From 44f56321b5f9f4af9d01f605982da1150f667b71 Mon Sep 17 00:00:00 2001 From: moralejo Date: Sun, 27 Oct 2024 22:17:46 +0000 Subject: [PATCH 01/16] Notebook for quick simulation of LST1 observations The telescope performance is stored in the csv files The background is obtained from good-quality real data, the gamma IRF from MC --- ...LST1_backg_irf_gheffi_0.40_theffi_0.40.csv | 191 +++ ...LST1_backg_irf_gheffi_0.70_theffi_0.70.csv | 191 +++ ...LST1_backg_irf_gheffi_0.90_theffi_0.90.csv | 191 +++ ...LST1_gamma_irf_gheffi_0.40_theffi_0.40.csv | 351 +++++ ...LST1_gamma_irf_gheffi_0.70_theffi_0.70.csv | 351 +++++ ...LST1_gamma_irf_gheffi_0.90_theffi_0.90.csv | 351 +++++ notebooks/LST1_observation_simulator.ipynb | 1159 +++++++++++++++++ 7 files changed, 2785 insertions(+) create mode 100644 lstchain/data/LST1_backg_irf_gheffi_0.40_theffi_0.40.csv create mode 100644 lstchain/data/LST1_backg_irf_gheffi_0.70_theffi_0.70.csv create mode 100644 lstchain/data/LST1_backg_irf_gheffi_0.90_theffi_0.90.csv create mode 100644 lstchain/data/LST1_gamma_irf_gheffi_0.40_theffi_0.40.csv create mode 100644 lstchain/data/LST1_gamma_irf_gheffi_0.70_theffi_0.70.csv create mode 100644 lstchain/data/LST1_gamma_irf_gheffi_0.90_theffi_0.90.csv create mode 100644 notebooks/LST1_observation_simulator.ipynb diff --git a/lstchain/data/LST1_backg_irf_gheffi_0.40_theffi_0.40.csv b/lstchain/data/LST1_backg_irf_gheffi_0.40_theffi_0.40.csv new file mode 100644 index 000000000..ac81aa50f --- /dev/null +++ b/lstchain/data/LST1_backg_irf_gheffi_0.40_theffi_0.40.csv @@ -0,0 +1,191 @@ +ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_cut +6.0,0.012589254117941675,0.0199526231496888,0.08662540530202503,0.32,0.14980508270919676 +6.0,0.0199526231496888,0.03162277660168379,3.654012468122838,0.25416790232400216,0.2232647189688829 +6.0,0.03162277660168379,0.05011872336272725,4.506734624017561,0.22836642194149148,0.24282859738626 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np\n", + "import astropy.units as u\n", + "import matplotlib.pyplot as plt\n", + "from pyirf.spectral import CRAB_MAGIC_JHEAP2015, PowerLaw, LogParabola\n", + "from pyirf.statistics import li_ma_significance\n", + "from scipy.stats import moyal\n", + "from gammapy.modeling.models import EBLAbsorptionNormSpectralModel" + ] + }, + { + "cell_type": "markdown", + "id": "c30c3f8d", + "metadata": {}, + "source": [ + "## Set cut efficiency" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ef3576a4", + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# Cut efficiency: the same for the gammaness cut and the theta cut\n", + "# Options: 0.4, 0.7, 0.9\n", + "#\n", + "# Recommended: \n", + "# 0.7: standard cuts (safer for spectral analysis)\n", + "# 0.4: tight cuts, better for detection of weak sources\n", + "#\n", + "cut_efficiency = 0.7" + ] + }, + { + "cell_type": "markdown", + "id": "5dd2e6fd", + "metadata": {}, + "source": [ + "## Load files which contain the instrument response function and show table contents\n", + "They characterize the average performance of LST1 within 1 degree off-axis (computed from diffuse gamma MC and\n", + "real data for the cosmic ray rates)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0844c0a4", + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# Load files for the requested efficiency:\n", + "#\n", + "datadir = os.environ['LSTCHAIN'] + '/data/'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c59f8666", + "metadata": {}, + "outputs": [], + "source": [ + "input_filename = datadir + f'LST1_gamma_irf_gheffi_{cut_efficiency:.2f}_theffi_{cut_efficiency:.2f}.csv'\n", + "gamma_data = pd.read_csv(input_filename)\n", + "\n", + "input_filename = datadir + f'LST1_backg_irf_gheffi_{cut_efficiency:.2f}_theffi_{cut_efficiency:.2f}.csv'\n", + "background_data = pd.read_csv(input_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ad3a988e", + 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ZD_degEtrue_min_TeVEtrue_max_TeVAeff_m2emig_mu_locemig_mu_scaleemig_model
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36.000.0223870.0281848.858634e+031.2126840.190464moyal
46.000.0281840.0354811.299283e+041.0507730.184902moyal
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ZD_degEreco_min_TeVEreco_max_TeVBckgRate_per_secondTheta_cut_degGammaness_cut
06.000.0125890.0199530.1587530.3200000.112810
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190 rows × 6 columns

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" + ], + "text/plain": [ + " ZD_deg Ereco_min_TeV Ereco_max_TeV BckgRate_per_second Theta_cut_deg \\\n", + "0 6.00 0.012589 0.019953 0.158753 0.320000 \n", + "1 6.00 0.019953 0.031623 11.244392 0.320000 \n", + "2 6.00 0.031623 0.050119 17.278245 0.320000 \n", + "3 6.00 0.050119 0.079433 10.838075 0.320000 \n", + "4 6.00 0.079433 0.125893 2.151692 0.267352 \n", + ".. ... ... ... ... ... \n", + "185 66.44 7.943282 12.589254 0.002647 0.133676 \n", + "186 66.44 12.589254 19.952623 0.001408 0.134832 \n", + "187 66.44 19.952623 31.622777 0.000598 0.138468 \n", + "188 66.44 31.622777 50.118723 0.000177 0.133650 \n", + "189 66.44 50.118723 79.432823 0.000094 0.137851 \n", + "\n", + " Gammaness_cut \n", + "0 0.112810 \n", + "1 0.176081 \n", + "2 0.189099 \n", + "3 0.197615 \n", + "4 0.316888 \n", + ".. ... \n", + "185 0.387355 \n", + "186 0.398923 \n", + "187 0.426601 \n", + "188 0.474480 \n", + "189 0.536455 \n", + "\n", + "[190 rows x 6 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "background_data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "90114613", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available zeniths: [ 6. 9.579 16.08 23.16 30.39 37.66 44.92 52.16 59.34 66.44 ] (degrees)\n" + ] + } + ], + "source": [ + "# CHECK that we have the same pointing zenith values in both tables:\n", + "assert np.alltrue(np.unique(gamma_data.ZD_deg) == np.unique(background_data.ZD_deg))\n", + "\n", + "# Available zeniths:\n", + "zenith = np.unique(gamma_data.ZD_deg)\n", + "print('Available zeniths:', zenith, '(degrees)')" + ] + }, + { + "cell_type": "markdown", + "id": "dc762a1a", + "metadata": {}, + "source": [ + "## Select zenith bin" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a5a9386a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selected ZD = 16.08 degrees\n" + ] + } + ], + "source": [ + "# Choose the bins among those above. Just set the bin number (from 0)\n", + "# Make sure you choose values which make sense for the declination of your source\n", + "\n", + "zd_bin = 2\n", + "print('Selected ZD = ', zenith[zd_bin], 'degrees')\n", + "\n", + "# Cuts for tables:\n", + "zd_selection_gamma = abs(gamma_data.ZD_deg - np.unique(gamma_data.ZD_deg)[zd_bin])<0.01\n", + "zd_selection_backg = abs(background_data.ZD_deg - np.unique(background_data.ZD_deg)[zd_bin])<0.01" + ] + }, + { + "cell_type": "markdown", + "id": "8d1c2dcf", + "metadata": {}, + "source": [ + "## ON to OFF exposure" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0c58505b", + "metadata": {}, + "outputs": [], + "source": [ + "# Ratio of ON to OFF exposure (Li & Ma's \"alpha\")\n", + "# For standard wobble offset (0.4 deg) reasonable values are alpha=0.333 (3 off regions) above 0.2 TeV, \n", + "# and alpha=1 below 0.2 TeV. For testing sensitivity with the standard definition, set alpha=0.2\n", + "\n", + "alpha = 1 # 0.333 # 1" + ] + }, + { + "cell_type": "markdown", + "id": "aaf2cba7", + "metadata": {}, + "source": [ + "## Observation time" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7d8d3a2e", + "metadata": {}, + "outputs": [], + "source": [ + "effective_obs_time = 34 * u.h " + ] + }, + { + "cell_type": "markdown", + "id": "d662546b", + "metadata": {}, + "source": [ + "## Source redshift" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ea9bca3c", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "redshift = 0\n", + "\n", + "# We will apply the Dominguez EBL model to simulate the absorption\n", + "\n", + "\n", + "try:\n", + " os.environ['GAMMAPY_DATA']\n", + "except:\n", + " # SET HERE THE GAMMAPY_DATA ENV VARIABLE IN CASE IT IS NOT SET\n", + " # YOU MUST SET THE PATH TO THE CORRESPONDING DIRECTORY IN THE CONDA ENVIRONMENT YOU ARE USING:\n", + " os.environ['GAMMAPY_DATA'] = '/fefs/aswg/workspace/abelardo.moralejo/miniconda3/envs/lst-dev/lib/python3.11/site-packages/gammapy/gammapy-datasets/1.1'\n", + "\n", + "dominguez = EBLAbsorptionNormSpectralModel.read_builtin(\"dominguez\", redshift=redshift)" + ] + }, + { + "cell_type": "markdown", + "id": "e5766b4e", + "metadata": {}, + "source": [ + "## Source (intrinsic) spectrum " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e24f72ce", + "metadata": {}, + "outputs": [], + "source": [ + "# Set here the simulated intrinsic spectrum (must take as argument an astropy quantity with energy units)\n", + "\n", + "# Crab Nebula:\n", + "def intrinsic_dFdE(E):\n", + " return CRAB_MAGIC_JHEAP2015(E) # Crab Nebula, MAGIC log-parabola\n", + "\n", + "# def intrinsic_dFdE(E):\n", + "# return PowerLaw(normalization=5e-8 / (u.TeV * u.cm**2 * u.s), \n", + "# index=-2.0, \n", + "# e_ref=0.1 * u.TeV)(E)\n", + "\n", + "# def intrinsic_dFdE(E):\n", + "# return LogParabola(normalization=5e-8 / (u.TeV * u.cm**2 * u.s), \n", + "# a=-2.0, b =-0.2, \n", + "# e_ref=0.1 * u.TeV)(E)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "014476b0", + "metadata": {}, + "outputs": [], + "source": [ + "# After EBL absorption:\n", + "def dFdE(E):\n", + " return intrinsic_dFdE(E) * dominguez.evaluate(E, redshift, 1)" + ] + }, + { + "cell_type": "markdown", + "id": "8613146f", + "metadata": {}, + "source": [ + "## END of user settings\n", + "#" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "80627c34", + "metadata": {}, + "outputs": [], + "source": [ + "# Other settings (change only for tests)\n", + "\n", + "min_signal_to_backg_ratio = 0.01 # safeguard against systematics\n", + "min_signi_in_flux_point = 3 # minimum significance to display a flux point\n", + "integral_significance_threshold = 5 # \"Detection significance\"\n", + "\n", + "min_Aeff = 100 *u.m**2 # Minimum required Aeff in Etrue bins. \n", + " # Just to avoid Etrue bins with little MC stats, hence noisy!" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "24e8394c", + "metadata": {}, + "outputs": [], + "source": [ + "erecobins = background_data[zd_selection_backg].Ereco_min_TeV.to_numpy()\n", + "erecobins = np.append(erecobins, background_data[zd_selection_backg].Ereco_max_TeV.to_numpy()[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "056ea33b", + "metadata": {}, + "outputs": [], + "source": [ + "etruebins = gamma_data[zd_selection_gamma].Etrue_min_TeV.to_numpy()\n", + "etruebins = np.append(etruebins, gamma_data[zd_selection_gamma].Etrue_max_TeV.to_numpy()[-1])\n", + "\n", + "effective_area = gamma_data.Aeff_m2[zd_selection_gamma].to_numpy()*u.m**2\n", + "\n", + "# Model to characterize the energy migration matrix (gauss or moyal):\n", + "emig_model = gamma_data[zd_selection_gamma].emig_model.to_numpy()\n", + "\n", + "# Parameters to characterize the energy migration matrix:\n", + "loc = gamma_data[zd_selection_gamma].emig_mu_loc.to_numpy()\n", + "scale = gamma_data[zd_selection_gamma].emig_mu_scale.to_numpy()\n", + "\n", + "\n", + "# Now we extrapolate Aeff to higher Etrue by using the same value of the highest available energy:\n", + "# (it is better than having zeros!) We also assume the same E-migration\n", + "factor = etruebins[-1]/etruebins[-2] # step in energy in each bin\n", + "while etruebins[-1] < 80: # extend to 80 TeV at least\n", + " etruebins = np.append(etruebins, etruebins[-1]*factor)\n", + " effective_area = np.append(effective_area, effective_area[-1])\n", + " emig_model = np.append(emig_model, emig_model[-1])\n", + " loc = np.append(loc, loc[-1])\n", + " scale = np.append(scale, scale[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1b79d5b2", + "metadata": {}, + "outputs": [], + "source": [ + "etruebincenters = (etruebins[:-1]*etruebins[1:])**0.5\n", + "erecobincenters = (erecobins[:-1]*erecobins[1:])**0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "2038bb5b", + "metadata": {}, + "outputs": [], + "source": [ + "# Exclude too low eff. areas (in general, unreliable due to low MC stats)\n", + "\n", + "effective_area[effective_area= etruebincenters[np.where(effective_area>0)[0][0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "48a34458", + "metadata": {}, + "outputs": [], + "source": [ + "def integrate (dfde1, dfde2, e1, e2):\n", + " # We cannot let numpy deal with the units, sometimes rounding leads to wrong units in result!\n", + " # like TeV^(1e-15) :-D\n", + " # in power-law approximation:\n", + " gamma = np.log(dfde2/dfde1) / np.log(e2/e1)\n", + " e1tev = e1.to_value(u.TeV)\n", + " e2tev = e2.to_value(u.TeV)\n", + " \n", + " integral = dfde1.to_value(1/(u.TeV * u.cm**2 * u.s)) / (gamma+1) * e1tev**(-gamma) * (e2tev**(gamma+1) - e1tev**(gamma+1))\n", + " return integral # (1/u.s/u.cm**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "54b22527", + "metadata": {}, + "outputs": [], + "source": [ + "integrated_flux = []\n", + "for etruemin, etruemax in zip(etruebins[:-1], etruebins[1:]):\n", + " integrated_flux.append(integrate(dFdE(etruemin*u.TeV),\n", + " dFdE(etruemax*u.TeV),\n", + " etruemin*u.TeV, \n", + " etruemax*u.TeV))\n", + "integrated_flux = np.array(integrated_flux)\n", + "\n", + "# Too strong EBL absorption produces NaNs in high-E bins, jus trplace by 0's:\n", + "integrated_flux[np.isnan(integrated_flux)] = 0\n", + " \n", + "integrated_flux = np.array(integrated_flux) * 1/(u.s * u.cm**2)" + ] + }, + { + "cell_type": "markdown", + "id": "a0d668ca", + "metadata": {}, + "source": [ + "## Effective area" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "4eff20a0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,4))\n", + "plt.plot(etruebincenters, effective_area)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.xlabel('Etrue (TeV)')\n", + "plt.ylabel('Aeff (m2)')\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "id": "bb2fb8b7", + "metadata": {}, + "source": [ + "## Background rate" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "9c89f6e6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,4))\n", + "plt.plot(erecobincenters[ereco_mask], \n", + " background_data[zd_selection_backg].BckgRate_per_second.to_numpy()[ereco_mask])\n", + "plt.xlabel('Ereco (TeV)')\n", + "plt.ylabel('Background rate within theta cut\\n (events/s) in Ereco bins')\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "5164274a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total gamma rate after cuts: 0.897 events/s\n" + ] + } + ], + "source": [ + "total_gamma_rate = (integrated_flux*effective_area).to(1/u.s)\n", + "print(f'Total gamma rate after cuts: {total_gamma_rate.sum().to_value(1/u.s):.3f} events/s')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "6a415916", + "metadata": {}, + "outputs": [], + "source": [ + "# Number of realizations (random numbers taken from gaussian) for E-migration simulation \n", + "num_realizations = 10000" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "31415b87", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.1, 4167178.6876828154)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,4))\n", + "\n", + "total_bg_counts = effective_obs_time.to_value(u.s) * background_data[zd_selection_backg].BckgRate_per_second.to_numpy()\n", + "total_signal_counts = np.zeros(zd_selection_backg.sum())\n", + "\n", + "for ietrue in range(len(etruebincenters)):\n", + " etrue = etruebincenters[ietrue]\n", + " \n", + " gamma_rate = total_gamma_rate[ietrue]\n", + "\n", + " \n", + " if emig_model[ietrue] == 'gauss':\n", + " counts, _ = np.histogram(etrue*np.random.normal(loc[ietrue], scale[ietrue], num_realizations),\n", + " bins=erecobins)\n", + " \n", + " elif emig_model[ietrue] == 'moyal':\n", + " counts, _ = np.histogram(etrue*moyal.rvs(loc[ietrue], scale[ietrue], num_realizations),\n", + " bins=erecobins)\n", + " else:\n", + " continue\n", + " \n", + "\n", + " total_signal_counts += gamma_rate * effective_obs_time * counts / num_realizations\n", + " # print(gamma_rate * effective_obs_time * counts / num_realizations)\n", + "\n", + " \n", + "# Now set to zero values below the \"reliable minimum energy\":\n", + "total_signal_counts[~ereco_mask] = 0\n", + "total_bg_counts[~ereco_mask] = 0\n", + " \n", + "plt.errorbar(0.5*(erecobins[:-1]+erecobins[1:]), total_signal_counts, total_signal_counts**0.5,\n", + " label='gammas')\n", + "plt.errorbar(0.5*(erecobins[:-1]+erecobins[1:]), total_bg_counts, total_bg_counts**0.5,\n", + " label='Background')\n", + "\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.legend()\n", + "plt.xlabel('Ereco (TeV)')\n", + "plt.ylabel('Number of events after cuts in Ereco bins')\n", + "plt.grid()\n", + "plt.ylim(0.1, 2*np.max(total_bg_counts))\n", + "# plt.xlim(10, 80)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "b22d2e36", + "metadata": {}, + "outputs": [], + "source": [ + "significance = li_ma_significance(total_signal_counts+total_bg_counts,\n", + " total_bg_counts/alpha, alpha)\n", + "\n", + "# integrating from each Ereco to max Ereco\n", + "integral_signal_counts = np.cumsum(total_signal_counts[::-1])[::-1]\n", + "integral_bg_counts = np.cumsum(total_bg_counts[::-1])[::-1]\n", + "\n", + "integral_significance = li_ma_significance(integral_signal_counts+integral_bg_counts,\n", + " integral_bg_counts/alpha, alpha)" + ] + }, + { + "cell_type": "markdown", + "id": "d27e4dfc", + "metadata": {}, + "source": [ + "## Signal to background ratio and significance in Ereco bins" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "2a3d41c1", + "metadata": {}, + "outputs": [], + "source": [ + "signal_to_background_ratio = total_signal_counts / total_bg_counts" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "9b5e3aa8", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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ys7Nx4MCBpoZIRCbKL67En1fsxdFLxWjl6Yavnu+PvmYO2WsqdzcZPp3UB7Ht/FBSVYMJn+xH1qVis86x9Oa6WA91D0Gkv/XWspo+rD083WQ4eqkYW4/l3/0JRERmuts6hbcv4k5Exuw+ySotLUVmZiYyMzMBADk5OcjMzERubi4AYPbs2fjkk0/w2Wef4fjx43j55ZeRm5uLadOmSRg1EZnjXGEZHvtoD04XlCLYV4n/TBuA6Da+ksTi4eaKzyb3RUx4S6gqazDh0/04nmdaAYZTV0rwU5Yu4Xnxvg7WDBOtvBSYOkS39ta/Uk/xTjIRWZyp6xRachF3Imdh90nWwYMH0atXL/Tq1QuALqnq1asX3nzzTQDA448/jiVLlmD+/Pno2bMndu3ahc2bNyM8PFzKsInIRCfyVfjTir24eL0CEa098c20AWhvxR4gU3gpXLF6cl/0CGuBG+VqPPXJfpy6UnLX56Vs1/ViJUQHoVOgt7XDxLNDItDCQ47TBaX4/vAlq78eETUvpi7ObslF3Imchd0nWcOGDYMoirV+Vq9ebThmxowZOHfuHKqqqpCRkYF77723Sa/JOVlEtnE49zoeX7EPV0uq0CXIG/95YQBCW9rHmnTeSjk+nxKLbm18DXPFTheU1nv8uWtl2PT7ZQDW78XS81HKMW2obt7X+9tOYfepq/hf5iXsPXONPVtE1GSxEX4I9lWivtmvAnRVBhtaCJ6oubL7JEsKnJNFpKPRith75lqTv7jXdZ7fThfiqU/2o7hCjd5tW+Dr5wfA37v2EgpS8nWX44upsYgK9kFhaRXGf7wPOYVldR67fGcOtCIwoksAuobYbqjjxAHt4KN0xcXrFXj6s3S8tD4TT368D4Pf/QVbsvJsFgcROR+Zi4B5o6MAoFaipX88b3QU18siqoNTlHAnIsuz1LoodZ2npYccJZU1qNGKGNKxNVY8HQMPN/v8c9TCww1rn+2H8R/vw4n8Ejy5ch++fqE/wlt5Go65Vgn873ddQvOXER1tGt/OUwVQVdbU2s7yykRkCaOig7F8Qu9af8eDuE4WUYPs81sNEUnKUuui1Hee6+VqAECvsBb4ZGIfKFxllgncSvw8dYnWkyv34Y+CUoz/eD/WP98fIS3ckZ5ThG2XXKC5mTD2DGths7j05ZXrIkJ3pzl5UzbiooJ4p5mIGm1UdLDF1ikkai6YZBGREVPWRZm38Rju7egPpVwGl3ousg2dRy+vuAKuLo4xarm1lwJfPtcPT6zch7NXyzA25TfIXATcKKuERtS1QfZlFbZk5dnszq455ZXvXHyZiMgcdS3iTkT1Y5JVh5SUFKSkpECj0UgdCpHN3e2LOwBcUVUhat5WALoLr1wmQC5zgZvMBXKZC1xlAjRa8a7nyVdVOVQCEOCtxFfP9cdD//4VV0uqAAAuAqAVBQgQUVRWbdMheiyvTEREZJ8c4xayjbHwBTVn5n4h12hFVKq1KKmswbWyauSrKnHxesVdE6zGvp7UWnspjCaAa2/2Yrm63OrpS96UbZPqfiyvTEREZJ/Yk0VEBsfzVFiz55xJx346sQ96tW0JtUZ780eEWqNFdY3uceaFG/XOF7qdoyUAujkJVUbbIrxFXLpZdNCWQ/T05ZXziyvrHZYZ6KNgeWUiIiIbY08WESH3WjlmrT+MBz7cjUO5Nxo8Vr8uyrDOAfDzdEOgjxKhLT0Q0doTnQK9Ed3GF73atsQzA9o55foqd/a8yQQRT7bXQBAaPs4aGiqvrOfrLodao7V6LHTLpUuXMGHCBLRq1QoeHh7o2bMnMjIyDPtFUURSUhJCQkLg7u6OYcOG4dixYxJGTERElsYki6gZK1BV4h/fZ+G+93bg+8zLEEXgwW7BSBodBQFNWxfFWddXubPnzdUFCHS/+3HWoi+vHORr/HqtPN2glLvg1JVSvPBFBqpqOMfUFq5fv45BgwZBLpfjp59+QnZ2Nt577z20aNHCcMyiRYuwePFiLF26FAcOHEBQUBDi4uJQUlIiXeBERGRRHC5YBxa+IGeg0YpIzykCoBvi1r9DgCGhKS5X46NdZ7DqtxxUqnW9HPd28sff4jujW6huId0gX2WT10VxxvVV7jZET4Du/dmyh66+8sqHcq/j6U/3Y+epq3jpq0wsHd8LrjLeW7Omd999F2FhYVi1apVhW7t27Qz/FkURS5YswRtvvIFx48YBANasWYPAwECsW7cOL7zwQq1zVlVVoarq1hBVlUoFAFCr1VCr1VZ6J82Tvj3ZrtbDNrY+trH1mNOmgiiK1p+d7aBUKhV8fX1RXFwMHx8fqcNxOmq1Gps3b8YDDzwAuVwudThORb8AcFFpBRbFajAnXQY/L3fMTeiCizcq8NGOM4YFbHu1bYE5I7vUOX9In6g1dV0US53HXujX/wIAN5loaONqje492dMCwLv/uIqpqw+iWqPFI73a4L0/9ai37L69ccS/wVFRURg5ciQuXryInTt3ok2bNpgxYwaee+45AMDZs2fRvn17HDp0CL169TI87+GHH0aLFi2wZs2aWudMSkpCcnJyre3r1q2Dh4eH9d4MEREZKS8vx/jx4026LrEni8jJ3L4AsOLmGr+iqCvGMHN9puG4zoHe+OvIzrj/ngAId04ouslS66I42/oqt/fQFZVWGLbbYw/dkI7+WPZUb0xbm4ENhy/B3U2Gt8dG1/v/nJrm7NmzWL58OWbPno3XX38d6enpmDlzJhQKBZ555hnk5+cDAAIDA42eFxgYiPPnz9d5zrlz52L27NmGxyqVCmFhYYiPj3eY5NNRqNVqpKWlIS4ujjf/rIRtbH1sY+vRjyQwBZMsIidy5wLAoggcvCqg+ra6BzJBwLuPdccjvdo4dG+S1PRD9PadLkDh8X34bGJfoyGZ9uT+qEC8/3hPzFx/GOv258JDLsMbD97DRMsKtFot+vTpgwULFgAAevXqhWPHjmH58uV45plnDMfd2faiKNb7/0OhUEChUNTaLpfL+QXKSti21sc2tj62seWZ054cnE/kRO5cSFitBb44LcPtpSc0oog2LdztMhlwNDIXwTD3yt6HQI7uEYJ3x3UHAHzyaw6WbPtD4oicU3BwMKKiooy23XPPPcjNzQUABAUFAYChR0uvoKCgVu8WERE5LiZZRE7kzrLhIgS4y0TIBLHB46h5+HPfMCTdrPj4wc9/YMXOMxJH5HwGDRqEkydPGm07deoUwsPDAQAREREICgpCWlqaYX91dTV27tyJgQMH2jRWIiKyHiZZRE6iQFWJ7w9fNtomE0T8o5cGrnf8pjvaAsBkOZMGRWDOqM4AgIU/ncAXe89JG5CTefnll7Fv3z4sWLAAp0+fxrp167By5UokJiYC0A0TnDVrFhYsWIANGzYgKysLkyZNgoeHB8aPHy9x9EREZCmck1UHlnAnR1JeXYOPd+Vgxa4zKK82/sy6ugCetw0flqK8ONmfGcM6oLxKg6XbT+Mf/zsGdzdXPBYTKnVYTqFv377YsGED5s6di/nz5yMiIgJLlizBU089ZThmzpw5qKiowIwZM3D9+nX069cPqamp8Pb2ljByIiKyJCZZdUhMTERiYqKhfDCRPdJoRXx36CL+lXoSV1S6NXR6tW2B++8JwL+2nqp1vCMvAEyW90p8J5RV12DVb+cw59vf4S6X4cHu9lMV0ZE99NBDeOihh+rdLwgCkpKSkJSUZLugiIjIpphkETmgPacL8daPx5GdpyslGtrSHa+O6oKHugdDEAS09/dyiPLiJB1BEPDmQ1GoqNZg/YELeGn9Ybi7uWBopwCnWtOMiIhICkyyiOxMQwv3ni4owcLNJ/DziQIAgLfSFX+5rwOeGdAOSrnMcA5HKi9O0hEEAW8/0g0Vag3+l3kZz3+eAR93VxSV3VrRPpjJORERkdmYZBHZkS1ZeUjelG1Uhj3YV4mX4zriyMVifJV+ARqtCFcXARP6h2PmiI7w83Sr81z68uKbj9t/eXGSjsxFwL/+1APnrpXh9wvFRgkWAOQXV2L62kNYPqE3Ey0iIiITMckishNbsvIwfe0hiHdszyuuxJxvjxoex0UFYm5CF0T6e9k2QHJaLoKAK8V1l/UXoZvPl7wpG3FRQUzWiYiITMAki8gOaLQikjdl10qwbieXCVgzORYDO7S2WVzUPKTnFCH/ZvGUuojQJfvpOUUY0L6V7QIjIiJyUFwni8gOpOcUGQ0RrItaI0IQ2ItAlmfq4tRcxJqIiMg0TLLqkJKSgqioKPTt21fqUKiZ4JdckpKpi1NzEWsiIiLTNHq4YEZGBo4fPw5BEHDPPfegd+/eloxLUlwni2yNX3JJSrERfgj2VSK/uLLOIatcxJqIiMg8ZidZBQUFeOKJJ7Bjxw60aNECoiiiuLgYw4cPx/r16+Hv72+NOImcWs+wFlC4uqCqRlvnfn7JJWuSuQiYNzoK09ceggAYJVpcxJqIiMh8Zg8X/Mtf/gKVSoVjx46hqKgI169fR1ZWFlQqFWbOnGmNGImcmkYrYs5/jzSYYAH8kkvWNSo6GMsn9EaQr3FvaZCvkuXbiYiIzGR2T9aWLVuwbds23HPPPYZtUVFRSElJQXx8vEWDI3J2oijizf9lYdPvlyGXCZg2NBLfZlwyKoIRxMVgyUb0i1iv/i0H//zxOIJ8lPj11fuY3BMREZnJ7CRLq9VCLpfX2i6Xy6HV1n0nnojq9q/Uk/hyfy4EAXj/8Z54qHsIZt3fGek5RSgoqUSAt5ILCZNNyVwEjO4Rgn/+eBxXS6ug0Yr8/BEREZnJ7OGC9913H1566SVcvnzZsO3SpUt4+eWXMWLECIsGR+TMVu46g5TtZwAACx7phoe6hwDQfckd0L4VHu7ZBgPat+IXXLI5f28FvBSu0GhF5BaVSR0OERGRwzE7yVq6dClKSkrQrl07tG/fHh06dEBERARKSkrw73//2xoxEjmd9em5WLD5BADgtYQueDK2rcQREd0iCAIiWnsCAM5cZZJFRERkLrOHC4aFheHQoUNIS0vDiRMnIIoioqKicP/991sjPiKns/loHl7fcBQAMG1oe0wb2l7iiIhqi/T3xNFLxTjLJIuIiMhsjV4nKy4uDnFxcZaMhcjp7Tp1FS+tPwytCDwZ2xavjuosdUhEdYps7QUAOHu1VOJIiIiIHI9JSdaHH36I559/HkqlEh9++GGDxzpDGfeUlBSkpKRAo9FIHQo5kYzz1/HCFxlQa0Q82D0Yb42NhiBwvhXZp0h/3XDBs4XsySIiIjKXSUnW+++/j6eeegpKpRLvv/9+vccJguAUSVZiYiISExOhUqng6+srdTjkBI7nqTB5VToq1BoM7eSP9//ckwUtyK4Zkiz2ZBGZRaMVWSGWiExLsnJycur8NxHd3bnCMjz9aTpUlTXoE94Syyf0hpur2TVniGxKX/jierka18uq0dLTTeKIiOzflqw8JG/KNlrrMJhrHRI1S2Z/05s/fz7Ky8trba+oqMD8+fMtEhSRs8gvrsSET/ejsLQK9wT74NNJfeHh1uipkEQ24+HmihBfJQDgbCF7s4juZktWHqavPWSUYAG668D0tYewJStPosiISApmJ1nJyckoLa19wS0vL0dycrJFgiJyRBqtiL1nruF/mZew98w1FJZW4elP9+Pi9Qq0a+WBz6fEwte99kLeRPYq0l9f/ILzsogaotGKSN6UDbGOffptyZuyodHWdQQROSOzb6mLoljnZP3ff/8dfn5+FgmKyNHUNURELhOg1ogI8lHii6n94O+tkDBCIvNF+nvi19OFLH5BdBfpOUW1erBuJwLIK65Eek4RBrRvZbvAiEgyJidZLVu2hCAIEAQBnTp1Mkq0NBoNSktLMW3aNKsESWTP9ENE7rw/qdbotjx/bwTC/DxsHxhRE+nnZbH4BVHDCkrqT7Aac5yz0hcFAXSJaf8OASwKQk7L5CRryZIlEEURU6ZMQXJyslHVPTc3N7Rr1w4DBgywSpBE9qqhISJ6H+/OwcSBEbyQkMPhcEEi0wR4Ky16nDPSj/goKq3AolhgypoD8PNyZ1EQclomJ1kTJ04EAERERGDgwIGQyzm3hOhuQ0QADhEhxxV5syfr/LVyaLQibxQQ1SM2wg/BvkrkF1fWedNNABDkqyvn3hzdPuJDIbu1XV8UZPmE3ky0yOmYXfhi6NChhgSroqICKpXK6IeoOeEQEXJmbVq4Q+HqgmqNFhev164q60y++OILDBo0CCEhITh//jwA3QiO//3vfxJHRo5A5iJg3ugoALqE6nb6x/NGRzXLGxUsCkLNldlJVnl5OV588UUEBATAy8sLLVu2NPohak44RIScmYuLcNu8LOcdMrh8+XLMnj0bDzzwAG7cuAGNRgMAaNGiBZYsWSJtcOQwRkUHY/mE3gjyNf57H+SrbNY9NeYUBSFyJmYnWX/729/wyy+/YNmyZVAoFPjkk0+QnJyMkJAQfP7559aIkchuxUb4obVX/Yu0CtAtRNlch4iQ44v01yVZZ5y4+MW///1vfPzxx3jjjTcgk90ay9SnTx8cPXpUwsjI0YyKDsavr96Hr57rjw+e6ImvnuuPX1+9r9kmWABHfFDzZXYJ902bNuHzzz/HsGHDMGXKFAwZMgQdOnRAeHg4vvzySzz11FPWiJPILl0rrap3iENzHyJCziGy9c3iF05cxj0nJwe9evWqtV2hUKCszHnfN1mHzEXgHNzbcMQHNVdm92QVFRUhIiICAODj44OiIl337uDBg7Fr1y7LRkdkxyrVGjz3RQaul6sR5KNAoI/xOljNfYgIOQd9T5Yzl3GPiIhAZmZmre0//fQToqKizDpXUlKSYbkT/U9QUJBhvyiKSEpKQkhICNzd3TFs2DAcO3asqW+ByG7pi4LUd6uRIz7IWZndkxUZGYlz584hPDwcUVFR+M9//oPY2Fhs2rQJLVq0sEKItpeSkoKUlBTDuHyiO4miiL99ewS/X7iBFh5yrH9+AML8PJCeU4SCkkoEeOsuGOzBIkenL+Oe48Q9WX/729+QmJiIyspKiKKI9PR0fPXVV1i4cCE++eQTs8/XtWtXbNu2zfD49iGIixYtwuLFi7F69Wp06tQJb731FuLi4nDy5El4e3tb5P0Q2RN9UZDpaw+xKAg1K2YnWZMnT8bvv/+OoUOHYu7cuXjwwQfx73//GzU1NVi8eLE1YrS5xMREJCYmQqVSGa0HRqT3719OY9Pvl+HqImD5UzFod7M4AIeIkLPRF764oqpCaVUNvBRmXzbs3uTJk1FTU4M5c+agvLwc48ePR5s2bfDBBx/giSeeMPt8rq6uRr1XeqIoYsmSJXjjjTcwbtw4AMCaNWsQGBiIdevW4YUXXqjzfFVVVaiqqjI81lfyVavVUKvVZsdH9dO3J9vVskZ0bo1l43vgnZ9O4HppBQBA4SIiyEeJ1xK6YETn1mxzC+Ln2HrMaVOzr5Yvv/yy4d/Dhw/HiRMncPDgQbRv3x49evQw93REDufHI3lYnHYKAPDW2GgmVuTUfN3laO3lhsLSauRcLUO3UOe88fTcc8/hueeeQ2FhIbRaLQICAhp9rj/++AMhISFQKBTo168fFixYgMjISOTk5CA/Px/x8fGGYxUKBYYOHYo9e/bUm2QtXLgQycnJtbanpqbCw8Oj0XFS/dLS0qQOwSnN7nLr3//sowVQhuqcDGzOkSwkp8bPseWVl5u+nIlZSZZarUZ8fDxWrFiBTp06AQDatm2Ltm3bmhchkYM6cvEGXvkmEwAwdXAEnojlZ5+cX2RrLxSWFuFsYalTJlk5OTmoqalBx44d0bp1a8P2P/74A3K5HO3atTP5XP369cPnn3+OTp064cqVK3jrrbcwcOBAHDt2DPn5+QCAwMBAo+cEBgYa1uaqy9y5czF79mzDY5VKhbCwMMTHx8PHx8fk2Oju1Go10tLSEBcXZ1gTlCyLbWx9bGPrMWdNYLOSLLlcjqysLAgCx81S85NfXInnPj+ISrUWwzv74/UH7pE6JCKbiPT3RPq5Ipxx0rWyJk2ahClTpqBjx45G2/fv349PPvkEO3bsMPlcCQkJhn9369YNAwYMQPv27bFmzRr0798fAGpdQ0VRbPC6qlAooFAoam2Xy+X8AmUlbFvrYxtbH9vY8sxpT7OrCz7zzDP49NNPzX0akUOrqNbguc8P4oqqCh0DvPDhk704SZeaDWevMHj48GEMGjSo1vb+/fvXWXXQHJ6enujWrRv++OMPwzwtfY+WXkFBQa3eLSIicmxmz8mqrq7GJ598grS0NPTp0weenp5G+52l+AWRnlYr4q/f/I6jl4rh5+mGTyf2hbeSd4ao+TCsleWkPVmCIKCkpKTW9uLi4iZXma2qqsLx48cxZMgQREREICgoCGlpaYZ1uaqrq7Fz5068++67TXodIiKyL2YnWVlZWejduzcA4NSpU0b7OIyQnNEHP/+BH4/mQS4T8NGEGLRtxYnm1Lzoe7JyCsug1YpwcbJe3CFDhmDhwoX46quvDOXWNRoNFi5ciMGDB5t1rr/+9a8YPXo02rZti4KCArz11ltQqVSYOHEiBEHArFmzsGDBAnTs2BEdO3bEggUL4OHhgfHjx1vjrRERkUTMTrK2b99ujTiI7NLG3y/jg5//AAAseKQbF0ukZinMzwOuLgIq1BrkqyoR0sJd6pAsatGiRbj33nvRuXNnDBkyBACwe/duqFQq/PLLL2ad6+LFi3jyySdRWFgIf39/9O/fH/v27UN4eDgAYM6cOaioqMCMGTNw/fp19OvXD6mpqVwji4jIyTjfgidEFpJ54Qb+9s3vAIAX7o3En/qESRwRkTTkMhe0beWBs1fLcPZqmdMlWVFRUThy5AiWLl2K33//He7u7njmmWfw4osvws/PvBsr69evb3C/IAhISkpCUlJSEyImIiJ7xySLqA55xRV47vODqKrR4v57AjBnVJe7P4nIiUW29sLZq2XIKSzF4I6t7/4EBxMSEoIFCxZIHQYRETkJJllEdyivrsGzaw7iakkVugR5Y8kTrCRI1N7fE9uOw2nLuN+4cQPp6ekoKCiAVqs12vfMM89IFBURETkqJlnU7Gm0ItJzilBQUonWXgp8vvccjl1WobWXGz6Z2AdeCv6aEEW0vlnGvdD5kqxNmzbhqaeeQllZGby9vY2KOAmCwCSLiIjMxm+P1KxtycpD8qZs5BVXGm13dRGw4ukYhLZkJUEiAIj015dxd761sl555RVMmTLFUOmPiIioqUxKsjZu3GjyCceMGdPoYIhsaUtWHqavPQSxjn01WhFXS6psHhORvdKXcb90owKVag2UcpnEEVnOpUuXMHPmTCZYRERkMSYlWWPHjjV6LAgCRFE0eqzX1IUbiWxBoxWRvCm7zgQLAAQAyZuyERcVxPlYRABaebrBR+kKVWUNzl0rQ5cgH6lDspiRI0fi4MGDiIyMlDoUIiJyEiYlWbdPAt62bRteffVVLFiwAAMGDIAgCNizZw/+/ve/szITOYz0nKJaQwRvJwLIK65Eek4RBrRvZbvAiOyUIAiI9PdC5oUbOHvVuZKsBx98EH/729+QnZ2Nbt26QS6XG+3nCA0iIjKX2XOyZs2ahY8++giDBw82bBs5ciQ8PDzw/PPP4/jx4xYNkMgaCkrqT7AacxxRcxDp73kzyXKueVnPPfccAGD+/Pm19gmCwBEaRERkNrOTrDNnzsDX17fWdl9fX5w7d84SMRFZXYC30qLHETUH7Q3FL5yrwuCdJduJiIiaysXcJ/Tt2xezZs1CXl6eYVt+fj5eeeUVxMbGWjQ4S/jhhx/QuXNndOzYEZ988onU4ZCdiI3wQ7Bv/QmUACDYV4nYCD/bBUVk5yKduIw7ERGRJZndk/XZZ5/hkUceQXh4ONq2bQsAyM3NRadOnfD9999bOr4mqampwezZs7F9+3b4+Pigd+/eGDduHPz8+MW5uZO5CJg3OgrT1h6qtU9f5mLe6CgWvSC6ze1l3EVRNCp65OjKysqwc+dO5Obmorq62mjfzJkzJYqKiIgcldlJVocOHXDkyBGkpaXhxIkTEEURUVFRuP/+++3ugpueno6uXbuiTZs2AIAHHngAW7duxZNPPilxZGQP2rSou1xzkK8S80ZHYVR0sI0jIrJv4a08IAiAqrIG18qq0dpLIXVIFnH48GE88MADKC8vR1lZGfz8/FBYWAgPDw8EBAQwySIiIrOZPVwQ0E0Ejo+Px8yZM/HSSy8hLi7OKgnWrl27MHr0aISEhEAQhDp7ypYtW4aIiAgolUrExMRg9+7dhn2XL182JFgAEBoaikuXLlk8TnJMH+08AwAY2zMEXz3XHx880RNfPdcfv756HxMsojoo5TK0aeEOwLnmZb388ssYPXo0ioqK4O7ujn379uH8+fOIiYnBv/71L6nDIyIiB2R2TxYA/Pzzz/j5559RUFBQa8LwZ599ZpHAAN3wjR49emDy5Ml49NFHa+3/+uuvMWvWLCxbtgyDBg3CihUrkJCQgOzsbLRt29ZoLS+9hpLBqqoqVFXdWoBWpVIBANRqNdRqtQXeEd1O36ZStO25a2X4KUs3r/DZQeHoHOQNQFeSWqupgdZJiolJ2cbNRXNr44hWHrh4vQJ/5BejV6i3VV/LVm2amZmJFStWQCaTQSaToaqqCpGRkVi0aBEmTpyIcePG2SQOIiJyHmYnWcnJyZg/fz769OmD4OBgqw4RTEhIQEJCQr37Fy9ejKlTp+LZZ58FACxZsgRbt27F8uXLsXDhQrRp08ao5+rixYvo169fvedbuHAhkpOTa21PTU2Fh0fdQ8uo6dLS0mz+ml+fcYFWdEFUCy3OHNqNMzaPwLakaOPmprm0sVDqAsAFP6dnwavgiFVfq7y83Krn15PL5YZrWWBgIHJzc3HPPffA19cXubm5NomBiIici9lJ1kcffYTVq1fj6aeftkY8JquurkZGRgZee+01o+3x8fHYs2cPACA2NhZZWVm4dOkSfHx8sHnzZrz55pv1nnPu3LmYPXu24bFKpUJYWBji4+Ph4+M8C2/aC7VajbS0NMTFxdVa/NOaCkqq8Nf0XQBE/OOxfugT3tJmr21rUrVxc9Lc2vj6/lzs+uEE4BOIBx7oZdXX0o8msLZevXrh4MGD6NSpE4YPH44333wThYWF+OKLL9CtWzebxEBERM7F7CSruroaAwcOtEYsZiksLIRGo0FgYKDR9sDAQOTn5wMAXF1d8d5772H48OHQarWYM2cOWrVqVe85FQoFFIraE7nlcnmz+PIkFVu37xf7z0CtERET3hIDOgTY7HWlxM+w9TWXNu4YpFsn8dy1cqu/X1u154IFC1BSUgIA+Oc//4mJEydi+vTp6NChg0WHwBMRUfNhdpL17LPPYt26dfjHP/5hjXjMdudwxTvLCo8ZMwZjxoyxdVhkp1SVany57zwAYPrQ9hJHQ+R4Iv11a2XlFpVDrdFCLmtU/SS70qdPH8O//f39sXnzZgmjISIiZ2B2klVZWYmVK1di27Zt6N69e607jYsXL7ZYcA1p3bo1ZDKZoddKr6CgoFbvlrlSUlKQkpICjcZJqh+QwZf7clFSVYNOgV64r0vz6MUisqQgHyU83GQor9Ygt6gc7W+uneXIcnJyUFNTg44dOxpt/+OPPyCXy9GuXTtpAiMiIodl9i3II0eOoGfPnnBxcUFWVhYOHz5s+MnMzLRCiHVzc3NDTExMrcnmaWlpTR7OmJiYiOzsbBw4cKBJ5yH7UqnW4NNfcwAAL9zbHi5caJjIbIIgIKK1rjcrx0nKuE+aNMkwl/d2+/fvx6RJk2wfEBEROTyze7K2b99ujTjqVFpaitOnTxse5+TkIDMzE35+fmjbti1mz56Np59+Gn369MGAAQOwcuVK5ObmYtq0aTaLkRzHd4cuobC0CiG+SozpGSJ1OEQOK9LfC8cuq3C2sBRA00YO2IPDhw9j0KBBtbb3798fL774ogQRERGRo2vUOlm2cvDgQQwfPtzwWF/5b+LEiVi9ejUef/xxXLt2DfPnz0deXh6io6OxefNmhIeHSxUy2SmNVsSKXbpC7c8OiXSKeSREUtH3ZDnLgsSCIBgKX9yuuLiYw8aJiKhRzE6yhg8f3uDaWL/88kuTArrdsGHD6lxQ+HYzZszAjBkzLPaaAOdkOaOfsvJw/lo5WnjI8URsmNThEDm09v7OlWQNGTIECxcuxFdffQWZTAYA0Gg0WLhwIQYPHixxdERE5IjMTrJ69uxp9FitViMzMxNZWVmYOHGipeKSVGJiIhITE6FSqeDr6yt1ONREoijio526XqyJA9rBw82uO3CJ7F5ka12xC91wQce3aNEi3HvvvejcuTOGDBkCANi9ezdUKpVFbxwSEVHzYfa3zffff7/O7UlJSSgtdY4LLjmXX08XIuuSCu5yGSYObCd1OEQOL+JmT1ZhaTWKK9TwdXfs9cGioqJw5MgRLF26FL///jvc3d3xzDPP4MUXX4Sfn5/U4RERkQOy2C39CRMmIDY2Fv/6178sdUoii9D3Yj3eNwx+nm4SR0Pk+LwUrgj0UeCKqgpnr5aiV9uWUofUZCEhIViwYIHUYRCRCTRaEek5RSgoqUSAtxKxEX6QsWIw2RmLJVl79+6FUqm01OmILOLIxRv47fQ1uLoIeHZIhNThEDmNyNZeN5OsModMso4cOYLo6Gi4uLjgyJEjDR7bvXt3G0VFRHezJSsPyZuykVdcadgW7KvEvNFRGBUdLGFkRMbMTrLGjRtn9FgUReTl5eHgwYP4xz/+YbHApMTCF85D34s1pkcIQlt6SBwNkfOI9PfE3rPXHHZeVs+ePZGfn4+AgAD07NkTgiDUWWhJEAReC4jsxJasPExfewh3/qbmF1di+tpDWD6hNxMtshtmJ1l3FoJwcXFB586dMX/+fMTHx1ssMCmx8IVzOHu1FD9l5QMAXhjaXuJoiJxLpP/N4hcOWmEwJycH/v7+hn8TkX3TaEUkb8qulWABgAhAAJC8KRtxUUEcOkh2wewka9WqVdaIg8jiPt59FqII3H9PADoHeUsdDpFTibxZ/CKn0DGTrEceeQQ///wzWrZsiTVr1uCvf/0rPDws29u9cOFCvP7663jppZewZMkSALrRH8nJyVi5ciWuX7+Ofv36ISUlBV27drXoaxM5m/ScIqMhgncSAeQVVyI9pwgD2reyXWBE9Wj0iqwZGRlYu3YtvvzySxw+fNiSMRE12RVVJf6bcQkAMI29WEQW1/5mGfecwjJotQ2vZ2iPjh8/jrIyXYKYnJxs8eq4Bw4cwMqVK2vN51q0aBEWL16MpUuX4sCBAwgKCkJcXFydiyET0S0FJfUnWI05jsjazO7JKigowBNPPIEdO3agRYsWEEURxcXFGD58ONavX28YfkEkpc9+zUG1Rou+7VqiTzuWYCaytDYt3eEmc0FVjRaXblQgzM+x5jz27NkTkydPxuDBgyGKIv71r3/By8urzmPffPNNs85dWlqKp556Ch9//DHeeustw3ZRFLFkyRK88cYbhvnNa9asQWBgINatW4cXXnih8W+IyMkFeJtWXM3U44iszewk6y9/+QtUKhWOHTuGe+65BwCQnZ2NiRMnYubMmfjqq68sHqStsfCFYyuuUOPL/bkA2ItFZC0yFwHhrTzwR0EpzhaWOVyStXr1asybNw8//PADBEHATz/9BFfX2pdEQRDMTrISExPx4IMP4v777zdKsnJycpCfn280f1mhUGDo0KHYs2dPvUlWVVUVqqqqDI9VKhUAQK1WQ61WmxUbNUzfnmxX62lsG/cK9UZ4SwWuqCrrnJclAAj0UaJXqHez///Hz7H1mNOmZidZW7ZswbZt2wwJFqBbyDElJYWFL8gurN13HqVVNegc6I3hnQOkDofIaUX6e+qSrKulGNrJsUYxdO7cGevXrwegK+D0888/IyCg6X8v1q9fj0OHDuHAgQO19uXn6wrxBAYGGm0PDAzE+fPn6z3nwoULkZycXGt7amqqxeeRkU5aWprUITi9xrTx7C53O6IMW7f81Kh4nBE/x5ZXXl5u8rFmJ1larRZyubzWdrlcDq1Wa+7piCyqUq3Bqt/OAQBeGBoJF1YYIrIaXYXBKw5bYVDPUteuCxcu4KWXXkJqamqD60YKgvHfJVEUa2273dy5czF79mzDY5VKhbCwMMTHx8PHx6fpgZOBWq1GWloa4uLi6vyuQ03X1DbedvwK3vnpBPJVt+ZeBfko8VpCF9x/T2ADz2w++Dm2Hv1IAlOYnWTdd999eOmll/DVV18hJCQEAHDp0iW8/PLLGDFihLmnI7KobzMuorC0Cm1auGN0jxCpwyFyapGtdRUGHXGtrI0bNyIhIQFyuRwbN25s8NgxY8aYdM6MjAwUFBQgJibGsE2j0WDXrl1YunQpTp48CUDXoxUcfGstn4KCglq9W7dTKBRQKBS1tsvlcn6BshK2rfU1to0TuociProN0nOKUFBSiQBvJWIj/Fi2vQ78HFueOe1pdpK1dOlSPPzww2jXrh3CwsIgCAJyc3PRrVs3rF271tzTEVlMjUaLlbvOAgCeHRIBuazRxTOJyASOvFbW2LFjDYsRjx07tt7jzFmMeMSIETh69KjRtsmTJ6NLly549dVXERkZiaCgIKSlpaFXr14AgOrqauzcuRPvvvtuo98LUXMjcxFYpp3sntlJVlhYGA4dOoS0tDScOHECoigiKioK999/vzXiIzLZT1n5yC0qR0sPOR7vGyZ1OEROr/3NtbLyiitRXl0DDzezLymSuX2IoKWGC3p7eyM6Otpom6enJ1q1amXYPmvWLCxYsAAdO3ZEx44dsWDBAnh4eGD8+PEWiYGIiOyDWVfEmpoaKJVKZGZmIi4uDnFxcdaKi8gsoihi+Y4zAICJA9s51Jc9IkfVwsMNfp5uKCqrxtmrZYhuw0JBdzNnzhxUVFRgxowZhsWIU1NT4e3NBdOJiJyJWd9EXV1dER4e7vSlzVnC3fHs/qMQ2XkquMtlmDigndThEDUbka09UVRWjZxCx06yfv75Z/z8888oKCio1bP12WefNfq8O3bsMHosCAKSkpKQlJTU6HMSEZH9M3vSyt///nfMnTsXRUVF1ojHLiQmJiI7O7vOErxkPzRaEXvPXMP/Mi/hnZ9OAACeiA1DS083iSMjaj4i9MUvHHBell5ycjLi4+Px888/o7CwENevXzf6ISIiMpfZY6o+/PBDnD59GiEhIQgPD4enp6fR/kOHDlksOKL6bMnKQ/KmbOQVVxpt7xjgJVFERM2TofiFA1YY1Pvoo4+wevVqPP3001KHQkRETsLsJKuhKkxEtrAlKw/T1x6qc8X3NzZkwc/TDaOig+vYS0SWFunv+D1Z1dXVGDhwoNRhEBGREzE7yZo3b5414iAyiUYrInlTdp0Jll7ypmzERQVxzQwiG2hvSLJK77qorr169tlnsW7dOvzjH/+QOhQiInISLMFGDiU9p6jWEMHbidCVk07PKeIaGkQ20NbPEzIXAWXVGhSUVCHQRyl1SGarrKzEypUrsW3bNnTv3r3WYpOLFy+WKDIiInJUZidZLVu2rPNOpSAIUCqV6NChAyZNmoTJkydbJECi2xWU1J9gNeY4ImoaN1cXhLV0x7lr5ThztdQhk6wjR46gZ8+eAICsrCyjfY7YM0dERNIzO8l688038fbbbyMhIQGxsbEQRREHDhzAli1bkJiYiJycHEyfPh01NTV47rnnrBGz1bGEu/0K8DbtC5ypxxFR00X6e+HctXKcvVqGge1bSx2O2bZv3y51CERE5GTMTrJ+/fVXvPXWW5g2bZrR9hUrViA1NRX//e9/0b17d3z44YcOm2QlJiYiMTERKpUKvr6Ou+6LM4qN8EOwrxL5xZV1zssSAAT5KhEb4Wfr0IiarcjWnvgFjl38goiIyJLMTrK2bt2Kd999t9b2ESNG4JVXXgEAPPDAA3jttdeaHh3RHWQuAuaNjsL0tbWXCtAP6pk3OopFL4hsyNHLuD/yyCN3HQY/fvx4dO7cWYLoiIjIEZm9GLGfnx82bdpUa/umTZvg56frPSgrK4O3t3fToyOqw6joYCyf0LtWIhXkq8TyCb1Zvp3IxvRl3HMKHbMny9fXF7/88gsOHTpkSLYOHz6MX375BTU1Nfj666/Ro0cP/PbbbxJHSkREjsLsnqx//OMfmD59OrZv347Y2FgIgoD09HRs3rwZH330EQAgLS0NQ4cOtXiwRHq927aERitCALBgXDTatfJCbIQfe7CIJBDZWpdkXSgqR1WNBgpXmcQRmScoKAjjx4/H0qVL4eKiu/eo1Wrx0ksvwdvbG+vXr8e0adPw6quv4tdff5U4WiIicgRmJ1nPPfccoqKisHTpUnz33XcQRRFdunTBzp07DYs56ocNElnL3rPXAABRIT54MjZc4miImjd/bwW8FK4orapB7rVydAx0rJEMn376KX777TdDggUALi4u+Mtf/oKBAwdiwYIFePHFFzFkyBAJoyQiIkfSqHWyBg0ahEGDBtXaXlFRAXd39yYHRXQ3+24mWQMiuRYWkdQEQUCkvyeOXCzGmatlDpdk1dTU4MSJE+jUqZPR9hMnThiqzCqVSpZzJyIik5k9JysxMbHO7WVlZUhISGhyQESm2He2CAC44DCRndAPGXTE4hdPP/00pk6divfffx+//vorfvvtN7z//vuYOnUqnnnmGQDAzp070bVrV4kjJSIiR2F2T1Zqair+/ve/46233jJsKy0tZYJFNpNfXImcwjK4CEBflmonsguGCoMOWMb9/fffR2BgIBYtWoQrV64AAAIDA/Hyyy/j1VdfBQDEx8dj1KhRUoZJREQOpFFJ1uDBg9GqVSu8/PLLKCkpwciRI+Hq6oqffvrJGjESGdEPFYxu4wsfpVziaIgIuFVh8OxVx+vJkslkeOONN/DGG29ApVIBAHx8fIyOadu2rRShERGRgzI7yYqIiMDWrVsxbNgwuLi4YP369VAoFPjxxx/h6elpjRiJjOw9o0uy+nM+FpHdiGytXyvL8XqybndnckVERNQYjSp8ER0djR9++AH3338/+vXrhx9++MGpCl6kpKQgJSXFMOGZ7MteFr0gsjsRN+dk3ShXo6isGn6ebhJH1LDevXvj559/RsuWLdGrV68Gi1ocOlR78XMiIqKGmJRk1XcBUigUuHz5slGlQWe4GCUmJiIxMREqlQq+vr5Sh0O3uXSjArlF5ZC5COjTrqXU4RDRTe5uMrRp4Y5LNypw9mop/Dzte77kww8/DIVCAQAYO3astMEQEZHTMSnJ4gWI7MW+M7fmY3lzPhaRXYlo7alLsgrL0KedfSdZ8+bNq/PfRERElmBSksULENkLro9FZL8i/T3x6+lCh6sweOHCBQiCgNDQUABAeno61q1bh6ioKDz//PMSR0dERI7I7HWyDhw4gP3799favn//fhw8eNAiQRHVRz8fq3+kfd8lJ2qODGtlOViFwfHjx2P79u0AgPz8fNx///1IT0/H66+/jvnz50scHREROaJGLUZ84cKFWtsvXbpU70LFRJZwoagcF69XwNVFQF87H4pE1BwZ1spysAqDWVlZiI2NBQD85z//Qbdu3bBnzx6sW7cOq1evljY4IiJySGYnWdnZ2ejdu3et7b169UJ2drZFgiKqi36oYPdQX3gqGlUYk4isSL9W1vlrZajRaCWOxnRqtdpQBGPbtm0YM2YMAKBLly7Iy8uTMjQiInJQZidZCoUCV65cqbU9Ly8Prq784kvWc2uoIOdjEdmjEF93KOUuUGtEXLxeIXU4JuvatSs++ugj7N69G2lpaRg1ahQA4PLly2jVin9viIjIfGYnWXFxcZg7dy6Ki4sN227cuIHXX38dcXFxFg2OSE8URew/WwQAGNCeX3qI7JGLi4B2rW7Oyyp0nHlZ7777LlasWIFhw4bhySefRI8ePQAAGzduNAwjJCIiMofZXU/vvfce7r33XoSHh6NXr14AgMzMTAQGBuKLL76weIBEAHChqAKXblRALhMQE871sYjsVXt/L5zIL8HZq2W4r4vU0Zhm2LBhKCwshEqlQsuWt/6+PP/88/Dw8JAwMiIiclRmJ1lt2rTBkSNH8OWXX+L333+Hu7s7Jk+ejCeffBJyOdctIuvYe7YQANAjtAU83Dgslche6edlnXGwMu4ymcwowQKAdu3aSRMMERE5vEZ9W/X09OTaIWRT+24OFeR8LCL7pk+yHK2MOxERkSWZPSdLLzs7G1u2bMHGjRuNfogsTRRF7D1zcxFizscismsRrXVl3HMcrIy7pSxfvhzdu3eHj48PfHx8MGDAAPz000+G/aIoIikpCSEhIXB3d8ewYcNw7NgxCSMmIiJrMLsn6+zZs3jkkUdw9OhRCIIAURQBAIIgAAA0Go1lI6Rm7/y1cuSrKuEmc0HvtpyPRWTP9D1ZBSVVKKlUw1vZvIaRh4aG4p133kGHDh0AAGvWrMHDDz+Mw4cPo2vXrli0aBEWL16M1atXo1OnTnjrrbcQFxeHkydPwtvbW+LoiYjIUszuyXrppZcQERGBK1euwMPDA8eOHcOuXbvQp08f7Nixwwoh2l5KSgqioqLQt29fqUMh3Crd3jOsBdzdZBJHQ0QN8VHK0dpLt+ZUc+zNGj16NB544AF06tQJnTp1wttvvw0vLy/s27cPoihiyZIleOONNzBu3DhER0djzZo1KC8vx7p166QOnajZ0Wh1I2X+l3kJe89cg0YrSh0SORGze7L27t2LX375Bf7+/nBxcYGLiwsGDx6MhQsXYubMmTh8+LA14rSpxMREJCYmQqVSwdfXV+pwmj39UMH+HCpI5BAi/T1RWFqFs1fL0D20hdThmKSsrAw7d+5Ebm4uqqurjfbNnDmzUefUaDT45ptvUFZWhgEDBiAnJwf5+fmIj483HKNQKDB06FDs2bMHL7zwQp3nqaqqQlVVleGxSqUCoFtEWa1WNyo2qpu+Pdmu1mMvbbzt+BW889MJ5KsqDduCfJR4LaEL7r8nUMLIms5e2tgZmdOmZidZGo0GXl66MfetW7fG5cuX0blzZ4SHh+PkyZPmno6oQaIoYp9hEWI/iaMhIlO09/dEek6RwxS/OHz4MB544AGUl5ejrKwMfn5+KCwshIeHBwICAsxOso4ePYoBAwagsrISXl5e2LBhA6KiorBnzx4AQGCg8Re4wMBAnD9/vt7zLVy4EMnJybW2p6amssS8laSlpUkdgtOzhzaeXWuZiTJU52Rgc44U0ViePbSxsykvLzf5WLOTrOjoaBw5cgSRkZHo168fFi1aBDc3N6xcuRKRkZHmno6oQWcLy1BQUgU3V87HInIUkTeLX5xxkOGCL7/8MkaPHo3ly5ejRYsW2LdvH+RyOSZMmICXXnrJ7PN17twZmZmZuHHjBv773/9i4sSJ2Llzp2G/fg6zniiKtbbdbu7cuZg9e7bhsUqlQlhYGOLj4+Hj42N2fFQ/tVqNtLQ0xMXFcVkaK5G6jTVaESOX7DLqwbqdACDQR4mts+6FzKX+30t7JnUbOzP9SAJTmJ1k/f3vf0dZme7C+dZbb+Ghhx7CkCFD0KpVK3z99dfmno6oQfperN5tW0Ap53wsIkdwq4y7YyRZmZmZWLFiBWQyGWQyGaqqqhAZGYlFixZh4sSJGDdunFnnc3NzMxS+6NOnDw4cOIAPPvgAr776KgAgPz8fwcHBhuMLCgpq9W7dTqFQQKFQ1Noul8v5BcpK2LbWJ1UbHzxzDeevV0GXTtXt/PUqHL5Y4vAVjfk5tjxz2tPsJGvkyJGGf0dGRiI7OxtFRUVo2bJlg3fiiBrDMB+L62MROYxIf30Z91JotSJc7PxusFwuN1y/AgMDkZubi3vuuQe+vr7Izc1t8vlFUURVVRUiIiIQFBSEtLQ09OrVCwBQXV2NnTt34t13323y6xDR3RWU1N2D1djjiOrTqMWI9S5cuABBEBAaGmqpeIgMdPOxdIsQD2CSReQwwlq6Qy4TUKnWYs3ec+gS5IPYCD+7HXrTq1cvHDx4EJ06dcLw4cPx5ptvorCwEF988QW6detm1rlef/11JCQkICwsDCUlJVi/fj127NiBLVu2QBAEzJo1CwsWLEDHjh3RsWNHLFiwAB4eHhg/fryV3h0R3S7AW2nR44jqY3aSVVNTg+TkZHz44YcoLdVNavby8sJf/vIXzJs3j92SZDFnrpaisLQKClcX9GzbQupwiMhE245fgb4ScvKmbABAsK8S80ZHYVR0cAPPlMaCBQtQUlICAPjnP/+JiRMnYvr06ejQoQNWrVpl1rmuXLmCp59+Gnl5efD19UX37t2xZcsWxMXFAQDmzJmDiooKzJgxA9evX0e/fv2QmprKNbKIbCQ2wg/BvkrkF1eiroLtAoAgXyViI1hsi5rG7CTrxRdfxIYNG7Bo0SIMGDAAgK6se1JSEgoLC/HRRx9ZPEhqnvRDBWPCW0LhyvlYRI5gS1Yepq89VOvLS35xJaavPYTlE3rbXaLVp08fw7/9/f2xefPmRp/r008/bXC/IAhISkpCUlJSo1+DiBpP5iJg3ugoTF97CAJg9LdK39c+b3SU3fa8k+MwO8n66quvsH79eiQkJBi2de/eHW3btsUTTzzBJIsshkMFiRyLRisieVN2nXeHRei+wCRvykZcVBC/wBCRZEZFB2P5hN5I3pSNvOLb1smy4x53cjxmJ1lKpRLt2rWrtb1du3Zwc3OzRExExutjOXh1H6LmIj2nyOgLy51EAHnFlUjPKbKLql333XefScf98ssvVo6EiGxtVHQw4qKCkJ5ThIKSSgR4K+167ig5HrOTrMTERPzzn//EqlWrDCVlq6qq8Pbbb+PFF1+0eIDUPP1RUIprZdVQyl3QI7SF1OEQkQkcrWrXjh07EB4ejgcffJDziYmaIZmLYBc3fMg5mZRk3blGyLZt2xAaGooePXoAAH7//XdUV1djxIgRlo+QmiX9fKw+4X5wc3WROBoiMoWjVe165513sHr1anzzzTd46qmnMGXKFERHR0sdFhEROQGTkixfX1+jx48++qjR47CwMMtFRIRbSRbvMBE5Dker2jVnzhzMmTMHe/fuxWeffYZBgwahc+fOmDJlCsaPHw8fHx+pQyQiIgdlUpJlbglboqbQakXsz+EixESOxlGrdg0YMAADBgzABx98gG+++QYpKSn461//isuXLzPRIiKiRuE4LLI7J6+U4Hq5Gh5uMnQP9b37E4jIbuirdgX5Gg8JDPJV2mX59tsdOnQIO3fuxPHjxxEdHc15WkRE1GhmF74gsjZ9VcE+7fwgl/E+AJGjcaSqXZcvX8bq1auxevVqqFQqTJgwAfv370dUVJTUoRERkQNjkkV2Rz8fq3+kfczbICLzOULVrgceeADbt29HfHw8/u///g8PPvggXF15WSQioqbj1YTsim4+FhchJiLr27JlC4KDg5Gbm4vk5GQkJyfXedyhQ4dsHBkRETk6k5Os8ePHY+zYsRg1apTDTQR+5JFHsGPHDowYMQLffvut1OFQA47nq1BcoYanmwzd2nA+FhFZz7x586QOgYiInJTJSVbnzp3x7rvv4plnnsG9996Lhx9+GGPGjHGI8u0zZ87ElClTsGbNGqlDobvQDxXsG+EHV87HIiIrYpJFRETWYvK32Hnz5iEjIwOnT5/G2LFjsXHjRnTs2BG9e/dGUlISDh8+bM04m2T48OHw9vaWOgwywb6zHCpIRERERI7N7K6C0NBQzJgxA1u3bsXVq1fx2muv4Y8//sCIESMQHh6OF198EceOHTP5fLt27cLo0aMREhICQRDw/fff1zpm2bJliIiIgFKpRExMDHbv3m1u2OQANFwfi4iIiIicQJMKX3h7e+PPf/4z/vznP0Oj0WDHjh3YuHEj9u7di65du5p0jrKyMvTo0QOTJ0/Go48+Wmv/119/jVmzZmHZsmUYNGgQVqxYgYSEBGRnZ6Nt27YAgJiYGFRVVdV6bmpqKkJCQkx+P1VVVUbnUalUAAC1Wg21Wm3yecg0+jbV/zfrkgollTXwUriik78729wC7mxjsjy2sfWwTYmIyFFZrLqgTCbDiBEjMGLECLOel5CQgISEhHr3L168GFOnTsWzzz4LAFiyZAm2bt2K5cuXY+HChQCAjIyMxgd+m4ULF9ZZXSo1NRUeHh4WeQ2qLS0tDQDwy2UBgAzhHtVI3bpF2qCcjL6NyXrYxpZXXl4udQhERESNYtcl3Kurq5GRkYHXXnvNaHt8fDz27Nlj8debO3cuZs+ebXisUqkQFhaG+Ph4h6uo6AjUajXS0tIQFxcHuVyODV8cAlCI0f264IFB7aQOzync2cZkeWxj69GPJpBSUVER/Py4Zl9zoNGKSL+5hEh6ThH6dwiwywW0icgx2HWSVVhYCI1Gg8DAQKPtgYGByM/PN/k8I0eOxKFDh1BWVobQ0FBs2LABffv2rXWcQqGAQqGotV0ul/PLkxXJ5XIILjJknL8BABjcMYDtbWH8DFsf29jybN2e3bt3x5AhQzBlyhTExMTg1KlTeOihh3Dq1CmbxkG2tyUrD8mbslFUWoFFscCUNQfg5+WOeaOjMCo6WOrwiMgB2XWSpScIxneSRFGsta0hW7dutXRIZGHHLqtQUlUDH6Ur7glmryER2d7EiRORlZWF4cOHY8SIEdi9e3edN+TIuWzJysP0tYcgAlDIbm3PL67E9LWHsHxCbyZaRGQ2u06yWrduDZlMVqvXqqCgoFbvliWlpKQgJSUFGo3Gaq9Bxvae1VUVjI1oxeEZRGQTWq0WAODioiu0+8orrwAARo0ahSeffBJeXl748ssvJYuPrE+jFZG8KRtiHftEAAKA5E3ZiIsK4rWJiMxiUpL14YcfmnzCmTNnNjqYO7m5uSEmJgZpaWl45JFHDNvT0tLw8MMPW+x17pSYmIjExESoVCr4+vpa7XXoln03k6wB7Vm6nYhs44knnsDw4cMxffp0w7b09HQ899xzSE5Oxt69e/H222/jvffekzBKsqb0nCLkFVfWu18EkFdcifScIl6fiMgsJiVZ77//vkknEwTB7CSrtLQUp0+fNjzOyclBZmYm/Pz80LZtW8yePRtPP/00+vTpgwEDBmDlypXIzc3FtGnTzHodsl9qjRYHbk427h/JCeZEZBs7d+40qih7/PhxPPjgg/jnP/+Jl156Cbt378aECROYZDmxgpL6E6zGHEdEpGdSkpWTk2O1AA4ePIjhw4cbHuur+02cOBGrV6/G448/jmvXrmH+/PnIy8tDdHQ0Nm/ejPDwcKvFRLZ17LIKZdUa+LrLcU8Q52MRkW2UlZVBJtNNwjl//jwSEhLw7rvvYsqUKQCA4OBgFBYWShkiWVmAt9KixxER6Uk+J2vYsGEQxbpGQ98yY8YMzJgxw0YRcU6Wre3PuQ4A6BfhBxeOeSciG+nZsydmzZqFcePG4a233sKMGTMMCRYAbNmyBR06dJAwQrK22Ag/BPsqkV9cWee8LAFAkK8SsREcZUH105f/LyipRIC37vPCOXzUqCTr4sWL2LhxI3Jzc1FdXW20b/HixRYJTEqck2Vb+24OFeR4dyKypSVLluDxxx/HokWL8Nhjj+H//u//4Ovri549e2LXrl1ITk52imsa1U/mImDe6ChMX3sId34l1j+eNzqKX5ipXvry/7fP7Qv2VbL8P5mfZP38888YM2YMIiIicPLkSURHR+PcuXMQRRG9e/e2RozkxDRaIOO8rieLSRYR2VKfPn1w5swZw+Nu3bph7ty5yM/Ph7u7O1566SU8//zzEkZItjAqOhjLJ/Q2rJOlF8QvynQXt5f/vx3L/xPQiCRr7ty5eOWVVzB//nx4e3vjv//9LwICAvDUU09h1KhR1oiRnFhuGVCh1qKlhxydArylDoeImrGJEyfimWeeQUFBAVq2bAk3NzepQyIbGRUdjLioIOw7XYDC4/vw2cS+6N8hgD1YVC+W/6e7cTH3CcePH8fEiRMBAK6urqioqICXlxfmz5+Pd9991+IBknP7o1j3h6d/ZCvOxyIiyQmCgMDAQCZYzZDMRTDMveKcGrobc8r/U/NkdpLl6emJqqoqAEBISIjRUAtnqcKUkpKCqKgo9O3bV+pQnN4fqltJFhEREZEjYPl/uhuzk6z+/fvjt99+AwA8+OCDeOWVV/D2229jypQp6N+/v8UDlEJiYiKys7Nx4MABqUNxalU1WuSU6JIszsciImewcOFC9O3bF97e3ggICMDYsWNx8uRJo2NEUURSUhJCQkLg7u6OYcOG4dixYxJFTESNwfL/dDdmJ1mLFy9Gv379AABJSUmIi4vD119/jfDwcHz66acWD5Cck0Yr4j8HLkCtFeCjdEVka0+pQyIiarKdO3ciMTER+/btQ1paGmpqahAfH4+ysjLDMYsWLcLixYuxdOlSHDhwAEFBQYiLi0NJSYmEkROROfTl/+sbVCpAV2WQ5f+bL7MLX0RGRhr+7eHhgWXLllk0IHJ++nKnBaoKAAJKq9QYsmg7qzgRkcPbsmWL0eNVq1YhICAAGRkZuPfeeyGKIpYsWYI33ngD48aNAwCsWbMGgYGBWLduHV544QUpwiYiM91Z/v/2Ahgs/09AExYjrq6uRkFBAbRardH2tm3bNjkocl63lzvV/9lxAcudEpFtbdy4EQkJCZDL5di4cWODx44ZM6bRr1NcXAwA8PPT3c3OyclBfn4+4uPjDccoFAoMHToUe/bsqTPJqqqqMsyFBgCVSgUAUKvVUKvVjY6NatO3J9vVepypjUd0bo1l43vgnZ9OIF91a+5VkI8SryV0wYjOrSV5n87UxvbGnDY1O8k6deoUpk6dij179hhtF0URgiBAo9GYe0q7k5KSgpSUFKd4L/bkznKn+v8KAiCKLHdKRLYzduxY5OfnG+ZN1acp1zVRFDF79mwMHjwY0dHRAID8/HwAQGBgoNGxgYGBOH/+fJ3nWbhwIZKTk2ttT01NhYeHR6Nio4alpaVJHYLTc6Y2nt3lzi1lqM7JwOYcKaK5xZna2F6Ul5ebfKzZSdbkyZPh6uqKH374AcHBwRAE5/synJiYiMTERKhUKvj6+kodjtOoXe5UgI9cROXN7y+3lztlIQwisqbbR2HcOSLDUl588UUcOXIEv/76a619d1479Tcq6zJ37lzMnj3b8FilUiEsLAzx8fHw8fGxbNDNnFqtRlpaGuLi4iCXy6UOxymxja2PbWw9+pEEpjA7ycrMzERGRga6dKmVthM1qK4yph19RWRdF+56HBGRrV24cAHz5s3DZ599ZvZz//KXv2Djxo3YtWsXQkNDDduDgoIA6Hq0goNvDY0uKCio1bulp1AooFAoam2Xy+X8AmUlbFvrYxtbH9vY8sxpT7OrC0ZFRTnNelhkW3WVMe3hV3utdJY7JSJ7UFRUhDVr1pj1HFEU8eKLL+K7777DL7/8goiICKP9ERERCAoKMhrGU11djZ07d2LgwIEWiZuIiKRndpL17rvvYs6cOdixYweuXbsGlUpl9ENUH32501tE3NPiVpLFcqdE5OgSExOxdu1arFu3Dt7e3sjPz0d+fj4qKioA6IYJzpo1CwsWLMCGDRuQlZWFSZMmwcPDA+PHj5c4eiIishSzhwvef//9AIARI0YYbXemwhdkHfpyp9PWHgIAuAiAm0y3j+VOicgZLF++HAAwbNgwo+2rVq3CpEmTAABz5sxBRUUFZsyYgevXr6Nfv35ITU2Ft7e3jaMlIiJrMTvJ2r59uzXioGYiPioILT3kuF6uxu25VJCvkutkEZHDE8XaQ6DvJAgCkpKSkJSUZP2AiIhIEmYnWUOHDrVGHHaFJdyt5/CFG7heroaXwhX/frw7VH+k47OJfdG/QwB7sIjIpvSLAdfnxo0btgmEiIicjtlJ1pEjR+rcLggClEol2rZtW2cVJEfCEu7W88ORywCAuKhADO7YGpv/0M3VYoJFRLZ2t7/vvr6+eOaZZ2wUDREROROzk6yePXs2uDaWXC7H448/jhUrVkCpZJU4ukWrFbH5aB4A4MFuHBZIRNJatWqV1CEQEZGTMru64IYNG9CxY0esXLkSmZmZOHz4MFauXInOnTtj3bp1+PTTT/HLL7/g73//uzXiJQeWkXsdV1RV8Fa4Ykin1lKHQ0RERERkFWb3ZL399tv44IMPMHLkSMO27t27IzQ0FP/4xz+Qnp4OT09PvPLKK/jXv/5l0WDJsf14RNeLFdc1EApXGdRqrcQRERERERFZntk9WUePHkV4eHit7eHh4Th69CgA3ZDCvLy8pkdHTkNz21DB0d1DJI6GiIiIiMh6zE6yunTpgnfeeQfV1dWGbWq1Gu+88w66dOkCALh06RICAwMtFyU5vIPnilBQUgUfpSsGdeBQQSIiIiJyXmYPF0xJScGYMWMQGhqK7t27QxAEHDlyBBqNBj/88AMA4OzZs5gxY4bFg7UVlnC3vB9v9mKN7BoEN1ezc3siIiIiIodhdpI1cOBAnDt3DmvXrsWpU6cgiiIee+wxjB8/3rBa/dNPP23xQG2JJdwtSzdUMB8A8GB3VhUkIiIiIudmdpIFAF5eXpg2bZqlYyEnlZ5ThMLSKvi6yzlUkIiIiIicnklJ1saNG5GQkAC5XI6NGzc2eOyYMWMsEhg5jx+P6hYgHtU1CHIZhwoSERERkXMzKckaO3Ys8vPzERAQgLFjx9Z7nCAInMdERmo0WmzJ4lBBIiIiooZotCLSc4pQUFKJAG8lYiP8IHMRpA6LGsmkJEur1db5b6K72Z9ThMLSarT0kGNA+1ZSh0NERERkd7Zk5SF5UzbyiisN24J9lZg3OgqjonmT2hFx7BZZ1Q83FyAeFc2hgkRERER32pKVh+lrDxklWACQX1yJ6WsPYUsW1551RCZ/692/fz9++ukno22ff/45IiIiEBAQgOeffx5VVVUWD5Acl26ooO4Pw4PduAAxERER0e00WhHJm7Ih1rFPvy15UzY02rqOIHtmcpKVlJSEI0eOGB4fPXoUU6dOxf3334/XXnsNmzZtwsKFC60SJDmmvWev4Xq5Gq083dA/0k/qcIiIiIjsSnpOUa0erNuJAPKKK5GeU2S7oMgiTE6yMjMzMWLECMPj9evXo1+/fvj4448xe/ZsfPjhh/jPf/5jlSDJMf1421BBVw4VJCIiIjJSUFJ/gtWY48h+mPzN9/r16wgMDDQ83rlzJ0aNGmV43LdvX1y4cMGy0UkkJSUFUVFR6Nu3r9ShOCy1Rostx1hVkIiIiKg+Ad5Kix5H9sPkJCswMBA5OTkAgOrqahw6dAgDBgww7C8pKYFcLrd8hBJITExEdnY2Dhw4IHUoDmvPmWu4Ua5Gay839ItgVUEiIiKiO8VG+CHYV4n6CrUL0FUZjI3gtAtHY3KSNWrUKLz22mvYvXs35s6dCw8PDwwZMsSw/8iRI2jfvr1VgiTH8+MR3QLECdHBXOOBiIiIqA4yFwHzRkcBQK1ES/943ugofpdyQCYnWW+99RZkMhmGDh2Kjz/+GB9//DHc3NwM+z/77DPEx8dbJUhyLNU1Wmw9dgUAhwoSERERNWRUdDCWT+iNIF/jIYFBvkosn9Cb62Q5KJMWIwYAf39/7N69G8XFxfDy8oJMJjPa/80338DLy8viATqq5rxq929nClFcoYa/twJ927F7m4iIiKgho6KDERcV1Gy/Ozojk5MsPV9f3zq3+/nxy7Rec1+1+4ffdVUFH4gO4h8HIiIiIhPIXAQMaM957M6CdbUtrLmv2l1Vo0Fqtr6qIBcgJiIiIqLmh0mWBXHVbuDXPwpRUlmDQB8F+oS3lDocIiIiIiKbY5JlQVy1+9YCxA90C4YLhwoSERERUTNk9pwsql9zX7W7Uq1BWrauquBDrCpIREQ20pyLTRGRfWKSZUHNfdXu3X8UoqSqBsG+SvQK41BBIiKyvuZebIqI7BOHC1pQc1+1W78AMYcKEhGRLTT3YlNEZL+YZFlQQ6t2A7o5Wc66avftQwW5ADEREVkbi00RkT1jkmVh9a3aDQCjuwc77dCFnaeuoqxagzYt3NErrIXU4RARSWLXrl0YPXo0QkJCIAgCvv/+e6P9oigiKSkJISEhcHd3x7Bhw3Ds2DFpgnVwLDZFRPaMSZYVjIoOxq+v3oevnuuPD57oiRnD2gMADl+4Aa2T3lG7VVUwCILgfD11RESmKCsrQ48ePbB06dI69y9atAiLFy/G0qVLceDAAQQFBSEuLg4lJSU2jtTxNfdiU0Rk31j4og4pKSlISUmBRqNp9DluX7W7olqDL/aex8XrFdiXcw0D27e2VKh2oVKtwbbj+qGCXICYiJqvhIQEJCQk1LlPFEUsWbIEb7zxBsaNGwcAWLNmDQIDA7Fu3Tq88MILdT6vqqoKVVVVhscqlQoAoFaroVarLfwOHEdrD1coZHe/cdnaw9XkdtIf15zb1drYxtbHNrYec9qUSVYdEhMTkZiYCJVKBV9f3yafz91NhtE9Q7Bufy6+OXjR6ZKs7ScKUF6tQWhLd/QIbXp7ERE5o5ycHOTn5yM+Pt6wTaFQYOjQodizZ0+9SdbChQuRnJxca3tqaio8PDysFq8jWBR792MKj+/D5uPmnTctLa1xAZHJ2MbWxza2vPLycpOPZZJlI3+KCcW6/bnYfDQPyQ93hY9SLnVIFvPDUd1QwQe7B3OoIBFRPfLz8wEAgYGBRtsDAwNx/vz5ep83d+5czJ492/BYpVIhLCwM8fHx8PHxsU6wtnBpE5D5KlB+6dY2jzZAz3eBNqNNOsW241fw8teZAGBUAEN/JXr/8Z64/57AO59WL7VajbS0NMTFxUEud57rtD1hG1vPtuNX8M5PJ3C9tAL/7KPFPw66oKWXO15L6GLW7wHVTz+SwBRMsmykZ1gLdAzwwh8Fpdj0+2U81S9c6pAsory6Br8cLwAAPNSNQwWJiO7mzptRoig2eINKoVBAoVDU2i6Xyx33S+qF74A9jwF31gYsP6PbPuRbIGzcXU+T0D0UgovM4utkOXTbOgi2sWVtycrDjHW/QwSgkOn+nlRpBeRer8KMdb9j+YTeTlt8zZbM+cwyybIRQRDw5z5heHvzcXxz8KLTJFnbT1xFhVqDtn4eiG7jwHdUiYisLCgoCICuRys4+NaXnYKCglq9W05NqwEyXkKtBAu4uU0AMmYBbR4GXGR3Pd2o6GDERQUhPacIBSWVCPDWrUfpjMulENXlbssZCNAtZxAXFcTfCxtidUEbGturDVxdBGReuIFTV5yjktSPR3ULEHOoIBFRwyIiIhAUFGQ0T6K6uho7d+7EwIEDJYzMxq7uBsovNnCACJRf0B1nIn2xqYd7tsGA9q34RZKaFS5nYJ+YZNmQv7cC93UJAAB8c/CCxNE0XVlVDX45oRsq+GA3dkETEZWWliIzMxOZmZkAdMUuMjMzkZubC0EQMGvWLCxYsAAbNmxAVlYWJk2aBA8PD4wfP17awG2pIs+yxxE1c1zOwD5xuKCN/alPGFKzr+C7Q5cwZ1QXyGWOm+f+cqIAlWot2rXyQNcQDhUkIjp48CCGDx9ueKwvWDFx4kSsXr0ac+bMQUVFBWbMmIHr16+jX79+SE1Nhbe3t1Qh2567iTflTD2OqJkL8FZa9DiyDCZZNjassz9aeylQWFqFX04UYGTXIKlDajT9AsQcKkhEpDNs2DCIYv1rNwmCgKSkJCQlJdkuKHvjPwTwCL1ZVbCuthJ0+/2H2DoyIocUG+GHYF8l8osr6/uNQpCvbq4i2Y7jdqM4KLnMBY/2bgMA+OZgQ2PS7VtpVQ22n9QPFWRVQSIiMpGLDIj54OaDO2/Q3Xwcs8SkohdEpJuTOG90FIB6f6Mwb3QU5yraGJMsCfypTygAYPvJAocdH/vz8SuoqtEisrUn7gluRsNciIio6cLG6cq0e7Qx3u4RanL5diK6ZVR0MJZP6I0gX+MhgUG+SpZvlwiHC0qgQ4A3erVtgcO5N7Dh0CW8MLS91CGZTKMVkZ5ThE92nwUAPNCNQwWJiKgRwsbpyrRf3a0rcuEerBsiyB4sokbRL2ew73QBCo/vw2cT+6J/hwD2YEmESZZE/twnDIdzb+A/By/g+XsjHSJR2ZKVV2vBx/UHchHdxod3SIiIyHwuMiBwmNRREDkNmYuA2Ag/bD6OJq0Xp7+pzrXnGo9JlkQe6h6M5E3HcOZqGQ5fuIHebVtKHVKDtmTlYfraQ7UmVBaWVmP62kPsiiYiIiJyAnXdVA/2VWLe6Ch+1zMD52RJxFspxwM315ay9zWzGlpJXC95UzY02oaOICIiIiJ7pr+pfufixvnFlZi+9hC2ZHH9OlMxyZLQn/uEAQA2/Z6H8uoaiaOpH1cSJyIiInJuDd1U12/jTXXTMcmSUL8IP7T180BpVQ1+OpovdTj14kriRERERM6NN9Uti0mWhARBwJ9idOXcv8mw3yGDHm6mTd3jSuJEREREjok31S2LSZbEHo0JhSAA+84W4fy1MqnDqeVQ7nUkbTzW4DECdBMiuZI4ERERkWMy9WY5b6qbxumTrAsXLmDYsGGIiopC9+7d8c0330gdkpGQFu4Y0tEfAPBtxkWJo7lFqxWxctcZ/Pmjvbh0owKtvdwAcCVxIiICoNUAV3YA577S/VerkToiImqi2Ag/BPsqa33X0+NNdfM4fZLl6uqKJUuWIDs7G9u2bcPLL7+MsjL76jH6cx/dkMFvMy7axWTCorJqTF1zAAs2n0CNVsSD3YPxy1+H4SOuJE5ERBe+Aza2A34eDuwZr/vvxna67UTksGQuAuaNjgLAm+qW4PTrZAUHByM4WJcABAQEwM/PD0VFRfD09JQ4slvuvycQvu5y5BVX4rfThbi3k79ksew/ew0vrc9EvqoSbq4uSBrdFU/GhkEQBMNK4lycjoiombrwHbD7MeDO+mPll3Tbh3wLhI2TJDQiarpR0cFYPqF3rXWygrhOltkk78natWsXRo8ejZCQEAiCgO+//77WMcuWLUNERASUSiViYmKwe/fuRr3WwYMHodVqERYW1sSoLUspl2FszxAAwH8kWjNLoxXx75//wJMf70O+qhKR/p74X+IgjO/XFoJwK4mSuQgY0L4VHu7ZBgPat2KCRUTUXGg1QMZLqJVgAbe2Zczi0EEiBzcqOhi/vnofvnquPz54oie+eq4/fn31PiZYZpK8J6usrAw9evTA5MmT8eijj9ba//XXX2PWrFlYtmwZBg0ahBUrViAhIQHZ2dlo27YtACAmJgZVVVW1npuamoqQEF3ycu3aNTzzzDP45JNP6o2lqqrK6DwqlQoAoFaroVarm/Q+7+aRnsFYs/c8th7Lx9XicrTwkFv19W53taQKf/32KPacLTLEMu+he+CpcLXq+9af29pt25yxja2PbWw9bFM7c3U3UN7Q3GERKL+gOy5wmK2iIiIr0N9UbyqNVmy2I6AkT7ISEhKQkJBQ7/7Fixdj6tSpePbZZwEAS5YswdatW7F8+XIsXLgQAJCRkdHga1RVVeGRRx7B3LlzMXDgwHqPW7hwIZKTk2ttT01NhYeHhylvp0naeMhwqRx456ttuDfYNnOzTt4Q8MVpF5SoBbi5iPhThBax7hew82fb9ailpaXZ7LWaK7ax9bGNLa+8vFzqEOh2FXmWPY6InNqWrLxaww6Dm9GwQ8mTrIZUV1cjIyMDr732mtH2+Ph47Nmzx6RziKKISZMm4b777sPTTz/d4LFz587F7NmzDY9VKhXCwsIQHx8PHx8f89+Ama62PI+3Np/EieoWeOeBARY777bjV/DOTyeQr7r1IQ/0VqBrmxbYcqIAogh0CvDCB493R4cAL4u97t2o1WqkpaUhLi4Ocrnteu6aE7ax9bGNrUc/moDshLuJX4pMPY6InNaWrDxMX3uo1uDi/OJKTF97qFkUTbPrJKuwsBAajQaBgYFG2wMDA5Gfn2/SOX777Td8/fXX6N69u2G+1xdffIFu3brVOlahUEChUNTaLpfLbfLl6dGYtli09Q8cu1yCU1fL0TXEt8nn3JKVhxnrfr/5Ib/VPZt7oxq5NwoAAE/GtsW80VFQymVNfr3GsFX7NmdsY+tjG1se29PO+A8BPEJ1RS7qnJcl6Pb7D7F1ZERkRzRaEcmbsuudvSkASN6UjbioIKceOmjXSZbe7YUXAF3v1J3b6jN48GBotVprhGVxLT3dEBcViB+P5uGbgxfRdUzTkqyGPuR6LdzleGtstFN/yImIyAJcZEDMBzerCwowTrRuXkNiluiOI6JmKz2nyGiI4J1EAHnFlUjPKbLIvC97JXl1wYa0bt0aMpmsVq9VQUFBrd4tS0pJSUFUVBT69u1rtdeoz59urpn1feYlVNU0rULT3T7kAHCjQo30nKImvQ4RETUTYeN0Zdo92hhv9whl+XYiAgAUlDT83dPc4xyVXSdZbm5uiImJqTWhPC0trcECFk2VmJiI7OxsHDhwwGqvUZ8hHf0R5KPEjXI1fj5e0KRz8UNOREQWFzYOGHMOGLEdGLhO998xOUywiAgAEOCttOhxAHRLQ1zZAZz7SvdfB1gqQvLhgqWlpTh9+rThcU5ODjIzM+Hn54e2bdti9uzZePrpp9GnTx8MGDAAK1euRG5uLqZNmyZh1NYjcxHwaEwbpGw/g/8cvIAHujV+UqBGa1qFQrM+5ERERC4ylmknojrFRvgh2FeJ/OLK+mZvIshXV87dJBe+063Rd/sSEh6huuHLdnxzR/KerIMHD6JXr17o1asXAGD27Nno1asX3nzzTQDA448/jiVLlmD+/Pno2bMndu3ahc2bNyM8PFzKsK3qTzG6xZJ3nbqKvOIKs59fqdbgvdSTeO2/Rxs8ToCulKbJH3IiInJsDng3mIgci8xFwLzRUQBuL7kGo8fzRkeZVg/gwne6eaB3rtFXfkm3/cJ3TY7XWiTvyRo2bBhEseEelxkzZmDGjBk2ikg3JyslJQUajTQXn3atPREb4Yf0nCJ8d+gSEod3MPm5Px+/gqRNx3ChSJecdQ3xwbHLqvqmKJv+ISciIsfmoHeDicjxjIoOxvIJvWutkxVkzjpZWo3ub1ZDdQozZgFtHrbLgjuSJ1n2KDExEYmJiVCpVPD1bXoZ9cb4c58wpOcU4ZuDFzBjWPu7VlO8UFSO5E3Z2Hb8CgAgxFeJN0dHYWTXIGw9lt+0DzkRETk2/d3gO7+s6O8Gs2gFEVnYqOhgxEUFIT2nCAUllQjw1o2eMvnm/tXdtXuwjIhA+QXdcXY4fJlJlp16oFsQ5v0vC+eulePAuev1DumrqtHg411nsXT7aVSqtXB1EfDskEjMHNEBHm66/71N/pATEZHjcvC7wUTkuGQuQuPLtFfkWfY4G2OSZac83FzxUPcQfH3wAlK2n0ZecZtaydGvfxTizf9l4WxhGQCgf6Qf/vlwNDoGetc6X5M+5ERE5Lgc/G4wETVT7iaOtjL1OK1G93euIk/3HP8hVr2xxCTLjrVt5Q4A2HnqKnaeugpAV6hi5ogO+PX0Nfx4RJe5+3sr8PcH78GYHiEmL9JMRETNhIPfDSaiZsp/iG7eaPkl1N0TL+j2+w+5+7kkmJPKJKsOUhe+AIAtWXn4v62nam3PK67E3O+yAAAuAjBxYDu8HNcJPkq5rUMkIiJHYOm7wUREtuAi0yVBux8D6ivhFrPk7r1REs1JlbyEuz2ScjFiQLe+VfKm7AaPkcsE/C9xMOaN7soEi4jIwSxbtgwRERFQKpWIiYnB7t27rfdi+rvBtYop6wmAR5hpd4OJiGwpbJwuCfJoY7zdI9S05Oiuc1Khm5NqheUsmGTZofScIqNKgHVRa0SUVtXYKCIiIrKUr7/+GrNmzcIbb7yBw4cPY8iQIUhISEBubq51XlB/NxhAvavWmHI3mIhICmHjgDHngBHbgYHrdP8dk2Na75M5c1ItjMMF7VBBScMJlrnHERGR/Vi8eDGmTp2KZ599FgCwZMkSbN26FcuXL8fChQtrHV9VVYWqqirDY5VKBQBQq9VQq9WmvWjQaGDgt0DmqzfnN9zkEQr0fEe339RzOTF9e5rcrmQ2trH1OW0b+w269W+NVvdzN6V5ANxNO87v7u1lTpsyybJDAd5Kix5HRET2obq6GhkZGXjttdeMtsfHx2PPnj11PmfhwoVITk6utT01NRUeHh5mvLoMEP4FeN6x+XcAv2824zzOLy0tTeoQnB7b2PrYxgDgAXh+dffDsgBk3f3vYHl5ucmvzCSrDlIXvoiN8EOwrxL5xZX11VJBkK+y3rWziIjIPhUWFkKj0SAwMNBoe2BgIPLz8+t8zty5czF79mzDY5VKhbCwMMTHx8PHx8eq8TY3arUaaWlpiIuLg1zO+c7WwDa2PrbxbbQa4KduQPll1F+hsA2QcMSkIdP6kQSmYJJVh8TERCQmJkKlUsHX19fmry9zETBvdBSmrz1UXy0VzBsdxcWEiYgc1J3LbYiiWO8SHAqFAgqFotZ2uVzOL1BWwra1Prax9bGNAUAOxLx7s7ogUHeFwncAhWmjw8xpTxa+sFOjooOxfEJvBPka/08P8lVi+YTeGBXNUrtERI6mdevWkMlktXqtCgoKavVuERGRBTS1QmEjsSfLjo2KDkZcVBDSc4pQUFKJAG/dEEH2YBEROSY3NzfExMQgLS0NjzzyiGF7WloaHn74YQkjIyJyYmHjgDYP66oIVuTp1gX0H2LVqqpMsuyczEXAgPatpA6DiIgsZPbs2Xj66afRp08fDBgwACtXrkRubi6mTZsmdWhERM7LRQYEDrPZyzHJIiIisqHHH38c165dw/z585GXl4fo6Ghs3rwZ4eHhUodGREQWwiSrDlJXFyQiIuc2Y8YMzJgxQ+owiIjISlj4og6JiYnIzs7GgQMHpA6FiIiIiIgcDJMsIiIiIiIiC2KSRUREREREZEFMsoiIiIiIiCyIhS8aIIq6VaFVKpXEkTgntVqN8vJyqFQqrkhuJWxj62MbW4/+b6/+bzHp8NpkPfx9tj62sfWxja3HnOsSk6wGlJSUAADCwsIkjoSIqPkqKSmBr6+v1GHYDV6biIikZcp1SRB5i7BeWq0Wly9fhre3NwRBsNrr9O3bt0mVDM15vinHNnRMffvq2n63bSqVCmFhYbhw4QJ8fHxMir+x2MZsY1P2mdLGdz5mG1uvjUVRRElJCUJCQuDiwtHterw2mb7P3N9pW/4+1xeftZ7PNrb+89nG1n/+3Y5tzH5T271v375IT083+brEnqwGuLi4IDQ01OqvI5PJmvRLYM7zTTm2oWPq21fXdlO3+fj4WP2PANuYbWzKPlPas77nso1N22duG7MHqzZem0zf19jfaVv8Ptf32tZ6PtvY+s9nG1v/+Xc7tjH7TW13mUwGX19fk69LvDVoBxITE232fFOObeiY+vbVtd3UbbbANra+5tLGUrWvJV6bbUzm4OfN+tjG1sc2tj57auPG7De13c19nxwuSJJRqVTw9fVFcXGxTe60NEdsY+tjGxM5D/4+Wx/b2PrYxvaBPVkkGYVCgXnz5kGhUEgditNiG1sf25jIefD32frYxtbHNrYP7MkiIiIiIiKyIPZkERERERERWRCTLCIiIiIiIgtikkVERERERGRBTLKIiIiIiIgsiEkWERERERGRBTHJIofwyCOPoGXLlnjsscekDsVp/PDDD+jcuTM6duyITz75ROpwnBI/t0TOjb/jlsdrk/Xxc2sbLOFODmH79u0oLS3FmjVr8O2330odjsOrqalBVFQUtm/fDh8fH/Tu3Rv79++Hn5+f1KE5FX5uiZwbf8cti9cm2+Dn1jbYk0UOYfjw4fD29pY6DKeRnp6Orl27ok2bNvD29sYDDzyArVu3Sh2W0+Hnlsi58Xfcsnhtsg1+bm2DSRY12a5duzB69GiEhIRAEAR8//33tY5ZtmwZIiIioFQqERMTg927d9s+UCfS1Da/fPky2rRpY3gcGhqKS5cu2SJ0h8HPNZFj4++w7fHaZH38XDsOJlnUZGVlZejRoweWLl1a5/6vv/4as2bNwhtvvIHDhw9jyJAhSEhIQG5uruGYmJgYREdH1/q5fPmyrd6GQ2lqm9c1SlgQBKvG7Ggs8bkmIunw2mR7vDZZH69NDkQksiAA4oYNG4y2xcbGitOmTTPa1qVLF/G1114z69zbt28XH3300aaG6HQa0+a//fabOHbsWMO+mTNnil9++aXVY3VUTflc83NLJD1em2yP1ybr47XJvrEni6yquroaGRkZiI+PN9oeHx+PPXv2SBSVczOlzWNjY5GVlYVLly6hpKQEmzdvxsiRI6UI1yHxc03k2Pg7bHu8NlkfP9f2xVXqAMi5FRYWQqPRIDAw0Gh7YGAg8vPzTT7PyJEjcejQIZSVlSE0NBQbNmxA3759LR2uUzClzV1dXfHee+9h+PDh0Gq1mDNnDlq1aiVFuA7J1M81P7dE9onXJtvjtcn6eG2yL0yyyCbuHFMtiqJZ46xZXch8d2vzMWPGYMyYMbYOy6ncrY35uSWyb7w22R6vTdbHa5N94HBBsqrWrVtDJpPVujNYUFBQ604LWQbb3PrYxkSOjb/Dtsc2tz62sX1hkkVW5ebmhpiYGKSlpRltT0tLw8CBAyWKyrmxza2PbUzk2Pg7bHtsc+tjG9sXDhekJistLcXp06cNj3NycpCZmQk/Pz+0bdsWs2fPxtNPP40+ffpgwIABWLlyJXJzczFt2jQJo3ZsbHPrYxsTOTb+Dtse29z62MYORMrShuQctm/fLgKo9TNx4kTDMSkpKWJ4eLjo5uYm9u7dW9y5c6d0ATsBtrn1sY2JHBt/h22PbW59bGPHIYhiHSu/ERERERERUaNwThYREREREZEFMckiIiIiIiKyICZZREREREREFsQki4iIiIiIyIKYZBEREREREVkQkywiIiIiIiILYpJFRERERERkQUyyiIiIiIiILIhJFhERERERkQUxySJqpu69916sW7fOZq/32GOPYfHixTZ7PSIicjy8NpGzYJJFZAGTJk2CIAi1fkaNGiV1aHX64YcfkJ+fjyeeeAI7duyoM/bbf1avXl3neTIyMiAIAn799dc6948cORJjxowBALz55pt4++23oVKprPW2iIjoNrw28dpE0nGVOgAiZzFq1CisWrXKaJtCoaj3eLVaDblcbu2w6vThhx9i8uTJcHFxwcCBA5GXl2fY99JLL0GlUhm9F19f3zrPExMTgx49emDVqlUYPHiw0b4LFy5g27Zt+O677wAA3bt3R7t27fDll19i+vTpVnhXRER0J16beG0iabAni8hCFAoFgoKCjH5atmxp2C8IAj766CM8/PDD8PT0xFtvvQUA2LRpE2JiYqBUKhEZGYnk5GTU1NQYnnfjxg08//zzCAwMhFKpRHR0NH744QfD/v/+97/o2rUrFAoF2rVrh/fee6/BOAsLC7Ft2zbDXTw3NzejmN3d3Y3eS2BgIP79738jMjIS7u7u6NGjB7799lvD+aZOnYr//Oc/KCsrM3qd1atXw9/fHw8++KBh25gxY/DVV181onWJiKgxeG3itYkkIhJRk02cOFF8+OGHGzwGgBgQECB++umn4pkzZ8Rz586JW7ZsEX18fMTVq1eLZ86cEVNTU8V27dqJSUlJoiiKokajEfv37y927dpVTE1NFc+cOSNu2rRJ3Lx5syiKonjw4EHRxcVFnD9/vnjy5Elx1apVoru7u7hq1ap649iwYYPo6ekpajQak97L66+/Lnbp0kXcsmWLeObMGXHVqlWiQqEQd+zYIYqiKF67dk1UKBRGr6nVasXIyEhxzpw5RufevHmzqFAoxMrKygbbioiImo7XJl6bSDpMsogsYOLEiaJMJhM9PT2NfubPn284BoA4a9Yso+cNGTJEXLBggdG2L774QgwODhZFURS3bt0quri4iCdPnqzzdcePHy/GxcUZbfvb3/4mRkVF1Rvr+++/L0ZGRjb4XvQXstLSUlGpVIp79uwxOmbq1Knik08+aXj8+OOPi/fee6/h8S+//CICEE+cOGH0vN9//10EIJ47d67e1yciIsvgtYnXJpIO52QRWcjw4cOxfPlyo21+fn5Gj/v06WP0OCMjAwcOHMDbb79t2KbRaFBZWYny8nJkZmYiNDQUnTp1qvM1jx8/jocfftho26BBg7BkyRJoNBrIZLJaz6moqIBSqTTpPWVnZ6OyshJxcXFG26urq9GrVy/D46lTpyI+Ph6nT59Ghw4d8Nlnn2HQoEHo3Lmz0fPc3d0BAOXl5Sa9PhERNQ2vTbw2kTSYZBFZiKenJzp06HDXY26n1WqRnJyMcePG1TpWqVQa/vDXRxRFCIJQa1tDWrdujevXrzd4zO3xAcCPP/6INm3aGO27feL0/fffj/DwcKxevRpz5szBd999h6VLl9Y6X1FREQDA39/fpNcnIqKm4bWJ1yaSBpMsIgn17t0bJ0+erPcC2L17d1y8eBGnTp2q845hVFRUrRK1e/bsQadOneq8UwgAvXr1Qn5+Pq5fv240+bkuUVFRUCgUyM3NxdChQ+s9ThAETJ48GZ988glCQ0Ph4uKCP//5z7WOy8rKQmhoKFq3bt3g6xIRkXR4bSJqOiZZRBZSVVWF/Px8o22urq4N/tF+88038dBDDyEsLAx/+tOf4OLigiNHjuDo0aN46623MHToUNx777149NFHsXjxYnTo0AEnTpwwrHPyyiuvoG/fvvjnP/+Jxx9/HHv37sXSpUuxbNmyel+zV69e8Pf3x2+//YaHHnqowffk7e2Nv/71r3j55Zeh1WoxePBgqFQq7NmzB15eXpg4caLh2MmTJ2P+/Pl4/fXX8cQTT9S6MwoAu3fvRnx8fIOvSURElsNrE69NJBFpp4QROYeJEyeKAGr9dO7c2XAMAHHDhg21nrtlyxZx4MCBoru7u+jj4yPGxsaKK1euNOy/du2aOHnyZLFVq1aiUqkUo6OjxR9++MGw/9tvvxWjoqJEuVwutm3bVvy///u/u8b72muviU888US97+X2Ck5arVb84IMPxM6dO4tyuVz09/cXR44cKe7cubPWc+Pj40UAtSYji6IoVlRUiD4+PuLevXvvGh8RETUdr006vDaRFARRvMsgWSJyOleuXEHXrl2RkZGB8PBwm7xmSkoK/ve//yE1NdUmr0dERI6F1yZyJlyMmKgZCgwMxKefforc3FybvaZcLse///1vm70eERE5Fl6byJmwJ4uIiIiIiMiC2JNFRERERERkQUyyiIiIiIiILIhJFhERERERkQUxySIiIiIiIrIgJllEREREREQWxCSLiIiIiIjIgphkERERERERWRCTLCIiIiIiIgtikkVERERERGRB/w+K5VRnC7mwdQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10,4))\n", + "fig.add_subplot(1, 2, 1)\n", + "plt.plot(erecobincenters, signal_to_background_ratio)\n", + "plt.scatter(erecobincenters, signal_to_background_ratio)\n", + "plt.grid()\n", + "plt.xlabel('Ereco (TeV)')\n", + "plt.ylabel('Signal / background ratio')\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "\n", + "fig.add_subplot(1, 2, 2)\n", + "points_mask = ((signal_to_background_ratio >= min_signal_to_backg_ratio) &\n", + " (significance >= min_signi_in_flux_point))\n", + "plt.scatter(erecobincenters[points_mask], significance[points_mask])\n", + "\n", + "plt.scatter(erecobincenters[~points_mask], significance[~points_mask], color='orange')\n", + "\n", + "plt.grid()\n", + "plt.xlabel('Ereco (TeV)')\n", + "plt.ylabel('Li & Ma significance')\n", + "plt.xscale('log')" + ] + }, + { + "cell_type": "markdown", + "id": "ca520d68", + "metadata": {}, + "source": [ + "## Integral significance (for Ereco>xx)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "0e1df16e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "*************************\n", + "Detection successful! :-D\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,4))\n", + "mask = integral_significance >= integral_significance_threshold\n", + "plt.scatter(erecobins[:-1][mask], integral_significance[mask], \n", + " label=f'Significance >= {integral_significance_threshold} sigma')\n", + "plt.scatter(erecobins[:-1][~mask], integral_significance[~mask],\n", + " label=f'Significance < {integral_significance_threshold} sigma')\n", + "plt.grid()\n", + "plt.xlabel('Minimum Ereco (TeV)')\n", + "plt.ylabel('Li & Ma integral significance')\n", + "plt.xscale('log')\n", + "\n", + "if mask.sum() > 0:\n", + " print('*************************')\n", + " print('Detection successful! :-D')\n", + "else:\n", + " print('****************')\n", + " print('No detection :-C')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "04cd5759", + "metadata": {}, + "source": [ + "## Simulated SED from observation (Asimov dataset)\n", + "The fluxes & uncertainties are computed in the Ereco bins, and shamelessly placed at the same value in Etrue, and at the expected flux level. \n", + "In reality an energy unfolding process is needed... what is shown below is just a an estimate of what the spectrum will look like - but pretty accurate for the purpose of observation planning." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "64661959", + "metadata": {}, + "outputs": [], + "source": [ + "noff = total_bg_counts / alpha\n", + "non = total_signal_counts + total_bg_counts\n", + "# excess = non - alpha * noff (= total_signal_counts in Asimov dataset)\n", + "excess_error = (non + alpha**2 * noff)**0.5\n", + "relative_excess_error = excess_error / total_signal_counts" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "4d56445f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" - ], - "text/plain": [ - " ZD_deg Etrue_min_TeV Etrue_max_TeV Aeff_m2 emig_mu_loc \\\n", - "0 6.00 0.011220 0.014125 1.523558e+03 2.173221 \n", - "1 6.00 0.014125 0.017783 3.045447e+03 1.768732 \n", - "2 6.00 0.017783 0.022387 5.419198e+03 1.459977 \n", - "3 6.00 0.022387 0.028184 8.858634e+03 1.212684 \n", - "4 6.00 0.028184 0.035481 1.299283e+04 1.050773 \n", - ".. ... ... ... ... ... \n", - "345 66.44 11.220185 14.125375 1.919827e+06 0.936101 \n", - "346 66.44 14.125375 17.782794 1.964986e+06 0.933643 \n", - "347 66.44 17.782794 22.387211 2.261285e+06 0.938107 \n", - "348 66.44 22.387211 28.183829 2.116637e+06 0.945268 \n", - "349 66.44 28.183829 35.481339 2.213826e+06 0.943924 \n", - "\n", - " emig_mu_scale emig_model \n", - "0 0.254397 moyal \n", - "1 0.234880 moyal \n", - "2 0.216000 moyal \n", - "3 0.190464 moyal \n", - "4 0.184902 moyal \n", - ".. ... ... \n", - "345 0.148488 gauss \n", - "346 0.150116 gauss \n", - "347 0.146014 gauss \n", - "348 0.145841 gauss \n", - "349 0.152568 gauss \n", - "\n", - "[350 rows x 7 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "gamma_data" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "1d2b1efe", "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/html": [ - "
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ZD_degEreco_min_TeVEreco_max_TeVBckgRate_per_secondTheta_cut_degGammaness_cut
06.000.0125890.0199530.1587530.3200000.112810
16.000.0199530.03162311.2443920.3200000.176081
26.000.0316230.05011917.2782450.3200000.189099
36.000.0501190.07943310.8380750.3200000.197615
46.000.0794330.1258932.1516920.2673520.316888
.....................
