From 3d1ed5140279b5ae7984165d1800efb27ef30b0b Mon Sep 17 00:00:00 2001 From: Kilian Lieret Date: Sun, 5 Nov 2023 22:38:35 -0500 Subject: [PATCH] WIP --- notebooks/030_performance.ipynb | 648 +++++++++++++++++++++++--------- src/ocpaper231/data.py | 5 +- 2 files changed, 473 insertions(+), 180 deletions(-) diff --git a/notebooks/030_performance.ipynb b/notebooks/030_performance.ipynb index 5ccaa1b..19d9f68 100644 --- a/notebooks/030_performance.ipynb +++ b/notebooks/030_performance.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 156, + "execution_count": 8, "outputs": [ { "name": "stdout", @@ -22,6 +22,11 @@ "%autoreload 2\n", "\n", "from pytorch_lightning import Trainer\n", + "import torch\n", + "from pathlib import Path\n", + "\n", + "\n", + "import pickle\n", "\n", "from gnn_tracking.postprocessing.dbscanscanner import DBSCANHyperParamScannerFixed\n", "from gnn_tracking.training.tc import TCModule\n", @@ -35,21 +40,25 @@ "from ocpaper231.names import variable_manager as vm" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T23:03:19.783031Z", + "start_time": "2023-10-05T23:03:19.711855Z" + } } }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 2, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001B[32m[17:31:49] INFO: DataLoader will load 900 graphs (out of 900 available).\u001B[0m\n", - "\u001B[36m[17:31:49] DEBUG: First graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v6/part_1/data21000_s0.pt, last graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v6/part_1/data21999_s0.pt\u001B[0m\n", - "\u001B[32m[17:31:49] INFO: DataLoader will load 20 graphs (out of 1000 available).\u001B[0m\n", - "\u001B[36m[17:31:49] DEBUG: First graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v6/part_9/data29000_s0.pt, last graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v6/part_9/data29019_s0.pt\u001B[0m\n" + "\u001b[32m[19:03:07] INFO: DataLoader will load 900 graphs (out of 900 available).\u001b[0m\n", + "\u001b[36m[19:03:07] DEBUG: First graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v8/part_1/data21000_s0.pt, last graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v8/part_1/data21999_s0.pt\u001b[0m\n", + "\u001b[32m[19:03:07] INFO: DataLoader will load 20 graphs (out of 1000 available).\u001b[0m\n", + "\u001b[36m[19:03:07] DEBUG: First graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v8/part_9/data29000_s0.pt, last graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v8/part_9/data29019_s0.pt\u001b[0m\n" ] } ], @@ -59,47 +68,101 @@ "dm = get_dm(n_val=20)" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T23:03:07.079988Z", + "start_time": "2023-10-05T23:03:07.055455Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "chkpt_path = \"/home/kl5675/Documents/23/git_sync/hyperparameter_optimization2/scripts/pixel/lightning_logs/vagabond-tasteful-hyrax/checkpoints_persist/epoch=451-step=406800.ckpt\"" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T23:03:07.123226Z", + "start_time": "2023-10-05T23:03:07.078372Z" + } } }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 4, "outputs": [], "source": [ - "chkpt_path = model_exchange_path / \"tc\" / \"tc-04b2e3ce.ckpt\"" + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"" ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T23:03:07.157412Z", + "start_time": "2023-10-05T23:03:07.106927Z" + } + } + }, + { + "cell_type": "markdown", + "source": [], "metadata": { "collapsed": false } }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 29, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001B[36m[17:31:50] DEBUG: Getting class PreTrainedECGraphTCN from module gnn_tracking.models.track_condensation_networks\u001B[0m\n", - "\u001B[36m[17:31:50] DEBUG: Getting class ECForGraphTCN from module gnn_tracking.models.edge_classifier\u001B[0m\n", - "/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/utilities/parsing.py:196: UserWarning: Attribute 'ec' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['ec'])`.\n", - " rank_zero_warn(\n", + "\u001b[36m[19:10:36] DEBUG: Getting class PreTrainedECGraphTCN from module gnn_tracking.models.track_condensation_networks\u001b[0m\n", "/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/utilities/parsing.py:196: UserWarning: Attribute 'hc_in' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['hc_in'])`.\n", " rank_zero_warn(\n", - "\u001B[36m[17:31:51] DEBUG: Getting class MLGraphConstruction from module gnn_tracking.models.graph_construction\u001B[0m\n", - "\u001B[36m[17:31:51] DEBUG: Getting class GraphConstructionFCNN from module gnn_tracking.models.graph_construction\u001B[0m\n", - "\u001B[36m[17:31:51] DEBUG: Getting class PotentialLoss from module gnn_tracking.metrics.losses\u001B[0m\n", - "\u001B[36m[17:31:51] DEBUG: Getting class BackgroundLoss from module gnn_tracking.metrics.losses\u001B[0m\n", - "\u001B[36m[17:31:51] DEBUG: Getting class DBSCANHyperParamScanner from module gnn_tracking.postprocessing.dbscanscanner\u001B[0m\n" + "\u001b[36m[19:10:36] DEBUG: Getting class MLGraphConstruction from module gnn_tracking.models.graph_construction\u001b[0m\n", + "\u001b[36m[19:10:36] DEBUG: Getting class GraphConstructionFCNN from module gnn_tracking.models.graph_construction\u001b[0m\n", + "\u001b[36m[19:10:36] DEBUG: Getting class PotentialLoss from module gnn_tracking.metrics.losses\u001b[0m\n", + "\u001b[36m[19:10:36] DEBUG: Getting class DBSCANHyperParamScanner from module gnn_tracking.postprocessing.dbscanscanner\u001b[0m\n" ] } ], "source": [ - "lmodel = TCModule.load_from_checkpoint(chkpt_path)" + "lmodel = TCModule.load_from_checkpoint(chkpt_path, map_location=device)" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T23:10:36.298599Z", + "start_time": "2023-10-05T23:10:35.980526Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 30, + "outputs": [ + { + "data": { + "text/plain": " | Name | Type | Params\n--------------------------------------------------------\n0 | model | PreTrainedECGraphTCN | 1.9 M \n1 | preproc | MLGraphConstruction | 333 K \n2 | potential_loss | PotentialLoss | 0 \n--------------------------------------------------------\n1.9 M Trainable params\n333 K Non-trainable params\n2.2 M Total params\n8.950 Total estimated model params size (MB)" + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ModelSummary(lmodel)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T23:10:40.500463Z", + "start_time": "2023-10-05T23:10:40.437875Z" + } } }, { @@ -113,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "outputs": [], "source": [ "import numpy as np\n", @@ -130,18 +193,22 @@ ")" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:08:25.965489Z", + "start_time": "2023-10-05T22:08:25.933633Z" + } } }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "outputs": [ { "data": { - "text/plain": " | Name | Type | Params\n---------------------------------------------------------\n0 | model | PreTrainedECGraphTCN | 1.8 M \n1 | preproc | MLGraphConstruction | 1.3 M \n2 | potential_loss | PotentialLoss | 0 \n3 | background_loss | BackgroundLoss | 0 \n---------------------------------------------------------\n1.8 M Trainable params\n1.3 M Non-trainable params\n3.2 M Total params\n12.636 Total estimated model params size (MB)" + "text/plain": " | Name | Type | Params\n--------------------------------------------------------\n0 | model | PreTrainedECGraphTCN | 1.9 M \n1 | preproc | MLGraphConstruction | 333 K \n2 | potential_loss | PotentialLoss | 0 \n--------------------------------------------------------\n1.9 M Trainable params\n333 K Non-trainable params\n2.2 M Total params\n8.950 Total estimated model params size (MB)" }, - "execution_count": 6, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -150,12 +217,16 @@ "ModelSummary(lmodel)" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:08:26.006345Z", + "start_time": "2023-10-05T22:08:25.965396Z" + } } }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "outputs": [ { "name": "stderr", @@ -171,15 +242,19 @@ } ], "source": [ - "trainer = Trainer(accelerator=\"gpu\")" + "trainer = Trainer(accelerator=device)" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:08:26.334418Z", + "start_time": "2023-10-05T22:08:26.007569Z" + } } }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "outputs": [ { "name": "stderr", @@ -187,9 +262,9 @@ "text": [ "/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py:189: UserWarning: .validate(ckpt_path=\"last\") is set, but there is no last checkpoint available. No checkpoint will be loaded.\n", " rank_zero_warn(\n", - "You are using a CUDA device ('NVIDIA A100-SXM4-80GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", - "\u001B[32m[16:46:50] INFO: DataLoader will load 5 graphs (out of 1000 available).\u001B[0m\n", - "\u001B[36m[16:46:50] DEBUG: First graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v6/part_9/data29000_s0.pt, last graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v6/part_9/data29004_s0.pt\u001B[0m\n", + "You are using a CUDA device ('NVIDIA A100 80GB PCIe') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "\u001b[32m[18:08:27] INFO: DataLoader will load 20 graphs (out of 1000 available).\u001b[0m\n", + "\u001b[36m[18:08:27] DEBUG: First graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v8/part_9/data29000_s0.pt, last graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v8/part_9/data29019_s0.pt\u001b[0m\n", "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", "/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:432: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", " rank_zero_warn(\n" @@ -201,7 +276,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "e5a3177e20084d64868446b132f72ef2" + "model_id": "5fce6054414d4862a571396f50db9d58" } }, "metadata": {}, @@ -211,39 +286,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", - "NaN or Inf found in input tensor.\n", "NaN or Inf found in input tensor.\n", "NaN or Inf found in input tensor.