From d46df243717bc88e89a939d254726876df13e143 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 5 Feb 2025 12:48:44 +0000 Subject: [PATCH 001/140] change links to stable --- doc/examples/py_double_ml_cate.ipynb | 4 ++-- doc/examples/py_double_ml_cate_plr.ipynb | 2 +- doc/examples/py_double_ml_gate.ipynb | 2 +- doc/examples/py_double_ml_gate_plr.ipynb | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/doc/examples/py_double_ml_cate.ipynb b/doc/examples/py_double_ml_cate.ipynb index ffaa14bf..a6d4d2f6 100644 --- a/doc/examples/py_double_ml_cate.ipynb +++ b/doc/examples/py_double_ml_cate.ipynb @@ -33,7 +33,7 @@ "\n", "We define a data generating process to create synthetic data to compare the estimates to the true effect. The data generating process is based on the Monte Carlo simulation from [Oprescu et al. (2019)](http://proceedings.mlr.press/v97/oprescu19a.html).\n", "\n", - "The documentation of the data generating process can be found [here](https://docs.doubleml.org/dev/api/datasets.html#dataset-generators)." + "The documentation of the data generating process can be found [here](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)." ] }, { @@ -409,7 +409,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.2" + "version": "3.12.3" }, "vscode": { "interpreter": { diff --git a/doc/examples/py_double_ml_cate_plr.ipynb b/doc/examples/py_double_ml_cate_plr.ipynb index ef9dccd3..5293794b 100644 --- a/doc/examples/py_double_ml_cate_plr.ipynb +++ b/doc/examples/py_double_ml_cate_plr.ipynb @@ -30,7 +30,7 @@ "\n", "We define a data generating process to create synthetic data to compare the estimates to the true effect. The data generating process is based on the Monte Carlo simulation from [Oprescu et al. (2019)](http://proceedings.mlr.press/v97/oprescu19a.html).\n", "\n", - "The documentation of the data generating process can be found [here](https://docs.doubleml.org/dev/api/datasets.html#dataset-generators)." + "The documentation of the data generating process can be found [here](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)." ] }, { diff --git a/doc/examples/py_double_ml_gate.ipynb b/doc/examples/py_double_ml_gate.ipynb index adf83167..85853443 100644 --- a/doc/examples/py_double_ml_gate.ipynb +++ b/doc/examples/py_double_ml_gate.ipynb @@ -33,7 +33,7 @@ "\n", "We define a data generating process to create synthetic data to compare the estimates to the true effect. The data generating process is based on the Monte Carlo simulation from [Oprescu et al. (2019)](http://proceedings.mlr.press/v97/oprescu19a.html).\n", "\n", - "The documentation of the data generating process can be found [here](https://docs.doubleml.org/dev/api/datasets.html#dataset-generators). In this example the true effect depends only the first covariate $X_0$ and takes the following form\n", + "The documentation of the data generating process can be found [here](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators). In this example the true effect depends only the first covariate $X_0$ and takes the following form\n", "\n", "$$\n", "\\theta_0(X) = \\exp(2X_0) + 3\\sin(4X_0).\n", diff --git a/doc/examples/py_double_ml_gate_plr.ipynb b/doc/examples/py_double_ml_gate_plr.ipynb index b8308fc8..de784fc6 100644 --- a/doc/examples/py_double_ml_gate_plr.ipynb +++ b/doc/examples/py_double_ml_gate_plr.ipynb @@ -30,7 +30,7 @@ "\n", "We define a data generating process to create synthetic data to compare the estimates to the true effect. The data generating process is based on the Monte Carlo simulation from [Oprescu et al. (2019)](http://proceedings.mlr.press/v97/oprescu19a.html).\n", "\n", - "The documentation of the data generating process can be found [here](https://docs.doubleml.org/dev/api/datasets.html#dataset-generators). In this example the true effect depends only the first covariate $X_0$ and takes the following form\n", + "The documentation of the data generating process can be found [here](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators). In this example the true effect depends only the first covariate $X_0$ and takes the following form\n", "\n", "$$\n", "\\theta_0(X) = \\exp(2X_0) + 3\\sin(4X_0).\n", From b66fd86c25d2459d4635e55f99dbb0fd71f4e3e0 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 5 Feb 2025 16:12:20 +0000 Subject: [PATCH 002/140] add irm vs apo example notebook --- doc/examples/index.rst | 1 + doc/examples/py_double_ml_irm_vs_apo.ipynb | 618 +++++++++++++++++++++ 2 files changed, 619 insertions(+) create mode 100644 doc/examples/py_double_ml_irm_vs_apo.ipynb diff --git a/doc/examples/index.rst b/doc/examples/index.rst index f363e19e..d200adee 100644 --- a/doc/examples/index.rst +++ b/doc/examples/index.rst @@ -21,6 +21,7 @@ General Examples py_double_ml_pension.ipynb py_double_ml_sensitivity.ipynb py_double_ml_apo.ipynb + py_double_ml_irm_vs_apo.ipynb py_double_ml_learner.ipynb py_double_ml_firststage.ipynb py_double_ml_multiway_cluster.ipynb diff --git a/doc/examples/py_double_ml_irm_vs_apo.ipynb b/doc/examples/py_double_ml_irm_vs_apo.ipynb new file mode 100644 index 00000000..396f5fd2 --- /dev/null +++ b/doc/examples/py_double_ml_irm_vs_apo.ipynb @@ -0,0 +1,618 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python: IRM and APO Model Comparison\n", + "\n", + "In this simple example, we illustrate how the (binary) [DoubleMLIRM](https://docs.doubleml.org/stable/guide/models.html#binary-interactive-regression-model-irm) model and the [DoubleMLAPOS](https://docs.doubleml.org/stable/guide/models.html#average-potential-outcomes-apos-for-multiple-treatment-levels) differ.\n", + "\n", + "More specifically, we focus on the `causal_contrast()` method of [DoubleMLAPOS](https://docs.doubleml.org/stable/guide/models.html#average-potential-outcomes-apos-for-multiple-treatment-levels) in a binary setting to highlight, when both methods coincide." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import doubleml as dml\n", + "\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "\n", + "from doubleml.datasets import make_irm_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "\n", + "We rely on the [make_irm_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_irm_data.html) go generate data with a binary treatment." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " X1 X2 X3 X4 X5 X6 X7 \\\n", + "0 0.541060 0.195963 0.835750 1.180271 0.655959 1.044239 -1.214769 \n", + "1 1.340235 2.319420 2.193375 1.139508 1.458814 0.493195 1.313870 \n", + "2 -0.563563 -1.480199 0.943548 -0.400113 0.757559 0.131483 -0.574160 \n", + "3 -0.044176 -2.122421 -1.526582 -1.892828 -1.777867 -1.425325 0.020272 \n", + "4 -1.896263 -1.198493 -1.285483 -1.361623 -2.921778 -2.026966 -1.107156 \n", + "\n", + " X8 X9 X10 y d \n", + "0 -2.160836 -2.196655 -2.037529 4.704558 1.0 \n", + "1 0.127337 0.418400 1.070433 6.113952 1.0 \n", + "2 0.067212 -0.427654 -1.342117 -0.226479 0.0 \n", + "3 0.487524 -0.579197 -0.752909 0.367366 0.0 \n", + "4 -0.498122 0.568287 1.004542 0.913585 0.0 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_obs = 2000\n", + "\n", + "np.random.seed(42)\n", + "df = make_irm_data(\n", + " n_obs=n_obs,\n", + " dim_x=10,\n", + " theta=5.0,\n", + " return_type='DataFrame'\n", + ")\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, define the ``DoubleMLData`` object." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "dml_data = dml.DoubleMLData(\n", + " df,\n", + " y_col='y',\n", + " d_cols='d'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learners and Hyperparameters\n", + "\n", + "To simplify the comparison and keep the variation in learners as small as possible, we will use linear models." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "n_folds = 5\n", + "n_rep = 1\n", + "\n", + "dml_kwargs = {\n", + " \"obj_dml_data\": dml_data,\n", + " \"ml_g\": LinearRegression(),\n", + " \"ml_m\": LogisticRegression(random_state=42),\n", + " \"n_folds\": n_folds,\n", + " \"n_rep\": n_rep,\n", + " \"trimming_threshold\": 1e-2,\n", + " \"draw_sample_splitting\": False,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Remark:**\n", + "All results rely on the exact same predictions for the machine learning algorithms. If the more than two treatment levels exists the `DoubleMLAPOS` model fit multiple binary models such that the combined model might differ." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further, to remove all uncertainty from sample splitting, we will rely on externally provided sample splits." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from doubleml.utils import DoubleMLResampling\n", + "\n", + "rskf = DoubleMLResampling(\n", + " n_folds=n_folds,\n", + " n_rep=n_rep,\n", + " n_obs=n_obs,\n", + " stratify=df['d'],\n", + ")\n", + "all_smpls = rskf.split_samples()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Average Treatment Effect\n", + "\n", + "Comparing the effect estimates for the `DoubleMLIRM` and `causal_contrasts` of the `DoubleMLAPOS` model, we can numerically equivalent results for the ATE." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training IRM Model\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 5.001907 0.066295 75.449677 0.0 4.871972 5.131842\n" + ] + } + ], + "source": [ + "dml_irm = dml.DoubleMLIRM(**dml_kwargs)\n", + "dml_irm.set_sample_splitting(all_smpls)\n", + "print(\"Training IRM Model\")\n", + "dml_irm.fit()\n", + "\n", + "print(dml_irm.summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training APOS Model\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "0 0.038103 0.045984 0.828618 0.40732 -0.052023 0.128229\n", + "1 5.040010 0.047873 105.278454 0.00000 4.946180 5.133839\n", + "Evaluate Causal Contrast\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1 vs 0 5.001907 0.066295 75.449677 0.0 4.871972 5.131842\n" + ] + } + ], + "source": [ + "dml_apos = dml.DoubleMLAPOS(treatment_levels=[0,1], **dml_kwargs)\n", + "dml_apos.set_sample_splitting(all_smpls)\n", + "print(\"Training APOS Model\")\n", + "dml_apos.fit()\n", + "print(dml_apos.summary)\n", + "\n", + "print(\"Evaluate Causal Contrast\")\n", + "causal_contrast = dml_apos.causal_contrast(reference_levels=[0])\n", + "print(causal_contrast.summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a direct comparison, see" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IRM Model\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 5.001907 0.066295 75.449677 0.0 4.871972 5.131842\n", + "Causal Contrast\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1 vs 0 5.001907 0.066295 75.449677 0.0 4.871972 5.131842\n" + ] + } + ], + "source": [ + "print(\"IRM Model\")\n", + "print(dml_irm.summary)\n", + "print(\"Causal Contrast\")\n", + "print(causal_contrast.summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Average Treatment Effect on the Treated\n", + "\n", + "For the average treatment effect on the treated we can adjust the score in `DoubleMLIRM` model to `score=\"ATTE\"`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training IRM Model\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 5.540549 0.08364 66.242427 0.0 5.376617 5.704482\n" + ] + } + ], + "source": [ + "dml_irm_atte = dml.DoubleMLIRM(score=\"ATTE\", **dml_kwargs)\n", + "dml_irm_atte .set_sample_splitting(all_smpls)\n", + "print(\"Training IRM Model\")\n", + "dml_irm_atte.fit()\n", + "\n", + "print(dml_irm_atte.summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In to consider weighted effects in the `DoubleMLAPOS` model, we have to specify the correct weight, see [User Guide](https://docs.doubleml.org/stable/guide/heterogeneity.html#weighted-average-treatment-effects).\n", + "\n", + "As these weights include the propensity score, we will use the predicted propensity score from the previous `DoubleMLIRM` model.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training APOS Model\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "0 0.044929 0.073384 0.612246 0.540375 -0.098901 0.188760\n", + "1 5.585479 0.039895 140.003045 0.000000 5.507285 5.663672\n", + "Evaluate Causal Contrast\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1 vs 0 5.540549 0.08364 66.242427 0.0 5.376617 5.704482\n" + ] + } + ], + "source": [ + "p_hat = df[\"d\"].mean()\n", + "m_hat = dml_irm_atte.predictions[\"ml_m\"][:, :, 0]\n", + "\n", + "weights_dict = {\n", + " \"weights\": df[\"d\"] / p_hat,\n", + " \"weights_bar\": m_hat / p_hat,\n", + "}\n", + "\n", + "dml_apos_atte = dml.DoubleMLAPOS(treatment_levels=[0,1], weights=weights_dict, **dml_kwargs)\n", + "dml_apos_atte.set_sample_splitting(all_smpls)\n", + "print(\"Training APOS Model\")\n", + "dml_apos_atte.fit()\n", + "print(dml_apos_atte.summary)\n", + "\n", + "print(\"Evaluate Causal Contrast\")\n", + "causal_contrast_atte = dml_apos_atte.causal_contrast(reference_levels=[0])\n", + "print(causal_contrast_atte.summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The point estimates are equal but on closer comparison the standard errors and confidence intervals are larger in the causal contrast example.\n", + "\n", + "This is to be expected as `score=ATTE` actually " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training IRM Model\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 5.540549 0.08364 66.242427 0.0 5.376617 5.704482\n" + ] + } + ], + "source": [ + "dml_irm_weighted_atte = dml.DoubleMLIRM(score=\"ATE\", weights=weights_dict, **dml_kwargs)\n", + "dml_irm_weighted_atte .set_sample_splitting(all_smpls)\n", + "print(\"Training IRM Model\")\n", + "dml_irm_weighted_atte.fit()\n", + "\n", + "print(dml_irm_weighted_atte.summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In summary, see" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IRM Model ATTE Score\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 5.5405 0.0836 66.2424 0.0 5.3766 5.7045\n", + "IRM Model (Weighted)\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 5.5405 0.0836 66.2424 0.0 5.3766 5.7045\n", + "Causal Contrast (Weighted)\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1 vs 0 5.5405 0.0836 66.2424 0.0 5.3766 5.7045\n" + ] + } + ], + "source": [ + "print(\"IRM Model ATTE Score\")\n", + "print(dml_irm_atte.summary.round(4))\n", + "print(\"IRM Model (Weighted)\")\n", + "print(dml_irm_weighted_atte.summary.round(4))\n", + "print(\"Causal Contrast (Weighted)\")\n", + "print(causal_contrast_atte.summary.round(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sensitivity Analysis\n", + "\n", + "There exist also slight differences with respect to the bounds in the sensitivity analysis. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== Sensitivity Analysis ==================\n", + "\n", + "------------------ Scenario ------------------\n", + "Significance Level: level=0.95\n", + "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", + "\n", + "------------------ Bounds with CI ------------------\n", + " CI lower theta lower theta theta upper CI upper\n", + "d 4.773339 4.890665 5.001907 5.113149 5.217684\n", + "\n", + "------------------ Robustness Values ------------------\n", + " H_0 RV (%) RVa (%)\n", + "d 0.0 72.206256 66.239019\n" + ] + } + ], + "source": [ + "dml_irm.sensitivity_analysis()\n", + "print(dml_irm.sensitivity_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== Sensitivity Analysis ==================\n", + "\n", + "------------------ Scenario ------------------\n", + "Significance Level: level=0.95\n", + "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", + "\n", + "------------------ Bounds with CI ------------------\n", + " CI lower theta lower theta theta upper CI upper\n", + "1 vs 0 4.72215 4.844805 5.001907 5.159008 5.26271\n", + "\n", + "------------------ Robustness Values ------------------\n", + " H_0 RV (%) RVa (%)\n", + "1 vs 0 0.0 60.755008 54.175559\n" + ] + } + ], + "source": [ + "causal_contrast.sensitivity_analysis()\n", + "print(causal_contrast.sensitivity_summary)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 6fcd615b3179f8353d8e7192f32f58896c2e99a7 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 7 Feb 2025 11:47:07 +0000 Subject: [PATCH 003/140] update examples --- doc/examples/py_double_ml_apo.ipynb | 11955 ++++++++++++++++++- doc/examples/py_double_ml_irm_vs_apo.ipynb | 108 +- 2 files changed, 11986 insertions(+), 77 deletions(-) diff --git a/doc/examples/py_double_ml_apo.ipynb b/doc/examples/py_double_ml_apo.ipynb index d36d136b..68f353b0 100644 --- a/doc/examples/py_double_ml_apo.ipynb +++ b/doc/examples/py_double_ml_apo.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -55,9 +55,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Individual effects in each group:\n", + "[ 0. 1.75 7.03 9.43 10.4 10.49]\n", + "\n", + "Average Potential Outcomes in each group:\n", + "[210.04 211.79 217.06 219.47 220.44 220.53]\n", + "\n", + "Levels and their counts:\n", + "(array([0., 1., 2., 3., 4., 5.]), array([615, 487, 465, 482, 480, 471]))\n" + ] + } + ], "source": [ "# Parameters\n", "n_obs = 3000\n", @@ -97,9 +112,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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treatment_levelapothetaci_lowerci_upper
00.0210.036240210.077702208.768798211.386831
11.0211.785815211.881937210.545492213.218383
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" + ], + "text/plain": [ + " treatment_level apo theta ci_lower ci_upper\n", + "0 0.0 210.036240 210.077702 208.768798 211.386831\n", + "1 1.0 211.785815 211.881937 210.545492 213.218383\n", + "2 2.0 217.063017 217.069443 215.750701 218.388185\n", + "3 3.0 219.468907 219.404300 218.096418 220.712095\n", + "4 4.0 220.439699 220.503700 219.186589 221.820963\n", + "5 5.0 220.525064 220.417834 219.095104 221.740505" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.random.seed(42)\n", "\n", @@ -250,9 +395,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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treatment_levelapothetaci_lowerci_upper
00.0210.036240210.077702208.766940211.384677
11.0211.785815211.881937210.553004213.225427
22.0217.063017217.069443215.756200218.393654
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44.0220.439699220.503700219.192952221.828157
55.0220.525064220.417834219.095785221.741523
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" + ], + "text/plain": [ + " treatment_level apo theta ci_lower ci_upper\n", + "0 0.0 210.036240 210.077702 208.766940 211.384677\n", + "1 1.0 211.785815 211.881937 210.553004 213.225427\n", + "2 2.0 217.063017 217.069443 215.756200 218.393654\n", + "3 3.0 219.468907 219.404300 218.108259 220.723846\n", + "4 4.0 220.439699 220.503700 219.192952 221.828157\n", + "5 5.0 220.525064 220.417834 219.095785 221.741523" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dml_obj = dml.DoubleMLAPOS(\n", " dml_data,\n", @@ -323,9 +575,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " 2.5 % 97.5 %\n", + "0.0 208.642329 211.518478\n", + "1.0 210.419871 213.355065\n", + "2.0 215.622272 218.521233\n", + "3.0 217.975289 220.850038\n", + "4.0 219.058375 221.962364\n", + "5.0 218.968127 221.878746" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dml_obj.bootstrap(n_rep_boot=2000)\n", "ci_joint = dml_obj.confint(level=0.95, joint=True)\n", @@ -375,9 +713,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== Sensitivity Analysis ==================\n", + "\n", + "------------------ Scenario ------------------\n", + "Significance Level: level=0.95\n", + "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", + "\n", + "------------------ Bounds with CI ------------------\n", + " CI lower theta lower theta theta upper CI upper\n", + "0 208.792396 209.890855 210.075809 210.260762 211.359229\n", + "1 210.543052 211.663177 211.889638 212.115901 213.238619\n", + "2 215.752696 216.858952 217.074927 217.290901 218.398166\n", + "3 218.114989 219.212811 219.416052 219.619294 220.716615\n", + "4 219.189739 220.296099 220.510555 220.725010 221.830273\n", + "5 219.072605 220.184224 220.418741 220.652324 221.761224\n", + "\n", + "------------------ Robustness Values ------------------\n", + " H_0 RV (%) RVa (%)\n", + "0 0.0 99.916359 99.909571\n", + "1 0.0 99.877903 99.861019\n", + "2 0.0 99.893461 99.882928\n", + "3 0.0 99.907879 99.899250\n", + "4 0.0 99.898183 99.888352\n", + "5 0.0 99.878895 99.864664\n" + ] + } + ], "source": [ "dml_obj.sensitivity_analysis()\n", "print(dml_obj.sensitivity_summary)" @@ -392,9 +760,98 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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cf_ycf_drhodelta_theta
0.00.00.0000000.00.000006
1.00.00.000000-1.0-0.004253
2.00.00.0000001.00.003220
3.00.00.000000-1.0-0.004526
4.00.00.0034151.00.003404
5.00.00.000000-1.0-0.006055
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" + ], + "text/plain": [ + " cf_y cf_d rho delta_theta\n", + "0.0 0.0 0.000000 0.0 0.000006\n", + "1.0 0.0 0.000000 -1.0 -0.004253\n", + "2.0 0.0 0.000000 1.0 0.003220\n", + "3.0 0.0 0.000000 -1.0 -0.004526\n", + "4.0 0.0 0.003415 1.0 0.003404\n", + "5.0 0.0 0.000000 -1.0 -0.006055" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dml_obj.sensitivity_benchmark(benchmarking_set=['x4'])" ] @@ -426,9 +883,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1.0 vs 0.0 1.810306 0.180143 10.049264 0.0 1.454406 2.165707\n", + "2.0 vs 0.0 6.994208 0.145027 48.226969 0.0 6.710059 7.278035\n", + "3.0 vs 0.0 9.335446 0.135344 68.975592 0.0 9.068934 9.600776\n", + "4.0 vs 0.0 10.431998 0.141460 73.745022 0.0 10.155160 10.708837\n", + "5.0 vs 0.0 10.342362 0.155174 66.650234 0.0 10.039141 10.645583\n" + ] + } + ], "source": [ "causal_contrast_model = dml_obj.causal_contrast(reference_levels=0)\n", "print(causal_contrast_model.summary)" @@ -443,9 +913,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ates = causal_contrast_model.thetas\n", "ci_ates = causal_contrast_model.confint(level=0.95)\n", @@ -484,9 +965,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== Sensitivity Analysis ==================\n", + "\n", + "------------------ Scenario ------------------\n", + "Significance Level: level=0.95\n", + "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", + "\n", + "------------------ Bounds with CI ------------------\n", + " CI lower theta lower theta theta upper CI upper\n", + "1.0 vs 0.0 1.219585 1.518854 1.810306 2.101998 2.400905\n", + "2.0 vs 0.0 6.469676 6.708821 6.994208 7.279595 7.517798\n", + "3.0 vs 0.0 8.835344 9.060016 9.335446 9.610318 9.833065\n", + "4.0 vs 0.0 9.914598 10.148005 10.431998 10.716098 10.947855\n", + "5.0 vs 0.0 9.786986 10.040784 10.342362 10.643939 10.899654\n", + "\n", + "------------------ Robustness Values ------------------\n", + " H_0 RV (%) RVa (%)\n", + "1.0 vs 0.0 0.0 17.204893 14.534139\n", + "2.0 vs 0.0 0.0 51.844663 50.155423\n", + "3.0 vs 0.0 0.0 62.924443 61.490896\n", + "4.0 vs 0.0 0.0 65.700314 64.285001\n", + "5.0 vs 0.0 0.0 63.327958 61.784872\n" + ] + } + ], "source": [ "causal_contrast_model.sensitivity_analysis()\n", "print(causal_contrast_model.sensitivity_summary)" @@ -501,9 +1010,11393 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "contours": { + "coloring": "heatmap", + "labelfont": { + "color": "white", + "size": 12 + }, + "showlabels": true + }, + "hovertemplate": "cf_d: %{x:.3f}
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "causal_contrast_model.sensitivity_plot(idx_treatment=0)" ] @@ -532,7 +12425,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/doc/examples/py_double_ml_irm_vs_apo.ipynb b/doc/examples/py_double_ml_irm_vs_apo.ipynb index 396f5fd2..375e4879 100644 --- a/doc/examples/py_double_ml_irm_vs_apo.ipynb +++ b/doc/examples/py_double_ml_irm_vs_apo.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -171,7 +171,7 @@ "4 -0.498122 0.568287 1.004542 0.913585 0.0 " ] }, - "execution_count": 2, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -221,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -234,6 +234,7 @@ " \"ml_m\": LogisticRegression(random_state=42),\n", " \"n_folds\": n_folds,\n", " \"n_rep\": n_rep,\n", + " \"normalize_ipw\": True,\n", " \"trimming_threshold\": 1e-2,\n", " \"draw_sample_splitting\": False,\n", "}" @@ -256,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -282,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -291,7 +292,7 @@ "text": [ "Training IRM Model\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 5.001907 0.066295 75.449677 0.0 4.871972 5.131842\n" + "d 5.002585 0.066201 75.566262 0.0 4.872833 5.132337\n" ] } ], @@ -306,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -314,12 +315,12 @@ "output_type": "stream", "text": [ "Training APOS Model\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "0 0.038103 0.045984 0.828618 0.40732 -0.052023 0.128229\n", - "1 5.040010 0.047873 105.278454 0.00000 4.946180 5.133839\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "0 0.037800 0.045201 0.836262 0.403008 -0.050793 0.126392\n", + "1 5.040385 0.048482 103.965037 0.000000 4.945363 5.135407\n", "Evaluate Causal Contrast\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "1 vs 0 5.001907 0.066295 75.449677 0.0 4.871972 5.131842\n" + "1 vs 0 5.002585 0.066201 75.566262 0.0 4.872833 5.132337\n" ] } ], @@ -335,6 +336,27 @@ "print(causal_contrast.summary)" ] }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[1.01471103],\n", + " [1.01471103]]])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dml_apos.sensitivity_elements[\"sigma2\"]" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -344,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -353,10 +375,10 @@ "text": [ "IRM Model\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 5.001907 0.066295 75.449677 0.0 4.871972 5.131842\n", + "d 5.002585 0.066201 75.566262 0.0 4.872833 5.132337\n", "Causal Contrast\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "1 vs 0 5.001907 0.066295 75.449677 0.0 4.871972 5.131842\n" + "1 vs 0 5.002585 0.066201 75.566262 0.0 4.872833 5.132337\n" ] } ], @@ -378,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -386,8 +408,8 @@ "output_type": "stream", "text": [ "Training IRM Model\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 5.540549 0.08364 66.242427 0.0 5.376617 5.704482\n" + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 5.541136 0.082383 67.260366 0.0 5.379668 5.702605\n" ] } ], @@ -411,26 +433,20 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Training APOS Model\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Training APOS Model\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "0 0.044929 0.073384 0.612246 0.540375 -0.098901 0.188760\n", - "1 5.585479 0.039895 140.003045 0.000000 5.507285 5.663672\n", + "0 0.044364 0.072137 0.615000 0.538555 -0.097022 0.185751\n", + "1 5.585498 0.040281 138.663329 0.000000 5.506548 5.664447\n", "Evaluate Causal Contrast\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1 vs 0 5.540549 0.08364 66.242427 0.0 5.376617 5.704482\n" + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1 vs 0 5.541133 0.082736 66.974012 0.0 5.378975 5.703292\n" ] } ], @@ -465,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -473,8 +489,8 @@ "output_type": "stream", "text": [ "Training IRM Model\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 5.540549 0.08364 66.242427 0.0 5.376617 5.704482\n" + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 5.541133 0.082736 66.974012 0.0 5.378975 5.703292\n" ] } ], @@ -496,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -505,13 +521,13 @@ "text": [ "IRM Model ATTE Score\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 5.5405 0.0836 66.2424 0.0 5.3766 5.7045\n", + "d 5.5411 0.0824 67.2604 0.0 5.3797 5.7026\n", "IRM Model (Weighted)\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 5.5405 0.0836 66.2424 0.0 5.3766 5.7045\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 5.5411 0.0827 66.974 0.0 5.379 5.7033\n", "Causal Contrast (Weighted)\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1 vs 0 5.5405 0.0836 66.2424 0.0 5.3766 5.7045\n" + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1 vs 0 5.5411 0.0827 66.974 0.0 5.379 5.7033\n" ] } ], @@ -535,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -550,11 +566,11 @@ "\n", "------------------ Bounds with CI ------------------\n", " CI lower theta lower theta theta upper CI upper\n", - "d 4.773339 4.890665 5.001907 5.113149 5.217684\n", + "d 4.774269 4.891343 5.002585 5.113827 5.218325\n", "\n", "------------------ Robustness Values ------------------\n", " H_0 RV (%) RVa (%)\n", - "d 0.0 72.206256 66.239019\n" + "d 0.0 72.210512 66.247583\n" ] } ], @@ -565,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -580,11 +596,11 @@ "\n", "------------------ Bounds with CI ------------------\n", " CI lower theta lower theta theta upper CI upper\n", - "1 vs 0 4.72215 4.844805 5.001907 5.159008 5.26271\n", + "1 vs 0 4.774269 4.891343 5.002585 5.113827 5.218325\n", "\n", "------------------ Robustness Values ------------------\n", " H_0 RV (%) RVa (%)\n", - "1 vs 0 0.0 60.755008 54.175559\n" + "1 vs 0 0.0 72.210512 66.247583\n" ] } ], From 0ef5951bab395ea1a6864a51e63b68d327557229 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 7 Feb 2025 12:03:17 +0000 Subject: [PATCH 004/140] update icon links (pypi and discussions) --- doc/conf.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/doc/conf.py b/doc/conf.py index be565248..17270c7f 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -109,7 +109,13 @@ { "name": "PyPI", "url": "https://pypi.org/project/DoubleML/", - "icon": "fa-solid fa-cube", + "icon": "fa-brands fa-python", + "type": "fontawesome", + }, + { + "name": "Discussions", + "url": "https://github.com/DoubleML/doubleml-for-py/discussions", + "icon": "fa-solid fa-comments", "type": "fontawesome", }, ], From 703937c7835cf0202cb0445167ea40024cf07d0a Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 14 Feb 2025 14:27:57 +0000 Subject: [PATCH 005/140] update "check_switcher": True --- doc/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/conf.py b/doc/conf.py index 17270c7f..7c40a848 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -124,7 +124,7 @@ "version_match": version, }, "show_version_warning_banner": True, - "check_switcher": False, + "check_switcher": True, "announcement": "Interested to learn more? We offer DoubleML Trainings!", } From b19a53612f5d9189fb71d4c936134da45ed8b72a Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Mon, 17 Feb 2025 10:05:49 +0100 Subject: [PATCH 006/140] fix \bar{} in weights for ATTE --- doc/guide/scores/irm_score.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/guide/scores/irm_score.rst b/doc/guide/scores/irm_score.rst index a17a0116..c5c58847 100644 --- a/doc/guide/scores/irm_score.rst +++ b/doc/guide/scores/irm_score.rst @@ -39,6 +39,6 @@ whereas ``score='ATTE'`` changes weights to: \omega(Y,D,X) &= \frac{D}{\mathbb{E}_n[D]} - \omega(Y,D,X) &= \frac{m(X)}{\mathbb{E}_n[D]}. + \bar{\omega(Y,D,X)} &= \frac{m(X)}{\mathbb{E}_n[D]}. For more details on other weight specifications, see :ref:`weighted_cates`. From 0ee917165cbd0152fd115778cef00da207720c59 Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Mon, 17 Feb 2025 10:15:44 +0100 Subject: [PATCH 007/140] add ATTE score from paper again --- doc/guide/scores/irm_score.rst | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/doc/guide/scores/irm_score.rst b/doc/guide/scores/irm_score.rst index c5c58847..545d496a 100644 --- a/doc/guide/scores/irm_score.rst +++ b/doc/guide/scores/irm_score.rst @@ -39,6 +39,13 @@ whereas ``score='ATTE'`` changes weights to: \omega(Y,D,X) &= \frac{D}{\mathbb{E}_n[D]} - \bar{\omega(Y,D,X)} &= \frac{m(X)}{\mathbb{E}_n[D]}. + \bar{\omega(Y,D,X)} &= \frac{m(X)}{\mathbb{E}_n[D]}. + +This score is identical to the original presentation in Section 5.1. of Chernozhukov et al. (2018) + +.. math:: + + \psi_a(W; \eta) &= -\frac{D}{\mathbb{E}_n[D]} + \psi_b(W; \eta) &= \frac{D(Y-g(0,X))}{\mathbb{E}_n[D]} - \frac{m(X)(1-D)(Y-g(0,X))}{\mathbb{E}_n[D](1-m(X))}. For more details on other weight specifications, see :ref:`weighted_cates`. From 2bca11e76a2b2f451b456ba4390d76fa74d48cce Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Mon, 17 Feb 2025 10:40:21 +0100 Subject: [PATCH 008/140] remove sentence and clean output --- doc/examples/py_double_ml_irm_vs_apo.ipynb | 229 ++++++++++++++++----- 1 file changed, 179 insertions(+), 50 deletions(-) diff --git a/doc/examples/py_double_ml_irm_vs_apo.ipynb b/doc/examples/py_double_ml_irm_vs_apo.ipynb index 375e4879..029fa789 100644 --- a/doc/examples/py_double_ml_irm_vs_apo.ipynb +++ b/doc/examples/py_double_ml_irm_vs_apo.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -37,11 +37,163 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "X1", + "rawType": "float64", + "type": "float" + }, + { + "name": "X2", + "rawType": "float64", + "type": "float" + }, + { + "name": "X3", + "rawType": "float64", + "type": "float" + }, + { + "name": "X4", + "rawType": "float64", + "type": "float" + }, + { + "name": "X5", + "rawType": "float64", + "type": "float" + }, + { + "name": "X6", + "rawType": "float64", + "type": "float" + }, + { + "name": "X7", + "rawType": "float64", + "type": "float" + }, + { + "name": "X8", + "rawType": "float64", + "type": "float" + }, + { + "name": "X9", + "rawType": "float64", + "type": "float" + }, + { + "name": "X10", + "rawType": "float64", + "type": "float" + }, + { + "name": "y", + "rawType": "float64", + "type": "float" + }, + { + "name": "d", + "rawType": "float64", + "type": "float" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "a9c9733d-8cd0-4462-bce0-2ae659ce1f57", + "rows": [ + [ + "0", + "0.5410600009398328", + "0.19596320061200245", + "0.8357498628565287", + "1.1802705956535042", + "0.6559594659651191", + "1.0442386416501361", + "-1.2147691072994447", + "-2.1608355544486257", + "-2.1966545042626002", + "-2.037528713787469", + "4.704557861274168", + "1.0" + ], + [ + "1", + "1.3402346175283055", + "2.319419772908943", + "2.1933749531741857", + "1.1395084484535947", + "1.458813741008928", + "0.4931953747207535", + "1.3138695483024647", + "0.12733696559064114", + "0.4183996354335551", + "1.0704333391740974", + "6.113952046411651", + "1.0" + ], + [ + "2", + "-0.563562542185758", + "-1.4801994807149643", + "0.943547564913407", + "-0.40011333250307796", + "0.7575588742540096", + "0.1314831591519779", + "-0.5741602404435565", + "0.06721246035484671", + "-0.4276535739567427", + "-1.342116682640478", + "-0.22647889190152676", + "0.0" + ], + [ + "3", + "-0.04417623932069992", + "-2.1224207866044593", + "-1.5265821731248783", + "-1.892827997020228", + "-1.777867074929982", + "-1.4253250679555296", + "0.020271522476072307", + "0.48752421581642885", + "-0.5791968376193459", + "-0.752908771713609", + "0.3673655068161781", + "0.0" + ], + [ + "4", + "-1.8962633705814884", + "-1.1984925083423128", + "-1.28548282413299", + "-1.361623398270639", + "-2.9217779853782138", + "-2.0269659336859434", + "-1.1071561471956224", + "-0.49812231007907926", + "0.5682873816392543", + "1.0045423814924672", + "0.9135846262332494", + "0.0" + ] + ], + "shape": { + "columns": 12, + "rows": 5 + } + }, "text/html": [ "
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If the more than two treatment levels exists the `DoubleMLAPOS` model fit multiple binary models such that the combined model might differ." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Further, to remove all uncertainty from sample splitting, we will rely on externally provided sample splits." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from doubleml.utils import DoubleMLResampling\n", - "\n", - "rskf = DoubleMLResampling(\n", - " n_folds=n_folds,\n", - " n_rep=n_rep,\n", - " n_obs=n_obs,\n", - " stratify=df['d'],\n", - ")\n", - "all_smpls = rskf.split_samples()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Average Treatment Effect\n", - "\n", - "Comparing the effect estimates for the `DoubleMLIRM` and `causal_contrasts` of the `DoubleMLAPOS` model, we can numerically equivalent results for the ATE." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training IRM Model\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 5.002585 0.066201 75.566262 0.0 4.872833 5.132337\n" - ] - } - ], - "source": [ - "dml_irm = dml.DoubleMLIRM(**dml_kwargs)\n", - "dml_irm.set_sample_splitting(all_smpls)\n", - "print(\"Training IRM Model\")\n", - "dml_irm.fit()\n", - "\n", - "print(dml_irm.summary)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training APOS Model\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "0 0.037800 0.045201 0.836262 0.403008 -0.050793 0.126392\n", - "1 5.040385 0.048482 103.965037 0.000000 4.945363 5.135407\n", - "Evaluate Causal Contrast\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1 vs 0 5.002585 0.066201 75.566262 0.0 4.872833 5.132337\n" - ] - } - ], - "source": [ - "dml_apos = dml.DoubleMLAPOS(treatment_levels=[0,1], **dml_kwargs)\n", - "dml_apos.set_sample_splitting(all_smpls)\n", - "print(\"Training APOS Model\")\n", - "dml_apos.fit()\n", - "print(dml_apos.summary)\n", - "\n", - "print(\"Evaluate Causal Contrast\")\n", - "causal_contrast = dml_apos.causal_contrast(reference_levels=[0])\n", - "print(causal_contrast.summary)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For a direct comparison, see" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IRM Model\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 5.002585 0.066201 75.566262 0.0 4.872833 5.132337\n", - "Causal Contrast\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1 vs 0 5.002585 0.066201 75.566262 0.0 4.872833 5.132337\n" - ] - } - ], - "source": [ - "print(\"IRM Model\")\n", - "print(dml_irm.summary)\n", - "print(\"Causal Contrast\")\n", - "print(causal_contrast.summary)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Average Treatment Effect on the Treated\n", - "\n", - "For the average treatment effect on the treated we can adjust the score in `DoubleMLIRM` model to `score=\"ATTE\"`." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training IRM Model\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 5.541136 0.082383 67.260366 0.0 5.379668 5.702605\n" - ] - } - ], - "source": [ - "dml_irm_atte = dml.DoubleMLIRM(score=\"ATTE\", **dml_kwargs)\n", - "dml_irm_atte.set_sample_splitting(all_smpls)\n", - "print(\"Training IRM Model\")\n", - "dml_irm_atte.fit()\n", - "\n", - "print(dml_irm_atte.summary)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In order to consider weighted effects in the `DoubleMLAPOS` model, we have to specify the correct weight, see [User Guide](https://docs.doubleml.org/stable/guide/heterogeneity.html#weighted-average-treatment-effects).\n", - "\n", - "As these weights include the propensity score, we will use the predicted propensity score from the previous `DoubleMLIRM` model.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training APOS Model\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "0 0.044364 0.072137 0.615000 0.538555 -0.097022 0.185751\n", - "1 5.585498 0.040281 138.663329 0.000000 5.506548 5.664447\n", - "Evaluate Causal Contrast\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1 vs 0 5.541133 0.082736 66.974012 0.0 5.378975 5.703292\n" - ] - } - ], - "source": [ - "p_hat = df[\"d\"].mean()\n", - "m_hat = dml_irm_atte.predictions[\"ml_m\"][:, :, 0]\n", - "\n", - "weights_dict = {\n", - " \"weights\": df[\"d\"] / p_hat,\n", - " \"weights_bar\": m_hat / p_hat,\n", - "}\n", - "\n", - "dml_apos_atte = dml.DoubleMLAPOS(treatment_levels=[0,1], weights=weights_dict, **dml_kwargs)\n", - "dml_apos_atte.set_sample_splitting(all_smpls)\n", - "print(\"Training APOS Model\")\n", - "dml_apos_atte.fit()\n", - "print(dml_apos_atte.summary)\n", - "\n", - "print(\"Evaluate Causal Contrast\")\n", - "causal_contrast_atte = dml_apos_atte.causal_contrast(reference_levels=[0])\n", - "print(causal_contrast_atte.summary)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The point estimates are equal but on closer comparison the standard errors and confidence intervals are larger in the causal contrast example." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training IRM Model\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 5.541133 0.082736 66.974012 0.0 5.378975 5.703292\n" - ] - } - ], - "source": [ - "dml_irm_weighted_atte = dml.DoubleMLIRM(score=\"ATE\", weights=weights_dict, **dml_kwargs)\n", - "dml_irm_weighted_atte.set_sample_splitting(all_smpls)\n", - "print(\"Training IRM Model\")\n", - "dml_irm_weighted_atte.fit()\n", - "\n", - "print(dml_irm_weighted_atte.summary)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In summary, see" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IRM Model ATTE Score\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 5.5411 0.0824 67.2604 0.0 5.3797 5.7026\n", - "IRM Model (Weighted)\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 5.5411 0.0827 66.974 0.0 5.379 5.7033\n", - "Causal Contrast (Weighted)\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1 vs 0 5.5411 0.0827 66.974 0.0 5.379 5.7033\n" - ] - } - ], - "source": [ - "print(\"IRM Model ATTE Score\")\n", - "print(dml_irm_atte.summary.round(4))\n", - "print(\"IRM Model (Weighted)\")\n", - "print(dml_irm_weighted_atte.summary.round(4))\n", - "print(\"Causal Contrast (Weighted)\")\n", - "print(causal_contrast_atte.summary.round(4))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Sensitivity Analysis\n", - "\n", - "There exist also slight differences with respect to the bounds in the sensitivity analysis. " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== Sensitivity Analysis ==================\n", - "\n", - "------------------ Scenario ------------------\n", - "Significance Level: level=0.95\n", - "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", - "\n", - "------------------ Bounds with CI ------------------\n", - " CI lower theta lower theta theta upper CI upper\n", - "d 4.774269 4.891343 5.002585 5.113827 5.218325\n", - "\n", - "------------------ Robustness Values ------------------\n", - " H_0 RV (%) RVa (%)\n", - "d 0.0 72.210512 66.247583\n" - ] - } - ], - "source": [ - "dml_irm.sensitivity_analysis()\n", - "print(dml_irm.sensitivity_summary)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== Sensitivity Analysis ==================\n", - "\n", - "------------------ Scenario ------------------\n", - "Significance Level: level=0.95\n", - "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", - "\n", - "------------------ Bounds with CI ------------------\n", - " CI lower theta lower theta theta upper CI upper\n", - "1 vs 0 4.774269 4.891343 5.002585 5.113827 5.218325\n", - "\n", - "------------------ Robustness Values ------------------\n", - " H_0 RV (%) RVa (%)\n", - "1 vs 0 0.0 72.210512 66.247583\n" - ] - } - ], - "source": [ - "causal_contrast.sensitivity_analysis()\n", - "print(causal_contrast.sensitivity_summary)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Effect Heterogeneity" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import PolynomialFeatures\n", - "import pandas as pd\n", - "\n", - "# Assuming df is your DataFrame and df[\"X1\"] is the column you want to transform\n", - "X = df[[\"X1\"]]\n", - "\n", - "# Create a PolynomialFeatures object with the desired degree\n", - "poly = PolynomialFeatures(degree=2, include_bias=True)\n", - "\n", - "# Fit and transform the data\n", - "design_matrix = poly.fit_transform(X)\n", - "\n", - "basis = pd.DataFrame(design_matrix, columns=poly.get_feature_names_out([\"X1\"]))" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLBLP Object ==================\n", - "\n", - "------------------ Fit summary ------------------\n", - " coef std err t P>|t| [0.025 0.975]\n", - "1 4.980006 0.065959 75.501036 0.000000e+00 4.850728 5.109284\n", - "X1 0.921124 0.072210 12.756191 2.879412e-37 0.779595 1.062653\n", - "X1^2 0.002328 0.042443 0.054851 9.562573e-01 -0.080859 0.085515\n" - ] - } - ], - "source": [ - "cate = dml_irm.cate(basis)\n", - "print(cate)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2.5 % effect 97.5 %\n", - "0 3.379763 3.803741 4.227719\n", - "1 3.410537 3.827810 4.245082\n", - "2 3.441103 3.851882 4.262661\n", - "3 3.471463 3.875957 4.280451\n", - "4 3.501623 3.900036 4.298449\n", - ".. ... ... ...\n", - "95 5.629324 6.104665 6.580006\n", - "96 5.641677 6.129040 6.616403\n", - "97 5.653741 6.153418 6.653095\n", - "98 5.665518 6.177799 6.690081\n", - "99 5.677011 6.202184 6.727356\n", - "\n", - "[100 rows x 3 columns]\n" - ] - } - ], - "source": [ - "new_data = pd.DataFrame({\"X1\": np.linspace(np.quantile(df[\"X1\"], 0.1), np.quantile(df[\"X1\"], 0.9), 100)})\n", - "\n", - "# Convert the result to a DataFrame with the same column names\n", - "grid_basis = pd.DataFrame( poly.transform(new_data), columns=poly.get_feature_names_out([\"X1\"]))\n", - "\n", - "# Assuming cate is your model object that has a confint method\n", - "df_cate = cate.confint(grid_basis, level=0.95, joint=True, n_rep_boot=2000)\n", - "print(df_cate)" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "plt.rcParams['figure.figsize'] = 10., 7.5\n", - "\n", - "df_cate['x'] = new_data['X1']\n", - "fig, ax = plt.subplots()\n", - "ax.plot(df_cate['x'],df_cate['effect'], label='Estimated Effect')\n", - "ax.fill_between(df_cate['x'], df_cate['2.5 %'], df_cate['97.5 %'], alpha=.3, label='Confidence Interval')\n", - "\n", - "plt.legend()\n", - "plt.title('CATE')\n", - "plt.xlabel('x')\n", - "_ = plt.ylabel('Effect and 95%-CI')" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLBLP Object ==================\n", - "\n", - "------------------ Fit summary ------------------\n", - " coef std err t P>|t| [0.025 0.975]\n", - "1 0.013534 0.045659 0.296413 0.766915 -0.075956 0.103024\n", - "X1 0.001013 0.055947 0.018108 0.985553 -0.108641 0.110667\n", - "X1^2 0.023895 0.036482 0.654985 0.512477 -0.047608 0.095399\n", - "================== DoubleMLBLP Object ==================\n", - "\n", - "------------------ Fit summary ------------------\n", - " coef std err t P>|t| [0.025 0.975]\n", - "1 4.993540 0.047982 104.070731 0.000000e+00 4.899497 5.087583\n", - "X1 0.922137 0.045885 20.096828 7.866358e-90 0.832205 1.012070\n", - "X1^2 0.026223 0.022604 1.160093 2.460111e-01 -0.018081 0.070527\n" - ] - } - ], - "source": [ - "capo0 = dml_apos.modellist[0].capo(basis)\n", - "print(capo0)\n", - "capo1 = dml_apos.modellist[1].capo(basis)\n", - "print(capo1)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "df_capo0 = capo0.confint(grid_basis, level=0.95, joint=True, n_rep_boot=2000)\n", - "df_capo1 = capo1.confint(grid_basis, level=0.95, joint=True, n_rep_boot=2000)" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "plt.rcParams['figure.figsize'] = 10., 7.5\n", - "\n", - "df_cate['x'] = new_data['X1']\n", - "fig, ax = plt.subplots()\n", - "ax.plot(df_cate['x'], df_capo0['effect'], label='APO(0)')\n", - "ax.fill_between(df_cate['x'], df_capo0['2.5 %'], df_capo0['97.5 %'], alpha=.3, label='Confidence Interval')\n", - "\n", - "ax.plot(df_cate['x'], df_capo1['effect'], label='APO(1)')\n", - "ax.fill_between(df_cate['x'], df_capo1['2.5 %'], df_capo1['97.5 %'], alpha=.3, label='Confidence Interval')\n", - "\n", - "\n", - "plt.legend()\n", - "plt.title('Average Potential Outcomes')\n", - "plt.xlabel('X1')\n", - "_ = plt.ylabel('Effect and 95%-CI')" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "plt.rcParams['figure.figsize'] = 20., 7.5\n", - "\n", - "df_cate['x'] = new_data['X1']\n", - "\n", - "fig, (ax1, ax2) = plt.subplots(1, 2)\n", - "\n", - "# Plot CATE\n", - "ax1.plot(df_cate['x'], df_cate['effect'], label='Estimated Effect')\n", - "ax1.fill_between(df_cate['x'], df_cate['2.5 %'], df_cate['97.5 %'], alpha=.3, label='Confidence Interval')\n", - "ax1.legend()\n", - "ax1.set_title('CATE')\n", - "ax1.set_xlabel('x')\n", - "ax1.set_ylabel('Effect and 95%-CI')\n", - "\n", - "# Plot Average Potential Outcomes\n", - "ax2.plot(df_cate['x'], df_capo0['effect'], label='APO(0)')\n", - "ax2.fill_between(df_cate['x'], df_capo0['2.5 %'], df_capo0['97.5 %'], alpha=.3, label='Confidence Interval')\n", - "ax2.plot(df_cate['x'], df_capo1['effect'], label='APO(1)')\n", - "ax2.fill_between(df_cate['x'], df_capo1['2.5 %'], df_capo1['97.5 %'], alpha=.3, label='Confidence Interval')\n", - "ax2.legend()\n", - "ax2.set_title('Average Potential Outcomes')\n", - "ax2.set_xlabel('X1')\n", - "ax2.set_ylabel('Effect and 95%-CI')\n", - "\n", - "\n", - "# Ensure the same scale on y-axis\n", - "ax1.set_ylim(min(ax1.get_ylim()[0], ax2.get_ylim()[0]), max(ax1.get_ylim()[1], ax2.get_ylim()[1]))\n", - "ax2.set_ylim(min(ax1.get_ylim()[0], ax2.get_ylim()[0]), max(ax1.get_ylim()[1], ax2.get_ylim()[1]))\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [], - "source": [ - "orth_signal = -1.0 * (causal_contrast.scaled_psi.reshape(-1) - causal_contrast.thetas)\n", - "\n", - "causal_contrast_cate = dml.utils.DoubleMLBLP(orth_signal, basis)\n", - "causal_contrast_cate.fit()\n", - "df_causal_contrast_cate = causal_contrast_cate.confint(grid_basis, level=0.95, joint=True, n_rep_boot=2000)" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "plt.rcParams['figure.figsize'] = 10., 7.5\n", - "\n", - "df_cate['x'] = new_data['X1']\n", - "fig, ax = plt.subplots()\n", - "ax.plot(df_cate['x'],df_causal_contrast_cate['effect'], label='Estimated Effect')\n", - "ax.fill_between(df_cate['x'], df_causal_contrast_cate['2.5 %'], df_causal_contrast_cate['97.5 %'], alpha=.3, label='Confidence Interval')\n", - "\n", - "plt.legend()\n", - "plt.title('CATE')\n", - "plt.xlabel('x')\n", - "_ = plt.ylabel('Effect and 95%-CI')" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "plt.rcParams['figure.figsize'] = 20., 7.5\n", - "\n", - "df_cate['x'] = new_data['X1']\n", - "\n", - "fig, (ax1, ax2) = plt.subplots(1, 2)\n", - "\n", - "# Plot CATE\n", - "ax1.plot(df_cate['x'], df_cate['effect'], label='Estimated Effect')\n", - "ax1.fill_between(df_cate['x'], df_cate['2.5 %'], df_cate['97.5 %'], alpha=.3, label='Confidence Interval')\n", - "ax1.legend()\n", - "ax1.set_title('CATE')\n", - "ax1.set_xlabel('x')\n", - "ax1.set_ylabel('Effect and 95%-CI')\n", - "\n", - "# Plot Average Potential Outcomes\n", - "ax2.plot(df_cate['x'], df_causal_contrast_cate['effect'], label='Estimated Effect')\n", - "ax2.fill_between(df_cate['x'], df_causal_contrast_cate['2.5 %'], df_causal_contrast_cate['97.5 %'], alpha=.3, label='Confidence Interval')\n", - "ax2.legend()\n", - "ax2.set_title('CATE')\n", - "ax2.set_xlabel('X1')\n", - "ax2.set_ylabel('Effect and 95%-CI')\n", - "\n", - "\n", - "# Ensure the same scale on y-axis\n", - "ax1.set_ylim(min(ax1.get_ylim()[0], ax2.get_ylim()[0]), max(ax1.get_ylim()[1], ax2.get_ylim()[1]))\n", - "ax2.set_ylim(min(ax1.get_ylim()[0], ax2.get_ylim()[0]), max(ax1.get_ylim()[1], ax2.get_ylim()[1]))\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "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": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python: IRM and APO Model Comparison\n", + "\n", + "In this simple example, we illustrate how the (binary) [DoubleMLIRM](https://docs.doubleml.org/stable/guide/models.html#binary-interactive-regression-model-irm) class relates to the [DoubleMLAPOS](https://docs.doubleml.org/stable/guide/models.html#average-potential-outcomes-apos-for-multiple-treatment-levels) class.\n", + "\n", + "More specifically, we focus on the `causal_contrast()` method of [DoubleMLAPOS](https://docs.doubleml.org/stable/guide/models.html#average-potential-outcomes-apos-for-multiple-treatment-levels) in a binary setting to highlight, when both methods coincide." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import doubleml as dml\n", + "\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from doubleml.datasets import make_irm_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "\n", + "We rely on the [make_irm_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_irm_data.html) go generate data with a binary treatment." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " X1 X2 X3 X4 X5 X6 X7 \\\n", + "0 0.541060 0.195963 0.835750 1.180271 0.655959 1.044239 -1.214769 \n", + "1 1.340235 2.319420 2.193375 1.139508 1.458814 0.493195 1.313870 \n", + "2 -0.563563 -1.480199 0.943548 -0.400113 0.757559 0.131483 -0.574160 \n", + "3 -0.044176 -2.122421 -1.526582 -1.892828 -1.777867 -1.425325 0.020272 \n", + "4 -1.896263 -1.198493 -1.285483 -1.361623 -2.921778 -2.026966 -1.107156 \n", + "\n", + " X8 X9 X10 y d \n", + "0 -2.160836 -2.196655 -2.037529 4.704558 1.0 \n", + "1 0.127337 0.418400 1.070433 6.113952 1.0 \n", + "2 0.067212 -0.427654 -1.342117 -0.226479 0.0 \n", + "3 0.487524 -0.579197 -0.752909 0.367366 0.0 \n", + "4 -0.498122 0.568287 1.004542 0.913585 0.0 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_obs = 2000\n", + "\n", + "np.random.seed(42)\n", + "df = make_irm_data(\n", + " n_obs=n_obs,\n", + " dim_x=10,\n", + " theta=5.0,\n", + " return_type='DataFrame'\n", + ")\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, define the ``DoubleMLData`` object." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "dml_data = dml.DoubleMLData(\n", + " df,\n", + " y_col='y',\n", + " d_cols='d'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learners and Hyperparameters\n", + "\n", + "To simplify the comparison and keep the variation in learners as small as possible, we will use linear models." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "n_folds = 5\n", + "n_rep = 1\n", + "\n", + "dml_kwargs = {\n", + " \"obj_dml_data\": dml_data,\n", + " \"ml_g\": LinearRegression(),\n", + " \"ml_m\": LogisticRegression(random_state=42),\n", + " \"n_folds\": n_folds,\n", + " \"n_rep\": n_rep,\n", + " \"normalize_ipw\": True,\n", + " \"trimming_threshold\": 1e-2,\n", + " \"draw_sample_splitting\": False,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Remark:**\n", + "All results rely on the exact same predictions for the machine learning algorithms. If the more than two treatment levels exists the `DoubleMLAPOS` model fit multiple binary models such that the combined model might differ." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further, to remove all uncertainty from sample splitting, we will rely on externally provided sample splits." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from doubleml.utils import DoubleMLResampling\n", + "\n", + "rskf = DoubleMLResampling(\n", + " n_folds=n_folds,\n", + " n_rep=n_rep,\n", + " n_obs=n_obs,\n", + " stratify=df['d'],\n", + ")\n", + "all_smpls = rskf.split_samples()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Average Treatment Effect\n", + "\n", + "Comparing the effect estimates for the `DoubleMLIRM` and `causal_contrasts` of the `DoubleMLAPOS` model, we can numerically equivalent results for the ATE." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training IRM Model\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 5.002585 0.066201 75.566262 0.0 4.872833 5.132337\n" + ] + } + ], + "source": [ + "dml_irm = dml.DoubleMLIRM(**dml_kwargs)\n", + "dml_irm.set_sample_splitting(all_smpls)\n", + "print(\"Training IRM Model\")\n", + "dml_irm.fit()\n", + "\n", + "print(dml_irm.summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training APOS Model\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "0 0.037800 0.045201 0.836262 0.403008 -0.050793 0.126392\n", + "1 5.040385 0.048482 103.965037 0.000000 4.945363 5.135407\n", + "Evaluate Causal Contrast\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1 vs 0 5.002585 0.066201 75.566262 0.0 4.872833 5.132337\n" + ] + } + ], + "source": [ + "dml_apos = dml.DoubleMLAPOS(treatment_levels=[0,1], **dml_kwargs)\n", + "dml_apos.set_sample_splitting(all_smpls)\n", + "print(\"Training APOS Model\")\n", + "dml_apos.fit()\n", + "print(dml_apos.summary)\n", + "\n", + "print(\"Evaluate Causal Contrast\")\n", + "causal_contrast = dml_apos.causal_contrast(reference_levels=[0])\n", + "print(causal_contrast.summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a direct comparison, see" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IRM Model\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 5.002585 0.066201 75.566262 0.0 4.872833 5.132337\n", + "Causal Contrast\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1 vs 0 5.002585 0.066201 75.566262 0.0 4.872833 5.132337\n" + ] + } + ], + "source": [ + "print(\"IRM Model\")\n", + "print(dml_irm.summary)\n", + "print(\"Causal Contrast\")\n", + "print(causal_contrast.summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Average Treatment Effect on the Treated\n", + "\n", + "For the average treatment effect on the treated we can adjust the score in `DoubleMLIRM` model to `score=\"ATTE\"`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training IRM Model\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 5.541136 0.082383 67.260366 0.0 5.379668 5.702605\n" + ] + } + ], + "source": [ + "dml_irm_atte = dml.DoubleMLIRM(score=\"ATTE\", **dml_kwargs)\n", + "dml_irm_atte.set_sample_splitting(all_smpls)\n", + "print(\"Training IRM Model\")\n", + "dml_irm_atte.fit()\n", + "\n", + "print(dml_irm_atte.summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to consider weighted effects in the `DoubleMLAPOS` model, we have to specify the correct weight, see [User Guide](https://docs.doubleml.org/stable/guide/heterogeneity.html#weighted-average-treatment-effects).\n", + "\n", + "As these weights include the propensity score, we will use the predicted propensity score from the previous `DoubleMLIRM` model.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training APOS Model\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "0 0.044364 0.072137 0.615000 0.538555 -0.097022 0.185751\n", + "1 5.585498 0.040281 138.663329 0.000000 5.506548 5.664447\n", + "Evaluate Causal Contrast\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1 vs 0 5.541133 0.082736 66.974012 0.0 5.378975 5.703292\n" + ] + } + ], + "source": [ + "p_hat = df[\"d\"].mean()\n", + "m_hat = dml_irm_atte.predictions[\"ml_m\"][:, :, 0]\n", + "\n", + "weights_dict = {\n", + " \"weights\": df[\"d\"] / p_hat,\n", + " \"weights_bar\": m_hat / p_hat,\n", + "}\n", + "\n", + "dml_apos_atte = dml.DoubleMLAPOS(treatment_levels=[0,1], weights=weights_dict, **dml_kwargs)\n", + "dml_apos_atte.set_sample_splitting(all_smpls)\n", + "print(\"Training APOS Model\")\n", + "dml_apos_atte.fit()\n", + "print(dml_apos_atte.summary)\n", + "\n", + "print(\"Evaluate Causal Contrast\")\n", + "causal_contrast_atte = dml_apos_atte.causal_contrast(reference_levels=[0])\n", + "print(causal_contrast_atte.summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The same results can be achieved by specifying the weights for `DoubleMLIRM` class with `score='ATE'`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training IRM Model\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 5.541133 0.082736 66.974012 0.0 5.378975 5.703292\n" + ] + } + ], + "source": [ + "dml_irm_weighted_atte = dml.DoubleMLIRM(score=\"ATE\", weights=weights_dict, **dml_kwargs)\n", + "dml_irm_weighted_atte.set_sample_splitting(all_smpls)\n", + "print(\"Training IRM Model\")\n", + "dml_irm_weighted_atte.fit()\n", + "\n", + "print(dml_irm_weighted_atte.summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In summary, see" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IRM Model ATTE Score\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 5.5411 0.0824 67.2604 0.0 5.3797 5.7026\n", + "IRM Model (Weighted)\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 5.5411 0.0827 66.974 0.0 5.379 5.7033\n", + "Causal Contrast (Weighted)\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1 vs 0 5.5411 0.0827 66.974 0.0 5.379 5.7033\n" + ] + } + ], + "source": [ + "print(\"IRM Model ATTE Score\")\n", + "print(dml_irm_atte.summary.round(4))\n", + "print(\"IRM Model (Weighted)\")\n", + "print(dml_irm_weighted_atte.summary.round(4))\n", + "print(\"Causal Contrast (Weighted)\")\n", + "print(causal_contrast_atte.summary.round(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sensitivity Analysis\n", + "\n", + "The sensitvity analysis gives identical results." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== Sensitivity Analysis ==================\n", + "\n", + "------------------ Scenario ------------------\n", + "Significance Level: level=0.95\n", + "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", + "\n", + "------------------ Bounds with CI ------------------\n", + " CI lower theta lower theta theta upper CI upper\n", + "d 4.770463 4.917327 5.002585 5.087842 5.221186\n", + "\n", + "------------------ Robustness Values ------------------\n", + " H_0 RV (%) RVa (%)\n", + "d 0.0 79.97688 57.230886\n" + ] + } + ], + "source": [ + "dml_irm.sensitivity_analysis()\n", + "print(dml_irm.sensitivity_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== Sensitivity Analysis ==================\n", + "\n", + "------------------ Scenario ------------------\n", + "Significance Level: level=0.95\n", + "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", + "\n", + "------------------ Bounds with CI ------------------\n", + " CI lower theta lower theta theta upper CI upper\n", + "1 vs 0 4.770463 4.917327 5.002585 5.087842 5.221186\n", + "\n", + "------------------ Robustness Values ------------------\n", + " H_0 RV (%) RVa (%)\n", + "1 vs 0 0.0 79.97688 57.230886\n" + ] + } + ], + "source": [ + "causal_contrast.sensitivity_analysis()\n", + "print(causal_contrast.sensitivity_summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Effect Heterogeneity\n", + "\n", + "For conditional treatment effects the exact same methods do not exist.\n", + "Nevertheless, we will compare the `capo()` variant of the `DoubleMLAPO` class to the corresponding `cate()` method of the `DoubleMLIRM` class.\n", + "\n", + "For a simple case we will just use a polynomial basis of the first feature `X1`. To plot the data we will evaluate the methods on the corresponding grid of basis values." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "X = df[[\"X1\"]]\n", + "poly = PolynomialFeatures(degree=2, include_bias=True)\n", + "\n", + "basis_matrix = poly.fit_transform(X)\n", + "basis_df = pd.DataFrame(basis_matrix, columns=poly.get_feature_names_out([\"X1\"]))\n", + "\n", + "grid = pd.DataFrame({\"X1\": np.linspace(np.quantile(df[\"X1\"], 0.1), np.quantile(df[\"X1\"], 0.9), 100)})\n", + "grid_basis = pd.DataFrame( poly.transform(grid), columns=poly.get_feature_names_out([\"X1\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Apply the `cate()` method to the basis and evaluate on the transformed grid values." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLBLP Object ==================\n", + "\n", + "------------------ Fit summary ------------------\n", + " coef std err t P>|t| [0.025 0.975]\n", + "1 4.980006 0.065959 75.501036 0.000000e+00 4.850728 5.109284\n", + "X1 0.921124 0.072210 12.756191 2.879412e-37 0.779595 1.062653\n", + "X1^2 0.002328 0.042443 0.054851 9.562573e-01 -0.080859 0.085515\n" + ] + } + ], + "source": [ + "cate = dml_irm.cate(basis_df)\n", + "print(cate)\n", + "np.random.seed(42)\n", + "df_cate = cate.confint(grid_basis, level=0.95, joint=True, n_rep_boot=2000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The corresponding `apo()` method can be used for the treatment levels $0$ and $1$." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLBLP Object ==================\n", + "\n", + "------------------ Fit summary ------------------\n", + " coef std err t P>|t| [0.025 0.975]\n", + "1 0.013534 0.045659 0.296413 0.766915 -0.075956 0.103024\n", + "X1 0.001013 0.055947 0.018108 0.985553 -0.108641 0.110667\n", + "X1^2 0.023895 0.036482 0.654985 0.512477 -0.047608 0.095399\n", + "================== DoubleMLBLP Object ==================\n", + "\n", + "------------------ Fit summary ------------------\n", + " coef std err t P>|t| [0.025 0.975]\n", + "1 4.993540 0.047982 104.070731 0.000000e+00 4.899497 5.087583\n", + "X1 0.922137 0.045885 20.096828 7.866358e-90 0.832205 1.012070\n", + "X1^2 0.026223 0.022604 1.160093 2.460111e-01 -0.018081 0.070527\n" + ] + } + ], + "source": [ + "capo0 = dml_apos.modellist[0].capo(basis_df)\n", + "print(capo0)\n", + "np.random.seed(42)\n", + "df_capo0 = capo0.confint(grid_basis, level=0.95, joint=True, n_rep_boot=2000)\n", + "\n", + "capo1 = dml_apos.modellist[1].capo(basis_df)\n", + "print(capo1)\n", + "np.random.seed(42)\n", + "df_capo1 = capo1.confint(grid_basis, level=0.95, joint=True, n_rep_boot=2000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example the average potential outcome of the control group is zero (as can be seen in the outcome definition, see [documentation](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_irm_data.html#doubleml.datasets.make_irm_data)).\n", + "Let us visualize the effects" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [ + "nbsphinx-thumbnail" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_cate['x'] = grid_basis['X1']\n", + "\n", + "plt.rcParams['figure.figsize'] = 10., 7.5\n", + "fig, (ax1, ax2) = plt.subplots(1, 2)\n", + "\n", + "# Plot CATE\n", + "ax1.plot(df_cate['x'], df_cate['effect'], label='Estimated Effect')\n", + "ax1.fill_between(df_cate['x'], df_cate['2.5 %'], df_cate['97.5 %'], alpha=.3, label='Confidence Interval')\n", + "ax1.legend()\n", + "ax1.set_title('CATE')\n", + "ax1.set_xlabel('X1')\n", + "ax1.set_ylabel('Effect and 95%-CI')\n", + "\n", + "# Plot Average Potential Outcomes\n", + "ax2.plot(df_cate['x'], df_capo0['effect'], label='APO(0)')\n", + "ax2.fill_between(df_cate['x'], df_capo0['2.5 %'], df_capo0['97.5 %'], alpha=.3, label='Confidence Interval')\n", + "ax2.plot(df_cate['x'], df_capo1['effect'], label='APO(1)')\n", + "ax2.fill_between(df_cate['x'], df_capo1['2.5 %'], df_capo1['97.5 %'], alpha=.3, label='Confidence Interval')\n", + "ax2.legend()\n", + "ax2.set_title('Average Potential Outcomes')\n", + "ax2.set_xlabel('X1')\n", + "ax2.set_ylabel('Effect and 95%-CI')\n", + "\n", + "# Ensure the same scale on y-axis\n", + "ax1.set_ylim(min(ax1.get_ylim()[0], ax2.get_ylim()[0]), max(ax1.get_ylim()[1], ax2.get_ylim()[1]))\n", + "ax2.set_ylim(min(ax1.get_ylim()[0], ax2.get_ylim()[0]), max(ax1.get_ylim()[1], ax2.get_ylim()[1]))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `causal_contrast()` method does not currently not have a `cate()` method implemented. But the cate can be manually constructed via the the correct score function." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coef std err t P>|t| [0.025 0.975]\n", + "1 4.980006 0.065959 75.501036 0.000000e+00 4.850728 5.109284\n", + "X1 0.921124 0.072210 12.756191 2.879412e-37 0.779595 1.062653\n", + "X1^2 0.002328 0.042443 0.054851 9.562573e-01 -0.080859 0.085515\n", + "CATE (IRM) as comparison:\n", + " coef std err t P>|t| [0.025 0.975]\n", + "1 4.980006 0.065959 75.501036 0.000000e+00 4.850728 5.109284\n", + "X1 0.921124 0.072210 12.756191 2.879412e-37 0.779595 1.062653\n", + "X1^2 0.002328 0.042443 0.054851 9.562573e-01 -0.080859 0.085515\n" + ] + } + ], + "source": [ + "orth_signal = -1.0 * (causal_contrast.scaled_psi.reshape(-1) - causal_contrast.thetas)\n", + "\n", + "causal_contrast_cate = dml.utils.DoubleMLBLP(orth_signal, basis_df)\n", + "causal_contrast_cate.fit()\n", + "print(causal_contrast_cate.summary)\n", + "np.random.seed(42)\n", + "df_causal_contrast_cate = causal_contrast_cate.confint(grid_basis, level=0.95, joint=True, n_rep_boot=2000)\n", + "\n", + "print(\"CATE (IRM) as comparison:\")\n", + "print(cate.summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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tdy_meany_lower_quantiley_upper_quantileite_meanite_lower_quantileite_upper_quantile
02025-01-012025-03-01208.802144201.063218215.6392090.158468-2.3080352.931649
12025-01-012025-04-01209.826333203.069145217.3508600.124031-2.3266002.849099
22025-01-012025-05-01211.769134204.303741219.656927-0.150817-2.4904762.183467
32025-01-012025-06-01212.482365204.606826221.270151-0.085319-2.3227182.170312
42025-01-012025-07-01214.180131206.038682221.703445-0.017048-2.2361541.886426
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" + ], + "text/plain": [ + " t d y_mean y_lower_quantile y_upper_quantile \\\n", + "0 2025-01-01 2025-03-01 208.802144 201.063218 215.639209 \n", + "1 2025-01-01 2025-04-01 209.826333 203.069145 217.350860 \n", + "2 2025-01-01 2025-05-01 211.769134 204.303741 219.656927 \n", + "3 2025-01-01 2025-06-01 212.482365 204.606826 221.270151 \n", + "4 2025-01-01 2025-07-01 214.180131 206.038682 221.703445 \n", + "\n", + " ite_mean ite_lower_quantile ite_upper_quantile \n", + "0 0.158468 -2.308035 2.931649 \n", + "1 0.124031 -2.326600 2.849099 \n", + "2 -0.150817 -2.490476 2.183467 \n", + "3 -0.085319 -2.322718 2.170312 \n", + "4 -0.017048 -2.236154 1.886426 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# Create aggregation dictionary for means\n", + "def agg_dict(col_name):\n", + " return {\n", + " f'{col_name}_mean': (col_name, 'mean'),\n", + " f'{col_name}_lower_quantile': (col_name, lambda x: x.quantile(0.05)),\n", + " f'{col_name}_upper_quantile': (col_name, lambda x: x.quantile(0.95))\n", + " }\n", + "\n", + "# Calculate means and confidence intervals\n", + "agg_dictionary = agg_dict(\"y\") | agg_dict(\"ite\")\n", + "agg_df = df.groupby([\"t\", \"d\"]).agg(**agg_dictionary).reset_index()\n", + "\n", + "agg_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "d=2025-03-01 00:00:00
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"ATT(2025-03-01T00:00:00,2025-02-01T00:00:00,202... 2.338365 3.006497 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dml_obj = DoubleMLDIDMulti(\n", + " obj_dml_data=dml_data,\n", + " ml_g=LinearRegression(),\n", + " ml_m=LogisticRegression(),\n", + " gt_combinations=gt_combinations,\n", + ")\n", + "\n", + "dml_obj.fit()\n", + "\n", + "dml_obj.summary" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDBinary Object ==================\n", + "\n", + "------------------ Data summary ------------------\n", + "Outcome variable: y\n", + "Treatment variable(s): ['d']\n", + "Covariates: ['Z1', 'Z2', 'Z3', 'Z4']\n", + "Instrument variable(s): None\n", + "Time variable: t\n", + "Id variable: id\n", + "No. Observations: 1000\n", + "\n", + "------------------ Score & algorithm ------------------\n", + "Score function: observational\n", + "\n", + "------------------ Machine learner ------------------\n", + "Learner ml_g: LinearRegression()\n", + "Learner ml_m: LogisticRegression()\n", + "Out-of-sample Performance:\n", + "Regression:\n", + "Learner ml_g0 RMSE: [[1.40810973]]\n", + "Learner ml_g1 RMSE: [[1.51729928]]\n", + "Classification:\n", + "Learner ml_m Log Loss: [[0.6020031]]\n", + "\n", + "------------------ Resampling ------------------\n", + "No. folds: 5\n", + "No. repeated sample splits: 1\n", + "\n", + "------------------ Fit summary ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 0.055079 0.192801 0.28568 0.775124 -0.322803 0.432961\n" + ] + } + ], + "source": [ + "print(dml_obj.modellist[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== Sensitivity Analysis ==================\n", + "\n", + "------------------ Scenario ------------------\n", + "Significance Level: level=0.95\n", + "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", + "\n", + "------------------ Bounds with CI ------------------\n", + " CI lower theta lower \\\n", + "ATT(2025-03-01T00:00:00,2025-01-01T00:00:00,202... -0.420082 -0.093357 \n", + "ATT(2025-03-01T00:00:00,2025-02-01T00:00:00,202... 0.555740 0.820178 \n", + "ATT(2025-03-01T00:00:00,2025-02-01T00:00:00,202... 1.689218 1.978020 \n", + "ATT(2025-03-01T00:00:00,2025-02-01T00:00:00,202... 2.278650 2.560117 \n", + "\n", + " theta theta upper \\\n", + "ATT(2025-03-01T00:00:00,2025-01-01T00:00:00,202... 0.025576 0.144509 \n", + "ATT(2025-03-01T00:00:00,2025-02-01T00:00:00,202... 0.929571 1.038965 \n", + "ATT(2025-03-01T00:00:00,2025-02-01T00:00:00,202... 2.092555 2.207090 \n", + "ATT(2025-03-01T00:00:00,2025-02-01T00:00:00,202... 2.672431 2.784744 \n", + "\n", + " CI upper \n", + "ATT(2025-03-01T00:00:00,2025-01-01T00:00:00,202... 0.469062 \n", + "ATT(2025-03-01T00:00:00,2025-02-01T00:00:00,202... 1.306159 \n", + "ATT(2025-03-01T00:00:00,2025-02-01T00:00:00,202... 2.497881 \n", + "ATT(2025-03-01T00:00:00,2025-02-01T00:00:00,202... 3.065176 \n", + "\n", + "------------------ Robustness Values ------------------\n", + " H_0 RV (%) RVa (%)\n", + "ATT(2025-03-01T00:00:00,2025-01-01T00:00:00,202... 0.0 0.653054 0.000496\n", + "ATT(2025-03-01T00:00:00,2025-02-01T00:00:00,202... 0.0 22.749657 16.685089\n", + "ATT(2025-03-01T00:00:00,2025-02-01T00:00:00,202... 0.0 42.280193 36.340674\n", + "ATT(2025-03-01T00:00:00,2025-02-01T00:00:00,202... 0.0 50.825427 44.615741\n" + ] + } + ], + "source": [ + "dml_obj.sensitivity_analysis()\n", + "print(dml_obj.sensitivity_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'str' object is not callable", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdml_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msensitivity_summary\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mTypeError\u001b[0m: 'str' object is not callable" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 111d6bdfdf53aeb7b205d01614d3e59be0845e48 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Sun, 2 Mar 2025 11:00:59 +0000 Subject: [PATCH 014/140] add plots --- doc/examples/py_double_ml_panel.ipynb | 1620 ++++++++++++++----------- 1 file changed, 918 insertions(+), 702 deletions(-) diff --git a/doc/examples/py_double_ml_panel.ipynb b/doc/examples/py_double_ml_panel.ipynb index 87a13ff5..571b120e 100644 --- a/doc/examples/py_double_ml_panel.ipynb +++ b/doc/examples/py_double_ml_panel.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -13,12 +13,16 @@ "import plotly.graph_objects as go\n", "import plotly.express as px\n", "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", "from lightgbm import LGBMRegressor, LGBMClassifier\n", "from sklearn.linear_model import LinearRegression, LogisticRegression\n", "\n", "# simulate data\n", "n_obs = 5000\n", - "df = make_did_CS2021(n_obs, n_periods=8, n_periods_pre=3)\n", + "df = make_did_CS2021(n_obs, dgp_type=1, n_periods=8, n_pre_treat_periods=4)\n", "df[\"ite\"] = df[\"y1\"] - df[\"y0\"]\n" ] }, @@ -26,6 +30,29 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "['2025-07-01 00:00:00', '2025-05-01 00:00:00', '2025-06-01 00:00:00',\n", + " '2025-08-01 00:00:00', 'NaT']\n", + "Length: 5, dtype: datetime64[s]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"d\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, "outputs": [ { "data": { @@ -62,57 +89,57 @@ " \n", " 0\n", " 2025-01-01\n", - " 2025-03-01\n", - " 208.802144\n", - " 201.063218\n", - " 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "from plotly.subplots import make_subplots\n", + "\n", + "def plot_atts_plotly(df, level=0.95, \n", + " title='Coefficient Estimates by First Treatment Period',\n", + " height=800):\n", + " \"\"\"\n", + " Plot coefficient estimates with CIs over time using Plotly.\n", + " \n", + " Parameters\n", + " ----------\n", + " df : pandas.DataFrame\n", + " DataFrame with required columns\n", + " level : float, default=0.95\n", + " Confidence level for intervals\n", + " title : str\n", + " Plot title\n", + " height : int\n", + " Figure height in pixels\n", + " \"\"\"\n", + " # Setup\n", + " all_time_periods = sorted(df['Time Period'].unique())\n", + " first_treated_periods = sorted(df['First Treated'].unique())\n", + " n_periods = len(first_treated_periods)\n", + " \n", + " # Create figure with subplots\n", + " fig = make_subplots(rows=n_periods, cols=1,\n", + " subplot_titles=[f'First Treated: {period}' \n", + " for period in first_treated_periods],\n", + " vertical_spacing=0.05)\n", + " \n", + " # Color scheme - using RGB instead of RGBA\n", + " colors = {'pre': '#0073C2', 'post': '#EFC000'} # Colorblind-friendly without alpha\n", + " \n", + " for idx, period in enumerate(first_treated_periods):\n", + " row = idx + 1\n", + " period_data = df[df['First Treated'] == period]\n", + " i_period = all_time_periods.index(period)\n", + " \n", + " # Add treatment transition period\n", + " fig.add_vrect(\n", + " x0=all_time_periods[i_period - 1],\n", + " x1=period,\n", + " fillcolor=\"gray\",\n", + " opacity=0.2,\n", + " line_width=0,\n", + " row=row,\n", + " col=1,\n", + " name=\"Treatment transition\",\n", + " showlegend=(idx == 0)\n", + " )\n", + " \n", + " # Add treatment start line\n", + " fig.add_vline(\n", + " x=all_time_periods[i_period - 1],\n", + " line_dash=\"dot\",\n", + " line_color=\"red\",\n", + " opacity=0.7,\n", + " row=row,\n", + " col=1,\n", + " name=\"Treatment start\",\n", + " showlegend=(idx == 0)\n", + " )\n", + " # add zero line\n", + " fig.add_hline(\n", + " y=0,\n", + " line_dash=\"dash\",\n", + " line_color=\"black\",\n", + " opacity=0.5,\n", + " row=row,\n", + " col=1,\n", + " name=\"Zero effect\",\n", + " showlegend=(idx == 0)\n", + " )\n", + " \n", + " # Split data by treatment status\n", + " pre_treatment = period_data[period_data['Pre-Treatment']]\n", + " post_treatment = period_data[~period_data['Pre-Treatment']]\n", + " \n", + " # Plot pre-treatment data\n", + " if not pre_treatment.empty:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=pre_treatment['Time Period'],\n", + " y=pre_treatment['Estimate'],\n", + " error_y=dict(\n", + " type='data',\n", + " symmetric=False,\n", + " array=pre_treatment['Upper CI'] - pre_treatment['Estimate'],\n", + " arrayminus=pre_treatment['Estimate'] - pre_treatment['Lower CI'],\n", + " color=colors['pre'],\n", + " width=3\n", + " ),\n", + " mode='markers',\n", + " marker=dict(color=colors['pre'], size=8),\n", + " name='Pre-treatment',\n", + " showlegend=(idx == 0)\n", + " ),\n", + " row=row,\n", + " col=1\n", + " )\n", + " \n", + " # Add joint CIs\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=pre_treatment['Time Period'],\n", + " y=pre_treatment['Estimate'],\n", + " error_y=dict(\n", + " type='data',\n", + " symmetric=False,\n", + " array=pre_treatment['Upper CI Joint'] - pre_treatment['Estimate'],\n", + " arrayminus=pre_treatment['Estimate'] - pre_treatment['Lower CI Joint'],\n", + " color=colors['pre'],\n", + " width=3\n", + " ),\n", + " mode='markers',\n", + " marker=dict(color=colors['pre'], size=0),\n", + " name='Pre-treatment Joint CI',\n", + " showlegend=(idx == 0)\n", + " ),\n", + " row=row,\n", + " col=1\n", + " )\n", + " \n", + " # Plot post-treatment data (similar structure)\n", + " if not post_treatment.empty:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=post_treatment['Time Period'],\n", + " y=post_treatment['Estimate'],\n", + " error_y=dict(\n", + " type='data',\n", + " symmetric=False,\n", + " array=post_treatment['Upper CI'] - post_treatment['Estimate'],\n", + " arrayminus=post_treatment['Estimate'] - post_treatment['Lower CI'],\n", + " color=colors['post'],\n", + " width=3\n", + " ),\n", + " mode='markers',\n", + " marker=dict(color=colors['post'], size=8),\n", + " name='Post-treatment',\n", + " showlegend=(idx == 0)\n", + " ),\n", + " row=row,\n", + " col=1\n", + " )\n", + " \n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=post_treatment['Time Period'],\n", + " y=post_treatment['Estimate'],\n", + " error_y=dict(\n", + " type='data',\n", + " symmetric=False,\n", + " array=post_treatment['Upper CI Joint'] - post_treatment['Estimate'],\n", + " arrayminus=post_treatment['Estimate'] - post_treatment['Lower CI Joint'],\n", + " color=colors['post'],\n", + " width=3\n", + " ),\n", + " mode='markers',\n", + " marker=dict(color=colors['post'], size=0),\n", + " name='Post-treatment Joint CI',\n", + " showlegend=(idx == 0)\n", + " ),\n", + " row=row,\n", + " col=1\n", + " )\n", + " \n", + " # Update layout\n", + " fig.update_layout(\n", + " height=height,\n", + " title_text=title,\n", + " showlegend=True,\n", + " legend=dict(\n", + " orientation=\"h\",\n", + " yanchor=\"bottom\",\n", + " y=-0.2,\n", + " xanchor=\"center\",\n", + " x=0.5\n", + " ),\n", + " yaxis_title=\"Effect\",\n", + " xaxis_title=\"Time Period\"\n", + " )\n", + " \n", + " # Update axes\n", + " for i in range(n_periods):\n", + " fig.update_xaxes(title_text=\"Time Period\" if i == n_periods-1 else \"\", row=i+1, col=1)\n", + " fig.update_yaxes(title_text=\"Effect\" if i == 0 else \"\", row=i+1, col=1)\n", + " \n", + " return fig\n", + "\n", + "# Usage\n", + "fig = plot_atts_plotly(ci_df, title=\"Estimated Effects\")\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDBinary Object ==================\n", + "\n", + "------------------ Data summary ------------------\n", + "Outcome variable: y\n", + "Treatment variable(s): ['d']\n", + "Covariates: ['Z1', 'Z2', 'Z3', 'Z4']\n", + "Instrument variable(s): None\n", + "Time variable: t\n", + "Id variable: id\n", + "No. Observations: 5000\n", + "\n", + "------------------ Score & algorithm ------------------\n", + "Score function: observational\n", + "\n", + "------------------ Machine learner ------------------\n", + "Learner ml_g: LinearRegression()\n", + "Learner ml_m: LogisticRegression()\n", + "Out-of-sample Performance:\n", + "Regression:\n", + "Learner ml_g0 RMSE: [[1.49325335]]\n", + "Learner ml_g1 RMSE: [[1.39831852]]\n", + "Classification:\n", + "Learner ml_m Log Loss: [[0.63737225]]\n", + "\n", + "------------------ Resampling ------------------\n", + "No. folds: 5\n", + "No. repeated sample splits: 1\n", + "\n", + "------------------ Fit summary ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d -0.016796 0.069257 -0.24251 0.808385 -0.152537 0.118946\n" + ] + } + ], + "source": [ + "print(dml_obj.modellist[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -3475,97 +5133,97 @@ "\n", "------------------ Bounds with CI ------------------\n", " CI lower theta lower \\\n", - 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"ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 2.722520 2.833921 \n", - "ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,202... -0.156717 -0.042255 \n", - "ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,202... -0.148167 -0.042277 \n", - "ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,202... -0.261595 -0.153469 \n", - "ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,202... -0.041869 0.065194 \n", - "ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,202... -0.222299 -0.115752 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 0.794087 0.899700 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 1.788762 1.898172 \n", + "ATT(2025-05-01T00:00:00,2025-01-01T00:00:00,202... -0.227478 -0.113596 \n", + "ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,202... -0.156915 -0.038577 \n", + "ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,202... -0.314085 -0.199266 \n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.811576 0.924423 \n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 1.754570 1.870266 \n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 2.693624 2.812185 \n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 3.659888 3.775449 \n", + "ATT(2025-06-01T00:00:00,2025-01-01T00:00:00,202... -0.224755 -0.109209 \n", + "ATT(2025-06-01T00:00:00,2025-02-01T00:00:00,202... -0.161933 -0.048153 \n", + "ATT(2025-06-01T00:00:00,2025-03-01T00:00:00,202... -0.219529 -0.107667 \n", + "ATT(2025-06-01T00:00:00,2025-04-01T00:00:00,202... -0.191221 -0.081200 \n", + "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 0.797060 0.910939 \n", + "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 1.731408 1.847228 \n", + "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 2.711531 2.824122 \n", + "ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,202... -0.170057 -0.058933 \n", + "ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,202... -0.171734 -0.060707 \n", + "ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,202... -0.235038 -0.127097 \n", + "ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,202... -0.221675 -0.115415 \n", + "ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,202... -0.197229 -0.087152 \n", + "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 0.764956 0.876886 \n", + "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 1.812385 1.919570 \n", "\n", " theta theta upper \\\n", - "ATT(2025-05-01T00:00:00,2025-01-01T00:00:00,202... -0.022801 0.081223 \n", - "ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,202... 0.050044 0.149103 \n", - "ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,202... -0.056758 0.043961 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 1.136864 1.238248 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 2.152684 2.252849 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 2.983371 3.086262 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 3.968121 4.069077 \n", - "ATT(2025-06-01T00:00:00,2025-01-01T00:00:00,202... 0.036877 0.133494 \n", - "ATT(2025-06-01T00:00:00,2025-02-01T00:00:00,202... -0.064355 0.027401 \n", - "ATT(2025-06-01T00:00:00,2025-03-01T00:00:00,202... 0.041138 0.134239 \n", - "ATT(2025-06-01T00:00:00,2025-04-01T00:00:00,202... 0.108182 0.200176 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 1.025512 1.117946 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 1.964430 2.059159 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 2.926489 3.019056 \n", - "ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,202... 0.050586 0.143427 \n", - "ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,202... 0.047012 0.136300 \n", - "ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,202... -0.064625 0.024218 \n", - "ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,202... 0.153933 0.242671 \n", - "ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,202... -0.026693 0.062365 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 0.988183 1.076665 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 1.989144 2.080116 \n", + "ATT(2025-05-01T00:00:00,2025-01-01T00:00:00,202... -0.016796 0.080005 \n", + "ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,202... 0.056174 0.150925 \n", + "ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,202... -0.106566 -0.013865 \n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 1.017391 1.110360 \n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 1.964210 2.058153 \n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 2.908697 3.005208 \n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 3.871215 3.966980 \n", + "ATT(2025-06-01T00:00:00,2025-01-01T00:00:00,202... -0.014928 0.079353 \n", + "ATT(2025-06-01T00:00:00,2025-02-01T00:00:00,202... 