18566.447.94328212.5892540.0026470.1336760.387355
18666.4412.58925419.9526230.0014080.1348320.398923
18766.4419.95262331.6227770.0005980.1384680.426601
18866.4431.62277750.1187230.0001770.1336500.474480
18966.4450.11872379.4328230.0000940.1378510.536455
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190 rows × 6 columns

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" - ], - "text/plain": [ - " ZD_deg Ereco_min_TeV Ereco_max_TeV BckgRate_per_second Theta_cut_deg \\\n", - "0 6.00 0.012589 0.019953 0.158753 0.320000 \n", - "1 6.00 0.019953 0.031623 11.244392 0.320000 \n", - "2 6.00 0.031623 0.050119 17.278245 0.320000 \n", - "3 6.00 0.050119 0.079433 10.838075 0.320000 \n", - "4 6.00 0.079433 0.125893 2.151692 0.267352 \n", - ".. ... ... ... ... ... \n", - "185 66.44 7.943282 12.589254 0.002647 0.133676 \n", - "186 66.44 12.589254 19.952623 0.001408 0.134832 \n", - "187 66.44 19.952623 31.622777 0.000598 0.138468 \n", - "188 66.44 31.622777 50.118723 0.000177 0.133650 \n", - "189 66.44 50.118723 79.432823 0.000094 0.137851 \n", - "\n", - " Gammaness_cut \n", - "0 0.112810 \n", - "1 0.176081 \n", - "2 0.189099 \n", - "3 0.197615 \n", - "4 0.316888 \n", - ".. ... \n", - "185 0.387355 \n", - "186 0.398923 \n", - "187 0.426601 \n", - "188 0.474480 \n", - "189 0.536455 \n", - "\n", - "[190 rows x 6 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "background_data" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "90114613", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available zeniths: [ 6. 9.579 16.08 23.16 30.39 37.66 44.92 52.16 59.34 66.44 ] (degrees)\n" - ] - } - ], + "outputs": [], "source": [ "# CHECK that we have the same pointing zenith values in both tables:\n", "assert np.alltrue(np.unique(gamma_data.ZD_deg) == np.unique(background_data.ZD_deg))\n", @@ -488,18 +130,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "a5a9386a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selected ZD = 16.08 degrees\n" - ] - } - ], + "outputs": [], "source": [ "# Choose the bins among those above. Just set the bin number (from 0)\n", "# Make sure you choose values which make sense for the declination of your source\n", @@ -522,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "0c58505b", "metadata": {}, "outputs": [], @@ -544,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "7d8d3a2e", "metadata": {}, "outputs": [], @@ -562,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "ea9bca3c", "metadata": {}, "outputs": [], @@ -593,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "e24f72ce", "metadata": {}, "outputs": [], @@ -617,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "014476b0", "metadata": {}, "outputs": [], @@ -638,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "80627c34", "metadata": {}, "outputs": [], @@ -655,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "24e8394c", "metadata": {}, "outputs": [], @@ -666,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "056ea33b", "metadata": {}, "outputs": [], @@ -697,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "1b79d5b2", "metadata": {}, "outputs": [], @@ -708,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "2038bb5b", "metadata": {}, "outputs": [], @@ -723,7 +357,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "48a34458", "metadata": {}, "outputs": [], @@ -742,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "54b22527", "metadata": {}, "outputs": [], @@ -771,21 +405,10 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "4eff20a0", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(8,4))\n", "plt.plot(etruebincenters, effective_area)\n", @@ -806,21 +429,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "9c89f6e6", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(8,4))\n", "plt.plot(erecobincenters[ereco_mask], \n", @@ -834,20 +446,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "5164274a", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total gamma rate after cuts: 0.897 events/s\n" - ] - } - ], + "outputs": [], "source": [ "total_gamma_rate = (integrated_flux*effective_area).to(1/u.s)\n", "print(f'Total gamma rate after cuts: {total_gamma_rate.sum().to_value(1/u.s):.3f} events/s')" @@ -855,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "6a415916", "metadata": {}, "outputs": [], @@ -866,31 +470,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "31415b87", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.1, 4167178.6876828154)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig = plt.figure(figsize=(10,4))\n", "fig.add_subplot(1, 2, 1)\n", @@ -1026,29 +598,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "0e1df16e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "*************************\n", - "Detection successful! :-D\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(8,4))\n", "mask = integral_significance >= integral_significance_threshold\n", @@ -1083,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "64661959", "metadata": {}, "outputs": [], @@ -1097,21 +650,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "4d56445f", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(8,4))\n", "SED = (erecobincenters*u.TeV)**2 * dFdE(erecobincenters*u.TeV)\n", From 8de27506bd427eb88096fd8ad7126ec830dd2386 Mon Sep 17 00:00:00 2001 From: moralejo Date: Tue, 29 Oct 2024 23:53:57 +0000 Subject: [PATCH 03/16] Better performance files Replaced gauss by skewnorm for migration matrix parametrization Added example spectra Pulsar mode --- ...LST1_backg_irf_gheffi_0.40_theffi_0.40.csv | 340 ++++----- ...LST1_backg_irf_gheffi_0.70_theffi_0.70.csv | 358 ++++----- ...LST1_backg_irf_gheffi_0.90_theffi_0.90.csv | 358 ++++----- ...LST1_gamma_irf_gheffi_0.40_theffi_0.40.csv | 702 +++++++++--------- ...LST1_gamma_irf_gheffi_0.70_theffi_0.70.csv | 702 +++++++++--------- ...LST1_gamma_irf_gheffi_0.90_theffi_0.90.csv | 702 +++++++++--------- notebooks/LST1_observation_simulator.ipynb | 361 +++++++-- 7 files changed, 1868 insertions(+), 1655 deletions(-) diff --git a/lstchain/data/LST1_backg_irf_gheffi_0.40_theffi_0.40.csv b/lstchain/data/LST1_backg_irf_gheffi_0.40_theffi_0.40.csv index ac81aa50f..e8bf7c03b 100644 --- a/lstchain/data/LST1_backg_irf_gheffi_0.40_theffi_0.40.csv +++ b/lstchain/data/LST1_backg_irf_gheffi_0.40_theffi_0.40.csv @@ -1,191 +1,191 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_cut -6.0,0.012589254117941675,0.0199526231496888,0.08662540530202503,0.32,0.14980508270919676 -6.0,0.0199526231496888,0.03162277660168379,3.654012468122838,0.25416790232400216,0.2232647189688829 -6.0,0.03162277660168379,0.05011872336272725,4.506734624017561,0.22836642194149148,0.24282859738626 -6.0,0.05011872336272725,0.07943282347242814,1.5030621541123657,0.17930820879898315,0.3082568921420486 -6.0,0.07943282347242814,0.12589254117941673,0.13512067113746323,0.11869128089021427,0.5205997890139312 -6.0,0.12589254117941673,0.19952623149688797,0.016042078792318386,0.1,0.6802999880849291 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+66.44,17.782794100389236,22.38721138568341,3621539.9759411146,0.9341296501604117,0.1892115433229644,0.00033485180864913436,skewnorm +66.44,22.38721138568341,28.183829312644566,3483388.7993919547,0.7722802929786422,0.26542310146783166,1.4931862700122722,skewnorm +66.44,28.183829312644566,35.48133892335755,3691550.6997481054,0.7913556135004776,0.2510805767288358,1.2058824623523396,skewnorm diff --git a/notebooks/LST1_observation_simulator.ipynb b/notebooks/LST1_observation_simulator.ipynb index 35d109a29..e6cf40d02 100644 --- a/notebooks/LST1_observation_simulator.ipynb +++ b/notebooks/LST1_observation_simulator.ipynb @@ -14,7 +14,7 @@ "import matplotlib.pyplot as plt\n", "from pyirf.spectral import CRAB_MAGIC_JHEAP2015, PowerLaw, LogParabola\n", "from pyirf.statistics import li_ma_significance\n", - "from scipy.stats import moyal\n", + "from scipy.stats import moyal, norm, skewnorm\n", "from gammapy.modeling.models import EBLAbsorptionNormSpectralModel" ] }, @@ -49,15 +49,16 @@ "id": "5dd2e6fd", "metadata": {}, "source": [ - "## Load files which contain the instrument response function and show table contents\n", + "## Load files which contain the (approximate) instrument response function, and show table contents\n", "They characterize the average performance of LST1 within 1 degree off-axis (computed from diffuse gamma MC and\n", - "real data for the cosmic ray rates)" + "real data for the cosmic ray rates)\n", + "The Ereco/Etrue distributions in each Etrue bin is parametrized with moyal or a skewnorm function (whatever fits better) " ] }, { "cell_type": "code", "execution_count": null, - "id": "df170ecd", + "id": "95ac1d89", "metadata": {}, "outputs": [], "source": [ @@ -98,7 +99,7 @@ "execution_count": null, "id": "1d2b1efe", "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [], "source": [ @@ -138,7 +139,8 @@ "# Choose the bins among those above. Just set the bin number (from 0)\n", "# Make sure you choose values which make sense for the declination of your source\n", "\n", - "zd_bin = 2\n", + "zd_bin = 4\n", + "\n", "print('Selected ZD = ', zenith[zd_bin], 'degrees')\n", "\n", "# Cuts for tables:\n", @@ -165,7 +167,34 @@ "# For standard wobble offset (0.4 deg) reasonable values are alpha=0.333 (3 off regions) above 0.2 TeV, \n", "# and alpha=1 below 0.2 TeV. For testing sensitivity with the standard definition, set alpha=0.2\n", "\n", - "alpha = 1 # 0.333 # 1" + "alpha = 0.333 # 1" + ] + }, + { + "cell_type": "markdown", + "id": "1161f853", + "metadata": {}, + "source": [ + "## Pulsar mode\n", + "(overrides the setting of alpha above)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89f2cb17", + "metadata": {}, + "outputs": [], + "source": [ + "pulsar_mode = False # True # Set to True to activate it\n", + "\n", + "on_phase_interval = 0.043 # Crab P1: [-0.017, 0.026] \n", + "off_phase_interval = 0.35 # [0.52 - 0.87]\n", + "\n", + "alpha = on_phase_interval / off_phase_interval\n", + "print(f'alpha = {alpha:.4f}')\n", + "\n", + "# The spectrum is interpreted as average flux in full period (i.e. not just in the on-phase)" ] }, { @@ -183,7 +212,11 @@ "metadata": {}, "outputs": [], "source": [ - "effective_obs_time = 34 * u.h " + "#effective_obs_time = 103 * u.h # Crab pulsar paper\n", + "\n", + "# effective_obs_time = 8 * u.s # BOAT, 240 - 248 s\n", + "\n", + "effective_obs_time = 34 * u.h # Crab nebula. performance paper" ] }, { @@ -201,9 +234,10 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "redshift = 0\n", "\n", + "# redshift = 0.151 # BOAT GRB\n", + "\n", "# We will apply the Dominguez EBL model to simulate the absorption\n", "\n", "\n", @@ -238,15 +272,21 @@ "def intrinsic_dFdE(E):\n", " return CRAB_MAGIC_JHEAP2015(E) # Crab Nebula, MAGIC log-parabola\n", "\n", + "# Crab pulsar P1, from LST1 paper:\n", + "# def intrinsic_dFdE(E):\n", + "# return PowerLaw(normalization=1.27e-4 / (u.TeV * u.cm**2 * u.s), \n", + "# index=-1.811, e_ref=1*u.GeV)(E) * (1+(E/(6.8*u.GeV))**((4.09-1.811)/3))**-3\n", + "\n", + "# The BOAT (GRB 221009A) @ ~T0+240s: (set redshift above to 0.151)\n", "# def intrinsic_dFdE(E):\n", - "# return PowerLaw(normalization=5e-8 / (u.TeV * u.cm**2 * u.s), \n", - "# index=-2.0, \n", - "# e_ref=0.1 * u.TeV)(E)\n", + "# return PowerLaw(normalization=208e-8 / (u.TeV * u.cm**2 * u.s), \n", + "# index=-2.455, \n", + "# e_ref=1*u.TeV)(E)\n", "\n", "# def intrinsic_dFdE(E):\n", "# return LogParabola(normalization=5e-8 / (u.TeV * u.cm**2 * u.s), \n", - "# a=-2.0, b =-0.2, \n", - "# e_ref=0.1 * u.TeV)(E)" + "# a=-2, b =-0.05, \n", + "# e_ref=0.1*u.TeV)(E)" ] }, { @@ -279,12 +319,22 @@ "source": [ "# Other settings (change only for tests)\n", "\n", - "min_signal_to_backg_ratio = 0.01 # safeguard against systematics\n", - "min_signi_in_flux_point = 3 # minimum significance to display a flux point\n", + "backg_systematics_uncertainty = 0.005 # 0.5% (relative) will be added in quadrature to statistical uncertainty of flux points \n", + "min_signi_in_flux_point = 2 # minimum significance to display a flux point\n", + "\n", "integral_significance_threshold = 5 # \"Detection significance\"\n", + "integral_min_signal_to_backg_ratio = 0.05 # S/B must be larger or equal than this for detection\n", + "\n", + "if pulsar_mode: # unbiased background can be taken from off-phase\n", + " backg_systematics_uncertainty = 0\n", + " integral_min_signal_to_backg_ratio = 0\n", + "\n", "\n", "min_Aeff = 100 *u.m**2 # Minimum required Aeff in Etrue bins. \n", - " # Just to avoid Etrue bins with little MC stats, hence noisy!" + " # Just to avoid Etrue bins with little MC stats, hence noisy!\n", + "\n", + "# To exclude Ereco bins with too strong deviation of the true energies that fall inside them:\n", + "max_ereco_to_etrue_deviation = 0.5 #  abs(mean(Etrue)/ereco_bin_center - 1) < max_ereco_to_etrue_deviation" ] }, { @@ -310,12 +360,13 @@ "\n", "effective_area = gamma_data.Aeff_m2[zd_selection_gamma].to_numpy()*u.m**2\n", "\n", - "# Model to characterize the energy migration matrix (gauss or moyal):\n", + "# Model to characterize the energy migration matrix (skewnorm or moyal):\n", "emig_model = gamma_data[zd_selection_gamma].emig_model.to_numpy()\n", "\n", "# Parameters to characterize the energy migration matrix:\n", "loc = gamma_data[zd_selection_gamma].emig_mu_loc.to_numpy()\n", "scale = gamma_data[zd_selection_gamma].emig_mu_scale.to_numpy()\n", + "a = gamma_data[zd_selection_gamma].emig_mu_a.to_numpy()\n", "\n", "\n", "# Now we extrapolate Aeff to higher Etrue by using the same value of the highest available energy:\n", @@ -326,7 +377,8 @@ " effective_area = np.append(effective_area, effective_area[-1])\n", " emig_model = np.append(emig_model, emig_model[-1])\n", " loc = np.append(loc, loc[-1])\n", - " scale = np.append(scale, scale[-1])" + " scale = np.append(scale, scale[-1])\n", + " a = np.append(a, a[-1])" ] }, { @@ -427,6 +479,16 @@ "## Background rate" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "29dd2c44", + "metadata": {}, + "outputs": [], + "source": [ + "background_data[zd_selection_backg].BckgRate_per_second.to_numpy()" + ] + }, { "cell_type": "code", "execution_count": null, @@ -435,8 +497,13 @@ "outputs": [], "source": [ "plt.figure(figsize=(8,4))\n", - "plt.plot(erecobincenters[ereco_mask], \n", - " background_data[zd_selection_backg].BckgRate_per_second.to_numpy()[ereco_mask])\n", + "\n", + "bgrate = background_data[zd_selection_backg].BckgRate_per_second.to_numpy()\n", + "\n", + "if pulsar_mode: # Scale background to the on-phase:\n", + " bgrate *= on_phase_interval\n", + "\n", + "plt.plot(erecobincenters[ereco_mask], bgrate[ereco_mask])\n", "plt.xlabel('Ereco (TeV)')\n", "plt.ylabel('Background rate within theta cut\\n (events/s) in Ereco bins')\n", "plt.yscale('log')\n", @@ -464,60 +531,158 @@ "metadata": {}, "outputs": [], "source": [ - "# Number of realizations (random numbers taken from gaussian) for E-migration simulation \n", - "num_realizations = 10000" + "num_realizations_a = 100\n", + "num_realizations_b = 100\n", + "# Number of realizations (random numbers taken from skewnorm or moyal) for E-migration simulation\n", + "\n", + "num_realizations = num_realizations_a * num_realizations_b" ] }, { "cell_type": "code", "execution_count": null, - "id": "31415b87", + "id": "25b8fe0c", "metadata": {}, "outputs": [], "source": [ - "plt.figure(figsize=(8,4))\n", + "fine_etrue_binning = np.logspace(-2.5, 3.5, 601) # Just for calculation of mean Etrue within an Ereco bin\n", "\n", "total_bg_counts = effective_obs_time.to_value(u.s) * background_data[zd_selection_backg].BckgRate_per_second.to_numpy()\n", - "total_signal_counts = np.zeros(zd_selection_backg.sum())\n", + "if pulsar_mode:\n", + " total_bg_counts *= on_phase_interval\n", + "\n", + "total_signal_counts_2d = np.zeros(shape=[len(total_bg_counts), len(fine_etrue_binning)-1])\n", "\n", "for ietrue in range(len(etruebincenters)):\n", - " etrue = etruebincenters[ietrue]\n", " \n", " gamma_rate = total_gamma_rate[ietrue]\n", "\n", " \n", - " if emig_model[ietrue] == 'gauss':\n", - " counts, _ = np.histogram(etrue*np.random.normal(loc[ietrue], scale[ietrue], num_realizations),\n", - " bins=erecobins)\n", - " \n", - " elif emig_model[ietrue] == 'moyal':\n", - " counts, _ = np.histogram(etrue*moyal.rvs(loc[ietrue], scale[ietrue], num_realizations),\n", - " bins=erecobins)\n", - " else:\n", - " continue\n", - " \n", - "\n", - " total_signal_counts += gamma_rate * effective_obs_time * counts / num_realizations\n", - " # print(gamma_rate * effective_obs_time * counts / num_realizations)\n", + " emin= etruebins[ietrue]\n", + " emax = etruebins[ietrue+1]\n", "\n", + " etrue_values = np.exp(np.log(emin) + np.log(emax/emin) * np.random.uniform(0, 1, num_realizations_a))\n", " \n", + " for etrue in etrue_values:\n", + " # print(f'{ietrue}: {etrue:.4f} TeV, {gamma_rate}, {loc[ietrue]:.4f}, {scale[ietrue]:.4f}, {emig_model[ietrue]}')\n", + " # Possible alternative: simulate according to the (interpolated) events vs Etrue graph,\n", + " # instead of all energies being equal to the bin center\n", + "\n", + " if emig_model[ietrue] == 'skewnorm':\n", + " ereco = etrue*skewnorm.rvs(a[ietrue], loc[ietrue], scale[ietrue], \n", + " num_realizations_b) \n", + " elif emig_model[ietrue] == 'moyal':\n", + " ereco = etrue*moyal.rvs(loc[ietrue], scale[ietrue], \n", + " num_realizations_b)\n", + " else:\n", + " continue\n", + "\n", + " # counts, _ = np.histogram(ereco, bins=erecobins)\n", + " # Histogram in very fine Etrue bins, for later calculation of mean true energy of\n", + " # gammas within each Ereco bin:\n", + " counts, _, _ = np.histogram2d(ereco, etrue*np.ones_like(ereco), bins=[erecobins, fine_etrue_binning]) \n", + "\n", + "\n", + " # Integrate in Ereco bins and add to total:\n", + " total_signal_counts_2d += (gamma_rate * effective_obs_time * counts / num_realizations)\n", + "\n", + " \n", + "total_signal_counts = np.sum(total_signal_counts_2d, axis=1) # Integrate in Ereco bins\n", "# Now set to zero values below the \"reliable minimum energy\":\n", "total_signal_counts[~ereco_mask] = 0\n", "total_bg_counts[~ereco_mask] = 0\n", - " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31415b87", + "metadata": {}, + "outputs": [], + "source": [ "plt.errorbar(0.5*(erecobins[:-1]+erecobins[1:]), total_signal_counts, total_signal_counts**0.5,\n", - " label='gammas')\n", + " label='gammas', fmt='o')\n", "plt.errorbar(0.5*(erecobins[:-1]+erecobins[1:]), total_bg_counts, total_bg_counts**0.5,\n", " label='Background')\n", "\n", + "# PLOT GAMMAS VS. ETRUE FOR COMPARISON, correcting for the different bin width:\n", + "plt.plot(etruebincenters, total_gamma_rate*effective_obs_time.to_value(u.s)*\n", + " len(etruebincenters)/len(erecobincenters), '--', alpha=0.5, label='gammas vs. Etrue')\n", + "\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.legend()\n", "plt.xlabel('Ereco (TeV)')\n", "plt.ylabel('Number of events after cuts in Ereco bins')\n", "plt.grid()\n", - "plt.ylim(0.1, 2*np.max(total_bg_counts))\n", - "# plt.xlim(10, 80)" + "plt.ylim(0.1, 2*max(np.max(total_bg_counts), np.max(total_signal_counts)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "07a3be0c", + "metadata": {}, + "outputs": [], + "source": [ + "total_bg_counts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3769732", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "mean_etrue_vs_ereco = np.zeros_like(erecobincenters)\n", + "finebincenters = 0.5 * (fine_etrue_binning[1:]+fine_etrue_binning[:-1])\n", + "\n", + "for iereco in range(len(erecobincenters)):\n", + " mean_etrue_vs_ereco[iereco] = (np.nansum(finebincenters * total_signal_counts_2d[iereco]) / \n", + " np.nansum(total_signal_counts_2d[iereco]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4d6ac4d", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(12,4))\n", + "\n", + "fig.add_subplot(1, 2, 1)\n", + "plt.scatter(erecobincenters, mean_etrue_vs_ereco)\n", + "plt.xlabel('Ereco bin center (TeV)')\n", + "plt.ylabel('Mean Etrue in bin (TeV)')\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.grid()\n", + "\n", + "fig.add_subplot(1, 2, 2)\n", + "plt.scatter(erecobincenters, mean_etrue_vs_ereco / erecobincenters)\n", + "plt.xlabel('Ereco bin center (TeV)')\n", + "plt.ylabel('Mean Etrue in bin / Ereco bin center')\n", + "plt.ylim(0, 1.1*np.nanmax(mean_etrue_vs_ereco/erecobincenters))\n", + "plt.xscale('log')\n", + "plt.grid()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb040e44", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's not use bind with too high bias in the spectrum:\n", + "not_too_high_bias = abs(mean_etrue_vs_ereco / erecobincenters - 1) < max_ereco_to_etrue_deviation" ] }, { @@ -535,7 +700,8 @@ "integral_bg_counts = np.cumsum(total_bg_counts[::-1])[::-1]\n", "\n", "integral_significance = li_ma_significance(integral_signal_counts+integral_bg_counts,\n", - " integral_bg_counts/alpha, alpha)" + " integral_bg_counts/alpha, alpha)\n", + "integral_signal_to_background_ratio = integral_signal_counts / integral_bg_counts" ] }, { @@ -565,7 +731,7 @@ }, "outputs": [], "source": [ - "fig = plt.figure(figsize=(10,4))\n", + "fig = plt.figure(figsize=(12,4))\n", "fig.add_subplot(1, 2, 1)\n", "plt.plot(erecobincenters, signal_to_background_ratio)\n", "plt.scatter(erecobincenters, signal_to_background_ratio)\n", @@ -576,13 +742,18 @@ "plt.xscale('log')\n", "\n", "fig.add_subplot(1, 2, 2)\n", - "points_mask = ((signal_to_background_ratio >= min_signal_to_backg_ratio) &\n", - " (significance >= min_signi_in_flux_point))\n", - "plt.scatter(erecobincenters[points_mask], significance[points_mask])\n", "\n", - "plt.scatter(erecobincenters[~points_mask], significance[~points_mask], color='orange')\n", + "# Conditions to consider a bin reliable\n", + "reliable_points = ((signal_to_background_ratio >= backg_systematics_uncertainty) &\n", + " not_too_high_bias)\n", + "\n", + "plt.scatter(erecobincenters[reliable_points], significance[reliable_points], \n", + " label='Valid bins')\n", + "\n", + "plt.scatter(erecobincenters[~reliable_points], significance[~reliable_points], label='Unreliable bins', color='orange')\n", "\n", "plt.grid()\n", + "plt.legend()\n", "plt.xlabel('Ereco (TeV)')\n", "plt.ylabel('Li & Ma significance')\n", "plt.xscale('log')" @@ -604,9 +775,16 @@ "outputs": [], "source": [ "plt.figure(figsize=(8,4))\n", - "mask = integral_significance >= integral_significance_threshold\n", - "plt.scatter(erecobins[:-1][mask], integral_significance[mask], \n", - " label=f'Significance >= {integral_significance_threshold} sigma')\n", + "\n", + "mask = (integral_significance >= integral_significance_threshold)\n", + "\n", + "plt.scatter(erecobins[:-1][mask & reliable_points], integral_significance[mask & reliable_points], \n", + " label=f'Significance $\\geq$ {integral_significance_threshold} sigma')\n", + "\n", + "if (mask & ~reliable_points).sum() > 0:\n", + " plt.scatter(erecobins[:-1][mask & ~reliable_points], integral_significance[mask & ~reliable_points], \n", + " label=f'Significance >= {integral_significance_threshold}, \\nUNRELIABLE points!')\n", + "\n", "plt.scatter(erecobins[:-1][~mask], integral_significance[~mask],\n", " label=f'Significance < {integral_significance_threshold} sigma')\n", "plt.grid()\n", @@ -614,7 +792,7 @@ "plt.ylabel('Li & Ma integral significance')\n", "plt.xscale('log')\n", "\n", - "if mask.sum() > 0:\n", + "if (mask & reliable_points).sum() > 0:\n", " print('*************************')\n", " print('Detection successful! :-D')\n", "else:\n", @@ -630,8 +808,8 @@ "metadata": {}, "source": [ "## Simulated SED from observation (Asimov dataset)\n", - "The fluxes & uncertainties are computed in the Ereco bins, and shamelessly placed at the same value in Etrue, and at the expected flux level. \n", - "In reality an energy unfolding process is needed... what is shown below is just a an estimate of what the spectrum will look like - but pretty accurate for the purpose of observation planning." + "The fluxes & uncertainties are computed in the Ereco bins, then placed at the mean Etrue of the gamma events falling within the bin, and at the \"expected\" flux level. \n", + "In reality proper energy unfolding will be needed... what is shown below is just a an estimate of what the spectrum will look like - but pretty accurate for the purpose of observation planning." ] }, { @@ -644,8 +822,13 @@ "noff = total_bg_counts / alpha\n", "non = total_signal_counts + total_bg_counts\n", "# excess = non - alpha * noff (= total_signal_counts in Asimov dataset)\n", - "excess_error = (non + alpha**2 * noff)**0.5\n", - "relative_excess_error = excess_error / total_signal_counts" + "stat_excess_error = (non + alpha**2 * noff)**0.5\n", + "relative_stat_excess_error = stat_excess_error / total_signal_counts\n", + "\n", + "syst_excess_error = backg_systematics_uncertainty * total_bg_counts\n", + "relative_syst_excess_error = syst_excess_error / total_signal_counts\n", + "\n", + "total_relative_excess_error = np.hypot(relative_stat_excess_error, relative_syst_excess_error)\n" ] }, { @@ -655,26 +838,56 @@ "metadata": {}, "outputs": [], "source": [ - "plt.figure(figsize=(8,4))\n", - "SED = (erecobincenters*u.TeV)**2 * dFdE(erecobincenters*u.TeV)\n", - "SED_error = SED*relative_excess_error\n", - "intrinsic_SED = (erecobincenters*u.TeV)**2 * intrinsic_dFdE(erecobincenters*u.TeV)\n", + "SED = (mean_etrue_vs_ereco*u.TeV)**2 * dFdE(mean_etrue_vs_ereco*u.TeV)\n", "\n", - "if redshift > 0:\n", - " plt.plot(erecobincenters*u.TeV, intrinsic_SED, '--', color='lightgrey', label='intrinsic')\n", - "plt.plot(erecobincenters*u.TeV, SED, '--')\n", + "SED_stat_error = SED*relative_stat_excess_error\n", + "SED_total_error = SED*total_relative_excess_error\n", "\n", - "plt.errorbar(erecobincenters[points_mask]*u.TeV, SED[points_mask], \n", - " yerr=SED_error[points_mask], fmt='o', markersize=2, label='Observed')\n", + "displayed_points = reliable_points & (significance > min_signi_in_flux_point)\n", "\n", - "plt.yscale('log')\n", - "plt.xscale('log')\n", - "plt.xlabel('Energy')\n", - "plt.ylabel(f'SED ({SED.unit})')\n", - "plt.ylim(SED[points_mask].min().value*0.2, SED[points_mask].max().value*5)\n", - "plt.legend()\n", - "plt.grid()" + "if displayed_points.sum() > 0:\n", + "\n", + " plt.figure(figsize=(8,4))\n", + " intrinsic_SED = (mean_etrue_vs_ereco*u.TeV)**2 * intrinsic_dFdE(mean_etrue_vs_ereco*u.TeV)\n", + "\n", + " if redshift > 0:\n", + " plt.plot(mean_etrue_vs_ereco, intrinsic_SED, '--', color='lightgrey', label='intrinsic')\n", + "\n", + "\n", + " SED_fine = (fine_etrue_binning*u.TeV)**2 * dFdE(fine_etrue_binning*u.TeV)\n", + " plt.plot(fine_etrue_binning, SED_fine, '--')\n", + "\n", + "\n", + " if not pulsar_mode:\n", + " plt.errorbar(mean_etrue_vs_ereco[displayed_points], SED[displayed_points], \n", + " yerr=SED_total_error[displayed_points], fmt='o', markersize=2, \n", + " label='Observed, stat+syst')\n", + " plt.errorbar(mean_etrue_vs_ereco[displayed_points], SED[displayed_points], \n", + " yerr=SED_stat_error[displayed_points], fmt='o', markersize=2, \n", + " label='Observed, stat-only')\n", + "\n", + " plt.yscale('log')\n", + " plt.xscale('log')\n", + " plt.xlabel('Energy (TeV)')\n", + " plt.ylabel(f'SED ({SED.unit})')\n", + " plt.ylim(np.nanmin((SED-SED_total_error)[displayed_points]).value*0.1, \n", + " np.nanmax((SED+SED_total_error)[displayed_points]).value*5)\n", + "\n", + " plt.xlim(mean_etrue_vs_ereco[displayed_points][0]/3, mean_etrue_vs_ereco[displayed_points][-1]*3)\n", + " plt.legend()\n", + " plt.grid()\n", + " \n", + "else:\n", + " print(\"Nothing to display. No valid flux points!