\n" ] @@ -253,21 +295,16 @@ "_ = trainer.validate(lmodel, dm, ckpt_path=\"last\", verbose=False)" ], "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:12:36.393651Z", + "start_time": "2023-10-05T22:08:26.332074Z" + } } }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "outputs": [], "source": [ "class TracksVsDBSCANPlot(Plot):\n", @@ -301,61 +338,161 @@ " )" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:12:55.601011Z", + "start_time": "2023-10-05T22:12:55.565774Z" + } } }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 25, + "outputs": [], + "source": [ + "with Path(\"~/paperresults/vs_eps.pkl\").expanduser().open(\"wb\") as f:\n", + " pickle.dump(lmodel.cluster_scanner, f)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:15:29.747408Z", + "start_time": "2023-10-05T22:15:29.698221Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 22, "outputs": [ { "data": { - "text/plain": "" + "text/plain": "" }, - "execution_count": 65, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "p = TracksVsDBSCANPlot(\n", + "tvdp = TracksVsDBSCANPlot(\n", " mean_df=lmodel.cluster_scanner.get_results().df_mean,\n", - " model=chkpt_path.stem,\n", - " watermark=\"outdated\",\n", + " # model=model,\n", + " # watermark=\"outdated\",\n", ")\n", - "p.plot_var(\"double_majority_pt0.9\")\n", - "p.plot_var(\"lhc_pt0.9\")\n", - "p.plot_var(\"perfect_pt0.9\")\n", - "p.ax.legend()" + "tvdp.plot_var(\"double_majority_pt0.9\")\n", + "tvdp.plot_var(\"lhc_pt0.9\")\n", + "tvdp.plot_var(\"perfect_pt0.9\")\n", + "tvdp.ax.legend()" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:15:07.412843Z", + "start_time": "2023-10-05T22:15:06.941992Z" + } } }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 29, + "outputs": [ + { + "data": { + "text/plain": "0.9642037721968443" + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# vs pt and eta" + "tvdp.df[\"double_majority_pt0.9\"].max()" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:16:06.618149Z", + "start_time": "2023-10-05T22:16:06.326008Z" + } } }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 30, "outputs": [], "source": [ - "h_dfs = []\n", - "c_dfs = []" + "max_dm_idx = tvdp.df[\"double_majority_pt0.9\"].argmax()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:16:08.623012Z", + "start_time": "2023-10-05T22:16:08.587083Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 31, + "outputs": [ + { + "data": { + "text/plain": "0.5310526315789474" + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max_dm_eps = tvdp.df.loc[max_dm_idx][\"eps\"]\n", + "max_dm_eps" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:16:09.290008Z", + "start_time": "2023-10-05T22:16:09.255724Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 35, + "outputs": [ + { + "data": { + "text/plain": "fake_double_majority_pt0.9 0.009222\nperfect_pt0.9 0.858110\nlhc_pt0.9 0.980250\nName: 40, dtype: float64" + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tvdp.df.loc[max_dm_idx][[\"fake_double_majority_pt0.9\", \"perfect_pt0.9\", \"lhc_pt0.9\"]]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:17:22.775334Z", + "start_time": "2023-10-05T22:17:22.685708Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "# vs pt and eta" ], "metadata": { "collapsed": false @@ -363,33 +500,40 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 26, "outputs": [], "source": [ - "# eyeballed\n", - "best_eps = 0.45\n", - "best_k = 1" + "h_dfs = []\n", + "c_dfs = []" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:15:34.756031Z", + "start_time": "2023-10-05T22:15:34.720863Z" + } } }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 36, "outputs": [], "source": [ "from gnn_tracking.postprocessing.dbscanscanner import DBSCANPerformanceDetails\n", "\n", - "lmodel.cluster_scanner = DBSCANPerformanceDetails(eps=best_eps, min_samples=best_k)" + "lmodel.cluster_scanner = DBSCANPerformanceDetails(eps=max_dm_eps, min_samples=1)" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:23:01.919534Z", + "start_time": "2023-10-05T22:23:01.830958Z" + } } }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 37, "outputs": [ { "name": "stderr", @@ -408,12 +552,16 @@ "trainer = Trainer(accelerator=\"gpu\")" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:23:09.220792Z", + "start_time": "2023-10-05T22:23:09.111686Z" + } } }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 38, "outputs": [ { "name": "stderr", @@ -421,9 +569,9 @@ "text": [ "/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py:189: UserWarning: .validate(ckpt_path=\"last\") is set, but there is no last checkpoint available. No checkpoint will be loaded.\n", " rank_zero_warn(\n", - "You are using a CUDA device ('NVIDIA A100-SXM4-80GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", - "\u001B[32m[17:32:02] INFO: DataLoader will load 20 graphs (out of 1000 available).\u001B[0m\n", - "\u001B[36m[17:32:02] DEBUG: First graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v6/part_9/data29000_s0.pt, last graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v6/part_9/data29019_s0.pt\u001B[0m\n", + "You are using a CUDA device ('NVIDIA A100 80GB PCIe') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "\u001b[32m[18:23:10] INFO: DataLoader will load 20 graphs (out of 1000 available).\u001b[0m\n", + "\u001b[36m[18:23:10] DEBUG: First graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v8/part_9/data29000_s0.pt, last graph is /scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v8/part_9/data29019_s0.pt\u001b[0m\n", "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", "/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:432: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", " rank_zero_warn(\n" @@ -435,74 +583,95 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "e95cc95e87db4d0a8ac760d26a2c7524" + "model_id": "e02a192318f84d918dbd05bc5cffabcf" } }, "metadata": {}, "output_type": "display_data" - }, - { - "ename": "TypeError", - "evalue": "'NoneType' object is not iterable", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[148], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m _ \u001B[38;5;241m=\u001B[39m \u001B[43mtrainer\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvalidate\u001B[49m\u001B[43m(\u001B[49m\u001B[43mlmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdm\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mckpt_path\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mlast\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mverbose\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n", - "File \u001B[0;32m/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:630\u001B[0m, in \u001B[0;36mTrainer.validate\u001B[0;34m(self, model, dataloaders, ckpt_path, verbose, datamodule)\u001B[0m\n\u001B[1;32m 628\u001B[0m model \u001B[38;5;241m=\u001B[39m _maybe_unwrap_optimized(model)\n\u001B[1;32m 629\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstrategy\u001B[38;5;241m.\u001B[39m_lightning_module \u001B[38;5;241m=\u001B[39m model\n\u001B[0;32m--> 630\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mcall\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_and_handle_interrupt\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 631\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;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[43m_validate_impl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdataloaders\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mckpt_path\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mverbose\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdatamodule\u001B[49m\n\u001B[1;32m 632\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[0;32m/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:42\u001B[0m, in \u001B[0;36m_call_and_handle_interrupt\u001B[0;34m(trainer, trainer_fn, *args, **kwargs)\u001B[0m\n\u001B[1;32m 40\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m trainer\u001B[38;5;241m.\u001B[39mstrategy\u001B[38;5;241m.\u001B[39mlauncher \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m 41\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m trainer\u001B[38;5;241m.\u001B[39mstrategy\u001B[38;5;241m.\u001B[39mlauncher\u001B[38;5;241m.\u001B[39mlaunch(trainer_fn, \u001B[38;5;241m*\u001B[39margs, trainer\u001B[38;5;241m=\u001B[39mtrainer, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m---> 42\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mtrainer_fn\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\u001B[1;32m 44\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m _TunerExitException:\n\u001B[1;32m 45\u001B[0m _call_teardown_hook(trainer)\n", - "File \u001B[0;32m/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:673\u001B[0m, in \u001B[0;36mTrainer._validate_impl\u001B[0;34m(self, model, dataloaders, ckpt_path, verbose, datamodule)\u001B[0m\n\u001B[1;32m 668\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_data_connector\u001B[38;5;241m.\u001B[39mattach_data(model, val_dataloaders\u001B[38;5;241m=\u001B[39mdataloaders, datamodule\u001B[38;5;241m=\u001B[39mdatamodule)\n\u001B[1;32m 670\u001B[0m ckpt_path \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_checkpoint_connector\u001B[38;5;241m.\u001B[39m_select_ckpt_path(\n\u001B[1;32m 671\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstate\u001B[38;5;241m.\u001B[39mfn, ckpt_path, model_provided\u001B[38;5;241m=\u001B[39mmodel_provided, model_connected\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlightning_module \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m 672\u001B[0m )\n\u001B[0;32m--> 673\u001B[0m results \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_run\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mckpt_path\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mckpt_path\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 674\u001B[0m \u001B[38;5;66;03m# remove the tensors from the validation results\u001B[39;00m\n\u001B[1;32m 675\u001B[0m results \u001B[38;5;241m=\u001B[39m convert_tensors_to_scalars(results)\n", - "File \u001B[0;32m/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:975\u001B[0m, in \u001B[0;36mTrainer._run\u001B[0;34m(self, model, ckpt_path)\u001B[0m\n\u001B[1;32m 970\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_signal_connector\u001B[38;5;241m.\u001B[39mregister_signal_handlers()\n\u001B[1;32m 972\u001B[0m \u001B[38;5;66;03m# ----------------------------\u001B[39;00m\n\u001B[1;32m 973\u001B[0m \u001B[38;5;66;03m# RUN THE TRAINER\u001B[39;00m\n\u001B[1;32m 974\u001B[0m \u001B[38;5;66;03m# ----------------------------\u001B[39;00m\n\u001B[0;32m--> 975\u001B[0m results \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_run_stage\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 977\u001B[0m \u001B[38;5;66;03m# ----------------------------\u001B[39;00m\n\u001B[1;32m 978\u001B[0m \u001B[38;5;66;03m# POST-Training CLEAN UP\u001B[39;00m\n\u001B[1;32m 979\u001B[0m \u001B[38;5;66;03m# ----------------------------\u001B[39;00m\n\u001B[1;32m 980\u001B[0m log\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m: trainer tearing down\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "File \u001B[0;32m/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:1011\u001B[0m, in \u001B[0;36mTrainer._run_stage\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 1008\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstrategy\u001B[38;5;241m.\u001B[39mbarrier(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mrun-stage\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 1010\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mevaluating:\n\u001B[0;32m-> 1011\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_evaluation_loop\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1012\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpredicting:\n\u001B[1;32m 1013\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpredict_loop\u001B[38;5;241m.\u001B[39mrun()\n", - "File \u001B[0;32m/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/loops/utilities.py:177\u001B[0m, in \u001B[0;36m_no_grad_context.._decorator\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 175\u001B[0m context_manager \u001B[38;5;241m=\u001B[39m torch\u001B[38;5;241m.