDBwUGM7xEEoUVkpQNdwv0BOKyLoOLoBitHJNzK9V1c/v7+qFSqW34W1NX8uV7dgqcrM1ZSpauq72nCTYjkp5V16NCBUaNGGTN+QRCElojqk4i3shyd0pn9+9KRKwqtHZLQgPPnz7Nlyxaqq6ub9XxZlsnNzaW4uJihwUMJcg2iuLqYjRc2mjlS+9fkT9+ZM2eyfft2S8Rit65evUplZaXxvoeHh0h8BEEwC0mS6Dd8ApKsJ8elC5kb37N2SEI9dDodx44do7i4mPPnzzfrGEeOHGHbtm2cOnUKpULJjJgZACw5ucScobYLTf4ELi0tZcyYMURHR/Of//yHrKwsS8RlV9LT09m2bVuzs31BEISb8fDxIzbYE4AD1aFUn/zJugEJN1CpVAwfPpyuXbsSExPTrGMEBwcDtbPEampqmBY9DbVCzbH8YxzNO2rOcO1ek5Of5cuXk5WVxdNPP82yZcuIiIhg/Pjx/PDDD2i1WkvE2OY5OTnh7e2Ng4ODtUMRBMFOdRk4GndFJdUqd87uXAlVJdYOSQBqamqMP2s0Grp169bs8Z7e3t54eHhgMBi4ePEi3k7exqKH3538zizxthfN6nvx8fHhT3/6EwcOHGDv3r106tSJ+++/n6CgIGbPns2ZM2fMHWebNmTIEPr37y8GOAuCYDFKpZJ+Q0YSV7yR2MzvYPM/rB1Su3f27Fk2btxIcXGxWY4nSZJxva+zZ88iyzJ3dbkLgI3nN1JYJcZ7NVaLBp7k5OTw008/8dNPP6FUKpkwYQLHjh2ja9euvPvuu+aKsc1zcnISiY8gCBbnExBC9KiZSMiwbyGc22btkNotg8HA+fPnqa6uJjPTfLV4wsLCjKu95+fn09O3J7HesdQYaliVscps57F3TU5+tFoty5cv5/bbbyc8PJxly5Yxe/ZscnJy+Oqrr/jpp5/4+uuv+de//mWJeAVBEISbiRwG/R5BJzmQ/dP7UF1m7YjaJYVCQUJCAnFxcXTt2tVsx1Wr1YSFhQG1rT+SJHF3l7sBWHpqKXqDqB/XGE1OfgIDA3nssccIDw9n7969pKen88QTT6DRaIz7jB07Fk9PT3PGKQiCIDSSNvHv/NT5NXZ5zyD/p7etHU67Ul5ebvzZwcGB6Ohos7f81y12WlxcjCzLjI8cj8ZBQ1ZZFruyd5n1XPaqycnPu+++S3Z2Nh9++CFxcXH17uPl5dXsqXyCIAhCy6jdvPH1CwBJQXqRB4ZzO60dUrtw5swZNmzYQHZ2tkXP4+XlxciRIxk9ejSSJOGscmZqp6mAmPbeWE1Ofu6//37j+iKCIAiCbeo1dByO1FDiFMLJ5CVQU2HtkOyaLMvk5+cjyzIFBQUWPZckSfj4+Ji0KN0VUzvweWfWTi6XXrbo+e2BqLQnCIJghxwdHYnr0xeA425DKNksur8sSZIkBgwYwMCBA+nWrVurnVeWZWRZJtw9nMFBg5GRWXZqWaudv60SyY8gCIKdCo2KIdBdhaxQkZ6nRr6429oh2Z38/HzjzwqFgtDQ0Fab3bt//37WrFljbGm6O6Z24POKjBViva9bEMmPIAiCnZIkiT7DxqJCR75LJ85u+QK0lbd+otAop06dIikpiRMnTljl/NXV1dTU1HD16lUAEkISCHQNpLi6mE0XNlklprZCJD+CIAh2zMXFhZ7duxNYcYKgnJ8g5Q1rh2Q36lZov36l9tbk7+8PwJUrVwBM1vtaemqpVWJqK0TyIwiCYOeiunRnyIC+uOgKIfV/kLnP2iHZhdjYWBITE1t1jM/16pKfgoIC4/JS0zrVrvd1JO8Ix/KOWSWutkAkP4IgCHZOkiSk2InQ406QDVSt+TPoxELLzZGVlWXS0tOhQwerxeLq6opGo0GWZXJzcwHwcfZhTMQYAJacEtPeGyKSH0EQhHZCO/o/7A1/kk1e91O14wNrh9PmnDx5ktTUVPbu3Yssy9YOB7ix6wt+Hfi84fwGiqqKrBGWzRPJjyAIQjuhdPOlyKcPNSoNxzMuQXmetUNqU9zd3ZEkCQ8PD5tZr7Eu+akb9AzQq0Mvunh3oVpfzeqzq60Vmk0TyY8gCEI7oVAoiBswDIDz7vFUpsyzbkBtTFBQEGPHjiU2NtbaoRj5+fnh7e1NaGiosTtOkiRj68/SU0sxyNYZkG3LRPIjCILQjnTw98fXTYVBoeZkZgEUiKWIbubcuXPU1NQY71+/jqUtUKlUjBo1ih49eqBQ/PqRPj5yPBq1hsull9mVJdb7+i2R/LSC8mqdtUMQBEEAalsFuvYZDMA5z6FUbn3LyhHZrpMnT7Jv3z62b99utenszeWidmFKpymAmPZeH5H8tIKJ/0tl3pbTFFXU3HpnQRAEC/Pz88PH3RmDQs2pfD1kH7B2SDYpMDAQBwcHQkNDTVpVbJFOpyM7O9skSatb72t75nayyrKsFZpNsu3/TTtRVq1l3pYzDPlvEm+sP0FuqSg7LgiC9UiSRLe4/gBkavpg2PxPsJHZS7bEw8ODcePGERMTY+1QbkqWZTZs2MCuXbtMltuI8IhgUOAgZGSWZyy3YoS2RyQ/reDV3lqmRCkpr9Ezf/s5hr6ZzMurjnK5QKyyLAiCdfj5+dG/ayTjzr+G4nwynN1q7ZCsTpZlTp48SWlpqXGbo6OjFSNqHEmSjPWGrp/1BXBXl9rWn1VnV6GVta0em60SyU9rMOh4dnQMX8zsR58wT3Q6PV/vvkDi2ynM+f4gGbmltz6GIAiCGUmSRES3fqj6PVC7YfOr0MbGtZhbRkYGR44cISUlxVgxua2ob8o7wPCQ4QS4BlBUXcRR7VFrhGaTRPLTCvr27UtYWBgju/iz/A+D+WRKCP+I0xLvU8O6g5mMfnc7T327n7PXyqwdqiAI7U3Cn5Ed3SkpyoMjy6wdjVWFhYXh4eFBly5dUKvV1g6nSa5f6uL62WkqhYo7O98JwJ7qPVaJzRaJ5KcVBAYGolQqgdpvW4ryPDRKHdPCtLwaV8WUkBr2nMpi9DvbeP6HQ2QWiu4wQRBaR7XSlS1d/suWqL9TlfIOaNvvmERHR0dGjRpFdHS0tUNpMhcXF9zd3YEbW3/uiL4DlUJFpj6T4wXHrRGezRHJjxUMHz6cuLg4NBoNKklmiJ+O57tVcXdENduPXmLk29t4dc0xrpWKtXcEQbAsBwcHJLcO6BWOnHboDj9/Zu2QWo0syxw7dsxkhlTdF9W2qKGuL19nX24LvQ2AZafbd+teHZH8WIFarSY6OpqxY8eSkJBAYGAgkgS9vfXcGw01egNfpl4g4a1k3tp4kuKKttX3LAhC2yFJEt269wAgw3sE1bs+hsoi6wbVSi5evMiZM2fQ6XRUVlZaO5wWCwgIAGrX+frt2mMzOs8AYOPFjRRXF7d6bLZGJD9WJEkS/v7+DB06lNGjRxMSEsJ94waz+NF4eoV6okbLhvTTDH0riQ+TM0SxREEQLCIgIAAvT0/0CkdOucbDrnnWDqlVhIWFGYclODs7WzucFvP19SUuLo7hw4ff8Fgv314EKAKo1lezKmNV6wdnY0TyYyM8PT0ZNGgQPj4+DOnky6onB/PvRG+ejKnm92FlrE47zvD/S+KLneep0uqtHa4gCHZEkiS6dusG/NL68/MiKLbPonjXt4goFAoGDBjQpru6rqdSqYiOjkaj0dyw8KokScQ7xgPw/anv2/16XyL5sVGSJBHu44pCoSBKY+Cx6GruDSll+Y7DjHw7maU/X0Knb99vXkEQzCcwMBBPT0/0SidOeyRAyn+sHZLZybLMwYMHOX36tHGbrazO3hp6OvTETe3GpdJLpGWnWTscqxLJjw2Li4tjwoQJREdHo1QqCXU1MLNTDXcHF/H11gOMfnc7aw5lYzCIyqyCILSMJEl07doVgDyXTsgHv4XcE1aOyryuXLlCRkYGhw4doqSkxNrhWITBYODcuXPs3r0bvd60l8BRcmRS5CQAlpxaYo3wbIZIfmycs7OzMQnq0qULKpWKQGeZGE+J83nlPPPdASa8v4OtJ67eMMBNEAShKYKCgkhISCDR6QSSbIAt/7R2SGYVEBBAbGws/fr1M04LtzeSJHH06FEuX75sstRFnbqaP9szt5Ndlt3a4dkMkfy0EU5OTvTo0YOJEyfSrVs3XrxnJM+N7ozGUUVFaRGfrU/jzo93kXb2xje7IAhCY9RNwpBuexUkJZzeABdTrR1Wi8iybPxiKEkS3bt3JzIy0spRWY4kScZZX7+d8g4Q4R5BfGA8BtnQrqe9i+SnjXFwcKBr1674erjxx1HR7HhhBI90U3NHmJbxnjl8smYHjyzcTUauqBYtCEIz+Uaj7f0Q11yiYfM/2uyip7Iss3//ftLT09tVy3hdvZ8rV67U+/g9MfcAsOLMCmr0NfXuY+9E8tPGeTirGdIzGkcnZ9zVcHuIlsGOl/n711t57cdjlFSJGkGCIDRNaWkp6w1D2Rn2DDXZx+DEj9YOqVkKCws5f/48Fy5cqLcLyF7VJT9FRUVUV99YLHd46HD8XfwpqCpg04VNrR2eTVBZOwChZSRJolOnTkRFRXHx4kWOHD2OOxX8LqyGnMITzPzwIjOGdefOfqEoFe1nVoNwa3q9vs0t3tgYWq0WlUpFVVXVDQM+7Ym5r9PBwQGFovb7sJubG07OLpRodZzxGUW3rf+EmPGgbFvrXXl7exMfH4/BYMDX19fa4bQaJycnPDw8KC4u5urVq4SFhZk8Xrfe1wcHP2DpqaVM6jjJSpFaj0h+7IRCoSAyMpLw8HAyMjI4fPQYgc46HOQa/rriCN/sucirk7rRL8Lb2qEKVibLMleuXKGoqMjaoViELMsEBARw+fJlu57GbO7rrPsb4uDgYJz5tXv3bs74jKbz6S2o9y+C/o+YIXLLkmUZvV6PSlX78RYaGmrliKwjICCgweQH4Hedf8cnhz/h0LVDnMg/QaxPrBWitB6R/NgZhUJB586diYiI4PSZDG7zd+TY1gyOZpUwe9FOBkQH8ucJ3Qn0aPvVTIXmqUt8/Pz8cHFxsbsEwWAwUFZWhpubm7Elwx6Z8zoNBgPZ2dnk5OQQFhaGJEmEhITg7u5OSQmc8RlF15T/Qs+7wNHNTFdgfrIsk56eTmlpKcOGDWtzK7Obk7+/P6dOnWqwddfX2ZfRYaPZcGEDS08t5dXBr7ZugFYmkh875eDgQPduXekOTO0TwtyNJ4ioysBVusC/FmXTLTaGR4dH46S2j8qmQuPo9Xpj4uPj42PtcCzCYDBQU1ODk5OT3Sc/5rzODh06kJ2djU6nQ61WI0kSsbGx7Nmzh9O+Y4k+tQX17o9g+PNmiN4yysvLycrKQqfTkZ+fb5z11B516NCBKVOm4ODg0OA+d3W5iw0XNrDu3Dpm952Nh6NHK0ZoXfb7l0Ew8nVz5OUJnQn2ccdRCSMDanC6epRnPt3EhiPZ7WoWRHtX9y3QxcXFypEItqbuQ/L68UOhoaFoNBq0CmcyvEfBrveg7Jq1QrwlNzc3hg8fzsCBA9t14gO1vQA3S3wA+vj1Idormip9FWvOrmmlyGyDSH7aCRcXFyaOG018fDyoHPFwkBndoZzT+1N59sttnLxin9VOhfrZW1eX0HL1vSeur/pc6dEJaspg+/+1dmg3JcuyyYrsXl5ehISEWDEi29PQgHhJkrg75m4Alp5a2q7W+xLJTzsiSRJhYWHcMXkisd26Y0BJkIvMULdrPLpgG/9YfZTC8vZZ80EQhPqFhoYybtw4+gwZWbsh/QsoOGfdoH4hyzJ79+5l69atlJWJ2ma/VVNTQ1JSEmvWrGkwAbo96nZc1a5cLLnInpw9rRyh9Yjkpx1SKpV07xrL1MkTCQiNIFfvQmaFgkVpFxkxN4VFqefEoqmCzUlMTOTZZ5+1yrnnzJmDJEnccccddj19vj6SJKHRaCAqETqOAoMWtv7b2mEBtR/uhYWFVFVV2e1aXS2hVqspLy83joGqj4vahdujbgdgZcbK1gzPqkTy0445OjoybGB/nrxrAt8+Gk+MvwZ9TQ3aC/v4+5cb2XnmxtLogtDWabVaXnjhBXr06IGrqytBQUE88MADZGfXv87R66+/zqeffsr8+fNJS0vj8ccfv+nxMzIy0Gg0eHp6WiB66yob+jdy3HrAsRWQtc/a4eDo6Mjw4cMZMmQIQUFB1g7H5ly/1EVubm6D+03tNBWApEtJlNS0jyRSJD8CkiQxuJMv654ZyovDvHFXQ1/3ck7s3cG/lmwnr7TK2iEKgtlUVFSwf/9+Xn75Zfbv38+KFSs4ffo0kydPvmHfBQsWMHfuXDZv3sysWbPYvn07mzdv5oUXXqj32FqtlnvuuYdhw4ZZ+jJaXV5eHhv3nWdv+B/QKRxh8ytWWfbCYDCY1KhydnYmMDCw1eNoK+qqPd8s+enm041Onp2o1lez8fzG1grNqkTyIxiplAruGTOI7nF9qUGNp4NMrPIqn/6wgR/3nRezwgSbsnHjRjw8PFi0aFGTnufh4cHmzZuZMWMGMTExDBw4kP/973/s27ePS5cuGff74YcfeOWVV0hKSmLgwIEAREdHs2PHDlasWMFbb711w7H//ve/06VLF2bMmNGyi7NB3t7euLi4UIMDZ31GwYUdkLG1VWMwGAzs2bOHpKQkrl2z3VlntqQu+SkpKWnwb7gkScbWn9UZq1srNKsSyY9gQpIkYqOjuPuOSfiERKGXoZObjqIz6fz7uxSuld64TozQtsmyTEWNrtVvLUmmlyxZwowZM1i0aBEPPPAAixcvxs3NDTc3N9zd3Y0F+uq2ubm5sXjx4gaPV1xcjCRJJl1V06dPJycnh7i4OJN9w8LCOHPmDM8/b1rvJikpiWXLlvHhhx82+7psmUKhIDa2tgrwKf9J6CQH2PIKGFpvDJQsy+h0OgwGg10uzWIJjo6OeHl5AbXJY0MmRk1EKSk5nHeYc0W2MaDdkkSRQ6FeSqWSkYP6khfTkY0pu3Cigry8Ika/u41XJ3VjSlyQmC5tJyq1err+o/UXNzz+r7G4ODT9T9BHH33ESy+9xOrVqxkxYgQAkydPri3jQMOVj+u+Af9WVVUVf/3rX7n33ntxd3dvxpVAfn4+M2fO5Jtvvmn2MdqC8PBwTpw4QXl5OWf9xhFzdQ0cWQa97m6V8yuVSgYPHkxhYWG7Wqurpfz9/SksLLzpFw5fZ1+GBQ8jJTOFVWdXMafvnFaMsPWJlh/hpny9Pblv2gQCO3blgsGbogotzy49yJOL9pBTWGHt8IR2Zvny5Tz77LP89NNPxsQHQKPR0KlTJ+MtKirK5H6nTp1qZyz9hlar5e6778ZgMPDRRx81O67HHnuMe++9l4SEhGYfoy0waf3xm1jb+pP0GmgtNy7QYDBw5coV432lUikSnyYKDAwkMDDwll9Y67q+fjz7IzqDrhUisx7R8tMEa9eu5bnnnsNgMPDCCy/w6KOPWjukViFJEkP7dCO+l4FPUs7yv6TThOuz+P7HLAI7duPOwTGiFagNc1YrOf6vsVY5b1PFxcWxf/9+Fi5cSP/+/Y3vu8WLF99yFtb8+fO57777jPe1Wi0zZszg/PnzJCUltajFpq6Wyttvvw3Uds8YDAZUKhULFizg4YcfbvaxbU14eDjHjx+noqKCc4GT6Jy9HPZ9CQOfMPu5DAYDaWlpZGdn07dvX6Kiosx+jvbA19eX+Ph41q9ff9P9EkIS8HL0Iq8yj9TsVBJC7DeZF8lPI+l0OubMmUNycjLu7u706dOHO+64A2/v9rNKulqp4I+johkeqeHwzztxVMjos47wxuKL3D9+CKE+trvgodAwSZKa1f1kDR07dmTu3LkkJiaiVCr54IMPgKZ3e9UlPmfOnCE5ObnF65ylpaWZ1P9ZvXo1b775JqmpqQQHB7fo2LamrvXn8OHDSB1HQPZySPsQ+j8KSvO+jyRJwtXVFYVCIZZkaQVqpZqJURP55sQ3rMpYJZIfAfbu3Uu3bt2Mf8gmTJjApk2buOeee6wcWevrGRVAlP8EVm3dhWN1EdGOJazdsAmP0BjuSegmWoEEi+rcuTPJyckkJiaiUqmYN28eGo3G2K1lMBgoKSnB3d293gU/dTod06dPZ//+/axduxa9Xm/sVvH29r7lekj1qesKqpOeno5CoaB79+7NuELbFxERQUhICA6SHva9CsWX4Pgq6DHdrOeRJIlevXoRFRVl12OpWoMsy8iyTHZ2NuHh4Q3uN7XTVL458Q0pl1MoqirC08mz1WJsTVYf8/PGG2/Qv39/NBoNfn5+TJ06lVOnTpn1HNu3b2fSpEkEBdUO0l21alW9+3300UdERkbi5ORE37592bFjh/Gx7Oxsk29wISEhZGVlmTXOtsTN1YXfTx5NZLc+VOgV+DoaUOee4K3FG7lwrdTa4Ql2LiYmhqSkJL777juee+65Jj03MzOTNWvWkJmZSVxcnHE8RGBgIKmpqRaK2L4YF81UO8OAWbUbd71nlro/er2es2fPGgfnSpIkEh8zKC8vR6vVkp6ejk7X8HieGO8YYr1j0Rq0rDu/rhUjbF1WT362bdvGU089xe7du9m8eTM6nY4xY8ZQXl5e7/67du2qd4rjyZMnTQbFXa+8vJxevXoZm8jrs3TpUp599ln+9re/ceDAAYYNG8b48eONdT/qGyUvWjigX9eOzJh6OzXOtQMQnfTlTPpgJ1+lXsBgEHWBBPNJSUlh3rx5xvuxsbFcvXqVuXPnNuk4ERERxm/Bv70lJiaaJdaZM2eaFOKzV7IscyViGtke/eDKYTi/rcXH2717N/v37+fQoUNmilIAcHV1BWpbRm9VI2lKpymAfdf8sXq318aNptUkFy5ciJ+fH/v27bth5oTBYOCpp54iOjqaJUuWoFTWDpg8ffo0I0aMYPbs2TfU3gAYP34848ePv2kc77zzDo888ohxEPO8efPYtGkTH3/8MW+88QbBwcEmLT2ZmZnGMQa3otVq7bomhUqpYMbYoRw9e5mfdlyitLqcV9YcY92hTP55e2c6BXhZO8Q2oe49Ysn3ilarNQ7GvVnNj7as7otK3XXaK3Nfp8FgQJZltFqt8W/rrVy8eJEDBw7hHXovQcXpGHa+hz50SIP7N+Y9HhAQwNWrV/Hz82uVv5ut8XtnC+fW6XQoFAoMBgM5OTk3nTE3OmQ0b6e/zYmCExzLPUZnr863PL4lrsWSr4sk21jZ3oyMDKKjozly5Ei9/eXZ2dkkJCQQHx/P119/zfnz5xk+fDgTJ05k/vz5tzy+JEmsXLmSqVOnGrfV1NTg4uLCsmXLmDZtmnH7n/70Jw4ePMi2bdvQ6XTExsaSkpJiHPC8e/fuegdKfvjhh3z44Yfo9XpOnz7Nt99+224G6xlk2HVVYs1FBQl+Oob6acmsVNHVW0KpEC1l1qZSqQgICCA0NLRZY1sE+1VTU8Ply5e5cuXKTbtFrleXLAGMO/0SGm0uyV1eo8Q5rEWxyLIsWtYtwGAwoNPpkCQJtVp9032/K/+OY9pjDHYczATnCa0UoamKigruvfdeiouLzd71afWWn+vJssycOXMYOnRogwMFg4KCSEpKIiEhgXvvvZe0tDRGjRrFJ5980uzz5uXlodfrbyiC5u/vb+xKU6lUzJ07lxEjRmAwGHj++ecbnCHy1FNP8dRTT1FSUoKHhwcjRoxo8WyStuR24MmCCrYkJeOqghi1jkvljtw2ZAAxIe3ndWgqrVbL5s2bGT169C3/MDVXVVUVly9fxs3NDScnJ4ucw9pkWaa0tBSNRmPXH6Dmvs6qqiqcnZ1JSEho0ntj165dXLt2jUudHqDbibcZrj6CfkL9097re4/r9XpOnTpF586dUala/yOpNX7vbOHcWq2Wn376Cah974wYMQJnZ+cG99dkafjTtj9xnOO8O/Zd1Mqbx2eJa2loJXpzsKnk5+mnn+bw4cPs3LnzpvuFhYWxaNEihg8fTlRUFJ9//rlZfvl/e4zffvuYPHlyvYsf3oparW71Xypri/L34JEZk1i2dS8UZhLsWE367p1khMcydXA3a4dn0yz5ftHr9UiShEKhqHcmlD2o6wKqu057Ze7rVCgUxhaBprz/IiIiapMflx50BRTHlqO47RXwaHiK//XnSE9PJzMzk9LSUoYOHdrSy2g2a/6dbq1zS5KEl5cXhYWF5OfnExkZ2eC+CWEJ+Dr7kleZR1puGqPCRjXqHOa8Fku+Jjbzl+GPf/wja9asITk5mZCQkJvue/XqVWbNmsWkSZOoqKhg9uzZLTq3r68vSqXyhgHTubm5DZbEF25NqVRy95hB9BsynEKdGleVTHXmcT5cnkyV1r6rhwpCexESEoJSqaSsSkth5FQw6GDPx41+fufOnXFyciImJsZyQQpGfn5+QO3n6M2oFComRU0CYFXGKkuH1eqsnvzIsszTTz/NihUrSEpKumkmCrVdVKNGjSI2Ntb4nO+//54///nPzY7BwcGBvn37snnzZpPtmzdvZvDgwc0+rlCrU4gfD/5uIqVqbxQSeOnyeOyznWSK5TEEoc1TqVTGMiAXQ38ZM5n+JVQVN+r5Pj4+TJgwgQ4dOlgoQuF6oaGhDBkyhL59+95y37pZXzsyd5BXmWfp0FqV1ZOfp556im+++YZvv/0WjUbDlStXuHLlCpWVlTfsazAYGDduHOHh4SxduhSVSkVsbCxbtmzhyy+/5N133633HGVlZRw8eJCDBw8CcP78eQ4ePGicxg4wZ84cPvvsM7744gtOnDjB7NmzuXTpEk88Yf6S7e2Ro4Oah6eOwi0khg05zuy4WM7E93eSfDLX2qEJgtBCYWG1A5wLcQffGKgprV3yoh6yLLN//34qKn798tPY2WVCy7m5uREUFNSoLqWOnh3p4dsDvaxn3Tn7qvlj9eTn448/pri4mMTERJNiY0uXLr1hX4VCwRtvvMHy5ctNZqr06NGDLVu2MH16/dVF09PT6d27N7179wZqE53evXvzj3/8w7jPXXfdxbx58/jXv/5FXFwc27dvZ/369TethCk03fhBPXnjwZH0CvGguFLLP77fzfzVO9Dq9Ld+siAINsnf359Ro0bVLjY7+I+1G3d/ArqaG/bV6/VcunSJXbt23XSVccE21C12uipjlV39f1k9+Wmo2NjMmTPr3X/06NH1zkSIi4sjNDS03uckJibWe44vv/zSZL8nn3ySCxcuUF1dXW+dIcE8Qrxc+P6JQTw0MJTfR9XgXXOFj5euJyu/xNqhCYLQDAqFAm9v79oJIj1ngFsAlGbD0R9u2FepVOLh4UHv3r3tejaeLausrOTo0aPs27fvlvuOixyHo9KRjKIMjhccb4XoWofVkx+hfXJUKfnHlB74hUWhM0CgQxUbNm1m25Hz1g5NsFGJiYk8++yzVjn3nDlzkCSJO+64w2QBU+FGekmFfsDjtXdS/3fDkheSJJGYmHjTInuCZen1ek6cOMH58+dvWUjQ3cGdkWEjAVh1ZlUrRNc6RPIjWI0kSUxL6EP3/kMo1irxUBvIPp7Owg1p6PX2W5lXsC2PP/44kiSZLJ1xvddff51PP/2U+fPnk5aWxuOPP37T42VkZKDRaPD09DR/sDbu+PHj/Pjjj1wOGAcObpB7HN2pzezcuZO8vF8HzIoWH+tyc3PD1dUVWZZvudQFwNSOUwFYf3491fpqC0fXOkTyI1hdj6gg7pwynnzZDZUC3Moy+fD7DRSUVlk7NMHOrVq1ij179hAUFFTv4wsWLGDu3Lls3ryZWbNmsX37djZv3swLL7xQ7/5arZZ77rmHYcOGWTJsm6bVarmYcw36PAjAsX27yMnJYffu3aLVzIYEBAQAt57yDhAfGI+/iz8lNSUkX062dGitQiQ/gk3wcHVm1vSx6L3C0RmgoKyayR/t4nBmkbVDE2zUxo0b8fDwYNGiRc16flZWFk8//TSLFy+ud+bLDz/8wCuvvEJSUhIDBw4EIDo6mh07drBixQreeuutG57z97//nS5dujBjxoxmxdTW1U0Qyc3NpTLuEZCUdMv4iCBvVwYPHixmddmQuhp2DS0Ifj2lQsnkjrUFfu1lsVOR/Ag2Q6FQcPdtA+jUK57UEg8yC6uY/nEaX6edt+sFKq1OlqGmvPVvLZg5smTJEmbMmMGiRYt44IEHWLx4MW5ubri5ueHu7k5ISAju7u7GbW5ubixevNj4fIPBwP33389f/vIXunWrv+L49OnTycnJIS4uzmR7WFgYZ86cuWER5aSkJJYtW8aHH37Y7Otq61xdXY1jeS4V66D771DJNQwpWIa3t7eVoxOu5+fnhyRJlJWVUV5efsv962r+pGancrX81q1Fts6mlrcQBID+XcJYFh7IX5Yd4qfjVzh26AAfXTrF/bcn4uFqn+tRWZW2Av5Tf7ePRb2UDQ6uTX7aRx99xEsvvcTq1atrp1ZTu/RMfHw8UJvYlJWV4ebmZrLsw/XV2t98801UKhXPPPNMCy+iVn5+PjNnzuSbb74x+wKMbU1wcDB5eXmcOXOGmCHPwJHv4fgqKHwV3KzwPhPqpVar8fb2Jj8/n6tXrxIVFXXT/cPdw+nj14f9ufv58dyPPNrj0VaK1DJE8iPYJA9nNfPv78vC5GO45p1AIZWyZPV6Bg8aRI/IQGuHJ1jJ8uXLuXr1Kjt37mTAgAHG7RqNBo1GA9QmPyUlJbi7u9e75tW+fft477332L9/v9kG3j722GPce++9ojwGGFeEr6ysJE8ViG/UCDiXDLs/gttes3J0wvUCAgIoLS01/p/dytROU9mfu5/VGat5pPsjbXrgukh+BJslSRIPj+zOjsPOZBw7gLdaz6E9Ozl9uRO/S+ht7fDsh9qlthXGGudtori4OPbv38/ChQvp37+/8Y/v4sWLbzkLa/78+dx3333s2LGD3NxcY1ViqJ36+9xzzzFv3jwuXLjQ5LiSkpJYs2YNb7/9NlBbv8xgMKBSqViwYAEPP/xwk4/ZVsXGxnLx4kXKysrIzs7Gd8gztcnP/kUw5Dlrhydcp3PnzsTGxjY6iRkTMYY39r7BhZILHLp2iDi/OMsGaEEi+RFs3rCeHekY7Muqn7bRQVWN4WoGn6wo4LEpI1AqxbC1FpOkZnU/WUPHjh2ZO3cuiYmJKJVKPvjgA6Bp3V73338/t912m8lxx44dy/33389DDz3UrLjS0tJMZjKtXr2aN998k9TUVOO6V/ZMp9OhVCqRJAlJkujTpw95eXm1CaZbD/DvAVePoNi3EOhi7XCFX6hUTUsBXNWujA4fzZqza1iVsUokP4JgaUE+HsyacTtfrduBe3UuPvoCPvjhJ5743WgcVWIGSXvSuXNnkpOTSUxMRKVSMW/evCZ1e/n4+ODj42OyTa1WExAQ0OyVxWNjY03up6eno1Ao6N69e7OO15bU1NSwfft2OnToQM+ePZEkCX9/f5MxVgz+I6ychSL9MxSd/mO9YIV6ybKMVqs1WTaqIVM7TWXN2TVsurCJFwa8gLPKuRUiND/xtVloM1RKBY9MHo4qoDPVelibUcNDC3+mtOrmFUoF+xMTE0NSUhLfffcdzz0nulKsKTc3l8LCQi5cuEBVVQO1ubrfAe4hSOW5hBbsat0AhZu6du0a69atY+fOnY3av69/X4LdginTlrH10lYLR2c5IvkR2pxpw3oR1msI2dUOpJ7N5+4Fu7laUmntsAQLS0lJManCHBsby9WrV5k7d26Lj33hwgWzLp0xc+ZMioqKzHY8WxYSEkK/fv0YPnw4zs6/tgLIskxWVlZtcUMUMPAPAHTM3QiyKF1hK1xcXKisrKSgoICamhsXov0thaRgSsfaae+rMlZZODrLEcmP0CYlxAaxZNYgfN0cyM0v4vvVGzl5OdfaYQlCu1BTU2MyxikyMrLe5TwOHjzI5cuXyc7Ohr4PIju6o6nOQTq9sRWjFW7G1dUVjUaDLMsmS5DczOROtQUP9+bsJbvMCpMlzEAkP0Kb1SPEg+V/GMw9HfUEOOnYvXM7P5+8bO2wBMGuVVdXs23bNlJTU2+6XIUkScaKzxcvXgRHDYY+MwFQ7G6/hSBtkZeXFwDFxcWN2j/YLZj4gHhkZNacXWPJ0CxGJD9Cmxbu48qMCSPI16pwVcmcOribLftPWTssQbBbZWVllJaWUlhYSGXlzbub68oJXLlyherqagz9H8MgKVFk7oHLe1sjXKER6gpzlpaWNvo5dRWfV2esxtAGuzFF8iO0eSG+Htw1aSzXdI44KuHamcOs2HHI2mEJgl3y8fFh2LBhJCYm4ubmdtN93d3d8fLyQpZlLl++DJpALnsNrn1w13utEK3QGHUzJZuS/NwWfhuualcyyzLZf3W/pUKzGJH8CHbBS+PCzDvGkWtwRaUA/ZXTfL1pt7XDEgS7UF1dbdLK06FDh0Yv41HX+nPx4kUAzvpNqH3g5DrIyzBvoEKz1CU/JSUlyI1cc89Z5cy4iHFA2xz4LJIfwW64ODrwxPRx5CtrF1DMzsrijfXHG/3LLAjCjerG+Gzbtu2W3Vz1qUt+CgoKarvMnIMxdBoNyJD2gZmjFZrDzc0NPz8/IiIimrSI9NROUwH46eJPVGgrLBSdZYjkR7ArSqWCWVNHUOoWypdnHZm//TzPLTuEVt/2+qQFwRbodDq0Wi1arbbRa0Bdz8nJCX9/fzw9PY1TqQ0Dn6598NB3UHbNnOEKzaBUKhk+fDi9e/dGqWx80dheHXoR4R5Bpa6SLZe3WDBC8xPJj2B3FAoFD48fyL+m9UKpkFixP4tXv02hpKKBAmyCIDTI1dWV4cOHk5iYaOweaarBgwczevRovL1rW2XlsMEQ1Ad0VfDzp+YMV2hFkiQZBz6vOde2Zn2J5EewW3f2C+XTB/qSEKAjzimfxas2cqWw8QP6BKG9qqqqorCw0Hjfzc2t2YkP1LOGlCTVLnkBsPdTqGlbXSb2SqvVUlHRtP+LSVGTUEgK9ufuJ1+fb6HIzE8kP4JdG9nFn8dv60mFTsJXrWXNhs2czW5cIS/BtiQmJpq1CnNTzJkzB0mSuOOOO25a28YeVFdXk5KSwrZt20wSIHPQarW/jimJnQye4VBZAAcXm/U8QtNdunSJVatW8fPPPzfpef6u/gwKHATAgZoDlgjNIkTyI9i9Id0j6TtwCCVaBV5qPdu2pXDwbJa1wxKs7MSJE0yePBkPDw80Gg0DBw7k0qVLN+z3+uuv8+mnnzJ//nzS0tJ4/PHHb3rcjIwMNBpNvRWP2wKFQoGDgwMqlQq1Wm224xYVFbFhwwZ0Ol1tAqRUwaBfxv6kfQAG+04qbZ2rqyvQtOnudeoGPh+oOdBmav6I5EdoF7pHBjJq1AgKtEo0KpkjP6ey7ZCYZttenT17lqFDh9KlSxdSUlI4dOgQL7/8Mk5OTib7LViwgLlz57J582ZmzZrF9u3b2bx5My+88EK9x9Vqtdxzzz0MGzasNS7DItRqNcOGDWPEiBG3rOPTFO7u7sZkKjf3l6Voet8Hzl5QeAFOtK0xI/amrluzsrISrbZpi0WPCBuBRq2hWC7m56tNazmyFpH8CO1GhL83v5s4lmtaB5yUkHXyAOsPXLB2WEIzbdy4EQ8PDxYtWtTk5/7tb39jwoQJvPXWW/Tu3ZuoqCgmTpyIn5+fcZ8ffviBV155haSkJAYOHAhAdHQ0O3bsYMWKFbz11ls3HPfvf/87Xbp0YcaMGc2/MCswGAwmi1qq1WpjS4C5KBQKQkJCAGoLHgI4uEL/x2p/3vU+iLIUVuPg4ICjoyNQW8W7KRyVjsaaP21luQuR/Ajtiq+HKw/eMZ5cvQsbs9Q8/f0xvv+5fa8HJssyFdqKVr+1pP7SkiVLmDFjBosWLeKBBx5g8eLFuLm54ebmhru7OyEhIbi7uxu3ubm5sXhx7bgSg8HAunXr6Ny5M2PHjsXPz4/4+HhWrVplco7p06eTk5NDXFycyfawsDDOnDnD888/b7I9KSmJZcuW8eGHbWvdKoPBQGlpKRUVFc2ayt4UoaGhAOTk5PzaujBgFqicIHs/XNxl0fMLN3d9scOmmhxVu9hpUmYSpTW2P7FEdetdBMG+uDo5MGv6ODLXHMeQe4m/rjiMxknJ+B5B1g7NKip1lcR/G9/q591z7x5c1C5Nft5HH33ESy+9xOrVqxkxYgQAkydPJj6+9hoMBgNlZWW4ubmhUPz6/c7f3x+o7XIpKyvjv//9L6+99hpvvvkmGzdu5I477iA5OZnhw4c3Oab8/HxmzpzJN9980+jKx7ZCkiRUKhUKhcLk9bIEDw8PJEnCYDCQmZlJZGQkuHWAXvfAvoW1rT8RQy0ag9Awd3d38vLymjXup6t3V/wUfuTqc9l4YSN3dr7TAhGaj0h+hHZJrVLyn2ndAZlV+y5x+OfdOOl6MKJ3tLVDE25i+fLlXL16lZ07dzJgwADjdo1GY/zWajAYKCkpwd3dvd4P87rZRlOmTGH27NkAxMXFkZqayieffNKs5Oexxx7j3nvvJSEhoTmXZVWSJOHi4oIkSeTnW3aqsiRJKBQK9Ho9ly5dqk1+oHba+74v4cwmyD0BfrEWjUOoX0tafiRJoo9DHzZWbWRVxiqR/AiCrZIkidem9sCn+grhqkqyTh3kZ2cn+ncJtXZorcpZ5cyee/dY5bxNFRcXx/79+1m4cCH9+/dHkiQAFi9efMtZWPPnz+e+++7D19cXlUpF165dTR6PjY1l586dTY4Jaru81qxZw9tvvw3UdiUaDAZUKhULFizg4YcfbtZxLUWv16PVanF0dESSJGNS0hrqkp9r165RXV1dO87EpyN0mQgn19bW/bn9nVaJRTDl6+tLVFQUvr6+zXp+L4debK7ezOFrhzlXfI4oj6hmx1JUVFTv7EtzEcmP0K4pFRJ/mJbIlys24qeu5tiBPbg6O9A13N/aobUaSZKa1f1kDR07dmTu3LkkJiaiVCr54IPataGa0u3l4OBA//79OXXqlMmxT58+TXh4eLPiSktLM6n/s3r1at58801SU1MJDg5u1jEtRZZlysrKjPH+doabpUmSRN++fQkMDDQOsAWg/6O1yc/h72HMv2sHQwutytvb21iFuzk0Cg2DAwezI3sHa8+u5Zk+zzTrOBUVFWzbto2ioqJmx3IrIvkR2j1XJwfunXQbS3/chI9aR9qunbg6jSTc38vaoQn16Ny5M8nJySQmJqJSqZg3b16Tur0A/vKXv3DXXXeRkJDAiBEj2LhxIz/++CMpKSnNiik21rSbJj09HYVCQffu3Zt1PEuSJAlHR0eqq6vNWsenKUJDQ288d+Rw8IqEwvNwdDn0ecAqsQktMyFyAjuyd7D+/Hr+2PuPxtbZpnB2diY8PNyii1KL2V6CAHhpXJg8dhRFWiXuagMbtyZzrahp0z2F1hMTE0NSUhLfffcdzz33XJOfP23aND755BPeeustevTowWeffcby5csZOrR9DLZ1cnLC3d29SYtYWorxA06hgL4za39OX2i1eNo7nU5HYWEhlZWVzXp+QnACrmpXssqyOHjtYLOOIUkSvXr1YtCgQc16fmOI5EcQfhHk487IxARKdRLeaj1fr02mosayU3+FxktJSWHevHnG+7GxsVy9epW5c+c263gPP/wwZ86cobKykoMHDzJlyhQzRQozZ860aJN9U+n1esrLy02+STfnG7k5Xbt2je3bt3PkyJFfN8bdBwp17bT3nEPWC64dS09PZ8uWLc0eb+OscmZU2CgA1p1b1+jnFRYWcvjwYeN7tG4WoqWI5EcQrtMxyJd+8YPJqVTw7Rn4wzf7qdG1jXLtglCfujE+1dXVzf42bwk1NTVcvXqVixcv/pqUuXWA2Em1P4vWH6toyYyvOhOjJgKw6cImtIZbV4uuqalh+/btnDp1ijNnzjT7vE0hkh9B+I2eUUEMThhBuUHNttPX+POyQxgMovKs0DbVTWVXKpWtPrj5ZgICAlCr1VRVVf263AX82vV1ZBlU236xPHtTV6eqObV+6sQHxOPr7EtRdRGpWam33N/BwYFevXrh6+v7a/kDCxPJjyDUo1+ENx//vg8qhcSp85eZvyr519WoBaGNUavVNx0Abg1KpdJY8fnixYu/PhCZAN4doaasduCz0Kqub/lp7oBjpULJ+MjxAKw9t7ZRz4mIiCAxMbHVBuHbzm+CINiYxBg/3p7WhZkdq/HV5/PlelF6X2gb9Ho9paWlJgm7tcf41KeutEBWVtavS2tIkhj4bEV1i9lqtVqT9d6aqq7rK+VyCuXa8hseLygoIDU11WRJldZ8j4rkRxBuYmr/KPSetd9ONZVX+HZL21ixWGi/ZFmmvLwcrVZLRUWFtcO5KR8fH1xcXNDpdGRnZ//6QNx9oHSAnIOQfcBq8bVHKpUKF5faul8tGffT1bsrEe4RVOmr2Hppq8ljer2e1NRUsrKyOH78eIvibS6R/AjCLfx+7ECKHWtX+1YWXGD1rsNWjkgQGiZJEq6urqjVauOHmK2SJMnY+mPS9eXqA7G1C2WK1p/WZ45xP5IkGVt/1p417fpSKpUMHDiQwMDAGyqttxaR/AhCIzx8+zDyJE8UElRknmLL/lO3fpIgtKLrx2colUo0Go1NjfFpSHh4OL6+vsbxP0b9Hqr998gPUNX8Fgih6SIiIujZsyc+Pj4tOk5d8rPnyh7yKvNM3qO+vr4MHTrUotPZb8b2fzMEwQYoFAoemzaKXIMrKgVcOX2YvaczrR2WIAC13QglJSUm4yfaCo1Gw4gRI4iIiDB9IHwI+ESDtrx25pfQakJDQ4mJicHDw6Nlx9GE0qtDLwyygY3HNrJlyxab6YoVyY8gNJJKqeChqbeRq3XiUKGSJ5YcIyNXTMUVrK+iogK9Xm9TdXxa7PqBz/sWggWXOhAsp671p/JCJUVFRRw7dszKEdUSyY8gNIGLowP3Tx3DaX0HCip03P/5XrKL7OgDx4YlJiby7LPPWuXcc+bMQZIk7rjjDpMFTG2Fq6srjo6OuLq23cVAq6qqOHPmDGVl1y0rE3cvKB3hypHaqs9Cq5BlmeLiYjIzM1v8fh8bMRaVpOLLii/xDfald+/eZoqyZUTyIwhN5O7iyMKH4unYwZWrxZW890MSVwvFOmBtSVlZGU8//TQhISE4OzsTGxvLxx9/XO++r7/+Op9++inz588nLS2Nxx9//KbHzsjIQKPR4OnpaYHI66dQKHB1dW0TY3wa8vPPP3Pw4EEyM6/rTnbxhq6/LDsiBj63qpSUFNLS0lo06Fmv1+Pt5M3g4MGUyCUccz5mtTE+v9V2f1MEwYq8XR1Y9Eg893Qy0M+jguUbtlBcXmXtsIRGmj17Nhs3buSbb77hxIkTzJ49mz/+8Y+sXr3aZL8FCxYwd+5cNm/ezKxZs9i+fTubN2/mhRdeqPe4Wq2We+65h2HDhlk0fp1O16IidLbI398fqF3zy0TdwOejy6GquJWjap8kSTIWO2xu8pObm8v69evJz89nYmRt19e6c+ts5j0rkh9BaKZgT2fuHtWfCp1EB7WWb9ZsprLm1uvYCOaxceNGPDw8WLRoUZOfm5aWxoMPPkhiYiIRERHMmjWLXr16kZ6ebtznhx9+4JVXXiEpKYmBAwcCEB0dzY4dO1ixYgVvvfXWDcf9+9//TpcuXZgxY0bzL6wRqqurMRgMNvNBYg6+vr4A5Ofnm15X2CDwjQFtBRz+3krRtT8tXePr9OnTVFVVcfr0aRJDE3FWOZNZlsmha7axYK1IfgShBXpEBtK5Vz9qDOCnquKL1UnWDqnJZFnGUFHR6reWfHAvWbKEGTNmsGjRIh544AEWL16Mm5sbbm5uuLu7ExISgru7u3Gbm5sbixcvNj5/6NChrFmzhqysLGRZJjk5mdOnTzN27FjjPtOnTycnJ4e4uDiTc4eFhXHmzBmef/55k+1JSUksW7aMDz/8sNnX1VguLi44OTnZZNXm5vL09ESpVKLVaikuvq6Fx2Tg85di4HMraWmtn4EDBxIbG8uAAQNwUbtwW9htQNNWerck2+h8E4Q2bFDXCMoqqig4dwQ/qYRlKQe4M9E2BvU1hlxZyak+fVv9vDH79yE1owjfRx99xEsvvcTq1asZMWIEAJMnTyY+Ph4Ag8FAWVkZbm5uJmNg6rpVAN5//30ee+wxQkJCUKlUKBQKPvvsM4YOHdqsa8nPz2fmzJl88803xg8NczMYDMbrkSQJJyenFi0/YGsUCgU+Pj7k5uaSl5dnOmaq192w5VW4ehSy9kFIP2uF2W40p+Xn+i80KpWK7t27G+9PjJrIj+d+ZNOFTTw/4HnUitZZw6shIvkRBDMY3a8LC69cw63yCtVXMjh8PoCekYHWDsvuLF++nKtXr7Jz504GDBhg3K7RaIx/rA0GAyUlJTddyPP9999n9+7drFmzhvDwcLZv386TTz5JYGAgt912W5Pjeuyxx7j33ntJSEho3oXdgk6no6ysDEdHR5ydnS1yDlvQoUMHY/LTqVOnXx9w8YZu0+DwktqBzyL5sbjrW35kWb5lK2Nubi5arZZLly7RsWPHGx6PD4zH28mbgqoC0rLTSAixzO9KY4nkRxDM5IHxQ5j//VrcpWre23CE/z3aARcH2/8Vk5ydidm/zyrnbaq4uDj279/PwoUL6d+/v/EP8uLFi285C2v+/Pncd999VFZW8tJLL7Fy5UomTqwdiNmzZ08OHjzI22+/3azkJykpiTVr1vD2228Dv3QlGgyoVCoWLFjAww8/3ORjXk+n02EwGNBqtXbX3XW9unE/eXl5Nz7Y76Ha5Ofochj7Ojh7tm5w7YyLiwsKhQKDwUBFRcUtyyhcuXIFgOzsbKKiom54j6oUKsZHjmfxicWsPbdWJD+CYC+USgVTRg/n4S92c6pAyz9WH+PtO3tZO6xbkiSpWd1P1tCxY0fmzp1LYmIiSqWSDz74AGhat5dWq0Wr1d7QKqRUKk1WQW+KtLQ0k3ooq1ev5s033yQ1NZXg4OBmHfN6dQmPWq2228QHwNvbmyFDhtS/rEJoPHSIhWsnagc+x89q/QDbEYVCQVxcHI6Ojjg4ONxy/x49enDx4kWTLyW/dXvU7Sw+sZjkS8mUa8txVVuvLpVIfgTBjIJ9Pfjn9H7c++luftiXSXyEF3f2D7N2WHalc+fOJCcnk5iYiEqlYt68eU3q9nJ3d2f48OH85S9/wdnZmfDwcLZt28aiRYt45513mhVTbGysyf309HQUCoXJmIem0uv1KBQK4weJo6Njs4/VVqhUKoKCgup/UJJqW382PF9b8XnAY7XbBIupr/vqeiUlJWg0mtovUJKEUqlEqVQ2uH83n26Eu4dzseQiSZeSmNRxkrlDbjQx20sQzGxglA9zRncmyk3PtZN7OXLhirVDsjsxMTEkJSXx3Xff8dxzzzX5+UuWLKF///7cd999dO3alf/+97+8/vrrPPHEExaItunq6viUl5fb1XT2Fus5A1ROkHscLu+1djTtWk5ODps3b+bw4cONfo9KkmRS88eaRMuPIFjAH4Z3RHn1BL4qmR270ojwn4DG2f6/uVtSSkqKyf3Y2FiuXr3arGMFBASwcKHlKgbPnDmTmTNnNvv5dTV8mtsN15ZVVVWRkZFBRUWFyaB2AJy9oNsdcOjb2mnvYfFWibG9qKmp4dq1a+h0OsLDw00eq6ysxGAwNDlBnxg1kY8OfURaThp5lXn4OvuaO+xGES0/gmABSqWCSbclUKGX6OCgY+GP260dktCGODg4oNFocHNzs+sxPvWRJIkTJ05w8eJFqqurb9yhruLzsRVQWdi6wbUz5eXlpKamcujQjYUJo6KiGDZsGAMHDmzSsiph7mH09O2JQTaw6cImc4bbJCL5EQQLCengSXhMDwACpSKWbbONyqaCbaqb0VVHrVa36bW6msvR0dE4fis/P//GHUL6g1830FXBoaWtHF37Uvf/UF1dTU1NDXl5eSYD+wMCApr1Hp0QNQGAtWfXmifQZmh/v1mC0IoSe8dQ7uwHQGXOaY5caF43jWDfdDodpaWllJWVtcuurt+qm/J+wzpf8OvAZ6gd+CzGRFmMSqUy1pU6d+4cKSkppKamtnil93ER41BKSo7mH+ViyUVzhNpkIvkRBAv7/bihFOgccFbCtp2plFXZT1VewXzqZsy0t26u+nTo0AFooN4P/DLw2RmunYRLu1sxsvbn+tYfhUJhlnILPs4+DAoaBFhv4LNIfgTBwlQqJRNvS6BSL5FTLvPPNcesHZJgY1QqVbsd41OfupafwsJCdDrdjTs4eUD339X+vM9yA9eFXys9S5LEyJEjGTBggFm6YydG1c76WnturVVmNIrkRxBaQUgHLzr2Gsh3FxxYtj+b79MvWzskwcq0Wq1J94FSqRSJzy9cXFxwdnZGlmUKCgrq38k48HkVVDSwj9AiOTk5ODk5AbU1fTw9Pc02Dm1k6EicVc5cLr3MkbwjZjlmU4jkRxBayZDYEJ4b0wWAf6w+wvFMMVOlvapbq6u0tLTF4yfskSRJ+Pr6olarqaqqqn+n4L7g3wP01XBoSesG2A5kZ2eza9cuzp07BzR/dfeGuKhdGBFauzCxNbq+RPIjCK3oD8M7MqqzNzNCq9iSnEJppRj/0x4pFAoUCgVKpbJdzuhqjD59+jBlyhTCwhqokC5J0G9m7c9i4LPZubu74+TkhLe3N/Hx8QwaNMjs57g96nYANl7YiNagNfvxb0b81glCK1IoJP55ewyd3fUEOun4/MftooJvO6RQKMQYn1twcHC49WvTYwaoXSHvNFxMbZ3A2gk3NzdGjRrFwIEDCQsLw9PT0+znGBQ0yLjS++7s1h24LpIfQWhlIX7eBEfXrvkULBWybMdRK0cktIa6BVXrXL9ul3BzDX5BcHKHHmLgs7lcvnyZwsJfu+OdnZ0t+h5VKVSMjRgLwLrzrdv1JZIfQbCCEX1jqXCqndFSnnWKoxfrqWcimEhMTOTZZ5+1dhjNotPpGDFiBH/84x/rn70k1OvUqVOsW7eOjIyMhnfqO7P23+OrxcDnFrhy5Qq7d+9m27ZtlJWVmTxWVFTE6dOnycnJMft567q+ki4lUaGtMPvxGyKSH0GwknvHDaNYr8ZNJbN1+y4x/seO1c3kqhvnIzSOwWCgoqKi4Xo/AEF9IKAn6Gvg4LetF5yd8fHxwcfHh+DgYFxdXU0ey8nJ4dChQ1y6dMns5+3h24NQTSiVukqSLieZ/fgNEcmPIFiJWq1i7Ihh1BggyEnLwg1pYsxmA2bOnMm2bdt47733jIUAz549yyOPPEJkZCTOzs7ExMTw3nvv3fC8qVOn8vbbbxMYGIiPjw9PPfWUSfdTTk4OEydOxNnZmcjISL799lsiIiKYN2+ecZ/i4mJmzZqFn58f7u7ujBw50mS9o1dffZW4uDi+/vprIiIi8PDw4O677zbOkHnooYfYuXMnH3/8sbG768KFCxQWFnLffffRoUMHnJ2diY6OtuiCq23N9ZWeG+z6Mqn4/KUY+NxMarWahIQE+vXrd0NXV12tH3PP+IJfVnqPav2V3kXyIwhWFOrvQ1DHbhTVSKw5XcGea9YbA6LT6Rq8/XY6dkv3bar33nuPQYMG8dhjj5GTk0NOTg4hISGEhITw/fffc/z4cf7xj3/w0ksv8f3335s8Nzk5mbNnz5KcnMxXX33Fl19+yZdffml8/IEHHiA7O5uUlBSWL1/OggULyM3NNT4uyzITJ07kypUrrF+/nn379tGnTx9GjRplUoPm7NmzrFq1irVr17J27Vq2bdvGa6+91mD8oaGhvPzyyxw/fpwNGzZw4sQJPv74Y+MHvgDe3t4oFAqqq6spLy9veMced4KDG+SfgQs7Wy/ANu7ixYtcvvxrzTGVSlXvGJ+6Ks+lpaUWmaAxMbI2+UnLTiO/sp713CxA1SpnEQShQSP6deV4iZKLRzLIPidz35VSuod6t3ocK1eubPCxgIAAhg0bZry/Zs2aBuvTdOjQgcTEROP9devWUVNj2qV35513Nik2Dw8PHBwccHFxISAgwLj9n//8p/HnyMhIUlNTWbZsGePGjTNu9/Ly4oMPPkCpVNKlSxcmTpzI1q1beeyxxzh58iRbtmzh559/pl+/fgB89tlnREdHG5+fnJzMkSNHyM3NxdHREYC3336bVatW8cMPPzBr1iygtovmyy+/RKPRoNfrufPOO9m6dSs1NTUNxn/p0iV69+5tPHdERESTXhd7p1Qq8fLyIj8/n2vXruHm5lb/jo4a6DG9tuVn30KIHFb/foLRtWvX2Lt3L5Ik4erqird3w39z6mYl6nQ6qqqqjOt9mUuERwTdfbpzNP8oGy9s5L7Y+8x6/PqIlh9BsAF/SOxMQrQPWlni78sPUFbVujUv2qpPPvmEfv360aFDB9zc3Pj0009NvskCdOvWzWScTWBgoLFl59SpU6hUKvr06WN8vFOnTnh5eRnv79u3j7KyMnx8fHBzczPezp8/z9mzZ437RUREGL8hKxQKQkJCyM/PR61WNxj/H/7wB5YsWUJcXBzPP/88qaliuvZv3XKdrzp9f+n6Or4Gym+xr4Cvry/h4eFERkaavN/ro1AojIlnSUmJReKp6/paf269RY7/W6LlRxBsgEIh8dbvevDnLzYzPrCC+at3MGfGiFadCj1t2rQGH/ttHJMnT270vhMnTmxZYA34/vvvmT17NnPnzmXQoEFoNBr+7//+jz179pjs99vkQ5Ik48rpDTXhX7/dYDAQGBhISkrKDftdX/vk+vNIkoRarUaW5Zv+H44fP56LFy+ybt06tmzZwqhRo3jqqad4++23G3xOe1PXDXjL5CcoDgLjIOdg7cDnIc9YOrQ2TZIk+vfvb/z5VjQaDaWlpZSWluLv72/2eMZFjuP/0v+Pw3mHuVRyiTD3Bopbmolo+REEG+Hj6sBQfwMOCgiR8vlh5/FWPb9KpWrw9tsZSi3dtzkcHBxMutp27NjB4MGDefLJJ+nduzedOnUyaYlpjC5duqDT6Thw4IBxW0ZGBkVFRcb7ffr04cqVK6hUKjp16mRyu358jizLlJeXGxOn336g/Db+Oh06dGDmzJl88803zJs3jwULFjTpGuydj48Pnp6eBAQEGJPWBomBzzel1+s5ceKEyXu0sV+w6lo1LdXy4+vsy6DA1lvpXSQ/gmBDAt2UVDp4oZAg/+JJcksqrR2SzYiIiGDPnj1cuHCBvLw8OnXqRHp6Ops2beL06dO8/PLL/Pzzz006ZpcuXbjtttuYNWsWe/fu5cCBA8yaNcukuNttt93GoEGDmDp1Kps2beLChQukpqby97//nfT0dKA28TEYDFRXV1NdXd2o+A0GA//4xz9YvXo1GRkZHDt2jLVr1xIbG9uyF8rOODg4MHr0aHr37n3rpUC6TwcHDRSchfPbWyfANqKkpAS9Xs+pU6e4cuVKk58fFRXFqFGj6NmzpwWiq2Wc9XV+ncUr34vkRxBszNSRA6nUK/BxNLBoY+uWfLdlf/7zn1EqlXTt2pUOHTowbtw47rjjDu666y7i4+PJz8/nySefbPJxFy1ahL+/PwkJCUybNo3HHnsMjUZjXM1akiTWr19PQkICDz/8MJ07d+buu+/mwoULxub/uho+Dg4OxkHRt4r/0qVLODg48OKLL9KzZ08SEhJQKpUsWSIW6Ww2Rzfo+ctgelHx2YS7uztKpZKoqCiTQfeN5ebmhre3d7NbbhtjZNhInJROXCy5yLH8YxY7D4Aki4WFLKakpAQPDw/y8vLw8fGxdjiCjdNqtaxfv54JEyaQevg0ueeOU6WHjr2HMDgmyCznqKqq4vz580RGRho/3O2NwWCgpKQEd3f3Zi0ampmZSWhoqHEMzs38dkzPrcb4mFNLr/O3WuO9cf17/GYDweuj1+spKSm55eBccg7D/GGgUMOcE+DWwWIxtVRrnLvuPVl3rvHjx+Pg4GD285jrWp7f9jwbLmzgvtj7eKzjY/j6+lJcXGysNWQuouVHEGxQQp+ulOOEkxKS0/ah1d9irIPQbElJSaxZs4bz58+TmprK3XffTUREBAkJCTd9Xk1NDWVlZSbjUMRaXZZRXV3NqlWr2LJli0mBynoF9oTgvmDQwqHvWidAG5WRkUFaWprZ3qMXLlxg//79Fhv3A3B7x9rlLjac34DOYLmlYETyIwg2SJIkRgwZgNYAl0t0fL7jnLVDsltarZaXXnqJbt26MW3aNDp06EBKSspNv73KskxFRQVarbbBMT6C+Tg6Ohpbo64vLNmg3r+v/ffQd+124HNlZSWHDh0iKyvLbMtSXLhwgbNnz5osfmpug4IG4eXoRUFVAfuu7rPYeUTyIwg2KjzIH3VkPzZmO/De1gyyisTgZ0sYO3YsR48epaKigqtXr7Jy5UrCw8Nv+hxJknBzczP5UBYsq9FT3gG6TQOlI+QehyuHLRyZbXJ2dmbIkCF06dLllu/nxrL0jC8AtULNmIgxAGy+tNli5xHJjyDYsOn9IxgQ4U2lVs8/11h2AKBwa9cPkVSpVLi6uoqurlZy/Tpft+TsBTHja38+2L66vq4vpxAQEECPHj3M9h615Bpf16tb6X17luVm7InkRxBsmCRJ/HtqdyLcDARXX2Tz0Sxrh9Ru1dTUGKcLC62vrtJzQUHBrev9AMTdW/vvkWWgbx8V00+fPs3WrVst1hXbGi0/AL069CLYLZhKveVau0XyIwg2rrO/G492kenqaSB5934qa8SHb2uTZZnKykr0er0Y42MlGo3GWCiyUWNOOo4C1w5QkQcZWywfoJXV1NRw6tQpiouLb1jixVzqWn5+O9Df3K5f6d1SRPIjCDZOkiSGxtcufNnTvYr5W45YOaL2R5IkNBoNzs7OZl/UUWgcSZKaNu5HqYIeM2p/PvitBSOzDQ4ODgwfPpwePXrQsWNHi5zD2dkZpVJprGZuSSL5EQSBiNAgVBoflBJUXjnLmauWbXYWal3/7VahUJhUfhZaX0REBL169SIwMLBxT4i7p/bf0xuhohGzxNqg61si3d3d6dKli8Xeo3VfAqC29ceSojyiiPGKsdjxRfIjCG3E6KED0MvQSWPgk/XpFi//3t5VV1dTXFx867oyQqsJDg6mc+fOjS94F9AD/LuDvgaOrbBscFZw8uRJNm3aRHFxcaudc+DAgUyZMqXxCWgLPBj7oMWOLZIfQWgj3NzcCIvsBEBndQGr9lumX1+oHeOTk5ND586dycjIsHY4ZtG/f39WrLC/BOCWev3S+mNns770ej2ZmZlUV1c3a62u5qobe9UahgUPs9ixRfIjCG3IgN490CvUeDvKrE87THGlaJWwBEmS+N///sfEiRPp0qVLq523oKCAP/7xj8TExODi4kJYWBjPPPNMo77ZZ2Vl8fvf/x4fHx9cXFyIi4tj375fi8S9/PLL/PWvf7XoQNXWUFFRwcWLFxs35R2gx50gKSErHfLOWDa4VqRUKklISKBv377ExFiue8heieRHENoQlUrFoAF9Scl3Y0smzP3plLVDspqamhqzH7NuGntlZSVffPEFjz/+eKuO8cnOziY7O5u3336bI0eO8OWXX7Jx40YeeeSRmz6vsLCQIUOGoFar2bBhA8ePH2fu3Ll4enoa95k4cSLFxcVs2rTJwldhWefOnWPv3r2cO9fIqucaf+j0yxptdrDcxfU1dhwcHIiKimrV89fU1HDw4EFSU1PbdNe7SH4EoY0JDw3lvtv6IyPx9e6LHM4ssnZIrSIxMZGnn36aOXPm4Ovry+jRowF455136NGjB66uroSGhvLUU08ZB2OWl5fj7u7ODz/8YHKsH3/8EVdXV5MPkroxPlVVVWzYsKE20Rw0yPh4SkoKkiSxadMmevfujbOzMyNHjiQ3N5cNGzYQGxuLu7s799xzDxUVFc26xu7du7N8+XImTZpEx44dGTlyJK+//jo//vgjOl3D6xy9+eabhIaGsnDhQgYMGEBERASjRo0ymfWjVCqZMGEC333XthOAJs34qlPX9XVoKbThlq/jx4+zadMmsrOzrRaDUqnkzJkzZGVltemyDyL5EYQ2aHAnX6bGBaFC5r9rDqI3tPwbmE6nQ6fTmXybMxgM6HS6Gwr7tXTf5vrqq69QqVTs2rWL+fPnA7WzsN5//32OHj3KV199RXJyMq+88goArq6u3H333SxcuNDkOAsXLmT69OnGmSvwa6uPXq9n+/bt9OvXr94YXn31VT744ANSU1O5fPkyM2bMYN68eXz77besW7eOzZs387///c+4/3/+8x/c3NxuetuxY0eD11y3orVKpWpwnzVr1tCvXz/uvPNO/Pz86N27N59++ukN+w0YMOCm52oLfHx8gNrur0YnmTETwNEDSjLhQtu8flmWKSkpMf5rLUqlEldXV8DylZ4tqeHfJkEQbNofhwbSTX+WKn0V3+4+z/2DW9b8vXLlSgAmT56Mo6MjAKdOneLo0aNERkaaJANr1qxBr9czYcIE4x/CjIwMDh06RFhYGPHx8cZ9161bR01NDWPGjMHDw6NFMXbq1Im33nrLZNuzzz5r/DkyMpJ//vOfPPnkk8YP/0cffZTBgweTnZ1NUFAQeXl5rF27ls2bTdcNcnZ2RqVSoVaruXDhAkFBQfXG8NprrzFkyBAAHnnkEV588UXOnj1r7H6YPn06ycnJvPDCCwA88cQTzJgx46bXFRwcXO/2/Px8/v3vf/P444/f9Pnnzp3j448/Zs6cObz00kvs3buXZ555BkdHRx544AGT81y6dAmDwYBC0Ta/+6rVary8vCgsLCQvL4+wsLBGPMkJuk+DfV/Wdn1FDbd4nOYmSRIDBgwgNDS0wfdLa9FoNJSXl1NSUmKsvN3WtM13vyAIhPp54+GkJMBZJiX9KLmlVdYOyeLqa41JTk5m9OjRBAcHo9FomDlzJgUFBcYibAMGDKBbt24sWrQIgK+//pqwsDASEhLQarXGFilJknBwcECSJCorKxtcsLRnz57Gn/39/XFxcTEZd+Hv709ubq7xvre3N506dbrprb7CiSUlJUycOJGuXbsaW7IaYjAY6NOnD//5z3/o3bs3jz/+OI899hgff/yxyX7Ozs4YDIY23V0Bze36+mW5i+NroNqyNWrMRZZlk4HdCoXC6okPtN4aX5Ykkh9BaKMcHBzo06v2g3h4h2r+b13LKj9PmzaNadOmmUxjjYmJYdq0afTu3dtk38mTJzNt2jRcXFyM2zp16sS0adNuSFAmTpzItGnTGl+b5SbqWpnqXLx4kQkTJhjHyuzbt8/Y5XR9fZ5HH33U2PW1cOFCHnroIWpqaigtLaWiouKGLjlfX98Gl1BQq9XGnyVJMrlft+36GVXN6fYqLS1l3LhxuLm5sXLlyhvO8VuBgYF07drVZFtsbCyXLl0y2VZQUICLi0ubr1LdrOQndAB4R4G2HE78aKHIzOvYsWOkpKRw4sQJa4diorXW+LIk0e0lCG1Yx45RHD99BspLka9dJvVsJIM7+jbrWPWNKVEoFPV2j7R0X3NJT09Hp9Mxd+5c47mXLl16w36///3vef7553n//fc5duwYDz74a/G0+mZz9e7dm2+++cYsMTa126ukpISxY8fi6OjImjVrGmyBut6QIUM4dcp05t/p06cJDw832Xb06FH69OnThOhtU13yU1eE8lbJIQCSVDvwOfl1OPTtr9WfbZhSqTT511bYQ8uPSH4EoQ2TJInBA/qRnJxMf189768/QL8/jMJB1T4adTt27IhOp+N///sfkyZNMhkIfT0vLy/uuOMO/vKXvzBmzBhCQkKA2g8VpVJ5QwI0duxYXnzxRQoLC/Hy8mpRjN7e3nh7ezdq39LSUsaMGUNFRQXffPMNJSUlxm/XHTp0MH4Ijho1iqlTp3L//fcDMHv2bAYPHsx//vMfZsyYwd69e1mwYAELFiwwOf6OHTsYM2ZMi67HFjg5OTF06FC8vLwal/jU6XlXbfJzfgcUXQbPUMsFaQaxsbH4+fkZB3nbirqWn7oJDraWnDVG+/gLKQh2zNfXl8DgEBQS9HMr4dPtZ60dUquJi4vjnXfe4c0336R79+4sXryY119/vd59H3nkEWpqanjooYeM21QqVb0tPz169KBfv358//33Fou9Pvv27WPPnj0cOXKETp06ERgYaLxdv1L32bNnTbp8+vfvz8qVK/nuu+/o3r07//73v5k3bx733XefcZ+srCxSU1NNrr8tCwwMbFSrmAmvcAgfCshw+MYWQmuTZdk4IL2OrSU+AI6OjkyePJnJkye3ycQHRPIjCHahb+84kBQUayXmp5zhckHz6szYspSUFObNm3fD9tmzZ5OdnU1FRQUbN27k/vvvp7Cw0KTAH9SOD/L29mbUqFGNmnb/8ssv89577xk/iBITE5Fl2eS4M2fOpKioyOR5r776KgcPHmzi1WFyjvpuERERxv0uXLhwwyDo22+/nSNHjlBVVcWJEyd47LHHTB6fN28eM2fONLZ6tVt13V2HloCNFek7duwYe/bs4eeff7b5AoKOjo5tepFfkfwIgh1wdnZmwvhxnCGQkhqZV9ccs/k/nq2loqKCY8eOMXfuXGbOnNnowb4TJkzg8ccfJysry8IRtg4/Pz/+/e9/WzsMs9Hr9Rw/fpwdO3bcUFvqpmIng8oZ8s9A1r5b79+KvLy8kCQJHx+fNp1YtAUi+REEO+Hq6sprU7ujVkpsPZnLT8evWjskm/DWW28RFxeHv78///jHP3B2dm70B8uf/vQnQkNte1xIY/3lL3/B39/f2mGYjUKhICMjgytXrjQ4M69eTu4QO6n254PfWia4ZgoODmb8+PF06tTJ2qHc0rVr19i5c2ezWzmtTSQ/gmBHOvlpeGJIKHdFVPP+hsOUVze8JEJ7UF1dzcsvv4xWq2Xr1q14eHiIb9R2QpKk5k15h1+7vo4uB531ah7JsoxerzdZp+635RxslU6nIycnx6SmVVsikh9BsDPxHqX089EzyLOU97eetnY4VlNTU0N5eTllZWVtfiVzoX511YUbvcJ7ncjhoAmCqiKkjJ8sEFnjnDx5Er1eT2pqapt7j9bN+CotLW2TXewi+REEOxPXqydIEjEeBlIPZ3DqStutxdESKpUKpVJprNos2J+6lp/8/PymfQArlNCztvaSwoqzvuqWUImIiGhzy424urqiUCgwGAzGauptSdt6tQVBuCU3Nze6xMQAMDGkhldWHWmT38xaSqFQoNFocHJyEsmPnfLw8EClUqHVaikuLm7ak39Z6V06uwUHrXUqFXt4eKBWq01m8rUVkiSZtP60NSL5EQQ7FBsbi9rRER9HGZeqXJbvt48ZS7dSVVVlMvNHoVCIxMeOKRQKYx2cJo/78esCQb2RDDpCCtMsEN2NZFnm6NGjJslCW35/iuRHEASbolKp6BMXB8CoAC2fJp2gRte2xhQ0VXV1NRUVFZSVlbXLlq72ytfX19j602S/LHYaWrDLzFHV79SpU5w4cYJt27ah07X9yQhteY0vkfwIgp0KDQ3F28cHByX0cC1l2b7Lt35SG+bg4IBarW7zxdeEpuncuTNTpkwhNja26U/u/jtkhRrPyguQa/nFQyMjI/Hw8KB79+4WXfOutbi7u7fZ6xDJjyDYKUmS6B0XR7UmiA3ZDnyQlEGNrgnF4NoYSZJwc3Nr+pIHDcjPz8fPz48LFy6Y5XjmkpubS4cOHeym+GJLqVSq5g8WdvVB7jQaAMURyw98dnR05LbbbmuTY3zqExISwtSpU+nXr5+1Q2kykfwIgh3z9vZm+qiBuLk4kVNcxfojV6wdkllVVlZSXf1rnRZztvi88cYbTJo0ySofVGlpaYwcORJXV1c8PT1JTEyksrISqK3UfP/99/Pqq6+2ely2rjnTxQ097wZAcXQZ6M3bFSXLMgcOHODKlV9/79rarK6bactj6uznf0EQhHo5qZU8NaK2Yuy3ey/YzXiY8vJyKisrKS8vN/v4icrKSj7//HMeffRRsx63MdLS0hg3bhxjxoxh7969/Pzzzzz99NMmH5oPPfQQ33777Q3rirVXly9fZsOGDRw4cKDJz5U73Ua10g2p7CqcSzFrXOfOnSMjI4PU1FSqqqrMemyhZUTyIwjtwMhwR/7UtYYgVRXlNW2z6ysxMZGnn36aOXPm4Ovry4QJE3B0dOTTTz+ld+/euLq6EhoaylNPPUVZWRlQmyC5u7vzww8/mBzrxx9/xNXVtcFZKhs2bEClUjFo0CDjtpSUFCRJYtOmTfTu3RtnZ2dGjhxJbm4uGzZsIDY2Fnd3d+655x4qKpq/sOzs2bN55pln+Otf/0q3bt2Ijo5m+vTpODo6Gvfp0aMHAQEBrF27ttnnsScKhYKysrKmz/gCUDqQ5TWw9udD35k1rsjISIKDg+nXr5/ZumNtzfHjx9m4cSOXLl2ydihNIpIfQWgHKspKCXHW0c9HR2mVFoPhxtYfnU6HTqczaRkyGAzodLobFo5s6b7N9dVXX6FUKtm1axfz58/HxcUFR0dH3n//fY4ePcpXX31FcnKyccVzV1dX7r77bhYuXGhynIULFzJ9+nTjbJXf2r59e4PjGF599VU++OADUlNTuXz5MjNmzGDevHl8++23rFu3js2bN/O///3PuP9//vMf3NzcbnrbsWMHUDueZ8+ePfj5+TF48GD8/f0ZPnw4O3fuvCGO/v37k5bWOlO0bV1dscOSkhKTbtDGuuw9tPaHk2uhqon1gn7j+ve5QqFg0KBBhIWFteiYtqy6uprS0tI2N+OrbQ7TFgShSSIjIzl27Bi+ahkFMkWVWlxcTFc3X7lyJQCTJ082tjKcOnWKo0ePEhkZaZIMrFmzBr1ez4QJE4xrEWVkZHDo0CHCwsKIj4837rtu3TpqamoYM2YMHh4eLbqOqKgo/vnPf5osTvrss8+aXOc///lPnnzyST799FMAHn30UQYPHkx2djZBQUHk5eWxdu1aNm/e3OB5Lly4YKy++1uvvfYaQ4YMAeCRRx7hxRdf5OzZs0RFRQEwffp0kpOTeeGFFwB44oknmDFjxk2vKzg4GKjtJoHaBOvtt98mLi6ORYsWMWrUKI4ePUp0dLTJc9LT02963PbC0dERjUZDaWkp+fn5Df7fNaTIJRLZtzNS3mk4tgr6PtisOGRZJj09HU9PT+P/VVsdE9NY7u7uQNur9SNafgShHVCr1URGRgLgpICC8hr09bT+2DJZlunVqxdVVVUmY3ySk5MZPXo0wcHBaDQaZs6cSUFBgbHk/oABA+jWrRuLFi0C4OuvvyYsLIyEhIQGz1VZWdlgN0XPnj2NP/v7++Pi4mJMfOq2Xb/Yo7e3N506dbrpzdm5NhGtG7D7+OOP89BDD9G7d2/effddYmJi+OKLL0zicHZ2Ng6CFn5t/WnyOl8AkoShR+3AZw4taXYMWVlZXLhwgUOHDhm7Xu1dXetpW7tekfwIQjtR901UpZCRMFBQbto9MG3aNKZNm4aDg4NxW0xMDNOmTaN3794m+06ePJlp06bh4uJi3NapUyemTZt2Q3fRxIkTmTZtmvEbYnNJkoSHhwcuLi6o1WoALl68yIQJE+jevTvLly9n3759xi6n64vePfroo8aur4ULF/LQQw/d9Bu5r68vhYWF9T5Wd+66mK6/X7ft+llHTen2CgwMBKBr164mx4yNjb1hTEVBQYGxurHw6yKnzRr3Axi6TwckuJQKBeebdYzg4GC6du1KfHw8bm5uzTpGW1P3e11eXt6mJlOIbi9BaCdcXV3x9/cHwEUpc620Bm9XR5SK2iSgvmJlCoWi3qm5Ld23ser+mNYlKkql0qRFJj09HZ1Ox9y5c43nXrr0xnotv//973n++ed5//33OXbsGA8+ePNujd69e/PNN980O+7rNaXbKyIigqCgIE6dOmXy+OnTpxk/frzJtmPHjjFw4ECzxGgP6lp+CgsL0el0TX/fuQdBVCKcS4bDSyHxr416msFgQJIk461bt25NjLxtc3R0RK1Wo9VqRfIjCIJtioiIICsrCyelTLneQH55NX4a25yFIssylZWV6PX6Br9Fd+zYEZ1Ox//+9z8mTZpkHAj9W15eXtxxxx385S9/YcyYMYSEhNz03GPHjuXFF1+ksLAQLy+vFl2Ht7c33t7ejdpXkiT+8pe/8Morr9CrVy/i4uL46quvOHnypMmMtYqKCvbt28eLL77YotjsiYuLC35+fri5uTUv+YHaxU7PJdfO+hr+AtxivI7BYGDv3r0olUr69etn9+N76lO3wGlBQYG1Q2kS0e0lCO2Ip6cnKpUKSe2ILENeabXNjv0xGAxUV1ej1WobXLcpLi6Od955hzfffJPu3buzePFiXn/99Xr3feSRR6ipLoVbogAAGV1JREFUqeHhhx++5bl79OhBv379+P7771t0Dc3x7LPP8uKLLzJ79mx69erF1q1b2bx5Mx07djTus3r1asLCwhg8eHCrx2erJEli+PDh9O3bt/nTymNvBwc3KLwAl249k66goIDMzEwuXrzYYDdpe+Dl5dXiyQytTSQ/gtCOSJKEo6MjHi5OOKiU6Awy+WVNnxrcGpRKJW5ubri4uODg4EBKSgrz5s27Yb/Zs2eTnZ1NRUUFGzdu5P7776ewsBBPT0+T/XJycvDx8WHKlCmNOv/LL7/Me++9Zxy/k5iYiCzLJsedOXPmDYUGX331VQ4ePNiEK73RX//6Vy5fvkx5eTmpqakMHTrU5PF3332Xv//97y06h1APB1foOrX250bU/PH19WXAgAEMGjSo0a179qhPnz6MGDGiTVWvbjuRCoJgNpIk4edeO539Wlk1+mYsC2AJsiybDBZWq9UtLg5XUVHBsWPHeOONN3j88cdNBnTfzIQJE3j88cdtbg2t3Nxcpk+fzj333GPtUGySwWAgPz+/WUtdANDrl1lfx1aB9sbZdHX1rOqEhYUZx2wJbYdIfgShHZJlGSfJgJ8T6A0yeWU11g7JOMantLS0+R9c9XjrrbeIi4vD39+/yWNk/vSnPxEaGmq2WMzBz8+P559/vl2OL7kVWZZZt24dSUlJFBc3s1hh+BDwCIPqEji5zuQhg8HAnj172L59e4Ndse2ZLMttZtCzSH4EoR2SZZmqqkocFAaclJBXVo3Oyq0/sixTU1ODXq836wfLq6++ilarZevWre1m+nF7JUmSsVuyWfV+ABSKX1t/ftP1VV5eztWrVyksLBTrql1HlmVSUlLQarVtpvaUSH4EoR1SKBTGKs7u6trWn3wrt/4oFAo0Gg2urq4m61gJQlO0tN4P8GvyczYJSnKMmzUaDQkJCQwePNh4HqE26axb1qatFDsUyY8gtEOyLBsTDAeFAbWiduaXTt+6rT+yLJusBaZUKkXiYyVtpbviVurq/eTl5TX/mnw6Qmg8yAYMh5eZLFTr7e1tLEYp/KquVbWtLHMhkh9BaEfqqhFXVFSgVCqNg389HEAvt+7YH1mWqaiooKSkxGQAqWAdNTW1//dKpdLKkbSMl5cXCoWC6urqlrVC9Lobg6Qk7UI5ycnJxuVShPq1tWUuRJFDQWhHlEolnp6exrWn1Go1NTU1KJBAL3GtSIubyoBKafnvRXUDnHU6nUki1lIGg4Gamhqqqqra1NTbpjLndRoMBq5du4aLi0uLKnLbAqVSibe3N3l5eeTn5xs/lJus2x1of3qNUlypqqykrKzMuIivcKO21vLTtt/lgiA0WUBAAIAxAaqqqkKv11MjKyjXQnm+Cg9n9c0OYTZ1U9vN2dpQl1Rdv/K7PTL3dSoUCsLCwuziNXN3dycvL69lrRDOnjhGJzD81NuU9pqFn/908wVoh+qSTJH82Im1a9fy3HPPYTAYeOGFF3j00UetHZIgtIgkSQQGBuLn54dWqyUnJ4erV69Spvbin8nncVIrWfxoPJ4u5mmJuZ4syxQXF99QgNCctFot27dvJyEh4YZFR+2Jua/TwcHBblrKQkJCcHNza9agZL1eT0lJSe2yJr3uwfnYSpxPLIQJL4DSft9PLVXX8lNdXU1NTY3ZWnItRSQ/N6HT6ZgzZw7Jycm4u7vTp08f7rjjjnZdyVOwH0qlEqVSSWRkJJGRkciyzP9Sr3Ekq5iFe7J4cXysWc8nyzL79u3j/PnzDBw40GL1c5RKJTqdDicnJ7tOftrLdTaHv7+/cRHfptDr9aSmpnLt2jWGDRtGh46jwLUDlF+DM5uhywQLRGsf1Go1kiQREBCATqez+eTHPtJ8C9m7dy/dunUjODgYjUbDhAkT2LRpk7XDEgSLkCSJ2aOjAViUepE8Cyx7UTf7xl5mFgn2x+Q9qlRBjztrHziyzIpRtQ1qtZr4+HhcXFysHcot2XXys337diZNmkRQUBCSJLFq1aob9vnoo4+IjIzEycmJvn37smPHDuNj2dnZJmXLQ0JCbK7UvSCYS2lpKR7lmYyJcqZSq2f+trNmPb4kSfTr14+RI0cSFhZm1mMLwvVkWaawsJDMzMwmVQtXKpUMGTKExMRE/Pz8ajfWJT+nNkB12xjPItyaXSc/5eXl9OrViw8++KDex5cuXcqzzz7L3/72Nw4cOMCwYcMYP348ly5dAur/dmoPgwEFoT4ZGRmcO3eOiWG17/uvd18kt7SqRceUZZnMzEzj75IkSfj4+LQ4VkG4la1bt5KWlnbLisN6vd4kQaqbLWYU1Bu8O4KuEk6ut1S4dqOuUruts+sxP+PHj2f8+PENPv7OO+/wyCOPGAcxz5s3j02bNvHxxx/zxhtvEBwcbNLSk5mZSXx8fIPHq66uprr6166CurVlCgoKWnopQjug1WqpqKggPz/fKmM4vL29qaiooKKigkH+vuy6VMa7aw/w5zGdm3U8WZY5dOgQmZmZREdHExMTY+aI62ft17G1tMXrbO2YKyoqyMrKajDhrlurq6SkhEOHDhEZGVnvfoqICShz3sew+xv0IbeZLb7WfD0sfS6tVktZWRnff/89zs7OJCYmtviYdZ+dFukml9sJQF65cqXxfnV1taxUKuUVK1aY7PfMM8/ICQkJsizLslarlTt16iRnZmbKJSUlcqdOneS8vLwGz/HKK6/IgLiJm7iJm7iJm7iZ6Xb27Fmz5wR23fJzM3l5eej1+htmBPj7+3PlyhUAVCoVc+fOZcSIERgMBp5//vmbNtm/+OKLzJkzx3i/qKiI8PBwLl26hIeHh2UuRLAr/fv35+eff7Z2GG1ee3kd2+J12mLM1oypNc9t6XOZ+/jFxcWEhYVZZIZ1u01+6vx2DI8syybbJk+ezOTJkxt1LEdHx3rXJfLw8MDd3b1lgQrtglKpFO8VM2gvr2NbvE5bjNmaMbXmuS19Lksd3xL1p+x6wPPN+Pr6olQqja08dXJzc5tVH0IQzOGpp56ydgh2ob28jm3xOm0xZmvG1JrntvS5bPH/tiHSL+Nh7J4kSaxcuZKpU6cat8XHx9O3b18++ugj47auXbsyZcoU3njjjRafs6SkBA8PD4qLi23um44gCIIg2DJLfobadbdXWVkZGRkZxvvnz5/n4MGDeHt7ExYWxpw5c7j//vvp168fgwYNYsGCBVy6dIknnnjCLOd3dHTklVdeqbcrTBAEQRCEhlnyM9SuW35SUlIYMWLEDdsffPBBvvzyS6C2yOFbb71FTk4O3bt359133yUhIaGVIxUEQRAEobXYdfIjCIIgCILwW+12wLMgCIIgCO2TSH4EQRAEQWhXRPIjCHZs7dq1xMTEEB0dzWeffWbtcNqs9vI6tpfrtCTxGrYNYsyPINgpnU5H165dSU5Oxt3dnT59+rBnzx6LVEu1Z+3ldWwv12lJ4jVsO0TLjxWJbwiCJe3du5du3boRHByMRqNhwoQJbNq0ydphtTnt5XVsL9dpSeI1bF3Tpk3Dy8uL6dOnN/m5IvmxEp1Ox5w5c0hKSmL//v28+eabYvX3NuKNN96gf//+aDQa/Pz8mDp1KqdOnTLrObZv386kSZMICgpCkiRWrVpV734fffQRkZGRODk50bdvX3bs2GF8LDs7m+DgYOP9kJAQsrKyzBpnS3z88cf07NkTd3d33N3dGTRoEBs2bDDrOWztdXzjjTeQJIlnn322Wc9viK1dpyVkZWXx+9//Hh8fH1xcXIiLi2Pfvn1mO357eA3tzTPPPMOiRYua9VyR/FiJ+IbQdm3bto2nnnqK3bt3s3nzZnQ6HWPGjKG8vLze/Xft2oVWq71h+8mTJ29YXqVOeXk5vXr14oMPPmgwjqVLl/Lss8/yt7/9jQMHDjBs2DDGjx/PpUuXgNp16n7rt2vZWVNISAj//e9/SU9PJz09nZEjRzJlyhSOHTtW7/5t/XX8+eefWbBgAT179rzpfm39Oi2hsLCQIUOGoFar2bBhA8ePH2fu3Ll4enrWu794DduHESNGoNFomvdks68T305s27ZNvv322+XAwEAZkFeuXHnDPh9++KEcEREhOzo6yn369JG3b99ufGzZsmXyU089Zbz/1v+3d/8xUdd/HMCfyHHHySkFomIIA49fht0Iwgj74q+4rSbiFFxDQb2yoADZlB9Zrj/SFghOtoQ0TCAmksikpAVuyA9xigaBP0b+4nBUEgmGgofcvb5/NG+dd4fIj/C412O7Ld6f1/tnbJ/X5/15n6SlUXp6+n8xdDbGOjs7CQBVV1frXVOr1SSTyWjNmjU0ODioLW9tbaXZs2fTF1988cT2jf1+BQQE0Pvvv69T5uXlRSkpKUREdPr0aQoLC9Nei4+Pp8LCwuFOa0I8//zz9PXXX+uVm/o69vb2kru7O1VWVlJwcDAlJCQYjDP1eY6X5ORkWrRo0bBieQ1Nw2jvoY9UVVXR6tWrn7p/3vkZoSc9JfATgvm4e/cuABg81DhlyhSUl5ejsbERUVFR0Gg0uH79OpYuXYrQ0FAkJSWNqM+BgQFcuHABISEhOuUhISGor68HAAQEBODixYvo6OhAb28vysvLIZfLR9TfeFOr1SgqKsL9+/cRGBiod93U1/GDDz7AW2+9heXLlw8ZZ+rzHC9lZWXw9/dHeHg4Zs6cCV9fXxw4cMBgLK+haRjtPXTURpCwscfAQNbKTwjmQaPR0IoVK574VKpUKsnFxYXWrl1Lzs7OFBUVRRqNZlh9GPr96ujoIAB0+vRpnfKdO3eSh4eH9ufjx4+Tu7s7zZs3j7766qvhTeo/1NzcTDY2NmRpaUm2trZ04sSJIeNNcR0PHz5MPj4+1N/fT0Q05M7PI6Y4z/EkEolIJBJRamoq/fzzz5STk0PW1taUl5dntA6voekYyT30kZHu/EzqP2w6UR49IaSkpOiUG3tCmD59OsrLy7Fjx46JGC4bhQ8//BDNzc2oq6sbMs7Z2Rn5+fkIDg6Gm5sbcnNzx2Sn7/E2iEinLDQ0FKGhoaPuZ7x4enqiqakJPT09KCkpQXR0NKqrqzF//nyD8aa2jrdu3UJCQgIqKipgbW097HqmNs/xptFo4O/vj127dgEAfH19cenSJWRnZyMqKspgHV5D0zWce+ho8WuvcdDV1QW1Wo1Zs2bplM+aNUt72E4gECAjIwNLliyBr68vtm3bBnt7+4kYLhuhuLg4lJWVoaqqCk5OTkPG3r59G5s3b8aKFSvQ19eHxMTEUfU9Y8YMWFpa6h3e7Ozs1Pu9e5YJhUJIpVL4+/vj888/h0wmw969e43Gm9o6XrhwAZ2dnfDz84NAIIBAIEB1dTWysrIgEAigVqsN1jO1eY43R0dHvYTY29t7yFcgvIamazj3UACQy+UIDw9HeXk5nJyc0NDQMOw+eOdnHPETwuRERIiLi0NpaSlOnToFV1fXIeO7urqwbNkyeHt747vvvsPVq1exePFiiEQi7N69e0RjEAqF8PPzQ2VlJVatWqUtr6ysxMqVK0fU5rOAiKBSqQxeM8V1XLZsGVpaWnTKNm7cCC8vLyQnJ8PS0lKvjinOc7wFBQXp/XMSv/76K1xcXAzG8xpODk+6h47qG9JP/aKM6cFj7ytVKhVZWlrSsWPHdOLi4+Ppf//73388OjbWYmJiyNbWlk6dOkW///679tPX16cXq1aryc/Pj958801SqVTa8ubmZrK3t6fMzEyDffT29lJjYyM1NjYSAMrMzKTGxkZSKpXamKKiIrKysqLc3Fy6fPkybdmyhWxsbKitrW3sJz0OUlNTqaamhm7evEnNzc300Ucf0ZQpU6iiokIvdjKt45O+7TVZ5jmWzp07RwKBgHbu3ElXr16lwsJCmjp1Kn377bd6sbyGpmci7qGc/IyBx//HEf1zWCsmJkanzNvbW++wFjM9AAx+vvnmG4PxFRUV2sOu/9bY2Ejt7e0G61RVVRnsIzo6Wifuyy+/JBcXFxIKhfTyyy8b/Lr9s2rTpk3asTs4ONCyZcsMJj6PTJZ1fNKB58kyz7H2/fffk4+PD4lEIvLy8qL9+/cbjeU1NC0TcQ/lv+01Qvfu3cO1a9cA/HP4LjMzE0uWLIGdnR2cnZ1x5MgRrF+/Hjk5OQgMDMT+/ftx4MABXLp0yehWLWOMMWYOJvweOiYplBkazlMCPyEwxhhj+ib6Hso7P4wxxhgzK/xVd8YYY4yZFU5+GGOMMWZWOPlhjDHGmFnh5IcxxhhjZoWTH8YYY4yZFU5+GGOMMWZWOPlhjDHGmFnh5IcxxhhjZoWTH8bYMyU3NxchISHanzds2ICwsLCJG9AYeNo5tLS0wMnJCffv3x+/QTFmxjj5YYw9M1QqFXbs2IFPPvlkoocyoRYsWICAgADs2bNnoofC2KTEyQ9j7JlRUlICiUSC119/faKHMuE2btyI7OxsqNXqiR4KY5MOJz+MsTH3559/Yvbs2di1a5e27OzZsxAKhaioqDBar6ioCKGhoUO2rVKpEB8fj5kzZ8La2hqLFi1CQ0ODTkxZWRnc3d0hFouxZMkS5OXlwcLCAj09PUbb/fTTT+Hs7AyRSIQ5c+YgPj5ep8+kpCTMnTsXIpEI7u7uyM3NBQCo1WooFAq4urpCLBbD09MTe/fuHXIORIS0tDS4ublBLBZDJpPh6NGjOjFyuRx//fUXqqurh2yLMfb0BBM9AMbY5OPg4ICDBw8iLCwMISEh8PLywrp16xAbG6tznudxtbW1iIyMHLLtpKQklJSUIC8vDy4uLkhLS4NcLse1a9dgZ2eHtrY2rFmzBgkJCXjnnXfQ2NiIrVu3Dtnm0aNHsWfPHhQVFeHFF1/EH3/8gV9++UV7PSoqCmfOnEFWVhZkMhlu3ryJrq4uAIBGo4GTkxOKi4sxY8YM1NfXY/PmzXB0dERERITB/j7++GMcO3YM2dnZcHd3R01NDdatWwcHBwcEBwcDAIRCIWQyGWpra7F06dIhx88Ye0pj9vfhGWPsMbGxseTh4UGRkZHk4+ND/f39RmO7u7sJANXU1OiUR0dH08qVK4mI6N69e2RlZUWFhYXa6wMDAzRnzhxKS0sjIqLk5GTy8fHRaWP79u0EgLq7uw32nZGRQR4eHjQwMKB3rbW1lQBQZWXlcKZMRP/Me/Xq1UbnYG1tTfX19Tp1FAoFvf322zplq1atog0bNgy7X8bY8PBrL8bYuNm9ezcGBwdRXFyMwsJCWFtbG43t7+8HgCFjrl+/jocPHyIoKEhbZmVlhYCAAFy5cgUA0NraildeeUWnXkBAwJDjDA8PR39/P9zc3PDuu++itLQUg4ODAICmpiZYWlpqd2QMycnJgb+/PxwcHCCRSHDgwAG0t7cbjL18+TIePHiAN954AxKJRPvJz8/H9evXdWLFYjH6+vqGHDtj7Onxay/G2Li5ceMGfvvtN2g0GiiVSrz00ktGY+3t7WFhYYHu7m6jMUQEALCwsNArf1T27/9+vJ4xc+fORWtrKyorK3Hy5EnExsYiPT0d1dXVEIvFQ9YtLi5GYmIiMjIyEBgYiGnTpiE9PR1nz541GK/RaAAAJ06cwAsvvKBzTSQS6fx8584dzJs3b8j+GWNPj3d+GGPjYmBgAJGRkVi7di0+++wzKBQK3L5922i8UCjE/PnzcfnyZaMxUqkUQqEQdXV12rKHDx/i/Pnz8Pb2BgB4eXnpHYA+f/78E8crFosRGhqKrKwsnDp1CmfOnEFLSwsWLFgAjUZj9OBxbW0tXnvtNcTGxsLX1xdSqVRvB+ff5s+fD5FIhPb2dkilUp3P3LlzdWIvXrwIX1/fJ46dMfZ0eOeHMTYutm/fjrt37yIrKwsSiQQ//vgjFAoFfvjhB6N15HI56urqsGXLFoPXbWxsEBMTg23btsHOzg7Ozs5IS0tDX18fFAoFAOC9995DZmYmkpOToVAo0NTUhEOHDgHQ3zF65NChQ1Cr1Vi4cCGmTp2KgoICiMViuLi4wN7eHtHR0di0aZP2wLNSqURnZyciIiIglUqRn5+Pn376Ca6urigoKEBDQwNcXV0N9jVt2jRs3boViYmJ0Gg0WLRoEf7++2/U19dDIpEgOjoaANDW1oaOjg4sX758mCvOGBu2iT1yxBibjKqqqkggEFBtba22TKlUkq2tLe3bt89ovStXrpBYLKaenh5t2b8PCxMR9ff3U1xcHM2YMYNEIhEFBQXRuXPndNo5fvw4SaVSEolEtHjxYsrOziYARg9cl5aW0sKFC2n69OlkY2NDr776Kp08eVKnz8TERHJ0dCShUEhSqZQOHjxIREQPHjygDRs2kK2tLT333HMUExNDKSkpJJPJjM5Bo9HQ3r17ydPTk6ysrMjBwYHkcjlVV1drY3bt2kVyudzoWjHGRs6C6Akvwxlj7D8UEREBX19fpKamjlmbO3fuRE5ODm7dujVmbY4nlUoFd3d3HD58WOdwN2NsbPCZH8bYMyU9PR0SiWRUbezbtw8NDQ24ceMGCgoKkJ6ern2dZAqUSiW2b9/OiQ9j44R3fhhjk05iYiKOHDmCO3fuwNnZGevXr0dqaioEAj7myBjj5IcxxhhjZoZfezHGGGPMrHDywxhjjDGzwskPY4wxxswKJz+MMcYYMyuc/DDGGGPMrHDywxhjjDGzwskPY4wxxswKJz+MMcYYMyuc/DDGGGPMrPwfQiIdwBk0AKUAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_log_v = np.linspace(-m.log(10), m.log(10), 50)\n", - "\n", - "# draw the invariance curves\n", - "k_v = [kk**4 for kk in k_sqrt4_v]\n", - "x_v = [m.exp(xx) for xx in x_log_v]\n", - "\n", - "for kk in k_v: \n", - " y_f = SolidlySwapFunction(k=kk)\n", - " yy_v = [y_f(xx) for xx in x_v]\n", - " #yy_v = [y_f(xx, kk) for xx in x_v]\n", - " plt.loglog(x_v, yy_v, marker=None, linestyle='-', label=f\"k={kk**0.25:.0f}^4\")\n", - "\n", - "# draw the central tangents\n", - "C = 0.5**(0.25)\n", - "label=\"tangents\"\n", - "for kk in k_sqrt4_v:\n", - " yy_v = [C*kk - (xx-C*kk) for xx in x_v]\n", - " plt.loglog(x_v, yy_v, marker=None, linestyle='--', color=\"#aaa\", label=label)\n", - " label = \"\"\n", - "\n", - "# draw the rays\n", - "for mm in [2.6, 6]:\n", - " yy_v = [mm*xx for xx in x_v]\n", - " plt.loglog(x_v, yy_v, marker=None, linestyle='dotted', color=\"#aaa\", label=f\"ray (m={mm})\")\n", - " yy_v = [1/mm*xx for xx in x_v]\n", - " plt.loglog(x_v, yy_v, marker=None, linestyle='dotted', color=\"#aaa\")\n", - "\n", - "plt.grid(True, which=\"both\")\n", - "plt.legend()\n", - "plt.xlim(1, max(x_v))\n", - "plt.ylim(1, max(x_v))\n", - "plt.title(\"Invariance curves for different values of $\\sqrt[4]{k}$\")\n", - "plt.xlabel(\"x (log scale)\")\n", - "plt.ylabel(\"y (log scale)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "b53de9a6-0093-47c6-86f7-97f09c1f2573", - "metadata": {}, - "source": [ - "### As function of x/y, real space" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "2e383a09-2460-4d5d-a06d-c8386edc0594", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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q6+urrlu3rsnH9DaKqqpqqyQbIYQQQngtOUtFCCGEEG4ngUMIIYQQbieBQwghhBBuJ4FDCCGEEG4ngUMIIYQQbieBQwghhBBuJ4FDCCGEEG7X4VcatdvtnDhxgoCAgBavRieEEEJ4E1VVKSoqIjY2ttaK1E3V4QPHiRMnSEhI8HQ1hBBCiHbr6NGjLb60RYcPHM7rEBw9epTAwEAP16ZjslgsrFixgosvvhi9Xu/p6ngNaXfPkHb3DGl3z8jNzaVLly4NXtOnsTp84HAOowQGBkrgcBOLxYLJZCIwMFB+EbQiaXfPkHb3DGl3z7BYLAAumZIgk0aFEEII4XYSOIQQQgjhdhI4hBBCCOF2HX4OhxBCiLZLVVWsVis2m63B/SwWCzqdjvLy8jPuKxpPq9Wi0+laZdkICRxCCCE8wmw2c/LkSUpLS8+4r6qqREdHc/ToUVlTycVMJhMxMTEYDAa3HkcChxBCiFZnt9tJTU1Fq9USGxuLwWBoMEjY7XaKi4vx9/dv8QJUwkFVVcxmM1lZWaSmppKUlOTWtpXAIYQQotWZzWbsdjsJCQmYTKYz7m+32zGbzfj6+krgcCGj0Yher+fIkSNV7esu8l0TQgjhMRIePK+1vgfynRZCCCGE20ngEEIIIYTbSeAQQgghmmD06NE88MADHjn2jBkzUBSFyZMnt7vTgyVwCCGEEB5gsVh49NFHOeuss/Dz8yM2NpZp06Zx4sSJOvd/8cUXee+993jnnXfYtGkTd911V4Plp6SkEBAQQHBwsBtq33QSOIQQQggPKC0tZceOHTz99NPs2LGDxYsXc+DAASZOnFhr33fffZd58+axcuVK7rzzTtatW8fKlSt59NFH6yzbYrFwww03MGLECHe/jUaT02KFEEJ4nKqqlFnqHyKw2+2UmW3ozFaXn1Vh1GtbtJjY8uXLue6663jjjTeYNm1ao18XFBTEypUra2x74403GDJkCGlpaSQmJgLw5Zdf8uyzz7Jq1SoGDhwIQFJSEuvXr2fs2LGEhYXxyCOP1CjnqaeeolevXowdO5aNGzc2+725kgQOIYQQHldmsdHnmR89cuy9z43HZGjen8PPPvuMO++8k48++ohJkybx8ccfn3Go45133uHGG2+s87mCggIURakxDDJlyhSmTJlSa9/ExESSk5NrbV+1ahWLFi1i165dLF68uGlvyI0kcAghhBDN8NZbb/HEE0+wZMkSxowZA8DEiRMZOnRog6+Lioqqc3t5eTmPPfYYU6dOJTAwsFl1ysnJYfr06SxcuLDZZbiLBA4hhBAeZ9Rr2fvc+Hqft9vtFBUWERAY4JYhlab66quvyMjIYMOGDQwZMqRqe0BAAAEBAU0uz2KxcP3112O323nrrbea/HqnO+64g6lTpzJy5Mhml+EuMmlUCCGExymKgsmga/BmNGjPuE9zbs2ZvzFw4EAiIiJYsGABqqpWbf/444/x9/dv8Pbxxx/XKMtisXDttdeSmprKypUrW9QzsWrVKl599VV0Oh06nY7bb7+dgoICdDod77//frPLdQXp4RBCCCGaqFu3bsybN4/Ro0ej1Wp58803gaYPqTjDRnJyMqtXryYsLKxF9dq0aVON9TmWLFnCnDlz2LhxI3FxcS0qu6W8JnCodrunqyCEEKID6dGjB6tXr2b06NHodDrmz5/fpCEVq9XKlClT2LFjB9999x02m4309HQAQkNDm3W5+N69e9d4vH37djQaDf369WtyWa7mPYGjvNzTVRBCCNHB9OzZk1WrVlX1dMybN6/Rrz127BhLly4FqDrd1ckZZDoSrwkcdgkcQgghXGDNmjU1Hvfu3ZuMjIwml9O5c+ca8z/cYfr06UyfPt2tx2gsr5k0Kj0cQgghhOd4TeCwV1R4ugpCCCGE1/Jo4Fi3bh1XXHEFsbGxKIrCN998U+++d911F4qiMH/+/GYdS4ZUhBBCCM/xaOAoKSlhwIABVacT1eebb75hy5YtxMbGNvtYMqQihBBCeI5HJ41eeumlXHrppQ3uc/z4ce677z5+/PFHLrvssmYfy14mgUMIIYTwlDZ9lordbufmm2/m4Ycfpm/fvo16TUVFBRXV5msUFhYCYCkpxmKxuKWe3s7ZrtK+rUva3TOk3V3DYrGgqip2ux17I9ZJcp7N4XyNcB273Y6qqlgsFrTamsu8u/Jz3qYDx5w5c9DpdNx///2Nfs3s2bOZNWtWre2/b93KLl2bfrvt3umXWRatQ9rdM6TdW0an0xEdHU1xcTFms7nRrysqKnJjrbyT2WymrKyMdevWYbVaazxXWlrqsuO02b/Av/76K//85z/ZsWNHk9a5f/zxx5kxY0bV48LCQhISEujTpSsJEya4o6pez2KxsHLlSsaNG4der/d0dbyGtLtnSLu7Rnl5OUePHsXf3x9fX98z7q+qKkVFRQQEBDTr2ieifuXl5RiNRkaOHFnre5GTk+Oy47TZwLF+/XoyMzNJTEys2maz2XjooYeYP38+hw8frvN1Pj4++Pj41NqulJfLLwc30+v10sYeIO3uGdLuLWOz2VAUBY1G06irvzqHUZyvEa6j0WhQFKXOz7QrP+Nt9rt28803s3v3bnbt2lV1i42N5eGHH+bHH39scnl2F3YLCSGE8F6jR4/mgQce8MixZ8yYgaIoTJ48ucZF2toDjwaO4uLiqjABkJqayq5du0hLSyMsLIx+/frVuOn1eqKjo+nZs2eTj2UvKXFx7YUQQgjXOdN6Uy+++CLvvfce77zzDps2beKuu+5qsLyUlBQCAgIIDg52fWWbwaOBY/v27QwaNIhBgwYBjuQ2aNAgnnnmGZcfS3o4hBBCtFVnWm/q3XffZd68eaxcuZI777yTdevWsXLlSh599NE697dYLNxwww2MGDHCndVuEo/O4Rg9enSTLlxT37yNxpDAIYQQbZiqgqWB39N2u+N5sxZcPYdDb4IWTERdvnw51113HW+88QbTpk1r8uvPtN7Ul19+ybPPPsuqVauqriqblJTE+vXrGTt2LGFhYTzyyCM1XvPUU0/Rq1cvxo4dy8aNG5v1vlytzU4adTUZUhFCiDbMUgov1b+atAYIdtexnzgBBr9mvfSzzz7jzjvv5KOPPmLSpEl8/PHHZxzqeOedd7jxxhuBxq03NWXKFKZMmVJre2JiIsnJybW2r1q1ikWLFrFr1y4WL17cjHflHt4TOKSHQwghhAu99dZbPPHEEyxZsoQxY8YAMHHiRIYOHdrg66Kioqq+bs56Uw3Jyclh+vTpLFy4kMDAQJeU6SoSOIQQQnie3uToaaiH3W6nsKiIwIAA158Wqzc1+SVfffUVGRkZbNiwgSFDhlRtDwgIICAgoFFlNHe9qYbccccdTJ06lZEjR7qkPFdqs6fFupoEDiGEaMMUxTGs0dBNbzrzPs25NeOP/cCBA4mIiGDBggU15iJ+/PHH+Pv7N3j7+OOPgZrrTel0OnQ6HUeOHOGhhx6ic+fOzWrGVatW8eqrr1aVd/vtt1NQUIBOp+P9999vVpmu4jU9HGqpzOEQQgjhGt26dWPevHmMHj0arVZbddXzpgyp3HzzzVx00UU1nhs/fjw333wzt956a7PqtWnTphrrcyxZsoQ5c+awceNG4uLimlWmq3hN4LCVlnm6CkIIITqQHj16sHr1akaPHo1Op2P+/PlNGlIJCwsjLCysxraWrDcF0Lt37xqPt2/fjkajoV+/fs0qz5W8JnBgsWA3m9EYDJ6uiRBCiA6iZ8+erFq1qqqnY968eZ6uUpvlPYEDx6mxEjiEEEK0xJo1a2o87t27NxkZGS4puyXrTdVl+vTpTJ8+3aVlNpfXTBoFWYtDCCGE8BQJHEIIIYRwOwkcQgghhHA7CRxCCCGEcDsJHEIIIYRwOwkcQgghhHA7CRxCCCGEcDuvChy24mJPV0EIIYTwSl4VOOyFRZ6ughBCCOGVvCpw2IoKPV0FIYQQ7dzo0aN54IEHPHLsGTNmoCgKkydPrnGRtvbAqwKHvVAChxBCiLZl3759TJw4kaCgIAICAjjvvPNIS0urtd+LL77Ie++9xzvvvMOmTZu46667Giw3JSWFgIAAgoOD3VTzpvGqwGErkMAhhBCi7Th48CDDhw+nV69erFmzht9++42nn34aX1/fGvu9++67zJs3j5UrV3LnnXeybt06Vq5cyaOPPlpnuRaLhRtuuIERI0a0xttoFK+6eJutSOZwCCFEW6SqKmXWsnqft9vtlFnL0Fl0aDSu/V/ZqDOiKEqzX798+XKuu+463njjDaZNm9ak1z755JNMmDCBuXPnVm3r2rVrjX2+/PJLnn32WVatWsXAgQMBSEpKYv369YwdO5awsDAeeeSRGq956qmn6NWrF2PHjmXjxo3Ne2Mu5lWBw15Q4OkqCCGEqEOZtYyhnwz1yLG3TN2CSW9q1ms/++wz7rzzTj766CMmTZrExx9/fMahjnfeeYcbb7wRu93OsmXLeOSRRxg/fjw7d+6kS5cuPP7441x55ZVV+0+ZMoUpU6bUKicxMZHk5ORa21etWsWiRYvYtWsXixcvbtb7cgevChzSwyGEEMJV3nrrLZ544gmWLFnCmDFjAJg4cSJDhzYcnKKiogDIzMykuLiYl19+mRdeeIE5c+awfPlyJk+ezOrVqxk1alST65STk8P06dNZuHAhgYGBTX9TbuRVgcNeVIRqs6FotZ6uihBCiGqMOiNbpm6p93m73U5RUREBAQFuGVJpqq+++oqMjAw2bNjAkCFDqrYHBAQQEBDQqDLsdjsAkyZN4sEHHwRg4MCBbNy4kbfffrtZgeOOO+5g6tSpjBw5ssmvdTevmjQKYJfFv4QQos1RFAWT3tTgzagznnGf5tyaM39j4MCBREREsGDBAlRVrdr+8ccf4+/v3+Dt448/BiA8PBydTkefPn1qlN27d+86z1JpjFWrVvHqq6+i0+nQ6XTcfvvtFBQUoNPpeP/995tVpqt4TQ+HYjSC2YytsBBtUJCnqyOEEKId69atG/PmzWP06NFotVrefPNNoGlDKgaDgcGDB/Pnn3/WeP7AgQN06tSpWfXatGlTjfU5lixZwpw5c9i4cSNxcXHNKtNVvCZwaP39ITfXcWpsgqdrI4QQor3r0aMHq1evZvTo0eh0OubPn9+kIRWAhx9+mOuuu46RI0cyZswYli9fzrfffsuaNWuaVafevXvXeLx9+3Y0Gg39+vVrVnmu5DWBQxMYALm52GW1USGEEC7Ss2dPVq1aVdXTMW/evCa9/qqrruLtt99m9uzZ3H///fTs2ZOvvvqK4cOHu6nGnuM9gSPAMVtXFv8SQgjREqf3PvTu3ZuMjIxml3fbbbdx2223tbBWdZs+fTrTp093S9lN5TWTRrWVXVxyPRUhhBCi9XlP4Ah0BA65nooQQgjR+rwmcGj8K3s45BL1QgghRKvzmsDh7OGwFcry5kIIIURr85rAoamcw2GXSaNCCCFEq/OiwFF5lopcT0UIIYRodV4TOGRIRQghhPAc7wkcziGVfAkcQgghRGvznsARHAyANT/fo/UQQgghvJH3BI6QEADsBQWoFouHayOEEKK9Gj16NA888IBHjj1jxgwURWHy5Mk1LtLWHnhP4AgKgspLENukl0MIIUQbUFxczH333Ud8fDxGo5HevXvzn//8p859X3zxRd577z3eeecdNm3axF133dVg2SkpKQQEBBBc2cPvaV4TOBSt9tSwSm6eZysjhBBCAA8++CDLly9n4cKF7Nu3jwcffJC//e1vLFmypMZ+7777LvPmzWPlypXceeedrFu3jpUrV/Loo4/WWa7FYuGGG25gxIgRrfE2GsVrAgeANjQUAFterodrIoQQojpVVbGXljZ8Kys78z7NuKmq2qK6L1++nKCgID788MMmv3bTpk3ccsstjB49ms6dO3PnnXcyYMAAtm/fXrXPl19+ybPPPsuqVas477zzAEhKSmL9+vUsXryYuXPn1ir3qaeeolevXlx77bXNf2Mu5jVXiwXQhYRgBmy5EjiEEKItUcvK+PPsc864X/OvyVq/njt+RTGZmvXazz77jDvvvJOPPvqISZMm8fHHH59xqOOdd97hxhtvBGD48OEsXbqU2267jdjYWNasWcOBAwf45z//WbX/lClTmDJlSq1yEhMTSU5OrrV91apVLFq0iF27drF48eJmvS938KrA4Zw4as2TIRUhhBAt89Zbb/HEE0+wZMkSxowZA8DEiRMZOnRog6+Lioqq+vpf//oXd9xxB/Hx8eh0OjQaDf/9738ZPnx4s+qUk5PD9OnTWbhwIYGBgc0qw128K3A4h1RkDocQQrQpitFIzx2/1vu83W6nsKiIwIAANBrXzgZQjMYmv+arr74iIyODDRs2MGTIkKrtAQEBBFSu+9QY//rXv9i8eTNLly6lU6dOrFu3jnvuuYeYmBguuuiiJtfrjjvuYOrUqYwcObLJr3U3Lwscjh4OmcMhhBBti6IoDQ9r2O1orFY0JpPLA0dzDBw4kB07drBgwQIGDx6MUnkWZFOGVMrKynjiiSf4+uuvueyyywDo378/u3bt4tVXX21W4Fi1ahVLly7l1VdfBSrnxtjt6HQ63n33XW677bYml+kqXhU4dCGOHg45S0UIIURLdOvWjXnz5jF69Gi0Wi1vvvkm0LQhFYvFgsViqRWgtFotdru9WfXatGlTjfU5lixZwpw5c9i4cSNxcXHNKtNVvCpwnBpSkR4OIYQQLdOjRw9Wr17N6NGj0el0zJ8/v0lDKoGBgYwaNYqHH34Yo9FIp06dWLt2LR9++CGvvfZas+rUu3fvGo+3b9+ORqOhX79+zSrPlbwqcOiqhlSkh0MIIUTL9ezZk1WrVlX1dMybN69Jr//ss894/PHHufHGG8nNzaVTp068+OKL3H333W6qsed4VeCQs1SEEEK01Jo1a2o87t27NxkZzTthNzo6mgULFrigVnWbPn0606dPd1v5TeH5mTet6NTCX3mozRwfE0IIIUTTeTRwrFu3jiuuuILY2FgUReGbb76pes5isfDoo49y1lln4efnR2xsLNOmTePEiRPNPp6zhwObDXthYQtrL4QQQojG8mjgKCkpYcCAAVWze6srLS1lx44dPP300+zYsYPFixdz4MABJk6c2OzjaQwGNP7+gJypIoQQQrQmj87huPTSS7n00kvrfC4oKIiVK1fW2PbGG28wZMgQ0tLSSExMbNYxtaGh2IuLK9fi6NKsMoQQQgjRNO1q0mhBQQGKojR4qd2KigoqKiqqHhdWDp1Une8cHAxpaVRkZaO3WNxcY+9gqWxHi7Rnq5J29wxpd9ewWq2oqorNZmvUmhPOC6w5F7ISrmOz2VBVFavVWutz7crPebsJHOXl5Tz22GNMnTq1wfXhZ8+ezaxZs2ptX7FiBSaTiVirBX/gt7VrKagod2ONvc/pPVKidUi7e4a0e8soikJMTAy5ublNWgq8qKjIjbXyTkVFRZSUlLBq1apaV84tLS112XHaReCwWCxcf/312O123nrrrQb3ffzxx5kxY0bV48LCQhISErj44osJDAwkc/uvFO7dR6/oKMImTHB31b2CxWJh5cqVjBs3Dr1e7+nqeA1pd8+QdnedjIwMCgsL8fX1xWQyVS0PXhdVVSkpKcHPz6/B/UTjqapKaWkpRUVFxMTEMHDgwFr75OTkuOx4bT5wWCwWrr32WlJTU1m1atUZr37n4+ODj49Pre16vR69Xo8h2rGkrJqdI78sXMzZxqJ1Sbt7hrR7y8XFxaHVasnOzj7jvqqqUlZWhtFolMDhYiEhIURHR9fZrq78jLfpwOEMG8nJyaxevZqwsLAWl6mLiADAmpXV4rKEEEI0n3NYJTIy8oxzBSwWC+vWrWPkyJES9FxIr9ej1Wpb5VgeDRzFxcWkpKRUPU5NTWXXrl2EhoYSGxvLlClT2LFjB9999x02m4309HQAQkNDMRgMzTqmLjISAGtmZsvfgBBCiBbTarVn/KOn1WqxWq34+vpK4GinPBo4tm/fzpgxY6oeO+de3HLLLcycOZOlS5cC1BpXcl4spzmcPRyWLAkcQgghRGvxaOAYPXp0rRmx1TX0XHPpK3s4bDm5qFYriq5NjyoJIYQQHYJXXUsFKq+notWC3Y41Ry5TL4QQQrQGrwscilaLrnLyqUwcFUIIIVqH1wUOkImjQgghRGvzzsDhPDVWAocQQgjRKrwzcDh7OGRIRQghhGgV3hk4pIdDCCGEaFXeGTgiZbVRIYQQojV5aeCQSaNCCCFEa/LOwCHXUxFCCCFalVcGDudqo9acHFSr1cO1EUIIITo+rwwcNVcbzfF0dYQQQogOzysDh6LVooty9HJYTpzwcG2EEEKIjs8rAweAPjYWkMAhhBBCtAYJHBI4hBBCCLeTwCGBQwghhHA7CRwSOIQQQgi38+LAEQeAVQKHEEII4XZeHDgqeziOn0BVVQ/XRgghhOjYvDhwxABgLy3FXlDg4doIIYQQHZvXBg6Nry/asDBA5nEIIYQQ7ua1gQNk4qgQQgjRWiRwIIFDCCGEcDcJHDgmjgohhBDCfSRwID0cQgghhLt5d+CIk8AhhBBCtAbvDhzSwyGEEEK0CgkcgC0vD3tpqYdrI4QQQnRcXh04tIGBaIKCADAfPebh2gghhBAdl1cHDgBDp04AmI8c9mxFhBBCiA5MAkdV4Dji4ZoIIYQQHZcEjsREQAKHEEII4U4SODo7ejgshyVwCCGEEO4igUOGVIQQQgi385rAYbaZ69zuDBzWrCzsJSWtWSUhhBDCa3hN4Cg2F9e5XRsUhDY4GADz0aOtWCMhhBDCe3hP4LDUHTig2rCKzOMQQggh3EICB6cmjso8DiGEEMI9vCZwFJmL6n1OLxNHhRBCCLfymsBR3xwOkDNVhBBCCHfznsDR4ByOzoAEDiGEEMJdvCdwNNjD4Vht1Jadja24/v2EEEII0TxeEziKLPXP4dAGBKANDQWkl0MIIYRwB68JHA0NqQAYOncGwHwotRVqI4QQQngX7wkcDQypAPh07w5ARUpKa1RHCCGE8CpeEzhKzA0vWy6BQwghhHAfrwkchZbCBp/36ZEEQEVKcmtURwghhPAqXhM4zjSHw9nDYUk7ir28vDWqJIQQQngNrwkcZxpS0YaFOS7ipqpUHDzYOpUSQgghvITXBI6GljYHUBSlqpfDLPM4hBBCCJfymsBRYi3BZrc1uM+peRwSOIQQQghX8prAAY7Q0RCD80yVAzJxVAghhHAljwaOdevWccUVVxAbG4uiKHzzzTc1nldVlZkzZxIbG4vRaGT06NHs2bOn2ccrqCho8Hk5NVYIIYRwD48GjpKSEgYMGMCbb75Z5/Nz587ltdde480332Tbtm1ER0czbtw4iooano9Rn7zyvAaf90lyDKlYjh/HXtJwb4gQQgghGk/nyYNfeumlXHrppXU+p6oq8+fP58knn2Ty5MkA/O9//yMqKopPPvmEu+66q8nHO1Pg0IWEoA0Px5adTcXBgxj792/yMYQQQghRm0cDR0NSU1NJT0/n4osvrtrm4+PDqFGj2LhxY72Bo6KigoqKiqrHhYWnFvzKKsnCYrE0eFxDt66UZWdTuv9PdL17t/BdeAdnm56pbYVrSbt7hrS7Z0i7e4Yr27vNBo709HQAoqKiamyPioriSANXdJ09ezazZs2q87nNv21G/6e+weNGaHWEAPtXriTbx9C0Snu5lStXeroKXkna3TOk3T1D2r11lZaWuqysNhs4nBRFqfFYVdVa26p7/PHHmTFjRtXjwsJCEhISAIjsEsmEQRMaPF5BSSlZGzcSb7cxZELD+woHi8XCypUrGTduHHp9w4FOuI60u2dIu3uGtLtn5OTkuKysNhs4oqOjAUdPR0xMTNX2zMzMWr0e1fn4+ODj41PncwXmgjN+UE29ewFQceCAfKibSK/XS5t5gLS7Z0i7e4a0e+tyZVu32XU4unTpQnR0dI3uM7PZzNq1axk2bFizyjzTpFEA3549QaPBlpWNJTOzWccRQgghRE0e7eEoLi4mpdqaF6mpqezatYvQ0FASExN54IEHeOmll0hKSiIpKYmXXnoJk8nE1KlTm3W8xgQOjcmEoWsXzCkHKd+7F31kZLOOJYQQQohTPBo4tm/fzpgxY6oeO+de3HLLLXzwwQc88sgjlJWVcc8995CXl8fQoUNZsWIFAQEBzTpeXsWZAweAb58+VYEjYPToZh1LCCGEEKd4NHCMHj0aVVXrfV5RFGbOnMnMmTNdcrzG9HCAI3AULv2W8r17XXJcIYQQwtu12Tkc7lBqLaXCVnHG/Yx9+wJQvkcChxBCCOEKXhM4dBpHZ05jejl8Khf8sp48iTU31631EkIIIbyB1wSOYEMwALnlZw4QWn9/DJ06AVC+d587qyWEEEJ4Be8JHL7BQBPmcfTtAyDzOIQQQggX8J7A4RMMNK6HA8DXOY9DAocQQgjRYl4TOEJ8QoCmnakCUL5nj9vqJIQQQngLrwkcVUMqjV2Lo3LiqOXoUWwFBe6qlhBCCOEVvCdwNHFIRRscjD4uDoDyffvdVS0hhBDCK3hN4Ag3hgOQVZrV6NdUDavIPA4hhBCiRbwmcEQYIwDIKM1o9GuqJo7+8btb6iSEEEJ4C68JHJEmx0XYMksbfwVY44D+AJTt+s0tdRJCCCG8hdcEjgiTo4cjvyK/UcubA/ie1R80GiwnTmDJaHzPiBBCCCFq8prAEWgIxEfrA0BmSeN6ObT+fvj07AlA2c5d7qqaEEII0eF5TeBQFIUoUxTQtHkcxoEDACjbtcsd1RJCCCG8gtcEDmjePA7ToEEAlO3c6ZY6CSGEEN7AKwNHk3o4nIFj717sFY2b+yGEEEKImrwqcET5OYZUmtLDoY+PRxsWBhaLLHMuhBBCNJN3BY5mzOFQFAXjoIGATBwVQgghmksCRyNUzePYJfM4hBBCiObwqsDRnEmjAMaBAwEo3bkLVVVdXS0hhBCiw/PKwJFVmoXNbmv063z79gW9Hlt2Npbjx91VPSGEEKLD8qrAEW4MR6NosKm2Rl81FkDj64tvH8fl6uX0WCGEEKLpvCpw6DQ6wn0dV41t6rCKqXJYRQKHEEII0XReFTjg1LBKeml6k17nXI+jVM5UEUIIIZrM6wJHtF80AOklTQwcZ58NQMX+/djy811dLSGEEKJD87rAER8QD8CxomNNep0+MhJDt26gqpRs2+aOqgkhhBAdltcFjoSABACOFh1t8mv9hg4BoHTzFpfWSQghhOjovC5wxPs7ejiaEzhMQ88DoHSrBA4hhBCiKbwucDh7OI4XH8eu2pv0WtOQwQBUJKdgzc52ed2EEEKIjsrrAke0fzRaRUuFrYKs0qwmvVYXEoJPb8d6HCVbpJdDCCGEaCyvCxx6jZ4YvxigmfM4hlTO49iy1aX1EkIIIToyrwsccOpMlWbN4zhvKAAlWza7tE5CCCFER+aVgcM5j+NYcdNOjQUwDR4MWi2WI2lYTpxwddWEEEKIDsmrA0dzeji0/v749usLQIkMqwghhBCN4tWBo6mLfzn5OU+PlYmjQgghRKNI4GgGU+UCYCVbtqCqqsvqJYQQQnRUXhk4nJNG8yryKDYXN/n1prPPBr0e68mTWNLSXF09IYQQosPxysDhp/cj1DcUaN48Do3RiGnAAABKZJlzIYQQ4oy8MnBAtYu4NeNMFQDT+Y55HCW//OKyOgkhhBAdldcGjsSARAAOFxxu1uv9R44EHIFDNZtdVS0hhBCiQ/LawNEtuBsAKfkpzXq9b9++aMPCsJeUULpjhyurJoQQQnQ4Xhs4ugd3B+Bg/sFmvV7RaPAfMQKA4rXrXFYvIYQQoiPy2sDh7OFILUjFZrc1qwz/0aMAKF671mX1EkIIIToirw0ccf5xGHVGzHZzs85UAfC74ALQ6TAfOoT5aPPKEEIIIbyB1wYOjaKhS1AXoPnDKtqAAMeaHMiwihBCCNEQrw0ccGoeR3J+crPL8B/lOFtFhlWEEEKI+kngoPk9HAD+oxzzOEq3bMFeWuqSegkhhBAdjVcHjpaeGgtg6NYNfVwcqtksq44KIYQQ9fDqwOHs4ThceBiL3dKsMhRFOTWssk6GVYQQQoi6eHXgiPGLwaQzYbVbSSts/kXYnMMqxWvXydVjhRBCiDp4deBQFMUlwyqmoUNRfH2xnjxJxYHmT0AVQgghOqo2HTisVitPPfUUXbp0wWg00rVrV5577jnsdrvLjuGKiaMaX1/8hg4F5GwVIYQQoi5tOnDMmTOHt99+mzfffJN9+/Yxd+5cXnnlFd544w2XHcMVPRwAfs55HGvWtLRKQgghRIfTpgPHpk2bmDRpEpdddhmdO3dmypQpXHzxxWzfvt1lx0gKSQLgz9w/W1ROwIUXAlC2cyeWzMwW10sIIYToSHSerkBDhg8fzttvv82BAwfo0aMHv/32Gxs2bGD+/Pn1vqaiooKKioqqx4WFhQBYLBYsltpnovQI7AFAWlEaOSU5BBoCm1fZsDB8Bwyg/LffyP9hOcFTb2heOe2Qs13ral/hPtLuniHt7hnS7p7hyvZu04Hj0UcfpaCggF69eqHVarHZbLz44ovccEP9f8xnz57NrFmzam1fsWIFJpOpzteEakLJtefywQ8f0F3fvdn1DY6PJ/K330j77DM2Bgc1u5z2auXKlZ6ugleSdvcMaXfPkHZvXaUuXNCyTQeOzz//nIULF/LJJ5/Qt29fdu3axQMPPEBsbCy33HJLna95/PHHmTFjRtXjwsJCEhISuPjiiwkMrLv3Yt2GdaxIW4F/d38m9J3Q7PpaBg3iyLJlmA4f5uLBg9FFRDS7rPbEYrGwcuVKxo0bh16v93R1vIa0u2dIu3uGtLtn5OTkuKysNh04Hn74YR577DGuv/56AM466yyOHDnC7Nmz6w0cPj4++Pj41Nqu1+vr/ZD2j+zPirQV7M/b36IPsj4xEd8B/Sn/bTdlq1cTeuONzS6rPWqojYX7SLt7hrS7Z0i7ty5XtnWbnjRaWlqKRlOzilqt1qWnxQL0CesDwB85f7S4rMDxlwBQtPzHFpclhBBCdBRtOnBcccUVvPjiiyxbtozDhw/z9ddf89prr3HVVVe59Dh9wvqgoJBekk52WXaLygocfzEApdu3Y83KckX1hBBCiHavTQeON954gylTpnDPPffQu3dv/vGPf3DXXXfx/PPPu/Q4fno/ugR1AWBvzt4WlaWPi8O3f39QVQplcpMQQggBtPHAERAQwPz58zly5AhlZWUcPHiQF154AYPB4PJj9QvvB8Ce7D0tLitw/HhAhlWEEEIIpzYdOFqTK+dxBFQGjtLt27Fmt2yIRgghhOgIJHBUqt7D0dIrvhri4/A96yyw2ymSYRUhhBBCAodTz5Ce6BQdOeU5ZJRmtLi8wEscvRyFMqwihBBCSOBw8tX50j3EscqoK+ZxVA2rbNuG1YULpwghhBDtkQSOavqG9QVgV9auFpdliI/Ht18/GVYRQgghaEbgmD59OuvWrXNHXTzunKhzAPg141eXlFc1rPL9Dy4pTwghhGivmhw4ioqKuPjii0lKSuKll17i+PHj7qiXRwyOHgw41uIosZS0uLzASy8FRaF061bMxzpOOwkhhBBN1eTA8dVXX3H8+HHuu+8+Fi1aROfOnbn00kv58ssv2/1lg6P9oonzj8Om2tiVuavF5enj4jCdNxSAgq+/bnF5QgghRHvVrDkcYWFh/P3vf2fnzp1s3bqV7t27c/PNNxMbG8uDDz5IcnKyq+vZapzDKtsztrukvODJkwFH4FBdfA0YIYQQor1o0aTRkydPsmLFClasWIFWq2XChAns2bOHPn368Prrr7uqjq3q3KhzAdie7prAEXDRRWj8/bGcOEHp1q0uKVMIIYRob5ocOCwWC1999RWXX345nTp1YtGiRTz44IOcPHmS//3vf6xYsYKPPvqI5557zh31dbtzox2B44+cPyizlrW4PI3RSOBllwGQv3hxi8sTQggh2qMmB46YmBjuuOMOOnXqxNatW9m+fTt33303AQEBVfuMHz+e4OBgV9az1cT7xxNpisRqt/Jb1m8uKTN4suPqtkUrVmIrKnJJmUIIIUR70uTA8frrr3PixAn+/e9/M3DgwDr3CQkJITU1taV18whFUaqGVVx1eqxv//4YunVDLS+XU2SFEEJ4pSYHjptvvhlfX1931KXNcA6ruGoeh6IoVb0cBTKsIoQQwgvJSqN1cPZw7M7aTYWtwiVlBk2cCFotZb/9RsXBgy4pUwghhGgvJHDUoXNgZ8J8wzDbzfye9btLytRFROA/ciQga3IIIYTwPhI46qAoStV6HNsytrms3KDKYZX8JUtQrVaXlSuEEEK0dRI46jE0xrFC6MbjG11WZsCoUWhDQrBlZVO8fr3LyhVCCCHaOgkc9RgRNwKA3dm7yS/Pd0mZisFA0MQrAChYLMMqQgghvIf3BA5r0yZ/xvjH0D24O3bVzsYTruvlCKpc6rxozRqseXkuK1cIIYRoy7wncJRkN/klI+IdvRzrj7tu+MO3Z098+/YFi4XCb791WblCCCFEW+Y9gaO0GYGjcljll+O/YFddd+G1oKsdvRx5n30uF3QTQgjhFbwncDSjh2Ng5ED89f7kVeSxJ3uPy6oSNHEiGj8/zIcOUfLLLy4rVwghhGirJHA0QK/Rc37s+YBrh1W0/v4ET5kCQO4H/3NZuUIIIURb5UWBI6tZL3MOq6w/5trTWENuvgk0Gkp++YWK5GSXli2EEEK0NV4UOJrewwEwPG444LhcfXZZ88qoiyE+noCxYwHI/fAjl5UrhBBCtEXeEziaMWkUIMIUQe/Q3gAuPT0WIPSWaQAULF0qp8gKIYTo0LwncDRzSAWqnR7r4mEV4znn4Nu3L2pFBfmff+HSsoUQQoi2xIsCR/OHQ6pOjz3xC1a7666BoihKVS9H3scfo5rNLitbCCGEaEskcDTCWeFnEewTTJG5iF8zfnVhpSDwkkvQRURgzcqi8McfXVq2EEII0VZ4T+AoywVb83ontBotYxMdEzx/POzaUKAYDITcOBVwnCKrqqpLyxdCCCHaAu8JHKhQmtPsV4/vPB6An4785NJhFYDg665D8fGhfM8eynbscGnZQgghRFvgRYEDKDze7JcOjh5MiE8IeRV5bEvf5sJKgS4khKCJEwHI/d+HLi1bCCGEaAu8K3DkpzX7pTqNjos6XQS4flgFIHTazQAU/fQT5mPHXF6+EEII4UleFjiOtOjlVcMqaT9hsVtcUaMqPklJ+F1wAdjt5C382KVlCyGEEJ7mXYEjr2WB45yocwj1DaWgooCtJ7e6qFKnOE+Rzf/yS2zFJS4vXwghhPAU7wocLRhSAcewyrhO4wD3DKv4DR+OoUsX7MXFFCxe7PLyhRBCCE/xssDRsh4OODWs8nPaz1hsrh1WUTSaql6OnA8WYJeFwIQQQnQQXhY40qCF61ycHXk2Yb5hFJoL2Xxys4sqdkrQlVeii4zEeuIk+YsWubx8IYQQwhO8KHBowFoOxZktKkWr0bp1WEXj60v4X+8GIPvtt7GXlbn8GEIIIURr857AERDtuHfhsMqqtFWYba4f9gi++mr0cXHYsrLJ++RTl5cvhBBCtDbvCRzBCY77Fk4cBRgUOYgIYwRFliI2HN/Q4vJOpxgMhN97LwA5770nZ6wIIYRo97wncAQlOu7zDre4KK1Gy4QuEwD4OuXrFpdXl6CJV2Do0gVbfj55H8nqo0IIIdo3Lwoc8Y57FwypAExOmgzA+mPrySxt2byQuig6HRF/uw+AnPcXYCsocPkxhBBCiNbiPYHDhUMqAF2DuzIochA21cbSg0tdUubpAi65BJ8ePbAXFZHz/gK3HEMIIYRoDd4TOIIqA0cLVxutztnLsTh5MXbV7rJynRSNhoi/3w9A7kcfYc1p/tVuhRBCCE/yvsBRcBTsNpcUeXGni/HT+3G06Cjb07e7pMzT+V94Ib5nnYVaWkrOu++55RhCCCGEu3lP4AiMAZ0R7FbITXVJkSa9qWry6FfJX7mkzNMpikLE3/8OQN6nn2LJyHDLcYQQQgh38p7AodFCRE/H15l7XFbs1UlXA/DTkZ8oqHDPxE6/C4ZhPPccVLOZ7LffdssxhBBCCHfynsABENXXcZ+x12VF9gnrQ4+QHpjtZpYdWuaycqtTFIXIyl6O/C+/wnzsmFuOI4QQQriLdwWOyD6O+0zXBQ5FUaomj36V/BVqC6/VUh/T4MH4XXABWCxk//sttxxDCCGEcJc2HziOHz/OTTfdRFhYGCaTiYEDB/Lrr782r7Ao1wcOgMu7Xo5BY+BA3gH25ri27OqcZ6wULFlCxSHXzEMRQgghWkObDhx5eXlccMEF6PV6fvjhB/bu3cu8efMIDg5uXoHOHo7cQ2Bx3UXRgnyCuKjTRYD7Jo8CGPv3x//CC8FuJ/vNN912HCGEEMLV2nTgmDNnDgkJCSxYsIAhQ4bQuXNnxo4dS7du3ZpXoH8UGENBtUPWfpfW1Tl59PvU7ym1lLq07Ooi7v8bAIXff0/5fte+ByGEEMJddJ6uQEOWLl3K+PHjueaaa1i7di1xcXHcc8893HHHHfW+pqKigoqKiqrHhYWFAFgsFixWK9rI3miO/IL1xO+oEf1cVtcBYQOI94/nWPExliQvYUrSFJeVXZ22Wzf8L7mE4uXLOfncc8QtWICi8WxutFgsNe5F65B29wxpd8+QdvcMV7a3orprlqML+Pr6AjBjxgyuueYatm7dygMPPMA777zDtGnT6nzNzJkzmTVrVq3tn3zyCSaTibOOfUTXrJWkRF7KnrgbXFrfTRWbWFa2jDBNGH8P+DsaxT1BQJefT+d5r6Exm0m/ejKFQ4a45ThCCCG8W2lpKVOnTqWgoIDAwMAWldWmA4fBYODcc89l48aNVdvuv/9+tm3bxqZNm+p8TV09HAkJCWRnZxMYGIiy80N038/A3nUMthsWubS+pZZSLv3mUoosRcwbMY8xCWNcWn51+R9+RPYrr6AJDCRx6RJ0YWFuO9aZWCwWVq5cybhx49Dr9R6rh7eRdvcMaXfPkHb3jJycHGJiYlwSONr0kEpMTAx9+vSpsa1379589VX9EzN9fHzw8fGptV2v1zs+pDH9AdBk7kPj4g9tkD6I63pdx39//y8L/1zIxV0vdmn51YXfMo2i776jYt8+8l5/ndg5c9x2rMaqamPRqqTdPUPa3TOk3VuXK9u6TU8aveCCC/jzzz9rbDtw4ACdOnVqfqGRvRz3xelQmtuC2tVtaq+p6DQ6dmbuZFfmLpeX76TodMTMmgmKQsGSpZTU0+MjhBBCtAVtOnA8+OCDbN68mZdeeomUlBQ++eQT3n33Xe69997mF+oTAMGJjq8zXLfEuVOEKYLLu14OwId7P3R5+dUZ+/cnZOpUANJnzsJebShJCCGEaEvadOAYPHgwX3/9NZ9++in9+vXj+eefZ/78+dx4440tKzhmgOP+uHuu8HpLn1sAx/VVjhYedcsxnCIe+Du6iAjMR46Q8867bj2WEEII0VxtOnAAXH755fz++++Ul5ezb9++Bk+JbbSE8xz3aZtbXlYduod0Z3jccFRUPtr3kVuO4aQNCCDqyScAyHnvPVmBVAghRJvU5gOHWySe77g/ugXsdrccYnrf6QB8k/IN+eX5bjmGU8D48fiNGolqsZA+c6bbrucihBBCNJd3Bo6Y/qAzQlke5CS75RBDoofQO7Q3ZdYyvjjwhVuO4aQoCtFPP4Pi60vp1q0UfLPErccTQgghmso7A4dWD3HnOL5Oc8/ZHYqicEtfx1yOT/Z9QoXNvRM6DfFxhN97DwCZc+dizctz6/GEEEKIpvDOwAGQ6JzHscVth7i488VE+0WTU57DskPL3HYcp7Dp0/FJSsKWl0fmq6+6/XhCCCFEY0ngOOqeiaMAeo2em3rfBMAHez7ArrpnvoiTotcTXbmse8FXiyndts2txxNCCCEay3sDR/xgQHFcqr44022HuTrpavz1/qQWpLLyyEq3HcfJdPYggq+9FoCTM2ehms1uP6YQQghxJt4bOIzBEFm5bLqbTo8F8Df4M62P40Jzb+x8A4vd/Vc6jHxoBtqwMMwHD5Lz/vtuP54QQghxJt4bOAAShzruj7pvHgfAtL7TCPEJ4UjhEZakuP8MEm1QEFGPPQZA9lv/wXzkiNuPKYQQQjTEuwOHmxcAc/LT+3FHf8eCZf/57T+UW8vdejyAwMsvw2/Y+ahmM+mznpO1OYQQQniUdwcO58TRk7ugosith7q257XE+MWQWZrJZ/s/c+uxoHJtjmefRTEYKNm4kbyP3LviqRBCCNEQ7w4cwYkQ2hXsVji42q2H8tH6cM9AxzoZ7/3+HoXmQrceD8DQqRORDz8MQMbcVyjdsdPtxxRCCCHq4t2BQ1GgxyWOrw/86PbDXdH1CroFdaPQXMgHf3zg9uMBhNx0I4ETLgWrleMPPIA1J6dVjiuEEEJU592BA6DHeMd98o9uu66Kk1aj5W+D/gbAwn0LyS7LduvxwDG0EvP88xi6dsWamcnxf/wD1WZz+3GFEEKI6iRwJA4Dn0AoyYIT7h9yuDDxQs4KP4syaxnv7m6dy8lr/PyI/9c/UUwmSjdtJuuNN1rluEIIIYSTBA6dAbpd6Pj6wHK3H05RFB44+wEAFh1YxNGio24/JoBP9+7EPP8cADlvv0PRavfOWRFCCCGqk8AB1eZxuD9wAAyJGcKw2GFY7Vbe2vVWqxwTIOiyywi58UYATjz6GOZjx1rt2EIIIbybBA6ApHGAAum7ofBEqxzy/rPvB2DZoWX8mftnqxwTIOrRR/Ad0B97YSHH7/879gr3XsVWCCGEAAkcDn7hlddWoVXOVgHoG9aXiztdjIrKGztbb06FYjAQP38+2pAQyvfuJeOFF1vt2EIIIbyXBA4n59kqrRQ4AO4bdB9aRcvaY2vZnr691Y6rj4kh9tVXQFHIX7SI/MVft9qxhRBCeCcJHE7OeRyH1oClrFUO2SWoC5OTJgPw/ObnMdta78qu/hdcQPjf7gMgfdYsyvfvb7VjCyGE8D4SOJyi+kJQAljLINn9l5F3+vvZfyfUN5RDBYd4/4/WvbJr+N134zdyBGpFBcfu/zu2QvevfiqEEMI7SeBwUhTo5+ht4LdPW+2wQT5BPDr4UQDe3f0uqQWprXZsRaMhds4c9LGxWNLSOPH4E3KRNyGEEG4hgaO6AVMd98kroMT9q4A6XdrlUi6IuwCL3cJzm1r3yq66kBDi/vlPFL2e4p9/Jvf//q/Vji2EEMJ7SOCoLrIXxA5yXMzt9y9b7bCKovDU0Kcw6oxsz9jONynftNqxAYxn9SPqyScByHztdUq2bG3V4wshhOj4JHCcbsANjvvfPmnVw8YHxHPvwHsBeHX7q61ynZXqgq+7lqBJE8Fu5/hDD2HJzGzV4wshhOjYJHCcrt8U0Ojh5G+QsbdVD31j7xvpHdqbQnMhc7fNbdVjK4pC9MyZ+PTogS07m+MPzkC1WFq1DkIIITouCRyn8ws7tSZHK04eBdBpdDw77Fk0ioYfUn9gw/ENrXp8jdFI3D/no/Hzo+zXX8mYPVsmkQohhHAJCRx1GXC94373F2Cztuqh+4b15cbejuudvLD5BUotpa16fJ8uXYiZ/RIAeZ98SuacuRI6hBBCtJgEjrokjQdjKBSnQ+qaVj/8fQPvI8YvhuPFx/nPb/9p9eMHXnwx0bNmAZD7wQdkzZsnoUMIIUSLSOCoi84AZ01xfL2rdYdVAEx6E0+d9xQAH+79kL05rTuXBCDkumuJfvYZAHL++39kvT5fQocQQohmk8BRH+ewyv7voDS31Q8/Mn4kl3S+BLtqZ9amWVjtrTu0AxByww1EPeUIPjnvvkv2G613kTkhhBAdiwSO+sSeDdH9wVoO2z2zGNajQx4lwBDA3py9fLKvdU/TdQq96UainngcgOy3/kPWm//2SD2EEEK0bxI46qMoMOxvjq+3vAuW8lavQrgxnIfOeQiAN3e9ybGiY61eB4DQadOIfNSx/Hr2m2+S/Z/Wn1cihBCifZPA0ZC+V0FgHJRkwu+LPFKFq5Ku4pyocyizlvGPtf9o1SvKVhd263Qi/+EIP1n//BfZ77zrkXoIIYRonyRwNESrh6F3O77e9CZ4YNKkRtEwe/hsgn2C2ZOzh5e3vtzqdXAK+8tfiHjwQQCyXn+dHLnuihBCiEaSwHEm59wChgDI2g8pP3mkCjH+Mbw84mUUFBYdWMSSlCUeqQdA+F13En6/Y6gp85VXyVnwgcfqIoQQov2QwHEmvkGO0AGw0XNnaVwQdwF/HfhXAJ7f/Dx/5v7psbpE3HMP4fc6rvuSOWcO+QsXeqwuQggh2gcJHI0x9G5QtJC6Fk7u9lg17up/F8PjhlNhq+DBNQ9SaC70WF3C77uXsL86hpuy58wleONGj9VFCCFE2yeBozGCExwTSMExl8NDnPM5Yv1iOVp0lKc2POWxxbgURSHi/vsJu+MOACKXLKXg8889UhchhBBtnwSOxhp2n+P+j6+g4LjHqhHsG8xro19Dr9Gz+uhqFuxZ4LG6KIpCxIwHCb51OgBZL7xI3udfeKw+Qggh2i4JHI0VOwg6jwC71aO9HAB9w/vyxNAnAPjnjn+y9eRWj9VFURTCHnyQ3BHDAUh/9lnyv/zSY/URQgjRNkngaIrhDzjut/0X8tM8WpWrk65mUrdJ2FU7D697mIySDI/VRVEUsi+7jKCbHFe5Pfn0M+Qv/tpj9RFCCNH2SOBoim5joctIsJlh1YserYqiKDx53pP0DOlJbnku/1j7Dyx2iycrRPgjjxAydSqoKieffJKCJZ47fVcIIUTbIoGjKRQFLnJctp3dn0P67x6tjlFn5LXRrxGgD2BX1i5e2/6aR+ujKApRTz9F8PXXgapy4vEnKFi61KN1EkII0TZI4GiquLOh72RAhZXPero2JAYm8sLwFwBYuG8hyw8v92h9FEUh+plnCL7mGrDbOfHIo2TOm4dqbf2r3QohhGg7JHA0x9inQaOHgz/DoTWerg0XJl7I7f1uB+CZX57hUP4hj9ZH0WiInjWT0FscC6blvPdf0qbfiiUz06P1EkII4TkSOJojtCuce5vj65XPgN3u2foA9w26jyHRQyizlvHAmgcosZR4tD6KRkPU448RN/91NCYTpdu3kzr5akq2eO6MGiGEEJ4jgaO5Rj3iuMbKyd9gz2JP1wadRsfckXOJNEaSWpDKkxuexGr3/DBG4CWX0PnLL/FJSsKWnU3arbeS/e57qG0gpAkhhGg9Ejiayy8cLvi74+ufnwNrhWfrA4QZw5g3eh46jY6f037myQ1PYrPbPF0tfLp2ofMXnxM0aRLY7WS99hrH7rkXW0GBp6smhBCilUjgaInz7wH/aMg/Atvf93RtABgYOZB5o+ahU3R8n/o9z2x8pk2EDo3RSMzLs4l+/jkUg4HiNWtInXw1Zb//4emqCSGEaAUSOFrC4AejH3N8vXYulOZ6tj6VLky8kLmj5qJVtCw9uJRZm2ZhVz0/hKEoCiHXXEPnzz5Fn5CA5fhxjkydSt5nn3nsmjBCCCFaR7sKHLNnz0ZRFB544AFPV+WUQTdDRC8oy4UVT3m6NlXGdRrHyyNeRqNo+Drla57f/HybCB0Avn360OWrL/EfOxbVYiF95ixOPPIo9tJST1dNCCGEm7SbwLFt2zbeffdd+vfv7+mq1KTVwRX/AhTY9TGk/OzpGlW5pMslvDj8RRQUvjzwJS9teanN9CRoAwOJf/MNIh9+GLRaCr/9ltRrr6Xi4EFPV00IIYQbtIvAUVxczI033sh7771HSEiIp6tTW+JQGHqX4+tvH4CKYo9Wp7rLu17O8xc8j4LC539+ztxtc9tM6FAUhbDbb6PT/z5AFxGBOeUgqddcS8GyZZ6umhBCCBfTeboCjXHvvfdy2WWXcdFFF/HCCy80uG9FRQUVFafOGCksLATAYrFgsbjxWiMjH0O3/3uUgjRsK2diHz/bfcdqogmdJmC2mnluy3Ms3LcQRVV4YNADKIrikvKd7drc9tUPGED8F1+Q8egjlG3dxomH/kHJtu2EP/wPFIPBJXXsiFra7qJ5pN09Q9rdM1zZ3oraVv7drcdnn33Giy++yLZt2/D19WX06NEMHDiQ+fPn17n/zJkzmTVrVq3tn3zyCSaTya11jSj8g2EH56KisCHpSXL9e7j1eE21tWIrS8sc1zYZ6TOScb7jXBY6XMJuJ2zlSsJWrQagPD6eEzfdiLUt9moJIYQXKC0tZerUqRQUFBAYGNiistp04Dh69CjnnnsuK1asYMCAAQBnDBx19XAkJCSQnZ3d4sZqDO2396PZ/QlqWHesf1kDOl+3H7MpPj/wOXO2zwHgjn538Nf+f21xmRaLhZUrVzJu3Dj0en2LyytZt56Mxx/HXliIJjCQqNmz8Rs5osXldjSubnfRONLuniHt7hk5OTnExMS4JHC06SGVX3/9lczMTM4555yqbTabjXXr1vHmm29SUVGBVqut8RofHx98fHxqlaXX61vnQ3rpS3DoZ5ScFPS/vAYXef4Cb9Xd1PcmVEVl7ra5vPfHe+h1ev46oOWhA1zXxsFjL8Tv68Uce+BByn//nZP33kvY3XcR8be/oZz2/Rat+NkWNUi7e4a0e+tyZVu36UmjY8eO5ffff2fXrl1Vt3PPPZcbb7yRXbt21QobbYIxBC6rvEz8L/90LH3extzc52YeOuchAN7a9Rb//f2/Hq5Rbfq4ODp9vJCQqVMByHn7HdJu/wvW7GwP10wIIURztOnAERAQQL9+/Wrc/Pz8CAsLo1+/fp6uXv16Xw59rgTVBkvuBVvbm+Q0vd90/n62Y2n2f+74Jx/88YFnK1QHjcFA9DNPE/vqqygmE6WbN5N61WRKt2/3dNWEEEI0UZsOHO3ahFccvR3pv8PGf3m6NnX6y1l/4d6B9wIw79d5fLT3Iw/XqG5Bl19Gl0VfYOjeDWtWFkdumU72e++hymx1IYRoN9pd4FizZk29E0bbFP9IuORlx9erZ8Oxtvlf+d0D7uau/o41ROZum8sn+z7xcI3q5tOtG12++ILAK64Am42sea9xaOIkilavbjPrigghhKhfuwsc7Ur/66D3RLBb4POboTjT0zWq070D7+X2frcDMHvrbD7c82Gb/COuMZmInTuHmBdfQBsaijk1lWN/vYe0W2+jfN8+T1dPCCFEAyRwuJOiwKR/Q3gPKDoBi24Fm9XTtapFURT+fvbfmd53OgCvbH+FR9c9Sqml7V3bRFEUgq++mm4rfiTsjjtQDAbH3I7JV3PiiSexZLTNUCeEEN5OAoe7+QbCdR+DwR+ObICf2tZpsk6KojDjnBk8fO7D6BQdPxz+gRuW3cCh/EOerlqdtP7+RD40g67ff0/gZZeBqlKweDEHL7mErH//Wy4EJ4QQbYwEjtYQ0QOu/I/j601vwh9febY+9VAUhWl9p/H+Je8TaYzkUMEhrl92PT+k/uDpqtXLEB9H3LxX6fz5ZxgHDUItKyP7jTc5eMml5H/9Daq9bVwhVwghvJ0EjtbSZyIMf9Dx9ZL7IGOvZ+vTgEGRg/j8is8ZGj2UMmsZj6x7hNlbZmNpg6f3OhkHDKDTJx8TN/919PHxWDMzOfn446ROmULJ5i2erp4QQng9rwkcbWIS5IVPQ9fRYCmFz2+EsnxP16he4cZw3hn3DnecdQcAn+z/hOk/Tie9JN3DNaufoigEXnIJXb9fRuTDD6Px96di7z7Spk/n6D33UnEo1dNVFEIIr+U1gSO3xOzpKoBGC1e/D0EJkHsIvr4b2nCXv1aj5f6z7+fNC98kwBDA7qzdXPvttWw8sdHTVWuQxmAg7Pbb6LbiR8dKpVotxatWcWjiRNJffAlrXp6nqyiEEF7HawJHWm4bmUToFwbXfQRaHzjwA6x/1dM1OqNRCaP44vIv6B3am7yKPO5eeTf/+e0/2NW2G5YAdKGhRD/zNF2/XYr/6NFgtZL30UccHH8JOQs+wG5uAyFUCCG8hNcEjgMZRZ6uwimxg+DyyuutrH4Jkld6tj6NEB8Qz0cTPmJKjymoqLy16y3u+fke8svzPV21M/Lp2pWEt/9D4oL38enVC3thIZlz5nDosssp/HFF2xhuE0KIDs5rAseutHxPV6GmQTfBubcBKnx1u2OIpY3z0frw7PnP8sIFL+Cr9eWX479w7XfXsidnj6er1ih+559Pl6++dCwcFhGO5ehRjv/97xy56WbKfv/d09UTQogOzWsCx86j+Z6uQm2XvAxx50J5AXx8DRRleLpGjTKp+yQWTlhIYkAiJ0tOctvK29hSsaVd9BQoWi3BV19N9+XLCb/nHhRfX8p+/ZXD11zL8YcfwXLihKerKIQQHZLXBI5jeWVkFpV7uho16Xwc8zmCEiAnBf53BRRnebpWjdIztCefXf4ZFyVehMVu4duyb3lq01NtcnXSumj8/Ii4/290W/4DQVdeCUDht99y8NIJZL4+H1txiWcrKIQQHYzXBA6AHUfa4NkJgbFwy7cQEAvZf8KHk6Akx9O1apQAQwCvjX6NGYNmoEHDD4d/4MbvbyS1oP2cfqqPjib25dl0/vJLTIMHo1ZUkPPOOxwcP568z79Atba9peiFEKI98qrAsf1wGwwcAKFdYPp34B8NmXvgo0lQmuvpWjWKoijc1PsmbvO/jXBjOCn5KVz/3fX8ePhHT1etSYz9+pL44f+I//ebGDp1wpaTQ/qzz5J61VUUr9/g6eoJIUS7512Boy32cDiFdXP0dPhFQPrv8NFVbXphsNN11nXmk0s+YXD0YEqtpfxj7T+Ys3VOm16d9HSKohAwdixdv11K1BOPowkKoiI5haN33EHaX+6gZONGWSpdCCGayasCx54TBZRbbJ6uRv0iejhChykMTu6ChVdDeaGna9Vo4cZw3h33btWl7hfuW8itP97aroZYABSDgdBp0+j+43JCb7kF9HpKNmwg7bbbOXjJpWS/9x7WnPYx7CWEEG2F1wSOCH8DFpvK7mMFnq5KwyJ7w7QlYAyB49sdZ69UFHu6Vo2m0+h44JwH+NeYfxGgD+C3rN+YvGQyL215idzy9jFM5KQNDibq8cfo9t23hEy9AY2/P5a0NLLmvUby6DEce/BBSjZtkl4PIYRoBK8JHAMTgwHYfqQd/NGLPgtu/gZ8g+DoZvjkWjC3r7MmxiSO4YsrvmB0/GisqpVP93/KZYsv4/0/3qfCVuHp6jWJoVMnop95hqR1a4l58QV8B/QHi4WiH5aTduttHLz0UnL++1/p9RBCiAZ4T+BICAbg17Y6cfR0sQPh5q/BJxCO/AKfXg+WMk/XqkniA+J5Y+wb/Pfi/9I7tDfFlmJe//V1Jn49ke8Pfd/ml0Y/ncZkIvjqq+ny+ed0+XoxwTdcj8bPD8uRNDJfnXeq12Pz5naxJokQQrQmrwkcgzuHAvDLwWyKK9rJqY5x58BNX4HBH1LXwWdTwdLG1hJphKExQ/ns8s94cfiLRJoiOVFygkfXP8qNy25kR8YOT1evWXx79ybm2WdJWr+OmBeex7d/tV6P6bdy6JJLyfm//8Oa2w561IQQohV4TeDoHRNIl3A/yi12fvyj7V5ivZaEIXDjItCb4OAq+OJmsLavIQkAjaJhYreJfHfVd/xt0N8w6Uz8kfMHtyy/hQdXP8iRwiOermKzaEwmgqdMocsXlb0e11+Hxs8P85EjZL7yKimjRnN8xkOUbG4fK7EKIYS7eE3gUBSFqwbFAfDNruMerk0TdRoGUz8HnRGSV8Ci6e1ueMXJqDNyZ/87WTZ5Gdf0uAaNouGntJ+48psrmbN1Tru4GFx9fHv3JmbmTJLWrSX6+efwPessVIuFwu+/J2369Mpej/ex5rWTYT0hhHAhrwkcAFcOdASOX1KyyShsZ0MTXUbCDZ84Lmv/5/fwfxdDXvvsFQDHKbTPnP8MiycuZkTcCKyqlYX7FjLh6wn8b8//MNva76XjNX5+hFxzDV0WfUHnr74k+Lrr0JhMlb0er5AycpSj12PLVun1EEJ4Da8KHIlhJs7tFIJdhaW72uFFurpd6JjTYQqD9N3w7ihI+dnTtWqRbsHdeOuit3h33Lv0DOlJkbmIV7e/ysRvJrL88PJ2/wfZ2LcvMbNmkrR+HdHPzcK3X79TvR633MKhSyeQ8/4C6fUQQnR4XhU4AK4629HLsXhnOxtWceoyAu5cC7GDoCzPsTjY+nnQzv8wnx97Pp9f/jnPDXuOSGMkx4uP8/Dah7nph5vYlbnL09VrMY2fHyHXXkuXLxfV7PU4fJjMuXMdvR7/eJiSrdLrIYTomLwucFx2VgwGrYZ9JwvZn95+VvGsITgBbl0Og24GVPj5Ofj8pna1KmldtBotVyVdxbdXfcs9A+/BqDOyO2s3N/9wMzPWzOBo4VFPV9ElnL0e3detI3rWLHz79HH0enz3HWnTbuHQhMvIWfCB9HoIIToUrwscwSYDY3pFAPB1e+3lAND7wqQ34Yp/gtYA+7+D9y6ErD89XbMWM+lN/HXAX1l21TKuTroajaJh5ZGVTFwykbnb5lJQ0cZXi20krb8fIdddS5fFX9H5yy8JvuYaFJMJc2oqmXPmOM5wefgRSrdtk14PIUS753WBA6g6W2XJzhPY7O38F/k50+HWHxyXt89JdoSOvUs8XSuXiDBFMHPYTBZdsYgL4i7Aarfy0d6PmLB4Ah/u+bBdTyw9nbFfX2Kef46kdeuInjkTnz69Uc1mCr/9liM3T+PQZZeT88EH2PLzPV1VIYRoFq8MHGN6RRJs0pNeWM437bmXwyn+XLhrLXQaDuZi+GIarHwW7G34QnVN0COkB29f9DbvXPQOSSFJFJoLeWX7K0z6ZhIrDq/oUP/9a/39CLn+OrouXkznRYtO9XocOkTmy3NIHjmK44884pjrYW0nC9gJIQReGjh8dFruGtkNgNd/OoDZ2r6W2K6Tf6Tjom/n3+d4/Mt8WDgZSjrO9T2GxQ1j0eWLmDVsFuHGcI4VH+OhtQ8x7Ydp/Jb1m6er53LGs/pV9nqsJXrms/j0ruz1WPotadNuIXXUaGI+/oTCr7/BkpHp6eoKIUSDvDJwAEwf1pnIAB+O5ZXx6dY0T1fHNbQ6GP8iTHnfsTLpoTWOU2dP7PR0zVxGq9EyOWkyy65axl8H/BWjzsiurF3c9P1N/OXHv7Ds0LJ2d3G4M9H6+xNy/fWOuR6LviBoytVogoKwFxYSsHs3mc88Q8qoURyaOImMV15xrGpq7jjDTUKIjsFrA4fRoOX+sUkAvLEqmZL2cn2Vxuh3NfzlZwjtCgVH4f/Gw86PPV0rlzLpTdwz8B6+vfJbrup+FQoKW9K38Nj6xxjzxRhe3Pwi+3L2ebqaLqUoCsazziL2hRfosfEX4hd+RM5FY/HpfxYoChUHDpD7f++TNn06f553Pkf/eg+5n3yC+WjHOLtHCNG+eW3gALhucAKdwkxkF5tZ8Euqp6vjWlF94I7V0OMSsFXAknvguxlg7Vj/+Ub5RfHcBc+x/Orl/HXAX4nxi6HIXMRnf37Gtd9dy7XfXsun+z/tMGe2OClaLb4DBpAzbhwJH39M0sZfiJ33KkFXXok2PBy1tJTi1avJeO55Do67mIPjLyH9hRcpXrsWe1n7XBZfCNG+6TxdAU/SazXMGNeDv3+2i3fWHuLGoZ0I8TN4ulquYwyG6z+Fda/Amtmw/f8cK5Re8wEExXu6di4V6x/LPQPv4a7+d7Hl5BYWpyxmVdoq9uXuY9+WfczbPo+xiWOZnDSZwdGD0SgdK2vrQkIIuuwygi67DNVup+LPPylev4GS9esp3bkT85EjmI8cIW/hQhSDAdO55+I3YgT+I4Zj6NYNRVE8/RaEEB2cVwcOgCv6x/L22kPsO1nI22sP8viE3p6ukmtpNDD6UcfKpIv/Ase2wZuDYfiDMOxvoDd6uoYupdVoGRY3jGFxw8gvz+e7Q9+xOGUxyXnJfJ/6Pd+nfk+8fzxXdr+SSd0nEe0X7ekqu5yi0eDbuze+vXsTfucd2IqLKd28uSqAWE6coGTjRko2biRzzhx0MTH4Dx+O34jh+J1/PtqAAE+/BSFEB6SoHemcwjoUFhYSFBREQUEBgYGBde6zan8Gt32wHR+dhp8fGkV8iKmVa9lKcg/B13fD0S2Ox0GJMG4W9L0KWvAfrsVi4fvvv2fChAno9XoXVdZ1VFVlT84eFicv5ofUHyi2FAOgUTQMix3G5KTJjI4fjV7b9urekOa0u6qqmFNTKVm/nuL1GyjdurXmBFOtFuOggfgPH4HfiOH49u6NoulYvUEt1dY/7x2VtLtn5OTkEB4e3uDf0Mby+h4OgDE9IxnSJZStqbk8+PkuPr3jPHTaDvhLNrQr3PYj/PEVrHwGCtLgy1th67twyWxHL0gHpCgK/cL70S+8Hw8PfpiVR1ayOHkxv2b8yobjG9hwfAOhvqFc3vVyJidNpltwN09X2W0URcGna1d8unYl9JZbsJeVUbp9O8Xr11OyfgPm1FTKtv9K2fZfyZo/H21YGP7DL8Bv+Aj8LhiGLjTU029BCNFOSeDA8Uv4lSn9uexfG9h2OI9/rUphxrgenq6WeygKnDUFek6Ajf+CDfMhbRO8OwYG3QgXPgMBUZ6updsYdUYmdpvIxG4TOVJ4hK+Tv2bpwaVklWXx4d4P+XDvh/SP6M/k7pO5pMsl+On9PF1lt9IYjfiPGIH/iBEAmI8do2TDBkfvx6ZN2HJyKFiylIIlS0FR8O3XD/8Rw/EbPgJj/7NQdPIrRAjRODKkUs2SXcf5+2e70CjwyR3ncV7XsFaqpQcVHIOfZsLvixyPDQEw8iE47x7Q+TSqiPbe1Wm1W9lwfAOLkxez7tg6bKpjhVajzsj4zuOZnDSZgRED29zESne3u2o2U7pzFyUbHMMvFfv313heExiI37BhlQFkOPqojhtUq2vvn/f2StrdM1w5pCKB4zT/WPQbX/56jOhAX374+4iOddZKQ45uhR8ehRM7HI9DOsPFL0Cvy884v6Mj/SLILstm6cGlfJ38NYcLD1dt7xLUhau6X8UV3a4g3BjuuQpW09rtbsnIpOSXXxwB5JeN2Atqnmrs06MHfiOG4z9iBMazz0Zj6Jg/Ox3p896eSLt7hgSOJmhq4CipsHLFmxs4lFXCRb2jeG/aOW3uP1u3sdth9+eOHo/idMe2ziPgkpchul+9L+uIvwhUVWVn5k4WJy9mxZEVlFkda1foFB0j40cyOWkyF8RdgE7juSEFT7a7arNR/scfFK9bT/GG9ZTv/h2q/SpRTCb8hg6tCiCGhIRWrZ87dcTPe3sg7e4ZEjiaoKmBA2DPiQKu+vdGzDY7syb25ZZhnd1bybamohg2vA4b33AsGqZo4Oxb4MKnwK/2f/cd/RdBsbmY5YeX83Xy1+zO3l21PdIYycTuE7mq+1UkBia2er3aUrtb8/Icp9qu30Dxhg3YsrNrPG/o1Am/Cy7AOKA/vn36YOjSpd3O/2hL7e5NpN09QwJHEzQncAAs+CWVWd/uxaDV8PW9w+gbG+TGWrZReUccZ7Ps/cbx2CcIRj0CQ+4E3anucm/6RZCSl8LilMV8d/A78iryqrafG3Uuk5Mmc1GnizDqWmdtk7ba7jUWHtuwgdIdO+C0K9sqvr749uyJb98++PZx3Hy6d0dpB8MwbbXdOzppd8+QwNEEzQ0cqqpyx4fb+WlfJpEBPnxx1/l0Du/YZyzU6/AvsPwxxyqlAGHd4eIXocd4UBSv/EVgsVlYfXQ1i1MWs/H4RlQcP0b+en8u6XIJI+JGcG70uQQaWvYD2mAd2km724qLKd2yhZLNWyjft5eKvfuwl5bW3lGvxzcpqWYI6dkTja9v61e6Ae2l3TsaaXfPkHU4WoGiKLx6zQCuf3cz+9OLmPreZj6/63wSQjvoomAN6XwB3LkGdn0MPz8HOSnw6XXQ7UIYPxtCOu66FfXRa/Vc3PliLu58Mekl6XyT8g3fpHzD8eLjfHngS7488CUaRUOf0D4MjRnK0JihDIwc2Gq9H22J1t+fgLFjCRg7FnD0gJiPHKF8795qt33YCwqqHp96sRafrl0dAaQyiPj06o3W30vDvxDtmPRwnEFWUQXXvbuJQ1klJIaa+OKu84kOalv/cbWq8kJY/yps/g/YzKBosZ1zGz9WDGLcxGu9+j8Pu2pna/pWfjryE1tObqlxlguAXqNnYORAhkQP4byY8+gb3he9pvnt1ZH+41NVFcvxE5Tv3XMqhOzZiy0np/bOioKhU6caIcS3d2+0wcGtUteO1O7tibS7Z8iQShO0NHAApBeUc+07m0jLLaVrhB+f33k+EQGNW6Oiw8o9BCuehv3fAWDWmtCeMw3tubdCZC8PV65tSC9JZ2v6Vrac3MKWk1vIKM2o8bxJZ+KcqHMYGjOU82LOIykkqUkXlevov4BVVcWamVUthOyjfO9erCdP1rm/Pi6uZgjp0wdduOtPYe7o7d5WSbt7hgSOJnBF4AA4llfKtW9v4kRBOb2iA/j0jvO8Z42Ohhxai7r8MZTMat3gCUPh7GmOa7QYpOsbHH88jxQeYWv6Vjaf3My29G3kV+TX2CfEJ4TB0YOrhmASAxIbPCXbW38BW3Nzq8KH82ZJS6tzX11kZFX4cAYRXXR0i05199Z29zRpd8+QwNEErgocAIezS7j2nU1kFlXQLy6Qj/9yHkFG+eBbKsrZ/vlchur2oUn+ESpX6sQQ4FhG/expjuu0eMt6Jo1gV+0cyDvAlpNb2HxyM79m/Fq11odTtF80Q6Md4WNI9BCi/Gqu5Cm/gE+xFRZSvm9/jRBiPnSoxtogTtqQkFohRJ+Q0OgQIu3uGdLuniGBowlcGTgAUjKLuO6dzeSUmBmUGMxHtw/F38e7597W+EVQngu/fQI7PnQMuzhFnQXn3OIIIMYQz1W2jbLYLfyR/UfV8MtvWb9hsVtq7NM5sHPV8Mvg6MGYNCb5BdwAe0kJ5X8eqBFCKlJSap2iC6AJCMC3d+8aIcTQuTOKVltrX/nD5xnS7p4hgaMJXB04APaeKOSG9zZTUGahV3QA79x8Dp3CvHfooM5fBKoKhzc4gsfeJY4FxAB0vtBnkmMhsU7DpNejHmXWMnZm7qwKIHtz9ladegugoNAzpCfhpeFcd/51DIkdgknvhWdQNZG9ooKKA8k1Q8iff6KazbX2VYxGfHv1OtUb0qc3Pt26YVUU+cPnARI4PMNrAsfs2bNZvHgx+/fvx2g0MmzYMObMmUPPnj0bXYY7AgfA78cKuO1/28gqqiDIqOdfNwxiVI8Il5XfnpzxF0FpruPicL/+DzL3nNoe2s0x3DJwKvhHtl6F26GCigK2Z2xny8ktbD25lYMFB2s8r9Po6B/ev2r+R//w/ui18ku5MVSLhYpDhyjfU+003X37UMvKau2r6PUYkpLI8PcnafzF+J11Fj49erS5tUI6IgkcnuE1geOSSy7h+uuvZ/DgwVitVp588kl+//139u7di59f43oU3BU4ADIKy7l74a/sTMtHo8DD43tx96iu3nPtlUqN/kWgqnB8B+z4H/zxFZiLHds1Ouh5qaPXo9uFoKndjS1qyirNYuOxjSz+dTEn9Sc5WVLzzA2jzsjZkWc75n/EDKFXSC+00q6NptpsjrVCqoeQvXuxFxXV3lmrxadbN8cwTNeuGBLi0SckYkiIRxvkhSsUu4kEDs/wmsBxuqysLCIjI1m7di0jR45s1GvcGTgAKqw2Zi7dw6dbjwJw2VkxzJ3SHz8vmtfRrF8EFcWwZ7FjyOXYtlPbA+Nh0E0w6EYIbv3rk7Qnzna/9NJLyajIqBp+2Zq+ldzy3Br7BhoCGRI9hCExQxgaM5QugV28Lhi3lKqqWI4do2T3bvZ89x0JZjMVe/dhy8ur9zWaoCAM8fHoExMwxCc47ivDiC46us45IqJuEjg8w2tXGi2ovBx2aGhovftUVFRQUVFR9biwsBBwfFgtFkt9L2s2DfDcFb3pHe3P88v2s+z3k6RkFvHvqQPp5CWrkjrbtUntq/GBs25w3DL3otn1MZo/vkApPAZrX0ZdOwe164XYB92EmjQetHIK8umc7W21Won2jWZSl0lM6jIJVVVJKUhha/pWtmVs49eMXyk0F/JT2k/8lPYTABHGCAZHDWZI9BAGRw0mxi/Gk2+l3VCio/EJCyPHauXscePQ6XTYMjKo2LePiv37saQdxXLsGJajR7Hl5DhWTy0ooHzPntqF6XTo4+LQx8ejj49Hl5Dg+DrB8Vhj8o7fH43VrN8zosVc2d7tpodDVVUmTZpEXl4e69evr3e/mTNnMmvWrFrbP/nkE0xu/gE+VAgLDmgptCiYtCrTetjpHdwumrdN0NjNxOT/SqectUQUn1rXo1wXyNHQ4aSFjaLYV/4wNpVNtXHCdoKD1oMcsh4izZqGlZpnaoRpwkjUJhKljaq6BSgB0gvSAorZjD4nF31uDvqcXAyV9/rcXPR5eSg2W4Ovt/r7YwkLwxIaiiUsFHNoaNVjW0CATLgWraK0tJSpU6d615DKvffey7Jly9iwYQPx8fH17ldXD0dCQgLZ2dluGVI5XUZhOfd++hu/HStAo8BD45K4Y3jnDv2L22KxsHLlSsaNG+e6rs68VEevx2+foJRkVm22J5yHfdA01F6Xg5efldHcdi+3lrM7ezdbM7ayNX0re3P3YlfttfYL0AfQLbgb3YIct+7B3ekW1I0QX+8+rdkVn3fVZsOakVHVG2I9dgzLUcfXlmPHsFf2zNZH8fVFHx+HPj6hVs+IPi6uXVx1t6nc8ntGnFFOTg4xMTHeEzj+9re/8c0337Bu3Tq6dOnSpNe6ew5HXSqsNp5dsofPtjnmdVzYK5JZE/t22Au/uXVs1WaF5BWOiabJK8D5h9EnCPpf45hoGtPftcdsJ1zV7kXmInZk7GBv7l5S8lJIyU/hSOERbGrd/4GH+YbRPaQ7ScFJdA/uTveQ7nQP7o6f3jtODW+NuQS2ggLMR49hOZpW8z4tDUt6OthrB8QqioIuOhpDQgL6hHgMCYmO+8REDAkJaIKC2uU/QDKHwzO8Zg6Hqqr87W9/4+uvv2bNmjVNDhue4qPTMnvyWfSLC2LWt3tYtT+TjQezuX9sEn8Z3hWDrvHXy/B6Wh30muC4FZ6AnR/Dzg8hPw22/ddxC+8JXUZC5+GOm5/rr5/RkQUYAhiVMIpRCaOqtpltZlILUknJdwSQlLwUkvOTOV58nJzyHHJO5rDl5JYa5cT6xVaFj+7B3UkKSaJLUBd8tF5+3aFm0AYFYQwKwtivb63nVLMZy4kTmI8ew3w0DUvaUczHjlbeH0MtLcV68qTjmjNbt9Z6vSYgoDKMJGBITEAfX3mfkIA+OhpF16b/LIh2rE1/su69914++eQTlixZQkBAAOnp6QAEBQVhNLbty3wrisJN53ViaJdQnvrmD7ak5jJ3+Z989esxnr+yH8O6yR/FJguMhVEPw4iHIHWN4wyXfd9B9p+O27b3HPtF9oHOI04FEFP9k4xF3QxaAz1De9IztOaaN6WWUg7mHyQl3xFAnD0iWWVZnCg5wYmSE6w7tq5qf42iITEgkaSQpKog0j2kO4kBieg0bfrXT5ulGAwYOnfG0LlzredUVcWWk4P56FEsR4867iuDiCUtDWtWFvaioqrTfGvR6dDHxjoCSY0zaxzBROvvHb1Ywj3a9JBKfd1+CxYsYPr06Y0qwxNDKqdTVZWvdx7npe/3kV3sWNHwyoGxPHFZbyID2v+CQR7t6izNhcPrHauapq6HrH2194nqdyqAdBrWYQJIW+pizi/PP9Ubkp9Ccl4yKfkpFJrrnoug1+jpGtS1qkckKTiJ7iHdifGLadIVcz2hLbV7U9nLyrAcO3YqkFTrHbEcO4Z6hjMStKGhlWEkscZ6I/qERHQR4Sga933v2nO7t2deNaTSESiKwuSz4xnbK4pXV/zJwi1H+GbXCX7el8k/xvfkpvM6odW0vzHVNsEU6lgqvc8kx+PiLDiy4VQAyf4TMv5w3Lb8B1Aguh90rhyC6XS+XNvFBYJ9gzk3+lzOjT63apuqqmSVZVUNxzh7RlLyUyizlvFn3p/8mfdnjXJMOlONeSHOoZkw37B2Oe+grdEYjfgkJeGTlFTrOdVux5qRUSOMVPWSHD2KLT8fW24uZbm5lP32W63XKz4+jrki8TV7R/QxsegiI9AGBbk1kIi2r00Hjo4myKTn+Sv7cc258Tz1zR/sPlbAs0v3sOjXo7xw5VkMTAj2dBXbP/8I6HuV4wZQnHmqB+TwBsg+AOm/O26b/w0ojkmnnUc4bp3OB19ZHdIVFEUh0hRJpCmSYXHDqrbbVTsnik/U6g05VHCIUmspu7N3szt7d42ygn2CHWfJBHer6g3pHtydIB/5XrmKotGgj4lBHxMDQ4bUet5WVHQqiBw77f7kSdSKCswpBzGnHKyjdECnQxcaii48HG1EOLrwcHThEY77ysfasDB0ERFo/PwkYHZAbXpIxRXawpBKXWx2lU+2pjF3+X6Kyq0oCkwdksgj43sRZGpf3YXtqquzKL0yfFSGkJyUms8rGogZUDn/YyQknge+bedzU127avdGsNgtHC086pgbUjlRNSU/hbSitDpP2wWINEbW6g3pGtTVrRey62jt7gqqxYLl5MmaIaSyd8SakdHgaqx1UXx9KwOJI4xow8PRhISyL/0kA0ePxicqyhFQwsPR+MikZHfy2qXNm6OtBg6nrKIKZv+wj8U7jgMQ5mfgiQm9mXx2XLtJ+O36F3DhCTj8Cxxe5wgguYdqPq9oIXZgtQAyFHwCPFLV07Xrdm+Ccmt51Rkz1Seqnn79GCcFhTj/uFqn7nYJ7OKSC9p5S7u7kmqxYM3NxZqVjTU7C1t2Ntbs7MrHjptzm72kpEllawIDT4WTagHF0XsSVrVdGxoqS8k3gwSOJmjrgcNp86Ecnv7mD5IzHRc06xMTyG3Du3DFgBh8dG37h6RD/QIuOF6tB2Q95B2u+byihbizKwPICEgYCj7+Hqlqh2r3ZigyF9WYF+KcK3L6dWScdIqOToGd6B7Snc6BnYn2i3bcTNFE+UURYGhckPT2dnc3e2kp1pycqnDiDCPmjExO7NtLmFaLLScHW1b2GSe51qDRoK0c0qkeThxDOTUfawID280/fO4mgaMJ2kvgALDY7Ly/IZV//pxMqdmx6FK4vw83n9eJG89LJNy/bXYdduhfwPlH4cgvjgmoh9dD/pGaz2t0EHs2dKk8CybhPDC0zgJvHbrdWyCnLIeD+QdrDc0UW4obfJ2f3o9okyOERPlF1fm1SW+SdveQ09tdVVXshYWn9ZRU9p5kZTtCi7P3JCfHcbXqRlL0+sp5JhHVAkpYZc9J5XZnOGnjSzS0lASOJmhPgcMpv9TMp1uP8r+Nh0kvLAfAoNUwaWAst17QhT6xbet9eNUv4Py0U2fAHF4PBUdrPq/RQ9w51QLIUNC75xeSV7V7C6mqSkZpBsl5ySTnJ3Os6BjpJemkl6aTXpJOkbmOy87XIcAQQJQpCk2xhn6d+xHrH1sjlET5RWHUdew/QJ7Sks+7arViy8urCiA1A8qpYGLNzj7jsvKn0/j5nZoIe1pPibZ6OAkNRWmHP6cSOJqgPQYOJ4vNzvI/0vm/DansOppftf38rmHcNrwLF/aKbBOn03r1H768IzXXASk8VvN5rQHizj0VQOKHgN41a694dbu7WKmltCp8ZJRkkF5aeV9Sua0044w9JE7BPsFEmaJODdn4RZ96XBlKDHL14yZrrc+7vaLi1ByTqt6SavNMnGElKwu12nW7GkMbElIZRMJq9p5UDygRbesUYq9Zh8Pb6bUarhgQyxUDYtmRlsf7G1L54Y90Nh3KYdOhHDqFmbh1WGemnJuAv498Kz0ipJPjNugmR5dt3uGaAaToBKRtdNzWznHMAQnpDOFJjltYEoT3cHxtCpMrgHqISW+ia1BXugZ1rXefYnMx6SXpHCs8xs9bfiaiWwRZ5VlVgSS9JJ0yaxn5FfnkV+TXWmOkulDf0Bqh5PSAEmmMdMkEV9F0Gh8fNHFx6OPiGtxPVVXsJSVYs6pNgq3qLamce+IMJzk5YLNhy8tznLGTnNxwJXQ6dGFh6MLC6jyFWBscjCYgAG1QENrAQDT+/m0moDRE/kq1E2cnhnD21BBO5Jfx4aYjfLo1jSM5pcz8di/zVhzgusEJ3DKsc4e9QFy7oCgQ2sVxO3uaI4DkHjo1CTV1PRSnQ+5Bx+3A8pqvN4ZUCyDdHfdhSY7y5I+Px/kb/Olu6E4n/04U+BQwoX/N/7RVVaXQXFgjgDi/dvaapJekU2GrILc8l9zyXPbl1rEyLo4zbcKMYXXOI3EGlAhThCwP70GKoqD190fr7w9nuM6Xardjy88/dZZO1aTY2nNPbPn5YLVizcjAmpHR2Mo4AkhAAJqgQLQBgY4gEhiANjAIbVCg4/nAILSBAWgCHc879glstVOL5dPazsQGG3ns0l7cP7Y7X+04zoJfUjmUVcJ/N6Ty/i+pjO8bzW3Du3BupxCZZe1pigJh3Ry3c25xBJCidMhJdixAlp3iuM9JdkxOLcuDY1sdt+o0uspekR4Q1v1Uj0hQ+7iYobdQFIUgnyCCfIJqXYPGSVVVCioKqsLH6eHE+dhit5Bdlk12WTZ/5PxRZ1kaRUO4MbzGUM3poSTcGI5W07bPcvMGikbjWPQsNBR69mhwX9VsPnUKcU523acQF+RjLyjEVlSEWl4OlRNo7YWFcPx40+vn41MVPmoElYAAilx4MT8JHO2UyaBznL0yJJG1yVm8vyGV9cnZ/PBHOj/8kc5ZcUHcNrwzl50VK1enbSsUBQJjHLcuI2s+ZymDnIOVAaQyiGQnO26WEse20xYp0wOXaP3RZvWFiKRTPSLhPRzDPNIr0uYoikKwbzDBvsH0Cu1V5z6qqpJbnltrTonz64xSx81qt5JZmklmaSa72V1nWTpFR4QpovawjTOg+EUT6hva5q9f400UgwF9dDT66OhG7W83m7EXFmIrLMRWUIC9qAhbQSG2osLK7UXYCguqAoqtsAB7YRG2wkLsRUWgqqgVFVizsiArq1b5xTaby96bBI52TqNRGNMzkjE9IzmQUcSCX1JZvOM4vx8v4MHPf+Ol7/dzw5BExvWOom9sIJo2MMlU1EFvdFzjJbpfze2q6licLCf5VABxhpKCo/jYiuHYFsetOo0OQrtWBpBq80TCuneYi9d1VIriGE4JM4bRN6z25enBsTx8TllO7R6S0lMBJas0C6tq5WTJyXoXSQPQaXREmaLqn+jqF02QIUh6StoojcGApnLyaVOpdjv24mJH+HCGlsKaQUWTngFzXnZJXeUslQ4ot8TMp1vT+N/Gw2QWnZpFHe5vYGRSBKN6RjAyKYIQP9fMlJezJTzDUpLPhqUfMqJ3FLr81FO9IjkpYCmt/4Wm8NrzRMKTILgTaOV/kDNpL593m91Gdll2ncM3zjNwssqyUDnznwAFhUCfQIJ9ggkyOIaNgn2Cz3hv1BldNrTbXtq9o5GzVESDQv0M3DumO3eM6MoPf5xk2e6T/JKSTXaxmcU7j7N453E0CgxICGZ0j0hG9Yygf1yQ9H60NwY/Ck2dUPtOgOq/gO12x9kxp88TyU6GwuNQmg1p2Y4zZ6rT6CvnnFSbJ+KcN2IMbtW3JlpOq9ES5RdFlF8UAyIG1LmPxW4huzS7zuEbZ0DJLstGxTH3pKCioEl10Gv0VQHEGUKCfYKrwkuNkGIIItjXcS9n6HRMEjg6MINOw6SBcUwaGIfZamf7kVzW/pnF2gNZ7E8vYmdaPjvT8nn9pwOE+hkYmRTO6J6RjOwRQaiLej+EB2g0EBTvuHW7sOZzFcWn5oNUnyeSkwLWMsja77idzi+yjlN5uzt6RaSrvd3Sa/TE+McQ4x9T7z4Wm4UCsyNsOE/5dYYP59d1bbfYLVjsFrLKssgqqz03oCF+er8aPSnBPsH46/3JKssif38+oabQWj0qAYYAmYvSxkng8BIGnYZh3cIZ1i2cxyf05mRBGWv/zGLNn1n8kpJNbomZb3ad4JtdJ1AU6B8fzKgeEYzuGcGA+OA2scCYcAEff8fF6GIH1txutzsWLasKINXOpCk6ASWZjtuRX2q+TutTR69IZShpo1fZFU2j1+oJN4YTbmz8HAFVVSmzltUKI3XeVwszhRWFqKiUWEoosZRwouRErbLX7FhT5zE1ioZAQ2CtIFI9tNT12FfrK2f0tRIJHF4qJsjI9UMSuX5IIhabnR1H8lhzwBFA9p0s5Lej+fx2NJ9//ZxMiEnPiCRH+BjZI6LNXtNFtIBGA8GJjlv3sTWfqyiq7BFJrtkrknsQrOWQuddxO51/tCOIBESDfxT4R1a7RTl6TfzCpYekA1IUBZPehElvarD35HQ2u40icxEF5trhJLc0l93JuwmKDqLIUlQjzJRZy7Cr9qrHTWHQGBzhwzeo1hyV+kJKoE8geo0M+zSVBA6BXqthaNcwhnYN49FLepFRWO7o/TiQyfrkbPJKLSz97QRLf3P8t9E/Pqiq92NgQoiHay/czicAYgc5btXZbY5ryZw+TyQ72bHAmfPWEEXjmMRaI4hEnBZQKsOJMcQRjESHpdVoq04b7kSnGs9ZLBa+P/49E4bXnjRqtpmbPORTUFGAVbVitpvJLMsksyyzSXX11/s3qSclyCeIAH2AV/emSOAQtUQF+nLt4ASuHZyA1WZn59F81vyZyZo/s9hzopDdxwrYfayAN1alEGTUM7xbGMFlCoOLKogNldTvNTSVy7SHdIaki2o+V15QOVfkEBRnOIZjijMdXxdnVW7LAtV+arjmTIsqanSO4OFfLZD4VQaSqm2VgcU3SJaJ9yIGrYEIUwQRpohGv0ZVVUqtpafCSPmpIZ7Te1eqhxbnhf6KLcUUW4o5Xtz4hba0irYqfAQZap7RE+wbXDUkVD2kBBoCXXq2jydJ4BAN0mk1DO4cyuDOoTw8vheZReWsO5DNmj8dvR8FZRaW/ZEOaPl47lr6xgYyumcEo3tGMighGJ1W/iP1Sr5Bjqvmxp1T/z52G5TmVIaQakGkuDKAOL8uzoSyXLBbHfNJimqP69ei9TktiDjDSbVeE+c2H3/XvW/RbiiKgp/eDz+9H3H+DV83pTqr3UqRuajBeSnO+SjVe1bKbeXYVFvVsvZNqiuOISo/nR8mvQmjzoif3vG1SWfCT++HUWd07KP3w6QzVe1v1Bur9qm+v16jb/UQI4FDNElkgC9TzolnyjnxWG12fjuWz897M/h2+0GOlijsOVHInhOF/Hv1QQJ8dQzuHEpSpD/dq90CfKUXRODoIXEGAM5qeF+r2XE6b/UQUiOcVOs9qSgAWwUUpDluZ6L3q3t+SfVg4gwnLrrSr2i/dBodIb4hhPg2bTi53Fpe/4TZ8vxaE2idPSs21VZjIi1lLnofig6j3ngqoDiDS/WAojNhL7W75oBI4BAtoNNqOKdTKP1jA+hpPsCQkWPZlJrPmj+zWJecRX6phVX7M1m1v+bYaEyQb1X4SIoMqLz3d9lCZKID0hkgMNZxOxNLWWUQyarde1JyWlixlDqWjs9LddzOxCcInX8EF1Ro0S7+yrFMvV+14Rxnj4pfhCwtL2rw1fniq/Mlyi+q0a9xDvuUWkqr7kssJY6vndsrnyuxlNTYr2p/q2N7mbWMUksp5bZyAKyqo6fGOURUH1uZLG0u2qBwfx8mnx3P5LPjsdlVdh/L548ThaRkFJGcWUxKZjGZRRWcLCjnZEE565OzT3u9gW4R/iRF1QwiEQE+HWL8UrQSvdFxLZmQTmfet6L41HySM/We2MxQUYBSUUA4wL76Lz8PgDG07vklp0+INYXJmTqiTtWHfVzFardWhY8SawlllrJagcUZasosZWTlZrGPuq9q3FQSOIRbaDUKgxJDGJRYs9uxoNRCSlYRKZnFJGcUVwWR4/llZBebyS7OZUtqzfHNQF9djd6Q7lGOIBIbZJTVUUXL+Pg7bmHdGt5PVR0TYYszsRacYOeGHzk7KQ5tWc6pQFIVTjJBtTnmnZTlQtYZfllXnalT15yTauHEN9hxxpDBTybEimbTaXQEGAIIMAQ0av+cnBxexjXXUpHAIVpVkEnPOZ1COadTzQuIlVRYOZjlCB/OEJKSWcyRnBIKy63sSMtnR1p+jdcY9dqqXpBulfdJUQEkhBhlsqpwLUVxLO9uDEYN7sKJPQUMHDIBbV3X9LDboSzv1HBOnUM7zvvspp2pA46AYghwhI86b4H1b/Ot9pzBX3pWRKuSwCHaBD8fHf3jg+kfH1xje7nFxuGckqrekIOZxSRnFpGaXUKZxcbvxwv4/XjN6zsYtBq6RvjVmCeSFOVP5zA/DDoJIsLNNBrwC3Pcovo0vK/NWu1MnTrOzqm+vbzQ0XOi2h0TY5t4XZM6GfybHlrq2ibzVUQjSOAQbZqvXkuv6EB6RddcJttis5OWW0pyRjEpmUVVPSMHs4opt9jZn17E/vSak6G0GoVOYSa6nzZPpFuEP0aD/KcnPECrg4Aox+1MVNUxIbaiqPJWWO3rhrY5t1c+V14IdoujTHOx41ZU/+XrG0Xn24hw4tzeQHDR+chwUQcmgUO0S3qthm4RjrAA0VXb7XaV4/llJGcWVYaRU0M0xRVWDmWVcCirhBV7T/VdKwrEhxgrg0hAjVN4A+UUXtFWKAoYTI5bYwJKQ6wVzQwtp22zlFaWV+64lTTtIm21aPT1hhaN3o8+x7PQbNjnGN5qKODIPJc2SQKH6FA0GoWEUBMJoSYu7HXql7KqqmQUVpBcrTckJcMxPJNXauFobhlHc8tY/WfNX5jRgY5TeLtG+BHh70Oov4EwPx/C/A2E+hkI9/Mh0KiTs2hE+6Lzcdz8Gn9BtjrZrGBuZDipN8hU3lAdPS/Oyban0QJJAJnfn7lep89z8T29V6WhHpjqwUXmubiSBA7hFRRFITrIl+ggX0Yk1Vz+OKe4osZEVWcoySisIL2wnPTCcjakZNdTMug0CqF+jgASVhlIQv0MhPkZCPN3fB1eGVDCJKCIjkSrc1zjxtjCayrZ7Y71UMoL6w0otrJ8UvfvpmtsOBpLSf2Bpq3Nc9GbHENOOl/HXBcv/tmXwCG8Xpi/D2H+PpzXNazG9oIyi+PMmYxiDueUkFNsJqfETE5JBbklZnKLzRRVWLHaVTKLKsgsqmjU8fRahRCTI4yE1QgqBkIre0+qh5VAXwkoooPTaE79ga6H3WJhT9H3dJowAU1dZweBC+a5VHtsq/x5dtU8F3D0vOh8K3uYfKvdfBzrx+h8QGc89by+2vNN2l7t5tzWBnpqJHAIUY8go56zE0M4O7H+/97KLTbySs1VYSS3pOJUMCl2BBPH12ZyS8wUV1ix2JoeUEIrw4izpyTUz0CIUcfxDAX93kyigo1VYSXARwKK8FIun+dSXC2I1N/70qh5LuDoebGU1tzWWjS6ZoUZTZksbS5Em+Cr1xITZCQmyNio/cstNkfvSImZ7MpA4vi6ZljJrQwsJWYbFptj/klGYV0BRcvnh3bV2OIMKNXnmoRV6zmpMfQjAUWIulXNcwk7874NUVXHKrXWcrBUTq61VoC1rPK++vZqz1uqPe+8NXY/53bn2UjguPihuchxawJthdqy91+NBA4hWpGvXktssJHY4MYHlJzK4ZucykDi7DXJKipjf+oxtH7B5JVayC02NyKg1GbQak6bg1L30E5Y5fP+ElCEaDxFORVefINa99h2W7WAU36GEFN3CLLl5wFvuKQ6EjiEaMN89Vrigo3E1RFQLBYL33+fxoQJ56GvHNN2BpSc4oqaQaXq65rDPaVmG2abvWpybGMYtJqqnhPHhFifal/XDCuhfhJQhPAYjdZxirCh+ddisefkIIFDCFFLQwGlLmVmW9Uk2MbMQymzOAKK8wJ8jWHQaQjzMxBiMuDno8VXr8Vk0GLUazEatBj1OowGTeVjXeW9pnL7qX1Pf52PTiNBRoh2RAKHEF7MaNASbzARH2Jq1P7OgOIc2qlzHkpVeKmg3GLHbG1aQGksRXFcT+dUcHEEEt/Kx1VfVwsqvgYtJuf+znBT7fWnv06WwhfCdSRwCCEarakBpdRsrQonuaVmysw2x81S7d75deXjUrONcouNUrOVMov91NdmmyPA2Byz5lUVSs2O/Slxz/vVaZRTgaSqp0VDSYGGb3J34Oejr/V8zR4ZR+9N1dfVA07lflq54rHwEhI4hBBuYzLoMIXqSAhtXEBpDIvNEUKqh5ZSs43yal+XWZyhxVYZVE5trx54Si01X+cMN/bKiflWu0pRhZWiCutptdCQXFj/YnBNYdBp6ggqjkDiW9f2OgLOqZ6c2gHHVy9DT6JtkMAhhGhX9FoNeq2GADdd50ZVVcw2O+VmO6UWa60emeIyMxu3/kqvvmdRYaNmuKkMMKWn997Uce9ktjqGnQrKLA3UqmVOHzaqce8MLdWGm2rPs6l9XxVoDBoMWgk14swkcAghRDWKouCj0+Kj0xJE7VDz/+3df0yU9QMH8PdzD3CAnDhs3HnhGWxumEiQ8K0pA/uhf0AUm6tMK5Zu9QcWaHPaj2W5OKYNZ4tpozX7zuZsq0gq27AiSJ1B1iFfaimTEWkKMoUQvV/P5/sHcEKHhnIPn3zu/dqc8Lm75973+QPefD7PPef1euHpEMjPSgq8O+hGaZqA26cNr6z4hlZsPNrwNtJEtpv+YRXH64fHd/WCTSNjelGHt57GrMgMF5ioCBMiVQURJhMiVAWRqgkRJgUR6tXxSFVBxJivh+4TqQ4/xmSCAg3/O6/A1HYO0VGRgfGhYwYff+SxI8dUTVdvU00KC5IELBxERFPMZFIC2yIJ06J0eQ6fX8MV31CJueK5Wm4ms900evtq0OuHf3jvya8JDLh9GAjaego1Ff892RKSI12/pAQXln8qNH8vTJHDpWp0MRo9PvaYClTT+I+ZSFm7VcoTCwcRkQFFqCbEqSbEmfX5MS+EgNcvxtku8uHyqILj9mnw+QV8mgavX8A//P/oMZ9fg08T8PqH7uvVtKDbPT4N53p6ED8jAT4xVKjG3NevwauJMeN+bSjjeLx+Aa/fD+i3kzVlVNM1StLosnON1aPgkjS2GHkGb+zKpNfDwkFERDdMURRERSiIijAhPkaf82lGG7rQ3QHk5//nhrayhBDwa2L8QjPqa+9w6fH5h0vO38ZHHhtckq5+ffUx1y5TgfHrHGe85xopU9o4/cmvDb1Gty90n3syQnOH7nNfWDiIiMiwFGXkr/ahC+Pd6rTh8vSPheYaqz/jlanR439ffervu4iKEGVn4SAiIrpFmEwKokwKojA1F6Xr7e0NWeHgZfSIiIhIdywcREREpDsWDiIiItIdCwcRERHpjoWDiIiIdGf4d6kIMfSm5f7+fslJjMvr9WJwcBD9/f03falnunGcdzk473Jw3uX466+hC3+N/C6dDMMXjpHJmj17tuQkREREt6be3l7Ex8dP6hiKCEVt+RfTNA1nzpyBxWK5Za43f6vp7+/H7Nmz0dXVhenTp8uOEzY473Jw3uXgvMvR19cHh8OBCxcuYMaMGZM6luFXOEwmE5KSkmTHCAvTp0/nDwIJOO9ycN7l4LzLYTJN/pRPnjRKREREumPhICIiIt2xcNCkmc1mbN68GWazWXaUsMJ5l4PzLgfnXY5QzrvhTxolIiIi+bjCQURERLpj4SAiIiLdsXAQERGR7lg4iIiISHcsHHTTKioqkJ2dDYvFgsTERBQVFeG3336THSusVFRUQFEUlJWVyY4SFk6fPo0nn3wSM2fORGxsLDIyMnDs2DHZsQzN5/Ph1VdfRXJyMmJiYpCSkoItW7ZA0zTZ0QylsbERhYWFsNvtUBQFn3322ZjbhRB4/fXXYbfbERMTgyVLlqCtre2GnoOFg25aQ0MDSkpKcPToURw8eBA+nw/Lli3DpUuXZEcLC83NzaiurkZ6errsKGHhwoULWLx4MSIjI/HVV1/hl19+QWVl5aQv90zXt3XrVrz77ruoqqrCr7/+im3btuGtt97CO++8IzuaoVy6dAl33XUXqqqqxr1927Zt2L59O6qqqtDc3AybzYalS5cGPq9sIvi2WAqZnp4eJCYmoqGhAbm5ubLjGNrAwADuvvtu7Ny5E2+++SYyMjKwY8cO2bEMbdOmTTh8+DC+//572VHCykMPPQSr1Yr3338/MLZ8+XLExsZiz549EpMZl6IoqKmpQVFREYCh1Q273Y6ysjJs3LgRAOB2u2G1WrF161Y899xzEzouVzgoZPr6+gAACQkJkpMYX0lJCQoKCvDggw/KjhI2amtrkZWVhUcffRSJiYnIzMzEe++9JzuW4eXk5OCbb77BiRMnAAAtLS04dOgQ8vPzJScLHx0dHTh79iyWLVsWGDObzcjLy8ORI0cmfBzDf3gbTQ0hBNavX4+cnBykpaXJjmNo+/btw08//YTm5mbZUcLKqVOnsGvXLqxfvx4vv/wympqa8MILL8BsNuPpp5+WHc+wNm7ciL6+PqSmpkJVVfj9fpSXl+OJJ56QHS1snD17FgBgtVrHjFutVnR2dk74OCwcFBJr167F8ePHcejQIdlRDK2rqwulpaWoq6tDdHS07DhhRdM0ZGVlwel0AgAyMzPR1taGXbt2sXDo6KOPPsKHH36IvXv3Yv78+XC5XCgrK4PdbkdxcbHseGFFUZQx3wshgsauh4WDJu35559HbW0tGhsbkZSUJDuOoR07dgzd3d1YuHBhYMzv96OxsRFVVVVwu91QVVViQuOaNWsW7rzzzjFj8+bNwyeffCIpUXjYsGEDNm3ahBUrVgAAFixYgM7OTlRUVLBwTBGbzQZgaKVj1qxZgfHu7u6gVY/r4TkcdNOEEFi7di0+/fRTfPvtt0hOTpYdyfAeeOABtLa2wuVyBf5lZWVh1apVcLlcLBs6Wrx4cdDbvk+cOIE5c+ZIShQeBgcHYTKN/VWlqirfFjuFkpOTYbPZcPDgwcCYx+NBQ0MDFi1aNOHjcIWDblpJSQn27t2L/fv3w2KxBPb54uPjERMTIzmdMVkslqBzZKZNm4aZM2fy3BmdrVu3DosWLYLT6cRjjz2GpqYmVFdXo7q6WnY0QyssLER5eTkcDgfmz5+Pn3/+Gdu3b8fq1atlRzOUgYEBtLe3B77v6OiAy+VCQkICHA4HysrK4HQ6MXfuXMydOxdOpxOxsbFYuXLlxJ9EEN0kAOP+2717t+xoYSUvL0+UlpbKjhEWPv/8c5GWlibMZrNITU0V1dXVsiMZXn9/vygtLRUOh0NER0eLlJQU8corrwi32y07mqHU19eP+/O8uLhYCCGEpmli8+bNwmazCbPZLHJzc0Vra+sNPQevw0FERES64zkcREREpDsWDiIiItIdCwcRERHpjoWDiIiIdMfCQURERLpj4SAiIiLdsXAQERGR7lg4iIiISHcsHERERKQ7Fg4ikuqDDz7AvffeKzsGEemMhYOIpKqtrcUjjzwiOwYR6YyFg4h00dPTA5vNBqfTGRj74YcfEBUVhbq6OgDAlStXUFdXh4cffhhbtmzBggULgo6zcOFCvPbaa1OWm4j0wQ9vIyLdHDhwAEVFRThy5AhSU1ORmZmJgoIC7NixAwDw5ZdforS0FO3t7fjjjz8wZ84cHD16FNnZ2QCA48ePIyMjA+3t7UhJSZH4Sohoslg4iEhXJSUl+Prrr5GdnY2WlhY0NzcjOjoaAPDss8/CYrGgsrISAJCfn4877rgDO3fuBACsW7cOLpcL9fX10vITUWiwcBCRri5fvoy0tDR0dXXhxx9/RHp6OgBACAG73Y59+/YhLy8PAFBTU4PVq1fjzz//hKqquP3221FZWYmnnnpK5ksgohCIkB2AiIzt1KlTOHPmDDRNQ2dnZ6BwNDU1wePxICcnJ3DfwsJCmM1m1NTUwGw2w+12Y/ny5bKiE1EIsXAQkW48Hg9WrVqFxx9/HKmpqVizZg1aW1thtVqxf/9+FBQUQFXVwP0jIiJQXFyM3bt3w2w2Y8WKFYiNjZX4CogoVLilQkS62bBhAz7++GO0tLQgLi4O9913HywWC7744gukpaXhjTfeCFrBOHnyJObNmwcAOHz4MO655x4Z0YkoxFg4iEgX3333HZYuXYr6+vrAtsnvv/+O9PR0lJeX48UXX8T58+cRFxcX9Njc3Fz09vaira1tqmMTkU54HQ4i0sWSJUvg9XrHnKPhcDhw8eJFuN1u3H///eOWDSEEzp07hzVr1kxlXCLSGc/hIKIpl5SUhJdeeilovLu7G3v27MHp06fxzDPPSEhGRHrhlgoR/WsoioLbbrsNb7/9NlauXCk7DhGFEFc4iOhfg3//EBkXz+EgIiIi3bFwEBERke5YOIiIiEh3LBxERESkOxYOIiIi0h0LBxEREemOhYOIiIh0x8JBREREuvs/cFSv+NzRrncAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(0, 10, 100)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "\n", - "# draw the invariance curves\n", - "for kk in k_v: \n", - " y_f = SolidlySwapFunction(k=kk)\n", - " yy_v = np.array([y_f(xx) for xx in x_v])\n", - " #yy_v = [y_f(xx, kk) for xx in x_v]\n", - " plt.plot(x_v/yy_v, yy_v, marker=None, linestyle='-', label=f\"k={kk**0.25:.0f}^4\")\n", - " #plt.loglog(x_v/yy_v, yy_v, marker=None, linestyle='-', label=f\"k={kk**0.25:.0f}^4\")\n", - "\n", - "# # draw the central tangents\n", - "# C = 0.5**(0.25)\n", - "# label=\"tangents\"\n", - "# for kk in k_sqrt4_v:\n", - "# yy_v = np.array([C*kk - (xx-C*kk) for xx in x_v])\n", - "# plt.plot(yy_v/x_v, yy_v, marker=None, linestyle='--', color=\"#aaa\", label=label)\n", - "# label = \"\"\n", - "\n", - "# # draw the rays\n", - "# for mm in [2.6, 6]:\n", - "# yy_v = [mm*xx for xx in x_v]\n", - "# plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color=\"#aaa\", label=f\"ray (m={mm})\")\n", - "# yy_v = [1/mm*xx for xx in x_v]\n", - "# plt.plot(y_v/x_v, yy_v, marker=None, linestyle='dotted', color=\"#aaa\")\n", - "\n", - "plt.grid(True)\n", - "plt.legend()\n", - "plt.xlim(.1, 10)\n", - "plt.ylim(.1, 15)\n", - "plt.title(\"Invariance curves for different values of $\\sqrt[4]{k}$\")\n", - "plt.xlabel(\"x/y\")\n", - "plt.ylabel(\"y\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "05a4bda3-7597-4962-bb30-bfc75d56d79c", - "metadata": {}, - "source": [ - "### As function of x/y, log/log" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2cf7e55e-8d8f-4744-9406-06fac04cfb95", - "metadata": { - "lines_to_next_cell": 0, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(0, 10, 100)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "\n", - "# draw the invariance curves\n", - "for kk in k_v: \n", - " y_f = SolidlySwapFunction(k=kk)\n", - " yy_v = np.array([y_f(xx) for xx in x_v])\n", - " #yy_v = [y_f(xx, kk) for xx in x_v]\n", - " #plt.plot(x_v/yy_v, yy_v, marker=None, linestyle='-', label=f\"k={kk**0.25:.0f}^4\")\n", - " plt.loglog(x_v/yy_v, yy_v, marker=None, linestyle='-', label=f\"k={kk**0.25:.0f}^4\")\n", - "\n", - "# # draw the central tangents\n", - "# C = 0.5**(0.25)\n", - "# label=\"tangents\"\n", - "# for kk in k_sqrt4_v:\n", - "# yy_v = np.array([C*kk - (xx-C*kk) for xx in x_v])\n", - "# plt.plot(yy_v/x_v, yy_v, marker=None, linestyle='--', color=\"#aaa\", label=label)\n", - "# label = \"\"\n", - "\n", - "# # draw the rays\n", - "# for mm in [2.6, 6]:\n", - "# yy_v = [mm*xx for xx in x_v]\n", - "# plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color=\"#aaa\", label=f\"ray (m={mm})\")\n", - "# yy_v = [1/mm*xx for xx in x_v]\n", - "# plt.plot(y_v/x_v, yy_v, marker=None, linestyle='dotted', color=\"#aaa\")\n", - "\n", - "plt.grid(True, which=\"both\")\n", - "plt.legend()\n", - "plt.xlim(.1, 10)\n", - "plt.ylim(.1, 15)\n", - "plt.title(\"Invariance curves for different values of $\\sqrt[4]{k}$\")\n", - "plt.xlabel(\"x/y\")\n", - "plt.ylabel(\"y\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2324b5d7-fb94-4e0c-a106-bd04d837832b", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "7f477d24-a4e1-41a6-842f-8cd270d6395c", - "metadata": {}, - "source": [ - "## Fitting a hyperbolic curve\n", - "\n", - "_this code seems to have some issues and we may revisit it later_" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7bed8803-c8f9-4173-b8de-91e8742471eb", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "k = 5**4" - ] - }, - { - "cell_type": "markdown", - "id": "2be75c67-e2e0-4832-8df8-6b58ec7826e5", - "metadata": {}, - "source": [ - "### Determining the central region\n", - "\n", - "The central region is between the rays $m=2.6$ and $1/m=2.6$ (fan-shaped area in the real plot, and diagonal band in the log/log plot). We are fixing $k=5^4$ as a curve in the middle of our existing chart. The inner region in this case is determined by the equations $\\frac x y = m$ and $f(x,y)=k$" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c8eef7ce-906b-4219-b080-c22714068a63", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# x_mid = (k/2)**0.25\n", - "# # set up the invariant and the swap function\n", - "# iv = SolidlyInvariant()\n", - "# y_f = SolidlySwapFunction(k=k)\n", - "# ratio_f = lambda x: y_f(x)/x\n", - "\n", - "# # various consistency checks\n", - "# print(\"x,y mid = (k/2)^0.25 = \", x_mid)\n", - "# assert iseq(y_f(x_mid), x_mid) # at x_mid, y_mid = y(x_mid)\n", - "# assert iseq(ratio_f(x_mid), 1) # ditto, but with ratio_f\n", - "# assert iseq(f.goalseek(func=ratio_f, target = 1), x_mid) # ditto, but goalseek\n", - "# for xx in np.linspace(0.1, 10):\n", - "# assert iseq(iv.k_func(xx, y_f(xx)), k)\n", - "\n", - "# y_f.plot(0.1,10, show=False)\n", - "# plt.grid(True)\n", - "# plt.xlim(0, 10)\n", - "# plt.ylim(0, 10)\n", - "# plt.title(f\"Invariance curve for $k={k**0.25:.0f}^4$\")\n", - "# plt.xlabel(\"x\")\n", - "# plt.ylabel(\"y\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1eb3363d-fec1-40e5-a77e-dc8a4b89a8e5", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# x_v = np.linspace(0.1,10)\n", - "# #plt.plot(x_v, [m.log10(ratio_f(xx)) for xx in x_v])\n", - "# plt.plot(x_v, [(ratio_f(xx)) for xx in x_v])\n", - "# plt.grid(True)\n", - "# plt.xlim(0, 10)\n", - "# plt.ylim(0, 5)\n", - "# plt.title(f\"Ratio y/x for $k={k**0.25:.0f}^4$\")\n", - "# plt.xlabel(\"x\")\n", - "# plt.ylabel(\"y(x)/x\")\n", - "# print(f\"check that ratio = 1 for x = x_mid = {x_mid}\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e8f79b2b-4ec9-411d-81bc-f02342418e4b", - "metadata": {}, - "source": [ - "Here we finally determine the **central region**, defined by $m^{\\pm 1} = 2.6$. We find that, for our chosen value of $k$, the region is from 2.35 to 6.13 and centers at 4.2.\n", - "\n", - "More generally, scaling laws and experiments show that **in percentage terms this region is independent of k**. In other words, the central region is always\n", - "\n", - " 0.56 x_mid (43.9% below) ... 