0.042546 0.133246 \n", + "ATT(2025-06-01T00:00:00,2025-03-01T00:00:00,202... -0.017262 0.073144 \n", + "ATT(2025-06-01T00:00:00,2025-04-01T00:00:00,202... 0.007673 0.096546 \n", + "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 1.002159 1.093378 \n", + "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 1.939536 2.031845 \n", + "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 2.915487 3.006852 \n", + "ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,202... 0.033156 0.125246 \n", + "ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,202... 0.029941 0.120589 \n", + "ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,202... -0.038743 0.049611 \n", + "ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,202... -0.028387 0.058641 \n", + "ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,202... 0.003061 0.093275 \n", + "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 0.967370 1.057853 \n", + "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 2.007656 2.095742 \n", "\n", " CI upper \n", - "ATT(2025-05-01T00:00:00,2025-01-01T00:00:00,202... 0.204519 \n", - "ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,202... 0.258515 \n", - "ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,202... 0.163856 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 1.357860 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 2.367756 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 3.202754 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 4.184274 \n", - "ATT(2025-06-01T00:00:00,2025-01-01T00:00:00,202... 0.254397 \n", - "ATT(2025-06-01T00:00:00,2025-02-01T00:00:00,202... 0.135063 \n", - "ATT(2025-06-01T00:00:00,2025-03-01T00:00:00,202... 0.248819 \n", - "ATT(2025-06-01T00:00:00,2025-04-01T00:00:00,202... 0.313157 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 1.229496 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 2.172515 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 3.130439 \n", - "ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,202... 0.257869 \n", - "ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,202... 0.242055 \n", - "ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,202... 0.131876 \n", - "ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,202... 0.350124 \n", - "ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,202... 0.168787 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 1.182258 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 2.189327 \n", + "ATT(2025-05-01T00:00:00,2025-01-01T00:00:00,202... 0.194236 \n", + "ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,202... 0.268509 \n", + "ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,202... 0.100826 \n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 1.223333 \n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 2.173868 \n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 3.124557 \n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 4.082582 \n", + "ATT(2025-06-01T00:00:00,2025-01-01T00:00:00,202... 0.194864 \n", + "ATT(2025-06-01T00:00:00,2025-02-01T00:00:00,202... 0.246904 \n", + "ATT(2025-06-01T00:00:00,2025-03-01T00:00:00,202... 0.185215 \n", + "ATT(2025-06-01T00:00:00,2025-04-01T00:00:00,202... 0.206460 \n", + "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 1.207023 \n", + "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 2.148189 \n", + "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 3.119500 \n", + "ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,202... 0.236378 \n", + "ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,202... 0.231649 \n", + "ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,202... 0.157544 \n", + "ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,202... 0.164914 \n", + "ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,202... 0.203276 \n", + "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 1.169555 \n", + "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 2.203008 \n", "\n", "------------------ Robustness Values ------------------\n", " H_0 RV (%) RVa (%)\n", - "ATT(2025-05-01T00:00:00,2025-01-01T00:00:00,202... 0.0 0.665598 0.000623\n", - "ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,202... 0.0 1.527177 0.000450\n", - "ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,202... 0.0 1.701976 0.000383\n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.0 28.817682 25.972569\n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.0 47.453796 44.902375\n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.0 57.546883 55.029849\n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.0 67.867195 65.627010\n", - "ATT(2025-06-01T00:00:00,2025-01-01T00:00:00,202... 0.0 1.156036 0.000640\n", - "ATT(2025-06-01T00:00:00,2025-02-01T00:00:00,202... 0.0 2.113788 0.000570\n", - "ATT(2025-06-01T00:00:00,2025-03-01T00:00:00,202... 0.0 1.337051 0.000598\n", - "ATT(2025-06-01T00:00:00,2025-04-01T00:00:00,202... 0.0 3.518487 0.000542\n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 0.0 28.563154 25.669995\n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 0.0 46.292040 43.829727\n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 0.0 60.513136 58.282791\n", - "ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,202... 0.0 1.646089 0.000515\n", - "ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,202... 0.0 1.591088 0.000575\n", - "ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,202... 0.0 2.191384 0.000539\n", - "ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,202... 0.0 5.146174 1.594821\n", - "ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,202... 0.0 0.908971 0.000431\n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 0.0 28.720890 25.909809\n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 0.0 48.019244 45.590271\n" + "ATT(2025-05-01T00:00:00,2025-01-01T00:00:00,202... 0.0 0.527279 0.000614\n", + "ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,202... 0.0 1.789747 0.000634\n", + "ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,202... 0.0 3.440920 0.000385\n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.0 28.238182 25.301400\n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.0 46.558355 43.883954\n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.0 58.873179 56.544855\n", + "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.0 68.789553 66.616182\n", + "ATT(2025-06-01T00:00:00,2025-01-01T00:00:00,202... 0.0 0.480965 0.000608\n", + "ATT(2025-06-01T00:00:00,2025-02-01T00:00:00,202... 0.0 1.418822 0.000608\n", + "ATT(2025-06-01T00:00:00,2025-03-01T00:00:00,202... 0.0 0.580070 0.000609\n", + "ATT(2025-06-01T00:00:00,2025-04-01T00:00:00,202... 0.0 0.262494 0.000582\n", + "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 0.0 28.330418 25.347961\n", + "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 0.0 46.717868 44.214633\n", + "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 0.0 60.832069 58.634622\n", + "ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,202... 0.0 1.090854 0.000349\n", + "ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,202... 0.0 1.001204 0.000392\n", + "ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,202... 0.0 1.326905 0.000581\n", + "ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,202... 0.0 0.988785 0.000433\n", + "ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,202... 0.0 0.103171 0.000561\n", + "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 0.0 27.691876 24.740015\n", + "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 0.0 49.389812 47.023490\n" ] } ], From ae7a27190da519ffcd5ee12366b5317c8cf18cff Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 3 Mar 2025 16:01:29 +0000 Subject: [PATCH 017/140] add summary and loss --- doc/examples/index.rst | 1 + doc/examples/py_double_ml_panel.ipynb | 2002 ++++++++++++++++--------- 2 files changed, 1302 insertions(+), 701 deletions(-) diff --git a/doc/examples/index.rst b/doc/examples/index.rst index f363e19e..af77cfeb 100644 --- a/doc/examples/index.rst +++ b/doc/examples/index.rst @@ -26,6 +26,7 @@ General Examples py_double_ml_multiway_cluster.ipynb py_double_ml_ssm.ipynb py_double_ml_sensitivity_booking.ipynb + py_double_ml_panel.ipynb py_double_ml_did.ipynb py_double_ml_did_pretest.ipynb py_double_ml_basic_iv.ipynb diff --git a/doc/examples/py_double_ml_panel.ipynb b/doc/examples/py_double_ml_panel.ipynb index 89fc73e7..d952e83e 100644 --- a/doc/examples/py_double_ml_panel.ipynb +++ b/doc/examples/py_double_ml_panel.ipynb @@ -21,8 +21,8 @@ "from sklearn.linear_model import LinearRegression, LogisticRegression\n", "\n", "# simulate data\n", - "n_obs = 5000\n", - "df = make_did_CS2021(n_obs, dgp_type=1, n_periods=8, n_pre_treat_periods=4)\n", + "n_obs = 1000\n", + "df = make_did_CS2021(n_obs, dgp_type=1, n_periods=8, n_pre_treat_periods=4, time_type=\"datetime\")\n", "df[\"ite\"] = df[\"y1\"] - df[\"y0\"]\n" ] }, @@ -35,8 +35,8 @@ "data": { "text/plain": [ "\n", - "['2025-06-01 00:00:00', '2025-05-01 00:00:00', '2025-08-01 00:00:00',\n", - " 'NaT', '2025-07-01 00:00:00']\n", + "['2025-06-01 00:00:00', '2025-05-01 00:00:00', '2025-07-01 00:00:00',\n", + " '2025-08-01 00:00:00', 'NaT']\n", "Length: 5, dtype: datetime64[s]" ] }, @@ -90,56 +90,56 @@ " 0\n", " 2025-01-01\n", " 2025-05-01\n", - " 209.188890\n", - " 201.740740\n", - " 216.645895\n", - " -0.021438\n", - " -2.316422\n", - " 2.312522\n", + " 209.988086\n", + " 203.071942\n", + " 217.087669\n", + " 0.018839\n", + " -2.364419\n", + " 2.165378\n", " \n", " \n", " 1\n", " 2025-01-01\n", " 2025-06-01\n", - " 210.605421\n", - " 203.277117\n", - " 217.997342\n", - " 0.031009\n", - 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -2667,39 +336,50 @@ } ], "source": [ - "def plot_data(df, col_name='y'):\n", - " fig = px.line(\n", - " df,\n", + "def plot_data_seaborn_improved(df, col_name='y'):\n", + " \"\"\"\n", + " Create an improved plot with colorblind-friendly features\n", + " \n", + " Parameters:\n", + " -----------\n", + " df : DataFrame\n", + " The dataframe containing the data\n", + " col_name : str, default='y'\n", + " Column name to plot (will use '{col_name}_mean')\n", + " \"\"\"\n", + " plt.figure(figsize=(12, 7))\n", + " n_colors = df[\"First Treated\"].nunique()\n", + " color_palette = sns.color_palette(\"colorblind\", n_colors=n_colors)\n", + "\n", + " sns.lineplot(\n", + " data=df,\n", " x='t',\n", " y=f'{col_name}_mean',\n", - " color='d',\n", - " title=f'Average Values {col_name} by Group Over Time'\n", + " hue='First Treated',\n", + " style='First Treated',\n", + " palette=color_palette,\n", + " markers=True,\n", + " dashes=True,\n", + " linewidth=2.5,\n", + " alpha=0.8\n", " )\n", + " \n", + " plt.title(f'Average Values {col_name} by Group Over Time', fontsize=16)\n", + " plt.xlabel('Time', fontsize=14)\n", + " plt.ylabel(f'Average Value {col_name}', fontsize=14)\n", + " \n", "\n", - " for d_val in df['d'].unique():\n", - " mask = df['d'] == d_val\n", - " fig.add_traces(\n", - " go.Scatter(\n", - " x=df[mask]['t'].tolist() + df[mask]['t'].tolist()[::-1],\n", - " y=df[mask][f'{col_name}_upper_quantile'].tolist() + df[mask][f'{col_name}_lower_quantile'].tolist()[::-1],\n", - " fill='toself',\n", - " fillcolor='rgba(0,100,80,0.2)',\n", - " line=dict(color='rgba(255,255,255,0)'),\n", - " showlegend=False,\n", - " name=f'CI d={d_val}'\n", - " )\n", - " )\n", - "\n", - " fig.update_layout(\n", - " xaxis_title=\"Time\",\n", - " yaxis_title=f\"Average Value {col_name}\",\n", - " hovermode='x unified'\n", - " )\n", + " plt.legend(title='First Treated', title_fontsize=13, fontsize=12, \n", + " frameon=True, framealpha=0.9, loc='best')\n", + " \n", + " plt.grid(alpha=0.3, linestyle='-')\n", + " plt.tight_layout()\n", "\n", - " fig.show()\n", + " plt.show()\n", "\n", - "plot_data(agg_df)\n", - "plot_data(agg_df, col_name='ite')" + "# Call the function with your dataframes\n", + "plot_data_seaborn_improved(agg_df)\n", + "plot_data_seaborn_improved(agg_df, col_name='ite')" ] }, { @@ -2720,120 +400,185 @@ "Instrument variable(s): None\n", "Time variable: t\n", "Id variable: id\n", - "No. Observations: 1000\n", + "No. Observations: 5000\n", "\n", "------------------ DataFrame info ------------------\n", "\n", - "RangeIndex: 8000 entries, 0 to 7999\n", - "Columns: 11 entries, id to ite\n", - "dtypes: datetime64[s](2), float64(8), int64(1)\n", - "memory usage: 687.6 KB\n", + "RangeIndex: 40000 entries, 0 to 39999\n", + "Columns: 12 entries, id to First Treated\n", + "dtypes: datetime64[s](2), float64(8), int64(1), object(1)\n", + "memory usage: 3.7+ MB\n", "\n" ] } ], "source": [ - "dml_data = DoubleMLPanelData(df, y_col=\"y\", d_cols=\"d\", id_col=\"id\", t_col=\"t\", x_cols=[\"Z1\", \"Z2\", \"Z3\", \"Z4\"])\n", + "dml_data = DoubleMLPanelData(df, y_col=\"y\", d_cols=\"d\", id_col=\"id\", t_col=\"t\", x_cols=[\"Z1\", \"Z2\", \"Z3\", \"Z4\"], datetime_unit=\"M\")\n", "print(dml_data)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 58, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array(['2025-05-01T00:00:00', '2025-06-01T00:00:00',\n", - " '2025-07-01T00:00:00', '2025-08-01T00:00:00',\n", - " 'NaT'], dtype='datetime64[s]')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDMulti Object ==================\n", + "\n", + "------------------ Data summary ------------------\n", + "Outcome variable: y\n", + "Treatment variable(s): ['d']\n", + "Covariates: ['Z1', 'Z2', 'Z3', 'Z4']\n", + "Instrument variable(s): None\n", + "Time variable: t\n", + "Id variable: id\n", + "No. Observations: 5000\n", + "\n", + "------------------ Score & algorithm ------------------\n", + "Score function: observational\n", + "GT combinations: ['(2025-05,2025-01,2025-02)', '(2025-05,2025-01,2025-03)', '(2025-05,2025-02,2025-03)', '(2025-05,2025-01,2025-04)', '(2025-05,2025-02,2025-04)', '(2025-05,2025-03,2025-04)', '(2025-05,2025-01,2025-05)', '(2025-05,2025-02,2025-05)', '(2025-05,2025-03,2025-05)', '(2025-05,2025-04,2025-05)', '(2025-05,2025-01,2025-06)', '(2025-05,2025-02,2025-06)', '(2025-05,2025-03,2025-06)', '(2025-05,2025-04,2025-06)', '(2025-05,2025-01,2025-07)', '(2025-05,2025-02,2025-07)', '(2025-05,2025-03,2025-07)', '(2025-05,2025-04,2025-07)', '(2025-05,2025-01,2025-08)', '(2025-05,2025-02,2025-08)', '(2025-05,2025-03,2025-08)', '(2025-05,2025-04,2025-08)', '(2025-06,2025-01,2025-02)', '(2025-06,2025-01,2025-03)', '(2025-06,2025-02,2025-03)', '(2025-06,2025-01,2025-04)', '(2025-06,2025-02,2025-04)', '(2025-06,2025-03,2025-04)', '(2025-06,2025-01,2025-05)', '(2025-06,2025-02,2025-05)', '(2025-06,2025-03,2025-05)', '(2025-06,2025-04,2025-05)', '(2025-06,2025-01,2025-06)', '(2025-06,2025-02,2025-06)', '(2025-06,2025-03,2025-06)', '(2025-06,2025-04,2025-06)', '(2025-06,2025-05,2025-06)', '(2025-06,2025-01,2025-07)', '(2025-06,2025-02,2025-07)', '(2025-06,2025-03,2025-07)', '(2025-06,2025-04,2025-07)', '(2025-06,2025-05,2025-07)', '(2025-06,2025-01,2025-08)', '(2025-06,2025-02,2025-08)', '(2025-06,2025-03,2025-08)', '(2025-06,2025-04,2025-08)', '(2025-06,2025-05,2025-08)', '(2025-07,2025-01,2025-02)', '(2025-07,2025-01,2025-03)', '(2025-07,2025-02,2025-03)', '(2025-07,2025-01,2025-04)', '(2025-07,2025-02,2025-04)', '(2025-07,2025-03,2025-04)', '(2025-07,2025-01,2025-05)', '(2025-07,2025-02,2025-05)', '(2025-07,2025-03,2025-05)', '(2025-07,2025-04,2025-05)', '(2025-07,2025-01,2025-06)', '(2025-07,2025-02,2025-06)', '(2025-07,2025-03,2025-06)', '(2025-07,2025-04,2025-06)', '(2025-07,2025-05,2025-06)', '(2025-07,2025-01,2025-07)', '(2025-07,2025-02,2025-07)', '(2025-07,2025-03,2025-07)', '(2025-07,2025-04,2025-07)', '(2025-07,2025-05,2025-07)', '(2025-07,2025-06,2025-07)', '(2025-07,2025-01,2025-08)', '(2025-07,2025-02,2025-08)', '(2025-07,2025-03,2025-08)', '(2025-07,2025-04,2025-08)', '(2025-07,2025-05,2025-08)', '(2025-07,2025-06,2025-08)', '(2025-08,2025-01,2025-02)', '(2025-08,2025-01,2025-03)', '(2025-08,2025-02,2025-03)', '(2025-08,2025-01,2025-04)', '(2025-08,2025-02,2025-04)', '(2025-08,2025-03,2025-04)', '(2025-08,2025-01,2025-05)', '(2025-08,2025-02,2025-05)', '(2025-08,2025-03,2025-05)', '(2025-08,2025-04,2025-05)', '(2025-08,2025-01,2025-06)', '(2025-08,2025-02,2025-06)', '(2025-08,2025-03,2025-06)', '(2025-08,2025-04,2025-06)', '(2025-08,2025-05,2025-06)', '(2025-08,2025-01,2025-07)', '(2025-08,2025-02,2025-07)', '(2025-08,2025-03,2025-07)', '(2025-08,2025-04,2025-07)', '(2025-08,2025-05,2025-07)', '(2025-08,2025-06,2025-07)', '(2025-08,2025-01,2025-08)', '(2025-08,2025-02,2025-08)', '(2025-08,2025-03,2025-08)', '(2025-08,2025-04,2025-08)', '(2025-08,2025-05,2025-08)', '(2025-08,2025-06,2025-08)', '(2025-08,2025-07,2025-08)']\n", + "Control group: not_yet_treated\n", + "Anticipation periods: 0\n", + "------------------ Machine learner ------------------\n", + "Learner ml_g: LGBMRegressor(learning_rate=0.01, n_estimators=500, verbose=-1)\n", + "Learner ml_m: LGBMClassifier(learning_rate=0.01, n_estimators=500, verbose=-1)\n", + "Out-of-sample Performance:\n", + "Regression:\n", + "Learner ml_g0 RMSE: [[1.609668 1.99597102 1.63411853 2.54661976 2.03346623 1.59265116\n", + " 3.12650957 2.53697762 2.01848505 1.62289559 4.05305673 3.35262575\n", + " 2.73162542 2.14632763 5.15884007 4.39779591 3.65373048 2.90704249\n", + " 7.4357806 6.62484945 5.55458715 4.35032789 1.61832439 1.99323435\n", + " 1.64583311 2.5598477 2.07627822 1.57134888 3.35725724 2.72892217\n", + " 2.08132444 1.66185967 3.99756378 3.41594903 2.63822987 2.11889533\n", + " 1.66648225 5.25672263 4.46587065 3.63099963 2.8985437 2.23629061\n", + " 7.36887343 6.4273402 5.45282121 4.37451962 3.41791305 1.60363828\n", + " 2.0060245 1.62657377 2.54419867 2.01705474 1.56851854 3.35400844\n", + " 2.65458997 2.09673115 1.64634041 4.49801868 3.64788681 2.95389494\n", + " 2.24464719 1.73919302 5.22462445 4.36422018 3.62173921 2.87999451\n", + " 2.22378813 1.69340401 7.41560809 6.46312581 5.45969844 4.32684937\n", + " 3.36702316 2.53696583 1.60614653 1.97805736 1.64661148 2.54368814\n", + " 2.03793656 1.60057816 3.26848009 2.75515845 2.06316807 1.62462338\n", + " 4.47204587 3.70295229 2.89866228 2.22943063 1.69782966 6.35609013\n", + " 5.55514607 4.39942654 3.48046871 2.54504767 1.81698347 7.20825701\n", + " 6.5593845 5.42627864 4.31498061 3.42491263 2.53160508 1.81842156]]\n", + "Learner ml_g1 RMSE: [[1.81288678 2.50533598 1.88159685 3.30286165 2.45696805 1.70497671\n", + " 4.35252669 3.37132049 2.495426 1.82445549 5.15379337 4.34889006\n", + " 3.27014526 2.57262796 6.0629488 5.31999594 4.24298007 3.36008234\n", + " 7.08523464 6.20141244 5.23866179 4.37865084 1.78629445 2.5854703\n", + " 1.81864771 3.54614192 2.51462151 1.79676419 4.3403931 3.50275958\n", + " 2.56290654 1.81074244 5.50911722 4.44903432 3.52988972 2.533784\n", + " 1.8711304 6.48232283 5.49426252 4.46626223 3.50089991 2.68858051\n", + " 7.54019252 6.39410794 5.26572233 4.34339355 3.45351793 1.8688584\n", + " 2.58241336 1.88806243 3.30807795 2.54589767 1.80404723 4.18629823\n", + " 3.39722254 2.50559423 1.81209914 5.17734128 4.36608125 3.20981576\n", + " 2.53335651 1.76864545 6.04700315 5.27865612 4.30367795 3.49254229\n", + " 2.55906141 1.90861015 7.11673533 6.29357412 5.21164956 4.32100124\n", + " 3.31938069 2.61618638 1.76312786 2.48227172 1.79471417 3.40456044\n", + " 2.55866819 1.74797113 4.17166324 3.38599016 2.54281496 1.78753217\n", + " 5.16469675 4.28111717 3.35952848 2.48633968 1.85114229 6.35455489\n", + " 5.14677354 4.20771726 3.36921563 2.45633992 1.753428 7.34079369\n", + " 6.42017696 5.29920751 4.245377 3.41378336 2.48134163 1.86862072]]\n", + "Classification:\n", + "Learner ml_m Log Loss: [[0.50299058 0.50589822 0.5074863 0.50283078 0.50344871 0.50195721\n", + " 0.50361272 0.50576588 0.50419881 0.50635454 0.55981259 0.55639577\n", + " 0.55903573 0.56053003 0.61718421 0.61966507 0.62243193 0.61563976\n", + " 0.66565918 0.67338093 0.66364921 0.6713965 0.51564356 0.51782303\n", + " 0.51739953 0.51457947 0.51597003 0.5137619 0.57750561 0.57769253\n", + " 0.57795935 0.58085086 0.57717929 0.5824299 0.57966301 0.5782299\n", + " 0.57578142 0.64987443 0.64629056 0.654599 0.65503288 0.65009761\n", + " 0.71875509 0.71159607 0.72555221 0.71525088 0.7136845 0.51452789\n", + " 0.51975511 0.51586602 0.51152442 0.51341068 0.51109543 0.58157848\n", + " 0.58013024 0.58026799 0.57929193 0.65376688 0.65811886 0.65714205\n", + " 0.65497301 0.65314306 0.65694096 0.65226732 0.64681352 0.65633847\n", + " 0.65318649 0.65108903 0.72342404 0.71769281 0.71049815 0.72089877\n", + " 0.71806737 0.71671219 0.51433584 0.51091421 0.50726156 0.51191457\n", + " 0.50944754 0.51292994 0.58545535 0.57646218 0.58922204 0.58066676\n", + " 0.66895664 0.66948458 0.67051678 0.66769599 0.6676877 0.73854274\n", + " 0.73065792 0.73022571 0.73071207 0.73463666 0.73296758 0.73034902\n", + " 0.73467922 0.73399954 0.72617787 0.74278312 0.72948124 0.72895827]]\n", + "\n", + "------------------ Resampling ------------------\n", + "No. folds: 5\n", + "No. repeated sample splits: 1\n", + "\n", + "------------------ Fit summary ------------------\n", + " coef std err t P>|t| \\\n", + "ATT(2025-05,2025-01,2025-02) 0.020087 0.059370 0.338342 7.351056e-01 \n", + "ATT(2025-05,2025-01,2025-03) -0.117237 0.079539 -1.473951 1.404949e-01 \n", + "ATT(2025-05,2025-02,2025-03) -0.156237 0.063185 -2.472699 1.340971e-02 \n", + "ATT(2025-05,2025-01,2025-04) -0.037044 0.093136 -0.397736 6.908249e-01 \n", + "ATT(2025-05,2025-02,2025-04) -0.019166 0.077393 -0.247641 8.044123e-01 \n", + "... ... ... ... ... \n", + "ATT(2025-08,2025-03,2025-08) 1.123490 0.334177 3.361964 7.739031e-04 \n", + "ATT(2025-08,2025-04,2025-08) 1.480931 0.254240 5.824941 5.713272e-09 \n", + "ATT(2025-08,2025-05,2025-08) 1.444773 0.190461 7.585682 3.308465e-14 \n", + "ATT(2025-08,2025-06,2025-08) 1.205753 0.139658 8.633627 0.000000e+00 \n", + "ATT(2025-08,2025-07,2025-08) 1.127595 0.095938 11.753415 0.000000e+00 \n", + "\n", + " 2.5 % 97.5 % \n", + "ATT(2025-05,2025-01,2025-02) -0.096276 0.136451 \n", + "ATT(2025-05,2025-01,2025-03) -0.273131 0.038657 \n", + "ATT(2025-05,2025-02,2025-03) -0.280077 -0.032397 \n", + "ATT(2025-05,2025-01,2025-04) -0.219587 0.145500 \n", + "ATT(2025-05,2025-02,2025-04) -0.170852 0.132521 \n", + "... ... ... \n", + "ATT(2025-08,2025-03,2025-08) 0.468516 1.778465 \n", + "ATT(2025-08,2025-04,2025-08) 0.982630 1.979231 \n", + "ATT(2025-08,2025-05,2025-08) 1.071477 1.818069 \n", + "ATT(2025-08,2025-06,2025-08) 0.932029 1.479478 \n", + "ATT(2025-08,2025-07,2025-08) 0.939561 1.315630 \n", + "\n", + "[102 rows x 6 columns]\n" + ] } ], "source": [ - "dml_data.g_values" + "control_group = \"not_yet_treated\"\n", + "# control_group = \"never_treated\"\n", + "\n", + "ml_g = LGBMRegressor(n_estimators=500, learning_rate=0.01, verbose=-1)\n", + "ml_m = LGBMClassifier(n_estimators=500, learning_rate=0.01, verbose=-1)\n", + "\n", + "dml_obj = DoubleMLDIDMulti(\n", + " obj_dml_data=dml_data,\n", + " ml_g=ml_g,\n", + " ml_m=ml_m,\n", + " gt_combinations=\"all\",\n", + " control_group=control_group,\n", + " trimming_threshold=0.05,\n", + ")\n", + "\n", + "dml_obj.fit()\n", + "print(dml_obj)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array(['2025-01-01T00:00:00', '2025-02-01T00:00:00',\n", - " '2025-03-01T00:00:00', '2025-04-01T00:00:00',\n", - " '2025-05-01T00:00:00', '2025-06-01T00:00:00',\n", - " '2025-07-01T00:00:00', '2025-08-01T00:00:00'],\n", - " dtype='datetime64[s]')" + "" ] }, - "execution_count": 7, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dml_data.t_values" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[(np.datetime64('2025-05-01T00:00:00'), np.datetime64('2025-01-01T00:00:00'), np.datetime64('2025-02-01T00:00:00')), (np.datetime64('2025-05-01T00:00:00'), np.datetime64('2025-02-01T00:00:00'), np.datetime64('2025-03-01T00:00:00')), (np.datetime64('2025-05-01T00:00:00'), np.datetime64('2025-03-01T00:00:00'), np.datetime64('2025-04-01T00:00:00')), (np.datetime64('2025-05-01T00:00:00'), np.datetime64('2025-04-01T00:00:00'), np.datetime64('2025-05-01T00:00:00')), (np.datetime64('2025-05-01T00:00:00'), np.datetime64('2025-04-01T00:00:00'), np.datetime64('2025-06-01T00:00:00')), (np.datetime64('2025-05-01T00:00:00'), np.datetime64('2025-04-01T00:00:00'), np.datetime64('2025-07-01T00:00:00')), (np.datetime64('2025-05-01T00:00:00'), np.datetime64('2025-04-01T00:00:00'), np.datetime64('2025-08-01T00:00:00')), (np.datetime64('2025-06-01T00:00:00'), np.datetime64('2025-01-01T00:00:00'), np.datetime64('2025-02-01T00:00:00')), (np.datetime64('2025-06-01T00:00:00'), np.datetime64('2025-02-01T00:00:00'), np.datetime64('2025-03-01T00:00:00')), (np.datetime64('2025-06-01T00:00:00'), np.datetime64('2025-03-01T00:00:00'), np.datetime64('2025-04-01T00:00:00')), (np.datetime64('2025-06-01T00:00:00'), np.datetime64('2025-04-01T00:00:00'), np.datetime64('2025-05-01T00:00:00')), (np.datetime64('2025-06-01T00:00:00'), np.datetime64('2025-05-01T00:00:00'), np.datetime64('2025-06-01T00:00:00')), (np.datetime64('2025-06-01T00:00:00'), np.datetime64('2025-05-01T00:00:00'), np.datetime64('2025-07-01T00:00:00')), (np.datetime64('2025-06-01T00:00:00'), np.datetime64('2025-05-01T00:00:00'), np.datetime64('2025-08-01T00:00:00')), (np.datetime64('2025-07-01T00:00:00'), np.datetime64('2025-01-01T00:00:00'), np.datetime64('2025-02-01T00:00:00')), (np.datetime64('2025-07-01T00:00:00'), np.datetime64('2025-02-01T00:00:00'), np.datetime64('2025-03-01T00:00:00')), (np.datetime64('2025-07-01T00:00:00'), np.datetime64('2025-03-01T00:00:00'), np.datetime64('2025-04-01T00:00:00')), (np.datetime64('2025-07-01T00:00:00'), np.datetime64('2025-04-01T00:00:00'), np.datetime64('2025-05-01T00:00:00')), (np.datetime64('2025-07-01T00:00:00'), np.datetime64('2025-05-01T00:00:00'), np.datetime64('2025-06-01T00:00:00')), (np.datetime64('2025-07-01T00:00:00'), np.datetime64('2025-06-01T00:00:00'), np.datetime64('2025-07-01T00:00:00')), (np.datetime64('2025-07-01T00:00:00'), np.datetime64('2025-06-01T00:00:00'), np.datetime64('2025-08-01T00:00:00'))]\n" - ] - } - ], - "source": [ - "gt_combinations_d0 = [\n", - " (dml_data.g_values[0], dml_data.t_values[0], dml_data.t_values[1]),\n", - " (dml_data.g_values[0], dml_data.t_values[1], dml_data.t_values[2]),\n", - " (dml_data.g_values[0], dml_data.t_values[2], dml_data.t_values[3]),\n", - " (dml_data.g_values[0], dml_data.t_values[3], dml_data.t_values[4]),\n", - " (dml_data.g_values[0], dml_data.t_values[3], dml_data.t_values[5]),\n", - " (dml_data.g_values[0], dml_data.t_values[3], dml_data.t_values[6]),\n", - " (dml_data.g_values[0], dml_data.t_values[3], dml_data.t_values[7]),\n", - "]\n", - "gt_combinations_d1 = [\n", - " (dml_data.g_values[1], dml_data.t_values[0], dml_data.t_values[1]),\n", - " (dml_data.g_values[1], dml_data.t_values[1], dml_data.t_values[2]),\n", - " (dml_data.g_values[1], dml_data.t_values[2], dml_data.t_values[3]),\n", - " (dml_data.g_values[1], dml_data.t_values[3], dml_data.t_values[4]),\n", - " (dml_data.g_values[1], dml_data.t_values[4], dml_data.t_values[5]),\n", - " (dml_data.g_values[1], dml_data.t_values[4], dml_data.t_values[6]),\n", - " (dml_data.g_values[1], dml_data.t_values[4], dml_data.t_values[7]),\n", - "]\n", - "\n", - "gt_combinations_d2 = [\n", - " (dml_data.g_values[2], dml_data.t_values[0], dml_data.t_values[1]),\n", - " (dml_data.g_values[2], dml_data.t_values[1], dml_data.t_values[2]),\n", - " (dml_data.g_values[2], dml_data.t_values[2], dml_data.t_values[3]),\n", - " (dml_data.g_values[2], dml_data.t_values[3], dml_data.t_values[4]),\n", - " (dml_data.g_values[2], dml_data.t_values[4], dml_data.t_values[5]),\n", - " (dml_data.g_values[2], dml_data.t_values[5], dml_data.t_values[6]),\n", - " (dml_data.g_values[2], dml_data.t_values[5], dml_data.t_values[7]),\n", - "]\n", - "\n", - "gt_combinations = gt_combinations_d0 + gt_combinations_d1 + gt_combinations_d2\n", - "\n", - "print(gt_combinations)" + "dml_obj.bootstrap()\n", + "dml_obj.sensitivity_analysis()" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -2857,489 +602,154 @@ " \n", " \n", " \n", - " coef\n", - " std err\n", - " t\n", - " P>|t|\n", - " 2.5 %\n", - " 97.5 %\n", + " First Treated\n", + " Pre-treatment Period\n", + " Evaluation Period\n", + " Estimate\n", + " CI Lower\n", + " CI Upper\n", + " Pre-Treatment\n", + " RV\n", " \n", " \n", " \n", " \n", - " ATT(2025-05-01T00:00:00,2025-01-01T00:00:00,2025-02-01T00:00:00)\n", - " -0.174962\n", - " 0.163741\n", - " -1.068526\n", - " 2.852834e-01\n", - " -0.495889\n", - " 0.145965\n", - " \n", - " \n", - " ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,2025-03-01T00:00:00)\n", - " -0.116450\n", - " 0.174492\n", - " -0.667367\n", - " 5.045378e-01\n", - " -0.458449\n", - " 0.225548\n", - " \n", - " \n", - " ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,2025-04-01T00:00:00)\n", - " 0.019741\n", - " 0.170649\n", - " 0.115684\n", - " 9.079033e-01\n", - " -0.314724\n", - " 0.354207\n", - " \n", - " \n", - " ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,2025-05-01T00:00:00)\n", - " 0.991408\n", - " 0.154558\n", - " 6.414481\n", - " 1.413036e-10\n", - " 0.688480\n", - " 1.294335\n", - " \n", - " \n", - " ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,2025-06-01T00:00:00)\n", - " 2.179452\n", - " 0.185118\n", - " 11.773298\n", - " 0.000000e+00\n", - " 1.816627\n", - " 2.542277\n", - " \n", - " \n", - " ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,2025-07-01T00:00:00)\n", - " 3.094986\n", - " 0.172306\n", - " 17.962126\n", - " 0.000000e+00\n", - " 2.757272\n", - " 3.432700\n", - " \n", - " \n", - " ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,2025-08-01T00:00:00)\n", - " 4.189546\n", - " 0.180109\n", - " 23.261173\n", - " 0.000000e+00\n", - " 3.836539\n", - " 4.542553\n", - " \n", - " \n", - " ATT(2025-06-01T00:00:00,2025-01-01T00:00:00,2025-02-01T00:00:00)\n", - " -0.168708\n", - " 0.160845\n", - " -1.048885\n", - " 2.942309e-01\n", - " -0.483957\n", - " 0.146542\n", - " \n", - " \n", - " ATT(2025-06-01T00:00:00,2025-02-01T00:00:00,2025-03-01T00:00:00)\n", - " 0.101348\n", - " 0.156885\n", - " 0.646006\n", - " 5.182756e-01\n", - " -0.206140\n", - " 0.408837\n", - " \n", - " \n", - " ATT(2025-06-01T00:00:00,2025-03-01T00:00:00,2025-04-01T00:00:00)\n", - " -0.002634\n", - " 0.169704\n", - " -0.015520\n", - " 9.876175e-01\n", - " -0.335247\n", - " 0.329979\n", - " \n", - " \n", - " ATT(2025-06-01T00:00:00,2025-04-01T00:00:00,2025-05-01T00:00:00)\n", - " -0.169933\n", - " 0.174687\n", - " -0.972786\n", - " 3.306596e-01\n", - " -0.512313\n", - " 0.172447\n", - " \n", - " \n", - " ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,2025-06-01T00:00:00)\n", - " 0.980952\n", - " 0.181550\n", - " 5.403202\n", - " 6.546162e-08\n", - " 0.625120\n", - " 1.336783\n", - " \n", - " \n", - " ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,2025-07-01T00:00:00)\n", - " 2.041356\n", - " 0.179710\n", - " 11.359194\n", - " 0.000000e+00\n", - " 1.689132\n", - " 2.393580\n", - " \n", - " \n", - " ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,2025-08-01T00:00:00)\n", - " 3.282039\n", - " 0.171789\n", - " 19.105103\n", - " 0.000000e+00\n", - " 2.945340\n", - " 3.618739\n", - " \n", - " \n", - " ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,2025-02-01T00:00:00)\n", - " -0.189431\n", - " 0.153354\n", - " -1.235252\n", - " 2.167369e-01\n", - " -0.489999\n", - " 0.111137\n", - " \n", - " \n", - " ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,2025-03-01T00:00:00)\n", - " 0.070483\n", - " 0.150055\n", - " 0.469716\n", - " 6.385583e-01\n", - " -0.223619\n", - " 0.364586\n", - " \n", - " \n", - " ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,2025-04-01T00:00:00)\n", - " 0.121959\n", - " 0.147787\n", - " 0.825239\n", - " 4.092359e-01\n", - " -0.167697\n", - " 0.411616\n", + " ATT(2025-05,2025-01,2025-02)\n", + " 2025-05-01\n", + " 2025-01-01\n", + " 2025-02-01\n", + " 0.020087\n", + " -0.179384\n", + " 0.219559\n", + " True\n", + " 0.004923\n", " \n", " \n", - " ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,2025-05-01T00:00:00)\n", - " -0.116124\n", - " 0.143672\n", - " -0.808260\n", - " 4.189410e-01\n", - " -0.397717\n", - " 0.165468\n", + " ATT(2025-05,2025-01,2025-03)\n", + " 2025-05-01\n", + " 2025-01-01\n", + " 2025-03-01\n", + " -0.117237\n", + " -0.384471\n", + " 0.149998\n", + " True\n", + " 0.022780\n", " \n", " \n", - " ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,2025-06-01T00:00:00)\n", - " -0.038648\n", - " 0.154366\n", - " -0.250365\n", - " 8.023053e-01\n", - " -0.341199\n", - " 0.263904\n", + " ATT(2025-05,2025-02,2025-03)\n", + " 2025-05-01\n", + " 2025-02-01\n", + " 2025-03-01\n", + " -0.156237\n", + " -0.368525\n", + " 0.056051\n", + " True\n", + " 0.037449\n", " \n", " \n", - " ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,2025-07-01T00:00:00)\n", - " 0.981190\n", - " 0.151253\n", - " 6.487097\n", - " 8.750622e-11\n", - " 0.684741\n", - " 1.277640\n", + " ATT(2025-05,2025-01,2025-04)\n", + " 2025-05-01\n", + " 2025-01-01\n", + " 2025-04-01\n", + " -0.037044\n", + " -0.349961\n", + " 0.275874\n", + " True\n", + " 0.005514\n", " \n", " \n", - " ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,2025-08-01T00:00:00)\n", - " 2.209412\n", - " 0.159746\n", - " 13.830762\n", - " 0.000000e+00\n", - " 1.896315\n", - " 2.522509\n", + " ATT(2025-05,2025-02,2025-04)\n", + " 2025-05-01\n", + " 2025-02-01\n", + " 2025-04-01\n", + " -0.019166\n", + " -0.279189\n", + " 0.240858\n", + " True\n", + " 0.003669\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " coef std err \\\n", - "ATT(2025-05-01T00:00:00,2025-01-01T00:00:00,202... -0.174962 0.163741 \n", - "ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,202... -0.116450 0.174492 \n", - "ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,202... 0.019741 0.170649 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.991408 0.154558 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 2.179452 0.185118 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 3.094986 0.172306 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 4.189546 0.180109 \n", - "ATT(2025-06-01T00:00:00,2025-01-01T00:00:00,202... -0.168708 0.160845 \n", - "ATT(2025-06-01T00:00:00,2025-02-01T00:00:00,202... 0.101348 0.156885 \n", - "ATT(2025-06-01T00:00:00,2025-03-01T00:00:00,202... -0.002634 0.169704 \n", - "ATT(2025-06-01T00:00:00,2025-04-01T00:00:00,202... -0.169933 0.174687 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 0.980952 0.181550 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 2.041356 0.179710 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 3.282039 0.171789 \n", - "ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,202... -0.189431 0.153354 \n", - "ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,202... 0.070483 0.150055 \n", - "ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,202... 0.121959 0.147787 \n", - "ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,202... -0.116124 0.143672 \n", - "ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,202... -0.038648 0.154366 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 0.981190 0.151253 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 2.209412 0.159746 \n", + " First Treated Pre-treatment Period \\\n", + "ATT(2025-05,2025-01,2025-02) 2025-05-01 2025-01-01 \n", + "ATT(2025-05,2025-01,2025-03) 2025-05-01 2025-01-01 \n", + "ATT(2025-05,2025-02,2025-03) 2025-05-01 2025-02-01 \n", + "ATT(2025-05,2025-01,2025-04) 2025-05-01 2025-01-01 \n", + "ATT(2025-05,2025-02,2025-04) 2025-05-01 2025-02-01 \n", "\n", - " t P>|t| \\\n", - "ATT(2025-05-01T00:00:00,2025-01-01T00:00:00,202... -1.068526 2.852834e-01 \n", - "ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,202... -0.667367 5.045378e-01 \n", - "ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,202... 0.115684 9.079033e-01 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 6.414481 1.413036e-10 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 11.773298 0.000000e+00 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 17.962126 0.000000e+00 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 23.261173 0.000000e+00 \n", - "ATT(2025-06-01T00:00:00,2025-01-01T00:00:00,202... -1.048885 2.942309e-01 \n", - "ATT(2025-06-01T00:00:00,2025-02-01T00:00:00,202... 0.646006 5.182756e-01 \n", - "ATT(2025-06-01T00:00:00,2025-03-01T00:00:00,202... -0.015520 9.876175e-01 \n", - "ATT(2025-06-01T00:00:00,2025-04-01T00:00:00,202... -0.972786 3.306596e-01 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 5.403202 6.546162e-08 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 11.359194 0.000000e+00 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 19.105103 0.000000e+00 \n", - "ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,202... -1.235252 2.167369e-01 \n", - "ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,202... 0.469716 6.385583e-01 \n", - "ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,202... 0.825239 4.092359e-01 \n", - "ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,202... -0.808260 4.189410e-01 \n", - "ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,202... -0.250365 8.023053e-01 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 6.487097 8.750622e-11 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 13.830762 0.000000e+00 \n", + " Evaluation Period Estimate CI Lower CI Upper \\\n", + "ATT(2025-05,2025-01,2025-02) 2025-02-01 0.020087 -0.179384 0.219559 \n", + "ATT(2025-05,2025-01,2025-03) 2025-03-01 -0.117237 -0.384471 0.149998 \n", + "ATT(2025-05,2025-02,2025-03) 2025-03-01 -0.156237 -0.368525 0.056051 \n", + "ATT(2025-05,2025-01,2025-04) 2025-04-01 -0.037044 -0.349961 0.275874 \n", + "ATT(2025-05,2025-02,2025-04) 2025-04-01 -0.019166 -0.279189 0.240858 \n", "\n", - " 2.5 % 97.5 % \n", - "ATT(2025-05-01T00:00:00,2025-01-01T00:00:00,202... -0.495889 0.145965 \n", - "ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,202... -0.458449 0.225548 \n", - "ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,202... -0.314724 0.354207 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.688480 1.294335 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 1.816627 2.542277 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 2.757272 3.432700 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 3.836539 4.542553 \n", - "ATT(2025-06-01T00:00:00,2025-01-01T00:00:00,202... -0.483957 0.146542 \n", - "ATT(2025-06-01T00:00:00,2025-02-01T00:00:00,202... -0.206140 0.408837 \n", - "ATT(2025-06-01T00:00:00,2025-03-01T00:00:00,202... -0.335247 0.329979 \n", - "ATT(2025-06-01T00:00:00,2025-04-01T00:00:00,202... -0.512313 0.172447 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 0.625120 1.336783 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 1.689132 2.393580 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 2.945340 3.618739 \n", - "ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,202... -0.489999 0.111137 \n", - "ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,202... -0.223619 0.364586 \n", - "ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,202... -0.167697 0.411616 \n", - "ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,202... -0.397717 0.165468 \n", - "ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,202... -0.341199 0.263904 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 0.684741 1.277640 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 1.896315 2.522509 " + " Pre-Treatment RV \n", + "ATT(2025-05,2025-01,2025-02) True 0.004923 \n", + "ATT(2025-05,2025-01,2025-03) True 0.022780 \n", + "ATT(2025-05,2025-02,2025-03) True 0.037449 \n", + "ATT(2025-05,2025-01,2025-04) True 0.005514 \n", + "ATT(2025-05,2025-02,2025-04) True 0.003669 " ] }, - "execution_count": 9, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "control_group = \"not_yet_treated\"\n", - "control_group = \"never_treated\"\n", - "\n", - "ml_g=LinearRegression()\n", - "ml_m=LogisticRegression()\n", - "\n", - "# ml_g = LGBMRegressor()\n", - "# ml_m = LGBMClassifier()\n", - "\n", - "dml_obj = DoubleMLDIDMulti(\n", - " obj_dml_data=dml_data,\n", - " ml_g=ml_g,\n", - " ml_m=ml_m,\n", - " gt_combinations=gt_combinations,\n", - " control_group=control_group,\n", - ")\n", + "def create_ci_dataframe(dml_obj, level=0.95, joint=True, include_rvs=False):\n", + " \"\"\"\n", + " Create a DataFrame with coefficient estimates and confidence intervals from a DoubleML object.\n", + " \n", + " Parameters:\n", + " -----------\n", + " dml_obj : DoubleML object\n", + " The fitted DoubleML object\n", + " level : float, default=0.95\n", + " Confidence level for intervals\n", + " joint : bool, default=True\n", + " Whether to use joint confidence intervals\n", + " \n", + " Returns:\n", + " --------\n", + " DataFrame\n", + " DataFrame containing estimates and confidence intervals\n", + " \"\"\"\n", "\n", - "dml_obj.fit()\n", + " ci = dml_obj.confint(level=level, joint=joint)\n", "\n", - "dml_obj.summary" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "level = 0.95\n", + " # Create DataFrame\n", + " result_df = pd.DataFrame({\n", + " 'First Treated': [gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", + " 'Pre-treatment Period' : [gt_combination[1] for gt_combination in dml_obj.gt_combinations],\n", + " 'Evaluation Period': [gt_combination[2] for gt_combination in dml_obj.gt_combinations],\n", + " 'Estimate': dml_obj.coef,\n", + " 'CI Lower': ci.iloc[:, 0],\n", + " 'CI Upper': ci.iloc[:, 1],\n", + " 'Pre-Treatment': [gt_combination[2] < gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", + " })\n", + " if include_rvs:\n", + " result_df[\"RV\"] = dml_obj.sensitivity_params[\"rv\"]\n", + " return result_df\n", "\n", - "ci = dml_obj.confint(level=level)\n", - "dml_obj.bootstrap(n_rep_boot=5000)\n", - "ci_joint = dml_obj.confint(level=level, joint=True)" + "ci_df = create_ci_dataframe(dml_obj, include_rvs=True)\n", + "ci_df.head()" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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Time PeriodEstimateLower CIUpper CILower CI JointUpper CI JointFirst TreatedPre-Treatment
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ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,2025-06-01T00:00:00)2025-06-012.1794521.8166272.5422771.6334912.7254122025-05-01False
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True \n", - "ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,202... True \n", - "ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,202... True \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... False \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... False " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ci_df = pd.DataFrame({\n", - " 'Time Period': [gt_combination[2] for gt_combination in dml_obj.gt_combinations],\n", - " 'Estimate': dml_obj.coef,\n", - " 'Lower CI': ci.iloc[:, 0],\n", - " 'Upper CI': ci.iloc[:, 1],\n", - " 'Lower CI Joint': ci_joint.iloc[:, 0],\n", - " 'Upper CI Joint': ci_joint.iloc[:, 1],\n", - " 'First Treated': [gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", - " 'Pre-Treatment': [gt_combination[2] < gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", - "})\n", - "ci_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -3347,12 +757,13 @@ } ], "source": [ - "def plot_atts(df, level=0.95, figsize=(15, 10), \n", - " title='Coefficient Estimates by First Treatment Period'):\n", + "def plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8)):\n", " \"\"\"\n", - " Plot coefficient estimates with CIs over time, grouped by first treatment period.\n", + " Plot coefficient estimates with CIs over time, grouped by first treated period.\n", " \"\"\"\n", - " all_time_periods = sorted(df['Time Period'].unique())\n", + "\n", + " df = create_ci_dataframe(dml_obj, level=level, joint=joint)\n", + " all_time_periods = sorted(df['Evaluation Period'].unique())\n", " first_treated_periods = sorted(df['First Treated'].unique())\n", " n_periods = len(first_treated_periods)\n", " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", @@ -3374,13 +785,9 @@ " ax = axes[idx]\n", "\n", " i_period = all_time_periods.index(period)\n", - " # Add shaded treatment transition period\n", - " shade = ax.axvspan(all_time_periods[i_period - 1], period, color='gray', alpha=0.2)\n", - " if idx == 0:\n", - " legend_elements.append((shade, 'Treatment transition'))\n", - " \n", + "\n", " # Add treatment start line\n", - " line = ax.axvline(x=all_time_periods[i_period - 1], color='red', \n", + " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", " linestyle=':', alpha=0.7)\n", " if idx == 0:\n", " legend_elements.append((line, 'Treatment start'))\n", @@ -3395,53 +802,41 @@ " \n", " if not pre_treatment.empty:\n", " # Pre-treatment points\n", - " scatter_pre = ax.scatter(pre_treatment['Time Period'], \n", + " scatter_pre = ax.scatter(pre_treatment['Evaluation Period'], \n", " pre_treatment['Estimate'], \n", - " color=colors['pre'], alpha=0.8, s=50)\n", + " color=colors['pre'], alpha=0.8, s=10)\n", " # Regular CIs\n", - " error_pre = ax.errorbar(pre_treatment['Time Period'], \n", + " error_pre = ax.errorbar(pre_treatment['Evaluation Period'], \n", " pre_treatment['Estimate'],\n", - " yerr=[pre_treatment['Estimate'] - pre_treatment['Lower CI'],\n", - " pre_treatment['Upper CI'] - pre_treatment['Estimate']],\n", + " yerr=[pre_treatment['Estimate'] - pre_treatment['CI Lower'],\n", + " pre_treatment['CI Upper'] - pre_treatment['Estimate']],\n", " fmt='none', color=colors['pre'], alpha=1.0, \n", " capsize=5)\n", - " # Joint CIs\n", - " error_pre_joint = ax.errorbar(pre_treatment['Time Period'], \n", - " pre_treatment['Estimate'],\n", - " yerr=[pre_treatment['Estimate'] - pre_treatment['Lower CI Joint'],\n", - " pre_treatment['Upper CI Joint'] - pre_treatment['Estimate']],\n", - " fmt='none', color=colors['pre'], alpha=0.5, \n", - " capsize=5)\n", " if idx == 0:\n", " legend_elements.extend([\n", " (scatter_pre, 'Pre-treatment'),\n", " (error_pre, f'{int(level*100)}% CI'),\n", - " (error_pre_joint, f'{int(level*100)}% joint CI')\n", " ])\n", " \n", " # Similar structure for post-treatment\n", " if not post_treatment.empty:\n", - " scatter_post = ax.scatter(post_treatment['Time Period'], \n", + " scatter_post = ax.scatter(post_treatment['Evaluation Period'], \n", " post_treatment['Estimate'], \n", - " color=colors['post'], alpha=0.8, s=50)\n", + " color=colors['post'], alpha=0.8, s=10)\n", " if idx == 0:\n", " legend_elements.append((scatter_post, 'Post-treatment'))\n", " \n", - " ax.errorbar(post_treatment['Time Period'], post_treatment['Estimate'],\n", - " yerr=[post_treatment['Estimate'] - post_treatment['Lower CI'],\n", - " post_treatment['Upper CI'] - post_treatment['Estimate']],\n", + " ax.errorbar(post_treatment['Evaluation Period'], post_treatment['Estimate'],\n", + " yerr=[post_treatment['Estimate'] - post_treatment['CI Lower'],\n", + " post_treatment['CI Upper'] - post_treatment['Estimate']],\n", " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", - " ax.errorbar(post_treatment['Time Period'], post_treatment['Estimate'],\n", - " yerr=[post_treatment['Estimate'] - post_treatment['Lower CI Joint'],\n", - " post_treatment['Upper CI Joint'] - post_treatment['Estimate']],\n", - " fmt='none', color=colors['post'], alpha=0.5, capsize=5)\n", "\n", " ax.set_title(f'First Treated: {period}')\n", " ax.grid(True, alpha=0.3)\n", " \n", " if idx == 0:\n", " ax.set_ylabel('Effect')\n", - " ax.set_xlabel('Time Period')\n", + " ax.set_xlabel('Evaluation Period')\n", " \n", " # Create legend in a separate subplot at the bottom\n", " legend_ax = fig.add_subplot(gs[-1])\n", @@ -3454,1464 +849,23 @@ " mode='expand',\n", " borderaxespad=0.)\n", " \n", - " plt.suptitle(title, y=1.02) # Adjust title position\n", + " plt.suptitle(\"Estimated ATTs by Group\", y=1.02)\n", " plt.tight_layout()\n", " plt.show()\n", "\n", - "plot_atts(ci_df, title=\"Estimated Effects\")" + "plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8))" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 75, "metadata": {}, "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "error_y": { - "array": [ - 0.32092689393485985, - 0.3419986025467044, - 0.33446571082117504 - ], - "arrayminus": [ - 0.32092689393485985, - 0.34199860254670444, - 0.33446571082117504 - ], - "color": "#0073C2", - "symmetric": false, - 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -4919,743 +873,788 @@ } ], "source": [ - "import plotly.graph_objects as go\n", - "from plotly.subplots import make_subplots\n", + "import numpy as np\n", + "import pandas as pd\n", "\n", - "def plot_atts_plotly(df, level=0.95, \n", - " title='Coefficient Estimates by First Treatment Period',\n", - " height=800):\n", + "def plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8)):\n", " \"\"\"\n", - " Plot coefficient estimates with CIs over time using Plotly.\n", - " \n", - " Parameters\n", - " ----------\n", - " df : pandas.DataFrame\n", - " DataFrame with required columns\n", - " level : float, default=0.95\n", - " Confidence level for intervals\n", - " title : str\n", - " Plot title\n", - " height : int\n", - " Figure height in pixels\n", + " Plot coefficient estimates with CIs over time, grouped by first treated period.\n", + " CIs with the same x-value are jittered horizontally for better visibility.\n", + " Works with both numeric and datetime values.\n", " \"\"\"\n", - " # Setup\n", - " all_time_periods = sorted(df['Time Period'].unique())\n", + " df = create_ci_dataframe(dml_obj, level=level, joint=joint)\n", + " all_time_periods = sorted(df['Evaluation Period'].unique())\n", " first_treated_periods = sorted(df['First Treated'].unique())\n", " n_periods = len(first_treated_periods)\n", + " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", + " \n", + " # Check if we're dealing with datetime values\n", + " is_datetime = pd.api.types.is_datetime64_any_dtype(df['Evaluation Period'])\n", + " \n", + " # Adjust figure size to accommodate bottom legend\n", + " fig = plt.figure(figsize=figsize)\n", + " # Create subplot grid with space for legend at bottom\n", + " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", + " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", + "\n", + " if n_periods == 1:\n", + " axes = [axes]\n", " \n", - " # Create figure with subplots\n", - " fig = make_subplots(rows=n_periods, cols=1,\n", - " subplot_titles=[f'First Treated: {period}' \n", - " for period in first_treated_periods],\n", - " vertical_spacing=0.05)\n", + " # Create a list to store legend handles and labels\n", + " legend_elements = []\n", " \n", - " # Color scheme - using RGB instead of RGBA\n", - " colors = {'pre': '#0073C2', 'post': '#EFC000'} # Colorblind-friendly without alpha\n", + " # Define jitter amount - different handling for datetime vs numeric\n", + " if is_datetime:\n", + " # For datetime, calculate time difference between periods\n", + " if len(all_time_periods) > 1:\n", + " time_diff = (all_time_periods[1] - all_time_periods[0]).total_seconds()\n", + " jitter_seconds = time_diff * 0.1 # Use 5% of time difference for jitter\n", + " else:\n", + " jitter_seconds = 86400 * 0.05 # Default to 5% of a day if only one period\n", + " else:\n", + " jitter_amount = 0.1 # Standard numeric jitter\n", " \n", " for idx, period in enumerate(first_treated_periods):\n", - " row = idx + 1\n", " period_data = df[df['First Treated'] == period]\n", + " ax = axes[idx]\n", + "\n", " i_period = all_time_periods.index(period)\n", - " \n", - " # Add treatment transition period\n", - " fig.add_vrect(\n", - " x0=all_time_periods[i_period - 1],\n", - " x1=period,\n", - " fillcolor=\"gray\",\n", - " opacity=0.2,\n", - " line_width=0,\n", - " row=row,\n", - " col=1,\n", - " name=\"Treatment transition\",\n", - " showlegend=(idx == 0)\n", - " )\n", - " \n", + "\n", " # Add treatment start line\n", - " fig.add_vline(\n", - " x=all_time_periods[i_period - 1],\n", - " line_dash=\"dot\",\n", - " line_color=\"red\",\n", - " opacity=0.7,\n", - " row=row,\n", - " col=1,\n", - " name=\"Treatment start\",\n", - " showlegend=(idx == 0)\n", - " )\n", - " # add zero line\n", - " fig.add_hline(\n", - " y=0,\n", - " line_dash=\"dash\",\n", - " line_color=\"black\",\n", - " opacity=0.5,\n", - " row=row,\n", - " col=1,\n", - " name=\"Zero effect\",\n", - " showlegend=(idx == 0)\n", - " )\n", - " \n", + " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", + " linestyle=':', alpha=0.7)\n", + " if idx == 0:\n", + " legend_elements.append((line, 'Treatment start'))\n", + "\n", + " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", + " if idx == 0:\n", + " legend_elements.append((zero_line, 'Zero effect'))\n", + "\n", " # Split data by treatment status\n", " pre_treatment = period_data[period_data['Pre-Treatment']]\n", " post_treatment = period_data[~period_data['Pre-Treatment']]\n", " \n", - " # Plot pre-treatment data\n", " if not pre_treatment.empty:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=pre_treatment['Time Period'],\n", - " y=pre_treatment['Estimate'],\n", - " error_y=dict(\n", - " type='data',\n", - " symmetric=False,\n", - " array=pre_treatment['Upper CI'] - pre_treatment['Estimate'],\n", - " arrayminus=pre_treatment['Estimate'] - pre_treatment['Lower CI'],\n", - " color=colors['pre'],\n", - " width=3\n", - " ),\n", - " mode='markers',\n", - " marker=dict(color=colors['pre'], size=8),\n", - " name='Pre-treatment',\n", - " showlegend=(idx == 0)\n", - " ),\n", - " row=row,\n", - " col=1\n", - " )\n", + " pre_treatment = pre_treatment.copy()\n", " \n", - " # Add joint CIs\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=pre_treatment['Time Period'],\n", - " y=pre_treatment['Estimate'],\n", - " error_y=dict(\n", - " type='data',\n", - " symmetric=False,\n", - " array=pre_treatment['Upper CI Joint'] - pre_treatment['Estimate'],\n", - " arrayminus=pre_treatment['Estimate'] - pre_treatment['Lower CI Joint'],\n", - " color=colors['pre'],\n", - " width=3\n", - " ),\n", - " mode='markers',\n", - " marker=dict(color=colors['pre'], size=0),\n", - " name='Pre-treatment Joint CI',\n", - " showlegend=(idx == 0)\n", - " ),\n", - " row=row,\n", - " col=1\n", - " )\n", + " for x_val in pre_treatment['Evaluation Period'].unique():\n", + " mask = pre_treatment['Evaluation Period'] == x_val\n", + " count = mask.sum()\n", + " if count > 1:\n", + " if is_datetime:\n", + " # For datetime values, create timedelta jitters\n", + " jitter_range = np.linspace(-jitter_seconds, jitter_seconds, count)\n", + " jitters = [pd.Timedelta(seconds=float(j)) for j in jitter_range]\n", + " else:\n", + " # For numeric values, create standard jitters\n", + " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", + " \n", + " # Store the jitters for these points\n", + " pre_treatment.loc[mask, 'jitter_index'] = range(count)\n", + " for i, j in enumerate(jitters):\n", + " pre_treatment.loc[mask & (pre_treatment['jitter_index'] == i), 'jittered_x'] = x_val + j\n", + " \n", + " # For points without jitter (single point at x-value)\n", + " if 'jittered_x' not in pre_treatment.columns:\n", + " pre_treatment['jittered_x'] = pre_treatment['Evaluation Period']\n", + " else:\n", + " mask = ~pre_treatment['jittered_x'].notna()\n", + " pre_treatment.loc[mask, 'jittered_x'] = pre_treatment.loc[mask, 'Evaluation Period']\n", + " \n", + " # Pre-treatment points with jitter\n", + " scatter_pre = ax.scatter(pre_treatment['jittered_x'], \n", + " pre_treatment['Estimate'], \n", + " color=colors['pre'], alpha=0.8, s=10)\n", + " \n", + " # Regular CIs with jitter\n", + " error_pre = ax.errorbar(pre_treatment['jittered_x'], \n", + " pre_treatment['Estimate'],\n", + " yerr=[pre_treatment['Estimate'] - pre_treatment['CI Lower'],\n", + " pre_treatment['CI Upper'] - pre_treatment['Estimate']],\n", + " fmt='none', color=colors['pre'], alpha=1.0, \n", + " capsize=5)\n", + " if idx == 0:\n", + " legend_elements.extend([\n", + " (scatter_pre, 'Pre-treatment'),\n", + " (error_pre, f'{int(level*100)}% CI'),\n", + " ])\n", " \n", - " # Plot post-treatment data (similar structure)\n", + " # Similar structure for post-treatment with jittering\n", " if not post_treatment.empty:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=post_treatment['Time Period'],\n", - " y=post_treatment['Estimate'],\n", - " error_y=dict(\n", - " type='data',\n", - " symmetric=False,\n", - " array=post_treatment['Upper CI'] - post_treatment['Estimate'],\n", - " arrayminus=post_treatment['Estimate'] - post_treatment['Lower CI'],\n", - " color=colors['post'],\n", - " width=3\n", - " ),\n", - " mode='markers',\n", - " marker=dict(color=colors['post'], size=8),\n", - " name='Post-treatment',\n", - " showlegend=(idx == 0)\n", - " ),\n", - " row=row,\n", - " col=1\n", - " )\n", + " post_treatment = post_treatment.copy()\n", + " \n", + " for x_val in post_treatment['Evaluation Period'].unique():\n", + " mask = post_treatment['Evaluation Period'] == x_val\n", + " count = mask.sum()\n", + " if count > 1:\n", + " if is_datetime:\n", + " # For datetime values, create timedelta jitters\n", + " jitter_range = np.linspace(-jitter_seconds, jitter_seconds, count)\n", + " jitters = [pd.Timedelta(seconds=float(j)) for j in jitter_range]\n", + " else:\n", + " # For numeric values, create standard jitters\n", + " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", + " \n", + " # Store the jitters for these points\n", + " post_treatment.loc[mask, 'jitter_index'] = range(count)\n", + " for i, j in enumerate(jitters):\n", + " post_treatment.loc[mask & (post_treatment['jitter_index'] == i), 'jittered_x'] = x_val + j\n", + " \n", + " # For points without jitter (single point at x-value)\n", + " if 'jittered_x' not in post_treatment.columns:\n", + " post_treatment['jittered_x'] = post_treatment['Evaluation Period']\n", + " else:\n", + " mask = ~post_treatment['jittered_x'].notna()\n", + " post_treatment.loc[mask, 'jittered_x'] = post_treatment.loc[mask, 'Evaluation Period']\n", + " \n", + " scatter_post = ax.scatter(post_treatment['jittered_x'], \n", + " post_treatment['Estimate'], \n", + " color=colors['post'], alpha=0.8, s=10)\n", + " if idx == 0:\n", + " legend_elements.append((scatter_post, 'Post-treatment'))\n", " \n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=post_treatment['Time Period'],\n", - " y=post_treatment['Estimate'],\n", - " error_y=dict(\n", - " type='data',\n", - " symmetric=False,\n", - " array=post_treatment['Upper CI Joint'] - post_treatment['Estimate'],\n", - " arrayminus=post_treatment['Estimate'] - post_treatment['Lower CI Joint'],\n", - " color=colors['post'],\n", - " width=3\n", - " ),\n", - " mode='markers',\n", - " marker=dict(color=colors['post'], size=0),\n", - " name='Post-treatment Joint CI',\n", - " showlegend=(idx == 0)\n", - " ),\n", - " row=row,\n", - " col=1\n", - " )\n", + " # Error bars with jitter\n", + " ax.errorbar(post_treatment['jittered_x'], post_treatment['Estimate'],\n", + " yerr=[post_treatment['Estimate'] - post_treatment['CI Lower'],\n", + " post_treatment['CI Upper'] - post_treatment['Estimate']],\n", + " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", + "\n", + " ax.set_title(f'First Treated: {period}')\n", + " ax.grid(True, alpha=0.3)\n", + " \n", + " if idx == 0:\n", + " ax.set_ylabel('Effect')\n", + " ax.set_xlabel('Evaluation Period')\n", " \n", - " # Update layout\n", - " fig.update_layout(\n", - " height=height,\n", - " title_text=title,\n", - " showlegend=True,\n", - " legend=dict(\n", - " orientation=\"h\",\n", - " yanchor=\"bottom\",\n", - " y=-0.2,\n", - " xanchor=\"center\",\n", - " x=0.5\n", - " ),\n", - " yaxis_title=\"Effect\",\n", - " xaxis_title=\"Time Period\"\n", - " )\n", + " # Create legend in a separate subplot at the bottom\n", + " legend_ax = fig.add_subplot(gs[-1])\n", + " legend_ax.axis('off') # Hide axes for legend subplot\n", " \n", - " # Update axes\n", - " for i in range(n_periods):\n", - " fig.update_xaxes(title_text=\"Time Period\" if i == n_periods-1 else \"\", row=i+1, col=1)\n", - " fig.update_yaxes(title_text=\"Effect\" if i == 0 else \"\", row=i+1, col=1)\n", + " # Add legend using collected handles and labels\n", + " legend = legend_ax.legend(*zip(*legend_elements), \n", + " loc='center',\n", + " ncol=5, # Adjust number of columns as needed\n", + " mode='expand',\n", + " borderaxespad=0.)\n", " \n", - " return fig\n", + " plt.suptitle(\"Estimated ATTs by Group\", y=1.02)\n", + " plt.tight_layout()\n", + " plt.show()\n", "\n", - "# Usage\n", - "fig = plot_atts_plotly(ci_df, title=\"Estimated Effects\")\n", - "fig.show()" + "plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8))" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 76, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "================== DoubleMLDIDBinary Object ==================\n", - "\n", - "------------------ Data summary ------------------\n", - "Outcome variable: y\n", - "Treatment variable(s): ['d']\n", - "Covariates: ['Z1', 'Z2', 'Z3', 'Z4']\n", - "Instrument variable(s): None\n", - "Time variable: t\n", - "Id variable: id\n", - "No. Observations: 1000\n", - "\n", - "------------------ Score & algorithm ------------------\n", - "Score function: observational\n", - "\n", - "------------------ Machine learner ------------------\n", - "Learner ml_g: LinearRegression()\n", - "Learner ml_m: LogisticRegression()\n", - "Out-of-sample Performance:\n", - "Regression:\n", - "Learner ml_g0 RMSE: [[1.44914314]]\n", - "Learner ml_g1 RMSE: [[1.37504999]]\n", - "Classification:\n", - "Learner ml_m Log Loss: [[0.63114242]]\n", - "\n", - "------------------ Resampling ------------------\n", - "No. folds: 5\n", - "No. repeated sample splits: 1\n", - "\n", - "------------------ Fit summary ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d -0.174962 0.163741 -1.068526 0.285283 -0.495889 0.145965\n" + "/tmp/ipykernel_36280/2959045490.py:60: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[-0.05 0.05]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " pre_treatment.loc[mask, 'jitter'] = jitters\n" ] - } - ], - "source": [ - "print(dml_obj.modellist[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ + }, + { + "ename": "UFuncTypeError", + "evalue": "ufunc 'add' cannot use operands with types dtype(' 132\u001b[0m \u001b[43mplot_atts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdml_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.95\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfigsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m8\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[76], line 62\u001b[0m, in \u001b[0;36mplot_atts\u001b[0;34m(dml_obj, level, joint, figsize)\u001b[0m\n\u001b[1;32m 59\u001b[0m jitters \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m-\u001b[39mjitter_amount, jitter_amount, count)\n\u001b[1;32m 60\u001b[0m pre_treatment\u001b[38;5;241m.\u001b[39mloc[mask, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjitter\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m jitters\n\u001b[0;32m---> 62\u001b[0m jittered_x \u001b[38;5;241m=\u001b[39m \u001b[43mpre_treatment\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mEvaluation Period\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mpre_treatment\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mjitter\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;66;03m# Pre-treatment points with jitter\u001b[39;00m\n\u001b[1;32m 65\u001b[0m scatter_pre \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mscatter(jittered_x, \n\u001b[1;32m 66\u001b[0m pre_treatment[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEstimate\u001b[39m\u001b[38;5;124m'\u001b[39m], \n\u001b[1;32m 67\u001b[0m color\u001b[38;5;241m=\u001b[39mcolors[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpre\u001b[39m\u001b[38;5;124m'\u001b[39m], alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.8\u001b[39m, s\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m)\n", + "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/pandas/core/ops/common.py:76\u001b[0m, in \u001b[0;36m_unpack_zerodim_and_defer..new_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[1;32m 74\u001b[0m other \u001b[38;5;241m=\u001b[39m item_from_zerodim(other)\n\u001b[0;32m---> 76\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/pandas/core/arraylike.py:186\u001b[0m, in \u001b[0;36mOpsMixin.__add__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;129m@unpack_zerodim_and_defer\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__add__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__add__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[1;32m 100\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 101\u001b[0m \u001b[38;5;124;03m Get Addition of DataFrame and other, column-wise.\u001b[39;00m\n\u001b[1;32m 102\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;124;03m moose 3.0 NaN\u001b[39;00m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 186\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_arith_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moperator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/pandas/core/series.py:6135\u001b[0m, in \u001b[0;36mSeries._arith_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m 6133\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_arith_method\u001b[39m(\u001b[38;5;28mself\u001b[39m, other, op):\n\u001b[1;32m 6134\u001b[0m \u001b[38;5;28mself\u001b[39m, other \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_align_for_op(other)\n\u001b[0;32m-> 6135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbase\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mIndexOpsMixin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_arith_method\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/pandas/core/base.py:1382\u001b[0m, in \u001b[0;36mIndexOpsMixin._arith_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m 1379\u001b[0m rvalues \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(rvalues\u001b[38;5;241m.\u001b[39mstart, rvalues\u001b[38;5;241m.\u001b[39mstop, rvalues\u001b[38;5;241m.\u001b[39mstep)\n\u001b[1;32m 1381\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39merrstate(\u001b[38;5;28mall\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 1382\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marithmetic_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1384\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_construct_result(result, name\u001b[38;5;241m=\u001b[39mres_name)\n", + "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py:273\u001b[0m, in \u001b[0;36marithmetic_op\u001b[0;34m(left, right, op)\u001b[0m\n\u001b[1;32m 260\u001b[0m \u001b[38;5;66;03m# NB: We assume that extract_array and ensure_wrapped_if_datetimelike\u001b[39;00m\n\u001b[1;32m 261\u001b[0m \u001b[38;5;66;03m# have already been called on `left` and `right`,\u001b[39;00m\n\u001b[1;32m 262\u001b[0m \u001b[38;5;66;03m# and `maybe_prepare_scalar_for_op` has already been called on `right`\u001b[39;00m\n\u001b[1;32m 263\u001b[0m \u001b[38;5;66;03m# We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy\u001b[39;00m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)\u001b[39;00m\n\u001b[1;32m 266\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 267\u001b[0m should_extension_dispatch(left, right)\n\u001b[1;32m 268\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(right, (Timedelta, BaseOffset, Timestamp))\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 271\u001b[0m \u001b[38;5;66;03m# Timedelta/Timestamp and other custom scalars are included in the check\u001b[39;00m\n\u001b[1;32m 272\u001b[0m \u001b[38;5;66;03m# because numexpr will fail on it, see GH#31457\u001b[39;00m\n\u001b[0;32m--> 273\u001b[0m res_values \u001b[38;5;241m=\u001b[39m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleft\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 275\u001b[0m \u001b[38;5;66;03m# TODO we should handle EAs consistently and move this check before the if/else\u001b[39;00m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;66;03m# (https://github.com/pandas-dev/pandas/issues/41165)\u001b[39;00m\n\u001b[1;32m 277\u001b[0m \u001b[38;5;66;03m# error: Argument 2 to \"_bool_arith_check\" has incompatible type\u001b[39;00m\n\u001b[1;32m 278\u001b[0m \u001b[38;5;66;03m# \"Union[ExtensionArray, ndarray[Any, Any]]\"; expected \"ndarray[Any, Any]\"\u001b[39;00m\n\u001b[1;32m 279\u001b[0m _bool_arith_check(op, left, right) \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n", + "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/pandas/core/arrays/datetimelike.py:2200\u001b[0m, in \u001b[0;36mTimelikeOps.__array_ufunc__\u001b[0;34m(self, ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m 2192\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 2193\u001b[0m ufunc \u001b[38;5;129;01min\u001b[39;00m [np\u001b[38;5;241m.\u001b[39misnan, np\u001b[38;5;241m.\u001b[39misinf, np\u001b[38;5;241m.\u001b[39misfinite]\n\u001b[1;32m 2194\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(inputs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 2195\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m inputs[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[1;32m 2196\u001b[0m ):\n\u001b[1;32m 2197\u001b[0m \u001b[38;5;66;03m# numpy 1.18 changed isinf and isnan to not raise on dt64/td64\u001b[39;00m\n\u001b[1;32m 2198\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(ufunc, method)(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ndarray, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m-> 2200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__array_ufunc__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mufunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/pandas/core/arrays/base.py:2300\u001b[0m, in \u001b[0;36mExtensionArray.__array_ufunc__\u001b[0;34m(self, ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m 2297\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[1;32m 2298\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m-> 2300\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marraylike\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdefault_array_ufunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mufunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/pandas/core/arraylike.py:492\u001b[0m, in \u001b[0;36mdefault_array_ufunc\u001b[0;34m(self, ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m 488\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m\n\u001b[1;32m 490\u001b[0m new_inputs \u001b[38;5;241m=\u001b[39m [x \u001b[38;5;28;01mif\u001b[39;00m x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray(x) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m inputs]\n\u001b[0;32m--> 492\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mufunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mUFuncTypeError\u001b[0m: ufunc 'add' cannot use operands with types dtype('" ] }, - "execution_count": 15, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "dml_obj.gt_index" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDBinary Object ==================\n", - "\n", - "------------------ Data summary ------------------\n", - "Outcome variable: y\n", - "Treatment variable(s): ['d']\n", - "Covariates: ['Z1', 'Z2', 'Z3', 'Z4']\n", - "Instrument variable(s): None\n", - "Time variable: t\n", - "Id variable: id\n", - "No. Observations: 1000\n", - "\n", - "------------------ Score & algorithm ------------------\n", - "Score function: observational\n", - "\n", - "------------------ Machine learner ------------------\n", - "Learner ml_g: LinearRegression()\n", - "Learner ml_m: LogisticRegression()\n", - "Out-of-sample Performance:\n", - "Regression:\n", - "Learner ml_g0 RMSE: [[1.44914314]]\n", - "Learner ml_g1 RMSE: [[1.37504999]]\n", - "Classification:\n", - "Learner ml_m Log Loss: [[0.63114242]]\n", - "\n", - "------------------ Resampling ------------------\n", - "No. folds: 5\n", - "No. repeated sample splits: 1\n", - "\n", - "------------------ Fit summary ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d -0.174962 0.163741 -1.068526 0.285283 -0.495889 0.145965\n" - ] - } - ], - "source": [ - "print(dml_obj.modellist[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== Sensitivity Analysis ==================\n", - "\n", - "------------------ Scenario ------------------\n", - "Significance Level: level=0.95\n", - "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", - "\n", - "------------------ Bounds with CI ------------------\n", - " CI lower theta lower \\\n", - "ATT(2025-05-01T00:00:00,2025-01-01T00:00:00,202... -0.540615 -0.266085 \n", - "ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,202... -0.494061 -0.211491 \n", - "ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,202... -0.364503 -0.082964 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.643259 0.900005 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 1.774270 2.081722 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 2.721068 3.006202 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 3.801540 4.098575 \n", - "ATT(2025-06-01T00:00:00,2025-01-01T00:00:00,202... -0.534148 -0.268717 \n", - "ATT(2025-06-01T00:00:00,2025-02-01T00:00:00,202... -0.245643 0.011320 \n", - "ATT(2025-06-01T00:00:00,2025-03-01T00:00:00,202... -0.378753 -0.101450 \n", - "ATT(2025-06-01T00:00:00,2025-04-01T00:00:00,202... -0.549436 -0.255227 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 0.587975 0.891010 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 1.646503 1.943468 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 2.898776 3.181276 \n", - "ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,202... -0.540622 -0.287330 \n", - "ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,202... -0.270156 -0.023814 \n", - "ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,202... -0.216130 0.027312 \n", - "ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,202... -0.442095 -0.204722 \n", - "ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,202... -0.384643 -0.130414 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 0.644772 0.893375 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 1.856110 2.118015 \n", - "\n", - " theta theta upper \\\n", - "ATT(2025-05-01T00:00:00,2025-01-01T00:00:00,202... -0.174962 -0.083839 \n", - "ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,202... -0.116450 -0.021409 \n", - "ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,202... 0.019741 0.122447 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.991408 1.082810 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 2.179452 2.277182 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 3.094986 3.183770 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 4.189546 4.280517 \n", - "ATT(2025-06-01T00:00:00,2025-01-01T00:00:00,202... -0.168708 -0.068698 \n", - "ATT(2025-06-01T00:00:00,2025-02-01T00:00:00,202... 0.101348 0.191377 \n", - "ATT(2025-06-01T00:00:00,2025-03-01T00:00:00,202... -0.002634 0.096182 \n", - "ATT(2025-06-01T00:00:00,2025-04-01T00:00:00,202... -0.169933 -0.084639 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 0.980952 1.070893 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 2.041356 2.139244 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 3.282039 3.382802 \n", - "ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,202... -0.189431 -0.091532 \n", - "ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,202... 0.070483 0.164781 \n", - "ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,202... 0.121959 0.216607 \n", - "ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,202... -0.116124 -0.027527 \n", - "ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,202... -0.038648 0.053119 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 0.981190 1.069006 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 2.209412 2.300810 \n", - "\n", - " CI upper \n", - "ATT(2025-05-01T00:00:00,2025-01-01T00:00:00,202... 0.181603 \n", - "ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,202... 0.270995 \n", - "ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,202... 0.403329 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 1.335359 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 2.579487 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 3.466414 \n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 4.576976 \n", - "ATT(2025-06-01T00:00:00,2025-01-01T00:00:00,202... 0.195706 \n", - "ATT(2025-06-01T00:00:00,2025-02-01T00:00:00,202... 0.451147 \n", - "ATT(2025-06-01T00:00:00,2025-03-01T00:00:00,202... 0.377811 \n", - "ATT(2025-06-01T00:00:00,2025-04-01T00:00:00,202... 0.196924 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 1.366256 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 2.434355 \n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 3.666095 \n", - "ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,202... 0.160173 \n", - "ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,202... 0.412544 \n", - "ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,202... 0.459767 \n", - "ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,202... 0.208183 \n", - "ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,202... 0.307195 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 1.318433 \n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 2.564859 \n", - "\n", - "------------------ Robustness Values ------------------\n", - " H_0 RV (%) RVa (%)\n", - "ATT(2025-05-01T00:00:00,2025-01-01T00:00:00,202... 0.0 5.680066 0.000660\n", - "ATT(2025-05-01T00:00:00,2025-02-01T00:00:00,202... 0.0 3.663255 0.000545\n", - "ATT(2025-05-01T00:00:00,2025-03-01T00:00:00,202... 0.0 0.583935 0.000502\n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.0 28.029098 20.733198\n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.0 48.668419 41.410119\n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.0 63.846392 56.959575\n", - "ATT(2025-05-01T00:00:00,2025-04-01T00:00:00,202... 0.0 72.954066 66.854403\n", - "ATT(2025-06-01T00:00:00,2025-01-01T00:00:00,202... 0.0 5.008134 0.000476\n", - "ATT(2025-06-01T00:00:00,2025-02-01T00:00:00,202... 0.0 3.370819 0.000567\n", - "ATT(2025-06-01T00:00:00,2025-03-01T00:00:00,202... 0.0 0.081011 0.000486\n", - "ATT(2025-06-01T00:00:00,2025-04-01T00:00:00,202... 0.0 5.887337 0.000549\n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 0.0 28.158619 19.191185\n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 0.0 46.473959 39.461827\n", - "ATT(2025-06-01T00:00:00,2025-05-01T00:00:00,202... 0.0 61.534174 56.259800\n", - "ATT(2025-07-01T00:00:00,2025-01-01T00:00:00,202... 0.0 5.722851 0.000551\n", - "ATT(2025-07-01T00:00:00,2025-02-01T00:00:00,202... 0.0 2.251094 0.000428\n", - "ATT(2025-07-01T00:00:00,2025-03-01T00:00:00,202... 0.0 3.848794 0.000612\n", - "ATT(2025-07-01T00:00:00,2025-04-01T00:00:00,202... 0.0 3.913597 0.000614\n", - "ATT(2025-07-01T00:00:00,2025-05-01T00:00:00,202... 0.0 1.274773 0.000497\n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 0.0 28.732018 22.070345\n", - "ATT(2025-07-01T00:00:00,2025-06-01T00:00:00,202... 0.0 51.356140 46.080850\n" - ] - } - ], - "source": [ - "dml_obj.sensitivity_analysis()\n", - "print(dml_obj.sensitivity_summary)" + "def plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8)):\n", + " \"\"\"\n", + " Plot coefficient estimates with CIs over time, grouped by first treated period.\n", + " CIs with the same x-value are jittered horizontally for better visibility.\n", + " \"\"\"\n", + "\n", + " df = create_ci_dataframe(dml_obj, level=level, joint=joint)\n", + " all_time_periods = sorted(df['Evaluation Period'].unique())\n", + " first_treated_periods = sorted(df['First Treated'].unique())\n", + " n_periods = len(first_treated_periods)\n", + " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", + " \n", + " # Adjust figure size to accommodate bottom legend\n", + " fig = plt.figure(figsize=figsize)\n", + " # Create subplot grid with space for legend at bottom\n", + " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", + " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", + "\n", + " if n_periods == 1:\n", + " axes = [axes]\n", + " \n", + " # Create a list to store legend handles and labels\n", + " legend_elements = []\n", + " \n", + " # Define jitter amount\n", + " jitter_amount = 0.05 # Adjust this value to control jitter spread\n", + " \n", + " for idx, period in enumerate(first_treated_periods):\n", + " period_data = df[df['First Treated'] == period]\n", + " ax = axes[idx]\n", + "\n", + " i_period = all_time_periods.index(period)\n", + "\n", + " # Add treatment start line\n", + " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", + " linestyle=':', alpha=0.7)\n", + " if idx == 0:\n", + " legend_elements.append((line, 'Treatment start'))\n", + "\n", + " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", + " if idx == 0:\n", + " legend_elements.append((zero_line, 'Zero effect'))\n", + "\n", + " # Split data by treatment status\n", + " pre_treatment = period_data[period_data['Pre-Treatment']]\n", + " post_treatment = period_data[~period_data['Pre-Treatment']]\n", + " \n", + " if not pre_treatment.empty:\n", + " # Add jitter to x-values\n", + " # Group by evaluation period and assign jitter within each group\n", + " pre_treatment = pre_treatment.copy()\n", + " pre_treatment['jitter'] = 0\n", + " \n", + " for x_val in pre_treatment['Evaluation Period'].unique():\n", + " mask = pre_treatment['Evaluation Period'] == x_val\n", + " count = mask.sum()\n", + " if count > 1:\n", + " # Create evenly spaced jitter values\n", + " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", + " pre_treatment.loc[mask, 'jitter'] = jitters\n", + " \n", + " jittered_x = pre_treatment['Evaluation Period'] + pre_treatment['jitter']\n", + " \n", + " # Pre-treatment points with jitter\n", + " scatter_pre = ax.scatter(jittered_x, \n", + " pre_treatment['Estimate'], \n", + " color=colors['pre'], alpha=0.8, s=10)\n", + " \n", + " # Regular CIs with jitter\n", + " error_pre = ax.errorbar(jittered_x, \n", + " pre_treatment['Estimate'],\n", + " yerr=[pre_treatment['Estimate'] - pre_treatment['CI Lower'],\n", + " pre_treatment['CI Upper'] - pre_treatment['Estimate']],\n", + " fmt='none', color=colors['pre'], alpha=1.0, \n", + " capsize=5)\n", + " if idx == 0:\n", + " legend_elements.extend([\n", + " (scatter_pre, 'Pre-treatment'),\n", + " (error_pre, f'{int(level*100)}% CI'),\n", + " ])\n", + " \n", + " # Similar structure for post-treatment with jittering\n", + " if not post_treatment.empty:\n", + " # Add jitter to x-values\n", + " post_treatment = post_treatment.copy()\n", + " post_treatment['jitter'] = 0\n", + " \n", + " for x_val in post_treatment['Evaluation Period'].unique():\n", + " mask = post_treatment['Evaluation Period'] == x_val\n", + " count = mask.sum()\n", + " if count > 1:\n", + " # Create evenly spaced jitter values\n", + " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", + " post_treatment.loc[mask, 'jitter'] = jitters\n", + " \n", + " jittered_x = post_treatment['Evaluation Period'] + post_treatment['jitter']\n", + " \n", + " scatter_post = ax.scatter(jittered_x, \n", + " post_treatment['Estimate'], \n", + " color=colors['post'], alpha=0.8, s=10)\n", + " if idx == 0:\n", + " legend_elements.append((scatter_post, 'Post-treatment'))\n", + " \n", + " # Error bars with jitter\n", + " ax.errorbar(jittered_x, post_treatment['Estimate'],\n", + " yerr=[post_treatment['Estimate'] - post_treatment['CI Lower'],\n", + " post_treatment['CI Upper'] - post_treatment['Estimate']],\n", + " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", + "\n", + " ax.set_title(f'First Treated: {period}')\n", + " ax.grid(True, alpha=0.3)\n", + " \n", + " if idx == 0:\n", + " ax.set_ylabel('Effect')\n", + " ax.set_xlabel('Evaluation Period')\n", + " \n", + " # Create legend in a separate subplot at the bottom\n", + " legend_ax = fig.add_subplot(gs[-1])\n", + " legend_ax.axis('off') # Hide axes for legend subplot\n", + " \n", + " # Add legend using collected handles and labels\n", + " legend = legend_ax.legend(*zip(*legend_elements), \n", + " loc='center',\n", + " ncol=5, # Adjust number of columns as needed\n", + " mode='expand',\n", + " borderaxespad=0.)\n", + " \n", + " plt.suptitle(\"Estimated ATTs by Group\", y=1.02)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8))" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", "import numpy as np\n", - "dta = pd.read_csv(\"https://raw.githubusercontent.com/d2cml-ai/csdid/main/data/sim_data.csv\")\n", - "dta.head()\n", - "dta.loc[dta[\"G\"] == 0, \"G\"] = np.nan" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "dml_data = DoubleMLPanelData(dta, y_col=\"Y\", d_cols=\"G\", id_col=\"id\", t_col=\"period\", x_cols=[\"X\"])" + "import pandas as pd\n", + "from datetime import timedelta\n", + "\n", + "def plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8)):\n", + " \"\"\"\n", + " Plot coefficient estimates with CIs over time, grouped by first treated period.\n", + " CIs with the same x-value are jittered horizontally for better visibility.\n", + " Works with both numeric and datetime x-values.