\u001B[39mno_grad\n\u001B[1;32m 176\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m context_manager():\n\u001B[0;32m--> 177\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mloop_run\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\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/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/loops/evaluation_loop.py:115\u001B[0m, in \u001B[0;36m_EvaluationLoop.run\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 113\u001B[0m previous_dataloader_idx \u001B[38;5;241m=\u001B[39m dataloader_idx\n\u001B[1;32m 114\u001B[0m \u001B[38;5;66;03m# run step hooks\u001B[39;00m\n\u001B[0;32m--> 115\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_evaluation_step\u001B[49m\u001B[43m(\u001B[49m\u001B[43mbatch\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbatch_idx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdataloader_idx\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 116\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mStopIteration\u001B[39;00m:\n\u001B[1;32m 117\u001B[0m \u001B[38;5;66;03m# this needs to wrap the `*_step` call too (not just `next`) for `dataloader_iter` support\u001B[39;00m\n\u001B[1;32m 118\u001B[0m \u001B[38;5;28;01mbreak\u001B[39;00m\n", - "File \u001B[0;32m/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/loops/evaluation_loop.py:375\u001B[0m, in \u001B[0;36m_EvaluationLoop._evaluation_step\u001B[0;34m(self, batch, batch_idx, dataloader_idx)\u001B[0m\n\u001B[1;32m 372\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mbatch_progress\u001B[38;5;241m.\u001B[39mincrement_started()\n\u001B[1;32m 374\u001B[0m hook_name \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtest_step\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m trainer\u001B[38;5;241m.\u001B[39mtesting \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mvalidation_step\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m--> 375\u001B[0m output \u001B[38;5;241m=\u001B[39m \u001B[43mcall\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_strategy_hook\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrainer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mhook_name\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mstep_kwargs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvalues\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 377\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mbatch_progress\u001B[38;5;241m.\u001B[39mincrement_processed()\n\u001B[1;32m 379\u001B[0m hook_name \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mon_test_batch_end\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m trainer\u001B[38;5;241m.\u001B[39mtesting \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mon_validation_batch_end\u001B[39m\u001B[38;5;124m\"\u001B[39m\n", - "File \u001B[0;32m/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:287\u001B[0m, in \u001B[0;36m_call_strategy_hook\u001B[0;34m(trainer, hook_name, *args, **kwargs)\u001B[0m\n\u001B[1;32m 284\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m 286\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m trainer\u001B[38;5;241m.\u001B[39mprofiler\u001B[38;5;241m.\u001B[39mprofile(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m[Strategy]\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtrainer\u001B[38;5;241m.\u001B[39mstrategy\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mhook_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m):\n\u001B[0;32m--> 287\u001B[0m output \u001B[38;5;241m=\u001B[39m \u001B[43mfn\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\u001B[1;32m 289\u001B[0m \u001B[38;5;66;03m# restore current_fx when nested context\u001B[39;00m\n\u001B[1;32m 290\u001B[0m pl_module\u001B[38;5;241m.\u001B[39m_current_fx_name \u001B[38;5;241m=\u001B[39m prev_fx_name\n", - "File \u001B[0;32m/scratch/gpfs/kl5675/micromamba/envs/gnn/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py:379\u001B[0m, in \u001B[0;36mStrategy.validation_step\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 377\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprecision_plugin\u001B[38;5;241m.\u001B[39mval_step_context():\n\u001B[1;32m 378\u001B[0m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmodel, ValidationStep)\n\u001B[0;32m--> 379\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvalidation_step\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~/Documents/23/git_sync/gnn_tracking/src/gnn_tracking/training/tc.py:126\u001B[0m, in \u001B[0;36mTCModule.validation_step\u001B[0;34m(self, data, batch_idx)\u001B[0m\n\u001B[1;32m 124\u001B[0m out \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m(data)\n\u001B[1;32m 125\u001B[0m loss, metrics \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mget_losses(out, data)\n\u001B[0;32m--> 126\u001B[0m metrics \u001B[38;5;241m|\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_evaluate_cluster_metrics(out, data, batch_idx)\n\u001B[1;32m 127\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlog_dict_with_errors(\n\u001B[1;32m 128\u001B[0m metrics, batch_size\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtrainer\u001B[38;5;241m.\u001B[39mval_dataloaders\u001B[38;5;241m.\u001B[39mbatch_size\n\u001B[1;32m 129\u001B[0m )\n", - "\u001B[0;31mTypeError\u001B[0m: 'NoneType' object is not iterable" - ] } ], "source": [ "_ = trainer.validate(lmodel, dm, ckpt_path=\"last\", verbose=False)" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:24:01.147094Z", + "start_time": "2023-10-05T22:23:10.033451Z" + } } }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 39, "outputs": [], "source": [ "import itertools" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:24:01.190249Z", + "start_time": "2023-10-05T22:24:01.146219Z" + } } }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 51, "outputs": [], "source": [ - "h_dfs = lmodel.cluster_scanner.h_dfs\n", - "c_dfs = lmodel.cluster_scanner.c_dfs" + "with Path(\"~/paperresults/details.pkl\").expanduser().open(\"wb\") as f:\n", + " pickle.dump(lmodel.cluster_scanner, f)" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T22:55:19.537909Z", + "start_time": "2023-10-05T22:55:19.414149Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "with Path(\"~/paperresults/details.pkl\").expanduser().open(\"rb\") as f:\n", + " detail_scanner = pickle.load(f)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T23:03:26.316774Z", + "start_time": "2023-10-05T23:03:26.234951Z" + } } }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 10, + "outputs": [], + "source": [ + "h_dfs, c_dfs = detail_scanner.get_results()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T23:03:27.231122Z", + "start_time": "2023-10-05T23:03:27.196451Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "11 10 11 10\n" + "11 10 21 20\n" ] } ], @@ -513,102 +682,198 @@ ")\n", "import numpy as np\n", "\n", - "pts = np.array(np.linspace(0, 3, 10).tolist() + [5.0])\n", + "\n", + "pts = np.array(np.arange(0, 3, 0.3).tolist() + [5.0])\n", "vs_pt = tracking_metrics_vs_pt(h_dfs, c_dfs, pts=pts)\n", - "etas = np.array(np.linspace(-6, 6, 10).tolist() + [6.0])\n", + "etas = np.array(np.linspace(-4, 4, 20).tolist() + [4.0])\n", "vs_eta = tracking_metrics_vs_eta(h_dfs, c_dfs, etas=etas)\n", "print(len(pts), len(vs_pt), len(etas), len(vs_eta))" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T23:03:28.784016Z", + "start_time": "2023-10-05T23:03:27.822206Z" + } } }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 12, "outputs": [], "source": [ "class PerformancePlot(Plot):\n", - " def __init__(self, xs, df, var=\"pt\", **kwargs):\n", + " def __init__(self, xs, df, *, df_ul=None, var=\"pt\", y_label=\"Efficiency\", **kwargs):\n", " super().__init__(**kwargs)\n", " self.df = df\n", + " self.df_ul = df_ul\n", " self.xs = xs\n", " self.ax.set_xlabel(vm[var])\n", - " self.ax.set_ylabel(\"Efficiency\")\n", + " self.ax.set_ylabel(y_label)\n", + " self._legend_items = []\n", "\n", " def plot_var(\n", " self,\n", " var,\n", " color,\n", + " *,\n", + " label=None,\n", + " plot_ul=True,\n", " ):\n", - " self.ax.stairs(var, edges=self.xs, data=self.df, color=color)\n", + " stairs = self.ax.stairs(var, edges=self.xs, data=self.df, color=color, lw=1.5)\n", + " if self.df_ul is not None and plot_ul:\n", + " self.ax.stairs(\n", + " var, edges=self.xs, data=self.df_ul, color=color, lw=1.5, ls=\":\"\n", + " )\n", " mids = (self.xs[:-1] + self.xs[1:]) / 2\n", - " self.ax.errorbar(\n", + " if label is None:\n", + " label = vm[var].latex\n", + " bar = self.ax.errorbar(\n", " mids,\n", " var,\n", - " yerr=f\"{var}_std\",\n", + " yerr=f\"{var}_err\",\n", " ls=\"none\",\n", " color=color,\n", - " label=vm[var],\n", " data=self.df,\n", " )\n", + " self._legend_items.append(((stairs, bar), label))\n", "\n", " def add_blocked(self, a, b, label=\"Not trained for\"):\n", - " self.ax.axvspan(a, b, alpha=0.3, color=\"gray\", label=label, linestyle=\"none\")" + " span = self.ax.axvspan(\n", + " a, b, alpha=0.3, color=\"gray\", label=label, linestyle=\"none\"\n", + " )\n", + " self._legend_items.append(((span,), label))\n", + "\n", + " def add_legend(self, **kwargs):\n", + " all_handles = [item[0] for item in self._legend_items]\n", + " all_labels = [item[1] for item in self._legend_items]\n", + " self.ax.legend(all_handles, all_labels, **kwargs)" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T23:03:30.011230Z", + "start_time": "2023-10-05T23:03:29.972576Z" + } } }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 13, "outputs": [ { "data": { - 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" }, - "execution_count": 157, "metadata": {}, - "output_type": "execute_result" - }, + "output_type": "display_data" + } + ], + "source": [ + "p = PerformancePlot(df=vs_pt, xs=pts, var=vm[\"pt\"].latex + \" [GeV]\")\n", + "p.plot_var(\"double_majority\", \"C0\")\n", + "p.plot_var(\"lhc\", \"C1\")\n", + "p.plot_var(\"perfect\", \"C2\")\n", + "p.add_blocked(0, 0.9)\n", + "p.add_legend()\n", + "p.ax.get_figure().savefig(Path.home() / \"paperresults/vs_pt.pdf\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T23:03:31.661066Z", + "start_time": "2023-10-05T23:03:30.543851Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ { "data": { "text/plain": "