\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c63c390f", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 7f2bca0a345419a06e29e5e5aa37b739dc39cc43 Mon Sep 17 00:00:00 2001 From: moralejo Date: Wed, 30 Oct 2024 10:00:08 +0000 Subject: [PATCH 04/16] Better background rates (extrapolated to the highest E's, where statistics were poor) Silenced useless warnings --- ...LST1_backg_irf_gheffi_0.40_theffi_0.40.csv | 178 +++++++++--------- ...LST1_backg_irf_gheffi_0.70_theffi_0.70.csv | 128 ++++++------- ...LST1_backg_irf_gheffi_0.90_theffi_0.90.csv | 94 ++++----- notebooks/LST1_observation_simulator.ipynb | 76 +++++--- 4 files changed, 249 insertions(+), 227 deletions(-) diff --git a/lstchain/data/LST1_backg_irf_gheffi_0.40_theffi_0.40.csv b/lstchain/data/LST1_backg_irf_gheffi_0.40_theffi_0.40.csv index e8bf7c03b..b1f198695 100644 --- a/lstchain/data/LST1_backg_irf_gheffi_0.40_theffi_0.40.csv +++ b/lstchain/data/LST1_backg_irf_gheffi_0.40_theffi_0.40.csv @@ -8,16 +8,16 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 6.0,0.19952623149688797,0.31622776601683794,0.0049059443594680376,0.1,0.7519160350778854 6.0,0.31622776601683794,0.5011872336272722,0.0016774052026938128,0.1,0.8187521024602308 6.0,0.5011872336272722,0.7943282347242814,0.0007139553959658439,0.1,0.8703749276810416 -6.0,0.7943282347242814,1.2589254117941668,0.00026955556042065703,0.1,0.9016936131710661 -6.0,1.2589254117941668,1.9952623149688788,8.066996404733683e-05,0.1,0.9316904703903903 -6.0,1.9952623149688788,3.1622776601683795,4.102722039814362e-05,0.1,0.9396606170144983 -6.0,3.1622776601683795,5.01187233627272,1.110097464803121e-05,0.1,0.9500643114259029 -6.0,5.01187233627272,7.943282347242813,7.06861619118234e-06,0.1,0.9510363823031566 -6.0,7.943282347242813,12.589254117941662,-9.229844993002799e-07,0.1,0.9401611478282427 -6.0,12.589254117941662,19.952623149688787,1.6517809853830499e-06,0.1,0.9567406906007412 -6.0,19.952623149688787,31.622776601683793,-4.769331173746089e-07,0.1016591644784053,0.9477693994941487 -6.0,31.622776601683793,50.118723362727195,0.0,0.1,0.9310066460490418 -6.0,50.118723362727195,79.43282347242814,5.546826240300546e-05,0.32,0.1 +6.0,0.7943282347242814,1.2589254117941668,0.00030388143938634826,0.1,0.9016936131710661 +6.0,1.2589254117941668,1.9952623149688788,0.0001293413142127672,0.1,0.9316904703903903 +6.0,1.9952623149688788,3.1622776601683795,5.505165302648401e-05,0.1,0.9396606170144983 +6.0,3.1622776601683795,5.01187233627272,2.343168166640779e-05,0.1,0.9500643114259029 +6.0,5.01187233627272,7.943282347242813,9.973246497280277e-06,0.1,0.9510363823031566 +6.0,7.943282347242813,12.589254117941662,4.244921346729869e-06,0.1,0.9401611478282427 +6.0,12.589254117941662,19.952623149688787,1.8067694651723323e-06,0.1,0.9567406906007412 +6.0,19.952623149688787,31.622776601683793,7.947470144435928e-07,0.1016591644784053,0.9477693994941487 +6.0,31.622776601683793,50.118723362727195,3.273172783976941e-07,0.1,0.9310066460490418 +6.0,50.118723362727195,79.43282347242814,1.42659587511951e-06,0.32,0.1 9.579,0.012589254117941675,0.0199526231496888,0.07496221511832982,0.32,0.14954631782891498 9.579,0.0199526231496888,0.03162277660168379,3.1143848906358587,0.2489388571568171,0.21880920839480064 9.579,0.03162277660168379,0.05011872336272725,4.613326231585207,0.23467156439645145,0.24001297834835228 @@ -27,16 +27,16 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 9.579,0.19952623149688797,0.31622776601683794,0.005171553265496122,0.1,0.7502508793627872 9.579,0.31622776601683794,0.5011872336272722,0.001865983854968374,0.1,0.8134164858767834 9.579,0.5011872336272722,0.7943282347242814,0.0007358633567411192,0.1,0.8706827596053548 -9.579,0.7943282347242814,1.2589254117941668,0.00029631423582446114,0.1,0.903105147449207 -9.579,1.2589254117941668,1.9952623149688788,9.068830610373063e-05,0.1,0.9316411475681786 -9.579,1.9952623149688788,3.1622776601683795,4.413207063878484e-05,0.1,0.9390698016445345 -9.579,3.1622776601683795,5.01187233627272,1.4386956952662837e-05,0.1,0.9568974012918506 -9.579,5.01187233627272,7.943282347242813,6.951317635437314e-06,0.1,0.9571810800605608 -9.579,7.943282347242813,12.589254117941662,1.6161098841073004e-06,0.1,0.9550247825644098 -9.579,12.589254117941662,19.952623149688787,1.939843144372741e-06,0.1,0.9537668221860696 -9.579,19.952623149688787,31.622776601683793,8.080549420536502e-07,0.1,0.9466113932901232 -9.579,31.622776601683793,50.118723362727195,0.0,0.1,0.9113429102140364 -9.579,50.118723362727195,79.43282347242814,5.131737842028748e-05,0.32,0.1 +9.579,0.7943282347242814,1.2589254117941668,0.00029019269290702694,0.1,0.903105147449207 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52.16,0.03162277660168379,0.05011872336272725,0.0019554053555110216,0.32,0.1 @@ -143,14 +143,14 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 52.16,0.5011872336272722,0.7943282347242814,0.004105712193174192,0.1,0.7640496741110404 52.16,0.7943282347242814,1.2589254117941668,0.001638159631918383,0.1,0.809011634949459 52.16,1.2589254117941668,1.9952623149688788,0.000743727554210192,0.1,0.8312713822693032 -52.16,1.9952623149688788,3.1622776601683795,0.000323095627873684,0.1,0.8560218568749494 -52.16,3.1622776601683795,5.01187233627272,0.00017499456720929297,0.1,0.8610640097240986 -52.16,5.01187233627272,7.943282347242813,7.592803144801126e-05,0.1,0.8858931169655582 -52.16,7.943282347242813,12.589254117941662,4.7809820207785665e-05,0.1,0.9025033051679945 -52.16,12.589254117941662,19.952623149688787,2.988081584451767e-05,0.1,0.894107880719096 -52.16,19.952623149688787,31.622776601683793,6.5125185081998385e-06,0.1,0.8862347667752004 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59.34,0.03162277660168379,0.05011872336272725,0.0,0.32,0.1 @@ -163,13 +163,13 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 59.34,0.7943282347242814,1.2589254117941668,0.004804964695608002,0.1,0.7659993562236929 59.34,1.2589254117941668,1.9952623149688788,0.0014958161500865784,0.1,0.7939874191858363 59.34,1.9952623149688788,3.1622776601683795,0.000640994817720803,0.1,0.8175957633965458 -59.34,3.1622776601683795,5.01187233627272,0.00033333496216936,0.1,0.8121226351902138 -59.34,5.01187233627272,7.943282347242813,0.0001296515931577197,0.1,0.8266512716247353 -59.34,7.943282347242813,12.589254117941662,8.780436854221437e-05,0.1,0.8292790830485759 -59.34,12.589254117941662,19.952623149688787,4.248738162850868e-05,0.1,0.8293737894005931 -59.34,19.952623149688787,31.622776601683793,3.0691324203393205e-05,0.1,0.8354121746337494 -59.34,31.622776601683793,50.118723362727195,5.124092075941176e-06,0.1,0.8409636718231623 -59.34,50.118723362727195,79.43282347242814,3.5762188027080534e-06,0.1,0.8346364329941128 +59.34,3.1622776601683795,5.01187233627272,0.0002746823908280065,0.1,0.8121226351902138 +59.34,5.01187233627272,7.943282347242813,0.00011770830862450671,0.1,0.8266512716247353 +59.34,7.943282347242813,12.589254117941662,5.0440968849428864e-05,0.1,0.8292790830485759 +59.34,12.589254117941662,19.952623149688787,2.1615222988085098e-05,0.1,0.8293737894005931 +59.34,19.952623149688787,31.622776601683793,9.262666350032505e-06,0.1,0.8354121746337494 +59.34,31.622776601683793,50.118723362727195,3.9692853485396905e-06,0.1,0.8409636718231623 +59.34,50.118723362727195,79.43282347242814,1.700938540021639e-06,0.1,0.8346364329941128 66.44,0.012589254117941675,0.0199526231496888,0.0,0.32,0.1 66.44,0.0199526231496888,0.03162277660168379,0.0,0.32,0.1 66.44,0.03162277660168379,0.05011872336272725,0.0,0.32,0.1 @@ -183,9 +183,9 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 66.44,1.2589254117941668,1.9952623149688788,0.0036020326669464198,0.1,0.7594603806998884 66.44,1.9952623149688788,3.1622776601683795,0.0013513455207365842,0.1,0.7772482091153307 66.44,3.1622776601683795,5.01187233627272,0.0006775027183780024,0.1,0.7653316236510899 -66.44,5.01187233627272,7.943282347242813,0.0003680761770778734,0.1,0.7497217406946608 -66.44,7.943282347242813,12.589254117941662,0.00023054150763559698,0.1,0.7345757077104096 -66.44,12.589254117941662,19.952623149688787,9.043843099941784e-05,0.1,0.7553842611535082 -66.44,19.952623149688787,31.622776601683793,4.9664645636761276e-05,0.1,0.7705226017247755 -66.44,31.622776601683793,50.118723362727195,1.0232428798718365e-05,0.1,0.7916358634248051 -66.44,50.118723362727195,79.43282347242814,7.975881269249493e-06,0.1,0.8106709918015099 +66.44,5.01187233627272,7.943282347242813,0.00033966881627682326,0.1,0.7497217406946608 +66.44,7.943282347242813,12.589254117941662,0.00017029437907956354,0.1,0.7345757077104096 +66.44,12.589254117941662,19.952623149688787,8.537779789140115e-05,0.1,0.7553842611535082 +66.44,19.952623149688787,31.622776601683793,4.280451540552178e-05,0.1,0.7705226017247755 +66.44,31.622776601683793,50.118723362727195,2.1460222497564374e-05,0.1,0.7916358634248051 +66.44,50.118723362727195,79.43282347242814,1.0759172140646597e-05,0.1,0.8106709918015099 diff --git a/lstchain/data/LST1_backg_irf_gheffi_0.70_theffi_0.70.csv b/lstchain/data/LST1_backg_irf_gheffi_0.70_theffi_0.70.csv index 2b662b68e..f3e2ba3c7 100644 --- a/lstchain/data/LST1_backg_irf_gheffi_0.70_theffi_0.70.csv +++ b/lstchain/data/LST1_backg_irf_gheffi_0.70_theffi_0.70.csv @@ -10,14 +10,14 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 6.0,0.5011872336272722,0.7943282347242814,0.010091305532878039,0.1739656472302629,0.6283677225961126 6.0,0.7943282347242814,1.2589254117941668,0.004284656279599664,0.17348059554416378,0.6651146560298868 6.0,1.2589254117941668,1.9952623149688788,0.0015450244710438553,0.17624772620116916,0.724208825217094 -6.0,1.9952623149688788,3.1622776601683795,0.0007479326794440422,0.1775535739379539,0.7674645252753842 -6.0,3.1622776601683795,5.01187233627272,0.0002868484854450279,0.17268620694592587,0.7940713722648308 -6.0,5.01187233627272,7.943282347242813,0.00011659598583980067,0.17626078400309486,0.8155952548324541 -6.0,7.943282347242813,12.589254117941662,5.000751393141074e-05,0.1875924714679997,0.8099803960631745 -6.0,12.589254117941662,19.952623149688787,4.945120597770074e-05,0.16745612152278178,0.8145827139310521 -6.0,19.952623149688787,31.622776601683793,1.177010642502351e-05,0.2035040253875547,0.8166966955524747 -6.0,31.622776601683793,50.118723362727195,1.0796696091375844e-05,0.1278319007462821,0.7245412020766999 -6.0,50.118723362727195,79.43282347242814,5.546826240300546e-05,0.32,0.1 +6.0,1.9952623149688788,3.1622776601683795,0.0005477990781407422,0.1775535739379539,0.7674645252753842 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ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 9.579,0.5011872336272722,0.7943282347242814,0.01032639074384704,0.1745120474721881,0.6247461567451729 9.579,0.7943282347242814,1.2589254117941668,0.004707475782640064,0.17364226573653765,0.6546501202021747 9.579,1.2589254117941668,1.9952623149688788,0.0017924382704006246,0.17598988293186216,0.7246773797513005 -9.579,1.9952623149688788,3.1622776601683795,0.0008272980616760409,0.17801224280917452,0.7737687181061742 -9.579,3.1622776601683795,5.01187233627272,0.0002984426173831613,0.17091322945173035,0.8160090457289869 -9.579,5.01187233627272,7.943282347242813,0.00011834008318582145,0.16140656603972997,0.8276962270244919 -9.579,7.943282347242813,12.589254117941662,8.322332118793957e-05,0.18843251513484596,0.8094454349142098 -9.579,12.589254117941662,19.952623149688787,4.298335046517881e-05,0.18462404760506107,0.8264931239980831 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23.16,0.5011872336272722,0.7943282347242814,0.014351167983589378,0.17805703472453632,0.5826247541059284 23.16,0.7943282347242814,1.2589254117941668,0.005869436091868877,0.17616453857941652,0.6470008848062724 23.16,1.2589254117941668,1.9952623149688788,0.0025380630886366385,0.17228031578013675,0.7079716572463909 -23.16,1.9952623149688788,3.1622776601683795,0.0011701761500205975,0.1704164327776217,0.7491467671786793 -23.16,3.1622776601683795,5.01187233627272,0.0006458015211605517,0.18029526744332122,0.7793540495073453 -23.16,5.01187233627272,7.943282347242813,0.0002691507310421093,0.1835248164793628,0.8102841198456221 -23.16,7.943282347242813,12.589254117941662,0.0001521452765349848,0.18133806327636143,0.7984871443704252 -23.16,12.589254117941662,19.952623149688787,5.977536884163166e-05,0.1845877541112928,0.843535208980421 -23.16,19.952623149688787,31.622776601683793,3.0264106029615515e-06,0.16165480321673856,0.8289874768781574 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30.39,0.012589254117941675,0.0199526231496888,0.00044520408537425323,0.32,0.1 30.39,0.0199526231496888,0.03162277660168379,0.9155897850387641,0.32,0.1 30.39,0.03162277660168379,0.05011872336272725,11.10242449502949,0.32,0.1639943823130124 @@ -87,13 +87,13 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 30.39,0.7943282347242814,1.2589254117941668,0.007091993868415975,0.16971032286194984,0.6276184481944082 30.39,1.2589254117941668,1.9952623149688788,0.0031242303490547625,0.16715020923254043,0.6812090477125519 30.39,1.9952623149688788,3.1622776601683795,0.001462909122026292,0.17134919204561602,0.7369775023835263 -30.39,3.1622776601683795,5.01187233627272,0.0008096363045336298,0.18912003866642446,0.7489922589629406 -30.39,5.01187233627272,7.943282347242813,0.00029865210296694,0.1742045944084464,0.775382140614782 -30.39,7.943282347242813,12.589254117941662,0.0001293428092432525,0.18371269022815082,0.7964947089057927 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+30.39,31.622776601683793,50.118723362727195,8.8033288982448e-06,0.15025352913817633,0.8310774813706188 +30.39,50.118723362727195,79.43282347242814,3.3560342374281397e-06,0.13898001973606883,0.9083706576565244 37.66,0.012589254117941675,0.0199526231496888,0.0,0.32,0.1 37.66,0.0199526231496888,0.03162277660168379,0.005439726626267147,0.32,0.1 37.66,0.03162277660168379,0.05011872336272725,3.3375735922542797,0.32,0.11722132626955144 @@ -106,13 +106,13 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 37.66,0.7943282347242814,1.2589254117941668,0.010311179893023055,0.16986375115070904,0.5692501405024707 37.66,1.2589254117941668,1.9952623149688788,0.0053634770958229955,0.17073099613563647,0.5987840017675614 37.66,1.9952623149688788,3.1622776601683795,0.002468614581690785,0.17116008788909587,0.6455422465080286 -37.66,3.1622776601683795,5.01187233627272,0.001105631903910125,0.17245304908370357,0.6930177608297756 -37.66,5.01187233627272,7.943282347242813,0.0005304154746088491,0.17928511092161245,0.7351275510452387 -37.66,7.943282347242813,12.589254117941662,0.00015763882369647925,0.1730573517272854,0.7672332972520108 -37.66,12.589254117941662,19.952623149688787,0.00011029721745463537,0.16640732885712728,0.7835020740940228 -37.66,19.952623149688787,31.622776601683793,3.2786443976427326e-05,0.1778558255900631,0.7927161914703217 -37.66,31.622776601683793,50.118723362727195,2.390349686589518e-05,0.179220913351681,0.7806032848847845 -37.66,50.118723362727195,79.43282347242814,1.3981751973654678e-05,0.13876575937601177,0.7421657312459152 +37.66,3.1622776601683795,5.01187233627272,0.0011476689876657765,0.17245304908370357,0.6930177608297756 +37.66,5.01187233627272,7.943282347242813,0.0005680543914486575,0.17928511092161245,0.7351275510452387 +37.66,7.943282347242813,12.589254117941662,0.000242386379844833,0.1730573517272854,0.7672332972520108 +37.66,12.589254117941662,19.952623149688787,0.0001026359808893749,0.16640732885712728,0.7835020740940228 +37.66,19.952623149688787,31.622776601683793,5.369297807661835e-05,0.1778558255900631,0.7927161914703217 +37.66,31.622776601683793,50.118723362727195,2.4968087532488496e-05,0.179220913351681,0.7806032848847845 +37.66,50.118723362727195,79.43282347242814,6.854871039873389e-06,0.13876575937601177,0.7421657312459152 44.92,0.012589254117941675,0.0199526231496888,0.0,0.32,0.1 44.92,0.0199526231496888,0.03162277660168379,0.0005034818183207038,0.32,0.1 44.92,0.03162277660168379,0.05011872336272725,0.5086737255508019,0.32,0.1 @@ -126,12 +126,12 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 44.92,1.2589254117941668,1.9952623149688788,0.007250183920382882,0.16371647071121967,0.5602967285967493 44.92,1.9952623149688788,3.1622776601683795,0.0037801269469097875,0.16639429403474953,0.5875755949931851 44.92,3.1622776601683795,5.01187233627272,0.001986945749602008,0.1681963148550561,0.6201994770798541 -44.92,5.01187233627272,7.943282347242813,0.000729248130664041,0.16545570235079157,0.6666928181923851 -44.92,7.943282347242813,12.589254117941662,0.0004039831358698137,0.17154006394593865,0.6906162599772102 -44.92,12.589254117941662,19.952623149688787,0.0002486004553497884,0.1898042820273609,0.7281710124768749 -44.92,19.952623149688787,31.622776601683793,9.94483632868544e-05,0.19758690018354005,0.7421912549100427 -44.92,31.622776601683793,50.118723362727195,8.030276559540815e-05,0.18999858388994198,0.7253371508623293 -44.92,50.118723362727195,79.43282347242814,1.4570413467376696e-05,0.16762154445923916,0.7877360419394447 +44.92,5.01187233627272,7.943282347242813,0.0009890997311236382,0.16545570235079157,0.6666928181923851 +44.92,7.943282347242813,12.589254117941662,0.0005469293821485527,0.17154006394593865,0.6906162599772102 +44.92,12.589254117941662,19.952623149688787,0.0003444574615941554,0.1898042820273609,0.7281710124768749 +44.92,19.952623149688787,31.622776601683793,0.00019202748110055764,0.19758690018354005,0.7421912549100427 +44.92,31.622776601683793,50.118723362727195,9.134218318862706e-05,0.18999858388994198,0.7253371508623293 +44.92,50.118723362727195,79.43282347242814,3.657244211521825e-05,0.16762154445923916,0.7877360419394447 52.16,0.012589254117941675,0.0199526231496888,0.0,0.32,0.1 52.16,0.0199526231496888,0.03162277660168379,0.0,0.32,0.1 52.16,0.03162277660168379,0.05011872336272725,0.0019554053555110216,0.32,0.1 @@ -146,11 +146,11 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 52.16,1.9952623149688788,3.1622776601683795,0.004946975878343615,0.1555130647391105,0.5203952668639182 52.16,3.1622776601683795,5.01187233627272,0.003181292423022168,0.16069714049725675,0.5377443746126526 52.16,5.01187233627272,7.943282347242813,0.0014446801650994203,0.15515195503284768,0.5759227203435799 -52.16,7.943282347242813,12.589254117941662,0.000695927618856082,0.1611417616403506,0.6183355860929303 -52.16,12.589254117941662,19.952623149688787,0.0003482874033854663,0.16684434757697789,0.6528851954846248 -52.16,19.952623149688787,31.622776601683793,0.00012638338376595926,0.17931711428655356,0.6771689770598769 -52.16,31.622776601683793,50.118723362727195,8.556784436345245e-05,0.17588958187093684,0.6855806258278904 -52.16,50.118723362727195,79.43282347242814,4.172405261386783e-05,0.17897974316562515,0.7148069911355314 +52.16,7.943282347242813,12.589254117941662,0.0007591774354520192,0.1611417616403506,0.6183355860929303 +52.16,12.589254117941662,19.952623149688787,0.0003964787770481321,0.16684434757697789,0.6528851954846248 +52.16,19.952623149688787,31.622776601683793,0.00022310549993776125,0.17931711428655356,0.6771689770598769 +52.16,31.622776601683793,50.118723362727195,0.00010457236571103917,0.17588958187093684,0.6855806258278904 +52.16,50.118723362727195,79.43282347242814,5.274901346304115e-05,0.17897974316562515,0.7148069911355314 59.34,0.012589254117941675,0.0199526231496888,0.0,0.32,0.1 59.34,0.0199526231496888,0.03162277660168379,0.0,0.32,0.1 59.34,0.03162277660168379,0.05011872336272725,0.0,0.32,0.1 @@ -166,10 +166,10 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 59.34,3.1622776601683795,5.01187233627272,0.004671493509012499,0.14533876777071955,0.45309685949761846 59.34,5.01187233627272,7.943282347242813,0.0025009838820515594,0.149213170331759,0.46354371638170516 59.34,7.943282347242813,12.589254117941662,0.0015499149580913297,0.1586202192645271,0.4732790898387837 -59.34,12.589254117941662,19.952623149688787,0.0007302175520631691,0.15861096053452234,0.5008794110357272 -59.34,19.952623149688787,31.622776601683793,0.0003607337429762502,0.16900982400913736,0.5500547600738862 -59.34,31.622776601683793,50.118723362727195,9.852257885455641e-05,0.16487619115421753,0.5862800757784927 -59.34,50.118723362727195,79.43282347242814,7.410273662875863e-05,0.17042288768853997,0.5992219866891113 +59.34,12.589254117941662,19.952623149688787,0.0008498678511541781,0.15861096053452234,0.5008794110357272 +59.34,19.952623149688787,31.622776601683793,0.0005291796136841228,0.16900982400913736,0.5500547600738862 +59.34,31.622776601683793,50.118723362727195,0.00027617816908460685,0.16487619115421753,0.5862800757784927 +59.34,50.118723362727195,79.43282347242814,0.00016181676170701128,0.17042288768853997,0.5992219866891113 66.44,0.012589254117941675,0.0199526231496888,0.0,0.32,0.1 66.44,0.0199526231496888,0.03162277660168379,0.0,0.32,0.1 66.44,0.03162277660168379,0.05011872336272725,0.0,0.32,0.1 @@ -186,6 +186,6 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 66.44,5.01187233627272,7.943282347242813,0.00402018645901959,0.1313429094712646,0.40625462802117973 66.44,7.943282347242813,12.589254117941662,0.002493201006176852,0.13367578147823206,0.3873548159062976 66.44,12.589254117941662,19.952623149688787,0.0012408610863292337,0.13483173498613846,0.39892274658665067 -66.44,19.952623149688787,31.622776601683793,0.0005900299839963799,0.13846844758081744,0.42660129373129496 -66.44,31.622776601683793,50.118723362727195,0.00020105263183862104,0.13365007793852096,0.4744804124172203 -66.44,50.118723362727195,79.43282347242814,8.69474121791437e-05,0.13785123669460694,0.5364545721486894 +66.44,19.952623149688787,31.622776601683793,0.0006402176564767995,0.13846844758081744,0.42660129373129496 +66.44,31.622776601683793,50.118723362727195,0.000291777326549409,0.13365007793852096,0.4744804124172203 +66.44,50.118723362727195,79.43282347242814,0.0001518523513920558,0.13785123669460694,0.5364545721486894 diff --git a/lstchain/data/LST1_backg_irf_gheffi_0.90_theffi_0.90.csv b/lstchain/data/LST1_backg_irf_gheffi_0.90_theffi_0.90.csv index dfbbd6e06..44f63684e 100644 --- a/lstchain/data/LST1_backg_irf_gheffi_0.90_theffi_0.90.csv +++ b/lstchain/data/LST1_backg_irf_gheffi_0.90_theffi_0.90.csv @@ -12,12 +12,12 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 6.0,1.2589254117941668,1.9952623149688788,0.02740361620255927,0.32,0.33444928025707926 6.0,1.9952623149688788,3.1622776601683795,0.011288435510305356,0.32,0.410147075635551 6.0,3.1622776601683795,5.01187233627272,0.005975646622498844,0.32,0.45329619750767874 -6.0,5.01187233627272,7.943282347242813,0.0021776968868908782,0.32,0.47244032671019254 -6.0,7.943282347242813,12.589254117941662,0.0010404686853705168,0.32,0.4666984680217185 -6.0,12.589254117941662,19.952623149688787,0.00040044596793131055,0.3082496059580866,0.49147566817020794 -6.0,19.952623149688787,31.622776601683793,0.00017113045676107175,0.32,0.5412176576412443 -6.0,31.622776601683793,50.118723362727195,2.4702398733341905e-05,0.16578504144578401,0.3576959983091037 -6.0,50.118723362727195,79.43282347242814,5.546826240300546e-05,0.32,0.1 +6.0,5.01187233627272,7.943282347242813,0.003163268508234222,0.32,0.47244032671019254 +6.0,7.943282347242813,12.589254117941662,0.0016745079298216643,0.32,0.4666984680217185 +6.0,12.589254117941662,19.952623149688787,0.0008225144648288427,0.3082496059580866,0.49147566817020794 +6.0,19.952623149688787,31.622776601683793,0.00046923404012519956,0.32,0.5412176576412443 +6.0,31.622776601683793,50.118723362727195,6.667027008255354e-05,0.16578504144578401,0.3576959983091037 +6.0,50.118723362727195,79.43282347242814,0.00013148972333362836,0.32,0.1 9.579,0.012589254117941675,0.0199526231496888,0.1481991677785433,0.32,0.1 9.579,0.0199526231496888,0.03162277660168379,14.718800243714139,0.32,0.11015613325644188 9.579,0.03162277660168379,0.05011872336272725,24.348141102914983,0.32,0.1093508969147617 @@ -31,12 +31,12 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 9.579,1.2589254117941668,1.9952623149688788,0.02752388933883518,0.32,0.3652345161846954 9.579,1.9952623149688788,3.1622776601683795,0.011307859534533645,0.32,0.4509771334446142 9.579,3.1622776601683795,5.01187233627272,0.005469451122185254,0.32,0.48171932833846715 -9.579,5.01187233627272,7.943282347242813,0.002322463579198239,0.32,0.48881721036185327 -9.579,7.943282347242813,12.589254117941662,0.001097502716381317,0.32,0.4749441813144524 -9.579,12.589254117941662,19.952623149688787,0.00038240061695774615,0.32,0.4745695200280388 -9.579,19.952623149688787,31.622776601683793,9.346298819994572e-05,0.25631446180909623,0.5137827335639006 -9.579,31.622776601683793,50.118723362727195,6.185801477372505e-05,0.21602576938344725,0.2526272049317645 -9.579,50.118723362727195,79.43282347242814,5.131737842028748e-05,0.32,0.1 +9.579,5.01187233627272,7.943282347242813,0.0026454958594608396,0.32,0.48881721036185327 +9.579,7.943282347242813,12.589254117941662,0.001279588789821422,0.32,0.4749441813144524 +9.579,12.589254117941662,19.952623149688787,0.0006189189316555376,0.32,0.4745695200280388 +9.579,19.952623149688787,31.622776601683793,0.00019206303048174906,0.25631446180909623,0.5137827335639006 +9.579,31.622776601683793,50.118723362727195,6.59891033377573e-05,0.21602576938344725,0.2526272049317645 +9.579,50.118723362727195,79.43282347242814,7.00363958657308e-05,0.32,0.1 16.08,0.012589254117941675,0.0199526231496888,0.05712969752402746,0.32,0.1 16.08,0.0199526231496888,0.03162277660168379,10.510096452332292,0.32,0.10190888871381985 16.08,0.03162277660168379,0.05011872336272725,24.75223918502653,0.32,0.10832150005806346 @@ -50,12 +50,12 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 16.08,1.2589254117941668,1.9952623149688788,0.028423235938300165,0.32,0.36894744146794856 16.08,1.9952623149688788,3.1622776601683795,0.012697792669658139,0.32,0.4224723676467453 16.08,3.1622776601683795,5.01187233627272,0.005633864740117978,0.32,0.46335199119040127 -16.08,5.01187233627272,7.943282347242813,0.0025092023765650555,0.32,0.5108895048274227 -16.08,7.943282347242813,12.589254117941662,0.0010264408616312982,0.32,0.5988211248295849 -16.08,12.589254117941662,19.952623149688787,0.0002929678559160108,0.32,0.48258021345782603 -16.08,19.952623149688787,31.622776601683793,0.00016911633747753013,0.2933161018614291,0.4862742934905485 -16.08,31.622776601683793,50.118723362727195,5.992208176541516e-05,0.22404150054737482,0.5646455548300677 -16.08,50.118723362727195,79.43282347242814,9.063151052036456e-05,0.32,0.1 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23.16,1.9952623149688788,3.1622776601683795,0.014861057358120013,0.32,0.40461859697891644 23.16,3.1622776601683795,5.01187233627272,0.007831621051631366,0.32,0.4365758774608059 -23.16,5.01187233627272,7.943282347242813,0.0032997910315857963,0.32,0.4968352080195375 -23.16,7.943282347242813,12.589254117941662,0.0013809398737149052,0.32,0.48956967277584734 -23.16,12.589254117941662,19.952623149688787,0.0004402549571419662,0.32,0.5883215945363233 -23.16,19.952623149688787,31.622776601683793,0.00017276361584225454,0.32,0.4256245535221811 -23.16,31.622776601683793,50.118723362727195,9.027817400402867e-05,0.2268471004255254,0.5044199848424348 -23.16,50.118723362727195,79.43282347242814,6.119941593392974e-05,0.32,0.1 +23.16,5.01187233627272,7.943282347242813,0.004127181991047416,0.32,0.4968352080195375 +23.16,7.943282347242813,12.589254117941662,0.002174981536380379,0.32,0.48956967277584734 +23.16,12.589254117941662,19.952623149688787,0.001146192412609121,0.32,0.5883215945363233 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-30.39,7.943282347242813,12.589254117941662,0.001645255645098864,0.32,0.5051540074317398 -30.39,12.589254117941662,19.952623149688787,0.0007190834623417877,0.32,0.5078703223644796 -30.39,19.952623149688787,31.622776601683793,0.0004332113469978534,0.32,0.5751575238410231 -30.39,31.622776601683793,50.118723362727195,0.0001235283564014712,0.2519740959959312,0.36249544819835267 -30.39,50.118723362727195,79.43282347242814,4.090709716082094e-05,0.2198791765944912,0.46335389409984495 +30.39,5.01187233627272,7.943282347242813,0.004905081141780332,0.32,0.4721346454914295 +30.39,7.943282347242813,12.589254117941662,0.0023778003005578423,0.32,0.5051540074317398 +30.39,12.589254117941662,19.952623149688787,0.0011526688562140343,0.32,0.5078703223644796 +30.39,19.952623149688787,31.622776601683793,0.0005587708487437167,0.32,0.5751575238410231 +30.39,31.622776601683793,50.118723362727195,0.00016794813524384343,0.2519740959959312,0.36249544819835267 +30.39,50.118723362727195,79.43282347242814,6.199561135441974e-05,0.2198791765944912,0.46335389409984495 37.66,0.012589254117941675,0.0199526231496888,0.0,0.32,0.1 37.66,0.0199526231496888,0.03162277660168379,0.005439726626267147,0.32,0.1 37.66,0.03162277660168379,0.05011872336272725,4.033267102327914,0.32,0.1 @@ -108,11 +108,11 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 37.66,1.9952623149688788,3.1622776601683795,0.03382440263576279,0.32,0.32059189443595226 37.66,3.1622776601683795,5.01187233627272,0.01641770122605414,0.32,0.3776824343131596 37.66,5.01187233627272,7.943282347242813,0.008053027406759726,0.32,0.41732136463774966 -37.66,7.943282347242813,12.589254117941662,0.003237621687188236,0.32,0.4681958676935546 -37.66,12.589254117941662,19.952623149688787,0.0012991891239513848,0.32,0.4975242950021638 -37.66,19.952623149688787,31.622776601683793,0.0006883145056863597,0.32,0.4560346038262774 -37.66,31.622776601683793,50.118723362727195,0.0004235952762966507,0.32,0.3750940508406499 -37.66,50.118723362727195,79.43282347242814,3.5488550608510015e-05,0.18950220722004738,0.41521133819475003 +37.66,7.943282347242813,12.589254117941662,0.003950081044909462,0.32,0.4681958676935546 +37.66,12.589254117941662,19.952623149688787,0.0019375496286347814,0.32,0.4975242950021638 +37.66,19.952623149688787,31.622776601683793,0.0009503851998836204,0.32,0.4560346038262774 +37.66,31.622776601683793,50.118723362727195,0.00046617233169622414,0.32,0.3750940508406499 +37.66,50.118723362727195,79.43282347242814,8.019044891039412e-05,0.18950220722004738,0.41521133819475003 44.92,0.012589254117941675,0.0199526231496888,0.0,0.32,0.1 44.92,0.0199526231496888,0.03162277660168379,0.0005034818183207038,0.32,0.1 44.92,0.03162277660168379,0.05011872336272725,0.5484809263354772,0.32,0.1 @@ -128,10 +128,10 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 44.92,3.1622776601683795,5.01187233627272,0.026913273538198192,0.32,0.30028752922703805 44.92,5.01187233627272,7.943282347242813,0.01209136211856364,0.32,0.3605503283144242 44.92,7.943282347242813,12.589254117941662,0.005657276374754573,0.32,0.3926582316629572 -44.92,12.589254117941662,19.952623149688787,0.002416143555161545,0.32,0.4559466839610515 -44.92,19.952623149688787,31.622776601683793,0.0009525500986153963,0.32,0.47256881354857116 -44.92,31.622776601683793,50.118723362727195,0.0004299427191114581,0.32,0.4840381992257248 -44.92,50.118723362727195,79.43282347242814,0.00011309980172289235,0.26962765847382447,0.5393843603168154 +44.92,12.589254117941662,19.952623149688787,0.002646912371536695,0.32,0.4559466839610515 +44.92,19.952623149688787,31.622776601683793,0.0012384307639377004,0.32,0.47256881354857116 +44.92,31.622776601683793,50.118723362727195,0.0005794338994973615,0.32,0.4840381992257248 +44.92,50.118723362727195,79.43282347242814,0.00019247100719071677,0.26962765847382447,0.5393843603168154 52.16,0.012589254117941675,0.0199526231496888,0.0,0.32,0.1 52.16,0.0199526231496888,0.03162277660168379,0.0,0.32,0.1 52.16,0.03162277660168379,0.05011872336272725,0.0019554053555110216,0.32,0.1 @@ -147,10 +147,10 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 52.16,3.1622776601683795,5.01187233627272,0.043727538658971755,0.32,0.24930940071416305 52.16,5.01187233627272,7.943282347242813,0.02150721255993863,0.32,0.26471029380820676 52.16,7.943282347242813,12.589254117941662,0.011762951290919104,0.32,0.2937675351898848 -52.16,12.589254117941662,19.952623149688787,0.004267232186510297,0.32,0.340632595436056 -52.16,19.952623149688787,31.622776601683793,0.0018970793308773712,0.32,0.3737626001490424 -52.16,31.622776601683793,50.118723362727195,0.0007798542933852055,0.32,0.42187429178046265 -52.16,50.118723362727195,79.43282347242814,0.0004033181758834818,0.2981934958226351,0.46791605631265054 +52.16,12.589254117941662,19.952623149688787,0.006433517253196768,0.32,0.340632595436056 +52.16,19.952623149688787,31.622776601683793,0.003518687038951978,0.32,0.3737626001490424 +52.16,31.622776601683793,50.118723362727195,0.0019244773878451216,0.32,0.42187429178046265 +52.16,50.118723362727195,79.43282347242814,0.00091399013595741,0.2981934958226351,0.46791605631265054 59.34,0.012589254117941675,0.0199526231496888,0.0,0.32,0.1 59.34,0.0199526231496888,0.03162277660168379,0.0,0.32,0.1 59.34,0.03162277660168379,0.05011872336272725,0.0,0.32,0.1 @@ -168,8 +168,8 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 59.34,7.943282347242813,12.589254117941662,0.021372538870198188,0.32,0.21249691515716132 59.34,12.589254117941662,19.952623149688787,0.010162926277315474,0.32,0.23612639342909444 59.34,19.952623149688787,31.622776601683793,0.0051678215728923425,0.32,0.27812830854616394 -59.34,31.622776601683793,50.118723362727195,0.0017435639258569936,0.32,0.3079741076018832 -59.34,50.118723362727195,79.43282347242814,0.0007007075226042048,0.32,0.34640349847763846 +59.34,31.622776601683793,50.118723362727195,0.0026278238255907083,0.32,0.3079741076018832 +59.34,50.118723362727195,79.43282347242814,0.001336241578959414,0.32,0.34640349847763846 66.44,0.012589254117941675,0.0199526231496888,0.0,0.32,0.1 66.44,0.0199526231496888,0.03162277660168379,0.0,0.32,0.1 66.44,0.03162277660168379,0.05011872336272725,0.0,0.32,0.1 @@ -187,5 +187,5 @@ ZD_deg,Ereco_min_TeV,Ereco_max_TeV,BckgRate_per_second,Theta_cut_deg,Gammaness_c 66.44,7.943282347242813,12.589254117941662,0.038460607554884396,0.31068923577798857,0.1862295796307054 66.44,12.589254117941662,19.952623149688787,0.021556562587680985,0.31760805725788366,0.18385702585616567 66.44,19.952623149688787,31.622776601683793,0.010124930615833161,0.31224688339635853,0.19600365005165704 -66.44,31.622776601683793,50.118723362727195,0.004054931196847225,0.3033689680537544,0.2193029044907234 -66.44,50.118723362727195,79.43282347242814,0.0013720321458985804,0.28724537451748516,0.27689025508229353 +66.44,31.622776601683793,50.118723362727195,0.004644484386197977,0.3033689680537544,0.2193029044907234 +66.44,50.118723362727195,79.43282347242814,0.0020234883061995145,0.28724537451748516,0.27689025508229353 diff --git a/notebooks/LST1_observation_simulator.ipynb b/notebooks/LST1_observation_simulator.ipynb index e6cf40d02..43e8e237e 100644 --- a/notebooks/LST1_observation_simulator.ipynb +++ b/notebooks/LST1_observation_simulator.ipynb @@ -126,7 +126,7 @@ "id": "dc762a1a", "metadata": {}, "source": [ - "## Select zenith bin" + "## Zenith distance bin selection" ] }, { @@ -139,7 +139,7 @@ "# Choose the bins among those above. Just set the bin number (from 0)\n", "# Make sure you choose values which make sense for the declination of your source\n", "\n", - "zd_bin = 4\n", + "zd_bin = 3\n", "\n", "print('Selected ZD = ', zenith[zd_bin], 'degrees')\n", "\n", @@ -172,7 +172,7 @@ }, { "cell_type": "markdown", - "id": "1161f853", + "id": "845a97a9", "metadata": {}, "source": [ "## Pulsar mode\n", @@ -182,7 +182,7 @@ { "cell_type": "code", "execution_count": null, - "id": "89f2cb17", + "id": "3e91e336", "metadata": {}, "outputs": [], "source": [ @@ -216,7 +216,9 @@ "\n", "# effective_obs_time = 8 * u.s # BOAT, 240 - 248 s\n", "\n", - "effective_obs_time = 34 * u.h # Crab nebula. performance paper" + "effective_obs_time = 34 * u.h # Crab nebula. performance paper\n", + "\n", + "# effective_obs_time = 11.8 * u.h # 1ES 1011+496, February 2014 flare" ] }, { @@ -236,6 +238,7 @@ "source": [ "redshift = 0\n", "\n", + "# redshift = 0.212 # 1ES 1011\n", "# redshift = 0.151 # BOAT GRB\n", "\n", "# We will apply the Dominguez EBL model to simulate the absorption\n", @@ -272,7 +275,7 @@ "def intrinsic_dFdE(E):\n", " return CRAB_MAGIC_JHEAP2015(E) # Crab Nebula, MAGIC log-parabola\n", "\n", - "# Crab pulsar P1, from LST1 paper:\n", + "# Crab pulsar P1, from LST1 paper (smoothly broken power-law):\n", "# def intrinsic_dFdE(E):\n", "# return PowerLaw(normalization=1.27e-4 / (u.TeV * u.cm**2 * u.s), \n", "# index=-1.811, e_ref=1*u.GeV)(E) * (1+(E/(6.8*u.GeV))**((4.09-1.811)/3))**-3\n", @@ -283,6 +286,13 @@ "# index=-2.455, \n", "# e_ref=1*u.TeV)(E)\n", "\n", + "\n", + "# 1ES1011 February 2014 flare\n", + "# def intrinsic_dFdE(E):\n", + "# return PowerLaw(normalization=8.7e-10 / (u.TeV * u.cm**2 * u.s), \n", + "# index=-2.03, e_ref=0.25*u.TeV)(E)\n", + "\n", + "# A log-parabola spectrum:\n", "# def intrinsic_dFdE(E):\n", "# return LogParabola(normalization=5e-8 / (u.TeV * u.cm**2 * u.s), \n", "# a=-2, b =-0.05, \n", @@ -414,15 +424,22 @@ "metadata": {}, "outputs": [], "source": [ + "# Integrate dF/dE within a bin, assuming power-law approximation within it:\n", "def integrate (dfde1, dfde2, e1, e2):\n", " # We cannot let numpy deal with the units, sometimes rounding leads to wrong units in result!\n", " # like TeV^(1e-15) :-D\n", - " # in power-law approximation:\n", + " if (dfde1 == 0) | (dfde2 == 0): # May happen, e.g because of EBL or a strong cutoff\n", + " return 0\n", + " \n", + " # In power-law approximation:\n", " gamma = np.log(dfde2/dfde1) / np.log(e2/e1)\n", " e1tev = e1.to_value(u.TeV)\n", " e2tev = e2.to_value(u.TeV)\n", " \n", - " integral = dfde1.to_value(1/(u.TeV * u.cm**2 * u.s)) / (gamma+1) * e1tev**(-gamma) * (e2tev**(gamma+1) - e1tev**(gamma+1))\n", + " integral = (dfde1.to_value(1/(u.TeV * u.cm**2 * u.s)) / \n", + " (gamma+1) * e1tev**(-gamma) * \n", + " (e2tev**(gamma+1) - e1tev**(gamma+1))\n", + " )\n", " return integral # (1/u.s/u.cm**2)" ] }, @@ -482,7 +499,7 @@ { "cell_type": "code", "execution_count": null, - "id": "29dd2c44", + "id": "99353083", "metadata": {}, "outputs": [], "source": [ @@ -622,7 +639,7 @@ { "cell_type": "code", "execution_count": null, - "id": "07a3be0c", + "id": "d7d64f53", "metadata": {}, "outputs": [], "source": [ @@ -642,6 +659,8 @@ "finebincenters = 0.5 * (fine_etrue_binning[1:]+fine_etrue_binning[:-1])\n", "\n", "for iereco in range(len(erecobincenters)):\n", + " if np.nansum(total_signal_counts_2d[iereco]) == 0:\n", + " continue\n", " mean_etrue_vs_ereco[iereco] = (np.nansum(finebincenters * total_signal_counts_2d[iereco]) / \n", " np.nansum(total_signal_counts_2d[iereco]))" ] @@ -677,7 +696,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cb040e44", + "id": "71e29aa4", "metadata": {}, "outputs": [], "source": [ @@ -823,12 +842,14 @@ "non = total_signal_counts + total_bg_counts\n", "# excess = non - alpha * noff (= total_signal_counts in Asimov dataset)\n", "stat_excess_error = (non + alpha**2 * noff)**0.5\n", - "relative_stat_excess_error = stat_excess_error / total_signal_counts\n", + "\n", + "np.seterr(divide='ignore')\n", + "relative_stat_excess_error = stat_excess_error/total_signal_counts\n", "\n", "syst_excess_error = backg_systematics_uncertainty * total_bg_counts\n", - "relative_syst_excess_error = syst_excess_error / total_signal_counts\n", "\n", - "total_relative_excess_error = np.hypot(relative_stat_excess_error, relative_syst_excess_error)\n" + "relative_syst_excess_error = syst_excess_error / total_signal_counts\n", + "total_relative_excess_error = np.hypot(relative_stat_excess_error, relative_syst_excess_error)" ] }, { @@ -838,20 +859,21 @@ "metadata": {}, "outputs": [], "source": [ - "SED = (mean_etrue_vs_ereco*u.TeV)**2 * dFdE(mean_etrue_vs_ereco*u.TeV)\n", - "\n", - "SED_stat_error = SED*relative_stat_excess_error\n", - "SED_total_error = SED*total_relative_excess_error\n", - "\n", "displayed_points = reliable_points & (significance > min_signi_in_flux_point)\n", "\n", "if displayed_points.sum() > 0:\n", "\n", + " SED = ((mean_etrue_vs_ereco[displayed_points]*u.TeV)**2 * \n", + " dFdE(mean_etrue_vs_ereco[displayed_points]*u.TeV))\n", + "\n", + " SED_stat_error = SED*relative_stat_excess_error[displayed_points]\n", + " SED_total_error = SED*total_relative_excess_error[displayed_points]\n", + "\n", " plt.figure(figsize=(8,4))\n", - " intrinsic_SED = (mean_etrue_vs_ereco*u.TeV)**2 * intrinsic_dFdE(mean_etrue_vs_ereco*u.TeV)\n", + " intrinsic_SED = (finebincenters*u.TeV)**2 * intrinsic_dFdE(finebincenters*u.TeV)\n", "\n", " if redshift > 0:\n", - " plt.plot(mean_etrue_vs_ereco, intrinsic_SED, '--', color='lightgrey', label='intrinsic')\n", + " plt.plot(finebincenters, intrinsic_SED, '--', color='lightgrey', label='intrinsic')\n", "\n", "\n", " SED_fine = (fine_etrue_binning*u.TeV)**2 * dFdE(fine_etrue_binning*u.TeV)\n", @@ -859,19 +881,19 @@ "\n", "\n", " if not pulsar_mode:\n", - " plt.errorbar(mean_etrue_vs_ereco[displayed_points], SED[displayed_points], \n", - " yerr=SED_total_error[displayed_points], fmt='o', markersize=2, \n", + " plt.errorbar(mean_etrue_vs_ereco[displayed_points], SED, yerr=SED_total_error, \n", + " fmt='o', markersize=2, \n", " label='Observed, stat+syst')\n", - " plt.errorbar(mean_etrue_vs_ereco[displayed_points], SED[displayed_points], \n", - " yerr=SED_stat_error[displayed_points], fmt='o', markersize=2, \n", + " plt.errorbar(mean_etrue_vs_ereco[displayed_points], SED, yerr=SED_stat_error, \n", + " fmt='o', markersize=2, \n", " label='Observed, stat-only')\n", "\n", " plt.yscale('log')\n", " plt.xscale('log')\n", " plt.xlabel('Energy (TeV)')\n", " plt.ylabel(f'SED ({SED.unit})')\n", - " plt.ylim(np.nanmin((SED-SED_total_error)[displayed_points]).value*0.1, \n", - " np.nanmax((SED+SED_total_error)[displayed_points]).value*5)\n", + " plt.ylim(np.nanmin((SED-SED_total_error)).value*0.1, \n", + " np.nanmax((SED+SED_total_error)).value*5)\n", "\n", " plt.xlim(mean_etrue_vs_ereco[displayed_points][0]/3, mean_etrue_vs_ereco[displayed_points][-1]*3)\n", " plt.legend()\n", From dfc9adb73e4336b293f4617af06b94ae221915aa Mon Sep 17 00:00:00 2001 From: moralejo Date: Wed, 30 Oct 2024 11:31:32 +0000 Subject: [PATCH 05/16] Added option to simulate source extension --- notebooks/LST1_observation_simulator.ipynb | 75 ++++++++++++++++------ 1 file changed, 54 insertions(+), 21 deletions(-) diff --git a/notebooks/LST1_observation_simulator.ipynb b/notebooks/LST1_observation_simulator.ipynb index 43e8e237e..944005013 100644 --- a/notebooks/LST1_observation_simulator.ipynb +++ b/notebooks/LST1_observation_simulator.ipynb @@ -172,7 +172,7 @@ }, { "cell_type": "markdown", - "id": "845a97a9", + "id": "9798d5c3", "metadata": {}, "source": [ "## Pulsar mode\n", @@ -182,11 +182,11 @@ { "cell_type": "code", "execution_count": null, - "id": "3e91e336", + "id": "8d8cff5b", "metadata": {}, "outputs": [], "source": [ - "pulsar_mode = False # True # Set to True to activate it\n", + "pulsar_mode = False # Set to True to activate it\n", "\n", "on_phase_interval = 0.043 # Crab P1: [-0.017, 0.026] \n", "off_phase_interval = 0.35 # [0.52 - 0.87]\n", @@ -212,7 +212,7 @@ "metadata": {}, "outputs": [], "source": [ - "#effective_obs_time = 103 * u.h # Crab pulsar paper\n", + "# effective_obs_time = 103 * u.h # Crab pulsar paper\n", "\n", "# effective_obs_time = 8 * u.s # BOAT, 240 - 248 s\n", "\n", @@ -221,6 +221,40 @@ "# effective_obs_time = 11.8 * u.h # 1ES 1011+496, February 2014 flare" ] }, + { + "cell_type": "markdown", + "id": "f243b138", + "metadata": {}, + "source": [ + "## Source extension\n", + "For moderately extended sources, say below ~0.5 degrees radius\n", + "Set here the radius of the source within which (in 2d-gaussian approximation) the same fraction of the source is contained as the cut efficiency set above. That is, if you use 0.7 efficiency, set as source radius the angular distance within which 70% of the emission is expected to be contained.\n", + "We will simply increase the background by a factor (source_radius$^2$ + theta_cut$^2$) / (theta_cut$^2$), since we expect to integrate the same fraction of the signal within an angle sqrt(source_radius$^2$ + theta_cut$^2$) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ab033a7", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "source_radius = 0 # 0.3 * u.deg\n", + "theta_cut = background_data.Theta_cut_deg[zd_selection_backg].to_numpy()*u.deg\n", + "\n", + "if source_radius == 0:\n", + " print(\"Emission from a point-like source will be assumed!\")\n", + " background_increase_factor = np.ones_like(theta_cut.to_value())\n", + "else:\n", + " print(f'Source angular radius within which {cut_efficiency:.1%} of the emission is contained: {source_radius}')\n", + "\n", + "\n", + " background_increase_factor = (theta_cut**2 + \n", + " (source_radius.to_value(u.deg)*np.ones_like(theta_cut))**2) / (theta_cut**2)" + ] + }, { "cell_type": "markdown", "id": "d662546b", @@ -269,7 +303,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Set here the simulated intrinsic spectrum (must take as argument an astropy quantity with energy units)\n", + "# Set here the simulated intrinsic spectrum intrinsic_dFdE\n", + "# (it must take as argument an astropy quantity with energy units!)\n", "\n", "# Crab Nebula:\n", "def intrinsic_dFdE(E):\n", @@ -496,16 +531,6 @@ "## Background rate" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "99353083", - "metadata": {}, - "outputs": [], - "source": [ - "background_data[zd_selection_backg].BckgRate_per_second.to_numpy()" - ] - }, { "cell_type": "code", "execution_count": null, @@ -520,6 +545,8 @@ "if pulsar_mode: # Scale background to the on-phase:\n", " bgrate *= on_phase_interval\n", "\n", + "bgrate *= background_increase_factor\n", + "\n", "plt.plot(erecobincenters[ereco_mask], bgrate[ereco_mask])\n", "plt.xlabel('Ereco (TeV)')\n", "plt.ylabel('Background rate within theta cut\\n (events/s) in Ereco bins')\n", @@ -564,9 +591,7 @@ "source": [ "fine_etrue_binning = np.logspace(-2.5, 3.5, 601) # Just for calculation of mean Etrue within an Ereco bin\n", "\n", - "total_bg_counts = effective_obs_time.to_value(u.s) * background_data[zd_selection_backg].BckgRate_per_second.to_numpy()\n", - "if pulsar_mode:\n", - " total_bg_counts *= on_phase_interval\n", + "total_bg_counts = effective_obs_time.to_value(u.s) * bgrate\n", "\n", "total_signal_counts_2d = np.zeros(shape=[len(total_bg_counts), len(fine_etrue_binning)-1])\n", "\n", @@ -639,7 +664,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d7d64f53", + "id": "7295189b", "metadata": {}, "outputs": [], "source": [ @@ -696,7 +721,7 @@ { "cell_type": "code", "execution_count": null, - "id": "71e29aa4", + "id": "bfdf90f7", "metadata": {}, "outputs": [], "source": [ @@ -774,7 +799,7 @@ "plt.grid()\n", "plt.legend()\n", "plt.xlabel('Ereco (TeV)')\n", - "plt.ylabel('Li & Ma significance')\n", + "plt.ylabel('Li & Ma significance per Ereco bin')\n", "plt.xscale('log')" ] }, @@ -910,6 +935,14 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ca87a15", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From c7fcca9538a45204d2a68cb33d169eade294e60b Mon Sep 17 00:00:00 2001 From: moralejo Date: Wed, 30 Oct 2024 12:11:11 +0000 Subject: [PATCH 06/16] Search for gammapy data directory automatically --- notebooks/LST1_observation_simulator.ipynb | 33 ++++++++++++++-------- 1 file changed, 22 insertions(+), 11 deletions(-) diff --git a/notebooks/LST1_observation_simulator.ipynb b/notebooks/LST1_observation_simulator.ipynb index 944005013..cd68c5565 100644 --- a/notebooks/LST1_observation_simulator.ipynb +++ b/notebooks/LST1_observation_simulator.ipynb @@ -12,10 +12,13 @@ "import numpy as np\n", "import astropy.units as u\n", "import matplotlib.pyplot as plt\n", + "import gammapy\n", + "from gammapy.modeling.models import EBLAbsorptionNormSpectralModel\n", + "import subprocess\n", "from pyirf.spectral import CRAB_MAGIC_JHEAP2015, PowerLaw, LogParabola\n", "from pyirf.statistics import li_ma_significance\n", "from scipy.stats import moyal, norm, skewnorm\n", - "from gammapy.modeling.models import EBLAbsorptionNormSpectralModel" + "from pathlib import Path" ] }, { @@ -172,7 +175,7 @@ }, { "cell_type": "markdown", - "id": "9798d5c3", + "id": "ba8de510", "metadata": {}, "source": [ "## Pulsar mode\n", @@ -182,7 +185,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8d8cff5b", + "id": "8e3139e8", "metadata": {}, "outputs": [], "source": [ @@ -223,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "f243b138", + "id": "4ccf4294", "metadata": {}, "source": [ "## Source extension\n", @@ -235,7 +238,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2ab033a7", + "id": "cb352dca", "metadata": { "scrolled": false }, @@ -278,12 +281,20 @@ "# We will apply the Dominguez EBL model to simulate the absorption\n", "\n", "\n", + "# Make sure we have the necessary EBL absorption data:\n", "try:\n", " os.environ['GAMMAPY_DATA']\n", "except:\n", - " # SET HERE THE GAMMAPY_DATA ENV VARIABLE IN CASE IT IS NOT SET\n", - " # YOU MUST SET THE PATH TO THE CORRESPONDING DIRECTORY IN THE CONDA ENVIRONMENT YOU ARE USING:\n", - " os.environ['GAMMAPY_DATA'] = '/fefs/aswg/workspace/abelardo.moralejo/miniconda3/envs/lst-dev/lib/python3.11/site-packages/gammapy/gammapy-datasets/1.1'\n", + " # WE SET HERE THE GAMMAPY_DATA ENV VARIABLE IN CASE IT IS NOT SET\n", + " gammapy_dir = Path(gammapy.__file__).parent\n", + " gammapy_dir\n", + "\n", + " ebl_file = subprocess.run(['find', str(gammapy_dir), '-name', 'ebl_dominguez11.fits.gz'], \n", + " stdout=subprocess.PIPE).stdout.decode()\n", + " gammapy_data = ebl_file[:ebl_file.find('/ebl/')]\n", + " os.environ['GAMMAPY_DATA'] = gammapy_data\n", + " print('Set GAMMAPY_DATA to', gammapy_data)\n", + "\n", "\n", "dominguez = EBLAbsorptionNormSpectralModel.read_builtin(\"dominguez\", redshift=redshift)" ] @@ -664,7 +675,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7295189b", + "id": "e91f0070", "metadata": {}, "outputs": [], "source": [ @@ -721,7 +732,7 @@ { "cell_type": "code", "execution_count": null, - "id": "bfdf90f7", + "id": "5113ba34", "metadata": {}, "outputs": [], "source": [ @@ -939,7 +950,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1ca87a15", + "id": "f9c2b653", "metadata": {}, "outputs": [], "source": [] From d847700f46bc04868f63fa7b8e9fbe25a80f52fa Mon Sep 17 00:00:00 2001 From: moralejo Date: Thu, 31 Oct 2024 09:26:28 +0000 Subject: [PATCH 07/16] Improved range of S/B plot (starts at 1e-3 now) Added another observation example (GRB190114C) --- notebooks/LST1_observation_simulator.ipynb | 635 ++++++++++++++++++--- 1 file changed, 568 insertions(+), 67 deletions(-) diff --git a/notebooks/LST1_observation_simulator.ipynb b/notebooks/LST1_observation_simulator.ipynb index cd68c5565..5a378d7b6 100644 --- a/notebooks/LST1_observation_simulator.ipynb +++ b/notebooks/LST1_observation_simulator.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "8d7fb8ab-7034-47be-a392-17612fd611b7", "metadata": {}, "outputs": [], @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "id": "ef3576a4", "metadata": {}, "outputs": [], @@ -44,7 +44,7 @@ "# 0.7: standard cuts (safer for spectral analysis)\n", "# 0.4: tight cuts, better for detection of weak sources\n", "#\n", - "cut_efficiency = 0.7" + "cut_efficiency = 0.4" ] }, { @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "id": "95ac1d89", "metadata": {}, "outputs": [], @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "id": "c59f8666", "metadata": {}, "outputs": [], @@ -87,34 +87,404 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "id": "ad3a988e", "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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ZD_degEtrue_min_TeVEtrue_max_TeVAeff_m2emig_mu_locemig_mu_scaleemig_mu_aemig_model
06.000.0112200.0141256.018636e+022.1416140.237586NaNmoyal
16.000.0141250.0177831.229892e+031.7960290.218862NaNmoyal
26.000.0177830.0223872.391636e+031.4318090.201941NaNmoyal
36.000.0223870.0281843.886893e+031.2220060.185834NaNmoyal
46.000.0281840.0354815.221963e+031.0412030.175796NaNmoyal
...........................
34566.4411.22018514.1253751.069989e+060.8588290.1742591.269968skewnorm
34666.4414.12537517.7827941.139978e+060.8440600.1785651.444996skewnorm
34766.4417.78279422.3872111.294651e+060.8437950.1825841.540402skewnorm
34866.4422.38721128.1838291.192070e+060.8486140.1842901.672204skewnorm
34966.4428.18382935.4813391.231130e+060.8418080.1953921.734357skewnorm
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350 rows × 8 columns

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" + ], + "text/plain": [ + " ZD_deg Etrue_min_TeV Etrue_max_TeV Aeff_m2 emig_mu_loc \\\n", + "0 6.00 0.011220 0.014125 6.018636e+02 2.141614 \n", + "1 6.00 0.014125 0.017783 1.229892e+03 1.796029 \n", + "2 6.00 0.017783 0.022387 2.391636e+03 1.431809 \n", + "3 6.00 0.022387 0.028184 3.886893e+03 1.222006 \n", + "4 6.00 0.028184 0.035481 5.221963e+03 1.041203 \n", + ".. ... ... ... ... ... \n", + "345 66.44 11.220185 14.125375 1.069989e+06 0.858829 \n", + "346 66.44 14.125375 17.782794 1.139978e+06 0.844060 \n", + "347 66.44 17.782794 22.387211 1.294651e+06 0.843795 \n", + "348 66.44 22.387211 28.183829 1.192070e+06 0.848614 \n", + "349 66.44 28.183829 35.481339 1.231130e+06 0.841808 \n", + "\n", + " emig_mu_scale emig_mu_a emig_model \n", + "0 0.237586 NaN moyal \n", + "1 0.218862 NaN moyal \n", + "2 0.201941 NaN moyal \n", + "3 0.185834 NaN moyal \n", + "4 0.175796 NaN moyal \n", + ".. ... ... ... \n", + "345 0.174259 1.269968 skewnorm \n", + "346 0.178565 1.444996 skewnorm \n", + "347 0.182584 1.540402 skewnorm \n", + "348 0.184290 1.672204 skewnorm \n", + "349 0.195392 1.734357 skewnorm \n", + "\n", + "[350 rows x 8 columns]" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "gamma_data" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "id": "1d2b1efe", "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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ZD_degEreco_min_TeVEreco_max_TeVBckgRate_per_secondTheta_cut_degGammaness_cut
06.000.0125890.0199530.0851720.3200000.149805
16.000.0199530.0316233.4842740.2541680.223265
26.000.0316230.0501194.4781400.2283660.242829
36.000.0501190.0794331.4772600.1793080.308257
46.000.0794330.1258930.1278750.1186910.520600
.....................
18566.447.94328212.5892540.0001700.1000000.734576
18666.4412.58925419.9526230.0000850.1000000.755384
18766.4419.95262331.6227770.0000430.1000000.770523
18866.4431.62277750.1187230.0000210.1000000.791636
18966.4450.11872379.4328230.0000110.1000000.810671
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190 rows × 6 columns