1.46 x_mid (46.0% above)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5cb67fb8-bbd1-4972-bc01-fb4d56cbdfcd", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# assert iseq(f.goalseek(func=ratio_f, target = 1), x_mid)\n", - "# r = (\n", - "# f.goalseek(func=ratio_f, target = 2.6),\n", - "# f.goalseek(func=ratio_f, target = 1),\n", - "# f.goalseek(func=ratio_f, target = 1/2.6)\n", - "# )\n", - "# r, tuple(round(vv/r[1]*100-100,1) for vv in r), tuple(round(vv/r[1]*100,1) for vv in r)" - ] - }, - { - "cell_type": "markdown", - "id": "dc9531d4-8eab-4682-872c-a4bf23e79311", - "metadata": {}, - "source": [ - "Here we are asserting invariance with respect to $k$" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6db0ac58-7bb8-428d-ad0b-ab474daedc29", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# k_v = [kk**4 for kk in [5, 25, 100, 1000]]\n", - "# for kk in k_v:\n", - "# x_mid = (kk/2)**0.25\n", - "# y_f = SolidlySwapFunction(k=kk)\n", - "# ratio_f = lambda x: y_f(x)/x\n", - "# r0 = (\n", - "# f.goalseek(func=ratio_f, target = 2.6),\n", - "# f.goalseek(func=ratio_f, target = 1),\n", - "# f.goalseek(func=ratio_f, target = 1/2.6)\n", - "# )\n", - "# r = tuple(round(vv/r0[1],4) for vv in r0)\n", - "# print(r)\n", - "# x_min_r, _, x_max_r = r" - ] - }, - { - "cell_type": "markdown", - "id": "7b0ccebb-edf7-4b7b-8fcf-1cdeec0a6f52", - "metadata": {}, - "source": [ - "### Fitting with flat kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "da539d48-8d35-4c4c-a49e-7fc00d21e0bc", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# x_mid = (k/2)**0.25\n", - "# x_min, x_max = x_min_r*x_mid, x_max_r*x_mid\n", - "# # x_min, x_max = 0.2*x_min, 3*x_max # uncomment to see bigger plot\n", - "# k**0.25, x_min, x_mid, x_max " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "8067732f-18ea-478f-bbf3-1512cb1a122a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# iv = SolidlyInvariant()\n", - "# y_f = SolidlySwapFunction(k=k)\n", - "# fv = f.FunctionVector(kernel=f.Kernel(x_min=x_min, x_max=x_max, kernel=f.Kernel.FLAT))\n", - "# y_fv = fv.wrap(y_f)\n", - "# y_fv.plot(steps=100, show=False)\n", - "# match0_fv = y_fv.wrap(f.HyperbolaFunction(k=15, x0=0, y0=0))\n", - "# match0_fv.plot(steps=100, show=False)\n", - "# plt.title(f\"Invariance function $k={k**0.25:.0f}^4$ (fitted area only)\")\n", - "# plt.xlabel(\"x\")\n", - "# plt.ylabel(\"y\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "481e0f2c-d69e-4e07-85fb-f9099031e315", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# match0_fv = match0_fv.update(k=25)\n", - "# params0 = match0_fv.function().params()\n", - "# #del params0[\"k\"]\n", - "# params = y_fv.curve_fit(match0_fv.function(), params0, learning_rate=1, iterations=1000, tolerance=0.01, verbose=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "90558cf4-d0d5-456d-992d-184fbc1e84d0", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# match_f = match0_fv.function().update(**params)\n", - "# match_fv = y_fv.wrap(match_f)\n", - "# y_fv.plot(steps=100, show=False)\n", - "# match_fv.plot(steps=100, show=False)\n", - "# plt.title(f\"Invariance function $k={k**0.25:.0f}^4$ (fitted area only)\")\n", - "# plt.xlabel(\"x\")\n", - "# plt.ylabel(\"y\")\n", - "# print(\"params = \", params)\n", - "# print(match_fv.params())\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "ee5710ed-3686-40b1-873c-5388aeb76c67", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# iv = SolidlyInvariant()\n", - "# y_f = SolidlySwapFunction(k=k)\n", - "# fv = f.FunctionVector(kernel=f.Kernel(x_min=x_min, x_max=x_max, kernel=f.Kernel.FLAT))\n", - "# y_fv = fv.wrap(y_f)\n", - "# y_fv.plot(steps=100, show=False)\n", - "# match0_fv = y_fv.wrap(f.QuadraticFunction())\n", - "# match0_fv.plot(steps=100, show=False)\n", - "# plt.title(f\"Invariance function $k={k**0.25:.0f}^4$ (fitted area only)\")\n", - "# plt.xlabel(\"x\")\n", - "# plt.ylabel(\"y\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "b9b607a7-cb35-422c-a0e1-c2825a6b12ab", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# params0 = match0_fv.function().params()\n", - "# params = y_fv.curve_fit(match0_fv.function(), params0, learning_rate=0.1, iterations=100, tolerance=0.01, verbose=True)" - ] - }, - { - "cell_type": "markdown", - "id": "4d759acb-8e3d-44f0-bbf5-435f346f4404", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "## Fitting a hyperbolic curve (charts for paper)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "6b5ccc69-d8da-47b9-a59b-7e65c1ad111a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.5611, 1.0, 1.4589)\n" - ] - }, - { - "data": { - "text/plain": [ - "(6.0, 2.8309618715931557, 5.045378491522287, 7.3607026812818654)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "k = 6**4\n", - "\n", - "x_mid = (k/2)**0.25\n", - "y_f = SolidlySwapFunction(k=k)\n", - "ratio_f = lambda x: y_f(x)/x\n", - "r0 = (\n", - " f.goalseek(func=ratio_f, target = 2.6),\n", - " f.goalseek(func=ratio_f, target = 1),\n", - " f.goalseek(func=ratio_f, target = 1/2.6)\n", - ")\n", - "r = tuple(round(vv/r0[1],4) for vv in r0)\n", - "print(r)\n", - "x_min_r, _, x_max_r = r\n", - "x_min, x_max = x_min_r*x_mid, x_max_r*x_mid\n", - "fv_template = f.FunctionVector(kernel=f.Kernel(x_min=x_min, x_max=x_max, kernel=f.Kernel.FLAT))\n", - "\n", - "x_v = np.linspace(0,10,1000)\n", - "x_v[0] = x_v[1]/2\n", - "\n", - "k**0.25, x_min, x_mid, x_max " - ] - }, - { - "cell_type": "markdown", - "id": "c0568da5-fa18-467e-93d7-0b45cef266f0", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "### Generic curve fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "49f2245f-70ca-4f98-9985-984ec102c91a", - "metadata": { - "lines_to_next_cell": 0, - "tags": [] - }, - "outputs": [], - "source": [ - "# solidly\n", - "y_fv = fv_template.wrap(y_f)\n", - "yy_solidly_v = [y_fv(xx) for xx in x_v]\n", - "yp_solidly_v = [y_fv.p(xx) for xx in x_v]\n", - "ya = y_f(x_min)\n", - "\n", - "# constant product\n", - "ps=0.04\n", - "params_opt_L2s = {'k': 4999.920086411355, 'x0': 65.96403685971154, 'y0': 65.36154243491612}\n", - "params_opt = {'k': 4999.920086411355, 'x0': 65.96403685971154, 'y0': 65.36154243491612}\n", - "match_fv = fv_template.wrap(f.LCPMM.from_xpxp(xa=x_min, xb=x_max, pa=1+ps, pb=1-ps, ya=ya))\n", - "match_opt_fv = match_fv.wrap(match_fv.el[0].update(**params_opt))\n", - "yy_match_v = [match_fv(xx) for xx in x_v]\n", - "yp_match_v = [match_fv.p(xx) for xx in x_v]\n", - "yy_match_opt_v = [match_opt_fv(xx) for xx in x_v]\n", - "yp_match_opt_v = [match_opt_fv.p(xx) for xx in x_v]\n", - "\n", - "# rays\n", - "mm = 2.6\n", - "yy_ray1_v = [mm*xx for xx in x_v]\n", - "yy_ray2_v = [1/mm*xx for xx in x_v]\n", - "\n", - "# tangent\n", - "C = 0.5**(0.25)\n", - "kk = k**0.25\n", - "yy_tang_v = [C*kk - (xx-C*kk) for xx in x_v]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "db41e7d1-b0c6-4b66-870b-2411191b9877", - "metadata": { - "lines_to_next_cell": 0, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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udiYhhPiLfv36kZSUxLFjx5xdSqW1bNkyhg4dyooVKxg4cKCzyxGiypHbNEIIm2eeeYY2bdpQp04dMjIymDNnDqtXr2bq1KnOLq1SOnToEGfOnOHZZ5+ldevW3HHHHc4uSYgqScKIEMLGbDbz+uuvc/HiRRRFoWnTpsyaNYtx48Y5u7RK6fHHH2fTpk20bduW77//vsqumCyEs8ltGiGEEEI4lVMf7c3Ozuapp56ibt26uLu707VrV3bs2HHLfRISEmjXrh1ubm5ER0czZcqUCqpWCCGEEI7g1DDy4IMPsnr1ambNmsWBAwfo168fffr04fz588Vuf/r0aQYOHEiPHj3Ys2cPL7/8Mv/85z9ZsGBBBVcuhBBCCHtx2m2a/Px8vL29WbJkCYMGDbJ93rp1awYPHsy//vWvG/Z54YUXWLp0aZF1Lh599FH27dtX7DLeQgghhKj8nDaA1WQyYTabi0yjDdaZCTdu3FjsPlu2bLGtBXJN//79mTp1KkajEb1ef8M+hYWFFBYW2t5bLBYyMjIIDAyUwWZCCCFEKaiqSnZ2NuHh4becCLG0nBZGvL296dKlC++88w5NmjQhNDSUuXPnsm3bNho2bFjsPhcvXrxh0a7Q0FBMJhNpaWnFLhL1/vvv89ZbbznkGIQQQoia6OzZs3Zd6NGpj/bOmjWLSZMmERERgVarpW3btowdO5bdu3ffdJ+/Xs24dpfpZlc5XnrpJZ555hnb+8zMTCIjIzl27BgBAQGlqjctLY0dO3ZgNpsJCQmhXbt2RdYlcQTNnplof38DAHPcK1jaP+jQ/m7GaDSybt06evXqVewVqIqQbzBz59dbSM0xMLlfA+7tGOmUOkqqMpyzqkjOW+nJObu1zMxMLBYL/v7+RT6X81Z6GRkZxMTE2H3hRKeGkfr165OQkEBubi5ZWVnUqlWL0aNHExUVVez2YWFhN6wgmpqaesvlvl1dXYtdKyQgIKBES4RfLzAwEH9/fzZu3EhOTg5Hjx6la9eujg0kfZ4GFzOsfQe2vAdBYdDu5ou3OYrRaMTDw4PAwECn/qV9ZnAbXl50gBk705jUuwVerpV3qpzKcs6qGjlvpSfn7NZu9m+9nLeys/cwh0qxaq+npye1atXiypUr/PbbbwwbNqzY7bp06cLq1auLfLZq1Srat29fYT9IISEhdO/eHa1Wy8WLF9m0aVOJViUtlx7PQrcnra+XPQkHa+7TQ6Pa1yY6yJOMXAPfrj/l7HKEEJWYTKNVdTg1jPz222+sXLmS06dPs3r1anr16kWjRo24//77AestlgkTJti2f/TRRzlz5gzPPPMMhw8fZtq0aUydOpXJkydXaN0hISH06NEDrVbLpUuX2LRpk2N/6BUF+rwF7e4HVFj4MBxb5bj+KjGdVsOz/axLmX+34RRpOYW32UMIURMZDAZWrlzJwYMHsVgszi5H3IZTw0hmZiZ///vfady4MRMmTKB79+6sWrXKdpUjJSWF5ORk2/ZRUVH88ssvxMfH07p1a9555x0+//xzhg8fXuG1BwcH06NHD3Q6HeHh4Y5/MkdRYNDH0GIkWEzw03hIKv6po+rujuZhtIjwJddg5qt1J5xdjhCiEjp79iw5OTlcuHBBnpysApx6w33UqFGMGjXqpt+fMWPGDZ/FxsbecoBrRQoODuaOO+644fFkh9Fo4c7/QmEOHPsVfrgHJi6FiLYV038lodEovDCgMeOmbmPO1mQmdYuiToCHs8sSQlQi9erVw8XFBY1GI2GkCqgUY0aqsuuDSGFhIbt378ZkMjmuQ60eRs6Aej3AkA2z74bUw7fdrbrp3jCIbg0CMZgt/GeNLG0vhChKq9VSp04dIiIinF2KKAEJI3aiqipbtmzh5MmTbNy40bGBRO8GY+ZCRDvIvwIz74SM047rr5J6vn9jABbtOc/Ri9lOrkYIIURZSRixE0VRaN68OTqdjsuXLzs+kLh6w70/Q0hTyLkIM4dB1gXH9VcJtarjx8AWYagqfPjbUWeXI4SoBMxmMxs3biQpKUkGrlYhEkbsKCgoiJ49e9oCyYYNGxwbSDwCYPwi8I+Cq2esV0hy0x3XXyX0bL9GaDUKaw5fYmdShrPLEUI42fnz50lJSSExMVHGilQhEkbsLDAw0BZI0tLSHB9IvMNgwhLwDoe0o9YxJAVZjuuvkqkf7MXIdtYpiT9YeUTmFRCihgsODqZ58+Y0btxYwkgVImHEAQIDA4mNjUWv15OWlsbOnTsd26F/XWsg8QiElL0w9x4w5Dm2z0rkyT4NcdVp2JF0hXVHU51djhDCidzd3WnSpAn169d3dimiFCSMOEhAQAA9e/bEx8eH5s2bO77D4BgYtxBcfeDMJvhpApgMju+3Eqjl6859XesB8MGvRzFb5OqIEEJUJRJGHCggIIB+/frh5eVl+8yhtxHCW8PYn0DnDidWw6KHweLgqeoricfjGuDrrufopWx+3nXW2eUIISqYqqrs3buX9PR0uV1bBUkYcbDr71mmpKSQkJCA0Wh0XId1u8A9s0Gjh8RFsPwpqAF/MX099PyjdwMAPl51jDyDA8fpCCEqnYsXL3L8+HE2btwoT9FUQRJGKojJZGLnzp1cvnyZ9evXOzaQNOgDI6aCooHdM2HVqzUikIzvUpc6Ae6kZhfy3YaaN++KEDWZh4cHdevWJTo62rErqQuHkDBSQXQ6Hd27d8fFxYWMjAzWr1+PweDAMR1Nh8HQL62vt3wJCf92XF+VhKtOy3N/TIT2TcJJLmfLInpC1BS+vr507NiRFi1aOLsUUQYSRiqQv78/sbGxFRdI2twLA/7P+jr+Pdj6X8f1VUkMblGLVrWti+h99rtMEy+EEFWBhJEK5ufnZwskV65ccXwg6fwYxL1sfb3yRdgz23F9VQIajcJLA5sAMHf7WU6k5ji5IiGEI6mqysmTJykoKHB2KaIcJIw4wV8DyYkTJxzbYezz0OUJ6+ul/4DExY7tz8k6RwfSp0kIZovKByuPOLscIYQDZWRksHv3bn799VfM5prx9GB1JGHESfz8/IiLi6NBgwY0adLEsZ0pCvT7F7QZD6oFFjwIx9c4tk8ne/GOxmg1CqsPXWL7aZkmXojqymKxEBgYSEREhAxcrcIkjDiRr68vbdq0sT3+q6qq456yURQY8hk0uwssRvhxHCRtckxflUCDEG9Gd6gDwHu/HJZ5B4SopoKDg+nduzft27d3dimiHCSMVBKqqrJ9+3bi4+MpLHTQUyAaLdz1P2jYH0z58MNoOL/bMX1VAk/1aYiHi5a9Z6+y4kCKs8sRQjiQRiO/zqoy+dOrJPLz87l06RJXr14lISHBcYFE5wKjvod6PcCQbV1Y79Ihx/TlZCHebjzcMxqAf688SqFJ7icLUZ2kpqbKBGfVhISRSsLDw4O4uDhcXV3JzMx0bCDRu8OYuRDRDvKvwKw7If2kY/pysod6RBPs7UpyRh5ztiY7uxwhhJ1kZWWRkJDAL7/8IgNXqwEJI5WIj48PcXFxuLm5OT6QuHrDvT9DSDPIuQQz74TMc47py4k8XXU80zcGgM/XHicz34Ez3wohKkxubi6urq74+/vLwNVqQMJIJfPXQBIfH++45+c9AmDCYgioD5nJMHMY5Fx2TF9ONLJdbRqGeHE1z8iXa487uxwhhB3UqlWLwYMH065dO2eXIuxAwkgl5O3tbQskOTk5ZGVlOa4zrxCYsAR860D6CZh1l/XWTTWi02p4eZD18ekZm5NISst1ckVCCHvQaDS4ubk5uwxhBxJGKqlrgaRbt26EhIQ4tjO/OtZA4hkClw7AnJFQWL1mLu3VKISeMcEYzSrv/3rY2eUIIcohLy/P2SUIO5MwUol5e3sTFhZme5+Tk+O4WzaB9a23bNz84NwOmDcGjNVreuVXBzVBq1H4LfESW06mO7scIUQZ5Ofns2LFCn7//XdMJpOzyxF2ImGkisjOzmbdunXEx8eTn5/vmE5Cm8G4heDiBafXw/z7wFx9BnzGhHozpqN1IrR/rTiE2SIToQlR1aSlpaEoChqNBp1O5+xyhJ1IGKkiFEVBURSys7NJSEhwXCCp3Q7G/gg6Nzj2Kyx6BCzV57G5p/vE4O2mI/FCFgt2V7+nh4So7urUqcPgwYNp27ats0sRdiRhpIrw8vIiLi4ODw8PsrOzHXuFpF53GD0bNHo4uACWPwXVZDr1QC9X/tm7IQAf/naU3EK5zCtEVePm5oavr6+zyxB2JGGkCrk+kOTk5Dg2kDTsC8O/BUUDu2eiWfNatQkkE7rWpW6gB5ezC5mSUD0nexOiOpLJzaovCSNVjKenZ8UFkmZ3wdAvANBun0Kji4sd008Fc9VpeekO66O+/1t/ivNXHXT+hBB2YzAYWLZsGdu2bZOBq9WQhJEq6PpAotPpHDv7YJtxMOADABpfXIRm29eO66sC9W8WSqeoAApNFv698oizyxFC3EZKSgpGo5HMzEyZcbUakjBSRXl6etKrVy9iY2NxcXFxbGedH8Uc+zIA2jWvw87pju2vAiiKwmuDm6IosGTvBfYkV6+J3oSobiIjI/nb3/5Gq1atUBTF2eUIO5MwUoV5eHgUCSJJSUkOmwzI0u1pjocMsr5Z/jQc+Nkh/VSk5hG+jGhbG4B3lh9CrSZjYoSojhRFISAggNDQUGeXIhxAwkg1kZSUxI4dO4iPjyc31wHTnSsKh8JHYW57P6DCwofhyC/276eCPde/ER4uWnYnX2XZ/hRnlyOEEDWShJFqIiQkBE9PT3Jzcx0aSCwDPoCW94Bqtk6Kdire/v1UoBAfNx6LrQ/AB78eId8go/WFqEzMZjOrV6/m0KFD8jRNNSZhpJrw8PAgLi4OLy8v8vLyHBhINDDsK2g8GMyFMHcsnN1u/34q0EM9o4nwc+f81Xy+WS+P+gpRmZw/f56rV69y+vRpNBr5lVVdyZ9sNVJhgUSrgxHTILoXGHNh9ghI2W//fiqIm17LywOtj/r+N/4k567IIlxCVBbh4eF07NiR5s2by8DVakzCSDXj7u5+QyAxGAz270jnCvfMgcguUJgJs+6Cy8fs308FGdgizPao7/u/yKO+QlQWOp2OunXrUrduXWeXIhxIwkg1dC2QeHt7ExUV5bhHf108revY1GoFeWkwcxhcOeOYvhxMURTeHNoMjQIrDqTIqr5CCFGBJIxUU+7u7vTp04emTZs6tiM3Xxi3CIIbQ/YFmDkUsqrmUylNavlwbyfr/77eWpaIyWxxckVC1FyqqrJ161aSk5OxWOTvYnUnYaQau355bZPJxNatW8nJybF/R56BMH4x+NeDK0kw607IrZpXFp7pG4Ovu54jF7OZuz3Z2eUIUWNdvHiRs2fPsmfPHpkDqAaQMFJD7N27l7NnzxIfH092drb9O/CpBROWgHctuHwEZt8NBVn278fB/D1deLZfDAAfrz7G1TwHjLcRQtyWr68vTZs2JSYmRqZ/rwEkjNQQzZs3x8fHh/z8fMcFEv961kDiEQgpe+GH0WCoek+mjO0YSeMwb67mGflkddUdlCtEVebh4UGzZs1o0qSJs0sRFUDCSA3h5uZGbGwsPj4+FBQUOC6QBDeC8YvA1ReSN8OP94Kp0P79OJBOq+GNIc0AmL31DIdTqt4VHiGEqEokjNQgbm5uxMXF4evrawskWVkO+EVbqxXcOx/0HnByLSx4AMxVa8nvLvUDGdgiDItqHcwq96yFqBiqqpKYmEhGRob8vatBJIzUMK6ursTGxtoCyZYtWxzzFz6yE9zzA2hd4PAyWPoEVLER8S8PbIKrTsPWUxn8evCis8sRokbIyMjg0KFDxMfHYzJVrf/EiLKTMFIDXQskISEhdOrUyXGzGtbvBSNngKKFfXPh1+ehCv1Pp7a/B4/+sW7NuysOy7o1QlQAnU5HnTp1qFu3Lnq93tnliAoiYaSGuhZI/Pz8bJ855Fn+xoPgrimAAju+hd/fsn8fDvRobH3Cfd1k3RohKoivry+dO3embdu2zi5FVCAJIwKAy5cvs3LlSjIzM+3feMtRMPg/1tcb/wMbPrZ/Hw7i7qLllUHWieP+G3+SsxlV7+kgIaoiWYemZpEwImwDxnJzc0lISHBMIGl/P/T7l/X172/Dtv/Zvw8HGdgijK71Ayk0WXhr2SFnlyNEtXXmzBkKC6vW03fCPiSMCBRFoWvXrvj7+1NYWEh8fDxXr161f0dd/wGxL1hf//oc7P3B/n04gKIovD2sGTqNwprDl/j98CVnlyREtZOVlcX27dtZsWIFRqPR2eWICiZhRADg4uJCz5498ff3x2AwkJCQ4JhAEvcSdH7c+nrJ3+HQEvv34QANQrx5oEcUAG8uS6TAKINZhbAno9GIv78/oaGhMnC1BpIwImwqJJAoCvR/D9qMB9UCPz8Ax9fYtw8H+WfvhoT5uHE2I58pCTKYVQh7CgwMpE+fPnTq1MnZpQgnkDAiinBxcSE2NpaAgAAMBgPHjjlgOnRFgSGfQbO7wGK0ztKatMn+/diZp6uO1wZbB7N+HX+S5HQZzCqEvV2/wKeoOSSMiBvo9Xp69uxJ48aNad++vWM60Wjhrv9Bw/5gKrCuY3N+l2P6sqOBLcLo3iAIg8nCW8sSnV2OENVCenq6Y6YWEFWGhBFRLL1eT4sWLdBorD8iqqraf6ZWnQuM+h7q9QBDNsweDpcq99MqiqLw5tBm6LUKvx9JZc0hGcwqRHnk5+ezdu1aGbhaw0kYEbelqir79+/HaDRy5coV+zaud4cxcyGiPeRfgVl3QnrlHo/RIMSLB3tEAzKYVYjyysrKwsXFBS8vLxm4WoNJGBG3ZbFYbHOPbNq0iYyMDPt24OptXVgvtDnkXIKZd0LmOfv2YWf/6N2AcF83zl3J5+v4yh2ehKjMQkNDGTJkiAxcreEkjIjb0mq1dOnSBUVRMJlMrF+/3v6BxCMAxi+CgPqQmQwzh0FOqn37sCMPlz8Hs05JOElSWq6TKxKi6tJoNHh4eDi7DOFEEkZEiej1enQ6HYGBgRiNRhISEkhPT7dvJ14hMGEJ+NaB9BMw6y7rrZtKakDzMHo0tA5mfXNZoix3LkQpFRQUOLsEUUlIGBElpigKXbp0ISgoyHaFxO6BxK+ONZB4hcKlgzB7BBRm27cPO1EUhbf+GMwaf/Qyq2UwqxAlZjAYWLFiBWvXrsVgMDi7HOFkEkZEqeh0Onr06EFwcDAmk4m8PAfMtRFYH8YvBnd/OL8T5o4BY+X8H1R0sBcP97QOZn1r2SHyDTKYVYiSSEtLw2KxYDKZZOCqkDAiSk+n09G9e3d69OhBnTp1HNNJaFMYtwBcvCBpA8yfCObK+djf33s1IMLPnfNX8/k6/oSzyxGiSggPD2fw4MG0b99eVugVEkZE2eh0OsLCwmzv8/Pz7X/LJqIdjP0RdG5wbCUsegQsle/Kw/WDWb9JOMXJyzlOrkiIqsHd3Z2AgABnlyEqAQkjotwKCgqIj48nISGBy5cv27fxet1h9GzQ6OHgAlj+FFTCgaL9m4US1ygYg9nCq4sOymBWIW5BZlsVfyVhRJSbTqfDw8MDs9nMhg0b7B9IGvaF4d+BooHdM+G3lytdIFEUhXeGNcdNr2HLqXQW7j7v7JKEqJTMZjO//PILO3bskBlXhY2EEVFu18aQhIaGOi6QNLsThn5pfb31a4j/P/u2bwd1Ajz4598aAvDuL4e5kidPCAjxVxcvXiQ/P5/U1FRZFE/YODWMmEwmXn31VaKionB3dyc6Opq33377lpfw4uPjURTlhq8jR45UYOXir7RaLd26dSsSSFJT7TxpWZt74Y5/W18n/B9s/sK+7dvBQz2iiQn1IiPXwIerjju7HCEqnfDwcHr16kWbNm1k4KqwcWoY+eCDD5gyZQpffvklhw8f5t///jcffvghX3xx+18yR48eJSUlxfbVsGHDCqhY3MpfA8nGjRvtf4Wk0yPQ+zXr61Wvws7p9m2/nPRaDe/d1QKA+bvOczLLyQUJUckoikJQUBDh4eHOLkVUIk4NI1u2bGHYsGEMGjSIevXqMWLECPr168fOnTtvu29ISAhhYWG2L61WWwEVi9u5FkjCwsJwd3fHy8vL/p30eBa6PWV9vfxp2D/f/n2UQ/t6AdzTwfrI80+ntBhMMlhPCCFuxak37Lp3786UKVM4duwYMTEx7Nu3j40bN/Lpp5/edt82bdpQUFBA06ZNefXVV+nVq1ex2xUWFlJYWGh7n5Vl/a+q0WiUwVOlcO1clfScdejQAaPRiE6nc8x5jn0FTUEW2l3TUBc9glnjgtpooP37KaNn+zRgVeJFLuYZ+XbDKf7eq4GzS6oySvuzJqrGOVNVlY0bNxIaGkp0dHSlGC9SFc5bZeOoc6WoTnwGUVVVXn75ZT744AO0Wi1ms5l3332Xl1566ab7HD16lPXr19OuXTsKCwuZNWsWU6ZMIT4+np49e96w/Ztvvslbb711w+c//PCDLMxUgcxmM4qioNHY8WKcaqFN8rdEZmzCrOjYFv00l31a2K/9ctpxWWH2CS16ReXF1maC3JxdkRDOc222VbCudSXjRaqmvLw8xo4dS2ZmJj4+PnZr16lhZN68eTz33HN8+OGHNGvWjL179/LUU0/xySefMHHixBK3M2TIEBRFYenSpTd8r7grI3Xq1CElJYXAwEC7HEdNYDQaWb16NX379i311M2XL19m06ZNaDQaOnfuTEhIiP0Ks5jQLnoIzZFlqDp3zGN+Qo3sYr/2y8FgMHDnZ2s5nqWhZ8NAvhvfVv4BLoHy/KzVVFXhnJlMJs6fP4/ZbCY6OtrZ5QBV47xVNunp6dSqVcvuYcSp18mee+45XnzxRe655x4AWrRowZkzZ3j//fdLFUY6d+7M7Nmzi/2eq6srrq6uN3yu1+vlh68MynLeQkNDCQ8P58KFC2zdutU2psROFcGIaTBvLMqJ1eh+HAsTl0JEWzu1Xz4joy18eEDL+uPprDqSxuCWMmivpOTvaOlV5nOm1+tp0KBy3q6szOetsnHUeXLqANa8vLwbLttrtdpSz863Z88eatWqZc/ShB1ptVq6dOlCeHg4FouFTZs2cfHiRft1oHOB0bOgXg8wZMPsu+FSov3aL4dQd3i0ZxRgXUgvq0DuTQshxF85NYwMGTKEd999lxUrVpCUlMSiRYv45JNPuOuuu2zbvPTSS0yYMMH2/tNPP2Xx4sUcP36cxMREXnrpJRYsWMATTzzhjEMQJaTRaG4IJCkpKfbrQO8OY+ZCRHvIvwIz74S0yrFo3SM9oogK8uRydiEf/XbU2eUIUaFUVWXXrl2cO3dOpoEXN+XUMPLFF18wYsQIHn/8cZo0acLkyZN55JFHeOedd2zbpKSkkJycbHtvMBiYPHkyLVu2pEePHmzcuJEVK1Zw9913O+MQRClcCyQRERFYLBY2b95MZmam/Tpw9YZxP0NoC8hNhZnD4Gry7fdzMFe9lnfvbA7ArK1n2Hv2qnMLEqICpaenc+rUKbZv347ZXPkWuhSVg1PHjHh7e/Ppp5/e8lHeGTNmFHn//PPP8/zzzzu2MOEw1waxbt26FQ8PD7sOgALA3R/GL4IZAyHtGHw/FCatBG97jVEpm64Ngri7TQQL95zn5YUHWPpEN3RaWY1BVH8eHh40btwYVVVlXIa4KfnXUFS4a4GkVatWtqdL7PpQl1cwTFgCfnXhymnrFZLcdPu1X0YvD2qCr7ueQylZTNt02tnlCFEhPDw8aNGiBS1btnR2KaISkzAinEKj0diCiNlsZsuWLZw/b8eVbn3CrU/VeIfD5SMw+y4osOMtoTII8nLl5YGNAfhk9TGS0/OcWo8QQlQWEkaE0508eZLz58/bP5D417NeIfEIgpR9MGckGHLt134ZjGpfhy7RgRQYLbyy+IB9rwgJUckcO3aMq1evOrsMUQVIGBFO16BBA+rUqYOqqvYPJMEx1jEkbr5wdhvMHQPGAvu1X0qKovDe3S1w1WnYcDyNhbvteKxCVCJZWVns27ePNWvWFJl4UojiSBgRTqfRaOjYsSORkZG2QHLu3Dn7dVCrJdy7AFy84HQCzL8PzM6b7yMqyJOn+sQA8M6KQ6TlyD/UonqqXbs2ERERxU48KcT1JIyISuGvgWTr1q32DSR1OsCYeaBzg2O/wsKHweK8xwwf7BFF01o+XM0z8vayQ06rQwhH8fHxoUuXLnTu3NnZpYgqQMKIqDQURaFjx47UrVsXVVXZuXMnBoPBfh1E9YBRs0Cjh8SFsOyf4KRJmPRaDR8Mb4lGgaX7LrD2yCWn1CGEo8l6TKIkJIyISkVRFDp06ED9+vXp3r07Li4u9u0gph+MmAqKBvbMht9eAicNIm1R25cHe1gXDHt10UFyCk1OqUMIe7tw4YJ9/yMhqj0JI6LSURSFtm3bEhQUZPvMaLTjGI+mw2DY19bX26bA2nduvb0DPd0nhjoB7lzILJCp4kW1kJ+fz6ZNm1i2bBkFBc4bLC6qFgkjotK7evUqv/76a5FlAcqt9RgY9LH19YaPrV9O4O6i5b27WgDw/ZYkdidfcUodQthLfn4+vr6+BAQE4Obm5uxyRBUhYURUemfOnKGwsJBt27Zx5swZ+zXc4UHo+7b19e9vw7Zv7Nd2KfRoGMzwtrVRVXhxwX4MJllMTFRdAQEB9O3bl+7duzu7FFGFSBgRlV7Lli2JiooCYPv27fYNJN2ehNgXrK9/fR52z7Jf26Xw6qAmBHq6cOxSDlMSTjqlBiHsRVEUWYdGlIqEEVHpKYpCu3btiI62Dvbcvn07SUlJ9usg7iXo8oT19bJ/wsEF9mu7hPw9XXhjaDMAvlx7ghOp2RVegxDllZmZKbMKizKRMCKqhGuDWq8Fkh07dtgvkCgK9PsXtLsPVIt1DpKjv9qn7VIY0rIWvRuHYDBbeHHBASwW+UddVB0Gg4E1a9bwyy+/yMBVUWoSRkSVcS2Q1K9fH4CkpCT7/S9MUWDQJ9BiFFhM8NNEOBVvn7ZLXILCO3c2x9NFy84zV5izzY63o4RwsMzMTLRaLXq9XmZcFaUmYURUKYqi0KZNG1q3bk337t3tO6GSRgt3/hcaDwZzoXUdm+St9mu/BCL83Hl+gHVl3//79QjnrsjKvqJqCA4OZsiQIXTp0kUmOhOlJmFEVDmKotCwYUN0Op3tsytX7PRIrFYHI6ZB/b+BMc+60u+FvfZpu4TGd65L+7r+5BrMvLRQVvYVVYdWq8Xb29vZZYgqSMKIqPISExNZs2YNp06dsk+DOlcYPRsiu0JhFsy6C1IP26ftEtBoFP49oqVtZd/5O+24Ro8QDiCzrYrykjAiqjRVVW2zs+7atct+gcTFA8b+COFtIT8DZt4J6RX3yG10sBfP9vtzZd+LmTIgUFROZrOZX3/9lfj4eBm4KspMwoio0hRFoVWrVjRs2BCwBpKTJ+0UGtx8YNwCCGkGORdh5jDIrLirFA90j6ZVHT+yC0y8skhu14jKKS0tDYPBQG5urgxcFWUmYURUedcCSUyM9UrC7t27OXHihH0a9wiACYshsAFknoXvh0J2xaywq9UofDSiJS5aDb8fSWXx3vMV0q8QpREaGsqgQYPo2LGjDFwVZSZhRFQLiqLQsmVLWyDZs2eP/QKJVwhMWAK+kZBxEmbdCXkZ9mn7NhqGevNkH+tVnzeXHiI1Wy6Di8rHw8OD4OBgZ5chqjAJI6LauBZIGjVqZHtvN761rVdIvMIg9RDMvhsKsuzX/i083DOa5hE+ZOYbeW3xQbldIyoN+VkU9iJhRFQriqLQokULevfubZsczW4C61uvkLgHwIU98MNoMDh+HhC9VsOHI1qh0yj8lniJFQdSHN6nELejqiqrVq1i165dFBYWOrscUcVJGBHVjqIoBAYG2t4XFhbab3G9kMYwfhG4+kDyZvjxXjA5/h/iJrV8+HuvBgC8viSR9Bz5x184V2pqKllZWZw7d67InD9ClIWEEVGtmUwm1q9fz/bt2zl27Jh9Gg1vDff+DHoPOLkWfp4EZqN92r6Fv/dqQOMwbzJyDbyxNNHh/QlxKyEhIcTGxtK6dWu0Wq2zyxFVnIQRUa1ptVpq1aoFwL59++wXSCI7wZi5oHWFI8th8WNgMdun7Ztw0Vlv12g1Csv3p7Dy4EWH9ifErSiKQkhICHXr1nV2KaIakDAiqjVFUWjWrBlNmjQBrIHk6NGj9mk8Og5GzQSNDg7Mh+VPg4MH9LWo7csjPa0rF7+6+CBX82TmSyFE1SdhRFR7iqLQvHlzmjZtCsD+/fs5cuSIfRpvNADu/h8oGtj9Pfz2isMDyT//1pAGIV6k5RTy9rJDDu1LiL9SVZVNmzZx9OhR2+zHQpSXhBFRYzRr1swWSA4cOGC/eUiaD4ehX1hfb/0K4t+3T7s34abX8uGIlmgUWLjnPKsPVcwkbEIAZGRkcOHCBRITZdySsB8JI6JGadasGc2aNcPd3Z2wsDD7NdxmHNzxofV1wgew6TP7tV1cd5H+PPTH7ZqXFh7gSq7crhEVw8fHh3bt2tGkSRP0er2zyxHVhDyPJWqcpk2b0qBBA1xcXOzbcKeHwZADv78Fq19Ho3ED7Bh4/uLpPjGsPZzK8dQcXltykC/HtnVYX0Jco9friY6OdnYZopqRKyOiRro+iFy4cIHDhw/bp+Eez0CPyQBof3ueOukb7NNuMdz0Wj4Z1dr2dM3y/Rcc1pcQQjiShBFRo+Xk5LBlyxYOHjzIoUN2Ggza+1Xo9CgAbZK/Qzm81D7tFqNFbV/bZGivLT4oa9cIh9q/fz8XLlzAYrE4uxRRzUgYETWal5cXzZo1AyAxMdE+g/IUBfq/j6XVvSioaBc/AsdWlb/dm3iiVwOahftwJc/Iywtl7RrhGFlZWRw9epTNmzdjMMgYJWFfEkZEjde4cWNatmwJwKFDh0hMTCz/L3SNBvPATzjn1xnFYoSfxsPp9Xao9kYuOg0fj2qFXquw5vAlFu4+75B+RM2m0+mIiYmhXr16uLm5ObscUc1IGBECaNSokQMCiZbd9R7G0rA/mArgh3vg7A47VHujxmE+PNUnBoA3lyWSkpnvkH5EzeXh4UGrVq1o3769s0sR1ZCEESH+0KhRI1q1agXA4cOHOX++/FcYVEWH+e6p1tlajbkwZzik7C93u8V5pGc0rev4kV1g4vmf98vtGiFElSFhRIjrxMTE0KpVK+rWrUtERIR9GtW5wT0/QJ3OUJAJs+6Cy3aakv76brTW2zWuOg0bjqfxw/Zku/chaqZTp06RlZXl7DJENSZhRIi/iImJoUOHDiiKAlinvy73VQYXT7j3J6jVCvLSYOYwyDhth2qLqh/sxfMDGgPw7orDnM3Is3sfombJz89n165d/Pbbb+Tlyc+TcAwJI0IU4/ogsn37dg4cOFD+QOLmC+MWQXATyE6xBpJM+w82vb9rPTpGBZBnMDN5/j4sFrldI8rOZDIRHh5OSEgIHh4ezi5HVFMSRoS4hdTUVJKTkzl69Cj799thHIZnIExYDAHRcPWMNZDkXLZLrddoNAofjWiFh4uWbaczmLE5ya7ti5rF29ubbt260aNHD2eXIqoxCSNC3EJoaCht2rQB4NixY/YJJN5hMGEJ+NSG9OPWMST5V+xQ7Z8iAz14eWATAD5YeYSTl3Ps2r6oeTQa+XUhHEd+uoS4jQYNGtC2rXXdl2PHjrFv377yBxK/SJi4FDxD4NIBmD0CCrPtUO2f7u0USY+GQRSaLDzz416MZpk1U5ROamoqRqPR2WWIGkDCiBAlUL9+fVsgOX78uH0CSWB96xUSd384vxPmjgGj/eYHURSFf49oiY+bjn3nMvly7Qm7tS2qP4PBwIYNG1i2bBm5ubnOLkdUcxJGhCih+vXr065dOwBOnjxJdrYdrmSENoVxC8HFG5I2wI/jwFRY/nb/UMvXnX/d1QKAL9edYE+yfW8HieorLy8PT09PvLy8ZOCqcDgJI0KUQnR0NO3bt6dr1674+PjYp9GItnDvfNC5w4k18PMkMJvs0zYwtFU4Q1uFY7aoPPPTPvIM9mtbVF9+fn7079+fnj172p4uE8JRJIwIUUpRUVHUqlXL9r6goKD8t2zqdoExP4DWBY4sh8WPgsVczkr/9M6w5oT5uHE6LZf3fjlst3ZF9aYoiqxDIyqEhBEhyiE7O5vVq1ezZ8+e8geS+r1h1EzQ6ODAfFj+FNhpSndfDz0fjbROdT97azLrjqbapV1RPeXk5MhyAqJCSRgRohyuXLlCQUEBJ0+eZPfu3eX/B7zRHXD3t6BoYPdMWPmi3QJJ94ZB3N+tHgDP/7yfK7myDLy4kdlsZs2aNfz6668ycFVUGAkjQpRDZGQkHTp0AKzrd9glkDS/G4Z9ZX29bQr8/nY5q/zTCwMa0yDEi8vZhby8yA6zyopqJzMz07YEggxcFRVFwogQ5VSvXj06duwIWAPJrl27yv9LvvVYGPSx9fXGT2D9h+Ws0spNr+XT0a3RaRR+PXiRRXvsPx29qNoCAgIYMmQI3bp1k4GrosJIGBHCDurWrWsLJKdPn7ZPIOnwIPT7l/X12n/Blq/LWaVV8whfnu4bA8AbSxI5d0UWPxNF6XQ6/Pz8nF2GqEEkjAhhJ3Xr1qVTp06AdSyJyWSHR2i7/gPiXra+/u0l2Dm9/G0Cj/SMpl1df7ILTTz7kyymJ6zs8jMrRBlIGBHCjiIjI+nevTuxsbHo9Xr7NBr7PHR70vp6+dOw78dyN6nTavhk1J+L6U3deLrcbYqqTVVVfvvtN9avX09enlwtExVLwogQdlarVi1cXFxs71NTU8t3y0ZRoM9b0PFhQLXOQZK4uNx11g305PXBTQH48LejHE7JKnebourKyMggLy+PK1eu4Orq6uxyRA0jYUQIBzKbzWzevJkdO3aUP5AM+ABajwPVAgsegGO/lbu+0R3q0KdJCAazhafm7aXAaL+J1kTVEhgYyB133EHHjh3RarXOLkfUMBJGhHAgRVFQFIUzZ86wffv28gUSjQaGfg7Nh4PFBD+Oh1Px5a7v/4a3JMjLhaOXsvm/X4+Uqz1RtXl5eRWZXViIiiJhRAgH0mg0tG/fHkVRSE5OtkMg0cJd30CjQWAutK70m7y1XDUGebny4R+zs87YnCSzs9ZAMt+McDYJI0I4WEREBJ07dy4SSCwWS9kb1Oph5HSo/zcw5sGckXB+d7lq7NUohPu61gPgufn7uJxtv5WDReWmqirr1q1jz549FBQUOLscUUNJGBGiAtSuXZsuXbrY7wqJzhVGz4a63aAwC2bfDZcSy1Xji3c0plGoN2k5Bp7/eZ/8b7mGyMjIID09ndOnT8tYEeE0EkaEqCARERG2QOLj41P+2S1dPGDsjxDRHvKvwMw7Ie1EmZtz02v5fEwbXHQa1h29zMwtZ8pXn6gSAgIC6NGjB61atbLf4+hClJKEESEqUEREBP3796dp06b2adDVG8b9DGEtIDcVZg6FK2UPEY3CvHn5jsYAvPvLYY5ezLZPnaLSUhSFsLAw6tev7+xSRA0mYUSICubt7W17bTQaSUxMLN8YEnd/GL8YghpB1nn4fghkXShzcxO71qNXo2AMJgv/nLtHHvcVQjichBEhnERVVTZv3syhQ4fYunVr+QKJZxBMWAL+UXD1DHw/FHLK9lSMoij8e0Qredy3BtixYwfHjx/HaDQ6uxRRw0kYEcJJFEUhJiYGjUbD+fPnyx9IfGrBxKXgUxvSj1vHkORllKmpYG9XPhwhj/tWZ1lZWSQlJbFv3z7MZrn6JZzLqWHEZDLx6quvEhUVhbu7O9HR0bz99tu3/Qc5ISGBdu3a4ebmRnR0NFOmTKmgioWwr1q1atG1a1dbINmyZUv5AolfpDWQeIVCaiLMHg4FZZvmvVdjedy3OnN3d6dNmzY0bNgQNzc3Z5cjajinhpEPPviAKVOm8OWXX3L48GH+/e9/8+GHH/LFF1/cdJ/Tp08zcOBAevTowZ49e3j55Zf55z//yYIFCyqwciHsp1atWnTr1g2NRsOFCxfKH0gC61tv2bgHwIXd8MMoMOSWqSl53Lf60uv1NGjQgFatWjm7FCGcG0a2bNnCsGHDGDRoEPXq1WPEiBH069ePnTt33nSfKVOmEBkZyaeffkqTJk148MEHmTRpEh999FEFVi6EfYWFhRUJJLt27SpfgyFNYMJicPWF5C0wbywYSz+hlTzuK4SoCDpndt69e3emTJnCsWPHiImJYd++fWzcuJFPP/30pvts2bKFfv36Ffmsf//+TJ06FaPReMNz8oWFhRQW/nl5OSvLesnaaDTKoK1SuHau5JyVXGnPWWBgIJ07d2bPnj1ERUWV/1wHNUW5Zx7aH0agnIrH8tMEzMNnWGdwLYXoQDde6B/DOyuO8O4vh2kf6UNMqPftdywj+VkrvdKes2PHjuHr60tISEj557upwuRnrfQcda6cGkZeeOEFMjMzady4MVqtFrPZzLvvvsuYMWNuus/FixcJDQ0t8lloaCgmk4m0tLQbFnl6//33eeutt25oZ926dXh4eNjnQGqQ1atXO7uEKqe050xVVTZt2mS3/gPr/pMuJz9Ce/w3UqYMY2e9x0Ep3UXRQBWa+Gk4fBUenLqZZ1ua0Tv4uqr8rJVeSc6Zqqq2Xyh6vb5Gh5Fr5Get5PLy8hzSrlPDyI8//sjs2bP54YcfaNasGXv37uWpp54iPDyciRMn3nS/v/7luXYfu7i/VC+99BLPPPOM7X1WVhZ16tShV69eBAYG2ulIqj+j0cjq1avp27evzNJYQvY4Z2lpaZw6dYp27dqVY6rugagnW6P+NI6Iq9upZYnGPPjzUgeSTrGFDP5yCym5BvYRxesDm5SxnluTn7XSK805y8/P5/jx4+Tn59OpU6cKqrBykp+10ktPT3dIu04NI8899xwvvvgi99xzDwAtWrTgzJkzvP/++zcNI2FhYVy8eLHIZ6mpqeh0umLDhaurK66urjd8rtfr5YevDOS8lV5Zz5nJZGLHjh0UFhZisVjo2rVr2QNJ4wHWxfV+mohm/zw0Lp4w6GMoxf+Ka/nr+WhUK+6fvoNZ287SIyaEfs3CylZPCcjPWumV5Jzp9XratWtXQRVVDfKzVnKOOk9OHcCal5eHRlO0BK1We8snCbp06XLDJbVVq1bRvn17+WES1YpOp6Nz585otVouXrzIpk2byjcfRJMhcNc3gAI7p8KqV6GUT8f0ahTCQz2iAHju5/1cuJpf9nqEEOIPTg0jQ4YM4d1332XFihUkJSWxaNEiPvnkE+666y7bNi+99BITJkywvX/00Uc5c+YMzzzzDIcPH2batGlMnTqVyZMnO+MQhHCokJAQevTogVar5dKlS+UPJC1HwpDPrK+3fAnx75e6ief6N6ZlbV8y8408OW8PJnM5HkMWFers2bNkZ8t6Q6LycWoY+eKLLxgxYgSPP/44TZo0YfLkyTzyyCO88847tm1SUlJITk62vY+KiuKXX34hPj6e1q1b88477/D5558zfPhwZxyCEA4XHBxcJJBs3LgRk8lU9gbbTYQBH1hfJ3wAGz8t1e4uOg1fjGmDl6uOHUlX+Pz342WvRVQYg8HA9u3bWblype2pQiEqC6eOGfH29ubTTz+95aO8M2bMuOGz2NhYdu/e7bjChKhkrgWSDRs2kJqayrFjx8q38m/nR8GYB7+/BWveAL0HdHq4xLvXDfTkvbtb8M+5e/hi3Qk61w+ka/2gstcjHM5gMBASEkJBQUGRxRqFqAxkbRohqojg4GB69uxJZGQkjRs3Ln+DPZ6Bns9ZX//6HOyeVardh7YKZ3T7OqgqPP3jXtJzZLr4yszLy4sePXrQu3dveZxXVDoSRoSoQoKCgujUqZNt4LeqquUbQ9LrFej8d+vrpf+AAz+Xavc3hjalfrAnl7IKmTxfpouvCsr+iLgQjiNhRIgqSlVV9u7dy4YNG8o+hkRRoP+70O5+QIWFD8ORFSXe3cNFx5dj29qmi5+68XTZ6hAOlZ6eXr5xRkI4mIQRIaqo3NxckpKSuHz5cvkDyaBPoOU9oJph/n1wYk2Jd29Sy4fXBlvHr3yw8gj7z10tWx3CIcxmMxs2bGDZsmUycFVUWhJGhKiivLy86NmzJzqdjrS0tPIFEo0Ghn0FTYeB2QDzxkHSxhLvPq5TJAOahWE0q/xj7h6yC2Stj8oiNzcXV1dXXFxcZOCqqLQkjAhRhQUGBhIbG4ter7cFkjIvZKXVwd3fQcP+YMqHH0bD2R0l2lVRFD4Y3pIIP3fOpOfx6uKDMn6kkvDx8WHAgAH06tVLBq6KSkvCiBBVXEBAAD179rRPING5wKiZEBULhhyYMxxS9pdoV18PPZ+PaY1Wo7Bk7wV+3nWubDUIu1MURRYGFZWahBEhqoHrA0l6enr5FrPSu8GYuVCnMxRkwqw7IfVIiXZtVzeAZ/rGAPD6kkROpOaUvQ5Rbnl5eXKFSlQJEkaEqCYCAgKIjY2lc+fOhIWVcwE7F0+49ycIbwN56TBzGKSfLNGuj8XWp3uDIPKNZp74YTcFxnI8eizKTFVV1q5dy2+//SZTwItKT8KIENWIv78/derUsb3Pz88v+y0bN18YtxBCmkLORWsguXr2trtpNAqfjGpFoKcLRy5m887yQ2XrX5RLVlYWBoOBwsJCuUUjKj0JI0JUU/n5+cTHx7N+/XoMBkPZGvEIgAlLILABZJ6FmUMh++JtdwvxceM/o1ujKDBnWzJL9p4vW/+izHx9fRkyZAjdu3eXic5EpSdhRIhqqrCwEIPBQEZGRvkCiVcITFgKfpGQccp6hST39mNSesYE8/e4BgC8vPAApy7L+JGKptfrCQwMdHYZQtyWhBEhqik/Pz9iY2NxcXHhypUr5QskvhHWQOIdDpePWAe15l+97W5P9WlIp6gAcg1mHp8j40cqisVicXYJQpSKhBEhqjG7BpKAKOstG48guLgf5oyAwlsPjNRpNXwxpg1BXtbxI28tSyxb36LEVFVl9erVbNy4kdzcXGeXI0SJSBgRoprz8/MjLi7OFkgSEhLKHkiCY6yBxM0Pzu2AuWPAmH/LXUJ83Ph0dBsUBeZuP8viPTJ+xJEyMzPJysoiNTUVFxcXZ5cjRIlIGBGiBvD19SUuLg5XV1dMJlP5VvoNaw7jF4KLNyRtgB/Hganwlrt0bxjEP3o3BODlRQdk/hEH8vPzY8CAAXTo0AG9Xu/scoQoEQkjQtQQ1wJJXFwc7u7u5Wssoh3cOx/0HtZF9X6eBOZbr4vz5N8a0iU6kDyDmb/P2U2+QcaPOIq3t3eRR7yFqOwkjAhRg/j4+BQJIufPn6ew8NZXNW6qbhe45wfQusKR5bD4UbDcPGBoNQqfjWlNkJcrRy9l8+ZSGT8ihLCSMCJEDXX27Fk2b95MQkJC2QNJ/V7WtWw0OjgwH5Y/BbeYfjzE243P72mNRoEfd55l4W5Zv8aetm7dyt69e8nPv/U4HiEqGwkjQtRQvr6+uLm5kZmZWb5A0mgADP8OFA3sngkrX7xlIOnaIIgn/2Zdv+aVRQc5fkmmKrcHVVW5ePEiJ06ckNV5RZUjYUSIGsrHx4fY2FhbIImPj6egoKBsjTW7C4Z9ZX29bQr8/vYtN3+idwPb+jWPz9lNnuHW401EyXTu3JnmzZvj5ubm7FKEKBUJI0LUYD4+PsTFxeHm5kZWVhYJCQllDyStx8Kgj62vN34CCR/edFOtRuE/o1sT7O3K8dQcXl8i40fKS1EUwsLCaNy4sbNLEaLUJIwIUcN5e3vfEEjKvLhehweh37vW1+v+BZu/vOmmwd6ufDGmDRoFft51jvk7b78InxCiepIwIoSwBRJ3d3dCQ0PR6XRlb6zrE9DrFevrVa/A9m9vumnn6ECe6WsdP/LakoMcuZhV9n5rsIMHD2I2m8seIoVwMgkjQgjAGkj69OlDq1atyj8Asudz0P0Z6+tfJsOe2Tfd9PG4BvSMCabAaOGx2bvJLpDxI6WRl5fHiRMnMJvNZR+ELISTSRgRQti4ubnZgojZbGbPnj1le0xUUeBvr0Pnx63vlzwBB34udlONRuHT0a0J93XjdFouLy06eKuHccRf6PV6mjdvjkajwcvLy9nlCFEmEkaEEMXau3cvJ06cID4+vuyBpP970O5+QIWFD8PhZcVuGuDpwlf3tkWvVfjtUCrxKfJoaknp9XoaNGhQvltrQjiZhBEhRLEaNWqEh4cHOTk55Qskgz6BVmNANcP8++HYqmI3bRPpz2uDmwKw9IyGnWeulKd8IUQVImFECFEsLy8v4uLiigSSvLy80jek0cDQL61zkViM1oX1TsUXu+n4znUZ3CIMCwpP/rify9kyBuJWTpw4waVLl1Dlvpao4iSMCCFuytPT0z6BRKuDu7+FRoPAXAhzx8CZLTdspigK/xrWlDB3ldTsQv4xdzcms8UOR1L9GAwG9u3bx/r168nMzHR2OUKUi4QRIcQtXR9IcnNz2bRpU9n+J67Vw8jpUP9vYMyDOSPh/K4b+3PVMamRGU8XLVtPZfDx6mN2OIrqx2KxEBUVRVBQEL6+vs4uR4hykTAihLgtT09PevXqha+vL23atCn7o786Vxg9G+r1AEM2zLobLh64YbNQd3jvzmYA/Df+JKsPXSpP+dWSm5sbbdu2JS4uTtaiEVWehBEhRIl4eHjQt29fgoKCbJ+V6QqJiweMmQe1O0LBVZg5DFKP3LDZwBZh3N+tHgDP/LSXM+m5Zay8epMgIqoDCSNCiBK7/hff1atX+f3338nNLUNIcPWCcT9DrdaQlw4zh0L6yRs2e+mOJrSN9CO7wMSjs3dTYDSXo/rqIyUlpWznXYhKSsKIEKLUVFVl165dXLlyhfj4+LL9YnTzhfGLIKQZ5FyC74fClTNFNnHRafjq3rYEerpwOCWL15cctNMRVF1ms5lt27bxyy+/kJGR4exyhLALCSNCiFJTFIWuXbvi5eVFXl5e2QOJRwBMWAxBMZB1znqFJCulyCa1fN35/I8F9X7aeY4fdyTb5yCqqIKCAvz9/fH09MTf39/Z5QhhFxJGhBBl4u7uTlxcXJFAkpOTU/qGvEJgwhLwrwdXktD9cBeuxqKPqnZrEMSz/RoB8NqSRA6er7mPsnp6ehIbG0u/fv1kvIioNiSMCCHK7Fog8fb2Ll8g8QmHicvApzZK+gm6nPg35BW9BfFYbH3+1jgEg8nCY3N2cTXPYKejqJpk+ndRnUgYEUKUi7u7O7GxsXh7e5Ofn09iYmLZGvKLhIlLUb1C8S04i3buSMi/avu2RqPwyajW1Alw52xGPk/O24vZUrNmHr169SpmswziFdWPhBEhRLldu0ISFRVFu3btyt5QYH1M9y6iUOeN5uI+68Rohdm2b/t66Jkyrh2uOg0Jxy7z2ZqaMyGaxWJh48aNLFu2jCtXZN0eUb1IGBFC2IWbmxvt27cvcvugsLAMa8sExbC5/guobn5wbrt16njDn1PQNwv35f+GtwDg87UnasyEaHl5eSiKgqIo+Pj4OLscIexKwogQwiESExNZtWoV2dnZt9/4L7I8IjGP+QlcvCFpA/x4L5j+DDZ3tanNfV3rAfDMj3s5dbkM41SqGC8vLwYOHEjv3r3RarXOLkcIu5IwIoSwO5PJxLlz5ygoKCA+Pr5MgUQNbwv3zge9B5xcC/PvA7PR9v1XBjWhQz1/sgtNPDJrF7mFJjseQeWkKAre3t7OLkMIu5MwIoSwO51OR1xcHL6+vrZAkpWVVfqG6naxTh2vdYWjv8DCh8BsDR16rXVCtBBvV46n5vD8z/vLNj19FVBYWFhtj00IkDAihHAQV1dXYmNjyx9IomPhnjmg0UPiIlj6BFgsAIR4u/Hfce3QaxVWHEjhf+tP2fkonE9VVdatW8eqVavIzKy586uI6k3CiBDCYa4PJIWFhWUPJA37wsjpoGhh31xY8Qz8caWgXV1/Xh9iXeH3g5VH2HQizZ6H4HS5ubm2Lw8PD2eXI4RDSBgRQjjUtUDi5+dHYWEh6enpZWuoyRC4+3+AArumw8qXbIFkXKdIRrSrjUWFJ37Yzbkrebduqwrx8vJiyJAhdOvWDb1e7+xyhHAICSNCCIe7Fkg6depEVFRU2RtqMQKGfWV9ve2/8PtboKooisK/7mxO8wgfruQZeayarfDr4uJCaGios8sQwmEkjAghKoSLiwuRkZG294WFhWW7ZdPmXhj0sfX1xv/A+g8BcNNrmTKuHf4eeg6cz+S1xQer/KDPql6/ECUlYUQIUeEKCwtJSEhg3bp1XL16tfQNdHgQ+r1rfb3uXdj0OQC1/T34YkxbNArM33WOH7ZX7RV+4+Pj2bx5c5kejRaiKpEwIoSocIqioNFoMBgMJCQklC2QdH0Cer9qfb36Ndj+LQDdGwbx/IDGALy5NJHdyVVz6vScnBzS0tK4cOGCjBUR1Z6EESFEhXNxcaFnz574+/uXL5D0fA56TLa+/mUy7J4FwCM9o7mjeRhGs8pjs3eRml1gv+IriJeXF/369aNdu3a4ubk5uxwhHErCiBDCKa4FkoCAAFsgKdMCcL1fhc5/t75e+g/YPx9FUfhwZCsahnhxKauQJ+bswWi22PcAKoCvr2/5BvwKUUVIGBFCOE1xgaTUV0gUBfq/C+0nASosegQOLcHLVceU8e3wdtWxPSmDd1ccdsQhCCHsQMKIEMKp9Hq9LZDodLqyjY9QFBj4MbS+F1Qz/PwAHPuN+sFefDK6NQAzNifx086z9i3eQXbs2MH+/fvJz893dilCVAgJI0IIp7sWSHr16oWnp2fZGtFoYOgX0OxusBjhx/Fwch19m4bydJ8YAF5ddLDSD2jNy8sjKSmJo0ePYjJV/8X/hAAJI0KISkKv1xcJIhcvXiQjI6N0jWi01llaGw8GcyHMGwtnNvOP3g3o3ywUg9nCo7N2cSmr8g5odXNzo2vXrjRu3FhW6BU1hoQRIUSlY7FY2LZtG+vXry99INHqYcQ0aNAHjHkwZxSaC7v5eFRrYkK9SM0u5JFZuyrtDK0ajYaIiAhatGjh7FKEqDASRoQQlY6iKAQEBGA0GklISCj9ejY6Vxg9G+r1AEM2zL4Lr4xEvp3QHl93PXvPXq0WM7QKUV1IGBFCVDqKotClSxeCgoIwmUysX7++9IFE7w5j5kGdzlCQCbPuoq45mS/HtrHN0Pr95iSH1F9Whw4d4vTp0zJWRNQ4EkaEEJWSTqejR48eBAcHlz2QuHrBvT9BeBvIS4eZw+gRkMVLdzQB4J0Vh9l8Ms0B1ZeewWDg8OHD7Ny5s2xr9ghRhUkYEUJUWjqdju7duxcJJKVep8XNF8YthNDmkHMJvh/Cgy003NUmArNF5e9zdnM2I88xB1AKiqLQrFkzIiIi8Pf3d3Y5QlQoCSNCiErt+kASERGBl5dX6RvxCIDxiyEoBrLOo3w/lPf7BNAiwpcreUYenrWLPINzb43o9XoaN25M165dURTFqbUIUdEkjAghKr1rt2w6dOhQ9l/UXsEwYSn4R8HVM7j9cBff3h1JkJcLh1OyeO7n/TKgVQgnkTAihKgStFqtLYhYLBZ27tzJ5cuXS9eITy2YuBR860D6CcKWjObbEVHotQor9qfwdfxJB1R+e2fOnOHy5csShkSNJWFECFHlHD9+nNOnT7Nhw4bSBxK/SGsg8QqD1EO0ib+fd++oA8BHq46y9sglB1R8c2azmT179hAfH1/6YxGimnBqGKlXrx6Kotzw9fe//73Y7ePj44vd/siRIxVcuRDCmRo0aEBoaChms5kNGzaQmppaugYCoq2BxCMILu5n1JGnua99EKoKT87dy8nLOY4pvBgmk4natWvj4+NDcHBwhfUrRGVS6jBy3333sX79ert0vmPHDlJSUmxfq1evBmDkyJG33O/o0aNF9mvYsKFd6hFCVA1arZZu3boRFhaG2Wxm48aNpQ8kwY1gwhJw84NzO3g960261XUnu9DEQzN3klVgdETpN3B1daV9+/b069dPBq6KGqvUYSQ7O5t+/frRsGFD3nvvPc6fP1/mzoODgwkLC7N9LV++nPr16xMbG3vL/UJCQorsp9Vqy1yDEKJq0mq1dO3atXyBJKw5jF8Erj5okjcz3e1T6vpoOHU5l6fm7cVsqbgxHBJERE2mK+0OCxYsID09ndmzZzNjxgzeeOMN+vTpwwMPPMCwYcPKtvw31gl/Zs+ezTPPPHPbv5Rt2rShoKCApk2b8uqrr9KrV6+bbltYWEhhYaHt/bXJhIxGI0ZjxfzPpzq4dq7knJWcnLOyKe1569ChA9u3b+fSpUts3ryZfv36le7foZAWKPfMQ/vDSFzOJLCkjo7uefez9kgq//fLIZ7vH1OWwyiRy5cv4+Xlhbu7e7nakZ+1spHzVnqOOleKWs7h23v27GHatGl89913eHl5MW7cOB5//PFS3zr56aefGDt2LMnJyYSHhxe7zdGjR1m/fj3t2rWjsLCQWbNmMWXKFOLj4+nZs2ex+7z55pu89dZbN3z+ww8/4OHhUaoahRCVk6qqmEwmtFotGk3ZhsIFZR+i88mP0apGDrp3ZNiVf2BGy70NzHQMtv8VElVVbf+w63S6MtctREXKy8tj7NixZGZm4uPjY7d2yxVGUlJSmDlzJtOmTeP8+fMMHz6clJQU1q1bx7///W+efvrpErfVv39/XFxcWLZsWalqGDJkCIqisHTp0mK/X9yVkTp16pCSkkJgYGCp+qrJjEYjq1evpm/fvmW++lXTyDkrG3udN4vFUupf8MrJ39HOH49iNnAwaABDzo1Dp9Uy54EOtKnjV+ZailNQUMCOHTvIzs6mf//+5brdLD9rZSPnrfTS09OpVauW3cNIqW/TGI1Gli5dyvTp01m1ahUtW7bk6aef5t5778Xb2xuAefPm8dhjj5U4jJw5c4Y1a9awcOHC0pZD586dmT179k2/7+rqiqur6w2f6/V6+eErAzlvpSfnrGzKc96ys7PZsGEDbdu2JSwsrOQ7Nh4AI2fAj+NpnraS74O1TLg8lsd/2MfSJ7oR7le+2ynX0+v19O7dG4PBgIuLi93alJ+10pPzVnKOOk+lvi5Yq1YtHnroIerWrcv27dvZuXMnjz76qC2IgPUqh5+fX4nbnD59OiEhIQwaNKi05bBnzx5q1apV6v2EENXXsWPHyM3NZdOmTaSkpJRu58aDYPi3oGjomb2CT33mkZZTwEMzdzpkynh7BREhqrJSXxn5z3/+w8iRI3Fzc7vpNv7+/pw+fbpE7VksFqZPn87EiRPR6YqW89JLL3H+/HlmzpwJwKeffkq9evVo1qyZbcDrggULWLBgQWkPQwhRjbVp04bCwkLOnz/P5s2b6dq1a+n+09J8OJgKYfFj3GlYxlV3LW9eGMmzP+3jq7Ft0WjK9+RLdnY2Hh4e8iSgEH8o9ZWR8ePH3zKIlNaaNWtITk5m0qRJN3wvJSWF5ORk23uDwcDkyZNp2bIlPXr0YOPGjaxYsYK7777bbvUIIao+jUZD586diYiIwGKxsHnz5tJfIWk9FgZ9AsB96mKe1i/k14MX+ez34+WqTVVVNm3axPLly0lLSytXW0JUF6W+MmJv/fr1u+l6DDNmzCjy/vnnn+f555+vgKqEEFXdtUCydetWzp8/z6ZNm+jatetNn9YrVocHrFdIfnuJJ7ULKLDo+ez3ocSEejOoZdluDxcUFGAymTCbzfj6+papDSGqG6eHESGEcJRrgWTbtm2cO3eOI0eOUKtWrdJNMNblcTAXwpo3eUE/j0L0PDtfQ91AD5pHlD5MuLu7M2jQILKysmTQpBB/kDAihKjWNBoNnTp1wtvbm5iYmLLNdNr9aesVkvj3eV0/C4NRx0MzXVjy926E+JT+trWiKHJVRIjryCw7QohqT6PR0Lx58yJPruTklHIxvNgXrKEE+Jd+Ot1zVvLwrF0UGM0lbkJm+hSieBJGhBA1zvHjx1m5ciXnzp0r+U6KAn97Azo/DsAH+m+JPL+ClxYeuOm4t7/asGEDq1ev5sqVK2UpW4hqS8KIEKJGUVWVjIwMVFVl69atpQ8k/d+D9g+gQeUT/X8p2LeQb9afuu2u+fn5XLlyhczMzHKvRSNEdSNhRAhRoyiKQseOHYmMjLQFkrNnz5amARj4EbQeh06x8Ln+S3atmsOaQ5duuZu7uzuDBw+ma9eudp0eQYjqQMKIEKLGuRZI6tati6qqbNu2rXSBRKOBoZ9Di5HoFTNf6j5j/rzpHLmYdcvdXF1dS/dosRA1hIQRIUSNpCgKHTp0KEcg0cKdU7A0HoqrYuIz5SO+mjqNy9mFN2xazsXRhaj2JIwIIWqsa4GkXr16qKpa+idstDo0I6ZirN8fN8XIB4b3+M/U7294wmbz5s1s2bKF7OxsO1YvRPUhYUQIUaMpikL79u3p1q0bTZo0KX0DOhf0Y2aRFxmHh1LIS1de58tZ82xXQwoKCrhw4ULpBsoKUcNIGBFC1HiKohQZy2E0Gku3lo3OFY9xc8kM7Yy3ks/DZybzw+JlALi5udGnTx9atWpVZHVzIcSfJIwIIcR1TCYTGzduZOPGjZw5c6bkO7p44DtpAZf92+Cj5HHH3sdYm7AOsK5kHhMT46CKhaj6JIwIIcR1tFotPj4+AGzfvp2kpKSS7+zqRfAjS7ng2ZQAJYeWaydycN8OxxQqRDUiYUQIIa6jKApt27YlOjoagB07dpQukLj5EPr4LyS7NOB8WD9yN31H0pG9DqlViOpCwogQQvzFtUBSv359oPSBROvpj+9DSzge8DeSg/tgWvIkOZduP0urEDWVhBEhhCiGoii0adOmSCApzRgS78BwGjdpTHDGdurn7yXv24GYrpRiHhMhahAJI0IIcRPXAkmDBg1wcXHB19e3xPtqNBqat2xDaNyDnFVDCDGlcHXKQMi+9bTxQtREEkaEEOIWFEWhdevW9O3bFz8/v1Lv36RRI04NnMs5NYigwmSuThkAuWn2L1SIKkzCiBBC3IaiKHh4eNjeX758mVOnbj4G5Pjx4yQlJWEymQCI69Se+M7fcVH1xy/3FNnfDYa8DIfXLURVIWFECCFKIScnhw0bNrBr1y5Onjx5w/fNZjOJiYns2LGD9PR02+f3Dojj+4ZfcFn1xfvKYQpm3AkFmRVYuRCVl4QRIYQoBU9PT9ug1t27d98QSCwWCzExMQQHBxMSEmL7XFEUnr5nEP8X/AEZqhduqfswzhwOhaVcD0eIakjCiBBClIKiKLRs2dI2o+ru3bs5ceKE7ft6vZ6mTZsSFxeHoihF9nXRaXjl/uE87/42maoH+gs7MP8wGgx5FXoMQlQ2EkaEEKKUrgWSRo0aAbBnz54igeRWAjxdeHHSaB5TXiVbdUd7ZiPqj/eCscCRJQtRqUkYEUKIMlAUhRYtWhQJJAcOHCA9Pd22Yu/NNAjx4u/jRvOA6QVyVVeUk2th/kQwGSqidCEqHQkjQghRRtcCSePGjQkPDycpKYm1a9eWaMXfbg2CuGvYcB4wPkeBqodjK2HBJDAbK6ByISoXCSNCCFEOiqLQvHlz2rVrR2hoKB4eHkUGrt7KmI6RtO4xhIeMz2JQdXB4GSx6BCxmB1ctROUiYUQIIcpJURTc3Nzo2LEjAwYMYP/+/Rw9erRE+z7fvxE+zfvzqPEpjGjh4AJY8gRYLA6uWojKQ8KIEELYUWpqKidPnixxINFoFD4e2YqrtXvzD8M/MKGBfT/AiqfhNmNPhKguJIwIIUQ5ZWRkUFBgfRqmVq1aNG3aFID9+/dz5MiR2+7vptfy7YT2HPKL4xnD41hQYNcMWPmiBBJRI0gYEUKIclBVle3bt7N8+XIuXrwIQLNmzWyB5MCBAyUKJIFerky/vwMJrrE8b3zY+uG2KbD6dQkkotqTMCKEEOVgMBhwcXFBo9EQGBho+7xZs2Y0a9YMsAaSw4cP37at+sFe/G98O5bSi5eND1g/3Pw5xL/vkNqFqCx0zi5ACCGqMldXV3r37k1BQQF6vb7I965dHUlMTCQxMZGIiAh8fHxu2V6n6ED+PaIlT/1owRUDb+hnQcIHoHWBnpMddhxCOJOEESGEsAM3N7diP2/atCmKouDp6XnbIHLNnW0iOJuRx8erwVUx8aJuLqx9B3Ru0PUJe5YtRKUgYUQIIcooLy8PNzc3NJpb3/Fu0qRJkfdGo/GGqyh/9UTvBpzJyGPKriF4aUw8oZkPq14BnSt0fKjctQtRmciYESGEKKOtW7eyfPlyUlNTS7xPfn4+a9asITEx8ZbbKYrCe3e1oGv9QD4y3MkMzd3Wb/wyGXbPLE/ZQlQ6EkaEEKIMDAYDubm5GAyGEt9+AUhJSSEnJ4dDhw6RmJh4y3VsXHQa/juuHQ1DvHkzbzgLXYdZv7H0n7Dvx/IeghCVhoQRIYQoAxcXFwYNGkSvXr1uOl6kONHR0bRs2RKgRIHE113PtPs6EOTlxjOZo/jdawigwuJHIXFReQ9DiEpBwogQQpTRXx/nLalGjRrRqlUrAA4fPnzbQFInwIOpE9vjptfyYNpodgUMAtUCCx6EIyvKXL8QlYWEESGEKCWzufwL2cXExBQJJAcPHrxlIGlVx4/P7mkDioaRF8ZwPHQgWEzw00SUE2vKXY8QziRhRAghSmnz5s38/vvvZGRklKudmJgYWrduDcD58+cxmUy33L5/szBeGdgECxruSB5DSkR/sBjRLriPoOxbD4gVojKTR3uFEKIUDAYDqampWCyW2z6eWxINGzZEr9cTGhpaovYe6B7F2Yw8vt9yhr+dGc+mKDP+Z9fQ6dR/ILk71O9Z7pqEqGhyZUQIIUrh2sDVTp064e3tbZc269Wrh7u7u+39lStXbnrLRlEUXh/SjL5NQ8kzaeh7dhI5dWLRWQxof7wHzu6wS01CVCQJI0IIUUpubm5ERkY6pO0zZ86wZs0a9u/ff9NAotUofH5PG9pE+pFWAENTH+WiZxMUQy7MHg4X9jqkNiEcRcKIEEKU0K0GmNrLtXEjx44dY9++fTft091Fy3cT2lMv0INTmSrj8idjiugEhZkw6064eNDhtQphLxJGhBCihHbu3Mm2bdvIyspyWB/169enbdu2ABw/fvyWgSTQy5UZ93fE30PPiTx3/s6LqOHtIP8KzBwGl486rE4h7EnCiBBClIDBYCA5OZnk5OTbPvVSXvXr16ddu3aANZDs3bv3poGkXpAn/xvXBr1G5beT+bzt9w5qWEvIS4Pvh0L6SYfWKoQ9SBgRQogS0Ov19OrVi6ZNm+Lv7+/w/qKjo22B5MSJE7cMJK3r+DGxoQWNAtN3X+Xbep9ASFPIuQjfD4ErSQ6vV4jykDAihBAloCgKAQEBNGvWDEVRKqTP6Oho2rdvb+v/VloEqLw+2Lo68HvxqSxt9TUENoSs89YrJJnnHF6vEGUlYUQIISqxqKgoevfuTatWrW4bSO7tWIfH4uoD8MyKFLb2mA7+UXD1jDWQZF+siJKFKDUJI0IIcRuHDh3i0KFD5OfnO6X/wMBAWxAxm82cOHHiprdsnuvXiGGtwzFZVB5cdIFjd/wAvpGQcdIaSHIuV2TpQpSIhBEhhLgFs9nMsWPHSExMdOhTNCWhqipbtmxhz5497N69u9hAotEo/HtES7pEB5JTaGLc/AtcvOsn8A6HtKPWx37zyjeNvRD2JmFECCFuQVEU2rRpQ506dQgJCXF6LXXq1AHg1KlT7Nq1q9hA4qrTMmV8O2JCvUjNLmT8wlSyRy0AzxC4dNAaSPKvVmzxQtyChBEhhLgFjUZD3bp16dy5c4UNXL2VunXr0rFjRwBOnz5900Di665nxv0dCfVx5XhqDg/+kolh3CLwCISUfdaZWguce6VHiGskjAghRBVTt25dOnXqBFgDyZ49e4oNJOF+7sy4vyNerjq2nc7g2XgjlnGLwd0fzu+EOSOhMKeCqxfiRhJGhBDiJpKSkkhOTsZsNju7lBtERkbaAsmtamxSy4cp49qh0ygs23eBD/bpYfxicPWFs1th7j1gyKvAyoW4kYQRIYQohsVi4cCBA2zbto2UlBRnl1OsyMhIOnfujF6vR6O5+T/n3RsG8cHwlgB8k3CKmWf8YPxCcPGGpA0wbywYCyqoaiFuJGFECCGKYbFYiI6Oxs/Pj1q1ajm7nJuqU6cOffv2vWUYARjerjaT+8UA8MbSRH7LrA3jfga9J5xaBz+NB1NhRZQsxA0kjAghRDF0Oh3NmjWjb9++aLVaZ5dzSy4uLrbXV65cueljv3/v1YAxHeugqvDPuXvYaYmBsT+Czh2Or4L594PZWJGlCwFIGBFCiGrDZDKxYcMGTp48yfbt27FYLEW+rygK7wxrzt8ah1BosvDA9zs57tEaxswFrSscXQELHgCzYxcCFOKvJIwIIcRfXLp0iYyMjJvOclpZ6XQ62rZti6IoJCcnFxtIdFoNX45tS5tIPzLzjUyctp2UoM5wzxzQusChJbDoEbBUvkG7ovqSMCKEENdRVZU9e/bw+++/c/bsWWeXU2q1a9emS5cuKIrC2bNniw0k7i5apk3sQHSwJxcyC7hv2g4ya8fByO9Bo4ODP8OSJ+Av+wnhKBJGhBDiOmazGT8/P1xcXCr1wNVbiYiIKBJItm3bdkMg8fd0YeakjoR4u3L0UjYPzdxJQf3+MGIaKFrY9wMsf0oCiagQEkaEEOI6Op2Ozp07M3jwYPR6vbPLKbPrA8m5c+c4evToDdvU9vdgxv0d8XbVsf10Bk//uBdz46Fw9/9A0cDu7+HX56GK3a4SVY+EESGEKEZlf4KmJCIiIujatSuhoaE0bNiw2G2ahvvwzYR2uGg1/HrwIm8tS0RtPhyGfQUosONb+O0VCSTCoSSMCCHEHzIzMyksrF5zbYSHh9OjRw90Op3ts78OzO1aP4hPRrdCUWDmljN8HX8SWo+FIZ9ZN9j6Fax5UwKJcBgJI0II8YedO3eybNkyzp8/7+xS7Or6Bf4SExPZsmXLDWNIBrcM543BTQH48Lej/LTzLLSbCAM/sm6w6VOIf7+iShY1jFPDSL169VAU5Yavv//97zfdJyEhgXbt2uHm5kZ0dDRTpkypwIqFENWVyWSyXTEIDAx0cjWOkZOTw5EjRzh//nyxgeS+blE8FlcfgJcWHmDtkUvQ8SHo/0cISfgA1n9Y0WWLGsCpYWTHjh2kpKTYvlavXg3AyJEji93+9OnTDBw4kB49erBnzx5efvll/vnPf7JgwYKKLFsIUQ3pdDr69OnDHXfcgZubm7PLcQgvLy+6deuGRqPhwoULxQaS5/s34u62EZgtKo/P2c2e5CvQ5XHo85Z1g7X/gk2fOaF6UZ05NYwEBwcTFhZm+1q+fDn169cnNja22O2nTJlCZGQkn376KU2aNOHBBx9k0qRJfPTRRxVcuRCiuvL09HR2CQ4VFhZWJJBs3ry5yIq/iqLwwfCWxMYEU2C0MGnGDk5ezoHuT0GvV6wbrX4dtv7XOQcgqiXd7TepGAaDgdmzZ/PMM88Uub95vS1bttCvX78in/Xv35+pU6diNBqLfQyvsLCwyIC0rKwsAIxGI0ajrMFQUtfOlZyzkpNzVjbOOG8FBQW4uLjcdrG5yqq05ywwMJDOnTuzdetWUlJS2LRpEx07dizyBNFno1owYfpO9p/PYsLUbfz4UEdCuz6NxlCAdtPHsPJFzGiwtJvkkGOqCPJ3tPQcda4UtZLMd/zTTz8xduxYkpOTCQ8PL3abmJgY7rvvPl5++WXbZ5s3b6Zbt25cuHCh2AmK3nzzTd56660bPv/hhx/w8PCw3wEIIaoso9GIqqrodLoqG0jKwmKxYDJZ16Ep7thzjPCfg1rSChQiPFT+0cyMu1al6YWfaJi6AoA9kQ+QHFj81WxR/eTl5TF27FgyMzPx8fGxW7uV5srI1KlTueOOO24aRK7561WTa1nqZldTXnrpJZ555hnb+6ysLOrUqUOvXr2q7SA1RzAajaxevZq+fftW6YmgKpKcs7Kp6PNmMplYvXo1hYWFxMXF4eXl5fA+7a085+zy5cvk5OQQFRVV7Pc798hj9LfbOZ9jYHFaMN9NaIerdiDm1a+i3fENrZOn0aJ1O9QWo+xxKBVK/o6WXnp6ukParRRh5MyZM6xZs4aFCxfecruwsDAuXrxY5LPU1FR0Ot1Ng4Wrqyuurq43fK7X6+WHrwzkvJWenLOyqajzptfrGTx4MGlpafj7+zu8P0cqyzn7638ADQYDWq3WdsumfqgvM+7vyD3/28rW01d4YVEiX9zTBs3AD0A1oeycim7ZE+DiBs2H2+1YKpL8HS05R52nSnE9cvr06YSEhDBo0KBbbtelSxfbEzfXrFq1ivbt28sPkhCizDQaDSEhIc4uw+kKCwuJj49n06ZNRQa1No/wZcq4dui1Civ2p/D28kOoYJ2DpO0EUC2w4CE4tNRptYuqzelhxGKxMH36dCZOnFhkhkCw3mKZMGGC7f2jjz7KmTNneOaZZzh8+DDTpk1j6tSpTJ48uaLLFkJUA399rLWmy8nJIScnh0uXLt0QSLo3DOKjka0AmLE5yTpLq0YDgz+DVmNANcPP98PRX51VvqjCnB5G1qxZQ3JyMpMm3TgiOyUlheTkZNv7qKgofvnlF+Lj42ndujXvvPMOn3/+OcOHV81Lg0II59qxYwdr164lLS3N2aVUCoGBgfTo0QOtVsulS5fYuHGjbYArwLDWEbw6qAlgnaV13vZkayAZ9pX1Fo3FBD9NgONrnHUIoopy+piRfv363bBOwjUzZsy44bPY2Fh2797t4KqEENWdyWTi/PnzmM3mGvUEze0EBwfTo0cPNmzYQGpqKps2baJbt262K9cP9ogmPdfAf+NP8vKiA/h7utC/WRjc9Q2YjXB4KcwbC/f+BNFxzj0YUWXI30AhRI2k0+m44447aNu2bZUfuGpvwcHB9OzZE51OZwsk118heb5/I0a3r4NFhX/M3cPWU+mg1cPwqRBzB5gL4Yd7IGmjE49CVCUSRoQQNZa7uzv169e/6dQANVlQUJBttd/s7Owik0cqisK7dzWnX9NQDCYLD32/k8QLmaBzgVHfQ4M+YMqHOaMgeZsTj0JUFRJGhBBCFCsoKIiePXsSFxd3wzT5Oq2Gz8e0oVNUANmFJiZO28GZ9FzQucLo2dZbNMZcmDMCzu1yzgGIKkPCiBCixtm/fz87duywLQ8hbi4wMLDIRHCXL1+23bJx02v5dmJ7mtTyIS2nkPFTt5OaVQB6d7hnLtTtDoVZMPsuuLDXSUcgqgIJI0KIGsVsNnPq1CmSkpLIz893djlVSkpKCgkJCWzYsMEWSHzc9Hw/qQORAR4kZ+QxcfoOMvON4OIBY3+EOp2gIBNm3QkXDzr3AESlJWFECFGjaDQaunfvTsOGDWWis1JycXFBq9WSlpbGhg0bbIumhXi7MeuBjgR5uXI4JYuHZu6kwGgGVy+4dz5EtIP8KzBzGKQecfJRiMpIwogQokZRFIWgoCBat24tA1dLKTAwkNjYWPR6/Q2BpG6gJ99P6oC3q47tpzP4x9w9mMwWcPOFcQsgrCXkpcHMoZB2wslHIiobCSNCCCFKLCAggJ49e6LX60lPTy8SSJqF+/LtxPa46DSsPnSJlxcdsM4j5e4PE5ZASDPIuQTfD4GMU04+ElGZSBgRQtQYJ06c4PDhwxQUFDi7lCotICDAdoUkPT2d9evX28aQdI4O5IsxbdAo8NPOc/z7t6PWnTwCrIEkuDFkX4Dvh8LV5Fv0ImoSCSNCiBrBYrFw+PBhDh48yOXLl51dTpXn7+9vCyTe3t62VX4B+jcL4/27WwDw3/iTfLfhj6sgXsHWQBLYADLPwozBkHneGeWLSkbCiBCixmjevDm1atUiPDzc2aVUC/7+/vTp04cOHTrcMP5mdIdInh/QCIB/rTjMwt3nrN/wDoOJy8C/Hlw9Y71lk32xgisXlY2EESFEjaDRaIiKiqJ79+5F/hcvysfLy8sWRCwWC4cOHcJgMADwWGx9HuweBcBzP+9n7ZFL1p18wq2BxDcSMk5aA0lOqlPqF5WDhBEhhBB2sXfvXhITE1m/fj0GgwFFUXh5YBPubhOB2aLy+Jzd7EzKsG7sFwkTl4JPBKQdsz72m5vu3AMQTiNhRAhR7Z0/f55z585hsVicXUq1Fh0djYuLC1euXLEFEo1G4YMRLenVKJgCo4VJM3Zw9GK2dYeAKOsVEq8wSD0Es4ZBXoZzD0I4hYQRIUS1pqoqBw4cYMuWLZw5c8bZ5VRrfn5+xMXF2QJJQkICBoMBvVbD1/e2o11df7IKTEyYto2zGXnWnQLrW6+QeAbDxQMw+27rjK2iRpEwIoSo1iwWCxEREXh5eVG7dm1nl1Pt+fr6EhcXh6urK1evXrUFEncXLVMnticm1ItLWYVMmLadtJw/VgIObgQTloJ7AFzYA7NHQGG2cw9EVCgJI0KIak2r1dKiRQsGDBiAXq93djk1gq+vL7GxsbZAsmHDBlRVxc/DhZmTOhHh587ptFwmTttOVoF1wjRCm1of+3Xzg3PbYc4oMOQ69ThExZEwIoSoEWTq94p17QqJh4cHzZo1s53/MF/rOjaBni4kXsjiwRl/rGMDUKsljF8Erj6QvBnm3gNGWcywJpAwIoSottLT07l69aqzy6ixfHx8GDBgAGFhYUU+jw724vtJHa3r2CRl8Pic3RjNfwwujmhrXcvGxQtOr4d594JRZsyt7iSMCCGqrX379rF69WpOnz7t7FJqrOvndMnOzmbDhg0UFhbSPMKXqfd1wFWnYe2RVCbP34fFolo3rNPRutqv3gNO/g7zJ4LJ4KQjEBVBwogQoloym824u7uj1WqpVauWs8up8VRVZdu2bVy8eJH4+HgKCgroGBXAlHHt0GkUluy9wBtLE60L6wHU7Qpj5oHODY6thJ/vB7PRuQchHEbCiBCiWtJqtXTp0oWhQ4fi5ubm7HJqPEVR6NSpE25ubmRlZZGQkEBBQQG9Gofw8ahWKArM2nqGT1Yf+3On6Fi4Zw5oXeDIclj4MJhNzjsI4TASRoQQ1ZpOp3N2CeIP3t7exMXF3RBIhrWO4O1hzQH4Yu2JPxfWA2jQB0bNAo0eEhfCksfBYnbSEQhHkTAihKh2cnJybOujiMrlWiBxd3cnKyvLdstmfOe6PNf/z4X1ftp59s+dGg2AkdNB0cL+H2HZP0Fm061WJIwIIaqdPXv2sGzZMpKTk51diijG9YEkOzubffv2AfB4XH0e7hkNwIsL9rPyYMqfOzUZAsO/A0UDe2bDL8/CtfElosqTMCKEqFYsFgsFBQVYLBb8/f2dXY64CS8vL+Li4ggPD6dNmzaAdVzJS3c0ZnT7OlhU+OfcvWw8nvbnTs3vhru+ARTYOQ1WviiBpJqQMCKEqFY0Gg19+vShf//+eHt7O7sccQteXl5069YNFxcX22cWi4X37m7BHc3DMJgtPDxrJ3uSr/y5U8tRMOxL6+ttU2DVqxJIqgEJI0KIakdRFHx8fJxdhiil48ePs2rVKgyFBXx6T2t6NAwiz2DmvunXrfQL0GYcDP7U+nrLl/D7WxJIqjgJI0KIasNgMPw5T4WoUkwmE8eOHSMnJ4f4+HjMhkKmjGtHm0g/MvONjJ+6jeT0vD93aH8/DPzI+nrjf2Dde84pXNiFhBEhRLWxZ88efvnlFy5cuODsUkQp6XQ621o21wKJYjYw/b4ONAr1JjW7kHFTt5Gadd3U8B0fgv7vW1+v/zck/Ns5xYtykzAihKgWLBYLqamp5OXlySRnVZSnpydxcXF4enqSm5tLfHw8LpiY9UBHIgM8SM7IY/zU7VzNu+6x7S6PQ9+3ra/XvQsbPnFO8aJcJIwIIaoFjUbDwIED6datmzxFU4UVF0i8dBZmP9CJEG9Xjl7K5v4ZO8gzXDcTa7cnofdr1te/vwWbv3BO8aLMJIwIIaoNrVZLeHi4bbl6UTV5eHgUCSQXLlwgMtCDWQ90wtddz57kqzwyaxeFputmYu05GeJesr5e9Sps/a9zihdlImFECFHlyaDV6udaIGnTpg0NGjQAoFGYN9Pv74CHi5YNx9N4at5ezJbr/uxjX4Aek62vV74I2791QuWiLCSMCCGqvL179xIfH09aWtrtNxZVhoeHhy2IABiNRhoHufK/8e1x0Wr49eBFXl544M8wqijQ+1XrbRuAXybDzulOqFyUloQRIUSVZrFYSE5O5vLly5jNsoBadWU0GtmwYQPr1q2jTbg7n49pjUaBH3ee5f1fjxQNJH3egs5/t75f/pR1+nhRqUkYEUJUaddmXG3ZsiUhISHOLkc4iMlkorCwkLy8PNatW0f3et78390tAfjf+lN8ufbEnxsrCvR/Fzo+Yn2/5AnYN88JVYuSkjAihKjyPD09adSokQxcrcbc3d2Ji4vD29ub/Px84uPjGdjEn9cGNwXg49XHmLbx9J87KArc8QG0nwSosPgxOPCzc4oXtyVhRAghRJXg7u5ObGxskUAyunUwT/eJAeDt5Yf4acfZP3dQFBj4MbSdAKoFFj4MiYucVL24FQkjQogq68iRI+zatYvMzExnlyIqSHFXSO7vGMpDPaIAeHHhfpbvv24GXo0GBn8GrcaCaoYFD8Lh5U6qXtyMhBEhRJVksVg4fvw4p06dIisry9nliArk5uZGXFwcPj4+mM1mVFXl5YFNGNOxDhYVnpq3l3VHUv/cQaOxrvTbYhRYTDD/Pji60mn1ixtJGBFCVEmKotCpUyeioqIIDw93djmigrm5uREbG0tcXBy+vr4oisK/7mzB0FbhmCwqj87exZaT6X/uoNHCnf+FZneDxQg/jUc5+bvzDkAUIWFECFElKYpCSEgI7du3R6vVOrsc4QRubm74+vra3mekp/HmHdH0aRJCocnCg9/vYE/ylT930Org7v9Bk6FgNqCdP4HgrINOqFz8lYQRIYQQVV56ejobNmxg04b1vD+kId0aBJJrMHPf9B0cTrnuNp5WD8OnQqOBKOZCOp36D0rSBucVLgAJI0KIKujMmTMcPXqUgoKC228sagQvLy+8vLwoKChg66YNfHJnDG0j/cjMNzJ+6nZOXc75c2OdC4ycgaVBX7SqEe1P90LSJucVLySMCCGqFlVVOXz4MPv37+fChQu330HUCK6ursTGxuLr60tBQQHbNm/kixGNaVrLh7ScQsZ9t43zV/P/3EHninn4dC55t0Ax5sGckZC8zXkHUMNJGBFCVCmqqhITE0NQUBB16tRxdjmiErkWSPz8/CgsLGTX1k38d1RjooM9uZBZwL3fbiU1+7qraTo3tkc/iSUqFoy5MHs4nNvpvAOowSSMCCGqFI1GQ3R0NL169UKv1zu7HFHJuLq60rNnT1sg2bdjC9+NbU6EnztJ6XlMmLqdq3kG2/YWjQvmkbOgXg8wZMOsu+H8biceQc0kYUQIIUS1cv0VksDAQOqFBvDDQ50I8XblyMVsJk7fQU6h6c8d9B4wZh5EdoHCTJh1F6Tsc94B1EASRoQQVcalS5e4cOECFovF2aWISs7FxYXY2Fi6dOmCRqOhbqAnsx/shJ+Hnn1nr/Lg9zsoMF63yrOrF9w7H2p3hIKrMPNOuJTorPJrHAkjQogq4+DBg2zatIkTJ07cfmNR47m4uKDRWH/NqapKYWoS/xvdBC9XHVtPZfDEvH2Yrs+1rt4w7meIaAf5GfD9UEg94pziaxidswuorMxmM0aj0dllVBpGoxGdTkdBQQFms/n2Owi7nTO9Xi+TemGd/j0oKIi8vDwiIyOdXY6oYo4fP86RI0dwcXFhyqjWPDjvEAnH0sgM1DDIomIbfeTmC+MWwsyh1ls13w+B+1ZAcIwzy6/2JIz8haqqXLx4katXrzq7lEpFVVXCwsI4e/asLNNeQvY8Z35+foSFhdXoc6/RaGjVqhUtW7as0edBlE29evU4e/YsGRkZpJ/Yy5fDW/DY/CPsTdfwypJEPhzRGo3mj58rdz8Yv9h6ZeTSAWsguf8XCKzvzEOo1iSM/MW1IBISEoKHh4f8o/cHi8VCTk4OXl5etsue4tbscc5UVSUvL4/UVOuiX7Vq1bJniVWS/J0UZeHi4kLPnj1Zv349GRkZqEn7+XhIY55cfIIFuy/g7ebCG0Oa/vnz5REAE5bA94Mh9dCfV0gCopx7INWUhJHrmM1mWxAJDAx0djmVisViwWAw4ObmJmGkhOx1ztzd3QFITU0lJCSkRt6yuXr1KhqNBh8fH2eXIqowvV5fJJDoLx7hgRj47pieGZuT8HHT8Uy/Rn/u4BkIE5bCjEGQdvTPQOJf13kHUU3Jb5XrXBsj4uHh4eRKhCjq2s9kTR3HdODAAX777TcZuCrK7VogCQwMxGg00sjbyBsDGwLw+doTfJNwsugOXsEwcSkENoDMs9ZAknnOCZVXbxJGiiGXgUVlU5N/JlVVRavVoigKYWFhzi5HVAN6vZ4ePXoQGBiIVqtlXJconh9gvSLy/q9HmL31TNEdvMNg4jLwj4KrZ2DGYMiSpQjsScKIEKJSUxSFrl27MmTIELy8vJxdjqgm9Ho93bt3t932fDyuAY/HRgPw2pKDLNz9l6sfPuFw33LwqwtXTluvkGRfrOiyqy0JI8LmzTffpHXr1rb39913H3feeect94mLi+Opp56yva9Xrx6ffvppuWsZP3487733nt3brYy+/PJLhg4d6uwyKj1XV1dnlyCqmeuvOObl5dFWf55HO4WgqjB5/j5W7E8puoNvbesVEt86kH7CGkhyUiu46upJwkg1kZqayiOPPEJkZCSurq6EhYXRv39/tmzZUuY2P/vsM2bMmGG/Ikto//79rFixgn/84x8O6+N///sfcXFx+Pj4oCjKDY9yJyUl8cADDxAVFYW7uzv169fnjTfewGAwFNnu999/p2vXrnh7e1OrVi1eeOEFTCZTkW1UVeWjjz4iJiYGV1dX6tSpUyRoPfTQQ+zYsYONGzc67Hirqry8vBo7TkZUrMTERK5cuUIjznF/u0AsKjw5bw+/H75UdEP/utZA4hMBacesj//mpjmn6GpEwkg1MXz4cPbt28f333/PsWPHWLp0KXFxcWRkZJS5TV9fX/z8/OxXZAl9+eWXjBw5Em9vb4f1kZeXx4ABA3j55ZeL/f6RI0ewWCx88803JCYm8p///IcpU6YU2X7//v0MHDiQAQMGsGfPHubNm8fSpUt58cUXi7T11FNP8d133/HRRx9x5MgRli1bRseOHW3fd3V1ZezYsXzxxReOOdgqbP/+/SxbtoykpCRnlyKquTZt2hAcHIzJZKKlLoV7W/ljsqg8Nmc3G4//JWwERFkDiVcYXD4MM4dBXtn/rRUSRqqFq1evsnHjRj744AN69epF3bp16dixIy+99BKDBg2ybZecnMywYcPw8vLCx8eHUaNGcenSpZu2+9fbNLm5uUycOBEvLy9q1arFxx9/fMu6Jk2axODBg4t8ZjKZCAsLY9q0acXuY7FYmD9//m1vW0yfPh1fX19Wr159y+1u5qmnnuLFF1+kc+fOxX5/wIABTJ8+nX79+hEdHc3QoUOZPHkyCxcutG0zb948WrZsyeuvv06DBg2IjY3l/fff56uvviI7OxuAo0ePMmXKFJYsWcLQoUOJioqidevW9OnTp0h/Q4cOZfHixeTn55fpeKojVVXJzs7GbDbLI73C4XQ6Hd27d7cFkvZulxjV3BeDycJDM3ey/fRfwkZgfesYEs8QuHTQGkjyrzin+GpAwshtqKpKnsHklC9VVUtUo5eXF15eXixevJjCwsKbHsedd95JRkYGCQkJrF69mpMnTzJ69OgSn4vXX3+d+Ph4Fi1axKpVq4iPj2fXrl033f7BBx9k5cqVpKT8ed/1l19+IScnh1GjRhW7z/79+7l69Srt27e/absfffQRkydP5rfffqNv374AvPfee7bzcLOvDRs2lPhYi5OZmUlAQIDtfWFhIW5ubkW2cXd3p6CgwHZeVq5cSXR0NMuXLycqKop69erx4IMP3nDFqn379hiNRrZv316uGqsTRVHo06cPffr0wd/f39nliBrgr4Gks+dl7mziTb7RzKQZO9h79mrRHYIaWq+QeATBxf0w624oyHRK7VWdTHp2G/lGM01f/80pfR96uz8eLrf/I9LpdMyYMYP/b+/O46Kq+geOf+4w7LIoCoKCoLiAiKCoobnlgoqWVprlmrk9mpk+WWG/VouyrIfMJ800NEzNJ8MstdQU9w1z30UQFIncAGWH+/uDmBxZBASG5ft+veb1Yu499853zusw851zzz1nwoQJLFq0iHbt2tG9e3eGDx+Ot7c3AFu3buX48eNER0fj7OwMQFhYGK1bt+bQoUN06NCh2Ne4c+cOK1asYNmyZboEYPny5TRu3LjIYzp37kzLli0JCwvj1VdfBfJ6NIYOHVrkXRExMTEYGRlhb29f6P6goCCWL19OREQEbdq00W2fPHlykQlOvkaNGhW7vzhRUVF88cUXer1BAQEBhISEsGrVKoYNG0ZCQgLvv/8+gC4Bi4mJ4fLly/zvf//j22+/JScnhxkzZvD000+zbds23bksLS2xtbUlJiaG7t27lznOmkZRFElERKXKT0j27NlDYmIigU6Z/JlRj32XbjJ66QFWT/TH0+menjr7VnnzkCwbCPF/wIqn8ta2MZPevNKQnpEa4qmnniI+Pp7169cTEBBAREQE7dq10w1APXPmDM7OzrpEBMDT0xNbW1vOnDnzwPNHRUWRmZmJv7+/blu9evVo2bJlMUfl9Y6EhoYCeYNsN2zYwLhx44osn5aWhqmpaaHzanz66ad89dVX7N69Wy8RyY/F3d292Ef+TKalFR8fT79+/Rg6dCjjx4/Xbe/bty+ffPIJkydPxtTUlBYtWugui+XfLqiqKhkZGXz77bd07dqVHj16sHTpUrZv3865c+f0Xsfc3JzU1NQyxVjTqKpa4p5BIcqbVqulS5cuNG3alEcf7cKSMR1o52JLcno2o5Ye4GJiiv4BDq3zpo43s4Urh+C7oZBxxyCxV1fSM/IA5sZGnH4vwGCvXRpmZmb06dOHPn368NZbbzF+/Hjefvttxo4di6qqhX7BF7W9sHJlMXr0aF5//XX27dvHvn37cHV1pWvXrkWWz1+VNTMzExMTE719Xbt2ZcOGDaxZs6bAINHg4GC9O1QKs2nTpmJfuzDx8fH07NkTf39/Fi9eXGD/zJkzmTFjBteuXaNu3brExMQQFBSEm1ve+hUODg5otVpatPhnxU8PDw8gbwzPvcnczZs3adCgQaniq6lycnLYsmULPj4+D9WjJURZabVa2rdvr3u+bFxHnl+yl8NX7vDc1wdYM8kf1/qW/xzg6A2j18HyJyBuP6x8BkasARPLgicXBRi8Z+Tq1auMHDkSOzs7LCws8PHxKXYcQkREBIqiFHicPXu2QuJTFAULE61BHg8766anpyd3797V/R0bG0tcXJxu/+nTp0lKStJ9ORbH3d0dY2Nj9u/fr9t269Ytzp8/X+xxdnZ2DB48mNDQUEJDQ3n++eeLLZ8/z8np06cL7OvYsSO//vorwcHBfPLJJ3r7Jk+ezNGjR4t9FDcOpTBXr16lR48etGvXjtDQ0CLXl1EUBScnJ8zNzVm1ahXOzs60a9cOgE6dOpGdnU1U1D9TTOfXWZMm/6xvERUVRXp6Or6+vqWKsSZSVZXc3FxSU1PRauX3kqgabv0ZzwinG/RwMSUxJYMRSw5w9fZ9A86dfGHUj2BiBZd3w6rhkCWD0kvCoP/pt27dokuXLvTs2ZNNmzZhb29PVFRUiW4nPXfunN4I+9r8i/LGjRsMHTqUcePG4e3tjZWVFZGRkXz88cc88cQTAPTu3Rtvb29GjBhBSEgI2dnZTJkyhe7du5foS7pOnTqMHDmS1157jQYNGuDg4MAbb7xRogXgxo8fz8CBA8nJyWHMmDHFlm3QoAHt2rVj9+7dehOw5fP392fTpk3069cPrVbLjBkzgLzLNPcOLn2QhIQEEhISdGudnDhxAisrK1xcXKhXrx7x8fH06NEDFxcX5s2bx19//aU79t4pyT/55BP69euHRqPhxx9/5KOPPmLNmjUYGRmRm5urS2bGjRtHSEgIubm5TJ06lT59+uj1luzatYumTZvSrJksUa4oCsbGxrRt27bIsUNCVCZVVYmLiyMnJ4dBDknk5Fqx60oaI77ez5pJ/thb3zOQvbEfjFwLK56E6J2w+jkYvgqMzYp+AWHYZGTu3Lk4OzvrxhRA3kybJWFvb2+QOTCqojp16tCpUyf+85//EBUVRVZWFs7OzkyYMEE3L4aiKKxbt45p06bRrVs3NBoN/fr1K9XcFu+99x6ZmZk8/vjjWFlZ8e9//5ukpAePHO/duzeOjo60bt0aJyenB5afOHEiy5Yt48UXXyx0f5cuXdiwYQMDBgzAyMiIl156qcTvId+iRYt49913dc+7desG5A2wHTt2LJs3b+bixYtcvHixwCDdey9Zbdq0iQ8++ICMjAzatm3LTz/9RP/+/XX7NRoNP/30E9OnT6dbt25YWlrSv3//ArdFr1q1igkTJpT6fdRUiqLQuHHjWr0mj6g6FEXB39+fvXv3kpCQwGDHFLJz67AvPpURSw6weuIj2NW5Z4Zgl04w4n95g1mjtsGaUfDMCtDKLMJFUVQDjhLz9PQkICCAK1eusGPHDho1asSUKVOK/VCOiIigZ8+euLq6kp6ejqenJ//3f/9Hz549Cy2fkZGhd7trcnIyzs7OXLt2DTs7O72y6enpxMXF4erqWuCWzdouf84HKyurUn9BpKam0rhxY5YsWcKTTz75wPLp6el4eHiwcuVKvQGz1U1J6+zkyZP06dOHs2fPYmNjU2iZ9PR0YmJicHZ2rtFtU1VVsrOz2bJlC3369MHY2NjQIVULWVlZUmdlUNp6y8nJ4eDBg/z5558oGg3fx1pw6M9cPBpaETbODxtz/XMoMbsw+v45lOw0cpv3I+epb8DIpIizVw83btzA0dGRpKSkcp3/x6DJSP6H6syZMxk6dCgHDx7k5Zdf5quvvmL06NGFHnPu3Dl27txJ+/btycjIICwsjEWLFhEREaH7dXuvd955R+8XcL6VK1fqlmXPp9VqadiwIc7OzgUGT4rSy83N5c8//+S///0vP/30E0eOHCnxGIA9e/aQnJys18tQU23btg1VVenVq1eRZTIzM4mLiyMhIaHAdPM1SXZ2tm6V3pJcAhSisuUnzHl3fMGKaBOO39LSpI7KFM8czO6776BB8kk6XfoPRmoW8TZ+RLpNQVWq71io1NRUnnvuuZqVjJiYmODn58fevXt121566SUOHTpUqjVVBg0ahKIorF+/vsA+6RkpH2XpGYmJiaFZs2Y0btyYb775ptgv25roYXqT7lcbekZUVWXz5s2kpaWh1WoJCAiQX/klJD0jZVPWesvJyeHQoUMkJCRg59SEmVtvczstiw6udVk6qh3mJvoZiRL1O0b/G4WSk0muxxPkDP4KNNUzIamonhGD1oajoyOenp562zw8PFi7dm2pzvPII4+wYsWKQveZmpoWutqnsbFxgcaXk5ODoihoNBr5VXaf3NxcAF39lETTpk1r9VwRZamzomg0Gt3Azpr8ZdOjRw+io6OJioqq8e+1IkidlU1p683Y2JjOnTsTGxuLq6srYU2See7r/RyKucXU1cf4erQfZvdOzdCqHwwLg+9HojnzExqtCQz5CjSlm76hKqio9mXQb9wuXboUmPjp/Pnzerc8lsSRI0dwdHQsz9CEEAZQp04dWrVqJQNXRZVnZGSEm5sbiqLQprEN34xtR3Mb2HXhOi+u/IOsnFz9A1r2g6HL8npETvwPfnoRcnMLPXdtZNBkZMaMGezfv5/g4GAuXrzIypUrWbx4MVOnTtWVCQoK0hs/EhISwrp167hw4QKnTp0iKCiItWvXFnnnhRBCCFGRcnJySL9ylonN02hTN5etZxJ5+fuj5OTe1zPsMRCeWgqKERxbCT+/JAnJ3wx6maZDhw6Eh4cTFBTEe++9h5ubGyEhIYwYMUJX5tq1a8TGxuqeZ2Zm8sorr3D16lXMzc1p3bq17jZPIUT1FBUVRVJS0kNN2y+EoSiKkrcEhKoyulkGy6NM2HD8GmZaIz552huN5p6evtaDITcbfpwAR8LAyBgCP4Na3hto8BE0AwcOLLDM/L3y11bJ9+qrr+oWXRNCVH+qqnLhwgVSUlKwtbXVWz9JiOpAo9HwyCOPsH//fq5evcqYppksizJh7R9XMDPW8P5gL/1Lj22ehtwcCJ8Ekd/kXbrp/3GtTkhklKYQwuB8fHxwcXGRRERUW/kJSd4kiSpjm2XiaZvNdwdi+WDDmYKD+ds+A08syPv74GL47Q2oxQP+JRkRQhiUoig0bNiQTp06yZ0golrTaDR06tTpnoQkC0+bbJbsjuY/WwpZx8t3JAz6PO/v/f+FLW/V2oREkhFRrvKnnS+tc+fO0bBhQ1JSUh5cuIZ55ZVXyjSlvRCi6slPSJydnTHWGvHsI00BmL/tIv/dfrHgAe3HQuDfy0PsnQ+/v1crExJJRmqIsWPHoigKkydPLrBvypQpKIrC2LFjS3y+mJgYFEXh6NGj5RdkMd544w2mTp2KlZVVpbxeYdauXYunpyempqZ4enoSHh7+wGNOnDhB9+7dMTc3p1GjRrz33ntFzq2yZ88etFptgQUAX331VUJDQ4mOji6Pt1GtXL16lQsXLuhNTChEdafRaOjYsSO9evViVM82vN6/FQCf/HaOJbsuFTygw/i8MSMAuz+D7cGVGG3VIMlIDeLs7Mzq1atJS/tnyer09HRWrVqFi4uLASMr3pUrV1i/fj3PP/+8wWLYt28fzzzzDKNGjeLYsWOMGjWKYcOGceDAgSKPSU5Opk+fPjg5OXHo0CG++OIL5s2bx2effVagbFJSEqNHjy50Flp7e3v69u3LokWLyvU9VQdnz57l6NGjtTIREzWbRqPRzVA6uXszZvVohJdtNu9vOMO3+2IKHtBpEgR8mPf3zo8h4qPKC7YKkGTkQVQVMu8a5lHKrrp27drh4uLCjz/+qNv2448/4uzsjK+vr17ZX3/9lUcffRRbW1vs7OwYOHAgUVFRuv1ubm4A+Pr6oigKjz32mG7fN998Q+vWrTE1NcXR0bHAHC/Xr19nyJAhWFhY0Lx580Kn6b/XmjVraNu2rd7quMuWLcPW1pZ169bRokULzMzM6NOnD3Fxcboyx44do2fPnlhZWWFtbU379u2JjIwsRY39IyQkhD59+hAUFESrVq0ICgqiV69ehISEFHnMd999R3p6OsuWLcPLy4snn3yS2bNn89lnnxXoHZk0aRLPPfdckQv/Pf7446xatapMsVdXqqrSpEkT6tatW+LVuoWoju7cuUOj9BhGN8ukjW02b/10ilUHYwsW9J8Cfd/P+zviQ9jxSeUGakAGv7W3ystKheAHL3tfIWbHg4llqQ55/vnnCQ0N1c3V8s033zBu3DgiIiL0yt29e5eZM2fSpk0b7t69y1tvvcWQIUM4evQoGo2GgwcP0rFjR7Zu3Urr1q11C9wtXLiQV155hY8++oj+/fuTlJTEnj179M797rvv8vHHH/PJJ5/wxRdfMGLECC5fvky9evUKjXnnzp34+fkV2J6amsoHH3zA8uXLMTExYcqUKQwfPlz3eiNGjMDX15eFCxdiZGTE0aNHdQMgY2NjCyw1cL+RI0fqeiP27dvHjBkz9PYHBAQUm4zs27eP7t276y03EBAQQFBQEDExMbqZhENDQ4mKimLFihW8//77hZ6rY8eOxMXFcfny5VLPQFxdKYqCu7s77u7uhg5FiAplaWmJk5MTly9fZlSzTFZEwezwExgbaXi6fWP9wp2n5c1DsvUd2P5+3pTxXWcaJO7KJMlIDTNq1Cjdl6GiKOzZs4fVq1cXSEaeeuopvedLly7F3t6e06dP4+XlRYMGDQCws7OjYcOG5ObmkpycTHBwMP/+97+ZPn267tgOHTronWvs2LE8++yzAAQHB/PFF19w8OBB+vXrV2jMMTExtG/fvsD2rKwsFixYQKdOnQBYvnw5Hh4eukQpNjaWWbNm0apV3vXY5s2b6451cnJ64HiXexd5SkhIwMHBQW+/g4MDCQkJRR6fkJBQ4Bd9/jkSEhJo0qQJUVFRzJ49m127dhW7YnGjRo0A9JIYIUTNoCiK7nPy8uXLjGyWyXeX4NUfjmGi1fB42/t+8D46I28ekm1z4Pd38xKSLtMLOXPNIcnIgxhb5PVQGOq1S6l+/foEBgayfPlyVFUlMDCQ+vXrFygXFRXFm2++yf79+7l+/bpuUbfY2Fi8vLwKPfdff/1FfHz8A1ff9fb21v1taWmJlZUViYmJRZZPS0srdCVarVar12PSqlUrbG1tOXPmDB07dmTmzJmMHz+esLAwevfuzdChQ2nWrJnu2NL+4r5/PRRVVR+4Rkphx+Rvz8nJYcKECbz99tu0aNGi2PPkzzqamppaqpirqxs3bpCZmUnDhg1lHRpRK+QnJIqiEBMTw4imeQnJjO+PYqxR6N/mvvXVur2Sl5BEBOfd8qvRgv/Uwk9eA8iYkQdRlLxLJYZ4lPFDety4cSxbtozly5czbty4QssMGjSIGzdu8PXXX3PgwAHdQM3MzMwiz1vSpevvnytCURRdslOY+vXrc+vWrUL3FfZFlb/tnXfe4dSpUwQGBrJt2za9O2BiY2OpU6dOsY977zxq2LBhgV6QxMTEAr0l9yrqGMjrIUlJSeHIkSO89NJLaLVatFot7733HseOHUOr1bJt2zbdcTdv3gTQ9UjVdKdPn2b37t2cOXPG0KEIUWkURcHPzw9XV1cUYIRbJs0ss5m26ghbT/9Z8IAer0H31/L+/m027K+5g9ylZ6QG6tevny6pCAgIKLD/xo0bnDlzhq+++oquXbsCsHv3br0yJiYmQN4CUPmsrKxwdXXl999/p2fPnuUWr6+vL6dPny6wPTs7m8jISDp27AjkzUVy+/Zt3WUZgBYtWtCiRQtmzJjBs88+S2hoKEOGDCn1ZRp/f3+2bNmiN25k8+bNdO7cucjj/f39mT17NpmZmbr62rx5M05OTri6upKTk8OePXuoU6cOGk1e3v/ll1+ybds2fvjhB90gYYCTJ09ibGxM69ati425JlBVFWtra27evCkzropaJz8hURSFlDt38Myx4vyxBKZ89weLR7enR0t7/QN6BOWNIdn1Kfz6Wt4lm44TDBN8BZJkpAYyMjLS/eI0MjIqsL9u3brY2dmxePFiHB0diY2N5fXXX9crY29vj7m5Ob/++iuNGzfGxMQERVF46623mDJlCvb29vTv35+UlBT27NnDtGnTyhxvQEAA48ePJycnRy9eY2Njpk2bxvz58zE2NubFF1/kkUceoWPHjqSlpTFr1iyefvpp3NzcuHLlCocOHdKNhSntZZrp06fTrVs35s6dyxNPPMFPP/3E1q1b9ZK0BQsWEB4ezu+//w7Ac889x7vvvsvYsWOZPXs2Fy5cIDg4mLfeegtFUdBoNHh6emJtba1LRuzt7TEzMytwKWzXrl107dq1ViwSpygKbdu2pU2bNrp6EaI2URSF9u3bk5ubS1cUMnKOsOlkApPCDvPN2A50ca9/b2F47M28SzZ7QmDjK6BooMMLBou/IsgnQQ1lbW2t98v/XhqNhtWrV3P48GG8vLyYMWMGn3yifwuZVqtl/vz5fPXVVzg5OTFkyBAAxowZQ0hICF9++SWtW7dm4MCBXLhw4aFiHTBgAMbGxmzdulVvu4WFBa+99prullhzc3NWr14N5CVZN27cYPTo0bRo0YJhw4bRv39/3n333TLF0LlzZ1avXk1oaCje3t4sW7aM77//Xjd4FvJuWb739mcbGxu2bNnClStX8PPzY8qUKcycOZOZM0s/8n3VqlVMmFDzfu0URxIRUZvlr/SrNdIQ8owPk7yN8bLOZPzySA5G37y/MPR+B/z/nkZhw0w4vKyyQ65QilrUdJE1VHJyMjY2Nly/fh07Ozu9fenp6URHR+Pm5lbi8RG1Rf7dNPf+yi9PX375JT/99BO//fYbkDfPyMsvv8zt27fL/bUqS0nrbMOGDcyaNYvjx48XecdNTWmb+dP9FzfTblZWFhs3btQlqeLBpM7KpqrUW3x8PHv27EFV4fsYE87eMeXbFzrRvkld/YKq+vfYkS/znj++ANqNqtRYb9y4Qf369UlKSiryB29ZyE8TUSVMnDiRbt261cq1ae7evUtoaGixt/7WFKdOneLXX3/l3Llzhg5FiCrD0dGRpk2boijwjGsmHlYZjP3mIMev3NYvqCgQEAwdJ+U9Xz8Njq6s9HgrgiQjokrQarW88cYbBl2bxlCGDRumdzmoplJVVXdXVW25a0iIklAUhXbt2tGsWTMUBYY1yaRVnXRGLT3Iqfik+wtD/7ng9wKgwropcOx7g8RdniQZEVXS2LFjq/UlGlGQoih07tyZgQMHUrdu3QcfIEQtoigKvr6+uLu75yUkrpm0tEhj1NKDnEtIub8wDJiXt+IvKqybDCd+METY5UaSESFEpTI3N5eJzoQohKIo+Pj46O4EfKpJJkbZ6YxYcoCov+7oF9ZoIPA/0G40qLnw4wQ4+WMhZ60eJBkRQlS4jIwMsrOzDR2GEFVefkLSvHlzWrdpS4N6tly/k8FzX+8n5vpd/cIaDQz8HHxG5CUka8fD6Z8ME/hDkmRECFHhTp06xc8//8ylS5cMHYoQVV5+QuLl0YIV4zvRwqEO11PSee7r/cTdvG/JCI0GHv8CvIeDmgM/jIMzvxgm8IcgyYgQokKpqsrNmzfJzs7G0rJ0q1ALUdvVszRh2eh2/NsriybGKTy3ZD/xt9P0C2mMYPCX0GZo3myt/xsL5zYZJN6ykmRECFGhFEWhV69e9OzZE3t7+wcfIITQc/dmAg1MsnmySRbORsmMWHKAxOR0/UIaIxi8CLyegtws+H4UnP/NMAGXgSQjQogKpygK9evXl4GrQpSBu7s7LVu2BGCISxaOym2eW3KA63cy9AsaaWHIYvAc/HdCMhIubC14wipIkhFRJjdu3MDe3p6YmBhDh1IuOnTowI8/Vt+R6FVVTk4OtWySZyHKnaIotGnTRi8hcVBvMXLJAW7dvW+ldSMtPLUEPAZBTiasfg6ithVy1qpFkhFRJh9++CGDBg3C1dW10l7z5s2bTJs2jZYtW2JhYYGLiwsvvfQSSUlJDzz26tWrjBw5Ejs7OywsLPDx8eHw4cO6/W+++Savv/66blIuUT5Onz7Nb7/9xpUrVwwdihDVWn5Ckr9q+WCXLOrn3mTk0gMkpWbpFzYyhqe+gZaBkJMBq56FSxGVH3QpSDJSQ2VmZj64UBmlpaWxdOlSxo8fX2GvUZj4+Hji4+OZN28eJ06cYNmyZfz666+88ELxq1feunWLLl26YGxszKZNmzh9+jSffvoptra2ujKBgYEkJSXp1sYRD09VVa5evUpKSor0jghRDhRFwcvLS5eQ9GiYzcWEJEaHHiQl/b6ERGsCQ5dBi36QnQ4rh0P0zsoPuoQkGSmh7OxssrOz9T5Uc3Nzyc7OJicnp9zLllaPHj148cUXmTlzJvXr16dPnz4AfPbZZ7Rp0wZLS0ucnZ2ZMmUKd+7kTZ5z9+5drK2t+eEH/Zn7fv75ZywtLYtcJ2bTpk1otVr8/f112yIiIlAUhd9++w1fX1/Mzc157LHHSExMZNOmTXh4eGBtbc2zzz5Lampqoed9EC8vL9auXcugQYNo1qwZjz32GB988AE///xzsXNYzJ07F2dnZ0JDQ+nYsSOurq706tWLZs2a6coYGRkxYMAAVq1aVabYREH5A1f9/PxwcnIydDhC1Aj5CYm3tzcd/R/FwsyEY3G3GRt6iLsZ930Oak1g2LfQvC9kp8HKZyBmj2ECfwBJRkooPDyc8PBwvR6Hc+fOER4ezpEjR/TKrl+/nvDwcL0v3YsXLxIeHk5kZKRe2Q0bNhAeHk5ycrJuW1nHYSxfvhytVsuePXv46quvgLxl2ufPn8/JkydZvnw527Zt49VXXwXA0tKS4cOHExoaqnee0NBQnn766SLXidm5cyd+fn6F7nvnnXdYsGABe/fuJS4ujmHDhhESEsLKlSvZsGEDW7Zs4YsvvtCVDw4Opk6dOsU+du3aVeR7zl85srhF5tavX4+fnx9Dhw7F3t4eX19fvv766wLlOnbsWOxridIzNjbGzc0NIyMjQ4ciRI2hKAotW7bE260hYS90wtpMS/S1G7yw/BBpmfo/eNGawrAwaNYLslLhu6FweZ9hAi9GzV8mtBZxd3fn448/1tv28ssv6/52c3Njzpw5/Otf/+LLL/OWoB4/fjydO3cmPj4eJycnrl+/zi+//MKWLVuKfJ2YmJgif+m+//77dOnSBYAXXniBoKAgoqKiaNq0KQBPP/0027dv57XXXgNg8uTJDBs2rNj31ahRo0K337hxgzlz5jBp0qRij7906RILFy5k5syZzJ49m4MHD/LSSy9hamrK6NGj9V4nNjaW3NxcNBrJ04UQVZ9XIxsWPtmUuDNH+TX+T8Z/e4ilYzpgZnzPDwBjMxj+Hawanjd25LunYVQ4OHc0WNz3k2SkhIYMGQKg9wuvZcuWNG/evMDtio8//niBsu7u7n8vEa1fNjAwsEDZsg4KLay3Yvv27QQHB3P69GmSk5PJzs4mPT2du3fvYmlpSceOHWndujXffvstr7/+OmFhYbi4uNCtW7ciXyctLQ0zM7NC93l7e+v+dnBwwMLCQpeI5G87ePCg7nm9evWoV69eqd9rcnIygYGBeHp68vbbbxdbNjc3Fz8/P4KDgwHw9fXl1KlTLFy4UC8ZMTc3Jzc3l4yMDMzNzUsdk/jH+fPnSUxMpGXLlrJCrxAVrJ5xNvEaCGycxcYrCUwMO8ziUe3vS0jMYfgqWPVM3tiRsCdh9DpoXHgvd2WTn38lpNVq0Wq1esmERqNBq9UW6IIuj7Jlcf/slpcvX2bAgAG6sRaHDx/mv//9LwBZWf8Mdho/frzuUk1oaCjPP/98sfNB1K9fn1u3bhW6z9jYWPe3oih6z/O33TsmpiyXaVJSUujXrx916tQhPDy8wGvcz9HREU9PT71tHh4exMbG6m27efMmFhYWkog8JFVVuXTpEteuXSty3JEQovx4eHjQunVrAAY0zsIoOZ5/rThMRvZ9l2xMLODZ1dDkUchMgbAhcPVwIWesfNIzUoNFRkaSnZ3Np59+qktw1qxZU6DcyJEjefXVV5k/fz6nTp1izJgxxZ7X19eXFStWlEuMpb1Mk5ycTEBAAKampqxfv77IHpp7denShXPnzultO3/+PE2aNNHbdvLkSdq1a1eK6EVhFEWhS5cuREdH4+zsbOhwhKgVPD09URSFkydPMqBRFpuuxjP1O4UvR7THRHvPD1wTS3ju+7yxI7F78xKS0evBycdgsYP0jNRozZo1Izs7my+++IJLly4RFhbGokWLCpSrW7cuTz75JLNmzaJv3740bty42PMGBARw6tSpIntHSqNevXq4u7sX+8jvqUhJSaFv377cvXuXpUuXkpycTEJCAgkJCXp3KfXq1YsFCxbons+YMYP9+/cTHBzMxYsXWblyJYsXL2bq1Kl6sezatYu+ffs+9HsSYGVlhbe39wN7rYQQ5cfDwwMvLy8A+jfKgltXeXHlH2Tl3HeHpmkdGLEGnDtBehJ8+wRcO26AiP8hyUgN5uPjw2effcbcuXPx8vLiu+++48MPPyy07AsvvEBmZibjxo174HnbtGmDn59fob0sFenw4cMcOHCAEydO4O7ujqOjo+4RFxenKxcVFcX169d1zzt06EB4eDirVq3Cy8uLOXPmEBISwogRI3Rlrl69yt69e3n++ecr9T0JIUR58vDwoE2bNgA0MFfZfDqBl1YdKSQhsYIRP0DjDpB+Oy8hSThZ+QH/TVFr2WxEycnJ2NjYcP36dezs7PT2paenEx0djZubW4m6/2uS7777junTpxMfH4+JiUmB/bm5uSQnJ2NtbY1Go2Hjxo288sornDx5skbceTJr1iySkpJYvHhxuZ3z/jp7GNWlbcbGxnLz5k2aNm2KtbV1mc6RlZXFxo0bGTBggPSslJDUWdnU5Hq7du0aZ5I0TA77g8ycXAZ6OxLyjA9ao/s+i9KT4NvBEP8HWNjBmF/AwbPQc0LeXYz169fXTatQXqr/t4h4KKmpqZw6dYoPP/yQSZMmFZqIFGbAgAFMmjSJq1evVnCElcPe3p45c+YYOoxq78KFC1y4cIH4+HhDhyJErebo6MhjrRxYOLIdpkZwOTaOf//vKDm59/U/mNnk3ebr6AOpN2D5IEg8W+nxSjJSy3388cf4+Pjg4OBAUFBQqY6dPn16jRmgOGvWLBwcHAwdRrWmqiqenp40atSoUtcsEkIU7bFW9sztZsnoZpmk/XmZWYUlJOa2eQlJQ29IvZ6XkPx1vlLjlGSklnvnnXfIysri999/p06dOoYOR1RjiqLg6OhI586dq/SlJCFqE0VR8HTLm6Syj1M2d/+M4fW1x8i9PyGxqAejfwKHNnA3MS8huX6x0uKUZEQIIYSowVq0aEHbtm0B6O2YTcq1aGb/eLzohMS+NdxJgOUD4UZUpcQoyYgQ4qElJiYSFRWlN5meEKLqaNGiBT4+PgD0cswmKf4Sb647UXBFbUs7GLMeGnhAyrW8HpKblyo8PklGhBAP7dy5c/zxxx+cPVv5A9+EECXTvHlzXULymGM2169E8c76U4UkJPXzEpL6LSH5KiwbBLdiKjQ2SUaEEA+tYcOGWFtb4+bmZuhQhBDFaN68Ob6+vqAoxNwxYvm+y7z3y+mCCUkdexjzM9g1h+QreQnJ7djCT1oOJBkRQjy05s2bExAQIIOghagG3N3dCRwwgHF9fAAI3RND8MYzBRMSK4e8hKReM0iKhWUDIaVibtuXZEQIIYSoZSwsLHimgwvBQ9rQwDSX6IvnmbvpbMGExNoRxv4Cdd3g9mW03z9XIfFIMiKEKLPbt2/z559/FvwAE0JUC8PaOzGrbQ4BTlnEXzrLp7+dKyQhccpLSGyboNy+XCFxSDJSQ/To0YOXX37Z0GGUWXWPv7Y6e/YsO3fu5ORJw61pIYQoO61Wi5+PNwBdHbKJvXiGz7cWMuGZTWMY+wuqdaOC+8qBJCNCiDIzMzPD2Nj4gSs9CyGqrqZNm9K+fXsgLyGJPn+aLwpLSGxdyH5mZYXEIMlICWVnZxf5uHf5+vIqWxpjx45lx44dfP755yiKgqIoREVF8cILL+Dm5oa5uTktW7bk888/L3Dc4MGDmTdvHo6OjtjZ2TF16lS9uSKuXbtGYGAglpaWtG3blpUrV+Lq6kpISIiuTFJSEhMnTsTe3h5ra2see+wxjh07ptv/zjvv4OPjQ1hYGK6urtjY2DB8+HBSUlKKjD8mJqZUdSAMw8fHh0GDBmFra2voUIQQD6Fp06b4+fmhAo/aZxN17hRfbr9QsKCNS4W8vrZCzloDhYeHF7mvYcOGdO3aVfd8/fr1BZKOfA0aNKBHjx665xs2bCAzM7NAuaFDh5Y4ts8//5zz58/j5eXFe++9B0DdunVp3Lgxa9asoX79+uzdu5eJEyfi6OjIsGHDdMdu374dR0dHtm/fzsWLF3nmmWfw8fFhwoQJAIwePZrr16+zbds2MjMzefvtt0lMTNQdr6oqgYGB1KtXj40bN2JjY8NXX31Fr169OH/+PPXq1QMgKiqKdevW8csvv3Dr1i2GDRvGRx99xAcffFBo/A0aNCjx+xeGZWRkZOgQhBDlIP/W/EORkXSxz+aXo6fQGmmY2K1Zhb+2JCM1gI2NDSYmJlhYWNCwYUPd9nfffVf3t5ubG3v37mXNmjV6yUjdunVZsGABRkZGtGrVisDAQH7//XcmTJjA2bNn2bp1K4cOHaJdu3YkJyezePFiWrZsqTt++/btnDhxgsTERExNTQGYN28e69at44cffmDixIkA5ObmsmzZMqysrAAYNWoUv//+Ox988EGR8YuqKzU1FVVVsbS0NHQoQohy5ObmhqIo7PnjFAevq+zYeBYjjYYXHq3YOYQkGSmhIUOGFLlPURS9548//niJywYGBj5cYMVYtGgRS5Ys4fLly6SlpZGZmambfS9f69at9X7ZOjo6cuLECSBvVk2tVku7du10+93d3albt67u+eHDh7lz5w52dnZ6501LSyMq6p81DVxdXXWJSP7r3NvDIqqXs2fPEhUVhZeXFx4eHoYORwhRjlxdXXFxceFPq4vM//0Cc345jVaBMV0qLiGRZKSEtNqSV1VFlS2NNWvWMGPGDD799FP8/f2xsrLik08+4cCBA3rljI2N9Z4rikJubi5Akbdr3rs9NzcXR0dHIiIiCpS7dxxBca8jqp+MjAwA3WU4IUTNotFomNG7OTm5uRw7dZbjx44SplEY0MLqwQeXgSQjNYSJiYneOJVdu3bRuXNnpkyZott2b09FSbRq1Yrs7GyOHDmSN30wcPHiRW7fvq0r065dOxISEtBqtbi6upZb/KJq8/f35+7du1hYWBg6FCFEBVEUhUn+TvyafAIFOHDkD9LvVMz4EbmbpoZwdXXlwIEDxMTEcP36ddzd3YmMjOS3337j/PnzvPnmmxw6dKhU52zVqhW9e/dm4sSJHDx4kOPHjzN58mTMzc11l5t69+6Nv78/gwcP5rfffiMmJoa9e/fyf//3f0RGRpY5fuk1qfosLS0LXHYUQtQs1tbWdOrYERXo1CBHdxm/vEkyUkO88sorGBkZ4enpSYMGDejXrx9PPvkkzzzzDJ06deLGjRt6vSQl9e233+Lg4ECPHj0YOXIkL7zwAlZWVpiZmQF5mfPGjRvp1q0b48aNo0WLFgwfPpyYmBgcHBzKHH9sbMUtyCTKLjMzU3qwhKhlmjRpoktI2ttVzP+/otayeZyTk5OxsbHh+vXrBQZdpqenEx0djZubm+7LVuTJzc0lOTmZ5ORkmjRpwtatW+nVq5ehw6rS8uvM2toajebh8v6q0jZPnDhBVFQUbdq0oVmziumuzcrKYuPGjQwYMKDAWCNROKmzspF6K53Y2Fi2bd/O82PHkpSUhLW1dbmdW8aMiGJt27aNO3fu0Lp1ay5evMicOXNwdXWlW7duhg5NGMBff/1FVlaW7jZuIUTt4eLigq+Pb4WcW5IRUaysrCxmz57NpUuXqFOnDp07d+a7776TXxG1VM+ePUlMTKR+/fqGDkUIYQCNG1fM2jSSjIhiBQQEEBAQUK6XHET1pShKqcYCCSFESci3ihDigXJzc4ucd0YIIR6WJCOFkA9dUdUYuk1euHCBzZs3ExcXZ9A4hBA1kyQj98gfB5GammrgSITQl98mDTVWJzY2luTkZL0VnYUQorzImJF7GBkZYWtrq1szxcLCQiZ1+ltubi6ZmZmkp6fLmJESKo86U1WV1NRUEhMTsbW1NdgKud27dycuLg5nZ2eDvL4QomaTZOQ++avGyiJu+lRVJS0tTW/2VVG88qwzW1tbg65obGJiUmHzigghhCQj91EUBUdHR+zt7aVL+h5ZWVns3LmTbt26yW29JVRedWZsbGywHhEhhKgMkowUwcjISL4A7mFkZER2djZmZmaSjJRQTaizmJgY4uPjad68OQ0aNDB0OEKIGsrgF/+vXr3KyJEjsbOzw8LCAh8fHw4fPlzsMTt27KB9+/aYmZnRtGlTFi1aVEnRClG7REVFcfXqVW7cuGHoUIQQNZhBe0Zu3bpFly5d6NmzJ5s2bcLe3p6oqChsbW2LPCY6OpoBAwYwYcIEVqxYwZ49e5gyZQoNGjTgqaeeqrzghagF/Pz8iI6OxtXV1dChCCFqMIMmI3PnzsXZ2ZnQ0FDdtgd96C1atAgXFxdCQkIA8PDwIDIyknnz5kkyIkQ5s7GxwcfHx9BhCCFqOIMmI+vXrycgIIChQ4eyY8cOGjVqxJQpU5gwYUKRx+zbt4++ffvqbQsICGDp0qVkZWUVuDafkZFBRkaG7nlSUhIAN2/eLMd3UvNlZWWRmprKjRs3qu34h8omdVY2Um+lJ3VWNlJvpZf/3VnuEzGqBmRqaqqampqqQUFB6h9//KEuWrRINTMzU5cvX17kMc2bN1c/+OADvW179uxRATU+Pr5A+bffflsF5CEPechDHvKQRzk9oqKiyjUfMGjPSG5uLn5+fgQHBwPg6+vLqVOnWLhwIaNHjy7yuPvnbFD/ztAKm8shKCiImTNn6p7fvn2bJk2aEBsbi42NTXm8jVohOTkZZ2dn4uLisLa2NnQ41YLUWdlIvZWe1FnZSL2VXlJSEi4uLtSrV69cz2vQZMTR0RFPT0+9bR4eHqxdu7bIYxo2bEhCQoLetsTERLRaLXZ2dgXKm5qaYmpqWmC7jY2NNL4ysLa2lnorJamzspF6Kz2ps7KReiu98p6J26C39nbp0oVz587pbTt//jxNmjQp8hh/f3+2bNmit23z5s34+fnJNT8hhBCiGjJoMjJjxgz2799PcHAwFy9eZOXKlSxevJipU6fqygQFBeldspk8eTKXL19m5syZnDlzhm+++YalS5fyyiuvGOItCCGEEOIhGTQZ6dChA+Hh4axatQovLy/mzJlDSEgII0aM0JW5du0asbGxuudubm5s3LiRiIgIfHx8mDNnDvPnzy/xbb2mpqa8/fbbhV66EUWTeis9qbOykXorPamzspF6K72KqjNFVcv7/hwhhBBCiJIz+HTwQgghhKjdJBkRQgghhEFJMiKEEEIIg5JkRAghhBAGVeOSkZ07dzJo0CCcnJxQFIV169Y98JgdO3bQvn17zMzMaNq0KYsWLar4QKuQ0tZZREQEiqIUeJw9e7ZyAq4CPvzwQzp06ICVlRX29vYMHjy4wJw5hantba0s9Vbb29vChQvx9vbWTczl7+/Ppk2bij2mtrczKH291fZ2VpgPP/wQRVF4+eWXiy1XHu2txiUjd+/epW3btixYsKBE5aOjoxkwYABdu3blyJEjzJ49m5deeqnYWWBrmtLWWb5z585x7do13aN58+YVFGHVs2PHDqZOncr+/fvZsmUL2dnZ9O3bl7t37xZ5jLS1stVbvtra3ho3bsxHH31EZGQkkZGRPPbYYzzxxBOcOnWq0PLSzvKUtt7y1dZ2dr9Dhw6xePFivL29iy1Xbu2tXFe6qWIANTw8vNgyr776qtqqVSu9bZMmTVIfeeSRCoys6ipJnW3fvl0F1Fu3blVKTNVBYmKiCqg7duwosoy0tYJKUm/S3gqqW7euumTJkkL3STsrWnH1Ju3sHykpKWrz5s3VLVu2qN27d1enT59eZNnyam81rmektPbt20ffvn31tgUEBBAZGUlWVpaBoqoefH19cXR0pFevXmzfvt3Q4RhUUlISQLGLR0lbK6gk9ZZP2hvk5OSwevVq7t69i7+/f6FlpJ0VVJJ6yyftDKZOnUpgYCC9e/d+YNnyam8GXSivKkhISMDBwUFvm4ODA9nZ2Vy/fh1HR0cDRVZ1OTo6snjxYtq3b09GRgZhYWH06tWLiIgIunXrZujwKp2qqsycOZNHH30ULy+vIstJW9NX0nqT9gYnTpzA39+f9PR06tSpQ3h4eIFFRvNJO/tHaepN2lme1atX88cff3Do0KESlS+v9lbrkxEARVH0nqt/T0p7/3aRp2XLlrRs2VL33N/fn7i4OObNm1er/mnzvfjiixw/fpzdu3c/sKy0tX+UtN6kveXVwdGjR7l9+zZr165lzJgx7Nixo8gvVmlneUpTb9LOIC4ujunTp7N582bMzMxKfFx5tLdaf5mmYcOGJCQk6G1LTExEq9ViZ2dnoKiqn0ceeYQLFy4YOoxKN23aNNavX8/27dtp3LhxsWWlrf2jNPVWmNrW3kxMTHB3d8fPz48PP/yQtm3b8vnnnxdaVtrZP0pTb4Wpbe3s8OHDJCYm0r59e7RaLVqtlh07djB//ny0Wi05OTkFjimv9lbre0b8/f35+eef9bZt3rwZPz8/jI2NDRRV9XPkyJFa1f2rqirTpk0jPDyciIgI3NzcHniMtLWy1Vthalt7u5+qqmRkZBS6T9pZ0Yqrt8LUtnbWq1cvTpw4obft+eefp1WrVrz22msYGRkVOKbc2luphrtWAykpKeqRI0fUI0eOqID62WefqUeOHFEvX76sqqqqvv766+qoUaN05S9duqRaWFioM2bMUE+fPq0uXbpUNTY2Vn/44QdDvYVKV9o6+89//qOGh4er58+fV0+ePKm+/vrrKqCuXbvWUG+h0v3rX/9SbWxs1IiICPXatWu6R2pqqq6MtLWCylJvtb29BQUFqTt37lSjo6PV48ePq7Nnz1Y1Go26efNmVVWlnRWltPVW29tZUe6/m6ai2luNS0byb8+6/zFmzBhVVVV1zJgxavfu3fWOiYiIUH19fVUTExPV1dVVXbhwYeUHbkClrbO5c+eqzZo1U83MzNS6deuqjz76qLphwwbDBG8ghdUXoIaGhurKSFsrqCz1Vtvb27hx49QmTZqoJiYmaoMGDdRevXrpvlBVVdpZUUpbb7W9nRXl/mSkotqboqp/jzQRQgghhDCAWj+AVQghhBCGJcmIEEIIIQxKkhEhhBBCGJQkI0IIIYQwKElGhBBCCGFQkowIIYQQwqAkGRFCCCGEQUkyIoQQQgiDkmRECCGEEAYlyYgQQgghDEqSESGEEEIYlCQjQgiD++uvv2jYsCHBwcG6bQcOHMDExITNmzcbMDIhRGWQhfKEEFXCxo0bGTx4MHv37qVVq1b4+voSGBhISEiIoUMTQlQwSUaEEFXG1KlT2bp1Kx06dODYsWMcOnQIMzMzQ4clhKhgkowIIaqMtLQ0vLy8iIuLIzIyEm9vb0OHJISoBDJmRAhRZVy6dIn4+Hhyc3O5fPmyocMRQlQS6RkRQlQJmZmZdOzYER8fH1q1asVnn33GiRMncHBwMHRoQogKJsmIEKJKmDVrFj/88APHjh2jTp069OzZEysrK3755RdDhyaEqGBymUYIYXARERGEhIQQFhaGtbU1Go2GsLAwdu/ezcKFCw0dnhCigknPiBBCCCEMSnpGhBBCCGFQkowIIYQQwqAkGRFCCCGEQUkyIoQQQgiDkmRECCGEEAYlyYgQQgghDEqSESGEEEIYlCQjQgghhDAoSUaEEEIIYVCSjAghhBDCoCQZEUIIIYRB/T89VnXmyXpR3gAAAABJRU5ErkJggg==", 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", 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j3Llz2V1KjrVq1So6duzImjVraNu2bXaXI0SeILuQhBBWGzVqFNWrV8fX15f79+8zd+5cNmzYwPTp07O7tBzp1KlTXLlyhffee49q1arx0ksvZXdJQuQZEmCEEFZLSkrik08+4datW6hUKgICApg9ezb9+vXL7tJypNdff51du3ZRo0YNZs6cmWvvBC5ETiS7kIQQQgiR62T7adQ3btygX79+eHh44OjoSLVq1Th06FC6fbZt20bNmjWxt7enZMmSTJkyJUWbJUuWEBAQgF6vJyAggGXLltlqCEIIIYTIYtkaYB48eECDBg3Q6XSsW7eOU6dO8d1336V7WuXly5dp27YtjRo14siRI4wbN463336bJUuWmNvs2bOHnj170r9/f44ePUr//v3p0aMH+/bty4JRCSGEEMLWsnUX0ocffsiuXbvYsWOH1X0++OADVq5cmezeKSNGjODo0aPm29b37NmTqKgo1q1bZ27Tpk0bChYsaL5nihBCCCFyr2w9iHflypW0bt2a7t27s23bNooWLcrrr7/OsGHD0uyzZ88e8/1lnmrdujXTp0/HYDCg0+nYs2cP7777boo2kydPTnWb8fHxxMfHm5eNRiP379/Hw8NDDroTQgghMkBRFB49ekSRIkXSvcDli8rWAHPp0iV+//13Ro0axbhx49i/fz9vv/02er2eAQMGpNrn1q1bKW4e5+XlRWJiIvfu3cPHxyfNNmnds+XLL79kwoQJmTMoIYQQQnDt2rVMvyHps7I1wBiNRmrVqsXEiRMBqF69OidPnuT3339PM8BAyhuSPd0L9uz61NqkNZsyduxYRo0aZV6OjIykePHinDt3Dnd394wNKh03btzg8JEjqIDQ+xoqV6lCp2pFLfYDUJ1Yguaf91GhkFRjEMZmn8ALzg4ZDAa2bNlCs2bN0Ol0L7StnC6/jFXGmbfIOPOW/DLO+/fvU7ZsWZvf4DNbA4yPj0+yu52C6d4jzx6Q+1/e3t4pZlLu3LmT7Pb2abX576zMU3q9PtX707i7u5u3mRk8PDwoWLAge/bupb4jHL5wgS1uBelWy9dy5ybDwdURVrwJJ2eCsyO0+fKFQozBYMDR0REPD488/csE+WesMs68RcaZt+SXcT5l60MwsvUspAYNGnD27Nlk686dO0eJEiXS7FOvXj02bNiQbF1ISAi1atUy/0Ck1aZ+/fqZVPnz8/X1pX69eihADfckDh86wNLDVt54rXo/6PiT6fm+32H9RyCX8RFCCJEPZWuAeffdd9m7dy8TJ07kwoULzJs3j6lTp/LGG2+Y24wdOzbZ7qQRI0Zw5coVRo0axenTp/nrr7+YPn06o0ePNrcZOXIkISEhfP3115w5c4avv/6ajRs38s4772Tl8NJUrFgx6tU1hZjq7kkc2H+A5UduWNe5xgBoP9n0fO+vsOFjCTFCCCHynWwNMLVr12bZsmXMnz+fSpUq8fnnnzN58mT69u1rbhMeHs7Vq1fNy/7+/qxdu5atW7dSrVo1Pv/8c3766Sdefvllc5v69euzYMECZsyYQZUqVQgODmbhwoUEBgZm6fjS4+tbjPr16mNExclIDaMWhbIi1MoQU2swtP/B9Hz3z7DxUwkxQggh8pVsvxdS+/btad++fZqvBwcHp1jXpEkTDh8+nO52u3XrRrdu3V60PJsqVqwoHdq148i68xw7cI13F4YCWHdgb60hYEyCtaNh14+gUkOLT1/4wF4hRM6UlJSEwWDI7jJswmAwoNVqiYuLIykpKbvLsZm8NE6dTodGo8nWGrI9wOR3jo4OTOxSGYB1oVfYtHM/ilKLztWtOLC3zjDTzMu692HnD6YQ0/xjCTFC5DGPHz/m1q1b5NVb1ymKgre3N9euXcvT197KS+NUqVQUK1YMZ2fnbKtBAkwOoFar+KJTRQKMl3EgkcMHDgBYF2ICh4NihH8+gB3fgUoDzT+yccVCiKyiUqkIDw/HyckJT0/PXP+HLzVGo5Ho6GicnZ1teuGz7JZXxqkoCnfv3uX69euUKVMm22ZiJMDkEFqthhb1a7Nz924qF0ziyMEDqFQqOlWz4iJAdUeYQsz6sbD9G1BroOmHti9aCGFzGo0GRVHw9PTEwcEhu8uxCaPRSEJCAvb29rn6D7sleWmcnp6ehIWFYTAYsi3A5O5PMI8pWrQIjRqYDuytVCCJwwf2szL0unWd670Orf5ner71S9j6te0KFUJkubw48yJyr5zw8ygBJocpUqQIjRo0eL4QU/9NCPrc9HzrRNj2re0KFUIIIbKRBJgcqEgRHxo3NIWYigWS2LTrAKuP3bSuc4O3oeWT+zpt+cJ0XIwQQgiRx0iAyaF8fHxo3LAhj9GzOVzHyAWh1oeYhu9Ai09Mzzd9ZjpDSQghcpnx48dTrVo18/KgQYPo3Llzun2aNm2a7KKlfn5+TJ48+YVr6d+/v/m+fZm53Zzol19+oWPHjtldhkUSYHIwHx9v+ndtT+uqviQZFVOIOWpliGn0HjT/P9PzjeNN14oRQogscufOHV599VWKFy+OXq/H29ub1q1bs2fPnufe5o8//pjqtcFs7dixY6xZs4a33nrLZu8xdepUmjZtiqurKyqViocPHyZ7PSwsjKFDh+Lv74+DgwOlSpXi008/JSEhIVm7TZs2Ub9+fVxcXPDx8eGDDz4gMTExWRtFUZg0aRJly5ZFr9fj6+ubLJwNGzaMAwcOsHPnTpuNNzPIWUg5nEaj5uuXq6Aoph/gwwf2ghJIe2sudtf4fdN1Yrb8DzZ8YrpOTH3b/QIKIcRTL7/8MgaDgZkzZ1KyZElu377Npk2buH///nNv083NLRMrtN4vv/xC9+7dbXp35ZiYGNq0aUObNm0YO3ZsitfPnDmD0Wjkjz/+oHTp0pw4cYJhw4bx+PFjJk2aBJiCVtu2bfnoo4+YNWsWN27cYMSIESQlJZnbwL+325k0aRKVK1cmMjKSe/fumV/X6/X06dOHn3/+mYYNG9pszC9KAkwuoFGr+LxDOVauOo9GpRB6aB8qVSDtqloRYpqMMV2xd9tXEPJ/phBT7w3L/YQQOZKiKMQasucqrg46jVVnnzx8+JCdO3eydetWmjRpAkCJEiWoU6dOsnZXr17lrbfeYtOmTajValq3bs0vv/yCl5dXqtsdNGgQDx8+ZPny5YDpAn+vvfYaS5cuxcXFJdk98VIzZMgQ7ty5w+rVq83rEhMTKVasGBMnTmTIkCEp+hiNRhYvXsycOXPS3faMGTN45513+PvvvwkKCkq3bWqe7vbaunVrqq8/DTdPlSxZkrNnz/L777+bw8mCBQuoUqUKn3xiOoSgdOnSfPnll/Tu3ZtPP/0UFxcXTp8+ze+//86JEycoV65cmvV07NiRVq1aERsbm2NP35cAk0s4OtjTtHEjtm7fQTnXJEIP7gPq0K6qFdeJafqh6Tox27+B9eNMF7urO8LmNQshMl+sIYmAT9Zny3uf+qw1jnaW/2w4Ozvj7OzM8uXLqVu3Lnq9PkUbRVHo3LkzTk5ObNmyhcjISD744AN69uyZ5h/x/3r//ffZsmULy5Ytw9vbm3HjxnHo0KFkx80865VXXqFx48aEh4fj4+MDwNq1a4mOjqZHjx6p9jl27BgPHz6kVq1aadYxadIkvvzyS9avX0/dunUBmDhxYrLdMqlZt24djRo1smKkqYuMjMTd3d28HB8fj729fbI2Dg4OxMXFcejQIZo2bcqqVasoWbIkq1evpk2bNiiKQsuWLfnmm2+SbatWrVoYDAb2799vDqE5jRwDk4t4e3vRtEkjkhQVZZ+EmLVHrTjFWqWCZuOg0ZP/nfzzAeybattihRD5llarJTg4mJkzZ1KgQAEaNGjAuHHjOHbsmLnNxo0bOXbsGPPmzaNmzZrUqlWLmTNnsm3bNg48uRp5eqKjo5k+fTqTJk0iKCiIypUrM3PmzHTvMVS/fn3KlSvH7NmzzetmzJhB9+7d07wkflhYGBqNhsKFC6f6+tixY/n+++/ZunWrObwAjBgxgtDQ0GSPw4cPs337dg4fPkxoaGi6ociSixcv8vPPPzNixL//GW3dujW7d+9m/vz5JCUlcePGDb744gvAdGNkgEuXLnHlyhUWL17MrFmzCA4O5tChQynuHejk5ESBAgUICwt77hptTWZgchlvLy+aNmnM1m3bKetqfDITA20tzcSoVKaDehUj7Pwe1r2P2mgEfGxftBAi0zjoNJz6rHW2vbe1Xn75Zdq1a8eOHTvYs2cP//zzD9988w3Tpk1j0KBBnD59Gl9fX3x9fTEajQAEBARQoEABTp8+Te3atdPd/sWLF0lISKBevXrmde7u7unuFgHTLMzUqVMZM2YMd+7cYc2aNWzatCnN9rGxsej1+lR3nX333Xc8fvyYgwcPUrJkyWSvubu7J5vRANPuqKioKFxdXV/oSrw3b96kTZs2dO/enVdeecW8vlWrVnz77beMGDGC/v37o9fr+fjjj9m5c6f5arlGo5H4+HhmzZpF2bJlAZg+fTo1a9bk7NmzyT4/BwcHYmJinrtOW5MZmFzI26swzZo0JklRUcbVyOyNh/jnRLjljiqV6fTqBiMB0Kz/AL97m21crRAiM6lUKhzttNnyyOjVV+3t7QkKCuKTTz5h9+7dDBo0iE8//RQw7UJKbXtprU+t3fMYMGAAly5dYs+ePcyZMwc/P790d+MUKlSImJiYFGf7ADRq1IikpCQWLVqU4rWJEyead6U9fbi6ulKsWDFcXV1xdnZmx44dGa7/5s2bNGvWjHr16jF1asqZ9FGjRvHw4UOuXr3KvXv36NSpEwD+/v6A6RIdWq3WHF4AKlSoAJiOSXrW/fv38fT0zHCNWUUCTC7l5VWYZk2bcF0pyJ67Gt6cd4R/Ttyy3FGlMl3o7snZSFWvBaM6PNPG1QohhGmG5fHjx+bnV69e5dq1a+bXT506RWRkpPkPanpKly6NTqdj79695nUPHjzg3Llz6fbz8PCgc+fOzJgxgxkzZjB48OB02z89nubUqVMpXqtTpw7//PMPEydO5Ntvk1/53Ba7kG7cuEHTpk2pUaMGM2bMSHMWR6VSUaRIERwcHJg/fz6+vr7UqFEDgAYNGpCYmMjFixfN7Z9+ZiVKlDCvu3jxInFxcVSvXj1DNWYl2YWUi3kV9uTtbi24YgxleehN3pl/iB96VuOlKhbOTlKpIOhzkpIS0ez7He2690Crg5oDs6ZwIUSeFhERQffu3RkyZAhVqlTBxcWFgwcP8s0335hnBFq2bEmVKlXo27cv33//vfkg3iZNmlj1h93Z2ZmhQ4fy/vvv4+HhgZeXFx999JFVu2ZeeeUV2rdvT1JSEgMHpv/vnqenJzVq1GDnzp2pHhxcr1491q1bR5s2bdBqtbz77rtAxnch3bp1i1u3bnHhwgUAjh8/jouLC8WLF8fd3Z2bN2/StGlTihcvzqRJk7h79665r7e3t/n5t99+S5s2bVCr1SxdupSvvvqKRYsWmXchtWzZkho1ajBkyBAmT56M0WjkjTfeICgoKNmszI4dOyhZsiSlSpWy+HlmFwkwuZxGreK7HtVQYcQ9OoyjB/cCda0KMcYWnxF26SKl7obAqrdNp1jX6J8ldQsh8i5nZ2cCAwP54YcfuHjxIgaDAV9fX4YNG8a4ceMA0yzB8uXLeeutt2jatGmy06it9e233xIdHU3Hjh1xcXHhvffeIzIy0mK/li1b4uPjQ8WKFSlSpIjF9sOHDyc4OJg333wz1dcbNGjAmjVraNu2LRqNhrffftvqMTw1ZcoUJkyYYF5u3LgxYDrIeNCgQYSEhHDhwgUuXLhAsWLJj3l8dnfaunXr+N///kd8fDxVq1ZlxYoVvPTSS+bX1Wo1q1at4q233qJx48Y4OTnx0ksv8d13yW87M3/+fIYNG5bhcWQllfK8OxLzsKioKNzc3Lh37x4eHh7ZXY5V7j94yPoNm9CqjFx6pKZq7bq0qZx+iDEYDKxds4b22p1oDv4JqKDTr1C9b9YUnYUMBgNr166lbdu26HS67C7HZmSceYvBYCAkJAR/f39KliyZ4hTZvCKzDm61VkxMDEWKFOGvv/6ia9euFtvHxcVRrlw5FixYkOyg4YzK6nE+rxMnTtCiRQvOnTuX5sUD4+LiuHz5Mv7+/il+LiMiIihUqBCRkZG4urrarM6c+wmKDHEvWIAWzZqQqKgp6WLk6IG9rD9+w3JHlQpjq4lQexigwIo3IHS+zesVQoisZjQauXnzJh9//DFubm5W3+/H3t6eWbNmJbtabV528+ZNZs2alW1XPraW7ELKQwp7FqJFsyZs2rLNFGIOmm470LqKFadYt/3WdIr1wemw/DXT7qSqPbOmcCGEyAJXr17F39+fYsWKERwcjFZr/Z/AnHoxN1to1apVdpdgFQkweUxhz0K0bNaEjVu24e9s5NihfahUKlpZ2J1kCjGTTCHm0AxYPsK0rkrqV6cUQojcxs/P77lPvxY5j+xCyoM8PQvRsnlTDIoabwcjX6w4zIZTty13VKuh3fdQY6ApyCx7FY7/bfuChRBCiAySAJNHeRbyIKh5U04rRbkareb1uYfYaG2IaT8Zqvc3hZilw+DEEpvXK4QQQmSEBJg8zLOQB5/1qEv7Kj4YkhTGLznABmsO7FWrocNPUK2fKcQsGQYnl9m+YCGEEMJKcgxMHqfVqJncsxpOxBOgus7xQ3tBVZegShaOiVGroeNPpgBzdB78PdR0YG9Ap6wpXAghhEiHzMDkA1qNmvdbl0OnUePrZOT4wb1sOGHNTIwGOv0CVXqBkgR/D4FTK21fsBBCCGGBBJh8opCHO61bNMOgmELMiUN72XzKihtAqjXQ+Teo0hOMifD3YDi9yvYFCyGEEOmQAJOPeHi406plcxIUNcUcjZwKPcDpB1Z0VGug8+9QuYcpxCweJCFGCJErPL1lQUadPXsWb29vHj16lPlF5XCjR49+rtshZDUJMPlMIfeCtH4mxOhVBjafvmm5o1oDXaZA5e7PhJjVNq9XCJE7DRo0CJVKxYgRI1K89vrrr6NSqRg0aJDV2wsLC0OlUhEaGpp5Rabjo48+4o033sDFxSVL3i81S5YsISAgAL1eT0BAAMuWWT6Z4vjx4zRp0gQHBweKFi3KZ599lua1b3bt2oVWq01xk8oxY8YwY8YMLl++nBnDsBkJMPlQIfeCtHkSYuISVby76ARbztyx3FGtgc5ToNLLT0LMQDizxvYFCyFyJV9fXxYsWEBsbKx5XVxcHPPnz6d48eLZWFn6rl+/zsqVKxk8eHC21bBnzx569uxJ//79OXr0KP3796dHjx7s27cvzT5RUVEEBQVRpEgRDhw4wM8//8ykSZP4/vvvU7SNjIxkwIABtGjRIsVrhQsXplWrVkyZMiVTx5TZJMDkUx7uBWnVvCkH7uuISYRXZx9iy1krQoxGC12m/htiFg2EM2ttX7AQwkRRIOFx9jwyeBXbGjVqULx4cZYuXWpet3TpUnx9falevXqyths3bqRx48YUKFAADw8P2rdvz8WLF82v+/v7A1C9enVUKhVNmzY1v/bXX39RsWJF9Ho9Pj4+Ke4afe/ePbp06YKjoyNlypRh5cr0T0ZYtGgRVatWTXbX5+DgYAoUKMDy5cspW7Ys9vb2BAUFce3aNXObo0eP0qxZM1xcXHB1daVmzZocPHjQ+g/sGZMnTyYoKIixY8dSvnx5xo4dS4sWLZg8eXKafebOnUtcXBzBwcFUqlSJrl27Mm7cOL7//vsUszCvvvoqffr0SfPmlB07dmT+/Jx9Xzw5jTofK+DmSr/SCp5ehVl/6jZ/rd2DMbEWLSpaOMX6aYhRFDi5FBYNgJ6zodxL6fcTQrw4QwxMLJI97z3uJtg5ZajL4MGDmTFjBn37mu5y/9dffzFkyBC2bt2arF1MTAzvvPMOVatW5fHjx3zyySd06dKF0NBQ1Go1+/fvp06dOmzcuJGKFStiZ2cHwO+//86oUaP46quveOmll4iMjGTXrl3Jtj1hwgS++eYbvv32W37++Wf69u3LlStXcHd3T7Xm7du3U6tWrRTrY2Ji+N///sfMmTOxs7Pj9ddfp1evXub369u3L9WrV+f3339Ho9EQGhpqvlv61atXCQgISPez6tevn3nWY8+ePbz77rvJXm/dunW6AWbPnj00adIEvV6frM/YsWMJCwszh8AZM2Zw8eJF5syZwxdffJHqturUqcO1a9e4cuUKJUqUSLfu7CIBJp/TqOGHHlWYvHQnZXT3OXloLyrq0tyaENP1T0AxXeRuYX8JMUKIFPr372/+A6pSqdi1axcLFixIEWA6duyIq6srarVpx8D06dMpXLgwp06dolKlSnh6egLg4eGBt7e3ud8XX3zBe++9x8iRI83rateunWzbgwYNonfv3gBMnDiRn3/+mf3799OmTZtUaw4LC6NmzZop1hsMBn755RcCAwMBmDlzJhUqVDCHq6tXr/L+++9Tvnx5AMqUKWPuW6RIEQ4fPkx0dDTOzs7mcT7L1dXV/PzWrVt4eXkle93Ly4tbt26lWvPTPn5+fin6PH3N39+f8+fP8+GHH7Jjx450b2ZZtGhR82chAUbkWDqNmhFta7I2ZDM+DkmcPLQXCKR5RQt3sdZooes000zMqeVPQswcKJf6PwpCiEygczTNhGTXe2dQoUKFaNeuHTNnzkRRFNq1a0ehQoVStLt8+TLffPMN+/bt4969exiNRsA0c1GpUqVUt33nzh1u3ryZ6nEcz6pSpYr5uZOTEy4uLty5k/Yu89jYWOzt7VOs12q1yWZmypcvT4ECBTh9+jR16tRh1KhRvPLKK8yePZuWLVvSvXt3SpUqZe5bunRpoqKikgW19KhUqmTLiqKkWGdNn6frk5KS6NOnDxMmTKBs2bLpbsfBwQEwzTrlVHIMjADAvUAB2rZqQbyiwdvByMlD+9hy0oqL3Wm08PI00xV6jQZY1B/Orbd9wULkVyqVaTdOdjws/PFMy5AhQwgODmbmzJkMGTIk1Ta9e/cmIiKCP//8k3379pkPVk1ISEhzu0//yFrydDfOUyqVyhyQUlOoUCEePEj9GhOpBYin68aPH8/Jkydp164dmzdvTnbm0NWrV3F1daVYsWK4urri7Oyc4vHsGVve3t4pZlvu3LmTYlbmWWn1AdNMzKNHjzh48CBvvvkmWq0WrVbLZ599xtGjR9FqtWzevNnc7/79+wDmma+cSGZghJl7ATdeCmrBug2b8DbPxNSlmcXdSTp4ebppJub0SljYD3rOhbKtsqRuIUTO1qZNG3MQad26dYrXIyIiOHv2LH/88QdNmjQBYOfOncnaPD3mJSkpybzOxcUFPz8/Nm3aRLNmzTKt3urVq3Pq1KkU6xMTEzl48CB16tQBTNeKefjwoXmXEUDZsmUpW7Ys7777Lr1792bGjBl06dIlw7uQ6tWrx4YNG5IdBxMSEkL9+vXTrLtevXqMGzeOhIQE8+cVEhJCkSJF8PPzQ1EUjh8/nqzPb7/9xubNm/n777/Nx8gAnDhxAp1OR8WKFS19XNlGZmBEMh4F3Wgb1II4RYOXg5GTh/ay7YwVd7HW6KDbX1ChIyQlwMK+cH6D7QsWQuR4Go2G06dPc/r0aTQaTYrXCxYsiLu7O3/++ScXLlxg8+bNjBo1KlmbwoUL4+DgwD///MPt27eJjIwETLMe3333HT/99BPnz5/n8OHD/Pzzzy9Ub+vWrdmzZ0+ysASmmZy33nqLffv2cfjwYQYPHkzdunWpU6cOsbGxvPnmm2zdupUrV66wa9cuDhw4QIUKFYB/dyGVLFmS0qVLp/ooXLiw+b1GjhxJSEgIX3/9NWfOnOHrr79m48aNvPPOO+Y2v/zyS7LdZ3369EGv1zNo0CBOnDjBsmXLmDhxIqNGjUKlUqFWq6lUqVKyR+HChbG3t6dSpUo4Of17gPaOHTto1KiR1bNc2UECjEjBvaAb7Vq1INaoYccdLcPmHGb7ubuWO5pDTAdTiFnQF85vtH3BQogcz9XVNdkMw7PUajXTp0/n8OHDVKpUiXfffZdvv/02WRutVstPP/3EH3/8QZEiRejUyXRj2YEDBzJ58mR+++03KlasSPv27Tl//vwL1dq2bVt0Oh0bNyb/98vR0ZEPPvjAfPqxg4MDCxYsAEwhLSIiggEDBlC2bFl69OjBSy+9xIQJE56rhvr167NgwQJmzJhBlSpVCA4OZuHCheYDiMF0evizp5q7ubmxYcMGrl+/Tq1atXj99dcZNWpUijBojfnz5zNs2LDnqj2rqJS0LtGXj0VFReHm5sa9e/fw8PDI7nJsxmAwsHbtWvMv63/FxCUwctExNpy6jV6rZtrAWjQqY8X+0CSD6Uq9Z1aDRg+950Hplpk/gAywNNa8QsaZtxgMBkJCQvD396dkyZKpHliaFxiNxgwd3JoVfvvtN1asWMH69aZj+oKDg3nnnXd4+PDhc28zJ44zNWvWrOH999/n2LFjaZ6pFBcXx+XLl/H390/xcxkREUGhQoWIjIxMM7Rmhpz7CYps52hvx699atCyghcaJYmNW3ew9dR1yx01Oug2A8q3h6R4mN8HLshMjBAi9xg+fDiNGzfOl/dCevz4MTNmzEj3NOucQAKMSJedVs1vfWvwRmWoVCCJU4f2su2UFWcnae1MIaZcu2dCzCbbFyyEEJlAq9Xy0UcfZeu9kLJLjx49ku2qyqkkwAiL7LRq+rzUmFijBk97hVOH97DNmlOstXbQPfjfELOgD1zcbLGbEELkNIMGDXqh3Uci80mAEVYp6OZK+zYtiTVqKKRXOHVkD9ut2Z1kDjFtITEO5veGi1tsXq8QQoi8TQKMsJq7mysd2rQkxqg1hZjDezMQYmZC2ZeehJheEmKEEEK8EAkwIkMKurnS8SVTiPHQK+zeu5+d5+9Z7qi1gx4zoWybf0PMpa02r1cIIUTeJAFGZFhBVxc6vtSSiCR7FobpGDrzADvOW3GdGK0eesyCMq1NIWZeL7i0zfYFCyGEyHMkwIjnUtDVhcHd2lKzlDfxiUZemXmQ7eesuGKvVm+6a3WZ1pAYC/N6SogRQgiRYdkaYMaPH49KpUr2ePY26f81aNCgFO1VKlWyezUEBwen2iYuLi4rhpSv6LUafu1bg5YVClPKKYET+3aw5cQ1yx3NIabVvyHm8nbbFyyEECLPyPYZmIoVKxIeHm5+/PdGU8/68ccfk7W9du0a7u7udO/ePVk7V1fXZO3Cw8Pz7BUss5teq+GX3tXpVlLB017hXOg+tpy4armjVg89ZkPpIFOImdsDLu+wfcFCiDwpIiKCwoULExYWlt2lZIratWuzdOnS7C4jR8v2y+xptdp0Z12e5ebmhpubm3l5+fLlPHjwgMGDBydrZ2km57/i4+OJj483L0dFRQGmy3gbDAart5PbPB3bi45Ro4JOQU1ZtWEL7vpEzh/dT2JiEk0rFrPUE16egWbxQNSXNqHM60FSz/koJRq8UD2pyayx5nQyzrzl6fgURcFoNGI0GrO5Itt4ekebp+N8HhMnTqR9+/YUL148yz6n+/fvM378eDZs2MC1a9coVKgQnTp14rPPPkv2t+qpZ8d57do1PvzwQ/755x9iY2MpW7Ysf/75JzVr1gTgo48+YsyYMXTs2DFH3nbAaDSiKAoGgyHFDTqz6vcy2wPM+fPnKVKkCHq9nsDAQCZOnEjJkiWt6jt9+nRatmxJiRIlkq2Pjo6mRIkSJCUlUa1aNT7//HOqV6+e5na+/PLLVG+4tWXLFhwdHTM2oFxow4bMuWu0s07Fw1gVBe0ULp84xNFjxyiT8nc4BbVLb+q43MXr0TGY1519JUcT4VLecsfnkFljzelknHmHVqslLi6O6OhoEhISsruc55aQkICdnV26bZ73sv2xsbFMnz6dRYsWmf8DmhXOnTvH1atXGT9+POXLl+fatWuMGjWKq1evMnPmzDT7Xbt2jcaNG9OoUSMWLVqEp6cnly9fRqvVmutv1KgRDx8+ZNmyZQQFBWXVkKyWkJBAbGws27dvJzExMdlrMTExWVJDtt7Mcd26dcTExFC2bFlu377NF198wZkzZzh58qTFmyiGh4fj6+vLvHnz6NGjh3n93r17uXDhApUrVyYqKooff/yRtWvXcvToUcqUKZPqtlKbgfH19SU8PDzP38xxw4YNBAUFZdoN8aKiY1gVshkndSIPE1SUqFiTZhZnYoDEODSLB6C+tBlF50hSrwUoxetnSk1gm7HmRDLOvMVgMLBlyxb8/PxS3DTv6R8NjUaDSqUCMM/SqFSqZP8rzoy2GZ0FaN68ORUrVsTOzo7Zs2dTsWJFtmzZwg8//EBwcDCXLl3C3d2d9u3b89VXX6EoCmq1mmLFijFt2jS6detm3taqVavo06cPN2/eTPXS/kuXLuW1117j9u1/TyTYunUrLVq0YO3atYwbN44zZ85Qr1495s2bx6FDhxg9ejQ3btygbdu2TJs2LdP+s7p48WIGDBjAo0ePUtxLSFEUHj16xMSJE9mzZw/btqV/AsOQIUNISkpKNwxll7i4OMLCwvD19U31Zo4+Pj42v5ljts7AvPTSS+bnlStXpl69epQqVYqZM2davP13cHAwBQoUoHPnzsnW161bl7p165qXGzRoQI0aNfj555/56aefUt2WXq9Hr9enWK/T6fL0P45PZeY4PQq60bVdK5au2UABOwMrdx3FzsGJpuUKWyoCes+HBb1RXdyMdkFv6Pc3lMi8EGN6G/lO85L8Mk6VSoVarU4WIlasWAFAx44dzf9+nT17lhMnTuDv70+tWrXMbVevXk1SUhJt27bFyckJgAsXLnD06FGKFy+e7L4369atIyEhgVatWpl3g4SFhVk9M/6sWbNm8dprr7Fr1y5zQNFoNPz000/4+flx+fJlXn/9dT788EO+/PJLnJ2d6dWrFzNnzkz2H9OZM2fSrVu3VHfLAOzcuZNatWol+3yePv/ss8/45ZdfcHR0pEePHvTq1Qu9Xs+8efOIjo6mS5cu/Prrr3zwwQeAaVfUxIkT0x3XunXraNSoUaqvPXr0CFdX11Rnm57u2lq9ejWtW7emZ8+ebNu2jaJFi/L6668zbNiwZO0DAwP55ptvcuQuJLVajUqlSvV3MKt+J7N9F9KznJycqFy5MufPn0+3naIo/PXXX/Tv39/ilKRaraZ27doWtykyj6uzEy+3b8VvK3ex5lo8IbMO8ceAmjSzGGLsodc80+0GLm2BOd1sEmKEEFmjdOnSfPPNN8nWvfPOO+bn/v7+fP7557z22mt8+eWXALzyyivUr1+fmzdvUqRIEe7du8fq1avT3V0YFhZGkSJFUn3tiy++oEED03F1Q4cOZezYsVy8eNEcyLp168aWLVvMAWbEiBHJwlNqihYtmur6iIgIPv/8c1599dV0+1+6dInff/+dUaNGMW7cOPbv38/bb7+NXq9nwIAByd7n6tWrzzUDlh/kqAATHx/P6dOn00y2T23bto0LFy4wdOhQi9tUFIXQ0FAqV66cWWUKK7g4OTKqRwsuJR5m/cnbvDbrIL/0rETLKsXT76hzMM3EPL1S75xu0G8JlKiXJXULkVt06dIFINnun3LlylGmTBnzrp+nOnbsmKJt6dKlKVmyZIq27dq1S9HWz8/vuWp8dhboqS1btjBx4kROnTpFVFQUiYmJxMXF8fjxY1xdXalTpw4VK1Zk1qxZfPjhh8yePZvixYvTuHHjNN8nNjY2zTNNq1SpYn7u5eWFo6NjstkkLy8v9u/fb152d3fH3d09w2ONioqiXbt2BAQE8Omnn6bb1mg0UqtWLfNMT/Xq1Tl58iS///57sgDj4OCA0WgkPj4eBweHDNeU12VrpBs9ejTbtm3j8uXL7Nu3j27duhEVFcXAgQMBGDt2bLIv86np06cTGBhIpUqVUrw2YcIE1q9fz6VLlwgNDWXo0KGEhoYyYsQIm49HJKfTqPmlTw1equhF1+JxXDmxn41Hw6zo6AC95oN/EzA8hrnd4Opem9crRG6i1WrRarXJAoharUar1aY4KyQz2j6Pp7urnrpy5Qpt27alUqVKLFmyhEOHDvHrr78CJDsQ9JVXXmHGjBkAzJgxg8GDB6cIWs8qVKgQDx48SPW1Z3dnPN3l8SyVSpXsrKWJEyfi7Oyc7mPHjuSXfHj06BFt2rTB2dmZZcuWWdyF4uPjQ0BAQLJ1FSpU4OrV5JeguH//Po6OjhJe0pCtMzDXr1+nd+/e3Lt3D09PT+rWrcvevXvNZxWFh4en+EIjIyNZsmQJP/74Y6rbfPjwIcOHD+fWrVu4ublRvXp1tm/fTp06dWw+HpGSTqNmUrdKLFp5HWd1EldOHmQDCkFV/dPvaOcIvRfA/CcXuZvzMvRbCsUD0+8nhMixDh48SGJiIt999505FC1atChFu379+jFmzBh++uknTp48af5PbVqqV6/OnDlzMqXGjO5CioqKonXr1uj1elauXGnVNcfq16/P2bNnk607d+5cijNqT5w4QY0aNTJQff6SrQFmwYIF6b4eHBycYp2bm1u6p2j98MMP/PDDDy9amshETg72dO/QmsWrQnDVJXDt5CFCFGhVzZoQszB5iOm/FHwljAqRG5UqVYrExER+/vlnOnTowK5du5gyZUqKdgULFqRr1668//77tGrVimLF0j+TsXXr1owdO5YHDx5QsGDBF6oxI7uQHj16RKtWrYiJiWHOnDlERUWZT4P29PQ0z261aNGCLl268PrrrwOm44AaNmzIxIkT6dGjB/v372fq1KlMnTo12fZ37NhBq1atXmg8eZkcFSSyhLOjA907tCLaaIeLTuHaqUOEHLlsuePTEOPXCBIeweyucG2/5X5CiBynWrVqfP/993z99ddUqlSJuXPnmg/e/a+hQ4eSkJDAkCFDLG63cuXK1KpVK9XZHFs6dOgQ+/bt4/jx45QuXRofHx/z49q1f2+rcvHiRe7du2derl27NsuWLWP+/PlUqlSJzz//nMmTJ9O3b19zmxs3brB79+4UF2oV/8rW68DkVFFRUbi5uXHv3r08fx2YtWvX0rZt2yw77S06JpbFq0JwVifwyKCiaPkatK5hxemZCY9N90wK2wF2LtB/GfjWtvp9s2Os2UHGmbcYDAZCQkLw9/enZMmSefaWKEajkaioKFxdXc27lubOncvIkSO5efOmxbNNAdauXcvo0aM5ceJEjj1jJ7VxpuX9998nMjIyxaxMThEXF8fly5dTXJ8ITGdjFSpUyObXgcmZ37LIs5wdHejRoRWPjHbYaxR+WH+SjaesuIu1nRP0eWYmZk5XuH7Q9gULIbJUTEwMJ0+e5Msvv+TVV1+1KrwAtG3blldffZUbN27YuMKsUbhwYT7//PPsLiNHkwAjspyTowM9O7TihLEIZyLVvDb3EBsyEmJKNIT4KJjdRUKMEHnMN998Q7Vq1fDy8mLs2LEZ6jty5Eh8fX1tVFnWev/99/Hy8sruMnI0CTAiWzg5OvBZj3q0q+KDIUnhk78PsO7QRcsd7Zyg7yIo0eCZEHPI9gULIbLE+PHjMRgMbNq0CWdn5+wuR+RgEmBEttFq1PzYsxrdqhRiaKk4bp87bH2I6bMIitf/N8TckBAjhBD5iQQYka20GjWfdamKWmeHk5YnIeaC5Y56Z+i7+EmIiYRZEmKEECI/kQAjsp2jgz29OrTikaLHSQt3zh1hbYZCTL1nQsxh2xcshBAi20mAETnC0xATpehx1MLdc0dYczADIca3rinEzO4MN4/YvF4hhBDZSwKMyDEcHezp07G1OcTcO29tiHEx3bXaty7ERcKsThJihBAij5MAI3IUB3u9OcRExKv5YMVZ1h0Pt9zRHGICn4SYznAz1NblCiGEyCYSYESO42Cvp2+nNtywL0G0Ad6af8T6ENP3byhWB+IePpmJCbV1uUIIIbKBBBiRI9nr7fimR026VC9KolFhzob9rN5/zoqOrtBvSfIQE37U5vUKIVJq2rQp77zzTnaX8dxye/15nQQYkWNp1Comda/KK9Vd6eybwP2LR1mVoRBT+98Qc+u4zesVQgiRdSTAiBxNo1YxunMgUYo9Dlp4eOkoK/dlIMQUrQWxD9DO64przFXbFyxEFklMTEzzkZSUlOltM2rQoEFs27aNH3/8EZVKhUql4uLFiwwdOhR/f38cHBwoV64cP/74Y4p+nTt3ZtKkSfj4+ODh4cEbb7yBwWAwtwkPD6ddu3Y4ODjg7+/PvHnz8PPzY/LkyeY2kZGRDB8+nMKFC+Pq6krz5s05evTf2djx48dTrVo1Zs+ejZ+fH25ubvTq1YtHjx6lWX9YWFiGPwdhO9rsLkAIS+z1dvTr3IY5y9fjqokl8vJRVqLQMbCchY5u0H8pzO6K6sZB6l/4Cm43hGLVs6ZwIWxo2bJlab7m7e1No0aNzMsrV65MEVSe8vT0pGnTpublNWvWkJCQkKJd9+7dM1Tfjz/+yLlz56hUqRKfffYZAAULFqRYsWIsWrSIQoUKsXv3boYPH46Xlxdt2rQx992yZQs+Pj5s2bKFCxcu0LNnT6pVq8awYcMAGDBgAPfu3WPr1q3odDpGjRrFnTt3zP0VRaFdu3a4u7uzdu1a3Nzc+OOPP2jRogXnzp3D3d0dgIsXL7J8+XJWr17NgwcP6NGjB1999RX/+9//Uq3f09MzQ5+BsC2ZgRG5gt5OR7/OrYlSHLDXQOTlY6zYd8ZyxychxlikBvqkaLRzu8KtE7YvWIh8zs3NDTs7OxwdHfH29sbb2xu9Xs+ECROoXbs2/v7+9O3bl0GDBrF48eJkfQsWLMgvv/xC+fLlad++Pe3atWPTpk0AnDlzho0bN/Lnn38SGBhIjRo1mDZtGrGxseb+W7Zs4fjx4yxevJhatWpRpkwZJk2aRIECBfj777/N7YxGI8HBwVSqVIlGjRrRv39/8/ukVr9Go8mCT05YS2ZgRK7xNMTMXr4eN00sUZePs1Kjp2Mt//Q72ruR1Hsxkb8HUTDmEszqCANXgVfFrClcCBvo0qVLmq+pVKpkyx07drS6bbt27V6sMAumTJnCtGnTuHLlCrGxsSQkJFCtWrVkbSpWrJgsLPj4+HD8uOk4trNnz6LVaqlRo4b59dKlS1OwYEHz8qFDh4iOjsbDwyPZdmNjY7l48d/7rfn5+eHi4pLsfZ6dyRE5mwQYkavo7XT079ya2SvWs+tWItuPnUal09OhapH0O9q7sbvU+7x0byrq8CMws4OEGJGrabXW//Ntq7YZtWjRIt59912+++476tWrh4uLC99++y379u1L1k6n0yVbVqlUGI1GwLR7KDXPrjcajfj4+LB169YU7QoUKGDV+4icTwKMyHX0djoGdnmJs8tOkHTrOiMXHMGoKHSqVjTdfolaJ5L6/I16fjfTlXpndoCBq8ErIIsqFyJ/sbOzS3bszY4dO6hfvz6vv/66ed2zMyLWKF++PImJiRw5coSaNWsCcOHCBR4+fGhuU6NGDW7duoVWq8XPzy/T6hc5ixwDI3IlnVbD1y9XoXvNYuhUCicO7WX5ntOWO9q7Qf9l4FMNYiJMIeb2KZvXK0R+5Ofnx759+wgLC+PevXuULl2agwcPsn79es6dO8fHH3/MgQMHMrTN8uXL07JlS4YPH87+/fs5cuQIw4cPx8HBwbw7rGXLltSrV4/OnTuzfv16wsLC2L17N//3f//HwYMHn7t+mZ3JWSTAiFxLrVbx9ctVeKO6PWVdjURfOcGy3VaEEYeCMGA5+FSFmHumEHPHivAjhMiQ0aNHo9FoCAgIwNPTkzZt2tC1a1d69uxJYGAgERERyWZjrDVr1iy8vLxo3LgxXbp0YdiwYbi4uGBvbw+YdgWtXbuWxo0bM2TIEMqWLUuvXr0ICwvDy8vrueu/elUuxZCTqJS0dijmY1FRUbi5uXHv3r0UB4HlJQaDgbVr19K2bdsU+4JzE4MhkZnL1+NGDAlJ4Fi8Il3qB/ynTSpjjbn/5CJ3x8DJ07Q7qXD5bBhB5skr36kl+WmcISEh+Pv7U7JkSfMf6LzGaDQSFRWFq6sranXG/199/fp1fH192bhxIy1atLBBhZnjRceZk8TFxXH58mX8/f1T/FxGRERQqFAhIiMjcXV1tVkNufsTFALQ6bQM7NyaSJyw00DM1ZMs3WXFTIyjOwxYAd5V4PFdmNke7lhxarYQIltt3ryZlStXcvnyZXbv3k2vXr3w8/OjcePG2V2ayEISYESeYAoxrcwhJvZ6RkNM5SchpgPcPWv7goUQz81gMDBu3DgqVqxIly5d8PT0NF/UTuQfEmBEnpEsxKjh7uVTLD1sxT5rR3cYsPJJiLkDwe0lxAiRg7Vu3ZoTJ04QExPD7du3WbZsGSVKlMjuskQWkwAj8pSnISZC5cpfF/SMXnycZUeuW+74NMR4PRtirLjnkhBCiGwhAUbkOTqdluFdW9G8ih9GBUYtOsryw9csd3y6O8mrkinEzJQQI3IOOd9C5CQ54edRAozIk9RqFf/rXIk+gcXxd0ri8aVDnH5gxS+ck8eTmZhKEH3bFGLunbd9wUKk4em1R1K7waIQ2eXpz2N23h9KrsQr8iy1WsUXnSrxZ9JNnJR4iqsNLNt9ih5Nqqbf8WmImdkB7pw07U4atBoKlcmawoV4htFoxMHBgbt376LT6XL96bepMRqNJCQkEBcXlyfH91ReGafRaOTu3bs4Ojra9NYTlkiAEXmaWq1iaOeWzFi2ngLqxyTeOsfiHWq6N6qcfkcnDxi4EmZ2fCbErIFCpbOmcCGe4eXlxbVr17hy5Up2l2ITiqIQGxub7Gq6eVFeGqdaraZ48eLZOg4JMCLP02o19GvfnKmL1lDE0UjizTMs3q7QvXGV9Ds6FXoSYjrAnVOm3UmD1oBHqawpXIgndDodZcqUybO7kQwGA9u3b6dx48Z5+lTovDROOzu7bJ9FkgAj8gWtVkNxVw13Ex0pqI4mMfwsi7ZDD2tCzNPdSXdPQ3A7CTEiW6jV6jx7JV6NRkNiYiL29va5/g97evLLOLNK7t0JJ0QGqdUqBnRoxkOVC1o1HD19gUUHrDg7ydkTBq4CzwrwKNwUYiIydgddIYQQmUsCjMhXNBoNQ7oEcVPtxYIwOz5YekxCjBBC5EISYES+o9FoeLtrI/rW9UNR4IOlR1m004p7IEmIEUKIHEMCjMiXVCoVEzpWZGDd4nQuZsB48zgLt4Za7phaiLl3web1CiGESE4CjMi3VCoVn3asSIC3IxoVKHfOs2BLqOWOT0NM4QBTiJnZXkKMEEJkMQkwIl9Tq9UM7RLEQ7UbGhVw9zwLthyx3NHZ03R2koQYIYTIFhJgRL5nCjEtnwkxFyTECCFEDicBRghSDzHzNz9HiJFjYoQQIktIgBHiCXOI0RQAYP7+q8zdZ8Wl2589Jib6loQYIYTIAhJghHiGWq1maOcWXLP359hDLR8tO8GcvVaEGKdCEmKEECILSYAR4j/UajWjO9RkaEN/AL5dc4x51uxOkhAjhBBZRgKMEKlQqVT8X7sKvNrAl9fKxaO5d4G5mw5Z7phqiDlv+4KFECKfkQAjRBpUKhUftKuExqkAahVoIy4xd+NByx3NIabikxDTXkKMEEJksmwNMOPHj0elUiV7eHt7p9l+69atKdqrVCrOnEl+GfglS5YQEBCAXq8nICCAZcuW2XooIo9Sq9UM7dScSG1BU4i5fzkDIWalhBghhLCRbJ+BqVixIuHh4ebH8ePHLfY5e/Zssj5lypQxv7Znzx569uxJ//79OXr0KP3796dHjx7s27fPlsMQeZharWZIp+Y81Lo/E2IOWO4oIUYIIWwm2wOMVqvF29vb/PD09LTYp3Dhwsn6aDQa82uTJ08mKCiIsWPHUr58ecaOHUuLFi2YPHmyDUch8jrTTEyzZ0JMGHM2HbbcUUKMEELYhDa7Czh//jxFihRBr9cTGBjIxIkTKVmyZLp9qlevTlxcHAEBAfzf//0fzZo1M7+2Z88e3n333WTtW7dunW6AiY+PJz4+3rwcFRUFgMFgwGAwPMeocoenY8vLY3wqs8Y6oG1DZq7ZQUL0Q/7YcpM4nSsD65VIv5OdG/RZgnZeV1R3TqHMaEti/xXgUSb9fs8hv3ynMs68RcaZt2TV+FSKoihZ8k6pWLduHTExMZQtW5bbt2/zxRdfcObMGU6ePImHh0eK9mfPnmX79u3UrFmT+Ph4Zs+ezZQpU9i6dSuNGzcGwM7OjuDgYPr06WPuN2/ePAYPHpwspDxr/PjxTJgwIcX6efPm4ejomEmjFXmF0ajwz3U162+YZv66+CXR1Mfyr5Fd4iPqn/8Kt7hrxGnd2FVmHNH2PrYuVwghslRMTAx9+vQhMjISV1dXm71PtgaY/3r8+DGlSpVizJgxjBo1yqo+HTp0QKVSsXLlSsAUYGbOnEnv3r3NbebOncvQoUOJi4tLdRupzcD4+voSHh6eapDKKwwGAxs2bCAoKAidTpfd5dhUZo9VURS+33iBKdsvU9M9kQ5VvOjboqbljjERaOd2Mc3EOBXO9JmY/PKdyjjzFhln3hIREYGPj4/NA0y270J6lpOTE5UrV+b8eeuPEahbty5z5swxL3t7e3Pr1q1kbe7cuYOXl1ea29Dr9ej1+hTrdTpdnv4heyq/jBMyd6wfvFQBB+Lwjr6I+tE15m9WMaB1YPqd3Lxh4GqY2QHVnZPo5nQ2LXuWzZSansov36mMM2+RceYNWTW2bD+I91nx8fGcPn0aHx/rp9WPHDmSrH29evXYsGFDsjYhISHUr18/0+oUAkzXiXmrTTWi9YUAcIi6ysx/9lru6ORhuk6MVyWIvm26i/XdczauVggh8pZsDTCjR49m27ZtXL58mX379tGtWzeioqIYOHAgAGPHjmXAgAHm9pMnT2b58uWcP3+ekydPMnbsWJYsWcKbb75pbjNy5EhCQkL4+uuvOXPmDF9//TUbN27knXfeyerhiXxArVYzuEMTovSms+ccH10jeJ2VIWbASgkxQgjxnLI1wFy/fp3evXtTrlw5unbtip2dHXv37qVECdNZHeHh4Vy9etXcPiEhgdGjR1OlShUaNWrEzp07WbNmDV27djW3qV+/PgsWLGDGjBlUqVKF4OBgFi5cSGCghal9IZ6TWq1mSIcmPHoSYpyirzFj3R7LHSXECCHEc8vWY2AWLFiQ7uvBwcHJlseMGcOYMWMsbrdbt25069btRUoTIkNUKhWDOzRhxqptuMTfxTn6OlM3hDI8qFr6HZ+GmFkd4fYJU4ixwTExQgiR1+SoY2CEyM2ehphH9oXZdkvLxE3X+XWLFXejNs/EVDbNxAS3k5kYIYSwQAKMEJlIpVIxuH1jylWoCKj4dv1ZftlkRRhx8oABK0wh5vEdCTFCCGGBBBghMplKpeKtlmV5v3U5tCqFB5ePM331LssdJcQIIYTVJMAIYSNvNCvNh40LU97NiGvsTaY9d4g5a/tihRAil5EAI4QNvfJSHWIcvQFwi73Jn6t2Wu6UIsS0lxAjhBD/IQFGCBsb2K4RMU6miy0WiAvnz1U7sHgHDycP012sJcQIIUSqJMAIkQUGtm1IrDnE3GLaqp2WQ4yju4QYIYRIgwQYIbLIgLYNiXUqAoA+5haT/zn5fCHmzpksqFYIIXI2CTBCZKEBbRsQ61KU6ef1/LjtCpNCzmY8xMzsICFGCJHvSYARIosNaFOfAc0qA/Drlot8t+6E9SHGW0KMEEKABBghssXQhv582iGAog5GPCPPMHXldutCzAAJMUIIARJghMg2gxv481pdTxy14J5wh6krtmE0GtPvlCLEyDExQoj8SQKMENmoX+u6GNx8AXA33GXqiu0ZDDF3JcQIIfIlCTBCZLM+rf4NMR6JdzM4E1NFQowQIl+SACNEDtCnVV0SCxQHwCPxHn8stzbErEgeYuQ6MUKIfEICjBA5RO+gQHOIefDgPp+vPmXlgb3/hhjt3M64xF7PgmqFECJ7SYARIgfpHRRIYqEyzLigZ8buK0xYlbEQo3p8l/oXvoK7sjtJCJG3SYARIofp3awan3epAkDw7stMWr7P6t1Jildl7BOj0M7tAndOZ0G1QgiRPSTACJED9apTnG9erkLrIgb8Eq8xZdkWq0JMYt+lPHQogerx3SfXiZEQI4TImyTACJFD9ajtS4tKprOTPI33mbJsM0lJFkKMQ0F2l/4AxevJKdbB7SXECCHyJAkwQuRgPZrXAg9/ADyND/jDihBj0DqT2Hep6cDemHsSYoQQeZIEGCFyuO7Na0GhkgB4Kg+snolhwArwqSohRgiRJ0mAESIX6N6sJipPU4gprDxgyrJNGI1WnJ3Uf7mEGCFEniQBRohcolvTmqgLl8KowPawGMYuPf58Ieb2qSypVwghbEkCjBC5yMtNamBXojoHIrQsPHiND5cey3iImdlBQowQIteTACNELtMlsDQ/9KyGWgVrQq8xeckWEi0dEyMhRgiRx0iAESIX6lStKD/0rMawMnH4EsGUJRutCzHPHtgrIUYIkYtJgBEil+pUrSh+JUtjVMBLFcmUJRssh5j/np0kIUYIkUtJgBEiF+vcqBr2PmWfhJgopq/YQpKlY2JShBg5sFcIkftIgBEil+vUqCoORZ6EGPUjzj80ZmAmphrEREiIEULkOhJghMgDOjasikPRchgVKOmcxG/Lt1kZYpZLiBFC5EoSYITIIzo2qIJ9kTJExKuYcyqedxcdlRAjhMizJMAIkYe0DazIPYOOR4kaVh29yTsLQ58zxJzMinKFEOK5SYARIo+p7A4/96qKTqPi8pVr/PZ3CIbEpPQ7PQ0xRao/CTEdJMQIIXI0CTBC5EEtKxTml56V6eOfgI/6EX/8HUK8ITH9Tg4Fof8yCTFCiFxBAowQeVTrKr4U9KtAkgJemmimLpEQI4TIOyTACJGHta1bCbcSASQp4K15/Pwh5taJrClYCCGsJAFGiDzupcCKFChRMVmIiUuwJsQs/zfEzOooIUYIkaNIgBEiH2gTGEABv4okGk0h5qvF20lItHR2UgEJMUKIHEsCjBD5RJs6AbiXrMj+e1pmHo/h9bmHJcQIIXItCTBC5COtawfQvkVDdFoNG0/f5o25B4lNMKTf6b8hZmYHuHU8K8oVQog0SYARIp9pUtaTaQNq4aBVUTjuOtOWhGQgxNSA2Psws6OEGCFEtpIAI0Q+1LisJ791r0DFAkl4a2OYtmQ9MfHWhJhlULTmkxDTAcKPZUm9QgjxXxJghMinmlX1x6tMFQxG8NbGMn1pRkJMLYh98CTEHM2SeoUQ4lkSYITIx1rUKIf3syHGmpkYezfovxSK1Ya4h6bdSTdDs6JcIYQwkwAjRD7XvEY5vMtWNYUYnWkm5nFcQvqd7N2g31IoVscUYmZ1hJtHsqReIYQACTBCCKB59bL4lK2GwQge6lg+nL+XOIOFG0Dau0K/JeAbCHGRMKsT3DicNQULIfK9bA0w48ePR6VSJXt4e3un2X7p0qUEBQXh6emJq6sr9erVY/369cnaBAcHp9imSqUiLi7O1sMRIldrVr0MRcpVY36YA6vOPmLYrIMZCDF1n4SYznDjUJbUK4TI37J9BqZixYqEh4ebH8ePp31q5vbt2wkKCmLt2rUcOnSIZs2a0aFDB44cST517erqmmyb4eHh2Nvb23ooQuR6TauV4f+618PRTsOO8/d4e9ZeomMt7E7Su0C/v6F4PYiPhFld4LqEGCGEbWV7gNFqtXh7e5sfnp6eabadPHkyY8aMoXbt2pQpU4aJEydSpkwZVq1alazd05mcZx9CCOsElvRg5pA6FHdWEehwk+Bl660LMX3/huL1TSFmdme4fjBL6hVC5E/a7C7g/PnzFClSBL1eT2BgIBMnTqRkyZJW9TUajTx69Ah3d/dk66OjoylRogRJSUlUq1aNzz//nOrVq6e5nfj4eOLj483LUVFRABgMBgwGC2dk5GJPx5aXx/hUfhlrZo2zWlEXPm9fmutnj+KqjiN42T/0ad8cFwd92p3Ueug5D83C3qiv7kGZ3Zmk3otRitZ6oVpSI99n3iLjzFuyanwqRVGULHmnVKxbt46YmBjKli3L7du3+eKLLzhz5gwnT57Ew8PDYv9vv/2Wr776itOnT1O4cGEA9u7dy4ULF6hcuTJRUVH8+OOPrF27lqNHj1KmTJlUtzN+/HgmTJiQYv28efNwdHR8sUEKkYvdiDbiqklEr4Frj9UUc9Gg16rS7aNJiqfupe8oFH0Gg9qePaXf54FT6r97Qoi8JyYmhj59+hAZGYmrq6vN3idbA8x/PX78mFKlSjFmzBhGjRqVbtv58+fzyiuvsGLFClq2bJlmO6PRSI0aNWjcuDE//fRTqm1Sm4Hx9fUlPDzcqiCVWxkMBjZs2EBQUBA6nS67y7Gp/DJWW4xzz6kwrp0OxU4DtxP09OlgYSYGIOExmkV9UF/ZhWLnTFLvRSjF6mRKPSDfZ14j48xbIiIi8PHxsXmAyfZdSM9ycnKicuXKnD9/Pt12CxcuZOjQoSxevDjd8AKgVqupXbt2utvU6/Xo9Sn/QdbpdHn6h+yp/DJOyD9jzcxxNq5ahl1aLZePH8TLLp55qzczoFMrXJ3SOTBeV8B0TMy8HqjCdqCd38N0tlLxuplSk/lt5PvMU2SceUNWjS3bD+J9Vnx8PKdPn8bHxyfNNvPnz2fQoEHMmzePdu3aWdymoiiEhoamu00hRPoaVPSnZOVaxCdBfIKB1+Yc4nF8Yvqd7ByhzyLwbwwJ0TDnZbiyJ2sKFkLkedkaYEaPHs22bdu4fPky+/bto1u3bkRFRTFw4EAAxo4dy4ABA8zt58+fz4ABA/juu++oW7cut27d4tatW0RGRprbTJgwgfXr13Pp0iVCQ0MZOnQooaGhjBgxIsvHJ0ReUr+iP6Wr1mH+VSd2XX7IoBn7ibYmxPReCCWbPhNidmdJvUKIvC1bA8z169fp3bs35cqVo2vXrtjZ2bF3715KlCgBQHh4OFevXjW3/+OPP0hMTOSNN97Ax8fH/Bg5cqS5zcOHDxk+fDgVKlSgVatW3Lhxg+3bt1OnTubtfxciv6pboQTTB9fFxV7LgbAH/N+crTyIjk2/k50j9F4AJZuB4THM6QZhu7KmYCFEnpWtx8AsWLAg3deDg4OTLW/dutXiNn/44Qd++OGHF6hKCJGeqr4FmPtKID8s2UED1wfMWxFCn06tKOjskHYnnQP0ng8L+sDFzTC3G/RdDH4Ns65wIUSekqOOgRFC5A5VihXgtVZViUuCwnYJzFsZwv3omPQ76Ryg1zwo1QIMMTC3O1zekTUFCyHyHAkwQojnUqdCccpXCzSFGF0C81dssD7ElG75b4i5tC1rChZC5CkSYIQQz612+eKUrx5I7JOZmPkrQoiIshRi7KHnXCgdBImxMK8nXNqaJfUKIfIOCTBCiBdSu1xxAmrUJTZJRWE7AwtXhfDgcXz6nXT20GsulGn1b4i5uCVrChZC5AkSYIQQL6xWWV8qPgkxB+7AwBkHiIyxcD8UrR56zoEyrSExDub3Mh3gK4QQVpAAI4TIFDXLFqN6vSYcjXbi2PVI+k3fZ2WImQ1lX3oSYnrDhU1ZU7AQIleTACOEyDSVS3gyb1gg7k52nAt/yE+L13M38nH6nbR66DETyrV9JsRszJqChRC5lgQYIUSmKu/tyvxhdRlQ2kB5x1j+Xh3C3cjo9Dtp9dB9JpRrB0nxML8PnJcQI4RImwQYIUSmK+ftQvsmgcQkqihkl8jfqzdw56GlEGMH3YOhfHtTiFnQG85vyJJ6hRC5jwQYIYRNVC1VhOqB9c0hZsmaDdx+kJEQk2C6cu+5kCypVwiRu0iAEULYTJWSphDz+EmIWbrWihCj0ZlCTIWOphCzsC+c/SdL6hVC5B4SYIQQNlWlZBFq1v03xMxbvYmIaAvXidHooNtfENDpSYjpB2fXZU3BQohcQQKMEMLmKvsXoWbdBtyNV7Pgopo+f+7jnjUh5uXpENAZjAZY2B/OrM2SeoUQOZ8EGCFElqjs70OrVq1QdA6cvf2IPn/u5e6juPQ7PQ0xFbuYQsyiAXBmTdYULITI0STACCGyTOnCLiwYXhcvVz2JjyNZuGIdNyKi0u+k0ULXaVDpZXOIUZ2VmRgh8jsJMEKILFXS05l5rwTSy9+Alz6R1f9s5MY9K0JMl6lQqRsYE9EsHYLPw4NZU7AQIkeSACOEyHKlCrvQqFFDohNVuNslsTpkI9fvRabfSaOFLn9A5e6ojInUuvwrqjOrsqZgIUSOIwFGCJEtKhT3ol6DRqYQo0tizfpNXL9rXYgxVuqGmiQ0S1+Bk8uzpF4hRM4iAUYIkW3KF/eifsPGPHoyE7Nmwyau332Yfie1hqQOv3KtYANUShL8PQROLsuSeoUQOYcEGCFEtirnW5iGDZvwKFGNuy6J31fu5HaUhbOT1BoOlxiGsXJPUJLg76FwYmnWFCyEyBEkwAghsl1ZX08aNWrM0Ug9889Dr6l7uRVpIcSo1CS1/wmq9jGFmCWvwIklWVOwECLbSYARQuQIZYp58mrXlvgUcOTyvcf0mrqbq5YO7FVroNMvUK3fvyHm+N9ZU7AQIltJgBFC5Bi+7o4sGF6XYgXtqeEUyfqQjYTdvp9+J7UGOv4M1fuBYoSlw+DY4qwpWAiRbSTACCFyFF93R2YPqkHFAkYK6Ixs2LSFy7cshRg1dPgZagwwhZhlw+HYoqwpWAiRLSTACCFyHH+vgjRt1pRIg5oCOiMbN2/hkjUhpv2PUGPgkxDzKhxdmCX1CiGyngQYIUSOVMrHg+bN/g0xmzZv4WJ4RPqd1GpoPxlqDvo3xITOz4JqhRBZTQKMECLHKunjQYvmzcwhZvOWrVy4aUWIafcD1BoCKLD8NQidlyX1CiGyjgQYIUSO5u/tTssWphDjojUy/u8DXH8Qm34ntRrafge1hmIKMa/DkblZUq8QImtIgBFC5Hh+Xu4EtWjO+ruu7LyZRL+/DhBh4TIxppmY76D2K4ACK96Aw7OzolwhRBaQACOEyBVKeBXky35NKFnIiRsP45h5TsV5S7uTVCpoOwnqDAcUWPkWHJ6VJfUKIWxLAowQItfwdrNn/vC6VC6sp7d/Ant27eLc9bvpd1Kp4KVvoM6rmEPMoZlZUq8QwnYkwAghchUvV3t+6l0NFeCqM7Jj+3bOWhVivobA10zLq96GgzNsXqsQwnYkwAghcp1ihdxw1Gt5YNDgqjOyc/t2zly7k34nlQrafAl1Xzctr34HDv5l81qFELYhAUYIkSu52qlo3byxOcTs3rGD01etCDGtJ0LdN0zLq9+FA9NtX6wQItNJgBFC5FpFPNxo26oFDwwaXHRG9uzczqkrt9PvpFJB6/9BvTdNy2tGwf4/bV+sECJTSYARQuRqxQq50b61KcTEJMLr849y8W50+p1UKmj1BdR/y7S8drSEGCFyGQkwQohcr4iHKcRsfOBB2EMDvafu5cIdK0JM0OfQYKRpee1o2DfV9sUKITKFBBghRJ5QxMON6UPrU97bhTuP4vlswXZOhN1Kv5NKBS0nQIN3TMvr3oe9U2xeqxDixUmAEULkGR7OeuYNq0trPx3tvB9zYM9OToSFp99JpYKW46HhKNPyPx/A3t9tXqsQ4sVIgBFC5CnuTnZM6FGPh4lanLUKB/bs4vhlK0JMi0+g0Xum5X8+hD2/2r5YIcRzkwAjhMhzvN1d6PxSSyIMphBzcO8ujl2yIsQ0/xgav29aXj8Odv9i+2KFEM9FAowQIk/yKuhCl5eCzCHm8L5dHL14M/1OKhU0+wgajzEth3wEu36yfbFCiAyTACOEyLO8CjrTta0pxDhpFY7s383xq1bcdqD5R9DkQ9Pyho9h14+2L1YIkSESYIQQeVrhAs50a9uKCIOWg/c0DJx5hNPhUZY7NhsLTceanm/4BHb+YNtChRAZIgFGCJHnFSrgRPf2bbiqKsT9xwb6/LmXUzetCDFNP4Sm40zPN46HHd/btE4hhPUkwAgh8gUPVwdmD61L1WJuPIpNYOHazRw6f91yx6YfmI6LAdg0AbZPsm2hQgirSIARQuQbbg46Zg0NpH85NZXcDJw4uJeD565Z7thkDDT/P9PzzZ/D9m9tW6gQwqJsDTDjx49HpVIle3h7e6fbZ9u2bdSsWRN7e3tKlizJlCkpr5q5ZMkSAgIC0Ov1BAQEsGzZMlsNQQiRy7g56Hjz5SbcM+hw1CqcPLSPg2etCDGN3zedZg2w+QvY9o1tCxVCpCvDAWbQoEFs37490wqoWLEi4eHh5sfx48fTbHv58mXatm1Lo0aNOHLkCOPGjePtt99myZIl5jZ79uyhZ8+e9O/fn6NHj9K/f3969OjBvn37Mq1mIUTu5uHiRI8Orcwh5tSRfRywKsSMhhafmp5v+R9s/cq2hQoh0pThAPPo0SNatWpFmTJlmDhxIjdu3HihArRaLd7e3uaHp6dnmm2nTJlC8eLFmTx5MhUqVOCVV15hyJAhTJr07z7pyZMnExQUxNixYylfvjxjx46lRYsWTJ48+YXqFELkLR4ujvR6EmIcNAqnD+/lwBkrQkyjURD0men51i9h8/9AUWxbrBAiBW1GOyxZsoSIiAjmzJlDcHAwn376KS1btmTo0KF06tQJnU6Xoe2dP3+eIkWKoNfrCQwMZOLEiZQsWTLVtnv27KFVq1bJ1rVu3Zrp06djMBjQ6XTs2bOHd999N0Wb9AJMfHw88fHx5uWoKNPZCQaDAYPBkKHx5CZPx5aXx/hUfhmrjDNjnO11dHupOYvXbcZTZyD00D5UOj1VixdMv2Od11EroNn4CWz/hqREA8am40zXkMlE8n3mLfltnLamUpQX+6/DkSNH+Ouvv5g2bRrOzs7069eP119/nTJlyljsu27dOmJiYihbtiy3b9/miy++4MyZM5w8eRIPD48U7cuWLcugQYMYN26ced3u3btp0KABN2/exMfHBzs7O4KDg+nTp4+5zbx58xg8eHCykPKs8ePHM2HChBTr582bh6OjozUfgxAiF4tLVLj2KImV13TcjVPzWoUkSrhY7lfyzj9UvjEPgPOF23GqSI9MDzFC5DYxMTH06dOHyMhIXF1dbfY+GZ6BeVZ4eDghISGEhISg0Who27YtJ0+eJCAggG+++SbFTMh/vfTSS+bnlStXpl69epQqVYqZM2cyatSoVPuo/vOPw9P89ez61Nr8d92zxo4dm+z9oqKi8PX1pVmzZqkGqbzCYDCwYcMGgoKCMjxzltvkl7HKOJ9fdHwie2cf5uqVh0w9b89f/atRvYS7hV5tSTpQCU3IOMrcWUPJkv4Ym3+aaSFGvs+8Jb+MMyIiIkveJ8MBxmAwsHLlSmbMmEFISAhVqlTh3XffpW/fvri4mP7LsmDBAl577TWLAea/nJycqFy5MufPn0/1dW9vb27dupVs3Z07d9BqteagkVYbLy+vNN9Xr9ej1+tTrNfpdHn6h+yp/DJOyD9jlXFmXEGdjplDAhk84wA37tzj6L4dJCbUon6AX/od678BWjtYOxrN3l/QqIBWX2TqTIx8n3lLXh9nVo0twwfx+vj4MGzYMEqUKMH+/fs5ePAgI0aMMIcXMB1zUqBAgQwXEx8fz+nTp/Hx8Un19Xr16rFhw4Zk60JCQqhVq5b5A0urTf369TNcjxAif3HSa5kxuDbdS0EBO4WLxw6w62SY5Y51hkG7J1fp3fML/DNWDuwVwsYyHGB++OEHbt68ya+//kq1atVSbVOwYEEuX75scVujR49m27ZtXL58mX379tGtWzeioqIYOHAgYNq1M2DAAHP7ESNGcOXKFUaNGsXp06f566+/mD59OqNHjza3GTlyJCEhIXz99decOXOGr7/+mo0bN/LOO+9kdKhCiHzISa9lSJcg7hrssNfA5eMH2HXC8r9n1B4K7Sebnu/7HdZ9ICFGCBvKcIDp378/9vb2mfLm169fp3fv3pQrV46uXbtiZ2fH3r17KVGiBGA6xubq1avm9v7+/qxdu5atW7dSrVo1Pv/8c3766Sdefvllc5v69euzYMECZsyYQZUqVQgODmbhwoUEBgZmSs1CiLzP1dGefp1bc9egR6+ByycOsvP4Jcsdaw2Gjj8DKtj/B6wdDUajzesVIj96oYN4X9SCBQvSfT04ODjFuiZNmnD48OF0+3Xr1o1u3bq9SGlCiHzO1dGe/p1bM3v5ejx18YSdPIQCNKqc+mUezGoMAJUaVrwJB6aBYoS234Fa7twiRGaS3yghhEiDi6OeAZ1bczfRNBOzZd9R9l++b7lj9X7Q+TdABQf/gtXvyEyMEJlMAowQQqTD+UmIORfnyuyLOgbN2M++S1acJlqtD3T5wzQbc3gmrHpbQowQmUgCjBBCWODsoOe9Xi2pW9qTmIQkBs3Yz87T1y13rNoTukw1hZgjs2Hlm2BMsn3BQuQDEmCEEMIK9joNfw6oReOyhWjsGceVY3vYEnrBcscq3aHrn6DSQOhcWP66hBghMoEEGCGEsJK9TsOUvjWo4qnFTg03zx5hy5HUL7yZTOVu0G26KcQcWwDLRkiIEeIFSYARQogMcNTrGNSlNXcS7U0h5lwom60JMRW7QPcZoNbC8UWwdDgkJdq+YCHyKAkwQgiRQU72dgzu2po7iQ7YqeHWuVA2HT5nuWNAJ+gebAoxJ/6GpcMkxAjxnCTACCHEc3DU2zG4ayvuJDqgU8Pt80fZePis5Y4VOkCPWaDWwcmlsGQIJBlsX7AQeYwEGCGEeE7mEJNkCjHTt55l27m7ljuWbwc9Z4PGDk6tgL8HS4gRIoMkwAghxAtw1NsxpEtrDsd7seeOhmGzDrL17B3LHcu9BD3nmkLM6VWweBAkJti8XiHyCgkwQgjxghz0Oib0akirAC8SEo28M/cg6w9asTupbCvoNR80ejizGhYPhMR42xcsRB4gAUYIITKBnVbNr31r0L6iJ0NKxXD/4jH+2X/acscyLaH3fNDaw9m1sLC/hBghrCABRgghMolOo+a7XjXQ6B3QquHB5ROs23fKcsfSLaD3AlOIOb8eFvQFQ5ztCxYiF5MAI4QQmUiv0/LKy625a3RCq4aHYSdZa02IKdUM+iwCrQNc2AAL+kCihBgh0iIBRgghMplep+WVrq248yTERIadZPXek5Y7lmwCfReDzhEubkKzqB9qoxzYK0RqJMAIIYQN2Om0DOvaijtGZ7RqeHTlFKv3WjET498I+v4NOifUl7dS9+L3YIixfcFC5DISYIQQwkZMISaIu0ZnHhlUTPjnEuuOh1vu6NcA+i1BsXPCM/oUmoV9IOGx7QsWIheRACOEEDZkp9My7OVWXND5cTdOxZvzj7DmmBUhpkQ9knotwqC2R31lJ8ztAfHRti9YiFxCAowQQtiYTqvhy+416Vq9KElGhSnrDrBi1zGL/RTfQPaUfh/Fzhmu7IS53SXECPGEBBghhMgCGrWKb7tXZUCNQvTxiyf2xlmW7bQcYh44lSGpzxLQu8LV3TDnZYh/lAUVC5GzSYARQogsolGr+OTl2jxUu6BRQfzNsyzbcdRiP6VoTRiwHPRucG0vzO4KcVG2L1iIHEwCjBBCZCGtRs3wrq24h6spxISfY+l2yyGGpyHG3g2u74c5XSEu0ub1CpFTSYARQogsptGoGd41yBxiDLfO8fe2UMsdi9aAASvBvgBcPwCzu0DsQxtXK0TOJAFGCCGygTnEqNxQqyDp9nkW77Li3klFqsHAVeDgDjcOwezOEPvA1uUKkeNIgBFCiGyi0agZ3qUl91RuHIzQ8MHqiyw+eM1yR58qphDj6AE3j8CsThBz3/YFC5GDSIARQohs9DTE6L1LY1RUjFlyjEX7r1ru6F3pSYgpBOFHYVZHCTEiX5EAI4QQ2UyjUfN558oMqFcClaIQeuQgC7ccstzRqyIMWg1OnnDrOMzsCI8jbF+wEDmABBghhMgBVCoVEzpW5O3AglR3T4K7l1hszYG9hSvAwNXgVBhuH4eZHeDxPZvXK0R2kwAjhBA5hEql4u1O9bivKYhaBZr7YZx9YLTcsXB5GLQGnL3hzkkIbg/Rd21fsBDZSAKMEELkIGq1mmGdm5tDTAnHRBZuPWK5o2dZU4hx8YG7p2Fme4i+Y/uChcgmEmCEECKHMYcYtSnE6B9eYd7GA5Y7Fir9JMQUgbtnILgdPLpl+4KFyAYSYIQQIgdSq9UMat+IsGgNAEpEGDN3nLPc0aOU6cBe16Jw75wpxERZcfdrIXIZCTBCCJFDqdVqShdQc1/rwcwLej5dc54Zuy5b7uhRyjQT4+YLEReehJibti9YiCwkAUYIIXIwtVrF4HYNaVGzLAATVp1i+vYLlju6+5tmYtyKw/2LMKMtRF63cbVCZB0JMEIIkcOpVCrGtC7HG81K4ak3olwLZdb6fZY7FvQzhZgCxeHBZdNMzEMrrvQrRC4gAUYIIXIBlUrF6FblGF7TDTc7BYeoq8z8Z6/ljgVLwKC1pjDzIOxJiLHiSr9C5HASYIQQIpdQqVQMbd+QKL0nAI6PrhG8bo/ljgV8TcfEFPSHh1dgRjtTmBEiF5MAI4QQuYharWZIhyY8ehJinKKvM2OtFSHGrRgMXgvupSDyqulid/etOCBYiBxKAowQQuQyKpWKwR2a8Mi+MADOj6/z19rdlju6FjHNxHiUhshrT0LMJRtXK4RtSIARQohcSKVSMbh9Y6LtvQB4cCecXzadtdzR1ccUYgqVhajrpt1JERdtXK0QmU8CjBBC5FIqlYpB7RsR5eTLn+f1TNpwgZ82nbfc0cXbdANIz/Lw6KbpwN57VvQTIgeRACOEELmYSqViaNu6vB1UHoDvN5zj139CLXd08XoSYirAo3BTiLlrxZV+hcghJMAIIUQe8HrT0nz4UnkaFTZQ+NF5/ly1E0VR0u/k7Gm6TkzhihB9+0mIsWI3lBA5gAQYIYTII0Y0KUVQ+UIAFIgLZ9pqK0KMUyEYuAq8KsHjO6YQc+d0FlQrxIuRACOEEHnIgLYNiXMuAkCBuFvWzcQ4eZhCjHdleHzXdHbS7ZNZUK0Qz08CjBBC5DH9X2pgDjEF428xdeUOyyHG0R0GrASfqhBzD2Z2gFvHs6BaIZ6PBBghhMiD+r/UgHiXogC4J9zmD6tDzAooUh1iIkwhJvxYFlQrRMblmADz5ZdfolKpeOedd9JsM2jQIFQqVYpHxYoVzW2Cg4NTbRMXF5cFoxBCiJyjX5v6JLiaQszeS/f5at0ZyyHGoSD0Xw5FakDsA1OIuRlq81qFyKgcEWAOHDjA1KlTqVKlSrrtfvzxR8LDw82Pa9eu4e7uTvfu3ZO1c3V1TdYuPDwce3t7Ww5BCCFypL6t6xPnWYGtt3X8sf0SE9eetiLEFIABy6FoLYh7CLM6ws0jWVCtENbTZncB0dHR9O3blz///JMvvvgi3bZubm64ubmZl5cvX86DBw8YPHhwsnYqlQpvb2+ra4iPjyc+Pt68HBUVBYDBYMBgMFi9ndzm6djy8hifyi9jlXHmLZk1zl4NypGkc2T8qtPM2nUJ54QIRrStg1qdzv9hNY7QezGa+T1Q3ziAMrMjSX3+RilS44VqSY18n3lLVo1PpViM4rY1cOBA3N3d+eGHH2jatCnVqlVj8uTJVvXt0KED8fHxhISEmNcFBwfzyiuvULRoUZKSkqhWrRqff/451atXT3M748ePZ8KECSnWz5s3D0dHxwyPSQghcqLdt6GQLhE/ZyPnojQEuKtRq1Xp9tEmxVL34iQ8Hp/HoHZgT+kxPHAqlUUVi9woJiaGPn36EBkZiaurq83eJ1sDzIIFC/jf//7HgQMHsLe3z1CACQ8Px9fXl3nz5tGjRw/z+r1793LhwgUqV65MVFQUP/74I2vXruXo0aOUKVMm1W2lNgPj6+tLeHg4Hh4eLzzOnMpgMLBhwwaCgoLQ6XTZXY5N5ZexyjjzFluM8+8tB9FGXgfgttqDYe0bpD8TAxD/CM3C3qiv7UWxcyap92KUYrUzpR6Q7zOviYiIwMfHx+YBJtt2IV27do2RI0cSEhLyXMenBAcHU6BAATp37pxsfd26dalbt655uUGDBtSoUYOff/6Zn376KdVt6fV69Hp9ivU6nS5P/5A9lV/GCflnrDLOvCUzx9m7VT0WbtyH+sFVvIwRTFu9i9e6NE0/xOjcod8SmNcD1ZVdaOd3Ny0Xr5t2n+cg32fekFVjy7aDeA8dOsSdO3eoWbMmWq0WrVbLtm3b+Omnn9BqtSQlJaXZV1EU/vrrL/r374+dnV2676NWq6lduzbnz8uNyoQQAqBny0AU9xIAFDZG8PuyLSQlGdPvpHeGvovBrxEkRMOcl+HK7iyoVojUZVuAadGiBcePHyc0NNT8qFWrFn379iU0NBSNRpNm323btnHhwgWGDh1q8X0URSE0NBQfH5/MLF8IIXK1Hi3qgLsfAIWN9/l1+TaMRgtHFNg5QZ9F4N/k3xBzeYftixUiFdkWYFxcXKhUqVKyh5OTEx4eHlSqVAmAsWPHMmDAgBR9p0+fTmBgoLndsyZMmMD69eu5dOkSoaGhDB06lNDQUEaMGGHzMQkhRG7SvUVtVB7+PDLA3OPRfLT8hBUhxhH6LIRSzcEQA3O7w6WtWVKvEM/KEdeBSUt4eDhXr15Nti4yMpIlS5akOfvy8OFDhg8fToUKFWjVqhU3btxg+/bt1KlTJytKFkKIXKVb81oUKFubu/Fq5u+/yrhlxy2HGJ0D9JoPpVtCYizM6wkXNmVNwUI8ke3XgXnW1q1bky0HBwenaOPm5kZMTEya2/jhhx/44YcfMrkyIYTIu7rW8kOt0TFqUSgHTofxi/EOb3RtjkaT3oG99tBrHizsD+fXw/zepuUyLbOucJGv5egZGCGEEFmjc/WifP9yAINLx+PDA35duolESwf2avXQczaUawdJ8bCgN5xbnzUFi3xPAowQQggAOtfyx8nHdJE6Hx7y2xIrQ0z3YKjQAZISYEFfOLPW9sWKfE8CjBBCCLMuTWqg83oSYlQP+W3JRitCjB10mwEBncFogEX94fQq2xcr8jUJMEIIIZLp3LgGdt6lMSrgo4rkt7+tCDEaHbw8HSp1A2MiLBoIJ5dlTcEiX5IAI4QQIoVOjarj4PMkxKgj+ebv7VaEGC10nQpVeoGSBH8PheN/Z03BIt+RACOEECJVHRtVx7FIWY4/0DDtSDTvLjpqOcSoNdD5N6jW1xRilg6DowuzpmCRr0iAEUIIkaYODavSoH49NBo1q47eZOTCIxgS077VC2AKMR1/gRoDQDHCslchdF7WFCzyDQkwQggh0tW6kg+/9a2JnQb0D6/y298bSLAYYtTQ/keoORhQYPnrcHhWltQr8gcJMEIIISwKCvDipy5lCSyUSBHNI363OsT8ALWHAQqsfAsOzsiSekXeJwFGCCGEVdrUKoOLb3mMChTRPOK3xSHEGyyEGJUK2n4Lga+Zlle/A/v/tHmtIu+TACOEEMJqbetVxq14BYwKFNVG8/vfVoaYNl9CvTdNy2tHw94pti9W5GkSYIQQQmRIm7qVcCvxHCGm1RfQYKRp+Z8PYM+vti9W5FkSYIQQQmRYm8BKFCgRQJIC3ppoxi3YQ7ylY2JUKmg5ARq9Z1pePw52/Wj7YkWeJAFGCCHEc2kdWBF3vwAWXbFnyclIXptz2LoQ0/xjaPKBaXnDJ6h3TbZ5rSLvkQAjhBDiubWqU5FRneuh16rZfOYOb8/ZT2xCYvqdVCpoNg6ajgNAs/ULyt5abvNaRd4iAUYIIcQLaVimEH8Nqo2XI1TX3uCPv9cTE2+w3LHpB9DiEwAqhC9Fve0rUBQbVyvyCgkwQgghXliD0oX4sn1pCtgpFNXF8MeSEOtCTKP3SGr+KQCanZNg8xcSYoRVJMAIIYTIFM1rlKNwqcokGqGYLoapVoYYY723OF60j2lhxyTYOF5CjLBIAowQQohM07xmebzLmEJMUV0MU/8O4XGc5RBzqXAbklp9aVrYNRlC/k9CjEiXBBghhBCZqlmN8viUrWIKMXYx/LlkvVUhxlh7GLSdZFrY8wv8M1ZCjEiTBBghhBCZrmn1chQpV5VEI2iT4nh99n5iLJ2dBFBnGLSfbHq+73fTVXuNRpvWKnInCTBCCCFsokm1svhWqM7sK05su/iQwTMOWBdiag2Gjr8AKjgwDdaMkhAjUpAAI4QQwmYaVinNbwPq4qzXsu/yfcbM3sGj2ATLHWv0h86/ASo4NANWvS0hRiQjAUYIIYRN1SxRkFlD61Dfy0gjl3tMX7qeKGtCTLU+0OUPUKnhyGxY+SYYLVzpV+QbEmCEEELYXI3iBXmj5ZMbQNrF8deS9UTGxFvuWLUndP0TVBoInQvLX5MQIwAJMEIIIbJIg8qlKRFQA4MRiurjmLEshMjHcZY7Vu4G3aabQsyxhbB0OCRZcSyNyNMkwAghhMgy9SuXwr/ikxBj9yTExFgRYip2ge7BoNbCib9hyVBIsuJKvyLPkgAjhBAiS9WrVIpSlWo+mYmJZ86qLcQarLjeS0BH6DEL1Do4tRz+HgyJVhxLI/IkCTBCCCGyXGDFkpSqXAuDEc4/SOL3MxqiYq2YUSnfDnrNBY0dnF4FiwdJiMmnJMAIIYTIFoEB/lSo1ZBd9x25Eq1m0MxDRMZYEWLKtoZe80Gjh7NrYFF/SLTigGCRp0iAEUIIkW2ql/Jh1uDaOGkVTt+M5NuFG7n/yIpjYsq0hD4LQGsP5/6BBX3BYEU/kWdIgBFCCJGtKvi48EZAEgNKG6jqHM3sFeuJeBRjuWOp5tBnEWgd4MIGWNAbDLG2L1jkCBJghBBCZLuiTtCmXhUSjFBEn8CcFRu4F2VFiCnZBPr9DTonuLgZ5vWEBCv6iVxPAowQQogcoXqZ4lSoHkj8kxAzd+UG7loTYvwamkKMnTNc3gbzekB8tO0LFtlKAowQQogco0bZ4lR8JsTMWxnC3UgrQkyJ+tBvKdi5QNgOmNsd4h/ZvmCRbSTACCGEyFGqly1OpRqBxCdBEb2BmSs2EhFtxVlGxQNhwHLQu8LV3TDnZYiLsnm9IntIgBFCCJHjVCtTnMo16xJlULEiDPr8uY971oSYYrVMIcbeDa7tg9ldIC7S1uWKbCABRgghRI5UtYwvjVsEEa9x5OztR/T5c691IaZoTRiwEuwLwI2DMKszxD6wdbkii0mAEUIIkWOV8XJjwfC6eLnqefwoiuCl/xD+4LHljkWqwcBV4OAONw/DrE4Qc9/m9YqsIwFGCCFEjlbS05l5rwQyqHQCJRwSWLxmAzcfWHGWkU8VGLQaHAtB+FGY1REeR9i+YJElJMAIIYTI8UoVdqF+3brEJanw0RtYsmYDN+9bcZaRV0UYtAacCsOt4zCzAzy+Z/uChc1JgBFCCJErVCxZlJqB9YlLUuGtT2TJmo3csCbEFC5vCjHO3nDnJAS3h+g7ti9Y2JQEGCGEELlGgH8RatVtYAox9oksXbuR6/esCDGeZU0hxsUH7p6G4Hbw6JbtCxY2IwFGCCFErlLBz4fa9RoQ+2Qm5vflWwmPtOIeSIVKm0KMa1G4d84UYqJu2r5gYRMSYIQQQuQ65Uv4UKdeA85F27HooopeU/dy86EVIcajlCnEuPlCxAVTiIm8YfuCRaaTACOEECJXKl/Ch4Gdg/Au6MiViBh6Td3D1Qgrzk5y9zeFmALF4f4lCG4LD6/ZvmCRqXJMgPnyyy9RqVS88847abbZunUrKpUqxePMmTPJ2i1ZsoSAgAD0ej0BAQEsW7bMxtULIYTIDsUKOrJgeD2KuzsQYB/JynUhhN2x4sq7BUvAoLVQ0A8ehJlCzIMrti5XZKIcEWAOHDjA1KlTqVKlilXtz549S3h4uPlRpkwZ82t79uyhZ8+e9O/fn6NHj9K/f3969OjBvn37bFW+EEKIbFS0gAOzBlanfuEkvPRJrA7ZxOXbDy13LOBrCjHuJeHhVdPupPuXbV6vyBzZHmCio6Pp27cvf/75JwULFrSqT+HChfH29jY/NBqN+bXJkycTFBTE2LFjKV++PGPHjqVFixZMnjzZRiMQQgiR3fy8ClK/YSNiklR46ZNYu2GzdSHGragpxHiUhshrphATcdHm9YoXp83uAt544w3atWtHy5Yt+eKLL6zqU716deLi4ggICOD//u//aNasmfm1PXv28O677yZr37p163QDTHx8PPHx/95fIyrKdPdSg8GAwWDIwGhyl6djy8tjfCq/jFXGmbfIODOmhJc7SfXqs3fPbgo/CTFBzRrhX7hA+h0dCkHf5WjndUV17xzKjLYk9lsGHmXS75dB+e37tLVsDTALFizg8OHDHDhwwKr2Pj4+TJ06lZo1axIfH8/s2bNp0aIFW7dupXHjxgDcunULLy+vZP28vLy4dSvt8/2//PJLJkyYkGL9li1bcHR0zMCIcqcNGzZkdwlZJr+MVcaZt8g4M0at0fLYYKCwPomQTdtw1GvxsFdZ7Kf3fov60V/hGn2DpGlt2FXmQ6Lti2ZKTc/K699nTExMlrxPtgWYa9euMXLkSEJCQrC3t7eqT7ly5ShXrpx5uV69ely7do1JkyaZAwyASpX8B1VRlBTrnjV27FhGjRplXo6KisLX15dmzZrh4eFh7ZByHYPBwIYNGwgKCkKn02V3OTaVX8Yq48xbZJzPL+xWBLt37cTLQWFVuB2f9GxACXcr/kP6OAhl3svY3zlJ86vfk9h3GXiWz5Sa8sv3GRGRNfebyrYAc+jQIe7cuUPNmjXN65KSkti+fTu//PIL8fHxyY5tSUvdunWZM2eOednb2zvFbMudO3dSzMo8S6/Xo9frU6zX6XR5+ofsqfwyTsg/Y5Vx5i0yzowr4+uNrnETvl55iO03k+g3/SALhtfFr5BT+h0L+JhuADmrI6pbx9HN6QQDVoJ3pUypC/L+95lVY8u2g3hbtGjB8ePHCQ0NNT9q1apF3759CQ0NtSq8ABw5cgQfHx/zcr169VJMz4WEhFC/fv1MrV8IIUTO5lfEkwl9m1K6sDO3ouIYPH0X527et9zR0d0UWnyqQUyE6QaQ4cdsXq/ImGybgXFxcaFSpeSJ1snJCQ8PD/P6sWPHcuPGDWbNmgWYzjDy8/OjYsWKJCQkMGfOHJYsWcKSJUvM2xg5ciSNGzfm66+/plOnTqxYsYKNGzeyc+fOrBucEEKIHKGwiz3zh9VlyPTdBLlHsHHzFozNmlK+qIXDAxzdYcAKmNMVbhwyhZgBK6BItawoW1gh20+jTk94eDhXr141LyckJDB69GiqVKlCo0aN2LlzJ2vWrKFr167mNvXr12fBggXMmDGDKlWqEBwczMKFCwkMDMyOIQghhMhmni56fu1dBVc7FZ56I1u2bOX0dSuO03AoAP2XQbHaEPcQZnU0hRmRI2T7adTP2rp1a7Ll4ODgZMtjxoxhzJgxFrfTrVs3unXrlomVCSGEyM2Ke3nQollTNm3ZSiG90fT3pmkTKhQrlH5HezfotxTmdodre2FWZ9Oyb+0sqFqkJ0fPwAghhBCZxfdJiHmcpKaQ3si2rds4dfWu5Y72rtBvCZRoAPFRMLsLXJWru2c3CTBCCCHyDV8vD1o2b0Z0khoPvZHt27dz0poQo3eGvovBrxEkPDIdG3Nlt+0LFmmSACOEECJfKVbYnVbNmxOdqEaFwlvzDnH21iPLHe2coM8iKNkUEqJhzstweYfN6xWpkwAjhBAi3ylauCCtg5qzOdKDSw+T6P3nXs7cirLc0c4Rei+AUs3BEGM6NubiFtsXLFKQACOEECJfKlKoIH8MbkDlom7cf5zAR3N3cPTyHcsddQ7Qaz6UaQ2JsTC/F5zfaPuCRTISYIQQQuRbBRztmDM0kDYl9XT3jWHv7h2EXr5tuaPOHnrOhnLtIDEOFvSGs//YvmBhJgFGCCFEvubmqGP8y7WJSdLgbmdk3+6dHLlkRYjR6qF7MFToAEkJsLAfnF5t83qFiQQYIYQQ+Z63hxvtWrfg0ZMQc2DPTo5cumW5o9YOus2Ail3BaIDFA+HkcpvXKyTACCGEEAB4uf8bYgo+CTGHL1oRYjQ66PonVO4BxkT4ewgc/9v2BedzEmCEEEKIJ7wKutGhdcsnIUbh4N6dHLpkxYG9Gi10mQLV+oKSBEuHwdGFti84H5MAI4QQQjzDs6ArHdoE8ShJw7koNYNmHSb02kPLHdUa6PgL1BgAihGWvQpH5ti83vxKAowQQgjxH54FXOjctjVXVF48ikui/7R9HLn6wHJHtRra/wi1hgIKrHgDDs6web35kQQYIYQQIhXurk4EDw6kjp87j+MNzFmznb1nb1juqFZDu+8g8DXT8up3YP+fNq01P5IAI4QQQqTBSa9lxuDaDAnQULdQAicO7mHv2euWO6pU0OZLqP+WaXntaNT7p9i22HxGAowQQgiRDie9ltc7NyIqSYubncKJg3vZfcbKEBP0OTQcBYBmw/9R+vYaG1ebf0iAEUIIISxwd3Wma9tW/9/evUdFVe7/A3/vuTGCgEqCeLykRiIiYoCK1wzQEtSOJnpS1NI6dfylVqtO+jvnm34zsXPySB3LwizyAt7QzMSU1hHJRERkOJTmJSxJQbxxSx25PL8/iPk5gLBHmBk2vF9rzVruh+eZ+XzaPqu3e4bZphBz8sRRHD6Z1/hCSQJC/gcY/VcAQP9LW6H6brWVq20bGGCIiIhk6OjihCnjx6KkUgtXrcBpQzpSf5AZYsYsQeXoxQAAdcrbQMpKQAjrFtzKMcAQERHJ1MHFCU+Fh6GkUgsXrcDJrHQcPivje2IAVI14FT90jaw+SIkG/rOcIaYJGGCIiIgs4OrshKciwlBUqUPiLzrM/TwTqWeuyFp7ziMClaH/W33w7bvAN28yxNwnBhgiIiILubZ3QtSUcPT4gyeMFVWYt+E4Dv4o4waQAKqG/AV44h/VB9+9B+xfwhBzHxhgiIiI7oNeq8GHMx9BmI8HXNUV+G/6YSQbzstbPOTPQPi/qv989ENg3+sMMRZigCEiIrpPDho1Pnj6ETzbT0JXxyrknczE/hO58hYHzQUm/huABByLBb56Gaiqsmq9rQkDDBERURPoNCrMmhiC0iodnLUCl37MRFLmT/IWPzILePJDABKQ+Rmw5yWgqtKq9bYWDDBERERN5OTYDtMmjEWp0KG9Frh8+gS+On5O3mL/p4HJ6wBJVX3zxy/+whAjAwMMERFRM3BybIfpE8ahTDigvRa4ciYLezJkhhi/qcCU9YCkBv67Bdj5PFBZYd2CFY4BhoiIqJk4ttNj+sT/H2Kysv+LL7MvyVvsOxmYGgeoNMD3O4DEZ4HKcqvWq2QMMERERM2ond4B0yeOQyFcEX9eh0VbsvBFloy7WAOAz0Rg2iZArQNO7ga2zwEq7li1XqVigCEiImpm7fQOeHFKGCYN6oEqAbyyzYBdx3+Rt7jvE8D0eEDtAPz4FbAtCqgwWrdgBWKAISIisgKVSkL05AH40+AeGNG5HDd/zkL2NZnf9eIVBvwpAdDogTNfA1ueBspvWbdghWGAISIishKVSsJbE33wWA81nDRAz3bl2H7ktLzFD4UAT28DtI7AuW+AhOnAnZvWLVhBGGCIiIisSKNR4+mJ4/Ab9HDUAOWXTiH+25PyFvceDczYAWidgNwUID4SMJZZtV6lYIAhIiKyMp1Oh6njH8N1o1QdYn79AZsO/SBv8YPDgahdgM4Z+PlbYPNTgLHUugUrAAMMERGRDeh0Org7aXBTagdHDVB16SQ2HPxe3uIeQ4BZXwAOrsCFNGDjZOB2sVXrbekYYIiIiGxEpZLw1BNjcEvliHYa4EDWOXzyrcx7J3ULBGbvBvQdgF+PARueBG7dsGa5LRoDDBERkQ3pdDpMnxCGK/puOFyoxfK9pxCbKvPeSV0HAbP3AO06AZdOABsmATevW7fgFooBhoiIyMZ0Oh1ejBiKBY89BABY9fUpxCbnyFvs6QfM+QpwfADIzwY+nwj8dtWK1bZMDDBERER2IEkSXhnbFy+H9MY8LyPaXfsRHx/IlrfYoz8wZy/g5A5czgHiIoCyQusW3MIwwBAREdnR/DFe8OzgCL0acLx+Bh9+bZC30N0beCYJcPYErpwC4sKB0gKr1tqSMMAQERHZkUajwfQJYbijbQ+9GnAuOos1+7IghIxv7X3Aq/pKjEs34OoZ4LPxQLHM+y4pHAMMERGRnWk0GkyLCEO51hl6NeBafA5rkmSGGLc+wDN7AdcewPWfgLjxQFGe9Yu2MwYYIiKiFkCj0SAyIhTluuoQ07H0J7y394S8ENPxweoQ0/FB4MbP1SHmhsybRyoUAwwREVELUXMlpsLBBeVVQMLxS4je96O8ENOhBzAnCejUByi6UP120nWZ3zGjQAwwRERELYharUZkeCg03Qbg8m0VYlNzsXzvKXkhxvUP1Z+JeeBhoOTX6hBz9Zz1i7YDBhgiIqIWRq1WI2qUN5Y/6QsASDGcw7u7M+SFGBfP6hDTuR9Qml/9dtIVmXfAVhAGGCIiohZq5tCeiA7vjWe9jOhq/AX/+OKYvBDT3r36y+48fIGyy9VXYi7LvAO2QjDAEBERtWCRwx6G1qkDdCqg+50LWLkzHVVVMkKM0wPVtx3o4gfcvFr9PTEFMr/tVwEYYIiIiFowtVqNKU88Bjh2hFYF9KzIQ/TOo/JCjGMnYPaXQNdHgFvXq7+x91KW9Yu2gRYTYKKjoyFJEhYtWnTPOTt37kRYWBg6d+4MFxcXBAcHY//+/WZz4uLiIElSncft27et3AEREZF1qNVqTH58DKTfQ0yvyl/xdmIaKuWEmHYdgVlfAN2CgNtFwOeTgF8zrV2y1bWIAJORkYHY2Fj4+fk1OC81NRVhYWFISkpCZmYmxowZgwkTJiAryzxNuri4ID8/3+yh1+ut2QIREZFVqdVqTH7iMUhOnaBVAX2qLmLpjnR5IUbvCszcCXQfChiLgY1PAnnHrF6zNdk9wJSVlWHGjBlYt24dOnbs2ODcmJgYvP766wgKCoKXlxdWrFgBLy8v7Nmzx2yeJEno0qWL2YOIiEjpVCoVJj8+Bqr2nXC6RI3NJ67i1W0GVFRWNb5Y7wLMTAR6jgCMJcDGPwK/HLF+0VaisXcB8+fPR3h4OEJDQ7F8+XKL1lZVVaG0tBSdOnUyGy8rK0PPnj1RWVkJf39/vPXWWxg0aNA9n8doNMJoNJqOS0pKAADl5eUoLy+3qCYlqemtNfdYo630yj5bF/bZujRnnxGPjYD25GVsPv89vjBcQnllFd6d4guNupHrEioHYFo81NtmQvVzKsSmKaiM3Azx4Mgm11TDVudRErJ+H8s6tmzZgrfffhsZGRnQ6/V49NFH4e/vj5iYGFnr//nPf2LlypU4deoU3N3dAQBHjx7FuXPnMGDAAJSUlOC9995DUlISsrOz4eXlVe/zLF26FMuWLaszHh8fD0dHx/vuj4iIyJqyr0n4/KyEiG4VqBASQv4gobEMAwCqqjsYnPsePEpzUCHpcKz3Ilxx8W2Wmm7evImnn34axcXFcHFxaZbnrI/dAkxeXh4CAwNx4MABDBw4EAAsCjAJCQmYN28edu/ejdDQ0HvOq6qqwiOPPIJRo0bh/fffr3dOfVdgunfvjvz8fLi5uVnWmIKUl5cjOTkZYWFh0Gq19i7HqtpKr+yzdWGfrYu1+tyXlgPj5Z9QUQV8X+6O//vUUGjlpJiK21AnPgPVuWQItQMqp26A6BPS5HquXbsGT09PqwcYu72FlJmZicLCQgQEBJjGKisrkZqaijVr1sBoNEKtVte7duvWrZg7dy62b9/eYHgBqt8vDAoKwtmzZ+85x8HBAQ4ODnXGtVptq95MNdpKn0Db6ZV9ti7ss3Vp7j4jRvhjT3IxUHIVvtpCvL3jKN6cPhI6TSMhRqsFpscD2+dAOr0Xmu1RQOQGoO8TTarHVufQbh/iDQkJQU5ODgwGg+kRGBiIGTNmwGAw3DO8JCQkYM6cOYiPj0d4eHijryOEgMFggKenZ3O3QEREZHcqlQoTwkZD59oZGhXgqy3Em1u+hbGisvHFGh0Q+TngMwmovANsjQJO7Wl8XQtgtwDj7OwMX19fs4eTkxPc3Nzg61v9PtzixYsxa9Ys05qEhATMmjULq1atwtChQ1FQUICCggIUFxeb5ixbtgz79+9Hbm4uDAYD5s6dC4PBgBdeeMHmPRIREdmCSqXChNBRcOjgDo0KGKgrxNKEb3G7XEaIUWuBKZ8CvlOAqnJg22zgh13WL7qJ7P5r1A3Jz8/HhQsXTMcff/wxKioqMH/+fHh6epoeCxcuNM0pKirC888/j379+mHs2LG4ePEiUlNTMXjwYHu0QEREZBMqlQoRISOh7+AOtQT46q5gwaZjMkOMBvhjLOA3DRCVwI65QM4O6xfdBHb/Neq7paSkmB3HxcU1+PP6rF69GqtXr26+ooiIiBRCpVIhPGQk9h38DluyipB59Tqe23Ac62YFQq+t/6MZJmoN8ORaQKUFDJuAnc8BleWA/59sU7yFWvQVGCIiIrJMTYh5bfIwOOrU+PbsVcz7/Bhu3ZFxJUalBib+GwiYA4gq4IsXgRMbrV7z/WCAISIiaoWG9nZD3DOD0dVJQrDDRfxtcwpu3qlofKFKBYSvBoLmARDAl/8HOP6p1eu1FAMMERFRKzW4VycsG/MA3PUCQU7X8bfNh/CbUWaIGf8uMOTF6uOvXgbSY61brIUYYIiIiFqx0OFBcH7AE2oJGOx0HX/blIIyOSFGkoDHo4FhC6qP970GpH1g3WItwABDRETUikmShHGPDodL565QS8BQ5xv4+6aDKL0t455FkgSE/S8w8tXq4/1LgMMxVq1XLgYYIiKiVk6SJIwdPQyu7n+ASgKGOhfh75tSUCI3xDz2d2D0G9XH37wJpP7TugXLwABDRETUBkiShLBRwejwe4jprS3GrE+OoviWzBAzZjHw2N+qj/+zHDgYDdjvftAMMERERG2FJEkIHRWMzt37YNuv7WH4tQQzP0lH0c078p5g1GtA6LLqPx9aCfznLbuFGAYYIiKiNkSSJDw69BF8MncY3Jx0yLlYjOc//Q43fpMZYkYsAsatqP7zt6uA5L/bJcQwwBAREbVB3l1ckPD8UDzaVWCy+1X8z+aDuFZmlLc4eD7wxO+fgznyb+DrxTYPMQwwREREbdTDHs6YHdAZagkY0aEESzen4KrcEDPkeSAipvrP6WuBva8CVVVWq7U2BhgiIqI27NFhg9H5Dz2hkoARHUuwdPNBFJbelrc48Blg0gcAJOD4emDPgupbENgAAwwREVEbJkkSRgcHwf33EDO6UymWbT6IyyUyQ8ygmcAfPwYkFZC1Eep9r1m34N8xwBAREbVxkiRhVHAQPLo9CAB41K0MyzYfxKWiW/KeYOA0YMongKSG6uRO6xV6FwYYIiIigiRJGDk0EF2696oeqLiNabFp+PXGTXlP4DsFmBoHIWmsV+RdGGCIiIgIQHWIGTEkAN7+Qfjhtivyrt/CtI+P4sI1mSHGZyIqJ621bpG/Y4AhIiIiE0mSMMDrQWx9fhh6P+CEy8U3sWTzIZy/+pus9eKhUCtXWI0BhoiIiOro4qpH/LzB+Eu/SkzyLMPKrQfx05Uye5dlwgBDRERE9fJwbYfhPj0AAKHut/CPrSk4e7nUzlVVY4AhIiKiekmShMEB/ujZxwsAEOpxC+9uT8Gp/BI7V8YAQ0RERA2QJAlBgwai10MPAwBCPW5jdeIhfH+x2K51McAQERFRgyRJQoC/H3p7VYeYMI/bWJ2Yiv/+WmS3mhhgiIiIqFGSJOGRgX7o83Bf3KmScKZIYMa6dJy4cMMu9TDAEBERkSw1IWbsuHHwdO+MUmMFZq0/huM/X7d5LQwwREREZJHOHZwR92wQgnu7oYPaiLW7D+No7jWb1sAAQ0RERBZz1Gnw4fQBeNH7Dh7vasSnXx3Gd+eu2uz1GWCIiIjovnR0ccIAn34AgDDPO/g86Tsc+ck2V2IYYIiIiOi++fn2h7ePD4DqEJOYctwmr8sAQ0RERE0yoH9/9PPpDwAY41Fuk9dkgCEiIqIm8+3vA5/+vjZ7PY3NXomIiIhatf4+/VBaYpvbDPAKDBERETUbL6+HbPI6DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4LSbAREdHQ5IkLFq0qMF5hw4dQkBAAPR6PXr37o2PPvqozpzExET4+PjAwcEBPj4+2LVrl5WqJiIiIntoEQEmIyMDsbGx8PPza3De+fPnMX78eIwcORJZWVlYsmQJFixYgMTERNOctLQ0TJs2DVFRUcjOzkZUVBQiIyORnp5u7TaIiIjIRuweYMrKyjBjxgysW7cOHTt2bHDuRx99hB49eiAmJgb9+vXDvHnz8Oyzz+Ldd981zYmJiUFYWBgWL14Mb29vLF68GCEhIYiJibFyJ0RERGQrGnsXMH/+fISHhyM0NBTLly9vcG5aWhrGjh1rNjZu3DisX78e5eXl0Gq1SEtLw8svv1xnTkMBxmg0wmg0mo6Li4sBANevX7ewG2UpLy/HzZs3ce3aNWi1WnuXY1VtpVf22bqwz9alrfRZ8/9OIYRVX8euAWbLli04ceIEMjIyZM0vKCiAh4eH2ZiHhwcqKipw9epVeHp63nNOQUHBPZ83Ojoay5YtqzP+8MMPy6qLiIiIzF27dg2urq5We367BZi8vDwsXLgQBw4cgF6vl71OkiSz45qEd/d4fXNqj91t8eLFeOWVV0zHRUVF6NmzJy5cuGDV//j2VlJSgu7duyMvLw8uLi72Lseq2kqv7LN1YZ+tS1vps7i4GD169ECnTp2s+jp2CzCZmZkoLCxEQECAaayyshKpqalYs2YNjEYj1Gq12ZouXbrUuZJSWFgIjUYDNze3BufUvipzNwcHBzg4ONQZd3V1bdV/yWq4uLi0iT6BttMr+2xd2Gfr0lb6VKms+zFbu32INyQkBDk5OTAYDKZHYGAgZsyYAYPBUCe8AEBwcDCSk5PNxg4cOIDAwEDT+4n3mjNs2DDrNUNEREQ2ZbcrMM7OzvD19TUbc3Jygpubm2l88eLFuHjxIjZs2AAAeOGFF7BmzRq88soreO6555CWlob169cjISHB9BwLFy7EqFGj8M4772DSpEnYvXs3vvnmGxw+fNh2zREREZFV2f3XqBuSn5+PCxcumI579eqFpKQkpKSkwN/fH2+99Rbef/99TJkyxTRn2LBh2LJlCz777DP4+fkhLi4OW7duxZAhQ2S/roODA958881631ZqTdpKn0Db6ZV9ti7ss3Vhn81LEtb+PSciIiKiZtair8AQERER1YcBhoiIiBSHAYaIiIgUhwGGiIiIFKdNBZjo6GhIkoRFixY1OO/QoUMICAiAXq9H79698dFHH9WZk5iYCB8fHzg4OMDHxwe7du2yUtX3R06vO3fuRFhYGDp37gwXFxcEBwdj//79ZnPi4uIgSVKdx+3bt63cgTxy+kxJSam3hx9//NFsXks+p3L6nDNnTr199u/f3zSnpZ3PpUuX1qmlS5cuDa5R4v60tE8l701Le1Xq/rS0TyXuzxoXL17EzJkz4ebmBkdHR/j7+yMzM7PBNbbYp20mwGRkZCA2NhZ+fn4Nzjt//jzGjx+PkSNHIisrC0uWLMGCBQuQmJhompOWloZp06YhKioK2dnZiIqKQmRkJNLT063dhixye01NTUVYWBiSkpKQmZmJMWPGYMKECcjKyjKb5+Ligvz8fLOHJbd/sBa5fdY4ffq0WQ9eXl6mn7Xkcyq3z/fee8+sv7y8PHTq1AlTp041m9fSzmf//v3NasnJybnnXCXvT0v6VPretKTXGkrcn5b0qdT9eePGDQwfPhxarRb79u3DyZMnsWrVKnTo0OGea2y2T0UbUFpaKry8vERycrIYPXq0WLhw4T3nvv7668Lb29ts7M9//rMYOnSo6TgyMlI8/vjjZnPGjRsnpk+f3qx13w9Leq2Pj4+PWLZsmen4s88+E66urs1bZDOwpM+DBw8KAOLGjRv3nNNSz2lTzueuXbuEJEni559/No21tPP55ptvioEDB8qer9T9aWmf9VHK3rS0V6Xuz6aeUyXsTyGE+Otf/ypGjBhh0Rpb7dM2cQVm/vz5CA8PR2hoaKNz09LSMHbsWLOxcePG4fjx4ygvL29wzpEjR5qv6PtkSa+1VVVVobS0tM4NuMrKytCzZ09069YNERERdf4VaA/30+egQYPg6emJkJAQHDx40OxnLfWcNuV8rl+/HqGhoejZs6fZeEs7n2fPnkXXrl3Rq1cvTJ8+Hbm5ufecq+T9aUmftSlpbwL316sS92dTzqlS9ueXX36JwMBATJ06Fe7u7hg0aBDWrVvX4Bpb7dNWH2C2bNmCEydOIDo6Wtb8goKCOjd+9PDwQEVFBa5evdrgnNo3kbQ1S3utbdWqVfjtt98QGRlpGvP29kZcXBy+/PJLJCQkQK/XY/jw4Th79mxzlW0xS/v09PREbGwsEhMTsXPnTvTt2xchISFITU01zWmJ57Qp5zM/Px/79u3DvHnzzMZb2vkcMmQINmzYgP3792PdunUoKCjAsGHDcO3atXrnK3V/WtpnbUrZm4DlvSp1fzblnCplfwJAbm4u1q5dCy8vL+zfvx8vvPACFixYYLrFT31stk8tui6kMBcuXBDu7u7CYDCYxhq7DO/l5SVWrFhhNnb48GEBQOTn5wshhNBqtSI+Pt5szqZNm4SDg0PzFW+h++n1bvHx8cLR0VEkJyc3OK+yslIMHDhQvPTSS00p9741tc8aERERYsKECabjlnZOm9rnihUrhJubmzAajQ3Os/f5rK2srEx4eHiIVatW1ftzpe7P2hrr825K2Zv3YkmvNVr6/qyPJX0qaX9qtVoRHBxsNvbSSy+ZvR1Um632aau+ApOZmYnCwkIEBARAo9FAo9Hg0KFDeP/996HRaFBZWVlnTZcuXeokwMLCQmg0Gri5uTU4p3aatKX76bXG1q1bMXfuXGzbtq3RtypUKhWCgoLs9i+CpvR5t6FDh5r10NLOaVP6FELg008/RVRUFHQ6XYOvY+/zWZuTkxMGDBhwz3qUuj9ra6zPGkram/cit9e7tfT9WR+5fSptf3p6esLHx8dsrF+/fmb3KazNVvu0VQeYkJAQ5OTkwGAwmB6BgYGYMWMGDAYD1Gp1nTXBwcFITk42Gztw4AACAwOh1WobnDNs2DDrNdOI++kVABISEjBnzhzEx8cjPDy80dcRQsBgMMDT07O5W5DlfvusLSsry6yHlnZOm9LnoUOHcO7cOcydO7fR17H3+azNaDTi1KlT96xHqfuztsb6BJS3N+9FTq+1tfT9WR+5fSptfw4fPhynT582Gztz5kydz+7czWb7VPa1mlai9mX4N954Q0RFRZmOc3NzhaOjo3j55ZfFyZMnxfr164VWqxU7duwwzfnuu++EWq0WK1euFKdOnRIrV64UGo1GHD161JatNKqxXuPj44VGoxEffPCByM/PNz2KiopMc5YuXSq+/vpr8dNPP4msrCzxzDPPCI1GI9LT023ZSoMa63P16tVi165d4syZM+L7778Xb7zxhgAgEhMTTXOUcE4b67PGzJkzxZAhQ+p9jpZ2Pl999VWRkpIicnNzxdGjR0VERIRwdnY2/WZGa9mflvap5L1paa9K3Z+W9llDSftTCCGOHTsmNBqNePvtt8XZs2fF5s2bhaOjo9i0aZNpjr32aZsPMLNnzxajR482m5OSkiIGDRokdDqdePDBB8XatWvrPM/27dtF3759hVarFd7e3mabraVorNfRo0cLAHUes2fPNs1ZtGiR6NGjh9DpdKJz585i7Nix4siRI7ZrQobG+nznnXdEnz59hF6vFx07dhQjRowQe/furfM8Lf2cyvm7W1RUJNq1aydiY2PrfY6Wdj6nTZsmPD09hVarFV27dhWTJ08WP/zwg+nnrWV/Wtqnkvempb0qdX/ez99dpe3PGnv27BG+vr7CwcFBeHt716nfXvtUEkII+ddriIiIiOyvVX8GhoiIiFonBhgiIiJSHAYYIiIiUhwGGCIiIlIcBhgiIiJSHAYYIiIiUhwGGCIiIlIcBhgiIiJSHAYYIiIiUhwGGCIiIlIcBhgiIiJSHAYYIlKEK1euoEuXLlixYoVpLD09HTqdDgcOHLBjZURkD7yZIxEpRlJSEp588kkcOXIE3t7eGDRoEMLDwxETE2Pv0ojIxhhgiEhR5s+fj2+++QZBQUHIzs5GRkYG9Hq9vcsiIhtjgCEiRbl16xZ8fX2Rl5eH48ePw8/Pz94lEZEd8DMwRKQoubm5uHTpEqqqqvDLL7/YuxwishNegSEixbhz5w4GDx4Mf39/eHt741//+hdycnLg4eFh79KIyMYYYIhIMV577TXs2LED2dnZaN++PcaMGQNnZ2d89dVX9i6NiGyMbyERkSKkpKQgJiYGGzduhIuLC1QqFTZu3IjDhw9j7dq19i6PiGyMV2CIiIhIcXgFhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgU5/8BXKyntTXra7UAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot 1\n", - "plt.plot(x_v, yy_solidly_v, label=f\"Solidly (k={k})\")\n", - "plt.plot(x_v, yy_match_v, label=f\"Match (ps={ps})\")\n", - "#plt.plot(x_v, yy_match_opt_v, label=f\"Match (optimized)\")\n", - "plt.plot(x_v, yy_ray1_v, marker=None, linestyle='dotted', color=\"#aaa\", label=f\"ray (m={mm})\")\n", - "plt.plot(x_v, yy_ray2_v, marker=None, linestyle='dotted', color=\"#aaa\")\n", - "plt.plot(x_v, yy_tang_v, marker=None, linestyle='--', color=\"#aaa\", label=\"tangent\")\n", - "plt.grid(True)\n", - "plt.title(f\"Matching a Solidly curve\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()\n", - "plt.xlim(0, 10)\n", - "plt.ylim(0, 10)\n", - "plt.savefig(\"/Users/skl/Desktop/sol_img_matching1.jpg\")\n", - "plt.show()\n", - "\n", - "# plot 2\n", - "plt.plot(x_v, yy_solidly_v, label=f\"Solidly (k={k})\")\n", - "plt.plot(x_v, yy_match_v, label=f\"Match (ps={ps})\")\n", - "#plt.plot(x_v, yy_match_opt_v, label=f\"Match (optimized)\")\n", - "plt.plot(x_v, yy_ray1_v, marker=None, linestyle='dotted', color=\"#aaa\", label=f\"ray (m={mm})\")\n", - "plt.plot(x_v, yy_ray2_v, marker=None, linestyle='dotted', color=\"#aaa\")\n", - "plt.plot(x_v, yy_tang_v, marker=None, linestyle='--', color=\"#aaa\", label=\"tangent\")\n", - "plt.grid(True)\n", - "plt.title(f\"Matching a Solidly curve\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()\n", - "plt.xlim(1, 4)\n", - "plt.ylim(6, 9)\n", - "plt.savefig(\"/Users/skl/Desktop/sol_img_matching2.jpg\")\n", - "plt.show()\n", - "\n", - "# plot 3\n", - "plt.plot(x_v, yy_solidly_v, label=f\"Solidly (k={k})\")\n", - "plt.plot(x_v, yy_match_v, label=f\"Match (ps={ps})\")\n", - "#plt.plot(x_v, yy_match_opt_v, label=f\"Match (optimized)\")\n", - "plt.plot(x_v, yy_ray1_v, marker=None, linestyle='dotted', color=\"#aaa\", label=f\"ray (m={mm})\")\n", - "plt.plot(x_v, yy_ray2_v, marker=None, linestyle='dotted', color=\"#aaa\")\n", - "plt.plot(x_v, yy_tang_v, marker=None, linestyle='--', color=\"#aaa\", label=\"tangent\")\n", - "plt.grid(True)\n", - "plt.title(f\"Matching