", - "image/png": 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NN4vHjh2Tr9m5c6ccciMjI8X58+eLV199tTh16tRub/QgkWrdCoIgzpgxQ1y0aJGo0+lEAOKYMWPEyspKt9vqzkYPoaGhYlRUlHj11VeLc+fOlTd6OPfccx0C7IIFC+Q+Llq0qFtl2fbs2SNvBKHVasVZs2aJN9xwg3jFFVfIf7fOPvtsu3sSEhJEQRDEM844Q1ywYIF43XXXiTNmzJA3X/Dx8RG3bNni9DX+6U9/EgVBEAGIaWlp4jXXXCPOmzdProbgbKOH7vaPiJxjwCUil/oq4Ipi+4YPl112mRgWFiaqVCo54Bw4cMBlf6qqqsQ777xTjI2NFb29vcXY2FjxtttuEysrK8UlS5a4DLii2L4ZwQ033CAmJCSIPj4+okajEUePHi3Onz9f/Pvf/y5WV1fbXT+QAbetrU3885//LI4dO1YuqwbAoeZqQUGBuHLlSnHMmDGir6+vGBAQII4ePVq86aabHEak3fX555+Ls2fPFkNDQ0UfHx9x1KhR4urVq13ucOaKuwE3KytLzM3NFa+77joxMjJS9Pb2FlNTU8XHHntMNBqNDvddeOGFDvVonf3namON8vJy8dFHHxUzMjLkTT5iY2PF8847T8zKynIoX/fWW2+JS5YsEceOHSuGhYWJSqVSDAwMFCdOnCjef//94unTpzv9OnzzzTfiddddJ8bExIheXl5iaGioOGHCBPHBBx8UCwoKet0/InJOEEUXy1OJiIiIiIYgLjIjIiIiomGFAZeIiIiIhhUGXCIiIiIaVhhwiYiIiGhYYcAlIiIiomGFAZeIiIiIhhXVQHdgMKuqqsLOnTuRmJjY5TaQRERERNT/mpubkZ+fj0svvRTh4eEAGHA7tXPnTmRmZg50N4iIiIioC9nZ2bjhhhsAMOB2KjExEUD7Fyw9PR0AkJOTg8zMTLtjniK1/Ze//AXJyckeazc3NxcPPPCAx9vty7YHU5+lvwdd6Y+/G0Opbfa5f9pmn/un7aHY575sm33un7bZZ/falo7b/nvNgNsJaVpCeno6MjIy7M45O+YpycnJGDt27JBpty/bHgx9HjNmTLfa7cu/G0Oxbfa5f9pmn/un7aHY575sm33un7bZZ/fatp1OykVmRERERDSsMOB2k06nQ1ZWFnQ63UB3xW0RERG4/fbbERERMWTaHop97kt9+feur9oein3uS/w69327fWkofp37sm1+D/un7aHY577UnT4LoiiK/dCnIenQoUOYNGkSvv/++z4bZnf2vPfff7/P3pan7nN3ikJ//30hz+P3cOjj93Do4/dwaBuI75+zZ3IEl4iIiIiGlUETcI8fP44XX3wRS5cuxZlnngmVSgVBEPDEE0/0uu1t27ZhxowZCAkJgVqtxoQJE/DMM8/AbDZ7oOdERERENJgMmioKf/vb37Bu3TqPt3v33Xdj3bp1UKlUmDVrFgICAvDll1/ioYcewocffojPPvts0GzioNPphtzcUPrVUJzPRPb4PRz6+D0c+vg9HNoGy/dv0MzB/fvf/47jx49j4sSJyMjIwJNPPok333wTjz/+OB555JEetbl9+3YsWLAAAQEB2LNnjzwvo6qqCrNmzcLPP/+M++67D88++6zT+wdiHsnx48f75Tnkvu6WCSMiIqL+4yyvDZoR3FtuucXuc4Wi97MnnnzySQDAqlWr7AJqeHg4XnrpJZx//vnYsGEDHn30UQQFBfX6eUREREQ08AbNHFxPKykpwXfffQcAuP766x3OT58+HXFxcTCZTNixY0d/d4+IiIiI+siwDbiHDx8GAISGhiIpKcnpNZMnT7a7loiIiIiGvkEzRcHT8vLyAADx8fEur4mLi7O71pWcnByX53Q63YBPpCYiIiIazvR6PfR6vdNzznLasA24DQ0NAAC1Wu3ymoCAAABAfX19p21lZma6PJeVlYU1a9Z0v4NERERE5JZNmzZh7dq1bl8/bAOuJ2VnZyM9Pd3pOY7eEhEREfWt5cuXY968eU7P5eTkOAxGDtuAq9FoAABGo9HlNY2NjQCAwMDATttKT0/ndoFEREREA6S7U0KH7SKzxMREAEBRUZHLa6Rz0rVERERENPQN24A7ceJEAIDBYHC5iOzgwYMAwNFZIiIiomFk2Abc2NhYTJkyBQCwdetWh/N79+5FUVERfHx8cNlll/V394iIiIiojwz5gLt69WqkpaVh9erVDucefvhhAMBTTz2FQ4cOyccNBgNWrFgBALjjjju4ixkRERHRMDJoFpkdOnRIDp0AcPr0aQDtZSE++ugj+fgHH3xgN8lYr9fj+PHjTmujzZ8/H3fddRfWr1+Pc845BxdddBHUajW++OIL1NbWYtq0aXj88cf78FURERERUX8bNAG3vr4eBw4ccDheXFyM4uJi+XOTydStdtetW4dp06Zh48aN2L9/P8xmM1JSUrBq1Srcc8898Pb27nXfiYio5/6a/x7qLU0IVPnj3sSFA90dIhoGBk3AnTFjBkRR7PZ9mzdvxubNmzu9ZtGiRVi0aFEPe0ZERH3prwX/QompCjE+4Qy4ROQRQ34OLhERERGRLQZcIiIiIhpWGHCJiIiIaFhhwCUiIiKiYWXQLDIjIqK+x4oFRDQSMOASEY0grFhARCMBpygQERER0bDCEVwioj7AqQBERAOHAZeIqA9wKgAR0cBhwCWiIY0jpURE1BEDLhENaRwpJSKijhhw3ZCTkyP/WafTQafTDWBviIiIiEY2vV4PvV4PwD6nSRhw3ZCZmSn/OSsrC2vWrBm4zhARERGNcJs2bcLatWtdnmfAdUN2djbS09MBgKO3NKJxvuvQ19hiBoT//0hENEQtX74c8+bNA9A+gms7GAkw4LolPT0dGRkZA90NogHH+a5Dn7HFAvj9/0cioiGqqymjDLhEgxBHSolopOLPP/IEBlyiQYgjpUQ0Uv3x+DuoE+oQJAbx5x/1GAMuEZGNwuYKVJnret2OWbTIHw/Vn+x1e+FeQYj30/a6HaLBjtNoyBMYcGnEe6P+SzRaWxCg8MWywFkD3R0aQIXNFUjfdzOarCaPtVnRWotJ/7u91+34K3yQM+21YRlyufCNiDyNAZdGvM31X6G8rRaRymAG3BGuylyHJqsJ2Wc+hHR1fK/aunD/KjQqGhBg1WDPeU/1qq0cYyEyf34aVea6YRlwOWJHRJ7GgEvkQYP17W1PGYxvu/fF6F+6Oh4ZgaN61UaLyQr4tX/sbVuSHGNhr9sQbT729nvIaRNENFgx4NKQVdlah3qLsdftWMQ2+ePpplKH88Z6hdv9ufKHtYPy7W1P8+Tb7v86KwsR3kE9bqPx/0f/Glt6H7o9ESD7QrhXEPwVPsj8+eletyXADwBghbXX38PhPG2CiIY2Blwakipb67AiZz1M1t6P2tUFNwIKoM7SiHuOv+xw/kRljdtt+St88GnGk70KbAAw59AfUNFaC613MD7J+FOv2vIkT/ZL+oVg9qGHe9WOJwMb0P49DPfq3ffP0+L9tMiZ9ppH3h2Y+tWDaAOggALfnrOxx+0M92kTRDS0MeDSkFRvMcJkNeO+xKsQ6xPRq7YWGzbAYG1AkCoAz4+5zeF8VEqs22156i1bL0Elf/TU29ueYDKJgND+0RP98kRo81RgkwzWt93j/bQe6Zdg89Ej38NBNm0CGLzfQyLqPwy4NKTF+kQgxT+6V22oqpXtHwWl07bGDKKAOdA8vRjIE6HN04GN3DNYp00AnDpBRAy4RFgaOFMuE0ZE7hmM0yYATp0gonYMuDTisTQYUc8M1mkTREQMuESD0L0JV8p7sRMREVH3MOASDULcf52odwZryTfqmqcXHVL/GwwLPRlwqd9wS9yhLyRvAmotRgSr1APdlUGPX6uB4cnFbzQwPL3okPrfYFjoyYBL/YZb4g59IXkT0FzTjJAQv4HuyqDHr9XA8OTiNxoYni79R/1rsCz0ZMClLvXXjmHdUWyq7HV/iGh48tTiNxoYXHRInsCAS53qzx3DustH4YVAvv3rlqIqIwyNvd9C2NxmlT/+kF/d6/bCAnwQFz64vof8WhERDX0MuNSp/twxrLsCVepeb4k7EhRVGTF51Udoam3zWJuV9Sac/9invW7H31uJt+46H+GBPa9BbLsgpbdBsqq+BTes/++g/VodfOoKhlw3eOqXFBoYnvx/mvrf8Zb6ge4CAAZct+Tk5Mh/1ul00Ol0A9ibgdEfO4bRr178JAcNzWZo/Lxw55z0XrVlaDShqbUNf7/tXIyJ7t0vBFc++xUq602ICPTBv+6f2au2pDC54NndvWpHmX4GFF6tEMzeOP99zwTJD+6f0avQDXj2a3W8tA63vPwNDI0mBtwu9MUvdNS/rAvaI67VKnrkl0PqX22hBuAyoKy2GQjsu+fo9Xro9XoA9jlNwoDrhszMTPnPWVlZWLNmzcB1ZgjjjmHu2/DpMZTWNCM6xK/XAVcyJjoIZyWG9qoNL6VC/tjbtgDg4FNX9Hqk7cpnfX4Nkn/sXZAEPDcVwNNfK3KPJ3+ho4Ex+cdt7YvMFAL++8fZA90d6qZPi37G6uYPUdfU+6mNndm0aRPWrl3r8jwDrhuys7ORnt4eMkbi6K2nsHICdRQXru51mGSQJGc88QsdDQzhx///CPB7OATltAQA/VCGevny5Zg3b177M3Ny7AYjAQZct6SnpyMjI2Ogu0FERERE6HrKKAMuEdEQcby097VdPbmAh5UdiGiwYsAl8qDBWGLKE6GIBlZYgA/8vZW45eVvet2WJxflsbIDEQ1WDLhEHjLYy3GFBfh4oEc0EOLC1R5ZkAd4blGeVNlh/4kKjGns3WIuT44q8xc6IgIYcIk8ZrCW4wL4VvJw4IkFeYDnFuV5clTZ02Wh+AsdETHgEnnYYCzHReRpnhxV9nRZKP5CR0QMuETktjtmp8kbUBB5alSZZaGIyNMYcInIbZ7adIKIyJWQvAmotRgRrOIoPPUcAy4RERENGiF5E9Bc04yQEL+B7goNYQy4RIMQpwIQERH1HAMu0SDEqQBDH39JISIaOAy4RDSkDdYgyV9SiIgGDgMuEQ1pDJJERNQRA+4w9Eb9l2i0tiBA4YtlgbMGujtERJ3iqnki8jQG3GFoc/1XKG+rRaQymAGXiAY9rponIk9jwCUiIqJBY7DOq6ehhQGXiIiIBg3OqydPUAx0B4iIiIiIPIkBl4iIiIiGFU5RcENOTo78Z51OB51O12fPqmytQ73F2Ks2LGKb/PF0U2mv2io2VfbqfiIiIiJP0+v10Ov1AOxzmoQB1w2ZmZnyn7OysrBmzZo+eU5hcwVW5KyHyWruVTt1wY2AAqizNOKe4y/3ul8+Ci8EsnwPERERDRKbNm3C2rVrXZ5nwHVDdnY20tPbJ7335ehtlbkOJqsZ9yVehVifiB63s9iwAQZrA4JUAXh+zG297legSo0I76Bet0NERETkCcuXL8e8efMAtI/g2g5GAgy4bklPT0dGRka/PS/WJwIp/tE9vl9VrWz/KCh71Q4RERHRYNTVlFEuMiMiIiKiYYUBl4iIiIiGFQZcIiIiIhpWOAeXRrzsA+Uwmtqg9lEi8+zIge4OERER9RID7jC0NHAmGq0tCFD4DnRXhoStB8pR0WCGVuPFgEvD3h2z09DQbIbGz2uguyIbjH0ioqGNAXcYWhY4a6C7QESD1J1z0ge6Cw4GY5+IaGjjHFwiIiIiGlYYcImIiIhoWGHAJSIiIqJhhQGXiIiIiIYVLjKjIauq3oSGFnOv27FYRfljXkWjw/lmn2q32jleWtfrvhAREVHvMeDSkFRVb8I9//gWJou1123VNfsDUKCuqRUPvfW9w/ncpuNut+XvrURYgE+v+0REREQ9N+gC7rZt27Bx40b8+OOPaG1tRWpqKm644Qbcc8898PLqXo3EwsJCPPPMM/j8889RWFgIURSh0+lwwQUX4N5778WECRP66FVQX2toMcNkseLOOWmIDVX3qq3lW0+j2mhBkL83nr7esVxRVFyS222FBfggLrx3/SEiIqLeGVQB9+6778a6deugUqkwa9YsBAQE4Msvv8RDDz2EDz/8EJ999hn8/PzcauvAgQP4zW9+g4aGBsTExOCSSy6BUqnEDz/8gC1btmDr1q3YunUrrr766j5+VdSXYkPVSNIG9KoNlUKQPzpra0xiaK/aJyIiov41aBaZbd++HevWrUNAQAAOHDiAnTt34v3338fJkydx5plnYu/evXj00Ufdbu/WW29FQ0MDbr31VuTl5eHf//43/vWvf+HUqVN45JFHYLFYcOutt6KlpaUPXxURERER9bdBE3CffPJJAMCqVauQkZEhHw8PD8dLL70EANiwYQPq6rpeyGMwGPDTTz8BAJ544gm7qQ0KhQJr1qyBn58famtrkZOT48mXQUREREQDbFAE3JKSEnz33XcAgOuvv97h/PTp0xEXFweTyYQdO3Z02Z6Pj/uLfMLDw93vKBERERENeoMi4B4+fBgAEBoaiqQk5wt6Jk+ebHdtZwICAnD++ecDAB555BGYzb+WkrJarVizZg2am5sxZ84cxMXF9bb7RERERDSIDIpFZnl5eQCA+Ph4l9dIQVS6tiuvvvoqLrvsMrzyyiv4+OOPMXnyZCiVShw+fBglJSW48cYbsWHDBrfa6mwag06ng06nc6sdIiIiIuo+vV4PvV7v9JyznDYoAm5DQwMAQK12XV4pIKB9dXt9fb1bbY4ZMwbffPMNbrzxRnz22WcoKSmRz51xxhmYMWMGAgMD3WorMzPT5bmsrCysWbPGrXZocLr+7EgYTW1Q+ygHuitERETkxKZNm7B27Vq3rx8UAbcv7Nu3D1deeSVUKhW2bt2KWbNmwdvbG/v27cO9996Lm2++Gfv27cNrr73WZVvZ2dlIT3esjwqAo7fDQObZkQPdBSIiIurE8uXLMW/ePKfncnJyHAYjB0XA1Wg0AACj0ejymsbG9i1U3Rl1ra2txYIFC1BVVYVvvvkGZ599tnzuiiuuwBlnnIEzzzwTr7/+OjIzMzFz5sxO20tPT7er7EBERERE/ae7U0IHxSKzxMREAEBRUZHLa6Rz0rWd+fjjj1FZWYnk5GS7cCuxPb5r167ud5iIiIiIBq1BEXAnTpwIoL1+ratFZAcPHgQAt0ZSCwsLAXQ+2hsUFAQAqK6u7lZfiYiIiGhwGxQBNzY2FlOmTAEAbN261eH83r17UVRUBB8fH1x22WVdthcTEwMAOHbsmNONIcxmMw4dOgQALsuSEREREdHQNCgCLgA8/PDDAICnnnpKDp9A+6juihUrAAB33HGHPPIqWb16NdLS0rB69Wr52Jw5c6BWq9Hc3Izf/e538vxdAGhtbcU999yDwsJCeHl5YeHChX35soiIiIionw2KRWYAMH/+fNx1111Yv349zjnnHFx00UVQq9X44osvUFtbi2nTpuHxxx93uE+v1+P48eN2tdEiIiLw8ssvY9myZdi2bRt2796NKVOmwMvLCwcPHkRJSQkUCgXWr1+P5OTk/nyZRERERNTHBs0ILgCsW7cO77zzDs4991zs378fO3bsQGxsLJ566il8+eWX8PPzc7utzMxMHDx4EEuXLoVGo8EXX3yBTz75BCqVCjfccAO++eYb3HbbbX34aoiIiIhoIAyaEVzJokWLsGjRIrev37x5MzZv3uz03IQJE/DGG294qGdERERENBQMqhFcIiIiIqLeYsAlIiIiomGFAZeIiIiIhhUGXCIiIiIaVhhwiYiIiGhYYcAlIiIiomGFAZeIiIiIhhUGXCIiIiIaVhhwiYiIiGhYYcAlIiIiomGFAZeIiIiIhhUGXCIiIiIaVlQD3YGhICcnR/6zTqeDTqcbwN4QERERjWx6vR56vR6AfU6TMOC6ITMzU/5zVlYW1qxZM3CdISIiIhrhNm3ahLVr17o8z4DrhuzsbKSnpwMAR2+JiIiIBtjy5csxb948AO0juLaDkQADrlvS09ORkZEx0N0gIiIiInQ9ZZSLzIiIiIhoWGHAJSIiIqJhhQGXiIiIiIYVzsElIhpJfvgr0FoPeAcCZ9070L0hIuoTDLhERCPJj38FjCWAOmbwBFyGbiLyMAZcIiIaWIMxdBPRkMY5uEREREQ0rDDgEhEREdGwwikKRER9gfNKiYgGDAMuEVFf4LxSIqIBwykKRERERDSsMOASERER0bDCKQpENLRxrisREXXAgEtEQxvnuhIRUQecokBEREREwwoDLhERERENKwy4RERERDSsMOASERER0bDCRWZuyMnJkf+s0+mg0+kGsDdEREREI5ter4derwdgn9MkDLhuyMzMlP+clZWFNWvWDFxniIiIiEa4TZs2Ye3atS7PM+C6ITs7G+np6QDA0VsiIupSa2srqqurERUVNdBdIRqWli9fjnnz5gFoH8G1HYwEGHDdkp6ejoyMjIHuBhERDQEFBQXIy8sDAPj7+yMwMHCAe0Q0/HQ1ZZQBl4iIyAMMBgNOnDgBk8kkHysoKMC4ceMgCMIA9oxo5GHAJSL3cVtcIpcaGhpgMpmgUCggiiJEUYTBYEBlZSW0Wu1Ad2/o4M8Z8gAGXCJyH7fFJXIgiiIEQUBCQgIMBgMaGhogCAIEQYAoiigoKEBISAi8vLwGuqtDA3/OkAewDi4REVEvSEFWEATEx8fLn0vHjEajXM6IiPoHAy4REVEvSXNsIyIiEBYWZncMAAoLC9HU1DQgfSMaiRhwiYiIPEAURQBAQkICVCqV3SiuxWJBUVHRAPeQaORgwCUiIvIAaWqCRqOR69/ajuLq9XrU1tYOUO+IRhYGXCIiIg+Lj4+Hr6+vPIqrULT/c1tQUACr1TrAvSMa/hhwiYiIPEQaxfX29kZcXJzdMQCoqalBRUXFQHaRaERgwCUiIuoDMTExCAwMlMOtNF2hoKAAra2tA9k1omGPAZeIiMiDbEdsExISHMqGNTc3o6SkZIB7STS8caMHIho4DYVAS1Xv2rCaf/1Yeaj3ffINBzTxvW+HRjRptDYsLAwRERGoqKiwW3BWXFwMrVYLtVo9UF0kGtYYcIloYDQUAm+nAxYP1QZtrgC2Tep9Oyp/4LochlzqNmmEVmK1WqFQKJCQkICamhqYze2/jAmCgLa2NhQWFiI9PX2guks0rDHgEtHAaKlqD7cXZwMhvfhH/qM57eHWTwtc8Unv+lSTA+zKbO8bAy51oWOglYJra2srlEolAECpVEKtViMmJgb5+fl295eXlyMyMhKhoaH92W2iEYEBl4gGVkg6EJHR8/sVXr9+7E07RN0khdvm5mY0NTWhpqYGVVVVUCgUaGlpgbe3N7y9vREeHo7Q0FCUl5ejpaVFDsaiKKKgoABBQUFyICYiz2DAJRqMfvgr0FoPeAcCZ9070L0ZeWpyet8G5wYPeyaTCdXV1aiurkZtba3dFARRFGEymdDS0oL6+nr4+fnJ54FfR3/r6upQXl6O6OjogXoZRMMSAy7RYPTjXwFjCaCOYcDtT77h7XNwd2V6rk3ODR5WbCshlJaWoqKiAiaTCUB7sBUEQd7IQaqkAEAeuXWmoKAAYWFh8PHx6fsXQDRCMOASEUk08e0hsreVHQDODR6mBEGA2WzG6dOnUVVVJR+TyoBJmzyEhoaira0NCoUCDQ0NaG1thcVikac12JYNM5lMKC4uRkpKykC+NKJhhQGXiMiWJt4zIbIv5gYPtqkTI3DaREtLC44dO4ba2loAgEKhkIOqRqOBVqtFdHS0vDUvAJjNZhiNRhQUFKC+vh5tbW12i9OAX8uGaTSa/nw5RMMWA64bcnJ+/UdFp9NBp9MNYG+IesAT9WYBz4YjT4S1kWKwTp0YgdMmDAYDGhoa5IAqTUeIiopCUlKSPM3AdiGZl5cXgoODodFoYDAYcOLECVgsFgC/TmsQRRGFhYU444wzHMIvETnS6/XQ6/UA7HOahAHXDZmZv/6jkpWVhTVr1gxcZ4i6y9P1ZgHPziv1De99O8PdYJw6MQKnTZhMJuTl5dmNwCoUCiQmJiI+vv1r0HFbXtuwqlAooNVqIYoiSkpKUF9fb9d+ZWUlDAYDwsP5/wRRVzZt2oS1a9e6PM+A64bs7Gy5GDdHb2nI8VS9WcCz80qBEfkWd48N1qkTg23aBNBnf6+qqqrkebRSkPX390dERAQAx7q4rkRERMDLywu//PKLHJalNvPz8xEcHAyViv88E3Vm+fLlmDdvHoD2EVzbwUiAAdct6enpyMhgfU0a4npbbxZgzVn61WCdNgH0ydQJURTlebfAryOzsbGx8PPzszvmiu2ob2hoKJKTk3Hy5Em5fUEQ0NjYCL1ej7i4OI/1nWg46mrKKAMuERF132CcNgH02dSJtrY2NDY2Avh1GoI0t1Y61t25szExMaioqEBdXZ3dvYWFhYiIiICvr69nOk80AjHgEhFRzwzWaRN9wGKxoLm52W56go+PD7y9vXsUbqV7kpOT8eOPP8qL1aQyZEVFRRg1apTHXwfRSMGAS0REw4+Hq3S0NZng3VoJs6UNAgARItravKCsVnR5rzNSHA4CEOHdgPLqeigEAe3ZWUTJST20ygoEafw88wKGEk/PyaZ+FdJwDHHm5oHuBgMuERENI30xNxiAaA1Aa3P7/GApnPooa2E59guUsKAnlb1EUYAgiEiy+qK8+WxY/79tASJECCg4Vo0zfY5AEJzvgDbseWpONvWr2QByBAV2xZQNaD8YcImIaPjoZG5wT6YSSJQtrfA/WYKmllbg/8dwGxQK4KxkCEplj9oW/r9PvoKAyFw9yg31gCAA/z+KWw2gMlkHbVhgj/o8ZHm6Wgv1q/2/7MB5Rx+Fj7l2QPvBgEvkSdxQgWjg/f/c4I6hszfbJyhMJrQVi4DCBAhCezgVBFQrEqCN0Pa4bSnkhrRFo9J0wm4uriiKyK/3R0jKmfDy8upF74eYITAnm1yrVw+Of3MYcIk8hRsqEA0oh0D7/382GAywWCxyDVtvb2+o1Wp4e3tDqVS61a6Pjw80Gg1MJpN8DAAaGxsRFhbmVjuuSH2Stv2V/hMEAU1NTSgtLUVCQkKP2ycaiRhwiTyFGyoQDSgp0La2tqKpqQnV1dWoqKiAyWSCIAjy6CjQXotWo9EgMjISoaGh8PX1dTnNQBpNDQkJgcFgsNuGt6GhAWazuVcBFwBCQkLkNm0rNQBAUVERIiIi4O/v36tnEI0kbgfcc845B3feeScWLVo0st4qIeoubqhANCDa2tpQW1sLg8GAmpoaNDe3r+SWwq3t9rlWqxV1dXWoq6uDl5cXRo0ahZCQEHh5eTkNuoIgwM/PDz4+PmhpaZGP19TUoL6+vlc1a0VRhMVigZ+fH8xmsxxupX5YLBYUFRVhzJgxPX4G0Ujjdn2Tb7/9FosXL0ZcXBweffRRlJSU9GW/iIiIuiSFwdbWVpSVlSEvLw+lpaVyzVrpbX/ba23nuUp1Z0+ePImTJ0/KW+c6e0ZQUBB8fHzkY9J1xcXFsFgsPX4NgiDAy8tLnv4gHbOl1+tRU1PT42cQjTRuB9x//vOfmD59OioqKvDkk08iKSkJV199Nfbs2dOX/SMiInJJCoKFhYU4ffq0vNsY0B5CpTCrVqvh5eXl8h1Ii8WCiooKnDp1Ck1NTfL90jNEUYRSqURUVJTdMUEQUF9fj7KyMrtpBd0hiiJaWlrswrVKpZLPKRTt/1QXFBSgra2tR88gGmncnqKwaNEiLFq0CL/88gtefPFFvPXWW3j//ffxr3/9C2PHjsXtt9+OG2+8kXOEiKh/TbgXaK0HvEdYKaURTgqXzc3NOHHihDy6qVAoYLVaoVAo4OPjg5CQEOh0Onh5eUGhUMgLt0pKSlBXVyfPz5VIQTUpKQk+Pj4O0xV0Oh1KSkrQ2Nhod7yoqAiBgYEIDOze30PbxWRtbW0QRRFBQUEIDAxEUVGR3TNqa2tRV1eH0NDQnn7ZiEaMbm/BMnbsWLz88ssoKSnBCy+8gFGjRuHIkSNYsWIFYmJicM899+DkyZM97tC2bdswY8YMhISEQK1WY8KECXjmmWdgNpt73GZrayvWr1+P6dOny4sJYmNjMWfOHLzzzjs9bpeIBoGz7gWmrmn/SCOGFPz0ej3q6urk6QjSiG14eDjS09MxevRoaDQa+Pr6wtvbGyqVCkFBQUhPT8e4ceMQEBAAAHaLuyorK1FQUGD3HNuFX4mJiXI/pPtMJhOKiorQ0NAgH++KbXi2HQFua2tDSkoKgoOD5VFof39/jB07luGWyE0922MQQGBgIO666y4cO3YMn332GebOnYu6ujqsX78e6enpuOyyy/DFF190q827774bixYtwr59+zB16lTMnj0bhYWFeOihhzBr1ix5wUB3FBcXY+LEiVi5ciWOHz+OadOmYf78+UhISMDXX3+Nbdu2dbtNIiIaOFIQLC0tRWFhIaxWqxwEFQoFUlNTccYZZ8ijqbZh03ZEVKPRYMyYMdBqtXbtt7W1oaysDNXV1Xb3S/eGh4cjIiLCIcRWVVWhqKgILS0tDpUQnJHaKykpQUVFhfy5FLpjYmKgVCqRkpKCqVOnIiIiohtfJaKRrddlwoxGI06ePIlTp04B+HW+0KeffoqdO3dizpw5eOuttxAUFNRpO9u3