\n", + " \"\"\"\n", + "\n", + " df = create_ci_dataframe(dml_obj, level=level, joint=joint)\n", + " all_time_periods = sorted(df['Evaluation Period'].unique())\n", + " first_treated_periods = sorted(df['First Treated'].unique())\n", + " n_periods = len(first_treated_periods)\n", + " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", + " \n", + " # Determine if we're working with datetime values\n", + " is_datetime = pd.api.types.is_datetime64_any_dtype(df['Evaluation Period'])\n", + " \n", + " # Figure setup\n", + " fig = plt.figure(figsize=figsize)\n", + " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", + " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", + "\n", + " if n_periods == 1:\n", + " axes = [axes]\n", + " \n", + " legend_elements = []\n", + " \n", + " # Define jitter amount based on data type\n", + " if is_datetime:\n", + " # For datetime, calculate appropriate timedelta for jittering\n", + " if len(all_time_periods) > 1:\n", + " # Calculate average interval between periods\n", + " if isinstance(all_time_periods[0], pd.Timestamp):\n", + " interval = (all_time_periods[1] - all_time_periods[0]).total_seconds()\n", + " # Use 10% of interval for jittering\n", + " jitter_seconds = interval * 0.1\n", + " else:\n", + " jitter_seconds = 86400 # Default: 1 day in seconds\n", + " else:\n", + " jitter_seconds = 86400 # Default: 1 day in seconds\n", + " else:\n", + " # For numeric data\n", + " if n_periods > 1:\n", + " jitter_amount = min(0.2, (all_time_periods[1] - all_time_periods[0]) * 0.2)\n", + " else:\n", + " jitter_amount = 0.1\n", + " \n", + " for idx, period in enumerate(first_treated_periods):\n", + " period_data = df[df['First Treated'] == period]\n", + " ax = axes[idx]\n", + "\n", + " i_period = all_time_periods.index(period)\n", + "\n", + " # Add treatment start line\n", + " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", + " linestyle=':', alpha=0.7)\n", + " if idx == 0:\n", + " legend_elements.append((line, 'Treatment start'))\n", + "\n", + " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", + " if idx == 0:\n", + " legend_elements.append((zero_line, 'Zero effect'))\n", + "\n", + " # Process pre-treatment data\n", + " if not period_data[period_data['Pre-Treatment']].empty:\n", + " pre_treatment = period_data[period_data['Pre-Treatment']].copy()\n", + " pre_treatment['jitter'] = 0\n", + " \n", + " for x_val in pre_treatment['Evaluation Period'].unique():\n", + " mask = pre_treatment['Evaluation Period'] == x_val\n", + " count = mask.sum()\n", + " if count > 1:\n", + " if is_datetime:\n", + " # Create datetime jitters\n", + " jitter_range = np.linspace(-jitter_seconds, jitter_seconds, count)\n", + " pre_treatment.loc[mask, 'jitter'] = [pd.Timedelta(seconds=j) for j in jitter_range]\n", + " else:\n", + " # Create numeric jitters\n", + " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", + " pre_treatment.loc[mask, 'jitter'] = jitters\n", + " \n", + " # Apply jitter based on data type\n", + " jittered_x = pre_treatment['Evaluation Period'] + pre_treatment['jitter']\n", + " \n", + " scatter_pre = ax.scatter(jittered_x, \n", + " pre_treatment['Estimate'], \n", + " color=colors['pre'], alpha=0.8, s=10)\n", + " \n", + " error_pre = ax.errorbar(jittered_x, \n", + " pre_treatment['Estimate'],\n", + " yerr=[pre_treatment['Estimate'] - pre_treatment['CI Lower'],\n", + " pre_treatment['CI Upper'] - pre_treatment['Estimate']],\n", + " fmt='none', color=colors['pre'], alpha=1.0, \n", + " capsize=5)\n", + " if idx == 0:\n", + " legend_elements.extend([\n", + " (scatter_pre, 'Pre-treatment'),\n", + " (error_pre, f'{int(level*100)}% CI'),\n", + " ])\n", + " \n", + " # Process post-treatment data (same logic as pre-treatment)\n", + " if not period_data[~period_data['Pre-Treatment']].empty:\n", + " post_treatment = period_data[~period_data['Pre-Treatment']].copy()\n", + " post_treatment['jitter'] = 0\n", + " \n", + " for x_val in post_treatment['Evaluation Period'].unique():\n", + " mask = post_treatment['Evaluation Period'] == x_val\n", + " count = mask.sum()\n", + " if count > 1:\n", + " if is_datetime:\n", + " # Create datetime jitters\n", + " jitter_range = np.linspace(-jitter_seconds, jitter_seconds, count)\n", + " post_treatment.loc[mask, 'jitter'] = [pd.Timedelta(seconds=j) for j in jitter_range]\n", + " else:\n", + " # Create numeric jitters\n", + " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", + " post_treatment.loc[mask, 'jitter'] = jitters\n", + " \n", + " jittered_x = post_treatment['Evaluation Period'] + post_treatment['jitter']\n", + " \n", + " scatter_post = ax.scatter(jittered_x, \n", + " post_treatment['Estimate'], \n", + " color=colors['post'], alpha=0.8, s=10)\n", + " if idx == 0:\n", + " legend_elements.append((scatter_post, 'Post-treatment'))\n", + " \n", + " ax.errorbar(jittered_x, post_treatment['Estimate'],\n", + " yerr=[post_treatment['Estimate'] - post_treatment['CI Lower'],\n", + " post_treatment['CI Upper'] - post_treatment['Estimate']],\n", + " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", + "\n", + " ax.set_title(f'First Treated: {period}')\n", + " ax.grid(True, alpha=0.3)\n", + " \n", + " if idx == 0:\n", + " ax.set_ylabel('Effect')\n", + " ax.set_xlabel('Evaluation Period')\n", + " \n", + " # Create legend in a separate subplot at the bottom\n", + " legend_ax = fig.add_subplot(gs[-1])\n", + " legend_ax.axis('off')\n", + " \n", + " legend" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[(np.float64(2.0), np.int64(1), np.int64(2)), (np.float64(2.0), np.int64(1), np.int64(3)), (np.float64(2.0), np.int64(1), np.int64(4)), (np.float64(3.0), np.int64(1), np.int64(2)), (np.float64(3.0), np.int64(2), np.int64(3)), (np.float64(3.0), np.int64(2), np.int64(4)), (np.float64(4.0), np.int64(1), np.int64(2)), (np.float64(4.0), np.int64(2), np.int64(3)), (np.float64(4.0), np.int64(3), np.int64(4))]\n" + "ename": "UFuncTypeError", + "evalue": "ufunc 'multiply' cannot use operands with types dtype('O') and dtype(' 137\u001b[0m \u001b[43mplot_atts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdml_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.95\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfigsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m8\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[70], line 64\u001b[0m, in \u001b[0;36mplot_atts\u001b[0;34m(dml_obj, level, joint, figsize)\u001b[0m\n\u001b[1;32m 61\u001b[0m count \u001b[38;5;241m=\u001b[39m mask\u001b[38;5;241m.\u001b[39msum()\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m count \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 63\u001b[0m \u001b[38;5;66;03m# Create evenly spaced jitter values\u001b[39;00m\n\u001b[0;32m---> 64\u001b[0m jitters \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinspace\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mjitter_amount\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjitter_amount\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcount\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 65\u001b[0m pre_treatment\u001b[38;5;241m.\u001b[39mloc[mask, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjitter\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m jitters\n\u001b[1;32m 67\u001b[0m jittered_x \u001b[38;5;241m=\u001b[39m pre_treatment[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEvaluation Period\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m pre_treatment[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjitter\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/numpy/_core/function_base.py:164\u001b[0m, in \u001b[0;36mlinspace\u001b[0;34m(start, stop, num, endpoint, retstep, dtype, axis, device)\u001b[0m\n\u001b[1;32m 162\u001b[0m y \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m step\n\u001b[1;32m 163\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 164\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43my\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 166\u001b[0m \u001b[38;5;66;03m# sequences with 0 items or 1 item with endpoint=True (i.e. div <= 0)\u001b[39;00m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;66;03m# have an undefined step\u001b[39;00m\n\u001b[1;32m 168\u001b[0m step \u001b[38;5;241m=\u001b[39m nan\n", + "File \u001b[0;32mtimedeltas.pyx:2043\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.timedeltas.Timedelta.__mul__\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mUFuncTypeError\u001b[0m: ufunc 'multiply' cannot use operands with types dtype('O') and dtype('" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "gt_combinations_d0 = [\n", - " (dml_data.g_values[0], dml_data.t_values[0], dml_data.t_values[1]),\n", - " (dml_data.g_values[0], dml_data.t_values[0], dml_data.t_values[2]),\n", - " (dml_data.g_values[0], dml_data.t_values[0], dml_data.t_values[3]),\n", - "]\n", - "gt_combinations_d1 = [\n", - " (dml_data.g_values[1], dml_data.t_values[0], dml_data.t_values[1]),\n", - " (dml_data.g_values[1], dml_data.t_values[1], dml_data.t_values[2]),\n", - " (dml_data.g_values[1], dml_data.t_values[1], dml_data.t_values[3]),\n", - "]\n", + "import numpy as np\n", + "from datetime import timedelta\n", + "\n", + "def plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8)):\n", + " \"\"\"\n", + " Plot coefficient estimates with CIs over time, grouped by first treated period.\n", + " CIs with the same x-value are jittered horizontally for better visibility.\n", + " \"\"\"\n", + "\n", + " df = create_ci_dataframe(dml_obj, level=level, joint=joint)\n", + " all_time_periods = sorted(df['Evaluation Period'].unique())\n", + " first_treated_periods = sorted(df['First Treated'].unique())\n", + " n_periods = len(first_treated_periods)\n", + " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", + " \n", + " # Adjust figure size to accommodate bottom legend\n", + " fig = plt.figure(figsize=figsize)\n", + " # Create subplot grid with space for legend at bottom\n", + " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", + " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", + "\n", + " if n_periods == 1:\n", + " axes = [axes]\n", + " \n", + " # Create a list to store legend handles and labels\n", + " legend_elements = []\n", + " \n", + " # Define jitter amount\n", + " if n_periods > 1:\n", + " jitter_amount = all_time_periods[1] - all_time_periods[0]\n", + " else:\n", + " jitter_amount = 0.1\n", + " \n", + " for idx, period in enumerate(first_treated_periods):\n", + " period_data = df[df['First Treated'] == period]\n", + " ax = axes[idx]\n", "\n", - "gt_combinations_d2 = [\n", - " (dml_data.g_values[2], dml_data.t_values[0], dml_data.t_values[1]),\n", - " (dml_data.g_values[2], dml_data.t_values[1], dml_data.t_values[2]),\n", - " (dml_data.g_values[2], dml_data.t_values[2], dml_data.t_values[3]),\n", - "]\n", + " i_period = all_time_periods.index(period)\n", "\n", - "gt_combinations = gt_combinations_d0 + gt_combinations_d1 + gt_combinations_d2\n", + " # Add treatment start line\n", + " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", + " linestyle=':', alpha=0.7)\n", + " if idx == 0:\n", + " legend_elements.append((line, 'Treatment start'))\n", + "\n", + " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", + " if idx == 0:\n", + " legend_elements.append((zero_line, 'Zero effect'))\n", + "\n", + " # Split data by treatment status\n", + " pre_treatment = period_data[period_data['Pre-Treatment']]\n", + " post_treatment = period_data[~period_data['Pre-Treatment']]\n", + " \n", + " if not pre_treatment.empty:\n", + " # Add jitter to x-values\n", + " # Group by evaluation period and assign jitter within each group\n", + " pre_treatment = pre_treatment.copy()\n", + " pre_treatment['jitter'] = 0\n", + " \n", + " for x_val in pre_treatment['Evaluation Period'].unique():\n", + " mask = pre_treatment['Evaluation Period'] == x_val\n", + " count = mask.sum()\n", + " if count > 1:\n", + " # Create evenly spaced jitter values\n", + " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", + " pre_treatment.loc[mask, 'jitter'] = jitters\n", + " \n", + " jittered_x = pre_treatment['Evaluation Period'] + pre_treatment['jitter']\n", + " \n", + " # Pre-treatment points with jitter\n", + " scatter_pre = ax.scatter(jittered_x, \n", + " pre_treatment['Estimate'], \n", + " color=colors['pre'], alpha=0.8, s=10)\n", + " \n", + " # Regular CIs with jitter\n", + " error_pre = ax.errorbar(jittered_x, \n", + " pre_treatment['Estimate'],\n", + " yerr=[pre_treatment['Estimate'] - pre_treatment['CI Lower'],\n", + " pre_treatment['CI Upper'] - pre_treatment['Estimate']],\n", + " fmt='none', color=colors['pre'], alpha=1.0, \n", + " capsize=5)\n", + " if idx == 0:\n", + " legend_elements.extend([\n", + " (scatter_pre, 'Pre-treatment'),\n", + " (error_pre, f'{int(level*100)}% CI'),\n", + " ])\n", + " \n", + " # Similar structure for post-treatment with jittering\n", + " if not post_treatment.empty:\n", + " # Add jitter to x-values\n", + " post_treatment = post_treatment.copy()\n", + " post_treatment['jitter'] = 0\n", + " \n", + " for x_val in post_treatment['Evaluation Period'].unique():\n", + " mask = post_treatment['Evaluation Period'] == x_val\n", + " count = mask.sum()\n", + " if count > 1:\n", + " # Create evenly spaced jitter values\n", + " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", + " post_treatment.loc[mask, 'jitter'] = jitters\n", + " \n", + " jittered_x = post_treatment['Evaluation Period'] + post_treatment['jitter']\n", + " \n", + " scatter_post = ax.scatter(jittered_x, \n", + " post_treatment['Estimate'], \n", + " color=colors['post'], alpha=0.8, s=10)\n", + " if idx == 0:\n", + " legend_elements.append((scatter_post, 'Post-treatment'))\n", + " \n", + " # Error bars with jitter\n", + " ax.errorbar(jittered_x, post_treatment['Estimate'],\n", + " yerr=[post_treatment['Estimate'] - post_treatment['CI Lower'],\n", + " post_treatment['CI Upper'] - post_treatment['Estimate']],\n", + " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", "\n", - "print(gt_combinations)" + " ax.set_title(f'First Treated: {period}')\n", + " ax.grid(True, alpha=0.3)\n", + " \n", + " if idx == 0:\n", + " ax.set_ylabel('Effect')\n", + " ax.set_xlabel('Evaluation Period')\n", + " \n", + " # Create legend in a separate subplot at the bottom\n", + " legend_ax = fig.add_subplot(gs[-1])\n", + " legend_ax.axis('off') # Hide axes for legend subplot\n", + " \n", + " # Add legend using collected handles and labels\n", + " legend = legend_ax.legend(*zip(*legend_elements), \n", + " loc='center',\n", + " ncol=5, # Adjust number of columns as needed\n", + " mode='expand',\n", + " borderaxespad=0.)\n", + " \n", + " plt.suptitle(\"Estimated ATTs by Group\", y=1.02)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8))" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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coefstd errtP>|t|2.5 %97.5 %
ATT(2.0,1,2)0.9197710.06399614.3722480.0000000.7943411.045201
ATT(2.0,1,3)1.9858440.06459630.7425430.0000001.8592382.112450
ATT(2.0,1,4)2.9541310.06313346.7918480.0000002.8303923.077870
ATT(3.0,1,2)-0.0422520.065880-0.6413490.521296-0.1713740.086870
ATT(3.0,2,3)1.1079010.06533516.9572280.0000000.9798471.235955
ATT(3.0,2,4)2.0595760.06536731.5076920.0000001.9314582.187694
ATT(4.0,1,2)0.0061550.0684580.0899040.928364-0.1280210.140330
ATT(4.0,2,3)0.0606720.0665330.9119120.361815-0.0697300.191075
ATT(4.0,3,4)0.9570250.06762814.1513350.0000000.8244771.089573
\n", - "
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", "text/plain": [ - " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.919771 0.063996 14.372248 0.000000 0.794341 1.045201\n", - "ATT(2.0,1,3) 1.985844 0.064596 30.742543 0.000000 1.859238 2.112450\n", - "ATT(2.0,1,4) 2.954131 0.063133 46.791848 0.000000 2.830392 3.077870\n", - "ATT(3.0,1,2) -0.042252 0.065880 -0.641349 0.521296 -0.171374 0.086870\n", - "ATT(3.0,2,3) 1.107901 0.065335 16.957228 0.000000 0.979847 1.235955\n", - "ATT(3.0,2,4) 2.059576 0.065367 31.507692 0.000000 1.931458 2.187694\n", - "ATT(4.0,1,2) 0.006155 0.068458 0.089904 0.928364 -0.128021 0.140330\n", - "ATT(4.0,2,3) 0.060672 0.066533 0.911912 0.361815 -0.069730 0.191075\n", - "ATT(4.0,3,4) 0.957025 0.067628 14.151335 0.000000 0.824477 1.089573" + "
" ] }, - "execution_count": 21, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "control_group = \"not_yet_treated\"\n", - "control_group = \"never_treated\"\n", + "def plot_rvs(dml_obj, level=0.95, joint=True, figsize=(12, 8)):\n", + " \"\"\"\n", + " Plot coefficient estimates with CIs over time, grouped by first treated period.\n", + " \"\"\"\n", "\n", - "ml_g=LinearRegression()\n", - "ml_m=LogisticRegression()\n", + " df = create_ci_dataframe(dml_obj, level=level, joint=joint, include_rvs=True)\n", + " all_time_periods = sorted(df['Evaluation Period'].unique())\n", + " first_treated_periods = sorted(df['First Treated'].unique())\n", + " n_periods = len(first_treated_periods)\n", + " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", + " \n", + " # Adjust figure size to accommodate bottom legend\n", + " fig = plt.figure(figsize=figsize)\n", + " # Create subplot grid with space for legend at bottom\n", + " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", + " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", "\n", - "# ml_g = LGBMRegressor()\n", - "# ml_m = LGBMClassifier()\n", + " if n_periods == 1:\n", + " axes = [axes]\n", + " \n", + " # Create a list to store legend handles and labels\n", + " legend_elements = []\n", + " \n", + " for idx, period in enumerate(first_treated_periods):\n", + " period_data = df[df['First Treated'] == period]\n", + " ax = axes[idx]\n", "\n", - "dml_obj = DoubleMLDIDMulti(\n", - " obj_dml_data=dml_data,\n", - " ml_g=ml_g,\n", - " ml_m=ml_m,\n", - " gt_combinations=\"standard\",\n", - " control_group=control_group,\n", - ")\n", + " i_period = all_time_periods.index(period)\n", + "\n", + " # Add treatment start line\n", + " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", + " linestyle=':', alpha=0.7)\n", + " if idx == 0:\n", + " legend_elements.append((line, 'Treatment start'))\n", + "\n", + " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", + " if idx == 0:\n", + " legend_elements.append((zero_line, 'Zero effect'))\n", "\n", - "dml_obj.fit()\n", + " # Split data by treatment status\n", + " pre_treatment = period_data[period_data['Pre-Treatment']]\n", + " post_treatment = period_data[~period_data['Pre-Treatment']]\n", + " \n", + " if not pre_treatment.empty:\n", + " # Pre-treatment points\n", + " scatter_pre = ax.scatter(pre_treatment['Evaluation Period'], \n", + " pre_treatment['RV'], \n", + " color=colors['pre'], alpha=0.8, s=10)\n", + "\n", + " if idx == 0:\n", + " legend_elements.extend([\n", + " (scatter_pre, 'Pre-treatment'),\n", + " ])\n", + " \n", + " # Similar structure for post-treatment\n", + " if not post_treatment.empty:\n", + " scatter_post = ax.scatter(post_treatment['Evaluation Period'], \n", + " post_treatment['RV'], \n", + " color=colors['post'], alpha=0.8, s=10)\n", + " if idx == 0:\n", + " legend_elements.append((scatter_post, 'Post-treatment'))\n", + " \n", + " ax.set_title(f'First Treated: {period}')\n", + " ax.grid(True, alpha=0.3)\n", + " \n", + " if idx == 0:\n", + " ax.set_ylabel('Effect')\n", + " ax.set_xlabel('Evaluation Period')\n", + " \n", + " # Create legend in a separate subplot at the bottom\n", + " legend_ax = fig.add_subplot(gs[-1])\n", + " legend_ax.axis('off') # Hide axes for legend subplot\n", + " \n", + " # Add legend using collected handles and labels\n", + " legend = legend_ax.legend(*zip(*legend_elements), \n", + " loc='center',\n", + " ncol=5, # Adjust number of columns as needed\n", + " mode='expand',\n", + " borderaxespad=0.)\n", + " \n", + " plt.suptitle(\"Estimated ATTs by Group\", y=1.02)\n", + " plt.tight_layout()\n", + " plt.show()\n", "\n", - "dml_obj.summary" + "plot_rvs(dml_obj, level=0.95, joint=True, figsize=(12, 8))" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -5668,7 +1667,123 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_atts(df, level=0.95, figsize=(15, 10), \n", + " title='Coefficient Estimates by First Treatment Period'):\n", + " \"\"\"\n", + " Plot coefficient estimates with CIs over time, grouped by first treatment period.\n", + " \"\"\"\n", + " all_time_periods = sorted(df['Time Period'].unique())\n", + " first_treated_periods = sorted(df['First Treated'].unique())\n", + " n_periods = len(first_treated_periods)\n", + " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", + " \n", + " # Adjust figure size to accommodate bottom legend\n", + " fig = plt.figure(figsize=figsize)\n", + " # Create subplot grid with space for legend at bottom\n", + " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", + " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", + "\n", + " if n_periods == 1:\n", + " axes = [axes]\n", + " \n", + " # Create a list to store legend handles and labels\n", + " legend_elements = []\n", + " \n", + " for idx, period in enumerate(first_treated_periods):\n", + " period_data = df[df['First Treated'] == period]\n", + " ax = axes[idx]\n", + "\n", + " i_period = all_time_periods.index(period)\n", + "\n", + " # Add treatment start line\n", + " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", + " linestyle=':', alpha=0.7)\n", + " if idx == 0:\n", + " legend_elements.append((line, 'Treatment start'))\n", + "\n", + " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", + " if idx == 0:\n", + " legend_elements.append((zero_line, 'Zero effect'))\n", + "\n", + " # Split data by treatment status\n", + " pre_treatment = period_data[period_data['Pre-Treatment']]\n", + " post_treatment = period_data[~period_data['Pre-Treatment']]\n", + " \n", + " if not pre_treatment.empty:\n", + " # Pre-treatment points\n", + " scatter_pre = ax.scatter(pre_treatment['Time Period'], \n", + " pre_treatment['Estimate'], \n", + " color=colors['pre'], alpha=0.8, s=50)\n", + " # Regular CIs\n", + " error_pre = ax.errorbar(pre_treatment['Time Period'], \n", + " pre_treatment['Estimate'],\n", + " yerr=[pre_treatment['Estimate'] - pre_treatment['Lower CI'],\n", + " pre_treatment['Upper CI'] - pre_treatment['Estimate']],\n", + " fmt='none', color=colors['pre'], alpha=1.0, \n", + " capsize=5)\n", + " # Joint CIs\n", + " error_pre_joint = ax.errorbar(pre_treatment['Time Period'], \n", + " pre_treatment['Estimate'],\n", + " yerr=[pre_treatment['Estimate'] - pre_treatment['Lower CI Joint'],\n", + " pre_treatment['Upper CI Joint'] - pre_treatment['Estimate']],\n", + " fmt='none', color=colors['pre'], alpha=0.5, \n", + " capsize=5)\n", + " if idx == 0:\n", + " legend_elements.extend([\n", + " (scatter_pre, 'Pre-treatment'),\n", + " (error_pre, f'{int(level*100)}% CI'),\n", + " (error_pre_joint, f'{int(level*100)}% joint CI')\n", + " ])\n", + " \n", + " # Similar structure for post-treatment\n", + " if not post_treatment.empty:\n", + " scatter_post = ax.scatter(post_treatment['Time Period'], \n", + " post_treatment['Estimate'], \n", + " color=colors['post'], alpha=0.8, s=50)\n", + " if idx == 0:\n", + " legend_elements.append((scatter_post, 'Post-treatment'))\n", + " \n", + " ax.errorbar(post_treatment['Time Period'], post_treatment['Estimate'],\n", + " yerr=[post_treatment['Estimate'] - post_treatment['Lower CI'],\n", + " post_treatment['Upper CI'] - post_treatment['Estimate']],\n", + " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", + " ax.errorbar(post_treatment['Time Period'], post_treatment['Estimate'],\n", + " yerr=[post_treatment['Estimate'] - post_treatment['Lower CI Joint'],\n", + " post_treatment['Upper CI Joint'] - post_treatment['Estimate']],\n", + " fmt='none', color=colors['post'], alpha=0.5, capsize=5)\n", + "\n", + " ax.set_title(f'First Treated: {period}')\n", + " ax.grid(True, alpha=0.3)\n", + " \n", + " if idx == 0:\n", + " ax.set_ylabel('Effect')\n", + " ax.set_xlabel('Time Period')\n", + " \n", + " # Create legend in a separate subplot at the bottom\n", + " legend_ax = fig.add_subplot(gs[-1])\n", + " legend_ax.axis('off') # Hide axes for legend subplot\n", + " \n", + " # Add legend using collected handles and labels\n", + " legend = legend_ax.legend(*zip(*legend_elements), \n", + " loc='center',\n", + " ncol=5, # Adjust number of columns as needed\n", + " mode='expand',\n", + " borderaxespad=0.)\n", + " \n", + " plt.suptitle(title, y=1.02) # Adjust title position\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "plot_atts(ci_df, title=\"Estimated Effects\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -5704,58 +1819,58 @@ " \n", " \n", " \n", - " ATT(2.0,1,2)\n", - " 2\n", - " 0.919771\n", - " 0.794341\n", - " 1.045201\n", - " 0.743241\n", - " 1.096301\n", - " 2.0\n", - " False\n", + " ATT(2025-05,2025-01,2025-02)\n", + " 2025-02-01\n", + " 0.023003\n", + " -0.094992\n", + " 0.140998\n", + " -0.162497\n", + " 0.208504\n", + " 2025-05-01\n", + " True\n", " \n", " \n", - " ATT(2.0,1,3)\n", - " 3\n", - " 1.985844\n", - " 1.859238\n", - " 2.112450\n", - " 1.807660\n", - " 2.164028\n", - " 2.0\n", - " False\n", + " ATT(2025-05,2025-02,2025-03)\n", + " 2025-03-01\n", + " -0.128634\n", + " -0.253590\n", + " -0.003678\n", + " -0.325077\n", + " 0.067810\n", + " 2025-05-01\n", + " True\n", " \n", " \n", - " ATT(2.0,1,4)\n", - " 4\n", - " 2.954131\n", - " 2.830392\n", - " 3.077870\n", - " 2.779981\n", - " 3.128281\n", - " 2.0\n", - " False\n", + " ATT(2025-05,2025-03,2025-04)\n", + " 2025-04-01\n", + " 0.122700\n", + " 0.009588\n", + " 0.235812\n", + " -0.055124\n", + " 0.300524\n", + " 2025-05-01\n", + " True\n", " \n", " \n", - " ATT(3.0,1,2)\n", - " 2\n", - " -0.042252\n", - " -0.171374\n", - " 0.086870\n", - " -0.223977\n", - " 0.139473\n", - " 3.0\n", - " True\n", + " ATT(2025-05,2025-04,2025-05)\n", + " 2025-05-01\n", + " 0.911296\n", + " 0.791404\n", + " 1.031189\n", + " 0.722813\n", + " 1.099780\n", + " 2025-05-01\n", + " False\n", " \n", " \n", - " ATT(3.0,2,3)\n", - " 3\n", - " 1.107901\n", - " 0.979847\n", - " 1.235955\n", - " 0.927678\n", - " 1.288124\n", - " 3.0\n", + " ATT(2025-05,2025-04,2025-06)\n", + " 2025-06-01\n", + " 1.963113\n", + " 1.786896\n", + " 2.139329\n", + " 1.686082\n", + " 2.240143\n", + " 2025-05-01\n", " False\n", " \n", " \n", @@ -5763,259 +1878,587 @@ "" ], "text/plain": [ - " Time Period Estimate Lower CI Upper CI Lower CI Joint \\\n", - "ATT(2.0,1,2) 2 0.919771 0.794341 1.045201 0.743241 \n", - "ATT(2.0,1,3) 3 1.985844 1.859238 2.112450 1.807660 \n", - "ATT(2.0,1,4) 4 2.954131 2.830392 3.077870 2.779981 \n", - "ATT(3.0,1,2) 2 -0.042252 -0.171374 0.086870 -0.223977 \n", - "ATT(3.0,2,3) 3 1.107901 0.979847 1.235955 0.927678 \n", + " Time Period Estimate Lower CI Upper CI \\\n", + "ATT(2025-05,2025-01,2025-02) 2025-02-01 0.023003 -0.094992 0.140998 \n", + "ATT(2025-05,2025-02,2025-03) 2025-03-01 -0.128634 -0.253590 -0.003678 \n", + "ATT(2025-05,2025-03,2025-04) 2025-04-01 0.122700 0.009588 0.235812 \n", + "ATT(2025-05,2025-04,2025-05) 2025-05-01 0.911296 0.791404 1.031189 \n", + "ATT(2025-05,2025-04,2025-06) 2025-06-01 1.963113 1.786896 2.139329 \n", "\n", - " Upper CI Joint First Treated Pre-Treatment \n", - "ATT(2.0,1,2) 1.096301 2.0 False \n", - "ATT(2.0,1,3) 2.164028 2.0 False \n", - "ATT(2.0,1,4) 3.128281 2.0 False \n", - "ATT(3.0,1,2) 0.139473 3.0 True \n", - "ATT(3.0,2,3) 1.288124 3.0 False " + " Lower CI Joint Upper CI Joint First Treated \\\n", + "ATT(2025-05,2025-01,2025-02) -0.162497 0.208504 2025-05-01 \n", + "ATT(2025-05,2025-02,2025-03) -0.325077 0.067810 2025-05-01 \n", + "ATT(2025-05,2025-03,2025-04) -0.055124 0.300524 2025-05-01 \n", + "ATT(2025-05,2025-04,2025-05) 0.722813 1.099780 2025-05-01 \n", + "ATT(2025-05,2025-04,2025-06) 1.686082 2.240143 2025-05-01 \n", + "\n", + " Pre-Treatment \n", + "ATT(2025-05,2025-01,2025-02) True \n", + "ATT(2025-05,2025-02,2025-03) True \n", + "ATT(2025-05,2025-03,2025-04) True \n", + "ATT(2025-05,2025-04,2025-05) False \n", + "ATT(2025-05,2025-04,2025-06) False " ] }, - "execution_count": 23, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ci_df = pd.DataFrame({\n", - " 'Time Period': [gt_combination[2] for gt_combination in gt_combinations],\n", + " 'Time Period': [gt_combination[2] for gt_combination in dml_obj.gt_combinations],\n", " 'Estimate': dml_obj.coef,\n", " 'Lower CI': ci.iloc[:, 0],\n", " 'Upper CI': ci.iloc[:, 1],\n", " 'Lower CI Joint': ci_joint.iloc[:, 0],\n", " 'Upper CI Joint': ci_joint.iloc[:, 1],\n", - " 'First Treated': [gt_combination[0] for gt_combination in gt_combinations],\n", - " 'Pre-Treatment': [gt_combination[2] < gt_combination[0] for gt_combination in gt_combinations],\n", + " 'First Treated': [gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", + " 'Pre-Treatment': [gt_combination[2] < gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", "})\n", "ci_df.head()" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/matplotlib/cbook.py:1709: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_atts(df, level=0.95, figsize=(15, 10), \n", + " title='Coefficient Estimates by First Treatment Period'):\n", + " \"\"\"\n", + " Plot coefficient estimates with CIs over time, grouped by first treatment period.\n", + " \"\"\"\n", + " all_time_periods = sorted(df['Time Period'].unique())\n", + " first_treated_periods = sorted(df['First Treated'].unique())\n", + " n_periods = len(first_treated_periods)\n", + " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", + " \n", + " # Adjust figure size to accommodate bottom legend\n", + " fig = plt.figure(figsize=figsize)\n", + " # Create subplot grid with space for legend at bottom\n", + " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", + " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", + "\n", + " if n_periods == 1:\n", + " axes = [axes]\n", + " \n", + " # Create a list to store legend handles and labels\n", + " legend_elements = []\n", + " \n", + " for idx, period in enumerate(first_treated_periods):\n", + " period_data = df[df['First Treated'] == period]\n", + " ax = axes[idx]\n", + "\n", + " i_period = all_time_periods.index(period)\n", + "\n", + " # Add treatment start line\n", + " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", + " linestyle=':', alpha=0.7)\n", + " if idx == 0:\n", + " legend_elements.append((line, 'Treatment start'))\n", + "\n", + " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", + " if idx == 0:\n", + " legend_elements.append((zero_line, 'Zero effect'))\n", + "\n", + " # Split data by treatment status\n", + " pre_treatment = period_data[period_data['Pre-Treatment']]\n", + " post_treatment = period_data[~period_data['Pre-Treatment']]\n", + " \n", + " if not pre_treatment.empty:\n", + " # Pre-treatment points\n", + " scatter_pre = ax.scatter(pre_treatment['Time Period'], \n", + " pre_treatment['Estimate'], \n", + " color=colors['pre'], alpha=0.8, s=50)\n", + " # Regular CIs\n", + " error_pre = ax.errorbar(pre_treatment['Time Period'], \n", + " pre_treatment['Estimate'],\n", + " yerr=[pre_treatment['Estimate'] - pre_treatment['Lower CI'],\n", + " pre_treatment['Upper CI'] - pre_treatment['Estimate']],\n", + " fmt='none', color=colors['pre'], alpha=1.0, \n", + " capsize=5)\n", + " # Joint CIs\n", + " error_pre_joint = ax.errorbar(pre_treatment['Time Period'], \n", + " pre_treatment['Estimate'],\n", + " yerr=[pre_treatment['Estimate'] - pre_treatment['Lower CI Joint'],\n", + " pre_treatment['Upper CI Joint'] - pre_treatment['Estimate']],\n", + " fmt='none', color=colors['pre'], alpha=0.5, \n", + " capsize=5)\n", + " if idx == 0:\n", + " legend_elements.extend([\n", + " (scatter_pre, 'Pre-treatment'),\n", + " (error_pre, f'{int(level*100)}% CI'),\n", + " (error_pre_joint, f'{int(level*100)}% joint CI')\n", + " ])\n", + " \n", + " # Similar structure for post-treatment\n", + " if not post_treatment.empty:\n", + " scatter_post = ax.scatter(post_treatment['Time Period'], \n", + " post_treatment['Estimate'], \n", + " color=colors['post'], alpha=0.8, s=50)\n", + " if idx == 0:\n", + " legend_elements.append((scatter_post, 'Post-treatment'))\n", + " \n", + " ax.errorbar(post_treatment['Time Period'], post_treatment['Estimate'],\n", + " yerr=[post_treatment['Estimate'] - post_treatment['Lower CI'],\n", + " post_treatment['Upper CI'] - post_treatment['Estimate']],\n", + " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", + " ax.errorbar(post_treatment['Time Period'], post_treatment['Estimate'],\n", + " yerr=[post_treatment['Estimate'] - post_treatment['Lower CI Joint'],\n", + " post_treatment['Upper CI Joint'] - post_treatment['Estimate']],\n", + " fmt='none', color=colors['post'], alpha=0.5, capsize=5)\n", + "\n", + " ax.set_title(f'First Treated: {period}')\n", + " ax.grid(True, alpha=0.3)\n", + " \n", + " if idx == 0:\n", + " ax.set_ylabel('Effect')\n", + " ax.set_xlabel('Time Period')\n", + " \n", + " # Create legend in a separate subplot at the bottom\n", + " legend_ax = fig.add_subplot(gs[-1])\n", + " legend_ax.axis('off') # Hide axes for legend subplot\n", + " \n", + " # Add legend using collected handles and labels\n", + " legend = legend_ax.legend(*zip(*legend_elements), \n", + " loc='center',\n", + " ncol=5, # Adjust number of columns as needed\n", + " mode='expand',\n", + " borderaxespad=0.)\n", + " \n", + " plt.suptitle(title, y=1.02) # Adjust title position\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "plot_atts(ci_df, title=\"Estimated Effects\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "================== DoubleMLDIDMulti Object ==================\n", + "================== DoubleMLDIDBinary Object ==================\n", "\n", "------------------ Data summary ------------------\n", - "Outcome variable: Y\n", - "Treatment variable(s): ['G']\n", - "Covariates: ['X']\n", + "Outcome variable: y\n", + "Treatment variable(s): ['d']\n", + "Covariates: ['Z1', 'Z2', 'Z3', 'Z4']\n", "Instrument variable(s): None\n", - "Time variable: period\n", + "Time variable: t\n", "Id variable: id\n", - "No. Observations: 3979\n", + "No. Observations: 5000\n", "\n", "------------------ Score & algorithm ------------------\n", "Score function: observational\n", + "Treatment group: 2025-05\n", + "Pre-treatment period: 2025-01\n", + "Evaluation period: 2025-02\n", + "Control group: never_treated\n", + "Effective sample size: 2040\n", "\n", "------------------ Machine learner ------------------\n", - "Learner ml_g: LinearRegression()\n", - "Learner ml_m: LogisticRegression()\n", + "Learner ml_g: LGBMRegressor(learning_rate=0.01, n_estimators=500, verbose=-1)\n", + "Learner ml_m: LGBMClassifier(learning_rate=0.01, n_estimators=500, verbose=-1)\n", "Out-of-sample Performance:\n", "Regression:\n", - "Learner ml_g0 RMSE: [[1.42511349 1.41059066 1.39672734 1.4241224 1.40399012 1.4197266\n", - " 1.42642755 1.40479147 1.426991 ]]\n", - "Learner ml_g1 RMSE: [[1.40397143 1.43565579 1.39510891 1.41293383 1.42339918 1.38510924\n", - " 1.45999302 1.41755301 1.411329 ]]\n", + "Learner ml_g0 RMSE: [[1.75343356]]\n", + "Learner ml_g1 RMSE: [[1.78847566]]\n", "Classification:\n", - "Learner ml_m Log Loss: [[0.69109027 0.69060657 0.69075731 0.67921779 0.67980946 0.67925216\n", - " 0.66244357 0.66258341 0.66218262]]\n", + "Learner ml_m Log Loss: [[0.67570748]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 5\n", "No. repeated sample splits: 1\n", "\n", "------------------ Fit summary ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.919771 0.063996 14.372248 0.000000 0.794341 1.045201\n", - "ATT(2.0,1,3) 1.985844 0.064596 30.742543 0.000000 1.859238 2.112450\n", - "ATT(2.0,1,4) 2.954131 0.063133 46.791848 0.000000 2.830392 3.077870\n", - "ATT(3.0,1,2) -0.042252 0.065880 -0.641349 0.521296 -0.171374 0.086870\n", - "ATT(3.0,2,3) 1.107901 0.065335 16.957228 0.000000 0.979847 1.235955\n", - "ATT(3.0,2,4) 2.059576 0.065367 31.507692 0.000000 1.931458 2.187694\n", - "ATT(4.0,1,2) 0.006155 0.068458 0.089904 0.928364 -0.128021 0.140330\n", - "ATT(4.0,2,3) 0.060672 0.066533 0.911912 0.361815 -0.069730 0.191075\n", - "ATT(4.0,3,4) 0.957025 0.067628 14.151335 0.000000 0.824477 1.089573\n" + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 0.157743 0.118683 1.329112 0.183811 -0.074872 0.390358\n" ] } ], "source": [ - "print(dml_obj)" + "print(dml_obj.modellist[0])" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 11, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/opt/venv/lib/python3.12/site-packages/matplotlib/cbook.py:1709: FutureWarning:\n", + "================== Sensitivity Analysis ==================\n", "\n", - "Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - "\n" + "------------------ Scenario ------------------\n", + "Significance Level: level=0.95\n", + "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", + "\n", + "------------------ Bounds with CI ------------------\n", + " CI lower theta lower theta theta upper \\\n", + "ATT(2025-05,2025-01,2025-02) -0.120006 0.069369 0.157743 0.246118 \n", + "ATT(2025-05,2025-02,2025-03) -0.269324 -0.052141 0.055352 0.162846 \n", + "ATT(2025-05,2025-03,2025-04) -0.155909 0.096783 0.196838 0.296892 \n", + "ATT(2025-05,2025-04,2025-05) 0.815970 1.086885 1.182260 1.277636 \n", + "ATT(2025-05,2025-04,2025-06) 1.775847 2.105615 2.245718 2.385820 \n", + "ATT(2025-05,2025-04,2025-07) 2.716193 3.302991 3.443723 3.584455 \n", + "ATT(2025-05,2025-04,2025-08) 3.477819 4.144813 4.382113 4.619413 \n", + "ATT(2025-06,2025-01,2025-02) -0.240154 -0.080768 0.025464 0.131696 \n", + "ATT(2025-06,2025-02,2025-03) -0.391045 -0.204127 -0.104800 -0.005473 \n", + "ATT(2025-06,2025-03,2025-04) -0.175712 0.042373 0.135043 0.227713 \n", + "ATT(2025-06,2025-04,2025-05) -0.174499 0.030080 0.118779 0.207478 \n", + "ATT(2025-06,2025-05,2025-06) 0.811950 1.014461 1.114512 1.214563 \n", + "ATT(2025-06,2025-05,2025-07) 1.611025 1.907322 2.050087 2.192851 \n", + "ATT(2025-06,2025-05,2025-08) 2.744902 3.158602 3.307784 3.456965 \n", + "ATT(2025-07,2025-01,2025-02) -0.065555 0.102357 0.197566 0.292776 \n", + "ATT(2025-07,2025-02,2025-03) -0.209319 -0.037719 0.057542 0.152803 \n", + "ATT(2025-07,2025-03,2025-04) -0.081986 0.165769 0.228719 0.291668 \n", + "ATT(2025-07,2025-04,2025-05) -0.102946 0.068754 0.164268 0.259782 \n", + "ATT(2025-07,2025-05,2025-06) -0.264629 -0.086315 -0.000824 0.084667 \n", + "ATT(2025-07,2025-06,2025-07) 0.775759 0.948530 1.041953 1.135376 \n", + "ATT(2025-07,2025-06,2025-08) 1.747544 2.011390 2.137674 2.263957 \n", + "ATT(2025-08,2025-01,2025-02) -0.045196 0.106545 0.198593 0.290640 \n", + "ATT(2025-08,2025-02,2025-03) -0.142176 0.016219 0.108227 0.200234 \n", + "ATT(2025-08,2025-03,2025-04) -0.116497 0.048845 0.140670 0.232495 \n", + "ATT(2025-08,2025-04,2025-05) -0.304563 -0.149809 -0.050136 0.049538 \n", + "ATT(2025-08,2025-05,2025-06) -0.149991 0.015477 0.115210 0.214943 \n", + "ATT(2025-08,2025-06,2025-07) -0.344588 -0.188381 -0.088871 0.010638 \n", + "ATT(2025-08,2025-07,2025-08) 1.007429 1.167105 1.260098 1.353092 \n", + "\n", + " CI upper \n", + "ATT(2025-05,2025-01,2025-02) 0.450629 \n", + "ATT(2025-05,2025-02,2025-03) 0.393038 \n", + "ATT(2025-05,2025-03,2025-04) 0.555804 \n", + "ATT(2025-05,2025-04,2025-05) 1.565359 \n", + "ATT(2025-05,2025-04,2025-06) 2.738323 \n", + "ATT(2025-05,2025-04,2025-07) 4.233211 \n", + "ATT(2025-05,2025-04,2025-08) 5.313431 \n", + "ATT(2025-06,2025-01,2025-02) 0.292584 \n", + "ATT(2025-06,2025-02,2025-03) 0.187398 \n", + "ATT(2025-06,2025-03,2025-04) 0.456664 \n", + "ATT(2025-06,2025-04,2025-05) 0.442892 \n", + "ATT(2025-06,2025-05,2025-06) 1.417664 \n", + "ATT(2025-06,2025-05,2025-07) 2.494698 \n", + "ATT(2025-06,2025-05,2025-08) 3.916391 \n", + "ATT(2025-07,2025-01,2025-02) 0.463180 \n", + "ATT(2025-07,2025-02,2025-03) 0.325410 \n", + "ATT(2025-07,2025-03,2025-04) 0.565833 \n", + "ATT(2025-07,2025-04,2025-05) 0.435110 \n", + "ATT(2025-07,2025-05,2025-06) 0.255810 \n", + "ATT(2025-07,2025-06,2025-07) 1.311939 \n", + "ATT(2025-07,2025-06,2025-08) 2.524003 \n", + "ATT(2025-08,2025-01,2025-02) 0.443700 \n", + "ATT(2025-08,2025-02,2025-03) 0.358259 \n", + "ATT(2025-08,2025-03,2025-04) 0.399046 \n", + "ATT(2025-08,2025-04,2025-05) 0.202978 \n", + "ATT(2025-08,2025-05,2025-06) 0.377566 \n", + "ATT(2025-08,2025-06,2025-07) 0.167372 \n", + "ATT(2025-08,2025-07,2025-08) 1.514207 \n", + "\n", + "------------------ Robustness Values ------------------\n", + " H_0 RV (%) RVa (%)\n", + "ATT(2025-05,2025-01,2025-02) 0.0 5.291229 0.000398\n", + "ATT(2025-05,2025-02,2025-03) 0.0 1.556378 0.000506\n", + "ATT(2025-05,2025-03,2025-04) 0.0 5.815643 0.000347\n", + "ATT(2025-05,2025-04,2025-05) 0.0 31.296964 24.177074\n", + "ATT(2025-05,2025-04,2025-06) 0.0 38.339629 32.590509\n", + "ATT(2025-05,2025-04,2025-07) 0.0 51.766229 39.284928\n", + "ATT(2025-05,2025-04,2025-08) 0.0 42.612003 35.789261\n", + "ATT(2025-06,2025-01,2025-02) 0.0 0.727644 0.000662\n", + "ATT(2025-06,2025-02,2025-03) 0.0 3.162723 0.000372\n", + "ATT(2025-06,2025-03,2025-04) 0.0 4.341441 0.000551\n", + "ATT(2025-06,2025-04,2025-05) 0.0 3.996742 0.000610\n", + "ATT(2025-06,2025-05,2025-06) 0.0 28.659565 23.077695\n", + "ATT(2025-06,2025-05,2025-07) 0.0 35.208491 29.712370\n", + "ATT(2025-06,2025-05,2025-08) 0.0 48.478791 39.983373\n", + "ATT(2025-07,2025-01,2025-02) 0.0 6.124151 0.925003\n", + "ATT(2025-07,2025-02,2025-03) 0.0 1.823209 0.000447\n", + "ATT(2025-07,2025-03,2025-04) 0.0 10.471987 0.000637\n", + "ATT(2025-07,2025-04,2025-05) 0.0 5.103291 0.000407\n", + "ATT(2025-07,2025-05,2025-06) 0.0 0.029213 0.000421\n", + "ATT(2025-07,2025-06,2025-07) 0.0 28.688699 23.926366\n", + "ATT(2025-07,2025-06,2025-08) 0.0 39.954797 33.379184\n", + "ATT(2025-08,2025-01,2025-02) 0.0 6.359461 1.533646\n", + "ATT(2025-08,2025-02,2025-03) 0.0 3.519432 0.000341\n", + "ATT(2025-08,2025-03,2025-04) 0.0 4.558764 0.000582\n", + "ATT(2025-08,2025-04,2025-05) 0.0 1.520578 0.000468\n", + "ATT(2025-08,2025-05,2025-06) 0.0 3.457416 0.000617\n", + "ATT(2025-08,2025-06,2025-07) 0.0 2.683734 0.000357\n", + "ATT(2025-08,2025-07,2025-08) 0.0 33.626953 29.598398\n" ] - }, + } + ], + "source": [ + "dml_obj.sensitivity_analysis()\n", + "print(dml_obj.sensitivity_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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" + "array([0.0056365 , 0.03090225, 0.03031496, 0.20215469, 0.32155703,\n", + " 0.40301916, 0.42120061, 0.0171697 , 0.00725611, 0.00196753,\n", + " 0.02802333, 0.2309368 , 0.34055762, 0.38839082, 0.01142757,\n", + " 0.01075262, 0.00927836, 0.0160404 , 0.01690221, 0.25701313,\n", + " 0.4222928 , 0.01768095, 0.01798774, 0.00106278, 0.00850198,\n", + " 0.04722776, 0.0077089 , 0.30099369])" ] }, + "execution_count": 38, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "plot_atts(ci_df, title=\"Estimated Effects\")" + "dml_obj.sensitivity_analysis()\n", + "dml_obj.sensitivity_params[\"rv\"]" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 49, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Group Aggregation \n", + "/tmp/ipykernel_36280/1092211131.py:19: FutureWarning: \n", "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.489311 0.03425 43.483234 0.0 1.422182 1.55644\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "2.0 1.953249 0.052170 37.439853 0.0 1.850997 2.055501\n", - "3.0 1.583739 0.056193 28.183886 0.0 1.473602 1.693875\n", - "4.0 0.957025 0.067628 14.151335 0.0 0.824477 1.089573\n", - "------------------ Additional Information ------------------\n", - "Control Group: never_treated\n", - "Anticipation Periods: 0\n", - "Score: observational\n", - "\n" + "Setting a gradient palette using color= is deprecated and will be removed in v0.14.0. Set `palette='dark:#0173b2'` for the same effect.\n", + "\n", + " sns.stripplot(\n" ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "aggregated = dml_obj.aggregate(\"group\")\n", - "print(aggregated)" + "def plot_sensitivity(dml_obj, figsize=(12, 6)):\n", + " \"\"\"\n", + " Plot sensitivity analysis results for the ATT using jittered points.\n", + " \"\"\"\n", + " # Extract robustness values\n", + " rv_values = dml_obj.sensitivity_params[\"rv\"]\n", + "\n", + " \n", + " # Create a simple dataframe with just the values\n", + " sens_df = pd.DataFrame({\n", + " \"Robustness Value\": rv_values,\n", + " 'Pre-Treatment': [gt_combination[2] < gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", + " })\n", + " \n", + " # Create a figure\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " \n", + " # Plot jittered points\n", + " sns.stripplot(\n", + " data=sens_df,\n", + " y=\"Robustness Value\",\n", + " hue=\"Pre-Treatment\",\n", + " ax=ax,\n", + " size=8,\n", + " jitter=True,\n", + " alpha=0.7,\n", + " color=sns.color_palette(\"colorblind\")[0]\n", + " )\n", + "\n", + " \n", + " # Add title and labels\n", + " ax.set_title('Sensitivity Analysis - Robustness Values')\n", + " ax.set_ylabel('Robustness Value')\n", + " ax.set_xlabel('')\n", + " \n", + " # Remove x-axis ticks since we're just plotting the distribution\n", + " ax.set_xticks([])\n", + " \n", + " # Add legend\n", + " ax.legend()\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "plot_sensitivity(dml_obj)" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 39, "metadata": {}, "outputs": [ + { + "ename": "ValueError", + "evalue": "Could not interpret value `Sensitivity` for `x`. An entry with this name does not appear in `data`.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[39], line 20\u001b[0m\n\u001b[1;32m 17\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSensitivity Analysis for ATT\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 18\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[0;32m---> 20\u001b[0m \u001b[43mplot_sensitivity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdml_obj\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[39], line 15\u001b[0m, in \u001b[0;36mplot_sensitivity\u001b[0;34m(dml_obj, figsize)\u001b[0m\n\u001b[1;32m 13\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39mfigsize)\n\u001b[1;32m 14\u001b[0m \u001b[38;5;66;03m# Plot the sensitivity values\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbarplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msens_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSensitivity\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mFirst Treated\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcolorblind\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Add a title\u001b[39;00m\n\u001b[1;32m 17\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSensitivity Analysis for ATT\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/seaborn/categorical.py:2341\u001b[0m, in \u001b[0;36mbarplot\u001b[0;34m(data, x, y, hue, order, hue_order, estimator, errorbar, n_boot, seed, units, weights, orient, color, palette, saturation, fill, hue_norm, width, dodge, gap, log_scale, native_scale, formatter, legend, capsize, err_kws, ci, errcolor, errwidth, ax, **kwargs)\u001b[0m\n\u001b[1;32m 2338\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mlen\u001b[39m:\n\u001b[1;32m 2339\u001b[0m estimator \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 2341\u001b[0m p \u001b[38;5;241m=\u001b[39m \u001b[43m_CategoricalAggPlotter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2342\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2343\u001b[0m \u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2344\u001b[0m \u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2345\u001b[0m \u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2346\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2347\u001b[0m \u001b[43m \u001b[49m\u001b[43mlegend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlegend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2348\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2350\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 2351\u001b[0m ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n", + "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/seaborn/categorical.py:67\u001b[0m, in \u001b[0;36m_CategoricalPlotter.__init__\u001b[0;34m(self, data, variables, order, orient, require_numeric, color, legend)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__init__\u001b[39m(\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 58\u001b[0m data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 64\u001b[0m legend\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 65\u001b[0m ):\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;66;03m# This method takes care of some bookkeeping that is necessary because the\u001b[39;00m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;66;03m# original categorical plots (prior to the 2021 refactor) had some rules that\u001b[39;00m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;66;03m# don't fit exactly into VectorPlotter logic. It may be wise to have a second\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[38;5;66;03m# default VectorPlotter rules. If we do decide to make orient part of the\u001b[39;00m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;66;03m# _base variable assignment, we'll want to figure out how to express that.\u001b[39;00m\n\u001b[1;32m 78\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_format \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwide\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m orient \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", + "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/seaborn/_base.py:634\u001b[0m, in \u001b[0;36mVectorPlotter.__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m 629\u001b[0m \u001b[38;5;66;03m# var_ordered is relevant only for categorical axis variables, and may\u001b[39;00m\n\u001b[1;32m 630\u001b[0m \u001b[38;5;66;03m# be better handled by an internal axis information object that tracks\u001b[39;00m\n\u001b[1;32m 631\u001b[0m \u001b[38;5;66;03m# such information and is set up by the scale_* methods. The analogous\u001b[39;00m\n\u001b[1;32m 632\u001b[0m \u001b[38;5;66;03m# information for numeric axes would be information about log scales.\u001b[39;00m\n\u001b[1;32m 633\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_var_ordered \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m} \u001b[38;5;66;03m# alt., used DefaultDict\u001b[39;00m\n\u001b[0;32m--> 634\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massign_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 636\u001b[0m \u001b[38;5;66;03m# TODO Lots of tests assume that these are called to initialize the\u001b[39;00m\n\u001b[1;32m 637\u001b[0m \u001b[38;5;66;03m# mappings to default values on class initialization. I'd prefer to\u001b[39;00m\n\u001b[1;32m 638\u001b[0m \u001b[38;5;66;03m# move away from that and only have a mapping when explicitly called.\u001b[39;00m\n\u001b[1;32m 639\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhue\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstyle\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", + "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/seaborn/_base.py:679\u001b[0m, in \u001b[0;36mVectorPlotter.assign_variables\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m 674\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 675\u001b[0m \u001b[38;5;66;03m# When dealing with long-form input, use the newer PlotData\u001b[39;00m\n\u001b[1;32m 676\u001b[0m \u001b[38;5;66;03m# object (internal but introduced for the objects interface)\u001b[39;00m\n\u001b[1;32m 677\u001b[0m \u001b[38;5;66;03m# to centralize / standardize data consumption logic.\u001b[39;00m\n\u001b[1;32m 678\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_format \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlong\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 679\u001b[0m plot_data \u001b[38;5;241m=\u001b[39m \u001b[43mPlotData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 680\u001b[0m frame \u001b[38;5;241m=\u001b[39m plot_data\u001b[38;5;241m.\u001b[39mframe\n\u001b[1;32m 681\u001b[0m names \u001b[38;5;241m=\u001b[39m plot_data\u001b[38;5;241m.\u001b[39mnames\n", + "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/seaborn/_core/data.py:58\u001b[0m, in \u001b[0;36mPlotData.__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__init__\u001b[39m(\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 53\u001b[0m data: DataSource,\n\u001b[1;32m 54\u001b[0m variables: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, VariableSpec],\n\u001b[1;32m 55\u001b[0m ):\n\u001b[1;32m 57\u001b[0m data \u001b[38;5;241m=\u001b[39m handle_data_source(data)\n\u001b[0;32m---> 58\u001b[0m frame, names, ids \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_assign_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframe \u001b[38;5;241m=\u001b[39m frame\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnames \u001b[38;5;241m=\u001b[39m names\n", + "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/seaborn/_core/data.py:232\u001b[0m, in \u001b[0;36mPlotData._assign_variables\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 231\u001b[0m err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn entry with this name does not appear in `data`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 232\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err)\n\u001b[1;32m 234\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 235\u001b[0m \n\u001b[1;32m 236\u001b[0m \u001b[38;5;66;03m# Otherwise, assume the value somehow represents data\u001b[39;00m\n\u001b[1;32m 237\u001b[0m \n\u001b[1;32m 238\u001b[0m \u001b[38;5;66;03m# Ignore empty data structures\u001b[39;00m\n\u001b[1;32m 239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, Sized) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(val) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", + "\u001b[0;31mValueError\u001b[0m: Could not interpret value `Sensitivity` for `x`. An entry with this name does not appear in `data`." + ] + }, { "data": { - "text/html": [ - "
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", "text/plain": [ - " coef std err t P>|t| 2.5 % 97.5 %\n", - "0 1.489311 0.03425 43.483234 0.0 1.422182 1.55644" + "
" ] }, - "execution_count": 27, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "aggregated.overall_summary" + "# plot the robustness values for the ATT with seaborn\n", + "\n", + "def plot_sensitivity(dml_obj, figsize=(12, 8)):\n", + " \"\"\"\n", + " Plot sensitivity analysis results for the ATT.\n", + " \"\"\"\n", + " # vector of robustness values\n", + " sens_df = pd.DataFrame({\n", + " \"rv\": dml_obj.sensitivity_params[\"rv\"]\n", + " })\n", + "\n", + " # Create a figure\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " # Plot the sensitivity values\n", + " sns.barplot(data=sens_df, x='Sensitivity', y='First Treated', ax=ax, palette='colorblind')\n", + " # Add a title\n", + " ax.set_title('Sensitivity Analysis for ATT')\n", + " plt.show()\n", + "\n", + "plot_sensitivity(dml_obj)\n" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 42, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Time Aggregation \n", - "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.480607 0.035056 42.235223 0.0 1.411898 1.549316\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "2 0.919771 0.063996 14.372248 0.0 0.794341 1.045201\n", - "3 1.548219 0.051311 30.173021 0.0 1.447651 1.648787\n", - "4 1.973831 0.046620 42.339093 0.0 1.882458 2.065203\n", - "------------------ Additional Information ------------------\n", - "Control Group: never_treated\n", - "Anticipation Periods: 0\n", - "Score: observational\n", - "\n" + "ename": "AttributeError", + "evalue": "'DoubleMLDIDMulti' object has no attribute 'data'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[42], line 57\u001b[0m\n\u001b[1;32m 54\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n\u001b[1;32m 55\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[0;32m---> 57\u001b[0m \u001b[43mplot_sensitivity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdml_obj\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[42], line 19\u001b[0m, in \u001b[0;36mplot_sensitivity\u001b[0;34m(dml_obj, figsize)\u001b[0m\n\u001b[1;32m 13\u001b[0m sens_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame([\n\u001b[1;32m 14\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFirst Treated\u001b[39m\u001b[38;5;124m\"\u001b[39m: key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRobustness Value\u001b[39m\u001b[38;5;124m\"\u001b[39m: value} \n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m rv_data\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 16\u001b[0m ])\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(rv_data, (pd\u001b[38;5;241m.\u001b[39mSeries, np\u001b[38;5;241m.\u001b[39mndarray)):\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# If rv_data is a series or array, assume it corresponds to first_treated periods\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m first_treated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(\u001b[43mdml_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfirst_treat\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39munique())\n\u001b[1;32m 20\u001b[0m sens_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\n\u001b[1;32m 21\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFirst Treated\u001b[39m\u001b[38;5;124m\"\u001b[39m: first_treated,\n\u001b[1;32m 22\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRobustness Value\u001b[39m\u001b[38;5;124m\"\u001b[39m: rv_data\n\u001b[1;32m 23\u001b[0m })\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 25\u001b[0m \u001b[38;5;66;03m# If single value\u001b[39;00m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'DoubleMLDIDMulti' object has no attribute 'data'" ] } ], "source": [ - "aggregated_time = dml_obj.aggregate(\"time\")\n", - "print(aggregated_time)" + "import numpy as np\n", + "\n", + "def plot_sensitivity(dml_obj, figsize=(12, 8)):\n", + " \"\"\"\n", + " Plot sensitivity analysis results for the ATT.\n", + " \"\"\"\n", + " # Extract robustness values and convert to proper dataframe format\n", + " rv_data = dml_obj.sensitivity_params[\"rv\"]\n", + " \n", + " # Create a properly structured dataframe\n", + " if isinstance(rv_data, dict):\n", + " # If rv_data is a dictionary with keys as periods/groups\n", + " sens_df = pd.DataFrame([\n", + " {\"First Treated\": key, \"Robustness Value\": value} \n", + " for key, value in rv_data.items()\n", + " ])\n", + " elif isinstance(rv_data, (pd.Series, np.ndarray)):\n", + " # If rv_data is a series or array, assume it corresponds to first_treated periods\n", + " first_treated = sorted(dml_obj.data['first_treat'].unique())\n", + " sens_df = pd.DataFrame({\n", + " \"First Treated\": first_treated,\n", + " \"Robustness Value\": rv_data\n", + " })\n", + " else:\n", + " # If single value\n", + " sens_df = pd.DataFrame({\n", + " \"First Treated\": [\"Overall\"],\n", + " \"Robustness Value\": [rv_data]\n", + " })\n", + " \n", + " # Create a figure\n", + " fig, ax = plt.subplots(figsize=figsize)\n", + " \n", + " # Plot the sensitivity values\n", + " sns.barplot(\n", + " data=sens_df, \n", + " x=\"First Treated\", \n", + " y=\"Robustness Value\", \n", + " ax=ax, \n", + " palette='colorblind'\n", + " )\n", + " \n", + " # Add a title and labels\n", + " ax.set_title('Sensitivity Analysis for ATT')\n", + " ax.set_xlabel('First Treated Period')\n", + " ax.set_ylabel('Robustness Value')\n", + " \n", + " # Add horizontal line at y=1 for reference\n", + " ax.axhline(y=1, color='red', linestyle='--', alpha=0.7, label='Threshold')\n", + " \n", + " # Add legend\n", + " ax.legend()\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "plot_sensitivity(dml_obj)" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -6027,14 +2470,19 @@ "\n", "------------------ Overall Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.990268 0.038694 51.435514 0.0 1.914428 2.066107\n", + "2.763755 0.218875 12.627083 0.0 2.334768 3.192743\n", "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "-2.0 0.006155 0.068458 0.089904 0.928364 -0.128021 0.140330\n", - "-1.0 0.010572 0.040513 0.260955 0.794127 -0.068831 0.089975\n", - "0.0 0.994075 0.030745 32.333425 0.000000 0.933817 1.054333\n", - "1.0 2.022597 0.045646 44.310285 0.000000 1.933132 2.112062\n", - "2.0 2.954131 0.063133 46.791848 0.000000 2.830392 3.077870\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "-6 months 0.198593 0.092508 2.146769 0.031812 0.017281 0.379905\n", + "-5 months 0.152421 0.071982 2.117498 0.034218 0.011340 0.293503\n", + "-4 months 0.074895 0.066944 1.118773 0.263237 -0.056313 0.206103\n", + "-3 months 0.057963 0.077873 0.744331 0.456677 -0.094665 0.210592\n", + "-2 months 0.116883 0.076878 1.520356 0.128421 -0.033796 0.267561\n", + "-1 months 0.057215 0.077162 0.741485 0.458399 -0.094021 0.208450\n", + "0 months 1.150537 0.081138 14.179983 0.000000 0.991510 1.309565\n", + "1 months 2.145565 0.148218 14.475725 0.000000 1.855063 2.436067\n", + "2 months 3.376806 0.278366 12.130827 0.000000 2.831219 3.922393\n", + "3 months 4.382113 0.412502 10.623264 0.000000 3.573625 5.190602\n", "------------------ Additional Information ------------------\n", "Control Group: never_treated\n", "Anticipation Periods: 0\n", @@ -6044,128 +2492,9 @@ } ], "source": [ - "aggregated_eventstudy = dml_obj.aggregate(\"eventstudy\")\n", - "print(aggregated_eventstudy)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "masked_array(\n", - " data=[[[--, 0.0, 0.0, 0.3287533512064343],\n", - " [--, --, --, --],\n", - " [--, --, --, --],\n", - " [--, --, --, --]],\n", - "\n", - " [[--, 0.0, --, --],\n", - " [--, --, 0.0, 0.32674262734584447],\n", - " [--, --, --, --],\n", - " [--, --, --, --]],\n", - "\n", - " [[--, 0.0, --, --],\n", - " [--, --, 0.0, --],\n", - " [--, --, --, 0.34450402144772113],\n", - " [--, --, --, --]],\n", - "\n", - " [[--, --, --, --],\n", - " [--, --, --, --],\n", - " [--, --, --, --],\n", - " [--, --, --, --]]],\n", - " mask=[[[ True, False, False, False],\n", - " [ True, True, True, True],\n", - " [ True, True, True, True],\n", - " [ True, True, True, True]],\n", - "\n", - " [[ True, False, True, True],\n", - " [ True, True, False, False],\n", - " [ True, True, True, True],\n", - " [ True, True, True, True]],\n", - "\n", - " [[ True, False, True, True],\n", - " [ True, True, False, True],\n", - " [ True, True, True, False],\n", - " [ True, True, True, True]],\n", - "\n", - " [[ True, True, True, True],\n", - " [ True, True, True, True],\n", - " [ True, True, True, True],\n", - " [ True, True, True, True]]],\n", - " fill_value=1e+20)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "aggregated_time.additional_parameters[\"weight_masks\"][...,2]" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. ],\n", - " [0. , 0.50153374, 0. , 0. , 0.49846626,\n", - " 0. , 0. , 0. , 0. ],\n", - " [0. , 0. , 0.32875335, 0. , 0. ,\n", - " 0.32674263, 0. , 0. , 0.34450402]])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "aggregated_time.aggregation_weights" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(np.float64(2.0), np.int64(1), np.int64(2)),\n", - " (np.float64(2.0), np.int64(1), np.int64(3)),\n", - " (np.float64(2.0), np.int64(1), np.int64(4)),\n", - " (np.float64(3.0), np.int64(1), np.int64(2)),\n", - " (np.float64(3.0), np.int64(2), np.int64(3)),\n", - " (np.float64(3.0), np.int64(2), np.int64(4)),\n", - " (np.float64(4.0), np.int64(1), np.int64(2)),\n", - " (np.float64(4.0), np.int64(2), np.int64(3)),\n", - " (np.float64(4.0), np.int64(3), np.int64(4))]" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dml_obj.gt_combinations\n" + "agg_obj = dml_obj.aggregate(aggregation=\"eventstudy\")\n", + "print(agg_obj)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 144a30c65b5a01709f9a1a72611e41c660d536e2 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 12 Mar 2025 13:59:53 +0000 Subject: [PATCH 027/140] update nbs --- doc/examples/py_double_ml_panel.ipynb | 2328 ++++-------------- doc/examples/py_double_ml_panel_simple.ipynb | 518 +++- 2 files changed, 849 insertions(+), 1997 deletions(-) diff --git a/doc/examples/py_double_ml_panel.ipynb b/doc/examples/py_double_ml_panel.ipynb index db610263..a4e40fc4 100644 --- a/doc/examples/py_double_ml_panel.ipynb +++ b/doc/examples/py_double_ml_panel.ipynb @@ -67,77 +67,77 @@ " \n", " 0\n", " 0\n", - " 201.441702\n", - " 201.441702\n", - " 200.800554\n", - " 2025-05-01\n", + " 208.160364\n", + " 208.160364\n", + " 206.150875\n", + " 2025-07-01\n", " 2025-01-01\n", - " -0.952834\n", - " -1.363224\n", - " 0.520004\n", - " -1.355132\n", - " -0.641149\n", - " 2025-05\n", + " -1.221552\n", + " 0.665762\n", + " -0.20382\n", + " 0.617408\n", + " -2.009488\n", + " 2025-07\n", " \n", " \n", " 1\n", " 0\n", - " 194.901756\n", - " 194.901756\n", - " 194.833183\n", - " 2025-05-01\n", + " 204.174392\n", + " 204.174392\n", + " 203.462996\n", + " 2025-07-01\n", " 2025-02-01\n", - " -0.952834\n", - " -1.363224\n", - " 0.520004\n", - " -1.355132\n", - " -0.068573\n", - " 2025-05\n", + " -1.221552\n", + " 0.665762\n", + " -0.20382\n", + " 0.617408\n", + " -0.711396\n", + " 2025-07\n", " \n", " \n", " 2\n", " 0\n", - " 188.848925\n", - " 188.848925\n", - " 187.587438\n", - " 2025-05-01\n", + " 199.599967\n", + " 199.599967\n", + " 197.741360\n", + " 2025-07-01\n", " 2025-03-01\n", - " -0.952834\n", - " -1.363224\n", - " 0.520004\n", - " -1.355132\n", - " -1.261487\n", - " 2025-05\n", + " -1.221552\n", + " 0.665762\n", + " -0.20382\n", + " 0.617408\n", + " -1.858607\n", + " 2025-07\n", " \n", " \n", " 3\n", " 0\n", - " 180.295271\n", - " 180.295271\n", - " 179.251483\n", - " 2025-05-01\n", + " 195.233663\n", + " 195.233663\n", + " 195.721657\n", + " 2025-07-01\n", " 2025-04-01\n", - " -0.952834\n", - " -1.363224\n", - " 0.520004\n", - " -1.355132\n", - " -1.043788\n", - " 2025-05\n", + " -1.221552\n", + " 0.665762\n", + " -0.20382\n", + " 0.617408\n", + " 0.487994\n", + " 2025-07\n", " \n", " \n", " 4\n", " 0\n", - " 174.722549\n", - " 170.505867\n", - " 174.722549\n", + " 189.010286\n", + " 189.010286\n", + " 191.331624\n", + " 2025-07-01\n", " 2025-05-01\n", - " 2025-05-01\n", - " -0.952834\n", - " -1.363224\n", - " 0.520004\n", - " -1.355132\n", - " 4.216682\n", - " 2025-05\n", + " -1.221552\n", + " 0.665762\n", + " -0.20382\n", + " 0.617408\n", + " 2.321338\n", + " 2025-07\n", " \n", " \n", "\n", @@ -145,18 +145,18 @@ ], "text/plain": [ " id y y0 y1 d t Z1 \\\n", - "0 0 201.441702 201.441702 200.800554 2025-05-01 2025-01-01 -0.952834 \n", - "1 0 194.901756 194.901756 194.833183 2025-05-01 2025-02-01 -0.952834 \n", - "2 0 188.848925 188.848925 187.587438 2025-05-01 2025-03-01 -0.952834 \n", - "3 0 180.295271 180.295271 179.251483 2025-05-01 2025-04-01 -0.952834 \n", - "4 0 174.722549 170.505867 174.722549 2025-05-01 2025-05-01 -0.952834 \n", + "0 0 208.160364 208.160364 206.150875 2025-07-01 2025-01-01 -1.221552 \n", + "1 0 204.174392 204.174392 203.462996 2025-07-01 2025-02-01 -1.221552 \n", + "2 0 199.599967 199.599967 197.741360 2025-07-01 2025-03-01 -1.221552 \n", + "3 0 195.233663 195.233663 195.721657 2025-07-01 2025-04-01 -1.221552 \n", + "4 0 189.010286 189.010286 191.331624 2025-07-01 2025-05-01 -1.221552 \n", "\n", - " Z2 Z3 Z4 ite First Treated \n", - "0 -1.363224 0.520004 -1.355132 -0.641149 2025-05 \n", - "1 -1.363224 0.520004 -1.355132 -0.068573 2025-05 \n", - "2 -1.363224 0.520004 -1.355132 -1.261487 2025-05 \n", - "3 -1.363224 0.520004 -1.355132 -1.043788 2025-05 \n", - "4 -1.363224 0.520004 -1.355132 4.216682 2025-05 " + " Z2 Z3 Z4 ite First Treated \n", + "0 0.665762 -0.20382 0.617408 -2.009488 2025-07 \n", + "1 0.665762 -0.20382 0.617408 -0.711396 2025-07 \n", + "2 0.665762 -0.20382 0.617408 -1.858607 2025-07 \n", + "3 0.665762 -0.20382 0.617408 0.487994 2025-07 \n", + "4 0.665762 -0.20382 0.617408 2.321338 2025-07 " ] }, "execution_count": 2, @@ -211,56 +211,56 @@ " 0\n", " 2025-01-01\n", " 2025-05\n", - " 208.848891\n", - " 201.392159\n", - " 217.133574\n", - " 0.008437\n", - " -2.232558\n", - " 2.322668\n", + " 208.851382\n", + " 201.215583\n", + " 216.475755\n", + " -0.013452\n", + " -2.392223\n", + " 2.371590\n", " \n", " \n", " 1\n", " 2025-01-01\n", " 2025-06\n", - " 210.623628\n", - " 202.713088\n", - " 218.241705\n", - " 0.027477\n", - " -2.164135\n", - " 2.353258\n", + " 210.329416\n", + " 202.638201\n", + " 217.961796\n", + " -0.052635\n", + " -2.448709\n", + " 2.271408\n", " \n", " \n", " 2\n", " 2025-01-01\n", " 2025-07\n", - " 211.807238\n", - " 204.248429\n", - " 219.444427\n", - " 0.030659\n", - " -2.431588\n", - " 2.384421\n", + " 211.793469\n", + " 204.025050\n", + " 219.193304\n", + " -0.048091\n", + " -2.403457\n", + " 2.219086\n", " \n", " \n", " 3\n", " 2025-01-01\n", " 2025-08\n", - " 213.567017\n", - " 206.058098\n", - " 221.386857\n", - " 0.034377\n", - " -2.275534\n", - " 2.281529\n", + " 213.467868\n", + " 205.879919\n", + " 220.905510\n", + " 0.095641\n", + " -2.277543\n", + " 2.263161\n", " \n", " \n", " 4\n", " 2025-01-01\n", " Never Treated\n", - " 214.932726\n", - " 207.203539\n", - " 222.839093\n", - " 0.034802\n", - " -2.367436\n", - " 2.281742\n", + " 215.076606\n", + " 207.173377\n", + " 222.389427\n", + " -0.009199\n", + " -2.332194\n", + " 2.405225\n", " \n", " \n", "\n", @@ -268,18 +268,18 @@ ], "text/plain": [ " t First Treated y_mean y_lower_quantile y_upper_quantile \\\n", - "0 2025-01-01 2025-05 208.848891 201.392159 217.133574 \n", - "1 2025-01-01 2025-06 210.623628 202.713088 218.241705 \n", - "2 2025-01-01 2025-07 211.807238 204.248429 219.444427 \n", - "3 2025-01-01 2025-08 213.567017 206.058098 221.386857 \n", - "4 2025-01-01 Never Treated 214.932726 207.203539 222.839093 \n", + "0 2025-01-01 2025-05 208.851382 201.215583 216.475755 \n", + "1 2025-01-01 2025-06 210.329416 202.638201 217.961796 \n", + "2 2025-01-01 2025-07 211.793469 204.025050 219.193304 \n", + "3 2025-01-01 2025-08 213.467868 205.879919 220.905510 \n", + "4 2025-01-01 Never Treated 215.076606 207.173377 222.389427 \n", "\n", " ite_mean ite_lower_quantile ite_upper_quantile \n", - "0 0.008437 -2.232558 2.322668 \n", - "1 0.027477 -2.164135 2.353258 \n", - "2 0.030659 -2.431588 2.384421 \n", - "3 0.034377 -2.275534 2.281529 \n", - "4 0.034802 -2.367436 2.281742 " + "0 -0.013452 -2.392223 2.371590 \n", + "1 -0.052635 -2.448709 2.271408 \n", + "2 -0.048091 -2.403457 2.219086 \n", + "3 0.095641 -2.277543 2.263161 \n", + "4 -0.009199 -2.332194 2.405225 " ] }, "execution_count": 3, @@ -301,11 +301,8 @@ "agg_dictionary = agg_dict(\"y\") | agg_dict(\"ite\")\n", "# convert \"d\" to month period\n", "\n", - "df.fillna(\"Never treated\")\n", - "\n", "# fill NaT values since they are not supported by groupby\n", "agg_df = df.groupby([\"t\", \"First Treated\"]).agg(**agg_dictionary).reset_index()\n", - "\n", "agg_df.head()" ] }, @@ -316,7 +313,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", + "image/png": 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", "text/plain": [ "
" ] @@ -419,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -439,66 +436,30 @@ "\n", "------------------ Score & algorithm ------------------\n", "Score function: observational\n", - "GT combinations: ['(2025-05,2025-01,2025-02)', '(2025-05,2025-01,2025-03)', '(2025-05,2025-02,2025-03)', '(2025-05,2025-01,2025-04)', '(2025-05,2025-02,2025-04)', '(2025-05,2025-03,2025-04)', '(2025-05,2025-01,2025-05)', '(2025-05,2025-02,2025-05)', '(2025-05,2025-03,2025-05)', '(2025-05,2025-04,2025-05)', '(2025-05,2025-01,2025-06)', '(2025-05,2025-02,2025-06)', '(2025-05,2025-03,2025-06)', '(2025-05,2025-04,2025-06)', '(2025-05,2025-01,2025-07)', '(2025-05,2025-02,2025-07)', '(2025-05,2025-03,2025-07)', '(2025-05,2025-04,2025-07)', '(2025-05,2025-01,2025-08)', '(2025-05,2025-02,2025-08)', '(2025-05,2025-03,2025-08)', '(2025-05,2025-04,2025-08)', '(2025-06,2025-01,2025-02)', '(2025-06,2025-01,2025-03)', '(2025-06,2025-02,2025-03)', '(2025-06,2025-01,2025-04)', '(2025-06,2025-02,2025-04)', '(2025-06,2025-03,2025-04)', '(2025-06,2025-01,2025-05)', '(2025-06,2025-02,2025-05)', '(2025-06,2025-03,2025-05)', '(2025-06,2025-04,2025-05)', '(2025-06,2025-01,2025-06)', '(2025-06,2025-02,2025-06)', '(2025-06,2025-03,2025-06)', '(2025-06,2025-04,2025-06)', '(2025-06,2025-05,2025-06)', '(2025-06,2025-01,2025-07)', '(2025-06,2025-02,2025-07)', '(2025-06,2025-03,2025-07)', '(2025-06,2025-04,2025-07)', '(2025-06,2025-05,2025-07)', '(2025-06,2025-01,2025-08)', '(2025-06,2025-02,2025-08)', '(2025-06,2025-03,2025-08)', '(2025-06,2025-04,2025-08)', '(2025-06,2025-05,2025-08)', '(2025-07,2025-01,2025-02)', '(2025-07,2025-01,2025-03)', '(2025-07,2025-02,2025-03)', '(2025-07,2025-01,2025-04)', '(2025-07,2025-02,2025-04)', '(2025-07,2025-03,2025-04)', '(2025-07,2025-01,2025-05)', '(2025-07,2025-02,2025-05)', '(2025-07,2025-03,2025-05)', '(2025-07,2025-04,2025-05)', '(2025-07,2025-01,2025-06)', '(2025-07,2025-02,2025-06)', '(2025-07,2025-03,2025-06)', '(2025-07,2025-04,2025-06)', '(2025-07,2025-05,2025-06)', '(2025-07,2025-01,2025-07)', '(2025-07,2025-02,2025-07)', '(2025-07,2025-03,2025-07)', '(2025-07,2025-04,2025-07)', '(2025-07,2025-05,2025-07)', '(2025-07,2025-06,2025-07)', '(2025-07,2025-01,2025-08)', '(2025-07,2025-02,2025-08)', '(2025-07,2025-03,2025-08)', '(2025-07,2025-04,2025-08)', '(2025-07,2025-05,2025-08)', '(2025-07,2025-06,2025-08)', '(2025-08,2025-01,2025-02)', '(2025-08,2025-01,2025-03)', '(2025-08,2025-02,2025-03)', '(2025-08,2025-01,2025-04)', '(2025-08,2025-02,2025-04)', '(2025-08,2025-03,2025-04)', '(2025-08,2025-01,2025-05)', '(2025-08,2025-02,2025-05)', '(2025-08,2025-03,2025-05)', '(2025-08,2025-04,2025-05)', '(2025-08,2025-01,2025-06)', '(2025-08,2025-02,2025-06)', '(2025-08,2025-03,2025-06)', '(2025-08,2025-04,2025-06)', '(2025-08,2025-05,2025-06)', '(2025-08,2025-01,2025-07)', '(2025-08,2025-02,2025-07)', '(2025-08,2025-03,2025-07)', '(2025-08,2025-04,2025-07)', '(2025-08,2025-05,2025-07)', '(2025-08,2025-06,2025-07)', '(2025-08,2025-01,2025-08)', '(2025-08,2025-02,2025-08)', '(2025-08,2025-03,2025-08)', '(2025-08,2025-04,2025-08)', '(2025-08,2025-05,2025-08)', '(2025-08,2025-06,2025-08)', '(2025-08,2025-07,2025-08)']\n", - "Control group: not_yet_treated\n", + "Control group: never_treated\n", "Anticipation periods: 0\n", + "\n", "------------------ Machine learner ------------------\n", "Learner ml_g: LGBMRegressor(learning_rate=0.01, n_estimators=500, verbose=-1)\n", "Learner ml_m: LGBMClassifier(learning_rate=0.01, n_estimators=500, verbose=-1)\n", "Out-of-sample Performance:\n", "Regression:\n", - "Learner ml_g0 RMSE: [[1.609668 1.99597102 1.63411853 2.54661976 2.03346623 1.59265116\n", - " 3.12650957 2.53697762 2.01848505 1.62289559 4.05305673 3.35262575\n", - " 2.73162542 2.14632763 5.15884007 4.39779591 3.65373048 2.90704249\n", - " 7.4357806 6.62484945 5.55458715 4.35032789 1.61832439 1.99323435\n", - " 1.64583311 2.5598477 2.07627822 1.57134888 3.35725724 2.72892217\n", - " 2.08132444 1.66185967 3.99756378 3.41594903 2.63822987 2.11889533\n", - " 1.66648225 5.25672263 4.46587065 3.63099963 2.8985437 2.23629061\n", - " 7.36887343 6.4273402 5.45282121 4.37451962 3.41791305 1.60363828\n", - " 2.0060245 1.62657377 2.54419867 2.01705474 1.56851854 3.35400844\n", - " 2.65458997 2.09673115 1.64634041 4.49801868 3.64788681 2.95389494\n", - " 2.24464719 1.73919302 5.22462445 4.36422018 3.62173921 2.87999451\n", - " 2.22378813 1.69340401 7.41560809 6.46312581 5.45969844 4.32684937\n", - " 3.36702316 2.53696583 1.60614653 1.97805736 1.64661148 2.54368814\n", - " 2.03793656 1.60057816 3.26848009 2.75515845 2.06316807 1.62462338\n", - " 4.47204587 3.70295229 2.89866228 2.22943063 1.69782966 6.35609013\n", - " 5.55514607 4.39942654 3.48046871 2.54504767 1.81698347 7.20825701\n", - " 6.5593845 5.42627864 4.31498061 3.42491263 2.53160508 1.81842156]]\n", - "Learner ml_g1 RMSE: [[1.81288678 2.50533598 1.88159685 3.30286165 2.45696805 1.70497671\n", - " 4.35252669 3.37132049 2.495426 1.82445549 5.15379337 4.34889006\n", - " 3.27014526 2.57262796 6.0629488 5.31999594 4.24298007 3.36008234\n", - " 7.08523464 6.20141244 5.23866179 4.37865084 1.78629445 2.5854703\n", - " 1.81864771 3.54614192 2.51462151 1.79676419 4.3403931 3.50275958\n", - " 2.56290654 1.81074244 5.50911722 4.44903432 3.52988972 2.533784\n", - " 1.8711304 6.48232283 5.49426252 4.46626223 3.50089991 2.68858051\n", - " 7.54019252 6.39410794 5.26572233 4.34339355 3.45351793 1.8688584\n", - " 2.58241336 1.88806243 3.30807795 2.54589767 1.80404723 4.18629823\n", - " 3.39722254 2.50559423 1.81209914 5.17734128 4.36608125 3.20981576\n", - " 2.53335651 1.76864545 6.04700315 5.27865612 4.30367795 3.49254229\n", - " 2.55906141 1.90861015 7.11673533 6.29357412 5.21164956 4.32100124\n", - " 3.31938069 2.61618638 1.76312786 2.48227172 1.79471417 3.40456044\n", - " 2.55866819 1.74797113 4.17166324 3.38599016 2.54281496 1.78753217\n", - " 5.16469675 4.28111717 3.35952848 2.48633968 1.85114229 6.35455489\n", - " 5.14677354 4.20771726 3.36921563 2.45633992 1.753428 7.34079369\n", - " 6.42017696 5.29920751 4.245377 3.41378336 2.48134163 1.86862072]]\n", + "Learner ml_g0 RMSE: [[1.79152793 1.81326944 1.8054232 1.80694168 2.47094152 3.37239486\n", + " 4.3303301 1.80638932 1.78492247 1.79906886 1.77017951 1.79014112\n", + " 2.5257241 3.38825777 1.79112596 1.7628981 1.80707893 1.82459002\n", + " 1.7949473 1.80655601 2.52190091 1.80338096 1.79623925 1.78961074\n", + " 1.76971177 1.80947666 1.78232135 1.77580097]]\n", + "Learner ml_g1 RMSE: [[1.81001117 1.74736062 1.73951704 1.76736419 2.45595687 3.22069965\n", + " 4.06967562 1.82961382 1.82863951 1.77281948 1.75404664 1.83431622\n", + " 2.61593344 3.37869708 1.78179777 1.8436273 1.85393631 1.78308466\n", + " 1.90635713 1.86073807 2.53288771 1.80448992 1.79240671 1.80233959\n", + " 1.9042676 1.79439788 1.81557319 1.817387 ]]\n", "Classification:\n", - "Learner ml_m Log Loss: [[0.50299058 0.50589822 0.5074863 0.50283078 0.50344871 0.50195721\n", - " 0.50361272 0.50576588 0.50419881 0.50635454 0.55981259 0.55639577\n", - " 0.55903573 0.56053003 0.61718421 0.61966507 0.62243193 0.61563976\n", - " 0.66565918 0.67338093 0.66364921 0.6713965 0.51564356 0.51782303\n", - " 0.51739953 0.51457947 0.51597003 0.5137619 0.57750561 0.57769253\n", - " 0.57795935 0.58085086 0.57717929 0.5824299 0.57966301 0.5782299\n", - " 0.57578142 0.64987443 0.64629056 0.654599 0.65503288 0.65009761\n", - " 0.71875509 0.71159607 0.72555221 0.71525088 0.7136845 0.51452789\n", - " 0.51975511 0.51586602 0.51152442 0.51341068 0.51109543 0.58157848\n", - " 0.58013024 0.58026799 0.57929193 0.65376688 0.65811886 0.65714205\n", - " 0.65497301 0.65314306 0.65694096 0.65226732 0.64681352 0.65633847\n", - " 0.65318649 0.65108903 0.72342404 0.71769281 0.71049815 0.72089877\n", - " 0.71806737 0.71671219 0.51433584 0.51091421 0.50726156 0.51191457\n", - " 0.50944754 0.51292994 0.58545535 0.57646218 0.58922204 0.58066676\n", - " 0.66895664 0.66948458 0.67051678 0.66769599 0.6676877 0.73854274\n", - " 0.73065792 0.73022571 0.73071207 0.73463666 0.73296758 0.73034902\n", - " 0.73467922 0.73399954 0.72617787 0.74278312 0.72948124 0.72895827]]\n", + "Learner ml_m Log Loss: [[0.67557856 0.67288349 0.6648673 0.67501146 0.66570519 0.67115224\n", + " 0.68066941 0.69235836 0.70052703 0.69100833 0.69927306 0.69189097\n", + " 0.71101967 0.7033916 0.71521098 0.70924631 0.72316055 0.71177035\n", + " 0.71347509 0.71951124 0.72730388 0.75837297 0.74186801 0.74919736\n", + " 0.74636338 0.75038344 0.74348438 0.74928883]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 5\n", @@ -506,38 +467,73 @@ "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| \\\n", - "ATT(2025-05,2025-01,2025-02) 0.020087 0.059370 0.338342 7.351056e-01 \n", - "ATT(2025-05,2025-01,2025-03) -0.117237 0.079539 -1.473951 1.404949e-01 \n", - "ATT(2025-05,2025-02,2025-03) -0.156237 0.063185 -2.472699 1.340971e-02 \n", - "ATT(2025-05,2025-01,2025-04) -0.037044 0.093136 -0.397736 6.908249e-01 \n", - "ATT(2025-05,2025-02,2025-04) -0.019166 0.077393 -0.247641 8.044123e-01 \n", - "... ... ... ... ... \n", - "ATT(2025-08,2025-03,2025-08) 1.123490 0.334177 3.361964 7.739031e-04 \n", - "ATT(2025-08,2025-04,2025-08) 1.480931 0.254240 5.824941 5.713272e-09 \n", - "ATT(2025-08,2025-05,2025-08) 1.444773 0.190461 7.585682 3.308465e-14 \n", - "ATT(2025-08,2025-06,2025-08) 1.205753 0.139658 8.633627 0.000000e+00 \n", - "ATT(2025-08,2025-07,2025-08) 1.127595 0.095938 11.753415 0.000000e+00 \n", + "ATT(2025-05,2025-01,2025-02) -0.126335 0.125715 -1.004936 3.149277e-01 \n", + "ATT(2025-05,2025-02,2025-03) 0.081016 0.128130 0.632297 5.271925e-01 \n", + "ATT(2025-05,2025-03,2025-04) -0.094349 0.117890 -0.800311 4.235309e-01 \n", + "ATT(2025-05,2025-04,2025-05) 1.069615 0.129334 8.270197 2.220446e-16 \n", + "ATT(2025-05,2025-04,2025-06) 1.891446 0.181301 10.432646 0.000000e+00 \n", + "ATT(2025-05,2025-04,2025-07) 2.796395 0.258786 10.805820 0.000000e+00 \n", + "ATT(2025-05,2025-04,2025-08) 3.990323 0.330378 12.078046 0.000000e+00 \n", + "ATT(2025-06,2025-01,2025-02) -0.093047 0.107462 -0.865858 3.865678e-01 \n", + "ATT(2025-06,2025-02,2025-03) 0.080593 0.111061 0.725660 4.680474e-01 \n", + "ATT(2025-06,2025-03,2025-04) -0.131845 0.111625 -1.181149 2.375436e-01 \n", + "ATT(2025-06,2025-04,2025-05) 0.123388 0.122133 1.010270 3.123662e-01 \n", + "ATT(2025-06,2025-05,2025-06) 0.857444 0.113622 7.546480 4.463097e-14 \n", + "ATT(2025-06,2025-05,2025-07) 1.782152 0.185136 9.626188 0.000000e+00 \n", + "ATT(2025-06,2025-05,2025-08) 3.039536 0.293384 10.360281 0.000000e+00 \n", + "ATT(2025-07,2025-01,2025-02) 0.037523 0.096977 0.386923 6.988132e-01 \n", + "ATT(2025-07,2025-02,2025-03) -0.092156 0.100885 -0.913475 3.609928e-01 \n", + "ATT(2025-07,2025-03,2025-04) -0.013253 0.109006 -0.121584 9.032285e-01 \n", + "ATT(2025-07,2025-04,2025-05) 0.163007 0.102667 1.587726 1.123482e-01 \n", + "ATT(2025-07,2025-05,2025-06) -0.203583 0.098786 -2.060840 3.931834e-02 \n", + "ATT(2025-07,2025-06,2025-07) 0.948008 0.100933 9.392434 0.000000e+00 \n", + "ATT(2025-07,2025-06,2025-08) 1.980269 0.148701 13.317112 0.000000e+00 \n", + "ATT(2025-08,2025-01,2025-02) -0.030199 0.111913 -0.269845 7.872795e-01 \n", + "ATT(2025-08,2025-02,2025-03) -0.143455 0.093717 -1.530726 1.258372e-01 \n", + "ATT(2025-08,2025-03,2025-04) -0.027104 0.089255 -0.303666 7.613826e-01 \n", + "ATT(2025-08,2025-04,2025-05) -0.015802 0.098179 -0.160949 8.721339e-01 \n", + "ATT(2025-08,2025-05,2025-06) -0.047013 0.097550 -0.481934 6.298529e-01 \n", + "ATT(2025-08,2025-06,2025-07) -0.079947 0.098730 -0.809756 4.180807e-01 \n", + "ATT(2025-08,2025-07,2025-08) 0.853624 0.093793 9.101148 0.000000e+00 \n", "\n", " 2.5 % 97.5 % \n", - "ATT(2025-05,2025-01,2025-02) -0.096276 0.136451 \n", - "ATT(2025-05,2025-01,2025-03) -0.273131 0.038657 \n", - "ATT(2025-05,2025-02,2025-03) -0.280077 -0.032397 \n", - "ATT(2025-05,2025-01,2025-04) -0.219587 0.145500 \n", - "ATT(2025-05,2025-02,2025-04) -0.170852 0.132521 \n", - "... ... ... \n", - "ATT(2025-08,2025-03,2025-08) 0.468516 1.778465 \n", - "ATT(2025-08,2025-04,2025-08) 0.982630 1.979231 \n", - "ATT(2025-08,2025-05,2025-08) 1.071477 1.818069 \n", - "ATT(2025-08,2025-06,2025-08) 0.932029 1.479478 \n", - "ATT(2025-08,2025-07,2025-08) 0.939561 1.315630 \n", - "\n", - "[102 rows x 6 columns]\n" + "ATT(2025-05,2025-01,2025-02) -0.372731 0.120061 \n", + "ATT(2025-05,2025-02,2025-03) -0.170114 0.332147 \n", + "ATT(2025-05,2025-03,2025-04) -0.325410 0.136712 \n", + "ATT(2025-05,2025-04,2025-05) 0.816126 1.323105 \n", + "ATT(2025-05,2025-04,2025-06) 1.536103 2.246789 \n", + "ATT(2025-05,2025-04,2025-07) 2.289184 3.303607 \n", + "ATT(2025-05,2025-04,2025-08) 3.342794 4.637853 \n", + "ATT(2025-06,2025-01,2025-02) -0.303670 0.117575 \n", + "ATT(2025-06,2025-02,2025-03) -0.137083 0.298268 \n", + "ATT(2025-06,2025-03,2025-04) -0.350625 0.086935 \n", + "ATT(2025-06,2025-04,2025-05) -0.115989 0.362764 \n", + "ATT(2025-06,2025-05,2025-06) 0.634750 1.080138 \n", + "ATT(2025-06,2025-05,2025-07) 1.419293 2.145012 \n", + "ATT(2025-06,2025-05,2025-08) 2.464515 3.614557 \n", + "ATT(2025-07,2025-01,2025-02) -0.152549 0.227594 \n", + "ATT(2025-07,2025-02,2025-03) -0.289886 0.105575 \n", + "ATT(2025-07,2025-03,2025-04) -0.226901 0.200395 \n", + "ATT(2025-07,2025-04,2025-05) -0.038216 0.364230 \n", + "ATT(2025-07,2025-05,2025-06) -0.397200 -0.009965 \n", + "ATT(2025-07,2025-06,2025-07) 0.750183 1.145834 \n", + "ATT(2025-07,2025-06,2025-08) 1.688820 2.271718 \n", + "ATT(2025-08,2025-01,2025-02) -0.249545 0.189146 \n", + "ATT(2025-08,2025-02,2025-03) -0.327137 0.040227 \n", + "ATT(2025-08,2025-03,2025-04) -0.202041 0.147834 \n", + "ATT(2025-08,2025-04,2025-05) -0.208229 0.176626 \n", + "ATT(2025-08,2025-05,2025-06) -0.238207 0.144182 \n", + "ATT(2025-08,2025-06,2025-07) -0.273455 0.113560 \n", + "ATT(2025-08,2025-07,2025-08) 0.669793 1.037455 \n" ] } ], "source": [ - "control_group = \"not_yet_treated\"\n", - "# control_group = \"never_treated\"\n", + "# control_group = \"not_yet_treated\"\n", + "control_group = \"never_treated\"\n", + "\n", + "gt_combinations = \"all\"\n", + "gt_combinations = \"standard\"\n", "\n", "ml_g = LGBMRegressor(n_estimators=500, learning_rate=0.01, verbose=-1)\n", "ml_m = LGBMClassifier(n_estimators=500, learning_rate=0.01, verbose=-1)\n", @@ -546,9 +542,8 @@ " obj_dml_data=dml_data,\n", " ml_g=ml_g,\n", " ml_m=ml_m,\n", - " gt_combinations=\"all\",\n", + " gt_combinations=gt_combinations,\n", " control_group=control_group,\n", - " trimming_threshold=0.05,\n", ")\n", "\n", "dml_obj.fit()\n", @@ -557,16 +552,16 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 62, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -578,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -618,55 +613,55 @@ " 2025-05-01\n", " 2025-01-01\n", " 2025-02-01\n", - " 0.020087\n", - " -0.179384\n", - " 0.219559\n", + " -0.126335\n", + " -0.511271\n", + " 0.258601\n", " True\n", - " 0.004923\n", + " 0.040722\n", " \n", " \n", - " ATT(2025-05,2025-01,2025-03)\n", + " ATT(2025-05,2025-02,2025-03)\n", " 2025-05-01\n", - " 2025-01-01\n", + " 2025-02-01\n", " 2025-03-01\n", - " -0.117237\n", - " -0.384471\n", - " 0.149998\n", + " 0.081016\n", + " -0.311316\n", + " 0.473349\n", " True\n", - " 0.022780\n", + " 0.023928\n", " \n", " \n", - " ATT(2025-05,2025-02,2025-03)\n", + " ATT(2025-05,2025-03,2025-04)\n", " 2025-05-01\n", - " 2025-02-01\n", " 2025-03-01\n", - " -0.156237\n", - " -0.368525\n", - " 0.056051\n", + " 2025-04-01\n", + " -0.094349\n", + " -0.455328\n", + " 0.266630\n", " True\n", - " 0.037449\n", + " 0.025671\n", " \n", " \n", - " ATT(2025-05,2025-01,2025-04)\n", + " ATT(2025-05,2025-04,2025-05)\n", " 2025-05-01\n", - " 2025-01-01\n", " 2025-04-01\n", - " -0.037044\n", - " -0.349961\n", - " 0.275874\n", - " True\n", - " 0.005514\n", + " 2025-05-01\n", + " 1.069615\n", + " 0.673597\n", + " 1.465633\n", + " False\n", + " 0.246898\n", " \n", " \n", - " ATT(2025-05,2025-02,2025-04)\n", + " ATT(2025-05,2025-04,2025-06)\n", " 2025-05-01\n", - " 2025-02-01\n", " 2025-04-01\n", - " -0.019166\n", - " -0.279189\n", - " 0.240858\n", - " True\n", - " 0.003669\n", + " 2025-06-01\n", + " 1.891446\n", + " 1.336306\n", + " 2.446586\n", + " False\n", + " 0.295226\n", " \n", " \n", "\n", @@ -675,27 +670,27 @@ "text/plain": [ " First Treated Pre-treatment Period \\\n", "ATT(2025-05,2025-01,2025-02) 2025-05-01 2025-01-01 \n", - "ATT(2025-05,2025-01,2025-03) 2025-05-01 2025-01-01 \n", "ATT(2025-05,2025-02,2025-03) 2025-05-01 2025-02-01 \n", - "ATT(2025-05,2025-01,2025-04) 2025-05-01 2025-01-01 \n", - "ATT(2025-05,2025-02,2025-04) 2025-05-01 2025-02-01 \n", + "ATT(2025-05,2025-03,2025-04) 2025-05-01 2025-03-01 \n", + "ATT(2025-05,2025-04,2025-05) 2025-05-01 2025-04-01 \n", + "ATT(2025-05,2025-04,2025-06) 2025-05-01 2025-04-01 \n", "\n", " Evaluation Period Estimate CI Lower CI Upper \\\n", - "ATT(2025-05,2025-01,2025-02) 2025-02-01 0.020087 -0.179384 0.219559 \n", - "ATT(2025-05,2025-01,2025-03) 2025-03-01 -0.117237 -0.384471 0.149998 \n", - "ATT(2025-05,2025-02,2025-03) 2025-03-01 -0.156237 -0.368525 0.056051 \n", - "ATT(2025-05,2025-01,2025-04) 2025-04-01 -0.037044 -0.349961 0.275874 \n", - "ATT(2025-05,2025-02,2025-04) 2025-04-01 -0.019166 -0.279189 0.240858 \n", + "ATT(2025-05,2025-01,2025-02) 2025-02-01 -0.126335 -0.511271 0.258601 \n", + "ATT(2025-05,2025-02,2025-03) 2025-03-01 0.081016 -0.311316 0.473349 \n", + "ATT(2025-05,2025-03,2025-04) 2025-04-01 -0.094349 -0.455328 0.266630 \n", + "ATT(2025-05,2025-04,2025-05) 2025-05-01 1.069615 0.673597 1.465633 \n", + "ATT(2025-05,2025-04,2025-06) 2025-06-01 1.891446 1.336306 2.446586 \n", "\n", " Pre-Treatment RV \n", - "ATT(2025-05,2025-01,2025-02) True 0.004923 \n", - "ATT(2025-05,2025-01,2025-03) True 0.022780 \n", - "ATT(2025-05,2025-02,2025-03) True 0.037449 \n", - "ATT(2025-05,2025-01,2025-04) True 0.005514 \n", - "ATT(2025-05,2025-02,2025-04) True 0.003669 " + "ATT(2025-05,2025-01,2025-02) True 0.040722 \n", + "ATT(2025-05,2025-02,2025-03) True 0.023928 \n", + "ATT(2025-05,2025-03,2025-04) True 0.025671 \n", + "ATT(2025-05,2025-04,2025-05) False 0.246898 \n", + "ATT(2025-05,2025-04,2025-06) False 0.295226 " ] }, - "execution_count": 64, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -742,12 +737,20 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 9, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/matplotlib/cbook.py:1709: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] @@ -757,130 +760,63 @@ } ], "source": [ - "def plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8)):\n", - " \"\"\"\n", - " Plot coefficient estimates with CIs over time, grouped by first treated period.