a Solidly curve\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()\n", - "plt.xlim(2.8, 3)\n", - "plt.ylim(7, 7.5)\n", - "plt.savefig(\"/Users/skl/Desktop/sol_img_matching3.jpg\")\n", - "plt.show()\n", - "\n", - "# plot 4\n", - "plt.plot(x_v, yy_solidly_v, label=f\"Solidly (k={k})\")\n", - "plt.plot(x_v, yy_match_v, label=f\"Match (ps={ps})\")\n", - "#plt.plot(x_v, yy_match_opt_v, label=f\"Match (optimized)\")\n", - "plt.plot(x_v, yy_ray1_v, marker=None, linestyle='dotted', color=\"#aaa\", label=f\"ray (m={mm})\")\n", - "plt.plot(x_v, yy_ray2_v, marker=None, linestyle='dotted', color=\"#aaa\")\n", - "plt.plot(x_v, yy_tang_v, marker=None, linestyle='--', color=\"#aaa\", label=\"tangent\")\n", - "plt.grid(True)\n", - "plt.title(f\"Matching a Solidly curve\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend()\n", - "plt.xlim(4, 6)\n", - "plt.ylim(4, 6)\n", - "plt.savefig(\"/Users/skl/Desktop/sol_img_matching4.jpg\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "07c9019b-3d5f-4377-ac3c-a247c46be041", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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79+7hxIkTFZ4AcMCAAQCAFStWlNimX79+UCgUuHPnTrHHVNKtxE2bNsX//d//oXXr1kWCVVRUFDIzM/UCHBk+ngmhGuXLL798aptBgwZh8eLFGD16NN58800kJCRg0aJFRX4RA0Dr1q2xZcsWbN26FQ0bNoSxsTFat26NGTNmYOvWrRg2bBg++OADdOrUCRkZGTh8+DAGDx5c4p0GVWHOnDnYvXs3evfujTlz5sDExAQrV65EWloagKK3yRZmaWmJ7t274+uvv4adnR08PDxw+PBhrFmzBtbW1k/97I0bN+LHH3/E6NGj0bFjR1hZWeHBgwdYvXo1rly5go8++ghGRkYAgAULFsDf3x+9evXCf//7XxgZGWH58uW4fPkyNm/eXK4ZY1u0aIHXX38dS5cuhVKpRJ8+fXD58mUsWrSoTJcE8k2fPh2jRo2CIAiYOnVqmbZZvHgxnnvuOXTu3BkffPABGjdujNjYWPzxxx/48ccfYWFhgYEDB8LGxgaTJk3C/PnzoVAoEBgYiPv375e5tnwTJ07EwoULMXr0aJiYmBS54+uVV17Bpk2bMHDgQEyfPh2dOnWCUqnEgwcP8O+//2LYsGF48cUXS9x/z549IYoiTp06pbtMWVhcXBxefPFFvPHGG0hKSsLHH38MY2NjBAQElPtYAKBbt24YM2YMPvvsM8TGxmLw4MFQqVS4cOECTE1N8c4778DDwwPz58/HnDlzcPfuXfTv3x/16tVDbGwsTp8+DTMzM8ybNw8XL17E22+/jREjRqBJkyYwMjLCwYMHcfHiRb0zewB0t8VX599NqgTSjoslKlnhu2NKU9wdLmvXrhWbNWsmqlQqsWHDhuKCBQvENWvWFLlzITw8XOzbt69oYWEhAtAb5f/48WNx+vTpopubm6hUKkUHBwdx0KBB4vXr10VRLLg75uuvvy5SEwDx448/1r0u6e6YQYMGFdm2R48eRY7n6NGjYufOnUWVSiU6OTmJs2fPFhcuXCgCEBMTE0v9/jx48EB8+eWXxXr16okWFhZi//79xcuXL4vu7u56d5kU5+rVq+J7770ndujQQbS3txcVCoVYr149sUePHuLGjRuLtD969Kj4/PPPi2ZmZqKJiYnYpUsXcffu3XptynJ3jCiKYlZWlvjee++JDg4OorGxsdilSxcxODi4SN3F7a/wPlQqldi/f/9Sj7O44x4xYoRoa2srGhkZiW5ubuL48ePFzMxMXZvTp0+Lfn5+opmZmVi/fn3x448/FlevXl3s3THF9XNhfn5+IgDxtddeK/Z9tVotLlq0SGzTpo1obGwsmpubi82bNxffeust8datW6XuW6PRiB4eHuLUqVP11ud/3zZu3Ci+++67or29vahSqcRu3bqJZ8+e1Ws7btw40czMrNj9P3l3TP5nLlmyRGzVqpVoZGQkWllZib6+vkV+Fnbu3Cn26tVLtLS0FFUqleju7i4OHz5cPHDggCiKohgbGyuOHz9ebN68uWhmZiaam5uL3t7e4pIlS/TuSBNFURwzZozYunXrUr8XZHgEUSx0Ho6Iaoy+ffsiPDwcN2/elLoUg7V7924MHToUe/bswcCBA6UuRzLffPMNPv/8c0RGRupmaj106BB69eqF3377DcOHD5e4wmeTnJwMFxcXLFmyBG+88YbU5VA5cEwIUQ0wa9YsbNy4EYcOHcL27dvx8ssvIygoqMgpadK6evUq/vrrL7z33nto27atbpxCXTVt2jRYWVkVmcCttliyZAnc3Nx0E7xRzcExIUQ1gEajwUcffYSYmBgIggAvLy9s3LgRr7/+utSlGaSpU6fi+PHjaNeuHdavX19jn2BcWYyNjbFx48YiU6LXFpaWlggMDCzTHDJkWHg5hoiIiCQh6eWYI0eOYMiQIXBxcYEgCE+dx2H79u3w9/eHvb09LC0t4evri3379hVpt23bNnh5eUGlUsHLy0v3PBAiIiIyHJKGkLS0NLRp0wbLli0rU/sjR47A398fe/fuxblz59CrVy8MGTJE7xRjcHAwRo0ahTFjxiA0NBRjxozByJEjcerUqao6DCIiIqoAg7kcIwgCduzYgRdeeKFc27Vs2RKjRo3CRx99BED7BM3k5GTd8wsA6O5BL/woeCIiIpJWjR7Fk5ubi5SUFNjY2OjWBQcHY+bMmXrt+vXrh6VLl5a4n6ysLGRlZent99GjR7C1ta3zA9qIiIjKQxRFpKSkwMXFpdTJFIEaHkK++eYbpKWl6T1NNCYmpsiDvRwdHUt9zsaCBQswb968KquTiIiorrl///5THwZZY0PI5s2b8cknn2DXrl1wcHDQe+/JsxeiKJZ6RiMgIACzZs3SvU5KSoKbmxvCwsKe+nCo0qjVavz777/o1asXlEplhfdDlYv9YrjYN4aLfWOYDLFfUlJS4OnpWabfnzUyhGzduhWTJk3Cb7/9hj59+ui95+TkVOSsR1xcXKmPPVepVMU+V8TGxqZcz6l4klqthqmpKWxtbQ3mh4PYL4aMfWO42DeGyRD7Jb+OsgxnqHEzpm7evBnjx4/HL7/8gkGDBhV539fXF0FBQXrr9u/fDz8/v+oqkYiIiMpA0jMhqampuH37tu51WFgYQkJCYGNjAzc3NwQEBCAyMhIbNmwAoA0gY8eOxbfffosuXbrozniYmJjAysoKgPapmd27d8fChQsxbNgw7Nq1CwcOHMCxY8eq/wCJiIioRJKeCTl79ix8fHzg4+MDQPt8DB8fH93tttHR0YiIiNC1//HHH5GTk4Np06bB2dlZt0yfPl3Xxs/PD1u2bMG6devg7e2NwMBAbN26FZ07d67egyMiIqJSSXompGfPnihtmpLAwEC914cOHSrTfocPH17jnwpJRFSYRqOBWq2W7PPVajUUCgUyMzOh0Wgkq4P0SdUvSqUScrn8mfdTIwemVpfIx+nPNDCViKgypKam4sGDB6X+p62qiaIIJycn3L9/n/MnGRCp+kUQBDRo0ADm5ubPtB+GkFIcufUQLdydpC6DiOowjUaDBw8ewNTUFPb29pIFgNzcXKSmpsLc3PypE1BR9ZGiX0RRxMOHD/HgwQM0adLkmc6IMISU4tCNh3irz9PbERFVFbVaDVEUYW9vDxMTE8nqyM3NRXZ2NoyNjRlCDIhU/WJvb4/w8HCo1epnCiH8SSrF6bDHSMvKkboMIiJeAiGDUlk/jwwhpVBrcnHsdrzUZRAREdVKDCFPEXQ1VuoSiIiIaiWGkKf451oscjS5UpdBRFTnfPLJJ2jbtq3u9fjx4/HCCy+Uuk3Pnj0xY8YM3WsPD49Sn6JeVmPGjMEXX3xR6fs1RMuWLcPQoUOr5bMYQkphZaLA43Q1zt57LHUpREQ1SlxcHN566y24ublBpVLByckJ/fr1Q3BwcIX3+e233xaZP6o6XLx4EXv27ME777xTZZ+xatUq9OzZE5aWlhAEAYmJiXrvh4eHY9KkSfD09ISJiQkaNWqEjz/+GNnZ2Xrt/vnnH/j5+cHCwgLOzs54//33kZOjP7ZRFEUsWrQITZs2hUqlgqurq17AeuONN3DmzJlqmWmcd8eUokdTB/x5PRH7r8SiS0NbqcshIqoxXn75ZajVaqxfvx4NGzZEbGws/vnnHzx69KjC+8x/PEd1W7ZsGUaMGPFMT1V/mvT0dPTv3x/9+/dHQEBAkfevX7+O3Nxc/Pjjj2jcuDEuX76MN954A6mpqZg7dy4AbVgaOHAg5syZgw0bNiAyMhJTpkyBRqPBokWLdPuaPn069u/fj0WLFqF169ZISkpCfHzB+EeVSoXRo0fj+++/x3PPPVdlxwwwhJSqdwttCNl3JQZzB7fg6HQikpwoishQV/+Mpbm5uWWeLC0xMRHHjh3DoUOH0KNHDwCAu7s7OnXqpNcuIiIC77zzDv755x/IZDL0798f33//fYlPPR8/fjwSExOxc+dOAEBaWhr+85//YPv27bCwsMB///vfUuuaOHEi4uLi8Oeff+rW5eTkoEGDBvjiiy8wceLEYo/7t99+w88//1zqvtetW4cZM2bg999/h7+/f6lti5N/CamkmcHzA0q+hg0b4saNG1ixYoUuhGzZsgXe3t66R580btwYCxYswKuvvoqPP/4YFhYWuHbtGlasWIHLly+jWbNmJdYzdOhQ9O3bFxkZGVV6azhDSCn8GtnBWHkbkYkZuBqdjJYu0qRwIqJ8GWoNvD7aJ8lnB8/qgrL8K2hubg5zc3Ps3LkTXbp0gUqlKtJGFEW88MILMDMzw+HDh5GTk4OpU6di1KhRZX5Ex+zZs/Hvv/9ix44dcHJywocffohz587pjSMpbPLkyejevTuio6Ph7OwMANi7dy9SU1MxcuTIYre5ePEiEhMT0aFDhxLrWLRoERYsWIB9+/ahS5cuAIAvvvhC7xJHcf766y9069atDEdavKSkJNjY2OheZ2VlwdjYWK+NiYkJMjMzce7cOfTs2RO7d+9Gw4YN8eeff6J///4QRRF9+vTBV199pbevDh06QK1W4/Tp07ogWRU4JqQUJkZydG9iDwDYd4V3yRARlYVCoUBgYCDWr18Pa2trdO3aFR9++CEuXryoa3PgwAFcvHgRv/zyC9q3b4/OnTtj48aNOHz4MM6cOfPUz0hNTcWaNWuwaNEi+Pv7o3Xr1li/fn2pz0/x8/NDs2bNsHHjRt26devWYcSIESVOPx4eHg65XA4HB4di3w8ICMDixYtx6NAhXQABgClTpiAkJKTUpbRg8zR37tzB999/jzfffFO3rl+/fjhx4gQ2b94MjUaDyMhIfPbZZwC0D4QFgLt37+LevXv47bffsGHDBgQGBuLcuXNFnrdmZmYGa2trhIeHV7jGsuCZkKfo19IJ+6/GYv+VGMzybyp1OURUx5ko5bg6v1+1f25ubi7UGWllbv/yyy9j0KBBOHr0KIKDg/H333/jq6++wurVqzF+/Hhcu3YNrq6ucHV11W3j5eUFa2trXLt2DR07dix1/3fu3EF2djZ8fX1162xsbEq9xABoz4asWrUK//vf/xAXF4c9e/bgn3/+KbF9RkYGVCpVsZfjv/nmG6SlpeHs2bNo2LCh3ns2NjZ6ZxYqU1RUFPr3748RI0Zg8uTJSE5OBgD07dsXX3/9NaZMmYIxY8ZApVJh7ty5OHbsmG5W09zcXGRlZWHDhg1o2lT7O23NmjVo3749bty4off9MzExQXp6epUcQz6eCXmK3i0cIJcJuB6TgoiEqu0MIqKnEQQBpkYKSZbyjoszNjaGv78/PvroI5w4cQLjx4/Hxx9/DEB7Oaa4/ZW0vrh2FTF27FjcvXsXwcHB+Pnnn+Hh4VHqJRE7Ozukp6cXuQsFALp16waNRoNff/21yHtffPGF7rJUScvRo0fLXX9UVBR69eoFX19frFq1qsj7s2bNQmJiIiIiIhAfH49hw4YBADw9PQEAzs7OUCgUugACAC1atACgHaNT2KNHj2Bvb1/uGsuDZ0KewtrUCJ09bXDiTgL2X43B5G4Nn74REREV4eXlpRtU6uXlhYiICNy/f193NuTq1atISkrS/VIsTePGjaFUKnHy5Em4ubkBAB4/foybN2+WOobB1tYWL7zwAtatW4fg4GBMmDCh1M/JH19y9erVImNNOnXqhHfeeQf9+vWDXC7H7Nmzde9NmTKlxHEm+erXr1/q+0+KjIxEr1690L59e6xbtw4ymQy5uUXnsRIEAS4uLgCAzZs3w9XVFe3atQMAdO3aFTk5Obhz5w4aNWoEALh58yYA7eDhfHfu3EFmZiZ8fHzKVWN5MYSUQV8vR20IuRLLEEJE9BQJCQkYMWIEJk6cCG9vb1hYWODs2bP46quvdP8z79OnD7y9vfHaa69h6dKluoGpPXr0KNNYCXNzc0yaNAmzZ8+Gra0tHB0dMWfOnDI9xG3y5MkYPHgwNBoNxo0bV2pbe3t7tGvXDseOHSt2wKuvry/++usv9O/fHwqFAjNnzgRQ/ssxMTExiImJwe3btwEAly5dgoWFBdzc3GBjY4OoqCj07NkTbm5uWLRoER4+fAhAe3nF1NRUt5+vv/4a/fv3h0wmw/bt2/Hll1/i119/1V2O6dOnD9q1a4eJEydi6dKlyM3NxbRp0+Dv7693duTo0aNo2LChLqhUFV6OKYO+LZ0AAGfvPUJ8apbE1RARGTZzc3N07twZS5YsQffu3dGqVSvMnTsXb7zxBpYtWwZA+7/1nTt3ol69eujevTv69OmDhg0bYuvWrWX+nK+//hrdu3fH0KFD0adPHzz33HNo3779U7fr06cPnJ2d0a9fP90Zg9K8+eab2LRpU4nvd+3aFXv27MHcuXPx3Xfflbn+wlauXAkfHx+88cYbAIDu3bvDx8cHf/zxBwBg//79uH37Ng4ePIgGDRrA2dkZzs7ORc6m5N9x06FDB+zZswe7du3Sm2VWJpNh9+7dsLOzQ/fu3TFo0CC0aNECW7Zs0dvP5s2bdbVUJUGs6IW1Wiw5ORlWVlZISkqCpaUlAGDI98dwKTIJC19ujVEd3cq0H7Vajb1792LgwIFQKpVVWTKVA/vFcLFvisrMzERYWBg8PT2L3H5ZnXJzc5GcnAxLS8tqfWR8VUhPT4eLiwvWrl2Ll1566antMzMz0axZM2zZskVvIKwhqIp+uXz5Mnr37o2bN2+WOEFcaT+Xxf0OLUnN/kmqRn29tJPn/H05RuJKiIioInJzcxEVFYW5c+fCysqqzM9HMTY2xoYNG/RmFa3NoqKisGHDhmqZoZZjQspoQGtnfBN0E8duxyMpQw0rE/4vjYioJomIiICnpycaNGiAwMBAKBRl/xVYlRN2GZq+fftW22cxhJRRYwdzNHU0x83YVPxzLRYvtWsgdUlERFQOHh4eFb61l6oGL8eUw8DWedP8XoqWuBIiIqKajyGkHPJDyJGb8UjJVEtcDRERUc3GEFIOTRzM0cjeDNmaXPxzLU7qcoiIiGo0hpByEAQBg3hJhoiIqFIwhJTTgLwQcujmQ6Rm5UhcDRERUc3FEFJOzZ0s0NDODNk5uTh4nZdkiIiIKoohpJwEQcCA1tpp3Pde5CUZIiJDlj89fHnduHEDTk5OSElJqfyiDNx///tfvPvuu9XyWQwhFZB/l8y/N+KQxksyRERFjB8/HoIgYMqUKUXemzp1KgRBwPjx48u8v/DwcAiCgJCQkMorshRz5szBtGnTYGFhUS2fV5xt27bBy8sLKpUKXl5e2LFjx1O3uXTpEnr06AETExPUr18f8+fPL3FulOPHj0OhUBR5MN///vc/rFu3DmFhYZVxGKViCKkAL2dLuNuaIisnF//e4CUZIqLiuLq6YsuWLcjIyNCty8zMxObNm+HmVrZncEnhwYMH+OOPPzBhwgTJaggODsaoUaMwZswYhIaGYsyYMRg5ciROnTpV4jbJycnw9/eHi4sLzpw5g++//x6LFi3C4sWLi7RNSkrC2LFj0bt37yLvOTg4oG/fvli5cmWlHlNxGEIqQBAETlxGRNIQRSA7TZqlnLONtmvXDm5ubti+fbtu3fbt2+Hq6gofHx+9tn///Teee+45WFtbw9bWFoMHD8adO3d073t6egIAfHx8IAgCevbsqXtv7dq1aNmyJVQqFZydnfH222/r7Ts+Ph4vvvgiTE1N0aRJE92TaUvy66+/ok2bNmjQoGBm7MDAQFhbW2Pnzp1o2rQpjI2N4e/vj/v37+vahIaGolevXrCwsIClpSXat2+Ps2fPlv0bVsjSpUvh7++PgIAANG/eHAEBAejduzeWLl1a4jabNm1CZmYmAgMD0apVK7z00kv48MMPsXjx4iJnQ9566y2MHj26xAfyDR06FJs3b65Q7eXBadsraFBrZ6w4dAf/XItDalYOzFX8VhJRNVCnA188/fHzlU0GANOuASjfQ80mTJiAdevW4bXXXgOgDQwTJ07EoUOH9NqlpaVh1qxZaN26NdLS0vDRRx/hxRdfREhICGQyGU6fPo1OnTrhwIEDaNmyJYyMjAAAK1aswKxZs/Dll19iwIABSEpKwvHjx/X2PW/ePHz11Vf4+uuv8f333+O1117DvXv3YGNjU2zNR44cQYcOHYqsT09Px+eff47169fDyMgIU6dOxSuvvKL7vNdeew0+Pj5YsWIF5HI5QkJCdE+DjoiIgJeXV6nfq9dff1139iE4OBgzZ87Ue79fv36lhpDg4GD06NEDKpVKb5uAgACEh4frgty6detw584d/Pzzz/jss8+K3VenTp1w//593Lt3D+7u7qXW/Sz4m7OCWrpYoqGdGe7GpyHoagxe9OGzZIiInjRmzBjdL0FBEHD8+HFs2bKlSAh5+eWX9V6vWbMGDg4OuHr1Klq1agV7e3sAgK2tLZycnHTtPvvsM7z33nuYPn26bl3Hjh319jV+/Hi8+uqrAIAvvvgC33//PU6fPo3+/fsXW3N4eDjat29fZL1arcayZcvQuXNnAMD69evRokULXUCKiIjA7Nmz0bx5cwBAkyZNdNu6uLg8dTxL4cfex8TEwNHRUe99R0dHxMSU/CT3mJgYeHh4FNkm/z1PT0/cunULH3zwAY4ePVrqA/zq168PQPu9YAgxQIIgYEgbF3z7zy38ERLFEEJE1UNpCnwYVe0fm5ubC2SUfyC+nZ0dBg0ahPXr10MURQwaNAh2dnZF2t25cwdz587FyZMnER8fr/08aM8gtGrVqth9x8XFISoqqthxDYV5e3vr/mxmZgYLCwvExZU8ni8jIwPGxsZF1isUCr0zJM2bN4e1tTWuXbuGTp06YdasWZg8eTI2btyIPn36YMSIEWjUqJFu28aNG5da55MEQdB7LYpikXVl2SZ/vUajwejRozFv3jw0bdq01P2YmJgA0J79qUocE/IMhrTRnhI9eisej9OyJa6GiOoEQQCMzKRZnvILsCQTJ05EYGAg1q9fj4kTJxbbZsiQIUhISMBPP/2EU6dO6QZgZmeX/G9r/i/Kp8m/JJJPEARdyCmOnZ0dHj9+XOx7xYWA/HWffPIJrly5gkGDBuHgwYN6d7RERETA3Ny81KXwnUROTk5FznrExcUVOTtSWEnbANozIikpKTh79izefvttKBQKKBQKzJ8/H6GhoVAoFDh48KBuu0ePHgGA7gxUVeGZkGfQ2MEcXs6WuBqdjL8ux2B0Z8Md7U1EJJX+/fvrwkS/fv2KvJ+QkIBr167hxx9/RLdu3QAAx44d02uTPwZEo9Ho1llYWMDDwwP//PMPevXqVWn1+vj44OrVq0XW5+Tk4OzZs+jUqRMA7VwiiYmJussvANC0aVM0bdoUM2fOxKuvvop169bhxRdfLPflGF9fXwQFBemNC9m/fz/8/PxK3N7X1xcffvghsrOzdd+v/fv3w8XFBR4eHhBFEZcuXdLbZvny5Th48CB+//133ZgRALh8+TKUSiVatmxZas3PiiHkGQ1t64Kr0cn4IzSSIYSIqBhyuRzXrl3T/flJ9erVg62tLVatWgVnZ2dERETggw8+0Gvj4OAAExMT/P3332jQoAGMjY1hZWWFTz75BFOmTIGDgwMGDBiAlJQUHD9+HO+8806F6+3Xrx8mT54MjUajV69SqcQ777yD7777DkqlEm+//Ta6dOmCTp06ISMjA7Nnz8bw4cPh6emJBw8e4MyZM7qxLuW9HDN9+nR0794dCxcuxLBhw7Br1y4cOHBAL5wtW7YMO3bswLZt2wBAd6ll/Pjx+PDDD3Hr1i188cUX+OijjyAIAgRBKHJpy8HBAcbGxkXWHz16FN26dSvz2aaK4uWYZ5R/SeZU2CPEJGVKXA0RkWGytLTU+59+YTKZDFu2bMG5c+fQqlUrzJw5E19//bVeG4VCge+++w4//vgjXFxcMGzYMADAuHHjsHTpUixfvhwtW7bE4MGDcevWrWeqdeDAgVAqlThw4IDeelNTU7z//vu6W1tNTEywZcsWANpwlZCQgLFjx6Jp06YYOXIkBgwYgHnz5lWoBj8/P2zZsgXr1q2Dt7c3AgMDsXXrVt2gWEB763Hh25itrKwQFBSEBw8eoEOHDpg6dSpmzZqFWbNmlfvzN2/ejDfeeKNCtZeHIJY0lVodlpycDCsrKyQlJZX4l6aw4StO4Oy9x/i/QS0wuVtD3Xq1Wo29e/fqfqDJMLBfDBf7pqjMzEyEhYXB09Oz2MGS1SU3NxfJycmwtLSETFb7//+6fPly7Nq1C/v27QOgnSdkxowZSExMlLawJ1RFv+zZswezZ8/GxYsXS7yDprSfy/L8Dq39P0nVYGhb7dmQ3aHVP2KdiIgq35tvvonu3bvXyWfHpKWlYd26daXewltZGEIqwYBWzpAJQOiDJNxLSJO6HCIiekYKhQJz5syR9NkxUhk5cqTeZZ+qxBBSCewtVOjaWHvfO8+GEBHVPuPHjze4SzG1AUNIJckfoLorJKrEJxYSERFRAYaQStK/lRNUChluxaXiSlSy1OUQUS3D/9yQIamsn0eGkEpiaaxEHy/tTHbbz0dKXA0R1Rb581SUNnMoUXXL/3ksbt6X8uBkZZXo5Xb1sediNP4IjcSHA5s/fQMioqdQKBQwNTXFw4cPoVQqJbs9Njc3F9nZ2cjMzKwTt+jWFFL0S25uLh4+fAhTU9NnvoOGIaQSdWtiD1szI8SnZuPorXg816ie1CURUQ0nCAKcnZ0RFhaGe/fuSVaHKIrIyMiAiYnJUx+iRtVHqn6RyWRwc3N75s+UNIQcOXIEX3/9Nc6dO4fo6Gjs2LEDL7zwQonto6Oj8d577+HcuXO4desW3n33XSxdulSvTWBgICZMmFBk25KeiliZlHIZhrZ1wbrj4dh+IZIhhIgqhZGREZo0aSLpJRm1Wo0jR46ge/funEjOgEjVL0ZGRpVy5kXSEJKWloY2bdpgwoQJuvn1S5OVlQV7e3vMmTMHS5YsKbGdpaUlbty4obeuumYafMmnAdYdD8f+KzFIGdysWj6TiGo/mUwm6YypcrkcOTk5MDY2ZggxIDW9XyQNIQMGDMCAAQPK3N7DwwPffvstAGDt2rUlthMEAU5OTmXeb1ZWFrKysnSvk5O1d7eo1Wqo1eoy7wcAmjmYoJG9Ge48TMOei9GwzNsPGY78/mC/GB72jeFi3xgmQ+yX8tRSK8eEpKamwt3dHRqNBm3btsWnn34KHx+fEtsvWLCg2IcM7d+/H6ampuX+/BYmAu5AjvWHr+GdlkBQUFC590FVj/1iuNg3hot9Y5gMqV/S09PL3LbWhZDmzZsjMDAQrVu3RnJyMr799lt07doVoaGhaNKkSbHbBAQE6D1lMDk5Ga6urujbt2+ZHmD3JJ+kTOz55ghuJwt4lAWMGuxfI0+T1VZqtRpBQUHw92e/GBr2jeFi3xgmQ+yX/KsJZVHrQkiXLl3QpUsX3euuXbuiXbt2+P777/Hdd98Vu41KpYJKpSqyXqlUVqhT3eyU8G1oixN3EnD2oYDXK7gfqloV7V+qeuwbw8W+MUyG1C/lqaPW3+wtk8nQsWNH3Lp1q1o/90Wf+gCAMw9lnOmQiIioGLU+hIiiiJCQEDg7O1fr5w5o7QwTpQxxmQJC7idV62cTERHVBJJejklNTcXt27d1r8PCwhASEgIbGxu4ubkhICAAkZGR2LBhg65NSEiIbtuHDx8iJCQERkZG8PLyAgDMmzcPXbp0QZMmTZCcnIzvvvsOISEh+OGHH6r12MxVCvRv5YQdF6Lw+/lIdGpkX62fT0REZOgkDSFnz55Fr169dK/zB4eOGzcOgYGBiI6ORkREhN42he9yOXfuHH755Re4u7sjPDwcAJCYmIg333wTMTExsLKygo+PD44cOYJOnTpV/QE9YXg7F+y4EIU9l2Lw8dAcmKlq3RAcIiKiCpP0t2LPnj1LHS8RGBhYZN3TxlcsWbKk1InMqlNH93qwMxYRn6nB3kvRGNHBVeqSiIiIDEatHxMiJUEQ0MUhFwDw29kHEldDRERkWBhCqlhHOxEyATgd/gh3H6ZKXQ4REZHBYAipYtYqoFsTOwDAb+d4NoSIiCgfQ0g1GN5OO2fItnMPkKPJlbgaIiIiw8AQUg2eb2YPGzMjxKVk4fDNh1KXQ0REZBAYQqqBkUKmm0H117P3Ja6GiIjIMDCEVJORebfn/nMtDg9TsiSuhoiISHoMIdWkmZMF2rpaIydXxG/neDaEiIiIIaQaje7sBgDYfDoCubl8qB0REdVtDCHVaIi3CyyMFbj/KANHb8dLXQ4REZGkGEKqkYmRHC+3awAA+OXUPYmrISIikhZDSDXLvyRz4FocYpMzJa6GiIhIOgwh1aypowU6uNeDJlfE1jMcoEpERHUXQ4gEXuuiPRuy5XQENBygSkREdRRDiAQGtHKGtakSUUmZOHQjTupyiIiIJMEQIgFjpRzDdQNUIySuhoiISBoMIRJ5NW+A6r834hCZmCFxNURERNWPIUQijezN4dfIFrki8PNJ3q5LRER1D0OIhMb6egDQDlDNVGukLYaIiKiaMYRIqE8LB9S3NsHjdDX+CI2SuhwiIqJqxRAiIYVchte7uAMA1p8Ihyjydl0iIqo7GEIk9kpHV6gUMlyJSsb5iMdSl0NERFRtGEIkVs/MCMPaugAAAk9wgCoREdUdDCEGYJyfBwDgr0vRfJ4MERHVGQwhBqClixU6etRDTq7IycuIiKjOYAgxEPlnQ345HYHsnFxpiyEiIqoGDCEGol9LJzhZGuNhShb2XOLtukREVPsxhBgIpVyGMb7a23VXHw3j7bpERFTrMYQYkNc6u8FEKceVqGQE302QuhwiIqIqxRBiQKxNjTCig/bpuquPhklcDRERUdViCDEwE7t6QhCAg9fjcDsuRepyiIiIqgxDiIHxsDODfwtHAMCaY+HSFkNERFSFGEIM0ORuDQEA288/QEJqlsTVEBERVQ2GEAPU0aMe2jSwQlZOLjae5FTuRERUOzGEGCBBEDAp72zIxuB7yFRrJK6IiIio8jGEGKiBrZxQ39oECWnZ2H4+UupyiIiIKh1DiIFSyGWY+JwnAGDVkTvQ5HLyMiIiql0YQgzYKx1dYW2qRHhCOv66HC11OURERJWKIcSAmakUGJ/3YLvl/97hVO5ERFSrMIQYuHG+HjA1kuNqdDIO33wodTlERESVhiHEwNUzM8KrndwAACsO3ZG4GiIiosrDEFIDTO7mCaVcwKmwRzh377HU5RAREVUKhpAawNnKBC/61AcArDh0W+JqiIiIKgdDSA3xVo9GEATgwLU43Ijhg+2IiKjmYwipIRrZm2NAKycAwA//8mwIERHVfAwhNcjUno0BALsvRuF2XKrE1RARET0bSUPIkSNHMGTIELi4uEAQBOzcubPU9tHR0Rg9ejSaNWsGmUyGGTNmFNtu27Zt8PLygkqlgpeXF3bs2FH5xUugVX0r+Hs5QhSB7w/ekrocIiKiZyJpCElLS0ObNm2wbNmyMrXPysqCvb095syZgzZt2hTbJjg4GKNGjcKYMWMQGhqKMWPGYOTIkTh16lRlli6Z6b2bAAB2h0bhzkOeDSEioppL0hAyYMAAfPbZZ3jppZfK1N7DwwPffvstxo4dCysrq2LbLF26FP7+/ggICEDz5s0REBCA3r17Y+nSpZVYuXRa1bdCnxaOyBWBZQc5NoSIiGouhdQFVLbg4GDMnDlTb12/fv1KDSFZWVnIysrSvU5OTgYAqNVqqNXqCteSv+2z7KM403p44sC1WOwKicR/unvA086sUvdf21VVv9CzY98YLvaNYTLEfilPLbUuhMTExMDR0VFvnaOjI2JiYkrcZsGCBZg3b16R9fv374epqekz1xQUFPTM+3hSy3oyXHksw5xNR/F6k9xK339dUBX9QpWDfWO42DeGyZD6JT09vcxta10IAQBBEPRei6JYZF1hAQEBmDVrlu51cnIyXF1d0bdvX1haWla4DrVajaCgIPj7+0OpVFZ4P8VxbZOEl1aewrkEGb54vRs8bHk2pKyqsl/o2bBvDBf7xjAZYr/kX00oi1oXQpycnIqc9YiLiytydqQwlUoFlUpVZL1SqayUTq2s/RTWzsMOzzd3wMHrcVh+OBxLRrWt1P3XBVXRL1Q52DeGi31jmAypX8pTR62bJ8TX17fIaan9+/fDz89Pooqqzsw+TQEAO0MicTOWs6gSEVHNImkISU1NRUhICEJCQgAAYWFhCAkJQUREBADtZZKxY8fqbZPfPjU1FQ8fPkRISAiuXr2qe3/69OnYv38/Fi5ciOvXr2PhwoU4cOBAiXOK1GStG1hhQCsniCKwaN8NqcshIiIqF0kvx5w9exa9evXSvc4flzFu3DgEBgYiOjpaF0jy+fj46P587tw5/PLLL3B3d0d4eDgAwM/PD1u2bMH//d//Ye7cuWjUqBG2bt2Kzp07V/0BSWCWf1PsuxKD/VdjEXI/EW1draUuiYiIqEwkDSE9e/aEKIolvh8YGFhkXWnt8w0fPhzDhw9/ltJqjCaOFnjRpwG2nX+ARftu4OfJtTNsERFR7VPrxoTURTP6NIFSLuDY7XicuB0vdTlERERlwhBSC7jamGJ0JzcAwFf7bpTpbBEREZHUGEJqiWnPN4aJUo6Q+4k4cC1O6nKIiIieiiGklnCwMMaErh4AgK/+vo4cDWdRJSIiw8YQUou81aMRrE2VuBWXit/OPZC6HCIiolIxhNQiViZKvPN8EwDAN/tvIi0rR+KKiIiISsYQUsuM6eIOd1tTxKdmYdWRu1KXQ0REVCKGkFrGSCHD+/2bAwBWHbmL2ORMiSsiIiIqHkNILTSglRPauVkjQ63B4v03pS6HiIioWAwhtZAgCJgzqAUA4Ldz93E9puyPVSYiIqouDCG1VHt3Gwxo5YRcEfhi73VOYEZERAaHIaQWe79/cyjlAo7cfIiD1zmBGRERGRaGkFrMw84Mk55rCACY/+dVZOVoJK6IiIioAENILff2843hYKHCvYR0rDkWJnU5REREOgwhtZy5SoEPBmhv2V128DZv2SUiIoPBEFIHvNC2Ptq5WSM9W4Mv/7oudTlEREQAGELqBJlMwCdDW0IQgB0XInE2/JHUJRERETGE1BXeDawxsr0rAODjP65Ak8tbdomISFoMIXXI7P7NYGGswJWoZGwMDpe6HCIiquMYQuoQO3OV7rkyi/bf5CBVIiKSFENIHTO6kxvaulojNSsH8/+8KnU5RERUhzGE1DEymYDPX2wFmQDsuRiNQzc4kyoREUmDIaQOaulihQldPQEAH+26gkw1Z1IlIqLqxxBSR830bwpnK2NEPErHsoO3pS6HiIjqIIaQOspcpcDHQ1oCAH48cgc3YlIkroiIiOoahpA6rF9LR/Rp4Qi1RsT/fg9FjiZX6pKIiKgOYQipwwRBO0jVwliB0AdJWHucD7gjIqLqwxBSxzlaGmPuIC8AwDf7byIsPk3iioiIqK5gCCGM6NAA3ZrYISsnF+9vu4hcTulORETVgCGEIAgCvnixNUyN5Dgd9gibTt2TuiQiIqoDGEIIAOBqY4r/9WsGAPjyr+u4/yhd4oqIiKi2YwghnbG+HujkYYO0bA3e+y2UT9olIqIqxRBCOjKZgEUj2sAs77LMmmN3pS6JiIhqMYYQ0uNma4q5g7V3yyzadxPXY5IlroiIiGorhhAqYlRHV/Rp4YBsTS5mbg1FVg6fLUNERJWPIYSKEAQBC17yho2ZEa5FJ2PpgVtSl0RERLUQQwgVy95ChS9ebA0A+PHwHZy6myBxRUREVNswhFCJ+rdywoj2DZArAjO2huBxWrbUJRERUS3CEEKl+mRoSzS0N0N0UiZm/34RosjbdomIqHIwhFCpzFQKfP+qD4zkMhy4Fov1J8KlLomIiGoJhhB6qpYuVpgzqAUA4Iu913E5MkniioiIqDZgCKEyGevrDn8vR2RrcvHu5gtIy8qRuiQiIqrhGEKoTARBwNfDveFsZYy78Wn4YPsljg8hIqJnwhBCZWZtaoRlo32gkAnYHRqFQI4PISKiZ8AQQuXS3t1GNz7k8z3XcDb8kcQVERFRTcUQQuU23s8DQ9u4ICdXxNRN5xGXkil1SUREVAMxhFC5aad1b40mDuaIS8nC279cgFqTK3VZRERUw0gaQo4cOYIhQ4bAxcUFgiBg586dT93m8OHDaN++PYyNjdGwYUOsXLlS7/3AwEAIglBkyczk/9Yrk5lKgZVj2sNcpcDpsEf4Yu81qUsiIqIaRtIQkpaWhjZt2mDZsmVlah8WFoaBAweiW7duuHDhAj788EO8++672LZtm147S0tLREdH6y3GxsZVcQh1WiN7cywa0QYAsO54OLacjpC4IiIiqkkUUn74gAEDMGDAgDK3X7lyJdzc3LB06VIAQIsWLXD27FksWrQIL7/8sq6dIAhwcnKq7HKpGP1bOWFmn6ZYcuAm5u66jIb25ujkaSN1WUREVANIGkLKKzg4GH379tVb169fP6xZswZqtRpKpRIAkJqaCnd3d2g0GrRt2xaffvopfHx8StxvVlYWsrKydK+Tk5MBAGq1Gmq1usL15m/7LPuoCf7T3R03YpKw93Is3tp4FtundEGDeiZSl1WiutIvNRH7xnCxbwyTIfZLeWqpUSEkJiYGjo6OeuscHR2Rk5OD+Ph4ODs7o3nz5ggMDETr1q2RnJyMb7/9Fl27dkVoaCiaNGlS7H4XLFiAefPmFVm/f/9+mJqaPnPdQUFBz7wPQ9fLFLhoJseDNDVGrzyCGa00MJZLXVXp6kK/1FTsG8PFvjFMhtQv6enpZW5bo0IIoL3UUlj+rJ3567t06YIuXbro3u/atSvatWuH77//Ht99912x+wwICMCsWbN0r5OTk+Hq6oq+ffvC0tKywrWq1WoEBQXB399fd5amNuvcPRMvrzyJ6NRs/JXohBWj20IhN7wbsOpav9Qk7BvDxb4xTIbYL/lXE8qiRoUQJycnxMTE6K2Li4uDQqGAra1tsdvIZDJ07NgRt27dKnG/KpUKKpWqyHqlUlkpnVpZ+zF0bnZKrBrbAa+sOolDN+Px2V838dkLrYoER0NRV/qlJmLfGC72jWEypH4pTx2G99/UUvj6+hY55bR//3506NChxIMWRREhISFwdnaujhLrPB+3evj2FR8IArDpVARWHL4jdUlERGSgJA0hqampCAkJQUhICADtLbghISGIiNDe6hkQEICxY8fq2k+ZMgX37t3DrFmzcO3aNaxduxZr1qzBf//7X12befPmYd++fbh79y5CQkIwadIkhISEYMqUKdV6bHVZ/1ZO+GiwFwDgq79vYFdIpMQVERGRIZL0cszZs2fRq1cv3ev8cRnjxo1DYGAgoqOjdYEEADw9PbF3717MnDkTP/zwA1xcXPDdd9/p3Z6bmJiIN998EzExMbCysoKPjw+OHDmCTp06Vd+BESZ09UTk4wysPhaG//4WCnsLFfwa2UldFhERGRBJQ0jPnj1LfRx8YGBgkXU9evTA+fPnS9xmyZIlWLJkSWWUR8/ow4EtEJ2UiT2XovHmhnP45Y3O8G5gLXVZRERkIGrUmBCqWWQyAd+MbAPfhrZIzcrBuLWncSs2ReqyiIjIQDCEUJUyVsrx07gOaNPACo/T1Riz5jTuPyr7PeRERFR7MYRQlTNXKRA4oROaOJgjJjkTr685hbgUPlCQiKiuYwgpza0gICVW6ipqhXpmRtg4qTMa1DPBvYR0vL76FBJSs56+IRER1VoMIaX5fQLwTVNgcUtg6xjg2FIg7AiQWfbZ4KiAk5UxNk3uDAcLFW7GpmL0TwwiRER1WY2aMbXa2TYFUm8DyQ+0y7U/8t4QAPtmgEs7oH47oH57wLEVoDCStNyawN3WDJvf7IJXV53EjdgUvLb6FDZN7gxb86Iz1hIRUe3GEFKaNw8CKgGICgGizgOR54DI80DSfeDhde0S+ou2rdwIcGqtDSQuecHEyl3S8g1VI3tzXRC5HqMNIr+80QU2ZgxxRER1CUPI06gsAM9u2iVfapw2jESeKwgnGY/zQso5XTOFygJ+SlfI/j0HuHbUBhNLFwkOwvDkB5FX8oLIq6tOYuPkTnCwMJa6NCIiqiYMIRVh7gA0669dAEAUgcdhecEkL5REh0LISoF91lXgxNVC2zppw0j9vEs5Lj6AST1pjkNijezNsaXQpZmRK4Px8+TOaFDPVOrSiIioGjCEVAZBAGwaapfWw7XrNDlQR1/ClX3r4W2bA1l0CBB3FUiNAW7s0S75bBsXXMKp3157WUdZN84INLI3x29TfPHa6lMIT0jHiJXB2DipMxo7mEtdGhERVTGGkKoiVwCOrXDPrhdaDhwImVIJZKcDMRcLLttEnteeQUm4rV0u/ardVqYAHFvqjy+xbwbI5NIeUxVxtzXD71P88PqaU7gdl4pRPwZj/cROaFXfSurSiIioCjGEVCcjU8Cti3bJl/5IG0Z0A1/PAWkPgehQ7YK12nZKM8ClbcHdOC7tAGs37VmYWsDJyhhb3+yCcetO43JkMl5ddRIrx7RH18Z86B0RUW3FECI1UxugSR/tAmjHlyQ90D9bEh0CZKcC945rF922doXGl+QFEzNbSQ6jMtiaq/DLG10wef1ZnA57hPHrTmPhy954qV0DqUsjIqIqwBBiaAQBsHbVLi1f0K7L1QDxNwtCSeQ5IPYykB4P3NqnXfJZuxeMLanfDnBuAxiZSXIoFWFprMSGiZ3w3m+h2HMxGrN+DUVUYgam9WoMoZac9SEiIi2GkJpAJgccWmgXn9e169SZ2iBS+IxJwi0g8Z52ubJd206QAQ5e2rtw8sOJQwtArpTueJ7CWCnH96/4oL61CVYduYtF+28iMjED84e1glLOSX6JiGoLhpCaSmkMNOigXfJlJAJRF/LGl+SdMUmJ1oaV2MvAhY3adgpjwMk77xbhvFuFbRoBMsP5BS+TCfhwYAvUtzbBJ7uvYPPp+wiLT8Py19pzUjMiolqCIaQ2MbEGGvXSLvmSo56YWO0CkJUEPDitXfKpLLWXbgoHEytXyQe+jvPzQH1rE0zfcgEn7z7CsB+OYfXYjmjmZCFpXURE9OwYQmo7Sxft0mKw9nVuLvDoTsEdOVEXgOiLQFYyEH5Uu+Qztcu7jJMXTFx8AAvHaj+EPl6O2DGtKyavP4uIR+l4aflxLBnVFn1bOlV7LUREVHkYQuoamQywa6Jd2ozSrtPkAA+v5QWTvMs5sVe0A19vB2mXfJb1nwgmbatlxtemjhbYNa0rpm46j+C7CXhz4zlM69UIM/s0hYLjRIiIaiSGENJOrObUWru0H6ddpxv4WiiYPLwBJEdql+t/Fmxv07DgEo5LO8DZu0ruyKlnZoQNkzrh8z3XEHgiHD/8ewdnwx/j+1d94GBZN2aYJSKqTRhCqHjFDXzNStFOoFY4mDwOBx7d1S6Xf9e2E2SAffO8YOKjPXPi2ApQqJ69LLkMnwxtiXbu9RCw7SJOhT3CwO+O4rtXfODHic2IiGoUhhAqO5UF4PGcdsmX/qhgbElkXjBJidY+JyfuKhDys7ad3Eg7FX3+2JL67bRBpYJT0Q9t44KWLpaYtuk8rsek4LU1pzCjd1O8/XxjyGWcT4SIqCZgCKFnY2oDNO6jXfIlR+vfKhx1Hsh4nLfuQkE7pan2jpzCwcSmYZnvyGlkb46d07ri411XsPXsfSw5cBPHb8fjm5Ft4GrDJ/ESERk6hhCqfJbO2qX5QO1rUdRettEFkwsFU9FHBGuXfMZW2kDi4lMwzsSyfonBxFgpx8Lh3ujkaYOPdl3G6fBH6L/0CD4a4oWRHVw5yyoRkQFjCKGqJwiAjad2afWSdl2uBoi/pX/GJOYSkJkE3D2kXfKZOejfkVO/HWCmP/7j5fYN0MnTBu/9GorT4Y/w/rZLCLoaiwUvecPe4tnHohARUeVjCCFpyOSAQ3Pt0vZV7bqc7EK3CuedMYm7CqTFFX1GjpVrkVuFXW2ssPnNLvjp6F0s3n8TB67F4fzSI5g3tCUGezvzrAgRkYFhCCHDoTDSjhFxbgNggnadOkN7hkQXTPKekZN0X7tc+6Nge9vGkLu0w5T67dB/eDNMP5SD0NhsvLP5Aradf4BPh7XiWBEiIgPCEEKGTWkCuHbSLvkykwrdKpx3xiQpAki4rV0u/QoPADsFOeJtGuJQagNcuN0QM5Y0Qv/ne+N1X0+pjoaIiAphCKGax9gK8OyuXfKlxefdJlxwxkRIi4N9+i2MkN3CCNm/AICsQ0qEHfOEu7kbhNAkwLUDYN+swrcKExFRxTGEUO1gZgc08dcugPaOnOQoXSARoy5AHXEOqpxkNNfcBJJuAn8e0LZVmmqfKqy7K8cHsG1sUE8VJiKqjRhCqHYSBMCqvnZpMQQCACNRxKMH17H37z3IvHcWrWVhaCWEw0ydDtw/qV3yGZnnzWFSKJjU82QwISKqRAwhVHcIAmxcW2DU+Mb48de9WJRij3PhCfAUotHNNAKjXR+hSc4tCDGXtHOY3DuuXfKprACXQsHEuS1Qz6PMk6sREZE+hhCqk1zNgU0jOuDgzQR8vvcaAh/VR+B1wNPODDMGeGJw/VTIo0MKZnmNuQRkJQFhR7RLPmNr/bMlLj6AVQMGEyKiMmAIoTpLEAT0b+WMXs0dsDH4HpYfuoOw+DRM/+0yljmYY6b/8+g/YDRkMgHQqIG4a9qZXnXB5DKQmQjc/Ve75DO1KxRK2mq/WjgzmBARPYEhhOo8lUKOyd0a4tVObgg8EY5VR+7iVlwqpm46j2aOFnirR0MMaeMCpbM34OwNtBur3TAnSzuZWn4oibqgDSrp8cDtIO2Sz9yx6BkTcwdpDpiIyEAwhBDlMVMpMK1XY4zxdceao2FYcywMN2JTMOvXUCzadwMTn/PEK53cYK7K+2ujUBUEinzqTCD2St6ThUO0weThNSA1Frj5t3bJZ1m/YGxJ/lmTJ6ajJyKqzRhCiJ5gaazETP+mmNjVEz+fuod1x8MRlZSJz/Zcw3f/3MJrXdzxWmc3NKhXzOyrSmOgQXvtki87XTumJP9sSXQI8PAGkBypXa7/WdDWyq3gEk5+MDGpV8VHTEQkDYYQohJYmSoxrVdjTHrOEzsvRGLVkbu4G5+GFYfuYOXhO+jVzAGvd3FDj6YOkMtKGe9hZAq4ddYu+bJS9INJ1AXtbK9JEdql8HT09Tz1g4lzG+2EbURENRxDCNFTGCvleKWTG0Z2cEXQtVhsDL6HY7fjcfB6HA5ej0ODeiZ4tZMbXvSpDxdrk7LtVGUBuPtpl3z509FHXSi4lPM4rGC5sqOgrW1j/fElTt6AyrxSj5uIqKoxhBCVkUwmoF9LJ/Rr6YS7D1Ox6VQEfj/3AA8eZ+DrfTewaP8NdPG0xUvt6mNAa+eCsSNlVdx09OmPCgWTvHCi95yc3/IaCoBd0yeCSWvtWRgiIgPFEEJUAQ3tzTF3sBdm92uG3aFR+O3cA5wOe4TguwkIvpuAubsuo6+XEwa2dkaPpvYwMargs2lMbYBGvbRLvrQEIPqC/hmT5Egg/oZ2ubhF206QAfYt9G8VdmylHbdCRGQAGEKInoGxUo4RHVwxooMr7j9Kx84LkdhxIRJ349PwR2gU/giNgrFShp5NHdC/lRN6NXeAlYny2T7UzBZo3Ee75EuJ1Z/DJOqC9o6cuCvaJeRnbTuZAnBooX/GxMFLe6cPEVE1YwghqiSuNqZ4p3cTvP18Y4Q+SMLu0CjsuxKDB48z8PeVGPx9JQZKuYAO7jbo3tQePZrao4WzBYTKmMTMwhGw6Ac07VewLjlaP5REXdDOYRJzSbuc36BtJ1MCjl55twq31X51bMlgQkRVjiGEqJIJgoC2rtZo62qN/xvUAleikrHvSgz+uhyD23Gpuks2C/++DnsLFbo3sYdvI1t09rRBg3omlRNKAMDSWbs0H6h9LYpA0oOC24Tzg0nGY+24k+hQ4Px6bVuZMu+MSduCcOLQkpdyiKhSMYQQVSFBENCqvhVa1bfCe32bISw+DUduPsThmw8RfCcBD1OysO38A2w7/wAA4GRpjI6eNujkUQ8dPW3Q1MFCO2185RQDWLtqF6+h2nWiCCRG5IWSkIKvGY+AmIvaBflnTPIu5ejOmPhoz5gwmBBRBTGEEFUjTzszeNqZYZyfB7JyNDgb/hhHbj3E6bBHuPQgCTHJmdgdGoXdoVEAADMjOVq6WOUFGUu0qm+FRvbmpc9LUh6CANRz1y5ew7TrRBFIuq8fSqJDgPSEgks5FzZq28oUeYNf2xTM/OrYElCW8VZlIqrTGEKIJKJSyNG1sR26NtZO1Z6RrcGF+49xJuwxzoQ/wvmIx0jL1uB0+COcDn+k285EKUdTJws0tjdHY4eCxc3GtHLCiSAA1m7apfAZk6QHRc+YpMcDsZe0y4W8wa+C/IkzJm0Bp1YMJkRUhKQh5MiRI/j6669x7tw5REdHY8eOHXjhhRdK3ebw4cOYNWsWrly5AhcXF/zvf//DlClT9Nps27YNc+fOxZ07d9CoUSN8/vnnePHFF6vwSIienYmRHH6N7ODXSBtKcjS5uPMwDZcjk3ApMglXopJwJSoZ6dkahN5PROj9RL3tjeQyuNuawtXGFK71TNCgnilcbbRfG9QzgZWJsuLjTQpfymkxRLtOFLW3Bj95xiTtIRB7WbuEFAom9s31x5g4tuI8JkR1nKQhJC0tDW3atMGECRPw8ssvP7V9WFgYBg4ciDfeeAM///wzjh8/jqlTp8Le3l63fXBwMEaNGoVPP/0UL774Inbs2IGRI0fi2LFj6Ny581M+gchwKOQyNHOyQDMnC7zcvgEAQJMrIiw+FTdjU3E7rmC58zAVWTm5uBWXiltxqcXuz0gug72FSrc4FPqztYkRLE0UsDJRwtJYqf1qoiz9zIogAFYNtEuLwdp1oggkRxU9Y5IWV+h24U1528u0waTwGRO75pXyvSOimuGZQ4goigBQof9hDRgwAAMGDChz+5UrV8LNzQ1Lly4FALRo0QJnz57FokWLdCFk6dKl8Pf3R0BAAAAgICAAhw8fxtKlS7F58+Zy10hkSOQyAY0dLNDYwUJvfW6uiMjEDITFp+HB4wzcf5yu/fooHQ8epyM+NRvZmlxEJmYgMjGjzJ9nrtIGExMjOUyU2kWllGn/bCSHsSLvq1IOI7kAuUwGhVyAXNYcCmULyNxHQ+EJmKsfwj7lGmyTr8Em6SqsE6/AOCseiLuqXUJ/AQAoIIOf0hnxj7Yh3c4babatkG7jBRiZQSYIkAnamWt1fxYECHlf89cJggC5rPT389+TFWonlwlQKWSVd3cSET1VhUPImjVrsGTJEty6dQsA0KRJE8yYMQOTJ0+utOKeFBwcjL59++qt69evH9asWQO1Wg2lUong4GDMnDmzSJv84FKcrKwsZGVl6V4nJycDANRqNdRqdYXrzd/2WfZBla+29ouThRJOFtaAp3WR97LUGsSnZSMuJQvxKdmIS81CfEoWHqZmIT41G0kZaiRn5CA5U43kzBykZ2sAAKlZOUjNyqmkCu0AdMtbAAc8RmvZXbSWhaGVEIbWsjA4ComwV0cC9yKBe7sAABpRwB3RBZdET1zO9cSlXE9cFT2Qjsq/K0cuE2BqJNcuSjlMVXKYGilgaiSHmZH2z1YmCtQzNYKViRLWpkrUM1XCOu/PNmZGUMpllV6XIaitf29qOkPsl/LUUqEQMnfuXCxZsgTvvPMOfH19AUD3yz88PByfffZZRXb7VDExMXB0dNRb5+joiJycHMTHx8PZ2bnENjExMSXud8GCBZg3b16R9fv374ep6bNfsw4KCnrmfVDlq8v9Ui9vaaIs9KIQTS6QoQHSc4CMHCA7V0B2LqDORcFXjfarOu89TS6ggfaKjEYEcgsv0H4tvF6EFcLFdghDO+wUARGATe5jNEUYmolhaCqGoRnCYC8koqkQiaaIxMvyYwCAXFFAOJxxFZ64KmqXK/BAumgMMW9fuXm1iMV9RfFnOzS5IlIyc5CSWbHgJUCEhRKwMgKsjES9r/VUgJ1KRD0VUFk3N0mhLv+9MWSG1C/p6ellbluhELJixQr89NNPePXVV3Xrhg4dCm9vb7zzzjtVFkKAopd9irscVFyb0k6xBgQEYNasWbrXycnJcHV1Rd++fWFpaVnhWtVqNYKCguDv7w+l8hmn6qZKw34xXAV9819d36hTYiDEhEKIDtV9laXGoCGi0BBRGCwcB5AXLGwbQ3RuA9GpjfarY2vtE4ufIIpiXhjSfhVFEWpNLtKzNchQa5CWpUF6tgbp2dqzQmnZGmRka5CalYOkDDUep6uRmK5GYoYaienZeJyuRlKGGrmigGQ1kKwG7qcV/2+OUi6gvrUJ3GxM4FrPFB52pmjqYI4mDuawMzcy2MtB/HtjmAyxX/KvJpRFhUKIRqNBhw4diqxv3749cnIq69RtUU5OTkXOaMTFxUGhUMDW1rbUNk+eHSlMpVJBpSo6RbVSqayUTq2s/VDlYr8YLr2+sXHVLl6DCxronpUTovsqpEQBCbcgJNwCLv+e11AbTPTuynHyBoyL/8+FzTPUnJsrIj4tC3HJWYhJykRMciZi85aY5Cw8eJSO+4/TodaICE9IR3hCOoAEvX3UM1WiqaOFbkCyd31rNHOygJHCcC7x8O+NYTKkfilPHRUKIa+//jpWrFiBxYsX661ftWoVXnvttYrsskx8fX2xe/duvXX79+9Hhw4ddAft6+uLoKAgvXEh+/fvh5+fX5XVRUTVrLhn5aTGaaeeL3xXTvIDIOGWdrn0W0Fb28b6d+U4ewPGVs9UkkwmwMHCGA4WxmhVv/h9aXJFRCdlIOJROiIS0nHvUTruxKXiZmwK7j1Kx+N0NU6FPcKpsIJ5YYwUMrRwtkSbBlbwbmCNtq7aCesM9YwJUXk808DU/fv3o0uXLgCAkydP4v79+xg7dqzepY0ng0phqampuH37tu51WFgYQkJCYGNjAzc3NwQEBCAyMhIbNminjZ4yZQqWLVuGWbNm4Y033kBwcDDWrFmjd9fL9OnT0b17dyxcuBDDhg3Drl27cODAARw7dqyih0pENYG5A9DEX7vkS32Y91ycC3nhJFQ7G2zCbe2iO2MCwKYh4Jw386tzG+1i+iznRoqSy4S8eVtM4ddI/72MbA3uPNQGkhuxKbgalYyLD5KQlKEuNC/MPW2pZkbo7GmDLg1t0blhJU/vT1SNKhRCLl++jHbt2gEA7ty5AwCwt7eHvb09Ll++rGv3tKR+9uxZ9OrVS/c6P7yMGzcOgYGBiI6ORkREhO59T09P7N27FzNnzsQPP/wAFxcXfPfdd3pzjPj5+WHLli34v//7P8ydOxeNGjXC1q1bOUcIUV1kbg806aNd8qXFP3EpJxRIigAe3dUuV3YUtLVy054lyQ8mLm21YacKmBjJdc8ZyieKIiIepSP0QRIu3k/ExQdJuBiZiEdp2fjrsvahiID2Mk4nT+3TmXs2c0B9a85OSzVDhULIv//+Wykf3rNnT93A0uIEBgYWWdejRw+cP3++1P0OHz4cw4cPf9byiKg2MrMDGvfRLvnSEoCY0IKnCUeHagNJUoR2uf5nQVsL54IzJfmLZX3t5G2VTBAEuNuawd3WDEPbuAAAsnNycfFBIk6FPcLJuwk4G/4Yj9PV2HclFvuuxAIAmjlaoGdze/Rq5oAO7vWgqKW3DVPNx2fHEBGZ2QKNntcu+TKTtA/ry7+MEx0KxN8EUqK1y82/C9qa2hUNJvU8qiSYGClk6OBhgw4eNpjWqzGyc3JxKTIJJ27H49DNh7gQ8Rg38i7p/Hj4LuqZKtHXywkDWjvBr5GdQQ1yJWIIISIqjrEV4PGcdsmXlQrEXskLJSHar3HXtA/yu/OPdim8vZN33mUcH+1Xm0aArHJDgJFChvbu9dDevR7e6d0Ej9OyceTWQxy68RCHbsThcboaW8/ex9az92FprEAfL0cMaeOCbo3teIaEJMcQQkRUVipzwK2zdsmnztQ+E6fwpZzYK9ozKeFHtUs+I3PAqbX+GRO7ZoC88v4prmdmhGFt62NY2/rI0eTidNgj7L0cjb8vxyI+NQvbz0di+/lI2Fuo8EJbF7zUrgFaOFd8PiSiZ8EQQkT0LJTGQP322iVfTjbw8Lp+MIm5BGSnAhHB2iWfwlj7ROHCwcShBaAoOndReSnkMvg1toNfYzvMG9oK5+49xp6LUdh9MRoPU7Lw09Ew/HQ0DF7OlhjevgFebt8AViaGMdcE1Q0MIURElU1hlHdXjTeAMdp1mhztbcH5l3GiQ4Hoi0B2ChB5Vrvkkym1QST/jhzntoBjS0BZ8bte5DIBnTxt0MnTBnMGeeHwzYfYdu4B/rkei6vRyZj/51V8te86XmhbH693cS9xrhOiysQQQkRUHeQKwKG5dmnzinZdbi7wOKzQLcN54SQzEYi5qF0ubNS2FeSAfTP9MyZOxU9L/zRGChn8vRzh7+WIx2nZ2H0xCptORuBGbAq2nLmPLWfuo62rNcb6umOwtwsHs1KVYQghIpKKTAbYNtIurfLmOxJFIDGi0NmSEG1ASY8H4q5ql9D8CRrzpqXXuzPHGzCpV8IHFlXPzAhjfT0wpos7zt57jI3B9/DX5WiE3E9EyP1EfPX3DUx8zgPDfVwq++iJGEKIiAyKIAD13LWL11DtOlHU3hYc/cRcJsmRBdPSF5791do97zJOfjBpq50fpdSPFdDRwwYdPWzwMMULv569j8AT4YhJzsQXe6/ju39uo7OtDO2TM9HAluNGqHIwhBARGTpBACxdtEuzAQXrU+O040oKjzNJvFewXN1V0Nayvn4ocW4DWDgVO5eJvYUK03o1xuRunth1IQo/HrmDOw/T8E+UDEcWH8UrHd0wrVdjOFkZV/mhU+3GEEJEVFOZOxSdlj79kXYsSeEzJgm3tWdNkiOBG3sL2po5FJ1kzdpNF0xUCjlGdnTF8PYNsP9KFL764wLupgAbT97D1rP3MbqTG6b2bAQHS4YRqhiGECKi2sTUBmjYU7vky0wGYi8XespwKBB/A0iLA24HaZd8JvUK5jJx0o4xkdk2Ru/mDsi6q4FN8y74/t+7OB3+CIEnwrH5dARe7+KOqT0bwdb82W8rprqFIYSIqLYztgTc/bRLvuz0vNlfQwrOmMRdAzIeA2FHtEs+pSnkDi3hnWUJV5dHeG5oW5xIbotvDobjfEQi1hwLw69n7mPa840x3s8Dxkp5tR8i1UwMIUREdZGRKeDaUbvky8nSBpHo0LxLOhe1Z1DU6ZBFnoEnAOzRTk3fVaaEn30zxHg1x85oW/yT6Ijv/0rBxuB7+F//Zhji7QKZrPKfnUO1C0MIERFpKVTau2pc2hasy9UACbeR8+A8woJ3oZFpKmQxl4DMRAixl+GMy/gPgP+ogFwICEt3wtVt7tiwvzn8nuuNpm26ah8QSFQMhhAiIiqZTDtJmmjdEFcjTOExcCBkCgWQdL9g1te8syaylCg0kkWjEaKB9JPA/kBgP6CxcIHcpW3eA/28tV+tGlTJU4apZmEIISKi8hEE7V001m5AiyEF61MfAjGhSA2/gNsXj8Mq8So8ZbGQp0QBN6L078wxsSkIJPl35lTBU4bJsDGEEBFR5TC3Bxr3gXnjPmjbZzbOhj/CeztOQx53Ba1kYehmHg0/s0gYJ94CMh4Bdw9pl3xKs7w7c7wLzprYt9A+i4dqJYYQIiKqEh08bPDru32xPrgZFu+/gXVJGihSBLzTvQGmeqmhfHip4HJOzGVAnQbcP6ld8uke5uetnWTNyRtwagUYmUl2XFR5GEKIiKjKKOQyTHrOE4O9nfHxriv4+0oMlhy6j73XLPD1iBfg3X68tmH+U4YLT7QWcxHITCr0ML+f8/YqAHZN9MeYOLfRzpFCNQpDCBERVTlHS2OseL0d9lyKxke7ruBGbApeXH4Cb3ZviOm9m8BYWegpw94jtRsVfphf/i3DMRe1z9GJv6ldCj8zx8q10BiTvHBi6cIBsAaMIYSIiKqFIAgY7O0C34a2+PiPK/jzYjRWHLqDf67F4ttXfNDC2fLJDYo+zA8oeGZOTGjBHTqPw7R37CTdB27sKWhraps3+6t3wSWdep4cAGsgGEKIiKha2ZqrsGx0Owz2jsH/7byMm7GpGPbDcXzQvzkmdPWA8LQzF8U9MyczSTuupPBZk4fXgfQE4M5B7ZLPyEI7rqTwWRP75oCcTweubgwhREQkif6tnNDBox7+9/tFHLweh/l/XsXhmw/x9QhvOFiU86F4xlaAR1ftkk+dAcRdLTSXSah2qvrsFCAiWLvkkxsBDl6Fxpi0BRxbameWpSrDEEJERJKxM1dhzbgO2HjyHj7fcw2Hbz7EgKVH8fUIbzzf3PHZdq40Aeq31y75NDlAwi1tMCl81iQrKe85OiEFbQUZYNuk0G3DrbUP9eMMsJWGIYSIiCQlCALG+nqgS0NbvLv5Aq7HpGBi4FlM7dkIs/ybQiGvxPEbcoX2ll+HFkCbUdp1ogg8Dtcf/BodCqTGap82HH9DfwCshcsTwcQbqOfBAbAVwBBCREQGoamjBXZO64oFe69hffA9LD90BxciEvHdqz6wt1BV3QcLAmDjqV28hhWsT4kFYi5pB8DGXNIuCXeAlCjtcmtfQVuVZV4gaV0QTOybc6K1p2AIISIig2GslGPesFZo72GDD7ZdRPDdBAz+/ih+GN0OHTyqeR4QC0ftUngAbFYKEHu1YO6S6IvaJw9nJQP3jmuXfDKl9pZjp7xxJk6ttQNija2q9zgMGEMIEREZnKFtXODlbIEpP5/H7bhUvLLqJAIGtsDEstw9U5VUFoBbZ+2ST6PWzlkSU3gG2PyJ1vLOoGBTQft6HgXjS/Iv61g418nLOQwhRERkkBo7WGDXtK4I2H4Jf4RG4dM/r+JqVDK+eKkVVAq51OUVkCu1d9I4tgTavKJdJ4raOUt0weSSNpgk3deOP3kcDlzbXbAPU9uCyzj5c5rYNtY+xbgWYwghIiKDZaZS4NtX2sLHzRqf7bmGbecfICw+FSvHtC//bbzVqfCThpsPKlif/qjg7Eh+MHl4QzufyZMP9FOYaINN/jgT5zba24hr0W3DDCFERGTQBEHAhK6eaOxgjmmbzuN8RCJeWHYcq8Z2QKv6NWx8hakN0LCHdsmnztTOZ5IfSmIuFTzQL/Ksdsn3xG3Dgp0XjNTJ1X8clYQhhIiIaoRuTeyxc1pXTF5/Fnfj0zBiZTC+GdkGA1s7S13as1EaA/XbaZd8uRrgUVjB+JL8yzppcXq3DSsADAAg3vu80AP9as5twwwhRERUYzS0N8eOaV3x9i/ncfRWPKZuOo8PBjTHW90bSjtgtbLJ5IBdY+3S6qWC9SmxesFEjA6F8OguhJRo7YP9nrxt2LGVfjAxsNuGGUKIiKhGsTJRYt34jvhszzUEngjHl39dR3RiBj4a0hJyWS0KIsWxcAQs/IEm/gCAHLUa+3dvQ7+2DaCIv5Y3C+wl7eWdrGQg4oR2yad323D+QFjpbhtmCCEiohpHIZfhk6Et0aCeCT7bo53cLCY5E9++4gNjZe2+o+RJOXITiK6dgYbPFaws023DhTx527BTa8DSpcov5zCEEBFRjTW5W0M4WRlj1tZQ7LsSi9dWn8LqsR1Qz8xwLjlIorTbhnW3DEt/2zBDCBER1WiDvV1gb67CGxvO4ty9x3h5xQlsmNQJDerVnltZK0Xh24ZbDC5Yr3fbcF5AKfW2YS/96ekdvACVeYVKYgghIqIar3NDW/z+Hz+MX3tad+fMz5M7o5F9xX451inF3jacoZ2Ovtjbhs9pFx0BsG1UEEzMG5X5oxlCiIioVmjqaIHtU7vitdUncedhGkauDMbGSZ3h5WIpdWk1j9KkhNuG7+pPthZ7WXtXTsJt7XJlB5AllvljGEKIiKjWcLIyxq9v+WLs2tO4EpWMV1YFY92ETmjvXk/q0mo+mRywa6JdCt82nPoQiC0UTMJCAJwraS/6u6ySQomIiCRia67C5je7oIN7PSRn5mDMmlM4fjte6rJqL3N7oNHzQNfpwMurgTcPlnlThhAiIqp1LI2V2DCpE7o1sUN6tgYT1p3BP9dipS6LnsAQQkREtZKpkQKrx3VAv5aOyNbk4j8/n8fB6wwihoQhhIiIai2VQo5lo9thUGtnZGtyMWUjg4ghYQghIqJaTSmXYekrbTGwtZMuiPx7PU7qsggMIUREVAco5TJ8+4oPBrTSBpG3Np5jEDEADCFERFQnKOUyfPeqfhA5dINBREqSh5Dly5fD09MTxsbGaN++PY4ePVpq+x9++AEtWrSAiYkJmjVrhg0bNui9HxgYCEEQiiyZmZlVeRhERFQD5AeR/i0LgsipuwlSl1VnSRpCtm7dihkzZmDOnDm4cOECunXrhgEDBiAiIqLY9itWrEBAQAA++eQTXLlyBfPmzcO0adOwe/duvXaWlpaIjo7WW4yNjavjkIiIyMAp5TJ8P9oHzzd3QFZOLiatP4tLD5KkLqtOknTG1MWLF2PSpEmYPHkyAGDp0qXYt28fVqxYgQULFhRpv3HjRrz11lsYNWoUAKBhw4Y4efIkFi5ciCFDhujaCYIAJyenMteRlZWFrKws3evk5GQAgFqthlqtrtCx5W9f+CsZBvaL4WLfGK7a2DffjmyNyRvP41TYY4xdewqbJnVEE4ea9awZQ+yX8tQiWQjJzs7GuXPn8MEHH+it79u3L06cOFHsNllZWUXOaJiYmOD06dNQq9VQKpUAgNTUVLi7u0Oj0aBt27b49NNP4ePjU2ItCxYswLx584qs379/P0xNn/0pjEFBQc+8D6p87BfDxb4xXLWtb16yB6Lj5IhIU+PVlccxvZUGtjXwxLkh9Ut6enqZ20oWQuLj46HRaODo6Ki33tHRETExMcVu069fP6xevRovvPAC2rVrh3PnzmHt2rVQq9WIj4+Hs7MzmjdvjsDAQLRu3RrJycn49ttv0bVrV4SGhqJJkybF7jcgIACzZs3SvU5OToarqyv69u0LS8uKP/hIrVYjKCgI/v7+uoBE0mO/GC72jeGqzX3Tq3c2XltzBrfi0rAu3AKbJ3eEo2XNSCKG2C/5VxPKQvIH2AmCoPdaFMUi6/LNnTsXMTEx6NKlC0RRhKOjI8aPH4+vvvoKcrkcANClSxd06dJFt03Xrl3Rrl07fP/99/juu++K3a9KpYJKpSqyXqlUVkqnVtZ+qHKxXwwX+8Zw1ca+cbBSYtPkLhi+MhgRj9IxacMF/DrFF1YmNec4DalfylOHZANT7ezsIJfLi5z1iIuLK3J2JJ+JiQnWrl2L9PR0hIeHIyIiAh4eHrCwsICdnV2x28hkMnTs2BG3bt2q9GMgIqLawcHSGJsmd4aDhQo3YlPw1sazyMrRSF1WrSdZCDEyMkL79u2LXMcKCgqCn59fqdsqlUo0aNAAcrkcW7ZsweDBgyGTFX8ooigiJCQEzs7OlVY7ERHVPq42plg3oSPMVQqcvPsI7/0aitxcUeqyajVJL8fMmjULY8aMQYcOHeDr64tVq1YhIiICU6ZMAaAdqxEZGambC+TmzZs4ffo0OnfujMePH2Px4sW4fPky1q9fr9vnvHnz0KVLFzRp0gTJycn47rvvEBISgh9++EGSYyQiopqjpYsVfhzTHuPXncafF6PhbGWMOYO8pC6r1pI0hIwaNQoJCQmYP38+oqOj0apVK+zduxfu7u4AgOjoaL05QzQaDb755hvcuHEDSqUSvXr1wokTJ+Dh4aFrk5iYiDfffBMxMTGwsrKCj48Pjhw5gk6dOlX34RERUQ3UtbEdvh7eBjO2huCno2FwtDTG5G4NpS6rVpJ8YOrUqVMxderUYt8LDAzUe92iRQtcuHCh1P0tWbIES5YsqazyiIioDnrBpz5ikjPx5V/X8dmea3C0NMaQNi5Sl1XrSD5tOxERkSF6q3tDjPfzAAC891sozt17JG1BtRBDCBERUTEEQcDcwV7o6+WI7JxcvLnhHO4/KvtEXPR0DCFEREQlkMsELH2lLVq6WCIhLRuT1p9BSqbhTJFe0zGEEBERlcLUSIHV4zrAwUKFm7GpePuXC8jR5EpdVq3AEEJERPQUzlYmWD2uA4yVMhy++RCf7bkmdUm1AkMIERFRGXg3sMaSkW0BAIEnwrEhOFzSemoDhhAiIqIyGtDaGbP7NQMAzNt9FSdux0tcUc3GEEJERFQOU3s2wos+9aHJFTHtl/O8Y+YZMIQQERGVgyAIWPBSa7Sub4XH6Wq8tfEcMrL5sLuKYAghIiIqJ2OlHCvHtIetmRGuRifj/W0XIYp82F15MYQQERFVQH1rEyx/rR0UMgF/hEbhp6N3pS6pxmEIISIiqqDODW3x0RDtU3a//Os6jt56KHFFNQtDCBER0TMY08UdIzs0QK4IvP3LBQ5ULQeGECIiomcgCALmD2uFNq7WSMpQY+qm88jK4UDVsmAIISIiekbGSjmWv9YO9UyVuBSZhE//vCp1STUCQwgREVElqG9tgiWj2kIQgJ9PRmDnhUipSzJ4DCFERESVpGczB7zzfBMAQMD2S7gZmyJxRYaNIYSIiKgSTe/dBM81tkOGWoMpP59DalaO1CUZLIYQIiKiSiSXCfj2lbZwsjTG3YdpCNh+iROZlYAhhIiIqJLZmquwbLQPFDIBu0OjsOlUhNQlGSSGECIioirQwcMG7/dvDgD49M+ruBHD8SFPYgghIiKqIpOe80TPZvbIysnFO5vP80F3T2AIISIiqiIymYBFI9rA3kKFm7Gp+HQP5w8pjCGEiIioCtmZq7BkpHb+kF9OReCvS9FSl2QwGEKIiIiq2HNN7DClRyMAwPvbLuLBYz5fBmAIISIiqhaz/JvCx80ayZk5mL4lBDmaXKlLkhxDCBERUTVQymX47hUfWKgUOHfvMb7955bUJUmOIYSIiKiauNqYYsHLrQEAy/69jRN34iWuSFoMIURERNVosLcLXunoClEE/vtrKJIy1FKXJBmGECIiomo2d7AX3G1NEZWUiXl/XJG6HMkwhBAREVUzM5UCi0e2hUwAtl+IxN46etsuQwgREZEE2rvXw7RejQEAH+64hLjkTIkrqn4MIURERBJ5t3cTtKpvicR0NWb/frHOPW2XIYSIiEgiSrkMS0a2hUohw+GbD/FzHXvaLkMIERGRhJo4WuCDAdqn7X6x5xruPkyVuKLqwxBCREQksXG+Huja2BYZag1m/hpaZ2ZTZQghIiKSWP7Tdi2NFQi9n4jlh+5IXVK1YAghIiIyAM5WJvj0hVYAgO8P3sK16GSJK6p6DCFEREQGYmgbF/T1coRaI2L276FQ1/LLMgwhREREBkIQBHz2YitYmShxOTIZq47clbqkKsUQQkREZEAcLIzxyVAvAMDSAzdxIyZF4oqqDkMIERGRgXmhbX30aeGguyxTW++WYQghIiIyMIIg4PMXW8PSWIGLD5Lw09EwqUuqEgwhREREBsjR0hgfDWkJAFgSdBO342rfZRmGECIiIgP1crv66NnMHtmaXPz3t4vQ5NauZ8swhBARERkoQRCw4KXWsFApEHI/EWuO1a67ZSQPIcuXL4enpyeMjY3Rvn17HD16tNT2P/zwA1q0aAETExM0a9YMGzZsKNJm27Zt8PLygkqlgpeXF3bs2FFV5RMREVUpZysT/N/gFgCAb/bfxL2ENIkrqjyShpCtW7dixowZmDNnDi5cuIBu3bphwIABiIgo/imCK1asQEBAAD755BNcuXIF8+bNw7Rp07B7925dm+DgYIwaNQpjxoxBaGgoxowZg5EjR+LUqVPVdVhERESVamQHV/g1skVWTi7m7LgMUawdl2UkDSGLFy/GpEmTMHnyZLRo0QJLly6Fq6srVqxYUWz7jRs34q233sKoUaPQsGFDvPLKK5g0aRIWLlyoa7N06VL4+/sjICAAzZs3R0BAAHr37o2lS5dW01ERERFVLkEQ8MWLraFSyHDsdjx2XIiUuqRKoZDqg7Ozs3Hu3Dl88MEHeuv79u2LEydOFLtNVlYWjI2N9daZmJjg9OnTUKvVUCqVCA4OxsyZM/Xa9OvXr9QQkpWVhaysLN3r5GTtfP1qtRpqtbo8h6Unf9tn2QdVPvaL4WLfGC72jfTqWxnh7Z4N8c2B2/j0z6vo2rAeLIwEAIbVL+WpRbIQEh8fD41GA0dHR731jo6OiImJKXabfv36YfXq1XjhhRfQrl07nDt3DmvXroVarUZ8fDycnZ0RExNTrn0CwIIFCzBv3rwi6/fv3w9TU9MKHJ2+oKCgZ94HVT72i+Fi3xgu9o206ucCzqZyRKer8faag3i9sXYSM0Pql/T09DK3lSyE5BMEQe+1KIpF1uWbO3cuYmJi0KVLF4iiCEdHR4wfPx5fffUV5HJ5hfYJAAEBAZg1a5budXJyMlxdXdG3b19YWlpW5LAAaNNgUFAQ/P39oVQqK7wfqlzsF8PFvjFc7BvD4domESN/Oo0zD2V4q58P0u6cM6h+yb+aUBaShRA7OzvI5fIiZyji4uKKnMnIZ2JigrVr1+LHH39EbGwsnJ2dsWrVKlhYWMDOzg4A4OTkVK59AoBKpYJKpSqyXqlUVkqnVtZ+qHKxXwwX+8ZwsW+k17GhPcb5eiDwRDjm7bmJd5sYVr+Upw7JBqYaGRmhffv2RU4hBQUFwc/Pr9RtlUolGjRoALlcji1btmDw4MGQybSH4uvrW2Sf+/fvf+o+iYiIaor/9msGZytj3H+cgb8fSD7bRoVJejlm1qxZGDNmDDp06ABfX1+sWrUKERERmDJlCgDtZZLIyEjdXCA3b97E6dOn0blzZzx+/BiLFy/G5cuXsX79et0+p0+fju7du2PhwoUYNmwYdu3ahQMHDuDYsWOSHCMREVFlM1cp8OmwVpi84Sz+jRJwLToF3m42UpdVbpLGp1GjRmHp0qWYP38+2rZtiyNHjmDv3r1wd3cHAERHR+vNGaLRaPDNN9+gTZs28Pf3R2ZmJk6cOAEPDw9dGz8/P2zZsgXr1q2Dt7c3AgMDsXXrVnTu3Lm6D4+IiKjK9PFyRP+WjsiFgP/bdaVGTuku+cDUqVOnYurUqcW+FxgYqPe6RYsWuHDhwlP3OXz4cAwfPrwyyiMiIjJYcwc1x6HrMbgYmYzNpyPwehd3qUsql5p7IYmIiKiOc7BQYZCr9jbdr/6+jvjUrKdsYVgYQoiIiGqw55xEeDlbIDkzBwv2Xpe6nHJhCCEiIqrBZAIwb0gLCAKw7fwDnA57JHVJZcYQQkREVMO1dbXGKx3dAABzd16GWpMrcUVlwxBCRERUC/yvXzPUM1XiRmwK1p8Il7qcMmEIISIiqgXqmRkhYEALAMCSoJuITsqQuKKnYwghIiKqJYa3b4B2btZIy9bgsz+vSV3OUzGEEBER1RIymYDPXmgNmQDsuRSNIzcfSl1SqRhCiIiIahEvF0uM9/MEAHy06zKycjQSV1QyhhAiIqJaZqZ/EzhYqBCekI41x8KkLqdEDCFERES1jIWxEgEDmwMAlh28jZikTIkrKh5DCBERUS30Qtv6aOdmjfRsDb78yzAHqTKEEBER1UKCIGDe0FYQBGBnSBTOhhveTKoMIURERLVU6wZWeKWjKwDg4z+uQJMrSlyRPoYQIiKiWuy/fZvBwliBK1HJ2HrmvtTl6GEIISIiqsVszVWY5d8UAPD1vutISldLXFEBhhAiIqJa7vUu7mjqaI7H6WosOXBT6nJ0GEKIiIhqOaVcho+HtAQAbDx5D9djkiWuSIshhIiIqA7o2tgOA1o5QZMrYt4fVyGK0g9SZQghIiKqIz4c2AIqhQzBdxPw9+UYqcthCCEiIqorXG1M8Vb3hgCABX9dl/y5MgwhREREdchbPRrBwUKFiEfp2HDinqS1MIQQERHVIWYqBf7brxkA4LuDt5CQmiVZLQwhREREdczwdg3Q0sUSKZk5+PafW5LVwRBCRERUx8hkAuYMagEA2HQqArdiU6SpQ5JPJSIiIkn5NbKDv5cjNLkivtgrzVN2GUKIiIjqqA8HtoBCJuDfGw9x5ObDav98hhAiIqI6ytPODGN9PQAAn++5hhxNbrV+PkMIERFRHfZu78awMlHiRmwKfj37oFo/myGEiIioDrM2NcKMPk0AAIuDbiAls/qesssQQkREVMe93sUdDe3MEJ+ajeWH7lTb5zKEEBER1XFKuQwBA7W37K45Fob7j9Kr5XMZQoiIiAh9WjjAr5EtsnNysWj/jWr5TIYQIiIigiAI+DDvbMiukChcjkyq8s9kCCEiIiIAQKv6VhjW1gUAsOCvaxBFsUo/jyGEiIiIdP7btxmM5DIcv52AI7fiq/SzGEKIiIhIx9XGFK93cQcAfPnXdeTmVt3ZEIYQIiIi0vP2841hoVLgWnQydoZEVtnnMIQQERGRHhszI0zp2QgA8M3+m8hUa6rkcxhCiIiIqIiJXT3hZGmMyMQMbAy+VyWfwRBCRERERZgYyTHTXzud+7J/byMpvfKnc2cIISIiomK93K4BmjiYIylDjeWHb1f6/hlCiIiIqFgKuQzv928OAFh3PBxRiRmVun+GECIiIipR7xYO6ORhg+ycXCwOulmp+2YIISIiohIJgoAPBmrPhmw7/wDXY5Irbd8MIURERFSqdm71MKCVE0QR+Orvynu4HUMIERERPdXsfs0glwk4eD0OZ8MfVco+JQ8hy5cvh6enJ4yNjdG+fXscPXq01PabNm1CmzZtYGpqCmdnZ0yYMAEJCQm69wMDAyEIQpElMzOzqg+FiIio1mpob44R7RsAAL7ad6NSHm4naQjZunUrZsyYgTlz5uDChQvo1q0bBgwYgIiIiGLbHzt2DGPHjsWkSZNw5coV/Pbbbzhz5gwmT56s187S0hLR0dF6i7GxcXUcEhERUa31bu8mMFLIcDrsUaU83E7SELJ48WJMmjQJkydPRosWLbB06VK4urpixYoVxbY/efIkPDw88O6778LT0xPPPfcc3nrrLZw9e1avnSAIcHJy0luIiIjo2bhYm2BM3sPtvt737A+3U1RGURWRnZ2Nc+fO4YMPPtBb37dvX5w4caLYbfz8/DBnzhzs3bsXAwYMQFxcHH7//XcMGjRIr11qairc3d2h0WjQtm1bfPrpp/Dx8SmxlqysLGRlZeleJydrR/6q1Wqo1RWfIS5/22fZB1U+9ovhYt8YLvaNYZKiX954zh1bTkfgcmQy/gx9gAGt9P+jX55aBLEyLupUQFRUFOrXr4/jx4/Dz89Pt/6LL77A+vXrceNG8aNvf//9d0yYMAGZmZnIycnB0KFD8fvvv0OpVALQni25ffs2WrdujeTkZHz77bfYu3cvQkND0aRJk2L3+cknn2DevHlF1v/yyy8wNTWthKMlIiKqPfbel2HfAxkcTUS830YDuVDwXnp6OkaPHo2kpCRYWlqWuh/JQ8iJEyfg6+urW//5559j48aNuH79epFtrl69ij59+mDmzJno168foqOjMXv2bHTs2BFr1qwp9nNyc3PRrl07dO/eHd99912xbYo7E+Lq6or4+PinfgNLo1arERQUBH9/f11IIumxXwwX+8ZwsW8Mk1T9kpKpxvOLjyExQ40FL7bE8Hb1de8lJyfDzs6uTCFEsssxdnZ2kMvliImJ0VsfFxcHR0fHYrdZsGABunbtitmzZwMAvL29YWZmhm7duuGzzz6Ds7NzkW1kMhk6duyIW7dulViLSqWCSqUqsl6pVFZKp1bWfqhysV8MF/vGcLFvDFN194uNUompvRrhi73Xsezfu3ipvStUCrmulrKSbGCqkZER2rdvj6CgIL31QUFBepdnCktPT4dMpl+yXK496JJO6IiiiJCQkGIDChEREVXMWF8POFqqEJmYgV9OFX9X69NIenfMrFmzsHr1aqxduxbXrl3DzJkzERERgSlTpgAAAgICMHbsWF37IUOGYPv27VixYgXu3r2L48eP491330WnTp3g4uICAJg3bx727duHu3fvIiQkBJMmTUJISIhun0RERPTsjJVyvNtbO9byh39vIy0rp9z7kOxyDACMGjUKCQkJmD9/PqKjo9GqVSvs3bsX7u7a23+io6P15gwZP348UlJSsGzZMrz33nuwtrbG888/j4ULF+raJCYm4s0330RMTAysrKzg4+ODI0eOoFOnTtV+fERERLXZyA6uWHXkLu4lpGPd8TC8/XzxN4CURNIQAgBTp07F1KlTi30vMDCwyLp33nkH77zzTon7W7JkCZYsWVJZ5REREVEJlHIZZvk3xfQtIfjxyF283sW9XJdYJJ+2nYiIiGquId4uaO5kgZTMHPx45G65tmUIISIiogqTyQT8t28zAMC642GIT8l6yhaFtq2qooiIiKhu6N3CAW1crZGpzsWa42Fl3o4hhIiIiJ6JIAiY5d8UALD1zP0yb8cQQkRERM+sexM7tHevh+yc3DJvwxBCREREz0wQBLyXdzakrBhCiIiIqFL4NrJFB/d6ZW7PEEJERESVQhAEvN2rcZnbM4QQERFRpengaVPmtgwhREREJAmGECIiIpIEQwgRERFJgiGEiIiIJMEQQkRERJJgCCEiIiJJMIQQERGRJBhCiIiISBIMIURERCQJhhAiIiKSBEMIERERSYIhhIiIiCTBEEJERESSYAghIiIiSTCEEBERkSQYQoiIiEgSDCFEREQkCYYQIiIikgRDCBEREUmCIYSIiIgkwRBCREREkmAIISIiIkkwhBAREZEkGEKIiIhIEgwhREREJAmGECIiIpIEQwgRERFJgiGEiIiIJMEQQkRERJJgCCEiIiJJMIQQERGRJBhCiIiISBIMIURERCQJhhAiIiKSBEMIERERSYIhhIiIiCTBEEJERESSYAghIiIiSUgeQpYvXw5PT08YGxujffv2OHr0aKntN23ahDZt2sDU1BTOzs6YMGECEhIS9Nps27YNXl5eUKlU8PLywo4dO6ryEIiIiKgCJA0hW7duxYwZMzBnzhxcuHAB3bp1w4ABAxAREVFs+2PHjmHs2LGYNGkSrly5gt9++w1nzpzB5MmTdW2Cg4MxatQojBkzBqGhoRgzZgxGjhyJU6dOVddhERERURlIGkIWL16MSZMmYfLkyWjRogWWLl0KV1dXrFixotj2J0+ehIeHB9599114enriueeew1tvvYWzZ8/q2ixduhT+/v4ICAhA8+bNERAQgN69e2Pp0qXVdFRERERUFgqpPjg7Oxvnzp3DBx98oLe+b9++OHHiRLHb+Pn5Yc6cOdi7dy8GDBiAuLg4/P777xg0aJCuTXBwMGbOnKm3Xb9+/UoNIVlZWcjKytK9TkpKAgA8evQIarW6vIemo1arkZ6ejoSEBCiVygrvhyoX+8VwsW8MF/vGMBliv6SkpAAARFF8alvJQkh8fDw0Gg0cHR311js6OiImJqbYbfz8/LBp0yaMGjUKmZmZyMnJwdChQ/H999/r2sTExJRrnwCwYMECzJs3r8h6T0/P8hwSERER5UlJSYGVlVWpbSQLIfkEQdB7LYpikXX5rl69infffRcfffQR+vXrh+joaMyePRtTpkzBmjVrKrRPAAgICMCsWbN0r3Nzc/Ho0SPY2tqWut3TJCcnw9XVFffv34elpWWF90OVi/1iuNg3hot9Y5gMsV9EUURKSgpcXFye2layEGJnZwe5XF7kDEVcXFyRMxn5FixYgK5du2L27NkAAG9vb5iZmaFbt2747LPP4OzsDCcnp3LtEwBUKhVUKpXeOmtr6wocVfEsLS0N5oeDCrBfDBf7xnCxbwyTofXL086A5JNsYKqRkRHat2+PoKAgvfVBQUHw8/Mrdpv09HTIZPoly+VyAAXXnnx9fYvsc//+/SXuk4iIiKQh6eWYWbNmYcyYMejQoQN8fX2xatUqREREYMqUKQC0l0kiIyOxYcMGAMCQIUPwxhtvYMWKFbrLMTNmzECnTp10p32mT5+O7t27Y+HChRg2bBh27dqFAwcO4NixY5IdJxERERUlaQgZNWoUEhISMH/+fERHR6NVq1bYu3cv3N3dAQDR0dF6c4aMHz8eKSkpWLZsGd577z1YW1vj+eefx8KFC3Vt/Pz8sGXLFvzf//0f5s6di0aNGmHr1q3o3LlztR+fSqXCxx9/XORSD0mL/WK42DeGi31jmGp6vwhiWe6hISIiIqpkkk/bTkRERHUTQwgRERFJgiGEiIiIJMEQQkRERJJgCKlkK1asgLe3t27iGF9fX/z1119Sl0XFWLBgAQRBwIwZM6Qupc775JNPIAiC3uLk5CR1WQQgMjISr7/+OmxtbWFqaoq2bdvi3LlzUpdV53l4eBT5OyMIAqZNmyZ1aeUi+bTttU2DBg3w5ZdfonHjxgCA9evXY9iwYbhw4QJatmwpcXWU78yZM1i1ahW8vb2lLoXytGzZEgcOHNC9zp+IkKTz+PFjdO3aFb169cJff/0FBwcH3Llzp1JnlKaKOXPmDDQaje715cuX4e/vjxEjRkhYVfkxhFSyIUOG6L3+/PPPsWLFCpw8eZIhxECkpqbitddew08//YTPPvtM6nIoj0Kh4NkPA7Nw4UK4urpi3bp1unUeHh7SFUQ69vb2eq+//PJLNGrUCD169JCooorh5ZgqpNFosGXLFqSlpcHX11fqcijPtGnTMGjQIPTp00fqUqiQW7duwcXFBZ6ennjllVdw9+5dqUuq8/744w906NABI0aMgIODA3x8fPDTTz9JXRY9ITs7Gz///DMmTpz4TA9dlQJDSBW4dOkSzM3NoVKpMGXKFOzYsQNeXl5Sl0UAtmzZgvPnz2PBggVSl0KFdO7cGRs2bMC+ffvw008/ISYmBn5+fkhISJC6tDrt7t27WLFiBZo0aYJ9+/ZhypQpePfdd3WP0iDDsHPnTiQmJmL8+PFSl1JunDG1CmRnZyMiIgKJiYnYtm0bVq9ejcOHDzOISOz+/fvo0KED9u/fjzZt2gAAevbsibZt22Lp0qXSFkd60tLS0KhRI/zvf//DrFmzpC6nzjIyMkKHDh1w4sQJ3bp3330XZ86cQXBwsISVUWH9+vWDkZERdu/eLXUp5cYzIVXAyMgIjRs3RocOHbBgwQK0adMG3377rdRl1Xnnzp1DXFwc2rdvD4VCAYVCgcOHD+O7776DQqHQG+RF0jIzM0Pr1q1x69YtqUup05ydnYv856lFixZ6z/Qiad27dw8HDhzA5MmTpS6lQjgwtRqIooisrCypy6jzevfujUuXLumtmzBhApo3b47333+fd2MYkKysLFy7dg3dunWTupQ6rWvXrrhx44beups3b+oeMkrSW7duHRwcHDBo0CCpS6mQ/2/v7kFSDeMwjN9ERFDWIGGLgpuDSA5GtDk4RAhOQkgIrU2BDjaKOMa7BS4OLg2Ci4SEg0NIX4u0C6UkfUw5BA3SduBwtvcczr+Xrt/4TPfm5eMDEiH/2PHxsXZ2dhQMBjWdTnV2dqZer6dOp2M97cfz+XyKRqO/nS0tLcnv9/9xjv+rUCgonU4rFArp5eVFlUpF7+/vyufz1tN+tKOjI21vb6tarSqbzerm5ka1Wk21Ws16GiTNZjPV63Xl83nNz3vz49ybq7+x5+dn7e/vazKZaHV1VbFYTJ1OR6lUynoa8G2Nx2Pt7e3p7e1Na2tr2tra0tXVFd+4jSUSCbVaLZVKJZXLZYXDYTmOo1wuZz0Nkrrdrh4fH3VwcGA9xTUepgIAABM8TAUAACaIEAAAYIIIAQAAJogQAABggggBAAAmiBAAAGCCCAEAACaIEAAAYIIIAQAAJogQAABggggBAAAmiBAAnvD6+qr19XVVq9VfZ9fX11pYWNDFxYXhMgBu8Qd2ADzj/PxcmUxG/X5fkUhE8Xhcu7u7chzHehoAF4gQAJ5yeHiobrerRCKhwWCg29tbLS4uWs8C4AIRAsBTPj4+FI1GNRqNdHd3p1gsZj0JgEu8CQHgKcPhUE9PT5rNZnp4eLCeA+AvcBMCwDM+Pz+1ubmpjY0NRSIRnZyc6P7+XoFAwHoaABeIEACeUSwW1Ww2NRgMtLy8rGQyKZ/Pp3a7bT0NgAv8HAPAE3q9nhzHUaPR0MrKiubm5tRoNHR5eanT01PreQBc4CYEAACY4CYEAACYIEIAAIAJIgQAAJggQgAAgAkiBAAAmCBCAACACSIEAACYIEIAAIAJIgQAAJggQgAAgAkiBAAAmPgCcRfJb/lqjccAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot 1\n", - "plt.plot(x_v, yp_solidly_v, label=f\"Solidly (k={k})\")\n", - "plt.plot(x_v, yp_match_v, label=f\"Match (ps={ps})\")\n", - "#plt.plot(x_v, yp_match_opt_v, label=f\"Match (optimized)\")\n", - "# plt.plot(x_v, yy_ray1_v, marker=None, linestyle='dotted', color=\"#aaa\", label=f\"ray (m={mm})\")\n", - "# plt.plot(x_v, yy_ray2_v, marker=None, linestyle='dotted', color=\"#aaa\")\n", - "# plt.plot(x_v, yy_tang_v, marker=None, linestyle='--', color=\"#aaa\", label=\"tangent\")\n", - "plt.grid(True)\n", - "plt.title(f\"Matching a Solidly curve (prices)\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"p\")\n", - "plt.legend()\n", - "plt.xlim(0, 10)\n", - "plt.ylim(0, 2)\n", - "plt.savefig(\"/Users/skl/Desktop/sol_img_matchingp1.jpg\")\n", - "plt.show()\n", - "\n", - "# plot 2\n", - "plt.plot(x_v, yp_solidly_v, label=f\"Solidly (k={k})\")\n", - "plt.plot(x_v, yp_match_v, label=f\"Match (ps={ps})\")\n", - "#plt.plot(x_v, yp_match_opt_v, label=f\"Match (optimized)\")\n", - "# plt.plot(x_v, yy_ray1_v, marker=None, linestyle='dotted', color=\"#aaa\", label=f\"ray (m={mm})\")\n", - "# plt.plot(x_v, yy_ray2_v, marker=None, linestyle='dotted', color=\"#aaa\")\n", - "# plt.plot(x_v, yy_tang_v, marker=None, linestyle='--', color=\"#aaa\", label=\"tangent\")\n", - "plt.grid(True)\n", - "plt.title(f\"Matching a Solidly curve (prices)\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"p\")\n", - "plt.legend()\n", - "plt.xlim(x_min, x_max)\n", - "plt.ylim(0, 1.25)\n", - "plt.savefig(\"/Users/skl/Desktop/sol_img_matchingp2.jpg\")\n", - "plt.show()\n", - "\n", - "# plot 3\n", - "plt.plot(x_v, yp_solidly_v, label=f\"Solidly (k={k})\")\n", - "plt.plot(x_v, yp_match_v, label=f\"Match (ps={ps})\")\n", - "#plt.plot(x_v, yp_match_opt_v, label=f\"Match (optimized)\")\n", - "# plt.plot(x_v, yy_ray1_v, marker=None, linestyle='dotted', color=\"#aaa\", label=f\"ray (m={mm})\")\n", - "# plt.plot(x_v, yy_ray2_v, marker=None, linestyle='dotted', color=\"#aaa\")\n", - "# plt.plot(x_v, yy_tang_v, marker=None, linestyle='--', color=\"#aaa\", label=\"tangent\")\n", - "plt.grid(True)\n", - "plt.title(f\"Matching a Solidly curve (prices)\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"p\")\n", - "plt.legend()\n", - "plt.xlim(x_min, x_max)\n", - "plt.ylim(0.8, 1.2)\n", - "plt.savefig(\"/Users/skl/Desktop/sol_img_matchingp3.jpg\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "0340b4c7-8b83-45ca-8493-c442f313ace1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'k': 5000, 'x0': 60, 'y0': 60}\n", - "[minimize] converged in 13 iterations, norm=0.009877\n", - "x={'k': 4999.920086411355, 'x0': 65.96403685971154, 'y0': 65.36154243491612})\n", - "{'k': 4999.920086411355, 'x0': 65.96403685971154, 'y0': 65.36154243491612}\n" - ] - } - ], - "source": [ - "match1_fv = match_fv.update()\n", - "params0 = match1_fv.function().params()\n", - "params0 = dict(k=5000, x0=60, y0=60)\n", - "print(params0)\n", - "params = y_fv.curve_fit(match1_fv.function(), params0, learning_rate=0.5, \n", - " iterations=50, tolerance=0.01, verbosity=y_fv.MM_VERBOSITY_LOW)\n", - "print(params)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "52487ec3-8962-41e0-95e0-a4c5222bd918", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# params = y_fv.curve_fit(match1_fv.function(), params0, learning_rate=0.5, \n", - "# iterations=50, tolerance=0.01, norm=y_fv.CF_NORM_L2S, verbosity=y_fv.MM_VERBOSITY_HIGH)\n", - "# print(params)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "a2ea22ae-7e78-4294-b93f-ff31f83dfb85", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# params = y_fv.curve_fit(match1_fv.function(), params0, learning_rate=0.01, \n", - "# iterations=50, tolerance=0.01, norm=y_fv.CF_NORM_L2, verbosity=y_fv.MM_VERBOSITY_HIGH)\n", - "# print(params)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "587662f5-9e74-40ef-878e-5c8ecfb5d224", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# params = y_fv.curve_fit(match1_fv.function(), params0, learning_rate=0.02, \n", - "# iterations=50, tolerance=0.01, norm=y_fv.CF_NORM_L1, verbosity=y_fv.MM_VERBOSITY_HIGH)\n", - "# print(params)" - ] - }, - { - "cell_type": "markdown", - "id": "6e70c232-49fe-4353-af5e-1bebee56b2cc", - "metadata": {}, - "source": [ - "### Varying the price spread" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "065dcbd7-af34-4b99-bab9-9b06eed5365e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fv_flat = f.FunctionVector(kernel=f.Kernel(x_min=x_min, x_max=x_max, kernel=f.Kernel.FLAT))\n", - "fv_triang = f.FunctionVector(kernel=f.Kernel(x_min=x_min, x_max=x_max, kernel=f.Kernel.TRIANGLE))" - ] - }, - { - "cell_type": "markdown", - "id": "e506dbf6-7c58-4c0e-8b25-034d00afd578", - "metadata": {}, - "source": [ - "swap curves" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "9f0156ea-99c9-41ff-8f6c-519ba81f2ca3", - "metadata": { - "lines_to_next_cell": 2, - "tags": [] - }, - "outputs": [], - "source": [ - "# check different price spread curves\n", - "ps_v = np.linspace(0,0.15, 100)\n", - "ps_v[0] = ps_v[1]/2\n", - "dist_flat_l2_ps_v = []\n", - "dist_flat_l1_ps_v = []\n", - "dist_triang_l2_ps_v = []\n", - "dist_triang_l1_ps_v = []\n", - "for psps in ps_v:\n", - " psps = max(psps, 0.001)\n", - " match_ps_f = f.LCPMM.from_xpxp(xa=x_min, xb=x_max, pa=1+psps, pb=1-psps, ya=ya)\n", - " match_ps_flat_fv = fv_flat.wrap(match_ps_f)\n", - " match_ps_triang_fv = fv_triang.wrap(match_ps_f)\n", - " dist_flat_l2 = match_ps_flat_fv.dist_L2(y_f)\n", - " dist_flat_l1 = match_ps_flat_fv.dist_L1(y_f)\n", - " dist_triang_l2 = match_ps_triang_fv.dist_L2(y_f)\n", - " dist_triang_l1 = match_ps_triang_fv.dist_L1(y_f)\n", - " #print(psps, dist)\n", - " dist_flat_l2_ps_v.append(dist_flat_l2)\n", - " dist_flat_l1_ps_v.append(dist_flat_l1)\n", - " dist_triang_l2_ps_v.append(dist_triang_l2)\n", - " dist_triang_l1_ps_v.append(dist_triang_l1)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "db0a5ed0-5396-4766-99a9-f6e225db0d83", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(ps_v, dist_flat_l1_ps_v, color=\"blue\", label=\"L1 norm (flat)\")\n", - "plt.plot(ps_v, dist_flat_l2_ps_v, color=\"blue\", linestyle=\"--\", label=\"L2 norm (flat)\")\n", - "plt.plot(ps_v, dist_triang_l1_ps_v, color=\"red\", label=\"L1 norm (triangle)\")\n", - "plt.plot(ps_v, dist_triang_l2_ps_v, color=\"red\", linestyle=\"--\", label=\"L2 norm (triangle)\")\n", - "plt.grid()\n", - "plt.xlabel(\"boundary price spread vs middle (0.1=10%)\")\n", - "plt.ylabel(\"matching error on swap function (norm)\")\n", - "#plt.title(\"Optimal price spread\")\n", - "plt.xlim(0,None)\n", - "plt.ylim(0,0.03)\n", - "plt.legend()\n", - "plt.savefig(\"/Users/skl/Desktop/sol_img_optps.jpg\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "efa0062b-aefa-4464-bd9a-b3098f147778", - "metadata": {}, - "source": [ - "price curves" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "c3f962bb-64e8-426d-b8f5-3e3164df1cbe", - "metadata": { - "lines_to_next_cell": 2, - "tags": [] - }, - "outputs": [], - "source": [ - "# check different price spread curves\n", - "ps_v = np.linspace(0,0.15, 100)\n", - "ps_v[0] = ps_v[1]/2\n", - "dist_flat_l2_ps_v = []\n", - "dist_flat_l1_ps_v = []\n", - "dist_triang_l2_ps_v = []\n", - "dist_triang_l1_ps_v = []\n", - "for psps in ps_v:\n", - " psps = max(psps, 0.001)\n", - " match_ps_f = f.LCPMM.from_xpxp(xa=x_min, xb=x_max, pa=1+psps, pb=1-psps, ya=ya)\n", - " match_ps_flat_fv = fv_flat.wrap(match_ps_f)\n", - " match_ps_triang_fv = fv_triang.wrap(match_ps_f)\n", - " dist_flat_l2 = match_ps_flat_fv.distp_L2(y_f.p)\n", - " dist_flat_l1 = match_ps_flat_fv.distp_L1(y_f.p)\n", - " dist_triang_l2 = match_ps_triang_fv.distp_L2(y_f.p)\n", - " dist_triang_l1 = match_ps_triang_fv.distp_L1(y_f.p)\n", - " #print(psps, dist)\n", - " dist_flat_l2_ps_v.append(dist_flat_l2)\n", - " dist_flat_l1_ps_v.append(dist_flat_l1)\n", - " dist_triang_l2_ps_v.append(dist_triang_l2)\n", - " dist_triang_l1_ps_v.append(dist_triang_l1)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "ae76c7dd-398b-4c03-a778-dd72b0eea42f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(ps_v, dist_flat_l1_ps_v, color=\"blue\", label=\"L1 norm (flat)\")\n", - "plt.plot(ps_v, dist_flat_l2_ps_v, color=\"blue\", linestyle=\"--\", label=\"L2 norm (flat)\")\n", - "plt.plot(ps_v, dist_triang_l1_ps_v, color=\"red\", label=\"L1 norm (triangle)\")\n", - "plt.plot(ps_v, dist_triang_l2_ps_v, color=\"red\", linestyle=\"--\", label=\"L2 norm (triangle)\")\n", - "plt.grid()\n", - "plt.xlabel(\"boundary price spread vs middle (0.1=10%)\")\n", - "plt.ylabel(\"matching error on price function (norm)\")\n", - "#plt.title(\"Optimal price spread\")\n", - "plt.xlim(0,None)\n", - "plt.ylim(0,0.03)\n", - "plt.legend()\n", - "plt.savefig(\"/Users/skl/Desktop/sol_img_optpsp.jpg\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "380cdb45-6f04-40c8-b2d1-21be9c2ae278", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/analysis/202401 Solidly/202401 Solidly-2.py b/resources/analysis/202401 Solidly/202401 Solidly-2.py deleted file mode 100644 index a78e86b7f..000000000 --- a/resources/analysis/202401 Solidly/202401 Solidly-2.py +++ /dev/null @@ -1,685 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -import numpy as np -import math as m -import matplotlib.pyplot as plt -import pandas as pd -from sympy import symbols, sqrt, Eq -#import decimal as d - -import invariants.functions as f -from invariants.solidly import SolidlyInvariant, SolidlySwapFunction - -from testing import * -#D = d.Decimal -plt.rcParams['figure.figsize'] = [6,6] - -print("---") -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(f.Function)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(SolidlyInvariant)) -# - - -# # Solidly Analysis -- Notebook 2 - -# ## Introduction - -# ### Invariant function -# -# The Solidly invariant function is a stable swap curve -# -# $$ -# f(x,y) = x^3y+xy^3 = k -# $$ -# -# ### Swap equation -# -# Solving the invariance equation as $y=y(x; k)$ gives the following result for what we want to call the **swap equation** -# -# $$ -# y(x; k) = \frac{x^2}{\sqrt[3]{L(x; k)}} - \frac{\sqrt[3]{L(x;k)}}{3} -# $$ -# -# $$ -# L(x;k) = -\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6} -# $$ -# -# Using the function $y(x;k)$ we can easily derive the **actual swap equation** at point $(x; k)$ as -# -# $$ -# \Delta y = y(x+\Delta x; k) - y(x; k) -# $$ - -# #### Precision issues and L -# -# The above form of L -- that we want to denote $L_1$ -- is numerically not well conditioned for small $x$. In order to improve conditioning we rewrite $L$ into the format $L_2$ below -# -# $$ -# L_2(x;k) = \frac{27k}{2x} \left(\sqrt{1 + \frac{108x^8}{729k^2}} - 1 \right) -# $$ -# -# We note that for small $x$ the Taylor development below gives better results than finite precision numerics -# -# $$ -# \sqrt{1+\xi}-1 = \frac{\xi}{2} - \frac{\xi^2}{8} + \frac{\xi^3}{16} - \frac{5\xi^4}{128} + O(\xi^5) -# $$ - -# ### Price equation -# -# The derivative $p=dy/dx$ -- the **price equation** -- can be determined analytically but its complexity is such that perturbative calculation is preferrable. Importantly, we do not have how to invert it (ie write $x=x(p)$, which creates complications for our preferred method of optimization, the _marginal price optimization_. -# - -# ## Analysing the invariance curve - -# ### Overall shape in real space -# -# Here we draw the invariance curves for difference values of $k$ (or $\sqrt[4]{k}$ which is the quantity that scales linearly with currency amounts; see the notes in the first notebook regarding the scaling properties of the equation and its implications). More specifically we draw -# -# - the **invariance curves** for various values of $\sqrt[4]{k}$ -# -# - their **central tangents**, showing the curves are very flat in the core region, and finally -# -# - the **boundary rays** of the different regimes of the equation - -k_sqrt4_v = [2, 4, 6, 8] -k_v = [kk**4 for kk in k_sqrt4_v] - -# + -x_v = np.linspace(0, m.sqrt(10), 50) -x_v = [xx**2 for xx in x_v] -x_v[0] = x_v[1]/2 - -# draw the invariance curves -for kk in k_v: - y_f = SolidlySwapFunction(k=kk) - yy_v = [y_f(xx) for xx in x_v] - #yy_v = [y_f(xx, kk) for xx in x_v] - plt.plot(x_v, yy_v, marker=None, linestyle='-', label=f"k={kk**0.25:.0f}^4") - -# draw the central tangents -C = 0.5**(0.25) -label="tangents" -for kk in k_sqrt4_v: - yy_v = [C*kk - (xx-C*kk) for xx in x_v] - plt.plot(x_v, yy_v, marker=None, linestyle='--', color="#aaa", label=label) - label = "" - -# draw the rays -for mm in [2.6, 6]: - yy_v = [mm*xx for xx in x_v] - plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color="#aaa", label=f"ray (m={mm})") - yy_v = [1/mm*xx for xx in x_v] - plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color="#aaa") - -plt.grid(True) -plt.legend() -plt.xlim(0, max(x_v)) -plt.ylim(0, max(x_v)) -plt.title("Invariance curves for different values of $\sqrt[4]{k}$") -plt.xlabel("x") -plt.ylabel("y") -plt.savefig("/Users/skl/Desktop/image.jpg") -plt.show() -# - - -# ### In log/log space - -# + -x_log_v = np.linspace(-m.log(10), m.log(10), 50) - -# draw the invariance curves -k_v = [kk**4 for kk in k_sqrt4_v] -x_v = [m.exp(xx) for xx in x_log_v] - -for kk in k_v: - y_f = SolidlySwapFunction(k=kk) - yy_v = [y_f(xx) for xx in x_v] - #yy_v = [y_f(xx, kk) for xx in x_v] - plt.loglog(x_v, yy_v, marker=None, linestyle='-', label=f"k={kk**0.25:.0f}^4") - -# draw the central tangents -C = 0.5**(0.25) -label="tangents" -for kk in k_sqrt4_v: - yy_v = [C*kk - (xx-C*kk) for xx in x_v] - plt.loglog(x_v, yy_v, marker=None, linestyle='--', color="#aaa", label=label) - label = "" - -# draw the rays -for mm in [2.6, 6]: - yy_v = [mm*xx for xx in x_v] - plt.loglog(x_v, yy_v, marker=None, linestyle='dotted', color="#aaa", label=f"ray (m={mm})") - yy_v = [1/mm*xx for xx in x_v] - plt.loglog(x_v, yy_v, marker=None, linestyle='dotted', color="#aaa") - -plt.grid(True, which="both") -plt.legend() -plt.xlim(1, max(x_v)) -plt.ylim(1, max(x_v)) -plt.title("Invariance curves for different values of $\sqrt[4]{k}$") -plt.xlabel("x (log scale)") -plt.ylabel("y (log scale)") -plt.show() -# - - -# ### As function of x/y, real space - -# + -x_v = np.linspace(0, 10, 100) -x_v = [xx**2 for xx in x_v] -x_v[0] = x_v[1]/2 - -# draw the invariance curves -for kk in k_v: - y_f = SolidlySwapFunction(k=kk) - yy_v = np.array([y_f(xx) for xx in x_v]) - #yy_v = [y_f(xx, kk) for xx in x_v] - plt.plot(x_v/yy_v, yy_v, marker=None, linestyle='-', label=f"k={kk**0.25:.0f}^4") - #plt.loglog(x_v/yy_v, yy_v, marker=None, linestyle='-', label=f"k={kk**0.25:.0f}^4") - -# # draw the central tangents -# C = 0.5**(0.25) -# label="tangents" -# for kk in k_sqrt4_v: -# yy_v = np.array([C*kk - (xx-C*kk) for xx in x_v]) -# plt.plot(yy_v/x_v, yy_v, marker=None, linestyle='--', color="#aaa", label=label) -# label = "" - -# # draw the rays -# for mm in [2.6, 6]: -# yy_v = [mm*xx for xx in x_v] -# plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color="#aaa", label=f"ray (m={mm})") -# yy_v = [1/mm*xx for xx in x_v] -# plt.plot(y_v/x_v, yy_v, marker=None, linestyle='dotted', color="#aaa") - -plt.grid(True) -plt.legend() -plt.xlim(.1, 10) -plt.ylim(.1, 15) -plt.title("Invariance curves for different values of $\sqrt[4]{k}$") -plt.xlabel("x/y") -plt.ylabel("y") -plt.show() -# - - -# ### As function of x/y, log/log - -# + -x_v = np.linspace(0, 10, 100) -x_v = [xx**2 for xx in x_v] -x_v[0] = x_v[1]/2 - -# draw the invariance curves -for kk in k_v: - y_f = SolidlySwapFunction(k=kk) - yy_v = np.array([y_f(xx) for xx in x_v]) - #yy_v = [y_f(xx, kk) for xx in x_v] - #plt.plot(x_v/yy_v, yy_v, marker=None, linestyle='-', label=f"k={kk**0.25:.0f}^4") - plt.loglog(x_v/yy_v, yy_v, marker=None, linestyle='-', label=f"k={kk**0.25:.0f}^4") - -# # draw the central tangents -# C = 0.5**(0.25) -# label="tangents" -# for kk in k_sqrt4_v: -# yy_v = np.array([C*kk - (xx-C*kk) for xx in x_v]) -# plt.plot(yy_v/x_v, yy_v, marker=None, linestyle='--', color="#aaa", label=label) -# label = "" - -# # draw the rays -# for mm in [2.6, 6]: -# yy_v = [mm*xx for xx in x_v] -# plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color="#aaa", label=f"ray (m={mm})") -# yy_v = [1/mm*xx for xx in x_v] -# plt.plot(y_v/x_v, yy_v, marker=None, linestyle='dotted', color="#aaa") - -plt.grid(True, which="both") -plt.legend() -plt.xlim(.1, 10) -plt.ylim(.1, 15) -plt.title("Invariance curves for different values of $\sqrt[4]{k}$") -plt.xlabel("x/y") -plt.ylabel("y") -plt.show() -# - - - - -# ## Fitting a hyperbolic curve -# -# _this code seems to have some issues and we may revisit it later_ - -k = 5**4 - -# ### Determining the central region -# -# The central region is between the rays $m=2.6$ and $1/m=2.6$ (fan-shaped area in the real plot, and diagonal band in the log/log plot). We are fixing $k=5^4$ as a curve in the middle of our existing chart. The inner region in this case is determined by the equations $\frac x y = m$ and $f(x,y)=k$ - -# + -# x_mid = (k/2)**0.25 -# # set up the invariant and the swap function -# iv = SolidlyInvariant() -# y_f = SolidlySwapFunction(k=k) -# ratio_f = lambda x: y_f(x)/x - -# # various consistency checks -# print("x,y mid = (k/2)^0.25 = ", x_mid) -# assert iseq(y_f(x_mid), x_mid) # at x_mid, y_mid = y(x_mid) -# assert iseq(ratio_f(x_mid), 1) # ditto, but with ratio_f -# assert iseq(f.goalseek(func=ratio_f, target = 1), x_mid) # ditto, but goalseek -# for xx in np.linspace(0.1, 10): -# assert iseq(iv.k_func(xx, y_f(xx)), k) - -# y_f.plot(0.1,10, show=False) -# plt.grid(True) -# plt.xlim(0, 10) -# plt.ylim(0, 10) -# plt.title(f"Invariance curve for $k={k**0.25:.0f}^4$") -# plt.xlabel("x") -# plt.ylabel("y") -# plt.show() - -# + -# x_v = np.linspace(0.1,10) -# #plt.plot(x_v, [m.log10(ratio_f(xx)) for xx in x_v]) -# plt.plot(x_v, [(ratio_f(xx)) for xx in x_v]) -# plt.grid(True) -# plt.xlim(0, 10) -# plt.ylim(0, 5) -# plt.title(f"Ratio y/x for $k={k**0.25:.0f}^4$") -# plt.xlabel("x") -# plt.ylabel("y(x)/x") -# print(f"check that ratio = 1 for x = x_mid = {x_mid}") -# plt.show() -# - - -# Here we finally determine the **central region**, defined by $m^{\pm 1} = 2.6$. We find that, for our chosen value of $k$, the region is from 2.35 to 6.13 and centers at 4.2. -# -# More generally, scaling laws and experiments show that **in percentage terms this region is independent of k**. In other words, the central region is always -# -# 0.56 x_mid (43.9% below) ... 1.46 x_mid (46.0% above) - -# + -# assert iseq(f.goalseek(func=ratio_f, target = 1), x_mid) -# r = ( -# f.goalseek(func=ratio_f, target = 2.6), -# f.goalseek(func=ratio_f, target = 1), -# f.goalseek(func=ratio_f, target = 1/2.6) -# ) -# r, tuple(round(vv/r[1]*100-100,1) for vv in r), tuple(round(vv/r[1]*100,1) for vv in r) -# - - -# Here we are asserting invariance with respect to $k$ - -# + -# k_v = [kk**4 for kk in [5, 25, 100, 1000]] -# for kk in k_v: -# x_mid = (kk/2)**0.25 -# y_f = SolidlySwapFunction(k=kk) -# ratio_f = lambda x: y_f(x)/x -# r0 = ( -# f.goalseek(func=ratio_f, target = 2.6), -# f.goalseek(func=ratio_f, target = 1), -# f.goalseek(func=ratio_f, target = 1/2.6) -# ) -# r = tuple(round(vv/r0[1],4) for vv in r0) -# print(r) -# x_min_r, _, x_max_r = r -# - - -# ### Fitting with flat kernel - -# + -# x_mid = (k/2)**0.25 -# x_min, x_max = x_min_r*x_mid, x_max_r*x_mid -# # x_min, x_max = 0.2*x_min, 3*x_max # uncomment to see bigger plot -# k**0.25, x_min, x_mid, x_max - -# + -# iv = SolidlyInvariant() -# y_f = SolidlySwapFunction(k=k) -# fv = f.FunctionVector(kernel=f.Kernel(x_min=x_min, x_max=x_max, kernel=f.Kernel.FLAT)) -# y_fv = fv.wrap(y_f) -# y_fv.plot(steps=100, show=False) -# match0_fv = y_fv.wrap(f.HyperbolaFunction(k=15, x0=0, y0=0)) -# match0_fv.plot(steps=100, show=False) -# plt.title(f"Invariance function $k={k**0.25:.0f}^4$ (fitted area only)") -# plt.xlabel("x") -# plt.ylabel("y") -# plt.show() - -# + -# match0_fv = match0_fv.update(k=25) -# params0 = match0_fv.function().params() -# #del params0["k"] -# params = y_fv.curve_fit(match0_fv.function(), params0, learning_rate=1, iterations=1000, tolerance=0.01, verbose=True) - -# + -# match_f = match0_fv.function().update(**params) -# match_fv = y_fv.wrap(match_f) -# y_fv.plot(steps=100, show=False) -# match_fv.plot(steps=100, show=False) -# plt.title(f"Invariance function $k={k**0.25:.0f}^4$ (fitted area only)") -# plt.xlabel("x") -# plt.ylabel("y") -# print("params = ", params) -# print(match_fv.params()) -# plt.show() - -# + -# iv = SolidlyInvariant() -# y_f = SolidlySwapFunction(k=k) -# fv = f.FunctionVector(kernel=f.Kernel(x_min=x_min, x_max=x_max, kernel=f.Kernel.FLAT)) -# y_fv = fv.wrap(y_f) -# y_fv.plot(steps=100, show=False) -# match0_fv = y_fv.wrap(f.QuadraticFunction()) -# match0_fv.plot(steps=100, show=False) -# plt.title(f"Invariance function $k={k**0.25:.0f}^4$ (fitted area only)") -# plt.xlabel("x") -# plt.ylabel("y") -# plt.show() - -# + -# params0 = match0_fv.function().params() -# params = y_fv.curve_fit(match0_fv.function(), params0, learning_rate=0.1, iterations=100, tolerance=0.01, verbose=True) -# - - -# ## Fitting a hyperbolic curve (charts for paper) - - -# + -k = 6**4 - -x_mid = (k/2)**0.25 -y_f = SolidlySwapFunction(k=k) -ratio_f = lambda x: y_f(x)/x -r0 = ( - f.goalseek(func=ratio_f, target = 2.6), - f.goalseek(func=ratio_f, target = 1), - f.goalseek(func=ratio_f, target = 1/2.6) -) -r = tuple(round(vv/r0[1],4) for vv in r0) -print(r) -x_min_r, _, x_max_r = r -x_min, x_max = x_min_r*x_mid, x_max_r*x_mid -fv_template = f.FunctionVector(kernel=f.Kernel(x_min=x_min, x_max=x_max, kernel=f.Kernel.FLAT)) - -x_v = np.linspace(0,10,1000) -x_v[0] = x_v[1]/2 - -k**0.25, x_min, x_mid, x_max -# - - -# ### Generic curve fitting - - -# + -# solidly -y_fv = fv_template.wrap(y_f) -yy_solidly_v = [y_fv(xx) for xx in x_v] -yp_solidly_v = [y_fv.p(xx) for xx in x_v] -ya = y_f(x_min) - -# constant product -ps=0.04 -params_opt_L2s = {'k': 4999.920086411355, 'x0': 65.96403685971154, 'y0': 65.36154243491612} -params_opt = {'k': 4999.920086411355, 'x0': 65.96403685971154, 'y0': 65.36154243491612} -match_fv = fv_template.wrap(f.LCPMM.from_xpxp(xa=x_min, xb=x_max, pa=1+ps, pb=1-ps, ya=ya)) -match_opt_fv = match_fv.wrap(match_fv.el[0].update(**params_opt)) -yy_match_v = [match_fv(xx) for xx in x_v] -yp_match_v = [match_fv.p(xx) for xx in x_v] -yy_match_opt_v = [match_opt_fv(xx) for xx in x_v] -yp_match_opt_v = [match_opt_fv.p(xx) for xx in x_v] - -# rays -mm = 2.6 -yy_ray1_v = [mm*xx for xx in x_v] -yy_ray2_v = [1/mm*xx for xx in x_v] - -# tangent -C = 0.5**(0.25) -kk = k**0.25 -yy_tang_v = [C*kk - (xx-C*kk) for xx in x_v] -# + -# plot 1 -plt.plot(x_v, yy_solidly_v, label=f"Solidly (k={k})") -plt.plot(x_v, yy_match_v, label=f"Match (ps={ps})") -#plt.plot(x_v, yy_match_opt_v, label=f"Match (optimized)") -plt.plot(x_v, yy_ray1_v, marker=None, linestyle='dotted', color="#aaa", label=f"ray (m={mm})") -plt.plot(x_v, yy_ray2_v, marker=None, linestyle='dotted', color="#aaa") -plt.plot(x_v, yy_tang_v, marker=None, linestyle='--', color="#aaa", label="tangent") -plt.grid(True) -plt.title(f"Matching a Solidly curve") -plt.xlabel("x") -plt.ylabel("y") -plt.legend() -plt.xlim(0, 10) -plt.ylim(0, 10) -plt.savefig("/Users/skl/Desktop/sol_img_matching1.jpg") -plt.show() - -# plot 2 -plt.plot(x_v, yy_solidly_v, label=f"Solidly (k={k})") -plt.plot(x_v, yy_match_v, label=f"Match (ps={ps})") -#plt.plot(x_v, yy_match_opt_v, label=f"Match (optimized)") -plt.plot(x_v, yy_ray1_v, marker=None, linestyle='dotted', color="#aaa", label=f"ray (m={mm})") -plt.plot(x_v, yy_ray2_v, marker=None, linestyle='dotted', color="#aaa") -plt.plot(x_v, yy_tang_v, marker=None, linestyle='--', color="#aaa", label="tangent") -plt.grid(True) -plt.title(f"Matching a Solidly curve") -plt.xlabel("x") -plt.ylabel("y") -plt.legend() -plt.xlim(1, 4) -plt.ylim(6, 9) -plt.savefig("/Users/skl/Desktop/sol_img_matching2.jpg") -plt.show() - -# plot 3 -plt.plot(x_v, yy_solidly_v, label=f"Solidly (k={k})") -plt.plot(x_v, yy_match_v, label=f"Match (ps={ps})") -#plt.plot(x_v, yy_match_opt_v, label=f"Match (optimized)") -plt.plot(x_v, yy_ray1_v, marker=None, linestyle='dotted', color="#aaa", label=f"ray (m={mm})") -plt.plot(x_v, yy_ray2_v, marker=None, linestyle='dotted', color="#aaa") -plt.plot(x_v, yy_tang_v, marker=None, linestyle='--', color="#aaa", label="tangent") -plt.grid(True) -plt.title(f"Matching a Solidly curve") -plt.xlabel("x") -plt.ylabel("y") -plt.legend() -plt.xlim(2.8, 3) -plt.ylim(7, 7.5) -plt.savefig("/Users/skl/Desktop/sol_img_matching3.jpg") -plt.show() - -# plot 4 -plt.plot(x_v, yy_solidly_v, label=f"Solidly (k={k})") -plt.plot(x_v, yy_match_v, label=f"Match (ps={ps})") -#plt.plot(x_v, yy_match_opt_v, label=f"Match (optimized)") -plt.plot(x_v, yy_ray1_v, marker=None, linestyle='dotted', color="#aaa", label=f"ray (m={mm})") -plt.plot(x_v, yy_ray2_v, marker=None, linestyle='dotted', color="#aaa") -plt.plot(x_v, yy_tang_v, marker=None, linestyle='--', color="#aaa", label="tangent") -plt.grid(True) -plt.title(f"Matching a Solidly curve") -plt.xlabel("x") -plt.ylabel("y") -plt.legend() -plt.xlim(4, 6) -plt.ylim(4, 6) -plt.savefig("/Users/skl/Desktop/sol_img_matching4.jpg") -plt.show() -# + -# plot 1 -plt.plot(x_v, yp_solidly_v, label=f"Solidly (k={k})") -plt.plot(x_v, yp_match_v, label=f"Match (ps={ps})") -#plt.plot(x_v, yp_match_opt_v, label=f"Match (optimized)") -# plt.plot(x_v, yy_ray1_v, marker=None, linestyle='dotted', color="#aaa", label=f"ray (m={mm})") -# plt.plot(x_v, yy_ray2_v, marker=None, linestyle='dotted', color="#aaa") -# plt.plot(x_v, yy_tang_v, marker=None, linestyle='--', color="#aaa", label="tangent") -plt.grid(True) -plt.title(f"Matching a Solidly curve (prices)") -plt.xlabel("x") -plt.ylabel("p") -plt.legend() -plt.xlim(0, 10) -plt.ylim(0, 2) -plt.savefig("/Users/skl/Desktop/sol_img_matchingp1.jpg") -plt.show() - -# plot 2 -plt.plot(x_v, yp_solidly_v, label=f"Solidly (k={k})") -plt.plot(x_v, yp_match_v, label=f"Match (ps={ps})") -#plt.plot(x_v, yp_match_opt_v, label=f"Match (optimized)") -# plt.plot(x_v, yy_ray1_v, marker=None, linestyle='dotted', color="#aaa", label=f"ray (m={mm})") -# plt.plot(x_v, yy_ray2_v, marker=None, linestyle='dotted', color="#aaa") -# plt.plot(x_v, yy_tang_v, marker=None, linestyle='--', color="#aaa", label="tangent") -plt.grid(True) -plt.title(f"Matching a Solidly curve (prices)") -plt.xlabel("x") -plt.ylabel("p") -plt.legend() -plt.xlim(x_min, x_max) -plt.ylim(0, 1.25) -plt.savefig("/Users/skl/Desktop/sol_img_matchingp2.jpg") -plt.show() - -# plot 3 -plt.plot(x_v, yp_solidly_v, label=f"Solidly (k={k})") -plt.plot(x_v, yp_match_v, label=f"Match (ps={ps})") -#plt.plot(x_v, yp_match_opt_v, label=f"Match (optimized)") -# plt.plot(x_v, yy_ray1_v, marker=None, linestyle='dotted', color="#aaa", label=f"ray (m={mm})") -# plt.plot(x_v, yy_ray2_v, marker=None, linestyle='dotted', color="#aaa") -# plt.plot(x_v, yy_tang_v, marker=None, linestyle='--', color="#aaa", label="tangent") -plt.grid(True) -plt.title(f"Matching a Solidly curve (prices)") -plt.xlabel("x") -plt.ylabel("p") -plt.legend() -plt.xlim(x_min, x_max) -plt.ylim(0.8, 1.2) -plt.savefig("/Users/skl/Desktop/sol_img_matchingp3.jpg") -plt.show() - -# - - - -match1_fv = match_fv.update() -params0 = match1_fv.function().params() -params0 = dict(k=5000, x0=60, y0=60) -print(params0) -params = y_fv.curve_fit(match1_fv.function(), params0, learning_rate=0.5, - iterations=50, tolerance=0.01, verbosity=y_fv.MM_VERBOSITY_LOW) -print(params) - -# + -# params = y_fv.curve_fit(match1_fv.function(), params0, learning_rate=0.5, -# iterations=50, tolerance=0.01, norm=y_fv.CF_NORM_L2S, verbosity=y_fv.MM_VERBOSITY_HIGH) -# print(params) - -# + -# params = y_fv.curve_fit(match1_fv.function(), params0, learning_rate=0.01, -# iterations=50, tolerance=0.01, norm=y_fv.CF_NORM_L2, verbosity=y_fv.MM_VERBOSITY_HIGH) -# print(params) - -# + -# params = y_fv.curve_fit(match1_fv.function(), params0, learning_rate=0.02, -# iterations=50, tolerance=0.01, norm=y_fv.CF_NORM_L1, verbosity=y_fv.MM_VERBOSITY_HIGH) -# print(params) -# - - -# ### Varying the price spread - -fv_flat = f.FunctionVector(kernel=f.Kernel(x_min=x_min, x_max=x_max, kernel=f.Kernel.FLAT)) -fv_triang = f.FunctionVector(kernel=f.Kernel(x_min=x_min, x_max=x_max, kernel=f.Kernel.TRIANGLE)) - -# swap curves - -# check different price spread curves -ps_v = np.linspace(0,0.15, 100) -ps_v[0] = ps_v[1]/2 -dist_flat_l2_ps_v = [] -dist_flat_l1_ps_v = [] -dist_triang_l2_ps_v = [] -dist_triang_l1_ps_v = [] -for psps in ps_v: - psps = max(psps, 0.001) - match_ps_f = f.LCPMM.from_xpxp(xa=x_min, xb=x_max, pa=1+psps, pb=1-psps, ya=ya) - match_ps_flat_fv = fv_flat.wrap(match_ps_f) - match_ps_triang_fv = fv_triang.wrap(match_ps_f) - dist_flat_l2 = match_ps_flat_fv.dist_L2(y_f) - dist_flat_l1 = match_ps_flat_fv.dist_L1(y_f) - dist_triang_l2 = match_ps_triang_fv.dist_L2(y_f) - dist_triang_l1 = match_ps_triang_fv.dist_L1(y_f) - #print(psps, dist) - dist_flat_l2_ps_v.append(dist_flat_l2) - dist_flat_l1_ps_v.append(dist_flat_l1) - dist_triang_l2_ps_v.append(dist_triang_l2) - dist_triang_l1_ps_v.append(dist_triang_l1) - - -plt.plot(ps_v, dist_flat_l1_ps_v, color="blue", label="L1 norm (flat)") -plt.plot(ps_v, dist_flat_l2_ps_v, color="blue", linestyle="--", label="L2 norm (flat)") -plt.plot(ps_v, dist_triang_l1_ps_v, color="red", label="L1 norm (triangle)") -plt.plot(ps_v, dist_triang_l2_ps_v, color="red", linestyle="--", label="L2 norm (triangle)") -plt.grid() -plt.xlabel("boundary price spread vs middle (0.1=10%)") -plt.ylabel("matching error on swap function (norm)") -#plt.title("Optimal price spread") -plt.xlim(0,None) -plt.ylim(0,0.03) -plt.legend() -plt.savefig("/Users/skl/Desktop/sol_img_optps.jpg") -plt.show() - -# price curves - -# check different price spread curves -ps_v = np.linspace(0,0.15, 100) -ps_v[0] = ps_v[1]/2 -dist_flat_l2_ps_v = [] -dist_flat_l1_ps_v = [] -dist_triang_l2_ps_v = [] -dist_triang_l1_ps_v = [] -for psps in ps_v: - psps = max(psps, 0.001) - match_ps_f = f.LCPMM.from_xpxp(xa=x_min, xb=x_max, pa=1+psps, pb=1-psps, ya=ya) - match_ps_flat_fv = fv_flat.wrap(match_ps_f) - match_ps_triang_fv = fv_triang.wrap(match_ps_f) - dist_flat_l2 = match_ps_flat_fv.distp_L2(y_f.p) - dist_flat_l1 = match_ps_flat_fv.distp_L1(y_f.p) - dist_triang_l2 = match_ps_triang_fv.distp_L2(y_f.p) - dist_triang_l1 = match_ps_triang_fv.distp_L1(y_f.p) - #print(psps, dist) - dist_flat_l2_ps_v.append(dist_flat_l2) - dist_flat_l1_ps_v.append(dist_flat_l1) - dist_triang_l2_ps_v.append(dist_triang_l2) - dist_triang_l1_ps_v.append(dist_triang_l1) - - -plt.plot(ps_v, dist_flat_l1_ps_v, color="blue", label="L1 norm (flat)") -plt.plot(ps_v, dist_flat_l2_ps_v, color="blue", linestyle="--", label="L2 norm (flat)") -plt.plot(ps_v, dist_triang_l1_ps_v, color="red", label="L1 norm (triangle)") -plt.plot(ps_v, dist_triang_l2_ps_v, color="red", linestyle="--", label="L2 norm (triangle)") -plt.grid() -plt.xlabel("boundary price spread vs middle (0.1=10%)") -plt.ylabel("matching error on price function (norm)") -#plt.title("Optimal price spread") -plt.xlim(0,None) -plt.ylim(0,0.03) -plt.legend() -plt.savefig("/Users/skl/Desktop/sol_img_optpsp.jpg") -plt.show() - - diff --git a/resources/analysis/202401 Solidly/202401 Solidly-Freeze01.ipynb b/resources/analysis/202401 Solidly/202401 Solidly-Freeze01.ipynb deleted file mode 100644 index 706dc228f..000000000 --- a/resources/analysis/202401 Solidly/202401 Solidly-Freeze01.ipynb +++ /dev/null @@ -1,974 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 196, - "id": "96348e86-5892-417a-9e2d-2fda430683d0", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import math as m\n", - "import matplotlib.pyplot as plt\n", - "from sympy import symbols, sqrt, Eq\n", - "plt.rcParams['figure.figsize'] = [6,6]" - ] - }, - { - "cell_type": "markdown", - "id": "a14a57f8-e21f-4652-9d68-0cff0c4afead", - "metadata": {}, - "source": [ - "# Solidly Analysis (Freeze01)" - ] - }, - { - "cell_type": "markdown", - "id": "9bcaf580-1389-41dc-b329-c68a80c75d56", - "metadata": {}, - "source": [ - "## Equations" - ] - }, - { - "cell_type": "markdown", - "id": "58ab6488-5c7b-4103-bae1-9d79d9837f11", - "metadata": {}, - "source": [ - "### Invariant function\n", - "\n", - "The Solidly invariant function is \n", - "\n", - "$$\n", - " x^3y+xy^3 = k\n", - "$$\n", - "\n", - "which is a stable swap curve, but more convex than say curve. " - ] - }, - { - "cell_type": "code", - "execution_count": 197, - "id": "34a840d9-e684-406b-a8da-b1bbbe255f9f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def invariant_eq(x,y,k=0):\n", - " return x**3 * y + x * y**3 - k" - ] - }, - { - "cell_type": "markdown", - "id": "b6ee11bb-309c-4bb4-a9bc-45199287971e", - "metadata": {}, - "source": [ - "### Swap equation\n", - "\n", - "Solving the invariance equation as $y=y(x; k)$ gives the following result\n", - "\n", - "$$\n", - "y(x;k) = \\frac{x^2}{\\left(-\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\\right)^{\\frac{1}{3}}} - \\frac{\\left(-\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\\right)^{\\frac{1}{3}}}{3}\n", - "$$\n", - "\n", - "We can introduce intermediary variables $L(x;k), M(x;k)$ to write this a bit more simply\n", - "\n", - "$$\n", - "L = -\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\n", - "$$\n", - "\n", - "$$\n", - "M = L^{1/3} = \\sqrt[3]{L}\n", - "$$\n", - "\n", - "$$\n", - "y = \\frac{x^2}{\\sqrt[3]{L}} - \\frac{\\sqrt[3]{L}}{3} = \\frac{x^2}{M} - \\frac{M}{3} \n", - "$$\n", - "\n", - "Using the function $y(x;k)$ we can easily derive the swap equation at point $(x; k)$ as\n", - "\n", - "$$\n", - "\\Delta y = y(x+\\Delta x; k) - y(x; k)\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 198, - "id": "50f960e3-65e3-470c-a465-64c1a3fb51f2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{2}}{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}} - \\frac{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}}{3}$" - ], - "text/plain": [ - "x**2/(-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333 - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333/3" - ] - }, - "execution_count": 198, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x, k = symbols('x k')\n", - "\n", - "y = x**2 / ((-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**(1/3)) - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**(1/3)/3\n", - "y" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "id": "1799f486-222c-46ad-bd6d-a4c183d8d871", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{2}}{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}} - \\frac{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}}{3}$" - ], - "text/plain": [ - "x**2/(-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333 - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333/3" - ] - }, - "execution_count": 199, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L = -27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2\n", - "y2 = x**2 / (L**(1/3)) - (L**(1/3))/3\n", - "y2" - ] - }, - { - "cell_type": "markdown", - "id": "1ac5dc18-0a49-4d37-a49b-0f57ef5ebdc4", - "metadata": {}, - "source": [ - "Note that as above, $L$ (that we call $L_1$ now) is not particularly well conditioned. \n", - "\n", - "$$\n", - "L_1 = -\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\n", - "$$\n", - "\n", - "This alternative form works better\n", - "\n", - "$$\n", - "L_2(x;k) = \\frac{27k}{2x} \\left(\\sqrt{1 + \\frac{108x^8}{729k^2}} - 1 \\right)\n", - "$$\n", - "\n", - "Furthermore\n", - "\n", - "$$\n", - "\\sqrt{1+\\xi}-1 = \\frac{\\xi}{2} - \\frac{\\xi^2}{8} + \\frac{\\xi^3}{16} - \\frac{5\\xi^4}{128} + O(\\xi^5)\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 238, - "id": "1c208f81-5e12-4cd9-95a9-3cd1b3e0ea71", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def L1(x,k):\n", - " return -27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2\n", - "\n", - "def L2(x,k):\n", - " xi = (108 * x**8) / (729 * k**2)\n", - " lam1 = (m.sqrt(1 + xi) - 1)\n", - " lam2 = xi/2 - xi**2/8 \n", - " #lam2 = xi/2 - xi**2/8 + xi**3/16 - 0.0390625*xi**4\n", - " #lam2 = xi*(1/2 - xi*(1/8 - xi*(1/16 - 0.0390625*xi)))\n", - " lam = max(lam1, lam2)\n", - " # for very small xi we can get zero or close to zero in the full formula\n", - " # in this case the taulor approximation is better because for small xi it is always > 0\n", - " # we simply use the max of the two -- the Taylor gets negative quickly\n", - " L = lam * (27 * k) / (2 * x)\n", - " return L" - ] - }, - { - "cell_type": "code", - "execution_count": 201, - "id": "51a99f4c-1c36-4865-8046-52946214ec5b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(9.99999940631824e-8, 9.9999999962963e-08)" - ] - }, - "execution_count": 201, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L1(0.1, 1), L2(0.1,1)" - ] - }, - { - "cell_type": "code", - "execution_count": 202, - "id": "4abb21bd-64c3-437d-8c29-4be0b9a5c725", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{2}}{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}} - \\frac{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}}{3}$" - ], - "text/plain": [ - "x**2/(-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333 - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333/3" - ] - }, - "execution_count": 202, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "M = L**(1/3)\n", - "y3 = x**2 / M - M/3\n", - "y3" - ] - }, - { - "cell_type": "code", - "execution_count": 203, - "id": "7de2f57a-abca-4a23-b81d-3ce651b7855b", - "metadata": {}, - "outputs": [], - "source": [ - "assert y == y2\n", - "assert y == y3\n", - "assert y2 == y3" - ] - }, - { - "cell_type": "code", - "execution_count": 204, - "id": "285736b4-ac27-4804-8dcb-a8b96b6785de", - "metadata": {}, - "outputs": [], - "source": [ - "def swap_eq(x,k):\n", - " L,M,y = [None]*3\n", - " try:\n", - " #L = -27*k/(2*x) + m.sqrt(729*k**2/x**2 + 108*x**6)/2\n", - " L = L2(x,k)\n", - " M = L**(1/3)\n", - " y = x**2/M - M/3\n", - " except Exception as e:\n", - " print(\"Exception: \", e)\n", - " print(f\"x={x}, k={k}, L={L}, M={M}, y={y}\")\n", - " return y" - ] - }, - { - "cell_type": "code", - "execution_count": 205, - "id": "91cb13ac-a1fc-485b-9037-6447a4c49dd3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.6823278038280196\n" - ] - } - ], - "source": [ - "def swap_eq2(x, k):\n", - " # Calculating the components of the swap equation\n", - " term1_numerator = (2/3)**(1/3) * x**3\n", - " term1_denominator = (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3)\n", - "\n", - " term2_numerator = (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3)\n", - " term2_denominator = 2**(1/3) * 3**(2/3) * x\n", - "\n", - " # Swap equation calculation\n", - " y = -term1_numerator / term1_denominator + term2_numerator / term2_denominator\n", - "\n", - " return y\n", - "\n", - "# Example usage\n", - "x_value = 1 # Replace with the desired value of x\n", - "k_value = 1 # Replace with the desired value of k\n", - "print(swap_eq(x_value, k_value))" - ] - }, - { - "cell_type": "markdown", - "id": "4c115505-7076-47b4-9c3e-fd0dd826683c", - "metadata": {}, - "source": [ - "### Price equation\n", - "\n", - "The derivative $p=dy/dx$ is as follows\n", - "\n", - "$$\n", - "p=\\frac{dy}{dx} = 6^{\\frac{1}{3}}\\left(\\frac{-2 \\cdot 3^{\\frac{1}{3}} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} \\cdot \\left(-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}\\right) \\cdot \\left(3k \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} + \\sqrt{3} \\cdot \\left(-9k^2 + 4x^8\\right)\\right) + 2^{\\frac{1}{3}} \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} \\cdot \\left(\\frac{-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}}{x}\\right)^{\\frac{5}{3}} \\cdot \\left(-3k \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} + \\sqrt{3} \\cdot \\left(9k^2 - 4x^8\\right)\\right) + 4 \\cdot 3^{\\frac{1}{3}} \\cdot \\left(-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}\\right)^2 \\cdot \\left(27k^2 + 4x^8\\right)}{6 \\cdot x \\cdot \\left(\\frac{-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}}{x}\\right)^{\\frac{7}{3}} \\cdot \\left(27k^2 + 4x^8\\right)}\\right)\n", - "$$\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 206, - "id": "5c900f31-fee7-4726-b0af-31a35849b043", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.3136251299197979\n" - ] - } - ], - "source": [ - "def price_eq(x, k):\n", - " # Components of the derivative\n", - " term1_numerator = 2**(1/3) * x**3 * (18 * k * x + (m.sqrt(3) * (108 * k**2 * x**3 + 48 * x**11)) / (2 * m.sqrt(27 * k**2 * x**4 + 4 * x**12)))\n", - " term1_denominator = 3 * (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(4/3)\n", - " \n", - " term2_numerator = 18 * k * x + (m.sqrt(3) * (108 * k**2 * x**3 + 48 * x**11)) / (2 * m.sqrt(27 * k**2 * x**4 + 4 * x**12))\n", - " term2_denominator = 3 * 2**(1/3) * 3**(2/3) * x * (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(2/3)\n", - " \n", - " term3 = -3 * 2**(1/3) * x**2 / (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3)\n", - " \n", - " term4 = -(9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3) / (2**(1/3) * 3**(2/3) * x**2)\n", - " \n", - " # Combining all terms\n", - " dy_dx = (term1_numerator / term1_denominator) + (term2_numerator / term2_denominator) + term3 + term4\n", - "\n", - " return dy_dx\n", - "\n", - "# Example usage\n", - "x_value = 1 # Replace with the desired value of x\n", - "k_value = 1 # Replace with the desired value of k\n", - "print(price_eq(x_value, k_value))\n" - ] - }, - { - "cell_type": "markdown", - "id": "bd87b7d5-c0cd-4cfd-866b-ce305aa9d78f", - "metadata": {}, - "source": [ - "#### Inverting the price equation\n", - "\n", - "The above equations \n", - "([obtained thanks to Wolfram Alpha](https://chat.openai.com/share/55151f92-411c-43c1-a6ec-180856762a82), \n", - "the interface of which still sucks) are rather complex, and unfortunately they can't apparently be inverted analytically to get $x=x(p;k)$" - ] - }, - { - "cell_type": "markdown", - "id": "053180db-2679-4bf5-a8d6-d5d6e4e51f29", - "metadata": {}, - "source": [ - "## Charts" - ] - }, - { - "cell_type": "markdown", - "id": "99ffb5da-a7dd-4804-a2bf-1f32da169fad", - "metadata": {}, - "source": [ - "### Invariant equation" - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "id": "adfc7418-fa81-4108-9a4b-9c003ad315da", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "y_f = swap_eq" - ] - }, - { - "cell_type": "code", - "execution_count": 208, - "id": "3e8740bc-696c-4f0d-9acb-ebe8d8e27ae9", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k_v = [1**4, 2**4, 3**4, 5**4]\n", - "#k_v = [1**4]\n", - "x_v = np.linspace(0, m.sqrt(10), 50)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "y_v_dct = {kk: [y_f(xx, kk) for xx in x_v] for kk in k_v}\n", - "plt.grid(True)\n", - "for kk, y_v in y_v_dct.items(): \n", - " plt.plot(x_v, y_v, marker=None, linestyle='-', label=f\"k={kk}\")\n", - "plt.legend()\n", - "plt.xlim(0, max(x_v))\n", - "plt.ylim(0, max(x_v))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 209, - "id": "fcb63f18-df33-448e-9ef8-cd8733e3b84e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "7.105427357601002e-15" - ] - }, - "execution_count": 209, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kk = 10\n", - "xx = 2\n", - "invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk)" - ] - }, - { - "cell_type": "code", - "execution_count": 210, - "id": "81de37e3-4c86-4428-9c74-1ec98eed876f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_inv_dct = {kk: [invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk) for xx in x_v] for kk in k_v}\n", - "y_inv_lst = [v for lst in y_inv_dct.values() for v in lst]\n", - "#y_inv_lst\n", - "plt.hist(y_inv_lst, bins=10, color=\"blue\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 211, - "id": "bd4456bf-1c66-4c04-89d5-ff3302a3bd7a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{1: 0.0102200306584036,\n", - " 16: 0.007342191625435035,\n", - " 81: 0.9182468262089287,\n", - " 625: 10.463713766637625}" - ] - }, - "execution_count": 211, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{k: max([abs(vv) for vv in v]) for k,v in y_inv_dct.items()}" - ] - }, - { - "cell_type": "code", - "execution_count": 212, - "id": "7c236fa2-9b33-4693-bb9e-b72bab17f6e3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{1: 0.0, 16: 3.552713678800501e-15, 81: 2.842170943040401e-14, 625: 0.0}" - ] - }, - "execution_count": 212, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{k: min([abs(vv) for vv in v]) for k,v in y_inv_dct.items()}" - ] - }, - { - "cell_type": "code", - "execution_count": 213, - "id": "359b15ea-2a6e-4a0c-922a-e80c3f476782", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.06663890045814246, -0.9182468262089287)" - ] - }, - "execution_count": 213, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x_v[4], y_inv_dct[81][4]" - ] - }, - { - "cell_type": "code", - "execution_count": 214, - "id": "6f79282d-4f60-4a23-a209-7559750ddb4d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.10412328196584758, -10.463713766637625)" - ] - }, - "execution_count": 214, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x_v[5], y_inv_dct[625][5]" - ] - }, - { - "cell_type": "code", - "execution_count": 215, - "id": "99f4fbc6-967c-44fd-bd88-f32fbc030ae3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kk = 5**4\n", - "x_v = np.linspace(0, m.sqrt(10), 50)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "plt.grid(True)\n", - "plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f\"k={kk}\")\n", - "inv_dct = {xx: invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk) for xx in x_v}\n", - "plt.legend()\n", - "plt.xlim(0, max(x_v))\n", - "plt.ylim(0, max(x_v))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 216, - "id": "7cf25100-2a35-4d07-bab7-cbc92563191f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 216, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(inv_dct.keys(), inv_dct.values())" - ] - }, - { - "cell_type": "code", - "execution_count": 232, - "id": "621a8d45-7655-42e3-b8e7-71a6c44e19e6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kk = 5**4\n", - "x_v = np.linspace(0, m.sqrt(10), 5000)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "plt.grid(True)\n", - "plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f\"k={kk}\")\n", - "inv_dct = {xx: invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk) for xx in x_v[:700]}\n", - "plt.legend()\n", - "plt.xlim(0, max(x_v))\n", - "plt.ylim(0, max(x_v))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 233, - "id": "f2b078f1-7e68-4a2d-be32-26b0fa02254b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 233, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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"outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 237, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x_v[:700], [invariant_eq(x=xx, y=swap_eq(xx, kk), k=0) for xx in x_v[:700]])" - ] - }, - { - "cell_type": "markdown", - "id": "4066e383-dba2-4e49-b999-ef7322ada357", - "metadata": {}, - "source": [ - "### Numerical considerations\n", - "#### Comparing L1 with L2\n", - "\n", - "L1 and L2 are different expressions of the L term above. L2 is the naive formula, L1 is optimized. L2 can be zero for very small values (and it is not even continous; see 0.009 and 0.01 below) whilst L1 is *always* greater than zero." - ] - }, - { - "cell_type": "code", - "execution_count": 221, - "id": "0abe5692-f6da-4071-83db-c8bb995ff2be", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[(0, 1.0000000000000003e-28),\n", - " (0, 1.0000000000000001e-21),\n", - " (2.27373675443232e-13, 4.7829689999999975e-15),\n", - " (0, 1.0000000000000002e-14),\n", - " (2.27373675443232e-13, 1.9984014443252818e-13),\n", - " (1.25055521493778e-12, 1.279999999999999e-12),\n", - " (7.81199105404085e-10, 7.812499999988699e-10)]" - ] - }, - "execution_count": 221, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xs_v = [0.0001, 0.001, 0.009, 0.01, 0.015, 0.02, 0.05]\n", - "[(L1(xx,1), L2(xx, 1)) for xx in xs_v]" - ] - }, - { - "cell_type": "code", - "execution_count": 222, - "id": "a5b8067c-ca96-4586-bab2-d3fa5dc421db", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 222, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x_v, [L2(xx, 1) - L1(xx, 1) for xx in x_v])" - ] - }, - { - "cell_type": "code", - "execution_count": 223, - "id": "63c25d7d-81aa-4589-ae3e-a370ebc9a3a4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x_v, [L1(xx, 1) for xx in x_v])\n", - "plt.plot(x_v, [L2(xx, 1) for xx in x_v])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ea07ddd4-7b54-4bae-9fc9-d61daa2847bc", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/analysis/202401 Solidly/202401 Solidly-Freeze02.ipynb b/resources/analysis/202401 Solidly/202401 Solidly-Freeze02.ipynb deleted file mode 100644 index 344fcaacc..000000000 --- a/resources/analysis/202401 Solidly/202401 Solidly-Freeze02.ipynb +++ /dev/null @@ -1,2017 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 359, - "id": "96348e86-5892-417a-9e2d-2fda430683d0", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import math as m\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "from sympy import symbols, sqrt, Eq\n", - "plt.rcParams['figure.figsize'] = [6,6]" - ] - }, - { - "cell_type": "markdown", - "id": "a14a57f8-e21f-4652-9d68-0cff0c4afead", - "metadata": {}, - "source": [ - "# Solidly Analysis (Freeze02)" - ] - }, - { - "cell_type": "markdown", - "id": "9bcaf580-1389-41dc-b329-c68a80c75d56", - "metadata": {}, - "source": [ - "## Equations" - ] - }, - { - "cell_type": "markdown", - "id": "58ab6488-5c7b-4103-bae1-9d79d9837f11", - "metadata": {}, - "source": [ - "### Invariant function\n", - "\n", - "The Solidly invariant function is \n", - "\n", - "$$\n", - " x^3y+xy^3 = k\n", - "$$\n", - "\n", - "which is a stable swap curve, but more convex than say curve. " - ] - }, - { - "cell_type": "code", - "execution_count": 360, - "id": "34a840d9-e684-406b-a8da-b1bbbe255f9f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def invariant_eq(x, y, k=0, *, aserr=False):\n", - " \"\"\"returns f(x,y)-k or f(x,y)/k - 1\"\"\"\n", - " if aserr:\n", - " return (x**3 * y + x * y**3)/k-1\n", - " else:\n", - " return x**3 * y + x * y**3 - k" - ] - }, - { - "cell_type": "markdown", - "id": "b6ee11bb-309c-4bb4-a9bc-45199287971e", - "metadata": {}, - "source": [ - "### Swap equation\n", - "\n", - "Solving the invariance equation as $y=y(x; k)$ gives the following result\n", - "\n", - "$$\n", - "y(x;k) = \\frac{x^2}{\\left(-\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\\right)^{\\frac{1}{3}}} - \\frac{\\left(-\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\\right)^{\\frac{1}{3}}}{3}\n", - "$$\n", - "\n", - "We can introduce intermediary variables $L(x;k), M(x;k)$ to write this a bit more simply\n", - "\n", - "$$\n", - "L = -\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\n", - "$$\n", - "\n", - "$$\n", - "M = L^{1/3} = \\sqrt[3]{L}\n", - "$$\n", - "\n", - "$$\n", - "y = \\frac{x^2}{\\sqrt[3]{L}} - \\frac{\\sqrt[3]{L}}{3} = \\frac{x^2}{M} - \\frac{M}{3} \n", - "$$\n", - "\n", - "Using the function $y(x;k)$ we can easily derive the swap equation at point $(x; k)$ as\n", - "\n", - "$$\n", - "\\Delta y = y(x+\\Delta x; k) - y(x; k)\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 361, - "id": "50f960e3-65e3-470c-a465-64c1a3fb51f2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{2}}{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}} - \\frac{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}}{3}$" - ], - "text/plain": [ - "x**2/(-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333 - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333/3" - ] - }, - "execution_count": 361, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x, k = symbols('x k')\n", - "\n", - "y = x**2 / ((-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**(1/3)) - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**(1/3)/3\n", - "y" - ] - }, - { - "cell_type": "code", - "execution_count": 362, - "id": "1799f486-222c-46ad-bd6d-a4c183d8d871", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{2}}{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}} - \\frac{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}}{3}$" - ], - "text/plain": [ - "x**2/(-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333 - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333/3" - ] - }, - "execution_count": 362, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L = -27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2\n", - "y2 = x**2 / (L**(1/3)) - (L**(1/3))/3\n", - "y2" - ] - }, - { - "cell_type": "markdown", - "id": "1ac5dc18-0a49-4d37-a49b-0f57ef5ebdc4", - "metadata": {}, - "source": [ - "#### Precision issues and L\n", - "\n", - "Note that as above, $L$ (that we call $L_1$ now) is not particularly well conditioned. \n", - "\n", - "$$\n", - "L_1 = -\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\n", - "$$\n", - "\n", - "This alternative form works better\n", - "\n", - "$$\n", - "L_2(x;k) = \\frac{27k}{2x} \\left(\\sqrt{1 + \\frac{108x^8}{729k^2}} - 1 \\right)\n", - "$$\n", - "\n", - "Furthermore\n", - "\n", - "$$\n", - "\\sqrt{1+\\xi}-1 = \\frac{\\xi}{2} - \\frac{\\xi^2}{8} + \\frac{\\xi^3}{16} - \\frac{5\\xi^4}{128} + O(\\xi^5)\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 363, - "id": "1c208f81-5e12-4cd9-95a9-3cd1b3e0ea71", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def L1(x,k):\n", - " return -27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2\n", - "\n", - "def L2(x,k):\n", - " xi = (108 * x**8) / (729 * k**2)\n", - " #print(f\"xi = {xi}\")\n", - " if xi > 1e-10:\n", - " lam = (m.sqrt(1 + xi) - 1)\n", - " else:\n", - " lam = xi*(1/2 - xi*(1/8 - xi*(1/16 - 0.0390625*xi)))\n", - " # the relative error of this Taylor approximation is for xi < 0.025 is 1e-5 or better\n", - " # for xi ~ 1e-15 the full term is unstable (because 1 + 1e-16 ~ 1 in double precision)\n", - " # therefore the switchover should happen somewhere between 1e-12 and 1e-2\n", - " #lam1 = 0\n", - " #lam2 = xi/2 - xi**2/8 \n", - " #lam2 = xi/2 - xi**2/8 + xi**3/16 - 0.0390625*xi**4\n", - " #lam2 = xi*(1/2 - xi*(1/8 - xi*(1/16 - 0.0390625*xi)))\n", - " #lam = max(lam1, lam2)\n", - " # for very small xi we can get zero or close to zero in the full formula\n", - " # in this case the taulor approximation is better because for small xi it is always > 0\n", - " # we simply use the max of the two -- the Taylor gets negative quickly\n", - " L = lam * (27 * k) / (2 * x)\n", - " return L" - ] - }, - { - "cell_type": "code", - "execution_count": 364, - "id": "51a99f4c-1c36-4865-8046-52946214ec5b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(9.99999940631824e-8, 9.999997829801544e-08)" - ] - }, - "execution_count": 364, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L1(0.1, 1), L2(0.1,1)" - ] - }, - { - "cell_type": "code", - "execution_count": 365, - "id": "4abb21bd-64c3-437d-8c29-4be0b9a5c725", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{2}}{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}} - \\frac{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}}{3}$" - ], - "text/plain": [ - "x**2/(-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333 - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333/3" - ] - }, - "execution_count": 365, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "M = L**(1/3)\n", - "y3 = x**2 / M - M/3\n", - "y3" - ] - }, - { - "cell_type": "code", - "execution_count": 366, - "id": "7de2f57a-abca-4a23-b81d-3ce651b7855b", - "metadata": {}, - "outputs": [], - "source": [ - "assert y == y2\n", - "assert y == y3\n", - "assert y2 == y3" - ] - }, - { - "cell_type": "code", - "execution_count": 367, - "id": "285736b4-ac27-4804-8dcb-a8b96b6785de", - "metadata": {}, - "outputs": [], - "source": [ - "def swap_eq(x,k):\n", - " L,M,y = [None]*3\n", - " try:\n", - " #L = -27*k/(2*x) + m.sqrt(729*k**2/x**2 + 108*x**6)/2\n", - " L = L2(x,k)\n", - " M = L**(1/3)\n", - " y = x**2/M - M/3\n", - " except Exception as e:\n", - " print(\"Exception: \", e)\n", - " print(f\"x={x}, k={k}, L={L}, M={M}, y={y}\")\n", - " return y" - ] - }, - { - "cell_type": "code", - "execution_count": 368, - "id": "91cb13ac-a1fc-485b-9037-6447a4c49dd3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.6823278038280196\n" - ] - } - ], - "source": [ - "def swap_eq2(x, k):\n", - " # Calculating the components of the swap equation\n", - " term1_numerator = (2/3)**(1/3) * x**3\n", - " term1_denominator = (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3)\n", - "\n", - " term2_numerator = (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3)\n", - " term2_denominator = 2**(1/3) * 3**(2/3) * x\n", - "\n", - " # Swap equation calculation\n", - " y = -term1_numerator / term1_denominator + term2_numerator / term2_denominator\n", - "\n", - " return y\n", - "\n", - "# Example usage\n", - "x_value = 1 # Replace with the desired value of x\n", - "k_value = 1 # Replace with the desired value of k\n", - "print(swap_eq(x_value, k_value))" - ] - }, - { - "cell_type": "markdown", - "id": "4c115505-7076-47b4-9c3e-fd0dd826683c", - "metadata": {}, - "source": [ - "### Price equation\n", - "\n", - "The derivative $p=dy/dx$ is as follows\n", - "\n", - "$$\n", - "p=\\frac{dy}{dx} = 6^{\\frac{1}{3}}\\left(\\frac{-2 \\cdot 3^{\\frac{1}{3}} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} \\cdot \\left(-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}\\right) \\cdot \\left(3k \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} + \\sqrt{3} \\cdot \\left(-9k^2 + 4x^8\\right)\\right) + 2^{\\frac{1}{3}} \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} \\cdot \\left(\\frac{-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}}{x}\\right)^{\\frac{5}{3}} \\cdot \\left(-3k \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} + \\sqrt{3} \\cdot \\left(9k^2 - 4x^8\\right)\\right) + 4 \\cdot 3^{\\frac{1}{3}} \\cdot \\left(-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}\\right)^2 \\cdot \\left(27k^2 + 4x^8\\right)}{6 \\cdot x \\cdot \\left(\\frac{-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}}{x}\\right)^{\\frac{7}{3}} \\cdot \\left(27k^2 + 4x^8\\right)}\\right)\n", - "$$\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 369, - "id": "5c900f31-fee7-4726-b0af-31a35849b043", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.3136251299197979\n" - ] - } - ], - "source": [ - "def price_eq(x, k):\n", - " # Components of the derivative\n", - " term1_numerator = 2**(1/3) * x**3 * (18 * k * x + (m.sqrt(3) * (108 * k**2 * x**3 + 48 * x**11)) / (2 * m.sqrt(27 * k**2 * x**4 + 4 * x**12)))\n", - " term1_denominator = 3 * (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(4/3)\n", - " \n", - " term2_numerator = 18 * k * x + (m.sqrt(3) * (108 * k**2 * x**3 + 48 * x**11)) / (2 * m.sqrt(27 * k**2 * x**4 + 4 * x**12))\n", - " term2_denominator = 3 * 2**(1/3) * 3**(2/3) * x * (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(2/3)\n", - " \n", - " term3 = -3 * 2**(1/3) * x**2 / (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3)\n", - " \n", - " term4 = -(9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3) / (2**(1/3) * 3**(2/3) * x**2)\n", - " \n", - " # Combining all terms\n", - " dy_dx = (term1_numerator / term1_denominator) + (term2_numerator / term2_denominator) + term3 + term4\n", - "\n", - " return dy_dx\n", - "\n", - "# Example usage\n", - "x_value = 1 # Replace with the desired value of x\n", - "k_value = 1 # Replace with the desired value of k\n", - "print(price_eq(x_value, k_value))\n" - ] - }, - { - "cell_type": "markdown", - "id": "bd87b7d5-c0cd-4cfd-866b-ce305aa9d78f", - "metadata": {}, - "source": [ - "#### Inverting the price equation\n", - "\n", - "The above equations \n", - "([obtained thanks to Wolfram Alpha](https://chat.openai.com/share/55151f92-411c-43c1-a6ec-180856762a82), \n", - "the interface of which still sucks) are rather complex, and unfortunately they can't apparently be inverted analytically to get $x=x(p;k)$" - ] - }, - { - "cell_type": "markdown", - "id": "053180db-2679-4bf5-a8d6-d5d6e4e51f29", - "metadata": {}, - "source": [ - "## Charts" - ] - }, - { - "cell_type": "markdown", - "id": "99ffb5da-a7dd-4804-a2bf-1f32da169fad", - "metadata": {}, - "source": [ - "### Invariant equation" - ] - }, - { - "cell_type": "code", - "execution_count": 370, - "id": "adfc7418-fa81-4108-9a4b-9c003ad315da", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "y_f = swap_eq" - ] - }, - { - "cell_type": "code", - "execution_count": 371, - "id": "3e8740bc-696c-4f0d-9acb-ebe8d8e27ae9", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k_v = [1**4, 2**4, 3**4, 5**4]\n", - "#k_v = [1**4]\n", - "x_v = np.linspace(0, m.sqrt(10), 50)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "y_v_dct = {kk: [y_f(xx, kk) for xx in x_v] for kk in k_v}\n", - "plt.grid(True)\n", - "for kk, y_v in y_v_dct.items(): \n", - " plt.plot(x_v, y_v, marker=None, linestyle='-', label=f\"k={kk}\")\n", - "plt.legend()\n", - "plt.xlim(0, max(x_v))\n", - "plt.ylim(0, max(x_v))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c63f7026-4cc8-4f54-a34e-dc99939945b8", - "metadata": { - "tags": [] - }, - "source": [ - "Checking the invariant equation at a specific point (xx; kk)" - ] - }, - { - "cell_type": "code", - "execution_count": 372, - "id": "fcb63f18-df33-448e-9ef8-cd8733e3b84e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "5.773159728050814e-15" - ] - }, - "execution_count": 372, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kk = 625\n", - "xx = 3\n", - "invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True)" - ] - }, - { - "cell_type": "markdown", - "id": "ea922e57-a4d5-444c-8443-407674520fcc", - "metadata": {}, - "source": [ - "Calculating a histogram of relative errors, ie what the relative error in the invariant equation is at various points $xx$ of the swap equation and at various $kk$" - ] - }, - { - "cell_type": "code", - "execution_count": 373, - "id": "81de37e3-4c86-4428-9c74-1ec98eed876f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_inv_dct = {kk: [invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) for xx in x_v] for kk in k_v}\n", - "y_inv_lst = [v for lst in y_inv_dct.values() for v in lst]\n", - "#y_inv_lst\n", - "plt.hist(y_inv_lst, bins=200, color=\"blue\")\n", - "plt.title(\"Histogram of relative errors [f(x,y)/k - 1]\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f01529b5-7285-4c82-9145-0ea58a09877f", - "metadata": {}, - "source": [ - "Maximum relative error for different values of $k$" - ] - }, - { - "cell_type": "code", - "execution_count": 374, - "id": "bd4456bf-1c66-4c04-89d5-ff3302a3bd7a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{1: 3.1328071248282185e-08,\n", - " 16: 1.4596303516967168e-06,\n", - " 81: 1.3818783672903123e-07,\n", - " 625: 7.772002328376715e-07}" - ] - }, - "execution_count": 374, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{k: max([abs(vv) for vv in v]) for k,v in y_inv_dct.items()}" - ] - }, - { - "cell_type": "markdown", - "id": "9b5ef43c-9784-44fe-b680-c5262c36ec6b", - "metadata": { - "tags": [] - }, - "source": [ - "Minimum relative error for different values of $k$" - ] - }, - { - "cell_type": "code", - "execution_count": 375, - "id": "7c236fa2-9b33-4693-bb9e-b72bab17f6e3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{1: 0.0,\n", - " 16: 2.220446049250313e-16,\n", - " 81: 4.440892098500626e-16,\n", - " 625: 4.440892098500626e-16}" - ] - }, - "execution_count": 375, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{k: min([abs(vv) for vv in v]) for k,v in y_inv_dct.items()}" - ] - }, - { - "cell_type": "code", - "execution_count": 376, - "id": "99f4fbc6-967c-44fd-bd88-f32fbc030ae3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kk = 5**4\n", - "x_v = np.linspace(0, m.sqrt(10), 50)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "plt.grid(True)\n", - "plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f\"k={kk}\")\n", - "inv_dct = {xx: invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) for xx in x_v}\n", - "plt.legend()\n", - "plt.xlim(0, max(x_v))\n", - "plt.ylim(0, max(x_v))\n", - "plt.show()\n", - "plt.plot(inv_dct.keys(), inv_dct.values())\n", - "plt.title(f\"Relative error as a function of x for k={kk}\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7cf25100-2a35-4d07-bab7-cbc92563191f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "2d13ac33-bd7b-4507-b6e8-e77b51d4c328", - "metadata": {}, - "source": [ - "Same analysis, but much higher resolution" - ] - }, - { - "cell_type": "code", - "execution_count": 377, - "id": "621a8d45-7655-42e3-b8e7-71a6c44e19e6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "NUMPOINTS = 10000\n", - "kk = 5**4\n", - "x_v = np.linspace(0, m.sqrt(10), NUMPOINTS)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "plt.grid(True)\n", - "plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f\"k={kk}\")\n", - "inv_dct = {xx: invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) \n", - " for xx in x_v[int(0.15*NUMPOINTS):int(0.3*NUMPOINTS)] # <=== CHANGE RANGE HERE\n", - "}\n", - "plt.legend()\n", - "plt.xlim(0, max(x_v))\n", - "plt.ylim(0, max(x_v))\n", - "plt.show()\n", - "plt.plot(inv_dct.keys(), inv_dct.values())\n", - "plt.title(f\"Relative error as a function of x for k={kk} (highres)\")\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4066e383-dba2-4e49-b999-ef7322ada357", - "metadata": {}, - "source": [ - "### Numerical considerations\n", - "#### Comparing L1 with L2\n", - "\n", - "L1 and L2 are different expressions of the L term above. L2 is the naive formula, L1 is optimized. L2 can be zero for very small values (and it is not even continous; see 0.009 and 0.01 below) whilst L1 is *always* greater than zero." - ] - }, - { - "cell_type": "code", - "execution_count": 378, - "id": "0abe5692-f6da-4071-83db-c8bb995ff2be", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[(0, 1.0000000000000003e-28),\n", - " (0, 1.0000000000000001e-21),\n", - " (2.27373675443232e-13, 4.7829689999999975e-15),\n", - " (0, 1.0000000000000002e-14),\n", - " (2.27373675443232e-13, 1.7085937499999996e-13),\n", - " (1.25055521493778e-12, 1.279999999999999e-12),\n", - " (7.81199105404085e-10, 7.812499999988701e-10)]" - ] - }, - "execution_count": 378, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xs_v = [0.0001, 0.001, 0.009, 0.01, 0.015, 0.02, 0.05]\n", - "[(L1(xx,1), L2(xx, 1)) for xx in xs_v]" - ] - }, - { - "cell_type": "code", - "execution_count": 379, - "id": "a5b8067c-ca96-4586-bab2-d3fa5dc421db", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 379, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x_v, [L2(xx, 1) - L1(xx, 1) for xx in x_v])" - ] - }, - { - "cell_type": "code", - "execution_count": 380, - "id": "63c25d7d-81aa-4589-ae3e-a370ebc9a3a4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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FloatTaylor2Taylor4
