From 2f959801542c621ae97db5964c3fcbb93e9e40eb Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:43:58 +0200 Subject: [PATCH 01/20] Move statsmodels notebooks into directory --- .../Chapter01CorrAssocSimpsons.ipynb | 0 .../Chapter02PotentialOutcomes.ipynb | 0 Chapter03CREandFRT.ipynb => statsmodels/Chapter03CREandFRT.ipynb | 0 .../Chapter04CREandNeyman.ipynb | 0 .../Chapter05StratandPostStrat.ipynb | 0 .../Chapter06RegadjRerand.ipynb | 0 .../Chapter07MatchedPairs.ipynb | 0 .../Chapter08UnifyingFisherNeyman.ipynb | 0 .../Chapter09BridgingFinitePopAndSuperPop.ipynb | 0 .../Chapter10ObsStudiesSelBias.ipynb | 0 Chapter11Pscore.ipynb => statsmodels/Chapter11Pscore.ipynb | 0 .../Chapter12DoubleRobustATE.ipynb | 0 .../Chapter13DoubleRobustATT.ipynb | 0 Chapter15Matching.ipynb => statsmodels/Chapter15Matching.ipynb | 0 .../Chapter16UnconfDifficulties.ipynb | 0 Chapter17Evalue.ipynb => statsmodels/Chapter17Evalue.ipynb | 0 .../Chapter18SensitivityAnalysis.ipynb | 0 .../Chapter19RosenbaumPvalues.ipynb | 0 Chapter20OverlapRD.ipynb => statsmodels/Chapter20OverlapRD.ipynb | 0 .../Chapter21IVexperiments.ipynb | 0 .../Chapter22IVmixtureDist.ipynb | 0 .../Chapter23IVeconometrics.ipynb | 0 Chapter24IVfuzzyRD.ipynb => statsmodels/Chapter24IVfuzzyRD.ipynb | 0 .../Chapter25IVmendelian.ipynb | 0 .../Chapter26principalStratification.ipynb | 0 .../Chapter27mediationAnalysis.ipynb | 0 ChapterA.ipynb => statsmodels/ChapterA.ipynb | 0 utils.py => statsmodels/utils.py | 0 28 files changed, 0 insertions(+), 0 deletions(-) rename Chapter01CorrAssocSimpsons.ipynb => statsmodels/Chapter01CorrAssocSimpsons.ipynb (100%) rename Chapter02PotentialOutcomes.ipynb => statsmodels/Chapter02PotentialOutcomes.ipynb (100%) rename Chapter03CREandFRT.ipynb => statsmodels/Chapter03CREandFRT.ipynb (100%) rename Chapter04CREandNeyman.ipynb => statsmodels/Chapter04CREandNeyman.ipynb (100%) rename Chapter05StratandPostStrat.ipynb => statsmodels/Chapter05StratandPostStrat.ipynb (100%) rename Chapter06RegadjRerand.ipynb => statsmodels/Chapter06RegadjRerand.ipynb (100%) rename Chapter07MatchedPairs.ipynb => statsmodels/Chapter07MatchedPairs.ipynb (100%) rename Chapter08UnifyingFisherNeyman.ipynb => statsmodels/Chapter08UnifyingFisherNeyman.ipynb (100%) rename Chapter09BridgingFinitePopAndSuperPop.ipynb => statsmodels/Chapter09BridgingFinitePopAndSuperPop.ipynb (100%) rename Chapter10ObsStudiesSelBias.ipynb => statsmodels/Chapter10ObsStudiesSelBias.ipynb (100%) rename Chapter11Pscore.ipynb => statsmodels/Chapter11Pscore.ipynb (100%) rename Chapter12DoubleRobustATE.ipynb => statsmodels/Chapter12DoubleRobustATE.ipynb (100%) rename Chapter13DoubleRobustATT.ipynb => statsmodels/Chapter13DoubleRobustATT.ipynb (100%) rename Chapter15Matching.ipynb => statsmodels/Chapter15Matching.ipynb (100%) rename Chapter16UnconfDifficulties.ipynb => statsmodels/Chapter16UnconfDifficulties.ipynb (100%) rename Chapter17Evalue.ipynb => statsmodels/Chapter17Evalue.ipynb (100%) rename Chapter18SensitivityAnalysis.ipynb => statsmodels/Chapter18SensitivityAnalysis.ipynb (100%) rename Chapter19RosenbaumPvalues.ipynb => statsmodels/Chapter19RosenbaumPvalues.ipynb (100%) rename Chapter20OverlapRD.ipynb => statsmodels/Chapter20OverlapRD.ipynb (100%) rename Chapter21IVexperiments.ipynb => statsmodels/Chapter21IVexperiments.ipynb (100%) rename Chapter22IVmixtureDist.ipynb => statsmodels/Chapter22IVmixtureDist.ipynb (100%) rename Chapter23IVeconometrics.ipynb => statsmodels/Chapter23IVeconometrics.ipynb (100%) rename Chapter24IVfuzzyRD.ipynb => statsmodels/Chapter24IVfuzzyRD.ipynb (100%) rename Chapter25IVmendelian.ipynb => statsmodels/Chapter25IVmendelian.ipynb (100%) rename Chapter26principalStratification.ipynb => statsmodels/Chapter26principalStratification.ipynb (100%) rename Chapter27mediationAnalysis.ipynb => statsmodels/Chapter27mediationAnalysis.ipynb (100%) rename ChapterA.ipynb => statsmodels/ChapterA.ipynb (100%) rename utils.py => statsmodels/utils.py (100%) diff --git a/Chapter01CorrAssocSimpsons.ipynb b/statsmodels/Chapter01CorrAssocSimpsons.ipynb similarity index 100% rename from Chapter01CorrAssocSimpsons.ipynb rename to statsmodels/Chapter01CorrAssocSimpsons.ipynb diff --git a/Chapter02PotentialOutcomes.ipynb b/statsmodels/Chapter02PotentialOutcomes.ipynb similarity index 100% rename from Chapter02PotentialOutcomes.ipynb rename to statsmodels/Chapter02PotentialOutcomes.ipynb diff --git a/Chapter03CREandFRT.ipynb b/statsmodels/Chapter03CREandFRT.ipynb similarity index 100% rename from Chapter03CREandFRT.ipynb rename to statsmodels/Chapter03CREandFRT.ipynb diff --git a/Chapter04CREandNeyman.ipynb b/statsmodels/Chapter04CREandNeyman.ipynb similarity index 100% rename from Chapter04CREandNeyman.ipynb rename to statsmodels/Chapter04CREandNeyman.ipynb diff --git a/Chapter05StratandPostStrat.ipynb b/statsmodels/Chapter05StratandPostStrat.ipynb similarity index 100% rename from Chapter05StratandPostStrat.ipynb rename to statsmodels/Chapter05StratandPostStrat.ipynb diff --git a/Chapter06RegadjRerand.ipynb b/statsmodels/Chapter06RegadjRerand.ipynb similarity index 100% rename from Chapter06RegadjRerand.ipynb rename to statsmodels/Chapter06RegadjRerand.ipynb diff --git a/Chapter07MatchedPairs.ipynb b/statsmodels/Chapter07MatchedPairs.ipynb similarity index 100% rename from Chapter07MatchedPairs.ipynb rename to statsmodels/Chapter07MatchedPairs.ipynb diff --git a/Chapter08UnifyingFisherNeyman.ipynb b/statsmodels/Chapter08UnifyingFisherNeyman.ipynb similarity index 100% rename from Chapter08UnifyingFisherNeyman.ipynb rename to statsmodels/Chapter08UnifyingFisherNeyman.ipynb diff --git a/Chapter09BridgingFinitePopAndSuperPop.ipynb b/statsmodels/Chapter09BridgingFinitePopAndSuperPop.ipynb similarity index 100% rename from Chapter09BridgingFinitePopAndSuperPop.ipynb rename to statsmodels/Chapter09BridgingFinitePopAndSuperPop.ipynb diff --git a/Chapter10ObsStudiesSelBias.ipynb b/statsmodels/Chapter10ObsStudiesSelBias.ipynb similarity index 100% rename from Chapter10ObsStudiesSelBias.ipynb rename to statsmodels/Chapter10ObsStudiesSelBias.ipynb diff --git a/Chapter11Pscore.ipynb b/statsmodels/Chapter11Pscore.ipynb similarity index 100% rename from Chapter11Pscore.ipynb rename to statsmodels/Chapter11Pscore.ipynb diff --git a/Chapter12DoubleRobustATE.ipynb b/statsmodels/Chapter12DoubleRobustATE.ipynb similarity index 100% rename from Chapter12DoubleRobustATE.ipynb rename to statsmodels/Chapter12DoubleRobustATE.ipynb diff --git a/Chapter13DoubleRobustATT.ipynb b/statsmodels/Chapter13DoubleRobustATT.ipynb similarity index 100% rename from Chapter13DoubleRobustATT.ipynb rename to statsmodels/Chapter13DoubleRobustATT.ipynb diff --git a/Chapter15Matching.ipynb b/statsmodels/Chapter15Matching.ipynb similarity index 100% rename from Chapter15Matching.ipynb rename to statsmodels/Chapter15Matching.ipynb diff --git a/Chapter16UnconfDifficulties.ipynb b/statsmodels/Chapter16UnconfDifficulties.ipynb similarity index 100% rename from Chapter16UnconfDifficulties.ipynb rename to statsmodels/Chapter16UnconfDifficulties.ipynb diff --git a/Chapter17Evalue.ipynb b/statsmodels/Chapter17Evalue.ipynb similarity index 100% rename from Chapter17Evalue.ipynb rename to statsmodels/Chapter17Evalue.ipynb diff --git a/Chapter18SensitivityAnalysis.ipynb b/statsmodels/Chapter18SensitivityAnalysis.ipynb similarity index 100% rename from Chapter18SensitivityAnalysis.ipynb rename to statsmodels/Chapter18SensitivityAnalysis.ipynb diff --git a/Chapter19RosenbaumPvalues.ipynb b/statsmodels/Chapter19RosenbaumPvalues.ipynb similarity index 100% rename from Chapter19RosenbaumPvalues.ipynb rename to statsmodels/Chapter19RosenbaumPvalues.ipynb diff --git a/Chapter20OverlapRD.ipynb b/statsmodels/Chapter20OverlapRD.ipynb similarity index 100% rename from Chapter20OverlapRD.ipynb rename to statsmodels/Chapter20OverlapRD.ipynb diff --git a/Chapter21IVexperiments.ipynb b/statsmodels/Chapter21IVexperiments.ipynb similarity index 100% rename from Chapter21IVexperiments.ipynb rename to statsmodels/Chapter21IVexperiments.ipynb diff --git a/Chapter22IVmixtureDist.ipynb b/statsmodels/Chapter22IVmixtureDist.ipynb similarity index 100% rename from Chapter22IVmixtureDist.ipynb rename to statsmodels/Chapter22IVmixtureDist.ipynb diff --git a/Chapter23IVeconometrics.ipynb b/statsmodels/Chapter23IVeconometrics.ipynb similarity index 100% rename from Chapter23IVeconometrics.ipynb rename to statsmodels/Chapter23IVeconometrics.ipynb diff --git a/Chapter24IVfuzzyRD.ipynb b/statsmodels/Chapter24IVfuzzyRD.ipynb similarity index 100% rename from Chapter24IVfuzzyRD.ipynb rename to statsmodels/Chapter24IVfuzzyRD.ipynb diff --git a/Chapter25IVmendelian.ipynb b/statsmodels/Chapter25IVmendelian.ipynb similarity index 100% rename from Chapter25IVmendelian.ipynb rename to statsmodels/Chapter25IVmendelian.ipynb diff --git a/Chapter26principalStratification.ipynb b/statsmodels/Chapter26principalStratification.ipynb similarity index 100% rename from Chapter26principalStratification.ipynb rename to statsmodels/Chapter26principalStratification.ipynb diff --git a/Chapter27mediationAnalysis.ipynb b/statsmodels/Chapter27mediationAnalysis.ipynb similarity index 100% rename from Chapter27mediationAnalysis.ipynb rename to statsmodels/Chapter27mediationAnalysis.ipynb diff --git a/ChapterA.ipynb b/statsmodels/ChapterA.ipynb similarity index 100% rename from ChapterA.ipynb rename to statsmodels/ChapterA.ipynb diff --git a/utils.py b/statsmodels/utils.py similarity index 100% rename from utils.py rename to statsmodels/utils.py From 838934ccf766f6946e1245f21d6d386138390a0a Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:27 +0200 Subject: [PATCH 02/20] Add PyFixest notebook utilities --- pyfixest/utils.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 pyfixest/utils.py diff --git a/pyfixest/utils.py b/pyfixest/utils.py new file mode 100644 index 0000000..680857b --- /dev/null +++ b/pyfixest/utils.py @@ -0,0 +1,14 @@ +import numpy as np +import pandas as pd +import graphviz as gr + +def simulate(**kwargs): + values = {} + g = gr.Digraph() + for k,v in kwargs.items(): + parents = v.__code__.co_varnames + inputs = {arg: values[arg] for arg in v.__code__.co_varnames} + values[k] = v(**inputs) + for p in parents: + g.edge(p, k) + return pd.DataFrame(values), g From 399a4622d17fdd2544f1f1cdb969e173a18dd5d4 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:27 +0200 Subject: [PATCH 03/20] Add PyFixest Chapter 01 notebook --- pyfixest/Chapter01CorrAssocSimpsons.ipynb | 491 ++++++++++++++++++++++ 1 file changed, 491 insertions(+) create mode 100644 pyfixest/Chapter01CorrAssocSimpsons.ipynb diff --git a/pyfixest/Chapter01CorrAssocSimpsons.ipynb b/pyfixest/Chapter01CorrAssocSimpsons.ipynb new file mode 100644 index 0000000..e58860e --- /dev/null +++ b/pyfixest/Chapter01CorrAssocSimpsons.ipynb @@ -0,0 +1,491 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 1: Correlation, Association, and the Yule-Simpson Paradox" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import scipy as sp\n", + "import pyfixest as pf\n", + "\n", + "# viz\n", + "from IPython.core.interactiveshell import InteractiveShell\n", + "\n", + "InteractiveShell.ast_node_interactivity = \"all\"\n", + "\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Unadjusted and Adjusted Regression\n", + "Lalonde Observational Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# read CPS data\n", + "dat = pd.read_table(\"cps1re74.csv\", delimiter=\" \")\n", + "dat[\"u74\"] = np.where(dat[\"re74\"] == 0, 1, 0)\n", + "dat[\"u75\"] = np.where(dat[\"re75\"] == 0, 1, 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unadjusted regression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pf.feols(\"re78 ~ treat\", data=dat, vcov=\"HC2\").tidy().loc[\"treat\"]\n", + "# %%\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adjusted Regression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rhs = list(set(dat.columns) - {\"re78\", \"treat\"})\n", + "pf.feols(f're78 ~ treat + {\"+\".join(rhs)}', data=dat, vcov=\"hetero\").tidy().loc[\n", + " \"treat\"\n", + "]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fisher's exact test for contingency tables\n", + "Bertrand and Mullainathan (2004) experiment " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "call 0 1\n", + "race \n", + "black 2278 157\n", + "white 2200 235\n" + ] + } + ], + "source": [ + "resume = pd.read_csv(\"resume.csv\")\n", + "print(xtab := pd.crosstab(resume[\"race\"], resume[\"call\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SignificanceResult(statistic=1.5498841922408801, pvalue=4.758747107909523e-05)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sp.stats.fisher_exact(xtab)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## simpson's paradox \n", + "UCB admissions data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AdmitGenderDeptFreq
0AdmittedMaleA512
1RejectedMaleA313
2AdmittedFemaleA89
3RejectedFemaleA19
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ucb.loc[ucb.Dept == d, [\"Gender\", \"Admit\", \"Freq\"]],\n", + " index=\"Gender\",\n", + " columns=\"Admit\",\n", + " values=\"Freq\",\n", + " )\n", + " print(d, risk_difference(twoby2_d))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "econometrics", + "language": "python", + "name": "econometrics" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From cd390a2a3f0b11a6df043f3c230d096a59e90ab0 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:28 +0200 Subject: [PATCH 04/20] Add PyFixest Chapter 02 notebook --- pyfixest/Chapter02PotentialOutcomes.ipynb | 136 ++++++++++++++++++++++ 1 file changed, 136 insertions(+) create mode 100644 pyfixest/Chapter02PotentialOutcomes.ipynb diff --git a/pyfixest/Chapter02PotentialOutcomes.ipynb b/pyfixest/Chapter02PotentialOutcomes.ipynb new file mode 100644 index 0000000..d82e2e5 --- /dev/null +++ b/pyfixest/Chapter02PotentialOutcomes.ipynb @@ -0,0 +1,136 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 2: Potential Outcomes" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pandas : 2.1.1\n", + "matplotlib : 3.8.0\n", + "scipy : 1.11.3\n", + "numpy : 1.23.5\n", + "matplotlib_inline: 0.1.6\n", + "\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import scipy as sp\n", + "from IPython.core.interactiveshell import InteractiveShell\n", + "\n", + "InteractiveShell.ast_node_interactivity = \"all\"\n", + "\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "n = 500\n", + "Y0 = sp.stats.norm.rvs(size=n)\n", + "tau = -0.5 + Y0\n", + "Y1 = Y0 + tau" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Perfect doctor: treat if individual TE is positive" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.3555878913957384" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Z = tau >= 0\n", + "Y = Z * Y1 + (1 - Z) * Y0\n", + "np.mean(Y[Z == 1]) - np.mean(Y[Z == 0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clueless doctor: flip coin" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.4046654673989749" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Z = sp.stats.bernoulli.rvs(p=0.5, size=n)\n", + "Y = Z * Y1 + (1 - Z) * Y0\n", + "np.mean(Y[Z == 1]) - np.mean(Y[Z == 0])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "econometrics", + "language": "python", + "name": "econometrics" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From b316492c448b909d2ba3ed84f3f0a30e39f3650f Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:28 +0200 Subject: [PATCH 05/20] Add PyFixest Chapter 03 notebook --- pyfixest/Chapter03CREandFRT.ipynb | 351 ++++++++++++++++++++++++++++++ 1 file changed, 351 insertions(+) create mode 100644 pyfixest/Chapter03CREandFRT.ipynb diff --git a/pyfixest/Chapter03CREandFRT.ipynb b/pyfixest/Chapter03CREandFRT.ipynb new file mode 100644 index 0000000..1341da3 --- /dev/null +++ b/pyfixest/Chapter03CREandFRT.ipynb @@ -0,0 +1,351 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 3: The completely randomized experiment and the Fisher randomization test" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scipy : 1.11.3\n", + "matplotlib : 3.8.0\n", + "pandas : 2.1.1\n", + "matplotlib_inline: 0.1.6\n", + "numpy : 1.23.5\n", + "\n" + ] + } + ], + "source": [ + "import itertools\n", + "# %% library loads\n", + "import numpy as np\n", + "import scipy as sp\n", + "# viz\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "font = {'family' : 'IBM Plex Sans Condensed',\n", + " 'weight' : 'normal',\n", + " 'size' : 10}\n", + "plt.rc('font', **font)\n", + "plt.rcParams['figure.figsize'] = (6, 5)\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def permuter(N, N1):\n", + " combos = np.array(list(itertools.combinations(range(N), N1)))\n", + " # create an empty matrix of size N x (N choose N1)\n", + " matrix = np.zeros((N, len(combos)))\n", + " for i in range(len(combos)):\n", + " matrix[combos[i], i] = 1\n", + " return matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 1., 1., 1., 1., 1., 0., 0., 0., 0.],\n", + " [1., 1., 1., 0., 0., 0., 1., 1., 1., 0.],\n", + " [1., 0., 0., 1., 1., 0., 1., 1., 0., 1.],\n", + " [0., 1., 0., 1., 0., 1., 1., 0., 1., 1.],\n", + " [0., 0., 1., 0., 1., 1., 0., 1., 1., 1.]])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "permuter(5, 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## illustration using lalonde data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Laurence Wong's package has lalonde data\n", + "# !pip install git+https://github.com/laurencium/Causalinference\n", + "from causalinference.utils import lalonde_data\n", + "\n", + "y, z, _ = lalonde_data()\n", + "y = y * 1000 # stored in 1000s of dollars in causalinference" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1rW+le/fu6dKlSz74wQ+mb9+++epXv5ok+dWvfrVCaNB0e8X7778//fv3T//+/XPjjTeuUz1JctBBB+W4445LQ0NDTj311Gy++ebp2rVrBg4cmL/85S/58Ic/vAFevY1PUAAAAMAGd9hhh+WRRx7J5z//+Wy11Vapr6/P4MGDc8ghh2TChAk55ZRTVhhz66235mtf+1oGDx6c6urqbLnllhk9enRuuummfO5zn8s+++yTZMXbHO6zzz759Kc/nZqamrz++ut5/fXXyxkJ61JPklx++eX5yU9+kve9733p1q1bevTokWOOOSa33XbbJnOBw4qiKIrWLmJT8Morr2Tw4MFJll90Y9CgQa1c0RoYO3bT3BYAAJuMZ599NvX19amurs7222/f2uXABrW2x/uG+hxqRgEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFASFAAAAAAlQQEAAABQEhQAAADAWhoyZEgqKioyYcKE1i5lvatu7QIAAADWm7FjW7uCDWcjP7cbbrgh11xzTf7+979nxowZqa2tzbBhw/KZz3wmI0eOTOfOnTdqPWw8ggIAAABKS5cuzWGHHZbbb7+9bOvSpUtmz56d++67L/fdd19+9rOf5c4770zfvn1bsdJk7Nix6dWrV77+9a+3ah2bGqceAAAAUPriF7+Y22+/Pb17986Pf/zjzJkzJ4sXL86cOXPy29/+Nttuu20ee+yxHH744SmKolVrPeOMM3LRRRe1ag2bIkEBAAAASZIJEybkuuuuS9euXTN+/Ph8+ctfTs+ePZMkPXv2zOGHH5677rorffv2zd13350bbrihlStmQxAUAAAAkCS5/PLLkyQnnnhi3vWud7XYZ5tttslJJ52U4cOHZ6eddtqY5TXzyCOPtNq2N3WCAgAAALJ06dL87ne/S5J8/vOfX2Xf0aNHZ8KECdlhhx2atT/xxBM5+uijs/XWW6dz587p27dvDjrooNxxxx0trmfChAmpqKjIkCFDkiS33nprdtlll9TW1maLLbbIF7/4xUyfPr3ZmKuvvjoVFRV53/velyR5+eWXU1FRkYqKihx99NFlv5deeqlsT5I777wzu+22W7p3777CNQ0WLFiQs88+O+9973tTW1ub2travPe9780555yThQsXrvK12BS5mCEAAAB56qmnsmjRonTt2jXDhg1bZd+ampoV2m666aZ87nOfy7Jly5Ik3bp1y8yZM/O73/0uv/vd73LyySfn+9///krX+ec//zmf/OQnU1lZmYaGhixcuDBXX311Hn744Tz88MPlXRY6deqUnj17pr6+PgsXLkxFRUV69OhRbrMlf/3rX3PQQQelvr4+PXr0SNeuXctlr7/+evbbb7888cQT5fqT5NFHH82jjz6aX/3qV/nTn/6Ufv36rfI12ZSYUQAAAECefPLJJEnv3r3Lb+HX1EsvvZQjjzwyy5Yty0knnZSpU6dm4cKFmT9/fn7yk5+ke/fu+cEPfpCbbrqpxfGLFy/OuHHjctttt2XhwoWZM2dOzj///FRWVmbixIm55ppryr5HHHFE5syZU96VYeutt86cOXMyZ86c8tSJNzvnnHNy/fXXl+s+55xzymX/9V//lSeeeCK77rprHnjggSxZsiRLlizJAw88kPe///2ZOHFivvzlL6/V69HeCQoAAADIzJkzkyTV1Ws/8fz888/PwoUL86UvfSk/+MEPym/fu3fvnv/6r//KZZddliQ566yzWhw/bdq0fPWrX82BBx6YTp06pUePHvl//+//5VOf+lSSrPTUhTX1la98JYceemizmQTJ8usc/O53v0vfvn3zxz/+MR/4wAfK0xU+8IEP5I9//GP69OmT2267Lf/4xz/WqYb2RFAAAABA5s+f/5bH3njjjUmy0m/e//M//zO1tbV57LHH8vzzz6+wvLa2NnvttdcK7QcddFCStDhmbQwfPrzF9qa7Nhx22GHp1avXCst79+6dww47rFnfjkBQAAAAwFv22muvZcqUKUmSoUOHttinuro6b3/725MkDz300ArL+/Tp0+LpDltttVWS5RcbXBe1tbUttj/88MNJVl53krzjHe9I0nLdmypBAQAAANlss83e0rjXX3+9/LnpgoMt6dKlS5Jk6tSpa7zuptMgGhsb31Jtq9NU+/quu70TFAAAAJA+ffokSerq6tZq3NrePnBdZwesT2tTe1uqe0MTFAAAAFDeEnH27Nlr9Q1+9+7d12o7KzsNoDWsTe1tqe4NTVAAAABAdthhh3Tr1i2LFy/OU089tcq+S5YsKafiN93hIEmWLl26yjFJ0r9///VQ7frRVHt7q3tDExQAAACQzp075+Mf/3iS5Be/+MUq+1577bXZYYcd8utf/zoDBw7MgAEDkiTPPPNMi/3r6+vLOxfssssu67HqdfP+978/ycrrfuOytlT3hiYoAAAAIEly/PHHJ0kuvvjiPP744y32ee2113LGGWdkzpw5qaxc/pFyxIgRSZKf/vSnLY759a9/nQULFmTnnXcu737QFjTVfeONN2bu3LkrLJ89e3azWyh2FIICAAAAkiR77713Pve5z2Xx4sXZd999c+WVV5YfoOfMmZMbbrghw4cPz2uvvZZ99tknn/70p5MkJ510Umpra3PllVfmm9/8ZqZNm5YkWbRoUa666qocd9xxSZLTTjttvdXaFFKsi/e973056KCDMm3atBxwwAF56KGHUhRFiqLIQw89lAMOOCAzZszIwQcfnHe/+93NxrZ0O8dNhaAAAACA0lVXXZWDDjooM2fOzMiRI9OrV6907do1m2++eQ4//PA899xz2WOPPXLrrbeWY7bZZptce+21qampybnnnpt+/fqle/fuqa2tzTHHHJMFCxbk5JNPzqc+9an1VufAgQOTJJMnT07//v3Tv3//XHTRRWu9np/85CfZaaed8te//jW77rprunTpki5dumTXXXfNgw8+mJ122ilXXnnlCuOaTrc46KCD0qtXrxZnJLRXggIAAABKnTt3zm233Zbrr78+H//4x9O/f//U19enV69e2WuvvTJu3LiMHz9+hbsAfPKTn8wjjzySI488MoMGDUpdXV369OmTj3/84/n973+f73//++u1zu222y4nnnhiamtr8/rrr+f1119/S7cw7NevXx544IF873vfy84775zq6upUV1fnPe95T84+++w88MADzS7Y2GTMmDHZZpttsmjRosydOzdFUayPp9UmVBSb0rNpRa+88koGDx6cZHmiNWjQoFauaA2MHbtpbgsAgE3Gs88+m/r6+lRXV2f77bdv7XJgg1rb431DfQ41owAAAAAoCQoAAACAkqAAAAAAKAkKAAAAgJKgAAAAACgJCgAAAICSoAAAAAAoCQoAAACAkqAAAAAAKAkKAAAAgJKgAAAAACgJCgAAgDarsnL5R5aGhoYURdHK1cCGUxRFGhoakiRVVVWtWougAAAAaLM6deqUZPmHqKVLl7ZyNbDhLFq0qAzDmo771iIoAAAA2qzu3buXP8+bN68VK4ENpyiKzJo1q/y9R48erViNoAAAAGjDamtry59nzpyZmTNnltOzob0riiILFy7MK6+8kgULFiRJKioqmh33raG6VbcOAACwCp06dUrfvn0zffr0JMm0adMybdq0VFVVpaKiopWrg3Xz5mtvVFRUZKuttiqvzdFaBAUAAECb1qdPnyxbtixz584t28wqYFPTFBJsttlmrV2KoAAAAGjbKioqMnDgwPTu3Ttz5szJokWLBAVsEqqqqtKpU6f06NEjtbW1rT6ToImgAAAAaBe6dOmS/v37t3YZsMlrG3EFAAAA0CYICgAAAICSoAAAAAAoCQoAAACAkqAAAAAAKAkKAAAAgJKgAAAAACgJCgAAAICSoAAAAAAoCQoAAACAkqAAAAAAKAkKAAAAgJKgAAAAACgJCgAAAICSoAAAAAAoCQoAAACAkqAAAAAAKAkKAAAAgJKgAAAAACgJCgAAAICSoAAAAAAoCQoAAACAkqAAAAAAKAkKAAAAgJKgAAAAACgJCgAAAICSoAAAAAAobbSg4JRTTklFRUX5GDdu3GrH3HfffTnssMMycODAdO7cOQMGDMiIESNy7733btCxAAAA0FFt8KCgKIqMGjUq3//+97P55puna9euazTuyiuvzF577ZUbb7wxU6ZMSVVVVaZOnZqbbropw4cPX2XQsC5jAQAAoCPboEFBQ0NDjjnmmFx++eXZcsstM378+Gy55ZarHffMM89k1KhRaWxszGmnnZYZM2Zk0aJFmTlzZsaMGZPGxsaccMIJefLJJ9frWAAAAOjoNlhQUFdXlyOOOCJXX311Bg0alL/85S9597vfvUZjL7zwwtTV1WXkyJE588wz06dPnyRJ7969M3bs2Bx33HGpr6/PD3/4w/U6FgAAADq6DRYUPP3007ntttuy7bbb5p577snQoUPXeOytt96aJDnxxBNbXD569OgkyW233ZaiKNbbWAAAAOjoNlhQ8M53vjO33HJL7rnnngwZMmSNxz3//POZMmVKNt988wwbNqzFPttvv3369++fGTNmNDuFYF3GAgAAABv4GgX7779/Bg4cuFZjnn322STJ4MGDV9mvaflzzz23XsYCAAAASXVrF/BmkyZNSpLV3h2hW7duzfqv69jVeeWVV1a5fMqUKWu8LgAAAGir2lxQsGDBgrfcf13Grs7qZikAAADApmCDnnrwVixevHit+i9atGi9jKWDmD49qaho/pg+vb1tAgAAYINpczMK2qrJkyevcvmUKVOy6667bqRqAAAAYMNoc0HB6q4v8GZN1xtY17GrM2jQoLVaNwAAALRHbe7Ug9ra2rfcf13GAgAAAG0wKNh6662TJEuWLFllv6brCzT1X9exAAAAQBsMCrbbbrskq78mQNPypv7rOhYAAABoo0HBgAEDMmvWrDz99NMt9nn++eczderU9OnTJ8OGDVsvYwEAAIA2GBQkySGHHJIkufjii1tcftFFFyVJDj744FRUVKy3sQAAANDRtbm7HiTJ6NGj87Of/Szjxo1Lv3798rWvfS29e/fO7Nmzc+mll+ayyy5LdXV1Tj755PU6lg6gU6fksMNWbAMAACBJUlEURbGhVr7ffvvl7rvvbtbW0NCQJKmsrGz2jf7w4cPz5z//ufz9xz/+cY477rg0NjYmWX4rw6aLEFZUVOTyyy/Pscce2+J212XsW/XKK69k8ODBSZZfA6Fd3E5x7NhNc1utbPr0ZMstm7dNm5b07ds69QAAAJumDfU5dIOeetDQ0LDCo0ljY+NKlyXJyJEjM378+Bx66KHp379/6urq0q9fvxx66KEZP378Kj/or8tYAAAA6Mg26KkHEyZMWKfxe+21V/baa6+NPhYAAAA6qjZ5MUMAAACgdQgKAAAAgJKgAAAAACi1ydsjwgYzd27ypS81b/vJT5KePVunHgAAgDZGUEDHsmxZcsMNzdsuv7x1agEAAGiDnHoAAAAAlAQFAAAAQElQAAAAAJQEBQAAAEBJUAAAAACUBAUAAABASVAAAAAAlAQFAAAAQElQAAAAAJQEBQAAAEBJUAAAAACUBAUAAABASVAAAAAAlKpbuwDYqGpqkuHDV2wDAAAgiaCAjqZXr2TChNauAgAAoM1y6gEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFBy1wM6lvnzk1NPbd527rnJZpu1Tj0AAABtjKCAjmXJkuTyy5u3jR0rKAAAAPg3px4AAAAAJUEBAAAAUBIUAAAAACVBAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAperWLgA2qqqqZNiwFdsAAABIIiigo+ndO3niidauAgAAoM1y6gEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFBy1wM6loULkx/8oHnbyScn3bu3Tj0AAABtjKCAjmXRouSMM5q3jRolKAAAAPg3px4AAAAAJUEBAAAAUBIUAAAAACVBAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAperWLgA2qoqKZIstVmwDAAAgiaCAjmaLLZLp01u7CgAAgDbLqQcAAABASVAAAAAAlAQFAAAAQElQAAAAAJQEBQAAAEDJXQ/oWBYvTn72s+ZtxxyTdO3aOvUAAAC0MYICOpYFC5KvfrV526c/LSgAAAD4N6ceAAAAACVBAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFCqbu0CYKPq2zcpitauAgAAoM0yowAAAAAoCQoAAACAkqAAAAAAKAkKAAAAgJKgAAAAACi56wEdy9Klya23Nm875JCkc+fWqQcAAKCNERTQscybl3z6083bpk1bfttEAAAAnHoAAAAA/B9BAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFASFAAAAAAlQQEAAABQEhQAAAAApTYdFMydOzdnnXVWdt111/Tq1Ss1NTXp06dP9txzz/z3f/93li5dutKxt99+ew444ID069cvnTt3zuDBg3PkkUdm4sSJG/EZAAAAQPtS3doFrMyLL76Y4cOHZ/LkyWVb586dM2vWrNx777259957c/XVV+fPf/5zNt9882ZjTzvttHz3u98tf+/atWteeeWV/PznP89vf/vbXH/99Tn44IM32nMBAACA9qLNzig48cQTM3ny5PTo0SPXXHNN5s2blyVLlmTmzJm56KKLUl1dnUcffTRjx45tNu6uu+7Kd7/73dTU1OTiiy/OvHnzsmjRorz66qs59thjs3Tp0nz+85/PjBkzWueJAQAAQBvWJoOCpUuX5g9/+EOS5Lvf/W6OPPLIbLbZZkmS3r1758QTT8wJJ5yQJLnllluajf3+97+fJDnjjDPyta99rRw3cODAXHHFFTnwwAMzb968XH755Rvp2dCm9OmTTJvW/NGnT2tXBQAA0Ga0yaBg1qxZqaurS5J86EMfarFPU/vUqVPLtgULFuSuu+5KdXV1jj/++BbHff3rX0+S/M///M96rJh2o7Iy6du3+aOyTf4zAAAAaBVt8hNS3759U129ZpdPGDhwYPnzgw8+mLq6urzzne9Mz549W+y/xx57pKKiIo899ljmz5+/XuoFAACATUWbDAqqq6vLGQP33Xdfi32a2vfee++y7dlnn02SDB48eKXr7tq1a7bYYos0NjbmhRdeWE8VAwAAwKahzd714Lzzzsu+++6b73znO+nVq1c++clPpra2NrNnz87Pf/7zXHLJJenbt29OP/30csykSZOSLA8DVqVbt25l/3e/+91rVM8rr7yyyuVTpkxZo/UAAABAW9Zmg4IPfOADuffeezN27NgcddRROfLII9OlS5csWbIkNTU1+fSnP52zzjorb3vb28oxCxYsWKttrE3/Vc1SAAAAgE1Fmw0KkmThwoXp2bNnKioqUhRFlixZkmT5qQk9evTIwoULm/VfvHjxWq1/0aJF661W2olly5L772/etvvuSadOrVMPAABAG9Nmg4If/ehHGTVqVGpra3P++efn4IMPzsCBA/Pyyy/nl7/8ZX7wgx/k2muvza9//escfPDBG7yeyZMnr3L5lClTsuuuu27wOlhHc+cm++zTvG3atOV3PwAAAKBtBgUvvfRSTjjhhDQ0NOS3v/1t9t9//3LZO97xjpx11lkZOHBgjj/++Bx11FGZPHlyunfvvtprE7xZ07UK1sSgQYPWat0AAADQHrXJux7cfPPNqaury7Bhw5qFBG907LHHplu3bpk9e3b+93//N0lSW1u7VttZ2/4AAACwqWuTQUHTbQ633377lfapqKgolzf133rrrZOkvJbByjRdm6CpPwAAALBcmwwKGhoakiSVlasur6Kioln/7bbbLsmqryewePHizJgxIxUVFdl2223XR7kAAACwyWiTQcGAAQOS/N9MgZYURZHnnnsuSTJw4MAkyW677Zaampr885//zLx581ocd//996coiuy8887ZbLPN1nPlAAAA0L61yaBgn39flf6JJ57IPffc02Kfa665JgsWLEhlZWWGDx+eZPk1B/bdd9/U1dXliiuuaHHcRRddlCQ59NBD13/hAAAA0M61yaBg+PDh2W233VIURQ477LBcffXVmTVrVpLk9ddfzwUXXJCvfvWrSZLPfe5z2Wabbcqx3/jGN5Ikp59+ei699NIsWLAgyfLbF44aNSq33357evTokVGjRm3kZwUAAABtX5u8PWKS/OY3v8lHPvKR/Otf/8oXv/jFJEmnTp2ybNmyss/uu++eyy+/vNm4fffdN9/+9rfzve99LyeccEK+9rWvpUuXLlm8eHG5jl/84hfZYostNt6TAQAAgHaiTc4oSJbfkeDvf/97vv/97+eDH/xgevXqlcbGxvTp0yf77LNPfvSjH2XChAnp0aPHCmO/+93v5uabb87++++fPn36pKGhIVtttVWOOOKIPPTQQzn44INb4RkBAABA29dmZxQky685cPLJJ+fkk09e67Gf+MQn8olPfGL9FwUAAACbsDY7owAAAADY+Nr0jAI2rAkTNuK2xr71sWPXYSwAAABrx4wCAAAAoCQoAAAAAEpOPaBj2XzzZOLEFdsAAABIIiigo6muTnbcsbWrAAAAaLOcegAAAACUBAUAAABASVAAAAAAlAQFAAAAQMnFDOlY6uuTZ55p3jZ06PKLHAIAACAooIOZPTvZaafmbdOmJX37tk49AAAAbYxTDwAAAICSoAAAAAAoCQoAAACAkqAAAAAAKAkKAAAAgJKgAAAAACgJCgAAAICSoAAAAAAoCQoAAACAkqAAAAAAKAkKAAAAgJKgAAAAACgJCgAAAICSoAAAAAAoVbd2AbBR9eyZjB+/YhsAAABJBAV0NJ06JXvv3dpVAAAAtFlOPQAAAABKggIAAACgJCgAAAAASoICAAAAoORihnQsjY3JzJnN2/r0SSplZgAAAImggI5m5sxkyy2bt02blvTt2zr1AAAAtDG+RgUAAABKggIAAACgJCgAAAAASoICAAAAoCQoAAAAAEqCAgAAAKAkKAAAAABKggIAAACgJCgAAAAASoICAAAAoCQoAAAAAEqCAgAAAKAkKAAAAABKggIAAACgVN3aBcBG1aNH8tvfrtgGAABAEkEBHU3nzsnhh7d2FQAAAG2WUw8AAACAkqAAAAAAKAkKAAAAgJKgAAAAACgJCgAAAICSoICOZfr0pKKi+WP69NauCgAAoM0QFAAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFASFAAAAAAlQQEAAABQqm7tAmCjqq1NLr10xTYAAACSCAroaLp2TUaNau0qAAAA2iynHgAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACV3PaBjmTEj2WGH5m1PPZVssUXr1AMAANDGCAroWIpieVjw5jYAAACSOPUAAAAAeANBAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUBIUAAAAAKXq1i4ANqpu3ZIxY1ZsAwAAIImggI6me/dk7NjWrgIAAKDNcuoBAAAAUBIUAAAAACVBAQAAAFASFAAAAAAlQQEAAABQctcDOpZZs5I992zeds89Se/erVMPAABAGyMooGNpaEiefHLFNgAAAJI49QAAAAB4A0EBAAAAUBIUAAAAAKV2ExQsWLAgn/jEJ1JRUZH+/fvn3nvvXWX/++67L4cddlgGDhyYzp07Z8CAARkxYsRqxwEAAEBH1i6Cgvnz52f//ffP//zP/2THHXfMQw89lD322GOl/a+88srstddeufHGGzNlypRUVVVl6tSpuemmmzJ8+PCMGzduI1YPAAAA7UebDwoaGxtz+OGH569//Wve+9735p577sngwYNX2v+ZZ57JqFGj0tjYmNNOOy0zZszIokWLMnPmzIwZMyaNjY054YQT8uSbr3wPAAAAtP2g4Hvf+17++Mc/Ztttt82dd96ZzTfffJX9L7zwwtTV1WXkyJE588wz06dPnyRJ7969M3bs2Bx33HGpr6/PD3/4w41RPgAAALQrbTooeOaZZ/Ld7343VVVVufHGG8sP/aty6623JklOPPHEFpePHj06SXLbbbelKIr1VywAAABsAtp0UHD66adn2bJlGTlyZHbeeefV9n/++eczZcqUbL755hk2bFiLfbbffvv0798/M2bMcPoBAAAAvEmbDQpeeOGF3HDDDenZs2fOPPPMNRrz7LPPJskqr2HwxuXPPffcuhUJAAAAm5g2GxT8/Oc/T2NjY77whS9kwYIFOfLII7PNNtuktrY2w4YNy5gxYzJ//vxmYyZNmpQk6dq16yrX3a1bt2b9AQAAgOWqW7uAlbnpppuSJLvttlt23333TJkypVz21FNP5cwzz8zNN9+cu+++u7zA4YIFC9ZqG2vT/5VXXlnl8jfWBwAAAO1Vm5xRMH/+/EycODGdO3fO9ddfn6OPPjovvvhili1blpdffjmnnXZaKisr889//jPf+MY3ynGLFy9eq+0sWrRojfsOHjx4lY9dd911rbYNAAAAbVGbnFHwz3/+M42NjVm6dGmGDh2as88+u1y29dZbl9csOOuss/KLX/wiF1xwQTbbbLPWKpf2pEuX5PjjV2wDAAAgSRsNCmbMmFH+fPybP9S9of2ss87KkiVL8sgjj2T48OGrvTbBmzVdq2BNTJ48eZXLp0yZYlZBe7DZZslll7V2FQAAAG1WmwwK5s2blySpra3NNtts02Kf/v37p0+fPpk5c2Z5/YDa2tq12s7a9B80aNBarRsAAADaozZ5jYKmmQHdu3dfZb+mD/pLlixJsvy0hDf+vjJN1yZo6g8AAAAs1yaDgn79+iVZ/cUJFy5cmCTp2bNnkmS77bZLsvrTBJqWN/UHAAAAlmuTQcG73vWuVFZWZt68eXn11Vdb7DNjxozyWgbDhg1LsvyD/4ABAzJr1qw8/fTTLY57/vnnM3Xq1PTp06ccBwAAACzXJoOCHj16ZJ999kmSXHnllS32aWofMmRIdthhh7L9kEMOSZJcfPHFLY676KKLkiQHH3xwKioq1lfJAAAAsElok0FBknzrW99Kknzve9/L97///cyZMydJMnfu3Fx44YUZM2ZMkuTUU09t9oF/9OjRqampybhx4zJ27NjMmjUrSTJ79uycddZZueyyy1JdXZ2TTz554z4h2oY5c5K9927++PexBQAAQBsOCvbdd99897vfTX19fU455ZT07t07tbW12XzzzfP//t//S11dXY455piMHDmy2bihQ4fm0ksvTWVlZc4444z06dMn3bt3T+/evXP66acnSS655BKnHXRUdXXJ3Xc3f9TVtXZVAAAAbUabDQqS5Nvf/nbuuuuuHHrooenbt2+WLVuWPn365KMf/WhuuOGG/PSnP23x9IGRI0dm/PjxOfTQQ9O/f//U1dWlX79+OfTQQzN+/Pgce+yxrfBsAAAAoO2rbu0CVmefffYpr1ewNvbaa6/stddeG6AiAAAA2HS16RkFAAAAwMYlKAAAAABKggIAAACgJCgAAAAASoICAAAAoCQoAAAAAEqCAgAAAKAkKAAAAABKggIAAACgJCgAAAAASoICAAAAoFTd2gXARtWpU3LYYSu2AQAAkERQQEfTs2dy/fWtXQUAAECb5dQDAAAAoCQoAAAAAEqCAgAAAKAkKAAAAABKggIAAACg5K4HdCxz5yZf+lLztp/8ZPndEAAAABAU0MEsW5bccEPztssvb51aAAAA2iCnHgAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFASFAAAAACl6tYuADaqmppk+PAV2wAAAEgiKKCj6dUrmTChtasAAABos5x6AAAAAJQEBQAAAEBJUAAAAACUBAUAAABASVAAAAAAlNz1gI5l/vzk1FObt517brLZZq1TDwAAQBsjKKBjWbIkufzy5m1jxwoKAAAA/s2pBwAAAEBJUAAAAACUBAUAAABASVAAAAAAlAQFAAAAQElQAAAAAJQEBQAAAEBJUAAAAACUBAUAAABASVAAAAAAlAQFAAAAQElQAAAAAJQEBQAAAECpurULgI2qqioZNmzFNgAAAJIICuhoevdOnniitasAAABos5x6AAAAAJQEBQAAAEBJUAAAAACUBAUAAABASVAAAAAAlNz1gI5l4cLkBz9o3nbyyUn37q1TDwAAQBsjKKBjWbQoOeOM5m2jRgkKAAAA/s2pBwAAAEBJUAAAAACUBAUAAABASVAAAAAAlAQFAAAAQElQAAAAAJQEBQAAAEBJUAAAAACUBAUAAABASVAAAAAAlAQFAAAAQElQAAAAAJQEBQAAAECpurULgI2qoiLZYosV2wAAAEgiKKCj2WKLZPr01q4CAACgzXLqAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUHLXAzqWxYuTn/2sedsxxyRdu7ZOPQAAAG2MoICOZcGC5Ktfbd726U8LCgAAAP7NqQcAAABASVAAAAAAlAQFAAAAQElQAAAAAJQEBQAAAEBJUAAAAACUBAUAAABASVAAAAAAlAQFAAAAQElQAAAAAJQEBQAAAEBJUAAAAACUBAUAAABASVAAAAAAlKpbuwDYqPr2TYqitasAAABos8woAAAAAEqCAgAAAKAkKAAAAABKggIAAACg1G6CgpkzZ6ZHjx6pqKhIRUVFxo4du8r+EydOzFFHHZWtt946nTt3zpZbbpkDDjggt91228YpGAAAANqhdnPXg3PPPTfz589PdXV16uvrV9n397//fT71qU9l6dKlSZKuXbtm+vTp+cMf/pA//OEP+da3vpXvfe97G6Ns2pqlS5Nbb23edsghSefOrVMPAABAG9MuZhRMmTIll112WXbeeeccccQRq+w7e/bs/Od//meWLl2aL3/5y3nllVeyaNGizJ8/P5deemk6deqUs88+O3feeedGqp42Zd685NOfbv6YN6+1qwIAAGgz2kVQ8N3vfjeLFy/OmDFjUlFRscq+P/rRjzJnzpzsv//++fGPf5ytttoqSVJbW5tRo0blrLPOSrJ8hgIAAADQXJsPCl5++eX85Cc/yc4775xDDz10tf3/53/+J0ly4okntrj8+OOPT+fOnfOXv/wlc+bMWZ+lAgAAQLvX5oOCM844I8uWLVuj2QRLlizJww8/nCTZY489WuxTW1ub97znPWloaMh999233usFAACA9qxNBwXPPPNMrr322jWeTfDSSy+lvr4+PXr0SI8ePVbab/DgwUmS5557br3VCgAAAJuCNn3XgzFjxqShoWGNZhMkyaRJk5Isv8vBqnTr1q1Z/zXxyiuvrHL5lClT1nhdAAAA0Fa12aDg8ccfz29/+9s1nk2QJAsWLFirbaxN/6ZZCAAAALApa7OnHpx22mkpimKNZxMkyeLFi9dqG4sWLXorpQEAAMAmq03OKPjb3/6WW2+9da1mE2xokydPXuXyKVOmZNddd91I1QAAAMCG0SaDgm9/+9tJslazCZLVX5vgzZquVbAmBg0atFbrBgAAgPaozZ16cPfdd+dPf/rTW5pNUFtbu0H7AwAAwKauzQUF3/nOd5Ks/WyCJNl6662TJEuWLFllv6ZrEzT1BwAAAJZrc6ce3HvvvUmSo48+usXlTR/yzz333Fx00UVJkjlz5iRJhgwZkqqqqsydOzfz5s1Ljx49WlxH0/UGtttuu/VXOAAAAGwC2tyMgiZz585t8VFXV5ckWbp0adnWpEuXLtlll12SJPfff3+L6124cGEeffTRVFZWZvfdd9/wTwQAAADakTYXFBRFscrHUUcdlWT5qQlNbW/UdF2Diy++uMX1jxs3LkuXLs1ee+2VzTfffMM+GQAAAGhn2lxQsK5GjhyZnj175g9/+EOOPfbYvPbaa0mWzyS44oor8q1vfStJ8s1vfrM1y6S19OmTTJvW/NGnT2tXBQAA0Ga0uWsUrKvevXvnl7/8ZT71qU/lRz/6UX70ox+lW7duWbx4cTn74NRTT83+++/fypXSKiork759W7sKAACANmuTm1GQJB//+Mfz0EMP5YgjjshWW22V+vr69O7dOx/5yEdy880355xzzmntEgEAAKBNanczCq6++upcffXVq+33rne9K7/85S83fEEAAACwCdkkZxQAAAAAb42gAAAAACi1u1MPYJ0sW5bcf3/ztt13Tzp1ap16AAAA2hhBAR3L3LnJPvs0b5s2zZ0QAAAA/s2pBwAAAEBJUAAAAACUBAUAAABASVAAAAAAlAQFAAAAQElQAAAAAJQEBQAAAEBJUAAAAACUBAUAAABASVAAAAAAlAQFAAAAQElQAAAAAJQEBQAAAEBJUAAAAACUqlu7ANioNt88mThxxTYAAACSCAroaKqrkx13bO0qAAAA2iynHgAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUHIxQzqW+vrkmWeatw0duvwihwAAAAgK6GBmz0522ql527RpSd++rVMPAABAG+PUAwAAAKAkKAAAAABKggIAAACgJCgAAAAASoICAAAAoCQoAAAAAEpuj8imaezYltsXLlyx7fvfT7p33zDbAwAAaGfMKAAAAABKggIAAACgJCgAAAAASoICAAAAoCQoAAAAAEqCAgAAAKAkKAAAAABK1a1dAGxUXbokRx21YhsAAABJBAV0NFVVyZAhrV0FAABAm+XUAwAAAKAkKAAAAABKTj2gzRs7du3H7D1hfVexahPGvrU6AQAA2hozCgAAAICSGQV0LEWRmrpFzZrqarolFRWtVBAAAEDbIiigQ6mpW5QP/fWHzdru++BJqevUvZUqAgAAaFucegAAAACUBAUAAABASVAAAAAAlAQFAAAAQElQAAAAAJQEBQAAAEBJUAAAAACUBAUAAABASVAAAAAAlAQFAAAAQElQAAAAAJQEBQAAAEBJUAAAAACUBAUAAABAqbq1C4CNqb66c54YdtgKbQAAACwnKGCj2HvC2NYuIUlSVFZnet8dW7sMAACANsupBwAAAEBJUAAAAACUBAUAAABASVAAAAAAlAQFAAAAQMldD+hQapYtzIf++sNmbfd98KTUdereShUBAAC0LWYUAAAAACVBAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUHJ7RFgP9p4wNhn7718WdkvyjeYdvv/9pPui9bOxsWNX2wUAAOCtMqMAAAAAKAkKAAAAgJKgAAAAACgJCgAAAICSoAAAAAAouesBrCcTJiz/75xlKy67776kV6f1tJ2x674ON04AAABWxowCAAAAoGRGAR1KQ1Wn/Gu7A1ZoAwAAYDlBAR1KY1VNXttq19YuAwAAoM1y6gEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFBy1wM6lJq6RdnlocuatT20y6jU1XRrpYoAAADaFkEBHUtRpFPdohXaAAAAWM6pBwAAAEBJUAAAAACUBAUAAABASVAAAAAAlAQFAAAAQElQAAAAAJQEBQAAAEBJUAAAAACUBAUAAABASVAAAAAAlAQFAAAAQKnNBwUPP/xwRowYkS233DI1NTXp27dvDjzwwNxxxx2rHHffffflsMMOy8CBA9O5c+cMGDAgI0aMyL333ruRKgcAAID2p00HBaeffnp22WWX3HTTTZk+fXoqKyszY8aM3HHHHTnwwAPzwx/+sMVxV155Zfbaa6/ceOONmTJlSqqqqjJ16tTcdNNNGT58eMaNG7eRnwkAAAC0D202KLjqqqty1llnpVOnTjnvvPMyY8aMLF26NC+88EIOPfTQJMk3v/nNPP/8883GPfPMMxk1alQaGxtz2mmnZcaMGVm0aFFmzpyZMWPGpLGxMSeccEKefPLJ1nhatLKGqpq8tM3wZo+GqprWLgsAAKDNqG7tAlpSX1+fsWPHJkkuvPDCHH/88eWyt73tbbnuuuuy7bbbZurUqbnuuuvyne98p1x+4YUXpq6uLiNHjsyZZ55Ztvfu3Ttjx47NtGnTcsUVV+SHP/xhfvazn22050Tb0FjVKS8N2bu1y2h1//7n1ea1lzoBAGBT0iZnFNTV1WXUqFHZfffdM3LkyBWWd+3aNXvvvXeS5B//+EezZbfeemuS5MQTT2xx3aNHj06S3HbbbSmKYj1WDQAAAO1fmwwKunbtmm984xu57777Ul3d8qSHQYMGJUnmzp1btj3//POZMmVKNt988wwbNqzFcdtvv3369++fGTNmOP0AAAAA3qRNBgVrYtmyZUmSXr16lW3PPvtskmTw4MGrHNu0/LnnntswxQEAAEA71SavUbAmnnrqqSTLr1nQZNKkSUmWz0hYlW7dujXrvyZeeeWVVS6fMmXKGq8LAAAA2qp2GRS89tprGT9+fJLkoIMOKtsXLFiwVutZm/6rm6UAAAAAm4J2GRScdNJJqa+vz5577pk999yzbF+8ePFarWfRokXruzTauOq6xXnPY1c1a3t05y+mvmbVs1AAAAA6inYXFPz0pz/Nddddl+7du+fKK6/caNudPHnyKpdPmTIlu+6660aqhreqomhM90XTV2gDAABguXYVFNx555057rjjUlFRkWuuuSZDhw5ttnx11yZ4s6ZrFayJprssAAAAwKas3QQF99xzTz75yU+mrq4uF1xwQUaMGLFCn9ra2rVa59r2BwAAgE1du7g94sMPP5yDDjooixYtyje/+c2MHj26xX5bb711kmTJkiWrXF/TtQma+gMAAADLtfmgYOLEifnYxz6WefPm5dhjj83ZZ5+90r7bbbddktVfT6BpeVN/AAAAYLk2HRQ899xz+chHPpKZM2fms5/9bC677LJV9t9uu+0yYMCAzJo1K08//XSLfZ5//vlMnTo1ffr0ybBhwzZE2QAAANButdmgYNKkSdlvv/0yderUHHDAAbn22mtTWbn6cg855JAkycUXX9zi8osuuihJcvDBB6eiomK91QsAAACbgjYZFCxYsCAf/vCHM2nSpOyxxx658cYbU1NTs0ZjR48enZqamowbNy5jx47NrFmzkiSzZ8/OWWedlcsuuyzV1dU5+eSTN+RTAAAAgHapTQYFM2bMyLPPPpskefTRRzNgwID06tVrpY9f/epX5dihQ4fm0ksvTWVlZc4444z06dMn3bt3T+/evXP66acnSS655BKnHQAAAEAL2vztERcuXLjaPsuWLWv2+8iRI/OOd7wjF1xwQR588MHMnDkz/fr1y2677ZbRo0dn+PDhG6pcAAAAaNfaZFAwZMiQFEWxTuvYa6+9stdee62nigAAAKBjaJOnHgAAAACto03OKADajr0njN2o25uw98bdHgAA0JwZBQAAAEDJjAI6lMbK6rw68P0rtAEAALCcT0h0KA3VnfPs9h9v7TIAAADaLKceAAAAACVBAQAAAFASFAAAAAAlQQEAAABQEhQAAAAAJXc9oEOprl+SnSb+ulnbxJ0+m/rqLq1UEQAAQNsiKKBDqWhsSK+5L6/QBgAAwHJOPQAAAABKggIAAACgJCgAAAAASoICAAAAoCQoAAAAAEqCAgAAAKAkKAAAAABKggIAAACgJCgAAAAASoICAAAAoCQoAAAAAEqCAgAAAKAkKAAAAABK1a1dAGxMjZVVmbbFsBXaAAAAWE5QQIfSUN0lT+54eGuXAQAA0GY59QAAAAAoCQoAAACAkqAAAAAAKAkKAAAAgJKgAAAAACi56wEdSlX9kgx95rZmbc8MPTgN1V1aqaK1t/eEsa1dAgAAsAkTFNChVDY2ZMsZTzZre3b7A9PQSvUAAAC0NU49AAAAAEqCAgAAAKAkKAAAAABKggIAAACg5GKGQJs1dmxrV7Bm2kudAACwJswoAAAAAEqCAgAAAKAkKAAAAABKggIAAACgJCgAAAAASoICAAAAoCQoAAAAAEqCAgAAAKBU3doFwMZUVFZlTs9tVmgDAABgOUEBHUp9dZc8tvPRrV0GAABAm+XUAwAAAKAkKAAAAABKggIAAACgJCgAAAAASoICAAAAoOSuB3QoVfVLs+2Lf2rW9sLbPpyG6s6tVBEAAEDbIiigQ6lsrM9Wrz3crO2lbfZOQwQFAAAAiVMPAAAAgDcQFAAAAAAlQQEAAABQEhQAAAAAJUEBAAAAUBIUAAAAACVBAQAAAFASFAAAAACl6tYuAOCN9p4wdqNta8LeG29bAADQXphRAAAAAJQEBQAAAEBJUAAAAACUBAUAAABAycUM6VCKisos7NZ3hTYAAACWExTQodTXdM1Duxzf2mXQUY0du2lvDwCATYKvUgEAAICSoAAAAAAoCQoAAACAkqAAAAAAKAkKAAAAgJK7HtChVDYsy9aT72/WNmnw7mms6tRKFbEpWNObC+w9YUNWsaIJY5v/7iYIAACsCUEBHUpVQ12GvHx3s7ZXB+4iKAAAAPg3px4AAAAAJUEBAAAAUBIUAAAAACVBAQAAAFByMUOgw9r7zbcFAAAAzCgAAAAA/o+gAAAAACgJCgAAAICSoAAAAAAoCQoAAACAkqAAAAAAKLk9IkAHMXZsa1ew5tpTrQAAmxozCgAAAICSGQV0LBUVWVbTbYU2AAAAlhMU0KHU1XTL/buf3NplAAAAtFlOPQAAAABKggIAAACg5NQDANbZ3hPGrt8Vrm51beS2CG2kjNVqL3UCAG2DGQUAAABASVAAAAAAlJx6QIdS2VCX/lMfbdY2tf970lhV00oVAQAAtC2CAjqUqoZl+Y/n7mjWNr3vjoICAACAf3PqAQAAAFAyowCANmfChNUsH7sxqgAA6JjMKAAAAABKggIAAACgtEkHBffdd18OO+ywDBw4MJ07d86AAQMyYsSI3Hvvva1dGgAAALRJm2xQcOWVV2avvfbKjTfemClTpqSqqipTp07NTTfdlOHDh2fcuHGtXSIAAAC0OZtkUPDMM89k1KhRaWxszGmnnZYZM2Zk0aJFmTlzZsaMGZPGxsaccMIJefLJJ1u7VAAAAGhTNsm7Hlx44YWpq6vLyJEjc+aZZ5btvXv3ztixYzNt2rRcccUV+eEPf5if/exnrVgpwIazt1sDwAYzdmxrV7Dxbcj/p+y995saOuILDB1Me/ln3l7qXN82yRkFt956a5LkxBNPbHH56NGjkyS33XZbiqLYaHUBAABAW7fJBQXPP/98pkyZks033zzDhg1rsc/222+f/v37Z8aMGU4/AAAAgDfY5IKCZ599NkkyePDgVfZrWv7cc89t8JoAAACgvdjkgoJJkyYlSbp27brKft26dWvWHwAAANgEL2a4YMGCDdL/lVdeWeXyyZMnlz9PmTJlrWpoLdOXzmvtEja66mWL8uY9OW3p/NQXDettG3OXLUvetJUZS+ekrliy3rYBHd28eav+fzLNreYtjLdgXsd7C92gfze88uZVO2hhk9de/j/a1v939MbPnvX19ettvZtcULB48eK16r9o0aI16re6UxneaNddd12rGmhlj/xoA6z0nGa/ffmRDbAJ6MgeuLC1K2hXLvRysR5s0MPogTdvzEELtA3t6X9H06dPz5AhQ9bLuja5Uw8AAACAt26Tm1GwumsTvFnTtQpW542nFrRkyZIlefrpp9OvX7/07ds31dVt96WdMmVKOevhb3/7WwYMGNDKFdGWOV5YG44X1objhbXheGFtOWZYG+31eKmvr8/06dOTJO985zvX23rb7qfZt6i2tnaD9B80aNBq+2y33XZrte22YMCAAWv03CBxvLB2HC+sDccLa8PxwtpyzLA22tvxsr5ON3ijTe7Ug6233jrJ8m/4V6Xp2gRN/QEAAIBNMCho+lZ/dacKNC1vj7MAAAAAYEPZJIOCAQMGZNasWXn66adb7PP8889n6tSp6dOnT4YNG7aRKwQAAIC2a5MLCpLkkEMOSZJcfPHFLS6/6KKLkiQHH3xwKioqNlZZAAAA0OZtkkHB6NGjU1NTk3HjxmXs2LGZNWtWkmT27Nk566yzctlll6W6ujonn3xyK1cKAAAAbcsmGRQMHTo0l156aSorK3PGGWekT58+6d69e3r37p3TTz89SXLJJZc47QAAAADeZJMMCpJk5MiRGT9+fA499ND0798/dXV16devXw499NCMHz8+xx57bGuXCAAAAG1ORVEURWsXAQAAALQNm+yMAgAAAGDtCQoAAACAkqAAAAAAKAkKAAAAgJKgAAAAACgJCgAAAICSoAAAAAAoCQoAAACAkqAAAAAAKAkKOpj77rsvhx12WAYOHJjOnTtnwIABGTFiRO69997WLo3VOOWUU1JRUVE+xo0bt9ox67K/2+NYlnv44YczYsSIbLnllqmpqUnfvn1z4IEH5o477ljluPa4zx0v62b+/Pk5//zz8+53vztdu3ZNly5dsv322+fYY4/Ns88+u8qxt99+ew444ID069cvnTt3zuDBg3PkkUdm4sSJq91uexzLimbOnJkePXqU70tjx45dZf+JEyfmqKOOytZbb53OnTtnyy23zAEHHJDbbrtttdtqj2M7ugkTJjT7u2Vlj3PPPbfF8e3xfcV70vqzYMGCfOITn0hFRUX69++/2tewPe7zNn+8FHQYP/7xj4vKysoiSZGk6Nq1a/lzZWVlccUVV7R2ibSgsbGxOP7444skxeabb17ut9Xtr3XZ3+1xLMuddtpp5WuWpOjUqVOz33/wgx+0OK497nPHy7p5/vnni//4j/8oX7Oamppmr2e3bt2Ke+65p8Wx3/nOd5odV2987Tt37lzceuutK91uexxLy0466aQiSVFdXV0kKcaMGbPSvr/73e+Kzp07t/j6Jym+9a1vbVJjKYrx48eX/2/p2bPnSh8XXnjhCmPb4/uK96T1Z968ecUHP/jBIkmx4447FpMmTVpl//a4z9vD8SIo6CCefvrpoqampkhSnHbaacWMGTOKoiiKmTNnFmPGjCnf6J944olWrpQ3qq+vL44++ugiSbHlllsWjz32WLHNNtusNihYl/3dHsey3M9+9rMyHDjvvPPK1/CFF14oDj300PI1fO6555qNa4/73PGybhYtWlTssMMORZJin332Kf7+978XDQ0NxbJly4q77rqr2G677YokxX/8x3+sMPbPf/5z+cf/xRdfXMybN68oiqJ49dVXi2OPPbZIUvTo0aOYPn36JjGWlr322mtF165di5133rk48sgjVxkUzJo1q+jVq1eRpPjyl79cvPLKK0VRFMX8+fOLSy+9tAw0//jHP24SY1muKSg46qij1mpce3xf8Z60/jQ0NBQf/ehHiyTFe9/73mLWrFmr7N8e93l7OV4EBR3EV77ylSJJMXLkyBaXH3fccUWS4otf/OJGroyVWbZsWfHpT3+6SFIMGjSoePrpp4uiKNYoKFiX/d0ex1IUdXV1xdZbb10kKS677LIVli9atKjo379/kaQ466yzmi1rj/vc8bJuli1bVpxyyinFrrvuWixdunSF5U1/4CcpnnzyyWbLmv6AO/vss1tc94EHHlgkKc4444wVlrXHsbSsaabbzTffXBx11FGrDArOOeecIkmx//77t7j8vPPOK0OrTWEsy73VoKA9vq94T1p/zjzzzCJJse2225YfoFelPe7z9nK8CAo6iAEDBhRJVppM/etf/yqSFFtssUXR2Ni4kaujJY8//njRtWvXYtttty1efPHFsn1NgoJ12d/tcSzLg4Dzzjuv2H333Yu6uroW+3z2s58tkhSHHXZYs/b2uM8dL+tHSyFBURTFggULyqDgz3/+c9k+f/78oqampqiuri7mzJnT4tg777yz/CbojdrjWFr20ksvFZ06dSp23nnnorGxcbVBwW677VYkKX73u9+1uHz+/PlF586di6qqqmL27NntfizLvdWgoD2+r3hPWj+efvrpolOnTkVVVVXx6KOPrtGY9rjP28vxIijoAJ577rkiWX5++6o0fds4ceLEjVQZq/PHP/6xePXVV5u1rS4oWJf93R7Hsuaazif+yEc+Ura1x33ueNnwZs2aVQYF//jHP8r2P/3pT0WS4j3vec9Kxy5atKioqKgoKisryyn+7XUsLfviF79YziYoimKVQcHixYvLaxjMnTt3pets+mB+++23t+ux/J+3EhS0x/cV70nrT9NM2uOOO26N+rfHfd6ejhd3PegAmq5cPXjw4FX2a1r+3HPPbfCaWDP7779/Bg4cuFZj1mV/t8exrLlly5YlSXr16lW2tcd97njZ8H7/+98nSYYNG5addtqpbF+T175r167ZYost0tjYmBdeeKFdj2VFzzzzTK699trsvPPOOfTQQ1fb/6WXXkp9fX169OiRHj16rLRfS/9e2+NY1k17fF/xnrR+vPDCC7nhhhvSs2fPnHnmmWs0pj3u8/Z0vAgKOoBJkyYlWf6H0Kp069atWX/ap3XZ3+1xLGvuqaeeSpK87W1vK9va4z53vGw4c+bMyVVXXZUTTjghtbW1+dGPfpTKyv/7U6E97nPHy/o1ZsyYNDQ0ZMyYMamoqFht//a43x0z69+sWbNywgkn5B3veEcGDBiQPffcM1deeWUaGhqa9WuP+9zxsn78/Oc/T2NjY77whS9kwYIFOfLII7PNNtuktrY2w4YNy5gxYzJ//vxmY9rjPm9Px0t1q22ZjWbBggUbtD9ty7rs7/Y4ljXz2muvZfz48UmSgw46qGxvj/vc8bJ+/epXv8rxxx+fhoaGLFiwIJ06dcp//ud/5uSTT84OO+zQrG973OeOl/Xn8ccfz29/+9s1nk2QtM/97phZvxoaGvLRj340Dz/8cNk2derU3Hvvvfn973+fG2+8sQwk2+M+d7ysHzfddFOSZLfddsvuu++eKVOmlMueeuqpnHnmmbn55ptz9913Z/PNN0/SPvd5ezpezCjoABYvXrxW/RctWrSBKmFjWJf93R7HsmZOOumk1NfXZ88998yee+5ZtrfHfe54Wb+WLVuWuXPnln+MNDY25qWXXipnoLxRe9znjpf157TTTktRFGs8myBpn/vdMbN+3Xbbbendu3cef/zxLFmyJJMnT87pp5+eioqK3HLLLbniiivKvu1xnzte1t38+fMzceLEdO7cOddff32OPvrovPjii1m2bFlefvnlnHbaaamsrMw///nPfOMb3yjHtcd93p6OF0EBwCbupz/9aa677rp07949V155ZWuXQxtz9NFHpyiKMiC4/PLL8/jjj2fEiBE599xzW7s82oi//e1vufXWW9dqNgEd25AhQ/KVr3wln/zkJ3PLLbfkne98Zzp37pxBgwbljDPOyFFHHZUkufjii1u5UlrbP//5zzQ2Nmbp0qUZOnRozj777AwZMiQ1NTXZeuutc+aZZ+bb3/52kuQXv/jFCqcgsGEICjqA1Z0D82ZN58TQPq3L/m6PY1m1O++8M8cdd1wqKipyzTXXZOjQoc2Wt8d97njZMCoqKrLNNtvky1/+cn7zm98kSb7zne/k5ZdfLvu0x33ueFk/mv5IX5vZBEn73O+OmfVjyJAhGTduXK666qoWX9OvfOUrSZZf3O3VV19N0j73ueNl3c2YMaP8+fjjj2+xT1P7kiVL8sgjjyRpn/u8PR0vgoIOoLa2doP2p21Zl/3dHseycvfcc08++clPpq6uLueff35GjBixQp/2uM8dLxvefvvtl3e84x1paGjIzTffXLa3x33ueFl3d999d/70pz+9pdkE7XG/O2Y2ju222678uSmQbI/73PGy7ubNm5dk+WuzzTbbtNinf//+6dOnT5LklVdeKfuvjbawz9vT8SIo6AC23nrrJMsTuFVpOgemqT/t07rs7/Y4lpY9/PDDOeigg7Jo0aJ885vfzOjRo1vs1x73ueNl42j6I77pVk5J+9znjpd1953vfCfJ2s8mSNrnfnfMbBxbbLFF+XPTa90e97njZd01fcvevXv3VfZr+tDseNk43PWgA2j6Y2/y5Mmr7Ne0/I0JL+3Puuzv9jiWFU2cODEf+9jHMm/evBx77LE5++yzV9q3Pe5zx8vG0XQV8sbGxrJtTV77xYsXZ8aMGamoqMi2227brsey3L333ptk+fUsWtL0B+25556biy66KMnyW20my6efV1VVZe7cuZk3b1569OjR4jpa+vfaHsey5mbOnFn+3LNnzyTt833Fe9K669evX5LVX+hv4cKFSRwvG01BhzBgwIAiSfHUU0+1uPy5554rkhR9+vQpGhsbN3J1rI1tttmmSFJcccUVK+2zLvu7PY7l/zz77LNF//79iyTFZz/72aKhoWG1Y9rjPne8rLvrrrtula/NjjvuWCQpzjzzzLJt/vz5RU1NTVFTU1PMnTu3xXF/+tOfiiTFe97znmbt7XEsyyVZ68cb7bbbbkWS4o477mhx/QsWLCg6d+5cVFZWFrNmzWr3Y1luyZIlxVVXXbXS5Q8++GCRpKisrCzmzZtXtrfH9xXvSetm7ty5RWVlZZGkeOWVV1rsM3369PL/L0888UTZ3h73eXs5Xpx60EEccsghSVZ+ZdmmbwAOPvjgtZ5WSNuzLvu7PY5luUmTJmW//fbL1KlTc8ABB+Taa68tvxVelfa4zx0v6+aLX/xiPve5z+XCCy9scfn999+fJ598Mkmy7777lu21tbXZd999U1dX1+yWZm/U9Nq/+Vz29jiW5YqiWOWj6er1Y8aMKdveqOl1Xdm/13HjxmXp0qXZa6+9yvujt+exJA0NDdlnn33yxS9+Mddff32LfX70ox8lST70oQ9ls802K9vb4/uK96R106NHj+yzzz5JstK7MzW1DxkyJDvssEPZ3h73ebs5XloroWDjevrpp4uampoiSTFmzJhi5syZRVEUxaxZs4ozzzyzqKioKKqrq5sldLRNazKjYF32d3scy/JvTbfffvsiSbHHHnsUixYtWuOx7XGfO17Wzbhx44okRUVFRfHNb36zePXVV4uiWP6tzvXXX19stdVWRZJizz33XGHsn//85yJJ0alTp+KSSy4p5s+fXxRFUbz22mvF8ccfXyQpevToUUyfPn2TGMvqHXXUUeW/xZbMnDmz6NmzZ5Gk+MpXvlIebwsWLCguv/zyolOnTkWS4o9//OMmMZblvvGNbxRJipqamuKcc84pZsyYURTF8v9Pf+973yu/QX7zrI32+L7iPWndNf1/urq6ujjvvPOK2bNnF0VRFHPmzCkuuOCC8vUdN25cs3HtcZ+3l+NFUNCB/OhHPyr/p5yk6NatW/lzRUXFKj940jr23XffoqqqqtmjaZ9VVlY2a993332bjV2X/d0ex3Z0L774Yvlade/evejZs+cqH7/85S+bjW+P+9zxsm7OP//8Zq9f586dm00dHzp0aDF58uQWx377299u9lp37dq1/L1Tp07FrbfeutLttsexrNrqgoKiKIrbb7+9/HDd9O+1oqKi/P3UU0/dpMZSFPX19cWoUaOa/X/ljf/ukuanNr1Re3xf8Z607r773e82e826d+/e7N/cMccc0+JU/Pa4z9vD8SIo6GDuvvvu4tBDDy369+9f1NTUFP369SsOPfTQYsKECa1dGi0YPnx4szfUVT2GDx++wvh12d/tcWxH9sagYE0eLZ032h73ueNl3UycOLH44he/WAwaNKioqakpNttss2KXXXYpzjnnnPJb95W5+eabi/3337/YYostik6dOhVbbbVVccQRRxT/+Mc/Vrvd9jiWlVuToKAoiuIf//hHccQRRxRbbbVV0alTp6JPnz7FRz7ykeLmm29e7Tba41iWGz9+fPGJT3yi2HrrrYtOnToV/fr1Kz7xiU8U48ePX+W49vi+4j1p3d11113FoYceWmy55ZZFTU1NscUWWxQf/ehHixtuuGGV49rjPm/rx0tFUbzpRDIAAACgw3IxQwAAAKAkKAAAAABKggIAAACgJCgAAAAASoICAAAAoCQoAAAAAEqCAgAAAKAkKAAAAABKggIAAACgJCgAAAAASoICAAAAoCQoAAAAAEqCAgAAAKAkKAAAAABKggIAAACgJCgAAAAASoICAAAAoCQoAADahLPPPjvdunXLcccd19qlZO+9905FRUWuvvrq9bK+q6++OhUVFdl7771XWDZ27NhUVFTk6KOPXi/bAoB1JSgAANqEu+66K4sXL87//u//tnYpANChVbd2AQCwqbrlllvy2GOP5eijj86QIUNau5w278gjj8zMmTPz2c9+trVLAYAOTVAAABvILbfckmuuuSZ77723oGANHHnkkTnyyCNbuwwA6PCcegAAAACUBAUAsAEsXrw4Tz/9dGuXAQCw1gQFAHQYCxYsyNlnn533vve9qa2tTW1tbd773vfmnHPOycKFC1scU1FRkYqKirz00ktrvLyioiLdunXLgw8+mCTZZ599yn5vNn/+/Jxxxhl55zvfmW7dumWLLbbIbrvtlp/+9Kepr69vcZsvv/xyRo0ale222y5dunRJ7969s99+++VXv/pViqJYof9LL73UbPsPPPBA9txzz2y22WbZaqut8rWvfS3z588v+19++eXZaaed0q1bt2y11VY5+eSTs3jx4hZraVr/8ccfn7e//e3p0qVL+vTpkwMOOCC///3vVzqmJSu7+v+b67/33nszfPjw9OzZM7169cqIESPywgsvrNW2kmTy5Mk55phjMnDgwHTp0iXbbrvtap9rk/vvvz+HH354BgwYkE6dOmXAgAH59Kc/nb/+9a9rXcfqTJo0KV/96lfz9re/PZ07d06fPn2y//7757bbbmux/4QJE1JRUVGe7vKrX/0q7373u9OlS5dcdNFFZb9p06Zl1KhR2XHHHdO9e/dsscUW2WeffXLjjTeu9+cAQDtTAEAHMHXq1GLHHXcskhRJik6dOhWdOnUqf99pp52KqVOnrjCuafmLL77Y4npbWt6zZ8+iZ8+eRUVFRZGk6N69e9n2Rq+++moxdOjQch2dO3cuqqqqyt8/8YlPFHV1dc3G3HfffUXPnj3LPl27di0qKyvL3z/zmc8UDQ0Nzca8+OKL5fLJkycXvXr1Kqqqqsr6khT7779/URRFcfbZZxdJiurq6nJZkuKggw5q8fn/5S9/KTbbbLOyX01NTbNxZ5111mr2zP8ZM2ZMkaQ46qijVlr/E088UXTr1q2orKxs9rwHDBhQTJ8+fY239dRTTxV9+vRpVnfTa3/YYYcVw4cPL5IUV1111QpjL7nkkmavXbdu3cqfKyoqiksuuWSFMVdddVWRpBg+fPgaP++iKIr777+/6N27d7n+Ll26NHt9zzvvvBXGjB8/vkhSbLPNNsVvf/vbIklRWVlZ9OzZs7j88suLoiiK2bNnF29729ua/Xt443P66le/usavJQCbHjMKAOgQ/uu//itPPPFEdt111zzwwANZsmRJlixZkgceeCDvf//7M3HixHz5y19eL9uaM2dO5syZk6233jpJcvvtt5dtb67pmWeeyS677JK///3vWbJkSRYvXpw777wz22yzTW655Zb84Ac/KPvPnTs3hx9+eObOnZsjjzwyL774YhYtWpTFixfnxhtvTN++ffOb3/ym2bfGb/ab3/wmP/7xj7Nw4cLMmzcv//3f/52qqqrceeedufHGG/PLX/4yd955ZxYvXpxZs2blW9/6Vvkc/vznPzdb19y5c/OZz3wm8+fPz+c///k8++yzWbJkSWbNmpVLLrkkXbp0yZgxY/K3v/1tvbyuSXLeeeflxz/+cRYsWJD58+fn2muvTdeuXTNlypRccMEFa7SOoijyhS98ITNnzsx73vOe3H///Vm6dGkWLFiQ3//+9/nHP/6Ru+++u8WxDz74YE488cRUVVXl+9//fmbPnp2FCxdm9uzZOffcc1NVVZWvf/3r6+U5NzQ05D//8z8zf/78/OAHP8icOXOyePHiTJ8+PSeeeGKS5LTTTsuUKVNaHD9nzpxcc8015W0n58yZk+OOOy5Jctlll+XFF1/MdtttVx57S5YsyQ033JABAwbk6quvfkuzNADYRLR2UgEAG9rf//73IknRt2/fYvbs2SssnzlzZvnt8mOPPdZsWd7CjIIm22yzTZGkGD9+/ArLJk6cWH4b3dJMhn/84x9Ft27ditNOO61s++EPf1gkKT784Q+3WMuECROKJMWWW25ZLFu2rGx/4zfyP//5z1cY95nPfKZcfs8996yw/AMf+ECRpBg9enSz9qZ6PvKRj7RYz+mnn17OclgTazKj4Oqrr15h3EknnVQkKXbeeec12s5dd91VzsZ49dVXV1j+wgsvlN/cv3lGwUEHHVQkKb773e+2uO6xY8eWs0He6K3OKLjzzjuLa6+9doX2xsbGYocddiiSFNdcc02zZU0zCpIUEydObLHOgw8+eKWv53PPPVf87W9/a3EcAB2DGQUAbPJuuOGGJMlhhx2WXr16rbC8d+/eOeyww5r13dD+8Ic/JEn222+/9OvXb4Xl73rXu/Laa6/lzDPPLNuazh1f2cyH4cOHZ+jQoZk2bVr+8pe/tNjnYx/72AptBx54YJKktrY2e+yxx0qXP/fcc83am+oZOXJki9s64ogjkiS///3vW7x2wlvx8Y9/fIW2gw46KEny/PPPr9E6br/99iTJ4YcfnoEDB66w/G1ve1ve8573rNC+aNGi3HHHHUmSL33pSy2u+ytf+UqS5He/+12WLFmyRvWsykc+8pF84QtfWKG9oqIi73rXu5Ikr7zySotj+/Tpkx133LHFZZWVlWWfN3v729+eXXbZ5a2WDMAmQFAAwCbv4YcfTpIMHTp0pX3e8Y53JEkeeuihjVLTxIkTkyz/ULYyPXv2LH9ubGzMo48+mmTdnscWW2yxQlvTh+WWPjS+cfmCBQtarGdlz2GbbbZJsvyCjWv6IX51Wqp/q622WqG+VXn88ceTrPp17NSp0wptjz32WBoaGtKrV68Ww50k6d+/f3r27Jm6uro89thja1TPW9WlS5ckybJly1pcXltbu9KxO+20U5LkuuuuS0NDw/ovDoB2rbq1CwCADe31119PknTu3HmlfZo+dE2dOnWj1DRjxozV1vRGc+fOLb+hXt/Po7p61X8ONC1vbGxssZ7hw4eX31CvzLRp07LddtutcU1ro6m+NZ210PTa9O7de622sybHUbJ8H8ydO3e9HUt/+ctfcuWVV+bhhx/OzJkzy2Bg0aJFb3mdX//613PllVfmV7/6VR588MF87GMfy/ve977svvvuqwxQAOgYBAUAbPJWduvDlqzpt9Lram0/5K3Nc0g2/PN4Yz1vvLXiymys13VNvNUP2K2xD84777yceuqp67yeN9tiiy3y0EMPZezYsbn++utz2WWXlcve+9735oorrsiuu+663rcLQPvg1AMANnndu3df476rmq69PnXr1m2t+q/Nc0g2/PN4Yz1PPfVUiqJY5WP//fffoPWsjbV97Zts7H3wxBNPlHed+PrXv55//etfWbJkSfmaHnXUUeu0/q233jo/+9nPMnv27Pz973/PlVdemY985CN55JFH8rGPfSzTp09fp/UD0H4JCgDY5DWdT7506dKV9mmaRt+/f/9m7RUVFUmS+vr69VpT07n2q6rpjXr27FmeVvBWnsf69sZ6Fi9evEG3tb41vTazZs1aq3Frchwl628f3Hj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kycnJ5vP5bNeuXWZWuaDgySefdP8P3oMh+POXv/wlol91goKJEyeaz+ezlJQUk2Tx8fFunSZOnOimK+9FZF5ennXq1MnVlpiYGLYdO3fubHl5eRH9giea8+bNs/bt25skS0hIcP28Xq8tXrw46rpEs3fv3rATaq/Xa0lJSe73o48+2r755hs3/aZNm9y6BqcJvgju0qVLhcvz+/02YsQI1zcuLs5tR0nm8/ls6dKlEf2qExQMGTLEfD6f2z5JSUmu9g8//NBNV1JSYkOHDnU1xMfHl7tPm9V8/BYtWmQ9e/Z02zx0excUFNgHH3xgCQkJ5vF43HNHkrVq1cp27NgRMd+tW7dax44dw9YhLi7O/T5o0KCIk6VYQvfbO+64wz3PQ/eLlJQU++ijj8L6tWjRwiTZypUro853+vTpJskuuOCCcpd/ww03mM/nc8tLSEhw4/bqq6+66UKPB3/+85/dtvT5fGHB4JIlSyw9Pd3VHvp8kWTjxo2LqCE/P99OPvlkN03oukuyIUOGmN/vd9O/+uqrLmAKbq9gzTfccIObLjQo2Lx5szVp0sTi4+PN4/G4x8877zwzM5swYULE/iHJcnJyom636qxn6DYM/r9sv3PPPTesT2WPf7G88cYbYftm6HE6PT09LMSr7L5QV+NV3aCgpKTEevToETYWoes8dOhQKy0trXBbAWiYCAoAHNYqGxSUlJS4Fy+hJ09m0YOC77//3r3geeaZZ6yoqMgCgYCtXr3acnJyTJL96U9/illPrBO9YFBwyimn2FVXXWUbN26MmKYyQYHX67VHHnnEhR5bt261G2+80Z2Url69OqxfdYKCytRjVv6LzGAY0rNnT1u2bJkFAgELBAK2bNkyO/XUU02SnX/++RH9gieaF154od177722fft2Ky0ttaVLl9oxxxxjkqx3795R64lmzJgx7mR4wYIF7sXrmjVrbODAgSbJunXrFvVFbbT9oyLBE7qMjAx744037MCBA2Z28ATq8ssvN0nWsmVL2717d1i/6gQFQaEhVDSPPPKISbLmzZvbm2++acXFxRYIBGzNmjV2zjnnhJ28BdV0/O655x4bN26c7dq1y4qLi+29996z5s2bmyS79957bcCAAfbXv/7V9u7da/v377fXXnvNUlNTXXtZ2dnZJsl69OhhH3/8sR04cMAKCwvtnXfesVatWsV8XkYT3G8lWVZWlr3++uvuXeXVq1db//79TZK1bt3aioqKXL9rr73WJNn48eOjznfUqFEmyZ577rlK1VHRczP4/GvTpo3l5OTYZ599FhGG7Nq1y7KyskySXX755bZu3Trz+/22Y8cOmzJliiUnJ1tcXJwtX748rN8VV1xhkuyqq65y+/e+ffts6tSp7kR6xowZMbddrBPL0KDg0Ucftddff92Kiopsz549NnnyZBcKzZgxwzp16mTvv/++lZSU2I4dO+z//u//XN8FCxbUynoGt2Hnzp0tJyfHPvroIystLbVt27bZ7bff7pb33nvvxdz+Vb2Z4XHHHWeS7Oqrr7Zt27aZmdmWLVvsjjvuMI/HY4MHD47oU9G+UFfjVd32adOmmSRr2rSp5ebmWmlpqZWUlNj8+fPthBNOMK/XGzUQBXB4ICgAcFirbFBg9r8TvjfffDPq46EngjNnzjRJ1q9fv4j5+P1+mz59ern1VBQU9OjRI+ZlmZUJCmK9oxU8sRs9enTY4/URFAQv027evHnUy9q3b99uTZs2NUm2atWqsLbgieaoUaMi+r311ltuOwSvwijP1q1bLSEhwRITE239+vUR7UVFRXbCCSeYJJs9e3ZEe1WDgqKiImvWrJlJing32uzgRxKCJ6GTJk0Ka6uroKC0tNQyMzNNks2dOzeivbCw0Fq3bm1Dhw61ffv2mVntjN/VV18d0e+ZZ54p993fO++80wU3oVasWFFuPYsWLTLp4MeLKvMuZmhQ8Mknn0S079271zIyMkySvfzyy+7xOXPmmCQ744wzIvoEAgHLzMw0j8djP/74Y4U1mFU+KEhJSbGCgoKo0zz66KMmRb4rHnT//febJBsxYkTY4+vXr48ZLF133XXupLSsqgQFr7zySkR76NU2ZYNbM7PTTjvNJNmtt95aK+sZ3IZerzfisn8zc6HX2LFjI9qqExRs377dpINX4ARDwlBz5sxxz7NQFe0LdTVe1W2/6aabTJI9+OCDEX22bdtm77//ftT5ATg8cDNDAL86e/furXCauLiDh8emTZtGbRs+fHiNajjrrLPk8Xiq3f/iiy+O+vhtt90mSXrrrbeqPe/aMmPGDEnSsGHD1KRJk4j2I488UsOGDQubtqwhQ4ZEPDZo0CDFx8dLkr777rsK63jzzTdVUlKic845R23bto1oT0pKct+PHquOqvjggw+0bds2dezYUWeccUZEu8fj0e9///taW15lrFq1Snl5efL5fBo8eHBEe0pKir788kvNmjVLqampYbXVZPwGDRoU8Vjo8str/+9//xv2+MyZMyVJw4cPj1pP//79lZmZqS1btlTpBnRZWVnq1atXxONpaWlunEKfTwMGDFBycrKWLVumnTt3hvX54osvlJeXp1NOOUUtWrSodA2VceKJJ6p58+ZR24LbJlhvWZdeeqkk6Z133pGZucfbtm2rBx98MGqfrl27SpJ++OGHalZ8UHlj3KhRI/Xp0ydme6x9oKrrGXTCCSfouOOOi3g8JydHUuWOJ5UR/PuRnp6uxMTEiPYLLrjAPc+q4lCMV1WU93eyadOmOvfccw9ZLQBqH0EBAETRqVMnSdLixYvr5IVXo0aNatQ/2otPSWrfvr0kqaCgQFu3bq3RMmpqxYoVkg6+OI+lQ4cOkqTPPvssanuzZs0iHouPj1dmZqakyoU+tVFHVVRleZ9//rkCgUCNl1mR4LdxtG3b1r24L8vn84X9XlfjF3oCXV572bEN1tOuXbuY9bRp00aSqnT3+FjPJel/z6fQ+aWmpqp///7y+/2aP39+2PTvvvuupP+deNamWMeMQCDggpFY2ya4Xfbs2VPpk+Hk5GRJUnFxcVVLDVPeGEc7wQxtD90HamM9o9UiSS1btoxYXk00adJELVu21Pbt2zVv3rxamWdFamu8qqJz586SDgaFRUVFh2y5AA4Nb30XAACHWmVO0o8//niNHj1aL730kjp06KDs7Gyddtpp6tGjh3r37l3uyUV9Cn0hvHXrVmVlZdVbLfn5+ZIOvmMfS/DFbV5eXpXm7fUe/PNVmZPsuqyjpsvbv3+/fv7556jvkNembdu2VVhTWXW13YJjV1F72XeEg/Xcc889euihh6L2DZ7oFRQUVLqe8gSfT2VDt5ycHL377rt65513wq4ueuedd1z7obJ79253kta3b9+YQVBQQUFB2Lvq3333naZMmaIlS5Zo69atKioqkpnV6QlnZfeB0Od3TdezqsurqYkTJ+qKK67QkCFD1K9fP5155pnq3r27zjzzzBo93+tjvGIZOXKkHn/8cX3wwQdq166dsrOz1aNHD/Xq1Usnn3zyIa8HQO0iKADwq1BaWur+f+SRR1aqz7PPPqvOnTvr6aef1owZM9zl1UcccYTuuece/fGPf6yTWmtLYWFhvS5/3759lZ62tt7Jawh1VGV5wWXWdVBQnX2hoYxfULCe/fv3a//+/Ye0nrLbLycnR2PGjAl7t3jXrl1avny5srKy1L1791pdfnlCx2nPnj0VTh+6bXJzc3X++edXeZ+tDzVZz/owcuRIHXHEEZo4caJyc3O1aNEiSQevYBk5cqQmTZpU5SvLGtp4JSUlacmSJRo3bpymTZumF198US+++KKkg2H7E088oezs7HquEkB18dEDAL8K27dvd/8v71LqUF6vV7feeqvWrVunTZs2afbs2br55ptVVFSk22+/XS+88EJdlVsrqvMZ2NqUlpZW6Wlr+lGMhlRHVZZXW8usSHX2hYYyfkHBep555hnZwZsxx/yZMGFCrS677PZr3bq1unTpovz8fK1du1aStGTJEvn9fg0ePLhG9x+pqtBxWrt2bYXb5rzzzpN08BL1K664Qvv27dN5552n5cuX6+eff3bTTZ069ZCtQ2VUdz3rU05OjpYuXart27dr/vz5evjhh5WRkaEXX3xR119/fZXm1VDHq2nTppo0aZIKCgq0evVqvfzyyxo2bJjWrVunCy+80D0/ABx+CAoA/CoEP2PcsmXLmJ9TLU/r1q114YUX6oknntD06dMlSU888URtllgrgpeYSwr72EHwMt3QKyvqWkZGhiTpwIEDMacJXkocvOfAL6GOqiwvJSVFjRs3do/X1TgF9/nyaiqroYxf2XoqupqgNgWfT9E+whP8eMGHH34Y9u/5559/iKo7yOfzhX2UpbKWLVumH374QQkJCZo5c6Z69uyp9PT0uiqzxqq7ng3BEUccoQEDBui+++7Tp59+qtTUVL366qthAXZF6nK8auO4ExcXp86dO2vkyJH617/+pdtuu00lJSV6+umna6VGAIceQQGAX4Vp06ZJkoYOHVrjeZ1zzjmSIu/IfSiVlJREffzbb7+VJB111FFhJzfBd+O2bNkStV9owFBbTj31VEnSN998E3OaYFuPHj1qffn1VUdVlte9e/ewz1rX1TgFbzq2cePGqHeDl6TNmzeHnYA1lPErW8/XX38dc5oNGzZU+aZqsZ5L0v+eT8E7yocKBgVLlixx/yYlJWnAgAFVWn5NxcXFqVu3bpLK3zZl39ndvHmzpIM3Dox2RUh9X7pfVnXXs6HJzMx0N8utyt+Quhyv4HEnPz9ffr8/or06x52G8HcSQM0QFAD4xZs3b55effVVxcXFxfxarbI+//xzde/eXRs2bIhoW79+vaTIu8QfSrNnz476+KRJkyRF3kwteJfw5cuXR7zoKy4u1sKFC2u9xksuuUTSwa802717d0T7zp07w76Cr65ccMEFSkhI0MKFC7Vx48aI9uLiYv3973+vtTr69u2rZs2aae3atfrkk0+iTvP8889HXV5wnN5+++2IPt999125J+3l6dq1qzIzM7Vr1y53w71QgUBAo0ePVrdu3dy3fDSU8QsKrWfHjh0R7cXFxRoxYoR69OhRpXsybNmyRZ9++mnE44WFhW6cot2csFevXmrWrJk+/PBD7du3TytXrlT//v2r/NGT2hDcNsF6y/r+++912mmnafTo0e6x4MnmTz/9FHFyWFxc7D5r3pBUZz3rw08//aSzzz5bc+fOjWjz+/36/vvvJVXtb0hdjlfwuFNaWqr33nsvoj3aMUOSXn75ZZ177rlRrzpqCH8nAdQMQQGAX6yCggI98sgjuuiii2Rmuummm3TSSSdVqu+0adO0cuVK9e7dW//85z+1f/9+mZnWrFmjq6++WpKivnNY0Z24a0thYaEee+wx9z3u+fn5uvXWWzV37lzFxcXptttuC5u+X79+SkpKUmFhoX73u9+5AGT9+vW67LLLyr07eHXXqXv37srJyVFBQYGys7P12Wefuc/TfvbZZ8rOzta2bdt0/vnn1+kdsjMzM3XdddepuLhYgwYNUm5urnuhvXbtWl100UVau3atunXrVit3q09KStJdd90l6eCJzZw5c9wdyTdt2qRRo0Zp4cKFatmypa666qqwvgMHDpQkffrpp7rjjju0Y8cO+f1+ffLJJ7rtttvc1xFGU944xcfHa+zYsZKkq6++Wm+//bZKS0tlZvr222/129/+VgsWLJDX63UfI2go4xfUvXt3DR48WNu3b9fAgQO1fPlymZn8fr9WrFjhauzYsWOV7snQpk0bzZw5U7NmzXJXVPznP//RhRdeqLy8PLVu3Trsmw2C4uLilJ2dre+//16vvfaaSktLq7X/1MYx45prrlFWVpZyc3M1cuRIF4gVFRVp7ty56tu3r/bs2eOuypAObk+Px6PCwkKNHTvWhUFfffWVBg8eXO5l6IfqOFdWddazpqqzrkuXLlVubq4uueQSTZgwwYWz+fn5uvbaa5Wfn69WrVpF3C+nvGXV5Xg1adJEPXv2lCSNGTNGS5culd/v186dO/XQQw9FDeZKS0s1efJkLViwQGeddZY++ugj+f1+lZaW6v3339fDDz8sKfrfSQCHCQOAw1jfvn1NkiUlJZnP53M/aWlpJsn9jBo1ykpLS6POIzjNhg0b3GMlJSV22WWXuTaPx2NJSUnu92bNmtn69esj5hXs07hxY8vIyLCWLVuGtV955ZUmyR544IGY6zR16lSTZH379o1Z65o1a+y4444zSZaSkhK2ro8++mjU+T700ENh0yUkJJgku/LKK+2BBx5w/y9r/vz5bvqMjAzLyMiwGTNmuPbc3FyTZG3atInom5eXZ507d3bLTExMtMTERPd7586dLS8vL6JfmzZtTJLl5uZGXZeK2svau3ev21ckmdfrteTkZPf70Ucfbd98803UvtH2j4r4/X4bMWKE6xsfH2+pqanud5/PZ0uXLo3oFwgE7Oyzz446TlOnTnXrMHXq1Ii+99xzj0mytLQ0N05btmxx7SUlJTZ06NCwbRA6Fq1atbKvv/46bJ51NX7lbdMNGza49rK2bNliHTt2DKsnPj7e/d67d2/buXNn1GWWFbrffvHFF5acnGwejydsv0hOTrYPP/ww5jymT59ukqxJkyYmyTZu3FipZYd6/vnn3bKC47Z8+XLXXt7xINSSJUuscePGrvayx4U//OEPFggEwvoEj0dl+3Tq1Mnmzp3r9l2fz2ebNm1y/datW2eSLC4uztX8+OOPu/byxtCs/GNGRetcnfWsaBuW117R8S+WyZMnh+2boftVXFyczZo1K6JPRftCXY2XmdnChQvD6g0ed9q3b2/z5s2LOl4//vijde3aNeyY4vV63e+9evWyAwcOVLitADRMXFEA4BfhwIED2r17t/s5cOCAWrZsqWHDhum9997T1KlTFR8fX+n5eb1eTZs2TTNmzNCAAQPUvHlzBQIBHX300Ro9erRWrFihtm3bRvS74447dOKJJ2rv3r3Kz8+v0vfLV0VaWprmz5+vYcOGKSUlRenp6erTp4/mzJkT82sb77//fj3xxBNq3769EhMTlZmZqdtvv13PPvtsucvq37+/hg8froSEBOXn5ys/P7/SNxPLyMjQsmXLNH78eHXt2lVer1der1fdunXThAkTtGzZMneTuroU3F5PPvmkevXqpZSUFElSx44dddddd2nVqlVq3759rS0vLi5O//jHPzRt2jT169dP6enpKi0tVbt27TRmzBitXr1ap59+ekQ/j8ejOXPm6Oabb1ZmZqaSk5N1/PHH64UXXtCoUaPKXeZ1112nnj176sCBA26cQi9R9nq9mjlzpl566SX16dNHqamp8nq9OvHEE3X33Xfr888/j3iHs6GMX1BWVpY+/fRTjRs3TieddJK8Xq/S09N16qmnavLkyVq0aFG1vmqya9euevfdd3X66acrPj5eTZs21cUXX6wVK1aoT58+MfsNHDhQCQkJ2rVrl7p06aI2bdpUedkjRozQOeecIzNz4xa8AqUqzjzzTH355Ze6/vrr1bZtW/n9fmVlZWnAgAGaPXu2nnrqqYhvY3j++ed1//33q127dkpISJDP59Po0aOVm5ur7OxsDRs2TAkJCdq9e7cCgYDrd9xxx+mWW25Ro0aNXM2H6p4G1VnPmqju8e+mm27SsmXLdOmll6pVq1by+/1q2rSphgwZosWLF0e9X05F+0JdjtfZZ5+td999V7169VJycrJ8Pp+GDx+u+fPnKykpKeo6tmjRQh9//LEee+wxnXLKKUpNTVV8fLw6dOig++67TwsXLlRiYmIltzSAhsZjFuOuRgAAACjX2WefrdzcXN199921/rWMAADUF64oAAAAqKbglUq1cX8LAAAaCq4oAAAAqIb9+/fryCOPdJd019dN/gAAqG38RQMAAKiGJUuWqKioSNnZ2YQEAIBfFG99FwAAAHA4ueWWWzR9+nQVFhZKOngTOgAAfkmIvwEAAKpg9+7dys/PVyAQ0DXXXKPBgwfXd0kAANQq7lEAAAAAAAAcrigAAAAAAAAOQQEAAAAAAHAICgAAAAAAgENQAAAAAAAAHIICAAAAAADgEBQAAAAAAACHoAAAAAAAADgEBQAAAAAAwCEoAAAAAAAADkEBAAAAAABwCAoAAAAAAIBDUAAAAAAAAByCAgAAAAAA4BAUAAAAAAAAh6AAAAAAAAA4BAUAAAAAAMAhKAAAAAAAAA5BAQAAAAAAcAgKAAAAAACAQ1AAAAAAAAAcggIAAAAAAOAQFAAAAAAAAIegAAAAAAAAOAQFAAAAAADAISgAAAAAAAAOQQEAAAAAAHAICgAAAAAAgENQAAAAAAAAHIICAAAAAADgEBQAAAAAAACHoAAAAAAAADgEBQAAAAAAwCEoAAAAAAAADkEBAAAAAABwCAoAAAAAAIBDUAAAAAAAAByCAgAAAAAA4BAUAAAAAAAAh6AAAAAAAAA4BAUAAAAAAMAhKAAAAAAAAA5BAQAAAAAAcAgKAAAAAACAQ1AAAAAAAAAcggIAAAAAAOAQFAAAAAAAAIegAAAAAAAAOAQFAAAAAADAISgAAAAAAAAOQQEAAAAAAHD+HzcEwW2yPiBbAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 509, + "width": 517 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "f, ax = plt.subplots(1, 1)\n", + "ax.hist(y[z == 1], bins=20, color=\"blue\", alpha=0.5, label=\"Treated\")\n", + "ax.hist(y[z == 0], bins=20, color=\"red\", alpha=0.5, label=\"Control\")\n", + "ax.vlines(y[z == 1].mean(), 0, 100, color=\"blue\", linestyle=\"-\")\n", + "ax.vlines(y[z == 0].mean(), 0, 100, color=\"red\", linestyle=\"--\")\n", + "ax.set_xlabel(\n", + " \"\"\"outcome in dollars \\n \\n\n", + "Distribution of outcome by treatment status\n", + "\"\"\"\n", + ")\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### FRT " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.835321178307911" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(tauhat := sp.stats.ttest_ind(y[z == 1], y[z == 0], equal_var=True)[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.674145786280093" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(student := sp.stats.ttest_ind(y[z == 1], y[z == 0], equal_var=False)[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "27402.5" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# scipy's wilcoxon requires equal length vectors; so use mann-whitney\n", + "(W := sp.stats.mannwhitneyu(y[z == 1], y[z == 0])[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.13212058212058211" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(D := sp.stats.ks_2samp(y[z == 1], y[z == 0])[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def one_perm():\n", + " zperm = np.random.permutation(z)\n", + " return [\n", + " sp.stats.ttest_ind(y[zperm == 1], y[zperm == 0], equal_var=True)[0],\n", + " sp.stats.ttest_ind(y[zperm == 1], y[zperm == 0], equal_var=False)[0],\n", + " sp.stats.mannwhitneyu(y[zperm == 1], y[zperm == 0])[0],\n", + " sp.stats.ks_2samp(y[zperm == 1], y[zperm == 0])[0],\n", + " ]\n", + "\n", + "\n", + "MC = int(1e4)\n", + "result = np.zeros((MC, 4))\n", + "for i in range(MC):\n", + " result[i] = one_perm()\n", + "Tauhat, Student, Wilcox, Ks = np.split(result, 4, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.0033 0.0034 0.006 0.0399]]\n" + ] + } + ], + "source": [ + "print(\n", + " exact_pvalue := np.c_[\n", + " np.mean(Tauhat >= tauhat),\n", + " np.mean(Student >= student),\n", + " np.mean(Wilcox >= W),\n", + " np.mean(Ks >= D),\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 449, + "width": 527 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "f, ax = plt.subplots(2, 2)\n", + "\n", + "ax[0, 0].hist(Tauhat, bins=20, color=\"blue\", alpha=0.5)\n", + "ax[0, 0].vlines(tauhat, 0, 1000, color=\"blue\", linestyle=\"-\")\n", + "ax[0, 0].title.set_text(\"T-test (equal var)\")\n", + "\n", + "ax[0, 1].hist(Student, bins=20, color=\"blue\", alpha=0.5)\n", + "ax[0, 1].vlines(student, 0, 1000, color=\"blue\", linestyle=\"-\")\n", + "ax[0, 1].title.set_text(\"T-test (unequal var)\")\n", + "\n", + "ax[1, 0].hist(Wilcox, bins=20, color=\"blue\", alpha=0.5)\n", + "ax[1, 0].vlines(W, 0, 1000, color=\"blue\", linestyle=\"-\")\n", + "ax[1, 0].title.set_text(\"Wilcoxon\")\n", + "\n", + "ax[1, 1].hist(Ks, bins=20, color=\"blue\", alpha=0.5)\n", + "ax[1, 1].vlines(D, 0, 1000, color=\"blue\", linestyle=\"-\")\n", + "ax[1, 1].title.set_text(\"Kolmogorov-Smirnov\")\n", + "\n", + "f.subplots_adjust(hspace=0.3, wspace=0.2)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "econometrics", + "language": "python", + "name": "econometrics" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From fccc721d515df0babfae7b69d2797ae3ba933df9 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:28 +0200 Subject: [PATCH 06/20] Add PyFixest Chapter 04 notebook --- pyfixest/Chapter04CREandNeyman.ipynb | 418 +++++++++++++++++++++++++++ 1 file changed, 418 insertions(+) create mode 100644 pyfixest/Chapter04CREandNeyman.ipynb diff --git a/pyfixest/Chapter04CREandNeyman.ipynb b/pyfixest/Chapter04CREandNeyman.ipynb new file mode 100644 index 0000000..e704c87 --- /dev/null +++ b/pyfixest/Chapter04CREandNeyman.ipynb @@ -0,0 +1,418 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 4: Neymanian Repeated Sampling Inference in Completely Randomized Experiments" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import scipy as sp\n", + "import pyfixest as pf\n", + "import matplotlib.pyplot as plt\n", + "\n", + "font = {\"family\": \"IBM Plex Sans Condensed\", \"weight\": \"normal\", \"size\": 10}\n", + "plt.rc(\"font\", **font)\n", + "plt.rcParams[\"figure.figsize\"] = (7, 5)\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "np.set_printoptions(suppress=True)\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def coverfn(truepar, point, varest):\n", + " lowerCI = point - 1.96 * np.sqrt(varest)\n", + " upperCI = point + 1.96 * np.sqrt(varest)\n", + " return (lowerCI < truepar) & (truepar < upperCI)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def sampling_dist(z, y1, y0, n, n1, n0, tautrue=1):\n", + " zmc = np.random.choice(z, size=n, replace=True)\n", + " y = zmc * y1 + (1 - zmc) * y0\n", + " tauhat = np.mean(y[zmc == 1]) - np.mean(y[zmc == 0])\n", + " vhat = np.var(y[zmc == 1]) / n1 + np.var(y[zmc == 0]) / n0\n", + " return tauhat, vhat, coverfn(tautrue, tauhat, vhat)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulation Studies - Neyman CLT" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "n, n1, n0 = 100, 60, 40\n", + "tautrue = 1\n", + "\n", + "# Simulation setting 1\n", + "y0 = np.random.exponential(1, n)\n", + "y0_1 = -np.sort(-y0)\n", + "y1_1 = y0_1 + tautrue\n", + "tautrue_1 = np.mean(y1_1) - np.mean(y0_1)\n", + "z_1 = z_2 = z_3 = np.repeat([0, 1], [n0, n1])\n", + "MC = int(1e4)\n", + "res1 = np.zeros((MC, 3))\n", + "for i in range(MC):\n", + " res1[i] = sampling_dist(z_1, y1_1, y0_1, n=n, n1=n1, n0=n0, tautrue=1)\n", + "v1 = np.var(y1_1) / n1 + np.var(y0_1) / n0 - np.var(y1_1 - y0_1) / n\n", + "\n", + "# Simulation setting 2\n", + "y0_2 = np.sort(y0)\n", + "y1_2 = y1_1\n", + "(tautrue_2 := np.mean(y1_1) - np.mean(y0_2))\n", + "res2 = np.zeros((MC, 3))\n", + "for i in range(MC):\n", + " res2[i] = sampling_dist(z_2, y1_1, y0_2, n=n, n1=n1, n0=n0, tautrue=1)\n", + "v2 = np.var(y1_2) / n1 + np.var(y0_2) / n0 - np.var(y1_2 - y0_2) / n\n", + "\n", + "\n", + "# Simulation setting 3\n", + "y0_3 = np.random.permutation(y0_1)\n", + "y1_3 = y1_1\n", + "(tautrue_3 := np.mean(y1_3) - np.mean(y0_3))\n", + "res3 = np.zeros((MC, 3))\n", + "for i in range(MC):\n", + " res3[i] = sampling_dist(z_3, y1_3, y0_3, n=n, n1=n1, n0=n0, tautrue=1)\n", + "v3 = np.var(y1_3) / n1 + np.var(y0_3) / n0 - np.var(y1_3 - y0_3) / n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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dM8azaxYAACAFAoGAFi5cqEsvvTTm8xs3bpQkZWVlacyYMSnsDAC6wbqOoGhY5xOwhUdKRrZkHnJAdcMWglkArpLldAPILEtqdmnG3KXdOgBMktbXN2vG3KVaUrMryZ0BAAD0bqFQSKeddpouu+wyPfPMMzFfM3fuXEnSySefrMLCwlS2BwCH192DvyQpO0fqZ9mTzZ5ZAC5DMAvbrNnRqOr5H6o1GDr8iw/RGgypev6HWrOjMUmdAQAAIDs7W1VVVZKkiy66SLNnzz64umDfvn361a9+pXnz5kmSfvaznznVJgB0Lp5gNtbzBLMAXIZgFrYwTVOzFq2MO5Q9oDUY0vWLPpZpmod/MQAAABLyq1/9Stdcc43a29t10003aeDAgcrPz9eAAQP085//XOFwWL/4xS90xhlnON0qAEQjmAWQYQhmYYulm/Z0e31BZ9bV+7Rs016bOgIAAIBVdna2fv/73+vNN9/UOeeco6OOOkqhUEglJSU655xz9Oabb+qWW25xuk0AiI1gFkCG4fAv2OLJZVtsq1M5stiWWgAAAIjt1FNP1amnnup0GwDQfaF2qWlH5DWCWQBpjolZ9JjP365X19bbUuuVtXXy+dttqQUAAAAAyBBNOyUzHHntsMHssMjHDdsk1ucBcBGCWfRYXaNfobA9f7mFwqbqm/y21AIAAAAAZAjrtGtugZR3RNfvsQa3QZ/Uts/evgCgBwhm0WMtCR741ZnmgL31AAAAAABpLtZ+WcPo+j39SiXDEnuwzgCAixDMosf65mbbWq+gj731AAAAAABpLt6DvyQp27M/nO2qDgA4iGAWPTakyKvsrMN8p7KbcrIMlfTz2lILAAAAAJAhEglmY72OYBaAixDMoscKvR5NG1diS61p44ao0OuxpRYAAAAAIEMQzALIQASzsMXMScNdVQcAAAAAkEEaLYFq0bDuvc/6usZt9vQDADYgmO2GPXv2qF+/fjIMQ4Zh6Pbbb4+7xrx58w6+v6uPp556yv5PIAUqy4s1pqSgRzXGlhRqUvkAmzoCAAAAAGSEUIfUuCPyGhOzADJAjtMNpIPZs2fL5/MpJydHHR0dParVp08feb2d71DNzc3tUX2nGIahu2eM14y5S9UaDMX9/vzcbM2ZcbyMw52qCQAAAADoXXw7JdPy78z+3fxpy1jBrGlK/NsTgAswMXsYtbW1uu+++zR+/HhdeOGFPa7305/+VA0NDZ1+fOtb37Kha2dUlBZp7sUnKj83O6735edma+7FJ6qitChJnQEAAAAA0pZ1ytXTV8rv5k9bWoPZQJPkb7ClLQDoKYLZw7jzzjvV1tam2267jWnObqgaPUiLqiu7vdZgbEmhFlVXqmr0oCR3BgAAAABIS5Zgdl3gCI24aXH33tuvVDIs0ce+LTY1BgA9QzDbhS1btuiRRx7R+PHjdfbZZzvdTtqoKC3Sq9dO1sIrJunM44YoOysy0M7JMvT144Zq4RWT9Mq1VUzKAgAAAAA611wf8bDOjONskpxcqaAk8lrLLhuaAoCeY8dsF+644w4Fg0GmZRNgGIYqRxarcmSxfP521Tf51RwIqaBPtkr6eVXo9TjdIgAAAAAgHbTti3i4T3EePJ13hOSr7bQeADiFYLYT69at0xNPPMG0rA0KvR6CWAAAAABAYtoaIh42mAkEs13UAwCnEMx24rbbblMoFErKtOy2bdt0880367333lNbW5uOPfZYXXnllTr//PPjrrV9+/Yun6+tre3yeQAAAAAAXM0y4dqYyMRsF/UAwCkEszGsWrVKixYtSsq0bGNjoyZPnqzNmzcfvLZz5069/vrr+uEPf6j/+Z//iavesGHDbO0PAAAAAABXsQazZt/43u/t32U9AHAKh3/FcMstt8g0zaRMyz722GM66aSTVFNTo0AgoA0bNqi6ulqS9L//+7966aWXbL0fAAAAAABpzd8Q8bAhzmD2oRWWINZSDwCcwsSsxfLly/XCCy/YPi17/PHH6/LLL1dhYaHuvvtuZWXtz8RHjhypBx98UOvWrdNbb72l3/3ud/r617/e7brbtm3r8vna2lpNnDixR70DAAAAAOAY647ZOFcZRAW5TMwCcAmCWYuf//znkmT7tOyXvvQlPfroo50+X11drbfeekvvvvuuOjo6lJPTvf9pysrK7GoRAAAAAAD36eEqgyYRzAJwJ1YZHOLtt9/W66+/npTdsoczatQoSVJbW5v+9a9/pfTeAAAAAAC4UqhdCjZHXIp/YtbyessELgA4hWD2EDfffLMk+6dlu2PgwIEHf+33+1N6bwAAAAAAXClGiNpoDVoPIyrIZWIWgEuwyuAQ7777riTp0ksvjfl8a2urJGn27Nm69957JUkNDQ223HvPnj0Hf11UVGRLTQAAAAAA0lqMELXRuprgMKJWH7Ttk0xTSvFAFgBYEczG0NjY2OXzgUBAgUAg7povv/yyvvOd78R8fuPGjZKkQYMGqbi4OK7aAAAAAABkJH9DxMMWs4/aDxNljPjpSxGPhxmWYDbcLrW3SrnxBbwAYDdWGRzCNM0uP7773e9K2r/q4MC17mhsbNTEiRN18cUX6/3334/5mrlz50qSzjjjDHs+GQAAAAAA0p1lYjbe/bJSJ6sPWGcAwAUIZm0SCoV0+umny+v16vTTT1coFDr4XFFRkcaPH6+Ojg5Nnz5dDz30kJqamiRJdXV1+tGPfqQ33nhDHo9HN9xwg1OfAgAAAAAA7mIJUJusawm6wac8hU3L2gKCWQAuwCoDm9TW1urvf/+7JOnvf/+76urqVFpaevD5efPmqaOjQ3/6059UXV2t6upq5eXlqa2tTZKUnZ2thx56SF/84hcd6R8AAAAAANexHP7VEOfBX5JkKkuN6qsj1NxpXQBwAhOzNhk6dKimTJmi3NxcTZkyRUOGDIl4Pi8vT88++6yef/55fe1rX9ORRx6pUCik0tJSXXjhhfrHP/7R6aFjAAAAAAD0SlGrDBLbC9sQ6wAwAHAYE7NxmDdvnubNmxfzuezs7IMTs50xDEPnnHOOzjnnHPubAwAAAAAg01gO/2pMYJWBJDVaA11LXQBwAhOzAAAAAADAnWw4/EuKcQAYE7MAXICJWQAAAAAA4E5dHP414qcvdbtM1MQswSwAF2BiFgAAAAAAuJP18K8EJ2ajDg3j8C8ALkAwCwAAAAAA3Mm6yiDBHbNRh4YxMQvABQhmAQAAAACAO1kC1KiVBN0UdWgYwSwAFyCYBQAAAAAA7mOakr8h4lLUSoJuarSuQLDUBQAnEMwCAAAAAAD3CTZL4Y6IS1EBazdF75hlYhaA8whmAQAAAACA+8QIT6NWEnRT9CqDhoTqAICdCGYBAAAAAID7WMNTI1s+5SVUqsE6aRtokkIdsV8MAClCMAsAAAAAANzHOjGb11+SkVCphliTtv7GhGoBgF0IZgEAAAAAgPtYg1lv/4RLNSlGMMueWQAOI5gFAAAAAADu42+IfJx3RMKlAspVm5nbdX0ASDGCWQAAAAAA4D5RqwwSD2alGHtmmZgF4DCCWQAAAAAA4D4xd8wmrtG6Z5ZgFoDDCGYBAAAAAID7tDVEPu7hxGyjdc+stT4ApBjBLAAAAAAAcB+7VxmYrDIA4C4EswAAAAAAwH2sh3N5+/eoXNQqAw7/AuAwglkAAAAAAOA+HP4FIMMRzAIAAAAAAPexecdsA4d/AXAZglkAAAAAAOA+UcFs/x6Va+LwLwAuQzALAAAAAADcJdQuBX2R1zj8C0CGIZgFAAAAAADuEmualR2zADIMwSwAAAAAAHAXf0P0NW//HpVstO6Y9TdIptmjmgDQEwSzAAAAAADAXazTrJ6+Uk5uj0o2WHfMhoJSe2uPagJATxDMAgAAAAAAd7EGsz1cYyBJjdYds7HuAwApRDALAAAAAADcxbpjNq9/j0v6lCfJ6Po+AJBCBLMAAAAAAMBdkjAxayorOuBlYhaAgwhmAQAAAACAu0QFs/3tqWsNeAlmATiIYBYAAAAAALiLvyHysbe/LWVX7j7MfQAghQhmAQAAAACAuyRhlYEU4wAwJmYBOIhgFgAAAAAAuEuSgtkGEcwCcA+CWQAAAAAA4C5tDZGPbdox22j27fo+AJBCBLMAAAAAAMBdkjYxaw1mmZgF4ByCWQAAAAAA4C7WwNSmw7+iJ2YJZgE4h2AWAAAAAAC4h2lK/obIa3Yd/mXdMWu9DwCkEMEsAAAAAABwj2CzFO6IvGbXKgOTw78AuAfBLAAAAAAAcI9YB3Il7fCvRlvqAkAiCGYBAAAAAIB7WKdYjWypTz9bSjdYVxkEGqVQR+wXA0CS5TjdAHoPn79ddY1+tQRD6pubrSFFXhV6PU63BQAAAABwE0swuyecrxNvWmxL6QbrxKwk+RulvsW21AeAeBDMIqlM09TSTXs0f+kWvfZJvUJh8+Bz2VmGpo0r0cxJw1VZXizDMBzsFAAAAADgCpYDuaLWD/RAk2IFsw0EswAcQTCLpFmzo1GzFq3U+vrmmM+HwqYWr67T4tV1GlNSoLtnjFdFaVGKuwQAAAAAuIplYrbRun6gBwLKVZuZqzwj2On9ACBV2DGLpFhSs0sz5i7tNJS1Wl/frBlzl2pJza4kdwYAAAAAcDVLUBpz/UAPRO2ZJZgF4BCCWdhuzY5GVc//UK3BUFzvaw2GVD3/Q63ZwamYAAAAANBrtTVEPGyMtX6gB6JWI1juBwCpQjALW5mmqVmLVsYdyh7QGgzp+kUfyzTNw78YAAAAAJB5oiZm7VtlIMUIepmYBeAQglnYaummPd1eX9CZdfU+Ldu016aOAAAAAABpJYk7ZqUYQS/BLACHEMzCVk8u2+KqOgAAAACANONviHgYtXqgh6LqWe4HAKlCMAvb+PztenVtvS21XllbJ5+/3ZZaAAAAAIA0wuFfAHoJglnYpq7Rr1DYnt2wobCp+ia/LbUAAAAAAGnEchhXVJDaQ1FBL8EsAIcQzMI2LQke+NWZ5oC99QAAAAAAacASzNq9yqDJcvjXPz7bZGt9AOguglnYpm9utq31CvrYWw8AAAAA4HKhdinoi7hk/8RsZL3+arG1PgB0F8EsbDOkyKvsLMOWWjlZhkr6eW2pBQAAAABIE5ZpWUlqNG0OZi1Bb3+j2db6ANBdBLOwTaHXo2njSmypNW3cEBV6PbbUAgAAAACkCX9D1KVG2bvKwLoaoZ9aJNOe81IAIB4Es7DVzEnDXVUHAAAAAJBGLAdxtZh91K4cW2/RYAl6+xgdUnurrfcAgO4gmIWtKsuLNaakZz9mMrakUJPKB9jUEQAAAAAgbVhWGdi9X1bqZDVCjBUKAJBsBLOwlWEYunvGeOUneBBYfm625sw4XoZhz65aAAAAAEAasUzMNpn2rjGQJJ/yFDYt/+a03BcAUoFgFrarKC3S3ItPjDuczc/N1tyLT1RFaVGSOgMAAAAAuJolIG2w+eAvSTKVFb23lmAWgAMIZpEUVaMHaVF1ZbfXGowtKdSi6kpVjR6U5M4AAAAAAK5lOfzLug/WLg3WSdwYh44BQLIRzHbDnj171K9fPxmGIcMwdPvttydc669//aumT5+ukpIS9enTR8OGDdMll1yiNWvW2NewS1SUFunVaydr4RWTdOZxQ5SdFfmjIjlZhr5+3FAtvGKSXrm2iklZAAAAAOjtLJOrjUlYZSCJiVkArmDv0YYZavbs2fL5fMrJyVFHR0fCdW655RbdeeedBx/n5eVp+/btmj9/vhYtWqRnnnlG3/jGN+xo2TUMw1DlyGJVjiyWz9+u+ia/mgMhFfTJVkk/rwq9HqdbBAAAAAC4hb8x4mFUgGqTqN21HP4FwAFMzB5GbW2t7rvvPo0fP14XXnhhwnXeeOMN3XnnnfJ4PPrd736npqYmtba2aseOHbrqqqsUCAQ0c+ZM7d6928bu3aXQ69GowYUaP6y/Rg0uJJQFAAAAAEQK+CIeNpt5SbmNT5a6weak3AcAukIwexh33nmn2tradNttt8kwjMO/oRO/+c1vJEl33HGH/uu//kuFhYWSpCOPPFIPPPCAzjzzTDU1Nen++++3pW8AAAAAANJOoCniYbM1QLVJs5lvua8v9gsBIIkIZruwZcsWPfLIIxo/frzOPvvshOs0NzfrjTfeUE5Ojn7wgx/EfM21114rSfrLX/6S8H0AAAAAAEhr1onZZAWz1rqWQBgAUoFgtgt33HGHgsFgj6dlP/jgA7W3t+u4445TUVHsA65OOeUUGYahlStXyufjO3UAAAAAgF7IEsz6rJOtNokOZvl3OIDUI5jtxLp16/TEE0/0eFpWkmpqaiRJw4YN6/Q1eXl5GjhwoMLhsDZt2tSj+wEAAAAAkJZSNDHrs+6uJZgF4IAcpxtwq9tuu02hUKjH07KStHXrVkn7w9eu5OfnH3z98ccf363a27dv7/L52trabtUBAAAAAMBxKTr8i4lZAG5AMBvDqlWrtGjRIlumZaX9O2aT9fqupnABAAAAAEgboQ6pvTXiki9ph38RzAJwHqsMYrjllltkmqYt07KS1NbWFtfrW1tbD/8iAAAAAAAySTA6HGViFkAmY2LWYvny5XrhhRdsm5ZNtm3btnX5fG1trSZOnJiibgAAAAAASFCMcJQdswAyGcGsxc9//nNJsm1aVjr8blmrA7tmu6OsrCzedgAAAAAAcJ+ocNRQq/ok5VYxJ2bDYSmLHywGkDr8iXOIt99+W6+//rrt07IFBQVJfT0AAAAAAGnPGsz2KZSZpNgiekWCKbW3JOVeANAZgtlD3HzzzZLsnZaVpKOOOkqS5Pf7u3zdgd2yB14PAAAAAECvESOYTZaYh4qxzgBAirHK4BDvvvuuJOnSSy+N+fyB4HT27Nm69957JUkNDQ2HrTtq1ChJXe+DbWtr0+7du2UYhsrLy7vfNAAAAAAAmSDQFPk4icFsC8EsABdgYjaGxsbGmB/t7e2SpEAgcPBad0yaNEkej0erV69WU1NTzNe8//77Mk1T48ePV2Fh8v7yAQAAAA7Ys2eP+vXrJ8MwZBiGbr/9dqdbAtCbpXBiNqRstZqW/bUEswBSjGD2EKZpdvnx3e9+V9L+VQcHrnVHQUGBpkyZovb2dj3wwAMxX3NgAtfO3bYAAABAV2bPni2fz6ecHH6QDoALpDCYlWIdABZ7kAoAkoVg1iahUEinn366vF6vTj/9dIVCoYjnb7zxRknSrbfeqt///vdqbm6WJNXW1uqaa67RX//6V/Xr10/XXHNNynsHAABA71NbW6v77rtP48eP14UXXuh0OwCQ8mDWZz0AjIlZAClGMGuT2tpa/f3vf1cgENDf//531dXVRTw/ZcoU/fznP1cwGNQPf/hD9evXT/n5+TryyCN1//33Kzc3V08++aQGDhzo0GcAAACA3uTOO+9UW1ub7QffAkDCHJ+YJZgFkFoEszYZOnSopkyZotzcXE2ZMkVDhgyJes2dd96p559/Xl/72tdUXFysUCik0tJSXXjhhfrHP/6hb3zjGw50DgAAgN5my5YteuSRRzR+/HhWaQFwj6jDv/ol9XbNTMwCcBjLpOIwb948zZs3L+Zz2dnZ+vvf/37YGuecc47OOeccexsDAAAA4nDHHXcoGAwyLQvAXZiYBdDLMDELAAAA9CLr1q3TE088wbQsAPdxPJjl8C8AqcXELAAAANCL3HbbbQqFQrZOy27fvr3L52tra225D4AMx+FfAHoZglkAAACgl1i1apUWLVpk+7TssGHDbKsFoBdzfGKWYBZAarHKAAAAAOglbrnlFpmmyW5ZAO4UFcxy+BeAzMbELAAAANALLF++XC+88EJSdstu27aty+dra2s1ceJEW+8JIAPFnJjdk7TbMTELwGkEswAAAEAv8POf/1ySkjItW1ZWZms9AL1QOJzyYJYdswCcxioDAAAAIMO9/fbbev3115MyLQsAtmhvkWRGXkv5jtmmpN4PAKwIZgEAAIAMd/PNN0tKzrQsANgi0Bx9LdnBrJlv6YGJWQCpxSoDAAAAIMO9++67kqRLL7005vOtra2SpNmzZ+vee++VJDU0NKSgMwD4P7FC0dxUT8z6JNOU+AYWgBQhmAUAAAB6icbGxi6fDwQCCgQCKeoGAA5hDWY9+VJ2ciMLnzWYDXdIHX7Jkxf7DQBgM1YZAAAAABnONM0uP7773e9K2r/q4MA1AEgpy37XfwVzNeKnLyX1ls3Ww78k1hkASCmCWQAAAAAA4CxLIOqLFZraLGqVQYw+ACCZCGYBAAAAAICzLIFozNDUZkF5FDAt6xIsk7sAkEwEswAAAAAAwFnWYDYFE7NSJweAAUCKcPgXAAAA0MvNmzdP8+bNc7oNAL2ZAxOz0v4AuNg45N4EswBSiIlZAAAAAADgLMsKgZQFs0zMAnAQwSwAAAAAAHCWA4d/SQSzAJxFMAsAAAAAAJxlCURb5E3JbaN22XL4F4AUIpgFAAAAAADOijr8Kz8lt2ViFoCTCGYBAAAAAICzrKsMUnj4V1d9AEAyEcwCAAAAAABnRU3MpiaYjQqACWYBpBDBLAAAAAAAcJZlt2vUioEkYWIWgJMIZgEAAAAAgLOsE7OpCmaZmAXgIIJZAAAAAADgHNOM3jGbolUG0cFsU+wXAkASEMwCAAAAAADndASkcHvEpVRNzEYFwEzMAkghglkAAAAAAOCcGGFoqg7/YpUBACcRzAIAAAAAAOfEWB/A4V8AegOCWQAAAAAA4BxLGBo0sxWQJyW3jgqAO/xSRzAl9wYAglkAAAAAAOAcSzC7Pyw1UnJrn5kffTHYnJJ7AwDBLAAAAAAAcI41mE3Rflmpk5UJMVYrAEAyEMwCAAAAAADnRE3MxphiTZJW9VHYtEznsmcWQIoQzAIAAAAAAOdYJlR9KTr4az8jemqWYBZAihDMAgAAAAAA5zi4ykCKEQQTzAJIEYJZAAAAAADgnJiHf6VOVBBMMAsgRQhmAQAAAACAcxyemGWVAQCnEMwCAAAAAADnWILQ1O6YZWIWgHMIZgEAAAAAgHPYMQuglyKYBQAAAAAAzgk0RTxkxyyA3oJgFgAAAAAAOMfpw7+YmAXgEIJZAAAAAADgHOuOWccP/2qK/UIAsBnBLAAAAAAAcI7DE7NRQTATswBShGAWAAAAAAA4x+HDv5qVH3mBYBZAihDMAgAAAAAAZ4TapY62iEsc/gWgtyCYBQAAAAAAzogRgvrM/BgvTB4O/wLgFIJZAAAAAADgjBghKDtmAfQWBLMAAAAAAMAZUSGooVb1SWkLUUFw0CeFwyntAUDvRDALAAAAAACcYQ1m+/STZKS0hZiHjQWbU9oDgN6JYBYAAAAAADgjKpgtTHkLMVcnsM4AQAoQzAIAAAAAAGcEmiIfE8wC6EUIZgEAAAAAgDNcMDEbVpbk6Rt5kWAWQAoQzAIAAAAAAGe4IJiNeV/rJC8AJAHBLAAAAAAAcIZrg1kmZgEkH8EsAAAAAABwBsEsgF4sx+kG0Lv4/O2qa/SrJRhS39xsDSnyqtDrcbotAAAAAIATXBPMFkQ+JpgFkAIEs0g60zS1dNMezV+6Ra99Uq9Q2Dz4XHaWoWnjSjRz0nBVlhfLMAwHOwUAAAAApFTQLcFsv8jHBLMAUoBgFkm1ZkejZi1aqfX1zTGfD4VNLV5dp8Wr6zSmpEB3zxivitKiFHcJAAAAAHCEayZmOfwLQOqxYxZJs6Rml2bMXdppKGu1vr5ZM+Yu1ZKaXUnuDAAAAADgCq4NZpmYBZB8BLMx+Hw+zZkzR8cff7zy8vLk9Xo1evRoXXXVVaqpqYm73qWXXirDMA77UVdXl4TPxhlrdjSqev6Hag2G4npfazCk6vkfas2OxiR1BgAAAABwDYJZAL0YqwwsNm3apOnTp2v9+vWSJI/Ho1AopA0bNmjDhg2aP3++Xn31VZ1yyilx1+7bt69ycjr/kmdlZUZObpqmZi1aGXcoe0BrMKTrF32sV66tYucsAAAAAGSyqGC2n6Rg6vsgmAXggMxIAm3S1tams846S+vXr9dpp52mDz/8UH6/X36/X2+88YZGjRql1tZWfe9730uo/l//+lc1NDR0+jF48GCbPyNnLN20p9vrCzqzrt6nZZv22tQRAAAAAMCVmJgF0IsRzB4iJydH3/zmNzVx4kS98sorOuGEE5SVlSWPx6PTTjtNDz/8sCRp/fr1+vTTTx3u1r2eXLbFVXUAAAAAAC4UDrsomO0X+ZhgFkAKsMrgEB6PR7Nnz1YwGFRubm7U8xMmTDj469raWn3hC19IZXtpwedv16tr622p9craOvn87Sr0emypBwAAAABwkfYWSWbkNSZmAfQiTMzGECuUlaRg8N97bgYOHJiqdtJKXaNfobB5+Bd2Qyhsqr7Jb0stAAAAAIDLxAo/HQpmvz1vbeSFQJMjfQDoXQhm47B48WJJ0rHHHquKigqHu3GnlgQP/OpMc8DeegAAAAAAd5h611+jL+Y6E8w2Ky/yQsAnmfYMHQFAZ1hl0A0NDQ16/vnndf3116ugoEBz585VVlZimfb777+vX/ziF1qzZo08Ho++8pWv6Mc//rFOPPHEhOpt3769y+dra2sTqpuovrnZttYr6GNvPQAAAACAOxSqLfKCJ1/Kdiam8FmDWTMktbdJufmO9AOgdyCY7cSCBQv0gx/8QKFQSM3NzcrNzdVFF12kH//4xwnvll2+fLluvfVWBQKBg9c2b96s5557Tn/84x81Y8aMuGsOGzYsoV6SZUiRV9lZhi3rDHKyDJX089rQFQAAAADAbQoMSzDr1H5ZSc1mXvTFgI9gFkBSscqgE8FgUI2NjWpubpYkhcNhbd68WZ9++mnCNWfPnq077rhD9fX18vv9Wr58uaqqqtTR0aHvfe97qqurs6t9xxR6PZo2rsSWWtPGDeHgLwAAAADIUAXWiVkng1nrxKzEAWAAko5gthOXXnqpTNM8GMjef//9WrVqlc477zzNnj07rlpf//rXdcEFF+jXv/61fvKTn2jw4MHq06ePJkyYoBdffFFFRUVqbm7WH/7wh7j73LZtW5cfy5cvj7tmT82cNNxVdQAAAAAA7lNotEZecDCYDcqjgGn5oWIOAAOQZASzh2EYhoYPH64rrrhCTz/9tCTp5ptv1pYtW7pd4/zzz9eCBQt0xRVXRD1XVFSk73znO5KkN998M+7+ysrKuvwYOnRo3DV7qrK8WGNKCnpUY2xJoSaVD7CpIwAAAACA27hpYlbq5AAwAEgigtk4TJ06Vcccc4xCoZCef/552+qOGjVKkuIKe93MMAzdPWO88hM8CCw/N1tzZhwvwzBs7gwAAAAA4BbRwWw/Zxr5P1F7ZglmASQZwWycDoSoNTU1ttUcOHCgJMnv99tW02kVpUWae/GJcYez+bnZmnvxiaooLUpSZwAAAAAAN3DT4V8SE7MAUo9gNk5ZWfu/ZOFw2Laae/bskbR/rUEmqRo9SIuqK7u91mBsSaEWVVeqavSgJHcGAAAAAHBaIasMAPRyBLMWTz31lEzT7PT5jRs3SpKOPPLIbtf89NNP9c477xy25jHHHNPtmumiorRIr147WQuvmKQzjxui7KzI9QQ5WYa+ftxQLbxikl65topJWQAAAADoJdw2MeuLWmXA4V8Akivn8C/pPS677DLNmzdPO3fu1KxZs6Kef//99/XJJ59IkqZMmdKtmsuXL9fpp5+u4uJiLV++XIMGRU6DNjU16amnnpIkTZ8+vYefgTsZhqHKkcWqHFksn79d9U1+NQdCKuiTrZJ+XhV6PU63CAAAAABIMQ7/AtDbMTF7iEmTJkmSbrjhBv3sZz/Tzp07Je0PT5999lnNmDFDpmmqqqpKJ5988sH3ff755xo1apTy8/N18803R9Q89thjNWjQIG3evFmTJ0/WK6+8okAgIElau3atvvWtb2nfvn06+uijdcEFF6ToM3VOodejUYMLNX5Yf40aXEgoCwAAAAC9lNsmZqMP/2JiFkByEcweorq6WnPmzJFhGLrrrrtUWloqr9eroqIinX/++dqxY4fGjh2rBQsWRLzvvffe08aNG9XW1qann3464rmCggK9/vrr+sIXvqDPPvtM06dPV35+vrxeryoqKvT3v/9dgwYN0rPPPqu8PMtfAgAAAAAAZKioHbO5bpuYbXamEQC9BsGsxaxZs7Rq1SpddtllKisrUzgcVmFhoSZMmKC77rpLK1asUFlZWcR7Tj75ZI0cOVJer1czZsyIqnn00UdrxYoV+p//+R9NnDhR/fv3l2EYGjt27MH7nXDCCan6FAEAAAAAcFxft60ysE7MBglmASQXO2ZjGDdunP7whz90+/VHH320NmzY0OVr8vPz9cMf/lA//OEPe9oeAAAAAABpz7rK4IIn1mhpuPPDuJOtRd7IC+yYBZBkTMwCAAAAAICU6yt/xOOoidUU4/AvAKlGMAsAAAAAAFKrI6A+RkfEpahgNMVYZQAg1QhmAQAAAABAasU4WKvZ9MZ4YeqwygBAqhHMAgAAAACA1ApGh54tbpuYjREeA4CdCGYBAAAAAEBqWULPsGmoVX0cama/qFUK7S1SOORMMwB6BYJZAAAAAACQWpY1Ac3ySjKc6eX/tMRapcCeWQBJRDALAAAAAABSyxJ4Or3GQOrk8DHWGQBIIoJZAAAAAACQWtaJWet+VwdEHf4lMTELIKkIZgEAAAAAQGpZgtmYoWiKdShHftMTeTEQfUgZANiFYBYAAAAAAKSWZRK1OdZ+VwdErTMgmAWQRASzAAAAAAAgtSy7W5uV71AjkaJWKrDKAEASEcwCAAAAAIDUCjRFPHTDKgMpRh9MzAJIIoJZAAAAAACQWpZJVJ8LDv+SYq0yYGIWQPIQzAIAAAAAgNRy4eFfUoxVBpbJXgCwE8EsAAAAAABILcskaotLJmajAmJ2zAJIIoJZAAAAAACQWtZVBtYVAg6xTsw+/tYahzoB0BsQzAIAAAAAgNSyHv5lumSVgSUgLjDaHOoEQG9AMAsAAAAAAFLLusrAJROz1oC4QH6HOgHQGxDMAgAAAACA1HLrKgNLH33FxCyA5CGYBQAAAAAAqRXwRTxklQGA3ohgFgAAAAAApE6oQ+qIXBFgDUSdwioDAKmU43QD6N18/nbVNfrVEgypb262hhR5Vej1ON0WAAAAACBZgr6oS82mO4LZqFUGBsEsgOQhmEXKmaappZv2aP7SLXrtk3qFwubB57KzDE0bV6KZk4arsrxYhmE42CkAAAAAwHaB6GC2RS5ZZRA1McsqAwDJQzCLlFqzo1GzFq3U+vrmmM+HwqYWr67T4tV1GlNSoLtnjFdFaVGKuwQAAAAAJE0g+t+DbglmW6IO//JLpikxNAQgCdgxi5RZUrNLM+Yu7TSUtVpf36wZc5dqSc2uJHcGAAAAAEiZYOS/CZtNr0yXxBM+SzCbZZhSsMWhbgBkOnf8yYeMt2ZHo6rnf6jWYCiu97UGQ6qe/6HW7GhMUmcAAAAAgJQKNEU8dMu0rBR9+JekmKsXAMAOBLNIOtM0NWvRyrhD2QNagyFdv+hjmaZ5+BcDAAAAANwtYJ2YdcfBX1L0KgNJURO+AGAXglkk3dJNe7q9vqAz6+p9WrZpr00dAQAAAAAcY11lECsMdUhQHgVMy3E8TMwCSBKCWSTdk8u22FLnvjc32FIHAAAAAOAgS9AZc32Ag6JWKxDMAkgSglkklc/frlfX1ttS690Nu9XUFrSlFgAAAADAIZZVBjHXBzioxbpagVUGAJKEYBZJVdfoVyhs327Y1z6xJ+QFAAAAADgkGDmB6nNZMBu1WiFAMAsgOQhmkVQtCR741Zk//XOHrfUAAAAAACnm8lUGzVGrDJqcaQRAxiOYRVL1zc22td6yTXvk87fbWhMAAAAAkEKsMgAASQSzSLIhRV5lGfbVC5tSfZPfvoIAAAAAgNSyBJ0+axDqMFYZAEgVglkkVaHXo0nlA2yt2Rywdz0CAAAAACCFrKsMrKsDHNZsXa1g6RcA7EIwi6Q750ulttYr6GPvegQAAAAAQAq5PJiNWq3AKgMASUIwi6Q7Y9wQ22rlZBkq6eeuv7QBAADShc/n05w5c3T88ccrLy9PXq9Xo0eP1lVXXaWamhqn2wPQW0StMsh3qJHYolcZMDELIDlynG4Ama9fXq5OHlWs9zbs6XGtaeOGqNDrsaErAACA3mXTpk2aPn261q9fL0nyeDwKhULasGGDNmzYoPnz5+vVV1/VKaec4nCnADKeyydmWWUAIFWYmEVKXHPaKFvqzJw03JY6AAAAvUlbW5vOOussrV+/Xqeddpo+/PBD+f1++f1+vfHGGxo1apRaW1v1ve99z+lWAfQGlsO0mt1++BerDAAkCcEsUqKyvFhjSgp6VGPYEXn6ytFH2NQRAABA75GTk6NvfvObmjhxol555RWdcMIJysrKksfj0WmnnaaHH35YkrR+/Xp9+umnDncLIKOFQ1J7S8SlqCDUYS3WoJiJWQBJQjCLlDAMQ3fPGK/83MQP7tq2r01n/G6J1uxotLEzAACAzOfxeDR79mwtWbJEubm5Uc9PmDDh4K9ra2tT2RqA3ibG9GmLdXWAw5qtqxUCTMwCSA6CWaRMRWmR5l58Yo/C2fX1zZoxd6mW1OyysTMAAIDeIVYoK0nBYPDgrwcOHJiqdgD0RjFCTrdNzEatVggyMQsgOQhmkVJVowdpUXVlj9YatAZDqp7/IZOzAAAANlm8eLEk6dhjj1VFRYXD3QDIaDHWArjt8K8Wa1Ac8Emm6UwzADJajtMNoPepKC3SKz+q0uT/fkvb97UlVKM1GNL1iz7WK9dWyTAMmzsEAADoHRoaGvT888/r+uuvV0FBgebOnausrPhnN7Zv397l86xHAHCQZZVBm5mrkBL/qcpkiFplYIal9jYpN9+ZhgBkLIJZOGLZ53sTDmUPWFfv07JNe1U5stimrgAAADLfggUL9IMf/EChUEjNzc3Kzc3VRRddpB//+Mf6whe+kFDNYcOG2dwlgIxlmZh12xoDKcYqA2l/oEwwC8BmrDKAI55ctsVVdQAAAHqLYDCoxsZGNTfvn1oLh8PavHmzPv30U4c7A9ArWINZlx38JXUSFsdYwQAAPUUwi5Tz+dv16tp6W2q9srZOPn+7LbUAAAB6g0svvVSmaR4MZO+//36tWrVK5513nmbPnp1QzW3btnX5sXz5cps/CwBpy7LKIGqfqwsE5FGHaYlLCGYBJAGrDJBydY1+hcL2LE4PhU3VN/lV6PXYUg8AAKC3MAxDw4cP1xVXXKHy8nKdfvrpuvnmm3XBBRdo+PDhcdUqKytLUpcAMk4gMph14yoDyVCz8tRfLf++ZAmUAcAOTMwi5VqCIVvrNQfsrQcAANDbTJ06Vcccc4xCoZCef/55p9sBkMkCTREP3bjKQIoRGDMxCyAJCGaRcn1z7T1xs6CPu07wBAAASEejRo2SJNXU1DjcCYCMlgarDCSpxRoYB5iYBWA/glmk3JAir7KzDFtq5WQZKunnzu+wAgAApJOsrP3/NAiHww53AiCjWVcZmO4MZqMmZoNMzAKwH8EsUq7Q69G0cSW21Jo2bgj7ZQEAALrhqaeekml2vud/48aNkqQjjzwyVS0B6I0sKwGa5c5Bm+iJWYJZAPZzNJj90Y9+pO9973tOtgCHzJwU34ESya4DAACQyS677DJdcMEFuueee2I+//777+uTTz6RJE2ZMiWVrQHobayrDNJlYpZVBgCSwNFgdsGCBZo3b56TLcAhleXFGlNS0KMaY0sKNal8gE0dAQAAZK5JkyZJkm644Qb97Gc/086dOyVJTU1NevbZZzVjxgyZpqmqqiqdfPLJTrYKINNFTcy6NJi1BsZBglkA9mOVARxhGIbunjFe+QkeBJafm605M46XYdizqxYAACCTVVdXa86cOTIMQ3fddZdKS0vl9XpVVFSk888/Xzt27NDYsWO1YMECp1sFkOnSZZWBta9AkzONAMhoBLNwTEVpkeZefGLc4Wx+brbmXnyiKkqLktQZAABA5pk1a5ZWrVqlyy67TGVlZQqHwyosLNSECRN01113acWKFSorK3O6TQCZjlUGAHBQTk8LlJeXJ/zeffv29fT2SHNVowdpUXWlZi1aqfX1h/+LbmxJoebMOJ5QFgAAIAHjxo3TH/7wB6fbANCbWQJOVhkA6M16HMxu3rxZhmF0ecJrV9z4o+g+n08PPfSQnnjiCa1fv16maWrYsGGaOnWqrr/+eo0ePTrumqFQSPfdd5/++Mc/at26dQoGgzrqqKP0jW98QzfddJMGDOi9u1IrSov06rWTtWzTXs1ftlmvrq1XKPzv3085WYamjRuimZOGa1L5AFf+ngEAAAAAdIN1lYGZLqsMfLFfCAA90ONgtm/fvmptbdW9996r0tLSuN57+eWXq7nZXd912rRpk6ZPn67169dLkjwej0KhkDZs2KANGzZo/vz5evXVV3XKKad0u2Z7e7vOOOMMvfHGG5Kk7Oxs5eTkaN26dVq3bp0WLFig9957TyNGjEjGp5QWDMNQ5chiVY4sVm1Dmz6pbdSelnYV9/Xo2KFFGtrfnd9FBQAAAAB0k2lGrzJIl4lZVhkASIIeB7Pjx4/X+++/r9LSUp133nlxvfeqq67q6e1t1dbWprPOOkvr16/Xaaedpt/+9rcaP368QqGQ3n33XV155ZXasGGDvve972ndunXdrnvnnXfqjTfe0JFHHqm5c+fqjDPOUE5OjlatWqVrrrlG7777ri666CK99957Sfzs3M00TS3dtEfzl27Ra59ETsxmZxmaNq5EMycNV2V5MROzAAAAAJCOgi2SIn/a1rWrDKx9BZmYBWC/Hh/+9eUvf1mS9OGHH/a4Gafl5OTom9/8piZOnKhXXnlFJ5xwgrKysuTxeHTaaafp4YcfliStX79en376abdqtrW16X//938lSc8884zOOuss5eTsz8O/+MUv6qWXXtKQIUP0/vvv65133knOJ+Zya3Y0atq97+jChz/Qy2vqIkJZSQqFTS1eXacLH/5A0+59R2t2NDrUKQAAAAAgYTHWAbDKAEBvZkswa5qmVqxYEfd7E91Lmywej0ezZ8/WkiVLlJubG/X8hAkTDv66tra2WzXfeecd7du3TxMmTNBJJ50U9Xy/fv10+eWXS5L+8pe/JNh5+lpSs0sz5i7t1sFfkrS+vlkz5i7VkppdSe4MAAAAAGCrGAdoscoAQG/W42D29NNP1z333KMLLrgg7veuXLlSmzZt6mkLtosVykpSMBg8+OuBAwd2q9a7774rSV3upJ08ebIk9bqJ2TU7GlU9/0O1BkNxva81GFL1/A+ZnAUAAACANPKNOa9EPA6YHrX3fMNiUkStMgi3Sx0BZ5oBkLF6/CdgSUmJfvSjHyX03rKysp7ePqUWL14sSTr22GNVUVHRrffU1NRIkoYNG9bpaw48t2HDhrh72r59e5fPd3eyN9VM09SsRSvjDmUPaA2GdP2ij/XKtVXsnAUAAACANFBgtEU8brauC3CRmCsWAj4pp0/qmwGQsZLyralVq1bpi1/8YjJKO6KhoUHPP/+8rr/+ehUUFGju3LnKyuresPHWrVslSXl5nf94Rn5+/sH7+Hw+FRYWdru3rgJfN1u6aU+31xd0Zl29T8s27VXlyGKbugIAAAAAJEuBLMGsdV2Ai8Q8lCzgk/p276dnAaA7erzKIJZTTjlFL730UjJKp8yCBQvUv39/FRYW6ogjjtBVV12lc845R8uXL+9yLYFVc3N84WO8r09XTy7b4qo6AAAAAIDksgazbt0vK0lt6qOQafnpTA4AA2CzpEzMNjc365xzztFvfvMbXXfddcm4RdIFg0E1Nv57h2k4HNbmzZv16aef6gtf+EK367S1tR3+RYdobW2N6/Xbtm3r8vna2lpNnDgxrprJ5vO369W19bbUenlNrXz+dhV6PbbUAwAAAAAkR1/DH/HYzasMJEMt8qrfoWFyjMPLAKAnkjIxK+0PMm+44QZVV1crFEpsj6iTLr30UpmmeTCQvf/++7Vq1Sqdd955mj17ttPtHVRWVtblx9ChQ51uMUpdo1+hsGlLrbAp1TXGF34DAAAAAFIvnVYZSDHWGQQIZgHYKynB7H/913/JNPcHb4888ojOOOMMNTQ0JONWSWcYhoYPH64rrrhCTz/9tCTp5ptv1pYt3fsR+q52y8ZyYN9sJmtJ8MCvziz7fJ+t9QAAAAAA9rMe/tXi6olZqcUaHAeanGkEQMZKSjB777336oEHHlB2drYk6Y033lBlZaU2btyYjNulzNSpU3XMMccoFArp+eef79Z7CgoK4rpHvK9PR31zs22t97dP6mytBwAAAACwX19ZVhm4fGI2KjhmlQEAmyVtlUF1dbUWL16soqIiSdK6dev0la98Re+8806ybpkSo0aNkiTV1NR06/VHHXWUJMnv93f6mgN7ZQ8cNpbphhR5lWUc/nXd9V7NHvn87fYVBAAAAADYLmqVgYsP/5IkX9TELMEsAHslLZiVpNNPP13vv/++jj76aEnS3r179bWvfU2PPfZYMm+bVFlZ+79k4XC4W68/EOR2dUjXgecOvDbTFXo9OnnUQNvqhUxT9U2dB98AAAAAAOel3SqDqB2zPmcaAZCxkhrMStIxxxyj5cuXq6qqSpIUDAb1/e9/Xz/5yU+SfeuEPPXUUwf348ZyYB3DkUce2a16Bz7v9957r9PXvPvuuxGv7Q2+emyJrfWaA+l3wBwAAAAA9Cbpvsrg4b+vcqgTAJkq6cGsJA0YMECvv/66vvvd7x689tvf/lbf+ta3Dv4YvxtcdtlluuCCC3TPPffEfP7999/XJ598IkmaMmVKt2pOnjxZ/fv31wcffKBly5ZFPe/z+fToo49Kks4+++wEO08/k44eYGu9gj727q0FAAAAANjLOjGbbqsM+lpWMQBAT6UkmJUkj8ejxx57TL/61a8OXvvLX/6iqqoq7dy5M1VtdGnSpEmSpBtuuEE/+9nPDvbV1NSkZ599VjNmzJBpmqqqqtLJJ5988H2ff/65Ro0apfz8fN18880RNfPy8vTDH/5QknT++edr8eLFCoX2T3euWbNGZ511lmpra3XSSSfpP/7jP1LxabrC0P55tu2ZzckyVNLP3T8CAwAAAAC9nXXHbIvp7n/HWSdmCw2CWQD2Slkwe8BPf/pTPfvss8rLy5Npmvroo480YcIE/e53v9M777yjpqamVLd0UHV1tebMmSPDMHTXXXeptLRUXq9XRUVFOv/887Vjxw6NHTtWCxYsiHjfe++9p40bN6qtrU1PP/10VN1bbrlFp512mrZv366vf/3r6tOnj7xer4477ji98847Gjp0qP74xz+m6tN0hUKvR2dUDLGl1rRxQ1To9dhSCwAAAACQHH0NyyoDl0/MtkRNzHK2CQB7pTyYlaRzzz1X77zzjgoKCmQYhmprazVr1iyddtppOuKII1ReXq5vfetb+sUvfqEXXnhBW7duTVlvs2bN0qpVq3TZZZeprKxM4XBYhYWFmjBhgu666y6tWLFCZWVlEe85+eSTNXLkSHm9Xs2YMSOqpsfj0WuvvaY5c+boy1/+svr27SvDMDR69Ghdd911WrVqlUaMGJGiz9A9Zk4a7qo6AAAAAIDksU7Mun3HrM8SHFuDZQDoqZxU3zAYDGr+/Pm699571dLSIkkyDCPiwK3Nmzdry5Yt+stf/nLwWv/+/XX88cdr/PjxGj9+vC655JKk9Thu3Dj94Q9/6Pbrjz76aG3YsKHL1+Tk5GjWrFmaNWtWT9vLGJXlxRpTUqD19c0J1xhbUqhJ5fbuqwUAAAAA2Mw0o1cZyOWrDCyrFgrZMQvAZikLZv/1r3/pvvvu04MPPqjdu3cfvG6aprxer0pLS7Vp06aDAe2hQa0k7du3T2+//bbefvttSUpqMIvUMAxDd88Yrxlzl6o1GIr7/fm52Zoz43gZhk3LagEAAAAAydHepmwj8t/5rl9lYJ2YJZgFYLOkB7OrV6/W3XffraeeekrBYDAicC0qKtLVV1+ta6+9VoMHD1Zzc7M+/vhjrVy58uDH2rVr5ffv/3GBA+8liMscFaVFmnvxiaqe/2Fc4Wx+brbmXnyiKkqLktgdAAAAAMAWweiflGSVAYDeLmnB7EsvvaR77rlHb775pqTICdihQ4fquuuuU3V1tQoLCw9eLygo0Mknn6yTTz754LVQKKTPPvssIqz9+OOPk9U2HFA1epAWVVdq1qKV3VprMLakUHNmHE8oCwAAAADpIuCLusQqAwC9XVKC2WOOOUY1NTWSIgPZMWPG6Mc//rEuvvhi5ebmdqtWdna2xo0bp3Hjxumiiy5KRrtwgYrSIr167WQt27RX85dt1qtr6xUK//v3Tk6WoWnjhmjmpOGaVD6AqWkAAAAASCeWYLbdzFZAHoea6R7rqoU+RrsUapey3d03gPSRlGB2/fr1EQd6TZgwQT/5yU907rnnEqihU4ZhqHJksSpHFsvnb1d9k1/NgZAK+mSrpJ9XhV7+8gMAAACAtGRZZbA/9HR3PhBz1ULAJ+VzADUAeyRtlYFpmvrqV7+qn/70pzrttNOSdRtkqEKvhyAWAAAAADKFZWLW7WsMpE56JJgFYKOkBLPf/va3deONN+pLX/pSMsoDAAAAAIB0ErBMzLr84C+pk2A2xiFmAJCopASzCxcuTEZZ9DI+f7vqGv1qCYbUNzdbQ4pYZwAAAAAAaSkYOTFr3d/qRqay1Gx6VWD4/30xQDALwD5JW2UAJMI0TS3dtEfzl27Ra59EHgCWnWVo2rgSzZw0XJXlxewrBgAAAIB0YV1lYLp/lYG0f2q2QIcGs77OXwwAcSKYhWus2dGoWYtWan197O9AhsKmFq+u0+LVdRpTUqC7Z4xXRWlRirsEAAAAAMTNusogDXbMSvtXLpQYDf++ECSYBWCfLKcbACRpSc0uzZi7tNNQ1mp9fbNmzF2qJTW7ktwZAAAAAKDHgtYds/kONRKfqJULrDIAYCOCWThuzY5GVc//UK3BUFzvaw2GVD3/Q63Z0ZikzgAAAAAAtgg0RTyMebCWC0WtXGCVAQAbEczCUaZpatailXGHsge0BkO6ftHHMk3z8C8GAAAAADjDMmnqS4PDv6QYE7NBJmYB2IdgFo5aumlPt9cXdGZdvU/LNu21qSMAAAAAgO3S9PCv6FUGTbFfCAAJIJiFo55ctsVVdQAAAAAASWCZNG1Jk4nZ6FUGTMwCsA/BLBzj87fr1bX1ttR6ZW2dfP52W2oBAAAAAGxmXWVgpkcwyyoDAMlEMAvH1DX6FQrbsxs2FDZV3+S3pRYAAAAAwGbWVQZpcvhXszVA5vAvADYimIVjWhI88KszzQF76wEAAAAAbBK0BrPpMTEbFSCzygCAjQhm4Zi+udm21ivoY289AAAAAIANTDNq0rSZw78AgGAWzhlS5FWWYU+tbEMq6Zcef7EDAAAAQK/S4ZfCHRGXfMp3qJn4RO3CZZUBABsRzMIxhV6PJow4wpZaE0YMUKHXY0stAAAAAICN/NFTpj4zTYJZa4DMxCwAGxHMwlGm7BmZtecIMQAAAACA7WJMmUatCHApDv8CkEwEs3CMz9+uD7fss6XWii375PO321ILAAAAAGCjQGPkQ9OjoNLjJx6jJmZDQand70wzADIOwSwcU9foVyhsz6xrKGyqvom/HAEAAADAdSxTpk1pMi0rdbJygalZADYhmIVjWoIhW+u9u2GPrfUAAAAAADaw7JiNWg/gYr5YITJ7ZgHYhGAWjumbm21rvdkvf6o1OxoP/0IAAAAAQOpYgsyo9QAuFpBHQdPyb1eCWQA2IZiFY4YUeZWdZc/hX5Lkbw/r+kUfyzQ5CgwAAAAAXMPyo//pNDErGdEHlfkJZgHYg2AWjin0ejRtXImtNdfV+7Rs015bawIAAAAAesCfvhOzUow9s0zMArAJwSwcNXPScNtrPrlsi+01AQAAAAAJsgSZUROoLhfVL4d/AbAJwSwcVVlerDElBbbWfGVtnXz+dltrAgAAAAASZAlmm6wTqC4XNeHLKgMANiGYhaMMw9DdM8bL67Hvt2IobKq+yW9bPQAAAABAD1gmTH1pNjEbvcqAiVkA9iCYheMqSov00+nH2FqzORCytR4AAAAAIEGWCdP0OvwrRpAcaHSmEQAZh2AWrnDyyIG21ivok21rPQAAAABAgqImZtNslYE1SGZiFoBNCGbhCkOKvMrOMmyplZNlqKSf15ZaAAAAAIAesh7+lWYTs1GHf7FjFoBNCGbhCoVej6aNK7Gl1rRxQ1To9dhSCwAAAADQQ5YgM/0mZtkxCyA5CGbhGjMnDXdVHQAAAACADayrDNJ9YjbAxCwAexDMwjUqy4s1pqSgRzXGlhRqUvkAmzoCAAAAAPRIOCQF03vHbJN1YpZVBgBsQjAL1zAMQ3fPGK/83MQO7srPzdacGcfLMOzZVQsAAAAA6KFgc9SlqNUALhcVJLPKAIBNCGbhKhWlRfrPKaMSeu//O7FMFaVFNncEAAAAAEhYjOnSqNUALhd1WBmrDADYhGAWrrJmR6N+/8aGhN77xNItmr90s70NAQAAAAASF2O6tFleBxpJXMyJ2XDYmWYAZBSCWbiGaZqatWilWoOhhGvc+sJard7eYF9TAAAAAIDEWaZLfWaezDSLIqInfM2YKxoAIF7p9achMtrSTXu0vr5nf7mZpvSDP/5Tpmna1BUAAAAAIGGWVQa+NFtjIElN1lUGEntmAdiCYBau8eSyLbbU2bavTcs27bWlFgAAAACgBywTs1H7WtNAS6wwmT2zAGxAMAtX8Pnb9eraetvqPfbe57bVAgAAAAAkyLrKwLqvNQ2ElK0Ws0/kRSZmAdiAYBauUNfoVyhs3/qB1z+tl8/fbls9AAAAAEACLAFmOk7MSjH2zPqZmAXQcwSzcIWWHhz4FUvYlOqb/LbWBAAAAADEKWrHbPpNzEqSz7T0zSoDADYgmIUr9M3Ntr1mc8DesBcAAAAAECfLxGzMg7TSQFSgTDALwAYEs3CFIUVeZRn21izoY3/YCwAAAACIg/Xwr7SdmGWVAQD7EczCFQq9Hk39wmDb6uVkGSrp57WtHgAAAAAgAZaJ2aiAM034rDtmOfwLgA0IZuEal518tG21JpUXq9Drsa0eAAAAACAB/saIh1GHaKWJZnbMAkgCglm4RmV5scqOsGfKdcueFpmmaUstAAAAAECCAhly+BcTswCSgGAWrmEYhh646ERbam3b16Zlm/baUgsAAAAAkKAMWWUQNelrmQQGgEQQzMJVjivrr3FH9rOl1pPLtthSBwAAAACQIH+GTMxGrTJgYhZAzxHMwlV8/nZ9VmfPX3CvrK2Tz99uSy0AAAAAQAKiJmbTM5htsgbK7JgFYAOCWbhKXaNfobA9u2FDYVP1TX5bagEAAAAA4tQRkEKBiEvpe/gXO2YB2I9gFq7SEgzZWq85YG89AAAAAEA3xQgv03ViNmoFg5+JWQA9RzDbhRUrVui8887T4MGD5fF4NGjQIJ155pl6+eWX4651++23yzCMw34sW7YsCZ9J+uibm21zPX6LAwAAAIAjYhyQ5cuYiVmCWQA9R2rViVtvvVUTJkzQn/70J+3atUtZWVnavXu3Xn75ZZ155pn67W9/m1DdvLw8FRUVdfqRk5Nj82eSXoYUeZWdZdhWb9veNttqAQAAAADiYAkvg2a2AvI41EzPRAXKHX6pI+hMMwAyBsFsDI899ph++ctfKjc3V7/+9a+1e/duBQIBbdq0SWeffbYk6aabbtLGjRvjrn3//feroaGh048vf/nLdn86aaXQ69G0cSW21XvunztsqwUAAAAAiINllcH+/bL2DeKkUswVDOyZBdBDBLMWHR0duv322yVJ99xzj2688UYVFxdLko4++mgtXLhQQ4YMUUdHhxYuXOhgp5nrWyeU2lbrlbV18vnbbasHAAAAAOgmyx7WdN0vK3WygoF1BgB6iGDWor29Xddcc41OOukkXXnllVHP5+Xl6dRTT5Ukffzxxynurnc46gj7/rIOhU3VN/ltqwcAAAAA6CbLRGnUAVpppE191GFaIhSCWQA9RDBrkZeXpxtvvFHvvfdep/tey8rKJEmNjdGLzNFzre1hW+s1B0K21gMAAAAAdIMluGxO04O/9jOi+v/2//zNoV4AZAqC2QQEg/sXfPfv39/ZRjJU39xsW+sV9LG3HgAAAACgGwKZs8pAiu6/0Gh1qBMAmSL2SCi69Omnn0rav3M2EWvXrtWtt96qFStWyDRNjR8/Xj/60Y80derUuGtt3769y+dra2sT6tFJQ4q8ys4yFAqbPa6VbUgl/bw2dAUAAAAAiIt1x2xaT8xGT/wWqM2hTgBkCoLZOO3cuVNvvvmmJOmss86K+/2bN2/WrFmztG/fvoPXtm3bphdffFFz5szRrFmz4qo3bNiwuHtwu0KvR9PGlWjx6roe18rPzVFBH36bAwAAAEDKWXfMmukdzDaJiVkA9mKVQZxuuOEGdXR0qKqqSlVVVXG//95779Wll16qbdu2KRAIaM2aNTr33HMlSTfeeCMHiv2fmZOG21LHF+jQsk17bakFAAAAAIhDRu2YlZotwXIhE7MAeohRwjg8+uijWrhwofr27auHH344rvdOnjxZF198scaMGaObb7754PVx48bpmWee0dixY7Vx40bdd999euihh7pdd9u2bV0+X1tbq4kTJ8bVqxtUlheroE+2LQd3PblsiypHFtvQFQAAAACg26yrDNJ9x6wlWGZiFkBPEcx202uvvaarr75ahmHo8ccf19ixY+N6/5QpUzRlypSYz2VnZ+v73/++brrppoNrErqrrKwsrteni+ZAh1qDPQ9lJemVtXXy+dtV6PXYUg8AAAAA0A3WVQZK82DWeviXCGYB9AyrDLphyZIlOvfcc9Xe3q45c+bovPPOs/0eo0aNkiRt3brV9trpqK7RLxvO/pIkhcKm6pv89hQDAAAAAHRPwDoxm+arDKyHfxmsMgDQMwSzh7FixQqdddZZam1t1U033aTrrrsuKfcZOHCgJCkYDCoUsmdSNJ212DQte4AdKxEAAAAAAHHI+IlZglkAPUMw24U1a9bojDPOUFNTk6666ir96le/Stq99uzZI0kqKChQdnZ20u6TLvrm2vs1KOjD1xQAAAAAUsqyY9Z6eFa6se6YZWIWQE8RzHZiw4YN+upXv6o9e/boO9/5ju67774e1du5c6f++te/dvr8xo0bJUnHHHNMj+6TKYYUeZWdZdhSKyfLUEk/ry21AAAAAADdYJrRqwwybGK2HztmAfQQwWwMW7du1dSpU1VXV6fp06friSeeUFZW4l+qzz//XCeccIJmzpypmpqaqOdDoZAeeeQRSdL06dMTvk8mKfR6NG1ciS21po0bwsFfAAAAAJBKwWZJkQeHWHe0ppuoHbOsMgDQQwSzFs3NzTr99NO1detWnXLKKXruuefk8Rw+1GtsbNQJJ5ygvLw8XXLJJRHPHXXUUSovL1djY6NOPfVULVq0SG1t+/8A//zzz3XRRReppqZG/fv319VXX52UzysdzZw03FV1AAAAAADdZFljIElN6b7KwLpj1mBiFkDPEMxa7N69++BU60cffaShQ4eqf//+nX4sWLBAkrR69Wp99NFH8vv9WrhwYUTN7OxsvfDCCzrllFO0c+dOffvb31bfvn2Vn5+v8vJyPf3008rPz9dTTz2loUOHpvxzdqtJRw9QbnbPfotmZ0lfOfoImzoCAAAAAHSL5eAvSWpO91UGsSZmTbOTVwPA4RHMdqGlpUWNjY1dfgSDQUnScccdp/Hjx6tPnz664IILomoNHDhQb7zxhh5//HFNnjxZgwYNUjgc1ogRI3TllVdq1apVmjZtWqo/RVdb9vleBUPhHtUIhaVf/vVTmzoCAAAAAHSLZb9si9lH4TSPIKzBbI4RltqZmgWQuBynG3CbESNGyEzgO15FRUX66KOPunyNx+PRJZdcErXqALE9uWyLLXUee3+zygf11cWVI2ypBwAAAAA4jAw7+EuKXmUgaf/Khty+qW8GQEZI729XIWP5/O16dW29bfVufWGtVm9vsK0eAAAAAKALlh2zzWm+X1bq5PCyGCsbAKC7CGbhSnWNfoXC9u3qMU3pB3/8Z0LT0AAAAACAOFkCy0yYmO1QjtrM3MiLgehDzgCguwhm4UotwZDtNbfta9OyTXttrwsAAJBuVqxYofPOO0+DBw+Wx+PRoEGDdOaZZ+rll192ujUAmcK6yiADJmalGFOzBLMAeoBgFq7UNzc7KXUfe+/zpNQFAABIF7feeqsmTJigP/3pT9q1a5eysrK0e/duvfzyyzrzzDP129/+1ukWAWSCqInZzAhmm6x7Zv0EswASRzALVxpS5FV2lmF73dc/rZfP3257XQAAgHTw2GOP6Ze//KVyc3P161//Wrt371YgENCmTZt09tlnS5Juuukmbdy40eFOAaS9qB2z6b/KQIo1McuOWQCJI5iFKxV6PZo2rsT2umFTqm/y214XAADA7To6OnT77bdLku655x7deOONKi4uliQdffTRWrhwoYYMGaKOjg4tXLjQwU4BZATrKoMMmZiNWsnAKgMAPUAwC9eaOWl4Uuo2B+zfXwsAAOB27e3tuuaaa3TSSSfpyiuvjHo+Ly9Pp556qiTp448/TnF3ADJO1I7ZzJiYjTrEjIlZAD1AMAvXqiwv1shBfW2vW9AnOftrAQAA3CwvL0833nij3nvvPeXk5MR8TVlZmSSpsbExla0ByETWVQYZMjHbbJ2YZccsgB4gmIVrGYah333nS7b+Js0ypJJ+XhsrAgAAZI5gMChJ6t+/v7ONAEh/lknSJuukaZqKnpjlG1kAEhf7W+WAS1SUFukn04/RXS9/Zku9kn5eFXo9ttQCAADINJ9++qmk/Ttn47F9+/Yun6+trU24JwBpKmA9/CszJmajduWyygBADxDMwvUumDjMtmC2rtEvn7+dcBYAAMBi586devPNNyVJZ511VlzvHTZsWDJaApDOLIFl1KRpmoralcsqAwA9wCoDuF59U8C2Wqakd2t22VYPAAAgU9xwww3q6OhQVVWVqqqqnG4HQLqz7pjNkInZqF25TMwC6AEmZuF6LcGQrfWufWqlCrweVY0eZGtdAACAdPXoo49q4cKF6tu3rx5++OG4379t27Yun6+trdXEiRMTbQ9Augm1Sx1tEZeiVgCkqaiJ2QATswASRzAL1+ubm21rvUDI1BWPr9CzV5+kitIiW2sDAACkm9dee01XX321DMPQ448/rrFjx8Zdo6ysLAmdAUhbMaZIm6yBZppixywAO7HKAK43pMir7CzD1pr+jrCumv+hTNO0tS4AAEA6WbJkic4991y1t7drzpw5Ou+885xuCUAm8DdGXYpaAZCmolYysGMWQA8QzML1Cr0eTRtXYnvd7Q1tWri86x+7AwAAyFQrVqzQWWedpdbWVt1000267rrrnG4JQKawTJF2mFlqUx+HmrFXk/UQs/YWlf/0RY346UvONAQgrRHMIi3MnDQ8KXXvWvwpU7MAAKDXWbNmjc444ww1NTXpqquu0q9+9SunWwKQSSx7V/dPy9r7U5BOiXWIWYFaHegEQCYgmEVaqCwv1piSAtvr+gIdevOzf9leFwAAwK02bNigr371q9qzZ4++853v6L777nO6JQCZxjIxG3VgVhrzWSdmJRWqLcYrAeDwCGaRFgzD0N0zxivf5oPAJGne0s221wQAAHCjrVu3aurUqaqrq9P06dP1xBNPKCuLfxIAsJk/1sRsZmhVH4XNyOnfQoNgFkBi+K8wpI2K0iLNvfhE9cmx97ftuzW75fO321oTAADAbZqbm3X66adr69atOuWUU/Tcc8/J4/E43RaATGRZZRC1lzWNmcqKCpoLWWUAIEEEs0grVaMH6Z5vj7e1ZtiU6pv8ttYEAABwm927d6umpkaS9NFHH2no0KHq379/px8LFixwuGMAacsSzPpi7GVNZ9aguYCJWQAJynG6ASBeVaMHKsvYH6japTkQsq8YAACAy7W0tBz2NcFgMAWdAMhIGbzKQPq/A8AO2WbAxCyARBHMIu0Uej06edRALanZbVvNgj72764FAABwkxEjRsg0bfzONgB0JoMP/5Ikn3WVAROzABLEKgOkpe+eNNy2WjlZhkr6eW2rBwAAAAC9WqAXTMwegolZAIkimEVamnpMiW1TrtPGDVGhl4MvAAAAAMAWGT8xy45ZAPYgmEVaMgxDPzvzC7bUmjnJvulbAAAAAOj1LDtmrT/6n+6YmAVgF4JZpK0LJh6l0v49+wt+bEmhJpUPsKkjAAAAAECmT8w2MTELwCYEs0hbhmFo7sUnyutJ7Ldxfm625sw4XoZhHP7FAAAAAIDuCTRGPMy0iVlr0NxPBLMAEkMwi7RWUVqkhy/5svJz49s32ycnSw/OPEEVpUVJ6gwAAAAAeinLxGxzhk3MWg8zKzRYZQAgMQSzSHtVowdpUXWlxpQUdPs9gY6w7nzpU63Z0Xj4FwMAAAAAusc0M37HrM+yY7aAiVkACSKYRUaoKC3SzV//gvrkdP+39Pr6Zs2Yu1RLanYlsTMAAAAA6EXaWyUzFHEp04LZqIlZDv8CkCCCWWSENTsaddWT/1SgIxzX+1qDIVXP/5DJWQAAAACwg2WNgZR5h3/5OPwLgE0IZpH2TNPUrEUr1RoMHf7FMbQGQ5r19EqZpmlzZwAAAADQy/ijh16sE6bprinq8K9WSfx7EkD8CGaR9pZu2qP19c09qrH+X81auHybTR0BAAAAQC/V1hDxsNn0qkM5zvSSJI3qG/HYY4SUr4BD3QBIZwSzSHtPLttiS52b/7yafbMAAAAA0BNt+yIeNqj7hzSni0Yz+nPqr54NCwHonQhmkdZ8/na9urbellphU7r8sX+wbxYAAAAAEmUJZhvNvp28MH35lKcOMzJO6W8QzAKIH8Es0lpdo1+hsH27fNrDpr7/+Ar2zQIAAABAIqwTszGmS9OfEbXOoMhocagXAOmMYBZprSXBA7+6Utfk10Nvb7K9LgAAAABkPH9DxENrgJkprJPARSKYBRA/glmktb652UmpO/uVz1hpAAAAAADxipqYzdBg1rI7l1UGABJBMIu0NqTIq+wsw/a6pqQr57PSAAAAAADiYt0xm4GHf0nRgTOHfwFIBMEs0lqh16Np40qSUntng18Ll29LSm0AAAAAyEhtDREPM/HwLyl6RUN/dswCSADBLNLezEnDk1b7rsWfMjULAAAAAN1lXWWQsROzkZ9XPyZmASSAYBZpr7K8WGNKkvOXvS/QoTc/+1dSagMAAABAxonaMZuZwWz0jlkmZgHEj2AWac8wDN09Y7z65Ni/a1aS/vfNDUmpCwAAAAAZJ2rHbGauMmDHLAA7EMwiI1SUFul/LzghKbU/2tqgprZgUmoDAAAAQMYIhyV/Q8SljN0xa/m8ipiYBZAAgllkjK+NG6KKI/slpfY5972n9zfsZt8sAAAAAHQm6JPMcMSlTF1lYN2dSzALIBEEs8goPzvzC0mpu2l3qy585AP9x3+/pTU7GpNyDwAAAABIa5Y1BlLvOfyLVQYAEkEwi4xSObJYRw3IT1r9rXtbdd4D72tJza6k3QMAAAAA0lJbQ8TDdjNbrerjTC9JZt2dW2D4pVC7Q90ASFcEs8gohmHo/otOUJLOAZMkBTrCuuKJFUzOAgAAAMChLBOzDeorKYn/OHNQY6wVDZZgGgAOh2AWGaeitEh/uGxCUu/hbw/r+kUr2TkLAAAAAAdYgtmY4WWGsE7MSoq5ygEAukIwi4w0ecxgfe0Lg5N6j3X1zVq2aW9S7wEAAAAAaSNqYjZzg9l25ajZ9EZeJJgFECeCWWSsq08bmfR7PLlsS9LvAQAAAABpwd8Q8bDRjDFVmkGipmYJZgHEiWAWGWvU4MKk3+Pl1bVqagsm/T4AAAAA4Hoxd8xmrqhVDZZgGgAOh2AWGavQ69HEowck9R5hSWf9z7scBAYAAAAAvWjHrCQ1WCeCmZgFECeCWWS0a6eOTvo9tu5r09m/f1dz397IYWAAAAAAeq+2hoiHDZkezFp36BLMAogTwSwyWuXIYh01ID/p9wmZ0l0vf6b/+O+3mJ4FAAAA0DtZgtmoHawZJmqHLsEsgDgRzCKjGYah+y86QbnZRkrut3Vvq2bMXaolNbtScj8AAAAAcA3rjtmMP/zLOjHb4EgfANIXwWwXVqxYofPOO0+DBw+Wx+PRoEGDdOaZZ+rll19OuOYTTzyhU089VQMHDpTX61V5ebmuvvpqbd261cbOcaiK0iI9eukEZaUmm1VrMKTq+R8yOQsAAACgd7HumLUGlxmGHbMAeopgthO33nqrJkyYoD/96U/atWuXsrKytHv3br388ss688wz9dvf/jbumpdccom++93v6u2339bevXtlGIY+//xzPfjggzruuOO0YsWKJHwmkKSq0YN05zkVKbtfazCk6xd9zM5ZAAAAAL2HvyHiYdSP+meYqFUNBLMA4kQwG8Njjz2mX/7yl8rNzdWvf/1r7d69W4FAQJs2bdLZZ58tSbrpppu0cePGuGrOnz9f/fr10/z589Xa2qq2tjZt2LBB5557rpqamnT++eervb09WZ9Wr3fBxKM0piR137FdV+/Tsk17U3Y/AAAAAHBMu19qb424FHU4VoaJOtzMEkwDwOEQzFp0dHTo9ttvlyTdc889uvHGG1VcXCxJOvroo7Vw4UINGTJEHR0dWrhwYbfr/vrXv5YkPfjgg5o5c6a8Xq8kaeTIkXr66af1xS9+UZs3b46rJuJjGIbunjFeeZ7U/bZ/ctmWlN0LAAAAABwTI5TM9B2zUcEzE7MA4kQwa9He3q5rrrlGJ510kq688sqo5/Py8nTqqadKkj7++ONu1Vy3bp3WrVunkpISffvb34563uPx6D//8z8lSX/5y18Sbx6HVVFapIcu+bJSdBaYXllbJ5+fKWgAAAAAGS5GKNlk/VH/DBO1qqFtnxQOO9MMgLREMGuRl5enG2+8Ue+9955ycnJivqasrEyS1NjYvcOd3n33XUnSSSedpKys2F/yyZMnS5LeeeedeFtGnKpGD9IvU7RvNhQ2Vd/kT8m9AAAAAMAxbQ0RD5vMPIWU7UwvKRIVzJphKehzphkAaYlgNgHBYFCS1L9//269vqamRpI0bNiwTl9z4Lndu3d3O/BF4lK5b7Y5EErJfQAAAADAMZaJ2Ubr/tUMFHOHriWgBoCuxB4JRZc+/fRTSft3znbH1q1bJe2fxu1Mfn5+xOuPO+64btXevn17l8/X1tZ2q05vc2Df7Iy5S9UaTG5wWtAns79LDAAAAADWYLYhw9cYSFKLvGo3s+UxDvk3Zds+6YjhzjUFIK0QzMZp586devPNNyVJZ511Vrfe09zcHNc94nl9V1O46FpFaZHmXnyiqud/mNRwdk9zUKMGJ608AAAAADjPcvhX1I/5ZyRDjeqrgWr69yUOAAMQB1YZxOmGG25QR0eHqqqqVFVV1a33tLW1xXWP1tbWRFpDAqpGD9Ki6sqkrjW4bN4/tKRmV9LqAwAAAIDjoiZmM3+VgdTJAWAA0E1MzMbh0Ucf1cKFC9W3b189/PDDTrcjSdq2bVuXz9fW1mrixIkp6iY9VZQW6dVrJ2vZpr36/Zs1em/DHlvrtwZDqp7/oRZVV6qitMjW2gAAAADgCr1wx6wUI4C2TA4DQFcIZrvptdde09VXXy3DMPT4449r7Nix3X5vV7tlYzl03+zhlJWVxVUbsRmGocqRxaocWaxX19bpvxb+U4EO07b6rcGQrl/0sV65tkqGYdhWFwAAAACcNuKnL+l/PGv1zUOO1+gNO2YlqcEaQDMxCyAOrDLohiVLlujcc89Ve3u75syZo/POOy+u9xcUxPedwnhfD3tNGzdEz119so4aEF+gfjjr6n1atmmvrTUBAAAAwA2K1BLxuHfsmJUarQE0wSyAOBDMHsaKFSt01llnqbW1VTfddJOuu+66uGscddRRkiS/39/paw7dK3vg9XBORWmR3v7xabpp+jHKzrJvwvXJZVtsqwUAAAAAblFkRB5izY5ZADg8gtkurFmzRmeccYaampp01VVX6Ve/+lVCdUaNGiWp632wB54bOHCgiorYQ+oGhmGo+j9GauEVX7Gt5itr6+Tzt9tWDwAAAADcoH/UxGzvCGajVxk0ONIHgPREMNuJDRs26Ktf/ar27Nmj73znO7rvvvsSrlVVVSVJWrp0qUwz9t7Sd999N+K1cI8j8nNtqxUKm6pv6nxyGgAAAADSUZFhCWZ7yY7Z6FUGDY70ASA9EczGsHXrVk2dOlV1dXWaPn26nnjiCWVlJf6lGjt2rMaOHava2lotWrQo6vmOjg79/ve/lySdffbZCd8HydESDNla790Ne2ytBwAAAABOMhSO2jEbNUmaoTj8C0BPEMxaNDc36/TTT9fWrVt1yimn6LnnnpPH4zns+xobG3XCCScoLy9Pl1xySdTzN954oySpurpaCxYsUCAQkCRt2rRJ3/nOd7Ry5UoNHz5cF1xwgb2fEHqsb2724V8Uh9kvf6o1OxptrQkAAAAATilUq7KMyJ8Obeglh381WCdm/Q2O9AEgPRHMWuzevVs1NTWSpI8++khDhw5V//79O/1YsGCBJGn16tX66KOP5Pf7tXDhwqi6l19+uS666CI1NjbqoosuUn5+vvLy8jRy5Eg999xz6tevn5555hnl5tr3Y/Owx5Air60HgPnbw7p+0cedrrUAAAAAgHTS37LGQOpNh38xMQsgcQSzXWhpaVFjY2OXH8FgUJJ03HHHafz48erTp0+nU69PPvmkHn30UZ1yyikqKiqSaZoaPny4rrjiCn388ceaMGFCKj89dFOh16Np40psrbmu3qdlm/baWhMAAAAAnGBdYxAwc+RX7xg6itox294qtXOuCIDuyXG6AbcZMWJEQpOMRUVF+uijjw77ussvv1yXX355Iq3BQTMnDdfi1XW21nxy2RZVjiy2tSYAAAAApFp/oznicaMKJNn3U4duFnOXrr9B8gxJeS8A0g8Ts0A3VJYXa0yJvT+Ks3h1rXz+dltrAgAAAECq9VdkMNtb9stKMSZmJamtIeV9AEhPBLNANxiGobtnjJfXY9//ZUxJD7+z0bZ6AAAAAOCEIsuO2ZhhZYYKKVs+My/yIntmAXQTwSzQTRWlRfrp9GNsrfm/b2zU6u0NttYEAAAAgFSy7piN+eP9GSwqiCaYBdBNBLNAHE4eOdDWeqakH/zxnwntNQYAAAAAN4i9Y7b3iAqiCWYBdBPBLBCHIUVeZWfZu8R+2742Ldu019aaAAAAAJAqvXnHrBTj8/U3ONIHgPRDMAvEodDr0bRxJbbXfey9z22vCQAAAACpELVjtpcFs6wyAJAoglkgTjMnDbe95uuf1svnb7e9LgAAAAAkmzWYbehlqwwaWWUAIEEEs0CcKsuLNWxA3uFfGIewKdU3+W2tCQAAAACpYF1lEBVUZrioIJpgFkA3EcwCcTIMQzdN/4LtdT/e3mh7TQAAAABItujDv3rZKgPr6oa2Bkf6AJB+cpxuAEhHVaMHypBk2ljzVy99ovLifBV4PRpS5FWh12NjdQAAAABIjiJZVhn0sh2zTMwCSBTBLJCAQq9Hpx87WH/75F+21dzT0q5zH1gqScrOMjRtXIlmThquyvJiGYZh230AAAAAwDbtbfIakedl9LYds9YgevP2HRrhTCsA0gyrDIAEXXby0UmrHQqbWry6Thc+/IGm3fuO1uxgzQEAAAAAF4oxHdrQy3bMNlqCaOtqBwDoDMEskKDK8mKVHeFN+n3W1zdrxtylWlKzK+n3AgAAAIC4xNin6lN+6vtwkHXHbD+1SuGwQ90ASCcEs0CCDMPQAxedmJJ7tQZDqp7/IZOzAAAAANzFMjHbaOYr3MuiBuuEcJZhSgH+7Qbg8HrXn5aAzY4r66/qyclbaXCo1mBI1y/6WKZp55FjAAAAANADlmC2t60xkKQGxTjsjAPAAHQDwSzQQ//vxGEpu9e6ep+WbdqbsvsBAAAAQJesE7OxQsoM16Y+CprZlosNjvQCIL0QzAI9NKTIq+wsI2X3++WLa1N2LwAAAADokr8h4mFvnJiVjKgDwJiYBdAdBLNADxV6PZo2riRl9/ukzqdfEM4CAAAAcAMmZiXFCKQJZgF0A8EsYIOZk4an9H5/eG+znnh/c0rvCQAAAABR2DErKcaeWYJZAN1AMAvYoLK8WGNKUvsfILe9sFartzek9J4AAAAAEIGJWUlSo2n5vC0rHgAgFoJZwAaGYejuGeOVn5t9+BfbxJT0gz/+U6ZppuyeAAAAABDBcshVb52Yjd4x2+BIHwDSC8EsYJOK0iLNvfjElIaz2/a1admmvSm7HwAAAABEYGJWEjtmASSGYBawUdXoQVpUXamyI7wpu+cf3t2UsnsBAAAAQARrMGv9kf5eIurzJpgF0A0Es4DNKkqL9M6PT1PZEXkpud/rn/5LPn97Su4FAAAAABEsu1R76yqD6MO/GhzpA0B6IZgFkiArK0sPzkzNWgNTUn2TP+n3AQAAAIAI4ZDkb4y41GDdtdpLsMoAQCIIZoEkSeXO2Q8+Z88sAAAAgBSzhLJS752YjT78i2AWwOERzAJJdGDn7JiS5P7Hyf1vbpRpmkm9BwAAAABEiBE+9tbDv9gxCyARBLNAklWUFunVaydr4RWTdOZxQ5Rl2H+PHQ1tWraJqVkAAAAAKWTZo+o3PQoo15leHBa1YzYUkNrbnGkGQNogmAVSwDAMVY4s1v0XnaiVt35VxX09tt/jyWVbbK8JAAAAAJ2yTIX21v2yUicrHFoZngHQNYJZIMX65eXq/otOsL3uK2vr5PO3214XAAAAAGJq3R3xcF8v3S8rSU3qq5Bp+fHI1j3ONAMgbRDMAg74SvlAjRtaaGvNUNhUfZPf1poAAAAA0Knm+oiHu8z+zvThAmFlaa/6RV5s/pczzQBIGwSzgEN+ftaxttd8ZsU2ra9rYnIWAAAAQPJZgsddKnKoEXeICqYtwTUAWOU43QDQW1WWF2t4cb627Gm1rebcdz7X3Hc+lyHplNED9d2ThmvqMSUyjCScOAYAAACgd2NiNsIu0xJME8wCOAwmZgGHGIah//5/X0xKbVPSkprd+v7jH6ritle04IMtMk0zKfcCAAAA0EtZgsfd1mCyl9ltnRhmlQGAwyCYBRw08ehiTRhxRFLv0RIM62fPr9GEO1/X6u0NSb0XAAAAgF6keVfEw6iJ0V4mamK4hWAWQNcIZgGHXffVMSm5z+6WoL7x+/c0f+nmlNwPAAAAQIazrjJQf2f6cInoVQYEswC6RjALOKyyvFhjSgpSdr9b/rKWcBYAAABAz3QEpba9EZfYMds/8gI7ZgEcBsEs4DDDMHT3jPHKz81O2T1v+cta1hoAAAAASFzLrqhLvX6VQdSOWYJZAF0jmAVcoKK0SHMvPjGl4ewljy5XOBxO2f0AAAAAZBBL6NhuZqtRfR1qxh2igml/o9QRcKYZAGmBYBZwiarRg7SoujJlaw32tbVr8n+/pTU7GlNyPwAAAAAZxDIxu1tFMnt5xBBzlQN7ZgF0oXf/qQm4TEVpkV69drJ+eXZFSu63fV+bZsxdqiU10T+GBAAAAACdsh781cvXGEhSo/oqaFp+CpJgFkAXCGYBlzEMQ5PKB6Tsfq3BkKrnf8jkLAAAAIDuiwpm+zvTh6sY2qX+kZfYMwugCwSzgAsNKfIqO8tI2f1agyFdv+hjmaaZsnsCAAAASF/zXlse8Xg3E7OSYnwdCGYBdIFgFnChQq9H08aVpPSe6+p9WrZpb0rvCQAAACA9DTIaIh7vEsGsFGOlQwtr4wB0jmAWcKmZk4an/J5PLtuS8nsCAAAASD8DjaaIx6wy2C/q68DELIAuEMwCLlVZXqwxJQUpvecra+vk87en9J4AAMAZP/nJT2QYxsGPBx980OmWAKSRQWqIeMzhX/tFTQ4TzALoAsEs4FKGYejuGeOVn5t9+BfbJBQ2Vd/kT9n9AABA6pmmqWuuuUa/+c1vdMQRRygvL8/plgCkoUFG5OHBTMzuFz0x+y9H+gCQHghmARerKC3S3ItPTGk42xwIpexeAAAgtUKhkC6//HLdf//9Gjx4sN58800NHjzY6bYApJtgqwqNtohLu9kxKynW4V8EswA6RzALuFzV6EFaVF2ZsrUGBX1SFwIDAIDUaW9v14UXXqh58+aprKxM77zzjo4//nin2wKQjlqiw0ZWGewX9XUgmAXQBYJZIA1UlBbp1Wsna+EVk3TmcUNkJPFee5qDSawOAACc8tlnn+nFF19UeXm5lixZorFjxzrdEoB0ZQkb28xcNYu1KJK0S/0jL7S3SIFmR3oB4H45TjcAoHsMw1DlyGJVjiyWz9+uJet36YdPrVQobNp6n5mPfqCfn/kFnXdimQq9HltrAwAA5xx33HH685//rIqKCh155JFOtwMgnVkOtNo/JZrM8ZH0EXPXbnO91Ce1BzsDSA8Es0AaKvR6dOYXj1Rhnkffe/wfCnbYF862h0zd/uInuv3FTzTx6AG6dupoVY4slmHwH1oAAKS7r33ta0mpu3379i6fr62tTcp9ATjEGsxap0R7sVZ51WL2UV8j8O+LLbuk4pHONQXAtQhmgTRWNXqQ7jr3i7r+mY+TUn/553t14SMf6KgB+br/ohNUUcreKAAAEG3YsGFOtwAglSyrDKIOvOrldptF6msc8jWyBNkAcAA7ZoE098Wy5P9H0Na9rTrvgfe1pGZX0u8FAAAAwOUswSwHf0WKmiDmADAAnWBiFkhzQ4q8ys4ybN81axXoCOuKJ1bo2atOYnIWAABE2LZtW5fP19bWauLEiSnqBkDSRQWz/Z3pw6WigmomZgF0gmAWSHOFXo+mjSvR4tV1Sb+Xvz2sq59cobd/fJqyshi4BwAA+5WVlTndAoBUYsdsl6KCaoJZAJ0gWQEywMxJw1N2r237/Jr8329pzY7GlN0TAAAAgItE7Zjt51Aj7hS1c7eZlXAAYiOYPYyf/OQnMgzj4MeDDz6YUJ3bb789ok5nH8uWLbP5M0BvUFlerDElBSm73/Z9bTrvgff11PKt8vnbU3ZfAAAAAA4zzeiJWVYZRNglVhkA6B6C2U6YpqlrrrlGv/nNb3TEEUcoLy/Plrp5eXkqKirq9CMnh+0SiJ9hGLp7xnjl52an7J6BjrB++qfVOv6O1/SDP36o9zfulmkmd88tAAAAAIcFmqRQIOISqwwiRa8y4PAvALERzMYQCoV0+eWX6/7779fgwYP15ptvavDgwbbUvv/++9XQ0NDpx5e//GVb7oPep6K0SHMvPlF5ntT+3zpsSotX1+nChz/QtHvfYcUBAAAAkMlihIxRh131clFfj5Z/7Z80BgALglmL9vZ2XXjhhZo3b57Kysr0zjvv6Pjjj3e6LaBbqkYP0jNXnaSCPqmbnD3U+vpmzZi7VEtq2KEEAAAAZCTLj+U3mfkKKNehZtwpamI2FJT8DU60AsDlCGYtPvvsM7344osqLy/XkiVLNHbsWKdbAuJSUVqkhy52bvK6NRhS9fwPmZwFAAAAMlHUflmmZa32KMZhaKwzABADwazFcccdpz//+c9asmSJRowY4XQ7QEIqR6b2MDCr1mBIP/jjP7W+ronDwQAAAIBMYgkYd1sPusL+CWIvB4ABODyC2Ri+9rWv6cgjj3S6DSBhThwGZrV1b6u+du8Sjf/F3zgcDAAAl5g6dapycnIiPrZs2SJJuuaaayKuT5061eFuAbiSJZiN+rF97FdQEvmYiVkAMeQ43UBvtHbtWt16661asWKFTNPU+PHj9aMf/Sih//jdvn17l8/X1tYm2ibS3IHDwKrnf6jWYMixPkJhU4tX12nx6jqNKSnQ3TPGq6KU76oDAOCEUCikUCj2fxeEw+Go1wJAlKhglv+2j6mgRNq9/t+PmZgFEAPBbIpt3rxZs2bN0r59+w5e27Ztm1588UXNmTNHs2bNiqvesGHD7G4RGaRq9CAtqq7UrEUrtb6+2el2Dh4ONvfiE1U1epDT7QAA0Ou89dZbTrcAIN1F7Zjt70wfblcwOPIxE7MAYmCVQYrde++9uvTSS7Vt2zYFAgGtWbNG5557riTpxhtv1Mcff+xwh8g0FaVFevXayVp4xSSdPKrY6XY4HAwAAABIZ9Zglh2zsfUlmAVweEzMpsjkyZN18cUXa8yYMbr55psPXh83bpyeeeYZjR07Vhs3btR9992nhx56qNt1t23b1uXztbW1mjhxYsJ9IzMYhqHKkcWqHFmsV9fW6T8X/FPtIef2vbYGQ7p+0cd65doqGYbhWB8AAAAAum/ET1/SB322quSQ/4RnlUEnoiZmWWUAIBrBbIpMmTJFU6ZMiflcdna2vv/97+umm27Sm2++GVfdsrIyO9pDLzJt3BA9/4OT9YM/fqite9sc62NdvU/LNu1V5Ujnp3gBAAAAHJ6hsIrVFHGNVQad4PAvAN3AKgOXGDVqlCRp69atDneC3qCitEhv//g0/fF7E5Xnce6PgXv+tv7wLwIAAADgCkeoWTlG5EGBBLOdsAazLQSzAKIRzLrEwIEDJUnBYJATcJEShmHo5NGD9MxVJynPk+1ID8s379UHm/Y4cm8AAAAA8RlkNERd26vC1DeSDgoshx237JLC/FsfQCSCWZfYs2d/OFVQUKDsbGdCMvROFaVFeuiSE5Wf68zvu588t0qm6dy+WwAAAADdM8iIPMB3j1moDjYkxjThd6sjL5hhqZWhFACRCGZTZOfOnfrrX//a6fMbN26UJB1zzDGpagk4qGr0IC2qrtSYkoKU33vznlYt27Q35fcFAAAAEJ+BigxmWWPQub3qp7BpOeiYA8AAWBDMpsDnn3+uE044QTNnzlRNTU3U86FQSI888ogkafr06aluD5C0f3L21Wsna+EVk3TmcUOUZRz+PXa548W1CofDh38hAAAAAMdYVxnsMoucaSQNhJStPdY1DxwABsCCYNYmjY2NOuGEE5SXl6dLLrkk4rmjjjpK5eXlamxs1KmnnqpFixapra1N0v7Q9qKLLlJNTY369++vq6++2on2AUn7985WjizW/RedqI9v+5p+fd5x8uYk/4+Jz+p8OuaWV/Tnj3Yk/V4AAAAAEmNdZbBL/Z1pJE3stk4UE8wCsCCYjWHq1KnKycmJ+NiyZYsk6Zprrom4PnXqVEnS6tWr9dFHH8nv92vhwoUR9bKzs/XCCy/olFNO0c6dO/Xtb39bffv2VX5+vsrLy/X0008rPz9fTz31lIYOHZryzxeIpdDr0bcnHKVnrz4pJSsOgiFT1z69Uve/tSHp9wIAAAAQP+vE7G4mZrsUNVHMKgMAFgSzMYRCoaiPA8LhcMznjjvuOI0fP159+vTRBRdcEFVz4MCBeuONN/T4449r8uTJGjRokMLhsEaMGKErr7xSq1at0rRp01L2OQLdZV1xkJ3kHQe/eWUdk7MAAACACw1SQ8RjVhl0bZeswSwTswAicXxiDG+99Vbc7ykqKtJHH33U5Ws8Ho8uueSSqFUHgNsdWHFQObJYPn+76hrbdMkf/qHaRn9S7nfjc6v0zeOHKiuL7x0BAAAAbjHQaIp4zOFfXYv6+jAxC8CC1ANAXAq9Ho0u6afffWd80u4R7Ajrsfc2J60+AAAAgPhFHf5lnQhFBOtE8Xsff6oRP33JoW4AuBHBLICETDy6WBNGHJG0+r9/syZptQEAAADEKdSuYsMXcYkds12zfn2swTYAEMwCSNh1Xx2TtNr7Wjv0/x54X+9v3C3TNJN2HwAAAADd0LIr6hKrDLq2S/0jHg8yGp1pBIBrEcwCSFhlebGGF+cnrf6KLft04cMfaNq972jNDv4jBgAAAHCMZT9qh5mlfSpwqJn0YA2ujzCa5VGHM80AcCWCWQAJMwxD/zVldNLvs76+Wec98L7eWc8ppgAAAIAjmiMnZnerSCaRQpesO2YlqVgMnAD4N/4UBdAjXyxLzV6pQEdYl/zhH/rzRztScj8AAAAAh7BMzMYKHRGpUX0VNLMjrrHOAMChCGYB9MiQIq+ys4yU3e/ap1fqwbc3sHcWAAAASKXmuoiHHPx1eKaytEeRX6cSY59D3QBwI4JZAD1S6PVo2riSlN5z9svr9B///RZ7ZwEAAIBU2bc54mGtWexMH2mm1hwQ8XiYwXo2AP9GMAugx2ZOGp7ye27d26oZc5dqSU306bAAAAAAbLZ3c8TDLWZqhzPSlfXrdBTBLIBDEMwC6LHK8mKNKUn9iaytwZCq53/I5CwAAACQbPs+j3hIMNs9Wy1fp+FGfSevBNAbEcwC6DHDMHT3jPHKz80+/Itt1hoMadbTK9k5CwAAACRLu19q2hlxaas52KFm0suWcOTXiWAWwKEIZgHYoqK0SHMvPtGRcHb9v5q1cPm2lN8XAAAA6BUatkqKHIRgYrZ7rF+nMmOXFA471A0AtyGYBWCbqtGDtKi6UqMH9035vW/5y5qIfbM+f7tq6n1aua1BNfU++fztKe8JAAAAyAiWNQa7zX5qUZ5DzaQX6yqDPkaH5NvZyasB9DY5TjcAILNUlBbptev+QwuXb9PNf16tcIo2DITCpq54fIVu/caxWlKzW699Uq/QITfPMqRJ5QN0zpdKdca4IeqXl5uaxgAAAIB0tzcymGWNQfftUpFazD7qawT+fXHv51JRmXNNAXANJmYB2M4wDF34laP0+OUTZaTwvv6OsH72/Bq9vKYuIpSVpLApvb9xr258drW+eMffdNEjy/T+xt3spgUAAAAOh4O/esCIDrItX08AvRfBLICkqRo9SHeeM87pNmJ6b8MeXfjwB5p27ztas6PR6XYAAAAA92Jitke2Wb9eewlmAexHMAsgqS78ynAdNSDf6TY6tb6+WTPmLo3YTwsAAADgEJYJz61hJmbjETVhzMQsgP9DMAsgqQzD0P0XnaA+Oe7946Y1GFL1/A+ZnAUAAACswmFp35aIS1uYmI1LVDDLxCyA/+PepARAxqgoLdIj3/2yvB73/pHTGgzp2qdWan1dk1Zua1BNvU8+f7vTbQEAAADO8tVKoUDEJXbMxocdswA6k+N0AwB6h6rRg/TsVSdp1qKVWl/f7HQ7MW3Y1ayv3bvk4OMsQ5r6hcG67OSjVVleLMNI5VFmAAAAgAtYQsRWs492qb8zvaSpqCDb3yi17pXyBzjTEADXcO/4GoCMU1FapFevnayFV0zS9Ar3f5c9bEp/++RfuvDhD1T1mze0enuD0y0BAAAAqRXz4C8GFuKxwxyoDtMSvzA1C0AEswBSzDAMVY4s1gMzv6ynr5zk6t2zh9q+z69v3vee5i/d7HQrAAAAQOpYD/5iv2zcOpSjnWZx5MV9mx3pBYC7pEciAiAjfaW8WI9898vKz812upVuMU3plr+sPRjO+vztqqn3sZMWAAAAmcsyMct+2cRwABiAWNgxC8BRVaMHaVF1pat3z1rd8pe1enlNrT74fJ9CYfPg9ewsQ9PGlWjmpOGqOLKf6psCagmG1Dc3W0OKvCr0ehzsGgAAAEgAE7O22MYBYABiIJgF4LgDu2eXbtyjK+evUHMg5HRLh/X+xr1R10JhU4tX12nx6rqo57IM6eRRA/XVY0s06egBGto/j6AWAAAA7he1Y5aJ2URET8xudqQPAO5CMAvAFQzD0EmjBuqpKyt1/oNL1dbu/nA2HmFTWlKzW0tqdkvaH9SeUTFEMycNV2V5seoa/Vq7s1F7W9s1IN+jcUcWaWj/PIe7BgAAQK/Wtk/yN0Rc2sLEbEKiglkmZgGIYBaAy1SUFumhS05U9fwP1RrMrHD2UGFTB6drs7OkUDj6NUP69dH3qo7W904+WllZrAQHAABAilkOqAqZhnaYg5zpJc1FrYBo2im1+yWP15mGALgC/9IH4DoH9s6OKSlwupWUiBXKSlJdU0D/30uf6ZhbXtWfP9oR8RwHjwEAACDpLGsMdpoD1c58V0KiD00zpYYtjvQCwD34ExWAKx3YO7ts017NX7ZZr66tjzhoKyfL0LRxQ3TyqIG686VPMnq6NhgK69qnV2pHQ6u+dNQRmr90i177pL7Tg8cqy4tlGIaDHQMAACAjWH7cnjUGiWtRnnab/TTQaPr3xb2fS4PGOtcUAMcRzAJwLcMwVDmyWJUji+Xzt6u+ya/mQEgFfbJV0s978PCsYQPyMn71gST996vrO33u0IPHhhfn67+mjNYXy4o0pMjLIWMAAABIDAd/2WqrOTgymGXPLNDrEcwCSAuFXk+nAeOB1QezFq3U+vrmFHfmPlv2tOr6Zz6W9O9J2m+dUKqjjshXa3tYfXOzCWwBAABweJYds9E/jo94bDFLdII2/PuC5esLoPchmAWQEQ5dffD7N2v03oY9TrfkCodO0h6qJ6sPfP521TX61RIMEfICAABksqiJWVYZ9ETU128vE7NAb0cwCyBjHLr6oKktqPvf2qgH397kdFuudGhgO6akQHfPGK+K0qJOX2+appZu2hNzv22WIZ08aqC+emyJJh09QEP75xHUAgAApLuOgNQUeQAtqwx6ZmvY8vVjlQHQ6xHMAv8/e/cdHlWZ/n/8M+kJaRAgoSbSohTBAgsKiFiwI6tiBRUVsOxPbCi7Ftx1Ffe7WHZFwbKgKCBY1rWsuioqKKi4dAVCDSWJtISE9Jnz+yObMTOZhMzkzJyZzPt1XXM5c85znnMfDkMmt/fcD1qk5PgY3X/e8fpi0y+0NziGLQUlGjtnheaMO0XDerart3/D3qJG20Q4DGlZzgEtyzkgqSZRe17fjCZX41KBCwAAEIQKcyUZLptY/Kt56v35Hd4lORxSRIQ1AQGwHIlZAC2WzWbTU2MHaOycFS1+YbDmKq20a9L8H7V40hCXytllOfu9XljNYeiY1biNVeA2p80CAAAATOL2NfuDRpJKlGBRMC1DvR699gqpeJ+U0tmagABYjv8tA6BF69spRXPGnaKEmEirQwl6pZV23bN4rQyjJkm6YW+R10lZd7XVuMty9ju3bdhbpFHPfK1rXvpO/96Q75KUlX5ts3DNS99p1DNfa8PeIp/PDwAAAB8ddu8vSxuD5tqvVJUasa4b6TMLhDUSswBavGE922nxpCHqlZ5odShBb3NBsVZuPyTDMHT34jWmVBrXVuNu2FukZTn7NXbOiia3l/CU2AUAAEAAuCUMaWNgBlv9BcDoMwuENVoZAAgLfTul6JMpw7Vy+yHNX7lTn2wsqFepiRqvr9wlQ4apvXlLK+26/Y3/an9JhdfJXvc2C/SkBQAACIDD7olZKmbNkGu01/Ha/esGKmaBsEZiFkDYsNlsGtI9TUO6p6m4vEoFR8pVUmHXvsOlumvxWlVUO6wOMSh8vDFflXbz/yx2HSr1+djSSrtuff1H9emUov+49aSNsEmn92ir60/L1FnHp9OTFgAAwAxuCcNcB4lZM9RLcFMxC4Q1ErMAwlJSXLSzynJAl1QlxUc3u59qS2F3GPr85wKrw6hn9+Ey7T5cVm+7w5CW5RzQspwDSoyN1O8vOEFXD+pKghYAAMBXDod0eKfLJloZmKNeYpaKWSCs0WMWAEQfWneh2uWhpMKu37+7QUOfXMqiYQAAAL4qzpPsFS6bWPzLHLvr9ZjdaUkcAIIDiVkA+J/aPrQLbxmsC/plKDLCteIyKsKmC/pm6KELT9D5fevvR/DYW1imy2d/y6JhAAAAvnBLFpYZMfpFqZaE0tLUqzwuL5TKDlsSCwDr0coAAOpoqA9tYmyk0pN/XWTqpmGqt799Uqw27ivW/JU79fGG/JCtOo20SfYQjb2u8iqHy6JhAAAAaCK3vqe5RntJFCWYYa/RTnbDpkhbnQ/ch3ZInVpbFxQAy5CYBYAG1O1D29T9dZO6+UVlWrnjsP6zMV/fbD0ou1FnwSpJsgVny4CzTkjX55t+cVlgK1SVVtp1z+K1+njKsID1nK259+U6WmlXq5hIZaTENfr3CAAAIOi4L/xFGwPTVClK+4y26mKr882uwzukTidbFxQAy5CYBQA/qE3a9kxP1rjBmR6rb1vFRGruNzv10rLtyj9SUW+ODilxunnocerRPlG3vvHfgC1MduPpxykq0qaP1ucH5Hz+trmgWCu3H9KQ7ml+O4dhGFqx/aDmr9ilT38qcElqR0bYNKpPuq4bnKkh3dJYlMwDktkAAASZgzkuL1n4y1y7jPbqojqJ2QNbrQsGgKVIzAJAADRUfXvTsG66aVg35RWW6ae8Izp4tFJprWLUu0OyOqTGO8ctnjREdy9eoy0FJX6NMzs9SYO7tZEho8UkZiXp9ZW7/JaY3bC3qNF7Y3fU/Fl+tD5fvdIT9dTYAbRWEMlsAACC2t7/urzcYnS2KJCWKcforKHa+OuGvT9aFwwAS5GYBYAg0CE13iUR6652YbKV2w9p/sqd+mRjgemtBhJiIjVzbP+aPrvd0tQrPdHvieBA+XhjvorLq0yvwlyWs1+T5v/Y5GrmLQUlGjtnheaMO0XDerYzNZZQQjK7BpXCAICgVJwvFe122bTa0dOiYFqm1Y6eulGf/Lphzw+SYUj8z2gg7JCYBYAQ4WlhsrV7ivT3z3O082Bps+ZOiInUnHGnOJNfNptNT40doLFzVgSshYI/2R2GCo6Um5r02rC3yKukbK3SSntYL0oW7slsKoUBAMFu4hOz9WLMr6+PGPHaanS0LqAW6L9GD9cNZYdq+sy26WZNQAAsE2F1AAAA7yXFRatH+yRddnJnLb13hBbeMlgX9MtQZIT3iZzs9CQtnjSkXtKrb6cUzRl3ihJiIpsdb3x0hLq2SWj2PM1RUmFegtkwDN29eI3PSevaRckMI/QXWPNGc5PZG/YW+SmywNiwt0ijnvla17z0nf69Ib9e1XttpfA1L32nUc98HfLXCwAITSdFuPY7XevoLoPUgan2GO2030h227jKmmAAWIqKWQAIcZ4qaUsq7GoVE6Hdh8r01n/31Gt9EBVh06g+GbpucKYGd2vTYGXesJ7tmt3ftrYat3VCjKUVuImxzU8w11qx/WCz2zwEYlGyYGJWMvvjKcNCspLUH5XCtEIAAPiDe2J2jXt1J0xg0xpHD50TWaeX754fpBPHWhcSAEuQmAWAFsR9kbGe6ckaeUK6S8I2MTZS6clNT+C497f9eEO+mtreNjs9STPH9nd+ZX/OuFN8qphsrqgIm9KT40yb7/WVu0ybJ1wSs+GczDaz7YW/WiGQ5AUASJLs1TrRtt1l02oHiVl/WF0vMUvFLBCOSMwCQBhwT9h6y70qN7+oTCt3HNZ/Nubrm60HZTeaVo1rRgWuL0b1yTAtyVRcXqVPNhaYMpe/FiULRuGazDazUnjjviOmLppGv1sAQD37f1aCrcJl0xoSs36x2nBdUK1q71pFV5VJ0Q0vCAyg5SExCwDwSm2St2d6ssYNzvS6Gte9AtdTm4Vze6dr3d4i7TlcZkrM1w3ONGUeScovKq/XG9RX/liULBiFczLbrErhF7/ermc/zzGtFcKGvUWmJnkBAC3Enh9cXu5ytNchJTcwGM2x3nGcHIZNEbaaz5XRNruUt07q+huLIwMQSCRmAQDN4ks1bkN9cesmdjfsLdIVs79VWZWjWfFlpydpcLc2zZqjrqMmt2Ewc1GyYBXOyWyzKoX/8slmr/8MPbVCkPzT7xYA0EK4fZ1+Nf1l/aZECdpidNbxtt2/btzzA4lZIMywtCIAwFJJcdHq0T5JA7qkqkf7JGfCrW+nFL04/lTFRfn+oyohJlIzx/Y39WvYrWLMW0RMMndRsmAVrslsMyuFfU1s17ZCMP7XbqS5/W437C3yKQ4AQHDLeuBDZT3wobb+d6nL9tWOng0cATPU69+7lz6zQLghMQsACFrDerbTW7eepk6p3i/clRATqTnjTjH969cZKXGKjDAn0Wv2omTBKlyT2WZWCjdH7aJpZvW7Ner0lC4ur1JOQbHW7C5UTkGxisurzAobABBgyTqqHhH7XLax8Jd/1atIZgEwIOzQygAAENT6dkrR8vtHauH3u/X4Rz+rpKL6mMdkpydp5tj+fumJmRQXrVF90vXR+vxmz2XmomTBrDaZbUaSMpSS2WZXCjfH6yt3yZBhSr/bFdsOSjaxaBgAtDD9I7a5vK4wovWzYV6fftRXb2G1ot1Scb6UlGFNQAACjorZY7j//vtls9mcj9mzZzdrvtdee00jRoxQ27ZtFRcXp27duunWW29Vbm6uSREDQMtjs9l0zW+6av30c/WP60/V8J5t5V60GhVh04X9OmjhLYP18ZRhfl2oyKzFxMxclCyY1SazzRBKyWyzK4Wb4+ON+Zr7zU5T5po4/0dd89J3+veG/HrJ9tpFw6556TuNeuZrWh8AQAg5ybbV5fUGI0tV1HL51Vajk4qNeNeNVM0CYYV/ZRtgGIbuuOMOPf/882rdurXKy8tVVta81cHHjx+v+fPnS6pJMsTGxmrHjh2aPXu2FixYoM8//1ynnnqqGeEDQItks9k08oR0jTwhvcFFwwJhSLc09UpPbFb1odmLkgW76wZnmlJlHErJbDMrhZvL7jD0+c/m9LttStW6xKJhABBqTorIcXlNGwP/cyhCax3dNDRy468b9/wgnXCRdUEBCCgqZj2w2+2aMGGCnn/+ebVv315Lly5V+/btmzXn3LlzNX/+fCUnJ2v+/PkqLS1VWVmZtm7dqjFjxujIkSO64oorVFVFbzYAaIqGFg0LBJvNpqfGDlCCjxWR/liULNjVJrObI9SS2WZWCpvBivwwi4YBQKgwNMCtlQELfwXGasPtz5mKWSCskJh1U1VVpWuuuUbz5s1T586d9fXXX6t///7NnvfJJ5+UJM2ePVvXXXed4uJq+uN1795db775pk488UTt3LlTCxcubPa5AAD+17dTiuaMO8Xr5Ky/FiULduGazA6lCl9/8bRoGAAguGTZ8tXa5vpNoDWO7hZFE17q/Tnv+69kb9q3UwCEPhKzbjZt2qT3339f3bp107Jly5Sdnd3sOTdv3qzNmzcrPT1dV155Zb390dHRuuOOOyRJ7733XrPPBwAIjGE922nxpCFNrgTNTk/S4klDwvZr3eGYzDajUtgMkRbnszcXFGvl9kPWBgEAaJB7f9lfjFTtVVuLogkv9RYAqyqV9v9sTTAAAo7ErJt+/frpn//8p5YtW6asrCxT5ly+fLkk6bTTTlNEhOc/8uHDh0uSvv76a1POCQAIjL6dUvTJlOFaeMtgXdAvQ5Fuq5IFclGyUBBuyezmVgqblVA964T0en83A+31lbssPT8AoGEnRbgmZmv6y4bWt1RC1UGlaJfDrXXinh+sCQZAwLH4lwfnnnuuqfPl5NQ0Ue/SpUuDY2r3HThwQEVFRUpJCe9f3AEglNhsNg3pnqYh3dMsXZQsVNQms1duP6T5K3fqk40FLgtkRUXYNKpPhq4bnKnB3dqEXPsCd7WVwpPm/6jSSnuTj0uIidSdZ/XUE//e1OwYbjz9OEVF2kxZgM1XH2/MV3F5Fe8HAAhCAzwmZhEoq40eytQvv27Ys0o6dYJ1AQEIGBKzAZCbmytJio+Pb3BMQkKCy/h+/fo1ae49e/Y0uj8vL69J8wAAzJEUF03iqQnCLZldWyl89+I12lJQcszx2elJmjm2v/p0TNbb/93TpGMam2twtzYyZFiamLU7DBUcKW9x9xYAQl5lqU6w5bpsWmOQmA2kNY4eujTy2183sAAYEDZIzAZASYl3v0x5M76xKlwAAEJBuCSzfa0UfmrsAI2ds8KrattadRdNq+1325wkb3OVVHh/DQAAP8tbq2jbr/8+2w2b1jm6WRhQ+KlXoXxgs1RWKMWnWhEOgAAiMRsAZWVlXo0vLS31UyQAAMBKvlQKN6cVQt1F02r73fqa5DVDYqxvvXYBAH7k1s90s9FVpYqzKJjw9JORpQojWrG2ql837v1R6nGWdUEBCAgSsyFu9+7dje7Py8vToEGDAhQNAABoKm8qhX1theC+2JyvSV4zREXYlJ7ML/oAEHT2un5tnv6ygVelKG0wsnSKLefXjXtWkZgFwgCJ2QBorLesJ3X7zR5L586dvQ0HAACEILMWTfM2yZsYG6WSiupmxz+qT0ZYtKwAgJBSWSptW+qyaY3R3aJgwtsaRw+dElEnMbv5I2nE/dYFBCAgSMwGQGJiol/HAwCA8GDWomneJHkNw9A1L3/X7NivG5zZ7DkAACbb9IFUccT50mHYtMzetIWoYa6vHCfqJv371w15a6SCjVJ6H8tiAuB/JGYDoGvXrpKk8vLyBsfU7StbOx4AAKAhzV00ralJXsMwmr1oWHZ6kgZ3a+Pz8QAAP1nzhsvL5Y6+yleaRcGEt+WOflJSB6k479eNaxZIo/5sXVAA/C7C6gDCQY8eNT16GusHW7uvbdu2SklJaXAcAACA2ZLiotWjfZIGdElVj/ZJLgnf2kXDEmJ8W7grISZSM8f2b7C1AgDAIoW7pe1fuWx6y36GRcHAoQip/1WuG9e9KdmrPB8AoEUgMRsAw4YNkyStWLFChmF4HLN8+XKXsQAAAMGidtEwb5OzCTGRmjPulHqLkAEAgsDaRZJ+/f30iJGgTxynWhcPpAHXur4+ul/K+Y81sQAICBKzAZCdna3s7Gzl5eVp8eLF9fZXV1frueeekySNHj060OEBAAAcU+2iYb3Sm9YLPzs9SYsnDdGwnu38HBkAwGuGUa+Nwfv2IapQjEUBQZLUtqfUeZDrNrf7BKBlITFrkqKiIp188smKj4/X+PHj6+2fOnWqJGnSpElasGCBKioqJEnbt2/XVVddpTVr1igzM1NXX311QOMGAABoqtpFwxbeMlgX9MtQZIRre4KoCJsu7NdBC28ZrI+nDKNSFgCCVe4K6fAOl01LaGMQHE5yq5rd8rF09IA1sQDwO5vR0Hfrw9hZZ52lr75y7bVjt9slSRERES490s444wx9/vnnWr58ubMNQVRUlKqq6veBue666/TGG28454mJiXEuCJacnKzPPvtMAwcONPVa9uzZoy5dukiq6WPbuXNnU+cHAADhq7FFw8zE55nQxz0Egsw/b5fWvO58udXRUWdX/p8k+oFbaeeMC6XyIumv2VJ12a87Rj0hDbnNusAA+O2zDBWzHtjt9nqPWg6Hw+O+fv36acCAAYqNjW2w6vX111/XK6+8oqFDhyolJUWGYSgzM1O33HKL1q5da3pSFgAAwJ8aWzQMABCkKkqkje+6bKqpliUpa7WsBz5U1vTlerfiZNcdaxZYExAAv4uyOoBg9OWXX3p9TEpKilavXn3McRMmTNCECRN8iAoAAAAAgGb6+V9S1dFfX9si9K59qHXxoJ637MM1JvKbXzcUrJfy1kod+lsXFAC/oGIWAAAAAIBw4V592eNs/aLW1sQCj7519JGS3b4mTdUs0CJRMQsAAAAAQAuU9cCHLq93Tu0t7Vzmsu22jccHMiQ0gaEI/e3Qqfp/UXt+3bhusXTOn6SoGOsCA2A6KmYBAAAAAAgHaxe5vCw0WukzxykWBYPGvGU/w3VD2SFpy8fWBAPAb0jMAgAAAADQwqWoRPpxrsu29+ynqVIs3BiMco10fedwq2b+9m+SvdqagAD4BYlZAAAAAABaNEN/jn5FKilw2VqvKhNB5S37cNcNe36QvnnammAA+AWJWQAAAAAAWrDfRizTRZHfuWz7yn6i1hvHWRQRmuJf9tOU62jnuvHLGdLeH60JCIDpSMwCAAAAANBCdbb9okejX3XdGN9aU6smSrJZEhOapkIxmlJ1u+xGnfvkqJbevkWqKLEuMACmITELAAAAAEALFCm7nol+Xkm2Mpftk4uuV4HaWBQVvPFfo5ees49x3Xhom/TJ760JCICpSMwCAAAAANAC3Rb5nk6N2OKybXH1GfrYMciiiOCLv1dfqtWOHq4b//uq9PMH1gQEwDQkZgEAAIAw88033+jyyy9Xx44dFRsbqw4dOuiyyy7T8uXLrQ4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" + ] + }, + "metadata": { + "image/png": { + "height": 1241, + "width": 691 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "f, ax = plt.subplots(3, 2, figsize=(8, 15))\n", + "for i, (y0, y1, res, v) in enumerate(\n", + " zip([y0_1, y0_2, y0_3], [y1_1, y1_2, y1_3], [res1, res2, res3], [v1, v2, v3])\n", + "):\n", + " ax[i][0].plot(y0, y1, \"o\")\n", + " ax[i][0].set_xlabel(r\"$Y^0$\", fontsize=12)\n", + " ax[i][0].set_ylabel(r\"$Y^1$\", fontsize=12)\n", + " ax[i][1].hist(res[:, 0] - tautrue, bins=50, density=True)\n", + " ax[i][1].set_xlabel(r\"$\\hat{\\tau} - \\tau$\", fontsize=12)\n", + " x = np.linspace(-1, 1, 100)\n", + " y = sp.stats.norm.pdf(x, loc=0, scale=np.sqrt(v))\n", + " ax[i][1].plot(x, y, label=\"Neyman variance\")\n", + "ax[0][1].set_title(r\"$\\tau_i = \\tau$\")\n", + "ax[1][1].set_title(r\"Negative Corr between $Y^1$ and $Y^0$\")\n", + "ax[2][1].set_title(r\"Uncorrelated $Y^1$ and $Y^0$\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ConstantNegativeIndependent
Variance0.0310730.0062610.019115
Estimated Variance0.0300880.0300940.030080
Coverage0.9411000.9999000.982800
\n", + "
" + ], + "text/plain": [ + " Constant Negative Independent\n", + "Variance 0.031073 0.006261 0.019115\n", + "Estimated Variance 0.030088 0.030094 0.030080\n", + "Coverage 0.941100 0.999900 0.982800" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(\n", + " np.c_[\n", + " np.r_[np.var(res1[:, 0]), np.mean(res1[:, 1:3], axis=0)],\n", + " np.r_[np.var(res2[:, 0]), np.mean(res2[:, 1:3], axis=0)],\n", + " np.r_[np.var(res3[:, 0]), np.mean(res3[:, 1:3], axis=0)],\n", + " ],\n", + " columns=[\"Constant\", \"Negative\", \"Independent\"],\n", + " index=[\"Variance\", \"Estimated Variance\", \"Coverage\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Heavy Tails: things break down" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 836, + "width": 446 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "n, n1, n0 = 1000, 600, 400\n", + "tautrue = 1\n", + "\n", + "# cauchy contamination: prob combination = 0.1\n", + "eps = np.random.binomial(1, 0.1, n)\n", + "y0_1 = (1 - eps) * np.random.exponential(1, n) + eps * np.random.standard_cauchy(n)\n", + "y1_1 = y0_1 + tautrue\n", + "tautrue_1 = np.mean(y1_1) - np.mean(y0_1)\n", + "z_1 = z_2 = z_3 = np.repeat([0, 1], [n0, n1])\n", + "MC = int(1e4)\n", + "res1 = np.zeros((MC, 3))\n", + "for i in range(MC):\n", + " res1[i] = sampling_dist(z_1, y1_1, y0_1, n=n, n1=n1, n0=n0, tautrue=1)\n", + "v1 = np.var(y1_1) / n1 + np.var(y0_1) / n0 - np.var(y1_1 - y0_1) / n\n", + "v1\n", + "\n", + "# cauchy contamination: prob combination = 0.3\n", + "eps = np.random.binomial(1, 0.3, n)\n", + "y0_2 = (1 - eps) * np.random.exponential(1, n) + eps * np.random.standard_cauchy(n)\n", + "y1_2 = y0_2 + tautrue\n", + "tautrue_2 = np.mean(y1_2) - np.mean(y0_2)\n", + "res2 = np.zeros((MC, 3))\n", + "for i in range(MC):\n", + " res2[i] = sampling_dist(z_2, y1_2, y0_2, n=n, n1=n1, n0=n0, tautrue=1)\n", + "v2 = np.var(y1_2) / n1 + np.var(y0_2) / n0 - np.var(y1_2 - y0_2) / n\n", + "v2\n", + "\n", + "\n", + "# cauchy contamination: prob combination = 0.5\n", + "eps = np.random.binomial(1, 0.5, n)\n", + "y0_3 = (1 - eps) * np.random.exponential(1, n) + eps * np.random.standard_cauchy(n)\n", + "y1_3 = y0_3 + tautrue\n", + "tautrue_3 = np.mean(y1_3) - np.mean(y0_3)\n", + "res3 = np.zeros((MC, 3))\n", + "for i in range(MC):\n", + " res3[i] = sampling_dist(z_3, y1_3, y0_3, n=n, n1=n1, n0=n0, tautrue=1)\n", + "v3 = np.var(y1_3) / n1 + np.var(y0_3) / n0 - np.var(y1_3 - y0_3) / n\n", + "\n", + "v1, v2, v3\n", + "\n", + "f, ax = plt.subplots(3, 1, figsize=(5, 10))\n", + "for i, (res, v) in enumerate(zip([res1, res2, res3], [v1, v2, v3])):\n", + " ax[i].hist(res[:, 0] - tautrue, bins=100, density=True)\n", + " ax[i].set_xlabel(r\"$\\hat{\\tau} - \\tau$\", fontsize=12)\n", + " x = np.linspace(-10, 10, 1000)\n", + " y = sp.stats.norm.pdf(x, loc=0, scale=np.sqrt(v))\n", + " ax[i].plot(x, y)\n", + " ax[i].set_xlim(-4, 4)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Application" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from causalinference.utils import lalonde_data\n", + "\n", + "y, z, _ = lalonde_data()\n", + "y *= 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1794.3430782016621, 670.9967300240978)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n0, n1 = np.sum(1 - z), np.sum(z)\n", + "tauhat = np.mean(y[z == 1]) - np.mean(y[z == 0])\n", + "vhat = np.var(y[z == 1], ddof=1) / n1 + np.var(y[z == 0], ddof=1) / n0\n", + "sehat = np.sqrt(vhat)\n", + "tauhat, sehat" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Homoskedastic" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "reg_data = pd.DataFrame({\"y\": y, \"z\": z})\n", + "pf.feols(\"y ~ z\", data=reg_data).tidy()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Heteroskedastic" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# PyFixest's heteroskedastic covariance is HC1.\n", + "reg_data = pd.DataFrame({\"y\": y, \"z\": z})\n", + "pf.feols(\"y ~ z\", data=reg_data, vcov=\"hetero\").tidy()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "reg_data = pd.DataFrame({\"y\": y, \"z\": z})\n", + "pf.feols(\"y ~ z\", data=reg_data, vcov=\"HC2\").tidy()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "econometrics", + "language": "python", + "name": "econometrics" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 93dbb75eec4afed5043b20c7914f645e370e32e4 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:28 +0200 Subject: [PATCH 07/20] Add PyFixest Chapter 05 notebook --- pyfixest/Chapter05StratandPostStrat.ipynb | 847 ++++++++++++++++++++++ 1 file changed, 847 insertions(+) create mode 100644 pyfixest/Chapter05StratandPostStrat.ipynb diff --git a/pyfixest/Chapter05StratandPostStrat.ipynb b/pyfixest/Chapter05StratandPostStrat.ipynb new file mode 100644 index 0000000..2294b9e --- /dev/null +++ b/pyfixest/Chapter05StratandPostStrat.ipynb @@ -0,0 +1,847 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 5: Stratification and Post-Stratification in Randomized Experiments" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import scipy as sp\n", + "\n", + "# viz\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "font = {\"family\": \"IBM Plex Sans Condensed\", \"weight\": \"normal\", \"size\": 10}\n", + "plt.rc(\"font\", **font)\n", + "plt.rcParams[\"figure.figsize\"] = (6, 6)\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "col_0 0 1 2 3 4 5\n", + "row_0 \n", + "0 234 41 687 794 738 860\n", + "1 87 48 757 866 811 461" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.crosstab(z, block)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FRT" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def stat_SRE(z, y, x):\n", + " xlevels = np.unique(x)\n", + " K = len(xlevels)\n", + " PiK, TauK, Wk = np.zeros(K), np.zeros(K), np.zeros(K)\n", + " for k in range(K):\n", + " id = np.where(x == xlevels[k])\n", + " zk = z[id]\n", + " yk = y[id]\n", + " PiK[k] = zk.shape[0] / z.shape[0]\n", + " TauK[k] = np.mean(yk[zk == 1]) - np.mean(yk[zk == 0])\n", + " Wk[k] = sp.stats.mannwhitneyu(yk[zk == 1], yk[zk == 0])[0]\n", + " return np.sum(PiK * TauK), sum(Wk / PiK)\n", + "\n", + "\n", + "def zRandomSRE(z, x):\n", + " xlevels = np.unique(x)\n", + " K = len(xlevels)\n", + " zrandom = z.copy()\n", + " for k in range(K):\n", + " xk = xlevels[k]\n", + " zrandom[x == xk] = np.random.permutation(z[x == xk])\n", + " return zrandom" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.001\n", + "0.0\n" + ] + } + ], + "source": [ + "# observed test statistics\n", + "stat_obs = stat_SRE(z, y, block)\n", + "# null distribution\n", + "MC = int(1e3)\n", + "statSREMC = np.zeros((MC, 2))\n", + "for k in range(MC):\n", + " zrandom = zRandomSRE(z, block)\n", + " statSREMC[k] = stat_SRE(zrandom, y, block)\n", + "\n", + "print(np.mean(statSREMC[:, 0] <= stat_obs[0]))\n", + "print(np.mean(statSREMC[:, 1] <= stat_obs[1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 525, + "width": 509 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "f, ax = plt.subplots(2, 1)\n", + "ax[0].hist(statSREMC[:, 0], bins=50)\n", + "ax[0].vlines(stat_obs[0], 0, 50, color=\"red\")\n", + "ax[0].set_xlabel(r\"$\\hat{\\tau}_s$\")\n", + "ax[1].hist(statSREMC[:, 1], bins=30)\n", + "ax[1].vlines(stat_obs[1], 0, 50, color=\"red\")\n", + "ax[1].set_xlabel(r\"$W_s$\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neyman" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def neyman_SRE(z, y, x):\n", + " xlevels = np.unique(x)\n", + " K = len(xlevels)\n", + " PiK, TauK, varK = np.zeros(K), np.zeros(K), np.zeros(K)\n", + " for k in range(K):\n", + " id = np.where(x == xlevels[k])\n", + " zk, yk = z[id], y[id]\n", + " PiK[k] = zk.shape[0] / z.shape[0]\n", + " TauK[k] = np.mean(yk[zk == 1]) - np.mean(yk[zk == 0])\n", + " varK[k] = np.var(yk[zk == 1], ddof=1) / sum(zk) + np.var(\n", + " yk[zk == 0], ddof=1\n", + " ) / sum(1 - zk)\n", + " return np.sum(PiK * TauK), np.sum(PiK**2 * varK)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def sim_cluster(K, n, n1, n0):\n", + " x = np.repeat(range(K), n)\n", + " y0 = np.random.exponential(1, n * K)\n", + " y1 = y0 + 1\n", + " # block level assignment vector\n", + " zb = np.repeat([0, 1], [n0, n1])\n", + " MC = int(1e4)\n", + " TauHat, VarHat = np.zeros(MC), np.zeros(MC)\n", + " for k in range(MC):\n", + " z = np.concatenate([np.random.permutation(zb) for i in range(K)])\n", + " y = z * y1 + (1 - z) * y0\n", + " TauHat[k], VarHat[k] = neyman_SRE(z, y, x)\n", + " plt.hist(TauHat, bins=50)\n", + " plt.vlines(1, 0, 500, color=\"red\")\n", + " return np.var(TauHat), np.mean(VarHat)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.009649300175537943, 0.00978208282388499)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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class_level1.02.03.04.05.0