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" + ], + "text/plain": [ + " ZD_deg Ereco_min_TeV Ereco_max_TeV BckgRate_per_second Theta_cut_deg \\\n", + "0 6.00 0.012589 0.019953 0.085172 0.320000 \n", + "1 6.00 0.019953 0.031623 3.484274 0.254168 \n", + "2 6.00 0.031623 0.050119 4.478140 0.228366 \n", + "3 6.00 0.050119 0.079433 1.477260 0.179308 \n", + "4 6.00 0.079433 0.125893 0.127875 0.118691 \n", + ".. ... ... ... ... ... \n", + "185 66.44 7.943282 12.589254 0.000170 0.100000 \n", + "186 66.44 12.589254 19.952623 0.000085 0.100000 \n", + "187 66.44 19.952623 31.622777 0.000043 0.100000 \n", + "188 66.44 31.622777 50.118723 0.000021 0.100000 \n", + "189 66.44 50.118723 79.432823 0.000011 0.100000 \n", + "\n", + " Gammaness_cut \n", + "0 0.149805 \n", + "1 0.223265 \n", + "2 0.242829 \n", + "3 0.308257 \n", + "4 0.520600 \n", + ".. ... \n", + "185 0.734576 \n", + "186 0.755384 \n", + "187 0.770523 \n", + "188 0.791636 \n", + "189 0.810671 \n", + "\n", + "[190 rows x 6 columns]" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "background_data" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "id": "90114613", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available zeniths: [ 6. 9.579 16.08 23.16 30.39 37.66 44.92 52.16 59.34 66.44 ] (degrees)\n" + ] + } + ], "source": [ "# CHECK that we have the same pointing zenith values in both tables:\n", "assert np.alltrue(np.unique(gamma_data.ZD_deg) == np.unique(background_data.ZD_deg))\n", @@ -134,10 +504,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "id": "a5a9386a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selected ZD = 23.16 degrees\n" + ] + } + ], "source": [ "# Choose the bins among those above. Just set the bin number (from 0)\n", "# Make sure you choose values which make sense for the declination of your source\n", @@ -161,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "id": "0c58505b", "metadata": {}, "outputs": [], @@ -175,7 +553,7 @@ }, { "cell_type": "markdown", - "id": "ba8de510", + "id": "311a8237", "metadata": {}, "source": [ "## Pulsar mode\n", @@ -184,10 +562,18 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "8e3139e8", + "execution_count": 75, + "id": "a589ca87", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "alpha = 0.1229\n" + ] + } + ], "source": [ "pulsar_mode = False # Set to True to activate it\n", "\n", @@ -210,7 +596,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "id": "7d8d3a2e", "metadata": {}, "outputs": [], @@ -219,14 +605,14 @@ "\n", "# effective_obs_time = 8 * u.s # BOAT, 240 - 248 s\n", "\n", - "effective_obs_time = 34 * u.h # Crab nebula. performance paper\n", + "# effective_obs_time = 34 * u.h # Crab nebula. performance paper\n", "\n", - "# effective_obs_time = 11.8 * u.h # 1ES 1011+496, February 2014 flare" + "effective_obs_time = 11.8 * u.h # 1ES 1011+496, February 2014 flare" ] }, { "cell_type": "markdown", - "id": "4ccf4294", + "id": "16d22a98", "metadata": {}, "source": [ "## Source extension\n", @@ -237,12 +623,20 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "cb352dca", + "execution_count": 77, + "id": "4954c1de", "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Emission from a point-like source will be assumed!\n" + ] + } + ], "source": [ "source_radius = 0 # 0.3 * u.deg\n", "theta_cut = background_data.Theta_cut_deg[zd_selection_backg].to_numpy()*u.deg\n", @@ -268,14 +662,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 78, "id": "ea9bca3c", "metadata": {}, "outputs": [], "source": [ "redshift = 0\n", "\n", - "# redshift = 0.212 # 1ES 1011\n", + "redshift = 0.212 # 1ES 1011\n", "# redshift = 0.151 # BOAT GRB\n", "\n", "# We will apply the Dominguez EBL model to simulate the absorption\n", @@ -309,7 +703,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 79, "id": "e24f72ce", "metadata": {}, "outputs": [], @@ -318,8 +712,8 @@ "# (it must take as argument an astropy quantity with energy units!)\n", "\n", "# Crab Nebula:\n", - "def intrinsic_dFdE(E):\n", - " return CRAB_MAGIC_JHEAP2015(E) # Crab Nebula, MAGIC log-parabola\n", + "# def intrinsic_dFdE(E):\n", + "# return CRAB_MAGIC_JHEAP2015(E) # Crab Nebula, MAGIC log-parabola\n", "\n", "# Crab pulsar P1, from LST1 paper (smoothly broken power-law):\n", "# def intrinsic_dFdE(E):\n", @@ -334,9 +728,9 @@ "\n", "\n", "# 1ES1011 February 2014 flare\n", - "# def intrinsic_dFdE(E):\n", - "# return PowerLaw(normalization=8.7e-10 / (u.TeV * u.cm**2 * u.s), \n", - "# index=-2.03, e_ref=0.25*u.TeV)(E)\n", + "def intrinsic_dFdE(E):\n", + " return PowerLaw(normalization=8.7e-10 / (u.TeV * u.cm**2 * u.s), \n", + " index=-2.03, e_ref=0.25*u.TeV)(E)\n", "\n", "# A log-parabola spectrum:\n", "# def intrinsic_dFdE(E):\n", @@ -347,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 80, "id": "014476b0", "metadata": {}, "outputs": [], @@ -368,7 +762,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 81, "id": "80627c34", "metadata": {}, "outputs": [], @@ -395,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "id": "24e8394c", "metadata": {}, "outputs": [], @@ -406,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "id": "056ea33b", "metadata": {}, "outputs": [], @@ -439,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 84, "id": "1b79d5b2", "metadata": {}, "outputs": [], @@ -450,7 +844,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 85, "id": "2038bb5b", "metadata": {}, "outputs": [], @@ -465,7 +859,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "id": "48a34458", "metadata": {}, "outputs": [], @@ -491,7 +885,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "id": "54b22527", "metadata": {}, "outputs": [], @@ -520,10 +914,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "id": "4eff20a0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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FdzCvSADwEC4dbAMCApSWlqasrCxdcskl9duzsrJ08cUXt+rcmZmZyszMlN1ub22ZANDI+u+P6n8+2qa1+Ufqt1ks0vDuEbpgUJwmDYhVTBh3QAIAZzI92JaXl2vXrl31z/Py8pSTk6OIiAh169ZN8+bN0+zZs5Wenq6RI0fqueee0969e3XDDTe06roZGRnKyMiQ1WpVeHh4a98GAEiqG27w+Efb9MGmQkmSj0Ua2TNSk1PiNHFADLfzBIA2ZHqwXbduncaNG1f//OSKBddcc40WLlyoK664QocPH9ZDDz2kwsJCpaSkaNmyZUpKSjKrZABopKzKpqe/2K3nv8xTTa1DFot0eVqifj+hj2LDCbMA0B5MD7Zjx47VmZbSnTt3rubOndtOFQFA09kdht5at09/+3S7SsprJEmjekbqTxckKzm+aUsIAgCcw/RgCwDu6sudJXrkw1xtKyqTJPWI6qh7pvTXL/tHc/taADCB1wZbJo8BaKldB8v152Xf6fNtByVJ4R38det5vfWrXyQpgNt+AYBpvDbYMnkMQHMdrajRk//doVe/2Su7w5Cfj0WzRybp1vN6q1NwgNnlAYDX89pgCwBNVVPr0KI1+frnZztlraqVJP2yf4zmT+mnnl1CTK4OAHASwRYATuNYZY0+zS3WguW7lH+4UpLUPy5Mf7qgv0b3ijK5OgDATxFsAeBHiq1V+nRrkT7eWqSv9xyR3VG3aktUSKDunNRHl6YlyteHiWEA4IoItgC8Xn5JhT45EWY37D3WYF+/2FBdNDhe14zqrpBAPjIBwJV57ac0qyIA3sswDH1XWKZPthbpk61F9ct1nZTarZPOT4nVpAGxSorsaFKVAIDm8tpgy6oIgHdxOAxt2Hesrmd2S5H2Hqms3+frY9HIsyI1aUCMJg6IVUwYdwoDAHfktcEWgOczDENbC6x659v9+nBToQ6WVdfvC/Tz0Tl9umjSgFj9sn80y3UBgAcg2ALwOIfKqvV+zgG9vX5/g2EGoYF+Gt8/WpMGxOrcPl3UkTGzAOBR+FQH4BGqbHZ99t1BvfPtfq3Ycah+NYMAXx/9Mjla04cmaEyfKAX6+ZpcKQCgrRBsAbgtwzCUs++Y3vl2v/6TU1B/8wRJGpLYSTPSEnTRoDiGGQCAlyDYAnA7haXHtfTbA3rn2/3ac6iifntsWJCmp3bV9NQE9YrmjmAA4G28Ntiy3BfgXmx2hz7cVKi31+/XV7tLZNSNNFCQv4/OHxCrGWkJGtUzipsnAIAX89pgy3JfgPvYcqBUd769Sd8VWuu3De8eoUvTEjR5YKxCg/xNrA4A4Cq8NtgCcH3VtXb96/NdWvDFbtkdhjoH++vqkd01IzVB3SKDzS4PAOBiCLYAXNLGfcd059sbtaO4XJJ0wcA4PXjxAEWFBJpcGQDAVRFsAbiUKptdT/53p55buVsOQ4rsGKCHp6VoysA4s0sDALg4gi0Al7H++6O66+2N2n1ipYOpg+P1wNQBiujIcl0AgDMj2AIw3fEau/726Xa98FWeDEPqEhqoR6elaOKAWLNLAwC4EYItAFOtzTuiu97eqPzDlZKk6alddd+FydxUAQDQbARbAKaorKnVXz7erpfX5Msw6m6u8OfpKRrfL8bs0gAAbsprgy03aADMs2b3Yf3hnU3ae6Sul/aK9ETdc0F/hXdgPVoAQMt5bbDlBg1A+7NW2fSXj7fp1a/3SpLiw4P02IxBOrdPF5MrAwB4Aq8NtgDaj8Nh6N0NB/TYR9tUUl4tSZo5opvmT+7HXcMAAE5DsAXQprYWlOq+97dq/fdHJUlnRXXUI9NSNKpXlMmVAQA8DcEWQJsorbTpb1nb9erX38thSMEBvrp5fG9df3YPBfj5mF0eAMADEWwBOJXDYeitdfv0l0+260hFjSTpwkFx+uMF/RUX3sHk6gAAnoxgC8BpNu0/pnvf36qN+45JknpHh+jBiwdoVE+GHQAA2h7BFkCrHamo0ROfbNOb2ftkGFJIoJ9u+2VvXTOqu/x9GXYAAGgfBFsALWZ3GHp97V799ZPtKj1ukyRdMrSr5k/up+iwIJOrAwB4G4ItgBZZ//1R3ff+Fm0tsEqS+sWG6qGLUzS8R4TJlQEAvJXXBlvuPAY0n91h6Ju8w3ore5/eyymQJIUG+emOiX01a0Q3+THsAABgIq8Nttx5DGgau8PQ2rwj+nBzgT7eUqSS8pr6fZenJ+iu8/spKiTQxAoBAKjjtcEWwOnZHYbW5R/Rh5sLtWxzUf3dwiQpvIO/zh8Qq6tGdNOQxE7mFQkAwE8QbAFIqlt/dv3eo/pwU6GWbS7UwbIfwmxYkJ8mDYjVBYPiNLpXFCsdAABcEsEW8GIOh6EN+47qg02F+mhzkYqsVfX7QoP8NDE5VheeCLPcLQwA4OoItoAXqql1KHP5Lr21bp8KS38UZgP9NGFATH2YDfTzNbFKAACah2ALeJn8kgrd8uYGbdpfKqnuZgoTkmN0wcA4jelDmAUAuC+CLeBF3s85oHuWblZFjV2dgv11/0XJmpwSpyB/wiwAwP0RbAEvUFlTq/vf36ol6/dLkoZ3j9A/rhqiuPAOJlcGAIDzEGwBD7e1oFQ3v7FBew5VyMci3Ty+t24e34ubKQAAPA7BFvBQhmFo0Zrv9eiy71RT61BsWJCevHKIfnFWpNmlAQDQJgi2gAc6VlmjO9/epKzcYknSef2i9cRlgxXRMcDkygAAaDsEW8DDrM07olvf3KDC0ioF+Ppo/pR+unZUd1ksFrNLAwCgTXltsM3MzFRmZqbsdrvZpQBOYXcYyly+S0/+d4cchtQjqqOeumqoUrqGm10aAADtwmuDbUZGhjIyMmS1WhUezg9+uLei0irdtniDvt5zRJI0PbWrHro4RSGBXvstDgDwQvzUA9zcZ98V644lG3W00qbgAF89Mi1F01MTzC4LAIB2R7AF3NTxGrv+5+NtWrg6X5I0ID5MT101VGd1CTG3MAAATEKwBdzQ2rwjuvPtjfr+cKUkac7oHvrD5L7cDhcA4NUItoAbOV5j1xOfbNdLq/NkGFJceJAemz5QY/tGm10aAACmI9gCbiI7/4juXLJR+Sd6aS9PT9CfLkxWWJC/yZUBAOAaCLaAi/tpL21sWJAemzFQ4+ilBQCgAYIt4MLW5R/RnW9vUl5JhSTpsrS6XtrwDvTSAgDwUwRbwAVV2ez66yfb9cJXP+qlnT5Q4/rRSwsAwOkQbAEXs/77I7pzySbtoZcWAIBmIdgCLqLKZtf/fLKzvpc2JixQj08fRC8tAABNRLAFXEBemTQ1c43yTqx4cGlagu69IFnhwfTSAgDQVARbwESGYWjBF3v0jy2+MlSpmLBAPTZ9oMb3izG7NAAA3A7BFjCJw2HokQ+/04tf5Umy6JKh8XrgohR6aQEAaCGCLWCCWrtD85du1pL1+yVJM7rb9fj0FPn7E2oBAGgpgi3Qzqpr7brtzRx9tKVIPhbp8UtSFFiYY3ZZAAC4PR+zCwC8SWVNrX6zaL0+2lKkAF8fLZiVpkuGxptdFgAAHsFrg21mZqaSk5M1bNgws0uBl7BW2XT1C2u1cschdfD31QvXpuv8lFizywIAwGN4bbDNyMhQbm6usrOzzS4FXqCkvFpXPfe11n1/VGFBfnr118M1pncXs8sCAMCjMMYWaGOFpcc16/lvtOdQhaJCArRozgglx4eZXRYAAB6HYAu0obySCv3q+W904NhxxYcH6dVfj9BZXULMLgsAAI9EsAXayHeFVs1+Ya1Kyqt1VlRHvfLrEeraqYPZZQEA4LEItkAb+HbvUV374lpZq2rVPy5Mi+YMV5fQQLPLAgDAoxFsASf7aleJfrNonSpr7Ert1kkvXTucu4kBANAOCLaAE326tUg3vb5BNXaHxvSO0rOz0xQcwLcZAADtgZ+4gJO8u2G/7liySXaHoUkDYvTPq4Yq0M/X7LIAAPAaBFvACd5Yu1fzl26WJM1ITdD/zBgoP1+vXSYaAABTEGyBVlqc/UOovXZUd913YbJ8fCwmVwUAgPch2AKtsGTdPt19ItReN7ou1FoshFoAAMzA70qBFnpn/X7d9c4mGcYPPbWEWgAAzEOwBVrgvQ0HdMfbG2UY0uxfJOn+iwi1AACYjWALNNP7OQc0760cGYY0c0Q3PTh1AKEWAAAXQLAFmuGDTQX6/eIcOQzpymGJeuTiFCaKAQDgIgi2QBMt21yoW9+sC7WXpyfoz5cMJNQCAOBCCLZAE3y8pUi3vLFBdoehGakJenz6IEItAAAuhmALnEHdbXK/Va3D0CVDu+ovlxJqAQBwRS1ex9Zms6moqEiVlZXq0qWLIiIinFkX4BL+m1usjBOhdurgeP31ssHyJdQCAOCSmtVjW15ermeffVZjx45VeHi4unfvruTkZHXp0kVJSUn6zW9+o+zs7LaqFWhXy7cd1NzXvpXNbujCQXH638sJtQAAuLImB9u///3v6t69u/79739r/PjxWrp0qXJycrR9+3atWbNG999/v2prazVhwgSdf/752rlzZ1vWDbSpL7Yf1O9eWa8au0NTBsbqySuGyM+XkTsAALiyJg9FWL16tZYvX66BAweecv/w4cM1Z84cPfPMM3rhhRe0YsUK9e7d22mFAu1l5Y5D+u2JUHv+gFj948qhhFoAANxAk4PtkiVLmnRcYGCg5s6d2+KCADN9ubNEv1m0TjW1Dk1IjtE/rxoqf0ItAABugZ/YwAlZucX69aJsVdc69Mv+0cqcmaoAP75FAABwF83+qV1YWKhXX31Vy5YtU01NTYN9FRUVeuihh5xWHNAeDMPQvz7fqd8sWqcqm0Pj+0UrcxahFgAAd9Osn9zZ2dlKTk5WRkaGLr30UqWkpGjr1q31+8vLy/Xggw86vUigrVTW1OqmNzbor5/ukCRdMzJJz85OU6Cfr8mVAQCA5mpWsL3nnns0ffp0HT16VMXFxZowYYLOPfdcbdiwoa3qa7LKykolJSXpjjvuMLsUuIkDx47r0qfX6MNNhfL3teix6QP14MUpjKkFAMBNNesGDevXr1dmZqZ8fHwUGhqqzMxMJSUl6bzzztMnn3yibt26tVWdZ/Too49qxIgRpl0f7iU7/4hueGW9DlfUKLJjgJ6ZnaZh3bnJCAAA7qzZdx6rqqpq8Pyuu+6Sj4+PJk6cqBdffNFphTXHzp07tW3bNl100UXasmWLKTXAfbyxdq/ue3+LbHZDyXFh+vc16eraqYPZZQEAgFZq1u9cU1JStHr16kbb77jjDt1zzz266qqrml3AypUrddFFFyk+Pl4Wi0Xvvfdeo2MWLFigHj16KCgoSGlpaVq1alWj6z/22GPNvja8i83u0P3vb9H8pZtlsxu6YGCc3r5xJKEWAAAP0awe26uvvlorVqzQDTfc0GjfnXfeKcMw9PTTTzergIqKCg0ePFjXXXedZsyY0Wj/4sWLddttt2nBggUaPXq0nn32WU2ePFm5ubnq1q2b3n//ffXp00d9+vQ5Zej+qerqalVXV9c/t1qtkiSbzSabzdakmk8e19TjYb6jlTW65c2N+jrvqCTp9+f10o3n9pDFYpj+70h7grPRpuBMtCc4U0vbU1OPtxiGYTS7qjZisVj07rvvatq0afXbRowYodTU1AaBuX///po2bZoee+wxzZ8/X6+++qp8fX1VXl4um82m22+/Xffdd98pr/HAAw+ccuWG119/XcHBwU5/TzBfQaX0/DZfHa62KNDH0OzeDg2McJlmDwAAzqCyslIzZ85UaWmpwsLCTnucSwfbmpoaBQcHa8mSJbrkkkvqj7v11luVk5OjFStWNHj9woULtWXLFv31r3897TVO1WObmJiokpKSn/2L+jGbzaasrCxNmDBB/v7+zXiHaG///e6g7nh7sypq7Ers3EHPzBqiPjGhZpfVAO0JzkabgjPRnuBMLW1PVqtVUVFRZwy2zZ48JkmHDx/Wfffdp+XLl+vgwYNyOBwN9h85cqQlp22kpKREdrtdMTExDbbHxMSoqKioRecMDAxUYGBgo+3+/v7N/oZtyWvQPupuurBLf8uqW592VM9IZc5MVeeOASZXdnq0JzgbbQrORHuCMzW3PTX12BYF21/96lfavXu3rr/+esXExMhisbTkNE320/MbhnHKa1577bVtWgfcQ2VNre5cskkfbi6UJF07qrv+eEF/1qcFAMDDtSjYfvnll/ryyy81ePBgZ9fTQFRUlHx9fRv1zh48eLBRLy4gSaWVNs18/mttLbDK39eihy9O0ZXDzVtfGQAAtJ8WdWH169dPx48fd3YtjQQEBCgtLU1ZWVkNtmdlZWnUqFGtOndmZqaSk5M1bNiwVp0HrsPuMHTLmxu0tcCqqJAAvf6bXxBqAQDwIi3qsV2wYIHuvvtu3XfffUpJSWk07qGpk7Akqby8XLt27ap/npeXp5ycHEVERKhbt26aN2+eZs+erfT0dI0cOVLPPfec9u7de8olx5ojIyNDGRkZslqtCg8Pb9W54Br+N2u7Vuw4pCB/H708Z7gGxPPvCgCAN2lRsO3UqZNKS0s1fvz4BttPjn212+1NPte6des0bty4+ufz5s2TJF1zzTVauHChrrjiCh0+fFgPPfSQCgsLlZKSomXLlikpKaklpcNDfbS5UJnLd0uS/mfGIEItAABeqEXBdtasWQoICNDrr7/e6sljY8eO1ZlWHJs7d67mzp3b4mvAs+0oLtPtSzZKkn59dg9dPKSryRUBAAAztCjYbtmyRRs2bFDfvn2dXQ/QLKWVNv120TpV1tg1qmek7p7cz+ySAACASVo0eSw9PV379u1zdi3tislj7s/uMHTr4g3KP1yprp066F8zU+XHkl4AAHitFvXY3nzzzbr11lt15513auDAgY0mjw0aNMgpxbUlJo+5vyf/u0NfbD+kQD8fPTs7TREufPMFAADQ9loUbK+44gpJ0pw5c+q3WSyWFk0eA1ri4y2FeurzutU0Hp8xUCld+c8JAADerkXBNi8vz9l1AE22s7hMt79VN1lszugeumRogskVAQAAV9CiYMtSWzCLtcqm376yXhU1dv3irAjNn8JkMQAAUKfJM23WrFnT5JNWVFRo69atLSoIOB2Hw9Dv38xRXkmF4sODlDkzVf5MFgMAACc0ORVcffXVmjBhgt566y2Vl5ef8pjc3Fzdc8896tWrl7799lunFdkWWBXB/Tz52U59tu2gAvx89OzsdEWGBJpdEgAAcCFNHoqQm5urZ599Vvfdd59mzZqlPn36KD4+XkFBQTp69Ki2bdumiooKTZ8+XVlZWUpJSWnLuluNVRHcyydbi/TPz3ZKkh67ZKAGJvBvBgAAGmpysPX399dNN92km266Sd9++61WrVql/Px8HT9+XIMHD9bvf/97jRs3ThEREW1ZL7zQroM/TBa7dlR3zUhjshgAAGisRZPHUlNTlZqa6uxagEZOThYrr67V8B4R+uMF/c0uCQAAuChm3sBlORyG5i3O0Z5DFYoLD9KCWUwWAwAAp0dKgMv65+c79d/v6iaLPfOrNEUxWQwAAPwMgi1cUlZusZ78b91ksUenpWhwYidzCwIAAC7Pa4Mty325ru8PV2je4hxJ0tUjk3RZeqK5BQEAALfQ5GAbERGhkpISSdKcOXNUVlbWZkW1h4yMDOXm5io7O9vsUvAjVTa75r72rcqqa5WW1Fn3XphsdkkAAMBNNDnY1tTUyGq1SpJefvllVVVVtVlR8F5/XvadthZY1TnYX/+aOZTJYgAAoMmavNzXyJEjNW3aNKWlpckwDN1yyy3q0KHDKY998cUXnVYgvMeHmwq1aM33kqT/vWKI4sJP3b4AAABOpcnB9tVXX9Xf//537d69W5JUWlpKry2cJr+kQn94Z5Mk6caxPTWub7TJFQEAAHfT5GAbExOjxx9/XJLUo0cPvfLKK4qMjGyzwuA9qmx2Zbz+rcqra5We1Fm3T+hjdkkAAMANtWjy2Lhx4xQQENBmRcG7/Hhc7VMzh8qPcbUAAKAFmDwGUzGuFgAAOAuTx2AaxtUCAABnatHkMYvF4vaTxzIzM5WZmSm73W52KV7px+Nqh3VnXC0AAGg9r508lpGRoYyMDFmtVoWHh5tdjtd59MO6cbURHQP0z6sYVwsAAFqvycH2x/Ly8uq/rqqqUlBQkNMKguf7YFOBXvn6xLjaywczrhYAADhFi7rJHA6HHn74YXXt2lUhISHas2ePJOnee+/VCy+84NQC4VnySyp09zubJUlzx/bUWMbVAgAAJ2lRsH3kkUe0cOFC/eUvf2mw7NfAgQP1/PPPO604eJYfj6sd3j1C8xhXCwAAnKhFwXbRokV67rnnNGvWLPn6+tZvHzRokLZt2+a04uBZGFcLAADaUouSxYEDB9SrV69G2x0Oh2w2W6uLguf58bjav18xRLHhjMsGAADO1aJgO2DAAK1atarR9iVLlmjo0KGtLgqe5cfjajPG9dS5fbqYXBEAAPBELVoV4f7779fs2bN14MABORwOLV26VNu3b9eiRYv0wQcfOLtGuLEqm11zX/thXO3vf8m4WgAA0DZa1GN70UUXafHixVq2bJksFovuu+8+fffdd/q///s/TZgwwdk1wo098mGucgutimRcLQAAaGMt6rGVpEmTJmnSpEmNtufk5GjIkCGtqaldcOextvfBpgK9+vVeWSyMqwUAAG3PKd1npaWlWrBggVJTU5WWluaMU7a5jIwM5ebmKjs72+xSPNK+I5Waf3Jc7dheOodxtQAAoI21Kth+/vnnmjVrluLi4vTUU09pypQpWrdunbNqg5uyOwzd/tZGlVXXKi2ps277ZW+zSwIAAF6g2UMR9u/fr4ULF+rFF19URUWFLr/8ctlsNr3zzjtKTk5uixrhZp5ZsVtr848oJNBPf798CONqAQBAu2hW4pgyZYqSk5OVm5urp556SgUFBXrqqafaqja4oc37S/X3rB2SpAemDlC3yGCTKwIAAN6iWT22n376qW655RbdeOON6t2bXy+joeM1dt26eINqHYamDIzVjNSuZpcEAAC8SLN6bFetWqWysjKlp6drxIgR+te//qVDhw61VW1wM48uy9WeQxWKCQvUny8ZKIvFYnZJAADAizQr2I4cOVL//ve/VVhYqN/97nd688031bVrVzkcDmVlZamsrKyt6oSL+3xbsV79eq8k6W+XDVGn4ACTKwIAAN6mRbN6goODNWfOHH355ZfavHmzbr/9dj3++OOKjo7W1KlTnV0jXFxJebXuenuTJOn6s3vo7N5RJlcEAAC8Uaunq/ft21d/+ctftH//fr3xxhvOqAluxDAM/eHtTSopr1G/2FDdOamv2SUBAAAv5bR1mHx9fTVt2jT95z//cdYp4QZe+2avPtt2UAF+PnryyiEK8vc1uyQAAOClWGAULbb7ULke+TBXkvSH8/upX2yYyRUBAABvRrBFi9TUOnTbmzmqsjl0dq8oXTequ9klAQAAL+e1wTYzM1PJyckaNmyY2aW4pX98tkObD5SqU7C//nrZYPn4sLQXAAAwl9cG24yMDOXm5io7O9vsUtzO2rwjWvDFbknSny8ZqNjwIJMrAgAA8OJgi5axVtn0+8U5Mgzp0rQETRkYZ3ZJAAAAkgi2aKYH3t+qA8eOKzGig+6/KNnscgAAAOoRbNFk/7exQEs3HJCPRXryiiEKDfI3uyQAAIB6BFs0ScGx4/rju5slSTeN66W0pAiTKwIAAGiIYIszcjgM3f7WRlmrajU4sZNuPq+32SUBAAA0QrDFGT3/5R6t2XNYHfx99eQVQ+TvS7MBAACuh4SCn7W1oFRPfLJdknTfRcnqEdXR5IoAAABOjWCL06qutWve4o2y2Q1NSI7RlcMSzS4JAADgtAi2OK1/fb5L24vLFNkxQI9PHyiLhbuLAQAA10WwxSlt3l9af3exh6elKDIk0OSKAAAAfh7BFo1U19p1x5KNsjsMXTAojruLAQAAt0CwRSNPffbDEISHpg4wuxwAAIAmIdiigc37S/X0CoYgAAAA90OwRT2GIAAAAHdGsEU9hiAAAAB3RrCFJGnT/mMMQQAAAG7Na4NtZmamkpOTNWzYMLNLMV11rV13LtnEEAQAAODWvDbYZmRkKDc3V9nZ2WaXYjqGIAAAAE/gtcEWdX48BOERhiAAAAA3RrD1Yj8dgjCZIQgAAMCNEWy9GEMQAACAJyHYeimGIAAAAE9DsPVCP74Rw4UMQQAAAB6CYOuFnvpsl3YUl9cNQbg4xexyAAAAnIJg62V+OgQhomOAyRUBAAA4B8HWizAEAQAAeDKCrRf552c7taO4XFEhDEEAAACeh2DrJTbtP6ZnVuyRxBAEAADgmQi2XuCnQxDOT2EIAgAA8DwEWy/wwpd5DEEAAAAej2Dr4apr7Xrpq3xJ0t2T+zMEAQAAeCyCrYf7YGOhDpVVKyYsUFMHx5tdDgAAQJsh2HowwzD0wpd5kqSrR3ZXgB//3AAAwHORdDzY13uOKLfQqiB/H80a0c3scgAAANoUwdaDneytnZGaoE7BjK0FAACejWDrofJLKvTZtmJJ0pyze5hcDQAAQNsj2Hqol77Kk2FI4/p2Uc8uIWaXAwAA0OYIth6o9LhNS9bvlyRdf/ZZJlcDAADQPgi2HujNtXtVWWNXv9hQje4VaXY5AAAA7YJg62Fq7Q69vDpfkjRndA9ZLBZzCwIAAGgnBFsP89GWIhWUVikqJEBTh3BDBgAA4D0Ith7m5BJfs0YkKcjf1+RqAAAA2g/B1oOs//6ocvYdU4Cvj371iySzywEAAGhXbh9sy8rKNGzYMA0ZMkQDBw7Uv//9b7NLMs2LJ3prLx4Sry6hgSZXAwAA0L78zC6gtYKDg7VixQoFBwersrJSKSkpmj59uiIjvWs1gP1HK/XRlkJJ0vVjuCEDAADwPm7fY+vr66vg4GBJUlVVlex2uwzDMLmq9vfy6nw5DGl0r0j1iw0zuxwAAIB2Z3qwXblypS666CLFx8fLYrHovffea3TMggUL1KNHDwUFBSktLU2rVq1qsP/YsWMaPHiwEhISdNdddykqKqqdqncN5dW1enPtPknS9dw+FwAAeCnThyJUVFRo8ODBuu666zRjxoxG+xcvXqzbbrtNCxYs0OjRo/Xss89q8uTJys3NVbdu3SRJnTp10saNG1VcXKzp06fr0ksvVUxMzCmvV11drerq6vrnVqtVkmSz2WSz2ZpU88njmnp8W3vzm+9VVl2rs6KCNbpHZ5epC03jau0J7o82BWeiPcGZWtqemnq8xXCh39tbLBa9++67mjZtWv22ESNGKDU1VU8//XT9tv79+2vatGl67LHHGp3jxhtv1Pjx43XZZZed8hoPPPCAHnzwwUbbX3/99fohDe7EYUiPbPDV4WqLLuth19mxLvPPCQAA4BSVlZWaOXOmSktLFRZ2+iGXpvfY/pyamhqtX79ed999d4PtEydO1OrVqyVJxcXF6tChg8LCwmS1WrVy5UrdeOONpz3n/PnzNW/evPrnVqtViYmJmjhx4s/+Rf2YzWZTVlaWJkyYIH9//xa8M+fJyj2ow1/nKLyDn/70q/EKDnDpf1Kcgiu1J3gG2hScifYEZ2ppezr5G/YzcekUVFJSIrvd3mhYQUxMjIqKiiRJ+/fv1/XXXy/DMGQYhm666SYNGjTotOcMDAxUYGDjpbD8/f2b/Q3bktc428Kv90qSZo5IUnjHDqbWgtZxhfYEz0KbgjPRnuBMzW1PTT3WpYPtSRaLpcFzwzDqt6WlpSknJ8eEqsy35UCp1uYdkZ+PRdeM7G52OQAAAKYyfVWEnxMVFSVfX9/63tmTDh48eNrJYd7k5O1zLxgUp9jwIJOrAQAAMJdLB9uAgAClpaUpKyurwfasrCyNGjWqVefOzMxUcnKyhg0b1qrzmKXYWqX/21ggSZozmiW+AAAATB+KUF5erl27dtU/z8vLU05OjiIiItStWzfNmzdPs2fPVnp6ukaOHKnnnntOe/fu1Q033NCq62ZkZCgjI0NWq1Xh4eGtfRvtbtGafNU6DKUnddbgxE5mlwMAAGA604PtunXrNG7cuPrnJ1csuOaaa7Rw4UJdccUVOnz4sB566CEVFhYqJSVFy5YtU1JSklklm+54jV2vfVM3aYwbMgAAANQxPdiOHTv2jLfAnTt3rubOndtOFbm+pRv261ilTQmdO2jigFizywEAAHAJLj3GFo05HIZePDFp7NpR3eXrYznDKwAAALyD1wZbd508tmLnIe0+VKGQQD9dMSzR7HIAAABchtcG24yMDOXm5io7O9vsUprlZG/t5emJCg1ioWwAAICTvDbYuqPtRWVatbNEPhbputHdzS4HAADApRBs3chLX9X11k5MjlViRLDJ1QAAALgWgq2bOFxeraUbDkiSrh/DEl8AAAA/RbB1E29m71NNrUODEsKVntTZ7HIAAABcjtcGW3daFcHhMPTG2robMsz+RZIsFpb4AgAA+CmvDbbutCrCyp2HtP/ocYUF+enCQfFmlwMAAOCSvDbYupOTt8+dkZagDgG+JlcDAADgmgi2Lq6w9Lg+33ZQkjRrRDeTqwEAAHBdBFsXtzh7n+wOQ8N7RKhXdKjZ5QAAALgsgq0Lq7U79ObafZLorQUAADgTgq0LW779kIqsVYroGKDzU2LNLgcAAMCleW2wdYflvl775ntJ0mVpCQr0Y9IYAADAz/HaYOvqy33tO1KpFTsOSZKuGs4wBAAAgDPx2mDr6t7M3ivDkM7uFaXuUR3NLgcAAMDlEWxdkM3u0OLs/ZKkmUwaAwAAaBKCrQvKyi1WSXm1uoQGakJyjNnlAAAAuAWCrQs6OWnsivRE+fvyTwQAANAUpCYXk1dSoa92HZbFIl05PNHscgAAANwGwdbFvLF2ryRpbJ8uSugcbHI1AAAA7oNg60Kqa+1asq7uTmMzRySZXA0AAIB78dpg64o3aPh4S5GOVtoUFx6kcX27mF0OAACAW/HaYOuKN2h47Zu6YQhXDEuUH5PGAAAAmoX05CJ2Fpdpbd4R+fpYdOUw1q4FAABoLoKtizjZW3tev2jFhgeZXA0AAID7Idi6gOM1di39ljuNAQAAtAbB1gV8sKlA1qpaJXTuoHN6M2kMAACgJQi2LuD1E2vXXjW8m3x8LCZXAwAA4J4ItibbWlCqDXuPyc/HosvTudMYAABASxFsTfb6iUljk1Ji1SU00ORqAAAA3BfB1kTl1bV6b8MBSdKs4UwaAwAAaA2vDbaucOex/+QUqKLGrrOiOmpkz0jT6gAAAPAEXhtszb7zmGEYeu2b7yXVTRqzWJg0BgAA0BpeG2zNtml/qbYWWBXg56MZaQlmlwMAAOD2CLYmOTlp7IKBcYroGGByNQAAAO6PYGuC0uM2/WdjgSTuNAYAAOAsBFsTvLfhgI7b7OoTE6L0pM5mlwMAAOARCLbtzDCM+mEIM5k0BgAA4DQE23a2/vuj2l5cpiB/H12SyqQxAAAAZyHYtrOTvbVTB8crvIO/ydUAAAB4DoJtOzpaUaMPNhdKkmaOSDK5GgAAAM9CsG1H3+Qdls3u0ID4MA1OCDe7HAAAAI/iZ3YB3uT8lDitumucSsprmDQGAADgZATbdpbQOVgJnYPNLgMAAMDjeO1QhMzMTCUnJ2vYsGFmlwIAAAAn8Npgm5GRodzcXGVnZ5tdCgAAAJzAa4MtAAAAPAvBFgAAAB6BYAsAAACPQLAFAACARyDYAgAAwCMQbAEAAOARCLYAAADwCARbAAAAeASvv6WuYRiSJKvV2uTX2Gw2VVZWymq1yt/fv61Kg5egPcHZaFNwJtoTnKml7elkTjuZ207H64NtWVmZJCkxMdHkSgAAAPBzysrKFB4eftr9FuNM0dfDORwOFRQUKDQ0VBaLpcG+YcOGnfKWu1arVYmJidq3b5/CwsLaq9RWO937cdXrtOY8zXltU49tynFnOuZU+2lP7Xedlp6rua9zVptq6X53bFO0p9Yf39rPKE9qT5L7tSl+5v08wzBUVlam+Ph4+ficfiSt1/fY+vj4KCEh4ZT7fH19f/YvPSwszK2+yc/0flztOq05T3Ne29Rjm3LcmY75uf20p7a/TkvP1dzXOatNtXa/O7Up2lPrj2/tZ5QntSfJ/doUP/PO7Od6ak9i8tjPyMjIMLsEp2qv9+Os67TmPM15bVOPbcpxZzrGk9qUu7Wn1pyrua9zVpuiPbn2ddytPZ3pGE9qT5L7tSl+5jmH1w9FaAmr1arw8HCVlpa61f9e4ZpoT3A22hScifYEZ2rr9kSPbQsEBgbq/vvvV2BgoNmlwAPQnuBstCk4E+0JztTW7YkeWwAAAHgEemwBAADgEQi2AAAA8AgEWwAAAHgEgi0AAAA8AsEWAAAAHoFg24b27dunsWPHKjk5WYMGDdKSJUvMLgke4JJLLlHnzp116aWXml0K3NAHH3ygvn37qnfv3nr++efNLgdujs8jOJMzchPLfbWhwsJCFRcXa8iQITp48KBSU1O1fft2dezY0ezS4MaWL1+u8vJyvfzyy3r77bfNLgdupLa2VsnJyVq+fLnCwsKUmpqqb775RhEREWaXBjfF5xGcyRm5iR7bNhQXF6chQ4ZIkqKjoxUREaEjR46YWxTc3rhx4xQaGmp2GXBDa9eu1YABA9S1a1eFhoZqypQp+uSTT8wuC26MzyM4kzNyk1cH25UrV+qiiy5SfHy8LBaL3nvvvUbHLFiwQD169FBQUJDS0tK0atWqFl1r3bp1cjgcSkxMbGXVcGXt2abgfVrbvgoKCtS1a9f65wkJCTpw4EB7lA4XxOcVnM2Zbaqlucmrg21FRYUGDx6sf/3rX6fcv3jxYt1222364x//qA0bNmjMmDGaPHmy9u7dW39MWlqaUlJSGj0KCgrqjzl8+LCuvvpqPffcc23+nmCu9mpT8E6tbV+nGnlmsVjatGa4Lmd8XgE/5qw21arcZMAwDMOQZLz77rsNtg0fPty44YYbGmzr16+fcffddzf5vFVVVcaYMWOMRYsWOaNMuJG2alOGYRjLly83ZsyY0doS4cZa0r6++uorY9q0afX7brnlFuO1115r81rh+lrzecXnEU6lpW2qtbnJq3tsf05NTY3Wr1+viRMnNtg+ceJErV69uknnMAxD1157rcaPH6/Zs2e3RZlwI85oU8DpNKV9DR8+XFu2bNGBAwdUVlamZcuWadKkSWaUCxfH5xWcrSltyhm5iWB7GiUlJbLb7YqJiWmwPSYmRkVFRU06x1dffaXFixfrvffe05AhQzRkyBBt3ry5LcqFG3BGm5KkSZMm6bLLLtOyZcuUkJCg7OxsZ5cKN9SU9uXn56e//e1vGjdunIYOHao777xTkZGRZpQLF9fUzys+j9BUTWlTzshNfk6r2EP9dPyZYRhNHpN29tlny+FwtEVZcGOtaVOSmMWOn3Wm9jV16lRNnTq1vcuCmzpTe+LzCM31c23KGbmJHtvTiIqKkq+vb6OetIMHDzb63wbQFLQptCXaF5yJ9gRna682RbA9jYCAAKWlpSkrK6vB9qysLI0aNcqkquDOaFNoS7QvOBPtCc7WXm3Kq4cilJeXa9euXfXP8/LylJOTo4iICHXr1k3z5s3T7NmzlZ6erpEjR+q5557T3r17dcMNN5hYNVwZbQptifYFZ6I9wdlcok21aC0FD7F8+XJDUqPHNddcU39MZmamkZSUZAQEBBipqanGihUrzCsYLo82hbZE+4Iz0Z7gbK7QpiyGcYoVuwEAAAA3wxhbAAAAeASCLQAAADwCwRYAAAAegWALAAAAj0CwBQAAgEcg2AIAAMAjEGwBAADgEQi2AAAA8AgEWwAAAHgEgi0AeIkXXnhBEydObLfrffDBBxo6dKgcDke7XROAdyPYAkArXHvttbJYLI0e559/fv0xFotF7733nnlFSqqurtZ9992ne++9V5LUvXv3U9Z98jF27NjTnmvgwIH69a9/fcp9b7zxhvz9/VVcXKwLL7xQFotFr7/+elu8JQBohGALAK10/vnnq7CwsMHjjTfeaNY5bDZbG1VX55133lFISIjGjBkjScrOzq6v9Z133pEkbd++vX7b0qVLT3uu66+/Xm+99ZYqKysb7XvxxRd14YUXKiYmRpJ03XXX6amnnmqDdwQAjRFsAaCVAgMDFRsb2+DRuXNnSXU9o5J0ySWXyGKx1D9/4IEHNGTIEL344os666yzFBgYKMMw1L17dz355JMNzj9kyBA98MAD9c9LS0v129/+VtHR0QoLC9P48eO1cePGn63xzTff1NSpU+ufd+nSpb7WiIgISVJ0dHT9tm3btumcc85Rhw4dlJiYqFtuuUUVFRWSpNmzZ6u6ulpLlixpcI29e/fq888/1/XXX1+/berUqVq7dq327NnT5L9PAGgpgi0AtKHs7GxJ0ksvvaTCwsL655K0a9cuvfXWW3rnnXeUk5PTpPMZhqELLrhARUVFWrZsmdavX6/U1FSdd955OnLkyGlft2rVKqWnpzfpGps3b9akSZM0ffp0bdq0SYsXL9aXX36pm266SZIUGRmpiy++WC+99FKD17300kuKiYnR5MmT67clJSUpOjpaq1atatK1AaA1CLYA0EoffPCBQkJCGjwefvhhSXU9o5LUqVMnxcbG1j+XpJqaGr3yyisaOnSoBg0aJIvFcsZrLV++XJs3b9aSJUuUnp6u3r17669//as6deqkt99++5SvOXbsmI4dO6b4+PgmvZ8nnnhCM2fO1G233abevXtr1KhR+uc//6lFixapqqpKkjRnzhytXLmyvifWMAwtXLhQ1157rXx9fRucr2vXrsrPz2/StQGgNfzMLgAA3N24ceP09NNPN9h28tf7PycpKalB0G2K9evXq7y8XJGRkQ22Hz9+XLt37z7la44fPy5JCgoKavI1du3apddee61+m2EYcjgcysvLU//+/TVx4kQlJCTopZde0sMPP6zPP/9c+fn5uu666xqdr0OHDqccjwsAzkawBYBW6tixo3r16tWi1/2Uj4+PDMNosO3HE8scDofi4uL0xRdfNHptp06dTnmdyMhIWSwWHT16tEl1ORwO/e53v9Mtt9zSaF+3bt3q67z22mu1cOFCPfjgg3rppZd0zjnnqHfv3o1ec+TIkWYHeABoCYItALQxf39/2e32Jh3bpUsXFRYW1j+3Wq3Ky8urf56amqqioiL5+fnVT0Q7k4CAACUnJys3N7dJ69impqZq69atZwzr1113nR555BEtXbpUS5cu1TPPPNPomKqqKu3evVtDhw5tUq0A0BqMsQWAVqqurlZRUVGDR0lJSf3+7t2767PPPlNRUdEZe03Hjx+vV155RatWrdKWLVt0zTXXNBiz+stf/lIjR47UtGnT9Mknnyg/P1+rV6/Wn/70J61bt+605500aZK+/PLLJr2fP/zhD1qzZo0yMjKUk5OjnTt36j//+Y9uvvnmBsf16NFD48eP129/+1v5+/vr0ksvbXSur7/+WoGBgRo5cmSTrg0ArUGwBYBW+vjjjxUXF9fgcfbZZ9fv/9vf/qasrCwlJiaesedy/vz5Ouecc3ThhRdqypQpmjZtmnr27Fm/32KxaNmyZTrnnHM0Z84c9enTR1deeaXy8/Pr1449ld/85jdatmyZSktLz/h+Bg0apBUrVmjnzp0aM2aMhg4dqnvvvVdxcXGNjr3++ut19OhRXXnllQoODm60/4033tCsWbNOuQ8AnM1i/HQwFwDAI11++eUaOnSo5s+f3y7XO3TokPr166d169apR48e7XJNAN6NHlsA8BJPPPGEQkJC2u16eXl5WrBgAaEWQLuhxxYAAAAegR5bAAAAeASCLQAAADwCwRYAAAAegWALAAAAj0CwBQAAgEcg2AIAAMAjEGwBAADgEQi2AAAA8AgEWwAAAHiE/wdNxhFiFlJdlAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(8,4))\n", "plt.plot(etruebincenters, effective_area)\n", @@ -544,10 +949,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "id": "9c89f6e6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(8,4))\n", "\n", @@ -568,12 +984,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "id": "5164274a", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total gamma rate after cuts: 0.286 events/s\n" + ] + } + ], "source": [ "total_gamma_rate = (integrated_flux*effective_area).to(1/u.s)\n", "print(f'Total gamma rate after cuts: {total_gamma_rate.sum().to_value(1/u.s):.3f} events/s')" @@ -581,7 +1005,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 91, "id": "6a415916", "metadata": {}, "outputs": [], @@ -595,7 +1019,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "id": "25b8fe0c", "metadata": {}, "outputs": [], @@ -649,10 +1073,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 93, "id": "31415b87", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(0.1, 345586.71542930865)" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.errorbar(0.5*(erecobins[:-1]+erecobins[1:]), total_signal_counts, total_signal_counts**0.5,\n", " label='gammas', fmt='o')\n", @@ -674,17 +1119,32 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "e91f0070", + "execution_count": 94, + "id": "1ce3c375", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.61414955e+02, 4.37676163e+04, 1.72793358e+05, 9.97278449e+04,\n", + " 1.26368420e+04, 1.03932861e+03, 2.73475801e+02, 1.01725507e+02,\n", + " 4.07068517e+01, 1.62894029e+01, 6.51842715e+00, 2.60843769e+00,\n", + " 1.04380198e+00, 4.17691626e-01, 1.67145012e-01, 6.68853602e-02,\n", + " 2.67650908e-02, 1.07104168e-02, 4.38877140e-02])" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "total_bg_counts" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "id": "a3769732", "metadata": { "scrolled": false @@ -703,10 +1163,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "id": "e4d6ac4d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure(figsize=(12,4))\n", "\n", @@ -731,8 +1202,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "5113ba34", + "execution_count": 97, + "id": "80fcfbe2", "metadata": {}, "outputs": [], "source": [ @@ -742,7 +1213,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 98, "id": "b22d2e36", "metadata": {}, "outputs": [], @@ -769,7 +1240,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 99, "id": "2a3d41c1", "metadata": {}, "outputs": [], @@ -779,12 +1250,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 100, "id": "9b5e3aa8", "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure(figsize=(12,4))\n", "fig.add_subplot(1, 2, 1)\n", @@ -824,10 +1306,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 101, "id": "0e1df16e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "*************************\n", + "Detection successful! :-D\n" + ] + }, + { + "data": { + "image/png": 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ZD_degEtrue_min_TeVEtrue_max_TeVAeff_m2emig_mu_locemig_mu_scaleemig_mu_aemig_model
06.000.0112200.0141256.018636e+022.1416140.237586NaNmoyal
16.000.0141250.0177831.229892e+031.7960290.218862NaNmoyal
26.000.0177830.0223872.391636e+031.4318090.201941NaNmoyal
36.000.0223870.0281843.886893e+031.2220060.185834NaNmoyal
46.000.0281840.0354815.221963e+031.0412030.175796NaNmoyal
...........................
34566.4411.22018514.1253751.069989e+060.8588290.1742591.269968skewnorm
34666.4414.12537517.7827941.139978e+060.8440600.1785651.444996skewnorm
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34866.4422.38721128.1838291.192070e+060.8486140.1842901.672204skewnorm
34966.4428.18382935.4813391.231130e+060.8418080.1953921.734357skewnorm
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" - ], - "text/plain": [ - " ZD_deg Etrue_min_TeV Etrue_max_TeV Aeff_m2 emig_mu_loc \\\n", - "0 6.00 0.011220 0.014125 6.018636e+02 2.141614 \n", - "1 6.00 0.014125 0.017783 1.229892e+03 1.796029 \n", - "2 6.00 0.017783 0.022387 2.391636e+03 1.431809 \n", - "3 6.00 0.022387 0.028184 3.886893e+03 1.222006 \n", - "4 6.00 0.028184 0.035481 5.221963e+03 1.041203 \n", - ".. ... ... ... ... ... \n", - "345 66.44 11.220185 14.125375 1.069989e+06 0.858829 \n", - "346 66.44 14.125375 17.782794 1.139978e+06 0.844060 \n", - "347 66.44 17.782794 22.387211 1.294651e+06 0.843795 \n", - "348 66.44 22.387211 28.183829 1.192070e+06 0.848614 \n", - "349 66.44 28.183829 35.481339 1.231130e+06 0.841808 \n", - "\n", - " emig_mu_scale emig_mu_a emig_model \n", - "0 0.237586 NaN moyal \n", - "1 0.218862 NaN moyal \n", - "2 0.201941 NaN moyal \n", - "3 0.185834 NaN moyal \n", - "4 0.175796 NaN moyal \n", - ".. ... ... ... \n", - "345 0.174259 1.269968 skewnorm \n", - "346 0.178565 1.444996 skewnorm \n", - "347 0.182584 1.540402 skewnorm \n", - "348 0.184290 1.672204 skewnorm \n", - "349 0.195392 1.734357 skewnorm \n", - "\n", - "[350 rows x 8 columns]" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "gamma_data" ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "id": "1d2b1efe", "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "
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ZD_degEreco_min_TeVEreco_max_TeVBckgRate_per_secondTheta_cut_degGammaness_cut
06.000.0125890.0199530.0851720.3200000.149805
16.000.0199530.0316233.4842740.2541680.223265
26.000.0316230.0501194.4781400.2283660.242829
36.000.0501190.0794331.4772600.1793080.308257
46.000.0794330.1258930.1278750.1186910.520600
.....................