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0.0050510.0025220.0025220.002522
0.0101010.0050380.0050380.005038
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" - ], - "text/plain": [ - " Float Taylor2 Taylor4\n", - "x \n", - "0.005051 0.002522 0.002522 0.002522\n", - "0.010101 0.005038 0.005038 0.005038\n", - "0.020202 0.010051 0.010050 0.010051\n", - "0.030303 0.015038 0.015037 0.015038\n", - "0.040404 0.020002 0.019998 0.020002" - ] - }, - "execution_count": 381, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x1_v = np.linspace(0,1,100)\n", - "x1_v[0] = x1_v[1]/2\n", - "data = [(\n", - " xx, \n", - " m.sqrt(1+xx)-1,\n", - " xx * (0.5 - xx*1/8),\n", - " #xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128,\n", - " xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))),\n", - ") for xx in x1_v\n", - "]\n", - "df = pd.DataFrame(data, columns=['x', 'Float', 'Taylor2', 'Taylor4']).set_index(\"x\")\n", - "df.plot()\n", - "plt.grid()\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 382, - "id": "9f7fc799-1a9e-4eb9-a504-41200fb1d87d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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xSElJybdu2LBhSEpKwrp16zB69Oin7vP999/HggULEB0djfPnz2Py5Mk4c+YMxo8f/9TrHTlyBG+88QaCg4PRr18/pKSkICUlBXfv3i3WjEREtubWg3voHj0C2fJEOAgi1txORQen2sDoQ3ZRGACWBps0Y8YMtGjRAr6+vujUqRNcXV3Rp0+ffOucnZ3Rr18/lC9fvsDv/1twcDAmTJiACRMmoGnTpjh48CD27t2b55UTBYmMjEROTg7mzZsHNzc301ffvn2LMSERkW25cjcVPXf4IVdxCeWNAtal3EarSg2BUd8Azs9+tZ6t4MMTNsDf3z/PO0BWqVIFu3fvNuu6ycnJGDZsWL6XR86aNQuzZ882XZbL5Zg5cyZmzpxZ4H7q1q0LsYD3mIiMjERkZKRZWYiI7NFfqUkYsm80DMpbqGQU8HnKbXi6eAPDtgEOFaWOZ1EsDXbq7t27iI2NxXfffYeVK1dKHYeIyC4l3LoC/28CIChTUc1gxBcpt1G/dgdg0FfFfk8ea8TSYKdatGiBe/fuYcGCBWjYsKHUcYiI7M6J6xcwNu4tiMq7cNMbEJGSCvcGXYH+6wFlwW9+Z+tYGuzU1atXpY5ARGS3vvv7HMYffQdQPkBtvR5fJKfCrclAoNdKQGG/v1r5REgiIqJC2J/4K8YffRtQPEADnQ5fJt+GW4vRQO/Vdl0YAJYGIiIis20/9yOmnAgCFFnw1OqwITkV1dqGAt0XWdWbAJYU+65EREREFhJ56lss/m0SZAotvHK1WJ2SigqvzwJeDZU6WqlhaSAiInqG1T/FYHXidMjkerR+mIsVt+/AsdsioNVbUkcrVSwNRERET7H42E5EXvoIMrkRHXIeYvGduyjXZy3w0mCpo5U6lgYiIqInmPP9V9h2bRFkcgFdsnMwPz0D6gFfAp49pY4mCft/1gYVaPbs2fDy8pI6BhGR1Zp8KALbry2ATCagZ2Y2FtzNhnrI1jJbGACWBqslk8me+vXvt5W2FpcuXUKFChVQqVIlqaMQERVL8IGVOJASDsiAARmZ+ChTD5XfLqDB61JHkxQfnrBSycnJpj9HR0dj5syZOH/+vGlbuXLlpIiVh16vh0qlMv15yJAhaN++PeLj4yVORkRUdAG7F+Lkg00AAL8HGZj4UA75yH1ADS9pg1mBMnmmQRRF5OhzzPp6aHho9lpzvgr60KeCuLq6mr4qVqwImUxmuqxSqRAYGIhatWrB0dERTZs2RVRUlOm6GzduRNWqVaHVavPss1+/fhg5cmSBtycIAubMmYNatWpBo9HAy8sLBw8eNH3/6tWrkMlk2LZtGzp16gQHBwd89dVXpu9Pnz4djRo1wsCBAwtzVxARWQ1BEDBsx2xTYXj73gNM1JeDfNQ3LAz/KJNnGh4aHqL1ltaS3PbJoSfhqHIs1j5yc3Ph7e2NSZMmwdnZGQcOHICfnx/q1auH1q1bY8CAAQgODsbevXsxYMAAAEBaWhr279+PmJiYAve5fPlyLFmyBJ999hmaN2+O9evXo1evXvjjjz/yfDz2pEmTsGTJEmzYsMH0yZnfffcdtm/fjjNnzuDrr78u1mxERFIQBAH9t03FRe0BAMD4u/cRIKsMjNoDVPGQOJ31KJNnGmxdzZo1MXHiRHh5eaFevXp477334Ovri+3btwN49NDF0KFDsWHDBtN1Nm/ejFq1aqFTp04F7nPx4sWYNGkSBg8ejIYNG2LBggXw8vJCeHh4nnUhISHo27cvPDw8UKNGDaSnp8Pf3x+RkZFwdnYuqZGJiEqM3mhEr60TTIVhcvpdBKhcgdEHWRj+o0yeaSinLIeTQ08+c50gCMjMzESFChUgt9Dbg5ZTFv+5CEajEfPnz0d0dDSSkpKg1Wqh1Wrh5PT/H8P61ltv4eWXX0ZSUhJq1qyJDRs2wN/fHzKZLN/+MjIycOvWLbRr1y7P9nbt2uHs2bN5trVs2TLP5bfeegtDhw5Fhw4dij0XEVFp0+r16LF1PFKEY5CJIj5Mu4u+5esDfrsAp2pSx7M6ZbI0yGQysx4iEAQBBqUBjipHi5UGS1iyZAmWLVuG8PBwNG3aFE5OTggJCYFOpzOtad68OV566SVs3LgRvr6+OHfuHPbt2/fU/f63UIiimG/bv4sJ8Oihib1792Lx4sWm6wiCAKVSic8//xyjR48uzqhERCUmW6dFj6ggpOFnKEQRH99JR48qTYGh24BylaSOZ5XKZGmwdceOHUPv3r0xfPhwAI/KzcWLF+Hp6ZlnXUBAAJYtW4akpCR07twZ7u7uEAQh3/6cnZ1Ro0YNHD9+PM8Zg/j4eLRq1eqpWU6cOAGj0Wi6vGfPHixYsADx8fGoWbNmccYkIioxD3Jz8MbWsXggOwOlKGJxahped2kFDIkC1E7P3kEZxdJggxo0aICdO3ciPj4elStXxtKlS5GSkpKvNAwbNgwTJ07EunXrsHHjxqfu8/3338esWbNQv359eHl5YcOGDThz5gw2b9781Ov99zZ//fVXyOVyNGnSpGjDERGVsPTsTPTYFoAs+Z/QCCKWpd5B+9r/A/pvAFQOUsezaiwNNmjGjBm4cuUKfH194ejoiLfffht9+vTBgwcP8qxzdnZGv379cODAAfTp0+ep+wwODkZGRgYmTJiA1NRUNG7cGHv37s3zygkiIluXnHkfvbePxkPFRZQTBKy8fQetGvQE3lwLKFRSx7N6LA02wN/fP887QFapUgW7d+8267rJyckYNmyY6eWRj82aNQuzZ882XZbL5Zg5cyZmzpxZ4H7q1q1r1ntM/DcrEZG1uH4vDW/uGgWd4irKCwLWpKTC68UhQI9lgFwhdTybwNJgp+7evYvY2Fh89913WLlypdRxiIgkdSk9BUMPvA2D8iYqGo34LCUVL3qPBXw+Bgp4VRkVjKXBTrVo0QL37t3DggUL0LBhQ6njEBFJ5mZuJmYdGAWj8jaqGoxYl5KK59tOADpNZmEoJJYGO3X16lWpIxARSe70rcv4LPMLiKp0uBgM+CI5FXX/9yHQ9j2po9kklgYiIrJLP15NxDvfvg1RdR819QasS0mFe9dFQEu+f0xRlanSYO6HRVHJ4v1ARCXt20vnEHp0LERlJurq9Pj8dhrceq4GXhokdTSbZj1vc1iCFIpHz4r99zsmknRycnIAwPSx2kRElnTgr18R8sNbEJWZeF6nQ0RKOqr3/oKFwQLKxJkGpVIJR0dH3LlzByqVyuy3hBYEATqdDrm5uVb1NtLFIeVMoigiJycHqampqFSpkqnMERFZyvZzP2LOL6GA4iEaa7VYm5aBxLrBeLnRG1JHswtlojTIZDK4ubnhypUruHbtmtnXE0URDx8+RLly5Qr8oCdbZA0zVapUCa6urpLcNhHZr42nv8eisxMBhQ4v5Wqx+m4OHAdtw53f70kdzW6UidIAAGq1Gs8//3yhHqLQ6/X44Ycf0KFDB7s5lS71TCqVimcYiMjiPvv5IFb+MRWQ69HqYS5WZOjhNGIP9M81A36PkTqe3SgzpQF49K6HDg7mv6+4QqGAwWCAg4OD3ZQGe5yJiMq25fF78MX5WYDciHY5D7EsS4ZyI2MAl8aAXi91PLtSpkoDERHZl/k/RGPz5bmAXMDr2TlYkKuBZvQ+oGp9qaPZJZYGIiKySTO/3YhdNxYDMhHdsrLxsdEZ6tF7gUq1pY5mt1gaiIjI5kw8+BkOpawEZECfzCzMkj0H5ag9QAU+yboksTQQEZFNeXf/ChxNXwfIgEEZmZiidodi+G7AqarU0eweSwMREdmMgD0LcfL+JgDAyAcZCHNqCPmw7YBDRYmTlQ0sDUREZPUEQcCIrz/G2eztAICx9x4gqLIX5EOiALWTxOnKDpYGIiKyaoIgYND26fgrdx8AYPzd+whwaQcMiARU5r+MnoqPpYGIiKyWwWhE320f4IouFgAwKf0ehtf2Ad78DFDwvWZKW5E+fGD16tXw8PCAg4MDvL29cezYsaeuP3r0KLy9veHg4IB69eph7dq1+daEh4ejYcOGKFeuHNzd3REaGorc3NyixCMiIjugMxjQIyoEV3SxkIkiZqalY3j93kDfdSwMEil0aYiOjkZISAimTZuGhIQEtG/fHt26dcP169cLXH/lyhV0794d7du3R0JCAqZOnYrg4GDs3LnTtGbz5s2YPHkyZs2ahcTERERERCA6OhpTpkwp+mRERGSzHup16BY1DknGI5CLIj5Ou4sBjf2Anp8Ccr4VvVQK/fDE0qVLMWbMGAQEBAB4dIbg0KFDWLNmDebNm5dv/dq1a1G7dm2Eh4cDADw9PfHrr79i8eLF6NevHwDgxIkTaNeuHYYOHQoAqFu3LoYMGYKff/65qHMREZGNytLm4o2tgbiLU1CKIuanpsG3xTvA6zMBO/nwQFtVqNKg0+lw6tQpTJ48Oc92Hx8fxMfHF3idEydOwMfHJ882X19fREREQK/XQ6VS4dVXX8VXX32Fn3/+Ga1atcLly5cRExODkSNHPjGLVquFVqs1Xc7IyADw6AOZ9BZ6r/HH+7HU/qwBZ7J+9jYPwJlshTXM9CA3B713BCFD/htUooglqWno0CoM+nahgMFQ6P1Zw0yWVhIzmbuvQpWGtLQ0GI1GuLi45Nnu4uKClJSUAq+TkpJS4HqDwYC0tDS4ublh8ODBuHPnDl599VWIogiDwYB33nknXzn5t3nz5mH27Nn5tsfGxsLR0bEwYz1TXFycRfdnDTiT9bO3eQDOZCukminLoMOytM3QOvwNjSBgeWoanKv0w/4HDYGY4n1SJe+np8vJyTFrXZFePSH7z+khURTzbXvW+n9vP3LkCD755BOsXr0arVu3xqVLlzB+/Hi4ublhxowZBe5zypQpCAsLM13OyMiAu7s7fHx84OzsXJSx8tHr9YiLi0OXLl3s5hMhOZP1s7d5AM5kK6Sc6XbmA/TdHQCtw98oJwhYeTsNLf43D2JzPzQqxn55P5nn8dn6ZylUaahWrRoUCkW+swqpqan5ziY85urqWuB6pVKJqlUfveXnjBkz4OfnZ3qeRNOmTZGdnY23334b06ZNg1ye//maGo0GGo0m33aVSmXxH4yS2KfUOJP1s7d5AM5kK0p7phv309F3lz9ylddQXhCw+nY6mndfCTQbYLHb4P307H2Zo1CvnlCr1fD29s53SiQuLg5t27Yt8Dpt2rTJtz42NhYtW7Y0hczJyclXDBQKBURRNJ2VICIi+/N3+m302TkcucprqGg0Yt3tu2je+wuLFgaynEK/5DIsLAxffPEF1q9fj8TERISGhuL69esIDAwE8OhhgxEjRpjWBwYG4tq1awgLC0NiYiLWr1+PiIgITJw40bSmZ8+eWLNmDbZu3YorV64gLi4OM2bMQK9evaBQ8KU1RET26K/UJAzYPRw65U1UMRrxRep9NOm/CfDsIXU0eoJCP6dh0KBBSE9Px5w5c5CcnIwmTZogJiYGderUAQAkJyfnec8GDw8PxMTEIDQ0FKtWrUKNGjWwYsUK08stAWD69OmQyWSYPn06kpKSUL16dfTs2ROffPKJBUYkIiJr81vyNYyMGQWD8g6eMxjweVom6g/eBtR9Vepo9BRFeiJkUFAQgoKCCvxeZGRkvm0dO3bE6dOnnxxCqcSsWbMwa9asosQhIiIb8uvNv/HWoVEwKO/BzWDA5+kPUXfYbqBWS6mj0TPwsyeIiKjU/Hg1EeO+DYBRmQF3vR6f3dPDfcQ+wLWp1NHIDCwNRERUKr7/+xxCj74NozILHjo91maIqOEfA1RvKHU0MlORPrCKiIioMGLOn0LI0QAYFVl4XqfDF1kq1Bh1kIXBxrA0EBFRifr6j58w5cdACIoceGp1WPfQEc+N/gao4iF1NCokPjxBREQlJursD5h/OhSCQoeXcrX41FAZlUftBSoU/IaAZN1YGoiIqERsOHUYy357H6LcAO+HuVgBVziP2gM4VpE6GhURSwMREVncmpMxWPPnVIhyI9o8fIhlyrpwGr4TcKgodTQqBpYGIiKyqPAfd2P9hVkQ5QI65DzE4nINUW5INKApL3U0KiaWBiIispj5P0Rjy+VPIMpFdM7OwfwKXtAM/gpQlZM6GlkASwMREVnEh99tws7riwCZiG5Z2fi4SluoB6wHlGqpo5GFsDQQEVGxTY79AgduLQdkQJ/MLMx0fR2qN9cCCv6asSe8N4mIqFhCv1mNw6lrABkwMCMTU2v3gqJnOCDnWwHZG5YGIiIqsqB9y3Ds7noAwPAHGXi/wWDIu80HZDKJk1FJYGkgIqIiGb1rPn7J2AwAGHP/AYIbj4G880wWBjvG0kBERIUiCAJGfP0xzmZvBwAE3buPwObvQdbxA4mTUUljaSAiIrMJgoDBO2Yg8eFeAEDI3XsY88pUoE2QxMmoNLA0EBGRWYxGAf23fYBLukMAgEnp9zC8/Wzg5TESJ6PSwtJARETPZDAa0WdrKK4ZvgcATEu7h8GvLwK8hkicjEoTSwMRET2VVq9H7+j3kGT8ETJRxIfp99G366fAi29KHY1KGUsDERE90UO9Dj2j3sFt8WcoRBEfpd1Hz57rgIbdpI5GEmBpICKiAmVpc9Ez6m2kyRKgFEXMT8uAb58vgQavSx2NJMLSQERE+TzIzUHPqNG4J/8DKlHEorRMvN4/CqjbTupoJCGWBiIiyuNeTjb6fR2A+/Lz0AgCltzNQcfBO4FaLaWORhJjaSAiIpMMgxZ9dvgjU/k3ygkCwu/mou3Q3YDbS1JHIyvA0kBERACAlMx7WJG2HrkOSXASBHx6T4eX/fYDz3lKHY2sBEsDERHhxv00DNg1ArkOSahgFLDygYAWI74BqjWQOhpZEZYGIqIy7u/02xi6ezhylCmoZDRiZYYcL/l/A1SuK3U0sjL8sHMiojLsrztJGLJ7MHKUKahiNCL8joDGfgdYGKhALA1ERGXU7yk34Ld3KB4q01DdYMBn2eVwp/4UwLmG1NHISrE0EBGVQaeTLsP/wBDkKu/CxWDAZ9pKqD/iALSqSlJHIyvG5zQQEZUxP9+4hKDYEdAqM1FTb8Bq43OoN2oP9EonqaORleOZBiKiMuT41US8EzscWmUmauv1+EysiXr++4FylaSORjaApYGIqIz4/u9zGP/tSOiU2fDQ6fG5oh7qjNwLaCpIHY1sBEsDEVEZcOhCAiYeHQWd8iEa6HRYq2mMmsO/BtSOUkcjG8LSQERk5/Yl/oIpxwOgU2jhqdVhraMXagzdCqgcpI5GNoalgYjIju34/UfMOjEWeoUOTXO1WF3xFbgM3gwo1VJHIxvEV08QEdmpqLM/YOHpYBgURnjlahFe/TVU7bMakCukjkY2imcaiIjsUOTpbx8VBrkRLR/mYqVrV1Tts4aFgYqFpYGIyM6s++Ugws+GwSA34pWHD7G8dl9U7LUckPOffCoePjxBRGRHVp3Yh3V/TYNRLuLVnIdY3GA4nDrPAmQyqaORHWBpICKyE0uPf40vL30IQS6iU3YOFni+BcfXJksdi+wISwMRkR2YfzQaUVc+hiADumTnYG6zYDi8Ol7qWGRnWBqIiGzc7O82Yuf1RRBlQPesbMzxngTNK2OljkV2iKWBiMiGTY+LwN6kcIgyoFdmNj58ZSZULf2ljkV2iqWBiMhGTTq4Ft+krIIoA/plZGN6+3lQeg2SOhbZMZYGIiIbFHpgBQ6nrQNkwOAHWZjyv6WQN3lT6lhk51gaiIhszLt7FuHo/Y0AAL8HWZjYZSXknm9InIrKApYGIiIbMnbXJ4jP2AoAGHU/GyHd10H+fGeJU1FZwdJARGQDRFHEmK9n45esnQCAt+9nY1zPDZDX6yhxMipLWBqIiKycIAjw3zEdCQ/3AQDG3c9B4JtbgNqvSJyMyhqWBiIiKyYIAoZt+wC/aw8BAMbfz0FA/21ATW+Jk1FZxNJARGSlBEHAoK0h+Ev/PQBgwv1c+A/cBbg1kzgZlVUsDUREVshgNGLg1ndx0XAcADDpnhbDh+4FnvOUOBmVZSwNRERWxmA0ot+WQFwWfoJMFDH1gQGDhx8Aqj0vdTQq41gaiIisiM5gQL8tAbgqnoJcFDHjgYD+ft8AVTykjkbE0kBEZC1y9Tr03TIaN3AWClHEhxky9Bl5EKhUW+poRABYGoiIrEKOXou+m0ciSfYHlKKIOZkK9PT/BnCuIXU0IhOWBiIiiWVpc9F3y3Aky89DKYr4JFOF7qMOAeWfkzoaUR4sDUREEnqQm4P+W4YhRXEJakHE3GwH+I4+BDhVlToaUT4sDUREErn/MBv9tgxGqvIqNIKABQ/L4/XR3wCOVaSORlQglgYiIgnczclE/6hBuKO8gXKCgIXaSug0OgZwqCh1NKInYmkgIiplqVkZGBQ9AGnKW3AUBCzSVUOHUfsBTQWpoxE9lVzqAEREZUlK5j0MjO6HNOUtlBcELDO4ocOob1gYyCbwTAMRUSm5+eAuhm3vj7uqO6hgFLBUrIVX/PcAKgepoxGZhWcaiIhKwfX7dzB0e1/cVd1BRaMRy2X18MrIvSwMZFNYGoiIStjlu7cxbEc/3FOlo7LRiOWKRnh5xNeAUiN1NKJC4cMTREQl6MKdZIze0x8PVBmoajAiXNMEXkO2AAr+80u2h2caiIhKSGLqTYze0w8PVBmobjDg03It4DU0ioWBbBZLAxFRCTiXcg0B+/rjgSoTLgYDPi3fBk0HbwTkCqmjERUZSwMRkYUl3LqCsQcGIEOZDTeDASsrdsSLAyMAOf/JJdvGn2AiIgv69eYljPtmIDKVD1FTb8DKKp3RqN8aQCaTOhpRsbE0EBFZyE/Xz+O9Q4ORqcyFu16PlS498EKfFSwMZDdYGoiILODHq4kIjRuKLKUWdXV6rKzRDw16LmJhILvC0kBEVExH/j6HCd8OQ5ZSh3o6PT6tPRj1un8idSwii+PrfoiIiuG7S79h5okA5CgNaKDTYXk9f9R+fYrUsYhKBEsDEVER/ZaZjP0nZiFHYcQLWh2WvzAWtTqFSR2LqMSwNBARFUHM+V+xT7cGDxUCGml1WOH5Ltzavyd1LKISVaTnNKxevRoeHh5wcHCAt7c3jh079tT1R48ehbe3NxwcHFCvXj2sXbs235r79+9j3LhxcHNzg4ODAzw9PRETE1OUeEREJWrPH/H45OdAPFQIeFGrxadNJrAwUJlQ6NIQHR2NkJAQTJs2DQkJCWjfvj26deuG69evF7j+ypUr6N69O9q3b4+EhARMnToVwcHB2Llzp2mNTqdDly5dcPXqVezYsQPnz5/HunXrULNmzaJPRkRUAnb89gM+PvmoMDTL1SK86Qdwbfu21LGISkWhH55YunQpxowZg4CAAABAeHg4Dh06hDVr1mDevHn51q9duxa1a9dGeHg4AMDT0xO//vorFi9ejH79+gEA1q9fj7t37yI+Ph4qlQoAUKdOnaLORERUIqLPfo/Fp8cjVyHCK1eLkao+qNpypNSxiEpNoUqDTqfDqVOnMHny5DzbfXx8EB8fX+B1Tpw4AR8fnzzbfH19ERERAb1eD5VKhb1796JNmzYYN24c9uzZg+rVq2Po0KGYNGkSFIqC36ddq9VCq9WaLmdkZAAA9Ho99Hp9YcZ6osf7sdT+rAFnsn72Ng9gHzNFnfkWK37/AFq5CO+HWsxrPgM/pzjb9Ez/ZQ/3039xpsLt81kKVRrS0tJgNBrh4uKSZ7uLiwtSUlIKvE5KSkqB6w0GA9LS0uDm5obLly/ju+++w7BhwxATE4OLFy9i3LhxMBgMmDlzZoH7nTdvHmbPnp1ve2xsLBwdHQsz1jPFxcVZdH/WgDNZP3ubB7DdmX68fxHfCl9CJwdefqjFMIeB+DnFGYDtzvQ0nMk2WHKmnJwcs9YV6dUTsv+8w5koivm2PWv9v7cLgoDnnnsOn3/+ORQKBby9vXHr1i0sWrToiaVhypQpCAv7/5c2ZWRkwN3dHT4+PnB2di7KWPno9XrExcWhS5cupodNbB1nsn72Ng9g2zNF/hKDw3e/hF4OtH6oxcK2C1HB8w2bnulJOJNtKImZHp+tf5ZClYZq1apBoVDkO6uQmpqa72zCY66urgWuVyqVqFq1KgDAzc0NKpUqz0MRnp6eSElJgU6ng1qtzrdfjUYDjUaTb7tKpbL4D0ZJ7FNqnMn62ds8gO3N9NmJ3fjs/Azo5UDbHC0Wv/YpKjTyzbPG1mYyB2eyDZacydz9FOrVE2q1Gt7e3vlOicTFxaFt27YFXqdNmzb51sfGxqJly5amkO3atcOlS5cgCIJpzYULF+Dm5lZgYSAiKmlrftyBtX89Kgyv5mixpPOafIWBqKwp9Esuw8LC8MUXX2D9+vVITExEaGgorl+/jsDAQACPHjYYMWKEaX1gYCCuXbuGsLAwJCYmYv369YiIiMDEiRNNa9555x2kp6dj/PjxuHDhAg4cOIC5c+di3LhxFhiRiKhwVvwQhc8vzoZBDnTM1mKJzxco//zrUsciklyhn9MwaNAgpKenY86cOUhOTkaTJk0QExNjeolkcnJynvds8PDwQExMDEJDQ7Fq1SrUqFEDK1asML3cEgDc3d0RGxuL0NBQNGvWDDVr1sT48eMxadIkC4xIRGS+pUc2YePVhTDKgNeytVjQPRLl6hZ8JpWorCnSEyGDgoIQFBRU4PciIyPzbevYsSNOnz791H22adMGP/30U1HiEBFZxKLvNmDz9aUwyoDO2TrM7fEVytVuJXUsIqvBz54gIgIw//A6RN1cAUEG+Gbr8HHPKDi4t5A6FpFVYWkgojLvk9g1iL61GqIM6J6lx0d9d0Dt1lTqWERWh6WBiMq02d+swM7bn0OUydAjS4+P+n0NpWtjqWMRWSWWBiIqs2YeWIbddyIgymTolWXA7AF7oHyuodSxiKwWSwMRlUnT9y3CnrsbAZkMb2YZMWvQPiiqNZA6FpFVY2kgojJnyt752H9vMwCgX5YRM4ccgLyKh8SpiKwfSwMRlSkf7P4I3zzYBgAYkCVg+tBvIK9cR+JURLaBpYGIyowJX89CbObXAIDBWSKmDDsIeSV3iVMR2Q6WBiIqE0J3TsfhrD0AgGFZwAfDYyGvWEPiVES2haWBiOze+O1T8F3OfgCAX5YME/1iIXd2lTgVke1haSAiu/butvdx9OFBAIB/lgIT/A8DTtUkTkVkm1gaiMhuvbM1FMe1hwEAo7OVCB31LeBYReJURLaLpYGI7I4gCHgnOgTxuu8BAAHZaowfdRgoV1niZES2jaWBiOyKIAgI3PoeTuh/AACMzXbAu6PigHKVpA1GZAdYGojIbgiCgLejgnDS8CMA4J1sRwSNjgMcnCVORmQfWBqIyC4IgoAxW97Gr8aTAIBxOU4IHHMY0JSXOBmR/WBpICKbJwgCRm8eg1PCrwCA9x5WwNtj4gC1k8TJiOwLSwMR2TRBEDDqq1E4LZ4GAAQ/rIi3RscCakeJkxHZH5YGIrJZRqOAkV+NwFmchUwUMV5bBWPGHAJU5aSORmSXWBqIyCYZjEaM/MoPv+EcZKKIUF01jBp9EFA5SB2NyG6xNBCRzTEYjRixaSjOyf6ETBQRpnOB/+gYQKmROhqRXWNpICKbojca4bdxMP6Q/wW5KGKC3g0jRh8AlGqpoxHZPZYGIrIZOoMBfpsG4U/5BchFEe8bamL46P2AQiV1NKIygaWBiGyCzmCA38b++FPxNxSiiA+MtTF01F5AwX/GiEoLjzYisnq5eh38NvXHX4orUIgiJgkeGDJqNyBXSB2NqExhaSAiq5ar12H4xr44r7wGpShislgfg/y/ZmEgkoBc6gBERE+So9di+MY3TYVhiuwFDBq5i4WBSCI800BEVilHr4Xfxj64oLwJpShimswT/f2iATn/r0MkFZYGIrI62Vot/L7qhYvKW1CJIqYpXkS/YVEsDEQSY2kgIquSrdXCb1NPXFQlQy2ImKZqhr7DNgMymdTRiMo81nYishqZ2ocYvqmHqTDMVDdnYSCyIjzTQERWISP3IUZ81QN/q1KhEQTM0LyM3kM2sDAQWRGWBiKS3IPcHIz8qgf+Vt2BgyBgZrlX0HPQFywMRFaGD08QkaQe5OZgxFdv/H9hcGzHwkBkpXimgYgkc/9hNkZu7o7LqrsoJwiY5dQBbwxcI3UsInoClgYiksS9nCyM3NwdV9T3UE4Q8GGFTujef5XUsYjoKfjwBBGVukeFoZupMMx2fp2FgcgGsDQQUalKz87AiM1dcUV9H46CgDkVfdCt3wqpYxGRGVgaiKjUpGdnwH9LN1xVP4CTIGBO5W7o2neZ1LGIyEx8TgMRlYpMfS7e2t4TV9WZKC8ImFPlDXTpvVDqWERUCCwNRFTiUjPvY0v6UtxwyEF5QcBHVXuhc695UsciokLiwxNEVKJuZ97H2J09ccMhBxWMAj6u1oeFgchGsTQQUYlJybiLMVu74po6GxWMAj6q3hev9/xE6lhEVEQsDURUIm49SMeY6G64ps6Gs9GIsbrW6NB1ptSxiKgY+JwGIrK4Ww/SEbCtG26oH8LZaMRHLoOQafCSOhYRFRPPNBCRRd28n2YqDBWNRnxSYxja+0yVOhYRWQBLAxFZzM37aXh7+78KQy0/dOo6TepYRGQhfHiCiCzixr07eHtHd9xU56KS0Yi57v5o3+UDqWMRkQWxNBBRsd24m4q3d3bHTbUWlY1GfFJ7NNp3nih1LCKyMD48QUTFcv1uSp7CMLfOGBYGIjvFMw1EVGTX76Zg7M43cFOtQ2WjEfPrjEXb18dLHYuISghLAxEVydW0ZLyzqwduqnWoYjRinkcg2r4WLHUsIipBLA1EVGj/LQwLPN7BK6+9J3UsIiphfE4DERXK5bRbCNz16CGJqgYj5tcbx8JAVEbwTAMRme3ynSQE7e6JJLUeVQ1GLHj+PbTu8I7UsYiolLA0EJFZ/l0YqhmMmPfCeLRuP1bqWERUivjwBBE909+pNxC0u4fpDMO8F0LwCgsDUZnDMw1E9FR/p17HuD29kaQ2oJrBiAUNw9Dq1QCpYxGRBFgaiOiJLqZew7t7+uDWP4VhfqOJaNVutNSxiEgiLA1EVKCLt6/h3X2PCkN1gxELPN/Hy21HSR2LiCTE0kBE+TwqDL1xS2VEdYMRCxtPQss2I6WORUQSY2kgojzOJ1/Fewf6INlUGCajZZsRUsciIivA0kBEJueTryD4wJtIVhnxnMGIhS9OgfcrflLHIiIrwZdcEhEA4K/kv/HegTdxy1QYprIwEFEePNNARPgr+W8EH+hnOsOwuMk0NG89TOpYRGRlWBqIyrj/FoZFTWegeashUsciIivEhyeIyrDEpIsIPtAXySojXP4pDC1YGIjoCXimgaiMSky6iPHf9EeySoCLwYiFzWahxcuDpI5FRFaMpYGoDPpvYVjU7EM0f3mg1LGIyMqxNBCVMYlJFxH8TX+ksDAQUSGxNBCVIY/PMKSoBLgajFj40mw0bzlA6lhEZCNYGojKiH8/JPGoMMxB85b9pY5FRDaEr54gKgMSky6wMBBRsfFMA5Gde1QYBvx/YfD6CM29+0kdi4hsEM80ENkxFgYisiSeaSCyU/kLw8do7t1X6lhEZMN4poHIDrEwEFFJKFJpWL16NTw8PODg4ABvb28cO3bsqeuPHj0Kb29vODg4oF69eli7du0T127duhUymQx9+vQpSjSiMi9fYWj+CQsDEVlEoUtDdHQ0QkJCMG3aNCQkJKB9+/bo1q0brl+/XuD6K1euoHv37mjfvj0SEhIwdepUBAcHY+fOnfnWXrt2DRMnTkT79u0LPwkRFVwYWrwpdSwishOFLg1Lly7FmDFjEBAQAE9PT4SHh8Pd3R1r1qwpcP3atWtRu3ZthIeHw9PTEwEBARg9ejQWL16cZ53RaMSwYcMwe/Zs1KtXr2jTEJVhf91kYSCiklWoJ0LqdDqcOnUKkydPzrPdx8cH8fHxBV7nxIkT8PHxybPN19cXERER0Ov1UKlUAIA5c+agevXqGDNmzDMf7gAArVYLrVZrupyRkQEA0Ov10Ov1hRnriR7vx1L7swacyfoVZZ6/bl3EhLghpsIwt9kcNGnaw2r+TuztPgI4k63gTIXb57MUqjSkpaXBaDTCxcUlz3YXFxekpKQUeJ2UlJQC1xsMBqSlpcHNzQ0//vgjIiIicObMGbOzzJs3D7Nnz863PTY2Fo6OjmbvxxxxcXEW3Z814EzWz9x5bufcwY6sFUhWi3DRGzFMOQi3bqlw61ZMCScsPHu7jwDOZCs409Pl5OSYta5IL7mUyWR5LouimG/bs9Y/3p6ZmYnhw4dj3bp1qFatmtkZpkyZgrCwMNPljIwMuLu7w8fHB87Ozmbv52n0ej3i4uLQpUsX0xkRW8eZrF9h5vnr1kWsjpuJZLX46AzDSx/By6tXKSU1n73dRwBnshWcyTyPz9Y/S6FKQ7Vq1aBQKPKdVUhNTc13NuExV1fXAtcrlUpUrVoVf/zxB65evYqePXuavi8IwqNwSiXOnz+P+vXr59uvRqOBRqPJt12lUln8B6Mk9ik1zmT9njXPXzcvYGLcENz659MqFzafi+Yt+pRewCKwt/sI4Ey2gjM9e1/mKNQTIdVqNby9vfOdEomLi0Pbtm0LvE6bNm3yrY+NjUXLli2hUqnQqFEjnDt3DmfOnDF99erVC6+99hrOnDkDd3f3wkQkKhP+unkB4w8OMBWGRTZQGIjI9hX64YmwsDD4+fmhZcuWaNOmDT7//HNcv34dgYGBAB49bJCUlISNGzcCAAIDA7Fy5UqEhYXhrbfewokTJxAREYGoqCgAgIODA5o0aZLnNipVqgQA+bYTEXA+iYWBiKRR6NIwaNAgpKenY86cOUhOTkaTJk0QExODOnXqAACSk5PzvGeDh4cHYmJiEBoailWrVqFGjRpYsWIF+vXj+98TFdb5pAsI/qY/bqlEuBiMWOD1CQsDEZWaIj0RMigoCEFBQQV+LzIyMt+2jh074vTp02bvv6B9EJV1/y4Mz/1TGLy9+T4MRFR6+NkTRDbg/K2LeQrDQhYGIpIASwORlTt/6yKCY/qZCsN8r49ZGIhIEiwNRFbs/K2LGH/g0RmG6v8Uhpf54VNEJBGWBiIrdSn5b4w/0B9JagHVDUYsYGEgIokV6YmQRFSy0h6mY82hQUhSC6jGMwxEZCVYGoiszN/Jf2N7ZjiS1CKq/XOGoRULAxFZAT48QWRF/k6+jNBDg0yFYX6z2SwMRGQ1WBqIrMTfyZfx3v43cfOfhyTmNpmF1i8PkDoWEZEJSwORFbiccgXv7X8TN/4pDEPl/dGyBc8wEJF14XMaiCR2OeUK3tvXBzfUAqoajPi48QykpTpKHYuIKB+eaSCS0NXbV/Hevj64/k9hmNt0Flq17C91LCKiArE0EEnkauo1vLv3UWGoYhAwr+lMtG01SOpYRERPxNJAJIHrd67j3T29cU1tRGWjgHlNZ6BNq8FSxyIieiqWBqJSdv3OdYzb3ev/C8OL09CWhYGIbABLA1EpunHnBsbt7oWr/xSGTxpPQ7vWQ6WORURkFpYGolJyMy0J43b3NBWGjz2noP0rLAxEZDtYGohKwc20JATtegNX1EZUMgr4qNEkdGgzXOpYRESFwtJAVMJu3b2Fcbt64IraiIpGAR83moSObUdIHYuIqNBYGohK0K17yXhn5xu4rDagolHARy+8z8JARDaLpYGohKTcS8E727vjstoA538Kw2uv+ksdi4ioyFgaiErAo8LQDZc1jwrDnOcnsDAQkc1jaSCysNv3b+Od7d1x6Z/CMLtBKF5vP1rqWERExcbSQGRBt++n4p1t3XBJo0cFo4BZ9UPRuUOA1LGIiCyCpYHIQu48uIOgbV1x0VQYguHTkYWBiOwHSwORBaRnpOGd6K64oNGjvFHAzHrvwrfjWKljERFZFEsDUTGlZ6YjcKsvzmt0cBIEzPB4F107vSN1LCIii2NpICqGe5n3EBjlg7/+KQwz6wSh+2ssDERkn1gaiIroftZ9jI3qYioM02uPRff/jZM6FhFRiWFpICqC+1n3EbilCxI1WjgKAqa5v40erwdLHYuIqESxNBAV0oPsBwjc0gV/aHJRThAwtdYY9Ow8XupYREQljqWBqBAe5GQgcPP/F4YpNcegd5cwqWMREZUKlgYiM2U+zMQ7X3XG75qHcBAETKrhjzd9WBiIqOxgaSAyQ+bDTARu6oxzmofQCAI+cBuBfr7vSx2LiKhUsTQQPUP2wxy8s6kLftPkQCOI+MB1GAZ0nSR1LCKiUsfSQPQUOdocBG56HWc12VALIia6DMbAblOljkVEJAmWBqInyNHmIPDLzjijyYJaEDGh+kAM7j5d6lhERJJhaSAqQK4uF0FfdkGCJhMqUURYtf4Y2mOm1LGIiCTF0kD0H7m6XLwT2RmnNBlQiSJCq/TFsJ4fSh2LiEhyLA1E/5Kry0VQZBf8qnkApSgiuHJv+PWaI3UsIiKrwNJA9A+dXo9xkT74RXMfSlHEe5V6wL/3J1LHIiKyGiwNRHhUGIIiO+NnzT0oRRHjnLtjdJ/5UsciIrIqLA1U5hkMBoyL9MFJ9V0oRBHjKvgioO9CqWMREVkdlgYq0wwGA4I2dMFP6jQoRBFB5bsgoN8SqWMREVkllgYqswwGA97b4IsT6jTIRRFjnV7H2/2XSR2LiMhqsTRQmSQYjQiO7Ibj6lTIRRFvO3bCOwOWSx2LiMiqsTRQmSMYjQje0B3HVCmQiSLeKtcB4waulDoWEZHVY2mgMkUwGjF+wxs4qroFmShitKYt3h20WupYREQ2gaWBygxBEBAa2RNHVEkAgFHqVxAy5HOJUxER2Q6WBioTBEFAWGQvfKe8AQDwV7ZE6NAvJE5FRGRbWBrI7gmCgPcj38S3imsAAD9FC0wYtkHiVEREtoelgeyaIAiY9GU/xCouAwCGy1/CB8O/lDgVEZFtYmkguzZl40AclF8CAAyRNcEkv68kTkREZLtYGshuTflyIGJk5wEAg+GJqSOiJE5ERGTbWBrILs3YOBT7kQgAGCC+gGkjt0mciIjI9rE0kN2ZuWk4dovnAAD9hAaYOXKHxImIiOwDSwPZldlfjcQu4SwA4E2jBz70/xqQySRORURkH1gayG7M3zYWO4ynAQC9DLUxZ9QeFgYiIgtSSh2AyBKOJ32Fg05/AQB6GGrhI/+9LAxERBbGMw1k85bsCDYVhu76GvjEfz/kCoXEqYiI7A9LA9m0RdHvYrPuOADAV+eCeaNiWBiIiEoISwPZrCXbQrDp4REAQMesivjEbx8LAxFRCeJzGsgmLd85EV/mHIYok+F/2qro5BYMuYI/zkREJYlnGsjmfPr1JKzPPAhRJsNr2ipYOOIbnmEgIioF/K8Z2ZQ1u6fhi4wDEGQydNRWwtJRcRDBV0kQEZUGnmkgm/HZnpn47P4eCDIZXtU6I3zUt1Cq1FLHIiIqM1gayCZ8sW8O1tz7GkaZDO1yy2O5PwsDEVFpY2kgqxcZMw+r0rfBKJOhTa4TVvh/B7XaQepYRERlDksDWbVNBxdjRepmGGQytMp1xIqR30KtKSd1LCKiMomlgazWlthwhCdHQi+T4eVcB3w68ls4ODhJHYuIqMxiaSCrtO3bVVia9AV0chla5Grw6fBv4ehQXupYRERlGksDWZ2d33+GRdfXQCuXwStXjZXD4+Dk5Cx1LCKiMo+lgazK7h8isPDqCuTKZXgpV4WVQ2NRwamy1LGIiAgsDWRF9h3/EvP+XoocuRxNcpVYOTQWFStUlToWERH9g6WBrEJM/GbMvbAQOXI5XtQqsHrQQVSqUE3qWERE9C8sDSS5Qye34+O/5iJLIYenVo5P+x9A5UouUsciIqL/YGkgSX37y9f46I8PkamQo6FWjk/7HUD1KjWljkVERAVgaSDJHD29D7POzcADhRzPa2VY+eZeuFStJXUsIiJ6giKVhtWrV8PDwwMODg7w9vbGsWPHnrr+6NGj8Pb2hoODA+rVq4e1a9fm+f66devQvn17VK5cGZUrV0bnzp3x888/FyUa2YjjZw9iRsJkPFDIUV8LfNp7N1yr15E6FhERPUWhS0N0dDRCQkIwbdo0JCQkoH379ujWrRuuX79e4PorV66ge/fuaN++PRISEjB16lQEBwdj586dpjVHjhzBkCFD8P333+PEiROoXbs2fHx8kJSUVPTJyGr99PthTP91Au4p5fDQAZ/23ImaLvWkjkVERM9Q6NKwdOlSjBkzBgEBAfD09ER4eDjc3d2xZs2aAtevXbsWtWvXRnh4ODw9PREQEIDRo0dj8eLFpjWbN29GUFAQvLy80KhRI6xbtw6CIODbb78t+mRklX798wdMOTke6Uo56upELO8WDXe3F6SORUREZlAWZrFOp8OpU6cwefLkPNt9fHwQHx9f4HVOnDgBHx+fPNt8fX0REREBvV4PlUqV7zo5OTnQ6/WoUqXKE7NotVpotVrT5YyMDACAXq+HXq83e6anebwfS+3PGkg505kL8Zj80zikKeWorROx+PWNqOXyfLGz2Nv9ZG/zAJzJVnAm21ASM5m7r0KVhrS0NBiNRri45H05nIuLC1JSUgq8TkpKSoHrDQYD0tLS4Obmlu86kydPRs2aNdG5c+cnZpk3bx5mz56db3tsbCwcHR3NGcdscXFxFt2fNSjtmVIzr2Fb7udIVclQUydggGMQ/jp3A3+du2Gx27C3+8ne5gE4k63gTLbBkjPl5OSYta5QpeExmUyW57Ioivm2PWt9QdsBYOHChYiKisKRI0fg4ODwxH1OmTIFYWFhpssZGRlwd3eHj48PnJ0t8zkFer0ecXFx6NKlS4FnRGyRFDP9efU0Vh2dhhSVDDX0AhZ3/AINPVpabP/2dj/Z2zwAZ7IVnMk2lMRMj8/WP0uhSkO1atWgUCjynVVITU3NdzbhMVdX1wLXK5VKVK2a9y2CFy9ejLlz5+Lw4cNo1qzZU7NoNBpoNJp821UqlcV/MEpin1IrrZn+unoGk46OQbJKBle9gMUd1qHJC21K5Lbs7X6yt3kAzmQrOJNtsORM5u6nUE+EVKvV8Pb2zndKJC4uDm3bti3wOm3atMm3PjY2Fi1btswTctGiRfjoo49w8OBBtGxpuf+FknQu3fgDYXF+SFLJ8JxBwIJ2a9D0hYJ/ToiIyPoV+tUTYWFh+OKLL7B+/XokJiYiNDQU169fR2BgIIBHDxuMGDHCtD4wMBDXrl1DWFgYEhMTsX79ekRERGDixImmNQsXLsT06dOxfv161K1bFykpKUhJSUFWVpYFRiQpXLl1HiEHB+OGGqhmELDwlU/RwrOD1LGIiKgYCv2chkGDBiE9PR1z5sxBcnIymjRpgpiYGNSp8+iNeZKTk/O8Z4OHhwdiYmIQGhqKVatWoUaNGlixYgX69etnWrN69WrodDr0798/z23NmjULH374YRFHI6ncSLmE8Qf645oaqGoQMP/lpfB+8X9SxyIiomIq0hMhg4KCEBQUVOD3IiMj823r2LEjTp8+/cT9Xb16tSgxyArdvHMV7+3tiysaoIpBwMctFqB1M1+pYxERkQXwsyfIYlLSbiB4V2/8rRFRyShgjtfHeLV5D6ljERGRhbA0kEXcuZeMcV/3xEWNAGejgNkvfoiO3m9KHYuIiCyIpYGKLf3BbQRt744LGiMqGAXMbDQF/2s9QOpYRERkYSwNVCz3M9MxLrob/tIY4CQImP78+/BtO1zqWEREVAJYGqjIHmTdQ9AWH/yh0cNREDDFIwTd2/tLHYuIiEoISwMVSXZOJt7d7INzDjo4CAIm1Q5C705vSR2LiIhKEEsDFVpObjbGbXodZxxyoRFEvF8rAH1fHyd1LCIiKmEsDVQoudocvLuxM045PIRaEBHmNgIDu4RKHYuIiEoBSwOZTafT4r0vu+AXTRaUoojx1QdjaNcPpI5FRESlhKWBzGIw6PFeZGf8pMmAUhQxrko/jOgxXepYRERUilga6JkMBgPGb+iCeM19KEQRgRV7IqDXbKljERFRKWNpoKcSjEa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TCy+8AAC4du1atcuPjo7GZ599hmeffRZbtmzB3r17kZKSAh8fH5SUlNS73ZMnT8bly5eRkJAAAFi7di3KysqM/khfvXoVeXl5kMvlVdqelZVVY7vr0+/qftmZKr9+/brJ8oCAAMPr5izb1HIdHR3h4+NjVC6RSKBSqaostzH8/PPPGD9+PNq0aYNVq1YhKSkJKSkpmDx5MkpLS43qTp48Gc7Ozvjqq68AAJ9//jmcnZ0N43iqY6n363YHDx5EREQEOnXqhM2bN5s1gPnFF19E9+7d8eijjyIvLw95eXkoLi4GoAvq+fn5dW4HcOv799hjj1X5/s2fPx9CCNy4ccNonur67OXlZfRcLpfXWH7nZ3anhQsX4vnnn8fAgQOxfv16JCcnIyUlBaNHjza5TatUKpNl5eXlKCoqqrGuo6MjWrdubdbnW5fviEKhQHR0NEpLS9GrVy9ERETU0GNj9X0/i4qKMHToUOzZswcffPABdu7ciZSUFPz8888AUO/fh3XdNlq3bm30XP89169fIpFg27ZtiIqKwoIFC9CnTx/4+Pjg5ZdfRmFhYb3aaC08G8iOhIWFGQbVjhgxAhqNBt9++y1++uknPPbYY/D29gYAzJo1y2jg6u26dOlisjw/Px+bNm3C7Nmz8eabbxrKy8rKqvyirauoqCgEBARg2bJliIqKwrJlyzBw4ECjEfze3t5o3bo14uLiTC7D3d292uXXp9/V7RkwVd66dWtkZmZWKdef8qpff23LNrXciooK5OTkGAUWIQSysrLQv39/s5ZTF6tWrUJISAi+//57o3aWlZVVqatUKjFx4kR8++23eO2117Bs2TJER0ejVatWNa7DUu+X3sGDBzFq1CgEBQUhPj4eSqXSrPmOHj2KjIwMk6f5jxgxAkql0mhgs7n0/Vm8eHG1Z13duSensa91U51Vq1Zh+PDh+PLLL43Kq/tDlpWVZbJMLpdXOV0+KyvLaC9QRUUFrl+/XuUPbEO3qaNHj+Ldd99F//79kZKSgoULF2LmzJkm299Ytm/fjitXrmDnzp2GvSkA6vX9uF1dtw1zBAUFYenSpQCAU6dO4YcffkBsbCzKy8sN/2g0B9yzYscWLFgAT09PvPvuu9BqtejSpQs6deqEQ4cOoV+/fian6v7oSyQSCCGq/If67bffVtn9e2e6r41UKkVMTAw2btyIv/76C/v27avy3/m4ceNw/fp1aDQak+2uLmQBaFC/zTFy5EgcP34cBw4cMCr/7rvvIJFIMGLEiHovF9D9Qbnd+vXrcfPmTcPrjUkikUAulxv9AcnKyqpyNpDeyy+/jGvXruGxxx5DXl5ejadO640cOdLwy/523333HVxcXBp0GnVqaipGjRqFtm3bIiEhoU7XF1q3bh127NhhNL3xxhsAdIdK9GfdVKe67/2QIUPQqlUrHD9+vNrvn/4/96YmkUiqbNOHDx+ucihO7+effzbaW1NYWIjffvsNQ4cONTocAQCrV682ev7DDz+goqLCrIuzmfsduXnzJh5//HEEBwdjx44dmD59Ot58803s2bOn1nU0hH77uPO9+/rrr6vUrcvvQ0v9LtHr3Lkz3n77bfTo0aPKOmwd96zYMU9PT8yaNQuvv/461qxZg6effhpff/017rvvPkRFRWHSpElo06YNbty4gRMnTuDAgQP48ccfTS7Lw8MD99xzDz766CN4e3sjODgYu3btwtKlS6v8J929e3cAwDfffAN3d3c4OTkhJCSkyn9Ut5s8eTLmz5+P6OhoODs744knnjB6fcKECVi9ejXGjBmDV155BQMGDIBMJsOlS5ewY8cOPPjgg3j44YerXX59+22Of/3rX/juu+8wduxYvPfeewgKCsLvv/+OL774As8//3yV4/HmioiIQFRUFN544w0UFBRgyJAhOHz4MGbPno3evXsjJiam3m2ujv400hdeeAGPPfYYLl68iPfffx/+/v4mr4bcuXNnjB49Gn/88Qfuvvtu3HXXXbWuY/bs2di0aRNGjBiBd999F15eXli9ejV+//13LFiwwOw9IXdKS0vDqFGjAAAffvghTp8+bdTmDh06VDmkdjtTIUl/imnfvn0Ney2rU9P3fvHixZg4cSJu3LiBxx57DL6+vsjJycGhQ4eQk5NTZc9GUxk3bhzef/99zJ49G8OGDUNaWhree+89hISEoKKiokp9qVSKiIgIzJw5E1qtFvPnz0dBQYHhdOPb/fzzz3B0dERERASOHTuGd955B3fddRfGjx9fa7vM/Y4899xzuHDhAvbu3QtXV1d88sknSEpKwoQJE3Dw4MFa9/LV1+DBg+Hp6YnnnnsOs2fPhkwmw+rVq3Ho0KEqdXv06AEAmD9/Pu677z5IpVL07NnTZEBt7N8lhw8fxvTp0/H444+jU6dOkMvl2L59Ow4fPmy0h7xZsOrwXmoUNZ2xUFJSItq1ayc6depkGDV+6NAhMX78eOHr6ytkMplQqVTi3nvvFV999ZVhPlNnA126dEk8+uijwtPTU7i7u4vRo0eLo0ePVhnRLoRuVH1ISIiQSqVGpwffeTbQ7QYPHiwAiKeeesrk62q1Wnz88cfirrvuEk5OTsLNzU2EhoaKadOmidOnT9f6PpnT75rey2HDholu3bqZXHZGRoaIjo4WrVu3FjKZTHTp0kV89NFHRmca6Efwf/TRR7W2Va+kpES88cYbIigoSMhkMuHv7y+ef/55kZuba1SvMc8G+s9//iOCg4OFQqEQYWFhYsmSJYYzPExZvny5ACDWrVtn8nXccTaQEEIcOXJE3H///UKpVAq5XC7uuusuo1PIhbj1Hfzxxx/N6ov+s6tuunP5dVmmOWcDCVH9914IIXbt2iXGjh0rvLy8hEwmE23atBFjx4416p/+fdZfCuB21Z25BUC8+OKLRmXmftfKysrEa6+9Jtq0aSOcnJxEnz59xMaNG6tsp/rlzZ8/X8yZM0e0bdtWyOVy0bt3b7FlyxajZer7sH//fnH//fcLNzc34e7uLp588klx9epVs/okRO3fkSVLlpj8XM+cOSM8PDzEQw89ZCir7mygO9+f6r5zpr4HiYmJIjw8XLi4uAgfHx/x7LPPigMHDlRpU1lZmXj22WeFj4+PkEgkAoDhcgWmfnc29HfJ7dvb1atXxaRJk0RoaKhwdXUVbm5uomfPnuLTTz81OouoOZAIccdVnYiI6uDRRx9FcnIyzp8/b9ZZTtT8nD9/HiEhIfjoo4/w2muv1Vg3NjYWc+bMQU5OTr3GWBCZwsNARFRnZWVlOHDgAPbu3YsNGzZg4cKFDCpEZDEMK0RUZ5mZmRg8eDA8PDwwbdo0vPTSS9ZuEhHZMR4GIiIiIpvGU5eJiIjIpjGsEBERkU1jWCEiIiKbxgG2DaDVanHlyhW4u7s32eWxiYiI7IEQAoWFhQgICDC6oaQpDCsNcOXKlSp3BSUiIiLzXbx4EW3btq2xDsNKA+jvJ3Px4kV4eHjUaxlqtRrx8fGIjIy0m+tU2GOfAPvsF/vUfNhjv+yxT4B99ssSfSooKEBgYKBZ92ZjWGkA/aEfDw+PBoUVFxcXeHh42NWX2t76BNhnv9in5sMe+2WPfQLss1+W7JM5wyg4wJaIiIhsGsMKERER2TSGFSIiIrJpHLNCRERkBiEEKioqoNFoaqynVqvh6OiI0tLSWus2F/Xpk1QqhaOjY6Nc2oNhhYiIqBbl5eXIzMxEcXFxrXWFEFCpVLh48aLdXIOrvn1ycXGBv78/5HJ5g9bPsEJERFQDrVaL9PR0SKVSBAQEQC6X1/gHW6vVoqioCG5ubrVe7Ky5qGufhBAoLy9HTk4O0tPT0alTpwa9FwwrRERENSgvL4dWq0VgYCBcXFxqra/ValFeXg4nJye7Cit17ZOzszNkMhkyMjIM89aXfbyLREREFmYvwaMpNdZ7xneeiIiIbBrDChEREdk0hhUiIiKyaQwrREREdmrSpEmQSCRVptGjR9d5WQsXLsTAgQPh7u4OX19fPPTQQ0hLS7NAq6tiWCEiIrJjo0ePRmZmptG0du1ak3XVanW1ZYmJiXj++eeRnJyMhIQEVFRUIDIyEjdv3rRo+wGeukxERFRnQgiUqE1fyVWr1aKkXAPH8gqLnEHkLJPW6cJsCoUCKpXK5GsSiQRffvkl/vjjD2zduhWvvfYaJBIJNm7ciJdffhkffPABzp8/D7VajZ9++gkeHh6GPi1btgy+vr7Yv38/7rnnnkbpW3UYVoiIiOqoRK1B13e3WGXdx9+Lgou88f58z549G/PmzcOnn34KqVSKZcuW4cyZM/jhhx+wfv16SKVSk/Pl5+cDALy8vBqtLdVhWCEiIrJjmzZtgpubm1HZG2+8gXfeeQcAEB0djcmTJxu9Xl5ejpUrV8LHxweAbm/R7YQQmDlzJu6++250797dgq3XYVghIiKqI2eZFMffizL5mlarRWFBIdw93C12GKguRowYgS+//NKo7Pa9If369asyT1BQkCGomDJ9+nQcPnwYu3fvrlNb6othhYiIqI4kEkm1h2K0Wi0q5FK4yB1t4qq3rq6u6NixY42vm1Om99JLL+HXX3/Fn3/+ibZt2zZKG2vDsEJERES1EkLgpZdewsaNG7Fz506EhIQ02boZVoiIiOxYWVkZsrKyjMocHR3h7e1dp+W89tprWL9+PX755Re4u7sblqlUKuHs7Nxo7TWFYYWIiMiOxcXFwd/f36isS5cuOHnyZJ2W87///Q8AMHz4cKPyZcuWYdKkSQ1pYq0YVoiIiOzU8uXLsXz58mpfF0JUKYuNjUVsbGyV8tzcXKPrrDQl64/8ISIiIqoBwwoRERHZNIYVIiIismkMK0RERGTTGFaIiIjIpjGsEBERkU1jWCEiIiKbxrBCRERENo1hhYiIiGwawwoRERHZNIYVIiIiOzVp0iRIJJIq0+jRoxu03Hnz5kEikWDGjBmN09Ba8N5AREREdmz06NFYtmyZUZlCoTBZV61WQyaTVSmTSqWG5ykpKfjmm2/Qs2fPxm9sNRhWiIiI6koIQF1s+jWtVvdauRSwxE3/ZC6ARGJ2dYVCAZVKZfI1iUSCL7/8En/88Qe2bt2K1157DRKJBBs3bsTLL7+MDz74AOfPn4darQYAFBUV4amnnsKSJUvwwQcfNEp3zGHxsPLFF1/go48+QmZmJrp164ZFixZh6NCh1dbftWsXZs6ciWPHjiEgIACvv/46nnvuOaM669evxzvvvIOzZ8+iQ4cO+PDDD/Hwww/Xab1CCMyZMwfffPMNcnNzMXDgQHz++efo1q1b474BRERkf9TFwNwAky85AGhlyXX/+wogd220xc2ePRvz5s3Dp59+CqlUimXLluHMmTP44YcfsH79eqO9KtOnT8fYsWMxatSoJg0rFh2z8v3332PGjBl46623cPDgQQwdOhT33XcfLly4YLJ+eno6xowZg6FDh+LgwYP497//jZdffhnr16831ElKSsITTzyBmJgYHDp0CDExMRg/fjz27NlTp/UuWLAACxcuxGeffYaUlBSoVCpERESgsLDQcm8IERFRE9u0aRPc3NyMpvfff9/wenR0NCZPnoz27dsjKCgIAFBeXo6VK1eid+/e6NmzJyQSCdavX4+DBw9i3rx5Td4Hi+5ZWbhwIaZMmYJnn30WALBo0SJs2bIFX375pcnOfvXVV2jXrh0WLVoEAAgLC8O+ffvw8ccf49FHHzUsIyIiArNmzQIAzJo1C7t27cKiRYuwdu1as9YrhMCiRYvw1ltv4ZFHHgEArFixAn5+flizZg2mTZtmybelRldT4+AiiuGmkEIC83fzNZo67Fq8bSbjZxoNVHn7IUkDIJVWs8w7ygx1JDW0487XJLc9SIzLa/y5ukcAEofK57fV15dpNHAvuQhknwBk8tvmdaisp//Z4bbyO6fbyh2k1dchItsmc9Ht4TBBq9WioLAQHu7ucLDUYaA6GDFiBL788kujMi8vL8PP/fr1qzJPUFAQfHx8DM8vXryIWbNmYcuWLXBycqpjgxvOYmGlvLwc+/fvx5tvvmlUHhkZicTERJPzJCUlITIy0qgsKioKS5cuNQz6SUpKwr/+9a8qdfQBx5z1pqenIysry2hdCoUCw4YNQ2JiYrVhpaysDGVlZYbnBQUFAHSDj/TH8+pKP5/+sXjTLPhVnKvXsmyFI4CBAJBu5YY0MhmAewHgpGXXIyC5LchUHvOWSCvLpLcFHemteg6ORmVCIr1V5uB42yQ1+lkCB/TKzAY2xUPjqACk+tdkukepTPez1BFwkANSRwgHma5cKq+cZEaPwqhccevRUa57dJDW/iY0wJ3blL2wx341lz6p1WoIIaDVaqHVam+94Ohssr4QApBpIGQu0Frinw8hdJNZVQVcXFzQvn37Kq/p++Ls7GzULyEEXF1djcr279+PnJwc9O/f31Cm0Wjw559/4rPPPkNJSYnR4aLb1yGEqDJIF6jb526xsHLt2jVoNBr4+fkZlfv5+SErK8vkPFlZWSbrV1RU4Nq1a/D396+2jn6Z5qxX/2iqTkZGRrV9mjdvHubMmVOlPD4+Hi4udUu6d0pISAAAyDRtcU1r+mORAFBIAWcp4Owo4GTZ3/n1IoEZG1A1G5npeXVlkirzCOPXTSxbt7w7ngtR9TVx+zK0ukehe11SuQ6J0NcXhnZKhNZoufpl3irXAkL3aNb7cns7tRVm1ze9DPM4AAgCgBsNWl2daOEArYMjtBIZNA5yaCWO0DrIoJHIqjzqXtc9ahxk0DrIoZHoftaVKXSPEnnlc12Zi4MCf27+CRWVr+v2dtkH/e8Ke2LrfXJ0dIRKpUJRURHKy8vNns8WhhWo1WpUVFQY/rk2paSkxOj1srIyaDQao7L+/fvj77//Nppv+vTp6NSpE1555RXcvHnT5LLLy8tRUlKCP//8ExUVxr/XiourGaBsgsUH2EruSJVCiCpltdW/s9ycZTZWndvNmjULM2fONDwvKChAYGAgIiMj4eHhUe18NVGr1UhISEBERITudLExY1BSrsGFG8VIv16M89duIu1qEfZl5CKroMxoXpWHAmN7qDCuhz+6BbjX2PamVKVPdqK2fok7HqtWEIDQVk6aWz9r9c/FrXKtFoAW0Gp0ZVp9+a3nEnFnWYVuPm1F5XRbuX4ZGrVuPk0FICqgKS/DmVMn0bFDMKS4bV6NWldXq771XFsBaMorf1Yb6kBTDom2AtCUGZ5DUw5U6J5LNMbfWwdo4aAtB1AOaEz/gmtswtFJt+tc5gLIXSEqH/XPIXeDqHw0PFe4VT53g1C4Awp3QO4OKNx0/1E38fZmj9tVc+lTaWkpLl68CDc3N7MOgQghUFhYCHd36/9elslk0Gg0VYKBo6MjvL29Aej2rNz+N0yhUEAqlRqVubu7GyZ9nzw8PKBSqTBo0KBq119aWgpnZ2fcc889Vd67mgLUnSwWVry9vSGVSqvsRcnOzq6yR0NPpVKZrO/o6IjWrVvXWEe/THPWqz+FKysrC/7+/ma1DdB9gKbOTZfJZA3e0G5fhkwmQ3dXJ3QPvHVMUQiBizdKkHzuOhLPXsO2E9nIKijD0r8zsPTvDHTwccWkISF4tE8buMht44z0xnhfbJE99UurVuN0wWZ0GjYGUkv1SYjKEFMGVJRXPpZVBppSXVlFSeVjaeVUVllWBqhLbj1Xl1Y+3vlzse65uhhCXQJNaQEctbf+A5bol1ui24XU4D8fDo668KJwBxQeusnptkcnpYmpFeDcSvfopKz34TB7+v7p2XqfNBoNJBIJHBwczBqDoj98op/HmiQSCbZs2YI2bdoYlXfp0gUnT+qOad/ZL30Yub2suj7V1kcHBwdIJBKTn3FdPnOL/VWTy+Xo27cvEhISjE4rTkhIwIMPPmhynvDwcPz2229GZfHx8ejXr5+hU+Hh4UhISDAatxIfH4/Bgwebvd6QkBCoVCokJCSgd+/eAHS7qnbt2oX58+c3Qu8bn0QiQbvWLmjX2gXj+weiVK3BzrQc/Hb4CraduIqzOTfxzsaj+CQ+DU8NbId/hAfDz6PpB0ERVSGR6MarOMoB09ehalQVajU2b96MMffdBxkqdGGmvKjyuhfFgPomUG5qKtJNZfrHwqrPywoB/WG6klzdVF8KJeCsBJw975i8ABcv3c8urW89lyvNHqdApLd8+XIsX7682teFie9UbGwsYmNja132zp0769+wOrLov+AzZ85ETEwM+vXrh/DwcHzzzTe4cOGC4bops2bNwuXLl/Hdd98BAJ577jl89tlnmDlzJqZOnYqkpCQsXbrUcJYPALzyyiu45557MH/+fDz44IP45ZdfsHXrVuzevdvs9eovETx37lx06tQJnTp1wty5c+Hi4oLo6GhLviWNxkkmxejuKozurkJRWQXW77+E//2djozrxfh8x1l88+c5PDmgHV4e2Qnebk3wF4LI1kgklYd5XADX1o2zTP3FvsoKdMGltKDy5wLdz6X5lT/n33pumPKAkjxdWAKAsnzdlGf6Ug53kgG4Hw6QnPYGXL11Qcalte5nV5/Kn31uTW4+ur04NnJ4mKghLBpWnnjiCVy/fh3vvfceMjMz0b17d2zevNlwHndmZqbRtU9CQkKwefNm/Otf/8Lnn3+OgIAA/N///Z/htGUAGDx4MNatW4e3334b77zzDjp06IDvv/8eAwcONHu9APD666+jpKQEL7zwguGicPHx8XB3d7fkW2IRbgpHTBwcjKcHBWHriatY+lc69p6/ge+SMrB+/yX8854OeHZoCFwVtnF4iKjZcnDQjVlRuNV/GRXlxuFFv4em5Matn4tv6J4X37j1c3kRHKAFbmbrJrPaK7sVXNz8ADdfwNUXcFfpfnZTAe5+ukd5w04SILIkiTC1D4jMUlBQAKVSifz8/AYNsN28eTPGjBnTqMdsk85ex3/+OIFDl/IBAN5uCvy/qM4Y3y/Q4gO+LNUna7PHfrFPzYe6pAjbN/2Ie8N7QVaWBxRf1003rwE3cyqna0DxNaAoR7fXpi4USl2IMUz+gEfArUePAF3gacRTz5vLZ1VaWor09HSEhISYNcBWq9WioKAAHh4eVh+z0ljq26ea3ru6/A3lv9p2KrxDa2x8cQh+P5KJj7akIeN6Md5YfwQbDl7GvEd6IsS78S7VTERNwFGBUrkX4NcdMOcPe0WZLsAUZVc+Xq2csnWPhVdvlamLbx2WupZW/TIlUl2Q8WijCy/KtrrJow2gbAMo2+kOS/HQEzUyhhU7JpFIMK5nACK7qrAi8Tw+SUhD8rkbGL3oT8wY1RnPDg2BTGofqZ+I7uCouBUmaiKEbvxNYRZQmFn5eAUoyLztsbJcaICCy7qp2vU6Va43EGgVCLRqB7QK0j33DNIdcmqmext4IKLuGus9Y1hpAeSODph6T3tEdVPhrY1H8Nfpa5gfdxKbDl/Bfyf0RkffBhx/J6LmTSKpPN3aA/DpXH09rUa3V6bgClBwCcivDC35l3SPeRd1e2kqSoHrZ3STKVJFZYgJgoOyHTpcLYHkpBbw7gB4hehOB7cx+kNUxcXFcHY2fdVaMk1/fZeGHuZjWGlB2rV2wXeTB+DnA5fx/u/HcexKAR74bDfee7A7Hutby39fRNSyOUgBD3/dhL6m61SU3wow+Rd1ASbvApCXoZvyL+uus1MZZqQAugPA+nW3luHqA3iGAF7tdVPrDrcenZSW76cJUqkUrVq1Qna2bmCzi4tLjWP/tFotysvLUVpaaldjVurSJyEEiouLkZ2djVatWpm8FH9dMKy0MBKJBI/2bYuhnbwx4/tUJJ69jtd+PITEs9fw/oPdecYQEdWfo1y3d8QrxPTrmgpdmMk9D+RlQHM9HZnHEhHgUg6H3PO6s570g4Uv7a06v6sP4NUB8O4ItO4ItO6ke/Rqr1u3BekvJqoPLDURQqCkpATOzs5Wv4JtY6lvn1q1amV47xqCf5laKF8PJ6ycMhBf7DiDT7eews8HLiP1Yh6+erovOvvZ3m5YIrIDUkfduBVP3WUktGo19hdvht+YMXCQyXSndN9IB26cA3LTgevndD9fP1N5ynZlkLmYbLxciRTwDAa8OwPenQCfLoBPqO65U/3O1LyTRCKBv78/fH19a70Bn1qtxp9//ol77rnHps9yqov69EkmkzV4j4oew0oLJnWQ4KWRnTCwfWu8vPYgzuXcxCNfJGJxdG+M6OJr7eYRUUvjpAQCeummO5UW6ILLjbPAtTPA9dO6EHPtDFBeqCu/cRY49YfxfO4BuvDiG6YLMPrHeoYYqVRa6x9gqVSKiooKODk52U1YsXafGFYIA0K8sPmVoXh+1X7sSb+BKctT8NbYrpg8JNhudmESUTPn5GE6yAihO1Pp2qlbU06abiqqPLOp8ApwbofxfMpAwLcr4Ne18rGbbk+M1D7Chb1hWCEAgJerHCunDMQ7G4/i+30X8f6m4ziTXYg5D3SH3NE+BogRkR2SSG4N/G0/zPi1krzK4HJSN2Wf0D0WZuoGAOdfBE5vuVXfQabb6+LXDVB1B1Q9AFVP3b2ZyKoYVshA7uiA/zzaA5383PDh5hNYu/ciLt4owdcxfTnwloiaH+dWQLuBuul2xTd0oeXqMV2AuXoMyD6uu6/T1SO66fBt9T3a6oKLf0/A/y5dgFG25cXvmhD/ApERiUSCZ4e2R4i3K15aexC7z1zD00v3YNmk/mjlYtnR9kRETcLFCwgarJv0hNCdZn31GHD1KJB1RDflpuuuK1NwyXg8jLOX7pCUf6/Kw1O9dYeWGGAsgmGFTBoZ5ofVzw7EpGUpOHghD098nYyVUwbA16P2+2IQETU7EsmtM5VCx9wqLy3QBZjMQ0DWYSDzMJBzQnea9dntuknPpTUQ0AcOqrvgl68BbvYHWgU0fV/sEMMKVat3O0/8MC0cMUv3IO1qIR7/OgmrpgxEoBfvzkpELYSTBxAUrpv01KW6w0aZqcCVg8CVVN3z4uvAmQRIzyRgEAAs+lR3u4E2/YA2fYG2/XR7YmT8p6+uGFaoRl1U7vjpucF4amkyMq4X47GvEvH9P8MRzBshElFLJXMC2vTRTXrqUt3ho8sHoL20DzdP/wW30kxI8i7oDi8d+1lXz0GmG/vSdoAuvLQbVPv9m4hhhWrXrrULfnpuMJ7+dg9OZxfhqW/34Ptpg9DWk3tYiIgA6AJM235A237Q9HkG2zdvxph774Ys5whwaR9web/u8Wa27ufL+4E9lfN6tAECBwCBg3SDgf166C6gRwZ8N8gsfh5OWDN1EJ74Jgnncm7iySXJ+GFaOPyVvKkXEZFJTh5A++G6CagcxJsBXEzR3U7g4l7dIN6Cy8CxDboJAORuQNv+ugHA7Qbpfpa17N+1DCtkNh93BdY8qwssGdeLEb1kD77/5yAOuiUiModEorstgGcw0PNxXVn5Td1elgt7dLcRuJgClOXrLmKnv5Cdg0w35iV4CBA0BAgcCCjcrNULq2BYoTpRKXV7WMZ/lYT0azfx1Ld7sO6fg9DaTWHtphERNT9yVyDkHt0EAFqNbrDuhWQgI1E3FWVVBplk4K9PAAdHIKAPEDIUCL5bd/hIbt+H5RlWqM7atHLG2qmDMP7rJJzOLsKUFfuwduogOMsb54ZVREQtloO08sq5PYABU3WHjm6cuxVcMnbrBuxe2qub/vpEt+clcEBl6Bmm2wtj4btQNzWGFaqXdq1dsOrZgXjsq0SkXszDS2sP4Kun+8JRykvzExE1GokEaN1BN/WJ0ZXlZgDndwPn/wLS/9JdsC7jb920cx4gc9UdMmo/Qjdexjes2V+sjmGF6q2jrxu+/Uc/PPXtHmw9kY13fz2GDx/qbu1mERHZN/3F63o/pdvzkpsOnNsFpO8C0v/UXe/ldLxuAgA3FdBhBNBhpO7R1du67a8HhhVqkH7BXvjvhN54fvV+rNlzAQFKJ0wbGmztZhERtQwSCeDVXjf1ewbQaoHsY8C5ncDZHbfGvBxaq5sA3f2NOo7STW0HNIvTpG2/hWTzRndXIfb+bpj96zF8HH8Kvm5ycLgtEZEVODjcGvMy+CXdxeou7gHObtPdGiDriO7WAZmHdONdFEqgw3CgYwTQKRJw97N2D0xiWKFGMXFwMK7kl+DrXefw1i/H8GKYtVtERESQOQHth+mmiPeAwqu6U6JPJ+gCTEkucPwX3QTobgfQOUo3+ffWhR8bwLBCjeaNqFCcv3YTW45dxf/SpHi0oBSBrWXWbhYREem5+wF3TdBNWg1w+QBwJgE4tUV3ryP9tGs+4OanCy1dxgCBg2tZsGXZRmQiu+DgIMHC8b3Q2dcNBWoJXliTilK1xtrNIiIiUxykQGB/YMS/gWm7gFfTgAc+A8Lu111Ft+gqcOA7YO0EOC7sjAFnPwUKs6zTVKusleyWq8IRXz7VCy6OAkcuF+DN9YchhLB2s4iIqDbuKt3p0U+sAl4/Bzz9M9B/KqAMhKSiFN5FJwAXL6s0jWGFGl07Lxc801kLqYMEG1Ov4Os/z1m7SUREVBeOCqDjSGDsx8CMI1A/uwsH2z0LSK1zsTmGFbKIzkqBt+7rAgBYEHcSiWevWblFRERULxIJ4NcNmZ4DrNYEhhWymKcHBuKxvm2hFcDLa1ORXVhq7SYREVEzxLBCFiORSPD+g93Rxc8d14rK8PLag9BoOX6FiIjqhmGFLMpZLsXnT/WBi1yK5HM3sGjrKWs3iYiImhmGFbK4jr5umPdIDwDA4u1nsDMt28otIiKi5oRhhZrEg73a4KmB7QAA//o+FZn5JVZuERERNRcMK9Rk3hnXFd0CPJBbrMZrPx6CluNXiIjIDAwr1GScZFIsfrI3nGVS/H3mOv73d7q1m0RERM0Awwo1qfY+bnh7nO4uhwvi0nD8SoGVW0RERLaOYYWaXPSAdhgV5otyjRYzvj/I+wcREVGNGFaoyUkkEvzn0Z7wdpPj1NUizI87ae0mERGRDWNYIavwdlPgo8fuAgAs+/s8/jyVY+UWERGRrWJYIasZEeqLmEFBAIDXfzqMglK1lVtERES2iGGFrOrfY8IQ1NoFWQWlmPv7CWs3h4iIbBDDClmVs1yK+Y/2BACsS7mIv07zcBARERljWCGrG9S+Nf4Rrjsc9Ob6Iygqq7Byi4iIyJYwrJBNeGN0KNp6OuNyXgn+8wcPBxER0S0MK2QTXBWOhsNBq5IvIPHsNSu3iIiIbAXDCtmMIR29EV15s8M31x9BSTkvFkdERAwrZGNm3RcKf6UTLtwoxmc7Tlu7OUREZAMYVsimuDvJEPtANwDAN3+ew6mrhVZuERERWRvDCtmcqG4qjArzg1oj8NaGI9BqhbWbREREVsSwQjZpzoPd4CKXIuV8Ln7cf9HazSEiIitiWCGb1KaVM/41qjMAYN4fJ3G9qMzKLSIiImthWCGb9cyQYIT5eyCvWI0PN/PaK0RELRXDCtksR6kD5j7cHRIJ8POBy0g+d93aTSIiIitgWCGb1rudJ6IH6K69EvvrMVRotFZuERERNTWGFbJ5r0V2gdJZhpNZhViz94K1m0NERE2MYYVsnqerHK9F6gbbfhJ/Cjdullu5RURE1JQYVqhZiB4YhDB/D+SXqPFJfJq1m0NERE2IYYWaBamDBLH3dwUArNl7AUcv51u5RURE1FQYVqjZGNi+Ne6/KwBCAHN+OwYheGVbIqKWgGGFmpVZ94XCWaa7su2vh65YuzlERNQEGFaoWQlo5YwXR3QAACyIS0OpWmPlFhERkaUxrFCz8+zQ9vBXOuFyXgmW/X3e2s0hIiILY1ihZsdJJsX/i+oCAPhixxneN4iIyM4xrFCz9FCvNujexgOFZRX477bT1m4OERFZEMMKNUsODhL8e0wYAGD1ngs4m1Nk5RYREZGlMKxQszW4gzdGhflCoxX4zx8nrd0cIiKyEIYVatbevC8MUgcJEo5f5V2ZiYjsFMMKNWsdfd0Md2Weu/kELxRHRGSHGFao2XtlVCe4yqU4fCkffxzNsnZziIiokTGsULPn7abAlKHtAQAfx6ehQqO1couIiKgxMayQXZg6NASeLjKcy7mJnw9ctnZziIioETGskF1wd5LhheEdAQCLtp7iZfiJiOyIRcNKbm4uYmJioFQqoVQqERMTg7y8vBrnEUIgNjYWAQEBcHZ2xvDhw3Hs2DGjOmVlZXjppZfg7e0NV1dXPPDAA7h06VKd1n3o0CE8+eSTCAwMhLOzM8LCwvDf//63sbpOVhATHgSVhxOu5Jdi9Z4L1m4OERE1EouGlejoaKSmpiIuLg5xcXFITU1FTExMjfMsWLAACxcuxGeffYaUlBSoVCpERESgsLDQUGfGjBnYsGED1q1bh927d6OoqAjjxo2DRnPrv+na1r1//374+Phg1apVOHbsGN566y3MmjULn332WeO/EdQknGRSvDKqEwDg8x1nUFRWYeUWERFRY3C01IJPnDiBuLg4JCcnY+DAgQCAJUuWIDw8HGlpaejSpUuVeYQQWLRoEd566y088sgjAIAVK1bAz88Pa9aswbRp05Cfn4+lS5di5cqVGDVqFABg1apVCAwMxNatWxEVFWXWuidPnmy07vbt2yMpKQk///wzpk+fbqm3hSzs8b5t8c2f55B+7SaW/pVuCC9ERNR8WSysJCUlQalUGsICAAwaNAhKpRKJiYkmw0p6ejqysrIQGRlpKFMoFBg2bBgSExMxbdo07N+/H2q12qhOQEAAunfvjsTERERFRdVr3QCQn58PLy+vavtUVlaGsrJbN80rKCgAAKjVaqjVajPelar089V3fltk7T7NuLcDXvnhML756ywm9AuAl6u8UZZr7X5ZAvvUfNhjv+yxT4B99ssSfarLsiwWVrKysuDr61ul3NfXF1lZpq+FoS/38/MzKvfz80NGRoahjlwuh6enZ5U6+vnrs+6kpCT88MMP+P3336vt07x58zBnzpwq5fHx8XBxcal2PnMkJCQ0aH5bZK0+aQXQ1lWKSzc1eOu77bg/qHFPZeZn1TzYY58A++yXPfYJsM9+NWafiouLza5b57ASGxtr8g/27VJSUgAAEomkymtCCJPlt7vzdXPmubNOXdZ97NgxPPjgg3j33XcRERFR7TpmzZqFmTNnGp4XFBQgMDAQkZGR8PDwqLF91VGr1UhISEBERARkMlm9lmFrbKFPzu2z8dyaVCRek+GDfwxF60bYu2IL/Wps7FPzYY/9ssc+AfbZL0v0SX90whx1DivTp0/HhAkTaqwTHByMw4cP4+rVq1Vey8nJqbLnRE+lUgHQ7Rnx9/c3lGdnZxvmUalUKC8vR25urtHelezsbAwePNhQx9x1Hz9+HPfeey+mTp2Kt99+u8Z+KRQKKBSKKuUymazBH15jLMPWWLNPUT0C0KNNOo5czsfypIt4877QRls2P6vmwR77BNhnv+yxT4B99qsx+1SX5dT5bCBvb2+EhobWODk5OSE8PBz5+fnYu3evYd49e/YgPz/fECruFBISApVKZbSbqby8HLt27TLM07dvX8hkMqM6mZmZOHr0qKGOues+duwYRowYgYkTJ+LDDz+s61tBNkwikWBG5eDa75LO43pRWS1zEBGRrbLYqcthYWEYPXo0pk6diuTkZCQnJ2Pq1KkYN26c0QDX0NBQbNiwAUDlH5gZMzB37lxs2LABR48exaRJk+Di4oLo6GgAgFKpxJQpU/Dqq69i27ZtOHjwIJ5++mn06NHDcHaQOevWB5WIiAjMnDkTWVlZyMrKQk5OjqXeEmpi94b6omdbJYrLNfjmz3PWbg4REdWTRa+zsnr1avTo0QORkZGIjIxEz549sXLlSqM6aWlpyM/PNzx//fXXMWPGDLzwwgvo168fLl++jPj4eLi7uxvqfPrpp3jooYcwfvx4DBkyBC4uLvjtt98glUrNXvePP/6InJwcrF69Gv7+/oapf//+FnxHqCkZ713JwDXuXSEiapYsdjYQAHh5eWHVqlU11hFCGD2XSCSIjY1FbGxstfM4OTlh8eLFWLx4cb3XXds6yD6M6OKLu9oqcehSPr758xz+PSbM2k0iIqI64r2ByK7p9q50BqAbu5JTyL0rRETNDcMK2b3hXXxwV2ArlKq1WPIXx64QETU3DCtk9yQSCV4Zqbsj86rkDOTeLLdyi4iIqC4YVqhFGNHFF139PVBcrsGyxPPWbg4REdUBwwq1CBKJBC+O0O1dWf53Ou/ITETUjDCsUIsxursK7X1cUVBagVXJGdZuDhERmYlhhVoMqYMEzw/rAAD49q90lKo1Vm4RERGZg2GFWpSHerdBm1bOuFZUhu9TLlq7OUREZAaGFWpRZFIHPDesPQDg611nUV6htXKLiIioNgwr1OI83i8Q3m4KXMkvxcbUy9ZuDhER1YJhhVocJ5kUU4eGAAC+2nkWWq2oZQ4iIrImhhVqkZ4aFAQPJ0ecu3YTCSeuWrs5RERUA4YVapHcFI54elAQAOCbP3kJfiIiW8awQi3WpMHBkEsdsD8jF/vO37B2c4iIqBoMK9Ri+Xo44ZE+bQAAX3PvChGRzWJYoRbt2aG605gTjl/FmewiK7eGiIhMYVihFq2jrxtGhfkBAL79i3tXiIhsEcMKtXj6i8T9fOAysgtLrdwaIiK6E8MKtXj9gr3Qp10rlGu0WP73eWs3h4iI7sCwQgRgWuUNDlclZ6CorMLKrSEiotsxrBABiAjzQ3tvVxSUVuCnfbzBIRGRLWFYIQLg4CDBM0OCAQDLEs/zEvxERDaEYYWo0iN92sLDyREZ14ux7WS2tZtDRESVGFaIKrkqHPHkwHYAgP/tTrdya4iISI9hheg2E8ODIXWQIOncdRy7km/t5hARERhWiIwEtHLGfd1VAID/7T5v3cYQEREAhhWiKqbcHQIA+O3QFV4kjojIBjCsEN2hdztP9K68SNyq5AvWbg4RUYvHsEJkgn7vyurkDJSqNVZuDRFRy8awQmTC6G4qBCidcP1mOX5NvWLt5hARtWgMK0QmOEod8I/BwQCA5YnnIQQvEkdEZC0MK0TVeKJfIBSODjieWYADF3Kt3RwiohaLYYWoGp6ucjzYKwAAsCIxw8qtISJquRhWiGrwj/BgAMDmI5nILiyzbmOIiFoohhWiGnRvo0TfIE9UaAW+33fJ2s0hImqRGFaIavGP8CAAwLqUS6jQWrkxREQtEMMKUS3u6+4PH3cFsgvLcPiGxNrNISJqcRhWiGohd3RA9ADd3Zj/yuImQ0TU1Pibl8gM0QPbwdFBgnOFEhzPLLB2c4iIWhSGFSIz+Hk4IaqrHwBg1Z6LVm4NEVHLwrBCZKanBgYCADYdzkR+idrKrSEiajkYVojM1C+oFVTOAiVqLTYevGzt5hARtRgMK0RmkkgkGOKnO3d59Z4M3i+IiKiJMKwQ1UF/HwFnmQNOXS3CvgzeL4iIqCkwrBDVgbMjMK6nPwBgdTLvF0RE1BQYVojq6Mn+bQEAm49m4cbNciu3hojI/jGsENVRjzZK9GijRHmFFuv3835BRESWxrBCVA9PDdRd0XbN3gvQajnQlojIkhhWiOrh/rsC4K5wRPq1m0g6d93azSEismsMK0T14KpwxEO92wDQncZMRESWw7BCVE/RlYeC4o9dRXZhqZVbQ0RkvxhWiOopzN8Dfdq1QoVWYP1+XtGWiMhSGFaIGmBCf93ele9TLvCKtkREFsKwQtQAY3v6w1UuxfnrxdiTfsPazSEisksMK0QN4KpwxAO9dANtv0+5aOXWEBHZJ4YVogaa0D8QALD5SCbyi9VWbg0Rkf1hWCFqoJ5tlQhVuaOsQouNqRxoS0TU2BhWiBpIIpEY9q6s3cuBtkREjY1hhagRPNS7DeSODjiZVYgjl/Ot3RwiIrvCsELUCFq5yHFfdxUAYB0H2hIRNSqGFaJGor/myq+pV1BcXmHl1hAR2Q+GFaJGMqi9F4Jbu6CorAKbDmdauzlERHaDYYWokUgkEjzeTzfQ9qf9l6zcGiIi+8GwQtSIHunTBg4SYG/6DWRcv2nt5hAR2QWGFaJG5K90xt2dfAAA67l3hYioUTCsEDWyx/q2BQCsP3AZWi2vuUJE1FAMK0SNLLKrH9ydHHE5rwRJ565buzlERM0ewwpRI3OSSfHAXQEAgB/38ZorREQNxbBCZAH6s4LijmWhoJQ3NyQiagiGFSILuKutEp183VCq1uJ3XnOFiKhBGFaILEAikRgG2vJQEBFRwzCsEFnIw73bQOogwYELeTibU2Tt5hARNVsMK0QW4uvhhGGddddc4RVtiYjqz6JhJTc3FzExMVAqlVAqlYiJiUFeXl6N8wghEBsbi4CAADg7O2P48OE4duyYUZ2ysjK89NJL8Pb2hqurKx544AFcumT8x6Au675+/Tratm0LiURSa/uI6uLxykNBGw9ehobXXCEiqheLhpXo6GikpqYiLi4OcXFxSE1NRUxMTI3zLFiwAAsXLsRnn32GlJQUqFQqREREoLCw0FBnxowZ2LBhA9atW4fdu3ejqKgI48aNg0ajqde6p0yZgp49ezZOp4luc2+YLzycHJGZX4pkXnOFiKheLBZWTpw4gbi4OHz77bcIDw9HeHg4lixZgk2bNiEtLc3kPEIILFq0CG+99RYeeeQRdO/eHStWrEBxcTHWrFkDAMjPz8fSpUvxySefYNSoUejduzdWrVqFI0eOYOvWrXVe95dffom8vDy89tprlnorqAVTOEoxrvKaKz8fuGzl1hARNU+OllpwUlISlEolBg4caCgbNGgQlEolEhMT0aVLlyrzpKenIysrC5GRkYYyhUKBYcOGITExEdOmTcP+/fuhVquN6gQEBKB79+5ITExEVFSU2es+fvw43nvvPezZswfnzp2rtU9lZWUoKyszPC8oKAAAqNVqqNX1u5aGfr76zm+L7LFPQP379UAPP6zZcwFxRzPx7tjOcJFbbLOrM3v8rOyxT4B99sse+wTYZ78s0ae6LMtivzWzsrLg6+tbpdzX1xdZWVnVzgMAfn5+RuV+fn7IyMgw1JHL5fD09KxSRz+/OesuKyvDk08+iY8++gjt2rUzK6zMmzcPc+bMqVIeHx8PFxeXWuevSUJCQoPmt0X22Ceg7v0SAmitkOJ6mQYfr01APx/bG7tij5+VPfYJsM9+2WOfAPvsV2P2qbi42Oy6dQ4rsbGxJv9g3y4lJQWA7loTdxJCmCy/3Z2vmzPPnXVqW/esWbMQFhaGp59+usbl3m7WrFmYOXOm4XlBQQECAwMRGRkJDw8Ps5dzO7VajYSEBEREREAmk9VrGbbGHvsENKxfZ53PYPGOczgPX7w7pq+FWlh39vhZ2WOfAPvslz32CbDPflmiT/qjE+aoc1iZPn06JkyYUGOd4OBgHD58GFevXq3yWk5OTpU9J3oqlQqAbs+Iv7+/oTw7O9swj0qlQnl5OXJzc432rmRnZ2Pw4MGGOrWte/v27Thy5Ah++uknALogAwDe3t546623TAYyhUIBhUJRpVwmkzX4w2uMZdgae+wTUL9+Pdq3HRbvOIe/z15HbokGvh5OFmpd/djjZ2WPfQLss1/22CfAPvvVmH2qy3LqHFa8vb3h7e1da73w8HDk5+dj7969GDBgAABgz549yM/PN4SKO4WEhEClUiEhIQG9e/cGAJSXl2PXrl2YP38+AKBv376QyWRISEjA+PHjAQCZmZk4evQoFixYYPa6169fj5KSEsO6U1JSMHnyZPz111/o0KFDXd8WohoFe7uiT7tWOHAhD78euoJnh7a3dpOIiJoNi41ZCQsLw+jRozF16lR8/fXXAIB//vOfGDdunNHg2tDQUMybNw8PP/wwJBIJZsyYgblz56JTp07o1KkT5s6dCxcXF0RHRwMAlEolpkyZgldffRWtW7eGl5cXXnvtNfTo0QOjRo0ye913BpJr164Z5m3VqpWl3hZqwR7u0xYHLuRh/YHLDCtERHVg0eusrF69Gj169EBkZCQiIyPRs2dPrFy50qhOWloa8vPzDc9ff/11zJgxAy+88AL69euHy5cvIz4+Hu7u7oY6n376KR566CGMHz8eQ4YMgYuLC3777TdIpdI6rZuoKd3f0x8yqQQnMgtwMsv8Y7VERC2dRc+h9PLywqpVq2qsox8roieRSBAbG4vY2Nhq53FycsLixYuxePHiBq37dsOHD6/SFqLG1MpFjntDfbHl2FVsOHAZs8bUb1A2EVFLw3sDETWhh3vrLr+/gZffJyIyG8MKURMaEeoDpbMM2YVl2MPL7xMRmYVhhagJKRylGNNDd1r+xlRefp+IyBwMK0RN7MFeunsF/XE0C6VqTS21iYiIYYWoiQ0I9oK/0gmFpRXYmZZj7eYQEdk8hhWiJubgIMEDlXdi/vUQDwUREdWGYYXICh6oPBS09UQ2Ckrt586sRESWwLBCZAVd/T3Q0dcN5RVabDlq+i7kRESkw7BCZAUSiQQP9dIfCrpi5dYQEdk2hhUiK3ngrjYAgL/PXEN2YamVW0NEZLsYVoispF1rF/Ru1wpaAfx+ONPazSEislkMK0RW9FAv3d6Vjak8FEREVB2GFSIrGtPDH1IHCQ5dzMP5azet3RwiIpvEsEJkRT7uCgzp6A0A+I0DbYmITGJYIbKy+3vq7hW0ieNWiIhMYlghsrLIbirIpQ5Iu1qIU1cLrd0cIiKbw7BCZGVKZxnu6ewDANjEQ0FERFUwrBDZgPvv0h0K+u1wJoQQVm4NEZFtYVghsgEjw/ygcHRA+rWbOHalwNrNISKyKQwrRDbATeGIkWG+AIDfDvNQEBHR7RhWiGzEuJ66ewX9zkNBRERGGFaIbMSILr5wkUtxKbcEqRfzrN0cIiKbwbBCZCOc5VJEdPUDAPx2iNdcISLSY1ghsiGGQ0FHrkCr5aEgIiKAYYXIptzT2RvuTo64WlCGlPM3rN0cIiKbwLBCZEMUjlJEdVMB4OX3iYj0GFaIbMzYynsF/XE0CxoeCiIiYlghsjVDOnjDw8kR14p4KIiICGBYIbI5ckcHRFYeCtp8hIeCiIgYVohs0Ngetw4F8awgImrpGFaIbNCQjrqzgnIKy7AvI9fazSEisiqGFSIbJHd0MFwgjoeCiKilY1ghslG3DgVl8lAQEbVoDCtENuruTt5wV+guEHfgAg8FEVHLxbBCZKMUjlKMqjwU9DsPBRFRC8awQmTDxugPBR3hWUFE1HIxrBDZsKGdvOGmcERWQSkOXsyzdnOIiKyCYYXIhjnJpBgV5guAZwURUcvFsEJk4+4zHArKhBA8FERELQ/DCpGNG9bZB65yKa7kl+LwpXxrN4eIqMkxrBDZOCeZFMNDdYeC4o5lWbk1RERNj2GFqBkYXXljw7ijWTwUREQtDsMKUTMwItQXcqkD0q/dxKmrRdZuDhFRk2JYIWoG3BSOGNrJG4Bu7woRUUvCsELUTIzuXnkoiONWiKiFYVghaiZGhflB6iDBicwCZFy/ae3mEBE1GYYVombC01WOQe29AABbuHeFiFoQhhWiZuT2s4KIiFoKhhWiZiSyMqwcuJCHrPxSK7eGiKhpMKwQNSN+Hk7o064VACD+OPeuEFHLwLBC1MwYzgrioSAiaiEYVoiamdHddDc23JN+Azdullu5NURElsewQtTMtGvtgjB/D2i0AltPXLV2c4iILI5hhagZiurmBwBIOM6wQkT2j2GFqBmK7Kobt/LX6RyUlGus3BoiIstiWCFqhsL83dHW0xmlai3+PJ1j7eYQEVkUwwpRMySRSBDRVXcoKP4YDwURkX1jWCFqpvSHgradvIoKjdbKrSEishyGFaJmqn+wJ1q5yJBXrMa+jFxrN4eIyGIYVoiaKUepA0aG8lAQEdk/hhWiZiyy8hTm+ONZEEJYuTVERJbBsELUjN3TyQdOMgdcyi3BicxCazeHiMgiGFaImjFnuRRDO/kA4I0Nich+MawQNXM8hZmI7B3DClEzNzLUFw4S4HhmAS7lFlu7OUREjY5hhaiZa+2mQL9gLwDcu0JE9olhhcgORFYeCuJdmInIHjGsENmBUWG6sLI3/QbyS9RWbg0RUeNiWCGyA8Herujo64YKrcCuU7yxIRHZF4YVIjsxMswXALCNh4KIyM4wrBDZiYjKQ0E7TmZDzRsbEpEdYVghshO923nCy1WOgtIKpJy/Ye3mEBE1GoYVIjshdZBgRBf9oaBsK7eGiKjxWDSs5ObmIiYmBkqlEkqlEjExMcjLy6txHiEEYmNjERAQAGdnZwwfPhzHjh0zqlNWVoaXXnoJ3t7ecHV1xQMPPIBLly7Va93Lly9Hz5494eTkBJVKhenTpze020RWE9FVF1a2nrjKGxsSkd2waFiJjo5Gamoq4uLiEBcXh9TUVMTExNQ4z4IFC7Bw4UJ89tlnSElJgUqlQkREBAoLb92kbcaMGdiwYQPWrVuH3bt3o6ioCOPGjYNGo6nTuhcuXIi33noLb775Jo4dO4Zt27YhKiqqcd8EoiY0tJMP5FIHZFwvxtmcIms3h4ioUThaasEnTpxAXFwckpOTMXDgQADAkiVLEB4ejrS0NHTp0qXKPEIILFq0CG+99RYeeeQRAMCKFSvg5+eHNWvWYNq0acjPz8fSpUuxcuVKjBo1CgCwatUqBAYGYuvWrYiKijJr3bm5uXj77bfx22+/YeTIkYY2dOvWzVJvCZHFuSocEd6hNXadykHC8Wx09HW3dpOIiBrMYmElKSkJSqXSEBYAYNCgQVAqlUhMTDQZVtLT05GVlYXIyEhDmUKhwLBhw5CYmIhp06Zh//79UKvVRnUCAgLQvXt3JCYmIioqyqx1JyQkQKvV4vLlywgLC0NhYSEGDx6MTz75BIGBgSb7VFZWhrKyMsPzgoICAIBarYZaXb8Lcennq+/8tsge+wQ0n36N6KwLK1uPZ+HZIe1qrNtc+lQX9tgnwD77ZY99AuyzX5boU12WZbGwkpWVBV9f3yrlvr6+yMoyfSt7fbmfn59RuZ+fHzIyMgx15HI5PD09q9TRz2/Ous+dOwetVou5c+fiv//9L5RKJd5++21ERETg8OHDkMvlVeafN28e5syZU6U8Pj4eLi4uJvtkroSEhAbNb4vssU9AM+hXGQA44sCFXPzwy2a4yWqfxeb7VA/22CfAPvtlj30C7LNfjdmn4mLzb7xa57ASGxtr8g/27VJSUgAAEomkymtCCJPlt7vzdXPmubNObevWarVQq9X4v//7P8NemrVr10KlUmHHjh0mx67MmjULM2fONDwvKChAYGAgIiMj4eHhUWP7qqNWq5GQkICIiAjIZGb8VWkG7LFPQPPq1/dXknAiqxCO7e7CmN5tqq3XnPpkLnvsE2Cf/bLHPgH22S9L9El/dMIcdQ4r06dPx4QJE2qsExwcjMOHD+Pq1apX0szJyamy50RPpVIB0O0Z8ff3N5RnZ2cb5lGpVCgvL0dubq7R3pXs7GwMHjzYUKe2deuX37VrV8PrPj4+8Pb2xoULF0y2T6FQQKFQVCmXyWQN/vAaYxm2xh77BDSPfkV0U+FEViF2pF3HEwOCa63fHPpUV/bYJ8A++2WPfQLss1+N2ae6LKfOZwN5e3sjNDS0xsnJyQnh4eHIz8/H3r17DfPu2bMH+fn5hlBxp5CQEKhUKqPdTOXl5di1a5dhnr59+0ImkxnVyczMxNGjRw11zFn3kCFDAABpaWmGOjdu3MC1a9cQFBRU17eFyKaMqrz0/p+nc1BWoamlNhGRbbPYqcthYWEYPXo0pk6diuTkZCQnJ2Pq1KkYN26c0eDa0NBQbNiwAYDu0M2MGTMwd+5cbNiwAUePHsWkSZPg4uKC6OhoAIBSqcSUKVPw6quvYtu2bTh48CCefvpp9OjRw3B2kDnr7ty5Mx588EG88sorSExMxNGjRzFx4kSEhoZixIgRlnpbiJpE9wAlfNwVKC7XYG86r2ZLRM2bRa+zsnr1avTo0QORkZGIjIxEz549sXLlSqM6aWlpyM/PNzx//fXXMWPGDLzwwgvo168fLl++jPj4eLi73zoF89NPP8VDDz2E8ePHY8iQIXBxccFvv/0GqVRap3V/9913GDhwIMaOHYthw4ZBJpMhLi7O7nbbUcvj4CDBvbyaLRHZCYudDQQAXl5eWLVqVY117rzKpkQiQWxsLGJjY6udx8nJCYsXL8bixYsbtG4PDw8sXboUS5curbEeUXM0ItQX3++7iB1p2ZgtutY6SJ2IyFbx3kBEduruTt6Gq9meu3bT2s0hIqo3hhUiO+WmcMTA9l4AgO08FEREzRjDCpEduzdUN25l+0mGFSJqvhhWiOyYPqyknL+BglL7ufQ3EbUsDCtEdiyotSs6+LiiQivw16lr1m4OEVG9MKwQ2Tn93pVtJ6te1ZmIqDlgWCGyc/eG6m4xsSstBxqtqKU2EZHtYVghsnP9gj3h7uSI6zfLcehSnrWbQ0RUZwwrRHZOJnXAPZ19AAA7eFYQETVDDCtELQAvvU9EzRnDClELMLyLDyQS4HhmATLzS6zdHCKiOmFYIWoBWrsp0CuwFQDdQFsiouaEYYWohRhReShoRxoPBRFR88KwQtRCDO+iG2T795nrKK/QWrk1RETmY1ghaiG6Byjh7SZHUVkF9mXcsHZziIjMxrBC1EI4OEgMpzBz3AoRNScMK0QtCMetEFFzxLBC1IIM7eQNBwlw6moRLufxFGYiah4YVohakFYucvRp5wkA2Mm9K0TUTDCsELUw+rOCdpzkuBUiah4YVohamOGV41YSz15DGU9hJqJmgGGFqIXpFuABH3cFiss12JeRa+3mEBHVimGFqIWRSCQYXnkK85+nrlm5NUREtWNYIWqB9IeCdjKsEFEzwLBC1ALd3ckbUgcJzl27ieul1m4NEVHNGFaIWiClswx9K09hPp4nsXJriIhqxrBC1EINqzyF+STDChHZOIYVohZqWOUg21P5Et6FmYhsGsMKUQvV1d8D3m5ylGslOHAhz9rNISKqFsMKUQvl4CDB0I6tAQB/nuZZQURkuxhWiFqwuzt6AwD+OnPdyi0hIqoewwpRCzakY2tIIHAyqxBXC3gOMxHZJoYVohastascbV11P/95ijc2JCLbxLBC1MKFtRIAOG6FiGwXwwpRCxfWSnfa8l+nc6DRCiu3hoioKoYVohYuyB1wd3JEXrEaRy7nW7s5RERVMKwQtXBSCTC4vRcAYFcax60Qke1hWCEi3NNJdwrzrlPZVm4JEVFVDCtEhKGVYSX1Yh7yi9VWbg0RkTGGFSKCv9IJnXzdoBXA7jM8K4iIbAvDChEBAO6pvLEhDwURka1hWCEiALfuwvznqWsQgqcwE5HtYFghIgDAgBAvKBwdkFVQirM5RdZuDhGRAcMKEQEAnGRSDAjRncL85ymOWyEi28GwQkQG+rOC/jrN660Qke1gWCEig6GddONWks/dQFmFxsqtISLSYVghIoNQlTu83RQoUWuwPyPX2s0hIgLAsEJEt5FIJIZDQbt5F2YishEMK0Rk5Na4FYYVIrINDCtEZOTujrqwcvRKPq4XlVm5NUREDCtEdAdfDyeEqtwhBPD32evWbg4REcMKEVV1a9wKT2EmIutjWCGiKvSnMP91mpfeJyLrY1ghoioGhHhB7uiAzHxeep+IrI9hhYiqcJJJMZCX3iciG8GwQkQm6c8K2n2GYYWIrIthhYhM0o9bSTp7nZfeJyKrYlghIpNuv/T+gYw8azeHiFowhhUiMsnBQYIhHVsDABLP8lAQEVkPwwoRVWtIR156n4isj2GFiKqlH2R7+FIe8kvUVm4NEbVUDCtEVK2AVs5o7+MKrQCSz/HS+0RkHQwrRFQj/d6Vv3kKMxFZCcMKEdVoCK+3QkRWxrBCRDUa1L41HCTAuZybuJJXYu3mEFELxLBCRDVSOsvQs20rADwURETWwbBCRLXiuBUisiaGFSKq1a1xK9chhLBya4iopWFYIaJa9QlqBWeZFNeKypB2tdDazSGiFoZhhYhqpXCUYkCIFwBgN69mS0RNjGGFiMzCcStEZC0MK0RkFv24lT3pN1BeobVya4ioJWFYISKzhKrc0dpVjuJyDVIv5lm7OUTUglg0rOTm5iImJgZKpRJKpRIxMTHIy8urcR4hBGJjYxEQEABnZ2cMHz4cx44dM6pTVlaGl156Cd7e3nB1dcUDDzyAS5cu1XndKSkpGDlyJFq1agVPT09ERkYiNTW1EXpOZH8cHCQYrD8r6HSOlVtDRC2JRcNKdHQ0UlNTERcXh7i4OKSmpiImJqbGeRYsWICFCxfis88+Q0pKClQqFSIiIlBYeOsMhBkzZmDDhg1Yt24ddu/ejaKiIowbNw4ajcbsdRcWFiIqKgrt2rXDnj17sHv3bnh4eCAqKgpqNe8uS2TKkA6tAQCJZ3lTQyJqOo6WWvCJEycQFxeH5ORkDBw4EACwZMkShIeHIy0tDV26dKkyjxACixYtwltvvYVHHnkEALBixQr4+flhzZo1mDZtGvLz87F06VKsXLkSo0aNAgCsWrUKgYGB2Lp1K6Kiosxad1paGnJzc/Hee+8hMDAQADB79mz07NkTFy5cQIcOHSz11hA1W/pxK6kX83CzrAKuCov9CiEiMrDYb5qkpCQolUpDWACAQYMGQalUIjEx0WRYSU9PR1ZWFiIjIw1lCoUCw4YNQ2JiIqZNm4b9+/dDrVYb1QkICED37t2RmJiIqKgos9bdpUsXeHt7Y+nSpfj3v/8NjUaDpUuXolu3bggKCjLZp7KyMpSVlRmeFxQUAADUanW998bo57OnvTn22CfAPvtV1z6p3GVo28oJl/JKkXQmG8M6+1iyefVij58TYJ/9ssc+AfbZL0v0qS7LslhYycrKgq+vb5VyX19fZGVlVTsPAPj5+RmV+/n5ISMjw1BHLpfD09OzSh39/Oas293dHTt37sSDDz6I999/HwDQuXNnbNmyBY6Opt+WefPmYc6cOVXK4+Pj4eLiYnIecyUkJDRofltkj30C7LNfdelTW7kDLsEBa7bux80ztntWkD1+ToB99sse+wTYZ78as0/FxcVm161zWImNjTX5B/t2KSkpAACJRFLlNSGEyfLb3fm6OfPcWae2dZeUlGDy5MkYMmQI1q5dC41Gg48//hhjxoxBSkoKnJ2dq8w/a9YszJw50/C8oKAAgYGBiIyMhIeHR43tq45arUZCQgIiIiIgk8nqtQxbY499AuyzX/XpU8WhTCT/dARZUGLMmHALt7Du7PFzAuyzX/bYJ8A++2WJPumPTpijzmFl+vTpmDBhQo11goODcfjwYVy9erXKazk5OVX2nOipVCoAuj0j/v7+hvLs7GzDPCqVCuXl5cjNzTXau5KdnY3Bgwcb6tS27jVr1uD8+fNISkqCg4ODoczT0xO//PKLyT4qFAooFIoq5TKZrMEfXmMsw9bYY58A++xXXfo0tItur+WJrEIUlQt4usot2bR6s8fPCbDPftljnwD77Fdj9qkuy6nz2UDe3t4IDQ2tcXJyckJ4eDjy8/Oxd+9ew7x79uxBfn6+IVTcKSQkBCqVymg3U3l5OXbt2mWYp2/fvpDJZEZ1MjMzcfToUUMdc9ZdXFwMBwcHoz0w+udare3u2iayNl93J3TydYMQQPI5nhVERJZnsVOXw8LCMHr0aEydOhXJyclITk7G1KlTMW7cOKPBtaGhodiwYQMA3aGbGTNmYO7cudiwYQOOHj2KSZMmwcXFBdHR0QAApVKJKVOm4NVXX8W2bdtw8OBBPP300+jRo4fh7CBz1h0REYHc3Fy8+OKLOHHiBI4dO4ZnnnkGjo6OGDFihKXeFiK7oD8r6O+zvPQ+EVmeRa+zsnr1avTo0QORkZGIjIxEz549sXLlSqM6aWlpyM/PNzx//fXXMWPGDLzwwgvo168fLl++jPj4eLi7uxvqfPrpp3jooYcwfvx4DBkyBC4uLvjtt98glUrNXndoaCh+++03HD58GOHh4Rg6dCiuXLmCuLg4o0NQRFRVOK+3QkRNyKIXSfDy8sKqVatqrCOEMHoukUgQGxuL2NjYaudxcnLC4sWLsXjx4gatOyIiAhERETXWIaKqBrVvDQcJcC7nJrLyS6FSOlm7SURkx3hvICKqM6WzDN3bKAEAiTwUREQWxrBCRPUyuEPluJUzPBRERJbFsEJE9TK4ctxK0tlrVQ7nEhE1JoYVIqqX/sFekEkluJJfivPXzb8SJRFRXTGsEFG9OMul6N1Od2FGjlshIktiWCGiehtSOW4lkeNWiMiCGFaIqN4Gd6wct3LuOrRajlshIstgWCGierurbSs4y6S4cbMcp7ILrd0cIrJTDCtEVG9yRwf0C9aNW0ni1WyJyEIYVoioQQa11x0K4k0NichSGFaIqEH09wnak36D41aIyCIYVoioQXq0UcJVLkVesRonsgqs3RwiskMMK0TUIDKpA/qHeAHguBUisgyGFSJqsHCOWyEiC2JYIaIGu33ciobjVoiokTGsEFGDdQtQwt3JEYWlFTh2Jd/azSEiO8OwQkQNJnWQYCDHrRCRhTCsEFGj0F9vJYnjVoiokTGsEFGj0I9bSUm/AbVGa+XWEJE9YVghokYRpvJAKxcZbpZrcOQyx60QUeNhWCGiRuHAcStEZCEMK0TUaHi9FSKyBIYVImo04R28AQD7zueivILjVoiocTCsEFGj6eznhtaucpSoNThyOc/azSEiO8GwQkSNRiKRYGB73biV5HM3rNwaIrIXDCtE1KgGhnDcChE1LoYVImpU+ovD7Tufy+utEFGjYFghokbVydcNni4ylKg1OHyJ11shooZjWCGiRqW73or+Lsw8FEREDcewQkSNjoNsiagxMawQUaPTj1vZf573CSKihmNYIaJG18XP3XCfoKO8TxARNRDDChE1OgcHCQYE6w4F7UnnoSAiahiGFSKyiIG8TxARNRKGFSKyiEGVg2z3nc9FBcetEFEDMKwQkUWEqjzg4eSIorIKHLtSYO3mEFEzxrBCRBYhdZBgAC+9T0SNgGGFiCxGfyiIg2yJqCEYVojIYvTXW0lJvwGNVli5NUTUXDGsEJHFhPl7wN3JEYVlFTjOcStEVE8MK0RkMdLbrrfCcStEVF8MK0RkUQNCdGFl73mOWyGi+mFYISKL0l8cLuX8DWg5boWI6oFhhYgsqluAB1zkUuQVq3Equ9DazSGiZohhhYgsSiZ1QN8gTwDAXp7CTET1wLBCRBbHmxoSUUMwrBCRxRkG2abfgBAct0JEdcOwQkQWd1dgK8ilDsgpLMP568XWbg4RNTMMK0RkcU4yKXoFtgIA7E3n9VaIqG4YVoioSegPBXHcChHVFcMKETWJgfqbGp5jWCGiumFYIaIm0aedJ6QOElzOK8GlXI5bISLzMawQUZNwVTiiexslAN3VbImIzMWwQkRNZuBtpzATEZmLYYWImgwvDkdE9cGwQkRNpn+wFyQS4FzOTeQUllm7OUTUTDCsEFGTUbrI0MXPHQDHrRCR+RhWiKhJ6cet7DnHi8MRkXkYVoioSQ0IaQ2A41aIyHwMK0TUpPqHeAIA0q4WIr9EbeXWEFFzwLBCRE3K190Jwa1dIARwICPX2s0homaAYYWImlz/ylOY93KQLRGZgWGFiJqcPqzsY1ghIjMwrBBRk+tfeUbQoYv5KFVrrNwaIrJ1DCtE1OSCW7vA202Bco0Why/lW7s5RGTjGFaIqMlJJBIMqDwriBeHI6LaMKwQkVXox60wrBBRbRhWiMgq9GFl//lcaLTCyq0hIlvGsEJEVhHm7wE3hSMKyypwMqvA2s0hIhvGsEJEViF1kKBPUOW4FV56n4hqwLBCRFYzIFg/yJZXsiWi6jGsEJHV9LttkK0QHLdCRKYxrBCR1fQKbAWZVILswjJcuFFs7eYQkY2yaFjJzc1FTEwMlEollEolYmJikJeXV+M8QgjExsYiICAAzs7OGD58OI4dO2ZUp6ysDC+99BK8vb3h6uqKBx54AJcuXTKq8+GHH2Lw4MFwcXFBq1atTK7rwoULuP/+++Hq6gpvb2+8/PLLKC8vb0iXiagOnGRS9GzbCgCwl+NWiKgaFg0r0dHRSE1NRVxcHOLi4pCamoqYmJga51mwYAEWLlyIzz77DCkpKVCpVIiIiEBhYaGhzowZM7BhwwasW7cOu3fvRlFREcaNGweN5tZlu8vLy/H444/j+eefN7kejUaDsWPH4ubNm9i9ezfWrVuH9evX49VXX22czhORWXi9FSKqjaOlFnzixAnExcUhOTkZAwcOBAAsWbIE4eHhSEtLQ5cuXarMI4TAokWL8NZbb+GRRx4BAKxYsQJ+fn5Ys2YNpk2bhvz8fCxduhQrV67EqFGjAACrVq1CYGAgtm7diqioKADAnDlzAADLly832b74+HgcP34cFy9eREBAAADgk08+waRJk/Dhhx/Cw8OjUd8PIjJtQIgnvtoF7OMgWyKqhsXCSlJSEpRKpSGoAMCgQYOgVCqRmJhoMqykp6cjKysLkZGRhjKFQoFhw4YhMTER06ZNw/79+6FWq43qBAQEoHv37khMTDSEFXPa1717d0NQAYCoqCiUlZVh//79GDFiRJV5ysrKUFZWZnien6+7p8mNGzegVqvNWu+d1Go1iouLcf36dchksnotw9bYY58A++yXLfQpxE1AlBfjzOVinMq4gtZuigYtzxb6ZAn22C977BNgn/2yRJ/0R0zMGVxvsbCSlZUFX1/fKuW+vr7Iysqqdh4A8PPzMyr38/NDRkaGoY5cLoenp2eVOtUtt7p13bkeT09PyOXyapczb948wx6b24WEhJi9XiKqXpdF1m4BETW1wsJCKJXKGuvUOazExsaa/IN9u5SUFAC6m5XdSQhhsvx2d75uzjzm1KltPbUtZ9asWZg5c6bhuVarxY0bN9C6des6r1uvoKAAgYGBuHjxot0cerLHPgH22S/2qfmwx37ZY58A++yXJfokhEBhYaHREY7q1DmsTJ8+HRMmTKixTnBwMA4fPoyrV69WeS0nJ6fKHg09lUoFQLfXw9/f31CenZ1tmEelUqG8vBy5ublGe1eys7MxePBgs/uhUqmwZ88eo7Lc3Fyo1epq26dQKKBQGO+iru5Mo7ry8PCwmy+1nj32CbDPfrFPzYc99sse+wTYZ78au0+17VHRq/PZQN7e3ggNDa1xcnJyQnh4OPLz87F3717DvHv27EF+fn61oSIkJAQqlQoJCQmGsvLycuzatcswT9++fSGTyYzqZGZm4ujRo3UKK+Hh4Th69CgyMzMNZfHx8VAoFOjbt6/ZyyEiIiLLstipy2FhYRg9ejSmTp2K5ORkJCcnY+rUqRg3bpzR4NrQ0FBs2LABgO6wzIwZMzB37lxs2LABR48exaRJk+Di4oLo6GgAuhQ2ZcoUvPrqq9i2bRsOHjyIp59+Gj169DCcHQTorqGSmpqKCxcuQKPRIDU1FampqSgqKgIAREZGomvXroiJicHBgwexbds2vPbaa5g6dardJWEiIqJmTVjQ9evXxVNPPSXc3d2Fu7u7eOqpp0Rubq5RHQBi2bJlhudarVbMnj1bqFQqoVAoxD333COOHDliNE9JSYmYPn268PLyEs7OzmLcuHHiwoULRnUmTpwoAFSZduzYYaiTkZEhxo4dK5ydnYWXl5eYPn26KC0tbey3oUalpaVi9uzZTb5eS7LHPglhn/1in5oPe+yXPfZJCPvsl7X7JBGCN+QgIiIi28V7AxEREZFNY1ghIiIim8awQkRERDaNYYWIiIhsGsNKA33xxRcICQmBk5MT+vbti7/++qvG+rt27ULfvn3h5OSE9u3b46uvvqpSZ/369ejatSsUCgW6du1qOLW7Ieu1Zp+WLFmCoUOHwtPTE56enhg1apTR9XcA3ZWRJRKJ0aS/SKCt9mv58uVV2iyRSFBaWtqg9VqzT8OHDzfZp7FjxxrqWPqzqkufMjMzER0djS5dusDBwQEzZswwWc/a21Rdl29Ov2xhu2rsPtnCNmWJfjW37ernn39GREQEfHx84OHhgfDwcGzZsqVKvSbdrqxyDpKdWLdunZDJZGLJkiXi+PHj4pVXXhGurq4iIyPDZP1z584JFxcX8corr4jjx4+LJUuWCJlMJn766SdDncTERCGVSsXcuXPFiRMnxNy5c4Wjo6NITk6u93qt3afo6Gjx+eefi4MHD4oTJ06IZ555RiiVSnHp0iVDndmzZ4tu3bqJzMxMw5Sdnd3g/liyX8uWLRMeHh5Gbc7MzGzQeq3dp+vXrxv15ejRo0IqlRpdXsCSn1Vd+5Seni5efvllsWLFCtGrVy/xyiuvVKlj7W3KUv2y9nZliT5Ze5uyVL+a23b1yiuviPnz54u9e/eKU6dOiVmzZgmZTCYOHDhgqNPU2xXDSgMMGDBAPPfcc0ZloaGh4s033zRZ//XXXxehoaFGZdOmTRODBg0yPB8/frwYPXq0UZ2oqCgxYcKEeq+3LizRpztVVFQId3d3sWLFCkPZ7NmzxV133VX/htfCEv1atmyZUCqVjbreumiKz+rTTz8V7u7uoqioyFBmyc+qIe/XsGHDTP6hsPY21dDlV9evOzX1dmWJPll7m2ro8s39rJrTdqXXtWtXMWfOHMPzpt6ueBionsrLy7F//35ERkYalUdGRiIxMdHkPElJSVXqR0VFYd++fVCr1TXW0S+zPuu1dp/uVFxcDLVaDS8vL6Py06dPIyAgACEhIZgwYQLOnTvXgN7cYsl+FRUVISgoCG3btsW4ceNw8ODBBq3XFvp0u6VLl2LChAlwdXU1KrfEZ2Wp98ua21RTLF+vKbcrS/bJWttUUyxfr7ltV1qtFoWFhUbfraberhhW6unatWvQaDRVbnro5+eHrKwsk/NkZWWZrF9RUYFr167VWEe/zPqs19p9utObb76JNm3aGN0eYeDAgfjuu++wZcsWLFmyBFlZWRg8eDCuX7/eoD4BlutXaGgoli9fjl9//RVr166Fk5MThgwZgtOnT9d7vdbu0+327t2Lo0eP4tlnnzUqt9RnZan3y5rbVFMsX68ptytL9cma21RTLB9ontvVJ598gps3b2L8+PGGsqberup812UyJpFIjJ4LIaqU1Vb/znJzllnX9daFJfqkt2DBAqxduxY7d+6Ek5OTofy+++4z/NyjRw+Eh4ejQ4cOWLFiBWbOnFmvfpjTzob0a9CgQRg0aJDh9SFDhqBPnz5YvHgx/u///q/e660LS35WS5cuRffu3TFgwACjckt/VpZ4v6y9TVl6+dbarhq7T7awTVl6+c1tu1q7di1iY2Pxyy+/wNfXt87LbKz3kntW6snb2xtSqbRKQszOzq6SJPVUKpXJ+o6OjmjdunWNdfTLrM96rd0nvY8//hhz585FfHw8evbsWWNbXF1d0aNHD8N/VA1h6X7pOTg4oH///oY2N+fPqri4GOvWravy358pjfVZWer9suY21RTLt8Z2Zek+6TXlNtUUy29u29X333+PKVOm4IcffjDaYwc0/XbFsFJPcrkcffv2RUJCglF5QkICBg8ebHKe8PDwKvXj4+PRr18/yGSyGuvol1mf9Vq7TwDw0Ucf4f3330dcXBz69etXa1vKyspw4sQJ+Pv716MnxizZr9sJIZCammpoc3P9rADghx9+QFlZGZ5++ula29JYn5Wl3i9rblOWXr61titLv2d6TblNNcXym9N2tXbtWkyaNAlr1qwxOsVar8m3qzoPySUD/WlZS5cuFcePHxczZswQrq6u4vz580IIId58800RExNjqK8/dfRf//qXOH78uFi6dGmVU0f//vtvIZVKxX/+8x9x4sQJ8Z///Kfa08GqW6+t9Wn+/PlCLpeLn376yei0vMLCQkOdV199VezcuVOcO3dOJCcni3Hjxgl3d/dG6ZOl+hUbGyvi4uLE2bNnxcGDB8UzzzwjHB0dxZ49e8xer631Se/uu+8WTzzxhMn1WvKzqmufhBDi4MGD4uDBg6Jv374iOjpaHDx4UBw7dszwurW3KUv1y9rblSX6ZO1tylL90msu29WaNWuEo6Oj+Pzzz42+W3l5eYY6Tb1dMaw00Oeffy6CgoKEXC4Xffr0Ebt27TK8NnHiRDFs2DCj+jt37hS9e/cWcrlcBAcHiy+//LLKMn/88UfRpUsXIZPJRGhoqFi/fn2d1mtrfQoKChIAqkyzZ8821HniiSeEv7+/kMlkIiAgQDzyyCMmN3Zb6teMGTNEu3bthFwuFz4+PiIyMlIkJibWab221ichhEhLSxMARHx8vMl1WvqzqmufTH23goKCjOpYe5uyRL9sYbtq7D7ZwjZliX4J0by2q2HDhpns08SJE42W2ZTblUSIyhF2RERERDaIY1aIiIjIpjGsEBERkU1jWCEiIiKbxrBCRERENo1hhYiIiGwawwoRERHZNIYVIiIismkMK0RERGTTGFaIiIjIpjGsEBERkU1jWCEiIiKbxrBCRHYlJycHKpUKc+fONZTt2bMHcrkc8fHxVmwZEdUXb2RIRHZn8+bNeOihh5CYmIjQ0FD07t0bY8eOxaJFi6zdNCKqB4YVIrJLL774IrZu3Yr+/fvj0KFDSElJgZOTk7WbRUT1wLBCRHappKQE3bt3x8WLF7Fv3z707NnT2k0ionrimBUiskvnzp3DlStXoNVqkZGRYe3mEFEDcM8KEdmd8vJyDBgwAL169UJoaCgWLlyII0eOwM/Pz9pNI6J6YFghIrvz//7f/8NPP/2EQ4cOwc3NDSNGjIC7uzs2bdpk7aYRUT3wMBAR2ZWdO3di0aJFWLlyJTw8PODg4ICVK1di9+7d+PLLL63dPCKqB+5ZISIiIpvGPStERERk0xhWiIiIyKYxrBAREZFNY1ghIiIim8awQkRERDaNYYWIiIhsGsMKERER2TSGFSIiIrJpDCtERERk0xhWiIiIyKYxrBAREZFN+/+fxkUmvrW7iAAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x2_v = np.linspace(0,0.2,100)\n", - "x1_v[0] = x1_v[1]/2\n", - "data = [(\n", - " xx, \n", - " m.sqrt(1+xx)-1,\n", - " xx * (0.5 - xx*1/8),\n", - " #xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128,\n", - " xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))),\n", - ") for xx in x2_v\n", - "]\n", - "df = pd.DataFrame(data, columns=['x', 'Float', 'Taylor2', 'Taylor4']).set_index(\"x\")\n", - "df.plot()\n", - "plt.grid()\n", - "df2 = df.copy()\n", - "df2[\"Err2\"] = df2[\"Taylor2\"]/df2[\"Float\"] - 1\n", - "df2[\"Err4\"] = df2[\"Taylor4\"]/df2[\"Float\"] - 1\n", - "plt.show()\n", - "df2.plot(y=[\"Err2\", \"Err4\"])\n", - "plt.grid()\n", - "plt.title(\"Relative error of Taylor 2 4 term approximations\")\n", - "plt.ylim(-0.001, 0.0001)\n", - "df2.head()" - ] - }, - { - "cell_type": "markdown", - "id": "4446b5dd-a4c8-450f-81bd-d7a909895bf8", - "metadata": {}, - "source": [ - "### Decimal vs float\n", - "#### Precision\n", - "\n", - "we compare $\\sqrt{1+\\xi}-1$ for float, Taylor and Decimal\n", - "\n", - "$$\n", - "\\sqrt{1+\\xi}-1 = \\frac{\\xi}{2} - \\frac{\\xi^2}{8} + \\frac{\\xi^3}{16} - \\frac{5\\xi^4}{128} + O(\\xi^5)\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 383, - "id": "824c7650-acd7-4336-924e-9c927f0e2ebe", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(1e-18, 1.3721439741813515)" - ] - }, - "execution_count": 383, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import decimal as d\n", - "D = d.Decimal\n", - "d.getcontext().prec = 1000 # Set the precision to 30 decimal places (adjust as needed)\n", - "xd_v = [1e-18*1.5**nn for nn in np.linspace(0, 103, 500)]\n", - "xd_v[0], xd_v[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 384, - "id": "8252b418-74e6-429f-9162-1574ac04580f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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FloatTaylor2Taylor4Dec
x
1.000000e-180.05.000000e-195.000000e-190.0
1.087295e-180.05.436476e-195.436476e-190.0
1.182211e-180.05.911055e-195.911055e-190.0
1.285412e-180.06.427062e-196.427062e-190.0
1.397623e-180.06.988114e-196.988114e-190.0
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" - ], - "text/plain": [ - " Float Taylor2 Taylor4 Dec\n", - "x \n", - "1.000000e-18 0.0 5.000000e-19 5.000000e-19 0.0\n", - "1.087295e-18 0.0 5.436476e-19 5.436476e-19 0.0\n", - "1.182211e-18 0.0 5.911055e-19 5.911055e-19 0.0\n", - "1.285412e-18 0.0 6.427062e-19 6.427062e-19 0.0\n", - "1.397623e-18 0.0 6.988114e-19 6.988114e-19 0.0" - ] - }, - "execution_count": 384, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fmt = lambda x: x\n", - "fmt = float\n", - "data = [(\n", - " xx, \n", - " m.sqrt(1+xx)-1,\n", - " xx * (0.5 - xx*1/8),\n", - " #xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128,\n", - " xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))),\n", - " fmt(D(1+xx).sqrt()-1),\n", - ") for xx in xd_v\n", - "]\n", - "df = pd.DataFrame(data, columns=['x', 'Float', 'Taylor2', 'Taylor4', 'Dec']).set_index(\"x\")\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 385, - "id": "fefe53dc-7047-4506-bd8b-c6bc86d9bf56", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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] - }, - { - "cell_type": "markdown", - "id": "dfde558e-f3f6-4de1-ba87-60ddbfa9138d", - "metadata": {}, - "source": [ - "#### Timing" - ] - }, - { - "cell_type": "code", - "execution_count": 390, - "id": "6c6e54f3-7f43-4215-9c2d-39ad115bd009", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "import decimal as d\n", - "D = d.Decimal" - ] - }, - { - "cell_type": "code", - "execution_count": 391, - "id": "a16c06d8-8c87-42e8-917b-508affddc17c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def time_func(func, *args, N=None, **kwargs):\n", - " \"\"\"times the calls to func; func is called with args and kwargs; returns time in msec per 1m calls\"\"\"\n", - " if N is None:\n", - " N = 10_000_000\n", - " start_time = time.time()\n", - " for _ in range(N):\n", - " func(*args, **kwargs)\n", - " end_time = time.time()\n", - " return (end_time - start_time)/N*1_000_000*1000" - ] - }, - { - "cell_type": "code", - "execution_count": 392, - "id": "9a313fce-2b46-43b7-a416-98d5ab0073dd", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def time_func1(func, arg, N=None):\n", - " \"\"\"times the calls to func; func is called with arg; returns time in msec per 1m calls\"\"\"\n", - " if N is None:\n", - " N = 10_000_000\n", - " start_time = time.time()\n", - " for _ in range(N):\n", - " func(arg)\n", - " end_time = time.time()\n", - " return (end_time - start_time)/N*1_000_000*1000" - ] - }, - { - "cell_type": "markdown", - "id": "b313973b-ae68-4f0f-bd6c-5e1aa2bb25ea", - "metadata": {}, - "source": [ - "identify function (`lambda`)" - ] - }, - { - "cell_type": "code", - "execution_count": 393, - "id": "9a7ee59f-ac92-4752-9286-f5f64b6882f4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(42.34120845794678, 27.79741287231445)" - ] - }, - "execution_count": 393, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "time_func(lambda x: x, 1), time_func1(lambda x: x, 1)" - ] - }, - { - "cell_type": "markdown", - "id": "a6f31082-4975-4c77-a634-d68a98a8c7d9", - "metadata": {}, - "source": [ - "ditto, defined with `def`" - ] - }, - { - "cell_type": "code", - "execution_count": 394, - "id": "ef6a6f1f-13d2-48be-b12c-27e197ed276e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(40.04268646240234, 27.441811561584473)" - ] - }, - "execution_count": 394, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def idfunc(x):\n", - " return x\n", - "time_func(idfunc, 1), time_func1(idfunc, 1)" - ] - }, - { - "cell_type": "markdown", - "id": "f9c02ca3-1414-4981-82a0-0f8c932916d4", - "metadata": {}, - "source": [ - "sin, sqrt, exp etc as reference" - ] - }, - { - "cell_type": "code", - "execution_count": 395, - "id": "c3ef665b-0255-4ff4-ba77-491b6f82ee2b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(51.58989429473877,\n", - " 51.671695709228516,\n", - " 53.81209850311279,\n", - " 39.70539569854736,\n", - " 44.649386405944824,\n", - " 45.37239074707031)" - ] - }, - "execution_count": 395, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(time_func(m.sin, 1), time_func(m.cos, 1), time_func(m.tan, 1), \n", - " time_func(m.sqrt, 1), time_func(m.exp, 1), time_func(m.log, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 396, - "id": "c5300f07-35ac-464a-814a-a6767f3c4f11", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(38.92111778259277,\n", - " 38.546013832092285,\n", - " 41.330814361572266,\n", - " 27.349305152893066,\n", - " 31.124615669250485,\n", - " 42.095494270324714)" - ] - }, - "execution_count": 396, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(time_func1(m.sin, 1), time_func1(m.cos, 1), time_func1(m.tan, 1), \n", - " time_func1(m.sqrt, 1), time_func1(m.exp, 1), time_func1(m.log, 1))" - ] - }, - { - "cell_type": "markdown", - "id": "2bc8cf1a-9ad6-46ff-975d-ff0816471149", - "metadata": {}, - "source": [ - "**float** calculation" - ] - }, - { - "cell_type": "code", - "execution_count": 397, - "id": "74a9d3db-0239-4708-982d-5196b80ac910", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(77.3056983947754, 64.60719108581543)" - ] - }, - "execution_count": 397, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "time_func(lambda xx: m.sqrt(1+xx)-1, 1), time_func1(lambda xx: m.sqrt(1+xx)-1, 1)" - ] - }, - { - "cell_type": "markdown", - "id": "4f4abe46-5247-4307-8230-f7ef66788f30", - "metadata": {}, - "source": [ - "**taylor** calculations" - ] - }, - { - "cell_type": "code", - "execution_count": 398, - "id": "00b7850a-b625-4b5d-a3d0-8697eaaeee96", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(71.11051082611084, 59.263992309570305)" - ] - }, - "execution_count": 398, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "time_func(lambda xx: xx * (0.5 - xx*1/8), 1), time_func1(lambda xx: xx * (0.5 - xx*1/8), 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 399, - "id": "cb211a08-bbcf-463a-81e3-07fc2de66914", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(104.2957067489624, 91.90921783447264)" - ] - }, - "execution_count": 399, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(time_func(lambda xx: xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))), 1),\n", - "time_func1(lambda xx: xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))), 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 400, - "id": "922cd929-78d3-42e3-8d1f-90e91fbeb438", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(135.984206199646, 120.62640190124513)" - ] - }, - "execution_count": 400, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(time_func(lambda xx: xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128, 1),\n", - "time_func1(lambda xx: xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128, 1))" - ] - }, - { - "cell_type": "markdown", - "id": "7449ffef-1cd2-440f-8130-a451ad849ebe", - "metadata": { - "tags": [] - }, - "source": [ - "**decimal** calculations" - ] - }, - { - "cell_type": "code", - "execution_count": 401, - "id": "7f07e127-a034-4a27-99f0-8bb6a150f158", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(2829.4801712036133, 3080.589771270752)" - ] - }, - "execution_count": 401, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d.getcontext().prec = 30\n", - "ONE = D(1)\n", - "(time_func(lambda xx: D(1+xx).sqrt()-1, 1, N=100_000),\n", - " time_func(lambda xx: ONE+xx.sqrt()-1, ONE, N=100_000))" - ] - }, - { - "cell_type": "code", - "execution_count": 402, - "id": "34efbb1e-f424-4078-830b-3b0db3cee19b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(11736.392974853516, 12309.575080871582)" - ] - }, - "execution_count": 402, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d.getcontext().prec = 100\n", - "ONE = D(1)\n", - "(time_func(lambda xx: D(1+xx).sqrt()-1, 1, N=10_000),\n", - " time_func(lambda xx: ONE+xx.sqrt()-1, ONE, N=10_000))" - ] - }, - { - "cell_type": "code", - "execution_count": 403, - "id": "068d3189-bc7c-45fa-973e-7415e983d95b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(607185.1253509521, 645433.9027404785)" - ] - }, - "execution_count": 403, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d.getcontext().prec = 1_000\n", - "ONE = D(1)\n", - "(time_func(lambda xx: D(1+xx).sqrt()-1, 1, N=1_000),\n", - " time_func(lambda xx: ONE+xx.sqrt()-1, ONE, N=1_000))" - ] - }, - { - "cell_type": "markdown", - "id": "338a845c-5103-46fb-9a0f-8a7584159dad", - "metadata": {}, - "source": [ - "decimal conversions" - ] - }, - { - "cell_type": "code", - "execution_count": 404, - "id": "ce909177-cb11-4bf2-b210-0bcd9b53a10e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(397.92513847351074,\n", - " 280.1520824432373,\n", - " Decimal('0.999999999999999999999999999999'))" - ] - }, - "execution_count": 404, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d.getcontext().prec = 30\n", - "ONE = D(\"0.\"+\"9\"*d.getcontext().prec)\n", - "PI = m.pi\n", - "(time_func(lambda xx: D(xx), PI, N=1_000_000),\n", - " time_func(lambda: float(ONE), N=1_000_000),\n", - " ONE\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 405, - "id": "21f146ca-522c-44a9-b9ef-a9275ff026c1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(400.1290798187256,\n", - " 526.4580249786377,\n", - " Decimal('0.9999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999'))" - ] - }, - "execution_count": 405, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d.getcontext().prec = 100\n", - "ONE = D(\"0.\"+\"9\"*d.getcontext().prec)\n", - "(time_func(lambda xx: D(xx), PI, N=1_000_000),\n", - " time_func(lambda: float(ONE), N=1_000_000),\n", - " ONE\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 406, - "id": "13db7008-08da-436b-9885-01575e26e8d5", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(394.96421813964844,\n", - " 1885.3051662445068,\n", - " Decimal('0.9999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999'))" - ] - }, - "execution_count": 406, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d.getcontext().prec = 1000\n", - "ONE = D(\"0.\"+\"9\"*d.getcontext().prec)\n", - "(time_func(lambda xx: D(xx), PI, N=1_000_000),\n", - " time_func(lambda: float(ONE), N=1_000_000),\n", - " ONE\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dbf0b416-29b5-412b-bba8-e304ec3a751d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/analysis/202401 Solidly/202401 Solidly-Freeze03.ipynb b/resources/analysis/202401 Solidly/202401 Solidly-Freeze03.ipynb deleted file mode 100644 index 49fa72b48..000000000 --- a/resources/analysis/202401 Solidly/202401 Solidly-Freeze03.ipynb +++ /dev/null @@ -1,2128 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "96348e86-5892-417a-9e2d-2fda430683d0", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import math as m\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "from sympy import symbols, sqrt, Eq\n", - "import decimal as d\n", - "D = d.Decimal\n", - "plt.rcParams['figure.figsize'] = [6,6]" - ] - }, - { - "cell_type": "markdown", - "id": "a14a57f8-e21f-4652-9d68-0cff0c4afead", - "metadata": {}, - "source": [ - "# Solidly Analysis (Freeze03)" - ] - }, - { - "cell_type": "markdown", - "id": "9bcaf580-1389-41dc-b329-c68a80c75d56", - "metadata": {}, - "source": [ - "## Equations" - ] - }, - { - "cell_type": "markdown", - "id": "58ab6488-5c7b-4103-bae1-9d79d9837f11", - "metadata": {}, - "source": [ - "### Invariant function\n", - "\n", - "The Solidly invariant function is \n", - "\n", - "$$\n", - " x^3y+xy^3 = k\n", - "$$\n", - "\n", - "which is a stable swap curve, but more convex than say curve. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "34a840d9-e684-406b-a8da-b1bbbe255f9f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def invariant_eq(x, y, k=0, *, aserr=False):\n", - " \"\"\"returns f(x,y)-k or f(x,y)/k - 1\"\"\"\n", - " if aserr:\n", - " return (x**3 * y + x * y**3)/k-1\n", - " else:\n", - " return x**3 * y + x * y**3 - k" - ] - }, - { - "cell_type": "markdown", - "id": "b6ee11bb-309c-4bb4-a9bc-45199287971e", - "metadata": {}, - "source": [ - "### Swap equation\n", - "\n", - "Solving the invariance equation as $y=y(x; k)$ gives the following result\n", - "\n", - "$$\n", - "y(x;k) = \\frac{x^2}{\\left(-\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\\right)^{\\frac{1}{3}}} - \\frac{\\left(-\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\\right)^{\\frac{1}{3}}}{3}\n", - "$$\n", - "\n", - "We can introduce intermediary variables $L(x;k), M(x;k)$ to write this a bit more simply\n", - "\n", - "$$\n", - "L = -\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\n", - "$$\n", - "\n", - "$$\n", - "M = L^{1/3} = \\sqrt[3]{L}\n", - "$$\n", - "\n", - "$$\n", - "y = \\frac{x^2}{\\sqrt[3]{L}} - \\frac{\\sqrt[3]{L}}{3} = \\frac{x^2}{M} - \\frac{M}{3} \n", - "$$\n", - "\n", - "Using the function $y(x;k)$ we can easily derive the swap equation at point $(x; k)$ as\n", - "\n", - "$$\n", - "\\Delta y = y(x+\\Delta x; k) - y(x; k)\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "50f960e3-65e3-470c-a465-64c1a3fb51f2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{2}}{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}} - \\frac{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}}{3}$" - ], - "text/plain": [ - "x**2/(-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333 - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333/3" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x, k = symbols('x k')\n", - "\n", - "y = x**2 / ((-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**(1/3)) - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**(1/3)/3\n", - "y" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1799f486-222c-46ad-bd6d-a4c183d8d871", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{2}}{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}} - \\frac{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}}{3}$" - ], - "text/plain": [ - "x**2/(-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333 - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333/3" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L = -27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2\n", - "y2 = x**2 / (L**(1/3)) - (L**(1/3))/3\n", - "y2" - ] - }, - { - "cell_type": "markdown", - "id": "1ac5dc18-0a49-4d37-a49b-0f57ef5ebdc4", - "metadata": {}, - "source": [ - "#### Precision issues and L\n", - "\n", - "Note that as above, $L$ (that we call $L_1$ now) is not particularly well conditioned. \n", - "\n", - "$$\n", - "L_1 = -\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\n", - "$$\n", - "\n", - "This alternative form works better\n", - "\n", - "$$\n", - "L_2(x;k) = \\frac{27k}{2x} \\left(\\sqrt{1 + \\frac{108x^8}{729k^2}} - 1 \\right)\n", - "$$\n", - "\n", - "Furthermore\n", - "\n", - "$$\n", - "\\sqrt{1+\\xi}-1 = \\frac{\\xi}{2} - \\frac{\\xi^2}{8} + \\frac{\\xi^3}{16} - \\frac{5\\xi^4}{128} + O(\\xi^5)\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1c208f81-5e12-4cd9-95a9-3cd1b3e0ea71", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def L1(x,k):\n", - " return -27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2\n", - "\n", - "def L2(x,k):\n", - " xi = (108 * x**8) / (729 * k**2)\n", - " #print(f\"xi = {xi}\")\n", - " if xi > 1e-5:\n", - " lam = (m.sqrt(1 + xi) - 1)\n", - " else:\n", - " lam = xi*(1/2 - xi*(1/8 - xi*(1/16 - 0.0390625*xi)))\n", - " # the relative error of this Taylor approximation is for xi < 0.025 is 1e-5 or better\n", - " # for xi ~ 1e-15 the full term is unstable (because 1 + 1e-16 ~ 1 in double precision)\n", - " # therefore the switchover should happen somewhere between 1e-12 and 1e-2\n", - " #lam1 = 0\n", - " #lam2 = xi/2 - xi**2/8 \n", - " #lam2 = xi/2 - xi**2/8 + xi**3/16 - 0.0390625*xi**4\n", - " #lam2 = xi*(1/2 - xi*(1/8 - xi*(1/16 - 0.0390625*xi)))\n", - " #lam = max(lam1, lam2)\n", - " # for very small xi we can get zero or close to zero in the full formula\n", - " # in this case the taulor approximation is better because for small xi it is always > 0\n", - " # we simply use the max of the two -- the Taylor gets negative quickly\n", - " L = lam * (27 * k) / (2 * x)\n", - " return L\n", - "\n", - "def L3(x,k):\n", - " \"\"\"going via decimal\"\"\"\n", - " x = D(x)\n", - " k = D(k)\n", - " xi = (108 * x**8) / (729 * k**2)\n", - " lam = (D(1) + xi).sqrt() - D(1)\n", - " L = lam * (27 * k) / (2 * x)\n", - " return float(L)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "51a99f4c-1c36-4865-8046-52946214ec5b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(9.99999940631824e-8, 9.9999999962963e-08)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L1(0.1, 1), L2(0.1,1)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "4abb21bd-64c3-437d-8c29-4be0b9a5c725", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{2}}{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}} - \\frac{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}}{3}$" - ], - "text/plain": [ - "x**2/(-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333 - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333/3" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "M = L**(1/3)\n", - "y3 = x**2 / M - M/3\n", - "y3" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7de2f57a-abca-4a23-b81d-3ce651b7855b", - "metadata": {}, - "outputs": [], - "source": [ - "assert y == y2\n", - "assert y == y3\n", - "assert y2 == y3" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "285736b4-ac27-4804-8dcb-a8b96b6785de", - "metadata": {}, - "outputs": [], - "source": [ - "def swap_eq(x,k):\n", - " \"\"\"using floats only\"\"\"\n", - " L,M,y = [None]*3\n", - " try:\n", - " #L = -27*k/(2*x) + m.sqrt(729*k**2/x**2 + 108*x**6)/2\n", - " L = L2(x,k)\n", - " M = L**(1/3)\n", - " y = x**2/M - M/3\n", - " except Exception as e:\n", - " print(\"Exception: \", e)\n", - " print(f\"x={x}, k={k}, L={L}, M={M}, y={y}\")\n", - " return y\n", - "\n", - "def swap_eq_dec(x,k):\n", - " \"\"\"using decimals for the calculation of L\"\"\"\n", - " L,M,y = [None]*3\n", - " try:\n", - " #L = -27*k/(2*x) + m.sqrt(729*k**2/x**2 + 108*x**6)/2\n", - " L = L3(x,k)\n", - " M = L**(1/3)\n", - " y = x**2/M - M/3\n", - " except Exception as e:\n", - " print(\"Exception: \", e)\n", - " print(f\"x={x}, k={k}, L={L}, M={M}, y={y}\")\n", - " return y" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "91cb13ac-a1fc-485b-9037-6447a4c49dd3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.6823278038280196\n" - ] - } - ], - "source": [ - "def swap_eq2(x, k):\n", - " # Calculating the components of the swap equation\n", - " term1_numerator = (2/3)**(1/3) * x**3\n", - " term1_denominator = (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3)\n", - "\n", - " term2_numerator = (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3)\n", - " term2_denominator = 2**(1/3) * 3**(2/3) * x\n", - "\n", - " # Swap equation calculation\n", - " y = -term1_numerator / term1_denominator + term2_numerator / term2_denominator\n", - "\n", - " return y\n", - "\n", - "# Example usage\n", - "x_value = 1 # Replace with the desired value of x\n", - "k_value = 1 # Replace with the desired value of k\n", - "print(swap_eq(x_value, k_value))" - ] - }, - { - "cell_type": "markdown", - "id": "4c115505-7076-47b4-9c3e-fd0dd826683c", - "metadata": {}, - "source": [ - "### Price equation\n", - "\n", - "The derivative $p=dy/dx$ is as follows\n", - "\n", - "$$\n", - "p=\\frac{dy}{dx} = 6^{\\frac{1}{3}}\\left(\\frac{-2 \\cdot 3^{\\frac{1}{3}} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} \\cdot \\left(-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}\\right) \\cdot \\left(3k \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} + \\sqrt{3} \\cdot \\left(-9k^2 + 4x^8\\right)\\right) + 2^{\\frac{1}{3}} \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} \\cdot \\left(\\frac{-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}}{x}\\right)^{\\frac{5}{3}} \\cdot \\left(-3k \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} + \\sqrt{3} \\cdot \\left(9k^2 - 4x^8\\right)\\right) + 4 \\cdot 3^{\\frac{1}{3}} \\cdot \\left(-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}\\right)^2 \\cdot \\left(27k^2 + 4x^8\\right)}{6 \\cdot x \\cdot \\left(\\frac{-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}}{x}\\right)^{\\frac{7}{3}} \\cdot \\left(27k^2 + 4x^8\\right)}\\right)\n", - "$$\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "5c900f31-fee7-4726-b0af-31a35849b043", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.3136251299197979\n" - ] - } - ], - "source": [ - "def price_eq(x, k):\n", - " # Components of the derivative\n", - " term1_numerator = 2**(1/3) * x**3 * (18 * k * x + (m.sqrt(3) * (108 * k**2 * x**3 + 48 * x**11)) / (2 * m.sqrt(27 * k**2 * x**4 + 4 * x**12)))\n", - " term1_denominator = 3 * (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(4/3)\n", - " \n", - " term2_numerator = 18 * k * x + (m.sqrt(3) * (108 * k**2 * x**3 + 48 * x**11)) / (2 * m.sqrt(27 * k**2 * x**4 + 4 * x**12))\n", - " term2_denominator = 3 * 2**(1/3) * 3**(2/3) * x * (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(2/3)\n", - " \n", - " term3 = -3 * 2**(1/3) * x**2 / (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3)\n", - " \n", - " term4 = -(9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3) / (2**(1/3) * 3**(2/3) * x**2)\n", - " \n", - " # Combining all terms\n", - " dy_dx = (term1_numerator / term1_denominator) + (term2_numerator / term2_denominator) + term3 + term4\n", - "\n", - " return dy_dx\n", - "\n", - "# Example usage\n", - "x_value = 1 # Replace with the desired value of x\n", - "k_value = 1 # Replace with the desired value of k\n", - "print(price_eq(x_value, k_value))\n" - ] - }, - { - "cell_type": "markdown", - "id": "bd87b7d5-c0cd-4cfd-866b-ce305aa9d78f", - "metadata": {}, - "source": [ - "#### Inverting the price equation\n", - "\n", - "The above equations \n", - "([obtained thanks to Wolfram Alpha](https://chat.openai.com/share/55151f92-411c-43c1-a6ec-180856762a82), \n", - "the interface of which still sucks) are rather complex, and unfortunately they can't apparently be inverted analytically to get $x=x(p;k)$" - ] - }, - { - "cell_type": "markdown", - "id": "053180db-2679-4bf5-a8d6-d5d6e4e51f29", - "metadata": {}, - "source": [ - "## Charts" - ] - }, - { - "cell_type": "markdown", - "id": "99ffb5da-a7dd-4804-a2bf-1f32da169fad", - "metadata": {}, - "source": [ - "### Invariant equation" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "adfc7418-fa81-4108-9a4b-9c003ad315da", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "y_f = swap_eq" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3e8740bc-696c-4f0d-9acb-ebe8d8e27ae9", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k_v = [1**4, 2**4, 3**4, 5**4]\n", - "#k_v = [1**4]\n", - "x_v = np.linspace(0, m.sqrt(10), 50)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "y_v_dct = {kk: [y_f(xx, kk) for xx in x_v] for kk in k_v}\n", - "plt.grid(True)\n", - "for kk, y_v in y_v_dct.items(): \n", - " plt.plot(x_v, y_v, marker=None, linestyle='-', label=f\"k={kk}\")\n", - "plt.legend()\n", - "plt.xlim(0, max(x_v))\n", - "plt.ylim(0, max(x_v))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c63f7026-4cc8-4f54-a34e-dc99939945b8", - "metadata": { - "tags": [] - }, - "source": [ - "Checking the invariant equation at a specific point (xx; kk)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "fcb63f18-df33-448e-9ef8-cd8733e3b84e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "5.773159728050814e-15" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kk = 625\n", - "xx = 3\n", - "invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True)" - ] - }, - { - "cell_type": "markdown", - "id": "ea922e57-a4d5-444c-8443-407674520fcc", - "metadata": {}, - "source": [ - "Calculating a histogram of relative errors, ie what the relative error in the invariant equation is at various points $xx$ of the swap equation and at various $kk$" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "81de37e3-4c86-4428-9c74-1ec98eed876f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_inv_dct = {kk: [invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) for xx in x_v] for kk in k_v}\n", - "y_inv_lst = [v for lst in y_inv_dct.values() for v in lst]\n", - "#y_inv_lst\n", - "plt.hist(y_inv_lst, bins=200, color=\"blue\")\n", - "plt.title(\"Histogram of relative errors [f(x,y)/k - 1]\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f01529b5-7285-4c82-9145-0ea58a09877f", - "metadata": {}, - "source": [ - "Maximum relative error for different values of $k$" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "bd4456bf-1c66-4c04-89d5-ff3302a3bd7a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{1: 2.9978242110928477e-12,\n", - " 16: 2.220890138460163e-12,\n", - " 81: 9.826917057864648e-12,\n", - " 625: 7.190470441287289e-12}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{k: max([abs(vv) for vv in v]) for k,v in y_inv_dct.items()}" - ] - }, - { - "cell_type": "markdown", - "id": "9b5ef43c-9784-44fe-b680-c5262c36ec6b", - "metadata": { - "tags": [] - }, - "source": [ - "Minimum relative error for different values of $k$" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "7c236fa2-9b33-4693-bb9e-b72bab17f6e3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{1: 0.0, 16: 2.220446049250313e-16, 81: 4.440892098500626e-16, 625: 0.0}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{k: min([abs(vv) for vv in v]) for k,v in y_inv_dct.items()}" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "99f4fbc6-967c-44fd-bd88-f32fbc030ae3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kk = 5**4\n", - "x_v = np.linspace(0, m.sqrt(20), 50)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "plt.grid(True)\n", - "plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f\"k={kk}\")\n", - "inv_dct = {xx: invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) for xx in x_v}\n", - "plt.legend()\n", - "plt.xlim(0, max(x_v))\n", - "plt.ylim(0, max(x_v))\n", - "plt.show()\n", - "plt.plot(inv_dct.keys(), inv_dct.values())\n", - "plt.title(f\"Relative error as a function of x for k={kk}\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "2d13ac33-bd7b-4507-b6e8-e77b51d4c328", - "metadata": {}, - "source": [ - "Same analysis as above, but much higher resolution" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "621a8d45-7655-42e3-b8e7-71a6c44e19e6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "NUMPOINTS = 10000\n", - "kk = 5**4\n", - "x_v = np.linspace(0, m.sqrt(20), NUMPOINTS)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "plt.grid(True)\n", - "plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f\"k={kk}\")\n", - "inv_dct = {xx: invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) \n", - "# for xx in x_v[int(0.2*NUMPOINTS):int(0.5*NUMPOINTS)] # <=== CHANGE RANGE HERE\n", - " for xx in x_v # <=== CHANGE RANGE HERE\n", - "}\n", - "plt.legend()\n", - "plt.xlim(0, max(x_v))\n", - "plt.ylim(0, max(x_v))\n", - "plt.show()\n", - "plt.plot(inv_dct.keys(), inv_dct.values())\n", - "plt.title(f\"Relative error as a function of x for k={kk} (highres)\")\n", - "plt.grid()\n", - "plt.show()\n", - "plt.plot(inv_dct.keys(), inv_dct.values())\n", - "plt.title(f\"Relative error as a function of x for k={kk} (highres)\")\n", - "plt.grid()\n", - "plt.ylim(0,1e-13)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "49f8b5cb-ee4c-4ff5-a893-03bd61d52137", - "metadata": {}, - "source": [ - "same as above, but using decimal" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "7175fe6d-be86-428b-9a0b-fbc2beabacd1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/xd/7hy1yb2x4392l3378tjw70g80000gn/T/ipykernel_70005/2221901752.py:21: RuntimeWarning: divide by zero encountered in scalar divide\n", - " y = x**2/M - M/3\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "NUMPOINTS = 10000\n", - "kk = 5**4\n", - "x_v = np.linspace(0, m.sqrt(20), NUMPOINTS)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "plt.grid(True)\n", - "plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f\"k={kk}\")\n", - "inv_dct = {xx: invariant_eq(x=xx, y=swap_eq_dec(xx, kk), k=kk, aserr=True) \n", - "# for xx in x_v[int(0.15*NUMPOINTS):int(0.3*NUMPOINTS)] # <=== CHANGE RANGE HERE\n", - " for xx in x_v \n", - "}\n", - "plt.legend()\n", - "plt.xlim(0, max(x_v))\n", - "plt.ylim(0, max(x_v))\n", - "plt.show()\n", - "plt.plot(inv_dct.keys(), inv_dct.values())\n", - "plt.title(f\"Relative error as a function of x for k={kk} (highres)\")\n", - "plt.grid()\n", - "plt.show()\n", - "plt.plot(inv_dct.keys(), inv_dct.values())\n", - "plt.title(f\"Relative error as a function of x for k={kk} (highres)\")\n", - "plt.grid()\n", - "plt.ylim(0,1e-13)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4066e383-dba2-4e49-b999-ef7322ada357", - "metadata": {}, - "source": [ - "### Numerical considerations\n", - "#### Comparing L1 with L2\n", - "\n", - "L1 and L2 are different expressions of the L term above. L2 is the naive formula, L1 is optimized. L2 can be zero for very small values (and it is not even continous; see 0.009 and 0.01 below) whilst L1 is *always* greater than zero." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "0abe5692-f6da-4071-83db-c8bb995ff2be", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[(0, 1.0000000000000003e-28),\n", - " (0, 1.0000000000000001e-21),\n", - " (2.27373675443232e-13, 4.7829689999999975e-15),\n", - " (0, 1.0000000000000002e-14),\n", - " (2.27373675443232e-13, 1.7085937499999996e-13),\n", - " (1.25055521493778e-12, 1.279999999999999e-12),\n", - " (7.81199105404085e-10, 7.812499999988701e-10)]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xs_v = [0.0001, 0.001, 0.009, 0.01, 0.015, 0.02, 0.05]\n", - "[(L1(xx,1), L2(xx, 1)) for xx in xs_v]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "a5b8067c-ca96-4586-bab2-d3fa5dc421db", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x_v, [L2(xx, 1) - L1(xx, 1) for xx in x_v])" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "63c25d7d-81aa-4589-ae3e-a370ebc9a3a4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x_v, [L1(xx, 1) for xx in x_v])\n", - "plt.plot(x_v, [L2(xx, 1) for xx in x_v])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "09e238cb-680a-4e86-80cd-e06f6a5f39da", - "metadata": {}, - "source": [ - "## Generic numerical questions" - ] - }, - { - "cell_type": "markdown", - "id": "3d21a34f-35e0-4eed-a434-4ca7ee56dbb9", - "metadata": {}, - "source": [ - "### Square root term\n", - "\n", - "Here we are looking at the term $\\sqrt{1+\\xi}-1$ to understand up to which point we need the Tayler approximation, and whether there is a point going for T4 instead of T4. As a reminder\n", - "\n", - "$$\n", - "\\sqrt{1+\\xi}-1 = \\frac{\\xi}{2} - \\frac{\\xi^2}{8} + \\frac{\\xi^3}{16} - \\frac{5\\xi^4}{128} + O(\\xi^5)\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "d50b4540-91c0-43ba-bc8f-06721338d655", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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FloatTaylor2Taylor4
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" - ], - "text/plain": [ - " Float Taylor2 Taylor4\n", - "x \n", - "0.005051 0.002522 0.002522 0.002522\n", - "0.010101 0.005038 0.005038 0.005038\n", - "0.020202 0.010051 0.010050 0.010051\n", - "0.030303 0.015038 0.015037 0.015038\n", - "0.040404 0.020002 0.019998 0.020002" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x1_v = np.linspace(0,1,100)\n", - "x1_v[0] = x1_v[1]/2\n", - "data = [(\n", - " xx, \n", - " m.sqrt(1+xx)-1,\n", - " xx * (0.5 - xx*1/8),\n", - " #xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128,\n", - " xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))),\n", - ") for xx in x1_v\n", - "]\n", - "df = pd.DataFrame(data, columns=['x', 'Float', 'Taylor2', 'Taylor4']).set_index(\"x\")\n", - "df.plot()\n", - "plt.grid()\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "9f7fc799-1a9e-4eb9-a504-41200fb1d87d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x2_v = np.linspace(0,0.2,100)\n", - "x1_v[0] = x1_v[1]/2\n", - "data = [(\n", - " xx, \n", - " m.sqrt(1+xx)-1,\n", - " xx * (0.5 - xx*1/8),\n", - " #xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128,\n", - " xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))),\n", - ") for xx in x2_v\n", - "]\n", - "df = pd.DataFrame(data, columns=['x', 'Float', 'Taylor2', 'Taylor4']).set_index(\"x\")\n", - "df.plot()\n", - "plt.grid()\n", - "df2 = df.copy()\n", - "df2[\"Err2\"] = df2[\"Taylor2\"]/df2[\"Float\"] - 1\n", - "df2[\"Err4\"] = df2[\"Taylor4\"]/df2[\"Float\"] - 1\n", - "plt.show()\n", - "df2.plot(y=[\"Err2\", \"Err4\"])\n", - "plt.grid()\n", - "plt.title(\"Relative error of Taylor 2 4 term approximations\")\n", - "plt.ylim(-0.001, 0.0001)\n", - "df2.head()" - ] - }, - { - "cell_type": "markdown", - "id": "4446b5dd-a4c8-450f-81bd-d7a909895bf8", - "metadata": {}, - "source": [ - "### Decimal vs float\n", - "#### Precision\n", - "\n", - "we compare $\\sqrt{1+\\xi}-1$ for float, Taylor and Decimal\n", - "\n", - "$$\n", - "\\sqrt{1+\\xi}-1 = \\frac{\\xi}{2} - \\frac{\\xi^2}{8} + \\frac{\\xi^3}{16} - \\frac{5\\xi^4}{128} + O(\\xi^5)\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "824c7650-acd7-4336-924e-9c927f0e2ebe", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(1e-18, 1.3721439741813515)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import decimal as d\n", - "D = d.Decimal\n", - "d.getcontext().prec = 1000 # Set the precision to 30 decimal places (adjust as needed)\n", - "xd_v = [1e-18*1.5**nn for nn in np.linspace(0, 103, 500)]\n", - "xd_v[0], xd_v[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "8252b418-74e6-429f-9162-1574ac04580f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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FloatTaylor2Taylor4Dec
x
1.000000e-180.05.000000e-195.000000e-195.000000e-19
1.087295e-180.05.436476e-195.436476e-195.436476e-19
1.182211e-180.05.911055e-195.911055e-195.911055e-19
1.285412e-180.06.427062e-196.427062e-196.427062e-19
1.397623e-180.06.988114e-196.988114e-196.988114e-19