treatment
Soccer Player1619151010
Physician1720151110
Placebo1519161210
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" + ], + "text/plain": [ + "class_level 1.0 2.0 3.0 4.0 5.0\n", + "treatment \n", + "Soccer Player 16 19 15 10 10\n", + "Physician 17 20 15 11 10\n", + "Placebo 15 19 16 12 10" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dat_chong = pd.read_stata(\"chong.dta\")\n", + "pd.crosstab(dat_chong.treatment, dat_chong.class_level)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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class_level1.02.03.04.05.0
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01519161210
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" + ], + "text/plain": [ + "class_level 1.0 2.0 3.0 4.0 5.0\n", + "z \n", + "0 15 19 16 12 10\n", + "1 17 20 15 11 10" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "use_vars = [\"treatment\", \"gradesq34\", \"class_level\", \"anemic_base_re\"]\n", + "dat_physician = dat_chong.loc[dat_chong.treatment != \"Soccer Player\", use_vars]\n", + "dat_physician[\"z\"] = np.where(dat_physician.treatment == \"Physician\", 1, 0)\n", + "dat_physician[\"y\"] = dat_physician.gradesq34\n", + "pd.crosstab(dat_physician.z, dat_physician.class_level)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.40589046478271484, 0.04096197815071462)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(\n", + " tauS := neyman_SRE(\n", + " dat_physician.z.values, dat_physician.y.values, dat_physician.class_level.values\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.4633431335975384, 0.03624964630443229)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dat_physician[\"sps\"] = pd.Categorical(\n", + " dat_physician[\"class_level\"].astype(str)\n", + " + \"_\"\n", + " + dat_physician[\"anemic_base_re\"].astype(str)\n", + ")\n", + "(\n", + " tauSPS := neyman_SRE(\n", + " dat_physician.z.values, dat_physician.y.values, dat_physician.sps.values\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EstimateStd. Errorz valuePr(>|z|)
Stratify0.4058900.2023912.0054800.044912
Stratify and post-stratify0.4633430.1903932.4336090.014949
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" + ], + "text/plain": [ + " Estimate Std. Error z value Pr(>|z|)\n", + "Stratify 0.405890 0.202391 2.005480 0.044912\n", + "Stratify and post-stratify 0.463343 0.190393 2.433609 0.014949" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "seS = np.sqrt(tauS[1])\n", + "seSPS = np.sqrt(tauSPS[1])\n", + "\n", + "pvalS = 2 * (1 - sp.stats.norm.cdf(abs(tauS[0] / seS)))\n", + "pvalSPS = 2 * (1 - sp.stats.norm.cdf(abs(tauSPS[0] / seSPS)))\n", + "\n", + "pd.DataFrame(\n", + " np.r_[\n", + " np.c_[tauS[0], seS, tauS[0] / seS, pvalS],\n", + " np.c_[tauSPS[0], seSPS, tauSPS[0] / seSPS, pvalSPS],\n", + " ],\n", + " columns=[\"Estimate\", \"Std. Error\", \"z value\", \"Pr(>|z|)\"],\n", + " index=[\"Stratify\", \"Stratify and post-stratify\"],\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "econometrics", + "language": "python", + "name": "econometrics" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 82c4894a9de99c9c4c345f485baa9c37d570a064 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:28 +0200 Subject: [PATCH 08/20] Add PyFixest Chapter 06 notebook --- pyfixest/Chapter06RegadjRerand.ipynb | 393 +++++++++++++++++++++++++++ 1 file changed, 393 insertions(+) create mode 100644 pyfixest/Chapter06RegadjRerand.ipynb diff --git a/pyfixest/Chapter06RegadjRerand.ipynb b/pyfixest/Chapter06RegadjRerand.ipynb new file mode 100644 index 0000000..ec639cf --- /dev/null +++ b/pyfixest/Chapter06RegadjRerand.ipynb @@ -0,0 +1,393 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 6: Rerandomization and Regression Adjustment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import scipy as sp\n", + "import pyfixest as pf\n", + "\n", + "# viz\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "font = {\"family\": \"IBM Plex Sans Condensed\", \"weight\": \"normal\", \"size\": 10}\n", + "plt.rc(\"font\", **font)\n", + "plt.rcParams[\"figure.figsize\"] = (6, 6)\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regression Adjustment" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_17987/2898410316.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " angrist2[\"y\"] = angrist2.GPA_year1.fillna(angrist2.GPA_year1.mean())\n" + ] + } + ], + "source": [ + "angrist = pd.read_stata(\"star.dta\")\n", + "angrist2 = angrist.query(\"control == 1 | sfsp == 1\")\n", + "angrist2[\"y\"] = angrist2.GPA_year1.fillna(angrist2.GPA_year1.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "y, z, x = (\n", + " angrist2.y.values,\n", + " angrist2.sfsp.values,\n", + " angrist2.loc[:, [\"female\", \"gpa0\"]].values,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### unadjusted regression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "unadj_data = pd.DataFrame({\"y\": y, \"z\": z})\n", + "unadj_fit = pf.feols(\"y ~ z\", data=unadj_data, vcov=\"HC2\")\n", + "unadj_res = unadj_fit.tidy().loc[\n", + " \"z\", [\"Estimate\", \"Std. Error\", \"t value\", \"Pr(>|t|)\"]\n", + "]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### adjusted (Lin 2013) regression" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# standardize x\n", + "x = (x - x.mean(axis=0)) / x.std(axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lin_data = pd.DataFrame({\"y\": y, \"z\": z, \"x\": x})\n", + "lin_fit = pf.feols(\"y ~ z * x\", data=lin_data, vcov=\"HC2\")\n", + "lin_res = lin_fit.tidy().loc[\n", + " \"z\", [\"Estimate\", \"Std. Error\", \"t value\", \"Pr(>|t|)\"]\n", + "]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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coefsetp
unadjusted0.05180.0780.6690.504
adjusted0.06820.0740.9250.355
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" + ], + "text/plain": [ + " coef se t p\n", + "unadjusted 0.0518 0.078 0.669 0.504\n", + "adjusted 0.0682 0.074 0.925 0.355" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(\n", + " np.c_[unadj_res, lin_res].T,\n", + " columns=[\"coef\", \"se\", \"t\", \"p\"],\n", + " index=[\"unadjusted\", \"adjusted\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rerandomization simulation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TBD" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def Mahalanobis2(z, x):\n", + " x1 = x[z == 1, :]\n", + " x0 = x[z == 0, :]\n", + " n0, n1 = x0.shape[0], x1.shape[0]\n", + " diff = x1.mean(axis=0) - x0.mean(axis=0)\n", + " covdiff = (n1 + n0) / (n1 * n0) * np.cov(x.T)\n", + " M = np.sum(diff * np.linalg.solve(covdiff, diff))\n", + " return M" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def rRem(x, n1, n0, a):\n", + " n = n1 + n0\n", + " z = np.random.choice(np.repeat([0, 1], [n0, n1]), size=n, replace=False)\n", + " M = Mahalanobis2(z, x)\n", + " while M > a:\n", + " z = np.random.permutation(z)\n", + " M = Mahalanobis2(z, x)\n", + " return z" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m1_data = pd.DataFrame(\n", + " {\"y\": y[z == 1], \"x\": x[z == 1], \"x_sq\": x[z == 1] ** 2}\n", + ")\n", + "m1lm = pf.feols(\"y ~ x + x_sq\", data=m1_data)\n", + "sigma1 = np.sqrt(np.sum(m1lm.resid() ** 2) / (len(m1_data) - len(m1lm.coef())))\n", + "\n", + "m0_data = pd.DataFrame(\n", + " {\"y\": y[z == 0], \"x\": x[z == 0], \"x_sq\": x[z == 0] ** 2}\n", + ")\n", + "m0lm = pf.feols(\"y ~ x + x_sq\", data=m0_data)\n", + "sigma0 = np.sqrt(np.sum(m0lm.resid() ** 2) / (len(m0_data) - len(m0lm.coef())))\n", + "\n", + "imputation_data = pd.DataFrame({\"x\": x, \"x_sq\": x**2})\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def design_adjustment_fig(rescale):\n", + " a = 0.05\n", + " MC = 1000\n", + " n, n1, n0 = len(z), sum(z), sum(1 - z)\n", + "\n", + " y1impute = m1lm.predict(imputation_data) + np.random.normal(\n", + " 0, sigma1 * rescale, n\n", + " )\n", + " y0impute = m0lm.predict(imputation_data) + np.random.normal(\n", + " 0, sigma1 * rescale, n\n", + " )\n", + " tauimpute = np.mean(y1impute - y0impute)\n", + "\n", + " TauHatCRE = np.zeros(MC)\n", + " TauHatRegCRE = np.zeros(MC)\n", + " TauHatReM = np.zeros(MC)\n", + " TauHatRegReM = np.zeros(MC)\n", + "\n", + " for i in range(MC):\n", + " zCRE = np.random.permutation(z)\n", + " yCRE = zCRE * y1impute + (1 - zCRE) * y0impute\n", + " TauHatCRE[i] = np.mean(yCRE[zCRE == 1]) - np.mean(yCRE[zCRE == 0])\n", + " cre_data = pd.DataFrame({\"y\": yCRE, \"z\": zCRE, \"x\": x})\n", + " TauHatRegCRE[i] = pf.feols(\"y ~ z * x\", data=cre_data).coef().loc[\"z\"]\n", + "\n", + " ZReM = rRem(x, int(n1), int(n0), a)\n", + " yRem = ZReM * y1impute + (1 - ZReM) * y0impute\n", + " TauHatReM[i] = np.mean(yRem[ZReM == 1]) - np.mean(yRem[ZReM == 0]) - tauimpute\n", + " rem_data = pd.DataFrame({\"y\": yRem, \"z\": ZReM, \"x\": x})\n", + " TauHatRegReM[i] = pf.feols(\"y ~ z * x\", data=rem_data).coef().loc[\"z\"]\n", + "\n", + " data = [\n", + " TauHatCRE - tauimpute,\n", + " TauHatRegCRE - tauimpute,\n", + " TauHatReM - tauimpute,\n", + " TauHatRegReM - tauimpute,\n", + " ]\n", + " fig, ax = plt.subplots()\n", + " ax.violinplot(data, showmeans=True, showmedians=True)\n", + " ax.set_title(\"TauHats for rescale = {}\".format(rescale))\n", + " ax.set_xticks([1, 2, 3, 4])\n", + " ax.set_xticklabels([\"TauHatCRE\", \"TauHatRegCRE\", \"TauHatReM\", \"TauHatRegReM\"])\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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pairpotcrossselfdiff
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" + ], + "text/plain": [ + " pair pot cross self diff\n", + "0 1 1 23.500 17.375 6.125\n", + "1 2 1 12.000 20.375 -8.375\n", + "2 3 1 21.000 20.000 1.000\n", + "3 4 2 22.000 20.000 2.000\n", + "4 5 2 19.125 18.375 0.750" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ZeaMays from HistData r package\n", + "ZeaMays = pd.read_csv(\"ZeaMays.csv\")\n", + "ZeaMays.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Randomization distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.026337473677785578" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t_obs = ZeaMays[\"diff\"].mean()\n", + "abs_diff = np.abs(ZeaMays[\"diff\"].values)\n", + "n_pairs = ZeaMays.shape[0]\n", + "t_ran = (\n", + " np.array([np.sum(MP_enumerate(i, 15) * abs_diff) for i in range(1, 2**15)])\n", + " / n_pairs\n", + ")\n", + "(p_value := np.mean(t_ran >= t_obs))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.05, 0.9, 'p-value = 0.026')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ], + "text/plain": [ + " x_control x_treatment y_control y_treatment diffx diffy\n", + "1 12.9 12.0 54.6 60.6 -0.9 6.0\n", + "2 15.1 12.3 56.5 55.5 -2.8 -1.0\n", + "3 16.8 17.2 75.2 84.8 0.4 9.6\n", + "4 15.8 18.9 75.6 101.9 3.1 26.3\n", + "5 13.9 15.3 55.3 70.6 1.4 15.3\n", + "6 14.5 16.6 59.3 78.4 2.1 19.1\n", + "7 17.0 16.0 87.0 84.2 -1.0 -2.8\n", + "8 15.8 20.1 73.7 108.6 4.3 34.9" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataxy = np.array(\n", + " [\n", + " 12.9,\n", + " 12.0,\n", + " 54.6,\n", + " 60.6,\n", + " 15.1,\n", + " 12.3,\n", + " 56.5,\n", + " 55.5,\n", + " 16.8,\n", + " 17.2,\n", + " 75.2,\n", + " 84.8,\n", + " 15.8,\n", + " 18.9,\n", + " 75.6,\n", + " 101.9,\n", + " 13.9,\n", + " 15.3,\n", + " 55.3,\n", + " 70.6,\n", + " 14.5,\n", + " 16.6,\n", + " 59.3,\n", + " 78.4,\n", + " 17.0,\n", + " 16.0,\n", + " 87.0,\n", + " 84.2,\n", + " 15.8,\n", + " 20.1,\n", + " 73.7,\n", + " 108.6,\n", + " ]\n", + ")\n", + "dataxy = dataxy.reshape(-1, 4)\n", + "diffx = dataxy[:, 1] - dataxy[:, 0]\n", + "diffy = dataxy[:, 3] - dataxy[:, 2]\n", + "dataxy = np.c_[dataxy, diffx, diffy]\n", + "dataxy = pd.DataFrame(\n", + " dataxy,\n", + " columns=[\"x_control\", \"x_treatment\", \"y_control\", \"y_treatment\", \"diffx\", \"diffy\"],\n", + " index=np.arange(1, 9),\n", + ")\n", + "dataxy" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((13.425,), 4.6363374553628)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tauhat = (dataxy.diffy.mean(),)\n", + "sehat = np.sqrt(dataxy.diffy.var(ddof=1) / dataxy.shape[0])\n", + "tauhat, sehat" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With a regression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "unadj_data = pd.DataFrame({\"diffy\": dataxy.diffy})\n", + "unadj_res = pf.feols(\"diffy ~ 1\", data=unadj_data)\n", + "unadj_t = unadj_res.tstat().loc[\"Intercept\"]\n", + "unadj_res.tidy()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "adj_data = pd.DataFrame({\"diffy\": dataxy.diffy, \"diffx\": dataxy.diffx})\n", + "adj_res = pf.feols(\"diffy ~ diffx\", data=adj_data)\n", + "adj_t = adj_res.tstat().loc[\"diffx\"]\n", + "adj_res.tidy()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "warnings.simplefilter(\"once\", category=UserWarning)\n", + "\n", + "\n", + "def randist(x):\n", + " z_mpe = MP_enumerate(x, 8)\n", + " diffy_mpe = dataxy.diffy * z_mpe\n", + " diffx_mpe = dataxy.diffx * z_mpe\n", + " m0_data = pd.DataFrame({\"diffy\": diffy_mpe})\n", + " m1_data = pd.DataFrame({\"diffy\": diffy_mpe, \"diffx\": diffx_mpe})\n", + " m0 = pf.feols(\"diffy ~ 1\", data=m0_data)\n", + " m1 = pf.feols(\"diffy ~ diffx\", data=m1_data)\n", + " return m0.tstat().loc[\"Intercept\"], m1.tstat().loc[\"diffx\"]\n", + "\n", + "\n", + "t_randist = np.r_[[randist(i) for i in range(1, 2**8 + 1)]]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.03125, 0.0078125)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_unadjusted = np.mean(np.abs(t_randist[:, 0]) >= np.abs(unadj_t))\n", + "p_adjusted = np.mean(np.abs(t_randist[:, 1]) >= np.abs(adj_t))\n", + "p_unadjusted, p_adjusted" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.05, 0.9, 'p-value = 0.008')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 526, + "width": 974 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "f, ax = plt.subplots(1, 2, figsize=(12, 6))\n", + "ax[0].hist(t_randist[:, 0], bins=50)\n", + "ax[0].axvline(unadj_t, color=\"red\")\n", + "ax[0].set_title(\"Unadjusted Randomization Distribution\")\n", + "ax[0].annotate(\n", + " f\"p-value = {p_unadjusted:.3f}\", xy=(0.05, 0.9), xycoords=\"axes fraction\"\n", + ")\n", + "ax[1].hist(t_randist[:, 1], bins=50)\n", + "ax[1].axvline(adj_t, color=\"red\")\n", + "ax[1].set_title(\"Adjusted Randomization Distribution\")\n", + "ax[1].annotate(f\"p-value = {p_adjusted:.3f}\", xy=(0.05, 0.9), xycoords=\"axes fraction\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "econometrics", + "language": "python", + "name": "econometrics" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5f0ebfb16388797fe047d423433b477a4075337f Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:28 +0200 Subject: [PATCH 10/20] Add PyFixest Chapter 08 notebook --- pyfixest/Chapter08UnifyingFisherNeyman.ipynb | 579 +++++++++++++++++++ 1 file changed, 579 insertions(+) create mode 100644 pyfixest/Chapter08UnifyingFisherNeyman.ipynb diff --git a/pyfixest/Chapter08UnifyingFisherNeyman.ipynb b/pyfixest/Chapter08UnifyingFisherNeyman.ipynb new file mode 100644 index 0000000..332f9d4 --- /dev/null +++ b/pyfixest/Chapter08UnifyingFisherNeyman.ipynb @@ -0,0 +1,579 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 8: Unification of the Fisherian and Neymanian Inferences in Randomized Experiments" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from joblib import Parallel, delayed\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pyfixest as pf\n", + "import scipy as sp\n", + "import seaborn as sns\n", + "\n", + "# viz\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "font = {\"family\": \"IBM Plex Sans Condensed\", \"weight\": \"normal\", \"size\": 10}\n", + "plt.rc(\"font\", **font)\n", + "plt.rcParams[\"figure.figsize\"] = (6, 6)\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\", category=FutureWarning)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FRT Simulation Study (sec 8.3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def cafrt_stat_n(z, y):\n", + " data = pd.DataFrame({\"y\": y, \"z\": z})\n", + " m1 = pf.feols(\"y ~ z\", data=data)\n", + " m2 = pf.feols(\"y ~ z\", data=data, vcov=\"HC2\")\n", + " est, vse, rse = m1.coef().loc[\"z\"], m1.se().loc[\"z\"], m2.se().loc[\"z\"]\n", + " return est, est / vse, est / rse\n", + "\n", + "\n", + "def cafrt_stat_f(z, y, x):\n", + " covariates = pd.DataFrame(np.asarray(x))\n", + " covariates.columns = [f\"x{i}\" for i in range(covariates.shape[1])]\n", + " data = covariates.assign(y=y, z=z)\n", + " rhs = \" + \".join(covariates.columns)\n", + " m1 = pf.feols(\"y ~ z + \" + rhs, data=data)\n", + " m2 = pf.feols(\"y ~ z + \" + rhs, data=data, vcov=\"HC2\")\n", + " est, vse, rse = m1.coef().loc[\"z\"], m1.se().loc[\"z\"], m2.se().loc[\"z\"]\n", + " return est, est / vse, est / rse\n", + "\n", + "\n", + "def cafrt_stat_l(z, y, x):\n", + " covariates = pd.DataFrame(np.asarray(x))\n", + " covariates.columns = [f\"x{i}\" for i in range(covariates.shape[1])]\n", + " covariates = covariates - covariates.mean()\n", + " data = covariates.assign(y=y, z=z)\n", + " rhs = \" + \".join(covariates.columns)\n", + " m1 = pf.feols(\"y ~ z * (\" + rhs + \")\", data=data)\n", + " m2 = pf.feols(\"y ~ z * (\" + rhs + \")\", data=data, vcov=\"HC2\")\n", + " est, vse, rse = m1.coef().loc[\"z\"], m1.se().loc[\"z\"], m2.se().loc[\"z\"]\n", + " return est, est / vse, est / rse\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def cafrt_pvalue(z, y, x, nfrt, k=6):\n", + " covariates = pd.DataFrame(np.asarray(x))\n", + " covariates.columns = [f\"x{i}\" for i in range(covariates.shape[1])]\n", + " residual_data = covariates.assign(y=y)\n", + " rr = pf.feols(\"y ~ \" + \" + \".join(covariates.columns), data=residual_data).resid()\n", + " cafrt_stat_obs = np.r_[\n", + " cafrt_stat_n(z, y),\n", + " cafrt_stat_f(z, y, x),\n", + " cafrt_stat_l(z, y, x),\n", + " cafrt_stat_n(z, rr),\n", + " ]\n", + "\n", + " def run_cafrt_pvalue(*args):\n", + " zperm = np.random.permutation(z)\n", + " return np.r_[\n", + " cafrt_stat_n(zperm, y),\n", + " cafrt_stat_f(zperm, y, x),\n", + " cafrt_stat_l(zperm, y, x),\n", + " cafrt_stat_n(zperm, rr),\n", + " ]\n", + "\n", + " results = Parallel(n_jobs=k)(delayed(run_cafrt_pvalue)(i) for i in range(nfrt))\n", + " cafrt_stat_perm = np.vstack(results)\n", + " summ = 1 * (np.abs(cafrt_stat_perm) - np.abs(cafrt_stat_obs) >= 0)\n", + " return summ.mean(axis=0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.742, 0.742, 0.816, 0.722, 0.722, 0.822, 0.68 , 0.66 , 0.772,\n", + " 0.72 , 0.72 , 0.822])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nfrt = int(500)\n", + "n, r = 100, 0.2\n", + "n1, n0 = int(r * n), int((1 - r) * n)\n", + "sigma1, sigma0 = 1, 0.5\n", + "x = np.random.uniform(low=-1, high=1, size=n)\n", + "y1 = x**3 + np.random.normal(scale=sigma1, size=n)\n", + "y0 = -(x**3) + np.random.normal(scale=sigma0, size=n)\n", + "y1, y0 = y1 - y1.mean(), y0 - y0.mean()\n", + "zz = np.r_[np.ones(n1), np.zeros(n0)]\n", + "\n", + "\n", + "def simulation_frt():\n", + " z = np.random.permutation(zz)\n", + " y = z * y1 + (1 - z) * y0\n", + " return cafrt_pvalue(z, y, x, nfrt, k=8)\n", + "\n", + "\n", + "simulation_frt()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 23s, sys: 2.2 s, total: 1min 25s\n", + "Wall time: 1min 34s\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[0.01 , 0.01 , 0.024, 0.004, 0.004, 0.028, 0.01 , 0.004, 0.022,\n", + " 0.004, 0.004, 0.026],\n", + " [0.274, 0.274, 0.446, 0.37 , 0.372, 0.556, 0.102, 0.066, 0.27 ,\n", + " 0.368, 0.368, 0.564],\n", + " [0.134, 0.134, 0.286, 0.102, 0.1 , 0.316, 0.148, 0.122, 0.322,\n", + " 0.1 , 0.1 , 0.314],\n", + " [0.936, 0.936, 0.952, 0.708, 0.71 , 0.824, 0.456, 0.41 , 0.57 ,\n", + " 0.71 , 0.71 , 0.822],\n", + " [0.08 , 0.08 , 0.208, 0.176, 0.19 , 0.372, 0. , 0. , 0.01 ,\n", + " 0.204, 0.204, 0.422]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "nmc = 500\n", + "res = []\n", + "for i in range(nmc):\n", + " res.append(simulation_frt())\n", + "simres = np.vstack(res)\n", + "simres[:5, :]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "simdata = [\n", + " pd.DataFrame(\n", + " {\n", + " \"fac1\": \"Unstudentized\",\n", + " \"fac2\": \"Neyman\",\n", + " \"frt_pv\": simres[:, 0],\n", + " }\n", + " ),\n", + " pd.DataFrame(\n", + " {\n", + " \"fac1\": \"Studentized OLS\",\n", + " \"fac2\": \"Neyman\",\n", + " \"frt_pv\": simres[:, 1],\n", + " }\n", + " ),\n", + " pd.DataFrame(\n", + " {\n", + " \"fac1\": \"Studentized EHW\",\n", + " \"fac2\": \"Neyman\",\n", + " \"frt_pv\": simres[:, 2],\n", + " }\n", + " ),\n", + " # fisher\n", + " pd.DataFrame(\n", + " {\n", + " \"fac1\": \"Unstudentized\",\n", + " \"fac2\": \"Fisher\",\n", + " \"frt_pv\": simres[:, 3],\n", + " }\n", + " ),\n", + " pd.DataFrame(\n", + " {\n", + " \"fac1\": \"Studentized OLS\",\n", + " \"fac2\": \"Fisher\",\n", + " \"frt_pv\": simres[:, 4],\n", + " }\n", + " ),\n", + " pd.DataFrame(\n", + " {\n", + " \"fac1\": \"Studentized EHW\",\n", + " \"fac2\": \"Fisher\",\n", + " \"frt_pv\": simres[:, 5],\n", + " }\n", + " ),\n", + " # lin\n", + " pd.DataFrame(\n", + " {\n", + " \"fac1\": \"Unstudentized\",\n", + " \"fac2\": \"Lin\",\n", + " \"frt_pv\": simres[:, 6],\n", + " }\n", + " ),\n", + " pd.DataFrame(\n", + " {\n", + " \"fac1\": \"Studentized OLS\",\n", + " \"fac2\": \"Lin\",\n", + " \"frt_pv\": simres[:, 7],\n", + " }\n", + " ),\n", + " pd.DataFrame(\n", + " {\n", + " \"fac1\": \"Studentized EHW\",\n", + " \"fac2\": \"Lin\",\n", + " \"frt_pv\": simres[:, 8],\n", + " }\n", + " ),\n", + " # rosenbaum\n", + " pd.DataFrame(\n", + " {\n", + " \"fac1\": \"Unstudentized\",\n", + " \"fac2\": \"Rosenbaum\",\n", + " \"frt_pv\": simres[:, 9],\n", + " }\n", + " ),\n", + " pd.DataFrame(\n", + " {\n", + " \"fac1\": \"Studentized OLS\",\n", + " \"fac2\": \"Rosenbaum\",\n", + " \"frt_pv\": simres[:, 10],\n", + " }\n", + " ),\n", + " pd.DataFrame(\n", + " {\n", + " \"fac1\": \"Studentized EHW\",\n", + " \"fac2\": \"Rosenbaum\",\n", + " \"frt_pv\": simres[:, 11],\n", + " }\n", + " ),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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GxiokJETVqlXTTTfdpLffftvxe+nMlcdrf/75Z915552qWbOmAgICtHXr1mKNHxZjADDdu+++a0gyJBkLFiwo1jrx8fGGJKNhw4ZOl9v7mzRpkrFq1SojJCTE8Vz+R7du3Yzs7GynfTzzzDNO17E/GjdubPzyyy9Ox+XqMWnSpALtu3fvbkgyunfv7vb9ulrfbs+ePUbdunWdvmZgYKDx/vvvG5MmTXI858rUqVONgIAAl+OPjIw0li9f7nTd/P3n5uYat99+u9M+goODjc8//9zle3T1ePfddwu0HzZsmCHJiI+PL/B8/s9TcR6HDh0qsH5ubq7x6KOPul2nevXqxtatW11ux3Xr1hmVK1d2um54eLixevVql+MvbfZxSDKOHTtWoj7sn/thw4YVWnbo0CGjdu3aLrelzWYznnrqKaf95v/9SEpKcvn7VbNmTePgwYMux/fzzz8bsbGxbn+mo0aNMvLy8pyOP/9ncNq0aQXWi4mJKdE2AwBclpmZaQQGBhqSjN69e5eoj/wZxNnDXVa4MgfkV9S+Ojc31xg8eLDL1+3Tp0+h/Ygz3txPLVy40LDZbE77uO+++1y+R1ePKzPqypUrHctWrlxZYJkn+ctZpv3mm2+MChUquM0MU6dOdboNDcMw0tPTjZ49e7pcf8yYMW7HX5To6GhDkjFgwACP1nv88ccdr3nmzJlCy+3LbrzxRo/6zf/ZO3XqlEfrmqWk79WuqJ/frFmz3P4tU6NGDWP16tWF1rva36P8vPX3VEZGhtGxY8cC644bN64kmw0AyjSyIlnRzupZsSTHt50p6pi3Mw0bNnT5Wdy7d69jXM8880yh5Tt37nQsX7t2rZGVleXIMq+99lqh9mvXrnW0d3Z8t7R4Y3u7+x3OzMw0brzxRref01tuucVITU0ttO7VHiO3O336tNGpUye3Y+jYsaPTvzEMo+Dx2nXr1hnh4eEF1t2yZUuJthvMVfZPVQZQpMcee0xdu3ZVnz59FBcXp19++UUzZ87U2bNntXr1ak2fPl1PPvlkgXXWr1/vuGK4UqVKevTRR9W8eXNdvHhRq1ev1gcffKB9+/bp1ltv1datW1WlShVJ0uzZs5Wenq7//e9/ev311yVJs2bNUvXq1SVJTZs29fr7y8zMVO/evR1TyHTp0kV33323YmNjdfToUX366ad6/PHHNXToULf9vPXWW3rmmWckSY0aNdLQoUPVuHFj5ebmavPmzXrnnXd0/vx59e/fX7/88ovbM1E/+eQTrVu3Tn/961/VsmVLZWRk6KOPPtLy5ct16dIljRgxQocOHVJkZGSBdaTLV7F88MEHBZ6TpHbt2hVre/Ts2bPAelc6ffq0Ro0apdzcXLVv377QvRCfe+45x1SRbdu21aBBg1SvXj1lZGRo7dq1WrBggU6ePKnbb79dO3fuVExMTIH1k5OTdccdd+jMmTOSpNtvv119+vRRpUqVdPDgQS1atEjjx4/3m3vOjBo1SsePH5ck3XzzzbrzzjtVqVIlHTp0SPPmzdPevXv10ksvqWLFipowYYLLfqZOnaqQkBBNnTpVDRo00IkTJ/T2229r165dOnHihMaOHasvvvii0HrJycm6+eabdfr0aUVERGjw4MG64YYbFBUVpSNHjmjBggXavn27Zs2apUaNGumJJ55wOYZTp07pxRdf1PDhw9W+fXvFxcUpJCTk6jcSAPix0NBQtW/fXj/++KO++uor/fDDD477EBfXoEGDHPeK69+/vyTp3nvv1aBBgyRJERERXh2z3cSJE7Vw4UJJUvXq1fXwww+rRYsWunDhgjZt2qT58+fr888/d9uHN/dTmZmZ+utf/6r+/fvrxhtvVOXKlbVhwwbNmjVLGRkZWrRokfr3719gCuo//elPuvPOO3Xy5EnHTEOPPfaYevbsKUmqWrVqsbeHu/wlSVOmTNGWLVsUGBioO+64o8CyX375RXfccYeysrJUqVIlDR06VNdee61CQkK0b98+vfPOOzp06JDjqtW77767UP9//OMfHbexadCggYYPH67GjRvr9OnTWrt2rRITE9WkSZNiv5/8Dh8+rPPnz0uS6tat69G6+dvv2LFD3bp1K9EYrtS1a1fH5+/ZZ59VYmKiV/otqw4fPqzHHntMeXl5Cg8P16hRo9SuXTtdunRJmzZt0oIFC/T777+rT58+2rx5sxo3buy0n5L8Htl58++pt99+W8ePH9dzzz2nxo0bq0KFCo4rpwDAn5AVyYqS9bOilTVu3FiVK1fWmTNnnF6pnf+5Jk2aKCQkRPHx8Tp06FCR7a+//nrfDNoCpk2b5pj9NCEhQQ8//LBq166tU6dO6ZNPPtHKlSv1zTffaNCgQfryyy9d9lOSY+TS5Su0+/Xrpx9++EE2m019+vTRrbfeqmrVqun06dNatmyZvvjiC23YsEEPPPCAli1b5vb9jBs3Tp07d9att96q+Ph4BQUFqX79+le/oVD6zK6qA/Dtldo1atQwZsyYUWj57t27jYiICEO6fMX1lcaOHWtIl69ydnbW0saNG42qVasaTz/9dJHvyd1Znd64Uvuf//ynY/nEiROdrv/qq68WOBPrSmfPnnWc7ThgwAAjKyurUJt9+/Y5rj52dvZm/rPQbrzxRiM5OblQm4ceesjRZuHChU7HWpwryg2j6DNiXRkyZIghyQgKCjK2bdtWYNmePXscZwD/6U9/cnqW6/r16x1X/v/tb38rtHzMmDGO8c+ZM6fQ8tzcXONPf/qTo015vlL75MmTjjOAnV3VlJ2dbTz88MNG/fr1jT179hRabv/9iI6ONh5++OFCn8u0tDSjVatWjjNyjx8/XqiPBx54wJBkVKtWzdi1a1eh5ZcuXTLuuusuQ5IRERFhnD17tsDy/Gc1R0VFub1CHwBQMh9++GGB/2unTp1qnDx5skR9uctMdt64+ubYsWNGaGioIclo06aNcfr06UJtdu/ebVSpUsXxWs6uvvHmfqpWrVrGp59+WqiP77//3nG1xc033+z0vRbnKiHDKPpKWVe+++47x3pPPvlkoeVdu3Y1JBlNmjQxTpw4UWh5Wlqa4yqFOnXqGDk5OQWWb9y40dF/r169jIyMjEJ9rFmzxggLCyvR+PO/b1d525V33nnHse68efMKLc+fnz1x/vx5o0aNGo71+/fvb2zcuNGjPkpbSd+rnbvPX/6/d5xd8bJ3716jYcOGxrBhwwrN1OWN3yNv/z118803O/0cA4A/IiuSFa2eFQ3DuldqG4Zh3HLLLYZ0eebJK40bN86QZNSrV8/x3N13321IMpo2bVqovf1z36hRo2KPzxd8faV2ixYtHMsuXLhQaPnbb79tREZGGkuXLi20zBvHyOfMmWNIMgICAowPP/zQ6fjzzyb5zTffFFqef8ZLZ8eoUTZxT22gnGvYsKHGjBlT6PmEhATH/ST27dun06dPF1h+4MABRzv72Zz5tW/fXr/++muB+z+bZc6cOZIuX8ns6p4zf/nLXxxnUTozf/58XbhwQZGRkXrrrbecXn3aqFEjxxXtH3/8sc6dO+eyvwkTJqhGjRqFnp86darj3kY//vijy/V9Zfny5frvf/8rSXryySfVqlWrAssTExOVm5urunXr6l//+pfjfnb5XX/99XrwwQclXb7PUl5enmNZdna2o/8777xTI0aMKLR+QECApk2bZsmzP7/55hstXbrU7cOTez4dOnTIcX+Ze++9t9Dy4OBgzZo1S5s2bXK7PS5duqRXX3210OcyMjJSzz77rCTJMAxt2LChwPJTp045rvqfOnWq05kSgoKC9J///EcBAQFKT0/XokWLXI5jxIgRat26tcvlAICSGThwoCNjpKWl6ZlnnlFsbKw6duyop556St99952ysrJMHmVBCxcudIxp7ty5qly5cqE2CQkJeu2111z24e39VPfu3Qtd1SJJ3bp106233irp8v2G7fvm0pKRkeG4sqdhw4Z6/vnnCyzfvn271qxZI0maMWOG4uLiCvURGRmpf/3rX5KkY8eOafny5QWWv/POO5KksLAwzZ8/X2FhYYX66NKli55++ukSvYcLFy44vnaWD93J3z5/P1erQoUKWrJkiSpWrCjp8lUgHTp0UN26dTV8+HDNnz9fycnJXns9bzp16lSRmfPKXFcU+99vkZGR6t27d6HljRs31vr16zV37lwFBwe77Kekv0fe/nvq5Zdfdvo5BgB/RFYkK1o9K17pp59+KjLrXDnGK128eLHIPuyPixcvuu3Lfl/tkydPOmZTzD9WqeBV19ddd50kae/evUpLS3Pa3kr30y7O9k5KSvKoT3u27N27t6Kiogotf+ihh3Tw4EH169fPbT8lPUY+Y8YMSZdnmnA2Q5AkjR8/3vH/gr0+4EyrVq2cHqNG2cT040A517lzZ5fLunbt6phm+ujRo44pxPNzd8DDPqW4mQ4fPqzdu3dLkgYPHuz2INsNN9zgmGbnSvZpmzt37qxKlSq57MNeGM/KytIPP/yg22+/3Wk7V9u9Ro0aaty4sfbs2aMjR464fB1fSE9P1yOPPCLp8nQ6EydOLNTGvh1uvfVWBQW53kX07NlTs2bNUlJSknbv3q3mzZtLkn744QelpqZKkh544AGX6wcGBqp9+/bau3dvid+PLzz88MNFtjl79qzj4KknXP0uBQQEOP3dy69FixaFpnm369q1q+PrKz9T3333nbKzsyVJffr0cdl/rVq1lJCQoF27dmnlypUaPXq0y3EAAHzjlVdecRxI+vXXX5WXl6eNGzdq48aNevnll1WhQgXdd999evrppxUfH2/2cPXVV19Jklq2bOn2hCd3B3u8vZ8qKvd++eWXOn/+vM6dO+c273nbpEmTHAeFZs2apfDw8ALL7fkrIiLC7UmYHTp0UGRkpC5evKiVK1fqtttucyyz/zxuvvlmpweN7Ep68M1bxWj7FObe0rlzZ23dulVPPfWUFi9erNzcXB07dkzz5s3TvHnzZLPZ1L17d/3lL39x+xkrbdu3b3dM/+pKv379tHTpUo/7DgoKcvk3UbVq1Ypcv6S/R97+e4rcCQAFkRXJilbOileaPn26pk+f7rZNfHy8Dh8+7HJ5cnJykXmpuDp27Oj4+qefflLt2rUlXZ7ieuvWrZIKFrXbt2/vWL5lyxbHsbeMjAzt2rWrUHuzFWd7f/LJJ7rzzjs97vtqawMlOUaelJSkLVu2SHL/uy9Jf/jDH7R7926tWrXKZRtyZfnCldpAOefuvjj5D2pcedaZvUi5c+dO/fLLL74ZnBds377d8bV9zK64K9Ju2rRJklSzZk23feQPfvbQ40xxtvuV29zXnn32WR06dEg2m02zZ88udFZmamqq9u3bJ6nk28FbPw93du/e7fbMQ/tJDlaQkJCgwMBASdKHH35Y4n5K+nts/1zbbDbFxsa6fQ37z9Td59pdkAUAXL077rhDv/zyi9auXasJEyaoXbt2jrPXL1y4oNmzZ6tZs2ZasmSJySP9v32+N/KXt/ZTJd1f+tKWLVscVyA9+OCDuvHGGwu1sW+H6tWrO3KDMwEBAY73kX87pKamOg4E+Sp/WVl8fLwWLVqkI0eO6D//+Y969+6t6OhoSZdnslm1apX69u2rIUOGeDTjjnR5liN3uTM9Pd0Xb6lE7D/71NTUIq98cudqc6e3/p4idwJAYWRF18iKZEV37FdqSwXvib1nzx7Hzzt/kfraa691nCSYv/22bduUm5sryfMTAFJSUtzmyrVr13r+xnzI/llZtmzZVWXekhwjt3/mpeJny5MnT+rEiRNO25Ary5fy+b8UgGLJH4TsO2S7hx9+WDNmzFBWVpY6deqkhx9+WL169VLr1q2dTnNjlqNHjzq+djYVSnFkZGTo7Nmzki5PiTR37txirZeSklKi17Nv9yu3uS9t3rxZ//nPfyRd/tl27969UJsTJ044pliaPHmyy6ncr5R/O3jj51GU999/v9BUTPlNmjSp2GO/0rFjxxxna3pDTEyMhgwZonnz5mnRokU6evSohgwZok6dOikhIUGhoaFX/Rrufo9/++03SZcP6Nr/0C1KST/XAADv6dy5szp37qyXXnpJ586d01dffaU33nhDa9euVUZGhu677z5t2LBBbdu2NWV8aWlpjux0Nfv70txPudtf+kpubq4efvhh5eTkqEaNGo4pIa9k3w6HDx8u9tTe+bfDsWPHHF/7Kn9VqFDBK/3Yi82+UKtWLT322GN67LHHlJubqx9//FELFy7UO++8o6ysLC1cuFBxcXH65z//Wew+R44c6XZ2pUOHDqlevXoej/XGG2/Ud9995/F67tx3332aNGmSzpw5o759+2rYsGHq3bu32rZtq7p163rlNVz9HpX231MA4O/Iiq6RFS8r7ax4pQULFmjIkCFX1UfDhg21f//+YrVt1KiR42p3Z6pVq6Z69erp8OHDBYrUmzdvliSFhoYW+H2JiYlR48aNtXfvXkcb6f8K3BEREYVu6ViUX375xe2V5927d3d7tbE73tjeVxo7dqwefPBBHThwQNdcc43++Mc/qkePHmrRooXXMr2rY+T2z7wk9ejRo9j9paSkFFkER9nHldqABXh6XzpvretO06ZNtXTpUsXGxurChQt67bXXdNttt6lmzZqqU6eORo0aZYkruL0xFaJ9umxPeXv6RF/JycnRww8/rNzcXLcH8ryxHbx5n8TyYubMmbrvvvskSevWrdPo0aPVunVrxcTEqFu3bkpMTHRMp+VtJfmZlpXPNQD4i4oVK+q+++7TmjVrNGXKFEnSpUuX9Pe//920MXlrf1/e91PTpk3Tzz//LEn6z3/+43Iay6vdDqWRv/IXtT29z2T+9t4qjhclMDBQnTt31htvvKFNmzY5Dry9/vrrOn36dKmMobRVqVJFX3/9tRo1aqTs7Gy99dZbuvPOOxUfH6/q1atryJAhWrdunU9eu7z/PQUAVkZWLKgs7VvKU1YsC+xTkNu3ufR/Req2bdsqJCSkQHv7FOT5i+D2ddu3b+/2qvnyYPjw4frHP/6hsLAwHT58WM8884w6d+6sihUrqkWLFpo4caJOnTrlk9cmW8IdrtQGLCD/NNB5eXnFWsfe7sr7rHjTrbfeqoMHD2rJkiX66quvtHHjRh04cEDHjx/X7NmzNWfOHM2YMcNxn+by4KabbtKYMWOK1dZbVzz42j//+U9t27ZNkjRjxgyX92bO7/7779fAgQOL1X/Tpk2vanye8uQqciuIjIzUe++9p6efflrvv/++1qxZo61bt+r8+fNas2aN1qxZo8TERH333XeqWrWqT8YQEBBgienHAABX5+mnn9aiRYu0a9cuffvttzIMw2cnOJam8rifOnjwoCZNmiRJ6tu3r+65554i14mNjdWbb75ZrP7dTePnC/mvRvb0wGj+g0sluar5arVs2VJPPfWUnn76aWVlZen777/XXXfdVax13d3n0Yrat2+vXbt26fPPP9eyZcv0448/as+ePTp16pQWLlyohQsX6umnn9bUqVN9Noby+PcUAJQVZMWyo7xlxbKgQ4cO+uCDD5ScnKwTJ06oZs2ajoK1s/tjX3fddVq4cKH27t2rtLQ0RUVFOYraJbmfdo8ePTw+OdRsTz31lIYPH65Fixbpf//7nzZv3qzk5GT9+uuv+vXXXzVz5kx9+eWXBe5Z7m1///vf1axZs2K1Le1j1DAHRW3AAvJP/VLce1TY7zURGRnpkzHZhYeHa8iQIY4pTFJTU/X1119rwoQJOnLkiMaOHauePXuqSZMmPh2HK9642iN/kbdu3bq68847r7pPq9i/f7/jTN3+/fu7PYCXfzs0bdq0RNuhtK6+KYtatGihF198UdLlK5a2b9+uxMREJSYmatu2bRo/frwWLFjg1dfM/zMtT59rAPBXgYGB6tSpk3bt2qXU1FSdOXNGVapUKfVxeGt/X573U6NGjVJGRoaio6P1xhtvuG1r3w4RERGWzV/x8fGKjo7W+fPnC9xupjjyt2/ZsqW3h1YsXbt2dXx96NAhU8ZQWoKCgtS/f3/H9Jbp6elauXKlnn76aW3fvl1///vf1bNnT6f37Cyp8vz3FACUJWTFsqO8ZcWyIP99tXfu3KmaNWtq586dkpwXqe1Xaufl5Wn37t269tprtXv3bkme30+7LIuNjdX48eM1fvx4SZens//ggw8ct7257777tHfvXq/ejz3/736XLl0KZHmA6ccBC6hVq5bj6/z3jHDlwoULjnvTePP+v8URExOje++9V59//rmky/e8WLx4cYn7s98Tp6T3q6lTp47ja3uh31Ph4eGOKX5KOr2JVY0cOVKZmZmKiYnRzJkz3batWbOm4wzekm4Hb/w8/IHNZlPr1q315ptvasCAAZKkJUuW6NKlS159Hfv/LXl5efw8AKCcqF69uuPrkt6+Iv89CUuSwaKiohzZ6Wr2L+V1PzV37lzH/ZL/8Y9/FJnX7dvByvnLZrM5DuytW7eu2LNLSdKaNWskXb4voqtpNX3NG783ZVVERIR69+6tb7/91jHL1/vvv+/V1yjPf08BQFlDVrS+8pgVy4Jrr73WUXjdv3+/zpw5ozNnzkhyXtRu27atY4rxffv26dixY8rMzHTZ3l/UqVNHTzzxhF5++WVJl08Y3bBhg1dfI3+thGyJK1HUBiwgISHBERg3b95cZPuNGzc6vi7u9Bve1rJlS8fZnkeOHClxP/ar1FNSUly2cXf1eqtWrRxf28+ucyUnJ8flMvtBuuLcJ3zHjh1FtrGCt99+WytXrpQkvfLKK4qLi3PbPiYmRo0bN5ZU8u3grZ+HP+nRo4ckKSMjw+v3orF/rqWif6bHjx/XuXPnvPr6AIDiOXHihH788cditbWfAGmz2VS5cuUSvV7+WYJKmsHsV9x6I39J5Wc/dfLkSf3lL3+RJHXu3FmjR48uch37djh9+rSSk5Pdtt23b5/jYJpdTEyMYxpnX+Yv+7SYycnJWrZsWbHW2bZtmzZt2iRJGjRoUIlf2xnDMPTJJ58Uq23+E4fNuGLNCqpXr67mzZtLurq/31wpj39PAYBVkBXJipL1s6LVhYeHq0WLFpIuF7X37dsnSYqLi1N8fHyh9hEREY7stG/fPkf7+vXrq0aNGqU0auuyH8+UvJ8tPfndv3TpkuMKevgHitqABYSHh6tz586SpP/97386duyY2/Zz5851fH3TTTf5ZEx79uzR2LFjXZ6ReenSJcf98a5mCnR7qNq7d69+//13p23sV3c4U79+fSUkJEiSFi5c6PbeJO7OGuvdu7eky+/bfj8VZ5KTk9WpUyfdf//9ysjIcNnObMnJyXryySclSd26ddMf//jHYq1n3w4rV65UUlKSy3bbtm1T27ZtNX78+AJX6nTu3FnR0dGSpP/+978u18/LyyvWCRxlnWEYeuGFF7Rq1SqXbfL/gejt2wncdNNNCgkJkXT598OdP//5z2rbtq22bNni1TEAANzbsGGDrrvuOvXu3dtxoMSVjIwMff3115IuHygMDQ0t0Wvmv4+tq5yVk5Pj9uDpbbfdJulycWrbtm0u27nLX+VxPzVu3DidOXNGISEheuutt4p1H8vbb7/d8bW77WAYhu6//37dcMMNhabQtv88vv32W508edJlH1dzFcXAgQMdUwE+/vjjRd5bOycnR48++qgkKTg4WMOGDSvxa1/pwoULjlvr/Otf/yqyff7id/6DZOVJUlKSRo4cqYsXL7psY8+dvriFVXn7ewoArIKsSFYsK1mxLLBPQb5//37t379fktzeD9qeG/O395ertD///HNNmzbN5XJfHs+Mi4tT27ZtJUmLFi1yO0vUrFmzdO211xb5fwTKD4ragEXYD/JkZ2dr6NChLqeDef/99/Xee+9JulzQ7dKli9fHcvToUXXo0EEzZ87U4MGDnR5seOONNxxTJbdp06bEr2W/B0leXp6effbZQstPnjypf/7zn277GDFihCTp559/1uTJk522+fe//+2Y2seZoUOHOu4x8+CDDzo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tjMsfq0Z4sL5+7BY9MOkPnb/yvyPnf919Rh8u2aPXutd3+WMCAADkBRkSADyfYRj683CcJq48qDUHzjttG17KT72iK+mB5lVUL7J0IfWw4FksFkWUDlBE6QC1rlFO7wy/X2nhtVSm4a2yVKjjcL5V+89p1f5zqhQWqH6tq2pA62oKDaKWU9JQ1C5AY8aM0ezZs1W2bFl9//33CgjI+xE0J06ccDo9JiZGrVq1ym8X3c4wDC3fe1ap6VnPsOlav4JK+RfM29PX20sT+jfTwGkbtfFonHn//jNX9Ow32zR1YAuO9AEAAG7hivwoFf8MeSU5LdsOgFL+Prq9foTrRvm5TvXwYM0c2kp9J/+pyyn/u2TO1DVHVKdCaT3YgjObAACAe5AhAcCzGYah3/ee1YQVB/VXDtfLble7nAa2qa4uN0Xo/7F33+FtleffwL9He1ny3o6dvfcAQkiAQFhhb2iBsspqyyjdLVB4u6At/QEthVJGSthhr7BCgISQvRMnjveekq0tnfP+4cT2kex4xNbR+H6uy5et55wj3Yod6dZzP0OrHt5Jf9FI7GhCe9luWGs3Y8OuA3h7WzXe3FKNAw0dvZ5f3ebGwx/vx7/WlOB7xxfixpNGI92ij3DUpBQWtUfI3/72N/z+97+H0WjEO++8g3Hjxg3pfvLz84c5suiyv749bNnxKTnWEd8fwaBV4+lr5+HSJ9ehuL77xfHzfQ34y0f78MuzJ4/o4xMRERGFGq78EYj/HHJNcfigyKWTM6HXjOz+1lNzbfjPtfPwvWc2wB/sXh7u12/uxOh0M+YXpY7o4xMRERGFYg5JRBS9JEnC2gNN+Ovq/dhRZe/zPKNWjQvn5OHaE4rialb2YOUlG3HbyeNw65Kx2FPrwOubq/DG5io4PIGwczu8ATz5ZQmeW1eKK+aPwg+XjEGOzahA1BRJ8T/MQwFPPfUU7rnnHmg0Grz22ms48cQTlQ4pKgWCYtiy42adGovHp0fk8W1GLZ65dj5SzTpZ+7/XHsJHu2ojEgMRERERwPxxMKpaXShpdMraJmcnoWiEB0UecdyYNPy/C6bL2vxBCbe/uAWN7X3vBUZEREQ03JhDEhFFrw2HmnH5v7/Ftf/9rs+CdrpFh5+dORHf/nIp/nDh9IQuaPckCAKm5tpw37lTseFXp+EvF8/AtDxrr+d6/CKeW1eGJX9Zgz99uA/tHn+Eo6VIYlF7mL344ou49dZbIQgCnn/+eZxzzjlKhxS1tlW2ocMrH2Fz8sRM6LUjO8Omp4JUE/519Rxo1fJlKu99bQdKm5x9XEVEREQ0fJg/DpwkSWHLjhu1apw0ISOicVw2vwA3Lhota2to9+InL29FUJT6uIqIiIho+DCHJCKKTntqHPj+Mxtw+VPfyrY/7Skv2YgHz5+Kr39+Km47eRz3hj4Ko06Ny+YX4N07FuGt20/E8hk56G3XMV9QxJNfluCUR77EKxsr+Nk8TrGoPYzeeustXHfddRBFEY8//jiuuuoqpUOKWm5fEBvLWmVt+SlGjM2IzAybno4bk4b7z5sqa2v3BnDr/zbD4w9GPB4iIiJKHMwfB6e4vgMNIbOhjx+TCmMEB0Ue8cuzJ+OkkBWG1pU049FPiyMeCxERESUW5pBERNGnzu7Bva9txzmPfRU2GPuI0elm/PXSmVhz78n4/glFMCjwWTZWCYKAWQXJePyqOfjs7iW4dG4+NKrw6nZThxc/f2Mnznv8a2w41NzLPVEsY1F7mKxevRpXXHEFAoEAHnzwQdx2221KhxTVvittCdsHcdG4dAi9DbGJgKsWjMKFs/Nkbfvq2vGnD/cpEg8RERHFP+aPgxMQRawrkXcMpJi0mJprUyQetUrAo5fPQrbVIGt//IuD+JYfnImIiGiEMIckIoouTm8Af/ukGKc8sgavba6C1MsE4bxkI/5y8Qx8ctdiXDw3H1o1S3PHYkyGBQ8fGRxwfGGvxe3dNQ5c/tS3+NWbO7kkeRzh/5wBstvtmDNnDoxGI6655hrZsW3btuHCCy+E1+vF3Xffjd/85jcKRRkb2j1+7Khuk7VNzEpCVkiHYCQJgoD/d+E0TMiyyNqfW1eGL4sbFYqKiIiIYhnzx+G1u8YBh0e+dc2J49Kh7uXDa6SkWfR4/KrZsg/QkgTc8+p2OPihmYiIiIaAOSQRUWyQJAlvb6vGKY+swf99dgDuXlZ9zUzS48Hzp+Lzny7BZfMLoGExe1jlp5jw4AXTsPquxVg6KbPXc1ZuqMAZf1+LdQd7nz1PsSUh/wctXboUGo1G9lVeXg4AuP3222XtS5cuBQDs3LkTW7duhcfjwUsvvSS7v23btsHlcgEAnnnmGSQnJx/1q6KiIrJPOMpsqWhDz+0M1IKAhWPTlAvoMJNOg39ePQcGrfy/xb2vbUer06dQVERERBQNmD8qKyhK2Fwu37omN9mAMemR37om1LyiVNx7xkRZW3WbG/e9vVuhiIiIiChaMIckIopP++o6ZwH/5OVtYVtkAYBZp8ZPl03Al/eegu+fUAS9hsuMj6QxGRY8c918vHD9AozPtIQdr7F7cPUzG/Dnj/bBH7KCMMUWjdIBKCEYDCIY7H2vZFEUw84FgOnTp2PWrFnYu3cvLrvssj7v22639/v4oY+RSNy+IHZVy/+NpuZaYTVqFYpIblxmEn519mT8rkcnZEO7F79ctRP/+t4cxZZHJyIiImUxf1RWcX072kNmaZ8wJi1qcrObThqDNfsbsb7HsuNvbq3GqZMyce7MXAUjIyIiIiUxhyQiii92tx9//6QYK74tR1AMX2dcJQBXLBiFO08bj8wk5VamTVSLJ2Tgw5+chBXfluPhj/fD5et+D5Yk4F9rSvDtoWb86+q5yLbx9xOLErKovWbNmkFfY7PZsHXr1l6PXXfddbjuuuuOLagEsa2yDYEeL/aCAMwtTFEwonDfP74Qn+1tkC07/tHuOry+uQqXzitQMDIiIiJSCvNH5UiShE1l8lnaOTYD8pKNCkUUTqUS8NfLZuKMR9fKiu+/fnMn5hWlIMcWPbESERFR5DCHJCKKD5Ik4YOddbj/3d1o7GVmNtBZUP3NOZMxISspwtFRTxq1Cj84cTROm5yFX6zagW8ONsuOb61ow7mPf40nvzcHcwtTFYqShiohlx8nZXgDQWyvapO1TcpKippZ2kcIgoCHL5mBFJM8rgfe3YPKFpdCURERERElppJGJ1pc8q1g5hWlRM0s7SNyk4146IJpsjaHJ4B7Xt0OsZcR/ERERERERBT9atrcuPH5Tbh95ZZeC9r5KUY8fc08PP+D+SxoR5GCVBNWXH8cfnPOZGjV8v6DxnYvrnjqW7yxuUqh6GioWNSmiNld44A3IF/2aF5RdI6EybQa8MeLZsjaOrwB3PXKtl6XFSEiIiKikRG6l3a6RYfRacrvpd2b82fl4byQ5cbXlTTjv9+UKhQRERERERERDUVQlPDsN6U4/W9f4rN9DWHH9RoV7jxtPD69ewlOn5IVdQOvqXNVtRtPGoNVt56IglT5Cmr+oIR7XtuOJ744CElizSdWsKhNESFJEnZUyff6GZthRqpZp1BE/TtzWjYum5cva9tU3opn2SlJREREFBH1Dg/qHB5Z27zC1KjuLHjw/GnIDdmb6+GP9+NQY4dCEREREREREdFgHGrswKVPrsMD7+6Bs8e+zEecOikTn969BHeeNgEGrVqBCGkwpufb8O4di3DS+PSwYw9/vB/3v7ObK6zFCBa1KSLKm12wu/2yttmjomsv7d787typGJVqkrU9sno/Kpq5DDkRERHRSAsdFGnRazA+06JQNANjM2nxyGUz0bPu7g2I+MWqnfyQTEREREREFMVEUcIzX5firH98hS0VbWHH0y16PH7VbDxz7TwUhNQNKLolm3R49rr5uGHR6LBjz68vx6/f2sXP7DGARW2KiNC9tNMturAZLNHIotfgkUtnyto8fhG/WLWDS1IQERERjSC3P4j99e2ytul5NqhU0TtL+4iFY9Nx3cIiWdt3pS148bsKZQIiIiIiIiKioypvduKKp77Fg+/tCdtGFQCuXFCAz+5eguUzcqN69TDqm0atwm+XT8Fvzpkcduyl7yrwSw5Gj3osatOIs7v9KAuZ2TwjPzlmXvgXjE7F948vlLWtK2nGyxsrFYqIiIiIKP7tqXEg2OPDpEoApuZaFYxocH66bCLyU+R7dv3pg72obnMrFBERERERERGFkiQJKzdU4MxHv8J3ZS1hx4vSTHj55uPxx4tmwGbSKhAhDbcbTxqD/7tyNjQhg+Zf2VSJB97dzQmNUUyjdAAU/3aEzNLWaVSYlJ2kTDBD9POzJuHzfQ2yTsg/vL8Xp0zMRHYMzDgnIiIiiiWiJIXlkOMzk2DWx87HF7Negz9dNAPfe2ZDV5vTF8Sv39yJZ6+bHzMDPImIiIiIiPozb9481NXVKR3G4OnMUB3/fahGze71sLjvMxzc+hYu+YcvwoENTm1tLQDA6XQqHEnsOG9mLnRqFe5YuQWBHgPqn19fjnSLHj9aOl7B6KgvsdMrRDEpKErYU