b8e6desQEBCAPXv2ICOjfeV4VVUVZs2ahb179+LRRx/Fs88+63bfmpub8Zvf/AbHjh3DmjVr8PDDD9vNv2pqasKJEyd68KqJiGigCIKAtrY2lJeXy58D7fNWU1JS5Hmy0gipq9JfQHvITUpKQn19vbwwDWhfiHbq1ClMnTrVoXyXIAhITk5GQ0MDWlpa7M5VVFTAYrEgLS0N3t7edv3oON3BYrGgurraYdF2cHAwACAiIgKhoaFyCbLe7MRGNNL0eAT3xIkTuOuuuxATE4M77rgDR48exaxZs7B9+3bU1tbilVdeQWJiIj755BPcd999Xbb35JNPAgBWrVolh1ug/Tfll156CQCwYcMG1NXVud3HP//5zzh27BhuvfVWZGVlOSwu8Pf3x1lnneV2e0RENLCkUVFpaoLt8ZCQEISFhcmfuxsGfX19ER8fL99nOy+2sLDQ7lopqPr5+SE+Ph5+fn4O56urq/HLL79Ar9fb9UP62NbWhubmZuj1euTm5qKpqcmu1q7tiLLy/7cBtr2fiLrWrRFcURTxn//8Bxs3bsQXX3wh/09+880346677sK4cePka2+55RZcd911mDBhAj788MNO2y0pKcF3330HALj++usdzk+fPh1xcXEoKirCjh07cN1113XZV7PZjL/97W8AgAceeKA7L5OIiHpBFEW0trbCbDbLb7d7mrSozLbCQVJSkjyQ4W4YtFgsqK2tRWVlpd19Uru5ubnyIrWOdDodRFFEQUEBWltb7Z4p1dgtLS2FVquFt7c3fH190dDQgNbWVtTW1qK+vt7uWSqVCrGxsXJps47BmIjc53bAffrpp/Hyyy+jsLAQoigiLi4OK1aswO9+9zuXk97VajWmT5+ON998s9O2Dx8+DAAIDQ1FUlKS02smT56MoqIiHD582K2Ae+jQIVRVVSE6Ohqpqan4+eef8a9//QulpaUICQnB+eefjzlz5sjlV4jIDaxYQF2wWCyor69HaWkpampqkJGRAbVa3eV9oijAnRgnCAJaW1vlcCgJDAyEn5+f2yO3VqsVjY2NKC0tRVlZWYe+/DqKGxsb67D5g+10g6ioKAiCgBMnTjgdaW1oaJAXnjkjLYzz8fFBfHy8PALNUEvUO24H3NWrVwMApk2bhpUrV2LBggVubU04btw4XHDBBZ1ek5eXBwDyW0TOxMXF2V3blZ9++gkAEBsbi1WrVuGZZ56xm/D/9NNPY+LEidi+fXunzwWAnJwcl+d0Oh10Op1bfSIa8lipgDrR2NiIsrIylJeXy5VvioqKkJaW5vIeURQBERCE9p/PDQ0N8PHxgbe3t8uwajQa5Zqx0s91jUbjVh+lmrPl5eUoKSmR+ymNmkrtabVapKSkyBs7ONvZDGifQhAdHQ1RFFFWVmYXZjsubrOdiys9z2q1QqlUIiYmRp47TESO9Ho99Hq903POcprbAXfx4sVYuXIlJk6c2K0O3X///bj//vs7vUb6gdDZb/nS21wdf2t3xWAwAGgfHf72229x++2346677kJUVJT8+eHDh3H55Zfj0KFDnW4/nJmZ6fJcVlYW1qxZ41afiNzGkVIagmpqalBcXAzg15HJsrIyREZGIiQkxO5au9FOAWi0BuBEwxgYf/gBqamp0Ol0TncUkwKi7W5koijKC7o6G/lsbW2FwWBAcXExjEaj3E/g193NAgICMGrUKHlhdFfzX6U+RUdHIzQ0FCdPnkR9fb28s5ntfbaBXHqeRqPBqFGjul0/l2ik2bRpE9auXev29W4H3M2bN/ekPwNG+iFiNptx3XXXYcOGDfK5iy++GJ9//jnGjBmDI0eO4J///CduvPFGl21lZ2cjPT3d6TmO3lKf4EgpDSG2b9dL2+RKJbusVivy8/MRGBjotBqAxWLB6aZ46Ju9AW810NaGmpoaBAYGQq1WO52LKpWMlNoXBKHTSgNtbW2oq6tDcXGxXPpLqq4gBU0vLy8kJyfLP9PdXdhle97Pzw/jxo1DfX09SkpKUFNTA6vVKj9D6rOXlxd8fHwQExODyMjI7n65iUak5cuXY968eU7P5eTkOAxGuh1wTSYTysvLERIS4vKtoIaGBtTU1CAqKkr+bdodUnvSb9TOSNsvuvtbrm0fly9f7nA+Pj4el19+Od5//33s2rWr04Cbnp5uV9mBiIh+JY1Menl5IS4uDidOnJDDoyAIqKurQ0VFhbwoSwqFRUVFyMvLg7U1HEA9lGhDG4CKigoEBATA39/fLkBK9/r6+gKA3L4oivL2urasViuMRqP81qYUWm2nBwDtU+CSkpLk0dyelOOSrlcoFAgODkZwcDCam5vR0tKCxsZGuy2DlUolgoODOc+WqBu6OyXU7RVW69atQ1JSEg4ePOjymoMHDyIpKQkbN250uwPAr7vCFBUVubxGOme7g0xnkpOTnf7Z2TWu5nQQEfXYhHuByVntH0eQ6OhoeQcu2wBXUFAgz5s1GAw4cOAATp8+3T7SCysEiGhD+yhsQECA04EU2zJbHevbNjY2wmKxyIG3paUFxcXFOHLkCEpLS+V5r9KoryiKCAsLw9lnn42UlBQ59No+p6ekdnx9fRESEoK4uDgkJCQgISEB4eHhCAkJYbgl6mNuj+B++OGHiImJwcyZM11eM3PmTERHR+Pf//437rnnHrc7Ic3rNRgMyMvLc1pJQQrW7o6kZmRkyD/oqqqq5EVqtqqqqgCgz8rYENEINsKmmdguokpISEB9fb3dPNmWlhacOnUKJpNJniYgz3+FAoAAH6EVsSkpiIiIgI+Pj8s5uFL4tQ3RZrMZRqMRarVanmcrre/oOM/W398fqampcgUgd4NtW1ubW5suOKu2wE0aiPqX2yO4p06dwhlnnNHldWPHjsXJkye71YnY2FhMmTIFALB161aH83v37kVRURF8fHxw2WWXudVmVFQUpk+fDgDYtWuXw3mz2Yw9e/YAAKZOndqt/hIRkSMpwIWEhMjbytqGOr1ej+rqagiCII+kSiO4MaoSnBlwGjExMfD19e109zGlUumwaK2xsREVFRU4duwYcnJy0NDQ4PAcpVKJUaNGYerUqXbh1tVuZx1VVFQ4lBRz92vCcEvUv9wOuNXV1QgPD+/yuvDwcLmCQXc8/PDDAICnnnoKhw4dko8bDAasWLECAHDHHXc4bPm7evVqpKWlyWXMbGVlZQFo39Hsf//7n3zcYrHgvvvuQ25uLjQaDZYtW9bt/hIRkSNplDQ1NVUu9dWxXJbtdeHh4ThTfRrJ3nkIUDa7VZtcpVLJIdh2ZLS0tFR+Z67jqG1MTAzOO+88xMTEAHB/1FZSXV2NU6dOITc3FyaTya4iAhENPm5PUQgNDXWrBm1eXl6Pyp3Mnz8fd911F9avX49zzjkHF110EdRqNb744gvU1tZi2rRpePzxxx3u0+v1OH78uNN5tBdddBEef/xxPProozj//PMxdepUREVF4dChQ8jPz4efnx/efvttrmIlIvIAaZ4r0L4w2dU1QPvUsNjYWISGhsL7+wagta1bzwgMDERpaald0LQdjZWCbUhICFJTU+UylN0JtlJ7zc3NKC4uRltbG6xWK4qLi5GSksJRWaJBzO2AO2nSJOzcuRNHjhyx25LX1i+//IIDBw7gN7/5TY86s27dOkybNg0bN27E/v37YTabkZKSglWrVuGee+7pVmUGySOPPIKpU6fihRdewIEDB/Ddd98hKioKS5cuxUMPPdRpAXIiInKftMvYsWPH5Hm2zvj4+CAtLQ1qtbrHITEqKgqFhYVoampyCLmiKMLX1xepqanyO489WUAmvZ6TJ0/Kr0cURRQVFSEqKsqtHdqoB1gHnDzA7YB70003YceOHZg/fz7ee+89nHXWWXbnf/jhByxcuBCiKGLp0qU97tCiRYuwaNEit6/fvHlzlzV6L7nkElxyySU97hMREXVNFEV5ni3QPle2rc1+ZFYQBJhMJtTW1vZoga/ttITExEQcPXrU4RqNRoPx48fLW+xK93VXfX09CgoK7GrniqKIqKgo+Pv7d7s9ctMIW6BJfcPtgHvllVdiwYIF+OCDDzBp0iRMmjRJHv08duwYvv/+e4iiiN/+9re45ppr+qzDREQ0+EihMywsDA0NDaiqqpLDrZeXF8xms13ILCwsRHh4uFzTtjukdrRaLYqLi1FfX283ims0GlFVVYWIiAioVCq358razudtaGhAUVERampq5GeKogi1Wo3o6GhOTyAa5NxeZAYA77zzDu655x54eXnh4MGDyM7ORnZ2Ng4ePAgvLy+sXLkS7777bl/1lYiIBikp8AUEBCAkJARKpRK+vr5IT0/HmDFjoFQq5ekD0lv/ndU+74oUWqXa6B3n35aUlMgLnrsz3xZoLyF57NgxVFZWynN5pXCblpbGbXWJhgC3R3CB9pWrzz33HFatWoWvvvoKBQUFANp3BZs1a5ZcFoaIiEYeKSSGh4fD29sbarUafn5+EAQBkZGR8qIwSUlJCSIjI9GTuCi1ExoaiqioKIfyXY2NjcjLy4NKpUJYWJjcP+nejnVppakTp0+fRkVFhd0zACAoKAjJycnQaDSsaUs0BHQr4EoiIiK6NU+WiIiGPyn0+fj4IDw83G7aQHx8PAwGg1xdQapPW1BQgHEi0JO4KAXN1NRUNDQ0oKmpyW5jhZaWFhw7dgw6nQ7R0dF2m0dIH00mEywWC8rKylBcXCz313b+cGBgIOLi4uSRW4ZbosGvRwGXiIiGN2ejnd0Zuey4m5evry9iYmKQm5trF3wNBgMqzSHQoqTbfZTaUalUSElJQUFBAerq6uzOm81mFBYWorKyEv7+/vI2uYIgoL6+HhaLBXV1dTCbzQAgb9krhVutVivX9CWioaPbAddkMuHgwYMoKSlBS0uLy+sWL17cq44REVEfcLMEkxRQpe1pzWazXJWgpyOYcXFxqKioQGNjo90uYwUtUQhVHOvRiIvtVAWlUonc3Fy7kCtpaWlBc3MzDAaD000anG3nm5iYCK1W24NeEdFA69bPk/Xr12PNmjVOf3h0xIBLRDQIOSnBZBtaRVGExWKB2WxGeXk5jEYj2traYDQa4evrC19fXwQGBiIiIgI+Pj5uPdJ29DchIQFHjx61W3BmtPqh1BqNeLi32YMrQUFBSE9Px+nTp2EwGOSwKgVp22BruyDNdmMIpVKJ6OhoectgIhqa3A64b775Ju6++24AQFpaGtLT07mSlIhoGJDCbXNzs1ziq6qqSg59ktbWVtTX16OiogL5+fmIi4tDREQE/P39uxzZlc5FREQgLCwMVVVV9mXDzHGIaNPDrxevQ5oKkZaWhqqqKuj1etTW1tpVQrAN27avTxAEea5ubzagIKLBwe2A+8ILL0AQBLzxxhscnSUiGkCeXsVvNptRXV2Nqqoq1NbWOsxHdcZisaCgoACVlZU444wz5I0POuub7ShuTU2NPM9VgAiLqEKhKRJjevE6pOcqlUpERkZCq9WitrYWNTU1aGhoQHNzM0wmE/z8/GA2m+Hv74+AgAD4+vpCq9VyxJZoGHE74Obk5OCcc85huCUi6mfNzc3IycmBRqPBqFGjPNKmFDabmppQXl6O8vJyeV2F7dv3HXVcdNbY2Ijjx48jMTFRXsDlinSPRqOBTqdDcXGx3fX61nBE1tYiODjYY68vJCQEISEhsFqtEEURra2tcoUEPz8/eY4xEQ0vbgdcX19fuaA20XASUvgGFJZGWFUBqIlfNtDdIZJZrVbk5eXJGyLU19cjLi4Ovr6+PR7Ftd0QoaWlBbm5uaiqqnK4xnZnMlEU4e3tjcbGRphMJrS2tto9v66uDoWFhVAoFAgKCnKrb/Hx8aiqqmoP1ZHnQWE1warwQUFBAQIDA+VFXz1lO6dYWtAGAH5+9pMgGG6Jhie3A+7kyZNx8uTJvuzLoJWTkyP/WafTQafTDWBvyNNCCjfDy1QOs08kAy4NGm1tbfjmm29gsVgA/FqX9dSpUxg3blyPpyhI91VWVuLEiRPydARpdFXagSwiIgLR0dFQqVRyOGxra4PJZEJ+fj6qq6thsVjkBVy1tbXIz8/H+PHjOy0pJp3z9vZGXFwcTp48CUF3HkQAEEXU1NSgoqICUVFRPXp9rl4vEQ0ver0eer0egH1Ok7j9K/Lq1avx/fff45NPPvFc74aIzMxMTJo0CZMmTcKmTZsGujtENMxJQTM6OhpA+1xYab5qVVUVampq5Ou606b0X1lZGXJzc2E2m6FQKOTQ6ePjg5iYGJx55plITEyEt7e33UioUqmEv78/UlNTkZCQAAB21Qlqamq6tf1uTEwMAgMD7SobAEBBQQFaW1vdfl1ms1m+vjtfEyIaujZt2iRns8zMTIfzbo/gpqSk4JFHHsGCBQtw11134YorrkB8fLzLt5Hi4+N73utBJjs7G+np6QDA0Vsi6jfJyckoLy+HyWSyC5qnTp3ClClTurX5gnRNfX09ioqK0NzcbDfPNjAwEMnJyfL8146h0/YZKpUKcXFxaGhosNvWVhRF5Ofnywu2uhrFlRacHTlyxO55zc3NKCkpQWJiYqevzWKxoKamBqWlpQgICEBCQgJUKhW30iUaAZYvX4558+YBaB/B7Rhy3Q640g8aURTx3HPP4bnnnnN5rSAI8ttqw0F6ejoyMjIGuhtENELYBsCUlBQcPXoUwK/zSY1GI0pKShATE9Otdqurq3H06FH557MUKuPi4pCSkiJf5yogdpzPKu3uZRtOrVarPI2iq9cIAGFhYYiIiJCDsqS4uBhBQUEIDg6WqzlI91itVjQ2NqKkpAQVFRXyKG5wcDDCwsIYbolGgK6mjLodcOPj4/lDg4ion0g/b7VaLUpKSlBXV2dXtis3NxdRUVFQKpVujVhKoVCaNyttcRsfHy8HZdsFaLY6juaWl5fj9OnTLqcRSOXGgoOD3S4bVltba7d4ra2tDQUFBbBarQgPD5e33W1paUFlZSVKS0vloK5QKGA0GlFeXo6AgAC3N6AgouHL7YCbn5/fh90gIqKOpLCXmpqK77//Xp7vKs3Jzc3NdbtsmEKhgFqthp+fH5qbmwG0b7oQGRkpj8h2FWwbGhpw8uRJ1NfXy20CcLqRwsmTJ7ucRiGdU6vViImJQWFhoTzXGGivzmA0GhETEwOr1QqLxYLa2lq5/x2f39bW1uvqC0Q0PPRk628iIuoHtnVjo6KiUFZWZrfdrDRNoaudxKRzgYGB0Gq1KCgoQGxsLFJTU10+27Y9s9mM06dPo6ysTO5Xx+1t29ra7MKs0WhEaWmpvFCuK7GxsWhoaEB1dbUc5IFfN5To+HWxfb63tzeSk5M9VnmBiIY+/qpLRDQEpKSkyCW5gF9HL6Xyje5sk+vl5YWQkBBERkYiIiICgGPVgY6jtiUlJfjf//4nh1tpeoPUD61Wi4kTJ8rVEGz7kZubaxd8XfVNqtAQHx+P0NBQh77Y9ke6Xnp+fHw8zj33XIZbIrLT7YD73//+F4sWLUJsbCx8fHxw8803y+c+//xzPPzww/IPQiIi6h0p0Hl5edlttiONctbU1MBgMABwr0RWYGAgRo8ejaCgILn9js8D2qst/Pjjjzh58qT81r/tqGlQUBDGjh2L1NRUBAQEyIvUpD4oFApYLBbk5eW59RqlviUlJclzdzt+DSQKhQJRUVE455xzkJyczPUhROSgW1MUnnjiCWRlZdn9oLH9c1BQEJ5++mnExsZixYoVnuslEREhPj4eer1eLvElOXXqlFw9oKsFZ9LIr7PrpDq5BoMBxcXFqKurs6tcALTvahkTEwOtVmtXJzcoKAjh4eHyrmjSvw3FxcWIjo7uchqFRK1WIz09HaWlpSgtLYUoirBYLPD29kZAQAD8/f2h1WoRGBjYnS8dEY0wbgfcTz75BI899hhiY2Px17/+FRdeeCEiIyPtrpk6dSoiIiLw0UcfMeBSnwtX1cPbmAfU+/euIavl14/1px3PVxrda6fGcScVIk/oWDbsyJEjAH4Nqc3NzSgqKkJcXFy32nR2rKGhAYWFhWhoaJCfAbQH48jISOh0OgQEBNgt5pL6odVqYTAY7MqJSWXDpB3OuiJtOJGUlITo6GhYLBaYTCZoNBq0tbXB19fX7ddIRCOX2wF33bp18PHxwSeffIKxY8e6vG7ChAkjdktf6j/K1io8n7QZPjnm3jfmXQcIAFrrgL33OJ7/7oT7ban8Ad/w3veJCPZVCaSP4eHhCAkJQU1NjV3ZsLy8PERFRcHLy6vbGx1I19fU1CAnJ0cu/2Vb5SAqKgpardZpCS7pWb6+vvD394fRaJRHgwVBQHV1NQwGA8LCwrrsm+05b29v+Pj4QK1WA2ifQ0xE5A63A+53332HqVOndhpugfayM/v37+91x4g6ozQ3wEdhRmXSCkTEuFcmyaUfFgNmA+AdBEx93vF8YjcWr/iGA5rhs4sfDayOu4hJH1NTU/Hdd9/ZlQ2zWq3Izc3FmDFjevQcq9WK0tJStLa2ysFZFEX4+fkhLi4OERERUCqVnbajVqvlMl8d5812ZxpFx9dPRNRdbgdco9Ho1irVuro6ea4WUV9r9Y0BAlO6vrAzCtWvH521FdH9wEDUXR136hJFES0tLaiuroZCoZDfpvf19YVGo0F8fDwKCwvlewFAr9cjOjoaGo2m26O4xcXFqKyslJ8PtI/IjhkzRt6+t6v+K5VKBAUFoaWlRe5Tb6ZREBH1lNsBNzIyEqdOneryuuPHj/MHGDmVfaAcRlMb1D5KZJ4d2fUNRCOINLLZ2NiIuro6u+oIHa8LDAy0Gw0FYDffdeLEid0Kt1arFQaDQb5HFEUoFAq5ooF0rKupBVar1WF6g628vDzodDqoVKpuB3Aiou5wu0zY9OnT8cMPP2Dfvn0ur/noo49w6tQpzJw50yOdo+Fl64FyvPpfPbYeKB/orhANGlIIbG1thV6vR25uLnJzc+VwK5Xnsv2vrq4ORUVFdgFSmq5QV1eHiooKu7a70traivr6ervrNRqN3ULirsKoFIptR247nrNarTh92slCTiIiD3M74N53330QBAFXXnkltm/fLu8BLvn0009xyy23wMvLC3feeafHO0pENNxIo5jS/Nf8/HzU1NTIYVU6J739bxsazeb2BZbOgqcUIjvbYMFWXV2d3UI2oD3gAnB7ypkgCLBYLGhsbIQgCPDy8kJAQIBDP/R6PRoaGtzuGxFRT7gdcDMyMvDcc8+hqqoKV111FYKDgyEIAt5//30EBwfj8ssvR0VFBZ577jmcccYZfdlnIqJhQRAEtLW14fjx48jPz0dra6scbAFApVIhKCgIaWlpOOOMM3DWWWchPT0d0dHRcmUB27f6pT+bTCbk5+d3+Xzb6Q3S6KttW9I5d9uprKyExWKBKIpQqVRIT0+Xg7ltmJVGpzlFgYj6Src2eli5ciXS0tKQlZWF7777DqIoyrUSx48fjyeeeAJXXHFFn3SUiGg4KikpQXl5+7Qd2614NRoN4uLioNVqAdgH2YiICFgsFhQWFqK8vBwmk8khLObn50On08HHx8flfFfpWFtbmzxqLAVRk8mE1tZWeHt7uz3/VtrkQeqvWq1GUlISTp06BUEQEBQUhIiICLcWrRER9Ua3Ai4AXHrppbj00kthMBiQl5cHq9WKuLg46HS6vugfEdGw1dDQgKKiIvlzKdxqtVqMGjVKrvvaMWBarVaoVCokJycjJCQEx48fR0tLCwDYlQ07ffp0p++oSe2GhoZCpVLBYrHIIddoNKKhoUFezNbZ/WazGYWFhXaL4qStgGNjY1FTUwN/f39EREQ4bBJBRNQXuh1wJWFhYQgLC/NkX2iQqqo3oaGl9xsqWKyi/DGvorFXbdXWNyOm1z0iGlgGgwFms9luWkBwcLAcbqXR1I4B0zYghoSEIDU1FUVFRairq5PbAYCKigrExMQgKCjI6Sis7echISFymTAAaG5uhsFggL+/P/z8/Bzut/28qqrKbhRaoVDYlZVMS0sDwI0aiKj/9Djg0shQVW/CPf/4FiZL72sb1zX7A1CgrqkVD731fa/aSvIpx8REwN+Hf4Wp74iiCKvV2uUGBz3R2toqh0Lb+a6xsbFu70ZmOwKrUChw9OhReRRWGsU9efIkJk+e3Glb3t7eCAoKQk1NDSwWi3xvRUUFRFHEqFGjHEZdBUFAa2srTp8+Lb8OoH10OTAwEAEBAXL/GGyJqL+5TAdbtmwBACxYsAAajUb+3F2LFy/uXc9oUGhoMcNkseLOOWmIDVX3qq3lW0+j2mhBkL83nr4+vVdteRvzgBwgJMC7V+0QuWKxWFBfXw+DwYD4+HinW9Q6425915aWFjQ3N9vNe1Wr1QgJCQHg3gIs6RqFQoHQ0FAkJCTIFRSkObWNjY3Q6/XQ6XRO+yYdCwkJQVVVFWpra+V7LRYL9Ho9WltbERISgtDQUJhMJrS1taGurg4lJSXytAopFAcGBiItLY2hlogGlMuAu3TpUgiCgHPOOQcajUb+3F3DKeDm5OTIf9bpdCNyvnFsqBpJ2oBetaFSCPLH3raFev/e3W+jJn4pFJZGWFW97BMNC1arFU1NTaioqLBbwJWamtrpfR2nE9iW3XL2s9NoNDpcr1arezVaHBcXh4qKCrkMlyQ3NxeRkZEOlRJsn69WqxEZGQmTyYTm5mb5nCiKMBgMMBgM8iiuVP0B+HW6hNVqhZeXF6Kjoztd2EZE5Al6vR56vR6AfU6TuAy4ixcvlle92n4+EmVmZsp/zsrKwpo1awauM+RxNfHLBroLNIjU1dXh5MmTaGpqko+VlpYiMjJSrg1rq2OwNRqNKC4uRkhICLRarcsRU4k0Wgq0b43r7Bp3SPekpKTghx9+kD+XFoHl5eUhJcX5ttbSteHh4WhtbUVeXp7d/R372rFOLwD4+/tj9OjRrJBARP1i06ZNWLt2rcvzLgPu5s2bO/18JMnOzkZ6evtb6iNx9JZoJPH19bWrSCCFuIKCAowdO9blQqu2tjbk5uaipKREPhcYGAhfX1+766SPUoDurHxXd0ijrcHBwQgPD7cr2QUARUVFiI6OdrpgTPqzl5cX4uLi5NfrrMau7Udph7KYmBjEx8dzWgIR9Zvly5dj3rx5ANpHcG0HIwEuMnNLeno6MjIyBrobRNTHRFGEn58fEhISkJeXJx8D2isFGAwGhIeHO4zalpSUIDc3V37bHgBqamoQEBCA2NhYp6HY2Wit7Ta3PQ25ABAdHY2qqiq5HWl+7KlTp3DmmWd22rZCoUBSUhLUajVKSkrQ0NAgj9J23IY3MjIS0dHRCAwMHLHv8BHRwOhqyigDLhFRB9JcVmmerBQQ8/PzERQUJI9U1tTU4NSpU3bXAe1v5UtzWc1ms93IZscRUVs1NTVISEjodZ1YPz8/BAcHo7a2Vu6PIAgwGAyorq5GaGioyxAtHddqtQgNDYXRaJR3KLNYLFCr1fJ5f3/PzYUnIvIkt3+Kvv3220hOTsann37q8ppPP/0UycnJeO+99zzSORperj87Er87X4frz44c6K4QOSW99a5QKJCQkCAfs61IUFVVBZPJhCNHjuDHH3+E0WiUa79arVZYrVb4+PggMTERcXFxDm/bS6OfgYGBDlvYShss9JaPj488QtzRqVOn7F6rs6+BRKlUIigoCKmpqUhLS8O4ceOQlJSExMREhlsiGtTcHsF9++23UVtbi1mzZrm8ZubMmaipqcFbb72FhQsXeqSDNHxkMtjSECAFPK1Wi7KyMlRXV9uFvlOnTtlNRbDdXlcQBERFRSE6OhpqtdrpSKzUlre3NzQajbzdOQA0NjaipaXF6WI2d0kBPSAgwOG4IAhoampCSUkJYmK63irF1XxjIqLBzu0R3J9++gnjx4+Ht7fruqM+Pj6YMGECfvzxR490johoIEgjmwkJCVAqlXYjrbZ1X22rCISGhmLcuHFITU2FRqPpcpqBSqWCv7+/w8KtiooKj7wGaUtcZ6O0ubm5dtvyuoPhloiGErcDbllZmVu/8cfExKCsrKxXnSIiGgyCgoLkjRcktkFXql07evRopKWlISwszK06tqIoQqVSQaPROIyMVlZWOmy52x1SWxaLxa4EmdSeQqGQKz4QEQ1Xbgdcf39/GAyGLq8zGAydjvISEQ12giDAZDLh2LFjDuW2pPMKhQLx8fEYO3YsdDpdj37u6XQ6eVteqV0Acvjsyaip1JZKpbL7XGpP+ry0tBRGo7Fbo7hEREOF2wF37Nix2LdvH6qrq11eU11djb179yItLc0jnSMiGgg1NTU4cOAAysrKHDY7ANpDo9Vqhb+/vzzNoDukUKlUKuXFbFK7giCgrq5O3qGnu6S+SBUUpG14bYOsNMos7f7D6QdENNy4HXCvuuoqGI1GZGZm2u3wI2lubsaNN96I5uZmLjAjoiFNEAR5kZazEU4pEBYWFsJsNvfqWa42X8jLy+t2RQVprrDFYpHfcRNFEeHh4Rg1apTcd2mRnLe3tzzVgohoOHG7isLy5cvx6quvYufOnRg9ejSuv/56eaT22LFjePvtt1FaWooxY8ZgxYoVfdZhIqLe6jgloKOgoCBotVo0NjbKI7VBQUEoKyuTQ6RUkaC0tNRuFNZdtiXJEhMT5dFUqe3W1lbk5+cjISHBoSJCZ20C7dsNNzU12VV4iI6ORn5+PlpbW6HRaJCamipvxU5ENNy4HXD9/Pywc+dOLFiwAN9//z2ee+45u/OiKGLixIn44IMPWB+RiAatjtvrSlUSOm7AEBISgoiICHh5eUGn00GtVsNsNqOqqsouGBcVFSEiIqJHP/ekdiIjI1FRUQGDweCw4EwURSQnJ8vtd1auSxRF1NTU4MSJE/IorVKplMuOpaWlobm5WV4w3FXQJyIaqrq1k1lsbCy+/fZbfPjhh/j0009RUFAAAIiPj8fs2bMxb948/qAkokFJCobSz6iioiKcPn0aEyZMQEhIiMN5tVqNlJQUqFQq+VhiYiJqa2thsVgAtAdDi8WCwsLCHq89kJ47atQo1NXV2bUtiqK8scTo0aOh0Whc7oRmtVpRW1uL4uJitLa2yqO3YWFh8qYSoaGhDs8lIhqOur1VryAImDdvHubNm9cX/SEi6hNSmKuqqkJubq68liAvL09ehNWRbWUEURQREBAAnU6HoqIiu+vLysoQGRnpUFLM3X6JoghfX1+kpKSgsLAQzc3NducbGhpw5MgRaLVaREZG2k1ZkLYErqmpgV6vR2trq9xfAIiIiOj060FENBx1O+ASEQ1V1dXVOHLkCADIo7X19fXQ6/XQ6XRujWrGx8ejsrISLS0tAH7dyaygoABBQUFdbvDQmaioKIiiiIKCAphMJru+mEwmFBUVoaioCAEBAfDz84PVakVrayssFosciqXArFKpkJCQIAdcBloiGkl6/pOYiGiICQ0Nleej2lZHyM3NlTdFcFVRQDrn5eWF+Ph4u2OCIKC2thbl5eU96pdtO5GRkUhNTZVLedmGbuljY2MjKisrYTAY0NDQgObmZrk2r7RwTavVQqvV9qg/RERDncsR3OTkZAiCgF27diEpKQnJycluNyoIAk6fPu2RDhIReYIUFFNTU3H48GE50AqCALPZjLy8PKSkpLjVVnR0NMrLy1FXV2c3MlpQUICwsLAebfogtaNUKuVR17KyMhgMBoeRZdtAbEuqmBAfH4/4+PhejSYTEQ1lLgNufn6+/INf+txdfCuMiAYbKRRKJcAqKirsRmyLiopc1qTt2IYgCEhISMCRI0fkUCkIAlpaWlBSUoKkpKQe91NqPyIiAqGhoSgpKUF5eblcE9e2Dx1Hm0NDQ5GSkgK1Wt3j5xMRDQcuA25eXh4AyOVkpM+JiIa6lJQUVFRUyKO40jza06dPY9y4cZ3+ki6dCw0NRXh4uByUJVLZMHdr17pqX9rpLD4+HpGRkaitrUVZWRlaWlrQ1tYGq9UKhUIBjUYDb29vaLXaHi1yIyIajlwGXGknH2k/854UMiciGkykUU8fHx8kJibK70xJQbeqqgo1NTV2ZcOckc4lJiaipqZGfqdLCsqFhYU444wzet1X6Vne3t6IjIxEZGSkXEasubkZarUaFoulR1MiiIiGM5cTtJKSkvDAAw/In9900014/fXX+6VTg01OTg4OHTqEQ4cO9Xh/eCLyDE9tK5uYmAgfHx+HIHvy5EkAzrfolUjn/P39ER0d7XC9tGmDJ9jW5gXa5+iqVCpoNBooFAqGWyIakfR6vZzNpJ0gbbkMuNJ2lJLNmzdj7969fdPLQS4zMxOTJk3CpEmTsGnTpoHuDtGIZLtFLgB5p67uBl7bIGq7qMx2+92SkhK324uPj4e/v7/cP2lhV0FBgdxHd0n96uw1cY0DERGwadMmOZtlZmY6nHc5RcHf399jIxBDXXZ2NtLT0wEAOp1ugHtDNPLYBtvW1lbk5+dDoVAgNTW1R4FPuker1aKkpAR1dXVyiS2gvWxYVFSUwza+HduwnSd77NgxCIIgT3eQ6uvGxsZ22R+LxYL6+npYLBaEh4ez+gERUReWL18ubzqWk5PjEHJdBtz09HTs2rULr7/+OlJTUwG0l6z5+uuv3XrwBRdc0NM+Dzrp6enIyMgY6G4QjVhSwCwoKEB+fr68q5g782Vdsd0i9+DBg3YLztra2pCbm4tRo0a51VZUVBTKy8tRU1Nj14/CwkKEhITIVQ2cba9rNBpRWloKvV4PtVoNb29vBAcHd+u1EBGNNDqdrtNBR5cBd8WKFbj55pvxu9/9Tj62c+dO7Ny5s8uHSvuzExF5QmtrK44ePYra2loAkKcSlJeXIzAwsNORVlekEVhp+129Xm83faGkpATR0dFQq9Vulw2rr6+XpyUIgoDW1lYUFBQgISEBarVabsNqtcJkMqG8vBylpaXy9rotLS0wGAxQq9Xw8vLqzZeMiGhEcxlwly1bBq1Wi/feew+FhYX46quvoNVqkZaW1p/9IyKCIAgICgpCXV2dHECtVitqa2tRWVmJqKioXs1NTUlJQXl5uUPZsFOnTmHChAlulQ0LDg5GVFQUSktL7ebQVlZWwmq1IikpCV5eXjAajWhsbER5eTkaGxsBtFdfEAQBbW1t0Ov1CA0NZckvIqJecBlwAeDyyy/H5ZdfDqD9B/CcOXNGbCUFIho4Xl5eCA8PR11dnTyKC7SPeFZWViI4OBi+vr49HsVVqVRISkqSd2CUgm5NTQ2qqqoQHh7uVtmw+Ph41NfXo7Gx0W4zhqqqKjmcq1QqtLS0yM+X5u0CgEqlQnJyMsMtEVEvuVzJ8PXXX+PEiRPy50uWLMH06dP7pVNERB2p1WpotVp5MZgUNuvr61FRUQGgdxUG4uLinO5idurUKbntrsqG+fj4ID4+3ukmDxaLBRaLRQ630kIyKdzGxsbivPPOk8uOERFRz7kMuDNmzMBTTz0lf56fn4+qqqp+6RQRUUcKhQIhISEIDw+3O242m2EwGNDQ0ACgd2XDpAW1UjvS9ruFhYVutxceHo7Y2Fi7GrtSYO64wEwURYSFhWHq1KlITU1l9QQiIg/pdIqC7T8Ue/bs6dX+6kREveXn54eIiAjU1dXBZDLJc2WlOa0BAQF2C7/cJV0bFhaGkJAQh2oIeXl50Ol08PLycmvBWWRkJLy9vXHy5Ek0NTXZXadUKuHl5QV/f3/ExcVxOgIRUR9wGXA1Gg137SKiQScoKAgREREoLi6W58q2tbWhpqYG1dXVCAsL61G7ttUQampq5M+lEH369OkuF9naBt+QkBBMmDABVVVVaGxsRHNzMwIDA+Hj4wO1Ws1SYEREfchlwB0/fjy+/PJLPPbYY/LbdqdOncKWLVvcanjx4sWe6SERDXnSu0HORj47O+eMt7c3wsPDUVtbK1chAICmpiZUVFQgKCgIKpWqx6O4Go0GoaGhqK6ututfWVkZYmJioNFo3G7bx8cHMTExcjvchYyIqH+4DLgPPvggFi5ciD/96U/ysX379mHfvn1uNcyASzSy2QY66WNzczNEUYTFYoEoivDy8oKfn59d8HMnCAYEBECr1TpUK6irq0NlZSV0Ol2Pw6QgCFCr1fIoLgC7smETJ07s8aYSDLlERP3DZcCdO3cuvv32W2zfvh0FBQXYvHkzUlJSMG3atP7sHxENMbYLq6xWK5qbm+XR1fr6ennhlkqlQltbG/z9/REQEICIiAiEh4fLAbCzMKhSqRAaGoqamhrU1NTIx23LhjmriOAOhUJhV3JMFEV5KkRdXR3Ky8sRGRnZrbadLTIjIqK+0+kiswkTJmDChAkAgM2bN2P69Omsg0tEnZJCXGNjI6qrq1FdXW1Xu1a6pq2tDaIowmg0wmg0ory8HOHh4dBqtdBqtV2GQX9/f0RGRsq7h0lhtKGhARUVFUhISOjxSKtU5stZRYbTp08jMjKSI7JERINYpwHXVlZWFiZOnNiXfSGiIUwKexaLBQaDARUVFaitrZW3rpXq13ZkO2JbVVWFqqoqmEwmREREdLp5g0KhQHBwMMLDw1FeXi4fl8qGBQcHIygoqEcjrVLlA9sSYlI7ra2tyM/PR2JiovtfHCIi6lcuiy5u2bIF+/fvlz/PysrCvHnzALQXVpeKlXf09ttv49577+1xh7Zt24YZM2YgJCQEarUaEyZMwDPPPAOz2dzjNm09+OCD8tunTzzxhEfaJBrpbENkWVkZ8vLyYDAY5JFVabqCKIp2/0kBsuNCs7y8PBw/flyeGuCKr68vtFqtHISlOrJGoxEVFRV2z3D3dQCQf75JO4/Z9g0ACgoK0NLS0q22iYio/7gMuEuXLsXf//53p+dCQkJw++23Oz332WefYd26dT3qzN13341FixZh3759mDp1KmbPno3CwkI89NBDmDVrFpqbm3vUrmT//v147rnn+JYikYdJUw6OHz+OU6dOyeEP+DX8+vv7IyEhAUlJSUhJSUFERAT8/Pzs2rAdLa2pqcHp06fl/+87Bknp88DAQGi1WgBwKBvW081p6urqALRXbBg1apTd65D+XFBQIPebiIgGF7enKNiyHXHxlO3bt2PdunUICAjAnj17kJGRAQCoqqrCrFmzsHfvXjz66KN49tlne9R+U1MTli5dCp1OhylTpmD79u0e7D0RFRYWyrWzpaoDQHsA1el0iIqKcgiDZrMZ1dXVKCgokIOs7TV6vR6iKCIpKclhkwXpo5eXF8LCwlBTUyPvZga0V2woKytDUFAQvL293ZqqIAiC3I406hwREYGWlhbk5eXJryk+Pp4b3xARDWKDZl/IJ598EgCwatUqOdwC7dtevvTSSwCADRs2yCMr3bV69WqcPHkSr7zyCoKCgnrfYSKSf9E1GAwoKSkBADkYAkBUVBTGjRsnl+2yHaGVyoRFRkbijDPOQHR0tN05qZ2qqio5OLsKqAEBAQ4Lv6SyYcXFxZ3ea6uxsRGnTp2SF8BpNBooFArExcVBEASEhobinHPOQXJyMqcnEBENYoMi4JaUlOC7774DAFx//fUO56dPn464uDiYTCbs2LGj2+3v3r0bL774IhYvXozLLrus1/0lonZSyCsqKoLFYrEbXU1ISEBaWpo8etpx9NU2cAYEBGDUqFFyyLXV2toKvV6P+vp6AM4rGyiVSoSGhjrsYmaxWFBYWIjKykr5mG3Itj3W0NCAgoICGI1GuW8hISHy3N5zzz0X48ePl+f7Sq+DiIgGn0ERcA8fPgwACA0Ndfm23+TJk+2udVdjYyNuuukmREZG4oUXXuhVP4nIUWVlpVwGzHZkVpoXa1sX1xUpMCYnJ0Or1coVF2w3iMjPzwfgOlT6+fkhOjrabjqCdO3JkydRUlIiL3qT2hFFEWazGTU1NcjNzZWDsCiKUKvVdtMqvL297V4PERENXj2ag+tpeXl5ANrntbkSFxdnd6277r//fuTl5eGDDz5ASEhIj/qXk5Pj8pxOp4NOp+tRu0TDgRRubd+yj4mJgVqtlo93RbpXpVIhLi4ObW1tMBgMDs+pqKiAVqt1GjIFQUBQUBBiYmLknxO2pb1OnTqF6upqhIWFISgoCC0tLWhpaUF9fT0qKyvlaRWSyMhIp3N3GW6JiPqfXq+Xp6t15CynDYqAKy0Mkf5BdEYqvC69TemOzz77DJs2bcK1116L+fPn97h/mZmZLs9lZWVhzZo1PW6baCiT5rlKfwbaF5hJUwV6UoNWo9EgMjISRqPRrhSX1WpFeXk5wsLCoFQqnbatVCoRHx+Puro6VFdX27UtiiIMBgMMBgOUSiXa2trkj1K/RVGEUqmETqeTf+FmoCUiGnibNm3C2rVr3b6+04B76tQpbNmypVvnTp065fbD+1JdXR1uvvlmRERE4MUXX+xVW9nZ2UhPT3d6jqO3NJKZTCY0NzfbLe5SqVRQqVQ9eitfuickJARarRaFhYV2i8YaGxthMBhc7nQmXTt69GgcO3YM9fX1drV0pY/SVIWOdXoBICwszOlcYCIiGjjLly+X92PoKCcnx2EwstOAu2/fPuzbt8/huCAILs/15B81jUYDoL04uyuNjY0A2ksOuePuu+9GcXEx3nnnHYSHh3erPx2lp6fbVXYgonZtbW1QqVRobW0F8Osorre3d49GPm1LfwUHB8NgMNj9XGhtbUVDQ4M8iuvsflEU4evri1GjRqG0tBQlJSUOC8qcVXRQKpVITk5GTExMt/tNRER9q7tTQl0G3Pj4+H57a07a8rKoqMjlNdI5d7fH/OCDD6BSqfDSSy/JZcYkx44dAwC89tpr2LVrF6KiovDPf/6z+x0nGuHa2trQ2trqMILb0tICf3//HrUptRMYGIigoCA0NTXZhVLbWrfOSD+31Go1Ro0aBT8/P1RUVKC+vt6hgoJKpYJSqYRWq0VCQoK8axkREQ1tLn+aSyuW+8PEiRMBtNfSzMvLc1pJ4eDBgwDQrZFUi8WCPXv2uDyfn5+P/Px8JCQkdLPHRAS0j9T6+fnZTVOQAi/Qs3d0pOtVKhUCAwNRXl5utwCstrYWTU1N0Gg0nbYvnYuNjYVOp0NDQwMMBoPcx8DAQKhUKgQFBdntqEZEREPfoCgTFhsbiylTpgAAtm7d6nB+7969KCoqgo+Pj9t1bGtrax32vZf+W7JkCQDg8ccfhyiK/RrmiYYTURTlRVpSoLRYLHYLvHraLgBERETIC8qAX8NvTU2N3efO2IZshUKB4OBgpKSkIDU1FWeccQZiY2MRFRXFcEtENAwNioALAA8//DAA4KmnnsKhQ4fk4waDAStWrAAA3HHHHQ67kK1evRppaWlYvXp1/3WWiAC01561LQcmBdG6ujq7mrPdJbWlVCrt5tB3DLru7CTWsQavs40eiIhoeBk0AXf+/Pm466670NjYiHPOOQdz5szBwoULkZqaip9//hnTpk3D448/7nCfXq/H8ePHXdZGI6K+5awkWHNzc6eLRt1hO6/X9hjwa7nA3ixkY/kvIqLha9AEXABYt24d3nnnHZx77rnYv38/duzYgdjYWDz11FP48ssv+VYi0SAUGBgIHx8fu2MNDQ3yKG5PSeHWtm1p1FX6WcBRWCIicmbQLRletGgRFi1a5Pb1mzdvxubNm7v1jJ7cQ0TOaTQa+Pj4wGQy2VU7qKysRGhoaKcbuHTGtmat7TFpLr3tNURERLYG1QguEQ09giAgJSVF/rNE2gLXbDYD6P5oq3S9yWRyuF8KzRzBJSIiZxhwiajXgoKCEBgY6DCyqtfrUVdX1+1yYdL1JpMJtbW1dscVCoU8bYEjuERE5AwDLhF5hO121rYBtbS0FHV1dfJxd9guJpPq6krHNBoNgoODPdt5IiIaVhhwicgj/Pz8EBMT4xBia2pqkJ+fD5PJ5NaIq22psdzcXFitVrs2tVqtXUkyIiKijhhwichjkpOT4e3tDQB2C85qa2tx/PhxNDY2OtzTMagKgoDW1laUlpaipaVFHr0VRRFqtRoRERHydURERM4w4BKRxyiVSowZM8ahbBgAVFdXIycnB3q9HhaLRT7eMagaDAb88MMPKC8vt9uUwc/PD6NHj5YDNBERkSuDrkwYEQ1toaGhiImJQWFhISwWi92uY0ajESdOnEBJSQkiIyPh6+sLX19fNDU1oa2tDZWVlXbb8AqCAKvVCi8vL8TGxsoL2Th6S0REnWHAJSKPEgQBsbGxAIDc3FynNWsbGxvtpisoFApYrVaHz6Uwm5SUhOjo6H56BURENNQx4BKRxykUCsTHx8NqtaKystJu217bObcdN25QKBQQRVEOu2FhYUhOTu7xZhFERDQyMeASUZ+Jj49HRESEvMBMCq62o7m2FRGk82q1GrGxsYiIiIBKxR9TRETUPfyXg4j6jCAIUKvVGD9+POrq6qDX61FbWwur1Wo3JUGpVMLLywsqlQoxMTEMtkRE1Cv8F4SI+ow0UqtSqRAWFoawsDA0NTXBZDKhqakJAOTyXwqFAkFBQQPZXSIiGiYYcN2Qk5Mj/1mn00Gn0w1gb4iGJmnBmL+/P/z9/RESEjLQXSIioiFKr9dDr9cDsM9pEgZcN2RmZsp/zsrKwpo1awauM0RDFEt7ERGRp2zatAlr1651eZ4B1w3Z2dlIT08HAI7eEhEREQ2w5cuXY968eQDaR3BtByMBBly3pKenIyMjY6C7QUREREToesoot+olIiIiomGFAZeIiIiIhhUGXCIiIiIaVhhwiYiIiGhYYcAlIiIiomGFAZeIiIiIhhUGXCIiIiIaVhhwiYiIiGhYYcAlIiIiomGFAZeIiIiIhhUGXCIiIiIaVhhwiYiIiGhYYcAlIiIiomGFAZeIiIiIhhUGXCIiIiIaVhhwiYiIiGhYYcAlIiIiomGFAZeIiIiIhhXVQHdgKMjJyZH/rNPpoNPpBrA3RERERCObXq+HXq8HYJ/TJAy4bsjMzJT/nJWVhTVr1gxcZ4j6mNlsRltb20B3w21KpRJeXl4D3Q0iIupHmzZtwtq1a12eZ8B1Q3Z2NtLT0wGAo7c0rJnNZhw/fhzNzc0D3RW3+fn5YcyYMR4LuYmJiSgoKAAAbNu2DQsXLnR63cUXX4wvvvgCb7zxBpYuXeqRZw9Vu3fvxsyZM3HhhRdi9+7dA90dl3rTzzfeeAMbN25ETk4OmpqaAAB5eXlITEz0fEeJqEvLly/HvHnzALSP4NoORgIMuG5JT09HRkbGQHeDqM+1tbWhubkZKpUKKtXg//FgsVjQ3NyMtra2PhnF/cMf/oD58+f3y9dixowZ2LNnD7766ivMmDGj2/dLwZyhy/M+/vhj3HTTTfD19cXFF1+MsLAwAEBAQMAA94xo5Opqyujg/xeMiPqdSqWCt7f3QHfDLRaLpU/a9ff3x4kTJ/D3v/8dt912W588Y7iYOnUqcnJy4O/vP9Bd6RPbtm0DAKxfvx6/+93vBrg3ROQOVlEgInJi5cqVAIA//vGP8lvS5Jy/vz/S0tIQHx8/0F3pE4WFhQCAUaNGDXBPiMhdDLhERE5cdtlluPDCC6HX6/H88893+/5//vOfuOiiixAaGgofHx8kJCTgpptuwokTJ+yu2717NwRBwJ49ewAAM2fOhCAI8n+bN2/u9DmbN2+GIAjyvOGkpCS7+6W5ptJzZsyYgaamJjz22GNIT0+Hv7+/3ZSGb7/9Fg8++CCmTp2KqKgoeHt7IzIyEnPnzsWuXbuc9sG2bVv5+fkQBAGJiYkQRRGvvPIKJk2aBLVajaCgIFxyySX45ptvXL625uZmPPfcczjnnHMQHBwMX19fjBkzBg8++CAMBoPL+7Zs2YIpU6bA398foaGhmD17Nv773/92+nV0ZunSpRAEAV999RUA++9Nx3nX3377LRYtWoTo6Gh4e3tDq9Vi7ty5+Pzzzztte/PmzThy5AiuueYa6HQ6KJVKLmQm8gBOUSAicuHpp5/GOeecg2eeeQa33XabPPeyM6IoYunSpdiyZQtUKhUuuOACaLVaHDp0CG+88QbeeecdvP/++5g9ezYAICoqCkuWLMGnn36K8vJyXHrppYiKipLbS01N7fR5qampWLJkCd577z0YjUZcddVVdnNDbdsCgJaWFsyYMQNHjx7FBRdcgAkTJtiFxYcffhhfffUVxo4dK4fR06dP46OPPsJHH32EF154QR7d7o5ly5Zh69atOP/883HFFVfghx9+wOeff46vv/4ae/bswdlnn213fWlpKWbPno2ff/4ZoaGhmDJlCjQaDQ4dOoS//OUv2LZtG3bv3o2EhAS7+1auXIn169dDoVBg+vTpiI6Oxk8//YQZM2bgzjvv7Fafp0+fDgBOvzfSOQB49dVXcdttt8FqtWLixImYMWMGCgoK5K/ZmjVrkJWV5fQZ+/fvx2233QadTocLLrgAzc3N0Gg03eonETliwCUicuHss8/GlVdeiX/961/405/+hL/+9a9d3rNp0yZs2bIF4eHh+Pzzz3HWWWcBaA++a9euxdq1a3HdddfhxIkTiIiIQFpaGjZv3owZM2agvLwcq1at6tYis+nTp2P69OnYvXs3jEYjnn322U4XmR04cADjx4/HqVOnHMIvANx333148803HRZvfPPNN5g9ezYeeOABLFy4EDExMW73saCgALt378aRI0cwevRoAO0LGm+99Va8/vrreOyxx7Bz5075