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" - ], - "text/plain": [ - " Float Taylor2 Taylor4 Dec\n", - "x \n", - "1.000000e-18 0.0 5.000000e-19 5.000000e-19 5.000000e-19\n", - "1.087295e-18 0.0 5.436476e-19 5.436476e-19 5.436476e-19\n", - "1.182211e-18 0.0 5.911055e-19 5.911055e-19 5.911055e-19\n", - "1.285412e-18 0.0 6.427062e-19 6.427062e-19 6.427062e-19\n", - "1.397623e-18 0.0 6.988114e-19 6.988114e-19 6.988114e-19" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fmt = lambda x: x\n", - "fmt = float\n", - "ONE = D(1)\n", - "data = [(\n", - " xx, \n", - " m.sqrt(1+xx)-1,\n", - " xx * (0.5 - xx*1/8),\n", - " #xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128,\n", - " xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))),\n", - " fmt((ONE+D(xx)).sqrt()-1),\n", - ") for xx in xd_v\n", - "]\n", - "df = pd.DataFrame(data, columns=['x', 'Float', 'Taylor2', 'Taylor4', 'Dec']).set_index(\"x\")\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": 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", 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", 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u878XLVrE888/z759+zK3XXnWhzNZLBY2bdpEq1atePrpp6levTrLli1jyJAhBAQE0K9fP2cPUUSkVLJcTmLVoGtosDeVdA/4sksTmt47nYbVKzh7aHkqmzMNhgFpl+x7WZLtb2vPy46luCHjceFXXoGBgZhMpsz3ZrOZkSNHUqtWLfz9/WnZsmWWB4DNmTOHypUrk5qamqXPAQMGMHTo0By/z2az8dJLL1GrVi18fHxo06YNK1asyPz88OHDmEwmvvjiC7p164avry9z587lmWee4eWXX6ZTp040aNCAxx57jD59+rBkyZJCHBgREfeXlnyBVXd2pv7fgWFh12ac6focd1wd6uyh5atszjRYkuG1Gvk28wAqOvq7nzkB3uWK1EVKSgrh4eE8/fTTBAQEsGzZMgYPHkz9+vXp0KEDt912G4899hjffvstt912GwCnT5/m+++/Jyoq5yVIp0+fzptvvsmHH35IWFgYn376Kf/73//Ys2cPDRs2zGz39NNP8+abbzJr1ix8fHxy7OvChQs0bdq0SDWKiLijtKTzrBnUlfr7LFg8YUHXFkTXeZCoW1piMpmcPbx8lc2ZBhdXs2ZNxowZQ5s2bahfvz6PPvoovXv35ssvMy6e8fPz46677mLWrFmZ+8ybN49atWrRrVu3HPucMmUKTz/9NHfccQeNGzdm8uTJtGnThmnTpmVpN2rUKG655Rbq1atHjRrZg9dXX33Fr7/+yj333OOwekVE3EHqxTOsubMr9fZZSPOEede24otK9zDjzjYE+pmdPTy7lM2ZBrN/xm/8+bDZbCRevEhAhQp4eDgoX5n9i9yF1Wpl0qRJLFq0iNjYWFJTU0lNTaVcuX9mMEaMGMHVV19NbGwsNWvWZNasWQwbNizHJJuYmMiJEyfo3Llzlu2dO3dm165dWba1a9cu13GtXbuWYcOG8fHHH9O8efMiViki4j4uX0hg7aDu1DuQTpoXLOwWxqLyg/i/7g0JrxPk7OHZrWyGBpPJvlMENhuYrRltHRUaHODNN99k6tSpTJs2jZYtW1KuXDlGjRpFWlpaZpuwsDBat27NnDlz6N27N7/99hvfffddnv3+N1AYhpFt27+Dyb+tW7eOfv368dZbbzFkyJBCViYi4n4uX0hg7Z3XU/eQlTQv+C4yggWeA+hYP4iHrrvK2cMrkLIZGlzc+vXruemmm7j77ruBjBmR/fv3Z7uO4L777mPq1KnExsbSo0cPQkNDsdls2foLCAigRo0abNiwga5du2Zu37RpE+3bt893PGvXruXGG29k8uTJ3H///UWsTkTEfSSfiyf6zh7UPWwl1Qy/DLyeT5L6UtHfzLSBYXh6lP7rGP6t9Pz6LHa76qqrWL16NZs2beKPP/7ggQceID4+Plu7QYMGERsby8cff8y9996bZ59PPvkkkydPZtGiRezbt4+xY8eyc+dOHn/88Tz3W7t2LTfccAOPPfYYAwYMID4+nvj4eM6ePVukGkVEXN2lMyeIvrMHdQ5bSTHD0ZH/47WkvgC8cWtrggN9nTzCglNocEHPPfccbdu2pXfv3nTr1o3g4GD69++frV1AQAADBgygfPnyOX7+b4899hj/93//x//93//RsmVLVqxYwbfffpvlzomczJ49m+TkZCZOnEhISEjm65ZbbilChSIiri3pzDE23NmTOoetXPaG5KcG8tK5HgAMjahDz2bVnTzCwtHpCRcwbNiwLCtABgUF8c0339i1b1xcHIMGDcp2e+QLL7zAhAkTMt97eHjw/PPP8/zzz+fYT926dTFyWGNi9uzZzJ49266xiIiUBRdPHmLzoH7UPm7jsjd4PDeUDxKv43TSaZoEV2BcX9e9JV2hwU2dPXuWVatW8eOPP/LOO+84ezgiImVCYtwBfh58E6HHbSR7g/nF4WyufDPrt/2Jr9mDd+4KK9XLROdHocFNtW3blnPnzjF58mQaN27s7OGIiLi9Cyf2seXuWwg9YeOSD/i+9ADWtsN44/1NALzYrzlXVSvdy0TnR6HBTR0+fNjZQxARKTPOH/+DrYMHEBpncMkX/F55iNo9RnLj2xtItxnc0DKEgS6wTHR+FBpERESK4NzR39g2ZCA14w0u+kGF1x6jSZ+RPLFoJ0fOJFOzoh+vucgy0flRaBARESmks4d3sWPondQ8mREYAiY9QZPe97N423G+2XkCTw+TSy0TnR+FBhERkUI4c2g7u4bdTY0Eg0R/qPT6UzTqcQ+HTiXx3NLfAXiih2stE50fhQYREZECOrX/V36/dyghpwwulIMqU8Zx1XVDSEu38djCHSSnWelYP4gHu7nWMtH5UWgQEREpgIS/fmbPvfcQfBrOl4OqU5/jqq53AfDGyj/5PTbRZZeJzo9Cg4iIiJ1O7t3An/eNIPgsnC8P1adNoP41twPw074EPl4fA7juMtH50TLSZdSECRNo06aNs4chIuIy4vdEs2/4CKqdhXMVIOTtVzMDQ8LFFMZ8sQuAYZ3quuwy0flRaCilTCZTnq9/LytdWhw4cIAKFSpQsWJFZw9FRMShTuz+gb/ue4Cq5+BsANR8dyJ1IzKesWOzGfzfF7s4cymNJsEVGBvZxMmjLT46PVFKxcXFZf73okWLeP7559m3b1/mNj8/P2cMKwuLxYLZbM787zvvvJMuXbqwadMmJ49MRMRxYnes5NCDo6h6Hs4EQp33phAafkPm5x+vP8T6/afdYpno/JTJmQbDMEi2JNv1upx+2e629rxyeuhTToKDgzNfgYGBmEymzPdms5mRI0dSq1Yt/P39admyJQsWLMjcd86cOVSuXJnU1NQsfQ4YMIChQ4fm+H02m42XXnqJWrVq4ePjQ5s2bVixYkXm54cPH8ZkMvHFF1/QrVs3fH19mTt3bubn48ePp0mTJtx+++0FORQiIqXasW3LiBk5iirn4XRFqPv+1CyBYdex87yxMuMXOndYJjo/ZXKm4XL6ZTrM7+CU795y1xb8zf5F6iMlJYXw8HCefvppAgICWLZsGYMHD6Z+/fp06NCB2267jccee4xvv/2W2267DYDTp0/z/fffExUVlWOf06dP58033+TDDz8kLCyMTz/9lP/973/s2bMny+Oxn376ad58801mzZqV+eTMH3/8kS+//JKdO3fy9ddfF6k2EZHS4sgv33L8kaepnAinKsJVH75NjdY9Mj+/mGLh0QU73GqZ6PyUyZkGV1ezZk3GjBlDmzZtqF+/Po8++ii9e/fmyy+/BDJOXdx1113MmjUrc5958+ZRq1YtunXrlmOfU6ZM4emnn+aOO+6gcePGTJ48mTZt2jBt2rQs7UaNGsUtt9xCvXr1qFGjBmfOnGHYsGHMnj2bgICA4ipZRKREHdmyhOMPP01QIiRUgoafvJ8lMBiGwfhvfufoWfdaJjo/ZXKmwc/Ljy13bcm3nc1m4+LFi1SoUAEPD8fkKz+vol+LYLVamTRpEosWLSI2NpbU1FRSU1MpV65cZpsRI0Zw9dVXExsbS82aNZk1axbDhg3L8S91YmIiJ06coHPnzlm2d+7cmV27dmXZ1q5duyzvR4wYwV133UXXrl2LXJeISGkQs+kr4h57jqAkOBkETT/5mOrNrsnS5uvtsSx1w2Wi81MmQ4PJZLLrFIHNZiPdKx1/s7/DQoMjvPnmm0ydOpVp06bRsmVLypUrx6hRo0hLS8tsExYWRuvWrZkzZw69e/fmt99+47vvvsuz3/8GCsMwsm37dzCBjFMT3377LVOmTMncx2az4eXlxUcffcS9995blFJFRErUwfULSRg1gUqX4GRlaDZzJtWadMrSxp2Xic5PmQwNrm79+vXcdNNN3H333UBGuNm/fz9NmzbN0u6+++5j6tSpxMbG0qNHD0JDQ7HZbNn6CwgIoEaNGmzYsCHLjMGmTZto3759nmPZvHkzVqs18/3SpUuZPHkymzZtombNmkUpU0SkRB1YN4/To1+h4iWIr2KixazZVG2Y9d9Ad18mOj8KDS7oqquuYvHixWzatIlKlSrx1ltvER8fny00DBo0iDFjxvDxxx8zZ86cPPt88skneeGFF2jQoAFt2rRh1qxZ7Ny5k3nz5uW533+/c+vWrXh4eNCiRYvCFSci4gT7f/yMs2MmEZgMcVVNtPrsc6rUD8/W7vUVGctEV3LTZaLzo9Dggp577jliYmLo3bs3/v7+3H///fTv358LFy5kaRcQEMCAAQNYtmwZ/fv3z7PPxx57jMTERP7v//6PhIQEmjVrxrfffpvlzgkREXf05+pPSHzqTQIuQ1w1E23mLCCobuts7X7al8AnG9x7mej8KDS4gGHDhmVZATIoKIhvvvnGrn3j4uIYNGhQ5u2RV7zwwgtMmDAh872HhwfPP/88zz//fI791K1b1641Jv47VhGR0uyPFR+QNG46FS5DbLCJ8M8WUalOy2zt/rtMdA83XSY6PwoNburs2bOsWrWKH3/8kXfeecfZwxERKXX2LnuXS8++Q/kUiA0xEf75V1Sq1SxbO8MweObr38vEMtH5UWhwU23btuXcuXNMnjyZxo0bO3s4IiKlyp7vZnB5/PuUT4XjNUy0n7uEwBo5/1v5zc5Y1vxxErOniWl3tHHrZaLzU+D7CKOjo+nXrx81atTAZDLlO01+ZW2A/76aN2+e2Wb27Nk5tklJSSlwQZLh8OHDXLhwgTFjxjh7KCIipcqe76aRMv59yqXCsZoedJj3ba6B4WRiCi8s3QPAqB6NaBJcthexK3BouHTpEq1bt7Z7ynv69OnExcVlvo4dO0ZQUFDm8sZXBAQEZGkXFxeHr2/Zu8hERESKj+WP77C++Cn+qXCslgcR878jICTn2yYzTkv8RmJKOq1qBfJA1/olPNrSp8CnJyIjI4mMjLS7fWBgIIGBgZnvv/nmG86dO8c999yTpd2VBzKJiIgUh91fTaL+/I34pcHRUA86z1tG+Wp1c23/9fZYfvgzAW9PD6bc1hovz9KzyJ+zlPg1DTNnzqRHjx7UqVMny/akpCTq1KmD1WqlTZs2vPzyy4SFheXaz5Wlk69ITEwEMh7RbLFYsrS1WCyZKxXmtLhRbq7cLXBlX3dQGmqy2WwYhoHFYsHTs+jnBq8c7/8ed1fnjnWpJtfhbnXt+uJVPCctwtcCR+p40PGz7/CpVDPX+uITU5jwXcZpiceub0C9IN9S+WfhqONk7/4mw95nNee0s8nEkiVL8l0D4Iq4uDhCQ0OZP39+lkco//zzzxw4cICWLVuSmJjI9OnTiYqKYteuXbmuE/Diiy9muWXwivnz5+Pvn3WJaC8vL4KDgwkNDcXb29v+AqVYpKWlcezYMeLj40lPT3f2cETEzVl+W8xVi37FxwIxdUyk3vMUHn6Vcm1vGPDRnx7sPe9BnfIGj7ew4unmazglJydz1113ceHChTwfPliioWHixIm8+eabnDhxIs8f3jabjbZt29K1a1dmzJiRY5ucZhpCQ0M5ffp0toJTUlI4duwYdevWLdB1EoZhZD6wyl2eXlYaakpJSeHw4cOEhoY65LoVi8XC6tWr6dmzJ2az+zw0xh3rUk2uw13q2j7vBXymLMEnHQ7X8yRl2JP07ndbnjUt3h7L2CV7MHuaWPpQBA2rlS/BEReMo45TYmIiVapUyTc0lNjpCcMw+PTTTxk8eHC+v+17eHhw9dVXs3///lzb+Pj4ZFuwCMBsNmf7g7NarZhMJjw8PAr04Kkr0/dX9nUHpaEmDw8PTCZTjseqKBzdX2nhjnWpJtfhynX98unT+L71Ld7pcLiBF9fMWcGPG7bmWVP8hRReXb4PgNE9G9OsZu4zEqVJUY+TvfuW2E+NdevWceDAAYYPH55vW8Mw2LlzJyEhISUwstLr37erms1mqlevTs+ePfn000/d5hoLEZHisOWTJ/F5MyMwxDQ0c/3CtfgGVMtzH8MwGPv1bi6mpNMmtCIjutQrodG6jgKHhqSkJHbu3MnOnTsBiImJYefOnRw9ehSAcePGMWTIkGz7zZw5kw4dOuT4IKMJEyawcuVKDh06xM6dOxk+fDg7d+5k5MiRBR2e2+nTpw9xcXEcPnyY5cuXc9111/H4449z44036noAEZEc/PzhE/hN/R5vKxxqZKbHgmh8KlTOd78vtx5n7b5TeHvpboncFPhPZOvWrYSFhWXe2TB69GjCwsIyn1kQFxeXGSCuuHDhAosXL851luH8+fPcf//9NG3alF69ehEbG0t0dHS+j2UuC3x8fAgODqZmzZq0bduWZ555hqVLl7J8+XJmz54NZPz53n///VSrVo2AgACuv/56du3alaWfb7/9lnbt2uHv70+DBg0YMGCAE6oRESlem957lHLTV2C2wqEm3vRcuB7v8hXz3e/E+cu8/P1eAP6vZyOuKsXXMThTga9p6NatW54PLrryg+zfAgMDSU5OznWfqVOnMnXq1IIOpdAMw8C4fDnfdjabDdvly9i8vMBB5/9Nfn5FvgDx+uuvp3Xr1nz99dcMHz6cG264gaCgIKKioggMDOTDDz+ke/fu/PXXXwQFBbFs2TJuueUWnn32WT777DPOnj1LdHS0Q+oRESktNr79IAHvr8XLBoea+dBz7jq8/QPz3S/jtMRvXExNJ6x2Re7rokWcclMmnz1hXL7MvrbZn5Oem5MO/O7G27dh+s8toYXRpEkTdu/ezU8//cRvv/1GQkJC5oWhU6ZM4ZtvvuGrr77i/vvv59VXX+WOO+5gwoQJ2Gw2EhMT6dy5c5HHICJSWqyfdj8VP1qPlw0ONveh99wNmP3smy1Y9Osxov86hc/fpyU8PdzjbrniUCZDgzswDAOTycS2bdtISkqicuWs5+suX77MwYMHAdi5cycjRoxwxjBFRIpd9JvDCfpkE54GHGzpS+856+0ODLHnL/PKsj8AGNOrMQ2q6rREXspkaDD5+dF4+7Z829lsNhIvXiSgQgWH3Z5o8vNzSD9//PEH9erVw2azERISwtq1a7O1qVixIgB+DvpOEZHSZu3rw6g6awseBhxs7UefORvx8rHv3zzDMBi7eDdJqemE16nEvdfobon8lM3QYDLZd4rAZsMjPR0Pf/9StU7Djz/+yG+//cYTTzxBrVq1iI+Px8vLi7p16+bYvlWrVvzwww/ZnvchIuLK1k4aTNXPtmYEhjB/IudsxtNs/6q/C389xvr9p/Hx8uCNW1vptIQdymRocCWpqanEx8djtVo5efIkK1asYOLEidx4440MGTIEDw8PIiIi6N+/P5MnT6Zx48acOHGCqKgo+vfvT7t27XjhhRfo3r07DRo04Pbbb+f8+fNs2LCBp59+2tnliYgUyo+v3En1uTvxAA6Glydy9sYCBYbj55J55e+7JZ7s3Zj6Oi1hF4WGUm7FihWEhITg5eVFpUqVaN26NTNmzGDo0KGZsx9RUVE8++yz3HvvvZw6dYrg4GC6du1K9erVgYw7Xr788ktefvllJk2aRIUKFejataszyxIRKbQfXrqd4Pm/ZQSGdhWInLWhQIEh47TEb1xKs9KuTiXu6azTEvZSaCjFZs+eneMtrP9VoUIFZsyYketzOgBuueUWbrnllsy7J/JaW1xEpLRa88Kt1FyU8fTJgx0C6fvpRjwK+LTchVuPs+HAaXzNHryhuyUKRKFBRERcwurx/an1VcZzIQ5GVKTvJxsKHBjOpMCbK/4C4KneTahXpZzDx+nOFBpERKTUWzWuH6FLDgBwqHMl+n60vsCBwWYzWHDQg0tpVtrXDWJYp7rFMFL3VnpuCRAREcnByqdv+CcwdK1MZCECA8CCrcfZn+iBr9mD129thYdOSxSYQoOIiJRaK8ZEUnvpIQAOdatK5PvrChUYjp1N5vWVGaclxvRsSF2dligUnZ4QEZFSafkTvai7/BgAMddXJ/LtHwoVGGw2gye/2kVympUGFQwGd6jt6KGWGWUqNOT1oC0pOToOIpKf5Y91p+6qEwDE9Aih7zs/FrqvuVuO8POhs/iZPbjrqjSdliiCMnF6wvPvZJqWlubkkQiQ+cRTs9ns5JGISGkU9ch1mYHhcJ+aRQoMR88kMzHqTwCe7NWIKr4OGWKZVSZmGry8vPD39+fUqVOYzWa7l4S22WykpaWRkpJSqpaRLgpn1mQYBsnJySQkJFCxYsXMMCciAmCzWlnxaHfq/ZjxbOHDfWsT+dbKwvdnMxjz1S4uW6x0rB/EoPahrFjxu6OGWyaVidBgMpkICQkhJiaGI0eO2L2fYRhcvnwZPz8/TCb3mM4qDTVVrFiR4OBgp3y3iJRONquV5Q91o/660wAc7VeXyDeWF6nPOZsP80vMWfy9PXl9QGudlnCAMhEaALy9vWnYsGGBTlFYLBaio6Pp2rWr20ylO7sms9msGQYRycJmtbJ8ZFfqrz8LwNH+Deg96fsi9XnkzCUmr8hYCGpcZBNqV/bHYrEUeaxlXZkJDQAeHh74+tp/QsvT05P09HR8fX3dJjS4Y00i4rpsVitRI7rQYNM5AI4NaETvV5cWrU+bwZNf7uayxUpE/coM6lDHEUMVysiFkCIiUvrYrFaihnfODAzHb2tCryIGBoDZmw7zy+G/T0toESeHUmgQEZESZ023EHVPBA1+vgBA7B3N6fnykiL3G3P6Eq+vzLhb4pm+TQkN8i9yn/KPMnV6QkREnM9qSWP5PZ1psDUJG3ByUCt6PLeoyP3abAZPfbWLFIuNzldVZpAWcXI4hQYRESkxVksay4d0osGOS9iAhCFhXP/MfIf0PWvTYX49fI5y3p5MHtDKbe56K010ekJEREpEeupllg+OyAgMJkgYdjXXOSgwHDqVxOsrMk5LPHtDM2pV0mmJ4qCZBhERKXbpqZdZMbgzDXZfxmqCM8M7ct2YWQ7p22ozePKr3aSm2+jSsAp3tg91SL+SnWYaRESkWFkuJ7FiUKfMwHB2RGeudVBgAJi1MYZtR85R3seLSTotUaw00yAiIsXGcjmJVYOuocHeVNI94MIDXen6+IcO6//gqSTeWJmxiNOzNzSlZkU/h/Ut2WmmQUREikVa8gVW3dmZ+n8HhsSHr+caBwYGq83gyS93ZZ6WuONqnZYobgoNIiLicGlJ51lzZxfq/5mGxRMuPtqTzg+/69DvmLnhENuPnqeCj5fulighCg0iIuJQqRfPsObOrtTbZyHNE5Ifj6TTgzMc+h0HEpKYsuovAMbf2JQaOi1RIhQaRETEYVIST/Hjnd2otz8jMKQ8cSMd73/Lod9htRmM+XIXaek2rm1Uldvb6bRESdGFkCIi4hCXLySw9s7u1D2UTpoXpI65iQ7DJjn8ez5ef4idx85TwdeLSQNa6rRECdJMg4iIFFnyuXjW3nE9dQ+lk+oFlqcH0L4YAsP+kxd5a3XGaYnnbmxGSKBOS5QkhQYRESmSS2dOEH1nD+rGWEkxg3XcQNoNfsXh35NutWWelriucVVuC6/l8O+QvOn0hIiIFNgfcYkMnrkF42IcL22bRP3jBpe9YVa3TkTtaQ/PRDn8Ow3DwGZABV8vJt6iuyWcQaFBREQKxDAMXvh2D7YLsbyy/XXqxWYEhk+uvYYo//5gM4rtu00meKV/C4IDfYvtOyR3Cg0iIlIgK/ec5OCBP3h12xTqnjBI9oa0cUN48YYneLGYv9vH7Emgn7mYv0Vyo9AgIiJ2S0238sHSNby6dQp14gwu+YDPhPsJ7/+Es4cmJUChQURE7Pb5sp94cPVr1I43uOQLfq88RPMbH3X2sKSEKDSIiIhdDv+1k9pvPUboSYMkXyg/8VGaRj7k7GFJCVJoEBGRfJ09vJsDI+4k9CRc9IPyE0fRtM8Dzh6WlDCt0yAiInk6c2g7O4bcQc2TkOgP58c+RDMFhjKpwKEhOjqafv36UaNGDUwmE998802e7deuXYvJZMr2+vPPP7O0W7x4Mc2aNcPHx4dmzZqxZMmSgg5NREQc7Myhbeweejc1Egwu+MOK2wfRa6CuYSirChwaLl26ROvWrXnnnXcKtN++ffuIi4vLfDVs2DDzs82bNzNw4EAGDx7Mrl27GDx4MLfffjtbtmwp6PBERMRBrGcP8Mfwewg+ZXC+HEzv1p+h9+kuibKswNc0REZGEhkZWeAvqlatGhUrVszxs2nTptGzZ0/GjRsHwLhx41i3bh3Tpk1jwYIFBf4uEREpmpP7NhH08SdUPwvny8ObXW+hzfWDqFulnLOHJk5UYhdChoWFkZKSQrNmzRg/fjzXXXdd5mebN2/miSeyptfevXszbdq0XPtLTU0lNTU1831iYiIAFosFi8XikDFf6cdR/ZUGqsl1uGNdqsk1nPxjPQdHPkz1s3CuArxxzW0crngNH3St69J1uuOxclRN9u5f7KEhJCSEjz76iPDwcFJTU/n888/p3r07a9eupWvXrgDEx8dTvXr1LPtVr16d+Pj4XPudOHEiEyZMyLZ91apV+Pv7O7SG1atXO7S/0kA1uQ53rEs1lV7Wk3uoMutzqp2DswEwvetAdnhdzYBqKWz8yT1qdJdj9W9FrSk5OdmudsUeGho3bkzjxo0z30dERHDs2DGmTJmSGRqAbA8eMQwjz4eRjBs3jtGjR2e+T0xMJDQ0lF69ehEQEOCQsVssFlavXk3Pnj0xm91j2VLV5DrcsS7VVLrF7l7DsYmfU+U8nAmE72+4k1+Sw6lfpRwvD4vA7OnaN9y507G6wlE1XZmtz49T1mno2LEjc+fOzXwfHBycbVYhISEh2+zDv/n4+ODj45Ntu9lsdvhfhuLo09lUk+twx7pUU+lzfPsKjj80mioXMgKDz6RX+HJVxr+xz93YDH/f7P/euipXP1Y5KWpN9u7rlNi4Y8cOQkJCMt9HRERkm1pZtWoVnTp1KumhiYiUOUd//Y4jI5+g8gU4XRHqf/Q2Hx+rjdUwcc1VlenWuKqzhyilRIFnGpKSkjhw4EDm+5iYGHbu3ElQUBC1a9dm3LhxxMbGMmfOHCDjzoi6devSvHlz0tLSmDt3LosXL2bx4sWZfTz++ON07dqVyZMnc9NNN7F06VLWrFnDhg0bHFCiiIjk5siWJRx/5BmCLsKpStDw4/c54t+SVXt/xoTBuD6N8jxVLGVLgUPD1q1bs9z5cOW6gqFDhzJ79mzi4uI4evRo5udpaWmMGTOG2NhY/Pz8aN68OcuWLaNv376ZbTp16sTChQsZP348zz33HA0aNGDRokV06NChKLWJiEgeYjZ9RdzjzxF0ERKCoPEnH1GtyTWMeDfjF7ZO1Q0aVa/g5FFKaVLg0NCtWzcMw8j189mzZ2d5/9RTT/HUU0/l2++tt97KrbfeWtDhiIhIIRxcv5CEJyZQKQlOVoZmM2dSrUknvtp2nN9jEynv40Xf0BRnD1NKGde+FFZERArswLp5nBo1gYpJEF/FRPPZn1GtSSeS09J5Y2XGEv8PdatHefe6VlAcQKFBRKQM2f/jZ5we/QqBlyCuqomWn31O1YbtAfhg3SFOJqYSGuTHkI51nDxSKY30aGwRkTJi35qZnH9qCoHJEFfNROvZc6lcvy0AcRcu81H0QQDGRTbFx0u/U0p2+lshIlIG/LnyIxKfnEJAMpyobiLs80WZgQHg9RX7SLHYaF83iMgWwU4cqZRmmmkQEXFze6Pe49Izb1M+BWJDTITP+ZJKoc0zP9957DxLdsQCMP7GprrFUnKl0CAi4sb2fDeDy+Pfp3wqHA8xcfXcr6lYs0nm54Zh8Mr3ewG4pW1NWtWq6KSRiitQaBARcVO/fTOVtBc+olwqHKvpQce5SwkIuSpLm2W/xbH1yDn8zJ481btJLj2JZFBoEBFxQ7sWv076hFn4p8GxWh5EzPuOCtXrZ2mTYrEyaXnGLZYPXFuf4EBfZwxVXIhCg4iIm9n5xURsr8zBPw2OhnrQed4yylerm63drI2HOX7uMsEBvtzftX72jkT+Q6FBRMSNbF/wMqbX5uNngaO1Pei8YAXlK4dma3fqYirv/pTxHKGn+jTG31s/DiR/+lsiIuImts97AY9JX+BjgSN1PekybxXlKtfIse1bq/8iKTWdVrUC6d+mZgmPVFyVQoOIiBv49bNnMb/xNT7pcKSeJ13nr8G/Us7rLfwZn8iiXzMeLDj+hmZ4eOgWS7GPQoOIiIv75dOn8XnrW7zT4XB9L7ot+AG/wGo5ts24xfIPbAb0bRlM+3pBJTxacWUKDSIiLmzLJ0/iO/V7vK0Qc5UX3ResxadC5Vzb//hnAhsOnMbb04OxfZqW4EjFHSg0iIi4qJ8/Go3/9OWYrRDTyEyP+dF4l6+Ya3uL1carUX8AcM81dald2b+ERiruQqFBRMQFbXr/MSq8vRovG8Q09qbHvGi8ywfmuc+8n49w6NQlKpfz5pHrrsqzrUhOFBpERFzMxrcfJOD9tXjZ4FBTb3rOi8bbP+/AcCHZwrQf9gMwulcjKviaS2Ko4mYUGkREXMiG6Q8Q+GF0RmBo5kOveRsw+5XPd7/pP+znfLKFxtUrMLBd9nUbROyh0CAi4iKi37qPoI834mnAwRa+9P58vV2B4dCpJOZsPgxkPMXSy9OjmEcq7kqhQUTEBax9fRhVZ23Bw4CDrfzo8/lGvHz87Nr3tag/SbcZXN+kGl0aVi3mkYo7U9wUESnlfpo05J/A0Ma/QIFh04HTrPnjJJ4eJp7pq6dYStFopkFEpBT76bW7qDZnBx7AwbbliPxsE55mb7v2tdoMXvp+LwB3d6jNVdUqFONIpSxQaBARKaV+eOl2guf/lhEY2pUnctZGuwMDwJdbj/Fn/EUCfL0Y1aNR8Q1UygyFBhGRUmjNC7dSc9EeAA62D6DvrE14eHravX9SajpTVv0FwGPdG1KpnP1hQyQ3Cg0iIqXM6uduptaXfwJwsGMgfWduLFBgAHh/7QFOJ6VSr0o5hkTULYZRSlmk0CAiUoqsGvc/QpdkLMJ0qHMl+n60vsCB4fi5ZD5eHwPAuMgmeHvpmndxDP1NEhEpJVY+fcM/gaFLZSILERgAJq/YR1q6jYj6lenZrLqjhyllmGYaRERK0KFTSfx18mK27UkfDqb56lgA9nYOwv+Rhaz6I6HA/Z9KSuO7XScwmTIWcjKZTEUes8gVCg0iIiXk1MVU+r29gUtp1izbn4x9jet/PQvAhrYBvFrlaViwq0jfdXt4KM1r5P08CpGCUmgQESkhn26M4VKalSrlfaj792Op+255ioi/A8OWDpVY2XEK7Yr4PZXKefNUn8ZF7EUkO4UGEZEScCHZwuebjwAw8ZaW9GxWnahHrqPez2cAONy7BsOm/8AwJ45RJD+6EFJEpATM2XyYpNR0mgRX4LqGlYl6qBv11sQDcKRvKJHTf3DyCEXyp9AgIlLMLqWm8+nGjFsgR3apy8pHr6fejycBOHJjXfq8tcqZwxOxm0KDiEgxW/DLUc4lW6hTyQevD+6i/tpTABy9qT59pix38uhE7KdrGkREilFqupWP1x8Cw8qje56lwS+JABwb0Ijery518uhECkahQUSkGC3eFsvJC8m8EjOB5ruTATh+axN6vbLEySMTKTidnhARKSbpVhvv//QXLx98gfC/A0PswOb0VGAQF6WZBhGRYrJ0+2FG/jKGtntTsQEnB7Wix3OLnD0skULTTIOISDGwpKVimXRLZmBIGBLG9QoM4uIUGkREHCw99TLLBnWk9Z5UbCaIGxLOdc/Md/awRIpMoUFExIHSUy+zYnBnGv+Wgs0Ev97Ugh7PzHX2sEQcQqFBRMRBLJeTWDGoEw12X8ZqgiXXNqLfeAUGcR8FDg3R0dH069ePGjVqYDKZ+Oabb/Js//XXX9OzZ0+qVq1KQEAAERERrFy5Mkub2bNnYzKZsr1SUlIKOjwREaewpCSxatA1NPg9hXQP+KJrY2w3TKRyeR9nD03EYQocGi5dukTr1q1555137GofHR1Nz549iYqKYtu2bVx33XX069ePHTt2ZGkXEBBAXFxclpevr29BhyciUuJslmR+GtKN+ntT/w4MTVlQ5X5GdK3n7KGJOFSBb7mMjIwkMjLS7vbTpk3L8v61115j6dKlfPfdd4SFhWVuN5lMBAcHF3Q4IiJOlXbpPF6fvUr9/VbSPeCHG9vyucdd3Blei5BAP2cPT8ShSnydBpvNxsWLFwkKCsqyPSkpiTp16mC1WmnTpg0vv/xyllDxX6mpqaSmpma+T0zMWJrVYrFgsVgcMtYr/Tiqv9JANbkOd6zL3WpKu3SetYOv56r9ViyeEDu8G9MSbsTDBMM71XHpOt3tWIFqsqef/JgMwzAK+yUmk4klS5bQv39/u/d54403mDRpEn/88QfVqlUD4Oeff+bAgQO0bNmSxMREpk+fTlRUFLt27aJhw4Y59vPiiy8yYcKEbNvnz5+Pv79/oeoREbGXkXoRr9kTaXDIRpon/HVrK+aVu5udZz0Ir2JjSEObs4coYrfk5GTuuusuLly4QEBAQK7tSjQ0LFiwgPvuu4+lS5fSo0ePXNvZbDbatm1L165dmTFjRo5tcpppCA0N5fTp03kWXBAWi4XVq1fTs2dPzGazQ/p0NtXkOtyxLnepKSUxgQ2D+1D3UDppXvDXbWE0vWc6/d7/BcOAZY9E0Kh6BWcPs0jc5Vj9m2rKXWJiIlWqVMk3NJTY6YlFixYxfPhwvvzyyzwDA4CHhwdXX301+/fvz7WNj48PPj7Zr0o2m80O/8tQHH06m2pyHe5YlyvXlHwung1396ZujJVUL0gdczPelTowc/NxDAN6NqtO81pB+XfkIlz5WOVGNeW8vz1KZJ2GBQsWMGzYMObPn88NN9yQb3vDMNi5cychISElMDoREftcOnOC6Dt7ZAQGM9jG3U7bQRM4kwLf7ooD4OHrrnLyKEWKT4FnGpKSkjhw4EDm+5iYGHbu3ElQUBC1a9dm3LhxxMbGMmfOHCAjMAwZMoTp06fTsWNH4uPjAfDz8yMwMBCACRMm0LFjRxo2bEhiYiIzZsxg586dvPvuu46oUUSkyJLOHGPjnX2oc9RGihl4dhBt7xiPxWLhxxMeWG0G11xVhTahFZ09VJFiU+CZhq1btxIWFpZ5Z8Po0aMJCwvj+eefByAuLo6jR49mtv/www9JT0/n4YcfJiQkJPP1+OOPZ7Y5f/48999/P02bNqVXr17ExsYSHR1N+/bti1qfiEiRJSUcZuMdfah91MZlbzA9N4SwO8YDkHAxlZ8TTIBmGcT9FXimoVu3buR17eTs2bOzvF+7dm2+fU6dOpWpU6cWdCgiIsXu4slDbB7Uj9rHbSR7g9cL99B6wFOZn8/adIR0w0RYaCAd67vPtQwiOSnxdRpERFxFYtwBtgy6idATNpJ9wHvC/bTs/0Tm5+eT01jwyzEAHry2PiaTyVlDFSkRCg0iIjk4H/snv959C7XiDC75gN8rD9K832NZ2szedJhLaVZq+ht0a1TFSSMVKTkKDSIi/3Hu2B62DbktIzD4gv+rj9DshoeztElKTWfWxsMA9Kxp0yyDlAl6NLaIyL+cO/Ib2+++jZpxBkl+UH7SqGyBAWD+liNcuGyhXmV/Wlcu9Bp5Ii5FoUFE5G9nYnawY/BAapw0SPSHgDfG0KTPA9napVisfLw+BoD7u9bDQ5MMUkYoNIiIAKcPbmPXkEGEJGQEhqApY2ncY3iObb/cdpxTF1OpEejL/1ppETopO3RNg4iUeQl//czee+8h5DRcKAdV3hrPVdcOyrGtxWrjg7UHAXjg2gZ4e+l3Lyk79LddRMq0hD83sfeee6h+Gs6Xg6rTXsg1MAAs3XmC2POXqVLem4FXh5bgSEWcT6FBRMqs+D3R/HHvcKqfgXPlIfjtl2nQ5Y5c21ttBu+tzVhGf/g19fE1e5bUUEVKBZ2eEJEyKe73tewf8SDVzsHZClDrndeo0+HmPPdZuSeeQ6cuEeDrxd0da5fQSEVKD4UGESlzTuxaw8EHHqXqeTgbAKHvvk7tq/vluY9hGLz7U8Ysw7BOdang616PVhaxh05PiEiZcnz7Cg7d/yhVzsOZQKj9/pR8AwPA2r9OsedEIv7entzTuV7xD1SkFNJMg4iUGUd//Y5jDz9F5UQ4XRHqvz+NmmG97dr3vb9nGe5qX5tK5byLcZQipZdCg4iUCUe2LOH4o88QlAinKsFVH75DjVbd7dp3y6Ez/Hr4HN6eHozoWr+YRypSeik0iIjbO7z5a0489ixBFyEhCBp//CHBzbvavf87f88y3NquFtUDfItrmCKlnq5pEBG3dmjDF8Q9+iyVLsLJytDkk48LFBh2Hz/P+v2n8fQwMbJrg2IcqUjpp5kGEXFbB6Lnc/qJl6l4CU5WgWafzqJao44F6uPKHRP/a12D2pX9i2OYIi5DoUFE3NKBn+ZwesxEAi9BfFUTLWd/TpUG4QXqY//Ji6zccxKAh7pplkFEoUFE3M5fa2Zx7qnXCUyGuGomWs+eS+X6bQvcz3t/P2Oid/PqNKxewdHDFHE5Cg0i4lb+XPkRiWOnEnAZTlQ30XbOIirVaVngfo6eSebbXScAeOS6ho4epohLUmgQEbfxx/L3SBr3NhVSIDbYRPjnX1IptHmh+vog+iBWm0HXRlVpWSvQwSMVcU0KDSLi8pbujGXf8ne5ftESyqfAsRATP9z1Kks3pAE7CtXnyt/jAXhY1zKIZFJoEBGXdjoplahZr3H/2h/xT4UjISaebzeahIPewIki9d2+bhAd6ld2zEBF3IBCg4i4tO8/eYn7f/oR/zQ4UtNE3JgPeDAgtMj9enmY6N082AEjFHEfCg0i4rJ2fjmJVp9+jV8aHKnlwTXzl1G+Wl1nD0vEbSk0iIhL2rHwFYzX5uGXBodqmejw+fcKDCLFTMtIi4jL2T7vRUyvZgSGg6Emdox4lyohely1SHHTTIOIuJStn4/Ha/JifNLhQG0PnmnzDIu7tXP2sETKBIUGEXEZv8wai8+bS/FOh4P1PBnXchxN69ejkVZrFCkRCg0i4hK2fPIkvlO/x9sKMVd58VaHl0m6ZGZQhzrOHppImaFrGkSk1Pv5o9H4XQkMjcx4TfiKQ5fMVPI306eFbosUKSkKDSJSqm16/zHKTVuO2QqHGpvpMT+ahb8lAnBreC18zZ5OHqFI2aHTEyJSam185yEC3vsJLxscaupNz3nRJKSa+WlfAgB3tq/t5BGKlC2aaRCRUmnD9Af+CQzNfOg1fyPe/oEs+vUYhgGdGlSmftXyzh6mSJmimQYRKXXWv3UflT7eiKcBB1v40vvz9Zj9ymOx2lj46zEAXQAp4gQKDSJSqqx74x4qf/pzRmBo5Uefzzfi5eMHwA9/nOTUxVSqlPehZ7PqTh6pSNmj0CAipcZPk4ZQ7bNf8TDgYBt/+ny2ITMwAMzbchSA29vVwttLZ1dFSppCg4iUCj+9dhfV5uzAAzgYVo7IOZvwNHtnfn749CXW7z+NyaQLIEWcRaFBRJzux5cHUn3e7ozA0K48kbM2ZgkMAAt+zZhluLZRVUKD/J0wShFRaBARp1rzwq3UXLQHgIPtK9B31mY8PLOuvZCabuXLrccBuEuzDCJOo5OCIuI0q5+7+Z/A0DEwx8AAsOL3eM5eSiM4wJfrm1Qr6WGKyN8KHBqio6Pp168fNWrUwGQy8c033+S7z7p16wgPD8fX15f69evzwQcfZGuzePFimjVrho+PD82aNWPJkiUFHZqIuJBVz95ErS//BOBgp0r0nbkxx8AAMP/vCyDvaB+Kl6d+1xFxlgL/33fp0iVat27NO++8Y1f7mJgY+vbtS5cuXdixYwfPPPMMjz32GIsXL85ss3nzZgYOHMjgwYPZtWsXgwcP5vbbb2fLli0FHZ6IuIAfxvcndPFfABzqUpm+H6/PNTAcSLjIlpizeJhg4NWhJTlMEfmPAl/TEBkZSWRkpN3tP/jgA2rXrs20adMAaNq0KVu3bmXKlCkMGDAAgGnTptGzZ0/GjRsHwLhx41i3bh3Tpk1jwYIFBR2iiJRiacvfoNHaMwAcurYKke+tzTUwwD+3WXZvWp2QQL9c24lI8Sv2eb7NmzfTq1evLNt69+7N1q1bsVgsebbZtGlTcQ9PRErQmqduoMXfgSHm+ur5BoYUi5XF2/6+ALKDLoAUcbZiv3siPj6e6tWzrtxWvXp10tPTOX36NCEhIbm2iY+Pz7Xf1NRUUlNTM98nJmY89c5isWSGkaK60o+j+isNVJPrcLe6Vo+JpN7KWAAOdQ+m17RVWG02rDZbrvss3RFLYko6tSr6ElG3Yqn8s3C343SFO9almvLvJz8lcsulyWTK8t4wjGzbc2rz323/NnHiRCZMmJBt+6pVq/D3d+w93KtXr3Zof6WBanId7lCXZelEmm+6AMCeaypi7jWKqKiofPd7/zdPwESbgEusXLG8mEdZNO5wnHLijnWppuySk5PtalfsoSE4ODjbjEFCQgJeXl5Urlw5zzb/nX34t3HjxjF69OjM94mJiYSGhtKrVy8CAgIcMnaLxcLq1avp2bMnZrPZIX06m2pyHe5Ql81qZc0TvWn0d2CI6VMT83WP2lXTH3EXObx5M14eJp6543qqVvApiSEXmDscp5y4Y12qKXdXZuvzU+yhISIigu+++y7LtlWrVtGuXbvMAiMiIli9ejVPPPFEljadOnXKtV8fHx98fLL/I2I2mx3+l6E4+nQ21eQ6XLUum9XKqke7U3/tKQCO3FiXnhO/JSoqyq6avtiecSqjd/NgagSV/kdgu+pxyo871qWact7fHgUODUlJSRw4cCDzfUxMDDt37iQoKIjatWszbtw4YmNjmTNnDgAjR47knXfeYfTo0YwYMYLNmzczc+bMLHdFPP7443Tt2pXJkydz0003sXTpUtasWcOGDRsKOjwRKQVsVivLH7yW+tEZFz0evak+fSYvs/u86aXUdL7ZcQKAQboAUqTUKPDdE1u3biUsLIywsDAARo8eTVhYGM8//zwAcXFxHD16NLN9vXr1iIqKYu3atbRp04aXX36ZGTNmZN5uCdCpUycWLlzIrFmzaNWqFbNnz2bRokV06NChqPWJSAmzWa0sv79LZmA4dnNDek9eVqA+vt11gqTUdOpVKUdEg8rFMUwRKYQCzzR069Yt80LGnMyePTvbtmuvvZbt27fn2e+tt97KrbfeWtDhiEgpYrNaiRremQY/Z1zDcPzWxvR65ZsC9zNvyxEg4zkTeV0QLSIlS+uxiohD2KxWou7plBkYYgc2p2chAsPu4+f5PTYRby8PBoTXcvAoRaQoFBpEpMisljSihkbQ4JdEbMCJu1rSY8JXhepr3s8Zpzf7tggmqJx3Pq1FpCTp0dgiUiRWSxrLh3aiwfZL2ICTd7eh+/jCLf+emGLh211/XwDZsY4DRykijqDQICKFZrWksXxwBA12JmMzQcLQq7l+7JxC9/fNjlguW6w0ql6ednUqOXCkIuIIOj0hIoWSnnqZ5Xd3zAwMp+/tyHVFCAyGYWSemtAFkCKlk2YaRKTALJeTWDm4Cw1+T8FqgrMjOnPt6E+K1Oe2I+fYd/IivmYPbm6rCyBFSiOFBhEpEMvlJFbefQ0N9qSS7gEXHuhK18c/LHK/8/9+BPb/Wtcg0M+9VusTcRc6PSEidktLvsCquzpnBobEh67jGgcEhnOX0vj+tzgA7uqgCyBFSiuFBhGxS1rSedbc2ZX6f6SR7gEXH+lB50fec0jfi7cfJy3dRvMaAbSuFeiQPkXE8XR6QkTylZZ0njV3daXeXxYsnpD8WB86PTDVIX0bhpF5amJQhzq6AFKkFNNMg4jkKfXiGdbcmREY0jzh8hM30tFBgQFg86EzHDp9iXLenvyvTQ2H9SsijqeZBhHJ1eULCay9qzv1DqaT5gWp/3cTHe6Z5NDvmPf3LEP/sJqU99E/SSKlmWYaRCRHyefiWXvH9dQ9mE6qF6Q9eQvtHRwYTl1MZdWeeCDj1ISIlG4KDSKSzaUzJ4i+swd1Y6ykmsE27nauHvqqw7/ny23HsFgN2oRWpFmNAIf3LyKOpblAEcki6cwxNtwVSZ0jVlLMwLODaHvHeId/j81msOCXKxdA1nZ4/yLieAoNIpIpKeEwGwfdQJ1jNi57g8f4IbS5fVyxfNf6A6c5dvYyAb5e3NhKF0CKuAKFBpEy5sJlC7e8t5HDZ5KzbA9KP8lLW9+g7gmDy97wSbeurNzZGnZGOfT7DZsno7esxmYYANzSthZ+3p4O/Q4RKR4KDSJlzMrf4zl46lKWbVWt8by89U3qxBkk+8CHXbuxyv9GsBnFMAJTZr/lvD0Z2qluMXyHiBQHhQaRMmbl33crPNitAfd0qktS/H4OPvAkteIMLvmA9bkRvNLrIV4phu+2pKfzww8/0L17d8xeXlTwNWuWQcSFKDSIlCGXUtNZf+A0ADe1qYF3YgyHHhiYERh8wf/VR2h2w8PF9v0Wi4VAb6hWwQezWQ+lEnE1Cg0iZci6v06Rlm6jTmV/qqUeYdvQgdSMN0jygwoTR9GkzwPOHqKIlGJap0GkDLmykFK/kER2Dh5IjXiDi34Q8PoYBQYRyZdmGkTKiLR0Gz/8mUDt9IO0/fB9Qk5Boj8EvfE0DbsPc/bwRMQFKDSIlBE/HzpD5Yt/8vyWDwg5DRfKQZU3n+Wqbnc7e2gi4iIUGkTKiI3rV/HCzx8QfAbOl4Nq016gQZc7nD0sEXEhCg0iZUDcnmgiPnmF6ufgXHkImfEy9Trd6uxhiYiL0YWQIm4u7ve17L/vAaqfg7MVoNr0VxQYRKRQFBpE3NiJXWs4cN+DVD0HZwJg9d0PcVXnAc4eloi4KIUGETd1fPsKDt3/KFXOZwSGyV3uIrybAoOIFJ6uaRBxQ8e2LePIQ2OofAFOV4SJnQZzwDeMbo2rOntoIuLCNNMg4maO/LKUow9mBIZTFeGPR59lr3drOl1VmQq+WrpZRApPoUHEjRze/DWxD48lKBFOVYJGMz/k+3N1AejdPNi5gxMRl6fQIOImDm34grhHn6XSRUgIgsYzP8ZW82p2H7+AyQQ9mlZ39hBFxMUpNIi4gQPR8zn5+AtUTIKTlaHp7FlUb3YNq/eeBCC8diWqVvBx8ihFxNXpQkgRF3fgpzmcHjORipcgvqqJFp/OpmrD9gCs/PsBVTo1ISKOoNAg4sL+WjOLc0+9TmAyxFUz0Xr2XCrXbwvA+eQ0tsScBaBXc52aEJGiU2gQcVF/rvqYxKffIuAynKhuIuyzhQTVbZX5+Q9/JGC1GTQJrkCdyuWcOFIRcRcKDSIu6I/l75M0bgYVUiA22ET4519SKbR5ljZXTk300qkJEXEQhQYRF7Pn+7e5PP49yqdAbIiJdp8vpmKtplnaXE6zEr3/FAC9munUhIg4hkKDiAv5fek0Up//kHKpcLyGifZzlxBYo3G2dtH7T5FisVGzoh/NawQ4YaQi4o4UGkRcxO6vp2B5cSb+aXCslgcdP19KQMhVObZdtSfjVsvezYMxmUwlOUwRcWMKDSIuYNdXk7C+9Bn+aXA01INO876nQrV6ObZNt9r44c+M0KC7JkTEkQq1uNN7771HvXr18PX1JTw8nPXr1+fadtiwYZhMpmyv5s3/uWhr9uzZObZJSUkpzPBE3MruvwODXxocre1B54Urcg0MAL/EnOV8soWgct60q1OpBEcqIu6uwKFh0aJFjBo1imeffZYdO3bQpUsXIiMjOXr0aI7tp0+fTlxcXObr2LFjBAUFcdttt2VpFxAQkKVdXFwcvr6+hatKxE1Yfvsaj9fm45cGR+p6cs2C1ZSvHJrnPqv+XgWye5NqeHlq0VcRcZwC/4vy1ltvMXz4cO677z6aNm3KtGnTCA0N5f3338+xfWBgIMHBwZmvrVu3cu7cOe65554s7UwmU5Z2wcG6TUzKth0LXuCqRb/ga4HD9TzpumAN5SrXyHMfwzBYpVUgRaSYFOiahrS0NLZt28bYsWOzbO/VqxebNm2yq4+ZM2fSo0cP6tSpk2V7UlISderUwWq10qZNG15++WXCwsJy7Sc1NZXU1NTM94mJiQBYLBYsFou9JeXpSj+O6q80UE2uYfvn4/F561t80uFwfU86z1mBuXzlfGv8LfYCJy6k4O/tSYe6gaXuz8Qdj5U71gTuWZdqyr+f/JgMwzDs7fTEiRPUrFmTjRs30qlTp8ztr732Gp999hn79u3Lc/+4uDhCQ0OZP38+t99+e+b2n3/+mQMHDtCyZUsSExOZPn06UVFR7Nq1i4YNG+bY14svvsiECROybZ8/fz7+/v72liRS6qTtWECjr3bhnQ4H63tgGToWD1/7bptcdtSDVbEetA6ycW9jWzGPVETcRXJyMnfddRcXLlwgICD3f28KdffEf2/hMgzDrtu6Zs+eTcWKFenfv3+W7R07dqRjx46Z7zt37kzbtm15++23mTFjRo59jRs3jtGjR2e+T0xMJDQ0lF69euVZcEFYLBZWr15Nz549MZvNDunT2VRT6fbLp09R/stdeFshpqEX6YOfpveNt9hd19szNgKXGHx9a/q2DinewRaCOx2rK9yxJnDPulRT7q7M1uenQKGhSpUqeHp6Eh8fn2V7QkIC1avnfWuXYRh8+umnDB48GG9v7zzbenh4cPXVV7N///5c2/j4+ODjk/1Rv2az2eF/GYqjT2dTTaXPpg8ep8LbqzBbIaaxmWs/+4E16zbZXdehU0kcOHUJLw8TPZuHlOo/C1c/Vjlxx5rAPetSTTnvb48CXQjp7e1NeHg4q1evzrJ99erVWU5X5GTdunUcOHCA4cOH5/s9hmGwc+dOQkJK329KIsVh47sPU2FGRmA41MSbHgvW412uYoH6uHLXRESDygT6udc/iCJSOhT49MTo0aMZPHgw7dq1IyIigo8++oijR48ycuRIIOO0QWxsLHPmzMmy38yZM+nQoQMtWrTI1ueECRPo2LEjDRs2JDExkRkzZrBz507efffdQpYl4jo2TB9J4Ifr8LLBoWY+9Jy7Dm//gl/EqAdUiUhxK3BoGDhwIGfOnOGll14iLi6OFi1aEBUVlXk3RFxcXLY1Gy5cuMDixYuZPn16jn2eP3+e+++/n/j4eAIDAwkLCyM6Opr27dsXoiQR1xE9dQRBH23A04CDLXzp/fl6zH7lC9xPQmIKO46eB6BnU60CKSLFo1AXQj700EM89NBDOX42e/bsbNsCAwNJTk7Otb+pU6cyderUwgxFxGWtm3IPlWf+nBEYWvrRZ+5GvHz8CtXXlVMTbUIrEhyoRdFEpHjo2RMiTvDT5CFUm/0rHgYcbO1PnzkbCh0Y4J9TE1rQSUSKk0KDSAn76bW7qDZnBx7AwbByRM7ZhKc57zuK8nLhsoXNB88AekCViBQvhQaREvTjywOpPm93RmAIL0/k7I1FCgwAa/clkG4zuKpaeRpULfj1ECIi9lJoECkhP7x4GzUW/g7AwasrEDlrI55eRb818p9TE5plEJHipdAgUgLWPHcLNb/8A4CDHQPpO3MjHp6eRe43xWJl7b5TAPRqpusZRKR4KTSIFLPVz95ErcV/AXCwU0X6frzBIYEBYOOB0ySnWQkJ9KVVrUCH9CkikhuFBpFitGrsjYR+cxCAg9cE0ffDaIcFBoBVezJutezVrLpdz38RESmKAi0jLSL2W/lU38zAcOjaKg4PDFabwZo//g4NutVSREqAQoNIMVjxf32o/W0MAIeur0bke2sdGhgAth4+y5lLaQT6mWlfL8ihfYuI5ESnJ0TssOPoOU4mptjV9tK7d9Hsp4w7GvZ2rUq5hxay6o+EAn9nerqVXWdMeO45iZdX9sDx3a44ALo3qYbZU/lfRIqfQoNIPnYeO8/N722yq+24Yy/TddsFANaFBzIp6GmYu70I3+7Jp3/tyrOFTk2ISElRaBDJx/q/Mm5prFbBh9pB/jm2MWxW/rflKdr/HRg2R1RhTfvJtCvC9xqGwdlz5wiqVCnXixzrVinH9U2qFeFbRETsp9Agko9tR88B8FC3BgzrXC/b5zarlRWPXE+9X84DcOSGOtz75gruLeL3WiwWoqKi6Nu3PWZz0ReBEhEpKp0IFcmDzWZkPnK6bZ1K2T+3Wln+YDfq/ZRxzcKR/9Wnz5srSnKIIiIlRqFBJA+HTidx4bIFX7MHTUMCsnxms1pZfn9X6kefBuDYzVfR5/VlzhimiEiJUGgQycO2IxmnJlrXqpjlDgWb1UrUfddQf+NZAI4PaESvid85ZYwiIiVFoUEkD9uPnAeynpqwWa0sv7cTDTZnfBZ7e1N6vrrUCaMTESlZCg0iebhyEWR47YzQYLWkETU0gvpbErEBJ+5sSY+XvnbiCEVESo7unhDJxfnkNA4kJAEZMw1WSxrLh3WmwbYkbMDJu1vTffxC5w5SRKQEKTSI5GLHsfMA1K9SjkBvWD44ggY7k7EBCUPCuf6ZuU4dn4hISVNoEMnF9r8vgmxbw5/ld0fQYFcyNhOcGtae657+zMmjExEpeQoNIrnYduQcXkYa13x1Pw32pmI1wZnhEXQb86mzhyYi4hQKDSI5SLfa2HMkjol7n6fx/nSsJjh7/zVc+8THzh6aiIjTKDSI5GDPkRM8v/N5mh1IJ90DLjxwLV0f/8DZwxIRcSqFBpH/SEu+wJFRfTMDQ+LD13PNw+86e1giIk6ndRpE/iUt6Txr7uhCw78sWDxh28AIOiswiIgACg0imVIvnmHNnV2p95eFNE+Yd21r6t852dnDEhEpNRQaRIDLFxL48Y5u1NtvIc0LPr+2LV8EDqZN7YrOHpqISKmhaxqkzEs+F8+6QT2oe8hKqhccGt6br072pEn1CgT4mp09PBGRUkMzDVKmXTpzgug7/w4MZrCOvY19jR8AIKx2pXz2FhEpWxQapMxKOnOM9Xf1os5hKylmsD1zF+F3v5T5OOzwOgoNIiL/ptMTUiYlJRxm46AbqHPMxmVv8Bg/hLDbx5GabmV37AVAoUFE5L8UGqTMuXjyEJsH9aP2cRvJ3uD1wj20HvAUAHtOJJKWbiOonDd1K/s7eaQiIqWLQoOUKYlxB/j57psIjbWR7APeE+6nZf8nMj/PfEhV7YqYTCZnDVNEpFRSaJAy48KJffxy982EnjC45AN+rzxI836PZWmz/ejfoUGnJkREslFokDLh/PE/2Dp4ALXiDC75gv+rj9DshoeztDEMI/MiyLa6c0JEJBvdPSFu79yR39h29wBqxhkk+UG5iY9nCwwAJy6kcDIxFU8PE61rVSz5gYqIlHKaaRC3dvbwLnYOuZMaCQaJ/lDx9TE07jE8x7ZXZhma1wjAz9uzJIcpIuISFBrEbZ0+uI3dwwYTciojMARNGUvD64fm2n67Tk2IiORJoUHc0qn9v/D7PcMIOW1woRxUeWs8V107KM99dBGkiEjeFBrE7ST89TN777mH4DNwvhxUm/YCDbrckec+yWnp7DmRCGhRJxGR3Cg0iFs5uXcDf943gupn4Vx5CJnxMvU63ZrvfruPX8BqMwgO8KVGoG8JjFRExPUU6u6J9957j3r16uHr60t4eDjr16/Pte3atWsxmUzZXn/++WeWdosXL6ZZs2b4+PjQrFkzlixZUpihSRkWv2cd+4aPoNpZOFsBarz9ql2BAf65CLJtHS3qJCKSmwKHhkWLFjFq1CieffZZduzYQZcuXYiMjOTo0aN57rdv3z7i4uIyXw0bNsz8bPPmzQwcOJDBgweza9cuBg8ezO23386WLVsKXpGUSdaTe4gZ+ShVz8HZAAh973XqRtxi9/47juoiSBGR/BQ4NLz11lsMHz6c++67j6ZNmzJt2jRCQ0N5//3389yvWrVqBAcHZ748Pf+5pW3atGn07NmTcePG0aRJE8aNG0f37t2ZNm1agQuSsid29xqqzvycqufhTCDUfn8Kta/uZ/f+/17USdcziIjkrkDXNKSlpbFt2zbGjh2bZXuvXr3YtGlTnvuGhYWRkpJCs2bNGD9+PNddd13mZ5s3b+aJJ57I0r537955hobU1FRSU1Mz3ycmZlzEZrFYsFgs9paUpyv9OKq/0sDdajq+fTmxjz1NlQtwuiKEvvsWwa16FKi+mNOXOJdswdvLg0ZV/UvNn427HStQTa7EHetSTfn3k58ChYbTp09jtVqpXr16lu3Vq1cnPj4+x31CQkL46KOPCA8PJzU1lc8//5zu3buzdu1aunbtCkB8fHyB+gSYOHEiEyZMyLZ91apV+Ps79umEq1evdmh/pYE71GSN3Ub12V9SORFOVYTT9w7m7PE0dh2PKlA/vySYAE9q+llZs2pFsYy1KNzhWP2XanId7liXasouOTnZrnaFunvivxeKGYaR68VjjRs3pnHjxpnvIyIiOHbsGFOmTMkMDQXtE2DcuHGMHj06831iYiKhoaH06tWLgICAAtWTG4vFwurVq+nZsydms9khfTqbu9R0ZMsSEl75kkoXIaESnBk+jD53PVqomjYt3Qscp3vrevTt3cjxgy0kdzlW/6aaXIc71qWacndltj4/BQoNVapUwdPTM9sMQEJCQraZgrx07NiRuXPnZr4PDg4ucJ8+Pj74+Phk2242mx3+l6E4+nQ2V67p0IYvSBj1ApWSICEIGnz4PucPXSh0TTuPXQCgXd3KpfLPxJWPVW5Uk+twx7pUU87726NAF0J6e3sTHh6ebRpk9erVdOrUye5+duzYQUhISOb7iIiIbH2uWrWqQH1K2XAgej4nH88IDCcrQ9PZs6jepHOh+0tMsfBXwkUg43ZLERHJXYFPT4wePZrBgwfTrl07IiIi+Oijjzh69CgjR44EMk4bxMbGMmfOHCDjzoi6devSvHlz0tLSmDt3LosXL2bx4sWZfT7++ON07dqVyZMnc9NNN7F06VLWrFnDhg0bHFSmuIMDP83h9JiJVLwE8VVNtPh0NlUbti/SBUA7j57HMKB2kD/VKmhRJxGRvBQ4NAwcOJAzZ87w0ksvERcXR4sWLYiKiqJOnToAxMXFZVmzIS0tjTFjxhAbG4ufnx/Nmzdn2bJl9O3bN7NNp06dWLhwIePHj+e5556jQYMGLFq0iA4dOjigRHEHf62ZxbmnXicwGeKqmWg9ey6V67ctcr+ZizrVrljkvkRE3F2hLoR86KGHeOihh3L8bPbs2VneP/XUUzz11FP59nnrrbdy6632rd4nZcufqz4m8em3CLgMJ6qbCPtsAUF1Wzuk7ysPqdL6DCIi+dOzJ6RU+2P5BySNm06FFIgNNhH++ZdUCm3ukL6tNoOdR88DerKliIg9FBqk1Nq77F2Sn32H8ikQG2Ki3eeLqVirqcP6359wkYup6ZTz9qRx9QoO61dExF0pNEip9PvSaaQ8/yHlUuF4DRPt5y4hsEbj/HcsgCvXM7QOrYiXZ6Ge3SYiUqYoNEiJm70xhm1/nxbISe2D87n+i+8plwZHa5j44a5JfLsuGdiRY3ubzUbcCQ9WJe3Gw8P+H/57YjPWZ9D1DCIi9lFokBJ1MjGFF7/bm+vnfS59S/d10filQUxNE8+1HcOZA57AiXx69oAzuS87npeI+pULtZ+ISFmj0CAl6mBCEgDVKvjwYLcGWT4z//IBzWZlBIYjoR6cHP0RjwTUyLdPq9XK3r17adasWZanp9ojOMCXiAYKDSIi9lBokBJ16PQlAFrUDOSezvUyt29f8DKmT7/F1wJH6nhyzfzllK8calefFouFqHN76BtRx+2WhhURKU0UGqREHf47NNSrUi5z27a5z+M5+Ut8LHCkridd5q2iXOX8ZxhERKRkKTRIiYr5T2j49bNnML+xBJ90OFzfk2vnrcG/UrAzhygiIrlQaJASdSU01K9Sjl8+fRqft77FOx1irvLiunk/4BdYzckjFBGR3Cg0SImxWG0cPZsMQOrKVyj33kq8rRDT0Ez3+T/hU0EXJIqIlGYKDVJijp+7TLrN4O7EOVR+bzdmK8Q0MtNjfjTe5Ss6e3giIpIPhQYpMTGnkxh0fhYDo/fgZYNDTbzpOT8ab/9AZw9NRETsoLVzpcScWPAkd1wJDM18FBhERFyMZhqkRERPHUGbL37F04A/m3pz47wNmP3KO3tYIiJSAAoNUuyip9xL0MzNeBqwu7EZj7FfKTCIiLggnZ6QYrV28lAq/x0YfmvqzbNNJtAgpIqzhyUiIoWgmQYpNj+9Nohqc7bjARwI82ds7fHYTN7U/9dqkCIi4jo00yDF4sdX7sgMDAfDy1Nv8kpsJm8q+Zup6O/t7OGJiEghKDSIw/3w4m2EzN2VERiurkDk7I0cOW8Bsj5zQkREXItOT4hDrXnuFmp++QcAB9sH0HfWJjw8PYk5nfFI7HpVdAGkiIir0kyDOMzqZ2/6JzBEVMwMDPDPI7HrV9VMg4iIq1JoEIdYNfZGai3+C4BD1wTR95MNmYEBsj/dUkREXI9CgxTZyqf6EvrNQQAOda1C5IfRWQIDKDSIiLgDhQYpkhX/14fa38YAcOi6akS+vzZbYDh3KY3zyRkXQtatrNAgIuKqdCGkFNryJ3pSd/lxAA53D6bvOz9iMpmytbtyPUNIoC9+3p7ZPhcREdeg0CCFEvVod+qtPgFATM8a9H37h1zb6tSEiIh70OkJKRCb1UrUw90yA8PhPrXyDAzAv263VGgQEXFlCg1iN5vVyvJHrqfeDycBOHJDHSKnrc53P800iIi4B4UGsYvNamX5g92o/1MCAEf/V48+b66wa99Dp7RGg4iIO1BokHzZrFaWP9CV+tGnATh281X0fj3Kvn1tBofPXJlp0GqQIiKuTKFB8mSzWokacQ31N5wF4PiARvSa+J3d+8cnppBiseHlYaJWJb/iGqaIiJQAhQbJlc1qJerezjTYdB6A2Nua0vPVpQXq48r1DLWD/DF76q+biIgr07/ikiOrJY2oYRE02HIBG3Dizpb0ePnrAvdzSBdBioi4Da3TINlYLWksH9aZBtuSsAEn725N9/ELC9XXYYUGERG3odAgWVgtaSwfEkGDHcnYgIQh4Vz/zNxC95d5u6XunBARcXk6PSGZ0lMvs/zuvwODCU7d057rihAYQGs0iIi4E800CJARGFbc3ZkGv13GaoKzwyPoNubTIvVpsdo4ejYZgPq63VJExOVppkGwXE5ixaBONPjtMukecO6BLnQtYmAAOHY2GavNwM/sSfUAHweMVEREnEmhoYxLS77AqkHX0OD3FNI9IPHBbnQZ9ZFD+v73qYmcnn4pIiKuRacnyrC05Ausvqsr9f9MywgMD19P54ffdVj/ughSRMS9FGqm4b333qNevXr4+voSHh7O+vXrc2379ddf07NnT6pWrUpAQAARERGsXLkyS5vZs2djMpmyvVJSUgozPLFD2qXzrLmzC/X/TMPiCUmP9XJoYIB/1mior4sgRUTcQoFDw6JFixg1ahTPPvssO3bsoEuXLkRGRnL06NEc20dHR9OzZ0+ioqLYtm0b1113Hf369WPHjh1Z2gUEBBAXF5fl5evrW7iqJE9G6kXWDb6eevsspHlC8qi+RIyc7vDviTmlOydERNxJgU9PvPXWWwwfPpz77rsPgGnTprFy5Uref/99Jk6cmK39tGnTsrx/7bXXWLp0Kd999x1hYWGZ200mE8HBwQUdjhRQSmICXrMnUu+QjTQvSH2iHx2Hv14s36XbLUVE3EuBQkNaWhrbtm1j7NixWbb36tWLTZs22dWHzWbj4sWLBAUFZdmelJREnTp1sFqttGnThpdffjlLqPiv1NRUUlNTM98nJiYCYLFYsFgs9paUpyv9OKo/Z0tJTGDD4N40OGQj1QtSR99E28EvF0t9l1LTiU/MOL1UK9CnWP8M3e04XeGOdakm1+GOdamm/PvJj8kwDMPeTk+cOEHNmjXZuHEjnTp1ytz+2muv8dlnn7Fv3758+3jjjTeYNGkSf/zxB9WqVQPg559/5sCBA7Rs2ZLExESmT59OVFQUu3btomHDhjn28+KLLzJhwoRs2+fPn4+/v7+9JZUZtpTz+Hw6mXpHDFLNsP/2cLxb3VZs33f8Eryx24tyXgavXW0ttu8REZGiS05O5q677uLChQsEBATk2q5Qd0/89/Y5wzDsuqVuwYIFvPjiiyxdujQzMAB07NiRjh07Zr7v3Lkzbdu25e2332bGjBk59jVu3DhGjx6d+T4xMZHQ0FB69eqVZ8EFYbFYWL16NT179sRsNjukT2e4dO4EPw++gTpHDFLMcHBge24Y/X6x1hT1Wzzs3k3jGpXo27d9sX0PuM9x+i93rEs1uQ53rEs15e7KbH1+ChQaqlSpgqenJ/Hx8Vm2JyQkUL169Tz3XbRoEcOHD+fLL7+kR48eebb18PDg6quvZv/+/bm28fHxwccn+4JBZrPZ4X8ZiqPPkpJ05hhb7u5LnaM2LnuDMfZOzOVaF3tNR89lnJqoX7V8if3ZufJxyos71qWaXIc71qWact7fHgW6e8Lb25vw8HBWr16dZfvq1auznK74rwULFjBs2DDmz5/PDTfckO/3GIbBzp07CQkJKcjw5D8unjzEpoF9qP13YPB8fiitbhtXIt+tiyBFRNxPgU9PjB49msGDB9OuXTsiIiL46KOPOHr0KCNHjgQyThvExsYyZ84cICMwDBkyhOnTp9OxY8fMWQo/Pz8CAwMBmDBhAh07dqRhw4YkJiYyY8YMdu7cybvvOnbdgLIkMe4APw++idDjNpK9wfzicFrdMqbELgDSGg0iIu6nwKFh4MCBnDlzhpdeeom4uDhatGhBVFQUderUASAuLi7Lmg0ffvgh6enpPPzwwzz88MOZ24cOHcrs2bMBOH/+PPfffz/x8fEEBgYSFhZGdHQ07dsX77lwV/TEop18u+tEnm2qWE/w0i9vUTvO4JIPfNjten7c2gy2RgFg2DwZvWV1nn0UldWWcX2tVoMUEXEfhboQ8qGHHuKhhx7K8bMrQeCKtWvX5tvf1KlTmTp1amGGUqYYhsHSnbHY8rjfpXp6LC9tnUbteINLvvBe1x786NeHrDuZyLMTB6lb2V9PtxQRcSN69oQLSUpNz/xZv/6p6/DxynpJysUTfxAz8klqxhsk+QEvPMyk6+/L0saSns4PP/xA9+7dMXsV7+GvVM4bs6eeiSYi4i4UGlxIYko6AN6eHtSq5JflNtezh3dx+IFB1DxpcNEPAiaPpkmvEdn6sFgsBHpDtQo+bnf1sIiIFC+FBhdyITnjIsYAP3OWwHDm0HZ2DbubGgkGif5Q6fWnaNTjHmcNU0RE3JRCgwtJTLkSGv45bKcPbuO3YYMJOWVwoRxUmTKOq64b4qwhioiIG1NocCGJlzNCQ6BfxmmFhL9+Zu+99xB8Gs6Xg6pTn+Oqrnc5c4giIuLGFBpcyIW/Q0OAr5mEPzexd/hwqp+B8+Wh+rQJ1L/mdiePUERE3JkubXchVy6ErJP8G3/ekxEYzlWAkLdfVWAQEZFip5kGF3LhsoXGab/TY+5sqp6HswFQ651J1Gl/k7OHJiIiZYBCgwuxxUQzfuNsqlyAM4FQ570phIbn/ywPERERR1BocBHHti2j48w3qXwBTgdC/Q+mUTOst7OHJSIiZYiuaXABR3/9jqMPjqHyBThVEY49+YICg4iIlDiFhlLuyJYlHHvoKYISIaESvNL5Hio2vtbZwxIRkTJIpydKsZhNXxH32HMEJUFCEMzq+xh/JdUmwFfLP4uISMnTTEMpdXD9QuIfe45KSXCyMjT9dCZ/ejQC/lncSUREpCQpNJRCB9bN49SoCVRMgvgqJprP/oxqTTrluIy0iIhISdFPn1Jm/4+fcXbMJAKTIa6qiVazP6dKg3AsVhvJaVYAnZ4QERGnUGgoRf5c9TEXxr5FQDLEVTPRZs4Cguq2Bv557gRABV8dNhERKXn66VNK/LH8A5KemU7AZTgRbKLtZ4uoVKdl5udXlpAu7+OFl6fOKomISMlTaCgF9i57l0vPvkP5FIgNMdHu88VUrNU0S5sL/3nCpYiISElTaHCyPd/N4PL49ymfCsdrmGg/dwmBNRpna3fl9IROTYiIiLPoJ5AT/bbkTdJe/IRyqXCspgcd5y4lIOSqHNtqpkFERJxNocFJdi1+nfQJs/BPg2O1PIiY9x0VqtfPtf0/t1sqNIiIiHMoNDjBzkWvYnt1Lv5pcDTUg87zllG+Wt0897ky06DbLUVExFkUGkrY9gUvY3ptPn4WOFrbg84LVlC+cmi++yVezrh7QqcnRETEWRQaStD2eS/gMekLfCxwpK4nXeatolzlGnbtmznToNUgRUTESfQTqIT8+tmzmN/4Gp90OFzPk2vnr8G/UrDd+1+5pkEzDSIi4iwKDSXgl1lj8XlzKd7pcLiBF93m/4BfYLUC9ZGoaxpERMTJFBqK2ZZPnsR36vd4WyGmoZnu83/Cp0LlAveTqFsuRUTEyRQaitHPHz6B/4wVmK0Q08hMj/nReJevWKi+riwjrVsuRUTEWRQaismm9x6lwjtr8LLBoSbe9Jwfjbd/YKH70+JOIiLibAoNxWDj2w8S8P7ajMDQzIeec9cVKTAYhvHPNQ26e0JERJxEP4EcbP20+6n04Xo8jYzA0GveBsx+5YvUZ3KalXSbAehCSBERcR6FBgeKfnM4QZ9swtOAgy186f35+iIHBvjndksvDxP+3p5F7k9ERKQwFBocZO3rw6g6awseBhxs7UefORvx8vFzSN//LOxkxmQyOaRPERGRglJocIC1kwZT9bOtGYEhzJ/IOZvxNHs7rH8tIS0iIqWBQkMR/fjKnVSfuxMP4GB4eSJnb3RoYIB/P6xKh0tERJxHP4WK4IeXbid4/m8ZgaFdeSJnOT4wwL9Wg9RMg4iIOJFCQyGteWEANRftBeBg+wD6ztqEh2fxXKR4QaFBRERKAYWGQlg9vj+1vtoHwMGOgfSdubHYAgP8c/eEbrcUERFn8nD2AFzNqnH/ywwMhzpXKvbAALoQUkRESgeFhgJY+fQNhC7ZD8ChrpWJ/Gh9sQcG+PfpCU0MiYiI8yg02GnFmEhqLz0EwKFuVYl8f12JBAb45/SEZhpERMSZChUa3nvvPerVq4evry/h4eGsX78+z/br1q0jPDwcX19f6tevzwcffJCtzeLFi2nWrBk+Pj40a9aMJUuWFGZoxWLNk32p8/1hAGKur07kuz+VWGCAf99yqdAgIiLOU+DQsGjRIkaNGsWzzz7Ljh076NKlC5GRkRw9ejTH9jExMfTt25cuXbqwY8cOnnnmGR577DEWL16c2Wbz5s0MHDiQwYMHs2vXLgYPHsztt9/Oli1bCl+Zg1i+m0TdFccBiOlZg77vrS3RwAD/3HKpmQYREXGmAp8kf+uttxg+fDj33XcfANOmTWPlypW8//77TJw4MVv7Dz74gNq1azNt2jQAmjZtytatW5kyZQoDBgzI7KNnz56MGzcOgHHjxrFu3TqmTZvGggULCltbkZxLSuGH0b1ovuE8AHuvC6bcA3NZ8XtciY/l1MVUQLdcioiIcxUoNKSlpbFt2zbGjh2bZXuvXr3YtGlTjvts3ryZXr16ZdnWu3dvZs6cicViwWw2s3nzZp544olsba4EjZykpqaSmpqa+T4xMREAi8WCxWIpSFk52vjWPTSPPgXAj1cH8UbgGJi7vcj9FkV5b1ORa7uyvyP+jEoLd6wJ3LMu1eQ63LEu1ZR/P/kpUGg4ffo0VquV6tWrZ9levXp14uPjc9wnPj4+x/bp6emcPn2akJCQXNvk1ifAxIkTmTBhQrbtq1atwt/f396ScpVQ5wZsdXdzIrgSXzUcSz2MIvdZFKHlDH7fvJY9Dnpe1erVqx3TUSnijjWBe9almlyHO9almrJLTk62q12h7uH775MWDcPI8+mLObX/7/aC9jlu3DhGjx6d+T4xMZHQ0FB69epFQEBA/kXY4fJNkfy0bgPLel6P2ewepwYsFgurV6+mZ8+eqqmUc8e6VJPrcMe6VFPurszW56dAoaFKlSp4enpmmwFISEjINlNwRXBwcI7tvby8qFy5cp5tcusTwMfHBx8fn2zbzWaz4/4ylAtwfJ+lhGpyHe5Yl2pyHe5Yl2rKeX97FOjuCW9vb8LDw7NNg6xevZpOnTrluE9ERES29qtWraJdu3aZg8ytTW59ioiISMkr8OmJ0aNHM3jwYNq1a0dERAQfffQRR48eZeTIkUDGaYPY2FjmzJkDwMiRI3nnnXcYPXo0I0aMYPPmzcycOTPLXRGPP/44Xbt2ZfLkydx0000sXbqUNWvWsGHDBgeVKSIiIkVV4NAwcOBAzpw5w0svvURcXBwtWrQgKiqKOnXqABAXF5dlzYZ69eoRFRXFE088wbvvvkuNGjWYMWNG5u2WAJ06dWLhwoWMHz+e5557jgYNGrBo0SI6dOjggBJFRETEEQp1IeRDDz3EQw89lONns2fPzrbt2muvZfv2vG9XvPXWW7n11lsLMxwREREpAXr2hIiIiNhFoUFERETsotAgIiIidlFoEBEREbsoNIiIiIhdFBpERETELgoNIiIiYheFBhEREbGLQoOIiIjYRaFBRERE7KLQICIiInZRaBARERG7KDSIiIiIXQr1lMvSyDAMABITEx3Wp8ViITk5mcTERMxms8P6dSbV5DrcsS7V5DrcsS7VlLsrPzuv/CzNjduEhosXLwIQGhrq5JGIiIi4posXLxIYGJjr5yYjv1jhImw2GydOnKBChQqYTCaH9JmYmEhoaCjHjh0jICDAIX06m2pyHe5Yl2pyHe5Yl2rKnWEYXLx4kRo1auDhkfuVC24z0+Dh4UGtWrWKpe+AgAC3+Qt2hWpyHe5Yl2pyHe5Yl2rKWV4zDFfoQkgRERGxi0KDiIiI2EWhIQ8+Pj688MIL+Pj4OHsoDqOaXIc71qWaXIc71qWais5tLoQUERGR4qWZBhEREbGLQoOIiIjYRaFBRERE7KLQICIiInYp86Hhvffeo169evj6+hIeHs769evzbL9u3TrCw8Px9fWlfv36fPDBByU0UvsVpKa1a9diMpmyvf78888SHHHeoqOj6devHzVq1MBkMvHNN9/ku09pP04FrckVjtPEiRO5+uqrqVChAtWqVaN///7s27cv3/1K87EqTE2ucKzef/99WrVqlbkgUEREBMuXL89zn9J8nKDgNbnCcfqviRMnYjKZGDVqVJ7tivNYlenQsGjRIkaNGsWzzz7Ljh076NKlC5GRkRw9ejTH9jExMfTt25cuXbqwY8cOnnnmGR577DEWL15cwiPPXUFrumLfvn3ExcVlvho2bFhCI87fpUuXaN26Ne+8845d7V3hOBW0pitK83Fat24dDz/8MD///DOrV68mPT2dXr16cenSpVz3Ke3HqjA1XVGaj1WtWrWYNGkSW7duZevWrVx//fXcdNNN7NmzJ8f2pf04QcFruqI0H6d/+/XXX/noo49o1apVnu2K/VgZZVj79u2NkSNHZtnWpEkTY+zYsTm2f+qpp4wmTZpk2fbAAw8YHTt2LLYxFlRBa/rpp58MwDh37lwJjK7oAGPJkiV5tnGF4/Rv9tTkasfJMAwjISHBAIx169bl2sbVjpU9NbnisTIMw6hUqZLxySef5PiZqx2nK/KqyZWO08WLF42GDRsaq1evNq699lrj8ccfz7VtcR+rMjvTkJaWxrZt2+jVq1eW7b169WLTpk057rN58+Zs7Xv37s3WrVuxWCzFNlZ7FaamK8LCwggJCaF79+789NNPxTnMYlfaj1NRuNJxunDhAgBBQUG5tnG1Y2VPTVe4yrGyWq0sXLiQS5cuERERkWMbVztO9tR0hSscp4cffpgbbriBHj165Nu2uI9VmQ0Np0+fxmq1Ur169Szbq1evTnx8fI77xMfH59g+PT2d06dPF9tY7VWYmkJCQvjoo49YvHgxX3/9NY0bN6Z79+5ER0eXxJCLRWk/ToXhasfJMAxGjx7NNddcQ4sWLXJt50rHyt6aXOVY/fbbb5QvXx4fHx9GjhzJkiVLaNasWY5tXeU4FaQmVzlOCxcuZPv27UycONGu9sV9rNzmKZeF9d/HaBuGkeejtXNqn9N2ZypITY0bN6Zx48aZ7yMiIjh27BhTpkyha9euxTrO4uQKx6kgXO04PfLII+zevZsNGzbk29ZVjpW9NbnKsWrcuDE7d+7k/PnzLF68mKFDh7Ju3bpcf8i6wnEqSE2ucJyOHTvG448/zqpVq/D19bV7v+I8VmV2pqFKlSp4enpm+w08ISEhW0q7Ijg4OMf2Xl5eVK5cudjGaq/C1JSTjh07sn//fkcPr8SU9uPkKKX1OD366KN8++23/PTTT/k+rt5VjlVBaspJaTxW3t7eXHXVVbRr146JEyfSunVrpk+fnmNbVzlOBakpJ6XtOG3bto2EhATCw8Px8vLCy8uLdevWMWPGDLy8vLBardn2Ke5jVWZDg7e3N+Hh4axevTrL9tWrV9OpU6cc94mIiMjWftWqVbRr1w6z2VxsY7VXYWrKyY4dOwgJCXH08EpMaT9OjlLajpNhGDzyyCN8/fXX/Pjjj9SrVy/ffUr7sSpMTTkpbccqJ4ZhkJqamuNnpf045SavmnJS2o5T9+7d+e2339i5c2fmq127dgwaNIidO3fi6emZbZ9iP1YOuZzSRS1cuNAwm83GzJkzjb179xqjRo0yypUrZxw+fNgwDMMYO3asMXjw4Mz2hw4dMvz9/Y0nnnjC2Lt3rzFz5kzDbDYbX331lbNKyKagNU2dOtVYsmSJ8ddffxm///67MXbsWAMwFi9e7KwSsrl48aKxY8cOY8eOHQZgvPXWW8aOHTuMI0eOGIbhmsepoDW5wnF68MEHjcDAQGPt2rVGXFxc5is5OTmzjasdq8LU5ArHaty4cUZ0dLQRExNj7N6923jmmWcMDw8PY9WqVYZhuN5xMoyC1+QKxykn/717oqSPVZkODYZhGO+++65Rp04dw9vb22jbtm2WW6mGDh1qXHvttVnar1271ggLCzO8vb2NunXrGu+//34Jjzh/Balp8uTJRoMGDQxfX1+jUqVKxjXXXGMsW7bMCaPO3ZVbo/77Gjp0qGEYrnmcClqTKxynnOoBjFmzZmW2cbVjVZiaXOFY3XvvvZn/RlStWtXo3r175g9Xw3C942QYBa/JFY5TTv4bGkr6WOnR2CIiImKXMntNg4iIiBSMQoOIiIjYRaFBRERE7KLQICIiInZRaBARERG7KDSIiIiIXRQaRERExC4KDSIiIg4WHR1Nv379qFGjBiaTiW+++cbp3zds2DBMJlOWV8eOHQv0PQoNIiIiDnbp0iVat27NO++8U6q+r0+fPsTFxWW+oqKiCvQ9Zf7R2CIiIo4WGRlJZGRkrp+npaUxfvx45s2bx/nz52nRogWTJ0+mW7duxfJ9V/j4+BAcHFyo7wDNNIhIMTp16hTBwcG89tprmdu2bNmCt7c3q1atcuLIRJzrnnvuYePGjSxcuJDdu3dz22230adPn2J/NPfatWupVq0ajRo1YsSIESQkJBRofz17QkSKVVRUFP3792fTpk00adKEsLAwbrjhBqZNm+bsoYmUCJPJxJIlS+jfvz8ABw8epGHDhhw/fpwaNWpktuvRowft27fPErId8X1XLFq0iPLly1OnTh1iYmJ47rnnSE9PZ9u2bfj4+NjVt05PiEix6tu3LyNGjGDQoEFcffXV+Pr6MmnSJGcPS8Rptm/fjmEYNGrUKMv21NRUKleuDMDhw4epV69env08/PDDBbpmYuDAgZn/3aJFC9q1a0edOnVYtmwZt9xyi119KDSISLGbMmUKLVq04IsvvmDr1q34+vo6e0giTmOz2fD09GTbtm14enpm+ax8+fIA1KxZkz/++CPPfipVqlSkcYSEhFCnTp0CnRJRaBCRYnfo0CFOnDiBzWbjyJEjtGrVytlDEnGasLAwrFYrCQkJdOnSJcc2ZrOZJk2aFOs4zpw5w7FjxwgJCbF7H4UGESlWaWlpDBo0iIEDB9KkSROGDx/Ob7/9RvXq1Z09NJFik5SUxIEDBzLfx8TEsHPnToKCgmjUqBGDBg1iyJAhvPnmm4SFhXH69Gl+/PFHWrZsSd++fR36fbVr1yYpKYkXX3yRAQMGEBISwuHDh3nmmWeoUqUKN998s/1fZIiIFKMxY8YYdevWNS5cuGBYrVaja9euxg033ODsYYkUq59++skAsr2GDh1qGIZhpKWlGc8//7xRt25dw2w2G8HBwcbNN99s7N69u1i+Lzk52ejVq5dRtWpVw2w2G7Vr1zaGDh1qHD16tEDfo7snRKTYrF27lp49e/LTTz9xzTXXAHD06FFatWrFxIkTefDBB508QhEpCIUGERERsYsWdxIRERG7KDSIiIiIXRQaRERExC4KDSIiImIXhQYRERGxi0KDiIiI2EWhQUREROyi0CAiIiJ2UWgQERERuyg0iIiIiF0UGkRERMQuCg0iIiJil/8HZ+bwDEqD+vMAAAAASUVORK5CYII=", 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{ - "cell_type": "markdown", - "id": "dfde558e-f3f6-4de1-ba87-60ddbfa9138d", - "metadata": {}, - "source": [ - "#### Timing" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "6c6e54f3-7f43-4215-9c2d-39ad115bd009", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "import decimal as d\n", - "D = d.Decimal" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "a16c06d8-8c87-42e8-917b-508affddc17c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def time_func(func, *args, N=None, **kwargs):\n", - " \"\"\"times the calls to func; func is called with args and kwargs; returns time in msec per 1m calls\"\"\"\n", - " if N is None:\n", - " N = 10_000_000\n", - " start_time = time.time()\n", - " for _ in range(N):\n", - " func(*args, **kwargs)\n", - " end_time = time.time()\n", - " return (end_time - start_time)/N*1_000_000*1000" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "9a313fce-2b46-43b7-a416-98d5ab0073dd", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def time_func1(func, arg, N=None):\n", - " \"\"\"times the calls to func; func is called with arg; returns time in msec per 1m calls\"\"\"\n", - " if N is None:\n", - " N = 10_000_000\n", - " start_time = time.time()\n", - " for _ in range(N):\n", - " func(arg)\n", - " end_time = time.time()\n", - " return (end_time - start_time)/N*1_000_000*1000" - ] - }, - { - "cell_type": "markdown", - "id": "b313973b-ae68-4f0f-bd6c-5e1aa2bb25ea", - "metadata": {}, - "source": [ - "identify function (`lambda`)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "9a7ee59f-ac92-4752-9286-f5f64b6882f4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(41.66769981384277, 27.7008056640625)" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "time_func(lambda x: x, 1), time_func1(lambda x: x, 1)" - ] - }, - { - "cell_type": "markdown", - "id": "a6f31082-4975-4c77-a634-d68a98a8c7d9", - "metadata": {}, - "source": [ - "ditto, defined with `def`" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "ef6a6f1f-13d2-48be-b12c-27e197ed276e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(39.624619483947754, 27.41990089416504)" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def idfunc(x):\n", - " return x\n", - "time_func(idfunc, 1), time_func1(idfunc, 1)" - ] - }, - { - "cell_type": "markdown", - "id": "f9c02ca3-1414-4981-82a0-0f8c932916d4", - "metadata": {}, - "source": [ - "sin, sqrt, exp etc as reference" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "c3ef665b-0255-4ff4-ba77-491b6f82ee2b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(51.15928649902344,\n", - " 50.94408988952637,\n", - " 59.79418754577637,\n", - " 39.32750225067139,\n", - " 44.03328895568848,\n", - " 45.121097564697266)" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(time_func(m.sin, 1), time_func(m.cos, 1), time_func(m.tan, 1), \n", - " time_func(m.sqrt, 1), time_func(m.exp, 1), time_func(m.log, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "c5300f07-35ac-464a-814a-a6767f3c4f11", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(38.51971626281738,\n", - " 38.30740451812744,\n", - " 39.91541862487793,\n", - " 26.962804794311523,\n", - " 30.146718025207516,\n", - " 42.35830307006836)" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(time_func1(m.sin, 1), time_func1(m.cos, 1), time_func1(m.tan, 1), \n", - " time_func1(m.sqrt, 1), time_func1(m.exp, 1), time_func1(m.log, 1))" - ] - }, - { - "cell_type": "markdown", - "id": "2bc8cf1a-9ad6-46ff-975d-ff0816471149", - "metadata": {}, - "source": [ - "**float** calculation" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "74a9d3db-0239-4708-982d-5196b80ac910", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(82.55062103271484, 65.61510562896729)" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "time_func(lambda xx: m.sqrt(1+xx)-1, 1), time_func1(lambda xx: m.sqrt(1+xx)-1, 1)" - ] - }, - { - "cell_type": "markdown", - "id": "4f4abe46-5247-4307-8230-f7ef66788f30", - "metadata": {}, - "source": [ - "**taylor** calculations" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "00b7850a-b625-4b5d-a3d0-8697eaaeee96", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(72.03409671783446, 60.68711280822753)" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "time_func(lambda xx: xx * (0.5 - xx*1/8), 1), time_func1(lambda xx: xx * (0.5 - xx*1/8), 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "cb211a08-bbcf-463a-81e3-07fc2de66914", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(110.19370555877684, 96.72987461090088)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(time_func(lambda xx: xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))), 1),\n", - "time_func1(lambda xx: xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))), 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "922cd929-78d3-42e3-8d1f-90e91fbeb438", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(148.4793186187744, 120.42548656463623)" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(time_func(lambda xx: xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128, 1),\n", - "time_func1(lambda xx: xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128, 1))" - ] - }, - { - "cell_type": "markdown", - "id": "7449ffef-1cd2-440f-8130-a451ad849ebe", - "metadata": { - "tags": [] - }, - "source": [ - "**decimal** calculations" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "7f07e127-a034-4a27-99f0-8bb6a150f158", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(2821.168899536133, 3099.2603302001953)" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d.getcontext().prec = 30\n", - "ONE = D(1)\n", - "(time_func(lambda xx: D(1+xx).sqrt()-1, 1, N=100_000),\n", - " time_func(lambda xx: ONE+xx.sqrt()-1, ONE, N=100_000))" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "34efbb1e-f424-4078-830b-3b0db3cee19b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(11717.677116394043, 12349.104881286621)" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d.getcontext().prec = 100\n", - "ONE = D(1)\n", - "(time_func(lambda xx: D(1+xx).sqrt()-1, 1, N=10_000),\n", - " time_func(lambda xx: ONE+xx.sqrt()-1, ONE, N=10_000))" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "068d3189-bc7c-45fa-973e-7415e983d95b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(608450.8895874023, 646713.7336730957)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d.getcontext().prec = 1_000\n", - "ONE = D(1)\n", - "(time_func(lambda xx: D(1+xx).sqrt()-1, 1, N=1_000),\n", - " time_func(lambda xx: ONE+xx.sqrt()-1, ONE, N=1_000))" - ] - }, - { - "cell_type": "markdown", - "id": "338a845c-5103-46fb-9a0f-8a7584159dad", - "metadata": {}, - "source": [ - "decimal conversions" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "ce909177-cb11-4bf2-b210-0bcd9b53a10e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(399.7950553894043,\n", - " 280.5027961730957,\n", - " Decimal('0.999999999999999999999999999999'))" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d.getcontext().prec = 30\n", - "ONE = D(\"0.\"+\"9\"*d.getcontext().prec)\n", - "PI = m.pi\n", - "(time_func(lambda xx: D(xx), PI, N=1_000_000),\n", - " time_func(lambda: float(ONE), N=1_000_000),\n", - " ONE\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "21f146ca-522c-44a9-b9ef-a9275ff026c1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(396.4359760284424,\n", - " 523.1471061706543,\n", - " Decimal('0.9999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999'))" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d.getcontext().prec = 100\n", - "ONE = D(\"0.\"+\"9\"*d.getcontext().prec)\n", - "(time_func(lambda xx: D(xx), PI, N=1_000_000),\n", - " time_func(lambda: float(ONE), N=1_000_000),\n", - " ONE\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "13db7008-08da-436b-9885-01575e26e8d5", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(398.88906478881836,\n", - " 1889.2371654510498,\n", - " Decimal('0.9999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999'))" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d.getcontext().prec = 1000\n", - "ONE = D(\"0.\"+\"9\"*d.getcontext().prec)\n", - "(time_func(lambda xx: D(xx), PI, N=1_000_000),\n", - " time_func(lambda: float(ONE), N=1_000_000),\n", - " ONE\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dbf0b416-29b5-412b-bba8-e304ec3a751d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/analysis/202401 Solidly/202401 Solidly-Freeze04.ipynb b/resources/analysis/202401 Solidly/202401 Solidly-Freeze04.ipynb deleted file mode 100644 index 67b3e8f17..000000000 --- a/resources/analysis/202401 Solidly/202401 Solidly-Freeze04.ipynb +++ /dev/null @@ -1,2224 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "96348e86-5892-417a-9e2d-2fda430683d0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "---\n", - "Function v1.0-beta2 (17/Jan/2024)\n", - "SolidlyInvariant v1.0-beta2 (17/Jan/2024)\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import math as m\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "from sympy import symbols, sqrt, Eq\n", - "import decimal as d\n", - "\n", - "import invariants.functions as f\n", - "from invariants.solidly import SolidlyInvariant, SolidlySwapFunction\n", - "\n", - "from testing import *\n", - "D = d.Decimal\n", - "plt.rcParams['figure.figsize'] = [6,6]\n", - "\n", - "print(\"---\")\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(f.Function))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(SolidlyInvariant))" - ] - }, - { - "cell_type": "markdown", - "id": "a14a57f8-e21f-4652-9d68-0cff0c4afead", - "metadata": {}, - "source": [ - "# Solidly Analysis (Freeze04)" - ] - }, - { - "cell_type": "markdown", - "id": "9bcaf580-1389-41dc-b329-c68a80c75d56", - "metadata": {}, - "source": [ - "## Equations" - ] - }, - { - "cell_type": "markdown", - "id": "58ab6488-5c7b-4103-bae1-9d79d9837f11", - "metadata": {}, - "source": [ - "### Invariant function\n", - "\n", - "The Solidly invariant function is \n", - "\n", - "$$\n", - " x^3y+xy^3 = k\n", - "$$\n", - "\n", - "which is a stable swap curve, but more convex than say curve. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "34a840d9-e684-406b-a8da-b1bbbe255f9f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def invariant_eq(x, y, k=0, *, aserr=False):\n", - " \"\"\"returns f(x,y)-k or f(x,y)/k - 1\"\"\"\n", - " if aserr:\n", - " return (x**3 * y + x * y**3)/k-1\n", - " else:\n", - " return x**3 * y + x * y**3 - k" - ] - }, - { - "cell_type": "markdown", - "id": "b6ee11bb-309c-4bb4-a9bc-45199287971e", - "metadata": {}, - "source": [ - "### Swap equation\n", - "\n", - "Solving the invariance equation as $y=y(x; k)$ gives the following result\n", - "\n", - "$$\n", - "y(x;k) = \\frac{x^2}{\\left(-\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\\right)^{\\frac{1}{3}}} - \\frac{\\left(-\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\\right)^{\\frac{1}{3}}}{3}\n", - "$$\n", - "\n", - "We can introduce intermediary variables $L(x;k), M(x;k)$ to write this a bit more simply\n", - "\n", - "$$\n", - "L = -\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\n", - "$$\n", - "\n", - "$$\n", - "M = L^{1/3} = \\sqrt[3]{L}\n", - "$$\n", - "\n", - "$$\n", - "y = \\frac{x^2}{\\sqrt[3]{L}} - \\frac{\\sqrt[3]{L}}{3} = \\frac{x^2}{M} - \\frac{M}{3} \n", - "$$\n", - "\n", - "Using the function $y(x;k)$ we can easily derive the swap equation at point $(x; k)$ as\n", - "\n", - "$$\n", - "\\Delta y = y(x+\\Delta x; k) - y(x; k)\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "50f960e3-65e3-470c-a465-64c1a3fb51f2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{2}}{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}} - \\frac{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}}{3}$" - ], - "text/plain": [ - "x**2/(-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333 - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333/3" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x, k = symbols('x k')\n", - "\n", - "y = x**2 / ((-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**(1/3)) - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**(1/3)/3\n", - "y" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1799f486-222c-46ad-bd6d-a4c183d8d871", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{2}}{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}} - \\frac{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}}{3}$" - ], - "text/plain": [ - "x**2/(-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333 - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333/3" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L = -27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2\n", - "y2 = x**2 / (L**(1/3)) - (L**(1/3))/3\n", - "y2" - ] - }, - { - "cell_type": "markdown", - "id": "1ac5dc18-0a49-4d37-a49b-0f57ef5ebdc4", - "metadata": {}, - "source": [ - "#### Precision issues and L\n", - "\n", - "Note that as above, $L$ (that we call $L_1$ now) is not particularly well conditioned. \n", - "\n", - "$$\n", - "L_1 = -\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\n", - "$$\n", - "\n", - "This alternative form works better\n", - "\n", - "$$\n", - "L_2(x;k) = \\frac{27k}{2x} \\left(\\sqrt{1 + \\frac{108x^8}{729k^2}} - 1 \\right)\n", - "$$\n", - "\n", - "Furthermore\n", - "\n", - "$$\n", - "\\sqrt{1+\\xi}-1 = \\frac{\\xi}{2} - \\frac{\\xi^2}{8} + \\frac{\\xi^3}{16} - \\frac{5\\xi^4}{128} + O(\\xi^5)\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1c208f81-5e12-4cd9-95a9-3cd1b3e0ea71", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def L1(x,k):\n", - " return -27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2\n", - "\n", - "def L2(x,k):\n", - " xi = (108 * x**8) / (729 * k**2)\n", - " #print(f\"xi = {xi}\")\n", - " if xi > 1e-5:\n", - " lam = (m.sqrt(1 + xi) - 1)\n", - " else:\n", - " lam = xi*(1/2 - xi*(1/8 - xi*(1/16 - 0.0390625*xi)))\n", - " # the relative error of this Taylor approximation is for xi < 0.025 is 1e-5 or better\n", - " # for xi ~ 1e-15 the full term is unstable (because 1 + 1e-16 ~ 1 in double precision)\n", - " # therefore the switchover should happen somewhere between 1e-12 and 1e-2\n", - " #lam1 = 0\n", - " #lam2 = xi/2 - xi**2/8 \n", - " #lam2 = xi/2 - xi**2/8 + xi**3/16 - 0.0390625*xi**4\n", - " #lam2 = xi*(1/2 - xi*(1/8 - xi*(1/16 - 0.0390625*xi)))\n", - " #lam = max(lam1, lam2)\n", - " # for very small xi we can get zero or close to zero in the full formula\n", - " # in this case the taulor approximation is better because for small xi it is always > 0\n", - " # we simply use the max of the two -- the Taylor gets negative quickly\n", - " L = lam * (27 * k) / (2 * x)\n", - " return L\n", - "\n", - "def L3(x,k):\n", - " \"\"\"going via decimal\"\"\"\n", - " x = D(x)\n", - " k = D(k)\n", - " xi = (108 * x**8) / (729 * k**2)\n", - " lam = (D(1) + xi).sqrt() - D(1)\n", - " L = lam * (27 * k) / (2 * x)\n", - " return float(L)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "51a99f4c-1c36-4865-8046-52946214ec5b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(9.99999940631824e-8, 9.9999999962963e-08)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L1(0.1, 1), L2(0.1,1)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "4abb21bd-64c3-437d-8c29-4be0b9a5c725", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{2}}{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}} - \\frac{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}}{3}$" - ], - "text/plain": [ - "x**2/(-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333 - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333/3" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "M = L**(1/3)\n", - "y3 = x**2 / M - M/3\n", - "y3" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7de2f57a-abca-4a23-b81d-3ce651b7855b", - "metadata": {}, - "outputs": [], - "source": [ - "assert y == y2\n", - "assert y == y3\n", - "assert y2 == y3" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "285736b4-ac27-4804-8dcb-a8b96b6785de", - "metadata": {}, - "outputs": [], - "source": [ - "def swap_eq(x,k):\n", - " \"\"\"using floats only\"\"\"\n", - " L,M,y = [None]*3\n", - " try:\n", - " #L = -27*k/(2*x) + m.sqrt(729*k**2/x**2 + 108*x**6)/2\n", - " L = L2(x,k)\n", - " M = L**(1/3)\n", - " y = x**2/M - M/3\n", - " except Exception as e:\n", - " print(\"Exception: \", e)\n", - " print(f\"x={x}, k={k}, L={L}, M={M}, y={y}\")\n", - " return y\n", - "\n", - "def swap_eq_dec(x,k):\n", - " \"\"\"using decimals for the calculation of L\"\"\"\n", - " L,M,y = [None]*3\n", - " try:\n", - " #L = -27*k/(2*x) + m.sqrt(729*k**2/x**2 + 108*x**6)/2\n", - " L = L3(x,k)\n", - " M = L**(1/3)\n", - " y = x**2/M - M/3\n", - " except Exception as e:\n", - " print(\"Exception: \", e)\n", - " print(f\"x={x}, k={k}, L={L}, M={M}, y={y}\")\n", - " return y" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "91cb13ac-a1fc-485b-9037-6447a4c49dd3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.6823278038280196\n" - ] - } - ], - "source": [ - "def swap_eq2(x, k):\n", - " # Calculating the components of the swap equation\n", - " term1_numerator = (2/3)**(1/3) * x**3\n", - " term1_denominator = (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3)\n", - "\n", - " term2_numerator = (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3)\n", - " term2_denominator = 2**(1/3) * 3**(2/3) * x\n", - "\n", - " # Swap equation calculation\n", - " y = -term1_numerator / term1_denominator + term2_numerator / term2_denominator\n", - "\n", - " return y\n", - "\n", - "# Example usage\n", - "x_value = 1 # Replace with the desired value of x\n", - "k_value = 1 # Replace with the desired value of k\n", - "print(swap_eq(x_value, k_value))" - ] - }, - { - "cell_type": "markdown", - "id": "4c115505-7076-47b4-9c3e-fd0dd826683c", - "metadata": {}, - "source": [ - "### Price equation\n", - "\n", - "The derivative $p=dy/dx$ is as follows\n", - "\n", - "$$\n", - "p=\\frac{dy}{dx} = 6^{\\frac{1}{3}}\\left(\\frac{-2 \\cdot 3^{\\frac{1}{3}} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} \\cdot \\left(-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}\\right) \\cdot \\left(3k \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} + \\sqrt{3} \\cdot \\left(-9k^2 + 4x^8\\right)\\right) + 2^{\\frac{1}{3}} \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} \\cdot \\left(\\frac{-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}}{x}\\right)^{\\frac{5}{3}} \\cdot \\left(-3k \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} + \\sqrt{3} \\cdot \\left(9k^2 - 4x^8\\right)\\right) + 4 \\cdot 3^{\\frac{1}{3}} \\cdot \\left(-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}\\right)^2 \\cdot \\left(27k^2 + 4x^8\\right)}{6 \\cdot x \\cdot \\left(\\frac{-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}}{x}\\right)^{\\frac{7}{3}} \\cdot \\left(27k^2 + 4x^8\\right)}\\right)\n", - "$$\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "5c900f31-fee7-4726-b0af-31a35849b043", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.3136251299197979\n" - ] - } - ], - "source": [ - "def price_eq(x, k):\n", - " # Components of the derivative\n", - " term1_numerator = 2**(1/3) * x**3 * (18 * k * x + (m.sqrt(3) * (108 * k**2 * x**3 + 48 * x**11)) / (2 * m.sqrt(27 * k**2 * x**4 + 4 * x**12)))\n", - " term1_denominator = 3 * (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(4/3)\n", - " \n", - " term2_numerator = 18 * k * x + (m.sqrt(3) * (108 * k**2 * x**3 + 48 * x**11)) / (2 * m.sqrt(27 * k**2 * x**4 + 4 * x**12))\n", - " term2_denominator = 3 * 2**(1/3) * 3**(2/3) * x * (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(2/3)\n", - " \n", - " term3 = -3 * 2**(1/3) * x**2 / (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3)\n", - " \n", - " term4 = -(9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3) / (2**(1/3) * 3**(2/3) * x**2)\n", - " \n", - " # Combining all terms\n", - " dy_dx = (term1_numerator / term1_denominator) + (term2_numerator / term2_denominator) + term3 + term4\n", - "\n", - " return dy_dx\n", - "\n", - "# Example usage\n", - "x_value = 1 # Replace with the desired value of x\n", - "k_value = 1 # Replace with the desired value of k\n", - "print(price_eq(x_value, k_value))\n" - ] - }, - { - "cell_type": "markdown", - "id": "bd87b7d5-c0cd-4cfd-866b-ce305aa9d78f", - "metadata": {}, - "source": [ - "#### Inverting the price equation\n", - "\n", - "The above equations \n", - "([obtained thanks to Wolfram Alpha](https://chat.openai.com/share/55151f92-411c-43c1-a6ec-180856762a82), \n", - "the interface of which still sucks) are rather complex, and unfortunately they can't apparently be inverted analytically to get $x=x(p;k)$" - ] - }, - { - "cell_type": "markdown", - "id": "053180db-2679-4bf5-a8d6-d5d6e4e51f29", - "metadata": {}, - "source": [ - "## Charts" - ] - }, - { - "cell_type": "markdown", - "id": "99ffb5da-a7dd-4804-a2bf-1f32da169fad", - "metadata": {}, - "source": [ - "### Invariant equation" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "adfc7418-fa81-4108-9a4b-9c003ad315da", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "y_f = swap_eq" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3e8740bc-696c-4f0d-9acb-ebe8d8e27ae9", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k_v = [1**4, 2**4, 3**4, 5**4]\n", - "#k_v = [1**4]\n", - "x_v = np.linspace(0, m.sqrt(10), 50)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "y_v_dct = {kk: [y_f(xx, kk) for xx in x_v] for kk in k_v}\n", - "plt.grid(True)\n", - "for kk, y_v in y_v_dct.items(): \n", - " plt.plot(x_v, y_v, marker=None, linestyle='-', label=f\"k={kk}\")\n", - "plt.legend()\n", - "plt.xlim(0, max(x_v))\n", - "plt.ylim(0, max(x_v))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c63f7026-4cc8-4f54-a34e-dc99939945b8", - "metadata": { - "tags": [] - }, - "source": [ - "Checking the invariant equation at a specific point (xx; kk)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "fcb63f18-df33-448e-9ef8-cd8733e3b84e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "5.773159728050814e-15" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kk = 625\n", - "xx = 3\n", - "invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True)" - ] - }, - { - "cell_type": "markdown", - "id": "ea922e57-a4d5-444c-8443-407674520fcc", - "metadata": {}, - "source": [ - "Calculating a histogram of relative errors, ie what the relative error in the invariant equation is at various points $xx$ of the swap equation and at various $kk$" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "81de37e3-4c86-4428-9c74-1ec98eed876f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_inv_dct = {kk: [invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) for xx in x_v] for kk in k_v}\n", - "y_inv_lst = [v for lst in y_inv_dct.values() for v in lst]\n", - "#y_inv_lst\n", - "plt.hist(y_inv_lst, bins=200, color=\"blue\")\n", - "plt.title(\"Histogram of relative errors [f(x,y)/k - 1]\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f01529b5-7285-4c82-9145-0ea58a09877f", - "metadata": {}, - "source": [ - "Maximum relative error for different values of $k$" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "bd4456bf-1c66-4c04-89d5-ff3302a3bd7a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{1: 2.9978242110928477e-12,\n", - " 16: 2.220890138460163e-12,\n", - " 81: 9.826917057864648e-12,\n", - " 625: 7.190470441287289e-12}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{k: max([abs(vv) for vv in v]) for k,v in y_inv_dct.items()}" - ] - }, - { - "cell_type": "markdown", - "id": "9b5ef43c-9784-44fe-b680-c5262c36ec6b", - "metadata": { - "tags": [] - }, - "source": [ - "Minimum relative error for different values of $k$" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "7c236fa2-9b33-4693-bb9e-b72bab17f6e3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{1: 0.0, 16: 2.220446049250313e-16, 81: 4.440892098500626e-16, 625: 0.0}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{k: min([abs(vv) for vv in v]) for k,v in y_inv_dct.items()}" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "99f4fbc6-967c-44fd-bd88-f32fbc030ae3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kk = 5**4\n", - "x_v = np.linspace(0, m.sqrt(20), 50)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "plt.grid(True)\n", - "plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f\"k={kk}\")\n", - "inv_dct = {xx: invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) for xx in x_v}\n", - "plt.legend()\n", - "plt.xlim(0, max(x_v))\n", - "plt.ylim(0, max(x_v))\n", - "plt.show()\n", - "plt.plot(inv_dct.keys(), inv_dct.values())\n", - "plt.title(f\"Relative error as a function of x for k={kk}\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "2d13ac33-bd7b-4507-b6e8-e77b51d4c328", - "metadata": {}, - "source": [ - "Same analysis as above, but much higher resolution" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "621a8d45-7655-42e3-b8e7-71a6c44e19e6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# NUMPOINTS = 10000\n", - "# kk = 5**4\n", - "# x_v = np.linspace(0, m.sqrt(20), NUMPOINTS)\n", - "# x_v = [xx**2 for xx in x_v]\n", - "# x_v[0] = x_v[1]/2\n", - "# plt.grid(True)\n", - "# plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f\"k={kk}\")\n", - "# inv_dct = {xx: invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) \n", - "# # for xx in x_v[int(0.2*NUMPOINTS):int(0.5*NUMPOINTS)] # <=== CHANGE RANGE HERE\n", - "# for xx in x_v # <=== CHANGE RANGE HERE\n", - "# }\n", - "# plt.legend()\n", - "# plt.xlim(0, max(x_v))\n", - "# plt.ylim(0, max(x_v))\n", - "# plt.show()\n", - "# plt.plot(inv_dct.keys(), inv_dct.values())\n", - "# plt.title(f\"Relative error as a function of x for k={kk} (highres)\")\n", - "# plt.grid()\n", - "# plt.show()\n", - "# plt.plot(inv_dct.keys(), inv_dct.values())\n", - "# plt.title(f\"Relative error as a function of x for k={kk} (highres)\")\n", - "# plt.grid()\n", - "# plt.ylim(0,1e-13)\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "49f8b5cb-ee4c-4ff5-a893-03bd61d52137", - "metadata": {}, - "source": [ - "same as above, but using decimal" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "7175fe6d-be86-428b-9a0b-fbc2beabacd1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# NUMPOINTS = 10000\n", - "# kk = 5**4\n", - "# x_v = np.linspace(0, m.sqrt(20), NUMPOINTS)\n", - "# x_v = [xx**2 for xx in x_v]\n", - "# x_v[0] = x_v[1]/2\n", - "# plt.grid(True)\n", - "# plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f\"k={kk}\")\n", - "# inv_dct = {xx: invariant_eq(x=xx, y=swap_eq_dec(xx, kk), k=kk, aserr=True) \n", - "# # for xx in x_v[int(0.15*NUMPOINTS):int(0.3*NUMPOINTS)] # <=== CHANGE RANGE HERE\n", - "# for xx in x_v \n", - "# }\n", - "# plt.legend()\n", - "# plt.xlim(0, max(x_v))\n", - "# plt.ylim(0, max(x_v))\n", - "# plt.show()\n", - "# plt.plot(inv_dct.keys(), inv_dct.values())\n", - "# plt.title(f\"Relative error as a function of x for k={kk} (highres)\")\n", - "# plt.grid()\n", - "# plt.show()\n", - "# plt.plot(inv_dct.keys(), inv_dct.values())\n", - "# plt.title(f\"Relative error as a function of x for k={kk} (highres)\")\n", - "# plt.grid()\n", - "# plt.ylim(0,1e-13)\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4066e383-dba2-4e49-b999-ef7322ada357", - "metadata": {}, - "source": [ - "### Numerical considerations\n", - "#### Comparing L1 with L2\n", - "\n", - "L1 and L2 are different expressions of the L term above. L2 is the naive formula, L1 is optimized. L2 can be zero for very small values (and it is not even continous; see 0.009 and 0.01 below) whilst L1 is *always* greater than zero." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "0abe5692-f6da-4071-83db-c8bb995ff2be", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[(0, 1.0000000000000003e-28),\n", - " (0, 1.0000000000000001e-21),\n", - " (2.27373675443232e-13, 4.7829689999999975e-15),\n", - " (0, 1.0000000000000002e-14),\n", - " (2.27373675443232e-13, 1.7085937499999996e-13),\n", - " (1.25055521493778e-12, 1.279999999999999e-12),\n", - " (7.81199105404085e-10, 7.812499999988701e-10)]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xs_v = [0.0001, 0.001, 0.009, 0.01, 0.015, 0.02, 0.05]\n", - "[(L1(xx,1), L2(xx, 1)) for xx in xs_v]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "a5b8067c-ca96-4586-bab2-d3fa5dc421db", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x_v, [L2(xx, 1) - L1(xx, 1) for xx in x_v])" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "67804275-7f8b-41ef-bafd-18264189d3c8", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x_v, [L1(xx, 1) for xx in x_v])\n", - "plt.plot(x_v, [L2(xx, 1) for xx in x_v])\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "30ea5427-a3b0-4530-925e-7809f91996a3", - "metadata": {}, - "source": [ - "## Curvature and regions\n", - "\n", - "### Overview\n", - "\n", - "Here we look at the different _regions_ of the curve, most importantly the central, flat, region and its boundaries. Firstly we note that the invariance equation is homogenous\n", - "\n", - "$$\n", - " (\\lambda x)^3(\\lambda y)+(\\lambda x)(\\lambda y)^3 = \n", - " \\lambda^4 (x^3y+xy^3) = \\lambda^4 k\n", - "$$\n", - "\n", - "In other words, if a point $(x, y)$ is on curve $k$, then the point $(\\lambda x, \\lambda y)$ is on the curve $\\lambda^4 k$, and in fact there is a 1:1 relationship between _all_ points on the curve $k$ and _all_ points on the curve $\\lambda^4 k$ using this relationship. \n", - "\n", - "**Important side note:** This scaling relation also shows that the financially important quantity is $\\sqrt[4]{k}$, in the sense that this quantity scales linearly with the financial size of the curve.\n", - "\n", - "The points $(\\lambda x, \\lambda y)$ are _rays_ that come from the origin of the coordinate system. We now identify the ray where the curvature starts to bite, and this will be the boundary of our approximation\n", - "\n", - "Below we draw the rays as well as the **central tangents**, ie the tangents going through the point $x=y$. For a curve $k$, a the central point we have $2x^4=k$ and therefore it is at $(x,y) = (\\sqrt[4]{k/2}, \\sqrt[4]{k/2})$. The slope at this point is -1, so the equation is\n", - "\n", - "$$\n", - "t(x;k) = \\sqrt[4]{\\frac k 2} - (x-\\sqrt[4]{\\frac k 2})\n", - "$$\n", - "\n", - "We also note that $\\sqrt[4]{k/2} = \\sqrt[4]{k} \\sqrt[4]{0.5}$" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "844a1cea-6306-45c0-8f91-7478d729d4f5", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(0, m.sqrt(10), 50)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "k_sqrt4_v = [2, 3.5, 5, 6.5]\n", - "\n", - "# draw the invariance curves\n", - "k_v = [kk**4 for kk in k_sqrt4_v]\n", - "for kk in k_v: \n", - " y_f = SolidlySwapFunction(k=kk)\n", - " yy_v = [y_f(xx) for xx in x_v]\n", - " #yy_v = [y_f(xx, kk) for xx in x_v]\n", - " plt.plot(x_v, yy_v, marker=None, linestyle='-', label=f\"k={kk}\")\n", - "\n", - "# draw the central tangents\n", - "C = 0.5**(0.25)\n", - "for kk in k_sqrt4_v:\n", - " yy_v = [C*kk - (xx-C*kk) for xx in x_v]\n", - " plt.plot(x_v, yy_v, marker=None, linestyle='--', color=\"#aaa\")\n", - "\n", - "# draw the rays\n", - "for mm in [2.6, 6]:\n", - " yy_v = [mm*xx for xx in x_v]\n", - " plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color=\"#aaa\", label=f\"ray (m={mm})\")\n", - " yy_v = [1/mm*xx for xx in x_v]\n", - " plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color=\"#aaa\")\n", - "\n", - "plt.grid(True)\n", - "plt.legend()\n", - "plt.xlim(0, max(x_v))\n", - "plt.ylim(0, max(x_v))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "aca368bd-13af-404d-a1aa-192c51ca56a7", - "metadata": {}, - "source": [ - "### best hyperbola fit\n", - "\n", - "We now try the best possible (levered) hyperbola fit for one of those curves. Note that the levered hyperbola has the equation \n", - "\n", - "$$\n", - "y-y_0 = \\frac{k}{x-x_0}\n", - "$$\n", - "\n", - "and has therefore three free paramters, $(k, x_0, y_0)$. We fit those numerically." - ] - }, - { - "cell_type": "markdown", - "id": "0a297999-b281-4893-9abb-7b8546c6a000", - "metadata": {}, - "source": [ - "#### Unfitted hyperbola for demonstration\n", - "\n", - "Here we create four charts\n", - "1. The target curve, and a (bad) fit for demonstration, shown over a sufficiently wide range\n", - "2. The difference between the target curve and the fit\n", - "3. Target curve and fit, withing the kernel area\n", - "4. Difference, within kernel area (title contains L2 norm)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "cb21aa13-a3eb-4ac1-bc9e-d23cd017f114", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x1_v = np.linspace(0,1,100)\n", - "x1_v[0] = x1_v[1]/2\n", - "data = [(\n", - " xx, \n", - " m.sqrt(1+xx)-1,\n", - " xx * (0.5 - xx*1/8),\n", - " #xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128,\n", - " xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))),\n", - ") for xx in x1_v\n", - "]\n", - "df = pd.DataFrame(data, columns=['x', 'Float', 'Taylor2', 'Taylor4']).set_index(\"x\")\n", - "df.plot()\n", - "plt.grid()\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "9f7fc799-1a9e-4eb9-a504-41200fb1d87d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x2_v = np.linspace(0,0.2,100)\n", - "x1_v[0] = x1_v[1]/2\n", - "data = [(\n", - " xx, \n", - " m.sqrt(1+xx)-1,\n", - " xx * (0.5 - xx*1/8),\n", - " #xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128,\n", - " xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))),\n", - ") for xx in x2_v\n", - "]\n", - "df = pd.DataFrame(data, columns=['x', 'Float', 'Taylor2', 'Taylor4']).set_index(\"x\")\n", - "df.plot()\n", - "plt.grid()\n", - "df2 = df.copy()\n", - "df2[\"Err2\"] = df2[\"Taylor2\"]/df2[\"Float\"] - 1\n", - "df2[\"Err4\"] = df2[\"Taylor4\"]/df2[\"Float\"] - 1\n", - "plt.show()\n", - "df2.plot(y=[\"Err2\", \"Err4\"])\n", - "plt.grid()\n", - "plt.title(\"Relative error of Taylor 2 4 term approximations\")\n", - "plt.ylim(-0.001, 0.0001)\n", - "df2.head()" - ] - }, - { - "cell_type": "markdown", - "id": "4446b5dd-a4c8-450f-81bd-d7a909895bf8", - "metadata": {}, - "source": [ - "### Decimal vs float\n", - "#### Precision\n", - "\n", - "we compare $\\sqrt{1+\\xi}-1$ for float, Taylor and Decimal\n", - "\n", - "$$\n", - "\\sqrt{1+\\xi}-1 = \\frac{\\xi}{2} - \\frac{\\xi^2}{8} + \\frac{\\xi^3}{16} - \\frac{5\\xi^4}{128} + O(\\xi^5)\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "824c7650-acd7-4336-924e-9c927f0e2ebe", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(1e-18, 1.3721439741813515)" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import decimal as d\n", - "D = d.Decimal\n", - "d.getcontext().prec = 1000 # Set the precision to 30 decimal places (adjust as needed)\n", - "xd_v = [1e-18*1.5**nn for nn in np.linspace(0, 103, 500)]\n", - "xd_v[0], xd_v[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "8252b418-74e6-429f-9162-1574ac04580f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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FloatTaylor2Taylor4Dec
x
1.000000e-180.05.000000e-195.000000e-195.000000e-19
1.087295e-180.05.436476e-195.436476e-195.436476e-19
1.182211e-180.05.911055e-195.911055e-195.911055e-19
1.285412e-180.06.427062e-196.427062e-196.427062e-19
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" - ], - "text/plain": [ - " Float Taylor2 Taylor4 Dec\n", - "x \n", - "1.000000e-18 0.0 5.000000e-19 5.000000e-19 5.000000e-19\n", - "1.087295e-18 0.0 5.436476e-19 5.436476e-19 5.436476e-19\n", - "1.182211e-18 0.0 5.911055e-19 5.911055e-19 5.911055e-19\n", - "1.285412e-18 0.0 6.427062e-19 6.427062e-19 6.427062e-19\n", - "1.397623e-18 0.0 6.988114e-19 6.988114e-19 6.988114e-19" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fmt = lambda x: x\n", - "fmt = float\n", - "ONE = D(1)\n", - "data = [(\n", - " xx, \n", - " m.sqrt(1+xx)-1,\n", - " xx * (0.5 - xx*1/8),\n", - " #xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128,\n", - " xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))),\n", - " fmt((ONE+D(xx)).sqrt()-1),\n", - ") for xx in xd_v\n", - "]\n", - "df = pd.DataFrame(data, columns=['x', 'Float', 'Taylor2', 'Taylor4', 'Dec']).set_index(\"x\")\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": 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", 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", 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", 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{ - "cell_type": "markdown", - "id": "dfde558e-f3f6-4de1-ba87-60ddbfa9138d", - "metadata": {}, - "source": [ - "#### Timing" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "6c6e54f3-7f43-4215-9c2d-39ad115bd009", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "import decimal as d\n", - "D = d.Decimal" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "a16c06d8-8c87-42e8-917b-508affddc17c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(139.65392112731934, 128.91793251037598)" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# def timer(func, *args, N=None, **kwargs):\n", - "# \"\"\"times the calls to func; func is called with args and kwargs; returns time in msec per 1m calls\"\"\"\n", - "# if N is None:\n", - "# N = 10_000_000\n", - "# start_time = time.time()\n", - "# for _ in range(N):\n", - "# func(*args, **kwargs)\n", - "# end_time = time.time()\n", - "# return (end_time - start_time)/N*1_000_000*1000\n", - "\n", - "# def timer1(func, arg, N=None):\n", - "# \"\"\"times the calls to func; func is called with arg; returns time in msec per 1m calls\"\"\"\n", - "# if N is None:\n", - "# N = 10_000_000\n", - "# start_time = time.time()\n", - "# for _ in range(N):\n", - "# func(arg)\n", - "# end_time = time.time()\n", - "# return (end_time - start_time)/N*1_000_000*1000\n", - "\n", - "# def timer2(func, arg1, arg2, N=None):\n", - "# \"\"\"times the calls to func; func is called with arg1, arg2; returns time in msec per 1m calls\"\"\"\n", - "# if N is None:\n", - "# N = 10_000_000\n", - "# start_time = time.time()\n", - "# for _ in range(N):\n", - "# func(arg1, arg2)\n", - "# end_time = time.time()\n", - "# return (end_time - start_time)/N*1_000_000*1000\n", - "#-\n", - "\n", - "# identify function (`lambda`)\n", - "\n", - "timer(lambda x: x, 1), timer1(lambda x: x, 1)\n", - "\n", - "\n", - "# ditto, defined with `def`\n", - "\n", - "def idfunc(x):\n", - " return x\n", - "timer(idfunc, 1), timer1(idfunc, 1)\n", - "\n", - "# sin, sqrt, exp etc as reference\n", - "\n", - "(timer(m.sin, 1), timer(m.cos, 1), timer(m.tan, 1), \n", - " timer(m.sqrt, 1), timer(m.exp, 1), timer(m.log, 1))\n", - "\n", - "(timer1(m.sin, 1), timer1(m.cos, 1), timer1(m.tan, 1), \n", - " timer1(m.sqrt, 1), timer1(m.exp, 1), timer1(m.log, 1))\n", - "\n", - "# **float** calculation\n", - "\n", - "timer(lambda xx: m.sqrt(1+xx)-1, 1), timer1(lambda xx: m.sqrt(1+xx)-1, 1)\n", - "\n", - "# **taylor** calculations\n", - "\n", - "timer(lambda xx: xx * (0.5 - xx*1/8), 1), timer1(lambda xx: xx * (0.5 - xx*1/8), 1)\n", - "\n", - "(timer(lambda xx: xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))), 1),\n", - "timer1(lambda xx: xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))), 1))\n", - "\n", - "(timer(lambda xx: xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128, 1),\n", - "timer1(lambda xx: xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128, 1))\n", - "\n", - "# **decimal** calculations" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "9a313fce-2b46-43b7-a416-98d5ab0073dd", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 30\n", - "# ONE = D(1)\n", - "# (timer(lambda xx: D(1+xx).sqrt()-1, 1, N=100_000),\n", - "# timer(lambda xx: ONE+xx.sqrt()-1, ONE, N=100_000))" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "d647f240-1eaf-4183-92cb-9b5da5f9f616", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 100\n", - "# ONE = D(1)\n", - "# (timer(lambda xx: D(1+xx).sqrt()-1, 1, N=10_000),\n", - "# timer(lambda xx: ONE+xx.sqrt()-1, ONE, N=10_000))" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "8b67ff58", - "metadata": {}, - "outputs": [], - "source": [ - "# d.getcontext().prec = 1_000\n", - "# ONE = D(1)\n", - "# (timer(lambda xx: D(1+xx).sqrt()-1, 1, N=1_000),\n", - "# timer(lambda xx: ONE+xx.sqrt()-1, ONE, N=1_000))" - ] - }, - { - "cell_type": "markdown", - "id": "338a845c-5103-46fb-9a0f-8a7584159dad", - "metadata": {}, - "source": [ - "decimal conversions" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "ce909177-cb11-4bf2-b210-0bcd9b53a10e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 30\n", - "# ONE = D(\"0.\"+\"9\"*d.getcontext().prec)\n", - "# PI = m.pi\n", - "# (timer(lambda xx: D(xx), PI, N=1_000_000),\n", - "# timer(lambda: float(ONE), N=1_000_000),\n", - "# ONE\n", - "# )" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "21f146ca-522c-44a9-b9ef-a9275ff026c1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 100\n", - "# ONE = D(\"0.\"+\"9\"*d.getcontext().prec)\n", - "# (timer(lambda xx: D(xx), PI, N=1_000_000),\n", - "# timer(lambda: float(ONE), N=1_000_000),\n", - "# ONE\n", - "# )" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "13db7008-08da-436b-9885-01575e26e8d5", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 1000\n", - "# ONE = D(\"0.\"+\"9\"*d.getcontext().prec)\n", - "# (timer(lambda xx: D(xx), PI, N=1_000_000),\n", - "# timer(lambda: float(ONE), N=1_000_000),\n", - "# ONE\n", - "# )" - ] - }, - { - "cell_type": "markdown", - "id": "dfd8e821-c895-4399-8e0a-de36dd7eddb2", - "metadata": {}, - "source": [ - "`L2` (using Taylor) vs `L3` (using decimal)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "c2d71012-8abf-47b6-99b0-d6cd39587612", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 30\n", - "# r = ( \n", - "# timer2(L2, 1, 625, N=1_000_000),\n", - "# timer2(L3, 1, 625, N=10_000),\n", - "# )\n", - "# r, r[1]/r[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "0e184b46-e40c-4954-9cb2-cb866f5b6df1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 100\n", - "# r = ( \n", - "# timer2(L2, 1, 625, N=1_000_000),\n", - "# timer2(L3, 1, 625, N=10_000),\n", - "# )\n", - "# r, r[1]/r[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "e9a07613-c587-4ad0-ba92-1cb55a913c2c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 1000\n", - "# r = ( \n", - "# timer2(L2, 1, 625, N=1_000_000),\n", - "# timer2(L3, 1, 625, N=10_000),\n", - "# )\n", - "# r, r[1]/r[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "771d4692-3260-43c8-a335-7486f6a228a7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Decimal('1.999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999')" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "D(2).sqrt()**2" - ] - }, - { - "cell_type": "markdown", - "id": "de71bd17-e929-4624-8652-20e76d1eb796", - "metadata": { - "tags": [] - }, - "source": [ - "checking the performance of exponential on vectors (result: np.exp is faster than 10**; it may be worth pre-calculating np.log(10) for small vectors)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "87d9b988-2b6e-49b3-a7de-1a1991dee052", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "v1 = 10**np.linspace(1,2, 10)\n", - "v3 = 10**np.linspace(1,2, 1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "d147a08a-7e8c-442c-9490-e0334d7b6c24", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# r = (\n", - "# timer1(lambda x: 10**x, v1, N=100_000),\n", - "# timer1(lambda x: 10**x, v3, N=100_000)\n", - "# )\n", - "# r, r[1]/r[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "d4b9e3e2-71cb-4728-bc73-594b65605740", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# r = (\n", - "# timer1(lambda x: np.exp(v1*np.log(10)), v1, N=100_000),\n", - "# timer1(lambda x: np.exp(v3*np.log(10)), v3, N=100_000)\n", - "# )\n", - "# r, r[1]/r[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "e6c50eed-67e3-43c9-8a9c-bd8303a687c9", - "metadata": { - "lines_to_next_cell": 0, - "tags": [] - }, - "outputs": [], - "source": [ - "# LOG10 = np.log(10)\n", - "# r = (\n", - "# timer1(lambda x: np.exp(v1*LOG10), v3, N=100_000),\n", - "# timer1(lambda x: np.exp(v3*np.log(10)), v3, N=100_000)\n", - "# )\n", - "# r, r[1]/r[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b9992f2-709f-45f2-98d1-3df6e7a922dd", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/analysis/202401 Solidly/202401 Solidly.ipynb b/resources/analysis/202401 Solidly/202401 Solidly.ipynb deleted file mode 100644 index 8ed6d037c..000000000 --- a/resources/analysis/202401 Solidly/202401 Solidly.ipynb +++ /dev/null @@ -1,1747 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 63, - "id": "96348e86-5892-417a-9e2d-2fda430683d0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---\n", - "Function v0.9.4 (22/Jan/2024)\n", - "SolidlyInvariant v0.9 (18/Jan/2024)\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import math as m\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "from sympy import symbols, sqrt, Eq\n", - "import decimal as d\n", - "\n", - "import invariants.functions as f\n", - "from invariants.solidly import SolidlyInvariant, SolidlySwapFunction\n", - "\n", - "from testing import *\n", - "D = d.Decimal\n", - "plt.rcParams['figure.figsize'] = [6,6]\n", - "\n", - "print(\"---\")\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(f.Function))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(SolidlyInvariant))" - ] - }, - { - "cell_type": "markdown", - "id": "a14a57f8-e21f-4652-9d68-0cff0c4afead", - "metadata": {}, - "source": [ - "# Solidly Analysis" - ] - }, - { - "cell_type": "markdown", - "id": "9bcaf580-1389-41dc-b329-c68a80c75d56", - "metadata": {}, - "source": [ - "## Equations" - ] - }, - { - "cell_type": "markdown", - "id": "58ab6488-5c7b-4103-bae1-9d79d9837f11", - "metadata": {}, - "source": [ - "### Invariant function\n", - "\n", - "The Solidly invariant function is \n", - "\n", - "$$\n", - " x^3y+xy^3 = k\n", - "$$\n", - "\n", - "which is a stable swap curve, but more convex than say curve. " - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "34a840d9-e684-406b-a8da-b1bbbe255f9f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def invariant_eq(x, y, k=0, *, aserr=False):\n", - " \"\"\"returns f(x,y)-k or f(x,y)/k - 1\"\"\"\n", - " if aserr:\n", - " return (x**3 * y + x * y**3)/k-1\n", - " else:\n", - " return x**3 * y + x * y**3 - k" - ] - }, - { - "cell_type": "markdown", - "id": "b6ee11bb-309c-4bb4-a9bc-45199287971e", - "metadata": {}, - "source": [ - "### Swap equation\n", - "\n", - "Solving the invariance equation as $y=y(x; k)$ gives the following result\n", - "\n", - "$$\n", - "y(x;k) = \\frac{x^2}{\\left(-\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\\right)^{\\frac{1}{3}}} - \\frac{\\left(-\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\\right)^{\\frac{1}{3}}}{3}\n", - "$$\n", - "\n", - "We can introduce intermediary variables $L(x;k), M(x;k)$ to write this a bit more simply\n", - "\n", - "$$\n", - "L = -\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\n", - "$$\n", - "\n", - "$$\n", - "M = L^{1/3} = \\sqrt[3]{L}\n", - "$$\n", - "\n", - "$$\n", - "y = \\frac{x^2}{\\sqrt[3]{L}} - \\frac{\\sqrt[3]{L}}{3} = \\frac{x^2}{M} - \\frac{M}{3} \n", - "$$\n", - "\n", - "Using the function $y(x;k)$ we can easily derive the swap equation at point $(x; k)$ as\n", - "\n", - "$$\n", - "\\Delta y = y(x+\\Delta x; k) - y(x; k)\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "50f960e3-65e3-470c-a465-64c1a3fb51f2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{2}}{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}} - \\frac{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}}{3}$" - ], - "text/plain": [ - "x**2/(-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333 - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333/3" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x, k = symbols('x k')\n", - "\n", - "y = x**2 / ((-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**(1/3)) - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**(1/3)/3\n", - "y" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "1799f486-222c-46ad-bd6d-a4c183d8d871", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{2}}{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}} - \\frac{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}}{3}$" - ], - "text/plain": [ - "x**2/(-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333 - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333/3" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L = -27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2\n", - "y2 = x**2 / (L**(1/3)) - (L**(1/3))/3\n", - "y2" - ] - }, - { - "cell_type": "markdown", - "id": "1ac5dc18-0a49-4d37-a49b-0f57ef5ebdc4", - "metadata": {}, - "source": [ - "#### Precision issues and L\n", - "\n", - "Note that as above, $L$ (that we call $L_1$ now) is not particularly well conditioned. \n", - "\n", - "$$\n", - "L_1 = -\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\n", - "$$\n", - "\n", - "This alternative form works better\n", - "\n", - "$$\n", - "L_2(x;k) = \\frac{27k}{2x} \\left(\\sqrt{1 + \\frac{108x^8}{729k^2}} - 1 \\right)\n", - "$$\n", - "\n", - "Furthermore\n", - "\n", - "$$\n", - "\\sqrt{1+\\xi}-1 = \\frac{\\xi}{2} - \\frac{\\xi^2}{8} + \\frac{\\xi^3}{16} - \\frac{5\\xi^4}{128} + O(\\xi^5)\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "1c208f81-5e12-4cd9-95a9-3cd1b3e0ea71", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def L1(x,k):\n", - " return -27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2\n", - "\n", - "def L2(x,k):\n", - " xi = (108 * x**8) / (729 * k**2)\n", - " #print(f\"xi = {xi}\")\n", - " if xi > 1e-5:\n", - " lam = (m.sqrt(1 + xi) - 1)\n", - " else:\n", - " lam = xi*(1/2 - xi*(1/8 - xi*(1/16 - 0.0390625*xi)))\n", - " # the relative error of this Taylor approximation is for xi < 0.025 is 1e-5 or better\n", - " # for xi ~ 1e-15 the full term is unstable (because 1 + 1e-16 ~ 1 in double precision)\n", - " # therefore the switchover should happen somewhere between 1e-12 and 1e-2\n", - " #lam1 = 0\n", - " #lam2 = xi/2 - xi**2/8 \n", - " #lam2 = xi/2 - xi**2/8 + xi**3/16 - 0.0390625*xi**4\n", - " #lam2 = xi*(1/2 - xi*(1/8 - xi*(1/16 - 0.0390625*xi)))\n", - " #lam = max(lam1, lam2)\n", - " # for very small xi we can get zero or close to zero in the full formula\n", - " # in this case the taulor approximation is better because for small xi it is always > 0\n", - " # we simply use the max of the two -- the Taylor gets negative quickly\n", - " L = lam * (27 * k) / (2 * x)\n", - " return L\n", - "\n", - "def L3(x,k):\n", - " \"\"\"going via decimal\"\"\"\n", - " x = D(x)\n", - " k = D(k)\n", - " xi = (108 * x**8) / (729 * k**2)\n", - " lam = (D(1) + xi).sqrt() - D(1)\n", - " L = lam * (27 * k) / (2 * x)\n", - " return float(L)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "51a99f4c-1c36-4865-8046-52946214ec5b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(9.99999940631824e-8, 9.9999999962963e-08)" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L1(0.1, 1), L2(0.1,1)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "4abb21bd-64c3-437d-8c29-4be0b9a5c725", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{x^{2}}{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}} - \\frac{\\left(- \\frac{27 k}{2 x} + \\frac{\\sqrt{\\frac{729 k^{2}}{x^{2}} + 108 x^{6}}}{2}\\right)^{0.333333333333333}}{3}$" - ], - "text/plain": [ - "x**2/(-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333 - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**0.333333333333333/3" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "M = L**(1/3)\n", - "y3 = x**2 / M - M/3\n", - "y3" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "7de2f57a-abca-4a23-b81d-3ce651b7855b", - "metadata": {}, - "outputs": [], - "source": [ - "assert y == y2\n", - "assert y == y3\n", - "assert y2 == y3" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "285736b4-ac27-4804-8dcb-a8b96b6785de", - "metadata": {}, - "outputs": [], - "source": [ - "def swap_eq(x,k):\n", - " \"\"\"using floats only\"\"\"\n", - " L,M,y = [None]*3\n", - " try:\n", - " #L = -27*k/(2*x) + m.sqrt(729*k**2/x**2 + 108*x**6)/2\n", - " L = L2(x,k)\n", - " M = L**(1/3)\n", - " y = x**2/M - M/3\n", - " except Exception as e:\n", - " print(\"Exception: \", e)\n", - " print(f\"x={x}, k={k}, L={L}, M={M}, y={y}\")\n", - " return y\n", - "\n", - "def swap_eq_dec(x,k):\n", - " \"\"\"using decimals for the calculation of L\"\"\"\n", - " L,M,y = [None]*3\n", - " try:\n", - " #L = -27*k/(2*x) + m.sqrt(729*k**2/x**2 + 108*x**6)/2\n", - " L = L3(x,k)\n", - " M = L**(1/3)\n", - " y = x**2/M - M/3\n", - " except Exception as e:\n", - " print(\"Exception: \", e)\n", - " print(f\"x={x}, k={k}, L={L}, M={M}, y={y}\")\n", - " return y" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "91cb13ac-a1fc-485b-9037-6447a4c49dd3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.6823278038280196\n" - ] - } - ], - "source": [ - "def swap_eq2(x, k):\n", - " # Calculating the components of the swap equation\n", - " term1_numerator = (2/3)**(1/3) * x**3\n", - " term1_denominator = (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3)\n", - "\n", - " term2_numerator = (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3)\n", - " term2_denominator = 2**(1/3) * 3**(2/3) * x\n", - "\n", - " # Swap equation calculation\n", - " y = -term1_numerator / term1_denominator + term2_numerator / term2_denominator\n", - "\n", - " return y\n", - "\n", - "# Example usage\n", - "x_value = 1 # Replace with the desired value of x\n", - "k_value = 1 # Replace with the desired value of k\n", - "print(swap_eq(x_value, k_value))" - ] - }, - { - "cell_type": "markdown", - "id": "4c115505-7076-47b4-9c3e-fd0dd826683c", - "metadata": {}, - "source": [ - "### Price equation\n", - "\n", - "The derivative $p=dy/dx$ is as follows\n", - "\n", - "$$\n", - "p=\\frac{dy}{dx} = 6^{\\frac{1}{3}}\\left(\\frac{-2 \\cdot 3^{\\frac{1}{3}} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} \\cdot \\left(-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}\\right) \\cdot \\left(3k \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} + \\sqrt{3} \\cdot \\left(-9k^2 + 4x^8\\right)\\right) + 2^{\\frac{1}{3}} \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} \\cdot \\left(\\frac{-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}}{x}\\right)^{\\frac{5}{3}} \\cdot \\left(-3k \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}} + \\sqrt{3} \\cdot \\left(9k^2 - 4x^8\\right)\\right) + 4 \\cdot 3^{\\frac{1}{3}} \\cdot \\left(-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}\\right)^2 \\cdot \\left(27k^2 + 4x^8\\right)}{6 \\cdot x \\cdot \\left(\\frac{-9k + \\sqrt{3} \\cdot x \\cdot \\sqrt{\\frac{27k^2 + 4x^8}{x^2}}}{x}\\right)^{\\frac{7}{3}} \\cdot \\left(27k^2 + 4x^8\\right)}\\right)\n", - "$$\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "5c900f31-fee7-4726-b0af-31a35849b043", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1.3136251299197979\n" - ] - } - ], - "source": [ - "def price_eq(x, k):\n", - " # Components of the derivative\n", - " term1_numerator = 2**(1/3) * x**3 * (18 * k * x + (m.sqrt(3) * (108 * k**2 * x**3 + 48 * x**11)) / (2 * m.sqrt(27 * k**2 * x**4 + 4 * x**12)))\n", - " term1_denominator = 3 * (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(4/3)\n", - " \n", - " term2_numerator = 18 * k * x + (m.sqrt(3) * (108 * k**2 * x**3 + 48 * x**11)) / (2 * m.sqrt(27 * k**2 * x**4 + 4 * x**12))\n", - " term2_denominator = 3 * 2**(1/3) * 3**(2/3) * x * (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(2/3)\n", - " \n", - " term3 = -3 * 2**(1/3) * x**2 / (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3)\n", - " \n", - " term4 = -(9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3) / (2**(1/3) * 3**(2/3) * x**2)\n", - " \n", - " # Combining all terms\n", - " dy_dx = (term1_numerator / term1_denominator) + (term2_numerator / term2_denominator) + term3 + term4\n", - "\n", - " return dy_dx\n", - "\n", - "# Example usage\n", - "x_value = 1 # Replace with the desired value of x\n", - "k_value = 1 # Replace with the desired value of k\n", - "print(price_eq(x_value, k_value))\n" - ] - }, - { - "cell_type": "markdown", - "id": "bd87b7d5-c0cd-4cfd-866b-ce305aa9d78f", - "metadata": {}, - "source": [ - "#### Inverting the price equation\n", - "\n", - "The above equations \n", - "([obtained thanks to Wolfram Alpha](https://chat.openai.com/share/55151f92-411c-43c1-a6ec-180856762a82), \n", - "the interface of which still sucks) are rather complex, and unfortunately they can't apparently be inverted analytically to get $x=x(p;k)$" - ] - }, - { - "cell_type": "markdown", - "id": "053180db-2679-4bf5-a8d6-d5d6e4e51f29", - "metadata": {}, - "source": [ - "## Charts" - ] - }, - { - "cell_type": "markdown", - "id": "99ffb5da-a7dd-4804-a2bf-1f32da169fad", - "metadata": {}, - "source": [ - "### Invariant equation\n", - "\n", - "_(see Freeze04 for the latest version)_" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "adfc7418-fa81-4108-9a4b-9c003ad315da", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "y_f = swap_eq" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "3e8740bc-696c-4f0d-9acb-ebe8d8e27ae9", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# k_v = [1**4, 2**4, 3**4, 5**4]\n", - "# #k_v = [1**4]\n", - "# x_v = np.linspace(0, m.sqrt(10), 50)\n", - "# x_v = [xx**2 for xx in x_v]\n", - "# x_v[0] = x_v[1]/2\n", - "# y_v_dct = {kk: [y_f(xx, kk) for xx in x_v] for kk in k_v}\n", - "# plt.grid(True)\n", - "# for kk, y_v in y_v_dct.items(): \n", - "# plt.plot(x_v, y_v, marker=None, linestyle='-', label=f\"k={kk}\")\n", - "# plt.legend()\n", - "# plt.xlim(0, max(x_v))\n", - "# plt.ylim(0, max(x_v))\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c63f7026-4cc8-4f54-a34e-dc99939945b8", - "metadata": { - "tags": [] - }, - "source": [ - "Checking the invariant equation at a specific point (xx; kk)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "fcb63f18-df33-448e-9ef8-cd8733e3b84e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# kk = 625\n", - "# xx = 3\n", - "# invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True)" - ] - }, - { - "cell_type": "markdown", - "id": "ea922e57-a4d5-444c-8443-407674520fcc", - "metadata": {}, - "source": [ - "Calculating a histogram of relative errors, ie what the relative error in the invariant equation is at various points $xx$ of the swap equation and at various $kk$" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "81de37e3-4c86-4428-9c74-1ec98eed876f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# y_inv_dct = {kk: [invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) for xx in x_v] for kk in k_v}\n", - "# y_inv_lst = [v for lst in y_inv_dct.values() for v in lst]\n", - "# #y_inv_lst\n", - "# plt.hist(y_inv_lst, bins=200, color=\"blue\")\n", - "# plt.title(\"Histogram of relative errors [f(x,y)/k - 1]\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f01529b5-7285-4c82-9145-0ea58a09877f", - "metadata": {}, - "source": [ - "Maximum relative error for different values of $k$" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "bd4456bf-1c66-4c04-89d5-ff3302a3bd7a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# {k: max([abs(vv) for vv in v]) for k,v in y_inv_dct.items()}" - ] - }, - { - "cell_type": "markdown", - "id": "9b5ef43c-9784-44fe-b680-c5262c36ec6b", - "metadata": { - "tags": [] - }, - "source": [ - "Minimum relative error for different values of $k$" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "7c236fa2-9b33-4693-bb9e-b72bab17f6e3", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# {k: min([abs(vv) for vv in v]) for k,v in y_inv_dct.items()}" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "99f4fbc6-967c-44fd-bd88-f32fbc030ae3", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# kk = 5**4\n", - "# x_v = np.linspace(0, m.sqrt(20), 50)\n", - "# x_v = [xx**2 for xx in x_v]\n", - "# x_v[0] = x_v[1]/2\n", - "# plt.grid(True)\n", - "# plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f\"k={kk}\")\n", - "# inv_dct = {xx: invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) for xx in x_v}\n", - "# plt.legend()\n", - "# plt.xlim(0, max(x_v))\n", - "# plt.ylim(0, max(x_v))\n", - "# plt.show()\n", - "# plt.plot(inv_dct.keys(), inv_dct.values())\n", - "# plt.title(f\"Relative error as a function of x for k={kk}\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "2d13ac33-bd7b-4507-b6e8-e77b51d4c328", - "metadata": {}, - "source": [ - "Same analysis as above, but much higher resolution" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "621a8d45-7655-42e3-b8e7-71a6c44e19e6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# NUMPOINTS = 10000\n", - "# kk = 5**4\n", - "# x_v = np.linspace(0, m.sqrt(20), NUMPOINTS)\n", - "# x_v = [xx**2 for xx in x_v]\n", - "# x_v[0] = x_v[1]/2\n", - "# plt.grid(True)\n", - "# plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f\"k={kk}\")\n", - "# inv_dct = {xx: invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) \n", - "# # for xx in x_v[int(0.2*NUMPOINTS):int(0.5*NUMPOINTS)] # <=== CHANGE RANGE HERE\n", - "# for xx in x_v # <=== CHANGE RANGE HERE\n", - "# }\n", - "# plt.legend()\n", - "# plt.xlim(0, max(x_v))\n", - "# plt.ylim(0, max(x_v))\n", - "# plt.show()\n", - "# plt.plot(inv_dct.keys(), inv_dct.values())\n", - "# plt.title(f\"Relative error as a function of x for k={kk} (highres)\")\n", - "# plt.grid()\n", - "# plt.show()\n", - "# plt.plot(inv_dct.keys(), inv_dct.values())\n", - "# plt.title(f\"Relative error as a function of x for k={kk} (highres)\")\n", - "# plt.grid()\n", - "# plt.ylim(0,1e-13)\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "49f8b5cb-ee4c-4ff5-a893-03bd61d52137", - "metadata": {}, - "source": [ - "same as above, but using decimal" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "id": "7175fe6d-be86-428b-9a0b-fbc2beabacd1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# NUMPOINTS = 10000\n", - "# kk = 5**4\n", - "# x_v = np.linspace(0, m.sqrt(20), NUMPOINTS)\n", - "# x_v = [xx**2 for xx in x_v]\n", - "# x_v[0] = x_v[1]/2\n", - "# plt.grid(True)\n", - "# plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f\"k={kk}\")\n", - "# inv_dct = {xx: invariant_eq(x=xx, y=swap_eq_dec(xx, kk), k=kk, aserr=True) \n", - "# # for xx in x_v[int(0.15*NUMPOINTS):int(0.3*NUMPOINTS)] # <=== CHANGE RANGE HERE\n", - "# for xx in x_v \n", - "# }\n", - "# plt.legend()\n", - "# plt.xlim(0, max(x_v))\n", - "# plt.ylim(0, max(x_v))\n", - "# plt.show()\n", - "# plt.plot(inv_dct.keys(), inv_dct.values())\n", - "# plt.title(f\"Relative error as a function of x for k={kk} (highres)\")\n", - "# plt.grid()\n", - "# plt.show()\n", - "# plt.plot(inv_dct.keys(), inv_dct.values())\n", - "# plt.title(f\"Relative error as a function of x for k={kk} (highres)\")\n", - "# plt.grid()\n", - "# plt.ylim(0,1e-13)\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4066e383-dba2-4e49-b999-ef7322ada357", - "metadata": {}, - "source": [ - "### Numerical considerations\n", - "\n", - "_(see Freeze04 for the latest version)_\n", - "\n", - "#### Comparing L1 with L2\n", - "\n", - "L1 and L2 are different expressions of the L term above. L2 is the naive formula, L1 is optimized. L2 can be zero for very small values (and it is not even continous; see 0.009 and 0.01 below) whilst L1 is *always* greater than zero." - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "0abe5692-f6da-4071-83db-c8bb995ff2be", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[(0, 1.0000000000000003e-28),\n", - " (0, 1.0000000000000001e-21),\n", - " (2.27373675443232e-13, 4.7829689999999975e-15),\n", - " (0, 1.0000000000000002e-14),\n", - " (2.27373675443232e-13, 1.7085937499999996e-13),\n", - " (1.25055521493778e-12, 1.279999999999999e-12),\n", - " (7.81199105404085e-10, 7.812499999988701e-10)]" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xs_v = [0.0001, 0.001, 0.009, 0.01, 0.015, 0.02, 0.05]\n", - "[(L1(xx,1), L2(xx, 1)) for xx in xs_v]" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "a5b8067c-ca96-4586-bab2-d3fa5dc421db", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# plt.plot(x_v, [L2(xx, 1) - L1(xx, 1) for xx in x_v])" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "67804275-7f8b-41ef-bafd-18264189d3c8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# plt.plot(x_v, [L1(xx, 1) for xx in x_v])\n", - "# plt.plot(x_v, [L2(xx, 1) for xx in x_v])\n", - "# plt.grid()\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "30ea5427-a3b0-4530-925e-7809f91996a3", - "metadata": {}, - "source": [ - "## Curvature and regions\n", - "\n", - "_(note that from here onwards we are using the library functions we've developed on the way rather than the explicit functions defined above)_\n", - "\n", - "### Overview\n", - "\n", - "Here we look at the different _regions_ of the curve, most importantly the central, flat, region and its boundaries. Firstly we note that the invariance equation is homogenous\n", - "\n", - "$$\n", - " (\\lambda x)^3(\\lambda y)+(\\lambda x)(\\lambda y)^3 = \n", - " \\lambda^4 (x^3y+xy^3) = \\lambda^4 k\n", - "$$\n", - "\n", - "In other words, if a point $(x, y)$ is on curve $k$, then the point $(\\lambda x, \\lambda y)$ is on the curve $\\lambda^4 k$, and in fact there is a 1:1 relationship between _all_ points on the curve $k$ and _all_ points on the curve $\\lambda^4 k$ using this relationship. \n", - "\n", - "**Important side note:** This scaling relation also shows that the financially important quantity is $\\sqrt[4]{k}$, in the sense that this quantity scales linearly with the financial size of the curve.\n", - "\n", - "The points $(\\lambda x, \\lambda y)$ are _rays_ that come from the origin of the coordinate system. We now identify the ray where the curvature starts to bite, and this will be the boundary of our approximation\n", - "\n", - "Below we draw the rays as well as the **central tangents**, ie the tangents going through the point $x=y$. For a curve $k$, a the central point we have $2x^4=k$ and therefore it is at $(x,y) = (\\sqrt[4]{k/2}, \\sqrt[4]{k/2})$. The slope at this point is -1, so the equation is\n", - "\n", - "$$\n", - "t(x;k) = \\sqrt[4]{\\frac k 2} - (x-\\sqrt[4]{\\frac k 2})\n", - "$$\n", - "\n", - "We also note that $\\sqrt[4]{k/2} = \\sqrt[4]{k} \\sqrt[4]{0.5}$" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "844a1cea-6306-45c0-8f91-7478d729d4f5", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(0, m.sqrt(10), 50)\n", - "x_v = [xx**2 for xx in x_v]\n", - "x_v[0] = x_v[1]/2\n", - "k_sqrt4_v = [2, 3.5, 5, 6.5]\n", - "\n", - "# draw the invariance curves\n", - "k_v = [kk**4 for kk in k_sqrt4_v]\n", - "for kk in k_v: \n", - " y_f = SolidlySwapFunction(k=kk)\n", - " yy_v = [y_f(xx) for xx in x_v]\n", - " #yy_v = [y_f(xx, kk) for xx in x_v]\n", - " plt.plot(x_v, yy_v, marker=None, linestyle='-', label=f\"k={kk}\")\n", - "\n", - "# draw the central tangents\n", - "C = 0.5**(0.25)\n", - "for kk in k_sqrt4_v:\n", - " yy_v = [C*kk - (xx-C*kk) for xx in x_v]\n", - " plt.plot(x_v, yy_v, marker=None, linestyle='--', color=\"#aaa\")\n", - "\n", - "# draw the rays\n", - "for mm in [2.6, 6]:\n", - " yy_v = [mm*xx for xx in x_v]\n", - " plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color=\"#aaa\", label=f\"ray (m={mm})\")\n", - " yy_v = [1/mm*xx for xx in x_v]\n", - " plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color=\"#aaa\")\n", - "\n", - "plt.grid(True)\n", - "plt.legend()\n", - "plt.xlim(0, max(x_v))\n", - "plt.ylim(0, max(x_v))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "aca368bd-13af-404d-a1aa-192c51ca56a7", - "metadata": {}, - "source": [ - "### best hyperbola fit\n", - "\n", - "We now try the best possible (levered) hyperbola fit for one of those curves. Note that the levered hyperbola has the equation \n", - "\n", - "$$\n", - "y-y_0 = \\frac{k}{x-x_0}\n", - "$$\n", - "\n", - "and has therefore three free paramters, $(k, x_0, y_0)$. We fit those numerically." - ] - }, - { - "cell_type": "markdown", - "id": "0a297999-b281-4893-9abb-7b8546c6a000", - "metadata": {}, - "source": [ - "#### Unfitted hyperbola for demonstration\n", - "\n", - "(focus of Freeze04)\n", - "\n", - "Here we create four charts\n", - "1. The target curve, and a (bad) fit for demonstration, shown over a sufficiently wide range\n", - "2. The difference between the target curve and the fit\n", - "3. Target curve and fit, withing the kernel area\n", - "4. Difference, within kernel area (title contains L2 norm)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "cb21aa13-a3eb-4ac1-bc9e-d23cd017f114", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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FloatTaylor2Taylor4