\n", - " \"\"\"\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from matplotlib.lines import Line2D\n", "\n", - " df = create_ci_dataframe(dml_obj, level=level, joint=joint)\n", - " all_time_periods = sorted(df['Evaluation Period'].unique())\n", - " first_treated_periods = sorted(df['First Treated'].unique())\n", - " n_periods = len(first_treated_periods)\n", - " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", - " \n", - " # Adjust figure size to accommodate bottom legend\n", - " fig = plt.figure(figsize=figsize)\n", - " # Create subplot grid with space for legend at bottom\n", - " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", - " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", "\n", - " if n_periods == 1:\n", - " axes = [axes]\n", - " \n", - " # Create a list to store legend handles and labels\n", - " legend_elements = []\n", + "def add_jitter(data, is_datetime, jitter_value):\n", + " \"\"\"\n", + " Adds jitter to duplicate x-values for better visibility.\n", " \n", - " for idx, period in enumerate(first_treated_periods):\n", - " period_data = df[df['First Treated'] == period]\n", - " ax = axes[idx]\n", - "\n", - " i_period = all_time_periods.index(period)\n", - "\n", - " # Add treatment start line\n", - " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", - " linestyle=':', alpha=0.7)\n", - " if idx == 0:\n", - " legend_elements.append((line, 'Treatment start'))\n", + " Args:\n", + " data (DataFrame): The subset of the dataset to jitter.\n", + " is_datetime (bool): Whether the x-values are datetime objects.\n", + " jitter_value (float or timedelta): Jitter amount.\n", "\n", - " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", - " if idx == 0:\n", - " legend_elements.append((zero_line, 'Zero effect'))\n", - "\n", - " # Split data by treatment status\n", - " pre_treatment = period_data[period_data['Pre-Treatment']]\n", - " post_treatment = period_data[~period_data['Pre-Treatment']]\n", - " \n", - " if not pre_treatment.empty:\n", - " # Pre-treatment points\n", - " scatter_pre = ax.scatter(pre_treatment['Evaluation Period'], \n", - " pre_treatment['Estimate'], \n", - " color=colors['pre'], alpha=0.8, s=10)\n", - " # Regular CIs\n", - " error_pre = ax.errorbar(pre_treatment['Evaluation Period'], \n", - " pre_treatment['Estimate'],\n", - " yerr=[pre_treatment['Estimate'] - pre_treatment['CI Lower'],\n", - " pre_treatment['CI Upper'] - pre_treatment['Estimate']],\n", - " fmt='none', color=colors['pre'], alpha=1.0, \n", - " capsize=5)\n", - " if idx == 0:\n", - " legend_elements.extend([\n", - " (scatter_pre, 'Pre-treatment'),\n", - " (error_pre, f'{int(level*100)}% CI'),\n", - " ])\n", - " \n", - " # Similar structure for post-treatment\n", - " if not post_treatment.empty:\n", - " scatter_post = ax.scatter(post_treatment['Evaluation Period'], \n", - " post_treatment['Estimate'], \n", - " color=colors['post'], alpha=0.8, s=10)\n", - " if idx == 0:\n", - " legend_elements.append((scatter_post, 'Post-treatment'))\n", - " \n", - " ax.errorbar(post_treatment['Evaluation Period'], post_treatment['Estimate'],\n", - " yerr=[post_treatment['Estimate'] - post_treatment['CI Lower'],\n", - " post_treatment['CI Upper'] - post_treatment['Estimate']],\n", - " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", - "\n", - " ax.set_title(f'First Treated: {period}')\n", - " ax.grid(True, alpha=0.3)\n", - " \n", - " if idx == 0:\n", - " ax.set_ylabel('Effect')\n", - " ax.set_xlabel('Evaluation Period')\n", - " \n", - " # Create legend in a separate subplot at the bottom\n", - " legend_ax = fig.add_subplot(gs[-1])\n", - " legend_ax.axis('off') # Hide axes for legend subplot\n", - " \n", - " # Add legend using collected handles and labels\n", - " legend = legend_ax.legend(*zip(*legend_elements), \n", - " loc='center',\n", - " ncol=5, # Adjust number of columns as needed\n", - " mode='expand',\n", - " borderaxespad=0.)\n", + " Returns:\n", + " DataFrame with an additional 'jittered_x' column.\n", + " \"\"\"\n", + " if data.empty:\n", + " return data\n", + " \n", + " data = data.copy()\n", + " # Initialize jittered_x with original values\n", + " data['jittered_x'] = data['Evaluation Period']\n", + " \n", + " for x_val in data['Evaluation Period'].unique():\n", + " mask = data['Evaluation Period'] == x_val\n", + " count = mask.sum()\n", + " if count > 1:\n", + " # Create evenly spaced jitter values\n", + " if is_datetime:\n", + " jitters = [pd.Timedelta(seconds=float(j)) \n", + " for j in np.linspace(-jitter_value, jitter_value, count)]\n", + " else:\n", + " jitters = np.linspace(-jitter_value, jitter_value, count)\n", + " \n", + " # Apply jitter to each duplicate point\n", + " data.loc[mask, 'jitter_index'] = range(count)\n", + " for i, j in enumerate(jitters):\n", + " data.loc[mask & (data['jitter_index'] == i), 'jittered_x'] = x_val + j\n", " \n", - " plt.suptitle(\"Estimated ATTs by Group\", y=1.02)\n", - " plt.tight_layout()\n", - " plt.show()\n", + " return data\n", "\n", - "plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8))" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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l+gw7IiKiB1B0dLQAYPYVHR0thBBi06ZN4tFHHxWBgYFCo9GI6tWri9GjR4tbt24pjrdmzRpRq1Yt4eTkJACI2NhYIYTxY+ILHj2/ceNGk/EcPHhQUT5nzhwBQCQkJMhl3333nWjWrJlwdXUVNWrUEAsXLhSffPKJACAuXbok14uLixM9e/YUnp6eAoAijrS0NDF9+nRRp04dodFohL+/v4iIiBCLFy8Wer1erpeYmCiefvpp4eXlJby9vcXTTz8tjhw5orhGpkyYMEEAEBcuXDBbZ+7cuQKAOHbsmFy2ceNGxfUz5956Fy9eFM8++6yoXbu2cHV1FX5+fqJTp05i165dRR5HiPzvqHHjxuLQoUMiPDxcuLq6itDQUPHBBx+Y3adHjx4CgPj999+LPX5hWVlZYunSpSI8PFx4eXkJZ2dnERQUJB5//HERExMjDAaDXNdcXynw1VdfiZYtWwqtViv8/PzEsGHDxPXr143qbdiwQdSqVUtoNBrRokULsWPHDjFixAgRGhoq17l06ZIAIBYtWiTeffddERISIrRarejQoYPi+yEiIiL7phLCwjHRRERERFTunnjiCfz99984f/58eYdSKi5fvoyaNWti0aJFmDp1anmHQ0RERCXENZiIiIiIHMStW7fw448/4umnny7vUIiIiIgUuAYTERERkZ27dOkSfvvtN6xduxYuLi4YPXp0eYdEREREpMARTERERER27pdffsHTTz+NS5cu4dNPP0VQUFB5h0RERESkwDWYiIiIiIiIiIjIJhzBRERERERERERENmGCiYiIiIiIiIiIbMIEExERERERERER2YQJJiIiIiIiIiIisgkTTEREREREREREZBMmmIiIiIiIiIiIyCZMMBERERERERERkU2YYCIiIiIiIiIiIpswwURERERERERERDZhgomIiIiIiIiIiGzCBBMREREREREREdmECSYiIiIiIiIiIrIJE0xERERERERERGQTJpiIiIiIiIiIiMgmTDAREREREREREZFNmGAiIiIiIiIiIiKbMMFEREREREREREQ2YYKJiIiIiIiIiIhswgQTERERERERERHZhAkmIiIiIiIiIiKyCRNMRERERERERERkEyaYiIiIiIiIiIjIJkwwERERERERERGRTZhgIiIiIiIiIiIimzDBRERERERERERENmGCiYiIiIiIiIiIbMIEExERERERERER2YQJJiIiIiIiIiIisgkTTEREREREREREZBMmmIiIiIiIiIiIyCZMMBERERERERERkU2YYCIiIiIiIiIiIpswwURERERERERERDZhgomIiIiIiIiIiGzCBBMREREREREREdmECSYiIiIiIiIiIrIJE0xERERERERERGQTJpiIiIiIiIiIiMgmTDAREREREREREZFNmGAiIiIiIiIiIiKbMMFEREREREREREQ2YYKJiIiIiIiIiIhswgQTERERERERERHZhAkmIiIiIiIiIiKyCRNMRERERERERERkEyaYiIiIysDly5ehUqmwbt268g6lQqtRowaioqLKOwwiIiKiBx4TTERERCWwbt06qFQqk69XX321TNqcP38+tmzZUmy9yMhIs7EVfs2dO7dM4rzXtm3b7ltb1jh9+jSmTZuGFi1awNPTE8HBwejZsycOHTpksv6NGzcwaNAg+Pj4wMvLC3369MHFixcVda5du4Z58+ahbdu28PX1hb+/PyIjI7Fr1y6j4xXVh+Li4iw+j48//hgNGzaEq6sr6tati+XLlxvVmTt3rsl2XF1dLW5HkiS88847qFmzJlxdXdGsWTN88cUXRvX+/PNPjBs3Dq1atYKLiwtUKpXFbRT4/fff0b59e7i5uSEoKAgTJ05Eenq6Ub2cnBy88sorqFKlCnQ6HcLCwrBz506r2rLk+gGWff9FsSZWS8+fiIjInjiXdwBERESO7PXXX0fNmjUVZU2aNEFoaCiysrLg4uJSam3Nnz8fAwYMQN++fYusN3PmTDz//PPy54MHD+L999/HjBkz0LBhQ7m8WbNmpRZbUbZt24YVK1bYXZJp7dq1+Pjjj9G/f3+MGzcOKSkpWL16Nf73v//hp59+QpcuXeS66enp6NSpE1JSUjBjxgy4uLjgvffeQ8eOHXH06FFUqlQJALB161YsXLgQffv2xYgRI2AwGPDZZ5+ha9eu+OSTT/DMM88YxWGqD/n4+Fh0DqtXr8aYMWPQv39/TJkyBXv37sXEiRORmZmJV155xaj+ypUr4eHhIX92cnKyqB0gv1+9/fbbGDlyJNq0aYOtW7di6NChUKlUGDx4sFxv27ZtWLt2LZo1a4ZatWrh7NmzFrcBAEePHkXnzp3RsGFDLFmyBNevX8fixYtx7tw5bN++XVE3KioKmzZtwuTJk1G3bl2sW7cOPXr0QGxsLNq3b19sW5ZeP0u//6JYGqs1509ERGRXBBEREVktOjpaABAHDx606Tjp6ekW13V3dxcjRoywuo2NGzcKACI2NrbUYrHG+PHjRVn9yhEaGlqiayKEEIcOHRJpaWmKsjt37oiAgADRrl07RfnChQsFAPHnn3/KZadOnRJOTk5i+vTpctmJEydEQkKCYt/s7GzRoEEDUa1aNUW5rX0oMzNTVKpUSfTs2VNRPmzYMOHu7i6SkpLksjlz5ggARrFZ6vr168LFxUWMHz9eLpMkSXTo0EFUq1ZNGAwGuTwuLk5kZmYKIUr23Xfv3l0EBweLlJQUuWzNmjUCgNixY4dcduDAAQFALFq0SC7LysoStWvXFuHh4cW2Y831s/T7N8eaWC09fyIiInvDKXJERERlwNQaTFFRUfDw8MCFCxfQo0cPeHp6YtiwYQCAc+fOoX///ggKCoKrqyuqVauGwYMHIyUlBQCgUqmQkZGBTz/9VJ7eZMvaQwVTpk6ePImhQ4fC19dXMYpiw4YNaNWqFXQ6Hfz8/DB48GBcu3ZNcYy9e/di4MCBqF69OrRaLUJCQvDiiy8iKytLcc4rVqyQz6HgVUCSJCxduhSNGzeGq6srKleujNGjR+Pu3buKtoQQePPNN1GtWjW4ubmhU6dO+Oeff0ye24ULF3DhwoVir0GrVq0Uo3kAoFKlSujQoQNOnTqlKN+0aRPatGmDNm3ayGUNGjRA586d8fXXX8tljRs3hr+/v2JfrVaLHj164Pr160hLSzMZS1paGvLy8oqNubDY2FgkJiZi3LhxivLx48cjIyMDP/74o9E+QgikpqZCCGFVW1u3bkVubq6iLZVKhbFjx+L69evYv3+/XF65cmXodDqrjl8gNTUVO3fuxFNPPQUvLy+5fPjw4fDw8FBc602bNsHJyQmjRo2Sy1xdXfHcc89h//79iv56584dnD59GpmZmXKZNdfP0u8fAK5evYrTp08ryiyN1ZrzJyIisjdMMBEREdkgJSUFd+7cUbyKYjAY0K1bNwQGBmLx4sXo378/9Ho9unXrhj/++AMTJkzAihUrMGrUKFy8eBHJyckAgPXr10Or1aJDhw5Yv3491q9fj9GjR9sc/8CBA5GZmYn58+dj5MiRAIC33noLw4cPR926dbFkyRJMnjwZu3fvxsMPPyzHAwAbN25EZmYmxo4di+XLl6Nbt25Yvnw5hg8fLtcZPXo0unbtKp9Dwavw9pdffhnt2rXDsmXL8MwzzyAmJgbdunVDbm6uXG/27Nl47bXX0Lx5cyxatAi1atXCo48+ioyMDKNz6ty5Mzp37lziaxIXF6dIEkmShOPHj6N169ZGddu2bYsLFy6YTRwVPqabmxvc3NyMtnXq1AleXl5wc3ND7969ce7cOYviPHLkCAAYxdWqVSuo1Wp5e2G1atWCt7c3PD098dRTT+H27dsWt+Xu7q6YYgnkn3/hWGz1999/w2AwGJ2TRqNBixYtFO0cOXIE9erVUyRiCsd09OhRueyDDz5Aw4YN8eeffyr2B4q/ftZ+/8OHDze6TpbGas35ExER2RuuwURERGSDwuv0FChqdEhOTg4GDhyIBQsWyGVHjx7FpUuXsHHjRgwYMEAunz17tvz+qaeewpgxY1CrVi089dRTpRQ90Lx5c3z++efy5ytXrmDOnDl48803MWPGDLm8X79+aNmyJT788EO5fOHChYqRKqNGjUKdOnUwY8YMXL16FdWrV0d4eDjq1asnj8oobN++fVi7di1iYmIwdOhQubxTp0547LHHsHHjRgwdOhQJCQl455130LNnT3z//ffyCKiZM2di/vz5pXYtgPxRWfv378esWbPksqSkJOTk5CA4ONiofkHZzZs3Ub9+fZPHPH/+PDZv3oyBAwcq1jxyc3NDVFSUnGA6fPgwlixZgoiICPz1118ICQkpMtZbt27ByckJgYGBinKNRoNKlSrh5s2bcpmvry9eeOEFhIeHQ6vVYu/evVixYgX+/PNPHDp0yCjxYaqtypUrGy3YXfj8S8OtW7cUx723rb179yrqFvedFNeWJdfP1u/fmlitOX8iIiJ7wwQTERGRDVasWIF69epZtc/YsWMVn729vQEAO3bsQI8ePUyOcikrY8aMUXzevHkzJEnCoEGDFKOxgoKCULduXcTGxsoJpsLJpYyMDGRlZSEiIgJCCBw5cgTVq1cvsu2NGzfC29sbXbt2VbRVMHUtNjYWQ4cOxa5du6DX6zFhwgRFgmPy5MkmE0yXL1+26hoUiI+Px9ChQ1GzZk1MmzZNLi+Y8qfVao32KXgKW+FpgYVlZmZi4MCB0Ol0ePvttxXbBg0ahEGDBsmf+/bti27duuHhhx/GW2+9hVWrVhUZb1ZWFjQajcltrq6uipgmTZqk2N6/f3+0bdsWw4YNw4cffljskw+zsrJKdP7WKu5aF27Hmpjmzp1rtMi8pdfP2u9/z549Js/Lkv2tOX8iIiJ7wwQTERGRDdq2bWty6ow5zs7OqFatmqKsZs2amDJlCpYsWYKYmBh06NABvXv3xlNPPSUnn8rKvU8vO3fuHIQQqFu3rsn6hZ+Kd/XqVcyePRvfffed0ZpJBWtHFeXcuXNISUkxGkFSID4+HkD+qCoARjEFBATA19e32HYskZGRgccffxxpaWnYt2+fYm2mgkRaTk6O0X7Z2dmKOoXl5eVh8ODBOHnyJLZv344qVaoUG0f79u0RFhaGXbt2yWUJCQmK9Zk8PDzg4eEBnU4HvV5v8jjZ2dnFroM0dOhQvPTSS9i1a5ecYIqLi1PU8fb2hk6ng06ns/r8S6K4a124HVtjsvT6lfT7v7ctS/a35vyJiIjsDRNMRERE95FWq4VabbwE4rvvvouoqChs3boV//d//4eJEydiwYIF+OOPP4wSUqXp3j9YJUmCSqXC9u3bTT7CviDxkpeXh65duyIpKQmvvPIKGjRoAHd3d9y4cQNRUVGQJKnYtiVJQmBgIGJiYkxuDwgIKMEZWU+v16Nfv344fvw4duzYgSZNmii2+/n5QavVytOXCisoM5U8GjlyJH744QfExMTgkUcesTiekJAQnDlzRv7cpk0bOckGAHPmzMHcuXMRHByMvLw8xMfHK5J0er0eiYmJFiW0QkJCkJSUJH++d2pWdHQ0oqKiEBwcjNjYWAghFKPIijr/kiho39y1LtxOcHAwbty4YbKeJTFZev1K+v3f25YlsVpz/kRERPaGCSYiIiI70bRpUzRt2hSzZs3C77//jnbt2mHVqlV48803AcBo/ZuyULt2bQghULNmzSKn/v399984e/YsPv30U8Wi3jt37jSqay7u2rVrY9euXWjXrl2RIzNCQ0MB5I94qlWrllyekJBgNHLKWpIkYfjw4di9eze+/vprdOzY0aiOWq1G06ZNcejQIaNtBw4cQK1ateDp6akof/nllxEdHY2lS5diyJAhVsV08eJFRXItJiZGMTWq4Bq0aNECAHDo0CH06NFD3n7o0CFIkiRvN0cIgcuXL6Nly5Zy2b3fX+PGjeW21q5di1OnTqFRo0by9gMHDihisVWTJk3g7OyMQ4cOKaYP6vV6HD16VFHWokULxMbGIjU1VbGGlKUxWXr9SvL9m2rLklitOX8iIiJ7w6fIERERlbPU1FQYDAZFWdOmTaFWqxVTZdzd3RVPcSsL/fr1g5OTE+bNm2e0WLkQAomJiQAgj24qXEcIgWXLlhkd093dHQCMYh80aBDy8vLwxhtvGO1jMBjk+l26dIGLiwuWL1+uaG/p0qUmz+HChQu4cOFC0Sf6rwkTJuCrr77Chx9+iH79+pmtN2DAABw8eFCRZDhz5gx+/vlnDBw4UFF30aJFWLx4MWbMmGG09lFhCQkJRmXbtm3D4cOH8dhjj8ll7dq1Q5cuXeRXQYLpkUcegZ+fH1auXKk4xsqVK+Hm5oaePXsW2dbKlSuRkJCgaKtwO126dJFH1PTp0wcuLi748MMP5bpCCKxatQpVq1ZFRESE2fO0hre3N7p06YINGzYonsy2fv16pKenK671gAEDkJeXh48++kguy8nJQXR0NMLCwhSLpN+5cwenT59GZmamXGbN9bPm+7969SpOnz6tKLM0VmvOn4iIyN5wBBMREVE5+/nnn/HCCy9g4MCBqFevHgwGA9avXw8nJyf0799frteqVSvs2rULS5YsQZUqVVCzZk2EhYWVaiy1a9fGm2++ienTp+Py5cvo27cvPD09cenSJXz77bcYNWoUpk6digYNGqB27dqYOnUqbty4AS8vL3zzzTcmRxS1atUKADBx4kR069YNTk5OGDx4MDp27IjRo0djwYIFOHr0KB599FG4uLjg3Llz2LhxI5YtW4YBAwYgICAAU6dOxYIFC/D444+jR48eOHLkCLZv3w5/f3+j9jp37gyg+MW+ly5dig8//BDh4eFwc3PDhg0bFNufeOIJOTk2btw4rFmzBj179sTUqVPh4uKCJUuWoHLlynjppZfkfb799ltMmzYNdevWRcOGDY2O2bVrV1SuXBkAEBERgZYtW6J169bw9vbGX3/9hU8++QQhISGKJ/iZo9Pp8MYbb2D8+PEYOHAgunXrhr1792LDhg1466234OfnJ9cNDQ3Fk08+iaZNm8LV1RX79u3Dl19+iRYtWmD06NHFtlWtWjVMnjwZixYtQm5uLtq0aYMtW7Zg7969iImJUUynvHLlCtavXw8AckKmYBReaGgonn766SLbeuuttxAREYGOHTti1KhRuH79Ot599108+uijimRYWFgYBg4ciOnTpyM+Ph516tTBp59+isuXL+Pjjz9WHPODDz7AvHnzEBsbi8jISKuvn6XfPwAMHz4cv/zyiyIZak2slp4/ERGR3RFERERktejoaAFAHDx40OT2S5cuCQAiOjpaLhsxYoRwd3c3qnvx4kXx7LPPitq1awtXV1fh5+cnOnXqJHbt2qWod/r0afHwww8LnU4nAIgRI0ZYFOvGjRsFABEbGyuXzZkzRwAQCQkJJvf55ptvRPv27YW7u7twd3cXDRo0EOPHjxdnzpyR65w8eVJ06dJFeHh4CH9/fzFy5Ehx7Ngxo/M2GAxiwoQJIiAgQKhUKnHvrx8fffSRaNWqldDpdMLT01M0bdpUTJs2Tdy8eVOuk5eXJ+bNmyeCg4OFTqcTkZGR4sSJEyI0NNToOoSGhorQ0NBir8uIESMEALOvS5cuKepfu3ZNDBgwQHh5eQkPDw/x+OOPi3PnzinqFFxXc6/C38HMmTNFixYthLe3t3BxcRHVq1cXY8eOFXFxccXGfu/1q1+/vtBoNKJ27drivffeE5IkKeo8//zzolGjRsLT01O4uLiIOnXqiFdeeUWkpqZa3E5eXp6YP3++CA0NFRqNRjRu3Fhs2LDBqF5sbKzZ8+/YsaNFbe3du1dEREQIV1dXERAQIMaPH28y1qysLDF16lQRFBQktFqtaNOmjfjpp5+M6hV8L4WvfwFLrp8Qln3/QgjRsWNHoz5uTazWnD8REZE9UQlxz/h3IiIiIiIiIiIiK3ANJiIiIiIiIiIisgkTTEREREREREREZBMmmIiIiIiIiIiIyCZMMBERERERERERkU2YYCIiIiIiIiIiIpswwURERERERERERDZxLu8AbCFJEm7evAlPT0+oVKryDoeIiIiIiIiIqEIRQiAtLQ1VqlSBWm1+nJJDJ5hu3ryJkJCQ8g6DiIiIiIiIiKhCu3btGqpVq2Z2u0MnmDw9PQHkn6SXl1c5R2MbSZKQkJCAgICAIjOCRLZiXyNHwb5K9oj9khwJ+yvZI/ZLciTsr/lSU1MREhIi52DMcegEU8G0OC8vrwqRYMrOzoaXl9cD3XGp7LGvkaNgXyW7YzBA2r4dIiUFXoMGQa3RlHdEREXifZTsEfslORL2V6XiliZy6AQTERER0X1jMEC1ejV0ej3Qrx/ABBMREVGFZci4hdy0m5CSkpADP4sTTE7uQXB2Dy7j6OwTE0xEREREllCrIdq1Q256OjT8V0wiIqIKLe34GiQfeBMAEGfFfj5hs+AbPrtsgrJzTDARERERWUKjAV55BRnx8XDn6CUiIqIKzbPZSLjW7Im7SUnw9csfwSQZshD3dSQAIGjQHqiddUb7ObkH3edI7QcTTEREREREREREhTi7B0Otqww14qENDMxPMOVmyNu1Ac2hdnEvxwjtD8d3ExERERERERGRTZhgIiIiIrJETg5UUVHwnjgRyMkp72iIiIiI7AqnyBERERFZQgggKQkqvT7/PREREdkFQ8Yt5GVYsxR3vgf5iW9lgQkmIiIiIktoNBBLlyItMRGVuMg3ERGR3Sj8xDdreDR5Dl7NRpndLkkSpKQkGNwbQeNZ1ZYQHwhMMBERERFZQq0GatVCnodH/nsiIiKyC57NRsKtdi9FmaknvqUeX4P0E2vlOuknPkb6iY+LPX5625nwi5hTqjFXREwwEREREREREZHDcnYPNprqZuqJb77hr8Gr2UhlPROJKHmbJOFuUhI8QhqVXfAVCBNMRERERJYwGIDYWGiSk4E+fQBOkyMiInIoliai5G2SBDXi4eweeN9idGRMMBERERFZwmCAatkyuOn1QM+eTDARERERFcIEExEREZEl1GqI1q2Rm5EBDddgIiIiIlJggomIiIjIEhoNMHs2MuLj4c7RS0REREQKTDAREREREREREZWAIeMW8jLirN7PyT3IaD0oR8cEExERERERERFRCaQdX4PkA29avZ9P2Cz4hs8ug4jKDxNMRERERJbIyYHqhRfglZUFrFkD6HTF70NEREQVmmezkXCr3UtRJhmyEPd1JAAgaNAeqJ2Nf2dwcg+6H+HdV0wwEREREVlCCODWLaj1+vz3RERE9MBzdg82muom5WbI77UBzaF2cb/fYZULJpiIiIiILKHRQCxciLTERFTiIt9ERERECkwwEREREVlCrQYaNkRefHz+eyIiIiKS8bcjIiIiIiIiIiKyCRNMRERERJbIywP27YPLgQP574mIiIhIxilyRERERJbIzYXqnXfgrtcDXbsCLi7lHRERERGR3WCCiYiIiMgSajXQpAkMGRnQcA0mIiIiIgUmmIiIiIgsodFAzJ+P9Ph4uPEpckREREQK5frPb3PnzoVKpVK8GjRoUJ4hEREREREREREVSZ94srxDsDvlPoKpcePG2LVrl/zZ2bncQyIiIiIiIiIiUkg9tkp+H//jEHg/NBneLV8ox4jsS4lGMDk5OSE+Pt6oPDExEU5OTlYdy9nZGUFBQfLL39+/JCERERERlS29HqpJk+A5axag15d3NERERFTKihqVpL9zAqnHVsqfhRBIPfI+9HdO3I/QHEKJhgsJIUyW5+TkQGPlmgTnzp1DlSpV4OrqivDwcCxYsADVq1c3e/ycnBz5c2pqKgBAkiRIkmRVu/ZGkiQIIRz+PMj+sa+Ro2BfJbtjMACXLsEpJweSwQCwb5Kd432U7BH7Jd0vhfuYJElm/7+dfPS/UUm3fxwGr5YT4d1inLxfQX/Vp16FZPgvH6Fy9YWUmQB96lU4+zUqUduOwtKfV6sSTO+//z4AQKVSYe3atfDw8JC35eXl4ddff7VqDaWwsDCsW7cO9evXx61btzBv3jx06NABJ06cgKenp1H9BQsWYN68eUblCQkJyM7OtuZU7I4kSUhJSYEQAmo+mYbKEPsaOQr2VbI7kgT1xIlIT0+Ha3Iy1Onp5R0RUZF4HyV7xH5J94swZMrvb144DI2fca4iN/UKkk/9jILMRqpLXaT+sxPpHm3g4hWq6K95hkpI0zWDR9ZOAECKVBnQVYXaUAnp98zwKtx2QkICVM4ZpX+C91FaWppF9axKML333nsA8kcwrVq1SjEdTqPRoEaNGli1apW53Y10795dft+sWTOEhYUhNDQUX3/9NZ577jmj+tOnT8eUKVPkz6mpqQgJCUFAQAC8vLysORW7I0kSVCoVAgICeKOlMsW+Ro6CfZXskRQYiLyEBPZLcgi8j5I9Yr+k+yX58HsoGIYi9o+CttDIpAKZmX8hL+s4CuZoeatvQ2QmwNc5EW6BbZT9NSgI7imPIOW3/ASTV+45eD00Cd512hi1LeVm4FrBMZ3uwDWwddmc5H3i6upqUT2rEkyXLl0CAHTq1AmbN2+Gr6+v9ZEVwcfHB/Xq1cP58+dNbtdqtdBqtUblarW6QtycVCpVhTkXsm/sa+Qo2FfJHrFfkiNhfyV7xH5JZU1/5wTSj/+3XhJEHtKPvg/36pHQ+DeRizVe1aF21iKvoFr2Xaidtfnl//bPwv3Vp8UYpPw2HQBQuWcMXIOMk0sAkPr3R/L7O9uGOvxi4Jb+rJZoDabY2NiS7Fas9PR0XLhwAU8//XSZHJ+IiIioxPLygEOH4Hz3LtC5M8A/jIioAjNk3EJeRpzV+zm5B8HZPbgMIiKynCHtGoThv2V01K4+EJnxMKRdUyaY/JvAq/lY3N2XnzRSqVTwemiSoo45mkqNTJabWwxcFxJp0XEdWYkSTP3790fbtm3xyiuvKMrfeecdHDx4EBs3brToOFOnTkWvXr0QGhqKmzdvYs6cOXBycsKQIUNKEhYRERFR2cnNheqNN+Ch1wMPPwy4uJR3REREZSbt+BokH3jT6v18wmbBN3x2GUREZDlnzxConP+b1iVlJ0PtrIOzZ4hRXa/mY+QEU2DPL8yOSrKUpcmtiqhECaZff/0Vc+fONSrv3r073n33XYuPc/36dQwZMgSJiYkICAhA+/bt8ccffyAgIKAkYRERERGVHbUaqFsXeZmZHL1ERBWeZ7ORcKvdS1EmGbIQ93UkACBo0B6onXVG+zm5B92P8IiKVNKRSeZGJVnDmuRWRVOiBFN6ejo0Go1RuYuLC1JTUy0+zpdfflmS5omIiIjuP40G4t13kRYfD52J34OIiCoSZ/dgo6luUu5/T8LSBjSH2sX9fodFZLHSHplkKVum3Tm6Ev3zW9OmTfHVV18ZlX/55Zdo1Mj2jB8RERERERERUWkojZFJ1vBqPkZ+H9jzC3i3GH9f2y8vJRrB9Nprr6Ffv364cOECHnnkEQDA7t278cUXX1i8/hIRERERERE5PmsWBJckCVJSEnLgBxfPKlwQnCq8+53cKk8lSjD16tULW7Zswfz587Fp0ybodDo0a9YMu3btQseOHUs7RiIiIqLyp9dDNXMmPDIzgXffBVxdi9+HiOgBUJIFwePABcGJKpoSJZgAoGfPnujZs2dpxkJERERkvyQJOHUKznp9/nsiIgJg+YLgeVl3YMhIQGpqCry8vOHs7o+c+CPFHt/JPYgjnYgcQIkTTMnJydi0aRMuXryIqVOnws/PD3/99RcqV66MqlWrlmaMREREROXPxQVixgxk3L0LjYtLeUdDRGQ3LF0Q/O7+1+WRTolWHJ8jnYgcQ4kSTMePH0eXLl3g7e2Ny5cv4/nnn4efnx82b96Mq1ev4rPPPivtOImIiIjKl5MT8L//ITc+Pv89ERFZxbPZSLjW7Im7SUnw9fODWq02O9IpL+uOvJ+TrhJHOhE5gBIlmKZMmYKoqCi888478PT0lMt79OiBoUOHllpwREREREREZH/0iSetfuy7s3sw1LrKUCMe2sDA/ARTMSOdrMGRTkTlq0QJpoMHD2L16tVG5VWrVkVcnGVPDyAiIiJyKJIE/P03nJOSAH9/QK0u74iIiO6r1GOr5PfxPw6B90OT4d3yhVJvx9I1ne7l5B5U6rEQkeVKlGDSarVITU01Kj979iwCAgJsDoqIiIjI7hQ8RU6vB8LCAOcSL2VJRORw9HdOIPXYSvmzEAKpR96HLiQSGv8mpdqWpWs6EZF9KdE/vfXu3Ruvv/46cnNzAQAqlQpXr17FK6+8gv79+5dqgERERER2QaUCQkIgVamS/56I6AFiSLsGYciWP6tdfSAMWTCkXSt2X33iSYvaKO16RHR/lSjB9O677yI9PR2BgYHIyspCx44dUadOHXh6euKtt94q7RiJiIiIyp9WC7FiBVLffhvQass7GiKi+8rZMwQqZ1f5s5SdDJWzDs6eISbr3zudLuXIB/elHhGVnxKN7fb29sbOnTvx22+/4dixY0hPT8dDDz2ELl26lHZ8REREREREVM40/k3g1Xws7u6bDiB/FovXQ5NMTo8rajqds18ji+oVPu79nJ5HRCVn8QgmPz8/3LmT/6jIZ599FmlpaWjXrh3GjRuHadOmMblERERERERUgXk1HyO/D+z5BbxbjDdZz9LpdKVdj4jKl8UJJr1eLy/s/emnnyI7O7uYPYiIiIgqEL0eqtmz4bFwIaDXl3c0RETlSlOpkdltlk6nK+16RFS+LJ4iFx4ejr59+6JVq1YQQmDixInQ6YwfDQkAn3zySakFSERERGQXJAk4ehTOen3+eyIiMqmo6XRSofunpdPurJmeR1QUQ8Yt5GXEKcokQ5b8PifhGNTO/+U5JEmClJQEg3sjaDyrlsoxCzi5Bxk9LdHRWZxg2rBhA9577z1cuHABAJCSksJRTERERPTgcHGBmDIFmcnJ0Li4lHc0RER2zav5GDkhFNjzC7gGtbkv9YiKknZ8DZIPvGl2e9zXkSbL09vOhF/EnFI9pk/YLPiGzza7nyOyOMFUuXJlvP322wCAmjVrYv369ahUqVKZBUZERERkV5ycgMhI6OPj898TEZFFippOV5b1iO7l2Wwk3Gr3sri+JEm4m5QEjxDzfc7aYxZwcg+yeh97Z3GCyc/PD2fPnoW/vz86deoEjUZTlnEREREREREREZUaZ/dgq6alSZIENeLh7B5YasesyCxOMBUs8u3v749PP/0UCxcuhKenZ1nGRkRERGQ/JAk4dw5OiYmAvz+gtvhZKURFMrV+hyWKWr/DkHELuWk3ISUlIQd+UFvYXyvimiBkP0z1S1Nr1eRl3UFe1h253ElXCSoXD6N692L/pdJkaX+914PcD7nINxEREZEl9HqoXnoJnno9sGUL4Gzxr1FERSpu/Q5zPJo8B69mo0xuSz2+Bukn1gIArEldVcQ1Qch+FO7rpvqlubVqLK3H/kulqaT99UHuhyVa5FulUnGRbyIiInqwqFRAYCCk7Oz890SlxNT6HZIhS/7jJWjQHvlfyQsnjtJPfIz0Ex9b1IZHk+fg0ehp+Zj+j34CjX9jkyNFcuKPFHu8B/lf6KnkPJuNhGvNnriblARfP/Mj60z1SyddQLHHr4hr2pBlyuJJbpb2V1PHfFBxkW8iIiIiS2i1EGvXIjU+Hq5abXlHQxWIqfU7pNwM+b02oDnULu4AAN/w1+DVbKSyrplkVMHitL5+fnDxrIL0U5/L+9zdPxfeD02GlJ1UotFTD/K/0D8orPmDvXBCqCAZxKlEdD+VxZPcnN2DodZVhhrx0AYGWpxgepBZNba7R48e+OKLL3Dp0iUAwNtvv40xY8bAx8cHAJCYmIgOHTrg5MmTpR4oEREREdGDSJ94Un4ke3HJKJXaBdrAlvnl/y5Oqw0MhCHpJFKPrZTrCSGQeuR9+HdZjSpDDyiPVyhhVTDS6V4P8r/QPyhK+ge7pXXjwEQllR4+yc0+WJVg+umnn5CTkyN/nj9/PgYNGiQnmAwGA86cOVOqARIRERERPWhSj62S38f/OATeD02Gd8sXSlzXkHYNwvDf8hZqVx+IzHiIvGw5IVUg+dC78vuCkU7m2qaKy5o/2E2NYDLn3pF1RKWBT3KzDzatTimEKK04iIiIiOybXg8sXAj39HRg3jzA1bW8I6IKSn/nhMnRRrqQSGj8m1hc19mvkVzu7BkClfN/fVbKTobaWQdnz5ASt00VW1n9wV54ZB2nHBFVLPyJJiIiIrKEJEF14ABcjhwBJKm8o6EKzORoI0MWDGnXSlxX498EXs3Hyp9VKhW8HppklDSypm0iIqLCrBrBpFKpoLrnqSn3fiYiIiKqkJydIcaPR2ZyMjTONg0CJyqSpaONrK3r1XwM7u6bDgAI7PmFvK5TSY9HRERUmFW/HQkhEBUVBe2/T07Jzs7GmDFj4O6e/1SLwuszEREREVUozs5At27Qx8fnvycqIwWjjQqSQeZGGxVVV62rhJz4I5CSkpCD/MdrF36ql5BykRN/xOTj4N1q90ba0Q/yC6RcuNUfKNeX6/HpX0REdA+VsGIhpWeeecaietHR0SUOyBqpqanw9vZGSkoKvLy87kubZUWSJMTHxyOQc5GpjLGvkaNgXyV7xH5J94uUm4ErK3wBAMGDfzM52qiounf3v17kE8Bsxad/UUnxPkqOhP01n6W5F6v++e1+JY6IiIiI7I4QwNWrUN+5AwSYf0ISUWnTVGpUfKV76no2GwnXmj3lp3WZ+8PI1Aimop4AJtfjo72JiOgeHN9NREREFZIh4xbyMuKs3s/s1J+cHKheeAFeej2wZQvg5mZ7kEQw3VcLT2fLSTgGtbMOgOnHwZurWxintBERUVljgomIiIgqpLTja0o0RaioqT+GSi7QiyyIO0ehdv3vj/jcu+eRl3kbTm6V4eJbx2g//nFPRSmur8Z9HWnxsUzVjQOntBERUdljgomIiIgqJM9mI+FWu5eiTDJkyX+ABw3aA7WzzuQUocKLGReW8lQQMv75EdgcaVUs/OOeimKqr5pjagSTOZIkyVPkXDyrlEqsRERE5jDBRERERBWSs3uw0aghKTdDfq9Su0Ab2LLEiyF7NHkOupBHkPjLS5Ay86c3qXSVoVar4d12JtRaT7luQdKKI53IFFN9tTRIkgQ14qF9wBenJSKi+4MJJiIiInpgpB5bJb+P/3EIvB+abHL0SNrJ9fJj2tVuQfBoMBQeDZ40GhGijz8KCEnez8k9ECIzHjk39yHjzJdWx8eRTkREROSomGAiIiKiB4L+zgmkHlspfxZCIPXI+9CFREIb2FJRL/PCd//tqHZB5vnN8KzzJLQxP8MjPR3a6dOhdnWF5JkIlbOrXFXKTobaWQfPxs/Au9WLcnnu3fMmRzr5PbxYMZKJT+YiIiIiR8UEExERET0QDGnXIAzZ8me1qw9EZjwMadeg8W9SfL3Uq1Dt/z+o1OnIie8mL/LtVru3PNoJUi7c6g+E2tVH0baQ9CZHOqk1HorkFhEREZGjYoKJiIiIHgjOniEmRxs5e4ZYVs8nFGm9ApCS8ovZRb6lzDikHl6C1MNLFOVudfoBqv/WwMnLiIdarYakT1csKM41mIiIiMhRMcFEREREDwSNfxN4NR+Lu/umAwBUKhW8HpqkGL1UZL2gFvAYsAjZ156Br59fkYsmpx5fg/QTa+XPmec3K7aLrNvIA5CwfZiinGswERERkaNigomIiIgqJEPGLeRlxCnKtFXC5fe+4XOh8W+MzCs75ce+A/lPfDNVLyf+CCSp8DQ386ONfMNfg1ezkUblljxFjoiIiMgRMcFEREREFVLa8TVIPvCm2e13/u9Zi45jql4cih5tZO6x81xviYiIiCoqJpiIiIioQvJsNhJutXsVWy8v647RCCYnXYBxxZwciGnTkJubC+cl78KlUs3SDJeIiIjIoZV7gmnFihVYtGgR4uLi0Lx5cyxfvhxt27Yt77DKzK3UbNxKzTYqlySBpLsZ8NOnQK1WGW0P9nJFsJerUTkRkT0wd28rTsF9zZb7orm2z9/JQFxaDoI8tajj7252f6q4zI0iKrHsbAh9AJCTA41/C6jd3Erv2EREREQOrlwTTF999RWmTJmCVatWISwsDEuXLkW3bt1w5swZBAYGlmdoZWb1/it4fedZq/eb3bUe5narXwYRkb2y5Q92/tFM95st9zYANt0XeV+l+8bVFWLjRiTHxyPQlfdZIiIiosJUQghRXo2HhYWhTZs2+OCDDwAAkiQhJCQEEyZMwKuvvlrs/qmpqfD29kZCQgK8vLyMtqvVajg7/5dD0+v1Zo+lUqng4uJSorq5ubkwdxnvrXs1MQ23UnMUdTYcvoYVf1wHAFTx1GDoQ9UwuGVVGAwGFBw2yFOLYC+tYj+NRmNRDPfWNRgMikVKbanr4uIClUpVpnXz8vKQl5dXKnWdnZ3lp/7YQ11JkmAwGEzWe2PXebz18wWzxzFndtd6mN21rtnjSpKExMREBAcH5z8i20QMt1JzEJeW30/VTmo4/Rvv+YR0XL+bgSBPLWr7exgdu6qPDtV880eKCCGQm5trNs7CP59lVRco+mfZHu8RpVUXuL/3iMJ9pkDhe1uQpxZDmwdhYPMqijpBnvn3tbi0HDg7O+PfH2WsO3AZH+y/BgAIdlNjcMuqGNSimryfs7MzqnjnJ1Ov383AjeQseduFO+mY+v1JxGXm/xxW9nCBk9oJS3o3Qk0/HaQ8SW773vuqI90jAMDJyQlOTk52U7e4n09r6trrPUKSJMTHxyMwMFDxFDneI0pWl79H2Fa3uJ9PlUqFxMRE+R9ueY/g7xH2cI9wdnaW76OSJPEeUYZ17eF3A0e/R9z7//0H9R6RmpqKgIAApKSkmMy9FCi3EUx6vR6HDx/G9OnT5TK1Wo0uXbpg//79JvfJyclBTs5/f8CkpqYCABYvXgytVmtUv06dOhg27L/H/77zzjtmO1ZoaCiioqLkz++99x4yMzNN1g0ODsaoUaPkzx988AGSk5NN1g0ICMC4cePkz9998SkSEhLkz/HpOfjy+G2geT8AgKsT8O3ft/B0q6r486dNuHXrlsnjurm54eWXX5Y/r1+/HleuXDFZ18XFBTNmzJA/f/HFFzh//rzJugAwZ84c+f2mTZtw6tQps3WnT58u/w/lu+++w7Fjx8zWnTp1Ktzd85MP27dvx6FDh8zWnTRpEnx8fAAAO3fuNNsnAGDs2LHyL06//PILfvnlF7N1n3/+eVStWhUA8Pvvv2PXrl1m644YMQI1atQAABw8eBDbt283W3fIkCGoVy9/JMaxY8ewdetWs3UHDBiAxo0bAwD++ecfbNq0yWS9DMkFGx7rgnr18kdYXL16BQvXfYODPvlTSD3O/oymgW5oHJT/A96uXQQaN26CYC8tLl++jE8//dTkcYUQaNWqFXr27AkAuHHjBtauXauo84u+Cn7NrWJq9yINranGZ2O7AwCSk5OxbNkys3Vbt24tx5CRkYHFixebrdu8eXP07dsXQP69Y8GCBWbrNmzYEIMGDZI/v/XWW2br2uM9YvXq1Yp7RGE+Pj6YNGmS/Pnjjz+223uEqXtbzKZvkbE3C4Eexvdr4L97xD9xqfh66zYgsCkAIOv3jYj+TUJmiyryvpMmTYKPhwaSJOHEgb2Ke8S5OxlIPZUAPDQAAOCvysJd4QZ3jRPSzh+tMPcIAOjTpw9atGgBADh79iy++OILs3W7d+8uT0Ev6h4BAF26dEG7du0AmL5HFNaxY0dERkYCAOLj47Fy5UqzdcPDw/Hoo48CcNx7hF6vR0ZGBtzd3eU/OADeIwrj7xH57OEe0atXL1StWhWSJOH8+fO8R4C/R9jDPeK1116DEAKSJPEewd8j7P4eIYRQ/H//Qb1HFM7DFKXcEkx37txBXl4eKleurCivXLkyTp8+bXKfBQsWYN68eUblGRkZJrOiqampiI+Plz+np6ebzZ6mpaUZ1c3KyrKoblpaGjIyMkzWdXV1LbJuSmo2fJzykPLv55aVVEjNzsWVG3FFHrcgk2pJDAX/SlAgNTXVbF0AVtctuOmnpKQUWTchIUHebkndgqxtcnJykXXv3PlvYVZL6hZkZe/evVtk3cTERLj9u76GJXULrltSUlKRdZOSkiyqqwYQiHRU0+T/MF9KTUTizavAvwmmQEMSEm4kQNJWgo/O5b+62TlITEw0e1whBNLT0xEfHw+1Wo07d+4Y1W2AKwhB/s2kebNmCKxRH6v/uIr98fk/P1Uv7oJKSHioqjc8Xf+7jUR4NpTPrbjvOCUlRa6bmZlpcd2CP+7Muffn3pq69niPKMzJycniuuV9j7j33taqkgrnNQakpKbDXWX6GhfcI67cSEagNg8FrQTpgBx9nmLfwveIS/EpuJjx3x/6mSpX+Pr4oOB/yTqRC18PCYl3EhF/PVmu6w49PKD81yFHukfcW7eon/uCGEtS19Q9orDk5OQS1XXIe0RqKnDoEFS5ucho1QqqQv8qyXvEf/h7RD57uUe4u7tDCMF7BH+PsKt7REpKCoQQvEfYwT2Cv0cUfY8QQiA7O3/ZEpVK9cDeIyxNMJXbFLmbN2+iatWq+P333xEeHi6XT5s2Db/88gsOHDhgtI+pEUwhISG4ffu2A02RS8ettP/W1bl0JwMv/3AStzLzh3BW9XCGSu2Exb0aobq3FgL5xw3y1CLYk1PkbK3ryMNWfzwZh5FfHkb8v92naaAWd9L0WNG/GR5rEGjxUFRJkpCUlISgoCCzU+TujeH/ziXi+a+P4nZ6rsm2TcXLoe0Vd2i7JXVPxqViwKeHcDkt/2ehho8WaiHw9dMt0SjI9LDagp/lf+JS0WftH7icmv891/RQQaV2wtfDW8n7Fv65n7vjNN7cbf100hmdamFW59qKMke+R9hDXXsc2l5UXcDKe0RqKjB0KPQ5OXDeuFGxyDfvESWry98jbKtryRS5pKQkBAQEAOAUOWvrAvw9oiR1LZkil5CQgICAAE6R4+8Rdn+PkCRJ7q8P+hS5ypUr2+8UOX9/fzg5OeH27duK8tu3byMoKMjkPlqt1uRUOFdXV7hasNimJXVKUtdUTOas++tWkYvR3kg3ADBgcMwRRXlxi9FaE0Ph/wE4Ql21Wq3o6BWtbuGbTlFqVvKATqcDsvMz2Sl6FVx1OtSp7GPUX4s6riRJ8v94TN0kTQn1dYOrizOA3GLbLqzgfwCWKKu6ZfVzX1b3CHuoWxo/yw/VcMW4h+th2o/5w96FSo3JkbXwUA3jBzj8t6D9f8n33s2q4v19lwEAOSoXDHuoGuCswck7BXWy5QXtx7arhb5Njad0WvIUuaK+R0e7R9hDXcA+fu7L7B7h4QFp+HBkp6bC1cMD6iJ+VniPsJ+69vKzXB73CEmSoFKpLP7/vaXHLWldwD5+7vl7RNnWLe7ns3C/tKbv8B5Rsrrl/XPv6PcISZKg1Wrh6uqqWHuxwINyjygq4VVYuSWYNBoNWrVqhd27d8vzHSVJwu7du/HCCy+UV1hlbnR4KHo3rmxUfj4hHbfi7yA40B91AowXT+ZTwR48pp4i16dxZfmPbn2ewNMtgqDPk/DX9WS5Tmk8Ra482ybHZKrPhIf6yu/ndauPJkGeiv4C5PeZ4p4CdzNNj0V7LmLRnouK8oLEu7l+91A1H+tPhKgozs7AoEHIjo+HlxW/LBMRERE9CMr1t6MpU6ZgxIgRaN26Ndq2bYulS5ciIyMDzzzzTHmGVabM/SHUoooX4uOdjZ5KQw+u4v7ojkvLKfKPbkdtmxxTcX0m6sujJstnd61nNvEuSQJJd5Pg5+sHtVpltJ3JTCIiIiIi+1GuCaYnn3wSCQkJmD17NuLi4tCiRQv89NNPRgt/Ez2IzP3RXZzS+KO7PNsmx2RLnzGXeJckCfGaHAQGejPxTvZBCCAlBarUVODfNW2IiIiIKF+5j+9+4YUXKvSUOKKSKs/pZpzqRtZin6EHQk4OVE8/DW+9HtiyBSi0yDcRERHRg67cE0y2KLyiuaOTJAlpaWlmFw8jKi3sa+Qo2FfJ7mRnQ+TmIsdggCY1FeoinopDZA94HyV7xH5JjoT9NV9BzqWop0kCDp5gSktLAwCEhISUcyRERET0QAkOLu8IiIiIiO6rtLQ0eHt7m92uEsWloOyYJEm4efMmPD09oVIZLwDrSFJTUxESEoJr167By8urvMOhCox9jRwF+yrZI/ZLciTsr2SP2C/JkbC/5hNCIC0tDVWqVClyJJdDj2BSq9WoVq1aeYdRqry8vB7ojkv3D/saOQr2VbJH7JfkSNhfyR6xX5IjYX9FkSOXCjy4kwiJiIiIiIiIiKhUMMFEREREREREREQ2YYLJTmi1WsyZMwdarba8Q6EKjn2NHAX7Ktkj9ktyJOyvZI/YL8mRsL9ax6EX+SYiIiIiIiIiovLHEUxERERERERERGQTJpiIiIiIiIiIiMgmTDAREREREREREZFNmGAiIiIiIiIiIiKbMMFEREREREREREQ2YYKpCAsWLECbNm3g6emJwMBA9O3bF2fOnFHUyc7Oxvjx41GpUiV4eHigf//+uH37trz92LFjGDJkCEJCQqDT6dCwYUMsW7ZMcYw9e/ZApVIZveLi4oqMTwiB2bNnIzg4GDqdDl26dMG5c+fk7ZcvX8Zzzz2HmjVrQqfToXbt2pgzZw70en0pXB0qTY7e1wCgd+/eqF69OlxdXREcHIynn34aN2/etPHKkD2qCP21QE5ODlq0aAGVSoWjR4+W7IKQXagI/bJGjRpGx3377bdtvDJkbypCXwWAH3/8EWFhYdDpdPD19UXfvn1LflGo3Dl6vzR3XJVKhYMHD5bCFSJ74uj9FQDOnj2LPn36wN/fH15eXmjfvj1iY2NtvDJ2QJBZ3bp1E9HR0eLEiRPi6NGjokePHqJ69eoiPT1drjNmzBgREhIidu/eLQ4dOiT+97//iYiICHn7xx9/LCZOnCj27NkjLly4INavXy90Op1Yvny5XCc2NlYAEGfOnBG3bt2SX3l5eUXG9/bbbwtvb2+xZcsWcezYMdG7d29Rs2ZNkZWVJYQQYvv27SIqKkrs2LFDXLhwQWzdulUEBgaKl156qZSvFNnK0fuaEEIsWbJE7N+/X1y+fFn89ttvIjw8XISHh5fiVSJ7URH6a4GJEyeK7t27CwDiyJEjtl8cKjcVoV+GhoaK119/XXHcwvFTxVAR+uqmTZuEr6+vWLlypThz5oz4559/xFdffVWKV4nuN0fvlzk5OYrj3bp1Szz//POiZs2aQpKkUr5aVN4cvb8KIUTdunVFjx49xLFjx8TZs2fFuHHjhJubm7h161YpXqn7jwkmK8THxwsA4pdffhFCCJGcnCxcXFzExo0b5TqnTp0SAMT+/fvNHmfcuHGiU6dO8ueCjnv37l2LY5EkSQQFBYlFixbJZcnJyUKr1YovvvjC7H7vvPOOqFmzpsXtUPmoCH1t69atQqVSCb1eb3Fb5Jgctb9u27ZNNGjQQPzzzz9MMFVAjtgvQ0NDxXvvvWfxcalicLS+mpubK6pWrSrWrl1r8XHJ8Thav7yXXq8XAQEB4vXXX7e4HXJcjtZfExISBADx66+/ynVSU1MFALFz506L27JHnCJnhZSUFACAn58fAODw4cPIzc1Fly5d5DoNGjRA9erVsX///iKPU3CMwlq0aIHg4GB07doVv/32W5GxXLp0CXFxcYq2vb29ERYWVqK2yb44el9LSkpCTEwMIiIi4OLiUuTxyfE5Yn+9ffs2Ro4cifXr18PNzc2yEyWH4oj9EgDefvttVKpUCS1btsSiRYtgMBiKP1lyaI7WV//66y/cuHEDarUaLVu2RHBwMLp3744TJ05YftJk9xytX97ru+++Q2JiIp555pkij00Vg6P110qVKqF+/fr47LPPkJGRAYPBgNWrVyMwMBCtWrWy/MTtEBNMFpIkCZMnT0a7du3QpEkTAEBcXBw0Gg18fHwUdStXrmx2Xubvv/+Or776CqNGjZLLgoODsWrVKnzzzTf45ptvEBISgsjISPz1119m4yk4fuXKlS1u+/z581i+fDlGjx5d7PlS+XHkvvbKK6/A3d0dlSpVwtWrV7F161aLz5sckyP2VyEEoqKiMGbMGLRu3drqcyb754j9EgAmTpyIL7/8ErGxsRg9ejTmz5+PadOmWXXu5Fgcsa9evHgRADB37lzMmjULP/zwA3x9fREZGYmkpCTrLgDZJUfsl/f6+OOP0a1bN1SrVq3Y8yXH5oj9VaVSYdeuXThy5Ag8PT3h6uqKJUuW4KeffoKvr6/V18CeOJd3AI5i/PjxOHHiBPbt21fiY5w4cQJ9+vTBnDlz8Oijj8rl9evXR/369eXPERERuHDhAt577z2sX78eMTExiqTQ9u3b4eTkZFXbN27cwGOPPYaBAwdi5MiRJT4HKnuO3NdefvllPPfcc7hy5QrmzZuH4cOH44cffoBKpSrxuZB9c8T+unz5cqSlpWH69OkljpnsmyP2SwCYMmWK/L5Zs2bQaDQYPXo0FixYAK1WW+JzIfvliH1VkiQAwMyZM9G/f38AQHR0NKpVq4aNGzfyHzIrAEfsl4Vdv34dO3bswNdff13i+MlxOGJ/FUJg/PjxCAwMxN69e6HT6bB27Vr06tULBw8eRHBwcInPpbwxwWSBF154AT/88AN+/fVXRRY8KCgIer0eycnJiuzo7du3ERQUpDjGyZMn0blzZ4waNQqzZs0qts22bdvKPyS9e/dGWFiYvK1q1aq4deuW3FbhDnj79m20aNFCcaybN2+iU6dOiIiIwEcffWTxedP95+h9zd/fH/7+/qhXrx4aNmyIkJAQ/PHHHwgPD7f4GpDjcNT++vPPP2P//v1Gf7C3bt0aw4YNw6effmrZBSC75Kj90pSwsDAYDAZcvnxZ8QsuVQyO2lcLyhs1aiRv12q1qFWrFq5evWrh2ZO9ctR+WVh0dDQqVaqE3r17W3TO5Lgctb/+/PPP+OGHH3D37l14eXkBAD788EPs3LkTn376KV599VXrLoQ9Ke9FoOyZJEli/PjxokqVKuLs2bNG2wsWD9u0aZNcdvr0aaPFw06cOCECAwPFyy+/bHHbXbp0EU888USRsQUFBYnFixfLZSkpKUaL3V2/fl3UrVtXDB48WBgMBovbp/urIvS1e125ckUAELGxsRbHQo7B0fvrlStXxN9//y2/duzYIQCITZs2iWvXrlkcC9kXR++XpmzYsEGo1WqRlJRkcSxk/xy9rxZ8LrzIt16vF4GBgWL16tUWx0L2xdH7ZeG6NWvW5FOzKzhH76/fffedUKvVIi0tTbFvvXr1xFtvvWVxLPaICaYijB07Vnh7e4s9e/YoHkuYmZkp1xkzZoyoXr26+Pnnn8WhQ4eMHs3+999/i4CAAPHUU08pjhEfHy/Xee+998SWLVvEuXPnxN9//y0mTZok1Gq12LVrV5Hxvf3228LHx0ds3bpVHD9+XPTp00fx+MPr16+LOnXqiM6dO4vr168r2if74uh97Y8//hDLly8XR44cEZcvXxa7d+8WERERonbt2iI7O7uUrxaVN0fvr/e6dOkSnyJXATh6v/z999/Fe++9J44ePSouXLggNmzYIAICAsTw4cNL+UpReXP0viqEEJMmTRJVq1YVO3bsEKdPnxbPPfecCAwMZDLUgVWEfimEELt27RIAxKlTp0rpypA9cvT+mpCQICpVqiT69esnjh49Ks6cOSOmTp0qXFxcxNGjR0v5at1fTDAVAYDJV3R0tFwnKytLjBs3Tvj6+go3NzfxxBNPKBI4c+bMMXmM0NBQuc7ChQtF7dq1haurq/Dz8xORkZHi559/LjY+SZLEa6+9JipXriy0Wq3o3LmzOHPmjLw9Ojra7DmQfXH0vnb8+HHRqVMn4efnJ7RarahRo4YYM2aMuH79eqlcH7Ivjt5f78UEU8Xg6P3y8OHDIiwsTHh7ewtXV1fRsGFDMX/+fCbpKyBH76tC5I9Yeumll0RgYKDw9PQUXbp0ESdOnLD52lD5qQj9UgghhgwZIiIiImy6FmT/KkJ/PXjwoHj00UeFn5+f8PT0FP/73//Etm3bbL425U0lhBAWzKQjIiIiIiIiIiIySV3eARARERERERERkWNjgomIiIiIiIiIiGzCBBMREREREREREdmECSYiIiIiIiIiIrIJE0xERERERERERGQTJpiIiIiIiIiIiMgmTDAREREREREREZFNmGAiIiKiB8Lly5ehUqlw9OjRMm9r3bp18PHxKfN2ykpkZCQmT55s0zEc/RoQERGRdZhgIiIionIXFRUFlUpl9HrsscfKO7Ri1ahRA0uXLlWUPfnkkzh79myZtx0ZGSlfK1dXVzRq1AgffvihzcfdvHkz3njjjVKIkIiIiB4UzuUdABEREREAPPbYY4iOjlaUabXacorGNjqdDjqd7r60NXLkSLz++uvIzMzEZ599hvHjx8PX1xdDhgyx+lh6vR4ajQZ+fn5lECkRERFVZBzBRERERHZBq9UiKChI8fL19QUADB06FE8++aSifm5uLvz9/fHZZ58BAH766Se0b98ePj4+qFSpEh5//HFcuHDBbHumpnBt2bIFKpVK/nzhwgX06dMHlStXhoeHB9q0aYNdu3bJ2yMjI3HlyhW8+OKL8kgic8deuXIlateuDY1Gg/r162P9+vWK7SqVCmvXrsUTTzwBNzc31K1bF999912x183NzQ1BQUGoVasW5s6dq9gvOTkZzz//PAICAuDl5YVHHnkEx44dk/edO3cuWrRogbVr16JmzZpwdXWVz6vwFLm7d+9i+PDh8PX1hZubG7p3745z584ZXc/q1avDzc0NTzzxBBITE4uNnYiIiCoOJpiIiIjI7g0bNgzff/890tPT5bIdO3YgMzMTTzzxBAAgIyMDU6ZMwaFDh7B7926o1Wo88cQTkCSpxO2mp6ejR48e2L17N44cOYLHHnsMvXr1wtWrVwHkTyWrVq0aXn/9ddy6dQu3bt0yeZxvv/0WkyZNwksvvYQTJ05g9OjReOaZZxAbG6uoN2/ePAwaNAjHjx9Hjx49MGzYMCQlJVkVs06ng16vBwAMHDgQ8fHx2L59Ow4fPoyHHnoInTt3Vhzz/Pnz+Oabb7B582az61NFRUXh0KFD+O6777B//34IIdCjRw/k5uYCAA4cOIDnnnsOL7zwAo4ePYpOnTrhzTfftCpuIiIicmxMMBEREZFd+OGHH+Dh4aF4zZ8/HwDQrVs3uLu749tvv5Xrf/755+jduzc8PT0BAP3790e/fv1Qp04dtGjRAp988gn+/vtvnDx5ssQxNW/eHKNHj0aTJk1Qt25dvPHGG6hdu7Y8QsjPzw9OTk7w9PSUR12ZsnjxYkRFRWHcuHGoV68epkyZgn79+mHx4sWKelFRURgyZAjq1KmD+fPnIz09HX/++adFsebl5WHDhg04fvw4HnnkEezbtw9//vknNm7ciNatW6Nu3bpYvHgxfHx8sGnTJnk/vV6Pzz77DC1btkSzZs2Mjnvu3Dl89913WLt2LTp06IDmzZsjJiYGN27cwJYtWwAAy5Ytw2OPPYZp06ahXr16mDhxIrp162ZR3ERERFQxMMFEREREdqFTp044evSo4jVmzBgAgLOzMwYNGoSYmBgA+aOVtm7dimHDhsn7nzt3DkOGDEGtWrXg5eWFGjVqAIA82qgk0tPTMXXqVDRs2BA+Pj7w8PDAqVOnrD7mqVOn0K5dO0VZu3btcOrUKUVZ4QSPu7s7vLy8EB8fX+SxP/zwQ3h4eECn02HkyJF48cUXMXbsWBw7dgzp6emoVKmSIml36dIlxdTB0NBQBAQEFBm7s7MzwsLC5LJKlSqhfv36cvynTp1SbAeA8PDwIuMmIiKiioWLfBMREZFdcHd3R506dcxuHzZsGDp27Ij4+Hjs3LkTOp1O8ZS5Xr16ITQ0FGvWrEGVKlUgSRKaNGkiTxe7l1qthhBCUVYw5avA1KlTsXPnTixevBh16tSBTqfDgAEDzB7TVi4uLorPKpWq2Cl+w4YNw8yZM6HT6RAcHAy1Ov/fD9PT0xEcHIw9e/YY7VN4fSh3d3eb4yYiIiJigomIiIgcQkREBEJCQvDVV19h+/btGDhwoJyQSUxMxJkzZ7BmzRp06NABALBv374ijxcQEIC0tDRkZGTISZZ71yD67bffEBUVJa/zlJ6ejsuXLyvqaDQa5OXlFdlWw4YN8dtvv2HEiBGKYzdq1KjY8y6Ot7e3ycTcQw89hLi4ODg7O8ujuUqiYcOGMBgMOHDgACIiIgD8d70L4m/YsCEOHDig2O+PP/4ocZtERETkeJhgIiIiIruQk5ODuLg4RZmzszP8/f3lz0OHDsWqVatw9uxZxQLZvr6+qFSpEj766CMEBwfj6tWrePXVV4tsLywsDG5ubpgxYwYmTpyIAwcOYN26dYo6devWxebNm9GrVy+oVCq89tprRiOKatSogV9//RWDBw+GVqtVxFvg5ZdfxqBBg9CyZUt06dIF33//PTZv3qx4Il1p69KlC8LDw9G3b1+88847qFevHm7evIkff/wRTzzxBFq3bm3RcerWrYs+ffpg5MiRWL16NTw9PfHqq6+iatWq6NOnDwBg4sSJaNeuHRYvXow+ffpgx44d+Omnn8rs3IiIiMj+cA0mIiIisgs//fQTgoODFa/27dsr6gwbNgwnT55E1apVFWsaqdVqfPnllzh8+DCaNGmCF198EYsWLSqyPT8/P2zYsAHbtm1D06ZN8cUXX2Du3LmKOkuWLIGvry8iIiLQq1cvdOvWDQ899JCizuuvv47Lly+jdu3aZtcy6tu3L5YtW4bFixejcePGWL16NaKjoxEZGWn5BbKSSqXCtm3b8PDDD+OZZ55BvXr1MHjwYFy5cgWVK1e26ljR0dFo1aoVHn/8cYSHh0MIgW3btskjyP73v/9hzZo1WLZsGZo3b47/+7//w6xZs8ritIiIiMhOqcS9iw8QERERERERERFZgSOYiIiIiIiIiIjIJkwwERERERERERGRTZhgIiIiIiIiIiIimzDBRERERERERERENmGCiYiIiIiIiIiIbMIEExERERERERER2YQJJiIiIiIiIiIisgkTTEREREREREREZBMmmIiIiIiIiIiIyCZMMBERERERERERkU2YYCIiIiIiIiIiIpswwURERERERERERDZhgomIiIiIiIiIiGzCBBMREREREREREdmECSYiIiIiIiIiIrIJE0xERERERERERGQTJpiIiIiIiIiIiMgmTDARERGVgcuXL0OlUmHdunXlHUqFVqNGDURFRZV3GEREREQPPCaYiIiISmDdunVQqVQmX6+++mqZtDl//nxs2bKl2HqRkZFmYyv8mjt3bpnEea9t27bdt7ascfr0aUybNg0tWrSAp6cngoOD0bNnTxw6dMhk/Rs3bmDQoEHw8fGBl5cX+vTpg4sXLyrqXLt2DfPmzUPbtm3h6+sLf39/REZGYteuXUbHK6oPxcXFWXweH3/8MRo2bAhXV1fUrVsXy5cvN1v3q6++Qnh4ONzd3eHj44OIiAj8/PPPFrUjSRLeeecd1KxZE66urmjWrBm++OILo3p//vknxo0bh1atWsHFxQUqlcricynw+++/o3379nBzc0NQUBAmTpyI9PR0o3o5OTl45ZVXUKVKFeh0OoSFhWHnzp1WtWXp9bPk+y+KNbFaev5ERET2xLm8AyAiInJkr7/+OmrWrKkoa9KkCUJDQ5GVlQUXF5dSa2v+/PkYMGAA+vbtW2S9mTNn4vnnn5c/Hzx4EO+//z5mzJiBhg0byuXNmjUrtdiKsm3bNqxYscLukkxr167Fxx9/jP79+2PcuHFISUnB6tWr8b///Q8//fQTunTpItdNT09Hp06dkJKSghkzZsDFxQXvvfceOnbsiKNHj6JSpUoAgK1bt2LhwoXo27cvRowYAYPBgM8++wxdu3bFJ598gmeeecYoDlN9yMfHx6JzWL16NcaMGYP+/ftjypQp2Lt3LyZOnIjMzEy88sorirpz587F66+/jgEDBiAqKgq5ubk4ceIEbty4YVFbM2fOxNtvv42RI0eiTZs22Lp1K4YOHQqVSoXBgwfL9bZt24a1a9eiWbNmqFWrFs6ePWvR8QscPXoUnTt3RsOGDbFkyRJcv34dixcvxrlz57B9+3ZF3aioKGzatAmTJ09G3bp1sW7dOvTo0QOxsbFo3759sW1Zev0s/f6LYmms1pw/ERGRXRFERERktejoaAFAHDx40KbjpKenW1zX3d1djBgxwuo2Nm7cKACI2NjYUovFGuPHjxdl9StHaGhoia6JEEIcOnRIpKWlKcru3LkjAgICRLt27RTlCxcuFADEn3/+KZedOnVKODk5ienTp8tlJ06cEAkJCYp9s7OzRYMGDUS1atUU5bb2oczMTFGpUiXRs2dPRfmwYcOEu7u7SEpKksv2798vVCqVWLJkSYnaun79unBxcRHjx4+XyyRJEh06dBDVqlUTBoNBLo+LixOZmZlCiJJ99927dxfBwcEiJSVFLluzZo0AIHbs2CGXHThwQAAQixYtksuysrJE7dq1RXh4eLHtWHP9LP3+zbEmVkvPn4iIyN5wihwREVEZMLUGU1RUFDw8PHDhwgX06NEDnp6eGDZsGADg3Llz6N+/P4KCguDq6opq1aph8ODBSElJAQCoVCpkZGTg008/ladR2bL20Ny5c6FSqXDy5EkMHToUvr6+ilEUGzZsQKtWraDT6eDn54fBgwfj2rVrimPs3bsXAwcORPXq1aHVahESEoIXX3wRWVlZinNesWKFfA4FrwKSJGHp0qVo3LgxXF1dUblyZYwePRp3795VtCWEwJtvvolq1arBzc0NnTp1wj///GPy3C5cuIALFy4Uew1atWoFDw8PRVmlSpXQoUMHnDp1SlG+adMmtGnTBm3atJHLGjRogM6dO+Prr7+Wyxo3bgx/f3/FvlqtFj169MD169eRlpZmMpa0tDTk5eUVG3NhsbGxSExMxLhx4xTl48ePR0ZGBn788Ue5bOnSpQgKCsKkSZMghLB6utXWrVuRm5uraEulUmHs2LG4fv069u/fL5dXrlwZOp3OquMXSE1Nxc6dO/HUU0/By8tLLh8+fDg8PDwU13rTpk1wcnLCqFGj5DJXV1c899xz2L9/v6K/3rlzB6dPn0ZmZqZcZs31s/T7B4CrV6/i9OnTijJLY7Xm/ImIiOwNE0xEREQ2SElJwZ07dxSvohgMBnTr1g2BgYFYvHgx+vfvD71ej27duuGPP/7AhAkTsGLFCowaNQoXL15EcnIyAGD9+vXQarXo0KED1q9fj/Xr12P06NE2xz9w4EBkZmZi/vz5GDlyJADgrbfewvDhw1G3bl0sWbIEkydPxu7du/Hwww/L8QDAxo0bkZmZibFjx2L58uXo1q0bli9fjuHDh8t1Ro8eja5du8rnUPAqvP3ll19Gu3btsGzZMjzzzDOIiYlBt27dkJubK9ebPXs2XnvtNTRv3hyLFi1CrVq18OijjyIjI8PonDp37ozOnTuX+JrExcUpkkSSJOH48eNo3bq1Ud22bdviwoULZhNHhY/p5uYGNzc3o22dOnWCl5cX3Nzc0Lt3b5w7d86iOI8cOQIARnG1atUKarVa3g4Au3fvRps2bfD+++8jICBAXnPqgw8+sLgtd3d3xRRLIP/8C8diq7///hsGg8HonDQaDVq0aKFo58iRI6hXr54iEVM4pqNHj8plH3zwARo2bIg///xTsT9Q/PWz9vsfPny40XWyNFZrzp+IiMjecA0mIiIiGxRep6eAEMJs/ZycHAwcOBALFiyQy44ePYpLly5h48aNGDBggFw+e/Zs+f1TTz2FMWPGoFatWnjqqadKKXqgefPm+Pzzz+XPV65cwZw5c/Dmm29ixowZcnm/fv3QsmVLfPjhh3L5woULFSNVRo0ahTp16mDGjBm4evUqqlevjvDwcNSrV08elVHYvn37sHbtWsTExGDo0KFyeadOnfDYY49h48aNGDp0KBISEvDOO++gZ8+e+P777+URUDNnzsT8+fNL7VoA+aOy9u/fj1mzZsllSUlJyMnJQXBwsFH9grKbN2+ifv36Jo95/vx5bN68GQMHDoSTk5Nc7ubmhqioKDnBdPjwYSxZsgQRERH466+/EBISUmSst27dgpOTEwIDAxXlGo0GlSpVws2bNwEAd+/exZ07d/Dbb7/h559/xpw5c1C9enVER0djwoQJcHFxKTZZeevWLVSuXNlowe7C518abt26pTjuvW3t3btXUbe476S4tiy5frZ+/9bEas35ExER2RuOYCIiIrLBihUrsHPnTsWrOGPHjlV89vb2BgDs2LFDMYXnfhgzZozi8+bNmyFJEgYNGqQYlRUUFIS6desiNjZWrls4uZSRkYE7d+4gIiICQgiLRlps3LgR3t7e6Nq1q6KtgqlrBW3t2rULer0eEyZMUCQ4Jk+ebPK4ly9fxuXLl624Cvni4+MxdOhQ1KxZE9OmTZPLC6b8abVao31cXV0Vde6VmZmJgQMHQqfT4e2331ZsGzRoEKKjozF8+HD07dsXb7zxBnbs2IHExES89dZbxcablZUFjUZjcpurq6scU8F0uMTERKxduxZTp07FoEGD8OOPP6JRo0Z48803LWqrJOdvreKudeF2rIlp7ty5EEIgMjJSsb8l18/a73/Pnj1GSWZLY7Xm/ImIiOwNRzARERHZoG3btianzpjj7OyMatWqKcpq1qyJKVOmYMmSJYiJiUGHDh3Qu3dvPPXUU3Lyqazc+/Syc+fOQQiBunXrmqxf+Kl4V69exezZs/Hdd98ZrZlUsHZUUc6dO4eUlBSjESQF4uPjAeSPqgJgFFNAQAB8fX2LbccSGRkZePzxx5GWloZ9+/Yp1mYqSKTl5OQY7Zedna2oU1heXh4GDx6MkydPYvv27ahSpUqxcbRv3x5hYWHYtWuXXJaQkKBYn8nDwwMeHh7Q6XTQ6/Umj5OdnS3HVPBfFxcXxQg5tVqNJ598EnPmzJFHnMXFxSmO4+3tDZ1OB51OZ/X5l0Rx17pwO7bGZO31s7UtS/a35vyJiIjsDRNMRERE95FWq4VabTyA+N1330VUVBS2bt2K//u//8PEiROxYMEC/PHHH0YJqdJ07x+skiRBpVJh+/btiulcBQoSL3l5eejatSuSkpLwyiuvoEGDBnB3d8eNGzcQFRUFSZKKbVuSJAQGBiImJsbk9oCAgBKckfX0ej369euH48ePY8eOHWjSpIliu5+fH7RarTx9qbCCMlPJo5EjR+KHH35ATEwMHnnkEYvjCQkJwZkzZ+TPbdq0kZNsADBnzhzMnTsXwcHByMvLQ3x8vCJJp9frkZiYKMfk5+cHV1dX+Pj4GH2nBfvdvXsX1atXN5qaFR0djaioKAQHByM2NhZCCMUosqLOvyQK2jd3rQu3ExwcjBs3bpisZ0lM1ly/knz/97ZlSazWnD8REZG9YYKJiIjITjRt2hRNmzbFrFmz8Pvvv6Ndu3ZYtWqVPIXp3vVvykLt2rUhhEDNmjVRr149s/X+/vtvnD17Fp9++qliUW9TUwTNxV27dm3s2rUL7dq1K3JkRmhoKID8EU+1atWSyxMSEoxGTllLkiQMHz4cu3fvxtdff42OHTsa1VGr1WjatCkOHTpktO3AgQOoVasWPD09FeUvv/wyoqOjsXTpUgwZMsSqmC5evKhIrsXExCimRhVcgxYtWgAADh06hB49esjbDx06BEmS5O1qtRotWrTAwYMHodfrFdPCCtb+KWjv3u+vcePGcltr167FqVOn0KhRI8X5F47FVk2aNIGzszMOHTqEQYMGyeV6vR5Hjx5VlLVo0QKxsbFITU1VLJ5taUzWXD9rv39TbVkSqzXnT0REZG+4BhMREVE5S01NhcFgUJQ1bdoUarVaMVXG3d1d8RS3stCvXz84OTlh3rx5RuvICCGQmJgIAPJImMJ1hBBYtmyZ0THd3d0BwCj2QYMGIS8vD2+88YbRPgaDQa7fpUsXuLi4YPny5Yr2li5davIcLly4gAsXLhR9ov+aMGECvvrqK3z44Yfo16+f2XoDBgzAwYMHFUmGM2fO4Oeff8bAgQMVdRctWoTFixdjxowZmDRpktljJiQkGJVt27YNhw8fxmOPPSaXtWvXDl26dJFfBQmmRx55BH5+fli5cqXiGCtXroSbmxt69uwplz355JPIy8vDp59+KpdlZ2cjJiYGjRo1kkfGFG6nS5cu8oiaPn36wMXFBR9++KG8vxACq1atQtWqVREREWH2PK3h7e2NLl26YMOGDYons61fvx7p6emKaz1gwADk5eXho48+kstycnIQHR2NsLAwxSLpd+7cwenTpxVrnFlz/az5/q9evYrTp08ryiyN1ZrzJyIisjccwURERFTOfv75Z7zwwgsYOHAg6tWrB4PBgPXr18PJyQn9+/eX67Vq1Qq7du3CkiVLUKVKFdSsWRNhYWGlGkvt2rXx5ptvYvr06bh8+TL69u0LT09PXLp0Cd9++y1GjRqFqVOnokGDBqhduzamTp2KGzduwMvLC998843JEUWtWrUCAEycOBHdunWDk5MTBg8ejI4dO2L06NFYsGABjh49ikcffRQuLi44d+4cNm7ciGXLlmHAgAEICAjA1KlTsWDBAjz++OPo0aMHjhw5gu3bt8Pf39+ovc6dOwNAsQt9L126FB9++CHCw8Ph5uaGDRs2KLY/8cQTcnJs3LhxWLNmDXr27ImpU6fCxcUFS5YsQeXKlfHSSy/J+3z77beYNm0a6tati4YNGxods2vXrqhcuTIAICIiAi1btkTr1q3h7e2Nv/76C5988glCQkIUT/AzR6fT4Y033sD48eMxcOBAdOvWDXv37sWGDRvw1ltvwc/PT647evRorF27FuPHj8fZs2dRvXp1rF+/HleuXMH3339fbFvVqlXD5MmTsWjRIuTm5qJNmzbYsmUL9u7di5iYGMXUuytXrmD9+vUAICdkCkbhhYaG4umnny6yrbfeegsRERHo2LEjRo0ahevXr+Pdd9/Fo48+qki8hYWFYeDAgZg+fTri4+NRp04dfPrpp7h8+TI+/vhjxTE/+OADzJs3D7GxsfJC39ZcP0u/fwAYPnw4fvnlF0Uy1JpYLT1/IiIiuyOIiIjIatHR0QKAOHjwoMntly5dEgBEdHS0XDZixAjh7u5uVPfixYvi2WefFbVr1xaurq7Cz89PdOrUSezatUtR7/Tp0+Lhhx8WOp1OABAjRoywKNaNGzcKACI2NlYumzNnjgAgEhISTO7zzTffiPbt2wt3d3fh7u4uGjRoIMaPHy/OnDkj1zl58qTo0qWL8PDwEP7+/mLkyJHi2LFjRudtMBjEhAkTREBAgFCpVOLeXz8++ugj0apVK6HT6YSnp6do2rSpmDZtmrh586ZcJy8vT8ybN08EBwcLnU4nIiMjxYkTJ0RoaKjRdQgNDRWhoaHFXpcRI0YIAGZfly5dUtS/du2aGDBggPDy8hIeHh7i8ccfF+fOnVPUKbiu5l6Fv4OZM2eKFi1aCG9vb+Hi4iKqV68uxo4dK+Li4oqN/d7rV79+faHRaETt2rXFe++9JyRJMqp3+/ZtMWLECOHn5ye0Wq0ICwsTP/30k8Xt5OXlifnz54vQ0FCh0WhE48aNxYYNG4zqxcbGmj3/jh07WtTW3r17RUREhHB1dRUBAQFi/PjxIjU11aheVlaWmDp1qggKChJarVa0adPG5DkVfC+Fr38BS6+fJd+/EEJ07NjRqI9bE6s1509ERGRPVELcM/6diIiIiIiIiIjIClyDiYiIiIiIiIiIbMIEExERERERERER2YQJJiIiIiIiIiIisgkTTEREREREREREZBMmmIiIiIiIiIiIyCZMMBERERERERERkU2cyzsAW0iShJs3b8LT0xMqlaq8wyEiIiIiIiIiqlCEEEhLS0OVKlWgVpsfp+TQCaabN28iJCSkvMMgIiIiIiIiIqrQrl27hmrVqpnd7tAJJk9PTwD5J+nl5VXO0dhGkiQkJCQgICCgyIwgka3Y18hRsK+SPWK/JEfC/kr2iP2SHAn7a77U1FSEhITIORhzHDrBVDAtzsvLq0IkmLKzs+Hl5fVAd1wqe+xr5CjYV8kesV+SwzAYIG3fDpGSAq9Bg6DWaMo7IiIAvI+SY2F/VSpuaSKHTjAREREREZEJBgNUq1dDp9cD/foBTDARUQVmyLiFvIw4q/dzcg+Cs3twGUT0YGKCiYiIiIioolGrIdq1Q256OjT8V3ciquDSjq9B8oE3rd7Po8lz8Go2yux2lS4QgJMNkT1YSiXBNHfuXMybN09RVr9+fZw+fdrsPhs3bsRrr72Gy5cvo27duli4cCF69OhRGuEQERERET3YNBrglVeQER8Pd45eIqIKzrPZSLjV7qUokwxZiPs6EgDg3mgEMk5+arRf+omPkX7iY7PH9W47E6g1ulRjrchKbQRT48aNsWvXrv8O7Gz+0L///juGDBmCBQsW4PHHH8fnn3+Ovn374q+//kKTJk1KKyQiIiIiIiIiquCc3YONprpJuRnye5+2r8K7xTjl9kIJqKBBe6B21hkdV6ULRFKGUTGZUWoJJmdnZwQFBVlUd9myZXjsscfw8ssvAwDeeOMN7Ny5Ex988AFWrVpVWiERERERERER0QPO2T0Iap/airLCCShtQHOoXdyN9pMkCciIL/P4KopSSzCdO3cOVapUgaurK8LDw7FgwQJUr17dZN39+/djypQpirJu3bphy5YtRbaRk5ODnJwc+XNqaiqA/C9dkiTbTqCcSZIEIYTDnwfZP/Y1chTsq2SP2C/JYeTkAKNGwSsnB1J0NKAz/pd5ovLA+yjdL4X7mCRJwD19rrjtBeXsr7D4/EslwRQWFoZ169ahfv36uHXrFubNm4cOHTrgxIkT8PT0NKofFxeHypUrK8oqV66MuLiiV31fsGCB0VpPAJCQkIDs7GzbTqKcSZKElJQUCCH4+EMqU+xr5CjYV8kesV+Sw8jOhk9cHPIMBsTfvg21m1t5R0QEgPdRun+EIVN+n5CQAJVzhlXbAfbXAmlpaRbVK5UEU/fu3eX3zZo1Q1hYGEJDQ/H111/jueeeK40mAADTp09XjHxKTU1FSEgIAgIC4OXlVWrtlAdJkqBSqRAQEPBAd1wqe+xr5CjYV8kesV+Sw5AkSCtWIDUpCYHVqkFdxPqoRPcT76N0v0i5Gbj27/uAgACjKXDFbQfYXwu4urpaVK9M/k/j4+ODevXq4fz58ya3BwUF4fbt24qy27dvF7uGk1arhVarNSpXq9UV4stWqVQV5lzIvrGvkaNgXyV7xH5JDkGtBurUgRQfD7WzM/sr2RXeR+m+KNS/TPa34rb/i/0VFp97mSSY0tPTceHCBTz99NMmt4eHh2P37t2YPHmyXLZz506Eh4eXRThERERERERERCYZMuIg9KlG5ZIkQUpKQg78TCZZnNyDjJ5e9yArlQTT1KlT0atXL4SGhuLmzZuYM2cOnJycMGTIEADA8OHDUbVqVSxYsAAAMGnSJHTs2BHvvvsuevbsiS+//BKHDh3CRx99VBrhEBERERE92AwGIDYWmuRkoE8fQKMp74iIiOxW+j/rkHJwodnt5laL9mjyHLyajTK57UFMPpVKgun69esYMmQIEhMTERAQgPbt2+OPP/5AQEAAAODq1auKbF9ERAQ+//xzzJo1CzNmzEDdunWxZcsWNGnSpDTCISIiIiJ6sBkMUC1bBje9HujZkwkmIqIieDSOgnvdfooyyZCFuK8ji9wv/cTHSD/xscltPmGz4Bs+u7RCdAilkmD68ssvi9y+Z88eo7KBAwdi4MCBpdE8EREREREVplZDtG6N3IwMaB7gdUOIiCzh7B4EtU9tRZmU+99T5SoP+BlOmv8WAS+cfAoatAdqZ53RMZ3ci15juiLi4ySIiIiIiCoajQaYPRsZ8fFw5+glIiKbaAKaw1nrKX8unHzSBjQ3+QS6BxH/OYOIiIiIiIiIiGzCBBMREREREREREdmECSYiIiIiooomJweq0aPhNXUqkJNT3tEQEdEDgGswERERERFVNEIAt25BrdfnvyciIipjTDAREREREVU0Gg3EwoVIS0xEJS7yTURUZgwZcRD6VKv3c3IPgrN7cBlEVH6YYCIiIiIiqmjUaqBhQ+TFx+e/JyKiMpH+zzqkHFxo9X4+YbPgGz67DCIqP0wwERERERERERGVgEfjKLjX7acokwxZiPs6EgAQNGgP1M46o/2c3IPuR3j3FRNMREREREQVTV4e8NtvcLl7F+jRg6OYiIjKiLN7ENQ+tRVlUm6G/F4b0BxqF/f7HVa5YIKJiIiIiKiiyc2F6p134K7XA127Ai4u5R0RERFVcPynDCIiIiKiikatBpo0gaF+fY5eIqIHnj7xZHmH8EDgCCYiIiIioopGo4GYPx/p8fFw41PkiOgBlHpslfw+/sch8H5oMrxbvlCOEVV8/OcMIiIiIiIiIqow9HdOIPXYSvmzEAKpR96H/s6Jcoyq4mOCiYiIiIiIiIgqDEPaNQhDtvxZ7eoDYciCIe2ayfq2TKHj9Lv/MMFERERERFTR6PVQTZoEz1mzAL2+vKMhIrqvnD1DoHJ2lT9L2clQOevg7Bkil907hS7lyAdmj6dPOqX4bNW+D1ACigkmIiIiIqKKRpKAS5fgdPVq/nsiogeIxr8JvJqPlT+rVCp4PTQJGv8mACybQlc4iZSw7Sk5iWTtvsUloCoSJpiIiIiIiCoajQbi9deRPm0awEW+iegB5NV8jPw+sOcX8G4xXv5c3BS6opJItuxb0THBRERERERU0ajVQIsWMDRpkv+eiOgBpqnUSPG5uCl0xkkkbzmJZP2+Ra//VJHw/zZERERERERE9MAobgqdcRIpRU4iWb+v8fpPFRUTTEREREREFU1eHnDwIJyPHs1/T0RECkVNoSsuiWTLvhUZE0xERERERBVNbi5Ub7wBjyVLgNzc8o6GiMiu3TuFDlAmkQJ6bFAkkazZ994EVEXmXN4BEBERERFRKVOrgbp1kZeZyTWYiIhspPFrWPJ9TSSgKiommIiIiIiIKhqNBuLdd5EWHw8dnyJHJWDIuIW8jDiz2/Oy7iAv645RuZOuEpx0AWb3U+kCATiVRohEZGeYYCIiIiIiIiKFtONrkHzgzVI/rnfbmUCt0aV+XCIqf6WSYFqwYAE2b96M06dPQ6fTISIiAgsXLkT9+vXN7rNu3To888wzijKtVovs7GwzexAREREREdH94NlsJNxq91KUSYYsxH0dCQAI6PUNRG66YrvIy0HizlEAgKBBe6B21hkdV6ULRFJG2cRMROWrVBJMv/zyC8aPH482bdrAYDBgxowZePTRR3Hy5Em4u7ub3c/LywtnzpyRP6tUqtIIh4iIiIjowabXQzVzJjwyM4F33wVcXYvfh6gQZ/dgOLsHK8qk3P8yQ27VH4Haxd1oe0GCSRvQ3Gg7AEiSBGTEl0HERFTeSiXB9NNPPyk+r1u3DoGBgTh8+DAefvhhs/upVCoEBQWVRghERERERFRAkoBTp+Cs1+e/J7rPDBlxEPpUo3JJkiAlJSEHflCbWIDeyT3IKLFFRI6hTNZgSklJAQD4+fkVWS89PR2hoaGQJAkPPfQQ5s+fj8aNG5utn5OTg5ycHPlzamr+DUuSpPxMuAOTJAlCCIc/D7J/7GvkKNhXyR6xX5LDcHKC9OqrSE9Oho+TE5NMVCoK3/skSTLqV4W3p52IRuqhd8wey9zy4d5tZ8Lnf6/ZFCcRYF1/tWS7Uf1/5abdhHRPMlUYsuT32bePQGViuqgjJVMt/b2n1BNMkiRh8uTJaNeuHZo0aWK2Xv369fHJJ5+gWbNmSElJweLFixEREYF//vkH1apVM7nPggULMG/ePKPyhIQEh1+7SZIkpKSkQAhhMpNPVFrY18hRsK+SPWK/JEci1aqFlJQU6BMT2V+pVAhDpvw+ISEBKucMs9tzKj8O7aMdldvzsqHf3R8A4NxpE5xcjP/oztEFIj6eU+jIdtb0V0u2O2myTG67ffBD5J1aYTaO25seMVnu3HgyXJpOKeYs7ENaWppF9Uo9wTR+/HicOHEC+/btK7JeeHg4wsPD5c8RERFo2LAhVq9ejTfeeMPkPtOnT8eUKf99AampqQgJCUFAQAC8vLxK5wTKiSRJUKlUCAgI4C8AVKbY18hRsK+SPWK/JEfC/kqlTcrNwLV/3wcEBJhcg6lge2D1xqa3785/71+1PmBQLhKezwDghtkYHGnUB5Uva/qrJdudtZ4mt1VuMw5S86esji+/LwdavV95cLVwHb9STTC98MIL+OGHH/Drr7+aHYVkjouLC1q2bInz58+braPVaqHVao3K1Wp1hfifpkqlqjDnQvaNfY0cBfsq2SP2S3IIkgT88w9ckpKgZoKJSkuhfmTyPmjF9szT64ucQmeOR5Pn4NVslMltTD6RQin2V6Pthd67eFaB2qVu6cRspyz9f0ipJJiEEJgwYQK+/fZb7NmzBzVr1rT6GHl5efj777/Ro0eP0giJiIiIiOjBVfAUOb0eCAsDnMtk6VWiEvNoNAIe9foryiRDFuK+jixyv/QTHyP9xMcmt/mEzYJv+OzSCpGIrFQq/6cZP348Pv/8c2zduhWenp6Ii8tfss3b2xs6Xf682uHDh6Nq1apYsGABAOD111/H//73P9SpUwfJyclYtGgRrly5gueff740QiIiIiIienCpVEBICKSsrPz3RPeZPvEkXIPamN3u5B6kmHIE5E87KhA0aA/UhRZGLpx8undb4WMSUfkplQTTypUrAQCRkZGK8ujoaERFRQEArl69qhhWdffuXYwcORJxcXHw9fVFq1at8Pvvv6NRo0alERIRERER0YNLq4VYsQKp8fFwNbHEBJGtTCWQUo+tkt/H/zgE3g9NhnfLF0zvn3QKzsFtzR5fpXaBNrCl/Llw8unebURlTZ9wDHma/9Zokgo9JS4n4ZjZhOeDNmWz1KbIFWfPnj2Kz++99x7ee++90mieiIiIiIiIylhRCST9nRNIPbZS3i6EQOqR96ELiYTGv4nR/gnbnoLPQxMVCaiijm9N8oqotJl7EhwAs9M6H8Qpm5yMTUREREREREUqLoFkSLsGYciWt6tdfSAy42FIuwaNf5Ni9y9qO4Bik1dEZanygJ/hpHEvvmIhD+KUTSaYiIiIiIgqGr0eqtdfh0dGBrBgAWDhI6aJzCkugeTsGQKV83/9TMpOhtpZB2fPEDP7e0Nk3pb3L+r4AIpsm6isaQKaG60ZRsaYYCIiIiIiqmgkCTh6FM56ff57IhsVl0DS+DeBV/OxuLtvOgBApVLB66FJcgLIeP8UOBXav7jjF7WNyJBxC3kZcYqy4tZJKrzdkBEHoU81u/3eNZgKPIjrLBWFCSYiIiIioorGxQViyhRkJidD4+JS3tFQBVBcAgkAvJqPkbcH9vxCsQh4cfsXt724tunBlnZ8DZIPvGl2u7l1kgqk/7MOKQcXmt1ubg2mB3GdpaIwwUREREREVNE4OQGRkdDHx+e/JyoFRSWQ7qWpZPx08ML7B/TYALd7niJX1PGtaZsePJ7NRsKtdi+r9pEMWXLiyaNxFNzr9jOuI0m4m5QEXz8/qNVqo+0P4jpLRWGCiYiIiIiIiKxiKoFk1f5+DYvcLqRc5MQfkT8Xnq5077YCnK704HJ2D7b6u5dyMwrtHwS1T23jOpIENeKhDQw0mWAiJSaYiIiIiIjsmKm1RYolSVDdzoJThhrw9wf4hxE5mKKmNPGx8GStkqzRBAAqXSAAjgK1FBNMRERERER2rLi1RczxvlgLnqerA1u2AM78tZ/sS15GHPJSzivKCv/BX6nrR1A5aY32c9JVgpMuwOQxOV2JzCnpGk3ebWcCtUaXUVQVD/9PQ0RERERkx0ytLVJ47ZCAXt9A5KYrd8rNhfpiDHKqqyHuHIPa1RX34nQiKkvFPZUr5eBCZJz6zOz+iTtHmSznKCUqiZKs0QTkj2BKyii+HuVjgomIiIiIqByVaApcIfq4P00//ajWv//dvNvkfj5hs+DZbGSJ2mZyiopT3FO5zCWXPJo8B69mppNLAEcpUcmUZI0mIH8NJmTEl0FEFRMTTERERERE5aikU+AKmHr6UeERTv49NwJ5mUb7Oekq4e7+N5B+Yq3VbXIUCRXHlqdyMXlJ5JiYYCIiIiIiKkfFTYELGrTHaPHZwtul7CSjR7YXfjpS1uVtyPgnukSxqd2C4NFgKDwaPKko5ygSKg6fykX04GGCiYiIiIioHJmaulE4QaQNaA61i7tie/Khd+X38T8OgfdDk+Hd8gW5LPXQCvl95oX/g9dDUxRJIsUIp0c/gca/MQAg9+55JP7yEqTMf6fNqV2QeX4zPBs9BY1/E9tOlBxKcU/dyrz6s9HaXyIvR37Pp3IRPXiYYCIiIiIiciD6OyeQemyl/FkIgdQj70MXEgmNf5N/t6/6bwehNkoSFU5Q3d0/V05Q5WXEAUKSt6ldfSAy42FIu8YE0wOmuKmbCd/3L3J/PpWL6MHDBBMRERERkQMxpF2DMGTLn+9NAhnSrkFI/40kUbv5QOT8t72oBJWzZwhUzv89cU7KTobaWQdnz5D7c3JkN4p76lZe1h3kZd0xKnfSVYKTLsDsfnwqF1HFxQQTEREREZEd0yeeVKyxVFwSyNkzBCqtDtD/uz03FU6FtheVoHKr2R1ezcfi7r7pAACVSgWvhyZx9NIDqKRP3SoOn8pFVHFxVTUiIiIiIjtTeIpb/I9DkHLkA/mzxr8JvJqPlT/fmwQqbvu9Caq8jHhApYakT0dO/BFoq4TL23zD58K1SgRy4o8oXoaMW6V/0kRE5NA4gomIiIiIyI4Ut8YSAHg1HyOPMgrs+YXRU+S8mo2Wtwd0X///7N15eBNV2wbwO+mSpPu+AKWUUhZBKLLUFpAiZRGUHQRRqJ+yKyAiCiKLCKjwsrwoioBVATdQAZXlBSyKsghIQWQVKXtpWbovaTvn+6N26DRJm9CWJuX+XVcukjNnZs5MnpwmD2fOwKlWhLysOAFVvFzkXEchgJStQw3acuN//2e0jR4R0+EZOePuD5KIiGocJpiIiIiIiKxIeXMslebo/YDhRvLuzMFkp3JGXvIRxeKSo5Q8Hp4Je4/6Bpsoay4dO+eAco+DiIjuL0wwERERERFZEUsn2i7ISoLQpyvKpKxU+Xnakf8g68w6k/tL3T/baDlHKRERkSWYYCIiIiIisiKlL2Erb6LtzL8+QdrBd0xuz1RyyaXZc3BrPtLkehylRERElmCCiYiIiIjIypScY8n70WWwdw5QXOYmFeTIzx0D2sKn+6cG21BpvJCeo4anlxfUasN7+9g5B1TJXcKIiOj+xAQTEREREZEVy7u6F8lljFBK+b6/0XL3tq9DXX8UNH5+RhNMRERElYkJJiIiqlbX0nNxLT3XoFySBG7dzoKXPg1qtcpgeaCbFoFuWoNyIkuZikFjSsZlbQ8dY5DuCZemsXAO62fZSvn5UH3+PXK3fwBMnQpoGatERFS1KjXB9P7772PBggVISkpCixYtsGzZMrRt29Zk/fXr1+ONN95AYmIiwsLC8M4776BHjx6V2SQiuguW/NgqiT/4718ViZkV+y7gzR1nLF53RpeGmNWtEeOVTDI3Nlbsu4CVBy5avP0REXUxKjLY6LIbWXrcyNLLr32cHeHj7FjuNhmXZIy9cwDUHqGWrZSbC/Hzm5D0ekCSqqZhREREJVRagumrr77CpEmT8OGHHyIiIgJLlixBt27dcPr0afj5+RnU37t3L4YMGYL58+fj8ccfx+eff44+ffrgjz/+QLNmxicwrAn4P/Vkrur80VzRH/x0/6lIzIyKDEavpv6K8hx9ITos3wsA+OTJ5mgW6G6wbnGcM17JlLuNjRERdTGsVR05BveMjYLO0Q4A8OHeRKz6/RIAYOWBi3eVmCoL4/L+VJB1DYVZSYqyknMs5aUchdpeZ7BemXMo2dtDPPccctLS4GjPixaIiKjqqYQQojI2FBERgTZt2uC9994DAEiShKCgILz44ot47bXXDOo/+eSTyMrKwg8//CCXPfzwwwgPD8eHH35o1j7T09Ph7u6OlJQUuLm5GSxXq9WwL/EHVa/XG9QpplKp4ODgcFd18/PzYeo0lq47Y+sJvLXrnMltm/L6o6F4I6aBoszR8c7/hJbVhtJ1CwoKIJXxP1mW1HVwcIBKparSuoWFhSgsLKyUuvb29vIcBNZQV5IkFBQUGK03Z+ffmPuT5bEyo0tDzOgSZnK7kiTh5s2bCAwMhFqtNtqGa+l5SMrIAwCo7dSwU6vx2aFL+O+viQCAACc7DGlZC4PC6yjWq+2hQx1PZwCAEAL5+fkm21ny81lVdYGyP8vW2EdUVl3g3vYRJWOmWE5+AR796CAA4JPB4Wji6wRJUrYhwFWDQDcNAOVn+Z0dpzB1+1kAQD1XO7zYoT7Gta8vr1ey7uXbWbiSmqPY7trDl/D+/stF+3BxxNOt6mBwy9oolCRIhZLBvovZUh8BAHZ2drCzs7OauuV9Pi2pWxl9hLG4zCuUEP3hAQBFiSN7leHnIsBVAzetHbxn7QIAZMx9DM4ae+j1elxJy8HJC9eKJk1WFb1PpWO9WYArrqVm42b2nX7C28kR205dvxOXrho8/VBtDG5ZGwUFBSj+eJaOy5rSR5jC7xFFMg/NR9rvc00uN8W97etwaT3V5HKVSoWbN2/K/9nLPoLfI6yhj7C3t0dycjL8/PwgSRL7iCqsaw3fDWy9j5AkSY5XtVp93/YR6enp8PX1RVpamtHcS7FK+e8MvV6Pw4cPY+rUO3/g1Go1YmJisG/fPqPr7Nu3D5MmTVKUdevWDRs3bjS5n7y8POTl3fmimJ6eDgBYuHAhNBqNQf0GDRpg6NCh8ut3333XZGAFBwcjNjZWfr148WJkZ2cbrRsYGIiRI+/c0vW9995Damqq0bq+vr4YO3as/Fpz+mc8r01X1DmalImDHkWXEtZxdcDglnUwKLw2vv32W9y4cQMAkLXvKOYeuNN2JycnvPLKK/LrNWvW4MKFC0bb4ODggGnTpsmvv/jiC/z9999G6wLAzJkz5ecbNmzAyZMnTdadOnWq/Adl8+bNOHr0qMm6kydPhrNzUfJh69atOHTokMm6EyZMgIeHBwBgx44dJuMIAMaMGSN/cfr555/x888/m6z7/PPPo3bt2gCKRtHt3LnTZN3hw4ejXr16AICDBw9i69atJusOGTIEDRs2BAAcPXoUmzZtMll3wIABaNq0KQDgr7/+woYNG4zWy5IcsLZ7DBo2LPqf7IsXL+CdT76RY8XlzE940M8JTQOKPuDt2kWhadNmCHTTIDExEZ9+ang3GaCo02zVqhV69uwJALhy5QpWrVplsr0dO3aEb+OH8P1fd/5nNWfv14j7TUJ2eC34udz57EVGRqJW164AgNTUVCxdutTkdlu3bi23ISsrCwsXLjRZt0WLFujTpw+Aov5m/vz5Jus2adIEgwYNkl/PnWv6C7s19hErVqxASkqK0boeHh6YMGGC/Hr16tW4du2a0brW0EfsvZgGeHcGAMzefgqP4hxq6Y23F7jTR/yVlI7Fa74F/B4EAKTu+RpzfpFwpkS8lewjjh/Yo+gjkjPz8OWx60CLovlK7HPT8N2fdnimVW2knkmoMX0EAPTu3Rvh4eEAgDNnzuCLL74wWfexxx6TL1svq48AgJiYGLRr1w6AeX1EdHQ0ACA5ORkffPCBybqRkZHoWs19REjDJgCK/hY1r+WKhfON9xF6oQbwEADgeFIa2gR54t1334Ver0dWVhacnZ3lHxylY31ih/rI3rdB0UfsKxWXWjvguz+v4ZlWtfHzd2vuyz4C4PeIYkP6xiB48OMAgDOnT2N3GW2I6dwZ9UOLLpc7dzkN68v4O/fEE0+gdu3akCQJf//9N/sI8HuENfQRb7zxBoQQkCSJfQS/R1h9HyGEUPzdv1/7iJJ5mLJUSoLpxo0bKCwshL+/8hIHf39/nDp1yug6SUlJRusnJSUZrQ8A8+fPx+zZsw3Ks7KyjGZF09PTkZycLL/OzMw0mT3NyMgwqJuTk2NW3YyMDGRlZRmtq9VqFXXVOalwy70pv07NycfNq6nAv0mDZu4STiZeQWEDJ3jm34I+986bX3IPxZlUc9pQ/L8ExdLT003WBWBx3eJOPy0trcy6KSkp8nJz6hZnbVNTU8usW5yEM7ducVb29u3bZda9efMmnJyczK5bfN5u3bpVZt1bt26ZVVcNwA+ZqONY9GE+n34TN69elGPFr+AWUq6kQNJ4w0PncKdubh5u3rxpcrtCCGRmZiI5ORlqtRo3btwos72pqanIvpKEetp8nP+3LFCnQp6+EGnpmXBWFSjqFh9bee9xWlqaXDc7O9vsusU/7kwp/bm3pK419BFl1bWzszO7bnX3Eak5+Th/PRXwLnrd0LkACcevQWhS4aFzMKgP3OkjLlxJhZ+mEMV7CdDBIN7K6iPS0nPhYVeItH9ft/BSQe+QjwtXkqCvQX1E6bplfe6L23g3dc3pI+6mbnX1ERkZGShOMKUkp5iseyI5B/j3Cv9Z3x1E/wcDkJmZifz8fOTmFl2+rFKpjMb6/xL+RnDyLWhVd/6HuXRctvJWIT23KC7vxz6iZF1+jwBuZqnhgaIfpSn5t3Ep3dV0Gwr84f1v3ZtZmaa3KwRS//kHbrm5EP+OXmYfwe8R1tJHpKWlQQjBPoLfI6y+jxBCKP7u3699hLkJpkq5RO7q1auoXbs29u7di8jISLl8ypQp+Pnnn3HgwAGDdRwdHfHpp59iyJAhctny5csxe/ZsXL9+3eh+jI1gCgoKwvXr123mErnSdbedSsboDUeR/O9UO+GBzkjOzMeKgS0QE+rJoe3gsNXiYaA/nkjCiC8Py7HyoJ8GNzL0eL9/c3Rv7Gf2UFRJknDr1i0EBASYvESudBtOpWShT9xBnL9dtPNgl6LL5r4e1goPBLgp6nJoe80f2m5O3dJ9W4tAFySn5eD9fs3QvbHhvHzAnc/yX0np6L1qPxLTi97nEBcVVGo7RbyV9bk/kZSOAZ8eQmJGUVmwuyPs7ezwbWxrNPZ1rvbPPYe2V28fkVMgyZe+7XsxCi38nQ3qlI6heh4a2KnV+Oqp5mjs54KUlBT4+vpCrVYbjfWUTD3e691EEeumtvltbGs09Nbdd31EMX6PqFjdMj+fubmwGzoUhXo97L/7DtBq2UdYWBfg94i7qWvOJXLF/SgvkeP3CGvvIyRJUvzdv1/7iPT0dPj7+9+bS+R8fHxgZ2dnkBi6fv06AgICjK4TEBBgUX0A0Gg0Ri+F02q10Jpx61Vz6txNXWNtMuVWnlBM3KyHHdQOGiC3KHF2PUMPldoOWfpC/JVyJ2NZ3sTNlrSh5B8AW6irVqsVgV7T6pbsdMoS4u0CnU4H5BbFRZpeBa1Ohwb+HgbxWtZ2JUmS//AY6yQB4xOMP9E0QJ6DKU/lgGceqg3YO+LEjTv1iuL0zrEX/wEwR1XVrarPfVX1EdZQtzI+yw38PRTxejunAFqtxmi8lvZgLQ+M7dAAU378d8i8gwYTO4bioXrGE1PXM/XKeLV3RK8WQXfiVVJhUMtA5EvAn9fv/I9Nef2qrfUR1lAXsI7PfVl134u/c9nG4LVHMOmR+nixQ31FnaScNGQLOwBFX+DddI5IztQjKUcgXKuFRqOBVquFWq02GusaB3u4O+sU/WPpuMwtBJ4JL4rLqvp7b819xL2say2f5eroI4RWi0KVqujvvb09+4i7qMvvEZbXLe/zKUkSVP/GpSWxwz7i7upW9+fe1vsISZIUf/dLu1/6iLISXiVVSoLJ0dERrVq1wq5du+RrFyVJwq5du/DCCy8YXScyMhK7du3CxIkT5bIdO3YoRkDVROXd0eZaZj6AfAxe+4einHeVoWaBbhgXVU/+0a1SqfDSIyFoFmg6g3y3yovTpIw8LNj9Dxbs/kdRzjilYpbEq7GEZmSwp/x8ZteGaBbojj8upyrqFP8QZ7ySuY5fS8f7exPl10IILNlzHp0a+Chis66HDjqHO18uU3PyoXOwQ10Pw7t4mYr1n/+5xbik6qXVQqxfj9TkZPhZ8EOFiIjoblXaPUsnTZqE4cOHo3Xr1mjbti2WLFmCrKwsPPvsswCAYcOGoXbt2vKEWRMmTEDHjh3xn//8Bz179sSXX36JQ4cO4aOPPqqsJlklY7fjBoC/UzJxLfkGAv180MDXxWB5RW87T7anvB/ds7s1QrMAV5M/uivCVJyWh3FKJY0p8aP7q2daoW1dT6P1yksQxX51zGh58Q9xxiuZ62JqDnLy71xW4KFzQHKmHhdTcxQJprISpFdSs/HX9Sx46dOgVhddMmGsb76Rpcfap1rK5T7OjvBxLv9/1BmXREREZKsqLcH05JNPIiUlBTNmzEBSUhLCw8Oxbds2eSLvixcvKoaURUVF4fPPP8f06dMxbdo0hIWFYePGjWjWrFllNckqmfrxH17LDcnJ9vLtD4nK/dH9ZYLR8sr43+/KSFLR/cVYQjRHf+eHfH6BZJAMBYpizVSCSJIEbt2+BS9PL/mHfOl1i/9lvJI5LBmZZCpB+tH+i5iz86zJfVRl30xERERkzSplku/qkp6eDnd393InmrIFxXdqYIKJihn7wW6O8n5sM9aoKszafrrMhKgpZf3oZqxSVVgY/7ecOAr21OHljvXRv3ktownSDsv3AgD2jI2CzrEoMZWckYdz11Lg7uYOP1eNWaOSACZCqRrk50P64ANkZmTAZfJkqC2Yd4OoKvHvO9kSxmsRc3MvlTaCiYgqF3+MkC3hZWpkK4yNTCovQVqcaCqNo5LIqhUWQvW//8FRrwdeeqm6W0NERPcBJpiIiKjCmBAlW9TU3xWAZQnSkpdu1jZyaR2R1bC3h3j6aeSmp8PRgjs4ERER3S3+tSEiIqIaqby5wRKupMmXvpVUVsJUkiQkO+bBz8/9vh4qTzbA3h4YNAi5yclwY4KJiIjuAf61ISIiohqpvJsl8NI3IiIiosrDBBMRERHVSJwbjO5rQgBpaVClpwO+vtXdGiIiug8wwUREREQ1EucGo/taXh5UzzwDd70e2LgRcHKq7hYREVENZ9MJJiEEgKJb5tk6SZKQkZEBrVbLOR2oSjHWyFYwVskaMS7JZuTmQuTnI6+gAI7p6VAXFFR3i4gAsB8l28J4LVKccynOwZhi0wmmjIwMAEBQUFA1t4SIiIiIyEoFBlZ3C4iIqAbIyMiAu7u7yeUqUV4KyopJkoSrV6/C1dUVKpWquptTIenp6QgKCsKlS5fg5uZW3c2hGoyxRraCsUrWiHFJtoTxStaIcUm2hPFaRAiBjIwM1KpVq8yRXDY9gkmtVqNOnTrV3YxK5ebmdl8HLt07jDWyFYxVskaMS7IljFeyRoxLsiWMV5Q5cqnY/XsRIRERERERERERVQommIiIiIiIiIiIqEKYYLISGo0GM2fOhEajqe6mUA3HWCNbwVgla8S4JFvCeCVrxLgkW8J4tYxNT/JNRERERERERETVjyOYiIiIiIiIiIioQphgIiIiIiIiIiKiCmGCiYiIiIiIiIiIKoQJJiIiIiIiIiIiqhAmmIiIiIiIiIiIqEKYYCrD/Pnz0aZNG7i6usLPzw99+vTB6dOnFXVyc3Mxbtw4eHt7w8XFBf3798f169fl5UePHsWQIUMQFBQEnU6HJk2aYOnSpYpt7N69GyqVyuCRlJRUZvuEEJgxYwYCAwOh0+kQExODs2fPyssTExPx3HPPISQkBDqdDqGhoZg5cyb0en0lnB2qTLYeawDQq1cv1K1bF1qtFoGBgXjmmWdw9erVCp4ZskY1IV6L5eXlITw8HCqVCgkJCXd3Qsgq1IS4rFevnsF233777QqeGbI2NSFWAeDHH39EREQEdDodPD090adPn7s/KVTtbD0uTW1XpVLh4MGDlXCGyJrYerwCwJkzZ9C7d2/4+PjAzc0N7du3R3x8fAXPjBUQZFK3bt1EXFycOH78uEhISBA9evQQdevWFZmZmXKd0aNHi6CgILFr1y5x6NAh8fDDD4uoqCh5+erVq8X48ePF7t27xblz58SaNWuETqcTy5Ytk+vEx8cLAOL06dPi2rVr8qOwsLDM9r399tvC3d1dbNy4URw9elT06tVLhISEiJycHCGEEFu3bhWxsbFi+/bt4ty5c2LTpk3Cz89PvPzyy5V8pqiibD3WhBBi0aJFYt++fSIxMVH89ttvIjIyUkRGRlbiWSJrURPitdj48ePFY489JgCII0eOVPzkULWpCXEZHBws3nzzTcV2S7afaoaaEKsbNmwQnp6e4oMPPhCnT58Wf/31l/jqq68q8SzRvWbrcZmXl6fY3rVr18Tzzz8vQkJChCRJlXy2qLrZerwKIURYWJjo0aOHOHr0qDhz5owYO3ascHJyEteuXavEM3XvMcFkgeTkZAFA/Pzzz0IIIVJTU4WDg4NYv369XOfkyZMCgNi3b5/J7YwdO1Z06tRJfl0cuLdv3za7LZIkiYCAALFgwQK5LDU1VWg0GvHFF1+YXO/dd98VISEhZu+HqkdNiLVNmzYJlUol9Hq92fsi22Sr8bplyxbRuHFj8ddffzHBVAPZYlwGBweLxYsXm71dqhlsLVbz8/NF7dq1xapVq8zeLtkeW4vL0vR6vfD19RVvvvmm2fsh22Vr8ZqSkiIAiF9++UWuk56eLgCIHTt2mL0va8RL5CyQlpYGAPDy8gIAHD58GPn5+YiJiZHrNG7cGHXr1sW+ffvK3E7xNkoKDw9HYGAgunTpgt9++63Mtpw/fx5JSUmKfbu7uyMiIuKu9k3WxdZj7datW1i3bh2ioqLg4OBQ5vbJ9tlivF6/fh0jRozAmjVr4OTkZN6Bkk2xxbgEgLfffhve3t5o2bIlFixYgIKCgvIPlmyarcXqH3/8gStXrkCtVqNly5YIDAzEY489huPHj5t/0GT1bC0uS9u8eTNu3ryJZ599tsxtU81ga/Hq7e2NRo0a4bPPPkNWVhYKCgqwYsUK+Pn5oVWrVuYfuBVigslMkiRh4sSJaNeuHZo1awYASEpKgqOjIzw8PBR1/f39TV6XuXfvXnz11VcYOXKkXBYYGIgPP/wQ33zzDb755hsEBQUhOjoaf/zxh8n2FG/f39/f7H3//fffWLZsGUaNGlXu8VL1seVYe/XVV+Hs7Axvb29cvHgRmzZtMvu4yTbZYrwKIRAbG4vRo0ejdevWFh8zWT9bjEsAGD9+PL788kvEx8dj1KhRmDdvHqZMmWLRsZNtscVY/eeffwAAs2bNwvTp0/HDDz/A09MT0dHRuHXrlmUngKySLcZlaatXr0a3bt1Qp06dco+XbJstxqtKpcLOnTtx5MgRuLq6QqvVYtGiRdi2bRs8PT0tPgfWxL66G2Arxo0bh+PHj+PXX3+9620cP34cvXv3xsyZM9G1a1e5vFGjRmjUqJH8OioqCufOncPixYuxZs0arFu3TpEU2rp1K+zs7Cza95UrV9C9e3cMHDgQI0aMuOtjoKpny7H2yiuv4LnnnsOFCxcwe/ZsDBs2DD/88ANUKtVdHwtZN1uM12XLliEjIwNTp0696zaTdbPFuASASZMmyc+bN28OR0dHjBo1CvPnz4dGo7nrYyHrZYuxKkkSAOD1119H//79AQBxcXGoU6cO1q9fz//IrAFsMS5Lunz5MrZv346vv/76rttPtsMW41UIgXHjxsHPzw979uyBTqfDqlWr8MQTT+DgwYMIDAy862OpbkwwmeGFF17ADz/8gF9++UWRBQ8ICIBer0dqaqoiO3r9+nUEBAQotnHixAl07twZI0eOxPTp08vdZ9u2beUPSa9evRARESEvq127Nq5duybvq2QAXr9+HeHh4YptXb16FZ06dUJUVBQ++ugjs4+b7j1bjzUfHx/4+PigYcOGaNKkCYKCgrB//35ERkaafQ7IdthqvP7000/Yt2+fwQ/21q1bY+jQofj000/NOwFklWw1Lo2JiIhAQUEBEhMTFV9wqWaw1VgtLn/ggQfk5RqNBvXr18fFixfNPHqyVrYalyXFxcXB29sbvXr1MuuYyXbZarz+9NNP+OGHH3D79m24ubkBAJYvX44dO3bg008/xWuvvWbZibAm1T0JlDWTJEmMGzdO1KpVS5w5c8ZgefHkYRs2bJDLTp06ZTB52PHjx4Wfn5945ZVXzN53TEyM6Nu3b5ltCwgIEAsXLpTL0tLSDCa7u3z5sggLCxODBw8WBQUFZu+f7q2aEGulXbhwQQAQ8fHxZreFbIOtx+uFCxfEn3/+KT+2b98uAIgNGzaIS5cumd0Wsi62HpfGrF27VqjVanHr1i2z20LWz9Zjtfh1yUm+9Xq98PPzEytWrDC7LWRdbD0uS9YNCQnhXbNrOFuP182bNwu1Wi0yMjIU6zZs2FDMnTvX7LZYIyaYyjBmzBjh7u4udu/erbgtYXZ2tlxn9OjRom7duuKnn34Shw4dMrg1+59//il8fX3F008/rdhGcnKyXGfx4sVi48aN4uzZs+LPP/8UEyZMEGq1WuzcubPM9r399tvCw8NDbNq0SRw7dkz07t1bcfvDy5cviwYNGojOnTuLy5cvK/ZP1sXWY23//v1i2bJl4siRIyIxMVHs2rVLREVFidDQUJGbm1vJZ4uqm63Ha2nnz5/nXeRqAFuPy71794rFixeLhIQEce7cObF27Vrh6+srhg0bVslniqqbrceqEEJMmDBB1K5dW2zfvl2cOnVKPPfcc8LPz4/JUBtWE+JSCCF27twpAIiTJ09W0pkha2Tr8ZqSkiK8vb1Fv379REJCgjh9+rSYPHmycHBwEAkJCZV8tu4tJpjKAMDoIy4uTq6Tk5Mjxo4dKzw9PYWTk5Po27evIoEzc+ZMo9sIDg6W67zzzjsiNDRUaLVa4eXlJaKjo8VPP/1UbvskSRJvvPGG8Pf3FxqNRnTu3FmcPn1aXh4XF2fyGMi62HqsHTt2THTq1El4eXkJjUYj6tWrJ0aPHi0uX75cKeeHrIutx2tpTDDVDLYel4cPHxYRERHC3d1daLVa0aRJEzFv3jwm6WsgW49VIYpGLL388svCz89PuLq6ipiYGHH8+PEKnxuqPjUhLoUQYsiQISIqKqpC54KsX02I14MHD4quXbsKLy8v4erqKh5++GGxZcuWCp+b6qYSQggzrqQjIiIiIiIiIiIySl3dDSAiIiIiIiIiItvGBBMREREREREREVUIE0xERERERERERFQhTDAREREREREREVGFMMFEREREREREREQVwgQTERERERERERFVCBNMRERERERERERUIUwwERERERERERFRhTDBRERERPeFxMREqFQqJCQkVPm+PvnkE3h4eFT5fqpKdHQ0Jk6cWKFt2Po5ICIiIsswwURERETVLjY2FiqVyuDRvXv36m5auerVq4clS5Yoyp588kmcOXOmyvcdHR0tnyutVosHHngAy5cvr/B2v/32W8yZM6cSWkhERET3C/vqbgARERERAHTv3h1xcXGKMo1GU02tqRidTgedTndP9jVixAi8+eabyM7OxmeffYZx48bB09MTQ4YMsXhber0ejo6O8PLyqoKWEhERUU3GEUxERERkFTQaDQICAhQPT09PAMBTTz2FJ598UlE/Pz8fPj4++OyzzwAA27ZtQ/v27eHh4QFvb288/vjjOHfunMn9GbuEa+PGjVCpVPLrc+fOoXfv3vD394eLiwvatGmDnTt3ysujo6Nx4cIFvPTSS/JIIlPb/uCDDxAaGgpHR0c0atQIa9asUSxXqVRYtWoV+vbtCycnJ4SFhWHz5s3lnjcnJycEBASgfv36mDVrlmK91NRUPP/88/D19YWbmxseffRRHD16VF531qxZCA8Px6pVqxASEgKtVisfV8lL5G7fvo1hw4bB09MTTk5OeOyxx3D27FmD81m3bl04OTmhb9++uHnzZrltJyIiopqDCSYiIiKyekOHDsX333+PzMxMuWz79u3Izs5G3759AQBZWVmYNGkSDh06hF27dkGtVqNv376QJOmu95uZmYkePXpg165dOHLkCLp3744nnngCFy9eBFB0KVmdOnXw5ptv4tq1a7h27ZrR7Xz33XeYMGECXn75ZRw/fhyjRo3Cs88+i/j4eEW92bNnY9CgQTh27Bh69OiBoUOH4tatWxa1WafTQa/XAwAGDhyI5ORkbN26FYcPH8ZDDz2Ezp07K7b5999/45tvvsG3335rcn6q2NhYHDp0CJs3b8a+ffsghECPHj2Qn58PADhw4ACee+45vPDCC0hISECnTp3w1ltvWdRuIiIism1MMBEREZFV+OGHH+Di4qJ4zJs3DwDQrVs3ODs747vvvpPrf/755+jVqxdcXV0BAP3790e/fv3QoEEDhIeH4+OPP8aff/6JEydO3HWbWrRogVGjRqFZs2YICwvDnDlzEBoaKo8Q8vLygp2dHVxdXeVRV8YsXLgQsbGxGDt2LBo2bIhJkyahX79+WLhwoaJebGwshgwZggYNGmDevHnIzMzE77//blZbCwsLsXbtWhw7dgyPPvoofv31V/z+++9Yv349WrdujbCwMCxcuBAeHh7YsGGDvJ5er8dnn32Gli1bonnz5gbbPXv2LDZv3oxVq1ahQ4cOaNGiBdatW4crV65g48aNAIClS5eie/fumDJlCho2bIjx48ejW7duZrWbiIiIagYmmIiIiMgqdOrUCQkJCYrH6NGjAQD29vYYNGgQ1q1bB6BotNKmTZswdOhQef2zZ89iyJAhqF+/Ptzc3FCvXj0AkEcb3Y3MzExMnjwZTZo0gYeHB1xcXHDy5EmLt3ny5Em0a9dOUdauXTucPHlSUVYywePs7Aw3NzckJyeXue3ly5fDxcUFOp0OI0aMwEsvvYQxY8bg6NGjyMzMhLe3tyJpd/78ecWlg8HBwfD19S2z7fb29oiIiJDLvL290ahRI7n9J0+eVCwHgMjIyDLbTURERDULJ/kmIiIiq+Ds7IwGDRqYXD506FB07NgRycnJ2LFjB3Q6neIuc0888QSCg4OxcuVK1KpVC5IkoVmzZvLlYqWp1WoIIRRlxZd8FZs8eTJ27NiBhQsXokGDBtDpdBgwYIDJbVaUg4OD4rVKpSr3Er+hQ4fi9ddfh06nQ2BgINTqov8/zMzMRGBgIHbv3m2wTsn5oZydnSvcbiIiIiImmIiIiMgmREVFISgoCF999RW2bt2KgQMHygmZmzdv4vTp01i5ciU6dOgAAPj111/L3J6vry8yMjKQlZUlJ1lKz0H022+/ITY2Vp7nKTMzE4mJiYo6jo6OKCwsLHNfTZo0wW+//Ybhw4crtv3AAw+Ue9zlcXd3N5qYe+ihh5CUlAR7e3t5NNfdaNKkCQoKCnDgwAFERUUBuHO+i9vfpEkTHDhwQLHe/v3773qfREREZHuYYCIiIiKrkJeXh6SkJEWZvb09fHx85NdPPfUUPvzwQ5w5c0YxQbanpye8vb3x0UcfITAwEBcvXsRrr71W5v4iIiLg5OSEadOmYfz48Thw4AA++eQTRZ2wsDB8++23eOKJJ6BSqfDGG28YjCiqV68efvnlFwwePBgajUbR3mKvvPIKBg0ahJYtWyImJgbff/89vv32W8Ud6SpbTEwMIiMj0adPH7z77rto2LAhrl69ih9//BF9+/ZF69atzdpOWFgYevfujREjRmDFihVwdXXFa6+9htq1a6N3794AgPHjx6Ndu3ZYuHAhevfuje3bt2Pbtm1VdmxERERkfTgHExEREVmFbdu2ITAwUPFo3769os7QoUNx4sQJ1K5dWzGnkVqtxpdffonDhw+jWbNmeOmll7BgwYIy9+fl5YW1a9diy5YtePDBB/HFF19g1qxZijqLFi2Cp6cnoqKi8MQTT6Bbt2546KGHFHXefPNNJCYmIjQ01ORcRn369MHSpUuxcOFCNG3aFCtWrEBcXByio6PNP0EWUqlU2LJlCx555BE8++yzaNiwIQYPHowLFy7A39/fom3FxcWhVatWePzxxxEZGQkhBLZs2SKPIHv44YexcuVKLF26FC1atMD//vc/TJ8+vSoOi4iIiKyUSpSefICIiIiIiIiIiMgCHMFEREREREREREQVwgQTERERERERERFVCBNMRERERERERERUIUwwERERERERERFRhTDBREREREREREREFcIEExERERERERERVQgTTEREREREREREVCFMMBERERERERERUYUwwURERERERERERBXCBBMREREREREREVUIE0xERERERERERFQhTDAREREREREREVGFMMFEREREREREREQVwgQTERERERERERFVCBNMRERERERERERUIUwwERERERERERFRhTDBREREREREREREFcIEExERURVITEyESqXCJ598Ut1NqdHq1auH2NjY6m4GERER0X2PCSYiIqK78Mknn0ClUhl9vPbaa1Wyz3nz5mHjxo3l1ouOjjbZtpKPWbNmVUk7S9uyZcs925clTp06hSlTpiA8PByurq4IDAxEz549cejQIaP1r1y5gkGDBsHDwwNubm7o3bs3/vnnH0WdS5cuYfbs2Wjbti08PT3h4+OD6Oho7Ny502B7ZcVQUlKS2cexevVqNGnSBFqtFmFhYVi2bJlBnXr16pncV1hYmFn7kSQJ7777LkJCQqDVatG8eXN88cUXBvV+//13jB07Fq1atYKDgwNUKpXZx1Js7969aN++PZycnBAQEIDx48cjMzPToF5eXh5effVV1KpVCzqdDhEREdixY4dF+zLn/AHmvf9lsaSt5h4/ERGRNbGv7gYQERHZsjfffBMhISGKsmbNmiE4OBg5OTlwcHCotH3NmzcPAwYMQJ8+fcqs9/rrr+P555+XXx88eBD//e9/MW3aNDRp0kQub968eaW1rSxbtmzB+++/b3VJplWrVmH16tXo378/xo4di7S0NKxYsQIPP/wwtm3bhpiYGLluZmYmOnXqhLS0NEybNg0ODg5YvHgxOnbsiISEBHh7ewMANm3ahHfeeQd9+vTB8OHDUVBQgM8++wxdunTBxx9/jGeffdagHcZiyMPDw6xjWLFiBUaPHo3+/ftj0qRJ2LNnD8aPH4/s7Gy8+uqrcr0lS5YYJCguXLiA6dOno2vXrmbt6/XXX8fbb7+NESNGoE2bNti0aROeeuopqFQqDB48WK63ZcsWrFq1Cs2bN0f9+vVx5swZs7ZfLCEhAZ07d0aTJk2waNEiXL58GQsXLsTZs2exdetWRd3Y2Fhs2LABEydORFhYGD755BP06NED8fHxaN++fbn7Mvf8mfv+l8Xctlpy/ERERFZFEBERkcXi4uIEAHHw4MEKbSczM9Psus7OzmL48OEW72P9+vUCgIiPj6+0tlhi3Lhxoqq+cgQHB9/VORFCiEOHDomMjAxF2Y0bN4Svr69o166dovydd94RAMTvv/8ul508eVLY2dmJqVOnymXHjx8XKSkpinVzc3NF48aNRZ06dRTlFY2h7Oxs4e3tLXr27KkoHzp0qHB2dha3bt0qc/05c+YIAOK3334rd1+XL18WDg4OYty4cXKZJEmiQ4cOok6dOqKgoEAuT0pKEtnZ2UKIu3vvH3vsMREYGCjS0tLkspUrVwoAYvv27XLZgQMHBACxYMECuSwnJ0eEhoaKyMjIcvdjyfkz9/03xZK2mnv8RERE1oaXyBEREVUBY3MwxcbGwsXFBefOnUOPHj3g6uqKoUOHAgDOnj2L/v37IyAgAFqtFnXq1MHgwYORlpYGAFCpVMjKysKnn34qX9pUkbmHZs2aBZVKhRMnTuCpp56Cp6enYhTF2rVr0apVK+h0Onh5eWHw4MG4dOmSYht79uzBwIEDUbduXWg0GgQFBeGll15CTk6O4pjff/99+RiKH8UkScKSJUvQtGlTaLVa+Pv7Y9SoUbh9+7ZiX0IIvPXWW6hTpw6cnJzQqVMn/PXXX0aP7dy5czh37ly556BVq1ZwcXFRlHl7e6NDhw44efKkonzDhg1o06YN2rRpI5c1btwYnTt3xtdffy2XNW3aFD4+Pop1NRoNevTogcuXLyMjI8NoWzIyMlBYWFhum0uKj4/HzZs3MXbsWEX5uHHjkJWVhR9//LHM9T///HOEhIQgKiqq3H1t2rQJ+fn5in2pVCqMGTMGly9fxr59++Ryf39/6HQ6i46lWHp6Onbs2IGnn34abm5ucvmwYcPg4uKiONcbNmyAnZ0dRo4cKZdptVo899xz2LdvnyJeb9y4gVOnTiE7O1sus+T8mfv+A8DFixdx6tQpRZm5bbXk+ImIiKwNE0xEREQVkJaWhhs3bigeZSkoKEC3bt3g5+eHhQsXon///tDr9ejWrRv279+PF198Ee+//z5GjhyJf/75B6mpqQCANWvWQKPRoEOHDlizZg3WrFmDUaNGVbj9AwcORHZ2NubNm4cRI0YAAObOnYthw4YhLCwMixYtwsSJE7Fr1y488sgjcnsAYP369cjOzsaYMWOwbNkydOvWDcuWLcOwYcPkOqNGjUKXLl3kYyh+lFz+yiuvoF27dli6dCmeffZZrFu3Dt26dUN+fr5cb8aMGXjjjTfQokULLFiwAPXr10fXrl2RlZVlcEydO3dG586d7/qcJCUlKZJEkiTh2LFjaN26tUHdtm3b4ty5cyYTRyW36eTkBCcnJ4NlnTp1gpubG5ycnNCrVy+cPXvWrHYeOXIEAAza1apVK6jVanm5qXVPnjyJp556yux9OTs7Ky6xBIqOv2RbKurPP/9EQUGBwTE5OjoiPDxcsZ8jR46gYcOGikRMyTYlJCTIZe+99x6aNGmC33//XbE+UP75s/T9HzZsmMF5Mretlhw/ERGRteEcTERERBVQcp6eYkIIk/Xz8vIwcOBAzJ8/Xy5LSEjA+fPnsX79egwYMEAunzFjhvz86aefxujRo1G/fn08/fTTldR6oEWLFvj888/l1xcuXMDMmTPx1ltvYdq0aXJ5v3790LJlSyxfvlwuf+eddxQjVUaOHIkGDRpg2rRpuHjxIurWrYvIyEg0bNhQHpVR0q+//opVq1Zh3bp1ikRHp06d0L17d6xfvx5PPfUUUlJS8O6776Jnz574/vvv5RFQr7/+OubNm1dp5wIoGpW1b98+TJ8+XS67desW8vLyEBgYaFC/uOzq1ato1KiR0W3+/fff+PbbbzFw4EDY2dnJ5U5OToiNjZUTTIcPH8aiRYsQFRWFP/74A0FBQWW29dq1a7Czs4Ofn5+i3NHREd7e3rh69arJddetWwcA8gi68ly7dg3+/v4GE3aXPP7KcO3aNcV2S+9rz549irrlvSfl7cuc81fR99+Stlpy/ERERNaGI5iIiIgq4P3338eOHTsUj/KMGTNG8drd3R0AsH37dsUlPPfC6NGjFa+//fZbSJKEQYMGKUZlBQQEICwsDPHx8XLdksmlrKws3LhxA1FRURBCmDXSYv369XB3d0eXLl0U+yq+dK14Xzt37oRer8eLL76oSHBMnDjR6HYTExORmJhowVkokpycjKeeegohISGYMmWKXF58yZ9GozFYR6vVKuqUlp2djYEDB0Kn0+Htt99WLBs0aBDi4uIwbNgw9OnTB3PmzMH27dtx8+ZNzJ07t9z25uTkwNHR0egyrVZrsk2SJOHLL79Ey5YtDUbalLWvuzl+S5V3rkvux5I2zZo1C0IIREdHK9Y35/xZ+v7v3r3bIMlsblstOX4iIiJrwxFMREREFdC2bVujl86YYm9vjzp16ijKQkJCMGnSJCxatAjr1q1Dhw4d0KtXLzz99NNy8qmqlL572dmzZyGEMHnr+pJ3xbt48SJmzJiBzZs3G8yZVDx3VFnOnj2LtLQ0gxEkxZKTkwEUjaoCYNAmX19feHp6lrsfc2RlZeHxxx9HRkYGfv31V8XcTMWJtLy8PIP1cnNzFXVKKiwsxODBg3HixAls3boVtWrVKrcd7du3R0REBHbu3CmXpaSkKOZncnFxgYuLC3Q6HfR6vdHt5ObmmpwH6eeff8aVK1fw0ksvGSxLSkpSvHZ3d4dOp4NOp7P4+O9Geee65H4q2iZzz9/dvv+l92XO+pYcPxERkbVhgomIiOge0mg0UKsNBxD/5z//QWxsLDZt2oT//e9/GD9+PObPn4/9+/cbJKQqU+kfrJIkQaVSYevWrYrLuYoVJ14KCwvRpUsX3Lp1C6+++ioaN24MZ2dnXLlyBbGxsZAkqdx9S5IEPz8/+XKt0nx9fe/iiCyn1+vRr18/HDt2DNu3b0ezZs0Uy728vKDRaOTLl0oqLjOWPBoxYgR++OEHrFu3Do8++qjZ7QkKCsLp06fl123atJGTbAAwc+ZMzJo1C4GBgSgsLERycrIiSafX63Hz5k2TCa1169ZBrVZjyJAhBstKX5oVFxeH2NhYBAYGIj4+HkIIxSiyso7/bhTv39S5LrmfwMBAXLlyxWg9c9pk7vm72/e/9L7Maaslx09ERGRtmGAiIiKyEg8++CAefPBBTJ8+HXv37kW7du3w4Ycf4q233gIAg/lvqkJoaCiEEAgJCUHDhg1N1vvzzz9x5swZfPrpp4pJvY1dImiq3aGhodi5cyfatWtX5siM4OBgAEUjnurXry+Xp6SkGIycspQkSRg2bBh27dqFr7/+Gh07djSoo1ar8eCDD+LQoUMGyw4cOID69evD1dVVUf7KK68gLi4OS5YsMZrIKcs///yjSK6tW7dOcWlU8TkIDw8HABw6dAg9evSQlx86dAiSJMnLS8rLy8M333yD6Ohoo8mK0u9f06ZN5X2tWrUKJ0+exAMPPCAvP3DggKItFdWsWTPY29vj0KFDGDRokFyu1+uRkJCgKAsPD0d8fDzS09MVk2eb2yZzz9/dvP/G9mVOWy05fiIiImvDOZiIiIiqWXp6OgoKChRlDz74INRqteJSGWdnZ8Vd3KpCv379YGdnh9mzZxvMIyOEwM2bNwFAHt1Uso4QAkuXLjXYprOzMwAYtH3QoEEoLCzEnDlzDNYpKCiQ68fExMDBwQHLli1T7G/JkiVGj+HcuXM4d+5c2Qf6rxdffBFfffUVli9fjn79+pmsN2DAABw8eFCRZDh9+jR++uknDBw4UFF3wYIFWLhwIaZNm4YJEyaY3GZKSopB2ZYtW3D48GF0795dLmvXrh1iYmLkR3GC6dFHH4WXlxc++OADxTY++OADODk5oWfPnka3n5qaanJy75L7iYmJkUfU9O7dGw4ODli+fLlcVwiBDz/8ELVr10ZUVJTJ47SEu7s7YmJisHbtWsWd2dasWYPMzEzFuR4wYAAKCwvx0UcfyWV5eXmIi4tDRESEYpL0Gzdu4NSpU4o5ziw5f5a8/xcvXsSpU6cUZea21ZLjJyIisjYcwURERFTNfvrpJ7zwwgsYOHAgGjZsiIKCAqxZswZ2dnbo37+/XK9Vq1bYuXMnFi1ahFq1aiEkJAQRERGV2pbQ0FC89dZbmDp1KhITE9GnTx+4urri/Pnz+O677zBy5EhMnjwZjRs3RmhoKCZPnowrV67Azc0N33zzjdERRa1atQIAjB8/Ht26dYOdnR0GDx6Mjh07YtSoUZg/fz4SEhLQtWtXODg44OzZs1i/fj2WLl2KAQMGwNfXF5MnT8b8+fPx+OOPo0ePHjhy5Ai2bt0KHx8fg/117twZAMqd6HvJkiVYvnw5IiMj4eTkhLVr1yqW9+3bV06OjR07FitXrkTPnj0xefJkODg4YNGiRfD398fLL78sr/Pdd99hypQpCAsLQ5MmTQy22aVLF/j7+wMAoqKi0LJlS7Ru3Rru7u74448/8PHHHyMoKEhxBz9TdDod5syZg3HjxmHgwIHo1q0b9uzZg7Vr12Lu3Lnw8vIyWGfdunXQaDSKuDJHnTp1MHHiRCxYsAD5+flo06YNNm7ciD179mDdunWKyykvXLiANWvWAICckCkehRccHIxnnnmmzH3NnTsXUVFR6NixI0aOHInLly/jP//5D7p27apIvEVERGDgwIGYOnUqkpOT0aBBA3z66adITEzE6tWrFdt87733MHv2bMTHx8sTfVty/sx9/wFg2LBh+PnnnxXJUEvaau7xExERWR1BREREFouLixMAxMGDB40uP3/+vAAg4uLi5LLhw4cLZ2dng7r//POP+L//+z8RGhoqtFqt8PLyEp06dRI7d+5U1Dt16pR45JFHhE6nEwDE8OHDzWrr+vXrBQARHx8vl82cOVMAECkpKUbX+eabb0T79u2Fs7OzcHZ2Fo0bNxbjxo0Tp0+fluucOHFCxMTECBcXF+Hj4yNGjBghjh49anDcBQUF4sUXXxS+vr5CpVKJ0l8/PvroI9GqVSuh0+mEq6urePDBB8WUKVPE1atX5TqFhYVi9uzZIjAwUOh0OhEdHS2OHz8ugoODDc5DcHCwCA4OLve8DB8+XAAw+Th//ryi/qVLl8SAAQOEm5ubcHFxEY8//rg4e/asok7xeTX1KPkevP766yI8PFy4u7sLBwcHUbduXTFmzBiRlJRUbttLn79GjRoJR0dHERoaKhYvXiwkSTKol5aWJrRarejXr59F2y9WWFgo5s2bJ4KDg4Wjo6No2rSpWLt2rUG9+Ph4k8ffsWNHs/a1Z88eERUVJbRarfD19RXjxo0T6enpBvVycnLE5MmTRUBAgNBoNKJNmzZi27ZtBvWK35eS57+YuefPnPdfCCE6duxoEOOWtNWS4yciIrImKiFKjX8nIiIiIiIiIiKyAOdgIiIiIiIiIiKiCmGCiYiIiIiIiIiIKoQJJiIiIiIiIiIiqhAmmIiIiIiIiIiIqEKYYCIiIiIiIiIiogqxr+4GVIQkSbh69SpcXV2hUqmquzlERERERERERDWKEAIZGRmoVasW1GrT45RsOsF09epVBAUFVXcziIiIiIiIiIhqtEuXLqFOnToml9t0gsnV1RVA0UG6ublVc2sqRpIkpKSkwNfXt8yMIFFFMdbIVjBWyRoxLsmWMF7JGjEuyZYwXoukp6cjKChIzsGYYtMJpuLL4tzc3GpEgik3Nxdubm73deBS1WOska1grJI1YlySLWG8kjViXJItYbwqlTc1kU0nmIiIiIiIiMhGFBQAW7dCk5YGDBoEODpWd4uohivIuobCrCSL17NzDoC9c2AVtKhmY4KJiIiIiIiIql5BAVQrVkCn1wP9+jHBRFUu49hKpB54y+L1PCKmwzNyRhW0qGazmgTT22+/jalTp2LChAlYsmRJdTeHiIiIiIiIKpNaDdGuHfIzM+HIy43oHnBtPgJOoU8oyqSCHCR9HQ0ACBi0G2p7ncF6ds4B96J5NY5VJJgOHjyIFStWoHnz5tXdFCIiIiIiIqoKjo7Aq68iKzkZzhy9RPeAvXOgwaVuUn6W/Fzj2wJqB+d73awaq9oTTJmZmRg6dChWrlyJt94qe+haXl4e8vLy5Nfp6ekAiibekiSpSttZ1SRJghDC5o+DrB9jjWwFY5WsEeOSbAnjlawR45KqW8nYkyQJKCMWGa9FzD3+ak8wjRs3Dj179kRMTEy5Cab58+dj9uzZBuUpKSnIzc2tqibeE5IkIS0tDUIIzk5PVYqxRraCsUrWiHFJtoTxStaIcUnVTRRky89TUlKgss8yWZfxWiQjI8OsetWaYPryyy/xxx9/4ODBg2bVnzp1KiZNmiS/Tk9PR1BQEHx9feHm5lZVzbwnJEmCSqWCr6/vfR24VPUYa2QrGKtkjRiXZEsYr2R18vKAkSPhkZcHx7g4qHWGc98QVTUpPwuX/n3u6+tb5iVy7EeLaLVas+pVW4Lp0qVLmDBhAnbs2GF2YzUaDTQajUG5Wq2uEW+2SqWqMcdC1o2xRraCsUrWiHFJtoTxSlZFpYK4fRtqvR7qf2OT6J4rEXfm9I/sR2H2sVdbgunw4cNITk7GQw89JJcVFhbil19+wXvvvYe8vDzY2dlVV/OIiIiIiIioMjk6QixZgoybN+HNSb6JapxqSzB17twZf/75p6Ls2WefRePGjfHqq68yuURERERERFSTqNVA/foodHFRjCIhopqh2hJMrq6uaNasmaLM2dkZ3t7eBuVERERERERERGS9qv0uckRERERERHQfKCgA4uPhmJoK9O4N8DI5qiQFWddQmJVkVl2pIKfEeklw9Aitqmbdd6wqwbR79+7qbgIRERERERFVhYICqJYuhZNeD/TsyQQTVZqMYyuReuAti9fL/OsTeLWbUwUtuj9ZVYKJiIiIiIiIaii1GqJ1a+RnZcGRczBRJXJtPgJOoU8oyqSCHCR9HQ0ACBi0G2p7nUG5Y0Ab5CUfMbldSZIg3bqFPHhBrVbDzjkA9s6BVXIMNQETTERERERERFT1HB2BGTOQlZwMZ45eokpk7xxokPiR8rPk5xrfFlA7OBuUp3w/wKztF1985xExHZ6RMyrW2BqMCSYiIiIiIiIiuu+YGtlUXC5JEm7fugVPrzsjmMg0JpiIiIiIiIiI6L5jamRTcbkkSVAjGRo/P6h5WWe5mGAiIiIiIiKiqpeXB9ULL8AtJwdYuRLQ6aq7RURG5aUclUcwlZyDqSycn4kJJiIiIiIiIroXhACuXYNary96TmSlii+Vk1+bsQ7nZ2KCiYiIiIiIiO4FR0eId95Bxs2b8OYk32TFSs/B5OGmw/UNjyqWlcb5mZhgIiIiIiIiontBrQaaNEFhcnLRcyIrVXoOJkdPZ4NlZIifaiIiIiIiIiIiqhAmmIiIiIiIiKjqFRYCv/4KhwMHip4TUY3CS+SIiIiIiIio6uXnQ/Xuu3DW64EuXQAHh+puERFVIiaYiIiIiIiIqOqp1UCzZijIyoIj52AiqnGYYCIiIiIiIqKq5+gIMW8eMpOT4cS7yBHVOEwbExERERERERFRhXAEExERERERERHdd/Q3T0Ab0MaidfJSjkJtr5NfF+bcQGHODZP17XTesNP5GpY7B8DeOdCifVs7JpiIiIiIiIio6un1UL38MlxzcoD33gO02upuEd2H0o9+KD9P/nEI3B+aCPeWL5i9ftLX0ZXSDo+I6fCMnFEp27IWTDARERERERFR1ZMk4Px52On1Rc+J7jH9jeNIP/qB/FoIgfQj/4UuKBr27iFmbSNg0G55BJNUkCMnnLy7fASVnaZou4V5uLljJADA94kNcHANMtiOnXNARQ7FKjHBRERERERERFXP0RHizTeReesWvDjJN1WDgoxLEAW58mu11gMiOxkFGZfMTjBpfFtA7eAMAJDys+Ryl4YDFeXFCSanup3l8pqOCSYiIiIiIiKqemo1EB6OguTkoudE95i9axBU9ncuzZRyU6G218HeyAgjshw/1URERERERERU4zn6NINbizHya5VKBbeHJsDRp5minv7mCZPbKGvZ/Y4JJiIiIiIiIqp6hYXAwYOwT0goek5UDdxajJaf+/X8Au7h4wAYTv6dduQ9+XX6sY9MLqM7mGAiIiIiIiKiqpefD9WcOXBZtAjIz6/u1hDB0fsBAKYn/9bf/Av56ReQcWyF4bIbx83ax/004okJJiIiIiIiIqp6ajUQFobCkBDOwURWxejk3wU5KMi4BCnnBkRBjtFlppQ1Gqomq9ZP9fz589GmTRu4urrCz88Pffr0wenTp6uzSURERERERFQVHB0h/vMfZMyeDfAucmRFjE3+rfp38m+1zgcqe53RZSUVj1QyORrKzBFPtqxaE0w///wzxo0bh/3792PHjh3Iz89H165dkZWVVf7KREREREREREQVZHLyb++mcHALhmvzUYbLfJoZHalU1mioms6+One+bds2xetPPvkEfn5+OHz4MB555JFqahUREREREdH9rSDrGgqzkixez845APbOgVXQIqKq5dZiNG7/OhVA0eTf2oA2kCSpaFnzkUj7TbnM1Eglr0feNRgNpTYy4qkmqtYEU2lpaWkAAC8vL6PL8/LykJeXJ79OT08HAEiSJL/xtkqSJAghbP44yPox1shWMFbJGjEuyZYwXqki0o9+hLTf51q8nnvb1+Hx8BvGF+r1wPTpcMnOhrRwIaDVGq9HVAlK9n25N/6C1r+1QbkkScC/r0uW23s2lvMMpfvR4mX69IuQCu7kJ1RaT0jZKRBqR7g0HyMnpKCyg0vLCbD3esBm+2Nz2201CSZJkjBx4kS0a9cOzZo1M1pn/vz5mD17tkF5SkoKcnNzjaxhOyRJQlpaGoQQUHPCO6pCjDWyFYxVskaMS7IljFeqCBHYB5quUcqywlzod/UHADh2/gYqO8MEUZ7OD8nJycY3mpsLj6NHIQoKkJyUBLWTU6W3m6hY1plv5DmBzu2YDafQvnAOfRyiIFuuk5KSApV90RQ9xsqL+1EpP8tgWX6BNzJ0zeGSswMAkCb5A7raUBd4wz6wL4CiBJMq8iPkeTU2/bmwARkZGWbVs5oE07hx43D8+HH8+uuvJutMnToVkyZNkl+np6cjKCgIvr6+cHNzuxfNrDKSJEGlUsHX15dfAKhKMdbIVjBWyRoxLsmWMF6pYvwMSqT8LFzaVfTcv2E01A7Olm2ysBDSzJnITE2FX+3aUDs4VEI7iQzpb/6FvLNLUTzuxlV/BurTC+HRMAL2nvVQPBuSr6+vHMdSfpZBeXE/6u3hhCul1/Hzg3Pao0j7rSjB5JZ/Fm4PTYB7gzaKbdUKbWX5Z8XKaM0cbWgVCaYXXngBP/zwA3755RfUqVPHZD2NRgONRmNQrlara8QfTZVKVWOOhawbY41sBWOVrBHjkmwJ45UqVYk4uqu4UquBqCgUJCdD7eDAuKQqI2VeBkqMSLLTukFkJ0PKvAy1R325XBHHJuK7uB81tswjfLR8KZx/z3XQBrQpc1u2ytz2V2uCSQiBF198Ed999x12796NkJCQ6mwOEREREREREdk4e9egez7RtqP3A1W2bVtRrQmmcePG4fPPP8emTZvg6uqKpKSiuxS4u7tDp9NVZ9OIiIiIiIjIAuXeeU6SIP39N1Rpachr2RJq+6Kfo7zzHFU2R59mcGsxRr4rnEqlgttDE+Do00wxn1Jlyks5CrV9UR5DKsgxWl5STYz7ak0wffBB0S39oqOjFeVxcXGIjY299w0iIiIiIiKichn70Zx+bCUyj68ya/3ss3eee0RMh2fkjMpsHhHcWoyWE0x+Pb+4c/laFUn6Otqi8poY99V+iRwRERERERHZFlM/msuiC34Mzvuyodfr4fDs/0GlcQQA2Om8kZd8xKB+TRzhQdXjXly+FjBot9GRSqbYOQdUYWuqh1VM8k1ERERERES2o/SPaakgR046lVxWsjznwlbk1Pp3hfh95e6jJo7woJpL49vC5u8WV1FMMBEREREREZFFSv+YLjmvTcllJcsDBu0G1BrcvnULHm46XN/wqFxuao4aIrIdTDARERERERFRldP4tgDsdFAjGY6e5Y/0KMxKMjppOC+do+rEybxNY4KJiIiIiIiILKK/ecLySZP1eqjmz4dLVhbw5nS52NL5nFyaPQe35iMNyu+nH/JUtYqTRZIkQbp1C/qCO4kjTuZtGhNMREREREREVK70ox/Kz5N/HAL3hybCveUL5m9AkoCEBNjr9UXP/2VqziZTMo+vRubx1Qbl99MPeapapWPwuhnrlJX4vF8wwURERERERERl0t84jvSjH8ivhRBIP/Jf6IKi4ejTzLyNODhATJqE7NRUuDk4yMUqtQM0fi0BKOds8un6MRx9mhaVm5hEvKT76Yc8Va3iGJMkCbdv3YKnlxfUanWZ63AEHRNMREREREREVI6CjEsQBbnya7XWAyI7GQUZl8xOMOlTT8MxOhr65GSk/3VnBFLJ0VAlR0nd3jdLLi+ZeCqZkCKqCsUT1UuSBDWSofHzKzfBRADPEBEREREREZXJ3jUIKnut/FrKTYXKXgd71yCDuvqbJ+TnpS+rS0tYjvz0C8g4tkIuLx4NlfX3RqOjpIpGT5XazpH3Ku3YiKhyMMFEREREREREZXL0aQa3FmPk1yqVCm4PTZBHLxlLABlcVlcoIWf7IoiEeAh9tlyu1npAFORAf+NPw1FSBTnIvfKrycQTEVkPXiJHRERERFSNrqXn4lp6bvkVSwl00yLQTVt+RaJK4tZiNG7/OhUA4NfzC/kucqbmZ1Kp7ZUJI3sPeHxxEsLtS9x4Wgvxb7mUmwq1vQ6OPg8ajJIqnmupopfnERlTfLc4oGier9LlxXeRy0PRHEycZ6lsTDAREREREVWjFfsu4M0dZyxeb0aXhpjVrVEVtIiofI7eD8jPTc3PBECZMMpLheSuAfzrwvXBdkg7+EZRnX9HQzk36IP81HNyEqu4XFu7vdHEk7HL84gsYeqOhaXLk/79l3cqLBsTTERERERE1WhUZDB6NfVXlOXoC9Fh+V4AwJ6xUdA52hmsx9FLZC2Mzc+kttdBW7s93PKz7iSMHNXI/+9c5AX2h4+ns5xgKjkaytQoKbcWYwwSTxy9ROYyNVLJu8tHUNlpDOrb6bxhp/M1uIsc71RYNiaYiIiIiIiqkbFL3bLyCuTn4bXd4azh13ayXsXzMxlLANm7hygSRo5+rZCcnKxcv8RoqJKElI+85CMAAE2tSLncM3IWHH2aysuK8fIlMsXUSKWbO0YaLS8eqcS7yFmGf6mIiIiIiIioQkyNPCrJVCLJ1OgSU0mBG//7P6PlvHyJTAkYtFuOMXNwpNLdYYKJiIiIiIiIKo2pRBL0emDRIjhnZgLTp8jFphJJprg0ew5uzQ1HnjApQKZofFtA7eBc3c2o8ZhgIiIiIiIioiqXl5wA1YkdQH4+9Mnd5PLy5sExKOelcERWiQkmIiIiqpF463ciIuuStKkbEPHvi++7y+XlzYNDRLaBCSYiIiKqkXjrd7pfmEqmSpLArdtZ8NKnQa1WGSxnMpXutYBBuwG1RnFXrrLwkjci28IEExEREdVIvPU73S+YTCVbofFtAdjpeFcuohqKCSYiIiKqkXjrd7pflJdM/XlsJJwdDWOdyVQqS0HWNRRmJSnKSt7hreSd38papihPTgBSUiFSU5EnGkFtV5Tk55xKRDUDv1URWSnOHUJEdG9Y0t8mZ+Th3LUbcL+cDz9XDXycHc1aj30zVURSei7SSiRHLRVeyw2uWvNilahYxrGVSD3wlsnlZd35zdSypPWd7jxPuFPOuZaIagYmmIisFIe7ky1hQpRs0V/XM9C2rudd97eWGBFRF6Mig8utx88EGfN2/N9Y/fulu14/KSMPZ29kW7we4/H+5tp8BJxCnzC6LP3YSmQeX2XxNl2axMJ1/SXo9Xo4vDUHam3RKCfOtURUMzDBdI9xEkYyV3XOHcJkAVmqIgnRUZHB7Bfpnvlgb6L8/Mk1hzHpkfoW9bdFI5hS4O7mLo9gMlZ3xb4LWHngory9lQcuKl6bwv8koGIlY3Xb6RRM7lgfg1vWlsvK+05Qcnnc75fwdvw5i9vAeLy/2TsHmrxszTPyDbg1H2HxNu2cA6Du4o/U5GT4cQ4mohqn2hNM77//PhYsWICkpCS0aNECy5YtQ9u2bau7WVWGo1LIXOXNHeJgr8ZDdTyqZN+MU7JURRKiFY03JkTJXMevpeP9Ej/ahRBYsuc8OjXwMehPTc3VJEkSkj2F4oeRsb55VjetwYilkp+JTwaHo1mAq0EbGZMEGMaqnQr45s8kDGsdhGaBbgDKn0+s5PJ2IZ441KKDYnnJeFw1sDm0DoZ9tI+zI/64nGq0jexD729lJZ/KI0lSJbeGyPI5w4px/q/KVa0Jpq+++gqTJk3Chx9+iIiICCxZsgTdunXD6dOn4efnV51NqzLl/Qj75MnmaBbobrAe/4ATYPx/3l/sUL/S91NunPKHEZVSkYRoRSenZUKUzHUxNQc5+YXyaw+dA5Iz9biYmiP/aDem+FI6U0z1zaU/Ewvj/5afz9x+usr6cLJ9dxurJZWMy/HfHcfER0IV8Vayj35+/TGL28g+lIisyd3OGcb5vypXtSaYFi1ahBEjRuDZZ58FAHz44Yf48ccf8fHHH+O1114zezt6vR56vd6gXK1Ww97eXlHPFJVKBQcHh7uqm5+fDyGEWXV9dHbw1jop6izafecL5+z/nZG/AJTebuk2OTremayxrDaUrltQUFDm/xxYUtfBwQEqlapK6xYWFqKwsLBS6trb28v/42wNdSVJQkGB6Uk77ezsYPfv3TWOXUnFe7+clZdJ+Xosjj+D9nXd0DTQTVG3rO1KkqRon7G63lq1HKfF2y35w2jGj8cxoUMIxrVX/jAq3j9QNDIgPz/f5LGV/HxWVV2g7M+yNfYRlVUXqP4+YtnPd2LmyTWHMSGqLsa2q2dQrzjeSn6W07LvjEhSF+ajmZ/yB1XJus+3rYPHGnorlufkF+DRjw4CAD55soX8g6xQkiAVFrU3wFVj8D7ach9hDXXL+3xaUrcq+ohAJztoS1yNkZqTD61KQqCTnUEslI7fSY/Ux6iIOpAkSf7eoVar8de1dEXfXDwqqrhvLmaqXqcGPmjko7sv+wiA3yNM1Q10soNWdef4bmdkQ6tWI9DJDpIkGVxWpM/Xw6FEfWPxtvjnc4q41Off+Vyv7t8UzWp7wK54VJ6+AB2X75OXPRCg7IPVdmrU8Sj6nlCT+ojSdQF+j7ibumV+PvV62H/wAZwyM4GpU1Fgb88+ogrrWsN3g3vVR2gbx8K3bnf5tVqlgl2Jz72pNtg5BaCgoMBkH1H67/792keU1Z6Sqi3BpNfrcfjwYUydOlUuU6vViImJwb59+4yuk5eXh7y8PPl1eno6AGDhwoXQaDQG9Rs0aIChQ4fKr999912TARscHIzY2Fj59eLFi5GdbXwyxMDAQIwcOVJ+/d577yE1NdVoXV9fX4wdO1Z+vWLFCqSkpMivkzPz8OWx60CLfgAAlZDw3z3/IDrUC79t/hLXrl0zul0nJye88sor8us1a9bgwoULRus6ODhg2rRp8usvvvgCf//9t9G6ADBz5kz5+YYNG3Dy5EmTdadOnSr/Qdm8eTOOHj1qsu7kyZPh7OwMANi6dSsOHTpksu6ECRPg4eEBANixY4fJmACAMWPGyCPefv75Z/z8888m6z7//POoXbto/oK9e/di586dJusOHz4c9erVAwAcPHgQW7duNVl3yJAhaNiwIQDg6NGj2LRpk8m6AwYMQNOmTQEAf/31FzZs2GCybu/evREeHg4A2H/0L9z4+UvgoQEAgLz93yA7Nw8Lru9EmI8zHnvsMfny0sTERHz66adGtymEQKtWrdCzZ08AwJUrV7BqlelJGjt27Ajfxg/hg73n5bK0PV9hzi8SzoTXgp/Lnc9eZGQkunbtCgBITU3F0qVLTW63devWchuysrKwcOFCk3VbtGiBPn36ACjqO+bPn2+ybpMmTTBo0CD59dy5c03WtYU+oiQPDw9MmDBBfr169Wqr7SOM9W3vrP4Cp3/IUcRMSSX7iEn/XQugaFTTE2OnIqKOm2IESck+4viBPQZ9xN6LaYB3ZwDAG98ewOTHHsK49iHYvXt3je0jzpw5gy+++MJkXXP7CACIiYlBu3btAJjXR0RHRwMAkpOT8cEHH5isaw19RHv44HPUA1B02VGj81uw8eN92Fiijqm/zWd++BieGjWysrLg7OwMlUqFszeycONkitw3e+rskZKpx3//uxR1Xe4kAUzVu3A7Gzu+XH3f9RHF+D2iiLE+IuxiGi7+24+l/7YekXVcsfHjfbD/t4+QxJ0fz+++uwCOJRJMpePNQ2ePK4nnseDd7QjzKTqHJfvJSTPm4LkBT+DdEX0AANO//EXe1qQZc/BwHVdFHxwTEwP/Ou0gSVKN6yP4PaJIlfURBQWYefQoHPPyIBUUYMPGjewjwO8R1txHCCEUf/fv1z6iZB6mLNWWYLpx4wYKCwvh76+8LMLf3x+nTp0yus78+fMxe/Zsg/KsrCyjGcn09HQkJyfLrzMzM01mLjMyMgzq5uTkmFU3IyMDWVlZRutqtdoy66al58LDrhBp/75u6a1Cem4+LlxJKnO7kiSZ3QZ7e3tF3fT0dJN1AVhct7jTT0tLK7NuSkqKvNycusVZ0tTU1DLr3rhxQ35uTt3irOzt27fLrHvz5k04OTmZXbf4vN26davMurdu3bqrup7IRYAW+OffZS7qArjpACfkIysrC7dv35br3rx50+R2hRDIzMxEcnIy1Go1bty4UWYbUlNTkX0lCfW0+ShOMQXqVMjTFyItPRPOqgJF3eI2lPcep6WlyXWzs7PNrqvX68usW/pzb0lda+wjSrKzszO7bnX3EaX7tlbeKvztWGAQMyUV9xEXbmUj4XwS4Fr098HbQcK5pFuo7aSCh85Brmuqj0jNycf566nAv4OaGjgX4n8Jf6Otj6pG9xFlfe6L23g3dc3pI+6mbnX1EREN6uDzf3/3rH48BD+scTSobyx+03PzkXopE45OdsjNLRphp1Kp4IR8Rd8cYJeLOp5qOKfrkZV153+YTdXzQTYO34d9RMm6/B5hvI94wNsRu/5d3quhB3x1dsjKypI/99n6O/F19WYqfHV3RhEbizd3d8DpdtF3htL9pLeDhP1nLuPgqUQAwN5TFwB4yctK98E1uY/g94giVdZHSBJS+/dHVlYW7G7dYh/B7xFW30cIIRR/9+/XPsLcBJNKlDXWsQpdvXoVtWvXxt69exEZGSmXT5kyBT///DMOHDhgsI6xEUxBQUG4fv063NwMr0e3hSFpJ5LSMeDTQ0jMKPqSUN/DESq1Hb6NbY2G3qaHzAMc2n43dW192OrCXacxbUfR18V6rnYY3yEEo6NCjNYt6xK5W7duISAgAGq12qw2nErJQp+4gzh/u6hzDXZRw06txtfDWimGzXNou/G6NWpou5l1S/dt9Tw0UAuBr59paXCpRbHiz/LWU8l47ovDSM4p2m5zP0ekZOTj/f7N0b2xn6IuYPiZ23YqGaM3HEXyv1fZNQ9wxo2sfKwY2AJdw7yr/XPPoe3V20fkFEjwnlX0sz3trW5wgHnxa6dW46unmqOxnwtSUlLg6+srv09Lfj4n980hnlq89Eh9PN+mtsF2jdUb2y7kvuwjivF7hOm6WfpC+L75EwAgZcajcP73RgnFdRftPocpW4r+U7aeqx1ebB+CMe1C5G2WjLdQTw1ebB+CkRFBAAz7yQf9NLiRVYCPnnwIAPDcl38gObvwzrIMvaIPrsl9BL9H3FFVfYS9vb3cj0qSxD6iCutaw3cDW+8jJElS/N2/X/uI9PR0+Pv7Iy0tzWjupVi1jWDy8fGBnZ0drl+/rii/fv06AgICjK6j0WiMXgqn1Wqh1ZY/ubA5de6mrrE2mXIrTyjvdmTviF4tgvDfXxMBALmFwNDwQORLwF8pdzKW5d2pw5I2lPwDYAt11Wq1ItBrWt2SnU5JRXfGUmaXO4QFAP9+WZzV80E0C3DFiRvKu2cVx4qp7UqSJP/hMdZJ3tm3crtPNA2Q4zRP5YBnHqoN2Dsq9l+07zvHXnJOpvJUVd2q+txXVR9hDXUr47P8UD0txj7SEFN+LBr2LlRqTIyuj4fqlX8Dh2BPJ+i0GuDf/7VJ16ug1enQwN/D6Hkv/Zlr4O8BnU4H5Batn5pbCI2DPYI9i+Z5sobPfWX0EdZYF7COz31ZdQtLTGysVqmh1RjGsLH4nfBIUfxKkgSNRgOtVit/gX8xupH8Q/6LZ1qjbV1Po/1oyT58ZrfGaBbgioSr6Yo6lfn33pr7iHtZ11o+y5bWLSwx2lOr0UBb4i5xx6+l4/19dy5Xkuwc8N6Bq+jyQG15zrmScbn26VaICL4zV13pfjJNr4JWo0GwZ9FoCp3GEcgusaycPrgm9RGl8XuE5XXL+3xKkgSVSmVx7LCPuLu61f3dwNb7CGN/90u6X/oIq5+DydHREa1atcKuXbvk6x0lScKuXbvwwgsvVFezqlx5dzu6mqHHgt3/YMHufxTlvFPH/ae8WIn9MsFoeWXESnn7TsrIY5xSucZE1ZN/oH/1TKsy78JVUrNAN4wrsa5KpcJLj4SYvHOSsR/yvZv6ywlRfaHAM+EB0BdKittt8xbbVBZT8XstPRd/Xc+Clz4NanXR/2jnlLhUKb+gKM5W7LuAlQcumtx+VfbhVPNZepe5B/yVd34tr5+1pA8msogQQHIy1DduAL6+1d0aIqpk1XoXuUmTJmH48OFo3bo12rZtiyVLliArK0u+q1xNZOx23AAgSQK3bt+Cl6eX/IW1JP4Iuv+YipXyVEasVOe+yTYZS/IY+9FdWnHMlF43MvhOMmpm14ZoFuhusH5xgogJUTKlvLhMuJIGnaPh/2gGumnhVmK0SNMSP84/2n8Rc3aeNVinWIfle42Wj4ioi1GRweW2mf0omaOuhw46hzuxm5qTD52DHep66MzeRskk6uxujdAswFXuZ0v2waWXFWOSnu5KXh5Uzz8PN70e2LgRcHIqdxUish3VmmB68sknkZKSghkzZiApKQnh4eHYtm2bwcTfNYmpP8aSJCHZMQ9+fu5Gh97R/ac6v7jxSyNZqrwkj6kf3TO6FN0RpczRel8dM7nurG6NmBAlk+42LkdE1MWwVnXk1yUTUZHBnljWIwTubu7wc9XAx9m8yzDYr1JlKj0CqVAATzZXjtRUJFOvpsPZUfm1v+RyUyPqylrGJD3dNQsu0SEi21Jtk3xXhvT0dLi7u5c70ZQtKL5Tg5+fHxNMVKUYa1QVjI0UMYepEUyAeSM7+YOdymJuXJZ3OZsp/IFNVSkrrwCurxfdtjxj7mNw1tibXP5c2yCs/v3SXe9r1cDm0DoYjubzcXY0mURlH0x3i99FyZYwXouYm3up1hFMRERUM1T0hwZHdlJVMDcuZ3XTmnX5GqBMfNa24HIkoqr0WqcGGBNVz6C8OF43nM7AqjISUM+vL3ukKBERkTmYYCIiIqL7miUJUiY+yRoFuGkRqjH8Wl8cr02DAzHaSAKqPByhRERElmCCiYiIiIioBgt006K2BydTJiuQnw988AGcMjKAyZM5HxNRDcMEExERERFRNbqbOx6WXE5kMwoLofrf/+Co1wMvvVTdrSGiSsYEExERERFRNbrbOx4WM5aAAgB/F0cYlhJVI3t7iKefRm56Ohzt+VOUqKbhp5qIiIiIqBqNigxGr6b+JpeXd6dDUwmoN2LCMCbco6LNI6o89vbAoEHITU6GGxNMRDUOP9VERERERNWovInmLbnTYUn+Lo5AbnpFmkZERGQ2JpiIiIiIiKyYJXc6LEmSJCQzwUTWRAggLQ2q9HTA17e6W0NElYwJJiIiIiIiIqp6eXlQPfMM3PV6YONGwIl3NySqSWw6wSSEAACkp9v+/8xIkoSMjAxotVqo1erqbg7VYIw1shWMVbJGjEuyJYxXsjq5uRD5+cgrKIBjejrUBQXV3SKiMrEfLVKccynOwZhi0wmmjIwMAEBQUFA1t4SIiIiIiIjMFhhY3S0gIgtlZGTA3d3d5HKVKC8FZcUkScLVq1fh6uoKlUpV3c2pkPT0dAQFBeHSpUtwc3Or7uZQDcZYI1vBWCVrxLgkW8J4JWvEuCRbwngtIoRARkYGatWqVeZILpsewaRWq1GnTp3qbkalcnNzu68Dl+4dxhrZCsYqWSPGJdkSxitZI8Yl2RLGK8ocuVTs/r2IkIiIiIiIiIiIKgUTTEREREREREREVCFMMFkJjUaDmTNnQqPRVHdTqIZjrJGtYKySNWJcki1hvJI1YlySLWG8WsamJ/kmIiIiIiIiIqLqxxFMRERERERERERUIUwwERERERERERFRhTDBREREREREREREFcIEExERERERERERVQgTTGWYP38+2rRpA1dXV/j5+aFPnz44ffq0ok5ubi7GjRsHb29vuLi4oH///rh+/bq8/OjRoxgyZAiCgoKg0+nQpEkTLF26VLGN3bt3Q6VSGTySkpLKbJ8QAjNmzEBgYCB0Oh1iYmJw9uxZeXliYiKee+45hISEQKfTITQ0FDNnzoRer6+Es0OVydZjDQB69eqFunXrQqvVIjAwEM888wyuXr1awTND1qgmxGuxvLw8hIeHQ6VSISEh4e5OCFmFmhCX9erVM9ju22+/XcEzQ9amJsQqAPz444+IiIiATqeDp6cn+vTpc/cnhaqdrcelqe2qVCocPHiwEs4QWRNbj1cAOHPmDHr37g0fHx+4ubmhffv2iI+Pr+CZsQKCTOrWrZuIi4sTx48fFwkJCaJHjx6ibt26IjMzU64zevRoERQUJHbt2iUOHTokHn74YREVFSUvX716tRg/frzYvXu3OHfunFizZo3Q6XRi2bJlcp34+HgBQJw+fVpcu3ZNfhQWFpbZvrffflu4u7uLjRs3iqNHj4pevXqJkJAQkZOTI4QQYuvWrSI2NlZs375dnDt3TmzatEn4+fmJl19+uZLPFFWUrceaEEIsWrRI7Nu3TyQmJorffvtNREZGisjIyEo8S2QtakK8Fhs/frx47LHHBABx5MiRip8cqjY1IS6Dg4PFm2++qdhuyfZTzVATYnXDhg3C09NTfPDBB+L06dPir7/+El999VUlniW612w9LvPy8hTbu3btmnj++edFSEiIkCSpks8WVTdbj1chhAgLCxM9evQQR48eFWfOnBFjx44VTk5O4tq1a5V4pu49JpgskJycLACIn3/+WQghRGpqqnBwcBDr16+X65w8eVIAEPv27TO5nbFjx4pOnTrJr4sD9/bt22a3RZIkERAQIBYsWCCXpaamCo1GI7744guT67377rsiJCTE7P1Q9agJsbZp0yahUqmEXq83e19km2w1Xrds2SIaN24s/vrrLyaYaiBbjMvg4GCxePFis7dLNYOtxWp+fr6oXbu2WLVqldnbJdtja3FZml6vF76+vuLNN980ez9ku2wtXlNSUgQA8csvv8h10tPTBQCxY8cOs/dljXiJnAXS0tIAAF5eXgCAw4cPIz8/HzExMXKdxo0bo27duti3b1+Z2yneRknh4eEIDAxEly5d8Ntvv5XZlvPnzyMpKUmxb3d3d0RERNzVvsm62Hqs3bp1C+vWrUNUVBQcHBzK3D7ZPluM1+vXr2PEiBFYs2YNnJyczDtQsim2GJcA8Pbbb8Pb2xstW7bEggULUFBQUP7Bkk2ztVj9448/cOXKFajVarRs2RKBgYF47LHHcPz4cfMPmqyercVlaZs3b8bNmzfx7LPPlrltqhlsLV69vb3RqFEjfPbZZ8jKykJBQQFWrFgBPz8/tGrVyvwDt0JMMJlJkiRMnDgR7dq1Q7NmzQAASUlJcHR0hIeHh6Kuv7+/yesy9+7di6+++gojR46UywIDA/Hhhx/im2++wTfffIOgoCBER0fjjz/+MNme4u37+/ubve+///4by5Ytw6hRo8o9Xqo+thxrr776KpydneHt7Y2LFy9i06ZNZh832SZbjFchBGJjYzF69Gi0bt3a4mMm62eLcQkA48ePx5dffon4+HiMGjUK8+bNw5QpUyw6drItthir//zzDwBg1qxZmD59On744Qd4enoiOjoat27dsuwEkFWyxbgsbfXq1ejWrRvq1KlT7vGSbbPFeFWpVNi5cyeOHDkCV1dXaLVaLFq0CNu2bYOnp6fF58Ca2Fd3A2zFuHHjcPz4cfz66693vY3jx4+jd+/emDlzJrp27SqXN2rUCI0aNZJfR0VF4dy5c1i8eDHWrFmDdevWKZJCW7duhZ2dnUX7vnLlCrp3746BAwdixIgRd30MVPVsOdZeeeUVPPfcc7hw4QJmz56NYcOG4YcffoBKpbrrYyHrZovxumzZMmRkZGDq1Kl33WaybrYYlwAwadIk+Xnz5s3h6OiIUaNGYf78+dBoNHd9LGS9bDFWJUkCALz++uvo378/ACAuLg516tTB+vXr+R+ZNYAtxmVJly9fxvbt2/H111/fdfvJdthivAohMG7cOPj5+WHPnj3Q6XRYtWoVnnjiCRw8eBCBgYF3fSzVjQkmM7zwwgv44Ycf8Msvvyiy4AEBAdDr9UhNTVVkR69fv46AgADFNk6cOIHOnTtj5MiRmD59ern7bNu2rfwh6dWrFyIiIuRltWvXxrVr1+R9lQzA69evIzw8XLGtq1evolOnToiKisJHH31k9nHTvWfrsebj4wMfHx80bNgQTZo0QVBQEPbv34/IyEizzwHZDluN159++gn79u0z+MHeunVrDB06FJ9++ql5J4Cskq3GpTEREREoKChAYmKi4gsu1Qy2GqvF5Q888IC8XKPRoH79+rh48aKZR0/WylbjsqS4uDh4e3ujV69eZh0z2S5bjdeffvoJP/zwA27fvg03NzcAwPLly7Fjxw58+umneO211yw7EdakuieBsmaSJIlx48aJWrVqiTNnzhgsL548bMOGDXLZqVOnDCYPO378uPDz8xOvvPKK2fuOiYkRffv2LbNtAQEBYuHChXJZWlqawWR3ly9fFmFhYWLw4MGioKDA7P3TvVUTYq20CxcuCAAiPj7e7LaQbbD1eL1w4YL4888/5cf27dsFALFhwwZx6dIls9tC1sXW49KYtWvXCrVaLW7dumV2W8j62XqsFr8uOcm3Xq8Xfn5+YsWKFWa3hayLrcdlybohISG8a3YNZ+vxunnzZqFWq0VGRoZi3YYNG4q5c+ea3RZrxARTGcaMGSPc3d3F7t27FbclzM7OluuMHj1a1K1bV/z000/i0KFDBrdm//PPP4Wvr694+umnFdtITk6W6yxevFhs3LhRnD17Vvz5559iwoQJQq1Wi507d5bZvrffflt4eHiITZs2iWPHjonevXsrbn94+fJl0aBBA9G5c2dx+fJlxf7Juth6rO3fv18sW7ZMHDlyRCQmJopdu3aJqKgoERoaKnJzcyv5bFF1s/V4Le38+fO8i1wNYOtxuXfvXrF48WKRkJAgzp07J9auXSt8fX3FsGHDKvlMUXWz9VgVQogJEyaI2rVri+3bt4tTp06J5557Tvj5+TEZasNqQlwKIcTOnTsFAHHy5MlKOjNkjWw9XlNSUoS3t7fo16+fSEhIEKdPnxaTJ08WDg4OIiEhoZLP1r3FBFMZABh9xMXFyXVycnLE2LFjhaenp3BychJ9+/ZVJHBmzpxpdBvBwcFynXfeeUeEhoYKrVYrvLy8RHR0tPjpp5/KbZ8kSeKNN94Q/v7+QqPRiM6dO4vTp0/Ly+Pi4kweA1kXW4+1Y8eOiU6dOgkvLy+h0WhEvXr1xOjRo8Xly5cr5fyQdbH1eC2NCaaawdbj8vDhwyIiIkK4u7sLrVYrmjRpIubNm8ckfQ1k67EqRNGIpZdffln4+fkJV1dXERMTI44fP17hc0PVpybEpRBCDBkyRERFRVXoXJD1qwnxevDgQdG1a1fh5eUlXF1dxcMPPyy2bNlS4XNT3VRCCGHGlXRERERERERERERGqau7AUREREREREREZNuYYCIiIiIiIiIiogphgomIiIiIiIiIiCqECSYiIiIiIiIiIqoQJpiIiIiIiIiIiKhCmGAiIiIiIiIiIqIKYYKJiIiIiIiIiIgqhAkmIiIiIiIiIiKqECaYiIiI6L6QmJgIlUqFhISEKt/XJ598Ag8PjyrfT1WJjo7GxIkTK7QNWz8HREREZBkmmIiIiKjaxcbGQqVSGTy6d+9e3U0rV7169bBkyRJF2ZNPPokzZ85U+b6jo6Plc6XVavHAAw9g+fLlFd7ut99+izlz5lRCC4mIiOh+YV/dDSAiIiICgO7duyMuLk5RptFoqqk1FaPT6aDT6e7JvkaMGIE333wT2dnZ+OyzzzBu3Dh4enpiyJAhFm9Lr9fD0dERXl5eVdBSIiIiqsk4gomIiIisgkajQUBAgOLh6ekJAHjqqafw5JNPKurn5+fDx8cHn332GQBg27ZtaN++PTw8PODt7Y3HH38c586dM7k/Y5dwbdy4ESqVSn597tw59O7dG/7+/nBxcUGbNm2wc+dOeXl0dDQuXLiAl156SR5JZGrbH3zwAUJDQ+Ho6IhGjRphzZo1iuUqlQqrVq1C37594eTkhLCwMGzevLnc8+bk5ISAgADUr18fs2bNUqyXmpqK559/Hr6+vnBzc8Ojjz6Ko0ePyuvOmjUL4eHhWLVqFUJCQqDVauXjKnmJ3O3btzFs2DB4enrCyckJjz32GM6ePWtwPuvWrQsnJyf07dsXN2/eLLftREREVHMwwURERERWb+jQofj++++RmZkpl23fvh3Z2dno27cvACArKwuTJk3CoUOHsGvXLqjVavTt2xeSJN31fjMzM9GjRw/s2rULR44cQffu3fHEE0/g4sWLAIouJatTpw7efPNNXLt2DdeuXTO6ne+++w4TJkzAyy+/jOPHj2PUqFF49tlnER8fr6g3e/ZsDBo0CMeOHUOPHj0wdOhQ3Lp1y6I263Q66PV6AMDAgQORnJyMrVu34vDhw3jooYfQuXNnxTb//vtvfPPNN/j2229Nzk8VGxuLQ4cOYfPmzdi3bx+EEOjRowfy8/MBAAcOHMBzzz2HF154AQkJCejUqRPeeusti9pNREREto0JJiIiIrIKP/zwA1xcXBSPefPmAQC6desGZ2dnfPfdd3L9zz//HL169YKrqysAoH///ujXrx8aNGiA8PBwfPzxx/jzzz9x4sSJu25TixYtMGrUKDRr1gxhYWGYM2cOQkND5RFCXl5esLOzg6urqzzqypiFCxciNjYWY8eORcOGDTFp0iT069cPCxcuVNSLjY3FkCFD0KBBA8ybNw+ZmZn4/fffzWprYWEh1q5di2PHjuHRRx/Fr7/+it9//x3r169H69atERYWhoULF8LDwwMbNmyQ19Pr9fjss8/QsmVLNG/e3GC7Z8+exebNm7Fq1Sp06NABLVq0wLp163DlyhVs3LgRALB06VJ0794dU6ZMQcOGDTF+/Hh069bNrHYTERFRzcAEExEREVmFTp06ISEhQfEYPXo0AMDe3h6DBg3CunXrABSNVtq0aROGDh0qr3/27FkMGTIE9evXh5ubG+rVqwcA8miju5GZmYnJkyejSZMm8PDwgIuLC06ePGnxNk+ePIl27dopytq1a4eTJ08qykomeJydneHm5obk5OQyt718+XK4uLhAp9NhxIgReOmllzBmzBgcPXoUmZmZ8Pb2ViTtzp8/r7h0MDg4GL6+vmW23d7eHhEREXKZt7c3GjVqJLf/5MmTiuUAEBkZWWa7iYiIqGbhJN9ERERkFZydndGgQQOTy4cOHYqOHTsiOTkZO3bsgE6nU9xl7oknnkBwcDBWrlyJWrVqQZIkNGvWTL5crDS1Wg0hhKKs+JKvYpMnT8aOHTuwcOFCNGjQADqdDgMGDDC5zYpycHBQvFapVOVe4jd06FC8/vrr0Ol0CAwMhFpd9P+HmZmZCAwMxO7duw3WKTk/lLOzc4XbTURERMQEExEREdmEqKgoBAUF4auvvsLWrVsxcOBAOSFz8+ZNnD59GitXrkSHDh0AAL/++muZ2/P19UVGRgaysrLkJEvpOYh+++03xMbGyvM8ZWZmIjExUVHH0dERhYWFZe6rSZMm+O233zB8+HDFth944IFyj7s87u7uRhNzDz30EJKSkmBvby+P5robTZo0QUFBAQ4cOICoqCgAd853cfubNGmCAwcOKNbbv3//Xe+TiIiIbA8TTERERGQV8vLykJSUpCizt7eHj4+P/Pqpp57Chx9+iDNnzigmyPb09IS3tzc++ugjBAYG4uLFi3jttdfK3F9ERAScnJwwbdo0jB8/HgcOHMAnn3yiqBMWFoZvv/0WTzzxBFQqFd544w2DEUX16tXDL7/8gsGDB0Oj0SjaW+yVV17BoEGD0LJlS8TExOD777/Ht99+q7gjXWWLiYlBZGQk+vTpg3fffRcNGzbE1atX8eOPP6Jv375o3bq1WdsJCwtD7969MWLECKxYsQKurq547bXXULt2bfTu3RsAMH78eLRr1w4LFy5E7969sX37dmzbtq3Kjo2IiIisD+dgIiIiIquwbds2BAYGKh7t27dX1Bk6dChOnDiB2rVrK+Y