elEUsWjRIvz888+4+eab8fzzz8uhz2KxYNWqVXjuueewbNkyfPnll/J9H3/8MdavXw+1Wo1PPvkE559/vnzuz3/+Mx5++GG3+wwAt9xyC2655ZZOvzc///wzVqxYAVEUsWXLFtx4443yuU8++QTz58/HmjVrcN555+E3v/mNwzNeffVVrFq1Cn/605+gUPBNVSJP4f9NRESdePLJJ6FSqfDSSy/J0wA68+yzzwIAHnvsMTncAoAgCMjKysL48eNRW1uLV199ta+63KUNGzY4DbcAMGfOHKcrk88991zcfvvtMJvN+Pe//93tZ7744otyuAXa6xf/6U9/AgDs2bMHZrNZPrdz507s27cPZ511Fl5++WW7EU2VSoVnnnkG48aNw1dffYUjR47I51544QUAwB133GEXbgFg9erVdt8PT1m3bh0sFgsWLFhgF26B9q/lrbfeCgD4y1/+4vT+0aNH44knnmC4JfIw/h9FRNSJMWPG4KabboLJZMKjjz7a6bXFxcU4ffo0AGDJkiUO5wVBwLJlywBAntfZ37RarUP468hgMGDLli148MEH8bvf/Q5Lly7F0qVL5XnCx48f79YzVSqVPCXDVlRUFEJCQmAymeymSXz88ccAgKuuusppiTaFQoELLrgAQPtb/ED7yO7evXsBwKEepmTx4sXd6rc7pDnOrmoh33zzzQCA//73v043UJk/fz6USqXH+0U00nGKAhFRF9asWYPs7Gy89dZbuP/++zF+/Hin15WUlAAAwsLCEBgY6PSalJQUu2v7W1c1cl999VXcc889MBqNLq+pr6/v1jN1Op3LOsWBgYGoqalBS0uLfCw3NxcA8Oijj3b5S0VlZSWA9lAutZGUlOT0WlfHe0P6PrpqW/p+t7S0wGAwQKvV2p1nzWKivsGAS0TUBZ1Oh5UrV+LPf/4zVq9eLY8wDkV+fn4uz33//fdYvnw5lEolnn76acydOxfx8fHw9/eHIAh45ZVXsHz5coii2K1ndvftd6vVCqB9frEUEF0ZO3Zst9oebDr7fhBRzzHgEhG54aGHHsIrr7yCHTt24Ouvv3Z6jbTwymAwoL6+3ukorjQ62Z1FWv1l27ZtEEURd955Jx588EGH8ydPnuyXfsTFxQEAfvvb3+L+++93656wsDD4+PjAZDIhPz/fafDNz8/3ZDcBtH8fT58+jdzcXIwbN87hvPT99vX1RWhoqMefT0TOcQ4uEZEbgoKC5FX4zsIfAMTGxsojjs7q14qiKB+fOXOm3Tlp57ie7szW2/sBoLq6GgAcSm8B7W+xv//++z1uuzvmzJkD4NfA7Q6VSoVp06YBAN566y2n17z55pue6aANqaqCq3rFr7/+OgDg/PPPHxLbXxMNFwy4RERuuv322xEfH48DBw643KBAGnF8/PHH8eOPP8rHRVHEE088gR9++AHBwcEOW77GxsYCAH755Zce9a239wNAeno6AOAf//gHGhoa5OMtLS1YsWIF8vLyetx2d/z2t7/FlClT8O2332LZsmXyPFtbNTU1ePnll+0C/d133w2gvWKDtPhM8swzz+DQoUMe7+vKlSuhUqmwfft2ZGdn25377LPPsGnTJgBweySaiDyDv04SkYPejAL2p/7up4+PD/74xz9i6dKlLrfvXb58Ofbv348333wTkydPxoUXXihv9HD8+HH4+flh69atiIiIsLvvqquuwhtvvIEHH3wQu3btglarhSAIuOmmm3Deeed12berrroKX331FTIzM3HJJZcgJCQEAPDAAw9gzJgxbr2+ZcuWYd26dTh8+DCSkpJw/vnnQ6lU4r///S+am5uxcuVKrFu3zq22ekOhUGD79u24/PLL8Y9//APvvfceJkyYgPj4eLS2tiI3Nxc///wz2trasHTpUnlkdO7cubj99tuxceNGnH/++bjgggug0+nw008/IScnp0/6f+aZZ2Ljxo34/e9/jxtvvBHPP/880tLSUFBQgP3790MURaxZswaXXHKJR59LRJ1jwCUimVKphJ+fH5qbm4dMyPXz8+vXMks33ngjnnvuOfz8889OzwuCgC1btmDOnDl45ZVX8P3338NoNCIqKgpLly7FqlWrnAbOyy+/HK+++ir+9re/4csvv5QD9PTp090KuL///e/R0NCA7Oxs7NixQ64okJmZ6XbADQ4OxsGDB5GVlYWdO3fik08+QVhYGC655BJkZWXJZbj6Q3R0NP73v/9h8+bNeOedd/DTTz/h22+/RWhoKKKjo3Hbbbdh3rx58PX1tbtvw4YNmDRpEjZu3Ij//e9/8PHxwZQpU7BhwwYA6JOAfuutt2LChAl49tlnsXfvXvz0008ICgrCZZddhpUrVzrd4IGI+pYgdnc57Ahy6NAhTJo0Cd9//z0yMjL6/nn1J3Hdjkfw/JjbkOIf3efPc0deRSMeeut7PH3DJCRpA7q+ob/Unwb23gNMfx4I7HyVdW+5Gw6GC7PZ7LRe52ClVCpdlqAiIqL+9el3b2H2d5n4dEo2Zk+5oV+e6SyvcQSXiOx4eXkxMBIR0ZDGRWZERERENKxwBHcYyj5QDqOpDWofJTLPjhzo7hARERH1KwbcYWjrgXJUNJih1Xgx4BIREdGIw4DrhpycHPnPOp0OOp1uAHszdIUUvgGFpRFWVQBq4pcNdHeIiIhoiNLr9dDr9QDsc5qEAdcNmZmZ8p+zsrKwZs2aPn1eSXUzFI2NPb7fYhXlj3kVPW8HAIqrjb2631ZI4WZ4mcph9olkwCUiIqIe27RpE9auXevyPAOuG7Kzs+Udfvpy9LasthkAsP7THCgainvcTl2zPwAF6ppa8dBb3/e6Xz4qBTS+XFVPREREg8Py5csxb948AO0juLaDkQADrlvS09P7pQ5uXZMZAHDteYmYFJ7Y43aWbz2NaqMFQf7eePr69F73S+PrhfBAn163Q0REROQJXU0ZZcAdhLSBfr3aVEGlEOSPg2pzBiIiIqJ+wIA7DF1/dqRcJswjmisBc33v27Fafv1Yf7p3bTX2fAoHERERDW8MuMOQR0uDNVcCX68A2ky9b8u7DhAAtNa1b7PbW0ofwCuw9+1Qn3vxkxw0NJuh8fPCnXN6P22GiIioMwy41DlzfXu4Pes+ICC2d239sBgwGwDvIGDq873vm1cg4BfR+3aoz2349BhKa5oRHeLHgEtERH2OAZfcExALBKb0rg2F6tePvW2LiIiIyAXFQHeAiIiIiMiTGHCJiLohMTERgiAgPz9/QJ6fn58PQRCQmJjY7XsHuu+DxYwZMyAIAnbv3t1vz1y6dCkEQcDmzZv77ZlEIxkDLhER0QDZvHkzBEHA0qVLB7orRMMK5+BSv6mJXwqFpRFWFWvzEtHA2bJlC5qamhAfH99vz/zzn/+MVatW9elumET0KwZc6jc18csGugtERP0abCVd7bpERJ7FKQpERB0cPXoUV199NcLDw+Hn54dx48bh2WefRVtbm8t7qqur8fDDD2Ps2LHw9/eHRqPBpEmT8Mwzz6C5udnh+q7emnZnrq3FYsEzzzyDsWPHws/PD+Hh4Vi0aBGOHTvW3ZcMAHjvvfcwe/ZsREREwNvbGzExMcjMzMTRo0d71J7E9rVYrVasX78e48ePh7+/P3Q6HW677TZUV1cDAEwmEx5//HGkpaXBz88P0dHRWLlyJYxGo0O7DQ0NePXVV3HllVdi1KhRUKvVUKvVOPPMM/GHP/wBtbW1TvvT2Rxci8WCl19+Geeddx6CgoLg6+uLUaNG4a677kJJSYnT9gRBgCC07yD5xhtv4Nxzz0VQUJDdfGdnc3ATExOxbFn7L/7/+Mc/5HYEQcCMGTNgtVqRnJwMQRDwzTffuPz6rlixAoIg4MEHH3R5DdFIw4BLRGRj7969mDp1Kt577z0EBQVh/vz50Ol0ePjhh3HNNdc4vSc3NxcZGRn485//jMrKSlx22WWYNWsWTp48iYceegjTp09HTU2Nx/t6zTXX4JFHHkF0dDTmz5+PoKAgbNu2DVOmTOk0EHVksVhwzTXX4Oqrr8bu3bsxevRozJ8/HxEREXjrrbcwefJkfPrppx7pc2ZmJlatWoWYmBhceumlsFqt2LRpEy6++GIYjUZcfPHFePbZZzFmzBhcfPHFaGpqwvr163H11Vc7tPXjjz/i1ltvxd69exEVFYW5c+di+vTp0Ov1ePLJJzFlyhQYDAa3+2YymTBnzhz8/ve/x+HDhzFt2jTMnz8fJpMJL774Is466ywcOnTI5f133nknbrnlFqhUKlx++eU4++yz5eDrzMKFCzFt2jQAQEpKCpYsWSL/N3v2bCgUCtxxxx0AgA0bNjhto76+Hm+++SYUCgVWrFjh9mslGu4G7RSFbdu2YePGjfjxxx/R2tqK1NRU3HDDDbjnnnvg5eXV5f1msxlff/01Pv30U+zevRsnT56E0WhEWFgYpk6diuXLl+Pyyy/vh1dCRENFS0sLrr/+ehiNRtx999149tlnoVS2b3n9008/4aKLLkJVVZXDfddffz0KCgowb948bN26FWq1GgBQWVmJ2bNn49ChQ7jjjjvw1ltveayvBQUFMBqNOHjwIMaPHw8AaGtrwz333IMXX3wR1113HY4fPw4fH58u28rKysK7776Ls88+G2+//TaSkpLkc++99x6uvfZaXH/99cjNzUVwcHCv+qxSqZCTk4OEhAQAgMFgwLnnnovDhw/j3HPPhZ+fH3JzcxEWFgYAyMvLw6RJk/DJJ59g3759ciAE2kdAd+3ahZkzZ0Kh+HW8pqmpCb///e+xZcsWPPbYY9i4caNb/cvKysKuXbuQkpKCXbt2yaPnZrMZv//97/Haa69h4cKFOHbsGLy9vR3u37JlC/bt24dzzjnHrec9++yz2Lx5M/bt24fp06c7rbBw880347HHHsN7772Hv/71r4iMtN+pcsuWLWhsbMTcuXN7VFmDaLgalCO4d999NxYtWoR9+/Zh6tSpmD17NgoLC/HQQw9h1qxZTt/u62jPnj3ySEBxcTGmT5+OK6+8EhEREfjwww9xxRVXYPny5RBFsR9eERENBe+//z6KiooQFxeHZ555Rg63ADB+/Hj84Q9/cLhn7969OHDgAPz9/fHKK6/I4RYAIiIi8MorrwAA/vnPf6K4uNij/X3kkUfkcAsASqUSf/nLXxATE4OCggK8//77XbZRXV2N559/Hr6+vnj//fftwi3QPsq4fPly1NTUIDs7u9d9Xr9+vRxuASAsLAy///3vAQBHjhzBa6+9JodbAEhKSkJmZiYA4IsvvrBrKzY2FhdddJFduAUAf39//O1vf4NKpcK2bdvc6ldLS4schJ9//nm7sOjl5YX169cjMjISeXl5eO+995y2cf/997sdbt0VFBSEJUuWoLW1Fa+++qrDeanP0kgvEbUbdAF3+/btWLduHQICAnDgwAHs3LkT77//Pk6ePIkzzzwTe/fuxaOPPtplOwqFAldddRW+/vpr6PV6fPTRR3jnnXfw888/45///CeUSiVeeeUVvPnmm/3wqohoKJDmZC5atMjpO0VLlixxec/s2bMdRtcAYNKkSZgwYQKsViv27Nnj0f4664+Pj488lcKdOq9fffUVmpubMW3aNMTExDi9ZsaMGQCA/fv397ivAKBSqXDJJZc4HB81ahSA9sVf48aNc3m+tLTUabv79+/H008/jdtvvx3Lli3D0qVLsWLFCnh7e6OystKt6SEHDx5EY2MjQkNDMXfuXIfz/v7+uPbaawG0f82cWbhwYZfP6Ym77roLgiBg06ZNsFgs8vEvvvgCx44dw5gxY/Cb3/ymT55NNFQNuikKTz75JABg1apVyMjIkI+Hh4fjpZdewvnnn48NGzbg0UcfRVBQkMt2Zs2ahVmzZjk9d8011+Dzzz/Ha6+9hi1btmDx4sWefRFENCRJI6wdRzElISEhCAoKQl1dnXxMWnjk6h6gfX7ljz/+6HKRUk8EBwe7nC4g9cWdEePc3FwA7WGps/miQPuUi97Q6XRQqRz/2QkIaC8d6Kq6gUajAdA+ymqroqICV111Ffbu3dvpc+vr6xESEtLpNe5+H22v7aivpgiMGTMGl1xyCXbu3Int27fLQVoavZUWmRHRrwZVwC0pKcF3330HoH1OW0fTp09HXFwcioqKsGPHDlx33XU9ftbEiRMBAEVFRT1ug4ior1it1l634c4ULOk5qampdvNbnUlLS+tVfzpOJeju+Y5uueUW7N27F+eeey7Wrl2LCRMmICQkRB59j46Ohl6v77epaH5+fn3W9sqVK7Fz505s3LgRCxcuRFFREf7zn/8gICCAm0QQOTGoAu7hw4cBAKGhoS5/i548eTKKiopw+PDhXgXckydPAgDrEhKRTHqL3tVWtrW1tXajt7b3SCOhzkjnbKcASIuUGhoanN5TUFDQaV9ra2tRW1vrdBRX6n9sbGynbQBAXFwcgPZRwqG0jazRaMSOHTugUCiwY8cOh6+D0WhEWVmZ2+1J35u8vDyX1zj7PvaX2bNnY/To0di9ezd++eUXbN26FW1tbbjxxhsRGBjY7/0hGuwGVcCVfrB0VoRb+mHc2Q+hrpSVlck/yK+66qour8/JyXF5jsW7iYaPCy+8EK+99hreffddPPnkkw7zcLds2eJwjzQ/9dNPP0V5ebnDPNzDhw/jhx9+gEKhwAUXXCAfl0KSq5q1H3/8cZf9ffPNN3HnnXfaHWttbcU777xj17fOXHTRRfD29sbu3btRUVEBrVbb5T2DQV1dHdra2lxO1cjOzu7WyO3kyZMREBCA6upq/Oc//8G8efPszjc3N+Of//wnAGDmzJm96rst6Rcd27m1zgiCgDvvvBN33nkn/vrXv+Kjjz4CwMVlNHLo9Xro9Xqn55zltEG1yEwaybBdhdyRNFervr6+R8+wWCzIzMxEXV0dzjzzTCxfvrzLezIzMzFp0iSn/23atKlH/SCiwWfhwoWIiYlBYWEhVq9ebTdN4MiRI3jiiScc7pk+fTrOPvtsNDc3Y/ny5WhqapLPVVVVyT9jrr32WvkXdACYOnUqAgMDcfToUYfFrtu2bcP69eu77O/jjz+OI0eOyJ9brVY89NBDKC4uRlxcnFu/wEdGRuLOO++E0WjE3Llz8fPPPztcYzKZ8J///KfHG0j0hcjISISEhKC2ttbh6/e///0Pq1ev7lZ7vr6+uP322wEA9913n90IutlsxsqVK1FWVoakpCSPLiaTRtnd2Uxj6dKlCAoKwuuvv46KigrMnDkTZ5xxhsf6QjSYbdq0yWUWkyqt2BpUI7j94bbbbsMXX3yBsLAwvPfee05rGXaUnZ2N9PR0p+c4eks0fPj5+eGtt97CZZddhueeew7bt2+XNwvYvXs35s6di++//95h+sDWrVsxa9Ys/Pvf/0ZSUhIuuOACmM1mfPXVV6ivr0dGRoZDoX4/Pz+sXbsW99xzDxYvXoy//e1viImJQU5ODo4ePYpHHnkEjz/+uMu+xsfHY9KkScjIyMCMGTMQFhaG7777DqdPn4ZarcbWrVvh6+vr1ut+6qmnoNfrsXXrVpx11lmYMGECkpOToVKpUFxcjB9++AFGoxGffPJJr+fheopSqcRjjz0mf/02btyI5ORkFBYWYv/+/cjMzMTXX3/d5VQPW2vXrsXBgwfxxRdfID09HTNnzoRGo8E333yDwsJChIWFYdu2bW79u+Guc845B9HR0Th8+DAyMjJw5plnwsvLC2PGjMEDDzxgd21AQACWLVuGF154AQBHb2lkWb58ucM7K5KcnByHkDuoAq60UtbZloySxsZGAOjRnKOVK1fitddeQ0hICD7//HOMHj3arfvS09PtKjoQ0fB14YUX4sCBA8jKysLu3bvxwQcfIDk5GX/84x9x//33IzU11eGe5ORkHDp0CM8++yy2b9+Ojz76CAqFAmPGjME111yDu+66y+kCpLvvvhuhoaFYt24dDh8+jF9++QWTJ0/GCy+8gNTU1E4DriAIePfdd/+vvXuPiqre/z/+HAVJuSkiSoLK0lI0wwtaK7RIK9FleO16SDA9Wa2OoRmZ5REzy9RMRRPynpcy89jR1MIsNLKLlzQV0DRJ9IuJKEhe4TC/P/wxiYCNyMwextdjrb0YP5+9P/sFo+Pb7Wd/NpMnT2bJkiVs2bIFd3d3BgwYwBtvvHFdV/ZcXFxYtmwZUVFRzJs3jx9//JG9e/fi7u6Ov78/Dz/8MJGRkaWmWDiC2NhYgoKCmDx5Mmlpaezbt49WrVoxe/Zsnn322WuuiACUWXnAzc2NL774grlz5/Lhhx/y7bffcvHiRQIDA/nXv/7FK6+8UuXzb2vVqsWXX37Ja6+9xvfff8/u3bspLi7mvvvuK1PgAvTo0YPp06cTGBhInz59qjSLiCO73imhDlXgliyxcq2VDUr6rnc5lpdeeomZM2dSt25dkpOTLasoiIhc7Y477qjwIQkV3YDm4+PDW2+9ZVnq0FqDBg2qcKnC8uaQNmvWrFT7mDFjGDNmjFXnqih7iZ49e9KzZ0+rxroeV2e+Wnh4+DX7Y2JiKlwpoE+fPhUWehV9vyXTSOrUqVOmz8XFheeee87y8AlrWDPXd9GiRRXexHfHHXfw3//+16pzlTxs47nnniv1IBIRKc2h5uCWFJ25ubkV3kS2fft2gOu6ohoXF8e0adPw9vYmOTmZ0NDQGw8rIiLVTn5+PhkZGdSoUcPyAInqYs+ePaxYsQIPDw+r7h8RuZk5VIEbEBBAp06dgMtz2q6WmppKVlYWbm5u9OrVy6oxR48ezZQpU/D29mbjxo2W8UVE5OaRkZHB448/zh133EFBQQF9+/at8EEZjmbo0KE88cQTdO3alaKiIl5//XV8fHyMjiXi0BxqigJc/u+2fv36MWnSJHr27Gm5Upubm8vzzz8PXJ5Yf/VTzF599VVWr15Nv379ePvtt4HLz2l/5513LNMSVNyKGOOFiFYUnC/Es3bZx99K9ZKamsq8efOs3n/q1Kn4+vraMJF1jh8/zieffIKfnx9Dhw5l6tSpRkey2vz586lRowaBgYGMGjWKuLg4oyOJODyHK3D79u3L8OHDmTlzJnfffTfdu3fH3d2dTZs2kZeXR1hYWLk3XmRnZ7N//37LGmlr1qxh4sSJwOUn9JQ80vBqvr6+1eqDTqQ6+lfP8lchkern4MGDLF682Or94+PjHaLADQ8Pr5KnwxnBXk9iE3EmDlfgAsyYMYOwsDBmz57N1q1bKSwspHnz5owePZoRI0ZYtUTLqVOnLK+3b99umbt7taZNm6rAFRGx0rVu+BIRcRQOWeACPProozz66KNW73/1Har6EBYRERG5OTnUTWYiIiIiIjdKBa6IiIiIOBUVuCIiIiLiVFTgioiIiIhTUYErIiIiIk5FBa6IiIiIOBWHXSZMRJzHtMxPOVN0Di+XOoxsNtDoOCIi4uRU4IqIzU37/T8cu3iSxm6+KnBFRMTmNEVBRERERJyKClwRkSs0a9YMk8lEZmampS08PByTyURKSophuURExHoqcEVERETEqWgOrhXS09Mtr/39/fH39zcwjYiIiMjNLTs7m+zsbKB0nVZCBa4VoqKiLK/HjRtHfHy8cWFEREREbnJJSUmMHz++wn5NUbDC0qVL2bFjBzt27GDYsGFGxxERA+3atYv+/fvj6+uLm5sbrVu35t1338VsNld4zNdff80jjzxCQEAAbm5uNGjQgE6dOjFu3Dhyc3PtmF5ExDkMGzbMUpstXbq0TL+u4FohODiYDh06GB1DRAz25ZdfMm3aNJo3b86DDz5IdnY2qampjBo1iqysLKZPn17mmOHDh5OQkABAu3bt6Nq1K/n5+ezfv5833niD+++/n/DwcPt+IyIi1dzfTRlVgSsiYqVJkyaRmJhY6n9yvv76ax544AFmzZrFqFGjCAgIsPQlJCSQkJBA/fr1WblyJffff3+p8X766SfN6RcRsQFNURARsVL//v3LTFPq1q0bPXr04H//+x/ffPONpb2oqIgJEyYA8MEHH5QpbgE6d+5MYGCgbUOLiNyEVOCKiFjp4YcfLrc9ODgYgGPHjlnaduzYQU5ODr6+vvTr188u+URE5DIVuCIiVmrSpEm57V5eXgBcuHDB0vb7778D0LJlS0wmk+3DiYiIhQpcEREr1aihj0wRkepAn9YiIjZQcrX3wIED11xCTEREqp4KXBERGwgNDcXX15ecnBw+++wzo+OIiNxUVOCKiNiAi4sLr732GgDPPPMMW7ZsKbPPtm3bOHr0qL2jiYg4Pa2DKyJiIy+++CL79+8nMTGR++67j/bt29OyZUvOnDlDRkYGv/32G998802ptXNFROTGqcAVEbERk8nEnDlz6NOnD4mJifzwww/s3buXunXrEhQURHR0NHfeeafRMUVEnI4KXBGRK2RmZpZpS0lJueYx8fHxxMfHV9gfERFBRETEjQUTERGraQ6uiIiIiDgVFbgiIiIi4lQ0RUFEbG5k0/6cKTqHl0sdo6OIiMhNQAWuiNjcyGYDjY4gIiI3EU1REBERERGnogJXRERERJyKClwRERERcSoqcEVERETEqegmMyukp6dbXvv7++Pv729gGhEREZGbW3Z2NtnZ2UDpOq2EClwrREVFWV6PGzfumk8sEhERERHbSkpKYvz48RX2q8C1wtKlSwkODgbQ1VsRERERgw0bNozIyEjg8hXcKy9GggpcqwQHB9OhQwejY4hUX7umwaUzUMsL2o00Oo2IiFRzfzdlVAWuiNje7mlw9hi4N1aBKyIiNqcC1wnVO7KQGkV/Uuziwekmg42OIyIiImJXWibMCdU7sgjfw7Opd2SR0VFEpIqsXbuWrl274uXlhclkwmQykZKSYnQsERGHpCu4Dsb3f5fwOH8UztzAIMVFf309c+jGAv159MaOF5EbtmvXLgYMGEBxcTHdunXD398fk8lEo0aN7JYhMzOToKAgmjZtSmZmpt3OKyJSGSpwHUidC8eZk5OB24k0+PUGBqqVDybgUj6kjrjxYDXdwNXrxscRkUr57LPPKCwsZMyYMUycONHoOCIiDk8FrgNxK8zDzVxMRmAUrZp2rPxAuwZBYS7U8obO7914MFcvqN3gxscRkUo5cuQIALfddpvBSUREqgfNwXVA525pCF7NK7/V+P//bqnhcmPjlGwqbuUmUjK/FWDu3Ll07NgRd3d36tatS69evfjhhx8qPLaoqIh58+YRHh6Oj48Pbm5uBAUF8dxzz5GVlVVm/5SUFEwmE+Hh4Zw7d45///vfBAcHU6dOHZo1a0Z8fDwmk4mFCxcCMHjwYEu