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" - ], - "text/plain": [ - " Float Taylor2 Taylor4\n", - "xi \n", - "0.005051 0.002522 0.002522 0.002522\n", - "0.010101 0.005038 0.005038 0.005038\n", - "0.020202 0.010051 0.010050 0.010051\n", - "0.030303 0.015038 0.015037 0.015038\n", - "0.040404 0.020002 0.019998 0.020002" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x1_v = np.linspace(0,1,100)\n", - "x1_v[0] = x1_v[1]/2\n", - "data = [(\n", - " xx, \n", - " m.sqrt(1+xx)-1,\n", - " xx * (0.5 - xx*1/8),\n", - " #xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128,\n", - " xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))),\n", - ") for xx in x1_v\n", - "]\n", - "df = pd.DataFrame(data, columns=['xi', 'Float', 'Taylor2', 'Taylor4']).set_index(\"xi\")\n", - "oldfs = plt.rcParams['figure.figsize']\n", - "plt.rcParams['figure.figsize'] = [12,6]\n", - "#plt.figure(figsize=(12, 6))\n", - "df.plot()\n", - "plt.grid(True)\n", - "plt.rcParams['figure.figsize'] = oldfs\n", - "plt.savefig(\"/Users/skl/Desktop/image.jpg\")\n", - "#plt.grid()\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "9f7fc799-1a9e-4eb9-a504-41200fb1d87d", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# x2_v = np.linspace(0,0.2,100)\n", - "# x1_v[0] = x1_v[1]/2\n", - "# data = [(\n", - "# xx, \n", - "# m.sqrt(1+xx)-1,\n", - "# xx * (0.5 - xx*1/8),\n", - "# #xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128,\n", - "# xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))),\n", - "# ) for xx in x2_v\n", - "# ]\n", - "# df = pd.DataFrame(data, columns=['x', 'Float', 'Taylor2', 'Taylor4']).set_index(\"x\")\n", - "# df.plot()\n", - "# plt.grid()\n", - "# df2 = df.copy()\n", - "# df2[\"Err2\"] = df2[\"Taylor2\"]/df2[\"Float\"] - 1\n", - "# df2[\"Err4\"] = df2[\"Taylor4\"]/df2[\"Float\"] - 1\n", - "# plt.show()\n", - "# df2.plot(y=[\"Err2\", \"Err4\"])\n", - "# plt.grid()\n", - "# plt.title(\"Relative error of Taylor 2 4 term approximations\")\n", - "# plt.ylim(-0.001, 0.0001)\n", - "# df2.head()" - ] - }, - { - "cell_type": "markdown", - "id": "4446b5dd-a4c8-450f-81bd-d7a909895bf8", - "metadata": {}, - "source": [ - "### Decimal vs float\n", - "#### Precision\n", - "\n", - "we compare $\\sqrt{1+\\xi}-1$ for float, Taylor and Decimal\n", - "\n", - "$$\n", - "\\sqrt{1+\\xi}-1 = \\frac{\\xi}{2} - \\frac{\\xi^2}{8} + \\frac{\\xi^3}{16} - \\frac{5\\xi^4}{128} + O(\\xi^5)\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "824c7650-acd7-4336-924e-9c927f0e2ebe", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# import decimal as d\n", - "# D = d.Decimal\n", - "# d.getcontext().prec = 1000 # Set the precision to 30 decimal places (adjust as needed)\n", - "# xd_v = [1e-18*1.5**nn for nn in np.linspace(0, 103, 500)]\n", - "# xd_v[0], xd_v[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "8252b418-74e6-429f-9162-1574ac04580f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# fmt = lambda x: x\n", - "# fmt = float\n", - "# ONE = D(1)\n", - "# data = [(\n", - "# xx, \n", - "# m.sqrt(1+xx)-1,\n", - "# xx * (0.5 - xx*1/8),\n", - "# #xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128,\n", - "# xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))),\n", - "# fmt((ONE+D(xx)).sqrt()-1),\n", - "# ) for xx in xd_v\n", - "# ]\n", - "# df = pd.DataFrame(data, columns=['x', 'Float', 'Taylor2', 'Taylor4', 'Dec']).set_index(\"x\")\n", - "# df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "id": "fefe53dc-7047-4506-bd8b-c6bc86d9bf56", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# df.plot()\n", - "# # plt.xlim(0, None)\n", - "# # plt.ylim(0, 100)\n", - "# plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "7ae2dc71-107f-43ea-bf79-a3304b99b068", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# df.iloc[:80].plot()\n", - "# plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "3d78cb69-7484-4991-8331-acf4af7d931d", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# df.iloc[:100].plot()\n", - "# plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "2e0e3893-e838-4533-9c27-40b5260f406d", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# LOC = 480\n", - "# df.iloc[LOC:].plot()\n", - "# plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "2ad1b51e-2b18-4be1-8cfa-fe2a831dfa5d", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# df2 = pd.DataFrame([\n", - "# (df[\"Float\"]-df[\"Taylor4\"])/df[\"Taylor4\"],\n", - "# (df[\"Taylor2\"]-df[\"Taylor4\"])/df[\"Taylor4\"],\n", - "# (df[\"Dec\"]-df[\"Taylor4\"])/df[\"Taylor4\"],\n", - "# ]).transpose()\n", - "# df2.columns = [\"Float\", \"Taylor2\", \"Dec\"]\n", - "# df2" - ] - }, - { - "cell_type": "markdown", - "id": "dfde558e-f3f6-4de1-ba87-60ddbfa9138d", - "metadata": {}, - "source": [ - "#### Timing\n", - "\n", - "(focus of Freeze03)" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "6c6e54f3-7f43-4215-9c2d-39ad115bd009", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "import decimal as d\n", - "D = d.Decimal" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "a16c06d8-8c87-42e8-917b-508affddc17c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(131.56676292419434, 120.24784088134766)" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# def timer(func, *args, N=None, **kwargs):\n", - "# \"\"\"times the calls to func; func is called with args and kwargs; returns time in msec per 1m calls\"\"\"\n", - "# if N is None:\n", - "# N = 10_000_000\n", - "# start_time = time.time()\n", - "# for _ in range(N):\n", - "# func(*args, **kwargs)\n", - "# end_time = time.time()\n", - "# return (end_time - start_time)/N*1_000_000*1000\n", - "\n", - "# def timer1(func, arg, N=None):\n", - "# \"\"\"times the calls to func; func is called with arg; returns time in msec per 1m calls\"\"\"\n", - "# if N is None:\n", - "# N = 10_000_000\n", - "# start_time = time.time()\n", - "# for _ in range(N):\n", - "# func(arg)\n", - "# end_time = time.time()\n", - "# return (end_time - start_time)/N*1_000_000*1000\n", - "\n", - "# def timer2(func, arg1, arg2, N=None):\n", - "# \"\"\"times the calls to func; func is called with arg1, arg2; returns time in msec per 1m calls\"\"\"\n", - "# if N is None:\n", - "# N = 10_000_000\n", - "# start_time = time.time()\n", - "# for _ in range(N):\n", - "# func(arg1, arg2)\n", - "# end_time = time.time()\n", - "# return (end_time - start_time)/N*1_000_000*1000\n", - "#-\n", - "\n", - "# identify function (`lambda`)\n", - "\n", - "timer(lambda x: x, 1), timer1(lambda x: x, 1)\n", - "\n", - "\n", - "# ditto, defined with `def`\n", - "\n", - "def idfunc(x):\n", - " return x\n", - "timer(idfunc, 1), timer1(idfunc, 1)\n", - "\n", - "# sin, sqrt, exp etc as reference\n", - "\n", - "(timer(m.sin, 1), timer(m.cos, 1), timer(m.tan, 1), \n", - " timer(m.sqrt, 1), timer(m.exp, 1), timer(m.log, 1))\n", - "\n", - "(timer1(m.sin, 1), timer1(m.cos, 1), timer1(m.tan, 1), \n", - " timer1(m.sqrt, 1), timer1(m.exp, 1), timer1(m.log, 1))\n", - "\n", - "# **float** calculation\n", - "\n", - "timer(lambda xx: m.sqrt(1+xx)-1, 1), timer1(lambda xx: m.sqrt(1+xx)-1, 1)\n", - "\n", - "# **taylor** calculations\n", - "\n", - "timer(lambda xx: xx * (0.5 - xx*1/8), 1), timer1(lambda xx: xx * (0.5 - xx*1/8), 1)\n", - "\n", - "(timer(lambda xx: xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))), 1),\n", - "timer1(lambda xx: xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))), 1))\n", - "\n", - "(timer(lambda xx: xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128, 1),\n", - "timer1(lambda xx: xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128, 1))\n", - "\n", - "# **decimal** calculations" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "9a313fce-2b46-43b7-a416-98d5ab0073dd", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 30\n", - "# ONE = D(1)\n", - "# (timer(lambda xx: D(1+xx).sqrt()-1, 1, N=100_000),\n", - "# timer(lambda xx: ONE+xx.sqrt()-1, ONE, N=100_000))" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "d647f240-1eaf-4183-92cb-9b5da5f9f616", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 100\n", - "# ONE = D(1)\n", - "# (timer(lambda xx: D(1+xx).sqrt()-1, 1, N=10_000),\n", - "# timer(lambda xx: ONE+xx.sqrt()-1, ONE, N=10_000))" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "id": "8b67ff58", - "metadata": {}, - "outputs": [], - "source": [ - "# d.getcontext().prec = 1_000\n", - "# ONE = D(1)\n", - "# (timer(lambda xx: D(1+xx).sqrt()-1, 1, N=1_000),\n", - "# timer(lambda xx: ONE+xx.sqrt()-1, ONE, N=1_000))" - ] - }, - { - "cell_type": "markdown", - "id": "338a845c-5103-46fb-9a0f-8a7584159dad", - "metadata": {}, - "source": [ - "decimal conversions" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "id": "ce909177-cb11-4bf2-b210-0bcd9b53a10e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 30\n", - "# ONE = D(\"0.\"+\"9\"*d.getcontext().prec)\n", - "# PI = m.pi\n", - "# (timer(lambda xx: D(xx), PI, N=1_000_000),\n", - "# timer(lambda: float(ONE), N=1_000_000),\n", - "# ONE\n", - "# )" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "id": "21f146ca-522c-44a9-b9ef-a9275ff026c1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 100\n", - "# ONE = D(\"0.\"+\"9\"*d.getcontext().prec)\n", - "# (timer(lambda xx: D(xx), PI, N=1_000_000),\n", - "# timer(lambda: float(ONE), N=1_000_000),\n", - "# ONE\n", - "# )" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "13db7008-08da-436b-9885-01575e26e8d5", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 1000\n", - "# ONE = D(\"0.\"+\"9\"*d.getcontext().prec)\n", - "# (timer(lambda xx: D(xx), PI, N=1_000_000),\n", - "# timer(lambda: float(ONE), N=1_000_000),\n", - "# ONE\n", - "# )" - ] - }, - { - "cell_type": "markdown", - "id": "dfd8e821-c895-4399-8e0a-de36dd7eddb2", - "metadata": {}, - "source": [ - "`L2` (using Taylor) vs `L3` (using decimal)" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "c2d71012-8abf-47b6-99b0-d6cd39587612", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 30\n", - "# r = ( \n", - "# timer2(L2, 1, 625, N=1_000_000),\n", - "# timer2(L3, 1, 625, N=10_000),\n", - "# )\n", - "# r, r[1]/r[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "id": "0e184b46-e40c-4954-9cb2-cb866f5b6df1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 100\n", - "# r = ( \n", - "# timer2(L2, 1, 625, N=1_000_000),\n", - "# timer2(L3, 1, 625, N=10_000),\n", - "# )\n", - "# r, r[1]/r[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "id": "e9a07613-c587-4ad0-ba92-1cb55a913c2c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# d.getcontext().prec = 1000\n", - "# r = ( \n", - "# timer2(L2, 1, 625, N=1_000_000),\n", - "# timer2(L3, 1, 625, N=10_000),\n", - "# )\n", - "# r, r[1]/r[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "771d4692-3260-43c8-a335-7486f6a228a7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Decimal('1.999999999999999999999999999')" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "D(2).sqrt()**2" - ] - }, - { - "cell_type": "markdown", - "id": "de71bd17-e929-4624-8652-20e76d1eb796", - "metadata": { - "tags": [] - }, - "source": [ - "checking the performance of exponential on vectors (result: np.exp is faster than 10**; it may be worth pre-calculating np.log(10) for small vectors)" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "87d9b988-2b6e-49b3-a7de-1a1991dee052", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "v1 = 10**np.linspace(1,2, 10)\n", - "v3 = 10**np.linspace(1,2, 1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "id": "d147a08a-7e8c-442c-9490-e0334d7b6c24", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# r = (\n", - "# timer1(lambda x: 10**x, v1, N=100_000),\n", - "# timer1(lambda x: 10**x, v3, N=100_000)\n", - "# )\n", - "# r, r[1]/r[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "d4b9e3e2-71cb-4728-bc73-594b65605740", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# r = (\n", - "# timer1(lambda x: np.exp(v1*np.log(10)), v1, N=100_000),\n", - "# timer1(lambda x: np.exp(v3*np.log(10)), v3, N=100_000)\n", - "# )\n", - "# r, r[1]/r[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "id": "e6c50eed-67e3-43c9-8a9c-bd8303a687c9", - "metadata": { - "lines_to_next_cell": 0, - "tags": [] - }, - "outputs": [], - "source": [ - "# LOG10 = np.log(10)\n", - "# r = (\n", - "# timer1(lambda x: np.exp(v1*LOG10), v3, N=100_000),\n", - "# timer1(lambda x: np.exp(v3*np.log(10)), v3, N=100_000)\n", - "# )\n", - "# r, r[1]/r[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b9992f2-709f-45f2-98d1-3df6e7a922dd", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/analysis/202401 Solidly/202401 Solidly.py b/resources/analysis/202401 Solidly/202401 Solidly.py deleted file mode 100644 index cda9e3819..000000000 --- a/resources/analysis/202401 Solidly/202401 Solidly.py +++ /dev/null @@ -1,879 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -import numpy as np -import math as m -import matplotlib.pyplot as plt -import pandas as pd -from sympy import symbols, sqrt, Eq -import decimal as d - -import invariants.functions as f -from invariants.solidly import SolidlyInvariant, SolidlySwapFunction - -from testing import * -D = d.Decimal -plt.rcParams['figure.figsize'] = [6,6] - -print("---") -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(f.Function)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(SolidlyInvariant)) - - -# - - -# # Solidly Analysis - -# ## Equations - -# ### Invariant function -# -# The Solidly invariant function is -# -# $$ -# x^3y+xy^3 = k -# $$ -# -# which is a stable swap curve, but more convex than say curve. - -def invariant_eq(x, y, k=0, *, aserr=False): - """returns f(x,y)-k or f(x,y)/k - 1""" - if aserr: - return (x**3 * y + x * y**3)/k-1 - else: - return x**3 * y + x * y**3 - k - - -# ### Swap equation -# -# Solving the invariance equation as $y=y(x; k)$ gives the following result -# -# $$ -# y(x;k) = \frac{x^2}{\left(-\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6}\right)^{\frac{1}{3}}} - \frac{\left(-\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6}\right)^{\frac{1}{3}}}{3} -# $$ -# -# We can introduce intermediary variables $L(x;k), M(x;k)$ to write this a bit more simply -# -# $$ -# L = -\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6} -# $$ -# -# $$ -# M = L^{1/3} = \sqrt[3]{L} -# $$ -# -# $$ -# y = \frac{x^2}{\sqrt[3]{L}} - \frac{\sqrt[3]{L}}{3} = \frac{x^2}{M} - \frac{M}{3} -# $$ -# -# Using the function $y(x;k)$ we can easily derive the swap equation at point $(x; k)$ as -# -# $$ -# \Delta y = y(x+\Delta x; k) - y(x; k) -# $$ - -# + -x, k = symbols('x k') - -y = x**2 / ((-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**(1/3)) - (-27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2)**(1/3)/3 -y -# - - -L = -27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2 -y2 = x**2 / (L**(1/3)) - (L**(1/3))/3 -y2 - - -# #### Precision issues and L -# -# Note that as above, $L$ (that we call $L_1$ now) is not particularly well conditioned. -# -# $$ -# L_1 = -\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6} -# $$ -# -# This alternative form works better -# -# $$ -# L_2(x;k) = \frac{27k}{2x} \left(\sqrt{1 + \frac{108x^8}{729k^2}} - 1 \right) -# $$ -# -# Furthermore -# -# $$ -# \sqrt{1+\xi}-1 = \frac{\xi}{2} - \frac{\xi^2}{8} + \frac{\xi^3}{16} - \frac{5\xi^4}{128} + O(\xi^5) -# $$ - -# + -def L1(x,k): - return -27*k/(2*x) + sqrt(729*k**2/x**2 + 108*x**6)/2 - -def L2(x,k): - xi = (108 * x**8) / (729 * k**2) - #print(f"xi = {xi}") - if xi > 1e-5: - lam = (m.sqrt(1 + xi) - 1) - else: - lam = xi*(1/2 - xi*(1/8 - xi*(1/16 - 0.0390625*xi))) - # the relative error of this Taylor approximation is for xi < 0.025 is 1e-5 or better - # for xi ~ 1e-15 the full term is unstable (because 1 + 1e-16 ~ 1 in double precision) - # therefore the switchover should happen somewhere between 1e-12 and 1e-2 - #lam1 = 0 - #lam2 = xi/2 - xi**2/8 - #lam2 = xi/2 - xi**2/8 + xi**3/16 - 0.0390625*xi**4 - #lam2 = xi*(1/2 - xi*(1/8 - xi*(1/16 - 0.0390625*xi))) - #lam = max(lam1, lam2) - # for very small xi we can get zero or close to zero in the full formula - # in this case the taulor approximation is better because for small xi it is always > 0 - # we simply use the max of the two -- the Taylor gets negative quickly - L = lam * (27 * k) / (2 * x) - return L - -def L3(x,k): - """going via decimal""" - x = D(x) - k = D(k) - xi = (108 * x**8) / (729 * k**2) - lam = (D(1) + xi).sqrt() - D(1) - L = lam * (27 * k) / (2 * x) - return float(L) - - -# - - -L1(0.1, 1), L2(0.1,1) - -M = L**(1/3) -y3 = x**2 / M - M/3 -y3 - -assert y == y2 -assert y == y3 -assert y2 == y3 - - -# + -def swap_eq(x,k): - """using floats only""" - L,M,y = [None]*3 - try: - #L = -27*k/(2*x) + m.sqrt(729*k**2/x**2 + 108*x**6)/2 - L = L2(x,k) - M = L**(1/3) - y = x**2/M - M/3 - except Exception as e: - print("Exception: ", e) - print(f"x={x}, k={k}, L={L}, M={M}, y={y}") - return y - -def swap_eq_dec(x,k): - """using decimals for the calculation of L""" - L,M,y = [None]*3 - try: - #L = -27*k/(2*x) + m.sqrt(729*k**2/x**2 + 108*x**6)/2 - L = L3(x,k) - M = L**(1/3) - y = x**2/M - M/3 - except Exception as e: - print("Exception: ", e) - print(f"x={x}, k={k}, L={L}, M={M}, y={y}") - return y - - -# + -def swap_eq2(x, k): - # Calculating the components of the swap equation - term1_numerator = (2/3)**(1/3) * x**3 - term1_denominator = (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3) - - term2_numerator = (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3) - term2_denominator = 2**(1/3) * 3**(2/3) * x - - # Swap equation calculation - y = -term1_numerator / term1_denominator + term2_numerator / term2_denominator - - return y - -# Example usage -x_value = 1 # Replace with the desired value of x -k_value = 1 # Replace with the desired value of k -print(swap_eq(x_value, k_value)) - - -# - - -# ### Price equation -# -# The derivative $p=dy/dx$ is as follows -# -# $$ -# p=\frac{dy}{dx} = 6^{\frac{1}{3}}\left(\frac{-2 \cdot 3^{\frac{1}{3}} \cdot x \cdot \sqrt{\frac{27k^2 + 4x^8}{x^2}} \cdot \left(-9k + \sqrt{3} \cdot x \cdot \sqrt{\frac{27k^2 + 4x^8}{x^2}}\right) \cdot \left(3k \cdot x \cdot \sqrt{\frac{27k^2 + 4x^8}{x^2}} + \sqrt{3} \cdot \left(-9k^2 + 4x^8\right)\right) + 2^{\frac{1}{3}} \cdot \sqrt{\frac{27k^2 + 4x^8}{x^2}} \cdot \left(\frac{-9k + \sqrt{3} \cdot x \cdot \sqrt{\frac{27k^2 + 4x^8}{x^2}}}{x}\right)^{\frac{5}{3}} \cdot \left(-3k \cdot x \cdot \sqrt{\frac{27k^2 + 4x^8}{x^2}} + \sqrt{3} \cdot \left(9k^2 - 4x^8\right)\right) + 4 \cdot 3^{\frac{1}{3}} \cdot \left(-9k + \sqrt{3} \cdot x \cdot \sqrt{\frac{27k^2 + 4x^8}{x^2}}\right)^2 \cdot \left(27k^2 + 4x^8\right)}{6 \cdot x \cdot \left(\frac{-9k + \sqrt{3} \cdot x \cdot \sqrt{\frac{27k^2 + 4x^8}{x^2}}}{x}\right)^{\frac{7}{3}} \cdot \left(27k^2 + 4x^8\right)}\right) -# $$ -# -# - -# + -def price_eq(x, k): - # Components of the derivative - term1_numerator = 2**(1/3) * x**3 * (18 * k * x + (m.sqrt(3) * (108 * k**2 * x**3 + 48 * x**11)) / (2 * m.sqrt(27 * k**2 * x**4 + 4 * x**12))) - term1_denominator = 3 * (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(4/3) - - term2_numerator = 18 * k * x + (m.sqrt(3) * (108 * k**2 * x**3 + 48 * x**11)) / (2 * m.sqrt(27 * k**2 * x**4 + 4 * x**12)) - term2_denominator = 3 * 2**(1/3) * 3**(2/3) * x * (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(2/3) - - term3 = -3 * 2**(1/3) * x**2 / (9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3) - - term4 = -(9 * k * x**2 + m.sqrt(3) * m.sqrt(27 * k**2 * x**4 + 4 * x**12))**(1/3) / (2**(1/3) * 3**(2/3) * x**2) - - # Combining all terms - dy_dx = (term1_numerator / term1_denominator) + (term2_numerator / term2_denominator) + term3 + term4 - - return dy_dx - -# Example usage -x_value = 1 # Replace with the desired value of x -k_value = 1 # Replace with the desired value of k -print(price_eq(x_value, k_value)) - -# - - -# #### Inverting the price equation -# -# The above equations -# ([obtained thanks to Wolfram Alpha](https://chat.openai.com/share/55151f92-411c-43c1-a6ec-180856762a82), -# the interface of which still sucks) are rather complex, and unfortunately they can't apparently be inverted analytically to get $x=x(p;k)$ - -# ## Charts - -# ### Invariant equation -# -# _(see Freeze04 for the latest version)_ - -y_f = swap_eq - -# + -# k_v = [1**4, 2**4, 3**4, 5**4] -# #k_v = [1**4] -# x_v = np.linspace(0, m.sqrt(10), 50) -# x_v = [xx**2 for xx in x_v] -# x_v[0] = x_v[1]/2 -# y_v_dct = {kk: [y_f(xx, kk) for xx in x_v] for kk in k_v} -# plt.grid(True) -# for kk, y_v in y_v_dct.items(): -# plt.plot(x_v, y_v, marker=None, linestyle='-', label=f"k={kk}") -# plt.legend() -# plt.xlim(0, max(x_v)) -# plt.ylim(0, max(x_v)) -# plt.show() -# - - -# Checking the invariant equation at a specific point (xx; kk) - -# + -# kk = 625 -# xx = 3 -# invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) -# - - -# Calculating a histogram of relative errors, ie what the relative error in the invariant equation is at various points $xx$ of the swap equation and at various $kk$ - -# + -# y_inv_dct = {kk: [invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) for xx in x_v] for kk in k_v} -# y_inv_lst = [v for lst in y_inv_dct.values() for v in lst] -# #y_inv_lst -# plt.hist(y_inv_lst, bins=200, color="blue") -# plt.title("Histogram of relative errors [f(x,y)/k - 1]") -# plt.show() -# - - -# Maximum relative error for different values of $k$ - -# + -# {k: max([abs(vv) for vv in v]) for k,v in y_inv_dct.items()} -# - - -# Minimum relative error for different values of $k$ - -# + -# {k: min([abs(vv) for vv in v]) for k,v in y_inv_dct.items()} - -# + -# kk = 5**4 -# x_v = np.linspace(0, m.sqrt(20), 50) -# x_v = [xx**2 for xx in x_v] -# x_v[0] = x_v[1]/2 -# plt.grid(True) -# plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f"k={kk}") -# inv_dct = {xx: invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) for xx in x_v} -# plt.legend() -# plt.xlim(0, max(x_v)) -# plt.ylim(0, max(x_v)) -# plt.show() -# plt.plot(inv_dct.keys(), inv_dct.values()) -# plt.title(f"Relative error as a function of x for k={kk}") -# plt.show() -# - - -# Same analysis as above, but much higher resolution - -# + -# NUMPOINTS = 10000 -# kk = 5**4 -# x_v = np.linspace(0, m.sqrt(20), NUMPOINTS) -# x_v = [xx**2 for xx in x_v] -# x_v[0] = x_v[1]/2 -# plt.grid(True) -# plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f"k={kk}") -# inv_dct = {xx: invariant_eq(x=xx, y=swap_eq(xx, kk), k=kk, aserr=True) -# # for xx in x_v[int(0.2*NUMPOINTS):int(0.5*NUMPOINTS)] # <=== CHANGE RANGE HERE -# for xx in x_v # <=== CHANGE RANGE HERE -# } -# plt.legend() -# plt.xlim(0, max(x_v)) -# plt.ylim(0, max(x_v)) -# plt.show() -# plt.plot(inv_dct.keys(), inv_dct.values()) -# plt.title(f"Relative error as a function of x for k={kk} (highres)") -# plt.grid() -# plt.show() -# plt.plot(inv_dct.keys(), inv_dct.values()) -# plt.title(f"Relative error as a function of x for k={kk} (highres)") -# plt.grid() -# plt.ylim(0,1e-13) -# plt.show() -# - - -# same as above, but using decimal - -# + -# NUMPOINTS = 10000 -# kk = 5**4 -# x_v = np.linspace(0, m.sqrt(20), NUMPOINTS) -# x_v = [xx**2 for xx in x_v] -# x_v[0] = x_v[1]/2 -# plt.grid(True) -# plt.plot(x_v, [y_f(xx, kk) for xx in x_v], marker=None, linestyle='-', label=f"k={kk}") -# inv_dct = {xx: invariant_eq(x=xx, y=swap_eq_dec(xx, kk), k=kk, aserr=True) -# # for xx in x_v[int(0.15*NUMPOINTS):int(0.3*NUMPOINTS)] # <=== CHANGE RANGE HERE -# for xx in x_v -# } -# plt.legend() -# plt.xlim(0, max(x_v)) -# plt.ylim(0, max(x_v)) -# plt.show() -# plt.plot(inv_dct.keys(), inv_dct.values()) -# plt.title(f"Relative error as a function of x for k={kk} (highres)") -# plt.grid() -# plt.show() -# plt.plot(inv_dct.keys(), inv_dct.values()) -# plt.title(f"Relative error as a function of x for k={kk} (highres)") -# plt.grid() -# plt.ylim(0,1e-13) -# plt.show() -# - - -# ### Numerical considerations -# -# _(see Freeze04 for the latest version)_ -# -# #### Comparing L1 with L2 -# -# L1 and L2 are different expressions of the L term above. L2 is the naive formula, L1 is optimized. L2 can be zero for very small values (and it is not even continous; see 0.009 and 0.01 below) whilst L1 is *always* greater than zero. - -xs_v = [0.0001, 0.001, 0.009, 0.01, 0.015, 0.02, 0.05] -[(L1(xx,1), L2(xx, 1)) for xx in xs_v] - -# + -# plt.plot(x_v, [L2(xx, 1) - L1(xx, 1) for xx in x_v]) - -# + -# plt.plot(x_v, [L1(xx, 1) for xx in x_v]) -# plt.plot(x_v, [L2(xx, 1) for xx in x_v]) -# plt.grid() -# plt.show() -# - - -# ## Curvature and regions -# -# _(note that from here onwards we are using the library functions we've developed on the way rather than the explicit functions defined above)_ -# -# ### Overview -# -# Here we look at the different _regions_ of the curve, most importantly the central, flat, region and its boundaries. Firstly we note that the invariance equation is homogenous -# -# $$ -# (\lambda x)^3(\lambda y)+(\lambda x)(\lambda y)^3 = -# \lambda^4 (x^3y+xy^3) = \lambda^4 k -# $$ -# -# In other words, if a point $(x, y)$ is on curve $k$, then the point $(\lambda x, \lambda y)$ is on the curve $\lambda^4 k$, and in fact there is a 1:1 relationship between _all_ points on the curve $k$ and _all_ points on the curve $\lambda^4 k$ using this relationship. -# -# **Important side note:** This scaling relation also shows that the financially important quantity is $\sqrt[4]{k}$, in the sense that this quantity scales linearly with the financial size of the curve. -# -# The points $(\lambda x, \lambda y)$ are _rays_ that come from the origin of the coordinate system. We now identify the ray where the curvature starts to bite, and this will be the boundary of our approximation -# -# Below we draw the rays as well as the **central tangents**, ie the tangents going through the point $x=y$. For a curve $k$, a the central point we have $2x^4=k$ and therefore it is at $(x,y) = (\sqrt[4]{k/2}, \sqrt[4]{k/2})$. The slope at this point is -1, so the equation is -# -# $$ -# t(x;k) = \sqrt[4]{\frac k 2} - (x-\sqrt[4]{\frac k 2}) -# $$ -# -# We also note that $\sqrt[4]{k/2} = \sqrt[4]{k} \sqrt[4]{0.5}$ - -# + -x_v = np.linspace(0, m.sqrt(10), 50) -x_v = [xx**2 for xx in x_v] -x_v[0] = x_v[1]/2 -k_sqrt4_v = [2, 3.5, 5, 6.5] - -# draw the invariance curves -k_v = [kk**4 for kk in k_sqrt4_v] -for kk in k_v: - y_f = SolidlySwapFunction(k=kk) - yy_v = [y_f(xx) for xx in x_v] - #yy_v = [y_f(xx, kk) for xx in x_v] - plt.plot(x_v, yy_v, marker=None, linestyle='-', label=f"k={kk}") - -# draw the central tangents -C = 0.5**(0.25) -for kk in k_sqrt4_v: - yy_v = [C*kk - (xx-C*kk) for xx in x_v] - plt.plot(x_v, yy_v, marker=None, linestyle='--', color="#aaa") - -# draw the rays -for mm in [2.6, 6]: - yy_v = [mm*xx for xx in x_v] - plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color="#aaa", label=f"ray (m={mm})") - yy_v = [1/mm*xx for xx in x_v] - plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color="#aaa") - -plt.grid(True) -plt.legend() -plt.xlim(0, max(x_v)) -plt.ylim(0, max(x_v)) -plt.show() -# - - -# ### best hyperbola fit -# -# We now try the best possible (levered) hyperbola fit for one of those curves. Note that the levered hyperbola has the equation -# -# $$ -# y-y_0 = \frac{k}{x-x_0} -# $$ -# -# and has therefore three free paramters, $(k, x_0, y_0)$. We fit those numerically. - -# #### Unfitted hyperbola for demonstration -# -# (focus of Freeze04) -# -# Here we create four charts -# 1. The target curve, and a (bad) fit for demonstration, shown over a sufficiently wide range -# 2. The difference between the target curve and the fit -# 3. Target curve and fit, withing the kernel area -# 4. Difference, within kernel area (title contains L2 norm) -# - -# + -k_sqrt4 = 5 -kernel = f.Kernel(x_min=1, x_max=7, kernel=f.Kernel.FLAT) - -######## FIRST CHART -- WIDE CURVES -x_v = np.linspace(0, m.sqrt(10), 50) -x_v = [xx**2 for xx in x_v] -x_v[0] = x_v[1]/2 - -# draw the invariance curve -k_v = [kk**4 for kk in k_sqrt4_v] -k = k_sqrt4**4 -y1_f = SolidlySwapFunction(k=k) -yy_v = [y1_f(xx) for xx in x_v] -plt.plot(x_v, yy_v, marker=None, linestyle='-', label=f"k={k} ({k_sqrt4})") - -# draw the central tangent -C = 0.5**(0.25) -yy_v = [C*k_sqrt4 - (xx-C*k_sqrt4) for xx in x_v] -plt.plot(x_v, yy_v, marker=None, linestyle='--', color="#aaa") - -# draw the rays -for mm in [2.6, 6]: - yy_v = [mm*xx for xx in x_v] - plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color="#aaa", label=f"ray (m={mm})") - yy_v = [1/mm*xx for xx in x_v] - plt.plot(x_v, yy_v, marker=None, linestyle='dotted', color="#aaa") - -# draw the hyperbola -hyperbola_p = dict(x0=-1, y0=-1, k=25) -y2_f = f.HyperbolaFunction(**hyperbola_p) -yy_v = [y2_f(xx) for xx in x_v] -plt.plot(x_v, yy_v, marker=None, linestyle='--', color="red", label=f"hyperbola {hyperbola_p}") - -plt.grid() -plt.legend() -plt.xlim(0, max(x_v)) -plt.ylim(0, max(x_v)) -plt.show() - - -######## SECOND CHART -- DIFFERENCE -dy_f = f.FunctionVector({y1_f: 1, y2_f:-1}, kernel=kernel) -yy_v = [dy_f(xx) for xx in x_v] -plt.plot(x_v, yy_v, marker=None) -plt.grid() -plt.xlim(0, max(x_v)) -plt.ylim(-8,2) -#plt.legend() -plt.title("difference") -plt.show() - - -######## THIRD CHART -- CURVES WITHIN KERNEL -x_v = np.linspace(kernel.x_min, kernel.x_max, 100) - -# draw the invariance curve -k_v = [kk**4 for kk in k_sqrt4_v] -k = k_sqrt4**4 -y1_f = SolidlySwapFunction(k=k) -yy_v = [y1_f(xx) for xx in x_v] -plt.plot(x_v, yy_v, marker=None, linestyle='-', label=f"k={k} ({k_sqrt4})") - -# draw the hyperbola -hyperbola_p = dict(x0=-1, y0=-1, k=25) -y2_f = f.HyperbolaFunction(**hyperbola_p) -yy_v = [y2_f(xx) for xx in x_v] -plt.plot(x_v, yy_v, marker=None, linestyle='--', color="red", label=f"hyperbola {hyperbola_p}") - -plt.grid() -plt.legend() -plt.xlim(*kernel.limits) -#plt.ylim(0, None) -plt.show() - - -######## FOURTH CHART -- DIFFERENCE -dy_f = f.FunctionVector({y1_f: 1, y2_f:-1}, kernel=kernel) -yy_v = [dy_f(xx) for xx in x_v] -plt.plot(x_v, yy_v, marker=None) -plt.grid() -plt.xlim(*kernel.limits) -#plt.legend() -norm = dy_f.norm() -plt.title(f"difference [norm={norm:.2f}]") -plt.show() - -y1_f, y2_f, dy_f -# - - -# ## Generic numerical questions -# -# _(see Freeze04 for the latest results)_ - -# ### Square root term -# -# Here we are looking at the term $\sqrt{1+\xi}-1$ to understand up to which point we need the Tayler approximation, and whether there is a point going for T4 instead of T4. As a reminder -# -# $$ -# \sqrt{1+\xi}-1 = \frac{\xi}{2} - \frac{\xi^2}{8} + \frac{\xi^3}{16} - \frac{5\xi^4}{128} + O(\xi^5) -# $$ - -x1_v = np.linspace(0,1,100) -x1_v[0] = x1_v[1]/2 -data = [( - xx, - m.sqrt(1+xx)-1, - xx * (0.5 - xx*1/8), - #xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128, - xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))), -) for xx in x1_v -] -df = pd.DataFrame(data, columns=['xi', 'Float', 'Taylor2', 'Taylor4']).set_index("xi") -oldfs = plt.rcParams['figure.figsize'] -plt.rcParams['figure.figsize'] = [12,6] -#plt.figure(figsize=(12, 6)) -df.plot() -plt.grid(True) -plt.rcParams['figure.figsize'] = oldfs -plt.savefig("/Users/skl/Desktop/image.jpg") -#plt.grid() -df.head() - -# + -# x2_v = np.linspace(0,0.2,100) -# x1_v[0] = x1_v[1]/2 -# data = [( -# xx, -# m.sqrt(1+xx)-1, -# xx * (0.5 - xx*1/8), -# #xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128, -# xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))), -# ) for xx in x2_v -# ] -# df = pd.DataFrame(data, columns=['x', 'Float', 'Taylor2', 'Taylor4']).set_index("x") -# df.plot() -# plt.grid() -# df2 = df.copy() -# df2["Err2"] = df2["Taylor2"]/df2["Float"] - 1 -# df2["Err4"] = df2["Taylor4"]/df2["Float"] - 1 -# plt.show() -# df2.plot(y=["Err2", "Err4"]) -# plt.grid() -# plt.title("Relative error of Taylor 2 4 term approximations") -# plt.ylim(-0.001, 0.0001) -# df2.head() -# - - -# ### Decimal vs float -# #### Precision -# -# we compare $\sqrt{1+\xi}-1$ for float, Taylor and Decimal -# -# $$ -# \sqrt{1+\xi}-1 = \frac{\xi}{2} - \frac{\xi^2}{8} + \frac{\xi^3}{16} - \frac{5\xi^4}{128} + O(\xi^5) -# $$ - -# + -# import decimal as d -# D = d.Decimal -# d.getcontext().prec = 1000 # Set the precision to 30 decimal places (adjust as needed) -# xd_v = [1e-18*1.5**nn for nn in np.linspace(0, 103, 500)] -# xd_v[0], xd_v[-1] - -# + -# fmt = lambda x: x -# fmt = float -# ONE = D(1) -# data = [( -# xx, -# m.sqrt(1+xx)-1, -# xx * (0.5 - xx*1/8), -# #xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128, -# xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))), -# fmt((ONE+D(xx)).sqrt()-1), -# ) for xx in xd_v -# ] -# df = pd.DataFrame(data, columns=['x', 'Float', 'Taylor2', 'Taylor4', 'Dec']).set_index("x") -# df.head() - -# + -# df.plot() -# # plt.xlim(0, None) -# # plt.ylim(0, 100) -# plt.grid() - -# + -# df.iloc[:80].plot() -# plt.grid() - -# + -# df.iloc[:100].plot() -# plt.grid() - -# + -# LOC = 480 -# df.iloc[LOC:].plot() -# plt.grid() - -# + -# df2 = pd.DataFrame([ -# (df["Float"]-df["Taylor4"])/df["Taylor4"], -# (df["Taylor2"]-df["Taylor4"])/df["Taylor4"], -# (df["Dec"]-df["Taylor4"])/df["Taylor4"], -# ]).transpose() -# df2.columns = ["Float", "Taylor2", "Dec"] -# df2 -# - - -# #### Timing -# -# (focus of Freeze03) - -import time -import decimal as d -D = d.Decimal - -# + -# def timer(func, *args, N=None, **kwargs): -# """times the calls to func; func is called with args and kwargs; returns time in msec per 1m calls""" -# if N is None: -# N = 10_000_000 -# start_time = time.time() -# for _ in range(N): -# func(*args, **kwargs) -# end_time = time.time() -# return (end_time - start_time)/N*1_000_000*1000 - -# def timer1(func, arg, N=None): -# """times the calls to func; func is called with arg; returns time in msec per 1m calls""" -# if N is None: -# N = 10_000_000 -# start_time = time.time() -# for _ in range(N): -# func(arg) -# end_time = time.time() -# return (end_time - start_time)/N*1_000_000*1000 - -# def timer2(func, arg1, arg2, N=None): -# """times the calls to func; func is called with arg1, arg2; returns time in msec per 1m calls""" -# if N is None: -# N = 10_000_000 -# start_time = time.time() -# for _ in range(N): -# func(arg1, arg2) -# end_time = time.time() -# return (end_time - start_time)/N*1_000_000*1000 -#- - -# identify function (`lambda`) - -timer(lambda x: x, 1), timer1(lambda x: x, 1) - - -# ditto, defined with `def` - -def idfunc(x): - return x -timer(idfunc, 1), timer1(idfunc, 1) - -# sin, sqrt, exp etc as reference - -(timer(m.sin, 1), timer(m.cos, 1), timer(m.tan, 1), - timer(m.sqrt, 1), timer(m.exp, 1), timer(m.log, 1)) - -(timer1(m.sin, 1), timer1(m.cos, 1), timer1(m.tan, 1), - timer1(m.sqrt, 1), timer1(m.exp, 1), timer1(m.log, 1)) - -# **float** calculation - -timer(lambda xx: m.sqrt(1+xx)-1, 1), timer1(lambda xx: m.sqrt(1+xx)-1, 1) - -# **taylor** calculations - -timer(lambda xx: xx * (0.5 - xx*1/8), 1), timer1(lambda xx: xx * (0.5 - xx*1/8), 1) - -(timer(lambda xx: xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))), 1), -timer1(lambda xx: xx * (0.5 - xx*(1/8 - xx*(1/16 - 5/128*xx))), 1)) - -(timer(lambda xx: xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128, 1), -timer1(lambda xx: xx/2 - xx**2/8 + xx**3/16 - xx**4 * 5 / 128, 1)) - -# **decimal** calculations - -# + -# d.getcontext().prec = 30 -# ONE = D(1) -# (timer(lambda xx: D(1+xx).sqrt()-1, 1, N=100_000), -# timer(lambda xx: ONE+xx.sqrt()-1, ONE, N=100_000)) - -# + -# d.getcontext().prec = 100 -# ONE = D(1) -# (timer(lambda xx: D(1+xx).sqrt()-1, 1, N=10_000), -# timer(lambda xx: ONE+xx.sqrt()-1, ONE, N=10_000)) - -# + -# d.getcontext().prec = 1_000 -# ONE = D(1) -# (timer(lambda xx: D(1+xx).sqrt()-1, 1, N=1_000), -# timer(lambda xx: ONE+xx.sqrt()-1, ONE, N=1_000)) -# - - -# decimal conversions - -# + -# d.getcontext().prec = 30 -# ONE = D("0."+"9"*d.getcontext().prec) -# PI = m.pi -# (timer(lambda xx: D(xx), PI, N=1_000_000), -# timer(lambda: float(ONE), N=1_000_000), -# ONE -# ) - -# + -# d.getcontext().prec = 100 -# ONE = D("0."+"9"*d.getcontext().prec) -# (timer(lambda xx: D(xx), PI, N=1_000_000), -# timer(lambda: float(ONE), N=1_000_000), -# ONE -# ) - -# + -# d.getcontext().prec = 1000 -# ONE = D("0."+"9"*d.getcontext().prec) -# (timer(lambda xx: D(xx), PI, N=1_000_000), -# timer(lambda: float(ONE), N=1_000_000), -# ONE -# ) -# - - -# `L2` (using Taylor) vs `L3` (using decimal) - -# + -# d.getcontext().prec = 30 -# r = ( -# timer2(L2, 1, 625, N=1_000_000), -# timer2(L3, 1, 625, N=10_000), -# ) -# r, r[1]/r[0] - -# + -# d.getcontext().prec = 100 -# r = ( -# timer2(L2, 1, 625, N=1_000_000), -# timer2(L3, 1, 625, N=10_000), -# ) -# r, r[1]/r[0] - -# + -# d.getcontext().prec = 1000 -# r = ( -# timer2(L2, 1, 625, N=1_000_000), -# timer2(L3, 1, 625, N=10_000), -# ) -# r, r[1]/r[0] -# - - -D(2).sqrt()**2 - -# checking the performance of exponential on vectors (result: np.exp is faster than 10**; it may be worth pre-calculating np.log(10) for small vectors) - -v1 = 10**np.linspace(1,2, 10) -v3 = 10**np.linspace(1,2, 1000) - -# + -# r = ( -# timer1(lambda x: 10**x, v1, N=100_000), -# timer1(lambda x: 10**x, v3, N=100_000) -# ) -# r, r[1]/r[0] - -# + -# r = ( -# timer1(lambda x: np.exp(v1*np.log(10)), v1, N=100_000), -# timer1(lambda x: np.exp(v3*np.log(10)), v3, N=100_000) -# ) -# r, r[1]/r[0] - -# + -# LOG10 = np.log(10) -# r = ( -# timer1(lambda x: np.exp(v1*LOG10), v3, N=100_000), -# timer1(lambda x: np.exp(v3*np.log(10)), v3, N=100_000) -# ) -# r, r[1]/r[0] -# - - - diff --git a/resources/analysis/202401 Solidly/DictVector.ipynb b/resources/analysis/202401 Solidly/DictVector.ipynb deleted file mode 100644 index 9f141ebd6..000000000 --- a/resources/analysis/202401 Solidly/DictVector.ipynb +++ /dev/null @@ -1,506 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "a1a1f2ee-2732-46d9-9260-2d5dcf183238", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "DictVector v0.9 (18/Jan/2024)\n" - ] - } - ], - "source": [ - "import invariants.vector as dv\n", - "\n", - "from testing import *\n", - "#plt.rcParams['figure.figsize'] = [12,6]\n", - "\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(dv.DictVector))" - ] - }, - { - "cell_type": "markdown", - "id": "fe0298aa-1a94-4ece-af7c-ea0989e1260e", - "metadata": {}, - "source": [ - "# Dict Vectors (Invariants Module)" - ] - }, - { - "cell_type": "markdown", - "id": "3019cc9c-f892-4631-a7b7-77745325f5b0", - "metadata": {}, - "source": [ - "## Basic dict vector functions" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7ae202ea-bfd0-4746-a082-664a511228a5", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "vec1 = dict(a=1, b=2)\n", - "vec2 = dict(b=3, c=4)\n", - "vec3 = dict(c=1, a=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "05a521f5-a5e2-41c5-a660-8233ddf989cc", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(dv.norm(vec1)**2, 1+4)\n", - "assert iseq(dv.norm(vec2)**2, 9+16)\n", - "assert iseq(dv.norm(vec3)**2, 1+9)\n", - "assert iseq(dv.norm(vec1)**2, dv.sprod(vec1, vec1))\n", - "assert iseq(dv.norm(vec2)**2, dv.sprod(vec2, vec2))\n", - "assert iseq(dv.norm(vec3)**2, dv.sprod(vec3, vec3))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1dccb31f-b1c5-4e8d-84c9-5115230bb218", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert dv.eq(vec1, vec1)\n", - "assert dv.eq(vec2, vec2)\n", - "assert dv.eq(vec3, vec3)\n", - "assert not dv.eq(vec1, vec2)\n", - "assert not dv.eq(vec3, vec2)\n", - "assert not dv.eq(vec1, vec3)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "469379a9-3a90-418d-b009-ac0135abf5d6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert dv.add(vec1, vec2) == dict(a=1, b=5, c=4)\n", - "assert dv.add(vec1, vec3) == dict(a=4, b=2, c=1)\n", - "assert dv.add(vec2, vec3) == dict(a=3, b=3, c=5)\n", - "assert dv.add(vec1, vec2) == dv.add(vec2, vec1)\n", - "assert dv.add(vec1, vec3) == dv.add(vec3, vec1)\n", - "assert dv.add(vec2, vec3) == dv.add(vec3, vec2)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "6bd2c8cb-68dd-45f2-ba9c-b9367d5c23da", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert dv.add(vec1, vec1) == dv.smul(vec1, 2)\n", - "assert dv.add(vec2, vec2) == dv.smul(vec2, 2)\n", - "assert dv.add(vec3, vec3) == dv.smul(vec3, 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "768ff970-7524-4226-a0a3-4697941cd9a4", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert dv.DictVector.dict_add == dv.add\n", - "assert dv.DictVector.dict_sub == dv.sub\n", - "assert dv.DictVector.dict_smul == dv.smul\n", - "assert dv.DictVector.dict_sprod == dv.sprod\n", - "assert dv.DictVector.dict_norm == dv.norm\n", - "assert dv.DictVector.dict_eq == dv.eq" - ] - }, - { - "cell_type": "markdown", - "id": "5cf321bf-0710-414d-ac85-a0cc9baef879", - "metadata": {}, - "source": [ - "## DictVector object" - ] - }, - { - "cell_type": "markdown", - "id": "cb340de1-1f42-4663-a95f-636322cd89ee", - "metadata": {}, - "source": [ - "null vector" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "22e0720e-a8c9-4959-9268-4142ccb02fb0", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(DictVector(vec={}), DictVector(vec={'a': 0, 'b': 0, 'x': 0}))" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vec0 = dv.DictVector.null()\n", - "vec0a = dv.DictVector()\n", - "vec0b = dv.DictVector.n(a=0, b=0, x=0)\n", - "\n", - "assert bool(vec0) is False\n", - "assert bool(vec0a) is False\n", - "assert bool(vec0b) is False\n", - "assert vec0 == vec0a\n", - "assert vec0 == vec0b\n", - "assert vec0a == vec0b\n", - "assert len(vec0) == 0\n", - "assert len(vec0a) == 0\n", - "assert len(vec0b) == 0\n", - "assert vec0.norm == 0\n", - "assert vec0a.norm == 0\n", - "assert vec0b.norm == 0\n", - "assert not \"a\" in vec0\n", - "assert not \"a\" in vec0a\n", - "assert not \"a\" in vec0b\n", - "vec0, vec0b" - ] - }, - { - "cell_type": "markdown", - "id": "0ced5528-3bce-4744-94e3-295f24af0419", - "metadata": {}, - "source": [ - "non-null vector" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d57adb73-dd59-420c-81d8-12b8717f7342", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "DictVector(vec={'a': 1, 'b': 2, 'x': 0})" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vec1 = dv.DictVector.n(a=1, b=2, x=0)\n", - "vec1b = dv.DictVector(vec1.vec)\n", - "assert bool(vec1) is True\n", - "assert bool(vec1b) is True\n", - "assert vec1[\"a\"] == 1\n", - "assert vec1[\"b\"] == 2\n", - "assert vec1[\"c\"] == 0 # !!! <<== missing elements are 0!\n", - "assert vec1[\"x\"] == 0\n", - "assert \"a\" in vec1\n", - "assert \"b\" in vec1\n", - "assert not \"c\" in vec1\n", - "assert not \"x\" in vec1\n", - "assert vec1 == vec1b\n", - "vec1" - ] - }, - { - "cell_type": "markdown", - "id": "67d07744-3918-449c-a80f-27e2fb55c768", - "metadata": {}, - "source": [ - "various ways of creating a vector" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e4fb783f-561c-4797-8001-2a15314a5f33", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "veca = dv.DictVector(dict(a=1, b=2, x=0))\n", - "vecb = dv.DictVector.new(a=1, b=2, x=0)\n", - "vecc = dv.DictVector.new(dict(a=1, b=2, x=0))\n", - "vecd = dv.DictVector.n(a=1, b=2, x=0)\n", - "vece = dv.DictVector.n(dict(a=1, b=2, x=0))\n", - "vecf = dv.V(a=1, b=2, x=0)\n", - "vecg = dv.V(dict(a=1, b=2, x=0))\n", - "assert veca == vecb\n", - "assert veca == vecc\n", - "assert veca == vecd\n", - "assert veca == vece\n", - "assert veca == vecf\n", - "assert veca == vecg" - ] - }, - { - "cell_type": "markdown", - "id": "c374a662-f5d9-413d-af4f-e4b19299f408", - "metadata": { - "tags": [] - }, - "source": [ - "vector arithmetic" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7ace0ffe-8971-4e1a-bdcb-3dbbeff87c32", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert vec0 + vec1 == vec1\n", - "assert vec0b + vec1 == vec1\n", - "assert vec1 + vec1 == 2*vec1\n", - "assert vec1 + vec1 == vec1*2\n", - "assert 3*vec1 == vec1*3\n", - "assert +vec1 == vec1\n", - "assert -vec1 == vec1 * (-1)\n", - "assert -vec1 == -1 * vec1\n", - "assert bool(0*vec1) is False\n", - "assert 0*vec1 == vec0\n", - "assert 0*vec1 == vec0b\n", - "assert 0*vec1 == vec1*0\n", - "assert (0*vec1).norm == 0\n", - "assert 2*3*vec1 == 6*vec1\n", - "assert 2*vec1*3 == vec1*6\n", - "assert 2*3*vec1/6 == vec1" - ] - }, - { - "cell_type": "markdown", - "id": "0c29cfed-16d8-481b-8f9f-f1a0f8eb3690", - "metadata": {}, - "source": [ - "vector base" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "2f724785-7df8-4e0c-b415-6926ab9c417f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "labels = \"abcdefghijklmnop\"\n", - "base = {l:dv.DictVector({l:1})for l in labels}\n", - "for x in base.values():\n", - " for y in base.values():\n", - " if x == y:\n", - " #print(x,y,x*y)\n", - " assert x*y == 1\n", - " else:\n", - " assert x*y == 0\n", - " \n", - "assert base[\"a\"] * dv.V(a=1, b=2) == 1\n", - "assert base[\"b\"] * dv.V(a=1, b=2) == 2\n", - "assert base[\"c\"] * dv.V(a=1, b=2) == 0\n", - "assert base[\"a\"]+2*base[\"b\"] == dv.V(a=1, b=2)" - ] - }, - { - "cell_type": "markdown", - "id": "c7ab4c1d-535e-4a19-b1e6-bea6a1ebe750", - "metadata": { - "tags": [] - }, - "source": [ - "floor / ceil / round / abs" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "79e2502b-0c0a-4d06-9f93-e3a9ce0d7969", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "vec2 = dv.V(a=1.2345, b=9.8765, c=3.5, d=1)\n", - "assert m.floor(vec2) == dv.V(a=1, b=9, c=3, d=1)\n", - "assert m.ceil(vec2) == dv.V(a=2, b=10, c=4, d=1)\n", - "assert m.ceil(vec2) - m.floor(vec2) == dv.V(a=1, b=1, c=1)\n", - "assert round(vec2) == dv.V(a=1, b=10, c=4, d=1)\n", - "assert round(vec2, 1) == dv.V(a=1.2, b=9.9, c=3.5, d=1)\n", - "assert abs(vec2) == vec2\n", - "assert abs(-vec2) == vec2" - ] - }, - { - "cell_type": "markdown", - "id": "3ced1a6b-4764-4107-85bc-e3003cb482a3", - "metadata": {}, - "source": [ - "incremental actions" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "1c6f5f4f-a32f-42f2-8641-8a6c1289da13", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "DictVector(vec={'b': 0.0, 'a': 0.0, 'c': 0.0})" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "v = dv.V()\n", - "assert not v\n", - "v += dv.V(a=1, b=2)\n", - "assert v\n", - "assert v == dv.V(a=1, b=2)\n", - "v *= 2\n", - "assert v == 2*dv.V(a=1, b=2)\n", - "v += dv.V(a=3, c=3)\n", - "assert v == dv.V(a=5, b=4, c=3)\n", - "v /= 2\n", - "assert v == 0.5 * dv.V(a=5, b=4, c=3)\n", - "v -= v\n", - "assert bool(v) is False\n", - "assert not v\n", - "v" - ] - }, - { - "cell_type": "markdown", - "id": "95c21f6d-ed05-4226-82a2-d6401417c76b", - "metadata": {}, - "source": [ - "generic base vector " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "7ea69e47-14a4-4c8b-8c33-9a371ae1e0d5", - "metadata": { - "lines_to_next_cell": 0, - "tags": [] - }, - "outputs": [], - "source": [ - "class Foo():\n", - " pass\n", - "\n", - "@dv.dataclass(frozen=True)\n", - "class Bar():\n", - " val: str\n", - " \n", - "foo1 = Foo()\n", - "foo2 = Foo()\n", - "assert foo1 != foo2\n", - "\n", - "bar1 = Bar(\"bang\")\n", - "bar1a = Bar(\"bang\")\n", - "assert bar1 == bar1a\n", - "assert not bar1 is bar1a\n", - "\n", - "va = dv.V({foo1: 3, foo2:4})\n", - "assert len(va) == 2\n", - "assert va.norm == 5\n", - "\n", - "va = dv.V({bar1: 3, foo1:4})\n", - "assert len(va) == 2\n", - "assert va.norm == 5\n", - "\n", - "va = dv.V({bar1: 3, bar1a:4})\n", - "assert len(va) == 1\n", - "assert va.norm == 4\n", - "\n", - "va = dv.V({bar1: 3})\n", - "vb = dv.V({bar1a: 3})\n", - "assert va == vb\n", - "assert not va is vb" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6edc87f2-8b9f-4675-8c04-393835facf30", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/analysis/202401 Solidly/DictVector.py b/resources/analysis/202401 Solidly/DictVector.py deleted file mode 100644 index 6516aade3..000000000 --- a/resources/analysis/202401 Solidly/DictVector.py +++ /dev/null @@ -1,230 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -import invariants.vector as dv - -from testing import * -#plt.rcParams['figure.figsize'] = [12,6] - -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(dv.DictVector)) -# - - -# # Dict Vectors (Invariants Module) - -# ## Basic dict vector functions - -vec1 = dict(a=1, b=2) -vec2 = dict(b=3, c=4) -vec3 = dict(c=1, a=3) - -assert iseq(dv.norm(vec1)**2, 1+4) -assert iseq(dv.norm(vec2)**2, 9+16) -assert iseq(dv.norm(vec3)**2, 1+9) -assert iseq(dv.norm(vec1)**2, dv.sprod(vec1, vec1)) -assert iseq(dv.norm(vec2)**2, dv.sprod(vec2, vec2)) -assert iseq(dv.norm(vec3)**2, dv.sprod(vec3, vec3)) - -assert dv.eq(vec1, vec1) -assert dv.eq(vec2, vec2) -assert dv.eq(vec3, vec3) -assert not dv.eq(vec1, vec2) -assert not dv.eq(vec3, vec2) -assert not dv.eq(vec1, vec3) - -assert dv.add(vec1, vec2) == dict(a=1, b=5, c=4) -assert dv.add(vec1, vec3) == dict(a=4, b=2, c=1) -assert dv.add(vec2, vec3) == dict(a=3, b=3, c=5) -assert dv.add(vec1, vec2) == dv.add(vec2, vec1) -assert dv.add(vec1, vec3) == dv.add(vec3, vec1) -assert dv.add(vec2, vec3) == dv.add(vec3, vec2) - -assert dv.add(vec1, vec1) == dv.smul(vec1, 2) -assert dv.add(vec2, vec2) == dv.smul(vec2, 2) -assert dv.add(vec3, vec3) == dv.smul(vec3, 2) - -assert dv.DictVector.dict_add == dv.add -assert dv.DictVector.dict_sub == dv.sub -assert dv.DictVector.dict_smul == dv.smul -assert dv.DictVector.dict_sprod == dv.sprod -assert dv.DictVector.dict_norm == dv.norm -assert dv.DictVector.dict_eq == dv.eq - -# ## DictVector object - -# null vector - -# + -vec0 = dv.DictVector.null() -vec0a = dv.DictVector() -vec0b = dv.DictVector.n(a=0, b=0, x=0) - -assert bool(vec0) is False -assert bool(vec0a) is False -assert bool(vec0b) is False -assert vec0 == vec0a -assert vec0 == vec0b -assert vec0a == vec0b -assert len(vec0) == 0 -assert len(vec0a) == 0 -assert len(vec0b) == 0 -assert vec0.norm == 0 -assert vec0a.norm == 0 -assert vec0b.norm == 0 -assert not "a" in vec0 -assert not "a" in vec0a -assert not "a" in vec0b -vec0, vec0b -# - - -# non-null vector - -vec1 = dv.DictVector.n(a=1, b=2, x=0) -vec1b = dv.DictVector(vec1.vec) -assert bool(vec1) is True -assert bool(vec1b) is True -assert vec1["a"] == 1 -assert vec1["b"] == 2 -assert vec1["c"] == 0 # !!! <<== missing elements are 0! -assert vec1["x"] == 0 -assert "a" in vec1 -assert "b" in vec1 -assert not "c" in vec1 -assert not "x" in vec1 -assert vec1 == vec1b -vec1 - -# various ways of creating a vector - -veca = dv.DictVector(dict(a=1, b=2, x=0)) -vecb = dv.DictVector.new(a=1, b=2, x=0) -vecc = dv.DictVector.new(dict(a=1, b=2, x=0)) -vecd = dv.DictVector.n(a=1, b=2, x=0) -vece = dv.DictVector.n(dict(a=1, b=2, x=0)) -vecf = dv.V(a=1, b=2, x=0) -vecg = dv.V(dict(a=1, b=2, x=0)) -assert veca == vecb -assert veca == vecc -assert veca == vecd -assert veca == vece -assert veca == vecf -assert veca == vecg - -# vector arithmetic - -assert vec0 + vec1 == vec1 -assert vec0b + vec1 == vec1 -assert vec1 + vec1 == 2*vec1 -assert vec1 + vec1 == vec1*2 -assert 3*vec1 == vec1*3 -assert +vec1 == vec1 -assert -vec1 == vec1 * (-1) -assert -vec1 == -1 * vec1 -assert bool(0*vec1) is False -assert 0*vec1 == vec0 -assert 0*vec1 == vec0b -assert 0*vec1 == vec1*0 -assert (0*vec1).norm == 0 -assert 2*3*vec1 == 6*vec1 -assert 2*vec1*3 == vec1*6 -assert 2*3*vec1/6 == vec1 - -# vector base - -# + -labels = "abcdefghijklmnop" -base = {l:dv.DictVector({l:1})for l in labels} -for x in base.values(): - for y in base.values(): - if x == y: - #print(x,y,x*y) - assert x*y == 1 - else: - assert x*y == 0 - -assert base["a"] * dv.V(a=1, b=2) == 1 -assert base["b"] * dv.V(a=1, b=2) == 2 -assert base["c"] * dv.V(a=1, b=2) == 0 -assert base["a"]+2*base["b"] == dv.V(a=1, b=2) -# - - -# floor / ceil / round / abs - -vec2 = dv.V(a=1.2345, b=9.8765, c=3.5, d=1) -assert m.floor(vec2) == dv.V(a=1, b=9, c=3, d=1) -assert m.ceil(vec2) == dv.V(a=2, b=10, c=4, d=1) -assert m.ceil(vec2) - m.floor(vec2) == dv.V(a=1, b=1, c=1) -assert round(vec2) == dv.V(a=1, b=10, c=4, d=1) -assert round(vec2, 1) == dv.V(a=1.2, b=9.9, c=3.5, d=1) -assert abs(vec2) == vec2 -assert abs(-vec2) == vec2 - -# incremental actions - -v = dv.V() -assert not v -v += dv.V(a=1, b=2) -assert v -assert v == dv.V(a=1, b=2) -v *= 2 -assert v == 2*dv.V(a=1, b=2) -v += dv.V(a=3, c=3) -assert v == dv.V(a=5, b=4, c=3) -v /= 2 -assert v == 0.5 * dv.V(a=5, b=4, c=3) -v -= v -assert bool(v) is False -assert not v -v - - -# generic base vector - -# + -class Foo(): - pass - -@dv.dataclass(frozen=True) -class Bar(): - val: str - -foo1 = Foo() -foo2 = Foo() -assert foo1 != foo2 - -bar1 = Bar("bang") -bar1a = Bar("bang") -assert bar1 == bar1a -assert not bar1 is bar1a - -va = dv.V({foo1: 3, foo2:4}) -assert len(va) == 2 -assert va.norm == 5 - -va = dv.V({bar1: 3, foo1:4}) -assert len(va) == 2 -assert va.norm == 5 - -va = dv.V({bar1: 3, bar1a:4}) -assert len(va) == 1 -assert va.norm == 4 - -va = dv.V({bar1: 3}) -vb = dv.V({bar1a: 3}) -assert va == vb -assert not va is vb -# - - - diff --git a/resources/analysis/202401 Solidly/Functions-Freeze01.ipynb b/resources/analysis/202401 Solidly/Functions-Freeze01.ipynb deleted file mode 100644 index 5eecefa01..000000000 --- a/resources/analysis/202401 Solidly/Functions-Freeze01.ipynb +++ /dev/null @@ -1,1713 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "0278c025-06e6-416b-9525-c2a4a8ae9128", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "Function v1.0-beta5 (18/Jan/2024)\n", - "Kernel v1.0-beta3 (18/Jan/2024)\n" - ] - } - ], - "source": [ - "import invariants.functions as f\n", - "from invariants.kernel import Kernel\n", - "# from invariants.invariant import Invariant\n", - "# from invariants.bancor import BancorInvariant, BancorSwapFunction\n", - "# from invariants.solidly import SolidlyInvariant, SolidlySwapFunction\n", - "import numpy as np\n", - "import math as m\n", - "import matplotlib.pyplot as plt\n", - "#import pandas as pd\n", - "#from sympy import symbols, sqrt, Eq\n", - "\n", - "from testing import *\n", - "plt.rcParams['figure.figsize'] = [12,6]\n", - "\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(f.Function))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(Kernel))" - ] - }, - { - "cell_type": "markdown", - "id": "7e212348-81d0-49f2-8d41-c7842a387634", - "metadata": {}, - "source": [ - "# Functions and integration kernels" - ] - }, - { - "cell_type": "markdown", - "id": "e831972e-e8b3-4e29-a6ec-103ddb874bd2", - "metadata": {}, - "source": [ - "## Functions" - ] - }, - { - "cell_type": "markdown", - "id": "64d064b4-c2f0-42f4-84d1-5fed091f461b", - "metadata": { - "tags": [] - }, - "source": [ - "### Built in functions\n", - "#### QuadraticFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "214f13cc-e573-42d9-94d9-8f7ad1ae6281", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "qf = f.QuadraticFunction(a=1, b=0, c=-10)\n", - "assert qf.params() == {'a': 1, 'b': 0, 'c': -10}\n", - "assert qf.a == 1\n", - "assert qf.b == 0\n", - "assert qf.c == -10" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f4828c9c-eafa-4da3-81a0-7e1949148d07", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "qf2 = qf.update(c=-5)\n", - "assert raises(qf.update, k=1)\n", - "assert qf2.params() == {'a': 1, 'b': 0, 'c': -5}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a169eb1c-a5bb-41c2-a64c-677fa5a581ed", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(-5,5)\n", - "y1_v = [qf(xx) for xx in x_v]\n", - "y2_v = [qf2(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"qf\")\n", - "plt.plot(x_v, y2_v, label=\"qf2\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "718fab97-6490-4888-912a-4c18aaa38451", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(-5,5)\n", - "y1_v = [qf(xx) for xx in x_v]\n", - "y2_v = [qf.p(xx) for xx in x_v]\n", - "y3_v = [qf.pp(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"f\")\n", - "plt.plot(x_v, y2_v, label=\"f'\")\n", - "plt.plot(x_v, y3_v, label=\"f''\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "markdown", - "id": "156af9c4-9461-4bf6-8d42-af54e15dfcf3", - "metadata": {}, - "source": [ - "#### TrigFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d2a5640a-6642-4458-9199-ad0efa016113", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "qf = f.TrigFunction()\n", - "assert qf.params() == {'amp': 1, 'omega': 1, 'phase': 0}\n", - "assert qf.amp == 1\n", - "assert qf.omega == 1\n", - "assert qf.phase == 0\n", - "assert int(qf.PI) == 3\n", - "\n", - "qf2 = qf.update(phase=1.5*qf.PI)\n", - "assert qf2.params() == {'amp': 1, 'omega': 1, 'phase': 1.5*qf.PI}" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5bd195a5-2db9-4fb7-bb0a-999f9ab1511e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(0,4*qf.PI, 100)\n", - "y1_v = [qf(xx) for xx in x_v]\n", - "y2_v = [qf2(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"qf\")\n", - "plt.plot(x_v, y2_v, label=\"qf2\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "markdown", - "id": "aa09589f-4748-48a9-86af-513da43d514c", - "metadata": {}, - "source": [ - "#### HyperbolaFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8cd24f4f-8721-42c0-b993-e874c2258307", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "qf = f.HyperbolaFunction()\n", - "assert qf.params() == {'k': 1, 'x0': 0, 'y0': 0}\n", - "assert qf.k == 1\n", - "assert qf.x0 == 0\n", - "assert qf.y0 == 0\n", - "\n", - "qf2 = qf.update(y0=0.5)\n", - "# assert qf2.params() == {'amp': 1, 'omega': 1, 'phase': 1.5*qf.PI}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "8c3909a6-4705-4433-aa3e-66c1d07c8615", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(1, 10, 100)\n", - "y1_v = np.array([qf(xx) for xx in x_v])\n", - "y2_v = np.array([qf2(xx) for xx in x_v])\n", - "assert iseq(min(y2_v-y1_v), 0.5)\n", - "assert iseq(max(y2_v-y1_v), 0.5)\n", - "plt.plot(x_v, y1_v, label=\"qf\")\n", - "plt.plot(x_v, y2_v, label=\"qf2\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "markdown", - "id": "18e5f995-a251-446b-8152-6fc4b70bd8a3", - "metadata": {}, - "source": [ - "### Derivatives" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b0c9d852-742f-4a1d-8dc6-4a1fc801db3c", - "metadata": {}, - "outputs": [], - "source": [ - "qf = f.QuadraticFunction(a=1, b=2, c=3)\n", - "qfp = qf.p_func()\n", - "qfpp = qf.pp_func()\n", - "assert qf.params() == {'a': 1, 'b': 2, 'c': 3}\n", - "assert qfp.func is qf\n", - "assert qfpp.func is qf" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "bb3df983-030d-429c-b3e1-b855f0000eef", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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KTEyU2+22up0O4XQ6meEGAAAAAsx/1mTroU+3S5LuPmuwLh3T0+KOIAVA6PZyOBwEUQAAAABd0sKt+br73U2SpJtP6aubT+1ncUfw6tIbqQEAAABAV7dyb7FmzVunJo+hi0f30O/OGmx1S2iD0A0AAAAAndTWnArdMHeNGho9On1Ikv5y0fAuv3l0Z0PoBgAAAIBOaG9hla5+aZUq6xs1rk+snpoxSkEOIp6/4R0BAAAAgE4mu6RGV/xrpYqq6jUkJUovXDNGIU72sPJHhG4AAAAA6ETyK+p05YsrlVtep34J4Xr1+nGKDnVa3Ra+BaEbAAAAADqJkuoGXfmvlTpQXKOesaF6/YYTFR/hsrotfAdCNwAAAAB0AuW1bl314krtKqhSclSI5t1wopKjQ6xuC9/Dp6H7mWee0YgRIxQVFaWoqCiddNJJ+vTTT1u+bhiGZs+erdTUVIWGhmry5MnasmWLL1sCAAAAgE6nur5R181ZrS05FYoLD9ZrN4xXz9gwq9vCUfBp6O7Ro4f+8pe/aM2aNVqzZo1+8pOf6Pzzz28J1g8//LAee+wxPfXUU1q9erWSk5M1depUVVZW+rItAAAAAOg06txNuvGVNVp7oFRRIUF69frx6p8YYXVbOEo+Dd3nnnuupk+froEDB2rgwIF64IEHFBERoa+//lqGYejxxx/XPffco4suukjp6emaO3euampqNG/ePF+2BQAAAACdgrvJo1mvr9OKPcUKD3Zo7nXjNDQ1yuq2cAyCOuoXampq0ltvvaXq6mqddNJJ2rdvn/Ly8jRt2rSWY1wul0499VStWLFCN9988xG/T319verr61s+r6iokCS53W653W7fngT8ivf95n2Hv+Iahb/jGoW/4xqFv/P1NdrkMXTnWxv1+fYCuYLseu7KUUpPieD3hB84lvfA56F706ZNOumkk1RXV6eIiAi99957Gjp0qFasWCFJSkpKand8UlKSDhw48K3f76GHHtK999572PiCBQsUFsY9DYFo4cKFVrcAfCeuUfg7rlH4O65R+DtfXKMeQ3pjj12rCu1y2AzN7O9W8bav9cm24/5L4Qeoqak56mN9HroHDRqkzMxMlZWV6Z133tE111yjxYsXt3zdZrO1O94wjMPG2rr77rt15513tnxeUVGhnj17atq0aYqKYplFIHG73Vq4cKGmTp0qp5PnEsL/cI3C33GNwt9xjcLf+eoaNQxDf/54u1YVZstht+mJS0fqjGFJ3/9CdBjviuuj4fPQHRwcrP79+0uSxowZo9WrV+uJJ57Qb3/7W0lSXl6eUlJSWo4vKCg4bPa7LZfLJZfr8OfQOZ1OfhgHKN57+DuuUfg7rlH4O65R+LvjeY0ahqG/zt+hV1dmy2aTHr1khM7J6HFcvjeOn2N5vzv8Od2GYai+vl5paWlKTk5utxSjoaFBixcv1oQJEzq6LQAAAACw3D+/3K1nF++RJN1/QbouHEXg7ux8OtP9+9//XmeddZZ69uypyspKvfnmm1q0aJHmz58vm82mO+64Qw8++KAGDBigAQMG6MEHH1RYWJhmzJjhy7YAAAAAwO+8tGyfHl2wU5J0z/QhumJ8b4s7wvHg09Cdn5+vq666Srm5uYqOjtaIESM0f/58TZ06VZJ01113qba2VrfccotKS0s1fvx4LViwQJGRkb5sCwAAAAD8ypursnTfR1slSXecPkA3ntLX4o5wvPg0dL/44ovf+XWbzabZs2dr9uzZvmwDAAAAAPzWB5mHdPd7myRJN53SV7efNsDijnA8dfg93QAAAAAA04ItebrzPxtkGNIV43vp7rMGf+fTnND5ELoBAAAAwAJLdxXq1nnr1eQxdNGo7vrz+ekE7i6I0A0AAAAAHezrvcW68ZU1amjy6MxhyXr44hGy2wncXRGhGwAAAAA60Mq9xbr25dWqc3s0eVCC/vGzUQpyEM26Kt5ZAAAAAOggq/eX6No5q1XrbtLJA+L17JWjFRxELOvKeHcBAAAAoAOs2V+imS+tUk1Dkyb1j9cLV49RiNNhdVvwMUI3AAAAAPjYuqxSzXx5taobmjShXxyBO4AQugEAAADAh9ZnleqaF1epqr5RJ/WN04vXjFVoMIE7UBC6AQAAAMBHNmSX6eoXV6myvlHj02L14swxBO4AQ+gGAAAAAB/YeLBMV764UpX1jRrXJ1YvzRyrsOAgq9tCByN0AwAAAMBxtvlQua7810pV1jVqTO9uevnasQp3EbgDEaEbAAAAAI6jzYfKdcW/VqqirlGje3fTnOvGEbgDGKEbAAAAAI6TrTkVuvLFlSqvdWtUrxjNuXasIgjcAY3QDQAAAADHwbbcCl3xr69VVuPWyJ4xmnvdOEWGOK1uCxYjdAMAAADAj7Qjr1JX/GulSmvcGtkjWq9cN05RBG5IYp0DAAAAAPwIu/KrdNXLa1RS3aDh3aP1yvXjFR1K4IaJmW4AAAAA+IHyaqSrXl6j4uoGpXeP0msEbnwDM90AAAAA8APsKazWU1sdqnQ3aGhKc+AOI3CjPWa6AQAAAOAY7S2s0tUvr1Gl26bByZF6/YbxigkLtrot+CFCNwAAAAAcg31F1frZC1+roLJeKWGG5s4crW7hBG4cGcvLAQAAAOAo7cqv1Ix/rVRhZb0GJkboml5liiVw4zsw0w0AAAAAR2FrToUue/5rFVbWa3BypF65drQiuIUb34OZbgAAAAD4HhsPlumqF1epvNZtPhbsunGKCLZZ3RY6AWa6AQAAAOA7rD1QqiteWKnyWrdG9YrRazeM5x5uHDVmugEAAADgW6zcW6zr5qxWdUOTxvWJ1UvXjlWEixiFo8fVAgAAAABHsGxXkW54ZbXq3B5N7B+nF64eo7BgIhSODVcMAAAAAHzDl9sLdPNra9XQ6NHkQQl69srRCnE6rG4LnRChGwAAAADaWLAlT7PmrZO7ydDUoUl6asYouYII3PhhCN0AAAAA0OyjjTm6481MNXoMnT08RY9fniGng/2n8cMRugEAAABA0rvrDurXb22Qx5AuGtVdD188QkEEbvxIhG4AAAAAAe/NVVm6+71NMgzp8rE99cCFw+Ww8xxu/HiEbgAAAAAB7ZWv9uuPH2yRJF19Um/NPneY7ARuHCeEbgAAAAAB619L9+r+j7dJkm6YlKZ7zh4im43AjeOH0A0AAAAgIP3zy9165LMdkqRZU/rp19MGEbhx3BG6AQAAAAQUwzD094U79Y8vdkuS7pw6UL88bYDFXaGrInQDAAAACBiGYegvn27Xc0v2SpJ+d9Zg/fzUfhZ3ha6M0A0AAAAgIHg8hu79cIvmfnVAkvSnc4fq2olpFneFro7QDQAAAKDLczd5dNfbG/Xe+kOSpAcuTNcV43tb3BUCAaEbAAAAQJdW527SrNfX6fPtBQqy2/S3S0fq/IzuVreFAEHoBgAAANBlVdS5dcPcNVq1r0SuILueufIE/WRwktVtIYAQugEAAAB0SUVV9brmpVXaklOhSFeQ/nXNGI3vG2d1WwgwhG4AAAAAXc6hslpd9a+V2ltUrbjwYM29bpzSu0db3RYCEKEbAAAAQJeyu6BKV724UrnldeoeE6pXrx+nvgkRVreFAEXoBgAAANBlbDpYrmteXqWS6gb1SwjXq9ePV2pMqNVtIYARugEAAAB0CV/tKdaNr6xRVX2jRvSI1pxrxyk2PNjqthDgCN0AAAAAOr2FW/M1a946NTR6dGLfWL1w9RhFhjitbgsgdAMAAADo3N5dd1C/eXujmjyGTh+SpKdmjFKI02F1W4AkQjcAAACATuylZft030dbJUk/PaGH/vrT4Qpy2C3uCmhF6AYAAADQ6RiGocf/t0tPfL5LknTtxD76f2cPld1us7gzoD1CNwAAAIBOxeMxdN9HWzVnxX5J0p1TB+q2n/SXzUbghv8hdAMAAADoNNxNHt319ka9t/6QJOne84bpmgl9rG0K+A6EbgAAAACdQp27SbNeX6fPtxfIYbfpb5eM1AWjulvdFvCdCN0AAAAA/F55jVs3vrpGq/aVyBVk19NXnKDThiRZ3RbwvQjdAAAAAPxaTlmtZr68SjvzqxTpCtK/rhmj8X3jrG4LOCqEbgAAAAB+a3tehWa+tFp5FXVKinJpzrXjNCQlyuq2gKNG6AYAAADgl1bsKdLNr6xVZX2j+idGaO5149Q9JtTqtoBjQugGAAAA4Hf+uyFHv/pPptxNhsb1idXzV49WTFiw1W0Bx4zQDQAAAMBvGIahfy3dpwc+2SZJmj48WY9dmqEQp8PizoAfhtANAAAAwC94PIb+/PFWvbx8vyTp2ol99P/OHiq73WZtY8CPQOgGAAAAYLk6d5N+9Z8N+nhTriTpnulDdMPJabLZCNzo3AjdAAAAACzV9hncTodNj14yUudndLe6LeC4IHQDAAAAsMyhslrNfGmVdhWYz+B+7urRmtAv3uq2gOOG0A0AAADAEttyKzTz5VXKr6hXclSI5lw3VoOTeQY3uhZCNwAAAIAOt2J3kW5+1XwG98CkCM25dpxSeQY3uiBCNwAAAIAO9UHmIf36rQ3mM7jTYvXCVWMUHea0ui3AJwjdAAAAADqEYRh6fslePfTpdknS2SNS9LdLRvIMbnRphG4AAAAAPtfkMfTnj7Zqzor9kqTrJ6XpnulDeAY3ujxCNwAAAACfqnM36f/+nalPN+dJkv5w9hDdcHJfi7sCOgahGwAAAIDPFFTW6cZX1mpDdpmCHXb97dKROndkqtVtAR2G0A0AAADAJ7bnVej6OWt0qKxWMWFOPXvlaJ3YN87qtoAORegGAAAAcNx9ub1At85bp+qGJqXFh+ulmWOVFh9udVtAhyN0AwAAADiu5izfp/s+2iqPIZ3YN1bPXjlaMWHBVrcFWILQDQAAAOC4aGzy6L6PtuqVrw5Iki4d00P3XzBcwUF2izsDrOPTq/+hhx7S2LFjFRkZqcTERF1wwQXasWNHu2MMw9Ds2bOVmpqq0NBQTZ48WVu2bPFlWwAAAACOs4o6t66fu0avfHVANpv0u7MG668/HUHgRsDz6e+AxYsXa9asWfr666+1cOFCNTY2atq0aaqurm455uGHH9Zjjz2mp556SqtXr1ZycrKmTp2qyspKX7YGAAAA4DjJLqnRxc+s0OKdhQpx2vXMFaP181P7yWbjGdyAT5eXz58/v93nL7/8shITE7V27VqdcsopMgxDjz/+uO655x5ddNFFkqS5c+cqKSlJ8+bN08033+zL9gAAAAD8SGsPlOrmV9eoqKpBiZEuvXjNWA3vEW11W4Df6NB7usvLyyVJsbGxkqR9+/YpLy9P06ZNaznG5XLp1FNP1YoVK44Yuuvr61VfX9/yeUVFhSTJ7XbL7Xb7sn34Ge/7zfsOf8U1Cn/HNQp/xzXq/z7amKvfvrdFDY0eDUmO1HNXjlJKdEjAvGdco4HrWN5zm2EYhg97aWEYhs4//3yVlpZq6dKlkqQVK1Zo4sSJOnTokFJTU1uOvemmm3TgwAF99tlnh32f2bNn69577z1sfN68eQoLC/PdCQAAAACQJBmG9NlBmz496JAkpXfz6OoBHrkcFjcGdJCamhrNmDFD5eXlioqK+s5jO2ym+9Zbb9XGjRu1bNmyw772zXs9DMP41vs/7r77bt15550tn1dUVKhnz56aNm3a954suha3262FCxdq6tSpcjqdVrcDHIZrFP6OaxT+jmvUP9W7m/T797fq04O5kqTrJ/bWb6YNlMMeePdvc40GLu+K66PRIaH7tttu03//+18tWbJEPXr0aBlPTk6WJOXl5SklJaVlvKCgQElJSUf8Xi6XSy6X67Bxp9PJhR6geO/h77hG4e+4RuHvuEb9R3FVvW56dZ3WHihVkN2m+y9I1+XjelndluW4RgPPsbzfPt293DAM3XrrrXr33Xf1xRdfKC0trd3X09LSlJycrIULF7aMNTQ0aPHixZowYYIvWwMAAABwDHblV+qCp5dr7YFSRYUEae514wjcwFHw6Uz3rFmzNG/ePH3wwQeKjIxUXl6eJCk6OlqhoaGy2Wy644479OCDD2rAgAEaMGCAHnzwQYWFhWnGjBm+bA0AAADAUVqys1CzXl+nyvpG9Y4L04vXjFX/xAir2wI6BZ+G7meeeUaSNHny5HbjL7/8smbOnClJuuuuu1RbW6tbbrlFpaWlGj9+vBYsWKDIyEhftgYAAADgexiGoVe+OqD7PtqqJo+hcX1i9exVoxUbHmx1a0Cn4dPQfTQbo9tsNs2ePVuzZ8/2ZSsAAAAAjkGdu0l//GCz/rPmoCTpohO666GLhssVxBblwLHo0Od0AwAAAPB/+RV1uvnVtcrMLpPdJt191hDdcHLatz5hCMC3I3QDAAAAaLH2QKl+/tpaFVbWKzrUqadmjNLJAxKsbgvotAjdAAAAACRJ/16dpf/3/hY1NHk0KClSz189Wr3jwq1uC+jUCN0AAABAgHM3eXTfh1v16tcHJElnDkvW3y4dqXAXcQH4sfhdBAAAAASwoqp63fL6Oq3aVyKbTfrV1IGaNaU/928DxwmhGwAAAAhQmw6W6+ZX1yinvE4RriA9flmGTh+aZHVbQJdC6AYAAAAC0PvrD+m372xUfaNHfePD9fzVY9Q/McLqtoAuh9ANAAAABJDGJo/+On+7Xli6T5L0k8GJevzyDEWFOC3uDOiaCN0dqM7dpBCnw+o2AAAAEKDKahp02xvrtXRXkSTp1in99X9TB8ph5/5twFfsVjcQKD7IPKTTH1usTQfLrW4FAAAAAWh7XoXOe2q5lu4qUqjToaevOEG/PmMQgRvwMUJ3B2jyGHpm0R4dLK3VT59doTdXZVndEgAAAALIp5tyddHTK5RVUqOesaF695YJmj48xeq2gIBA6O4ADrtN/775JJ0+JFENjR797t1N+s1bG1TnbrK6NQAAAHRhHo+hvy3YoV+8vk41DU2a2D9O/501SUNSoqxuDQgYhO4OEh3q1PNXjdFvzhgku016a+1B818bi2usbg0AAABdUFlNg254ZY2e/GK3JOmGSWmae+04dQsPtrgzILAQujuQ3W7TrCn99er14xUXHqytuRU658ml+t/WfKtbAwAAQBey8WCZzv7HMn2xvUCuILseu3Sk/nDOUAU5+Os/0NH4XWeBif3j9dEvJ+mEXjGqqGvUDa+s0SOfbVeTx7C6NQAAAHRihmHo1a8P6OJnvtKhslr1jgvTu7dM0EUn9LC6NSBgEbotkhIdqjdvOkkzJ/SRJP3zyz26+qWVKq6qt7YxAAAAdErV9Y36v39n6v+9v1kNTR6dMSxJH942ScNSo61uDQhohG4LBQfZNfu8YXri8gyFOh1avrtY5zy5TOuySq1uDQAAAJ3I7oJKXfDP5Xo/M0cOu033TB+iZ68cragQp9WtAQGP0O0Hzs/org9unai+CeHKLa/TZc99pbkr9sswWG4OAACA7/bfDTk676nl2lVQpcRIl9686UTdeEpf2Ww8fxvwB4RuPzEwKVL/vXWSpg9PlrvJ0J/+u0V3/DtTNQ2NVrcGAAAAP1Tf2KQ/frBZv3xjvWoamjShX5w+/uXJGtsn1urWALRB6PYjEa4g/XPGCfrD2UPksNv0QWaOLvjncu0prLK6NQAAAPiRg6U1uvS5r/XKVwckSbf9xHxCTkKky+LOAHwTodvP2Gw23XByX71x44lKjHRpZ36Vzn9quT7dlGt1awAAAPADX+4o0DlPLtOG7DJFhzr18syx+tW0QXLYWU4O+CNCt58alxarj345SePTYlVV36hfvL5OD3y8Ve4mj9WtAQAAwAJNHkN/W7BD1768WmU1bo3sEa2PfzlJUwYnWt0agO9A6PZjiZEhev2G8br5lL6SpBeW7tMVL6xUQUWdxZ0BAACgIxVV1euqF1fqyS92S5KuPqm3/vPzk9SjW5jFnQH4PoRuPxfksOvu6UP07JUnKMIVpFX7S3TWE0u1aEeB1a0BAACgA6zeX6Kz/7FUK/YUKyzYoScuz9B956fLFeSwujUAR4HQ3UmcmZ6i/946UYOTI1Vc3aCZL6/Wg59sU0Mjy80BAAC6IsMw9MKSvbr8+a+VX1Gv/okR+mDWRJ2f0d3q1gAcA0J3J9I3IULvz5qoq0/qLUl6fsleXfLsCmUV11jcGQAAAI6nspoG/fy1tXrgk21q8hg6d2SqPpg1UQOSIq1uDcAxInR3MiFOh+47P13PXjla0aFObThYrrP/sVQfbsixujUAAAAcByv3FuusJ5bqsy35cjps+vP5w/SPyzMU7gqyujUAPwC/czupM9OTNbxHtG5/Y73WHCjVbW+s1/LdRfrTucMUGsz9PQAAAJ1NY5NHT36xW09+sUseQ+oTF6Ynf3aChveItro1AD8CM92dWPeYUL1504m67Sf9ZbNJb67O1nlPLdP2vAqrWwMAAMAxOFRWq5+98LWe+NwM3D89oYc++uXJBG6gCyB0d3JBDrt+NW2QXr9+vBIiXdpVUKXzn1qu11cekGEYVrcHAACA7/Hpplyd9fgSrd5fqghXkB6/LEN/u3SkIlhODnQJhO4uYkL/eH16+8maPChB9Y0e3fPeZt3y+jqV17qtbg0AAABHUNvQpN+/t0m/eH2dKuoaNbJnjD7+5SRdMIrdyYGuhNDdhcRHuPTSNWN1z/QhCrLb9OnmPE1/YqnWHii1ujUAAAC0sT2vQuc9tUzzVmbJZpN+Mbmf3v75SeodF251awCOM0J3F2O323TjKX319i8mqFdsmA6V1erS577S04t2y+NhuTkAAICVDMPQK1/t13lPLdeugiolRLr06nXj9dszB8vp4K/mQFfE7+wuKqNnjD765SSdMyJFTR5DD8/foWteXqWCyjqrWwMAAAhIpdUNuunVtfrjB1vU0OjRlEEJmn/7yZo0IN7q1gD4EKG7C4sKcerJn43SX386XCFOu5buKtL0J5Zqyc5Cq1sDAAAIKF/tMZ+9vXBrvoIddv3xnKF6aeZYxUW4rG4NgI8Rurs4m82my8b20oe3TtKgpEgVVTXo6pdW6aFPtqm+scnq9gAAALq0xiaP/rZgh2b862vlVdSpb0K43ps1QddNSpPNZrO6PQAdgNAdIAYkReqDWyfqivG9JEnPLdmr859azjO9AQAAfCS7pEaXPf+1nvxitwxDumxMT3102yQNS+XZ20AgIXQHkBCnQw9cOFzPXjlaseHB2p5XqfOeXK7nl+xRE5usAQAAHDcfb8zV9H+YT5GJdAWZt/xdPEJhwTx7Gwg0hO4AdGZ6subfcbJOG5yohiaPHvxku2a88LUOltZY3RoAAECnVl7r1v/9O1Oz5q1TZV2jTugVo09uP1nnjky1ujUAFiF0B6jEyBD965oxeuii4QoLdmjlvhKd+fhSvbUmW4bBrDcAAMCxWrqrUGc+vkTvrT8ku0267Sf99Z+bT1LP2DCrWwNgIda3BDCbzaafjeulCf3idOd/NmjtgVL95u2N+t+2fD144XB20wQAADgKNQ2N+sun2/XKVwckSWnx4frbpSN1Qq9uFncGwB8w0w31jgvXf24+Sb85Y5CC7DZ9tiVfZzy+VJ9vy7e6NQAAAL+29kCppj+xtCVwX3NSb338y0kEbgAtmOmGJMlht2nWlP46dWCC/u/fmdpVUKXr567Rz8b10h/OHqJwF5cKAACAV0OjR4//b6eeXbxHHkNKiQ7RIxeP1KQB8Va3BsDPMNONdtK7R+vD2ybp+klpkqQ3VmU177xZYnFnAAAA/mFbboXO/+dyPb3IDNwXndBd8+84hcAN4IgI3ThMiNOh/3fOUM27YbxSo0N0oLhGlzz7lR75bLsaGj1WtwcAAGCJJo+hZxbt0XlPLdO23ArFhgfr2StH67FLMxQd6rS6PQB+itCNbzWhf7w+veMUXTiquzyG9M8v9+jCp5drV36l1a0BAAB0qP1F1br0ua/01/nb5W4yNHVokj674xSdmZ5sdWsA/ByhG98pOtSpv1+WoaevOEExYU5tyanQ2U8u04vL9snj4dFiAACgazMMQ69+fUBnPbFUaw+UKtIVpEcvGannrxqthEie9ALg+7E7Fo7K9OEpGtO7m37z9kYt3lmoP3+0Vf/bmq+HLx7BsycBAECXlFteq7ve3qilu4okSRP6xemRS0aqe0yoxZ0B6EyY6cZRS4wK0Zxrx+r+C9IV6nToq73Fmvb3JXp5ObPeAACg6zAMQ++vP6Qz/r5ES3cVyRVk15/OHarXrh9P4AZwzAjdOCY2m01Xnthbn9x+ssalxarW3aR7P9yqS577SrsLqqxuDwAA4EcprqrXrHnrdMe/M1VR16iRPaL18S9P1rUT02S326xuD0AnROjGD5IWH643bzxRf74gXeHBDq09UKrp/1iqf365W+4mdjgHAACdi2EYem/9QZ3+2GJ9silPQXabfjV1oN75xQT1T4ywuj0AnRj3dOMHs9ttuurE3vrJ4ET9/t1NWryzUI98tkOfbMrVwxeP0LDUaKtbBAAA+F4HS2v0h/c3a9GOQknS4ORIPXrJSKV35+8yAH48Zrrxo3WPCdWca8fq0UtGKjrU3OH8/KeW69HPdqi+scnq9gAAAI7I4zE0Z/k+Tfv7Ei3aUahgh12/OWOQPrxtEoEbwHHDTDeOC5vNpotH99ApA+P1x/e3aP6WPD315W59tiVPf714hE7o1c3qFgEAAFrsyq/Ub9/ZqHVZZZKksX266aGLRrCUHMBxR+jGcZUYGaJnrxqtTzbl6o8fbNaugir99JkVum5imn49bZBCgx1WtwgAAAJYQ6NHzy7eo6e+2K2GJo8iXEH67VmDdcW4XmyUBsAnCN3wienDU3RS3zjd99FWvbf+kF5ctk8Lt+brLz8drgn94q1uDwAABKDM7DL99u2N2pFfKUk6bXCi/nxBulJ5DBgAHyJ0w2e6hQfr75dl6LyRqfr9e5uUVVKjGS+s1IzxvXT3WYMVGeK0ukUAABAAahoa9bcFO/Xy8n3yGFJceLD+dN4wnTsiRTYbs9sAfIuN1OBzUwYnasH/naIZ43tJkuatzNK0vy/Rl9sLLO4MAAB0dct2FemMx5foxWVm4L5oVHctvPNUnTcylcANoEMw040OERni1IMXDtc5I1J097ubdKC4RtfOWa0LR3XXH84eorgIl9UtAgCALqSspkH3f7xNb689KMl82soDF6Zr8qBEizsDEGiY6UaHmtAvXvNvP0U3TEqT3Sa9t/6QTntssd5YlSWPx7C6PQAA0MkZhqGPN+bq9MeW6O21B2WzSTMn9NFn/3cKgRuAJZjpRocLDXboD+cM1dnNs97b8yp197ub9NaabN1/wXANTY2yukUAANAJ5ZXX6f99sFkLt+ZLkvonRuivPx2h0b15dCkA6zDTDcuM6tVNH902SX84e4jCgx1al1Wmc59apj9/tFVV9Y1WtwcAADoJd5NH/1q6V6f9bZEWbs2X02HTL08boI9/OYnADcByzHTDUkEOu244ua/OHpGiP3+0VZ9sytOLy/bpo405+uM5wzR9eDKbnAAAgG+1cm+x/vjBlpbHgI3qFaO/XDRCg5IjLe4MAEyEbviFlOhQPX3FaC3aUaA/frBFWSU1mjVvnU4ZmKD7zhumPvHhVrcIAAD8SEFlnR76ZLveW39IkhQbHqzfnTlYF4/uIbudf7AH4D9YXg6/MnmQ+XixX542QMEOu5bsLNS0x5foif/tUp27yer2AACAxRqbPJqzfJ9Oe3Sx3lt/SDabdMX4XvriV6fq0rE9CdwA/A4z3fA7IU6H7pw6UBdkpOqPH2zRst1F+vv/dur9zEO67/xhOnlAgtUtAgAAC6w9UKr/9/5mbc2tkCSN6BGtP5+frpE9Y6xtDAC+A6EbfqtvQoRevX6cPtqYqz9/tFX7iqp11YurdM6IFP2/c4YqNtRhdYsAAKADFFc36LH/bdV/1pjP3I4Odeo3ZwzSz8b1koOZbQB+jtANv2az2XTuyFSdOihBjy3YqVe+2q+PNuZq0Y5C3X5aP8XxaG8AALqsJo+hZXk2/fGJZSqvNZ9scumYHvrtmYMVF+GyuDsAODqEbnQKUSFOzT5vmC4e3UP3vL9ZG7LL9MAnO9Qj3KGeI8o0ti9LzgEA6Eo2ZJfpD+9v0qZDDkmNGpISpfsvGKbRvWOtbg0AjgmhG51KevdovfuLCXpzdZb++ul2Haxu1KUvrNKlo3vq12cMUkIk/+oNAEBnVlbToIc/26E3VmXJMKQQh6HfnDlE10xIU5CDPYABdD6EbnQ6DrtNV4zvrdMGxumXL32pVYV2/XtNtj7elKtZU/rrukl95Arifm8AADoTj8fQW2uz9ZdPt6u0xi1JumBkikYHZevyE3sRuAF0Wvz0QqcVF+HSFf09+veN4zSiR7Sq6hv11/nbNfWxJZq/OVeGwQ3fAAB0BuuySvXTZ1fot+9sUmmNWwOTIvTvm07UIxcPV1Sw1d0BwI/DTDc6vRN6xej9WybqvfWH9Nf525VVUqOfv7ZOJ/aN1f87Z6iGpUZb3SIAADiCg6U1+uv8HfpwQ44kKTzYof+bOlDXTOgjp8Mut9ttcYcA8OP5dKZ7yZIlOvfcc5Wamiqbzab333+/3dcNw9Ds2bOVmpqq0NBQTZ48WVu2bPFlS+ii7Habfjq6h7789WTd9pP+cgXZ9fXeEp3z5DL97p2NKqyst7pFAADQrLLOrYfnb9dP/rZYH27Ikc1m7kr+xa8n64aT+8rJUnIAXYhPf6JVV1dr5MiReuqpp4749YcffliPPfaYnnrqKa1evVrJycmaOnWqKisrfdkWurBwV5B+NW2QPv/VqTpnRIoMQ3pzdbamPLpIzy7eo/rGJqtbBAAgYDV5DM1bmaUpjy7S04v2qKHRo5P6xumj2ybp4YtHKikqxOoWAeC48+ny8rPOOktnnXXWEb9mGIYef/xx3XPPPbroooskSXPnzlVSUpLmzZunm2++2ZetoYvr0S1MT804QTMnlOi+j7Zq48Fy/eXT7Zq3Mku/nz5YZwxLls1ms7pNAAACxtJdhXrg423anmdOrvSND9fvpw/RaUMS+TMZQJdm2T3d+/btU15enqZNm9Yy5nK5dOqpp2rFihXfGrrr6+tVX9+6VLiiokKS5Ha7ue8nwHjf7+9630d2j9RbN47T+xty9LeFu1vu9x6f1k2/P2uQhqZEdVS7CEBHc40CVuIaRUfYXVClv3y2U4t3FkmSokODdNuUfvrZ2J4KDrKrsbHxW1/LNQp/xzUauI7lPbcZHbTFs81m03vvvacLLrhAkrRixQpNnDhRhw4dUmpqastxN910kw4cOKDPPvvsiN9n9uzZuvfeew8bnzdvnsLCwnzSO7qG+ibpf4fs+iLHpkbDJpsMnZhoaHpPDzujAgBwnFW5pfnZdi3Pt8kjm+w2QycnGzqju0fhTqu7A4Afp6amRjNmzFB5ebmior57Is/y3cu/uZzIMIzvXGJ09913684772z5vKKiQj179tS0adO+92TRtbjdbi1cuFBTp06V03l0f3pfKOlQWa0e+WyXPt6cp68KbNpY7tQtp/bVNSf1liuIjVtw/PyQaxToSFyj8IX6Ro9e/TpLTy/eq8o6cxb79MEJuuuMgUqLDz+m78U1Cn/HNRq4vCuuj4ZloTs5OVmSlJeXp5SUlJbxgoICJSUlfevrXC6XXC7XYeNOp5MLPUAd63vfJ8Gpf145WjP3l+i+D7dq06FyPbJgl95YfVB3Th2o8zO6y2Hn3jIcP/x8gr/jGsXxYBiG5m/O00Ofmo/vlKShKVH6wzlDNKFf/I/63lyj8Hdco4HnWN5vy6b10tLSlJycrIULF7aMNTQ0aPHixZowYYJVbSGAjO0Tqw9mTdSjl4xUYqRLB0trded/NujsfyzV59vy1UF3XgAA0OltPFimy577Wr94fZ2ySmqUGOnSwxeP0Ie3TfrRgRsAOjufznRXVVVp9+7dLZ/v27dPmZmZio2NVa9evXTHHXfowQcf1IABAzRgwAA9+OCDCgsL04wZM3zZFtDCbrfp4tE9NH14sl5evl/PLt6j7XmVun7uGo3p3U13nTlY49JirW4TAAC/tLugSn9fuFMfb8qVJIU47brplH66+ZS+CndZfhcjAPgFn/40XLNmjaZMmdLyufde7GuuuUZz5szRXXfdpdraWt1yyy0qLS3V+PHjtWDBAkVGRvqyLeAwYcFBmjWlv64Y30vPLN6jOcv3a82BUl363FeaMihBvzljsIamsmcAAACSdLC0Rk/8b5feWXdQHkOy2aQLM7rr12cMUmpMqNXtAYBf8Wnonjx58ncu0bXZbJo9e7Zmz57tyzaAoxYTFqy7zxqiayek6R9f7NK/V2fryx2FWrSzUOeNTNWvpg5Srzh2yQcABKaCyjo9/eUevb7ygNxN5t/xpg5N0q+mDdTgZP5xGgCOhHU/wBEkR4fowQuH64ZJaXps4U59tDFXH2Tm6OONuZoxvpdu/Ul/JUaGWN0mAAAdorzGreeW7NHLy/er1t0kSZrYP06/njZIo3p1s7g7APBvhG7gO/RNiNBTM07Qz08t18Of7dCSnYV65asDemvNQV0/KU03ndpXUSHsVAkA6Jqq6xs1Z4W554n38V8ZPWP0mzMGaWJ/NkgDgKNB6AaOQnr3aL1y3Tit2FOkh+fvUGZ2mZ76crdeW3lAt0zup6tP6qMQp8PqNgEAOC7qG5s0b2WW/vnlbhVVNUiSBiVF6tdnDNLpQxJls/FoTQA4WoRu4BhM6Bev926J04Kt+Xrksx3aXVClBz/ZrpeW7dcdpw/QxaN7KMhh2ZP4AAD4URqbPHpn3UE98b9dyimvkyT1jgvTnVMH6pwRqXLYCdsAcKwI3cAxstlsOmNYsk4fkqR31x3U3xfuVE55nX737iY9v2Svbv1Jf503MpXwDQDoNDweQx9vytXfF+7U3qJqSVJyVIh+edoAXTKmh5z8mQYAPxihG/iBHHabLhnTU+eOTNXrzUvw9hZV687/bNATn+/SrMn9deEJ3fmLCgDAbxmGoS93FOiRz3ZqW26FJKlbmFOzpvTXlSf25tYpADgOCN3AjxTidOj6SWm6bGxPvfrVAb2wdK8OFNfornc26onPd+mWKf108egecgXxFxcAgH/weAz9b1u+nvpytzYeLJckRbiCdOPJfXXdpD6KZJNQADhuCN3AcRLhCtIvJvfTNRN66/Wvs/Tckr06VFare97brKe+2K2fn9pPl43tyawBAMAyTc3LyP/5xW7tyK+UJIU47brmpD76+an91C082OIOAaDrIXQDx1lYcJBuPKWvrjqpt95YlaVnF+9Rbnmd/vTfLfrnl7t186n9NGNcL4UGE74BAB3D3eTRe+sP6ZlFe7Sv+Z7tCFeQrj6pt66blKb4CJfFHQJA10Xo7iiL/iLtXSSlZEipGebH+AGSneDVVYU4Hbp2Ypp+Nq6X3lp7UM98uVs55XX680db9cyi3brx5L668sTeCnfx2xAA4Bt17ia9tSZbzy42V19JUkyYU9dOSNPMCX0UHcYycgDwNf6231H2L5OyvjLLyxkuJQ9vDeEpI6X4gZKDt6UrCXE6dNWJvXXZmJ56d91B/XPRbmWX1OqhT7fr2cV7dMPJfXX1Sb25fw4AcNxU1zdq3sosPb90rwor6yVJ8REu3XRKmmaM760I/sEXADoMP3E7ytmPSYfWSrmZUk6mlLdRcldL2V+b5RUU2j6Ip2ZI8YMI4l1AcJBdl4/rpZ+O7qEPMnP0zy93a19RtR75bIeeW7xH10/qq5kT+yg6lPANAPhhymvdemXFfr20fJ9Ka9ySpNToEP18cj9dOoZ9RQDACiS5jpIw0KyMn5mfe5qk4t1mAG8bxBuqpIOrzPIKCpGS0tsH8YTBkoNw1hk5HXZdPLqHLshI1Ucbc/XkF7u0p7Baf//fTv1r6V5dM6GPZk7sw/11AICjVlxVr5eW79MrKw6osr5RktQnLky/mNxPF47qoeAgHl8JAFYhdFvF7pASBpk18jJzzOMxg3juhtYgnrtBaqiUDq0xy8vhkpLT29wjPlJKGCIFsetoZxHksOuCUd117shUfbo5V09+bu4k+9SXu/X80r366Qk9dP2kNPVPjLC6VQCAn8qvqNPzS/Zq3sos1bqbJEkDkyI0a0p/nT08RUEOwjYAWI3Q7U/s9tYZ8RGXmGMej1Sy1wzhLUF8o1Rfbi5XP7S29fWOYClpWPvN2hKHEsT9nMNu0zkjUjU9PUULtubrmUW7teFgud5YlaU3VmXptMGJuuHkvjqxb6xsNpvV7QIA/MCu/Eq9tHyf3ll7SA1NHknS8O7RmjWlv6YNTZLdzp8XAOAvCN3+zm6X4vubNfxic8zjkUr3tQnhmeaMeF25lLPeLG8WtzulpKHtg3jSMCmIpcv+xm636cz0ZJ0xLEmr95fqhaV79b9t+fp8e4E+316g9O5RuvHkvpo+PEVOZi4AIOAYhqGlu4r0r2X7tGRnYcv42D7dNGtKf506MIF/nAUAP0To7ozsdimun1npPzXHDEMq3d8+iOdkSnVlzcvVN0jr5ja/3iklDjGXpKdmSCmjzCDuDLHgZPBNNptN49JiNS4tVnsLq/TS8n16e+1BbT5UodvfzNRfP92uayem6fJxPdnxHAACQJ27Se+vP6SXlu/TzvwqSZLNJk0bmqTrJ/XVuLRYizsEAHwXQndXYbNJsWlmDbvQHDMMqexA673h3iBeW2Ju2pa3UVr/qnmsPci8Jzx1ZPOsuDeIh1pzPpAk9U2I0P0XDNedUwfpta8P6JWv9iunvE4PfLJNT3y+Sz8b11MzJ6apewzvEwB0NQWVdXrtqwN6bWWWSqobJEnhwQ5dOranrp2Qpl5xYRZ3CAA4GoTursxmk7r1MWvYBeaYYUjl2e1nw3MzpZpiKX+TWetfa369w9wlve2u6UnpUjB/yHe02PBg/fK0AbrplL76IPOQXli6T7sLqvTC0n16afl+nT08RTee3FfDe0Rb3SoA4EfalluhF5ft038zc1ru1+4eE6prJ/bRpWN7KopVTgDQqRC6A43NJsX0MmvoeeaYYUgVhw4P4tWFUsEWszJfb3693QziKRmty9OTh0vB4VacTcAJcTp02dheumR0Ty3eWagXlu7Vij3F+u+GHP13Q47Gp8XqplP6asqgRDbRAYBOxOMx9OWOAr24bJ9W7CluGT+hV4yun9RXZwxLYidyAOikCN0wg3h0D7OGnGOOGYZUkXP4PeLVBVLBVrM2zGt+vV2KH9j+8WXJIyQXj7ryFbvdpimDEzVlcKI2HyrXi8v26cMNOVq5r0Qr95Wob0K4Zk7oowtHdee+bwDwYzUNjXpn7UG9vHy/9hZVSzKfanFWerKun5SmUb26WdwhAODHInTjyGw2Kbq7WYPPbh2vyD08iFflSYXbzdr4pvcbSPED2u+anjJCckV27HkEgPTu0fr7ZRm668xBmrN8v+atzNLewmr98YMt+sun23V+RnddeWIvDUtl6TkA+Ivc8lq98tUBzVuZpfJatyQpMiRIPxvXS9dM6MNeHQDQhRC6cWyiUswadFbrWGVe+0eX5WRKlTlS0U6zNv2n+UCbueP6N4N4CGHweEiJDtXd04fottMG6K012Xp9ZZZ2F1S1PO97VK8YXTm+t84ekaIQp8PqdgEg4DR5DC3ZVah5K7P0xfYCNXkMSVLvuDBdO6GPLhnTU+Eu/moGAF0NP9nx40UmS4PONMurquDwe8QrDknFu83a/HbrsbH9Wpele+8VD43pwBPoWiJcQbp2YppmTuijlftK9NrXB/TZljytzyrT+qwy/fnjrbr4hB664sTeSovnXnwA8LX8ijr9Z3W23lydrUNltS3j49Nidf2kNJ02JEkO9uEAgC6L0A3fiEiUBk4zy6uqsPnRZetbH2NWni2V7DFr8zutx3ZLa79respIKZT72o6FzWbTiX3jdGLfOBVW1us/a7I1b2WWDpXV6l/L9ulfy/ZpUv94XXliL50+hA16AOB48ngMLd1dpHkrD+h/21pntaNDnfrpCT00Y3xP9U/klisACASEbnSciARpwOlmeVUXtV+WnpsplWVJpfvM2vJe67Hd+rTfNT0hvSO779QSIl2aNaW/fn5qPy3aUaDXvj6gRTsLtWx3kZbtLlJSlEuXj+2ln43rpeToEKvbBYBOq6CyTm+tOag3VmXpYGnrrPbYPt30s3G9NH04t/gAQKAhdMNa4fFS/9PN8qopab8sPXeDVLq/tba+L0lySjo9OF6O2rek7qOaZ8VHSWGxHXsOnYjDbtNpQ5J02pAkZZfU6I1VWfrPmmzlV9Tric936akvd+v0IYm68sTemtgvnseOAcBR8HgMLd9TpHkrs7Rwa74am2e1o0KCdNEJPTRjfC8NTGJWGwACFaEb/icsVur3E7O8akvbz4bnZEql+xTeUCRt/9Asr+ierbPhKaPMj+HxHXoKnUHP2DDddeZg3XH6QM3fkqfXvj6gVftK9NmWfH22JV994sJ0+bheunBUdyVFMfsNAN9UWFmvt9Zm681V2coqqWkZH93bnNU+e3iKQoOZ1QaAQEfoRucQ2k3qO9msZu7KIq364AWd2DtEjvxNZhAv2WPeJ16eLW3/qPX1UT0Ov0c8IrFDT8FfBQfZdd7IVJ03MlU78yv1+tcH9O66Q9pfXKO/fLpdD8/fron943Xx6B6aNjSZv0ACCGgej6EVe4r1xqosLdiaJ3eTOasdGRKki0Z118/G99Lg5CiLuwQA+BNCNzqvkGgVRQ6V58Tpcjid5lhduZS7sXnDtkwziBfvlioOmtU2iEemfiOIZ0iRSR19Fn5lYFKk7j0/Xb89a7A+3JCjt9ce1Or9pVq6q0hLdxUpwhWk6cOTddEJPTSuTyzLzwEEjO15FXpv/SH9NzNHueV1LeOjesXoZ+N66dwRqfyjJADgiAjd6FpCoqW0k83yqq9sDuKZrcvTi3aZzxLfkSPt+KT12MiU9pu1pWSYzyUPMGHBQbpsbC9dNraXDhRX6911h/Tu+oPKLqnVf9Yc1H/WHFSPbqG6aFR3XXRCD/Xh0WMAuqC88jr9d8Mhvbc+R9tyK1rGo0KCdH5Gd/1sXC8NTWVWGwDw3Qjd6PpckVKfiWZ51VdJeZu+EcR3SpW5Zu38tPXYiKT2s+EpI6WoVMkWGLO8vePC9X9TB+r20wZozYFSvbvuoD7emKuDpbX6xxe79Y8vdmt072766Qk9dPaIFEWHOq1uGQB+sKr6Rs3fnKf31x/S8j1FMszV43I6bPrJ4ERdOKq7pgxOlCuIWW0AwNEhdCMwuSKk3ieZ5dVQbQbxtrumF26XqvKlXZ+Z5RWe0D6Ip2ZIUd27dBC3220alxarcWmxmn3eMC3Ymq931h7U0l2FWnugVGsPlGr2h1s0dWiSfnpCd50yIIFnfwPoFNxNHi3bVaT31h/Sgq15qnN7Wr42tk83XTCqu84enqKYsGALuwQAdFaEbsArOFzqdaJZXg01Uv7m9rumF26Xqgul3QvN8gqLb78sPTXD3Em9CwbxEKejZfO1goo6vZ95SO+sPaQd+ZX6eGOuPt6Yq/iIYJ2f0V0XjuquYalRsnXB/x8AdF6GYWjjwXK9t/6QPtyQo+Lqhpav9Y0P14WjuuuCUd3VMzbMwi4BAF0BoRv4LsFhUs9xZnm5a6X8LVLO+uYgvkEq3CbVFEl7PjfLKzT28F3TY3p3qSCeGBWim07ppxtP7qutuRV6Z+0hfZB5SEVVDXpx2T69uGyf+sSF6azhKZqenqL07gRwANbJLqnR++sP6b3MQ9pbWN0yHhcerHNHpuqiE7prePdofk4BAI4bQjdwrJyhUo8xZnm568wgnru+eVZ8g1SwVaotkfZ8YZZXaDczfLddnt6tT6cP4jabTcNSozUsNVp3Tx+sJTsL9c66g/p8W4H2F9fomUV79MyiPerRLVTTh6forPRkZfSM4S+2AHxub2GV5m/J02eb87ThYHnLeIjTrmlDk3XhqO6aNCBeTm6JAQD4AKEbOB6cIVKP0WZ5NdY3B/HM1uXp+Vul2lJp7yKzvEJimoN4m+XpsX07bRB3Ouw6bUiSThuSpOr6Rn25o0CfbsrTF9sLdLC0Vs8v2avnl+xVanSIzkxP0fThyTqhVzceQQbguDAMQ1tzK/TZ5jzN35KnnflVLV+z2aSJ/eJ1wajuOjM9WREu/ioEAPAt/qQBfCXIJXU/wSyvxnpzBjx3Q5sgvkWqK5P2LTbLyxUtpYxoszx9lNQtTbJ3rpmYcFeQzhmRqnNGpKq2oUmLdxbok015+nxbvnLK6/TS8n16afk+JUW5dOawZJ01PEVj+8TKQQAHcAw8HkPrs0s1vzloZ5fUtnwtyG7ThP7xOnNYsqYOTVJCpMvCTgEAgYbQDXSkIJcZnlNHSd5J8cYG857wtpu15W+R6sul/UvN8nJFSckj2t8nHtuv0wTx0GCHzkxP0ZnpKapzN2npriJ9uilXC7fmK7+iXnO/OqC5Xx1QfIRLZ6YnaXp6isalxbILOoAjcjd5tGpfieZvztNnW/JUUFnf8rUQp12nDkzQmenJ+smgJEWH8ThDAIA1CN2A1YKCW5eW6xpzrMlt7pLeLohvluorpAPLzPIKjjRnxNveJx7XX7L79zNkQ5wOTR2apKlDk1Tf2KQVu4v18aZcLdiSp6Kqer32dZZe+zpLseHBmjbUXKo+oV+cwlkKCgS0OneTlu0q0vwtefrftnyV1bhbvhbpCtJpQxJ1ZnqyThmYoLBgfl4AAKzHn0aAP3I4peThZukqc6ypUSra0T6I522SGiqlA8vN8nKGNwfxjNZZ8fgBfhvEXUEOTRmcqCmDE9Vw4XB9tbdYn27K1Wdb8lRS3aA3V2frzdXZCnbYNS4tVpMHJWjyoET1SwhnIzYgABRV1WvprkL9b1uBFm0vUHVDU8vXvP8wd0Z6sib0i5MryD9/zgEAAhehG+gsHEFS0jCzRl1hjjU1SkU7zRDuvU88b6PkrpayvjLLyxlmhvh2QXyg+X39SHCQuST01IEJuv+CdK3cV6LPtuTpyx0Fyi6p1bLdRVq2u0j3f7xNPWNDNWVQoqYMStSJfeMUGsxftoGuwN3k0fqsMi3eWaDFOwu1+VBFu6+nRIfojGHJOjM9mT0gAAB+z7/+tg3g2DiCpKShZmXMMMc8TVLRrva7puc2B/HslWZ5BYWaQdz7DPGUDClhsN8E8SCHXRP7x2ti/3gZhqG9RdX6crv5l/CVe0uUXVKrV746oFe+OiBXkF0n9o3TlEEJmjI4Ub3jwq1uH8AxOFhao8U7C7VkZ6FW7C5WZX1ju68PTYnS5EEJOmNYskb04DnaAIDOwz/+Zg3g+LE7pMTBZo283BzzNEnFu9vvmp67QWqokg6uMssrKERKSm+dDU8ZKSUOMZe8W8hms6lfQoT6JUTohpP7qrq+USv2FGvRjgIt2lGoQ2W1WryzUIt3Fmr2h1vVNz5cpw5K0JRBiRqXFqsQJ7PggD+pczfp673FLUF7T2F1u6/Hhgfr5AHxOmVAgk4eGK/EyBCLOgUA4MchdAOBwO6QEgaZNeJSc8zjkUr2tL9HPHeDeY/4oTVmeTlc5rL2trumJwwxN4GzSLgrqGUjNsMwtKugSl9uNwP46v0l2ltUrb1F1Xp5+X6FOh2a0C9OE/vH68S+cRqcHMkzwYEOZhiGdhdUtfzj2Kp9Japv9LR83WG3aVTPGPP2kkEJSk+N5vcpAKBLIHQDgcpuNzdXix8gjbjEHPN4pJK9zSF8vRnCczeajy/LWWeWlyNYShzaPognDjUfi9bBbDabBiZFamBSpG4+tZ8q69xavrtIi3YU6ssdBcqvqNfn2wv0+fYCSVJ0qFNj+8TqxL6xOrFvnIakRHFPKHCcGYah7JJardpfolX7irVsV5FyyuvaHZMaHaJTByXolAEJmtA/XtGhPNYLAND1ELoBtLLbpfj+Zg2/2BzzeKTSfd+4R3yDVFfe/N+ZbV7vNO8vb/v4ssRhkrNjl4VGhjhbngduGIa25VZq8c5Cfb23WGv2l6i81q3/bcvX/7blNx8fpHF9YjW+b6zGp8VpWGoUzwYHjpHHY2hnQaVW7SvRqn0lWr2/RPkV9e2OCQ6ya3xarE4dmKDJgxLULyGCe7MBAF0eoRvAd7Pbpbh+ZqX/1BwzjOYgvqH98vS6subZ8Q2SXml+fZB5T3jLrumjzKXqHRTEbTabhqZGaWhqlH4xuZ8amzzanFOhlXuLtXJfiVbvK1FlXWO7mfAIV5DG9Omm8WlxGt83VsO7R8tJCAfaaWj0aNOhcq3eb/4+WnOgVOW17nbHOB02jegR07KyZHwaTxkAAAQeQjeAY2ezSbF9zRp2oTlmGFLZgW/cI54p1ZaazxPP2yStf7X59Y5vBPEMKTldcob6vPUgh10ZPWOU0TNGN5/aT00eQ1tzKrRyX7G+3lusVftKVFHXqEU7CrVoR6EkKSzYodG9u+nEvnEa07ub0rtHK9zFj08ElpqGRq07UKZVzSF7fXap6tyedsd4f6+M7ROrsX1iNapXDJsYAgACHn9rBHB82GxStz5mDbvAHDMMqTz78CBeUyzlbzYr87Xm1zvMx5W1vUc8KV0KDvNp2w67TcN7RGt4j2jdcHJfNXkMbc+r0Nd7S7Ryb7FW7S9RWY1bS3cVaemuIkmS3Sb1T4zQiB4xGtkjWiN6xGhwSqRcQYQLdA0ej6H9xdXadKhcmw6Wa/WBUm05VK5Gj9HuuG5h5v4I49LMkM2tGQAAHI7QDcB3bDYpppdZQ88zxwxDKj/YvAw9szWIVxdKBVvMyny9+fV2KX5Q+yCePFwK9t0zuB12m4alRmtYarSun5Qmj8fQjvzKluXomdllyi2v0878Ku3Mr9Lbaw9KMpfRDkmJ0oge0RrRPUYjekarTzcecQT/5zGkPYXV2p7fHLIPlWtrToWqvvGcbEnqHhOqsX26aWxarMb1iVX/RO7JBgDg+xC6AXQsm02K6WnWkHPMMcOQKnLaP7osN1OqypcKt5m14Y3m19ul+IGtzxBPzZCSR0iuCJ+0a7ebYXpISpRmTkyTJBVU1GnjwXJtPFimDc0fS2vczWPlkrIkSaFOu1JCHMq07VBGr24a2SNGvePCCCmwTJPH0L6iquYZ7AptPFiqTdkO1X+9/LBjXUF2DU2NUnpqtE7obd6X3aObb1eeAADQFRG6AVjPZpOiu5s1+OzW8Yrc9rPhOZlSVZ5UuN2sjW96v4H56LN294gPl0KifNJuYlSITh8aotOHJkkyH410sLRWGw6WaePBcm3ILtPmQ+WqbmjSXrdNe1cckFYckGQ+riy9e5QGJJqPOBuQFKGBiZGKDuNRSTi+3E0e7Suq1qaD5uz1lpxybcmpUE1D0zeOtCnEadfQlCgN7x6t9O7m7Rb9EyJYKg4AwHFA6Abgv6JSzBp0VutYZd7hu6ZX5khFO83a9J/mA23mjuttg3jKCCkk+ri3abPZ1DM2TD1jw3TOiFRJ5ozijtwyvf7JUtni+2hTTqW25VSovNat5buLtXx3cbvvkRDp0sCkCA1IbA7iSZGEcRyVyjq39hZWa3dBlfYUVrV8PFBcc9g92JIU6nRoWGqU0rtHa0hyuEr2bNDMC89UaIjLgu4BAOj6CN0AOpfIZLMGntE6VlVw+GZtFYek4t1mbX679djYvt8I4iOl0Jjj3qbDbtOAxAiNSzQ0ffoQOZ1ONTR6tCOvUttyK7Qzv1I7C6q0O79SOeV1KqysV2Fl/feGcXOGPEIxYcHHvWf4L8MwVFBZ3xKo9xRUaXdhlfYUVCuvou5bXxce7DCXiHeP1vDm6psQIYfdvMXB7Xbrk9wNzGgDAOBDhG4AnV9EojRwmlleVYXN94avb71PvDxbKtlr1pZ3W4/tltY+hKdmSKHdjnubwUH2lp3S26qsc2t3QZV25VdpV0GlduZXadf3hPH4CJd6xYaqR7cw9ehmfuzZ/HlqTAg7qXdCHo+hoqp6HSqrVU5ZnQ6UeGevq7W3oEqVR9jYzCsh0qX+CRHqlxje/DFC/RMjlBwVwh4CAABYjNANoGuKSJAGnG6WV3WxOQvedka8LEsq3WfWlvdaj43p3X7X9JQMKSzWJ61Ghjg1qlc3jerVPui3hPECM4Tvag7mh8pqVVRVr6Kqeq3LKjvi90yKcplBvFv7YN6jW6hSY0IVHMTMZkeraWhUTlmdcspqm4N168ecsjrlltfK3XT4cnAvu03qHReuft8I1/0SIhQdym0IAAD4K0I3gMARHif1P80sr5qS5iDe5j7x0v1S2QGztn7QemxML3MmvCWIjzK/p498Wxivqm/U3sIqHSyt1cHSmuaP5n9nl9Sq1t2k/Ip65VfUa+2B0sO+r80mJUeFqHtMqJKiQhQbHqy4iGDFRbgUH25+jA0PVnxEsKJCnLLbmSn9NoZhqNbdpNIat0qrG1RS3aDSmgblV9Qpp6yuTaiuVWmN+3u/n9373nQLVfeYUPVLMGes+yVGqHdcGCsYAADohAjdAAJbWKzU7ydmedWWtg/huRvMJellWWZt+7D12OierUvSU0aZ/x2R4NOWI1xBGtEjRiN6xBz2NcMwVFLd0C6It/2YXVqjOrdHueV1yi3/9nuBvYLstuZQ7lKcN5yHu5o/muPRoU6FBTuaK0hhLofCnI5Od5+wx2MG6PJad0t4LqluUFlN+89LaxpUWu1u+by+0XPUv0aEK0jdY0LVvVuoUmNClBpjhuvU5kqKdHW6/98AAMB3I3QDwDeFdpP6TjbLq7ZMytvYfsO2kj3mfeLl2dL2j1qPjeoupWTInjRcieVuqWqM1K17h7Rus9nMgBzh0sieMYd93TAMFbeE8hoVVdarpLpBRdUNKq6qV3FVg4qb/7uirlGNHnMDr4LK+mPuJdhhbwngYa6g9sH8G//tCnLIZpNsMmfivfchm2O29l9r/tx7vt5xSapze1TnblJdY5PqGprMzxubVNvQpLpG82v17ibVuptajq11N6ne7VFD09GH5yOda7dwp7qFBatbWLASIl3NwTpU3ZvDdWpMqKJCWAYOAECgIXQDwNEIjZHSTjHLq65cyt3YvGFbphnEi3ebO6dXHJJjx8c6SZKe+JsUmdL+/vDUDHMX9g5ms9kUH+FSfIRLGUcI5W3VNzaptNqtoqp6FVc3qKTaDOVFVc0BvTmcV9Y1qqahSdUN5sem5sdUNTR51FDjUZm+f1m1P/lmgI4ND1ZMmFOx4Uf+vFt4sMKDHWxYBgAAjojQDQA/VEi0lHayWV71lc1BPFOeQ+tUvWu5IurzZKvMlSpzpZ2fth4bkXz4Zm1RKR17Dt/BFeRQcrRDydEhR/0awzDU0ORRbUOTqhuaVNscxKvrm1TrbjQ/tgnoNc0f6xs9MgxJMmQYkmFIHsOQIfO/DRlq/p+MduOtn0tSSJBDIU67Qp0OhTjN/w5p+W9H83jbsbbHtn5OgAYAAMcLoRsAjidXpNRnotRnoprcbn3xySeaftrJchZvb3+feNFOqSpP2jnfLK+IpG9s1pYhRaW2rp/2czabTa4gc7l4TJjV3QAAAFiP0A0AvuaKlHpPMMuroVrK29T+HvGiHVJVvrRrgVle4QmHL02P6t5pgjgAAEAgI3QDgBWCw6VeJ5rl1VAt5W9pH8QLt0vVhdLuhWZ5hcW32TU9w/wY3ZMgDgAA4GcI3QDgL4LDpZ7jzPJy10p5m1tDeO4GqWCrVFMk7fncLK/Q2OYQ3mZ5ekxvgjgAAICFCN0A4M+coVLPsWZ5uWul/K1S7vrWWfGCbVJtibTnC7O8Qrsdfo94tz4EcQAAgA5C6AaAzsYZKvUYbZaXu86cAW+ZEc80g3ltqbR3kVleIdGHB/HYvgRxAAAAHyB0A0BX4AyRup9glldjvRnEvcvSczPNe8bryqV9S8zyckVLKSOa7xMf1RrE7fYOPhEAAICuhdANAF1VkMsM0KmjWscaG6TCba2z4bkbzHvG68ul/UvN8nJFSckj2m/YFtefIA4AAHAMCN0AEEiCgpuXlo+UdI051uQ2d0lvu2t6/mapvkI6sMwsr+AIM4i33bAtfoBkd3T0mQAAAHQKhG4ACHQOp5Q83CxdZY41NZpB3DsbnpNpPle8oUrKWmGWlzPcfG3bx5fFDySIAwAAiNANADgSR5CUnG7WqCvNsaZGqWhn+83a8jZJ7mop+2uzvJxhZhBvu2Fb/CDz+wIAAAQQv/jbz9NPP61HHnlEubm5GjZsmB5//HGdfPLJVrcFAGjLESQlDTUrY4Y55mmSina1D+K5G5uD+EqzvIJCzRDfdtf0hMEEcQAA0KVZ/jedf//737rjjjv09NNPa+LEiXruued01llnaevWrerVq5fV7QEAvovdISUONmvk5eaYp0kq3t3+HvG8jebS9IOrzfIKCpGS0tvfI544xFzyDgAA0AVYHrofe+wxXX/99brhhhskSY8//rg+++wzPfPMM3rooYcs7g4AcMzsDilhkFkjLzPHPB6pZE/7IJ67QWqolA6tMcvL4ZKShrW/RzxhiLkJHAAAQCdjaehuaGjQ2rVr9bvf/a7d+LRp07RixYojvqa+vl719fUtn1dUVEiS3G633G6375qF3/G+37zv8Fdco98Q3cesIReYnxseqXSfbLkbZMvb0Pxxo2z1FVLOOrOaGY5gGYlDZSSPkJE8UkbKyOYg7rLiTLoMrlH4O65R+Duu0cB1LO+5paG7qKhITU1NSkpKajeelJSkvLy8I77moYce0r333nvY+IIFCxQWFuaTPuHfFi5caHULwHfiGv0+IZLGS7HjpW4ehTcUKrpmn2Jq9iumZr+ia/cruKlGttxMc5a8mcfmUEVIT5WF9VF5WB+VhfZRRWhPeewsTT9WXKPwd1yj8Hdco4GnpqbmqI+1fHm5JNlstnafG4Zx2JjX3XffrTvvvLPl84qKCvXs2VPTpk1TVFSUT/uEf3G73Vq4cKGmTp0qp5O/ZMP/cI0eJ4Yhd9mBNrPh5kd7XZliavcrpna/VNx8qD1IShhizoinjJSRnCEjaah57zgOwzUKf8c1Cn/HNRq4vCuuj4aloTs+Pl4Oh+OwWe2CgoLDZr+9XC6XXK7DlxM6nU4u9ADFew9/xzV6HCQOMGvExebnhiGVZbXfNT0nU7baEil/k2z5m6QNr5vH2hzm5mxtd01PTpecoVaciV/iGoW/4xqFv+MaDTzH8n5bGrqDg4M1evRoLVy4UBdeeGHL+MKFC3X++edb2BkAwK/ZbFK33mYNbf7zwjCk8uzWTdq8gbymSMrfbFbma82vd5iPK/OG8JSR5nPFg7lNCQAAHF+WLy+/8847ddVVV2nMmDE66aST9PzzzysrK0s///nPrW4NANCZ2GxSTC+zhp5njhmGVHHoG7umZ0rVhVLBFrMyvTPidil+UPtd05OHS8HhFpwMAADoKiwP3ZdddpmKi4t13333KTc3V+np6frkk0/Uu3dvq1sDAHR2NpsU3cOsIeeYY4YhVeS0nw3PzZSq8qXCbWZteMP7DaT4gYcHcVekBScDAAA6I8tDtyTdcsstuuWWW6xuAwAQCGw2Kbq7WYOnt45X5B52j7iq8qSiHWZt/Lf3G0hx/b8RxEdIIWzmCQAADucXoRsAAMtFpZg16KzWsco8c0a8bRCvzJGKd5m16a3WY+P6m/eGt2zYNlIKie7QUwAAAP6H0A0AwLeJTDZr4BmtY1UFhwfxioNS8W6zNr/Temxs3/a7pqeMlEJjOvAEAACA1QjdAAAci4hEacBUs7yqi76xNH2DVJ4llew1a8u7rcd263N4EA+L7cATAAAAHYnQDQDAjxUeL/U/3Syv6mIzgLcN42VZUul+s7a+33psTO/294inZBDEAQDoIgjdAAD4Qnic1P80s7xqSg7fNb10v1R2wKytH7QeG9OrdSY8NUNKGWV+TwAA0KkQugEA6ChhsVK/KWZ51ZYefo946T5zVrwsS9r239Zjo3u2D+EpI6WIhI49BwAAcEwI3QAAWCm0m9R3slletWVS3sb2Qbxkj1Sebdb2j1qPjerefll6aoZ53zkAAPALhG4AAPxNaIyUdopZXnXlUu7G5vvEm2fGi3dLFYfM2vFx67GRKYcH8cjkDjwBAADgRejuAIZhqLax1uo2upzGxkY1GA2qbayVW26r2wEOwzWK48rhlHqMNsuroUrK22zOiuduND8W75Gq8qRd883yCk+SUkZIySOaPw5XY0gc1yj8Gj9H4e+4Rn0nNChUNpvN6jaOC5thGIbVTfwYFRUVio6OVnl5uaKioqxu54hq3DUaP2+81W0AAAAAQKewcsZKhTnDrG7jWx1LDrV3UE8AAAAAAAQclpd3gNCgUK2csdLqNrqcxsZGffbZZzrjjDMUFMSlDP/DNQq/5q5VU85GbVv0loZ2a5S9YLNUuEMymg4/NjS2zbL05o9R3aUusuwP/oufo/B3XKO+ExoUanULxw1XRgew2Wx+vTSis3LLrWBbsEKDQuV0Oq1uBzgM1yj8mjNM7t6TlJtQoVHTp5vXqLtWyt8i5axvfZ54wTapplja+6VZXqGxbR5flmF+jOlNEMdxxc9R+DuuURwNQjcAADA5Q6UeY8zyctdJBVvaP76sYJtUW3KEIN7NDOIpGa2BvFsaQRwAENAI3QAA4Ns5Q6Tuo83yaqyXCra2D+L5W6TaUmnvIrO8QqJbg7h3VrxbmmRnWxkAQGAgdAMAgGMT5JJSR5nl1dhgBnFvCM/NNIN4Xbm0b4lZXq5o877wlJHm90jJkGL7EsQBAF0SoRsAAPx4QcHmTHZqhuSdFG9skAq3NYfw5nvE8zZL9eXS/qVmebmizE3aUjNaZ8bj+hPEAQCdHqEbAAD4RlBwc4Ae2TrW5JYKt39jafpmqb5COrDMLK/giDZBPMP8PvEDJLujQ08DAIAfg9ANAAA6jsMpJQ83S1eZY02NUtGO9kE8b5PUUCVlrTDLyxluvrbtrunxAwniAAC/RegGAADWcgRJScPMGnWFOdbUKBXv+kYQ3yi5q6Xsr83ycoZJSenfCOKDzO8LAIDF+NMIAAD4H0eQlDjErIyfmWOeJqlolxnCcze0BvGGKungKrO8gkKl5PT2jy9LGGzOtAMA0IEI3QAAoHOwO6TEwWaNvNwc8zRJxXva75qeu1FqqJQOrjbLKyjEnE1v+/iyxCEEcQCATxG6AQBA52V3SAkDzRpxqTnm8Ugle5uD+PrmndM3mJu1HVprlpcj+AhBfKi5CRwAAMcBoRsAAHQtdrsU39+s4RebYx6PVLqvTQjPlHI2mI8vy1lvljeLO4LN4O1dlp6SYQbzIJc15wMA6NQI3QAAoOuz26W4fmZ5g7hhNAfxzDbL0zdIdWXNy9QzpXVzm1/vNJeitzxHfJQZxJ0hFpwMAKAzIXQDAIDAZLNJsX3NSr/IHDMMqezAN4J4plRbam7alrex9fX2IClhiJQ6snl5ujeIh3b4qQAA/BehGwAAwMtmk7r1MWvYBeaYYUhlWW2WpWeaH2uKpfxNZq1/rfn1DnNGvO2u6UnpUnBYx58LAMAvELoBAAC+i80mdett1tDzzDHDkMoPtg/hOZlSTZGUv9mszDZBPGFQ+83akocTxAEgQBC6AQAAjpXNJsX0NGvIueaYYUgVOYcH8eoCqWCrWRvmNb/eLsUPar9ZW/JwyRVhxdkAAHyI0A0AAHA82GxSdHezBp9tjhmGVJnb5hniG8z/rsqTCreZtfFN7zeQ4ge2hvDUDCl5BEEcADo5QjcAAICv2GxSVKpZg6e3jlfkHn6PeGWuVLTDrI3/9n4DKX5A847pGa1BPCSqo88EAPADEboBAAA6WlSKWYPObB2rzD88iFcckop2mrXprdZj4/q3v0c8ZYQUEt2RZwAAOEqEbgAAAH8QmSRFTpMGTmsdqypsH8JzN0jl2VLxbrM2v916bGzfbwTxkVJoTAeeAADgSAjdAAAA/ioiQRow1Syv6qJvbNa2QSrPkkr2mrXl3dZju/U5PIiHxXbgCQAACN0AAACdSXi81P90s7yqi1tnwr2BvOyAVLrfrK3vtx4b07v9rumpowjiAOBDhG4AAIDOLjxO6n+aWV41JYffI1663wzjZQekbf9tPTa6l3lfeGqGlDLK/Bge35FnAABdFqEbAACgKwqLlfpNMcurtrQ5iG9oDeIle83l6eVZ0vaPWo+N6tH+8WUpI6WIxA49BQDoCgjdAAAAgSK0m9R3slletWVS3sb2m7UV75YqDprVNohHpn4jiGeYG8ABAL4VoRsAACCQhcZIaaeY5VVX0T6I52SaQbwyR9qRI+34pPXYiOTDg3hUSsf1DwB+jtANAACA9kKipD6TzPKqr5TyNrUP4kU7pao8aed8s7wiktovS0/JkKJSJZutI88CAPwCoRsAAADfzxUp9Z5glld9lRnEvcvSczKloh1SVb606zOzvMIT2s+Gp2ZIUd0J4gC6PEI3AAAAfhhXhNT7JLO8GqqlvM1tdk3fIBVul6oLpd0LzfIKi//G48sypOieBHEAXQqhGwAAAMdPcLjUa7xZXg01Uv6W9o8vK9gm1RRJez43yys0tiWE25KGK6y+RDKMjj0HADiOCN0AAADwreAwqedYs7zcdc1BfH37IF5bIu35QtrzhYIkTZVk7Lu/9d5w76x4tz7MiAPoFAjdAAAA6HjOEKnHaLO83HVSwZaWEG7kZMrI3yJ7bam0d5FZXiExbZamNwfy2L4EcQB+h9ANAAAA/+AMkbqPNktSo9ut+R99oDNH95azoM194gVbpboyad9is7xc0VLKiDb3iI+SuqVJdnvHnwsANCN0AwAAwG957E4zQPdqszS9scEM3m03a8vfLNWXS/uXmuXlipKSR7TfrC22H0EcQIchdAMAAKBzCQo2w3NqhuRdnd7kNu8Jb7tZW95mqb5COrDMLK/gSHNGvO094nH9JLujQ08DQGAgdAMAAKDzczibg/QI6YSrzbEmt/m4Mu9seG6m+VzxhkrpwHKzvIIjpOTh7YN4/ACCOIAfjdANAACArsnhNIN08nBJV5ljTY1S0Y7W2fCczOYgXiVlfWWWlzPcfG3bzdriB0oO/goN4OjxEwMAAACBwxEkJQ0za9QV5lhTo1S86xtBfKPkrpayvzbLKyi0TRDPMD/GDyKIA/hW/HQAAABAYHMESYlDzMr4mTnmaZKKdrXfrC1vozkjfnCVWV5BIVJSevsgnjDYnGkHEPAI3QAAAMA32R1S4mCzRl5ujnmapOI97YN47gbzHvFDa8zycrik5PQ294iPlBKHEsSBAEToBgAAAI6G3SElDDRrxKXmmMcjlextDuLrW4N4fYV0aK1ZXo5gc1l7283aEoeau7ED6LII3QAAAMAPZbdL8f3NGn6xOebxSKX72j++LHeDVFduBvOc9ZI3izuCzeDddrO2pGFSkMuKswHgA4RuAAAA4Hiy283nfsf1k9J/ao4ZhhnE2z6+LCdTqitrDuWZbV7vNO8vb3uPeOIwyRnSoacB4PggdAMAAAC+ZrNJsX3NSr/IHDMMqexA+13TczOl2lJz07a8jZJeMY+1B0kJQ6TU5tnw1FHmjLgz1IqzAXAMCN0AAACAFWw2qVsfs4ZdYI4ZhlSW1X42PDdTqimW8jeZtf615tc7zBlx77L01AxzF/XgsI4/FwDfitANAAAA+AubTerW26yh55ljhiGVH/zGrumZUnWhlL/ZrMzXm19vNx9XlpJhhvHUDPO54sHhlpwOAEI3AAAA4N9sNimmp1lDzjXHDEOqyGk/G56TKVUXSAVbzdowr/n1dil+YPvHlyWPkFwRVpwNEHAI3QAAAEBnY7NJ0d3NGnx263hF7uFBvCpPKtxu1sY3vd9Aih/Q/vFlKSMkV2THngcQAAjdAAAAQFcRlWLWoLNaxyrzzCXpbYN4ZY5UtNOsTf9pPtAmxfVvXZbuXaIeEtXRZwF0KYRuAAAAoCuLTDZr4BmtY1UFh++aXnFIKt5l1ua3W4+N7df+8WUpI6WQ6A48AaBzI3QDAAAAgSYiURo4zSyvqsLmTdrWt27YVp4tlewxa/M7rcd2Szs8iId269hzADoJQjcAAAAAKSJBGnC6WV7VRYfvml6WJZXuM2vLe63HduvTftf0lAwpLLYDTwDwT4RuAAAAAEcWHi/1P90sr5qSwzdrKzsgle43a+v7rcfG9Gq/WVvqKII4Ag6hGwAAAMDRC4uV+v3ELK+aktaZcO+mbaX7zFnxsixp239bj43u2WY2fJT5MTy+Y88B6ECEbgAAAAA/Tlis1G+KWV61pVLuxvaz4iV7zfvEy7Ol7R+1HhvV4/B7xCMSO/IMAJ8hdAMAAAA4/kK7SX1PNcurtkzK29Q+iBfvlioOmtU2iEemfiOIZ0iRSR3XP3CcELoBAAAAdIzQGCntZLO86iqkvI3tnyVetMt8lviOHGnHJ63HRqYcvllbVEpHngFwzAjdAAAAAKwTEiX1mWSWV31l84x42yC+U6rMNWvnp63HRiS1nw1PGSlFpUo2W4eeBvBtCN0AAAAA/IsrUuo9wSyvhmoziLd9fFnhdqkqX9r1mVle4Qnf2DU9Q4rqThCHJQjdAAAAAPxfcLjU60SzvBpqpPzN7R9fVrhdqi6Udi80yyssvv2y9NQMcyd1gjh8zKeh+4EHHtDHH3+szMxMBQcHq6ys7LBjsrKyNGvWLH3xxRcKDQ3VjBkz9Oijjyo4ONiXrQEAAADo7ILDpJ7jzPJy10p5m5sfX5Yp5WyQCrdJNUXSns/N8gqNbb8sPTVDiulNEMdx5dPQ3dDQoEsuuUQnnXSSXnzxxcO+3tTUpLPPPlsJCQlatmyZiouLdc0118gwDD355JO+bA0AAABAV+QMlXqONcvLXSflb5Fy17fOihdsk2pLpD1fmOUV2s0M4G2Xp3frQxDHD+bT0H3vvfdKkubMmXPEry9YsEBbt25Vdna2UlNTJUl/+9vfNHPmTD3wwAOKioryZXsAAAAAAoEzROox2iyvxvrmIJ7ZGsTzt5rPF9+7yCyvkJg2S9ObA3ls347rH52apfd0f/XVV0pPT28J3JJ0xhlnqL6+XmvXrtWUKVMOe019fb3q6+tbPq+oqJAkud1uud1u3zcNv+F9v3nf4a+4RuHvuEbh77hG4Vt2KXG4WSOvMoca66XCbbLlbpAtb6NseRtkK9gqW12ZtG+xWc0MV5TsScM1tC5Kno3Vcvc4QeqWJtns1pwOOtSx/FyyNHTn5eUpKan9A+67deum4OBg5eXlHfE1Dz30UMsMelsLFixQWFiYT/qEf1u4cOH3HwRYiGsU/o5rFP6OaxQdL0HSaVLyabIlNiqq7qCia/Yrpna/Ymr2K6o2W476CjmylmuAJH1oPsLMbQ9VWVgflYf1UVlob5WHpanKlUQQ74JqamqO+thjDt2zZ88+Yuhta/Xq1RozZsxRfT/bEe6NMAzjiOOSdPfdd+vOO+9s+byiokI9e/bUtGnTWI4eYNxutxYuXKipU6fK6XRa3Q5wGK5R+DuuUfg7rlH4K0+TW56iHfIcXKecNR+rl7NU9sKtcjbWKqFqmxKqtrUcawRHyEgeLiN5pIyUkTKSM6S4fgTxTs674vpoHHPovvXWW3X55Zd/5zF9+vQ5qu+VnJyslStXthsrLS2V2+0+bAbcy+VyyeVyHTbudDr5YRygeO/h77hG4e+4RuHvuEbhd5xOqccouZPStTE/Xj2mT5fDLqlwR/t7xPM2y9ZQJVvWV1LWV62vD46Qkoe336wtfoBkd1hwMvghjuVn0jGH7vj4eMXHxx/ry47opJNO0gMPPKDc3FylpKRIMpeJu1wujR49+nteDQAAAAB+wuGUktPNGnWlOdbUKBXt/EYQ3yQ1VJkhvG0Qd4Y3B/E2zxKPHyg5LL0jGMeBT9/BrKwslZSUKCsrS01NTcrMzJQk9e/fXxEREZo2bZqGDh2qq666So888ohKSkr061//WjfeeCNLxQEAAAB0bo4gKWmoWRkzzDFPkxnEvSE8d4OUu1FyV0vZX5vlFRRqBvG2u6YnDCaIdzI+fbf++Mc/au7cuS2fjxo1SpL05ZdfavLkyXI4HPr44491yy23aOLEiQoNDdWMGTP06KOP+rItAAAAALCG3SElDjEr42fmmKdJKt7dGsRzMqW8jeaM+MFVZnkFhUhJ6e2DeOIQc6YdfsmnoXvOnDnf+oxur169eumjjz7yZRsAAAAA4L/sDilhkFkjLzPHPB4ziHtnw3OaPzZUSofWmOXlcElJw1qXpadmSAlDpKDgDj8VHI51CQAAAADgb+x2KWGgWSMuNcc8Hqlkb/Ns+PrmpekbpPoKKWedWV6OYClxaPsgnjhUCjp8U2r4FqEbAAAAADoDu12K72/W8IvNMY9HKt3XfrO23A1SXXnzf2e2eb3TvL+8Zdf0keZSdYK4TxG6AQAAAKCzstvN537H9ZPSf2qOGYYZxNuG8JxMqa6sdXZ8XfPeW/Yg857wliA+ylyq7gyx4my6JEI3AAAAAHQlNpsU29es9IvMMcOQyg4cHsRrS8zHmOVtkta/ah5rDzLvCW/7+LLkdMkZasnpdHaEbgAAAADo6mw2qVsfs4ZdYI4ZhlSe3X7X9NxMqaZYyt9kVuZrza93mI8ra3uPeFK6FBzW0WfS6RC6AQAAACAQ2WxSTC+zhp5njhmGVH6weRl6ZmsQry6UCraYlfl68+vtZhD3ProsNcN8rnhwuCWn468I3QAAAAAAk80mxfQ0a8g55phhSBU57UN4TqZUXSAVbDVrwxvNr7dL8QPb3COeYQZxV4QFJ+MfCN0AAAAAgG9ns0nR3c0afHbreEXu4UG8Kk8q3G7Wxje930CKH9A+iKeMkFyRHXseFiF0AwAAAACOXVSKWYPOah2rzGvdpM0bxCtzpKKdZm36T/OBNimuf5sQPtKskKiOPgufI3QDAAAAAI6PyGSzBp7ROlZVcPhmbRWHpOJdZm16q/XY2H5S9xOkC583H4fWBRC6AQAAAAC+E5EoDZxmlldVYfNmbeubg/gGcyf1kj3mfeFdJHBLhG4AAAAAQEeLSJAGnG6WV3WROQveWG9ZW75A6AYAAAAAWC88Xup/+vcf18l0nTl7AAAAAAD8DKEbAAAAAAAfIXQDAAAAAOAjhG4AAAAAAHyE0A0AAAAAgI8QugEAAAAA8BFCNwAAAAAAPkLoBgAAAADARwjdAAAAAAD4CKEbAAAAAAAfIXQDAAAAAOAjhG4AAAAAAHyE0A0AAAAAgI8QugEAAAAA8BFCNwAAAAAAPkLoBgAAAADARwjdAAAAAAD4SJDVDfxYhmFIkioqKizuBB3N7XarpqZGFRUVcjqdVrcDHIZrFP6OaxT+jmsU/o5rNHB586c3j36XTh+6KysrJUk9e/a0uBMAAAAAQCCprKxUdHT0dx5jM44mmvsxj8ejnJwcRUZGymazWd0OOlBFRYV69uyp7OxsRUVFWd0OcBiuUfg7rlH4O65R+Duu0cBlGIYqKyuVmpoqu/2779ru9DPddrtdPXr0sLoNWCgqKoofcvBrXKPwd1yj8Hdco/B3XKOB6ftmuL3YSA0AAAAAAB8hdAMAAAAA4COEbnRaLpdLf/rTn+RyuaxuBTgirlH4O65R+DuuUfg7rlEcjU6/kRoAAAAAAP6KmW4AAAAAAHyE0A0AAAAAgI8QugEAAAAA8BFCNwAAAAAAPkLoRpdSX1+vjIwM2Ww2ZWZmWt0OIEnav3+/rr/+eqWlpSk0NFT9+vXTn/70JzU0NFjdGgLc008/rbS0NIWEhGj06NFaunSp1S0BkqSHHnpIY8eOVWRkpBITE3XBBRdox44dVrcFfKuHHnpINptNd9xxh9WtwA8RutGl3HXXXUpNTbW6DaCd7du3y+Px6LnnntOWLVv097//Xc8++6x+//vfW90aAti///1v3XHHHbrnnnu0fv16nXzyyTrrrLOUlZVldWuAFi9erFmzZunrr7/WwoUL1djYqGnTpqm6utrq1oDDrF69Ws8//7xGjBhhdSvwUzwyDF3Gp59+qjvvvFPvvPOOhg0bpvXr1ysjI8PqtoAjeuSRR/TMM89o7969VreCADV+/HidcMIJeuaZZ1rGhgwZogsuuEAPPfSQhZ0BhyssLFRiYqIWL16sU045xep2gBZVVVU64YQT9PTTT+v+++9XRkaGHn/8cavbgp9hphtdQn5+vm688Ua9+uqrCgsLs7od4HuVl5crNjbW6jYQoBoaGrR27VpNmzat3fi0adO0YsUKi7oCvl15ebkk8XMTfmfWrFk6++yzdfrpp1vdCvxYkNUNAD+WYRiaOXOmfv7zn2vMmDHav3+/1S0B32nPnj168skn9be//c3qVhCgioqK1NTUpKSkpHbjSUlJysvLs6gr4MgMw9Cdd96pSZMmKT093ep2gBZvvvmm1q1bp9WrV1vdCvwcM93wW7Nnz5bNZvvOWrNmjZ588klVVFTo7rvvtrplBJijvUbbysnJ0ZlnnqlLLrlEN9xwg0WdAyabzdbuc8MwDhsDrHbrrbdq48aNeuONN6xuBWiRnZ2t22+/Xa+99ppCQkKsbgd+jnu64beKiopUVFT0ncf06dNHl19+uT788MN2f1FsamqSw+HQFVdcoblz5/q6VQSoo71GvX8Y5+TkaMqUKRo/frzmzJkju51/94Q1GhoaFBYWprfeeksXXnhhy/jtt9+uzMxMLV682MLugFa33Xab3n//fS1ZskRpaWlWtwO0eP/993XhhRfK4XC0jDU1Nclms8lut6u+vr7d1xDYCN3o9LKyslRRUdHyeU5Ojs444wy9/fbbGj9+vHr06GFhd4Dp0KFDmjJlikaPHq3XXnuNP4hhufHjx2v06NF6+umnW8aGDh2q888/n43UYDnDMHTbbbfpvffe06JFizRgwACrWwLaqays1IEDB9qNXXvttRo8eLB++9vfcisE2uGebnR6vXr1avd5RESEJKlfv34EbviFnJwcTZ48Wb169dKjjz6qwsLClq8lJydb2BkC2Z133qmrrrpKY8aM0UknnaTnn39eWVlZ+vnPf251a4BmzZqlefPm6YMPPlBkZGTLXgPR0dEKDQ21uDtAioyMPCxYh4eHKy4ujsCNwxC6AcDHFixYoN27d2v37t2H/UMQi41glcsuu0zFxcW67777lJubq/T0dH3yySfq3bu31a0BLY+ymzx5crvxl19+WTNnzuz4hgDgR2B5OQAAAAAAPsIuPgAAAAAA+AihGwAAAAAAHyF0AwAAAADgI4RuAAAAAAB8hNANAAAAAICPELoBAAAAAPARQjcAAAAAAD5C6AYAAAAAwEcI3QAAAAAA+AihGwAAAAAAHyF0AwAAAADgI4RuAAAAAAB85P8DSub/LazYmWsAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(-5,5)\n", - "y1_v = [qf(xx) for xx in x_v]\n", - "y2_v = [qfp(xx) for xx in x_v]\n", - "y3_v = [qfpp(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"f\")\n", - "plt.plot(x_v, y2_v, label=\"f'\")\n", - "plt.plot(x_v, y3_v, label=\"f''\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "5fbfdc73-3c3b-46f3-b465-8a72cf989548", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(-2.0000018174926066,\n", - " -1.9999998989657501,\n", - " 1.9999999488316007,\n", - " 2.000000751212651)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y2a_v = [qf.p(xx) for xx in x_v] # calculate the derivative from the original object\n", - "y3a_v = [qf.pp(xx) for xx in x_v] # ditto second derivative\n", - "y3b_v = [qfp.p(xx) for xx in x_v] # calculate the second derivative as derivative from the derivative object\n", - "assert y2a_v == y2_v # those are literally two ways of getting the same result\n", - "assert y3a_v == y3_v # ditto\n", - "assert iseq(min(y3_v), -2) # check that the second derivative is correct\n", - "assert iseq(max(y3_v), -2) # ditto\n", - "assert iseq(min(y3b_v), 2) # ditto, but the other way\n", - "assert iseq(max(y3b_v), 2) # ditto\n", - "min(y3_v), max(y3_v), min(y3b_v), max(y3b_v)" - ] - }, - { - "cell_type": "markdown", - "id": "02deebe2-3397-4efb-8e41-d50014dbba9d", - "metadata": {}, - "source": [ - "### Custom function" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "7accd13d-4da5-4d9f-94a6-575b5bb4cc6f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.41421356237309515, -0.3535533907028654, 0.08838838549962702)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "@f.dataclass(frozen=True)\n", - "class MyFunction(f.Function):\n", - " k: float = 1\n", - " \n", - " def f(self, x):\n", - " return (m.sqrt(1+x)-1)*self.k\n", - "mf = MyFunction()\n", - "mf2 = mf.update(k=2)\n", - "mf(1),mf.p(1),mf.pp(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b76d484d-5041-4d3c-90a2-43cebdb6161c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(0,10)\n", - "y1_v = [mf(xx) for xx in x_v]\n", - "y2_v = [mf2(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"mf\")\n", - "plt.plot(x_v, y2_v, label=\"nf2\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "markdown", - "id": "66461504-3d04-44c0-bc41-caa4ea47f696", - "metadata": {}, - "source": [ - "## Kernel" - ] - }, - { - "cell_type": "markdown", - "id": "d117bbf1-0988-4ef5-a40f-18fdd3f83a6f", - "metadata": { - "tags": [] - }, - "source": [ - "### Integration function" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ad760927-1132-4f93-9fd6-967c36efaed6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "integrate = Kernel.integrate_trapezoid\n", - "ONE = lambda x: 1\n", - "LIN = lambda x: 2*x\n", - "SQR = lambda x: 3*x*x" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "18785493-71e6-4952-978e-b755e3bdc84e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(integrate(ONE, 0, 1, 2), 1) # trapezoid integrates constant perfectly\n", - "assert iseq(integrate(ONE, 0, 1, 100), 1)\n", - "assert iseq(integrate(LIN, 0, 1, 2), 1) # ditto linear\n", - "assert iseq(integrate(LIN, 0, 1, 100), 1)\n", - "assert iseq(integrate(SQR, 0, 1, 100), 1, eps=1e-3)\n", - "assert iseq(integrate(SQR, 0, 1, 1000), 1, eps=1e-6)" - ] - }, - { - "cell_type": "markdown", - "id": "ba333451-0dfe-4409-a574-d8f77e1e1104", - "metadata": {}, - "source": [ - "### Default kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "2f02cf1c-fa10-4a2e-9472-d371d2c3b260", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k = Kernel(steps=1000)\n", - "assert k.x_min == 0\n", - "assert k.x_max == 1\n", - "assert set(k.kernel(xx) for xx in np.linspace(k.x_min, k.x_max, 50)) == {1}\n", - "assert iseq(k.integrate(ONE), 1)\n", - "assert iseq(k.integrate(LIN), 1)\n", - "assert iseq(k.integrate(SQR), 1)\n", - "x_v = np.linspace(-0.5, 1.5, 1000)\n", - "plt.plot(x_v, [k.k(xx) for xx in x_v], label=\"default kernel\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "3b9e2eb4-6bde-4b66-866c-3ac72970bf1c", - "metadata": { - "tags": [] - }, - "source": [ - "### Flat kernels" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "ffeeb416-d951-4f78-84a3-342ebbe1956f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k = Kernel(x_max=2, kernel=lambda x: 0.5, steps=1000)\n", - "assert k.x_min == 0\n", - "assert k.x_max == 2\n", - "assert set(k.kernel(xx) for xx in np.linspace(k.x_min, k.x_max, 50)) == {0.5}\n", - "assert iseq(k.integrate(ONE), 1)\n", - "assert iseq(k.integrate(LIN), 2)\n", - "assert iseq(k.integrate(SQR), 4)\n", - "x_v = np.linspace(-0.5, 2.5, 1000)\n", - "plt.plot(x_v, [k.k(xx) for xx in x_v], label=\"flat kernel 0..2\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "24eee0bd-2db9-47ba-870f-546912ec4028", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k = Kernel(x_max=4, kernel=lambda x: 0.25, steps=1000)\n", - "assert k.x_min == 0\n", - "assert k.x_max == 4\n", - "assert set(k.kernel(xx) for xx in np.linspace(k.x_min, k.x_max, 50)) == {0.25}\n", - "assert iseq(k.integrate(ONE), 1)\n", - "assert iseq(k.integrate(LIN), 4)\n", - "assert iseq(k.integrate(SQR), 16)\n", - "x_v = np.linspace(-0.5, 4.5, 1000)\n", - "plt.plot(x_v, [k.k(xx) for xx in x_v], label=\"flat kernel 0..4\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "49522d4f-9149-4b8d-9bc2-fdf90ac1769e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(4.0, 16.000008000000012)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "k.integrate(LIN), k.integrate(SQR)" - ] - }, - { - "cell_type": "markdown", - "id": "25309e0f-4cfe-4910-850b-da56d8e59e36", - "metadata": {}, - "source": [ - "### Triangle and sawtooth kernels" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "86546a13-cdb3-49c3-ab9c-a5af1e331b43", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kf = Kernel(x_min=1, x_max=3, kernel=Kernel.FLAT, steps=1000)\n", - "kl = Kernel(x_min=1, x_max=3, kernel=Kernel.SAWTOOTHL, steps=1000)\n", - "kr = Kernel(x_min=1, x_max=3, kernel=Kernel.SAWTOOTHR, steps=1000)\n", - "kt = Kernel(x_min=1, x_max=3, kernel=Kernel.TRIANGLE, steps=1000)\n", - "x_v = np.linspace(0.5, 3.5, 1000)\n", - "plt.plot(x_v, [kf.k(xx) for xx in x_v], label=\"flat\")\n", - "plt.plot(x_v, [kl.k(xx) for xx in x_v], label=\"sawtooth left\")\n", - "plt.plot(x_v, [kr.k(xx) for xx in x_v], label=\"sawtooth right\")\n", - "plt.plot(x_v, [kt.k(xx) for xx in x_v], label=\"triangle\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "335de4b7-cdce-4f69-ab18-b1e3dfd375bd", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(kf.integrate(ONE), 1)\n", - "assert iseq(kl.integrate(ONE), 1)\n", - "assert iseq(kr.integrate(ONE), 1)\n", - "assert iseq(kt.integrate(ONE), 1)\n", - "\n", - "assert iseq(kf.integrate(LIN), 4)\n", - "assert iseq(kl.integrate(LIN), 10/3)\n", - "assert iseq(kr.integrate(LIN), 14/3)\n", - "assert iseq(kt.integrate(LIN), 4)\n", - "\n", - "assert iseq(kf.integrate(SQR), 13)\n", - "assert iseq(kl.integrate(SQR), 9)\n", - "assert iseq(kr.integrate(SQR), 17)\n", - "assert iseq(kt.integrate(SQR), 12.5)" - ] - }, - { - "cell_type": "markdown", - "id": "31758d9a-b0d5-4842-8844-a64c50b7396f", - "metadata": {}, - "source": [ - "### Gaussian kernels" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "28ca49c4-4bb1-433a-a0ff-beb685950dbe", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kf = Kernel(x_min=1, x_max=3, kernel=Kernel.FLAT, steps=1000)\n", - "kg = Kernel(x_min=1, x_max=3, kernel=Kernel.GAUSS, steps=1000)\n", - "kw = Kernel(x_min=1, x_max=3, kernel=Kernel.GAUSSW, steps=1000)\n", - "kn = Kernel(x_min=1, x_max=3, kernel=Kernel.GAUSSN, steps=1000)\n", - "x_v = np.linspace(0.5, 3.5, 1000)\n", - "plt.plot(x_v, [kf.k(xx) for xx in x_v], label=\"flat\")\n", - "plt.plot(x_v, [kg.k(xx) for xx in x_v], label=\"gauss\")\n", - "plt.plot(x_v, [kw.k(xx) for xx in x_v], label=\"gauss wide\")\n", - "plt.plot(x_v, [kn.k(xx) for xx in x_v], label=\"gauss narrow\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "56110cff-696d-48a5-a957-a04d32e20298", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(kf.integrate(ONE), 1)\n", - "assert iseq(kg.integrate(ONE), 1, eps=1e-3)\n", - "assert iseq(kw.integrate(ONE), 1, eps=1e-3)\n", - "assert iseq(kn.integrate(ONE), 1, eps=1e-3)" - ] - }, - { - "cell_type": "markdown", - "id": "fe63fcfa-4fd9-43d7-8c0b-4bfd51e714d1", - "metadata": {}, - "source": [ - "## Function Vector" - ] - }, - { - "cell_type": "markdown", - "id": "91a19e24-da99-40f5-b16d-734e9d429743", - "metadata": {}, - "source": [ - "### vector operations and consistency" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "5400e8ef-8e97-4275-8485-b464ddd313b1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[FunctionVector::eq] called; funcs_eq=True, kernel_eq=True\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "knl = Kernel(x_min=1, x_max=3, kernel=Kernel.FLAT, steps=1000)\n", - "f1 = f.QuadraticFunction(a=3, c=1)\n", - "f2 = f.QuadraticFunction(b=2)\n", - "f3 = f.QuadraticFunction(a=3, b=2, c=1)\n", - "f1v = f.FunctionVector({f1: 1}, kernel=knl)\n", - "f2v = f.FunctionVector({f2: 1}, kernel=knl)\n", - "fv = f.FunctionVector({f1: 1, f2: 1}, kernel=knl)\n", - "assert fv == f1v + f2v\n", - "x_v = np.linspace(1, 3, 100)\n", - "y1_v = [f1(xx) for xx in x_v]\n", - "y2_v = [f2(xx) for xx in x_v]\n", - "y3_v = [f3(xx) for xx in x_v]\n", - "yv_v = [fv(xx) for xx in x_v]\n", - "y_diff = np.array(yv_v) - np.array(y3_v)\n", - "plt.plot(x_v, y1_v, label=\"f1\")\n", - "plt.plot(x_v, y2_v, label=\"f2\")\n", - "plt.plot(x_v, y3_v, label=\"f3\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "06d7ed49-1934-4943-8405-8fcbc9b3ac93", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "assert max(y_diff)<1e-10\n", - "assert min(y_diff)>-1e-10\n", - "plt.plot(x_v, yv_v, linewidth=3, label=\"vector\")\n", - "plt.plot(x_v, y3_v, linestyle=\"--\", color=\"#ccc\", label=\"f3\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()\n", - "plt.plot(x_v, y_diff)\n", - "plt.grid()\n", - "max(y_diff), min(y_diff)" - ] - }, - { - "cell_type": "markdown", - "id": "2f88e041-7084-4be7-81ec-7112877b2af0", - "metadata": {}, - "source": [ - "check that you can't add vectors with different kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "418bd7a3-29e2-49e1-9a5f-20faa1de2ecd", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "f1v = f.FunctionVector({f1: 1}, kernel=knl)\n", - "f2v = f.FunctionVector({f2: 1}, kernel=knl)\n", - "assert not raises(lambda: f1v+f2v)\n", - "assert not raises(lambda: f1v-f2v)\n", - "\n", - "f1v = f.FunctionVector({f1: 1}, kernel=knl)\n", - "f2v = f.FunctionVector({f2: 1}, kernel=None)\n", - "assert raises(lambda: f1v+f2v)\n", - "assert raises(lambda: f1v-f2v)" - ] - }, - { - "cell_type": "markdown", - "id": "7ad75da5-1701-4b2f-8d92-afee912bd73a", - "metadata": {}, - "source": [ - "### integration" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "45e38a6a-7af1-40b0-a707-58779d77dee7", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "f1v = f.FunctionVector({f1: 1}, kernel=knl)\n", - "f2v = f.FunctionVector({f2: 1}, kernel=knl)\n", - "#f1v.kernel, f2v.kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "622fde1e-6276-44b1-b2af-be33e9ce0cea", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "knl = f1v.kernel\n", - "assert f1v.kernel == f2v.kernel\n", - "assert f1v.kernel == fv.kernel\n", - "x_v = np.linspace(knl.x_min, knl.x_max)\n", - "plt.plot(x_v, [f1v(xx) for xx in x_v], label=\"f1\")\n", - "plt.plot(x_v, [f2v(xx) for xx in x_v], label=\"f2\")\n", - "plt.plot(x_v, [fv(xx) for xx in x_v], label=\"f=f1+f2\")\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "6d235d83-9593-4253-b602-f1e471436990", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(f1v.integrate(), 13+1)\n", - " # assert iseq(kf.integrate(ONE), 1)\n", - " # assert iseq(kf.integrate(SQR), 13)\n", - "\n", - "assert iseq(f2v.integrate(), 4)\n", - " # assert iseq(kf.integrate(LIN), 4)\n", - "\n", - "assert iseq(fv.integrate(), 18)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "39c7a0ee-bcbf-46c3-90a3-995bfbf395ed", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "4.000000000000001" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f2v.integrate()" - ] - }, - { - "cell_type": "markdown", - "id": "7b9f01e7-26a5-4301-8d37-90e5103166d5", - "metadata": {}, - "source": [ - "### goal seek and minimize" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "2ed23a10-1175-4841-89e7-c80c8e55d787", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f1 = f.QuadraticFunction(a=1, c=-4)\n", - "f1v = f.FunctionVector({f1: 1})\n", - "x_v = np.linspace(-2.5, 2.5, 100)\n", - "y1_v = [f1(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"f\")\n", - "#plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "375bce7a-9ee8-4b73-aeda-e4d6542032b7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.00030468016160726646" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert iseq(f1v.goalseek(0, x0=1), 2)\n", - "assert iseq(f1v.goalseek(0, x0=-1), -2)\n", - "assert iseq(f1v.goalseek(-3, x0=1), 1)\n", - "assert iseq(f1v.goalseek(-3, x0=-1), -1)\n", - "assert iseq(0, f1v.minimize1(x0=5), eps=1e-3)\n", - "f1v.minimize1(x0=5)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "d668c6c9-4074-453c-b301-eecb52952fbd", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f2 = f.QuadraticFunction(a=3, b=2, c=1)\n", - "f2v = f.FunctionVector({f2: 1})\n", - "x_v = np.linspace(-2.5, 2.5, 100)\n", - "y2_v = [f2(xx) for xx in x_v]\n", - "plt.plot(x_v, y2_v, label=\"f\")\n", - "#plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "19676a10-a38d-45ba-890e-e34115dfc9d4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.8685170919424989, -0.3332480000000852)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert iseq(f2v.goalseek(5), 0.8685170919424989, eps=1e-4)\n", - "assert iseq(f2v.minimize1(), -0.3332480000000852, eps=1e-4)\n", - "f2v.goalseek(5), f2v.minimize1()" - ] - }, - { - "cell_type": "markdown", - "id": "122ce720-6bcc-4eba-a16f-9f100c44b9ad", - "metadata": {}, - "source": [ - "## Restricted and apply kernel\n", - "\n", - "restricted functions (`f_r`, more generally `restricted(func)`) are zero outside the kernel domain; kernel-applied functions (`f_k`, more generally `apply_kernel(func)`) is multiplied with the kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "9642d905-3733-404a-8f29-47dcf9956af4", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "func = f.TrigFunction()" - ] - }, - { - "cell_type": "markdown", - "id": "8d18a0f1-f434-41ab-9001-b451f745d92a", - "metadata": {}, - "source": [ - "### Flat kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "06b27591-5c31-44ef-a677-2d0073bdbe69", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kernel = Kernel(0, 1, Kernel.FLAT)\n", - "fv = f.FunctionVector({func: 1}, kernel=kernel)\n", - "f_r = fv.restricted(fv.f)\n", - "f_k = fv.apply_kernel(fv.f) \n", - "\n", - "assert not fv.f(-0.5) == 0\n", - "assert not fv.f(1.5) == 0\n", - "assert f_r(-0.5) == fv.f_r(-0.5) == 0\n", - "assert f_r(1.5) == fv.f_r(1.5) == 0\n", - "assert f_r(0.5) == fv.f_r(0.5) == fv.f(0.5)\n", - "assert f_r(0.25) == fv.f_r(0.25) == fv.f(0.25)\n", - "assert f_r(0.75) == fv.f_r(0.75) == fv.f(0.75)\n", - "\n", - "assert f_k(-0.5) == fv.f_k(-0.5) == 0\n", - "assert f_k(1.5) == fv.f_k(1.5) == 0\n", - "assert f_k(0.5) == fv.f_k(0.5) == fv.f(0.5) * kernel(0.5)\n", - "assert f_k(0.25) == fv.f_k(0.25) == fv.f(0.25) * kernel(0.25)\n", - "assert f_k(0.75) == fv.f_k(0.75) == fv.f(0.75) * kernel(0.75)\n", - "\n", - "fv.plot(fv.f, x_min=-1, x_max=2, title=\"full function [self.f]\")\n", - "fv.plot(fv.f_r, x_min=-1, x_max=2, title=\"restricted function [self.f_r]\")\n", - "fv.plot(fv.f_k, x_min=-1, x_max=2, title=\"flat kernel applied [self.f_k]\")" - ] - }, - { - "cell_type": "markdown", - "id": "c86dcd7b-8c96-4532-a89a-d4e48eae6e30", - "metadata": {}, - "source": [ - "### Sawtooth-Left kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "9610b767-1c87-4665-9dbb-5e463f65ca24", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kernel = Kernel(0, 1, Kernel.SAWTOOTHL)\n", - "fv = f.FunctionVector({func: 1}, kernel=kernel)\n", - "f_r = fv.restricted(fv.f)\n", - "f_k = fv.apply_kernel(fv.f) \n", - "\n", - "assert not fv.f(-0.5) == 0\n", - "assert not fv.f(1.5) == 0\n", - "assert f_r(-0.5) == fv.f_r(-0.5) == 0\n", - "assert f_r(1.5) == fv.f_r(1.5) == 0\n", - "assert f_r(0.5) == fv.f_r(0.5) == fv.f(0.5)\n", - "assert f_r(0.25) == fv.f_r(0.25) == fv.f(0.25)\n", - "assert f_r(0.75) == fv.f_r(0.75) == fv.f(0.75)\n", - "\n", - "assert f_k(-0.5) == fv.f_k(-0.5) == 0\n", - "assert f_k(1.5) == fv.f_k(1.5) == 0\n", - "assert f_k(0.5) == fv.f_k(0.5) == fv.f(0.5) * kernel(0.5)\n", - "assert f_k(0.25) == fv.f_k(0.25) == fv.f(0.25) * kernel(0.25)\n", - "assert f_k(0.75) == fv.f_k(0.75) == fv.f(0.75) * kernel(0.75)\n", - "\n", - "fv.plot(fv.f, x_min=-1, x_max=2, title=\"full function [self.f]\")\n", - "fv.plot(fv.f_r, x_min=-1, x_max=2, title=\"restricted function [self.f_r]\")\n", - "fv.plot(fv.f_k, x_min=-1, x_max=2, title=\"sawtooth-left kernel applied [self.f_k]\")" - ] - }, - { - "cell_type": "markdown", - "id": "329818e4-76ad-4932-ab66-1f67865ac683", - "metadata": {}, - "source": [ - "## Curve fitting" - ] - }, - { - "cell_type": "markdown", - "id": "19533f44-0164-4bfe-a475-d2c7155f167c", - "metadata": {}, - "source": [ - "### norm and curve distance\n", - "\n", - "We have various ways of measuring the distance between a FunctionVector (that includes a kernel) and a Function, all being based on the L2 norm with kernel applied\n", - "\n", - "- Use `FunctionVector.distance2` for the squared distance between the FunctionVector and the Function, or `distance` for the squareroot thereof*\n", - "\n", - "- Wrap the Function in a FunctionVector with the same kernel using the `wrap` method, substract the two FunctionVectors from each other, and use `norm2` or `norm`\n", - "\n", - "*in optimization you typically want to use the squared function because it behaves better and you don't have to calculate the square root" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "868211e4-8759-4de8-bb8e-8ffe8ac87827", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# create the template function vector\n", - "fv_t = f.FunctionVector(kernel=Kernel(x_min=-1, x_max=1, kernel=Kernel.FLAT))\n", - "assert fv_t.f(0) == 0\n", - "\n", - "# create target and match functions and wrap them in FunctionVector\n", - "f0 = f.TrigFunction(phase=1/2)\n", - "f0v = fv_t.wrap(f0)\n", - "f1v = fv_t.wrap(f.QuadraticFunction(c=0))\n", - "f2v = fv_t.wrap(f.QuadraticFunction(a=-2, c=1))\n", - "\n", - "# check norms and distances\n", - "diff1 = (f0v-f1v).norm()\n", - "diff2 = (f0v-f2v).norm()\n", - "assert iseq( (f0v-f1v).norm2(), (f0v-f1v).norm()**2)\n", - "assert iseq( (f0v-f2v).norm2(), (f0v-f2v).norm()**2)\n", - "assert iseq(f1v.distance2(f0), (f0v-f1v).norm2())\n", - "assert iseq(f2v.distance2(f0), (f0v-f2v).norm2())\n", - "assert iseq(f1v.distance(f0), (f0v-f1v).norm())\n", - "assert iseq(f2v.distance(f0), (f0v-f2v).norm())\n", - "\n", - "# plot\n", - "f0v.plot(show=False, label=\"f0 [target function]\")\n", - "f1v.plot(show=False, label=f\"f1 [match 1]: dist={diff1:.2f}\")\n", - "f2v.plot(show=False, label=f\"f2 [match 2]: dist={diff2:.2f}\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e9a593ae-189c-4954-8c51-59adda51bc26", - "metadata": {}, - "source": [ - "### curve fitting" - ] - }, - { - "cell_type": "markdown", - "id": "a69b11ff-ebaa-4045-852c-c4e10e27d788", - "metadata": {}, - "source": [ - "#### flat kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "809c3d8e-4f2d-4103-8234-beab6844c875", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "({'a': -2.266725245480411,\n", - " 'b': -4.999979597020143e-07,\n", - " 'c': 0.7553958307274233},\n", - " QuadraticFunction(a=-2.266725245480411, b=-4.999979597020143e-07, c=0.7553958307274233))" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fv_template = f.FunctionVector(kernel=Kernel(x_min=-1, x_max=1, kernel=Kernel.FLAT))\n", - "target_f = f.TrigFunction(phase=1/2)\n", - "target_fv = fv_template.wrap(target_f)\n", - "f_match0 = f.QuadraticFunction()\n", - "params0 = dict(a=0, b=0, c=0)\n", - "params = target_fv.curve_fit(f_match0, params0)\n", - "f_match = f_match0.update(**params)\n", - "params, f_match" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "79e5a8fb-2046-4691-95ba-be04ae0dd8bc", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "FunctionVector(vec={QuadraticFunction(a=-2.266725245480411, b=-4.999979597020143e-07, c=0.7553958307274233): 1}, kernel=Kernel(x_min=-1, x_max=1, kernel=. at 0x1347f74c0>, kernel_name='builtin-flat', method='trapezoid', steps=100))" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_match_v = target_fv.wrap(f_match)\n", - "diff = (target_fv-f_match_v).norm()\n", - "target_fv.plot(show=False, label=\"target function\")\n", - "f_match_v.plot(show=False, label=f\"match (dist={diff:.2f})\")\n", - "plt.title(f\"Best fit (a={params['a']:.2f}, b={params['b']:.2f}, c={params['c']:.2f}); dist={diff:.2f}\")\n", - "plt.legend()\n", - "f_match_v" - ] - }, - { - "cell_type": "markdown", - "id": "72950948-71b6-4bb0-9618-71d2f1d3fd00", - "metadata": { - "tags": [] - }, - "source": [ - "#### skewed kernel (sawtooth-left)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "59598e82-3652-4c73-bf0f-927d8fd5077b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(Kernel(x_min=-1, x_max=1, kernel=. at 0x134741300>, kernel_name='builtin-sawtoothl', method='trapezoid', steps=100),\n", - " {'a': -1.8836343582517845, 'b': 0.2661645670906654, 'c': 0.7347668924372053},\n", - " QuadraticFunction(a=-1.8836343582517845, b=0.2661645670906654, c=0.7347668924372053))" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fv_template = f.FunctionVector(kernel=Kernel(x_min=-1, x_max=1, kernel=Kernel.SAWTOOTHL))\n", - "target_f = f.TrigFunction(phase=1/2)\n", - "target_fv = fv_template.wrap(target_f)\n", - "f_match0 = f.QuadraticFunction()\n", - "params0 = dict(a=0, b=0, c=0)\n", - "params = target_fv.curve_fit(f_match0, params0)\n", - "f_match = f_match0.update(**params)\n", - "target_fv.kernel, params, f_match" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "1ed9e83c-0131-46cb-ad96-39cf34a8b376", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "FunctionVector(vec={QuadraticFunction(a=-1.8836343582517845, b=0.2661645670906654, c=0.7347668924372053): 1}, kernel=Kernel(x_min=-1, x_max=1, kernel=. at 0x134741300>, kernel_name='builtin-sawtoothl', method='trapezoid', steps=100))" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_match_v = target_fv.wrap(f_match)\n", - "diff = (target_fv-f_match_v).norm()\n", - "target_fv.plot(show=False, label=\"target function\")\n", - "f_match_v.plot(show=False, label=f\"match (dist={diff:.2f})\")\n", - "plt.title(f\"Best fit (a={params['a']:.2f}, b={params['b']:.2f}, c={params['c']:.2f}); dist={diff:.2f}\")\n", - "plt.legend()\n", - "f_match_v" - ] - }, - { - "cell_type": "markdown", - "id": "71ec9291-2816-4c64-ae95-610fa169e81d", - "metadata": {}, - "source": [ - "## High dimensional minimization" - ] - }, - { - "cell_type": "markdown", - "id": "f651576a-81a6-4f6e-8f9c-0dfe50a9bdf7", - "metadata": {}, - "source": [ - "### Example\n", - "\n", - "here we use as example the function\n", - "\n", - "$$\n", - "f(x,y) = (x-2)^2 + (y-2)^2\n", - "$$\n", - "\n", - "which obviously should be minimal at $(x,y) = (2,2)$" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "ad59954b-c98d-447b-a9b0-7f139140adfe", - "metadata": {}, - "outputs": [], - "source": [ - "func = lambda x,y: (x-2)**2 + (y-2)**2" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "f1329b5b-a229-47b5-bdac-4b8bdbf48565", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "((2.0002364190731674, 1.9999073648139465), array([ 0.00078973, -0.00030712]))" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r, dxdy = f.minimize(func, x0=[20, -5], learning_rate=None, return_path=True)\n", - "assert iseq(r[-1][0], 2, eps=1e-3)\n", - "assert iseq(r[-1][1], 2, eps=1e-3)\n", - "r[-1], dxdy" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "5cc79156-daf9-41df-bec2-c84d5b46e551", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x,y = zip(*r)\n", - "plt.scatter(x,y)\n", - "plt.title(\"Convergence path\")\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "fefd7a80-655f-45ad-926a-be010ce1971a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "({'x': 2.0002364190731674, 'y': 1.9999073648139465},\n", - " {'x': 0.0007897302440762718, 'y': -0.0003071172868030315})" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r, dxdy = f.minimize(func, x0=dict(x=20, y=-5), learning_rate=None, return_path=True)\n", - "assert iseq(r[-1][\"x\"], 2, eps=1e-3)\n", - "assert iseq(r[-1][\"y\"], 2, eps=1e-3)\n", - "r[-1], dxdy" - ] - }, - { - "cell_type": "markdown", - "id": "dbc4281c-414e-46a2-9089-667e8fdbc416", - "metadata": {}, - "source": [ - "### Testing e_i, e_k and bump" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "2bf759f5-47d1-4273-80c8-800e55d89fe8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "e_i = f.FunctionVector.e_i\n", - "e_k = f.FunctionVector.e_k\n", - "bump = f.FunctionVector.bump" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "ddef7258-a871-41eb-bd00-264b8cfc2260", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert np.array_equal(e_i(1,5), np.array([0., 1., 0., 0., 0.]))