+uQtU3JsUKrjq1FAix6Df7fhdNw3bMbu9ravQH85q2dePqaeeyUJCIiIhpGlS0uODwBWdvMAptC0QzdovHpuHxeAV7Z1L3Cz5r9jXhzazUumpOvYGRERERERETDp66uDtXV1UqHMSiGwplIO+tuqJLSwo752+rQ/ME/4K3cqUBkQydx6exBOXNaNv71vbm47cXN8Ae7/+3++kkx0ix6XHXcKAWjo96wqE0jqrTJCY9fvv/EjPzY65AEgJMnZuKiOXlYtaX7zfnTvQ14Z3sNzp+Vp2BkRERERPEldFBkhkWPbGtsro7zq3Mm44v9DWho93a1/f69PThpfAYykvQKRkZERERERDS8BJUK1tQMpcM4OpUahrkXQj/9DAhC+OQ77941cH/3KgwBLwzpWQoEOHj2pnqlQ4hZp0/Jwt8um4Ufv7wVPVcd/+3bu1CUZsLCcenKBUdhWNSmEbU3pEMy12ZAikmnUDTH7nfLp2BtcROaOro7JR94t7NTMtUcu8+LiIiIKFp4/UGUNMqXTJuSa43ZlXFsRi0eumAabl6xuautzeXH/e/sxhNXz1EwMiIiIiIiouFlTc3AfSvXKh1GnxwePz7cWYc6hyfsWNBlR+snT+LB//sPgJsiH9wxuOfMyZBEsf8TqVfnzsxFi9OH+97Z3dUWFCXc+uIWvH37iShKNysYHfUUW2tAU0xxegMobZZ3SE7OtSoUzfBINunw4PlTZW0tTh8eem+PQhERERERxZfi+g4EeyyZphKAiVlJCkZ07JZNzcbyGTmytvd31uLTPRxNT0REREREFAmlTU68tKGi14K2u3QLav57OzylWxSIjKLBtQuLcNvJY2VtdrcfNzy/Ee0ev0JRUSgWtWnE7K9vly3XoFEJmJAZ2x2SAHDW9BycOTVb1rZqazXWFjcqFBERERFR/Ahdenx0uhlGnVqhaIbP/edNRbJJK2v77du7+OGYiIiIiIhoBImihG8ONuGd7TXwBOSzmdWCgMXj09H4+gMQnW3KBEhR46fLJmLZFPmS8yWNTvxy1U5IEvcrjwYsatOIkCQJe2rkHZLjMi3QaeLjT+6B86ciySBfvf/Xb+2EyxdQKCIiIiKi2Nfi9IWNmp+SE9sr/RyRbtHjN+dMkbXV2j145OP9CkVEREREREQU35zeAFZtrcam8tawYzajFpfNz8fsUSkAWLAkQKUS8PfLZ2FStnxy5ns7arHyuwqFoqKe4qPCSFGnscOLZqdP1hYvHZIAkGU14JdnTZa1Vba48einBxSKiIiIiCj27Q2ZpW3UqlGYFj97V108Jw+LxqXL2l74thybe+lgISIiIiIioqGrs3vw8sZKVLe5w46Ny7DgygUFyEwyKBAZRTOzXoOnr5kHa8ikxgfe3RM2kZMij0VtGhHF9R2y20kGDfJTjApFMzKumF+ABUWpsrb/fHUIu6rtCkVEREREFLskSUJxfbusbVJOEtQqQaGIhp8gCPjDhdNh0HZ/DJMk4JerdsAXsgweERERERERDc3uGjte31KFDq98ZVWVACyZkIGzp2dDr4n9ba5oZBSkmvDwpTNlbb6AiDtf2QqPP6hQVASwqE0joLcOyYlZSRCE+OmQBDqXovjDRdOhU3f/NxIl4Odv7EAgyE5JIiIiosGod3jh8Mg7HCZlJfVxduwalWbCXadNkLUV13fg31+WKBQRERERERFRfAiKEr7Y34BP9zYgKMqXFLfoNbh0bgFmFSTHXa2Cht8ZU7Nx3cIiWVtxfQf+/mmxMgERABa1aQTUOTxoD+mQnBCHHZJA5z7hd5w6Tta2u8aBZ74uVSgiIiIiotgUOijSZtQiI0mvUDQj64ZFozE1V741z2OfH8TBho4+riAiIiIiIqKjcfuCeHNrNXZUha+kmp9sxJULCpBt43LjNHC/PHtS2La6T609hM3lLQpFRCxq07ALXXo8xaRFukWnUDQj75YlYzEhyyJr+/unxahodikUEREREVFskSQJB0IKuvG40s8RGrUKf7poBnqurO4LivjVqp0QQ2YTEBERERER0dG1OH14ZVPv+2fPKkjGhbPzYNJpermSqG96jRp/u3wmtOruD++SBNzz6nYuQ64QFrVpWHV2SMpn2YyP4w5JANBpVPjjRTPQ8yl6/CJ+9eZOSBI7JYmIiIj6U2P3hO11Nj5k0GC8mZ5vww2LRsvavitrwcsbKxWKiIiIiIiIKPZUtLjw6qZK2N1+WbtaJWDZlCwsmZABlSp+6xM0siZlW3HX6fItxMqaXXjii4MKRZTYWNSmYVXT5oHTKx+hMiEzvjskAWBuYQq+f3yhrO3rg01YtaVaoYiIiIiIYkfo0uNpZh3SLfG59HhPd50+AfkpRlnbHz/ciwaHR6GIiIiIiIiIYsfOajve2lYNb0CUtXfun52PySFLRxMNxc0njcHsUcmytie/LOEWYgpgUZuGVViHpEWHtATokASAe8+YiJyQPTkeen8Pmju8CkVEREREFP1ESQr7IBjvs7SPMOk0+MOF02Vt7Z4A7ntnt0IRERERERERRT9JkvDVgUZ8vq8BoYulZibpcfn8AmRZuX82DQ+NWoU/XjQdmh4z/v1BCb95a6eCUSUmFrVp2EiShJImeYfkhMwkhaKJvCSDFg+eP03W1ury48H39igUEREREVH0q7N74PKFrPSTlTg55OIJGbhodp6s7cNddfh4d51CEREREREREUWvgCjio1112FLRFnZsXIYFl8zNh0XP/bNpeE3KtuKGk+RbiH17qAXC6OMUiigxsahNw6be4Q1benx8Aiw93tNpU7JwzvQcWdtb22rwxf4GhSIiIiIiim4ljfJBkWkWHVJMOoWiUcZvlk9Bqln+nH/39i44PP4+riAiIiIiIko8Hn8Qb22tQXEvyz7PK0zB2dOzoVWz7EUj4ydLxyMvWb6FmGrWhRC0ibFacTTg/24aNqEdkikmLVLMidUhCQD3nTcFVoN8JNhv3twFpzegUERERERE0UmSJJQ0OmVtY9MTa1AkAKSadfjt8smytnqHF3/5aJ9CEREREREREUWXdo8fr2+uQnWbW9auEoDTJ2fhxHHpEAShj6uJjp1Jp8H9502VtQnmFFjnX6hQRImHRW0aNqFF7bEZidchCQCZSQb86mx5p2R1mxt/+6RYoYiIiIiIolOz0we7Wz4beWyGWaFolHXBrDwsnpAha/vftxXYVNaiUERERERERETRoanDi1c3VaHZ6ZO169QqnDczF1NyrQpFRonmtMmZWDQuXdZmPe4SCKZkZQJKMCxq07BocfrQ6grtkEzMojYAXD6/AMeNTpW1PftNKbZXtikTEBEREVEUOhQyS9ui1yAjKTGX7RIEAf/vgmkwatWy9l+s2glvINjHVURERERERPGt1u7G65ur0BGyEqpJp8Ylc/NRmJaYA6NJGYIg4DfLJ0PVY1EAlc4Aw1zO1o4EFrVpWBwKmaVt1qmRZU3MDkmg84XtjxdNh07T/V9MlDo7Jf1BUcHIiIiIiKJH+Eo/5oReLq4g1YR7lk2QtR1s6MC/1pQoFBEREREREZFyKlpceHNrNbwBeZ96ikmLy+cVJOygaFLWpGwrLp9fIGvTjV+I5g6vQhElDk3/pxD1L3QvxDEZloTukAQ6/w1+snQ8Hv54f1fb3loH/vNVKW49eayCkREREREpr93jR0O7/ANfIq/0c8R1C4vw9rYa7Ky2d7U98cVBnDM9B+OzkhSMjIiIiIiIKHIONnTgo111CEqSrD3HZsB5M3NhCFnlimJTR1szAKC2thb5+fkKRzMIhiSoz38IgtYAABAEFb491IJzZuQoHFh8Y1GbjpnTG0CdwyNrS9S9EEPdvHgM3t1eg3117V1tj35ajLOmZaMonf9GRERElLhClx7Xa1TISzYqFE300KhV+NPF03He498gKHZ23viDEn6xaide++EJUKkSe+AoERERERHFvz21Dny6px5SSHthmgnnTM+BVs1FiOOFKIpd36urqxWOZnBs361C8olXdd0+2NiBBocHmVaDglHFNxa16ZiVN7tkt3VqFfJS2CEJAFq1Cn+8aDou+tc6HBlQ5g2I+NWbO/Hijccl/Gx2IiIiSlxlzfKi9uh0Mwu2h03NteHGk0bj318e6mrbXN6KF7+rwPePL1QwMiIiIiIiopG1rbINXxY3hrVPyLRg2dRsqPm5MU4JsKVnKh3E4JSsQ3DOuVAbu1dVW3+oGefPylMwqPjGojYds9KQDsmCVCM0Ko6UOmL2qBRce0IRnltX1tW2rqQZr22uwmXzCvq+kIiIiChO+YMiKlvdsrbRXMVG5s6lE/DhzjpUtHQPIP3zh/tw+uQsZNs46puIiIiIiOKLJEnYUNqCDaUtYcem5VpxyqRMqDhJLG6p9Sbct3Kt0mEM2gO/+w2Sl1zbdbus2YVauxs5Nk78HAmsPNIxCYoSKkJmarNDMtxPz5iI3JDOx//3/l40huwjSURERJQIqlrdXUtrA4AgAKNSTQpGFH2MOjX+cOF0WVuHN4Dfvb0LkhS6CB8REREREVHskiQJXx1o6rWgPbcwBaeyoE1Rqn3rewg6W2VtG8ta+zibjhWL2nRMau1u+IKirK0wjUXtUBa9Bg9dOE3WZnf78as3d7JTkoiIiBJO6NLjOTYDDFq1QtFEr0Xj03HJ3HxZ2+o99Xhne41CEREREREREQ0vSZLwZXEjtla2hR1bODYNi8alcxtPilqS3wv7hjdkbaVNTk5oHCEsatMxKQuZpZ2RpIdFz1Xte3PqpCycOzNX1vbJnnq8saVaoYiIiIiIIk+SJJQ1yYvaRRwU2adfnz0ZaWadrO23b+1Cnd2jUERERERERETDQ5IkfLG/Edur7GHHTpmYgflFqQpERTQ4Hds+QtDtkLVtKg9fdYCOHYvadEzCOyS5bOTR3HfulLBOyQfe2Y3qNncfVxARERHFl1aXHw5PQNbGonbfUsw6PHiBfMUfhyeAn72xgyv+EBERERFRzJIkCZ/va8DOanlBWwBwxtQszMhPViQuosGS/B50bP1Q1nagvgOtLp9CEcUvFrVpyBweP5qd8v+U3E/76NItevzhIvneiO3eAH72+naIIjsliYiIKP6FDoq06DVIt+j6OJsA4OzpOTh/lnzFn7XFjXhxQ4VCEREREREREQ2dKEn4dG8DdtXIZ7cKAnDmtGxMyrYqFBnR0HRs/xhadfcy+RKAzeXcW3u4sahNQxbaIWnQqJBlNSgUTew4Y2o2LpqTJ2v75mAzXlhfpkxARERERBFU2hy+0g/3R+vf78+bhiyrXtb2hw/2ojzk35OIiIiIiCiaiZKET/fUY0+tvKCtEoCzpmVjQlaSQpERDZ3kdYatLrCvrh0uX6D3C2hIWNSmIatoke+nPSrNBBU7JAfkvnOnIscmHwDwp4/24VBjh0IREREREY08f1BEbZt8L+hCLj0+IDaTFn++eIaszeUL4p5XtyPIFX+IiIiIiCgGiKKE1bvrsbeuXdauEjpXqBqfyYI2xa7ZBclQ96iRBUUpbHl9OjYsatOQiKKEyhb5PtDcC3HgbEYtHr5kpqzN4xdx96vbEQiKCkVFRERENLKqW90I9tgHWhCAglSjghHFlpMnZuKq40bJ2jaVt+Lprw4pFBEREREREdHAiKKEj/fUYX+9vKCtFgScMyMHYzMsCkVGNDzMeg0mZMv/jndU2REQWfMZLixq05DUt3vgCym+FqSaFIomNi0an45rTyiUtW2rbMOjnx5QKCIiIiKikRW60k+21QC9Rq1QNLHp12dPxqiQvPuvq/djR1WbMgERERERERH1Q5IkfLK3HsX18pVK1arOgvaYdBa0KT7MLkiR3Xb5gjhQzxV6hwuL2jQk5c3yDsk0sw4WvUahaGLXL86ajNHp8hnuT6w5iG8ONikUEREREdHICS1qF3JQ5KCZ9Rr89bKZ6Lnrjz8o4Y6VW9Hu8SsXGBERERERUS8kScLn+xqwL2TJcbVKwLkzcsL6x4liWUaSHvnJ8hXptla0QZK4bdhwYFGbhiRsP212SA6JUafG3y+fBY2qu1dSkoA7X9mGpg6vgpERERERDa8ObwDNTp+sbVQac8ihmF+UittOHitrq2hx4Vdv7uIHZSIiIiIiihqSJGFtcRN21Thk7UcK2oXc0pTi0OxRybLbjR1e1LR5lAkmzrCoTYPmDQRR55D/B2SH5NDNKkjGz86cKGtrbPfi7le3QxTZKUlERETxIXRQpE6jQlaSQaFoYt+dp03A3EL5smbvbq/BKxsrFYqIiIiIiIiomyRJ+KakGdtCtkpSCcDy6SxoU/wqSjfDZtTK2nZW2xWKJr6wqE2DVtXqRs8JIGpBQF7Icgo0ODcuGoOTJ2bI2tYWN+Lprw4pFBERERHR8AotahekGKHqsVoNDY5WrcI/rpgFq0G+BdD97+5GcX17H1cRERERERFFxnelLdhc3iprEwTgrGk5KOKS4xTHVIKAGXk2WdvBhg64fAGFIoofLGrToFWE7Kedk2yAVs0/pWOhUgl45NKZyEzSy9of/ng/tlS09nEVERERUWyQJCksh+T2NccuP8WEv1wyU9bm8Yu4Y+UWuH1BhaIiIiIiIqJEt7m8Fd+WtoS1nzElG+MyLQpERBRZk3OtUPcYyB+UJOypdRzlChoIViJp0Lif9shIt+jx6OWzIPSYsBQQJfxo5Va0hOw/SURERBRLmjp8cPvlRVbmkMPjzGnZuOaEQllbcX0Hfvs299cmIiIiIqLI217Zhq8PNoW1nz45CxOzkxSIiCjyjFo1xocM4NhV7eDn9GPEojYNSrvHjza3X9bGDsnhs3BcOn50yjhZW3WbG7e/uAX+oKhQVERERETHprJVPijSatAg2aRTKJr486uzJ2NyjlXW9vrmKjy/rkyZgIiIiIiIKCHpJizCmuLGsPZTJmZgSq61lyuI4tf0kCXI7W5/2KRRGhwWtWlQqlrdstt6jQoZIUtm07H58dLxWFCUKmtbf6gZf/hgr0IRERERER2b0ByygIMih5VBq8bjV82GSaeWtT/4/l6sL2lWKCoiIiIiIkok5iknw7jo2rD2k8anY0Z+cuQDIlJYjs2AdIt8QP/OartC0cSHhC9q//znP4cgCF1fTz755DHd3wsvvICTTz4Z6enpMBgMGDNmDG699VZUVFQMU8TKCu2QzE8xQtVzvWw6Zhq1Co9fPRtZVvlggWe/KcPrm6sUioqIiIiOYP44SIIK1b3kkDS8xmZY8NdL5ftrB0UJt6/cgqpWjgQnIiJSGnNIIopnQsFspJ1zFwRBXnI6YUwa5oxKUSgqImUJghA2W7u0yQmXL6BQRLFPo3QASpEkCXfccQf++c9/IiUlBR6PB263u/8Lj+Kaa67BihUrAHT+ser1epSWluLJJ5/EypUr8dlnn2HevHnDEb5iQpeOzE+J/lk2HW2ds1Nqa2uRn5+vcDSDkFYE9bKfQlBru5p+9eZOjMu0YFZBsnJxERERJSjmj0OjThsFX8g2KgXMIUeMauZ5UE0/p+t2i9OHm1/YjDduXQhjyExuIiIiGnnMIYko3n2+rx6qRTdCUMk/b8wvSsGC0al9XEWUGCZmJWHtgSYExc69tEUJ2F/Xjtkc7DEkCVnUDgaDuPHGG/Hcc88hMzMTq1evxvnnn4/y8vIh3+ezzz6LFStWwGq14oknnsAll1wCg8GAkpIS3HvvvXjzzTdx6aWXori4GFqttv87jEIqSzraPfIRJLEwy0YUxa7v1dXVCkczCNXVMIt6pJ9zd1eTLyDihys24d07FiHTalAwOCIiosTC/HHoNDmTZLdTTFqY9dH/MSR2c8gnkWHMgGncgq6mPbUO3Pv6djx25WwIXGWJiIgoYphDElG8+/pAE2753xYIavlnvNkFyThhTJpCURFFD71WjXEZFuyvb+9q21PrwKyCZH4+H4Lo700aZn6/H9/73vfw6quvIj8/H59++ikmTpx4zPf75z//GQDw5JNP4sorr+xqHzt2LF555RXMmzcPO3bswEsvvYRrrrnmmB9PCZpceYekUatGmlnXx9nRSIAtPVPpIAanbjccm96Bdd55XU31Di9uemETVt50fEx0CBMREcU65o/HJjSHjIWVfuRiL4f0rXsB2pRcaNO6Z5i/t6MWYzMsuOv0CQpGRkRElDiYQxJRvNtwqBk3vrARvoB8Za7peTacND6dBTuiw6bkWmVF7aYOHxrbvZy4OAQJVxHbt28f3n33XYwZMwafffYZioqKjvk+9+/fj/379yMrKwuXX3552HGtVos77rgDN998M95+++2YTShDZ9nkpxhj6o1JrTfhvpVrlQ5j0B64+mS40wthLOreI3F7lR23r9yCp6+ZB61adZSriYiI6FgxfzwGKjU0WeNlTQUxsNJPT7GaQz5466VQn/trqPTmrrZ/fHYAmVY9rj6uUMHIiIiIEgNzSCKKZ1srWnH9cxvh8csL2t7ir3HKqdfFVN2AaKQVpBiRZNDIVkLeU+tgUXsIEq4aNn36dLz11lv46quvhiWZBICvv/4aALBw4UKoVL3/ky5evBgAsHZt7HWIHdFbUZsiQBLR9M6fIXU0yZrX7G/Ez9/YAUmSFAqMiIgoMTB/HDp9zngIWvmHtDzmkBEh2uvR9M7DkCR5J9Nv39qFj3fXKRQVERFR4mAOSUTxale1Hdf+9zs4fUFZu3PvWri/fo4FbaIQgiBgco5V1ravrh0BUezjCupLwhW1AWDZsmXIzc0dtvs7cOAAAKCgoKDPc44ca2pqgt1uH7bHjhRNah5UZvnG9QUxt3Rk7BLdDgQ//z+kmOR7Ia3aUo0/f7RfoaiIiIgSB/PHoTGMmim7nWbRwaRLuMWiFOM+tAnixpdlbaIE/OilrdhY1qJQVERERImDOSQRxZvi+nZ8/5kNcPSYcQoAYsVWNL33V4ATsIh6NSWkqO0NiChvdikUTexij9IwqKioAAAYjX3POjGZugvAFRUVmD59+oDuu6qq6qjHa2trB3Q/x8pQME1226xTIzmkwEojzFGPZ66bj6ue/la2rMuTX5YgM0mP6xeNVjA4IiIiGoyRzB+B6Mkh9aPkOWRBMgdFRppU/CV+9Ivf4bHPD3a1+QIibnhuI167ZSEmZicpGB0RERENRqLkkEQUnQ41duCqpzeg1eWXtS+ZkIHPVv4HEIN9XElENqMWuckG1LR5utr21bVjbIZFwahiD4vaw6Cjo2PEzj/ayMtQTz31FJKTk496Tk5ODq688kpZ20svvXTUpPTKK68EdCbsEeqwO5ANAMhPMQFiAO1b3x9QbKZJi6G2pHbd9rdUw3NoY7/XCWoNLLOXy9o8ZVvhbyrv91pNSi6MYxfI2jq2fwzJ7+73WkPhLGgzirpuB90OuHZ/3u91AGCecQZUuu4PF766g/BW7er3OpUhCeZpS2VtruJ1uPG670MSRahUKqx5/VlcmySi1eWTnffORzWwGc/CxXPzu9r+9re/DSjeiy66SLYMVllZGVatWjWga++++27Z7TVr1mDLli39XldUVISLLrpI1vb888+jubm532uXLFmCuXPndt1ub2/H008/PaB4r7nmGqSnp3fd3rlzJz755JN+r7NYLLj55ptlbe+++27XCOmjmTZtGpYtWyZre/zxx+Hz+fq4otvy5csxYcKErts1NTV4+eWXj3JFt9tvvx16vb7r9vr167F+/fp+rxvKa8QRJ5xwAk444YSu216vF0888cSA4r3iiitko9eLi4vx3nvv9XudTqfDHXfcIWtbvXo1du3q///c+PHjce6558rannrqqQG9Rp9++umyjoGmpia88MIL/V4HADfddBOSkroLCJs3b8aXX37Z73VpaWm49tprZW2rVq1CWVlZv9fOmTMHJ598sqyNrxHhEuU1oq2trd9zaOSNZP4IREkOedVVEIwCdgd75JCpRkhBPzqYQ4YZyRxS2PU+rkvyw+WXdzI9+HQNHrr1MhSld++7zfeHcIny/gAwh2QOKccckq8RRzB/jB6JkEMewfcHvj+E4vuDsu8PQVFCi9OHUyUJ6LHDlE6twr2X/RhTf9c9c9tbsw++mn39PqbanArT5MWyNtfetQg6+19VSpc7Cfrc7u1Sj/Vz5l133nn4loD2TW/1el00f87sK/7h/pwZdDT0e60uayz0BfIBVX39mwLy2AP2emhsWV3HAvZ6uA/0/3kEAJLmXSC77a3cCV99Sb/Xqa2ZME1YKGtz7voMoqe932v1+fKB/KLPDeeOj/s8f2lQgtfQ+bn8I+9ElDYJ8AaC0GvU8DeWwVO+rd/HFLRGWGaeIWtzl3yHQGtNv9dq0wthKJota7v26iugUauhUqmO+loeLTkki9rDwO3u/4WnJ5drZJYU6OjogFqtPuo5Nput13ja2/v+D2qxdI4U0fboBMtLNgISIPkG9txD9/KDGBzQtZI6/E9UCvgGdm0g/M1a8rsHdm3oqDJJGvBzDV1iRRIDA3yu4TPfpYAXSZbukTpHfk/mkG1JtEIQP319O4KihMvmF8jO7U8gEAi7PdBrQ3m93gFd29v/AafTOaBrQ5MwSZIGHK8YskeF3+8f8nP1eDwDutbj8YS1tbe3DyiZ9Pvlox6DweCI/26G8hrR8zFCDTTeYFD+f26gvxudThfWdiy/m46OjgFdG/q7EUVxwM9VCnmN8Pl8A7rWYDCEtUXid8PXiPh7jRhsxxaNjGjJH4ERzCHNZgABaHu8xufajIAkMofs9eKRzSEFhOeQXp8Xlz+1HitvOr5rdDjfH8IlyvtDb5hDhmMOydeIUInwGsH8MXokQg55BN8f+P4wkDj4/hB+XqjhfH8wAEDodtkiYNDKXwukgH9gn2V04fFKfs8AP7f5QxqOrVZhtXYvDd3X/UTz58w+4x/mz5lD+t2ExhSiZ+wI3WNaHHj/QW9xDCze8Ne0Af8dioGQhqP3AWgAaA7/H1JBQlCUcLChA1NzbZAG2N/RaxzH8HdoNpuhP/z+d7TXmWjJIVnUjnKVlZVHPV5bW4sFCzpH+FgsFtnIvN70XIKoZ9vRrqutb4BgtMKP7jenvBQjIACCru/ljnoShJDt21XqAV0r9PJGIWh0A7tWE56ICtoBxqsKScoFYcDPFYL8nV1QaQYWrzY8mRQ0erR3dHTNssnJyek65vQG0O7tfNH0S2pIEvCzN3bAL4q4+rjCfv8WjtBoNGG3B3ptKL1eP6Bre/s7NJvNvSZeoUI/YAiCMOB4VSr536FWqx3QtRZL+BIgBoNhQNf29iEhKSlpQMmkVitPHtRqtSK/m/5eI3o+RqiBxhv6QXigv5vePnAey++mt991b0J/NyqVasDPVQh5jdDpdAO61mw2h7VF4nfD14j4e40I7eCh+BQNOWRdUyugM3blkGlmHYw6NaSgyByy14tHPoeUALQ4ffAHOzsK/JIa9Q4vrnjqW6y88TiMz0ri+0MvEuX9oTfMIcMxh+RrRKhEeI1g/pg4oiGHPILvD3x/GEgcfH8IPy/Usb4/iFLnDO2AKC+OatUqpJp0oR9jAACCRjv0zzJawwA/t4UUXY+xVuFwOLruyJae2ft1Ufw5095Uf+SgPP5h/pw5pN8Njv676Rm7SRX6u1EN/DN1L3EMLN7w1zRBa4AQDC/Oh52nCvmbGEAfgMcfRECUIB4eIbKvrh1Tc20QBtrf0cvfzbH8HTqdTng9nrD6U6hoySEFKXQ4V4IqKipCeXk5/vWvf+GWW24Z1LXnnXce3n33Xfz85z/Hn/70pz7PO5JkrFu3TrZ8zrGoqqrqWhqosrIS+fn5/VwxeAWLL4F64Q+6bhu1atx00uiwpCla3XPmZEiiCLXejIff7X9JmmjzwFWLYW+qR15enmxvI0mS8KcP9+Hfaw+FX3PeVFy7sCiCURIRUayIRO6QKGI1fwQi83cw6nt/hCp/Rtft6Xk2nDqp986BaBSvOWSby4er/7MBu2scsvPTzDq8eNNxmJRtDb0rIiJKcMwfhxdzSCKKFS1OH654aj2K6+WzLSdlJ+Hlm49Hsqm7QJafn4/q6mrY0rNw38q1kQ71mMX6579Yjj+WYwcGH/+B+nZ8sKtO1nbDiaNhMSgzB7mvvoPhMBJ5g6r/U6g/Ax11N9TzlSZkjpfdzk02xExBO54JgoBfnDUJt58yNuzYfe/sxn++Ci92ExERUXSI9/wxKEoQMsfJ2vKShza6moZXskmHlTcej5n58uVAm50+XPnUt9hdY1coMiIiIupPvOeQRBQ97G4/vv/MhrCC9tgMM/5343GygjYRDdzodDN0anlptrhhaFsTJCIWtYfBqFGjAPS+Z8URPffuOHJ+rAgtarNDMnoIgoCfLpuInywdH3bsoff34pGP94ftrUNERETKi/f8cV+dA4JOvswfc8joYTNpseLG4zBnVLKsvdXlxxVPfYv1Jc3KBEZERERHFe85JBFFhw5vANc9+13Y6k6FaSasvOl4pFvCl2smooHRqFUYmynfAuJA/fDvPR2vWNQeBuPGdc5COdq+M0eOpaenw2az9XletKl3eCBYs2RteSnskIwmgiDgrtMn4KfLJoQde/yLg7jn1e3wBUQFIiMiIqK+xHP+CADflbbIbtuMWsWW0qLeWQ1avHDDcZhflCJrb/cEcO1/v8Pb26oVioyIiIj6Eu85JBEpz+0L4vrnNmJrRZusPS/ZiBdvPA5Z1vC9lolocCZkyfeirnN44HD3v4c3sag9LE466SQAwPr16/ucFfv111/Lzo0VoR2SOrWKI7Gi1B2njscvz5oU1r5qazW+/8wGtDh9CkRFREREvYnn/BEIzyE5Szs6WfQaPH/9Ahw/JlXW7guK+MnL2/Dop8UQRa76Q0REFC3iPYckImV5/EHcvGJT2Oe5zCQ9Vt50HPJTTH1cSUSDUZBigl4jL88ebOBs7YFgUXsYTJw4ERMnTkRtbS1effXVsOOBQACPP/44AOD888+PdHjHZEOpfOnB3GQDVNxPO2r9cMlY/OHC6VCF/Io2lLbgvMe/xt5aR+8XEhERUUTFc/4oSRKL2jHEpNPguR8swBlTs8KOPfrpAdy+cguc3oACkREREVGoeM4hiUhZ3kAQt/5vM7460CRrTzPrsPKm41CYZu7jSiIaLLVKwNgMi6yN+2oPDIvaA2S32zFnzhwYjUZcc801Ycd/9rOfAQB++MMfYuXKlfB6vQCAQ4cO4YorrsC2bdtQWFiIK6+8MqJxH6uwDkkuPR71rjpuFP5z7TwYtWpZe1WrGxf9cx1WbalSKDIiIqLEkqj5Y0ljB5pDVohhDhndDFo1/nn1XFy3sCjs2Ie76nDBE9/gID9gExERRUSi5pBEpBx/UMQdK7fii/2NsnabUYv/3XgcxmUm9XElEQ3VhCx5Ubve4eUS5AOQkEXtpUuXQqPRyL7Ky8sBALfffrusfenSpQCAnTt3YuvWrfB4PHjppZfC7vP666/H1VdfDbvdjquvvhomkwlGoxFjx47FG2+8AavVitdeew06nS6iz/VY2N1+HAhZ8oCzbGLDqZOy8OoPT0B2yB4nbn8Qd7+6Hb94YwdcPs64ISIiGijmjwO3ubxVdlvsaIGV+2lHPbVKwH3nTsHvlk8JW/XnQEMHznv8G6zaUtXnUqdEREQUjjkkEUW7QFDEj1/aik/21Mvak/QarLhhASbnWBWKjCi+5aeYYAhZgjy0HkfhErKoHQwGw76OEEWx12PTp0/HrFmzoNfr+xzp+L///Q/PPPMMFi1aBJvNBkmSUFhYiJtuugnbt2/H/PnzI/L8hovNqMWmX5+G4Jf/gmPT2wg0lCAzydD/hRQVpufb8M6PTsScUclhx17eWImz//EVNpW1hF9IREREYZg/Dtxl8wrw6d1LEPz2f+jY/QV8ZZsgcPuamCAIAq5fNBrP/WBB2EAEl69zcORtL25Bc4dXoQiJiIhiC3NIIopmQVHCXa9ux4e76mTtZp0az9+wADPyk5UJjCgBqFUCxmbKZ2sf4App/UrIKRNr1qwZ9DU2mw1bt27t97zrr78e119//RCiik5pFj2kym1orX4fYnoW1FeeqXRINAiZSQa8dPPx+P27e/DihgrZsbJmFy7993rcuGg07lk2EYaQ5cqJiIioG/PHgRMEAeMyLZAOfoXm6mrY0rMA3K50WDQIiydk4J07FuHWF7dgb61DduzDXXX4rrQFD10wDWdNz1EoQiIiotjAHJKIolVQlHDva9vx7vYaWbtRq8Zz1y/AnFEpCkVGlDjGZ1qwu6b7M3e9w4t2jx9JBq2CUUW3hJypTZRI9Bo1/t+F0/GPK2bBpJMXriUJePqrUpz9f19hS0VrH/dARERERImmKN2MN29biCsXFIQda3b6cOuLW/Cjl7aiJWT/dCIiIiIiim6iKOFXq3Zi1dZqWbtBq8J/r5uP+UWpCkVGlFjyU0zQhyxBfqjRqVA0sSGiM7V9Ph/3cyEapI62ZgBAbW0t8vPzj+3OkjKhXngdhIyxsuZDjU5c+MTXkA59C3HbW4DbfmyP04vs7Gxs2rRp2O+Xjm7evHmoq6vr/8Qoxr8dImIOSTR4w5lDCqPmQLXgaggG+dJo726vwTvfHYC44z1IxWsASTymx+kN84DIi4f8EeDfDhExhyQi6o0kSfjt27vwyqZKWbtOo8LT18zDCWPTFIqMKPGoVQJGp5uxr6572fGDjR2YWZCsXFBRLqJF7bS0NJx++uk455xzcM455yA7OzuSD08Uk0RR7PpeXV3dz9n9qQb2b4d1/vlIPun7EDTdH+4EQQVh7EKgYDYcG96A47s3IQW4X2Ksq6urG4a/GyIiZTGHJBq8Yc0hq6uh2v4V0pbdBtPEhbJDgt4M9fzL4R+zEK1f/Bfuko3H9likOOaPRBQvmEMSEclJkoQHetmmUqdW4d/fn4uTxmcoFBlR4hqbYZEVtavb3HD7gzByu9heRbSo7Xa78dZbb+Htt9+GIAiYM2cOli9fjuXLl2POnDmRDIUoBgmwpWcOz10dWof25lKYllwPTcYY2SGVzojkk74H6+yz4N78FvwlGwApOOSHcrQ0QhKHf9YODY6gUsGaGluJKf92iOgI5pBEx2L4ckjf189AqtkJ48LvQaU3y45p0wqQecl98FfvhmfTKgSbyo7psZgHKC8W80eAfztE1I05JBFRN0mS8P/e34vn1pXJ2jUqAU9cPQenTBymfmciGpTCNBPUKgFBUQLQuWVsaZMTU3KsCkcWnSJa1G5ubsbq1avxwQcf4OOPP8amTZuwadMmPPDAA8jJycE555yD5cuX47TTToPRaIxkaERRT6034b6Va4f1PkVRwtbKNnxX2gJfUN7xo7KkwbzkBljP+CHmFqZgSo4VGrWqj3vq2wNXLYa9qX64QqYhsqZmDPvfz0jj3w4RHcEckmjoRiKHdHoDWFvciOKGjrBj2ryp0OZNRUGqEfMLU5GfYoQgCIN+DOYByovF/BHg3w4RdWMOSUTUSZIk/Pmj/fjP16WydrVKwGNXzsbpU7IUioyItGoVClNNONTUvZd2SUMHi9p9GHyF6hjYbDZceumlePbZZ1FTU4PNmzfjoYcewvHHH4/6+no8/fTTuOCCC5CWlobly5fjySefRGVlZf93TERDolIJmFuYgmtOKMS03N5fJB2eAL7Y34hn15Vhc3krvP6hz9omIiIaCuaQRNHFrNfgrOk5uHRuPrKs+l7PqWxxY9XWary2uQqHGjsgSlKEoyQiokTHHJKIqHuG9pNflsjaVQLw6OWzcNb0HIUiI6IjxmZaZLfLW1zwB7n6VG8iOlM71OzZszF79mz86le/gt1ux9q1a7u+joykFAQB06dP71oe6Pjjj1cyZKK4ZNZrsHRyFmbkJ2PtgUZUtbrDznH5gvj6YBO+PdSMCVlJmJ5nQ5ZVP6SZN0RERMeCOSRRdMhNNuLyeQXYX9eOb0qa0eENhJ1Ta/fg3R21SDJoMC3Xhim5Vlj0in4MJSKiBMUckogSjShKeODd3Xh+fbmsXRCAv142E+fOzFUoMiLqaUy6GYLQufQ4AARFCeXNLowLKXZThGdqH43NZsO5556Lhx9+GBs2bEBbWxueeuop2Gw27Ny5E3/84x9x4oknIisrCz/4wQ/w4YcfKh0yUdzJSNLjotl5OG9mLrKthl7PCYgS9tQ68MqmSqz8rgLbK9vg8oV3YBIREUUCc0giZQmCgEk5VlxzQiEWjUuHSafu9bx2TwDrDzXjv9+U4r0dNShtcnbtGUZERBRpzCGJKN6JooRfv7Wr14L2Xy6egQtn5ysUGRGFMmjVyEuWb4VS2mM5cuoWVUPk9+/fj6+++gpr167FV199hYqKCkiSBK1Wi0WLFkGtVuPrr7/G888/jxdeeAE33XQTnnzySaXDJoorgiBgdLoZRWkmVLW6sbG8BZUt4TO3AaCpw4c1xY348kAjClJMGJ9lwbgMCwza3jsziYiIRgJzSCLladUqzC1Mwcx8G3bXOrC5vBXtnvCBj5IElDQ6UdLohF6jwtgMCyZkWZCfYoJaxRWAiIgocphDElG8CooSfv7GDry+uUrWrhKAv102CxfMzlMoMiLqy+h0s2wF3dImJyRJ4kq5IRQtatvtdnzyySf44IMP8NFHH6G+vh5A5z4P6enp+N73vofly5fjjDPOgNXaud+vx+PBl19+icsuuwwrV65kMkk0QgRBQEGqCQWpJtTZPdhc3opDTR3obUKNJAEVLS5UtLjwxb4GFKSYUHS4ME5ERDTcmEMSRS+NWoWZ+cmYlmvD/vp2bKtoQ2OHt9dzvQERe2od2FPrgEGrwph0C4rSTBjFHJKIiEYAc0giSgSBoIifvrYdb22rkbWrVQL+ccUsLJ/BJceJotGYdDO+OtDUddvtD6Le4UW2rfcVdRNVxIva27dvx4cffogPPvgA3377LYLBIKTDC8WH7lnT2wgEg8GAM844AyaTCQ0NDZEOnyghZdsMOGdGDpzeAHbXOrC72g5HLzNvAECUgPIWF8pbXPgSQNLF/w+qA99CaK9Au8ePJIM2ssETEVFcYA5JFFvUKgFTcqyYnJ2E+nYvdlXbsb+uHYE+lhz3+LsL3CoBsJx9L6R96wF3LQJBERp11OycRUREMYQ5JBElEn9QxJ2vbMP7O2pl7Vq1gMeunIMzp2UrFBkR9SfZpEOKSYtWl7+r7VBTB4vaISJa1M7Ly0NdXR2AzlGQBoMBp59+elcCWVBQMOD7uuyyy+BwOEYqVCLqhVmvwYKiVMwvTEFFiwu7axwobXL22TkJAOrkbFjnXwAAmPnAakzLs2FBUSqOG5OGBUWpsJlY5CYioqNjDkkUuwRBQLbVgGyrASeNT8f+unbsq2tHrd3T5zWiBGhyJiElZxIAYNbvP8G8ohQsGJ2K40anYUa+DVoWuYmIqB/MIYkokXj8Qfz4pa1Yvade1q5Tq/Cv783B0slZCkVGRAM1Jt2CzRWtXbdLm5xYODZdwYiiT0SL2rW1tcjNzcU555yD5cuX47TTToPRaOz/wl784x//GOboiGigBEFAYZoZhWlm+IMiSpucKK5vR1mzC8GjFLhFCdhRZceOKjv+83UpBAGYlG3F/KIUzCpIxoz8ZIxJN0PF/RSJiKgH5pBE8UGvUWNGfmfO5/D4cbC+A8UN7ah39L48+REd3gDW7G/Emv2NAACjVo25hSmYU5iCWQU2zMhPRrpFH4mnQEREMYQ5JBElig5vADe/sAnrSppl7XqNCk9dMw9LJmQoFBkRDcbodLOsqN3U4YPD7YfVyImBR0S0qL1582bMnj07kg9JRCNMq1ZhQlYSJmQlwRcQUdbs7PxqcsHtDx71WkkC9tY6sLfWgRfWlwMAkvQazCiwYWZ+MmYWJGNmfjKyrPpelwEjIqLEwBySKP5YDVrMOVyYtrv9KG1yorTJiepWN4JS34Mkgc69xb4+2ISvD3bvN5afYjycP3bmkdPybDDrI77bFhERRRHmkEQUS+bNm9e1usSg6MxQn/ojCOmjZc1SwAfXp0/g6uf2DVOER1db27nkudPpjMjjEcWjHJsBBo0KnoDY1Vba5MTMgmTlgooyEf2Un5aWhqqqKuTn50fyYYkoQnSa7gK3JEloaPfi2Scfg5A1HvqciRDU/b/ktHsD+OZgM7452D2yMM2sw5Rca+dXjhVTc60YnW6BmjO6iYgSAnNIovhmM2oxqyAZswqS4Q+KqGxx4fVXVkKVOQ7atIEtDVvV6kZVqxvv7+zsTBMEoCjN3JU/Tsm1YmqOFZlW7kdGRJQomEMSUSypq6tDdXX1oK5RJ6Uh87JfQ5M+StYuel1oeP0BeKt2D2eIAyIdZRVPIjo6lUpAUboZ++rau9pKm1nU7imiRe2ioiKkpKSgubm5/5MPKygoQENDA7zeoy9JR0TRRRAEZFkN8G57F/ameuSNKsKrn2/EhkMt2FDajK0VbfD2GHF0NM1OH7460ISvDnTPxjFoVZiUfbiD8nBn5aRsK4w69Ug9JSIiUghzSKLEoVWrMCbDAve6/3XmkGMn48lVn3blkMX1HQO6H0lC1+zv93fUdrWnW/SygZJTcq0oSjNzsCQRURxiDklEsUhQqWBN7X+5cJU1C5az7oHKkiZrF90OOD/+OwyeJhjSI7ePtr2pvv+TiKhfRWnyonZ1qxuBoAiNWqVgVNEj4uuxSf0sJRcqEAggEAiMUDREFDFBPxaOTcfCsekAAG8giB1VdnxX2oLtlW3YVtmGhvaBf2j0+EVsO3zdESoBKEo3Y1J252zxI98L2VFJRBTzmEMSJSiPA8tn5GL5jFwAQIvTh+9KW7ClohXbKtuws8re75Y3PTV1eLG2uBFrixu72oxaNSZmJ2FiVhImdH23IMPCLXCIiGIdc0giijXW1Azct3LtUc+ptbvx7vbasDzYotfgouOnI2X5/0YyxF7dc+ZkSOLAJjARUd9GpZkgADiSwQRECdVtbhSmmZUMK2qMaFF7x44d2LZtm6zN5/NhxYoVA04q3W43VCqOQCCKN3qNGvOLUjG/KLWrrc7uwbbKNmyvasOOqjbsqLSj3TvwD5OiBBxqdOJQoxMf7Ozeg0avUWFcpiWkozIJuTYDOyqJiKIQc0gi6kuqWYczp2XjzGnZAIBAUMTBxo7DgyTt2FHVhn117QgOYtlDtz8YNljyyGNNyJLnkOOzkmAzaofzKRER0TBhDklEieBgQwc+2l0Xlu+mmLS4cHYekgzMVYlimVGrRpbVgDqHp6utvMXFovZhI1rUfvPNN/HAAw/IikZutxvXXXfdgO9DkiRMnTp1BKIjomiTbTPgTFt3J6UoSqhsdWF3jQN7ahzYU+vA7ho76h2DWwbMGxCxu8aB3TUOWXuSXoMJh2dzT8yyYGymBWMyLMixGqDizG4iIsUwhySigdKoO7ekmZRtxeXzO9s8/iCK69t75I8O7K11wOUb+IxuoHNW+LeHWvDtoRZZe47N0DWze3xWEsZkmDE23QKbiR2IRERKYg5JRPFua0Ur1vbYnvGIzCQ9LpiVx20ZieJEYZpJXtRudgHjFQwoioxoUfvkk0/u+nnnzp1YtWoV1Go1TjzxxAFdr1KpkJubi3vvvXeEIiSiaKZSCShMM6MwzYyzp+d0tTd1eLs6KffUdBa6DzU5MchVxdDuDWBzeSs2l7fK2g1aFYrSzBibYcGYDHPnV7oFozPMsHK0IxHRiGMOSUTHwqBVY0Z+MmbkJ3e1iaKE8hYXdtfYZcXuxkFsf3NErd2DWrsHa/Y3ytrTzDqMTj+cO2ZYMObwz6NSzdBpOOuPiGikMYckonglSRLWHmgKW1kI6Cx+nT0th/kmURwpTDNhQ2n34OoWpw/tHj9XYsAIF7WXLFmCJUuWAOjck0an08FiseCLL74YyYclojiXbtFj8YQMLJ6Q0dXm8gWwr64dxXXt2F/fjuL6duyv60BTx+A7Kj1+Efvq2rGvrr2Xx9YhL8WEghQj8lNMyE8xHv7q/Nmg5YhIIqJjxRySiIabSiVgdLoZo9PNXftzA0BDuwd7a+U5ZHF9Ozz+we8H2Oz0odnpw6aQAZNqlYDcZAPyk02yvLEgtfN7ltUANVcJIiI6ZswhiSgeeQNBrN5dj0NNzrBjU3OtOHViJlecJIozWVYD9BoVvIHuz6XlzS5My7MpGFV0GNGituyBNBqMGjUKycnJkXpIIkogJp0Gc0alYM6oFFl7c4e3s4Oyrh376zs6Oyrr2ge1V3dPTR0+NHX4sL2XkZFAZ8E9P8WIHJsBGUl6ZFj0yLTqD//c2ZZu0UGj5uhJIqKBYA5JRCMpM8mAzCQDlvQYLBkUJVS1urC/rrPAve/w90ONTgQGsVd3z/urbHGjssXd63GNSkBushF5yUZkHckbk/TITDL0+FkPm1ErW1KXiIj6xhySiOJBm8uH93bUotnpCzt2wtg0zC9MYX5IFIdUgoBRqSYcaOjoamNRu1PEitoAUFZWFsmHI6Io0NHWDACora1Ffn6+wtH0YEqBkJwLJOdBSM6FYMsFrFkQtIZjutumDi+aOrzYVtn3OYIApJp0SLfokWzSwmbUItmkRbJJB5uxx22jruu41aCFxaDhLB4iSkjMIYkST1TmkCo1kJTVmTsm5wHJuRBs2YAlA4Jq6Kv1BEQJFS0uVLS4jnqeVi0gw6JH2uEc0mrUIrlH3mgzamEzdbbZeuSQJp2anZ1ElJCYQxJRLKtoceHDnbXwBOQrCKkE4PTJWZiUY1UoMiKKhMI0eVG7otUFUZQSfmWGiBa1iSjxiKLY9b26ulrhaHqqBrArrFVtSYUmJQ/a1Dxo0/KhSe38WWPLhqAantnVktS9POVgJek1sBo7OzGths6fj3RYWo0aWA2Hbx8+juQ8qB1eQGuAJEns0CQiIqKYEL05ZEV4k0oNjS0L2tR8aNPyoEnNhzY1D/r0URCMw9fZ6A9KqLF7UGP3DOo6jUo4au4YmlsifTQ0bgGC0YhAUOQKQ0REREQRtr2yDV8eaIQUskiQQavCOdNzkJ9iUiYwIoqYwjSz7LYvIKLO4UFuslGhiKLDiBS116xZg3Xr1uGcc87BzJkzu9rXrl075PtcvHjxcIRGRIoRYEvPVDqIgXE3ANUNEKu3oqmlEZIoIq9gFNZs2oWKFheqWt2Hv7p/bhlCgXoo2r0BtHsDqG7rffnKUJrlv8ORuU2PfXEQerUKeq0aeo0KJp0aZr0GZp0GZr0aFr0GJr0GFp0GJr0aKhbAiSjCmEMSUbhYySGDgL0csJejddPh/DEvD7sPlKKsySnLHSu7ckjXkPbuHqyAKKHF6Rtwvqo58xfIO/zzE2tKoFYJ0GtU0GtUMGjVXbljzzzSrNfAotdAr1FxECURRRxzSCKKF4LWANOSm7CmuDHsWJpZh3Nn5sJm1CoQGRFFmkWvQZpFh+aO7s9x5c0uFrVH4k4vvPBCOBwOvPvuu1i/fn1X+8knnzykD7iCICAQGNr+t0QUHdR6E+5bOfQPlEp54KrFsDfVA2IQ4zKTMC4zqdfznIcLzUc6KxvbvWhs96Lh8PfG9s5lyYeyD+NwkSTAExDDli3qjUoAkg7P+u7tS6fhjB0iGn7MIYkoVCzmkF35IwCbUYuZBcmYWZAcdp4kSWh2+roK3DVtbjR1+NDg8KCxozuHbHX5I/wM5IKiBJcvCJcvCODosejUKliNml7zR6tBm/BL5RHRyGAOSURxwZaLnLN+A21aQdihsRlmLJuSzf44ogRTlGqWF7VbnDhhbJqCESlvRIraRUVF2L59O0aPHi1rHzVqFEdtE1FcMus1mJCVhAlZvRe9AUAUJbS6fF2dlE0dXthdfrS5/bC7/V0/t7l8nbfdfrS5/IoUwkUJXTH0xmrQIM2iR5pZhzRL5/7gKSYd9/wmomPCHJKIEokgCEi36JFu0WNWL0XvI7yBIJo7fF0DJltdvsN5o68rX5R/98HhUaYY4wuKaOrwoakjfFa4WhCQYtYizaJHulnXlUsmGTR8jSeiY8Ickohi3aubKqE+65cQNLqwYwuKUnH8mFS+nhEloMI0EzZXtHbdrnd44fIFYNIl7s7SI/LMN27ciPr6euTk5Mjay8rKRuLhiIhigkoldHbeWfSYlD2wayRJgtsfhN3th8MdgMPjh+Nwsdnh9sPhCXTf9nSfc+S43e2DIAz/KE6HJwCHJ4DSJmf38xOAFJMOmVY9sq0GZFsNSLPoWegmogFjDklEFE6vUSM32TioZeaCooQOjzwv7C1X7C2XrG1ug6A1DPvzCEpSV8F7f492nVqFdIsOWTZDVw7JQjcRDQZzSKLhMW/ePNTV1SkdxjHLzs7Gpk2blA5jQNo9ftz3zm6s2lIdVtDWaVQ4fXIWxmVaFIqOiJSWk2yAVi3AH+ye9FbR4sKkbKuCUSlrRIraGo0GeXl5/Z9IRERHJQgCTDoNTDoNcmyDvz4/vwA1jS1Izi3CLX99Gd5AEN6ACLc/CJc3CKc3AKcvAKc3ePh7AEOdGC5KQLPTh2anD3tr2wEAapWAzCQ9sg53UOYmG5Bk4N4/RNQ75pBERMNDrRJgM2lhM2kRvoDl0eXn56O6th7JuYX40eNvwRsQ4fUfziF9wbDc0ekNwu0PDjlWX1BEjd2DGrunq82oVSPbZkDW4cGSOTYjl9skoj4xhyQaHnV1daiurlY6jISxrqQJ9762A9Vt7rBjmUl6nD09h/tnEyU4jUqF/BSTbGJZeTOL2kRE1IeOtmYAQG1tLfLz8xWOZvBqa2shiSLa6yuQkaTv9/wjM8Md7kDX8uM9vzq8g1vKMihKqLV7UNujk9Jm1CI/xYi8ZCPyU4wsckcpjtAmIiIamnjIHyGK6GiuR4opfAnM3gRFCR3e7lnfoV/egDioGNz+IEqbnF2dNyoByEwydOaQKUbkssgdtZhDEhHFPkGlgjU1Q+kwBs3R0ghJHFzOoQS3L4g/f7QPz60r6/W4d89nuPT2m6FRMdchIqAwVV7UrmhxQZKkhF3ZSpGidllZGYqKisLam5ub8cILL+DgwYOw2Ww47bTTcOqpp0Y+QCKiw8TDybAoijE9WlUa4PTrnjPDs23hy04GgiJaXX40d3jR5PShucOLZqcP7YPYt/FI5+buGgeAziJ3XrIRBSlGjEozJfSeINGEI7QpGjGHJKJYkGj5I3B4ZrhRC5ux95nhbn8QLR0+NDm9aO7wdeWSvgEWu0UJqHN4UOfwYFN5KwQByEoyIC/FiFGpJuQmG9jxGyWYQ1I0Yg5JNDjW1Azct3Kt0mEM2gNXLYa9qV7pMI5qS0UrfvradhxqdIYdk3xuNH34D2ibDkDzo1sUiI6IolFhmkl22+ULorHDi8yk4d8yKhZEvHLw4x//GM8//zw+//xzzJ07t6t948aNOOuss9Da2gpJ6vzw/Oc//xkXX3wxXnnllYQddUBE0UKALT1T6SAGbbiTeY1ahYwkfdisb28giOYOHxravah3eFBn96DN7R9YjIeL3HtqO4vcmUl6FKaZoM6eALQ0DWv8NHgcoU3RgjkkEcUe5o9HGLVq5B2eZX2EJHXO7m7q8KHe4enMIR0eePz9v39LPYrcm8tboVEJyE8xojDNDJU1C4jyDu1EwBySogVzSCKKBm0uH/780X68vLECUi/jBo8fk4qv/3oLXMU7YEvPinyARBS1kk062Ixa2Hv0tZc3u1jUjoRVq1bh8ccfR25uLlJTU2XHbr31VrS0tOCiiy7C0qVLUVJSgn/+859444038H//93/4yU9+EslQiYhk1HpTTI5SvefMyRHplNFr1MhNNiI3ubuj0uMPdnVO1h1egnwgS082tHvR0O5F0jk/h/k0F9BQjBc3lGPppKxeZ4/TyOIIbYoGzCGJKBYxfzw6QRCQZNAiyaDF6HQzgM5Ct8MTQJ29O4dsaPegv0njAVFCWbMLZc0uWC/9A0xtdVA17Mcne+qxaFw6jDr1iD8fkmMOSdGAOSQRKU0UJby+pQp/+nAfWpy+sON6jQo/P3MSrltYhFG/b1YgQiKKBYWpJuyotnfdrmp1Y36RcvEoKaJF7ccffxyCIOCxxx7D6NGju9qLi4uxZcsWLFu2DK+//npX+7x583DVVVfhhRdeYDJJRBRjDFo1CtPMKEzr7qRs6vChqtWF6jY3qlrd/Ra5VXoTUDALv35zF36NXZieZ8Npk7Nw2pRMTMmxcvQ8UYJgDklElBgEoXsZ84nZSQAAf1BETZu7K3+sd/Rf5NYkZwPJ2bjphU3Qa1Q4aXw6TpuchVMnZybsjAaiRMQckoiUtKvajvvf2Y1N5a29Hp+Zb8NfL5uFcZmWCEdGRLGmIKSoXdPmRkAUE3ILpogWtXfu3AlBEHDuuefK2tetWwcAuOyyy2TtF110EVQqFfbv3x+xGImIaGQIgtC1dPnsUSldRe7qNjcqWlyoanXBHzx6D+XOajt2Vtvx90+LkWszYOnkLJw2JQvHj0mFXsMZOETxijkkEVHi0qpVsoGS/qCIWrsHVa0uVLS4UO/wHvV6b0DEp3sb8OneBgDAzIJknD45E6dNycLErCQOkiSKY8whiUgJZU1OPLJ6P97bUdvrcZNOjZ8sHY8bFo2GRp14BSkiGrz8Hts4AZ0rVdXZPchPMfVxRfyKaFHb7XZDq9VCo5E/7ObNmyEIAubMmSNr12q1UKvVUCXgaAMionjXs8g9qyAZQVFCrd2NsmYXKppdaOw4egdljd2DFd+WY8W35TDr1Fg8IQOnT8nCqZMykWzSRehZEFEkMIckIqIjtGoVRqWaMCrVhIVjAbcviIoWF8pbnChvdsHlCx71+u2Vbdhe2YZHVhcjL9mI06dkYdmULMwfnQotO5aJ4gpzSCKKpIZ2D/7vswN4+btKBPpYVubMqdn43blTZNv3ERH1x6BVIzNJj4b27v7yylY3i9ojraioCHv37sX27dsxc+ZMAIAoivjoo49gsVgwY8YM2fk7duyA3+/HpEmTIhkmEREpQK0SkJ9i6nwzHgf8/obl8FsLkDptEWyTFqLdE+jzWqcviA931eHDXXVQqwQcPyYVy6Zk4/QpWfygQBQHmEMSEVFfjDo1JmYnYWJ2EiRJwkN3XI1gShGSpyyEOnviUZcqr25z47l1ZXhuXRlsRi2WTsrEsqnZWDwhHSZdRLtLiGgEMIckokiobHHhma9L8crGSrj9vQ+uG5VqwgPnTcUpkzIjHB0RxYv8FKO8qN3iwglj0hSMSBkR/ZS2ZMkS7NmzB5dddhl++9vfIjU1FS+99BIOHTqEH/zgB2EjIX/5y19CEARccsklkQyTiIiigOR2wFn5OZLb9mPLi/dhY2kLPtlbj0/31qOyxd3ndUFRwjcHm/HNwWbc985uzMi3YdmULCybmo3xmRYuMUkUg5hDEhHRQAiCALG1Co4Dm5FU/S227T+ENcUN+HRPA74sbkSHt+9Bkna3H6u2VmPV1urD+3Bn4IypWVg6OQupZq4CRBSLmEMS0UjaU+PAv9eW4L0dtQj2MYrOatDg1pPH4QcnFsGg5bZ5RDR0BakmbKlo67pd7/DAFxCh0yTWCjMRLWrff//9+PDDD3HgwAFce+21AABJkpCZmYn7779fdu7VV1+Njz76CGPGjMGPf/zjSIZJRERRRqtWYeG4dCwcl47fLZ+CAw0d+HRvPT7dU4+tlW2QjjIDZ0eVHTuq7HhkdTGK0kw4Y2o2lk3NwuyCFKhULHATxQLmkERENBQpZh0unJ2PC2fnwxcQsaG0GZ/tbcAne+pR3db3IMnOfbg7B1OqBGB+USqWTc3GsilZKEhNvCX+iGIVc0giGm6+wznCyg0V+PpgU5/n6TUqXHdiEW5bMg42kzaCERJRvMq1GaES0LUSlSgBNXY3itLMygYWYREtamdkZGDr1q14/PHH8d1330GlUmHatGm48847kZYmnyY/ffp0rFu3Du+99x5sNlskwyQioigmCAImZCVhQlYSbjt5HJo6vPh8bwNW76nHVwca4Q2IfV5b1uzCv9cewr/XHkK6RY/Tp3QuMblwbBr0Go6YJYpWzCGJiOhY6Q7Pvj5pfAbuO3cK9tW149M99Vi9px47q+19XidKwIbSFmwobcGD7+3BlBwrlk3NwrIp2Zick8RVgIiiGHNIIhouJY0deGVjJd7YXIVmp6/P89QqAZfOzcdPThuPHBu3wyOi4aPTqJBtNaDG7ulqq2xxsag90mw2G37961/3e94vfvEL3HbbbbBarRGIioiIYlW6RY/L5hfgsvkFcPkCWFvchNW76/DZvgbY3f4+r2vq8OKl7yrx0neVMOvUOHlSJpZNycIpkzJhNXAULVG0YQ5JRETDRRAETM6xYnKOFT9aOh41bW58sqceH++uw4bSlj6XEAWAPbUO7Kl14NFPD6Ag1YhlUzpncM8rSoWaqwARRR3mkEQ0VHV2D97fWYv3d9TIlvztjUmnxhXzR+GGk0YjL5nFbCIaGfmpJllRu6q179Wn4lXEi9qDwUSSiIgGw6TT4Mxp2ThzWjb8QREbS1uwek89Vu+uk73hh3L6gnh/Ry3e31ELrVrACWPTsWxKFk6fkoUsqyGCz4CIhgNzSCIiGozcZCOuXViEaxcWoc3lw+f7GrB6dz2+LG6E2x/s87rKFjee+boUz3xdilSzDksnZeKMqdlYND6d+2YSxSDmkERU1erCp3vq8f7OWmwsa+33/DSzDtctLML3TyhEskkXgQiJKJEVpBjxXWn37YZ2Lzz+YEJ99ojqojYREdFQ9dyH+75zp2BXtQOr99Rh9e567K9v7/M6f1DC2uJGrC1uxG/e2oXZo5I7Z+BMzcLYDMuAH3/evHmoq6sbjqeiiNraWgCA0+lUOBIiIiKiyEk26XDRnHxcNCcfbl8QXx/sXAXo0731aHX1vQpQi9OH1zZX4bXNVTBq1VgyIQNnTMvCqROzBrWXJnNIIiKiyPEFRGwqa8Ga4kZ8sa8BBxo6BnTdgqJUXLGgAGdPz0moYhIRKSvbZoBGJSDQY2WpqlY3xmUOvM861kW8qO33+/H888/jww8/RElJCTo6OiCKfe9/CnQuDVZSUhKhCImIKN4IgoDp+TZMz7fhnmUTUdrkxCeHC9ybK1oh9b3CJLZWtGFrRRv+/NE+jM0w44yp2Vg2NRsz8mxQHWWJybq6OlRXV4/As4ks6SjLbxJFEnNIIiKKNKNOjdMPr94TCIrYVN6K1bs7lymvbut7qT+3P4iPdtfho911UKsEHD8mtWuQZH/7azKHJBpezCGJqCePP4jtlW3YWNaCDaUt2FzeCpev71VZeko163DxnDxcPn9UQhWQiCh6aFQq5CYbUdHi6mqrbHUl1GtSRIvaTU1NWLp0KXbt2gXpaBWEEILAfamIiGj4jE434+bFY3Hz4rFoaPfgs70NWL27Dt8cbIYv2HcHR0mjE/9cU4J/rilBttWA06dkYdnULBw3Og06jarXawSVCtbUjJF6KiPG3lSvdAhEXZhDEhGR0jRqFY4fk4bjx6Tht8snY0+tA6t312P1nnrsrXX0eV1QlPDNwWZ8c7AZ972zGzPybVg2JQvLpmZjfKalz/cq5pBEx445JBF1eAPYXN6K70qbsbG0Fdsq247a7xMqyaDBsinZWD4jByeOS++z74eIKFIKUkKK2j1+TgQRLWr/7Gc/w86dO6FSqXDxxRdj+fLlyMvLg0bDVdCJiEgZmUkGXLlgFK5cMArtHj++LG7E6t31+GJfA9q9gT6vq3N4sOLbcqz4thxJBg2WTMjA4gkZWDw+A9m27n24rakZuG/l2kg8lWF1z5mTIfUzg4EoUphDEhFRNBEEAVNzbZiaa8Ndp09ARbOrc5ubPfXYVNaCo01S3lFlx44qOx5ZXYzR6WacPLEzhzxudCpMuu73NeaQRMeOOSRR4lEZrRDyZ+LB9/ZgY1kLdlXbj/q+3JsUkxanTMzEOTNysGh8OvQaLi9ORNEjP9UElDR33W51+dHhDcCiT4z8JqLP8t1334UgCHjkkUdw5513RvKhiYiI+pVk0GL5jFwsn5ELX0DE+kPNWL27Dp/sqUdDu7fP69o9Aby3oxbv7ejcQ3BClgWquZfCoFsLuBojFT5R3GIOSURE0WxUmgk3njQGN540Bs0d3s5VgPbUYe2BJvgCfRd4S5ucKG1y4tlvyqBTqzCvKAXClDOg9X8BiH0vb05EA8Mckij+tXv8qGnzoLrNjaSLfo/klDwAwDNflw7qfmbk23DyxEycMjEDM/KToT7KdnNERErKTNJDp1HJPmdUtbgwKceqYFSRE9GitsfjgSAIuPHGGyP5sH365ptv8Pe//x3r1q1Dc3MzUlNTsXDhQtx1111YtGjRoO/vwIEDePTRR/HFF1+gvLwcfr8f6enpmDt3Lm644QZccMEFw/8kiIhoROg0KiyZkIElEzLw4PnTsK2qrXOJyd11ONTkPOq1xfUdUE0+DVmTT4MU8OONLVXISzYiL9mIbJsBWjWXqyIaDOaQFwz/kyAiohGRZtHjsvkFuGx+AZzeAL460IiPd9fjs731cHj6XgXIFxSxrqQZ6jkXIXfORRDddny4sxa5KZ05ZJpZxyWRiQYpmnJI5o8DN2/ePNTV1SkdxjHLzs7Gpk2blA4j7rh8AVS1ulHZ4kJlqxt2t7/rmPpwQbs/ggBMzrZiwehUHDc6FfNHpyLdoh+pkImIhpVKEJCfbJT1T1e2ulnUHgmTJk3Cjh07YDAY+j95hD399NO45ZZbIB5eFstoNKKurg6rVq3CW2+9hSeeeAK33HLLgO/v/fffx8UXXwyvt3MmnyAI0Gq1qK2txXvvvYf33nsP1113HZ599tkReT5ERDRyVCoBc0alYM6oFPzirEk42NCBj3d3LjG5vbLtqNcKGi2qWt2oau2cbaMSgCyrAbmHi9y5NgP0Wi5lRXQ0zCGZQxIRxSKzXoMzp+XgzGk58AdFfFfa0plD7q5HncNz1GtVRhuKGzpQ3NABANBrVF35Y16yERlJes4iI+pHtOSQzB8Hp66uDtXV1UqHQVHCFxBR3XakiO1CU4dv0PehVQuYnmfDgtFpOG50KuYUpsBm1I5AtEREkVGQagoparsgSVJCDIKN6FSx3/72twgGg/jss88i+bBh9u/fj9tvvx2iKOK3v/0tmpqa4HK50NzcjPvuuw+iKOJHP/oR9uzZM6D783g8+MEPfgCv14spU6bgiy++gNvthsfjQWlpKW644QYAwHPPPYc333xzJJ8aERFFwLhMC24/ZRzevv1EfPvLpfjDhdNx1rRsJBn6HysmSkCt3YPN5a14Z3sNnlx7CM+tK8OHO2uxqbwFFS0uePzBCDwLotjBHJI5JBFRrNOqVThxXDp+f/40rP/lqXjnjhNx9+kTML8oZUDFaW9ARGmTE18fbMIrmyrxry9L8PLGCny+rwG7qu2od3gQ4F7WRDLRkEMyfxw6QaWCLT0r5r4EFVdmO1ZtLh+2VrTijS1V+PfaEryzvQZbK9sGXNCWAj6cOC4Nd542HitvOg477jsDq247Eb84axJOmZTJgjYRxbyCFKPsdrsnIFu5Ip5FdKb2eeedh9WrV+Pf//43zGbzkJbXGQ5///vf4ff7cfPNN+P3v/99V3tqairuv/9+NDQ04F//+hceeeQR/Pe//+33/tatW4fGxs49U19++WVMnz6961hRURGefvppbN26FVu2bMFbb72FCy+8cPifFBERKSLbZsBVx43CVceNQiAoYnuVHV8daMTfV34IpI+GoOp/Frbd7Yfd7e+aiQMAVoMGGUl6pJn1SDFrkWLSIcWkg07DD8iUeJhDMockIoongiBgRn4yZuQn48dLx8Ph8WN9STO+OtCIFZ9shpCU0e99BEUJ9Q4v6h3erjaV0Ln8eYalM39MNemQYtbBZtBCxVndlICiIYdk/jh01tQM3LdyrdJhDNoDVy2Gvale6TBiiiRJaGz34kBDBw41OtHiGtxs7COrmez54Fm07d+ADLUbL1aUj1C0RETKSzXrYNKp4fJ1T4yqanMj2aRTMKrIiGhRe8yYMQAAt9uN119/HRaLBWlpaf1eJwgCSkpKhi2Od955BwDwk5/8pNfjd911F/71r3/h3XffHdCU/fr6zkQlKSlJlkweIQgCFi5ciC1btsTFnjBERNQ7jVqFuYUpmFuYgkeuOxU1TW1Im3YSFv3gN6huc6Ox3QtpgPfl8ATg8ARQ0ijfv9usU8Nq1CLJoEGSQQurQQOLXgOjTg2TTgOjVg2tWkiI5WYocTCHZA5JRBTPrAYtzpiajTOmZuO5O85CvTOIlKknYc4ld6C6zY32o+zF3ZMoAY3tXjS2e2XtKgFdeWPXd6MWZp26M4fUduaSXM6c4k005JDMH4l6J0kSmp0+FNe3o7i+Y1AzDLVqAXnJRhSkmFCQakKaRQeVIGDrnz+Gr6keyBvY3tpERLFKEDpfBw/0mCBV0+rGtFybglFFRkSL2mVlZbLb7e3taG9v7/e64eyYLykpQW1tLVJSUjBlypRezxk/fjyys7NRV1eHPXv2YOrUqUe9z5ycnAE/fm5u7qDiJSKi2CV5nfCXb8XiCZ2zbXwBEbV2N6rb3Khp86DO4UFQHGiZu5PTF4TTF0Stve9z1CoBRq0aJp0aBq0aOrUKWo0AnVoFnUYFrVp1uO3wd7UAvUYNnUYFvUbF2eAUdZhDMockIkokgbY6+PavxRlTHwIAODx+1LR155AtzsHNYBOl7pWBAHef5+k0qq4cUq8JyRcPfw/NK498P5JLEkUTpXNI5o9E4ZzeAPbXtWNPrQPNA3w/UwlAjs2IghQjClJNyLIaOBCLiBJeXoq8qF3V1neeH08iWtT+4osvIvlwvTpw4AAAoKCg4KjnFRQUoK6uDgcPHuw3oZw9ezZsNhvsdjt27twZNlJSkiSsX78eAHDyyScPPXgiIoppOo0KhWlmFKaZAQABUURLhw8N7d7DXx40dfgGXegOFRQldHgD6PAObFZPbwruXgXR54Lkc+Ol7ypg1mtg1qk7v+s1sBo0SDPrYdarOSucRhxzSOaQRESJzGrQwpqtxaRsKwDA4w+i4fCM7IZ2DxocXrQNwx56voAIX0A8pv348u98DZLXBdHnxmubKrtyxyN5ZIpJhxSzFnpN/1v0EB0rpXNI5o9EnURRQlmzE3tqHShtcmIgXR5GrRqj080YnW7GqFQTB04REYXISw7fV9vh9sNq1CoUUWREtKi9ZMmSSD5cryoqKgAARqPxqOeZTCbZ+Udjs9nw0EMP4Uc/+hGuuOIK/POf/8QJJ5wArVaLiooKPPTQQ9i8eTOOP/54fO973zv2J0FERHFBo1Ih02pAptXQ1RYUJbQ4fWho75yF0+L0odXlh8PtH/DS5cNBUGugNloBoxUN7V4gZBnLI3RqFVLNnZ2TmUkG5NgMSLfoOWqahhVzSOaQRETUzaBVY1SqCaNSTV1t3kAQTe3dOWSry48Wpw9uf/Ao9zT8VFo9oNVDjRTU2D19nmfRa5Bq1iHVrEO2tTOHTDJoOFiShpXSOSTzR0p0Tm8Au2rs2Flth9Pb//uR1aDB+KwkjM0wI9tq4HsCEdFRpJl1MGhV8PjFrrbqNjeL2vGmo6Oj/5OGcP4dd9yBrKws/OlPf8LJJ58MQRCg1Wrh8/mQmpqKe++9F/fffz/U6sGNRq6qqjrq8dra2kHdHxERRTe1SkBGkh4ZSXpZe0AU0Xa4uO3wBNDu6f7u9Abh9gePeYb3UPiCIuocnUup763tXMpPqxaQn2JCYaoJYzLMSDLEdzJFiYE5JBERRTO9Ro28FCPyUuTFM48/iFaXDw63PH9s9wTg8gXh8QcjOnDyiCOrClW0uLraLHoNitJMh1c2MkGr5qw8im2xlj8CzCHp2EmShDqHB9sr7TjQ0N7vrGyLXoPxWRZMyExCllXPQjYR0QAd2Ve7pNHZ1Vbd5sbkHKuCUY08RYvazc3N2L59OxoaGtDR0YEbb7xxxB/T7R7cuvIul6v/kw7zer2wWjv/YCRJgs/XuS+I0WiEwWCAy+XqGn05UP0tUURERIlBo1Ih3aJHukXf63FJkuAPSnD7g3D7gnD5AnD7g/D6RfiCnV/+wOGfAyL8QanHz53fA8NUFPcHJZQ2OVHa5MSa4kbk2AyYkJWESdlJMGi51CQdO+aQ/WMOSUREQOes7hybETm23o9LkgSPXwzPIXvkiJ15ZGfu6O+ZVwZEeIMipGGqind4A9hV48CuGgc0KgGj082YlJ2EojQzVFwFiIZBpHPIWMsfAeaQNHSiJKGksQOby1tR7+h9pbcjNCoB4zItmJJjRX6KkYVsIqIhCitqt8b/vtqKFLXfeOMN/PWvf8WGDRtk7UeSSUmSsGzZMsybNw+///3vodVG9wwvSZJw7bXXYsWKFRg/fjxef/11nHDCCUhKSsL+/fvx8MMP48EHH8SKFSvw6aefYuzYsUqHTEREcUYQBOg0AnQaFWxDXGYmKEpdHZd//OEFELRGaCypuPxnD8PpDaDDF4DLG0SHN4BWlw/+4MB6MGvtHtTaPfj6YBPGZ1owuyBZtuQ60UAxh2QOSUREw0sQBBh1ahh1asA8+OslSULgSA4ZEPGX2y+BoDNCY0nDRXf9AU5vAE5fAE5vEA6PH3a3f0BF8IAo4UBDBw40dMCi12BarhUz8pM74yQapHjKIZk/UrQJiCL21bZjc0Ur2lz+o56bbTVgaq4V47Ms0Gv4ek5EdKxC99Vuc/vh9AZg1sfvIt0Rf2Y//OEP8Z///AeSJEEQBGRmZqKhoUF2zsGDB1FcXIzPP/8cxcXFeP3114dtxFZ/+9iEGsioxpUrV2LFihVISUnBV199haysrK5j8+bNwyuvvAIAePXVV3Hbbbfh448/HvDjV1ZWHvV4bW0tFixYMOD7IyIi6otadbhTE2r4Gw5BEkUE9GZMywuf2iNJEjq8ga59vxs7vKi1e476ITYoSthX1459de0oTDNhflFqWPJF1BfmkMwhiYgo+giCAK1agFatglkP+OoOQBJFqPVmzCpIDjs/IIqwH97vu9npQ0O7F7V2t2wvwFAd3gC+LW3BpvJWTMuzYW5hCixx3FFHw0vJHDLW8keAOSQNnDcQxM5qO7ZVtMHp63u/bLVKwMSsJMzMt3FwOxHRMEtP0kOnUcEXkO+rPSErScGoRlZEPwWsXLkSTz/9NFJSUvDXv/4VV155JfR6PVJSUuBwOLrOGz9+PHbs2IFTTjkFb731FlauXImrr756WGKwWCzDfv6rr74KALjyyitlyWRPd999N1599VV88sknaG1tRUpKyoAePz8/f+DBEhERRYggCEgyaJFk0KIwrXtaj8vXuTdiebMLpU1OeAO9d1CWN3eeMybdjEXj0pFi1kUqdIpBzCGZQxIRUXzQqFRIs+iRZtFj/OE2SZLQ7PShosWFsiYnqtrcvc7mDogStlW2YVe1HXMKUzB3VAp0Gu67TX1TOoeMtfwRYA5J/XP7gthS0YodVXb4gn0PSEoyaDAzPxlTcq0wchsyIqIRoRIE5NoMKGvu3sKkqjW+i9oRzf6feuopCIKAJ554Atdddx30+t73BQUAm82Gv/3tb5AkCU899dSwxTBq1CgAgMfjOep5R/axOXL+0Rw4cABAZxLcl4kTJwLo/LB28ODBAcVKREQUa0w6DSZlW3HG1GzcdNIYnDczFxMyLehrG8RDTU6s2FCOrw80wX+UD8SU2JhDMockIqL4JQgC0i16zBmVgovm5OPGRaNxysQMZCb1/n4fECV8V9qCF9aX4UBDe4SjpViidA7J/JHiidsfxDcHm/DsulJsKm/ts6CdbtHhzKnZuO6EIswtTGFBm4hohOWlyFeGqWmL7321IzpTe/v27QCAc889d0DnL1q0CCqVCvv27Ru2GMaNGweg/+V0jhw/cv7RBIOdS6yoVH2PEei5bNGR84mIiOKZWiVgdLoZo9PNcPkC2FXjwLaKNrj98vdBSQI2V7TiYGMHlk7KREFq/8vuUWJhDik/n4iIKJ6ZdBrMyE/GjPxkNDg82FrZhv317WGzt52+ID7YWYexGe04ZWJmXO8dSEOjdA7J/JHigcffOTN7e+XRZ2bnpxgxrzAFo1JNw7YFFBER9S8/2QSguet2s9MHty8Ioy4+BxVFdKa21+uFVqsd8J4yGo0GOp0O7e3DN/J23LhxyMnJQUtLS59JaklJCerq6pCWloYpU6b0e585OTkAukdL9qa4uLjr59zc3EFGTUREFNtMOg0WFKXiBycWYfH49F5Ha9vdfqzaWo1vDjYhKPay5iQlLOaQnZhDEhFRosm0GnDG4Rl/03Kt6K1MUtLoxIsbKlDW5Ix4fBTdlM4hmT9SLPP4g1hf0oxnvynDxrK+Z2aPy7Dg8vkFuHhOPgrTzCxoExFFWEaSHlq1/LW3Oo5na0e0qF1UVAS/34+9e/cO6Pw9e/bA4/GgqKhoWOM477zzAAD/+Mc/ej3+6KOPAugcyTmQN+JTTjkFQOe+Ni0tLb2e88QTTwAAxowZM6DlhIiIiOKRVq3C7FEpuHZhIeYVpkDdy/vspvJWvL65Cu0evwIRUjRiDskckoiIEpvVqMXSyVm4+rhRKOxlVR+3P4i3t9fgqwONEDk4kg6LhhyS+SPFGkFrgH7Wcjz7TRm+K2vptZgtAJiUnYRrji/EOTNykG01RD5QIiIC0LlSZo5NPoCPRe1hcu6550KSJPz85z+HKB5938xAIIB77rkHgiB0JYDD5a677oJWq8WTTz6J+++/vysJbG1txYMPPognnngCGo0G9957b9c1drsdc+bMgdFoxDXXXCO7v1tuuQU2mw0NDQ1YunQpPv/8c7hcLoiiiJKSEtxyyy14/vnnAQC//OUvh/W5EBERxSK9Ro0Tx6XjygUFyLGFfwCuc3jw8sZK1NrjNwmjgWMOyRySiIgIANIsepw/KxdnTs3udeWfLRVteHt7DTx+LrlM0ZFDMn+kWOENBCFMPAV5P3waxrkXHrWY/f3jC3HG1GykmHWRD5SIiMLkJSdOUTuiGw7de++9WLFiBT788EPMnDkT119/PcaMGYNAIAAA+OSTT+ByubBv3z48//zz2LdvH/Ly8mSJ3XCYOHEiHn/8cdx666144IEH8MADD8BkMsHlcgHo3Hvmscceky37s3PnTmzduhUA8NJLL+GFF17oOpaVlYVXX30Vl1xyCbZt24alS5dCEARoNBr4/d2zzG6//XbceOONw/pciIiIYlmaRY9L5+Zja2UbvjnYhJ4Ta1y+IN7YUo3TJmdiUrZVuSBJccwhmUMSEREdIQgCJmYnYVSqCZ/tq0dJo3zZ8YoWF17ZVInzZ+Yi2cSCSyKLhhyS+SNFu6AoYdWWKjz66QGo51/R53kTs5KwYHQqUlnIJiKKOqFF7cZ2L7z+IPS9DAKNdREtaqenp+Ozzz7DFVdcgZ07d+KnP/2p7PiZZ57Z9bMkSZgxYwZefvllpKWlDXssN998MyZNmoS//e1v2LBhA5qbm5GVlYXjjz8ed911F5YsWSI7f/r06Zg1axb27t2Lyy67LOz+li1bhp07d+LRRx/FJ598grKyMvh8PuTm5uK4447DD3/4Q5xxxhnD/jyIiIhinSAImDMqBXnJRny4qw52d3dnTFCU8PHuenj8ImYVJCsXJCmKOSRzSCIiolBGnRrnTM/Bjio7vjrQhKDUPTqyzeXHa5urcMGsPGQk6RWMkpQULTkk80eKRpIk4ePddXhkdTEONnT0ed6ELAuOG53GYjYRURTLsuqhVgkI9pgtVGP3YHS6WcGoRkZEi9oAMHnyZGzZsgVvvfUW3njjDWzevBn19fVwOp0wm83IysrCvHnzcPHFF+P888+HWj1yIwkWL16MxYsXD+hcm83WNUqyL4WFhfj73/8+HKERERElnCyrAVfML8AHu2pR2SJfJufL4kZ4/UEsGJ06oL3mKP4whyQiIqJQgiBgZkEyMq16vLejFi5f97Ljnav+VOG8mbnIDZm9QokjWnJI5o8UTb4+0ISHP96H7VX2Ps8ZnW7GCWPSODCIiCgGaNQqZFsNsmXHq1vdLGoPF7VajYsvvhgXX3yxEg9PREREUcqgVeOCmXlYe6Ax7AP2t6UtCEoSFo5NVyg6UhpzSCIiIupNjs2Iy+cX4L3ttWjs8Ha1ewMi3txajQtn57GwncCYQxJ12lrRioc/3o91Jc19nuOp2An/9vdw3qP/jWBkw6OjrfN51dbWIj8/X+FoBq+2thYA4HQ6+zmTiChcXopRXtSO0321I17Ubmlpwb59+9Da2oqOjs6lTSwWC1JTUzFx4kSkpqZGOiQiIiKKIiqVgCUTMmDWa8I+bG8sa4VGrcKCIuYLiYY5JBERER2N1aDFxXPz8O72WlknXkCU8Pa2Glw0Jw9ZVoOCEZISmEMSAQcbOvCXj/Zh9Z76Ps+ZmmvFjhUPoX7TatjSsyIY3fARRbHre3V1tcLRDJ3UY/lgIqKBCt1Xu77dA19AhE6jUiiikRGRovbOnTvx1FNP4a233kJNTc1Rz83Ly8OFF16Im266CdOmTYtEeERERBRlBEHA/KJU6DUqfLG/UXZsfUkzdGoV99hOAMwhiYiIaDD0GjUumJWL93fWoqzZ1dXuC3bO2L5kbj7SLVxKN94xhyTq1NDuwaOfHsArGytl+6z2NDrdjLtPn4Bzpudg1F92RzjCkSLAlp6pdBCDZm/qe9ABEVF/cmwGqATgyMu9JAF1Dg9GpZqUDWyYjXhR++6778Zjjz0GURQhSf2PMqqqqsJjjz2Gf/7zn/jxj3+MRx55hHtnEhERJagZ+clQCQI+29cga/+yuBFmvRrjM5MUioxGGnNIIiIiGgqNWoXlMzoL26VN3Uu4egMi3t5Wg8vnF8CiV2Q3PooA5pBEgNMbwFNrD+Hprw7B5Qv2ek621YCfnDYel8zNh1YdX7P41HoT7lu5VukwBu2eMydDOjzbnIhosLRqFTKTDKhzeLraatvccVfUHtF3rJtuugn/+Mc/IIoirr76anz00Ueoq6uD3++HKIqyL7/fj7q6Onz00Ue4+uqrIYoiHn30Udx0000jGSIRERFFuWl5NiyZkBHW/vHuetTa43N/mETHHJKIiIiOhVol4Oxp2ShIlS/D2OEN4J3tNfAFWDSIR8whKdEFgiJe3FCOJQ+vwT8+O9BrQTvFpMWvz56MNfeejCsXjIq7gjYRUSLLSZZvtVNj9/RxZuwasXetTz75BM888wwMBgM++OADvPDCC1i2bBkyMzOhVqvDzler1cjMzMSyZcuwYsUKvP/++9Dr9Xj22Wfx2WefjVSYREREFANmFSTjhDFpsragKOHd7bWwu/0KRUUjgTkkERERDQeNWoVzZ+Qixybv3Gts9+LDXbUQBzCLl2IHc0hKZJIkYfXuOix7dC1+/eYuNHV4w84xaFW445Rx+PJnp+CmxWNg0Ib/vyAiotiWa5MP6Ky1uyH2sf1ErBqxovYzzzwDQRBw33334Ywzzhj09WeeeSZ+97vfQZIkPP300yMQIREREcWS+UUpmJZrlbW5/UG8v6MW/iBn28QL5pBEREQ0XLSHC9s2o1bWXtbswoZDLQpFRSOBOSQlqq0Vrbjs3+tx84rNONToDDuuEoDL5uVjzU9PwU/PmAirQdvLvRARUTwIHczpD0pocoYPdIplI1bUXr9+PQDg+uuvH/J93HDDDQCAdevWDUtMREREFLsEQcApEzNRmCbfC6axw4vP9zUMaM88in7MIYmIiGg4GXVqnD8rFwatvAvsu7IWlDR2KBQVDTfmkJRoGhwe3P3qNlz4z3XYWNba6zmnTMzABz85CX+5ZCayQwodREQUf8x6DZJDBnPWtMXXEuQjVtSuq6uDyWRCenr6kO8jIyMDZrMZ9fX1wxgZERERxSqVSsBZ07KRYpInaPvq2rGjyq5QVDScmEMSERHRcEsx6XDO9BwIgrx99e56tLp8ygRFw4o5JCUKX0DEv78swSmPrMGqLdW9njM9z4aVNx6HZ3+wAJOyrb2eQ0RE8SlsX+02t0KRjAzNSN2x3++H2Ww+5vvRaDRwuVzDEBEREcWSjrZmAEBtbS3y8/MVjmbwamtrAQBOZ/jyX3Rs9Bo1ls/IxSsbK+Hrsez4VweakJtsREaSPub/fo7Izs7Gpk2blA4jophDEhHRsYj1HIA55MjJTzHhpHHpWHugqavNFxTx0a46XDovHxqVKub/foDEzB8B5pCUGL7Y14AH39uDQ029v0fkpxhx7xkTce6MXKhUQq/nEBFRfMtNNmJvbXvX7Rq7G5IkQQgd3RmjRqyoTUREdCxEUez6Xl3d++jjWCCJXBJ7JKSadVg2NQvv7ajtagtKEj7cVYsrF4yKm78fIiIiGpx4yQGYQ46MWQXJqHd4sb++u6Ovod2L9SXNOGl8Rtz8/RBRfCltcuLB9/bg830NvR636DX48dJxuHZhEfQadYSjIyKiaJJrM8puO71BtHsCsIYsSx6rWNQmIqIoJ8CWnql0EINmb+KSdSNtbIYFc0YlY0tFW1dbq8uPL4sbe5wVm38/jpZGSKLY/4lERETUh9jMAZhDjixBELB0ciYa2j1odfm72rdUtKEg1dTzzJj7+2H+SBR/jiw1/tjnB2WrlPV06dx83HvmRGQmcc9sIiICUkxaGLQqePzd7xs1djeL2gPh8Xjwq1/96pjvg4iIEpdab8J9K9cqHcag3XPmZHYqRcDCsemoanWjod3b1ba7xgHj2AVwHfg2Zv9+HrhqcUJ3ajOHJCKiYxWrOQBzyJGnVatw1rQcvLKxEkGpe0b8J3vqoTJYEHQ5YvLvJ9HzR4A5JMWXrRWt+MUbO2UrS/Q0syAZD5w3FbMKkiMbGBERRTVBEJBrM8q2qqhp82BStlXBqIbPiBa1vV4v/vznPx/TfcTTWu9EREQ0vNQqAWdOy8ZL31XAH+zulExddhs8lbsBiZ3CsYg5JBEREY2kjCQ9Fo1Pl63w4/IFkXLqTWh6768KRkbHgjkkxQOnN4BHVu/Hc+vKIPWyE0W6RY+fnzkRF8/J577ZRETUq9zk0KK2W8FohteIFbUXL17MJJCIiIhGXIpJhyUTMvDp3u79xdSWVKSe9kO0fvIvBSOjoWAOSURERJEwM9+GsmYnyptdXW3mqafAue9r+Cp3KRgZDQVzSIoHm8tbcOcr21DZEl58UAnAD04cjTtPG48kQ3wsIUtERCMjN1m+JUWz0wePPwiDVq1QRMNnxIraa9asGam7JiIiIpKZkmPFwYYOlMk6JU+G+9BGBaOioWAOSURERJEgCAJOm5SFFRvK4Qt0r+6TdsYdqFtxj4KR0VAwh6RYFgiKeOzzg3js8wMQe5mdPTnHir9cPAPT822RD46IiGJORpIeapWAYI83lVq7B6PTzQpGNTxUSgdAREREdKwEQcDSSVnQaeSpTfLJ18s6KYmIiIiIjrAYNFgyIUPWprakwLboaoUiIqJEU9niwuVPfYt/fBZe0NZpVPjZmRPxzh0nsqBNREQDplGpkGXVy9pq7fGxBDmL2kRERBQXeuuU1CSlYf2hZoUiIiIiIqJoNzk7KWzWimX6UlTH0d6DRBSd3tleg7P/8RU2l7eGHZtflIKP71yM204eB62aXfhERDQ4uTaj7HZNm0ehSIYX3xGJiIgobkzOTsKoVJOsbXtlGxoc8ZG4EREREdHwEgQBp0zMgFYt34/5i30NsiUbiYiGS1CU8IcP9uLHL21FuzcgO6ZWCbjn9Al4+eYT4mKZWCIiUkZusryoXefwxEVuy6I2ERERxY0jnZJSwNfVJgH4bF8DJCn2EzciIiIiGn5JBi2OH5Mma2t2+rClInz2JBHRsbC7/PjBcxvx1NpDYcdGpZrw+i0n4EdLx0OtEnq5moiIaGBybAbZ7aAooaE99if9sKhNREREcSXZpIN9/auytoZ2L/bUOhSKiIiIiIii3az8ZPjq5UWmjWUt6AiZRUlENFQH6ttx/hNfY21xY9ixi+fk44OfnITZo1IUiIyIiOKNQatGmlkna4uHJchZ1CYiIqK449i4Cv7mSlnbNweb4Q0EFYqIiIiIiKKZSiWgZfXjkCSxq80flPDNwSYFoyKiePH5vnpc+M91KGt2ydq1agF/vGg6/nrZTFj0GoWiIyKieJSTLJ+tXdPmViiS4cOiNhEREcWfYAAtnz4la3L7g/iutEWhgIiIiIgo2vnqDqJjxyeytn117ai1x34HIBEp59WNlbjphc1hKz+kW/R46abjceWCUQpFRkRE8SzXJt9Xu9buifntGVnUJiIiorjkKdsKd8lGWdu2yja0unx9XEFEREREia5t7QqIXvlMyi+LG2O+A5CIIk+SJDzxxUH87I0dCIry15DpeTa8c8eJmFeUqlB0REQU73KT5UVttz+INpdfoWiGB4vaREREFLfa1q6ASui+LUrgEpJERERE1CfR1QbHhjdkbfUOL4rrOxSKiIhi1Z8/2o+HP94f1n7+rFy8dssJYcUGIiKi4WQ1aGDWqWVtNTG+AhGL2kRERBS3gvZ6zB6VImsraXTGxR4yRERERDQyOrZ9iGSTVta2rqQJAVHs4woiIjnV3Evx5JclYe23nTwWj14+CwatuperiIiIho8gCGEDqGraPApFMzxY1CYiIqK4Nr8oBQatPOX5+mATl5AkIiIiot6JQSwaly5rcngC2FllVyggIoolKUtvhmryaWHt9507BT87cxIEQejlKiIiouEXXtSO7Yk+LGoTERFRXNNr1FgQsk9Zrd2DQ01OhSIiIiIiomg3Jt2MHJtB1vZdWQu8/qBCERFRLDDMvRDWeefJ2lQC8MilM/GDE0crFBURESWq0Hy2ze2HyxdQKJpjp1E6ACIiIqKRNj3fhm2VbXB4upO2bw42YXS6GSqOkiciIiKiEIIgYNG4dLy2uaqrzeMXsbmiFQvHph/lSiJKVBvLWmCYtVzWJolB+Nc9iztXbMSdyoQ1YLW1tQAAp5MDwImI4kWGRQ+tWoA/2L1iZU2bB+MyLQpGNXQsahMREVHc06hUWDg2HR/trutqa3X5sb+uHZNzrApGRkRERETRKjfZiLEZZpQ0dhd4tlW2YVZBMkw6dqkRUbddNXasK2mWtUliEE3vPgLXvq8UimpoJJFbdRERxQuVSkC21YDK1u5lx2vtbha1iYiIiKLZhCwLNlfo0dju7WrbUNqCCVlJUKs4W5uIiIiIwi0cm45DjU4cKfH4gxI2l7fipPEZisZFRNGjrMmJz/c1hLW7v34e2qZi2NKzFIhq8OxN9UqHQEREIyA32Sgrate0eRSM5tiwqE1EREQJQRAEHD8mFe9ur+1qs7v92FvrwLQ8m4KREREREVG0SjXrMCk7CXvr2rvatlfZMWdUCsx6dqsRJboGhwcf7KqFFDK5ufWLZ3H/Q39UJqghuufMyZBEUekwiIhomIXuq93Q7oE/KEKrVikU0dDFXsREREREQzQ6zYxsqzyR21DaggA/uBMRERFRHxaMTkXPhX2CooSNZS3KBUREUcHpDeDdHbWyfUoBwL7+VTi3f6RQVERERHI5NiN6rlEpSkC9IzZna7OoTURERAlDEAScMDZN1tbhDWBvbXsfVxARERFRoks26TAlxypr21XtgNMbUCgiIlJaQBTx/s5adIS8Djh3f4G2tS8oFBUREVE4nUaF9CS9rK3WzqI2ERERUdQrSDEiL9koa9tc3gpRlPq4goiIiIgS3YLRqVAL3XNcgpKErRVtygVERIr6Yl9jWEEgL9mI5o8eUygiIiKivuWGLEFe0+bu48zoxqI2ERERJRRBELBgdKqsze7240BDh0IREREREVG0SzJoMTknSda2o7oNHn9QoYiISCm7a+zYU+uQtVkNGpw9PRsQuYIDERFFnxybfIJPnd0DSYq9CT4sahMREVHCKUgxIssqX3ZnY1lLTCZzRERERBQZcwtTZPsR+oMStle2KRUOESmgsd2LL/Y3ytq0agHLZ+TCpNMoFBUREdHR5YTM1PYERLS6/ApFM3QsahMREVHCEQQB8wrls7WbnT6UNjkVioiIiIiIol2ySYfxWRZZ27bKNvgCokIREVEkeQNBfLCzFsGQratOm5yFjP/f3p1Hx1WfecJ/Sou1WvIuyQtmM8YGE+BNCJ0QmzDTdPISwmFI6CwdQsMZaEIyjNOdSSbTYJbpOZOcDJAT6HagIQ10yKQJhBDSPUMPL8SQMGQZszTGxjYQL0gYeZEta5fu+wcjxyVbsssu6ZZUn885dXD97q+unroWqq/1/O69Q+5VCgCFZHJlWdRWZC++am4bf5cg19QGAIrSCTNrYlr1pKyxX7+509naAAAMa+jCyK6+gfiXt9pSqgYYS0+veyd2dWaf1faeufVxUsPkYV4BAIUhk8kccLZ2c1tXStUcOU1tAKAoZTKZeO+xU7PGWnZ3xdZd42+VIgAAY2Pm5Io4bkZN1tj/2bQz+gacrQ0T2Wtv74m1LXuyxhrqKuKcBTNSqggAcjO0qf3WOPwdqKY2AFC0TmqYHHWV2Zfe+fWbO1OqBgCA8eB9QxZG7u3uj7XNe4aZDYx3e7p64/9buy1rbFJpSfy/pzZFWYlfrwMwPjTVV2U939nRG5mKmmFmFyafugBA0SotycT/Mz/7l5KbdnREy+7xd/kdAADGRlN9Vcydmv1Lwd/8bmcMDLiNDUw0SZLEP695O7r7sq/G8OGTZ0ZdVXlKVQFA7mZOrojSkkzWWOmsE1Kq5shoagMARW1xU11UTyrNGvvNmztSqgYAgPHgvUMWRrZ19sb6be0pVQOMln/Zujs278y+POvChslxcmNdShUBwJEpLclEY132JcjLNLUBAMaPstKSOOOYKVljG9/ZGzs7etIpCACAgnfMtOqYNbkia+y3v9sZSeJsbZgodnf1xrMbWrPGaivK4sMLZ6ZUEQAcnaH31S6ddWJKlRwZTW0AoOgtmVMfFWXZsejFzbvSKQYAgIKXyWTifcdOyxp7p7073trlNjYwESRJEk++ui16+rMvO/6vF82KivLSYV4FAIVtaFO7bOZxESXj53NNUxsAKHoVZaVx6pz6rLE1zbuju7c/pYoAACh0x8+sifoh99RdvXlnStUA+bSuZU9s2tGRNba4qS7mT69JqSIAOHpN9VVZzzPlFTFp1nEpVZM7TW0AgIg4bW59ZDK/f97bn8Qrb+1OryAAAApaSSYT75mbvTBy4zt7o62zN6WKgHzo6u2PVeuzLzteU1EaSxfMSKkiAMiPqkmlMaU6e1FmxZxFKVWTO01tAICIqKssjxNn1maNvbBlVwwMuC8iAAAHt3h2XUwqHXIbmy270ikGyItfbGyNziFX7frwQpcdB2BimD3kbG1N7XHiF7/4RXziE5+I2bNnR0VFRTQ1NcUll1wSzz777FHv+7XXXotFixZFJpOJ97///bF169Y8VAwAjKbT503Jer6nqy9eb92bTjEUJPkRANhfRVlpLJ5dlzX2ytbd0dM3MMwrKEYy5PjR3NYZ/7I1+4pdx8+oiROGLIAGgPFq6H21K+acnFIluSvapvbdd98dS5cujYcffjiam5ujtLQ0Wlpa4pFHHolly5bFypUrj3jfa9asiQ996EOxdu3a+OQnPxmrVq2KOXPm5LF6AGA0NNVXxqzJFVlj7ovIIPkRADiYoQsje/oHYk2z29jwLhly/EiSJJ5e907WWFlJJpYtnJlSRQCQf0Ob2mV1syKqp6ZUTW6Ksqm9bt26uPbaa2NgYCCuv/76aG1tjY6Ojti+fXusWLEiBgYG4ktf+lKsWbMm532//fbb8Ud/9Eexbdu2uPLKK+OHP/xhVFRUHPqFAEDqMplMnHHMlKyxt3Z1xbbdXekURMGQHwGA4dRXlcfxM2qyxl7YvCuSxG1sip0MOb680rw7tu3pzho7+/jpUVdZPswrAGD8mVYzKSrKstvDmZknpFRNboqyqX3bbbdFb29vXHXVVXHzzTfH9OnTIyJi2rRpceONN8Y111wTfX198a1vfSvnfX/2s5+NLVu2xMUXXxx33XVXZDKZfJcPAIyiBbMmR82k7HulvbB5VzrFUDDkRwBgJEMXRrZ19sYb293GptjJkONHd19//HLD9qyxadWTDrgSAwCMd5lMJhqHnK2dmXl8StXkpiib2o899lhERFx33XUH3b58+fKIiPjpT3+a06ra733ve/Hkk0/G/Pnz4/7774+SkqI8vAAwrpWWZOK0uVOyxta9vSf2dvelUxAFQX4EAEYyZ0pVzKidlDX2wqZd6RRDwZAhx49fvbEjOnv7s8aWnjQjSkssFgBg4pldX5X13JnaBWrjxo3R3NwcU6dOjcWLFx90zoIFC6KxsTFaW1sP+/I/PT09ccMNN0RExDe/+c2ora3NW80AwNg6dU5d1i8vBpKIl7e2pVgRaZIfAYBDyWQyB5zRuXlnZ7S2dx/8BUx4MuT4sauj54Crcx0/oybmT685+AsAYJwbel/tmDovOnoK/4Seomtqr1+/PiIi5s2bN+K8we0bNmw4rP0++OCDsWXLlvjgBz8Yl1566dEVCQCkqnpSWZzcODlr7KUtbdE/4L6IxUh+BAAOx8KGyVFVnn0bmxfdxqZoyZDjx3Mbt8f+/9QryUR8aMGM9AoCgFHWUFcZ+9+5JFNSGi9tKfwTeoquqb1p06aIiKiqqhpxXnV1ddb8Q7nvvvsi4t3LBj3zzDNx/vnnR0NDQ0yZMiU+8IEPxL333pvTZYQAgHQNPdOms7c/Nr7Tnk4xpEp+BAAOR1lpSSyZU581tu7tPdHd1z/MK5jIZMjxoaWtK17blv3vvPfMmxJTqicN8woAGP8mlZXEzNqKfc+TZGBc/N6zLO0Cxlp7e25/KYczv7W1NVatWhU1NTVRUlIS5513XvT1/f40/eeeey6ee+65eOqpp+KBBx7I6etv2bJlxO3Nzc057Q8AODwzaiti9pTKeGtX176xl7e2xUkNk0d4FRPReMuPETIkAKRlyZz6+PXvdsRgT7G3P4l1LXvitLlTUq2LsSdDFr4kSeLZDa1ZYxVlJXHWsdNSqggAxs7i2XWx6Qf3RNuG38T0ZHd89hvr0y7pkIruTO3Ozs6c5nd0dBxyzvPPPx8DAwNx6qmnxooVK+K73/1uvPPOO9Hd3R0vvvhiXHLJJRER8fd///fx/e9/P6evP2/evBEfZ511Vk77AwAO39Azbbbs7Iwde3tSqoa0jLf8GCFDAkBaaivL4vgZ2ffhfWlrmzNni5AMWfje3N4RW3dl/z2979hpUTnkNgIAMBG9Z+6U6Fr9k+h64/9E9OaWW9JSdE3t0fDiiy9GxLvB8hvf+EZcccUVMWPGjJg0aVKcdtpp8dBDD8XSpUsjIuKv//qv0ywVAMjBibNqD7gv4r9sLfz7y1D45EcAmLiGLozc3t4TLbu7hpkNh0+GzJ8kSeK5jduzxiZXlsV75tYP8woAIG1Fd/nxQ93HZqjB+9qMpLX13cvUHHfccfHRj370gO2ZTCa+8IUvxKpVq+I3v/lN9Pb2Rnl5+WF9/c2bN4+4vbm5ecKtkgSAQlFWUhKLZ9fFb3+3c9/Ymubd8YETpkdZqbWBxWK85ccIGRIA0nTMtOqoqyyL3V2/vyz0y1vaoqk+t0zB+CZDFrYN29rjnfburLGzj/fvPAAoZEXX1K6trc37/N27d0dExCmnnDLsnMFtPT09sW3btpgzZ85hff25c+ce1jwAYHScOqSp3d03EOu3tceiproUq2Isjbf8GCFDAkCaMplMLJlTH7/Y7yzQ17a1x9KT+l3WuIjIkIVrIEnif7++I2tsanV5nNw4OaWKAIDDUXRLz4455piIiOjqGvmyT4P3sRmcP5LBlZc1NTXDztk/mB7qawMAhWNK9aQ4Zlr2WRMvbXEJ8mIiPwIAuVo8uy5KMr9/3j+QxJrm3ekVxJiTIQvXay17YkdHT9bYHxw/PUoymWFeAQAUgqJrap944okRcejL6QxuH5w/koaGhoiI6Owc/kbqe/fu3ffn+nr3ZgGA8WTofRFbdnfFO3u6h5nNRCM/AgC5qp5UFifOyj7z9uUtbZEkSUoVMdZkyMI0MJDE/34j+yztGbWTDvj/FQAoPEXZ1G5qaoodO3bE2rVrDzpn48aN0dLSEtOnT4/Fixcfcp9nnHFGRESsWbNm2DmvvvpqRETMmjUrZsyYcQSVAwBpOX5GTdRUZF8q8uWtztYuFvIjAHAkTpszJev5rs7e2LJz+GYkE4sMWZjWb2uPts7erLE/OH56ZJylDQAFr+ia2hERH//4xyMi4tvf/vZBt99+++0REXHhhRceVqA577zzYvLkybFhw4Z4+umnDzrn7rvvjoiIj3zkI7kXDACkqqQkE6fOzj7LYW3L7ujpG0ipIsaa/AgA5Gr2lMqYVj0pa8zCyOIiQxaWJEni129mn6U9a3JFHDdj+Mu5AwCFoyib2suXL4/y8vJYuXJl3HjjjbFjx7thZufOnXHLLbfEnXfeGWVlZfGVr3xl32va2trizDPPjKqqqrjsssuy9ldVVRXLly+PiIg//uM/jkceeSR6et69L8vmzZvjyiuvjCeeeCLKy8vjL/7iL8boXQIA+XTK7LrY/9dMvf1JrGvZk1o9jC35EQDIVSaTiSVzsxdGbnynPfZ296VUEWNNhiwsb7Tuje17s++l/b5jpzlLGwDGiaJsai9cuDDuuOOOKCkpiZtuuimmT58eNTU1MW3atLjhhhsiIuI73/lO1mV/Xn755Vi9enV0dXXFD37wgwP2+Z/+03+KCy64ILZt2xaXXHJJVFdXR01NTRxzzDFx7733RklJSaxcuTKWLFkyZu8TAMifyZXlB6zg/5e3nGlTLORHAOBInNw4OcpKft8wG0gi1jTvTrEixpIMWTjePUt7Z9bYtOpJccJMZ2kDwHhRlE3tiIirrroqnnrqqbjooouisbExent7o6GhIS666KJ46qmn4s/+7M+y5i9ZsiROP/30qKioiE9/+tMH7G/SpEnx2GOPxd/+7d/GOeecE7W1tdHX1xdz586Nz3zmM/HrX/86rrjiirF6ewDAKBh6ps22Pd3xzp7ulKphrMmPAECuKstL46SGyVlja97aHUmSpFQRY02GLAxbdnZGy+6urLH3HjvVWdoAMI6UpV1AmpYuXRpLly49rLn19fWxevXqEeeUlJTElVdeGVdeeWU+ygMACswx06qjtqIs2ve7ZOQrb7XFuQtnpVgVY0l+BABydcrsuqyzs3d19sZbu7piztSqFKtiLMmQ6Rt6L+26yrIDFpwAAIWtaM/UBgDIVUkmE4ub6rLG1rbsib7+gZQqAgCg0DXVV8bU6vKssVea3cYGxkpLW1ds3tmZNfb/zJ8apSXO0gaA8URTGwAgB4tnZze1u/sGYuM7e1OqBgCAQpfJZOKU2dm3sVn/dnt09/WnVBEUl6FnaVdPKj1gsTIAUPg0tQEAclBfVR5zh1wq0pk2AACM5OTGybH/rXv7BpJ47e329AqCItHa3h2vt2YvQj7zmKlRVurX4gAw3vj0BgDI0SlDztbevKMzdnf2plQNAACFrqaiLI6bXpM19spbFkbCaPvt73ZmPa8oK4klc+qHmQ0AFDJNbQCAHJ04szYqyrJj1Jrm3SlVAwDAeDB0YeTbu7ujtb07pWpg4mvv7ovX3t6TNXb6vCkxqcyvxAFgPPIJDgCQo7LSkljYMDlrbE3z7hhIkpQqAgCg0B07vSZqJpVmja15y8JIGC0vbdkVA/v9E62sJBPvmTcltXoAgKOjqQ0AcASGnmmzp6svNu/oSKkaAAAKXUlJJhY1ZWfItS17on/AwkjIt77+gXh5a/Yl/k9umhxV5aXDvAIAKHSa2gAAR2BWXWXMrK3IGnvFmTYAAIxg8ZCFkZ29/fH6O+0pVQMT19qWPdHVO5A1dvrcKekUAwDkhaY2AMARGnq29uvv7I3O3v6UqgEAoNBNrZ4Us6dUZo290mxhJORTkiTxwuZdWWPzp1XH9CGLkgGA8UVTGwDgCC1snBylJZl9z/uTJNa17EmxIgAACt0ps+uznv9ue0fs6epNqRqYeDbv7Izte3uyxk4/Zko6xQAAeaOpDQBwhCrLS+OEmTVZY6860wYAgBEsmFUbk0qzfyW31sJIyJvVm3ZmPZ9aXR7zp1WnVA0AkC+a2gAAR2FxU/YlyLft6Y7W9u6UqgEAoNCVl5bEgobarLG1zXsiSZKUKoKJY+fennhze0fW2OnzpkQmkxnmFQDAeKGpDQBwFOZNq46aitKssbXNzrQBAGB4ixqzF0bu6OiJt/dYGAlHa+i9tCvKSmLRkIXIAMD4pKkNAHAUSjKZOHnILyXXtuyOAWfaAAAwjNlTKqOusixrbK3b2MBR6ertjzVD/j86dU59lJf6FTgATAQ+0QEAjtKixslZz/f29MfmHR3DzAYAoNhlMpkDzh5d9/ae6B+wMBKO1Ctv7Y6+/f4fymQi3jO3PsWKAIB80tQGADhK02srYtbkiqyxV12CHACAEQxtanf1DsSb2/emVA2Mb0mSxMtb27LGFsysjcmV5SlVBADkm6Y2AEAeDP2l5MZ32qO7rz+lagAAKHT1VeUxu74ya+xVlyCHI7JpR0e0dfZmjZ02b0o6xQAAo0JTGwAgD05qqI2SzO+f9w0ksWFbe3oFAQBQ8IYujHyjdW909lgYCbkaepb29JpJBywaAQDGN01tAIA8qJ5UFsdOr8kacwlyAABGsqChNkr3Wxk5kES89rYMCblo7+qL11uzL92/ZE59ZDKZYV4BAIxHmtoAAHky9Eybrbs6D7gEHgAADKooK40TZg5ZGNniEuSQi1feaosk+f3z8tJMnNw0Ob2CAIBRoakNAJAnx86ojoqy7Hi11n0RAQAYwaLG7IWRb+/ujh17e1KqBsaXgYEk/uWt7H9zLWyYHBVlpSlVBACMFk1tAIA8KSspiYUN2WcEvNqyJ5L9TxsAAID9HDOtOqonZTfgXrUwEg7LG9v3Rnt3X9bYkrn1KVUDAIwmTW0AgDwaegnyts7eaG7rSqkaAAAKXUlJJk5uzF4YubZlTwxYGAmH9PKWtqznDXUVMWtyZUrVAACjSVMbACCPGuoqYmp1edaYM20AABjJ0IWR7d19sWVnZ0rVwPjQ1tkbv9vRkTV22pwp6RQDAIw6TW0AgDzKZDJx8pBfSr62rT36+gdSqggAgEI3o7YiZtZWZI1ZGAkje3lr9lnaFWUlsaChNqVqAIDRpqkNAJBnQy8f2dM3EK+37k2pGgAAxoNFTdkZcsO29ujpszASDqZvYCDWvJW98GNRU12Ul/p1NwBMVD7lAQDyrK6yPOZOrcoac6YNAAAjOalhcmQyv3/eN5DEhm3t6RUEBWzjtr3R2dufNbZkTn1K1QAAY0FTGwBgFCwecgny3+3oiL3dfSlVAwBAoaupKItjp9dkjb3aYmEkHMwrzdmXHp87pSqm1UxKqRoAYCxoagMAjIITZtZGeenvT7VJkoh1b+9JsSIAAArdoiG3sdmyszN2d/amVA0Upt2dvbF5R2fW2Clz6oaZDQBMFJraAACjYFJZSZw4szZrzCXIAQAYyXEzaqKiLPvXdWtbLIyE/b0y5N9VFQf5txcAMPFoagMAjJKTh1yCvLW9J1rbu1OqBgCAQldWWhILGrKbc2tbdkeSJClVBIVlIElizVvZTe2FjZOjrNSvuQFgovNpDwAwSuZOrYrairKsMWfaAAAwkkWN2Qsjd3b0xrY9FkZCRMTmHR3R3t2XNXbKbJceB4BioKkNADBKSjKZWNiQfV/EdS17nGkDAMCwmuoro67Swkg4mFeGnKU9c3JFzJpcmVI1AMBY0tQGABhFJzdlN7Xbu/tiy87OlKoBAKDQZTKZOHnI2drrWvbEwICFkRS3zp7+2PhOe9bYKU3O0gaAYqGpDQAwimbUVsSM2klZY860AQBgJEMXRnb29semHR0pVQOFYW3L7th/bUdpSSYWNk4e/gUAwISiqQ0AMMqGnmmzYVt79PUPpFQNAACFbmr1pGioq8gaszCSYpYkyQGXHj9hZk1UlpemVBEAMNY0tQEARtnQ+2r39A/E6617U6oGAIDxYOjCyI3vtEdPn4WRFKe3d3fH9r09WWOnzK5PqRoAIA2a2gAAo6y2sizmTavKGnOmDQAAIzmpoTYymd8/7xtIDrifMBSLV95qy3peV1kW86ZWDTMbAJiINLUBAMbA0DNtfrd9b3T09KVUDQAAha56UlnMn1adNfZqy+5hZsPE1ds/EK+9nb2gY/Hsusjsv+oDAJjwNLUBAMbAiTNro6zk9790GUgi1r/tTBsAAIY3dGHk5h2d0d5tYSTFZeO29ujpz770/uKmumFmAwATlaY2AMAYmFRWEsfPrMkac6YNAAAjOX5mTUwqzf713Tq3saHIrBny76b506pjcmV5StUAAGnR1AYAGCNDz7R5e3d37OzoSakaAAAKXXlpSZwwK3th5FoLIykie7p6Y/OOzqyxRc7SBoCipKkNADBG5k+rjqry0qyxtc60AQBgBIuGLIxsbe+J1vb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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 1488, + "width": 986 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# create a figure with subplots\n", + "fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(10, 15))\n", + "\n", + "# loop through each dataframe and plot histogram\n", + "for i, df in enumerate(simdata):\n", + " row = i // 3\n", + " col = i % 3\n", + " sns.histplot(\n", + " data=df,\n", + " x=\"frt_pv\",\n", + " hue=\"fac2\",\n", + " ax=axes[row, col],\n", + " kde=True,\n", + " stat=\"density\",\n", + " legend=False,\n", + " )\n", + " axes[row, col].set_ylim(0, 1.7)\n", + " axes[row, col].set_title(f\"{df['fac1'][0]} - {df['fac2'][0]}\")\n", + "[ax.axhline(y=1, linestyle=\"--\", color=\"grey\", linewidth=1) for ax in axes.flat]\n", + "# adjust spacing between subplots\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Case Study" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def cre_stat(z, y, x):\n", + " covariates = pd.DataFrame(np.asarray(x))\n", + " covariates.columns = [f\"x{i}\" for i in range(covariates.shape[1])]\n", + " neyman_data = pd.DataFrame({\"y\": y, \"z\": z})\n", + " lin_data = (covariates - covariates.mean()).assign(y=y, z=z)\n", + " rhs = \" + \".join(covariates.columns)\n", + " tau_n_fit = pf.feols(\"y ~ z\", data=neyman_data, vcov=\"HC2\")\n", + " tau_l_fit = pf.feols(\"y ~ z * (\" + rhs + \")\", data=lin_data, vcov=\"HC2\")\n", + " return np.r_[\n", + " tau_n_fit.coef().loc[\"z\"],\n", + " tau_n_fit.se().loc[\"z\"],\n", + " tau_n_fit.tstat().loc[\"z\"],\n", + " tau_l_fit.coef().loc[\"z\"],\n", + " tau_l_fit.se().loc[\"z\"],\n", + " tau_l_fit.tstat().loc[\"z\"],\n", + " ]\n", + "\n", + "\n", + "def cre_frt(z, y, x, n_frt=1e3):\n", + " test_stat = cre_stat(z, y, x)\n", + " # normal dist pvalue\n", + " asy_p_n = 2 * sp.stats.norm.cdf(-np.abs(test_stat[2]))\n", + " asy_p_l = 2 * sp.stats.norm.cdf(-np.abs(test_stat[5]))\n", + " # FRT p value\n", + " null_dist = Parallel(n_jobs=8)(\n", + " delayed(cre_stat)(np.random.permutation(z), y, x) for i in range(n_frt)\n", + " )\n", + " null_dist = np.vstack(null_dist)\n", + " comparisons = 1 * (np.abs(null_dist) >= np.abs(test_stat))\n", + " frt_p_n, frt_p_l = comparisons[:, 2].mean(), comparisons[:, 5].mean()\n", + "\n", + " restable = np.c_[\n", + " np.r_[test_stat[0], test_stat[3]],\n", + " np.r_[test_stat[1], test_stat[4]],\n", + " np.r_[asy_p_n, asy_p_l],\n", + " np.r_[frt_p_n, frt_p_l],\n", + " ]\n", + " res = pd.DataFrame(\n", + " restable,\n", + " index=[\"Neyman\", \"Lin\"],\n", + " columns=[\"Estimate\", \"Std. Error\", \"Asy. p-value\", \"FRT p-value\"],\n", + " )\n", + " return res, null_dist\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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class_level1.02.03.04.05.0
treatment
Soccer Player1619151010
Physician1720151110
Placebo1519161210
\n", + "
" + ], + "text/plain": [ + "class_level 1.0 2.0 3.0 4.0 5.0\n", + "treatment \n", + "Soccer Player 16 19 15 10 10\n", + "Physician 17 20 15 11 10\n", + "Placebo 15 19 16 12 10" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dat_chong = pd.read_stata(\"chong.dta\")\n", + "pd.crosstab(dat_chong.treatment, dat_chong.class_level)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "use_vars = [\"treatment\", \"gradesq34\", \"class_level\", \"anemic_base_re\"]\n", + "dat_physician = dat_chong.loc[dat_chong.treatment != \"Soccer Player\", use_vars]\n", + "dat_physician[\"z\"] = np.where(dat_physician.treatment == \"Physician\", 1, 0)\n", + "dat_physician[\"y\"] = dat_physician.gradesq34\n", + "dat_physician[\"x\"] = np.where(dat_physician.anemic_base_re == \"Yes\", 1, 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "res = []\n", + "for i in range(1, 6):\n", + " dd = dat_physician.loc[dat_physician.class_level == i,]\n", + " res.append(cre_frt(dd.z, dd.y, dd.x, n_frt=int(1e3))[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[ Estimate Std. Error Asy. p-value FRT p-value\n", + " Neyman 0.567059 0.426303 0.183460 0.189\n", + " Lin 0.588021 0.418414 0.159915 0.164,\n", + " Estimate Std. Error Asy. p-value FRT p-value\n", + " Neyman 0.193421 0.438494 0.659139 0.650\n", + " Lin 0.265317 0.409260 0.516801 0.524,\n", + " Estimate Std. Error Asy. p-value FRT p-value\n", + " Neyman 1.305000 0.49441 0.008303 0.016\n", + " Lin 1.501344 0.46183 0.001151 0.001,\n", + " Estimate Std. Error Asy. p-value FRT p-value\n", + " Neyman -0.273485 0.413089 0.507940 0.529\n", + " Lin -0.312505 0.417092 0.453708 0.477,\n", + " Estimate Std. Error Asy. p-value FRT p-value\n", + " Neyman -0.050000 0.379136 0.895080 0.918\n", + " Lin -0.066667 0.278936 0.811103 0.805]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "econometrics", + "language": "python", + "name": "econometrics" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From abe6f0f2e3296cb9d727ca7646f850051801dd1b Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:28 +0200 Subject: [PATCH 11/20] Add PyFixest Chapter 09 notebook --- ...hapter09BridgingFinitePopAndSuperPop.ipynb | 217 ++++++++++++++++++ 1 file changed, 217 insertions(+) create mode 100644 pyfixest/Chapter09BridgingFinitePopAndSuperPop.ipynb diff --git a/pyfixest/Chapter09BridgingFinitePopAndSuperPop.ipynb b/pyfixest/Chapter09BridgingFinitePopAndSuperPop.ipynb new file mode 100644 index 0000000..a835351 --- /dev/null +++ b/pyfixest/Chapter09BridgingFinitePopAndSuperPop.ipynb @@ -0,0 +1,217 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 9: Bridging Finite and Super-population Causal Inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from joblib import Parallel, delayed\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pyfixest as pf\n", + "\n", + "np.random.seed(42)\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def linestimator(Z, Y, X):\n", + " X = (X - X.mean(axis=0)) / X.std(axis=0)\n", + " n, p = X.shape\n", + " # fully interacted OLS\n", + " covariates = pd.DataFrame(X, columns=[f\"x{i}\" for i in range(p)])\n", + " data = covariates.assign(y=Y, z=Z)\n", + " m = pf.feols(\"y ~ z * (\" + \" + \".join(covariates.columns) + \")\", data=data, vcov=\"HC2\")\n", + " est, vehw = m.coef().loc[\"z\"], m.se().loc[\"z\"] ** 2\n", + " # super-population correction\n", + " inter = m.coef().loc[[f\"z:x{i}\" for i in range(p)]].to_numpy()\n", + " # (Ī²_1 - Ī²_0)' Ī£ (Ī²_1 - Ī²_0) / n\n", + " superCorr = (inter @ np.cov(X.T) @ inter) / n\n", + " vsuper = vehw + superCorr\n", + " return est, np.sqrt(vehw), np.sqrt(vsuper)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.052230404017171474, 0.1475302340448403, 0.1633386978782156)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def onerepl(*args):\n", + " n = 500\n", + " X = np.random.normal(0, 1, n * 2).reshape(n, 2)\n", + " Y0 = X[:, 0] + X[:, 0] ** 2 + np.random.uniform(-0.5, 0.5, n)\n", + " Y1 = X[:, 1] + X[:, 1] ** 2 + np.random.uniform(-1, 1, n)\n", + " Z = np.random.binomial(1, 0.6, n)\n", + " Y = Y0 * (1 - Z) + Y1 * Z\n", + " return linestimator(Z, Y, X)\n", + "onerepl()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "nrep, k = 2_000, 8\n", + "results = Parallel(n_jobs=k)(delayed(onerepl)(i) for i in range(nrep))\n", + "simres = np.vstack(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0033900784280582142, 0.13559452100782549, 0.15029308662381266)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# bias, estimated EHW SE, estimated super-population SE\n", + "simres[:, 0].mean(), simres[:, 1].mean(), simres[:, 2].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.14731850757623555" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# empirical SD\n", + "simres[:, 0].std()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.926" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# EHW coverage\n", + "np.mean(\n", + " (simres[:, 0] - 1.96 * simres[:, 1]) * (simres[:, 0] + 1.96 * simres[:, 1]) <= 0\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "EHW has below nominal coverage for superpopulation." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9515" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# superpop coverage\n", + "np.mean(\n", + " (simres[:, 0] - 1.96 * simres[:, 2]) * (simres[:, 0] + 1.96 * simres[:, 2]) <= 0\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Superpopn is above nom coverage for superpopulation." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "metrics", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 92f52f0962a2acc441f00f6240c5399ac69d2aab Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:28 +0200 Subject: [PATCH 12/20] Add PyFixest Chapter 10 notebook --- pyfixest/Chapter10ObsStudiesSelBias.ipynb | 33 +++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 pyfixest/Chapter10ObsStudiesSelBias.ipynb diff --git a/pyfixest/Chapter10ObsStudiesSelBias.ipynb b/pyfixest/Chapter10ObsStudiesSelBias.ipynb new file mode 100644 index 0000000..cad6cf5 --- /dev/null +++ b/pyfixest/Chapter10ObsStudiesSelBias.ipynb @@ -0,0 +1,33 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 10: Observational Studies, Selection Bias, and Nonparametric Identification of Causal Effects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "Y(z) \\bot Z \\mid X\n", + "$$" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "econometrics", + "language": "python", + "name": "econometrics" + }, + "language_info": { + "name": "python", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 86200d29c264b78213ecb6dbb09ba1e7c03c835a Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:29 +0200 Subject: [PATCH 13/20] Add PyFixest Chapter 11 notebook --- pyfixest/Chapter11Pscore.ipynb | 560 +++++++++++++++++++++++++++++++++ 1 file changed, 560 insertions(+) create mode 100644 pyfixest/Chapter11Pscore.ipynb diff --git a/pyfixest/Chapter11Pscore.ipynb b/pyfixest/Chapter11Pscore.ipynb new file mode 100644 index 0000000..23277b3 --- /dev/null +++ b/pyfixest/Chapter11Pscore.ipynb @@ -0,0 +1,560 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 11: The Central Role of the Propensity Score in Observational Studies for Causal Effects" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from joblib import Parallel, delayed\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pyfixest as pf\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from causalinference import CausalModel\n", + "font = {'family' : 'IBM Plex Sans Condensed',\n", + " 'weight' : 'normal',\n", + " 'size' : 10}\n", + "plt.rc('font', **font)\n", + "plt.rcParams['figure.figsize'] = (6, 6)\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "np.random.seed(42)\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[CausalInference](https://github.com/apoorvalal/CausalinferencePy) library for Estimators discussed in Imbens and Rubin (2015)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## regression" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "nhanes_bmi = pd.read_csv(\"nhanes_bmi.csv\")\n", + "nhanes_bmi.head()\n", + "z, y, X = (\n", + " nhanes_bmi.School_meal.values,\n", + " nhanes_bmi.BMI.values,\n", + " nhanes_bmi.iloc[:, 3:].values,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def regEst(y, z, X):\n", + " covariates = pd.DataFrame(np.asarray(X))\n", + " covariates.columns = [f\"x{i}\" for i in range(covariates.shape[1])]\n", + " data = covariates.assign(y=y, z=z)\n", + " rhs = \" + \".join(covariates.columns)\n", + " m0 = pf.feols(\"y ~ z\", data=data, vcov=\"HC2\")\n", + " m1 = pf.feols(\"y ~ z + \" + rhs, data=data, vcov=\"HC2\")\n", + " lin_data = (covariates - covariates.mean()).assign(y=y, z=z)\n", + " m2 = pf.feols(\"y ~ z * (\" + rhs + \")\", data=lin_data, vcov=\"HC2\")\n", + " res = np.c_[\n", + " np.r_[m0.coef().loc[\"z\"], m0.se().loc[\"z\"]],\n", + " np.r_[m1.coef().loc[\"z\"], m1.se().loc[\"z\"]],\n", + " np.r_[m2.coef().loc[\"z\"], m2.se().loc[\"z\"]],\n", + " ]\n", + " return pd.DataFrame(res, index=[\"est\", \"se\"], columns=[\"naive\", \"fisher\", \"lin\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " naive fisher lin\n", + "est 0.533904 0.061248 -0.016954\n", + "se 0.225701 0.220883 0.223635" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regEst(y, z, X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## propensity score stratification" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ps_data = pd.DataFrame(np.asarray(X))\n", + "ps_data.columns = [f\"x{i}\" for i in range(ps_data.shape[1])]\n", + "ps_data[\"z\"] = z\n", + "psmod = pf.feglm(\n", + " \"z ~ \" + \" + \".join(ps_data.columns.drop(\"z\")),\n", + " data=ps_data,\n", + " family=\"logit\",\n", + ")\n", + "pscores = psmod.predict(ps_data, type=\"response\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 390, + "width": 1189 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "f, ax = plt.subplots(1, 3, figsize=(12, 4))\n", + "ax[0].hist(pscores[z == 1], bins=5, alpha=0.5, density=True)\n", + "ax[0].hist(pscores[z == 0], bins=5, alpha=0.5, density=True, edgecolor=\"black\")\n", + "ax[1].hist(pscores[z == 1], bins=10, alpha=0.5, density=True)\n", + "ax[1].hist(pscores[z == 0], bins=10, alpha=0.5, density=True, edgecolor=\"black\")\n", + "ax[2].hist(pscores[z == 1], bins=30, alpha=0.5, density=True, label=\"treated\")\n", + "ax[2].hist(\n", + " pscores[z == 0],\n", + " bins=30,\n", + " alpha=0.5,\n", + " density=True,\n", + " label=\"control\",\n", + " edgecolor=\"black\",\n", + ")\n", + "ax[2].legend()\n", + "\n", + "# Hide the y-axis\n", + "ax[0].set_ylim(0, 4.5), ax[1].set_ylim(0, 4.5), ax[2].set_ylim(0, 4.5)\n", + "ax[0].set_xlim(0, 1), ax[1].set_xlim(0, 1), ax[2].set_xlim(0, 1)\n", + "ax[0].set_yticks([]), ax[1].set_yticks([]), ax[2].set_yticks([])\n", + "ax[0].set_yticklabels([]), ax[1].set_yticklabels([]), ax[2].set_yticklabels([])\n", + "ax[0].set_title(\"5 bins\"), ax[1].set_title(\"10 bins\"), ax[2].set_title(\"30 bins\")\n", + "f.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/alal/Desktop/code/00_causal/CausalinferencePy/causalinference/core/summary.py:102: RuntimeWarning: invalid value encountered in divide\n", + " return (mean_t - mean_c) / np.sqrt((sd_c**2 + sd_t**2) / 2)\n", + "/home/alal/Desktop/code/00_causal/CausalinferencePy/causalinference/estimators/ols.py:21: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n", + "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n", + " olscoef = np.linalg.lstsq(Z, Y)[0]\n", + "/home/alal/Desktop/code/00_causal/CausalinferencePy/causalinference/core/summary.py:28: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " self._dict[\"Y_t_sd\"] = np.sqrt(data[\"Y_t\"].var(ddof=1))\n", + "/home/alal/anaconda3/envs/metrics/lib/python3.11/site-packages/numpy/core/_methods.py:261: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/home/alal/Desktop/code/00_causal/CausalinferencePy/causalinference/core/summary.py:33: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " self._dict[\"X_t_sd\"] = np.sqrt(data[\"X_t\"].var(0, ddof=1))\n", + "/home/alal/anaconda3/envs/metrics/lib/python3.11/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n", + " ret = um.true_divide(\n", + "/home/alal/Desktop/code/00_causal/CausalinferencePy/causalinference/core/summary.py:27: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " self._dict[\"Y_c_sd\"] = np.sqrt(data[\"Y_c\"].var(ddof=1))\n", + "/home/alal/Desktop/code/00_causal/CausalinferencePy/causalinference/core/summary.py:32: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " self._dict[\"X_c_sd\"] = np.sqrt(data[\"X_c\"].var(0, ddof=1))\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 5 10 20 50 80\n", + "est -0.116096 -0.177604 -0.199688 -0.264742 -0.203770\n", + "se 0.281897 0.279457 0.272488 0.256721 0.244644" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = CausalModel(y, z, X)\n", + "m.est_propensity()\n", + "m.cutoff = 0\n", + "res = []\n", + "for nn in [5, 10, 20, 50, 80]:\n", + " m.blocks = nn\n", + " m.stratify()\n", + " m.est_via_blocking(adj=0)\n", + " ests = m.estimates[\"blocking\"]\n", + " res.append(np.array([ests[\"ate\"], ests[\"ate_se\"]]))\n", + "\n", + "pd.DataFrame(np.c_[res].T, index=[\"est\", \"se\"], columns=[5, 10, 20, 50, 80])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## weighting" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ate': -1.5162837769469517, 'ate_se': nan}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m.reset()\n", + "m.est_propensity()\n", + "m.cutoff = 0\n", + "m.trim()\n", + "m.est_via_weighting(estimand=\"ate\")\n", + "m.estimates[\"weighting\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ate': -0.15566888253380995, 'ate_se': nan}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m.reset()\n", + "m.est_propensity()\n", + "m.cutoff = 0\n", + "m.trim()\n", + "m.est_via_weighting(estimand=\"ate\", hajekize=True)\n", + "m.estimates[\"weighting\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ate': -1.5162837769469517, 'ate_se': nan}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m.reset()\n", + "m.est_propensity()\n", + "m.cutoff = 0.01\n", + "m.trim()\n", + "m.est_via_weighting(estimand=\"ate\")\n", + "m.estimates[\"weighting\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ate': -0.15566888253380995, 'ate_se': nan}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m.reset()\n", + "m.est_propensity()\n", + "m.cutoff = 0.01\n", + "m.trim()\n", + "m.est_via_weighting(estimand=\"ate\", hajekize=True)\n", + "m.estimates[\"weighting\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ate': -1.3034023808081816, 'ate_se': nan}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m.reset()\n", + "m.est_propensity()\n", + "m.cutoff = 0.05\n", + "m.trim()\n", + "m.est_via_weighting(estimand=\"ate\")\n", + "m.estimates[\"weighting\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ate': -0.10280308535143234, 'ate_se': nan}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m.reset()\n", + "m.est_propensity()\n", + "m.cutoff = 0.05\n", + "m.trim()\n", + "m.est_via_weighting(estimand=\"ate\", hajekize=True)\n", + "m.estimates[\"weighting\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ate': -0.0859162644736197, 'ate_se': nan}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m.reset()\n", + "m.est_propensity()\n", + "m.cutoff = 0.1\n", + "m.trim()\n", + "m.est_via_weighting(estimand=\"ate\")\n", + "m.estimates[\"weighting\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ate': 0.15180636527279034, 'ate_se': nan}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m.reset()\n", + "m.est_propensity()\n", + "m.cutoff = 0.1\n", + "m.trim()\n", + "m.est_via_weighting(estimand=\"ate\", hajekize=True)\n", + "m.estimates[\"weighting\"]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "metrics", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5b2a1c1c7fe85570ccfed56c598eefa6acf7b020 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:29 +0200 Subject: [PATCH 14/20] Add PyFixest Chapter 12 notebook --- pyfixest/Chapter12DoubleRobustATE.ipynb | 935 ++++++++++++++++++++++++ 1 file changed, 935 insertions(+) create mode 100644 pyfixest/Chapter12DoubleRobustATE.ipynb diff --git a/pyfixest/Chapter12DoubleRobustATE.ipynb b/pyfixest/Chapter12DoubleRobustATE.ipynb new file mode 100644 index 0000000..e219e57 --- /dev/null +++ b/pyfixest/Chapter12DoubleRobustATE.ipynb @@ -0,0 +1,935 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 12: The Doubly Robust or the Augmented Inverse Probability Score Weighting Estimator for the Average Causal Effect" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from joblib import Parallel, delayed\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pyfixest as pf\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "font = {'family' : 'IBM Plex Sans Condensed',\n", + " 'weight' : 'normal',\n", + " 'size' : 10}\n", + "plt.rc('font', **font)\n", + "plt.rcParams['figure.figsize'] = (6, 6)\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "np.random.seed(42)\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def OS_est(z, y, x, lb=0, ub=1):\n", + " covariates = pd.DataFrame(np.asarray(x))\n", + " covariates.columns = [f\"x{i}\" for i in range(covariates.shape[1])]\n", + " data = covariates.assign(y=y, z=z)\n", + " rhs = \" + \".join(covariates.columns)\n", + " pscore_fit = pf.feglm(\"z ~ \" + rhs, data=data, family=\"logit\")\n", + " pscore = np.clip(pscore_fit.predict(data, type=\"response\"), lb, ub)\n", + " # fitted potential outcomes through native PyFixest OLS\n", + " outcome1_fit = pf.feols(\"y ~ \" + rhs, data=data.loc[data.z == 1])\n", + " outcome0_fit = pf.feols(\"y ~ \" + rhs, data=data.loc[data.z == 0])\n", + " outcome1 = outcome1_fit.predict(data)\n", + " outcome0 = outcome0_fit.predict(data)\n", + " # outcome model\n", + " ace_reg = (outcome1 - outcome0).mean()\n", + " # ipw\n", + " y_treat = (y * z / pscore).mean()\n", + " y_control = (y * (1 - z) / (1 - pscore)).mean()\n", + " one_treat = (z / pscore).mean()\n", + " one_control = ((1 - z) / (1 - pscore)).mean()\n", + " ace_ipw0 = y_treat - y_control\n", + " ace_ipw = y_treat / one_treat - y_control / one_control\n", + " # aipw\n", + " r_treat, r_control = (\n", + " (z * (y - outcome1) / pscore).mean(),\n", + " ((1 - z) * (y - outcome0) / (1 - pscore)).mean(),\n", + " )\n", + " ace_dr = ace_reg + r_treat - r_control\n", + " return np.array([ace_reg, ace_ipw0, ace_ipw, ace_dr])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Nuisance propensity-score and outcome models use PyFixest logit and OLS fits.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def OS_ATE(z, y, x, n_boot=2 * 1e2, truncps=(0, 1)):\n", + " point_est = OS_est(z, y, x, *truncps)\n", + " n = len(z)\n", + "\n", + " # nonparametric bootstrap\n", + " def bootfn(*args):\n", + " # draw indices\n", + " ids = np.random.choice(np.arange(n), size=n, replace=True)\n", + " return OS_est(z[ids], y[ids], x[ids, :], *truncps)\n", + "\n", + " boot_est = Parallel(n_jobs=-1)(delayed(bootfn)(i) for i in range(int(n_boot)))\n", + " boot_est = np.vstack(boot_est)\n", + " # return boot_est\n", + " boot_se = boot_est.std(axis=0)\n", + "\n", + " res = pd.DataFrame(\n", + " [point_est, boot_se],\n", + " index=[\"point_est\", \"boot_se\"],\n", + " columns=[\"omod\", \"ipw0\", \"ipw\", \"aipw\"],\n", + " )\n", + " return res\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , -0.03158827, -0.10764208, -0.16381696, -0.09518803,\n", + " 0.10472859, 0.16304548, 0.16203658, 0.11038457])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def simu11(n=500):\n", + " x = np.random.normal(size=(n, 2))\n", + " x1 = np.c_[np.ones(n), x]\n", + " beta_z = np.array([0, 1, 1])\n", + " pscore = 1 / (1 + np.exp(-x1 @ beta_z))\n", + " z = np.random.binomial(1, pscore)\n", + " beta_y1, beta_y0 = np.array([1, 2, 1]), np.array([1, 2, 1])\n", + " y1, y0 = x1 @ beta_y1, x1 @ beta_y0\n", + " y = z * y1 + (1 - z) * y0 + np.random.normal(size=n)\n", + " ce = OS_ATE(z, y, x)\n", + " return np.r_[(y1 - y0).mean(), ce.iloc[0, :], ce.iloc[1, :]]\n", + "\n", + "\n", + "simu11()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.04582345, -0.22549646, -0.46072515, -0.52516757, -0.17942783,\n", + " 0.12001089, 0.25216989, 0.24312848, 0.13680289])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def simu01(n=500):\n", + " x = np.random.normal(size=(n, 2))\n", + " x1 = np.c_[np.ones(n), x, np.exp(x)]\n", + " beta_z = np.array([-1, 0, 0, 1, -1])\n", + " pscore = 1 / (1 + np.exp(-x1 @ beta_z))\n", + " z = np.random.binomial(1, pscore)\n", + " beta_y1, beta_y0 = np.array([1, 2, 1, 0, 0]), np.array([1, 1, 1, 0, 0])\n", + " y1, y0 = x1 @ beta_y1, x1 @ beta_y0\n", + " y = z * y1 + (1 - z) * y0 + np.random.normal(size=n)\n", + " ce = OS_ATE(z, y, x)\n", + " return np.r_[(y1 - y0).mean(), ce.iloc[0, :], ce.iloc[1, :]]\n", + "\n", + "\n", + "simu01()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.37558253, 0.30140973, 0.34046859, 0.33410275, 0.34599118,\n", + " 0.1020255 , 0.10175789, 0.09951637, 0.10378403])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def simu10(n=500):\n", + " x = np.random.normal(size=(n, 2))\n", + " x1 = np.c_[np.ones(n), x, np.exp(x)]\n", + " beta_z = np.array([0, 1, 1, 0, 0])\n", + " pscore = 1 / (1 + np.exp(-x1 @ beta_z))\n", + " z = np.random.binomial(1, pscore)\n", + " beta_y1, beta_y0 = np.array([1, 0, 0, 0.2, -0.1]), np.array([1, 0, 0, -0.2, 0.1])\n", + " y1, y0 = x1 @ beta_y1, x1 @ beta_y0\n", + " y = z * y1 + (1 - z) * y0 + np.random.normal(size=n)\n", + " ce = OS_ATE(z, y, x)\n", + " return np.r_[(y1 - y0).mean(), ce.iloc[0, :], ce.iloc[1, :]]\n", + "\n", + "\n", + "simu10()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.35452903, 0.05032412, 0.19088308, 0.1505216 , 0.22355156,\n", + " 0.1339793 , 0.16197421, 0.13353598, 0.17576791])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def simu00(n=500):\n", + " x = np.random.normal(size=(n, 2))\n", + " x1 = np.c_[np.ones(n), x, np.exp(x)]\n", + " beta_z = np.array([-1, 0, 0, 1, -1])\n", + " pscore = 1 / (1 + np.exp(-x1 @ beta_z))\n", + " z = np.random.binomial(1, pscore)\n", + " beta_y1, beta_y0 = np.array([1, 0, 0, 0.2, -0.1]), np.array([1, 0, 0, -0.2, 0.1])\n", + " y1, y0 = x1 @ beta_y1, x1 @ beta_y0\n", + " y = z * y1 + (1 - z) * y0 + np.random.normal(size=n)\n", + " ce = OS_ATE(z, y, x)\n", + " return np.r_[(y1 - y0).mean(), ce.iloc[0, :], ce.iloc[1, :]]\n", + "\n", + "\n", + "simu00()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def simstudy(f, n, truth=0):\n", + " est = [f() for _ in range(n)]\n", + " est = np.vstack(est)\n", + "\n", + " bias = est[:, 1:5] - truth\n", + " return pd.DataFrame(\n", + " [bias.mean(axis=0), bias.std(axis=0), est[:, 5:].mean(axis=0)],\n", + " index=[\"bias\", \"true se\", \"est se\"],\n", + " columns=[\"omod\", \"ipw0\", \"ipw\", \"aipw\"],\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Both well specified" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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omodipw0ipwaipw
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est se0.1041400.2665420.2323760.119668
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omodipw0ipwaipw
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omodipw0ipwaipw
bias-0.052806-0.0025810.0000060.000859
true se0.1143740.1641340.1541600.155509
est se0.1124470.1485460.1355250.137609
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" + ], + "text/plain": [ + " omod ipw0 ipw aipw\n", + "bias -0.052806 -0.002581 0.000006 0.000859\n", + "true se 0.114374 0.164134 0.154160 0.155509\n", + "est se 0.112447 0.148546 0.135525 0.137609" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "simstudy(simu10, 500, truth=0.2 * np.exp(1 / 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "both bad" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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omodipw0ipwaipw
bias-0.0727040.089918-0.0710390.133596
true se0.1254040.2509220.1889950.258324
est se0.1259690.2261540.1579420.222932
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" + ], + "text/plain": [ + " omod ipw0 ipw aipw\n", + "bias -0.072704 0.089918 -0.071039 0.133596\n", + "true se 0.125404 0.250922 0.188995 0.258324\n", + "est se 0.125969 0.226154 0.157942 0.222932" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "simstudy(simu00, 500, truth=0.2 * np.exp(1 / 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "AIPW has the worst bias and variance when both are bad, verifying the Kang and Schafer (2007) result." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## application" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Scale covariates with NumPy; this is preprocessing, not estimation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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BMISchool_mealageChildSexblackmexampir200_plusWICFood_StampfsdchbiAnyInsRefSexRefAge
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" + ], + "text/plain": [ + " BMI School_meal age ChildSex black mexam pir200_plus WIC \\\n", + "0 15.18 0 6 0 0 0 1 0 \n", + "1 17.93 0 6 1 0 1 0 1 \n", + "2 15.15 1 5 1 0 1 0 0 \n", + "3 15.69 1 11 0 0 0 0 0 \n", + "4 37.40 0 14 0 0 1 0 0 \n", + "\n", + " Food_Stamp fsdchbi AnyIns RefSex RefAge \n", + "0 0 0 1 1 51 \n", + "1 0 1 1 1 27 \n", + "2 0 0 0 0 24 \n", + "3 0 0 1 1 44 \n", + "4 0 0 0 0 48 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nhanes_bmi = pd.read_csv(\"nhanes_bmi.csv\").iloc[:, 1:]\n", + "nhanes_bmi.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "z, y = nhanes_bmi.School_meal, nhanes_bmi.BMI\n", + "covariates = nhanes_bmi.iloc[:, 2:].values\n", + "covariate_range = np.ptp(covariates, axis=0)\n", + "x = (covariates - covariates.min(axis=0)) / np.where(covariate_range == 0, 1, covariate_range)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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omodipw0ipwaipw
point_est-0.016954-1.516536-0.155755-0.019291
boot_se0.2270120.4842430.2467630.230823
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" + ], + "text/plain": [ + " omod ipw0 ipw aipw\n", + "point_est -0.016954 -1.516536 -0.155755 -0.019291\n", + "boot_se 0.227012 0.484243 0.246763 0.230823" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(causaleffects := OS_ATE(z.values, y.values, x, n_boot=1e3))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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omodipw0ipwaipw