+8PDwUmOdPn2acePG0a5dOzw9PalTpw5t27blzTff5Ny5cxVm3rFjB9HR0QQFBXHLLbfg4+NDSEgIL7/8Mr///jsAMTExBAUFAfD7779bMlz5sxIRcSS6guuETjeJsayiICKVM3LkSKZPn05YWBh9+vRhz549bNiwgY0bN/LJJ5/Qr1+/UvsXFBQQGRlJSkoKHh4edOzYkQYNGrBnzx4SExNZuXIlGzdupH379mXOdeHCBcLDw0lLS+Pee+8lJCSE3Nxc2rVrR3R0NKmpqRw6dIiwsDBatGgBQKtWrSzHp6WlERERQVZWFv7+/nTp0gVXV1d++uknxo4dy6pVq0hJScHb27vUeadMmcLo0aMpLi7m9ttvp0+fPpw/f56DBw8ydepU2rRpQ0xMDF26dOHPP/9k1apVuLu7M3DgQBv8xEVEqo4KXCekpcFEblxiYiJfffUV3bp1s7RNmTKFuLg4Bg8eTFhYGH5+fpa+Z599lpSUFHr37s38+fNL9U2fPp0RI0bw2GOPkZ6eTs2aNUud68cff+TOO+/k4MGDZW4c69u3LzExMRw6dIihQ4cSExNTqv/8+fNERkaSlZXF66+/ztixY6lVqxYA586dY+jQoXz00UeMGDGCBQsWWI5bs2YNcXFx3HLLLSxevJhHH3201LhpaWmWq7NDhw7lgQceYNWqVfj6+rJo0aLr/4GKiNiRpiiIiJRj2LBhpYpbgJdffpnQ0FDy8/OZN2+epT09PZ2PPvqIW2+9leXLl5cqbgFiY2Pp1asXv/76Kxs2bCj3fLNmzarUqgiLFy/m0KFD9O7dmwkTJliKW4A6derwwQcf4Ofnx5IlSzh9+rSlb9y4cQBMnDixTHEL0Lp1a8sjykVEqhsVuCIi5YiOji63fdCgQQCl1qBdv349ZrOZnj174unpWe5xJXNmt27dWqbPz8+Prl27VirnunXrAHjsscfK7ffw8CA0NJSioiK2bdsGwPHjx9m1axc1atRgyJAhlTqviIgj0xQFEZFylNxUVVH70aN/rRH922+/ATB//nzmz59/zXFzcnLKtDVr1qySKf8691NPPcVTTz1l1blLVmXw9/cvMy9XRMQZqMAVEakEs9lseV1cXAxAu3btCAkJueZxd911V5m22rVrVzpHybkjIiJo2LDhNfdt2rRppc8jIlKdqMAVESnH4cOHadeuXZn2kqd4BQQEWNoCAwMBCAsLY9asWfaIV+rcGRkZDBkyxOrVDZo0aQJAdnY2+fn5uoorIk5Hc3BFRMqxZMmSa7ZfuQ5tz549gcsrE1y4cMHm2a5Ucu5PPvnE6mMaNWpESEgIxcXFpVZWuJaSm9eKioquP6SIiJ2pwBURKcecOXNK3UgG8N577/HTTz/h6elZ6uas9u3bM2DAALKysujfv7/lKu+Vzp49y7Jly/jjjz+qNOczzzxD06ZNWblyJa+88goFBQVl9jl+/Dhz584t1VayisJrr73GqlWryhyTlpZGenq65dcNGjSgVq1aHD9+nFOnTlXp9yAiUtU0RUFEpBwly4R17dqVxo0bs3fvXvbs2UPNmjVZsGBBmSW9Fi5cSF5eHhs2bKBly5aEhIQQFBSE2WwmMzOT3bt3c+nSJdLT0/92ruz1cHd3Z926dfTu3ZvJkyfzwQcfcOeddxIQEMC5c+c4cOAA6enp+Pn58c9//tNyXL9+/Zg4cSKvv/46AwcOpFWrVoSEhFge9JCWlsbChQstS4W5uroSGRnJp59+Srt27ejSpQt16tQBKLVkmoiII1CBKyJSjvfee4+WLVuSlJTEtm3bcHV1JSIigrFjx3LPPfeU2d/T05Pk5GRWrFjB0qVL2bFjB7t27cLLywt/f3/+8Y9/EBkZSfPmzas8a5s2bfjll19ITExk9erV/PLLL3z//ff4+voSEBDAqFGjyjx5DWDMmDF069aNmTNnsmXLFv7zn//g6elJYGAgcXFxZdYBTkpKon79+mzYsIFPP/2UwsJCQAWuiDgek/nKW4GllJ07d9KxY0eWLl1quYrh7++Pv7+/Tc73xbZlBKW+wc7bXqLDbffZ5Bxy/Vq2bGl0hOpvcQCcPQbujSH66N/vb6CSp3fpo1FE5Pp9sW0ZEdui+KLTUiI6/cNm58nOziY7Oxu4/LCdqKgoduzYQYcOHQBdwbVKVFSU5fW4ceOIj483LoyIiIjITS4pKYnx48dX2K8C1wpXX8EVEREREeMMGzaMyMhI4K8ruFdSgWuF4OBgyyVvEamEkJFw6QzU8jI6iYiIOIG/mzKqAldEbK/dSKMTWE1zb0VEqj+tgysiIiIiTkUFroiIiIg4FRW4IiIiIuJUVOCKiIiIiFNRgSsiIiIiTkUFroiIiIg4FRW4IiIiIuJUVOCKiIiIiFNRgSsiIiIiTkUFroiIiIg4FRW4IiIiIuJUHK7AXblyJeHh4dSrVw93d3dCQkKYPHkyhYWFDjWmLZw6eZqEL05y+lS+0VGkErKzs4mPjyc7O9voKFJJeg+rP72H1Z/ew+rt1MnTxK+7/NVIDlXgxsbG8uijj/Ldd9/RuXNnIiIiOHLkCK+88grdunXj/PnzDjGmrZw6mcfs5FPkqcCtlrKzsxk/frw+lKsxvYfVn97D6k/vYfV26mQe49df/mokhylwP/vsM2bMmIGHhwc//vgjX375JatWreLXX3+lbdu2pKamMnbsWMPHFBERERHH5jAF7ltvvQXA6NGj6dChg6Xd19eX999/H4BZs2aRn2/91U1bjCkiIiIijs0hCtxjx46xbds2AJ588sky/V26dCEwMJCLFy+yfv16w8aE6jk36MSJEyQkJHDixIlqM3Z1zGxLtvx9Z6uxq2NmW9LP2fbj2lJ1/Dnbcmy9h/YZuzpmtqXryewQBe7PP/8MgI+PD0FBQeXuExoaWmpfI8aE6jk3KCcnh9mzZ5OTk1Ntxq6OmW3Jlr/vbDV2dcxsS/o5235cW6qOP2dbjq330D5jV8fMtnQ9mR2iwD18+DAATZo0qXCfwMDAUvsaMaaIiIiIOD4XowMAFBQUAODu7l7hPh4eHgCcOXPGbmOWrLCwfv160tPTgb+K4ZI2X19fGjRoYFWmv5OV+X8AHMvKZt++fVUyJsBvv/1W6mtVstXYjpT57NmzVu1X8nuk5GtVqo5jK7N9xlZm+4xdHTPbcmxlts/Y1TFzSS2Tlfl/7Ny5s8rGzcnJsUw9vbIug79qs1IrY5kdwMSJE82AOSwsrMJ9xowZYwbMDz30kN3GXLp0qRnQpk2bNm3atGnT5uDb0qVLLTWcQ1zB9fT0BK59pezPP/8EwMvLy25j9ujRg4SEBOrUqYObm1u5+1TlFVwRERERKSsnJ4eTJ0+W23fx4kXOnTtHjx49LG0OUeA2a9YMgKysrAr3Kekr2dceY/r6+vLCCy9YdT4RERERcQwOcZNZ+/btAcjNza3whq/t27cDlFrP1t5jioiIiIjjc4gCNyAggE6dOgGwfPnyMv2pqalkZWXh5uZGr169DBtTRERERByfQxS4AGPGjAFg0qRJpe66y83N5fnnnwfghRdewNvbu9Rxr776Kq1ateLVV1+tsjHtbeXKlYSHh1OvXj3c3d0JCQlh8uTJFBYWGppL/t7+/ftJSEggJiaGtm3b4uLigslk4s033zQ6mlihsLCQTZs28fLLL9OpUyfq1q2Lq6srjRo1IjIyknXr1hkdUaywbNkyBg0aREhICH5+fri6uuLt7U3nzp15++23LfdbSPURFxeHyWTS52k1ERMTY3m/KtouXLhg10wOMQcXoG/fvgwfPpyZM2dy99130717d9zd3dm0aRN5eXmEhYUxYcKEMsdlZ2ezf//+chf9reyY9hQbG8uMGTNwcXGhW7dueHh48PXXX/PKK6+wdu1akpOTqV27tqEZpWJz5sxhxowZRseQStq8eTMPPvggAI0aNaJLly64u7uTlpbG2rVrWbt2Lc888wyJiYmYTCaD00pF5syZw9atWwkODqZDhw74+Pjwxx9/8P3337Nt2zYWLFjA5s2bufXWW42OKlbYunUr7777LiaTCbPZbHQcuQ5hYWG0aNGi3L6aNWvaN4xVa27Z0YoVK8z33nuv2cvLy1y7dm3zHXfcYZ40aZL54sWL5e4fHR1tBszR0dFVNqa9rF692gyYPTw8zDt27LC05+TkmNu2bWsGzC+99JKBCeXvzJ071zxq1CjzsmXLzOnp6eannnrKDJgnTJhgdDSxwqZNm8wDBgwwb9mypUzfxx9/bK5Zs6YZMC9evNiAdGKtH374wZybm1um/eTJk+YuXbqYAfPjjz9uQDK5XmfPnjXfdttt5saNG5v79u2rz9NqoqQWW7hwodFRLByuwL2ZdOrUyQyY33zzzTJ93377rRkwu7m5mfPy8gxIJ5VR8odcH8jOYciQIWbA3L17d6OjSCVt2bLFDJh9fHyMjiJWGD58uBkwr1u3Tp+n1YgjFrgOMwf3ZnPs2DHLEzmefPLJMv1dunQhMDCQixcvsn79envHExH+Wo3lWssNimNzcbk8E6+itczFcaSkpJCQkMCgQYN087fcMIeZg3uz+fnnnwHw8fEhKCio3H1CQ0PJysri559/5oknnrBnPBEBfv31VwD8/f0NTiKVUVBQQHx8PACRkZHGhpFr+vPPP3n66adp2LAh06dPNzqOVNI333zDnj17KCgooH79+nTu3JlevXoZ8g9MFbgGKVmbt0mTJhXuExgYWGpfEbGf48ePs2jRIgAGDBhgbBixSnJyMsuXL6e4uNhyk1lBQQERERG88847RseTaxg1ahSHDx9m9erV1KtXz+g4UkkffvhhmTZ/f38WLFhARESEXbNoioJBCgoKAHB3d69wHw8PDwDOnDljl0wicllRURFRUVHk5+fTtm1bhg0bZnQksUJaWhqLFy9myZIlJCcnU1BQwJNPPsmiRYsMXw5SKpacnExSUhKPP/44ffv2NTqOVEJISAgzZsxg7969nDlzhj/++IPk5GTuuecesrOziYyMJCUlxa6ZVOCKiFzl2WefZdOmTdSvX59PP/2UWrVqGR1JrBAbG4vZbObSpUscPHiQd999lw0bNtC6dWu2bNlidDwpR35+PkOGDKFBgwYkJCQYHUcqacSIEQwfPpw2bdrg6emJn58fDz74IKmpqfTp04fCwkJiY2PtmkkFrkE8PT0BOHv2bIX7lCxO7uXlZZdMIgIvvvgi8+fPp169emzcuJHbb7/d6EhynVxdXWnevDkjR45kw4YNnD59mqioKM6fP290NLlKbGwsR48eZdasWfj6+hodR6qYyWRi/PjxAOzevduuN+xqDq5BmjVrBlz77uySvpJ9RcS2XnrpJWbOnEndunVJTk62rKIg1dddd91F69at2bdvH9u3b6dr165GR5IrrF69GhcXF95//33ef//9Un0ZGRkAzJ8/n6+++opGjRrx8ccfGxFTbkBwcLDl9dGjRy33F9maClyDlPzFmZuby+HDh8tdSWH79u0AdOjQwa7ZRG5GcXFxTJs2DW9vb5KTkwkNDTU6klSRknsdTpw4YXASKU9RURGbN2+usD8zM5PMzEyaNm1qx1RSVXJzcy2vS/732h40RcEgAQEBdOrUCYDly5eX6U9NTSUrKws3NzetByhiY6NHj2bKlCl4e3uzceNGy59Nqf5OnjzJ7t27ATTdxAHl5eVhvvzQqTJbdHQ0ABMmTMBsNpOZmWlsWKmUkqvuXl5etGzZ0m7nVYFroDFjxgAwadIkdu7caWnPzc3l+eefB+CFF17Q3b8iNvT666/zzjvvULduXRW31VBaWhrLli3jwoULZfoOHDjAI488wsWLF7n77rtp27atAQlFnNuuXbtYs2YNRUVFpdqLi4uZP3++pdYZPnw4rq6udsulKQoG6tu3L8OHD2fmzJncfffddO/eHXd3dzZt2kReXh5hYWFMmDDB6JhyDTt37rT8YwTg0KFDACQlJfH5559b2levXq2HBTigNWvWMHHiRABatGjB7Nmzy93P19eXqVOn2jOaWOnEiRNERUUxbNgw2rdvT0BAAJcuXeLIkSPs3LmT4uJigoODWbFihdFRRZxSZmYm/fr1o169enTo0IGGDRuSl5fH3r17OXLkCABPPPEE48aNs2suFbgGmzFjBmFhYcyePZutW7dSWFhI8+bNGT16NCNGjNDyRA7uzJkz/Pjjj2Xajx49ytGjRy2/vnjxoj1jiZVOnTpleb19+3bLvPerNW3aVAWug2rTpg0TJ07k22+/JSMjg59//pnCwkJ8fHzo3r07/fv3Z/DgwXpUr4iNhISEEBsby/bt28nIyOC7777DbDbTsGFDBg4cyODBgw2Zamkym81mu59VRERERMRGNAdXRERERJyKClwRERERcSoqcEVERETEqajAFRERERGnogJXRERERJyKClwRERERcSoqcEVERETEqajAFRERERGnogJXRERERJyKClwRERERcSoqcEVEbnLh4eGYTCZSUlKMjiIiUiVU4IqIiIiIU1GBKyIiIiJORQWuiIiTMJlMmEwmAObOnUvHjh1xd3enbt269OrVix9++KHU/ikpKZhMJjZv3gzA/fffbxnDZDKxaNEie38LIiJVwsXoACIiUrVGjhzJ9OnTCQsLo0+fPuzZs4cNGzawceNGPvnkE/r16wdAo0aNiI6O5osvvuCPP/6gR48eNGrUyDJOixYtjPoWRERuiMlsNpuNDiEiIjeu5Opt7dq1+fzzz+nWrZulb8qUKcTFxeHt7c2BAwfw8/Oz9IWHh7N582a++eYbwsPD7R1bRKTKaYqCiIiTGTZsWKniFuDll18mNDSU/Px85s2bZ1AyERH7UIErIuJkoqOjy20fNGgQgJYDExGnpwJXRMTJBAUFXbP96NGj9owjImJ3KnBFRG4yuvVCRJydClwRESdz+PDhctszMzMBCAgIsGMaERH7U4ErIuJklixZcs32q1dKqFWrFgBFRUU2zSUiYi8qcEVEnMycOXPK3Ej23nvv8dNPP+Hp6cmQIUNK9ZVc0d23b5+9IoqI2JTWwRURcRIl6+DGxsYyY8YMunbtSuPGjdm7dy979uyhZs2afPzxxwwcOLDUcevWraN3797UqlWLhx56CD8/P0wmE08//TT33HOPEd+KiMgNUYErIuIkSgpcs9lMYmIiSUlJ7N+/H1dXV+655x7Gjh1bYcE6b9485syZQ0ZGBufOnQNg4cKFxMTE2Cu+iEiVUYErIuIkrixwRURuZpqDKyIiIiJORQWuiIiIiDgVFbgiIiIi4lRcjA4gIiJVQ3NvRUQu0xVcEREREXEqKnBFRERExKmowBURERERp/L/ALNobSh7/l6WAAAAAElFTkSuQmCC" 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "p = PerformancePlot(df=vs_pt, xs=pts, watermark=\"outdated\", model=chkpt_path.stem)\n", + "p = PerformancePlot(df=vs_eta, xs=etas, var=vm[\"eta\"].latex)\n", "p.plot_var(\"double_majority\", \"C0\")\n", "p.plot_var(\"lhc\", \"C1\")\n", "p.plot_var(\"perfect\", \"C2\")\n", - "p.add_blocked(0, 0.9)\n", - "p.ax.legend()" + "p.ax.legend()\n", + "p.add_legend()\n", + "p.ax.get_figure().savefig(Path.home() / \"paperresults/vs_eta.pdf\")" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T23:03:32.523931Z", + "start_time": "2023-10-05T23:03:31.846236Z" + } } }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 15, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_245785/1904660633.py:46: UserWarning: The label '_ignore' of (, ) starts with '_'. It is thus excluded from the legend.\n", + " self.ax.legend(all_handles, all_labels, **kwargs)\n" + ] + }, { "data": { - "text/plain": "" + "text/plain": "
", + "image/png": 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" }, - "execution_count": 159, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + } + ], + "source": [ + "p = PerformancePlot(\n", + " df=vs_eta,\n", + " df_ul=None,\n", + " xs=etas,\n", + " var=vm[\"eta\"].latex,\n", + " y_label=r\"$f^{\\,\\,\\mathrm{DM}}_{p_T>0.9}$\",\n", + ")\n", + "p.plot_var(\"fake_double_majority\", \"C0\", label=\"_ignore\")\n", + "p.add_legend()\n", + "p.ax.get_figure().savefig(Path.home() / \"paperresults/fake_vs_eta.pdf\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T23:03:35.138165Z", + "start_time": "2023-10-05T23:03:34.628122Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_245785/1904660633.py:46: UserWarning: The label '_' of (, ) starts with '_'. It is thus excluded from the legend.\n", + " self.ax.legend(all_handles, all_labels, **kwargs)\n" + ] }, { "data": { - "text/plain": "
", - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -616,13 +881,40 @@ ], "source": [ "p = PerformancePlot(\n", - " df=vs_eta, xs=etas, var=\"eta\", watermark=\"outdated\", model=chkpt_path.stem\n", + " df=vs_pt,\n", + " df_ul=None,\n", + " xs=pts,\n", + " var=vm[\"pt\"].latex + \" [GeV]\",\n", + " y_label=r\"$f^{\\,\\,\\mathrm{DM}}$\",\n", ")\n", - "p.plot_var(\"double_majority\", \"C0\")\n", - "p.plot_var(\"lhc\", \"C1\")\n", - "p.plot_var(\"perfect\", \"C2\")\n", - "p.ax.legend()" + "p.add_blocked(0, 0.9)\n", + "# p.ax.set_ylim(0., 0.05)\n", + "p.plot_var(\"fake_double_majority\", \"C0\", label=\"_\")\n", + "p.add_legend()\n", + "\n", + "left, bottom, width, height = [0.45, 0.25, 0.4, 0.5]\n", + "ax2 = p.ax.get_figure().add_axes([left, bottom, width, height])\n", + "p = PerformancePlot(df=vs_pt, df_ul=None, xs=pts, var=\"\", y_label=\"\", ax=ax2)\n", + "p.add_blocked(0, 0.9)\n", + "p.ax.set_xlim(0.75, 5.8)\n", + "p.ax.set_ylim(0.0, 0.03)\n", + "p.plot_var(\"fake_double_majority\", \"C0\", label=\"_\")\n", + "\n", + "p.ax.get_figure().savefig(Path.home() / \"paperresults/fake_vs_eta.pdf\")" ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-10-05T23:04:07.385077Z", + "start_time": "2023-10-05T23:04:06.486303Z" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], "metadata": { "collapsed": false } diff --git a/src/ocpaper231/data.py b/src/ocpaper231/data.py index 00166f4..36012ad 100644 --- a/src/ocpaper231/data.py +++ b/src/ocpaper231/data.py @@ -8,15 +8,16 @@ def get_dm(*, n_val=5, setup=True) -> TrackingDataModule: """Get default tracking data module""" dm = TrackingDataModule( + identifier="point_clouds_v8", train={ "dirs": [ - "/scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v6/part_1/" + "/scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v8/part_1/" ], # If you run into memory issues, reduce this }, val={ "dirs": [ - "/scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v6/part_9/" + "/scratch/gpfs/IOJALVO/gnn-tracking/object_condensation/point_clouds_v8/part_9/" ], "stop": n_val, },