0UqvV+PLLL3H48GE0a9YML730EhYsWFDm/ry8vLB27Vps2bIFDz74IL744gvMmjVLUWfRokXw9PREVFQUnnjiCXTr1g0PPfSQos6bb76JxMREhIaGmpzLqE+fPli6dCkWLlyIpk2bYsWKFYiLi0N0dLT5J8hCKpUKW7ZswSOPPIJnn30WDRs2xODBg3HhwgX4+/tbtK24uDi0atUKjz/+OCIjIyGEwJYtW+QRZA8//DBWrlyJpUuXokWLFvjf//6H6dOnV8VhERERkZVSidKTDxAREREREREREVmAI5iIiIiIiIiIiKhCmGAiIiIiIiIiIqIKYYKJiIiIiIiIiIgqhAkmIiIiIiIiIiKqECaYiIiIiIiIiIioQphgIiIiIiIiIiKiCmGCiYiIiIiIiIiIKoQJJiIiIiIiIiIiqhAmmIiIiIiIiIiIqEKYYCIiIiIiIiIiogphgomIiIiIiIiIiCqECSYiIiIiIiIiIqoQJpiIiIiIiIiIiKhCmGAiIiIiIiIiIqIKYYKJiIiIiIiIiIgqhAkmIiIiIiIiIiKqECaYiIiIiIiIiIioQphgIiIiqgKJiYlQqVT45JNPqrspNVq9evUQGxtb3c0gIiIiuu8xwURERHQXPvnkE6hUKqOP1157rUr2OW/ePGzcuLHcetHR0SbbVvIxa9asKmlnaVu2bLln+7LEqVOnMGXKFISHh8PV1RWBgYHo2bMnDh06ZLT+lStXMGjQIHh4eMDNzQ29e/fGP//8o6hz6dIlzJ49G23btoWnpyd8fHwQHR2NnTt3GmyvrBhKSkoy+zhWr16NJk2aQKvVIiwsDMuWLTNab+fOnejUqRN8fHzg4eGBtm3bYs2aNWbvR5IkvPvuuwgJCYFWq0Xz5s3xxRdfGNT7/fffMXbsWLRq1QoODg5QqVRm76PY3r170b59ezg5OSEgIADjx49HZmamQb28vDy8+uqrqFWrFnQ6HSIiIrBjxw6L9mXu+TPn/S+LJW019/iJiIisiX11N4CIiMiWvfnmmwgJCVGUNWvWDMHBwcjJyYGDg0Ol7WvevHkYMGAA+vTpU2a9119/Hc8//7z8+uDBg/jvf/+LadOmoUmTJnJ58+bNK61tZdmyZQvef/99q0syrVq1CqtXr0b//v0xduxYpKWlYcWKFXj44Yexbds2xMTEyHUzMzPRqVMnpKWlYdq0aXBwcMDixYvRsWNHJCQkwNvbGwCwadMmvPPOO+jTpw+GDx+OgoICfPbZZ+jSpQs+/vhjPPvsswbtMBZDHh4eZh3DihUrMHr0aPTv3x+TJk3Cnj17MH78eGRnZ+PVV1+V623evBl9+vRBZGQkZs2aBZVKha+//hrDhg3DjRs38NJLL5W7r9dffx1vv/02RowYgTZt2mDTpk146qmnoFKpMHjwYLneli1bsGrVKjRv3hz169fHmTNnzDqWYgkJCejcuTOaNGmCRYsW4fLly1i4cCHOnj2LrVu3KurGxsZiw4YNmDhxIsLCwvDJJ5+gR48eiI+PR/v27cvdl7nnz9z3vyzmttWS4yciIrIqgoiIiCwWFxcnAIiDBw9WaDuZmZlm13V2dhbDhw+3eB/r168XAER8fHyltcUS48aNE1X1lSM4OPiuzokQQhw6dEhkZGQoym7cuCF8fX1Fu3btFOXvvPOOACB+//13uezkyZPCzs5OTJ06VS47fvy4SElJUaybm5srGjduLOrUqaMor2gMZWdnC29vb9GzZ09F+dChQ4Wzs7O4deuWXNalSxdRq1YtkZubK5fl5+eL0NBQ0bx583L3dfnyZeHg4CDGjRsnl0mSJDp06CDq1KkjCgoK5PKkpCSRnZ0thLi79/6xxx4TgYGBIi0tTS5buXKlACC2b98ulx04cEAAEAsWLJDLcnJyRGhoqIiMjCx3P5acP3Pff1Msaau5x09ERGRteIkcERFRFTA2B1NsbCxcXFxw7tw59OjRA66urhg6dCgA4OzZs+jfvz8CAgKg1WpRp04dDB48GGlpaQAAlUqFrKwsfPrpp/JlVBWZe6h4FMuJEyfw1FNPwdPTUzGKYu3atWjVqhV0Oh28vLwwePBgXLp0SbGNPXv2YODAgahbty40Gg2CgoLw0ksvIScnR3HM77//vnwMxY9ikiRhyZIlaNq0KbRaLfz9/TFq1Cjcvn1bsS8hBN566y3UqVMHTk5O6NSpE/766y+jx3bu3DmcO3eu3HPQqlUruLi4KMq8vb3RoUMHnDx5UlG+YcMGtGnTBm3atJHLGjdujM6dO+Prr7+Wy5o2bQofHx/FuhqNBj169MDly5eRkZFhtC0ZGRkoLCwst80lxcfH4+bNmxg7dqyifNy4ccjKysKPP/4ol6Wnp8PT0xMajUYus7e3h4+PD3Q6Xbn72rRpE/Lz8xX7UqlUGDNmDC5fvox9+/bJ5f7+/mZt05j09HTs2LEDTz/9NNzc3OTyYcOGwcXFRXGuN2zYADs7O4wcOVIu02q1eO6557Bv3z5FvN64cQOnTvQCd0UAAGIJSURBVJ1Cdna2XGbJ+TP3/QeAixcv4tSpU4oyc9tqyfETERFZGyaYiIiIKiAtLQ03btxQPMpSUFCAbt26wc/PDwsXLkT//v2h1+vRrVs37N+/Hy+++CLef/99jBw5Ev/88w9SU1MBAGvWrIFGo0GHDh2wZs0arFmzBqNGjapw+wcOHIjs7GzMmzcPI0aMAADMnTsXw4YNQ1hYGBYtWoSJEydi165deOSRR+T2AMD69euRnZ2NMWPGYNmyZejWrRuWLVuGYcOGyXVGjRqFLl26yMdQ/Ci5/JVXXkG7du2wdOlSPPvss1i3bh26deuG/Px8ud6MGTPwxhtvoEWLFliwYAHq16+Prl27Iisry+CYOnfujM6dO9/1OUlKSlIkiSRJwrFjx9C6dWuDum3btsW5c+dMJo5KbtPJyQlOTk4Gyzp16gQ3Nzc4OTmhV69eOHv2rFntPHLkCAAYtKtVq1ZQq9XycqBoXq6//voLb7zxBv7++2+cO3cOc+bMwaFDhzBlyhSz9uXs7Ky4xBIoOv6SbamoP//8EwUFBQbH5OjoiPDwcMV+jhw5goYNGyoSMSXblJCQIJe99957aNKkCX7//XfF+kD558/S93/YsGEG58nctlpy/ERERNaGczARERFVQMl5eooJIUzWz8vLw8CBAzF//ny5LCEhAefPn8f69esxYMAAuXzGjBny86effhqjR49G/fr18fTTT1dS64EWLVrg888/l19fuHABM2fOxFtvvYVp06bJ5f369UPLli2xfPlyufydd95RjFQZOXIkGjRogGnTpuHixYuoW7cuIiMj0bBhQ3lURkm//vorVq1ahXXr1uGpp56Syzt16oTu3btj/fr1eOqpp5CSkoJ3330XPXv2xPfffy+PgHr99dcxb968SjsXQNGorH379mH69Oly2a1bt5CXl4fAwECD+sVlV69eRaNGjYxu8++//8a3336LgQMHws7OTi53cnJCbGysnGA6fPgwFi1ahKioKPzxxx8ICgoqs63Xrl2DnZ0d/Pz8FOWOjo7w9vbG1atX5bI33ngD58+fx9y5c/HWW2/J+//mm2/Qu3fvcs5K0b78/f0NJuwuefyV4dq1a4rtlt7Xnj17FHXLe0/K25c556+i778lbbXk+ImIiKwNRzARERFVwPvvv48dO3YoHuUZM2aM4rW7uzsAYPv27YpLeO6F0aNHK15/++23kCQJgwYNUozKCggIQFhYGOLj4+W6JZNLWVlZuHHjBqKioiCEMGukxfr16+Hu7o4uXboo9lV86Vrxvnbu3Am9Xo8XX3xRkeCYOHGi0e0mJiYiMTHRgrNQJDk5GU899RRCQkIUo3qKL/kreXlZMa1Wq6hTWnZ2NgYOHAidToe3335bsWzQoEGIi4vDsGHD0KdPH8yZMwfbt2/HzZs3MXfu3HLbm5OTA0dHR6PLtFqtok0ajQYNGzbEgAED8MUXX2Dt2rVo3bo1nn76aezfv9+sfd3N8VuqvHNdcj+WtGnWrFkQQiA6Olqxvjnnz9L3f/fu3QZJZnPbasnxExERWRuOYCIiIqqAtm3bGr10xhR7e3vUqVNHURYSEoJJkyZh0aJFWLduHTp06IBevXrh6aeflpNPVaX03cvOnj0LIQTCwsKM1i95V7yLFy9ixowZ2Lx5s8GcScVzR5Xl7NmzSEtLMxhBUiw5ORlA0agqAAZt8vX1haenZ7n7MUdWVhYef/xxZGRk4Ndff1XMzVScSMvLyzNYLzc3V1GnpMLCQgwePBgnTpzA1q1bUatWrXLb0b59e0RERGDnzp1yWUpKimJ+JhcXF7i4uECn00Gv1xvdTm5urqJNL7zwAvbv348//vgDanXR/y8OGjQITZs2xYQJE3DgwAEARZfyleTu7g6dTgedTmfx8d+N8s51yf1UtE3mnr+7ff9L78uc9S05fiIiImvDBBMREdE9pNFo5B/4Jf3nP/9BbGwsNm3ahP/9738YP3485s+fj/379xskpCpT6R+skiRBpVJh69atisu5ihUnXgoLC9GlSxfcunULr776Kho3bgxnZ2dcuXIFsbGxkCSp3H1LkgQ/Pz+sW7fO6HJfX9+7OCLL6fV69OvXD8eOHcP27dvRrFkzxXIvLy9oNBr58qWSisuMJY9GjBiBH374AevWrcOjjz5qdnuCgoJw+vRp+XWbNm3kJBsAzJw5E7NmzUJgYCAKCwuRnJysSNLp9XrcvHlTbpNer8fq1asxZcoURew5ODjgsccew3vvvQe9Xg9HR0eDS7Pi4uIQGxuLwMBAxMfHQwihGEVW1vHfjeL9mzrXJfcTGBiIK1euGK1nTpvMPX93+/6X3pc5bbXk+ImIiKwNE0xERERW4sEHH8SDDz6I6dOnY+/evWjXrh0+/PBDec6c0vPfVIXQ0FAIIRASEoKGDRuarPfnn3/izJkz+PTTTxWTehu7RNBUu0NDQ7Fz5060a9euzJEZwcHBAIpGPNWvX18uT0lJMRg5ZSlJkjBs2DDs2rULX3/9NTp27GhQR61W48EHH8ShQ4cMlh04cAD169eHq6urovyVV15BXFwclixZgiFDhljUpn/++UeRXFu3bp3i0qjicxAeHg4AOHToEHr06CEvP3ToECRJkpffvHkTBQUFRu9Sl5+fD0mS5GWl37+mTZvK+1q1ahVOnjyJBx54QHH8JdtSUc2aNYO9vT0OHTqEQYMGyeV6vR4JCQmKsvDwcMTHxyM9PV0xeba5bTL3/N3N+29sX+a01ZLjJyIisjacg4mIiKiapaeno6CgQFH24IMPQq1WKy6VcXZ2VtzFrSr069cPdnZ2mD17tsE8MkII3Lx5EwDk0U0l6wghsHTpUoNtOjs7A4BB2wcNGoTCwkLMmTPHYJ2CggK5fkxMDBwcHLBs2TLF/pYsWWL0GM6dO4dz586VfaD/evHFF/HVV19h+fLl6Nevn8l6AwYMwMGDBxVJhtOnT+Onn37CwIEDFXUXLFiAhQsXYtq0aZgwYYLJbaakpBiUbdmyBYcPH0b37t3lsnbt2iEmJkZ+FCeYHn30UXh5eeGDDz5QbOODDz6Ak5MTevbsCQDw8/ODh4cHvvvuO8UlYZmZmfj+++/RuHFjOcFXcj8xMTHyiJrevXvDwcEBy5cvl9cXQuDDDz9E7dq1ERUVZfI4LeHu7o6YmBisXbtWcWe2NWvWIDMzU3GuBwwYgMLCQnz00UdyWV5eHuLi4hAREaGYJP3GjRs4deqUYo4zc89f8b7Mff8vXryIU6dOKcrMbaslx09ERGRtOIKJiIiomv3000944YUXMHDgQDRs2BAFBQVYs2YN7Ozs0L9/f7leq1atsHPnTixatAi1atVCSEgIIiIiKrUtoaGheOuttzB16lQkJiaiT58+cHV1xfnz5/Hdd99h5MiRmDx5Mho3bozQ0FBMnjwZV65cgZubG7755hujI4patWoFABg/fjy6desGOzs7DB48GB07dsSoUaMwf/58JCQkoGvXrnBwcMDZs2exfv16LF26FAMGDICvry8mT56M+fPn4/HHH0ePHj1w5MgRbN26FT4+Pgb769y5MwCUO9H3kiVLsHz5ckRGRsLJyQlr165VLO/bt6+cHBs7dixWrlyJnj17YvLkyXBwcMCiRYvg7++Pl19+WV7nu+++w5QpUxAWFoYmTZoYbLNLly7w9/cHAERFRaFly5Zo3bo13N3d8ccff+Djjz9GUFCQ4g5+puh0OsyZMwfjxo3DwIED0a1bN+zZswdr167F3Llz4eXlBaAoGTh58mRMnz4dDz/8MIYNG4bCwkKsXr0aly9fNmijMXXq1MHEiROxYMEC5Ofno02bNti4cSP27NmDdevWKS6nvHDhAtasWQMAckKmeBRecHAwnnnmmTL3NXfuXERFRaFjx44YOXIkLl++jP/85z/o2rWrIvEWERGBgQMHYurUqUhOTkaDBg3w6aefIjExEatXr1Zs87333sPs2bMRHx8vT/Rt7vkDzH//AWDYsGH4+eefFclQS9pq7vETERFZHUFEREQWi4uLEwDEwYMHjS4/f/68ACDi4uLksuHDhwtnZ2eDuv/884/4v//7PxEaGiq0Wq3w8vISnTp1Ejt37lTUO3XqlHjkkUeETqcTAMTw4cPNauv69esFABEfHy+XzZw5UwAQKSkpRtf55ptvRPv27YWzs7NwdnYWjRs3FuPGjROnT5+W65w4cULExMQIFxcX4ePjI0aMGCGOHj1qcNwFBQXixRdfFL6+vkKlUonSXz8++ugj0apVK6HT6YSrq6t48MEHxZQpU8TVq1flOoWFhWL27NkiMDBQ6HQ6ER0dLY4fPy6Cg4MNzkNwcLAIDg4u97wMHz5cADD5OH/+vKL+pUuXxIABA4Sbm5twcXERjz/+uDh79qyiTvF5NfUo+R68/vrrIjw8XLi7uwsHBwdRt25dMWbMGJGUlFRu20ufv0aNGglHR0cRGhoqFi9eLCRJMqi3bt060bZtW+Hh4SF0Op2IiIgQGzZsMHs/hYWFYt68eSI4OFg4OjqKpk2birVr1xrUi4+PN3n8HTt2NGtfe/bsEVFRUUKr1QpfX18xbtw4kZ6eblAvJydHTJ48WQQEBAiNRiPatGkjtm3bZlCv+H0pef6LmXv+zHn/hRCiY8eOBjFuSVstOX4iIiJrohKi1Ph3IiIiIiIiIiIiC3AOJiIiIiIiIiIiqhAmmIiIiIiIiIiIqEKYYCIiIiIiIiIiogphgomIiIiIiIiIiCqECSYiIiIiIiIiIqoQ++puQEVIkoSrV6/C1dUVKpWquptDRERERERERFSjCCGQkZGBWrVqQa02PU7JphNMV69eRVBQUHU3g4iIiIiIiIioRrt06RLq1KljcrlNJ5hcXV0BFB2km5tbNbemYiRJQkpKCnx9fcvMCBJVFGONbAVjlawR45JsCeOVrBHjkmwJ47VIeno6goKC5ByMKTadYCq+LM7Nza1GJJhyc3Ph5uZ2XwcuVT3GGtkKxipZI8Yl2RLGK1kjxiXZEsarUnlTE/EMERERERERERGVVFAA/PgjNDt3Fj2nctn0CCYiIiIiIiIiur8VZF1DYVaSxevZOQfA3jnQxEYLoFqxAjq9HujXD3B0rGAraz4mmIiIiIiIiIjIZmUcW4nUA29ZvJ5HxHR4Rs4wvlCthmjXDvmZmXDk5XFmYYKJiIiIiIiIiGyWa/MRcAp9QlEmFeQg6etoAEDAoN1Q2+sM1rNzDjC9UUdH4NVXkZWcDGeOXjILE0xEREREREREZLPsnQMNLnWT8rPk5xrfFlA7ON/rZt13OM6LiIiIiIiIiIgqhAkmIiIiIiIiIqKS8vKgio2F+/jxQF5edbfGJvASOSIiIiIiIiKikoQAbt2CSq8vek7lYoKJiIiIiIiIiKgkR0eIJUuQcfMmvDnJt1mYYCIiIiIi+v/27jy8iWr9A/g3aZs0TVe6A4WyFYrIjtiiUq5AcWNxQRERvIqsggsuqFcQF1T4Kcp1QVBUQEUFgcsqIDuICJRF9soi2NIWum9pm/f3R+k0aZM2JS1JyvfzPH1IzpycORnenJm8mTlDRERkSq0GmjdHibd36WOqFrcSERERERERERHZhQkmIiIiIiIiIiJTxcXAxo3QbNtW+piqxUvkiIiIiIiIiIhMFRdD9eGH8DIYgLvuAjgPU7WYYCIiIiIiIiIiMqVWQ7p2RVFuLjScg8kmTDAREREREREREZnSaIDXXkNuSgr0PHvJJkzDERERERERERGRXZhgIiIiIiIiIiIiuzDBRERERERERERkqrAQqlGj4DtpElBY6OjeuATOwUREREREREREZEoESEqC2mAofUzVYoKJiIiIiIiIiOqV4txk5XFh6gGo3XU2vc5NHwZ3fTig0UDefRfZly4hkJN824QJJiIiIiIiIiKqV3L+/Ep5nPxDnM2v8+/+KgJiXgPUaiA6GiUpKaWPqVpMMBERERERERFRveJ9wwhk7nkXABA2eDPU7joYi/OVZFNZGQCU5KehJD8NAOCmC0Rhyn4AgNFohPHyZRSiAdQWkkzK2U4EgAkmIiIiIiIiIqpn3PVhymNtcAeoPfQwFuVWKgOA9F3TkLH7TattJVspV852IgBMMBERERERERHRdcDavEzahjEI6ve1WV3Jz8alLeMBAEF9FkET3LJSe24mSSxigomIiIiIiIiIrgNXOy+TIW0ffG54oPY7VM84dKaq6dOno1u3bvDx8UFISAgGDhyI48ePO7JLRERERERERFQPed8wQnkcNngzGj68G2GDN1cqq1jufeO/r10nXZhDz2DasmULxo0bh27duqG4uBgvv/wy+vbtiyNHjkCv1zuya0RERERERNetpKwCJGUV1Ph14b6eCPf1rIMeEdmvJvMyGTISlfKSvIsoLM6xaR3X88TfDk0wrV271uz5V199hZCQEOzduxe33Xabg3pFRERERER0fZuz6yymrT9R49e91icKU+Nb10GPiK6e4dIReIZ1q9FrTC+nu/jTv2x+3fU88bdTzcGUmZkJAGjQoIHF5YWFhSgsLFSeZ2VlAbhy60Cjse47WIeMRiNExOXfBzk/xhq5CsYqOSPGJbkSxivZY2T3CNwdHWJWll9cgp6f7AIAbBkbA527W6XXhftqq4w5xiVdKxkJnymPL64aCt9OE+Bzw3ClzGg0AhVyCWVlAKBvOQSZe94FAATfsxZuej9Icb6SbAq9/1eorkwSbspNH1bv4tvW9+M0CSaj0Yinn34aPXr0QLt27SzWmT59Ol5//fVK5ampqSgoqPnpm87EaDQiMzMTIgK12qFTY1E9x1gjV8FYJWfEuCRXwngle7gBaKwxL8tDifK4kXshvDSVE0woKERKQZbVdhmXdC0UZZ1FxtFf4X3leZZHK2T9uR5+nuW5htTUVKjccyHFeZXKAEBMrojLLAmBG4IgKK+biYZQwavyynMB5KbU5ttxuOzsbJvqOU2Cady4cTh8+DC2b99utc7kyZPx7LPPKs+zsrIQERGB4OBg+Pr6Xotu1hmj0QiVSoXg4GAOtFSnGGvkKhir5IwYl+RKGK9U23INxcrj4JBg6DU1/zrJuKRrIS9vH0ryD0KuPPdTX4TkpSLA/TLSrpQFBwcrczD9XaEMAIx+uvLyRo3grvOzWre+8/S0bV41p0gwjR8/HitXrsTWrVvRuHFjq/W0Wi20Wm2lcrVaXS8GJ5VKVW/eCzk3xhq5CsYqOSPGJbkSxivVJrVKbfb4auOKcUl1TePbBGp3rXLOnRSkQ+2uhYdvhFJHiUGTODSLS/fydIna3b3quvWcre/ToVtDRDB+/Hj8/PPP+PXXX9GsWTNHdoeIiIiIiIiIXJwmqB18O4xRnqtUKvh2nghNYFsH9qr+c2iCady4cVi4cCG+/fZb+Pj4IDk5GcnJycjPz3dkt4iIiIiIiIjIhfl2GK08DrnrO/h1HFezBkpKLD8mqxyaYPr000+RmZmJuLg4hIeHK3+LFy92ZLeIiIiIiIiIqJ64qjOXioosPyarHDoHk4hUX4mIiIiIiIicSsKFTOgs3UWuCuG+ngj11lRfkcgZmM47dOVxcW6yUlSYegBqd51NTbnpw+CuD6/V7jkjp5jkm4iIiIiIiFzHrZ/srPFrXusThdf6tKqD3hDVAY2m0uOcP79SipJ/iLO5Kf/uryIg5rVa6pjzYoKJiIiIiIiIamTb2FizM5jyDSVK0qnisjLhvrbd6pzI2RguH4V7+E3wvmEEMve8CwAIG7wZancdjMX5SrKprKwiN33YteyuwzDBRERERERERDXSsZEf9Nryr5O5hcVWl5kyGo113jei2pB14DPlcerqR+DfeQJ82j2mlKnUHtCGdIKxKFcp0wZ3gNpDf0376UwcOsk3EREREREREZEzMaQdRtbeT5TnUmRE1v6PcHnnVKUsZdUQZO7/rwN657x4BhMRERERERER0RXF2X9DigqU52qNL4yGJOQc+04pExFk7f8I7v4tlLLrfeJvJpiIiIiIiIiIiK5w94mASusJXLny01icBZXKDTCWXwqq9vSH5KUg58gCpex6n/ibCSYiIiIiIiIiois0Qe3g22ks0rdPBgCo3NTwbj0Cuce/R0nhZQCAsSADancdvNsOQ97JnwBw4m8mmIiIiIiIiIiITPh2GK0kmILvXAiv8Jvg5hlQnnRSqeDbeSJ0jW9TXnO9T/zNSb6JiIiIiIiI6LpiuHSk6gomdzzU+LcGUJp0KhNy13fw6zjO7G5z1/vE30wwEREREREREVG9V10yyCzpZDBYfnyFJrBt6d3mDnyqlJVN/G3aTrWJrHqECSYiIiIiIiIiqtesJYMu75yqlJklnVSq8hebPjZRnP03pNjkbnOe/pDifGQd/Nxym/UcE0xEREREREREVK9ZSgYZDdnIOfadUqacgZR2GNBqy19s+tiEu08EVO6eynNjQQagckP+mV8st1nPMcFERERERERERPWatWQQjMVKWdkZSMXZf9vUpiaoHXw7jFGeq1Qq6JrfBUjJVbfpyphgIiIiIiIiIqJ6zVIyyPuGEVBrvJUyY0EGVO46uPtE2NxupYm/O4yplMiqaZuuigkmIiIiIiIiIqr3KiaDAm95s1LSybfzRGiC2lU7ybclmsC2FhNZSpv1nLujO0BEREREdD1LyipAUlZB9RUrCPf1RLivZ/UVia6xhAuZ0GncLC4zGgWX03PRwJAJtbp04mTGMjmCJrAtgNKkU/r2yQBKk06eYd1KKxiN5ZVNH9vAapv1HBNMREREREQONGfXWUxbf6LGr3utTxSmxreugx4RVe/Pi9m4qUmAxWW3frKzRm0xlslZlCWdAAAeHpYf29NmPccEExERERGRA42KaYr+N4SaleUbSpQv6dvGxlo8G4RnfNC19unOM8rjBxfsxbO3NcdTtzavVM80ZivGstZdjcvpl9EgoIHZGUxETsfNzfJjsooJJiIiIiIiB7J0eVBuYfldjcJ8tMg0eV6mukvrQr014Fciqi2Hk7LwsUmCSUQwa9tp9GoZhHbhvmZ1Ozbyg15b+lXTNJY7NvKDzkONFE0hQkL8oFZzSmByXsW5ycpjQ+oBlGj0MBbnK2WFqQegdtehKPu8UpZz4ke4ezeEysO7Ur2K3PRhcNeH11HvHYMJJiIiIiIiJ/blnr8x/ddTNX7df3q3wpiO/rXfIbouncvIR35R+a3X/XUeSMkx4FxGfqUEk7XL5xIuZF45g8l8DiZrODcTOVLO4fnK44s//avS8uQf4iqVXVr/pE31AMC/+6sIiHntqvvnjJhgIiIionqJEydTffHvbhG4r735r9y2XEIX6q0BCrKuSR+p/mvir4POozzOMvKLoPNwQxP/0jMzbLl8jnMzkSvxbvUwMv94DwAQes9auPkEIOvgXOQcnlfztto9Dt/25sknN31YrfTTmTDBRERERPUSJ06m+iLM1xMttOaH7RUvO9JrKx/WG41GpDDBRLWkXbgvxsVG4oVVRwGU3nr9mduaoV24b5WXzzVr4KWUm87BpPPxQ89PdgEA5j3QHp4elZOkQXoN9p3PqFTOHwLoWnA3SQBpgtvD3TcIATH/gW/7kTVuqz5eDmcJE0xERERUL3HiZCKi2jXGJMG0eFgX5TK4qi6fM00wmc7BpPcvv6zuiR8P1qgf/CGAKirOTUKJyZxJACzOl1SxzMOvGdQa30r1Kr7ekHUMJQX6Suu9XhJHtmKCiYiIiOql6iZOtnbWBxERVe+GUB/lcXWXz1WnqrvO8YcAskX2wbnI2P2m1eWW5kFK/iEO/t1fhV/X56qsB1iegwmon/Mo2YNHVURERERELiw5q8DiXeaMRqlyMmVeZkS1parL53ItxGZFVd11jj8EkC182o+EV4t7qq1nLM5XkkhhgzfDw6+Z2fKwwZvN7vhmNBqRfvkyAho0sHjXw/o4j5I9+GklIiKi61pNJgM3/cLeyF/HL+fkFK72LnO8zIhqk7XL54iuBXd9uE2XqhmLcpXH2uAOUHvoLZYBAAwGGN99Fz45OdC+/jrUntznV4cJJiIishvv1kWujJOBk7Ozdsv3MtXdZW7p8C7IKzJWel2QXoNfjqcgLddgsd0gvQZBeo3FZRy/qSqml88RuSyjEardu+FhMADGymMoVcYEExER2Y1f0MkVlX1pr8lk4KVnMF1Gg4AGaGTj3CJEV8OWW76XuZRfVCkBZXqZ0e/nMvDOpsRa7R/HbyKq99zdIePGIS8jAxp3pk5swa1E5KR4Rgi5Et6ti1yFtS/ttk4GfiEjD5fTS8trMk5zbKaaqOqW7+3CS+92VJME1GM3ReD+Dg3NykzH6Iq3iC8oKlHu6sXxm4iuW+7uQHw8DCkppY+pWtxK15i1g1FOwkgV8YwQciX23K2L4yJdK7Z8aa/O57+dwxsbTtZ43RybqSaquuV7u3DfGsdymI8WrYLNL3UzHaMf7NjIbIzOLSxWEkwe7mp0buxfS++MiIjqMyaYrjEmDchWjjwjhGdP0bXEcZGuleq+tNviyZuboEe4BxoENFASnzxbj2pbdbd8r41YNlVxjqeanB1FVJeqm3+MqE6JAOfOQZ2WBgQHO7o3LoEJpmusuqTBVw+2R7twv0qv44Hp9ae6M0Lq8hdFfuGna6m6cXHL2BjoNZV3V2WfDyZEyVbVfWm3xvQLTrivJ9xC9QgJ8VNuV2zpbL2KcWlrnDIuCaj6lu9AzWP5yMVsdG8aaFZmLYlUG2f6EVnz58VsdG1c+buO6TjLBCc5jcJCqMaPh6/BACxbBnh5ObpHTo8JpmvM0oHjzE3lt5V9/ZcTePq2FhxEyaJrtcPlfDpU26r6BbK6ZKrGTVVlMpUJUbJVdV/aTV3teFsW64xLsldVt3y3JZZNY/jhhfvMji+rSiLV9tlRRJXG01ub4YEoPebsPGtefltz9GoZZBabhUUleHdTIkJ9tGgZpK92XUzSU63z9YUU1PyHzOuVwxNMH3/8MWbMmIHk5GR06NABs2fPxk033eTobl0z/JWIbHUtY8WRZ09R/WFPQrSqL0YVVXtm6EMd0S6s8u2SeQB6farqS3uZqsbbtqHelepbinUm6qk2Wbrle1WxXN0xQ1VJpKs904/IEkux+NH202isaYhPd10wK5+17TTc1Sqz2EzOMQAAHlq4z6b1MUlPtcrTE7JwITJTUhDiyf2zLRyaYFq8eDGeffZZfPbZZ+jevTtmzZqF+Ph4HD9+HCEhIY7s2jVTcQfvp/PAxZwi/kpElTj6F0Werkw1YU9CtKavre7M0CnrjjNeySJLX9qBqsfbigmmquK1YiKeiXqqKxVjubrjy6qSSDU504+oOpbG07ScQpxJz0OehXFWALPYDPPWQK1W4/3+bdHI15NJeiIn59AE0/vvv4+RI0fiscceAwB89tlnWLVqFb788ku89NJLNrdjMBhgMBgqlavVarib3E7QUp0yKpUKHh4eV1W3qKgIInJVdcO93OCpMirPM0128FW1CwAaTfndQGpSt7i4GEajsVbqenh4QKVS1WndkpISlJSU1Epdd3d3Zc4MZ6hrNBpRXFxsta6bmxvc3Ep3no19tWaxkp6dB0+1GuFebjAYDGZ1q2rXaDSa9c+WPhxNyTX7AmUsMuCDTSdwSxNf3GBywGnaBxFBUVGR1XZNP591VReo+rPsCmPE1dYFHDtGJKZkIi+//HRif50HLmbmIzElE1GBlg/+yj7L5zLykVdQvn19NEBKdoHZa6v63P+ZlIX/bi2/y5fRaFS+8EeH6B3+ua+rMcIZ6lb3+XSGMaK4xDxeLX2WK+6by758l423ZX9qtdpirKfkGCzG+uwt5YlP00T99ThGlOFxhPW6hqLyz52hyACPKzFpWtfScsDy8aWnm0qJ4ahAT4zq1hAv/5JYWqGkGBNvbakkkUbFNFESTIsevBFdmwSYfVbq8xjB44hylsYIa3FpWtd0WcVYTM/Og06jQWSAF7w83AAUKeWeajViGvuYxabGXY1JcS0wuGMjZOaVj7VtQ3SV5me83sYIW+o6w7GBs40RxqLyz57BYIBaPICS8v6JiNnn02g0mu33r9cxoqr+mHJYgslgMGDv3r2YPHmyUqZWq9G7d2/s2rXL4msKCwtRWFioPM/KygIAzJw5E1qttlL9li1bYujQocrz9957z2oQNm3aFCNGjFCef/DBB8jLy7NYNzw8HE8++aTy/L///S8yMjIs1g0ODsbYsWOV53PmzEFqaqpZnVbnMnEu8HYAgJsKmHhrJNqGeuPzzz9HUlKSxXa9vLzw/PPPK88XLFiAs2fPWqzr4eGBl19+WXn+3Xff4dSpUxbrAsCUKVOUxz/99BOOHj1qte7kyZOVHcqKFStw4MABq3UnTZoEvb702uk1a9bgjz/+sFp34sSJ8Pf3BwCsX7/eakwAwJgxY5Qz3rZs2YItW7ZYrfvEE0+gUaNGAICdO3diw4YNVusOHz4ckZGRAIA9e/ZgzZo1VusOGTIEUVFRAIADBw5g+fLlVuvef//9uOGGGwAAf/75J3766SerdQcMGICOHTsCADTZyWh1Zp0SK1k7fkRMYx8s+3IXlgG44447lMtLz5w5g6+//tpimyKCLl264K677gIAXLhwAfPmzbPah549eyI/rC0KTA4oCn9bgryCQsy4uAGtTK6Hj4mJQd++fQEAGRkZ+PDDD62227VrV6UPubm5mDlzptW6HTp0wMCBAwGUjh3Tp0+3Wjc6OhqDBw9Wnr/11ltW67rKGFHG398fEydOVJ5/8cUXTjtGpOQUwnDwItDhXgBAVr4B+Ot3rP1mC/Z5Vx6vgfIxIsJPC9XZBCDkRgDA3798C4gRa/O2Ka+taow4mZaLtKOpQOf7AQC+xlykG71wNj0PKUf/qLdjxIkTJ/Ddd99ZrWvrGAEAvXv3Ro8ePQDYNkbExcUBAFJSUvDpp59aresMY0SzqGgApeOWUYx4623LY0TFffMzt0Zi1YLP8LPBgNzcXOj1eqhUKouxrvdQY/OPX2KfW/mXm5ScQnxvUk8lRny07S/EtWiADYvnX3djRBkeR5SyNEYYRA2gMwDgvfdmQHPlS3rZGGGU8vgyXV6mYgwPaabCsi9nY9mV5abt987ahpu0ATAaWwAAzp49p7Tzv28+w7oKbdfnMYLHEaWsjRGW4rJsjLAWk6axmLXjR7zxn1dwc1M9xtzsjhfWHFfKy45rTdex6MF2iGkRCqPRiFWrVgPwqNR+mettjLCGxxGlunbtivi4zijJTUZBfj6+WbAAAKBXZeL2Kyd9/vjBCJTAHZER4Wh65XUZf36Ln1ZuQ4F4ASUlwOHDKCoqQlGHW1Cg9r9uxwjTPExVHJZgSktLQ0lJCUJDzecnCA0NxbFjxyy+Zvr06Xj99dcrlefm5lrMimZlZSElJUV5npOTYzV7mp2dXalufn6+TXWzs7ORm5trsa6np2e1ddsGarDxyuPP745EdKgeKSkpVbZrNBpt7oO7u7tZ3aysLKt1AdS4btmBYWZmZpV1U1NTleW21C3LkmZkZFRZNy0tTXlsS92yrGx6enqVdS9dugSvK3cKsKVu2Xa7fPlylXUvX758VXUvXbpkFiv9o/wRrHNTXp+enm5W11q7IoKcnBykpKRArVYjLS2tyj5kZGSgUVgeOvoD63JKy7zVxfDVAV4oMnttRkaG0ofq/o8zMzOVunl5eTbXNVz5cmdNxc99Teo66xhRxs3Nzea6jh4j9CogJtwLa688b+1tRPMoX+iyspGba3kbl40RwWpgUJQv5mSUlgdqBNHBXtCripXXVjVGeKEIYZ7AX1eeh7gVItLXC0HIw4V6PkZUVdfWMaJiXVvGiKup66gxIjs7G2UJptSUVKt1TcfbL+5uhjahesxalYOioiIUXJnsU6VSWYz1+9qH4OymYuTmlo8RmVkF8HcrQeaV510CVcgqKMLZC8nX5RhhWpfHEZbHiCKUn6WUl5eLIhiV5SkpKcgzlFhcXqbi8aUmJxU/7yvvg2n7uSot9py9jCzdaQDAuQvJyrIzeYA7VGZt/3EuHb7h5xHqral3YwSPI0pZGyMsxWXZGGEtJisev97b0gsZGRkY2NIbL5iUlx3Xmq4jUF2g9CMrKxNAEADgn0sZCNaZXyJ3vY0R1vA4Asryi799iOI/ZwEA+lu4yvcW7ytJvfTysqwt49HX9KrjW0v/+T1diz25sdftGGFrgkklVZ0XWYf++ecfNGrUCDt37kRMTIxS/sILL2DLli3YvXt3pddYOoMpIiICFy9ehK9v5YhxxlPSzl3KQVK2+Sz0+UVG9J67BwCweXR3eGlK6xcXF0NQ2m6YjxbhPua/+vPU9prXdfXTVjPzChE87VcAQOpr/4Le5Nrzmlwid/nyZYSFhUGtVtvch/c3J+KF1aXJ30gfN0y4tRlGxzaz2l+e2u6cp7bbUre2xohcQ4kSr7ueikWncB+bP/eZeQUInFp6OLr1yS7o2qSB1bqWPnOztiTi5fWlKaZIfy2e7dkCY3s0c4rPPU9td+wYkV9sVGIr8814eKD6+M18Mx56jTsMBgOMRiNSU1MRHBys/D9VjPVuEQGVxogjyVm4/+s/cCa79P8+0l8LN7UaS0d0RVSg7rocIwAeR1RV1zSuTPf5ZXVzDcXwe3VdpeVlTF+fPq0P9Bp3s8+y6fKr8Z/erTClb1S9GyN4HFHO0hhhLS41Go3VmKz4Gj8vLVJTU+HlF4CA19ZXWT/jjb7w1pb2ecaG45j8S+nZk5E+bnjqlmYY06P8WPR6GyNsqesMxwaOHCNQmIqS3GRAROmDMT8NxoJLFSvDTX1lDBWB0cMfbrpAoKQEsn07cnJy4DNwGDz8m1y3Y0RWVhZCQ0ORmZlpMfdSxmFnMAUFBcHNzQ0XL140K7948SLCwsIsvkar1Vq8FM7T0xOeNszqbkudq6lrqU/WfLUvqcrbFsd9VjmxBlR/R4Sa9KHiNdLOXletVpsFen2razromErKKkBSlnl2Od/kl6FjaQVWJzcM9/W02q7RaFR2PJauIy5ft3kiNCay/Av+1LtuRLswHxxJM69Tuu7y9162A7BFXdWtq899XY0RzlC3tj7LJaryg4l2YX7QaKqK9QIA5fFkGuvirqkUa0CBEuuWPnNPxbVWEkzfP9pNubuSs3zua2OMcMa6gHN87svqWhrLTGPr4D/ZVsdRX5PPjFpVOlZ6enrCaDRCq9XC09Oz/GC/QqyX1TXVOdITY2+LUua1EZUaE29rjhsb+tv8voD6NUZcq7rO8lmuaV3TuPLUauGpNf8cqlXqKpebvj4ltwjZl61/FuY90B6eHpU/C0F6DYL0lrd12fjrymOELXgcYV63qri0FpMVX6NWq6FSqczmErNW303tBrVajcNJWfhszwWl3Ojmgf/u/gd92jayOAH99TBG2FrX0ccGDh0jNI0An0Y2t2eJsUkM8lJSoA8JqTT/HXD9jBFOPweTRqNBly5dsHHjRuWaaKPRiI0bN2L8+PGO6lads3TbYgAwGgWX0y+jQUADqNWqSst5R4Trz5xdZ6tMRpbdRaOi2rg9a3XrHvF9Qp2tm64/1cVbz08sz4tQFm/VJRGKio3Ydz6j0ust3X2O6perHUdHdm+CR7s0Vp4nXMhUElEp2YVITEqD3/kihPhoEaTXmMVbWd20XAPScssPxoL0GsQ0Lb+N/OvxrdEuzKdSbDIuyZLkrAJkFpqfAWAp7qwtn//733hnU6LV9p/48aDFcu7XyVk4+m7KRGQbh95F7tlnn8Xw4cPRtWtX3HTTTZg1axZyc3OVu8rVR9YOHI1GI1I0hQgJ8bOYGaXrj7VkZHVq44uJI9dNrqm6JI+lLz9AaczYm3h3ZDKWnJutY9mcXWcxd3f5pMZzd58ze24thiyxtS4T9WSqujH07Y0nMf+P81ZfX13cPXZTBO7v0LDG/eJ+nZxFE38ddCZn2WWY3HmbqM6IACkpUKelAcHBju6NS3BogunBBx9EamoqXnvtNSQnJ6Njx45Yu3ZtpYm/ia5HjvwVm7+gU03Zm+SxJ/HOhChZY+tYNjW+NNFpi9IzmFLh5+unnMFkiaUzmKzVrdhnuv5UN4ZaSy6N7N4Eo2KaVoo3ACgoKlHOTErOLqx0S3eA+3tyHqY/RFn7gWrADaH4aPsZAECJAJNua8azl6huFRZC9cQT8DUYgGXLgCuTspN1Dk0wAcD48ePr9SVxRETXA0cmefgFiexVkxgyGo1ICRCEWJmLgehq2DOGhvt6Yuq643Zdakx0NSqenWwtMWRanpxVgGaBlb+kW/shylp5v9bBGH9L86vqN1GN1GDOInKCBBMREbk+JnmIiK6evWMo5/gkR6jq0kxry77c8zfe6Fc5qbltbKzFS+lN5RtKlHZf6tWyBj0lukqenpAff0RGSgpCajDp9vWMCSYiIiIiIhfGOT7JEWxJCgHmiaF/d4uwWKdjIz/oTe5IZ2leMlPJ2YWVJr4H+IMXkaMxwURERERERERmqpt83pqKSZ5ck0RQcnYh0vOLcDk9F7q88q+iFS+3q3jzhYp4Aw8i58QEExEREREREZmpi7u01nSupbKJ7G3Fs5eoVhUVAZ9+Cq/sbGDSJM7HZAMmmIiIiIiIiMhMXdzAY9vYWGjd1VXODVaxLSaNyGFKSqD65RdoDAbgmWcc3RuXwAQTERERERERmamL5E7HRn7Qeag5Nxi5Bnd3yCOPoCArCxp3pk5swa1EREREREREdqtu3qaEC5lXzmDKRQNDpnIGE89UIqfk7g4MHoyClBT4MsFkE24lIiIiIiIisltdzNtERK6DCSYiIiIiIiKymy3zNhmNUmkOJp69RE5JBMjMhCorCwgOdnRvXAITTERERERERGQ3Wy51MxqNnIOJXENhIVTDhsHPYACWLQO8vBzdI6fn0gkmEQEAZGVlObgn9jMajcjOzoanpycHWqpTjDVyFYxVckaMS3IljFdyRoxLchkFBZCiIhQWF0OTlQV1cbGje+QwZTmXshyMNS6dYMrOzgYAREREOLgnRERERERERFQvhYc7ugdOITs7G35+flaXq6S6FJQTMxqN+Oeff+Dj4wOVSuXo7tglKysLERER+Pvvv+Hr6+vo7lA9xlgjV8FYJWfEuCRXwnglZ8S4JFfCeC0lIsjOzkbDhg2rPPPQpc9gUqvVaNy4saO7Uat8fX2v68Cla4exRq6CsUrOiHFJroTxSs6IcUmuhPGKKs9cKsOLXomIiIiIiIiIyC5MMBERERERERERkV2YYHISWq0WU6ZMgVardXRXqJ5jrJGrYKySM2JckithvJIzYlySK2G81oxLT/JNRERERERERESOxzOYiIiIiIiIiIjILkwwERERERERERGRXZhgIiIiIiIiIiIiuzDBREREREREREREdmGCiYiIiIiIiIiI7MIEUxWmT5+Obt26wcfHByEhIRg4cCCOHz9uVqegoADjxo1DYGAgvL29cd999+HixYvK8gMHDmDIkCGIiIiATqdDdHQ0PvzwQ7M2Nm/eDJVKVekvOTm5yv6JCF577TWEh4dDp9Ohd+/eOHnypLL8zJkzePzxx9GsWTPodDq0aNECU6ZMgcFgqIWtQ7XJ1WMNAPr3748mTZrA09MT4eHhGDZsGP755x87tww5o/oQr2UKCwvRsWNHqFQqJCQkXN0GIadQH+IyMjKyUrvvvPOOnVuGnE19iFUAWLVqFbp37w6dToeAgAAMHDjw6jcKOZyrx6W1dlUqFfbs2VMLW4iciavHKwCcOHECAwYMQFBQEHx9fXHLLbdg06ZNdm4ZJyBkVXx8vMyfP18OHz4sCQkJcuedd0qTJk0kJydHqTN69GiJiIiQjRs3yh9//CE333yzxMbGKsu/+OILmTBhgmzevFkSExNlwYIFotPpZPbs2UqdTZs2CQA5fvy4JCUlKX8lJSVV9u+dd94RPz8/WbZsmRw4cED69+8vzZo1k/z8fBERWbNmjYwYMULWrVsniYmJsnz5cgkJCZHnnnuulrcU2cvVY01E5P3335ddu3bJmTNnZMeOHRITEyMxMTG1uJXIWdSHeC0zYcIEueOOOwSA7N+/3/6NQw5TH+KyadOmMm3aNLN2TftP9UN9iNWffvpJAgIC5NNPP5Xjx4/Ln3/+KYsXL67FrUTXmqvHZWFhoVl7SUlJ8sQTT0izZs3EaDTW8tYiR3P1eBURadWqldx5551y4MABOXHihIwdO1a8vLwkKSmpFrfUtccEUw2kpKQIANmyZYuIiGRkZIiHh4f8+OOPSp2jR48KANm1a5fVdsaOHSu9evVSnpcFbnp6us19MRqNEhYWJjNmzFDKMjIyRKvVynfffWf1de+99540a9bM5vWQY9SHWFu+fLmoVCoxGAw2r4tck6vG6+rVq6VNmzby559/MsFUD7liXDZt2lQ++OADm9ul+sHVYrWoqEgaNWok8+bNs7ldcj2uFpcVGQwGCQ4OlmnTptm8HnJdrhavqampAkC2bt2q1MnKyhIAsn79epvX5Yx4iVwNZGZmAgAaNGgAANi7dy+KiorQu3dvpU6bNm3QpEkT7Nq1q8p2ytow1bFjR4SHh6NPnz7YsWNHlX05ffo0kpOTzdbt5+eH7t27X9W6ybm4eqxdvnwZixYtQmxsLDw8PKpsn1yfK8brxYsXMXLkSCxYsABeXl62vVFyKa4YlwDwzjvvIDAwEJ06dcKMGTNQXFxc/Zsll+Zqsbpv3z5cuHABarUanTp1Qnh4OO644w4cPnzY9jdNTs/V4rKiFStW4NKlS3jssceqbJvqB1eL18DAQLRu3RrffPMNcnNzUVxcjDlz5iAkJARdunSx/Y07ISaYbGQ0GvH000+jR48eaNeuHQAgOTkZGo0G/v7+ZnVDQ0OtXpe5c+dOLF68GE8++aRSFh4ejs8++wxLlizBkiVLEBERgbi4OOzbt89qf8raDw0NtXndp06dwuzZszFq1Khq3y85jivH2osvvgi9Xo/AwECcO3cOy5cvt/l9k2tyxXgVEYwYMQKjR49G165da/yeyfm5YlwCwIQJE/D9999j06ZNGDVqFN5++2288MILNXrv5FpcMVb/+usvAMDUqVPx6quvYuXKlQgICEBcXBwuX75csw1ATskV47KiL774AvHx8WjcuHG175dcmyvGq0qlwoYNG7B//374+PjA09MT77//PtauXYuAgIAabwNn4u7oDriKcePG4fDhw9i+fftVt3H48GEMGDAAU6ZMQd++fZXy1q1bo3Xr1srz2NhYJCYm4oMPPsCCBQuwaNEis6TQmjVr4ObmVqN1X7hwAf369cMDDzyAkSNHXvV7oLrnyrH2/PPP4/HHH8fZs2fx+uuv49FHH8XKlSuhUqmu+r2Qc3PFeJ09ezays7MxefLkq+4zOTdXjEsAePbZZ5XH7du3h0ajwahRozB9+nRotdqrfi/kvFwxVo1GIwDglVdewX333QcAmD9/Pho3bowff/yRP2TWA64Yl6bOnz+PdevW4Ycffrjq/pPrcMV4FRGMGzcOISEh2LZtG3Q6HebNm4d77rkHe/bsQXh4+FW/F0djgskG48ePx8qVK7F161azLHhYWBgMBgMyMjLMsqMXL15EWFiYWRtHjhzB7bffjieffBKvvvpqteu86aablA9J//790b17d2VZo0aNkJSUpKzLNAAvXryIjh07mrX1zz//oFevXoiNjcXnn39u8/uma8/VYy0oKAhBQUGIiopCdHQ0IiIi8NtvvyEmJsbmbUCuw1Xj9ddff8WuXbsqfWHv2rUrhg4diq+//tq2DUBOyVXj0pLu3bujuLgYZ86cMTvApfrBVWO1rLxt27bKcq1Wi+bNm+PcuXM2vntyVq4al6bmz5+PwMBA9O/f36b3TK7LVeP1119/xcqVK5Geng5fX18AwCeffIL169fj66+/xksvvVSzDeFMHD0JlDMzGo0ybtw4adiwoZw4caLS8rLJw3766Sel7NixY5UmDzt8+LCEhITI888/b/O6e/fuLYMGDaqyb2FhYTJz5kylLDMzs9Jkd+fPn5dWrVrJQw89JMXFxTavn66t+hBrFZ09e1YAyKZNm2zuC7kGV4/Xs2fPyqFDh5S/devWCQD56aef5O+//7a5L+RcXD0uLVm4cKGo1Wq5fPmyzX0h5+fqsVr23HSSb4PBICEhITJnzhyb+0LOxdXj0rRus2bNeNfses7V43XFihWiVqslOzvb7LVRUVHy1ltv2dwXZ8QEUxXGjBkjfn5+snnzZrPbEubl5Sl1Ro8eLU2aNJFff/1V/vjjj0q3Zj906JAEBwfLI488YtZGSkqKUueDDz6QZcuWycmTJ+XQoUMyceJEUavVsmHDhir7984774i/v78sX75cDh48KAMGDDC7/eH58+elZcuWcvvtt8v58+fN1k/OxdVj7bfffpPZs2fL/v375cyZM7Jx40aJjY2VFi1aSEFBQS1vLXI0V4/Xik6fPs27yNUDrh6XO3fulA8++EASEhIkMTFRFi5cKMHBwfLoo4/W8pYiR3P1WBURmThxojRq1EjWrVsnx44dk8cff1xCQkKYDHVh9SEuRUQ2bNggAOTo0aO1tGXIGbl6vKampkpgYKDce++9kpCQIMePH5dJkyaJh4eHJCQk1PLWuraYYKoCAIt/8+fPV+rk5+fL2LFjJSAgQLy8vGTQoEFmCZwpU6ZYbKNp06ZKnXfffVdatGghnp6e0qBBA4mLi5Nff/212v4ZjUb5z3/+I6GhoaLVauX222+X48ePK8vnz59v9T2Qc3H1WDt48KD06tVLGjRoIFqtViIjI2X06NFy/vz5Wtk+5FxcPV4rYoKpfnD1uNy7d690795d/Pz8xNPTU6Kjo+Xtt99mkr4ecvVYFSk9Y+m5556TkJAQ8fHxkd69e8vhw4ft3jbkOPUhLkVEhgwZIrGxsXZtC3J+9SFe9+zZI3379pUGDRqIj4+P3HzzzbJ69Wq7t42jqUREbLiSjoiIiIiIiIiIyCK1oztARERERERERESujQkmIiIiIiIiIiKyCxNMRERERERERERkFyaYiIiIiIiIiIjILkwwERERERERERGRXZhgIiIiIiIiIiIiuzDBREREREREREREdmGCiYiIiK4LZ86cgUqlQkJCQp2v66uvvoK/v3+dr6euxMXF4emnn7arDVffBkRERFQzTDARERGRw40YMQIqlarSX79+/RzdtWpFRkZi1qxZZmUPPvggTpw4UefrjouLU7aVp6cn2rZti08++cTudpcuXYo33nijFnpIRERE1wt3R3eAiIiICAD69euH+fPnm5VptVoH9cY+Op0OOp3umqxr5MiRmDZtGvLy8vDNN99g3LhxCAgIwJAhQ2rclsFggEajQYMGDeqgp0RERFSf8QwmIiIicgparRZhYWFmfwEBAQCAhx9+GA8++KBZ/aKiIgQFBeGbb74BAKxduxa33HIL/P39ERgYiLvvvhuJiYlW12fpEq5ly5ZBpVIpzxMTEzFgwACEhobC29sb3bp1w4YNG5TlcXFxOHv2LJ555hnlTCJrbX/66ado0aIFNBoNWrdujQULFpgtV6lUmDdvHgYNGgQvLy+0atUKK1asqHa7eXl5ISwsDM2bN8fUqVPNXpeRkYEnnngCwcHB8PX1xb/+9S8cOHBAee3UqVPRsWNHzJs3D82aNYOnp6fyvkwvkUtPT8ejjz6KgIAAeHl54Y477sDJkycrbc8mTZrAy8sLgwYNwqVLl6rtOxEREdUfTDARERGR0xs6dCj+97//IScnRylbt24d8vLyMGjQIABAbm4unn32Wfzxxx/YuHEj1Go1Bg0aBKPReNXrzcnJwZ133omNGzdi//796NevH+655x6cO3cOQOmlZI0bN8a0adOQlJSEpKQki+38/PPPmDhxIp577jkcPnwYo0aNwmOPPYZNmzaZ1Xv99dcxePBgHDx4EHfeeSeGDh2Ky5cv16jPOp0OBoMBAPDAAw8gJSUFa9aswd69e9G5c2fcfvvtZm2eOnUKS5YswdKlS63OTzVixAj88ccfWLFiBXbt2gURwZ133omioiIAwO7du/H4449j/PjxSEhIQK9evfDmm2/WqN9ERETk2phgIiIiIqewcuVKeHt7m/29/fbbAID4+Hjo9Xr8/PPPSv1vv/0W/fv3h4+PDwDgvvvuw7333ouWLVuiY8eO+PLLL3Ho0CEcOXLkqvvUoUMHjBo1Cu3atUOrVq3wxhtvoEWLFsoZQg0aNICbmxt8fHyUs64smTlzJkaMGIGxY8ciKioKzz77LO69917MnDnTrN6IESMwZMgQtGzZEm+//TZycnLw+++/29TXkpISLFy4EAcPHsS//vUvbN++Hb///jt+/PFHdO3aFa1atcLMmTPh7++Pn376SXmdwWDAN998g06dOqF9+/aV2j158iRWrFiBefPm4dZbb0WHDh2waNEiXLhwAcuWLQMAfPjhh+jXrx9eeOEFREVFYcKECYiPj7ep30RERFQ/MMFERERETqFXr15ISEgw+xs9ejQAwN3dHYMHD8aiRYsAlJ6ttHz5cgwdOlR5/cmTJzFkyBA0b94cvr6+iIyMBADlbKOrkZOTg0mTJiE6Ohr+/v7w9vbG0aNHa9zm0aNH0aNHD7OyHj164OjRo2ZlpgkevV4PX19fpKSkVNn2J598Am9vb+h0OowcORLPPPMMxowZgwMHDiAnJweBgYFmSbvTp0+bXTrYtGlTBAcHV9l3d3d3dO/eXSkLDAxE69atlf4fPXrUbDkAxMTEVNlvIiIiql84yTcRERE5Bb1ej5YtW1pdPnToUPTs2RMpKSlYv349dDqd2V3m7rnnHjRt2hRz585Fw4YNYTQa0a5dO+VysYrUajVExKys7JKvMpMmTcL69esxc+ZMtGzZEjqdDvfff7/VNu3l4eFh9lylUlV7id/QoUPxyiuvQKfTITw8HGp16e+HOTk5CA8Px+bNmyu9xnR+KL1eb3e/iYiIiJhgIiIiIpcQGxuLiIgILF68GGvWrMEDDzygJGQuXbqE48ePY+7cubj11lsBANu3b6+yveDgYGRnZyM3N1dJslScg2jHjh0YMWKEMs9TTk4Ozpw5Y1ZHo9GgpKSkynVFR0djx44dGD58uFnbbdu2rfZ9V8fPz89iYq5z585ITk6Gu7u7cjbX1YiOjkZxcTF2796N2NhYAOXbu6z/0dHR2L17t9nrfvvtt6teJxEREbkeJpiIiIjIKRQWFiI5OdmszN3dHUFBQcrzhx9+GJ999hlOnDhhNkF2QEAAAgMD8fnnnyM8PBznzp3DSy+9VOX6unfvDi8vL7z88suYMGECdu/eja+++sqsTqtWrbB06VLcc889UKlU+M9//lPpjKLIyEhs3boVDz30ELRarVl/yzz//PMYPHgwOnXqhN69e+N///sfli5danZHutrWu3dvxMTEYODAgXjvvfcQFRWFf/75B6tWrcKgQYPQtWtXm9pp1aoVBgwYgJEjR2LOnDnw8fHBSy+9hEaNGmHAgAEAgAkTJqBHjx6YOXMmBgwYgHXr1mHt2rV19t6IiIjI+XAOJiIiInIKa9euRXh4uNnfLbfcYlZn6NChOHLkCBo1amQ2p5Farcb333+PvXv3ol27dnjmmWcwY8aMKtfXoEEDLFy4EKtXr8aNN96I7777DlOnTjWr8/777yMgIACxsbG45557EB8fj86dO5vVmTZtGs6cOYMWLVpYncto4MCB+PDDDzFz5kzccMMNmDNnDubPn4+4uDjbN1ANqVQqrF69Grfddhsee+wxREVF4aGHHsLZs2cRGhpao7bmz5+PLl264O6770ZMTAxEBKtXr1bOILv55psxd+5cfPjhh+jQoQN++eUXvPrqq3XxtoiIiMhJqaTi5ANEREREREREREQ1wDOYiIiIiIiIiIjILkwwERERERERERGRXZhgIiIiIiIiIiIiuzDBREREREREREREdmGCiYiIiIiIiIiI7MIEExERERERERER2YUJJiIiIiIiIiIisgsTTEREREREREREZBcmmIiIiIiIiIiIyC5MMBERERERERERkV2YYCIiIiIiIiIiIrswwURERERERERERHZhgomIiIiIiIiIiOzCBBMREREREREREdmFCSYiIiIiIiIiIrILE0xERERERERERGQXJpiIiIiIiIiIiMguTDAREREREREREZFdmGAiIiIiIiIiIiK7MMFERERERERERER2YYKJiIiIiIiIiIjswgQTERERERERERHZhQkmIiIiIiIiIiKyCxNMRERERERERERkFyaYiIiIiIiIiIjILkwwERERERERERGRXZhgIiIiIiIiIiIiuzDBREREREREREREdmGCiYiIiIiIiIiI7MIEExERERERERER2YUJJiIiIiIiIiIisgsTTEREREREREREZBcmmIiIiIiIiIiIyC7uju5AbSkpKUFRUZGju0FEREREREREVC94eHjAzc3Nproun2ASESQnJyMjI8PRXSEiIiIiIiIiqlf8/f0RFhYGlUpVZT2XTzCVJZdCQkLg5eVV7RsmIiIiIiIiIqKqiQjy8vKQkpICAAgPD6+yvksnmEpKSpTkUmBgoKO7Q0RERERERERUb+h0OgBASkoKQkJCqrxczqUn+S6bc8nLy8vBPSEiIiIiIiIiqn/Kci7VzXvt0mcwleFlcURERERE15ekrAIkZRXU+HXhvp4I9/Wsgx4REdVPtuZc6kWCiYiIiIiIri9zdp3FtPUnavy61/pEYWp86zroERHR9Y0JJiIiqjV5eXkYNmwY1q9fj+zsbKSnp0Oj0VQq8/f3d3RXiYjIxY2KaYr+N4SaleUbSnDrJzsBANvGxkKnqTxXCM9eIiKqGy49B5MrUqlUVf5NnTq1TtY7YsQIDBw4sE7avhpfffWV3V8wa6MNU1OnTkXHjh1rrT0ie2zevLnKsaJXr16O7qJFX3/9NbZt24adO3ciKSkJfn5+FsvsFRcXh6efftr+DhPVkhEjRiifT41Gg5YtW2LatGkoLi6u1fVc69ivj8cPVH+E+3qic2N/s7+Ojcr3MR0b+VVa3rmxf60mmLKzs/H000+jadOm0Ol0iI2NxZ49e8zqmI4PZX/9+vVTlhcWFmLYsGHw9fVFVFQUNmzYYPb6GTNm4KmnnrKpP1lZWXjllVfQpk0beHp6IiwsDL1798bSpUshIgC4DyXnUVf7zrLj6IyMjGrrRkZGYtasWXatryac7fNX29+BeQbTNZaUlKQ8Xrx4MV577TUcP35cKfP29lYeiwhKSkrg7s7/prpUtp2JnElsbKzZeFFmxYoVGD16NMaOHXvVbRsMBmg0Gnu6Z1ViYiKio6PRrl27KsuI6qN+/fph/vz5KCwsxOrVqzFu3Dh4eHhg8uTJZvXq8jMI8PiB6Fp64okncPjwYSxYsAANGzbEwoUL0bt3bxw5cgSNGjVS6pWND2W0Wq3y+PPPP8fevXuxa9curFmzBg8//DAuXrwIlUqF06dPY+7cufjjjz+q7UtGRgZuueUWZGZm4s0330S3bt3g7u6OLVu24IUXXsC//vUvJmjJ6di673SkkpISqFQqqNU8P6da4sLy8/PlyJEjkp+f7+iuXJX58+eLn5+f8nzTpk0CQFavXi2dO3cWDw8P2bRpk5SUlMjbb78tkZGR4unpKe3bt5cff/xReV1xcbH8+9//VpZHRUXJrFmzlOVTpkwRAGZ/mzZtktOnTwsAWbx4sdxyyy3i6ekpXbt2lePHj8vvv/8uXbp0Eb1eL/369ZOUlBSzvs+dO1fatGkjWq1WWrduLR9//LGyrKzdJUuWSFxcnOh0Omnfvr3s3LnT7H2a/k2ZMsXiNkpISJC4uDjx9vYWHx8f6dy5s+zZs6fKNr755hvp0qWLeHt7S2hoqAwZMkQuXrxY5XaeP39+pfbmz59/lf+zRHXjyJEj4uPjI6+88opZ+aFDh6Rfv36i1+slJCREHnnkEUlNTVWW9+zZU8aNGycTJ06UwMBAiYuLExGRzZs3S7du3USj0UhYWJi8+OKLUlRUVGUftm3bpowXjRs3lqeeekpycnKU9Zh+hnr27GmxTESkoKBAnnvuOWnYsKF4eXnJTTfdJJs2bTJb1/bt26Vnz56i0+nE399f+vbtK5cvX5bhw4dX+ryePn3avo1LZKfhw4fLgAEDzMr69OkjN998s7LszTfflPDwcImMjBQRkXPnzskDDzwgfn5+EhAQIP37968ylq3FPo8fiMrlFBSJ6rkVonpuheQUVL1Ps1deXp64ubnJypUrzco7d+5stq+2ND6YGjNmjLz44otKmwCUz058fLwsXbrUpv6MGTNG9Hq9XLhwodKy7OxsZR/fs2dPmThxok1tEtWlqvadly9flmHDhom/v7/odDrp16+fnDhxQql35swZufvuu8Xf31+8vLykbdu2smrVKmVfYvo3fPhwi+uveJxalh4p+56+fPlyiY6OFjc3Nzl9+nS1x69paWny0EMPScOGDUWn00m7du3k22+/NXu/Ve3H165dKx07dhRPT0/p1auXXLx4UVavXi1t2rQRHx8fGTJkiOTm5irtVbefL2t3w4YN0qVLF9HpdBITEyPHjh1T3qet34Ftzb3U3wRTfn7pn9FYXlZUVFpmMNhet7Cw+rpXyVqCqX379vLLL7/IqVOn5NKlS/Lmm29KmzZtZO3atZKYmCjz588XrVYrmzdvFhERg8Egr732muzZs0f++usvWbhwoXh5ecnixYtFpHSHMnjwYOnXr58kJSVJUlKSFBYWKh++sraPHDkiN998s3Tp0kXi4uJk+/btsm/fPmnZsqWMHj1a6efChQslPDxclixZIn/99ZcsWbJEGjRoIF999ZWIiFm7K1eulOPHj8v9998vTZs2laKiIiksLJRZs2aJr6+v0p/s7GyL2+iGG26QRx55RI4ePSonTpyQH374QRISEqps44svvpDVq1dLYmKi7Nq1S2JiYuSOO+6ocjufP39ennvuObnhhhuU9vLy8uz+PybnVlhYaPWvYqKlqrqGCmOKtXr2SE9Pl1atWsk999wjRpPxJz09XYKDg2Xy5Mly9OhR2bdvn/Tp00d69eql1OnZs6d4e3vL888/L8eOHZNjx47J+fPnxcvLS8aOHStHjx6Vn3/+WYKCgqr8snbq1CnR6/XywQcfyIkTJ2THjh3SqVMnGTFihIiIXLp0SUaOHCkxMTGSlJQkly5dslgmIvLEE09IbGysbN26VU6dOiUzZswQrVarHDjs379ftFqtjBkzRhISEuTw4cMye/ZsSU1NlYyMDImJiZGRI0cqn9fi4mK7ti/VP4f+yZRVR5Ll0D+Z12R9lg6S+/fvL507d5bhw4eLt7e3DBs2TA4fPiyHDx8Wg8Eg0dHR8u9//1sOHjwoR44ckYcfflhat25tdbywFvs8fiAqdy0TTFlZWcqXN1M9evRQflARKR0f/Pz8JDg4WKKiomT06NGSlpamLP/ss8+kR48ekpeXJz///LOEh4eL0WiUhQsXVpmYMlVSUiIBAQHy5JNPVluXCSaqSmHqIcn9a7UUph6q83VVte/s37+/REdHy9atWyUhIUHi4+OlZcuWynH3XXfdJX369JGDBw9KYmKi/O9//5MtW7ZIcXGxLFmyRADI8ePHJSkpSTIyMiyu/9KlS9K4cWOZNm2asl8RKf2e7uHhIbGxsbJjxw45duyY5ObmVnv8ev78eZkxY4bs379fEhMT5aOPPhI3NzfZvXu3iFS/H7/55pvN9qE9e/aUvn37yr59+2Tr1q0SGBgo77zzjtL/6vbzZe12795dNm/eLH/++afceuutEhsbKyKlCW1bvwMzwXT33aV/psG0eHFp2Ucfmde9777ScpOzXGTZstKyGTPM6z78cGn52bN2999agmnZsmVKWUFBgXh5eSm/3pV5/PHHZciQIVbbHjdunNx3333Kc0sf3rIDuXnz5ill3333nQCQjRs3KmXTp0+X1q1bK89btGhhlokVEXnjjTckJibGart//vmnAJCjR49afO/W+Pj4KAeeFdnaxp49ewSAchBqaTuLlP5S26FDh2rbo/pjypQpVv8WLlxoVvfNN9+0Wrdipv/dd9+1WO9qlZSUyB133CHR0dGSlZVltuyNN96Qvn37mpX9/fffyk5VpPRAslOnTmZ1Xn75ZWndurVZsurjjz8Wb29vKSkpsdiPxx9/vNKB67Zt20StVivj8MSJE80Oqi2VnT17Vtzc3Cr9wnr77bfL5MmTRURkyJAh0qNHD4v9KHtPPDgmaz7amijN39ogYVPXSfO3NshHWxPrfJ2m+1mj0Sjr168XrVYrkyZNkuHDh0toaKhZ4mjBggWVPoOFhYWi0+lk3bp1VtdjKfZ5/EBU7lommEREYmJipGfPnnLhwgUpLi6WBQsWiFqtlqioKKXOd999J8uXL5eDBw/Kzz//LNHR0dKtWzflxxGDwSBjx46VyMhI6dq1q2zbtk0uXbokzZs3l3Pnzskrr7wiLVq0kL59+8r58+ct9uPixYsCQN5///1q+8x9KFmTsW+2nPuilZyd01jOfdFKMvbNrtP1Wdt3Dhw4UADIjh07lLppaWmi0+nkhx9+EBGRG2+8UaZOnWqx3bL9Ynp6erV9aNq0qXzwwQdmZWVn9iQkJChlthy/WnLXXXfJc889pzyvaj9umqyePn26AJDExPJjmFGjRkl8fLyI2Laft9TuqlWrBIBy7G7rd2BbE0y8ON8Jde3aVXl86tQp5OXloU+fPmZ1DAYDOnXqpDz/+OOP8eWXX+LcuXPIz8+HwWCwebKu9u3bK49DQ0vvxHHjjTealaWkpAAAcnNzkZiYiMcffxwjR45U6hQXF1eauNe03fDwcABASkoK2rRpY1O/AODZZ5/FE088gQULFqB379544IEH0KJFiypfs3fvXkydOhUHDhxAeno6jEYjAODcuXNo27atUs90OxM5s5dffhm7du3C77//Dh8fH7NlBw4cwKZNm8zmbyuTmJiIqKgoAECXLl3Mlh09ehQxMTFQqVRKWY8ePZCTk4Pz58+jSZMmldo7cOAADh48iEWLFillIgKj0YjTp08jOjrapvdz6NAhlJSUKH0rU1hYiMDAQABAQkICHnjgAZvaIzJ1OCkLs7adhoigoa8W6XlFmLXtNHq1DEK7cN86XffKlSvh7e2NoqIiGI1GPPzww5g6dSrGjRuHG2+80WzepQMHDuDUqVOVPtMFBQVITEzEtm3bcMcddyjlc+bMwdChQ6tcP48fiK69BQsW4N///jcaNWoENzc3dO7cGUOGDMHevXuVOg899JDy+MYbb0T79u3RokULbN68Gbfffjs8PDzw8ccfm7X72GOPYcKECdi/fz+WLVuGAwcO4L333sOECROwZMmSSv2QKxN4E10tQ9phZO3/CCICtT4cxoJ0ZO3/CLqIOGiC6m4eTUv7znvvvRcrV65E9+7dlXqBgYFo3bo1jh49CgCYMGECxowZg19++QW9e/fGfffdZ7b/qGjRokUYNWqU8nzNmjW49dZbrdbXaDRm7dly/FpSUoK3334bP/zwAy5cuACDwYDCwkJ4eXnZtC0q7le9vLzQvHlzs7Lff/8dgO37+Yrtmu5XLR3v26v+Jph+/LH0X5MJ9HDvvUD//oBbhduVLlxYue5ddwHx8UDFiby++KJy3Vqm1+uVxzk5OQCAVatWmU0UWNqF0j58//33mDRpEv7v//4PMTEx8PHxwYwZM7B7926b1ufh4aE8LvuyWbGsLElT1p+5c+eafeABwK3CdrXUblk7tpo6dSoefvhhrFq1CmvWrMGUKVPw/fffY9CgQRbr5+bmIj4+HvHx8Vi0aBGCg4Nx7tw5xMfHw2AwmNU13c50fXr55ZetLqs4id/zzz9vta5pkgZArd4Z4vvvv8fMmTOxatUqtGrVqtLynJwc3HPPPXj33XcrLSvbgQC1E+85OTkYNWoUJkyYUGlZTXZQOTk5cHNzw969eyuNG2WJMp1OZ19n6bp1LiMfeUUlaOirhVqlQoCXB5KyCnEuI7/OE0y9evXCp59+Co1Gg4YNG5pNsl3xM5iTk4MuXbqYJWzLBAcHQ6PRICEhQSkrS+BUhccPRNdeixYtsGXLFuTm5iIrKwvh4eF48MEHzb4UVtS8eXMEBQXh1KlTuP322yst37RpE/7880/MmzcPzz//PO68807o9XoMHjwY//3vfy22GRwcDH9/fxw7dqzW3htdX4qz/4YU50OtD4dKpYbaMwDG3CQUZ/9dpwkmS/vOFStWVPu6J554AvHx8Vi1ahV++eUXTJ8+Hf/3f/9n9Y6L/fv3N9v/VNw3VqTT6cyO8W05fp0xYwY+/PBDzJo1CzfeeCP0ej2efvrpSt9Dram4/zN9XlZWcb9a1X7eWrtA3e1X62+CydPC7Ufd3Uv/artuHWrbti20Wi3OnTuHnj17WqyzY8cOxMbGmt1VKjEx0ayORqOplTulhYaGomHDhvjrr7+q/SW1KjXpT1RUFKKiovDMM89gyJAhmD9/PgYNGmSxjWPHjuHSpUt45513EBERAQA23XWjpn2i+qEmd3Gqq7pVSUhIwOOPP4533nkH8fHxFut07twZS5YsQWRkZI3uGBUdHY0lS5ZARJQdzY4dO+Dj44PGjRtbXdeRI0fQsmXLmr8ZE506dUJJSQlSUlKs/nLUvn17bNy4Ea+//rrF5fy8kjVN/HXw8nBDel4RArw8kJ5XBJ2HG5r4133SUq/X2/z56Ny5MxYvXoyQkBD4+lpOfFlqy9bY5/ED0bWl1+uh1+uRnp6OdevW4b333rNa9/z587h06ZLZD0FlCgoKMG7cOCxatAhubm4oKSlRzk4qKiqyGv9qtRoPPfQQFixYgClTpqBhw4Zmy3NycuDp6cm7S5JV7j4RULnrYCxIL00uFaRD5a6Du09Ena7X0r4zOjoaxcXF2L17N2JjYwEAly5dwvHjx82uSImIiMDo0aMxevRoTJ48GXPnzsVTTz2lHIubfl58fHwqnTUM2L5fseX4dceOHRgwYAAeeeQRAKVJnBMnTpj1ubb2Y7bs521R2/tV3mfPyfn4+GDSpEl45pln8PXXXyMxMRH79u3D7Nmz8fXXXwMAWrVqhT/++APr1q3DiRMn8J///Ad79uwxaycyMhIHDx7E8ePHkZaWhqKioqvu0+uvv47p06fjo48+wokTJ3Do0CHMnz8f77//vs1tREZGIicnBxs3bkRaWhry8vIq1cnPz8f48eOxefNmnD17Fjt27MCePXuUy3AstdGkSRNoNBrMnj0bf/31F1asWIE33njD5j6dPn0aCQkJSEtLQ2Fhoc3vh6i2paWlYeDAgYiLi8MjjzyC5ORks7/U1FQAwLhx43D58mUMGTIEe/bsQWJiItatW4fHHnusyp3F2LFj8ffff+Opp57CsWPHsHz5ckyZMgXPPvus1Vuwvvjii9i5cyfGjx+PhIQEnDx5EsuXL8f48eNr9N6ioqIwdOhQPProo1i6dClOnz6N33//HdOnT8eqVasAAJMnT8aePXswduxYHDx4EMeOHcOnn36KtLQ0AKWf1927d+PMmTNIS0vj2Q2kaBfui6dvbQaVSoWkrEKoVCo8c1uzOj97qaaGDh2KoKAgDBgwANu2bcPp06exefNmTJgwAefPn7f6Oltj/3o+fiC6ltatW4e1a9fi9OnTWL9+PXr16oU2bdrgscceA1Ca2Hn++efx22+/4cyZM9i4cSMGDBiAli1bWvzx6I033sCdd96pXOLSo0cPLF26FAcPHsR///tf9OjRw2pf3nrrLURERKB79+745ptvcOTIEZw8eRJffvklOnXqpJzxQGSJJqgdfDtNKD1LJjcJKpUKvp0n1unZS9a0atUKAwYMwMiRI7F9+3YcOHAAjzzyCBo1aoQBAwYAKL1iYN26dTh9+jT27duHTZs2Kd8TmzZtCpVKhZUrVyI1NbXK2I+MjMTWrVtx4cIF5TjTEluOX1u1aoX169dj586dOHr0KEaNGoWLFy9WWl9tHMPasp+3Ra1/B652NicnZutEU87K2iTfFScjMxqNMmvWLGndurV4eHhIcHCwxMfHy5YtW0SkdIKvESNGiJ+fn/j7+8uYMWPkpZdeMpusKyUlRfr06SPe3t6VbjO8f//+KvtgaULNRYsWSceOHUWj0UhAQIDcdtttyi1ULbWbnp6urLfM6NGjJTAw0OpthgsLC+Whhx6SiIgI0Wg00rBhQxk/frzZ/7elNr799luJjIwUrVYrMTExsmLFCrP+WNvOBQUFct9994m/v3+Vt2gkuha++uqrSrcNNf1r2rSpUvfEiRMyaNAg5Taubdq0kaefflqZPNjaZJ6bN2+Wbt26iUajkbCwMHnxxRcr3T2vot9//10ZS/R6vbRv317eeustZbktk3yLlN+9KjIyUjw8PCQ8PFwGDRokBw8eNOtfbGysaLVa8ff3l/j4eOVze/z4cbn55ptFp9Mpt3glMuUMd5GrbllSUpI8+uijEhQUJFqtVpo3by4jR46UzEzrfbYU+zx+ICp3rSf5Xrx4sTRv3lzZl44bN87sjlV5eXnSt29fCQ4OFg8PD2natKmMHDlSkpOTK7V16NAhadmypeTk5ChlJSUlMmbMGPH19ZVu3brJyZMnq+xPRkaGvPTSS9KqVSvRaDQSGhoqvXv3lp9//rna4wIiEcffRa7M5cuXZdiwYeLn5yc6nU7i4+OVu7WJiIwfP15atGghWq1WgoODZdiwYWZ3Z5w2bZqEhYWJSqWS4cOHW+3Drl27pH379qLVaqUsPWLthhLVHb9eunRJBgwYIN7e3hISEiKvvvqqPProo2bv0db9uKU+VJyQu7r9vKV29+/fb3bsbOt3YFtzLyoR150RrqCgAKdPn0azZs3geY0vXSMiIiIiIueSW1gMn1fWAACy37oDei0vCSMispetuReOuERERERE5HKSsgqQlFVgVpZvKL88POFCJnQat4ovQ7ivJ8J9+eM0EVFtY4KJiIiIiIhczpxdZzFt/Qmry2/9ZKfF8tf6RGFqfOu66hYR0XWLCSYiIiIiInI5o2Kaov8NoTV+Hc9eIiKqG0wwERERERGRy+GlbkREzsXyvahdjAvPU05ERERERERE5LRszbm4dILJw8MDAJCXl+fgnhARERERERER1T9lOZeyHIw1Ln2JnJubG/z9/ZGSkgIA8PLygkqlcnCviIiIiIiIiIhcm4ggLy8PKSkp8Pf3h5tb5TtzmlKJi19fJiJITk5GRkaGo7tCRERERERERFSv+Pv7IywsrNoTelw+wVSmpKQERUVFju4GEREREREREVG94OHhUe2ZS2XqTYKJiIiIiIiIiIgcw6Un+SYiIiIiIiIiIsdjgomIiIiIiIiIiOzCBBMREREREREREdmFCSYiIiIiIiIiIrILE0xERERERERERGQXJpiIiIiIiIiIiMguTDAREREREREREZFd/h9vU9z1lIDVVQAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", "\n", "def plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8)):\n", " \"\"\"\n", - " Plot coefficient estimates with CIs over time, grouped by first treated period.\n", - " CIs with the same x-value are jittered horizontally for better visibility.\n", - " Works with both numeric and datetime values.\n", + " Plots coefficient estimates with confidence intervals over time, grouped by first treated period.\n", + " \n", + " Args:\n", + " dml_obj: The DML object containing estimated treatment effects.\n", + " level (float): Confidence level for the intervals (default 0.95).\n", + " joint (bool): Whether to use joint confidence intervals (default True).\n", + " figsize (tuple): Figure size as (width, height) (default (12, 8)).\n", + " \n", + " Returns:\n", + " None. Displays the plot.\n", " \"\"\"\n", " df = create_ci_dataframe(dml_obj, level=level, joint=joint)\n", " all_time_periods = sorted(df['Evaluation Period'].unique())\n", @@ -888,154 +824,56 @@ " n_periods = len(first_treated_periods)\n", " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", " \n", - " # Check if we're dealing with datetime values\n", " is_datetime = pd.api.types.is_datetime64_any_dtype(df['Evaluation Period'])\n", " \n", - " # Adjust figure size to accommodate bottom legend\n", " fig = plt.figure(figsize=figsize)\n", - " # Create subplot grid with space for legend at bottom\n", - " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", + " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3] * n_periods + [0.5])\n", " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", - "\n", " if n_periods == 1:\n", " axes = [axes]\n", " \n", - " # Create a list to store legend handles and labels\n", - " legend_elements = []\n", - " \n", - " # Define jitter amount - different handling for datetime vs numeric\n", - " if is_datetime:\n", - " # For datetime, calculate time difference between periods\n", - " if len(all_time_periods) > 1:\n", - " time_diff = (all_time_periods[1] - all_time_periods[0]).total_seconds()\n", - " jitter_seconds = time_diff * 0.1 # Use 5% of time difference for jitter\n", - " else:\n", - " jitter_seconds = 86400 * 0.05 # Default to 5% of a day if only one period\n", - " else:\n", - " jitter_amount = 0.1 # Standard numeric jitter\n", + " jitter_value = (all_time_periods[1] - all_time_periods[0]).total_seconds() * 0.1 if is_datetime and len(all_time_periods) > 1 else 0.1\n", " \n", " for idx, period in enumerate(first_treated_periods):\n", " period_data = df[df['First Treated'] == period]\n", " ax = axes[idx]\n", - "\n", " i_period = all_time_periods.index(period)\n", - "\n", - " # Add treatment start line\n", - " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", - " linestyle=':', alpha=0.7)\n", - " if idx == 0:\n", - " legend_elements.append((line, 'Treatment start'))\n", - "\n", - " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", - " if idx == 0:\n", - " legend_elements.append((zero_line, 'Zero effect'))\n", - "\n", - " # Split data by treatment status\n", - " pre_treatment = period_data[period_data['Pre-Treatment']]\n", - " post_treatment = period_data[~period_data['Pre-Treatment']]\n", + " \n", + " ax.axvline(x=all_time_periods[i_period], color='red', linestyle=':', alpha=0.7)\n", + " ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", + " \n", + " pre_treatment = add_jitter(period_data[period_data['Pre-Treatment']], is_datetime, jitter_value)\n", + " post_treatment = add_jitter(period_data[~period_data['Pre-Treatment']], is_datetime, jitter_value)\n", " \n", " if not pre_treatment.empty:\n", - " pre_treatment = pre_treatment.copy()\n", - " \n", - " for x_val in pre_treatment['Evaluation Period'].unique():\n", - " mask = pre_treatment['Evaluation Period'] == x_val\n", - " count = mask.sum()\n", - " if count > 1:\n", - " if is_datetime:\n", - " # For datetime values, create timedelta jitters\n", - " jitter_range = np.linspace(-jitter_seconds, jitter_seconds, count)\n", - " jitters = [pd.Timedelta(seconds=float(j)) for j in jitter_range]\n", - " else:\n", - " # For numeric values, create standard jitters\n", - " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", - " \n", - " # Store the jitters for these points\n", - " pre_treatment.loc[mask, 'jitter_index'] = range(count)\n", - " for i, j in enumerate(jitters):\n", - " pre_treatment.loc[mask & (pre_treatment['jitter_index'] == i), 'jittered_x'] = x_val + j\n", - " \n", - " # For points without jitter (single point at x-value)\n", - " if 'jittered_x' not in pre_treatment.columns:\n", - " pre_treatment['jittered_x'] = pre_treatment['Evaluation Period']\n", - " else:\n", - " mask = ~pre_treatment['jittered_x'].notna()\n", - " pre_treatment.loc[mask, 'jittered_x'] = pre_treatment.loc[mask, 'Evaluation Period']\n", - " \n", - " # Pre-treatment points with jitter\n", - " scatter_pre = ax.scatter(pre_treatment['jittered_x'], \n", - " pre_treatment['Estimate'], \n", - " color=colors['pre'], alpha=0.8, s=10)\n", - " \n", - " # Regular CIs with jitter\n", - " error_pre = ax.errorbar(pre_treatment['jittered_x'], \n", - " pre_treatment['Estimate'],\n", - " yerr=[pre_treatment['Estimate'] - pre_treatment['CI Lower'],\n", - " pre_treatment['CI Upper'] - pre_treatment['Estimate']],\n", - " fmt='none', color=colors['pre'], alpha=1.0, \n", - " capsize=5)\n", - " if idx == 0:\n", - " legend_elements.extend([\n", - " (scatter_pre, 'Pre-treatment'),\n", - " (error_pre, f'{int(level*100)}% CI'),\n", - " ])\n", + " ax.scatter(pre_treatment['jittered_x'], pre_treatment['Estimate'], color=colors['pre'], alpha=0.8, s=10)\n", + " ax.errorbar(pre_treatment['jittered_x'], pre_treatment['Estimate'],\n", + " yerr=[pre_treatment['Estimate'] - pre_treatment['CI Lower'],\n", + " pre_treatment['CI Upper'] - pre_treatment['Estimate']],\n", + " fmt='none', color=colors['pre'], alpha=1.0, capsize=5)\n", " \n", - " # Similar structure for post-treatment with jittering\n", " if not post_treatment.empty:\n", - " post_treatment = post_treatment.copy()\n", - " \n", - " for x_val in post_treatment['Evaluation Period'].unique():\n", - " mask = post_treatment['Evaluation Period'] == x_val\n", - " count = mask.sum()\n", - " if count > 1:\n", - " if is_datetime:\n", - " # For datetime values, create timedelta jitters\n", - " jitter_range = np.linspace(-jitter_seconds, jitter_seconds, count)\n", - " jitters = [pd.Timedelta(seconds=float(j)) for j in jitter_range]\n", - " else:\n", - " # For numeric values, create standard jitters\n", - " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", - " \n", - " # Store the jitters for these points\n", - " post_treatment.loc[mask, 'jitter_index'] = range(count)\n", - " for i, j in enumerate(jitters):\n", - " post_treatment.loc[mask & (post_treatment['jitter_index'] == i), 'jittered_x'] = x_val + j\n", - " \n", - " # For points without jitter (single point at x-value)\n", - " if 'jittered_x' not in post_treatment.columns:\n", - " post_treatment['jittered_x'] = post_treatment['Evaluation Period']\n", - " else:\n", - " mask = ~post_treatment['jittered_x'].notna()\n", - " post_treatment.loc[mask, 'jittered_x'] = post_treatment.loc[mask, 'Evaluation Period']\n", - " \n", - " scatter_post = ax.scatter(post_treatment['jittered_x'], \n", - " post_treatment['Estimate'], \n", - " color=colors['post'], alpha=0.8, s=10)\n", - " if idx == 0:\n", - " legend_elements.append((scatter_post, 'Post-treatment'))\n", - " \n", - " # Error bars with jitter\n", + " ax.scatter(post_treatment['jittered_x'], post_treatment['Estimate'], color=colors['post'], alpha=0.8, s=10)\n", " ax.errorbar(post_treatment['jittered_x'], post_treatment['Estimate'],\n", - " yerr=[post_treatment['Estimate'] - post_treatment['CI Lower'],\n", - " post_treatment['CI Upper'] - post_treatment['Estimate']],\n", - " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", - "\n", + " yerr=[post_treatment['Estimate'] - post_treatment['CI Lower'],\n", + " post_treatment['CI Upper'] - post_treatment['Estimate']],\n", + " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", + " \n", " ax.set_title(f'First Treated: {period}')\n", " ax.grid(True, alpha=0.3)\n", - " \n", " if idx == 0:\n", " ax.set_ylabel('Effect')\n", " ax.set_xlabel('Evaluation Period')\n", " \n", - " # Create legend in a separate subplot at the bottom\n", " legend_ax = fig.add_subplot(gs[-1])\n", - " legend_ax.axis('off') # Hide axes for legend subplot\n", - " \n", - " # Add legend using collected handles and labels\n", - " legend = legend_ax.legend(*zip(*legend_elements), \n", - " loc='center',\n", - " ncol=5, # Adjust number of columns as needed\n", - " mode='expand',\n", - " borderaxespad=0.)\n", + " legend_ax.axis('off')\n", + " legend_elements = [\n", + " Line2D([0], [0], color='red', linestyle=':', alpha=0.7, label='Treatment start'),\n", + " Line2D([0], [0], color='black', linestyle='--', alpha=0.5, label='Zero effect'),\n", + " Line2D([0], [0], marker='o', color=colors['pre'], linestyle='None', label='Pre-treatment', markersize=5),\n", + " Line2D([0], [0], marker='o', color=colors['post'], linestyle='None', label='Post-treatment', markersize=5),\n", + " ]\n", + " legend_ax.legend(handles=legend_elements, loc='center', ncol=5, mode='expand', borderaxespad=0.)\n", " \n", " plt.suptitle(\"Estimated ATTs by Group\", y=1.02)\n", " plt.tight_layout()\n", @@ -1046,1172 +884,110 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 10, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/tmp/ipykernel_36280/2959045490.py:60: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[-0.05 0.05]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", - " pre_treatment.loc[mask, 'jitter'] = jitters\n" - ] - }, - { - "ename": "UFuncTypeError", - "evalue": "ufunc 'add' cannot use operands with types dtype(' 132\u001b[0m \u001b[43mplot_atts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdml_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.95\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfigsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m8\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[76], line 62\u001b[0m, in \u001b[0;36mplot_atts\u001b[0;34m(dml_obj, level, joint, figsize)\u001b[0m\n\u001b[1;32m 59\u001b[0m jitters \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m-\u001b[39mjitter_amount, jitter_amount, count)\n\u001b[1;32m 60\u001b[0m pre_treatment\u001b[38;5;241m.\u001b[39mloc[mask, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjitter\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m jitters\n\u001b[0;32m---> 62\u001b[0m jittered_x \u001b[38;5;241m=\u001b[39m \u001b[43mpre_treatment\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mEvaluation Period\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mpre_treatment\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mjitter\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;66;03m# Pre-treatment points with jitter\u001b[39;00m\n\u001b[1;32m 65\u001b[0m scatter_pre \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mscatter(jittered_x, \n\u001b[1;32m 66\u001b[0m pre_treatment[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEstimate\u001b[39m\u001b[38;5;124m'\u001b[39m], \n\u001b[1;32m 67\u001b[0m color\u001b[38;5;241m=\u001b[39mcolors[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpre\u001b[39m\u001b[38;5;124m'\u001b[39m], alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.8\u001b[39m, s\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m)\n", - "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/pandas/core/ops/common.py:76\u001b[0m, in \u001b[0;36m_unpack_zerodim_and_defer..new_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[1;32m 74\u001b[0m other \u001b[38;5;241m=\u001b[39m item_from_zerodim(other)\n\u001b[0;32m---> 76\u001b[0m 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\u001b[38;5;124;03m moose 3.0 NaN\u001b[39;00m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 186\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_arith_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moperator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/pandas/core/series.py:6135\u001b[0m, in \u001b[0;36mSeries._arith_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m 6133\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_arith_method\u001b[39m(\u001b[38;5;28mself\u001b[39m, other, op):\n\u001b[1;32m 6134\u001b[0m \u001b[38;5;28mself\u001b[39m, other \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_align_for_op(other)\n\u001b[0;32m-> 6135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbase\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mIndexOpsMixin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_arith_method\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/pandas/core/base.py:1382\u001b[0m, in \u001b[0;36mIndexOpsMixin._arith_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m 1379\u001b[0m rvalues \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(rvalues\u001b[38;5;241m.\u001b[39mstart, rvalues\u001b[38;5;241m.\u001b[39mstop, rvalues\u001b[38;5;241m.\u001b[39mstep)\n\u001b[1;32m 1381\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39merrstate(\u001b[38;5;28mall\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 1382\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marithmetic_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1384\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_construct_result(result, name\u001b[38;5;241m=\u001b[39mres_name)\n", - "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py:273\u001b[0m, in \u001b[0;36marithmetic_op\u001b[0;34m(left, right, op)\u001b[0m\n\u001b[1;32m 260\u001b[0m \u001b[38;5;66;03m# NB: We assume that extract_array and ensure_wrapped_if_datetimelike\u001b[39;00m\n\u001b[1;32m 261\u001b[0m \u001b[38;5;66;03m# have already been called on `left` and `right`,\u001b[39;00m\n\u001b[1;32m 262\u001b[0m \u001b[38;5;66;03m# and `maybe_prepare_scalar_for_op` has already been called on `right`\u001b[39;00m\n\u001b[1;32m 263\u001b[0m \u001b[38;5;66;03m# We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy\u001b[39;00m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)\u001b[39;00m\n\u001b[1;32m 266\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 267\u001b[0m should_extension_dispatch(left, right)\n\u001b[1;32m 268\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(right, (Timedelta, BaseOffset, Timestamp))\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 271\u001b[0m \u001b[38;5;66;03m# Timedelta/Timestamp and other custom scalars are included in the check\u001b[39;00m\n\u001b[1;32m 272\u001b[0m \u001b[38;5;66;03m# because numexpr will fail on it, see GH#31457\u001b[39;00m\n\u001b[0;32m--> 273\u001b[0m res_values \u001b[38;5;241m=\u001b[39m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleft\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 275\u001b[0m \u001b[38;5;66;03m# TODO we should handle EAs consistently and move this check before the if/else\u001b[39;00m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;66;03m# (https://github.com/pandas-dev/pandas/issues/41165)\u001b[39;00m\n\u001b[1;32m 277\u001b[0m \u001b[38;5;66;03m# error: Argument 2 to \"_bool_arith_check\" has incompatible type\u001b[39;00m\n\u001b[1;32m 278\u001b[0m \u001b[38;5;66;03m# \"Union[ExtensionArray, ndarray[Any, Any]]\"; expected \"ndarray[Any, Any]\"\u001b[39;00m\n\u001b[1;32m 279\u001b[0m _bool_arith_check(op, left, right) \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n", - "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/pandas/core/arrays/datetimelike.py:2200\u001b[0m, in \u001b[0;36mTimelikeOps.__array_ufunc__\u001b[0;34m(self, ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m 2192\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 2193\u001b[0m ufunc \u001b[38;5;129;01min\u001b[39;00m [np\u001b[38;5;241m.\u001b[39misnan, np\u001b[38;5;241m.\u001b[39misinf, np\u001b[38;5;241m.\u001b[39misfinite]\n\u001b[1;32m 2194\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(inputs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 2195\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m inputs[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[1;32m 2196\u001b[0m ):\n\u001b[1;32m 2197\u001b[0m \u001b[38;5;66;03m# numpy 1.18 changed isinf and isnan to not raise on dt64/td64\u001b[39;00m\n\u001b[1;32m 2198\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(ufunc, method)(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ndarray, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m-> 2200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__array_ufunc__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mufunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/pandas/core/arrays/base.py:2300\u001b[0m, in \u001b[0;36mExtensionArray.__array_ufunc__\u001b[0;34m(self, ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m 2297\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[1;32m 2298\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m-> 2300\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marraylike\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdefault_array_ufunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mufunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/pandas/core/arraylike.py:492\u001b[0m, in \u001b[0;36mdefault_array_ufunc\u001b[0;34m(self, ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m 488\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m\n\u001b[1;32m 490\u001b[0m new_inputs \u001b[38;5;241m=\u001b[39m [x \u001b[38;5;28;01mif\u001b[39;00m x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray(x) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m inputs]\n\u001b[0;32m--> 492\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mufunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mUFuncTypeError\u001b[0m: ufunc 'add' cannot use operands with types dtype('" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8)):\n", - " \"\"\"\n", - " Plot coefficient estimates with CIs over time, grouped by first treated period.\n", - " CIs with the same x-value are jittered horizontally for better visibility.\n", - " \"\"\"\n", - "\n", - " df = create_ci_dataframe(dml_obj, level=level, joint=joint)\n", - " all_time_periods = sorted(df['Evaluation Period'].unique())\n", - " first_treated_periods = sorted(df['First Treated'].unique())\n", - " n_periods = len(first_treated_periods)\n", - " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", - " \n", - " # Adjust figure size to accommodate bottom legend\n", - " fig = plt.figure(figsize=figsize)\n", - " # Create subplot grid with space for legend at bottom\n", - " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", - " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", - "\n", - " if n_periods == 1:\n", - " axes = [axes]\n", - " \n", - " # Create a list to store legend handles and labels\n", - " legend_elements = []\n", - " \n", - " # Define jitter amount\n", - " jitter_amount = 0.05 # Adjust this value to control jitter spread\n", - " \n", - " for idx, period in enumerate(first_treated_periods):\n", - " period_data = df[df['First Treated'] == period]\n", - " ax = axes[idx]\n", - "\n", - " i_period = all_time_periods.index(period)\n", - "\n", - " # Add treatment start line\n", - " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", - " linestyle=':', alpha=0.7)\n", - " if idx == 0:\n", - " legend_elements.append((line, 'Treatment start'))\n", - "\n", - " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", - " if idx == 0:\n", - " legend_elements.append((zero_line, 'Zero effect'))\n", - "\n", - " # Split data by treatment status\n", - " pre_treatment = period_data[period_data['Pre-Treatment']]\n", - " post_treatment = period_data[~period_data['Pre-Treatment']]\n", - " \n", - " if not pre_treatment.empty:\n", - " # Add jitter to x-values\n", - " # Group by evaluation period and assign jitter within each group\n", - " pre_treatment = pre_treatment.copy()\n", - " pre_treatment['jitter'] = 0\n", - " \n", - " for x_val in pre_treatment['Evaluation Period'].unique():\n", - " mask = pre_treatment['Evaluation Period'] == x_val\n", - " count = mask.sum()\n", - " if count > 1:\n", - " # Create evenly spaced jitter values\n", - " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", - " pre_treatment.loc[mask, 'jitter'] = jitters\n", - " \n", - " jittered_x = pre_treatment['Evaluation Period'] + pre_treatment['jitter']\n", - " \n", - " # Pre-treatment points with jitter\n", - " scatter_pre = ax.scatter(jittered_x, \n", - " pre_treatment['Estimate'], \n", - " color=colors['pre'], alpha=0.8, s=10)\n", - " \n", - " # Regular CIs with jitter\n", - " error_pre = ax.errorbar(jittered_x, \n", - " pre_treatment['Estimate'],\n", - " yerr=[pre_treatment['Estimate'] - pre_treatment['CI Lower'],\n", - " pre_treatment['CI Upper'] - pre_treatment['Estimate']],\n", - " fmt='none', color=colors['pre'], alpha=1.0, \n", - " capsize=5)\n", - " if idx == 0:\n", - " legend_elements.extend([\n", - " (scatter_pre, 'Pre-treatment'),\n", - " (error_pre, f'{int(level*100)}% CI'),\n", - " ])\n", - " \n", - " # Similar structure for post-treatment with jittering\n", - " if not post_treatment.empty:\n", - " # Add jitter to x-values\n", - " post_treatment = post_treatment.copy()\n", - " post_treatment['jitter'] = 0\n", - " \n", - " for x_val in post_treatment['Evaluation Period'].unique():\n", - " mask = post_treatment['Evaluation Period'] == x_val\n", - " count = mask.sum()\n", - " if count > 1:\n", - " # Create evenly spaced jitter values\n", - " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", - " post_treatment.loc[mask, 'jitter'] = jitters\n", - " \n", - " jittered_x = post_treatment['Evaluation Period'] + post_treatment['jitter']\n", - " \n", - " scatter_post = ax.scatter(jittered_x, \n", - " post_treatment['Estimate'], \n", - " color=colors['post'], alpha=0.8, s=10)\n", - " if idx == 0:\n", - " legend_elements.append((scatter_post, 'Post-treatment'))\n", - " \n", - " # Error bars with jitter\n", - " ax.errorbar(jittered_x, post_treatment['Estimate'],\n", - " yerr=[post_treatment['Estimate'] - post_treatment['CI Lower'],\n", - " post_treatment['CI Upper'] - post_treatment['Estimate']],\n", - " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", - "\n", - " ax.set_title(f'First Treated: {period}')\n", - " ax.grid(True, alpha=0.3)\n", - " \n", - " if idx == 0:\n", - " ax.set_ylabel('Effect')\n", - " ax.set_xlabel('Evaluation Period')\n", - " \n", - " # Create legend in a separate subplot at the bottom\n", - " legend_ax = fig.add_subplot(gs[-1])\n", - " legend_ax.axis('off') # Hide axes for legend subplot\n", - " \n", - " # Add legend using collected handles and labels\n", - " legend = legend_ax.legend(*zip(*legend_elements), \n", - " loc='center',\n", - " ncol=5, # Adjust number of columns as needed\n", - " mode='expand',\n", - " borderaxespad=0.)\n", - " \n", - " plt.suptitle(\"Estimated ATTs by Group\", y=1.02)\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from datetime import timedelta\n", - "\n", - "def plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8)):\n", - " \"\"\"\n", - " Plot coefficient estimates with CIs over time, grouped by first treated period.\n", - " CIs with the same x-value are jittered horizontally for better visibility.\n", - " Works with both numeric and datetime x-values.\n", - " \"\"\"\n", - "\n", - " df = create_ci_dataframe(dml_obj, level=level, joint=joint)\n", - " all_time_periods = sorted(df['Evaluation Period'].unique())\n", - " first_treated_periods = sorted(df['First Treated'].unique())\n", - " n_periods = len(first_treated_periods)\n", - " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", - " \n", - " # Determine if we're working with datetime values\n", - " is_datetime = pd.api.types.is_datetime64_any_dtype(df['Evaluation Period'])\n", - " \n", - " # Figure setup\n", - " fig = plt.figure(figsize=figsize)\n", - " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", - " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", - "\n", - " if n_periods == 1:\n", - " axes = [axes]\n", - " \n", - " legend_elements = []\n", - " \n", - " # Define jitter amount based on data type\n", - " if is_datetime:\n", - " # For datetime, calculate appropriate timedelta for jittering\n", - " if len(all_time_periods) > 1:\n", - " # Calculate average interval between periods\n", - " if isinstance(all_time_periods[0], pd.Timestamp):\n", - " interval = (all_time_periods[1] - all_time_periods[0]).total_seconds()\n", - " # Use 10% of interval for jittering\n", - " jitter_seconds = interval * 0.1\n", - " else:\n", - " jitter_seconds = 86400 # Default: 1 day in seconds\n", - " else:\n", - " jitter_seconds = 86400 # Default: 1 day in seconds\n", - " else:\n", - " # For numeric data\n", - " if n_periods > 1:\n", - " jitter_amount = min(0.2, (all_time_periods[1] - all_time_periods[0]) * 0.2)\n", - " else:\n", - " jitter_amount = 0.1\n", - " \n", - " for idx, period in enumerate(first_treated_periods):\n", - " period_data = df[df['First Treated'] == period]\n", - " ax = axes[idx]\n", - "\n", - " i_period = all_time_periods.index(period)\n", - "\n", - " # Add treatment start line\n", - " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", - " linestyle=':', alpha=0.7)\n", - " if idx == 0:\n", - " legend_elements.append((line, 'Treatment start'))\n", - "\n", - " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", - " if idx == 0:\n", - " legend_elements.append((zero_line, 'Zero effect'))\n", - "\n", - " # Process pre-treatment data\n", - " if not period_data[period_data['Pre-Treatment']].empty:\n", - " pre_treatment = period_data[period_data['Pre-Treatment']].copy()\n", - " pre_treatment['jitter'] = 0\n", - " \n", - " for x_val in pre_treatment['Evaluation Period'].unique():\n", - " mask = pre_treatment['Evaluation Period'] == x_val\n", - " count = mask.sum()\n", - " if count > 1:\n", - " if is_datetime:\n", - " # Create datetime jitters\n", - " jitter_range = np.linspace(-jitter_seconds, jitter_seconds, count)\n", - " pre_treatment.loc[mask, 'jitter'] = [pd.Timedelta(seconds=j) for j in jitter_range]\n", - " else:\n", - " # Create numeric jitters\n", - " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", - " pre_treatment.loc[mask, 'jitter'] = jitters\n", - " \n", - " # Apply jitter based on data type\n", - " jittered_x = pre_treatment['Evaluation Period'] + pre_treatment['jitter']\n", - " \n", - " scatter_pre = ax.scatter(jittered_x, \n", - " pre_treatment['Estimate'], \n", - " color=colors['pre'], alpha=0.8, s=10)\n", - " \n", - " error_pre = ax.errorbar(jittered_x, \n", - " pre_treatment['Estimate'],\n", - " yerr=[pre_treatment['Estimate'] - pre_treatment['CI Lower'],\n", - " pre_treatment['CI Upper'] - pre_treatment['Estimate']],\n", - " fmt='none', color=colors['pre'], alpha=1.0, \n", - " capsize=5)\n", - " if idx == 0:\n", - " legend_elements.extend([\n", - " (scatter_pre, 'Pre-treatment'),\n", - " (error_pre, f'{int(level*100)}% CI'),\n", - " ])\n", - " \n", - " # Process post-treatment data (same logic as pre-treatment)\n", - " if not period_data[~period_data['Pre-Treatment']].empty:\n", - " post_treatment = period_data[~period_data['Pre-Treatment']].copy()\n", - " post_treatment['jitter'] = 0\n", - " \n", - " for x_val in post_treatment['Evaluation Period'].unique():\n", - " mask = post_treatment['Evaluation Period'] == x_val\n", - " count = mask.sum()\n", - " if count > 1:\n", - " if is_datetime:\n", - " # Create datetime jitters\n", - " jitter_range = np.linspace(-jitter_seconds, jitter_seconds, count)\n", - " post_treatment.loc[mask, 'jitter'] = [pd.Timedelta(seconds=j) for j in jitter_range]\n", - " else:\n", - " # Create numeric jitters\n", - " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", - " post_treatment.loc[mask, 'jitter'] = jitters\n", - " \n", - " jittered_x = post_treatment['Evaluation Period'] + post_treatment['jitter']\n", - " \n", - " scatter_post = ax.scatter(jittered_x, \n", - " post_treatment['Estimate'], \n", - " color=colors['post'], alpha=0.8, s=10)\n", - " if idx == 0:\n", - " legend_elements.append((scatter_post, 'Post-treatment'))\n", - " \n", - " ax.errorbar(jittered_x, post_treatment['Estimate'],\n", - " yerr=[post_treatment['Estimate'] - post_treatment['CI Lower'],\n", - " post_treatment['CI Upper'] - post_treatment['Estimate']],\n", - " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", - "\n", - " ax.set_title(f'First Treated: {period}')\n", - " ax.grid(True, alpha=0.3)\n", - " \n", - " if idx == 0:\n", - " ax.set_ylabel('Effect')\n", - " ax.set_xlabel('Evaluation Period')\n", - " \n", - " # Create legend in a separate subplot at the bottom\n", - " legend_ax = fig.add_subplot(gs[-1])\n", - " legend_ax.axis('off')\n", - " \n", - " legend" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "UFuncTypeError", - "evalue": "ufunc 'multiply' cannot use operands with types dtype('O') and dtype(' 137\u001b[0m \u001b[43mplot_atts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdml_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.95\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfigsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m8\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[70], line 64\u001b[0m, in \u001b[0;36mplot_atts\u001b[0;34m(dml_obj, level, joint, figsize)\u001b[0m\n\u001b[1;32m 61\u001b[0m count \u001b[38;5;241m=\u001b[39m mask\u001b[38;5;241m.\u001b[39msum()\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m count \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 63\u001b[0m \u001b[38;5;66;03m# Create evenly spaced jitter values\u001b[39;00m\n\u001b[0;32m---> 64\u001b[0m jitters \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinspace\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mjitter_amount\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjitter_amount\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcount\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 65\u001b[0m pre_treatment\u001b[38;5;241m.\u001b[39mloc[mask, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjitter\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m jitters\n\u001b[1;32m 67\u001b[0m jittered_x \u001b[38;5;241m=\u001b[39m pre_treatment[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEvaluation Period\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m pre_treatment[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjitter\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", - "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/numpy/_core/function_base.py:164\u001b[0m, in \u001b[0;36mlinspace\u001b[0;34m(start, stop, num, endpoint, retstep, dtype, axis, device)\u001b[0m\n\u001b[1;32m 162\u001b[0m y \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m step\n\u001b[1;32m 163\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 164\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43my\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 166\u001b[0m \u001b[38;5;66;03m# sequences with 0 items or 1 item with endpoint=True (i.e. div <= 0)\u001b[39;00m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;66;03m# have an undefined step\u001b[39;00m\n\u001b[1;32m 168\u001b[0m step \u001b[38;5;241m=\u001b[39m nan\n", - "File \u001b[0;32mtimedeltas.pyx:2043\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.timedeltas.Timedelta.__mul__\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mUFuncTypeError\u001b[0m: ufunc 'multiply' cannot use operands with types dtype('O') and dtype('" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "from datetime import timedelta\n", - "\n", - "def plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8)):\n", - " \"\"\"\n", - " Plot coefficient estimates with CIs over time, grouped by first treated period.\n", - " CIs with the same x-value are jittered horizontally for better visibility.\n", - " \"\"\"\n", - "\n", - " df = create_ci_dataframe(dml_obj, level=level, joint=joint)\n", - " all_time_periods = sorted(df['Evaluation Period'].unique())\n", - " first_treated_periods = sorted(df['First Treated'].unique())\n", - " n_periods = len(first_treated_periods)\n", - " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", - " \n", - " # Adjust figure size to accommodate bottom legend\n", - " fig = plt.figure(figsize=figsize)\n", - " # Create subplot grid with space for legend at bottom\n", - " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", - " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", - "\n", - " if n_periods == 1:\n", - " axes = [axes]\n", - " \n", - " # Create a list to store legend handles and labels\n", - " legend_elements = []\n", - " \n", - " # Define jitter amount\n", - " if n_periods > 1:\n", - " jitter_amount = all_time_periods[1] - all_time_periods[0]\n", - " else:\n", - " jitter_amount = 0.1\n", - " \n", - " for idx, period in enumerate(first_treated_periods):\n", - " period_data = df[df['First Treated'] == period]\n", - " ax = axes[idx]\n", - "\n", - " i_period = all_time_periods.index(period)\n", - "\n", - " # Add treatment start line\n", - " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", - " linestyle=':', alpha=0.7)\n", - " if idx == 0:\n", - " legend_elements.append((line, 'Treatment start'))\n", - "\n", - " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", - " if idx == 0:\n", - " legend_elements.append((zero_line, 'Zero effect'))\n", - "\n", - " # Split data by treatment status\n", - " pre_treatment = period_data[period_data['Pre-Treatment']]\n", - " post_treatment = period_data[~period_data['Pre-Treatment']]\n", - " \n", - " if not pre_treatment.empty:\n", - " # Add jitter to x-values\n", - " # Group by evaluation period and assign jitter within each group\n", - " pre_treatment = pre_treatment.copy()\n", - " pre_treatment['jitter'] = 0\n", - " \n", - " for x_val in pre_treatment['Evaluation Period'].unique():\n", - " mask = pre_treatment['Evaluation Period'] == x_val\n", - " count = mask.sum()\n", - " if count > 1:\n", - " # Create evenly spaced jitter values\n", - " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", - " pre_treatment.loc[mask, 'jitter'] = jitters\n", - " \n", - " jittered_x = pre_treatment['Evaluation Period'] + pre_treatment['jitter']\n", - " \n", - " # Pre-treatment points with jitter\n", - " scatter_pre = ax.scatter(jittered_x, \n", - " pre_treatment['Estimate'], \n", - " color=colors['pre'], alpha=0.8, s=10)\n", - " \n", - " # Regular CIs with jitter\n", - " error_pre = ax.errorbar(jittered_x, \n", - " pre_treatment['Estimate'],\n", - " yerr=[pre_treatment['Estimate'] - pre_treatment['CI Lower'],\n", - " pre_treatment['CI Upper'] - pre_treatment['Estimate']],\n", - " fmt='none', color=colors['pre'], alpha=1.0, \n", - " capsize=5)\n", - " if idx == 0:\n", - " legend_elements.extend([\n", - " (scatter_pre, 'Pre-treatment'),\n", - " (error_pre, f'{int(level*100)}% CI'),\n", - " ])\n", - " \n", - " # Similar structure for post-treatment with jittering\n", - " if not post_treatment.empty:\n", - " # Add jitter to x-values\n", - " post_treatment = post_treatment.copy()\n", - " post_treatment['jitter'] = 0\n", - " \n", - " for x_val in post_treatment['Evaluation Period'].unique():\n", - " mask = post_treatment['Evaluation Period'] == x_val\n", - " count = mask.sum()\n", - " if count > 1:\n", - " # Create evenly spaced jitter values\n", - " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", - " post_treatment.loc[mask, 'jitter'] = jitters\n", - " \n", - " jittered_x = post_treatment['Evaluation Period'] + post_treatment['jitter']\n", - " \n", - " scatter_post = ax.scatter(jittered_x, \n", - " post_treatment['Estimate'], \n", - " color=colors['post'], alpha=0.8, s=10)\n", - " if idx == 0:\n", - " legend_elements.append((scatter_post, 'Post-treatment'))\n", - " \n", - " # Error bars with jitter\n", - " ax.errorbar(jittered_x, post_treatment['Estimate'],\n", - " yerr=[post_treatment['Estimate'] - post_treatment['CI Lower'],\n", - " post_treatment['CI Upper'] - post_treatment['Estimate']],\n", - " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", - "\n", - " ax.set_title(f'First Treated: {period}')\n", - " ax.grid(True, alpha=0.3)\n", - " \n", - " if idx == 0:\n", - " ax.set_ylabel('Effect')\n", - " ax.set_xlabel('Evaluation Period')\n", - " \n", - " # Create legend in a separate subplot at the bottom\n", - " legend_ax = fig.add_subplot(gs[-1])\n", - " legend_ax.axis('off') # Hide axes for legend subplot\n", - " \n", - " # Add legend using collected handles and labels\n", - " legend = legend_ax.legend(*zip(*legend_elements), \n", - " loc='center',\n", - " ncol=5, # Adjust number of columns as needed\n", - " mode='expand',\n", - " borderaxespad=0.)\n", - " \n", - " plt.suptitle(\"Estimated ATTs by Group\", y=1.02)\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_rvs(dml_obj, level=0.95, joint=True, figsize=(12, 8)):\n", - " \"\"\"\n", - " Plot coefficient estimates with CIs over time, grouped by first treated period.\n", - " \"\"\"\n", - "\n", - " df = create_ci_dataframe(dml_obj, level=level, joint=joint, include_rvs=True)\n", - " all_time_periods = sorted(df['Evaluation Period'].unique())\n", - " first_treated_periods = sorted(df['First Treated'].unique())\n", - " n_periods = len(first_treated_periods)\n", - " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", - " \n", - " # Adjust figure size to accommodate bottom legend\n", - " fig = plt.figure(figsize=figsize)\n", - " # Create subplot grid with space for legend at bottom\n", - " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", - " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", - "\n", - " if n_periods == 1:\n", - " axes = [axes]\n", - " \n", - " # Create a list to store legend handles and labels\n", - " legend_elements = []\n", - " \n", - " for idx, period in enumerate(first_treated_periods):\n", - " period_data = df[df['First Treated'] == period]\n", - " ax = axes[idx]\n", - "\n", - " i_period = all_time_periods.index(period)\n", - "\n", - " # Add treatment start line\n", - " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", - " linestyle=':', alpha=0.7)\n", - " if idx == 0:\n", - " legend_elements.append((line, 'Treatment start'))\n", - "\n", - " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", - " if idx == 0:\n", - " legend_elements.append((zero_line, 'Zero effect'))\n", - "\n", - " # Split data by treatment status\n", - " pre_treatment = period_data[period_data['Pre-Treatment']]\n", - " post_treatment = period_data[~period_data['Pre-Treatment']]\n", - " \n", - " if not pre_treatment.empty:\n", - " # Pre-treatment points\n", - " scatter_pre = ax.scatter(pre_treatment['Evaluation Period'], \n", - " pre_treatment['RV'], \n", - " color=colors['pre'], alpha=0.8, s=10)\n", - "\n", - " if idx == 0:\n", - " legend_elements.extend([\n", - " (scatter_pre, 'Pre-treatment'),\n", - " ])\n", - " \n", - " # Similar structure for post-treatment\n", - " if not post_treatment.empty:\n", - " scatter_post = ax.scatter(post_treatment['Evaluation Period'], \n", - " post_treatment['RV'], \n", - " color=colors['post'], alpha=0.8, s=10)\n", - " if idx == 0:\n", - " legend_elements.append((scatter_post, 'Post-treatment'))\n", - " \n", - " ax.set_title(f'First Treated: {period}')\n", - " ax.grid(True, alpha=0.3)\n", - " \n", - " if idx == 0:\n", - " ax.set_ylabel('Effect')\n", - " ax.set_xlabel('Evaluation Period')\n", - " \n", - " # Create legend in a separate subplot at the bottom\n", - " legend_ax = fig.add_subplot(gs[-1])\n", - " legend_ax.axis('off') # Hide axes for legend subplot\n", - " \n", - " # Add legend using collected handles and labels\n", - " legend = legend_ax.legend(*zip(*legend_elements), \n", - " loc='center',\n", - " ncol=5, # Adjust number of columns as needed\n", - " mode='expand',\n", - " borderaxespad=0.)\n", - " \n", - " plt.suptitle(\"Estimated ATTs by Group\", y=1.02)\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "plot_rvs(dml_obj, level=0.95, joint=True, figsize=(12, 8))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "level = 0.95\n", - "\n", - "ci = dml_obj.confint(level=level)\n", - "dml_obj.bootstrap(n_rep_boot=5000)\n", - "ci_joint = dml_obj.confint(level=level, joint=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_atts(df, level=0.95, figsize=(15, 10), \n", - " title='Coefficient Estimates by First Treatment Period'):\n", - " \"\"\"\n", - " Plot coefficient estimates with CIs over time, grouped by first treatment period.\n", - " \"\"\"\n", - " all_time_periods = sorted(df['Time Period'].unique())\n", - " first_treated_periods = sorted(df['First Treated'].unique())\n", - " n_periods = len(first_treated_periods)\n", - " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", - " \n", - " # Adjust figure size to accommodate bottom legend\n", - " fig = plt.figure(figsize=figsize)\n", - " # Create subplot grid with space for legend at bottom\n", - " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", - " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", - "\n", - " if n_periods == 1:\n", - " axes = [axes]\n", - " \n", - " # Create a list to store legend handles and labels\n", - " legend_elements = []\n", - " \n", - " for idx, period in enumerate(first_treated_periods):\n", - " period_data = df[df['First Treated'] == period]\n", - " ax = axes[idx]\n", - "\n", - " i_period = all_time_periods.index(period)\n", - "\n", - " # Add treatment start line\n", - " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", - " linestyle=':', alpha=0.7)\n", - " if idx == 0:\n", - " legend_elements.append((line, 'Treatment start'))\n", - "\n", - " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", - " if idx == 0:\n", - " legend_elements.append((zero_line, 'Zero effect'))\n", - "\n", - " # Split data by treatment status\n", - " pre_treatment = period_data[period_data['Pre-Treatment']]\n", - " post_treatment = period_data[~period_data['Pre-Treatment']]\n", - " \n", - " if not pre_treatment.empty:\n", - " # Pre-treatment points\n", - " scatter_pre = ax.scatter(pre_treatment['Time Period'], \n", - " pre_treatment['Estimate'], \n", - " color=colors['pre'], alpha=0.8, s=50)\n", - " # Regular CIs\n", - " error_pre = ax.errorbar(pre_treatment['Time Period'], \n", - " pre_treatment['Estimate'],\n", - " yerr=[pre_treatment['Estimate'] - pre_treatment['Lower CI'],\n", - " pre_treatment['Upper CI'] - pre_treatment['Estimate']],\n", - " fmt='none', color=colors['pre'], alpha=1.0, \n", - " capsize=5)\n", - " # Joint CIs\n", - " error_pre_joint = ax.errorbar(pre_treatment['Time Period'], \n", - " pre_treatment['Estimate'],\n", - " yerr=[pre_treatment['Estimate'] - pre_treatment['Lower CI Joint'],\n", - " pre_treatment['Upper CI Joint'] - pre_treatment['Estimate']],\n", - " fmt='none', color=colors['pre'], alpha=0.5, \n", - " capsize=5)\n", - " if idx == 0:\n", - " legend_elements.extend([\n", - " (scatter_pre, 'Pre-treatment'),\n", - " (error_pre, f'{int(level*100)}% CI'),\n", - " (error_pre_joint, f'{int(level*100)}% joint CI')\n", - " ])\n", - " \n", - " # Similar structure for post-treatment\n", - " if not post_treatment.empty:\n", - " scatter_post = ax.scatter(post_treatment['Time Period'], \n", - " post_treatment['Estimate'], \n", - " color=colors['post'], alpha=0.8, s=50)\n", - " if idx == 0:\n", - " legend_elements.append((scatter_post, 'Post-treatment'))\n", - " \n", - " ax.errorbar(post_treatment['Time Period'], post_treatment['Estimate'],\n", - " yerr=[post_treatment['Estimate'] - post_treatment['Lower CI'],\n", - " post_treatment['Upper CI'] - post_treatment['Estimate']],\n", - " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", - " ax.errorbar(post_treatment['Time Period'], post_treatment['Estimate'],\n", - " yerr=[post_treatment['Estimate'] - post_treatment['Lower CI Joint'],\n", - " post_treatment['Upper CI Joint'] - post_treatment['Estimate']],\n", - " fmt='none', color=colors['post'], alpha=0.5, capsize=5)\n", - "\n", - " ax.set_title(f'First Treated: {period}')\n", - " ax.grid(True, alpha=0.3)\n", - " \n", - " if idx == 0:\n", - " ax.set_ylabel('Effect')\n", - " ax.set_xlabel('Time Period')\n", - " \n", - " # Create legend in a separate subplot at the bottom\n", - " legend_ax = fig.add_subplot(gs[-1])\n", - " legend_ax.axis('off') # Hide axes for legend subplot\n", - " \n", - " # Add legend using collected handles and labels\n", - " legend = legend_ax.legend(*zip(*legend_elements), \n", - " loc='center',\n", - " ncol=5, # Adjust number of columns as needed\n", - " mode='expand',\n", - " borderaxespad=0.)\n", - " \n", - " plt.suptitle(title, y=1.02) # Adjust title position\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "plot_atts(ci_df, title=\"Estimated Effects\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Time PeriodEstimateLower CIUpper CILower CI JointUpper CI JointFirst TreatedPre-Treatment
ATT(2025-05,2025-01,2025-02)2025-02-010.023003-0.0949920.140998-0.1624970.2085042025-05-01True
ATT(2025-05,2025-02,2025-03)2025-03-01-0.128634-0.253590-0.003678-0.3250770.0678102025-05-01True
ATT(2025-05,2025-03,2025-04)2025-04-010.1227000.0095880.235812-0.0551240.3005242025-05-01True
ATT(2025-05,2025-04,2025-05)2025-05-010.9112960.7914041.0311890.7228131.0997802025-05-01False
ATT(2025-05,2025-04,2025-06)2025-06-011.9631131.7868962.1393291.6860822.2401432025-05-01False
\n", - "