point_est-0.016954-0.713539-0.053634-0.043381
boot_se0.2257280.4908540.2390520.229507
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" + ], + "text/plain": [ + " omod ipw0 ipw aipw\n", + "point_est -0.016954 -0.713539 -0.053634 -0.043381\n", + "boot_se 0.225728 0.490854 0.239052 0.229507" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(causaleffects2 := OS_ATE(z.values, y.values, x, n_boot=1e3, truncps=(0.1, 0.9)))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "metrics", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 926e9311f1d4fbe56a24d2db2b2d59abb6b568a5 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:29 +0200 Subject: [PATCH 15/20] Add PyFixest Chapter 13 notebook --- pyfixest/Chapter13DoubleRobustATT.ipynb | 481 ++++++++++++++++++++++++ 1 file changed, 481 insertions(+) create mode 100644 pyfixest/Chapter13DoubleRobustATT.ipynb diff --git a/pyfixest/Chapter13DoubleRobustATT.ipynb b/pyfixest/Chapter13DoubleRobustATT.ipynb new file mode 100644 index 0000000..0ddb25d --- /dev/null +++ b/pyfixest/Chapter13DoubleRobustATT.ipynb @@ -0,0 +1,481 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 13: The Average Causal Effect on the Treated Units and Other Estimands" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from joblib import Parallel, delayed\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pyfixest as pf\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "font = {'family' : 'IBM Plex Sans Condensed',\n", + " 'weight' : 'normal',\n", + " 'size' : 10}\n", + "plt.rc('font', **font)\n", + "plt.rcParams['figure.figsize'] = (6, 6)\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "np.random.seed(42)\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def ATT_est(z, y, x, omod=None, pmod=None, ub=1):\n", + " # E[Y | Z = 1]\n", + " y0mean = y[z == 1].mean()\n", + " nn, nn1 = len(z), z.sum()\n", + " covariates = pd.DataFrame(np.asarray(x))\n", + " covariates.columns = [f\"x{i}\" for i in range(covariates.shape[1])]\n", + " data = covariates.assign(y=y, z=z)\n", + " rhs = \" + \".join(covariates.columns)\n", + " # PyFixest is the default for nuisance functions; custom ML fits remain optional.\n", + " if pmod is None:\n", + " pscore = pf.feglm(\"z ~ \" + rhs, data=data, family=\"logit\").predict(\n", + " data, type=\"response\"\n", + " )\n", + " else:\n", + " pscore = pmod.fit(x, z).predict_proba(x)[:, 1]\n", + " pscore = np.clip(pscore, None, ub)\n", + " if omod is None:\n", + " outcome0 = pf.feols(\"y ~ \" + rhs, data=data.loc[data.z == 0]).predict(data)\n", + " else:\n", + " outcome0 = omod.fit(x[z == 0, :], y[z == 0]).predict(x)\n", + " # outcome regression with PyFixest\n", + " ace_reg0 = pf.feols(\"y ~ z + \" + rhs, data=data).coef().loc[\"z\"]\n", + " ace_reg = y0mean - outcome0[z == 1].mean()\n", + " # ipw\n", + " odds = pscore / (1 - pscore)\n", + " ace_ipw0 = y0mean - (odds * (1 - z) * y).mean() * (nn / nn1)\n", + " ace_ipw = y0mean - (odds * (1 - z) * y).mean() / (odds * (1 - z)).mean()\n", + " # aipw\n", + " res0 = y - outcome0\n", + " ace_dr = ace_reg - (odds * (1 - z) * res0).mean() * (nn / nn1)\n", + " return np.array([ace_reg0, ace_reg, ace_ipw0, ace_ipw, ace_dr])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The default nuisance estimators are PyFixest logit and OLS.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def OS_ATT(z, y, x, omod=None, pmod=None, n_boot=2 * 1e2, Utruncps=1):\n", + " n = len(z)\n", + " point_est = ATT_est(z, y, x, omod, pmod, Utruncps)\n", + "\n", + " def bootfn(*args):\n", + " # draw indices\n", + " ids = np.random.choice(np.arange(n), size=n, replace=True)\n", + " return ATT_est(z[ids], y[ids], x[ids, :], omod, pmod, Utruncps)\n", + "\n", + " boot_est = Parallel(n_jobs=-1)(delayed(bootfn)(i) for i in range(int(n_boot)))\n", + " boot_est = np.vstack(boot_est)\n", + " boot_se = boot_est.std(axis=0)\n", + " res = pd.DataFrame(\n", + " [point_est, boot_se],\n", + " index=[\"point_est\", \"boot_se\"],\n", + " columns=[\"omod0\", \"omod\", \"ipw0\", \"ipw\", \"aipw\"],\n", + " )\n", + " return res\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## application" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nhanes_bmi = pd.read_csv(\"nhanes_bmi.csv\").iloc[:, 1:]\n", + "nhanes_bmi.head()\n", + "\n", + "z, y = (\n", + " nhanes_bmi.School_meal.values,\n", + " nhanes_bmi.BMI.values,\n", + ")\n", + "covariates = nhanes_bmi.iloc[:, 2:].values\n", + "covariate_range = np.ptp(covariates, axis=0)\n", + "x = (covariates - covariates.min(axis=0)) / np.where(covariate_range == 0, 1, covariate_range)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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omod0omodipw0ipwaipw
point_est0.061248-0.350718-1.992439-0.350810-0.187104
boot_se0.2187050.2447700.7058750.3203660.272267
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omod0omodipw0ipwaipw
point_est0.061248-0.350718-0.597019-0.192312-0.229505
boot_se0.2239130.2529210.7114880.3363430.276487
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" + ], + "text/plain": [ + " omod0 omod ipw0 ipw aipw\n", + "point_est 0.061248 -0.350718 -0.597019 -0.192312 -0.229505\n", + "boot_se 0.223913 0.252921 0.711488 0.336343 0.276487" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(causaleffects := OS_ATT(z, y, x, n_boot=1e3, Utruncps=0.9))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "with more flexible nuisance functions" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor\n", + "\n", + "rfc, rfr = (\n", + " GradientBoostingClassifier(max_depth=3, random_state=0),\n", + " GradientBoostingRegressor(max_depth=3, random_state=0),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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omod0omodipw0ipwaipw
point_est0.061248-0.1711715.234295-0.197837-0.212641
boot_se0.2308240.2879140.5093900.3129490.302220
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" + ], + "text/plain": [ + " omod0 omod ipw0 ipw aipw\n", + "point_est 0.061248 -0.171171 5.234295 -0.197837 -0.212641\n", + "boot_se 0.230824 0.287914 0.509390 0.312949 0.302220" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(causaleffects := OS_ATT(z, y, x, omod=rfr, pmod=rfc, n_boot=1e3, Utruncps=0.9))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## bonus: balancing weights\n", + "\n", + "[calibration](https://github.com/google/empirical_calibration) package" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import empirical_calibration as ec\n", + "from itertools import combinations_with_replacement\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Polynomial basis: linear and quadratic terms." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Degree-two polynomial expansion, implemented with NumPy to avoid sklearn.\n", + "quadratic_terms = [\n", + " x[:, i] * x[:, j]\n", + " for i, j in combinations_with_replacement(range(x.shape[1]), 2)\n", + "]\n", + "X = np.column_stack([np.ones(len(x)), x, *quadratic_terms])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.5635223070320698" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "entr_weights, success = ec.calibrate(\n", + " covariates=X[z == 0, :],\n", + " target_covariates=X[z == 1, :],\n", + " objective=ec.Objective.ENTROPY,\n", + ")\n", + "y[z == 1].mean() - np.sum(y[z == 0] * entr_weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.6197354773671719" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "l2_weights, success = ec.calibrate(\n", + " covariates=X[z == 0, :],\n", + " target_covariates=X[z == 1, :],\n", + " objective=ec.Objective.QUADRATIC,\n", + ")\n", + "y[z == 1].mean() - np.sum(y[z == 0] * l2_weights)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "metrics", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 56afc4771a86442c2fc9aef8065ef012848dd0a4 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:29 +0200 Subject: [PATCH 16/20] Add PyFixest Chapter 15 notebook --- pyfixest/Chapter15Matching.ipynb | 614 +++++++++++++++++++++++++++++++ 1 file changed, 614 insertions(+) create mode 100644 pyfixest/Chapter15Matching.ipynb diff --git a/pyfixest/Chapter15Matching.ipynb b/pyfixest/Chapter15Matching.ipynb new file mode 100644 index 0000000..caa6ada --- /dev/null +++ b/pyfixest/Chapter15Matching.ipynb @@ -0,0 +1,614 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 15: Matching in Observational Studies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from joblib import Parallel, delayed\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pyfixest as pf\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "font = {'family' : 'IBM Plex Sans Condensed',\n", + " 'weight' : 'normal',\n", + " 'size' : 10}\n", + "plt.rc('font', **font)\n", + "plt.rcParams['figure.figsize'] = (6, 6)\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "np.random.seed(42)\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## experimental data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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treatmentageeducationblackhispanicmarriednodegreeearnings1974earnings1975earnings1978
01.037.011.01.00.01.01.00.00.09930.0460
11.022.09.00.01.00.01.00.00.03595.8940
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31.027.011.01.00.00.01.00.00.07506.1460
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" + ], + "text/plain": [ + " treatment age education black hispanic married nodegree \\\n", + "0 1.0 37.0 11.0 1.0 0.0 1.0 1.0 \n", + "1 1.0 22.0 9.0 0.0 1.0 0.0 1.0 \n", + "2 1.0 30.0 12.0 1.0 0.0 0.0 0.0 \n", + "3 1.0 27.0 11.0 1.0 0.0 0.0 1.0 \n", + "4 1.0 33.0 8.0 1.0 0.0 0.0 1.0 \n", + "\n", + " earnings1974 earnings1975 earnings1978 \n", + "0 0.0 0.0 9930.0460 \n", + "1 0.0 0.0 3595.8940 \n", + "2 0.0 0.0 24909.4500 \n", + "3 0.0 0.0 7506.1460 \n", + "4 0.0 0.0 289.7899 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import empirical_calibration as ec\n", + "import empirical_calibration.data.lalonde as lalonde\n", + "\n", + "treat, ctrl = lalonde.experimental_treated(), lalonde.experimental_control()\n", + "lalonde_exp = pd.concat([treat, ctrl])\n", + "lalonde_exp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "y, z = lalonde_exp.earnings1978.values, lalonde_exp.treatment.values\n", + "X = lalonde_exp.drop(columns=[\"earnings1978\", \"treatment\"]).values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def reg_adjust(z, y, X):\n", + " covariates = pd.DataFrame(np.asarray(X))\n", + " covariates.columns = [f\"x{i}\" for i in range(covariates.shape[1])]\n", + " data = covariates.assign(y=y, z=z)\n", + " rhs = \" + \".join(covariates.columns)\n", + " tau_n_fit = pf.feols(\"y ~ z\", data=data, vcov=\"HC2\")\n", + " tau_f_fit = pf.feols(\"y ~ z + \" + rhs, data=data, vcov=\"HC2\")\n", + " lin_data = (covariates - covariates.mean()).assign(y=y, z=z)\n", + " tau_l_fit = pf.feols(\"y ~ z * (\" + rhs + \")\", data=lin_data, vcov=\"HC2\")\n", + " resmat = np.r_[\n", + " np.c_[tau_n_fit.coef().loc[\"z\"], tau_n_fit.se().loc[\"z\"], tau_n_fit.tstat().loc[\"z\"]],\n", + " np.c_[tau_f_fit.coef().loc[\"z\"], tau_f_fit.se().loc[\"z\"], tau_f_fit.tstat().loc[\"z\"]],\n", + " np.c_[tau_l_fit.coef().loc[\"z\"], tau_l_fit.se().loc[\"z\"], tau_l_fit.tstat().loc[\"z\"]],\n", + " ]\n", + " return pd.DataFrame(\n", + " resmat, index=[\"neyman\", \"fisher\", \"lin\"], columns=[\"coef\", \"se\", \"t\"]\n", + " )\n", + "\n", + "\n", + "reg_adjust(z, y, X)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Matching" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def one_nn_att(X, z, y, k=1, bias_correction=False):\n", + " \"\"\"Matching estimator for the ATT using k-nearest neighbours.\n", + "\n", + " Nearest-neighbour matching has no PyFixest equivalent, so sklearn is used\n", + " only to select matches. Optional bias correction uses PyFixest OLS.\n", + " \"\"\"\n", + " from sklearn.neighbors import KNeighborsRegressor\n", + "\n", + " n, n1 = len(z), z.sum()\n", + " mod = KNeighborsRegressor(n_neighbors=k)\n", + " treat_nn_mod = mod.fit(X[z == 0, :], y[z == 0])\n", + " Y0hat = treat_nn_mod.predict(X[z == 1, :])\n", + " point_est = y[z == 1].mean() - Y0hat.mean()\n", + " # store neighbour index for each treated\n", + " _, neighbours = mod.kneighbors(X[z == 1, :])\n", + " if bias_correction:\n", + " full_data = pd.DataFrame(np.asarray(X))\n", + " full_data.columns = [f\"x{i}\" for i in range(full_data.shape[1])]\n", + " control_data = full_data.loc[z == 0].copy()\n", + " control_data[\"y\"] = y[z == 0]\n", + " bias_fit = pf.feols(\n", + " \"y ~ \" + \" + \".join(full_data.columns), data=control_data\n", + " )\n", + " muhat = bias_fit.predict(full_data)\n", + " # bias correction term is Ī¼^0(x_i) - Ī¼^0(x_j) for each\n", + " bias_corr_term = muhat[z == 1] - muhat[z == 0][neighbours.flatten()]\n", + " point_est = y[z == 1].mean() - Y0hat.mean() - bias_corr_term.mean()\n", + " ######################################################################\n", + " # abadie/imbens variance\n", + " ######################################################################\n", + " # 1/N1^2 āˆ‘ (Y_i - \\hat{Y}_i - \\hat{Ļ„})^2\n", + " first_term = 1 / (n1**2) * np.sum(y - treat_nn_mod.predict(X) - point_est) ** 2\n", + " # second term\n", + " # 1/N1^2 āˆ‘ (K_i^2 - K_sq) Ļƒ^2\n", + " mod2 = KNeighborsRegressor(n_neighbors=k)\n", + " ctrl_nn_mod = mod2.fit(X[z == 1, :], y[z == 1])\n", + " Yhat_all = np.zeros(n)\n", + " Yhat_all[z == 1], Yhat_all[z == 0] = Y0hat, ctrl_nn_mod.predict(X[z == 0, :])\n", + " sigma2 = 1 / 2 * np.sum((y - Yhat_all) ** 2)\n", + " # K terms: counts are the number of times each ctrl obs is matched with a treated obs\n", + " ctrl_index, counts = np.unique(neighbours, return_counts=True)\n", + " K, Ksq = np.zeros(n), np.zeros(n)\n", + " K[ctrl_index], Ksq[ctrl_index] = counts / k, counts / (k**2)\n", + " # conditional variance of Y given X, W\n", + " second_term = 1 / (n1**2) * np.sum((K**2 - Ksq) * sigma2)\n", + " v_ai = first_term + second_term\n", + " ######################################################################\n", + " # otsu and rai\n", + " ######################################################################\n", + " if bias_correction:\n", + " psi = z * (y - muhat) - (1 - z) * (K / k) * (y - muhat)\n", + " v_or = (1 / (n1**2)) * np.sum((psi - point_est * n1 / n) ** 2)\n", + " return point_est, np.sqrt(v_ai / n), np.sqrt(v_or)\n", + " return point_est, np.sqrt(v_ai / n)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# wrapper function that bootstraps the standard error\n", + "def nn_att(X, z, y, k=1, bias_correction=False, n_boot=1e3):\n", + " analytic_est = one_nn_att(X, z, y, k=k, bias_correction=bias_correction)\n", + " # bootstrap (invalid?)\n", + " n = len(z)\n", + "\n", + " def bootfn(*args):\n", + " # draw indices\n", + " ids = np.random.choice(np.arange(n), size=n, replace=True)\n", + " return one_nn_att(X[ids,], z[ids], y[ids], k=1, bias_correction=bias_correction)\n", + "\n", + " boot_est = Parallel(n_jobs=-1)(delayed(bootfn)(i) for i in range(int(n_boot)))\n", + " boot_est = np.stack(boot_est)\n", + " boot_se = boot_est[:, 0].std()\n", + " if bias_correction:\n", + " return pd.DataFrame(\n", + " np.c_[*analytic_est, boot_se], columns=[\"est\", \"ai_se\", \"or_se\", \"boot_se\"]\n", + " )\n", + " return pd.DataFrame(\n", + " np.c_[*analytic_est, boot_se], columns=[\"est\", \"ai_se\", \"boot_se\"]\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "No bias correction" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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estai_seboot_se
02011.153187585.758733853.85284
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" + ], + "text/plain": [ + " est ai_se boot_se\n", + "0 2011.153187 585.758733 853.85284" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nn_att(X, z, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "bias correction with OLS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nn_att(X, z, y, bias_correction=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Bootstrap SE is more in-line with the OLS SE, while AI SE looks too small." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### observational" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# read CPS data\n", + "dat = pd.read_table(\"cps1re74.csv\", delimiter=\" \")\n", + "dat[\"u74\"], dat[\"u75\"] = 1 * (dat.re74 == 0), 1 * (dat.re75 == 0)\n", + "z, y = dat.treat.values, dat.re78.values\n", + "X = dat.drop(columns=[\"treat\", \"re78\"]).values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Regression is bad." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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coefset
neyman-8506.495361583.442609-14.579832
fisher1067.546135628.4388791.698727
lin-4265.8005133211.771843-1.328177
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" + ], + "text/plain": [ + " coef se t\n", + "neyman -8506.495361 583.442609 -14.579832\n", + "fisher 1067.546135 628.438879 1.698727\n", + "lin -4265.800513 3211.771843 -1.328177" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reg_adjust(z, y, X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "No bias correction : Much better than OLS" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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estai_seboot_se
01521.3765031244.056155862.221261
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" + ], + "text/plain": [ + " est ai_se boot_se\n", + "0 1521.376503 1244.056155 862.221261" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nn_att(X, z, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "bias correction with OLS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nn_att(X, z, y, bias_correction=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "AI SE is now much bigger than boot, potentially because the second term (conditional variance) dominates. Otsu and Rai (2017) SEs look reasonable throughout." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "metrics", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From a5df741cd1904e6d05755d0e0379a65d40baadb4 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:29 +0200 Subject: [PATCH 17/20] Add PyFixest Chapter 16 notebook --- pyfixest/Chapter16UnconfDifficulties.ipynb | 340 +++++++++++++++++++++ 1 file changed, 340 insertions(+) create mode 100644 pyfixest/Chapter16UnconfDifficulties.ipynb diff --git a/pyfixest/Chapter16UnconfDifficulties.ipynb b/pyfixest/Chapter16UnconfDifficulties.ipynb new file mode 100644 index 0000000..cf8122a --- /dev/null +++ b/pyfixest/Chapter16UnconfDifficulties.ipynb @@ -0,0 +1,340 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 16: Difficulties of Unconfoundedness in Observational Studies for Causal Effects" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import scipy as sp\n", + "import pyfixest as pf\n", + "\n", + "# viz\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "font = {\"family\": \"IBM Plex Sans Condensed\", \"weight\": \"normal\", \"size\": 10}\n", + "plt.rc(\"font\", **font)\n", + "plt.rcParams[\"figure.figsize\"] = (10, 10)\n", + "%matplotlib inline\n", + "\n", + "from utils import *\n", + "\n", + "np.random.seed(42)\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "U1\n", + "\n", + "U1\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "U1->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Z\n", + "\n", + "Z\n", + "\n", + "\n", + "\n", + "U1->Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "U2\n", + "\n", + "U2\n", + "\n", + "\n", + "\n", + "U2->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Y\n", + "\n", + "Y\n", + "\n", + "\n", + "\n", + "U2->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n = int(1e6)\n", + "df, g = simulate(\n", + " U1=lambda: np.random.normal(size=n),\n", + " U2=lambda: np.random.normal(size=n),\n", + " X=lambda U1, U2: U1 + U2 + np.random.normal(size=n),\n", + " Z=lambda U1: U1 + np.random.normal(size=n),\n", + " Y=lambda U2: U2 + np.random.normal(size=n),\n", + ")\n", + "\n", + "g" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## M-bias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### continuous treatment\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pf.feols(\"Y ~ Z\", df).coef().loc[\"Z\"], pf.feols(\"Y ~ Z + X\", df).coef().loc[\"Z\"]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### binary treatment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[\"Z\"] = df.Z >= 0\n", + "pf.feols(\"Y ~ Z\", df).coef().loc[\"Z\"], pf.feols(\"Y ~ Z + X\", df).coef().loc[\"Z\"]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Z-bias" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "Z\n", + "\n", + "Z\n", + "\n", + "\n", + "\n", + "X->Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Y\n", + "\n", + "Y\n", + "\n", + "\n", + "\n", + "Z->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "U\n", + "\n", + "U\n", + "\n", + "\n", + "\n", + "U->Z\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "U->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n = int(1e6)\n", + "df, g = simulate(\n", + " U=lambda: np.random.normal(size=n),\n", + " X=lambda: np.random.normal(size=n),\n", + " Z=lambda X, U: X + U + np.random.normal(size=n),\n", + " Y=lambda U, Z: U + 0 * Z + np.random.normal(size=n),\n", + ")\n", + "\n", + "g" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pf.feols(\"Y ~ Z\", df).coef().loc[\"Z\"], pf.feols(\"Y ~ Z + X\", df).coef().loc[\"Z\"]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adjusted comparison is more biased." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### stronger association" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[\"Z\"] = 2 * df.X + df.U + np.random.normal(size=n)\n", + "pf.feols(\"Y ~ Z\", df).coef().loc[\"Z\"], pf.feols(\"Y ~ Z + X\", df).coef().loc[\"Z\"]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[\"Z\"] = 10 * df.X + df.U + np.random.normal(size=n)\n", + "pf.feols(\"Y ~ Z\", df).coef().loc[\"Z\"], pf.feols(\"Y ~ Z + X\", df).coef().loc[\"Z\"]\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "metrics", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From c418d3a7847d24ba15ec66a773ec50322c43b320 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:29 +0200 Subject: [PATCH 18/20] Add PyFixest Chapter 17 notebook --- pyfixest/Chapter17Evalue.ipynb | 2993 ++++++++++++++++++++++++++++++++ 1 file changed, 2993 insertions(+) create mode 100644 pyfixest/Chapter17Evalue.ipynb diff --git a/pyfixest/Chapter17Evalue.ipynb b/pyfixest/Chapter17Evalue.ipynb new file mode 100644 index 0000000..5910b2d --- /dev/null +++ b/pyfixest/Chapter17Evalue.ipynb @@ -0,0 +1,2993 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 17: E-Value : Evidence for Causation in Observational Studies with Unmeasured Confounding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import scipy as sp\n", + "import pyfixest as pf\n", + "\n", + "# viz\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "font = {\"family\": \"IBM Plex Sans Condensed\", \"weight\": \"normal\", \"size\": 10}\n", + "plt.rc(\"font\", **font)\n", + "plt.rcParams[\"figure.figsize\"] = (10, 10)\n", + "%matplotlib inline\n", + "\n", + "from utils import *\n", + "\n", + "np.random.seed(42)\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With observed conditional risk ration $\\text{RR}^{\\text{obs}}_{ZY \\mid X}$, we can calculate the E-value as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def evalue(rr):\n", + " return rr + np.sqrt(rr * (rr - 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10.7297803585806" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Analysis\n", + "p1 = 397 / (397 + 78557)\n", + "p0 = 51 / (51 + 108778)\n", + "\n", + "## Relative Risk\n", + "(rr := p1 / p0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Asymptotic Variance of Log of RR\n", + "logrr = np.log(p1 / p0)\n", + "se = np.sqrt(1 / 397 + 1 / 51 - 1 / (397 + 78557) - 1 / (51 + 108778))\n", + "upper = np.exp(logrr + 1.96 * se)\n", + "lower = np.exp(logrr - 1.96 * se)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.7297803585806\n", + "20.94733418446287\n" + ] + } + ], + "source": [ + "## point estimate\n", + "print(rr)\n", + "## e-value based on rr\n", + "print(evalue(rr))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8.017414334809697\n", + "15.518182180917343\n" + ] + } + ], + "source": [ + "## lower CI\n", + "print(lower)\n", + "## e-value based on lower CI\n", + "print(evalue(lower))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_30947/463092966.py:13: RuntimeWarning: divide by zero encountered in divide\n", + " y = RR * (RR - 1) / (x - RR) + RR\n", + "/tmp/ipykernel_30947/463092966.py:14: RuntimeWarning: divide by zero encountered in divide\n", + " y_L = RR_L * (RR_L - 1) / (x_L - RR_L) + RR_L\n" + ] + }, + { + "data": { + "image/png": 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cCSH2g4KGadCzR6yOoWXD1sNGixYtAAC3b9+mxekIMQMKGqZDzyDhFcuyePjwIR4+fGiyKZuGlg1b70bx8vLiFqW7fPmywNUQYvtYluXetyho8IueRcIrjUaDtWvXYu3atdBoNCa5D0PLxvPnz6FUKk1yH5bC0JVy4cIFgSshxPaJRCI4OTlxq4NS0OAPPZPE6ri6uqJRo0Zo27Yt8vPzhS7HpChsEGJehsBBQYNfNECUWB2GYZCYmCh0GWZBYYMQ02FZFiqVCjKZjFs0kpgGRTdCLJghbNy4cQMFBQUCV0OI7dDr9SgoKIBGo4FSqaTDApgYhQ1CLJifnx98fHyg1+tpJVFCeKLX66FUKrlZJ3q9nsKGiVHYIFbpvffeQ7169fDll18KXYpJMQxDXSmE8Khk0KBZJ+ZBzy6xSvn5+UhNTbWLg5RR2CCEHxQ0hEMDRAmvxGIx2rVrx/1uKvaysBdAYYMQPlDQEBaFDcIrsViMXr16mfx+7OX4KMD/wsbVq1e5kfOEkMqjoCE8eqaJVbKX46MAQL169eDh4QGNRkMriRJSDWq1mgsatI6GMOjZJrxiWRZPnz7F06dPTTq6255aNhiGQUxMDADg9OnTAldDiPWRyWQQi8UQiUSQy+UUNARAzzjhlUajwerVq7F69WqTLVcO2M/xUQzatGkDgMIGIdXBMAzkcjkFDQHRmA1ilXx8fBAaGgpfX1+7GMdAYYOQytPr9WAYxmhV0JLniXlR2CBWqVatWrh7967QZZiNoRvlzp07ePz4MTw8PASuiBDLZFgZ1DAIlAKGZaD2JEKsQJ06ddCgQQMAwJkzZwSuhhDLZAgaLMtCr9ejsLBQ6JLI/6OwQYiVoK4UQspXPGgARbNOHB0dBa6KGFDYIFbrnXfeQd26dfHVV18JXYpZUNggpGxlBQ3qQrEsFDaI1crPz0d6ejrS0tKELsUsDGHjzJkzdNAoQv4fBQ3rQANECa9EIhFatWrF/W5Kfn5+AIDs7GyT3o+liIqKgkwmQ25uLu7cucON4SDEXlHQsB7UskF4JZFI8NJLL+Gll16CRGLaLOvr6wsAyMrKMun9WAoHBwe0aNECAHWlEEJBw7pQ2CBWy95aNoD/daWcOnVK4EoIEZZIJOK+0FDQsHwUNgivWJZFfn4+8vPzTT6uwN5aNgCgffv2AIC///5b4EoIEZ6joyMcHBwoaFgBChuEVxqNBitWrMCKFStMulw58L+WjUePHpn8vixFx44dAQBXrlyBQqEQuBpChCeTyShoWAEKG8RqeXp6okGDBmjfvj2eP38udDlm4e/vj9DQUOj1eupKIXZFr9cjPz8fOp1O6FJINVDYIFZLJBLh9u3bOHHiBNzd3YUux2wMrRsnTpwQuBJCzMMwGFSv10OpVHKHiyfWg8IGIVaGwgaxJyVnndAB1awThQ1CrIwhbJw6dQpqtVrgaggxHZreajsobBCr9uGHHyIwMBAff/yx0KWYTaNGjeDh4YHCwkJcuHBB6HIIMQmdTkdBw4ZQ2CBWTaVSISMjw26WLAeKmpE7dOgAgLpSiG3S6XRQKpVc0BCLxRQ0rByFDcIrkUiEqKgoREVFmXy5csA+19oAgE6dOgGgsEFsT1lBQy6XU9CwcnRsFMIriUSCV155xWz3FxAQAADIzMw0231aguKDRFmWpTdiYjN0Oh0FDRtELRvEqgUGBgIA0tPTBa7EvKKjoyGXy/H48WMkJiYKXQ4hvHFwcIBMJqOgYWMobBBesSwLtVoNtVptlsOgG8JGVlYWtFqtye/PUjg4OHBLlx89elTgagjhl4ODAwUNG0Nhg/BKo9Fg+fLlWL58uVmWEPf29oZEIoFer7e7cRvdu3cHAPz1118CV0JI9el0ujK/KFDQsC00ZoNYNZFIhHbt2oFhGBQWFgpdjll169YNABAfHw+9Xm+WAbmE8Kn4YFC5XM4dxZXYHnplidU7duwYANjdAletWrVCrVq1kJubiytXrqB58+ZCl0RIpZWcdaJWqyls2DD6KkSIlZJKpejcuTMA6koh1qXkgl2GwaDEdlHYIDbDHANSLQ2N2yDWxhA0DGjWiX2gsEGs3rZt2xAYGIgxY8YIXYrZGcJGQkKCWQbkElITFDTsF4UNYvUkEgkyMjKQmpoqdClmFxUVBXd3dzx//hznz58XuhxCykVBw75R2CC8EolEiIiIQEREhNlmRxjW2sjIyDDL/VkSkUiErl27AqD1NojlYlkWSqWSO09Bw/5Q2CC8kkgkGDJkCIYMGWK2keXFw4ZerzfLfVoSQ1fKkSNHBK6EkLIxDANHR0cAFDTsFYUNYvV8fX0hEomg1WqNmmntRY8ePQAUHSfFHh8/sQ4SiQROTk4UNOwUhQ1i9aRSKXf017y8PIGrMb9GjRohKCgIKpUKCQkJQpdDCACU2cooFospaNgpiwgbcXFxaNKkCXx8fBAaGoo1a9Zw161YsQLh4eHw9/dHbGwskpOTBayUvIharcbSpUuxdOlSsy6yZehKscewwTAM+vTpAwA4cOCAwNUQUjQYND8/3+4W2iPlEzxs6HQ6XL58Gb///jsePHiAX375BTNmzMCTJ08QHx+Pb775BvHx8cjIyEC/fv0wduxYoUsmFqh169bo1KmT3a5ASGGDWIri3ZkqlYqmZBMAFhA2xGIxli1bhpCQEADAw4cPERoaChcXF/zyyy8YNWoU/P39wTAMZs6ciXPnzuHx48cCV00szZo1a3D48GGEhYUJXYogunfvDrFYjNu3b+PevXtCl0PslFarNZp1IpFI7PYLADEmeNgwGDt2LFxcXDBz5kzs27cPEokEycnJXF88UBRMPD09cefOnXL3o1KpkJeXZ3QixNa5ublxh5w/ePCgwNUQe1RW0HB0dKQxGgSABYWNjRs3QqFQYNmyZejTpw/y8/NRUFBQ6g+VYZgKR9wvX74cbm5u3Klu3bqmLp1YEHuc+mpAXSlEKBQ0yItYTNgAihYoGjRoEFxcXBAfHw8nJ6dSx7tgWRZOTk7l7mPBggVQKBTcKS0tzdRlEwtw8+ZNhIaGYtWqVUKXIhhD2Dhy5AgNzCNmQ0GDVIZFhI1Hjx4ZnReJRNDr9QgJCUF2djZ3uVarRU5ODurXr1/uvmQyGVxdXY1OxPZ5eHggIyMDeXl50Gq1QpcjiObNm8Pb2xvPnz/HyZMnhS6H2AEKGqSyBA8bf/31F2JiYrgWiCNHjiAtLQ3t2rXDoEGDsHnzZmRmZoJlWaxevRqtWrWCp6enwFWT8ohEIjRo0AANGjQw23LlAODp6cm1eCkUCrPdryURiUTo1asXAOCPP/4QuBpiD0QiERcsKGiQiggeNrp374758+cjNjYWfn5++M9//oOdO3fC09MT3bp1w8SJE9GlSxcEBARg//792Lhxo9AlkwpIJBKMGDECI0aMMOsodIZhUK9ePQDA06dPzXa/lubll18GAPz2228CV0LsgUgkgpOTE6RSKQUNUiGGLTkowsbk5eXBzc0NCoWCulRsXJ8+fXDw4EH0798fO3fuhIODg9AlmZ1CoYCXlxc0Gg1u3bqF8PBwoUsihFgxvj5DBW/ZIIQvhpYNe+1GAYqmwBqOArt3715hiyE2R6vVorCwUOgyiBWisEF4pVar8dFHH+Gjjz4y+4wI6kYp8q9//QsAhQ3CL8NgUI1GQ4GDVBmFDcI7jUYjyBLFERERqFevnt0PIO7fvz8A4O+//y4104uQ6ig568TGe9+JCVDYIDbDcOyczp07C12KoOrVq4eoqCjo9XqalUJqrKzprXK5XMCKiDWisEGIDRowYAAA6kohNUNBg/CFwgaxOVqt1u6PNGkYt3HgwAHqXyfVQkGD8InCBrEpmzZtwocffoi///5b6FIEFR0dDX9/f+Tn5+PIkSNCl0OsjEajMQoaUqmUggapEQobxKaIxWIAQEpKirCFCIxhGAwcOBAAsHPnToGrIdam+Ewyw4JdhNQEhQ3CK8NKnvXq1RNkNUF3d3cAwL1798x+35ZmyJAhAIA9e/bQgdlIlTg5OUEkElHQILwx33rSxC5IpVKMGTNGsPs3hI3k5GTBarAUHTt2hI+PDx48eIAjR46gb9++QpdErATDMHBycqLlxwlvqGWD2JQ6deoAAO7evStwJcITi8UYPHgwAGDHjh0CV0MsmVarLbV2BgUNwicKG8SmGMLGnTt3aOEh/K8rZffu3dSVQspkGAyqVCrpf4aYDIUNwiu1Wo3PPvsMn332mSAfboZuFIVCgdzcXLPfv6UxdKU8ffqUZqWQUoovPa7T6ex+yjgxHQobhHcFBQUoKCgQ5L4dHBzQsGFDvP7661CpVILUYEnEYjFeffVVANSVQoyVPMaJVCq1yyMlE/OgsEFszvDhw7Fhwwb4+/sLXYpFKD4rhb65EqDsoEGzTogpUdggxMZ16tQJPj4+ePLkCQ4ePCh0OURgFDSIEChsEJukVqvx8OFDocuwCGKxGMOGDQMA/PTTTwJXQ4REQYMIhcIGsTm3bt2Cm5sbt4ImAf79738DAH799Vfk5eUJXA0RglarpaBBBENhg9gcFxcX6PV63LlzR+hSLEZ0dDQaNWqEwsJC7Nq1S+hyiADEYjEkkqJ1HB0cHChoELOisEF4xTAM/P394e/vL9iiQIa1Nh4+fIhnz54JUoOlYRiGa9348ccfBa6GCIFhGDg6OsLR0REymUzocoidobBBeCWVSjFhwgRMmDABUqlUkBocHR3h6ekJgFYSLW7EiBEAgPj4eKSlpQlcDTGHslYFFer/ktg3ChvEJtWvXx8AkJSUJHAlliM4OBidO3cGy7LYunWr0OUQE1Or1cjPz4derxe6FEIobBDbFBYWBoDCRknFu1JoaWrbpVaroVKpwLIsCgoK6LUmgqOwQXil0WiwatUqrFq1StAFpBo2bAgASExMFKwGSzR48GDIZDJcv34d58+fF7ocYgKGoGEglUrpoGpEcBQ2CK9YloVCoYBCoRD021Tbtm0xcuRIdO/eXbAaLFHt2rUxaNAgAMCGDRsErobwrWTQcHBwoMGgxCJQ2CA2qUuXLvjpp58wduxYoUuxOBMmTAAAbNmyBfn5+QJXQ/hCQYNYMgobhNiZLl26oH79+nj27BkdnM1GUNAglo7CBrFZWq0Wd+7cwaNHj4QuxaKIRCKMHz8eAPDdd98JXA2pKQoaxBpQ2CA2a+jQoWjQoAG2b98udCkWZ/To0RCLxTh58iRu3LghdDmkBoqPjaKgQSwVhQ1isxo0aACAZqSUxc/PDy+//DIAGihq7WQyGRwcHChoEItGYYPwimEYeHl5wcvLS/Dpdo0bNwYA3Lx5U9A6LJVhoOgPP/xg1AxPrI9MJqOgQSyaROgCiG2RSqV48803hS4DANCoUSMAFDbK07t3bwQGBiI9PR1xcXHcgl/EsqnVaojFYojFYqFLIaTSqGWD2CxD2MjIyKADspVBIpFg8uTJAIA1a9YIXA2pDJVKBZVKhYKCAuh0OqHLIaTSKGwQm1W7dm34+voCoNaN8kyYMAEODg44c+YMzpw5I3Q5pAIqlQpqtZo7T2GDWBMKG4RXGo0GX3/9Nb7++mtBlys3MLRu0CDRsnl7e2PYsGEAgC+//FLgakh5SgYNw6BQQqwFhQ3CK5ZlkZOTg5ycHIs4+NPIkSOxaNEiREVFCV2KxZo+fToAYPv27Xjw4IHA1ZCSKGgQW0Bhg9i08ePHY+nSpRQ2KtCqVSu0adMGGo0G3377rdDlkGIoaBBbQWGDEMK1bqxbt84iur8IBQ1iWyhsEJuXnp6OP/74g2akVGDIkCHw8fFBZmYmrbhqAXQ6HQUNYlMobBCb16lTJ7z00ku4cOGC0KVYLAcHB8yYMQMA8Nlnn1nEeBt7JhaL4ejoCICCBrENFDaIzWvWrBkA4OrVqwJXYtmmTJkCZ2dnXLlyBYcOHRK6HLsnlUrh7OxMQYPYBAobhFcMw8DNzQ1ubm6CL1duQGGjctzd3bklzD/77DOBq7E/Za2bIRLRWzSxDfSXTHgllUoxa9YszJo1C1KpVOhyAFDYqIrZs2dDLBbjyJEjOH/+vNDl2A3DqqA0OJfYKgobxOYZwsa1a9doLMILBAUFYfjw4QCodcNcis86KSwshF6vF7giQvhHYYPYvPDwcEilUjx79gz3798XuhyLN2/ePADAjh07kJycLHA1tq3k9FZHR0fqOiE2if6qCa80Gg2+++47fPfddxbTJCyVSrnDzVNXyotFRUWhT58+0Ov1WL58udDl2KzCwsJSQcNSuh4J4RuFDcIrlmWRmZmJzMxMi+qyePvtt7F582a0bNlS6FKswnvvvQcA2LRpE1JSUoQtxgYVFhYahXEKGsTWUdggdmH48OH497//DX9/f6FLsQrt27dHbGwstFotPvroI6HLsSkUNIg9spiwcfnyZXTp0gV+fn4IDQ3Fpk2bABRNB5NIJPD19eVOQ4cOFbZYQuzA4sWLAQAbN26ksS48UalUFDSIXbKIsFFYWIh+/fphwYIFyMrKQlxcHCZPnowHDx7gyZMnCAwMRHZ2NneKi4sTumRihY4fP45Vq1bhyZMnQpdiFTp27IgePXpAq9XS2A2eSCQS7ncKGsSeWETYcHR0xOnTp9GnTx8ARUeh9PDwwJ07d5CbmwtPT0+BKyS24I033sDs2bNx9uxZoUuxGobWje+//x6pqakCV2P9xGIxnJycKGgQu2MRYQMAAgMDud/z8vLw9OlThIaGcmFj0qRJ8PPzQ9euXXHt2rVy96NSqZCXl2d0IgQAoqOjAYCOkVIFnTp1Qrdu3aDRaPDhhx8KXY5NEIvFFDSI3bGYsFHc7NmzMW7cOPj5+cHZ2Rm+vr5YuHAh0tLS0KNHDwwbNqzc2y5fvpxbLtvNzQ1169Y1Y+UEAJycnODk5CR0GaUYZqJQ2KiaDz74AEBR68bNmzcFrsa6FBYWQqVSCV0GIYJjWAuan8iyLGbMmIH09HTs2LHDqH/T4Pnz53BxccGjR4/g4eFR6nqVSmX0z52Xl4e6detCoVDA1dXVpPUTYanVam5swYIFC0odwOrw4cPo2bMnQkNDcffuXSFKtFoDBgzA3r17MWjQIOzatUvocqxC8VknDg4OkMlkAldESNXl5eXBzc2txp+hFtOyodPpMG7cOGRmZiIuLs4oaBRf+MZwcK/ymiFlMhlcXV2NToQA/+tGSU5OpkGiVfTRRx9BJBLhl19+wenTp4Uux+KVnN5Kq4ISe2cR/wFarRYjRoyAUqnE9u3bjYLEnj170KdPH+7DYd26dYiJiaEQQaqsTp06CA4OBgBcvHhR2GKsTJMmTTBq1CgAwFtvvWVRC7ZZGlpHg5DSLCJspKenIy4uDkeOHEFgYCC3nsY777yD/v37o1OnToiKioKfnx8OHTqELVu2CF0yKYdGo8GmTZuwadMmi1muvDhD6wYd0bTqli5dCplMhoSEBBw4cEDociwSBQ1CylZ6UIQAgoODK/ymtHTpUixdutSMFZHqYlmWWwDKEr/9vv3225g/fz6aN28udClWJygoCNOmTcPKlSvx1ltvoVevXhCLxUKXZTFKBg25XF7muDNC7JFFtGwQYi6tW7dG27Zt4ejoKHQpVmnBggVwd3fH1atXsX79eqHLsRhKpZKCBiEVoLBBCKk0Dw8PrpVx4cKFNNAWRS14Op2OO09Bg5DSKGwQu7Nv3z68+eab+Ouvv4QuxSpNnjwZERERePz4MXVvomiGnJOTE0QiEQUNQspBYYPYnd9++w1r166lQY7VJJVKsWrVKgDAmjVrcOPGDWELsgAikQhOTk4UNAgpB4UNYnfatm0LADh16pTAlVivnj17YsCAAdDpdJg9e7ZFDgY2JbVaXeoxG9YAIoSURmGD8E4qlVr0dD9D2Dh37pxFTs+1FitWrICDgwMOHTqEPXv2CF2OWbAsC6VSCZVKhcLCQrsLWYRUF4UNwisHBwe88847eOedd0otF24pwsPDUbt2bSiVSly5ckXocqxWWFgY5s2bBwCYPn06nj17JnBFpsWyLAoLC6HVagEULUao1+sFrooQ60Bhg9gdkUhEXSk8effddxEaGoqMjAy89957QpdjMiWDBlA064TWGSGkcihsELtkCBv//POPwJVYN7lcjrVr1wIAvvzyS5tcmbW8oEGDQQmpPAobhFdarRZbt27F1q1bjd6cLU27du0AAFlZWQJXYv169eqFESNGQK/XY+LEiRb9ulcVBQ1C+EFhg/BKr9cjKSkJSUlJFt2f3blzZ2RkZODIkSNCl2ITPv/8c9SuXRsXLlzAmjVrhC6HFxQ0COEPhQ1ilxwdHeHv7y90GTbDx8cHn376KYCilUXv3LkjcEU1p1arKWgQwhMKG8Tu0fRFfowbNw7du3dHQUEBxo4da9EtW5Xh4ODADQCloEFIzVDYIHbr7t276NOnD2JiYoQuxSaIRCJs2LABtWrVwokTJ7B69WqhS6oRhmEgl8tpZVBCeEBhg9itOnXq4NChQzh37hyys7OFLscmBAcHY+XKlQCAd955B7du3RK4ospjWbbMVUFpeishNUdhg9gtd3d3REZGAgCOHTsmcDW2Y8KECejZsycKCwsxZswYoyOiWirDyqAFBQXUrUaICVDYIHatS5cuAICEhASBK7EdDMNgw4YNcHV1xalTp/Dxxx8LXVKFDEFDp9NBr9dDqVQKXRIhNofCBuGVg4MDFi9ejMWLF1vscuXFde7cGQC1bPCtbt26+OKLLwAAixcvttjF04oHDQOZTCZgRYTYJgobxK4Zwsa1a9fw4MEDgauxLaNGjcLw4cOh0+kwYsQIKBQKoUsyUlbQcHJyojEahJgAhQ1i17y8vNC8eXMAoAW+eMYwDNauXYuQkBCkpKRg0qRJFjMegoIGIeZFYYPwSqvVYseOHdixY4fVLFvdv39/9OjRA7Vr1xa6FJvj5uaGrVu3QiwWY/v27di0aZPQJVHQIEQAFDYIr/R6PW7cuIEbN25YzaJO77//Pg4fPox+/foJXYpNatu2LT744AMAwLRp03D9+nXBaqGgQYgwKGwQQkzuP//5D2JjY1FQUIBBgwYhLy9PkDoYhgHDMNx5ChqEmAeFDUL+X3Z2tlUtQmVNxGIxtm7disDAQNy+fRtjx44VbPyGXC6HVCqloEGIGVHYIATAhg0b4Ofnhzlz5ghdis3y8vLCzp07IZVK8csvv2DFihWC1eLo6EhBgxAzorBBCICWLVsCKFrcS61WC1yN7WrTpg23/sbbb7+No0ePmvT+DGM0rGX8ECG2isIGIQAiIyPh7e2N/Px8nDx5UuhybNqkSZMwevRo6PV6DB06FPfu3TPJ/RiChlarRUFBAQUOQgREYYMQFB2xtHfv3gCAffv2CVyNbTOsvxEdHY1Hjx6hf//+vA8YLWvWiaWs8UGIPaKwQXgllUqxYMECLFiwAFKpVOhyquTll18GAPz+++8CV2L75HI59u7dCz8/P1y/fh3Dhg3jbV2WkkHDcKh4GqNBiHAobBBeMQwDBwcHODg4GE0xtAa9e/eGRCLBzZs3cefOHaHLsXkBAQHYu3cvHB0dsX//fsybN6/G+6SgQYhlorBByP9zc3PjjgL722+/CVyNfWjVqhU2b94MAFi9ejXWrVtX7X1R0CDEclHYILzSarXYs2cP9uzZYzXLlRc3d+5c/Pjjjxg1apTQpdiNIUOG4P333wcATJ06tVpBz1aDxtmzZ9G5c2cUFBQIXYrFS0hIQGxsLM0ms1AUNgiv9Ho9Ll++jMuXL1vl6P++ffvi9ddfh4eHh9Cl2JV3333XaIZKVWcEabVamwsaGo0GY8aMwaZNm+Dk5IQnT55gxIgRCAwMREBAAGbMmGE06PXQoUOIjo6Gr68vWrRogYMHD5a77zVr1iAsLAz+/v7o0qWL0RLy33//PZydneHr68uddu3a9cJ6L1++jC5dusDPzw+hoaGljoOzYsUKhIeHw9/fH7GxsUhOTi5zP0qlEm+++SaCgoIQGBiI119/HU+ePOGuHzVqFNzd3Y3qy8rKQpcuXdCpUyd88sknL6yVmB+FDUKI4BiGwXfffYd+/fqhsLAQL7/8MhITEyt9e6lUCplMBoZhbGZl0J9//hkdOnRAaGgoAGDs2LEIDQ1Famoqrly5gt9++w27d+8GACgUCowYMQJr1qxBdnY2vv76a4wcORIKhaLUfo8cOYKVK1ciPj4emZmZmDRpEgYPHsyFtdzcXMyfPx/Z2dnc6dVXX62w1sLCQvTr1w8LFixAVlYW4uLiMHnyZDx48AAAEB8fj2+++Qbx8fHIyMhAv379MHbs2DL39dFHHyE9PR23bt3C/fv34eLigrfeeou7Pjc3F7t37zaqz8/PDwAwb948bNiwAYWFhVV8tompUdggpISMjAx8+umnWLZsmdCl2BWpVIq4uDi0adMGT548Qe/evZGenl7p2zs4OMDZ2RkikW28re3duxfDhg3jzn/33XdYtGgRRCIRPDw80L59ey6QHTlyBFFRUWjfvj0AoF27doiOjsaRI0dK7ffAgQN45ZVXEBgYCAAYMWIEcnJyuEHRubm58PT0rFKtjo6OOH36NPr06QOgaCyOh4cHt89ffvkFo0aNgr+/PxiGwcyZM3Hu3Dk8fvy4zPrGjRvHtU6V7FqrqD5nZ2d06tTJ5IvFkaqzjf9KQnh0//59vPXWW/jkk0+gUqmELseuODs74/fff0fDhg2RlpaG3r17IycnBzqdDvHx8di2bRvi4+Oh1WrLHBNkbTOgKnLhwgVERkZy5728vODg4MCdv3fvHsLCwgAAycnJ8PX1Nbq9n58fkpKSSu1Xo9GU6uJkWRYPHz4EUPRh/uzZM7Rq1QqBgYGYOnVqpf4PDOEFAPLy8vD06VOuVaZkfWKxGJ6enmXO+ipZX/HaDPWdOnUKYWFhCA8Px9dff210+8jISJw7d+6F9RLzqnbYOHv2LJ91EGIx2rZti4CAADx79gyHDh0Suhy74+npiYMHDyIgIAA3btxAmzZtEBQUhG7dumHEiBHo3r07GjdujF27dlnlIOTKys7OLvcb/I8//gi1Wo2BAwcCAAoKCkoFLYZhyhxY2r59e/zyyy+4c+cO9Ho9tmzZgufPn8Pf3x8AEBERAYZh8M8//+DChQs4c+YMvvzyyyrVPnv2bIwbN47r3qhqfd988w0UCgUKCwuxceNGrjYA6NixI7y8vHDnzh3ExcVhwYIFRuHCy8uL674hlqPaYWPixIlYu3Ytn7UQYhFEIhHXR71z506Bq7FP9erVw5EjR1C7dm3cu3cPmZmZAP43+PPBgweYMGECdu3aZXcrg27btg3Lly/H77//zrV0ODk5lXoeWJaFk5NTqdsPGTIE//73vxEbG4tGjRrh5s2bqF+/PurXrw8AmDFjBt5++21IpVJ4e3tj9OjRSEhIqFRtLMti+vTpyM3Nxeeff85dXpX6PvjgA9SuXRuNGjVC586d8fz5c/Tv35+7fv369RgwYAAAoHnz5ujZs2el6yPCqXbYOHHiBE6cOIGRI0fStCxic4YMGQIA+PXXX6krRSBhYWGQyWRGlxU/WivLsvjPf/5jlbOeKsPHxwePHj0yumz9+vX48MMPcfjwYaNuiZCQEGRnZxttm5mZyXWzFMcwDD766COkpKTg9u3bSExMxPz587nrS04dZRimUqsB63Q6jBs3DpmZmYiLi4NEIim3Pq1Wi5ycHC7gFOfh4YGff/4ZWVlZOHr0KP766y/Mnj270vXl5OTAx8fnhfUS86p22HB2dsaWLVvQvn17dOzYEXv37kVKSgqPpRFrJJVKMW/ePMybN8/qlisvrn379vDz84NCoShzkB0xvePHj5dqDler1WBZFnq9HgUFBUhNTcXx48cFqtC0oqOjceXKFe78F198gdWrV+Ovv/4y6lYAgNjYWFy6dImbMmzoAunRowcAYPr06fj4449L3cfHH38MtVqNMWPGcJcNHToUX3zxBViWRV5eHrZu3YqePXtWWKtWq8WIESOgVCqxffv2Uv/7gwYNwubNm5GZmQmWZbF69Wq0atUKnp6eUKlU6NGjR6kuS7VajZEjR2LKlClo0KABgKJpsU2bNsWJEycAAElJSYiPj0f37t252125cgWtWrWqsF5ifjUaIHrixAn8888/ePbsGX788Uf0798fHh4eaNu2LV/1ESvDMAycnZ3h7Oxs1YP1inel7NixQ+Bq7FNWVhaAor8pR0dHAEXfngsKCoyO4mrYztb861//wvbt27nzn3/+OdLS0hAVFcWtL9G1a1cARavfbt26FVOnToWvry+mTJmCLVu2oHbt2gCAu3fvIjU1ldtXYWEhJk2ahJMnT2Lr1q1G/6urVq3CwYMH4evri2bNmqFjx46YOHEiAGDjxo3w9vYuNaU2PT0dcXFxOHLkCAIDA7n63nnnHQBAt27dMHHiRHTp0gUBAQHYv38/Nm7cCKBoQGhiYqJRK056ejp69OiBFi1aYO7cudzlcrkc69atw6xZs+Dn54f+/ftj1apVaNq0KYCisSHHjx9Ht27davr0E54xbDU7PJs0aQJvb29MmzYNr7zyCte0qdVqkZSUhMaNG/NaaHXl5eXBzc0NCoUCrq6uQpdDTEitVmP58uUAgAULFhiN3K+OhIQE9O3bFyNHjsR3333HR4mkCgzfWJ2cnCASibigUdLRo0e5D11botFoEBUVhX379iEkJETocgAAly5dQteuXfHgwYNSXVyWwLAS7aJFiwSuxHbw9RkqefEmZfv555/RrFmz0juUSCwmaBDz02q13MqFhgObWauOHTsiJycHzs7OQpdilzp16oT69etzXSkMw4BhGKOBhv7+/ujUqZNQJZqUVCrFpk2bMGbMGOzfv7/MwZTmtnPnTqxdu9Yig8axY8eQkJCAP/74Q+hSSBmq/UkwZMgQREZGolmzZtyprME+xL7o9XpuGtqL+nktnVgspqAhILFYjOXLl+ONN97gjn1SVkNsZmYm6tatK0CFphcTE2NRMy0+/PBDoUsoV+fOnWl8lQWr9piNQ4cOYcyYMZDJZNi1axcGDx6M4OBgLF261KbnvhP7dP36deTl5Qldht0ZPHgwvv/+e7i7uxvNOvH19YWnpycyMzPRvn17o2N7EEIsT7XDRlBQEPr164f//Oc/+PHHH3Hx4kVcuHABWVlZ+M9//sNnjYQIasSIEWjatCl+/vlnoUuxeWW1XAwePBj37t3D0aNHsXXrVhw9ehTp6ek4f/48GjZsiPT0dHTs2NFmZ6UQYgt4Xa68Tp06+Oqrr7B//34+d0uIoJo3bw4A2Lx5s7CF2DjDdNayDhEuFovRtWtXDB8+HF27doVYLEZQUBD+/vtvtG/fHk+fPkXPnj1pETZCLFS1w8YPP/yA8+fPlzq6nl6vpyPuEZsycuRIMAyDv//+u9zDYpOa0ev1UCqV0Ov1UKlU0Gg0lbqdh4cHDh8+jFdeeQUqlYpbI4IQYlmqHTZOnz6N2bNno27duggPD8err76K2bNno1u3bnj55ZervL/Lly+jS5cu8PPzQ2hoKDZt2sRdt2LFCoSHh8Pf3x+xsbH0hk/MKiAggFsc6ccffxS4GttTPGgARbNOqnKIeLlcjp07d+LNN98Ey7KYOXMmpk2bVunAQggxvWqHja+//hrHjh1DTk4O4uPjMXnyZDRs2BDvvPNOlQ/aU1hYiH79+mHBggXIyspCXFwcJk+ejAcPHiA+Ph7ffPMN4uPjkZGRgX79+mHs2LHVLZuQahk9ejSAokWNdDqdwNXYjrKChmFdjaoQi8VYs2YNPv74YzAMg6+++gp9+/ZFbm6uKcomhFRRtRf14lt6errRIYoDAgIQFxeH7du3w8vLC++99x6AohUEXV1dkZqaCg8Pj1L7UalURseyyMvLQ926dWlRLzNhWZZbXdDNzc2sq4jyvahXcUqlEv7+/nj69Cn++OMP9O3bl7d92yu+gkZJv/76K0aOHIn8/HyEhYXht99+Q6NGjfgomRC7w9eiXlX6r75//z4OHTpU7vLAhiMzVkfxoJGXl4enT58iNDQUycnJRgccEovF8PT0xJ07d8rcz/Lly+Hm5sadbHX+vaViGAa1a9dG7dq1rXq58pLkcjlGjRoFADQrhQeGwaCGoCESiXgJGgAwYMAAnDx5EvXq1cOdO3fQtm1bHDhwoMb7JYRUX6X/s7dt24awsDD06dMH9evX5/qu79+/j48//hgxMTEICgripajZs2dj3Lhx8PPzQ0FBQakPLYZhyj3S7IIFC6BQKLhTWloaLzURMn36dOzatQvr168XuhSrZggahkZVkUgEuVzOS9AwiIyMxJkzZ9CxY0coFAr069cPH3zwgc0eIZYQS1fp/+4PPvgA06dPx9WrV9GzZ09MmTIFCxcuRP369bFp0ya0adMGv/zyS42KYVkW06dPR25uLj7//HMAgJOTU6m59yzLlrt0r0wmg6urq9GJmI9Op8OhQ4dw6NAhmxvbEBYWhkGDBln10WwtQfH/Z1MEDQNvb28cPnwYEydOBMuyWLRoEV566SU8fvyY9/sihFSs0suV3717FzNnzkS9evXw1VdfISgoCP/88w+uXr3Ky7FQdDodJkyYAIVCgbi4OO6YGiEhIcjOzua202q1yMnJoaXRLZROp8M///wDANx6CLZIp9OBYRiTfEjaOrFYDLlcDpVKBUdHR5M+hzKZDN988w3at2+PKVOm4MCBA4iOjsaOHTsQExNjsvslhBir9H+5RqOBXC4HUDS+Qi6XY8WKFbwEDa1WixEjRkCpVGL79u1G3xwHDRqEzZs3IzMzEyzLYvXq1WjVqhU8PT1rfL+EVMeqVasQEhKCffv2CV2K1RKLxbyN0aiM0aNH49SpU2jQoAFSU1PRsWNHrFmzpswVSwkh/KvSf/rWrVtx8+bNohuKRHB3d+eliPT0dMTFxeHIkSMIDAyEr68vfH198c4776Bbt26YOHEiunTpgoCAAOzfvx8bN27k5X4JqY7MzEykpaVh1apVQpdiFQwLdQktMjISZ8+exaBBg6DRaDB9+nQMGDAAOTk5QpdGiM2r9NTXzp074/Lly3j+/Dnc3d2hUCgwdepUtG/fHk2bNkV4eLhFHk6cr2k7pHJMOf3UUu47NTUVoaGh0Ol0uHz5MiIjI01yP7ag+GBQqVQKR0dHoUsCy7L48ssvMX/+fKjVavj6+uKHH35Ar169hC6NEItj9qmvx44dg0KhwM2bN7FmzRrMnj0bV69exeTJk9G0aVM4OzvTmy6xC0FBQXj11VcBgFo3KlBy1olOp7OIbguGYTBjxgycPXsWERERyM7ORu/evTFnzhyLaIEhxBZVucO0QYMGGDZsGD799FMcOXIEubm5uHv3Ln766adqLVNOiDWaNWsWAGDLli14+PChsMVYoLKmtzo5OVnU2iuRkZE4d+4cpk6dCgD473//i5iYGFy6dEnYwgixQbws6hUSEoIhQ4Zg2rRpvBZHiKVq27YtYmJioFarsW7dOqHLsSjWEDQM5HI51qxZg99++w2enp64cuUKWrdujcWLF5d59FlCSPXwtqhXmzZteFvUi1gvqVSKKVOmYMqUKTa9HgXDMFzrxldffUUfTP/PmoJGcS+//DKuXbuGQYMGQavV4v3330erVq1w4cIFoUsjxCbwtqhXTExMjRf1ItaPYRh4e3vD29vb4j9gamrw4MF48803cfDgQbMOhLVU1ho0DHx8fLBz505s374dnp6euHr1KmJiYvDuu+/SWA5CaqjSs1FkMhlu376NevXqIT09HUFBQejatSu++uorXtbaMBWajWI/hJwJQ4CCggJu1VhrCxolPXz4ENOmTcOOHTsAAOHh4fj666/Ro0cPgSsjxLzMPhvFlIt6Eduh0+kQHx+P+Ph4m1uu/EU0Go3QJQjKsOy4tQcNoGip87i4OOzYsQO+vr64ffs2YmNj8frrr+PBgwdCl0eI1bGIRb2I7dDpdEhISEBCQoLdhI1Hjx5h4sSJiIqKglarFbocwRgOEW/tQaO4wYMHIzExEVOnTgXDMNiyZQsaNmyIdevW0UHdCKmCSoeNjh07YvHixWjSpAk8PT1RWFiI1atXIy4uDjdu3LDrN1li3+RyOXbv3o3ExES7Ovy8Xq8vtW4GwzA2EzQMateujTVr1uD06dOIjo6GQqHAlClT0LZtW+44QISQitGiXoTUkLOzM+bMmQMAeP/99+2iO8UwGFSpVFrEQl3m0Lp1a5w5cwZffPEFXFxccPbsWbRv3x4jR45EWlqa0OURYtFoUS9CeDBt2jR4eXkhKSkJmzZtErock9LpdNysE51OZ1czNcRiMaZPn47bt2/jjTfeAMMw2Lp1Kxo2bIglS5YgPz9f6BIJsUi8HHLRsKjXRx99xMfuCLE6Li4uWLhwIQBg6dKlUCqVAldkGjqdzqg1QyQSQSaTCVyV+fn6+mLDhg04d+4cOnXqBKVSiaVLl6Jhw4b48ccf7Wa8EiGVZZ7jOxNiByZPnoy6desiIyMDX331ldDl8K5k0DAcJt7WxmhURXR0NBISErBjxw4EBwcjIyMDo0aNQosWLfD777/bTRcTIS9CYYMQnshkMixZsgQAsHbtWpv6dltW0JDL5XYdNAwYhuFmrSxfvhxubm64evUq+vfvj44dO+L48eNCl0iI4ChsEF5JJBKMHz8e48ePh0QiEbocsxs1ahSWL1+OM2fOQCwWC10OLyhoVI6joyPefvttJCcn46233oKjoyNOnjyJzp0746WXXqIDvBG7RmGD8EokEiEgIAABAQEQiezvz0sikeDtt9+Gh4eH0KXwQq/XU9Coojp16uDjjz/G3bt3MXnyZIjFYvzxxx9o0aIFBg4cSMdbIXbJ/j4NCDETlmVx6tQpocuoEYZhuNBIQaNq/P39sXbtWiQmJmLYsGFgGAZ79uxBy5Yt0b9/f5w5c0boEgkxGwobhFc6nQ5///03/v77b5sas1BVOp0Offr0Qbt27RAfHy90OdXGMAzkcjkcHBwoaFRTgwYNsG3bNly/fh0jR46ESCTC77//jjZt2qBv3760MBixCxQ2CK90Oh0OHz6Mw4cP23XYEIvFCAsLAwDMnDnTqp8LhmEgk8koaNRQ48aN8dNPP+HmzZsYM2YMxGIxDhw4gPbt26Nr1674/fffaQl0YrMobBBiIu+//z7c3d1x5coVfP3110KXUyk6nQ75+fn0oWdCDRo0wMaNG3H79m1uIHVCQgL69++Ppk2bYsOGDSgsLBS6TEJ4RWGDEBPx8PDAsmXLAAALFy5ERkaGwBVVzLAyqGEpcgocphUaGorvvvsO9+7dw/z58+Hq6orExESMHz8ewcHB+Oijj5Cbmyt0mYTwgsIGISY0adIktG3bFs+ePcP06dOFLqdchqBhIBKJqNvETAIDA/Hpp58iNTUVK1asQGBgIB48eICFCxeibt26mDJlCq5duyZ0mYTUCIUNQkxIJBLh22+/hUQiwe7du/Hrr78KXVIpJYMGzToRhpubG+bOnYvk5GT8+OOPiIqKQkFBAdatW4dmzZqha9eu2Llzp10c6I/YHgobhJhYs2bNMG/ePDRs2BCenp5Cl2OEgoblkUqleP3113Hx4kX89ddfePXVVyEWi5GQkIAhQ4YgJCQEH3zwAbKzs4UulZBKo7BBiBksXrwYly9fRocOHYQuhUNBw7IxDINu3bph586dSElJwbvvvgtvb29kZGRg0aJFCAoKwtChQ3Ho0CEaX0MsHoUNwiuJRILRo0dj9OjRdrlceXkcHR2Njo4q9GHZKWhYl8DAQHzwwQdITU3Fli1b0K5dO2g0GuzYsQO9e/dGSEgIlixZgvv37wtdKiFlorBBeCUSiRAcHIzg4GC7XK78RXQ6HVauXIlGjRrh8ePHgtWh1Wq53yloWA+ZTIYRI0bg5MmTuHjxIqZNm4batWsjNTUVS5cuRUhICHr37o0dO3YIHmgJKY4+DQgxI7VajfXr1yMlJQVTp04VrA6ZTAapVEpBw4o1b94cX375JTIzM7FlyxZ0794dLMvi0KFDGDp0KPz9/fHmm2/i5MmTdKh7IjgKG4RXOp0OZ86cwZkzZ6x61UxTkcvl+PHHHyEWi7F9+3Zs3bpVsFocHR0paNgAuVyOESNG4MiRI7hz5w4WLlwIf39/5ObmYu3atejQoQPCwsKwaNEi3Lp1S+hyiZ2isEF4pdPpsH//fuzfv5/CRjlatWqF9957D0DROhy3b982+X1qtdoyXw8KGralfv36+PDDD3H//n0cPHgQo0aNgrOzM5KTk/HBBx+gUaNGaN26NVavXo0HDx4IXS6xIxQ2CBHAwoUL0aVLFzx//hxDhw416fLUWq0WSqUSBQUFFADthEQiQa9evfDDDz/gwYMH2Lp1K1566SWIxWKcO3cOs2bNgr+/P7p3746vv/6aptESk6OwQYgAJBIJtm7dCi8vL1y+fBmzZ882yf0YgoaBWq02yf0Qy+Xs7Izhw4fj999/R2ZmJr788ku0adMGer0eR48exdSpU+Hv74+uXbtizZo1yMrKErpkYoMobBAiEH9/f/z0009wcHBAWFgY74P4SgYNiUQCR0dHXu+DWBdvb29MmzYNp06dwr179/DZZ5+hTZs2YFkWCQkJmD59OgICAtCpUyd88cUXSEtLE7pkYiMobBAioF69eiE5ORlz587ldfxEeUGDxmgQg+DgYMybNw+nTp3C/fv38fnnn6Ndu3ZgWRYnTpzAzJkzERQUhBYtWmDx4sU4f/48zWoh1UZhgxCBBQQEcL8/ffoUjx49qtH+KGiQqgoKCsLs2bNx8uRJpKamYtWqVejYsSNEIhEuXbqE999/H61atULdunUxefJk/PHHHyYdZ0RsD4UNQizErVu30KZNGwwePLjaB9uioEFqqm7dupg5cyaOHz+OBw8e4IcffsCrr74KZ2dnZGRk4JtvvsFLL70ET09PDBw4EOvXr6fuFvJCFDYIryQSCYYPH47hw4fTcuVVpNPpkJWVhYSEBMyYMaPKTdZ6vd4mg8bZs2fRuXNno+XVSdkSEhIQGxvL20BgT09PjBo1Cjt37sSjR4+wf/9+TJkyBQEBAcjPz8eePXswYcIEBAUFoUmTJpg7dy4OHTpErR6kFAobhFcikQjh4eEIDw+n5cqrKCIiAlu3bgXDMFi3bh0+//zzKt1eJBJxA0BtJWhoNBqMGTMGmzZtgpOTEwBg3bp1cHd3R2BgYKntO3fuDE9PT/j6+sLX17fMbQyCgoLg4+PDbduuXTvuuvv37+Pll19GQEAA6tati+XLl1e63rFjx8LV1RWvv/660XU6nQ4SiYS7P19fXwwdOrTM/SQnJ0MqlRptO3fuXO76o0ePonXr1vDz80Pjxo3xxx9/AAC6dOmCTp064ZNPPqlUvVXh6OiIPn364Ouvv0ZaWhouXLiApUuXol27dhCJRLhx4wY+//xz9O7dG3Xq1EHfvn2xevVq3Lp1i8Z6EIC1cQqFggXAKhQKoUshJqZSqdglS5awS5YsYVUqldDlVNuKFStYACwAdtu2bVW+vUajMUFVwti8eTM7YcIE7vy8efPY7t27s7t372YDAgJKbd+kSRP23r17ldq3q6srq9PpyrwuKiqK/eabb1iWZdl79+6xrq6u7Pnz5yvcX15eHtutWzd27ty57HvvvceOHDnS6PqcnBy2Xr16lart7NmzbJcuXcq8LjMzk/Xy8uLq2bVrF1u7dm1Wq9WyLMuyz58/Z+vVq8cqlcpK3RcfcnNz2bi4OPaNN95gAwICuL9fwykoKIgdO3Ysu3nzZjY9Pd1sdZGa4+szlL56El7pdDpcunQJly5dogWkqmnOnDmYOXMmAGD06NGIj48vd1u2jG+MttR9tXfvXgwbNow736tXLxw4cAC1a9cuc/vc3Fx4enq+cL8ajQYODg7ltr7t27cPEydOBFA0a6Np06ZITEyscJ9OTk546623sGLFijL3W9naXrStn58fLly4gOjoaABA//798fTpU25hLmdnZ3Tq1AlHjx6t1H3xwd3dHUOGDMGGDRuQlpaGq1ev4rPPPkNsbCwcHByQmpqKjRs3YtSoUQgMDETDhg0xZcoUxMXF4eHDh2arkwiHwgbhlU6nw6+//opff/2VwkY1MQyDzz//HIMHD4Zarca8efPAsix0Oh3i4+Oxbds2xMfHQ6VS4fnz59UeTGoNLly4gMjISO58z549IZVKy91eqVRi27ZtCAoKQtOmTfHLL7+Uud3jx4/h4eGBpUuXIiAgAK1bt8axY8e464vPENLr9UhNTUVYWFiFtYrFYvTu3bvc6w0BYtKkSfDz80PXrl1x7dq1crd1dXXFwIED4efnh5dfftloEGbx7qF79+7B1dUVXl5e3GWRkZE4d+5chfWaCsMwaNq0KebNm4c///wTubm5OHDgAN566y20bt0aIpEIt2/fxrp16/Daa6/Bx8cHzZo1w8yZM7Fnz54az8YilonCBiEWSCQS4ccff8TUqVPxxx9/YPfu3QgODka3bt0wYsQI9OzZE82aNeOmINpqsMvOzq50a4BWq0WPHj3QuHFj3L9/H6tWrcKoUaOQkZFR5vYtWrTAoEGDkJaWhmnTpmHw4MFGA2wNli1bhhYtWqBNmzY1eizOzs7w9fXFwoULkZaWhh49ehi12hTn7e0NmUyGDRs2ICUlBf7+/pgyZUqZj3nGjBlYvHgxHBwcuMu9vLws5tgnzs7O6N27Nz7++GOcOXMGubm52Lt3L2bNmoWoqCgAwLVr1/DFF19g4MCB8PLyQuPGjTFx4kRs3rwZycnJNObDFvDRp2PJaMyGeQk5bsJWxmyUtGvXLpZhGK7/WyKRsC4uLtwpLi5O6BJNxsnJqczLjx49WuaYjZJatmzJ7ty5s1L35eHhwZ47d87osk8//ZRt27Yt++zZs0rtw2Dx4sWlxmyU9OzZMxYA++jRoxfu7+rVq6yLi4vRZSqVih04cCA7bdq0Uttv3LiRnTp1apVqFkpOTg67c+dO9s0332QjIiJKjfcAwPr5+bFDhgxhV69ezV64cIEbn0JMj6/PUNvp3CXEBul0OsycOdPom51MJuN+12q1mDt3LgYNGgSxWCxEiSbl4+ODR48eVbp1Q61WG33DZxim3G6XF227ePFiHDlyBAcPHkStWrWq+QjKv0/DTKHy6tNoNNx1DMMYjcVRKpUYOHAgGjZsiNWrV5e6bU5ODnx8fHip2dQ8PT3x6quv4tVXXwVQ1MV18uRJnDhxAidOnMDZs2eRlZWFHTt2YMeOHQAAFxcXtG3bFm3atOFOxbuRiOWhbhRCLNjx48eRnp5udFlBQQH0ej00Gg2USiXS0tJw/PhxgSo0rejoaFy5cqVS22ZlZaFJkya4ceMGAODUqVO4e/cu2rZtCwCYPn06Pv74YwDA+fPnERMTwz23u3btglwuR8OGDQEUDdJNSEjAgQMH4Orqystj2bNnD/r06YMnT54AKJrCGxMTA1dXV6hUKvTo0QOHDh0CAKxatQpjxozhXutvv/0WPXv2BAA8f/4cffr0QbNmzcoMGgBw5coVtGrVipe6zc3DwwP9+/fHJ598gr///hsKhQIJCQlYtmwZ+vbtC1dXVzx79gx//vknPvzwQ/Tv3x/e3t4IDQ3F8OHDsWrVKvzzzz+01oeFoZYNQiyY4QicEokEMpkMBQUFYFkW+fn5ZW5na/71r39h+/bt6N69OwDgjTfewN69e6HRaPD8+XOuxePq1avw8/PDhx9+iCFDhiA3Nxc+Pj74+eef4e3tDQC4e/cuN7alZcuWGD9+PLp27Yr8/HyEhITgl19+4VqN/vvf/6JOnTpGg0J79uyJH3/8ERs3bsRbb72FpKQkuLm5GdXbsGFDPH78mAsJBw4cQGhoKM6cOYP+/fvj4sWLiIqKgkajQWRkJLZs2QKgqBUjMTGRGxw5adIk3LlzBw0aNAAAtG/fHl999RUA4Ny5czh27Bhu3bqFH3/8kbvvxYsXY8qUKSgoKMDx48fx3Xff8ftiCEQul6Nz587o3LkzgKLWvmvXruGff/7B6dOncfr0aSQmJuLevXu4d+8efv75ZwBFLUZRUVFcy0erVq0QHh5uky2A1oBhWdseeZOXlwc3NzcoFArevqGQ8qnVam4BpAULFhg1U9vyfZtKfHw8evbsCblcDqBoZoQhcBR39OhRdO3aVYAKTUuj0SAqKgr79u1DSEiI0OUAAC5duoSuXbviwYMHRl1aluL9998HACxatEjgSsxHoVDg7NmzXPg4ffp0mVNqnZyc0Lx5c0RHR6Nly5aIjo5G48aNK5zhZO/4+gyllg3CK4lEgsGDB3O/k5rp1KkT/P39uaZ3nU5XKmj4+PigU6dOQpRnclKpFJs2bcKYMWOwf/9+bhVRIe3cuRNr1661yKBx7NgxJCQkcCuK2gs3NzfExsYiNjYWQNH6M/fv3+eCx5kzZ3Dp0iXk5+fj5MmTOHnyJHdbmUyGyMhILnxER0ejadOmFvn6WjOLadnQaDSYOHEidu3ahX/961/46aefABS9ucpkMqMBYp07d0ZcXFyl9kstG/bDFls2AOCXX37Ba6+9BolEUmY/9L///W9s3rxZgMoIsR46nQ5JSUk4f/48Lly4wJ3y8vJKbSuVShEREYHIyEg0a9YMkZGRiIyMhK+vr9UfAqCqbKpl49mzZxgwYACio6Mxa9YsJCcnc9c9efIEgYGBSElJEa5AQgQ0aNAgbN++HTNnzjQaLBoQEIDevXtj/fr1AlZHiHUQi8Vo1KgRGjVqhJEjRwIo6pZMTk7mgochiOTm5uLy5cu4fPmy0T48PT2NwkdkZCQiIiIsosXN0llE2DAs89u7d28sWbLE6LqqLPELACqVCiqVijtfVmolpqPX67llnRs3bkwHY6sGjUYDvV5v1Iw7aNAgDBgwAMePH0dWVhb8/PzQqVMno8Fuz58/xw8//IA333zT7r59EVIdIpEIYWFhCAsL4w6KZ+iCuXLlCq5cuYKrV6/iypUruH37Nh49eoSjR48aLQXPMAwaNGiAyMhING3aFBEREYiIiECDBg1spnWVDxYRNipa5rf4Er979+5Fw4YNsWbNGjRt2rTM7ZcvX46lS5easlxSAa1Wi507dwKwra4Mc9FoNEZdJcUDh1gsLncQKMuyGDZsGPbt24eTJ09i/fr13KBSQkjlMQyD4OBgBAcH41//+hd3uVKpxI0bN7jwYTjl5OTg9u3buH37NvfeBxT9v4aFhSEiIgKNGzfmQkjDhg3tsiXEIsJGRYov8fvVV19h+fLlGDZsWLnHFFiwYAHmzJnDnc/Ly0PdunXNVS4h1VYyaFRlOBXDMBg4cCAOHDiArVu34ubNm9i9ezeCgoJMUSohdkcul6Nly5Zo2bKl0eUPHjzAlStXcPnyZdy4cYM7PXv2DLdu3cKtW7ewe/dubntDmCkeQBo1aoTw8HB4eHiY+2GZjcWHjWbNmmHTpk3c+dmzZ2PRokXcgZRKkslkNIqYWJ2SQUMqlcLR0bFK+xg3bhxCQ0MxZMgQXLhwAa1atUJcXJxNToklxFL4+PigZ8+e3KJrQNEXhczMTNy4cQOJiYlGIeTx48fcmiAlZw3VqVMH4eHhCA8PR4MGDYx+d3Z2NvdD45XFhw2gakv8EmJt+AgaBt26dcP58+cxcOBAXLx4EbGxsVi5ciVmzJhB4zgIMROGYRAQEICAgACjEAIULSVfMoDcunUL6enpyM3NxalTp3Dq1KlS+/T39+fCR/EwEhoaahXd1RYfNvbs2YMvvvgCu3btgru7u9ESv4RYOz6DhkG9evVw4sQJTJw4EVu2bMHKlSsxduxY+p8hxAJ4eXnBy8uLWxHVoKCgAHfu3OHGf9y+fRtJSUncwNTMzExkZmYiPj7e6HYikQh169ZFaGgoQkNDUb9+fe730NBQ1KlTxyK+aFhM2Chvmd9//vmn3CV+CbFmJYOGg4MDb12ATk5O+PHHH9G6dWu0bNmSggYhFs7JyYmbTltSbm4ukpKSuPBRPIw8f/4c9+/fx/37941myRi4ubkZhY/iYSQoKMhsvQQWs6iXqdCiXuZFy5VXjuH4JoZ/Pz6DRkU2bdqEzMxMvPXWW3SMCEKsHMuyePDgAZKTk5GcnIy7d+9yvycnJyMzM7PC24vFYtStWxchISGoV68eNwvH8HtgYCAKCgpsZ1EvYjvEYjEGDBjA/U7KxjAMnJycUFBQAKlUapagkZ6ejjfffBNKpRIHDhzAxo0bUb9+fZPfLyHENBiGga+vL3x9fdG+fftS1xcUFCAlJaXcMFJYWIiUlJRyF80Ui8Xw9/fnpVYKG4RXYrEYzZs3F7oMqyASieDs7Gy2/tSAgAB89dVXmDFjBo4fP47IyEh8+umnmDJlCi2+RogNcnJy4qbXlqTX65GdnY3k5GTcv3+fCx0pKSlct4xarUZaWhovtVA3CrEZlt6NotVqIRaLBR+slZKSgrFjx3IDzbp3744NGzYgODhY0LoIIZbDEEauX7+OXr161fgzlL7OEF7p9Xpu8JJerxe6HIuhVquhVCpRWFhYpcW6TCE4OBhHjhzBF198Ablcjr/++gvR0dG0tD8hhCMSieDv7482bdrwsz9e9kLI/9Nqtdi2bRu2bdsGrVYrdDkWQa1Wc8fr0Wq1FvG8iEQiTJ8+HVeuXEHHjh0xadIkavkjhJgMjdkgxISKBw2gaNaJJS1IFxYWhoSEBKMAdOnSJXz77bdYtmwZ3N3dBayOEGIrqGWDEBMpK2hY4lL6IpGIG9/CsiwmT56MtWvXIjw8HF9//bVFtMQQQqwbhQ1CTMBagkZJDMPg448/RuPGjfHo0SNMnToVUVFROHjwoNClEUKsGIUNQnhmrUHDoGvXrrh8+TLWrFkDDw8P3LhxA3369EG/fv2QmJgodHmEECtEYYMQHmk0GqsOGgZSqRRTp05FUlIS5syZA6lUiv379+P06dNCl0YIsUIUNgjhkUQi4VZOtdagUZy7uztWrlyJ69evY/r06Xj99de5606cOIH09HQBqyOEWAsKG4RXYrEYffv2Rd++fe1yuXKGYSCXy+Ho6Gj1QaO4Bg0a4IsvvoBEUjSBTaVSYcSIEQgLC8Ps2bNfeAwGQoh9o7BBeCUWixETE4OYmBi7CRslF+liGMaipreawsOHDxEcHAyVSoVVq1YhJCQEkyZNwt27d4UujRBigShsEFIDKpUKBQUFgq8Kam5169ZFQkICDh48iE6dOkGtVuPbb79FeHg4RowYgaSkJKFLJIRYEAobhFd6vZ47mI+tL1euUqmgVquh1+vtMnAwDINevXrh2LFjOH78OPr16we9Xo9t27bh6dOnQpdHCLEgtIIo4ZVWq8UPP/wAwDIPhsYXQ9AwkEqlgh9gTUgdO3bEvn37cOnSJezbtw+tW7fmrvvggw/g6emJUaNGwdnZWcAqCSFCoZYNQqqoZNCQyWQ2G6qqqnnz5li4cCF3PicnB8uWLcObb76JwMBAzJ8/HykpKcIVSAgRBIUNQqqAgkbVyOVyfPLJJwgNDcXTp0+xYsUKhIaGom/fvvjll1+g0WiELpEQYgYUNgipJAoaVVerVi3MnDkTt2/fxt69exEbGwuWZXHgwAG8+uqr+PLLL4UukRBiBhQ2CKkECho1IxaL0b9/f/z5559ISkrC22+/jcDAQIwYMYLb5ujRo/jhhx/w7NkzASslhJgChQ1CKqH4zBoKGjUTFhaG5cuX4/79+/D19eUu//DDDzFmzBj4+Phg+PDh2LdvH3WzEGIjKGwQUglyuRwSiYSCBo9Eov+9/bAsi+7duyM8PBxKpRI///wzXn75Zfj7+2P69Ok4c+aMgJUSQmqKwgbhlVgsRmxsLGJjY21uBVG5XE5Bw0QYhsHChQtx8+ZNnDlzBjNmzICXlxcePXqENWvW4N133zXa3t7WNCHE2lHYILwSi8Xo0KEDOnToYNVhQ6VS2fyiZJaIYRi0bt0aq1evRkZGBv744w8MHz7c6ABwGRkZCAoKwvTp05GQkACdTidgxYSQyqBFvQgpwTAYVKPRwMnJyai5n5iPVCrlDupX3K+//or09HSsWbMGa9asgbe3N15++WW8/PLLiI2NhYuLi0AVE0LKQ++ihFd6vR4ZGRnIyMiwypaBwsJCbtYJy7L0rdkCjRs3Dr///jvGjh0Ld3d3PHz4EN9//z0GDRoET09PHD16VOgSCSElUNggvNJqtVi/fj3Wr18PrVYrdDlVUlhYaDT7wdHR0eaP3mqNZDIZXnrpJXz//fd48OAB/vzzT8yaNQv169eHTqdDixYtuG03bNiA//znPzh8+DAKCwsFrJoQ+0ZhgxBQ0LBWUqkUsbGx+O9//4ukpCQkJyejdu3a3PXr16/HZ599hp49e8Ld3R29evXCZ599hkuXLlllyxsh1orGbBC7R0HDNjAMg6CgIKPLZs+ejX379uHPP/9EVlYW/vzzT/z5558AgPDwcNy8edOuD6BHiLlQ2CB2jYKGbRs6dCiGDh0KlmVx48YNLmwkJCSgadOmXNBgWRYdOnRA/fr10blzZ3Tu3Bnh4eEURAjhCYUNYre0Wi0FDTvBMAyaNGmCJk2aYNasWVCr1cjNzeWuv3fvHv755x/8888/+OmnnwAAPj4+XPDo2bMnGjZsKFT5hFg9GrNB7JZEIuEW6aKgYV8cHByMlkr39/fHn3/+iffeew9dunSBTCbDgwcPsGPHDkyfPh3r1q3jts3Pz8cff/yBx48fC1E6IVaJWjaIXZPJZJBKpbSWhp1zdHTkVr4FitZaOXPmDI4dO4Zjx46hZ8+e3LanT5/GSy+9BABo0KAB2rZti3bt2qFt27Zo1qwZJBJ6WyWkJPqvILwSi8Xo0qUL97ul0el0peqioEFKkslk6NSpEzp16oSFCxcaXZefn4+GDRvi1q1bSEpKQlJSEn788UcARUva//TTTxg0aBAAQKlUQiKRUKsZsXsUNgivxGIxunbtKnQZZTIMBjUcVI2Q6ujfvz/69++P3NxcnDlzBv/88w9OnTqF06dPQ6FQGM2I2bx5M2bOnInIyEi0bNkS0dHRaNmyJZo2bUrH2SF2hd5xiV0oPutEqVSiVq1aNNOA1EidOnXQp08f9OnTB0DR6rlJSUkICQnhtrl69SpUKhXOnj2Ls2fPcpdLpVJERERg+/bt3MBTlUoFBwcH+rskNonCBuEVy7LIyckBAHh5eVnEG2dZ01stoS5iW0QiUakZK1988QVmzZqFCxcu4Pz589zPJ0+e4PLly/D29ua2XbBgATZt2oTIyEg0a9YMzZo1Q2RkJJo2bYpatWqZ++EQwisKG4RXGo0Ga9euBVD05il0U3HJoEFdKMScRCIRwsLCEBYWhqFDhwIoCuT3799HYmIi3N3duW2vXbuGJ0+eICEhAQkJCUb7CQ4OxuXLl+Hq6goASElJQa1ateDp6Wm+B0NIDdC7LrFZSqXS6PgsFDSIJWAYBsHBwQgODja6fO/evUhMTMTVq1dx5coVXL16FVevXkVWVhby8vKMjmY7ffp0/P777/D09ESjRo3QuHFjo5/BwcHUekcsCr3zEpukVCqNZplQ0CCWztHRES1atDA6kBwAPHr0COnp6Ubh4dmzZ9x1J06cwIkTJ7jrXFxcoFAouPMbN26EWq1GgwYNEBYWhsDAQJqBRczObt591Wo1d+hwYjrFn2NzP9/F76+wsJCbbujo6Ai9Xk+vP7FKrq6uiIiIMPr7PXToEPLz83H79m3cunULN2/e5H66u7sbdR1++umnuHnzJndeJpMhNDQU9evXR2RkJBYvXsxdx7IstYgQI3y9bzIsy7K87MlC5eXlwc3NDW+//TYcHR2FLocQQkyqZGA4dOgQcnJykJubiydPnhgd7dbX1xeTJ0/mzq9duxYqlQq1a9c2Orm7u8Pd3Z0bM0LsR2FhIT7++GMoFIoavf5207JBCCH2oGTLRK9evbjfdTod8vLy8PjxY+Tm5hotNsayLB4/fgytVounT5+W2m/JYHLkyBFIJBIukLi6usLV1dUiF/MjwrOblo2cnBxK5WagVquxcuVKAMDcuXPNOhtFyPsmxNqxLIuMjAzcv3/f6JSSkoKUlBRER0dj69at3LZ16tRBQUGB0T4YhoGPjw9iY2OxYcMG7vI//vgDtWvXRmBgIPz8/GhFVSuSl5cHLy8vatmoLAcHB/rwMQOxWIx27doBKBqUaepvOSzLorCwEGKx2Oj1pdebkKoLDQ1FaGhomdcV757RaDR46623uCCSkpKCjIwMqNVqZGdn49mzZ9z/H8uyGDlyJBdMRCIRfH19ERgYiLp166J9+/aYM2cOdz93796Fp6cnXF1dafyIBeDrfdRuwgYxD7FYbNRsa0qGoKHVaqHVaumNiRATKv7/JZVKsWjRIqPr9Xo9Hj16hLS0NKOWC6VSiZYtWyI9PR3p6enQaDTIzMxEZmYmzpw5A41Gw4UNlmXRtGlTFBYWQi6Xw9/fH35+ftypVatW+Pe//83t++nTp3Bzc6P/fStAYYNYpeJBw4Cm8xEiHJFIBG9vb6NVUQHAyckJx44dA1AUSHJycpCWloa0tDSkp6ejbt263LbPnz+HTCZDYWEhlEol7t69i7t373LXv/LKK1zYYFkWvr6+3E9/f3/4+PhwNbRo0QKvvvoqd9vc3FzUrl2b3icEYjFhQ6PRYOLEidi1axf+9a9/4aeffuKuW7FiBb799ls8f/4cERER+Pbbb8tt6iPCYlmWm+Nvqm8cZQUNuVxuNMqeEGJ5RCIRfHx84OPjg1atWpW63sXFBU+fPkVBQQGysrKMTpmZmWjcuDG3rUKhgEqlAgCkpqYiNTXVaF8DBw7kwoYhkOh0Onh5eXGBxNvbGz4+PmjZsiVef/117rZpaWlwd3eHs7MztZrwxCLCxrNnzzBgwABER0dj1qxZSE5O5q6Lj4/HN998g4SEBPj5+eG///0vxo4dW2o5X2IZNBoNVq9eDcA0y5WXFzQkEgmto0GIjXByckL9+vVRv379crepXbs2CgsLkZ2dzQWShw8f4uHDh3jw4AGioqK4bfPy8ri1Rx48eIAHDx4Y7WvQoEFc2GBZFqGhodBqtXBwcICnpyc8PDy4n+3bt8fs2bO52x4+fBiurq7cNjTWpGwWETacnJzw1ltvoXfv3liyZInRdb/88gtGjRoFf39/AMDMmTPx3nvv4fHjx/Dw8BCgWiKUioIGIcT+yGQy1KtXD/Xq1atwOzc3N6jVajx69IgLJIZQ8vDhQzRp0oTbNi8vDxKJBFqtFmq1mhtfYsCyLBc2WJZFnz59oNPpuOslEgnq1KkDT09PxMbGcl++AODLL7+EXC7n1i0pfnJxcbHpLh6LeJcWi8Xo3bt3mdclJyejWbNmRtt6enrizp07ZYYNlUrFNa0BRX84xPpR0CCE1IRUKuUGmlbEzc0NBQUFKCgowOPHj/Ho0SOjn8WDjVKpRGRkJHd9QUEBtFotF2YiIiK4bVmWxaxZs8rt7u3RowcOHz7MnX/11VchkUiMAolhgbV69eohJibGaN+W3ppi8e/UBQUFpZ5EhmFKze82WL58OZYuXWqO0ogZsSxr9O2BggYhxFQYhoGzszOcnZ0RFBRU7nZOTk64cOECd16pVOLx48fcyc3NjbtOo9Fg5MiRePLkSalTYWEhateuzW2r1+uxZ8+eSgcTHx8faDQauLm5lTpFRkbiP//5D7ftr7/+ColEAldXV6PtXF1dTdqyYvHv1k5OTii57hjLsnBycipz+wULFhjN2c7LyzMa7Uysk0gkglwuh1KphKOjIwUNQojFkcvlCAwMRGBgYKnrHBwcsHnz5jJvV1hYaDTmjGVZbNq0qcxg8uTJE0RGRnLb6vV6PH78GHq9vsyVX7OysozCxhtvvIHc3Nwy6+jUqRM3c8iwLV+TMSz+HTskJATZ2dncea1Wi5ycnHIHDslkMshkMnOVR8xILBbT6HBCiM1xdHQ0OnaXWCw2Wk+kIgzDICMjA0+ePEFeXh4UCgV3ysvL48Y7GrRu3RqPHj0y2s4QdEp+ifv1118RGxtbw0dXxOLDxqBBgzBx4kSMGzcOfn5+WL16NVq1agVPT0+hSyMmxLIsNBpNqdksFDQIIeR/GIaBr68vfH19K7X9gQMHSl2mUqmgUCiMuqoB4L///S/q1KmDuLi4GtdpMWGjYcOGePz4MQoKCqDX63HgwAGEhobizJkzmDhxIrp06YL8/HxERERg48aNQpdLyiESibj589Xt/2NZFkqlEjqdDnq9no7WSwghJiSTyUotxgYAo0aN4m2ShcWEjVu3bpV73fz58zF//nwzVkOqSyKR4KWXXqr27YsHDQBc64YtTwkjhBBbR+/gxGKUDBpA0QBhChqEEGLdLKZlg9gGlmW5aclOTk6VHmNRXtAw9VFjCSGEmB59ZSS80mg0WLFiBVasWMEtD/wiFDQIIcS2UdgggqKgQQghto+6UYigVCoVFzQYhoFcLqegQQghNoZaNoigDDNNKGgQQojtopYNIijDMuQsy1LQIIQQG0Vhg5iV4Tg3xWep0NRWQgixbfQuT8zGMBhUqVSWOrgeIYQQ20UtG4RXIpEIUVFR3O8GJWedFBYWQi6XC1IjIYQQ86KwQXglkUjwyiuvGF1WMmgwDFPqAGuEEEJsF3WjEJMqK2jQrBNCCLEv1LJBeGU4NDxQ1MqhVCqh1+sBUNAghBB7RWGD8Eqj0WD58uUAgBkzZkAiKfoTo6BBCCH2i8IGMZni01zp6K2EEGK/7CZsqNVqqNXqUpeLRCLu27dhu/IwDAOpVFqtbTUaTbnTPU21LQCjgZhV2Var1XLdH1XZtvhzotFowDAMXF1duaDxov1KpVJuDY6qbKvT6Yzuu+RrU3Lb4sdiKUkikXD1WsK2er0eWq223G3FYjHXYmQJ2xbvSqvptsX/P021LVDx/zK9R5S9bXXfI8pS3f/7qr5HVPQ/R+8RZW9b0d9wVdhN2Fi5ciUcHR1LXd6gQQOMGDGCO1/R0Urr1auHMWPGcOdXr17NHU69JH9/f0yYMIE7/9VXX0GhUJS5rZeXF958803u/HfffYecnJwyt3Vzc8OsWbO485s2bUJmZmaZ2zo5OWH+/Pnc+S1btuD+/ftlbiuVSvHOO+9w5+Pi4pCUlFTmtgCwePFi7vfdu3fjxo0bpbZZt24dAGDBggXcH+/vv/+Oy5cvl7vfefPmwdnZGQBw8OBBnDt3rtxtZ86cidq1awMAjhw5gn/++Ye7buXKlUbbTpkyBd7e3gCA48ePIyEhodz9jh8/HgEBAQCAU6dO4fDhw+VuO3r0aAQHBwMAzp8/j/3795e77fDhwxEeHg4AuHr1Kn799ddytx08eDCaNGkCAEhMTMTOnTvL3XbAgAFo3rw5AODOnTvYtm1budv27dsXMTExAIDU1FT88MMP5W4bGxuLDh06AACysrKwfv36crft0qULunbtCgDIycnB2rVry922Xbt26NWrFwBAoVBg9erV5W7bqlUrvPTSSwCAgoICrFixotxto6KiuJlQxbvzyhIREYEhQ4Zw5yvalt4jipjiPcJgwYIFXDgx13tESfQeUaTke8Q333xT7rZVQe3ahBBCCDEphrXxpRzz8vLg5uaGnJwcuLq6lrqemkjL3rYqTaQajQYFBQWQyWTQarVcq8LcuXPh4OBgtiZSpVJZ6r7L21boZk9LaSKlbpQi1I1S9W2pG8U+3iNyc3Ph5eUFhUJR5mdoZdlNN4qDg0OlFpKqymJTVdm2+D+/NWxb/M21IizLQq1WQyQSQafTGd2urOe8svut6rZisdjovip6vYv/M1Vmv0JvKxKJKv23ZgnbVmXRNkvYFjDd/z29R1jOtpbwv2zL7xEvYjdhg/CPZVkUFBRw3yxYloVIJEJERAQAOsAaIYSQIhQ2SLWUDBrFp7cWH3hHCCGE0FdPUmUVBQ1CCCGkJPp0IFVCQYMQQkhVUTcKqbTKBA21Ws2tWVB87jwhhBD7RV9HSaVpNBpq0SCEEFJl9ElBKq34mhkUNAghhFQWdaOQKnF0dATLstziN4QQQsiL0FdTUi69Xl/mCnYUNAghhFQFhQ1SJr1eD6VSCaVSWeGSuYQQQsiLUNggpRiChl6vB8uyKCwsFLokQgghVozGbBAjxYMGUNRlIpfLK317kUiEBg0acL8TQgghFDYIR6/Xo6CggDvqo0gkglwur1JokEgkGDFihKlKJIQQYoXoqycBwE/QIIQQQspCnySEggYhhBCTom4UO2dYgpyvoKFWq7FixQoAwLx582i5ckIIIdSyYe8YhuECAV8tGhqNBhqNho/yCCGE2ABq2SBwcHAAwzAQi8XUdUIIIYR3FDbsUFnLjUulUoGqIYQQYuvoa6yd0ev1yM/Pp24OQgghZkNhw44Un3VSWFgIrVYrdEmEEELsAIUNO1HW9FaxWCxwVYQQQuwBjdmwA2UFDScnJ5McvZVhGNSrV4/7nRBCCKGwYePMGTSAooGmY8aMMcm+CSGEWCfqRrFh5g4ahBBCSFkobNgoChqEEEIsBXWj2Ci9Xi9I0FCr1Vi9ejUAYObMmbRcOSGEEAobtkoikUAul0OtVkMul5u1RaOgoMBs90UIIcTyWUU3yqJFi+Dq6gpfX1/udObMGaHLsngSiYS6TgghhAjOKlo2cnNz8cUXX9AshwrodDrodDrqtjATlmWh1Wqh0+mELoUQQqpNLBZDIpGY/Eup1YQNT09PocuwWDqdDkqlEizLgmVZyGQyoUuyaWq1GllZWdRdRAixCU5OTvDz8zPpl1WrCRvJyclo0qQJ8vPzMWrUKLz//vtlbqtSqaBSqbjzeXl55ipTEMWDhuF8WQdaI/zQ6/W4d+8exGIx/P39uSPmEkKItWFZFmq1Gjk5Obh37x4aNGhgsiN/W0XYaNmyJZycnHD16lXcv38f3bp1Q2RkJAYPHlxq2+XLl2Pp0qUCVGl+JYOGWCw2+2BQe6NWq6HX61G3bl04OTkJXQ4hhNSIXC6HVCrF/fv3oVar4ejoaJL7sYoBosuWLcP48eMhEokQEhKCwYMHIyEhocxtFyxYAIVCwZ3S0tLMXK15WGrQYBgG/v7+8Pf3F7wWUzJV+ieEEHMzx/uZVbRsqNVqo74khmEglUrL3FYmk9n8mAVLDRpA0XLlEyZMELoMQgghFsTiv56xLIsOHTpg165dAIAHDx5gz5496Nmzp8CVCcOSgwYhhBBSFosPGwzD4LvvvsPq1avh6+uL9u3bY8qUKejbt6/QpQmisLCQggYhhBCrYhXdKM2bN8exY8eELsMiyOVyFBQUQCQSWWTQ0Gg0+OqrrwAAU6dOLbe7ixBr9/jxYzRu3BhnzpxBcHCw0OWQahg8eDDat2+POXPmCF2KzbP4lg1izHCcE0sMGkBRt5dhcK6hBYZYjs6dO4NhGDAMAwcHBzRu3Bhbt26t8jZ86tq1a41rrsp21TVmzBi8/fbb3Pnly5ejf//+RkHDUmrlgyX+rVTVsWPH0L9/f27A+p49e4yuX7RoEZYtW2aSJRJedN8GX3/9NUJCQuDo6IiWLVvi+PHj5e4zODiYe76Ln6ZOncpts3btWkRGRsLV1RWurq5o164d9u/fz/fDqzIKGxau+AHVDEQikUUGDWLZWJbFpUuXsGLFCmRlZeHWrVvo06cPRo0ahXv37lV6Gz78/fffOHz4sNFlhw8fxt9//13lms1Rt16vx759+zBgwAAAgFKpxIYNGzB+/HiLq7U8Xbt2xaZNmyq1rSX9rdREfn4+oqKisGbNmjKvj4yMRHBwMLZs2WL2+waA7du3Y9asWVi4cCEuXryITp06oW/fvkhNTS1z+7NnzyIrK4s7/fnnnwCAIUOGcNsEBgbi448/xrlz53Du3Dl0794dAwYMwPXr1/l9gFXF2jiFQsECYBUKhdClVJlWq2Xz8vLYgoICoUupNJVKxS5ZsoRdsmQJq1KpbO6+lUole+PGDVapVJpk/6Z069YtFgB77do17rKrV6+yANj9+/dXehs+pKamsoMHD2anTJnCRkdHs1OmTGFfe+01Nj09vco1m6PuY8eOsd7e3qxOp2NZlmV37drFenp6WmSt5enSpQu7cePGSm1rSX8rfAHA7t69u9TlS5YsYTt16iTIfcfExLCTJ082uqxRo0bs22+/Xan9zpw5k61fvz6r1+sr3M7d3Z1dv359uddX9L7G12cotWxYKJ1Oxy2HrdVqoVarBa6IWLvz58/D3d0dERERAID09HQsXLgQMpkMzZo1q/Q2fKhbty527NgBNzc3XLhwAbVr18bPP/+MgICAKtdsjrr37t2L/v37c+sRHDt2DK1atbLIWvlgSX8rphYTE4MzZ84YrTxt8NFHH6FWrVoVnirq9qiIWq3G+fPn0atXL6PLe/XqhZMnT1bq9j/99BPeeOONclu6dTodfv75Z+Tn56Ndu3bVqpMvVjFA1N4UDxpA0awTGmhp2dRqdaUCoWEGUXFKpbJSB3RzcHCo0bELLly4AIVCARcXF+j1eiiVSsjlcqxbt477kK/MNgAwfPhwJCYmcufv37+P5s2bo1GjRoiJicHYsWO5y3v16oVbt24Z1ZKRkYG5c+fC3d0d0dHRePLkCYYNG4aVK1ca3U9l66nsdlOmTCmzvh07dmDYsGG4ceMGgKIm8A4dOuCbb75BmzZtsHfvXqxYsYLbT0pKCvz9/av8/FZ2u/Ke36NHj5b7GEo+xzXB599KdZ9zcwkICIBKpUJ2djbq1atndN3kyZMxdOjQF96+Oh49egSdTgcfHx+jy318fJCdnf3C2+/ZswdPnz4t8wClV69eRbt27VBYWIhatWph9+7dXCgUCrVsWJiygoalDgYl/8P+/0HwKnPi87ZVcf78eUydOhWXLl3CsWPH0KVLF8ycOdPozaoy2wDAtm3bcOnSJVy6dAnffvstHB0d8cUXX+DatWto2rQpt921a9fK/JabkpKC8ePHY+3atXBxccHatWsxfvx4pKSkVLnmqmxXXn2RkZHw8vLiZr1NmDAB48ePR5s2bZCYmIj09HTExsZyt1MqlaWWdeaz1vKe34oeQ0klv5UfP34ckydPrtS3cj7/VqrznBe3ZMmSMgdFFj+dO3euzMdRGYbwX9aBFevUqYOwsLAKTyW/PFRVyfd2tpLHttqwYQP69u1bKvQCQMOGDXHp0iWcOnUKU6ZMwejRo7lQJ5gadcJYAWsas2EYo2E45efnv7AvztKo1Wr2q6++Yr/66itWrVab9b6FHLOhUqnYZ8+evfBU1vibgoKCSt22po+pdu3a7LZt27jz169fZ0UiEZucnFylbYpLTk5mAwIC2IMHD7Isy7IeHh5sfn4+d/3HH3/MLlmypMK6unTpUqOaq7JdRfXt2LGDHTFiBPvll1+yw4YNM9pmwIABRvsZMWIEO3z4cJPWyrKln98XPYbiHj9+zCYlJXGnmJgY9pNPPjG6rLzxYHz+rVTnOS8uJyeHTUxMrPBUmTFUKGfcxKlTp1gAbE5OTqnrli1bxjo7O1d4OnbsWLXuW6VSsWKxmP3ll1+MLp8xYwbbuXPnCveXkpLCikQids+ePS+8b5Zl2R49erATJ04s93pzjNmgsGEhbCFoCI0GiJbv7t27LAD26tWrRpeHh4ezy5Ytq/Q2xeXm5rKNGzdmv/vuO5ZlWTY9PZ2tX7++0TYjR45kd+3aZbKaq7Ldi+rTaDRs/fr12aioKPbZs2fcNu3atWM3bNhgdLvPPvuMjYqKMlmtLFv6+a3MY6hIZQeI8vm3Ut3n3BTKCxvr169nAwMDy7xNycBW1qkyA/jLu++YmBh2ypQpRpc1btz4hQNEFy9ezPr6+rIajeaF982yLNu9e3d29OjR5V5PA0TtBHWdEFM7f/48JBIJwsPDjS7v2bMndu/eXeltDNRqNQYOHIhXXnmFm/55/fr1Us355TXx81VzVbZ7UX0SiQTNmzfHkiVLUKtWLQDAw4cPcfbsWbz88stGt+vduzeuX7+OJ0+emKTWsp7fyjwGPvD5t1Kd55xPz58/57qjAODevXu4dOmS0dTS48ePlxqkaVCTbpTK3PecOXOwfv16fP/990hMTMTs2bORmpqKyZMnAwDWrFmDHj16GO1Xr9dj48aNGD16NCSS0sMu33nnHRw/fhwpKSm4evUqFi5ciPj4eIwcObLSz5spUNiwACKRiBvlTkGDmMKFCxcQHh5eaoBpz549cf78eaSnp1dqG4Nx48bB19cXy5Yt4y67e/cugoKCuPO5ubnIzc1FgwYNTFZzVbarTH03b95EZGQkd/63335DmzZt4O3tbbTvZs2aoVWrVoiLizNJrWU9v5V9DDXF599KdZ5zPp07dw4tWrRAixYtABR9uLdo0QKLFi0CUHT4h927d5vk4JEvum8AeO2117Bq1Sq8//773ErZf/zxBzdQ9dGjR7h7967Rfg8fPozU1FS88cYbZd7vgwcP8O9//xsNGzZEjx49cPr0aRw4cED444nVqF3EClhLN4per2cLCwutvuvEXsds2JN3332X7dChA1tYWGh0+e+//862a9eOValUrFarZSdOnMguWrRIoCpLe1F9arWadXd3N/of7N+/P/vJJ5+Uub99+/axjRs35tbe4Et5z29lHoOlqc5zbk5r1qxhe/bsKch9WxLqRrEjDMNAJpNZfYsGy7LIyclBTk4OLVdug1JSUvDhhx8iKysLbdq0QfPmzdG8eXO888476NevH6KjoxEeHo6IiAjI5XIsXLhQ6JI5L6ovKSkJYWFhRv+DHTt2xPDhw8vd36RJk5CRkcFbjRU9v5V5DJamOs+5OUmlUnz55ZeC3Le9YVgb/0TIy8uDm5sbFAoFXF1dhS4HwP8W6bLF7hK1Wo3ly5cDABYsWFCjdSEs8b4LCwtx79497lgGhBBi7Sp6X+PrM5RaNsxMq9VyizgVFBTQt39CCCE2j8KGGRmChoFhUCghhBBiy+jTzkxKBg2JRAJHR0eb60YhhBBCSqKwYQYUNAghhNgzOhCbidlb0GAYBm5ubtzvhBBCCIUNE7K3oAEUTSWbNWuW0GUQQgixINSNYkIajYb73R6CBiGEEFIWatkwIUdHRxQWFgJAjQ9DTAghhFgrChsmxDCM3bVmaDQabNq0CQAwZswYSKVSYQsihBAiOAobPNJqtUYHVQPsb5Aky7LIzMzkfieEEEJozAZPDINBCwoKoNfrhS6HEGJijx8/hre3N1JSUoQuhVTT4MGD8fnnnwtdhl2gsMGD4rNOWJY1GhhKiCXp3LkzGIYBwzBwcHBA48aNsXXr1ipvw6euXbvWuOaqbFddY8aMwdtvv82dX758Ofr374/g4GCLq5UPlvi3UlXHjh1D//794e/vD4ZhsGfPHqPrFy1ahGXLliEvL4/3+9ZqtXj33XcREhICuVyO0NBQvP/++y/8Mvr1119zxyhp2bIljh8/bnT98uXL0bp1a7i4uMDb2xuvvPIKbt26xXv9fKOwUUMajcZoeqtUKoVMJhOwIkLKxrIsLl26hBUrViArKwu3bt1Cnz59MGrUKNy7d6/S2/Dh77//xuHDh40uO3z4MP7+++8q12yOuvV6Pfbt24cBAwYAAJRKJTZs2IDx48dbXK3l6dq1Kzee6kUs6W+lJvLz8xEVFYU1a9aUeX1kZCSCg4OxZcsW3u/7k08+wbp167BmzRokJibi008/xWeffVbhUWa3b9+OWbNmYeHChbh48SI6deqEvn37IjU1ldsmISEBU6dOxalTp/Dnn39Cq9WiV69eyM/P5/0x8KpGB6i3AgqFggXAKhQK3vetVqvZvLw87qRUKnm/D2ujUqnYJUuWsEuWLGFVKpXN3bdSqWRv3Lhhla/1rVu3WADstWvXuMuuXr3KAmD3799f6W34kJqayg4ePJidMmUKGx0dzU6ZMoV97bXX2PT09CrXbI66jx07xnp7e7M6nY5lWZbdtWsX6+npaZG1lqdLly7sxo0bK7WtJf2t8AUAu3v37lKXL1myhO3UqRPv9/fSSy+xb7zxhtFlgwYNYl9//fVybxMTE8NOnjzZ6LJGjRqxb7/9drm3efjwIQuATUhIqHatFb2v8fUZSi0b1aTRaLhprUBRiwYdcpxYsvPnz8Pd3R0REREAgPT0dCxcuBAymQzNmjWr9DZ8qFu3Lnbs2AE3NzdcuHABtWvXxs8//4yAgIAq12yOuvfu3Yv+/ftzg7+PHTuGVq1aWWStfLCkvxVTi4mJwZkzZ6BSqUpd99FHH6FWrVoVnkp2cxh07NgRR44cwe3btwEAly9fxokTJ9CvX78yt1er1Th//jx69epldHmvXr1w8uTJcutXKBQAgDp16lTq8QqFZqNUAwWNijk5OQldgtmwAo7RkUqlVZrtdOHCBSgUCri4uECv10OpVEIul2PdunXch3xltgGA4cOHIzExkTt///59NG/eHI0aNUJMTAzGjh3LXd6rV69SfcoZGRmYO3cu3N3dER0djSdPnmDYsGFYuXKl0f1Utp7KbjdlypQy69uxYweGDRuGGzduAChqfu/QoQO++eYbtGnTBnv37sWKFSu4/aSkpMDf37/Kz29ltyvv+T169Gi5j4HPfns+/1aq+5ybS0BAAFQqFbKzs1GvXj2j6yZPnoyhQ4e+8PZleeutt6BQKNCoUSOIxWLodDosW7YMw4cPL3P7R48eQafTwcfHx+hyHx8fZGdnl3kblmUxZ84cdOzYEU2bNq2wTqFR2KginU5HQaMCDg4OmD9/vtBlmI1Go8Hy5csFue8FCxbAwcGh0tufP38eU6dOxYwZM/D06VPMmzcP7dq1w5gxY6q0DQBs27aN+/3MmTMYMGAAvvjiC7z55pt44403uOuuXbtW5rfclJQUjB8/HrGxsejatSvWrl2Lw4cPIyUlxejNu7L1VHa7a9eulVlfZGQkvLy8cOzYMXTu3BkTJkzA+PHj0aZNGyQmJiI9PR2xsbHc7ZRKZan/ez5rLe/5regxlPTRRx/ho48+Mqr51KlTmDZtGnfZ/v370alTp1K35fNvpTrPeXFLlizB0qVLS9VY3NmzZ0u1NFWWYcHFgoKCUtfVqVOn2i0G27dvx08//YStW7eiSZMmuHTpEmbNmgV/f3+MHj263NuV/ALBsmy5XyqmTZuGK1eu4MSJE9Wq0ZwobFSRWCyGg4MD1Go1BQ1iVS5evIiJEyciLCwMQNGo92bNmmHixIkICQmp9DbF3bt3D4MGDcIPP/yAZs2aITExEU2aNOGuL++DsEOHDqUuK/5hXpWaq7JdRfVNnz4d33zzDa5cuQKWZbkP5b1796Jnz55GqwB7enriyZMnJq21rOf3RY+huJLfykeOHIlXX30VgwYN4i4r71s5n38r1XnOi5s2bRqGDRtWZp0GxWcEVVVubi4AwMvLq9R1JQNbWcoLbPPnz8fbb7/N1d6sWTPcv38fy5cvLzNseHp6QiwWl2rFePjwYanWDqDoudu7dy+OHTuGwMDACmu0CDUa8WEFTDVAVKPR8Lo/UnNCDBDV6/WsSqUS5KTX6ytd9927d1kA7NWrV40uDw8PZ5ctW1bpbYrLzc1lGzduzH733Xcsy7Jseno6W79+faNtRo4cye7atavSdVa15qps96L6NBoNW79+fTYqKop99uwZt027du3YDRs2GN3us88+Y6OiokxWK8uWfn4r8xgqUtkBonz+rVT3OTcFlDNAdP369WxgYGCZt3n8+DGblJRU4amgoKDM29apU4f9+uuvjS776KOP2AYNGpRbY0xMDDtlyhSjyxo3bmw0QFSv17NTp05l/f392du3b5e7r6qgAaIWgi1jJUyJhBqFymJYrnzTpk12sd6IYX0BIU5VGa9x/vx5SCQShIeHG13es2dP7N69u9LbGKjVagwcOBCvvPIKN/3z+vXrpb5hl/etm6+aq7Ldi+qTSCRo3rw5lixZglq1agEo+lZ59uxZvPzyy0a36927N65fv861bvBda1nPb2UeAx/4/FupznPOp+fPn+PSpUu4dOkSgKKWokuXLhlNJT1+/HipQZkGderUQVhYWIWn8o571b9/fyxbtgz79u1DSkoKdu/ejc8//xwDBw7ktlmzZg169OjBnZ8zZw7Wr1+P77//HomJiZg9ezZSU1MxefJkbpupU6dy3TMuLi7Izs5Gdna20RIMlojCxgtoNBo8f/4cWq1W6FKsAsuyuH//Pu7fv0/LlVuQCxcuIDw8vNQYj549e+L8+fNIT0+v1DYG48aNg6+vL5YtW8ZddvfuXQQFBXHnc3NzkZubiwYNGpis5qpsV5n6bt68icjISO78b7/9hjZt2sDb29to382aNUOrVq0QFxdnklrLen4r+xhqis+/leo853w6d+4cWrRogRYtWgAo+jBv0aIFFi1aBAAoLCzE7t27MWHCBN7v+8svv8TgwYPx5ptvonHjxpg3bx4mTZqEDz74gNvm0aNHuHv3Lnf+tddew6pVq/D++++jefPmOHbsGP744w+jgatr166FQqFA165d4efnx522b9/O+2PgVY3aRaxATZqASq6jYZhjT8pH62zYvnfffZft0KEDW1hYaHT577//zrZr145VqVSsVqtlJ06cyC5atEigKkt7UX1qtZp1d3c36p7q378/+8knn5S5v3379rGNGzfm/X2hvOe3Mo/B0lTnOTenNWvWsD179hTkvi0JdaMIqKzprcUPsEaIPUpJScGHH36IrKwstGnTBs2bN0fz5s3xzjvvoF+/foiOjkZ4eDgiIiIgl8uxcOFCoUvmvKi+pKQkhIWFGXVPdezYsdypiv369cOkSZOQkZHBW40VPb+VeQyWpjrPuTlJpdIKV/Qk/GFY1rbbuvPy8uDm5gaFQgFXV9dK3YbW0ag+tVrNTQWt6tRMa7jvwsJC3Lt3jzt2ASGEWLuK3teq8xlaFvqqXgIFDUIIIYRfNKWimJJBw8HBgQ6qRgghhNQQhY3/p1arjdbGp6BRfVKpVOgSCCGEWBAKG/+v+AAlChrV5+DgwA1mI4QQQgAKGxzDt3G9Xk9BgxBCCOERhY1iqPmfVJZerxe6BEII4YU53s/sNmyo1WowDEMBg2darZZbVXHo0KE2t6y7g4MDRCIRMjMz4eXlVeVlwwkhxFKwLAu1Wo2cnByIRCKTLlVgW58ElVRyMCgFDv7o9XokJSVxv9sakUiEkJAQZGVlITMzU+hyCCGkxpycnBAUFGTShSvtLmyUDBq2+IFITMvBwQFBQUHQarXQ6XRCl0MIIdUmFoshkUhM3kJrV2GDprcSvhi64KhVjBBCXsxuwoZhjIYBBQ1CCCHEPKxiufJ79+6hV69e8Pf3R3h4OD799NMq74NaNAghhBBhWEXYGDt2LHr16oWMjAwkJCTgu+++w9GjR6u1LwoahBBCiHlZfDfK48ePcfr0aRw+fBgMw8DPzw9jx47F7t270a1bt1Lbq1Qqo1YMhUIBAHj27BlkMhlYljW6nvBLrVZzx5fJy8sz+1FfhbpvQgixRXl5eQCKpsnWCGvhzp49y/r7+xtd9v3337N9+vQpc/vFixezAOhEJzrRiU50ohNPp7t379bos9ziWzYKCgpKTclhGAYFBQVlbr9gwQLMmTOHO//06VPUq1cPqampcHNzM2mtQsrLy0PdunWRlpYGV1dXocsxGXqctoUep22xl8cJ2M9jVSgUCAoKQp06dWq0H4sPG05OTqWab1iWhZOTU5nby2SyMsdkuLm52fQfhIGrqys9ThtCj9O20OO0PfbyWGu64JfFDxANDg5Gbm4utFotd1lmZibCwsIErIoQQgghlWXxYcPT0xMxMTFYtWoVWJZFdnY2Nm3ahIEDBwpdGiGEEEIqweLDBgBs3LgRBw8ehL+/Pzp16oTx48eje/fulbqtTCbD4sWLbX66Kz1O20KP07bQ47Q99vJY+XqcDFtyQAQhhBBCCI+somWDEEIIIdaLwgYhhBBCTIrCBiGEEEJMisIGIYQQQkyKwgYhhBBCTMqmwwYfh6a3BosWLYKrqyt8fX2505kzZ4Quq8Y0Gg3Gjh0LV1dXvP7660bXrVixAuHh4fD390dsbCySk5MFqrLmynucOp0OEonE6HUdOnSogJXW3OXLl9GlSxf4+fkhNDQUmzZt4q6zpde0vMdpS69pXFwcmjRpAh8fH4SGhmLNmjXcdbb0WgLlP1Zbej2LO3bsGKRSKcaMGcNdVuPXtEZHVrFwXbp0YT/77DNWr9ezmZmZbFhYGPvXX38JXRbvpk6dym7cuFHoMniVl5fHduvWjZ07dy773nvvsSNHjuSuO3r0KBsWFsZmZGSwer2eXblyJdu5c2cBq62+ih5nTk4OW69ePeGK45lSqWT9/f3Z/fv3syxbdJBFmUzGZmdn29RrWtHjtJXXVKvVsu+88w6bnJzMsizLXrx4kWUYhs3NzbWp15JlK36stvJ6FqfRaNjo6Gj27bffZkePHs2yLD/vuTbbsmE4NP2sWbNKHZre1uTm5sLT01PoMnjl5OSEt956CytWrCi1Jv8vv/yCUaNGwd/fHwzDYObMmTh37hweP34sULXVV9HjtLXX1dHREadPn0afPn0AAK1atYKHhwfu3LljU69pRY/TVl5TsViMZcuWISQkBADw8OFDhIaGwsXFxaZeS6Dix2orr2dxq1evRo8ePdCwYUPuMj5eU5sNG/fu3UOdOnUgkfzvWHN+fn5ISkoSsCrTyM3NRXJyMpo0aYLg4GAsWrRI6JJqTCwWo3fv3mVel5ycDF9fX6NtPT09cefOHXOVx5uKHqfhjWzSpEnw8/ND165dce3aNTNXyK/AwEDu97y8PDx9+hShoaE29ZoC5T9OW3tNx44dCxcXF8ycORP79u2DRCKxudfSoKzHamuvZ2ZmJr799lu89957Rpfz8ZrabNio6qHprVnLli3h5OSEq1ev4ujRo9i8eTN27twpdFkmYy+vrbOzM3x9fbFw4UKkpaWhR48eGDZsmNBl8Wb27NkYN24c/Pz8bPo1Lf44be013bhxIxQKBZYtW4Y+ffogPz/fZl/Lsh6rrb2ec+bMwbvvvgsXFxejy/l4TW02bFT10PTWbNmyZRg/fjxEIhFCQkIwePBgJCQkCF2WydjLa9usWTNs2rQJQUFBkEgkmD17Nq5fv261zdEGLMti+vTpyM3Nxeeffw7ANl/Tsh6nLb6mIpEIgwYNgouLC+Lj423ytTQo+Vht6fX866+/kJaWVmowPsDP/6fkxZtYp+KHpjd0pdjqoenVajUcHBy48wzDQCqVCliRaYWEhCA7O5s7r9VqkZOTg/r16wtYlWkUf20N3yys+bXV6XSYMGECFAoF4uLiuP9NW3tNy3ucgO28po8ePTIaryASiaDX623utQTKf6yA7byeBw8exM2bN+Hn5wcAUCqVUKvVuH79Otq2bVvj19RmWzbs5dD0LMuiQ4cO2LVrFwDgwYMH2LNnD3r27ClwZaYzaNAgbN68GZmZmWBZFqtXr0arVq1sbqDWnj170KdPHzx58gQAsG7dOsTExMDV1VXgyqpHq9VixIgRUCqV2L59u9Ebsi29phU9Tlt5Tf/66y/ExMQgLS0NAHDkyBGkpaWhXbt2NvVaAhU/Vlt5PQHgk08+wePHj5GdnY3s7GysXr0ar732Gs6ePcvPa1rjeTIW7O7du2xsbCzr6+vLhoWFsR9//LHQJZnExYsX2U6dOrE+Pj5saGgou3LlSqFL4kV4eDjr4eHByuVyViaTsR4eHmzr1q1ZlmXZTz/9lA0LC2P9/PzYHj16sHfu3BG42uor73FqtVp20aJFbN26dVlfX1+2V69ebFJSktDlVtu9e/dYAKyXlxfr4+PDnRYsWMCyrO28phU9Tlt6Tb/++ms2PDyc9fX1ZaOjo42WFbCV19KgvMdqS69nSRs3buSmvrJszV9TOsQ8IYQQQkzKZrtRCCGEEGIZKGwQQgghxKQobBBCCCHEpChsEEIIIcSkKGwQQgghxKQobBBCCCHEpChsEEIIIcSkKGwQQgghxKQobBBCCCHEpChsEEJ40blzZzAMA4Zh4ODggMaNG2Pr1q1V3oYQYnsobBBCaoxlWVy6dAkrVqxAVlYWbt26hT59+mDUqFG4d+9epbchhNgmChuEkBpLSkrCs2fP0KdPH/j6+iIkJATjxo2DTqfDrVu3/q9d+2dpJAqjMH4Wg6NCwIBFQAhENEgwYGUnVhlIbLTTTtHKIH4AEbWYQksbxcI2pY2WIWI/YcCoTeI/Yhu1iliYrRwUm9nN3tXI8+vuyynudId3buAMgJ+JsgGgZa7rKhKJKJlMSpJqtZpWV1dlWZZSqVTgDICfKfTVFwDQ/kqlkp6enhQOh/X6+qpGo6Hu7m7t7e2pv78/cEaSZmdndXl56Z9vb281Ojqq4eFhjY2NaX5+3p/bts1WBGgDbDYAtMx1XeVyOXmep9PTU01MTGhlZUVzc3N/lJGkfD4vz/PkeZ729/fV1dWlnZ0dlctljYyM+LlyucxGBGgTv5rNZvOrLwGgvUUiEe3u7mpmZkaSdHFxoVQqpUqlong8Hjjz3vX1tcbHx3VwcCDbttXX16e7uzv19PRIkra2tvT8/Kz19fX/9JUA/habDQAtubq60uPj44etQzKZ1ODgoPL5fODMew8PD5qcnNTGxoZs29b9/b16e3v9oiFJZ2dnbDaANkHZANAS13UVCoWUSCQ+zNPptA4PDwNn3ry8vGh6elpTU1NaXFyUJJ2fn38qFvxGAdoHZQNAS0qlkhKJhDo7Oz/M0+m0XNdVrVYLlHmzsLCgaDQqx3H8WbVaVSwW88/1el31el1DQ0OGvgrAv8SbDQDfxtramorFogqFgizL8ufHx8dyHEcnJyfq6OjQ0tKSotGoNjc3v/C2AIKibAD4Fm5ubhSPxzUwMKBwOOzPs9msHMfR8vKyjo6OZFmWMpmMtre3P21KAHxPlA0AAGAUbzYAAIBRlA0AAGAUZQMAABhF2QAAAEZRNgAAgFGUDQA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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure 17.1\n", + "# bias factor and hyperbola\n", + "RR = rr\n", + "RR_L = lower\n", + "xmax = 40\n", + "x = np.arange(0, xmax, 0.01)\n", + "f, ax = plt.subplots(1, 1, figsize=(6, 6))\n", + "ax.plot(x, x, linestyle=\"--\", color=\"grey\", alpha=0.1, linewidth=2)\n", + "ax.set_xlabel(r\"$RR_{ZU}$\")\n", + "ax.set_ylabel(r\"$RR_{UY}$\")\n", + "x = np.arange(RR, xmax, 0.01)\n", + "x_L = np.arange(RR_L, xmax, 0.01)\n", + "y = RR * (RR - 1) / (x - RR) + RR\n", + "y_L = RR_L * (RR_L - 1) / (x_L - RR_L) + RR_L\n", + "ax.axhline(y=RR, linestyle=\"-\", color=\"grey\")\n", + "ax.axhline(y=RR_L, linestyle=\"--\", color=\"grey\")\n", + "ax.axvline(x=RR, linestyle=\"-\", color=\"grey\")\n", + "ax.axvline(x=RR_L, linestyle=\"--\", color=\"grey\")\n", + "ax.plot(x, y, linestyle=\"-\", color=\"black\")\n", + "ax.plot(x_L, y_L, linestyle=\"--\", color=\"black\")\n", + "high = RR + np.sqrt(RR * (RR - 1))\n", + "high_L = RR_L + np.sqrt(RR_L * (RR_L - 1))\n", + "ax.scatter(high, high, marker=\"o\", color=\"black\")\n", + "ax.scatter(high_L, high_L, marker=\"o\", color=\"black\")\n", + "ax.text(high_L + 5, high_L, \"(15.52, 15.52)\")\n", + "ax.text(high + 5, high, \"(20.95, 20.95)\")\n", + "ax.set_xlim(0, 40)\n", + "ax.set_ylim(0, 40)\n", + "ax.legend(\n", + " [\n", + " r\"$RR_{ZU} * RR_{UY} / (RR_{ZU} + RR_{UY} - 1) = 10.73$\",\n", + " r\"$RR_{ZU} * RR_{UY} / (RR_{ZU} + RR_{UY} - 1) = 8.02$\",\n", + " ],\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## application" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " mar MINDEXSUM PTbirth smoking drinking hispanic black \\\n", + "0 1 4 0 1 0 0 0 \n", + "1 1 1 0 1 0 0 0 \n", + "2 2 0 0 0 0 0 0 \n", + "3 2 0 0 1 0 0 0 \n", + "4 1 0 0 0 0 0 0 \n", + "\n", + " nativeamerican asian agebelow20 ageabove35 somecollege preeclampsia \n", + "0 0 0 0.0 0.0 1.0 0.0 \n", + "1 0 0 0.0 0.0 0.0 0.0 \n", + "2 0 0 1.0 0.0 0.0 NaN \n", + "3 1 0 0.0 0.0 0.0 0.0 \n", + "4 0 0 0.0 0.0 1.0 0.0 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "NCHS2003 = pd.read_table(\"NCHS2003.txt\", sep=\"\\s+\")\n", + "NCHS2003.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_logit = pf.feglm(\n", + " \"\"\"PTbirth ~ ageabove35 + mar + smoking + drinking + somecollege\n", + " + hispanic + black + nativeamerican + asian\"\"\",\n", + " data=NCHS2003,\n", + " family=\"logit\",\n", + " vcov=\"hetero\",\n", + ")\n", + "est, se = y_logit.coef().loc[\"ageabove35\"], y_logit.se().loc[\"ageabove35\"]\n", + "est, se\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.306834493057289\n", + "1.9400658087603366\n" + ] + } + ], + "source": [ + "est, lower_ci = np.exp(est), np.exp(est - 1.96 * se)\n", + "print(est)\n", + "print(evalue(est))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.295552682600102\n", + "1.9143451120885522\n" + ] + } + ], + "source": [ + "print(lower_ci)\n", + "print(evalue(lower_ci))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, ax = plt.subplots(1, 3, figsize=(10, 4))\n", + "RR1 = np.arange(1, 1.5, 0.01)\n", + "ax[0].plot(RR1, evalue(RR1), linestyle=\"-\", color=\"black\")\n", + "ax[0].set_xlabel(r\"$RR$\")\n", + "ax[0].set_ylabel(r\"E-Value\")\n", + "ax[0].set_xlim(1, 1.5)\n", + "\n", + "RR2 = np.arange(1, 3, 0.01)\n", + "ax[1].plot(RR2, evalue(RR2), linestyle=\"-\", color=\"black\")\n", + "ax[1].set_xlabel(r\"$RR$\")\n", + "ax[1].set_ylabel(r\"$RR$\")\n", + "ax[1].set_xlim(1, 3)\n", + "\n", + "RR3 = np.arange(1, 10, 0.01)\n", + "ax[2].plot(RR3, evalue(RR3), linestyle=\"-\", color=\"black\")\n", + "ax[2].set_xlabel(r\"$RR$\")\n", + "ax[2].set_ylabel(r\"$RR$\")\n", + "ax[2].set_xlim(1, 10)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "metrics", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 2d4c39268edf2832bad8c7661d50723ae908e8b8 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:29 +0200 Subject: [PATCH 19/20] Add PyFixest Chapter 18 notebook --- pyfixest/Chapter18SensitivityAnalysis.ipynb | 304 ++++++++++++++++++++ 1 file changed, 304 insertions(+) create mode 100644 pyfixest/Chapter18SensitivityAnalysis.ipynb diff --git a/pyfixest/Chapter18SensitivityAnalysis.ipynb b/pyfixest/Chapter18SensitivityAnalysis.ipynb new file mode 100644 index 0000000..39fd83a --- /dev/null +++ b/pyfixest/Chapter18SensitivityAnalysis.ipynb @@ -0,0 +1,304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 18: Sensitivity Analysis for the Average Causal Effect with Unmeasured Confounding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import pyfixest as pf\n", + "from utils import *\n", + "\n", + "np.random.seed(42)\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def OS_est(z, y, x, lb=0, ub=1, e1=1, e0=1):\n", + " covariates = pd.DataFrame(np.asarray(x))\n", + " covariates.columns = [f\"x{i}\" for i in range(covariates.shape[1])]\n", + " data = covariates.assign(y=y, z=z)\n", + " rhs = \" + \".join(covariates.columns)\n", + " pscore = pf.feglm(\"z ~ \" + rhs, data=data, family=\"logit\").predict(\n", + " data, type=\"response\"\n", + " )\n", + " pscore = np.clip(pscore, lb, ub)\n", + " # fitted potential outcomes\n", + " outcome1 = pf.feols(\"y ~ \" + rhs, data=data.loc[data.z == 1]).predict(data)\n", + " outcome0 = pf.feols(\"y ~ \" + rhs, data=data.loc[data.z == 0]).predict(data)\n", + "\n", + " ## outcome regression estimator\n", + " ace_reg = (\n", + " np.mean(z * y)\n", + " + np.mean((1 - z) * outcome1 / e1)\n", + " - np.mean(z * outcome0 * e0)\n", + " - np.mean((1 - z) * y)\n", + " )\n", + " ## IPW estimators\n", + " w1 = pscore + (1 - pscore) / e1\n", + " w0 = pscore * e0 + (1 - pscore)\n", + " ace_ipw0 = np.mean(z * y * w1 / pscore) - np.mean((1 - z) * y * w0 / (1 - pscore))\n", + " ace_ipw = np.mean(z * y * w1 / pscore) / np.mean(z / pscore) - np.mean(\n", + " (1 - z) * y * w0 / (1 - pscore)\n", + " ) / np.mean((1 - z) / (1 - pscore))\n", + " ## doubly robust estimator\n", + " aug = outcome1 / pscore / e1 + outcome0 * e0 / (1 - pscore)\n", + " ace_dr = ace_ipw0 - np.mean((z - pscore) * aug)\n", + "\n", + " return np.array([ace_reg, ace_ipw0, ace_ipw, ace_dr])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "nhanes_bmi = pd.read_csv(\"nhanes_bmi.csv\").iloc[:, 1:]\n", + "z, y, x = (\n", + " nhanes_bmi.School_meal.values,\n", + " nhanes_bmi.BMI.values,\n", + " nhanes_bmi.iloc[:, 2:].values,\n", + ")\n", + "x = (x - x.mean(0)) / x.std(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "E1 = np.array([1 / 2, 1 / 1.7, 1 / 1.5, 1 / 1.3, 1, 1.3, 1.5, 1.7, 2])\n", + "E0 = E1.copy()\n", + "est = np.zeros((len(E1), len(E0)))\n", + "\n", + "for i in range(len(E1)):\n", + " for j in range(len(E0)):\n", + " est[i, j] = OS_est(z, y, x, e1=E1[i], e0=E0[j])[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"source": [ + "pd.DataFrame(est, columns=E0, index=E1).round(2)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "metrics", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From ca91daa4c1627b7ade57f51541c314396dbb13fb Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Thu, 30 Jul 2026 23:44:29 +0200 Subject: [PATCH 20/20] Add PyFixest Chapter 19 notebook --- pyfixest/Chapter19RosenbaumPvalues.ipynb | 1414 ++++++++++++++++++++++ 1 file changed, 1414 insertions(+) create mode 100644 pyfixest/Chapter19RosenbaumPvalues.ipynb diff --git a/pyfixest/Chapter19RosenbaumPvalues.ipynb b/pyfixest/Chapter19RosenbaumPvalues.ipynb new file mode 100644 index 0000000..a39b20b --- /dev/null +++ b/pyfixest/Chapter19RosenbaumPvalues.ipynb @@ -0,0 +1,1414 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 19: Rosenbaum-Style p-Values for Matched Observational Studies with Unobserved Confounding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import scipy as sp\n", + "\n", + "# viz\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "font = {\"family\": \"IBM Plex Sans Condensed\", \"weight\": \"normal\", \"size\": 10}\n", + "plt.rc(\"font\", **font)\n", + "plt.rcParams[\"figure.figsize\"] = (10, 10)\n", + "%matplotlib inline\n", + "\n", + "from utils import *\n", + "\n", + "np.random.seed(42)\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "\n", + "%load_ext watermark\n", + "%watermark --iversions\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "dat = pd.read_table(\"cps1re74.csv\", sep=\"\\s+\")\n", + "dat[\"u74\"] = (dat[\"re74\"] == 0).astype(int)\n", + "dat[\"u75\"] = (dat[\"re75\"] == 0).astype(int)\n", + "y, z = dat.re78.values, dat.treat.values\n", + "X = dat[\n", + " [\n", + " \"age\",\n", + " \"educ\",\n", + " \"black\",\n", + " \"hispan\",\n", + " \"married\",\n", + " \"nodegree\",\n", + " \"re74\",\n", + " \"re75\",\n", + " \"u74\",\n", + " \"u75\",\n", + " ]\n", + "].values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neighbors import NearestNeighbors\n", + "\n", + "# Matching is outside PyFixest's estimator scope.\n", + "matches = (\n", + " NearestNeighbors(n_neighbors=1)\n", + " .fit(X[z == 0, :])\n", + " .kneighbors(X[z == 1, :], n_neighbors=1, return_distance=False)\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1521.376503243242" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ytreated, yctrl = y[z == 1], y[z == 0][matches.flatten()]\n", + "datamatched = np.c_[ytreated, yctrl]\n", + "matched_means = datamatched.mean(axis=0)\n", + "matched_means[0] - matched_means[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "# run sensitivity analysis in R\n", + "import rpy2.robjects as ro\n", + "\n", + "sens = ro.packages.importr(\"sensitivitymw\")\n", + "ro.numpy2ri.activate()\n", + "\n", + "Gamma = np.arange(1, 1.4, 0.001)\n", + "Pvalue = [sens.senmw(datamatched, gamma)[0][0] for gamma in Gamma]" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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