\n", - "assert e_k(\"b\", dict(a=1, b=2, c=3)) == {'a': 0, 'b': 1, 'c': 0}\n", - "assert bump(dict(a=1, b=2, c=3), \"b\", 0.25) == {'a': 1, 'b': 2.25, 'c': 3}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0fbd4fa4-2808-4d83-9438-127141de87e5", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/analysis/202401 Solidly/Functions.ipynb b/resources/analysis/202401 Solidly/Functions.ipynb deleted file mode 100644 index 23e3c0e7b..000000000 --- a/resources/analysis/202401 Solidly/Functions.ipynb +++ /dev/null @@ -1,1707 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 78, - "id": "0278c025-06e6-416b-9525-c2a4a8ae9128", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Function v0.9.1 (19/Jan/2024)\n", - "Kernel v0.9 (18/Jan/2024)\n" - ] - } - ], - "source": [ - "import invariants.functions as f\n", - "from invariants.kernel import Kernel\n", - "import numpy as np\n", - "import math as m\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from testing import *\n", - "plt.rcParams['figure.figsize'] = [12,6]\n", - "\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(f.Function))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(Kernel))" - ] - }, - { - "cell_type": "markdown", - "id": "7e212348-81d0-49f2-8d41-c7842a387634", - "metadata": {}, - "source": [ - "# Functions and integration kernels" - ] - }, - { - "cell_type": "markdown", - "id": "e831972e-e8b3-4e29-a6ec-103ddb874bd2", - "metadata": {}, - "source": [ - "## Functions" - ] - }, - { - "cell_type": "markdown", - "id": "64d064b4-c2f0-42f4-84d1-5fed091f461b", - "metadata": { - "tags": [] - }, - "source": [ - "### Built in functions\n", - "#### QuadraticFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "214f13cc-e573-42d9-94d9-8f7ad1ae6281", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "qf = f.QuadraticFunction(a=1, b=0, c=-10)\n", - "assert qf.params() == {'a': 1, 'b': 0, 'c': -10}\n", - "assert qf.a == 1\n", - "assert qf.b == 0\n", - "assert qf.c == -10" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "f4828c9c-eafa-4da3-81a0-7e1949148d07", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "qf2 = qf.update(c=-5)\n", - "assert raises(qf.update, k=1)\n", - "assert qf2.params() == {'a': 1, 'b': 0, 'c': -5}" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "a169eb1c-a5bb-41c2-a64c-677fa5a581ed", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(-5,5)\n", - "y1_v = [qf(xx) for xx in x_v]\n", - "y2_v = [qf2(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"qf\")\n", - "plt.plot(x_v, y2_v, label=\"qf2\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "id": "718fab97-6490-4888-912a-4c18aaa38451", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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dRZ7TkO6qWYwKBUMMJ8raVLqzsd71SlI8by4u3Uhh0rrjpuOIiIiIiEgWsOrgJTYdu4qfj42hrSuYjpPlqXRnY34+Noa39lwK8tmqI1xLSDWcSEREREREvJnL9Z8pd98GJSmSO5fhRFmfSnc2d2f1IlQqHMqNlDQ+XnnYdBwREREREfFiP+48Q8z5G4QE+PBEs3Km42QLKt3ZnM1m8dyvGx98s+EEZ64nGU4kIiIiIiLeKCXNyaglBwEY0LQseYL8DCfKHlS6c4CmFfJTv0xeUp0uxiw9aDqOiIiIiIh4oambTnL6WhIFQvx5qFFp03GyDZXuHMCyLJ5rFwHA7O2nOXjhhuFEIiIiIiLiTeJT0hi/wnM76uBW5cnlZzecKPtQ6c4hokrkoV2VQrjc8N7PB0zHERERERERLzLhl6NcSUildHgQPWsXNx0nW1HpzkGeaVcRu81i2f4LbDl+1XQcERERERHxApdupDBhzVEAnmlbEV+7amJ60u9mDlI2fzA9axcD4J1FMbjdbsOJRERERETEtPErDpGY6qR6sTDaRxYyHSfbUenOYQa3rECAr41tJ67x857zpuOIiIiIiIhBRy7F892mkwA81y4Cy7IMJ8p+VLpzmEJhATzapAwA7/wcQ0qa03AiEREREREx5e2FMaS53LSMKEDDcuGm42RLKt050GNNy5I/xJ8TVxL5dsMJ03FERERERMSA9Ucus2z/Bew2ixc6VDIdJ9tS6c6Bgvx9eLpNBQA+XH6IawmphhOJiIiIiEhmcrrcvDl/PwD31StBuQLBhhNlXyrdOVSPWsWJKBRCXHIa45YfMh1HREREREQy0eztp9l3Lo6QAB8GtyxvOk62ptKdQ9ltFi91rAzAlI0nOHop3nAiERERERHJDImpaXyw5AAAA5uXI1+wv+FE2ZtKdw7WuHw4LSIKkOZy8/aiGNNxREREREQkE3zxy1EuxKVQPG8u+jYsZTpOtqfSncP9q0MEdpvF0n0X2HDkiuk4IiIiIiKSgS7EJfP56qOA54iwAF+74UTZn0p3DleuQAi965YAYOTCfbhcbsOJREREREQko3yw+ABJDic1S+SmY9XCpuPkCCrdwpBW5Qnx92HPmTjm7DhjOo6IiIiIiGSAvWdj+WH7aQBe6lQZy7IMJ8oZVLqFfMH+PNmiHADvLz5AUqrTcCIREREREUlPbrebkQv243ZD5+pFqFkij+lIOYZKtwDQr2EpiubOxfm4ZCasOWo6joiIiIiIpKMVMRdZf+QKfj42nm1b0XScHEWlWwAI8LXzfPsIAD5bfYSLccmGE4mIiIiISHpwOF28tXA/AA81Kk3xvIGGE+UsKt3ym07VChNVIjeJqU5GLTloOo6IiIiIiKSDaZtPcuRSAnmD/HiieVnTcXIclW75jWVZvNSxMgAzt51i39k4w4lEREREROR2xCY5GLvsEABDW1cgNMDXcKKcJ0NL9y+//ELnzp0pUqQIlmUxd+7c//rvbrebESNGUKRIEXLlykWzZs3Yu3dvRkaSv1GrZB46ViuM2w1vLdyP260jxEREREREsqpPVh7makIq5QoEc2+d4qbj5EgZWroTEhKoXr0648eP/8P//t577zF69GjGjx/Pli1bKFSoEK1bt+bGjRsZGcsMV9bZEfz5dhH42W2sPXyZVQcumY4jIiIiIiL/wKmriUxadxyAFztUwseuC51N8MnID96+fXvat2//h//N7XYzduxYXnzxRbp37w7A5MmTKViwIFOnTuWxxx77w/dLSUkhJSXlt1/HxXkugXY4HDgcjnT+CtKPbdMnWHtn44rqg7tyN/APMR3pTxUK8aVPgxJMXHucNxfso36pMK/8A/rv19ubX3fJ2bRGxdtpjYq30xoVb+fta/TthftIdbpoWDYvjcrk9tqcWdGt/F5a7ky6ftiyLObMmUPXrl0BOHr0KGXLlmX79u1ERUX99nZdunQhd+7cTJ48+Q8/zogRI3jttdd+9/zUqVMJDPTeXfia7X+RsORTAKTZAjidpz4nwptxPVdp8MJD6RPT4M0ddhLSLO4u7aRxIV1mLiIiIiKSVRy7AWP3+GDh5plqTooGmU6UvSQmJtK7d29iY2MJDQ39y7fN0En3Xzl//jwABQsW/K/nCxYsyIkTJ/70/V544QWGDRv226/j4uIoXrw4bdq0+dsv1qimdXHunoFtxzf4XD1CqSurKHVlFe6CVXFF9cEV2cPrpt/JBU/y2vwYll0I4IXejQnxsk0XHA4HS5cupXXr1vj6elc2EdAaFe+nNSreTmtUvJ23rlG3203PCZuBWHrUKsYjXauYjpTt/PuK65thrHT/m/U/U1632/275/4/f39//P39f/e8r6+vVy3038ldGJoMgcaD4cR62PY17PsR68Ju7D8/g335qxDZHWo9CEVrecX0+/4GpZmy6RRHLiXwxdqTv53j7W28/rWXHE9rVLyd1qh4O61R8Xbetkbn7TxL9KlYAv3sPNM2wquyZRe38ntq7EbdQoUKAf+ZeP/bxYsXfzf9zlYsC0o1grsmwPAYaPs25I8ARyLsmAITW8JnjWHTF5B03WhUX7uNf3WoBMBX645x6mqi0TwiIiIiIvLXkh1O3v05BoABTctSIDTAcCIxVrpLly5NoUKFWLp06W/Ppaamsnr1aho2bGgqVuYKzAsNnoAnNsJDi6H6veATABf2wKJnYFQEzHkcTm4CQ0d3tYgoQKNy+UhNc/He4gNGMoiIiIiIyM35ev1xTl9LolBoAI80KWM6jpDBpTs+Pp7o6Giio6MBOHbsGNHR0Zw8eRLLshgyZAhvvfUWc+bMYc+ePfTr14/AwEB69+6dkbG8j2VBifrQ7TPP9Lv9+1CgCqQlwc6p8FUb+KQBbPwUEq9mcjSLFztUxrI8l6lsP3ktUz+/iIiIiIjcnCvxKXy84jAAz7StSC4/u+FEAhlcurdu3UpUVNRvu5MPGzaMqKgoXnnlFQCeffZZhgwZwhNPPEHt2rU5c+YMS5YsISTEuzYUy1S58kC9R+HxdfDwMoi6H3wD4dJ++Pl5z/R71iNwfF2mTb8rFwnl7lrFAHhz/j4yacN7ERERERG5BWOXHeJGShqRRUPpFlXUdBz5VYZupNasWbO/LGiWZTFixAhGjBiRkTGyJsuC4nU8j7Zvwe7vPZuvnd8Nu2d6HvnKQ61+nsvSg/JlaJzhbSoyb+c5tp+8zsLd5+lYrXCGfj4REREREbl5hy/eYOrmkwC81LEyNpv5jZnFw9g93XILAsKgTn94bA08sgJq9gXfILhyCJa8CKMj4IeH4NgvGTb9LhgawICmZQF45+f9pKQ5M+TziIiIiIjIrXtrYQxOl5s2lQtSv0zGDuTk1qh0ZyWW5TlO7M4P4ekD0GksFK4BzlTYMwsmd4aPasG6cRB/Kd0//SN3lKZgqD+nriYxef3xdP/4IiIiIiJy69YeusyKmIv42CyvPeY3J1Ppzqr8Q6D2g/DYanh0NdR+CPxC4OoRWPoKjK4EM/vCkRXgcqXLpwz08+GZtp4/xB+tOMzVhNR0+bgiIiIiIvLPOF1u3lywD4AHGpSkTP5gw4nkf6l0ZwdFakCnMZ6dz+/8CIrWBpcD9s2Fb7vBhzVgzSi4ceG2P1X3qKJUKRLKjeQ0xi07eNsfT0RERERE/rlZ204Tc/4GoQE+DG5Z3nQc+QMq3dmJfzDU7AOPLIcBa6HOI+AfCtdPwPLXYUxlmH4fHFoGrn92T7bNZvFix0oATNl0kkMXbqTnVyAiIiIiIjcpPiWN95ccAGBQy/LkDvQznEj+iEp3dlWoKnT8AIYfgK6fQvF64EqDmPnw3V0wrgasfg/izt7yh25YNpzWlQvidLl5bZ6OEBMRERERMeGjFYe4dCOFUvkCeaBBSdNx5E+odGd3foFQozc8vASe2Aj1HoeA3BB7ElaOhDFVYNq9cHDxLU2/X+5YGT8fG2sPX2bJvtu/bF1ERERERG7e0UvxfLX2GACvdK6Mv4/dcCL5MyrdOUmBStD+Hc+9392+gBINwe2CAwthak8YWxVWvg2xp//2Q5XIF8hjd5QB4I35+0h26AgxEREREZHM4Ha7eX3+PhxONy0iCtAioqDpSPIXVLpzIt9cUL0XPLQIntwMDQZCrrwQdwZWv+Mp39/1hJgF4Ez70w/zeLOyFA4L4PS1JL745WgmfgEiIiIiIjnXipiLrDpwCV+7xcudKpuOI39DpTuny18R2o6EYfvhri+hVBPP9PvQYpjeG8ZGwoo34dqJ371roJ8P/+rg2VTtk1WHOXM9KbPTi4iIiIjkKClpTl6f7zki7OHGZSgdHmQ4kfwdlW7x8A2Aqj2g33wYuA0aDoLAcLhxDn55H8ZVh2+7w76fwOn47d06VStMvdJ5SXa4eGvhfoNfgIiIiIhI9jdxzTFOXEmkYKg/T7UoZzqO3ASVbvm98HLQ5g3P9Pvur6FMM8ANR5bDzAdgdGVYNgKuHsWyLEbcWQWbBQt2nWP9kctms4uIiIiIZFPnYpMYv+IwAC+0r0SQv4/hRHIzVLrlz/n4QZVu0OdHGLQDGg+DoAKQcBHWjoEPo+CbLlS6upy+dYsA8NpP+0hzugwHFxERERHJft5ZFEOSw0ntknnoUqOI6Thyk1S65ebkLQOtXoVh+6Dnt1C2JWDB0VXwfT9ePtSDEQHTSbl4kO82nTSdVkREREQkW9l87Co/Rp/FsmDEnVWwLMt0JLlJuh5Bbo3dFyrf6XlcOwE7voXt32KLP08/fqKf/09sXlKFG36DCKnRDXz8TScWEREREcnSnC43r/60F4B765YgsmiY4URyKzTpln8uT0lo8RIM3Qv3TMNdvi1ObNRlLyHzH4NREbD4Rbh00HRSEREREZEsa9rmk+w/F0dogA9Pt6loOo7cIpVuuX12H4jogHXfTPb0+IWxad05684LSVdhw3j4uA5M6gA7Z4BDx4qJiIiIiNysawmpfLDkAADD21Qkb5Cf4URyq1S6JV1Vj6zKschBNE75kJG5X8NdsT1YNjixDuY86pl+L3oeLup4MRERERGRvzN66UGuJzqIKBTCffVKmI4j/4BKt6S7F9pXIsDPlwnnyzM34gMYsgeavwhhxSH5Omz6FD6pD1+2geipkJpoOrKIiIiIiNfZdzaO7zadAODVzlXwsau+ZUV61STdFQoLYGCLcgC8vTCG+ICC0PRZGLwT7psFEZ3AssOpTTD3cc/0e+EzcH6P4eQiIiIiIt7B7XYz4qe9uNzQsVphGpTNZzqS/EMq3ZIhHm5cmpL5Arl4I4WPVhzyPGmzQ/lWcM93nqPHWrwMuUtCSixs/gI+awQTWsL2byE1wewXICIiIiJi0Lxd59h8/CoBvjZe7FDJdBy5DSrdkiH8fey80qkyAF+tPcbRS/H//QYhheCOp2FQNDwwByp3AZsPnNkKPw2EDyrC/KFwbmfmhxcRERERMSgxNY23Fnj2QHqyWTmK5M5lOJHcDpVuyTAtKxWkecX8OJxu3pi/74/fyGaDsi2g5zcwbD+0eg3yloHUG7D1K/j8DviiGWydBCk3MjW/iIiIiIgJH688zPm4ZIrnzcUjd5QxHUduk0q3ZKiXO1XG126x8sAlVsRc+Os3Di4AjYfAwG3Q5yeIvAtsvnB2B8wf4pl+/zQIzmwDtzsz4ouIiIiIZKoTVxKY8MsxAF7uWJkAX7vhRHK7VLolQ5XJH8xDjUsD8Pq8faSkOf/+nWw2KNMUenwFw2OgzZuQrzw4EmD7ZJjQAj5vgm3bV/g4tfO5iIiIiGQfb8zfT6rTRZPy4bSuXNB0HEkHKt2S4Z5qUZ4CIf4cv5LIl2uP3do7B4VDw6dg4BbotxCq9gS7P5zfjf3nZ2m7ZxD2eYPg1BZNv0VEREQkS1t14CLL9l/Ax2bxaucqWJZlOpKkA5VuyXDB/j680CECgPErDnM+NvnWP4hlQalGcNcEz/S73Tu4wyvi40rFtmsqfNkKPm0Em76ApOvp+wWIiIiIiGSw1DQXr8/z7IP0YKNSlCsQbDiRpBeVbskUXWsUpWaJ3CSmOnl70f7b+2CBeaH+46Q9upY15V/CVbUX+ATAxb2w6BkYVRHmDICTGzX9FhEREZEs4ev1xzh6OYHwYH8GtSxvOo6kI5VuyRSWZfF6l0gsC36MPsuW41fT44NyNbgCzjs/9ky/278PBapAWjLsnAZftYVP6sOGTyAxHT6fiIiIiEgGuBiXzLhlhwB4rl1FQgJ8DSeS9KTSLZkmsmgY99QpAcCrP+7F6UrHKXSuPFDvUXh8HfRfDlH3g28gXIqBxS/AqAiY9QgcX6fpt4iIiIh4lXd+jiEh1UmN4rm5q2Yx03Eknal0S6Z6uk0FQgN82HcujmmbT6b/J7AsKFYbuvw6/e44CgpVBWcK7J4JX3eA8XVg/UeQcCX9P7+IiIiIyC3YduIas7efAeC1O6tgs2nztOxGpVsyVb5gf4a1rgDAB0sOcD0xNeM+WUAY1OkPj62BR1ZCzb7gGwRXDsGSl2B0BHz/IBxdDS5XxuUQEREREfkDTpebET/tBaBn7WJUL57bbCDJECrdkunur1+SigVDuJ7oYPTSgxn/CS0LitaEOz+Epw9A53FQJAqcqbB3NnxzJ4yvBWvHQvyljM8jIiIiIgJ8v/UUu8/EEuLvwzNtI0zHkQyi0i2Zzsdu49U7KwMwZeMJ9p2Ny7xP7h8CtfrBo6vg0dVQ+yHwC4GrR2HZq57p98w+cGSFpt8iIiIikmFiEx28t/gAAENaVyB/iL/hRJJRVLrFiIZlw+lYtTAuN7w0dzeu9NxU7WYVqQGdxnju/b7zIyhaG1xpsO9H+LYbfFgD1oyCG+czP5uIiIiIZGvvLY7hakIq5QoE06dBSdNxJAOpdIsxL3WqRJCfne0nrzNj6ylzQfyDoWYfeGQ5DFgHdR8F/zC4fgKWvw6jK8P0++DQMnA5zeUUERERkWxhx8lrTP11U+E3u0bia1cty8706ooxhcNyMaxNRQDeWRTDlfgUw4mAQpHQ4X3P9Lvrp1C8HridEDMfvrsLxtWA1e9D3FnTSUVEREQkC0pzunhxzh7cbrirZjHql8lnOpJkMJVuMapvg5JULhxKbJKDtxbGmI7zH36BUKM3PLwEntgI9R6HgNwQexJWvgljImHavXBwsabfIiIiInLTJm84wb5zcYTl8uVfHbR5Wk6g0i1G+dhtjOwWiWXBrO2n2XDEC8/OLlAJ2r/jmX53+wJKNPRMvw8shKk9YWw1WPUOxJ42nVREREREvNi52CRGL/FsnvZ8+wjyBWvztJxApVuMiyqRh951SwCeTdVS07x013DfXFC9Fzy0CJ7cDA0GQq68EHcaVr0NY6vCdz0hZiE400ynFREREREv8/q8fSSkOqlZIje9ahc3HUcyiUq3eIVn20YQHuzHkUsJTFhz1HScv5e/IrQdCcP2w11fQqkm4HbBocUw/V4YGwkr3oRrJ0wnFREREREvsCLmAov2nMdusxjZrSo2m2U6kmQSlW7xCmGBvrzU0XN294fLD3HySqLhRDfJNwCq9oB+82HgNmg4CALzwY1z8Mv7MK46TLkL9v0ETofptCIiIiJiQFKqk1d+3AvAw41LU6lwqOFEkplUusVrdKlRhIZl85GS5uKVn/bgdhs4u/t2hJeDNm/AsBi4+2so0wxww+FlMPMBz9Fjy16Dq8cMBxURERGRzDR+5SFOX0uiSFgAg1uWNx1HMplKt3gNy7J4o2skfnYbqw5c4uc9501H+md8/KBKN+jzIwzaAY2HQVABSLgIa0fDhzXgmy6wdw6kpZpOKyIiIiIZ6NCFG3zxi+f2yVfvrEKQv4/hRJLZVLrFq5TNH8yAZmUBGDFvL/EpWXxDsrxloNWrMGwf9PwWyrYELDi6Cr7vB6MrwZKX4coRw0FFREREJL253W5enLsHh9NNq0oFaFO5oOlIYoBKt3idJ5qVpWS+QC7EpTB6yUHTcdKH3Rcq3wkPzIbBO+GOZyC4ECRehvUfwkc14etOsPsHSEsxnVZERERE0sGs7WfYfOwquXztjLizCpalzdNyIpVu8ToBvnZe7xIJwNfrj7HnTKzhROksT0lo8RIM3Qv3TIXybcGywfE1MOthGBUBi1+ES9nkBw4iIiIiOdC1hFTeWrgfgMGtylMsT6DhRGKKSrd4paYV8tOpWmFcbnhx7h6criy2qdrNsPtAREe4byYM3gVNn4fQopB0FTaMh4/rwFftYecMcCSZTisiIiIit+C9xTFcTUilQsFgHm5c2nQcMUilW7zWy50qE+Lvw85T15m2+aTpOBkrd3Fo/gIM2Q33zoCKHTzT75PrYc6jnun3oufg4n7TSUVERETkb2w7cZVpm08BMLJbVXztql05mV598VoFQwN4um1FAN79OYZLN3LAvc42O1RsB/dO81x+3vwlCCsOyddh02fwSX34sg1ET4XULHKWuYiIiEgO4nC6eHHOHgB61i5GnVJ5DScS01S6xavdX78kVYuGcSM5jZEL9pmOk7lCi0DTZzwbr903CyI6gWWHU5tg7uOe6feCp+H8btNJRURERORXk9YdI+b8DfIE+vJ8+0qm44gXUOkWr2a3WbzVrSo2C+ZGn2Xd4cumI2U+mx3Kt4J7vvMcPdbyFchdElJiYcsE+KwxTGgB27+BlHjTaUVERERyrDPXkxiz9BAAL3SoRN4gP8OJxBuodIvXq1osjAfqlwTg5bl7SElzGk5kUEghaDIcBkXDA3Ogchew+cCZbfDTU57p9/yhcDbadFIRERGRHGfET3tJcjipWyovPWoWMx1HvIRKt2QJw9tWJH+IP0cvJ/D56qOm45hns0HZFtDzGxi2H1q9BnnLQOoN2PoVfNEUPm8KWydByg3TaUVERESyvaX7LrB03wV8bBZvdovEZtOZ3OKh0i1ZQmiAL690qgzA+JWHOX45wXAiLxJcABoPgYHboM9PEHkX2HzhXDTMHwIfVISfBnmm4e5sePSaiIiIiGGJqWmM+GkvAP2blKFCwRDDicSbqHRLltGpWmGalA8nNc3Fyz/uwa0C+d9sNijTFHp8BcNjoM2bkK8cOBJg+2TPfd+fN4EtEyE51nRaERERkWxj/MqjnLmeRNHcuRjUspzpOOJlVLoly7Asize6ROLnY2PNocss3HPBdCTvFRQODZ+CgVuh30Ko2hPs/p6dzhcM99z7PfdJOLVF028RERGR23A2ASatPwHA612qEOjnYziReBuVbslSSoUHMbC556eHIxfGkJRmOJC3sywo1QjumuCZfrd9G/JHgCMRoqfAl63g00aw6QtIum46rYiIiEiW4nK5mXnMTprLTdsqBWlZqaDpSOKFVLoly3msaRnKhAdxKT6Vhae0hG9aYF5o8AQ8sREeWgzV7wWfALi4FxY9A6MqwpwBcHKjpt8iIiIiN2HWjrMcu2ER6Gfn1c5VTMcRL6XGIlmOv4+dN7pGArDmvMXuM7o/+ZZYFpSoD90+80y/278PBapAWjLsnAZftYVP6sOGTyDxqum0IiIiIl7pakIq7y0+CMCgFmUpkjuX4UTirVS6JUtqVC6cO6sVxo3FKz/tx+nSZPYfyZUH6j0Kj6+Dh5dB1P3gGwiXYmDxC557v2c9AsfXafotIiIi8v+8vXA/15McFAl006d+CdNxxIupdEuW9UL7CuSyu9lzNo5vNxw3HSdrsywoXge6fOyZfnccBYWqgjMFds+ErzvA+Dqw/iNIuGI6rYiIiIhRm45e4fttpwHoWcaJr121Sv6cVodkWeHB/nQq4QLg/cUHOH0t0XCibCIgDOr0h8fWwCMroWZf8A2CK4dgyUswOgK+fxCOrgaXy3RaERERkUyV7HDy/OzdAPSqXZTSOpJb/oZKt2RpDQu6qV0yNwmpTl6co7O705VlQdGacOeH8PQB6DwOikSBMxX2zoZv7oTxtWDtWIi/ZDqtiIiISKYYt/wQxy4nUCDEn2fbVDAdR7IAlW7J0mwWjOxSBT8fG6sPXmLOjjOmI2VP/iFQqx88ugoeXQ21HwK/ELh6FJa96pl+z+wDR1Zo+i0iIiLZ1p4zsXzxy1EA3uwaSWguX8OJJCtQ6ZYsr0z+IIa0Kg/A6/P3celGiuFE2VyRGtBpjOfe7zvHQ9Ha4EqDfT/Ct93gwxrwywdw47zppCIiIiLpxuF08ewPu3C63HSsVpg2VQqZjiRZhEq3ZAuPNClDlSKhXE90MGLeXtNxcgb/YKj5ADyyHAasg7qPgn8YXD8BK96A0ZVh+n1waCm4nKbTioiIiNyWL345yr5zceQO9GWEzuSWW6DSLdmCr93Gu3dVw26zWLDrHIv3asqaqQpFQof3PdPvrp9C8XrgdkLMfPiuB4yrDqvfg7izppOKiIiI3LLDF+MZt/wQAK90qkz+EH/DiSQrUemWbCOyaBiP3VEGgJfn7iE2yWE4UQ7kFwg1esPDS+CJjVBvAATkhthTsHIkjKkCU++BAz9r+i0iIiJZgsvl5vlZu0hNc9G0Qn66RRU1HUmyGJVuyVYGtSxPmfxBXLyRwlsL9puOk7MVqATt3/VMv7t9ASUbgdsFBxfBtF4wtiqsfBuunzKdVERERORPTdl0gq0nrhHkZ+et7lWxLMt0JMliVLolWwnwtfPeXdWwLJix9RTrDl82HUl8c0H1XvDgQnhyCzQYCLnyQtwZWP0OjKsG390NMQvAmWY6rYiIiMhvTl9L5N1FMQA83z6CorlzGU4kWZFKt2Q7tUvlpU/9kgA8P3sXiakqcl4jfwVoO9Iz/b7rSyjVxDP9PrQEpveGsZGw4k24dsJ0UhEREcnh3G43L87ZQ0Kqkzql8nBfvZKmI0kWpdIt2dIz7Tw/iTx1NYlRSw6ajiP/y8cfqvaAfvPhqe3QaDAEhsONc/DL+56N177tDvt+AqfuzRcREZHMN3v7GVYfvISfj4137qqGzabLyuWfMV66R4wYgWVZ//UoVEhn3sntCfb3YWS3SAC+WneM7SevGU4kfypfWWj9OgzbD3d/DWWaA244shxmPuA5emzZCLh61HBQERERySku3Ujh9fn7ABjSqjxl8wcbTiRZmfHSDVClShXOnTv322P37t2mI0k20KxiAbrXLIrbDc/9sIuUNO2W7dV8/KBKN+gzFwbtgMbDIKgAJFyEtWPgwyj4pgvsnQNpqabTioiISDY24qe9xCY5qFIklEealDEdR7I4H9MBAHx8fG56up2SkkJKSspvv46LiwPA4XDgcOgy1Jzk36/3X73uz7ctz+oDlzh0MZ7xyw8yqEW5zIontyOkODT9FzR+BuvQEmw7JmMdXYl1dBUcXYU7MBxXtXtw1bgf8nnva3oza1TEJK1R8XZao2LCkn0XWLD7HHabxcgulcHlxPEnR51qjeZct/KaW263252BWf7WiBEjeP/99wkLC8Pf35969erx1ltvUabMH/9EacSIEbz22mu/e37q1KkEBgZmdFzJgnZcsfj6oB275ebpqk6KBJlOJP9ErpRLlLyymhJX15DL8Z/bBS4FV+JEvmacy10bl83XYEIRERHJ6hLT4O1oO3EOi1ZFXXQu4TIdSbxUYmIivXv3JjY2ltDQ0L98W+Ole9GiRSQmJlKhQgUuXLjAm2++SUxMDHv37iVfvny/e/s/mnQXL16cy5cv/+0XK9mLw+Fg6dKltG7dGl/fPy9bbrebJ6ftZOn+i1QrGsqMR+riY/eKOyvkn3ClYR1eim3HN1hHlmO5PX8ZunPlxVW1J66oPhBewXBIj5tdoyKmaI2Kt9Malcz2r7l7+X7bGUrnC2Tekw3w97X/5dtrjeZccXFxhIeH31TpNn55efv27X/7/1WrVqVBgwaULVuWyZMnM2zYsN+9vb+/P/7+/r973tfXVws9h7qZ1/7NblXZeGw1u87EMWXzGR65Q/fmZF2+UOVOzyP2NOyYAtu/wYo7g33zZ9g3fwYlGkKtflD5Ts854aYT6/uTeDmtUfF2WqOSGdYdvsz3284A8N7d1QkODLjp99UazXlu5fX2unFfUFAQVatW5dChQ6ajSDZSMDSAlzpWAmDU0gMcv5xgOJGki7Bi0Ox5GLIbes+Eih3AssPJ9TDnURhVERY9Bxf2mU4qIiIiXiwxNY3nZ+8CoE+DktQplddwIslOvK50p6SksH//fgoXLmw6imQzPWsXp1G5fCQ7XDw/exeG76yQ9GSzQ4W2cO80GLoHmr8EYSUgORY2fQafNoCJrWHHd5CaaDqtiIiIeJlRSw5y6moSRXPn4tl2EabjSDZjvHQ//fTTrF69mmPHjrFp0yZ69OhBXFwcffv2NR1NshnLsni7WzVy+drZePQq07ecMh1JMkJoEWj6DAyOhvtmQaXOnun36c3w4xMwKgIWPA3ndTShiIiIwPaT1/hq3TEARnaLJNjf+B24ks0YL92nT5/m3nvvpWLFinTv3h0/Pz82btxIyZIlTUeTbKhEvkCeblsRgLcW7Od8bLLhRJJhbHYo3wp6TYFh+6DlK5CnFKTEwpYJ8FljmNACtn8DKfGm04qIiIgBKWlOnvthF243dK9ZlGYVC5iOJNmQ8R/jTJ8+3XQEyWH6NSzFvJ1niT51nZfm7mZCn9pYlmU6lmSkkELQZDg0GgrHVsO2ryFmAZzZ5nn8/C+o2sOz+VqRGobDioiISGb5eOURDl2MJzzYj5c7VjYdR7Ip45Nukcxmt1m816MavnaLZfsvMn/XOdORJLPYbFC2OfScDMP2Q+vXIW8ZSL0B2ybBF03h86awdRKk3DCdVkRERDLQ/nNxfLLyMACv3RlJniA/w4kku1LplhypQsEQBjYvD8CIn/ZyNSHVcCLJdMH5odFgeGo79J0HkT3A7gfnomH+EPigIvz0lGcSrk33REREspU0p4vnZu0izeWmTeWCdKhayHQkycZUuiXHerxZWSoWDOFKQiqvz9trOo6YYllQ+g7o8SUMi4E2IyG8AjgSPPd7T2gBnzeBzRM8u6GLiIhIljdp3XF2nY4lJMCHN7pG6lZDyVAq3ZJj+fnYeLdHNWwWzI0+y4qYC6YjiWlB+aDhQHhyMzy4CKr1Aru/Z6fzhU97dj6f+ySc2qLpt4iISBZ1/HICo5YeAOCljpUoGBpgOJFkdyrdkqPVKJ6bhxuXBuBfs/cQm+QwnEi8gmVByYbQ/QsYHgPt3oH8EeBIhOgp8GUr+LQhbPockq6ZTisiIiI3yeVy89ysXSQ7XDQql4+etYubjiQ5gEq35HjDWlekdHgQ5+OSee0nXWYu/yMwL9R/HJ7YCA8tgRr3gU8uuLgPFj3rmX7PfgxObND0W0RExMt9te4Ym45dJdDPztvdqumycskUKt2S4+Xys/PB3dWxWTB7xxl+3qPdzOUPWBaUqAddP/FMvzt8AAUjIS0Zdk2HSe3g43qw4RNIvGo6rYiIiPyPQxdu8N7if19WXpkS+QINJ5KcQqVbBKhVMg8DmpYF4F9z9nDpRorhROLVcuWGuo/AgLXQfwVEPQC+gXD5ACx+wTP9ntUf68Q6Tb9FRES8gMPpYtjMnaSmuWhWMT/31tVl5ZJ5VLpFfjW4VXkiCoVwNSGVf83ZjVtlSf6OZUGxWtBlPAw/AB1HQ6Fq4EyB3d/jM6ULLfc/h23jx5Bw2XRaERGRHOvjlYfZfSaWsFy+vHuXLiuXzKXSLfIrfx87Y3rVwNdusXTfBX7Ydtp0JMlKAkKhzsMwYA08ugpq9cPtF0Rwynnsy1/1TL+/fxCOrgaXy3RaERGRHGPX6et8tOIwAG90jdRu5ZLpVLpF/p9KhUMZ2roCAK/P28eZ60mGE0mWVCQKOo8jbdAeoos/iKtwFLgcsHc2fHMnfFQT1o6B+Iumk4qIiGRryQ4nw2buxOly07FaYe6sXsR0JMmBVLpF/sdjd5SlZonc3EhJ45nvd+Jy6TJz+Yf8QzgR3hznQ0vhsV+g9sPgFwLXjsGyETC6EszsA4eXa/otIiKSAT5YfIDDF+PJH+LPm10iTceRHEqlW+R/2G0Wo3rWIJevnfVHrjB5w3HTkSQ7KFwdOo2Gpw9Al4+hWB1wpcG+H2FKd/iwBvzyAdw4bzqpiIhItrDx6BW+XHcMgHfvqkqeID/DiSSnUukW+QOlw4P4V4cIAN5ZFMORS/GGE0m24RcEUfdD/2UwYB3UfRT8w+D6CVjxBoyuDNPvg0NLweU0nVZERCRLik9J4+nvd+J2wz11itMioqDpSJKDqXSL/In765ekSflwUtI8R0ykOXX5r6SzQpHQ4X3Pud9dP4Pi9cHthJj58F0PGFcdVr8HcWdNJxUREclS3py/j9PXkiiWJxcvdapsOo7kcCrdIn/Csize61GNkAAfdp66zqerjpiOJNmVXyDUuBceXgxPbIR6j0NAbog9BStHwpgqMPUeOPAzONNMpxUREfFqK2IuMH3LKSwLPri7OsH+PqYjSQ6n0i3yFwqH5eL1LlUAGLf8EHvOxBpOJNlegUrQ/h3Pud/dJ0DJRuB2wcFFMK0XjKsGK9+C66dMJxUREfE6VxNSefaH3QA83Kg09cvkM5xIRKVb5G91rVGUdlUKkeZyM2xmNMkO3WcrmcA3AKr1hAcXwpNboMFAyJUX4s7A6ndhbFX47m6IWaDpt4iICOB2u3l57h4ux6dQrkAwT7etaDqSCKDSLfK3LMtiZLdIwoP9OHghnjFLD5qOJDlN/grQdqTn3u+7voRSTQA3HFoC03t7Lj9f/gZcO2E6qYiIiDE/7TzLgt3n8LFZjOlZgwBfu+lIIoBKt8hNyRfsz9vdqwHwxZqjbDl+1XAiyZF8/KFqD+g3H57aDo0GQ2A4xJ+HNR94Nl77trvnGDKnw3RaERGRTHM+NplXftwLwMAW5ahaLMxwIpH/UOkWuUmtKxekR61iuN0wfOZOElJ0Sa8YlK8stH4dhu2Hu7+GMs0BNxxZDjP7eI4eWzYCrh41HFRERCRjud1unpu1i9gkB1WLhvFk83KmI4n8F5VukVvwSufKFM2di5NXExm5cL/pOCLg4wdVukGfuTBoBzQeBsEFIeEirB0DH0bBN11gz2xISzWdVkREJN1N3XyS1Qcv4edjY0yv6vjaVXHEu2hFityC0ABf3r/bc5n51E0nWXngouFEIv9P3jLQ6lUYuhd6TYFyrQALjq6CHx6E0ZVgyctw+bDppCIiIunixJUERi7wDEKebVuRcgVCDCcS+T2VbpFb1LBsOP0algLguR92cT1R00PxMnZfqNQZ7p8Fg3fCHc9CSGFIvAzrP4TxteDrTrD7B0hLMZ1WRETkH3G63AyfuZPEVCf1SufloUalTUcS+UMq3SL/wHPtIiiTP4iLN1J+27RDxCvlKQktXoQhe+CeaVC+LVg2OL4GZj0MoyJg8YtwSbvyi4hI1jJxzVG2nrhGkJ+dD+6ujs1mmY4k8odUukX+gVx+dkb3rIHdZvHTzrPM33XWdCSRv2b3gYgOcN9MGLIbmr0AoUUh6SpsGA8f14Gv2sPOGeBIMp1WRETkL8Wcj2PUEs8PjF/pXJnieQMNJxL5cyrdIv9QjeK5eaJZWQBemruHi3HJhhOJ3KSwYtDseU/57v09VOwIlh1Oroc5j8KoirDoObiwz3RSERGR30lNczFsxk5SnS5aRBSgZ+3ipiOJ/CWVbpHb8FSL8lQpEsr1RAfPz96N2+02HUnk5tnsUKEN3DsVhu6B5i9BWAlIjoVNn8GnDWBia9jxHaQmmk4rIiICwEcrDrHvXBx5An15566qWJYuKxfvptItchv8fGyM7lkDP7uNFTEXmbHllOlIIv9MaBFo+gwMjvZswFapM9h84PRm+PEJz73fC56G87tNJxURkRxsx8lrfLzScwrHm12rUiAkwHAikb+n0i1ymyoWCmF4mwoAvDF/HyeuJBhOJHIbbHbPUWO9pniOHmv5CuQpBSmxsGUCfNYYJrSAbZMhJd50WhERyUESU9MYPnMnLjfcWb0IHasVNh1J5KaodIukg/5NylC3VF4SUp0Mmh6Nw+kyHUnk9oUUgibD4akd8MBcqNzVM/0+sw3mDfLc+z1vCJyNNptTRERyhBE/7eXo5QQKhvrzepcqpuOI3DSVbpF0YLdZjLmnBqEBPuw8dZ3RS3X8kmQjNhuUbQ49J8Ow/dDqNchbBlLjYdsk+KIpfH4HbP0KkuNMpxURkWxo3s6zzNx6GsuCsb2iyB3oZzqSyE1T6RZJJ0Vz5+Ldu6oB8NnqI6w9dNlwIpEMEFwAGg+Bgdug7zyo0h3sfnBuJ8wf6rn3+6enPNNwbSwoIiLp4NTVRP4127OnyJPNytGgbD7DiURujUq3SDpqX7Uw99YtgdsNQ2dGcyU+xXQkkYxhs0HpO+DuSTAsBtqMhHzlwZEA27/x3Pf9WRPYPMGzG7qIiMg/kOZ0MXj6Dm6kpFGzRG4GtypvOpLILVPpFklnr3SqTLkCwVy6kcIzP+zSMWKS/QXlg4YDYeAW6LcQqvUCuz9c2A0Ln4YPKsLcJ+DUZk2/RUTkloxbfojtJ68T4u/DuHui8LWrvkjWo1Urks5y+dn56N4o/Hw8x4h9vf646UgimcOyoFQj6P4FDI+Bdu9A/ghIS4Lo7+DL1vBpQ9j4GSRdM51WRES83IYjVxj/6/Fgb3WvSvG8gYYTifwzKt0iGaBS4VBe7FAJgLcXxrD3rC6vlRwmMC/Ufxye2AgPLYHqvcEnAC7ug5+f89z7PfsxOLFB028REfmdawmpDJ0RjdsNPWsXo3P1IqYjifxjKt0iGaRPg5K0qlSAVKeLQdN2kJiaZjqSSOazLChRD7p9CsMPQIcPoGAkpCXDrukwqR18XA82fAyJV02nFRERL+B2u3l21i7OxyVTJn8QI+7U8WCStal0i2QQy7J4r0d1Cob6c+RSAq/P22c6kohZuXJD3UdgwFrovwKiHgDfQLh8ABb/y3Pu96z+cHytpt8iIjnYlI0nWLrvAn52Gx/eE0Wgn4/pSCK3RaVbJAPlDfJjTK8aWBZM33KKBbvOmY4kYp5lQbFa0GW8Z/rdcTQUqgbOVNj9PXzdEcbXhnUfQoKO3hMRyUlizsfxxoL9ADzXPoLIomGGE4ncPpVukQzWsGw4TzQrC8Dzs3dx+lqi4UQiXiQgFOo8DAPWwKOroFY/8AuGK4dh6cuee7+/7wdHV4HLZTariIhkqKRUJ4Om7SA1zUXzivl5qFEp05FE0oVKt0gmGNKqAlElcnMjOY3B06NJc6o8iPxOkSjoPM6z83nncVCkJrgcsHcOfNMFPqoJa8dA/EXTSUVEJAO8uWAfBy/Ekz/En/fvro5lWaYjiaQLlW6RTOD76z1JIf4+bDtxjQ+XHzIdScR7+Yd4Jt6ProTH1kCd/uAfCteOwbIRMLoSzHgADi/X9FtEJJv4ec85vtt0EoDRPasTHuxvOJFI+lHpFskkxfMG8ma3SADGrzzMxqNXDCcSyQIKV4OOozzT7y4fQ7E64EqD/T/BlO7wYXX45X2I034JIiJZ1dnrSTw3azcAjzUtQ5Py+Q0nEklfKt0imahLjaLcXasYLjcMnRHNtYRU05FEsga/IIi6H/ovg8fXQ93HwD8Mrp+EFW/CmCow/T44tBRcTtNpRUTkJjldboZMjyY2yUH1YmEMb13RdCSRdKfSLZLJRtxZhTLhQZyLTea5Wbtw62gkkVtTsAp0eM8z/e76GRSvD24nxMyH73rAuOqw6l2IPWM6qYiI/I3xKw6z+fhVgvzsfHhvFH4+qieS/WhVi2SyIH8fPrw3Cl+7xZJ9F367f0lEbpFfINS4Fx5eDE9sgnqPQ0BuiD0Fq96CsZEw9R44sAicaabTiojI/9h6/Crjlh8E4M1ukZTMF2Q4kUjGUOkWMSCyaBjPtYsA4I35+zhw/obhRCJZXIEIaP+O59zv7hOgZGNwu+DgIph2D4ytCivfguunTCcVEREgNtHB4OnRuNzQPaoo3aKKmY4kkmFUukUMeahRaZpVzE9KmotB03aQ7NB9qCK3zTcAqvWEBxfAk1ugwUDIlRdunIXV73rK95QesH8+OB2m04qI5Ehut5t/zdnNmetJlMoXyOtdI01HEslQKt0ihthsFh/c7TkS48CFG4xcsN90JJHsJX8FaDvSc+93j6+g9B2AGw4vhRn3wZhIWP46XDtuOqmISI4yY8spFuw+h4/NYtw9UQT7+5iOJJKhVLpFDAoP9md0z+oAfLvxBIv3njecSCQb8vGHyLug7zx4ajs0GgJB+SH+PKwZBeNqwLfdYN+Pmn6LiGSwwxdvMGLeXgCeaVuR6sVzmw0kkglUukUMu6NCfh67owwAz83axbnYJMOJRLKxfGWh9WswdB/cPRnKNAfccGQFzOwDoyvB0lfhyhHTSUVEsp1kh5OnpkWT7HDRpHw4jzQpYzqSSKZQ6RbxAsPbVKRasTCuJzoYMj0ap0vHiIlkKB8/qNIV+syFQdHQZDgEF4SES7BuLHxUEybfCXtmQ1qK2awiItnEO4ti2H8ujnxBfozqWR2bzTIdSSRTqHSLeAE/Hxvj7okiyM/OpmNX+XD5IdORRHKOvKWh5SswdC/0mgLlWgMWHFsNPzzomX4veQkuHzadVEQky1q89zxfrz8OwAd3V6dASIDZQCKZSKVbxEuUDg9iZLeqAHy44hCrDlw0nEgkh7H7QqXOcP8PMGQX3PEshBSGxCuw/iMYXwsmdYRd34Mj2XRaEZEs4/jlBJ6euROA/o1L0zyigOFEIplLpVvEi3SNKsp99UrgdsOQGdGcvpZoOpJIzpS7BLR4EYbsgXumQYV2YNngxFqY3R9GR8DPL8DFGNNJRUS8WlKqkwFTtnEjJY06pfLwXPsI05FEMp1Kt4iXeaVz5d/u737yu+2kpOn8bhFj7D4Q0QF6z4Ahu6HZCxBaFJKuwcZP4JN68FU7iJ4GDm2CKCLy/7ndbl7+cQ8x528QHuzH+N418bWrfkjOo1Uv4mX8fex83LsmYbl82Xk6ljfn6/xuEa8QVgyaPe8p372/h4odwbLDyQ0wdwCMqggLn4ULe00nFRHxCjO2nOKHbaexWfDhvVEUDNV93JIzqXSLeKHieQMZe08NLMtzfvfcHWdMRxKRf7PZoUIbuHcqDN0DzV+CsBKQHAubP4dPG8LEVrBjCqQmmE4rImLEnjOxvPKT54eQT7etSMOy4YYTiZij0i3ipZpXLMBTzcsB8MLs3Ry8cMNwIhH5ndAi0PQZGBwN98/ybMRm84HTW+DHJ2FUBCwYDud2mU4qIpJpYhMdDJiyjdQ0F60qFWDAHWVNRxIxSqVbxIsNblWBJuXDSXJ4NiGJT0kzHUlE/ojNDuVaeY4cG7oPWr4KeUpBShxsmQifN4EvmsO2yZASbzqtiEiGcbncDJsZzelrSRTPm4tRd9fQedyS46l0i3gxu81ibK8aFA4L4OilBJ77YRdut9t0LBH5KyEFockweGoHPDAXKncFmy+c3Q7zBnnu/Z43BM7uMBxURCT9fbr6CMtjLuLnY+PT+2oRFuhrOpKIcSrdIl4uX7A/H99XE1+7xYLd55i07rjpSCJyM2w2KNscek6GYfuh9euQtwykxsO2SfBFM/j8Dtj6FSTHmU4rInLb1h++zKglBwB4o0sVIouGGU4k4h1UukWygJol8vBih0oAvLVwP1uPXzWcSERuSXB+aDQYntoOfedDZA+w+8G5nTB/qOfe7x8HwultoKtZRCQLOh+bzFPTduByw921itGrTgnTkUS8hkq3SBbRt2EpOlcvQprLzZNTt3M5PsV0JBG5VZYFpZtAjy9hWAy0GQnhFcCRADu+hYkt4LMmsHmCZzd0EZEswOF0MXDqdq4kpFKpcChvdI00HUnEq6h0i2QRlmXxTveqlCsQzIW4FAZN24HTpYmYSJYVlA8aDoQnN8ODi6BaL7D7w4XdsPBp+KAizH0CTm3W9FtEvNo7i2LYeuIaIQE+fHZ/TQJ87aYjiXgVlW6RLCTI3/OXWaCfnfVHrjB66QHTkUTkdlkWlGwI3b+A4THQ7l3IXwnSkiD6O/iytefs742fQdI102lFRP7Lgl3n+HLtMQBG3V2dkvmCDCcS8T4q3SJZTLkCIbxzVzUAPl55hOX7LxhOJCLpJjAv1B8AT2yAh5ZAjfvAJxdc3Ac/P+e593v2Y3Big6bfImLckUvxPPvDTgAea1qGNlUKGU4k4p1UukWyoDurF6Ffw1IADJ0RzamriWYDiUj6siwoUQ+6fuKZfnf4AApGQloy7JoOk9rBx/Vgw8eQqI0VRSTzJaam8fiUbSSkOqlXOi/PtKloOpKI11LpFsmi/tWhElElchOXnMbj320j2eE0HUlEMkKu3FD3ERiwFvqvgKgHwDcQLh+Axf/ynPs9qz8cX6vpt4hkCrfbzb9m7+bghXgKhPjzUe8ofOyqFSJ/Rn86RLIoPx8bH/euSd4gP/acieO1eXtNRxKRjGRZUKwWdBkPww9ApzFQqBo4U2H39/B1RxhfG9Z9CAmXTacVkWxsyqaTzI0+i91mMb53TQqEBJiOJOLVVLpFsrAiuXMx7p4aWBZM23yKH7adNh1JRDJDQCjUfggGrIFHV0GtfuAXDFcOw9KXPfd+f/8gHF0FLpfhsCKSnew8dZ035u0D4Pl2EdQtnddwIhHvp9ItksU1KZ+foa0qAPDinN3sOxtnOJGIZKoiUdB5nGf63flDKFITXA7YOxu+6QIf1YS1YyD+oumkIpLFXUtI5YnvtpPqdNGuSiH6NyltOpJIluAVpfuTTz6hdOnSBAQEUKtWLdasWWM6kkiWMrB5OZpVzE9KmosnvttGXLLDdCQRyWz+wVCrLzy6Eh5bA3X6g38oXDsGy0bA6Eow4wE4vFzTbxG5ZS6XmyEzojlzPYlS+QJ57+5qWJZlOpZIlmC8dM+YMYMhQ4bw4osvsmPHDpo0aUL79u05efKk6WgiWYbNZjGmZw2K5s7F8SuJPD1zJ25tqCSScxWuBh1HeXY+7/IxFKsLrjTY/xNM6Q4fVodf3ocb50wnFZEs4qMVh1l98BIBvjY+vb8WoQG+piOJZBk+pgOMHj2ahx9+mP79+wMwduxYFi9ezKeffsrbb7/9u7dPSUkhJSXlt1/HxXkupXU4HDgcmu7lJP9+vfW6ewT7WXzYqxr3TNzMkn0X+GTlIR7VZV9GaY2KcZYfRPbyPC7uw7bjW2x7ZmJdPwkr3sRn5dvUDa2B84ANyrcGm910YpH/ou+j3mHNocuMXX4QgNc7V6ZceC69Jr/SGs25buU1t9wGx2GpqakEBgby/fff061bt9+eHzx4MNHR0axevfp37zNixAhee+213z0/depUAgMDMzSvSFaw9rzF98fsWLh5JMJFlTyaeIvIf9hcqRS5voVSl1eSL+Hgb88n+ubjRL6mnMx3B8l+2hhJRDwuJsHo3XaSnBYNC7joVVa3p4gAJCYm0rt3b2JjYwkNDf3LtzVaus+ePUvRokVZt24dDRs2/O35t956i8mTJ3PgwIHfvc8fTbqLFy/O5cuX//aLlezF4XCwdOlSWrduja+vLnH6N7fbzcs/7WPG1jME+/vw/aN1KVcg2HSsHElrVLxd2rk9nJ33NmVubMJKvg6A27LhLtsKV1Qf3OVagc34RXGSg+n7qFlxSQ56fL6JY1cSqVkiN988WBt/H+N3p3oVrdGcKy4ujvDw8Jsq3V7xN+n/bsLgdrv/dGMGf39//P39f/e8r6+vFnoOpdf+997oWo1jl5PYfPwqj0+N5scnGxMWqN8jU7RGxWsVjmRPsfso0eZLfA8tgm2TsU6sxTq8BNvhJRBSBKLuh5p9IHdx02klB9P30czndLkZ+sMOjl1JpEhYAJ8/UJvgXL//N7h4aI3mPLfyehv9UVV4eDh2u53z58//1/MXL16kYMGChlKJZH1+PjY+vb/mbxurPTl1O2lOXQ4mIn/CJwCq9YQHF8CTW6DBQMiVF26chV/eg7FVYUoP2D8fnLpvUSQneHvhfn75deO0L/rUJn+ICrfIP2W0dPv5+VGrVi2WLl36X88vXbr0vy43F5Fbly/Ynwl9ahPoZ2ft4cuMXLjfdCQRyQryV4C2Iz07n/f4Cko1AdxweCnMuA/GRMLy1+HacdNJRSSDfL/1FBPXHgNg1N01iCwaZjiRSNZm/KaMYcOGMXHiRL766iv279/P0KFDOXnyJAMGDDAdTSTLq1wklNE9qwMwad1xZmzRUXwicpN8/CHyLug3H57aDo0GQ1B+iD8Pa0bBuOrwbTfYOxfSUk2nFZF0su3ENV6csweAQS3L07FaYcOJRLI+4/d09+rViytXrvD6669z7tw5IiMjWbhwISVLljQdTSRbaBdZmKGtKjBm2UFemruHMvmDqVNKOxOLyC3IVxZavw7NX4IDC2Hb13B0JRxZ4XkE5Yca93nu/c5X1nRaEfmHzl5P4rFvt5HqdNG2SkGGtCxvOpJItmB80g3wxBNPcPz4cVJSUti2bRt33HGH6Ugi2cqgluXoWLUwDqebAd9u48z1JNORRCQr8vGDKl2hz1wYFA1NhkNwQUi4BOvGwkc1YXJn2DML0lL++mOJiFdJSnXy6LdbuRyfQkShEEb3rIHN9scbG4vIrfGK0i0iGcuyLN6/uxqVC4dyJSGV/pO3kpiaZjqWiGRleUtDy1dg6F7oNQXKtQYsOPYL/PAQjK4Ei1+Ey4dMJxWRv+F2u3nmh53sORNH3iA/JvSpTZC/8QtiRbINlW6RHCLQz4cJfWsTHuzH/nNxDJ+5E5fLbTqWiGR1dl+o1Bnu/wGG7II7nvUcNZZ4BTaMh/G1YVJH2DUTHMmm04rIH/h45WHm7zqHj83i0/tqUjxvoOlIItmKSrdIDlI0dy4+u78WvnaLRXvO8+EKTaBEJB3lLgEtXoQhu+He6VChHVg2OLEWZj8CoyPg5xfgYozppCLyq8V7z/PBkoMAvNE1knpl8hlOJJL9qHSL5DC1S+VlZNeqAIxddohFu88ZTiQi2Y7dByq2h94zYMgeaPYChBaDpGuw8RP4pB581Q6ip4FDe0yImBJzPo6hM6IB6NugJPfWLWE2kEg2pdItkgP1rFOcBxuVAmDYzJ3sPRtrNpCIZF9hRaHZ855Lz3t/DxU7gmWHkxtg7gAYVREWPgsX9ppOKpKjXIlP+XWPFycNy+bjpU6VTUcSybZUukVyqBc7VKJJ+XCSHE4e/WYbl+O107CIZCCbHSq0gXunejZfa/EShJWA5FjY/Dl82hAmtoIdUyA1wXRakWwtNc3F499t5/S1JErmC+Tj3jXxtasWiGQU/ekSyaF87DbG31uT0uFBnLmexONTtpGa5jIdS0RygtDCcMczMHgn3D/LsxGbzQdOb4Efn4RREbBgOJzfbTqpSLbjdrsZMW8vm49dJdjfh4l9apMnyM90LJFsTaVbJAcLC/RlQp/ahPj7sOX4NV6euwe3Wzuai0gmsdmgXCvPkWND90HLVyFPKUiJgy0T4bPGMKEFbJsMKfGm04pkC1M2nmDqppNYFoy7pwblC4aYjiSS7al0i+Rw5QoE82HvKCwLZmw9xeT1x01HEpGcKKQgNBkGT+2AB+ZClW5g84Uz22DeIM+93/OGwNlow0FFsq71hy8zYt4+AJ5tG0HLSgUNJxLJGVS6RYTmFQvwQvsIAN5YsJ+1hy4bTiQiOZbNBmWbw91fw7D90Pp1yFsGUuNh2yT4oil8fgds/QqS40ynFckyTlxJ4Imp23G63HStUYQBTcuYjiSSY6h0iwgAjzQpQ/eaRXG63Dw5dTvHLmsjIxExLDg/NBoMT22HvvMgsgfY/eDcTpg/1HPv948D4fQ20K0xIn/qRrKD/pO3cj3RQfViYbxzVzUsyzIdSyTHUOkWEQAsy+KtblWpUTw3sUkO+k/eQlyyw3QsERGwLCh9B/T4EobFQNu3ILwCOBJgx7cwsQV81gQ2T4Ck66bTingVp8vN0BnRHLoYT4EQfz5/oDYBvnbTsURyFJVuEflNgK+dLx6oRaHQAI5cSmDQtB2kObWjuYh4kaB80OBJeHIzPLgIqvUCuz9c2A0Ln/ZMv+c+Aac2a/otAry/+ADL9l/Ez8fGF31qUygswHQkkRxHpVtE/kuB0AC+6FMLfx8bqw5c4uUf92pHcxHxPpYFJRtC9y9geAy0exfyV4K0JIj+Dr5sDZ80gI2fQdI102lFjJiy8QSfrT4CwLt3ea5mE5HMp9ItIr9TrVhuxt3j2dF82uaTfLLqiOlIIiJ/LjAv1B8AT2yAh5ZAjfvAJxdc2g8/P+eZfs9+FE6s1/Rbcoxl+y7wyo97ABjSqjzdoooZTiSSc6l0i8gfahdZiBGdqwCeS9Nmbz9tOJGIyN+wLChRD7p+4pl+d/gACkZCWjLsmgGT2sPHdWH9eEi4YjqtSIaJPnWdgdO243JDz9rFGNyyvOlIIjmaSreI/Km+DUvx6B2eI0We/WEX6w7rKDERySJy5Ya6j8CAtdB/BUQ9AL6BcPkgLHkRRkfADw/DsTWafku2cuJKAg9/vYVkh4s7KuRnZLeq2qlcxDCVbhH5S8+3i6BTtcKkudwM+HYb+8/pXFwRyUIsC4rVgi7jYfgB6DgaClUDZyrs+QEmd4KPasG6cRB/yXRakdtyNSGVfpO2cCUhlSpFQvnkvpr42vXPfRHT9KdQRP6SzWYxqmd16pbOy42UNB6ctIVzsUmmY4mI3LqAUKjzMAxYA4+uglr9wC8Yrh6Bpa/A6ErwfT84ugpcOrlBspZkh5P+k7dw7HICRXPnYlK/OgT7+5iOJSKodIvITfD3sTPhgdqUKxDM+bhk+n2lM7xFJIsrEgWdx3mm350/hCI1weWAvXPgmy7wURSsGQ03LphOKvK3nC43g6fvYPvJ64QG+DD5oToUCNXRYCLeQqVbRG5KWKAvXz9Yh/wh/hy4cIPHvtlGapomQSKSxfkHQ62+8OhKeGwN1OkP/qFw7Tgsfw3GVIYZD8DhZZp+i1dyu928MX8fi/dewM9uY2LfOpQrEGI6loj8PyrdInLTiuUJZFK/OgT52dlw9ArPzdqlM7xFJPsoXA06jvLsfN7lYyhWF1xpsP8nmHIXfFgdfnkf4s6ZTirym4lrjvH1+uMAjO7luR1MRLyLSreI3JLIomF8cn8t7DaLOTvO8MGSA6YjiYikL78giLof+i+Fx9dD3ccgIAyun4QVb8KYKjCtNxxcAi6n6bSSg83beZaRC/cD8GKHSnSqVsRwIhH5IyrdInLLmlbIz9vdqwLw8cojfLfphOFEIiIZpGAV6PCe597vbp9DiQbgdsKBBTD1bhhbDVa9C7FnTCeVHGbT0SsMn7kTgH4NS9G/SWnDiUTkz2hLw0xyPuE8CY4E0zGylbS0NC46L3I09ig+PlrKma1WOejbNIBvN5zglYUXwDeS+mXymY7lVbRGxdtpjd6iknU8jytHPZecxyyApPOw7j1Y/wGUbASVu3qKuV2/n+lBa/SPnbiSwODpO0izO2lSMR+9G/tzNPao6Vg5ktZoxvCx+VAytKTpGOnGcmfxGzLj4uIICwsjNjaW0NBQ03H+1LBVw1h6YqnpGCIiIiIiIl6taHBRfr7rZ9Mx/tKt9FD9OCaTBPkGkcc/j+kY2YobN6mpqfj5+WFhmY6TY7mB+GQHqU43NgtCc/lit/R6gNaoeD+t0XTkdkJaMqSlgPv/7XJu9wMff8//6vf4lmmN/jc3buKS0khzubHbLEIDfLDp71yjtEYzRph/mOkI6UqlO5O80egN0xGyHYfDwcKFC+nQoQO+vr6m4+Ro8Slp9Pp8A3vPxhEeHsSsxxuSN8jPdCzjtEbF22mNZoC0FIiZD9smw7HV/3k+uKBnc7aafSBPKWPxshqt0f9wOF08PHkrvxy8RHiwH7Mfb0SJfIGmY+V4WqNyM7SRmojctmB/Hyb1q0PR3Lk4djmB/pO3kOzQjr4ikgP5+EPkXdD3J3hqOzQaAkH5If4CrBkF46rDt91g71xISzWdVrIIt9vNi3N288vBS+TytfNl3zoq3CJZiEq3iKSLAqEBTH6oDqEBPmw/eZ0h06NxurL0lhEiIrcnX1lo/RoM3Qc9v4GyLTzPH1kB3/eFMZVh6atw5YjZnOL1xi0/xMytp7FZML53FNWL5zYdSURugUq3iKSbcgVCmNi3Dn52Gz/vPc8b8/eRxfdqFBG5fT5+ULkLPDAHBkVDk+Gey80TLsG6sfBRTZjcGfbM8lyeLvL/zNx6irHLDgHwRtdIWlYqaDiRiNwqlW4RSVd1S+dldK/qAHy9/jhfrj1mOJGIiBfJWxpavgJD90Kv76Bca8CCY7/ADw/B6Eqw5CW4fNh0UvECqw9e4oXZuwF4snlZ7quXfY5QEslJVLpFJN11qlaEFztUAuDNBfuZte204UQiIl7G7guVOsH9P8CQXXDHsxBSBBKvwPqPYHwtmNQRdn0PjmTTacWA7Sev8fiUbThdbrpFFeXpNhVNRxKRf0ilW0QyRP8mpXmoUWkAnvlhJz/vOWc4kYiIl8pdAlq8CEN2w73ToUI7sGxwYi3M7g+jI+DnF+BijOmkkkn2no2l31ebSUx10qR8OO/eVQ1LR4OJZFkq3SKSISzL4qWOlehZuxguNzw1bQerDlw0HUtExHvZfaBie+g9A4bsgWb/gtBikHQNNn4Cn9SDr9rBzungSDKdVjLI4Yvx9PlyM3HJadQumYfPH6iFn4/+yS6SlelPsIhkGJvN4u3u1ehUrTAOp5vHvt3GpqNXTMcSEfF+YUWh2XOeS897fw8RncCyw8kNMOcxGFURFj4LF/aZTirp6NTVRO6fuIkrCalEFg3lqwfrEOjnYzqWiNwmlW4RyVB2m8WYXjVoGVGAlDQXD0/eys5T103HEhHJGmx2qNAG7vnOs/lai5cgrAQkx8Lmz+HTBjCxNeyYAqkJptPKbbgQl8x9EzdxPi6Z8gWC+eaheoQG+JqOJSLpQKVbRDKcr93Gx/fVpGHZfMSnpNF30mYOnL9hOpaISNYSWhjueAYG74T7Z0OlO8HmA6c3w49PwqgIWDAczu0ynVRu0dWEVO6fuImTVxMpkTeQKf3rkTfIz3QsEUknKt0ikikCfO1M6FObqBK5uZ7o4L6Jmzh2WVMZEZFbZrNBuZbQ61sYth9ajYA8pSElDrZMhM+bwBfNYdtkSIk3nVb+Rlyygz5fbeLQxXgKhQbwXf96FAwNMB1LRNKRSreIZJogfx++7leXyoVDuRyfwn0TNnLmujYDEhH5x4ILQOOh8NR26PMjVOkGNl84ux3mDfLc+z1vMJzdYTqp/IHE1DQemrSFPWfiyBfkx5T+9SieN9B0LBFJZyrdIpKpwgJ9+ebhupTNH8TZ2GTum7CRizd0Bq2IyG2x2aBMM7j7axgeA63fgLxlITUetn0NXzSDz++ALV9CcpzZrAJAssPJo99sY+uJa4QG+PDtw/UoVyDYdCwRyQAq3SKS6cKD/ZnSvx7F8uTi+JVEHpi4mWsJqaZjiYhkD0Hh0GgQPLUN+s6HqneD3Q/O7YQFwzzT7x8Hwumt4HabTpsjOZwuBk7dwdrDlwn0s/P1Q3WpXCTUdCwRySAq3SJiROGwXEztX58CIf4cuHCDfpM2cyPZYTqWiEj2YVlQugncNRGGH4C2b0F4RXAkwo5vYWJL+KwxbPoCkq6bTptjOF1unv5+J8v2X8DPx8bEvrWpWSKP6VgikoFUukXEmBL5Avnu1x1ad56O5eHJW0lKdZqOJSKS/QTmhQZPwpOb4MGfodo94BMAF/bAomc8O5/PeRxObtL0OwO53W5emrubH6PP4mOz+Oz+mjQsG246lohkMJVuETGqfMEQvnmoLiH+Pmw+dpUBU7aRkqbiLSKSISwLSjaA7p977v1u/x7krwRpSbBzKnzVBj5pABs/g8SrptNmK263m5EL9jNt8ylsFoy9pwYtIgqajiUimUClW0SMiywaxtcP1SGXr53VBy8xeFo0aU6X6VgiItlbrjxQ7zF4YgM8vBRq3Ac+ueDSfvj5Oc/0e/ajcGK9pt/pYOyyQ0xcewyAd+6qRqdqRQwnEpHMotItIl6hVsm8TOhTGz+7jZ/3nufZH3bhcukfeSIiGc6yoHhd6PqJZ/rd4QMoGAnOFNg1Aya1h4/rwvrxkHDFdNosacIvRxm3/BAAr3auTM/axQ0nEpHMpNItIl6jcflwxveOwm6zmL3jDK/8tAe3pisiIpknV26o+wgMWAv9V0DNPuAbBJcPwpIXYXQE/PAwHFuj6fdN+m7TCUYu3A/A020q8GCj0oYTiUhmU+kWEa/SpkohRvesjmXBlI0neefnGBVvEZHMZllQrBbc+ZFn+t1pDBSuDs5U2PMDTO4EH9WCdeMg/pLptF5r7o4zvDR3DwADmpblyeblDCcSERNUukXE63SpUZS3ulUF4PPVR/l45WHDiUREcrCAUKj9EDz2Czy6Cmo9CH7BcPUILH0FRleC7/vB0VXg0n4c/7Z473mGf78Ttxv6NCjJc+0qYlmW6VgiYoCP6QAiIn/k3rolSEhJ480F+/lgyUF87TYea1rWdCwRkZytSJTn0eZN2DMLtn0NZ7fD3jmeR55SULOvZ1O2kJy7M/fy/Rd4auoOnC433WsWZUTnKircIjmYJt0i4rX6NynDsNYVAHh7UQwf/boJjYiIGOYfDLX6wqMr4bE1UPth8A+Fa8dh+WswpjLMeAAOL8tx0++f95xnwJRtpDpddKhaiPfuqobNpsItkpOpdIuIVxvUsjxPt/EU71FLDzJ6yQHd4y0i4k0KV4NOoz33fnf5GIrVBVca7P8JptwFH1aHX96HuHOmk2a4eTvP8uTU7TicbjpXL8K4e6Lwseuf2yI5nb4LiIjXG9iiPP/qEAHAhysOa3M1ERFv5BcEUfdD/6Xw+Hqo+xgEhMH1k7DiTRhTBab1hoNLwOU0nTbdzd5+msHTf72kPKooY3vVwFeFW0RQ6RaRLOLRO8ryaufKgGdztdfn71PxFhHxVgWrQIf3YPgB6PY5lGgAbiccWABT74ax1WDVuxB7xnTSdDFzyymGf78Tlxt61S7O+3dXx65LykXkVyrdIpJlPNioNCO7RQIwad1xXv5xDy6XireIiNfyzQXV74GHfoYnNkH9JyFXHog7DavegrGRMLUXHFgEzjTTaf+Rbzee4NlZu3C74f76JXi7e1UVbhH5LyrdIpKl3FevJO/1qPbbOd4vzN6NU8VbRMT7FYiAdm/BsBjoPhFKNga3Cw7+DNPugbFVYcVIz+XoWcRXa4/x8q/ncD/UqDRvdInUpmki8jsq3SKS5fSsXZzRPatjs2DG1lM88/1O0pw5a3dcEZEsyzcAqt0NDy6AgVuh4VMQmA9unIVf3vNcej6lB+yfD06H6bR/6rPVR3h9/j4ABjQty8udKulYMBH5QzqnW0SypG5RxfC12xg8PZrZO86Q6nQxRpvWiIhkLeHlPWd+t3gZYubDtslwbDUcXgqHl+ITVIBKwfXgehXIX8502t98uPwQo5ceBDynbAxtVV6FW0T+lEq3iGRZnaoVwdduY+DU7czfdQ6H08VH99bEz0fFW0QkS/Hxh8i7PI8rR2D7NxD9HVbCRSokzIOP50GZ5lCrH1TsAD5+RmK63W5GLz3IRysOA/B0mwoMbFHeSBYRyTr0L1MRydLaVinE5w/Uws/HxuK9F3h8yjaSHdnvKBoRkRwjX1lo/RoM3UfaXZO4GOLZQJOjK+H7vjCmMix9xVPOM5Hb7eadRTG/Fe5/dYhQ4RaRm6LSLSJZXouIgkzsUxt/HxvLYy7yyDdbVbxFRLI6Hz/cEZ3ZUO5ZHE9ugyZPQ3AhSLgE68bBRzVhcmfYMwvSUjI0itvt5vX5+/j8l6MAvNq5Mo/eUTZDP6eIZB8q3SKSLdxRIT+THqxDLl87aw5d5sFJW0hMzZrHz4iIyP/IXRJavgxD90Cv76B8G8CCY7/ADw/B6Eqw+EW4fCjdP7XL5ealuXuYtO44ACO7RfJgo9Lp/nlEJPtS6RaRbKNh2XC+ebguwf4+bDh6hX5fbSE+RcVbRCTbsPtCpU5w3/cwZBc0fQ5CikDiFdgwHsbXhkkdYNdMcCTf9qdzutw8P3sX3206iWXBez2qcV+9kunwhYhITqLSLSLZSp1Sefn24bqEBPiw+fhVHvhyE7FJ3nvkjIiI/EO5S0Dzf8GQ3XDvDKjQHiwbnFgHsx+B0RHw8wtwMeYfffg0p4unv9/JzK2nsVkwumd1etYuns5fhIjkBCrdIpLtRJXIw9T+9ckd6MuOk9e5f+Imriemmo4lIiIZwe4DFdtB7+kwZA80fxHCikPSNdj4CXxSD75sC9HTwJF0Ux/S4XQxeEY0c3acwW6z+PDeKLpFFcvgL0REsiuVbhHJlqoWC2Nq//rkDfJj95lY7p2wiSvxGbvRjoiIGBZWFJo+C4N3wn0/QEQnsOxwaiPMHQCjKsLCZ+DC3j/9EKlpLgZO3c6CXefwtVt8cl9NOlUrkolfhIhkNyrdIpJtVS4SyvRH6xMe7M/+c3Hc/fkGTl1NNB1LREQyms0O5VvDPd/BsH3Q4mXP5ejJsbD5C/i0IUxsBdu/hdSE394tPiWNhydvYfHeC/j52Pj8gVq0rVLI4BciItmBSreIZGsVCoYw47H6FAkL4OilBLp/up69Z2NNxxIRkcwSUgjueBoG7YT7Z0PlLmDzgdNb4KeBMCoC5g/j6pGt9Pp8A2sOXSbQz86XfWvTIqKg6fQikg2odItItlc2fzCzn2hERKEQLt1IodfnG1l3+LLpWCIikplsNijXEnp+A8P2Q6sRkKc0pMTB1i/J+21L3ro8iP6Bq5nZL5Im5fObTiwi2YRKt4jkCIXCApg5oAENyuQjPiWNfpM2M3fHGdOxRETEhOAC0HgoPLWdg+2mspiGpLrtVLcd5SXX50ROrwfzBsPZHaaTikg2oNItIjlGaIAvXz9Uh87Vi+BwuhkyI5rPVx/B7XabjiYiIgYsjbnEnQtsPJY8kIfzfUtC01chXzlIjYdtX8MXzeCzJrDlS0iOMx1XRLIolW4RyVH8feyM61WD/o1LA/D2ohhen78Pl0vFW0QkJ5m66SSPfbuVZIeL5hXz8/mAtgQ1HwYDt0Lf+VD1brD7wfldsGCYZ+fzHwfC6a2gH9aKyC0wWrpLlSqFZVn/9Xj++edNRhKRHMBms3ipU2Ve6lgJgEnrjvPUtB0kO5yGk4mISEZzu92MXnqQf83ZjcsNPWsXY0Kf2gT6+XjewLKgdBO4ayIMPwBt34LwiuBIhB3fwsSW8Flj2DwBkq4b/VpEJGvwMR3g9ddf55FHHvnt18HBwQbTiEhO0r9JGQqEBjB8ZjQLdp/jUnwKEx6oTVigr+loIiKSAdKcLl6cs4cZW08BMKhFOYa2roBlWX/8DoF5ocGTUP8JOLnRc8n5vrlwYQ8sfBqWvAxVukGtflC8rqewi4j8D+OlOyQkhEKFbv78w5SUFFJSUn77dVyc5/4ah8OBw+FI93zivf79eut1l9vRvnJ+cvepyRNTd7L52FV6fLaOL/vUonBYwG1/bK1R8XZao+Lt0nONJqamMWjGLlYfvIzNghGdK3FvneKkpaXd3AcoUtvzaPUmtj3fY9vxDdal/bBzKuycijt/BK6oPrgie0Ku3LedV7IGfR/NuW7lNbfcBncQKlWqFCkpKaSmplK8eHHuvvtunnnmGfz8/P70fUaMGMFrr732u+enTp1KYGBgRsYVkWzsTAJ8tt9OnMMizM/NgEpOiuhbiohIthDvgM/32zmZYOFruelbwUXVvLf5T2C3mzyJRyh5eSVFr23Cx50KgNPy5WzuuhwPb8bVoAqafotkU4mJifTu3ZvY2FhCQ0P/8m2Nlu4xY8ZQs2ZN8uTJw+bNm3nhhRfo0qULEydO/NP3+aNJd/Hixbl8+fLffrGSvTgcDpYuXUrr1q3x9dXlwHL7zlxP4uFvtnPkUgIhAT582rsG9Urn/ccfT2tUvJ3WqHi79FijJ64m8vDk7Zy4mkjuXL58cX8UUSVyp2/Q5Dhse37wTL8v7vntaXe+8riiHsBVtRcE5kvfzyleQd9Hc664uDjCw8NvqnSn++XlfzaJ/v+2bNlC7dq1GTp06G/PVatWjTx58tCjRw/effdd8uX7429M/v7++Pv7/+55X19fLfQcSq+9pJdS+X2Z9XhD+k/eytYT13ho8nbG9KpBx2qFb+vjao2Kt9MaFW/3T9fortPXeejrLVyOT6Vo7lx883BdyubPgP2DfPNBg8eg/qNwdrvn3u/ds7CuHMK+7BXsK9+ESnd67v0u1VjT72xI30dznlt5vdO9dA8cOJB77rnnL9+mVKlSf/h8/fr1ATh8+PCflm4RkYyUO9CPKf3rMXj6DhbvvcDAadu5eKMyDzYqbTqaiIjcglUHLvLEd9tJTHVSuXAoXz9YhwKht79fx1+yLChay/No+xbs/sFTwM9Fw54fPI+8ZaFWX6jeG4LzZ2weEfEK6V66w8PDCQ8P/0fvu2PHDgAKF769qZKIyO0I8LXzyX21GPHTXr7deILX5u3jfGwyz7WLwGbTdEJExNv9sO00z8/aRZrLTeNy4Xx6f01CAjJ5CukfArUf9DzORsP2ybDre7h6BJa+AsvfgEqdfp1+3wE2oyf5ikgGMrZ7+YYNG9i4cSPNmzcnLCyMLVu2MHToUO68805KlChhKpaICAB2m8XrXapQKCyA9xcf4PNfjnIhLpn3elTHz0f/MBIR8UZut5tPVh3h/cUHAOhao4h3fN8uUsPzaP0G7J3tmX6f2QZ753geeUpBzb5Q4z4IKWg2q4ikO2Ol29/fnxkzZvDaa6+RkpJCyZIleeSRR3j22WdNRRIR+S+WZfFk83IUDA3g+Vm7mBt9los3Uvi4d03yBP35KQsiIpL5UtNcvPrTXqZtPgnAY03L8FxbL7tCyT8YavbxPM7vhm2TYdcMuHYclr8GK0dCxfae6XeZFpp+i2QTxkp3zZo12bhxo6lPLyJy03rUKkZ4sB9PfLed9Ueu0OXjdUzoU5uKhUJMRxMREeByfAqPT9nGluPXsCx4pVMW2IujUFXo+AG0fg32zvVMv09vhv3zPI/cJSCqD0TdD6G69VIkK9OPz0REbkKzigWY9XhDiuXJxcmriXT7ZB0/7zlnOpaISI6350wsd360li3HrxHs78PEPrW9v3D/f35BEHUf9F8Kj2+AegMgIAyun4SVb8KYKjCtNxxcAi6n6bQi8g+odIuI3KRKhUP5aWBjGpbNR2KqkwFTtjNm6UFcLrfpaCIiOdKP0We469P1nI1Npkx4EHOfbETLSln4nuiClaH9uzD8AHT7Ako0ALcTDiyAqXfD2Gqw6h2IPW06qYjcApVuEZFbkDfIj28eqsuDjUoBMG75IQZM2UZ8SprZYCIiOYjT5ebtRfsZPD2alDQXzSrmZ86TjShXIAPO4DbBNxdU7wUP/QxPbob6T0KuPBB3Gla9DWOrwtReELMQnPr7R8TbqXSLiNwiH7uNVztX4f0e1fCz21iy7wLdP1nHiSsJpqOJiGR7sYkOHvp6C5+vPgrA483K8mXfOoTlyuQjwTJL/orQ7i0YFgN3fQmlmoDbBQd/hun3wthIWDHSczm6iHgllW4RkX/o7trFmf5YfQqE+HPwQjx3jl/HmkOXTMcSEcm2Dl+8QddP1rH64CUCfG2Mu6cGz7WLwO5NO5RnFN8AqNoD+s2Hgdug4SAIzAc3zsEv73kuPZ9yl2cTNqfDdFoR+X9UukVEbkPNEnmY91RjahTPTWySg75fbWbimqO43brPW0QkPS3bd4GuH6/n2OUEiubOxQ8DGtKlRlHTscwILwdt3oBh+6HHJCjdFHDD4WUw437P5mvLXoOrx0wnFRFUukVEblvB0ACmP1qfHrWK4XLDmwv2M3zmTpId2mVWROR2ud3w6eqjPPLtVuJT0qhbKi8/DmxEZNEw09HM8/GHyO7Q9yd4ajs0GgJB+SH+AqwdDR/WgG+6wt45kJZqOKxIzmXsnG4RkewkwNfO+z2qUaVIKG8u2M/sHWc4fPEGd2XhTXRFRExLTE3j60M2oq8cBuD++iV4pVMV/Hw0N/qdfGU9Z343fxEOLvKc+31kBRxd6XkEhnuOJqvZ1/O2IpJpVLpFRNKJZVk82Kg0FQuG8MTU7ew6E8fxi3Yial2nXtn8puOJiGQpp64m8sjkLcRcseFrt3i9SyT31i1hOpb38/GDyl08j2vHYfs3sGOKZ/q9bpznUfoOT/mu1NkzLReRDKUfE4qIpLOG5cKZN7AxEQWDiXNY3P/VFmZs0a6yIiI3a/2Ry9w5fi0xF+IJ8XXz7YO1Vbj/iTyloOUrMHQv9PoOyrcBLDj2C8x6GEZFwOIX4fIh00lFsjWVbhGRDFA8byDTH6lL9bwuHE43z83azas/7sHhdJmOJiLitdxuN1+vO8YDX27mWqKDyCKhDK/qpFbJPKajZW12X6jUCe77HobsgqbPQUgRSLoKG8bD+NowqQPsmgmOZNNpRbIdlW4RkQwS5O/DgxVcDGlZDoDJG07wwJebuBKfYjiZiIj3SUlz8tysXYyYtw+ny023qKJM61+HPLr6OX3lLgHN/wVDdsO9M/i/9u48rsoycf/455zDYUeQ3QUQwQVFRVFxD801m/ayMou+U9OiTn79tUzbtEyNM99WxxmbsWnSFpsaK5s0TcvcynDDFTcQBAUERFmV9fz+eJR0NEdLfA6c6/163S/lcJTryOPhXOd+7vuh8ziwWOHAt/DJvfBKF1jyGyjcZXZSkRZDpVtEpAlZLDA5uSNv3tkXXw83vt9fwjV//pbNOUfNjiYi4jRyS6q4dc73fLTxIFYLPHlVHK/e0gtPu83saC2XzQ26jIXb/wnTdhgbsPlHwIljkPoGzB4Ab42BLR9ATZXZaUWaNZVuEZHLYFS3MBZOHkR0sA+Hjh3nlr+uY/bKDBoadD1vEXFti7flc9Wf1pCWc4xWnm7Mvbs/9w7riMViMTua6/BvB1c8Cg9thYkLoOvVYLFB7vew8H5j7fcXj8DhnWYnFWmWVLpFRC6T2FA/PpsymGt6taWuwcH/Ld3Dnf9YT2GZ1s+JiOs5XlPP459sY/L8zZSfqKNPZACLfz2UYZ11tQfTWG3QaRTc+j5MT4cRT0NAFFSXwvo58MYg+PtI2Pwu1FSanVak2VDpFhG5jFp52pl5awL/d1NPvOw21mYUM27mGr7ZU2h2NBGRy2Z3QRnX/HktH6zPNZbhDI/hw/sGEhHobXY0OcUvHIY9DL/eApM+hbhrwOoGBzfAv6cYs9+LpkP+NrOTijg9XadbROQys1gs3NI3gj6RrZn6QRq78su4++0N3DMkmkfHdsXdTe+HikjL5HA4eC81hxcWpVNd10ConwevTUhgcGyw2dHkx1itEDPCGBWFsGU+bJoLR7Ng41vGaNsbElMg/kbw8DM7sYjT0Ss7ERGTxIb68umDg0gZ1AGAv6/N4sY3viO7WKfsiUjLc6yqhvvf28TTC3dQXdfA8C4hLHloqAp3c+IbCkOmwdTNcOe/ofsNYLVDXhp8/pAx+/35Q8bHItJIpVtExESedhvPXtOdN+/sS4C3ne2HShn/pzV8mnbQ7GgiIpfMhuwSrpq5hi93HsZus/DU+Dj+kdKPIF9dD6xZslqh4xVw89vw/3bD6BcgKBZqKoxZ8DnJ8NehsOEtOFFmdloR06l0i4g4gVHdwljy0FCSogOprKnnfz/cyvSPtlBZXWd2NBGRn6y+wcHMr/Yx4W/ryCs9QYcgbz55YDD3DNXu5C2GTzAMmgpTNkLKYuhxM9g8oGAbLJ5uXPf7sylwcCM4dMUOcU0q3SIiTqKNvxfz7x3A9FGdsVrgk82HuHrWWnYcKjU7mojIRcsvPc7tb37Pa1/tpcEBN/Rpx6JfD6VHe3+zo0lTsFigwxC48e/G7PeYGRDcBWqrIO1d+PuV8NchsP5NOH7M7LQil5VKt4iIE7FZLfz6yk58eN9A2vp7klVcyfWzv+WttVk4NEMgIs3E8vTDjJu5htSsEnzcbbw2oRev3pKAr4f28HUJ3oEw8EGYnAp3L4Vet4GbJxzeAV88bKz9/vQByEnV7Le4BJd55quvr6e2ttbsGJeF3W7HZrOZHUNEfoZ+HQL54qGhPPbxNr7ceZjfLUpn7b4iXr65l9ZAiojTOlFbzx+W7Gbud9kA9Gjnz59u6010sI+5wcQcFgtEDTTG2Bmw9UPYPA8K02HrfGOExEHiXdBzglHWRVqgFl+6HQ4HBQUFHDt2zOwol1VAQADh4eFaLyXSjAV4u/PXOxJ5LzWH3y1K55s9RYybuYbXJyQwSLv9ioiTySyqYMp84zKIgC6DKGfyag0D7oek+4z13Zvmwo6PoWgXLP0NLH8Gul9nXHoscqBR2EVaiBZfuk8V7tDQULy9vVt8CXU4HFRVVVFYWAhAmzZtTE4kIj+HxWJh0oAo+nVozdT5aewrrGDiW6k8mBzDtJGdsdv0YlZEzOVwOPjXpoM889lOjtfWE+Tjzsu39GJ4l1Czo4kzslggop8xxv4etn0Em+bB4e2w7UNjBHeGPncZp6X7BJmdWORna9Glu76+vrFwBwW5zn9YLy8vAAoLCwkNDdWp5iItQNfwVvx7yhCeX5TOB+tz+Ms3mazYXcRLN/Ukvp02JRIRc+QdO85TC3ewYrfxZv/g2CBeuyWB0FaeJieTZsHTH/rfC/3ugbzNxuz39o+heC8sexK+fg7ifmHMfncYqtlvabZa9BTJqTXc3t7eJie5/E49ZldZxy7iCrzcbcy4oQd/ub0Prb3t7Mov49q/fMsfluzmRG292fFExIU0NDh47/sDjH5tNSt2F+Jus/Lo2C68+z9JKtxy8SwWaJcI18yCh/fA1a9DmwSorzFOQZ/3C5iVCGtfh4oik8OKXLwWXbpPaemnlJ+LKz5mEVcxvmcblk+/gl/0akt9g4O/rsrkqplrWJ9VYnY0EXEBWcWV3Prm9zy1cAcV1XX0iQzgi4eG8GByLFarXn/Iz+ThB33vhvtWwa9WQd//AXc/KMmEr56BV+Pgo7sgcwU0NJidVuSCtOjTy0VEWqpgXw9m3daba3q15amF29lfXMktf1vHpAFRPDq2C36edrMjikgLU1ffwN/XZvHa8r1U1zXg7W7j0TFdmDSwAzaVbWkKbROMMep3sPMT4/TzQ5sgfaExAqKMnc8T7gC/MFOjipyPS8x0i4i0VKO6hbHsf6/gtv4RALz7/QHGvLaab06urxQRuRTS88q4braxnKW6roGhnYL5ctowUgZHq3BL0/PwhT53wr0r4P610O9e8GgFxw7A18/Da93gnxNh31fQoOVW4nxUup2Uw+HgV7/6FYGBgVgsFrZs2WJ2JBFxUv5edmbc0JP59yQRGehNXukJ7p67gf/9cAsllTVmxxORZqy6rp5Xlu3hmj+vZcehMvy97Lx8cy/e+Z/+RAS63p454gTCe8D4l+H/7YZrZ0P7/tBQB7sXwfs3wswEWPUSlOWZnVSkkUq3k1q6dClz585l0aJF5OfnEx8fb3YkEXFyg2KNmad7h0ZjtcCnaYcY9eoqPt+ah8PhMDueiDQzmw6UcNXMNcxakUFdg4Nx8eEsnz6MmxLba+8YMZ+7D/SeCPcshwfWQdL9xm7opTnwzQvwWjx8cBvs/VKz32I6rel2UpmZmbRp04ZBgwaZHUVEmhEvdxtPju/G+J5teWzBNvYcLmfqB2l8tiWPF66LJ9xfuwqLyPlVVtfx0pd7mLcuG4fD2EPid9d2Z1yPNmZHEzm3sG4w7o8w8llI/8y47nfOd7DnC2O0ag99JkHvO8C/vdlpxQW5XOl2OBwcN+HSOl522wW/K5ySksK8efMAYxfyqKgosrOzmzCdiLQ0CREBfD51CLNXZvCXbzL4atdhUvcf4YnxcdzaL0KzVCJyTqv3FvH4J9s5dOw4ADcntuep8d3w99bmjNIM2L2g163GKNpjlO+t86HsIKycAav+CJ1GQ5+7jF9tLleFxCQud6Qdr62n22+/vOxfN/35MXi7X9g/98yZM4mJiWHOnDls2LABm83WxOlEpCVyd7MybWRnxsW34dGPt7E19xiPf7Kdf2/J4w839iAqyMfsiCLiJI5V1fDC4l0s2HQQgPatvZhxQw+GdgoxOZnITxTSBcb+Hq78rbHee9NcyF4De5caw6+NMfPdexK0jjI7rbRwWtPthPz9/fHz88NmsxEeHk5IiH7gichP1yXcj08eGMRT4+PwtFtZt/8IY15fzZur91PfoLXeIq5uyfZ8Rr66mgWbDmKxwN2DO/DltGEq3NIy2D2hx02QsgimbIRBU8E7CMrzYfVLMLMXvHcj7Poc6mvNTistlMvNdHvZbaQ/P8aUrysiYhab1cI9Qzsyuls4v/lkG99lHuHFL3bxSdohnr46jkExwWZHFJHLbN/hcl5YvItVe4sAiA315Y839iQxqrXJyUSaSHAnGP0CjHgadi82Zr+zVkHGV8bwDYOEicblyQKjzU4rLYjLlW6LxXLBp3mLiLQ0kUHevH9PEh9tzOXFxbvYlV/G7W+mMqpbGE9cFUd0sE45F2npSipreP2rvbyfmkN9gwO7zcJ9w2KYemUsHm6aJBAX4OYB8TcYo2Q/bH4H0t6DisOw9lVjdBwOiXdBl/Hg5m52Ymnm1D5FRFyMxWJhQr9IRnULZ+ZXe3kvNYfl6YdZuaeQuwZ2YOqVnfD30qZJIi1NTV0D76zLZubX+yg/UQfA6JNvuHXQG27iqgI7GrueJz8Be5cYm69lroD93xjDO9i4NFmfuyAoxuy00kypdIuIuKhAH3eeuzaeOwZE8eIXu1i5p4i/r83i480HmT6qM7f1j8TNpq0/RJo7h8PB8vTDzFiym6ziSgDi2rTS0hKR07m5Q7drjXE0Gza/e3L2uwC+nWmMDkMhMQXifmHMlotcIJVuEREX1ynMj7l392flnkJeXLyLfYUVPP3ZTt5Zd4Anx8eR3CXU7Igi8hOl55XxwuJ0vss8AhjX3H5kTGduSozAZtWlA0XOqXUHuPJpSH4c9n1prP3et9zY/Tx7DXgFQsLtxux3gNZ+y3+n0u2kpk2bxrRp08yOISIuJLlLKENig/lgfQ6vLt/LvsIKUt7eQHKXEJ4aH0dsqJ/ZEUXkAhWVV/PKsj18uDEXh8O4hOA9Q6J5cHgsvh56+SdyQWxu0HW8MY7lGjPfm9+B8jxY92dY92dskQNpb+kFdSPArqVZcm561hURkUZuNiuTBnbgmoR2zPp6H/PWZbNyTxFr9hVzR1Ik00Z2prWPNpQRcVYnauv5x7dZzP4mk4pqY9321T3b8NjYrkQEepucTqQZC4iA4Y/DsEeMnc43z4O9S7HmrCORdThm/hN63WZsvhYaZ3ZacTIq3SIichZ/LztPXd2NiQOimPHFLpalH2beugN8mnaIX1/ZiTsHdsDdTeu9RZyFw+Hgi+0FzFiyi4NHjwPQq70/T1/djb4dAk1OJ9KC2Nygy1hjlB6iftM7VK97E+8TRyD1DWNEDDDWfne7Ftz1ZpeodIuIyHlEB/sw586+fJdZzO8WGZcYe2HxLt5PzeGJq+IYGReKxaJ1oSJm2nbwGL9blM6G7KMAhLfy5LFxXbi2VzusWrct0nT829Ew9GGWl3VlfFcv3La8C3uWQO73xljyGPSaYBTwsO5mpxUTqXSLiMh/NSgmmEVTh/Cvjbm8vGwvWcWV3PvORgbFBPHk+Di6t/U3O6KIy8kvPc5LX+7hk82HAPCy27jvio78alhHvN31Ek/ksrFYccRcCV3HQnnBD2u/jx2A9XOM0b6fsfFa/A3grkv0uRo9I4uIyAWxWS3c2j+Sq3u1ZfY3Gfx9bRbfZR5h/J/WMqpbGFNHxNKzfYDZMUVavNySKt5YlcmCjQepqW8A4Ibe7XhkbBfa+HuZnE7ExfmFw7CHYch0yFpp7Hy+ezEc3GCML5+AHjcbs99tepocVi4XlW4REbkovh5uPDq2K7f1j+SlL/fw+bY8lqcfZnn6Ya7oHMLUEbFaQyrSBPYXVTB7ZSafph2ivsEBQFJ0II9fFUdCRIC54UTkTFYrxIwwRkUhbJlvFPCjWbDxLWO07W2U7/gbwUNXCGnJVLpFROQniQj05k+39eahkZ34yzcZfLYlj1V7i1i1t4iBHYOYOiKWgTFBWvMt8jPtKSjnL99ksGhbHie7NkM7BTN1RCf6R+sNLhGn5xsKQ6bBoF8b1/neNBd2fQ55acb48knocZNRwNv2NjmsNAWVbhER+VliQnx59ZYEpl3ZmTdWZbBg00HW7T/Cuv1HSIxqzdQRsVzROUTlW+Qi7ThUyp9XZLB0Z0HjbSPjQpkyopNmtkWaI6sVOl5hjMpi2PqBUcCPZBi/bpoL4T2N8t3jZvBsZW5euWRUukVE5JKIDPJmxg09mTKiE3NWZfLBhlw2HThKytsb6NnenynDYxkZF6bdlEX+i7Sco8xakcGK3YUAWCwwLj6cycNjtWmhSEvhEwyDpsLAKXDgO6Nwp38GBdtg8XRY9pSx6Vri3dAu0XgikGZLpdtJORwO7rvvPhYsWMDRo0dJS0sjISHB7FgiIv9VuwAvnrs2nsnDY3lzzX7e+z6HbQdL+dW7m+ga7seUEbGMi2+DTeVb5Ayp+48wa0UGazOKAbBa4JpebZk8PJZOYVrvKdIiWSzQYbAxxv0Rtv7TKODFe4xd0NPeg7B4Y+fznreAV4DZieUnUOl2UkuXLmXu3LmsXLmSjh078vDDDxMbG8uzzz5rdjQRkQsS2sqTJ8d34/4rYnhrbRbvrDvA7oJypsxPIyZkL5OHx3JNr7a42axmRxUxjcPhYG1GMbO+zmB9dgkAblYLN/RpxwPJsUQH69JCIi7DOxAGPggDHoDcVKN87/wUDu+AJY/A8t9C9+sh8S6ISNLsdzOi0u2kMjMzadOmDYMGDQLAzU3fKhFpnoJ8PXh0bFfuGxbD299l8Y+1WWQWVTL9o628/tU+HkyO4YY+7XF3U/kW1+FwOFixu5BZKzLYknsMAHeblZv7tuf+K2KICPQ2N6CImMdigcgBxhg7A7Z9ZBTwwnTYOt8YIV2Ntd89JxhlXZya6zU5hwNqqy7/17V7X/C7USkpKcybNw8Ai8VCVFQUycnJTRhORKTp+XvbmTayM78cEs273x/g72uyyCmp4jefbGfm1/u4rX8kE/pFENbK0+yoIk2m/EQtC7fk8f73xpkfAB5uVm5PiuS+YTGE++v4F5HTeLWGpPug/6/g4EajfO/4GIp2w9LfwPJnoNu1RgGPGqTZbyfleqW7tgp+3/byf90n8sD9wk4RmzlzJjExMcyZM4cNGzZgs9l45JFHmjigiMjl4edp58HkWFIGdWB+ag5zVu8nv/QEry7fy8yv9zEqLoyJAyIZHBOsTdekxdhxqJT3U3P4bMshqmrqAfBxt3HHwCjuGdKRED8PkxOKiFOzWCCinzHG/v7k7Pc8OLwdtn9kjKBORvnudRv4BJmdWE7jeqW7GfD398fPzw+bzUZ4eDgAc+fONTeUiMgl5u3uxj1DO3LHgCiW7ijg/dQDbMg+ytKdBSzdWUBUkDe394/kpsT2BPmqkEjzU1VTx6Kt+byfeoCtB0sbb48J8WFiUhQ39mmPv7fdxIQi0ix5+kP/e6HfPZC3GTa+DTs+gSP7YNmT8PVzEPcLo4B3GKrZbyfgeqXb7m3MOpvxdUVE5CyedhvX9W7Hdb3bsaegnPmpB/hk8yEOHKlixpLdvLJsL2Pjw5mYFEn/6EBd71uc3t7D5cxPzeHjzQcpP1EHgN1mYVx8Gx3HInLpWCzG5cTaJcKY3xunnW+aC/lbjN/v+BgCOxo7nydMBN8QsxO7LNcr3RbLBZ/mLSIil1eXcD+euzaex8Z15fOtecxPzWHrwVL+vTWPf2/NIzbUl4lJkdzQWzOE4lxO1NazdEcB81NzGnchB4gM9Ob2JOOMjWCdsSEiTcWzFfS92xh5W2DzPNj2LyjZD189AytegK7jjZ3Po5PBqs1LLyfXK90iIuL0vN3dmNAvkgn9Itl+sJT56w+wMC2PjMIKnvs8nT8s2c0verVlYlIkCREBmjUU02QVV/LB+hz+tTGXo1W1ANisFkbGhTIxKYohsdqbQEQus7YJxhj1O9j5ibH2+9BGSF9ojIAoo3wn3AF+YeZmdREq3SIi4tR6tPdnRvuePH5VHJ+lHeL91Bx2F5SzYNNBFmw6SLc2rZg4IJJrE9rh66Efa9L0ausbWJ5+mPdTD/BtxpHG29v4e2oXfhFxHh6+0OdOYxRsN8r3to/g2AH4+nn45vfQeSwk3g0xw8FqMztxi6VXJyIi0iy08rQzaWAH7hgQxeacY7yfeoBF2/JJzy/jyU938PvFu7gyLoxx8eFc0SUEb3f9iJNLp66+ge/3l7BkRz5f7iyguKIGMFatJXcOYWJSFMldQnCz6ZRNEXFC4T1g/Msw6nljtnvTXMhNhd2LjOEfCX0mQe87oJUJV3pq4fSKxElNmzaNadOmmR1DRMTpWCwWEqNakxjVmt9e3Y0Fmw4yPzWH/cWVjWu/Pe1WkjuHMq5HOCO6huLnqfXfcvGq6+r5NqOYJdsLWL7rMMdOnj4OEOzrwYR+7bm1XyQRgdosVUSaCXdvSLjdGIW7jNnvrR9AaQ588yKsnHFy9jsFYkdq9vsSUekWEZFmK8DbnXuGduSXQ6JJyz3G0h0FfLE9n4NHjzdeeszdZmVIp2DGxoczKi6M1j7uZscWJ3a8pp5Ve4tYuiOfr3cVUl5d1/i51t52RncLZ2yPcAbHBOPuplltEWnGQuNg3B9g5DOQ/m9j87UD38KeL4zRqh30nmTMgPu3Nztts6bSLSIizZ7FYqFPZGv6RLbm8XFd2ZlXxpId+SzZUcD+okpW7C5kxe5CbFYLAzsGMTY+nDHdwwnx027SAhXVdazYXcjSHfl8s7uI47X1jZ8L9fNgTPdwxsWH0z86UKePi0jLY/eCXhOMUbTXKN9b5kPZIVj1B1j9fxA7yth8rdMYsKlCXiz9i4mISItisViIb+dPfDt/Hh7dhX2FFSzZXsCSHfnsLihnbUYxazOKefqzHfSLCmRsfDhj48NpG+BldnS5jEqralm+6zBLd+Szel8xNXUNjZ9rF+DF2HijaPeJbK3dx0XEdYR0hjEvwpW/hV2fG2u/s9fAvi+N4dfGWPfdexK0jjI7bbOh0i0iIi2WxWKhc5gfncP8eGhkJ7KLK1myo4ClO/LZerCU9dklrM8u4flF6fSKCGBcfDjJXULoHOqnotXCOBwODh49zpp9xSzZkc+6zCPUNTgaPx8d7NNYtHu089dl6ETEtbl5QI+bjFGc8cPsd3k+rH4JVr8MMSOMtd9dxoFNe6ecj0q3iIi4jA7BPjyQHMMDyTEcOnacpScL+MYDR9mae4ytucf4w5LdBHjb6dchkKToQPpHB9KtTSudVtzMOBwOMosqWZ9VwvqsI6RmlZBfeuKM+3QJ8zOKdo9wuoT5qWiLiJxLcCyM/h2MeBr2LDZmv/evhMyvjeETCr0nGpcmC+xodlqnpNItIiIuqV2AF78cEs0vh0RTWHaCL9MPs2xnARuzj3Ksqpbl6YdZnn4YAF8PNxKjWtM/2ijiPdsHaBMtJ9PQ4GB3QTnrs44YZzBklTRe1usUN6uFnu39Gy8t1zHE16S0IiLNkJs7dL/eGCX7YfO7kPYeVBbC2teMEX2FMfvd9Wrj/gKodIuIiBDaypNJA6KYNCCK2voGdhwqZX1WCalZJWzILqH8RB2r9haxam8RAB5uVvpEnizhHQPpHdEaL3ddVuVyqqtvYGdeGalZR07OZpdQdqLujPt4uFnpHRlA/+ggkqID6R0ZoOu3i4hcCoEdjV3Phz8Be5YYs9+ZKyBrlTG8g43LkiWmQFCM2WlNp588IiIip7HbrPSObE3vyNbcd0UM9Q0OdheUGSV8v7EGvKSyhnX7j7Bu/xH4Guw2Cz3bBzTOhPeOaI2/t9a3XUpVNXXszDO+D9/vP8LmA0eprKk/4z4+7jYST1sW0LO9Px5uejNERKTJ2OzQ7RpjHD0Am98xZr8rCuC7Pxmjw1CjfMf9wlgr7oJUup2Uw+HgvvvuY8GCBRw9epS0tDQSEhLMjiUi4nJsVgvd2/rTva0/dw+OPrlWuILUkyU8NesIh8uq2XTgKJsOHOWNlZkAhPh5EBviS6cwX2JDfxghvh5aO3wepcdrySisIKOwnIzCCvYVVpBRWMHBo8fPum8rT7eTb3QE0T86kO5ttfZeRMQ0raPgyqch+XFjp/NN82DfMmP38+w14BVozH73ucvYJd2FqHQ7qaVLlzJ37lxWrlxJx44defjhh4mNjeXZZ58FIDk5mZSUFFJSUkzNKSLiaiwWC7GhfsSG+jExKQqHw0FuyXFST27WtT6rhJySKorKqykqrzZmw0/j72UnNtSXTqFnlvG2/l4us2O6w+GguKLmnOW6sLz6R/9csK8H/aNbN5bsLmHaZV5ExOnY3KDreGMcyzVmvtPeNa77ve7PxogabJTvbtcY1wlv4Zq0dL/44ossXryYLVu24O7uzrFjx866T05ODpMnT2bFihV4eXlx++238/LLL+Pu7toL7zMzM2nTpg2DBg0CwM1N74+IiDgji8VCZJA3kUHe3Nw3AoCK6joyTyuSp4plTkkVpcdrG2fFT+ftbiMmxCjjMaG+RAf7EOTjTpCvO4E+HgR42ZtNwXQ4HJSdqKOksoaSymqOVNSQU1JFZlEF+w5XkFFUwbGq2h/98238Pc94Q6JTqB+xob4E+rj2awMRkWYnIAKGPw7DHoGMr4xLj+1dCge+NcaSR6HXbZB4F4TGmZ22yTRpk6upqeHmm29m4MCBvPXWW2d9vr6+nvHjxxMSEsLatWs5cuQId911Fw6Hg1mzZjVlNKeWkpLCvHnzAOPFXFRUFMnJyeaGEhGRC+br4UaviAB6RQSccfuJ2nqyiiuNMn64nIyTJTT7SCVVNfVsP1TK9kOl5/w7rRYI8HYn0McYQT4//P6H2zzO+PhS7bBeV9/A0arakyW65ocyXVnD0coajpx2+6nbTr8G9rlYLBDR2tuY8Q/zPXkqvh8xIT74eWo9vIhIi2Jzgy5jjVF6CLa8b6z/Ls2F1DeMEZFkrP3udh24e5ud+JJq0tL93HPPATB37txzfn7ZsmWkp6eTm5tL27ZtAXjllVdISUnhxRdfpFWrVmf9merqaqqrfzj1rKysDIDa2lpqa89817y2thaHw0FDQwMNDQ2A8e778bqz14U1NS83rwtew/faa6/RsWNH3nzzTVJTU7HZbDz66KONj+WU0x/Xf2poaMDhcFBbW4vN1jI3kTn1/f7P77uIs9AxKv/JBsQGexEb7AXdQhpvr61vIKfkOJlFFWQUVpJZVEnu0arGolt2oo4GB43F9kL5uNvOPzvugLo6G09tXgE/crcGh4Oqmnoc5+/QP/r1W/u4E+hjJ7yVJ7EhvsSG+hAT4kPHYB887ef++aT/M3KKnkfF2ekY/Qm8Q2HQ/8KAX2PJWok17R0se5diyU2F3FQcSx6jocctNAx/Gtx9zE77oy7me27qOcvr1q0jPj6+sXADjBkzhurqajZt2sTw4cPP+jMzZsxoLPOnW7ZsGd7eZ74j4ubmRnh4OBUVFdTUGC9SjtcdZ/Ti0Zf4kfx3y8Yvw8vtwtYrWCwW7HY7Foul8THNnDkT+OFNhoULF57x8X+qqanh+PHjrF69mrq6unPep6VYvny52RFEzkvHqFyMDkAHH+C01xn1DVBZBxW1UFFnMX6thcpTvz/5ucpaCxV1UFkLDVjO2t373CxQ/99/Tlhw4O0GvnbwcQNfuwPfUx+f9vtTt/vYwW6tA05bo10DHITsg5B9Mf8o4vL0PCrOTsfoz+A9AY/uY4g8soaoIyvxqS7i+PbP+bpuqHFalJOqqqq64PuaWroLCgoICws747bWrVvj7u5OQUHBOf/M448/zvTp0xs/LisrIyIigtGjR581M37ixAlyc3Px9fXF09MTALdacx6yn58f3vYLP03C09MTq9V6ztn+C3HixAm8vLwYNmxY42NvaWpra1m+fDmjRo3CbtepiOJ8dIyKWRoajDXVpcdrcfDjU9S1tXV8++23DB48GLv9x38++nm44e9l187gctnpeVScnY7RS+l2cDRQl7Uaz7oTXNV5rNmBzuvHJj/P5aIb6LPPPnvOmebTbdiwgb59+17Q33euU64dDsePnort4eGBh8fZ13ez2+1nHej19fVYLBasVitWq/FCwcfdh9TbUy8o26V0MaeXww//LqdyXyyr1do4Y97SnwBc4TFK86ZjVMwQ4uFOiP/571NbW8s+L+gU7q9jVJyankfF2ekYvYS6jDI7wQW5mO/3RZfuKVOmcOutt573Ph06dLigvys8PJzU1DML8NGjR6mtrT1rBvxSsVgsFzXjLCIiIiIiIvJTXXTpDg4OJjg4+JJ88YEDB/Liiy+Sn59PmzZtAGNttoeHB4mJiZfka4iIiIiIiIiYpUkXOOfk5FBSUkJOTg719fVs2bIFgNjYWHx9fRk9ejTdunVj0qRJvPTSS5SUlPDwww9z7733/uS1zCIiIiIiIiLOokl3RPntb39L7969eeaZZ6ioqKB379707t2bjRs3AmCz2Vi8eDGenp4MHjyYW265heuuu46XX365KWM1C9OmTSM7O9vsGCIiIiIiIvIzNOlM99y5c3/0Gt2nREZGsmjRoqaMISIiIiIiImIKXftDREREREREpImodIuIiIiIiIg0EZVuERERERERkSai0i0iIiIiIiLSRFyidDc0NJgd4bJzxccsIiIiIiLibJp093Kzubu7Y7VaycvLIyQkBHd3dywWi9mxmpTD4aCmpoaioiKsVivu7u5mRxIREREREXFZLbp0W61WoqOjyc/PJy8vz+w4l5W3tzeRkZFYrS5xMoOIiIiIiIhTatGlG4zZ7sjISOrq6qivrzc7zmVhs9lwc3Nr8bP6IiIiIiIizq7Fl24Ai8WC3W7HbrebHUVERERERERciM49FhEREREREWkiKt0iIiIiIiIiTUSlW0RERERERKSJNPs13Q6HA4CysjKTk8jlVltbS1VVFWVlZVqvL05Jx6g4Ox2j4ux0jIqz0zHquk71z1N99HyafekuLy8HICIiwuQkIiIiIiIi4krKy8vx9/c/730sjgup5k6soaGBvLw8/Pz8dIksF1NWVkZERAS5ubm0atXK7DgiZ9ExKs5Ox6g4Ox2j4ux0jLouh8NBeXk5bdu2xWo9/6rtZj/TbbVaad++vdkxxEStWrXSk5w4NR2j4ux0jIqz0zEqzk7HqGv6bzPcp2gjNREREREREZEmotItIiIiIiIi0kRUuqXZ8vDw4JlnnsHDw8PsKCLnpGNUnJ2OUXF2OkbF2ekYlQvR7DdSExEREREREXFWmukWERERERERaSIq3SIiIiIiIiJNRKVbREREREREpImodIuIiIiIiIg0EZVuERERERERkSai0i0tSnV1NQkJCVgsFrZs2WJ2HBEAsrOz+eUvf0l0dDReXl7ExMTwzDPPUFNTY3Y0cXGzZ88mOjoaT09PEhMTWbNmjdmRRACYMWMG/fr1w8/Pj9DQUK677jr27NljdiyRHzVjxgwsFgvTpk0zO4o4IZVuaVEeffRR2rZta3YMkTPs3r2bhoYG/va3v7Fz505ee+01/vrXv/LEE0+YHU1c2Icffsi0adN48sknSUtLY+jQoYwbN46cnByzo4mwatUqJk+ezPfff8/y5cupq6tj9OjRVFZWmh1N5CwbNmxgzpw59OzZ0+wo4qR0nW5pMZYsWcL06dP5+OOP6d69O2lpaSQkJJgdS+ScXnrpJd544w32799vdhRxUUlJSfTp04c33nij8ba4uDiuu+46ZsyYYWIykbMVFRURGhrKqlWrGDZsmNlxRBpVVFTQp08fZs+ezQsvvEBCQgKvv/662bHEyWimW1qEw4cPc++99/Luu+/i7e1tdhyR/6q0tJTAwECzY4iLqqmpYdOmTYwePfqM20ePHs13331nUiqRH1daWgqg501xOpMnT2b8+PGMHDnS7CjixNzMDiDyczkcDlJSUrj//vvp27cv2dnZZkcSOa/MzExmzZrFK6+8YnYUcVHFxcXU19cTFhZ2xu1hYWEUFBSYlErk3BwOB9OnT2fIkCHEx8ebHUek0T//+U82b97Mhg0bzI4iTk4z3eK0nn32WSwWy3nHxo0bmTVrFmVlZTz++ONmRxYXc6HH6Ony8vIYO3YsN998M/fcc49JyUUMFovljI8dDsdZt4mYbcqUKWzbto0PPvjA7CgijXJzc3nooYd477338PT0NDuOODmt6RanVVxcTHFx8Xnv06FDB2699VY+//zzM14o1tfXY7PZmDhxIvPmzWvqqOKiLvQYPfXDOC8vj+HDh5OUlMTcuXOxWvW+p5ijpqYGb29v/vWvf3H99dc33v7QQw+xZcsWVq1aZWI6kR9MnTqVhQsXsnr1aqKjo82OI9Jo4cKFXH/99dhstsbb6uvrsVgsWK1Wqqurz/icuDaVbmn2cnJyKCsra/w4Ly+PMWPGsGDBApKSkmjfvr2J6UQMhw4dYvjw4SQmJvLee+/pB7GYLikpicTERGbPnt14W7du3bj22mu1kZqYzuFwMHXqVD799FNWrlxJp06dzI4kcoby8nIOHDhwxm133303Xbt25bHHHtNSCDmD1nRLsxcZGXnGx76+vgDExMSocItTyMvLIzk5mcjISF5++WWKiooaPxceHm5iMnFl06dPZ9KkSfTt25eBAwcyZ84ccnJyuP/++82OJsLkyZOZP38+n332GX5+fo17Dfj7++Pl5WVyOhHw8/M7q1j7+PgQFBSkwi1nUekWEWliy5YtIyMjg4yMjLPeCNLJRmKWCRMmcOTIEZ5//nny8/OJj4/niy++ICoqyuxoIo2XsktOTj7j9rfffpuUlJTLH0hE5GfQ6eUiIiIiIiIiTUS7+IiIiIiIiIg0EZVuERERERERkSai0i0iIiIiIiLSRFS6RURERERERJqISreIiIiIiIhIE1HpFhEREREREWkiKt0iIiIiIiIiTUSlW0RERERERKSJqHSLiIiIiIiINBGVbhEREREREZEmotItIiIiIiIi0kT+P5iv6yg0KqbZAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(-5,5)\n", - "y1_v = [qf(xx) for xx in x_v]\n", - "y2_v = [qf.p(xx) for xx in x_v]\n", - "y3_v = [qf.pp(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"f\")\n", - "plt.plot(x_v, y2_v, label=\"f'\")\n", - "plt.plot(x_v, y3_v, label=\"f''\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "markdown", - "id": "156af9c4-9461-4bf6-8d42-af54e15dfcf3", - "metadata": {}, - "source": [ - "#### TrigFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "d2a5640a-6642-4458-9199-ad0efa016113", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "qf = f.TrigFunction()\n", - "assert qf.params() == {'amp': 1, 'omega': 1, 'phase': 0}\n", - "assert qf.amp == 1\n", - "assert qf.omega == 1\n", - "assert qf.phase == 0\n", - "assert int(qf.PI) == 3\n", - "\n", - "qf2 = qf.update(phase=1.5*qf.PI)\n", - "assert qf2.params() == {'amp': 1, 'omega': 1, 'phase': 1.5*qf.PI}" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "5bd195a5-2db9-4fb7-bb0a-999f9ab1511e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(0, 4, 100)\n", - "y1_v = [qf(xx) for xx in x_v]\n", - "y2_v = [qf2(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"qf\")\n", - "plt.plot(x_v, y2_v, label=\"qf2\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "markdown", - "id": "aa09589f-4748-48a9-86af-513da43d514c", - "metadata": {}, - "source": [ - "#### HyperbolaFunction" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "8cd24f4f-8721-42c0-b993-e874c2258307", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "qf = f.HyperbolaFunction()\n", - "assert qf.params() == {'k': 1, 'x0': 0, 'y0': 0}\n", - "assert qf.k == 1\n", - "assert qf.x0 == 0\n", - "assert qf.y0 == 0\n", - "\n", - "qf2 = qf.update(y0=0.5)\n", - "# assert qf2.params() == {'amp': 1, 'omega': 1, 'phase': 1.5*qf.PI}" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "8c3909a6-4705-4433-aa3e-66c1d07c8615", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(1, 10, 100)\n", - "y1_v = np.array([qf(xx) for xx in x_v])\n", - "y2_v = np.array([qf2(xx) for xx in x_v])\n", - "assert iseq(min(y2_v-y1_v), 0.5)\n", - "assert iseq(max(y2_v-y1_v), 0.5)\n", - "plt.plot(x_v, y1_v, label=\"qf\")\n", - "plt.plot(x_v, y2_v, label=\"qf2\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "markdown", - "id": "18e5f995-a251-446b-8152-6fc4b70bd8a3", - "metadata": {}, - "source": [ - "### Derivatives" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "b0c9d852-742f-4a1d-8dc6-4a1fc801db3c", - "metadata": {}, - "outputs": [], - "source": [ - "qf = f.QuadraticFunction(a=1, b=2, c=3)\n", - "qfp = qf.p_func()\n", - "qfpp = qf.pp_func()\n", - "assert qf.params() == {'a': 1, 'b': 2, 'c': 3}\n", - "assert qfp.func is qf\n", - "assert qfpp.func is qf" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "bb3df983-030d-429c-b3e1-b855f0000eef", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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KTEyU2+22up0O4XQ6meEGAAAAAsx/1mTroU+3S5LuPmuwLh3T0+KOIAVA6PZyOBwEUQAAAABd0sKt+br73U2SpJtP6aubT+1ncUfw6tIbqQEAAABAV7dyb7FmzVunJo+hi0f30O/OGmx1S2iD0A0AAAAAndTWnArdMHeNGho9On1Ikv5y0fAuv3l0Z0PoBgAAAIBOaG9hla5+aZUq6xs1rk+snpoxSkEOIp6/4R0BAAAAgE4mu6RGV/xrpYqq6jUkJUovXDNGIU72sPJHhG4AAAAA6ETyK+p05YsrlVtep34J4Xr1+nGKDnVa3Ra+BaEbAAAAADqJkuoGXfmvlTpQXKOesaF6/YYTFR/hsrotfAdCNwAAAAB0AuW1bl314krtKqhSclSI5t1wopKjQ6xuC9/Dp6H7mWee0YgRIxQVFaWoqCiddNJJ+vTTT1u+bhiGZs+erdTUVIWGhmry5MnasmWLL1sCAAAAgE6nur5R181ZrS05FYoLD9ZrN4xXz9gwq9vCUfBp6O7Ro4f+8pe/aM2aNVqzZo1+8pOf6Pzzz28J1g8//LAee+wxPfXUU1q9erWSk5M1depUVVZW+rItAAAAAOg06txNuvGVNVp7oFRRIUF69frx6p8YYXVbOEo+Dd3nnnuupk+froEDB2rgwIF64IEHFBERoa+//lqGYejxxx/XPffco4suukjp6emaO3euampqNG/ePF+2BQAAAACdgrvJo1mvr9OKPcUKD3Zo7nXjNDQ1yuq2cAyCOuoXampq0ltvvaXq6mqddNJJ2rdvn/Ly8jRt2rSWY1wul0499VStWLFCN9988xG/T319verr61s+r6iokCS53W653W7fngT8ivf95n2Hv+Iahb/jGoW/4xqFv/P1NdrkMXTnWxv1+fYCuYLseu7KUUpPieD3hB84lvfA56F706ZNOumkk1RXV6eIiAi99957Gjp0qFasWCFJSkpKand8UlKSDhw48K3f76GHHtK999572PiCBQsUFsY9DYFo4cKFVrcAfCeuUfg7rlH4O65R+DtfXKMeQ3pjj12rCu1y2AzN7O9W8bav9cm24/5L4Qeoqak56mN9HroHDRqkzMxMlZWV6Z133tE111yjxYsXt3zdZrO1O94wjMPG2rr77rt15513tnxeUVGhnj17atq0aYqKYplFIHG73Vq4cKGmTp0qp5PnEsL/cI3C33GNwt9xjcLf+eoaNQxDf/54u1YVZstht+mJS0fqjGFJ3/9CdBjviuuj4fPQHRwcrP79+0uSxowZo9WrV+uJJ57Qb3/7W0lSXl6eUlJSWo4vKCg4bPa7LZfLJZfr8OfQOZ1OfhgHKN57+DuuUfg7rlH4O65R+LvjeY0ahqG/zt+hV1dmy2aTHr1khM7J6HFcvjeOn2N5vzv8Od2GYai+vl5paWlKTk5utxSjoaFBixcv1oQJEzq6LQAAAACw3D+/3K1nF++RJN1/QbouHEXg7ux8OtP9+9//XmeddZZ69uypyspKvfnmm1q0aJHmz58vm82mO+64Qw8++KAGDBigAQMG6MEHH1RYWJhmzJjhy7YAAAAAwO+8tGyfHl2wU5J0z/QhumJ8b4s7wvHg09Cdn5+vq666Srm5uYqOjtaIESM0f/58TZ06VZJ01113qba2VrfccotKS0s1fvx4LViwQJGRkb5sCwAAAAD8ypursnTfR1slSXecPkA3ntLX4o5wvPg0dL/44ovf+XWbzabZs2dr9uzZvmwDAAAAAPzWB5mHdPd7myRJN53SV7efNsDijnA8dfg93QAAAAAA04ItebrzPxtkGNIV43vp7rMGf+fTnND5ELoBAAAAwAJLdxXq1nnr1eQxdNGo7vrz+ekE7i6I0A0AAAAAHezrvcW68ZU1amjy6MxhyXr44hGy2wncXRGhGwAAAAA60Mq9xbr25dWqc3s0eVCC/vGzUQpyEM26Kt5ZAAAAAOggq/eX6No5q1XrbtLJA+L17JWjFRxELOvKeHcBAAAAoAOs2V+imS+tUk1Dkyb1j9cLV49RiNNhdVvwMUI3AAAAAPjYuqxSzXx5taobmjShXxyBO4AQugEAAADAh9ZnleqaF1epqr5RJ/WN04vXjFVoMIE7UBC6AQAAAMBHNmSX6eoXV6myvlHj02L14swxBO4AQ+gGAAAAAB/YeLBMV764UpX1jRrXJ1YvzRyrsOAgq9tCByN0AwAAAMBxtvlQua7810pV1jVqTO9uevnasQp3EbgDEaEbAAAAAI6jzYfKdcW/VqqirlGje3fTnOvGEbgDGKEbAAAAAI6TrTkVuvLFlSqvdWtUrxjNuXasIgjcAY3QDQAAAADHwbbcCl3xr69VVuPWyJ4xmnvdOEWGOK1uCxYjdAMAAADAj7Qjr1JX/GulSmvcGtkjWq9cN05RBG5IYp0DAAAAAPwIu/KrdNXLa1RS3aDh3aP1yvXjFR1K4IaJmW4AAAAA+IHyaqSrXl6j4uoGpXeP0msEbnwDM90AAAAA8APsKazWU1sdqnQ3aGhKc+AOI3CjPWa6AQAAAOAY7S2s0tUvr1Gl26bByZF6/YbxigkLtrot+CFCNwAAAAAcg31F1frZC1+roLJeKWGG5s4crW7hBG4cGcvLAQAAAOAo7cqv1Ix/rVRhZb0GJkboml5liiVw4zsw0w0AAAAAR2FrToUue/5rFVbWa3BypF65drQiuIUb34OZbgAAAAD4HhsPlumqF1epvNZtPhbsunGKCLZZ3RY6AWa6AQAAAOA7rD1QqiteWKnyWrdG9YrRazeM5x5uHDVmugEAAADgW6zcW6zr5qxWdUOTxvWJ1UvXjlWEixiFo8fVAgAAAABHsGxXkW54ZbXq3B5N7B+nF64eo7BgIhSODVcMAAAAAHzDl9sLdPNra9XQ6NHkQQl69srRCnE6rG4LnRChGwAAAADaWLAlT7PmrZO7ydDUoUl6asYouYII3PhhCN0AAAAA0OyjjTm6481MNXoMnT08RY9fniGng/2n8cMRugEAAABA0rvrDurXb22Qx5AuGtVdD188QkEEbvxIhG4AAAAAAe/NVVm6+71NMgzp8rE99cCFw+Ww8xxu/HiEbgAAAAAB7ZWv9uuPH2yRJF19Um/NPneY7ARuHCeEbgAAAAAB619L9+r+j7dJkm6YlKZ7zh4im43AjeOH0A0AAAAgIP3zy9165LMdkqRZU/rp19MGEbhx3BG6AQAAAAQUwzD094U79Y8vdkuS7pw6UL88bYDFXaGrInQDAAAACBiGYegvn27Xc0v2SpJ+d9Zg/fzUfhZ3ha6M0A0AAAAgIHg8hu79cIvmfnVAkvSnc4fq2olpFneFro7QDQAAAKDLczd5dNfbG/Xe+kOSpAcuTNcV43tb3BUCAaEbAAAAQJdW527SrNfX6fPtBQqy2/S3S0fq/IzuVreFAEHoBgAAANBlVdS5dcPcNVq1r0SuILueufIE/WRwktVtIYAQugEAAAB0SUVV9brmpVXaklOhSFeQ/nXNGI3vG2d1WwgwhG4AAAAAXc6hslpd9a+V2ltUrbjwYM29bpzSu0db3RYCEKEbAAAAQJeyu6BKV724UrnldeoeE6pXrx+nvgkRVreFAEXoBgAAANBlbDpYrmteXqWS6gb1SwjXq9ePV2pMqNVtIYARugEAAAB0CV/tKdaNr6xRVX2jRvSI1pxrxyk2PNjqthDgCN0AAAAAOr2FW/M1a946NTR6dGLfWL1w9RhFhjitbgsgdAMAAADo3N5dd1C/eXujmjyGTh+SpKdmjFKI02F1W4AkQjcAAACATuylZft030dbJUk/PaGH/vrT4Qpy2C3uCmhF6AYAAADQ6RiGocf/t0tPfL5LknTtxD76f2cPld1us7gzoD1CNwAAAIBOxeMxdN9HWzVnxX5J0p1TB+q2n/SXzUbghv8hdAMAAADoNNxNHt319ka9t/6QJOne84bpmgl9rG0K+A6EbgAAAACdQp27SbNeX6fPtxfIYbfpb5eM1AWjulvdFvCdCN0AAAAA/F55jVs3vrpGq/aVyBVk19NXnKDThiRZ3RbwvQjdAAAAAPxaTlmtZr68SjvzqxTpCtK/rhmj8X3jrG4LOCqEbgAAAAB+a3tehWa+tFp5FXVKinJpzrXjNCQlyuq2gKNG6AYAAADgl1bsKdLNr6xVZX2j+idGaO5149Q9JtTqtoBjQugGAAAA4Hf+uyFHv/pPptxNhsb1idXzV49WTFiw1W0Bx4zQDQAAAMBvGIahfy3dpwc+2SZJmj48WY9dmqEQp8PizoAfhtANAAAAwC94PIb+/PFWvbx8vyTp2ol99P/OHiq73WZtY8CPQOgGAAAAYLk6d5N+9Z8N+nhTriTpnulDdMPJabLZCNzo3AjdAAAAACzV9hncTodNj14yUudndLe6LeC4IHQDAAAAsMyhslrNfGmVdhWYz+B+7urRmtAv3uq2gOOG0A0AAADAEttyKzTz5VXKr6hXclSI5lw3VoOTeQY3uhZCNwAAAIAOt2J3kW5+1XwG98CkCM25dpxSeQY3uiBCNwAAAIAO9UHmIf36rQ3mM7jTYvXCVWMUHea0ui3AJwjdAAAAADqEYRh6fslePfTpdknS2SNS9LdLRvIMbnRphG4AAAAAPtfkMfTnj7Zqzor9kqTrJ6XpnulDeAY3ujxCNwAAAACfqnM36f/+nalPN+dJkv5w9hDdcHJfi7sCOgahGwAAAIDPFFTW6cZX1mpDdpmCHXb97dKROndkqtVtAR2G0A0AAADAJ7bnVej6OWt0qKxWMWFOPXvlaJ3YN87qtoAORegGAAAAcNx9ub1At85bp+qGJqXFh+ulmWOVFh9udVtAhyN0AwAAADiu5izfp/s+2iqPIZ3YN1bPXjlaMWHBVrcFWILQDQAAAOC4aGzy6L6PtuqVrw5Iki4d00P3XzBcwUF2izsDrOPTq/+hhx7S2LFjFRkZqcTERF1wwQXasWNHu2MMw9Ds2bOVmpqq0NBQTZ48WVu2bPFlWwAAAACOs4o6t66fu0avfHVANpv0u7MG668/HUHgRsDz6e+AxYsXa9asWfr666+1cOFCNTY2atq0aaqurm455uGHH9Zjjz2mp556SqtXr1ZycrKmTp2qyspKX7YGAAAA4DjJLqnRxc+s0OKdhQpx2vXMFaP181P7yWbjGdyAT5eXz58/v93nL7/8shITE7V27VqdcsopMgxDjz/+uO655x5ddNFFkqS5c+cqKSlJ8+bN08033+zL9gAAAAD8SGsPlOrmV9eoqKpBiZEuvXjNWA3vEW11W4Df6NB7usvLyyVJsbGxkqR9+/YpLy9P06ZNaznG5XLp1FNP1YoVK44Yuuvr61VfX9/yeUVFhSTJ7XbL7Xb7sn34Ge/7zfsOf8U1Cn/HNQp/xzXq/z7amKvfvrdFDY0eDUmO1HNXjlJKdEjAvGdco4HrWN5zm2EYhg97aWEYhs4//3yVlpZq6dKlkqQVK1Zo4sSJOnTokFJTU1uOvemmm3TgwAF99tlnh32f2bNn69577z1sfN68eQoLC/PdCQAAAACQJBmG9NlBmz496JAkpXfz6OoBHrkcFjcGdJCamhrNmDFD5eXlioqK+s5jO2ym+9Zbb9XGjRu1bNmyw772zXs9DMP41vs/7r77bt15550tn1dUVKhnz56aNm3a954suha3262FCxdq6tSpcjqdVrcDHIZrFP6OaxT+jmvUP9W7m/T797fq04O5kqTrJ/bWb6YNlMMeePdvc40GLu+K66PRIaH7tttu03//+18tWbJEPXr0aBlPTk6WJOXl5SklJaVlvKCgQElJSUf8Xi6XSy6X67Bxp9PJhR6geO/h77hG4e+4RuHvuEb9R3FVvW56dZ3WHihVkN2m+y9I1+XjelndluW4RgPPsbzfPt293DAM3XrrrXr33Xf1xRdfKC0trd3X09LSlJycrIULF7aMNTQ0aPHixZowYYIvWwMAAABwDHblV+qCp5dr7YFSRYUEae514wjcwFHw6Uz3rFmzNG/ePH3wwQeKjIxUXl6eJCk6OlqhoaGy2Wy644479OCDD2rAgAEaMGCAHnzwQYWFhWnGjBm+bA0AAADAUVqys1CzXl+nyvpG9Y4L04vXjFX/xAir2wI6BZ+G7meeeUaSNHny5HbjL7/8smbOnClJuuuuu1RbW6tbbrlFpaWlGj9+vBYsWKDIyEhftgYAAADgexiGoVe+OqD7PtqqJo+hcX1i9exVoxUbHmx1a0Cn4dPQfTQbo9tsNs2ePVuzZ8/2ZSsAAAAAjkGdu0l//GCz/rPmoCTpohO666GLhssVxBblwLHo0Od0AwAAAPB/+RV1uvnVtcrMLpPdJt191hDdcHLatz5hCMC3I3QDAAAAaLH2QKl+/tpaFVbWKzrUqadmjNLJAxKsbgvotAjdAAAAACRJ/16dpf/3/hY1NHk0KClSz189Wr3jwq1uC+jUCN0AAABAgHM3eXTfh1v16tcHJElnDkvW3y4dqXAXcQH4sfhdBAAAAASwoqp63fL6Oq3aVyKbTfrV1IGaNaU/928DxwmhGwAAAAhQmw6W6+ZX1yinvE4RriA9flmGTh+aZHVbQJdC6AYAAAAC0PvrD+m372xUfaNHfePD9fzVY9Q/McLqtoAuh9ANAAAABJDGJo/+On+7Xli6T5L0k8GJevzyDEWFOC3uDOiaCN0dqM7dpBCnw+o2AAAAEKDKahp02xvrtXRXkSTp1in99X9TB8ph5/5twFfsVjcQKD7IPKTTH1usTQfLrW4FAAAAAWh7XoXOe2q5lu4qUqjToaevOEG/PmMQgRvwMUJ3B2jyGHpm0R4dLK3VT59doTdXZVndEgAAAALIp5tyddHTK5RVUqOesaF695YJmj48xeq2gIBA6O4ADrtN/775JJ0+JFENjR797t1N+s1bG1TnbrK6NQAAAHRhHo+hvy3YoV+8vk41DU2a2D9O/501SUNSoqxuDQgYhO4OEh3q1PNXjdFvzhgku016a+1B818bi2usbg0AAABdUFlNg254ZY2e/GK3JOmGSWmae+04dQsPtrgzILAQujuQ3W7TrCn99er14xUXHqytuRU658ml+t/WfKtbAwAAQBey8WCZzv7HMn2xvUCuILseu3Sk/nDOUAU5+Os/0NH4XWeBif3j9dEvJ+mEXjGqqGvUDa+s0SOfbVeTx7C6NQAAAHRihmHo1a8P6OJnvtKhslr1jgvTu7dM0EUn9LC6NSBgEbotkhIdqjdvOkkzJ/SRJP3zyz26+qWVKq6qt7YxAAAAdErV9Y36v39n6v+9v1kNTR6dMSxJH942ScNSo61uDQhohG4LBQfZNfu8YXri8gyFOh1avrtY5zy5TOuySq1uDQAAAJ3I7oJKXfDP5Xo/M0cOu033TB+iZ68cragQp9WtAQGP0O0Hzs/org9unai+CeHKLa/TZc99pbkr9sswWG4OAACA7/bfDTk676nl2lVQpcRIl9686UTdeEpf2Ww8fxvwB4RuPzEwKVL/vXWSpg9PlrvJ0J/+u0V3/DtTNQ2NVrcGAAAAP1Tf2KQ/frBZv3xjvWoamjShX5w+/uXJGtsn1urWALRB6PYjEa4g/XPGCfrD2UPksNv0QWaOLvjncu0prLK6NQAAAPiRg6U1uvS5r/XKVwckSbf9xHxCTkKky+LOAHwTodvP2Gw23XByX71x44lKjHRpZ36Vzn9quT7dlGt1awAAAPADX+4o0DlPLtOG7DJFhzr18syx+tW0QXLYWU4O+CNCt58alxarj345SePTYlVV36hfvL5OD3y8Ve4mj9WtAQAAwAJNHkN/W7BD1768WmU1bo3sEa2PfzlJUwYnWt0agO9A6PZjiZEhev2G8br5lL6SpBeW7tMVL6xUQUWdxZ0BAACgIxVV1euqF1fqyS92S5KuPqm3/vPzk9SjW5jFnQH4PoRuPxfksOvu6UP07JUnKMIVpFX7S3TWE0u1aEeB1a0BAACgA6zeX6Kz/7FUK/YUKyzYoScuz9B956fLFeSwujUAR4HQ3UmcmZ6i/946UYOTI1Vc3aCZL6/Wg59sU0Mjy80BAAC6IsMw9MKSvbr8+a+VX1Gv/okR+mDWRJ2f0d3q1gAcA0J3J9I3IULvz5qoq0/qLUl6fsleXfLsCmUV11jcGQAAAI6nspoG/fy1tXrgk21q8hg6d2SqPpg1UQOSIq1uDcAxInR3MiFOh+47P13PXjla0aFObThYrrP/sVQfbsixujUAAAAcByv3FuusJ5bqsy35cjps+vP5w/SPyzMU7gqyujUAPwC/czupM9OTNbxHtG5/Y73WHCjVbW+s1/LdRfrTucMUGsz9PQAAAJ1NY5NHT36xW09+sUseQ+oTF6Ynf3aChveItro1AD8CM92dWPeYUL1504m67Sf9ZbNJb67O1nlPLdP2vAqrWwMAAMAxOFRWq5+98LWe+NwM3D89oYc++uXJBG6gCyB0d3JBDrt+NW2QXr9+vBIiXdpVUKXzn1qu11cekGEYVrcHAACA7/Hpplyd9fgSrd5fqghXkB6/LEN/u3SkIlhODnQJhO4uYkL/eH16+8maPChB9Y0e3fPeZt3y+jqV17qtbg0AAABHUNvQpN+/t0m/eH2dKuoaNbJnjD7+5SRdMIrdyYGuhNDdhcRHuPTSNWN1z/QhCrLb9OnmPE1/YqnWHii1ujUAAAC0sT2vQuc9tUzzVmbJZpN+Mbmf3v75SeodF251awCOM0J3F2O323TjKX319i8mqFdsmA6V1erS577S04t2y+NhuTkAAICVDMPQK1/t13lPLdeugiolRLr06nXj9dszB8vp4K/mQFfE7+wuKqNnjD765SSdMyJFTR5DD8/foWteXqWCyjqrWwMAAAhIpdUNuunVtfrjB1vU0OjRlEEJmn/7yZo0IN7q1gD4EKG7C4sKcerJn43SX386XCFOu5buKtL0J5Zqyc5Cq1sDAAAIKF/tMZ+9vXBrvoIddv3xnKF6aeZYxUW4rG4NgI8Rurs4m82my8b20oe3TtKgpEgVVTXo6pdW6aFPtqm+scnq9gAAALq0xiaP/rZgh2b862vlVdSpb0K43ps1QddNSpPNZrO6PQAdgNAdIAYkReqDWyfqivG9JEnPLdmr859azjO9AQAAfCS7pEaXPf+1nvxitwxDumxMT3102yQNS+XZ20AgIXQHkBCnQw9cOFzPXjlaseHB2p5XqfOeXK7nl+xRE5usAQAAHDcfb8zV9H+YT5GJdAWZt/xdPEJhwTx7Gwg0hO4AdGZ6subfcbJOG5yohiaPHvxku2a88LUOltZY3RoAAECnVl7r1v/9O1Oz5q1TZV2jTugVo09uP1nnjky1ujUAFiF0B6jEyBD965oxeuii4QoLdmjlvhKd+fhSvbUmW4bBrDcAAMCxWrqrUGc+vkTvrT8ku0267Sf99Z+bT1LP2DCrWwNgIda3BDCbzaafjeulCf3idOd/NmjtgVL95u2N+t+2fD144XB20wQAADgKNQ2N+sun2/XKVwckSWnx4frbpSN1Qq9uFncGwB8w0w31jgvXf24+Sb85Y5CC7DZ9tiVfZzy+VJ9vy7e6NQAAAL+29kCppj+xtCVwX3NSb338y0kEbgAtmOmGJMlht2nWlP46dWCC/u/fmdpVUKXr567Rz8b10h/OHqJwF5cKAACAV0OjR4//b6eeXbxHHkNKiQ7RIxeP1KQB8Va3BsDPMNONdtK7R+vD2ybp+klpkqQ3VmU177xZYnFnAAAA/mFbboXO/+dyPb3IDNwXndBd8+84hcAN4IgI3ThMiNOh/3fOUM27YbxSo0N0oLhGlzz7lR75bLsaGj1WtwcAAGCJJo+hZxbt0XlPLdO23ArFhgfr2StH67FLMxQd6rS6PQB+itCNbzWhf7w+veMUXTiquzyG9M8v9+jCp5drV36l1a0BAAB0qP1F1br0ua/01/nb5W4yNHVokj674xSdmZ5sdWsA/ByhG98pOtSpv1+WoaevOEExYU5tyanQ2U8u04vL9snj4dFiAACgazMMQ69+fUBnPbFUaw+UKtIVpEcvGannrxqthEie9ALg+7E7Fo7K9OEpGtO7m37z9kYt3lmoP3+0Vf/bmq+HLx7BsycBAECXlFteq7ve3qilu4okSRP6xemRS0aqe0yoxZ0B6EyY6cZRS4wK0Zxrx+r+C9IV6nToq73Fmvb3JXp5ObPeAACg6zAMQ++vP6Qz/r5ES3cVyRVk15/OHarXrh9P4AZwzAjdOCY2m01Xnthbn9x+ssalxarW3aR7P9yqS577SrsLqqxuDwAA4EcprqrXrHnrdMe/M1VR16iRPaL18S9P1rUT02S326xuD0AnROjGD5IWH643bzxRf74gXeHBDq09UKrp/1iqf365W+4mdjgHAACdi2EYem/9QZ3+2GJ9silPQXabfjV1oN75xQT1T4ywuj0AnRj3dOMHs9ttuurE3vrJ4ET9/t1NWryzUI98tkOfbMrVwxeP0LDUaKtbBAAA+F4HS2v0h/c3a9GOQknS4ORIPXrJSKV35+8yAH48Zrrxo3WPCdWca8fq0UtGKjrU3OH8/KeW69HPdqi+scnq9gAAAI7I4zE0Z/k+Tfv7Ei3aUahgh12/OWOQPrxtEoEbwHHDTDeOC5vNpotH99ApA+P1x/e3aP6WPD315W59tiVPf714hE7o1c3qFgEAAFrsyq/Ub9/ZqHVZZZKksX266aGLRrCUHMBxR+jGcZUYGaJnrxqtTzbl6o8fbNaugir99JkVum5imn49bZBCgx1WtwgAAAJYQ6NHzy7eo6e+2K2GJo8iXEH67VmDdcW4XmyUBsAnCN3wienDU3RS3zjd99FWvbf+kF5ctk8Lt+brLz8drgn94q1uDwAABKDM7DL99u2N2pFfKUk6bXCi/nxBulJ5DBgAHyJ0w2e6hQfr75dl6LyRqfr9e5uUVVKjGS+s1IzxvXT3WYMVGeK0ukUAABAAahoa9bcFO/Xy8n3yGFJceLD+dN4wnTsiRTYbs9sAfIuN1OBzUwYnasH/naIZ43tJkuatzNK0vy/Rl9sLLO4MAAB0dct2FemMx5foxWVm4L5oVHctvPNUnTcylcANoEMw040OERni1IMXDtc5I1J097ubdKC4RtfOWa0LR3XXH84eorgIl9UtAgCALqSspkH3f7xNb689KMl82soDF6Zr8qBEizsDEGiY6UaHmtAvXvNvP0U3TEqT3Sa9t/6QTntssd5YlSWPx7C6PQAA0MkZhqGPN+bq9MeW6O21B2WzSTMn9NFn/3cKgRuAJZjpRocLDXboD+cM1dnNs97b8yp197ub9NaabN1/wXANTY2yukUAANAJ5ZXX6f99sFkLt+ZLkvonRuivPx2h0b15dCkA6zDTDcuM6tVNH902SX84e4jCgx1al1Wmc59apj9/tFVV9Y1WtwcAADoJd5NH/1q6V6f9bZEWbs2X02HTL08boI9/OYnADcByzHTDUkEOu244ua/OHpGiP3+0VZ9sytOLy/bpo405+uM5wzR9eDKbnAAAgG+1cm+x/vjBlpbHgI3qFaO/XDRCg5IjLe4MAEyEbviFlOhQPX3FaC3aUaA/frBFWSU1mjVvnU4ZmKD7zhumPvHhVrcIAAD8SEFlnR76ZLveW39IkhQbHqzfnTlYF4/uIbudf7AH4D9YXg6/MnmQ+XixX542QMEOu5bsLNS0x5foif/tUp27yer2AACAxRqbPJqzfJ9Oe3Sx3lt/SDabdMX4XvriV6fq0rE9CdwA/A4z3fA7IU6H7pw6UBdkpOqPH2zRst1F+vv/dur9zEO67/xhOnlAgtUtAgAAC6w9UKr/9/5mbc2tkCSN6BGtP5+frpE9Y6xtDAC+A6EbfqtvQoRevX6cPtqYqz9/tFX7iqp11YurdM6IFP2/c4YqNtRhdYsAAKADFFc36LH/bdV/1pjP3I4Odeo3ZwzSz8b1koOZbQB+jtANv2az2XTuyFSdOihBjy3YqVe+2q+PNuZq0Y5C3X5aP8XxaG8AALqsJo+hZXk2/fGJZSqvNZ9scumYHvrtmYMVF+GyuDsAODqEbnQKUSFOzT5vmC4e3UP3vL9ZG7LL9MAnO9Qj3KGeI8o0ti9LzgEA6Eo2ZJfpD+9v0qZDDkmNGpISpfsvGKbRvWOtbg0AjgmhG51KevdovfuLCXpzdZb++ul2Haxu1KUvrNKlo3vq12cMUkIk/+oNAEBnVlbToIc/26E3VmXJMKQQh6HfnDlE10xIU5CDPYABdD6EbnQ6DrtNV4zvrdMGxumXL32pVYV2/XtNtj7elKtZU/rrukl95Arifm8AADoTj8fQW2uz9ZdPt6u0xi1JumBkikYHZevyE3sRuAF0Wvz0QqcVF+HSFf09+veN4zSiR7Sq6hv11/nbNfWxJZq/OVeGwQ3fAAB0BuuySvXTZ1fot+9sUmmNWwOTIvTvm07UIxcPV1Sw1d0BwI/DTDc6vRN6xej9WybqvfWH9Nf525VVUqOfv7ZOJ/aN1f87Z6iGpUZb3SIAADiCg6U1+uv8HfpwQ44kKTzYof+bOlDXTOgjp8Mut9ttcYcA8OP5dKZ7yZIlOvfcc5Wamiqbzab333+/3dcNw9Ds2bOVmpqq0NBQTZ48WVu2bPFlS+ii7Habfjq6h7789WTd9pP+cgXZ9fXeEp3z5DL97p2NKqyst7pFAADQrLLOrYfnb9dP/rZYH27Ikc1m7kr+xa8n64aT+8rJUnIAXYhPf6JVV1dr5MiReuqpp4749YcffliPPfaYnnrqKa1evVrJycmaOnWqKisrfdkWurBwV5B+NW2QPv/VqTpnRIoMQ3pzdbamPLpIzy7eo/rGJqtbBAAgYDV5DM1bmaUpjy7S04v2qKHRo5P6xumj2ybp4YtHKikqxOoWAeC48+ny8rPOOktnnXXWEb9mGIYef/xx3XPPPbroooskSXPnzlVSUpLmzZunm2++2ZetoYvr0S1MT804QTMnlOi+j7Zq48Fy/eXT7Zq3Mku/nz5YZwxLls1ms7pNAAACxtJdhXrg423anmdOrvSND9fvpw/RaUMS+TMZQJdm2T3d+/btU15enqZNm9Yy5nK5dOqpp2rFihXfGrrr6+tVX9+6VLiiokKS5Ha7ue8nwHjf7+9630d2j9RbN47T+xty9LeFu1vu9x6f1k2/P2uQhqZEdVS7CEBHc40CVuIaRUfYXVClv3y2U4t3FkmSokODdNuUfvrZ2J4KDrKrsbHxW1/LNQp/xzUauI7lPbcZHbTFs81m03vvvacLLrhAkrRixQpNnDhRhw4dUmpqastxN910kw4cOKDPPvvsiN9n9uzZuvfeew8bnzdvnsLCwnzSO7qG+ibpf4fs+iLHpkbDJpsMnZhoaHpPDzujAgBwnFW5pfnZdi3Pt8kjm+w2QycnGzqju0fhTqu7A4Afp6amRjNmzFB5ebmior57Is/y3cu/uZzIMIzvXGJ09913684772z5vKKiQj179tS0adO+92TRtbjdbi1cuFBTp06V03l0f3pfKOlQWa0e+WyXPt6cp68KbNpY7tQtp/bVNSf1liuIjVtw/PyQaxToSFyj8IX6Ro9e/TpLTy/eq8o6cxb79MEJuuuMgUqLDz+m78U1Cn/HNRq4vCuuj4ZloTs5OVmSlJeXp5SUlJbxgoICJSUlfevrXC6XXC7XYeNOp5MLPUAd63vfJ8Gpf145WjP3l+i+D7dq06FyPbJgl95YfVB3Th2o8zO6y2Hn3jIcP/x8gr/jGsXxYBiG5m/O00Ofmo/vlKShKVH6wzlDNKFf/I/63lyj8Hdco4HnWN5vy6b10tLSlJycrIULF7aMNTQ0aPHixZowYYJVbSGAjO0Tqw9mTdSjl4xUYqRLB0trded/NujsfyzV59vy1UF3XgAA0OltPFimy577Wr94fZ2ySmqUGOnSwxeP0Ie3TfrRgRsAOjufznRXVVVp9+7dLZ/v27dPmZmZio2NVa9evXTHHXfowQcf1IABAzRgwAA9+OCDCgsL04wZM3zZFtDCbrfp4tE9NH14sl5evl/PLt6j7XmVun7uGo3p3U13nTlY49JirW4TAAC/tLugSn9fuFMfb8qVJIU47brplH66+ZS+CndZfhcjAPgFn/40XLNmjaZMmdLyufde7GuuuUZz5szRXXfdpdraWt1yyy0qLS3V+PHjtWDBAkVGRvqyLeAwYcFBmjWlv64Y30vPLN6jOcv3a82BUl363FeaMihBvzljsIamsmcAAACSdLC0Rk/8b5feWXdQHkOy2aQLM7rr12cMUmpMqNXtAYBf8Wnonjx58ncu0bXZbJo9e7Zmz57tyzaAoxYTFqy7zxqiayek6R9f7NK/V2fryx2FWrSzUOeNTNWvpg5Srzh2yQcABKaCyjo9/eUevb7ygNxN5t/xpg5N0q+mDdTgZP5xGgCOhHU/wBEkR4fowQuH64ZJaXps4U59tDFXH2Tm6OONuZoxvpdu/Ul/JUaGWN0mAAAdorzGreeW7NHLy/er1t0kSZrYP06/njZIo3p1s7g7APBvhG7gO/RNiNBTM07Qz08t18Of7dCSnYV65asDemvNQV0/KU03ndpXUSHsVAkA6Jqq6xs1Z4W554n38V8ZPWP0mzMGaWJ/NkgDgKNB6AaOQnr3aL1y3Tit2FOkh+fvUGZ2mZ76crdeW3lAt0zup6tP6qMQp8PqNgEAOC7qG5s0b2WW/vnlbhVVNUiSBiVF6tdnDNLpQxJls/FoTQA4WoRu4BhM6Bev926J04Kt+Xrksx3aXVClBz/ZrpeW7dcdpw/QxaN7KMhh2ZP4AAD4URqbPHpn3UE98b9dyimvkyT1jgvTnVMH6pwRqXLYCdsAcKwI3cAxstlsOmNYsk4fkqR31x3U3xfuVE55nX737iY9v2Svbv1Jf503MpXwDQDoNDweQx9vytXfF+7U3qJqSVJyVIh+edoAXTKmh5z8mQYAPxihG/iBHHabLhnTU+eOTNXrzUvw9hZV687/bNATn+/SrMn9deEJ3fmLCgDAbxmGoS93FOiRz3ZqW26FJKlbmFOzpvTXlSf25tYpADgOCN3AjxTidOj6SWm6bGxPvfrVAb2wdK8OFNfornc26onPd+mWKf108egecgXxFxcAgH/weAz9b1u+nvpytzYeLJckRbiCdOPJfXXdpD6KZJNQADhuCN3AcRLhCtIvJvfTNRN66/Wvs/Tckr06VFare97brKe+2K2fn9pPl43tyawBAMAyTc3LyP/5xW7tyK+UJIU47brmpD76+an91C082OIOAaDrIXQDx1lYcJBuPKWvrjqpt95YlaVnF+9Rbnmd/vTfLfrnl7t186n9NGNcL4UGE74BAB3D3eTRe+sP6ZlFe7Sv+Z7tCFeQrj6pt66blKb4CJfFHQJA10Xo7iiL/iLtXSSlZEipGebH+AGSneDVVYU4Hbp2Ypp+Nq6X3lp7UM98uVs55XX680db9cyi3brx5L668sTeCnfx2xAA4Bt17ia9tSZbzy42V19JUkyYU9dOSNPMCX0UHcYycgDwNf6231H2L5OyvjLLyxkuJQ9vDeEpI6X4gZKDt6UrCXE6dNWJvXXZmJ56d91B/XPRbmWX1OqhT7fr2cV7dMPJfXX1Sb25fw4AcNxU1zdq3sosPb90rwor6yVJ8REu3XRKmmaM760I/sEXADoMP3E7ytmPSYfWSrmZUk6mlLdRcldL2V+b5RUU2j6Ip2ZI8YMI4l1AcJBdl4/rpZ+O7qEPMnP0zy93a19RtR75bIeeW7xH10/qq5kT+yg6lPANAPhhymvdemXFfr20fJ9Ka9ySpNToEP18cj9dOoZ9RQDACiS5jpIw0KyMn5mfe5qk4t1mAG8bxBuqpIOrzPIKCpGS0tsH8YTBkoNw1hk5HXZdPLqHLshI1Ucbc/XkF7u0p7Baf//fTv1r6V5dM6GPZk7sw/11AICjVlxVr5eW79MrKw6osr5RktQnLky/mNxPF47qoeAgHl8JAFYhdFvF7pASBpk18jJzzOMxg3juhtYgnrtBaqiUDq0xy8vhkpLT29wjPlJKGCIFsetoZxHksOuCUd117shUfbo5V09+bu4k+9SXu/X80r366Qk9dP2kNPVPjLC6VQCAn8qvqNPzS/Zq3sos1bqbJEkDkyI0a0p/nT08RUEOwjYAWI3Q7U/s9tYZ8RGXmGMej1Sy1wzhLUF8o1Rfbi5XP7S29fWOYClpWPvN2hKHEsT9nMNu0zkjUjU9PUULtubrmUW7teFgud5YlaU3VmXptMGJuuHkvjqxb6xsNpvV7QIA/MCu/Eq9tHyf3ll7SA1NHknS8O7RmjWlv6YNTZLdzp8XAOAvCN3+zm6X4vubNfxic8zjkUr3tQnhmeaMeF25lLPeLG8WtzulpKHtg3jSMCmIpcv+xm636cz0ZJ0xLEmr95fqhaV79b9t+fp8e4E+316g9O5RuvHkvpo+PEVOZi4AIOAYhqGlu4r0r2X7tGRnYcv42D7dNGtKf506MIF/nAUAP0To7ozsdimun1npPzXHDEMq3d8+iOdkSnVlzcvVN0jr5ja/3iklDjGXpKdmSCmjzCDuDLHgZPBNNptN49JiNS4tVnsLq/TS8n16e+1BbT5UodvfzNRfP92uayem6fJxPdnxHAACQJ27Se+vP6SXlu/TzvwqSZLNJk0bmqTrJ/XVuLRYizsEAHwXQndXYbNJsWlmDbvQHDMMqexA673h3iBeW2Ju2pa3UVr/qnmsPci8Jzx1ZPOsuDeIh1pzPpAk9U2I0P0XDNedUwfpta8P6JWv9iunvE4PfLJNT3y+Sz8b11MzJ6apewzvEwB0NQWVdXrtqwN6bWWWSqobJEnhwQ5dOranrp2Qpl5xYRZ3CAA4GoTursxmk7r1MWvYBeaYYUjl2e1nw3MzpZpiKX+TWetfa369w9wlve2u6UnpUjB/yHe02PBg/fK0AbrplL76IPOQXli6T7sLqvTC0n16afl+nT08RTee3FfDe0Rb3SoA4EfalluhF5ft038zc1ru1+4eE6prJ/bRpWN7KopVTgDQqRC6A43NJsX0MmvoeeaYYUgVhw4P4tWFUsEWszJfb3693QziKRmty9OTh0vB4VacTcAJcTp02dheumR0Ty3eWagXlu7Vij3F+u+GHP13Q47Gp8XqplP6asqgRDbRAYBOxOMx9OWOAr24bJ9W7CluGT+hV4yun9RXZwxLYidyAOikCN0wg3h0D7OGnGOOGYZUkXP4PeLVBVLBVrM2zGt+vV2KH9j+8WXJIyQXj7ryFbvdpimDEzVlcKI2HyrXi8v26cMNOVq5r0Qr95Wob0K4Zk7oowtHdee+bwDwYzUNjXpn7UG9vHy/9hZVSzKfanFWerKun5SmUb26WdwhAODHInTjyGw2Kbq7WYPPbh2vyD08iFflSYXbzdr4pvcbSPED2u+anjJCckV27HkEgPTu0fr7ZRm668xBmrN8v+atzNLewmr98YMt+sun23V+RnddeWIvDUtl6TkA+Ivc8lq98tUBzVuZpfJatyQpMiRIPxvXS9dM6MNeHQDQhRC6cWyiUswadFbrWGVe+0eX5WRKlTlS0U6zNv2n+UCbueP6N4N4CGHweEiJDtXd04fottMG6K012Xp9ZZZ2F1S1PO97VK8YXTm+t84ekaIQp8PqdgEg4DR5DC3ZVah5K7P0xfYCNXkMSVLvuDBdO6GPLhnTU+Eu/moGAF0NP9nx40UmS4PONMurquDwe8QrDknFu83a/HbrsbH9Wpele+8VD43pwBPoWiJcQbp2YppmTuijlftK9NrXB/TZljytzyrT+qwy/fnjrbr4hB664sTeSovnXnwA8LX8ijr9Z3W23lydrUNltS3j49Nidf2kNJ02JEkO9uEAgC6L0A3fiEiUBk4zy6uqsPnRZetbH2NWni2V7DFr8zutx3ZLa79respIKZT72o6FzWbTiX3jdGLfOBVW1us/a7I1b2WWDpXV6l/L9ulfy/ZpUv94XXliL50+hA16AOB48ngMLd1dpHkrD+h/21pntaNDnfrpCT00Y3xP9U/klisACASEbnSciARpwOlmeVUXtV+WnpsplWVJpfvM2vJe67Hd+rTfNT0hvSO779QSIl2aNaW/fn5qPy3aUaDXvj6gRTsLtWx3kZbtLlJSlEuXj+2ln43rpeToEKvbBYBOq6CyTm+tOag3VmXpYGnrrPbYPt30s3G9NH04t/gAQKAhdMNa4fFS/9PN8qopab8sPXeDVLq/tba+L0lySjo9OF6O2rek7qOaZ8VHSWGxHXsOnYjDbtNpQ5J02pAkZZfU6I1VWfrPmmzlV9Tric936akvd+v0IYm68sTemtgvnseOAcBR8HgMLd9TpHkrs7Rwa74am2e1o0KCdNEJPTRjfC8NTGJWGwACFaEb/icsVur3E7O8akvbz4bnZEql+xTeUCRt/9Asr+ierbPhKaPMj+HxHXoKnUHP2DDddeZg3XH6QM3fkqfXvj6gVftK9NmWfH22JV994sJ0+bheunBUdyVFMfsNAN9UWFmvt9Zm681V2coqqWkZH93bnNU+e3iKQoOZ1QaAQEfoRucQ2k3qO9msZu7KIq364AWd2DtEjvxNZhAv2WPeJ16eLW3/qPX1UT0Ov0c8IrFDT8FfBQfZdd7IVJ03MlU78yv1+tcH9O66Q9pfXKO/fLpdD8/fron943Xx6B6aNjSZv0ACCGgej6EVe4r1xqosLdiaJ3eTOasdGRKki0Z118/G99Lg5CiLuwQA+BNCNzqvkGgVRQ6V58Tpcjid5lhduZS7sXnDtkwziBfvlioOmtU2iEemfiOIZ0iRSR19Fn5lYFKk7j0/Xb89a7A+3JCjt9ce1Or9pVq6q0hLdxUpwhWk6cOTddEJPTSuTyzLzwEEjO15FXpv/SH9NzNHueV1LeOjesXoZ+N66dwRqfyjJADgiAjd6FpCoqW0k83yqq9sDuKZrcvTi3aZzxLfkSPt+KT12MiU9pu1pWSYzyUPMGHBQbpsbC9dNraXDhRX6911h/Tu+oPKLqnVf9Yc1H/WHFSPbqG6aFR3XXRCD/Xh0WMAuqC88jr9d8Mhvbc+R9tyK1rGo0KCdH5Gd/1sXC8NTWVWGwDw3Qjd6PpckVKfiWZ51VdJeZu+EcR3SpW5Zu38tPXYiKT2s+EpI6WoVMkWGLO8vePC9X9TB+r20wZozYFSvbvuoD7emKuDpbX6xxe79Y8vdmt072766Qk9dPaIFEWHOq1uGQB+sKr6Rs3fnKf31x/S8j1FMszV43I6bPrJ4ERdOKq7pgxOlCuIWW0AwNEhdCMwuSKk3ieZ5dVQbQbxtrumF26XqvKlXZ+Z5RWe0D6Ip2ZIUd27dBC3220alxarcWmxmn3eMC3Ymq931h7U0l2FWnugVGsPlGr2h1s0dWiSfnpCd50yIIFnfwPoFNxNHi3bVaT31h/Sgq15qnN7Wr42tk83XTCqu84enqKYsGALuwQAdFaEbsArOFzqdaJZXg01Uv7m9rumF26Xqgul3QvN8gqLb78sPTXD3Em9CwbxEKejZfO1goo6vZ95SO+sPaQd+ZX6eGOuPt6Yq/iIYJ2f0V0XjuquYalRsnXB/x8AdF6GYWjjwXK9t/6QPtyQo+Lqhpav9Y0P14WjuuuCUd3VMzbMwi4BAF0BoRv4LsFhUs9xZnm5a6X8LVLO+uYgvkEq3CbVFEl7PjfLKzT28F3TY3p3qSCeGBWim07ppxtP7qutuRV6Z+0hfZB5SEVVDXpx2T69uGyf+sSF6azhKZqenqL07gRwANbJLqnR++sP6b3MQ9pbWN0yHhcerHNHpuqiE7prePdofk4BAI4bQjdwrJyhUo8xZnm568wgnru+eVZ8g1SwVaotkfZ8YZZXaDczfLddnt6tT6cP4jabTcNSozUsNVp3Tx+sJTsL9c66g/p8W4H2F9fomUV79MyiPerRLVTTh6forPRkZfSM4S+2AHxub2GV5m/J02eb87ThYHnLeIjTrmlDk3XhqO6aNCBeTm6JAQD4AKEbOB6cIVKP0WZ5NdY3B/HM1uXp+Vul2lJp7yKzvEJimoN4m+XpsX07bRB3Ouw6bUiSThuSpOr6Rn25o0CfbsrTF9sLdLC0Vs8v2avnl+xVanSIzkxP0fThyTqhVzceQQbguDAMQ1tzK/TZ5jzN35KnnflVLV+z2aSJ/eJ1wajuOjM9WREu/ioEAPAt/qQBfCXIJXU/wSyvxnpzBjx3Q5sgvkWqK5P2LTbLyxUtpYxoszx9lNQtTbJ3rpmYcFeQzhmRqnNGpKq2oUmLdxbok015+nxbvnLK6/TS8n16afk+JUW5dOawZJ01PEVj+8TKQQAHcAw8HkPrs0s1vzloZ5fUtnwtyG7ThP7xOnNYsqYOTVJCpMvCTgEAgYbQDXSkIJcZnlNHSd5J8cYG857wtpu15W+R6sul/UvN8nJFSckj2t8nHtuv0wTx0GCHzkxP0ZnpKapzN2npriJ9uilXC7fmK7+iXnO/OqC5Xx1QfIRLZ6YnaXp6isalxbILOoAjcjd5tGpfieZvztNnW/JUUFnf8rUQp12nDkzQmenJ+smgJEWH8ThDAIA1CN2A1YKCW5eW6xpzrMlt7pLeLohvluorpAPLzPIKjjRnxNveJx7XX7L79zNkQ5wOTR2apKlDk1Tf2KQVu4v18aZcLdiSp6Kqer32dZZe+zpLseHBmjbUXKo+oV+cwlkKCgS0OneTlu0q0vwtefrftnyV1bhbvhbpCtJpQxJ1ZnqyThmYoLBgfl4AAKzHn0aAP3I4peThZukqc6ypUSra0T6I522SGiqlA8vN8nKGNwfxjNZZ8fgBfhvEXUEOTRmcqCmDE9Vw4XB9tbdYn27K1Wdb8lRS3aA3V2frzdXZCnbYNS4tVpMHJWjyoET1SwhnIzYgABRV1WvprkL9b1uBFm0vUHVDU8vXvP8wd0Z6sib0i5MryD9/zgEAAhehG+gsHEFS0jCzRl1hjjU1SkU7zRDuvU88b6PkrpayvjLLyxlmhvh2QXyg+X39SHCQuST01IEJuv+CdK3cV6LPtuTpyx0Fyi6p1bLdRVq2u0j3f7xNPWNDNWVQoqYMStSJfeMUGsxftoGuwN3k0fqsMi3eWaDFOwu1+VBFu6+nRIfojGHJOjM9mT0gAAB+z7/+tg3g2DiCpKShZmXMMMc8TVLRrva7puc2B/HslWZ5BYWaQdz7DPGUDClhsN8E8SCHXRP7x2ti/3gZhqG9RdX6crv5l/CVe0uUXVKrV746oFe+OiBXkF0n9o3TlEEJmjI4Ub3jwq1uH8AxOFhao8U7C7VkZ6FW7C5WZX1ju68PTYnS5EEJOmNYskb04DnaAIDOwz/+Zg3g+LE7pMTBZo283BzzNEnFu9vvmp67QWqokg6uMssrKERKSm+dDU8ZKSUOMZe8W8hms6lfQoT6JUTohpP7qrq+USv2FGvRjgIt2lGoQ2W1WryzUIt3Fmr2h1vVNz5cpw5K0JRBiRqXFqsQJ7PggD+pczfp673FLUF7T2F1u6/Hhgfr5AHxOmVAgk4eGK/EyBCLOgUA4MchdAOBwO6QEgaZNeJSc8zjkUr2tL9HPHeDeY/4oTVmeTlc5rL2trumJwwxN4GzSLgrqGUjNsMwtKugSl9uNwP46v0l2ltUrb1F1Xp5+X6FOh2a0C9OE/vH68S+cRqcHMkzwYEOZhiGdhdUtfzj2Kp9Japv9LR83WG3aVTPGPP2kkEJSk+N5vcpAKBLIHQDgcpuNzdXix8gjbjEHPN4pJK9zSF8vRnCczeajy/LWWeWlyNYShzaPognDjUfi9bBbDabBiZFamBSpG4+tZ8q69xavrtIi3YU6ssdBcqvqNfn2wv0+fYCSVJ0qFNj+8TqxL6xOrFvnIakRHFPKHCcGYah7JJardpfolX7irVsV5FyyuvaHZMaHaJTByXolAEJmtA/XtGhPNYLAND1ELoBtLLbpfj+Zg2/2BzzeKTSfd+4R3yDVFfe/N+ZbV7vNO8vb/v4ssRhkrNjl4VGhjhbngduGIa25VZq8c5Cfb23WGv2l6i81q3/bcvX/7blNx8fpHF9YjW+b6zGp8VpWGoUzwYHjpHHY2hnQaVW7SvRqn0lWr2/RPkV9e2OCQ6ya3xarE4dmKDJgxLULyGCe7MBAF0eoRvAd7Pbpbh+ZqX/1BwzjOYgvqH98vS6subZ8Q2SXml+fZB5T3jLrumjzKXqHRTEbTabhqZGaWhqlH4xuZ8amzzanFOhlXuLtXJfiVbvK1FlXWO7mfAIV5DG9Omm8WlxGt83VsO7R8tJCAfaaWj0aNOhcq3eb/4+WnOgVOW17nbHOB02jegR07KyZHwaTxkAAAQeQjeAY2ezSbF9zRp2oTlmGFLZgW/cI54p1ZaazxPP2yStf7X59Y5vBPEMKTldcob6vPUgh10ZPWOU0TNGN5/aT00eQ1tzKrRyX7G+3lusVftKVFHXqEU7CrVoR6EkKSzYodG9u+nEvnEa07ub0rtHK9zFj08ElpqGRq07UKZVzSF7fXap6tyedsd4f6+M7ROrsX1iNapXDJsYAgACHn9rBHB82GxStz5mDbvAHDMMqTz78CBeUyzlbzYr87Xm1zvMx5W1vUc8KV0KDvNp2w67TcN7RGt4j2jdcHJfNXkMbc+r0Nd7S7Ryb7FW7S9RWY1bS3cVaemuIkmS3Sb1T4zQiB4xGtkjWiN6xGhwSqRcQYQLdA0ej6H9xdXadKhcmw6Wa/WBUm05VK5Gj9HuuG5h5v4I49LMkM2tGQAAHI7QDcB3bDYpppdZQ88zxwxDKj/YvAw9szWIVxdKBVvMyny9+fV2KX5Q+yCePFwK9t0zuB12m4alRmtYarSun5Qmj8fQjvzKluXomdllyi2v0878Ku3Mr9Lbaw9KMpfRDkmJ0oge0RrRPUYjekarTzcecQT/5zGkPYXV2p7fHLIPlWtrToWqvvGcbEnqHhOqsX26aWxarMb1iVX/RO7JBgDg+xC6AXQsm02K6WnWkHPMMcOQKnLaP7osN1OqypcKt5m14Y3m19ul+IGtzxBPzZCSR0iuCJ+0a7ebYXpISpRmTkyTJBVU1GnjwXJtPFimDc0fS2vczWPlkrIkSaFOu1JCHMq07VBGr24a2SNGvePCCCmwTJPH0L6iquYZ7AptPFiqTdkO1X+9/LBjXUF2DU2NUnpqtE7obd6X3aObb1eeAADQFRG6AVjPZpOiu5s1+OzW8Yrc9rPhOZlSVZ5UuN2sjW96v4H56LN294gPl0KifNJuYlSITh8aotOHJkkyH410sLRWGw6WaePBcm3ILtPmQ+WqbmjSXrdNe1cckFYckGQ+riy9e5QGJJqPOBuQFKGBiZGKDuNRSTi+3E0e7Suq1qaD5uz1lpxybcmpUE1D0zeOtCnEadfQlCgN7x6t9O7m7Rb9EyJYKg4AwHFA6Abgv6JSzBp0VutYZd7hu6ZX5khFO83a9J/mA23mjuttg3jKCCkk+ri3abPZ1DM2TD1jw3TOiFRJ5ozijtwyvf7JUtni+2hTTqW25VSovNat5buLtXx3cbvvkRDp0sCkCA1IbA7iSZGEcRyVyjq39hZWa3dBlfYUVrV8PFBcc9g92JIU6nRoWGqU0rtHa0hyuEr2bNDMC89UaIjLgu4BAOj6CN0AOpfIZLMGntE6VlVw+GZtFYek4t1mbX679djYvt8I4iOl0Jjj3qbDbtOAxAiNSzQ0ffoQOZ1ONTR6tCOvUttyK7Qzv1I7C6q0O79SOeV1KqysV2Fl/feGcXOGPEIxYcHHvWf4L8MwVFBZ3xKo9xRUaXdhlfYUVCuvou5bXxce7DCXiHeP1vDm6psQIYfdvMXB7Xbrk9wNzGgDAOBDhG4AnV9EojRwmlleVYXN94avb71PvDxbKtlr1pZ3W4/tltY+hKdmSKHdjnubwUH2lp3S26qsc2t3QZV25VdpV0GlduZXadf3hPH4CJd6xYaqR7cw9ehmfuzZ/HlqTAg7qXdCHo+hoqp6HSqrVU5ZnQ6UeGevq7W3oEqVR9jYzCsh0qX+CRHqlxje/DFC/RMjlBwVwh4CAABYjNANoGuKSJAGnG6WV3WxOQvedka8LEsq3WfWlvdaj43p3X7X9JQMKSzWJ61Ghjg1qlc3jerVPui3hPECM4Tvag7mh8pqVVRVr6Kqeq3LKjvi90yKcplBvFv7YN6jW6hSY0IVHMTMZkeraWhUTlmdcspqm4N168ecsjrlltfK3XT4cnAvu03qHReuft8I1/0SIhQdym0IAAD4K0I3gMARHif1P80sr5qS5iDe5j7x0v1S2QGztn7QemxML3MmvCWIjzK/p498Wxivqm/U3sIqHSyt1cHSmuaP5n9nl9Sq1t2k/Ip65VfUa+2B0sO+r80mJUeFqHtMqJKiQhQbHqy4iGDFRbgUH25+jA0PVnxEsKJCnLLbmSn9NoZhqNbdpNIat0qrG1RS3aDSmgblV9Qpp6yuTaiuVWmN+3u/n9373nQLVfeYUPVLMGes+yVGqHdcGCsYAADohAjdAAJbWKzU7ydmedWWtg/huRvMJellWWZt+7D12OierUvSU0aZ/x2R4NOWI1xBGtEjRiN6xBz2NcMwVFLd0C6It/2YXVqjOrdHueV1yi3/9nuBvYLstuZQ7lKcN5yHu5o/muPRoU6FBTuaK0hhLofCnI5Od5+wx2MG6PJad0t4LqluUFlN+89LaxpUWu1u+by+0XPUv0aEK0jdY0LVvVuoUmNClBpjhuvU5kqKdHW6/98AAMB3I3QDwDeFdpP6TjbLq7ZMytvYfsO2kj3mfeLl2dL2j1qPjeoupWTInjRcieVuqWqM1K17h7Rus9nMgBzh0sieMYd93TAMFbeE8hoVVdarpLpBRdUNKq6qV3FVg4qb/7uirlGNHnMDr4LK+mPuJdhhbwngYa6g9sH8G//tCnLIZpNsMmfivfchm2O29l9r/tx7vt5xSapze1TnblJdY5PqGprMzxubVNvQpLpG82v17ibVuptajq11N6ne7VFD09GH5yOda7dwp7qFBatbWLASIl3NwTpU3ZvDdWpMqKJCWAYOAECgIXQDwNEIjZHSTjHLq65cyt3YvGFbphnEi3ebO6dXHJJjx8c6SZKe+JsUmdL+/vDUDHMX9g5ms9kUH+FSfIRLGUcI5W3VNzaptNqtoqp6FVc3qKTaDOVFVc0BvTmcV9Y1qqahSdUN5sem5sdUNTR51FDjUZm+f1m1P/lmgI4ND1ZMmFOx4Uf+vFt4sMKDHWxYBgAAjojQDQA/VEi0lHayWV71lc1BPFOeQ+tUvWu5IurzZKvMlSpzpZ2fth4bkXz4Zm1RKR17Dt/BFeRQcrRDydEhR/0awzDU0ORRbUOTqhuaVNscxKvrm1TrbjQ/tgnoNc0f6xs9MgxJMmQYkmFIHsOQIfO/DRlq/p+MduOtn0tSSJBDIU67Qp0OhTjN/w5p+W9H83jbsbbHtn5OgAYAAMcLoRsAjidXpNRnotRnoprcbn3xySeaftrJchZvb3+feNFOqSpP2jnfLK+IpG9s1pYhRaW2rp/2czabTa4gc7l4TJjV3QAAAFiP0A0AvuaKlHpPMMuroVrK29T+HvGiHVJVvrRrgVle4QmHL02P6t5pgjgAAEAgI3QDgBWCw6VeJ5rl1VAt5W9pH8QLt0vVhdLuhWZ5hcW32TU9w/wY3ZMgDgAA4GcI3QDgL4LDpZ7jzPJy10p5m1tDeO4GqWCrVFMk7fncLK/Q2OYQ3mZ5ekxvgjgAAICFCN0A4M+coVLPsWZ5uWul/K1S7vrWWfGCbVJtibTnC7O8Qrsdfo94tz4EcQAAgA5C6AaAzsYZKvUYbZaXu86cAW+ZEc80g3ltqbR3kVleIdGHB/HYvgRxAAAAHyB0A0BX4AyRup9glldjvRnEvcvSczPNe8bryqV9S8zyckVLKSOa7xMf1RrE7fYOPhEAAICuhdANAF1VkMsM0KmjWscaG6TCba2z4bkbzHvG68ul/UvN8nJFSckj2m/YFtefIA4AAHAMCN0AEEiCgpuXlo+UdI051uQ2d0lvu2t6/mapvkI6sMwsr+AIM4i33bAtfoBkd3T0mQAAAHQKhG4ACHQOp5Q83CxdZY41NZpB3DsbnpNpPle8oUrKWmGWlzPcfG3bx5fFDySIAwAAiNANADgSR5CUnG7WqCvNsaZGqWhn+83a8jZJ7mop+2uzvJxhZhBvu2Fb/CDz+wIAAAQQv/jbz9NPP61HHnlEubm5GjZsmB5//HGdfPLJVrcFAGjLESQlDTUrY4Y55mmSina1D+K5G5uD+EqzvIJCzRDfdtf0hMEEcQAA0KVZ/jedf//737rjjjv09NNPa+LEiXruued01llnaevWrerVq5fV7QEAvovdISUONmvk5eaYp0kq3t3+HvG8jebS9IOrzfIKCpGS0tvfI544xFzyDgAA0AVYHrofe+wxXX/99brhhhskSY8//rg+++wzPfPMM3rooYcs7g4AcMzsDilhkFkjLzPHPB6pZE/7IJ67QWqolA6tMcvL4ZKShrW/RzxhiLkJHAAAQCdjaehuaGjQ2rVr9bvf/a7d+LRp07RixYojvqa+vl719fUtn1dUVEiS3G633G6375qF3/G+37zv8Fdco98Q3cesIReYnxseqXSfbLkbZMvb0Pxxo2z1FVLOOrOaGY5gGYlDZSSPkJE8UkbKyOYg7rLiTLoMrlH4O65R+Duu0cB1LO+5paG7qKhITU1NSkpKajeelJSkvLy8I77moYce0r333nvY+IIFCxQWFuaTPuHfFi5caHULwHfiGv0+IZLGS7HjpW4ehTcUKrpmn2Jq9iumZr+ia/cruKlGttxMc5a8mcfmUEVIT5WF9VF5WB+VhfZRRWhPeewsTT9WXKPwd1yj8Hdco4GnpqbmqI+1fHm5JNlstnafG4Zx2JjX3XffrTvvvLPl84qKCvXs2VPTpk1TVFSUT/uEf3G73Vq4cKGmTp0qp5O/ZMP/cI0eJ4Yhd9mBNrPh5kd7XZliavcrpna/VNx8qD1IShhizoinjJSRnCEjaah57zgOwzUKf8c1Cn/HNRq4vCuuj4aloTs+Pl4Oh+OwWe2CgoLDZr+9XC6XXK7DlxM6nU4u9ADFew9/xzV6HCQOMGvExebnhiGVZbXfNT0nU7baEil/k2z5m6QNr5vH2hzm5mxtd01PTpecoVaciV/iGoW/4xqFv+MaDTzH8n5bGrqDg4M1evRoLVy4UBdeeGHL+MKFC3X++edb2BkAwK/ZbFK33mYNbf7zwjCk8uzWTdq8gbymSMrfbFbma82vd5iPK/OG8JSR5nPFg7lNCQAAHF+WLy+/8847ddVVV2nMmDE66aST9PzzzysrK0s///nPrW4NANCZ2GxSTC+zhp5njhmGVHHoG7umZ0rVhVLBFrMyvTPidil+UPtd05OHS8HhFpwMAADoKiwP3ZdddpmKi4t13333KTc3V+np6frkk0/Uu3dvq1sDAHR2NpsU3cOsIeeYY4YhVeS0nw3PzZSq8qXCbWZteMP7DaT4gYcHcVekBScDAAA6I8tDtyTdcsstuuWWW6xuAwAQCGw2Kbq7WYOnt45X5B52j7iq8qSiHWZt/Lf3G0hx/b8RxEdIIWzmCQAADucXoRsAAMtFpZg16KzWsco8c0a8bRCvzJGKd5m16a3WY+P6m/eGt2zYNlIKie7QUwAAAP6H0A0AwLeJTDZr4BmtY1UFhwfxioNS8W6zNr/Temxs3/a7pqeMlEJjOvAEAACA1QjdAAAci4hEacBUs7yqi76xNH2DVJ4llew1a8u7rcd263N4EA+L7cATAAAAHYnQDQDAjxUeL/U/3Syv6mIzgLcN42VZUul+s7a+33psTO/294inZBDEAQDoIgjdAAD4Qnic1P80s7xqSg7fNb10v1R2wKytH7QeG9OrdSY8NUNKGWV+TwAA0KkQugEA6ChhsVK/KWZ51ZYefo946T5zVrwsS9r239Zjo3u2D+EpI6WIhI49BwAAcEwI3QAAWCm0m9R3slletWVS3sb2Qbxkj1Sebdb2j1qPjerefll6aoZ53zkAAPALhG4AAPxNaIyUdopZXnXlUu7G5vvEm2fGi3dLFYfM2vFx67GRKYcH8cjkDjwBAADgRejuAIZhqLax1uo2upzGxkY1GA2qbayVW26r2wEOwzWK48rhlHqMNsuroUrK22zOiuduND8W75Gq8qRd883yCk+SUkZIySOaPw5XY0gc1yj8Gj9H4e+4Rn0nNChUNpvN6jaOC5thGIbVTfwYFRUVio6OVnl5uaKioqxu54hq3DUaP2+81W0AAAAAQKewcsZKhTnDrG7jWx1LDrV3UE8AAAAAAAQclpd3gNCgUK2csdLqNrqcxsZGffbZZzrjjDMUFMSlDP/DNQq/5q5VU85GbVv0loZ2a5S9YLNUuEMymg4/NjS2zbL05o9R3aUusuwP/oufo/B3XKO+ExoUanULxw1XRgew2Wx+vTSis3LLrWBbsEKDQuV0Oq1uBzgM1yj8mjNM7t6TlJtQoVHTp5vXqLtWyt8i5axvfZ54wTapplja+6VZXqGxbR5flmF+jOlNEMdxxc9R+DuuURwNQjcAADA5Q6UeY8zyctdJBVvaP76sYJtUW3KEIN7NDOIpGa2BvFsaQRwAENAI3QAA4Ns5Q6Tuo83yaqyXCra2D+L5W6TaUmnvIrO8QqJbg7h3VrxbmmRnWxkAQGAgdAMAgGMT5JJSR5nl1dhgBnFvCM/NNIN4Xbm0b4lZXq5o877wlJHm90jJkGL7EsQBAF0SoRsAAPx4QcHmTHZqhuSdFG9skAq3NYfw5nvE8zZL9eXS/qVmebmizE3aUjNaZ8bj+hPEAQCdHqEbAAD4RlBwc4Ae2TrW5JYKt39jafpmqb5COrDMLK/giDZBPMP8PvEDJLujQ08DAIAfg9ANAAA6jsMpJQ83S1eZY02NUtGO9kE8b5PUUCVlrTDLyxluvrbtrunxAwniAAC/RegGAADWcgRJScPMGnWFOdbUKBXv+kYQ3yi5q6Xsr83ycoZJSenfCOKDzO8LAIDF+NMIAAD4H0eQlDjErIyfmWOeJqlolxnCcze0BvGGKungKrO8gkKl5PT2jy9LGGzOtAMA0IEI3QAAoHOwO6TEwWaNvNwc8zRJxXva75qeu1FqqJQOrjbLKyjEnE1v+/iyxCEEcQCATxG6AQBA52V3SAkDzRpxqTnm8Ugle5uD+PrmndM3mJu1HVprlpcj+AhBfKi5CRwAAMcBoRsAAHQtdrsU39+s4RebYx6PVLqvTQjPlHI2mI8vy1lvljeLO4LN4O1dlp6SYQbzIJc15wMA6NQI3QAAoOuz26W4fmZ5g7hhNAfxzDbL0zdIdWXNy9QzpXVzm1/vNJeitzxHfJQZxJ0hFpwMAKAzIXQDAIDAZLNJsX3NSr/IHDMMqezAN4J4plRbam7alrex9fX2IClhiJQ6snl5ujeIh3b4qQAA/BehGwAAwMtmk7r1MWvYBeaYYUhlWW2WpWeaH2uKpfxNZq1/rfn1DnNGvO2u6UnpUnBYx58LAMAvELoBAAC+i80mdett1tDzzDHDkMoPtg/hOZlSTZGUv9mszDZBPGFQ+83akocTxAEgQBC6AQAAjpXNJsX0NGvIueaYYUgVOYcH8eoCqWCrWRvmNb/eLsUPar9ZW/JwyRVhxdkAAHyI0A0AAHA82GxSdHezBp9tjhmGVJnb5hniG8z/rsqTCreZtfFN7zeQ4ge2hvDUDCl5BEEcADo5QjcAAICv2GxSVKpZg6e3jlfkHn6PeGWuVLTDrI3/9n4DKX5A847pGa1BPCSqo88EAPADEboBAAA6WlSKWYPObB2rzD88iFcckop2mrXprdZj4/q3v0c8ZYQUEt2RZwAAOEqEbgAAAH8QmSRFTpMGTmsdqypsH8JzN0jl2VLxbrM2v916bGzfbwTxkVJoTAeeAADgSAjdAAAA/ioiQRow1Syv6qJvbNa2QSrPkkr2mrXl3dZju/U5PIiHxXbgCQAACN0AAACdSXi81P90s7yqi1tnwr2BvOyAVLrfrK3vtx4b07v9rumpowjiAOBDhG4AAIDOLjxO6n+aWV41JYffI1663wzjZQekbf9tPTa6l3lfeGqGlDLK/Bge35FnAABdFqEbAACgKwqLlfpNMcurtrQ5iG9oDeIle83l6eVZ0vaPWo+N6tH+8WUpI6WIxA49BQDoCgjdAAAAgSK0m9R3slletWVS3sb2m7UV75YqDprVNohHpn4jiGeYG8ABAL4VoRsAACCQhcZIaaeY5VVX0T6I52SaQbwyR9qRI+34pPXYiOTDg3hUSsf1DwB+jtANAACA9kKipD6TzPKqr5TyNrUP4kU7pao8aed8s7wiktovS0/JkKJSJZutI88CAPwCoRsAAADfzxUp9Z5glld9lRnEvcvSczKloh1SVb606zOzvMIT2s+Gp2ZIUd0J4gC6PEI3AAAAfhhXhNT7JLO8GqqlvM1tdk3fIBVul6oLpd0LzfIKi//G48sypOieBHEAXQqhGwAAAMdPcLjUa7xZXg01Uv6W9o8vK9gm1RRJez43yys0tiWE25KGK6y+RDKMjj0HADiOCN0AAADwreAwqedYs7zcdc1BfH37IF5bIu35QtrzhYIkTZVk7Lu/9d5w76x4tz7MiAPoFAjdAAAA6HjOEKnHaLO83HVSwZaWEG7kZMrI3yJ7bam0d5FZXiExbZamNwfy2L4EcQB+h9ANAAAA/+AMkbqPNktSo9ut+R99oDNH95azoM194gVbpboyad9is7xc0VLKiDb3iI+SuqVJdnvHnwsANCN0AwAAwG957E4zQPdqszS9scEM3m03a8vfLNWXS/uXmuXlipKSR7TfrC22H0EcQIchdAMAAKBzCQo2w3NqhuRdnd7kNu8Jb7tZW95mqb5COrDMLK/gSHNGvO094nH9JLujQ08DQGAgdAMAAKDzczibg/QI6YSrzbEmt/m4Mu9seG6m+VzxhkrpwHKzvIIjpOTh7YN4/ACCOIAfjdANAACArsnhNIN08nBJV5ljTY1S0Y7W2fCczOYgXiVlfWWWlzPcfG3bzdriB0oO/goN4OjxEwMAAACBwxEkJQ0za9QV5lhTo1S86xtBfKPkrpayvzbLKyi0TRDPMD/GDyKIA/hW/HQAAABAYHMESYlDzMr4mTnmaZKKdrXfrC1vozkjfnCVWV5BIVJSevsgnjDYnGkHEPAI3QAAAMA32R1S4mCzRl5ujnmapOI97YN47gbzHvFDa8zycrik5PQ294iPlBKHEsSBAEToBgAAAI6G3SElDDRrxKXmmMcjlextDuLrW4N4fYV0aK1ZXo5gc1l7283aEoeau7ED6LII3QAAAMAPZbdL8f3NGn6xOebxSKX72j++LHeDVFduBvOc9ZI3izuCzeDddrO2pGFSkMuKswHgA4RuAAAA4Hiy283nfsf1k9J/ao4ZhhnE2z6+LCdTqitrDuWZbV7vNO8vb3uPeOIwyRnSoacB4PggdAMAAAC+ZrNJsX3NSr/IHDMMqexA+13TczOl2lJz07a8jZJeMY+1B0kJQ6TU5tnw1FHmjLgz1IqzAXAMCN0AAACAFWw2qVsfs4ZdYI4ZhlSW1X42PDdTqimW8jeZtf615tc7zBlx77L01AxzF/XgsI4/FwDfitANAAAA+AubTerW26yh55ljhiGVH/zGrumZUnWhlL/ZrMzXm19vNx9XlpJhhvHUDPO54sHhlpwOAEI3AAAA4N9sNimmp1lDzjXHDEOqyGk/G56TKVUXSAVbzdowr/n1dil+YPvHlyWPkFwRVpwNEHAI3QAAAEBnY7NJ0d3NGnx263hF7uFBvCpPKtxu1sY3vd9Aih/Q/vFlKSMkV2THngcQAAjdAAAAQFcRlWLWoLNaxyrzzCXpbYN4ZY5UtNOsTf9pPtAmxfVvXZbuXaIeEtXRZwF0KYRuAAAAoCuLTDZr4BmtY1UFh++aXnFIKt5l1ua3W4+N7df+8WUpI6WQ6A48AaBzI3QDAAAAgSYiURo4zSyvqsLmTdrWt27YVp4tlewxa/M7rcd2Szs8iId269hzADoJQjcAAAAAKSJBGnC6WV7VRYfvml6WJZXuM2vLe63HduvTftf0lAwpLLYDTwDwT4RuAAAAAEcWHi/1P90sr5qSwzdrKzsgle43a+v7rcfG9Gq/WVvqKII4Ag6hGwAAAMDRC4uV+v3ELK+aktaZcO+mbaX7zFnxsixp239bj43u2WY2fJT5MTy+Y88B6ECEbgAAAAA/Tlis1G+KWV61pVLuxvaz4iV7zfvEy7Ol7R+1HhvV4/B7xCMSO/IMAJ8hdAMAAAA4/kK7SX1PNcurtkzK29Q+iBfvlioOmtU2iEemfiOIZ0iRSR3XP3CcELoBAAAAdIzQGCntZLO86iqkvI3tnyVetMt8lviOHGnHJ63HRqYcvllbVEpHngFwzAjdAAAAAKwTEiX1mWSWV31l84x42yC+U6rMNWvnp63HRiS1nw1PGSlFpUo2W4eeBvBtCN0AAAAA/IsrUuo9wSyvhmoziLd9fFnhdqkqX9r1mVle4Qnf2DU9Q4rqThCHJQjdAAAAAPxfcLjU60SzvBpqpPzN7R9fVrhdqi6Udi80yyssvv2y9NQMcyd1gjh8zKeh+4EHHtDHH3+szMxMBQcHq6ys7LBjsrKyNGvWLH3xxRcKDQ3VjBkz9Oijjyo4ONiXrQEAAADo7ILDpJ7jzPJy10p5m5sfX5Yp5WyQCrdJNUXSns/N8gqNbb8sPTVDiulNEMdx5dPQ3dDQoEsuuUQnnXSSXnzxxcO+3tTUpLPPPlsJCQlatmyZiouLdc0118gwDD355JO+bA0AAABAV+QMlXqONcvLXSflb5Fy17fOihdsk2pLpD1fmOUV2s0M4G2Xp3frQxDHD+bT0H3vvfdKkubMmXPEry9YsEBbt25Vdna2UlNTJUl/+9vfNHPmTD3wwAOKioryZXsAAAAAAoEzROox2iyvxvrmIJ7ZGsTzt5rPF9+7yCyvkJg2S9ObA3ls347rH52apfd0f/XVV0pPT28J3JJ0xhlnqL6+XmvXrtWUKVMOe019fb3q6+tbPq+oqJAkud1uud1u3zcNv+F9v3nf4a+4RuHvuEbh77hG4Vt2KXG4WSOvMoca66XCbbLlbpAtb6NseRtkK9gqW12ZtG+xWc0MV5TsScM1tC5Kno3Vcvc4QeqWJtns1pwOOtSx/FyyNHTn5eUpKan9A+67deum4OBg5eXlHfE1Dz30UMsMelsLFixQWFiYT/qEf1u4cOH3HwRYiGsU/o5rFP6OaxQdL0HSaVLyabIlNiqq7qCia/Yrpna/Ymr2K6o2W476CjmylmuAJH1oPsLMbQ9VWVgflYf1UVlob5WHpanKlUQQ74JqamqO+thjDt2zZ88+Yuhta/Xq1RozZsxRfT/bEe6NMAzjiOOSdPfdd+vOO+9s+byiokI9e/bUtGnTWI4eYNxutxYuXKipU6fK6XRa3Q5wGK5R+DuuUfg7rlH4K0+TW56iHfIcXKecNR+rl7NU9sKtcjbWKqFqmxKqtrUcawRHyEgeLiN5pIyUkTKSM6S4fgTxTs674vpoHHPovvXWW3X55Zd/5zF9+vQ5qu+VnJyslStXthsrLS2V2+0+bAbcy+VyyeVyHTbudDr5YRygeO/h77hG4e+4RuHvuEbhd5xOqccouZPStTE/Xj2mT5fDLqlwR/t7xPM2y9ZQJVvWV1LWV62vD46Qkoe336wtfoBkd1hwMvghjuVn0jGH7vj4eMXHxx/ry47opJNO0gMPPKDc3FylpKRIMpeJu1wujR49+nteDQAAAAB+wuGUktPNGnWlOdbUKBXt/EYQ3yQ1VJkhvG0Qd4Y3B/E2zxKPHyg5LL0jGMeBT9/BrKwslZSUKCsrS01NTcrMzJQk9e/fXxEREZo2bZqGDh2qq666So888ohKSkr061//WjfeeCNLxQEAAAB0bo4gKWmoWRkzzDFPkxnEvSE8d4OUu1FyV0vZX5vlFRRqBvG2u6YnDCaIdzI+fbf++Mc/au7cuS2fjxo1SpL05ZdfavLkyXI4HPr44491yy23aOLEiQoNDdWMGTP06KOP+rItAAAAALCG3SElDjEr42fmmKdJKt7dGsRzMqW8jeaM+MFVZnkFhUhJ6e2DeOIQc6YdfsmnoXvOnDnf+oxur169eumjjz7yZRsAAAAA4L/sDilhkFkjLzPHPB4ziHtnw3OaPzZUSofWmOXlcElJw1qXpadmSAlDpKDgDj8VHI51CQAAAADgb+x2KWGgWSMuNcc8Hqlkb/Ns+PrmpekbpPoKKWedWV6OYClxaPsgnjhUCjp8U2r4FqEbAAAAADoDu12K72/W8IvNMY9HKt3XfrO23A1SXXnzf2e2eb3TvL+8Zdf0keZSdYK4TxG6AQAAAKCzstvN537H9ZPSf2qOGYYZxNuG8JxMqa6sdXZ8XfPeW/Yg857wliA+ylyq7gyx4my6JEI3AAAAAHQlNpsU29es9IvMMcOQyg4cHsRrS8zHmOVtkta/ah5rDzLvCW/7+LLkdMkZasnpdHaEbgAAAADo6mw2qVsfs4ZdYI4ZhlSe3X7X9NxMqaZYyt9kVuZrza93mI8ra3uPeFK6FBzW0WfS6RC6AQAAACAQ2WxSTC+zhp5njhmGVH6weRl6ZmsQry6UCraYlfl68+vtZhD3ProsNcN8rnhwuCWn468I3QAAAAAAk80mxfQ0a8g55phhSBU57UN4TqZUXSAVbDVrwxvNr7dL8QPb3COeYQZxV4QFJ+MfCN0AAAAAgG9ns0nR3c0afHbreEXu4UG8Kk8q3G7Wxje930CKH9A+iKeMkFyRHXseFiF0AwAAAACOXVSKWYPOah2rzGvdpM0bxCtzpKKdZm36T/OBNimuf5sQPtKskKiOPgufI3QDAAAAAI6PyGSzBp7ROlZVcPhmbRWHpOJdZm16q/XY2H5S9xOkC583H4fWBRC6AQAAAAC+E5EoDZxmlldVYfNmbeubg/gGcyf1kj3mfeFdJHBLhG4AAAAAQEeLSJAGnG6WV3WROQveWG9ZW75A6AYAAAAAWC88Xup/+vcf18l0nTl7AAAAAAD8DKEbAAAAAAAfIXQDAAAAAOAjhG4AAAAAAHyE0A0AAAAAgI8QugEAAAAA8BFCNwAAAAAAPkLoBgAAAADARwjdAAAAAAD4CKEbAAAAAAAfIXQDAAAAAOAjhG4AAAAAAHyE0A0AAAAAgI8QugEAAAAA8BFCNwAAAAAAPkLoBgAAAADARwjdAAAAAAD4SJDVDfxYhmFIkioqKizuBB3N7XarpqZGFRUVcjqdVrcDHIZrFP6OaxT+jmsU/o5rNHB586c3j36XTh+6KysrJUk9e/a0uBMAAAAAQCCprKxUdHT0dx5jM44mmvsxj8ejnJwcRUZGymazWd0OOlBFRYV69uyp7OxsRUVFWd0OcBiuUfg7rlH4O65R+Duu0cBlGIYqKyuVmpoqu/2779ru9DPddrtdPXr0sLoNWCgqKoofcvBrXKPwd1yj8Hdco/B3XKOB6ftmuL3YSA0AAAAAAB8hdAMAAAAA4COEbnRaLpdLf/rTn+RyuaxuBTgirlH4O65R+DuuUfg7rlEcjU6/kRoAAAAAAP6KmW4AAAAAAHyE0A0AAAAAgI8QugEAAAAA8BFCNwAAAAAAPkLoRpdSX1+vjIwM2Ww2ZWZmWt0OIEnav3+/rr/+eqWlpSk0NFT9+vXTn/70JzU0NFjdGgLc008/rbS0NIWEhGj06NFaunSp1S0BkqSHHnpIY8eOVWRkpBITE3XBBRdox44dVrcFfKuHHnpINptNd9xxh9WtwA8RutGl3HXXXUpNTbW6DaCd7du3y+Px6LnnntOWLVv097//Xc8++6x+//vfW90aAti///1v3XHHHbrnnnu0fv16nXzyyTrrrLOUlZVldWuAFi9erFmzZunrr7/WwoUL1djYqGnTpqm6utrq1oDDrF69Ws8//7xGjBhhdSvwUzwyDF3Gp59+qjvvvFPvvPOOhg0bpvXr1ysjI8PqtoAjeuSRR/TMM89o7969VreCADV+/HidcMIJeuaZZ1rGhgwZogsuuEAPPfSQhZ0BhyssLFRiYqIWL16sU045xep2gBZVVVU64YQT9PTTT+v+++9XRkaGHn/8cavbgp9hphtdQn5+vm688Ua9+uqrCgsLs7od4HuVl5crNjbW6jYQoBoaGrR27VpNmzat3fi0adO0YsUKi7oCvl15ebkk8XMTfmfWrFk6++yzdfrpp1vdCvxYkNUNAD+WYRiaOXOmfv7zn2vMmDHav3+/1S0B32nPnj168skn9be//c3qVhCgioqK1NTUpKSkpHbjSUlJysvLs6gr4MgMw9Cdd96pSZMmKT093ep2gBZvvvmm1q1bp9WrV1vdCvwcM93wW7Nnz5bNZvvOWrNmjZ588klVVFTo7rvvtrplBJijvUbbysnJ0ZlnnqlLLrlEN9xwg0WdAyabzdbuc8MwDhsDrHbrrbdq48aNeuONN6xuBWiRnZ2t22+/Xa+99ppCQkKsbgd+jnu64beKiopUVFT0ncf06dNHl19+uT788MN2f1FsamqSw+HQFVdcoblz5/q6VQSoo71GvX8Y5+TkaMqUKRo/frzmzJkju51/94Q1GhoaFBYWprfeeksXXnhhy/jtt9+uzMxMLV682MLugFa33Xab3n//fS1ZskRpaWlWtwO0eP/993XhhRfK4XC0jDU1Nclms8lut6u+vr7d1xDYCN3o9LKyslRRUdHyeU5Ojs444wy9/fbbGj9+vHr06GFhd4Dp0KFDmjJlikaPHq3XXnuNP4hhufHjx2v06NF6+umnW8aGDh2q888/n43UYDnDMHTbbbfpvffe06JFizRgwACrWwLaqays1IEDB9qNXXvttRo8eLB++9vfcisE2uGebnR6vXr1avd5RESEJKlfv34EbviFnJwcTZ48Wb169dKjjz6qwsLClq8lJydb2BkC2Z133qmrrrpKY8aM0UknnaTnn39eWVlZ+vnPf251a4BmzZqlefPm6YMPPlBkZGTLXgPR0dEKDQ21uDtAioyMPCxYh4eHKy4ujsCNwxC6AcDHFixYoN27d2v37t2H/UMQi41glcsuu0zFxcW67777lJubq/T0dH3yySfq3bu31a0BLY+ymzx5crvxl19+WTNnzuz4hgDgR2B5OQAAAAAAPsIuPgAAAAAA+AihGwAAAAAAHyF0AwAAAADgI4RuAAAAAAB8hNANAAAAAICPELoBAAAAAPARQjcAAAAAAD5C6AYAAAAAwEcI3QAAAAAA+AihGwAAAAAAHyF0AwAAAADgI4RuAAAAAAB85P8DSub/LazYmWsAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(-5,5)\n", - "y1_v = [qf(xx) for xx in x_v]\n", - "y2_v = [qfp(xx) for xx in x_v]\n", - "y3_v = [qfpp(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"f\")\n", - "plt.plot(x_v, y2_v, label=\"f'\")\n", - "plt.plot(x_v, y3_v, label=\"f''\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "5fbfdc73-3c3b-46f3-b465-8a72cf989548", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(-2.0000018174926066,\n", - " -1.9999998989657501,\n", - " 1.9999999488316007,\n", - " 2.000000751212651)" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y2a_v = [qf.p(xx) for xx in x_v] # calculate the derivative from the original object\n", - "y3a_v = [qf.pp(xx) for xx in x_v] # ditto second derivative\n", - "y3b_v = [qfp.p(xx) for xx in x_v] # calculate the second derivative as derivative from the derivative object\n", - "assert y2a_v == y2_v # those are literally two ways of getting the same result\n", - "assert y3a_v == y3_v # ditto\n", - "assert iseq(min(y3_v), -2) # check that the second derivative is correct\n", - "assert iseq(max(y3_v), -2) # ditto\n", - "assert iseq(min(y3b_v), 2) # ditto, but the other way\n", - "assert iseq(max(y3b_v), 2) # ditto\n", - "min(y3_v), max(y3_v), min(y3b_v), max(y3b_v)" - ] - }, - { - "cell_type": "markdown", - "id": "02deebe2-3397-4efb-8e41-d50014dbba9d", - "metadata": {}, - "source": [ - "### Custom function" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "7accd13d-4da5-4d9f-94a6-575b5bb4cc6f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.41421356237309515, -0.3535533907028654, 0.08838838549962702)" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "@f.dataclass(frozen=True)\n", - "class MyFunction(f.Function):\n", - " k: float = 1\n", - " \n", - " def f(self, x):\n", - " return (m.sqrt(1+x)-1)*self.k\n", - "mf = MyFunction()\n", - "mf2 = mf.update(k=2)\n", - "mf(1),mf.p(1),mf.pp(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "b76d484d-5041-4d3c-90a2-43cebdb6161c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(0,10)\n", - "y1_v = [mf(xx) for xx in x_v]\n", - "y2_v = [mf2(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"mf\")\n", - "plt.plot(x_v, y2_v, label=\"nf2\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "markdown", - "id": "66461504-3d04-44c0-bc41-caa4ea47f696", - "metadata": {}, - "source": [ - "## Kernel" - ] - }, - { - "cell_type": "markdown", - "id": "d117bbf1-0988-4ef5-a40f-18fdd3f83a6f", - "metadata": { - "tags": [] - }, - "source": [ - "### Integration function" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "id": "ad760927-1132-4f93-9fd6-967c36efaed6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "integrate = Kernel.integrate_trapezoid\n", - "ONE = lambda x: 1\n", - "LIN = lambda x: 2*x\n", - "SQR = lambda x: 3*x*x" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "18785493-71e6-4952-978e-b755e3bdc84e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(integrate(ONE, 0, 1, 2), 1) # trapezoid integrates constant perfectly\n", - "assert iseq(integrate(ONE, 0, 1, 100), 1)\n", - "assert iseq(integrate(LIN, 0, 1, 2), 1) # ditto linear\n", - "assert iseq(integrate(LIN, 0, 1, 100), 1)\n", - "assert iseq(integrate(SQR, 0, 1, 100), 1, eps=1e-3)\n", - "assert iseq(integrate(SQR, 0, 1, 1000), 1, eps=1e-6)" - ] - }, - { - "cell_type": "markdown", - "id": "ba333451-0dfe-4409-a574-d8f77e1e1104", - "metadata": {}, - "source": [ - "### Default kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "2f02cf1c-fa10-4a2e-9472-d371d2c3b260", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k = Kernel(steps=1000)\n", - "assert k.x_min == 0\n", - "assert k.x_max == 1\n", - "assert set(k.kernel(xx) for xx in np.linspace(k.x_min, k.x_max, 50)) == {1}\n", - "assert iseq(k.integrate(ONE), 1)\n", - "assert iseq(k.integrate(LIN), 1)\n", - "assert iseq(k.integrate(SQR), 1)\n", - "x_v = np.linspace(-0.5, 1.5, 1000)\n", - "plt.plot(x_v, [k.k(xx) for xx in x_v], label=\"default kernel\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "3b9e2eb4-6bde-4b66-866c-3ac72970bf1c", - "metadata": { - "tags": [] - }, - "source": [ - "### Flat kernels" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "ffeeb416-d951-4f78-84a3-342ebbe1956f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k = Kernel(x_max=2, kernel=lambda x: 0.5, steps=1000)\n", - "assert k.x_min == 0\n", - "assert k.x_max == 2\n", - "assert set(k.kernel(xx) for xx in np.linspace(k.x_min, k.x_max, 50)) == {0.5}\n", - "assert iseq(k.integrate(ONE), 1)\n", - "assert iseq(k.integrate(LIN), 2)\n", - "assert iseq(k.integrate(SQR), 4)\n", - "x_v = np.linspace(-0.5, 2.5, 1000)\n", - "plt.plot(x_v, [k.k(xx) for xx in x_v], label=\"flat kernel 0..2\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "24eee0bd-2db9-47ba-870f-546912ec4028", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k = Kernel(x_max=4, kernel=lambda x: 0.25, steps=1000)\n", - "assert k.x_min == 0\n", - "assert k.x_max == 4\n", - "assert set(k.kernel(xx) for xx in np.linspace(k.x_min, k.x_max, 50)) == {0.25}\n", - "assert iseq(k.integrate(ONE), 1)\n", - "assert iseq(k.integrate(LIN), 4)\n", - "assert iseq(k.integrate(SQR), 16)\n", - "x_v = np.linspace(-0.5, 4.5, 1000)\n", - "plt.plot(x_v, [k.k(xx) for xx in x_v], label=\"flat kernel 0..4\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "49522d4f-9149-4b8d-9bc2-fdf90ac1769e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(4.0, 16.000008000000012)" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "k.integrate(LIN), k.integrate(SQR)" - ] - }, - { - "cell_type": "markdown", - "id": "25309e0f-4cfe-4910-850b-da56d8e59e36", - "metadata": {}, - "source": [ - "### Triangle and sawtooth kernels" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "86546a13-cdb3-49c3-ab9c-a5af1e331b43", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kf = Kernel(x_min=1, x_max=3, kernel=Kernel.FLAT, steps=1000)\n", - "kl = Kernel(x_min=1, x_max=3, kernel=Kernel.SAWTOOTHL, steps=1000)\n", - "kr = Kernel(x_min=1, x_max=3, kernel=Kernel.SAWTOOTHR, steps=1000)\n", - "kt = Kernel(x_min=1, x_max=3, kernel=Kernel.TRIANGLE, steps=1000)\n", - "x_v = np.linspace(0.5, 3.5, 1000)\n", - "plt.plot(x_v, [kf.k(xx) for xx in x_v], label=\"flat\")\n", - "plt.plot(x_v, [kl.k(xx) for xx in x_v], label=\"sawtooth left\")\n", - "plt.plot(x_v, [kr.k(xx) for xx in x_v], label=\"sawtooth right\")\n", - "plt.plot(x_v, [kt.k(xx) for xx in x_v], label=\"triangle\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "335de4b7-cdce-4f69-ab18-b1e3dfd375bd", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(kf.integrate(ONE), 1)\n", - "assert iseq(kl.integrate(ONE), 1)\n", - "assert iseq(kr.integrate(ONE), 1)\n", - "assert iseq(kt.integrate(ONE), 1)\n", - "\n", - "assert iseq(kf.integrate(LIN), 4)\n", - "assert iseq(kl.integrate(LIN), 10/3)\n", - "assert iseq(kr.integrate(LIN), 14/3)\n", - "assert iseq(kt.integrate(LIN), 4)\n", - "\n", - "assert iseq(kf.integrate(SQR), 13)\n", - "assert iseq(kl.integrate(SQR), 9)\n", - "assert iseq(kr.integrate(SQR), 17)\n", - "assert iseq(kt.integrate(SQR), 12.5)" - ] - }, - { - "cell_type": "markdown", - "id": "31758d9a-b0d5-4842-8844-a64c50b7396f", - "metadata": {}, - "source": [ - "### Gaussian kernels" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "28ca49c4-4bb1-433a-a0ff-beb685950dbe", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kf = Kernel(x_min=1, x_max=3, kernel=Kernel.FLAT, steps=1000)\n", - "kg = Kernel(x_min=1, x_max=3, kernel=Kernel.GAUSS, steps=1000)\n", - "kw = Kernel(x_min=1, x_max=3, kernel=Kernel.GAUSSW, steps=1000)\n", - "kn = Kernel(x_min=1, x_max=3, kernel=Kernel.GAUSSN, steps=1000)\n", - "x_v = np.linspace(0.5, 3.5, 1000)\n", - "plt.plot(x_v, [kf.k(xx) for xx in x_v], label=\"flat\")\n", - "plt.plot(x_v, [kg.k(xx) for xx in x_v], label=\"gauss\")\n", - "plt.plot(x_v, [kw.k(xx) for xx in x_v], label=\"gauss wide\")\n", - "plt.plot(x_v, [kn.k(xx) for xx in x_v], label=\"gauss narrow\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "id": "56110cff-696d-48a5-a957-a04d32e20298", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(kf.integrate(ONE), 1)\n", - "assert iseq(kg.integrate(ONE), 1, eps=1e-3)\n", - "assert iseq(kw.integrate(ONE), 1, eps=1e-3)\n", - "assert iseq(kn.integrate(ONE), 1, eps=1e-3)" - ] - }, - { - "cell_type": "markdown", - "id": "fe63fcfa-4fd9-43d7-8c0b-4bfd51e714d1", - "metadata": {}, - "source": [ - "## Function Vector" - ] - }, - { - "cell_type": "markdown", - "id": "91a19e24-da99-40f5-b16d-734e9d429743", - "metadata": {}, - "source": [ - "### vector operations and consistency" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "id": "5400e8ef-8e97-4275-8485-b464ddd313b1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[FunctionVector::eq] called; funcs_eq=True, kernel_eq=True\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "knl = Kernel(x_min=1, x_max=3, kernel=Kernel.FLAT, steps=1000)\n", - "f1 = f.QuadraticFunction(a=3, c=1)\n", - "f2 = f.QuadraticFunction(b=2)\n", - "f3 = f.QuadraticFunction(a=3, b=2, c=1)\n", - "f1v = f.FunctionVector({f1: 1}, kernel=knl)\n", - "f2v = f.FunctionVector({f2: 1}, kernel=knl)\n", - "fv = f.FunctionVector({f1: 1, f2: 1}, kernel=knl)\n", - "assert fv == f1v + f2v\n", - "x_v = np.linspace(1, 3, 100)\n", - "y1_v = [f1(xx) for xx in x_v]\n", - "y2_v = [f2(xx) for xx in x_v]\n", - "y3_v = [f3(xx) for xx in x_v]\n", - "yv_v = [fv(xx) for xx in x_v]\n", - "y_diff = np.array(yv_v) - np.array(y3_v)\n", - "plt.plot(x_v, y1_v, label=\"f1\")\n", - "plt.plot(x_v, y2_v, label=\"f2\")\n", - "plt.plot(x_v, y3_v, label=\"f3\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "id": "06d7ed49-1934-4943-8405-8fcbc9b3ac93", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "assert max(y_diff)<1e-10\n", - "assert min(y_diff)>-1e-10\n", - "plt.plot(x_v, yv_v, linewidth=3, label=\"vector\")\n", - "plt.plot(x_v, y3_v, linestyle=\"--\", color=\"#ccc\", label=\"f3\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()\n", - "plt.plot(x_v, y_diff)\n", - "plt.grid()\n", - "max(y_diff), min(y_diff)" - ] - }, - { - "cell_type": "markdown", - "id": "2f88e041-7084-4be7-81ec-7112877b2af0", - "metadata": {}, - "source": [ - "check that you can't add vectors with different kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "418bd7a3-29e2-49e1-9a5f-20faa1de2ecd", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "f1v = f.FunctionVector({f1: 1}, kernel=knl)\n", - "f2v = f.FunctionVector({f2: 1}, kernel=knl)\n", - "assert not raises(lambda: f1v+f2v)\n", - "assert not raises(lambda: f1v-f2v)\n", - "\n", - "f1v = f.FunctionVector({f1: 1}, kernel=knl)\n", - "f2v = f.FunctionVector({f2: 1}, kernel=None)\n", - "assert raises(lambda: f1v+f2v)\n", - "assert raises(lambda: f1v-f2v)" - ] - }, - { - "cell_type": "markdown", - "id": "7ad75da5-1701-4b2f-8d92-afee912bd73a", - "metadata": {}, - "source": [ - "### integration" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "45e38a6a-7af1-40b0-a707-58779d77dee7", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "f1v = f.FunctionVector({f1: 1}, kernel=knl)\n", - "f2v = f.FunctionVector({f2: 1}, kernel=knl)\n", - "#f1v.kernel, f2v.kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "id": "622fde1e-6276-44b1-b2af-be33e9ce0cea", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "knl = f1v.kernel\n", - "assert f1v.kernel == f2v.kernel\n", - "assert f1v.kernel == fv.kernel\n", - "x_v = np.linspace(knl.x_min, knl.x_max)\n", - "plt.plot(x_v, [f1v(xx) for xx in x_v], label=\"f1\")\n", - "plt.plot(x_v, [f2v(xx) for xx in x_v], label=\"f2\")\n", - "plt.plot(x_v, [fv(xx) for xx in x_v], label=\"f=f1+f2\")\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "id": "6d235d83-9593-4253-b602-f1e471436990", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(f1v.integrate(), 13+1)\n", - " # assert iseq(kf.integrate(ONE), 1)\n", - " # assert iseq(kf.integrate(SQR), 13)\n", - "\n", - "assert iseq(f2v.integrate(), 4)\n", - " # assert iseq(kf.integrate(LIN), 4)\n", - "\n", - "assert iseq(fv.integrate(), 18)" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "39c7a0ee-bcbf-46c3-90a3-995bfbf395ed", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "4.000000000000001" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f2v.integrate()" - ] - }, - { - "cell_type": "markdown", - "id": "7b9f01e7-26a5-4301-8d37-90e5103166d5", - "metadata": {}, - "source": [ - "### goal seek and minimize" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "2ed23a10-1175-4841-89e7-c80c8e55d787", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f1 = f.QuadraticFunction(a=1, c=-4)\n", - "f1v = f.FunctionVector({f1: 1})\n", - "x_v = np.linspace(-2.5, 2.5, 100)\n", - "y1_v = [f1(xx) for xx in x_v]\n", - "plt.plot(x_v, y1_v, label=\"f\")\n", - "#plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "id": "375bce7a-9ee8-4b73-aeda-e4d6542032b7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.00030468016160726646" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert iseq(f1v.goalseek(target=0, x0=1), 2)\n", - "assert iseq(f1v.goalseek(target=0, x0=-1), -2)\n", - "assert iseq(f1v.goalseek(target=-3, x0=1), 1)\n", - "assert iseq(f1v.goalseek(target=-3, x0=-1), -1)\n", - "assert iseq(0, f1v.minimize1(x0=5), eps=1e-3)\n", - "f1v.minimize1(x0=5)" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "d668c6c9-4074-453c-b301-eecb52952fbd", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f2 = f.QuadraticFunction(a=3, b=2, c=1)\n", - "f2v = f.FunctionVector({f2: 1})\n", - "x_v = np.linspace(-2.5, 2.5, 100)\n", - "y2_v = [f2(xx) for xx in x_v]\n", - "plt.plot(x_v, y2_v, label=\"f\")\n", - "#plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "id": "19676a10-a38d-45ba-890e-e34115dfc9d4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.8685170919424989, -0.3332480000000852)" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assert iseq(f2v.goalseek(target=5), 0.8685170919424989, eps=1e-4)\n", - "assert iseq(f2v.minimize1(), -0.3332480000000852, eps=1e-4)\n", - "f2v.goalseek(target=5), f2v.minimize1()" - ] - }, - { - "cell_type": "markdown", - "id": "122ce720-6bcc-4eba-a16f-9f100c44b9ad", - "metadata": {}, - "source": [ - "## Restricted and apply kernel\n", - "\n", - "restricted functions (`f_r`, more generally `restricted(func)`) are zero outside the kernel domain; kernel-applied functions (`f_k`, more generally `apply_kernel(func)`) is multiplied with the kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "9642d905-3733-404a-8f29-47dcf9956af4", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "func = f.TrigFunction()" - ] - }, - { - "cell_type": "markdown", - "id": "8d18a0f1-f434-41ab-9001-b451f745d92a", - "metadata": {}, - "source": [ - "### Flat kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "id": "06b27591-5c31-44ef-a677-2d0073bdbe69", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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HbrefdtfviooK7d692/vxqFGjunXjNwCAbyjVAAAAnWTp0qX60Y9+dNr7rFixQhdeeGH3BAIAdDlKNQAAQCcpKyvTvn37Tnuf4cOHKy4urpsSAQC6GqUaAAAAAAA/sVEZAAAAAAB+Copzqt1utw4fPqy4uLjTbuwBAAAAAEBnME1T1dXVysjIOO2RjkFRqg8fPqzMzEyrYwAAAAAAwkxhYaH69et3yj8PilLt2cyjsLBQ8fHxFqc5NafTqeXLlysnJ0cOh8PqOAgCjBn4ijEDXzFm4CvGDHzFmIGvgmXMVFVVKTMz84ybSwZFqfZM+Y6Pjw/4Uh0bG6v4+PiAHhwIHIwZ+IoxA18xZuArxgx8xZiBr4JtzJxpCTIblQEAAAAA4CdKNQAAAAAAfqJUAwAAAADgJ0o1AAAAAAB+olQDAAAAAOAnSjUAAAAAAH6iVAMAAAAA4CdKNQAAAAAAfqJUAwAAAADgJ0o1AAAAAAB+olQDAAAAAOAnSjUAAAAAAH7yuVR//PHHuuqqq5SRkSHDMPT222+f8TGrVq1Sdna2oqOjNWjQIC1ZssSfrAAAAAAABBSfS3VNTY3Gjh2rp556ql3337dvny6//HJNmzZN+fn5+uUvf6m77rpLb7zxhs9hAQAAAAAIJBG+PmDWrFmaNWtWu++/ZMkS9e/fX08++aQkaeTIkVq/fr0ee+wxXXvttb4+PQAAAAAAAcPnUu2rvLw85eTktLjt0ksv1XPPPSen0ymHw9HqMfX19aqvr/d+XFVVJUlyOp1yOp1dG7gDPNkCOSMCC2MGvmLMwFeMGfiKMQNfMWbgq2AZM+3N1+Wluri4WGlpaS1uS0tLU2Njo0pLS5Went7qMYsWLdLChQtb3b58+XLFxsZ2WdbOkpuba3UEBBnGDHzFmIGvGDPwFWMGvmLMwFeBPmZqa2vbdb8uL9WSZBhGi49N02zzdo/77rtPCxYs8H5cVVWlzMxM5eTkKD4+vuuCdpDT6VRubq5mzpzZ5hV44JsYM/AVYwYepmnqr58VatlXxWr+sXrK+5VXVCgpMfGUP3d7RNk1f/ogZWcldVFaBBNeZ+Arxgx8FSxjxjNj+ky6vFT36dNHxcXFLW4rKSlRRESEkpOT23xMVFSUoqKiWt3ucDgC+ovuESw5ETgYM/AVYya8udymHvjnFr209kA7H2FoX3Xlae+xdl+5/vv68bpsdJ+OB0RI4HUGvmLMwFeBPmbam63LS/WUKVP0zjvvtLht+fLlmjBhQkB/AQEACER1Tpfu/vsmvbelWIYh3X3xMA3v0/OU929sdGnjxo0655xzFBFhb/M+r284qA+2lWj+yxv04LdG64eTs7oqPgAAIcfnUn38+HHt3r3b+/G+ffu0adMm9erVS/3799d9992nQ4cO6cUXX5QkzZs3T0899ZQWLFig2267TXl5eXruuef0yiuvdN5nAQBAGKisdeq2l9br833HFGm36Q+zx+mKs1vvTfJ1TqdTrgOmLj0r7ZRvZl8yMk2//r+v9MrnhfrV21/pSFWdFswcdsrp4gAA4CSfz6lev369xo8fr/Hjx0uSFixYoPHjx+s3v/mNJKmoqEgFBQXe+w8cOFDLli3TypUrNW7cOD300EP67//+b47TAgDAB0WVJ/TdP6/R5/uOKS4qQn+5ddIZC3V7Rdht+t01Y3T3JUMlSX/6aLd+8cZmNbrcnfL3AwAQyny+Un3hhRd6Nxpry9KlS1vdNn36dG3cuNHXpwIAAJJ2HanWTc9/rqLKOqXFR2npjyZpZHrnbtxpGIbuvmSYUuOi9au3v9Q/1h9U2fEGPXXDOYqJbHvaOAAA8ONKNQAA6D7r9x/TdUvyVFRZp8EpPfTG7VM7vVB/3Q3n9teSH2YrKsKmD7eX6IZn1+pYTUOXPR8AAMGOUg0AQIB6f0uxfvDsZ6o84dQ5/RP1+ryp6pcU2+XPm3NWH70891wlxDiUX1Ch65asUeGx9p3VCQBAuKFUAwAQgF7+7IBu/+sG1Te6dcnIVL08d7KSekR22/NPGNBLr8+booyEaO09WqNrn1mjrYfbd14nAADhhFINAEAAMU1TT+Tu1P1vfSW3KV0/MVNLfphtybrmoWlxemP+VA1Pi1NJdb1m/zlPa/aUdnsOAAACGaUaAIAA0ehy6743v9R/f7hLknTXxUO16DtjFGG37sd1ekKM/jFviiYN7KXq+kbd8vw6vbv5sGV5AAAINJRqAAACwIkGl+b9dYP+vq5QNkN6+NujA+as6IQYh168dZJmje6jBpdbd76Srxc+3Wd1LAAAAgKlGgAAi5XXNOgHz67VB9tKFBVh0zM/zNYPJ2dZHauFaIddT91wjm6akiXTlBa+s1W///f20x6zCQBAOKBUAwBgoYPltbpuyRptLKhQfHSE/jr3XF16Vh+rY7XJbjO08Oqz9J+XDpckLVm1Rz997Qs5XW6LkwEAYB1KNQAAFtlWVKVrn1mjPUdrlJ4Qrddvn6qJA3pZHeu0DMPQHTOG6NHrzpbdZujNjYc05y/rVVPfaHU0AAAsQakGAMACeXvK9L0leTpSVa9haT315vypGpYWZ3WsdvvehEw9e9MExTjs+njnUX3/f9eq9Hi91bEAAOh2lGoAALrZwfJa3bp0narrGzVpQC+99uOpSk+IsTqWz2aMSNXfbjtXSbEObT5YqXkvbWCNNQAg7FCqAQDoZo+8t0MnnC5lZyXpxTmTlBDrsDqS38b3T9Lrt09VjMOu9QfK9e7mIqsjAQDQrSjVAAB0ow0HyvXOF4dlGNLCq89StMNudaQOG5zSU7dfOFiS9Pt/b1ed02VxIgAAug+lGgCAbuJ2m3ro3a2SpO9m99PovgkWJ+o8t00bpPSEaB2qOKHnPuEMawBA+KBUAwDQTf75xWFtKqxQbKRdP8sZbnWcThUTadcvLhshSVq8YrdKqussTgQAQPegVAMA0A1ONLj0yHvbJUnzLxys1PhoixN1vqvHZmhsZqJqGlx6/P2dVscBAKBbUKoBAOgG/7t6r4oq69Q3MUZzpw2yOk6XsNkM/ebKUZKkf2wo1JbDlRYnAgCg61GqAQDoYkeq6vTMyj2SpF/MGhESm5OdSnZWkq4amyHTlB56dytHbAEAQh6lGgCALvZo8xFa5/RP1FVnp1sdp8v94rLhioqwae3eY1q+9YjVcQAA6FKUagAAutCXByv1xsaDkqRfXzlKhmFYnKjr9UuK1dxpAyVJi5ZtU0Oj2+JEAAB0HUo1AABdxDRPHqH17XEZGt8/yeJE3ef2C4coJS5K+8tq9WLefqvjAADQZSjVAAB0kfe+Ktbn+48p2mHTz5uPmwoXPaMi9J/Nx4b98cNdOlbTYHEiAAC6BqUaAIAuUOd06Xf/3iZJ+n/TBikjMcbiRN3v2ux+GpUer+q6Rv0hlyO2AAChiVINAEAXWLpmvwqPnVBqXJR+PH2w1XEsYbcZ+nXzEVt/+7xAu45UW5wIAIDOR6kGAKCTlR6v11Mf7ZYk/fyyEeoRFWFxIutMGZysS89Kk8tt6uF/bbM6DgAAnY5SDQBAJ3sid6eO1zdqTN8EfWd8X6vjWO6+WSPlsBtatfOoVuwosToOAACdilINAEAn2l5cpb9/XiCp6Qgtmy30j9A6kwG9e+iWqQMkSf/1r21yujhiCwAQOijVAAB0EtM09fC72+Q2pVmj+2jSwF5WRwoYP7loqHr1iNTukuN6pflNBwAAQgGlGgCATvLR9hJ9srtUkXab7ps10uo4ASUhxqF7Zg6TJP0hd6cqa50WJwIAoHNQqgEA6AROl1v/1bwR14/OH6D+ybEWJwo835+YqWFpPVVe69SfPtpldRwAADoFpRoAgE7wUt4B7S2tUXKPSP1kxhCr4wSkCLtN91/RdMTWX/L2a19pjcWJAADoOEo1AAAdVFHboD9+2HTldUHOMMVFOyxOFLimD0vRhcNT5HSZ+t0yjtgCAAQ/SjUAAB305Ae7VHnCqRF94jR7QqbVcQLer64YKbvNUO7WI1qzp9TqOAAAdAilGgCADthdclx/XXtAkvSrK0Ypws6P1jMZkhqnH57bX5L00Lvb5HKbFicCAMB//OQHAKADfrdsmxrdpi4ekarzh/a2Ok7QuPuSYYqPjtC2oiq9tr7Q6jgAAPiNUg0AgJ9W7zqqj7aXKMJm6JdXcISWL5J6ROqui4dKkh5bvlPH6xstTgQAgH8o1QAA+KHR5dbD7zZttHXjlCwNTulpcaLgc9OUARrYu4dKj9dr8YrdVscBAMAvlGoAAPzw6vpC7ThSrYQYh/6j+YorfBMZYdMvL2+6wv/sJ/tUeKzW4kQAAPiOUg0AgI+q6px6YvlOSdLdlwxVYmykxYmC1yUjUzV1cLIaGt36/XvbrY4DAIDPKNUAAPjo6Y92q6ymQYNSeuiHk7OsjhPUDMPQr64YJcOQ/rW5SOv3H7M6EgAAPqFUAwDgg8JjtXrh0/2Sms5bdnCEVoeNyojX9RObzvd+6N2tMk2O2AIABA9+EwAAwAd//eyAGlxuTR2crBnDU62OEzIWzByuGIddXxys1MaCcqvjAADQbpRqAADayely640NByVJN08dIMMwLE4UOlLionTF2emSpL9/zrnVAIDgQakGAKCdPtxWotLjDerdM0oXjeAqdWfzTAF/d3ORquucFqcBAKB9KNUAALTTq+sKJEnXZfdjLXUXyM5K0uCUHjrhdOmdL4qsjgMAQLvwGwEAAO1QVHlCq3YelSTNbr6iis5lGIaun9hfUtM54AAABANKNQAA7fD6+oNym9K5A3tpYO8eVscJWdec01cOu6EvCiu0rajK6jgAAJwRpRoAgDNwu03vldPrJ3GVuiv17hmlmaPSJEmvruNqNQAg8FGqAQA4gzV7ynSw/ITioiM0a3S61XFC3vcmNL1x8Vb+IdU5XRanAQDg9CjVAACcwd+bNyj79ri+inbYLU4T+qYNTVFGQrQqTzj1/pZiq+MAAHBalGoAAE7jWE2Dlm85IokNyrqL3Wbou81Xq5kCDgAIdJRqAABO4638Q2pwuTW6b7xG902wOk7Y+O6EfjKMpqn3B8pqrI4DAMApUaoBADgF0zS9Z1PPbj7qCd2jX1Kspg1NkST9g+O1AAABjFINAMAp5BdWaOeR44p22HT12Ayr44Sd65un27+2/qAaXW6L0wAA0DZKNQAAp/Dq501XSC8fk66EGIfFacLPJSPT1KtHpEqq67Vyx1Gr4wAA0CZKNQAAbThe36h3Nh+WJF3P1G9LREbYdO05fSVJf2fDMgBAgKJUAwDQhne/OKzaBpcG9e6hiQOSrI4Ttjw7rq/YUaKSqjqL0wAA0BqlGgCANrzavDnW7ImZMgzD4jTha0hqnCZkJcnlNvX6xoNWxwEAoBVKNQAA37CjuFr5BRWKsBn6zjn9rI4T9jxXq19dVyjTNC1OAwBAS5RqAAC+4dXm9buXjExTSlyUxWlwxdnp6hkVoQNltVq795jVcQAAaIFSDQDA19Q3uvRmftM049mTMi1OA0mKjYzQ1eOajjTznBsOAECgoFQDAPA1y7ccUUWtU+kJ0bpgaIrVcdDMc2b1sq+KVVnrtDgNAAAnUaoBAPgaz9Tv72b3k93GBmWBYkzfBI3oE6eGRrfe3nTI6jgAAHhRqgEAaFZ4rFaf7C6VYUjfncDU70BiGIb3avUrnxewYRkAIGBQqgEAaPaP5mO0zh/SW5m9Yi1Og2/69vi+ioywaXtxtb48VGl1HAAAJFGqAQCQJDW63HptffMGZRO5Sh2IEmMjNWt0H0nS35un6QMAYDVKNQAAkj7edVTFVXVKinVo5qg0q+PgFDxvePxz02HVNjRanAYAAEo1AACSpL9/3nTl8zvn9FNUhN3iNDiVyQOTlZUcq+P1jfrX5iKr4wAAQKkGAKCkuk4fbi+RxNTvQGezGfpe8yZyrzIFHAAQACjVAICw98aGQ3K5TZ3TP1HD0uKsjoMzuK75uLP1B8q1u6Ta6jgAgDBHqQYAhDXTNL27fl8/sb/FadAeafHRmjE8VZL0j+bN5QAAsAqlGgAQ1j7fd0z7SmvUI9KuK85OtzoO2slzZvUbGw6qodFtcRoAQDijVAMAwppnXe7V4zLUIyrC4jRorwuHpyg1LkplNQ36cNsRq+MAAMKYX6V68eLFGjhwoKKjo5Wdna3Vq1ef9v4vv/yyxo4dq9jYWKWnp+tHP/qRysrK/AoMAEBnqTzh1L++bNpBejZTv4NKhN2m707oJ4kzqwEA1vK5VL/66qu6++67df/99ys/P1/Tpk3TrFmzVFBQ0Ob9P/nkE910002aM2eOtmzZotdee03r1q3T3LlzOxweAICO+OemQ6pvdGtEnziN7ZdgdRz4yLML+Me7jupQxQmL0wAAwpXPpfqJJ57QnDlzNHfuXI0cOVJPPvmkMjMz9cwzz7R5/7Vr12rAgAG66667NHDgQJ1//vn68Y9/rPXr13c4PAAAHeG5wjl7YqYMw7A4DXyVldxDUwcnyzSl19ZztRoAYA2fSnVDQ4M2bNignJycFrfn5ORozZo1bT5m6tSpOnjwoJYtWybTNHXkyBG9/vrruuKKK/xPDQBAB311qFJbDlcp0m7Tt8f1tToO/OQ5V/y19QflcpsWpwEAhCOfdmQpLS2Vy+VSWlpai9vT0tJUXFzc5mOmTp2ql19+WbNnz1ZdXZ0aGxt19dVX609/+tMpn6e+vl719fXej6uqqiRJTqdTTqfTl8jdypMtkDMisDBm4CvGTOf522f7JUkzR6WqZ6QRsl/TUB8zFw9LVkJMhA5VnNCq7cWaNrS31ZGCXqiPGXQ+xgx8FSxjpr35DNM02/227uHDh9W3b1+tWbNGU6ZM8d7+X//1X3rppZe0ffv2Vo/ZunWrLrnkEt1zzz269NJLVVRUpP/8z//UxIkT9dxzz7X5PA888IAWLlzY6va//e1vio2NbW9cAADa1OCSfr3BrjqXofmjXBqewBXOYPbGPps+LrZpXC+3fjSc47UAAJ2jtrZWN9xwgyorKxUfH3/K+/lUqhsaGhQbG6vXXntN11xzjff2//iP/9CmTZu0atWqVo+58cYbVVdXp9dee8172yeffKJp06bp8OHDSk9vfSZoW1eqMzMzVVpaetpPxmpOp1O5ubmaOXOmHA6H1XEQBBgz8BVjpnO8lX9YP3/zK/VLitGHd58vmy1011OHw5jZXlytq57Ok8NuaPXPLlByzyirIwW1cBgz6FyMGfgqWMZMVVWVevfufcZS7dP078jISGVnZys3N7dFqc7NzdW3vvWtNh9TW1uriIiWT2O32yVJp+rzUVFRiopq/QPR4XAE9BfdI1hyInAwZuArxkzHvL7xsCTp+omZioqKtDhN9wjlMTMms5fG9kvQFwcr9c6XJbrtgkFWRwoJoTxm0DUYM/BVoI+Z9mbzeffvBQsW6Nlnn9Xzzz+vbdu26Z577lFBQYHmzZsnSbrvvvt00003ee9/1VVX6c0339QzzzyjvXv36tNPP9Vdd92lSZMmKSMjw9enBwCgQ/YcPa7P9x+TzZCuy860Og46ieec8b+vKzjlm/YAAHQFn65US9Ls2bNVVlamBx98UEVFRRo9erSWLVumrKwsSVJRUVGLM6tvueUWVVdX66mnntJPf/pTJSYm6qKLLtIjjzzSeZ8FAADt9I/mo5dmDE9Vn4Roi9Ogs1w1Nl0PvbtVe47WaGNBubKzelkdCQAQJnwu1ZI0f/58zZ8/v80/W7p0aavb7rzzTt15553+PBUAAJ3G6XLrjQ0HJZ08igmhIS7aoSvPTtdrGw7q758XUqoBAN3G5+nfAAAEqw+3laj0eINS4qI0Y0Sq1XHQya6f1PRGybubi1RdF9jHtAAAQgelGgAQNv75xSFJ0nfO6SuHnR+Boeac/kkanNJDJ5wu5W49YnUcAECY4DcKAEBYqG90adWOo5KkK8a0Ps4Rwc8wDF3e/G/7wTZKNQCge1CqAQBhIW9PmWoaXEqLj9LojASr46CLXDIyTZK0asdR1Te6LE4DAAgHlGoAQFjwTAe+ZGSabDbD4jToKmP6JigtPko1DS7l7SmzOg4AIAxQqgEAIc80Te904EtGpVmcBl3JZjN0cfPVaqaAAwC6A6UaABDyvjpUpSNV9YqNtGvKoGSr46CLzfSU6q0lMk3T4jQAgFBHqQYAhLzcrcWSpOnDUhTtsFucBl1tyuBkxUbaVVxVp68OVVkdBwAQ4ijVAICQl7utRNLJTawQ2qIddl0wNEWSlMsUcABAF6NUAwBC2sHyWm0rqpLNkGaMSLU6DrqJZ+38B5xXDQDoYpRqAEBI85SqCQN6qVePSIvToLtcNCJVNkPaWlSlg+W1VscBAIQwSjUAIKR90Dz1eyZTv8NKrx6RmpDVS5L0YfMYAACgK1CqAQAhq6rOqbV7m84q5iit8HPJqKbp/hytBQDoSpRqAEDIWrnjqBrdpoak9tTA3j2sjoNuNnNUH0nS2r1lqqpzWpwGABCqKNUAgJDlWU/Nrt/haWDvHhqc0kNOl6lVO45aHQcAEKIo1QCAkOR0ubViR/N66lHs+h2uvLuAMwUcANBFKNUAgJD0+b5jqq5rVO+ekRqXmWR1HFgkp7lUr9heIqfLbXEaAEAoolQDAEJSbvPU74tGpMpuMyxOA6uMy0xSco9IVdU1at2+Y1bHAQCEIEo1ACDkmKbpLdWspw5vdpuhi0Y0Tf/PZQo4AKALUKoBACFne3G1DlWcUFSETdOGplgdBxab2TwFPHfrEZmmaXEaAECooVQDAEKOZ9fvaUN7KybSbnEaWO38ob0VFWHTwfIT2nGk2uo4AIAQQ6kGAIQczzRfpn5DkmIjI3T+kN6STr7hAgBAZ6FUAwBCSnFlnTYfrJRhSBdTqtHs61PAAQDoTJRqAEBI+XB7U2kal5molLgoi9MgUFw0smmzsi8OVupIVZ3FaQAAoYRSDQAIKez6jbakxkVrXGaiJOnDbSXWhgEAhBRKNQAgZNTUN2rN7jJJUs4oSjVaOjkFvNjiJACAUEKpBgCEjI93HlWDy62s5FgNSe1pdRwEGE+p/nRPmWrqGy1OAwAIFZRqAEDI+Pqu34ZhWJwGgWZoak/17xWrhka3Vu8qtToOACBEUKoBACGh0eXWiu1Na2VnMvUbbTAMg13AAQCdjlINAAgJGw6Uq7zWqcRYhyZkJVkdBwHKs4HdR9uPyOU2LU4DAAgFlGoAQEj4oHnq90XDUxVh58cb2jZxQJISYhwqr3VqY0G51XEAACGA3zoAAEHPNM2TR2kx9RunEWG36aIRTWdWMwUcANAZKNUAgKC35+hx7S+rVaTdpguGpVgdBwHOMwX8A0o1AKATUKoBAEEvd2vTBmVTBierZ1SExWkQ6KYPT1Gk3aa9pTXaXXLc6jgAgCBHqQYABD3PemqmfqM9ekZFaPLgZEknxw4AAP6iVAMAgtrR6nrvhlOXjEy1OA2CxczmscIUcABAR1GqAQBBbcX2EpmmNKZvgtITYqyOgyDhmdWwoaBcpcfrLU4DAAhmlGoAQFBb7tn1eyRTv9F+6QkxGt03XqYpfbS9xOo4AIAgRqkGAAStEw0ufbL7qCTpklFM/YZv2AUcANAZKNUAgKD16e5S1Tnd6psYo1Hp8VbHQZCZ2TwFfPWuUtU5XRanAQAEK0o1ACBo5XqnfqfKMAyL0yDYjEqPV0ZCtE44Xfp0d6nVcQAAQYpSDQAISm63qQ+3c5QW/GcYhnfscLQWAMBflGoAQFDadLBCpccbFBcVoXMHJlsdB0FqprdUl8jtNi1OAwAIRpRqAEBQ8kz9nj48RZER/DiDf84dmKyeURE6Wl2vLw5WWB0HABCE+C0EABCUPDs2z2TqNzogMsKm6cNTJDEFHADgH0o1ACDo7C+t0a6S44qwGbpwGEdpoWNymt+YyeVoLQCAHyjVAICg47miOGlgLyXEOixOg2B34bBU2W2Gdh45rgNlNVbHAQAEGUo1ACDonDxKi6nf6LiEWIcmDeglqWnDMgAAfEGpBgAElfKaBq3bf0wS66nReWZ6p4AXW5wEABBsKNUAgKCyYkeJ3KY0ok+cMnvFWh0HIcIz62Hd/nJV1DZYnAYAEEwo1QCAoOJZT83Ub3Sm/smxGp4WJ5fb1ModR62OAwAIIpRqAEDQqG90aVVz4WHqNzrbTHYBBwD4gVINAAgaeXvKVNPgUmpclMb0TbA6DkLMJc2letXOo6pvdFmcBgAQLCjVAICg4Zn6ffHINNlshsVpEGrO7pug1LgoHa9v1Gd7j1kdBwAQJCjVAICgYJqmPtjadNxRDlO/0QVsNkMXj2QKOADAN5RqAEBQ+OpQlYqr6hTjsGvK4GSr4yBEzRyVKqlpVoRpmhanAQAEA0o1ACAo5DZP/b5gWG9FO+wWp0Gomjq4t2IcdhVV1mnL4Sqr4wAAggClGgAQFD5sLtUzR/WxOAlCWbTDrguG9ZZ0cg0/AACnQ6kGAAS8YzUN3quGnsIDdJULhzdNAV+zu8ziJACAYECpBgAEvLV7m8rNsLSeSo2LtjgNQt3U5jX7+YXlqm1otDgNACDQUaoBAAHv092lkprWuwJdrX+vWPVNjJHTZWrd/nKr4wAAAhylGgAQ8PL2NF2pnsqu3+gGhmF4x9qaPaUWpwEABDpKNQAgoBVVntDe0hrZDOncQZRqdI/zhjTNimBdNQDgTCjVAICA5ik1Y/omKCHGYXEahAvPWehfHa5UZa3T4jQAgEBGqQYABLRPm6ffTmE9NbpRWny0Bqf0kGlKeXu5Wg0AODVKNQAgYJmm6V1Pfd4Qpn6je3mmgOexrhoAcBqUagBAwNpXWqOiyjpF2m2akNXL6jgIM57Nyj7dw5VqAMCpUaoBAAFrTXOZGd8/UTGRdovTINxMHpQsw5B2lxxXSVWd1XEAAAGKUg0ACFie44w4nxpWSIyN1FkZ8ZJOvsEDAMA3UaoBAAHJ7WY9Nax3XvMbOpxXDQA4FUo1ACAgbS+uVnmtU7GRdp3dL9HqOAhTnqO1Pt1dJtM0LU4DAAhElGoAQEDyXBmcNLCXIiP4cQVrTBrYSxE2Q4cqTqjw2Amr4wAAAhC/pQAAApJnDatnB2bACrGRERrfP1HSyTPTAQD4Oko1ACDgOF1ufbbXU6rZpAzWmupdV81mZQCA1vwq1YsXL9bAgQMVHR2t7OxsrV69+rT3r6+v1/3336+srCxFRUVp8ODBev755/0KDAAIfZsPVqqmwaXEWIdGpcdbHQdhzjNbIm9PKeuqAQCtRPj6gFdffVV33323Fi9erPPOO09//vOfNWvWLG3dulX9+/dv8zHf+973dOTIET333HMaMmSISkpK1NjY2OHwAIDQtGZ30zTbKYOSZbMZFqdBuBvfP0nRDptKjzdo55HjGt4nzupIAIAA4nOpfuKJJzRnzhzNnTtXkvTkk0/q/fff1zPPPKNFixa1uv97772nVatWae/everVq5ckacCAAR1LDQAIaaynRiCJjLBp4oBeWr2rVGv2lFKqAQAt+FSqGxoatGHDBt17770tbs/JydGaNWvafMw///lPTZgwQY8++qheeukl9ejRQ1dffbUeeughxcTEtPmY+vp61dfXez+uqqqSJDmdTjmdTl8idytPtkDOiMDCmIGvwmHM1Dld2lBQLkmamJUY0p9rdwiHMdMdzh2QpNW7SvXJrqP64aR+VsfpUowZ+IoxA18Fy5hpbz6fSnVpaalcLpfS0tJa3J6Wlqbi4uI2H7N371598sknio6O1ltvvaXS0lLNnz9fx44dO+W66kWLFmnhwoWtbl++fLliY2N9iWyJ3NxcqyMgyDBm4KtQHjM7Kg01NNqV4DC1/fNV2sHs704RymOmO5jHJSlCn+4q0Tv/WiZ7GIxLxgx8xZiBrwJ9zNTW1rbrfj5P/5Ykw2j5k8Q0zVa3ebjdbhmGoZdfflkJCQmSmqaQX3fddXr66afbvFp93333acGCBd6Pq6qqlJmZqZycHMXHB+6GNU6nU7m5uZo5c6YcDofVcRAEGDPwVTiMmW25uyTt04WjMnTFFWOsjhP0wmHMdAeX29T/7lqhqrpG9R97nsb2S7A6UpdhzMBXjBn4KljGjGfG9Jn4VKp79+4tu93e6qp0SUlJq6vXHunp6erbt6+3UEvSyJEjZZqmDh48qKFDh7Z6TFRUlKKiolrd7nA4AvqL7hEsORE4GDPwVSiPmbX7mqZ+nz80JWQ/RyuE8pjpDg5Jkwcla/nWI/r8QIUmDAz9o94YM/AVYwa+CvQx095sPh2pFRkZqezs7FaX6XNzczV16tQ2H3Peeefp8OHDOn78uPe2nTt3ymazqV+/0F6TBADwTVWdU5sPVkiSpg4J/dKC4OLZOG/Nbs6rBgCc5PM51QsWLNCzzz6r559/Xtu2bdM999yjgoICzZs3T1LT1O2bbrrJe/8bbrhBycnJ+tGPfqStW7fq448/1n/+53/q1ltvPeVGZQCA8PT53mNym9KA5Fj1TeRnBALLec1v9Kzbf0z1jS6L0wAAAoXPa6pnz56tsrIyPfjggyoqKtLo0aO1bNkyZWVlSZKKiopUUFDgvX/Pnj2Vm5urO++8UxMmTFBycrK+973v6eGHH+68zwIAEBI8R2lNGcxVagSeIak9lRIXpaPV9dp4oEJTOPINACA/NyqbP3++5s+f3+afLV26tNVtI0aMCPid3QAA1luzp1SSdN4QygoCj2EYmjo4Wf+36bDy9pRSqgEAkvyY/g0AQFcoPV6v7cXVkqQpgygrCEyeddWf7mFdNQCgCaUaABAQ8ppLyog+cUru2foECCAQTG1emvBFYYWO1zdanAYAEAgo1QCAgOBZTz2V9dQIYJm9YpXZK0aNblPr9h2zOg4AIABQqgEAAYH11AgW5zW/8eMZswCA8EapBgBY7mB5rQ6U1cpuMzRpYC+r4wCn5dmg7FPOqwYAiFINAAgAnqnfZ/dLUFy0w+I0wOl5lihsLapSeU2DxWkAAFajVAMALJfnXU/N1G8EvpS4KA1L6ylJytvL1WoACHeUagCApUzT9K5NZZMyBIuprKsGADSjVAMALLXnaI2OVNUrMsKm7Kwkq+MA7eKZVbGG86oBIOxRqgEAlsprvtKX3T9J0Q67xWmA9jl3ULJshrT3aI2KK+usjgMAsBClGgBgKc8OyhylhWCSEOPQmL4JkpgCDgDhjlINALCM2216N3qawnpqBBnPmOVoLQAIb5RqAIBlthZVqfKEUz2jIjS2X4LVcQCfeGZX5O0plWmaFqcBAFiFUg0AsIxn2uykgb0UYedHEoLLhKxeirTbdLiyTvvLaq2OAwCwCL/BAAAs45k2y/nUCEYxkXaN758oiXXVABDOKNUAAEs0NLq1bv8xSZxPjeDlPa+addUAELYo1QAAS3xxsEK1DS716hGpEX3irI4D+MW7rnpvmdxu1lUDQDiiVAMALOG5sjdlULJsNsPiNIB/zu6XqNhIu47VNGh7cbXVcQAAFqBUAwAs4VmDOpXzqRHEIiNsmjSwlyTWVQNAuKJUAwC63YkGl/ILKiSxnhrBz7PR3po9rKsGgHBEqQYAdLv1B46pweVWRkK0BiTHWh0H6BDPG0Of7S2T0+W2OA0AoLtRqgEA3c5zlNaUwb1lGKynRnAblR6vhBiHahpc2nyw0uo4AIBuRqkGAHS7PM96as6nRgiw2QxNGdS8CzjrqgEg7FCqAQDdqvKEU18earqaxyZlCBWeo7U+5bxqAAg7lGoAQLf6bG+Z3KY0qHcPpSfEWB0H6BRTmtdVbygoV53TZXEaAEB3olQDALqVZ4dkrlIjlAxO6aG0+Cg1NLq18UC51XEAAN2IUg0A6Fbe86k5SgshxDAM75j+lHXVABBWKNUAgG5TUl2nnUeOS5J3YycgVHBeNQCEJ0o1AKDb5DWXjVHp8UrqEWlxGqBzTR3SdKV688FKVdc5LU4DAOgulGoAQLfxlOrzWE+NENQ3MUYDkmPlcpv6fN8xq+MAALoJpRoA0G0+ZT01QpxnF3CO1gKA8EGpBgB0i8JjtSo8dkIRNkMTB/ayOg7QJTyzMNawWRkAhA1KNQCgW3hKxtjMRPWMirA4DdA1PBvwbS+uVunxeovTAAC6A6UaANAtPDsinzeY9dQIXck9ozSiT5wkae1epoADQDigVAMAupxpmt5SPYX11AhxU1lXDQBhhVINAOhyu0uO62h1vaIibBrfP9HqOECX8pxXnce6agAIC5RqAECX+3R3U7mYOKCXoh12i9MAXevcQb1ktxnaX1arQxUnrI4DAOhilGoAQJc7OfWb9dQIfXHRDo3pmyBJWrObq9UAEOoo1QCALuVym94Nm84bwnpqhIeTR2uxrhoAQh2lGgDQpbYXV6mqrlE9oyI0OiPe6jhAt5gyqOkNpM/3HbM4CQCgq1GqAQBdav3+cknSOVlJirDzYwfhYXz/RNlthg5VnGBdNQCEOH67AQB0qXX7m67UTchKsjgJ0H16REVoVHrTzIz1+7laDQChjFINAOgypml6r1RPGECpRnjxjPkNB8otTgIA6EqUagBAlzlUcULFVXWKsBkal5lodRygW00c0EuStG4/pRoAQhmlGgDQZTxX6M7KiFdsZITFaYDu5VnysKO4SlV1TovTAAC6CqUaANBlvOupm6/YAeEkNT5a/XvFym1K+QUVVscBAHQRSjUAoMt411OzSRnClGfss1kZAIQuSjUAoEtUnnBqx5FqSVI2m5QhTHlmaaxnXTUAhCxKNQCgS2wsKJdpSgOSY5UaF211HMASE5vfUMovLJfT5bY4DQCgK1CqAQBdYkPzlbnsLNZTI3wNTumphBiH6pxubT1cZXUcAEAXoFQDALqEZ5OyiUz9Rhiz2Qzvuup1rKsGgJBEqQYAdLqGRrc2FVZIYudvgHXVABDaKNUAgE635XCl6hvdSop1aHBKD6vjAJaa0DxbY/2BcpmmaXEaAEBno1QDADrd+q+tpzYMw+I0gLXG9E1QpN2m0uP1OlBWa3UcAEAno1QDADrd+gNNa0cnsJ4aULTDrjH9EiQ1Xa0GAIQWSjUAoFOZpum9Us0mZUAT7xRwNisDgJBDqQYAdKp9pTUqq2lQZIRNo/smWB0HCAgTm4+WYwdwAAg9lGoAQKfyTG8d2y9BURF2i9MAgSG7+VitPUdrdKymweI0AIDORKkGAHQqz/RWjtICTkrqEakhqT0lSRtYVw0AIYVSDQDoVJ4r1ROyWE8NfJ3ne8KzkR8AIDRQqgEAnabseL32Hq2RdHK6K4Amntkbno38AAChgVINAOg0nqvUw9J6KjE20uI0QGDx7Ib/5cFK1TldFqcBAHQWSjUAoNN41opmZ7GeGvim/r1i1btnlBpcbn15qNLqOACATkKpBgB0Gs9xQZxPDbRmGIb3e4OjtQAgdFCqAQCdos7p0lfNV98mcKUaaJNnr4ENrKsGgJBBqQYAdIovCivkdJlKjYtSZq8Yq+MAAWmiZ7OyA+Vyu02L0wAAOgOlGgDQKTyblE0c0EuGYVicBghMozLiFeOwq/KEU7uPHrc6DgCgE1CqAQCdYn3zGlGO0gJOzWG3aVxmoiSO1gKAUEGpBgB0mNtttrhSDeDUPJuVrWezMgAICZRqAECH7SypVnVdo2Ij7RqZHmd1HCCgTWh+42ndAUo1AIQCSjUAoMM801jH909UhJ0fLcDpjO+fKJshFR47oSNVdVbHAQB0EL/5AAA6zDONlaO0gDOLi3ZoRJ94SayrBoBQQKkGAHSYZz31hAFsUga0h+d7ZT1TwAEg6PlVqhcvXqyBAwcqOjpa2dnZWr16dbse9+mnnyoiIkLjxo3z52kBAAGoqPKEDpafkM2QxvenVAPt4VlXzZVqAAh+PpfqV199VXfffbfuv/9+5efna9q0aZo1a5YKCgpO+7jKykrddNNNuvjii/0OCwAIPJ5SMCojXj2jIixOAwQHzw7gW4uqVFPfaHEaAEBH+Fyqn3jiCc2ZM0dz587VyJEj9eSTTyozM1PPPPPMaR/34x//WDfccIOmTJnid1gAQODZ4Jn6zXpqoN3SE2LUNzFGLrepTYUVVscBAHSAT5cUGhoatGHDBt17770tbs/JydGaNWtO+bgXXnhBe/bs0V//+lc9/PDDZ3ye+vp61dfXez+uqqqSJDmdTjmdTl8idytPtkDOiMDCmIGvAnHMfL6vTJI0vl98QOVCk0AcM2hyTv8EHao4oc/2lGpSVoLVcbwYM/AVYwa+CpYx0958PpXq0tJSuVwupaWltbg9LS1NxcXFbT5m165duvfee7V69WpFRLTv6RYtWqSFCxe2un358uWKjY31JbIlcnNzrY6AIMOYga8CZczUuaRtRXZJhip2b9SyQqsT4VQCZczgpKhqQ5Jd72/cpcF1O6yO0wpjBr5izMBXgT5mamtr23U/vxa/GYbR4mPTNFvdJkkul0s33HCDFi5cqGHDhrX777/vvvu0YMEC78dVVVXKzMxUTk6O4uPj/YncLZxOp3JzczVz5kw5HA6r4yAIMGbgq0AbM5/sLpP5+Qb1S4zWDddcYHUctCHQxgxOGlRcrdefztPBEw7lXDojYM54Z8zAV4wZ+CpYxoxnxvSZ+FSqe/fuLbvd3uqqdElJSaur15JUXV2t9evXKz8/Xz/5yU8kSW63W6ZpKiIiQsuXL9dFF13U6nFRUVGKiopqdbvD4QjoL7pHsORE4GDMwFeBMmbyDzb9sJk4MDkg8uDUAmXM4KRRfZMUFx2h6rpG7Smr0+i+gTMFXGLMwHeMGfgq0MdMe7P59JZoZGSksrOzW12mz83N1dSpU1vdPz4+Xl9++aU2bdrk/W/evHkaPny4Nm3apHPPPdeXpwcABJgNzWfsZmdxlBbgK7vN0DnNx9Ct38951QAQrHye/r1gwQLdeOONmjBhgqZMmaL/+Z//UUFBgebNmyepaer2oUOH9OKLL8pms2n06NEtHp+amqro6OhWtwMAgkujy638ggpJ0sQB7PwN+GPigCSt2nlU6w6U65bzBlodBwDgB59L9ezZs1VWVqYHH3xQRUVFGj16tJYtW6asrCxJUlFR0RnPrAYABL9tRdWqbXApPjpCQ1N7Wh0HCEoTmt+QWr//2Cn3qAEABDa/NiqbP3++5s+f3+afLV269LSPfeCBB/TAAw/487QAgACybv/Jqd82G0UA8MfYfomKsBk6UlWvg+UnlNkr8E85AQC0FBjbTAIAgs765vXUE5j6DfgtJtLu3aDM8z0FAAgulGoAgM9M09T6/eWSpAlsUgZ0iOd7yPM9BQAILpRqAIDPCo+dUEl1vRx2Q2MzE62OAwS1k+uqKdUAEIwo1QAAn3mmqY7pm6Boh93iNEBwmzCg6Ur1zpJqVdY6LU4DAPAVpRoA4LN1nqnfrKcGOqx3zygN7N1DpiltLOBqNQAEG0o1AMBn65t3/mY9NdA5PN9Lnl31AQDBg1INAPBJRW2DdpUcl9R0nBaAjvNMAV9/gCvVABBsKNUAAJ9saP6lf1BKDyX3jLI4DRAaPEspviisUEOj2+I0AABfUKoBAD7xXEmbmMV6aqCzDOrdQ716RKq+0a2vDldaHQcA4ANKNQDAJ5711NkDmPoNdBbDMLzLKdazrhoAggqlGgDQbvWNLn1xsOkq2kR2/gY61cQBns3KWFcNAMGEUg0AaLevDlWqodGt5B6RGpAca3UcIKRkNy+p2HCgXKZpWpwGANBelGoAQLudPJ86SYZhWJwGCC2j+8YrKsKmYzUN2ltaY3UcAEA7UaoBAO22vrlUM/Ub6HxREXaNzUyUJG1gCjgABA1KNQCgXdxuUxsONG9SxvnUQJeYkOVZV81mZQAQLCjVAIB22Vt6XOW1TkU7bDorI8HqOEBI8swC8RxdBwAIfJRqAEC7eKZ+j8tMVGQEPz6ArnBO/yQZhrSvtEalx+utjgMAaAd+KwIAtIt3k7Is1lMDXSUh1qFhqXGSTr6RBQAIbJRqAEC7eNZTTxjAemqgK3m+xzzfcwCAwEapBgCcUUl1nfaX1cowpHPYpAzoUp5SvY4r1QAQFCjVAIAz8hzvMzwtTvHRDovTAKHNs8Tiq0OVOtHgsjgNAOBMKNUAgDPy7ETM+dRA1+uXFKM+8dFqdJv64mCF1XEAAGdAqQYAnNH6/aynBrqLYRjKbv5eW8951QAQ8CjVAIDTqm1o1FeHqyRJE7hSDXSLiVmsqwaAYEGpBgCc1qbCCrncptITotU3McbqOEBY8LyBtbGgXC63aXEaAMDpUKoBAKflOSuXq9RA9xnRJ049Iu2qrmvUziPVVscBAJwGpRoAcFonNyljPTXQXSLsNu/xdZ7vQQBAYKJUAwBOyeU2tbH5F/pszqcGupXne47NygAgsFGqAQCntL24SsfrG9UzKkIj+sRbHQcIK54j7NazWRkABDRKNQDglDYWVEiSxvdPlN1mWBsGCDPjMpu+7w5VnFBxZZ3VcQAAp0CpBgCcUn5B0xWyc/oz9Rvobj2iIjQ8LU6StKmQq9UAEKgo1QCAU9r0tSvVALqf53svv/l7EQAQeCjVAIA2ldc0aG9pjaSmaagAut/45lkilGoACFyUagBAmzYdrJAkDerdQ4mxkdaGAcKU5w2tzYcq5HS5rQ0DAGgTpRoA0CbPlbFxTP0GLDOodw/FR0eozunWjuJqq+MAANpAqQYAtMmzSdl4NikDLGOzGRrnnQLOZmUAEIgo1QCAVtxuU5sKKyRJ41lPDVjK8z3IumoACEyUagBAK3tLj6u6rlHRDptG9ImzOg4Q1rw7gDe/0QUACCyUagBAKxubr4id3S9REXZ+VABW8mxWtq+0RuU1DdaGAQC0wm9KAIBW8jmfGggYibGRGpTSQ5K8yzIAAIGDUg0AaMW7SVkmm5QBgcDzvchmZQAQeCjVAIAWauobtfNI09E9XKkGAgPrqgEgcFGqAQAtbD5YKbcp9U2MUVp8tNVxAOhkqd5UWCG327Q2DACgBUo1AKCF/MKm6aXjuEoNBIzhaXGKcdhVXdeovaXHrY4DAPgaSjUAoAXvJmWcTw0EjAi7TWP6JUg6uTs/ACAwUKoBAF6mabLzNxCgvOuqKdUAEFAo1QAAr4PlJ1R6vF4Ou6GzMhKsjgPga9gBHAACE6UaAODl2Vl4VHq8oh12a8MAaMFzpXrnkWodr2+0NgwAwItSDQDw8p5P3Z/zqYFAkxYfrb6JMXKb0uaDFVbHAQA0o1QDALxYTw0EtnGsqwaAgEOpBgBIkuobXdp6uErSybWbAAKLZ1d+SjUABA5KNQBAkrTlcJUaXG4l94hUZq8Yq+MAaINnacamwnKZpmlxGgCARKkGADTb9LWp34ZhWBsGQJvOyoiXw26o9HiDDpafsDoOAECUagBAM8/O32xSBgSuaIddo9LjJZ38ngUAWItSDQCQ9LWdv5vXbAIITJ43vjivGgACA6UaAKCS6jodLD8hw5DOplQDAW08O4ADQEChVAMAvOuph6fFqWdUhLVhAJyWZ3f+rYerVN/osjgNAIBSDQDwrs0cx1VqIOBl9opRco9INbjc2tJ8DB4AwDqUagDAyfXUzdNKAQQuwzCYAg4AAYRSDQBhrtHl1uaDlZLY+RsIFmxWBgCBg1INAGFu55Hjqm1wKS4qQkNSelodB0A7eHbp50o1AFiPUg0AYS6/sOlK19jMRNlshsVpALTH2ZmJMgzpUMUJlVTVWR0HAMIapRoAwpznShfrqYHg0TMqQsPT4iSd3GgQAGANSjUAhDk2KQOC0zimgANAQKBUA0AYqzzh1J6jNZKkcZlsUgYEE88bYZsK2awMAKxEqQaAMPZF87TRAcmx6tUj0towAHzi2QF888FKNbrcFqcBgPBFqQaAMHZyPTVXqYFgMySlp+KiIlTb4NLOI8etjgMAYYtSDQBhzLPzN+upgeBjsxka61lXzRRwALAMpRoAwpRpmt4r1Z4NjwAEF88bYmxWBgDWoVQDQJjaV1qjyhNORUXYNKJPvNVxAPjhZKnmSjUAWIVSDQBhynNla0zfBEVG8OMACEaeXfv3HK1RZa3T4jQAEJ74LQoAwhTrqYHg16tHpAYkx0qSNh2ssDYMAIQpSjUAhCl2/gZCg+d7mCngAGANv0r14sWLNXDgQEVHRys7O1urV68+5X3ffPNNzZw5UykpKYqPj9eUKVP0/vvv+x0YANBxtQ2N2l5cLYkr1UCw82w0yGZlAGANn0v1q6++qrvvvlv333+/8vPzNW3aNM2aNUsFBQVt3v/jjz/WzJkztWzZMm3YsEEzZszQVVddpfz8/A6HBwD458uDlXK5TfWJj1Z6QozVcQB0gOeNsU2FFXK7TWvDAEAY8rlUP/HEE5ozZ47mzp2rkSNH6sknn1RmZqaeeeaZNu//5JNP6uc//7kmTpyooUOH6ne/+52GDh2qd955p8PhAQD+yS+skMRVaiAUjOgTr6gImypPOLWvrMbqOAAQdnwq1Q0NDdqwYYNycnJa3J6Tk6M1a9a06+9wu92qrq5Wr169fHlqAEAn2uRdT51oaQ4AHRcZYdOYvgmSTn5vAwC6T4Qvdy4tLZXL5VJaWlqL29PS0lRcXNyuv+Pxxx9XTU2Nvve9753yPvX19aqvr/d+XFVVJUlyOp1yOgP3uAhPtkDOiMDCmIGvOmPMmKapjc0bGo3JiGP8hTheZ8LD2H7xWn+gXBsOlOnqs9PO/IDTYMzAV4wZ+CpYxkx78/lUqj0Mw2jxsWmarW5ryyuvvKIHHnhA//d//6fU1NRT3m/RokVauHBhq9uXL1+u2NhY3wN3s9zcXKsjIMgwZuCrjoyZ8nqppDpCNsPUwc15KtnSicEQsHidCW3uMkOSXR9vKdQy+/5O+TsZM/AVYwa+CvQxU1tb2677+VSqe/fuLbvd3uqqdElJSaur19/06quvas6cOXrttdd0ySWXnPa+9913nxYsWOD9uKqqSpmZmcrJyVF8fLwvkbuV0+lUbm6uZs6cKYfDYXUcBAHGDHzVGWPm318VSxs3a1R6gr591eROTohAw+tMeBhfWacXHvtYRSdsuvCSixUb6dd1E0mMGfiOMQNfBcuY8cyYPhOfXnEjIyOVnZ2t3NxcXXPNNd7bc3Nz9a1vfeuUj3vllVd066236pVXXtEVV1xxxueJiopSVFRUq9sdDkdAf9E9giUnAgdjBr7qyJjZfMhzlFYS4y6M8DoT2vr3dqhPfLSKq+q0/Uitzh2U3OG/kzEDXzFm4KtAHzPtzebz7t8LFizQs88+q+eff17btm3TPffco4KCAs2bN09S01Xmm266yXv/V155RTfddJMef/xxTZ48WcXFxSouLlZlZaWvTw0A6ATs/A2EJs/3tOd7HADQPXwu1bNnz9aTTz6pBx98UOPGjdPHH3+sZcuWKSsrS5JUVFTU4szqP//5z2psbNQdd9yh9PR073//8R//0XmfBQCgXRoa3fryUNObmuP7J1mcBkBn8pbq5o0IAQDdw68FN/Pnz9f8+fPb/LOlS5e2+HjlypX+PAUAoAtsK6pSQ6NbibEODUgO/I0fAbSf542yjQUV7d5EFgDQcT5fqQYABC/PFazxmYn8wg2EmNEZCbLbDB2trtfhyjqr4wBA2KBUA0AYObmemqnfQKiJibRrZHqcJKaAA0B3olQDQBjJL6iQxCZlQKgan9n0hpnnex0A0PUo1QAQJsqO16vgWK0MQxqbmWh1HABdwPOG2SZ2AAeAbkOpBoAw4fkle0hKT8VHB+6ZkAD851na8eWhSjU0ui1OAwDhgVINAGGCqd9A6BuQHKvEWIcaGt3aVlRldRwACAuUagAIE/mFzTt/s0kZELIMw9D45uUdbFYGAN2DUg0AYcDlNvVFYaUkrlQDoc7zxlk+66oBoFtQqgEgDOwuOa7j9Y3qEWnX0NQ4q+MA6EKeN87YARwAugelGgDCgGca6Nn9EmW3GRanAdCVxmYmyjCkgmO1Kj1eb3UcAAh5lGoACANsUgaEj/hohwan9JQkbeJqNQB0OUo1AIQBNikDwot3s7JCNisDgK5GqQaAEFdV59SukuOSpHHNv2gDCG3ezcq4Ug0AXY5SDQAhbnNhpUxTyuwVo5S4KKvjAOgGnqUeXxRWyOU2rQ0DACGOUg0AIc6zSdn4TKZ+A+FiWFqcYiPtqmlwaVdJtdVxACCkUaoBIMRtaj6rlk3KgPBhtxka2y9REpuVAUBXo1QDQAgzTVP53lLNlWognHBeNQB0D0o1AISwgmO1OlbToMgIm0alx1sdB0A38m5Wxg7gANClKNUAEMI8V6hGZ8QrMoKXfCCceHb731VyXFV1TmvDAEAI4zcsAAhhnk3KxrFJGRB2UuKilNkrRqbZdAoAAKBrUKoBIITls0kZENY8b6h53mADAHQ+SjUAhKg6p0tbD1dJolQD4Wp88xRwzxtsAIDOR6kGgBD11aFKNbpNpcRFqW9ijNVxAFjg5A7g5TJN09owABCiKNUAEKI8m5SNz0yUYRjWhgFgiVEZ8Yq021Re69SBslqr4wBASKJUA0CI+nRPqSTpnCw2KQPCVVSEXWP6JUiSPtldanEaAAhNlGoACEFVdU592vwL9CUj0yxOA8BKnteA97cUW5wEAEITpRoAQtCK7SVyukwNSe2pIak9rY4DwEKXntVUqvP2lKmylvOqAaCzUaoBIAS991XTFanLzupjcRIAVhuU0lPD0+LU6Db14fYjVscBgJBDqQaAEHOiwaWVO45Kki4bTakGIF3a/FrgecMNANB5KNUAEGI+3nVUJ5wu9U2M0VkZ8VbHARAAPLNWVu08qtqGRovTAEBooVQDQIh5v/lK1KVn9eEoLQCSpJHpccrsFaP6RrdWNc9kAQB0Dko1AIQQp8utD7Y1rZlk6jcAD8MwvFer2QUcADoXpRoAQsjavWWqqmtU756RyuZ8agBf43mj7cNtJWpodFucBgBCB6UaAEKIZxOimaP6yG5j6jeAk8ZnJiklLkrV9Y1as6fU6jgAEDIo1QAQIlxuU+9vYeo3gLbZbIb3zGqmgANA56FUA0CIyC8oV+nxesVFR2jKoGSr4wAIQJedlS5JWr7liFxu0+I0ABAaKNUAECI8U78vGZmmyAhe3gG0du6gXkqIcaispkHr9x+zOg4AhAR+6wKAEGCapt7b4jlKK83iNAAClcNu08UjUyXJ+5oBAOgYSjUAhICtRVU6WH5C0Q6bLhiWYnUcAAHMc7TW8i1HZJpMAQeAjqJUA0AIeL956vf0YSmKjYywOA2AQHbBsBTFOOw6VHFCXx2qsjoOAAQ9SjUAhADPNE52/QZwJtEOu2aMaJrR8t6WIovTAEDwo1QDQJDbc/S4dh45rgiboYtGsJ4awJld2jwF3LPBIQDAf5RqAAhynvNmpw7prYQYh8VpAASDi0akKtJu056jNdpdUm11HAAIapRqAAhynvXUns2HAOBM4qIdOm9I03n2XK0GgI6hVANAEDtUcUJfHKyUYUgzRzH1G0D7eaeAc7QWAHQIpRoAgtjy5l+GJ2QlKSUuyuI0AILJJaPSZDOkrw5VqfBYrdVxACBoUaoBIIh51lNfytRvAD7q3TNKEwf0kiQt33rE4jQAELwo1QAQpMqO1+vzfcckUaoB+MdzDN/7rKsGAL9RqgEgSH2w7YjcpjS6b7wye8VaHQdAEPK8IbfuwDEdra63OA0ABCdKNQAEqffY9RtAB2UkxmhsvwSZppTLFHAA8AulGgCCUHWdU5/uLpN0cvomAPjj0tHsAg4AHUGpBoAgtHJnqRpcbg1K6aEhqXFWxwEQxDxTwNfsLlXlCafFaQAg+FCqASAILd9aIomp3wA6bnBKTw1N7alGt6mPtjMFHAB8RakGgCDT4JI+3lUqianfADrHyV3AKdUA4CtKNQAEmR2VhmobXMpIiNaYvglWxwEQAjxTwFfuLNGJBpfFaQAguFCqASDIbD5mSGraXMgwDIvTAAgFZ2XEq19SjOqcbq3eXWp1HAAIKpRqAAgiTpdbXzWXatZTA+gshmF4X1M8ezYAANqHUg0AQeTz/eWqdRnq1cOhCQN6WR0HQAjxrKv+aMdRNbotDgMAQYRSDQBBZPnWpk2EZo5Mld3G1G8Aneec/klKiYtSdV2jdlXx+gIA7UWpBoAg4Xabym2eljlzZKrFaQCEGpvN0MxRaZKkzWWUagBoL0o1AASJ/MJyHT3eoGi7qcmDkq2OAyAEedZVby435HKbFqcBgOBAqQaAIPH+lqap32clmYqK4OUbQOebPChZ8dEROu40lF9YYXUcAAgK/FYGAEHANE2991WxJOnsXlw9AtA1IiNsumh4iiR2AQeA9qJUA0AQ2FZUrYJjtYqKsGlkIqUaQNfJaV5XvXzrEZkmrzcAcCaUagAIAu9tabpKfcHQ3oqyWxwGQEg7f0iyIm2mDlXUacvhKqvjAEDAo1QDQBB4v3nqd84odv0G0LViIu3eGTGeZScAgFOjVANAgNt79Lh2HKlWhM3QjOa1jgDQlTx7N3hmyQAATo1SDQABzrPr95TByUqIcVicBkA4GJVkymE3tLvkuHaXVFsdBwACGqUaAAKc50rRpc3nxwJAV4uNkKYM6iXp5Bt7AIC2UaoBIIAVVZ7QF4UVMoyTO/ICQHfwvOa8zxRwADgtSjUABLDlzVeIsvsnKTU+2uI0AMLJJSNSZBjS5oOVOlRxwuo4ABCwKNUAEMA8O+9eNpqp3wC6V3LPKE0c0DwFnF3AAeCUKNUAEKCO1TTos31lklhPDcAalzW/9rALOACcGqUaAALUB1uPyG1Ko9Ljldkr1uo4AMJQzllN66rX7T+mo9X1FqcBgMDkV6levHixBg4cqOjoaGVnZ2v16tWnvf+qVauUnZ2t6OhoDRo0SEuWLPErLACEE8+VIaZ+A7BKv6RYjembINOUPtjGLuAA0BafS/Wrr76qu+++W/fff7/y8/M1bdo0zZo1SwUFBW3ef9++fbr88ss1bdo05efn65e//KXuuusuvfHGGx0ODwChqrrOqU92lUqiVAOwluc16D3WVQNAm3wu1U888YTmzJmjuXPnauTIkXryySeVmZmpZ555ps37L1myRP3799eTTz6pkSNHau7cubr11lv12GOPdTg8AISqlTuOqsHl1qDePTQ0tafVcQCEMc+eDmv2lKqqzmlxGgAIPBG+3LmhoUEbNmzQvffe2+L2nJwcrVmzps3H5OXlKScnp8Vtl156qZ577jk5nU45HI5Wj6mvr1d9/cl1O1VVVZIkp9MppzNwX8zn/GW9Co/Y9ZeDn8kwDKvjIAiYpqnyCsYMWjtY3nR8zcyRqWpsbPTe7nkNDOTXQgQWxgx89c0xk5UUpcEpPbTnaI2++8wa9Yzy6ddHhAF+n4GvTNNUhmFoZoD/bGrvz06fXhVLS0vlcrmUlpbW4va0tDQVF7c9Jai4uLjN+zc2Nqq0tFTp6emtHrNo0SItXLiw1e3Lly9XbGzgbtazYb9dNY2G9lVXWh0FQYUxg7YZMhVfuUvLlu1q9We5ubkWJEIwY8zAV18fM8OjDe2RXTuOHLcwEQIbv8/AN44UI+B/NtXW1rbrfn691fjNd6BM0zztu1Jt3b+t2z3uu+8+LViwwPtxVVWVMjMzlZOTo/j4eH8idwtHVpHWb9yksWPHym63Wx0HQcDlcumLL75gzKBNfRNjNLpvy9c8p9Op3NxczZw5s82ZPsA3MWbgq7bGzEyXW9ftPabaBpfF6RCI+H0GvnK5XCrcvingfzZ5ZkyfiU+lunfv3rLb7a2uSpeUlLS6Gu3Rp0+fNu8fERGh5OTkNh8TFRWlqKioVrc7HI6A/qLPPCtdzgP5uvzsjIDOicDhdDqlg5sYM/BZoL8eIvAwZuCrr48Zh0O6aFTr2YWAxO8z8J3T6dSyg5sC/mdTe7P5tFFZZGSksrOzW12mz83N1dSpU9t8zJQpU1rdf/ny5ZowYUJAfwEBAAAAADgTn3f/XrBggZ599lk9//zz2rZtm+655x4VFBRo3rx5kpqmbt90003e+8+bN08HDhzQggULtG3bNj3//PN67rnn9LOf/azzPgsAAAAAACzg85rq2bNnq6ysTA8++KCKioo0evRoLVu2TFlZWZKkoqKiFmdWDxw4UMuWLdM999yjp59+WhkZGfrv//5vXXvttZ33WQAAAAAAYAG/NiqbP3++5s+f3+afLV26tNVt06dP18aNG/15KgAAAAAAApbP078BAAAAAEATSjUAAAAAAH6iVAMAAAAA4CdKNQAAAAAAfqJUAwAAAADgJ0o1AAAAAAB+olQDAAAAAOAnSjUAAAAAAH6iVAMAAAAA4CdKNQAAAAAAfqJUAwAAAADgJ0o1AAAAAAB+olQDAAAAAOCnCKsDtIdpmpKkqqoqi5OcntPpVG1traqqquRwOKyOgyDAmIGvGDPwFWMGvmLMwFeMGfgqWMaMp396+uipBEWprq6uliRlZmZanAQAAAAAEE6qq6uVkJBwyj83zDPV7gDgdrt1+PBhxcXFyTAMq+OcUlVVlTIzM1VYWKj4+Hir4yAIMGbgK8YMfMWYga8YM/AVYwa+CpYxY5qmqqurlZGRIZvt1Cung+JKtc1mU79+/ayO0W7x8fEBPTgQeBgz8BVjBr5izMBXjBn4ijEDXwXDmDndFWoPNioDAAAAAMBPlGoAAAAAAPxEqe5EUVFR+u1vf6uoqCiroyBIMGbgK8YMfMWYga8YM/AVYwa+CrUxExQblQEAAAAAEIi4Ug0AAAAAgJ8o1QAAAAAA+IlSDQAAAACAnyjVAAAAAAD4iVLdQf/1X/+lqVOnKjY2VomJie16jGmaeuCBB5SRkaGYmBhdeOGF2rJlS9cGRcAoLy/XjTfeqISEBCUkJOjGG29URUXFaR9zyy23yDCMFv9Nnjy5ewKj2y1evFgDBw5UdHS0srOztXr16tPef9WqVcrOzlZ0dLQGDRqkJUuWdFNSBApfxszKlStbvZ4YhqHt27d3Y2JY5eOPP9ZVV12ljIwMGYaht99++4yP4TUmvPk6ZniNwaJFizRx4kTFxcUpNTVV3/72t7Vjx44zPi6YX2so1R3U0NCg7373u7r99tvb/ZhHH31UTzzxhJ566imtW7dOffr00cyZM1VdXd2FSREobrjhBm3atEnvvfee3nvvPW3atEk33njjGR932WWXqaioyPvfsmXLuiEtuturr76qu+++W/fff7/y8/M1bdo0zZo1SwUFBW3ef9++fbr88ss1bdo05efn65e//KXuuusuvfHGG92cHFbxdcx47Nixo8VrytChQ7spMaxUU1OjsWPH6qmnnmrX/XmNga9jxoPXmPC1atUq3XHHHVq7dq1yc3PV2NionJwc1dTUnPIxQf9aY6JTvPDCC2ZCQsIZ7+d2u80+ffqYv//977231dXVmQkJCeaSJUu6MCECwdatW01J5tq1a7235eXlmZLM7du3n/JxN998s/mtb32rGxLCapMmTTLnzZvX4rYRI0aY9957b5v3//nPf26OGDGixW0//vGPzcmTJ3dZRgQWX8fMihUrTElmeXl5N6RDIJNkvvXWW6e9D68x+Lr2jBleY/BNJSUlpiRz1apVp7xPsL/WcKW6m+3bt0/FxcXKycnx3hYVFaXp06drzZo1FiZDd8jLy1NCQoLOPfdc722TJ09WQkLCGf/9V65cqdTUVA0bNky33XabSkpKujouullDQ4M2bNjQ4vVBknJyck45PvLy8lrd/9JLL9X69evldDq7LCsCgz9jxmP8+PFKT0/XxRdfrBUrVnRlTAQxXmPgL15j4FFZWSlJ6tWr1ynvE+yvNZTqblZcXCxJSktLa3F7Wlqa988QuoqLi5Wamtrq9tTU1NP++8+aNUsvv/yyPvroIz3++ONat26dLrroItXX13dlXHSz0tJSuVwun14fiouL27x/Y2OjSktLuywrAoM/YyY9PV3/8z//ozfeeENvvvmmhg8frosvvlgff/xxd0RGkOE1Br7iNQZfZ5qmFixYoPPPP1+jR48+5f2C/bUmwuoAgeiBBx7QwoULT3ufdevWacKECX4/h2EYLT42TbPVbQge7R0zUut/e+nM//6zZ8/2/v/o0aM1YcIEZWVl6V//+pe+853v+JkagcrX14e27t/W7QhdvoyZ4cOHa/jw4d6Pp0yZosLCQj322GO64IILujQnghOvMfAFrzH4up/85CfavHmzPvnkkzPeN5hfayjVbfjJT36i66+//rT3GTBggF9/d58+fSQ1vRuTnp7uvb2kpKTVuzMIHu0dM5s3b9aRI0da/dnRo0d9+vdPT09XVlaWdu3a5XNWBK7evXvLbre3usJ4uteHPn36tHn/iIgIJScnd1lWBAZ/xkxbJk+erL/+9a+dHQ8hgNcYdAZeY8LTnXfeqX/+85/6+OOP1a9fv9PeN9hfayjVbejdu7d69+7dJX/3wIED1adPH+Xm5mr8+PGSmtbErVq1So888kiXPCe6XnvHzJQpU1RZWanPP/9ckyZNkiR99tlnqqys1NSpU9v9fGVlZSosLGzxxgyCX2RkpLKzs5Wbm6trrrnGe3tubq6+9a1vtfmYKVOm6J133mlx2/LlyzVhwgQ5HI4uzQvr+TNm2pKfn8/rCdrEaww6A68x4cU0Td1555166623tHLlSg0cOPCMjwn61xrLtkgLEQcOHDDz8/PNhQsXmj179jTz8/PN/Px8s7q62nuf4cOHm2+++ab349///vdmQkKC+eabb5pffvml+f3vf99MT083q6qqrPgU0M0uu+wy8+yzzzbz8vLMvLw8c8yYMeaVV17Z4j5fHzPV1dXmT3/6U3PNmjXmvn37zBUrVphTpkwx+/bty5gJQX//+99Nh8NhPvfcc+bWrVvNu+++2+zRo4e5f/9+0zRN89577zVvvPFG7/337t1rxsbGmvfcc4+5detW87nnnjMdDof5+uuvW/UpoJv5Omb+8Ic/mG+99Za5c+dO86uvvjLvvfdeU5L5xhtvWPUpoBtVV1d7f1eRZD7xxBNmfn6+eeDAAdM0eY1Ba76OGV5jcPvtt5sJCQnmypUrzaKiIu9/tbW13vuE2msNpbqDbr75ZlNSq/9WrFjhvY8k84UXXvB+7Ha7zd/+9rdmnz59zKioKPOCCy4wv/zyy+4PD0uUlZWZP/jBD8y4uDgzLi7O/MEPftDq2Imvj5na2lozJyfHTElJMR0Oh9m/f3/z5ptvNgsKCro/PLrF008/bWZlZZmRkZHmOeec0+IIiptvvtmcPn16i/uvXLnSHD9+vBkZGWkOGDDAfOaZZ7o5Mazmy5h55JFHzMGDB5vR0dFmUlKSef7555v/+te/LEgNK3iOO/rmfzfffLNpmrzGoDVfxwyvMWhrvHyzD4Xaa41hms0rwAEAAAAAgE84UgsAAAAAAD9RqgEAAAAA8BOlGgAAAAAAP1GqAQAAAADwE6UaAAAAAAA/UaoBAAAAAPATpRoAAAAAAD9RqgEAAAAA8BOlGgAAAAAAP1GqAQAAAADwE6UaAAAAAAA/UaoBAAAAAPDT/w86UvYaAleMHQAAAABJRU5ErkJggg==", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kernel = Kernel(0, 1, Kernel.FLAT)\n", - "fv = f.FunctionVector({func: 1}, kernel=kernel)\n", - "f_r = fv.restricted(fv.f)\n", - "f_k = fv.apply_kernel(fv.f) \n", - "\n", - "assert not fv.f(-0.5) == 0\n", - "assert not fv.f(1.5) == 0\n", - "assert f_r(-0.5) == fv.f_r(-0.5) == 0\n", - "assert f_r(1.5) == fv.f_r(1.5) == 0\n", - "assert f_r(0.5) == fv.f_r(0.5) == fv.f(0.5)\n", - "assert f_r(0.25) == fv.f_r(0.25) == fv.f(0.25)\n", - "assert f_r(0.75) == fv.f_r(0.75) == fv.f(0.75)\n", - "\n", - "assert f_k(-0.5) == fv.f_k(-0.5) == 0\n", - "assert f_k(1.5) == fv.f_k(1.5) == 0\n", - "assert f_k(0.5) == fv.f_k(0.5) == fv.f(0.5) * kernel(0.5)\n", - "assert f_k(0.25) == fv.f_k(0.25) == fv.f(0.25) * kernel(0.25)\n", - "assert f_k(0.75) == fv.f_k(0.75) == fv.f(0.75) * kernel(0.75)\n", - "\n", - "fv.plot(fv.f, x_min=-1, x_max=2, title=\"full function [self.f]\")\n", - "fv.plot(fv.f_r, x_min=-1, x_max=2, title=\"restricted function [self.f_r]\")\n", - "fv.plot(fv.f_k, x_min=-1, x_max=2, title=\"flat kernel applied [self.f_k]\")" - ] - }, - { - "cell_type": "markdown", - "id": "c86dcd7b-8c96-4532-a89a-d4e48eae6e30", - "metadata": {}, - "source": [ - "### Sawtooth-Left kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "9610b767-1c87-4665-9dbb-5e463f65ca24", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kernel = Kernel(0, 1, Kernel.SAWTOOTHL)\n", - "fv = f.FunctionVector({func: 1}, kernel=kernel)\n", - "f_r = fv.restricted(fv.f)\n", - "f_k = fv.apply_kernel(fv.f) \n", - "\n", - "assert not fv.f(-0.5) == 0\n", - "assert not fv.f(1.5) == 0\n", - "assert f_r(-0.5) == fv.f_r(-0.5) == 0\n", - "assert f_r(1.5) == fv.f_r(1.5) == 0\n", - "assert f_r(0.5) == fv.f_r(0.5) == fv.f(0.5)\n", - "assert f_r(0.25) == fv.f_r(0.25) == fv.f(0.25)\n", - "assert f_r(0.75) == fv.f_r(0.75) == fv.f(0.75)\n", - "\n", - "assert f_k(-0.5) == fv.f_k(-0.5) == 0\n", - "assert f_k(1.5) == fv.f_k(1.5) == 0\n", - "assert f_k(0.5) == fv.f_k(0.5) == fv.f(0.5) * kernel(0.5)\n", - "assert f_k(0.25) == fv.f_k(0.25) == fv.f(0.25) * kernel(0.25)\n", - "assert f_k(0.75) == fv.f_k(0.75) == fv.f(0.75) * kernel(0.75)\n", - "\n", - "fv.plot(fv.f, x_min=-1, x_max=2, title=\"full function [self.f]\")\n", - "fv.plot(fv.f_r, x_min=-1, x_max=2, title=\"restricted function [self.f_r]\")\n", - "fv.plot(fv.f_k, x_min=-1, x_max=2, title=\"sawtooth-left kernel applied [self.f_k]\")" - ] - }, - { - "cell_type": "markdown", - "id": "329818e4-76ad-4932-ab66-1f67865ac683", - "metadata": {}, - "source": [ - "## Curve fitting" - ] - }, - { - "cell_type": "markdown", - "id": "19533f44-0164-4bfe-a475-d2c7155f167c", - "metadata": {}, - "source": [ - "### norm and curve distance\n", - "\n", - "We have various ways of measuring the distance between a FunctionVector (that includes a kernel) and a Function, all being based on the L2 norm with kernel applied\n", - "\n", - "- Use `FunctionVector.distance2` for the squared distance between the FunctionVector and the Function, or `distance` for the squareroot thereof*\n", - "\n", - "- Wrap the Function in a FunctionVector with the same kernel using the `wrap` method, substract the two FunctionVectors from each other, and use `norm2` or `norm`\n", - "\n", - "*in optimization you typically want to use the squared function because it behaves better and you don't have to calculate the square root" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "id": "868211e4-8759-4de8-bb8e-8ffe8ac87827", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# create the template function vector\n", - "fv_t = f.FunctionVector(kernel=Kernel(x_min=-1, x_max=1, kernel=Kernel.FLAT))\n", - "assert fv_t.f(0) == 0\n", - "\n", - "# create target and match functions and wrap them in FunctionVector\n", - "f0 = f.TrigFunction(phase=1/2)\n", - "f0v = fv_t.wrap(f0)\n", - "f1v = fv_t.wrap(f.QuadraticFunction(c=0))\n", - "f2v = fv_t.wrap(f.QuadraticFunction(a=-2, c=1))\n", - "\n", - "# check norms and distances\n", - "diff1 = (f0v-f1v).norm()\n", - "diff2 = (f0v-f2v).norm()\n", - "assert iseq( (f0v-f1v).norm2(), (f0v-f1v).norm()**2)\n", - "assert iseq( (f0v-f2v).norm2(), (f0v-f2v).norm()**2)\n", - "assert iseq(f1v.dist2_L2(f0), (f0v-f1v).norm2())\n", - "assert iseq(f2v.dist2_L2(f0), (f0v-f2v).norm2())\n", - "assert iseq(f1v.dist_L2(f0), (f0v-f1v).norm())\n", - "assert iseq(f2v.dist_L2(f0), (f0v-f2v).norm())\n", - "\n", - "# plot\n", - "f0v.plot(show=False, label=\"f0 [target function]\")\n", - "f1v.plot(show=False, label=f\"f1 [match 1]: dist={diff1:.2f}\")\n", - "f2v.plot(show=False, label=f\"f2 [match 2]: dist={diff2:.2f}\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e9a593ae-189c-4954-8c51-59adda51bc26", - "metadata": {}, - "source": [ - "### curve fitting" - ] - }, - { - "cell_type": "markdown", - "id": "a69b11ff-ebaa-4045-852c-c4e10e27d788", - "metadata": {}, - "source": [ - "#### flat kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "id": "809c3d8e-4f2d-4103-8234-beab6844c875", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "({'a': -2.266725245480411,\n", - " 'b': -4.999979597020143e-07,\n", - " 'c': 0.7553958307274233},\n", - " QuadraticFunction(a=-2.266725245480411, b=-4.999979597020143e-07, c=0.7553958307274233))" - ] - }, - "execution_count": 117, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fv_template = f.FunctionVector(kernel=Kernel(x_min=-1, x_max=1, kernel=Kernel.FLAT))\n", - "target_f = f.TrigFunction(phase=1/2)\n", - "target_fv = fv_template.wrap(target_f)\n", - "f_match0 = f.QuadraticFunction()\n", - "params0 = dict(a=0, b=0, c=0)\n", - "params = target_fv.curve_fit(f_match0, params0)\n", - "f_match = f_match0.update(**params)\n", - "params, f_match" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "id": "79e5a8fb-2046-4691-95ba-be04ae0dd8bc", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "FunctionVector(vec={QuadraticFunction(a=-2.266725245480411, b=-4.999979597020143e-07, c=0.7553958307274233): 1}, kernel=Kernel(x_min=-1, x_max=1, kernel=. at 0x150366ac0>, kernel_name='builtin-flat', method='trapezoid', steps=100))" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_match_v = target_fv.wrap(f_match)\n", - "diff = (target_fv-f_match_v).norm()\n", - "target_fv.plot(show=False, label=\"target function\")\n", - "f_match_v.plot(show=False, label=f\"match (dist={diff:.2f})\")\n", - "plt.title(f\"Best fit (a={params['a']:.2f}, b={params['b']:.2f}, c={params['c']:.2f}); dist={diff:.2f}\")\n", - "plt.legend()\n", - "f_match_v" - ] - }, - { - "cell_type": "markdown", - "id": "72950948-71b6-4bb0-9618-71d2f1d3fd00", - "metadata": { - "tags": [] - }, - "source": [ - "#### skewed kernel (sawtooth-left)" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "id": "59598e82-3652-4c73-bf0f-927d8fd5077b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(Kernel(x_min=-1, x_max=1, kernel=. at 0x15069dbc0>, kernel_name='builtin-sawtoothl', method='trapezoid', steps=100),\n", - " {'a': -1.8836343582517845, 'b': 0.2661645670906654, 'c': 0.7347668924372053},\n", - " QuadraticFunction(a=-1.8836343582517845, b=0.2661645670906654, c=0.7347668924372053))" - ] - }, - "execution_count": 119, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fv_template = f.FunctionVector(kernel=Kernel(x_min=-1, x_max=1, kernel=Kernel.SAWTOOTHL))\n", - "target_f = f.TrigFunction(phase=1/2)\n", - "target_fv = fv_template.wrap(target_f)\n", - "f_match0 = f.QuadraticFunction()\n", - "params0 = dict(a=0, b=0, c=0)\n", - "params = target_fv.curve_fit(f_match0, params0)\n", - "f_match = f_match0.update(**params)\n", - "target_fv.kernel, params, f_match" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "id": "1ed9e83c-0131-46cb-ad96-39cf34a8b376", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "FunctionVector(vec={QuadraticFunction(a=-1.8836343582517845, b=0.2661645670906654, c=0.7347668924372053): 1}, kernel=Kernel(x_min=-1, x_max=1, kernel=. at 0x15069dbc0>, kernel_name='builtin-sawtoothl', method='trapezoid', steps=100))" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f_match_v = target_fv.wrap(f_match)\n", - "diff = (target_fv-f_match_v).norm()\n", - "target_fv.plot(show=False, label=\"target function\")\n", - "f_match_v.plot(show=False, label=f\"match (dist={diff:.2f})\")\n", - "plt.title(f\"Best fit (a={params['a']:.2f}, b={params['b']:.2f}, c={params['c']:.2f}); dist={diff:.2f}\")\n", - "plt.legend()\n", - "f_match_v" - ] - }, - { - "cell_type": "markdown", - "id": "71ec9291-2816-4c64-ae95-610fa169e81d", - "metadata": {}, - "source": [ - "## High dimensional minimization" - ] - }, - { - "cell_type": "markdown", - "id": "f651576a-81a6-4f6e-8f9c-0dfe50a9bdf7", - "metadata": {}, - "source": [ - "### Example\n", - "\n", - "here we use as example the function\n", - "\n", - "$$\n", - "f(x,y) = (x-2)^2 + (y-2)^2\n", - "$$\n", - "\n", - "which obviously should be minimal at $(x,y) = (2,2)$" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "id": "ad59954b-c98d-447b-a9b0-7f139140adfe", - "metadata": {}, - "outputs": [], - "source": [ - "func = lambda x,y: (x-2)**2 + (y-2)**2" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "id": "f1329b5b-a229-47b5-bdac-4b8bdbf48565", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "((2.0002364190731674, 1.9999073648139465), array([ 0.00078973, -0.00030712]))" - ] - }, - "execution_count": 122, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r, dxdy = f.minimize(func, x0=[20, -5], learning_rate=None, return_path=True)\n", - "assert iseq(r[-1][0], 2, eps=1e-3)\n", - "assert iseq(r[-1][1], 2, eps=1e-3)\n", - "r[-1], dxdy" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "id": "5cc79156-daf9-41df-bec2-c84d5b46e551", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x,y = zip(*r)\n", - "plt.scatter(x,y)\n", - "plt.title(\"Convergence path\")\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "id": "fefd7a80-655f-45ad-926a-be010ce1971a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "({'x': 2.0002364190731674, 'y': 1.9999073648139465},\n", - " {'x': 0.0007897302440762718, 'y': -0.0003071172868030315})" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r, dxdy = f.minimize(func, x0=dict(x=20, y=-5), learning_rate=None, return_path=True)\n", - "assert iseq(r[-1][\"x\"], 2, eps=1e-3)\n", - "assert iseq(r[-1][\"y\"], 2, eps=1e-3)\n", - "r[-1], dxdy" - ] - }, - { - "cell_type": "markdown", - "id": "dbc4281c-414e-46a2-9089-667e8fdbc416", - "metadata": {}, - "source": [ - "### Testing e_i, e_k and bump" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "id": "2bf759f5-47d1-4273-80c8-800e55d89fe8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "e_i = f.FunctionVector.e_i\n", - "e_k = f.FunctionVector.e_k\n", - "bump = f.FunctionVector.bump" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "id": "ddef7258-a871-41eb-bd00-264b8cfc2260", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert np.array_equal(e_i(1,5), np.array([0., 1., 0., 0., 0.]))\n", - "assert e_k(\"b\", dict(a=1, b=2, c=3)) == {'a': 0, 'b': 1, 'c': 0}\n", - "assert bump(dict(a=1, b=2, c=3), \"b\", 0.25) == {'a': 1, 'b': 2.25, 'c': 3}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0fbd4fa4-2808-4d83-9438-127141de87e5", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/analysis/202401 Solidly/Functions.py b/resources/analysis/202401 Solidly/Functions.py deleted file mode 100644 index 5d46c1502..000000000 --- a/resources/analysis/202401 Solidly/Functions.py +++ /dev/null @@ -1,561 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -import invariants.functions as f -from invariants.kernel import Kernel -import numpy as np -import math as m -import matplotlib.pyplot as plt - -from testing import * -plt.rcParams['figure.figsize'] = [12,6] - -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(f.Function)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(Kernel)) -# - - -# # Functions and integration kernels - -# ## Functions - -# ### Built in functions -# #### QuadraticFunction - -qf = f.QuadraticFunction(a=1, b=0, c=-10) -assert qf.params() == {'a': 1, 'b': 0, 'c': -10} -assert qf.a == 1 -assert qf.b == 0 -assert qf.c == -10 - -qf2 = qf.update(c=-5) -assert raises(qf.update, k=1) -assert qf2.params() == {'a': 1, 'b': 0, 'c': -5} - -x_v = np.linspace(-5,5) -y1_v = [qf(xx) for xx in x_v] -y2_v = [qf2(xx) for xx in x_v] -plt.plot(x_v, y1_v, label="qf") -plt.plot(x_v, y2_v, label="qf2") -plt.legend() -plt.grid() - -x_v = np.linspace(-5,5) -y1_v = [qf(xx) for xx in x_v] -y2_v = [qf.p(xx) for xx in x_v] -y3_v = [qf.pp(xx) for xx in x_v] -plt.plot(x_v, y1_v, label="f") -plt.plot(x_v, y2_v, label="f'") -plt.plot(x_v, y3_v, label="f''") -plt.legend() -plt.grid() - -# #### TrigFunction - -# + -qf = f.TrigFunction() -assert qf.params() == {'amp': 1, 'omega': 1, 'phase': 0} -assert qf.amp == 1 -assert qf.omega == 1 -assert qf.phase == 0 -assert int(qf.PI) == 3 - -qf2 = qf.update(phase=1.5*qf.PI) -assert qf2.params() == {'amp': 1, 'omega': 1, 'phase': 1.5*qf.PI} -# - - -x_v = np.linspace(0, 4, 100) -y1_v = [qf(xx) for xx in x_v] -y2_v = [qf2(xx) for xx in x_v] -plt.plot(x_v, y1_v, label="qf") -plt.plot(x_v, y2_v, label="qf2") -plt.legend() -plt.grid() - -# #### HyperbolaFunction - -# + -qf = f.HyperbolaFunction() -assert qf.params() == {'k': 1, 'x0': 0, 'y0': 0} -assert qf.k == 1 -assert qf.x0 == 0 -assert qf.y0 == 0 - -qf2 = qf.update(y0=0.5) -# assert qf2.params() == {'amp': 1, 'omega': 1, 'phase': 1.5*qf.PI} -# - - -x_v = np.linspace(1, 10, 100) -y1_v = np.array([qf(xx) for xx in x_v]) -y2_v = np.array([qf2(xx) for xx in x_v]) -assert iseq(min(y2_v-y1_v), 0.5) -assert iseq(max(y2_v-y1_v), 0.5) -plt.plot(x_v, y1_v, label="qf") -plt.plot(x_v, y2_v, label="qf2") -plt.legend() -plt.grid() - -# ### Derivatives - -qf = f.QuadraticFunction(a=1, b=2, c=3) -qfp = qf.p_func() -qfpp = qf.pp_func() -assert qf.params() == {'a': 1, 'b': 2, 'c': 3} -assert qfp.func is qf -assert qfpp.func is qf - -x_v = np.linspace(-5,5) -y1_v = [qf(xx) for xx in x_v] -y2_v = [qfp(xx) for xx in x_v] -y3_v = [qfpp(xx) for xx in x_v] -plt.plot(x_v, y1_v, label="f") -plt.plot(x_v, y2_v, label="f'") -plt.plot(x_v, y3_v, label="f''") -plt.legend() -plt.grid() - -y2a_v = [qf.p(xx) for xx in x_v] # calculate the derivative from the original object -y3a_v = [qf.pp(xx) for xx in x_v] # ditto second derivative -y3b_v = [qfp.p(xx) for xx in x_v] # calculate the second derivative as derivative from the derivative object -assert y2a_v == y2_v # those are literally two ways of getting the same result -assert y3a_v == y3_v # ditto -assert iseq(min(y3_v), -2) # check that the second derivative is correct -assert iseq(max(y3_v), -2) # ditto -assert iseq(min(y3b_v), 2) # ditto, but the other way -assert iseq(max(y3b_v), 2) # ditto -min(y3_v), max(y3_v), min(y3b_v), max(y3b_v) - - -# ### Custom function - -@f.dataclass(frozen=True) -class MyFunction(f.Function): - k: float = 1 - - def f(self, x): - return (m.sqrt(1+x)-1)*self.k -mf = MyFunction() -mf2 = mf.update(k=2) -mf(1),mf.p(1),mf.pp(1) - -x_v = np.linspace(0,10) -y1_v = [mf(xx) for xx in x_v] -y2_v = [mf2(xx) for xx in x_v] -plt.plot(x_v, y1_v, label="mf") -plt.plot(x_v, y2_v, label="nf2") -plt.legend() -plt.grid() - -# ## Kernel - -# ### Integration function - -integrate = Kernel.integrate_trapezoid -ONE = lambda x: 1 -LIN = lambda x: 2*x -SQR = lambda x: 3*x*x - -assert iseq(integrate(ONE, 0, 1, 2), 1) # trapezoid integrates constant perfectly -assert iseq(integrate(ONE, 0, 1, 100), 1) -assert iseq(integrate(LIN, 0, 1, 2), 1) # ditto linear -assert iseq(integrate(LIN, 0, 1, 100), 1) -assert iseq(integrate(SQR, 0, 1, 100), 1, eps=1e-3) -assert iseq(integrate(SQR, 0, 1, 1000), 1, eps=1e-6) - -# ### Default kernel - -k = Kernel(steps=1000) -assert k.x_min == 0 -assert k.x_max == 1 -assert set(k.kernel(xx) for xx in np.linspace(k.x_min, k.x_max, 50)) == {1} -assert iseq(k.integrate(ONE), 1) -assert iseq(k.integrate(LIN), 1) -assert iseq(k.integrate(SQR), 1) -x_v = np.linspace(-0.5, 1.5, 1000) -plt.plot(x_v, [k.k(xx) for xx in x_v], label="default kernel") -plt.legend() -plt.grid() -plt.show() - -# ### Flat kernels - -k = Kernel(x_max=2, kernel=lambda x: 0.5, steps=1000) -assert k.x_min == 0 -assert k.x_max == 2 -assert set(k.kernel(xx) for xx in np.linspace(k.x_min, k.x_max, 50)) == {0.5} -assert iseq(k.integrate(ONE), 1) -assert iseq(k.integrate(LIN), 2) -assert iseq(k.integrate(SQR), 4) -x_v = np.linspace(-0.5, 2.5, 1000) -plt.plot(x_v, [k.k(xx) for xx in x_v], label="flat kernel 0..2") -plt.legend() -plt.grid() -plt.show() - -k = Kernel(x_max=4, kernel=lambda x: 0.25, steps=1000) -assert k.x_min == 0 -assert k.x_max == 4 -assert set(k.kernel(xx) for xx in np.linspace(k.x_min, k.x_max, 50)) == {0.25} -assert iseq(k.integrate(ONE), 1) -assert iseq(k.integrate(LIN), 4) -assert iseq(k.integrate(SQR), 16) -x_v = np.linspace(-0.5, 4.5, 1000) -plt.plot(x_v, [k.k(xx) for xx in x_v], label="flat kernel 0..4") -plt.legend() -plt.grid() -plt.show() - -k.integrate(LIN), k.integrate(SQR) - -# ### Triangle and sawtooth kernels - -kf = Kernel(x_min=1, x_max=3, kernel=Kernel.FLAT, steps=1000) -kl = Kernel(x_min=1, x_max=3, kernel=Kernel.SAWTOOTHL, steps=1000) -kr = Kernel(x_min=1, x_max=3, kernel=Kernel.SAWTOOTHR, steps=1000) -kt = Kernel(x_min=1, x_max=3, kernel=Kernel.TRIANGLE, steps=1000) -x_v = np.linspace(0.5, 3.5, 1000) -plt.plot(x_v, [kf.k(xx) for xx in x_v], label="flat") -plt.plot(x_v, [kl.k(xx) for xx in x_v], label="sawtooth left") -plt.plot(x_v, [kr.k(xx) for xx in x_v], label="sawtooth right") -plt.plot(x_v, [kt.k(xx) for xx in x_v], label="triangle") -plt.legend() -plt.grid() -plt.show() - -# + -assert iseq(kf.integrate(ONE), 1) -assert iseq(kl.integrate(ONE), 1) -assert iseq(kr.integrate(ONE), 1) -assert iseq(kt.integrate(ONE), 1) - -assert iseq(kf.integrate(LIN), 4) -assert iseq(kl.integrate(LIN), 10/3) -assert iseq(kr.integrate(LIN), 14/3) -assert iseq(kt.integrate(LIN), 4) - -assert iseq(kf.integrate(SQR), 13) -assert iseq(kl.integrate(SQR), 9) -assert iseq(kr.integrate(SQR), 17) -assert iseq(kt.integrate(SQR), 12.5) -# - - -# ### Gaussian kernels - -kf = Kernel(x_min=1, x_max=3, kernel=Kernel.FLAT, steps=1000) -kg = Kernel(x_min=1, x_max=3, kernel=Kernel.GAUSS, steps=1000) -kw = Kernel(x_min=1, x_max=3, kernel=Kernel.GAUSSW, steps=1000) -kn = Kernel(x_min=1, x_max=3, kernel=Kernel.GAUSSN, steps=1000) -x_v = np.linspace(0.5, 3.5, 1000) -plt.plot(x_v, [kf.k(xx) for xx in x_v], label="flat") -plt.plot(x_v, [kg.k(xx) for xx in x_v], label="gauss") -plt.plot(x_v, [kw.k(xx) for xx in x_v], label="gauss wide") -plt.plot(x_v, [kn.k(xx) for xx in x_v], label="gauss narrow") -plt.legend() -plt.grid() -plt.show() - -assert iseq(kf.integrate(ONE), 1) -assert iseq(kg.integrate(ONE), 1, eps=1e-3) -assert iseq(kw.integrate(ONE), 1, eps=1e-3) -assert iseq(kn.integrate(ONE), 1, eps=1e-3) - -# ## Function Vector - -# ### vector operations and consistency - -knl = Kernel(x_min=1, x_max=3, kernel=Kernel.FLAT, steps=1000) -f1 = f.QuadraticFunction(a=3, c=1) -f2 = f.QuadraticFunction(b=2) -f3 = f.QuadraticFunction(a=3, b=2, c=1) -f1v = f.FunctionVector({f1: 1}, kernel=knl) -f2v = f.FunctionVector({f2: 1}, kernel=knl) -fv = f.FunctionVector({f1: 1, f2: 1}, kernel=knl) -assert fv == f1v + f2v -x_v = np.linspace(1, 3, 100) -y1_v = [f1(xx) for xx in x_v] -y2_v = [f2(xx) for xx in x_v] -y3_v = [f3(xx) for xx in x_v] -yv_v = [fv(xx) for xx in x_v] -y_diff = np.array(yv_v) - np.array(y3_v) -plt.plot(x_v, y1_v, label="f1") -plt.plot(x_v, y2_v, label="f2") -plt.plot(x_v, y3_v, label="f3") -plt.legend() -plt.grid() - -assert max(y_diff)<1e-10 -assert min(y_diff)>-1e-10 -plt.plot(x_v, yv_v, linewidth=3, label="vector") -plt.plot(x_v, y3_v, linestyle="--", color="#ccc", label="f3") -plt.legend() -plt.grid() -plt.show() -plt.plot(x_v, y_diff) -plt.grid() -max(y_diff), min(y_diff) - -# check that you can't add vectors with different kernel - -# + -f1v = f.FunctionVector({f1: 1}, kernel=knl) -f2v = f.FunctionVector({f2: 1}, kernel=knl) -assert not raises(lambda: f1v+f2v) -assert not raises(lambda: f1v-f2v) - -f1v = f.FunctionVector({f1: 1}, kernel=knl) -f2v = f.FunctionVector({f2: 1}, kernel=None) -assert raises(lambda: f1v+f2v) -assert raises(lambda: f1v-f2v) -# - - -# ### integration - -f1v = f.FunctionVector({f1: 1}, kernel=knl) -f2v = f.FunctionVector({f2: 1}, kernel=knl) -#f1v.kernel, f2v.kernel - -knl = f1v.kernel -assert f1v.kernel == f2v.kernel -assert f1v.kernel == fv.kernel -x_v = np.linspace(knl.x_min, knl.x_max) -plt.plot(x_v, [f1v(xx) for xx in x_v], label="f1") -plt.plot(x_v, [f2v(xx) for xx in x_v], label="f2") -plt.plot(x_v, [fv(xx) for xx in x_v], label="f=f1+f2") -plt.grid() -plt.show() - -# + -assert iseq(f1v.integrate(), 13+1) - # assert iseq(kf.integrate(ONE), 1) - # assert iseq(kf.integrate(SQR), 13) - -assert iseq(f2v.integrate(), 4) - # assert iseq(kf.integrate(LIN), 4) - -assert iseq(fv.integrate(), 18) -# - - -f2v.integrate() - -# ### goal seek and minimize - -f1 = f.QuadraticFunction(a=1, c=-4) -f1v = f.FunctionVector({f1: 1}) -x_v = np.linspace(-2.5, 2.5, 100) -y1_v = [f1(xx) for xx in x_v] -plt.plot(x_v, y1_v, label="f") -#plt.legend() -plt.grid() - -assert iseq(f1v.goalseek(target=0, x0=1), 2) -assert iseq(f1v.goalseek(target=0, x0=-1), -2) -assert iseq(f1v.goalseek(target=-3, x0=1), 1) -assert iseq(f1v.goalseek(target=-3, x0=-1), -1) -assert iseq(0, f1v.minimize1(x0=5), eps=1e-3) -f1v.minimize1(x0=5) - -f2 = f.QuadraticFunction(a=3, b=2, c=1) -f2v = f.FunctionVector({f2: 1}) -x_v = np.linspace(-2.5, 2.5, 100) -y2_v = [f2(xx) for xx in x_v] -plt.plot(x_v, y2_v, label="f") -#plt.legend() -plt.grid() - -assert iseq(f2v.goalseek(target=5), 0.8685170919424989, eps=1e-4) -assert iseq(f2v.minimize1(), -0.3332480000000852, eps=1e-4) -f2v.goalseek(target=5), f2v.minimize1() - -# ## Restricted and apply kernel -# -# restricted functions (`f_r`, more generally `restricted(func)`) are zero outside the kernel domain; kernel-applied functions (`f_k`, more generally `apply_kernel(func)`) is multiplied with the kernel - -func = f.TrigFunction() - -# ### Flat kernel - -# + -kernel = Kernel(0, 1, Kernel.FLAT) -fv = f.FunctionVector({func: 1}, kernel=kernel) -f_r = fv.restricted(fv.f) -f_k = fv.apply_kernel(fv.f) - -assert not fv.f(-0.5) == 0 -assert not fv.f(1.5) == 0 -assert f_r(-0.5) == fv.f_r(-0.5) == 0 -assert f_r(1.5) == fv.f_r(1.5) == 0 -assert f_r(0.5) == fv.f_r(0.5) == fv.f(0.5) -assert f_r(0.25) == fv.f_r(0.25) == fv.f(0.25) -assert f_r(0.75) == fv.f_r(0.75) == fv.f(0.75) - -assert f_k(-0.5) == fv.f_k(-0.5) == 0 -assert f_k(1.5) == fv.f_k(1.5) == 0 -assert f_k(0.5) == fv.f_k(0.5) == fv.f(0.5) * kernel(0.5) -assert f_k(0.25) == fv.f_k(0.25) == fv.f(0.25) * kernel(0.25) -assert f_k(0.75) == fv.f_k(0.75) == fv.f(0.75) * kernel(0.75) - -fv.plot(fv.f, x_min=-1, x_max=2, title="full function [self.f]") -fv.plot(fv.f_r, x_min=-1, x_max=2, title="restricted function [self.f_r]") -fv.plot(fv.f_k, x_min=-1, x_max=2, title="flat kernel applied [self.f_k]") -# - - -# ### Sawtooth-Left kernel - -# + -kernel = Kernel(0, 1, Kernel.SAWTOOTHL) -fv = f.FunctionVector({func: 1}, kernel=kernel) -f_r = fv.restricted(fv.f) -f_k = fv.apply_kernel(fv.f) - -assert not fv.f(-0.5) == 0 -assert not fv.f(1.5) == 0 -assert f_r(-0.5) == fv.f_r(-0.5) == 0 -assert f_r(1.5) == fv.f_r(1.5) == 0 -assert f_r(0.5) == fv.f_r(0.5) == fv.f(0.5) -assert f_r(0.25) == fv.f_r(0.25) == fv.f(0.25) -assert f_r(0.75) == fv.f_r(0.75) == fv.f(0.75) - -assert f_k(-0.5) == fv.f_k(-0.5) == 0 -assert f_k(1.5) == fv.f_k(1.5) == 0 -assert f_k(0.5) == fv.f_k(0.5) == fv.f(0.5) * kernel(0.5) -assert f_k(0.25) == fv.f_k(0.25) == fv.f(0.25) * kernel(0.25) -assert f_k(0.75) == fv.f_k(0.75) == fv.f(0.75) * kernel(0.75) - -fv.plot(fv.f, x_min=-1, x_max=2, title="full function [self.f]") -fv.plot(fv.f_r, x_min=-1, x_max=2, title="restricted function [self.f_r]") -fv.plot(fv.f_k, x_min=-1, x_max=2, title="sawtooth-left kernel applied [self.f_k]") -# - - -# ## Curve fitting - -# ### norm and curve distance -# -# We have various ways of measuring the distance between a FunctionVector (that includes a kernel) and a Function, all being based on the L2 norm with kernel applied -# -# - Use `FunctionVector.distance2` for the squared distance between the FunctionVector and the Function, or `distance` for the squareroot thereof* -# -# - Wrap the Function in a FunctionVector with the same kernel using the `wrap` method, substract the two FunctionVectors from each other, and use `norm2` or `norm` -# -# *in optimization you typically want to use the squared function because it behaves better and you don't have to calculate the square root - -# + -# create the template function vector -fv_t = f.FunctionVector(kernel=Kernel(x_min=-1, x_max=1, kernel=Kernel.FLAT)) -assert fv_t.f(0) == 0 - -# create target and match functions and wrap them in FunctionVector -f0 = f.TrigFunction(phase=1/2) -f0v = fv_t.wrap(f0) -f1v = fv_t.wrap(f.QuadraticFunction(c=0)) -f2v = fv_t.wrap(f.QuadraticFunction(a=-2, c=1)) - -# check norms and distances -diff1 = (f0v-f1v).norm() -diff2 = (f0v-f2v).norm() -assert iseq( (f0v-f1v).norm2(), (f0v-f1v).norm()**2) -assert iseq( (f0v-f2v).norm2(), (f0v-f2v).norm()**2) -assert iseq(f1v.dist2_L2(f0), (f0v-f1v).norm2()) -assert iseq(f2v.dist2_L2(f0), (f0v-f2v).norm2()) -assert iseq(f1v.dist_L2(f0), (f0v-f1v).norm()) -assert iseq(f2v.dist_L2(f0), (f0v-f2v).norm()) - -# plot -f0v.plot(show=False, label="f0 [target function]") -f1v.plot(show=False, label=f"f1 [match 1]: dist={diff1:.2f}") -f2v.plot(show=False, label=f"f2 [match 2]: dist={diff2:.2f}") -plt.legend() -plt.show() -# - - -# ### curve fitting - -# #### flat kernel - -fv_template = f.FunctionVector(kernel=Kernel(x_min=-1, x_max=1, kernel=Kernel.FLAT)) -target_f = f.TrigFunction(phase=1/2) -target_fv = fv_template.wrap(target_f) -f_match0 = f.QuadraticFunction() -params0 = dict(a=0, b=0, c=0) -params = target_fv.curve_fit(f_match0, params0) -f_match = f_match0.update(**params) -params, f_match - -f_match_v = target_fv.wrap(f_match) -diff = (target_fv-f_match_v).norm() -target_fv.plot(show=False, label="target function") -f_match_v.plot(show=False, label=f"match (dist={diff:.2f})") -plt.title(f"Best fit (a={params['a']:.2f}, b={params['b']:.2f}, c={params['c']:.2f}); dist={diff:.2f}") -plt.legend() -f_match_v - -# #### skewed kernel (sawtooth-left) - -fv_template = f.FunctionVector(kernel=Kernel(x_min=-1, x_max=1, kernel=Kernel.SAWTOOTHL)) -target_f = f.TrigFunction(phase=1/2) -target_fv = fv_template.wrap(target_f) -f_match0 = f.QuadraticFunction() -params0 = dict(a=0, b=0, c=0) -params = target_fv.curve_fit(f_match0, params0) -f_match = f_match0.update(**params) -target_fv.kernel, params, f_match - -f_match_v = target_fv.wrap(f_match) -diff = (target_fv-f_match_v).norm() -target_fv.plot(show=False, label="target function") -f_match_v.plot(show=False, label=f"match (dist={diff:.2f})") -plt.title(f"Best fit (a={params['a']:.2f}, b={params['b']:.2f}, c={params['c']:.2f}); dist={diff:.2f}") -plt.legend() -f_match_v - -# ## High dimensional minimization - -# ### Example -# -# here we use as example the function -# -# $$ -# f(x,y) = (x-2)^2 + (y-2)^2 -# $$ -# -# which obviously should be minimal at $(x,y) = (2,2)$ - -func = lambda x,y: (x-2)**2 + (y-2)**2 - -r, dxdy = f.minimize(func, x0=[20, -5], learning_rate=None, return_path=True) -assert iseq(r[-1][0], 2, eps=1e-3) -assert iseq(r[-1][1], 2, eps=1e-3) -r[-1], dxdy - -x,y = zip(*r) -plt.scatter(x,y) -plt.title("Convergence path") -plt.grid() - -r, dxdy = f.minimize(func, x0=dict(x=20, y=-5), learning_rate=None, return_path=True) -assert iseq(r[-1]["x"], 2, eps=1e-3) -assert iseq(r[-1]["y"], 2, eps=1e-3) -r[-1], dxdy - -# ### Testing e_i, e_k and bump - -e_i = f.FunctionVector.e_i -e_k = f.FunctionVector.e_k -bump = f.FunctionVector.bump - -assert np.array_equal(e_i(1,5), np.array([0., 1., 0., 0., 0.])) -assert e_k("b", dict(a=1, b=2, c=3)) == {'a': 0, 'b': 1, 'c': 0} -assert bump(dict(a=1, b=2, c=3), "b", 0.25) == {'a': 1, 'b': 2.25, 'c': 3} - - diff --git a/resources/analysis/202401 Solidly/Invariants.ipynb b/resources/analysis/202401 Solidly/Invariants.ipynb deleted file mode 100644 index ff22909c8..000000000 --- a/resources/analysis/202401 Solidly/Invariants.ipynb +++ /dev/null @@ -1,676 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "0278c025-06e6-416b-9525-c2a4a8ae9128", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imported m, np, pd, plt, os, sys, decimal; defined iseq, raises, require, Timer\n", - "Function v0.9 (18/Jan/2024)\n", - "BancorInvariant v0.9 (18/Jan/2024)\n" - ] - } - ], - "source": [ - "import invariants.functions as f\n", - "from invariants.invariant import Invariant\n", - "from invariants.bancor import BancorInvariant, BancorSwapFunction\n", - "from invariants.solidly import SolidlyInvariant, SolidlySwapFunction\n", - "import numpy as np\n", - "import math as m\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from testing import *\n", - "plt.rcParams['figure.figsize'] = [12,6]\n", - "\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(f.Function))\n", - "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(BancorInvariant))" - ] - }, - { - "cell_type": "markdown", - "id": "7e212348-81d0-49f2-8d41-c7842a387634", - "metadata": {}, - "source": [ - "# Invariants Module" - ] - }, - { - "cell_type": "markdown", - "id": "2fb31878-07de-4ff8-89a6-8f5917f26f2e", - "metadata": {}, - "source": [ - "## General invariants" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b2dc880c-13aa-42d6-b54b-0bf1a240aae9", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "inv = BancorInvariant()" - ] - }, - { - "cell_type": "markdown", - "id": "4701eb9f-5d92-475e-84f2-37ea7f0e27ce", - "metadata": {}, - "source": [ - "### goal seek" - ] - }, - { - "cell_type": "markdown", - "id": "3a1ce2b7-7c78-4a9a-96ee-5398eaaf4b18", - "metadata": {}, - "source": [ - "testing on $(x-1)(x+1)$" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "cbed40a9-442e-4e20-bd71-3f5360a7cf0a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "func = lambda x: x**2 - 1\n", - "assert iseq(inv.goalseek_gradient(func, x0=-0.1), -1)\n", - "assert iseq(inv.goalseek_gradient(func, x0=0.1), 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "422f9e88-ee87-4e46-ba0f-8547b4a40af9", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(inv.goalseek_bisect(func, x_lo=0, x_hi=10), 1)\n", - "assert iseq(inv.goalseek_bisect(func, x_lo=0, x_hi=-10), -1)" - ] - }, - { - "cell_type": "markdown", - "id": "7f55341d-8b52-4970-8d03-de548a90d6d2", - "metadata": {}, - "source": [ - "testing on AMM invariant $k/x$" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9428308b-f778-4060-b497-0b4d97a25609", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "assert iseq(inv.goalseek_gradient(lambda x: 100/x - 5), 20)\n", - "assert iseq(inv.goalseek_gradient(lambda x: 100/x - 20), 5)\n", - "assert iseq(inv.goalseek_gradient(lambda x: 100/x - 10), 10)\n", - "assert iseq(inv.goalseek_gradient(lambda x: 100/x - 50), 2)" - ] - }, - { - "cell_type": "markdown", - "id": "2f89d075-2bce-4744-ab36-000857b96791", - "metadata": {}, - "source": [ - "#### timing " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "495e4468-b029-4542-9374-fd1d3634e485", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(5.0, 4.9999999999999725, 4.999999997468219)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inv.y_func(20, k=100), inv.y_func_from_k_func(20, k=100), inv.y_func_from_k_func(20, k=100, method=inv.GS_BISECT)" - ] - }, - { - "cell_type": "markdown", - "id": "77f3461e-2db3-4348-8275-f75087722bb8", - "metadata": { - "tags": [] - }, - "source": [ - "note that the gradient method is almost certainly going to be faster than bisection, unless we are very good at bracketing (or put the tolerance very low)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9d045b81-c9f4-4658-ab04-2597ed387494", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "((365.97251892089844,\n", - " 1902.6994705200195,\n", - " 10183.59661102295,\n", - " 5233.502388000488),\n", - " (1, 5.199022801302932, 27.82612377850163))" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = (\n", - " timer(inv.y_func, x=20, k=100, N=1000), \n", - " timer(inv.y_func_from_k_func, x=20, k=100, method=inv.GS_GRADIENT, N=10_000),\n", - " timer(inv.y_func_from_k_func, x=20, k=100, method=inv.GS_BISECT, N=10_000),\n", - " timer(inv.y_func_from_k_func, x=20, k=100, x_lo=0.1, x_hi=10, method=inv.GS_BISECT, N=10_000),\n", - ")\n", - "r, (1, r[1]/r[0], r[2]/r[0])" - ] - }, - { - "cell_type": "markdown", - "id": "639c0f69-279e-42df-93b6-4f599b3f2160", - "metadata": { - "tags": [] - }, - "source": [ - "### Bancor invariant function" - ] - }, - { - "cell_type": "markdown", - "id": "f0ac97c3-6ccb-4d07-bc42-8df4f4be347a", - "metadata": {}, - "source": [ - "we are here comparing the analytic invariant function with the one obtained numerically; note: they are a good match!" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7d2aa8e1-7b01-44fc-8f5f-2cbcf73ccd60", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "f = BancorSwapFunction(k=100)\n", - "assert f(10) == 10\n", - "assert f(5) == 20\n", - "assert f(20) == 5\n", - "inv = BancorInvariant()\n", - "assert inv.y_func_is_analytic is True" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9af100b4-376a-44fe-8a66-e2c2c5253d91", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(0.5 , 3, 50)\n", - "y1_v = [inv.y_func(xx, k=100) for xx in x_v]\n", - "y2_v = [inv.y_func_from_k_func(xx, k=100) for xx in x_v]\n", - "plt.plot(x_v, y1_v, linewidth=3, label=\"analytic\")\n", - "plt.plot(x_v, y2_v, linestyle=\"--\", color = \"#ccc\", label=\"numeric\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e1ede0f7-dbe5-403a-9a3b-09ed326ef82a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_v = np.linspace(0.5, 3, 100)\n", - "y1_v = [inv.p_func(xx, k=100) for xx in x_v]\n", - "y2_v = [inv.y_func(xx, k=100) for xx in x_v]\n", - "plt.plot(x_v, y1_v, linewidth=3, color=\"red\", label=\"p [LHS]\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"price dy/dx [red]\")\n", - "ax2 = plt.twinx()\n", - "ax2.plot(x_v, y2_v, linewidth=3, color=\"grey\", label=\"y [RHS]\")\n", - "ax2.set_ylabel(\"swap function y [grey]\")\n", - "#plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "da4562a8-e5a7-44ba-b4a6-6f7cd4707d9c", - "metadata": {}, - "source": [ - "#### timing" - ] - }, - { - "cell_type": "markdown", - "id": "53810771-a370-414d-8157-7a53cfe77493", - "metadata": {}, - "source": [ - "however, whilst the results are comparable, runtime difference is substantial (unsurprisingly especially given the extremely simple formula for the analytic function); for 1e-6 tolerance the factor is 27x, and for 1e-3 tolerance the factor is not much better at 19x" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7ea215be-7021-46bc-9c5b-6fe03b458497", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((853.7769317626953, 1922.1305847167966), 2.2513264451270594)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = timer2(inv.y_func, 20, 100, N=1000), timer2(inv.y_func_from_k_func, 20, 100, N=1000)\n", - "r, r[1]/r[0]" - ] - }, - { - "cell_type": "markdown", - "id": "f359ea63-195f-410c-a08c-44a33b6a1bb1", - "metadata": {}, - "source": [ - "### Solidly invariant function" - ] - }, - { - "cell_type": "markdown", - "id": "86bdba9e-4ad9-4ee4-9aa5-fb35379b40ed", - "metadata": { - "tags": [] - }, - "source": [ - "The Solidly **invariant equation** is \n", - "$$\n", - " x^3y+xy^3 = k\n", - "$$\n", - "\n", - "which is a stable swap curve, but more convex than for example Curve. \n", - "\n", - "To obtain the **swap equation** we solve the above invariance equation \n", - "as $y=y(x; k)$. This gives the following result\n", - "$$\n", - "y(x;k) = \\frac{x^2}{\\left(-\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\\right)^{\\frac{1}{3}}} - \\frac{\\left(-\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\\right)^{\\frac{1}{3}}}{3}\n", - "$$\n", - "\n", - "We can introduce intermediary **variables L and M** ($L(x;k), M(x;k)$) \n", - "to write this a bit more simply\n", - "\n", - "$$\n", - "L(x,k) = L_1(x) \\equiv -\\frac{27k}{2x} + \\sqrt{\\frac{729k^2}{x^2} + 108x^6}\n", - "$$\n", - "$$\n", - "M(x,k) = L^{1/3}(x,k) = \\sqrt[3]{L(x,k)}\n", - "$$\n", - "$$\n", - "y = \\frac{x^2}{\\sqrt[3]{L}} - \\frac{\\sqrt[3]{L}}{3} = \\frac{x^2}{M} - \\frac{M}{3} \n", - "$$\n", - "\n", - "If we rewrite the equation for L as below we see that it is not \n", - "particularly well conditioned for small $x$\n", - "$$\n", - "L(x,k) = L_2(x) \\equiv \\frac{27k}{2x} \\left(\\sqrt{1 + \\frac{108x^8}{729k^2}} - 1 \\right)\n", - "$$\n", - "\n", - "For simplicity we introduce the **variable xi** $\\xi=\\xi(x,k)$ as\n", - "$$\n", - "\\xi(x, k) = \\frac{108x^8}{729k^2}\n", - "$$\n", - "\n", - "then we can rewrite the above equation as \n", - "$$\n", - "L_2(x;k) \\equiv \\frac{27k}{2x} \\left(\\sqrt{1 + \\xi(x,k)} - 1 \\right)\n", - "$$\n", - "\n", - "Note the Taylor expansion for $\\sqrt{1 + \\xi} - 1$ is \n", - "$$\n", - "\\sqrt{1+\\xi}-1 = \\frac{\\xi}{2} - \\frac{\\xi^2}{8} + \\frac{\\xi^3}{16} - \\frac{5\\xi^4}{128} + O(\\xi^5)\n", - "$$\n", - "\n", - "and tests suggest that it is very good for at least $|\\xi| < 10^{-5}$" - ] - }, - { - "cell_type": "markdown", - "id": "d9705af6-fcd5-4773-a461-103304ba2f0f", - "metadata": {}, - "source": [ - "### L functions" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "ca4e362f-5465-4149-b644-38aaf26fedfb", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "f = SolidlySwapFunction(k=100)\n", - "assert f.method == f.METHOD_DEC1000\n", - "inv = SolidlyInvariant()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "0b16e3f1-99f2-4fb9-819e-890be55ce2e9", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0009999999638239387,\n", - " 0.0009999999629629658,\n", - " 0.0009999999629629658,\n", - " 0.0009999999629629656,\n", - " 0.0009999999629629658,\n", - " False,\n", - " True,\n", - " True)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x,k = 1,1000\n", - "(\n", - " f._L1_float(x, k),\n", - " f._L1_dec100(x, k),\n", - " f._L1_dec1000(x, k),\n", - " f._L2_taylor(x, k),\n", - " f.L(x, k),\n", - " f.L(x, k) == f._L2_taylor(x, k),\n", - " f.L(x, k) == f._L1_dec100(x, k),\n", - " f.L(x, k) == f._L1_dec1000(x, k),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "58bf213a-9389-47d5-96ad-6f4d41b795b8", - "metadata": {}, - "outputs": [], - "source": [ - "# x,k = 1,10\n", - "# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k))\n", - "# x,k = 1,100\n", - "# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k))\n", - "# x,k = 1,1_000\n", - "# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k))\n", - "# x,k = 1,10_000\n", - "# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k))\n", - "# x,k = 1,100_000\n", - "# assert iseq(f._L1_dec(x, k), f._L2_taylor(x, k)) # not float !\n", - "# f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k)" - ] - }, - { - "cell_type": "markdown", - "id": "a07bf50f-8159-4f7a-ae3f-184ea37d229a", - "metadata": {}, - "source": [ - "### Numeric vs analytic and verification" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ec2be1c6-1dec-4306-8481-5c5026ce193d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(6, 6))\n", - "k = 1000\n", - "x_v = np.linspace(0.1 , 20, 500)\n", - "y1_v = [inv.y_func(xx, k=k) for xx in x_v]\n", - "y2_v = [inv.y_func_from_k_func(xx, k=k) for xx in x_v]\n", - "plt.plot(x_v, y1_v, linewidth=3, label=\"analytic\")\n", - "plt.plot(x_v, y2_v, linestyle=\"--\", color = \"#ccc\", label=\"numeric\")\n", - "plt.xlim(0,20)\n", - "plt.ylim(0,20)\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "e5448a58-9b9f-44a9-aab1-6e21a58b2427", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k = 100\n", - "x1_v = np.linspace(0, 200)\n", - "x1_v[0] = 0.0001\n", - "k_v = [inv.k_func(xx, inv.y_func_from_k_func(xx, k=100)) for xx in x1_v]\n", - "plt.plot(x1_v, k_v)\n", - "ylim = (99.999999, 100.000001)\n", - "assert min(k_v) > ylim[0]\n", - "assert max(k_v) < ylim[1]\n", - "plt.ylim(*ylim)\n", - "plt.title(f\"Verifying `y_func_from_k_func` for k=100 [ylim = {ylim}\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"k\")\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c68a9da8-9c58-4d3f-8388-68519107c458", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k = 100\n", - "x1_v = np.linspace(0, 200)\n", - "x1_v[0] = 0.0001\n", - "k_v = [inv.k_func(xx, inv.y_func(xx, k=100)) for xx in x1_v]\n", - "plt.plot(x1_v, k_v)\n", - "ylim = (99.999999, 100.000001)\n", - "assert min(k_v) > ylim[0]\n", - "assert max(k_v) < ylim[1]\n", - "plt.ylim(*ylim)\n", - "plt.title(f\"Verifying `y_func` for k=100 [ylim = {ylim}\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"k\")\n", - "plt.grid()" - ] - }, - { - "cell_type": "markdown", - "id": "3d0eaf6d-4beb-420f-b323-e465df639143", - "metadata": { - "tags": [] - }, - "source": [ - "### Curves at different k" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "a44ccaf0-7aea-4669-8f54-00ee9942acf7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(6, 6))\n", - "k_v = [5, 50, 250, 1000, 4000, 12000, 35000]\n", - "x_v = np.linspace(0.1 , 20, 500)\n", - "y_v_by_k = {kk: [inv.y_func(xx, k=kk) for xx in x_v] for kk in k_v}\n", - "for kk, y_v in y_v_by_k.items():\n", - " plt.plot(x_v, y_v, label=f\"{kk}\")\n", - "plt.xlim(0,20)\n", - "plt.ylim(0,20)\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.title(\"Swap curves for different values of k\")\n", - "plt.legend()\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "311d8b50-1f12-4fdf-9749-07c6f856a11f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:light" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/resources/analysis/202401 Solidly/Invariants.py b/resources/analysis/202401 Solidly/Invariants.py deleted file mode 100644 index 07f4aea28..000000000 --- a/resources/analysis/202401 Solidly/Invariants.py +++ /dev/null @@ -1,249 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.15.2 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# + -import invariants.functions as f -from invariants.invariant import Invariant -from invariants.bancor import BancorInvariant, BancorSwapFunction -from invariants.solidly import SolidlyInvariant, SolidlySwapFunction -import numpy as np -import math as m -import matplotlib.pyplot as plt - -from testing import * -plt.rcParams['figure.figsize'] = [12,6] - -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(f.Function)) -print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(BancorInvariant)) -# - - -# # Invariants Module - -# ## General invariants - -inv = BancorInvariant() - -# ### goal seek - -# testing on $(x-1)(x+1)$ - -func = lambda x: x**2 - 1 -assert iseq(inv.goalseek_gradient(func, x0=-0.1), -1) -assert iseq(inv.goalseek_gradient(func, x0=0.1), 1) - -assert iseq(inv.goalseek_bisect(func, x_lo=0, x_hi=10), 1) -assert iseq(inv.goalseek_bisect(func, x_lo=0, x_hi=-10), -1) - -# testing on AMM invariant $k/x$ - -assert iseq(inv.goalseek_gradient(lambda x: 100/x - 5), 20) -assert iseq(inv.goalseek_gradient(lambda x: 100/x - 20), 5) -assert iseq(inv.goalseek_gradient(lambda x: 100/x - 10), 10) -assert iseq(inv.goalseek_gradient(lambda x: 100/x - 50), 2) - -# #### timing - -inv.y_func(20, k=100), inv.y_func_from_k_func(20, k=100), inv.y_func_from_k_func(20, k=100, method=inv.GS_BISECT) - -# note that the gradient method is almost certainly going to be faster than bisection, unless we are very good at bracketing (or put the tolerance very low) - -r = ( - timer(inv.y_func, x=20, k=100, N=1000), - timer(inv.y_func_from_k_func, x=20, k=100, method=inv.GS_GRADIENT, N=10_000), - timer(inv.y_func_from_k_func, x=20, k=100, method=inv.GS_BISECT, N=10_000), - timer(inv.y_func_from_k_func, x=20, k=100, x_lo=0.1, x_hi=10, method=inv.GS_BISECT, N=10_000), -) -r, (1, r[1]/r[0], r[2]/r[0]) - -# ### Bancor invariant function - -# we are here comparing the analytic invariant function with the one obtained numerically; note: they are a good match! - -f = BancorSwapFunction(k=100) -assert f(10) == 10 -assert f(5) == 20 -assert f(20) == 5 -inv = BancorInvariant() -assert inv.y_func_is_analytic is True - -x_v = np.linspace(0.5 , 3, 50) -y1_v = [inv.y_func(xx, k=100) for xx in x_v] -y2_v = [inv.y_func_from_k_func(xx, k=100) for xx in x_v] -plt.plot(x_v, y1_v, linewidth=3, label="analytic") -plt.plot(x_v, y2_v, linestyle="--", color = "#ccc", label="numeric") -plt.legend() -plt.grid() - -x_v = np.linspace(0.5, 3, 100) -y1_v = [inv.p_func(xx, k=100) for xx in x_v] -y2_v = [inv.y_func(xx, k=100) for xx in x_v] -plt.plot(x_v, y1_v, linewidth=3, color="red", label="p [LHS]") -plt.xlabel("x") -plt.ylabel("price dy/dx [red]") -ax2 = plt.twinx() -ax2.plot(x_v, y2_v, linewidth=3, color="grey", label="y [RHS]") -ax2.set_ylabel("swap function y [grey]") -#plt.grid() -plt.show() - -# #### timing - -# however, whilst the results are comparable, runtime difference is substantial (unsurprisingly especially given the extremely simple formula for the analytic function); for 1e-6 tolerance the factor is 27x, and for 1e-3 tolerance the factor is not much better at 19x - -r = timer2(inv.y_func, 20, 100, N=1000), timer2(inv.y_func_from_k_func, 20, 100, N=1000) -r, r[1]/r[0] - -# ### Solidly invariant function - -# The Solidly **invariant equation** is -# $$ -# x^3y+xy^3 = k -# $$ -# -# which is a stable swap curve, but more convex than for example Curve. -# -# To obtain the **swap equation** we solve the above invariance equation -# as $y=y(x; k)$. This gives the following result -# $$ -# y(x;k) = \frac{x^2}{\left(-\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6}\right)^{\frac{1}{3}}} - \frac{\left(-\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6}\right)^{\frac{1}{3}}}{3} -# $$ -# -# We can introduce intermediary **variables L and M** ($L(x;k), M(x;k)$) -# to write this a bit more simply -# -# $$ -# L(x,k) = L_1(x) \equiv -\frac{27k}{2x} + \sqrt{\frac{729k^2}{x^2} + 108x^6} -# $$ -# $$ -# M(x,k) = L^{1/3}(x,k) = \sqrt[3]{L(x,k)} -# $$ -# $$ -# y = \frac{x^2}{\sqrt[3]{L}} - \frac{\sqrt[3]{L}}{3} = \frac{x^2}{M} - \frac{M}{3} -# $$ -# -# If we rewrite the equation for L as below we see that it is not -# particularly well conditioned for small $x$ -# $$ -# L(x,k) = L_2(x) \equiv \frac{27k}{2x} \left(\sqrt{1 + \frac{108x^8}{729k^2}} - 1 \right) -# $$ -# -# For simplicity we introduce the **variable xi** $\xi=\xi(x,k)$ as -# $$ -# \xi(x, k) = \frac{108x^8}{729k^2} -# $$ -# -# then we can rewrite the above equation as -# $$ -# L_2(x;k) \equiv \frac{27k}{2x} \left(\sqrt{1 + \xi(x,k)} - 1 \right) -# $$ -# -# Note the Taylor expansion for $\sqrt{1 + \xi} - 1$ is -# $$ -# \sqrt{1+\xi}-1 = \frac{\xi}{2} - \frac{\xi^2}{8} + \frac{\xi^3}{16} - \frac{5\xi^4}{128} + O(\xi^5) -# $$ -# -# and tests suggest that it is very good for at least $|\xi| < 10^{-5}$ - -# ### L functions - -f = SolidlySwapFunction(k=100) -assert f.method == f.METHOD_DEC1000 -inv = SolidlyInvariant() - -x,k = 1,1000 -( - f._L1_float(x, k), - f._L1_dec100(x, k), - f._L1_dec1000(x, k), - f._L2_taylor(x, k), - f.L(x, k), - f.L(x, k) == f._L2_taylor(x, k), - f.L(x, k) == f._L1_dec100(x, k), - f.L(x, k) == f._L1_dec1000(x, k), -) - -# + -# x,k = 1,10 -# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k)) -# x,k = 1,100 -# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k)) -# x,k = 1,1_000 -# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k)) -# x,k = 1,10_000 -# assert iseq(f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k)) -# x,k = 1,100_000 -# assert iseq(f._L1_dec(x, k), f._L2_taylor(x, k)) # not float ! -# f._L1_dec(x, k), f._L1_float(x, k), f._L2_taylor(x, k) -# - - -# ### Numeric vs analytic and verification - -fig = plt.figure(figsize=(6, 6)) -k = 1000 -x_v = np.linspace(0.1 , 20, 500) -y1_v = [inv.y_func(xx, k=k) for xx in x_v] -y2_v = [inv.y_func_from_k_func(xx, k=k) for xx in x_v] -plt.plot(x_v, y1_v, linewidth=3, label="analytic") -plt.plot(x_v, y2_v, linestyle="--", color = "#ccc", label="numeric") -plt.xlim(0,20) -plt.ylim(0,20) -plt.legend() -plt.grid() - -k = 100 -x1_v = np.linspace(0, 200) -x1_v[0] = 0.0001 -k_v = [inv.k_func(xx, inv.y_func_from_k_func(xx, k=100)) for xx in x1_v] -plt.plot(x1_v, k_v) -ylim = (99.999999, 100.000001) -assert min(k_v) > ylim[0] -assert max(k_v) < ylim[1] -plt.ylim(*ylim) -plt.title(f"Verifying `y_func_from_k_func` for k=100 [ylim = {ylim}") -plt.xlabel("x") -plt.ylabel("k") -plt.grid() - -k = 100 -x1_v = np.linspace(0, 200) -x1_v[0] = 0.0001 -k_v = [inv.k_func(xx, inv.y_func(xx, k=100)) for xx in x1_v] -plt.plot(x1_v, k_v) -ylim = (99.999999, 100.000001) -assert min(k_v) > ylim[0] -assert max(k_v) < ylim[1] -plt.ylim(*ylim) -plt.title(f"Verifying `y_func` for k=100 [ylim = {ylim}") -plt.xlabel("x") -plt.ylabel("k") -plt.grid() - -# ### Curves at different k - -fig = plt.figure(figsize=(6, 6)) -k_v = [5, 50, 250, 1000, 4000, 12000, 35000] -x_v = np.linspace(0.1 , 20, 500) -y_v_by_k = {kk: [inv.y_func(xx, k=kk) for xx in x_v] for kk in k_v} -for kk, y_v in y_v_by_k.items(): - plt.plot(x_v, y_v, label=f"{kk}") -plt.xlim(0,20) -plt.ylim(0,20) -plt.xlabel("x") -plt.ylabel("y") -plt.title("Swap curves for different values of k") -plt.legend() -plt.grid() - - diff --git a/resources/analysis/202401 Solidly/README.md b/resources/analysis/202401 Solidly/README.md deleted file mode 100644 index d3abf94ab..000000000 --- a/resources/analysis/202401 Solidly/README.md +++ /dev/null @@ -1,19 +0,0 @@ -# Solidly - -_January 2024_ - -The main notebook here is `202401 Solidly` which contains the analysis regarding the Solidly analysis we performed in January 2023. - -The other notebooks are in relation to the `invariants` library that we developed to perform the analysis - - -## Running the notebooks - -In order to run the notebooks, run - - ln -s ../../../fastlane_bot/tools/invariants invariants - ln -s ../../../fastlane_bot/testing.py testing.py - echo invariants >>.gitignore - echo testing.py >>.gitignore - -to link the library that is now part of fastlane_bot \ No newline at end of file diff --git a/resources/docs/202312 ArbBot Convergence.pdf b/resources/docs/202312 ArbBot Convergence.pdf new file mode 100644 index 000000000..7123dc93a Binary files /dev/null and b/resources/docs/202312 ArbBot Convergence.pdf differ diff --git a/resources/docs/BalancerArbitrage.py b/resources/docs/BalancerArbitrage.py deleted file mode 100644 index 07e7ccdc2..000000000 --- a/resources/docs/BalancerArbitrage.py +++ /dev/null @@ -1,365 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.13.1 -# kernelspec: -# display_name: Python 3 -# language: python -# name: python3 -# --- - -# # Balancer Arbitrage Code - -# ## Documentation - -# The $r_k$ are the asset weight factors in the pool -# $$ -# \forall r_{_{k}} \in \left \{ r_{_{1}}, r_{_{2}}, \cdots r_{_{n}} \right \}, \; r_{_{k}} > 0 -# $$ - -# They are normalized to sum up to unity -# $$ -# \sum_{k = 1} ^ {n} r_{_{k}} \equiv r_{_{1}} + r_{_{2}} \cdots + r_{_{n}} = 1 -# $$ - -# The $x_l$ are the token balances in the pool, in native units -# $$ -# \forall x_{_{k}} \in \left \{ x_{_{1}}, x_{_{2}}, \cdots x_{_{n}} \right \}, \; x_{_{k}} > 0 -# $$ - -# **Equation 1 (Pool Invariant)** - -# $$ -# \prod_{k = 1}^{n} -# x_{_{k}} ^ {r_{_{k}}} -# \equiv -# x_{_{1}} ^ {r_{_{1}}} -# x_{_{2}} ^ {r_{_{2}}} -# \cdots\ -# x_{_{n}} ^ {r_{_{n}}} -# = \kappa -# = {constant} -# $$ - -# **Equation 2 (Isolation)** -# $$ -# x_{_{i}} = -# \left( -# \kappa \prod_{\substack{ k = 1 \\ k \neq i}}^{n} x_{_{k}} ^ {- r_{_{k}} } -# \right) ^ { \frac{ 1 }{ r_{_{i}} }} -# $$ - -# **Equation 3 (Marginal Price)** -# -# Note: the $P_i$ are prices in any numeraire (they only ever appear as ratio so any numeraire factor will divide out) - -# $$ -# - \frac{ \partial x_{_{i}} } { \partial x_{_{j}} } -# = \frac {P_i} {P_j} -# = -# \frac{ -# x_{_{i}} -# } -# { -# x_{_{j}} -# } -# \left( -# \frac{ r_{_{i}} } { r_{_{j}} } -# \right) ^ { - 1 } -# = \frac{x_i\,r_j}{x_j\,r_i} -# $$ - -# **Equation 4 (Rebalancing)** - -# $$ -# x_i = -# \kappa P_{_{i}} r_{_{i}} \prod_{k = 1} ^ {n} \left( P_{_{k}} r_{_{k}} \right) ^ {- r_{_{k}}} -# $$ - -# $$ -# x_i = -# \frac{\kappa P_{_{i}} r_{_{i}}} -# {\prod_{k = 1} ^ {n} \left( P_{_{k}} r_{_{k}} \right) ^ {r_{_{k}}}} -# $$ - -# If we define $\pi_i = P_i r_i$ the "weighted price i" then the above formula becomes -# $$ -# x_i = -# \frac{ \kappa \pi_i } -# {\prod_{k = 1} ^ {n} \pi_k ^ {r_{_{k}}}} -# $$ - -# We can also substitute $\kappa$ using the invariant equation and token balances -# $$ -# x_i -# = P_i r_i \prod_{k = 1} ^ {n} \left( \frac {x_k}{P_k r_k} \right)^{r_k} -# = P_i r_i \prod_{k = 1} ^ {n} \left( \frac {x_k}{P_k r_k} \right)^{r_k} -# $$ - -# We can also substitute $\kappa$ using the invariant equation and token balances -# $$ -# x_i -# = -# P_i r_i \prod_{k = 1} ^ {n} \left( \frac {x_k}{P_k r_k} \right)^{r_k} -# = -# \pi_i \prod_{k = 1} ^ {n} \left( \frac {x_k}{\pi_k} \right)^{r_k} -# = -# \frac -# {P_i r_i \prod_{k = 1} ^ {n} x_k{}^{r_k}} -# {\prod_{k = 1} ^ {n} (P_k r_k)^{r_k}} -# = -# \frac -# {\pi_i \prod_{k = 1} ^ {n} x_k{}^{r_k}} -# {\prod_{k = 1} ^ {n} \pi_k{}^{r_k}} -# $$ - -# **Equation 5 (Delta x)** - -# $$ -# \forall \Delta{x_{_{k}}} \in \left \{ \Delta{x_{_{1}}}, \Delta{x_{_{2}}}, \cdots \Delta{x_{_{n}}} \right \}, \; \Delta{x_{_{k}}} > 0 -# $$ - -# $$ -# \Delta{x_{_{j}}} -# = -# x_{_{j}} -# \left( -# 1 - -# \left( -# \frac{ x_{_{i}} } { \left( x_{_{i}} + \Delta{x_{_{i}}} \right) } -# \right) ^ { \frac{ r_{i} } { r_{j} } } -# \right) -# $$ -# - -# $$ -# \Delta{x_{_{i}}} -# = -# x_{_{i}} -# \left( -# \left( -# \frac{ x_{_{j}} } { \left( x_{_{j}} - \Delta{x_{_{j}}} \right) } -# \right) ^ { \frac{ r_{j} } { r_{i} } } -# - 1 -# \right) -# $$ - -# ## Code - -from decimal import * -getcontext().prec = 100 -from math import prod -from typing import List, Dict, Tuple -from tabulate import tabulate - - -class BalancerArbitrage: - def __init__( - self, - x_: Dict[str, Decimal], - r_: Dict[str, Decimal], - P_: Dict[str, Decimal], - ): - self.ZERO = Decimal('0') - self.ONE = Decimal('1') - self.x_ = x_ - self.r_ = r_ - self.P_ = P_ - self.k, self.n = self.initialize_k_n() - self.kappa = self.calculate_kappa() - - def isclose_decimal( - self, - num_1: Decimal, - num_2: Decimal, - rel_tol: Decimal = Decimal('1') / Decimal('2') ** Decimal('256') - ) -> bool: - return abs(num_1 - num_2) <= max(abs(num_1), abs(num_2)) * rel_tol - - def initialize_k_n( - self - ) -> Tuple[List[str], int]: - assert all(val > self.ZERO for val in self.x_.values()), "Not all values in x_ are > 0" - assert all(val > self.ZERO for val in self.r_.values()), "Not all values in r_ are > 0" - assert all(val > self.ZERO for val in self.P_.values()), "Not all values in P_ are > 0" - if self.x_.keys() == self.r_.keys() and self.r_.keys() == self.P_.keys(): - return list(self.x_.keys()), int(len(self.x_.keys())) - else: - raise ValueError("Keys of input dictionaries do not match.") - - def calculate_kappa( - self - ) -> Decimal: - return prod(self.x_[k] ** self.r_[k] for k in self.k) - - def calculate_marginal_price( - self, - i: str, - j: str - ) -> Decimal: - return (self.x_[i] / self.x_[j]) / (self.r_[i] / self.r_[j]) - - def adjust_reserves_after_trade( - self, - i: str, # source - j: str, # target - Dx_i: Decimal, # source amount - Dx_j: Decimal # target amount - ) -> None: - self.x_[i] += Dx_i - self.x_[j] -= Dx_j - return None - - def trade_by_source( - self, - i: str, # source - j: str, # target - Dx_i: Decimal, # source amount - commit: bool = True - ) -> Decimal: - Dx_j = self.x_[j] * (self.ONE - (self.x_[i] / (self.x_[i] + Dx_i)) ** (self.r_[i] / self.r_[j])) - assert self.x_[j] >= Dx_j, f"Insufficient {j} reserves to support this trade. Something is wrong." - if commit: - self.adjust_reserves_after_trade(i, j, Dx_i, Dx_j) - return Dx_j - - def trade_by_target( - self, - i: str, # source - j: str, # target - Dx_j: Decimal, # target amount - commit: bool = True - ) -> Decimal: - Dx_i = self.x_[i] * ((self.x_[j] / (self.x_[j] - Dx_j)) ** (self.r_[j] / self.r_[i]) - self.ONE) - assert self.x_[j] >= Dx_j, f"Insufficient {j} reserves to support this trade. Something is wrong." - if commit: - self.adjust_reserves_after_trade(i, j, Dx_i, Dx_j) - return Dx_i - - def calculate_balanced_coordinate( - self, - i: str - ) -> Decimal: - return self.kappa * self.P_[i] * self.r_[i] * prod((self.P_[k] * self.r_[k]) ** (- self.r_[k]) for k in self.k) - - def determine_balanced_pool_state( - self - ) -> Dict[str, Decimal]: - return {i: self.calculate_balanced_coordinate(i) for i in self.k} - - def get_rebalance_trade_sets( - self, - balanced_coordinates_: Dict[str, Decimal] - ) -> Tuple[Dict[str, Decimal], Dict[str, Decimal]]: - target_x_ = {} - source_x_ = {} - for k in self.k: - difference = balanced_coordinates_[k] - self.x_[k] - if difference < 0: - target_x_[k] = abs(difference) - elif difference > 0: - source_x_[k] = abs(difference) - return (target_x_, source_x_) - - def get_largest_value_from_trade_set( - self, - trade_set: Dict[str, Decimal] - ) -> Tuple[str, Decimal]: - return max(trade_set.items(), key=lambda x: x[1]) - - def find_rebalancing_path( - self, - target_x_: Tuple[str, Decimal], - source_x_: Tuple[str, Decimal] - ) -> Tuple[Dict[str, Decimal], Dict[str, Decimal]]: - target_id, target_amount = self.get_largest_value_from_trade_set(target_x_) - source_id, source_amount = self.get_largest_value_from_trade_set(source_x_) - try: - target_amount = self.trade_by_source(source_id, target_id, source_amount) - message = f"Swap {source_amount:.18f} x_{source_id} for {target_amount:.18f} x_{target_id}" - except AssertionError: - source_amount = self.trade_by_target(source_id, target_id, target_amount) - message = f"Swap {source_amount:.18f} x_{source_id} for {target_amount:.18f} x_{target_id}" - return message - - def rebalance_pool( - self - ): - rebalance_instructions = [] - balanced_coordinates_ = self.determine_balanced_pool_state() - target_x_, source_x_ = self.get_rebalance_trade_sets(balanced_coordinates_) - while any(not self.isclose_decimal(v, self.ZERO) for v in target_x_.values()): - rebalance_instructions.append(self.find_rebalancing_path(target_x_, source_x_)) - target_x_, source_x_ = self.get_rebalance_trade_sets(balanced_coordinates_) - if len(target_x_) == 0 or len(source_x_) == 0: - break - return rebalance_instructions - - def update_oracle_prices( - self, - updated_P_: Dict[str, Decimal] - ) -> None: - assert all(val > self.ZERO for val in updated_P_.values()), "Not all values in P_ are > 0" - if self.P_.keys() == updated_P_.keys(): - self.P_ = updated_P_ - else: - raise ValueError("Keys do not match. Are these the correct oracle prices?.") - - def initialize_k_n( - self - ) -> Tuple[List[str], int]: - assert all(val > self.ZERO for val in self.x_.values()), "Not all values in x_ are > 0" - assert all(val > self.ZERO for val in self.r_.values()), "Not all values in r_ are > 0" - - if self.x_.keys() == self.r_.keys() and self.r_.keys() == self.P_.keys(): - return list(self.x_.keys()), int(len(self.x_.keys())) - - def state_printer( - self - ) -> None: - data1 = [[k, - f"{self.x_[k]:.18f}", - f"{self.r_[k]:.18f}", - f"${1/self.P_[k]:.2f}" # inverse of P_ for Oracle price - ] for k in self.k] - data2 = [[i] + [f"{self.calculate_marginal_price(i, j):.18f}" for j in self.k] for i in self.k] - print("Table 1: Reserves, Ratios, and Oracle Prices\n") - print(tabulate(data1, - headers=["token", "Reserve balance", "Reserve ratio", "Oracle price"], - tablefmt="pretty")) - print("\n") - print("Table 2: Exchange Rates\n") - print(tabulate(data2, - headers=[""] + self.k, - tablefmt="pretty")) - return(None) - - -# + -x_ = {'a' : Decimal('100'), 'b' : Decimal('75'), 'c' : Decimal('16') + Decimal('2')/Decimal('3'), 'd' : Decimal('18.75'), 'e' : Decimal('25')} -r_ = {'a' : Decimal('0.2'), 'b' : Decimal('0.3'), 'c' : Decimal('0.1'), 'd' : Decimal('0.15'), 'e' : Decimal('0.25')} -P_ = {'a' : Decimal('1')/Decimal('1.4'), 'b' : Decimal('1')/Decimal('2.55'), 'c' : Decimal('1')/Decimal('3'), 'd' : Decimal('1')/Decimal('4'), 'e' : Decimal('1')/Decimal('5')} - -pool = BalancerArbitrage(x_, r_, P_) -# - - -pool.state_printer() - -pool.rebalance_pool() - -pool.state_printer() - -updated_P_ = {'a' : Decimal('1'), 'b' : Decimal('1'), 'c' : Decimal('1'), 'd' : Decimal('1'), 'e' : Decimal('1')} -pool.update_oracle_prices(updated_P_) -pool.state_printer() - -pool.rebalance_pool() - -pool.state_printer() - - diff --git a/resources/docs/Weighted Constant Product.md b/resources/docs/Weighted Constant Product.md deleted file mode 100644 index 6092f8dbd..000000000 --- a/resources/docs/Weighted Constant Product.md +++ /dev/null @@ -1,53 +0,0 @@ -# Weighted Constant Product Formulas - -**Parameter definitions** - -- $x,y$ are the token balances in their native units -- $\alpha$ is the weight of the $x$ token ($1/2$ is standard constant product) -- $\lambda = {\alpha}/{1-\alpha}$ is the weight ratio, equivalent to $\alpha$ but providing a different parameterization -- $k$ the pool invariant - -Formula D1. (Definition of lambda) -$$ -\lambda = \frac{\alpha}{1-\alpha} -$$ - -Formula D2. (Reverse lambda) -$$ -\alpha = \frac{\lambda}{1-\lambda} -$$ - -Formula D3. (Lambda relationship) -$$ -\frac{1}{\lambda-1} = \alpha - 1 -$$ - -Formula 1. (Invariant) -$$ -x^\alpha y^{1-\alpha} = k^\alpha -$$ - -Formula 2, 3. (y in terms of x) -$$ -y(x) = -\left(\frac{k}{x}\right)^{\frac{\alpha}{1-\alpha}} = -\left(\frac{k}{x}\right)^\lambda -$$ - -Formula 4. (marginal price) -$$ -p = \frac{dy}{dx} = \lambda k^\lambda x^{\lambda-1} = \lambda \frac{y}{x} -$$ - -Formula 5. (x in terms of p) -$$ -x(p) = k^\alpha \left(\frac{p}{\lambda}\right)^{\alpha-1} -$$ - -Formula 6. (x in terms of p) -$$ -y(p) = k^\alpha \left(\frac{p}{\lambda}\right)^{\alpha} -$$ - - - diff --git a/resources/docs/Weighted Constant Product.py b/resources/docs/Weighted Constant Product.py deleted file mode 100644 index f682db6fe..000000000 --- a/resources/docs/Weighted Constant Product.py +++ /dev/null @@ -1,206 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:light -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.13.1 -# kernelspec: -# display_name: Python 3 -# language: python -# name: python3 -# --- - -import numpy as np -import sympy as sp -import matplotlib.pyplot as plt - -# # Weighted Constant Product Formulas - - -# ### Definitions - -# - $x,y$ are the token balances in their native units -# - $\alpha$ is the weight of the $x$ token ($1/2$ is standard constant product) -# - $\eta = {\alpha}/{1-\alpha}$ is the weight ratio, equivalent to $\alpha$ but providing a different parameterization -# - $k$ the pool invariant - -# #### Formula D1. (Definition of eta) -# $$ -# \eta = \frac{\alpha}{1-\alpha} -# $$ - -# #### Formula D2. (Reverse eta) **OK** -# $$ -# \alpha = \frac{\eta}{\eta+1} -# $$ - - -# #### Formula D3. -# $$ -# \frac{\eta}{\eta-1} = \frac \alpha {2\alpha -1} -# $$ - -# #### Formula D4. -# $$ -# \frac{1}{\eta-1} = \frac {1-\alpha} {2\alpha -1} -# $$ - -# #### Formula D5. -# $$ -# \eta + 1 = \frac{1}{1-\alpha} -# $$ - -# #### Formula D6. - -# $$ -# \eta(1-\alpha)=\alpha -# $$ - -# ### Operational formulas - -# #### Formula 1. (Invariant) -# $$ -# x^\alpha y^{1-\alpha} = k^\alpha -# $$ - -# #### Formula 2. (x in terms of y) -# $$ -# x(y) -# = \frac{k}{ y^{\frac{1-\alpha}{\alpha}} } -# = \frac{k}{ y^{\frac{1}{\eta}} } -# $$ - -# #### Formula 3. (y in terms of x) -# $$ -# y(x) = -# \left(\frac{k}{x}\right)^{\frac{\alpha}{1-\alpha}} = -# \left(\frac{k}{x}\right)^\eta -# $$ - - -# #### Formula 4. (marginal price) -# $$ -# p = \frac{dy}{dx} -# = \eta \frac{y}{x} = \eta k^\eta x^{\eta-1} -# = \eta k^{-1} y^{1+\frac 1 \eta} -# $$ - -# #### Formula 5. (price response function $x(p)$) -# $$ -# x(p) -# = -# \left(\frac \eta p\right)^{1-\alpha} k^\alpha -# $$ - -# #### Formula 6. (price response function $y(p)$) -# $$ -# y(p) = \left( \frac{kp}{\eta} \right)^\alpha -# $$ - - -# ## Reconciliation - -# $$ -# \prod_{l} -# x_l{} ^ {r_l} -# = \kappa -# $$ - -# $$ -# x_i {}^ {r_i} -# = -# \kappa \prod_{l \neq i} x_l ^ {- r_l} -# $$ - -# $$ -# - \frac{ \partial x_{_{i}} } { \partial x_{_{j}} } -# = \frac {P_i} {P_j} -# = -# \frac{x_i}{x_j} -# \left(\frac{ r_i } { r_j } \right) ^ { - 1 } -# = \frac{x_i\,r_j}{x_j\,r_i} -# $$ - -# In this equation -# $$ -# x_i = -# \frac -# {\kappa P_i r_i} -# {\prod_{l} \left( P_l\, r_l \right) ^ {r_l}} -# $$ - -# For $x$ we get Formula 5 starting with and simplifying the below formula using we choose the token balances $x=x_1, y=x_2$, the weights $\alpha_1=\alpha, \alpha_2=1-\alpha$, and the prices $p=p_1/p_2$ and the pool constant $\kappa = k^\alpha$: -# $$ -# x_1 = \frac{\kappa p_1 \alpha_1} -# {(p_1 \alpha_1)^{\alpha_1}\cdot (p_2 \alpha_2)^{\alpha_2}} -# $$ -# -# Formula 6 we get when we start with the same equation except with $x_2=\cdots$ and $\kappa p_2 \alpha_2$ in the numerator - -# ## Testing - - - -x, y, k, p, al, eta = sp.symbols(r"x y k p \alpha \eta", real=True, positive=True) - -eta_eq = sp.Eq(eta, al/(1-al)) -eta_eq - -reta_eq = sp.Eq(al, eta/(1+eta)) -reta_eq - - -pxl_eq = sp.Eq(x, (1/k)**(eta/(eta-1)) * (p/eta)**(1/(eta-1))) -pxl_eq - -pxa_eq = pxl_eq.subs(eta_eq.lhs, eta_eq.rhs).simplify() -pxa_eq - -pya_eq = sp.Eq(y, k**al * (p/eta)**al).subs(eta_eq.lhs, eta_eq.rhs) -pya_eq - -inv_eq.subs(pxa_eq.lhs, pxa_eq.rhs).subs(pya_eq.lhs, pya_eq.rhs).simplify() - - -al_eq = sp.Eq(al, sp.solve(eta_eq, al)[0]) -al_eq - -eta_eq2 = sp.Eq(eta/(eta-1), (eta/(eta-1)).subs(eta, eta_eq.rhs).simplify()) -eta_eq2 - -eta_eq3 = sp.Eq(1/(eta-1), (1/(eta-1)).subs(eta, eta_eq.rhs).simplify()) -eta_eq3 - -px_eq0 = sp.Eq(x, (1/k)**(al/(2*al-1)) * (p/eta)**(al-1)) -px_eq = sp.Eq(x_eq0.lhs, x_eq0.rhs.subs(eta, eta_eq.rhs)) -px_eq0 - -py_eq0 = sp.Eq(y, (1/k)**(al/(2*al-1)) * (p/eta)**((1-al)/(2*al-1))) -py_eq = sp.Eq(x_eq0.lhs, x_eq0.rhs.subs(eta, eta_eq.rhs)) -py_eq0 - -inv_eq = sp.Eq(x**al * y**(1-al), k**al) -inv_eq - -y_eq0 = sp.Eq(y, (k/x)**eta) -y_eq = sp.Eq(y_eq0.lhs, y_eq0.rhs.subs(eta, eta_eq.rhs)) -y_eq0 - -y_f1 = sp.solve(inv_eq, y)[0] -y_f1 - -y_f2 = (k/x)**(al/(1-al)) -y_f2 - -(y_f1-y_f2).simplify() - -(y_f1-y_eq.rhs).simplify() - -(sp.solve(inv_eq, y)[0]/y_eq.rhs).simplify() - -inv_eq.subs(x, px_eq.rhs).subs(y, py_eq.rhs).simplify() - - diff --git a/resources/sphinx/Makefile b/resources/sphinx/Makefile deleted file mode 100644 index d0c3cbf10..000000000 --- a/resources/sphinx/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = source -BUILDDIR = build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/resources/sphinx/autodoc_preprocess_topazeblue.py b/resources/sphinx/autodoc_preprocess_topazeblue.py deleted file mode 100644 index c80bb01a2..000000000 --- a/resources/sphinx/autodoc_preprocess_topazeblue.py +++ /dev/null @@ -1,50 +0,0 @@ -import re - -def setup(app): - app.connect('autodoc-process-docstring', pre_process_docstring) - - -# Regular expression pattern -_PATTERN = r'^\s*(:)(\w+)(:)\s+(.*)$' - -# Replacement function -def _replace(match): - #if match.group(2) == "returns" or match.group(2) == "rtype" or match.group(2) == "return" or match.group(2) == "raises" or match.group(2) == "raise" or match.group(2) == "except" or match.group(2) == "exception" or match.group(2) == "yields" or match.group(2) == "yield": - if match.group(2) in ["returns", "rtype", "return", "raises", "raise", "except", "exception", "yields", "yield"]: - return f"{match.group(1)}{match.group(2)}{match.group(3)} {match.group(4)}" - return f"{match.group(1)}param {match.group(2)}{match.group(3)} {match.group(4)}" - - -def pre_process_docstring(app, what, name, obj, options, lines): - """ - pre-processes docstrings in the format used in topaze.blue code - - This function is called before the docstring is parsed by autodoc. It modifies the docstring - lines in place, then passing them back to autodoc. Changes made are the following: - - 1. the first line of the docstring is usually a summary, and separated from the rest of the text - by a blank line. The first line is emphasized. - - 2. In topaze.blue code, for readability the `param` term in `:param variable: description` is implied, - ie it only uses `:variable: description`. This function adds `param` before the variable name. - - 3. If there is a line that ONLY has "---" plus whitespace then that line and everything after it - is removed - """ - # try: - # if len(lines)==1 or lines[1].strip() == "": - # lines[0] = f"**{lines[0].strip()}**" - # except: - # pass - new_lines = [] - for line in lines: - if line.strip() == "---": - break - if re.match(_PATTERN, line): - # If it matches, perform the replacement - new_line = re.sub(_PATTERN, _replace, line) - #print("Modified line:", new_line) - else: - new_line = line - new_lines.append(new_line) - lines[:] = new_lines # Update the original list with modified lines diff --git a/resources/sphinx/build/html.zip b/resources/sphinx/build/html.zip deleted file mode 100644 index 6ee108a5f..000000000 Binary files a/resources/sphinx/build/html.zip and /dev/null differ diff --git a/resources/sphinx/make.bat b/resources/sphinx/make.bat deleted file mode 100644 index 747ffb7b3..000000000 --- a/resources/sphinx/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=source -set BUILDDIR=build - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.https://www.sphinx-doc.org/ - exit /b 1 -) - -if "%1" == "" goto help - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd diff --git a/resources/sphinx/source/_static/custom.css b/resources/sphinx/source/_static/custom.css deleted file mode 100644 index 607764146..000000000 --- a/resources/sphinx/source/_static/custom.css +++ /dev/null @@ -1,25 +0,0 @@ -body1 {background-color: red} -dl.py > dd > p:first-child {font-weight: bolder;} - -em.property .pre {color: rgb(187, 186, 186)} /* "property" etc */ - -dl.py.class {margin-top: 20px;} -dl.py.class .descname {color:blue;} /* class names */ - -dl.py.method {margin-top: 20px;} -dl.py.method .descname {color: rgb(137, 183, 46);} /* method names */ - -dl.py.property {margin-top: 20px;} -dl.py.property .descname {color: rgb(137, 183, 46);} /* property names */ - -dl.py.exception {margin-top: 20px;} -dl.py.exception .descname {color: rgb(172, 1, 172);} /* exception names */ - -dl.field-list dt {color: rgb(187, 186, 186)} /* "Parameters::" etc */ - -dt.sig em.sig-param span.n {color: rgb(137, 183, 46)} /* method parameters in signature */ -dl.field-list dd strong {color: rgb(137, 183, 46)} /* method parameters in description */ - -cite {color: darkblue} /* items in `backticks` */ - -h1, h2, h3, h4, h5, h6 {color: rgb(1, 128, 128)} /* headings */ diff --git a/resources/sphinx/source/analyzer.rst b/resources/sphinx/source/analyzer.rst deleted file mode 100644 index 7318aaa62..000000000 --- a/resources/sphinx/source/analyzer.rst +++ /dev/null @@ -1,16 +0,0 @@ -Analyzer -======== - -.. automodule:: tools.analyzer -.. currentmodule:: tools.analyzer - -CPCAnalyzer class ------------------ - -.. autoclass:: CPCAnalyzer - :members: - -Helper classes --------------- - -.. autoclass:: AttrDict diff --git a/resources/sphinx/source/arbgraphs.rst b/resources/sphinx/source/arbgraphs.rst deleted file mode 100644 index de2c12471..000000000 --- a/resources/sphinx/source/arbgraphs.rst +++ /dev/null @@ -1,54 +0,0 @@ -ArbGraphs -========= - -.. automodule:: tools.arbgraphs -.. currentmodule:: tools.arbgraphs - - -ArbGraph --------- - -.. autoclass:: ArbGraph - :members: - -Component classes ------------------ - -Node -~~~~ - -.. autoclass:: Node - :members: - -Edge -~~~~ - -.. autoclass:: Edge - :members: - -Path -~~~~ - -.. autoclass:: Path - :members: - -Cycle -~~~~~ - -.. autoclass:: Cycle - :members: - -Amount -~~~~~~ - -.. autoclass:: Amount - :members: - -Helper classes --------------- - -TrackedStateFloat -~~~~~~~~~~~~~~~~~ - -.. autoclass:: TrackedStateFloat - :members: diff --git a/resources/sphinx/source/conf.py b/resources/sphinx/source/conf.py deleted file mode 100644 index ddf552435..000000000 --- a/resources/sphinx/source/conf.py +++ /dev/null @@ -1,106 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -# import os -# import sys -# sys.path.insert(0, os.path.abspath('.')) - - -# -- Project information ----------------------------------------------------- - -project = 'FLBTools' -copyright = '2023-24, Bprotocol foundation' -author = 'Stefan K Loesch' - - -# -- General configuration --------------------------------------------------- - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.autosummary', - 'sphinx.ext.mathjax', - 'sphinx.ext.napoleon', - 'autodoc_preprocess_topazeblue', -] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = [] - - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = 'alabaster' - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] - -# These paths are either relative to html_static_path -# or fully qualified paths (eg. https://...) -html_css_files = [ - 'custom.css', -] - -# -- Custom variables -------------------------------------------------------- - -import tools -version = tools.__VERSION__ -release = version -date = tools.__VERSION_DATE__ -author = tools.__AUTHOR__ -copyright = tools.__COPYRIGHT__ - -import tools.cpc -import tools.invariants -from tools.optimizer import * -margp_optimizer_vd = f"v{MargPOptimizer.__VERSION__} ({MargPOptimizer.__DATE__})" -optimizer_base_vd = f"v{OptimizerBase.__VERSION__} ({OptimizerBase.__DATE__})" -cpcarb_optimizer_vd = f"v{PairOptimizer.__VERSION__} ({PairOptimizer.__DATE__})" -convex_optimizer_vd = f"v{ConvexOptimizer.__VERSION__} ({ConvexOptimizer.__DATE__})" -pair_optimizer_vd = f"v{PairOptimizer.__VERSION__} ({PairOptimizer.__DATE__})" - -from tools.cpc import ConstantProductCurve, CPCContainer -#from tools.cpcbase import CurveBase -cpc_vd = f"v{ConstantProductCurve.__VERSION__} ({ConstantProductCurve.__DATE__})" -cpc_container_vd = f"v{CPCContainer.__VERSION__} ({CPCContainer.__DATE__})" -#curve_base_vd = f"v{CurveBase.__VERSION__} ({CurveBase.__DATE__})" - - - -# conf.py -rst_epilog = f""" -.. |date| replace:: {date} -.. |author| replace:: {author} -.. |copyright| replace:: {copyright} -.. |margp_optimizer_vd| replace:: {margp_optimizer_vd} -.. |pair_optimizer_vd| replace:: {pair_optimizer_vd} -.. |convex_optimizer_vd| replace:: {convex_optimizer_vd} -.. |cpcarb_optimizer_vd| replace:: {cpcarb_optimizer_vd} -.. |optimizer_base_vd| replace:: {optimizer_base_vd} -.. |cpc_vd| replace:: {cpc_vd} -.. |cpc_container_vd| replace:: {cpc_container_vd} -""" -#.. |xxx_optimizer_vd| replace:: {xxx_optimizer_vd} - - diff --git a/resources/sphinx/source/cpc.rst b/resources/sphinx/source/cpc.rst deleted file mode 100644 index 749fe97ec..000000000 --- a/resources/sphinx/source/cpc.rst +++ /dev/null @@ -1,40 +0,0 @@ -CPC -=== -CPC stands for *ConstantProductCurve*, ie the hyperbolic -curve implied by $xy=k$ when operating an AMM. Whilst this -module is still mostly focused on classes dealing with CPCs -it has been extended to deal with some non-constant-product -AMMs as well. - -The key classes defined in the modules are -`ConstantProductCurve` (typically imported as `CPC`) and -`CPCContainer`, the latter being a container object for -multiple CPCs, representing a market, or a segment thereof. - -The `CPC` class derives from and abstract base class -`CurveBase`. This class defines the functions that any curve -class that is used in the `Optimizer` module must implement. - -.. automodule:: tools.cpc - - -CPC ---- -version |cpc_vd| - -.. autoclass:: ConstantProductCurve - :members: - -CPCContainer ------------- -version |cpc_container_vd| - -.. autoclass:: CPCContainer - :members: - -CurveBase ---------- - -.. automodule:: tools.cpcbase -.. autoclass:: CurveBase - :members: diff --git a/resources/sphinx/source/index.rst b/resources/sphinx/source/index.rst deleted file mode 100644 index d07ebdb3e..000000000 --- a/resources/sphinx/source/index.rst +++ /dev/null @@ -1,41 +0,0 @@ -.. FLBTools documentation master file, created by - sphinx-quickstart on Mon Feb 5 11:21:21 2024. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -FastLaneBot Tools -================= - -.. automodule:: tools - :noindex: - -- |version| -- |release| -- |date| -- |author| -- |copyright| - - - -Table of Contents ------------------ - -.. toctree:: - :maxdepth: 3 - :caption: Contents: - - analyzer - arbgraphs - cpc - invariants - optimizer - - - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/resources/sphinx/source/invariants.rst b/resources/sphinx/source/invariants.rst deleted file mode 100644 index 7d354f3a3..000000000 --- a/resources/sphinx/source/invariants.rst +++ /dev/null @@ -1,55 +0,0 @@ -Invariants -========== - -.. automodule:: tools.invariants - - - - -Functions ---------- - -.. automodule:: tools.invariants.functions - -Function -^^^^^^^^ -.. autoclass:: Function - :members: - :exclude-members: - -FunctionVector -^^^^^^^^^^^^^^ - -.. autoclass:: FunctionVector - :members: - :exclude-members: - - -Invariant ---------- - -.. automodule:: tools.invariants.invariant -.. autoclass:: Invariant - :members: - :exclude-members: - -Helpers -------- - -Vector -^^^^^^ - -.. automodule:: tools.invariants.vector -.. autoclass:: DictVector - :members: - :exclude-members: - - - -Kernel -^^^^^^ - -.. automodule:: tools.invariants.kernel -.. autoclass:: Kernel - :members: - :exclude-members: \ No newline at end of file diff --git a/resources/sphinx/source/optimizer.rst b/resources/sphinx/source/optimizer.rst deleted file mode 100644 index 14e9967f8..000000000 --- a/resources/sphinx/source/optimizer.rst +++ /dev/null @@ -1,70 +0,0 @@ -Optimizer -========= -.. automodule:: tools.optimizer - - -Main Optimizer Modules ----------------------- - -All classes in this section derive from the -`CPCArbOptimizer` base class that is discussed in more -detail below: - -.. automodule:: tools.optimizer.cpcarboptimizer - :noindex: - -MargPOptimizer -^^^^^^^^^^^^^^ -version |margp_optimizer_vd| - -.. automodule:: tools.optimizer.margpoptimizer -.. autoclass:: MargPOptimizer - :members: - :exclude-members: margp_optimizer - - -PairOptimizer -^^^^^^^^^^^^^ -version |pair_optimizer_vd| - -.. automodule:: tools.optimizer.pairoptimizer -.. autoclass:: PairOptimizer - :members: - -ConvexOptimizer -^^^^^^^^^^^^^^^ -version |pair_optimizer_vd| - -.. automodule:: tools.optimizer.convexoptimizer -.. autoclass:: ConvexOptimizer - :members: - - - -Base Classes ------------- - -CPCArbOptimizer -^^^^^^^^^^^^^^^ -version |cpcarb_optimizer_vd| - - -.. automodule:: tools.optimizer.cpcarboptimizer -.. autoclass:: CPCArbOptimizer - :members: - - -Optimizer Base -^^^^^^^^^^^^^^ -version |optimizer_base_vd| - -.. automodule:: tools.optimizer.base -.. autoclass:: OptimizerBase - :members: - - -DCBase -^^^^^^ -.. automodule:: tools.optimizer.dcbase -.. autoclass:: DCBase - :members: \ No newline at end of file diff --git a/resources/sphinx/tools b/resources/sphinx/tools deleted file mode 120000 index 24c3935fc..000000000 --- a/resources/sphinx/tools +++ /dev/null @@ -1 +0,0 @@ -../../fastlane_bot/tools \ No newline at end of file diff --git a/run_tests b/run_tests deleted file mode 100755 index fa903c693..000000000 --- a/run_tests +++ /dev/null @@ -1,20 +0,0 @@ -#!/bin/bash -cd "$(dirname "$0")" - -pwd -rm -rf fastlane_bot/tests/nbtest/* -mkdir fastlane_bot/tests/nbtest/ -touch fastlane_bot/tests/__init__.py -touch fastlane_bot/tests/nbtest/__init__.py - -# convert .ipynb to .py here... -for notebook in resources/NBTest/*.ipynb; do - jupytext --to py "$notebook" -done - -python resources/NBTest/ConvertNBTest.py >/dev/null - -pytest fastlane_bot/tests -v $1 - - -