" - ], - "text/plain": [ - " Time Period Estimate Lower CI Upper CI \\\n", - "ATT(2025-05,2025-01,2025-02) 2025-02-01 0.023003 -0.094992 0.140998 \n", - "ATT(2025-05,2025-02,2025-03) 2025-03-01 -0.128634 -0.253590 -0.003678 \n", - "ATT(2025-05,2025-03,2025-04) 2025-04-01 0.122700 0.009588 0.235812 \n", - "ATT(2025-05,2025-04,2025-05) 2025-05-01 0.911296 0.791404 1.031189 \n", - "ATT(2025-05,2025-04,2025-06) 2025-06-01 1.963113 1.786896 2.139329 \n", - "\n", - " Lower CI Joint Upper CI Joint First Treated \\\n", - "ATT(2025-05,2025-01,2025-02) -0.162497 0.208504 2025-05-01 \n", - "ATT(2025-05,2025-02,2025-03) -0.325077 0.067810 2025-05-01 \n", - "ATT(2025-05,2025-03,2025-04) -0.055124 0.300524 2025-05-01 \n", - "ATT(2025-05,2025-04,2025-05) 0.722813 1.099780 2025-05-01 \n", - "ATT(2025-05,2025-04,2025-06) 1.686082 2.240143 2025-05-01 \n", - "\n", - " Pre-Treatment \n", - "ATT(2025-05,2025-01,2025-02) True \n", - "ATT(2025-05,2025-02,2025-03) True \n", - "ATT(2025-05,2025-03,2025-04) True \n", - "ATT(2025-05,2025-04,2025-05) False \n", - "ATT(2025-05,2025-04,2025-06) False " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ci_df = pd.DataFrame({\n", - " 'Time Period': [gt_combination[2] for gt_combination in dml_obj.gt_combinations],\n", - " 'Estimate': dml_obj.coef,\n", - " 'Lower CI': ci.iloc[:, 0],\n", - " 'Upper CI': ci.iloc[:, 1],\n", - " 'Lower CI Joint': ci_joint.iloc[:, 0],\n", - " 'Upper CI Joint': ci_joint.iloc[:, 1],\n", - " 'First Treated': [gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", - " 'Pre-Treatment': [gt_combination[2] < gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", - "})\n", - "ci_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/venv/lib/python3.12/site-packages/matplotlib/cbook.py:1709: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_atts(df, level=0.95, figsize=(15, 10), \n", - " title='Coefficient Estimates by First Treatment Period'):\n", - " \"\"\"\n", - " Plot coefficient estimates with CIs over time, grouped by first treatment period.\n", - " \"\"\"\n", - " all_time_periods = sorted(df['Time Period'].unique())\n", - " first_treated_periods = sorted(df['First Treated'].unique())\n", - " n_periods = len(first_treated_periods)\n", - " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", - " \n", - " # Adjust figure size to accommodate bottom legend\n", - " fig = plt.figure(figsize=figsize)\n", - " # Create subplot grid with space for legend at bottom\n", - " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", - " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", - "\n", - " if n_periods == 1:\n", - " axes = [axes]\n", - " \n", - " # Create a list to store legend handles and labels\n", - " legend_elements = []\n", - " \n", - " for idx, period in enumerate(first_treated_periods):\n", - " period_data = df[df['First Treated'] == period]\n", - " ax = axes[idx]\n", - "\n", - " i_period = all_time_periods.index(period)\n", - "\n", - " # Add treatment start line\n", - " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", - " linestyle=':', alpha=0.7)\n", - " if idx == 0:\n", - " legend_elements.append((line, 'Treatment start'))\n", - "\n", - " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", - " if idx == 0:\n", - " legend_elements.append((zero_line, 'Zero effect'))\n", - "\n", - " # Split data by treatment status\n", - " pre_treatment = period_data[period_data['Pre-Treatment']]\n", - " post_treatment = period_data[~period_data['Pre-Treatment']]\n", - " \n", - " if not pre_treatment.empty:\n", - " # Pre-treatment points\n", - " scatter_pre = ax.scatter(pre_treatment['Time Period'], \n", - " pre_treatment['Estimate'], \n", - " color=colors['pre'], alpha=0.8, s=50)\n", - " # Regular CIs\n", - " error_pre = ax.errorbar(pre_treatment['Time Period'], \n", - " pre_treatment['Estimate'],\n", - " yerr=[pre_treatment['Estimate'] - pre_treatment['Lower CI'],\n", - " pre_treatment['Upper CI'] - pre_treatment['Estimate']],\n", - " fmt='none', color=colors['pre'], alpha=1.0, \n", - " capsize=5)\n", - " # Joint CIs\n", - " error_pre_joint = ax.errorbar(pre_treatment['Time Period'], \n", - " pre_treatment['Estimate'],\n", - " yerr=[pre_treatment['Estimate'] - pre_treatment['Lower CI Joint'],\n", - " pre_treatment['Upper CI Joint'] - pre_treatment['Estimate']],\n", - " fmt='none', color=colors['pre'], alpha=0.5, \n", - " capsize=5)\n", - " if idx == 0:\n", - " legend_elements.extend([\n", - " (scatter_pre, 'Pre-treatment'),\n", - " (error_pre, f'{int(level*100)}% CI'),\n", - " (error_pre_joint, f'{int(level*100)}% joint CI')\n", - " ])\n", - " \n", - " # Similar structure for post-treatment\n", - " if not post_treatment.empty:\n", - " scatter_post = ax.scatter(post_treatment['Time Period'], \n", - " post_treatment['Estimate'], \n", - " color=colors['post'], alpha=0.8, s=50)\n", - " if idx == 0:\n", - " legend_elements.append((scatter_post, 'Post-treatment'))\n", - " \n", - " ax.errorbar(post_treatment['Time Period'], post_treatment['Estimate'],\n", - " yerr=[post_treatment['Estimate'] - post_treatment['Lower CI'],\n", - " post_treatment['Upper CI'] - post_treatment['Estimate']],\n", - " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", - " ax.errorbar(post_treatment['Time Period'], post_treatment['Estimate'],\n", - " yerr=[post_treatment['Estimate'] - post_treatment['Lower CI Joint'],\n", - " post_treatment['Upper CI Joint'] - post_treatment['Estimate']],\n", - " fmt='none', color=colors['post'], alpha=0.5, capsize=5)\n", - "\n", - " ax.set_title(f'First Treated: {period}')\n", - " ax.grid(True, alpha=0.3)\n", - " \n", - " if idx == 0:\n", - " ax.set_ylabel('Effect')\n", - " ax.set_xlabel('Time Period')\n", - " \n", - " # Create legend in a separate subplot at the bottom\n", - " legend_ax = fig.add_subplot(gs[-1])\n", - " legend_ax.axis('off') # Hide axes for legend subplot\n", - " \n", - " # Add legend using collected handles and labels\n", - " legend = legend_ax.legend(*zip(*legend_elements), \n", - " loc='center',\n", - " ncol=5, # Adjust number of columns as needed\n", - " mode='expand',\n", - " borderaxespad=0.)\n", - " \n", - " plt.suptitle(title, y=1.02) # Adjust title position\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "plot_atts(ci_df, title=\"Estimated Effects\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDBinary Object ==================\n", - "\n", - "------------------ Data summary ------------------\n", - "Outcome variable: y\n", - "Treatment variable(s): ['d']\n", - "Covariates: ['Z1', 'Z2', 'Z3', 'Z4']\n", - "Instrument variable(s): None\n", - "Time variable: t\n", - "Id variable: id\n", - "No. Observations: 5000\n", - "\n", - "------------------ Score & algorithm ------------------\n", - "Score function: observational\n", - "Treatment group: 2025-05\n", - "Pre-treatment period: 2025-01\n", - "Evaluation period: 2025-02\n", - "Control group: never_treated\n", - "Effective sample size: 2040\n", - "\n", - "------------------ Machine learner ------------------\n", - "Learner ml_g: LGBMRegressor(learning_rate=0.01, n_estimators=500, verbose=-1)\n", - "Learner ml_m: LGBMClassifier(learning_rate=0.01, n_estimators=500, verbose=-1)\n", - "Out-of-sample Performance:\n", - "Regression:\n", - "Learner ml_g0 RMSE: [[1.75343356]]\n", - "Learner ml_g1 RMSE: [[1.78847566]]\n", - "Classification:\n", - "Learner ml_m Log Loss: [[0.67570748]]\n", - "\n", - "------------------ Resampling ------------------\n", - "No. folds: 5\n", - "No. repeated sample splits: 1\n", - "\n", - "------------------ Fit summary ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 0.157743 0.118683 1.329112 0.183811 -0.074872 0.390358\n" - ] - } - ], - "source": [ - "print(dml_obj.modellist[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== Sensitivity Analysis ==================\n", - "\n", - "------------------ Scenario ------------------\n", - "Significance Level: level=0.95\n", - "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", - "\n", - "------------------ Bounds with CI ------------------\n", - " CI lower theta lower theta theta upper \\\n", - "ATT(2025-05,2025-01,2025-02) -0.120006 0.069369 0.157743 0.246118 \n", - "ATT(2025-05,2025-02,2025-03) -0.269324 -0.052141 0.055352 0.162846 \n", - "ATT(2025-05,2025-03,2025-04) -0.155909 0.096783 0.196838 0.296892 \n", - "ATT(2025-05,2025-04,2025-05) 0.815970 1.086885 1.182260 1.277636 \n", - "ATT(2025-05,2025-04,2025-06) 1.775847 2.105615 2.245718 2.385820 \n", - "ATT(2025-05,2025-04,2025-07) 2.716193 3.302991 3.443723 3.584455 \n", - "ATT(2025-05,2025-04,2025-08) 3.477819 4.144813 4.382113 4.619413 \n", - "ATT(2025-06,2025-01,2025-02) -0.240154 -0.080768 0.025464 0.131696 \n", - "ATT(2025-06,2025-02,2025-03) -0.391045 -0.204127 -0.104800 -0.005473 \n", - "ATT(2025-06,2025-03,2025-04) -0.175712 0.042373 0.135043 0.227713 \n", - "ATT(2025-06,2025-04,2025-05) -0.174499 0.030080 0.118779 0.207478 \n", - "ATT(2025-06,2025-05,2025-06) 0.811950 1.014461 1.114512 1.214563 \n", - "ATT(2025-06,2025-05,2025-07) 1.611025 1.907322 2.050087 2.192851 \n", - "ATT(2025-06,2025-05,2025-08) 2.744902 3.158602 3.307784 3.456965 \n", - "ATT(2025-07,2025-01,2025-02) -0.065555 0.102357 0.197566 0.292776 \n", - "ATT(2025-07,2025-02,2025-03) -0.209319 -0.037719 0.057542 0.152803 \n", - "ATT(2025-07,2025-03,2025-04) -0.081986 0.165769 0.228719 0.291668 \n", - "ATT(2025-07,2025-04,2025-05) -0.102946 0.068754 0.164268 0.259782 \n", - "ATT(2025-07,2025-05,2025-06) -0.264629 -0.086315 -0.000824 0.084667 \n", - "ATT(2025-07,2025-06,2025-07) 0.775759 0.948530 1.041953 1.135376 \n", - "ATT(2025-07,2025-06,2025-08) 1.747544 2.011390 2.137674 2.263957 \n", - "ATT(2025-08,2025-01,2025-02) -0.045196 0.106545 0.198593 0.290640 \n", - "ATT(2025-08,2025-02,2025-03) -0.142176 0.016219 0.108227 0.200234 \n", - "ATT(2025-08,2025-03,2025-04) -0.116497 0.048845 0.140670 0.232495 \n", - "ATT(2025-08,2025-04,2025-05) -0.304563 -0.149809 -0.050136 0.049538 \n", - "ATT(2025-08,2025-05,2025-06) -0.149991 0.015477 0.115210 0.214943 \n", - "ATT(2025-08,2025-06,2025-07) -0.344588 -0.188381 -0.088871 0.010638 \n", - "ATT(2025-08,2025-07,2025-08) 1.007429 1.167105 1.260098 1.353092 \n", - "\n", - " CI upper \n", - "ATT(2025-05,2025-01,2025-02) 0.450629 \n", - "ATT(2025-05,2025-02,2025-03) 0.393038 \n", - "ATT(2025-05,2025-03,2025-04) 0.555804 \n", - "ATT(2025-05,2025-04,2025-05) 1.565359 \n", - "ATT(2025-05,2025-04,2025-06) 2.738323 \n", - "ATT(2025-05,2025-04,2025-07) 4.233211 \n", - "ATT(2025-05,2025-04,2025-08) 5.313431 \n", - "ATT(2025-06,2025-01,2025-02) 0.292584 \n", - "ATT(2025-06,2025-02,2025-03) 0.187398 \n", - "ATT(2025-06,2025-03,2025-04) 0.456664 \n", - "ATT(2025-06,2025-04,2025-05) 0.442892 \n", - "ATT(2025-06,2025-05,2025-06) 1.417664 \n", - "ATT(2025-06,2025-05,2025-07) 2.494698 \n", - "ATT(2025-06,2025-05,2025-08) 3.916391 \n", - "ATT(2025-07,2025-01,2025-02) 0.463180 \n", - "ATT(2025-07,2025-02,2025-03) 0.325410 \n", - "ATT(2025-07,2025-03,2025-04) 0.565833 \n", - "ATT(2025-07,2025-04,2025-05) 0.435110 \n", - "ATT(2025-07,2025-05,2025-06) 0.255810 \n", - "ATT(2025-07,2025-06,2025-07) 1.311939 \n", - "ATT(2025-07,2025-06,2025-08) 2.524003 \n", - "ATT(2025-08,2025-01,2025-02) 0.443700 \n", - "ATT(2025-08,2025-02,2025-03) 0.358259 \n", - "ATT(2025-08,2025-03,2025-04) 0.399046 \n", - "ATT(2025-08,2025-04,2025-05) 0.202978 \n", - "ATT(2025-08,2025-05,2025-06) 0.377566 \n", - "ATT(2025-08,2025-06,2025-07) 0.167372 \n", - "ATT(2025-08,2025-07,2025-08) 1.514207 \n", - "\n", - "------------------ Robustness Values ------------------\n", - " H_0 RV (%) RVa (%)\n", - "ATT(2025-05,2025-01,2025-02) 0.0 5.291229 0.000398\n", - "ATT(2025-05,2025-02,2025-03) 0.0 1.556378 0.000506\n", - "ATT(2025-05,2025-03,2025-04) 0.0 5.815643 0.000347\n", - "ATT(2025-05,2025-04,2025-05) 0.0 31.296964 24.177074\n", - "ATT(2025-05,2025-04,2025-06) 0.0 38.339629 32.590509\n", - "ATT(2025-05,2025-04,2025-07) 0.0 51.766229 39.284928\n", - "ATT(2025-05,2025-04,2025-08) 0.0 42.612003 35.789261\n", - "ATT(2025-06,2025-01,2025-02) 0.0 0.727644 0.000662\n", - "ATT(2025-06,2025-02,2025-03) 0.0 3.162723 0.000372\n", - "ATT(2025-06,2025-03,2025-04) 0.0 4.341441 0.000551\n", - "ATT(2025-06,2025-04,2025-05) 0.0 3.996742 0.000610\n", - "ATT(2025-06,2025-05,2025-06) 0.0 28.659565 23.077695\n", - "ATT(2025-06,2025-05,2025-07) 0.0 35.208491 29.712370\n", - "ATT(2025-06,2025-05,2025-08) 0.0 48.478791 39.983373\n", - "ATT(2025-07,2025-01,2025-02) 0.0 6.124151 0.925003\n", - "ATT(2025-07,2025-02,2025-03) 0.0 1.823209 0.000447\n", - "ATT(2025-07,2025-03,2025-04) 0.0 10.471987 0.000637\n", - "ATT(2025-07,2025-04,2025-05) 0.0 5.103291 0.000407\n", - "ATT(2025-07,2025-05,2025-06) 0.0 0.029213 0.000421\n", - "ATT(2025-07,2025-06,2025-07) 0.0 28.688699 23.926366\n", - "ATT(2025-07,2025-06,2025-08) 0.0 39.954797 33.379184\n", - "ATT(2025-08,2025-01,2025-02) 0.0 6.359461 1.533646\n", - "ATT(2025-08,2025-02,2025-03) 0.0 3.519432 0.000341\n", - "ATT(2025-08,2025-03,2025-04) 0.0 4.558764 0.000582\n", - "ATT(2025-08,2025-04,2025-05) 0.0 1.520578 0.000468\n", - "ATT(2025-08,2025-05,2025-06) 0.0 3.457416 0.000617\n", - "ATT(2025-08,2025-06,2025-07) 0.0 2.683734 0.000357\n", - "ATT(2025-08,2025-07,2025-08) 0.0 33.626953 29.598398\n" + "================== Sensitivity Analysis ==================\n", + "\n", + "------------------ Scenario ------------------\n", + "Significance Level: level=0.95\n", + "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", + "\n", + "------------------ Bounds with CI ------------------\n", + " CI lower theta lower theta theta upper \\\n", + "ATT(2025-05,2025-01,2025-02) -0.430592 -0.218892 -0.126335 -0.033778 \n", + "ATT(2025-05,2025-02,2025-03) -0.229224 -0.020882 0.081016 0.182915 \n", + "ATT(2025-05,2025-03,2025-04) -0.399950 -0.204859 -0.094349 0.016161 \n", + "ATT(2025-05,2025-04,2025-05) 0.750803 0.955098 1.069615 1.184133 \n", + "ATT(2025-05,2025-04,2025-06) 1.433732 1.727613 1.891446 2.055279 \n", + "ATT(2025-05,2025-04,2025-07) 2.175267 2.604104 2.796395 2.988686 \n", + "ATT(2025-05,2025-04,2025-08) 3.253588 3.809880 3.990323 4.170766 \n", + "ATT(2025-06,2025-01,2025-02) -0.365539 -0.189067 -0.093047 0.002972 \n", + "ATT(2025-06,2025-02,2025-03) -0.193003 -0.011296 0.080593 0.172481 \n", + "ATT(2025-06,2025-03,2025-04) -0.412308 -0.230882 -0.131845 -0.032809 \n", + "ATT(2025-06,2025-04,2025-05) -0.147983 0.041654 0.123388 0.205121 \n", + "ATT(2025-06,2025-05,2025-06) 0.568676 0.754233 0.857444 0.960654 \n", + "ATT(2025-06,2025-05,2025-07) 1.338491 1.643520 1.782152 1.920784 \n", + "ATT(2025-06,2025-05,2025-08) 2.457779 2.909994 3.039536 3.169077 \n", + "ATT(2025-07,2025-01,2025-02) -0.209353 -0.051747 0.037523 0.126793 \n", + "ATT(2025-07,2025-02,2025-03) -0.356251 -0.190426 -0.092156 0.006114 \n", + "ATT(2025-07,2025-03,2025-04) -0.286034 -0.104592 -0.013253 0.078085 \n", + "ATT(2025-07,2025-04,2025-05) -0.103663 0.064232 0.163007 0.261781 \n", + "ATT(2025-07,2025-05,2025-06) -0.466753 -0.303900 -0.203583 -0.103265 \n", + "ATT(2025-07,2025-06,2025-07) 0.686492 0.853559 0.948008 1.042458 \n", + "ATT(2025-07,2025-06,2025-08) 1.610064 1.854018 1.980269 2.106521 \n", + "ATT(2025-08,2025-01,2025-02) -0.294763 -0.112158 -0.030199 0.051760 \n", + "ATT(2025-08,2025-02,2025-03) -0.393739 -0.239299 -0.143455 -0.047612 \n", + "ATT(2025-08,2025-03,2025-04) -0.266969 -0.119738 -0.027104 0.065531 \n", + "ATT(2025-08,2025-04,2025-05) -0.272828 -0.111128 -0.015802 0.079524 \n", + "ATT(2025-08,2025-05,2025-06) -0.294523 -0.135235 -0.047013 0.041210 \n", + "ATT(2025-08,2025-06,2025-07) -0.334927 -0.172113 -0.079947 0.012218 \n", + "ATT(2025-08,2025-07,2025-08) 0.609092 0.762704 0.853624 0.944545 \n", + "\n", + " CI upper \n", + "ATT(2025-05,2025-01,2025-02) 0.173019 \n", + "ATT(2025-05,2025-02,2025-03) 0.400319 \n", + "ATT(2025-05,2025-03,2025-04) 0.211306 \n", + "ATT(2025-05,2025-04,2025-05) 1.407086 \n", + "ATT(2025-05,2025-04,2025-06) 2.359689 \n", + "ATT(2025-05,2025-04,2025-07) 3.416271 \n", + "ATT(2025-05,2025-04,2025-08) 4.750998 \n", + "ATT(2025-06,2025-01,2025-02) 0.182120 \n", + "ATT(2025-06,2025-02,2025-03) 0.360349 \n", + "ATT(2025-06,2025-03,2025-04) 0.156584 \n", + "ATT(2025-06,2025-04,2025-05) 0.421432 \n", + "ATT(2025-06,2025-05,2025-06) 1.150286 \n", + "ATT(2025-06,2025-05,2025-07) 2.226798 \n", + "ATT(2025-06,2025-05,2025-08) 3.696603 \n", + "ATT(2025-07,2025-01,2025-02) 0.289634 \n", + "ATT(2025-07,2025-02,2025-03) 0.172829 \n", + "ATT(2025-07,2025-03,2025-04) 0.256322 \n", + "ATT(2025-07,2025-04,2025-05) 0.432246 \n", + "ATT(2025-07,2025-05,2025-06) 0.059592 \n", + "ATT(2025-07,2025-06,2025-07) 1.208356 \n", + "ATT(2025-07,2025-06,2025-08) 2.352818 \n", + "ATT(2025-08,2025-01,2025-02) 0.238131 \n", + "ATT(2025-08,2025-02,2025-03) 0.106646 \n", + "ATT(2025-08,2025-03,2025-04) 0.212378 \n", + "ATT(2025-08,2025-04,2025-05) 0.241368 \n", + "ATT(2025-08,2025-05,2025-06) 0.203540 \n", + "ATT(2025-08,2025-06,2025-07) 0.174686 \n", + "ATT(2025-08,2025-07,2025-08) 1.100115 \n", + "\n", + "------------------ Robustness Values ------------------\n", + " H_0 RV (%) RVa (%)\n", + "ATT(2025-05,2025-01,2025-02) 0.0 4.072170 0.000445\n", + "ATT(2025-05,2025-02,2025-03) 0.0 2.392754 0.000548\n", + "ATT(2025-05,2025-03,2025-04) 0.0 2.567065 0.000351\n", + "ATT(2025-05,2025-04,2025-05) 0.0 24.689828 20.518957\n", + "ATT(2025-05,2025-04,2025-06) 0.0 29.522560 25.061055\n", + "ATT(2025-05,2025-04,2025-07) 0.0 35.559468 28.456559\n", + "ATT(2025-05,2025-04,2025-08) 0.0 48.391241 29.199529\n", + "ATT(2025-06,2025-01,2025-02) 0.0 2.908570 0.000345\n", + "ATT(2025-06,2025-02,2025-03) 0.0 2.636212 0.000611\n", + "ATT(2025-06,2025-03,2025-04) 0.0 3.973795 0.000338\n", + "ATT(2025-06,2025-04,2025-05) 0.0 4.493919 0.000393\n", + "ATT(2025-06,2025-05,2025-06) 0.0 22.305524 17.559574\n", + "ATT(2025-06,2025-05,2025-07) 0.0 32.234337 26.281610\n", + "ATT(2025-06,2025-05,2025-08) 0.0 50.357219 34.953278\n", + "ATT(2025-07,2025-01,2025-02) 0.0 1.272295 0.000664\n", + "ATT(2025-07,2025-02,2025-03) 0.0 2.816085 0.000394\n", + "ATT(2025-07,2025-03,2025-04) 0.0 0.440845 0.000615\n", + "ATT(2025-07,2025-04,2025-05) 0.0 4.902133 0.000368\n", + "ATT(2025-07,2025-05,2025-06) 0.0 5.993485 1.238147\n", + "ATT(2025-07,2025-06,2025-07) 0.0 26.255130 21.462039\n", + "ATT(2025-07,2025-06,2025-08) 0.0 37.708391 32.937833\n", + "ATT(2025-08,2025-01,2025-02) 0.0 1.116214 0.000515\n", + "ATT(2025-08,2025-02,2025-03) 0.0 4.456499 0.000341\n", + "ATT(2025-08,2025-03,2025-04) 0.0 0.887418 0.000606\n", + "ATT(2025-08,2025-04,2025-05) 0.0 0.503821 0.000559\n", + "ATT(2025-08,2025-05,2025-06) 0.0 1.610184 0.000642\n", + "ATT(2025-08,2025-06,2025-07) 0.0 2.607636 0.000371\n", + "ATT(2025-08,2025-07,2025-08) 0.0 24.799894 20.568212\n" ] } ], @@ -2219,282 +995,6 @@ "dml_obj.sensitivity_analysis()\n", "print(dml_obj.sensitivity_summary)" ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.0056365 , 0.03090225, 0.03031496, 0.20215469, 0.32155703,\n", - " 0.40301916, 0.42120061, 0.0171697 , 0.00725611, 0.00196753,\n", - " 0.02802333, 0.2309368 , 0.34055762, 0.38839082, 0.01142757,\n", - " 0.01075262, 0.00927836, 0.0160404 , 0.01690221, 0.25701313,\n", - " 0.4222928 , 0.01768095, 0.01798774, 0.00106278, 0.00850198,\n", - " 0.04722776, 0.0077089 , 0.30099369])" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dml_obj.sensitivity_analysis()\n", - "dml_obj.sensitivity_params[\"rv\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_36280/1092211131.py:19: FutureWarning: \n", - "\n", - "Setting a gradient palette using color= is deprecated and will be removed in v0.14.0. Set `palette='dark:#0173b2'` for the same effect.\n", - "\n", - " sns.stripplot(\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_sensitivity(dml_obj, figsize=(12, 6)):\n", - " \"\"\"\n", - " Plot sensitivity analysis results for the ATT using jittered points.\n", - " \"\"\"\n", - " # Extract robustness values\n", - " rv_values = dml_obj.sensitivity_params[\"rv\"]\n", - "\n", - " \n", - " # Create a simple dataframe with just the values\n", - " sens_df = pd.DataFrame({\n", - " \"Robustness Value\": rv_values,\n", - " 'Pre-Treatment': [gt_combination[2] < gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", - " })\n", - " \n", - " # Create a figure\n", - " fig, ax = plt.subplots(figsize=figsize)\n", - " \n", - " # Plot jittered points\n", - " sns.stripplot(\n", - " data=sens_df,\n", - " y=\"Robustness Value\",\n", - " hue=\"Pre-Treatment\",\n", - " ax=ax,\n", - " size=8,\n", - " jitter=True,\n", - " alpha=0.7,\n", - " color=sns.color_palette(\"colorblind\")[0]\n", - " )\n", - "\n", - " \n", - " # Add title and labels\n", - " ax.set_title('Sensitivity Analysis - Robustness Values')\n", - " ax.set_ylabel('Robustness Value')\n", - " ax.set_xlabel('')\n", - " \n", - " # Remove x-axis ticks since we're just plotting the distribution\n", - " ax.set_xticks([])\n", - " \n", - " # Add legend\n", - " ax.legend()\n", - " \n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "plot_sensitivity(dml_obj)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Could not interpret value `Sensitivity` for `x`. An entry with this name does not appear in `data`.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[39], line 20\u001b[0m\n\u001b[1;32m 17\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSensitivity Analysis for ATT\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 18\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[0;32m---> 20\u001b[0m \u001b[43mplot_sensitivity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdml_obj\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[39], line 15\u001b[0m, in \u001b[0;36mplot_sensitivity\u001b[0;34m(dml_obj, figsize)\u001b[0m\n\u001b[1;32m 13\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39mfigsize)\n\u001b[1;32m 14\u001b[0m \u001b[38;5;66;03m# Plot the sensitivity values\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbarplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msens_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSensitivity\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mFirst Treated\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcolorblind\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Add a title\u001b[39;00m\n\u001b[1;32m 17\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSensitivity Analysis for ATT\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/seaborn/categorical.py:2341\u001b[0m, in \u001b[0;36mbarplot\u001b[0;34m(data, x, y, hue, order, hue_order, estimator, errorbar, n_boot, seed, units, weights, orient, color, palette, saturation, fill, hue_norm, width, dodge, gap, log_scale, native_scale, formatter, legend, capsize, err_kws, ci, errcolor, errwidth, ax, **kwargs)\u001b[0m\n\u001b[1;32m 2338\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mlen\u001b[39m:\n\u001b[1;32m 2339\u001b[0m estimator \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 2341\u001b[0m p \u001b[38;5;241m=\u001b[39m \u001b[43m_CategoricalAggPlotter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2342\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2343\u001b[0m \u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2344\u001b[0m \u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2345\u001b[0m \u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2346\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2347\u001b[0m \u001b[43m \u001b[49m\u001b[43mlegend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlegend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2348\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2350\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 2351\u001b[0m ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n", - "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/seaborn/categorical.py:67\u001b[0m, in \u001b[0;36m_CategoricalPlotter.__init__\u001b[0;34m(self, data, variables, order, orient, require_numeric, color, legend)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__init__\u001b[39m(\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 58\u001b[0m data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 64\u001b[0m legend\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 65\u001b[0m ):\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;66;03m# This method takes care of some bookkeeping that is necessary because the\u001b[39;00m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;66;03m# original categorical plots (prior to the 2021 refactor) had some rules that\u001b[39;00m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;66;03m# don't fit exactly into VectorPlotter logic. It may be wise to have a second\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[38;5;66;03m# default VectorPlotter rules. If we do decide to make orient part of the\u001b[39;00m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;66;03m# _base variable assignment, we'll want to figure out how to express that.\u001b[39;00m\n\u001b[1;32m 78\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_format \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwide\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m orient \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", - "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/seaborn/_base.py:634\u001b[0m, in \u001b[0;36mVectorPlotter.__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m 629\u001b[0m \u001b[38;5;66;03m# var_ordered is relevant only for categorical axis variables, and may\u001b[39;00m\n\u001b[1;32m 630\u001b[0m \u001b[38;5;66;03m# be better handled by an internal axis information object that tracks\u001b[39;00m\n\u001b[1;32m 631\u001b[0m \u001b[38;5;66;03m# such information and is set up by the scale_* methods. The analogous\u001b[39;00m\n\u001b[1;32m 632\u001b[0m \u001b[38;5;66;03m# information for numeric axes would be information about log scales.\u001b[39;00m\n\u001b[1;32m 633\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_var_ordered \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m} \u001b[38;5;66;03m# alt., used DefaultDict\u001b[39;00m\n\u001b[0;32m--> 634\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massign_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 636\u001b[0m \u001b[38;5;66;03m# TODO Lots of tests assume that these are called to initialize the\u001b[39;00m\n\u001b[1;32m 637\u001b[0m \u001b[38;5;66;03m# mappings to default values on class initialization. I'd prefer to\u001b[39;00m\n\u001b[1;32m 638\u001b[0m \u001b[38;5;66;03m# move away from that and only have a mapping when explicitly called.\u001b[39;00m\n\u001b[1;32m 639\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhue\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstyle\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", - "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/seaborn/_base.py:679\u001b[0m, in \u001b[0;36mVectorPlotter.assign_variables\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m 674\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 675\u001b[0m \u001b[38;5;66;03m# When dealing with long-form input, use the newer PlotData\u001b[39;00m\n\u001b[1;32m 676\u001b[0m \u001b[38;5;66;03m# object (internal but introduced for the objects interface)\u001b[39;00m\n\u001b[1;32m 677\u001b[0m \u001b[38;5;66;03m# to centralize / standardize data consumption logic.\u001b[39;00m\n\u001b[1;32m 678\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_format \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlong\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 679\u001b[0m plot_data \u001b[38;5;241m=\u001b[39m \u001b[43mPlotData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 680\u001b[0m frame \u001b[38;5;241m=\u001b[39m plot_data\u001b[38;5;241m.\u001b[39mframe\n\u001b[1;32m 681\u001b[0m names \u001b[38;5;241m=\u001b[39m plot_data\u001b[38;5;241m.\u001b[39mnames\n", - "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/seaborn/_core/data.py:58\u001b[0m, in \u001b[0;36mPlotData.__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__init__\u001b[39m(\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 53\u001b[0m data: DataSource,\n\u001b[1;32m 54\u001b[0m variables: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, VariableSpec],\n\u001b[1;32m 55\u001b[0m ):\n\u001b[1;32m 57\u001b[0m data \u001b[38;5;241m=\u001b[39m handle_data_source(data)\n\u001b[0;32m---> 58\u001b[0m frame, names, ids \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_assign_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframe \u001b[38;5;241m=\u001b[39m frame\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnames \u001b[38;5;241m=\u001b[39m names\n", - "File \u001b[0;32m/opt/venv/lib/python3.12/site-packages/seaborn/_core/data.py:232\u001b[0m, in \u001b[0;36mPlotData._assign_variables\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 231\u001b[0m err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn entry with this name does not appear in `data`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 232\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err)\n\u001b[1;32m 234\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 235\u001b[0m \n\u001b[1;32m 236\u001b[0m \u001b[38;5;66;03m# Otherwise, assume the value somehow represents data\u001b[39;00m\n\u001b[1;32m 237\u001b[0m \n\u001b[1;32m 238\u001b[0m \u001b[38;5;66;03m# Ignore empty data structures\u001b[39;00m\n\u001b[1;32m 239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, Sized) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(val) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", - "\u001b[0;31mValueError\u001b[0m: Could not interpret value `Sensitivity` for `x`. An entry with this name does not appear in `data`." - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot the robustness values for the ATT with seaborn\n", - "\n", - "def plot_sensitivity(dml_obj, figsize=(12, 8)):\n", - " \"\"\"\n", - " Plot sensitivity analysis results for the ATT.\n", - " \"\"\"\n", - " # vector of robustness values\n", - " sens_df = pd.DataFrame({\n", - " \"rv\": dml_obj.sensitivity_params[\"rv\"]\n", - " })\n", - "\n", - " # Create a figure\n", - " fig, ax = plt.subplots(figsize=figsize)\n", - " # Plot the sensitivity values\n", - " sns.barplot(data=sens_df, x='Sensitivity', y='First Treated', ax=ax, palette='colorblind')\n", - " # Add a title\n", - " ax.set_title('Sensitivity Analysis for ATT')\n", - " plt.show()\n", - "\n", - "plot_sensitivity(dml_obj)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'DoubleMLDIDMulti' object has no attribute 'data'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[42], line 57\u001b[0m\n\u001b[1;32m 54\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n\u001b[1;32m 55\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[0;32m---> 57\u001b[0m \u001b[43mplot_sensitivity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdml_obj\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[42], line 19\u001b[0m, in \u001b[0;36mplot_sensitivity\u001b[0;34m(dml_obj, figsize)\u001b[0m\n\u001b[1;32m 13\u001b[0m sens_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame([\n\u001b[1;32m 14\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFirst Treated\u001b[39m\u001b[38;5;124m\"\u001b[39m: key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRobustness Value\u001b[39m\u001b[38;5;124m\"\u001b[39m: value} \n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m rv_data\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 16\u001b[0m ])\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(rv_data, (pd\u001b[38;5;241m.\u001b[39mSeries, np\u001b[38;5;241m.\u001b[39mndarray)):\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# If rv_data is a series or array, assume it corresponds to first_treated periods\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m first_treated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(\u001b[43mdml_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfirst_treat\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39munique())\n\u001b[1;32m 20\u001b[0m sens_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\n\u001b[1;32m 21\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFirst Treated\u001b[39m\u001b[38;5;124m\"\u001b[39m: first_treated,\n\u001b[1;32m 22\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRobustness Value\u001b[39m\u001b[38;5;124m\"\u001b[39m: rv_data\n\u001b[1;32m 23\u001b[0m })\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 25\u001b[0m \u001b[38;5;66;03m# If single value\u001b[39;00m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'DoubleMLDIDMulti' object has no attribute 'data'" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "def plot_sensitivity(dml_obj, figsize=(12, 8)):\n", - " \"\"\"\n", - " Plot sensitivity analysis results for the ATT.\n", - " \"\"\"\n", - " # Extract robustness values and convert to proper dataframe format\n", - " rv_data = dml_obj.sensitivity_params[\"rv\"]\n", - " \n", - " # Create a properly structured dataframe\n", - " if isinstance(rv_data, dict):\n", - " # If rv_data is a dictionary with keys as periods/groups\n", - " sens_df = pd.DataFrame([\n", - " {\"First Treated\": key, \"Robustness Value\": value} \n", - " for key, value in rv_data.items()\n", - " ])\n", - " elif isinstance(rv_data, (pd.Series, np.ndarray)):\n", - " # If rv_data is a series or array, assume it corresponds to first_treated periods\n", - " first_treated = sorted(dml_obj.data['first_treat'].unique())\n", - " sens_df = pd.DataFrame({\n", - " \"First Treated\": first_treated,\n", - " \"Robustness Value\": rv_data\n", - " })\n", - " else:\n", - " # If single value\n", - " sens_df = pd.DataFrame({\n", - " \"First Treated\": [\"Overall\"],\n", - " \"Robustness Value\": [rv_data]\n", - " })\n", - " \n", - " # Create a figure\n", - " fig, ax = plt.subplots(figsize=figsize)\n", - " \n", - " # Plot the sensitivity values\n", - " sns.barplot(\n", - " data=sens_df, \n", - " x=\"First Treated\", \n", - " y=\"Robustness Value\", \n", - " ax=ax, \n", - " palette='colorblind'\n", - " )\n", - " \n", - " # Add a title and labels\n", - " ax.set_title('Sensitivity Analysis for ATT')\n", - " ax.set_xlabel('First Treated Period')\n", - " ax.set_ylabel('Robustness Value')\n", - " \n", - " # Add horizontal line at y=1 for reference\n", - " ax.axhline(y=1, color='red', linestyle='--', alpha=0.7, label='Threshold')\n", - " \n", - " # Add legend\n", - " ax.legend()\n", - " \n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "plot_sensitivity(dml_obj)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Event Study Aggregation \n", - "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "2.763755 0.218875 12.627083 0.0 2.334768 3.192743\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "-6 months 0.198593 0.092508 2.146769 0.031812 0.017281 0.379905\n", - "-5 months 0.152421 0.071982 2.117498 0.034218 0.011340 0.293503\n", - "-4 months 0.074895 0.066944 1.118773 0.263237 -0.056313 0.206103\n", - "-3 months 0.057963 0.077873 0.744331 0.456677 -0.094665 0.210592\n", - "-2 months 0.116883 0.076878 1.520356 0.128421 -0.033796 0.267561\n", - "-1 months 0.057215 0.077162 0.741485 0.458399 -0.094021 0.208450\n", - "0 months 1.150537 0.081138 14.179983 0.000000 0.991510 1.309565\n", - "1 months 2.145565 0.148218 14.475725 0.000000 1.855063 2.436067\n", - "2 months 3.376806 0.278366 12.130827 0.000000 2.831219 3.922393\n", - "3 months 4.382113 0.412502 10.623264 0.000000 3.573625 5.190602\n", - "------------------ Additional Information ------------------\n", - "Control Group: never_treated\n", - "Anticipation Periods: 0\n", - "Score: observational\n", - "\n" - ] - } - ], - "source": [ - "agg_obj = dml_obj.aggregate(aggregation=\"eventstudy\")\n", - "print(agg_obj)" - ] } ], "metadata": { diff --git a/doc/examples/py_double_ml_panel_simple.ipynb b/doc/examples/py_double_ml_panel_simple.ipynb index 5c7ecc3b..7eae4166 100644 --- a/doc/examples/py_double_ml_panel_simple.ipynb +++ b/doc/examples/py_double_ml_panel_simple.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -74,84 +74,84 @@ " \n", " \n", " ATT(2.0,1,2)\n", - " 0.920659\n", - " 0.064105\n", - " 14.361647\n", + " 0.918577\n", + " 0.063957\n", + " 14.362451\n", " 0.000000\n", - " 0.795014\n", - " 1.046303\n", + " 0.793224\n", + " 1.043930\n", " \n", " \n", " ATT(2.0,1,3)\n", - " 1.987829\n", - " 0.064660\n", - " 30.742884\n", + " 1.988461\n", + " 0.064665\n", + " 30.750246\n", " 0.000000\n", - " 1.861098\n", - " 2.114560\n", + " 1.861720\n", + " 2.115201\n", " \n", " \n", " ATT(2.0,1,4)\n", - " 2.955122\n", - " 0.063113\n", - " 46.822594\n", + " 2.954280\n", + " 0.063294\n", + " 46.675247\n", " 0.000000\n", - " 2.831422\n", - " 3.078821\n", + " 2.830225\n", + " 3.078334\n", " \n", " \n", " ATT(3.0,1,2)\n", - " -0.042606\n", - " 0.066026\n", - " -0.645298\n", - " 0.518734\n", - " -0.172015\n", - " 0.086802\n", + " -0.042247\n", + " 0.065929\n", + " -0.640793\n", + " 0.521657\n", + " -0.171465\n", + " 0.086972\n", " \n", " \n", " ATT(3.0,2,3)\n", - " 1.107568\n", - " 0.065475\n", - " 16.915916\n", + " 1.112732\n", + " 0.065561\n", + " 16.972543\n", " 0.000000\n", - " 0.979239\n", - " 1.235896\n", + " 0.984235\n", + " 1.241228\n", " \n", " \n", " ATT(3.0,2,4)\n", - " 2.057568\n", - " 0.065470\n", - " 31.427868\n", + " 2.059244\n", + " 0.065484\n", + " 31.446740\n", " 0.000000\n", - " 1.929250\n", - " 2.185886\n", + " 1.930898\n", + " 2.187589\n", " \n", " \n", " ATT(4.0,1,2)\n", - " 0.004233\n", - " 0.068274\n", - " 0.062002\n", - " 0.950562\n", - " -0.129581\n", - " 0.138048\n", + " 0.007441\n", + " 0.068490\n", + " 0.108650\n", + " 0.913480\n", + " -0.126796\n", + " 0.141679\n", " \n", " \n", " ATT(4.0,2,3)\n", - " 0.061837\n", - " 0.066472\n", - " 0.930267\n", - " 0.352233\n", - " -0.068446\n", - " 0.192119\n", + " 0.062091\n", + " 0.066437\n", + " 0.934592\n", + " 0.349999\n", + " -0.068122\n", + " 0.192304\n", " \n", " \n", " ATT(4.0,3,4)\n", - " 0.953010\n", - " 0.067443\n", - " 14.130675\n", + " 0.951063\n", + " 0.067523\n", + " 14.084959\n", " 0.000000\n", - " 0.820825\n", - " 1.085195\n", + " 0.818720\n", + " 1.083407\n", " \n", " \n", "\n", @@ -159,18 +159,18 @@ ], "text/plain": [ " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.920659 0.064105 14.361647 0.000000 0.795014 1.046303\n", - "ATT(2.0,1,3) 1.987829 0.064660 30.742884 0.000000 1.861098 2.114560\n", - "ATT(2.0,1,4) 2.955122 0.063113 46.822594 0.000000 2.831422 3.078821\n", - "ATT(3.0,1,2) -0.042606 0.066026 -0.645298 0.518734 -0.172015 0.086802\n", - "ATT(3.0,2,3) 1.107568 0.065475 16.915916 0.000000 0.979239 1.235896\n", - "ATT(3.0,2,4) 2.057568 0.065470 31.427868 0.000000 1.929250 2.185886\n", - "ATT(4.0,1,2) 0.004233 0.068274 0.062002 0.950562 -0.129581 0.138048\n", - "ATT(4.0,2,3) 0.061837 0.066472 0.930267 0.352233 -0.068446 0.192119\n", - "ATT(4.0,3,4) 0.953010 0.067443 14.130675 0.000000 0.820825 1.085195" + "ATT(2.0,1,2) 0.918577 0.063957 14.362451 0.000000 0.793224 1.043930\n", + "ATT(2.0,1,3) 1.988461 0.064665 30.750246 0.000000 1.861720 2.115201\n", + "ATT(2.0,1,4) 2.954280 0.063294 46.675247 0.000000 2.830225 3.078334\n", + "ATT(3.0,1,2) -0.042247 0.065929 -0.640793 0.521657 -0.171465 0.086972\n", + "ATT(3.0,2,3) 1.112732 0.065561 16.972543 0.000000 0.984235 1.241228\n", + "ATT(3.0,2,4) 2.059244 0.065484 31.446740 0.000000 1.930898 2.187589\n", + "ATT(4.0,1,2) 0.007441 0.068490 0.108650 0.913480 -0.126796 0.141679\n", + "ATT(4.0,2,3) 0.062091 0.066437 0.934592 0.349999 -0.068122 0.192304\n", + "ATT(4.0,3,4) 0.951063 0.067523 14.084959 0.000000 0.818720 1.083407" ] }, - "execution_count": 9, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -200,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -213,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -233,21 +233,21 @@ "\n", "------------------ Score & algorithm ------------------\n", "Score function: observational\n", - "GT combinations: ['(2.0,1,2)', '(2.0,1,3)', '(2.0,1,4)', '(3.0,1,2)', '(3.0,2,3)', '(3.0,2,4)', '(4.0,1,2)', '(4.0,2,3)', '(4.0,3,4)']\n", "Control group: never_treated\n", "Anticipation periods: 0\n", + "\n", "------------------ Machine learner ------------------\n", "Learner ml_g: LinearRegression()\n", "Learner ml_m: LogisticRegression()\n", "Out-of-sample Performance:\n", "Regression:\n", - "Learner ml_g0 RMSE: [[1.42730197 1.41113036 1.39623362 1.4271475 1.40723812 1.41836799\n", - " 1.423926 1.40487993 1.42254218]]\n", - "Learner ml_g1 RMSE: [[1.40496958 1.43560178 1.39842847 1.4127457 1.42704868 1.38713382\n", - " 1.45719691 1.41487036 1.41335501]]\n", + "Learner ml_g0 RMSE: [[1.42597918 1.40968087 1.40052964 1.42787771 1.40713307 1.42403057\n", + " 1.42802689 1.40320538 1.42776935]]\n", + "Learner ml_g1 RMSE: [[1.40358956 1.43518576 1.39551046 1.4138002 1.42577686 1.38380243\n", + " 1.45865143 1.41456292 1.40745099]]\n", "Classification:\n", - "Learner ml_m Log Loss: [[0.69060261 0.69041707 0.69043153 0.67919869 0.67914682 0.67934808\n", - " 0.66257322 0.66203852 0.66207553]]\n", + "Learner ml_m Log Loss: [[0.69177084 0.69122753 0.6907171 0.67931424 0.68023374 0.67927162\n", + " 0.66238847 0.66240294 0.66204557]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 5\n", @@ -255,15 +255,15 @@ "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.920659 0.064105 14.361647 0.000000 0.795014 1.046303\n", - "ATT(2.0,1,3) 1.987829 0.064660 30.742884 0.000000 1.861098 2.114560\n", - "ATT(2.0,1,4) 2.955122 0.063113 46.822594 0.000000 2.831422 3.078821\n", - "ATT(3.0,1,2) -0.042606 0.066026 -0.645298 0.518734 -0.172015 0.086802\n", - "ATT(3.0,2,3) 1.107568 0.065475 16.915916 0.000000 0.979239 1.235896\n", - "ATT(3.0,2,4) 2.057568 0.065470 31.427868 0.000000 1.929250 2.185886\n", - "ATT(4.0,1,2) 0.004233 0.068274 0.062002 0.950562 -0.129581 0.138048\n", - "ATT(4.0,2,3) 0.061837 0.066472 0.930267 0.352233 -0.068446 0.192119\n", - "ATT(4.0,3,4) 0.953010 0.067443 14.130675 0.000000 0.820825 1.085195\n" + "ATT(2.0,1,2) 0.918577 0.063957 14.362451 0.000000 0.793224 1.043930\n", + "ATT(2.0,1,3) 1.988461 0.064665 30.750246 0.000000 1.861720 2.115201\n", + "ATT(2.0,1,4) 2.954280 0.063294 46.675247 0.000000 2.830225 3.078334\n", + "ATT(3.0,1,2) -0.042247 0.065929 -0.640793 0.521657 -0.171465 0.086972\n", + "ATT(3.0,2,3) 1.112732 0.065561 16.972543 0.000000 0.984235 1.241228\n", + "ATT(3.0,2,4) 2.059244 0.065484 31.446740 0.000000 1.930898 2.187589\n", + "ATT(4.0,1,2) 0.007441 0.068490 0.108650 0.913480 -0.126796 0.141679\n", + "ATT(4.0,2,3) 0.062091 0.066437 0.934592 0.349999 -0.068122 0.192304\n", + "ATT(4.0,3,4) 0.951063 0.067523 14.084959 0.000000 0.818720 1.083407\n" ] } ], @@ -273,7 +273,359 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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First TreatedPre-treatment PeriodEvaluation PeriodEstimateCI LowerCI UpperPre-Treatment
ATT(2.0,1,2)2.0120.9185770.7440791.093075False
ATT(2.0,1,3)2.0131.9884611.8120302.164891False
ATT(2.0,1,4)2.0142.9542802.7815893.126971False
ATT(3.0,1,2)3.012-0.042247-0.2221260.137632True
ATT(3.0,2,3)3.0231.1127320.9338571.291606False
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" + ], + "text/plain": [ + " First Treated Pre-treatment Period Evaluation Period \\\n", + "ATT(2.0,1,2) 2.0 1 2 \n", + "ATT(2.0,1,3) 2.0 1 3 \n", + "ATT(2.0,1,4) 2.0 1 4 \n", + "ATT(3.0,1,2) 3.0 1 2 \n", + "ATT(3.0,2,3) 3.0 2 3 \n", + "\n", + " Estimate CI Lower CI Upper Pre-Treatment \n", + "ATT(2.0,1,2) 0.918577 0.744079 1.093075 False \n", + "ATT(2.0,1,3) 1.988461 1.812030 2.164891 False \n", + "ATT(2.0,1,4) 2.954280 2.781589 3.126971 False \n", + "ATT(3.0,1,2) -0.042247 -0.222126 0.137632 True \n", + "ATT(3.0,2,3) 1.112732 0.933857 1.291606 False " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def create_ci_dataframe(dml_obj, level=0.95, joint=True, include_rvs=False):\n", + " \"\"\"\n", + " Create a DataFrame with coefficient estimates and confidence intervals from a DoubleML object.\n", + " \n", + " Parameters:\n", + " -----------\n", + " dml_obj : DoubleML object\n", + " The fitted DoubleML object\n", + " level : float, default=0.95\n", + " Confidence level for intervals\n", + " joint : bool, default=True\n", + " Whether to use joint confidence intervals\n", + " \n", + " Returns:\n", + " --------\n", + " DataFrame\n", + " DataFrame containing estimates and confidence intervals\n", + " \"\"\"\n", + "\n", + " ci = dml_obj.confint(level=level, joint=joint)\n", + "\n", + " # Create DataFrame\n", + " result_df = pd.DataFrame({\n", + " 'First Treated': [gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", + " 'Pre-treatment Period' : [gt_combination[1] for gt_combination in dml_obj.gt_combinations],\n", + " 'Evaluation Period': [gt_combination[2] for gt_combination in dml_obj.gt_combinations],\n", + " 'Estimate': dml_obj.coef,\n", + " 'CI Lower': ci.iloc[:, 0],\n", + " 'CI Upper': ci.iloc[:, 1],\n", + " 'Pre-Treatment': [gt_combination[2] < gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", + " })\n", + " if include_rvs:\n", + " result_df[\"RV\"] = dml_obj.sensitivity_params[\"rv\"]\n", + " return result_df\n", + "\n", + "ci_df = create_ci_dataframe(dml_obj, include_rvs=False)\n", + "ci_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/matplotlib/cbook.py:1709: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n", + "/opt/venv/lib/python3.12/site-packages/matplotlib/cbook.py:1709: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8)):\n", + " \"\"\"\n", + " Plot coefficient estimates with CIs over time, grouped by first treated period.\n", + " CIs with the same x-value are jittered horizontally for better visibility.\n", + " Works with both numeric and datetime values.\n", + " \"\"\"\n", + " df = create_ci_dataframe(dml_obj, level=level, joint=joint)\n", + " all_time_periods = sorted(df['Evaluation Period'].unique())\n", + " first_treated_periods = sorted(df['First Treated'].unique())\n", + " n_periods = len(first_treated_periods)\n", + " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", + " \n", + " # Check if we're dealing with datetime values\n", + " is_datetime = pd.api.types.is_datetime64_any_dtype(df['Evaluation Period'])\n", + " \n", + " # Adjust figure size to accommodate bottom legend\n", + " fig = plt.figure(figsize=figsize)\n", + " # Create subplot grid with space for legend at bottom\n", + " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", + " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", + "\n", + " if n_periods == 1:\n", + " axes = [axes]\n", + " \n", + " # Create a list to store legend handles and labels\n", + " legend_elements = []\n", + " \n", + " # Define jitter amount - different handling for datetime vs numeric\n", + " if is_datetime:\n", + " # For datetime, calculate time difference between periods\n", + " if len(all_time_periods) > 1:\n", + " time_diff = (all_time_periods[1] - all_time_periods[0]).total_seconds()\n", + " jitter_seconds = time_diff * 0.1 # Use 5% of time difference for jitter\n", + " else:\n", + " jitter_seconds = 86400 * 0.05 # Default to 5% of a day if only one period\n", + " else:\n", + " jitter_amount = 0.1 # Standard numeric jitter\n", + " \n", + " for idx, period in enumerate(first_treated_periods):\n", + " period_data = df[df['First Treated'] == period]\n", + " ax = axes[idx]\n", + "\n", + " i_period = all_time_periods.index(period)\n", + "\n", + " # Add treatment start line\n", + " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", + " linestyle=':', alpha=0.7)\n", + " if idx == 0:\n", + " legend_elements.append((line, 'Treatment start'))\n", + "\n", + " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", + " if idx == 0:\n", + " legend_elements.append((zero_line, 'Zero effect'))\n", + "\n", + " # Split data by treatment status\n", + " pre_treatment = period_data[period_data['Pre-Treatment']]\n", + " post_treatment = period_data[~period_data['Pre-Treatment']]\n", + " \n", + " if not pre_treatment.empty:\n", + " pre_treatment = pre_treatment.copy()\n", + " \n", + " for x_val in pre_treatment['Evaluation Period'].unique():\n", + " mask = pre_treatment['Evaluation Period'] == x_val\n", + " count = mask.sum()\n", + " if count > 1:\n", + " if is_datetime:\n", + " # For datetime values, create timedelta jitters\n", + " jitter_range = np.linspace(-jitter_seconds, jitter_seconds, count)\n", + " jitters = [pd.Timedelta(seconds=float(j)) for j in jitter_range]\n", + " else:\n", + " # For numeric values, create standard jitters\n", + " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", + " \n", + " # Store the jitters for these points\n", + " pre_treatment.loc[mask, 'jitter_index'] = range(count)\n", + " for i, j in enumerate(jitters):\n", + " pre_treatment.loc[mask & (pre_treatment['jitter_index'] == i), 'jittered_x'] = x_val + j\n", + " \n", + " # For points without jitter (single point at x-value)\n", + " if 'jittered_x' not in pre_treatment.columns:\n", + " pre_treatment['jittered_x'] = pre_treatment['Evaluation Period']\n", + " else:\n", + " mask = ~pre_treatment['jittered_x'].notna()\n", + " pre_treatment.loc[mask, 'jittered_x'] = pre_treatment.loc[mask, 'Evaluation Period']\n", + " \n", + " # Pre-treatment points with jitter\n", + " scatter_pre = ax.scatter(pre_treatment['jittered_x'], \n", + " pre_treatment['Estimate'], \n", + " color=colors['pre'], alpha=0.8, s=10)\n", + " \n", + " # Regular CIs with jitter\n", + " error_pre = ax.errorbar(pre_treatment['jittered_x'], \n", + " pre_treatment['Estimate'],\n", + " yerr=[pre_treatment['Estimate'] - pre_treatment['CI Lower'],\n", + " pre_treatment['CI Upper'] - pre_treatment['Estimate']],\n", + " fmt='none', color=colors['pre'], alpha=1.0, \n", + " capsize=5)\n", + " if idx == 0:\n", + " legend_elements.extend([\n", + " (scatter_pre, 'Pre-treatment'),\n", + " (error_pre, f'{int(level*100)}% CI'),\n", + " ])\n", + " \n", + " # Similar structure for post-treatment with jittering\n", + " if not post_treatment.empty:\n", + " post_treatment = post_treatment.copy()\n", + " \n", + " for x_val in post_treatment['Evaluation Period'].unique():\n", + " mask = post_treatment['Evaluation Period'] == x_val\n", + " count = mask.sum()\n", + " if count > 1:\n", + " if is_datetime:\n", + " # For datetime values, create timedelta jitters\n", + " jitter_range = np.linspace(-jitter_seconds, jitter_seconds, count)\n", + " jitters = [pd.Timedelta(seconds=float(j)) for j in jitter_range]\n", + " else:\n", + " # For numeric values, create standard jitters\n", + " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", + " \n", + " # Store the jitters for these points\n", + " post_treatment.loc[mask, 'jitter_index'] = range(count)\n", + " for i, j in enumerate(jitters):\n", + " post_treatment.loc[mask & (post_treatment['jitter_index'] == i), 'jittered_x'] = x_val + j\n", + " \n", + " # For points without jitter (single point at x-value)\n", + " if 'jittered_x' not in post_treatment.columns:\n", + " post_treatment['jittered_x'] = post_treatment['Evaluation Period']\n", + " else:\n", + " mask = ~post_treatment['jittered_x'].notna()\n", + " post_treatment.loc[mask, 'jittered_x'] = post_treatment.loc[mask, 'Evaluation Period']\n", + " \n", + " scatter_post = ax.scatter(post_treatment['jittered_x'], \n", + " post_treatment['Estimate'], \n", + " color=colors['post'], alpha=0.8, s=10)\n", + " if idx == 0:\n", + " legend_elements.append((scatter_post, 'Post-treatment'))\n", + " \n", + " # Error bars with jitter\n", + " ax.errorbar(post_treatment['jittered_x'], post_treatment['Estimate'],\n", + " yerr=[post_treatment['Estimate'] - post_treatment['CI Lower'],\n", + " post_treatment['CI Upper'] - post_treatment['Estimate']],\n", + " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", + "\n", + " ax.set_title(f'First Treated: {period}')\n", + " ax.grid(True, alpha=0.3)\n", + " \n", + " if idx == 0:\n", + " ax.set_ylabel('Effect')\n", + " ax.set_xlabel('Evaluation Period')\n", + " \n", + " # Create legend in a separate subplot at the bottom\n", + " legend_ax = fig.add_subplot(gs[-1])\n", + " legend_ax.axis('off') # Hide axes for legend subplot\n", + " \n", + " # Add legend using collected handles and labels\n", + " legend = legend_ax.legend(*zip(*legend_elements), \n", + " loc='center',\n", + " ncol=5, # Adjust number of columns as needed\n", + " mode='expand',\n", + " borderaxespad=0.)\n", + " \n", + " plt.suptitle(\"Estimated ATTs by Group\", y=1.02)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8))" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [ { @@ -306,7 +658,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -339,7 +691,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { From adf9cbe3b6796694aa5d17ebad29cbf3deb5c95a Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 17 Mar 2025 16:26:10 +0000 Subject: [PATCH 028/140] update nbs --- doc/examples/py_double_ml_panel.ipynb | 923 ++++++++----------- doc/examples/py_double_ml_panel_simple.ipynb | 628 ++++--------- 2 files changed, 546 insertions(+), 1005 deletions(-) diff --git a/doc/examples/py_double_ml_panel.ipynb b/doc/examples/py_double_ml_panel.ipynb index a4e40fc4..0e311d10 100644 --- a/doc/examples/py_double_ml_panel.ipynb +++ b/doc/examples/py_double_ml_panel.ipynb @@ -11,6 +11,7 @@ "import pandas as pd\n", "\n", "from lightgbm import LGBMRegressor, LGBMClassifier\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", "\n", "from doubleml.did import DoubleMLDIDMulti\n", "from doubleml.data import DoubleMLPanelData\n", @@ -19,7 +20,7 @@ "\n", "# simulate data\n", "n_obs = 5000\n", - "df = make_did_CS2021(n_obs, dgp_type=4, n_periods=8, n_pre_treat_periods=4, time_type=\"datetime\")\n", + "df = make_did_CS2021(n_obs, dgp_type=1, n_periods=8, n_pre_treat_periods=4, time_type=\"datetime\")\n", "df[\"ite\"] = df[\"y1\"] - df[\"y0\"]" ] }, @@ -67,96 +68,96 @@ " \n", " 0\n", " 0\n", - " 208.160364\n", - " 208.160364\n", - " 206.150875\n", - " 2025-07-01\n", + " 214.177554\n", + " 214.177554\n", + " 215.868822\n", + " NaT\n", " 2025-01-01\n", - " -1.221552\n", - " 0.665762\n", - " -0.20382\n", - " 0.617408\n", - " -2.009488\n", - " 2025-07\n", + " -0.480522\n", + " 0.943682\n", + " -0.218748\n", + " 0.91184\n", + " 1.691268\n", + " Never Treated\n", " \n", " \n", " 1\n", " 0\n", - " 204.174392\n", - " 204.174392\n", - " 203.462996\n", - " 2025-07-01\n", + " 218.358132\n", + " 218.358132\n", + " 218.343452\n", + " NaT\n", " 2025-02-01\n", - " -1.221552\n", - " 0.665762\n", - " -0.20382\n", - " 0.617408\n", - " -0.711396\n", - " 2025-07\n", + " -0.480522\n", + " 0.943682\n", + " -0.218748\n", + " 0.91184\n", + " -0.014681\n", + " Never Treated\n", " \n", " \n", " 2\n", " 0\n", - " 199.599967\n", - " 199.599967\n", - " 197.741360\n", - " 2025-07-01\n", + " 220.434292\n", + " 220.434292\n", + " 219.332694\n", + " NaT\n", " 2025-03-01\n", - " -1.221552\n", - " 0.665762\n", - " -0.20382\n", - " 0.617408\n", - " -1.858607\n", - " 2025-07\n", + " -0.480522\n", + " 0.943682\n", + " -0.218748\n", + " 0.91184\n", + " -1.101598\n", + " Never Treated\n", " \n", " \n", " 3\n", " 0\n", - " 195.233663\n", - " 195.233663\n", - " 195.721657\n", - " 2025-07-01\n", + " 221.413949\n", + " 221.413949\n", + " 223.756078\n", + " NaT\n", " 2025-04-01\n", - " -1.221552\n", - " 0.665762\n", - " -0.20382\n", - " 0.617408\n", - " 0.487994\n", - " 2025-07\n", + " -0.480522\n", + " 0.943682\n", + " -0.218748\n", + " 0.91184\n", + " 2.342129\n", + " Never Treated\n", " \n", " \n", " 4\n", " 0\n", - " 189.010286\n", - " 189.010286\n", - " 191.331624\n", - " 2025-07-01\n", + " 224.374383\n", + " 224.374383\n", + " 223.932041\n", + " NaT\n", " 2025-05-01\n", - " -1.221552\n", - " 0.665762\n", - " -0.20382\n", - " 0.617408\n", - " 2.321338\n", - " 2025-07\n", + " -0.480522\n", + " 0.943682\n", + " -0.218748\n", + " 0.91184\n", + " -0.442342\n", + " Never Treated\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id y y0 y1 d t Z1 \\\n", - "0 0 208.160364 208.160364 206.150875 2025-07-01 2025-01-01 -1.221552 \n", - "1 0 204.174392 204.174392 203.462996 2025-07-01 2025-02-01 -1.221552 \n", - "2 0 199.599967 199.599967 197.741360 2025-07-01 2025-03-01 -1.221552 \n", - "3 0 195.233663 195.233663 195.721657 2025-07-01 2025-04-01 -1.221552 \n", - "4 0 189.010286 189.010286 191.331624 2025-07-01 2025-05-01 -1.221552 \n", + " id y y0 y1 d t Z1 Z2 \\\n", + "0 0 214.177554 214.177554 215.868822 NaT 2025-01-01 -0.480522 0.943682 \n", + "1 0 218.358132 218.358132 218.343452 NaT 2025-02-01 -0.480522 0.943682 \n", + "2 0 220.434292 220.434292 219.332694 NaT 2025-03-01 -0.480522 0.943682 \n", + "3 0 221.413949 221.413949 223.756078 NaT 2025-04-01 -0.480522 0.943682 \n", + "4 0 224.374383 224.374383 223.932041 NaT 2025-05-01 -0.480522 0.943682 \n", "\n", - " Z2 Z3 Z4 ite First Treated \n", - "0 0.665762 -0.20382 0.617408 -2.009488 2025-07 \n", - "1 0.665762 -0.20382 0.617408 -0.711396 2025-07 \n", - "2 0.665762 -0.20382 0.617408 -1.858607 2025-07 \n", - "3 0.665762 -0.20382 0.617408 0.487994 2025-07 \n", - "4 0.665762 -0.20382 0.617408 2.321338 2025-07 " + " Z3 Z4 ite First Treated \n", + "0 -0.218748 0.91184 1.691268 Never Treated \n", + "1 -0.218748 0.91184 -0.014681 Never Treated \n", + "2 -0.218748 0.91184 -1.101598 Never Treated \n", + "3 -0.218748 0.91184 2.342129 Never Treated \n", + "4 -0.218748 0.91184 -0.442342 Never Treated " ] }, "execution_count": 2, @@ -211,56 +212,56 @@ " 0\n", " 2025-01-01\n", " 2025-05\n", - " 208.851382\n", - " 201.215583\n", - " 216.475755\n", - " -0.013452\n", - " -2.392223\n", - " 2.371590\n", + " 209.300753\n", + " 201.766880\n", + " 217.039317\n", + " -0.026614\n", + " -2.304513\n", + " 2.419752\n", " \n", " \n", " 1\n", " 2025-01-01\n", " 2025-06\n", - " 210.329416\n", - " 202.638201\n", - " 217.961796\n", - " -0.052635\n", - " -2.448709\n", - " 2.271408\n", + " 210.202290\n", + " 202.774666\n", + " 217.882044\n", + " -0.001342\n", + " -2.241443\n", + " 2.269269\n", " \n", " \n", " 2\n", " 2025-01-01\n", " 2025-07\n", - " 211.793469\n", - " 204.025050\n", - " 219.193304\n", - " -0.048091\n", - " -2.403457\n", - " 2.219086\n", + " 212.076382\n", + " 204.560539\n", + " 219.492164\n", + " 0.069318\n", + " -2.108613\n", + " 2.353309\n", " \n", " \n", " 3\n", " 2025-01-01\n", " 2025-08\n", - " 213.467868\n", - " 205.879919\n", - " 220.905510\n", - " 0.095641\n", - " -2.277543\n", - " 2.263161\n", + " 213.364195\n", + " 205.573922\n", + " 221.975874\n", + " -0.041680\n", + " -2.449703\n", + " 2.358228\n", " \n", " \n", " 4\n", " 2025-01-01\n", " Never Treated\n", - " 215.076606\n", - " 207.173377\n", - " 222.389427\n", - " -0.009199\n", - " -2.332194\n", - " 2.405225\n", + " 215.096598\n", + " 206.449830\n", + " 225.569888\n", + " -0.050251\n", + " -2.313271\n", + " 2.264483\n", " \n", " \n", "\n", @@ -268,18 +269,18 @@ ], "text/plain": [ " t First Treated y_mean y_lower_quantile y_upper_quantile \\\n", - "0 2025-01-01 2025-05 208.851382 201.215583 216.475755 \n", - "1 2025-01-01 2025-06 210.329416 202.638201 217.961796 \n", - "2 2025-01-01 2025-07 211.793469 204.025050 219.193304 \n", - "3 2025-01-01 2025-08 213.467868 205.879919 220.905510 \n", - "4 2025-01-01 Never Treated 215.076606 207.173377 222.389427 \n", + "0 2025-01-01 2025-05 209.300753 201.766880 217.039317 \n", + "1 2025-01-01 2025-06 210.202290 202.774666 217.882044 \n", + "2 2025-01-01 2025-07 212.076382 204.560539 219.492164 \n", + "3 2025-01-01 2025-08 213.364195 205.573922 221.975874 \n", + "4 2025-01-01 Never Treated 215.096598 206.449830 225.569888 \n", "\n", " ite_mean ite_lower_quantile ite_upper_quantile \n", - "0 -0.013452 -2.392223 2.371590 \n", - "1 -0.052635 -2.448709 2.271408 \n", - "2 -0.048091 -2.403457 2.219086 \n", - "3 0.095641 -2.277543 2.263161 \n", - "4 -0.009199 -2.332194 2.405225 " + "0 -0.026614 -2.304513 2.419752 \n", + "1 -0.001342 -2.241443 2.269269 \n", + "2 0.069318 -2.108613 2.353309 \n", + "3 -0.041680 -2.449703 2.358228 \n", + "4 -0.050251 -2.313271 2.264483 " ] }, "execution_count": 3, @@ -313,7 +314,7 @@ "outputs": [ { "data": { - "image/png": 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", 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/8OCBvEBxfsfOL669lsaNG+d7LdpCi6FryY+DgwO6dOkCQHcR9PXr1yMtLQ2VK1dGaGjoM13386C9H//73//yvR9Lly4FoHs/fvrpJ7i5uWHBggUICAiAs7Mz2rZti4ULF+LBgwfPNecnc7927Vq+udevX18v98KqWLFigfE7d+7oxKtVq4Y333wTd+7cwZYtW+T4119/DeDx4ueFkfcJhfnlfvLkSYhHI/Jx8ODBAo+X3+fh7t27AABnZ2fY2dkZbFO5cmWdtiXlafc3MzMz34X388p7r+7fv1+oc8fFxQF4VGTNq7R9N75oPj4+BuM2NjYFbtcWnvIuHP+s94aIiJ4P/X+GJCIiegbaJyVlZWXpFT2Axz8kIyIi8Nlnn0GpVAIAmjRpgsqVK+PKlSs4cuQIGjVqhLi4OGzfvh0WFhbo2bOnznE0Gg0AIDg4GK+99lqBOTVo0EAvZmlpmW/7U6dO4f3334dSqcTs2bPRoUMH+Pj4wMrKCpIk4dtvv8X7779vcIRDQfIrAGivpVu3brC2ti7wGM7OzkU65zvvvIM1a9Zg7dq1WLhwISwtLeUftIMGDdLJ6Xldd360151f/I033pALEPnJO1KkcePGiI6Oxu+//44DBw7gyJEj2LVrF3bs2IFPP/0UmzdvRosWLUok9/xoc3d3d9crzD4pb/GipBj624waNQq7du3C119/jW7duuH27dvYunUrbGxsMGDAgEIfu0KFCnByckJiYiJOnjyJN95445lyLegz+Lzk1+eKojD9v2LFinB0dMTDhw9x7NgxeYRffnJzc/H3338DgN5Iu9L03WgMCkXB/47+tO15Peu9ISKi54OFKSIiKjExMTHyY+sTEhJw+PDhfNveu3cPO3fuRLt27QA8KtoMGDAA//vf//Djjz+iUaNGWLVqFXJzc9GjRw84ODjo7O/t7Q0AeP3117FkyZISvY4NGzZACIERI0bI/3qel6HpdM7OzjA3N0d2djZu3ryJGjVq6LWJjo42eD5vb29cvXoVH3/8MerWrfvM+efVrFkzVKpUCdevX8evv/6KBg0a4ODBg1AqlXrTpIpz3QUxMzMDAKSmphrcfvPmTYNx7d+2Y8eO+Oijj4p0TktLS3Tr1g3dunUD8GjUw5QpU/Dtt99i0KBB+Z6zpGhzd3Z21hvhUhJu3Lhh8Ae1tm95eXnpbWvdujWqVq2K/fv348KFC1izZg3UajX69u2b74gkQxQKBdq1a4eff/4Zq1atwujRo4t7GQXy9PQE8Og7JCUlxWCO2pEv2rZapqamUKlUSE1NNThV62l//xs3bhiMa++vhYVFoYrDCoUCHTp0wE8//YSff/4ZH330UYEj07Zu3Yrk5GT5HudVmr4bX3a8N0REpROn8hERUYmJiIiAWq1GgwYN5Ok8hl7aood2dJXWgAEDoFAosH79emRkZOQ7VQV4tK4I8OgHXd6pGiUhMTERAODr66u3LSsrC5s2bdKLm5qaIiQkBACwZs0ag8f95ZdfDMa116Jd96kkSZIkTwX64Ycf5Cl9b731lt6P+uJcd0G0x79+/TpycnL0tmvXknqS9n5oC2XPwtXVFXPmzAEA3Lp1Cw8fPnym42mLbXnXa8qrXr16cHFxwcWLF3HhwoVnOpchP//8s8G4dv0h7fo4eUmShBEjRgAAFixYgBUrVgAAhg8fXuTzT5gwASYmJjh16pQ8nbKkeXl5ySPlDBX3hBByvFmzZjrbtH3u0qVLevudPXsWt2/fLvDcq1atMhjX3t833njD4LpnhowbNw4mJiY4d+4cvvzyy3zbJScny9+JYWFhBkcJlpbvxpcd7w0RUenEwhQREZUYbdEjvwWatbQLDP/2228663h4eXmhVatWSElJwaRJk3D+/Hn4+PigefPmeseoVasWunbtitu3b6NLly4GRyOlp6dj9erVhV7jRcvf3x8AsHLlSp3RPllZWRg6dGi+oypGjhwJAPjyyy9x9OhRnW2LFy/GsWPHDO43btw4ODg4YMGCBZg/f77BIs6NGzfy/dH8NAMGDIBSqcS+ffvw7bffAjC86Hlxrzs/vr6+8PPzQ1JSEmbPnq2zbf/+/fjkk08M7texY0fUq1cPx48fx8CBAw2u9fLw4UN88803coHo5s2bWLFihcH1r7Zt2wYAcHR0LNIIIUO0I5KuXr0KlUqlt93U1BSffvophBDo3LkzDh06pNdGrVbjzz//1OsjhbF582asXbtWJ7Zx40Zs2rQJJiYmcgHqSQMGDIC9vT1++OEHxMXFoVmzZgZH9T1NjRo1sHDhQgCPCluTJk1CUlKSXju1Wl2s69PSjpSbPn06zpw5I8eFEPj888/xzz//wMHBAYMHD9bZr2XLlgCAadOmITs7W45HR0ejf//+Ty10njp1Si5kah06dEhek2vMmDGFvobAwEC5348dOxZz5szRK2hevnwZLVu2xLVr11ChQoV8R/GUlu/Glx3vDRFRKfXCnv9HRERl2v79+wUAYW5uLhITE5/avnbt2gKAmDdvnk587dq18iPGAYhPPvkk32OkpKSIFi1aCADCzMxM1KtXT/To0UN0795d1KtXT5iZmQkA4tKlS/I+2keih4aG5nvchw8fCl9fXwFAODs7i06dOomuXbsKNzc3YWtrK0aNGiUAiP79++vt+9577wkAQqlUiqZNm4pevXqJwMBAoVQqxZgxY/J95PyBAweEi4uLACDc3NxE8+bNRe/evUX79u1F5cqVBQDRoEGDp97X/GgfgQ5AuLq6ipycnBK77oLu6aZNm4QkSQKACA4OFt27dxd16tQRkiSJTz75RM7pSXfv3hXBwcECgLC2thaNGjUSPXv2FF26dBHBwcFCqVQKAPJj5E+fPi0ACFNTU7kf9OjRQ9SqVUsAEJIkiRUrVhT6fmkfVf/pp5/qbatbt64AIKpVqyZ69+4t3nnnHfHxxx/rtBk3bpx8bQEBAaJjx46iZ8+eomnTpsLBwUEAEMuWLSt0Ptq/y+jRowUAUa9ePREeHi4aNGggn2fBggUFHkO7LwCxadOmQp/bkIiICGFjYyN/9kJCQkT37t1FeHi4aNWqlXBycpK3TZo0SWffwnwGNRqN6Nu3rwAgTExMRIsWLUSvXr1EtWrVBABhaWkptm/frrff9evX5fvr4+MjunbtKpo0aSIsLS1Fy5YtRaNGjQQAsW/fPp39QkNDBQAxcuRIoVAoREBAgOjVq5cIDQ0VCoVCABCjRo0q1r1avHix/F3k7OwsOnToIHr27CkaNGggfzbq1Kkjbt++XeBxSsN3Y1HcuHFDzvXGjRv5tuvfv78AIH788UedeEGfwYL209L+TZ/8Wxfn3hAR0fPFwhQREZUI7Y/Ibt26Far9okWLBADh7++vE8/KypJ/1EqSJK5fv17gcdRqtVizZo1o27atKFeunDA1NRXOzs4iMDBQDBw4UGzevFmnCFPYH1/x8fFi6NChonLlysLc3FyUL19e9OnTR1y9elX8+OOP+RamNBqN+O6770Tt2rWFhYWFcHBwEG+++ab466+/xE8//SQAiF69ehk85/3798X//vc/Ubt2bWFrayvMzMyEl5eXaNSokfj000/F2bNnC8y5IJs2bZJ/JI4dO7ZEr/tp9/T3338Xr7/+urCyshLW1taiYcOGYt26dUIIkW9hSohHfeGbb74RzZo1E87OzsLExES4ubmJ4OBgMWzYMLFr1y65bUpKili0aJHo3Lmz8PPzEzY2NsLa2lpUrVpV9OvXT5w8ebJI96ugH8U3b94U4eHhwsPDQ5iYmAgAwtfXV6/d4cOHRe/evYWvr68wNzcXtra2omrVqqJTp05ixYoVhSrgamkLUzdu3BDr168XISEh8jU2btxYbNu27anH2LFjhwAgvL29RW5ubqHPnZ+EhAQxe/Zs0bx5c+Hu7i7MzMyEpaWl8Pb2Fm3bthULFiwQ9+7d09uvKAWQNWvWyMU8U1NT4e3tLQYMGCAuX76c7z4XL14UXbp0EY6OjsLc3FxUq1ZNfP755yInJyffYkXe+N69e0WLFi2Evb29sLS0FHXr1hURERFFvT06oqOjxbhx48Rrr70m7O3thZmZmShfvrx4++23xerVq4VarX7qMUrLd2NhldbClBBFvzdERPR8SUKU0KN1iIiIqECDBg3Cjz/+iPnz52Ps2LHGTodeMX369MHq1asxY8YMTJw40djplCpNmzbFgQMHsG/fPoPrdBEREdHzwzWmiIiIStCFCxeQnp6uE9NoNPjuu+8QEREBCwsL9OrVy0jZ0avq3LlzWLduHWxsbPD+++8bOx0iIiIiWeEeK0JERESFMnfuXKxfvx61atWCp6cn0tPTcfHiRURHR0OpVGLp0qXw8PAwdpr0inj33XeRnp6OHTt2IDc3F1OmTIGTk5Ox0yIiIiKSsTBFRERUgsLCwpCSkoJTp07hn3/+QW5uLtzc3BAWFobRo0ejYcOGxk6RXiHff/89FAoFvL298dFHH2H8+PHGTomIiIhIB9eYIiIiIiIiIiIio+AaU0REREREREREZBQsTBERERERERERkVFwjSk8elrSvXv3YGtrC0mSjJ0OEREREREREdFLSwiB1NRUlC9fHgpFwWOiWJgCcO/ePXh7exs7DSIiIiIiIiKiMuP27dvw8vIqsA0LUwBsbW0BPLphdnZ2Rs7m2Wg0GsTHx8PV1fWpVUmip2F/IkPYL6gksT9RcbHvUElif6LiYt+hklSW+lNKSgq8vb3lektBWJgC5Ol7dnZ2ZaIwlZWVBTs7u5e+I5PxsT+RIewXVJLYn6i42HeoJLE/UXGx71BJKov9qTDLJZWNKyUiIiIiIiIiopcOC1NERERERERERGQULEwREREREREREZFRsDBFRERERERERERGwcIUEREREREREREZBZ/KVwxqtRo5OTnGTsMgjUYDlUqFzMzMMrOKPz0bMzMzKJVKY6dBREREREREpIeFqSIQQuDu3btITEw0dir5EkJACIHExMRCPZaRXg1OTk7w9PRknyAiIiIiIqJShYWpItAWpdzd3WFtbV0qRyQJIaDRaKBQKFiEIGg0GqSnpyM2NhYA4OXlZeSMiIiIiIiIiB5jYaqQ1Gq1XJRyc3Mzdjr5YmGKnmRtbQ0AiI2NhYeHB6f1ERERERERUalR+ob85DFr1ixIkoTRo0cX2G7Dhg2oXr06LCwsEBQUhO3bt5d4Lto1pbQ/8oleJtp+W1rXRiMiIiIiIqJXU6ktTJ04cQLLly9HzZo1C2x35MgR9OrVC++88w5Onz6NTp06oVOnTjh//vxzyas0Tt8jehr2WyIiIiIiIiqNSuWv1bS0NPTu3RvfffcdHB0dC2y7ePFitG7dGuPGjYO/vz+mT5+O2rVrY8mSJS8oWyIiIiIiIiIiKo5SWZgaNmwY2rVrh5YtWz61bVRUlF67t956C1FRUc8rvVJv//79UCgUiIiIMHYqr6zo6GhIkoSpU6caOxUiIiIiIiKiUqvULX6+du1a/P333zhx4kSh2sfGxqJcuXI6sXLlyslPITMkOzsb2dnZ8vuUlBQAj55gptFoDO6j0WgghJBfxrJ//340b9483+1HjhyR/39J5/rPP/9gy5YtGDBgACpUqFBg26flmZevry9u3LhRAhkWXkREBJKSkp66fllxae+7sfuLljaPgvq4Idp+X5R9qOxjv6CSxP5ExcW+QyWJ/YmKi32HSlJZ6k9FuYZSVZi6ffs2Ro0ahd27d8PCwuK5nWfmzJmYNm2aXjw+Ph5ZWVkG91GpVMX6YV/StOfu2bMn2rRpo7e9UqVKcHBwQFpaGkxNTUs019OnT+Ozzz5DkyZN4OPjU2DbatWqYeXKlTqx7777DocOHcL8+fPh4uIix21sbF74PY2IiMDNmzcxcuTI53J87fWUli8V7RdcYmIiTE1Ni7RfcnIyhBBcp4pk7BdUktifqLjYd6gksT9RcbHvUEkqS/0pNTW10G1LVWHq1KlTiIuLQ+3ateWYWq3GX3/9hSVLliA7O1vvUffu7u64f/++Tuz+/ftwd3fP9zwTJ07E2LFj5fcpKSnw9vaGq6sr7OzsDO6TmZmJxMREKBQKo3YQ7blr166Nvn37Gmyj0WhgZmb21GMJIZCeng4bG5tCnVuSJDmHp90DDw8Pvfz+/PNPHDp0CJ07d37qiKvU1FTY2toWKq/iyHstz4P2uJIklYovFIVCAUmS4OTkBEtLy0Lvp9FoIEkSXF1dS8V1UOnAfkElif2Jiot9h0oS+xMVF/sOlaSy1J+KMtioVBWmWrRogXPnzunEBg4ciOrVq+Pjjz/WK0oBQEhICPbu3aszJWv37t0ICQnJ9zzm5uYwNzfXixdUcNH+sNe+jEV77vzyEEJg//79aNmyJX788UcMGDAAwKOpdc2aNcOPP/6I9PR0fP3117h27RomTpyIqVOn4sKFC5g6dSqOHDmCBw8ewNHREf7+/vjoo4/Qrl07TJ06VR5llneKXv/+/Yu8llXe3KOjo1GxYkV8+umn8Pf3x5w5c3Dx4kWEhYXJx92zZw/mzJmD48ePIysrC1WrVsXQoUPxwQcf6Bz3jz/+wPfff48TJ04gJiYG5ubmqF+/PiZPnozQ0FC5XYUKFXDz5k0AuoWpffv2oWnTpgCAq1ev4rPPPsOePXuQkJCA8uXLo3v37pg6dSqsra11znvo0CF8/PHH+Pvvv2FnZ4fu3bvLuRm7v2hp8yhOYbW4+1HZxn5BJYn9iYqLfYdKEvsTFRf7DpWkstKfipJ/qSpM2draIjAwUCdmbW0NZ2dnOd6vXz94enpi5syZAIBRo0YhNDQU8+fPR7t27bB27VqcPHkS33777QvP/0XKyMjAgwcPdGLm5uZPHf20aNEiJCQkYPDgwXB3d4e3tzcSEhLkYtMHH3wAX19fPHjwACdPnsSxY8fQrl07dOnSBTExMfj2228xadIk+Pv7AwAqV65cItezZcsWfPnllxgyZAg++OADeeTat99+iw8++AANGzbE5MmTYW1tjd27d2PIkCG4du0a5s6dKx8jIiICiYmJ6NevH7y8vHD37l2sWLECLVq0wL59+9C4cWP5HkycOBEPHjzAwoUL5f2113Tq1Ck0b94cDg4OeP/99+Hp6YkzZ87gyy+/xOHDh3HgwAF5OtyxY8fQsmVL2Nra4uOPP4aDgwPWrl2Lfv36lch9ISIiIiIiIirLSlVhqjBu3bqlU3lr1KgR1qxZgylTpmDSpEnw8/PDli1b9ApcZc2nn36KTz/9VCcWFhaGX375pcD9bt26hcuXL8PNzU2Obd26FXFxcVi3bh169OhhcL+aNWsiJCQE3377LVq1aiWPLCopFy5cwNmzZ+XiEADExMRg5MiR6NmzJ9asWSPHhw4dilGjRmHBggUYMmQIKlWqBODRGlZPjmb64IMPEBAQgJkzZ8qFqU6dOmHRokXIzMxEnz599HIZNGgQPDw8cOLECZ3phC1atECXLl2wevVqeSTamDFjoNFocPjwYVStWlXO74033iiZG0NERERERERUhpX6wtT+/fsLfA8A3bt3R/fu3V9MQqXEe++9p3fNBa2rpdWvXz+dohQA2NvbAwB27NiB1q1b57vO1vPUrl07naIUAGzcuBHZ2dl455139EaHdejQAV9++SX27NmD9957DwB0ilJpaWnymmQNGjTA0aNHC5XHuXPncPbsWUybNk3v6Y1vvPEGrK2t8ccff2DAgAGIi4tDVFQUunXrJhelAMDMzAxjxoxBeHh4ke8DERERERERvboMLWFU1pX6whQZ5ufnh5YtW+rFhRAF7pe3gKIVGhqKfv36ISIiAqtXr0a9evXQsmVLhIWFoUaNGiWWc1HzunTpEgAYvE6tvAvfX7t2DZMnT8auXbuQlJSk066w6zxpz2loRNqT57x+/ToAoHr16nptXtR9IyIiIiIiopdfWnYuUrJykaAyhSo1B3YWJrAxfzVKNq/GVZLMysrKYHzlypUYN24cduzYgYMHD2L+/Pn44osvsGjRIgwfPtwoeWmLbD/99BM8PDwM7qedxpeWloYmTZogPT0do0ePRlBQEGxtbaFQKDBz5kz8+eefhcpDe84PP/wQrVu3NtjG0dGxUMciIiIiIiIiepr4tGx8e/Qmdv8bj6SMLDhYWeDNaq4Y3NAXrjb6D24ra1iYIllgYCACAwMxbtw4JCUloUGDBpgwYQKGDRtmlKfL+fn5AQBcXFwKHDUFAHv37sW9e/fwww8/YODAgTrbpkyZotc+v2vRnlOpVD71nBUrVgQAXL58WW/bxYsXC9yXiIiIiIiIKC07F98evYkt52ORk6vBvZQcSAolNp+PBQCMbFypzI+cermfP0glIjExERqNRifm4OCAihUrIiMjA1lZWQAgP/EvMTHxheTVo0cPmJub49NPP0VmZqbe9uTkZHkNKO083CenMv7xxx84duyY3r42NjZ4+PChXvtatWohMDAQ33zzjTxVL6/c3Fz5+suVK4eGDRsiMjISV65ckdvk5OToPO2PiIiIiIiIyJDU7FzsvhKPTJUa0Q8zkKHS4E5yFjRC4I8r8UjNzjV2is9d2S67UaH89NNPWLhwITp37owqVarA1NQUBw4cwK5du9CjRw9YWloCAOrVqweFQoEvvvgCDx8+hLW1NSpWrIgGDRo8l7y8vLywbNkyvPvuu/D390ffvn3h6+uL+Ph4nDt3Dlu2bMHFixdRoUIFvPHGG3B3d8eHH36I6OhoeHl54Z9//sHPP/+MoKAgnDt3TufYDRs2xG+//Ybhw4ejUaNGUCqVaN68Odzc3PDzzz+jefPmqFmzJgYNGoSAgABkZGTgv//+w6+//oqZM2fKT+VbsGABmjZtitdffx3Dhg2Dg4MD1q5di9zcsv/lQURERERERM8mKVOF2JRs3E3JgnbcRKZKg7i0HCgkCclZKnjYWRg3yeeMhSlC06ZNcfr0afz222+IiYmBUqlExYoVMW/ePJ31pXx8fPDDDz9g9uzZGDJkCFQqFfr37//cClMAMHDgQFStWhXz5s3D8uXLkZSUBBcXF1SrVg3Tp0+Xn0To4OCAXbt2Yfz48fjqq6+Qm5uLOnXqYPv27fj+++/1ClNjxozB9evXsXHjRnzzzTfQaDTYt28f3NzcEBwcjNOnT2PmzJnYunUrvvnmG9ja2qJChQoYMGAAWrRoIR8nJCQEu3fvxoQJEzBr1izY29ujW7duGDJkCIKCgp7bfSEiIiIiIqKXmxACSoWE5Kxc5J3MY2mqgIu1GazNlLC3MDVegi+IJJ72GLdXQEpKCuzt7ZGcnAw7OzuDbTIzM3H16lX4+fnJI4hKIyEENBoNFArFC18Tikqv4vZfjUaDuLg4uLm5QaHgzF96hP2CShL7ExUX+w6VJPYnKi72HSoutUZg7v7/4GlngaM3H2LlyTsAAGtTCV6OVlBIEjoHur+0a0wVps6i9fJdHRERERERERHRSyojJxcTt1/GkehElLezwPhmVQAA+/57ADOFgK2ZCd6s5or3Gvq+lEWpoir7V0hEREREREREVArEp2VjTOQF/BufBgC4l5KFufv/w7S3qmFyy6pITM+Ck7UF7CxMXomiFMDCFBERERERERHRc/ffg3SM3nIe99Oy5ZilqRIfNa2M18rbQ6PRwDQnFc62dq/U1FAWpoiIiIiIiIiInqPjtx5i/G8XkZ6jlmMu1mZY1DEQ1dxs5JharTa0e5nGwhQRERERERER0XOy9UIsZuy5CnWeZ89VdrbG4k6BKGdrbsTMSgcWpoiIiIiIiIiISpgQAt9E3cQPx2/pxOv7OGJ2O/9XZg2pp+FdICIiIiIiIiIqQTm5Gny2+wp2/RunE+9Qwx2TWlSBifLVWUPqaViYIiIiIiIiIiIqISlZKny07SJO303WiQ9pVAED63lDkiQjZVY6sTBFRERERERERFQC7iZnYtSW87j5MFOOmSoV+F9LP7TxL2fEzEovFqaIiIiIiIiIiJ7R+ZgUjN16AQ8zVXLMztwEczvUQG0vB+MlVsqxMEVERERERERE9Az2/fcAU3ZcRo5aI8fK21ngy06B8HWyMmJmpR8LU0RERERERERExSCEwC+n72LRX9ch8sQD3W0x/+0AOFmZGS23lwULU0RERERERERERaTRCMw7cA0bztzTiTet7ILpravBwlRppMxeLnw+IeHEiRMYPnw4AgICYG1tDR8fH/To0QNXrlzRa3vp0iW0bt0aNjY2cHJyQt++fREfH6/T5vLlyxg/fjyCg4Nha2sLDw8PtGvXDidPntQ73tSpUyFJkt7LwsKi0PlrNBrMmTMHFStWhIWFBWrWrIlffvlFr92AAQMMnqt69eqFPhcRERERERFRpkqNj367qFeUCq/tidnt/FmUKgKOmCLMnj0bhw8fRvfu3VGzZk3ExsZiyZIlqF27No4ePYrAwEAAwJ07d9CkSRPY29tjxowZSEtLw7x583Du3DkcP34cZmaPhiiuWLEC33//Pbp27YqhQ4ciOTkZy5cvR8OGDbFz5060bNlSL4dly5bBxsZGfq9UFv5DPHnyZMyaNQuDBw9GvXr1EBkZifDwcEiShJ49e+q0NTc3x4oVK3Ri9vb2hT4XERERERERvdoepGdjbOQFXIpLk2MKScKHoZXRI7i8ETN7ObEwRRg7dizWrFkjF5YAICwsDEFBQZg1axZWrVoFAJgxYwbS09Nx6tQp+Pj4AADq16+PVq1aISIiAu+99x4AoFevXpg6dapOoWnQoEHw9/fH1KlTDRamunXrBhcXlyLnfvfuXcyfPx/Dhg3DkiVLAADvvvsuQkNDMW7cOHTv3l2nyGViYoI+ffoU+TxERERERERE1xPSMWrLecSmZssxCxMFvmjjjyaVnY2Y2cuLU/mMLC07FzEpWbh0PxUxKVlIy8594Tk0atRIpygFAH5+fggICMClS5fk2KZNm9C+fXu5KAUALVu2RNWqVbF+/Xo5VqdOHZ2iFAA4OzujcePGOsfLSwiBlJQUCCEMbs9PZGQkVCoVhg4dKsckScKQIUNw584dREVF6e2jVquRkpJSpPMQERERERHRq+3ErSS8s+6MTlHK2coM33Z/jUWpZ8DClBHFp2Vj8cHr6LXqFPr9chq9Vp3ClwevIz4t++k7P2dCCNy/f18exXT37l3ExcWhbt26em3r16+P06dPP/WYsbGx+Y6KqlSpEuzt7WFra4s+ffrg/v37hcrz9OnTsLa2hr+/v15O2u15ZWRkwM7ODvb29nBycsKwYcOQlpYGIiIiIiIiovz8dvE+Rmw+h7Scx4NJKjlZ4ceewfAvZ2vEzF5+nMr3DNKyc/Hfg/Ri7etoaYofjt/GxrOPF0rLyFFj9d93kaMWGFjPGw8zVUU+rhAClZwsYWf5bI+kXL16Ne7evYvPPvsMABATEwMA8PDw0Gvr4eGBxMREZGdnw9zc3ODxDh48iKioKEyZMkUn7ujoiOHDhyMkJATm5uY4ePAgvv76axw/fhwnT56EnZ1dgXnGxMSgXLlykCRJLycAuHfvnk5s/PjxqF27NjQaDXbu3ImlS5fizJkz2L9/P0xM+HEgIiIiIiKix4QQ+PboTaw4dksnXtfbAXPa1YCtBX9HPivewWfw34N0DN5wpsj72ZqbYHZ7f3x79CZSDUzdWx51E+1quOHj3y4Z3P4033QNQm2v4hemLl++jGHDhiEkJAT9+/cHAGRmZgKAwcKT9gl6mZmZBrfHxcUhPDwcFStWxPjx43W2jRo1Sud9165dUb9+ffTu3RtLly7FhAkTCsw1v3PmzUlr5syZOm169uyJqlWrYvLkydi4caPeQulERERERET06lKpNZi++wp2XI7TibevUQ6TWvjBVMlJaCWBd9EI7C1M8DBDlW/RKTU7F0kZubA3QuU1NjYW7dq1g729PTZu3CgvHG5paQkAyM7Wn2aYlZWl0yav9PR0tG/fHqmpqYiMjNRbe8qQ8PBwuLu7Y8+ePTp55X1pC06WlpZFzimvMWPGQKFQ6JyLiIiIiIiIXm0pWSoM33xOryj1fogvPmlVlUWpEsQ7aQTJWblwtDKFrbnhwpOtuQkcrEyQnPViF0JPTk5GmzZtkJSUhJ07d6J8+cePudROjdNO6csrJiYGTk5OeiOXcnJy0KVLF5w9exaRkZEIDAwsdC7e3t5ITEzUOX/e17p16+R4bGys3qLp2jzzXoMhlpaWcHZ21jkXERERERERvbruJWfh3fVn8PedZDlmopAw7a1qeLeBr95SMvRsOJXvGVRxscZ33V8r1r6OlqZ4P8QXG87c09vW/bXycLM2x4K3A4p8XO0aU0WVlZWFDh064MqVK9izZw9q1Kihs93T0xOurq44efKk3r7Hjx9HcHCwTkyj0aBfv37Yu3cv1q9fj9DQ0CJdQ3R0NGrVqiXHdu/erdMmIODRvQkODsaKFStw6dIlnZyPHTsmby9IamoqHjx4AFdX10LnR0RERERERGXTxdhUjNl6HokZj9d8tjEzwdwONVDX28F4iZVhLEw9AxtzEwR72hd7/2GvV4CZUsIfV+KRnqOGtZkSb1Z1xXsNfeFiYw7fYhxTCAGNRlOkfdRqNcLCwhAVFYXIyEiEhIQYbNe1a1esXLkSt2/fhre3NwBg7969uHLlCsaMGaPTdsSIEVi3bh2WL1+OLl265Hvu+Ph4vaLQsmXLEB8fj9atW8uxli1bGty/Y8eOGDNmDJYuXYolS5YAeHQPvvnmG3h6eqJRo0YAHhXeVCoVbG11n5Ywffp0CCF0zkVERERERESvngPXHmDyjsvIzn38m7q8nQUWdQxERWcrI2ZWtrEwZUSuNuYY2bgSBtb3QXKWCvYWj6b32eQzxe95+fDDD7F161Z06NABiYmJWLVqlc72Pn36AAAmTZqEDRs2oFmzZhg1ahTS0tIwd+5cBAUFYeDAgXL7RYsWYenSpQgJCYGVlZXe8Tp37gxra2sAgK+vL8LCwhAUFAQLCwscOnQIa9euRXBwMN5///2n5u7l5YXRo0dj7ty5UKlUqFevHrZs2YKDBw9i9erV8hpZsbGxqFWrFnr16oXq1asDAHbt2oXt27ejdevW6NixY/FvIBEREREREb3Ufjl9FwsPXEPeRWJqlLPFwo4BcLJ6tqfeU8FYmDIym/8vRHnYWRgth3/++QcAsG3bNmzbtk1vu7Yw5e3tjQMHDmDs2LGYMGECzMzM0K5dO8yfP19nfSnt8aKiohAVFaV3vBs3bsiFqd69e+PIkSPYtGkTsrKy4Ovri/Hjx2Py5MmwsipcRXrWrFlwdHTE8uXLERERAT8/P6xatQrh4eFyGwcHB7Rv3x67d+/GypUroVarUaVKFcyYMQMfffQRFAout0ZERERERPSq0WgEFvx1Hev+uasTb1LJGV+0qQ4LU6WRMnt1SOLJVaNfQSkpKbC3t0dycjLs7OwMtsnMzMTVq1fh5+f31Ce9GZN2Kp9CoeCCbCQrbv/VaDSIi4uDm5sbi3ckY7+gksT+RMXFvkMlif2Jiot95+WWqVJjyo7L+Ot6gk68Z7AnxjSpBIXixf6mLkv9qTB1Fi2OmCIiIiIiIiKiV0piRg7GRF7AxfupckwCMCa0MnrV8jReYq8gFqaIiIiIiIiI6JVxIyEDoyPP415KlhwzN1HgizbVEVrZxYiZvZpYmCIiIiIiIiKiV8LJ20kY/9tFpGbnyjEnK1MsfDsQNdxtC9iTnhcWpoiIiIiIiIiozNt+6T6m776CXM3jpbYrOllhUcdAlLc33gPJXnUsTBERERERERFRmSWEwIpjt/Dt0Zs68dpe9pjbvgbsLEyNlBkBLEwRERERERERURmlUmvwxZ6r+P3SfZ14m+pu+F+rqjBVvtxPvysLWJgiIiIiIiIiojInNSsX43+/iJO3k3Tigxv6YnADH0iSZJzESAcLU0RERERERERUpsSkZGH0lvO4npghx5SShCmtqqJ9jXJGzIyexMIUEREREREREZUZF2NTMXbrBSRk5MgxGzMTzG7vj/o+jkbMjAxhYYqIiIiIiIiIyoS/riVg8o5LyMrVyDF3W3Ms7hSISs7WRsyM8sPCFBERERERERG99Nb/cw/zD1yDRgg5Vt3NBgs7BsDF2tyImVFBWJgiIiIiIiIiopeWRiOw6OB1/HL6rk68cSVnfN66GqzMWPoozfhcRMKJEycwfPhwBAQEwNraGj4+PujRoweuXLmi1/bSpUto3bo1bGxs4OTkhL59+yI+Pl6nzeXLlzF+/HgEBwfD1tYWHh4eaNeuHU6ePKl3vKlTp0KSJL2XhYVFofPXaDSYM2cOKlasCAsLC9SsWRO//PJLvm2XLVuG4OBgWFpawtnZGc2bN8eZM2cKfT4iIiIiIiIqHbJUanz8+yW9olT318pjXvsaLEq9BPgXIsyePRuHDx9G9+7dUbNmTcTGxmLJkiWoXbs2jh49isDAQADAnTt30KRJE9jb22PGjBlIS0vDvHnzcO7cORw/fhxmZmYAgBUrVuD7779H165dMXToUCQnJ2P58uVo2LAhdu7ciZYtW+rlsGzZMtjY2MjvlUplofOfPHkyZs2ahcGDB6NevXqIjIxEeHg4JElCz549ddoOGjQIq1evRr9+/TB8+HCkp6fj9OnTiIuLK86tIyIiIiIiIiNJzMjB2MgLuHA/VY5JAEY3qYRetTwhSZLxkqNCY2GKMHbsWKxZs0YuLAFAWFgYgoKCMGvWLKxatQoAMGPGDKSnp+PUqVPw8fEBANSvXx+tWrVCREQE3nvvPQBAr169MHXqVJ1C06BBg+Dv74+pU6caLEx169YNLi4uRc797t27mD9/PoYNG4YlS5YAAN59912EhoZi3Lhx6N69u1zkWr9+PVauXIlff/0VnTt3LvK5iIiIiIiIqHSITszAqC3ncS8lS46ZKRWY3ro6mvsV/bclGQ+n8hmZOiupwPcvQqNGjXSKUgDg5+eHgIAAXLp0SY5t2rQJ7du3l4tSANCyZUtUrVoV69evl2N16tTRKUoBgLOzMxo3bqxzvLyEEEhJSYHIs0hdYURGRkKlUmHo0KFyTJIkDBkyBHfu3EFUVJQcX7BgAerXr4/OnTtDo9EgPT29SOciIiIiIiIi4/v7ThLeWfePTlHK0dIUy7vVZFHqJcTClBHlpt1DypmlyE27Z/C9MQkhcP/+fXkU0927dxEXF4e6devqta1fvz5Onz791GPGxsbmOyqqUqVKsLe3h62tLfr06YP79+8XKs/Tp0/D2toa/v7+ejlptwNASkoKjh8/jnr16mHSpEmwt7eHjY0NKlWqpFNUIyIiIiIiotJr5+U4DN98HinZuXLM19ESP/YMRqCHnREzo+LiVL4SkJseC3V6bJH2UVg6I+1CBDKu/w5V0jXY1xqO5NNLoIo/A6HOgW3QO9BkJhT6eEprd5hYuxc19XytXr0ad+/exWeffQYAiImJAQB4eHjotfXw8EBiYiKys7Nhbm74EZwHDx5EVFQUpkyZohN3dHTE8OHDERISAnNzcxw8eBBff/01jh8/jpMnT8LOruAvlpiYGJQrV05v7rA2z3v3HhX5rl27BiEE1q5dCxMTE8yZMwf29vZYvHgxevbsCTs7O7Ru3boQd4aIiIiIiIheNCEEfjh+G99ERevEa3naY16HGrCzMDVOYvTMWJgqARnXf0PquRVF2sfUOQAO9T+GKjkaqoQLeLBnCADAxKEyrCq0xsMjU6FKuFDo49nVfB+2gQOLlEN+Ll++jGHDhiEkJAT9+/cHAGRmZgKAwcKT9gl6mZmZBrfHxcUhPDwcFStWxPjx43W2jRo1Sud9165dUb9+ffTu3RtLly7FhAkTCsw1v3PmzQkA0tLSAAAJCQk4evQoGjRoAAB4++23UbFiRXz++ecsTBEREREREZVCuWoNZuz9D9su6g4IeauaGz5pVRVmJpwM9jLjX89IVAkXIFTpsK81XCduX2skMm//WaSiVEmKjY1Fu3btYG9vj40bN8oLh1taWgIAsrOz9fbJysrSaZNXeno62rdvj9TUVERGRuqtPWVIeHg43N3dsWfPHp288r60BSdLS8tC5aT934oVK8pFKQCwsbFBhw4dcPz4ceTm5uodh4iIiIiIiIwnLTsXI7ec1ytKDarvg+mtq7EoVQbwL2gkps4BkEytkXx6iU48+fSXsPRuDlPngBeeU3JyMtq0aYOkpCTs3LkT5cuXl7dpp8Zpp/TlFRMTAycnJ72RSzk5OejSpQvOnj2LyMhIBAYGFjoXb29vJCYm6pw/72vdunVyPDY2Vm/RdG2e2mvQ/m+5cuX0zuXm5gaVSsXF0ImIiIiIiEqR2JQsvLv+DE7cTpJjSknC/1pVxZBGFfSWdKGXE6fylQCrSu1hXk5/UfCCaNeYUiVcgKlzgM4aUxnRO+HYaGqR15h6FllZWejQoQOuXLmCPXv2oEaNGjrbPT094erqipMnT+rte/z4cQQHB+vENBoN+vXrh71792L9+vUIDQ0tdC5CCERHR6NWrVpybPfu3TptAgIeFe6Cg4OxYsUKXLp0SSfnY8eOyduBR4Upd3d33L17V+989+7dg4WFBWxtbQudIxERERERET0/l+NSMSbyAh6k58gxazMlZrergQa+jkbMjEoaC1MlwKSYC4/bBr4DSErYBgyAiU15OL0+HakXIuT3sPV+DtnqU6vVCAsLQ1RUFCIjIxESEmKwXdeuXbFy5Urcvn0b3t6Pctu7dy+uXLmCMWPG6LQdMWIE1q1bh+XLl6NLly75njs+Ph6urq46sWXLliE+Pl5nzaeWLVsa3L9jx44YM2YMli5diiVLHo0+E0Lgm2++gaenJxo1aiS3DQsLw+LFi7F79260atUKAPDgwQNERkaiefPmUCg4gJCIiIiIiMjYDt1IwKTtl5GpUsuxcjbmWNQpEFVcrI2YGT0Ppa4wtWzZMixbtgzR0dEAHo2M+eSTT9CmTRuD7SMiIjBwoO6i3+bm5vIaQ6WZiU152L02FEoLB4PvX5QPP/wQW7duRYcOHZCYmIhVq1bpbO/Tpw8AYNKkSdiwYQOaNWuGUaNGIS0tDXPnzkVQUJDO32DRokVYunQpQkJCYGVlpXe8zp07w9r60ZeJr68vwsLCEBQUBAsLCxw6dAhr165FcHAw3n///afm7uXlhdGjR2Pu3LlQqVSoV68etmzZgoMHD2L16tXyGlkAMHHiRKxfvx5du3bF2LFjYW9vj2+++QYqlQozZswo9v0jIiIiIiKikrHxzD3M3X8NmjzLtVRztcHCjgFwtTH8FHh6uZW6wpSXlxdmzZoFPz8/CCGwcuVKdOzYEadPn5anbz3Jzs4O//77r/z+ZZpn+mQR6kUXpQDgn3/+AQBs27YN27Zt09uuLUx5e3vjwIEDGDt2LCZMmAAzMzO0a9cO8+fP11lfSnu8qKgoREVF6R3vxo0bcmGqd+/eOHLkCDZt2oSsrCz4+vpi/PjxmDx5MqysrAqV/6xZs+Do6Ijly5cjIiICfn5+WLVqFcLDw3XalStXDocOHcJHH32EhQsXQqVSISQkBKtWrcJrr71WqHMRERERERFRydNoBL46fAOrTt3RiTeq4ISZbavDyqzUlS+ohEjiyVWjSyEnJyfMnTsX77zzjt62iIgIjB49GklJScU+fkpKCuzt7ZGcnAw7OzuDbTIzM3H16lX4+fkZfPpcaSGEgEajgUKheKkKdPR8Fbf/ajQaxMXFwc3NjVMdScZ+QSWJ/YmKi32HShL7ExUX+07JyM5V4387/8W+/x7oxLvW9MC4plWgVLwav23LUn8qTJ1Fq1RfqVqtxtq1a5Genp7vukcAkJaWBl9fX3h7e6Njx464cOHCC8ySiIiIiIiIiIrjYUYOhmw8p1eUGtW4Ej5u9uoUpV5lpXIs3Llz5xASEoKsrCzY2Nhg8+bNek+J06pWrRp++OEH1KxZE8nJyZg3bx4aNWqECxcuwMvLy+A+2dnZyM7Olt+npKQAeFSd1Gg0BvfRaDQQQsivl8HLkic9f9p+W1AfN0Tb74uyD5V97BdUktifqLjYd6gksT9RcbHvPJtbDzMxKvIC7iU/XiPa1ETCtDeroYWfy0v1+7sklKX+VJRrKJWFqWrVquGff/5BcnIyNm7ciP79++PAgQMGi1MhISE6o6kaNWoEf39/LF++HNOnTzd4/JkzZ2LatGl68fj4+HwXTVepVMX6YW8MpT0/evG0X3CJiYkwNTUt0n7JyckQQrz0Q0mp5LBfUElif6LiYt+hksT+RMXFvlN85+6n47P9t5Ga8/jJe3bmJpja1As17B9NaXvVlKX+lJqaWui2pbIwZWZmhipVqgAA6tSpgxMnTmDx4sVYvnz5U/c1NTVFrVq18N9//+XbZuLEiRg7dqz8PiUlBd7e3nB1dS1wjanExEQoFIqXooO8DDnSi6Ndc8zJyanIa0xJkgRXV1f2KZKxX1BJYn+i4mLfoZLE/kTFxb5TPH9cice0A/eQq1HA1OTRffN2tMCitwPg5VB613R+3spSf7KwsCh021JZmHqSRqPRmXpXELVajXPnzqFt27b5tjE3N9d5ipxWQUUn7Q977au0yjvMsTTnSS+Wtt8Wp7Ba3P2obGO/oJLE/kTFxb5DJYn9iYqLfafwhBCIOHEbS49EPwr8/0/W18rbY36HGrC3LPzsjrKqrPSnouRf6gpTEydORJs2beDj44PU1FSsWbMG+/fvx65duwAA/fr1g6enJ2bOnAkA+Oyzz9CwYUNUqVIFSUlJmDt3Lm7evIl3333XmJdBRERERERERP8vV63BrH3/IfJ8rE68VVVXTH2zGsxMXu5CDBVfqStMxcXFoV+/foiJiYG9vT1q1qyJXbt2oVWrVgCAW7du6VTeHj58iMGDByM2NhaOjo6oU6cOjhw5ku9i6URERERERET04qRn5+Lj3y/h2K2HOvEB9bwxJKQCFHzy3iut1BWmvv/++wK379+/X+f9woULsXDhwueYEREREREREREVR1xqNkZFnsd/D9LlmEKSMLFFFXQK9DBiZlRalLrCFBERERERERG9/K7Ep2H0lvOIT8+RY1amSsxq54+QCk5GzIxKExamiIiIiIiIiKhEHYlOxMTfLyFDpZZjbjbmWNgxAFVdbYyYGZU2LEwRERERERERUYn59VwMZv/5HzR5nhpf1dUGC98OgJutuREzo9KIhSkiIiIiIiIiemYajcCSwzfw86k7OvGQCk6Y1bY6rMxYgiB9fB4j4cSJExg+fDgCAgJgbW0NHx8f9OjRA1euXNFre+nSJbRu3Ro2NjZwcnJC3759ER8fr9Pm8uXLGD9+PIKDg2FrawsPDw+0a9cOJ0+e1Dve1KlTIUmS3svCwqLQ+Ws0GsyZMwcVK1aEhYUFatasiV9++UWvnaHzaF/apz4SERERERFR0eXkajBpx2W9olTnIA8sfDuARSnKF3sGYfbs2Th8+DC6d++OmjVrIjY2FkuWLEHt2rVx9OhRBAYGAgDu3LmDJk2awN7eHjNmzEBaWhrmzZuHc+fO4fjx4zAzMwMArFixAt9//z26du2KoUOHIjk5GcuXL0fDhg2xc+dOtGzZUi+HZcuWwcbm8TxjpVJZ6PwnT56MWbNmYfDgwahXrx4iIyMRHh4OSZLQs2dPud3PP/+st+/JkyexePFivPnmm4U+HxERERERET2WlKnC2K0XcC4mRSc+4o2K6FvHC5IkGSkzehmwMEUYO3Ys1qxZIxeWACAsLAxBQUGYNWsWVq1aBQCYMWMG0tPTcerUKfj4+AAA6tevj1atWiEiIgLvvfceAKBXr16YOnWqTqFp0KBB8Pf3x9SpUw0Wprp16wYXF5ci53737l3Mnz8fw4YNw5IlSwAA7777LkJDQzFu3Dh0795dLnL16dNHb//9+/dDkiT06tWryOcmIiIiIiJ61d16mIHRkRdwOylTjpkpFZj6VjW0qupqxMzoZcGpfIRGjRrpFKUAwM/PDwEBAbh06ZIc27RpE9q3by8XpQCgZcuWqFq1KtavXy/H6tSpo1OUAgBnZ2c0btxY53h5CSGQkpICkWdxvMKIjIyESqXC0KFD5ZgkSRgyZAju3LmDqKiofPfNzs7Gpk2bEBoaCi8vryKdl4iIiIiI6FV35l4yBq07o1OUsrcwxdKuQSxKUaGxMGVEyTmZiM9K03sl52Q+fefnTAiB+/fvy6OY7t69i7i4ONStW1evbf369XH69OmnHjM2NjbfUVGVKlWCvb09bG1t0adPH9y/f79QeZ4+fRrW1tbw9/fXy0m7PT/bt29HUlISevfuXahzERERERER0SO7r8Rj6KZzSM5SyTFvB0v8EPYaXitvb8TM6GXDqXwlIDYjBbGZqQAejdZ5zam8vO1uejLis9IAAEpJgSAnD3lbRq4K7f74DmqhgSRJsFSaQikpsLnlQNxITUByThYAwNLEFNXs3eT9rqU8QKoqGwBgY2qOKnZFnwL3NKtXr8bdu3fx2WefAQBiYmIAAB4eHnptPTw8kJiYiOzsbJibG37058GDBxEVFYUpU6boxB0dHTF8+HCEhITA3NwcBw8exNdff43jx4/j5MmTsLOzKzDPmJgYlCtXTm/OsjbPe/fuFXiN5ubm6NatW4HnICIiIiIiokeEEPj51B18deiGTjzIww4L3g6Ag6WpkTKjlxULUyVg6+2LWPHvUQCAqUKJw+1HyNvW3/gHv1x/NGrHwcwSf7R+X96WnpuD66mJUAsNTJVKVLF1AaABAHx3+Sj2xFwFAFS1d8Wq0MejehZd+AvH4m8BAGo5e2L5691L9HouX76MYcOGISQkBP379wcAZGY+GsVlqPCkfYJeZmamwe1xcXEIDw9HxYoVMX78eJ1to0aN0nnftWtX1K9fH71798bSpUsxYcKEAnPN75x5czIkJSUFv//+O9q2bQsHB4cCz0FERERERESAWiMwe99/2HwuRifews8Vn71VDWYmnJRFRcdeQzpiY2PRrl072NvbY+PGjfLC4ZaWlgAercv0pKysLJ02eaWnp6N9+/ZITU1FZGSk3tpThoSHh8Pd3R179uzRySvvS1twsrS0LHJOwKP1srKysjiNj4iIiIiIqBAycnIxZusFvaJU3zpemNGmOotSVGzsOSRLTk5GmzZtkJSUhJ07d6J8+cdTErVT47RT+vKKiYmBk5OT3silnJwcdOnSBWfPnkVkZCQCAwMLnYu3tzcSExN1zp/3tW7dOjkeGxurt2i6Ns+815DX6tWrYW9vj/bt2xc6JyIiIiIioldRXGo2Bm84i6jox7/RFJKEiS38MLJxJSgUUgF7ExWMU/lKwNveNVDfxRsA9NY66lExGM08qgB4tMZUXtYmZqhk6ySvMaWUJLnN4OoN0aNSMIBHa0zlNTqgic4aUyUhKysLHTp0wJUrV7Bnzx7UqFFDZ7unpydcXV1x8uRJvX2PHz+O4OBgnZhGo0G/fv2wd+9erF+/HqGhoYXORQiB6Oho1KpVS47t3r1bp01AQAAAIDg4GCtWrMClS5d0cj527Ji8/UkxMTHYt28fBgwYkO+aWERERERERARcjU/D6MgLiEt7PFPFylSJme380aiCkxEzo7KChakS4G5lB3crw4t0e1rbw9Pa8BMJrExMsSvPmlNaZgolKto653u+yiW82LlarUZYWBiioqIQGRmJkJAQg+26du2KlStX4vbt2/D2flSI27t3L65cuYIxY8botB0xYgTWrVuH5cuXo0uXLvmeOz4+Hq6uuo8RXbZsGeLj49G6dWs51rJlS4P7d+zYEWPGjMHSpUuxZMkSAI8KW9988w08PT3RqFEjvX3Wrl0LjUbDaXxEREREREQFiIpOxITfLyFDpZZjrtZmWNQpEFVdn75MC1FhsDBlRPZmhtc/etE+/PBDbN26FR06dEBiYiJWrVqls71Pnz4AgEmTJmHDhg1o1qwZRo0ahbS0NMydOxdBQUEYOHCg3H7RokVYunQpQkJCYGVlpXe8zp07w9raGgDg6+uLsLAwBAUFwcLCAocOHcLatWsRHByM99/XL9o9ycvLC6NHj8bcuXOhUqlQr149bNmyBQcPHsTq1avlNbLyWr16NcqXL4+mTZsW9VYRERERERG9Eracj8HMvf9Bk2fZFD8XayzqGAg3W848oZLDwhThn3/+AQBs27YN27Zt09uuLUx5e3vjwIEDGDt2LCZMmAAzMzO0a9cO8+fP15kSpz1eVFQUoqKi9I5348YNuTDVu3dvHDlyRF6M3NfXF+PHj8fkyZNhZWVVqPxnzZoFR0dHLF++HBEREfDz88OqVasQHh6u1/bff//FqVOnMHbsWCgUXGKNiIiIiIgoL41GYFlUNCJO3NaJN/BxxOx2/rA2ZxmBSpYknlw1+hWUkpICe3t7JCcnw87O8JS8zMxMXL16FX5+fvk+6a00EEJAo9FAoVDorXdFr67i9l+NRoO4uDi4ubmxkEcy9gsqSexPVFzsO1SS2J+ouMpa38nJ1WDqH/9i95V4nXjHQHdMaFYFJsqX/xpLs7LUnwpTZ9FiqZOIiIiIiIjoFZecqcKH2y7izL1knfjQRhUwoJ43Bz7Qc8PCFBEREREREdEr7E5SJkZtOY9bSZlyzFSpwKdvVsVb1dyMmBm9CliYIiIiIiIiInpFnb2Xgg+3XUBSpkqO2ZmbYN7bAajlafgJ80QliYUpIiIiIiIiolfQn1cf4H87LyNHrZFjnvYWWNwxEL5OhXsYFdGzYmGKiIiIiIiI6BUihMDqv+/iy4PXkfdpaEHudpj/dg04WpkZLTd69bAwRURERERERPSKUGsE5u7/D5vOxujEm1VxwfTW1WBuojRSZvSqYmGKiIiIiIiI6BWQkZOLidsv40h0ok68Tx0vjHi9IhQKPnmPXjwWpoiIiIiIiIjKuPi0bIyJvIB/49PkmEKS8FHTyuj+WnkjZkavOhamiIiIiIiIiMqw/x6kY/SW87ifli3HLE2V+KJNdTSu5GzEzIhYmCIiIiIiIiIqs47feojxv11Eeo5ajrlYm2FhxwBUd7M1YmZEj7AwRURERERERFQGbb0Qixl7rkItHj97r7KzNRZ1DIC7nYURMyN6jIUpIiIiIiIiojJECIFvom7ih+O3dOL1vB0wp30N2JizFEClB3sjERERERERURmRk6vB9D1XsPNynE68Qw13TGpRBSZKhZEyIzKMPZJw4sQJDB8+HAEBAbC2toaPjw969OiBK1eu6LW9dOkSWrduDRsbGzg5OaFv376Ij4/XaXP58mWMHz8ewcHBsLW1hYeHB9q1a4eTJ0/qHW/q1KmQJEnvZWFR+GGlGo0Gc+bMQcWKFWFhYYGaNWvil19+Mdh2/fr1aNiwIRwcHODs7IzQ0FD8/vvvhT4XERERERFRaZWSpcLwzef0ilIfhFTA/1r5sShFpRJHTBFmz56Nw4cPo3v37qhZsyZiY2OxZMkS1K5dG0ePHkVgYCAA4M6dO2jSpAns7e0xY8YMpKWlYd68eTh37hyOHz8OMzMzAMCKFSvw/fffo2vXrhg6dCiSk5OxfPlyNGzYEDt37kTLli31cli2bBlsbGzk90qlstD5T548GbNmzcLgwYNRr149REZGIjw8HJIkoWfPnnK7r776CiNHjkS7du0wa9YsZGVlISIiAu3bt8emTZvQpUuX4t5CIiIiIiIio7qbnIlRW87j5sNMOWaikPC/VlXR1r+cETMjKpgkRJ5V0F5RKSkpsLe3R3JyMuzs7Ay2yczMxNWrV+Hn5wdLS8sXnGHhCSGg0WigUCggSVKh9jly5Ajq1q0rF5YA4OrVqwgKCkK3bt2watUqAMDQoUMRERGBy5cvw8fHBwCwZ88etGrVCsuXL8d7770HADh16hSqVaumU2hKSEiAv78/qlatikOHDsnxqVOnYtq0aYiPj4eLi0uRr/fu3buoWLEi3nvvPSxZskS+B6Ghobhx4waio6PlIlfVqlXh4OCAY8eOyfcmJSUFnp6eaN68OSIjI4t8/pdFcfuvRqNBXFwc3NzcoFDwX1foEfYLKknsT1Rc7DtUktifqLhKS985H5OCsVsv4GGmSo7ZmZtgbocaqO3lYLS8qGhKS38qCYWps2i93FdaRqgzkpGbEg91RrJRzt+oUSOdohQA+Pn5ISAgAJcuXZJjmzZtQvv27eWiFAC0bNkSVatWxfr16+VYnTp1dIpSAODs7IzGjRvrHC8vIQRSUlJQ1DppZGQkVCoVhg4dKsckScKQIUNw584dREVFyfGUlBS4ubnpFOzs7OxgY2NTqouNRERERERE+dn33wO8v/GsTlGqvJ0Fvg8LZlGKXgosTJUCIjcHt7/qApGbY+xUZEII3L9/Xx7FdPfuXcTFxaFu3bp6bevXr4/Tp08/9ZixsbH5joqqVKkS7O3tYWtriz59+uD+/fuFyvP06dOwtraGv7+/Xk7a7VpNmzbFzp078dVXXyE6OhqXL1/GsGHDkJycjFGjRhXqfERERERERKWBEAJr/r6Dj3+7iBy1Ro4HlLPFjz2DUcHJyojZERUe15gqAblJschNjn16Q4USFt5BOqGc+9cgmVlAk5UKoc5B1s0zMHH0gImdm9xGk52BnFj9hcifZGLvDqV9ycwdXr16Ne7evYvPPvsMABATEwMA8PDw0Gvr4eGBxMREZGdnw9zc3ODxDh48iKioKEyZMkUn7ujoiOHDhyMkJATm5uY4ePAgvv76axw/fhwnT5586pC/mJgYlCtXTm/aojbPe/fuybEvv/wSDx48wMiRIzFy5EgAgIuLC/bu3YuQkJACz0NERERERFRaaDQC8w5cw4Yz93TiTSu7YHrrarAwLfyavUTGxsJUCUg9vRUP93/71HZKKwf4frwHwKPpeyI3B0KjQs69G8iJv4Hse5dgYucGTXY61BnJUFrZAwBUCbdw74d3n3p8x6bvwaHp4Ge7GEAeSRQSEoL+/fsDeLRGEQCDhSftE/QyMzMNbo+Li0N4eDgqVqyI8ePH62x7cqRS165dUb9+ffTu3RtLly7FhAkTCsw1v3PmzUnLysoK1apVg5eXF9q3b4/U1FQsXLgQXbp0wcGDB1GlSpUCz0VERERERGRsmSo1Ju+4jIPXE3TivWp5YnTjSlAoCrfWMFFpwcKUkWin76nib0CdlQqoc3H7q66QJCVMHDxQYeJ+o+QVGxuLdu3awd7eHhs3bpQXDteuwZSdna23T1ZWlk6bvNLT0+Ui0KFDh/TWnjIkPDwcH374Ifbs2SMXpmJjdUek2dvbw9LSEpaWloXOqXv37jAxMcG2bdvkWMeOHeHn54fJkydj3bp1T82NiIiIiIjIWB6kZ2Ns5AVcikuTYwpJwtjQSggL9jRiZkTFx8KUkUgmZvAe8SvU6QlQJd7B7a+6wnvEJpjYuUFhYQvJxOzpBylhycnJaNOmDZKSknDw4EGUL19e3qadGqed0pdXTEwMnJyc9EYu5eTkoEuXLjh79ix27dqFwMDAQufi7e2NxMREvfNr/fjjjxgwYAA8PDywb98+CCF0pvNp89Rew/Xr17Fz5058+63uyDYnJye88cYbOHz4cKFzIyIiIiIietGuJ6Rj1JbziE19/A/zFiYKfNHGH00qOxsxM6Jnw8JUCbCt9TYsK9V/ekPF43m+2ml6mswUmJf3h5lrRZiX94c65QEU5tbydgAwdfZB+UErnnp4E3v3oif//7KystChQwdcuXIFe/bsQY0aNXS2e3p6wtXVFSdPntTb9/jx4wgODtaJaTQa9OvXD3v37sX69esRGhpa6FyEEIiOjkatWrXk2O7du3XaBAQEAACCg4OxYsUKXLp0SSfnY8eOydsByIupq9VqvfOpVCrk5uYWOj8iIiIiIqIX6cStJIz/7SLSch7/bnGyMsXCtwNRw93WiJkRPTsWpkqAiYM7TByKVxQyK1cZuSnxj0ZJKc1g4fuaXhuFuRUsfIMLdTwhRJFzUKvVCAsLQ1RUFCIjI/NdCLxr165YuXIlbt++DW9vbwDA3r17ceXKFYwZM0an7YgRI7Bu3TosX74cXbp0yffc8fHxcHV11YktW7YM8fHxaN26tRxr2bKlwf07duyIMWPGYOnSpViyZAmAR/fgm2++gaenJxo1agQAqFKlChQKBdatW4f3339fHl11584dHDx4EG+88UZBt4iIiIiIiMgofrt4H5/vvgJ1nt96lZyssKhTIDzsLIyYGVHJYGGqFNBO6zPG9D0A+PDDD7F161Z06NABiYmJWLVqlc72Pn36AAAmTZqEDRs2oFmzZhg1ahTS0tIwd+5cBAUFYeDAgXL7RYsWYenSpQgJCYGVlZXe8Tp37gxra2sAgK+vL8LCwhAUFAQLCwscOnQIa9euRXBwMN5///2n5u7l5YXRo0dj7ty5UKlUqFevHrZs2YKDBw9i9erV8hpZrq6uGDRoEFasWIEWLVqgS5cuSE1NxdKlS5GZmYmJEyc+0z0kIiIiIiIqSUIIfHfsFr47elMnXtfbAXPa1YCtBX/OU9nAnlwK5J22Zwz//PMPAGDbtm06C4NraQtT3t7eOHDgAMaOHYsJEybAzMwM7dq1w/z583XWl9IeLyoqClFRUXrHu3HjhlyY6t27N44cOYJNmzYhKysLvr6+GD9+PCZPngwrK6tC5T9r1iw4Ojpi+fLliIiIgJ+fH1atWoXw8HCddsuWLcNrr72G77//Xi5E1atXDz/99BOaNGlSqHMRERERERE9byq1BtN3X8GOy3E68Xb+5TC5pR9MlQojZUZU8iRRnLlfZUxKSgrs7e2RnJwMOzs7g20yMzNx9epV+Pn5GXz6XGkhhIBGo4FCodBZDJxebcXtvxqNBnFxcXBzc4NCwf/40SPsF1SS2J+ouNh3qCSxP1FxPY++k5KlwrjfLuLvO8k68fca+uLdBj78nVeGlaXvosLUWbQ4YoqIiIiIiIioFLiXnIXRkedxIzFDjpkoJExpWRXtapQzYmZEzw8LU0RERERERERGdjE2FWO2nkdihkqO2ZiZYE77Gqjn42C8xIieMxamiIiIiIiIiIzowLUHmLzjMrJzNXLMw9YCizoFoJKztREzI3r+WJgiIiIiIiIiMpK1p+9iwYFryLv4c41ytljwdgCcrY3z5HaiF4mFKSIiIiIiIqIXTKMRWPjXdaz9565OvEklZ3zepjosTZVGyozoxWJhioiIiIiIiOgFylSpMWXHZfx1PUEnHhbsibFNKkGh4JP36NXBwhQRERERERHRC5KYkYMxkRdw8X6qHJMAjAmtjF61PI2XGJGRsDBFRERERERE9ALcSMjA6MjzuJeSJcfMTRT4vHV1NK3iYsTMiIyHhSkiIiIiIiKi5+zk7SSM/+0iUrNz5ZiTlSkWvB2IAHdbI2ZGZFwsTBERERERERE9R9sv3cf03VeQq3n87L0KjlZY3CkQ5e0tjJgZkfGxMEVERERERET0HAghsOLYLXx79KZOvLaXPea2rwE7C1MjZUZUeiiMnQARPZvo6GhIkoSIiAhjp0JERERERP9Ppdbgs91X9IpSbaq74atOQSxKEf2/UleYWrZsGWrWrAk7OzvY2dkhJCQEO3bsKHCfDRs2oHr16rCwsEBQUBC2b9/+grItGyIiIiBJEiwsLHD37l297U2bNkVgYKARMns2kiQV6rV///4Xks/SpUtZPCIiIiIiegWkZuVi5Jbz+O3ifZ34uw18MO2tajAzKXU/xYmMptRN5fPy8sKsWbPg5+cHIQRWrlyJjh074vTp0wgICNBrf+TIEfTq1QszZ85E+/btsWbNGnTq1Al///33S1lMMabs7GzMmjULX331lbFTKRE///yzzvuffvoJu3fv1ov7+/u/kHyWLl0KFxcXDBgw4IWcj4iIiIiIXryYlCyM3nIe1xMz5JhSkjC5pR86BLgbMTOi0qnUFaY6dOig8/6LL77AsmXLcPToUYOFqcWLF6N169YYN24cAGD69OnYvXs3lixZgm+++eaF5FxWBAcH47vvvsPEiRNRvnx5Y6dTaOnp6bC2ttaL9+nTR+f90aNHsXv3br34kzIyMmBlZVWiORIRERERUdl3MTYVY7deQEJGjhyzNlNiTvsaqO/jaMTMiEqvUleYykutVmPDhg1IT09HSEiIwTZRUVEYO3asTuytt97Cli1b8j1udnY2srOz5fcpKSkAAI1GA41GY3AfjUYDIYT8KklCI6DJVkNhroSkkEruuIXMU9tu4sSJ6N27N2bOnIkvv/zyqcdbtWoVFi1ahIsXL8LS0hJvvvkm5syZA29vbwDA8OHDsXLlSty/f1+v0BMeHo59+/bhzp07UCqVAIAdO3Zg5syZ+Pvvv6FQKNCkSRPMnj1bpyA5cOBAbNy4Ef/88w9GjhyJgwcPokWLFti8eXOhrzPvdTRr1gwPHjxAREQExo4di5MnT2Lw4MFYtGgRsrOzMWPGDKxZswa3b9+Gm5sbevbsienTp8Pc3Fw+xo8//ohVq1bh/PnzSE5ORuXKlTF8+HAMGTJEblOxYkXcvPlobrkkPfobh4aGYt++fQCApKQkTJ06Fb/++ivi4uLg7e2Nd999F+PGjYNC8XiYb1JSEsaMGYPNmzdDkiR07NgRo0ePlq8rv7+5dltBfdwQbb8vyj5U9rFfUElif6LiYt+hksT+RMWl0WigUCig0Wjw1/UETNn5L7JVj/tROVtzLOxYA5Wdrdm/6KnK0ndRUa6hVBamzp07h5CQEGRlZcHGxgabN29GjRo1DLaNjY1FuXLldGLlypVDbGxsvsefOXMmpk2bphePj49HVlaWwX1UKlWxftg/lQBUSVmIPXgL7o19YOpgATxjbaqo+WmLGb6+vujbty9WrFiB8ePHy6OmtNvzHnfGjBn49NNP0b17dwwaNAjx8fH4+uuvERoaipMnT8LBwQHdu3fH0qVL8dtvv6Fbt27yvhkZGdi2bRv69+8PSZKg0WiwatUqDBw4EG+++SZmzpyJjIwMLF++HI0bN8bJkydRoUIFOZfc3Fy0bt0ar7/+OubMmQMrK6siF1vyXntCQgLatm2LsLAwhIeHo1y5csjNzcXbb7+Nw4cP491334W/vz/Onz+PRYsW4cqVK/j111/lYyxbtgwBAQFo3749TExM8Ntvv2HYsGFQq9UYOnQoAGDBggUYNWoUbGxsMHHiRACAm5sbNBoNMjIy0LRpU9y9exeDBw+Gj48PoqKiMGnSJMTExGDBggVyrh07dsThw4fx/vvvo3r16tiyZYs8NbCgLzDtF1xiYiJMTQu/yKJGo0FycjKEEDoFMnq1sV9QSWJ/ouJi36GSxP5ERaVUKiFMzZGtUSAxW4mHCRm4mZgBOzMJ9zJVAIAqThaY1qw8bNXpiItLN3LG9DIoS99FqamphW5bKgtT1apVwz///IPk5GRs3LgR/fv3x4EDB/ItThXVxIkTdUZZpaSkwNvbG66urrCzszO4T2ZmJhITE6FQKPQ6iCo1G6rUHIP7FURhoQRyBW7/dgWqlBzcSf0PXm39YOZogZykLKizch+1M1XAwvXxVLXshAyos9V6xzO1NYOp7aORPEXpxNoRPAqFApMnT8bPP/+MuXPnYvHixXrbAeDmzZuYNm0apk+fjkmTJsnH6dq1K2rXro1vvvkGkyZNQpMmTeDp6YkNGzagR48ecrsdO3YgPT0dYWFhUCgUSEtLw+jRo/HOO+/g22+/ldsNGDAA1atXx6xZs+S4JEnIzs5Gt27dMHPmzEJfY155740kSYiNjcWyZcvw/vvvy/FVq1Zh79692L9/P9544w05HhgYiCFDhuDo0aNo1KgRAODAgQOwtLSU24wYMQJt2rTBokWLMHz4cABA586d8cknn8DFxQV9+/bVyWfRokW4du0a/v77b/j5+QEAPvjgA5QvXx7z5s3Dhx9+CG9vb0RGRuLgwYOYPXu2PHV16NChaN68uXwt+f3dFQoFJEmCk5OTTq5Po9FoIEkSXF1dX/ovRio57BdUktifqLjYd6gksT9RUSVkqPDd0Zv44984/BefBg0kdAnywORW/pjz53+o6GyFz1tXg5WZ0tip0kukLH0XWVhYFLptqSxMmZmZoUqVKgCAOnXq4MSJE1i8eDGWL1+u19bd3R337+s+6eD+/ftwd89/UTlzc3OdqVhahopOebflfZJbXsmXE/DguP7T7ApiU8kRDtVdcCvyMnIeZkFSKqAwVeDO9qvwauuHtFvJiD9yGwBg4WKFij0fL+Qed/gO0m8n6x3Tpb4nXOo9XhvqyTzzo20nSRIqV66Mvn37ymtNeXh46LXbvHkzNBoNwsLCkJCQIG/38PCAn58f9u/fj8mTJ0OSJHTv3h3Lly9Heno6bGxsAADr16+Hp6cnGjduDEmSsGfPHiQlJSE8PFzneCYmJmjQoAH279+vdy1Dhw4t9PUZus68zM3NMWjQIJ34xo0b4e/vD39/f52cWrRoAQDYv38/Xn/9dQDQmaaYnJwMlUqF0NBQ7Nq1CykpKbC3tzeYR95zNW7cGE5OTjrnatWqFWbPno2DBw+id+/e2LFjB0xMTHSu3cTEBCNGjMDBgwcN9s2859QWror6BVfc/ahsY7+gksT+RMXFvkMlif2JCistOxffHr2JzedicTc5EylZakiShJUn7wAAxjWvjEa+TlCU4DIt9OooK99FRcn/pbhSjUajsyZUXiEhIdi7d69ObPfu3fmuSVUqKCQ413JH7IFo5DzUnTqoSsnG/YM3YeNjDxjpi2zKlCnIzc3FrFmzDG6/evUqhBDw8/ODq6urzuvSpUuIi4uT24aFhSEzMxNbt24FAKSlpWH79u3o3r27XES5evUqAKB58+Z6x/vjjz90jgc8KsZ4eXmV2PV6enrCzMxM7xovXLigl0/VqlUBQCenw4cPo2XLlrC2toaDgwNcXV3lkWTJyfoFxCddvXoVO3fu1DtXy5Ytdc518+ZNeHh4yAU+rWrVqhX/4omIiIiIqEhSs3Ox69843ErKROoTM1kO3UhAZWdrFqWIiqDUjZiaOHEi2rRpAx8fH6SmpmLNmjXYv38/du3aBQDo168fPD095Wlco0aNQmhoKObPn4927dph7dq1OHnypM6UsFJHI5BwOhbuoRVwK+myTnHK1M4c5Rr7Iu1WMqAp2UXWC6tSpUro06cPvv32W0yYMEFvu3Z44Y4dO+SFy/PKWzhp2LAhKlSogPXr1yM8PBzbtm1DZmYmwsLCdI4HAD///LPBkW4mJrrd1NzcvESrx4amtmk0GgQFBcnrOz1Ju8D7tWvX0KJFC1SvXh0LFiyAt7c3zMzMsH37dixcuLBQa19pNBq0atUK48ePN7hdWwwjIiIiIiLjS8zIwaX7acjMs8i5JAGedhYwM1EiOUsFD7vCT2MietWVusJUXFwc+vXrh5iYGNjb26NmzZrYtWsXWrVqBQC4deuWTlGiUaNGWLNmDaZMmYJJkybBz88PW7ZsQWBgYH6nKHEO/i6w9jK8NlVBFOZKVAoPwu3fr0CVnANTO3N4tfWDuZMlJIUEK/dHBR6FqW4Rptwb3lBnl9c7nqmtmV6suKZMmYJVq1Zh9uzZetsqV64MIQQqVqxYqKJJjx49sHjxYqSkpGDdunWoUKECGjZsqHM84NFi4NpRQsZWuXJlnDlzBi1atChwyuC2bduQnZ2NrVu3wsfHR45rn7aXV37HqVy5MtLS0p567b6+vti7dy/S0tJ0in///vvv0y6HiIiIiIhKwMOMHKjUAiYKBYBHhSmFBPg4WMLKTAlrMyXsLQr/sCEiLXVWEiQzO533SgsH4yX0ApW6qXzff/89oqOjkZ2djbi4OOzZs0cuSgGP1vaJiIjQ2ad