18566.447.94328212.5892540.0001700.1000000.734576
18666.4412.58925419.9526230.0000850.1000000.755384
18766.4419.95262331.6227770.0000430.1000000.770523
18866.4431.62277750.1187230.0000210.1000000.791636
18966.4450.11872379.4328230.0000110.1000000.810671
\n", - "

190 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " ZD_deg Ereco_min_TeV Ereco_max_TeV BckgRate_per_second Theta_cut_deg \\\n", - "0 6.00 0.012589 0.019953 0.085172 0.320000 \n", - "1 6.00 0.019953 0.031623 3.484274 0.254168 \n", - "2 6.00 0.031623 0.050119 4.478140 0.228366 \n", - "3 6.00 0.050119 0.079433 1.477260 0.179308 \n", - "4 6.00 0.079433 0.125893 0.127875 0.118691 \n", - ".. ... ... ... ... ... \n", - "185 66.44 7.943282 12.589254 0.000170 0.100000 \n", - "186 66.44 12.589254 19.952623 0.000085 0.100000 \n", - "187 66.44 19.952623 31.622777 0.000043 0.100000 \n", - "188 66.44 31.622777 50.118723 0.000021 0.100000 \n", - "189 66.44 50.118723 79.432823 0.000011 0.100000 \n", - "\n", - " Gammaness_cut \n", - "0 0.149805 \n", - "1 0.223265 \n", - "2 0.242829 \n", - "3 0.308257 \n", - "4 0.520600 \n", - ".. ... \n", - "185 0.734576 \n", - "186 0.755384 \n", - "187 0.770523 \n", - "188 0.791636 \n", - "189 0.810671 \n", - "\n", - "[190 rows x 6 columns]" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "background_data" ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "id": "90114613", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available zeniths: [ 6. 9.579 16.08 23.16 30.39 37.66 44.92 52.16 59.34 66.44 ] (degrees)\n" - ] - } - ], + "outputs": [], "source": [ "# CHECK that we have the same pointing zenith values in both tables:\n", "assert np.alltrue(np.unique(gamma_data.ZD_deg) == np.unique(background_data.ZD_deg))\n", @@ -504,18 +134,10 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "id": "a5a9386a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selected ZD = 23.16 degrees\n" - ] - } - ], + "outputs": [], "source": [ "# Choose the bins among those above. Just set the bin number (from 0)\n", "# Make sure you choose values which make sense for the declination of your source\n", @@ -539,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "id": "0c58505b", "metadata": {}, "outputs": [], @@ -562,18 +184,10 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "id": "a589ca87", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "alpha = 0.1229\n" - ] - } - ], + "outputs": [], "source": [ "pulsar_mode = False # Set to True to activate it\n", "\n", @@ -596,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "id": "7d8d3a2e", "metadata": {}, "outputs": [], @@ -623,20 +237,12 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "id": "4954c1de", "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Emission from a point-like source will be assumed!\n" - ] - } - ], + "outputs": [], "source": [ "source_radius = 0 # 0.3 * u.deg\n", "theta_cut = background_data.Theta_cut_deg[zd_selection_backg].to_numpy()*u.deg\n", @@ -662,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "id": "ea9bca3c", "metadata": {}, "outputs": [], @@ -703,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": null, "id": "e24f72ce", "metadata": {}, "outputs": [], @@ -741,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "id": "014476b0", "metadata": {}, "outputs": [], @@ -762,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "id": "80627c34", "metadata": {}, "outputs": [], @@ -789,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "id": "24e8394c", "metadata": {}, "outputs": [], @@ -800,7 +406,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "id": "056ea33b", "metadata": {}, "outputs": [], @@ -833,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "id": "1b79d5b2", "metadata": {}, "outputs": [], @@ -844,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": null, "id": "2038bb5b", "metadata": {}, "outputs": [], @@ -859,7 +465,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "id": "48a34458", "metadata": {}, "outputs": [], @@ -885,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": null, "id": "54b22527", "metadata": {}, "outputs": [], @@ -914,21 +520,10 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": null, "id": "4eff20a0", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(8,4))\n", "plt.plot(etruebincenters, effective_area)\n", @@ -949,21 +544,10 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": null, "id": "9c89f6e6", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(8,4))\n", "\n", @@ -984,20 +568,12 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": null, "id": "5164274a", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total gamma rate after cuts: 0.286 events/s\n" - ] - } - ], + "outputs": [], "source": [ "total_gamma_rate = (integrated_flux*effective_area).to(1/u.s)\n", "print(f'Total gamma rate after cuts: {total_gamma_rate.sum().to_value(1/u.s):.3f} events/s')" @@ -1005,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": null, "id": "6a415916", "metadata": {}, "outputs": [], @@ -1019,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": null, "id": "25b8fe0c", "metadata": {}, "outputs": [], @@ -1073,31 +649,10 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "id": "31415b87", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.1, 345586.71542930865)" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.errorbar(0.5*(erecobins[:-1]+erecobins[1:]), total_signal_counts, total_signal_counts**0.5,\n", " label='gammas', fmt='o')\n", @@ -1119,32 +674,17 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": null, "id": "1ce3c375", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.61414955e+02, 4.37676163e+04, 1.72793358e+05, 9.97278449e+04,\n", - " 1.26368420e+04, 1.03932861e+03, 2.73475801e+02, 1.01725507e+02,\n", - " 4.07068517e+01, 1.62894029e+01, 6.51842715e+00, 2.60843769e+00,\n", - " 1.04380198e+00, 4.17691626e-01, 1.67145012e-01, 6.68853602e-02,\n", - " 2.67650908e-02, 1.07104168e-02, 4.38877140e-02])" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "total_bg_counts" ] }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "id": "a3769732", "metadata": { "scrolled": false @@ -1163,21 +703,10 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "id": "e4d6ac4d", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig = plt.figure(figsize=(12,4))\n", "\n", @@ -1202,7 +731,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "id": "80fcfbe2", "metadata": {}, "outputs": [], @@ -1213,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": null, "id": "b22d2e36", "metadata": {}, "outputs": [], @@ -1240,7 +769,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": null, "id": "2a3d41c1", "metadata": {}, "outputs": [], @@ -1250,23 +779,12 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": null, "id": "9b5e3aa8", "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig = plt.figure(figsize=(12,4))\n", "fig.add_subplot(1, 2, 1)\n", @@ -1306,29 +824,10 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, "id": "0e1df16e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "*************************\n", - "Detection successful! :-D\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(8,4))\n", "\n", @@ -1370,7 +869,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "id": "64661959", "metadata": {}, "outputs": [], From 4498893960715803f1b6361c85ed04f4d08861d9 Mon Sep 17 00:00:00 2001 From: moralejo Date: Thu, 31 Oct 2024 12:27:11 +0000 Subject: [PATCH 09/16] Re-introduced changes of previous commit (lost for some reason): GRB190114C example & modified axis range --- notebooks/LST1_observation_simulator.ipynb | 44 ++++++++++++---------- 1 file changed, 24 insertions(+), 20 deletions(-) diff --git a/notebooks/LST1_observation_simulator.ipynb b/notebooks/LST1_observation_simulator.ipynb index b9da964bf..db7bf1e92 100644 --- a/notebooks/LST1_observation_simulator.ipynb +++ b/notebooks/LST1_observation_simulator.ipynb @@ -44,7 +44,7 @@ "# 0.7: standard cuts (safer for spectral analysis)\n", "# 0.4: tight cuts, better for detection of weak sources\n", "#\n", - "cut_efficiency = 0.4" + "cut_efficiency = 0.7" ] }, { @@ -215,13 +215,15 @@ "metadata": {}, "outputs": [], "source": [ - "# effective_obs_time = 103 * u.h # Crab pulsar paper\n", + "# effective_obs_time = 103 * u.h # LST1 Crab pulsar paper\n", "\n", - "# effective_obs_time = 8 * u.s # BOAT, 240 - 248 s\n", + "# effective_obs_time = 2392 * u.s # GRB190114C, 62 - 2454 s, MAGIC\n", "\n", - "# effective_obs_time = 34 * u.h # Crab nebula. performance paper\n", + "# effective_obs_time = 8 * u.s # BOAT, 240 - 248 s, LHAASO\n", "\n", - "effective_obs_time = 11.8 * u.h # 1ES 1011+496, February 2014 flare" + "effective_obs_time = 34 * u.h # Crab nebula. LST1 performance paper\n", + "\n", + "# effective_obs_time = 11.8 * u.h # 1ES 1011+496, February 2014 flare, MAGIC" ] }, { @@ -275,8 +277,10 @@ "source": [ "redshift = 0\n", "\n", - "redshift = 0.212 # 1ES 1011\n", + "\n", "# redshift = 0.151 # BOAT GRB\n", + "# redshift = 0.212 # 1ES 1011\n", + "# redshift = 0.42 # GRB190114C\n", "\n", "# We will apply the Dominguez EBL model to simulate the absorption\n", "\n", @@ -318,14 +322,21 @@ "# (it must take as argument an astropy quantity with energy units!)\n", "\n", "# Crab Nebula:\n", - "# def intrinsic_dFdE(E):\n", - "# return CRAB_MAGIC_JHEAP2015(E) # Crab Nebula, MAGIC log-parabola\n", + "def intrinsic_dFdE(E):\n", + " return 1.2* CRAB_MAGIC_JHEAP2015(E) # Crab Nebula, MAGIC log-parabola\n", "\n", "# Crab pulsar P1, from LST1 paper (smoothly broken power-law):\n", "# def intrinsic_dFdE(E):\n", "# return PowerLaw(normalization=1.27e-4 / (u.TeV * u.cm**2 * u.s), \n", "# index=-1.811, e_ref=1*u.GeV)(E) * (1+(E/(6.8*u.GeV))**((4.09-1.811)/3))**-3\n", "\n", + "# GRB 190114C T0+62 s to T0+2454 s (set redshift above to 0.42)\n", + "# def intrinsic_dFdE(E):\n", + "# return PowerLaw(normalization=8.45e-9 / (u.TeV * u.cm**2 * u.s), \n", + "# index=-2.22, \n", + "# e_ref=0.46*u.TeV)(E)\n", + "\n", + "\n", "# The BOAT (GRB 221009A) @ ~T0+240s: (set redshift above to 0.151)\n", "# def intrinsic_dFdE(E):\n", "# return PowerLaw(normalization=208e-8 / (u.TeV * u.cm**2 * u.s), \n", @@ -334,9 +345,9 @@ "\n", "\n", "# 1ES1011 February 2014 flare\n", - "def intrinsic_dFdE(E):\n", - " return PowerLaw(normalization=8.7e-10 / (u.TeV * u.cm**2 * u.s), \n", - " index=-2.03, e_ref=0.25*u.TeV)(E)\n", + "# def intrinsic_dFdE(E):\n", + "# return PowerLaw(normalization=8.7e-10 / (u.TeV * u.cm**2 * u.s), \n", + "# index=-2.03, e_ref=0.25*u.TeV)(E)\n", "\n", "# A log-parabola spectrum:\n", "# def intrinsic_dFdE(E):\n", @@ -795,6 +806,7 @@ "plt.ylabel('Signal / background ratio')\n", "plt.yscale('log')\n", "plt.xscale('log')\n", + "plt.ylim(1e-3, 2*np.nanmax(signal_to_background_ratio))\n", "\n", "fig.add_subplot(1, 2, 2)\n", "\n", @@ -942,15 +954,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c63c390f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e5cbe88", + "id": "16d657ee", "metadata": {}, "outputs": [], "source": [] From a734bd92b1524ef367d2743f676ba4c23417abe9 Mon Sep 17 00:00:00 2001 From: moralejo Date: Thu, 31 Oct 2024 12:29:29 +0000 Subject: [PATCH 10/16] Removed test factor in source flux --- notebooks/LST1_observation_simulator.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/LST1_observation_simulator.ipynb b/notebooks/LST1_observation_simulator.ipynb index db7bf1e92..b073fa5c5 100644 --- a/notebooks/LST1_observation_simulator.ipynb +++ b/notebooks/LST1_observation_simulator.ipynb @@ -323,7 +323,7 @@ "\n", "# Crab Nebula:\n", "def intrinsic_dFdE(E):\n", - " return 1.2* CRAB_MAGIC_JHEAP2015(E) # Crab Nebula, MAGIC log-parabola\n", + " return CRAB_MAGIC_JHEAP2015(E) # Crab Nebula, MAGIC log-parabola\n", "\n", "# Crab pulsar P1, from LST1 paper (smoothly broken power-law):\n", "# def intrinsic_dFdE(E):\n", @@ -954,7 +954,7 @@ { "cell_type": "code", "execution_count": null, - "id": "16d657ee", + "id": "5215017d", "metadata": {}, "outputs": [], "source": [] From f0bd4fb97c4f3dba4f3af2b0cd156311c4c0074a Mon Sep 17 00:00:00 2001 From: Daniel Morcuende Date: Thu, 14 Nov 2024 09:00:45 +0100 Subject: [PATCH 11/16] use get resource files for package data --- notebooks/LST1_observation_simulator.ipynb | 178 +++++++++------------ 1 file changed, 77 insertions(+), 101 deletions(-) diff --git a/notebooks/LST1_observation_simulator.ipynb b/notebooks/LST1_observation_simulator.ipynb index b073fa5c5..9c74d8daa 100644 --- a/notebooks/LST1_observation_simulator.ipynb +++ b/notebooks/LST1_observation_simulator.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8d7fb8ab-7034-47be-a392-17612fd611b7", + "id": "0", "metadata": {}, "outputs": [], "source": [ @@ -18,12 +18,13 @@ "from pyirf.spectral import CRAB_MAGIC_JHEAP2015, PowerLaw, LogParabola\n", "from pyirf.statistics import li_ma_significance\n", "from scipy.stats import moyal, norm, skewnorm\n", - "from pathlib import Path" + "from pathlib import Path\n", + "from lstchain.io.io import get_resource_path" ] }, { "cell_type": "markdown", - "id": "c30c3f8d", + "id": "1", "metadata": {}, "source": [ "## Set cut efficiency" @@ -32,7 +33,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ef3576a4", + "id": "2", "metadata": {}, "outputs": [], "source": [ @@ -49,49 +50,31 @@ }, { "cell_type": "markdown", - "id": "5dd2e6fd", + "id": "3", "metadata": {}, "source": [ - "## Load files which contain the (approximate) instrument response function, and show table contents\n", - "They characterize the average performance of LST1 within 1 degree off-axis (computed from diffuse gamma MC and\n", - "real data for the cosmic ray rates)\n", - "The Ereco/Etrue distributions in each Etrue bin is parametrized with moyal or a skewnorm function (whatever fits better) " + "## Load files for the requested efficiency:" ] }, { "cell_type": "code", "execution_count": null, - "id": "95ac1d89", + "id": "4", "metadata": {}, "outputs": [], "source": [ - "#\n", - "# Load files for the requested efficiency:\n", - "#\n", - "datadir = os.environ['LSTCHAIN'] + '/data/'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c59f8666", - "metadata": {}, - "outputs": [], - "source": [ - "input_filename = datadir + f'LST1_gamma_irf_gheffi_{cut_efficiency:.2f}_theffi_{cut_efficiency:.2f}.csv'\n", - "gamma_data = pd.read_csv(input_filename)\n", + "input_gamma_filename = get_resource_path(f'data/LST1_gamma_irf_gheffi_{cut_efficiency:.2f}_theffi_{cut_efficiency:.2f}.csv')\n", + "gamma_data = pd.read_csv(input_gamma_filename)\n", "\n", - "input_filename = datadir + f'LST1_backg_irf_gheffi_{cut_efficiency:.2f}_theffi_{cut_efficiency:.2f}.csv'\n", - "background_data = pd.read_csv(input_filename)" + "input_bkg_filename = get_resource_path(f'data/LST1_backg_irf_gheffi_{cut_efficiency:.2f}_theffi_{cut_efficiency:.2f}.csv')\n", + "background_data = pd.read_csv(input_bkg_filename)" ] }, { "cell_type": "code", "execution_count": null, - "id": "ad3a988e", - "metadata": { - "scrolled": false - }, + "id": "5", + "metadata": {}, "outputs": [], "source": [ "gamma_data" @@ -100,10 +83,8 @@ { "cell_type": "code", "execution_count": null, - "id": "1d2b1efe", - "metadata": { - "scrolled": false - }, + "id": "6", + "metadata": {}, "outputs": [], "source": [ "background_data" @@ -112,7 +93,7 @@ { "cell_type": "code", "execution_count": null, - "id": "90114613", + "id": "7", "metadata": {}, "outputs": [], "source": [ @@ -126,7 +107,7 @@ }, { "cell_type": "markdown", - "id": "dc762a1a", + "id": "8", "metadata": {}, "source": [ "## Zenith distance bin selection" @@ -135,7 +116,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a5a9386a", + "id": "9", "metadata": {}, "outputs": [], "source": [ @@ -153,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "8d1c2dcf", + "id": "10", "metadata": {}, "source": [ "## ON to OFF exposure" @@ -162,7 +143,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0c58505b", + "id": "11", "metadata": {}, "outputs": [], "source": [ @@ -175,7 +156,7 @@ }, { "cell_type": "markdown", - "id": "311a8237", + "id": "12", "metadata": {}, "source": [ "## Pulsar mode\n", @@ -185,7 +166,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a589ca87", + "id": "13", "metadata": {}, "outputs": [], "source": [ @@ -202,7 +183,7 @@ }, { "cell_type": "markdown", - "id": "aaf2cba7", + "id": "14", "metadata": {}, "source": [ "## Observation time" @@ -211,7 +192,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7d8d3a2e", + "id": "15", "metadata": {}, "outputs": [], "source": [ @@ -228,7 +209,7 @@ }, { "cell_type": "markdown", - "id": "16d22a98", + "id": "16", "metadata": {}, "source": [ "## Source extension\n", @@ -240,10 +221,8 @@ { "cell_type": "code", "execution_count": null, - "id": "4954c1de", - "metadata": { - "scrolled": false - }, + "id": "17", + "metadata": {}, "outputs": [], "source": [ "source_radius = 0 # 0.3 * u.deg\n", @@ -262,7 +241,7 @@ }, { "cell_type": "markdown", - "id": "d662546b", + "id": "18", "metadata": {}, "source": [ "## Source redshift" @@ -271,7 +250,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ea9bca3c", + "id": "19", "metadata": {}, "outputs": [], "source": [ @@ -305,7 +284,7 @@ }, { "cell_type": "markdown", - "id": "e5766b4e", + "id": "20", "metadata": {}, "source": [ "## Source (intrinsic) spectrum " @@ -314,7 +293,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e24f72ce", + "id": "21", "metadata": {}, "outputs": [], "source": [ @@ -359,7 +338,7 @@ { "cell_type": "code", "execution_count": null, - "id": "014476b0", + "id": "22", "metadata": {}, "outputs": [], "source": [ @@ -370,7 +349,7 @@ }, { "cell_type": "markdown", - "id": "8613146f", + "id": "23", "metadata": {}, "source": [ "## END of user settings\n", @@ -380,7 +359,7 @@ { "cell_type": "code", "execution_count": null, - "id": "80627c34", + "id": "24", "metadata": {}, "outputs": [], "source": [ @@ -407,7 +386,7 @@ { "cell_type": "code", "execution_count": null, - "id": "24e8394c", + "id": "25", "metadata": {}, "outputs": [], "source": [ @@ -418,7 +397,7 @@ { "cell_type": "code", "execution_count": null, - "id": "056ea33b", + "id": "26", "metadata": {}, "outputs": [], "source": [ @@ -451,7 +430,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1b79d5b2", + "id": "27", "metadata": {}, "outputs": [], "source": [ @@ -462,7 +441,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2038bb5b", + "id": "28", "metadata": {}, "outputs": [], "source": [ @@ -477,7 +456,7 @@ { "cell_type": "code", "execution_count": null, - "id": "48a34458", + "id": "29", "metadata": {}, "outputs": [], "source": [ @@ -503,7 +482,7 @@ { "cell_type": "code", "execution_count": null, - "id": "54b22527", + "id": "30", "metadata": {}, "outputs": [], "source": [ @@ -515,7 +494,7 @@ " etruemax*u.TeV))\n", "integrated_flux = np.array(integrated_flux)\n", "\n", - "# Too strong EBL absorption produces NaNs in high-E bins, jus trplace by 0's:\n", + "# Too strong EBL absorption produces NaNs in high-E bins, just replace by 0's:\n", "integrated_flux[np.isnan(integrated_flux)] = 0\n", " \n", "integrated_flux = np.array(integrated_flux) * 1/(u.s * u.cm**2)" @@ -523,7 +502,7 @@ }, { "cell_type": "markdown", - "id": "a0d668ca", + "id": "31", "metadata": {}, "source": [ "## Effective area" @@ -532,7 +511,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4eff20a0", + "id": "32", "metadata": {}, "outputs": [], "source": [ @@ -547,7 +526,7 @@ }, { "cell_type": "markdown", - "id": "bb2fb8b7", + "id": "33", "metadata": {}, "source": [ "## Background rate" @@ -556,7 +535,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9c89f6e6", + "id": "34", "metadata": {}, "outputs": [], "source": [ @@ -580,10 +559,8 @@ { "cell_type": "code", "execution_count": null, - "id": "5164274a", - "metadata": { - "scrolled": true - }, + "id": "35", + "metadata": {}, "outputs": [], "source": [ "total_gamma_rate = (integrated_flux*effective_area).to(1/u.s)\n", @@ -593,7 +570,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6a415916", + "id": "36", "metadata": {}, "outputs": [], "source": [ @@ -607,7 +584,7 @@ { "cell_type": "code", "execution_count": null, - "id": "25b8fe0c", + "id": "37", "metadata": {}, "outputs": [], "source": [ @@ -661,7 +638,7 @@ { "cell_type": "code", "execution_count": null, - "id": "31415b87", + "id": "38", "metadata": {}, "outputs": [], "source": [ @@ -686,7 +663,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1ce3c375", + "id": "39", "metadata": {}, "outputs": [], "source": [ @@ -696,10 +673,8 @@ { "cell_type": "code", "execution_count": null, - "id": "a3769732", - "metadata": { - "scrolled": false - }, + "id": "40", + "metadata": {}, "outputs": [], "source": [ "mean_etrue_vs_ereco = np.zeros_like(erecobincenters)\n", @@ -715,7 +690,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e4d6ac4d", + "id": "41", "metadata": {}, "outputs": [], "source": [ @@ -743,7 +718,7 @@ { "cell_type": "code", "execution_count": null, - "id": "80fcfbe2", + "id": "42", "metadata": {}, "outputs": [], "source": [ @@ -754,7 +729,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b22d2e36", + "id": "43", "metadata": {}, "outputs": [], "source": [ @@ -772,7 +747,7 @@ }, { "cell_type": "markdown", - "id": "d27e4dfc", + "id": "44", "metadata": {}, "source": [ "## Signal to background ratio and significance in Ereco bins" @@ -781,7 +756,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2a3d41c1", + "id": "45", "metadata": {}, "outputs": [], "source": [ @@ -791,10 +766,8 @@ { "cell_type": "code", "execution_count": null, - "id": "9b5e3aa8", - "metadata": { - "scrolled": false - }, + "id": "46", + "metadata": {}, "outputs": [], "source": [ "fig = plt.figure(figsize=(12,4))\n", @@ -828,7 +801,7 @@ }, { "cell_type": "markdown", - "id": "ca520d68", + "id": "47", "metadata": {}, "source": [ "## Integral significance (for Ereco>xx)" @@ -837,7 +810,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0e1df16e", + "id": "48", "metadata": {}, "outputs": [], "source": [ @@ -871,7 +844,7 @@ }, { "cell_type": "markdown", - "id": "04cd5759", + "id": "49", "metadata": {}, "source": [ "## Simulated SED from observation (Asimov dataset)\n", @@ -882,7 +855,7 @@ { "cell_type": "code", "execution_count": null, - "id": "64661959", + "id": "50", "metadata": {}, "outputs": [], "source": [ @@ -903,7 +876,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4d56445f", + "id": "51", "metadata": {}, "outputs": [], "source": [ @@ -930,7 +903,7 @@ "\n", " if not pulsar_mode:\n", " plt.errorbar(mean_etrue_vs_ereco[displayed_points], SED, yerr=SED_total_error, \n", - " fmt='o', markersize=2, \n", + " fmt='o', markersize=4, \n", " label='Observed, stat+syst')\n", " plt.errorbar(mean_etrue_vs_ereco[displayed_points], SED, yerr=SED_stat_error, \n", " fmt='o', markersize=2, \n", @@ -954,18 +927,21 @@ { "cell_type": "code", "execution_count": null, - "id": "5215017d", + "id": "52", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -976,7 +952,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.6" } }, "nbformat": 4, From be3a45b4cb66d57433c2e7f954b52812c8aa6caf Mon Sep 17 00:00:00 2001 From: Daniel Morcuende Date: Thu, 14 Nov 2024 09:09:53 +0100 Subject: [PATCH 12/16] bring back removed section header and original markersize --- notebooks/LST1_observation_simulator.ipynb | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/notebooks/LST1_observation_simulator.ipynb b/notebooks/LST1_observation_simulator.ipynb index 9c74d8daa..b0dca5828 100644 --- a/notebooks/LST1_observation_simulator.ipynb +++ b/notebooks/LST1_observation_simulator.ipynb @@ -53,7 +53,9 @@ "id": "3", "metadata": {}, "source": [ - "## Load files for the requested efficiency:" + "## Load files which contain the (approximate) instrument response function, and show table contents \n", + "\n", + "They characterize the average performance of LST1 within 1 degree off-axis (computed from diffuse gamma MC and real data for the cosmic ray rates). The Ereco/Etrue distributions in each Etrue bin is parametrized with moyal or a skewnorm function (whatever fits better) \n" ] }, { @@ -63,6 +65,9 @@ "metadata": {}, "outputs": [], "source": [ + "#\n", + "# Load files for the requested efficiency:\n", + "#\n", "input_gamma_filename = get_resource_path(f'data/LST1_gamma_irf_gheffi_{cut_efficiency:.2f}_theffi_{cut_efficiency:.2f}.csv')\n", "gamma_data = pd.read_csv(input_gamma_filename)\n", "\n", @@ -903,7 +908,7 @@ "\n", " if not pulsar_mode:\n", " plt.errorbar(mean_etrue_vs_ereco[displayed_points], SED, yerr=SED_total_error, \n", - " fmt='o', markersize=4, \n", + " fmt='o', markersize=2, \n", " label='Observed, stat+syst')\n", " plt.errorbar(mean_etrue_vs_ereco[displayed_points], SED, yerr=SED_stat_error, \n", " fmt='o', markersize=2, \n", From 98f0bc8f1ca934d84362f124220af54be08b5ead Mon Sep 17 00:00:00 2001 From: Daniel Morcuende Date: Thu, 14 Nov 2024 10:56:13 +0100 Subject: [PATCH 13/16] [skip ci] typo --- notebooks/LST1_observation_simulator.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/LST1_observation_simulator.ipynb b/notebooks/LST1_observation_simulator.ipynb index b0dca5828..bfcce2c2e 100644 --- a/notebooks/LST1_observation_simulator.ipynb +++ b/notebooks/LST1_observation_simulator.ipynb @@ -727,7 +727,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Let's not use bind with too high bias in the spectrum:\n", + "# Let's not use bins with too high bias in the spectrum:\n", "not_too_high_bias = abs(mean_etrue_vs_ereco / erecobincenters - 1) < max_ereco_to_etrue_deviation" ] }, From 94e764adfadcac26433a994c5a61cea4acd14436 Mon Sep 17 00:00:00 2001 From: Abelardo Moralejo Olaizola Date: Fri, 15 Nov 2024 11:38:55 +0100 Subject: [PATCH 14/16] Added EBL optical depths file, taken from gammapy v1.1 (gammapy/gammapy-datasets/1.1/ebl/ebl_dominguez11.fits.gz) Improved SED plot Added plot on relative uncertainty of SED measurements --- 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data:\n", - "try:\n", - " os.environ['GAMMAPY_DATA']\n", - "except:\n", - " # WE SET HERE THE GAMMAPY_DATA ENV VARIABLE IN CASE IT IS NOT SET\n", - " gammapy_dir = Path(gammapy.__file__).parent\n", - " gammapy_dir\n", + "# try:\n", + "# os.environ['GAMMAPY_DATA']\n", + "# except:\n", + "# # WE SET HERE THE GAMMAPY_DATA ENV VARIABLE IN CASE IT IS NOT SET\n", + "# gammapy_dir = Path(gammapy.__file__).parent\n", + "# gammapy_dir\n", "\n", - " ebl_file = subprocess.run(['find', str(gammapy_dir), '-name', 'ebl_dominguez11.fits.gz'], \n", - " stdout=subprocess.PIPE).stdout.decode()\n", - " gammapy_data = ebl_file[:ebl_file.find('/ebl/')]\n", - " os.environ['GAMMAPY_DATA'] = gammapy_data\n", - " print('Set GAMMAPY_DATA to', gammapy_data)\n", + "# ebl_file = subprocess.run(['find', str(gammapy_dir), '-name', 'ebl_dominguez11.fits.gz'], \n", + "# stdout=subprocess.PIPE).stdout.decode()\n", + "# gammapy_data = ebl_file[:ebl_file.find('/ebl/')]\n", + "# os.environ['GAMMAPY_DATA'] = gammapy_data\n", + "# print('Set GAMMAPY_DATA to', gammapy_data)\n", "\n", "\n", - "dominguez = EBLAbsorptionNormSpectralModel.read_builtin(\"dominguez\", redshift=redshift)" + "# dominguez = EBLAbsorptionNormSpectralModel.read_builtin(\"dominguez\", redshift=redshift)" ] }, { @@ -895,7 +897,7 @@ " SED_stat_error = SED*relative_stat_excess_error[displayed_points]\n", " SED_total_error = SED*total_relative_excess_error[displayed_points]\n", "\n", - " plt.figure(figsize=(8,4))\n", + " plt.figure(figsize=(10,6))\n", " intrinsic_SED = (finebincenters*u.TeV)**2 * intrinsic_dFdE(finebincenters*u.TeV)\n", "\n", " if redshift > 0:\n", @@ -908,16 +910,16 @@ "\n", " if not pulsar_mode:\n", " plt.errorbar(mean_etrue_vs_ereco[displayed_points], SED, yerr=SED_total_error, \n", - " fmt='o', markersize=2, \n", - " label='Observed, stat+syst')\n", + " fmt='o', markersize=4, \n", + " label=f'Observed, stat + backg syst ({backg_systematics_uncertainty:.1%})')\n", " plt.errorbar(mean_etrue_vs_ereco[displayed_points], SED, yerr=SED_stat_error, \n", - " fmt='o', markersize=2, \n", + " fmt='o', markersize=4, \n", " label='Observed, stat-only')\n", "\n", " plt.yscale('log')\n", " plt.xscale('log')\n", - " plt.xlabel('Energy (TeV)')\n", - " plt.ylabel(f'SED ({SED.unit})')\n", + " plt.xlabel('Energy (TeV)', fontsize=14)\n", + " plt.ylabel(f'SED ( {SED.unit} )', fontsize=14)\n", " plt.ylim(np.nanmin((SED-SED_total_error)).value*0.1, \n", " np.nanmax((SED+SED_total_error)).value*5)\n", "\n", @@ -925,6 +927,19 @@ " plt.legend()\n", " plt.grid()\n", " \n", + " plt.figure(figsize=(10,2))\n", + " if not pulsar_mode:\n", + " plt.scatter(mean_etrue_vs_ereco[displayed_points], SED_total_error/SED, \n", + " s=8, label=f'Observed, stat + backg syst ({backg_systematics_uncertainty:.1%})')\n", + " plt.scatter(mean_etrue_vs_ereco[displayed_points], SED_stat_error/SED, \n", + " s=8, label='Observed, stat-only')\n", + " plt.xscale('log')\n", + " plt.yscale('log')\n", + " plt.xlabel('Energy (TeV)', fontsize=14)\n", + " plt.ylabel('Relative SED \\n uncertainty', fontsize=14)\n", + " plt.grid()\n", + " plt.show()\n", + " \n", "else:\n", " print(\"Nothing to display. No valid flux points!\")" ] @@ -947,6 +962,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -957,7 +977,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.11.9" } }, "nbformat": 4, From eee78fd204592f4ef44f380f875d3bd8706f22f3 Mon Sep 17 00:00:00 2001 From: Abelardo Moralejo Olaizola Date: Fri, 15 Nov 2024 15:03:26 +0100 Subject: [PATCH 15/16] Addressed several comments by reviewers --- notebooks/LST1_observation_simulator.ipynb | 66 +++++++++++++++------- 1 file changed, 47 insertions(+), 19 deletions(-) diff --git a/notebooks/LST1_observation_simulator.ipynb b/notebooks/LST1_observation_simulator.ipynb index 824d0bde1..919ab3748 100644 --- a/notebooks/LST1_observation_simulator.ipynb +++ b/notebooks/LST1_observation_simulator.ipynb @@ -19,7 +19,8 @@ "from pyirf.statistics import li_ma_significance\n", "from scipy.stats import moyal, norm, skewnorm\n", "from pathlib import Path\n", - "from lstchain.io.io import get_resource_path" + "from lstchain.io.io import get_resource_path\n", + "from lstchain import version" ] }, { @@ -156,6 +157,8 @@ "# For standard wobble offset (0.4 deg) reasonable values are alpha=0.333 (3 off regions) above 0.2 TeV, \n", "# and alpha=1 below 0.2 TeV. For testing sensitivity with the standard definition, set alpha=0.2\n", "\n", + "# Note: this setting will be overriden in pulsar mode! (see below)\n", + "\n", "alpha = 0.333 # 1" ] }, @@ -176,12 +179,12 @@ "outputs": [], "source": [ "pulsar_mode = False # Set to True to activate it\n", + "on_phase_interval = 0.043 # Crab P1: [-0.017, 0.026] Phase range for integrating the signal\n", + "off_phase_interval = 0.35 # [0.52 - 0.87] Phase range for estimating the background\n", "\n", - "on_phase_interval = 0.043 # Crab P1: [-0.017, 0.026] \n", - "off_phase_interval = 0.35 # [0.52 - 0.87]\n", - "\n", - "alpha = on_phase_interval / off_phase_interval\n", - "print(f'alpha = {alpha:.4f}')\n", + "if pulsar_mode:\n", + " alpha = on_phase_interval / off_phase_interval\n", + " print(f'alpha = {alpha:.4f}')\n", "\n", "# The spectrum is interpreted as average flux in full period (i.e. not just in the on-phase)" ] @@ -239,7 +242,6 @@ "else:\n", " print(f'Source angular radius within which {cut_efficiency:.1%} of the emission is contained: {source_radius}')\n", "\n", - "\n", " background_increase_factor = (theta_cut**2 + \n", " (source_radius.to_value(u.deg)*np.ones_like(theta_cut))**2) / (theta_cut**2)" ] @@ -585,7 +587,9 @@ "num_realizations_b = 100\n", "# Number of realizations (random numbers taken from skewnorm or moyal) for E-migration simulation\n", "\n", - "num_realizations = num_realizations_a * num_realizations_b" + "num_realizations = num_realizations_a * num_realizations_b\n", + "\n", + "np.random.seed(0)" ] }, { @@ -897,7 +901,7 @@ " SED_stat_error = SED*relative_stat_excess_error[displayed_points]\n", " SED_total_error = SED*total_relative_excess_error[displayed_points]\n", "\n", - " plt.figure(figsize=(10,6))\n", + " fig = plt.figure(figsize=(10,6))\n", " intrinsic_SED = (finebincenters*u.TeV)**2 * intrinsic_dFdE(finebincenters*u.TeV)\n", "\n", " if redshift > 0:\n", @@ -916,6 +920,26 @@ " fmt='o', markersize=4, \n", " label='Observed, stat-only')\n", "\n", + " ax = fig.axes[0]\n", + " \n", + " str = 'Source-independent analysis'\n", + " str += f'\\nZenith angle = {zenith[zd_bin]:.1f} degrees'\n", + " str += f'\\nCut efficiencies: {cut_efficiency:.0%}, {cut_efficiency:.0%} ($\\\\theta$, g/h)'\n", + " str += f'\\nEffective observation time = {effective_obs_time.to(u.h):.1f}'\n", + " if pulsar_mode:\n", + " str += '\\nPulsar mode'\n", + " str += f'\\nOn/Off exposure ratio $\\\\alpha$={alpha:.3f}'\n", + " if source_radius == 0:\n", + " str += '\\nPoint-like source'\n", + " else:\n", + " str += f'\\nSource radius: {source_radius:.2f}$^\\\\circ$ ({cut_efficiency:.0%} containment)'\n", + " str += f'\\n\\ncta-lstchain {version.version}'\n", + " \n", + " ax.text(0.05, 0.38, str, \n", + " transform=ax.transAxes, fontsize=12, verticalalignment='top',\n", + " bbox=dict(facecolor='white', alpha=0.8))\n", + "\n", + " \n", " plt.yscale('log')\n", " plt.xscale('log')\n", " plt.xlabel('Energy (TeV)', fontsize=14)\n", @@ -924,38 +948,42 @@ " np.nanmax((SED+SED_total_error)).value*5)\n", "\n", " plt.xlim(mean_etrue_vs_ereco[displayed_points][0]/3, mean_etrue_vs_ereco[displayed_points][-1]*3)\n", + "\n", " plt.legend()\n", " plt.grid()\n", + " plt.show()\n", + " \n", " \n", - " plt.figure(figsize=(10,2))\n", + " plt.figure(figsize=(10,3))\n", " if not pulsar_mode:\n", " plt.scatter(mean_etrue_vs_ereco[displayed_points], SED_total_error/SED, \n", - " s=8, label=f'Observed, stat + backg syst ({backg_systematics_uncertainty:.1%})')\n", + " s=12, label=f'Observed, stat + backg syst ({backg_systematics_uncertainty:.1%})')\n", " plt.scatter(mean_etrue_vs_ereco[displayed_points], SED_stat_error/SED, \n", - " s=8, label='Observed, stat-only')\n", + " s=12, label='Observed, stat-only')\n", " plt.xscale('log')\n", " plt.yscale('log')\n", " plt.xlabel('Energy (TeV)', fontsize=14)\n", " plt.ylabel('Relative SED \\n uncertainty', fontsize=14)\n", " plt.grid()\n", + " plt.legend()\n", " plt.show()\n", - " \n", + " \n", "else:\n", " print(\"Nothing to display. No valid flux points!\")" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "52", + "cell_type": "markdown", + "id": "c927ed19", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "### NOTE: in reality, other systematics from data-MC discrepancies (in Aeff, energy-dependent) may make the point-to point fluctuations larger than shown above. Systematic errors of around a few percent (independent in each bin) are to be expected.\n" + ] }, { "cell_type": "code", "execution_count": null, - "id": "53", + "id": "f3089dde", "metadata": {}, "outputs": [], "source": [] From 377060993addf17d38aa79f6e2306beb8d1b4d7f Mon Sep 17 00:00:00 2001 From: Abelardo Moralejo Olaizola Date: Fri, 15 Nov 2024 15:39:08 +0100 Subject: [PATCH 16/16] Addressed more comments by reviewer --- notebooks/LST1_observation_simulator.ipynb | 25 ++++++++++++++-------- 1 file changed, 16 insertions(+), 9 deletions(-) diff --git a/notebooks/LST1_observation_simulator.ipynb b/notebooks/LST1_observation_simulator.ipynb index 919ab3748..6afcba744 100644 --- a/notebooks/LST1_observation_simulator.ipynb +++ b/notebooks/LST1_observation_simulator.ipynb @@ -20,7 +20,7 @@ "from scipy.stats import moyal, norm, skewnorm\n", "from pathlib import Path\n", "from lstchain.io.io import get_resource_path\n", - "from lstchain import version" + "from lstchain.version import version as lstchain_version" ] }, { @@ -271,8 +271,14 @@ "# We will apply the Dominguez EBL model to simulate the absorption\n", "\n", "dominguez_ebl_file = get_resource_path(f'data/ebl_dominguez11.fits.gz')\n", - "dominguez = EBLAbsorptionNormSpectralModel.read(dominguez_ebl_file, redshift=redshift)\n", + "ebl_model = EBLAbsorptionNormSpectralModel.read(dominguez_ebl_file, redshift=redshift)\n", "\n", + "\n", + "###############################################################################################\n", + "#\n", + "# In case you want to use other EBL models available in gammapy (e.g. Franceschini) you can try \n", + "# the lines below: \n", + "#\n", "# Make sure we have the necessary EBL absorption data:\n", "# try:\n", "# os.environ['GAMMAPY_DATA']\n", @@ -286,9 +292,10 @@ "# gammapy_data = ebl_file[:ebl_file.find('/ebl/')]\n", "# os.environ['GAMMAPY_DATA'] = gammapy_data\n", "# print('Set GAMMAPY_DATA to', gammapy_data)\n", - "\n", - "\n", - "# dominguez = EBLAbsorptionNormSpectralModel.read_builtin(\"dominguez\", redshift=redshift)" + "#\n", + "#\n", + "# ebl_model = EBLAbsorptionNormSpectralModel.read_builtin(\"franceschini\", redshift=redshift)\n", + "#" ] }, { @@ -353,7 +360,7 @@ "source": [ "# After EBL absorption:\n", "def dFdE(E):\n", - " return intrinsic_dFdE(E) * dominguez.evaluate(E, redshift, 1)" + " return intrinsic_dFdE(E) * ebl_model.evaluate(E, redshift, 1)" ] }, { @@ -933,7 +940,7 @@ " str += '\\nPoint-like source'\n", " else:\n", " str += f'\\nSource radius: {source_radius:.2f}$^\\\\circ$ ({cut_efficiency:.0%} containment)'\n", - " str += f'\\n\\ncta-lstchain {version.version}'\n", + " str += f'\\n\\ncta-lstchain {lstchain_version}'\n", " \n", " ax.text(0.05, 0.38, str, \n", " transform=ax.transAxes, fontsize=12, verticalalignment='top',\n", @@ -974,7 +981,7 @@ }, { "cell_type": "markdown", - "id": "c927ed19", + "id": "d56d43a4", "metadata": {}, "source": [ "### NOTE: in reality, other systematics from data-MC discrepancies (in Aeff, energy-dependent) may make the point-to point fluctuations larger than shown above. Systematic errors of around a few percent (independent in each bin) are to be expected.\n" @@ -983,7 +990,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f3089dde", + "id": "29c63cec", "metadata": {}, "outputs": [], "source": []