79+74999/kZ2djfPnz6Nt27YvNGdTW3NYlbct8svC2QoWrtbwebs6bCrY6xSlzJ0sH7fLs/A5AJg7Wxk8nnbh85JQuXJl9OnTB8uXL9d7wmGXLl2gVCoxbdo0+Yl9Wtqn3OUVFhaG7OxsrFy5Ejt37tRZCB0A3nrrLdjZ2WHGjBlQqVR6ucTHx5fQVRVejx49cPfuXXz33Xd62zIzM5Ge/uipGtoRY3nvQ3JyMn788Ue9/aytrZGUlGTwXFFRUfKowLySkpKQm/toEfy2bdsiNzcXy5Ytk7er1Wp89dVXRbs4IiIiIiIqsvup2Ri84QzOxaSgS9CjmR5KBeBlZwYr00e/C96s6gpb81I3/oNKudy0e0g5sxSajFhYWlpCkxGLlDNLkZt2z9ipvRD8xBiZtgjl+VYVKC1MIJWiucjaJ/T9+++/CAgIkOOVK1fG559/jokTJyI6OhqdOnWCra0tbty4gc2bN+O9997DRx99JLevXbs2qlSpgsmTJyM7O1tnGh8A2NnZYdmyZejbty9q166Nnj17wtXVFbdu3cLvv/+O119/HUuWLHlh1w0Affv2xfr16/HBBx9g3759eP3116FWq3H58mWsX78eu3btQt26dfHmm2/CzMwMHTp0wPvvv4+0tDR89913cHNzQ0xMjM4x69Spg2XLluHzzz9HlSpV4ObmhubNm2PcuHHYunUr2rdvjwEDBqBOnTpIT0/HuXPnsHHjRkRHR8PFxQUdOnTA66+/jgkTJiA6Oho1atTAr7/+Wqh1rIiIiIiIqPjuJGVi6KZziEnNwsazMRjfrAqUCgknbichMzsH1mZKvFnNFe819IUNC1NUBOqsJKReiEDGtUioEv+FTfAIJJ5dBlXCBQCA3WtDy/zIKX5iSgFJIcHEqvQN96xSpQr69OmDlStX6m2bMGECqlatioULF2LatGkAHq279Oabb+Ltt9/Wax8WFoYvvvgCVapUQe3atfW2h4eHo3z58pg1axbmzp2L7Oxs+cl9AwcOLPmLewqFQoEtW7Zg4cKF+Omnn7B582ZYWVmhUqVKGDVqlDyFsVq1ati4cSOmTJmCjz76CO7u7hgyZAhcXV0xaNAgnWN+8sknuHnzJubMmYPU1FSEhoaiefPmsLKywoEDBzBjxgxs2LABP/30E+zs7FC1alVMmzZNfqqfQqHA1q1bMXr0aKxatQqSJOHtt9/G/PnzUatWrRd+j4iIiIiIXgXXE9Ix7NdzeJCeAwC4l5KFlSdvYXrr6lBrgMT0LDhZW8DOwoRFKSoypYUDbGv0R9a9I8i48RsyYk/D1NwKps4BsA0YUOaLUgAgiSfnYr2CUlJSYG9vj+TkZNjZGV4rKjMzE1evXoWfn5/BxbJLCyEENBoNFApFgWsj0auluP1Xo9EgLi4Obm5uL/3jSqnksF9QSWJ/ouJi36GSxP5E+bkYm4oRm88hJTtXjlVyssKSLkFwtTGHRqNBQkICnJ2d2XeoWDTZyUi/+iuUVuUQtz0cQjKBqX0FuL75HczdXt4BCIWps2jxk0NERERERET0hNN3kzFk01mdolR1Nxss7/4aXG0er++rVquNkR6VAeqMOCSdnAtTJ38kn/r/J8ILNaBRIfn0kldmjSkWpoiIiIiIiIjyOBKdiBGbzyFD9bjo9Fp5eyzrWhMOlqVvGRZ6+eSm3kb87vdg4dUUaZdXI/v+SZi7N0C5zjtgVq4uVAkXkHohAuqsJGOn+txxAiwRERERERHR//vz6gNM3nEJuZrHq9408HHEvA41YPH/T98jelaSqTUkhSnSLv8Cu6DBkCQT2NUZjQzhAKfXpyP1QsQrs8YUR0wRERERERERAfj94n1M3K5blGpa2QUL3g5gUYpKlNLCCc7NFkOTGY+MG7/DocFEKG28kZmZCYWVO+xeGwoTm/LGTvOF4IgpIiIiIiIieuVtOHMPc/b9pxNrU90Nn75ZDUoFHyxFJc/Epjycm38FpbkjFOZ20Gg08rZXYaSUFkdMERERERER0Sst4sRtvaJUt5rlMZVFKSohWfeiINQqvbipnS8U5gU/ta6sY2GqiIQQT29EVMqw3xIRERER6RNC4OvDN/D14Rs68X51vTG+WWUoWJSiEpB2eQ0S9o/Bw6ipEELz9B1eMZzKV0gmJo9uVW5u7lNaEpU+KtWjyry2HxMRERERveo0GoF5B65hw5l7OvGhjSpgYH0fI2VFZYkQAqlnlyP1QgQAIPPWXijMbGFf72NIEoueWvyVWkgmJiZQKpVISkqCnd2rPcyOXj7JyclQKpUsTBERERERAVBrBKbvvoLfL93XiX/UtDLCgj2NlBWVJUJokHxiDtL/26ITV5g7GCWf0oy/UgtJkiR4eHjgzp07sLCwgI2NTamscAohoNFooFAoSmV+9GIJIZCWloakpCR4eXmxTxARERHRKy8nV4MpOy9j338P5JhCkjClpR86BLgbMTMqK4RahYdRU5F5a69O3L72SNhUDzdSVqUXC1NF4OjoiIyMDNy/fx+xsbHGTscgIQSEEJAkiUUIAvCoqOrk5ARHR0djp0JEREREZFRZKjXG/34JUdGJcsxEIeHzNtXRws/ViJlRWaHJzUTiwQnIjjn2OCgp4NhgEqwqtTdeYqUYC1NFIEkSvLy84OHhgZycHGOnY5BGo0FiYiKcnJygUHBtewLMzMygVCqNnQYRERERkVGlZ+di9NYL+OdushwzUyowt0MNNKrgZMTMqKzQZKcg4cBY5Dw4L8ckpRkcX/8cll5NjJhZ6cbCVDEolUpYWloaOw2DNBoNTE1NYWlpycIUERERERERgORMFUZuOY+L91PlmJWpEgs7BqC2l4PxEqMyQ50Rj4R9o6BKvi7HJFMrODeZB/NytY2YWenHwhQRERERERGVWQ/SszFs0zlcT8yQY3bmJviqcxBquNsaMTMqK3JTbyNh30jkpsXIMYW5A5ybLYSZk78RM3s5sDBFREREREREZdK95CwM+/Us7iRnyTEnK1Ms7VITlV2sjZgZlRWqpGt48OcIaLIer1umtHaHc7PFMLXzNWJmLw/O9SIiIiIiIqIy52ZiBgZvOKNTlHK3Ncd33V9jUYpKjMLCEQrTx/3JxM4Xri2XsyhVBCxMERERERERUZlyJT4NgzecQVxathzzcbDEih7B8HG0MmJmVNYoLZzg3PxLKK3cYObsD5eWy6G0LmfstF4qnMpHREREREREZca5mBSM2nIeqdm5cszPxRpLugTBycrMiJlRWWVi7QGXFl9DYeEMhSkLn0XFEVNERERERERUJpy4lYRhv57TKUoFutvim241WZSiEpF5ez+EOkcvbmLrzaJUMbEwRURERERERC+9v64lYHTkeWSq1HKsjpcDvu4SBDsLUyNmRmWBEAIp51Yg8eAEPIyaCiE0xk6pzGBhioiIiIiIiF5qf/wbh/G/XUSO+nGx4PWKTljcKQBWZlzBhp6NEBokn5qP1HMrAACZt/5E8onZEEIYObOygZ9QIiIiIiIiemltOR+DGXuuIm+JoFVVV3z2VjWYKDkWg56N0OTiYdQ0ZN7crRNX2nhBkiQjZVW2sDBFREREREREL6U1f9/Bwr+u68TeDnDH5BZ+UChYNKBno8nNwsNDE5F1L+pxUFLAod7HsK7S0XiJlTEsTBEREREREdFLRQiBFcdu4dujN3XivWp5YkyTShzJQs9Mk5OKhAMfIif+rByTFKZwfP0zWHo3M2JmZQ8LU0RERERERPTSEEJg8cEbWP33HZ34uw188F5DXxal6JmpMx8gYd9oqJL+k2OSqRWcG8+GuXs9I2ZWNrEwRURERERERC8FjUZg5p9XseV8rE58ZOOK6FvH20hZUVmSm3YXCX+OQG7aPTmmMLeHc9OFMHOuYcTMyi4WpoiIiIiIiKjUy1Vr8Okf/+KPf+PlmARgQgs/dAnyMF5iVGaokv5Dwp+joM5KkGNKKzc4N1sMU/uKRsysbGNhioiIiIiIiEq1nFwNJmy/hIPXHxcMFJKEaW9VQ+vqbkbMjMqKnMRLSPhzJDQ5qXLMxNYbzs2/gom1uxEzK/tYmCIiIiIiIqJSKyMnFx9tu4gTt5PkmKlSgZltqyO0sovxEqMyRWnlDoWFo1yYMnWqBuemi6C0cDRyZmWfwtgJEBERERERERmSmpWL4ZvP6xSlLEwUWNQxgEUpKlFKC0e4NPsKSqtyMHerDZcWS1mUekE4YoqIiIiIiIhKncSMHIzYfB5X4tPkmI2ZCRZ3CkTN8nZGzIzKKqV1Obi0XAalpQskpZmx03llcMQUERERERERlSpxqdl4b8MZnaKUg6UpvulWk0UpemZCCGTc2AGhztHbZmJTnkWpF4yFKSIiIiIiIio17iRl4t31Z3DzYaYcc7Mxx3fdX0M1NxsjZkZlgRAapJxejIdR0/DwyCcQGrWxU3rlsTBFREREREREpcL1hHQM3nAGMalZcszT3gLfdX8NFZysjJgZlQVCk4uko9ORdnktACDz9n4knZgFIYRxE3vFcY0pIiIiIiIiMrpL91MxYvN5JGep5FglJyss6RIEVxtzI2ZGZYHIzUbi4SnIunvwcVCSYOZcA5IkGS8xYmGKiIiIiIiIjOv03WSM3nIeGarH06qqu9ngq85BcLA0NWJmVBZoctKQ+Nc4ZMedlmOSwgSOjabB0qeFETMjgIUpIiIiIiIiMqKo6ESM++0isnM1cuy18vZY1DEANub8yUrPRp2ViIR9o6F6eEWOSSYWcGo8GxYeDYyYGWnxU05ERERERERG8efVB5i84xJyNY/X+Gng44i5HWrA0lRpxMyoLMhNu4eEfaOQm3pbjinM7ODcdD7MXIKMmBnlxcIUERERERERvXC/X7yPz3ZfgSbPwtNNK7vgizbVYWbC53TRs1ElX0fCvtFQZ8TJMaWlK5ybL4apfSUjZkZPYmGKiIiIiIiIXqgNZ+5hzr7/dGJtqrvh0zerQangQtT0bHIenEfC/rHQ5KTIMRNbLzg3+xImNuWNmBkZwsIUERERERERvTARJ27j68M3dGJda3pgfNMqULAoRc8oO/4MEvaNhsjNlGOmjn5wbrYYSgsnI2ZG+eH4SCIiIiIiInruhBD4+vANvaJUv7re+LgZi1JUMkztKkBpXU5+b+YWDJcWy1iUKsVYmCIiIiIiIqLnSqMRmLv/GiJO3NaJD21UASPeqAhJYlGKSobC3B4uzb6E0todFp5vwKXpYijMbIydFhWAU/mIiIiIiIjouVFrBD7fcwW/XbyvE/+oaWWEBXsaKSsqy5RWbnBtuRwKS2dICpY9SjuOmCIiIiIiIqLnQqXWYNL2SzpFKYUk4ZNWVVmUomcmhED6f5sh1Dl625TW5ViUeknwr0REREREREQlLkulxvjfLyEqOlGOmSgkfN6mOlr4uRoxMyoLhEaNpOMzkXH9N2Tdi4LTGzMhKZTGTouKgSOmiIiIiIiIqESlZ+di5JbzOkUpM6UC898OYFGKnplQ5+Dh4cnIuP4bACDrzl9IOj7TyFlRcXHEFBEREREREZWY5EwVRm45j4v3U+WYlakSCzsGoLaXg/ESozJBo8pA4l/jkX3/pByTJCXM3esbMSt6FixMERERERERUYl4kJ6NYZvO4XpihhyzMzfBV52DUMPd1oiZUVmgznqIxANjkZNwSY5JSnM4NZ4Fi/IhRsyMngULU0RERERERPTMYlKyMOzXc7idlCnHnKxM8XWXmqjiYm3EzKgsUKffx4N9I5CbckuOKcxs4RQ6D+aurxkxM3pWLEwRERERERHRM7mZmIGhv55DXFq2HHO3NcfXXYLg42hlxMyoLFAlRyNh3yioMx4/3VFp4QznZotg6uhnxMyoJLAwRURERERERMV2JT4NIzafQ2KGSo75OFji6y5BcLezMGJmVBbkJFxEwv6x0GQnyTETm/JwbvYlTGy9jJcYlRgWpoiIiIiIiKhYzsWkYNSW80jNzpVjfi7WWNIlCE5WZkbMjMqC7PunkPDXOAjV4zXLTB0qw7nZYigtXYyYGZWkZy5MpaWl4cqVK0hPT0fjxo1LIiciIiIiIiIq5U7cSsKH2y4gU6WWY4HutljcKRB2FqZGzIzKguzYE0jYPxZC83gknplLEJxD50NhbmfEzKikKYq7Y3R0NDp27AhHR0fUq1cPzZo1k7cdPnwYNWrUwP79+4t83JkzZ6JevXqwtbWFm5sbOnXqhH///bfAfSIiIiBJks7LwoJDRomIiIiIiJ6Hg9cTMDryvE5Rqo6XA77uEsSiFJUIU8dqOlP1LMqHwLn5lyxKlUHFKkzdunULDRs2xPbt29GxY0eEhIRACCFvb9CgAR48eIBffvmlyMc+cOAAhg0bhqNHj2L37t1QqVR48803kZ6eXuB+dnZ2iImJkV83b94s8rmJiIiIiIioYH/8G4dx2y4iR62RY69XdMLiTgGwMuNqMVQyFOZ2cG62GCY2HrD0bQWnxnOgMLE0dlr0HBTrW+PTTz/Fw4cPceDAATRq1AjTpk1DVFTU44OamKBx48Y4fPhwkY+9c+dOnfcRERFwc3PDqVOn0KRJk3z3kyQJ7u7uRT4fERERERERFc6W8zGYsecqRJ5Yq6qu+OytajBRFntCDpFBSis3uLT6DgoLJ0gS+1dZVazC1K5du9C5c2c0atQo3za+vr74888/i52YVnJyMgDAycmpwHZpaWnw9fWFRqNB7dq1MWPGDAQEBBhsm52djezsx48xTUlJAQBoNBpoNBqD+7wsNBoNhBAv/XVQ6cD+RIawX1BJYn+i4mLfoZLE/lQ4v5y+i0V/3dCJdQgoh0nNq0Ah4ZW8f+w7JUMIDdL/XQfrKl0gmZjrbJPMnSDEozZlXVnqT0W5hmIVphITE1GhQoUC2wghdIo/xaHRaDB69Gi8/vrrCAwMzLddtWrV8MMPP6BmzZpITk7GvHnz0KhRI1y4cAFeXvqPj5w5cyamTZumF4+Pj0dWVtYz5WxsGo0GycnJEEJAoWBFmZ4N+xMZwn5BJYn9iYqLfYdKEvtTwYQQWHPuAX4+E6cT7+zvjMFBdnjwIN5ImRkf+86zE+oc5J6bB/X9w0i6eRimr02GpFAaOy2jKEv9KTU1tdBti1WYKleuHK5evVpgm3PnzsHHx6c4h5cNGzYM58+fx6FDhwpsFxISgpCQEPl9o0aN4O/vj+XLl2P69Ol67SdOnIixY8fK71NSUuDt7Q1XV1fY2b3cC6lpNBpIkgRXV9eXviOT8bE/kSHsF1SS2J+ouNh3qCSxP+VPCIEvD0Vj7YWHMDV5vKj5Ow28MbiBDyRJMmJ2xse+82w0qgw8PDQRmsQTUJiaAg//hvnNH+DQYLKxUzOKstSfivJAumIVplq1aoWff/4ZZ8+eRc2aNfW2Hzx4EH/++SdGjx5dnMMDAIYPH47ffvsNf/31l8FRTwUxNTVFrVq18N9//xncbm5uDnNzc724QqF46f/4wKP1tsrKtZDxsT+RIewXVJLYn6i42HeoJLE/6dNoBGbvv4bN52KAPPWnkY0rom8db+MlVsqw7xSPJjsZD/ePQU7CRTkmKc1g5dPslb6XZaU/FSX/Yl3plClTYGlpiSZNmuCLL76QC0A7duzA//73P7Ru3RouLi4YN25ckY8thMDw4cOxefNm/Pnnn6hYsWKRj6FWq3Hu3Dl4eHgUeV8iIiIiIqJXXa5ag092/fuoKPX/JAATW/ixKEXPTJ0Rh/g97+sUpRSm1nButhgWnm8YMTMyhmKNmKpQoQJ27dqFnj174n//+x8kSYIQAu3bt4cQAj4+Pti4cWOxCkPDhg3DmjVrEBkZCVtbW8TGxgIA7O3tYWn56NGQ/fr1g6enJ2bOnAkA+Oyzz9CwYUNUqVIFSUlJmDt3Lm7evIl33323OJdHRERERET0ysrJ1WDC9ks4eD1BjikkCdPeqobW1d2MmBmVBbkpt/Bg30io02PlmMLCCS7NFsHUsaoRMyNjKVZhCgAaNGiAq1evYtu2bTh27BgSExNhZ2eHBg0aoGPHjjAzMyvWcZctWwYAaNq0qU78xx9/xIABAwAAt27d0hkW9n/s3Xd4FGXbBfAz29N20xuht0Ag9K50kKJ0rJ+IBRULSLFgQ1TEQhErqBR7QwQElCZFem+hQ6jpdZNNts58f8R3w5oAySYwu8n5XZfXy9y7G07geTfJYeaZ7OxsjBkzBikpKQgKCkKbNm2wfft2NG3a1K0MRERERERE1VGB1Y7JfxzDnks5zplaqcCMAbHoVj9UvmBUJVizTiBz0wSI5mznTOkXidAeH0Glr9ge1eS93C6mAEClUmHo0KEYOnRoZeWBJEk3fM6mTZtcjufMmYM5c+ZUWgYiIiIiIqLqJs9sx/jlR3Ek2eic6VQKzBoUh/a1gmRMRlWBJe0AMjdPgmQrcM7UhroI6TEXSl+eiVedubXHVM+ePfHNN99c9znfffcdevbs6VYoIiIiIiIiunWyCqx48rfDLqWUv0aFT4fFs5SiCiu8/A8yN453KaU0IXEI7T2fpRS5V0xt2rQJ58+fv+5zLly4gM2bN7vz4YmIiIiIiOgWScuz4IlfD+NUer5zFuijxrwR8YiP1suYjKoC85WtyPrnRUgOq3OmjWyPkJ4fQ6Hl+iI3i6myMJlMUKvVN+vDExERERERUQVdzinEmF8P4Xx28Zks4f5afDGiBRqH+8uYjKoKTWg81Po6zmOfWj0R0m0mFGpf+UKRRynzHlMXL150Oc7JySkxAwCHw4FLly7ht99+Q506dSockIiIiIiIiCrfuUwTnl56BBmm4jNZahh0+GxYPKINOhmTUVWi0OoR0vMjZKwbA21kBxjavQBBuGnnyJAXKnMxVadOHQiCAAAQBAFz587F3Llzr/l8SZLwwQcfVDwhERERERERVarjqXl49vejyDXbnLN6wb74ZFhzhPlrZUxGVZHSJxShfRdBoTU4ewWi/ylzMTVq1CgIggBJkvDNN9+gRYsWaNmyZYnnKZVKBAcHo2fPnujXr19lZiUiIiIiIqIKOnAlFxOWH4XJ6nDOYsP98fHQ5gj04XYs5D7JYUP+8W/hF3s/FCrXs+6UukB5QpHHK3MxtXjxYuevN2/ejIcffhjjxo27GZmIiIiIiIjoJth5IRuT/0iAxS46Zy2iDfhwcBz8tWX+8ZCoBNFeiKx/XoIleResGQkI7voeBAXXFN2YW6skMTGxsnMQERERERHRTbTxTAZe+fMEbI7iUqpDrSB8cFdT+KiVMiYjbydajMjcPBHWjKMAAHPSNuTsfhdBHV+VORl5A9aXREREREREVdzq46mYtvYURElyzrrXD8X0/rHQqLgRNbnPUZCOzI3jYcs955wJal/41u0vYyryJmUqpnr27AlBEPD1118jJiYGPXv2LNMHFwQBGzZsqFBAIiIiIiIict+vh5Lw/sYzLrP+seGY2rcxlApuRE3us+ddQubGcbDnJztnCm0gQnrMgSa4iYzJyJuUqZjatGkTBEFAQUGB87gsuNs+ERERERGRfL7ecwmfbHPdimV4fBRe6N4ACpZSVAG27FPI2PgcRHOWc6b0jUBIz4+g1teWMRl5mzIVU6IoXveYiIiIiIiIPIckSfh8+wUs2nPRZT6qbU0806UOTyKgCrGkHUTW5skQbfnOmUpfG6E9PoLSL0LGZOSNuMcUERERERFRFSKKEmZtPotfDiW5zMd2roNH2teSKRVVFeak7cj6Zwokh8U504Q0QXC3OVDqAuULRl6LxRQREREREVEV4RAlvL3+FFYeS3WZT+5eH/e0rCFTKqoqChL/Qs7OtyBJDudMG9EWwV3fh0LtK2My8mYspoiIiIiIiKoAm0PEq3+ewN9nMpwzhSDgld4NMSguUsZkVBUUXvwb2TvecJn51OyOoM5vQlBq5AlFVQLvC0pEREREROTlzDYHJv1xzKWUUikETO8fy1KKKoU2sh3UgQ2cx77170JQl+kspajCWEwRERERERF5MZPFjnHLjmLH+eK7o2mUCswaFIfejcJkTEZViUITgJAec6HyrwH/Jv+HwPYvQ1Ao5Y5FVQAv5SMiIiIiIvJSuYU2jFt2FMdS85wzX7UScwbHoXVMoHzBqEpS+oQgrN9iKDQBckehKoRnTBEREREREXmhDJMFTyw57FJK6bUqfDY8nqUUVYhoN8N46HOIdnOJx1hKUWXjGVNEREREREReJtloxtNLj+BSTqFzFuyrxqfD4tEg1E/GZOTtRGseMjdPhjX9EGzZpxDc9QMIClYHdPNU6Iyp33//HXfffTfi4+PRoEHxJmgnTpzA+++/jytXrlQ4IBERERERERW7mF2AMb8ccimlIgO0+HJkC5ZSVCGOwkxkrB8La/ohAIA5aQdy9rwvcyqq6tyqPUVRxH333YclS5YAAHx8fFBYWPymGBQUhFdeeQUOhwNTpkypnKRERERERETV3Kn0fDz7+xFkFdics1qBPvh0WHNE6nUyJiNvZ89PQubfz8KeX3yCiUJrgF+DIfKFomrBrTOm5syZg19//RVPPPEEsrOzMXnyZJfHIyIicPvtt2PVqlWVEpKIiIiIiKi6O5JsxJNLDruUUg1C/fDl3S1YSlGF2HLOIGPd4y6llNI3HKG950ET0lTGZFQduFVMLV68GO3atcNnn30GvV4PQRBKPKdBgwZITEyscEAiIiIiIqLqbu+lHDy99AjyLHbnrFlkAOaPiEewr0bGZOTtrOmHkbF+LByFGc6ZKqAmQvt8AbWhrozJqLpwq5g6c+YMbr/99us+JyQkBJmZmW6FIiIiIiIioiJbEzMxftlRFNoczlmbmEB8Oqw59Dq1jMnI25mTdiJj4ziI1uI7O6qDGyO0zxdQ+UXKmIyqE7f2mPLx8UFubu51n3PhwgUEBga68+GJiIiIiIgIwNqTaXj9r5NwSJJz1qVuMN4b2ARalVLGZOTtCi6sQ86OaZDE4rPwtOGtEdztAyjU3ESfbh23zphq1aoV1qxZA7PZXOrjWVlZ+Ouvv9CxY8cKhSMiIiIiIqqulh9Nwat/nnAppXo3CsMHdzZlKUUVYjr9G7K3v+5SSuliuiKkx4cspeiWc6uYGjduHC5fvozhw4fj8uXLLo+dPXsWQ4cORW5uLsaNG1cpIYmIiIiIiKqTH/ZfxtvrT0G6ajYoLhLT+8VCrXTrxzgiAEBB4p/I2fMBcFXh6VtvIIJvmwFByf3K6NZz61K+wYMH48UXX8R7772H2rVrw8+vqFENDw9HZmYmJEnCa6+9hp49e1ZqWCIiIiIioqpMkiQs2H0R83dccJnf16oGJnStV+qNp4jKQ1fjdqiDGsKWfRoA4B97L/StxkEQWHiSPNxeeTNmzMCaNWtw5513wtfXF0qlEqIool+/fvjzzz8xbdq0ysxJRERERERUpUmShI+2JpYopR7rUIulFFUahcYfIT3mQhUQA32LJ6FvNZ6lFMnKrTOm/qdPnz7o06dPZWUhIiIiIiKqlkRRwrsbz+D3I8ku83G318WDbWrKlIqqKqUuGGH9v4VC5SN3FCL3z5giIiIiIiKiirM7RLy+5qRLKSUAmNKrIUspqhDRmo/cfXMg2gtLPMZSijyFW2dMXbx4sczPrVWrlju/BRERERERUZVntYuYsvo4tpzLdM4UgoBpdzRGv9hwGZORt3OYs5C58TnYsk/BbryA4K4fQFCq5Y5FVIJbxVSdOnXKdH2zIAiw2+03fB4REREREVF1U2hzYNKKBOy5lOOcqZUKzBgQi271Q+ULRl7PbkpG5t/jYM+7BAAwJ+9E7r5ZCGz/kszJiEpyq5gaNWpUqcVUbm4uDh06hMTERHTr1g116tSpaD4iIiIiIqIqJ89sx/jlR3Ek2eic6VQKzBoUh/a1gmRMRt7OlnsOmRufg6MgzTlT+oTCr9FIGVMRXZtbxdTixYuv+ZgkSZg1axbef/99LFiwwN1cREREREREVVJ2gRXP/H4Up9LznTN/jQpzhzRDfLRexmTk7awZR5G5aSJEa3HhqQqIQUiPj6Dyj5YxGdG1Vfrm54IgYPLkyYiLi8Pzzz9f2R+eiIiIiIjIa6XlWfD4r4ddSqlAHzXmjYhnKUUVYk7ejYy/n3UppdRBDRHa5wuWUuTRbtpd+dq2bYu///77Zn14IiIiIiIir3IltxBjfj2E89kFzlm4vxZfjGiBxuH+MiYjb1d48W9kbZ4I6aq772nCWiC012dQ6oJlTEZ0Y25dylcWZ8+e5cbnREREREREAM5lmvD00iPIMFmds2i9Dp8Pj0e0QSdjMvJ2pjPLkLPnPUCSnDNddBcE3TYdChXXFnm+Si2mRFHElStXsHjxYixfvhy9evWqzA9PRERERETkdY6n5uHZ348i12xzzuoG++KToc0RHqCVMRl5M0mSkH/sWxgPfeYy963bD4EdXoWguGnnoRBVKrdWqkKhKPWufP8jSRKCgoIwa9Yst4MRERERERF5u4NXcvHc8qMwWR3OWWy4Pz4e2hyBPmoZk5G3Kzi7rEQp5d/4buhbPwdBuGm79hBVOreKqa5du5ZaTCkUCgQFBaFdu3Z4+OGHER4eXuGARERERERE3mjnhWxM/iMBFrvonMVH6zF3cDP4a3k2C1WMT+2+MJ1ZBlvWSQCAPv5x+Mc9fN2TSIg8kVvvhps2barkGERERERERFXHxjMZeOXPE7A5ikupDrWC8MFdTeGjVsqYjKoKhdoPId3nIGP9WPg1GgH/RiPljkTkFtb0RERERERElWj18VRMW3sK4lWbUXevH4rp/WOhUfESK6o8Sl0wwvt/C0GpkTsKkdv4rkhERERERFRJlhxKwtQ1J11Kqf6x4Xh3YBOWUuQ2hzkHObtnQLQVlHiMpRR5uzKdMdWzZ0+3PrggCNiwYYNbryUiIiIiIvIm3+y9hI+3JrrMhsdH4YXuDaBQcN8fco/DlIqMjc/CbrwIe34yQrrNZBlFVUqZiil395TipmtERERERFTVSZKEz7dfwKI9F13mD7aJwbO31eXPReQ2W+55ZG4cD0dBKgDAkrIbufs/RGC7F2RORlR5ylRMiaJ44ycRERERERFVM6IoYdbms/jlUJLLfGznOni4XU2WUuQ2a+YxZG6aCNGS45yp/KPhH3u/fKGIbgJufk5EREREROQGUZTw1vpTWHks1WU+uXt93NOyhkypqCqwpO5D5pbnIV21p5TaUA8hPT+C0idUxmRElY/FFBERERERUTnZHCJe++skNpxOd84UgoBXejfEoLhIGZORtyu8tBnZ216FJNqcM01oc4R0mwWFVi9jMqKbo0LFlNlsxp49e5CUlASLxVLqc0aNGlWR34KIiIiIiMijWOwOPL/yOHacz3LOVAoBb/WLRe9GYTImI29nOvsHcnbPAKTi7XR00Z0QdNs7UKh8ZExGdPO4XUx9+umneO2115Cbm1vq45IkQRAEFlNERERERFRlmCx2TFiRgANXin8O0igVeP/OpuhSN1jGZOTt8o9/j9wDH7vMfGr3RlDHqRCUaplSEd18CndetHTpUjz77LOoWbMmZs6cCUmSMHjwYLzzzjvo168fJEnC8OHDsXDhwsrOS0REREREJIvcQhueWnrEpZTyVSvx8dBmLKXIbZIkwXjo8xKllF/DYQjq/CZLKary3CqmPvzwQ4SHh2PHjh2YMGECAKBly5Z48cUXsWrVKnz33XdYtmwZateuXalhiYiIiIiI5JBpsuKJJYdxLDXPOdNrVfhseDxaxwTKF4y8nunUL8hL+NplFtDsERjaPg9BcOtHdiKv4tYqP3z4MAYNGgRfX1/nzOFwOH99//33o2fPnnjzzTcrnpCIiIiIiEhGyUYzxvx6CGczTc5ZsK8a80e2QFxkgIzJqCrwrXcn1MGxzmND6+egj38cgiDImIro1nGrmLLZbAgLK97Uz8fHBzk5OS7PadGiBfbv31+hcERERERERHK6mF2AMb8cwqWcQucsMkCLL0e2QINQPxmTUVWhUPshpPscqA31ENRpKvxj75U7EtEt5dbm59HR0UhOTnYe165dGwcOHHB5zoULF6BSVeimf0RERERERLI5nZ6PZ34/gqwCm3NWK9AHnw5rjki9TsZkVNUodUEI6/8NBAV/hqbqx60zptq1a+dyNlS/fv2wbds2zJgxAwkJCZg/fz6WLl2Kdu3alftjz5gxA+3atUNAQADCw8MxZMgQnDx58oav+/XXXxEbGwudTofmzZtj9erV5f69iYiIiIiIAOBoshFPLDnsUko1CPXDl3e3YClFbnMUpCF7xxsQbQUlHmMpRdWVW8XUyJEjYbFYcP78eQDAlClTEBMTg1dffRXx8fEYO3Ys/P398f7775f7Y2/evBlPP/00du7ciXXr1sFms6Fv374wmUzXfM327dtx33334dFHH8WBAwcwZMgQDBkyBEePHnXn0yMiIiIiomps76UcPLX0CPIsduesWWQA5o+IR7CvRsZk5M3sxotIX/c4ChL/QtY/L0JyWOWOROQRBEmSpLI88aeffsKwYcOg0ZT+RpydnY2vvvoK586dQ+3atfHggw+iRo0aFQ6Ynp6O8PBwbN68GV27di31Offccw9MJhNWrlzpnHXs2BEtW7bEvHnzbvh7GI1GGAwG5ObmQq/XVziznERRRFpaGsLDw6FQ8A4OVDFcT1QarguqTFxP5C6uHapMoigiMzMTISEh2H4hGy+uPA6rQ3Q+3jrGgDmD4uCr4Rkt5Kqs70XWrJPI3PQcRHO2c+bXaAQC206+FTHJS1Slr23l6VnK/M56//33Izg4GA888AAeeeQRtGjRwuXxoKAgPP/88+4lvo7c3FwAQHBw8DWfs2PHDkycONFldscdd2DZsmWlPt9iscBisTiPjUYjgKJFIIpiqa/xFqIoQpIkr/88yDNwPVFpuC6oMnE9kbu4dqiymKwO5FnsyLSqYMwqRGJmAUJ9NUgymgEAXeoGYcaAWGhVCq43KqEs70WWtAPI3vI8JHvx5XsqfV34xf4f1xS5qEpf28rzOZS5mLrvvvuwbNkyfPzxx/jkk0/QunVrPProo7jvvvtgMBjcCnojoijiueeeQ5cuXdCsWbNrPi8lJQUREREus4iICKSkpJT6/BkzZmDatGkl5unp6TCbzRULLTNRFJGbmwtJkry+YSX5cT1RabguqDJxPZG7uHaoopRKJexqX3y56yLWnEjF5RwzLKKAYfFReKFnfUxfdwINAjWY1C4EuVmZcsclD3Wj9yJH2i7YD70LSSy+bE9haARF/DRk5gPIT7uFacnTVaWvbXl5eWV+bpmLqe+//x5GoxHff/89Fi5ciH379mH//v2YNGkShg0bhkcffRTdu3d3J+81Pf300zh69Ci2bt1aqR93ypQpLmdYGY1G1KxZE2FhYVXiUj5BEBAWFub1C5nkx/VEpeG6oMrE9UTu4tqhijJZHfhoayL+OJaOzAI7MgodEATgm72XIQB4sWdjdKkTBIVCkDsqebDrvRcVJP6J3IT3oFJKgFINANBEtEXQbe9CofaVIy55uKr0tU2nK/tNIsp1kbRer8fYsWMxduxYJCQkYMGCBfj+++/x/fff44cffkDdunXxyCOP4KGHHqrw/lLPPPMMVq5ciS1btiAmJua6z42MjERqaqrLLDU1FZGRkaU+X6vVQqvVlpgrFAqv/8sHAEEQqsznQvLjeqLScF1QZeJ6Indx7VBF5FutWHcqHRkFVqTn/+9slqISauOZDLzSuxFUKqV8AclrlPZelH/iR+Tun+vyPJ+aPRDUeRoEJTfQp2urKl/bypPf7c80Li4Os2fPxpUrV7BkyRL0798fFy5cwKuvvoo6depg4MCBWLp0abk/riRJeOaZZ/D777/j77//Rt26dW/4mk6dOmHDhg0us3Xr1qFTp07l/v2JiIiIiKjqyym04UJ24VWlVJFQPw38tCoYLTaZkpE3kyQJxkPzS5RSfg2GIOi26SyliEpR4dtKqFQqDBs2DMOGDUNKSgq++eYbLFy4EH/++SfWrFkDu91+4w9ylaeffho//PADli9fjoCAAOc+UQaDAT4+PgCAUaNGoUaNGpgxYwYAYPz48ejWrRtmzZqFgQMH4qeffsLevXvxxRdfVPTTIyIiIiKiKkghCDDbXDfnDffXIMRPAz+NEgadWqZk5K0kSUTu3pkwnXY9QSMg7iEExD8JQeBloVS6XGshrKIDkAAE+CDTUgAIgEahhEHjI3e8m65S73eanZ2NtLQ05OTkAChqi8vr888/B4AS+1UtWrQIo0ePBgBcvHjR5bSwzp0744cffsCrr76Kl19+GQ0bNsSyZcuuu2E6ERERERFVT1/svAAflQLDmkfi672XAQChfiqE+BadzdK3URgCtJX6oxJVA/nHvytRShlaPQv/Jg/IlIi8hVV0YOj6RbCLDtjtdqjUKqgEJX7v/bDc0W6JCr/b5ufn48cff8TChQuxe/duSJIEX19fjBo1Co8++mi5P15ZyqxNmzaVmI0cORIjR44s9+9HRERERETVxxc7L+DLnRcQrdfhhR4NAABbEzMhSCL8NEr0bRyGxzvWhj+LKboBhzkHgqb45lm+dQfCbryAgnOrAEGBwPZT4Ff/LhkTkjfJsJiQbSlAlNYfgiRBgHjjF1URbr/bbt68GQsXLsRvv/2GwsJCSJKEdu3a4dFHH8V9992HgICAysxJRERERETkNkmS8MXOC/hq10UAQJLRjPc3nsG0fo3xSu9GyDKZEeyng16nYilFN2TPT0JewmIExI2Gj48fxIKUf48fhiSJ8InpDp+a3eSOSV7EX6VBSmEeUswm1FQHyh3nlirXO+6VK1ewePFiLF68GOfOnYMkSQgJCcGYMWPw6KOP8tI5IiIiIiLyOJIkYd6OC1i4+6LL/IHWMWgZbYAoilBb8xASoPf6O2HRzecw5yAvYTEKzq6ALecs/OPHIuvw57BlJgAADK3GQakLkjkleRu1QolafoE4n5cFk80Kg0Ynd6RbpszFVP/+/bF+/Xo4HA4IgoDevXvj0UcfxZAhQ6DR8M4CRERERETkeSRJwufbL2DRHtdS6oUeDTCyRbTz2OFw3Opo5KWUukD4N/k/WJJ3wppxGGlrn4BarYY6JA4BcaNZSlGZZFsK8Pr+vzCu6e0I1PpAKSjgq1Kjnl8QdBoNlEL1KcnLXEytWbMGtWrVwsMPP4yHH34YtWrVupm5iIiIiIiIKkSSJHy2/TwW77nkMn+pZwMMj4++xquIrs+adQIF5/+CPv5JpK99DPCJBAAYWj0DlT/XFd3YpfwcjN+1DJdNOZiwazkWd723aKNzCXCIDigVSudd+aqDMldwa9asQWJiIqZOncpSioiIiIiIPJokSfhkW8lSakqvhiylyG2WlL3IPfAxdJEdkbtvNiR7AWDOBADkHvgE9vwkmROSN5h3cjsum3IAAGnmfEzdvwZhOn+EaH2BvEKEaH0RpvOHQeMjb9BbpMzFVJ8+fSAIws3MQkREREREVGGSJOHjrYn4Zq9rKfVyr4YY1jxKplTk7QovbUTmpgnwazAU+Se+hyV1L7TRnRF+169Qh8TBlpmAvITFcJhz5I5KHu7l+F5oaAgDANT0C8SUFr1kTiQv3m6CiIiIiIiqDEmSMPefRHy//7JzJgB4pXcjDG4WKV8w8mqmM78jZ8/7gCQh/8SP0DcfA0Gphb71cygQAxDc5S3nXfqUukC545IHupifjVr+RfuP+am1+LDDYMw+uhkvNO+BIK2vzOnkVX120yIiIiIioipNkiTM2XKuRCn1ah+WUuQeSZKQd3QRcna/B0gSAMCWmQBL+iEEdngZSr9oFBYWQuEbCX2Lp7jHFJUgSRLmndiOuzd+g+2p553zMJ0/ZrQdWO1LKYDFFBERERERVQH/K6V+PHDFORMAvNanEQbFsZSi8pMkEbn7ZsN4eL7L3K/RSOjjx0CpC3aZ80wp+i9REvH2oXVYeGo3REnClH2rcCwnRe5YHofFFBEREREReTVJkjBrc8lS6vW+jXEXSylyg+SwIXv7VJhO/eoy17d4EoY2EyEI/FGabkyAAOVVa6XQbsORLBZT/8U9poiIiIiIyGtJkoQPNp3Fr4eK74YmAHjjjsYY0CRCvmDktURbAbK2ToEleVfxUFAgsN3z8GswVL5g5HUEQcCLzXsiw2zCjrTzeK1lXwyo2UTuWB6nUoqprKwsmEwm1KxZszI+HBERERER0Q2VVkopBAFT+zZiKUVuyzu6wKWUEhRqBHV5Ez41e8iYirzF+bwsbEk9h1EN2gIAlAoFprcZgJO5aWgZUkPmdJ7J7fMPc3NzMX78eERERCAsLAx169Z1PrZr1y4MGDAA+/btq5SQREREREREVxNFCe9tPFOilJrGM6WoggKaPwZNSBwAQFD7IqTHhyylqEwOZSXhsa0/45NjW/FL4kHn3EelZil1HW4VU1lZWejQoQM+/vhj1KxZE02aNIH07x0KACA+Ph7btm3D999/X2lBiYiIiIiIgOJS6rfDyc6ZQhDwZr/G6BcbLmMyqgoUKh+EdJ8NbXhrhPb6DNqINnJHIi+QYTbhmR1LYbRZAACzjm7CttREmVN5B7eKqTfeeAOnTp3CTz/9hL1792LkyJEuj/v4+KBbt274+++/KyUkERERERERUFRKvbvxDJYecS2l3urXGHc0ZilF5Xf1SRb/o9AaENLrU2iCY2VIRN4oVOeHJ2I7OY/jg6LRPChKxkTew61iasWKFbjzzjtx9913X/M5derUweXLl90ORkREREREdDVRlPDO36fx+39Kqbf7x6IvSylygzlpJzLWPwHRml/iMUEQZEhE3kSURFgddufxA/Va4566LdEzqgE+7TQMeo1OxnTew61iKjk5GU2bNr3uc7RaLUwmk1uhiIiIiIiIriaKEqZvOI3lR4tvta4QBLwzIBZ9GoXJmIy8VcH5tcjaPAnW9MPI2vI8JIdV7kjkRawOO6buX4NX9/8JURIBFJWZE5p1xTttB0CjrJR7zVULbhVTISEhuHTp0nWfc+LECURF8bQ1IiIiIiKqGFGU8Pb601iRUFxKKQUBMwY0Qa+GLKWo/PJP/ozs7a9DkhwAAEvaAeSf/FnmVOQt8m0WjN+1DGuunMSm5LOYdXSz85JQhaCAQnD7PnPVklt/Wl27dsXy5cuveanesWPH8Ndff6F3794VCkdERERERNWbKEp4a/0p/HHsP6XUwCbo2TBUxmTkjSRJgvHQfOTum+My9603EP6x98uUirzNRVM2jmQVX1K87MJRXMjPljGRd3OrmHrllVfgcDjQpUsXfP/998jIyAAAHD9+HAsWLEDPnj2h1Wrx/PPPV2pYIiIiIiKqPkRRwrR1p7DyWKpzphQEvDuwCXo0YClF5SNJInL3vIe8hEUuc/8m/4fADq9CUChlSkbepmlgJN5u0x8KQYC/WoOPOg5FnYBguWN5LbcuemzevDl+/vlnPPjggxg1ahSAoua5WbNmkCQJAQEB+OWXX9CwYcNKDUtERERERNWDKEp4Y+1J/HkizTlTKYpKqW71WUpR+UgOK7K3v47CS5tc5vqWTyOg6YOyZCLvciDzCmr7ByFY6wsA6B7VAK+17INYQzjq6/meVBFu78Y1aNAgJCYm4uuvv8auXbuQlZUFvV6PDh064OGHH0ZoKP9iiIiIiIio/ERRwtS1J/HXf0qp9wY2Rdf6ITImI28k2kzI2vICLKn7ioeCAkEdXoZvvTvlC0ZeY+2Vk5h2YC3q60Mwr/MI+Ko0AICBNa9/UzgqmwptEx8cHIwJEyZUVhYiIiIiIqrmHKKEqWtOYs3J4lJKrVTgvYFNcHs9llJUPg5zFjI3Pgdb9innTFBqENRlOnxibpcxGXmLPy4m4K2D6wAAJ3LS8PLe1ZjVfhCUCm5wXln4J0lERERERB7BIUp47a8TJUqp9+9kKUXlZ89PQsa6x11KKYXaHyE9PmIpRWXWJjQGQf9evgcA/motREgyJqp63Dpj6ptvvrnhcxQKBfR6PRo3bozGjRu789sQEREREVE14RAlvPrXCaw/le6caZQKfHBXU3Suw02Fqfzyji6APa/4TvJKn1CEdJ8DdRD3Qqayi/Y1YG6HIXhi+68YUSceTzfpAoXAc3wqk1vF1OjRoyEIQpmfHxsbi48//hg9e/Z057cjIiIiIqIqzO4Q8epfJ7HhNEspqjyGtpNhN16ENeMIVAExCOnxEVT+0XLHIg9ntJrxxoE1eLpJF+em5rGB4fi5+4OI9NXLnK5qcquYWrRoEZYuXYo//vgDffv2RZcuXRAREYHU1FRs27YNa9euxaBBg9C1a1fs378fP//8MwYMGIB//vkH7dq1q+zPgYiIiIiIvJTdIeKVP0/g7zMZzplGqcCsQXHoWDtIxmTk7RQqH4R0m4WcfTNhaP0clDqWnHR9yQVGjN+1DOfzsnAyNx2Lbr8H4T4BAMBS6iZyq5gyGAxYu3YtNmzYgB49epR4fNOmTRgwYAAeeeQRTJw4EWPGjEGvXr3w7rvv4rfffqtwaCIiIiIi8n52h4iX/zyBjf8ppWYPikMHllJUTpLDCkGpcZkptHoEd35TpkTkbT49vg3n87IAAOnmfLy+fw3mdRkhc6qqz60LI9955x3cfffdpZZSANC9e3eMHDkSb7/9NgCgW7du6NevH7Zu3ep+UiIiIiIiqjJsDhFTVpcspeYMZilF5Zd//Hukr30UojVP7ijkxV6M74F6AUU3Wojy1eOleG5HdCu4VUwlJCQgJibmus+JiYlBQkKC87hp06bIyclx57cjIiIiIqIqxOYQ8dKq49h0triU0qqKSqn2tVhKUdlJkoTcA58g98DHsGWfRuaW5yHZLXLHIi+SVJDr/HWAWoePOg7B7ZH1sPC2e1AngJd/3gpuFVP+/v74559/rvucf/75B/7+/s5jk8mEgIAAd347IiIiIiKqImwOES+uOo4t5zKdM61KgQ8HN2MpReUiiQ7k7JqO/OPfOWfWtIMovLxJvlDkNSRJwqLTuzHi76+xI+28cx7uE4BZ7QchROcnX7hqxq1iavDgwdi2bRueeuoppKenuzyWkZGBp59+Gtu2bcPgwYOd84MHD6J+/foVS0tERERERF7Lai8qpf65qpTSqRSYO6QZ2tYMlC8YeR3JbkHWPy+h4NxKl7mhzQT41rlDplTkLURJxHtH/sbnx7fDLop4ae8qnMhJkztWteXW5uczZszAtm3bMG/ePCxatAgNGjRAeHg40tLScObMGVgsFsTGxmLGjBkAgJSUFBQWFmL06NGVmZ2IiIiIiLyE1S7ihVXHsC0xyzn7XynVOiZQvmDkdURrHjK3PA9r2kHnTBCUCOz0OkspKhMBAuyi6DwutNuwJ+MSYgPDZUxVfblVTIWEhGD37t1499138f333yMhIcG5n1SdOnXwwAMP4MUXX3ReyhcZGYn9+/dXXmoiIiIiIvIaVruI51cew/bzxaWUj1qJDwfHsZSicnEUZiBz43Ow5ZxxzgSVDsG3zYAuupOMycibCIKAl+J7It1swq70C3iheU8Mq9Nc7ljVllvFFAD4+fnhrbfewltvvYW8vDwYjUbo9XruI0VERERERE5Wu4jJK49hx1WllK9aiblDmqFlDYOMycjb2PMuIXPjONjzk50zhUaPkO6zoQltJmMy8gZXTLnYnHIW99dvDQBQKZSY0XYAErJT0C6slszpqje3i6mrBQQEsJAiIiIiIiIXVruISX8kYOeFbOfMV63ER0OboUU0SykqO2vWSWRumgDRXFxwKn3DEdJjLtSGujImI29wLCcFE3atQLalABqlEiPqtAAA+Ko0LKU8gFubnxMREREREV2Pxe7AxBUlS6mPhzZnKUXlYkndj8wNT7mUUip9LYT2+YKlFN1QlqUAT23/DdmWAgDAzCObsD31vKyZyJXbxdSlS5fwxBNPoH79+vDx8YFSqSzxn0pVKSdkERERERGRFzHbHJiwPAG7LpYspeKj9TImI29kOvULRJvJeawJaYLQ3l9A5RcpYyryFsFaXzzSqIPzONYQjibc5NyjuNUcnTt3Dh06dEB2djbi4uJgsVhQu3Zt6HQ6nDt3DjabDS1atEBgYGAlxyUiIiIiIk9mthWdKbXnUo5z5qcpKqWaR7GUovIL7PQGHOZxsKYfhjayPYJvfxcKta/csciDSZIEhyRCpVACAB6s3waphXlILjBiepsB8FGpZU5IV3PrjKlp06YhNzcXGzZswKFDhwAADz/8MI4fP47z589j0KBBMJlMWLJkSaWGJSIiIiIiz2W2OTDhP6WUv0aFT4fFs5QitylUOoR0nYmApqMQ0m0WSym6LrvowNuH1uG1/X9BlEQARXfhm9SsGz5odxdLKQ/kVjG1fv16DBgwAN26dXPOJEkCAERFReHnn38GALz88suVEJGIiIiIiDxdoc2B55YnYG+JUqo54iJ5oyQqG0kSIVqMJeYKrR76lk9BULJUoGsrsFsxafcf+OPiMWxIOo0PE7Y4uwqFoIBSwW22PZFbfysZGRmIjY11HqtUKhQUFDiPtVot+vTpg5UrV1Y8IRERERERebRCmwPjlx3Fvss5zlmAtqiUaspSispIctiQveMNZKx/stRyiuhGzudnYV/GJefxr4mHkJifdZ1XkCdwq5gKDQ2FyWRyOT5//rzLc1QqFXJyciqSjYiIiIiIPFyB1Y7xy47iwJVc50zPUorKSbQXIuufF1B4fi1sueeQuWUyRLtZ7ljkZZoGRuLN1v0gCICvSo05HYagXkCI3LHoBtza/Lxhw4Y4e/as87h9+/ZYs2YNzp07h3r16iE9PR1LlixB/fr1Ky0oERERERF5lgKrHeOXJ+Dgf0up4c0RG85SispGtOQic9NEWDMTnDNb5nHYso5DG95KxmTkDRKyUxDlq0ewtmjvsZ7RDfGyrTdiA8PR2MC773kDt86Y6t+/PzZu3Og8I+q5555DXl4e4uPj0a5dOzRq1AgpKSl49tlnKzMrERERERF5iAKrHeOWlSylPhsez1KKysxhSkX6+idcSilB7YuQ7rNZStENbUo+gye2/YoJu5aj0G5zzgfXbsZSyou4VUyNHTsWmzZtglJZdOvF7t2746effkLt2rVx9OhRRERE4KOPPsKYMWMqNSwREREREcnPZLHjmd+P4lCSayn1+Yh4NA73lzEZeRNb7nmkr3sc9tzzzplCF4TQXp9CG9lOvmDkFf68fBwv7V0Fq+jA8ZxUvLJvNRyiKHcscoNbl/Lp9Xp06NDBZTZy5EiMHDmyUkIREREREZFnMlnseHbZURxJLt6c2qBT47PhzdEojKUUlY01MwGZmyZCtBSXm0q/SIT2+AgqfS0Zk5G3iA+KhkHjg2xL0Y3Y1AolHJIIpXvn35CM3Pob69mzJ1577bXKzkJERERERB4s32LHs7+7llKBPmp8zlKKysGcvBsZG55xKaXUhnoI6/MlSykqsxp+BszpMAg+KjXuqdsSM9oOgEbp1rk3JDO3iqldu3bB4XBUdhYiIiIiIvJQRaXUERxJKS6lgnzUmDc8Hg1ZSlEZFVxYh6zNEyHZC50zTVg8QnvPg9I3TMZk5OlMNgte2rMS5/IynbOmgZH4odv/YWKzblAIPFPKW7lVJ8bGxuLChQuVnYWIiIiIiDxQnrmolEpIzXPOgnzU+Hx4POqH+smYjLxJ/qlfkbtvNiBJzpkuuguCbpsOhUonYzLydOnmfIzfuQxnjBlIyEnFwtvvQZiuqBCv4WeQOR1VlFuV4rPPPovly5fj2LFjlZ2HiIiIiIg8iNFswzP/KaWCfdWYN4KlFJWdJImwJO92KaV86/ZHcNf3WErRDX18bCvOGDMAAKmFeXh135+QrlpL5N3cOmOqXr166N69Ozp27IgnnngC7dq1Q0REBARBKPHcrl27VjgkERERERHdekazDc8sPYLjafnOWbBv0ZlS9UJYSlHZCYICwV3eRsbGcbCmH4J/7P3Qt3oGAi+/ojJ4vnl3nMxNQ2JeFsJ0/ni+eY9S+wfyTm4VU927d4cgCJAkCbNmzbruguBeVERERERE3sdotuHppUdw4qpSKsRXg3kj4lEn2FfGZOStBJUWId1movDSRvjVHyR3HPJwqYV5iPAJAAAEqHX4qONQvHNoA15p0Qvh/86panCrmHr99dfZThIRERERVVFGsw1P/XYEJ9NZSpF7RFsBJHsBlD6hLnOFJoClFN3QD2f349Pj2zC7wyB0CKsNAIjwCcDcjkPkDUY3hVvF1BtvvFHJMYiIiIiIyBPkFtrw1NIjOHVVKRXqp8G84fGozVKKysBhzkbmpgmAw4LQ3vOh0OrljkReQpREzE34Bz+eOwAAeHHPSnzR5W40MvCOjVUZL+glIiIiIiIAQE4ppVSYnwbzR7CUorKx5ychY93jsGWdgC03EZmbJ0G0m+WORV5CgACjrXi9FNht2JqaKGMiuhUqVEwdOHAAL7zwAgYNGoTevXs75xcuXMAvv/yCrKyscn/MLVu24K677kJ0dDQEQcCyZcuu+/xNmzZBEIQS/6WkpJT79yYiIiIiqq6yC6wY+9thl1Iq3F+LeSPiUSuIpRTdmC3nLDLWPQF73iXnzJ5/BY6CVBlTkTcRBAGvtOiNDmG1IAjAxGbd8Eij9nLHopvMrUv5AOCFF17ArFmznLdovHrPKUmScP/992PWrFkYP358uT6uyWRCixYt8Mgjj2DYsGFlft3Jkyeh1xefIhoeHl6u35eIiIiIqLrKLrDiqaVHcCbD5Jz9r5SqGegjYzLyFpb0Q8jaPBmiNc85U/nXQEjPj6DyryFjMvJ0KQVGbEk9h7vrtgQAqBRKvNfuThzMTELniDqyZqNbw60zphYtWoSZM2fizjvvxOHDhzFlyhSXx+vUqYP27dtjxYoV5f7Y/fv3x9tvv42hQ4eW63Xh4eGIjIx0/qdQ8CpFIiIiIqIbySqwYuxvrqVUhL8W81lKURmZr2xD5t/jXEopdVBDhPb5gqUUXdep3HQ8vPVnzDyyCUvPH3HOfVUallLViFvtzWeffYYmTZrgt99+Q7NmzaDRaEo8JzY2FqdPn65wwLJq2bIloqKi0KdPH2zbtu2W/b5ERERERN4qq8CKsUsO42xmcSkVGaDF/JHxiGEpRWVQkLgamVueh+SwOGfa8FYI7fU5lD4hMiYjT5dlKcAT235Fprno/ef9I39je+p5eUORLNy6lO/YsWMYM2YMVKprvzwiIgJpaWluByurqKgozJs3D23btoXFYsFXX32F7t27Y9euXWjdunWpr7FYLLBYit84jUYjAEAURYiieNMz30yiKEKSJK//PMgzcD1RabguqDJxPZG7uHYqLtNUdPne+axC5yxSr8Xnw5ojKkBbrf5suZ7ck3/iR+Qd/NhlpqvRFYGd3wSUmmrx58m1475AtQ7/V78N5p3cDgCoHxCKBgEh1frPsiqtp/J8Dm4VUyqVClar9brPSUpKgr+/vzsfvlwaN26Mxo0bO487d+6Ms2fPYs6cOfj2229Lfc2MGTMwbdq0EvP09HSYzd59xwhRFJGbmwtJkng5I1UY1xOVhuuCKhPXE7mLa6diMgtseGndBVwyFv9jbbifGm91i4LKYkRamlHGdLce11P5SJIEx+nFsCcucZkrY+6AteHTSM/MkSeYDLh2ykeSJIiQoBSK/qwG6GshMSQFSeY8vNqgK0SjCWlG0w0+StVVldZTXl7ejZ/0L7eKqebNm+Pvv/+Gw+GAUqks8XhBQQHWr1+PNm3auPPhK6x9+/bYunXrNR+fMmUKJk6c6Dw2Go2oWbMmwsLCXDZQ90aiKEIQBISFhXn9Qib5cT1RabguqDJxPZG7uHbcl2Gy4vW/jiClQIRapQYAROm1+Hx4c0TpdTKnkwfXU9lJogO5e95F4eVVUKvVzrl/04fg3/xxl5tiVQdcO2UnSiJmHd2MXJsZb7a6A4p/y6k3wgfCIUlQK0p2C9VNVVpPOl3Zv564VUw98sgjeOyxx/Dkk0/ik08+cXnMaDTiscceQ0pKCubOnevOh6+wgwcPIioq6pqPa7VaaLXaEnOFQuH1f/lA0R0Sq8rnQvLjeqLScF1QZeJ6Indx7ZRfer4FY387gos5hcC//UG0Xod5I+KrbSn1P1xPZSNBgmRzPRPC0Ho8/GPvkymR/Lh2bszisOPVfX9ic8pZAECETwDGx3UFACigcK+YqKKqynoqT363i6n169djwYIF+PnnnxEYGAig6Eyl48ePw2QyYfTo0RgxYkS5P3Z+fj7OnDnjPE5MTMTBgwcRHByMWrVqYcqUKbhy5Qq++eYbAMCHH36IunXrIi4uDmazGV999RX+/vtvrF271p1PjYiIiIioSkrLs2Dsb4eLSql/1TDoMG94PCKreSlFZScolAju/BYyNz0Ha/phBHZ8Db51+8kdizzc2bwMbE877zz+8dwBDKzZFA30ofKFIo/hdgX3ww8/YP78+ahbty6uXLkCSZKwd+9e1KpVC59//jkWLlzo1sfdu3cvWrVqhVatWgEAJk6ciFatWuH1118HACQnJ+PixYvO51utVkyaNAnNmzdHt27dcOjQIaxfvx69evVy91MjIiIiIqpS0vIsePI/pVSMQYf5I1qwlKJyE1RaBHf9ACE9P2IpRWXSNDAS01rfAUEAtEoV3m93J0spchIkSZIq+kEKCwuRnZ0NvV5/SzY8r2xGoxEGgwG5ublVYo+ptLQ0hIeHe/2pfyQ/ricqDdcFVSauJ3IX107ZpeVZ8MSSQ7icW3yTn5qBPpg3PB7hASW3t6iOuJ6uzZ53GYJSA6VvuNxRPBLXzrWdzE1DuM4fQVpf52zp+SNoZAhFs6Brb71TnVWl9VSensWtS/ny8/NdCigfHx/4+Pi486GIiIiIiOgmSc2z4Mn/lFK1An3wOUspKgNb9ilkbHwOCq0eYb3nQ6E1yB2JvMT21POYsm8V6gWE4PNOw6H790YLw+o0lzkZeSK3KriIiAg88MAD+OuvvyCKYmVnIiIiIiKiCkoxmkucKcVSisrKknYAGevHQjRnwZ57HpmbJ0G0F974hVTtrbtyChN3L0eh3YaE7BS8sv9PONgb0HW4VUzVr18fP/74IwYOHIjo6GhMmDAB+/btq+xsRERERETkhmSjGU8sOYwrV5VStYN8MG8ESym6scJLm5G5cTxEm6l4KDkAh1W+UOQ1Yg3h0KuL964TJQk20SFjIvJ0bhVThw8fxsGDBzFhwgQolUrMnTsX7du3R9OmTTFjxgyXzcmJiIiIiOjWSco148klh5FkLC6l6gT5Yt6IeIT5s5Si6zOdXYGsrVMgXVVCaSPbI6Tnp7yUj8qkpn8gZncYBK1ShSG1m2Fmu7ucl/IRlcbt3bTi4+Mxc+ZMXL58GWvWrMEDDzyAy5cv45VXXkG9evXQvXt3LFiwoDKzEhERERHRdZRWStUNLiqlQv1YStG1SZKEvIRvkLPrHUAqvuzKp3ZvhHSbCYXa9zqvpuqswG7F6/v/wvm8LOesWVAUvut2P6bE94LSyzfxppuvwitEEAT06dMH33zzDVJTU/Hdd9+hT58+2LZtG5544onKyEhERERERDeQlFu0p1RyXnEpVe/fUirETyNjMvJ0kiTCeGAujIc+c5n7NRqBoM5vQlBy/VDpsiwFeHL7Evx1+QTG71qGTHPx5Z+1/YMhCIKM6chbVGp1abfbYbFYYLFYIIoiJEmqzA9PRERERESluJJbiMd/PYSUPItzVi/YF5+PiEewL0sFujZJtCNn55vIP/GTyzyg+RgY2kyCIPBsF7q2uQlbcCInDQCQXGDEy/tWswegclNV9AM4HA6sXr0a3333HVauXAmz2QyFQoG+ffviwQcfrIyMRERERER0DZdzCvHEksNIyy8upeqH+OGz4c1ZStF1ifZCZG99GeakHcVDQUBg28nwazhcvmDkNSY1647juWk4n5eFIK0vnovryrOkqNzcLqZ27tyJ7777Dr/88gsyMzMhSRJatmyJBx98EPfffz8iIiIqMycREREREf3HpZxCPPmfUqpBqB8+G9YcQSyl6DpEmwmZG5+DNeOIcyYoVAjqPA0+tXrJmIw8XYbZhFCdHwBAr9FhbochmHZgLV5v1QfRvtwgn8rPrWKqYcOGOHfuHCRJQo0aNfD888/jwQcfRFxcXGXnIyIiIiKiUlzMLsCTSw4j3VR897SGoX74lKUUlYGg8oHSJ6T4WO2LkNvfgzaynYypyNMtOX8IHx7dgjkdBqNdWC0AQJSvHvO6jJA5GXkzty4YTklJwahRo7B+/XpcvHgR7777bqmllMViKeXVRERERERUEaWVUo3C/PHZ8HiWUlQmgqBAUOc3oQ1vDYU2EKE9P2EpRdckSRI+O74N7x/eCKvowAt7VuJ0brrcsaiKcOuMqbS0NPj4+Fzz8f3792PBggX46aefkJmZ6XY4IiIiIiJydSGrAE/+dhgZ/y2lhjWHwUctYzLyNoJSg+BuH0AszIRKX0vuOOThUgvznL822a3YmHIWDQ1hMiaiqsKtYqq0UionJwffffcdFixYgMOHD0OSpOuWV0REREREVD7ns4rOlMosKC6lGof547PhzaHXsZSia7Ok7IUqoCaUfq57ASvUflCo/WRKRd5CEAS82rIPMiwm7Em/hGea3oYH67eROxZVERW+K9/69euxYMECLF++HBaLBZIkoVOnTnj44Ydxzz33VEZGIiIiIqJqLzGzAE/+dghZBTbnLDbcH58OYylF11d4YT2yd7wBpX8NhPX5AgotN6imG0s35+OflEQMq9McAKBWKPF+2zuxN/MyukXWlzkdVSVuFVOXLl3CokWLsGjRIly8eNG5CfqVK1cwevRoLFy4sLJzEhERERFVW+cyTRj722GXUqpJuD8+YSlFN2A6tQQ5+2YBkgS78QIyN01ASK9PoVDx6ha6tnN5mRi/cxlSC/OgFAQMrt0MAOCn1rKUokpX5mLKZrNh2bJlWLBgATZs2ACHwwE/Pz888MADGDVqFHr27AmVSgWVqsInYRERERER0b9KK6WaRgTgk6HNEaDj995UOkmSkHd0AfKOfOUyV+lrQVCwzKRry7EU4vGtv8BoK7qZ2YzDGxDu449O4XXkDUZVVpm/kkVHRyMrKwuCIKBHjx4YNWoUhg0bBj8/Xo9MRERERHQznM0oKqWyC4tLqbiIAHzMUoquQ5JE5O6dBdPp31zm/rH3Qt9qHATBrZuzUzURqPXBffVbY/6JHQCA2v5BqOsfLHMqqsrK/NUsMzMTCoUCEyZMwAsvvICwMO6+T0RERER0s5z5t5TKuaqUahZZVEr5a1lKUekkhxXZO6ah8OIGl7m+5VPwb/IgBEGQKRl5OlESofi3tHykYXukFubhQn42Pmh3F/QanczpqCorc1U+evRo+Pj4YPbs2YiJicGgQYPw66+/wmq13vjFRERERERUZqfT8/HkEtdSqnmknqUUXZdoK0Dm5kmupZSgQGCHlxHQdBRLKSqVKIn4MGELpu5fA1ESARTdhe/F5j3xccehLKXopitzMbVw4UIkJydj/vz5aN26NVauXIl7770XEREReOKJJ7B169abmZOIiIiIqFo4lZ6Psb8dQa75qlIqSo+PhzZjKUXX5DDnIPPvp2FJ2eOcCUoNgm+bAb/6g2RMRp7M6rDjtf1/4Yez+7Hmykl8enyb8zGlQgGNku85dPOV6+Jif39/PPbYY9ixYwcSEhLw3HPPQaPR4Msvv0S3bt0gCAJOnjyJCxcu3Ky8RERERERV1qn0fIxdctillIqP1uOToc3gx1KKrsFuSkHG+sdhzTzunCnUfgjpMRc+NbvJmIw83Zm8DGxKPus8/u7sPpzKTZcxEVVHbu9616RJE8yaNQtXrlzBL7/8gr59+0IQBPzzzz+oX78+evXqhW+//bYysxIRERERVVkn0vIwdslhGC1256xFtAEfD2kGXw1LKSqdw5SKjHWPw2686JwpdMEI7f05tOGtZExG3qBpYCSmtuoLANAolJjeZgAaGbifNN1aFf4Kp1KpMGLECIwYMQKXL1/GokWLsHjxYmzcuBGbNm3Cgw8+WBk5iYiIiIiqrBNpeXj6tyMupVTLGgbMHRzHUoquS+EbBk1IHAoL0gAAKv9ohPT4CKqAGJmTkac6a8xAqM4PBo0PAKBvjcbIsRaioT4MrUJqyJyOqqNKvU9oTEwMXnvtNZw9exbr1q3DvffeW5kfnoiIiIioyjmemoen/lNKtWIpRWUkCAoEdZ4GbURbqIMaIrTPFyyl6Jr2pF/EY1t/wcTdK2BxFL/n3F23JUspks1N+0rXq1cv9OrV62Z9eCIiIiIir3csJQ/P/H4EeVeVUq1jDPhwcDP4qJUyJiNvIig1CO76PiA5oNAEyB2HPNTG5DN4Zd9q2EURR7KS8dr+P/Fu24FQCJV6vgpRuXEFEhERERHJICElD08vdS2l2sQEspSi6zKd+R12U3KJuULty1KKrqt+QAj8VBrnscluhcXhkDERUREWU0REREREt9jRZCOeXnoY+dbiUqptzUB8ODiOpRSVSpIkGA/NQ87u95D59zg4zNlyRyIvU8s/CLPaD4JGoUT/mFh82GEIfFRquWMRsZgiIiIiIrqVjiQb8fTSIzBZi89UaFczEHMGxUHHUopKIYkO5OyegbyExQAAe94lZG2eCMlhlTcYeTSLw463Dq7FxfziEjM+OBrfdLsfb7S6A2oF32/IM7CYIiIiIiK6RQ4nGfHM0iMosBWXUu1rBWHOYJZSVDrJYUX2tldQcHaFy9ynVm8ISs01XkXVXa61EE/t+A1/XDyGZ3f+jixLgfOxegEhEARBxnRErlhMERERERHdAoeScvHs766lVIdaQZg9qCm0KpZSVJJozUfmxvEovLSpeCgoENTpdfg3eUCuWOQFPkzYgiNZRXuRJRcY8dLeVZAkSeZURKVjMUVEREREdJMdvJKLcb8fdSmlOtUJxuxBcSylqFQOcxYyNoyFJe2AcyYotQjp+gF86w6QMRl5g+fiuqKWfxAAwKDR4dkmt/EsKfJYLKaIiIiIiG6iA1dyMW5ZyVJq5p1NoVHx23EqyZ5/BRlrx8CWfdo5U2gCENLzI+hqdJExGXmy7Ksu1zNofPBRxyFoHhyFBbfdg+bBUTImI7o+fiUkIiIiIrpJ9l/OwfhlR1F4VSnVmaUUXYct+xQy1j4Oe/4V50zpE4bQ3vOgDWshYzLyZH9cTMCQDYuwL+Oycxbta8BXXe52njlF5Kn41ZCIiIiI6CYorZTqUjcYH7CUomuwpB1AxvqxcJgznTNVQE2E9pkPdWB9GZORp5IkCV+d2oW3Dq5Dod2G5/eswFljhvNxXr5H3oBfEYmIiIiIKtneS0WllNkuOme31wvB+wNZSlHpbNmnkLlxPESbyTlTB8citM98qPyjZUxGnkyChAt5Wc7jfJsVf10+IWMiovLjV0UiIiIiokq052IOnltespR6b2ATllJ0TarABtDVuN15rI1sh9Ben0GpC5YxFXk6haDAay37oE1oDADgscYd8VQT7kNG3kUldwAiIiIioqpi98VsTFieAKujuJTqWi8E7w5sArWSpRRdmyAoENRpKkSrEQq1P4I6T4Og1MgdizxQlqUAW1MTMahWHABAo1Thg3Z3YWf6BfSObiRzOqLyYzFFRERERFQJSiulutcPxTsDYllKUZkISg2Cu34AQamBIHDNUEmX8nMwbtfvuGLKhQDgrn/LKX+1lqUUeS2+2xERERERVdDOCyVLqR4NQjGDpRSVQhLtyN0/F/b8pBKPKVQ6llJUqlxrIR7d9jOumHIBAO8cWo+daRdkTkVUcXzHIyIiIiKqgB3nszBphWsp1bNBKN7pHwsVSyn6D9FuRtY/LyH/xI/I3DgeDnPWjV9EBMCg8cGIOi2cx9G+BtT0C5QvEFEl4VdKIiIiIiI3bT+fhcl/HHMppXo1DMN0llJUCtFiRObGcTBf2QoAsOddQtaWFyFJkszJyJOJUvH7y5hGHTCoVhyaBUViwW33oIafQcZkRJWDe0wREREREblhW2IWnl95DLarSqk+jcLwVr9YKBWCjMnIEzkK0pG5cTxsueecM0Hlg4Dmj0EQuF6oJEmSMO/kDqQUGPFGqzsgCAIEQcCU+F6wiQ7oVGq5IxJVChZTRERERETltDUxEy+sPM5SisrEbryIjI3j4DClOGcKrQEh3WdDExInYzLyVHbRgemH1mPVpeMAgAifADzVpAsAQKlQQKngGZlUdXA1ExERERGVw5azmXj+D9czpe5oHM5SikplzTqO9PVPuJRSSt8IhPaez1KKrumUMR1rrpx0Hi8+vQcnc9NkTER087CYIiIiIiIqoy1nM/HiqmOwi8V7AvWLDcebdzRmKUUlWFL2ImPD0xDN2c6ZylAHYX2/hNpQR75g5PGaBkbi9ZZ9AQBKQcAbre5AY0O4zKmIbg5eykdEREREVAabz2bgpVXHXUqp/rHheKNvYyhYStF/FF78G9nbp0ISbc6ZJrQZQrrNgkLLDauppAv5WQjS+EKv0QEA+sXEIttaiPoBIWgfVkvmdEQ3D4spIiIiIqIb2HgmA1NWHYfjqrunDWgSgal9GrGUohJMp5ciZ+8HwFXrRRfdCUG3vQOFykfGZOSpDmZeweTdK1BPH4pPOg6FRln0o/p99VrJnIzo5uOlfERERERE1/H36ZKl1J1NWUpR6cxJO5Gz532XUsq3zh0I7voBSykq1dbURDyzYymMNgsOZl7B6/v/giiJN34hURXBYoqIiIiI6Bo2nE7HlNWupdRdTSPxWm+WUlQ6bVR7+NTu4zz2b3wPAjtNhaDgxSpUupp+BuiUxesjy1oIs8MuYyKiW4vFFBERERFRKdafSsfLq09AvKqUGhQXiVd7N2QpRdckCAoEdXwduqiO0LcYC33r5yAI/LGLrq22fzBmdRgMjUKJXtEN8UnHofBVaeSORXTLsLYnIiIiIvqPtSfT8NpfJ11KqcHNIvFyT5ZSdGOCUo3g7rNZSFGprA47PkzYgvvqtUZN/0AAQIvgaCy8/V400IdAwXVD1QxXPBERERHRVUorpYY2j2IpRSU4zDnI3PIC7PlJJR5jKUWlybOZMX7XMiw5fxjjdv2OLEuB87FGhjCWUlQtcdUTEREREf3rrxMlS6lhzaPwUo8GLKXIhd2Ugoz1j8N8eQsyN46Dw5wldyTyAh8mbMG+jMsAgCumXLy0dxWkq95viKojFlNERERERAD+PJ6KqWtcS6nh8VF4qSdLKXJlyz2HjHWPw268CACw511Gzs63ZE5F3uDZJrejpl8gACBArcWTjTtBEPj+QkUksxFBuqL/rU5YTBERERFRtbf6eCreWHvKpZQa2SIaL/ZowB8ayYU14wgy1j0JR0Gac6byj4KhzUQZU5EnM1rNzl8Han3wUcehaBIYgS9vuxutQ2NkTEaeRrJbceHDIZDsVrmj3FIspoiIiIioWlt1LBVv/OdMqbtbROP57vVZSpELc9IOZPz9LERr8dkM6sAGCO3zJVQBNWVMRp5q7ZWTGLx+IQ5kXnHOavgZsPj2e1EvIETGZOSxRIfcCW45FlNEREREVG2tPJaKaWtP4uodXu5tWQOTWUrRfxScX4OszZMh2YvPftGEt0Ro78+h9AmVMRl5IkmS8O2ZfXh1358w2a2YvHsFzuVlOh/n+wv9j6MgF3ZjOmzZV2BNOwdJFGHPTYXdmA67MR2Ogly5I950HldMbdmyBXfddReio6MhCAKWLVt2w9ds2rQJrVu3hlarRYMGDbB48eKbnpOIiIiIvNuKhBS8+Z9S6r5WNTCxWz3+0Egu8k/+jOztUyFJxWcy6GrcjtDuc6HQBMiYjDzZidxU56/zbBb8cTFBxjTkqSS7FRdm9sPZV+JxYeYdkPLTkbTwMVyaOxiXPh5WLS7rU8kd4L9MJhNatGiBRx55BMOGDbvh8xMTEzFw4EA8+eST+P7777FhwwY89thjiIqKwh133HELEhMRERGRt1l+NAXT158qUUpN6MpSiopJkoS8w18gL2GRy9y33kAEtn8ZgkIpUzLydIIgYGrLvsgwm3Ag8wpGNWiLp5p0ljsWeSBBpUGtiSvhyM+EPTcFFz8ahqjxS6EOqe18vKrzuGKqf//+6N+/f5mfP2/ePNStWxezZs0CADRp0gRbt27FnDlzWEwRERERUQnLjiZj+vrTLrMHWsdg/O11WUqRkySJyN3zPkxnlrnM/Zv8H/Qtn+ZaoRKMVjO2piZiQM0mAACNUoWZ7e/CPynFM6L/UvoaoPQ1QCwwQmWIgEJngDqkFlT6MLmj3TIeV0yV144dO9C7d2+X2R133IHnnntOnkBERERE5LGWHknGjA2updSDbWLw7G0spchVwblVJUopfatnENDk/+QJRB4tqSAXk/b8gfN5WQDgLKIC1DqWUlQqSRQhKIp3V9JGN4YtJxmKoBoyppKH1xdTKSkpiIiIcJlFRETAaDSisLAQPj4+JV5jsVhgsVicx0Zj0V01RFGEKIo3N/BNJooiJEny+s+DPAPXE5WG64IqE9cTucudtfP7kWS8+/dZl9mDbWvg6c61IUkSJEm6xiupqittPenq9IcueRfMF9cDggKG9i/Dt+4Avl8RAMBos8AmOiABEP11yLWZ8UqL3sixFuLFPSsRrPFB+7BacsckD5W74wcUnvoH4fd/CIVa65wLah/UHr8MUGq8/r2mPPm9vphyx4wZMzBt2rQS8/T0dJjN5lJe4T1EUURubi4kSYJC4XF725OX4Xqi0nBdUGXieiJ3lXftrDyZhU92J7vM7mkWipENfJGenn6zYpKXuNZ6kuo9ATE/F4ro3sj3a4v8tDQZU5JHCfDB4LULYJdEOBwO5DgsyLNZsXHAUwhSaqEqsCCN64X+Q5IkWPb8AMvObwAAhYufhe+dUyEo1QCK34sMBgMUed7dTeTl5ZX5uV5fTEVGRiI1NdVllpqaCr1eX+rZUgAwZcoUTJw40XlsNBpRs2ZNhIWFQa/X39S8N5soihAEAWFhYfwGnyqM64lKw3VBlYnridxVnrXz66EkzN+fAbVK7ZyNbheDJzvV5uV7BMmaC0lVdGe98PBwCPY8CBpD8RMi58qUjDxZpqUAgkoBhShAlCSEaQIgCXlQK5X4uvsDCNH5yR2RPIwkSche/wkK9/0Itfrfr0dJB+GfdQx+cX0AVK3vi3Q6XZmf6/XFVKdOnbB69WqX2bp169CpU6drvkar1UKr1ZaYKxQKr//LB4ruAFFVPheSH9cTlYbrgioT1xO5qyxr55eDSZi5+RxwVf/0SPtaLKUIAGDPT0JewmL4x94PbcpfkPwHIO/4dwiIGw2Vf7Tc8chDSZIEk92KlII8hPn4Fw0FIMpXjxCtL8J8AuQNSB7JdOxvGLd/6zIL7jseAc1db9pWVb4vKk9+j/tM8/PzcfDgQRw8eBAAkJiYiIMHD+LixYsAis52GjVqlPP5Tz75JM6dO4cXXngBJ06cwGeffYZffvkFEyZMkCM+EREREXmInw5cwQebzrjMHuvAUoqKOMw5yEtYDNPJn5Gx7nHoAmsiY93jKDi9FHkJi+Ew58gdkTyQKImYm7AFRpsZOVYz0s0m52MCBL630DX5NumBgNZDnMehd76EwC4PyhfIg3jcGVN79+5Fjx49nMf/u+TuoYcewuLFi5GcnOwsqQCgbt26WLVqFSZMmIC5c+ciJiYGX331Fe64444SH5uIiIiIqocf9l/GnC3nXGZjOtbG4x1ry5SIPI1SFwj/xvfAfGkjLCm7kbbqfgiCAF3N7giIGw2lLlDuiOSBThszsOT8IfSMbgiVoECOpRD+PipoBTWUgsed90EeRBAEhN71MiSHDT712iOg5UC5I3kMQeLtR2A0GmEwGJCbm1sl9phKS0tDeHi415/6R/LjeqLScF1QZeJ6Inddb+2wlKKyKDi/FtbU/dCExSNt9f1FG59r/BA5ZCW0Ea3ljkcebPWl4/BXaxCk8UGAWgedQgmlQgkIgEahhEFT+l7HVL2INgssVxLgU6fs7ydV6fui8vQs3v2ZEhERERFd5bt9JUupJzqxlCJXplNLkH/qF+hqdkfuvtlFQ7UfVP41kXvwU9jzk+QNSB4ly1KAfJvFeTygZhNcMuUizZyPWn6BQF5h0d5SOn+WUgQAEC0FSPluHFK+eQoFp7fLHcfjsZgiIiIioirh232XMPcf11JqbOc6eKwDSykqIkkS8o4uQs7emfBvfC/yT3wPS+pe6Gp2R8TgFVCHNoMtM4F7TJHTpfwcPPLPz5i8+w9YHXbn/IH6rdErupGMychTOQqNSP7mKZjP74PksCP1p8koTNwndyyPxmKKiIiIiLzeN3sv4aN/El1mT3Wug0fa15IpEXkaSRJhPDAXxsPzAQD5J36Ef+wDCGjxJEJ7fQabb0MEd3kLvvUHcY8pAgAcz0nFo9t+RlJBLvZnXsbr+/+CKIlyxyIP5sjPQvLiJ2G5fNQ5E1RaCGqtjKk8H4spIiIiIvJKSqUSALB4zyV8vNW1lHqmS108zFKK/iWJDuTsfBv5J35yzmyZCXCYsxDY7kUofCNRWFgIhW8k9C2egso/Wsa05Ck0CiUcYnERddqYgRyrWcZE5MnsxjQkLRoDa8op50zpF4Soh+dDF9NMxmSej8UUEREREXmVfIsdKXlWpNjUOJ1RAIUAROt1zsefva0uHmpXU8aE5EkkhxVZW6egIHF18VAQENj+RfjVGwClLsjl+TxTiv6nvj4UM9sPgkahRFxQJBbcdg+Ctb5yxyIPZMu6gqQFj8GWccE5U+nDEfXIV9BG8pLPG1HJHYCIiIiIqKzS8y34YucFrDuZjnOZ+bCJAoY1j8QLPRrg/Y1nMKJFFB5sw1KKipnOLof58hbnsaBQIbDTVPjW7iNjKvJUS84fQufwOoj2NQAAWoXUwCedhqGxIRw+KrXM6cgTWdMTkfz1U3DkpTtnqqAaiHroc6iDePZlWbCYIiIiIiKvkG+x44udF7DsaArS863ILLBDEAR8vfcyAOCNOxqjVQ2DzCnJ0/g1HA5bZgIKEv+CoNIh+LZ3oYvuKHcs8jCiJOLjY1vx/dn9qOkXiK9uuxtB/54d1TKkhszpyFM5Co1IXvQ4HKZs50wdVhdRoz6DSh8mYzLvwkv5iIiIiMgr5FnsWHcyHal5FmSYrC6PbU3MRGQAN5elkgRBgcAOr8K3bj+E9viIpRSV6qtTu/H92f0AgEumHDy3azlsokPmVOTplD56GDo/6DzWRDVG9MNfsJQqJ54xRUREREReIbvAihPp+ci3uP6wGBGghUalRK7Zhqir9pqi6kmSRAiC67+/CwoVgjq9IU8g8goj6sTjz8vHccWUC0EABsQ0gVqhlDsWeYHA20ZBshWi8OwuRDwwF0qfALkjeR2eMUVEREREHi/FaIbVIUGA4DKPCNAg2FcNP40SBh33f6nurFknkP7nKNjzLssdhbyAKBXfcS9Y64uPOw5FuI8/3m49APfUaylfMPJotuykErPA7o8javQ8llJuYjFFRERERB7tcJIRD/10AEeSjRjWPBIAIAhAVIAawT4aAEDfRmEI0PJigOrMknYAGRuegi3nDDI3PgtHQfqNX0TV1mVTDh7Y/AMOZxWXDDF+gVjaczT61OBd1Kh0+QnrcfnjYTDuWeIyFwQBgkojUyrvx2KKiIiIiDzW6uOpePK3w8gqsGHJ4WTc3zoGj7SviaYR/vDXKOGnUWJos0g83rE2/FlMVVuFl/9B5sbxkGwFAAB7fjKMh+fLnIo81fGcVDy69RecNWZg4q7lSMzLdD6mUfJ9hEqXd+APpP36MiSHHRkr30XewVVyR6oy+P86IiIiIvI4oihh3o4LWLTnonOWZDRj6ZFkvN6nEawOCVkmM4L9dNDrVCylqrGCxD+RvfMt4KrLsrRRHWBoO0nGVOTJfjp3ANmWohLTaLNg5tFN+LTTcJlTkSfL3fULMle/7zIzXzyIgJYDZUpUtfArOBERERF5lEKbA1PXnMTGMxku896NwvBG30bQqpQQRRFqax5CAvRQKHgRQHWVf+In5O7/0GXmU6sngjq9AUHJy2qodK+06I3kwjwczLyCJoEReLt1f7kjkQcTbRYYd//iMtN3uAch/Vh+VxZ+FSciIiIij5GWZ8Hjvx4qUUqN6Vgb7/SPhVZVfJcsh4O3cq+uJEmC8fCXJUopvwaDEdTlbZZSVMKJnDTnrzVKFWa2uwt3122BzzsPR5DWV8Zk5OkUai2iHvoMqsBoAEBg10cQ0n8yBP6jSKXhGVNERERE5BGOpeRh0h8JyDBZnTONUoGpfRuhb+NwGZORJ5EkEbn7ZsN0ynXz4YCmoxDQYiwEQbjGK6k6EiURnxzbhu/O7sObrfuhX0wsAECv0WFy8x4ypyNvodKHI+qhz1BwaisMHe+VO06Vw4qPiIiIiGS37lQ6xvx6yKWUCvHVYP6IeJZS5CSJdmTveKNEKaVv9Qz0LZ9iKUUuREnEtANr8d3ZfQCANw+uxa70CzKnIk8niSKyN86HIz/LZa4OjmEpdZOwmCIiIiIi2UiShC93XsDLq4/D6ijevLpRmD++vq8VmkXpZUxHnkS0m5G15UUUnl9bPBQUCOzwMgKa/J98wchjKQQFwnT+zmO7KOJifo58gcjjSQ470n57FdmbvkTyN0/BUWiUO1K1wGKKiIiIiGRhsTvwyp8n8MVO1zMYutUPwZcj4xERoJUpGXmignN/wJy0zXksKNQIvm06/OoPkjEVebqnm3TBgJpNoFIo8Hab/hhZt4XckchDSXYrUn9+AaajReW3NfUMUn+YAEkUb/BKqijuMUVEREREt1yGyYLJK44hITXPZT66XU2M7VQHCgUvySJXfg2Hw5Z5HAWJqyGofBDS9X1oI9vJHYs8zBVTLlZdPo4xjTpAEAQIgoBXW/TG3XVboGlgpNzxyEOJ1kKk/jgJhed2O2eCSgNDl4e4yfktwGKKiIiIiG6pU+n5mLA8AWn5FudMrVTglV4NMbBphIzJyJMJggKBHV4BBAF+DYZCE9pM7kjkYU7mpmHczmXIthQAAB5v3BEAoFIoWUrRNYnmfKR8Px7mi4ecM0GtQ+R9s+FTv72MyaoPFlNEREREdMtsOpOB1/46AbO9+NKIIB81PrirKVpEG2RMRp5GkkQIguuZCoJCiaCOr8mUiDxZns2Mp7f/BqOtqPD+6uROxPgaMKBmE5mTkSdzmHKQ8u0zsCSfcM4UWj9E/t9H0NXiZZ+3Cs9JIyIiIqKbTpIkfL3nEl5YecyllKoX7IvF97ZiKUUurJnHkL76AdiNF+WOQl4iQK3DuLiuzuMmgRHoGF5bxkTk6ex5GUhe9LhrKeVrQNTo+SylbjGeMUVEREREN5XVLuKdDaex6niqy7xznWC80z8Wflp+S0rFLCl7kfnPC5BsBcjYOA5hfb6A0jdc7ljkoWyiA2qFEgAwqFYcMi0mHMi8gnfbDoSvSiNzOvJkWes/gTX9nPNYGRCKqFGfQRNeT8ZU1RPPmCIiIiKimya7wIqxSw+XKKXua1UDcwbFsZQiF4WXNiNz0wRItqI9ghymFBgPfyFzKvJEkiThk2Nb8dyuZbA67M756AbtMKf9YJZSdEOhA16AtkYcAEBliET0w1+ylJIJiykiIiIiuinOZpgw+qeDOJxkdM6UgoCXezXExG71eec9cmE6+weytk6BJNqcM110JxjaTpYxFXkiu+jAtINr8c2ZvdiTfgnTDq6FKBVdIiwIApS8ixqVgULri8gHP4Zf056IfnQB1CE15Y5UbfH/sURERERU6bYlZuGRnw8iyWh2zvRaFT4d3hxDm0fJmIw8Uf6JH5CzazogFe8/5lOnL4K7fgCFSidjMvJESQVGbEk56zxed+UUDmQmyZiIvIHlyjFY0865zJQ+ekTc8z5UBt4RVk4spoiIiIio0kiShB/2X8bEFQkosDmc89pBPlh8Xyu0iQmULxx5HEmSYDw0D7n7P3KZ+zUagaBOb0BQ8FJPKqmWfxBmthsEtUIJlUKBt9r0R5vQGLljkQcrPL8fyYufRPI3T8GWdVnuOPQffKcnIiIiokphc4h4f+MZLDua4jJvXysI7w5oggAdv/WkYpIkInfPBzCd+d1lHtDsEQQ0HwNB4KWeVCypIBcCBET56gEArUNj8FbrfvBXa9E+rJbM6ciTFZzZgdQfJ0OyWwBrAZK/HovoRxdApedNFTwFvzsgIiIiogozmm14fuUx7L+c6zIfER+Nyd3rQ8n9pOgqksOG7J3TUHhhvcvc0Ho8/GPvkykVeapTuekYt/N3+Ku1WHDb3TBofAAAPaMbypyMPJ3p+Eak/ToF0lUb5GvC6kKh08uYiv6Ll/IRERERUYVcyCrA6J8OupRSCkHA890b4MWeDVhKkQvRbkbWPy+4llKCAkGdXmcpRSUczkrC49t+QZalABfzszFh13IU2m03fiERAEgSJLF47zq/Jj0Qce9MKDTcu86TsJgiIiIiIrftvpiN0T8dxKWcQufMX6PC3CHNcHfLaBmTkacqOPcHzEk7nMeCUoPg29+Db90BMqYiT1XD1+A8QwoA7JIIs4PFFJWNX9OeCBv6BiAI8G8xAOF3vwtBpZE7Fv0HL+UjIiIiIrcsOZSEDzadhShJzlmMQYfZg5qhboivjMnIk/k1HAFb1kkUnFsJQe2LkK4zoY1oLXcs8lAhOj983HEoHtv6CxobwvBeuzvhy2KBrkOSJJc96gJaDIDKEAldrZYQFDw3xxOxmCIiIiKicnGIEmZtPotfD7nenr1VDQM+uLMpDD5qmZKRNxAEAYHtpwCCAn4Nh0IT3ETuSORBJEnC/JM7cFtEXTQLigJQdBe+r267G1G+eqgVSpkTkqeSJAk5m7+CaM5D8B0TXMopnzosvz0ZiykiIiIiKrM8sx1TVh/HrovZLvNBcZF4qWcDqJX812hyJUkiBMF1XQgKJYI6vCxTIvJUdtGB6YfWY9Wl4/jt/GF8ddvdqO0fDKConCK6FkmSkLXuI+Ru+xYAIKh9ENxrrMypqKz4nQMRERERlcmlnEI88vNBl1JKADChaz282rshSykqwZp+GGmr7oXdeFHuKOQFvj27D6suHQcA5FrNGLdzGSxX3U2NqDSSKCJz5bvOUgoAcrYsgPlygoypqDz43QMRERER3dD+yzkY/eMBnM8ucM581UrMHhyH+1vHuFwyQQQA5uRdyNg4DnbjRWRsHAeHKVXuSOTh7q/XGs2Diy7fUwoCnoztBK2SF/nQtUmiA+nL3oBx72/FQ0FA6J1ToIuJky8YlQuLKSIiIiK6ruVHU/D00iMwWorPXIgK0GHBPS1xW90QGZORpyq8uAFZmydBspsBAA5TCoxHF8icijxRob34DntapQpz2g9G08AIzOkwBP1juP8YXZtktyHt1ynIP7S6eCgoEDZ0GvTthssXjMqN9TMRERERlUoUJXy0NRHf77/sMm8epcfMu5oi2Jd3xqKSTGeWIWfPe8BVd2vU1bgdgW0myZiKPNGp3HRM2LUcz8V1RZ8ajQAAeo0Oi26/l2dh0nWJNgvSfn4eBae3O2eCUoXwkTPg16SHjMnIHSymiIiIiKiEAqsdr/x5AlsTs1zm/WPD8WrvRtCoeOI9lZR37BsYD37mMvOt2x+BHV6FwLup0VX2ZVzG5N0rYLJb8caBNQjU6NAurBYAsJSi6xItBUj5/jmYL+x3zgSVBhH3zoRvw84yJiN3sZgiIiIiIhfJRjMmrkjAmQyTy/ypznUwul1N/tBIJUiSBOPBT5F//DuXuX/je6BvPb7EXfmIdqadh8luBQDYRAcWnNqNtqF8f6Ebs2VfhjX5hPNYofFFxAMfwqdOaxlTUUXwKwQREREROR1OMmL0TwdcSimdSoH3BjbFw+1r8YdGKkGSROTsfqdEKaWPfxz61s+xlKJSjW3SGf1iYgEA7cJqYnaHQXx/oTLRRjZCxAMfQlBpoPDRI/Khz1lKeTmeMUVEREREAIDVx1Px9vrTsDlE5yzcX4vZg+LQONxfxmTkqSSHFdnbX0fhpU0uc0PbSfBvNFKWTOSZJEnCP6nncHtEPQiCAIWgwGst+6CBPhT31WsFNS/1pHLwqdMaEffNgtI/FNrIhnLHoQriP18QERERVXOiKOGzbecxdc1Jl1KqaUQAvr6vJUspKpVoK0Dm5kkupZQgKBHUeRpLKXLhEEW8fWgdJu/+AwtP73bO1QolRjVoy1KKrsuWdQUZq96D5LC7zH0bdGIpVUXwjCkiIiKiaqzQ5sDUNSex8UyGy7xPozBM7dsIWhV/YKTSFSSugiVlj/NYUGoQfNsM6Gp0kTEVeRpJkvDi3pXYknIOADD/xA6EaP0wpHYzmZORN7CmJyL566fgyEuHWGhE2LC3ICh4fk1Vw79RIiIiomoqLc+CMb8cKlFKjelYG9P7x7KUouvyazgCvvXvAgAo1H4I6TGXpRSVIAgCbouo5zxWCgI0PEOKysCSfBLJC8fAkZcOAMg/sgY5WxbInIpuBp4xRURERFQNHUvJw6Q/EpBhsjpnGqUCU/s2Qt/G4TImI28hCAIC20+BICjh22AoNMGN5Y5EHmpI7WbItJjwzZm9eLftQHQKryN3JPJw1owLSF78BERzvnOmiWoMfTteJlwVsZgiIiIiqmbWnUrHG2tOwnrVflIhvhrMGhSHuMgAGZORJ5NEB4T/nOkiCAoEtn9JpkTkqc4YM/D92X14uUVv5/5RjzRsj/4xsYj2NcicjryBOjgGPvXaw3TsbwCArmY8Ih6YC6UPv0ZVRSymiIiIiKoJSZLw1a6L+GLnBZd5ozB/zB4Uh4gArUzJyNNZ0g4iZ9fbCO46E2pDHbnjkAfbn3EZk/esQL7NCock4Y1WfaEQFBAEgaUUlZmgUCJ8+HSk2iZBstsQcd8sKLS+cseim4TFFBEREVE1YLE7MG3tKaw7le4y714/FG/2awwfNfd8odKZk7Yj658pkBwWZG4ch9A+X0DlFyl3LPJAhXYbXtq3Cvm2okuE/7p8ArGGcNxfv7XMycgbOEw5UPoFOo8FlRrh93wAQRAgqDTyBaObjpufExEREVVxGSYLnvj1cIlSanS7mnhvYBOWUnRNBefXImvz85AcFgCAoyAN+QmLZE5FnspHpcY7bQY4L99rF1YTg2vFyZyKvEHe/hW49OEgmC8cdJkr1FqWUtUAiykiIiKiKuxUej4e+vEgElLznDO1UoFpdzTG013qQqEQZExHnsx0agmyd0yFJDmcM5+a3WFoM0nGVORpJEmC0Wp2HrcNrYk3Wt2BO2o0xpz2g+Gn5iXCdH25u35B+vI3IVoLkPL9eFiSjssdiW4xXspHREREVEVtOpOB1/46AbO9eJPzIB81PrirKVpEc68XKp0kSchPWAzj4fkuc996dxbdhU/BM+yoiEMU8c7h9TiSnYKvutwNvUYHAOhToxH61GgkczryBjlbFiFrw6fOY9Figun4Rmijm8iYim41FlNEREREVYwkSfhm72V8ui0R0lXz+iF+mD0oDtEGnWzZyLNJkgjjgY+Qf+Inl7l/7P3Qt3oWgsAz7KiI2W7Dy/tWY2tqIgBg4u4V+LTTMGiV/BGTbkySJGT//Tlytix0mQd2fQRBPcfKlIrkwncNIiIioirEahcxfcNprD6e6jLvUjcY0/vFwk/Lb/+odJLoQM6u6ShIXO0y17cYC/+mo1hKkQuT3YozeRnO46PZyTiUlYT2YbVkTEXeQBJFZP41G8ZdrgV4cO9nEHj7aHlCkay4xxQRERFRFZFdYMXYpYdLlFL3t66B2XfFsZSia5IcVmRtneJaSgkCAtu/iIC4h1hKUQkhOj983HEoDBoddEoVZrcfzFKKbkgSRWSseLtEKRUy4AWWUtUYvzshIiIiqgLOZpgwcUUCkozFmxArBQEv9WqAIc2iZExGnk60mZC15QVYUvc5Z4JChcBOU+Fbu4+MycjTnDFmwCra0TQwEgBQ2z8YczoMhgABcUGRMqcjTyeJItJ+exWmo2uLh4ICYYNeRUDrQfIFI9l57BlTn376KerUqQOdTocOHTpg9+7d13zu4sWLIQiCy386HfdOICIiouphW2IWHvn5oEsppdeq8Onw5iyl6IYKEle7llIqHYK7zmQpRS72Z1zG49t+wXO7luNifrZz3iwoiqUUlY0gQGW4aq0olAgfMZ2lFHlmMfXzzz9j4sSJmDp1Kvbv348WLVrgjjvuQFpa2jVfo9frkZyc7PzvwoULtzAxERER0a0nSRJ+2H8ZE1ckoMDmcM7rBPli8X2t0CYmUL5w5DX8Go6AX4MhAACFJgChPT6CLrqjvKHIoxzIvIJxO39Hvs2KHEshnt35OzLNJrljkZcRBAHBfZ6Fvv1ICCoNIu6dCf9mLMDJQy/lmz17NsaMGYOHH34YADBv3jysWrUKCxcuxEsvvVTqawRBQGQkm3oiIiKqHmwOEe9vPINlR1Nc5u1rBeHdAU0QoPPIb/PIAwmCAEO7FwBBCb8GQ6AOaih3JPIwTQzhaGQIw9HsovebGr4G3n2PykQSRQiK4vNhBEFASP/noW87HJqIBjImI0/icWdMWa1W7Nu3D71793bOFAoFevfujR07dlzzdfn5+ahduzZq1qyJwYMHIyEh4VbEJSIiIrrlcgtteOb3IyVKqZEtovHRkGYspei6JNFeYiYICgS2e56lFJVKp1JjTofBqO0fhN7RjfBhh8HwV2vljkUezmHKQdKXo5F/ZK3LXFAoWEqRC4/7riUjIwMOhwMREREu84iICJw4caLU1zRu3BgLFy5EfHw8cnNzMXPmTHTu3BkJCQmIiYkp8XyLxQKLxeI8NhqNAABRFCGKYiV+NreeKIqQJMnrPw/yDFxPVBquC6pMXE/ldz6rAJP+OIbLOcX7SQkKYFLXehjZIhqABFGU5At4i3DtuMeSdgC5u6YjqOsHUBvqyh3HY3A9uXKIIj44ugntw2qiZ1RRWRmg0mJep+EwaHRQCAr+Wf2La6d0dmM6Ur97Brb0RKQtfQ1QaeDbuKvcsTxeVVpP5fkcPK6YckenTp3QqVMn53Hnzp3RpEkTzJ8/H2+99VaJ58+YMQPTpk0rMU9PT4fZbC4x9yaiKCI3NxeSJEGh8LgT4sjLcD1RabguqDJxPZXP/uR8TN98Gaar9pPyUyvx8u0xaBOluu5+nFUN1075OdJ2wX7oXUiiFSlrn4Km/fsQfCJu/MJqgOupmMVhx4xT/2BX1mX8nngI7zTtjeaG4nWSgXwZ03kerp2SRGMqTL+/CDE3+d+JDcnLZ8D/wfoQlGpZs3m6qrSe8vLyyvxcjyumQkNDoVQqkZqa6jJPTU0t8x5SarUarVq1wpkzZ0p9fMqUKZg4caLz2Gg0ombNmggLC4Ner3c/vAcQRRGCICAsLMzrFzLJj+uJSsN1QZWJ66nslhxOxswtSZAkBdSqoj+rGoE6zL6rKeoE+8qc7tbj2imfwvN/ISfhPaiUEqBUA45c6NJWw9DuRbmjeQSup2K/JB7EvrwUqNRFPyq+d24HlvUaDV+VRuZknolrx5UkSUj5YwqUBRlQqotKKKUhEpGjPoM6uIbM6TxfVVpPOp2uzM/1uGJKo9GgTZs22LBhA4YMGQKg6C9nw4YNeOaZZ8r0MRwOB44cOYIBAwaU+rhWq4VWW/KaaIVC4fV/+UDRhnJV5XMh+XE9UWm4LqgycT1dn0OUMGvzWfx6KKloIBT9T6saBnxwZ1MYfKrvvz5z7ZRN/smfkbtvjsvMp1ZPBLad5LIpcXXH9VTk7notcSQnBeuunIJOqcKbrfvBX1P2HzCrI64dV+HDpiFpwWNwmLKgDqmFqIc+h8rAszPLqqqsp/Lk97hiCgAmTpyIhx56CG3btkX79u3x4YcfwmQyOe/SN2rUKNSoUQMzZswAALz55pvo2LEjGjRogJycHHzwwQe4cOECHnvsMTk/DSIiIqIKyTPbMWX1cey6mO0yH9wsEi/2aAC10ru/aaWbS5Ik5B35CnlHF7jM/RoMhqHdixAErh8qkm7OR6jWr+gHYkGBqS37AgDuq9cKzYKiZE5H3qaojPoMGas/QMTIGVD6B8sdiTycRxZT99xzD9LT0/H6668jJSUFLVu2xF9//eXcEP3ixYsu7Vt2djbGjBmDlJQUBAUFoU2bNti+fTuaNm0q16dAREREVCGXcgoxcXkCzmcXOGcCgOe61sN9rWpAEAT5wpHHkyQRuftmw3Rqics8oOkoBLQYy/VDTgczr2DS7hV4sEFbjG7YDgCgUaowvU3pV58Q/Zf58lFoQutAofN3zjQRDRA1eh7fa6hMBEmSqv5tW27AaDTCYDAgNze3SuwxlZaWhvDwcK8/9Y/kx/VEpeG6oMrE9VS6/Zdz8Pwfx2C02J0zX7US0wfE4ra6ITIm8xxcO9cmiXbk7HwLBefXuMz1LZ9GQNMHZUrl2arretqUfAav7vsTVrHohgqvteyDu2rFyZzKu1TXtfM/BWd2IPXHydBGNUbkqE+h0PjIHcmrVaX1VJ6exbs/UyIiIqIqZvnRFDy99IhLKRUVoMPCe1qylKIbkuwWZG150bWUEhQIbD+FpRSVkGUpdJZSALDmyknwvAUqK9PxjUj9YQIkuwXmS4eR+uMkSHar3LHIC3nkpXxERERE1Y0oSvhoayK+33/ZZd48So+ZdzVFsC/viEXXJ1rzkbllMqxpB50zQaFGUJc34VOzh3zByGMNq9McGRYTvjq5E72iG2Jaqzt46RWVSd6h1Uj//Q1AEp0zhdZPvkDk1VhMEREREcmswGrHK3+ewNbELJd5/9hwvNq7ETQqnuRON1Zw/k/XUkrlg5Cu70Mb2U6+UORRHKKI3y4cxrDazaFSKAEAYxp1QB3/IPSObggFN8SnMjDuXYqMlTOAq86u828xAGFDpkL4d10RlQeLKSIiIiIZJeWaMXFFAs5mmlzmT3epi4faxvDsBSozv4YjYM85B9OZ36HQ6BHSfTY0oc3kjkUewuKw45V9q7El5RyO56Ti9ZZ9IQgCBEFA3xqN5Y5HXiJn23fIWvuhy0zfdjhCBr4Iwcv3RCL5sJgiIiIiksnhJCMm/5GA7EKbc6ZTKfBmv1j0aBAqYzLyRoIgwNDueQhKNXwbDIHaUE/uSORBXtizEjvSzgMAVl06jjCdP55q0kXeUOQ1JElCzqYvkb3pC5e5ocuDCO4zjv+IQhXCYoqIiIhIBquPp+Lt9adhcxTvzxHur8XsQXFoHO5/nVcSFZEcNghKtctMEBQwtJkoUyLyZPfXb4U9GRdhF0VolSrEB0fLHYm8iHH3LyVKqaAeTyCw22MspajCeK4dERER0S0kihI+3ZaIqWtOupRScREB+Pq+liylqEwsKXuRunIkbLnn5I5CHuzqO+x1CKuNqa3uQKDGB591Go7bIurKmIy8jX+zO6AJKz4LM+SOCQjqPoalFFUKFlNEREREt0ihzYGXVh/H4j2XXOZ9GoVh/sh4hPppZUpG3qTw0mZkbpoAhykFmX+Phz0/Se5I5IEOZSXhye1LkGczO2d31GiMpb1Go3lwlIzJyBsp/QIROepTqENqIfSul2Ho/IDckagKYTFFREREdAuk5Vkw5pdD2Hgmw2U+pmNtTO8fC62KdzKiGzOd/QNZW6dAEov2JXMUpiP/2LcypyJPsznlLJ7e/hsOZF7B5N1/wOqwOx/zV7MApxuT7FbYjekuM5U+DDFP/QR922EypaKqisUUERER0U12LCUPD/10ACfT850zjVKB6f1j8XjH2rwUgsok/8QPyNk1HZCKLwH1qd2He0qRC6vDjtlHN8MqOgAABzKvYPGZvTKnIm8iWs1I/WkykheNgT3P9R9TBJVGplRUlbGYIiIiIrqJ1p1Kx5hfDyHDZHXOQnw1+GJkC/RtHC5jMvIWkiTBeGgecvd/5DL3azgcQZ2nldgAnao3jVKFDzsMhv7fM6N6RDXA6AZtZU5F3kK0FCDlu3EoOL0dtqzLSPn6KThMOXLHoiqOd+UjIiIiugkkScKXuy7iy50XXOaNw/wxe1AcwgN4OQ3dmCSJyN3zPkxnlrnMA+IeRkD84zzbjgAAoiQipTAP0b4GAEDdgBDM7jAYG5JOY3zc7VAIPB+BbsxRaETKt8/CciXBObPnpsCWfRlKv0D5glGVx2KKiIiIqJJZ7A5MW3sK60657s/RvX4o3uzXGD5q7idFNyY5bMjeOQ2FF9a7zA2tx8M/9j6ZUpGnsTjseHXfnzicnYwFt92NmH8LhPjgaMQHR8sbjryKOXGvSyml8NEj6sFPoK3RVMZUVB2wOiciIiKqRBkmC5749XCJUurhdrXw3sAmLKWoTER7IbL+ecG1lBIUCOr4GkspcsqzmfHszt+xOeUssi0FeHbn78iyFMgdi7yUX9OeCOk/GQCg9AtG9MNfsJSiW4JnTBERERFVkpNp+Zi4IgFp+RbnTK1U4NXeDTGgSYSMycibiBYjMjdPgjXjiHMmKDUI6vI2fGK6ypiMPI1SUMBy1R33MswmnMvLRLDWV8ZU5M0MHe8FBAV8G3SEOqSW3HGomuAZU0RERESVYNOZDDz2y0GXUirIR43PhzdnKUXlUnBhjWsppfZFSPc5LKWoBF+VBh92GIyafoHQq7X4tNMwtA2tKXcs8hLWtHPI3fFDibmhw90speiW4hlTRERERBUgSRK+2XsZn25LhHTVvH6IH2YPikO0QSdbNvJOfg1HwG68ANOpJVBoAxHSfTY0IbychooczkpCjrUQXSPrAwCCtL74uNNQWBx21A0IkTkdeQtL0gmkfPsMHAU5AABDp/vlDUTVGospIiIiIjdZ7SKmbziN1cdTXeZd6gZjer9Y+Gn5rRaVnyAIMLSZCEGhhm/9wVAb6sgdiTzElpSzeGXfn5AkCZ90GoaWITUAwHk3PqKyMF88jJTvx0E05wMAMv+aDaU+Av5xvWRORtUVL+UjIiIickN2gRVjlx4uUUrd37oGZt8Vx1KKykxyWEvMBEEBQ+vxLKXI6XBWEl7YsxIWhx1W0YFJu1fgYn623LHIyxSe24Pkb592llIAoKsZD9/6HWRMRdUdiykiIiKicjqbYcLonw7icJLROVMKAl7p3RATutaHQiHImI68iTl5N1L/GAlbzhm5o5CHaxYUie7/Xr4HAG1DayLSJ0DGRORtTCf/Qcr34yFZC50zn3rtETnqUyh0/jImo+qOxRQRERFROWxLzMIjPx9EktHsnOm1Knw6vDmGNIuSMRl5m8KLfyNr80Q4ClKR+fd42POvyB2JPIwoiZCkot3rFIICb7buh9YhMRheJx4z2g6ARskzM6ls8o+sRepPkyHZi8/Q9G3cFZEPfAiFxkfGZETcY4qIiIioTCRJwo8HrmDuP4kQpeJtzusE+WL24DjUDOQ39lR2pjPLkLPnfUASAQAOcybyj/+AwHbPy5yMPIXVYcdr+/9CXFAkRjVoCwDQKFWY23EINAolBIFnZlLZ5O1fgfQVbzvfbwDAr1lfhA97EwLLTfIAXIVEREREN2BziHh/4xksO5riMu9QKwgzBjRBgI7fUlHZ5R37BsaDn7nMfOv2g6HNBJkSkafJs5kxefcfOJB5BRuTzyBU64cBNZsAALQsEqgc7MY0ZKx616WUCmg1CKGDXoWg4AVU5Bm4EomIiIiuI7fQhmd+P1KilBrZIhpzhzRjKUVlJkkScg98UqKU8m98NwI7vg5BwbVERbamJuJAZvGlnR8c2Qij1XydVxCVTqUPR/jIdwGh6Ed/fYd7WUqRx+FXPyIiIqJrOJ9VgIkrEnApp3ijWIUgYFK3+ri7ZbSMycjbSJKInN3vouDsCpd5QPMxCGj2CC/LIhf9Y5rgfF4WFp3eA71ai1kdBkOv0ckdi7yUX2xXhA9/G9a0MwjqOZbvN+RxWEwRERERlWLXhWy8tOo48q1258xfo8K7A5ugQ+0gGZORt5EcVmRvn4rCSxtd5oa2k+DfaKRMqcjTnDVmoI5/MJT/nsnyZGxnSAD6xcSiXkCIvOHIa0iiiPwja+Df/A6Xs6L8m/cF0Fe+YETXwWKKiIiI6D9+PZSEmZvOumxyHmPQYc7gZqgT7CtjMvI2oq0AWf+8BEvK7uKhoEBQx9fhW7effMHIo/yTcg4v71uNvjUa4dUWfSAIAgRBwFNNusgdjbyIJIrIWPE28g6sgOXyYYQMeIFnR5FXYDFFRERE9C+HKGHW5rP49VCSy7x1jAHvD2wKg49apmTkjUSLEZmbJsCameCcCUoNgm+bAV0NFg5UZOWlY3j74DqIkoQ/Lh5DqM4fY2M7yx2LvIxktyFt6eswJawDABh3/wqFxg/BfZ6RORnRjbGYIiIiIgKQZ7Zjyurj2HUx22U+uFkkXuzRAGolN4ql8im8sNallFKo/RDcbSa04a1kTEWeJljji6vPaTlnzIQoiVAIfM+hspHsVqT+/CIKTv1TPFQooYlqLF8oonLgux0RERFVe5dyCvHIzwddSimFIGBC13p4pVdDllLkFt+Gw+Hf+G4AgEIXjJBen7GUohI6R9TBay2L9v4ZWrs53ms3kKUUlZvksDp/Lag0iLh3Jvyb9ZExEVHZ8YwpIiIiqtb2X87B838cg9FSvMm5r1qJ6QNicVtdbjhM7hMEAfrWzwEKDfzqD4JKX0vuSOQBrA47vji5Ew81bIsAddGd9gbUbIIYPwOaB0VxTyAqt/8VUSnfPgtLyklE3jsLPvXbyx2LqMxYTBEREVG1tfxoCmZsOA3HVZucR+t1mD0oDt760qIAAHkYSURBVPVD/WRMRt5IclghKDUuM0FQwNCKe7xQkXybBZN2r8CBzCs4kp2MjzsOhUZZ9CNZfHC0zOnIm0iS5FJiKjQ+iHzgQ9iyLkEb3UTGZETlx3NEiYiIqNoRRQkfbjmHt9efciml4qP1WHRvS5ZSVG7mpB1IXTEctuzTckchD/bqvj9xIPMKAOBA5hVMP7Re5kTkjezGdCQvfAyWlFMuc4XOn6UUeSUWU0RERFStFFjtmPRHAr7ff9llPqBJBD4fFo9gX801XklUuoLza5G1eTIchenI2Dge9rzLN34RVUvj4m5HgFoLAAhQazG0dnOZE5G3sWUnIWnhYzBfPISUb56GNf283JGIKozFFBEREVUbSblmPPLzIWxNzHLOBADPdKmLN/o2gkbFb42ofEynf0P2jqmQJAcAQDRnIf/kzzKnIk9iFx3OX9cLCMGs9oNQ0y8QX952N1qG1JAxGXkba8YFJC98DPbsorPuHKZsZKx4G9JVZ/4SeSN+90VERETVwuEkI0b/dABnM03OmU6lwPt3NsVD7Wpyw2EqF0mSkJewGDl7PgCu+qHQt96dMLR+Tr5g5FG2piZi5MZvkFSQ65y1DKmBX3qMQr0A3lyBys6SchrJC8fAbkxzztQhtRA+Yjq/fpHXYzFFREREVd7q46l48rfDyC60OWfh/lp8dXdLdG8QKmMy8kaSJMJ4YC6Mh+a5zP1j70dgh1cgKJQyJSNPsuJiAibvXoErplw8u+N3ZFsKnI8pFfwxjMrOfDkByYufgMNUfLavJqIhoh/5CipDhIzJiCoH3xGJiIioyhJFCZ9uS8TUNSdhc4jOeVxEAL6+ryUah/vLmI68kSQ6kLNrOvJP/OQy17cYC32rZ3nmAgEAHKKI384fhvjv2XSXTDn44ex+mVORNyo8vx8pX4+FWGh0zrQ14hD18Hwo/YNlTEZUeVhMERERUZVUaHPgxVXHsXjPJZd5n0ZhmD8yHqF+WpmSkbf6//buO06Sqlz4+K9S5zA5bg5sgiWzsEheSSrJcMFAuIoEfQUxR64RVAS9GDACBhDBKyAISBBQkoQlbF42pwk7eTpX1Xn/qJ4OE3Znl4XZWZ7vZ+ez3XWqqqt7znRXPf2c5ygnS+e/v0xy7f3FhZpGxeGfJzrvAglKiQJD17lhwZk0h+MAnD35AC6dvXCMj0qMN8nXn6Hl9/8PN1vMtgtMPoTGC36OEYyN4ZEJsWeZY30AQgghxheV7qUy4P1PqGKsD0eIYbX1Zbjq3qWsbO8vW37xkZO5eMEkCSCIXebmknQ++TkyrS8WlmmaQcXC/yE0+Z1jeGRib5F1bFb3bmdeZQMAVf4QNx55No9te52PTD9U3nfELkuufBJlZwr3QzMXUveB76P7AmN4VELseZIxJYQQYpcoO8uGH52FsrNjfShCDGtZSx/n3764LCjlM3S+c9psPn7kZLk4FLvMSXfT8dgnyoNShp+q466ToJQAoD+X4Yrn7uaSp+7k1c6theUTwhWcP+Mwed8Ru6X6tM8ROeAUAMJzT6T+3OskKCX2SZIxJYQQYtSc/k6UnUVlEijlopSSk22xV3l4VTv/89BKsiX1pGrCPn74nnnMbYiO4ZGJ8Sy96VGyHcsL93VflKrjrsNfe+AYHpXYW/Rm01z2zF9Y3dMOwFXP3cMv3/EBmXVPvGGarlN79jfwT5xP7PD3ycQKYp8lgSkhhBC42RR2Tyt2bytOTyt2Twt2TwvRQ84iMGk+TrLHC0jl0mRaVuJ0biCzeQlOrB1NNzBidZjR4sxmTqILzbDQA1JYWrw1lFL86rmN/OrZDWXLZ9VGuP6MedRFpZ6U2H2hGedg922if8Wf0ANV1JzwY6zKmWN9WGIvEbF8TAjFC4EpBfTlMjveSIhh9DxzG1bNFEIzi/XINMMkvuC/xvCohHjzSWBKCCH2cYOzmpRj0/HgD7G7W7B7vSBU6UwvpXyNswlMmo+ys2y68RzcdD/Z9rXg2Gy68b1omgGGyYzvLinbrvORn9L30t3owRhWRRNmZRNmZXP+djNmRRNWZROa6XtTn7t4e8jYDt/4xyoeXtVetvyEGTV845RZBC35hlm8MZqmETv4U2i6j9D092BGJ471IYm9iK7pfOuQU/l/z/6VzYke/vfIs5geq9n5hkLkKaXofvxXdD3+SzTTR8NHfkJwyiFjfVhCvGUkMLWPkaLEQry9pda/RHLF44XsJ7unlcDEA6j/r+8XV9IN+l6+D5VN7XR/dk8LAJrpY+L/+z+UcshsXsLG/z2Hif/vL5ixOtAMNLM8G8Xu9upruKleMqleMttWDLv/0H7H0PChG8qWpTe+ihGtwYzXS8q62KntiQyfuXcZy1r7ypZfdPgkLj1qMrouQ03FrnPtNLpZXsdF03RiB10+Rkck9jZPt65nY6KLc6cdDIDPMLnuiPeQtHPUB2XYsBg9pRSd//gxPU//wbtvZ2n945VM+H9/wYzVjvHRCfHWkMDUPmagKPG0q+4d60MRQrxBSincdD92TwtOPsg0kOE0kO1Uf+51+BuKw0kyW5bR88xtZfuxu7eV3dc0DTPeQK593cgPrumYsVp0yws4GSFvumvlutA0B6NyIr76GfS9fB+ablB14mVlm+e6tozqOer+cPlzdl223XIJysl5xxBvwKpspvr0z+Grm1Zcz86BYUp9q7e5lW39XHXvUtr6i0NmLEPnq4tmcvqc+jE8MjGepTb/i+7/XEP18Tfgq5o11ocj9kL3bVrGt19+GFcpKnxBTp0wG4CoFSBqSWFqsWu6HvlJISg1oPKESyQoJd5WJDC1D1KZBMp1cPo70QNRNNMa60MSQgzDzWVwelvRTD9mvHgRnevcQsttV+L0tOJmkzvch929rSwwZVY0DF0nn/VUylc/E930Y8QbMOP1mLF6zHhD/qceI1ozbLaSputohoUWiKL7QlQv+uSwx1V96mewOzeR69qC3b0Vu2srua4tZVMeA5iVTWX3nb52LygFoFxv2+6taEb5+1j3U7+j+8nfYlY2DRkeOHDbkG+s92mPv76drz24grRdLHJeGbS47j3zmN8UG8MjE+NZct0DdD37LVAuHY9/mtpFN2HGJo31YYm9yPLuVr65+B+F+998+R9MilQwd5jPXyFGIzzvnfS+8BfcdD9oGjXv/hKxw84Z68MS4i0lgal9wEBRYvAuQJ3ODWS2LPWG2ABOqpeO+65FD8YwgjH0YAw9ECN68BkEJs0v7ifVh92zDSPgraP5gpKNIMRuUq6D07cdI1ZX9nfU/a9bSCx9xCsynugCIH7keVSf9pnCOnowuuNsphJOb2vZfTNWj+4LYcYbMOLFYNPgOlP17//ubj83zfQx+cq7d1gfKjz72CHLlFK4ia5CsCrXtYXg5PL6CcNmWuUzvErZ+SBXrn3diK+VHogSmHgADR/+3/Jte1rRQxWFbDAxtpRSVITjKKVGvf6tL2zmp0+V/96nV4e5/ox5NMUlW0Hsnv6Vd9DzYnFosZvuJLH6LuKHXjWGRyX2NnMq6jl/xmH87vUXADh9whxm5c+5hdgd/qbZNHzof2n546eoPv0LRA88bawPSYi3nASm9gEDRYlxHdxcZkhR4ilfeAS7rx0GZU0Epx0BFANT6fUv0vqnzxZX0A181ZOZ8Mk/l23X89yfcZPd6KE4eiCGEYoXg16BGHowKnVhxD5NKYWb7PaG1vUUC4jbPa04+dns7L7toFwmf/GfZZk7dl/7kHpL9qDgkh6IolkBVC495LE1w/JmwBvIdKoqL8Drn7A/k7/8xJsaVNYCMbp609TFdi0rRdM0jEgVRqQKJh4w7Dr+xlk0XvgL7K4t5PLZUiqbGpL5OVDDakfcdB9uJjFk+dabP47dtQUjUo2Vz66KH/VB/M1zd+n5iDdOuYpcV5otj6+j+fip6FUhtB3UhMraLt95dDV/X17+N3P01Cq+e9psQj45rRG7TilF35Lf0Pfar8uWh2ecSeyQK8fmoMReJevYGJqOoesAfGLO0XRkEtQHo1wy6yj5IlfsEjfrnd/pvuIXKYFJ85l45b0YQcn4FW9Pcga3DxgoSgyQ69rMuu8eVyxKDDiJblDukO30fM2YAUNm5XId1DDb9b10N9mWVTs8Jj0QIXrwGVSfWvyWUbkOXY/dlA9ixdGDUfRgHH/T3LI3ZiHGmptJFINOPS0EJh1UVt8oufJftN4+um/Q7Z6WssCUGRta98buLg8aa5pG9JCz0HTDCz7ls56MeD1GqBItf2I8nPF+cqz7wwSnHgpTD93hetFDzsLXNBe7JPtquJkFzcrmsvvKdQtDG53+Dpz+Dtj0KtGDzyhbr//VB+l89Kf54YFe8MqszN+ubMaIVI/71/rNpBwXN+fiZh1cO/9/1sHJObhZFzfnYIRMzKDFpntXkupMkmtPMfEMr55PuLl4Yt63tgs369BvO9zw4iae6CoOb62zFWfMa+D8wydipGxyGQfN0NEMzftf13YY6BJCKZeeF28gserOsuXRuecTPfAy+TsXJHIZPvf8fUwIx/nS/JPQNA1N0/jaQe9E10b+PBZiOG4mQcsfr0T3Bak/97qy7HMJSom3MwlM7QOMkgCTUi5G1SR8DbNIb1iMm+zBTXUTPfTs/O1enJT3vxGuLNuPM8xF3XBvkCNNK1+2TrrfK5I8aLvuf908ZN2JV9yNXjWhcL/737+j/+X7vIysYKwwtFAPxUuysmIYkSr8Dfvt9FiEKKXsLHZvG3ZPK26mn/Ds48rat/72YtIbFpctqz71qrLAlLkLKftObyuU1IDyNexHaNaxxYBTbGjWE0DN6Z8dskwUReafSmT+qWXL3EzCGybYtbUQrApMKM/MsntbwXWG7G9wACvXuQm7ext29zbS618csr5m+jArGrGqJlL/wRvKLl6Vnd3hMMe9kXKVFzzKOcWAUj6QpGka0WnFz4vuZe0kt/XhZl3MkEXDcZMLbZsfWE3/+h6UM/RLjVKRaZVUzK5h/Z+XkulKg1Ikt/Wz/s9LmfzeuShXFQJK7c9uprctwabuFBUmEPEycg1N40t+P9Vreli/pmfkB9O0fKBK82qk6Rqamb9taFTNr6dirldg1rVdtv5jjbeOoRGfXUN4ovcZ62QdOl/aVgx86Vp5AGzgtlHc95Dbho4RMNFNuZjdE97oTMTKtel+7tsk1z1Ytjx20CeIzv3IHjpKMZ5tTye44rm7Wd3TzgvbN1ETiPDxWUcCSFBK7DIn1UvL7z5JZusyANr+8lXq3n+NjDQRAglM7XM0TUPzR9AtP7FDztj5BiXC807CVzsFJ9XrBbHSfRiR6mEeRPd+hsmmKjU4qDVc4AtAH7Se3bONbPvanR6vVTWBiVfcXbas9Y7Pe/VjCvW04kTmn0agZNiQm0li97YWgl5SHH7fk+vaSmbrMuzugdnsWgoZUE6is7Ce7g8T/vITZdsO7o8wtHj44HpHAJoVKA6vGwg4xRvw1c8sWy8040hCM458I09PjED3h/E37LfDgLURjFH3vu8Whwp2bcHuaSkrPg9gd+14qKCys+S2b0Dl0kMyKlr+8Cky21aUZVuF938ngQn77/6T2wEnbZPtTntBpaxDZEoFmuFdMCU293oZR6UBp5KsJe++u8NAkhn2lQWmklv66Fm5HQBfxaBsV8VOg1LoGtUHN7DlwdfJdpUPV812pWl9cgOTz5mDGfLem7f3ZdjamcRV4Jjeax3zm/zgPXOJPLQOJ5Xb8eMphbIVygYYGpR0UnZxVcelb21X4X6wIVIITLkZm+0v7HwI6c5MOnt2ISOsf0MPm+5bVQhcTT57DoGakNe2sYf2pzcNHwgr3B8mEFaSKaaZOhVzawtBvkxXilxvxlvf1Ag1FrM5nbSNa7tD97EXZ5wpBW5/ClW561nXys7Q+dRXSG/5d3GhplNx+BcIzzhzDx6lGC+Gq3f3eu921vRuL9z/09qXeO/kA6gOhIfbhRAjcvo72fa7y8m2vl5Yllr3AnbXFqxqmWBBCAlM7WNGU5R4JFZFI1ZF407Xm/Tpe1Gui8omvSBW/mcgE8tN9eKkewlMOqhsO5VJoJn+8lm5NA3dHylbz0nu4JvvEnpgaAAh27KKXOfmsmX+CfuXBabSGxbT8scriofgCxaCWHog6tXMymdlVRxzYVmAzenvxLUzGMG4FIcfI5mWVdjd3hC7gaBTxXEfw1c7tbBOcuWTdDxw3U735WYSuOl+9ECxDw471K63rey+HopT8+4vYkTrMCu8YXZ6ILrP94eebIqs64ACokE6MknQwKcbxH3BsT68UdH9YSIHnLzT9QJTDkEpp5B9Zfe1e1fBg5gVzUOW5bq24qb7ybasKgx79tVNJzBhf1zbJdebIbVpBV1P/hEjUo8eqkMLVKP7K9GsOJhhXFsNH0jKL5v2wf3x5Yt8967ppOWf6wuPP/O/D8YMeYGpzPYkXa+2DjnGXeFmy4M5uk8fuc0axbe+rqJjcQsNx01hY89Kst1plOt9seKrDNBw3GSMgIlSitsXbyG7rZdY/qW3NZhSGeL6M+cxsSLIKnd0BdN3RDNKst0cNahNH7Fttx+vJNCjXLcscFb6FuKkcqS373hWztEYyAYD6FnRQceLXnBNM3VmX3pYoa39P1uG7yv5jDPd0EHX0EcMjmn4q4PUH1PMoOt8uYVsTxpN1/FVBaicV8w27V3dgZNxygJuaArsNMpJoXIpcJKoXBIzXoO/ab/Cum62n8RrDxKYeTqb71/GhLPeQXL1/QQnHlB2gedmkig7gx6Mlw2BdrP9dD75OTJtxexYTbeoPPqbBCee8MZecDEujVTv7si6yXz5wEV8++WHqQmE+d8jz5aglNhldk8r2269jFzHxsIyI1xF4wU/k6CUEHkSmNrH7G5R4l1+HF1HC0S8C/pB072PxN88l6lfewplZ4sBrXT/kHo5AwGtIQGvTH/ZheFwmS2jGY7opMoDXyqbws6moGfoCXnFwg+X3e968rf0Pven/AEYGMEYkfmnDaql5dL9xK/Li8OXBbykOPxw3GzKy2rqHSgg3ooeiBA/6oNl62279TLcQcHL8NyTygJTg7NfdsTuacVXEpgKzzkBq7IZYyD7KVY/JHNQ0zRih79vV57euJF1bNKOTayk7ttTrevYlOjmiNpJvOfh3xC1fNg5G9MySeRy3PPO/+aeDUtoCMU4uXlWYbtl3S2kbZuAaVLhC9JUMuzYcV30fJ2OvVHkwHcTmntaIRDkpNLkutrJdrVhd3eQ6+nC7ushZ9bT+q8NRGdUEWqMohybbHc/SftcUD4UPgLGo5j598lMR5L1dy7DSXZjd5cOM+zI/3g0w/J+TB9GrH7I+6SbLWYlGYOCQW7OAbxso1EFinbCzTllszqaYR9W3I9uGYWspgGRaRVYMR+6ZaD7DHRT9/63DHRfyW1LRzN1pn94PpvuX02qs59gVYSJ75qJvyqIrRTff+x17l7SQihmYCjvhOWAiRX89ox5RAPe6UvzaTNQtotyVfF/R3lfnjjF266twM3fdpSX1eUqXEfhG5RtE6gLF7YzAsXTJKUUmqHvPCNsJ3YU7NrjgTBNGxoIKzxW+d/eiI+XD5w5trft0JyzIndgnUQ32e3r6Hypi/R2B5RLoNqlcl5xpqntL26jf/Vqb5IH10UpZ9jgL4AR6sWsSOWfg0N0WgXxOaew/s9LSG3czMZ7ljDpjKNpeXYb/WufRTMMahY043OeYvv930Pho9/5OJpugq6j7H6UOgBNm+9FA3UdX81+9D0QBV4EDTRKXp/8zaZ3TiMyuQKAVFuCTX9bNfAy03zajEIGWnJLL1v+sbawbWFPWumd8n1rDG2b8r65hT7Ys2I7HS9tK7Z9YF5hSGjXkja6lhS/QNEGHmvQY5Q97KBjMcM+Jpw2o3C//bnNJLf0AeCLB2g8qfgZ2/b0JlJtifLdaiU3Bj2eNkxbqDlK9cHFL0O3PbYOJ+1lL4YnxancvxjE3PrIWi+bb8hz0cr+G/y0Sx8vPqu6OCw3bdP29KbCKpUH1oMLm+5/ndT2flbdsYRpZ87G7s+S68mwUK/g2/6FTI/VULnWpstohZKMwkJmoT4wXNh7HzH83u/OyTrY/dnCembYKvytK1cVhi7vzdmJYvflOjax7XeXY3cX/37NeD2NF/xcglJClJDAlHjLaaYPM1oD0Zph2+MLPkB8wQeGLFeui5vpx0324KR6hwzBU0oRnnVcPpDVUwh+6YPqTrjpvlEf6+Dgl5suCXy5Dk6iC2Vny9fJJOh6/Jc73m8ggh6IUX3aZ8pqHOU6NpJY8URZcXgjVFFW32hfkFj+OKk1zxYCUXZPy7C1y3x104cEpsxYPdlBgSl7UFCxdKidHogWA0wlhcQHhtqZFeXD8oLTDic47fA3+hTHlO069OUypJxcWTDo2bYNLOluoS+bJmCYXDbn6ELbt17+B//YsoqMYzOnop5bjz2v0Hb72sW8uH0Tvzj6/XRlUoRMC03TcZSiM5uiP5fh5yueZl5lY1lg6oYlT/JKp5edcVTdFH585FmFtvP/dTtrercTMEwWNe3HVw96Z6HtKy/+nZ78MS6sm8o5U4oBnFtXP48C/IbJvIp65ld5AR/lKha3bsbnaPhcjepQhOoa77krpehZsR0nZePmHAK14bKhaRvvWYmdypVlJil7pOCDD2jM/wC9kHylFSse8AJTrkPlCReTeiqCcrJg58CIYVZ4x6n7vECRcnY8/Ew5OW+dbHLI0FG7u4Wtv/81oboAZkUTOXcKbrYRPZ+1Vhq00gMGRtBCt8qDQobPQMv/XwgiWXr57ZL1S9Uc1kTNYcN/IRGbXgXTq3b43Er5q4JMfNeMQpaCvypIb8bm8/cv46XN3t95Mn+x9v4Dm/jMcdMxSi7ewhP27JcwZshi6gfmDX+slUFmX+ZlGHkBsHzwazS3S5b5Yv6y519zRPOwgTAr6iM6o2qHAbfi7fLHGrCj4FNpsFMphbLt8nWV8j4vXacYOCr5H+XkM74czHgjeiBSuLBOrX2Otru+Qsp+P47y+m820wYUA1PKUSg7O+QzdDilE7FEp1USn1XFhr+8TG57m1efbPUS1t68kYnvOxGURv/6XlDgJPLDMhUoV0O5DrgZlKsAg4FXQzN9oIXKMgBznVtQuSToRj6gZZBa10tk8vH543dxkvnj1w3cXPH1c22Fndj589rp8y7JCLRTNpnO1LDr2ckcmTeYXWeV9EuATGea5FbvfMkZlBmZbk+S3LzzeqM7MvBeOKB/Q0/hNRsc8O5b05UPuO++QF24OCzXdule1g549e6ctM2mf6wh15vB1cBpSbDuz0uZfNYc0h3d9K/tYiKQZTujzT+d8v65BOu9L72Sm3rY/EBx+Na0Dx2Av9J7v+5ZuZ1tj67zGrThAl0j3DfKl006o/jZ27uqg/6NPV6mo8+g/uhi8KNvXZfXV4YE1vTiMkPzAnyFQFv54xk+o2wYt50fTj2QQfl2rKE33DBQgGzbWrb97nKcvuJwUKtqIg0X/GxUo1SEeDuRwJQYNzRdx8jXjhquKpSmadSeffVO9xPa7xjMaG35MMR0b3lx+HQfysmhGeV/IsMFT4YEr0ZZHN5N9w9Zntm6gs5//LhsmRmvZ9JV95cta/u/r2N3b/MysYLx4YvDB2NeIGa4OmGjtKuFZd1Mkr6X7i4LONk9rUy47LayYvvpDYvpff6une7P7h16CmjGGwrDozTThxlvGDJ01Vc3nQmf+LM3xM4f2unj7I1c5dKfy9KbS9ObSzM7XlcotLq4Ywv/allLby5NXy7DtYe9q5DN8vPlT3Hz6ucBiFg+Hjvt8sI+n2xZw13rXwWgyh8qC0wpBRnHu7DqyZbX/YlZJSegyuX4uhlcPv1IfrbmWf6wrlgY3G+UX2hkXHvEtnQuh2mDkXUxkuVBoNZ12zH6bHB1UpURWjdtzA9jc+hctR7dBp+r0xrsZpW/1WuzXdZ3F/tLYHoFp593DABJO8tf//ZvKnMWGhqxOTWcMG0BAF2ZJC+/vo6AraNrGpW+EKF80Nt2XbqzKS+zC42I5cPKZzu6SpFx7EJbLuOdmOuWn4qFH6BtSfF1qTnmO5gV9fl2b3vNsND9YZSdD0AVLpEdNLJo5EDLYvhMwpMrygJHfS88jtv9Osme3vyxBKg65GNUHPk+dJ+BFfNj97ax/f7vYVU2Uz+vqVjvqrKpEMDaG2i6hlUZoPmU6VghHxu6U1x171I2dRcvwHVN47PHT+f9B44uO/et4F3IGQz7YbQL/FVBao8YOhwUIDwxXriQ3hGlFCqX8b64Sfd7n23pfpxUAjfZR/e/lxE/6oNohkn1IY1UzK6h5/n/I/n6s2y8YaP3eZRJEJj7YZoWfRDlKFzHxU0laP/bb/ECODpg5G8bgA5q4LZBoLIGsypWqI81MERfI41GwlvfTZQft+ui6cXg0A4NZHrpGtWHNrP5gdfJ9YERq8Xt7fO+vNFNWv61lQmnzaB/Yz6gkuweui/dBFRhEgTN8KEZw5Q/cHMoxwbHRuGVILB7MuWr2Bly7V5QYeuvvocv1IsRqsDWZ5DrOrIQ0NJ0wxtOWHpOodTg9J6hypp38EqNkGm2S3ZwKEOb9syw1rGSsrPkXAelQdXBDbQ8uZ7lm7dgKxccRZMTJL29n5bH13v9aX037OKw4bJMxcGZkaWZUaUffyofaN7FGNzgTKtUa4KeFV4gxAiYZYGp/rXddC9v37UHGCTUFGXyOXMK9zffv5pUi3dOG5lcwcT3FOs8rrtjKdnudHlgzRhl8C3/E5lWSXy/4rls6782gu4978ikOKF8zT7luHQtbS/uS9u9xzNCViG4plzl/X3pI2d4jzQMNNOyakiWv692Gg0X/Mz7gl4IUUYCU+Jtx6pswhrl8MPBKo75b8L7n1woDu+keghMPLBsHTfTP6ri8LCTjKzCOkMvTDJblpHbvn6n+48echa1Z361uP9sim23XFpWHF4PRIkfeR5GuKKwnpPsQbk2uA4bbjiDqVfeTWbzkvKAU3cLwamHUXnCx8ses+PB64cch93TUhaYGq54+BCaju4P42bT6CXDyipPvIzK4z+OGa9HD1UMe6Kgmb69Jsss5zq0p/vpy2XozaaZU1FPxPK+mV7V085f1r9aCD5dfdDJ1AW9oSD3bFzKNa88WtjPg6d8nKp8kG1FTxt/WFMMfKScHKF8cC5QkknYn8viKrcQ0Ipa/pK28gus8rZBgSnDT8DWUSmb99cdwCX1h7LxvtVcdtxhBHoU7roEB3bEmJ0J0v7c5kLW0RGvhzgg1Yjl6jS3+6EkEe2oNRGmtXsnmoEWExYW2+a2BpnS5f3Oqzuhc2ux+PzkniADF0UBBY7rBYQGf0vpc4r9IuM6ZHUXVylv25JsqP5clg4nRTznfRxGTT8D0QZbubSXBJAn6hWFwFTaybGhvwtbU+QMhZOKMzCA9LnOjTylrcc1wDU13h9qojrfT19NtPLI1DZ0y8DwRfnIrCOIhwKoTBfrWpbz+pbXCCa6CPa3M8nJYMXqqT9jFh3pBJ2ZJAHDgv88hq4V3yt0LU1kcjXBhuKQ1EzHRpIrygv7DzDClZgVTZiVzViVzcQW/NeYniRrmkZfup91XQZfuG8F/dliQDPiM7n2XXNYMLlyB3sY35TrDBnenVz1b6+mYbqvEHCKHPyessL+ieWPs/3eb+MMZDXtQPTgMzDCFVgRH1bERz/bUJ3PUZojZeptxGcX+4FyHXr/PnRGyuFU7z+H+IJTCvcH6vaFzL+VrKWj1NWF9+xp5+5P979eJtO2Dt0XQbMiaP4ImhlGs0JghNCsEJoZRLPC6L4IylU4OYcJp85g499WYvelcZM+NCuIrzJM40lTyfVnqTmsiVBTFL3hXfjqZ2D3d6OvBZVJ4mZTuNkkdu8WwAKlMGMNVBxyTP6Je/91Pfl3HLuL0rCMr2pR8fUK+4hN0ejreMV7dlrC+32l+3BUAtNV+V1520fn/hdmtLqw/9T6xaTXPY9mBdCsALoVxKxoJDDl0MI6mqGT2rAY3R/G9FvEZlRAvq+UBiP8VUGiM6pKjr/k/bD0php8o7jO4CylQG3Iy1JS4Ksoz6YK1IRL9lXyIIN3u4O2wRMn+GuCaEEDW7lo4eLlSVcmSXfIK8zvuC61gQiWroOCvlyGrckeHKVwlMusWG3hPbot2c+6vg4cpXCVyylGcfbbezcuJZtuQQM6n9M5/Ng5hNrb6evqRwGOpghWBmk4fgodr7TsXhyudKZWd+TA1OAZrHfLoMBU6eMNDloNPpY9/niDsjQHvlR6I6x4sa8oV9H5SvGcQPcZhcCUm3NpfXLDG3osgAnvmkl0qveZk2rtZ8NflnsNmsbks2YVHi+xuZeeVR3EplWy8Z6VpLuSZLYlmHTmbNLbE6S3ZUjnjsF1tqORw6qsJfSOi0m1G+jdPYWsZF/cXzaMW4i3q702MPXTn/6UH/zgB7S0tHDggQdy4403csQRR4y4/p133snXvvY11q9fz8yZM/ne977H6aef/hYesXg7CEyaT2DS/B2u42+cxdSrn/OKwyd7ymtlpfu8oYj5DK3BAZrhsqgG18iC0WVlwfDZXJktS4esFzv8vWX3u5+6le5nbqfpwl9gt67G7tvOtj99Dk25XsAtf0Y6OBtJ94fQg7Ehx2f3tOJvKn67ZlY0ehfH8QaMWH1hqF1hmF28HiNaM2wtLn/DzCHL3gq267Cur5O+XIaefAZTY8h7fVuSvdy4/N/0ZjP0ZFN8cs4x7F/RiOPC4o7NXPn8X1EoUPD9Q85mVqweV8HL27dz+5qXCyf4z1S3MynsBVC2dmbJpB0MV8NQGg8t20xtuBJHKV5vS1LRYWIqDUPp3PvQSkKaDxyXzr4+5nRHMJWGqTTu/MNLWMoAR+GkEixK1eDHwIfBNSwh67NwXEWu3eX/bZmd307ni4lXSZo6tuMyoSPEd3oOJmql+Mas41l313KMrhQd21/nirMW0ruygwvb96N7U46Xlq/xnowG01Xce94o3JTOl/++HF3T0DWIO1EspQGKTD98/5+vowGGrpFxLTTlZVIksy7tzsCQGIXt5oNLQCLroA8EpnCxXVW4hOzuzfH35a0YukZXtp9+HKqUd8HRlbR5en0nuqaxJdXJimA/YZ9B1nDx1dcSiFehWzrbnT7+umkpWV2R1V0unTed2qpGDJ/BusQ2vrP0P6j8xcdN+x1GX9rG0KEnm+XPE7YMFHrhgw3Fv5MtmV7+4rzuFepJw/mV78AXCAJBXu5v5foc4KuEqkoeP/3yQsDx7o1L+MWKZwA40Yjw6XkHFQqzt3Ru5lvLn6GtvZUqf4jfH/fBwqyCXdkUSTuLhoal69QGIjiJLpxEFx3rF5NTDu2N84jXTuGouikA9DxzO+1LHoJYA77KZoLVk4g1z31T/vb6Mza9aZuNCR3D0PjvIybyl1e3saU3zcSKINefMY8pVXtv1qNynUIGrFnRWDY8Lvn6M2Q2Ly0LLrmZftxU+X1f3QyaL/ld2X633/997O7yGQB9TXPLZ5zU9OEzgobhZvrLvnwonfChdJ1Smm6g+YKorJe9plkB9EDUG4ruj+SHpHv3fTVTy7b11c+g8aJflq/vD5d9kaBbBlUnXjCq4x9MuYop753Lpr8tJ9epYcX8TDjdq09WfiE+D1/DNHQzSNXxw+/LzaZRuVTZlycAPmcyuR4fbrIbJ9GNm+wm2FQsJO+L+amcniD38pND9mlonRjG42XLGk/4QlkAuOOhe2DDw957Qf79IDzhBOoXvb/4PJWi5XefKBvuqPtC6OFKtq6vwAhVoIcrMUIV1Mw9sez8RNk53FwK3R8ZUqNuNEYarusql/75IdKORdq2aQ7Hqc9/oZK0s/x1wxLSTo60neOEphnMzQ+Xb0n28u1XHiHt2KSdHFdOO5aBV/PVzq18TD0A+T/1n099XyHQv6KnjSvizxQe/5Zjz2VWfp+PbV3N1S8UZ1W888TzmRzxAnTLNi7lRy//p9B2+vRiUNGK+Pnf2V4Aw9A28kDVAcw8Yy6r7l1KX2c/XXXQdNYMVEOAidO8972BzJmBmlADw2txBy1zvWW+eDGYF2qOMuH0mYVtSofsBhui1B09Mb+dF6gq3U/pYw1ZVlKfqpQRNPFVBFBu+WMVaNobyrLbYbBL2/OBsLIJKnYU5NsTmYMMzmgbFNktadMsjeiUCjb8dTmZrjQoRbo9yYa/LGPSmbNJtQRwI4vIpTegmRbKmkTrv1th0IDQmRcdhBn2Puu7lrTR/twWb5i9z2Dye+cUsrf6N3ST2NRbVrfRGG74fX5bb0im1C0T48deGZi64447uOqqq7jppptYsGABP/rRjzjllFNYuXIldXV1Q9Z/+umnOe+887jmmmt497vfzW233cZZZ53FSy+9xP77vznTc+/NDGPoxbx4a2mahuYPo/vDoy4OD1BxzIXEj/pgYZihk+pBN/1D1gvOONL7Nr004JXuG3KisbPC7wNKs7KcZA/hA04jsN9xZLpbyLqQ6W6h4YKb0HUNN9HNttuvQrmKVOc2OhJZHFflv5VUZAPVuJksROtQ0XpUuJZ1mSDO1h5vPRfc0IE4H7gjf1/hKm/YlKvA6Vc4fQrHbcNV3voD+x5Y37sPtpNfXtLmKgbd94IZrqKwbWu2g7TKkHZyhLUwMa3CO3bH5qXcSyjHxnVtmvVJVOjNOK4i6aZptZ/H7+iYSiOkJpHT6702laTJWYdfGZhK49ZnFhN2X8dUoLQsRxuVmGhYSuf5pctZ4q7HUAqNHOcbE7CUt8871i5jteX9zky6+UJXsRDtPZtWs8bnnfyHVCeXdE/KB2E0rPWt2HgnLpM1l4k045XS1dBbexj4xr4JgyYaC/d/u6KDVtO7PTujcXiiOJzlpQ1ddOVPBnVHo35WHdVzatl4zwr03hxBw4TeHNv+topJZ85GudDy4haG//JXQ6F4eGVb4aT1+KSPqVmvv2ZzcOcrxYvwkxNxVL6tX4NOsmQ1yGqQ0cL524qsq5HLZcnk7/f5TTK6S1pzySRztD200uvT5Ej4s+SCKWzNId5rErh7CQAZLUmrvxvX56Jw+GtLnNBW7yIwYXSxKdBSqPa8+qm1BF1veESfsZ0tgeLQpI/d8Sp+5c3U1GO2ss1fvMg/87cv4ieArkGXtZWtVn+hiO97b34Bv+5D1zS26evZqCcKbR/+/csYuo6uwVq1jnUqiQbcUnkYbe6paJVgVGmsbHqJ1ZktuC3tBDQ/V96zhEmtW9nPrsNOrUXZKbxyITq5rJn/jUCvkyCtbD69+BmqfSv5UP0iDF1j+otPY6x7gmw+6OfTTYyZZ7N1wVXoGty29Qna0x2cufZxfPHJHDjhSNxII26skXu7VpJ0s/h0k+mROo6smlEIRj7ftRZQ+A2TKZFKjpxQT08mSzLrYPldNM3hsKkhjp05m18+tYXPnzCDWMAk69iYul7I/NtTvLqFiXyQqK8QYCoLIqX7CUw+mPCsYwrb5bq2svU3H8XN9BeCNgCTv/hY2Xtucvnj9L7wl50ex+CAEIAejEL3oOMdtN5wwaURH2PQlx6+hv0Izz3RCxzlg0xW7dAs0wmX34Ge/ywbPLR9R3R/mOCUQ0a9/q7SdM2rT/buWbQ86afh2CnDBKUgtekJup+/lurjb8BXNXv4Y/UFwBcYsrzi2It2ehyBKYdQf+51OMlu3EQXTrLbu53swkkM3O7GzSYxBmU/DxdU1AcFx1Q2NbSOZTbpZXx1bSkuU4pMrB6tZgpmPgCd3vwa227+OP1OjpwvQi4QIRir48BLf1/Y7oYlT+Bufg0nm2Ju8yxOnbEAPVSBbvk585HfkrSzpOwcF8w8nItnHQlAznU595/FfVw571g+ON37Xacdmx8vLQbqmsMVhcCUoxT/aS/OSNaZKdbEChjl2Vrpkhp8wcFtJbXQhm63g7aS7YKDapRmXIfftL7IR884hM1PrKP5uKn8tvUlrqwv1gD1+paGthun2FbUjxUdei4HXmZaoHbPBt9rj2gecYhw0zun0fTOaUODX2rHgbbS4JgRMIY8npOyUa47pFZZ5QF1+baRA2uDlw1+vLJsPqUwgpaXaeZSVs/qzZk5deRAmBmw2PL318l2lWeZZ7vStDyxnuZTvWGgVs1kMEy0ET7DSicpcdI2TiqHU1K3a0BySx+dL7cM2X7kJ6J5k45YRqGepK8yQNOi4nt9z6oOcr0ZrwxA1FfIFAPI9XmZ9QMBLynOL95se2Vg6vrrr+fiiy/moou8k4KbbrqJ+++/n9/+9rd88YtfHLL+j3/8Y0499VQ+97nPAfCtb32Lhx9+mJ/85CfcdNNNb+mxj6X+jE3Gdgj7o3SlcvhNg4h/r/wV7xNUPvhhlwRLbFdhO8XbA8ERu+R/23ULwRbvthd8sV23bD3HDeAov3d726b8tvl91l2EXTPocW0HLZdAT/egZ/sxsv30bq6j9+4l2I6LoxSh/s0czGysXD9Wrh+fncBwMnz696/guAqF4razJrPmO4sg3U384Pcw88vP0vL3b9Gz+G9YpsW0Lz3Gsz0RevQYbVsr+fuvni17XSz34+RiPi8AkcD7acsAr+zoxSxUMDHzs28Zyrs9sMxA4QKbSwoxN9i9VLkOOi5ZTFb5iif+050NNOZ0DBRhJ0LUiWOiMBQ0GO0YgKV0gq5LyHHyj6M4XS/O6PeaX/FIyLvwdHH4eO9kqhzvBOnFgOLJYL5GCQbn9DYSdL2TC1P5MPNBQoVBRi9+0FvKwCi06WTcYH72J42gW3xuhgpiKqekrXji4ncrCYwwdEdXBjojnTmXn1SYJedb9qDzDaOkLRw0qT+sifX3r6K3vbzAbro9yabH1jHlXfux/JVtpHIuWbwgUk4jHzTSyGre73LgqFdbOn26KgScSj0Z1PlX0Fvu7NK3fcNfqBtYxJxJw04n5lchJqUPHNoAhJ1KZiWOReHi4mKUvK5BJ8bE9HxcXJTmYKmSgtZuiOrcpEKboQxcvOCocgz8etTbp+bQn1Ek8U5Ae3xZbMuL7GnobEoUT3ZbfSkylpN/PjqvdRezErf5HfpMBbZNVpk81dHJU8yF2rlsaV6G0jZSm+2jKZ1ldn81lbl2KnMd+FlLTsvRkbJJJjLcvtW70P3vjetoyDm4mtcJXMfl6RaDx5/yaumsC7YTy21l3qb/oG98CfeVBwHQgROsLG2+CO2+KO1WM3drc1geOZheq4rVoWdwNO8i+8yJ85lVXcGhf/kZDy46i6aAjy2ZFMc8+Gf+854r+MKJM4gFLNJ2jmP//lPvNdHg0/OO49xpB6OUoquvg0sf/x22P4xm+vnEnKM5sWkmmZbVtCx7lPtWP4/fzuC3Mxw4cX/mn+PVINya7OFXK5/lHff8D8FkJ5W+IP580CXnOvRk0/nJ0zRiVoBq1yY86xhaU30s7tiCL9VNdedmwqYXUARv2OeyljWYlU2Yms7UaFUhcOQqlc8c1PIBwkFDXYbJlB2o0QSgGaZ3f9B2VkUT8SPPG5rFVMhmKt4eXIcvMm8RkXmL2JndHfb+VtB0DasqSMPJXn2ywRdNiTV/o/s/14By6Xj809QsugkrNnmPHoMZq8OMDf3CdIBSipzrYCm3MGHL1mQPral+co1zMA0/k3StEMBaaYW4a/lTpB2bSn+ID9UUgwutqT6Sdg4XRcAwaS6Z2GJDoovfLf03y9tbOa5hOj844j24+cBXa7KXbF8nAMGSAswAD29dxbuevY0pXZuo9IfYmM980nxBPtjfTZ/pJ2kFadryIr1924gddjY+3Sgk3FQnOskluvL1wnQCg4KXpQGmoW32iG2psu1GDj6FLR+V/hABwyRoWGUzKU4Ixzl94hyChkXAMAlbxb+BQ6sn8O1DTyNgmATy2z3RuhaF4uOnLuCX65/jydZ1XHnAceyr9uQsgKWTiQxWOvPinqBbBvt99OBh28ywxayPH7qTwJpbsmxgVkS3LDjmL8nS9cUD1C2cWGgbyGwCUCia3jmdTfeuJNOVxOnvRA9W4KsM0XjiVHpWbsfwm7i6toMJVUArOa8tnYBhcDDIze3isE+lcDMObsYpDNt2B80q27NiO4mN3pfWwcZoWWBqyz/WkNpW/HzSzNIg1wgz7g6aSMVfGSwLvNqpHLrpzcor2VxisL0uapHNZnnxxRf50pe+VFim6zqLFi3imWeeGXabZ555hquuuqps2SmnnMLdd9897PqZTIZMplhfpbc3X0DWdXH3xFjvMdCRzPHkmu0cEg2y7PH1TDx+Ck/0JDl6ShXVQat8ePxIqa4li3VLL05l67hlb4a6r/hG6dpucey48k7CbFfhugqHfJDGLQ/SOK7CVt403Y7ybjuWgQPeehkbN5sP0ihFzmcUs2QyNm7OC+a4+f04zkDGTD7gk1/XUeA4Kp+JUxL8AW+IkutiOwor54Dt7S8L9Or5oJGjCGQdNLc0W4dCBs4Oaw6UtGV06Cv5YKmxFQMfQUkd+kva6mzF4LdpbYTbI+nRDZJ6BVCBqRR1TnEK+k49wr8rvJpQllLUOYBSaNtTmEDEZ5DZblN50YOE42FC/ggb73uBie/5X0InfZfE9gQ9rX6en/AtsjkHQ4HlKnL5D5dJOcX+WRNDuZjA38I6dr5tfsblsLS3XFcKC83bHgAbrwKod/FmquIHf1ZP4eIAil4dfhMrfmjuZ/dyaCoM6LSZDqtKzl+nZU32T+cL8WLiK/nWK+0W6xQZSscqNGn5n3wbxd+zjkFOK+5Dxy1pM3HQ0TAG8pRKfiMalgoAGprygljFFoOAW7wADTl6se+4PsySCqg+VWwrnztrZI7m/biaVnbb1TVcDSoiPrSAgaFrRHMum00bpYPSNaZV+pngN9A1DR/Q3pUkdsIUWrrTJLpS3oxdmkaoMkjjCVNoN+CJ/avptd3C34uCQtaaq2CKKmauKRTb8n9PrlLUqPJ1XaXwqWK743ongaMo3bZHeb/PocE+Ex+mPUzBZCDgRAk40WHbYrk6YrnhL2RrMpOpzkzMB63csveRilwjYbsCV3MArawtbFegKwOFg66ssjZLBUgYtWwOVNHuC9HhK2YRbwy+QlLv8p5nSf/a7J9Kr9lBVbaDylwC0Oi06grtLg612d786zP4uWeZmO5gYroDQ23Bcl+lxTeRXrMKpXn9+eC+Dbx7+TLs1ArOzbzOFL/JSz84hXmfewjL0FBAb9d2Mi/+jkyyh3NXPk3AzuB3Mkx59a+s1w3cTD85J8cFvdv53cHvZW3VZJJ2Ftd1SW9eQvKxm5jSV3zvs3OJwuf79lSC+zYtY2IuTX0mSdTyMxBWzDh2WV2xoGHhpPpwXZclndv42ksPYDk5vpLoZnq0Gl/+gro/l+HrT/+Z1qj3u71/0ccw/WHQdDpQvJ5JkTZ9ZAw/Z8w8AisYQw9E+U9vB491t/HyAz/Hb5jct+ijXl84+xvctWEJD27fBIaPuD/Ijw89q/Ac7lr/Ki9s34RZO5tqf5hPzzu2cMwPbF7O693bMbVOagMR3jdlfqGOzb9a1tKeSWBqOjWBMAvzwzcBXu3cRtLJYmo6Vf4Q06LFYsNbkz0oBYauEzIsYiUZRo7repMAvIUXGb25jFe8GnB1Bz3roAGWbhCz/PSvuJ2+l28srO+mu0is+j9ih1wxZF8Zx6Y7myLl5Eg7NjOi1Zj5YeRr+zp4tXObNzTNsfnI9EMx8sPi/rntdR7aspKUkyPnOvzsqOKQ+JtWPMPt6xaTdnJU+II8dPLHC7+D3678D/ds8jI2m4J1/PWkYmbWjS89wMOrveFnM6I1fGTyATT8969xk93c/fJDbNy+gVAuxVTDYEbNhPwwwy6cTIqEFUChSNk5XNcl1+/1f73krzTlC5ed5wZ0k1Au/+VLybmhyqaoSPcSya9b2d9GqnYykUPOBMCvm2RzKT757M1Uv/xn1gWi+UlYKqiunUtHuJqgYeG6Lqkty8i1rQV/mBM1F4Jx9HAFtb5Q4VgqrACXzV6YDxSZzIrWFtomheP87pjzCkGkCl+g0HZARQMPvPNjZb/PgbZZsVq+fuA7h21rDEZpDBbfo3tzGf560kVef3Idrtz/eD69//FYmjFurwvetkwt/7m9a5xUHyqbRDk57P4e7J5cYXbcQCw/SYmTI9e2FSN0KOBlwpkRH5POnMLmh9aTWL6SUHMTTSc2EmjwE5o4mfpjvWC4clW+3paLm3W8n/zELUqpwjDEQH2Yiv1rcXMumqaV9z9NofsM3Kyz2yMxdUsv26eTsQv70q3yx3MGPY7KedeDdnLHswqXqpxfR/0xxeL7r9/6Sv65QfVhTdQcUZxZefPfVpUHu6zhAl/6sAGwfS2by3W9frEvvP/synPY6wJT27dvx3Ec6uvry5bX19ezYsWKYbdpaWkZdv2WluHTHa+55hq+8Y1vDFne3t5OOp0eZou9mx4I8cS6Hg6Khljxl+XEOlNs357iuLNns/nRdWxY301fOle4VlElMRWlVNly8NoerrPY6vemg69Lupy2PcdAZZc7Kg22md5F5LS04ow+t2z73XFNrJixcUwajs/HDV3gOyXZ74vScFSGEfNBRqPVgF+WJFacl4AZ+Sv9jSbcGi62XdgPE99YzUZe9MHfSybB+nCvV7QZ4Gk/PFoyguDCHnijg1XuCcKr+evlmAPnl3wZ/+cQrMwHb2pt+Ej5JEn4DIe1922kZr9qAhUR1v95BZl2h/V3rmDSWbNJdCTZtKqFk/pyZB0XNMUSQyeZP+igk2RuNj/jEUDOTy7fltXaiatgIW/A7xRf6Jyew9G8X4KOjuEWI0xKKZRWDBTl7By6pmHo4A6cemhgKYhYCiM/bEjLaOD9QwcGZqbWABsNZyD4pEMQrZCMoJR3/BoazX6dEyaHMXRvn40rM1Tgzd52cMyielocXfPqIk15VceX9T5slaahdM2buErXQA+A4d3H0L3/B76tNIozxpxdF+DMuiC6ls8c25hANzV0XePiKj9azPKORYHWnUU3NExTRzc0dFPHMDWMgfujqC1wxg5by+mBIE+u6+Hgc2az+u4V9HeliFQGmXnWbF7qTXJstcVnjqrd+Y7eoIH3rGLgy1vmQkkAC1y3uGwg0OXmg1tO/r7yvjQtDA0tLivZRnnvbQPrOPk3UGfIOt5+B9Yt3Y+TP2bXHdgm35ZfVgzgecc3ELwbWNdbFioe18A2hfsT8vfLj8dVMEnNLB6TBm4o/zopiLsHYDsOtnJwFZiGd3L8z/r30q+fRJYcmspQlUmi3AZytncyGsnUUJXeRlKPEbMzg+p6eAFYBYUvK9q1OFk7i6O8APP0ZBvN3aswu1dzTm87cfsKYpl+NA0sXIKmRronRfKZ27Fdl/0SncV+mEmSyRfqzynvxM1KJ7FzNn09vbT52silbXK2XXZcTn83bW1tALT1bsfO2aR0y/u92Q455T0323HKtrN1H4lUCretje2dndg5G1vBs43zmTj1MIKRajRfiHWJbjrTKeyc9z7W1dFBbMYpBGeexvNbl/Gb9S8V9nnGUR9C6ToOsHTjyzy26TVI9mPpRuEYAVYlU7zW7WW4xK1AWduLW9bycJtXz60xEOVDtcVhag+sXcJTHd6QqenhKo4NFWsZ/m75s7zUvQ2AA+L1zNi/+E32ta/+g5X5jJoFVRP4nzknFNo+sfheNuZnljqhdiqf3+8dhbaPvPAXtmeSGJrGuxtmcem04iwHlyy+l4zrYGo672rYj7PztQaVUnxh6cMYmoap6byzbgbH1ngXcBnH5mdr/4Oh65iazrHVk9k/7p3jJe0sf9u2ivfsdxCnPfRL/LqBrrwSBgrFHSddyPcf+j6Jjtf4L02nSfNOJpbVn8Yv2sOk/34TGdfhugNOZkJ+aN0jbWv44eqnC8f8+8POocbvfT79c9tKfra2WKfo2HAjkXz22dJtG3l4U/G8dFtrC0Z+qE5vXy/9aS/g0+sky353djpd6Ce9lLc5qUyhrTvZT3tnN/gbwN/A+qY2/uXz6idNC1fynoPenZ8TEe5c9ihrurZCzqY70UdbWxtOeBLm0Zew+PWn6OltJ2SniFc0lz1ete6jKpfBrxsYCnK54gVn1PDhGl5gK6AZpJxi//zctKOwUj00BGJYuk4ul4Xcdujdzs9P/jL+eFPh86f1mVvIPH87AKWDIzXDx5pQHC0QQwvGOT7WQPDEfOAwkaEt0YazfS1oBrFgDC0QAz1HN+VZu3uS67r09PQQj8fR83+j4++qYO/gZSHZ4NqFoA6ujRauRivJgnN7W3C7tqDcnDcDppNDD1VgTizPiso8/yeUnQYnV7I/B+VkC9vh2vjmvBNrv+ML2zkdG0jed3V+OxvyjxO99O7ymoAPfpfcqid2+ryMxrlE3n8DAPGgga4cDH+GiadOY1O2hYmnzUC3erG7t6ECUXpSI1xE6IDf+0mV/E0SBm12sHCdU/r3yn4B4vs1e59RjkLZLiqnvOBWLv9jq/z/5fcH1rHDqmyfqVQKO//ZnsplytrSfSkce/RBqOEk0sX3OKUU2VQxMaQv0Yebb3NzLj3rO4fdx2hohoZm6UQX1OFv8j7XnL4c/a92olk6uqkRmBHDjHnv327aJteRQbPy2Vumt71u6d55+Rhnc1mWRTwYo6enp+x9eTzq6+sb9bp7XWDqrfClL32pLMOqt7eXiRMnUltbSyw2tND03q4rleOweJjX7lpGsitFVGmku1Ks/esKpp09m26fydYXtuzSTLeb+2zWp703bC3nUpo92p+DfuVlltjKZeA78zfyN2yZRuFNwDBcNM17QB2wzGI3NXQHTduFJzIMXSvfp16yz6FtNtobDFYbuoZllmTJaHbhm5zBbXpJ27C0Yk4PGsVvhTTwginQVOHHjgcwdY1oLod/QzEyNa8pTHNlBEPXCCYT+Ff1FFIegroPQ9Opn11D9dxaVv1lKVpvzqtd0ZVm9V+WMvO988i4OTa+uh2VP+xPH3MAejyAoWu89tor6Csy3hS9wPffNRcz4g13ee655/GtzOafp8bUilD+9dBoT7v05juZqetMi4QL+9iWcunLzxIXsnRevOr4Ql+55Y8PEdjgncTXB3UevbxYB+bu//sX5rYMhmkQ8fuZHK9CM7wPn+5cGkyNgGUR9PmwLNNrMzTvA0r3Aj1W1Ed4UjEymlqQANcrNGoEzbKaEe5xTnHq4T31gbaXlcg7bqbfy8w8ew6b8pmZL/WlOG5mHdUhi5GG0ol9gyoLxikc91qcbIJc19ZCIXanextO91acnq24XZtQ2QQ3n1ODm0vSn5lBKtOH2Z9hznt/jhGuosl1sHtbMZWN1tPCCyedT72y0WsibLYsdNelPhRDuTlcO02INLqTQ9M0lNKotAyOC/uYWlvNfoEUFUYHmbBGv64IBEJkrRBZK0CwcUahRmW95TI1XsPaGe9gczbBh+ccQ3P1BHR/hJf6u/nf5U+TMi0ypo/bjj+fyfnMoUi2A9PyPh8enLuIT5z0UeqCXp9/Zv0r5JY8Xjipaqwrzr4Z6t1Y2A6gsb6+8B4R7AgX2oKGr6yOZrA1VGgLBQJlbf4twRHbfBsChbZwKFTWZq3xF9oioXBZm+n3Yaa9tlg4Ut7m82HmZ66MR6JlbbppYrr5x4uU77PDyZBxvfdoFSg+P8d1WZ4sZrS9Y+J+hbbebJrHXtxQaJvfOLnQtjXZw+2Ll3DC9Lm0ZhLUByOEdBPdMMi6Nik7w0NdLThaNaeaXUzWXSJzL6Si4WS2/ueewj4D8Rh1Fd4+G51ezPXF30+ksoK6iJeZW5/Zjrmp2BatqqA2P0SzLlGFubXYFq+uKkxWUNtThdnqtblATW1NoUZaTWclZnu+zdDLXq/atkoivUEChklNMFbWdnjfNAJBr60xVN72EXUkp6UT3naBMHU1dVBXB3MO50NHf8B7zvnhbr6SYXO/qDuP3IHH5WtidXkZWMkunGQPlflsLDdfNys2YQax/GO+q66ObMtqtgZKvnXLmzB5VtnEKNt1G9eyhqwHCtLd3k83GJnuIXVkt979WbLbisE/PRCj6pQriRz07sIyu3sbiWWPoIcqMEKVZQXhNV9olz6P3VQv1UEdwxcYMnnM3migTt5AsEbZWZSTw6qeVFYTLtu2llznRm89u5gJVNgu/6P7QsSPPr/sMboe+Sm57evL1ostOJfw3BML66Q3vUr7HZ9H5QNEysmOOFNo02V/wldXHKLavfweuh//Vdk6gelHUnfoKWXLNrx295A6e6UGfsvhWUdSUdKPsqqXTHL7kJXraqrKhji3RytIDNtPy/l8ZqGfuv3b2fST95PdtorQ/FOYcNpX2H7fp0mvegqjopFJn/q/YWsj703qPlxXyOZCUVYsP/ROP07aLmZ35WdcHv5+8XZpZnu8Ok71wPt+xqbL3FRoq6ippDLfluvP0m1ufmNPxobKykoidd55ezLXR8/WbYXmhvkTCdV5mZL9G7rZ/PTWYXejaRSHKg5kahWGLpZkb1kGlQfWFWqE5foy2IlcYX0z6tut6wGlFLmuNFseWUfz8VOxaoefgXy8CASG1nAcyV4XmKqpqcEwDFpby2csaG1tpaFh+CnmGxoadml9v9+P3z+0CKGu6+i7MXPJWAu6sOzxDSS6UmVBjUxXiq2Pr2dqvvZLNrsLEZZC9KPk/9G07a7SfQ7XNtztgUXFpJn8ba3kNoVaIQO3436DgybEvG9qdY3GjX1E+nNehkzE4rTZlRi65mXBLO8k2p8rPl2tuG9XuQzkm+majqnrhaeRdnOFtoWTo5x25DTMfNZNx59fwrS9LIhFsyq49Jg5mLpGTjms/uXAcFVFhS9I1PKjoWErh4393d5QJqWoD8WI5S96Mo7N2pIhK+85uJajFx4AwCvr1rPmj8VvP06fW8PBBzSi3BzPruilbVWqkNzU5DcImFB/aCObH1hDpiuJ3zDR88MDsl1JWh7fwORTp7Nq2YZCQcijJsepa/a+xU13bKBn/VZsTeFoilMnxIhUeN88r90UZVXrBq9NVxw1bx66YaAZOqvb1/Bk+xYcTRHy+zjukAVexo+ps3n7erYmOgj5fFSGwmXF/U874yhcXGL+ECGfVfb3e877jhuhQ8GkEVt2LNww/PAsAN0//t47dlVtxM8ps+vJ2A6HvW8uKUPjlOaY1LLbhyg7g5vtwc31o3IJ3GwClevHzf+oXNK7n+3HzQ20Jag86JP4DyqvWdS75GZ6X/oZ2kve0PyBS9hMpg0zXMm677+TTCaJUjY6Gst++gF0wyJTPZGpV9yNEa3Dsvw0+SO4Ti+Z1v+gmaAZCs0wMEyDZiPApOxT6JtfgM3QgXdSF50bYY6uEd7vfVQc9tmy45pFNz/tvh0tankXJt33kuz1oRkWs3Qft9X6UJoPxzAxX/sxPaYfTfexoGIW9yz6b28YuHKpCYbBSZPa8A+OdDRumDoJWzNwNAOzawm24QfD4siQSXy/g7AxcDQNzUmhGT40w8dhtRPRNe9LHkMrPweZXVHPotx+2K5LzOcva2sMx5kdr8dWLk2hWFlbyPQRswLYyiVolL8vOkoVPg8t3RixzRzUZiu3uJ0xeLvSNnPENrNku1zJ8sHH4pZ8Zg9po7yteFuhUtspvSLKoBM/5Aois88j1LGlbLus6xT2GTR9ZW0ZNXJb1nULbXXBKLPj9QRNr4aR0ii0HVzdzH/PPAJ/vr6RVvK7PWvy/hxdP7VQ+6j09frs/BP47Pxiplqpj8w8bNjlAMc2Th+xrTEcH7ENwF89Aaon7HCd4ViVjdR94HvF4YTJbtx0L0agfPZFNcKkK4OZ4coh5+DuoALxbroX3Sr/W8i1r6Xr4RsZjmZY+YBVBUa4ktpzvlk2M2K2bS1Of4e3TrgSlMuGH5/FtKvuLTyGch2c/o5i0MXODgnoDAR7gpMPxsjPCgiQaVlFatVTZesa4Soq3lEe/Nn+t2vIdW8t2beNcrJl+8bJETn4DKpP/lRhOyfZzaYfnDzkeU/69H0YFcVroMSr99Pz1O+HrDeYGW+g8pgLy5alN7xEZvNrZcvcuSeV/Q50TcdJjC7bRSv5+wKGneAHJzekL+imhZMZuuoQ7qD9W8MXnR9yHCOst6NjU6afif/v/7D72rG7t7LhB8cy+dP34Dv3h95jmL7xcV2pg2EOHYsSK6k3NVoDRfQHglS6r+TzwjJpOnEqbs7ByTqEGiKFNl3XCTdHywJcA0Mfd4UZKHlPdVRZ0oTpL/k8zKkdJlS4+WGLJHacrVR9UENhn32ru2h/thhcm3354YUhhttf2Er3svZCUMvwlQe4BoYo+qqDGD6DTfevIt2TYtP9rzPxXcPNODt+7MrfwF53NeHz+Tj00EN59NFHOeusswAvtfbRRx/lk5/85LDbHHXUUTz66KNceeWVhWUPP/wwRx111FtwxGMvrcOE4yfT1ZEk0ZmkS/MGS4Uqg8SPn0JrR4Jn4xZZR3mzI+nk/9e8IVElyzRNw9BgWtzPRL+BqWuEbZeWvlxhONMxVQHwmxi6RijrkOnNecOZNG84kq6TH07lBXcMLf94+eFXuq551Xh07xgMHX4+pQLT0DF0Dbcrid2dT9XW4L65TRi6l0mzbUsbPdt7UHj394vXFCJQK3vaaE33YbsuIdPHUbWTC6/RE61r2JLoxVYOlZEIvzrmwELbT554lBUdXdgoJlVV8c3ji/3m8+m7aOvuwVEuh1ZP4KKZh3hpycrhK68+SXcmhdIUxzZM50PTD8FJtOHm+vjskldIOjagOLayigu1Li/V2LW5qHpVfvpZxclWkCO3vkJ42rvJ+qv4ysT1hcc+pzLMMe42lHLosW1u2e7VYFJKcV4oy5H+DMp1SNoufwjGCmNnLrFcwAtMmREft03bipvpxsl0cfHyX9DyuvfNUa9dyS21xamlrzI2UaWnSa4/m6bTzmTtHe20dPaBBrpyidSHOfBdM1jV/zrfr38QR1PYuuJXz90Cfh+aFSaZi3DjBIugrhM1TU5b24YKRtGtMFMaKmk/cQIRy0/M56d20iQMTQPX4TS3llPUAiKWb8jMW6fuoKhmY7xiVH8jYs+J+E1Clk5HRwfV1dXj46TrbUApF5Xtx7UT3v+FwFI/yi4PMKlcAjeXIHbgpVgVM8r20/vqz+lf8addfnwn1T5kmW6F0YY52fVPrkepPqZ9+2VQYHdtZtOvLuTAS/+AWdHkDb81/Uz+zP2FbZLr/0HX06ObjUjTtMKXGJo+tA6YcjK42d4hy4d9XiW3g5MX0bTfWWXtdqKD7v9ciwlMLVneVXI7DhxZcn/bi+CrO4jaRTdxVN0UjsrXeVJ2htZ734tm+EC3WGj4OFr3AlikLDr//Xi+zcd5ho8PRi00w4ceKH+O3z70NABSm/8FQHrbf9AMC023+OHs/clxII5uYup+3EwP6N5+vnvIaaTcnBcIs8q/4fzCASfSb2ewXZcJgwIdH5t1JEk7h61c5leWFzo+c/L+hUDerHj5UN8jaid5E4Aot5B5NqApFMdWTuHzfIAzuMicBuDipDoxrDA6UKPlsHCo3P8iIrPPA6A2EOGU5lmFIFJNoDiUfGa8hv85+JRCDaPSouLHNkzjH6dcQsAw8RlG2efTiU0zObFpJsM5pGYCh9QMH+yZEK5gQrhi2LbxxAjGiMw7aafr1Z7zTaqGzFrYXbydX+6rH/paDjtzYaii7L6b6BqyzgDl5HD62nH6vPcnbVAh9d7n76L3+TtB09F0g8YLb0L1lb+X2V1b2fS/Z+/0eQI0fORGQjOK55DZrSvofPSnZev4aqcNCUylNywm2752p/sfMoPnCDNlKqf8Ynrw8x7J4BkgR9p26P5Hfzk5ZFtz6Hs0ztBggGYF0Xzp/HuZiWZ6XyZohuXNeJe/b8bKS7sYgSjRQ84stJPfdvD0itGD3kNg8sGFfRb2beZvmz7vcX3FLEEj/15hRKowozUYVZPw1U7DjL35ZQ32VpqmoZneqAOC5X1HN3Uq5g3/2lgRH5PPmTNkuXK9oYrFLK2SDK2SzC0nf9uKFPuTZniztw6sq/uLv/NdLiQ/0vMdZeF6O5Ej17vjyGpkWiW+igDr/rSETFcaPWiQ682w+e+rmXD6+A5OjdZeF5gCuOqqq7jgggs47LDDOOKII/jRj35EIpEozNJ3/vnn09zczDXXXAPAFVdcwXHHHccPf/hD3vWud/GnP/2JF154gV/+8pdj+TTeMj7TwF/j58QLD2TD31aS6cngj/uZ/J5Z9Po1qvxxvn1A8ZsTrxaKmw8WeX9QGcemNdVHznXIug6TI5WFk8H2dD+vdG4l6zjYyuXcxilE8yetq3raeXTrKmzlknUcPj77yELbU63r+L8Nr5F1bHKuy48WnInfMMC1uXPdy/xhzWKyro3tOjwQOxuUA8rmJ30vc9vW1SgUEcPk8QWfLxz7TckV/F/ba7jZXmpNnTtj070x48rhro5tPNrTD0ox2aezwBdDud548ge2J3k25WBGmtmfRj5U8vq90PkoryT6AcXB6TQtd/84v53NmmQza1w/oIhuf5T+7m8UtttafQFb8kX059Q4RKdW0vnv60htfJS1zjy6839es1q309VRnNZ5PfPJ5t9YDulspa+nBX/9oVjBGtZGi7UTunztuG1/A0BTBq9bxXFdXWozWq4jf+3lY62v+GaeMYr78Pt9bIykcMx+HK2LrFE86bD0LBsD3YX7abMVS0+QWX8T6oDTmXrWXFbcs4REV4KKqiDTzz4Au8Ik2LmBc3ybiOIQwSGS6iWX9t6M36E0jkFhukAWWAEDl39zZ5/LUYdcSalM+ytsf/gSL3vADJGyQmhWGM0Ko5thNCvkXdxa4fxFrtduxSbjKwmqgXdhDow4Ha/YsxznDRZfEwXKyZUEjEoCSoMCSSrXT3T/j2KEyocGdP/nWhKv373Ljxueec6QwJRm7d5wTDc3zMxyvhH2pWkk1txFdO5H6HzuO1Qe/nVIbMKwNDofv4zqY7+PERqU8ewOvWAaDc0YLjC1m/saLsjl7l7tB00f7mIvg92/ZZi1d8yMTyEy6wNDlnc99VXUDlIMckCq5L5P0wn6K2k45/4h685edRNOuhNN9y7uOvL/a7qPU/OBNM3woaV99LX5vIs/w8cX9n8fml5+AZjtXAF2muv3m+1to/vAsHBSHWi6RaXh568nXTDse/nUaDVPv/tTtKd6meUDDAM304fui6JluqkLhPlDs4/IrHMJTigWhG8Ox/lWPmA3WG0gwukTh14QAfgNszBjo9g9ui+I7gtCxa7NzqaUovEjP/GGFia6C8MMraqJZeu56VHWL9F09EB51rOT7KH+3B9i5AOFdm8byrGxe1oplKnwBShMQ7izYx5cj2cUQR0AzNEFjiiZkRBGDjjtcmBK07y/52GyhqzaKahcGi0f/MGwMAf9Lo1oDbEF/zU0qDMQ0DHMQnDHqiwP2Ib3P5nApIMK+9YMC90aOvRn0qfv3fFzGIERqaL2zK/tdL3ApPkEJs3f6XrDKbzH6UO/iBFvjKZrGD5vJsBdFZ4YZ9oHDxi2LTqjkkBdOJ+VVR7sckY5dBGNsuzQwmRgeEMBS5UGrYala1Qf3MCWB18n25WvbJffd643Q+u/NtB8ygzM0CjfK8apvfLT9r/+679ob2/n61//Oi0tLRx00EE8+OCDhQLnGzduLPuWfuHChdx222189atf5ctf/jIzZ87k7rvvZv/997ICLW+SiN8k6ST49iv3c/FJx9Py+BomHtfAlUvv5upDT8OX2ES6s4fVfV1csuRFbyYbpfj+9OksiIVRTo7XcgZXrC2O+/3VOz7A/HgtPYt/zAt9Kb7S4gVuUIqmep2pho1yc7yccPllT74bKcU71/2Kpkg1NSf+Ly2pPv7VUvwGaONf30M41w3ANqeOjU7xg23b327DyP9tZ5wGck6+0OmgPEtf/k3fSbWTcpL0vHRfoU3ZE3FcL4U6lcmSWP1IcUN7Cq4b9wrvDRr3btrJwsl71k3hJItD30xVD3gXI86gcYRWybFl8xfpAx/+VqH6L2QHbefHBXQslDfrG4BrY2g6s+J1mLqOTzeotteVbXOS3oWFi4liklYsxxnH5mPGVkwUForZweIHenMozk+POofcxkfILP83TVrxgmymluQ2awm+/HEMzN1oVc9FAbes/wcfO3MRW55YS/Nx0/jV+kf4dM2ZTI5U8EGjfOhs8TUZ+cRtuAtelfOCaF7KehY30z3i9qVCU08fEpiyezfQdv95aGYwH8QKovsiaOZAYCuIZkXybWH0fBDMiDThrz2wbF8DxY/H85hu8ebzUtYzhaFtI2UmRWafh26Fy7bt/s/3SG1+ApXr36VASWjau4cEpoYLvoyGmx0mmDToOAfTDF8xUGxF0K0Iui8yNJAE+OsPo/r4H6H7iutq+b9NN9ND7ys/w022oZlBrKpZ3vJkG4lVd2IceDlGoKLseQennIZys5B/v1DuQC2T0tu29zvJ37fiU4cclxGqJzL7vPLtC3VRcsV9D3osPTA0e3NvDXLt1v5KhqcPlu1YjpMc/n1/R8Iz3zdkWc8L15HdvmSn22qaAYYPTbfQDD+1p/wWI1SLm+nBSLVzz6KLvGCDFcXN9qKcLHqylfjhX8SK7e6AbbG30DSNwOSDdrpefOGHiB7+PtxUT3FIYWl2Vv62cu2yYtcAbqoHI1zB+u8t8rLalQNWmK2//ZgXbNANJnzSy6hC7fzLmKHZQN7fpmaYxaBLYOi5kL9xjvc+aQ4K6gz85Jf7m/cftH8/Ne/+0tCsoXh51lDssPcSnnvSoCyj0kygkb/Qq33Pl3f6vM1YHTWnf26n6w27bbSmbHjleKWZPiZfeffwGWBir2P4TYzaNxYGUYOKN1cd1EB0WlVZgGpAqDkKOkODXSWzNHYsbqHhuCls7F5BpitdGGpoxfzUHzO5rP7XvmqvfYaf/OQnRxy69/jjjw9Z9v73v5/3v//9b/JR7Z2cdDcql+Dhtf8i66S57NTT+cbSB7l3wyt8df6x2H1b6HrqK/QpP8lcceaermVP0a17+Sy5+IFA8RvDXD54k1h1F7YbxbWnFdr6t60irXvfsyo3jrKnFNpSfZtx8jOmWIO+OciVzDdn4Q5pM/LLrJITY3vQOXJxnxqDy4SbJdvlBrVVkaNeyxIJRWkcVNBynl/DSPVi4ZYFfAAW6Z0cTB8WiuZBbZ+cvj92sB5T12kY2Kfm/Ul9x1xTeJ6hQc/1T75BJ+SajlLetLC/P+6DhcX9K24n2T8VdBOfbvJFzfQuQHQTTZ+S/98kpJt8SC+2hWqL6fBB0+Lw2knkzHeQiUS8kxHN2478Nt7t/P+agRGsoSfTwRX7H4PfH2DC+44k6fZyRXQhRqoNq3I/4odcibKTuAP1ZnIJr/aMnSy5IE+iconCxZFuhRhsuCyL0dCGuXhWOW+KQWWncOzUkPaRBJqPwX9ceWDKzXTR8td3F7K0vCBWpBDw0qxwPugVKmZ1mWGMUC3+uoNHeCSxN1HKRdmpwrC3wZlJoWlnDMn06H7hOrLtr+T7fBKV7fcuZHYiNPnkIQEf5aRw07s+A42bTQxZNtosJy9oG0HzeRmJwwWh/I0LqDzyq8VAUn5dzZe/vwtBMCNUNySIVmgLVBCdd6F3O1jNxE/fj67rhKa9i+i8C8uCUoXj1w00PQjm0ILLu8KKTyF+yBVvaB+FfVXMpOGcB0oCWtn8LFFZlGsXllESSFNudthAnqZbhKa9u2R9uyRYlhkUSCsG4DRz6Hurcp2yekujtceCXN7OhvwNATvM4ipbTzlgp1ADeV35fRmBCuJ2ks6nrya1/iHcbD+apuFvOIL4op9LUOptSLf86FYdZmzXCk3XnPEVUIrp33gRpVzs7m1s/OWFNP33rzHj3t+oZpjUnvm1EQI6Zj5o5EMzLYxIeYAlPOdEpv7P8zv9kqv2zK/u2hPO03Sd2OHv3el6RqSqrPaV2PO0QIyu3jR143ASLbF7Bg+r88UD+OLDF/qumFtLxdwdD/FUjjd74rQPzWfTfatIdyexYv63zTA+2IsDU2L0jEAFmquhmUEeXvccv12/hJzrYOommhmgf6VXL8Qa9E1otixQVJ4enHUc0M18W/nJbWlAyELhy2fbWLi44A2tA2r8YeZVNmDpBpauo5fMXjNFS3Oq3lHI8tFKju0QrZeIYef3qXBLio2eM3k+x9RPo+/pr6P3rik7ro9ZbXxU78LSTa9YqlGX/8bL4lOaCYZLzbEfQPcPqo8x/SBy3atLAjTFYM1/DQraaCUBoHdMOGDIviJzPkxo2ruoLQn8oOVTmLX8N2aaWQgqoZsjDj2LzD6vUB/jjbIq98Oq3G/U68f7t9K39Fai8y4gYWeIGja8fifReRdgRpqIzD531PtSThY3lxz2gtaqmEHs4E8Wg1rZfHDAThaLLOcDXK6dKKTSD3dB7eZ2bwrp4QJmKtsPysXN9kG2j9EOWvPVzqf2neVDiJVrs+3OE/PDE/NBrZLMkWLmSagwVFG3wuiBSglyjUC5zjDD3JIEmo8esm7P4huxe9YW+9ZAwW47ucOhGYEJJwwJjjj9W8h1rd7l4x0uAKuZO85MKl9Zyw9tDcMwGS3+hsNLMpkiXn/yRcqymjQrPGyQYDArPg0rPm2n6+0JZqSJ2IGXo/libG9ro66umtigTKm9naYbGMNkUu0O3R+j8sjdu0AdQtOoe/cdZQEx3FwxODYo02wg20wzhz+pDjQcns9KKgbJCvse2P9AYM7NgVJ7NsgF3mdnnhlpIn7Q5aTW3e9lsugWlUd/Eys+Zbf2Ld6erEFD0nRfED0QxYzXl9UJipbMArgrdpSJJIQQpTRDxzB0AjUGk87Yjy2Pe7PyvV2CUiCBqX2GphsYoVq0bAKfk8YP+H1RdDdHrmMZADEczjVaC0O3pmrFrJJ6clx72Lvyw8hMZsfrvELooXpmo/MbuvHpBpZhEjcme0Ev3eRY3eK4QvDGAL0Z3ecFaxbWT2Fh/ZTCY/Sv8E5eNd0YtF0xUKPpFkdpJgtLl5V809QcjtMcjuOefMPAfJ7FoNFuDruKzrtgt7YbjndSPGWP7W+seBeMl6H5YqTa2ojWNRA78LLdumDUDB/GCFkWVnzqsENthuMNm0qjcolhh8EMHHMxsDUQ1Ermh1j1o+yUF5goyagaLtvAtXc3yDVcJleyOFSRkYu0Dnk+scnUv/uOIctb/uqdIJdmankBiVBJLa7ifS+7K7rHglxOuhvNFyu7vyv9wgtU9nvZRk4Gq3Jowdve136Nk2zNZzMlymsv5fpRdnqYPUPTeU8PCfRm214m27F01MdXOE47AVSULRsuU29nNM0YNkPEX3+IF3DKB410X6Q4NM7KZyjlM5U0M7jD2mn+uoPHbRDTCFTgum7ZffHGaZqOFZu88xVHqXLhN3a+Up5SCpQz4jDHqoXf9P6uCwGx3DCZZSVDKfNDLUuDZnb/VnoW/wTN8IOlY0bq6X3lJsxIM2ak6Q0/X/E2JnWChBBjSNM1rMoAzadMxwr53jZBKZDA1D5DuQ6kOrB0nQnYaJqGqZL4fWEqT70VpXLUaSafyQdyikPCrEIAaNowwYOGs+4B9kyoZVeybHZG90uq7Jttb7tg9Gb6GHkYjxmdMOogo1KuN8wwlxy2OKkRqCJ6wMXeOoXhicl8wGughlDKqw/kFrMNhw1y7eZwxWGDXMrFSXmzKjL60YrogSoaz/n7kOVtf/8wbq4fvSRTS8tnbhXrc3lZXUa4GSNYTd+y3xOb/3GCwTBusoW+pbcQnXch2e1LsHvWFYKAhcyk0qymbH9ZtoTui9L4voeHHFdq/YPYfZuHLN8ZlUug+cqL2+5OMAmGr7/kq97fyyrJZyQVhncWMpQi3utXCDBFvBo5wwTNg5NOIjhp5zNaCTHeeLMi5rOCh7Er2bvDcdLd9C29hVzHUgITTyQy/zL6X/05uY6l9C29Zdxl3Ym9h9QJEkLsDTRNozvRQ11414Ynj3cSmNoHOOlurxDoKZeCbqH7ot4QJDeHkWpHD9fKSZoQJTRN9wIYg4IYA4xQHbEDPjqqfXkZQF4Aq3SoSeGxzCDR/f+7ZBhZoiTAlRh2qCKMUEdrF2pnlRpuuCKAk2zDzfaOaqhi5dHfpn/57+lf9nvsvk1E5l9GZ/5iECDQtJC+pTfv0nG5uQRKqSGBm92fGS6BPuh3alVMR9mpfCCpOIxyuEylsqLewwS/I7PPhT0YYBdC7LrS+mTReReScMJUHf2tQpBcznfE7pI6QUIIMXYkMLUPGCgEWqwJZBC2LPqW3kZ03gVykibEm6g4VHH4OjNGoJLY/I/vdD9KKa8Yt53EzSVGGLqlEZ17fj4QVlJgvlCfK4lrJ4YMdRs2yKXULmVz9a+4ndgBF5PrXEmuYylt/7gEy7KwqucRnXchvYt/POp9FQ/CK0CuDQqcmeEGVLZn+IBRPpNr8ExvuhXGCFQPeYg9VeRaCLH3KK1PVhxuLplSQgghxHglgal9xJ6sCSSEeOtpmuYFaKwQRnD4qZN1K0TsoMt3ui/lOoUAlxqxXpYiMuvcfIDLG9Y4sH5p0fmBoYq5jqWkNj1G/LDP0PX01YW9xA/+JGakCbt/a74A98DwNi8rqZipFB4y9E0zw8MWSK465tqdv2BCiLe1vW24uRBCCCF2nwSm9iFykiaEAG8yBM0XHTKsrWwdTSd+yKd2uq/CUEUnh3KSdP37a2XtPYt/QtXR36L2lJtHrCkjhBBCCCGEECOReUyFEEKMSDN8GIFKNMNH//LbyHWvxqqeR93Jv8CqnlcoODxcsXAhhBBCCCGE2Bn5elsIIcROScFhIYQQQgghxJtBMqaEEEKMykDBYT3UQCqVQg95BYfNSNNYH5oQQgghhBBinJLAlBBCiFEbnBklmVJCCCGEEEKIN0ICU0IIIYQQQgghhBBiTEhgSgghhBBCCCGEEEKMCQlMCSGEEEIIIYQQQogxIYEpIYQQQgghhBBCCDEmJDAlhBBCCCGEEEIIIcaEBKaEEEIIIYQQQgghxJiQwJQQQgghhBBCCCGEGBMSmBJCCCGEEEIIIYQQY0ICU0IIIYQQQgghhBBiTEhgSgghhBBCCCGEEEKMCQlMCSGEEEIIIYQQQogxIYEpIYQQQgghhBBCCDEmJDAlhBBCCCGEEEIIIcaEBKaEEEIIIYQQQgghxJiQwJQQQgghhBBCCCGEGBMSmBJCCCGEEEIIIYQQY0ICU0IIIYQQQgghhBBiTEhgSgghhBBCCCGEEEKMCQlMCSGEEEIIIYQQQogxIYEpIYQQQgghhBBCCDEmzLE+gL2BUgqA3t7eMT6SN851Xfr6+ggEAui6xB3FGyP9SQxH+oXYk6Q/id0lfUfsSdKfxO6SviP2pH2pPw3EVwbiLTsigSmgr68PgIkTJ47xkQghhBBCCCGEEELsG/r6+ojH4ztcR1OjCV/t41zXZevWrUSjUTRNG+vDeUN6e3uZOHEimzZtIhaLjfXhiHFO+pMYjvQLsSdJfxK7S/qO2JOkP4ndJX1H7En7Un9SStHX10dTU9NOs78kYwrQdZ0JEyaM9WHsUbFYbNx3ZLH3kP4khiP9QuxJ0p/E7pK+I/Yk6U9id0nfEXvSvtKfdpYpNWB8D1oUQgghhBBCCCGEEOOWBKaEEEIIIYQQQgghxJiQwNQ+xu/3c/XVV+P3+8f6UMQ+QPqTGI70C7EnSX8Su0v6jtiTpD+J3SV9R+xJb9f+JMXPhRBCCCGEEEIIIcSYkIwpIYQQQgghhBBCCDEmJDAlhBBCCCGEEEIIIcaEBKaEEEIIIYQQQgghxJiQwNRb4JprruHwww8nGo1SV1fHWWedxcqVK8vWSafTfOITn6C6uppIJMJ73/teWltbC+2vvPIK5513HhMnTiQYDDJnzhx+/OMfl+3j8ccfR9O0IT8tLS07PD6lFF//+tdpbGwkGAyyaNEiVq9eXbbOd77zHRYuXEgoFKKiouKNvSDiDRnv/Wn9+vV89KMfZerUqQSDQaZPn87VV19NNpvdA6/O29d47xcAZ5xxBpMmTSIQCNDY2MhHPvIRtm7d+gZfGbE79oX+NCCTyXDQQQehaRovv/zy7r0gYtT2hb4zZcqUIfu99tpr3+ArI3bHvtCfAO6//34WLFhAMBiksrKSs846a/dfFDFq473/jLRfTdN4/vnn98ArJHbFeO9PAKtWreLMM8+kpqaGWCzGO97xDv75z3++wVdmD1HiTXfKKaeom2++WS1ZskS9/PLL6vTTT1eTJk1S/f39hXUuvfRSNXHiRPXoo4+qF154QR155JFq4cKFhfbf/OY36lOf+pR6/PHH1Zo1a9Tvf/97FQwG1Y033lhY55///KcC1MqVK9W2bdsKP47j7PD4rr32WhWPx9Xdd9+tXnnlFXXGGWeoqVOnqlQqVVjn61//urr++uvVVVddpeLx+J57ccQuG+/96YEHHlAXXniheuihh9SaNWvUPffco+rq6tRnPvOZPfxKvb2M936hlFLXX3+9euaZZ9T69evVU089pY466ih11FFH7cFXSYzWvtCfBnzqU59Sp512mgLU4sWL3/iLI3ZoX+g7kydPVt/85jfL9lt6/OKtsy/0p7vuuktVVlaqn//852rlypVq6dKl6o477tiDr5IYyXjvP5lMpmx/27ZtUx/72MfU1KlTleu6e/jVEjsz3vuTUkrNnDlTnX766eqVV15Rq1atUpdffrkKhUJq27Zte/CV2j0SmBoDbW1tClBPPPGEUkqp7u5uZVmWuvPOOwvrLF++XAHqmWeeGXE/l19+uTrhhBMK9wc6cVdX16iPxXVd1dDQoH7wgx8UlnV3dyu/369uv/32IevffPPNEpjay4zn/jTg+9//vpo6deqoH0fs3L7QL+655x6laZrKZrOjfizx5hiv/envf/+7mj17tlq6dKkEpsbIeOw7kydPVjfccMOo9yveOuOtP+VyOdXc3Kx+/etfj3q/4s0z3vrPYNlsVtXW1qpvfvObo34c8eYZb/2pvb1dAerJJ58srNPb26sA9fDDD4/6sd4sMpRvDPT09ABQVVUFwIsvvkgul2PRokWFdWbPns2kSZN45plndrifgX2UOuigg2hsbOSd73wnTz311A6PZd26dbS0tJQ9djweZ8GCBTt8bLH32Bf600iPLXbfeO8XnZ2d/PGPf2ThwoVYlrXD/Ys333jsT62trVx88cX8/ve/JxQKje6Jij1uPPYdgGuvvZbq6moOPvhgfvCDH2Db9s6frHjTjbf+9NJLL7FlyxZ0Xefggw+msbGR0047jSVLloz+SYs9Zrz1n8HuvfdeOjo6uOiii3a4b/HWGG/9qbq6mlmzZvG73/2ORCKBbdv84he/oK6ujkMPPXT0T/xNYo71AbzduK7LlVdeydFHH83+++8PQEtLCz6fb0jtpvr6+hHHkj799NPccccd3H///YVljY2N3HTTTRx22GFkMhl+/etfc/zxx/Pcc89xyCGHDLufgf3X19eP+rHF3mNf6E+vv/46N954I9ddd92onrPYufHcL77whS/wk5/8hGQyyZFHHsl99923S89d7HnjsT8ppbjwwgu59NJLOeyww1i/fv3uPHXxBo3HvgPwqU99ikMOOYSqqiqefvppvvSlL7Ft2zauv/76XX4NxJ4zHvvT2rVrAfif//kfrr/+eqZMmcIPf/hDjj/+eFatWiVfyr2FxmP/Gew3v/kNp5xyChMmTBjVcxZvnvHYnzRN45FHHuGss84iGo2i6zp1dXU8+OCDVFZW7tbrsCdJYOot9olPfIIlS5bw73//e7f3sWTJEs4880yuvvpqTj755MLyWbNmMWvWrML9hQsXsmbNGm644QZ+//vf88c//pFLLrmk0P7AAw9gGMZuH4cYe+O9P23ZsoVTTz2V97///Vx88cW7/RxEufHcLz73uc/x0Y9+lA0bNvCNb3yD888/n/vuuw9N03b7uYg3Zjz2pxtvvJG+vj6+9KUv7fYxizduPPYdgKuuuqpwe/78+fh8Pi655BKuueYa/H7/bj8X8caMx/7kui4AX/nKV3jve98LwM0338yECRO48847y/Yp3lzjsf+U2rx5Mw899BB//vOfd/v4xZ4zHvuTUopPfOIT1NXV8a9//YtgMMivf/1r3vOe9/D888/T2Ni4289lT5DA1Fvok5/8JPfddx9PPvlkWaS7oaGBbDZLd3d3WYS1tbWVhoaGsn0sW7aMk046iY9//ON89atf3eljHnHEEYU/mDPOOIMFCxYU2pqbm9m2bVvhsUo7Y2trKwcddNDuPE3xFhnv/Wnr1q2ccMIJLFy4kF/+8pejft5ix8Z7v6ipqaGmpob99tuPOXPmMHHiRJ599lmOOuqoUb8GYs8Zr/3pscce45lnnhkSRDjssMP40Ic+xK233jq6F0DstvHad4azYMECbNtm/fr1ZRcL4q0zXvvTwPK5c+cW2v1+P9OmTWPjxo2jfPbijRqv/afUzTffTHV1NWecccaonrN484zX/vTYY49x33330dXVRSwWA+BnP/sZDz/8MLfeeitf/OIXd+2F2NPGusjV24HruuoTn/iEampqUqtWrRrSPlAo7a677iosW7FixZBCaUuWLFF1dXXqc5/73Kgfe9GiRerss8/e4bE1NDSo6667rrCsp6dHip/vxfaF/rR582Y1c+ZMde655yrbtkf9+GJk+0K/GGzDhg0KUP/85z9HfSxizxjv/WnDhg3qtddeK/w89NBDClB33XWX2rRp06iPRey68d53hvOHP/xB6bquOjs7R30sYs8Y7/1p4H5p8fNsNqvq6urUL37xi1Efi9g9473/lK47depUmcF6jI33/nTvvfcqXddVX19f2bb77bef+s53vjPqY3mzSGDqLXDZZZepeDyuHn/88bIpH5PJZGGdSy+9VE2aNEk99thj6oUXXhgyTfprr72mamtr1Yc//OGyfbS1tRXWueGGG9Tdd9+tVq9erV577TV1xRVXKF3X1SOPPLLD47v22mtVRUWFuueee9Srr76qzjzzzCFTS27YsEEtXrxYfeMb31CRSEQtXrxYLV68eEjHFm++8d6fNm/erGbMmKFOOukktXnz5rLHF7tvvPeLZ599Vt14441q8eLFav369erRRx9VCxcuVNOnT1fpdHoPv1piZ8Z7fxps3bp1MivfW2S8952nn35a3XDDDerll19Wa9asUX/4wx9UbW2tOv/88/fwKyVGY7z3J6WUuuKKK1Rzc7N66KGH1IoVK9RHP/pRVVdXJ4HOt8C+0H+UUuqRRx5RgFq+fPkeemXE7hjv/am9vV1VV1erc845R7388stq5cqV6rOf/ayyLEu9/PLLe/jV2nUSmHoLAMP+3HzzzYV1UqmUuvzyy1VlZaUKhULq7LPPLrtQv/rqq4fdx+TJkwvrfO9731PTp09XgUBAVVVVqeOPP1499thjOz0+13XV1772NVVfX6/8fr866aST1MqVK8vWueCCC4Z9fMlkeOuN9/508803j/gcxO4b7/3i1VdfVSeccIKqqqpSfr9fTZkyRV166aVq8+bNe+T1EbtmvPenwSQw9dYZ733nxRdfVAsWLFDxeFwFAgE1Z84c9d3vflcC5GNkvPcnpbwMqc985jOqrq5ORaNRtWjRIrVkyZI3/NqIndsX+o9SSp133nlq4cKFb+i1EG/cvtCfnn/+eXXyySerqqoqFY1G1ZFHHqn+/ve/v+HXZk/QlFJq6AA/IYQQQgghhBBCCCHeXPpYH4AQQgghhBBCCCGEeHuSwJQQQgghhBBCCCGEGBMSmBJCCCGEEEIIIYQQY0ICU0IIIYQQQgghhBBiTEhgSgghhBBCCCGEEEKMCQlMCSGEEEIIIYQQQogxIYEpIYQQQgghhBBCCDEmJDAlhBBCCCGEEEIIIcaEBKaEEEIIIcbY8ccfj6ZpY30YQgghhBBvOXOsD0AIIYQQYl+yqwEmpdSbdCRCCCGEEHs/CUwJIYQQQuxBV1999ZBlP/rRj+jp6Rm2DeB3v/sdyWTyzT40IYQQQoi9jqbkazohhBBCiDfVlClT2LBhg2RHCSGEEEIMIjWmhBBCCCHG2HA1pm655RY0TeOWW27hb3/7GwsWLCAUCtHc3MzXvvY1XNcF4NZbb+XAAw8kGAwyadIkfvCDHwz7GEopfvvb33L00UcTi8UIhUIcdthh/Pa3v33Tn58QQgghxEhkKJ8QQgghxF7sr3/9K//4xz8466yzOProo7n//vv59re/jVKKeDzOt7/9bc4880yOP/54/vKXv/D5z3+e+vp6zj///MI+lFJ86EMf4vbbb2fmzJl88IMfxOfz8fDDD/PRj36UZcuWcd11143hsxRCCCHE25UM5RNCCCGEeJPtbCjf8ccfzxNPPFHWfsstt3DRRRdhWRZPPfUUhx9+OAB9fX3MmDGD/v5+YrEYTz31FNOmTQNg06ZNzJgxg1mzZvHqq68W9vWrX/2Kj3/841x00UX84he/wLIsALLZLO973/v429/+xgsvvMChhx76Zr0EQgghhBDDkqF8QgghhBB7sQ9/+MOFoBRANBrl3e9+N8lkkssuu6wQlAKYOHEi73jHO1i2bBm2bReW/+QnPyEcDvPTn/60EJQC8Pl8fOc73wHg9ttvfwuejRBCCCFEORnKJ4QQQgixFzvooIOGLGtsbNxhm+M4tLa20tzcTDKZ5LXXXqOpqYnvfe97Q9bP5XIArFixYo8etxBCCCHEaEhgSgghhBBiLxaLxYYsM01zp20DAaeuri6UUmzZsoVvfOMbIz5OIpHYE4crhBBCCLFLJDAlhBBCCLEPGwheHXroobzwwgtjfDRCCCGEEOWkxpQQQgghxD4sGo0yZ84cli9fTnd391gfjhBCCCFEGQlMCSGEEELs4z71qU+RTCa5+OKLhx2yt27dOtavX//WH5gQQggh3vZkKJ8QQgghxD7ukksu4dlnn+XWW2/lqaeeYtGiRTQ1NdHa2sqKFSt47rnnuO2225gyZcpYH6oQQggh3mYkMCWEEEIIsY/TNI1bbrmF008/nV/96lfcd9999Pf3U1dXx8yZM7nuuutYtGjRWB+mEEIIId6GNKWUGuuDEEIIIYQQQgghhBBvP1JjSgghhBBCCCGEEEKMCQlMCSGEEEIIIYQQQogxIYEpIYQQQgghhBBCCDEmJDAlhBBCCCGEEEIIIcaEBKaEEEIIIYQQQgghxJiQwJQQQgghhBBCCCGEGBMSmBJCCCGEEEIIIYQQY0ICU0IIIYQQQgghhBBiTEhgSgghhBBCCCGEEEKMCQlMCSGEEEIIIYQQQogxIYEpIYQQQgghhBBCCDEmJDAlhBBCCCGEEEIIIcaEBKaEEEIIIYQQQgghxJj4/07ugWlcX+iGAAAAAElFTkSuQmCC", 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", "text/plain": [ "
" ] @@ -440,91 +441,95 @@ "Anticipation periods: 0\n", "\n", "------------------ Machine learner ------------------\n", - "Learner ml_g: LGBMRegressor(learning_rate=0.01, n_estimators=500, verbose=-1)\n", - "Learner ml_m: LGBMClassifier(learning_rate=0.01, n_estimators=500, verbose=-1)\n", + "Learner ml_g: LinearRegression()\n", + "Learner ml_m: LogisticRegression()\n", "Out-of-sample Performance:\n", "Regression:\n", - "Learner ml_g0 RMSE: [[1.79152793 1.81326944 1.8054232 1.80694168 2.47094152 3.37239486\n", - " 4.3303301 1.80638932 1.78492247 1.79906886 1.77017951 1.79014112\n", - " 2.5257241 3.38825777 1.79112596 1.7628981 1.80707893 1.82459002\n", - " 1.7949473 1.80655601 2.52190091 1.80338096 1.79623925 1.78961074\n", - " 1.76971177 1.80947666 1.78232135 1.77580097]]\n", - "Learner ml_g1 RMSE: [[1.81001117 1.74736062 1.73951704 1.76736419 2.45595687 3.22069965\n", - " 4.06967562 1.82961382 1.82863951 1.77281948 1.75404664 1.83431622\n", - " 2.61593344 3.37869708 1.78179777 1.8436273 1.85393631 1.78308466\n", - " 1.90635713 1.86073807 2.53288771 1.80448992 1.79240671 1.80233959\n", - " 1.9042676 1.79439788 1.81557319 1.817387 ]]\n", + "Learner ml_g0 RMSE: [[1.40819196 1.44505628 1.41728035 1.379876 1.39455406 1.37787884\n", + " 1.41904666 1.43565448 1.40797579 1.38017852 1.40451927 1.38072431\n", + " 1.43039705 1.37694706 1.34678838 1.34816616 1.37326228 1.37028313\n", + " 1.42504859 1.40519018 1.41838418 1.38843531 1.40401396 1.44212802\n", + " 1.4162496 1.38217708 1.39642643 1.37807542 1.4217658 1.44046735\n", + " 1.41240661 1.38467958 1.40290054 1.38110169 1.43555287 1.37725986\n", + " 1.41760164 1.34963967 1.34923315 1.36906865 1.36790292 1.37882656\n", + " 1.42638524 1.40408103 1.42022176 1.38230774 1.45470794 1.4070382\n", + " 1.44664494 1.41549701 1.38641625 1.40214879 1.3782188 1.41428144\n", + " 1.43695538 1.40921613 1.38186793 1.40181613 1.38614742 1.43599508\n", + " 1.37406388 1.41734029 1.3449007 1.35422125 1.37337241 1.36970938\n", + " 1.37931347 1.38973654 1.4245557 1.40297224 1.42138834 1.38065336\n", + " 1.45184981 1.45946997 1.40136093 1.44723397 1.41798682 1.38054239\n", + " 1.39672286 1.37835191 1.42119314 1.4368779 1.40631144 1.3798683\n", + " 1.40614563 1.38467314 1.4307338 1.37330371 1.41948619 1.34386942\n", + " 1.35453612 1.36734589 1.36875058 1.37659987 1.38817831 1.42855774\n", + " 1.4040472 1.42513802 1.3833212 1.45272282 1.4632959 1.36559363]]\n", + "Learner ml_g1 RMSE: [[1.47605877 1.43614981 1.43189302 1.41568368 1.45445651 1.46455328\n", + " 1.40513321 1.45989737 1.48957798 1.45307455 1.44203462 1.4623187\n", + " 1.46718391 1.43394086 1.38222077 1.42826691 1.464996 1.43138203\n", + " 1.44008356 1.45884963 1.46468759 1.43461302 1.4260418 1.37827039\n", + " 1.41634728 1.3602931 1.31745734 1.40214917 1.40656252 1.34977232\n", + " 1.40854993 1.37663541 1.40770326 1.39217878 1.43824775 1.39905778\n", + " 1.38000488 1.42700417 1.39766271 1.4244126 1.45105544 1.42307302\n", + " 1.43541971 1.42357496 1.43008494 1.4558015 1.36171327 1.44502331\n", + " 1.37754443 1.39154287 1.34879869 1.42305005 1.37082086 1.41723063\n", + " 1.53079569 1.4165877 1.40730615 1.41443007 1.43728109 1.4068946\n", + " 1.40884084 1.47446069 1.3632237 1.44705699 1.46048099 1.3975671\n", + " 1.41686714 1.38395203 1.4054631 1.51285144 1.4687299 1.40211314\n", + " 1.45000125 1.44030773 1.41158239 1.43214102 1.41042458 1.45950582\n", + " 1.41411585 1.38231938 1.43923629 1.47058911 1.44001136 1.37001004\n", + " 1.42089026 1.39028541 1.43794863 1.38073535 1.34795159 1.43850685\n", + " 1.4121238 1.37989255 1.39914325 1.4266645 1.38634561 1.45871682\n", + " 1.48979386 1.44037473 1.43479917 1.42681476 1.41880263 1.41376113]]\n", "Classification:\n", - "Learner ml_m Log Loss: [[0.67557856 0.67288349 0.6648673 0.67501146 0.66570519 0.67115224\n", - " 0.68066941 0.69235836 0.70052703 0.69100833 0.69927306 0.69189097\n", - " 0.71101967 0.7033916 0.71521098 0.70924631 0.72316055 0.71177035\n", - " 0.71347509 0.71951124 0.72730388 0.75837297 0.74186801 0.74919736\n", - " 0.74636338 0.75038344 0.74348438 0.74928883]]\n", + "Learner ml_m Log Loss: [[0.6402197 0.63989553 0.63958696 0.63878686 0.6386713 0.63947377\n", + " 0.63932819 0.63832396 0.63823896 0.63978397 0.64174643 0.6394592\n", + " 0.63814901 0.64072084 0.63928224 0.64275765 0.63857932 0.63827519\n", + " 0.6387454 0.63841224 0.63902581 0.63875985 0.65284347 0.65432609\n", + " 0.65229215 0.6525085 0.65261234 0.65388986 0.65270304 0.65188196\n", + " 0.65434595 0.65251652 0.65305233 0.65333667 0.65322817 0.65326683\n", + " 0.65282586 0.65269046 0.65267178 0.65268175 0.65331524 0.65393183\n", + " 0.65381077 0.65325546 0.65522282 0.65399914 0.65208396 0.67407657\n", + " 0.67359855 0.67321106 0.67375347 0.67369195 0.67411811 0.6757009\n", + " 0.67405671 0.67316274 0.67355969 0.67423525 0.67583733 0.67492287\n", + " 0.67259788 0.67359116 0.67277904 0.67354769 0.67383592 0.67425601\n", + " 0.67452952 0.67374397 0.67489846 0.67551743 0.67355545 0.67291432\n", + " 0.67516199 0.67478959 0.68637751 0.68705498 0.68633705 0.68699719\n", + " 0.68670008 0.68653345 0.68628361 0.68719356 0.68669908 0.68615908\n", + " 0.68598557 0.68701368 0.68647181 0.68662731 0.68724774 0.68610139\n", + " 0.68681593 0.68848259 0.68637051 0.68603932 0.68657004 0.68700917\n", + " 0.68752109 0.6881993 0.68638602 0.6884052 0.68700503 0.68560155]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 5\n", "No. repeated sample splits: 1\n", "\n", "------------------ Fit summary ------------------\n", - " coef std err t P>|t| \\\n", - "ATT(2025-05,2025-01,2025-02) -0.126335 0.125715 -1.004936 3.149277e-01 \n", - "ATT(2025-05,2025-02,2025-03) 0.081016 0.128130 0.632297 5.271925e-01 \n", - "ATT(2025-05,2025-03,2025-04) -0.094349 0.117890 -0.800311 4.235309e-01 \n", - "ATT(2025-05,2025-04,2025-05) 1.069615 0.129334 8.270197 2.220446e-16 \n", - "ATT(2025-05,2025-04,2025-06) 1.891446 0.181301 10.432646 0.000000e+00 \n", - "ATT(2025-05,2025-04,2025-07) 2.796395 0.258786 10.805820 0.000000e+00 \n", - "ATT(2025-05,2025-04,2025-08) 3.990323 0.330378 12.078046 0.000000e+00 \n", - "ATT(2025-06,2025-01,2025-02) -0.093047 0.107462 -0.865858 3.865678e-01 \n", - "ATT(2025-06,2025-02,2025-03) 0.080593 0.111061 0.725660 4.680474e-01 \n", - "ATT(2025-06,2025-03,2025-04) -0.131845 0.111625 -1.181149 2.375436e-01 \n", - "ATT(2025-06,2025-04,2025-05) 0.123388 0.122133 1.010270 3.123662e-01 \n", - "ATT(2025-06,2025-05,2025-06) 0.857444 0.113622 7.546480 4.463097e-14 \n", - "ATT(2025-06,2025-05,2025-07) 1.782152 0.185136 9.626188 0.000000e+00 \n", - "ATT(2025-06,2025-05,2025-08) 3.039536 0.293384 10.360281 0.000000e+00 \n", - "ATT(2025-07,2025-01,2025-02) 0.037523 0.096977 0.386923 6.988132e-01 \n", - "ATT(2025-07,2025-02,2025-03) -0.092156 0.100885 -0.913475 3.609928e-01 \n", - "ATT(2025-07,2025-03,2025-04) -0.013253 0.109006 -0.121584 9.032285e-01 \n", - "ATT(2025-07,2025-04,2025-05) 0.163007 0.102667 1.587726 1.123482e-01 \n", - "ATT(2025-07,2025-05,2025-06) -0.203583 0.098786 -2.060840 3.931834e-02 \n", - "ATT(2025-07,2025-06,2025-07) 0.948008 0.100933 9.392434 0.000000e+00 \n", - "ATT(2025-07,2025-06,2025-08) 1.980269 0.148701 13.317112 0.000000e+00 \n", - "ATT(2025-08,2025-01,2025-02) -0.030199 0.111913 -0.269845 7.872795e-01 \n", - "ATT(2025-08,2025-02,2025-03) -0.143455 0.093717 -1.530726 1.258372e-01 \n", - "ATT(2025-08,2025-03,2025-04) -0.027104 0.089255 -0.303666 7.613826e-01 \n", - "ATT(2025-08,2025-04,2025-05) -0.015802 0.098179 -0.160949 8.721339e-01 \n", - "ATT(2025-08,2025-05,2025-06) -0.047013 0.097550 -0.481934 6.298529e-01 \n", - "ATT(2025-08,2025-06,2025-07) -0.079947 0.098730 -0.809756 4.180807e-01 \n", - "ATT(2025-08,2025-07,2025-08) 0.853624 0.093793 9.101148 0.000000e+00 \n", + " coef std err t P>|t| \\\n", + "ATT(2025-05,2025-01,2025-02) 0.102392 0.070229 1.457976 0.144847 \n", + "ATT(2025-05,2025-01,2025-03) -0.025312 0.068922 -0.367252 0.713431 \n", + "ATT(2025-05,2025-02,2025-03) -0.131312 0.068425 -1.919061 0.054977 \n", + "ATT(2025-05,2025-01,2025-04) -0.003033 0.068161 -0.044492 0.964512 \n", + "ATT(2025-05,2025-02,2025-04) -0.105774 0.071330 -1.482872 0.138108 \n", + "... ... ... ... ... \n", + "ATT(2025-08,2025-03,2025-08) 1.019466 0.064371 15.837414 0.000000 \n", + "ATT(2025-08,2025-04,2025-08) 1.034559 0.064665 15.998823 0.000000 \n", + "ATT(2025-08,2025-05,2025-08) 1.027387 0.065064 15.790351 0.000000 \n", + "ATT(2025-08,2025-06,2025-08) 1.039214 0.066547 15.616158 0.000000 \n", + "ATT(2025-08,2025-07,2025-08) 1.045628 0.063143 16.559774 0.000000 \n", "\n", " 2.5 % 97.5 % \n", - "ATT(2025-05,2025-01,2025-02) -0.372731 0.120061 \n", - "ATT(2025-05,2025-02,2025-03) -0.170114 0.332147 \n", - "ATT(2025-05,2025-03,2025-04) -0.325410 0.136712 \n", - "ATT(2025-05,2025-04,2025-05) 0.816126 1.323105 \n", - "ATT(2025-05,2025-04,2025-06) 1.536103 2.246789 \n", - "ATT(2025-05,2025-04,2025-07) 2.289184 3.303607 \n", - "ATT(2025-05,2025-04,2025-08) 3.342794 4.637853 \n", - "ATT(2025-06,2025-01,2025-02) -0.303670 0.117575 \n", - "ATT(2025-06,2025-02,2025-03) -0.137083 0.298268 \n", - "ATT(2025-06,2025-03,2025-04) -0.350625 0.086935 \n", - "ATT(2025-06,2025-04,2025-05) -0.115989 0.362764 \n", - "ATT(2025-06,2025-05,2025-06) 0.634750 1.080138 \n", - "ATT(2025-06,2025-05,2025-07) 1.419293 2.145012 \n", - "ATT(2025-06,2025-05,2025-08) 2.464515 3.614557 \n", - "ATT(2025-07,2025-01,2025-02) -0.152549 0.227594 \n", - "ATT(2025-07,2025-02,2025-03) -0.289886 0.105575 \n", - "ATT(2025-07,2025-03,2025-04) -0.226901 0.200395 \n", - "ATT(2025-07,2025-04,2025-05) -0.038216 0.364230 \n", - "ATT(2025-07,2025-05,2025-06) -0.397200 -0.009965 \n", - "ATT(2025-07,2025-06,2025-07) 0.750183 1.145834 \n", - "ATT(2025-07,2025-06,2025-08) 1.688820 2.271718 \n", - "ATT(2025-08,2025-01,2025-02) -0.249545 0.189146 \n", - "ATT(2025-08,2025-02,2025-03) -0.327137 0.040227 \n", - "ATT(2025-08,2025-03,2025-04) -0.202041 0.147834 \n", - "ATT(2025-08,2025-04,2025-05) -0.208229 0.176626 \n", - "ATT(2025-08,2025-05,2025-06) -0.238207 0.144182 \n", - "ATT(2025-08,2025-06,2025-07) -0.273455 0.113560 \n", - "ATT(2025-08,2025-07,2025-08) 0.669793 1.037455 \n" + "ATT(2025-05,2025-01,2025-02) -0.035254 0.240037 \n", + "ATT(2025-05,2025-01,2025-03) -0.160397 0.109773 \n", + "ATT(2025-05,2025-02,2025-03) -0.265422 0.002799 \n", + "ATT(2025-05,2025-01,2025-04) -0.136625 0.130560 \n", + "ATT(2025-05,2025-02,2025-04) -0.245579 0.034031 \n", + "... ... ... \n", + "ATT(2025-08,2025-03,2025-08) 0.893302 1.145631 \n", + "ATT(2025-08,2025-04,2025-08) 0.907819 1.161300 \n", + "ATT(2025-08,2025-05,2025-08) 0.899863 1.154910 \n", + "ATT(2025-08,2025-06,2025-08) 0.908784 1.169644 \n", + "ATT(2025-08,2025-07,2025-08) 0.921870 1.169385 \n", + "\n", + "[102 rows x 6 columns]\n" ] } ], @@ -533,11 +538,14 @@ "control_group = \"never_treated\"\n", "\n", "gt_combinations = \"all\"\n", - "gt_combinations = \"standard\"\n", + "#gt_combinations = \"standard\"\n", "\n", "ml_g = LGBMRegressor(n_estimators=500, learning_rate=0.01, verbose=-1)\n", "ml_m = LGBMClassifier(n_estimators=500, learning_rate=0.01, verbose=-1)\n", "\n", + "ml_g = LinearRegression()\n", + "ml_m = LogisticRegression()\n", + "\n", "dml_obj = DoubleMLDIDMulti(\n", " obj_dml_data=dml_data,\n", " ml_g=ml_g,\n", @@ -552,23 +560,37 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "(
,\n", + " [,\n", + " ,\n", + " ,\n", + " ])" ] }, - "execution_count": 7, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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First TreatedPre-treatment PeriodEvaluation PeriodEstimateCI LowerCI UpperPre-TreatmentRV
ATT(2025-05,2025-01,2025-02)2025-05-012025-01-012025-02-01-0.126335-0.5112710.258601True0.040722
ATT(2025-05,2025-02,2025-03)2025-05-012025-02-012025-03-010.081016-0.3113160.473349True0.023928
ATT(2025-05,2025-03,2025-04)2025-05-012025-03-012025-04-01-0.094349-0.4553280.266630True0.025671
ATT(2025-05,2025-04,2025-05)2025-05-012025-04-012025-05-011.0696150.6735971.465633False0.246898
ATT(2025-05,2025-04,2025-06)2025-05-012025-04-012025-06-011.8914461.3363062.446586False0.295226
\n", - "
" - ], - "text/plain": [ - " First Treated Pre-treatment Period \\\n", - "ATT(2025-05,2025-01,2025-02) 2025-05-01 2025-01-01 \n", - "ATT(2025-05,2025-02,2025-03) 2025-05-01 2025-02-01 \n", - "ATT(2025-05,2025-03,2025-04) 2025-05-01 2025-03-01 \n", - "ATT(2025-05,2025-04,2025-05) 2025-05-01 2025-04-01 \n", - "ATT(2025-05,2025-04,2025-06) 2025-05-01 2025-04-01 \n", - "\n", - " Evaluation Period Estimate CI Lower CI Upper \\\n", - "ATT(2025-05,2025-01,2025-02) 2025-02-01 -0.126335 -0.511271 0.258601 \n", - "ATT(2025-05,2025-02,2025-03) 2025-03-01 0.081016 -0.311316 0.473349 \n", - "ATT(2025-05,2025-03,2025-04) 2025-04-01 -0.094349 -0.455328 0.266630 \n", - "ATT(2025-05,2025-04,2025-05) 2025-05-01 1.069615 0.673597 1.465633 \n", - "ATT(2025-05,2025-04,2025-06) 2025-06-01 1.891446 1.336306 2.446586 \n", - "\n", - " Pre-Treatment RV \n", - "ATT(2025-05,2025-01,2025-02) True 0.040722 \n", - "ATT(2025-05,2025-02,2025-03) True 0.023928 \n", - "ATT(2025-05,2025-03,2025-04) True 0.025671 \n", - "ATT(2025-05,2025-04,2025-05) False 0.246898 \n", - "ATT(2025-05,2025-04,2025-06) False 0.295226 " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "================== Sensitivity Analysis ==================\n", + "\n", + "------------------ Scenario ------------------\n", + "Significance Level: level=0.95\n", + "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", + "\n", + "------------------ Bounds with CI ------------------\n", + " CI lower theta lower theta theta upper \\\n", + "ATT(2025-05,2025-01,2025-02) -0.109525 0.006676 0.102392 0.198107 \n", + "ATT(2025-05,2025-01,2025-03) -0.235913 -0.122100 -0.025312 0.071477 \n", + "ATT(2025-05,2025-02,2025-03) -0.338899 -0.226328 -0.131312 -0.036296 \n", + "ATT(2025-05,2025-01,2025-04) -0.209205 -0.096719 -0.003033 0.090654 \n", + "ATT(2025-05,2025-02,2025-04) -0.317787 -0.200853 -0.105774 -0.010694 \n", + "... ... ... ... ... \n", + "ATT(2025-08,2025-03,2025-08) 0.825698 0.931629 1.019466 1.107304 \n", + "ATT(2025-08,2025-04,2025-08) 0.841346 0.947790 1.034559 1.121329 \n", + "ATT(2025-08,2025-05,2025-08) 0.831890 0.939009 1.027387 1.115764 \n", + "ATT(2025-08,2025-06,2025-08) 0.840852 0.950433 1.039214 1.127995 \n", + "ATT(2025-08,2025-07,2025-08) 0.856187 0.960048 1.045628 1.131208 \n", + "\n", + " CI upper \n", + "ATT(2025-05,2025-01,2025-02) 0.313291 \n", + "ATT(2025-05,2025-01,2025-03) 0.184722 \n", + "ATT(2025-05,2025-02,2025-03) 0.076587 \n", + "ATT(2025-05,2025-01,2025-04) 0.202716 \n", + "ATT(2025-05,2025-02,2025-04) 0.107376 \n", + "... ... \n", + "ATT(2025-08,2025-03,2025-08) 1.213296 \n", + "ATT(2025-08,2025-04,2025-08) 1.227770 \n", + "ATT(2025-08,2025-05,2025-08) 1.222845 \n", + "ATT(2025-08,2025-06,2025-08) 1.237489 \n", + "ATT(2025-08,2025-07,2025-08) 1.235229 \n", + "\n", + "[102 rows x 5 columns]\n", + "\n", + "------------------ Robustness Values ------------------\n", + " H_0 RV (%) RVa (%)\n", + "ATT(2025-05,2025-01,2025-02) 0.0 3.205910 0.000443\n", + "ATT(2025-05,2025-01,2025-03) 0.0 0.793576 0.000335\n", + "ATT(2025-05,2025-02,2025-03) 0.0 4.121994 0.598492\n", + "ATT(2025-05,2025-01,2025-04) 0.0 0.098408 0.000605\n", + "ATT(2025-05,2025-02,2025-04) 0.0 3.331779 0.000561\n", + "... ... ... ...\n", + "ATT(2025-08,2025-03,2025-08) 0.0 29.652040 26.857627\n", + "ATT(2025-08,2025-04,2025-08) 0.0 30.317103 27.492452\n", + "ATT(2025-08,2025-05,2025-08) 0.0 29.691389 26.881212\n", + "ATT(2025-08,2025-06,2025-08) 0.0 29.860784 27.007606\n", + "ATT(2025-08,2025-07,2025-08) 0.0 30.930192 28.159719\n", + "\n", + "[102 rows x 3 columns]\n" + ] } ], "source": [ - "def create_ci_dataframe(dml_obj, level=0.95, joint=True, include_rvs=False):\n", - " \"\"\"\n", - " Create a DataFrame with coefficient estimates and confidence intervals from a DoubleML object.\n", - " \n", - " Parameters:\n", - " -----------\n", - " dml_obj : DoubleML object\n", - " The fitted DoubleML object\n", - " level : float, default=0.95\n", - " Confidence level for intervals\n", - " joint : bool, default=True\n", - " Whether to use joint confidence intervals\n", - " \n", - " Returns:\n", - " --------\n", - " DataFrame\n", - " DataFrame containing estimates and confidence intervals\n", - " \"\"\"\n", - "\n", - " ci = dml_obj.confint(level=level, joint=joint)\n", - "\n", - " # Create DataFrame\n", - " result_df = pd.DataFrame({\n", - " 'First Treated': [gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", - " 'Pre-treatment Period' : [gt_combination[1] for gt_combination in dml_obj.gt_combinations],\n", - " 'Evaluation Period': [gt_combination[2] for gt_combination in dml_obj.gt_combinations],\n", - " 'Estimate': dml_obj.coef,\n", - " 'CI Lower': ci.iloc[:, 0],\n", - " 'CI Upper': ci.iloc[:, 1],\n", - " 'Pre-Treatment': [gt_combination[2] < gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", - " })\n", - " if include_rvs:\n", - " result_df[\"RV\"] = dml_obj.sensitivity_params[\"rv\"]\n", - " return result_df\n", - "\n", - "ci_df = create_ci_dataframe(dml_obj, include_rvs=True)\n", - "ci_df.head()" + "dml_obj.sensitivity_analysis()\n", + "print(dml_obj.sensitivity_summary)" ] }, { @@ -740,19 +665,53 @@ "execution_count": 9, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDAggregation Object ==================\n", + " Group Aggregation \n", + "\n", + "------------------ Overall Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1.745067 0.026549 65.731185 0.0 1.693033 1.797101\n", + "------------------ Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "2025-05 2.401745 0.035213 68.206319 0.0 2.332729 2.470761\n", + "2025-06 1.969894 0.034375 57.305166 0.0 1.902519 2.037268\n", + "2025-07 1.507049 0.038340 39.307637 0.0 1.431904 1.582194\n", + "2025-08 1.013121 0.049838 20.328317 0.0 0.915440 1.110801\n", + "------------------ Additional Information ------------------\n", + "Control Group: never_treated\n", + "Anticipation Periods: 0\n", + "Score: observational\n", + "\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "/opt/venv/lib/python3.12/site-packages/matplotlib/cbook.py:1709: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n" + "/opt/venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + " warnings.warn(\n" ] }, { "data": { - "image/png": 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", "text/plain": [ - "
" + "(
,\n", + " )" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" ] }, "metadata": {}, @@ -760,126 +719,9 @@ } ], "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from matplotlib.lines import Line2D\n", - "\n", - "\n", - "def add_jitter(data, is_datetime, jitter_value):\n", - " \"\"\"\n", - " Adds jitter to duplicate x-values for better visibility.\n", - " \n", - " Args:\n", - " data (DataFrame): The subset of the dataset to jitter.\n", - " is_datetime (bool): Whether the x-values are datetime objects.\n", - " jitter_value (float or timedelta): Jitter amount.\n", - "\n", - " Returns:\n", - " DataFrame with an additional 'jittered_x' column.\n", - " \"\"\"\n", - " if data.empty:\n", - " return data\n", - " \n", - " data = data.copy()\n", - " # Initialize jittered_x with original values\n", - " data['jittered_x'] = data['Evaluation Period']\n", - " \n", - " for x_val in data['Evaluation Period'].unique():\n", - " mask = data['Evaluation Period'] == x_val\n", - " count = mask.sum()\n", - " if count > 1:\n", - " # Create evenly spaced jitter values\n", - " if is_datetime:\n", - " jitters = [pd.Timedelta(seconds=float(j)) \n", - " for j in np.linspace(-jitter_value, jitter_value, count)]\n", - " else:\n", - " jitters = np.linspace(-jitter_value, jitter_value, count)\n", - " \n", - " # Apply jitter to each duplicate point\n", - " data.loc[mask, 'jitter_index'] = range(count)\n", - " for i, j in enumerate(jitters):\n", - " data.loc[mask & (data['jitter_index'] == i), 'jittered_x'] = x_val + j\n", - " \n", - " return data\n", - "\n", - "\n", - "def plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8)):\n", - " \"\"\"\n", - " Plots coefficient estimates with confidence intervals over time, grouped by first treated period.\n", - " \n", - " Args:\n", - " dml_obj: The DML object containing estimated treatment effects.\n", - " level (float): Confidence level for the intervals (default 0.95).\n", - " joint (bool): Whether to use joint confidence intervals (default True).\n", - " figsize (tuple): Figure size as (width, height) (default (12, 8)).\n", - " \n", - " Returns:\n", - " None. Displays the plot.\n", - " \"\"\"\n", - " df = create_ci_dataframe(dml_obj, level=level, joint=joint)\n", - " all_time_periods = sorted(df['Evaluation Period'].unique())\n", - " first_treated_periods = sorted(df['First Treated'].unique())\n", - " n_periods = len(first_treated_periods)\n", - " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", - " \n", - " is_datetime = pd.api.types.is_datetime64_any_dtype(df['Evaluation Period'])\n", - " \n", - " fig = plt.figure(figsize=figsize)\n", - " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3] * n_periods + [0.5])\n", - " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", - " if n_periods == 1:\n", - " axes = [axes]\n", - " \n", - " jitter_value = (all_time_periods[1] - all_time_periods[0]).total_seconds() * 0.1 if is_datetime and len(all_time_periods) > 1 else 0.1\n", - " \n", - " for idx, period in enumerate(first_treated_periods):\n", - " period_data = df[df['First Treated'] == period]\n", - " ax = axes[idx]\n", - " i_period = all_time_periods.index(period)\n", - " \n", - " ax.axvline(x=all_time_periods[i_period], color='red', linestyle=':', alpha=0.7)\n", - " ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", - " \n", - " pre_treatment = add_jitter(period_data[period_data['Pre-Treatment']], is_datetime, jitter_value)\n", - " post_treatment = add_jitter(period_data[~period_data['Pre-Treatment']], is_datetime, jitter_value)\n", - " \n", - " if not pre_treatment.empty:\n", - " ax.scatter(pre_treatment['jittered_x'], pre_treatment['Estimate'], color=colors['pre'], alpha=0.8, s=10)\n", - " ax.errorbar(pre_treatment['jittered_x'], pre_treatment['Estimate'],\n", - " yerr=[pre_treatment['Estimate'] - pre_treatment['CI Lower'],\n", - " pre_treatment['CI Upper'] - pre_treatment['Estimate']],\n", - " fmt='none', color=colors['pre'], alpha=1.0, capsize=5)\n", - " \n", - " if not post_treatment.empty:\n", - " ax.scatter(post_treatment['jittered_x'], post_treatment['Estimate'], color=colors['post'], alpha=0.8, s=10)\n", - " ax.errorbar(post_treatment['jittered_x'], post_treatment['Estimate'],\n", - " yerr=[post_treatment['Estimate'] - post_treatment['CI Lower'],\n", - " post_treatment['CI Upper'] - post_treatment['Estimate']],\n", - " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", - " \n", - " ax.set_title(f'First Treated: {period}')\n", - " ax.grid(True, alpha=0.3)\n", - " if idx == 0:\n", - " ax.set_ylabel('Effect')\n", - " ax.set_xlabel('Evaluation Period')\n", - " \n", - " legend_ax = fig.add_subplot(gs[-1])\n", - " legend_ax.axis('off')\n", - " legend_elements = [\n", - " Line2D([0], [0], color='red', linestyle=':', alpha=0.7, label='Treatment start'),\n", - " Line2D([0], [0], color='black', linestyle='--', alpha=0.5, label='Zero effect'),\n", - " Line2D([0], [0], marker='o', color=colors['pre'], linestyle='None', label='Pre-treatment', markersize=5),\n", - " Line2D([0], [0], marker='o', color=colors['post'], linestyle='None', label='Post-treatment', markersize=5),\n", - " ]\n", - " legend_ax.legend(handles=legend_elements, loc='center', ncol=5, mode='expand', borderaxespad=0.)\n", - " \n", - " plt.suptitle(\"Estimated ATTs by Group\", y=1.02)\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8))" + "aggregated = dml_obj.aggregate(\"group\")\n", + "print(aggregated)\n", + "aggregated.plot_effects()" ] }, { @@ -891,110 +733,73 @@ "name": "stdout", "output_type": "stream", "text": [ - "================== Sensitivity Analysis ==================\n", - "\n", - "------------------ Scenario ------------------\n", - "Significance Level: level=0.95\n", - "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", + "================== DoubleMLDIDAggregation Object ==================\n", + " Event Study Aggregation \n", "\n", - "------------------ Bounds with CI ------------------\n", - " CI lower theta lower theta theta upper \\\n", - "ATT(2025-05,2025-01,2025-02) -0.430592 -0.218892 -0.126335 -0.033778 \n", - "ATT(2025-05,2025-02,2025-03) -0.229224 -0.020882 0.081016 0.182915 \n", - "ATT(2025-05,2025-03,2025-04) -0.399950 -0.204859 -0.094349 0.016161 \n", - "ATT(2025-05,2025-04,2025-05) 0.750803 0.955098 1.069615 1.184133 \n", - "ATT(2025-05,2025-04,2025-06) 1.433732 1.727613 1.891446 2.055279 \n", - "ATT(2025-05,2025-04,2025-07) 2.175267 2.604104 2.796395 2.988686 \n", - "ATT(2025-05,2025-04,2025-08) 3.253588 3.809880 3.990323 4.170766 \n", - "ATT(2025-06,2025-01,2025-02) -0.365539 -0.189067 -0.093047 0.002972 \n", - "ATT(2025-06,2025-02,2025-03) -0.193003 -0.011296 0.080593 0.172481 \n", - "ATT(2025-06,2025-03,2025-04) -0.412308 -0.230882 -0.131845 -0.032809 \n", - "ATT(2025-06,2025-04,2025-05) -0.147983 0.041654 0.123388 0.205121 \n", - "ATT(2025-06,2025-05,2025-06) 0.568676 0.754233 0.857444 0.960654 \n", - "ATT(2025-06,2025-05,2025-07) 1.338491 1.643520 1.782152 1.920784 \n", - "ATT(2025-06,2025-05,2025-08) 2.457779 2.909994 3.039536 3.169077 \n", - "ATT(2025-07,2025-01,2025-02) -0.209353 -0.051747 0.037523 0.126793 \n", - "ATT(2025-07,2025-02,2025-03) -0.356251 -0.190426 -0.092156 0.006114 \n", - "ATT(2025-07,2025-03,2025-04) -0.286034 -0.104592 -0.013253 0.078085 \n", - "ATT(2025-07,2025-04,2025-05) -0.103663 0.064232 0.163007 0.261781 \n", - "ATT(2025-07,2025-05,2025-06) -0.466753 -0.303900 -0.203583 -0.103265 \n", - "ATT(2025-07,2025-06,2025-07) 0.686492 0.853559 0.948008 1.042458 \n", - "ATT(2025-07,2025-06,2025-08) 1.610064 1.854018 1.980269 2.106521 \n", - "ATT(2025-08,2025-01,2025-02) -0.294763 -0.112158 -0.030199 0.051760 \n", - "ATT(2025-08,2025-02,2025-03) -0.393739 -0.239299 -0.143455 -0.047612 \n", - "ATT(2025-08,2025-03,2025-04) -0.266969 -0.119738 -0.027104 0.065531 \n", - "ATT(2025-08,2025-04,2025-05) -0.272828 -0.111128 -0.015802 0.079524 \n", - "ATT(2025-08,2025-05,2025-06) -0.294523 -0.135235 -0.047013 0.041210 \n", - "ATT(2025-08,2025-06,2025-07) -0.334927 -0.172113 -0.079947 0.012218 \n", - "ATT(2025-08,2025-07,2025-08) 0.609092 0.762704 0.853624 0.944545 \n", - "\n", - " CI upper \n", - "ATT(2025-05,2025-01,2025-02) 0.173019 \n", - "ATT(2025-05,2025-02,2025-03) 0.400319 \n", - "ATT(2025-05,2025-03,2025-04) 0.211306 \n", - "ATT(2025-05,2025-04,2025-05) 1.407086 \n", - "ATT(2025-05,2025-04,2025-06) 2.359689 \n", - "ATT(2025-05,2025-04,2025-07) 3.416271 \n", - "ATT(2025-05,2025-04,2025-08) 4.750998 \n", - "ATT(2025-06,2025-01,2025-02) 0.182120 \n", - "ATT(2025-06,2025-02,2025-03) 0.360349 \n", - "ATT(2025-06,2025-03,2025-04) 0.156584 \n", - "ATT(2025-06,2025-04,2025-05) 0.421432 \n", - "ATT(2025-06,2025-05,2025-06) 1.150286 \n", - "ATT(2025-06,2025-05,2025-07) 2.226798 \n", - "ATT(2025-06,2025-05,2025-08) 3.696603 \n", - "ATT(2025-07,2025-01,2025-02) 0.289634 \n", - "ATT(2025-07,2025-02,2025-03) 0.172829 \n", - "ATT(2025-07,2025-03,2025-04) 0.256322 \n", - "ATT(2025-07,2025-04,2025-05) 0.432246 \n", - "ATT(2025-07,2025-05,2025-06) 0.059592 \n", - "ATT(2025-07,2025-06,2025-07) 1.208356 \n", - "ATT(2025-07,2025-06,2025-08) 2.352818 \n", - "ATT(2025-08,2025-01,2025-02) 0.238131 \n", - "ATT(2025-08,2025-02,2025-03) 0.106646 \n", - "ATT(2025-08,2025-03,2025-04) 0.212378 \n", - "ATT(2025-08,2025-04,2025-05) 0.241368 \n", - "ATT(2025-08,2025-05,2025-06) 0.203540 \n", - "ATT(2025-08,2025-06,2025-07) 0.174686 \n", - "ATT(2025-08,2025-07,2025-08) 1.100115 \n", - "\n", - "------------------ Robustness Values ------------------\n", - " H_0 RV (%) RVa (%)\n", - "ATT(2025-05,2025-01,2025-02) 0.0 4.072170 0.000445\n", - "ATT(2025-05,2025-02,2025-03) 0.0 2.392754 0.000548\n", - "ATT(2025-05,2025-03,2025-04) 0.0 2.567065 0.000351\n", - "ATT(2025-05,2025-04,2025-05) 0.0 24.689828 20.518957\n", - "ATT(2025-05,2025-04,2025-06) 0.0 29.522560 25.061055\n", - "ATT(2025-05,2025-04,2025-07) 0.0 35.559468 28.456559\n", - "ATT(2025-05,2025-04,2025-08) 0.0 48.391241 29.199529\n", - "ATT(2025-06,2025-01,2025-02) 0.0 2.908570 0.000345\n", - "ATT(2025-06,2025-02,2025-03) 0.0 2.636212 0.000611\n", - "ATT(2025-06,2025-03,2025-04) 0.0 3.973795 0.000338\n", - "ATT(2025-06,2025-04,2025-05) 0.0 4.493919 0.000393\n", - "ATT(2025-06,2025-05,2025-06) 0.0 22.305524 17.559574\n", - "ATT(2025-06,2025-05,2025-07) 0.0 32.234337 26.281610\n", - "ATT(2025-06,2025-05,2025-08) 0.0 50.357219 34.953278\n", - "ATT(2025-07,2025-01,2025-02) 0.0 1.272295 0.000664\n", - "ATT(2025-07,2025-02,2025-03) 0.0 2.816085 0.000394\n", - "ATT(2025-07,2025-03,2025-04) 0.0 0.440845 0.000615\n", - "ATT(2025-07,2025-04,2025-05) 0.0 4.902133 0.000368\n", - "ATT(2025-07,2025-05,2025-06) 0.0 5.993485 1.238147\n", - "ATT(2025-07,2025-06,2025-07) 0.0 26.255130 21.462039\n", - "ATT(2025-07,2025-06,2025-08) 0.0 37.708391 32.937833\n", - "ATT(2025-08,2025-01,2025-02) 0.0 1.116214 0.000515\n", - "ATT(2025-08,2025-02,2025-03) 0.0 4.456499 0.000341\n", - "ATT(2025-08,2025-03,2025-04) 0.0 0.887418 0.000606\n", - "ATT(2025-08,2025-04,2025-05) 0.0 0.503821 0.000559\n", - "ATT(2025-08,2025-05,2025-06) 0.0 1.610184 0.000642\n", - "ATT(2025-08,2025-06,2025-07) 0.0 2.607636 0.000371\n", - "ATT(2025-08,2025-07,2025-08) 0.0 24.799894 20.568212\n" + "------------------ Overall Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "2.448411 0.029846 82.033602 0.0 2.389914 2.506909\n", + "------------------ Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "-6 months 0.118584 0.063783 1.859198 0.062999 -0.006427 0.243596\n", + "-5 months 0.021101 0.043800 0.481754 0.629981 -0.064746 0.106947\n", + "-4 months -0.041159 0.033809 -1.217373 0.223462 -0.107424 0.025107\n", + "-3 months -0.006282 0.029213 -0.215052 0.829727 -0.063539 0.050974\n", + "-2 months -0.013714 0.027415 -0.500233 0.616911 -0.067447 0.040019\n", + "-1 months -0.002954 0.026243 -0.112548 0.910389 -0.054390 0.048482\n", + "0 months 0.984152 0.025663 38.349080 0.000000 0.933853 1.034450\n", + "1 months 1.944027 0.031349 62.012347 0.000000 1.882584 2.005470\n", + "2 months 2.956757 0.038780 76.243869 0.000000 2.880750 3.032765\n", + "3 months 3.908710 0.055238 70.761776 0.000000 3.800446 4.016974\n", + "------------------ Additional Information ------------------\n", + "Control Group: never_treated\n", + "Anticipation Periods: 0\n", + "Score: observational\n", + "\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "dml_obj.sensitivity_analysis()\n", - "print(dml_obj.sensitivity_summary)" + "aggregated_eventstudy = dml_obj.aggregate(\"eventstudy\")\n", + "print(aggregated_eventstudy)\n", + "aggregated_eventstudy.plot_effects()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/doc/examples/py_double_ml_panel_simple.ipynb b/doc/examples/py_double_ml_panel_simple.ipynb index 7eae4166..f1957c88 100644 --- a/doc/examples/py_double_ml_panel_simple.ipynb +++ b/doc/examples/py_double_ml_panel_simple.ipynb @@ -74,84 +74,84 @@ " \n", " \n", " ATT(2.0,1,2)\n", - " 0.918577\n", - " 0.063957\n", - " 14.362451\n", + " 0.920815\n", + " 0.063970\n", + " 14.394375\n", " 0.000000\n", - " 0.793224\n", - " 1.043930\n", + " 0.795435\n", + " 1.046195\n", " \n", " \n", " ATT(2.0,1,3)\n", - " 1.988461\n", - " 0.064665\n", - " 30.750246\n", + " 1.983448\n", + " 0.064744\n", + " 30.635238\n", " 0.000000\n", - " 1.861720\n", - " 2.115201\n", + " 1.856552\n", + " 2.110344\n", " \n", " \n", " ATT(2.0,1,4)\n", - " 2.954280\n", - " 0.063294\n", - " 46.675247\n", + " 2.956340\n", + " 0.063267\n", + " 46.728168\n", " 0.000000\n", - " 2.830225\n", - " 3.078334\n", + " 2.832340\n", + " 3.080341\n", " \n", " \n", " ATT(3.0,1,2)\n", - " -0.042247\n", - " 0.065929\n", - " -0.640793\n", - " 0.521657\n", - " -0.171465\n", - " 0.086972\n", + " -0.044035\n", + " 0.065853\n", + " -0.668684\n", + " 0.503697\n", + " -0.173105\n", + " 0.085035\n", " \n", " \n", " ATT(3.0,2,3)\n", - " 1.112732\n", - " 0.065561\n", - " 16.972543\n", + " 1.105935\n", + " 0.065468\n", + " 16.892674\n", " 0.000000\n", - " 0.984235\n", - " 1.241228\n", + " 0.977620\n", + " 1.234251\n", " \n", " \n", " ATT(3.0,2,4)\n", - " 2.059244\n", - " 0.065484\n", - " 31.446740\n", + " 2.065059\n", + " 0.065618\n", + " 31.471099\n", " 0.000000\n", - " 1.930898\n", - " 2.187589\n", + " 1.936451\n", + " 2.193667\n", " \n", " \n", " ATT(4.0,1,2)\n", - " 0.007441\n", - " 0.068490\n", - " 0.108650\n", - " 0.913480\n", - " -0.126796\n", - " 0.141679\n", + " 0.000656\n", + " 0.068395\n", + " 0.009596\n", + " 0.992344\n", + " -0.133395\n", + " 0.134707\n", " \n", " \n", " ATT(4.0,2,3)\n", - " 0.062091\n", - " 0.066437\n", - " 0.934592\n", - " 0.349999\n", - " -0.068122\n", - " 0.192304\n", + " 0.062161\n", + " 0.066457\n", + " 0.935357\n", + " 0.349604\n", + " -0.068092\n", + " 0.192414\n", " \n", " \n", " ATT(4.0,3,4)\n", - " 0.951063\n", - " 0.067523\n", - " 14.084959\n", + " 0.951881\n", + " 0.067878\n", + " 14.023318\n", " 0.000000\n", - " 0.818720\n", - " 1.083407\n", + " 0.818842\n", + " 1.084920\n", " \n", " \n", "\n", @@ -159,15 +159,15 @@ ], "text/plain": [ " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.918577 0.063957 14.362451 0.000000 0.793224 1.043930\n", - "ATT(2.0,1,3) 1.988461 0.064665 30.750246 0.000000 1.861720 2.115201\n", - "ATT(2.0,1,4) 2.954280 0.063294 46.675247 0.000000 2.830225 3.078334\n", - "ATT(3.0,1,2) -0.042247 0.065929 -0.640793 0.521657 -0.171465 0.086972\n", - "ATT(3.0,2,3) 1.112732 0.065561 16.972543 0.000000 0.984235 1.241228\n", - "ATT(3.0,2,4) 2.059244 0.065484 31.446740 0.000000 1.930898 2.187589\n", - "ATT(4.0,1,2) 0.007441 0.068490 0.108650 0.913480 -0.126796 0.141679\n", - "ATT(4.0,2,3) 0.062091 0.066437 0.934592 0.349999 -0.068122 0.192304\n", - "ATT(4.0,3,4) 0.951063 0.067523 14.084959 0.000000 0.818720 1.083407" + "ATT(2.0,1,2) 0.920815 0.063970 14.394375 0.000000 0.795435 1.046195\n", + "ATT(2.0,1,3) 1.983448 0.064744 30.635238 0.000000 1.856552 2.110344\n", + "ATT(2.0,1,4) 2.956340 0.063267 46.728168 0.000000 2.832340 3.080341\n", + "ATT(3.0,1,2) -0.044035 0.065853 -0.668684 0.503697 -0.173105 0.085035\n", + "ATT(3.0,2,3) 1.105935 0.065468 16.892674 0.000000 0.977620 1.234251\n", + "ATT(3.0,2,4) 2.065059 0.065618 31.471099 0.000000 1.936451 2.193667\n", + "ATT(4.0,1,2) 0.000656 0.068395 0.009596 0.992344 -0.133395 0.134707\n", + "ATT(4.0,2,3) 0.062161 0.066457 0.935357 0.349604 -0.068092 0.192414\n", + "ATT(4.0,3,4) 0.951881 0.067878 14.023318 0.000000 0.818842 1.084920" ] }, "execution_count": 4, @@ -194,7 +194,6 @@ ")\n", "\n", "dml_obj.fit()\n", - "\n", "dml_obj.summary" ] }, @@ -202,6 +201,38 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/doubleml/did/did_multi.py:1298: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + " warnings.warn(\n", + "/opt/venv/lib/python3.12/site-packages/matplotlib/cbook.py:1709: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n", + "/opt/venv/lib/python3.12/site-packages/matplotlib/cbook.py:1709: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n" + ] + }, + { + "data": { + "image/png": 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hw5ozZ45at27tOZVTkm699VY999xzGjBggG677TalpqZq/vz5Ovfccz2Lr5+oXbt2uvjii3XXXXfJ5XJ5Gm8PPPBApZ4jAADwI79+9x8AAKi0RYsWmZLK/Vm0aJFpmqa5YsUKs3///mZcXJwZHh5utmzZ0hw3bpx56NAhr/t79dVXzTPOOMMMCQkxJZnr1q0zTdM0e/XqZfbq1csTt27dOlOSmZSUVGY+W7Zs8Rp/9NFHTUlmWlqaZ+y9994zO3fubDqdTrN169bmrFmzzNdff92UZO7Zs8cTl5KSYg4ePNiMjo42JXnlkZ2dbU6ePNls166dGR4ebjZq1Mi86KKLzGeeecZ0u92euCNHjpg33XSTGRMTY9atW9e86aabzG3btnn9jspy7733mpLM3bt3lxvz2GOPmZLM7777zjOWlJTk9fsrz8lx//vf/8xbb73VbNu2rel0Os0GDRqYffr0MdeuXVvh/ZhmcY3OPfdc85tvvjETEhJMp9NptmrVynzppZfKvc2gQYNMSeaXX355yvs/UV5enjl79mwzISHBjImJMUNDQ80mTZqYV155pbl06VKzsLDQE1vea6XE22+/bZ533nmmw+EwGzRoYN54443m77//XiruzTffNM844wwzPDzc7Nq1q7lmzRpz9OjRZqtWrTwxe/bsMSWZTz/9tPnss8+a8fHxpsPhMC+55BKv+gAAgNrLZpqVnHcNAACAgHXttdfqhx9+0K+//urvVE6LvXv3qk2bNnr66ac1adIkf6cDAACqgTWlAAAAgtyhQ4f04Ycf6qabbvJ3KgAAAB6sKQUAABCk9uzZoy+++EILFy5UWFiYxo0b5++UAAAAPJgpBQAAEKQ2bNigm266SXv27NGSJUvUpEkTf6cEAADgwZpSAAAAAAAA8DlmSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAgKC1d+9e2Ww2LV682N+pBLXWrVtrzJgx/k4DAAAEGJpSAAAgYC1evFg2m63Mn4ceeqhGHnPGjBlatWrVKeN69+5dbm4n/jz22GM1kufJPvroI589VlX88ssveuCBB9S1a1dFR0eradOmGjx4sL755ptK34fL5dKDDz6oZs2aKSIiQj169FBycnINZg0AAE6HUH8nAAAA8Gc9/vjjatOmjddYx44d1apVK+Xl5SksLOy0PdaMGTM0bNgwDRkypMK4f/7znxo7dqzn8pYtW/TCCy9oypQpOvvssz3jnTt3Pm25VeSjjz7S3Llza11jauHChXrttdd03XXX6e6771ZmZqYWLFigv/zlL/r444/Vt2/fU97HmDFjtGLFCk2YMEHt27fX4sWLNWjQIK1bt04XX3yxD54FAACoDppSAAAg4A0cOFAXXHBBmdc5nc5T3j43N1dRUVGnNad+/fqVyuOFF15Qv3791Lt3b5/mUpuNHDlSjz32mOrUqeMZu/XWW3X22WfrscceO2VT6uuvv9by5cv19NNPa9KkSZKkm2++WR07dtQDDzygL7/8skbzBwAA1cfpewAAIGiVtabUmDFjVKdOHe3evVuDBg1SdHS0brzxRknSrl27dN1116lJkyZyOp1q0aKFRowYoczMTEmSzWZTbm6ulixZ4jn97s+spfTYY4/JZrPpv//9r2644QbVr1/fa2bPm2++qW7duikiIkINGjTQiBEj9Ntvv3ndx//7f/9Pw4cPV8uWLeVwOBQfH6+//e1vysvL83rOc+fO9TyHkp8ShmFo9uzZOvfcc+V0OtW4cWONGzdOx44d83os0zQ1ffp0tWjRQpGRkerTp49++umnMp/b7t27tXv37lP+Drp16+bVkJKkhg0b6pJLLtHPP/98ytuvWLFCISEhuuOOOzxjTqdTt912mzZt2lTq9wUAAGoPZkoBAICAl5mZqfT0dK+xRo0alRtfWFioAQMG6OKLL9YzzzyjyMhIud1uDRgwQC6XS/fee6+aNGmiAwcO6IMPPlBGRobq1q2rN954Q2PHjlX37t09TZC2bdv+6fyHDx+u9u3ba8aMGTJNU5L05JNP6pFHHtH111+vsWPHKi0tTS+++KIuvfRSbdu2TfXq1ZMkJSUl6fjx47rrrrvUsGFDff3113rxxRf1+++/KykpSZI0btw4HTx4UMnJyXrjjTdKPf64ceO0ePFi3XLLLbrvvvu0Z88evfTSS9q2bZu++OILz+mPU6dO1fTp0zVo0CANGjRI3377rfr37y+3213qPi+//HJJxY3B6khJSamwhiW2bdumDh06KCYmxmu8e/fukqTt27crPj6+WjkAAICaRVMKAAAEvLJO8Spp7pTF5XJp+PDhSkxM9Ixt375de/bsUVJSkoYNG+YZnzp1qmd71KhRuvPOO3XGGWdo1KhRpyl7qUuXLnrrrbc8l/ft26dHH31U06dP15QpUzzjQ4cO1XnnnaeXX37ZMz5r1ixFRER4Yu644w61a9dOU6ZM0f79+9WyZUslJCSoQ4cOSk5OLpX3559/roULF2rp0qW64YYbPON9+vTRFVdcoaSkJN1www1KS0vTU089pcGDB+v999/3zLT65z//qRkzZpy234VUPPtr06ZNevjhh08Ze+jQITVt2rTUeMnYwYMHT2tuAADg9OH0PQAAEPDmzp2r5ORkr59Tueuuu7wu161bV5K0Zs0aHT9+vEbyLM+dd97pdfmdd96RYRi6/vrrlZ6e7vlp0qSJ2rdvr3Xr1nliT2xI5ebmKj09XRdddJFM09S2bdtO+dhJSUmqW7eu+vXr5/VYJafVlTzW2rVr5Xa7de+993qd+jdhwoQy73fv3r3VmiWVmpqqG264QW3atNEDDzxwyvi8vDw5HI5S4yVriZ14GiMAAKhdmCkFAAACXvfu3ctd6LwsoaGhatGihddYmzZtNHHiRD333HNaunSpLrnkEl199dUaNWqUp2FVU07+5sBdu3bJNE21b9++zPgTv01w//79mjp1qt57771Sa0CVrIVVkV27dikzM1NxcXFlXp+amiqpePaWpFI5xcbGqn79+qd8nMrIzc3VlVdeqezsbH3++eel1poqS0REhFwuV6nx/Px8z/UAAKB2oikFAAAsx+FwyG4vPWH82Wef1ZgxY/Tuu+/qk08+0X333afExER99dVXpZpYp9PJjRPDMGSz2bR69WqFhISUii9p1hQVFalfv346evSoHnzwQZ111lmKiorSgQMHNGbMGBmGccrHNgxDcXFxWrp0aZnXx8bGVuMZVZ3b7dbQoUP1/fffa82aNerYsWOlbte0aVMdOHCg1PihQ4ckSc2aNTuteQIAgNOHphQAAMAJOnXqpE6dOunhhx/Wl19+qZ49e2r+/PmaPn26JHmdulZT2rZtK9M01aZNG3Xo0KHcuB9++EE7d+7UkiVLdPPNN3vGyzp9sby827Ztq7Vr16pnz54Vzipq1aqVpOKZVWeccYZnPC0trdQMraoyDEM333yzPv30U/373/9Wr169Kn3brl27at26dcrKyvJa7Hzz5s2e6wEAQO3EmlIAAACSsrKyVFhY6DXWqVMn2e12r9PDoqKilJGRUaO5DB06VCEhIZo2bVqpBdtN09SRI0ckyTOL6sQY0zQ1Z86cUvcZFRUlSaVyv/7661VUVKQnnnii1G0KCws98X379lVYWJhefPFFr8ebPXt2mc9h9+7d2r17d8VP9A/33nuv3n77bb388ssaOnRouXHp6en65ZdfvNb8GjZsmIqKivTKK694xlwulxYtWqQePXrwzXsAANRizJQCAACQ9Nlnn2n8+PEaPny4OnTooMLCQr3xxhsKCQnRdddd54nr1q2b1q5dq+eee07NmjVTmzZt1KNHj9OaS9u2bTV9+nRNnjxZe/fu1ZAhQxQdHa09e/Zo5cqVuuOOOzRp0iSdddZZatu2rSZNmqQDBw4oJiZG//nPf8qcudStWzdJ0n333acBAwYoJCREI0aMUK9evTRu3DglJiZq+/bt6t+/v8LCwrRr1y4lJSVpzpw5GjZsmGJjYzVp0iQlJibqyiuv1KBBg7Rt2zatXr1ajRo1KvV4l19+uSSdcrHz2bNn6+WXX1ZCQoIiIyP15ptvel1/7bXXehpqL730kqZNm6Z169apd+/ekqQePXpo+PDhmjx5slJTU9WuXTstWbJEe/fu1WuvvVbVXz0AAPAhmlIAAACSunTpogEDBuj999/XgQMHFBkZqS5dumj16tX6y1/+4ol77rnndMcdd+jhhx9WXl6eRo8efdqbUpL00EMPqUOHDnr++ec1bdo0SVJ8fLz69++vq6++WlLxgufvv/++Z+0rp9Opa6+9VuPHj1eXLl287m/o0KG69957tXz5cr355psyTVMjRoyQJM2fP1/dunXTggULNGXKFIWGhqp169YaNWqUevbs6bmP6dOny+l0av78+Vq3bp169OihTz7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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = dml_obj.plot_effects()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "level = 0.95\n", @@ -213,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -241,13 +272,13 @@ "Learner ml_m: LogisticRegression()\n", "Out-of-sample Performance:\n", "Regression:\n", - "Learner ml_g0 RMSE: [[1.42597918 1.40968087 1.40052964 1.42787771 1.40713307 1.42403057\n", - " 1.42802689 1.40320538 1.42776935]]\n", - "Learner ml_g1 RMSE: [[1.40358956 1.43518576 1.39551046 1.4138002 1.42577686 1.38380243\n", - " 1.45865143 1.41456292 1.40745099]]\n", + "Learner ml_g0 RMSE: [[1.42395531 1.41102464 1.40193447 1.42414489 1.40480083 1.42114621\n", + " 1.42658311 1.4065011 1.42432406]]\n", + "Learner ml_g1 RMSE: [[1.40260415 1.44143708 1.40004539 1.41289911 1.4243203 1.38294772\n", + " 1.45655191 1.41725691 1.40904307]]\n", "Classification:\n", - "Learner ml_m Log Loss: [[0.69177084 0.69122753 0.6907171 0.67931424 0.68023374 0.67927162\n", - " 0.66238847 0.66240294 0.66204557]]\n", + "Learner ml_m Log Loss: [[0.69042286 0.69154849 0.69040404 0.68002834 0.67987763 0.679984\n", + " 0.66215109 0.66199744 0.66241892]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 5\n", @@ -255,15 +286,15 @@ "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.918577 0.063957 14.362451 0.000000 0.793224 1.043930\n", - "ATT(2.0,1,3) 1.988461 0.064665 30.750246 0.000000 1.861720 2.115201\n", - "ATT(2.0,1,4) 2.954280 0.063294 46.675247 0.000000 2.830225 3.078334\n", - "ATT(3.0,1,2) -0.042247 0.065929 -0.640793 0.521657 -0.171465 0.086972\n", - "ATT(3.0,2,3) 1.112732 0.065561 16.972543 0.000000 0.984235 1.241228\n", - "ATT(3.0,2,4) 2.059244 0.065484 31.446740 0.000000 1.930898 2.187589\n", - "ATT(4.0,1,2) 0.007441 0.068490 0.108650 0.913480 -0.126796 0.141679\n", - "ATT(4.0,2,3) 0.062091 0.066437 0.934592 0.349999 -0.068122 0.192304\n", - "ATT(4.0,3,4) 0.951063 0.067523 14.084959 0.000000 0.818720 1.083407\n" + "ATT(2.0,1,2) 0.920815 0.063970 14.394375 0.000000 0.795435 1.046195\n", + "ATT(2.0,1,3) 1.983448 0.064744 30.635238 0.000000 1.856552 2.110344\n", + "ATT(2.0,1,4) 2.956340 0.063267 46.728168 0.000000 2.832340 3.080341\n", + "ATT(3.0,1,2) -0.044035 0.065853 -0.668684 0.503697 -0.173105 0.085035\n", + "ATT(3.0,2,3) 1.105935 0.065468 16.892674 0.000000 0.977620 1.234251\n", + "ATT(3.0,2,4) 2.065059 0.065618 31.471099 0.000000 1.936451 2.193667\n", + "ATT(4.0,1,2) 0.000656 0.068395 0.009596 0.992344 -0.133395 0.134707\n", + "ATT(4.0,2,3) 0.062161 0.066457 0.935357 0.349604 -0.068092 0.192414\n", + "ATT(4.0,3,4) 0.951881 0.067878 14.023318 0.000000 0.818842 1.084920\n" ] } ], @@ -275,358 +306,6 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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First TreatedPre-treatment PeriodEvaluation PeriodEstimateCI LowerCI UpperPre-Treatment
ATT(2.0,1,2)2.0120.9185770.7440791.093075False
ATT(2.0,1,3)2.0131.9884611.8120302.164891False
ATT(2.0,1,4)2.0142.9542802.7815893.126971False
ATT(3.0,1,2)3.012-0.042247-0.2221260.137632True
ATT(3.0,2,3)3.0231.1127320.9338571.291606False
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" - ], - "text/plain": [ - " First Treated Pre-treatment Period Evaluation Period \\\n", - "ATT(2.0,1,2) 2.0 1 2 \n", - "ATT(2.0,1,3) 2.0 1 3 \n", - "ATT(2.0,1,4) 2.0 1 4 \n", - "ATT(3.0,1,2) 3.0 1 2 \n", - "ATT(3.0,2,3) 3.0 2 3 \n", - "\n", - " Estimate CI Lower CI Upper Pre-Treatment \n", - "ATT(2.0,1,2) 0.918577 0.744079 1.093075 False \n", - "ATT(2.0,1,3) 1.988461 1.812030 2.164891 False \n", - "ATT(2.0,1,4) 2.954280 2.781589 3.126971 False \n", - "ATT(3.0,1,2) -0.042247 -0.222126 0.137632 True \n", - "ATT(3.0,2,3) 1.112732 0.933857 1.291606 False " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def create_ci_dataframe(dml_obj, level=0.95, joint=True, include_rvs=False):\n", - " \"\"\"\n", - " Create a DataFrame with coefficient estimates and confidence intervals from a DoubleML object.\n", - " \n", - " Parameters:\n", - " -----------\n", - " dml_obj : DoubleML object\n", - " The fitted DoubleML object\n", - " level : float, default=0.95\n", - " Confidence level for intervals\n", - " joint : bool, default=True\n", - " Whether to use joint confidence intervals\n", - " \n", - " Returns:\n", - " --------\n", - " DataFrame\n", - " DataFrame containing estimates and confidence intervals\n", - " \"\"\"\n", - "\n", - " ci = dml_obj.confint(level=level, joint=joint)\n", - "\n", - " # Create DataFrame\n", - " result_df = pd.DataFrame({\n", - " 'First Treated': [gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", - " 'Pre-treatment Period' : [gt_combination[1] for gt_combination in dml_obj.gt_combinations],\n", - " 'Evaluation Period': [gt_combination[2] for gt_combination in dml_obj.gt_combinations],\n", - " 'Estimate': dml_obj.coef,\n", - " 'CI Lower': ci.iloc[:, 0],\n", - " 'CI Upper': ci.iloc[:, 1],\n", - " 'Pre-Treatment': [gt_combination[2] < gt_combination[0] for gt_combination in dml_obj.gt_combinations],\n", - " })\n", - " if include_rvs:\n", - " result_df[\"RV\"] = dml_obj.sensitivity_params[\"rv\"]\n", - " return result_df\n", - "\n", - "ci_df = create_ci_dataframe(dml_obj, include_rvs=False)\n", - "ci_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/venv/lib/python3.12/site-packages/matplotlib/cbook.py:1709: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n", - "/opt/venv/lib/python3.12/site-packages/matplotlib/cbook.py:1709: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n" - ] - }, - { - "data": { - "image/png": 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b69VXX9WuXbv073//u8I5tWvXTmPGjFHr1q3VvHlzzZw5U02bNnWdeVSrVi0FBwcrMjLSdTZXSebOnavk5GTdcccdatGihSZOnKjBgwdr7ty5bnHJyckaMWKEmjVrpkcffVTZ2dn65ptvypRrYWGh3njjDe3cuVNXXHGFvvjiC33zzTdasWKFOnbsqObNm2vu3LmqWbOmVq5c6Xqew+HQa6+9pgsuuEBt27b12O+ePXv03nvvadGiRbr00kvVrl07LVu2TAcOHNCaNWskSfPmzVOfPn107733qkWLFrrrrrvUu3fvMuUNAAD8G8UoAABQZV1++eXasWOH2+3222+XJFmtVg0bNkzLli2TVHQW1LvvvquRI0e6nr9nzx6NGDFCTZo0UVRUlBo1aiRJrrOYKiI7O1uTJk1Sy5YtVbNmTdWoUUM//vhjuff5448/qkuXLm5tXbp00Y8//ujWdmoxKCIiQlFRUUpNTT3tvl988UXVqFFDYWFhuvXWW3XPPfdo7Nix+v7775Wdna3atWu7Ffj27dvndvliw4YNFRsbe9rcrVarOnfu7GqrXbu2zj33XFf+P/74o9vjkpSUlHTavAEAQGBgAXMAAFBlRUREqFmzZqU+PnLkSHXt2lWpqalKSUlRWFiY26/tXXXVVWrYsKFefvll1atXT06nU61bt3ZdsvZ3QUFBMgzDra34srNikyZNUkpKiubOnatmzZopLCxMQ4YMKXWfZyskJMTtvsViOeNlhiNHjtT999+vsLAwJSQkKCio6P8fs7OzlZCQoI0bN3o859T1rCIiIs46bwAAgNJQjAIAANXWxRdfrMTERL399ttau3athg4d6ireHD16VD///LNefvllXXrppZKkL7744rT7i42NVVZWlnJyclwFmb+vmfTll18qOTnZtS5Vdna29u/f7xZjs9lUWFh42tdq2bKlvvzyS40ePdpt3+eff/4Z3/eZREdHl1jEu/DCC3X48GFZrVbXWWIV0bJlSxUUFGjLli26+OKLJf3v712cf8uWLbVlyxa353399dcVfk0AAOA/KEYBAIAqKz8/X4cPH3Zrs1qtqlOnjuv+ddddpwULFuiXX35xW/w7JiZGtWvX1ksvvaSEhAT9/vvvmjx58mlfr3PnzgoPD9fUqVN11113acuWLVqyZIlbTPPmzbVq1SpdddVVslgsevDBBz3OVGrUqJE+//xzDR8+XHa73S3fYv/85z81bNgwXXDBBerRo4fef/99rVq1yu2X+Spbjx49lJSUpIEDB+rxxx9XixYtdPDgQX344YcaNGiQOnbsWKb9NG/eXAMGDNCtt96qhQsXKjIyUpMnT9Y555yjAQMGSJLuuusudenSRXPnztWAAQO0bt06ffzxx157bwAAoPpgzSgAAFBlffzxx0pISHC7XXLJJW4xI0eO1L///W+dc845bmswBQUFafny5dq2bZtat26te+65R0888cRpX69WrVp644039NFHH6lNmzZ66623NH36dLeYp556SjExMbr44ot11VVXqXfv3rrwwgvdYh5++GHt379fTZs2LXXtpYEDB2revHmaO3euWrVqpYULF2rx4sXq1q1b2f9A5WSxWPTRRx/psssu04033qgWLVpo+PDh+u2331S3bt1y7Wvx4sXq0KGDrrzySiUlJckwDH300UeuM9P+8Y9/6OWXX9a8efPUrl07ffLJJ3rggQe88bYAAEA1YzH+vjACAAAAAAAA4CWcGQUAAAAAAACfoRgFAAAAAAAAn6EYBQAAAAAAAJ+hGAUAAAAAAACfoRgFAAAAAAAAn6EYBQAAAAAAAJ+hGAUAAAAAAACfoRgFAAAAAAAAn6EYBQAAAAAAAJ+hGAUAAAAAAACfoRgFAAAAAAAAn6EYBQAAAAAAAJ+hGAUAAAAAAACfoRgFAAAAAAAAn6EYBQAAAAAAAJ+hGAUAAAAAAACfoRgFAAAAAAAAn6EYBQAAqpz9+/fLYrFoyZIlZqfi1xo1aqTk5GSz0wAAAAGGYhQAAPC5JUuWyGKxlHibPHmyV17z0Ucf1Zo1a84Y161bt1JzO/U2ffp0r+T5dx999JHPXqs8Dh48qFGjRuncc89VZGSkatasqU6dOmnp0qUyDKNM+8jPz9d9992nevXqKSwsTJ07d1ZKSoqXMwcAAGazmp0AAAAIXA8//LAaN27s1ta6dWs1bNhQubm5CgkJqbTXevTRRzVkyBANHDjwtHH333+/brnlFtf9rVu36tlnn9XUqVPVsmVLV3vbtm0rLbfT+eijj/TCCy9UuYLUkSNH9Oeff2rIkCFq0KCBTp48qZSUFCUnJ+vnn3/Wo48+esZ9JCcna+XKlZowYYKaN2+uJUuWqF+/ftqwYYMuueQSH7wLAABgBopRAADANH379lXHjh1LfCw0NPSMz8/JyVFERESl5tSzZ0+PPJ599ln17NlT3bp182kuVVnbtm21ceNGt7bx48frqquu0rPPPquZM2cqODi41Od/8803Wr58uZ544glNmjRJknTDDTeodevWuvfee/XVV195M30AAGAiLtMDAABVTklrRiUnJ6tGjRrau3ev+vXrp8jISI0cOVKStGfPHl1zzTWKj49XaGio6tevr+HDhysjI0OSZLFYlJOTo6VLl7ouszubtZKmT58ui8Wif//737ruuusUExPjdibPG2+8oQ4dOigsLEy1atXS8OHD9ccff7jt4//9v/+noUOHqkGDBrLb7UpMTNQ999yj3Nxct/f8wgsvuN5D8a2Y0+nUM888o1atWik0NFR169bVmDFjdPz4cbfXMgxDs2bNUv369RUeHq7LL79cP/zwQ4nvbe/evdq7d2+F/zaNGjXSiRMn5HA4Thu3cuVKBQcH67bbbnO1hYaG6uabb9bmzZs9/l4AAMB/cGYUAAAwTUZGho4cOeLWVqdOnVLjCwoK1Lt3b11yySWaO3euwsPD5XA41Lt3b+Xn5+vOO+9UfHy8Dhw4oA8++EDp6emKjo7W66+/rltuuUWdOnVyFT+aNm161vkPHTpUzZs316OPPupaJ+mRRx7Rgw8+qGHDhumWW25RWlqannvuOV122WXavn27atasKUlasWKFTpw4obFjx6p27dr65ptv9Nxzz+nPP//UihUrJEljxozRwYMHlZKSotdff93j9ceMGaMlS5boxhtv1F133aV9+/bp+eef1/bt2/Xll1+6LnOcNm2aZs2apX79+qlfv3767rvv1KtXrxILRt27d5dUVBAsi9zcXOXk5Cg7O1ubNm3S4sWLlZSUpLCwsNM+b/v27WrRooWioqLc2jt16iRJ2rFjhxITE8uUAwAAqF4oRgEAANP06NHDo+10i1/n5+dr6NChmj17tqttx44d2rdvn1asWKEhQ4a42qdNm+baHjVqlG6//XY1adJEo0aNqqTspXbt2unNN9903f/tt9/00EMPadasWZo6daqrffDgwbrgggv04osvutofe+wxt4LNbbfdpmbNmmnq1Kn6/fff1aBBAyUlJalFixZKSUnxyPuLL77QokWLtGzZMl133XWu9ssvv1x9+vTRihUrdN111yktLU2PP/64+vfvr/fff991ZtX9999fpnWdzmTevHmaMmWK63737t21ePHiMz7v0KFDSkhI8Ggvbjt48OBZ5wYAAKomLtMDAACmeeGFF5SSkuJ2O5OxY8e63Y+OjpYkrVu3TidOnPBKnqW5/fbb3e6vWrVKTqdTw4YN05EjR1y3+Ph4NW/eXBs2bHDFnlqIysnJ0ZEjR3TxxRfLMAxt3779jK+9YsUKRUdHq2fPnm6v1aFDB9WoUcP1WuvXr5fD4dCdd97pdonfhAkTStzv/v37y3xWlCSNGDFCKSkpevPNN11FsVMvNSxNbm6u7Ha7R3vxWmFl2QcAAKieODMKAACYplOnTqUuYF4Sq9Wq+vXru7U1btxYEydO1FNPPaVly5bp0ksv1dVXX61Ro0a5ClXe8vdfAtyzZ48Mw1Dz5s1LjD/11wF///13TZs2Te+9957HGk/Fa12dzp49e5SRkaG4uLgSH09NTZVUdLaWJI+cYmNjFRMTc8bXOZOGDRuqYcOGkooKU7fddpt69Oihn3/++bSX6oWFhSk/P9+jPS8vz/U4AADwTxSjAABAtWG32xUU5Hli95NPPqnk5GS9++67+uSTT3TXXXdp9uzZ+vrrrz2KV5Xp7wUTp9Mpi8WitWvXlvhLcjVq1JAkFRYWqmfPnjp27Jjuu+8+nXfeeYqIiNCBAweUnJwsp9N5xtd2Op2Ki4vTsmXLSnw8Nja2Au/o7A0ZMkQvv/yyPv/8c/Xu3bvUuISEBB04cMCj/dChQ5KkevXqeS1HAABgLopRAADAL7Rp00Zt2rTRAw88oK+++kpdunTRggULNGvWLElyu0TNW5o2bSrDMNS4cWO1aNGi1Lhdu3bpl19+0dKlS3XDDTe42ku6TLG0vJs2bar169erS5cupz2LqPispT179qhJkyau9rS0NI8zsipD8eV1Zzq7q3379tqwYYMyMzPdFjHfsmWL63EAAOCfWDMKAABUa5mZmSooKHBra9OmjYKCgtwuA4uIiFB6erpXcxk8eLCCg4M1Y8YMj4XYDcPQ0aNHJcl11tSpMYZhaN68eR77jIiIkCSP3IcNG6bCwkLNnDnT4zkFBQWu+B49eigkJETPPfec2+s988wzJb6HvXv3au/evad/oyoqZpXklVdekcVi0YUXXuhqO3LkiH766Se3Nb2GDBmiwsJCvfTSS662/Px8LV68WJ07d+aX9AAA8GOcGQUAAKq1zz77TOPHj9fQoUPVokULFRQU6PXXX1dwcLCuueYaV1yHDh20fv16PfXUU6pXr54aN26szp07V2ouTZs21axZszRlyhTt379fAwcOVGRkpPbt26fVq1frtttu06RJk3TeeeepadOmmjRpkg4cOKCoqCi98847JZ6p1KFDB0nSXXfdpd69eys4OFjDhw9X165dNWbMGM2ePVs7duxQr169FBISoj179mjFihWaN2+ehgwZotjYWE2aNEmzZ8/WlVdeqX79+mn79u1au3at6tSp4/F63bt3l6QzLmL+yCOP6Msvv1SfPn3UoEEDHTt2TO+88462bt2qO++8U82aNXPFPv/885oxY4Y2bNigbt26SZI6d+6soUOHasqUKUpNTVWzZs20dOlS7d+/X6+88koFewAAAFQHFKMAAEC11q5dO/Xu3Vvvv/++Dhw4oPDwcLVr105r167VP/7xD1fcU089pdtuu00PPPCAcnNzNXr06EovRknS5MmT1aJFCz399NOaMWOGJCkxMVG9evXS1VdfLaloIfP333/ftbZVaGioBg0apPHjx6tdu3Zu+xs8eLDuvPNOLV++XG+88YYMw9Dw4cMlSQsWLFCHDh20cOFCTZ06VVarVY0aNdKoUaPUpUsX1z5mzZql0NBQLViwQBs2bFDnzp31ySefqH///hV+n/3799fevXv16quvKi0tTaGhoWrbtq0WL16s0aNHl2kfr732mh588EG9/vrrOn78uNq2basPPvhAl112WYXzAgAAVZ/F+Ps55AAAAAAAAICXsGYUAAAAAAAAfIZiFAAAAAAAAHyGYhQAAAAAAAB8hmIUAAAAAAAAfIZiFAAAAAAAAHyGYhQAAAAAAAB8xmp2AmfD6XTq4MGDioyMlMViMTsdAAAAAACAgGUYhrKyslSvXj0FBZV+/lO1LkYdPHhQiYmJZqcBAAAAAACA//rjjz9Uv379Uh+v1sWoyMhISUVvMioqyuRszo7T6VRaWppiY2NPWz2E/2IMBDb6H4yBwEb/Bzb6H4yBwEb/BzZ/6//MzEwlJia66jWlqdbFqOJL86KiovyiGJWXl6eoqCi/GIAoP8ZAYKP/wRgIbPR/YKP/wRgIbPR/YPPX/j/TUkr+804BAACqo5MnpXnzFP7SS0XbAAAAfo5iFAAAgJkKC2X59FPZvvhCKiw0OxsAAACvq9aX6QEAAFR7VquM5GTlZmTIZuXQDAAA+D+OeAAAAMxktUqDBys/NbVoGwAA+J2CnEMqzDns0e50OuU8dkz5qlXimlHBEfGyRiT4IkWf4ogHAAAAAADAi7J2vqz0LbNKfdyzTFWkZucHFJM0zTtJmYhiFAAAgJkMQzp6VJZjx6TYWLOzAQAAXhDZ9laFN73Krc1ZkKvD/+omSao75DMF2yI8nhccEe+L9HyOYhQAAICZ8vNlufFGRTsc0po1Uni42RkBAIBKZo1I8Ljcznkyx7Vti20nqz3S12mZhl/TAwAAMFtwsFTCOhEAAAD+iDOjAAAAzBQaKmP1aqWnpiouNNTsbAAAALyO/4IDAAAAAACAz1CMAgAAAAAAgM9QjAIAADDTyZPS/PkKW7q0aBsAAAQcx7EfzU7BpyhGAQAAmKmwUJa1a2X/9FOpsNDsbAAAgI9kfr/AtZ320ShlbH/exGx8y9Ri1OzZs3XRRRcpMjJScXFxGjhwoH7++WczUwIAAPAtq1XG8OHKGzhQsvLbMgAABALHkd3K/H6+675hGMrc/qwcR3abmJXvmFqM2rRpk8aNG6evv/5aKSkpOnnypHr16qWcnBwz0wIAAPAdq1W67jrlDR5MMQoAgABRkPWHjII81/2g0GgZBbkqyPrDxKx8x9Qjno8//tjt/pIlSxQXF6dt27bpsssuMykrAAAAAAAA77FGJspiDXXdd+ZlKNgaJmtkoolZ+U6V+u+3jIwMSVKtWrVKfDw/P1/5+fmu+5mZmZIkp9Mpp9Pp/QS9yOl0yjCMav8+UHGMgcBG/4MxEMAMQ87sbCknR07WjApIfP7BGAhs9H9gstY6XzXajlXGl1OKGizBqnHBnbLWOr9aj4Wy5l5lilFOp1MTJkxQly5d1Lp16xJjZs+erRkzZni0p6WlKS8vr4RnVB9Op1MZGRkyDENBQawrH4gYA4GN/gdjIIDl5anmrbcqoqBAqa+8oqDwcLMzgo/x+QdjILDR/4HLSBgkqagYZfnHfOXXbqnU1FRzkzpLWVlZZYqrMsWocePGaffu3friiy9KjZkyZYomTpzoup+ZmanExETFxsYqKirKF2l6jdPplMViUWxsLBNQgGIMBDb6H4yBAJaXJ9ntkqS4uDiKUQGIzz8YA4GN/g9czpM5Kl4hKqHJhbLaI03NpzKEhoaeOUhVpBg1fvx4ffDBB/r8889Vv379UuPsdrvs/z1YO1VQUJBffGgtFovfvBdUDGMgsNH/YAwEqLAwOVetUnpqquLCwuj/AMXnH4yBwEb/B6hT+ttf+r+s78HUYpRhGLrzzju1evVqbdy4UY0bNzYzHQAAAN+zWIp+Rc9qLdoGAADwc6YWo8aNG6c333xT7777riIjI3X48GFJUnR0tMLCwsxMDQAAAAAAAF5g6jlg8+fPV0ZGhrp166aEhATX7e233zYzLQAAAN8pKJBefVVhb71VtA0AAODnTL9MDwAAIKAVFMiyZo3sDoc0Zoxks5mdEQAAqGQFOYdUmHPYrc1ZkOvadqR9r0JbhMfzgiPiZY1I8Hp+vlYlFjAHAAAIWFarjIEDlZ+VJZuVQzMAAPxR1s6Xlb5lVqmP/7XyihLba3Z+QDFJ07yVlmk44gEAADCT1SrddJNyU1MVSTEKAAC/FNn2VoU3vcqj3el06vixY4qpVavEX6ILjoj3RXo+xxEPAAAAAACAF1kjEkq83M7pdCpIqbLHxZVYjPJXgfNOAQAAqiLDKFq4vKCgaBsAAMDPcWYUAACAmfLzZRkyRDUdDmnNGik83OyMAAAAvIozowAAAAAAAOAznBkFAABgJrtdxltvKSMtTbF2u9nZAAAAeB1nRgEAAJjJYpEiImSEhxdtAwAA+DmKUQAAAAAAAPAZilEAAABmKiiQ3nxToatWFW0DAAD4OYpRAAAAZiookGX5coWuWUMxCgAABAQWMAcAADBTcLCMvn2Vn50tW3Cw2dkAAAB4HcUoAAAAM4WESGPHKjc1VZEhIWZnAwAA4HVcpgcAAAAAAACfoRgFAAAAAAAAn6EYBQAAYKa8PFkGDVLN5GQpL8/sbAAAALyOYhQAAIDZCgslp9PsLAAAAHyCBcwBAADMZLfLWLxYGWlpirXbzc4GAADA6yhGAQAAmMlikWrXllFYWLQNAADg57hMDwAAAAAAAD5DMQoAAMBMBQXSqlWyf/hh0TYAAICfoxgFAABgpoICWZYsUdjbb1OMAgAAAYE1owAAAMwUHCyje3c5srJkCw42OxsAAACvoxgFAABgppAQ6e67dSI1VTVCQszOBgAAwOu4TA8AAAAAAAA+QzEKAAAAAAAAPkMxCgAAwEx5ebKMGKHo22+X8vLMzgYAAMDrWDMKAADAbDk5sjgcZmcBAADgExSjAAAAzGS3y1iwQJlHjqiO3W52NgAAAF5HMQoAAMBMFotUr56cVmvRNgAAgJ9jzSgAAAAAAAD4DGdGAQAA+EhBziEV5hz+W2OBnF99peDsbOX37KmgkBCP5wVHxMsakeCjLAEAALyLYhQAAICPZO18WelbZpX6ePaKGSW21+z8gGKSpnkrLQAAAJ+iGAUAAOAjkW1vVXjTq9zanLmZOry6pySp7oB1Co6o6fG84Ih4X6QHAADgExSjAAAAfMQakeBxuZ3zZI5r21avo6z2SF+nBQAA4FMsYA4AAAAAAACfoRgFAAAAAAAAn6EYBQAAYKb8/JK3AQAA/BTFKAAAADMZRsnbAAAAfopiFAAAgJlsNtemI3uviYkAAAD4BsUoAAAAE2Xuesm1nfbxDcrY/ryJ2QAAAHgfxSgAAACTOI7sVub38133DcNQ5vZn5Tiy28SsAAAAvItiFAAAgEkKsv6QkZ/ruh8UEiWjIFcFWX+YmBUAAIB3UYwCAAAwiTUyUZYgu+u+80S6LNYwWSMTTcwKAADAu0wtRn3++ee66qqrVK9ePVksFq1Zs8bMdAAAAHzKVqe1otrd7rpvCQpS1IV3y1antYlZAQAAeJepxaicnBy1a9dOL7zwgplpAAAAmCaq4zjXduzVyxTdftxpogEAAKo/q5kv3rdvX/Xt29fMFAAAAKoMW62WZqcAAADgdaYWo8orPz9f+fn5rvuZmZmSJKfTKafTaVZalcLpdMowjGr/PlBxjIHARv+DMRC4Tu1zfzimQfnx+QdjILDR/4HN3/q/rO+jWhWjZs+erRkzZni0p6WlKS8vz4SMKo/T6VRGRoYMw1BQEOvKByLGQGCj/8EYCFxGznHXdtqffyo4sraJ2cAMfP7BGAhs9H9g87f+z8rKKlNctSpGTZkyRRMnTnTdz8zMVGJiomJjYxUVFWViZmfP6XTKYrEoNjbWLwYgyo8xENjofzAGApczK1h//Hc7tk4dWaNjTc0HvsfnH4yBwEb/BzZ/6//Q0NAyxVWrYpTdbpfdbvdoDwoK8otOs1gsfvNeUDGMgcBG/4MxEKBOOWgLCg2l/wMUn38wBgIb/R/Y/Kn/y/oeqlUxCgAAoDoryDmkwpzDbm3OglzXtuPoLhXaIjyeFxwRL2tEgtfzAwAA8AVTi1HZ2dn69ddfXff37dunHTt2qFatWmrQoIGJmQEAAFS+rJ0vK33LrFIf/2vlFSW21+z8gGKSpnkrLQAAAJ8ytRj17bff6vLLL3fdL14PavTo0VqyZIlJWQEAAHhHZNtbFd70KvfGwkI5v/9eWZmZiryki4KsIR7PC46I91GGAAAA3mdqMapbt24yDMPMFAAAAHzGGpHgebldXp6M56fL4nDIduUYBYWHm5McAACAj7BmFAAAgJmCgqTWrVWQkyObHyxcCgAAcCYUowAAAMxks8l49FFlp6Yq3GYzOxsAAACv47/fAAAAAAAA4DMUowAAAAAAAOAzXKYHAABgJodDlv/7P0Xm5krPPy+FhpqdEQAAgFdRjAIAADCT0ynt26dgh6NoGwAAwM9RjAIAADCTzSbj4YeVfeyYarGAOQAACAAUowAAAMwUFCS1b6+C1NSibQAAAD/HEQ8AAAAAAAB8hmIUAACAmQoLpa1bZd2xo2gbAADAz3GZHgAAgJlOnpRl5kzVcDikyy6TQkLMzggAAMCrKEYBAACYKShIat5chSdOsGYUAAAICBSjAAAAzGSzyXjySWWlpiqMX9MDAAABgP9+AwAAAAAAgM9QjAIAAAAAAIDPUIwCAAAwk8Mhy333qcbMmZLDYXY2AAAAXseaUQAAAGZyOqUff5TV4SjaBgAA8HMUowAAAMwUEiJj6lTlHD8uW0iI2dkAAAB4HcUoAAAAMwUHS//4h06mphZtAwAA+DnWjAIAAAAAAIDPcGYUAACAmZxOadcuWY8dk+rUkYL4v0IAAODfKEYBAACYyeGQ5f77VcPhkDp3lqwcngEAAP/G0Q4AAICZLBYpMVHO3NyibQAAAD9HMQoAAMBMdruMF15QZmqqQu12s7MBAADwOhYlAAAAAAAAgM9QjAIAAAAAAIDPUIwCAAAwk8Mhy7RpqvHYY5LDYXY2AAAAXseaUQAAAGZyOqUdO2R1OIq2AQAA/BzFKAAAADOFhMiYOFEn0tNlCwkxOxsAAACvoxgFAABgpuBgqVs3OVJTi7YBAAD8HGtGAQAAAAAAwGcoRgEAAJjJ6ZT27FHwf/7DmlEAACAgcJkeAACAmRwOWf7v/xTpcEhr1khWDs8AAIB/42gHAADATBaLFBcnZ15e0TYAAICfoxgFAABgJrtdxqJFykxNVajdbnY2AAAAXseaUQAAAAAAAPAZilEAAAAAAADwGYpRAAAAZnI4pEceUcTTTxdtAwAA+DnWjAIAADCT0ynLli0KcTgkp9PsbAAAALyOYhQAAICZrFYZ48bpRHq6bFYOzQAAgP/jiMfHDmXm6VBmnke702no2PEc1XJkKCjI82edE6JClRAV6osUAQCAL1mtUu/ecqSmFm0DAAD4OY54fGzh5t/0cMov5X7etJ4tNL33uV7ICAAAAAAAwHcoRvnYmKSGurpVXbe2XEehLn3xK0nSpjuSFGHz7BbOigIAwE8ZhvT77wo6ckSKjTU7GwAAAK+jGOVjJV1ul5Nf4NpuXy9KkaE2X6cFAADMkp8vy/jxinI4pDVrpPBwszMCAADwqiCzE5CkF154QY0aNVJoaKg6d+6sb775xuyUAAAAfCcqSkaNGmZnAQAA4BOmF6PefvttTZw4UQ899JC+++47tWvXTr1791ZqaqrZqQEAAHhfaKiMN95QxosvSqFclg8AAPyf6ZfpPfXUU7r11lt14403SpIWLFigDz/8UK+++qomT55cpn04HA45HA6P9qCgIFlP+VWakmKKWSwWhYSEVCj25MmTMgyjwrGOkwV/f9oZ9ytJNputQrEFBQVyOp2VEhsSEiKLxeLV2MLCQhUWFlZKrNVqVVBQUJWJdTqdKigocG0Xj+Xix4ODgxUcHOwRWxJfxBqGoZMnT1ZK7KmfT2/FSqf/LFfVOaJ4PJ/NfiXmiIrEmjlHFM8BTqezxDmiJMwRZxcrVZ05orCw0OM7oLTYsznmOBVzRPljvTVHnNrnFT2OKAlzxNnFSr6bI049DgwODvbK5545ouTYqjJHlPQdcLb7rQqfe+aIM8c6nU6P1/RlPaIisVLpc8Tpcj+VqcUoh8Ohbdu2acqUKa62oKAg9ejRQ5s3b/aIz8/PV35+vut+ZmamJGnu3Lmy2+0e8c2aNdPIkSNd9x9//PFSB1bDhg2VnJzsuv/000/rxIkTJcYmJCTotttuc91//vnnlZ6eXmJsbGys7rjjDtf9hQsXKi0tzS3GYQRJulBS0UAsnjhfeeUVHTp0qMT9hoeH65///Kfr/uuvv67ffvutxNiQkBBNnTrVdf+tt97Sr7/+WmKsJD300EOu7ZUrV+rHH38sNXbKlCmuQfjee+/p+++/LzV20qRJioiIkCStXbtW3377bamxd999t2rWrClJSklJKXE8FBs7dqzi4uIkSZs2bdKmTZtKjb3lllt0zjnnSJK++uorrV+/vtTY0aNHq1GjRpKkrVu3au3ataXGjhgxQi1atJAkff/993r33XdLjR0yZIhatWolSfrhhx+0cuVKSUUTX05OjiIiIlxfeAMGDFD79u0lSb/88oveeuutUvfbt29fderUSZK0f/9+LV26tNTYHj16qEuXLpKkAwcOaNGiRaXGdu3aVd26dZMkpaamav78+aXGJiUlqVevXpKk9PR0zZs3r9TYjh07qn///pKknJwczZ07t9TYdu3aaeDAgZKK5o3Zs2eXGtuyZUsNGzbMdf+RRx4pNbYqzRFOp1OGYWjhwoU6evRoibE1a9bU3Xff7brPHFFTkv/MEcVzwA033KA2bdpIcp8jSsIcUcQf5ojjx497fAdIZTuOKMYc8T/VbY649tprFRMTI6fTqZ07d1boOKIkzBFFqsMccepxYL169Sr13xrFmCP+pyrOEe+8847Hd0Cxyvi3RkmYI4qYPUcYhqFatWpp/PjxrjZf1iOKVdYccWrN5nRMLUYdOXJEhYWFqlvX/dfl6tatq59++skjfvbs2ZoxY4ZHe05OTonV1szMTLfL/bKzs0utymZlZXnE5ubmlik2KytLOTk5JcaGhoaeMfbkKVdLfrfnd7WsG3XG/TqdzjLnYLVa3WIzMzNLjZVU7tjiL4iMjIzTxqalpbkeL0tscUU1PT39tLFHjhxxbZcltrjaW3zgX5qjR48q/L+LyJYltvjvduzYsdPGHjt2rMRYwzCUl5cnSa4voVNjjx49etr9Hj9+vEKxR44cOW1senp6hWLP1McZGRmu2BMnTpQ51uFwnDb275/78sSaOUc4nU5lZGQoOzu71Njg4OAy75c54n+qyxxRPAeUNkeUhDnCM7ZazhHp6XJs2iTLyZPK6dRJllP+F7UsxxHFmCPcY6vbHGGxWGQYRoWPI84UyxxRteeIU48DK/vfGsWYI9xjq9oc8fd/B/w99mz/rXGmWOYI8+YIwzBcn+Xis9p8WY8oVllzRFmLURbjdOddednBgwd1zjnn6KuvvlJSUpKr/d5779WmTZu0ZcsWt/iSzoxKTEzUX3/9paioKI/9V9VLcP4e+8ymvZqa8h9JUtMYu+66tKnGXdKYU2er2KmzvrhMLy0tTbGxsVymV0mxUtW5BOdMc0Rx/9esWZPL9MoZ6y9zRPEYSEhIcI3LqvC5Z47wwRyRlSVjxAg58vNlXbFCQaf8mh6X4Jx9bHWYI4KCgnT06FHFxsbKMAwuwSlDrL/NEaceB3KZ3v8Eyhxx8uRJHT582O3fAZWx36rwuWeOKNtlekeOHFG9evVcfVedL9PLzMxU3bp1lZGRUWKdppipZ0bVqVNHwcHB+uuvv9za//rrL8XHx3vE2+32Ei/HCw0NVWgZFvwsS0xFYkvKqayxuw9lauG3/zv1rdCQnvlivy5vHqvWCaV33NnkcOqgqQ6xQUFBbh8Kf4s99R+ddrtdoaGhJX4J/X0yK+t+KzNWkusLoLrEeutz7405wmKxyG63l9j/Z7Pf8sZWhc99IM4RxXPAqQeRVeFzzxzh/Vh7RISct96q3IwM1ahRQ0GnGf/e+twzR5gb63Q6ZbFYFBQUVOHjiOoQK1WNz31VnCNOdxxYFT73zBHejQ0JCTntvwPOJgezP/fMEWeOdTqdstlsru+A8u63KnzuT40t65pRpv6ans1mU4cOHfTpp5+62pxOpz799FO3M6X82e/puco9+b/KdnRYiHJPFur39JJPyQMAAH7GapUGDFB+nz5F2wAAAH7O9COeiRMnavTo0erYsaM6deqkZ555Rjk5Oa5f1/N3DWqGKSzkf5XXjNyTCgsJVoOaYSZmBQAAAAAA4B2mF6OuvfZapaWladq0aTp8+LDat2+vjz/+2GNRc3/VOiFK4y5upHs/LPoVCYvFonsua1yuS/QAAEA1ZhhSaqqCjhyRYmPNzgYAAMDrTC9GSdL48ePdfsYw0Iw9pRj15qgL1blhbZMzAgAAPpOfL8sttyjK4ZDWrJFOWcAcAADAH1WJYhT+5/y6kWanAAAAfK0ci4QCAABUdxSjAAAAzBQaKmPFCqWnpiquHL+eAwAAUF2Z+mt6AAAAAAAACCwUowAAAAAAAOAzFKMAAADMdPKk9PzzCn/llaJtAAAAP8eaUT52KDNPhzLz3NpyHYWu7R0HMxVh8+yWhKhQJUSxjgQAAH6nsFCWTz6RzeGQ7rnH7GwAAAC8jmKUjy3c/JseTvml1Me7vri5xPZpPVtoeu9zvZUWAAAwi9UqY9Qo5WVmymbl0AwAAPg/jnh8bExSQ13dqq5Hu9Np6NjxY6oVU0tBQRaPxzkrCgAAP2W1SsOGKS81VVEUowAAQADgiMfHSrvczul0KtWWr7i4aAUFsZQXAAAAAADwT1Q9AAAAzGQYUkaGLJmZRdsAAAB+jjOjAAAAzJSfL8v11yva4ZDWrJHCw83OCAAAwKuqdTHK+O//HmZmZpqcydlzOp3KyspSaGgol+kFKMZAYKP/wRgIYHl5Mk6eVH5BgWyZmQoqKDA7I/gYn38wBgIb/R/Y/K3/i+szxhnO9q7WxaisrCxJUmJiosmZAAAAVIKEBLMzAAAAOGtZWVmKjo4u9XGLcaZyVRXmdDp18OBBRUZGymLx/AW66iQzM1OJiYn6448/FBUVZXY6MAFjILDR/2AMBDb6P7DR/2AMBDb6P7D5W/8bhqGsrCzVq1fvtGd6Veszo4KCglS/fn2z06hUUVFRfjEAUXGMgcBG/4MxENjo/8BG/4MxENjo/8DmT/1/ujOiilX/CxIBAAAAAABQbVCMAgAAAAAAgM9QjKoi7Ha7HnroIdntdrNTgUkYA4GN/gdjILDR/4GN/gdjILDR/4EtUPu/Wi9gDgAAAAAAgOqFM6MAAAAAAADgMxSjAAAAAAAA4DMUowAAAAAAAOAzFKMAAAAAAADgMxSjvGD27Nm66KKLFBkZqbi4OA0cOFA///zzGZ+3YsUKnXfeeQoNDVWbNm300UcfuT1uGIamTZumhIQEhYWFqUePHtqzZ4+33gbOQkXGwMsvv6xLL71UMTExiomJUY8ePfTNN9+4xSQnJ8tisbjd+vTp4823ggqoSP8vWbLEo29DQ0PdYpgDqoeK9H+3bt08+t9isah///6uGD7/1cf8+fPVtm1bRUVFKSoqSklJSVq7du1pn8MxgP8ob//z/e9fytv/fP/7n/KOAY4B/NucOXNksVg0YcKE08YF4nEAxSgv2LRpk8aNG6evv/5aKSkpOnnypHr16qWcnJxSn/PVV19pxIgRuvnmm7V9+3YNHDhQAwcO1O7du10xjz/+uJ599lktWLBAW7ZsUUREhHr37q28vDxfvC2UQ0XGwMaNGzVixAht2LBBmzdvVmJionr16qUDBw64xfXp00eHDh1y3d566y1vvx2UU0X6X5KioqLc+va3335ze5w5oHqoSP+vWrXKre93796t4OBgDR061C2Oz3/1UL9+fc2ZM0fbtm3Tt99+qyuuuEIDBgzQDz/8UGI8xwD+pbz9z/e/fylv/0t8//ub8o4BjgH819atW7Vw4UK1bdv2tHEBexxgwOtSU1MNScamTZtKjRk2bJjRv39/t7bOnTsbY8aMMQzDMJxOpxEfH2888cQTrsfT09MNu91uvPXWW95JHJWmLGPg7woKCozIyEhj6dKlrrbRo0cbAwYM8EKG8Kay9P/ixYuN6OjoUh9nDqi+KvL5f/rpp43IyEgjOzvb1cbnv3qLiYkxFi1aVOJjHAP4v9P1/9/x/e9/Ttf/fP8HhvLMARwD+IesrCyjefPmRkpKitG1a1fj7rvvLjU2UI8DODPKBzIyMiRJtWrVKjVm8+bN6tGjh1tb7969tXnzZknSvn37dPjwYbeY6Ohode7c2RWDqqssY+DvTpw4oZMnT3o8Z+PGjYqLi9O5556rsWPH6ujRo5WaKypfWfs/OztbDRs2VGJiosf/oDEHVF8V+fy/8sorGj58uCIiItza+fxXP4WFhVq+fLlycnKUlJRUYgzHAP6rLP3/d3z/+4+y9j/f//6rInMAxwD+Ydy4cerfv7/H93tJAvU4wGp2Av7O6XRqwoQJ6tKli1q3bl1q3OHDh1W3bl23trp16+rw4cOux4vbSotB1VTWMfB39913n+rVq+c26fTp00eDBw9W48aNtXfvXk2dOlV9+/bV5s2bFRwc7I30cZbK2v/nnnuuXn31VbVt21YZGRmaO3euLr74Yv3www+qX78+c0A1VZHP/zfffKPdu3frlVdecWvn81+97Nq1S0lJScrLy1ONGjW0evVqnX/++SXGcgzgf8rT/3/H93/1V57+5/vfP1V0DuAYwD8sX75c3333nbZu3Vqm+EA9DqAY5WXjxo3T7t279cUXX5idCkxSkTEwZ84cLV++XBs3bnRbxHL48OGu7TZt2qht27Zq2rSpNm7cqO7du1dq3qgcZe3/pKQkt/8xu/jii9WyZUstXLhQM2fO9Haa8JKKfP5feeUVtWnTRp06dXJr5/NfvZx77rnasWOHMjIytHLlSo0ePVqbNm0qc0EC1VtF+5/vf/9Qnv7n+98/VXQO4Big+vvjjz909913KyUlxePHCOCOy/S8aPz48frggw+0YcMG1a9f/7Sx8fHx+uuvv9za/vrrL8XHx7seL24rLQZVT3nGQLG5c+dqzpw5+uSTT8642F2TJk1Up04d/frrr5WRLipZRfq/WEhIiC644AJX3zIHVD8V6f+cnBwtX75cN9988xlj+fxXbTabTc2aNVOHDh00e/ZstWvXTvPmzSsxlmMA/1Oe/i/G97//qEj/F+P73z9UZAxwDOAftm3bptTUVF144YWyWq2yWq3atGmTnn32WVmtVhUWFno8J1CPAyhGeYFhGBo/frxWr16tzz77TI0bNz7jc5KSkvTpp5+6taWkpLj+p6Rx48aKj493i8nMzNSWLVvKfP0xfKciY0Aq+pWEmTNn6uOPP1bHjh3PGP/nn3/q6NGjSkhIONuUUYkq2v+nKiws1K5du1x9yxxQfZxN/69YsUL5+fkaNWrUGWP5/FcvTqdT+fn5JT7GMYD/O13/S3z/+7sz9f+p+P73T2UZAxwD+Ifu3btr165d2rFjh+vWsWNHjRw5Ujt27CjxssqAPQ4wd/10/zR27FgjOjra2Lhxo3Ho0CHX7cSJE66Y66+/3pg8ebLr/pdffmlYrVZj7ty5xo8//mg89NBDRkhIiLFr1y5XzJw5c4yaNWsa7777rrFz505jwIABRuPGjY3c3Fyfvj+cWUXGwJw5cwybzWasXLnS7TlZWVmGYRT9IsOkSZOMzZs3G/v27TPWr19vXHjhhUbz5s2NvLw8n79HlK4i/T9jxgxj3bp1xt69e41t27YZw4cPN0JDQ40ffvjBFcMcUD1UpP+LXXLJJca1117r0c7nv3qZPHmysWnTJmPfvn3Gzp07jcmTJxsWi8X45JNPDMPgGMDflbf/+f73L+Xtf77//U95x0AxjgH8199/TY/jgCIUo7xAUom3xYsXu2K6du1qjB492u15//rXv4wWLVoYNpvNaNWqlfHhhx+6Pe50Oo0HH3zQqFu3rmG3243u3bsbP//8sw/eEcqrImOgYcOGJT7noYceMgzDME6cOGH06tXLiI2NNUJCQoyGDRsat956q3H48GHfvjmcUUX6f8KECUaDBg0Mm81m1K1b1+jXr5/x3Xffue2XOaB6qOh3wE8//WRIch2snorPf/Vy0003GQ0bNjRsNpsRGxtrdO/e3a1fOQbwb+Xtf77//Ut5+5/vf/9Tke8AjgH829+LURwHFLEYhmF499wrAAAAAAAAoAhrRgEAAAAAAMBnKEYBAAAAAADAZyhGAQAAAAAAwGcoRgEAAAAAAMBnKEYBAAAAAADAZyhGAQAAAAAAwGcoRgEAAAAAAMBnKEYBAAAAAADAZyhGAQCAgLV//35ZLBbt2LHD66+1ZMkS1axZ0+uv4y3dunXThAkTzmof1f1vAAAAKgfFKAAAUCUlJyfLYrF43Pr06WN2amfUqFEjPfPMM25t1157rX755Revv3a3bt1cf6vQ0FCdf/75evHFF896v6tWrdLMmTMrIUMAABDorGYnAAAAUJo+ffpo8eLFbm12u92kbM5OWFiYwsLCfPJat956qx5++GGdOHFCr732msaNG6eYmBiNGDGi3PtyOByy2WyqVauWFzIFAACBiDOjAABAlWW32xUfH+92i4mJkSRdd911uvbaa93iT548qTp16ui1116TJH388ce65JJLVLNmTdWuXVtXXnml9u7dW+rrlXQZ2Zo1a2SxWFz39+7dqwEDBqhu3bqqUaOGLrroIq1fv971eLdu3fTbb7/pnnvucZ2hVNq+58+fr6ZNm8pms+ncc8/V66+/7va4xWLRokWLNGjQIIWHh6t58+Z67733zvh3Cw8PV3x8vJo0aaLp06e7PS89PV233HKLYmNjFRUVpSuuuELff/+967nTp09X+/bttWjRIjVu3FihoaGu93XqZXrHjx/XDTfcoJiYGIWHh6tv377as2ePx9+zQYMGCg8P16BBg3T06NEz5g4AAPwfxSgAAFAtjRw5Uu+//76ys7NdbevWrdOJEyc0aNAgSVJOTo4mTpyob7/9Vp9++v/bu/c4m+r9j+PvPfe7cRlmXELMiNwphgonGpFciogTfhFKRDqhjvuhUyrqVBwVFaVEKBT5oVwOwqBck0tpmBm3MTPMbX9/f8xv9rHNxRgze8/Mfj0fj/2w9nd/9tqfNd+111r7Y63vWi83Nzd1795dVqu1wJ+bmJioTp06af369dqzZ486duyoLl266NSpU5IyL2erWrWqpkyZopiYGMXExOQ4n6+++kojR47U888/r59//llDhgzRwIEDtWHDBru4yZMnq1evXtq3b586deqkvn376vz58zeVs6+vr1JTUyVJPXv2VGxsrNasWaNdu3apadOmuv/+++3m+euvv2rp0qVatmxZruNpDRgwQD/99JNWrlypbdu2yRijTp06KS0tTZK0fft2Pfnkkxo+fLiio6PVrl07TZs27abyBgAApRPFKAAAUGx98803CggIsHtMnz5dkhQVFSV/f3999dVXtvhPP/1UDz/8sAIDAyVJjzzyiHr06KHatWurcePG+vDDD7V//34dOHCgwDk1atRIQ4YMUf369RUeHq6pU6eqVq1atjOPypUrJ3d3dwUGBtrO5srJzJkzNWDAAD399NOKiIjQ6NGj1aNHD82cOdMubsCAAerTp49q166t6dOnKzExUTt27MhXrhkZGVq4cKH27dunv/zlL9q8ebN27NihJUuWqHnz5goPD9fMmTMVHBysL7/80va+1NRUffzxx2rSpIkaNmyYbb5Hjx7VypUr9f777+vee+9Vo0aNtGjRIp0+fVrLly+XJM2ePVsdO3bU3/72N0VERGjEiBGKiorKV94AAKB0oxgFAACKrXbt2ik6OtruMXToUEmSh4eHevXqpUWLFknKPAtqxYoV6tu3r+39R48eVZ8+fXT77bcrKChINWrUkCTbWUwFkZiYqDFjxqhu3boKDg5WQECADh48eNPzPHjwoFq3bm3X1rp1ax08eNCu7dpikL+/v4KCghQbG5vnvN99910FBATI19dXgwcP1qhRozRs2DDt3btXiYmJKl++vF2B7/jx43aXL1avXl0hISF55u7h4aEWLVrY2sqXL686derY8j948KDd65IUGRmZZ94AAMA1MIA5AAAotvz9/VW7du1cX+/bt6/atGmj2NhYrVu3Tr6+vnZ32+vSpYuqV6+uefPmqXLlyrJarapfv77tkrXrubm5yRhj15Z12VmWMWPGaN26dZo5c6Zq164tX19fPfroo7nO81Z5enraPbdYLDe8zLBv37566aWX5Ovrq7CwMLm5Zf7/Y2JiosLCwrRx48Zs77l2PCt/f/9bzhsAACA3FKMAAECJ1apVK1WrVk2ff/651qxZo549e9qKN+fOndPhw4c1b9483Xv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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "def plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8)):\n", - " \"\"\"\n", - " Plot coefficient estimates with CIs over time, grouped by first treated period.\n", - " CIs with the same x-value are jittered horizontally for better visibility.\n", - " Works with both numeric and datetime values.\n", - " \"\"\"\n", - " df = create_ci_dataframe(dml_obj, level=level, joint=joint)\n", - " all_time_periods = sorted(df['Evaluation Period'].unique())\n", - " first_treated_periods = sorted(df['First Treated'].unique())\n", - " n_periods = len(first_treated_periods)\n", - " colors = dict(zip(['pre', 'post'], sns.color_palette(\"colorblind\")[:2]))\n", - " \n", - " # Check if we're dealing with datetime values\n", - " is_datetime = pd.api.types.is_datetime64_any_dtype(df['Evaluation Period'])\n", - " \n", - " # Adjust figure size to accommodate bottom legend\n", - " fig = plt.figure(figsize=figsize)\n", - " # Create subplot grid with space for legend at bottom\n", - " gs = fig.add_gridspec(n_periods + 1, 1, height_ratios=[3]*n_periods + [0.5])\n", - " axes = [fig.add_subplot(gs[i]) for i in range(n_periods)]\n", - "\n", - " if n_periods == 1:\n", - " axes = [axes]\n", - " \n", - " # Create a list to store legend handles and labels\n", - " legend_elements = []\n", - " \n", - " # Define jitter amount - different handling for datetime vs numeric\n", - " if is_datetime:\n", - " # For datetime, calculate time difference between periods\n", - " if len(all_time_periods) > 1:\n", - " time_diff = (all_time_periods[1] - all_time_periods[0]).total_seconds()\n", - " jitter_seconds = time_diff * 0.1 # Use 5% of time difference for jitter\n", - " else:\n", - " jitter_seconds = 86400 * 0.05 # Default to 5% of a day if only one period\n", - " else:\n", - " jitter_amount = 0.1 # Standard numeric jitter\n", - " \n", - " for idx, period in enumerate(first_treated_periods):\n", - " period_data = df[df['First Treated'] == period]\n", - " ax = axes[idx]\n", - "\n", - " i_period = all_time_periods.index(period)\n", - "\n", - " # Add treatment start line\n", - " line = ax.axvline(x=all_time_periods[i_period], color='red', \n", - " linestyle=':', alpha=0.7)\n", - " if idx == 0:\n", - " legend_elements.append((line, 'Treatment start'))\n", - "\n", - " zero_line = ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n", - " if idx == 0:\n", - " legend_elements.append((zero_line, 'Zero effect'))\n", - "\n", - " # Split data by treatment status\n", - " pre_treatment = period_data[period_data['Pre-Treatment']]\n", - " post_treatment = period_data[~period_data['Pre-Treatment']]\n", - " \n", - " if not pre_treatment.empty:\n", - " pre_treatment = pre_treatment.copy()\n", - " \n", - " for x_val in pre_treatment['Evaluation Period'].unique():\n", - " mask = pre_treatment['Evaluation Period'] == x_val\n", - " count = mask.sum()\n", - " if count > 1:\n", - " if is_datetime:\n", - " # For datetime values, create timedelta jitters\n", - " jitter_range = np.linspace(-jitter_seconds, jitter_seconds, count)\n", - " jitters = [pd.Timedelta(seconds=float(j)) for j in jitter_range]\n", - " else:\n", - " # For numeric values, create standard jitters\n", - " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", - " \n", - " # Store the jitters for these points\n", - " pre_treatment.loc[mask, 'jitter_index'] = range(count)\n", - " for i, j in enumerate(jitters):\n", - " pre_treatment.loc[mask & (pre_treatment['jitter_index'] == i), 'jittered_x'] = x_val + j\n", - " \n", - " # For points without jitter (single point at x-value)\n", - " if 'jittered_x' not in pre_treatment.columns:\n", - " pre_treatment['jittered_x'] = pre_treatment['Evaluation Period']\n", - " else:\n", - " mask = ~pre_treatment['jittered_x'].notna()\n", - " pre_treatment.loc[mask, 'jittered_x'] = pre_treatment.loc[mask, 'Evaluation Period']\n", - " \n", - " # Pre-treatment points with jitter\n", - " scatter_pre = ax.scatter(pre_treatment['jittered_x'], \n", - " pre_treatment['Estimate'], \n", - " color=colors['pre'], alpha=0.8, s=10)\n", - " \n", - " # Regular CIs with jitter\n", - " error_pre = ax.errorbar(pre_treatment['jittered_x'], \n", - " pre_treatment['Estimate'],\n", - " yerr=[pre_treatment['Estimate'] - pre_treatment['CI Lower'],\n", - " pre_treatment['CI Upper'] - pre_treatment['Estimate']],\n", - " fmt='none', color=colors['pre'], alpha=1.0, \n", - " capsize=5)\n", - " if idx == 0:\n", - " legend_elements.extend([\n", - " (scatter_pre, 'Pre-treatment'),\n", - " (error_pre, f'{int(level*100)}% CI'),\n", - " ])\n", - " \n", - " # Similar structure for post-treatment with jittering\n", - " if not post_treatment.empty:\n", - " post_treatment = post_treatment.copy()\n", - " \n", - " for x_val in post_treatment['Evaluation Period'].unique():\n", - " mask = post_treatment['Evaluation Period'] == x_val\n", - " count = mask.sum()\n", - " if count > 1:\n", - " if is_datetime:\n", - " # For datetime values, create timedelta jitters\n", - " jitter_range = np.linspace(-jitter_seconds, jitter_seconds, count)\n", - " jitters = [pd.Timedelta(seconds=float(j)) for j in jitter_range]\n", - " else:\n", - " # For numeric values, create standard jitters\n", - " jitters = np.linspace(-jitter_amount, jitter_amount, count)\n", - " \n", - " # Store the jitters for these points\n", - " post_treatment.loc[mask, 'jitter_index'] = range(count)\n", - " for i, j in enumerate(jitters):\n", - " post_treatment.loc[mask & (post_treatment['jitter_index'] == i), 'jittered_x'] = x_val + j\n", - " \n", - " # For points without jitter (single point at x-value)\n", - " if 'jittered_x' not in post_treatment.columns:\n", - " post_treatment['jittered_x'] = post_treatment['Evaluation Period']\n", - " else:\n", - " mask = ~post_treatment['jittered_x'].notna()\n", - " post_treatment.loc[mask, 'jittered_x'] = post_treatment.loc[mask, 'Evaluation Period']\n", - " \n", - " scatter_post = ax.scatter(post_treatment['jittered_x'], \n", - " post_treatment['Estimate'], \n", - " color=colors['post'], alpha=0.8, s=10)\n", - " if idx == 0:\n", - " legend_elements.append((scatter_post, 'Post-treatment'))\n", - " \n", - " # Error bars with jitter\n", - " ax.errorbar(post_treatment['jittered_x'], post_treatment['Estimate'],\n", - " yerr=[post_treatment['Estimate'] - post_treatment['CI Lower'],\n", - " post_treatment['CI Upper'] - post_treatment['Estimate']],\n", - " fmt='none', color=colors['post'], alpha=1.0, capsize=5)\n", - "\n", - " ax.set_title(f'First Treated: {period}')\n", - " ax.grid(True, alpha=0.3)\n", - " \n", - " if idx == 0:\n", - " ax.set_ylabel('Effect')\n", - " ax.set_xlabel('Evaluation Period')\n", - " \n", - " # Create legend in a separate subplot at the bottom\n", - " legend_ax = fig.add_subplot(gs[-1])\n", - " legend_ax.axis('off') # Hide axes for legend subplot\n", - " \n", - " # Add legend using collected handles and labels\n", - " legend = legend_ax.legend(*zip(*legend_elements), \n", - " loc='center',\n", - " ncol=5, # Adjust number of columns as needed\n", - " mode='expand',\n", - " borderaxespad=0.)\n", - " \n", - " plt.suptitle(\"Estimated ATTs by Group\", y=1.02)\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "plot_atts(dml_obj, level=0.95, joint=True, figsize=(12, 8))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, "outputs": [ { "name": "stdout", @@ -636,29 +315,48 @@ " Group Aggregation \n", "\n", "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.487968 0.034215 43.488305 0.0 1.420908 1.555029\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1.488207 0.034341 43.336421 0.0 1.4209 1.555514\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "2.0 1.954536 0.052243 37.412432 0.0 1.852142 2.056931\n", - "3.0 1.582568 0.056295 28.111848 0.0 1.472231 1.692905\n", - "4.0 0.953010 0.067443 14.130675 0.0 0.820825 1.085195\n", + "2.0 1.953534 0.052242 37.393827 0.0 1.851142 2.055927\n", + "3.0 1.585497 0.056338 28.142811 0.0 1.475077 1.695917\n", + "4.0 0.951881 0.067878 14.023318 0.0 0.818842 1.084920\n", "------------------ Additional Information ------------------\n", "Control Group: never_treated\n", "Anticipation Periods: 0\n", "Score: observational\n", "\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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n9enTJ5Ikid69e8enP/3pWutefPHFeO211+Kuu+6Kc889Nzde06e21eU2sh3tNSKic+fOu2yWUkP1Wl91/T3p06dPZLPZ+Mc//lHl9tNP2tZjdOrUKc4///w4//zzY/369XHMMcfE9773PaEUALsNa0oBQBP04Ycfxr333huf+9zn4owzzqj2demll8a6devigQceiIiIUaNGxaZNm+I///M/c4+RzWarBVr77LNP9O/fP371q1/F+vXrc+OPPfZYvPjii3XqrXPnzjFixIi49dZbY8WKFdX2r169Ovf9qFGj4umnn47nn38+N/bee+/FzJkzqxyzdabN1pk6EREbN26Mm2++udrjFxcX13gL25lnnhnLly+v8hps9eGHH0Z5eXlERJx00kkREfHzn/+8Ss2NN95Y7bhd6fTTT48WLVrE5MmTqzzPiC3P+913342Iml+LJEniZz/7WbXHLC4ujogtM812pVGjRkVJSUlcd911sWnTpmr7P/4zrqutYemu7rW+6vp7MmbMmCgoKIhrrrmm2uy0j/9siouLa3xOW3+eW7Vr1y769u0blZWVu+BZAEDTYKYUADRBDzzwQKxbty5OOeWUGvcfeeSRUVpaGjNnzoyxY8fGmDFj4vDDD49vfvOb8cYbb0S/fv3igQceiPfeey8iqs7muO666+LUU0+NYcOGxfnnnx/vv/9+3HTTTdG/f/8qQdW2TJ06NY466qgYMGBAXHjhhfGpT30qVq1aFU8//XT885//jBdeeCEiIq666qr49a9/HSeccEJcdtllUVxcHNOnT4+ePXvGe++9l+tr6NCh0bFjxxg3blx8/etfj0wmEzNmzKgW3kREDB48OGbPnh0TJkyIz3zmM9GuXbs4+eST45xzzonf/va3cfHFF8eCBQti2LBhsXnz5njllVfit7/9bfzhD3+Iww47LAYNGhRnnXVW3HzzzbF27doYOnRozJ8/v84zxXZUnz594gc/+EFMnDgx3nrrrRgzZky0b98+Fi9eHPfdd1987WtfiyuvvDL69esXffr0iSuvvDKWL18eJSUl8bvf/a7G9ZkGDx4cEVsWbR81alS0aNEivvjFL+50ryUlJXHLLbfEOeecE4ceemh88YtfjNLS0li6dGk89NBDMWzYsLjpppvq9Zht2rSJAw88MGbPnh2f/vSno1OnTtG/f//o37//No/785//HBUVFdXGDz744B1an6muvyd9+/aN73znO/H9738/jj766Dj99NOjsLAwnnnmmdhnn31iypQpEbHlZ3DLLbfED37wg+jbt2907tw5jjvuuDjwwANjxIgRMXjw4OjUqVMsWrQo7rnnnrj00kvr3TMANFn5+dA/AGBnnHzyyUlRUVFSXl5ea815552XtGrVKlmzZk2SJEmyevXq5Oyzz07at2+fdOjQITnvvPOSJ598MomIZNasWVWOnTVrVtKvX7+ksLAw6d+/f/LAAw8kn//855N+/frlahYvXpxERPKTn/ykxvO/+eabybnnnpt07do1adWqVdKtW7fkc5/7XHLPPfdUqXvuueeSo48+OiksLEy6d++eTJkyJfn5z3+eRESycuXKXN2TTz6ZHHnkkUmbNm2SffbZJ7nqqquSP/zhD0lEJAsWLMjVrV+/Pjn77LOTPfbYI4mIpFevXrl9GzduTH70ox8lBx10UFJYWJh07NgxGTx4cDJ58uRk7dq1uboPP/ww+frXv57sueeeSXFxcXLyyScny5YtSyIimTRpUq2v+SfNmTOnWn+TJk1KIiJZvXp1jcf87ne/S4466qikuLg4KS4uTvr165eMHz8+efXVV3M1//jHP5KRI0cm7dq1S/baa6/kwgsvTF544YUkIpI77rgjV/fRRx8ll112WVJaWppkMplk6z/9avvZLViwIImIZM6cOVXG77jjjiQikmeeeaZa/ahRo5IOHTokRUVFSZ8+fZLzzjsvWbRoUa5m3LhxSXFxcbXnufV1+LinnnoqGTx4cNK6devtvtZbe63t6+PHDh8+PDnooIOqPUavXr2S0aNHVxuv6+9JkiTJ7bffnhxyyCG5uuHDhyfz5s3L7V+5cmUyevTopH379klEJMOHD0+SJEl+8IMfJIcffniyxx57JG3atEn69euXXHvttcnGjRtrfc4A0NxkkqSG/2IEAHYLc+fOjdNOOy2eeOKJGDZs2DZrBw0aFKWlpTWuXbSrXXHFFXHrrbfG+vXrm9Qi2QAA1J01pQBgN/Hhhx9W2d68eXP84he/iJKSkjj00ENz45s2bYqPPvqoSu3ChQvjhRdeiBEjRjR4X++++27MmDEjjjrqKIEUAEAzZk0pANhNXHbZZfHhhx/GkCFDorKyMu6999546qmn4rrrros2bdrk6pYvXx4jR46ML3/5y7HPPvvEK6+8EtOmTYuuXbvGxRdfvMv7GjJkSIwYMSIOOOCAWLVqVfzyl7+MsrKy+O53v7vLzwUAQOMhlAKA3cRxxx0X119/fTz44INRUVERffv2jV/84hfVFlbu2LFjDB48OKZPnx6rV6+O4uLiGD16dPzwhz+MPffcc5f39dnPfjbuueeeuO222yKTycShhx4av/zlL+OYY47Z5ecCAKDxsKYUAAAAAKmzphQAAAAAqRNKAQAAAJC63W5NqWw2G2+//Xa0b98+MplMvtsBAAAAaFaSJIl169bFPvvsEwUFtc+H2u1Cqbfffjt69OiR7zYAAAAAmrVly5ZF9+7da92/24VS7du3j4gtL0xJSUmeuwEAAABoXsrKyqJHjx65DKY2u10otfWWvZKSEqEUAAAAQAPZ3rJJFjoHAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHUt890AdZPdVF6v+oJWxQ3UCQAAAMDOE0o1EUumdqxXfe8rNjZQJwAAAAA7z+17AAAAAKTOTKkmotf496tsZzeVx7LbukdERI+v/dPtegAAAECTIpRqIrYVOhW0KhZKAQAAAE2K2/cAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASF1eQ6kpU6bEZz7zmWjfvn107tw5xowZE6+++up2j5szZ07069cvioqKYsCAAfHwww+n0C0AAAAAu0peQ6nHHnssxo8fH//1X/8V8+bNi02bNsWJJ54Y5eXltR7z1FNPxVlnnRVf/epX47nnnosxY8bEmDFj4qWXXkqxcwAAAAB2RiZJkiTfTWy1evXq6Ny5czz22GNxzDHH1FgzduzYKC8vjwcffDA3duSRR8agQYNi2rRp2z1HWVlZdOjQIdauXRslJSW7rPe0ZTeVx5KpHSMiotf496OgVXGeOwIAAACoe/bSqNaUWrt2bUREdOrUqdaap59+OkaOHFllbNSoUfH00083aG8AAAAA7Dot893AVtlsNq644ooYNmxY9O/fv9a6lStXRpcuXaqMdenSJVauXFljfWVlZVRWVua2y8rKcufLZrO7oPP8+Hjv2Ww2ogk/FwAAAKD5qGve0mhCqfHjx8dLL70UTzzxxC593ClTpsTkyZOrja9evToqKip26bnSlHy0Iff96tWrI9Oy9nW4AAAAANKybt26OtU1ilDq0ksvjQcffDAef/zx6N69+zZru3btGqtWraoytmrVqujatWuN9RMnTowJEybktsvKyqJHjx5RWlra5NeUWva/35eWllpTCgAAAGgUioqK6lSX11AqSZK47LLL4r777ouFCxdG7969t3vMkCFDYv78+XHFFVfkxubNmxdDhgypsb6wsDAKCwurjRcUFERBQaNaUqt+PtZ7k38uAAAAQLNR14wir6HU+PHj4+677477778/2rdvn1sXqkOHDtGmTZuIiDj33HOjW7duMWXKlIiIuPzyy2P48OFx/fXXx+jRo2PWrFmxaNGiuO222/L2PPIt+agiwkwpAAAAoAnJ6/SaW265JdauXRsjRoyIvffeO/c1e/bsXM3SpUtjxYoVue2hQ4fG3XffHbfddlsMHDgw7rnnnpg7d+42F0dvTpLs5ih//d5YNffU3NjSW/eOFb8bFeWv3xtJdnMeuwMAAACom0ySJEm+m0hTWVlZdOjQIdauXdvk1pTKVpbFqofGRsXS+bXWFPU8PrqMnh0FhU3ruQEAAADNQ12zFwsRNRFJdvN2A6mIiIql82PVQ2PNmAIAAAAaNaFUE7Hhzfu3G0htVbF0fmz4nwcauCMAAACAHSeUaiLK/n5r/epfqF89AAAAQJqEUk1A9qOKqFi2oF7HVCx7NLIfVTRQRwAAAAA7RyjVBCQb16V6HAAAAEBDE0o1AZnW7VM9DgAAAKChCaWagIKWRVHU49h6HVPU47goaFnUQB0BAAAA7ByhVBNRcvBF9asfWL96AAAAgDQJpZqItn1OjaKex9eptqjn8dH2U6c0cEcAAAAAO04o1URkClpEl9GztxtMFfU8PrqMnh2ZghYpdQYAAABQf0KpJqSgsCS6jnkwOn9udhR1H15lX1GP46Lz52ZH1zEPRkFhSZ46BAAAAKiblvlugPrJFLSI4r6nRZteJ8aSqR0jIqLnRSuiRZs989wZAAAAQN2ZKdUMZHzKHgAAANDECKUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUtcx3A9RNdlN5rduf3BcRUdCquMF7AgAAANhRQqkmYsnUjrXuW3Zb92pjva/Y2JDtAAAAAOwUt+8BAAAAkDozpZqIXuPfz3cLAAAAALuMUKqJsEYUAAAA0Jy4fQ8AAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1LXMdwMAAPyf7KbyetUXtCpuoE4AABqWUAoAoBFZMrVjvep7X7GxgToBAGhYbt8DAAAAIHVmSgEANCK9xr9fZTu7qTyW3dY9IiJ6fO2fbtcDAJoNoRQAQCOyrdCpoFWxUAoAaDbcvgcAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAABNRPJRRb5bAADYZYRSAACNUJLdHOWv3xur5p6aG1t6696x4nejovz1eyPJbs5jdwAAO69lvhsAAKCqbGVZrHpobFQsnV9tX8WyBVGxbEEU9Tw+uoyeHQWFJXnoEABg55kpBQDQiCTZzbUGUh9XsXR+rHporBlTAECTJZQCAGhENrx5/3YDqa0qls6PDf/zQAN3BADQMIRSAACNSNnfb61f/Qv1qwcAaCyEUgAAjUT2o4qoWLagXsdULHs0sj6VDwBogoRSAACNRLJxXarHAQDkk1AKAKCRyLRun+pxAAD5JJQCAGgkCloWRVGPY+t1TFGP46KgZVEDdQQA0HCEUgAAjUjJwRfVr35g/eoBABoLoRQAQCPSts+pUdTz+DrVFvU8Ptp+6pQG7ggAoGEIpQAAGpFMQYvoMnr2doOpop7HR5fRsyNT0CKlzgAAdi2hFABAI1NQWBJdxzwYnT83O4q6D6+yr6jHcdH5c7Oj65gHo6CwJE8dAgDsvJb5bgAAgOoyBS2iuO9p0abXibFkaseIiOh50Ypo0WbPPHcGALBrmCkFANBEZHzKHgDQjAilAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1OU1lHr88cfj5JNPjn322ScymUzMnTt3m/ULFy6MTCZT7WvlypXpNAwAAADALpHXUKq8vDwGDhwYU6dOrddxr776aqxYsSL31blz5wbqEAAAAICG0DKfJz/ppJPipJNOqvdxnTt3jj322GPXNwQAAABAKprkmlKDBg2KvffeO0444YR48skn890OAAAAAPWU15lS9bX33nvHtGnT4rDDDovKysqYPn16jBgxIv7yl7/EoYceWuMxlZWVUVlZmdsuKyuLiIhsNhvZbDaVvgEAdtTH/72SzWYj/PsFAGjk6pq3NKlQav/994/9998/tz106NB4880344YbbogZM2bUeMyUKVNi8uTJ1cZXr14dFRUVDdYrAMCukHy0Iff96tWrI9OyPI/dAABs37p16+pU16RCqZocfvjh8cQTT9S6f+LEiTFhwoTcdllZWfTo0SNKS0ujpKQkjRYBAHZYdlN5LPvf70tLS6OgVXFe+wEA2J6ioqI61TX5UOr555+Pvffeu9b9hYWFUVhYWG28oKAgCgqa5JJaAMDu5GP/XvHvFwCgKajrv1fyGkqtX78+3njjjdz24sWL4/nnn49OnTpFz549Y+LEibF8+fL41a9+FRERN954Y/Tu3TsOOuigqKioiOnTp8ejjz4af/zjH/P1FAAAAADYAXkNpRYtWhTHHntsbnvrbXbjxo2LO++8M1asWBFLly7N7d+4cWN885vfjOXLl0fbtm3j4IMPjj/96U9VHgMAAACAxi+TJEmS7ybSVFZWFh06dIi1a9daUwoAaPSym8pjydSOERHRa/z71pQCABq9umYvFiUAAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSl9dP3wMAoKrspvJatz+5LyIsfA4ANFlCKQCARmTrJ+3VZNlt3auN9b5iY0O2AwDQYNy+BwAAAEDqzJQCAGhEeo1/P98tAACkQigFANCIWCMKANhduH0PAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNS1zHcDAAAAALuT7KbyetUXtCpuoE7ySygFAAAAkKIlUzvWq773FRsbqJP82qHb91q0aBHvvPNOtfF33303WrRosdNNAQAAANC87dBMqSRJahyvrKyM1q1b71RDAAAAAM1Zr/HvV9nObiqPZbd1j4iIHl/7Z7O9Xe+T6hVK/fznP4+IiEwmE9OnT4927drl9m3evDkef/zx6Nev367tEAAAAKAZ2VboVNCqWChVkxtuuCEitsyUmjZtWpVb9Vq3bh377rtvTJs2bdd2CAAAAECzU69QavHixRERceyxx8a9994bHTvWb2EuAAAAAIjYwTWlFixYsKv7AAAAAGA3skOfvvf5z38+fvSjH1Ub//GPfxxf+MIXdropAAAAAJq3HQqlHn/88fjsZz9bbfykk06Kxx9/fKebAgAAAKB526FQav369dG6detq461atYqysrKdbgoAAACA5m2HQqkBAwbE7Nmzq43PmjUrDjzwwJ1uCgAAAIDmbYcWOv/ud78bp59+erz55ptx3HHHRUTE/Pnz4ze/+U3MmTNnlzYIAAAAQPOzQ6HUySefHHPnzo3rrrsu7rnnnmjTpk0cfPDB8ac//SmGDx++q3sEAAAAoJnZoVAqImL06NExevToXdkLAAAAALuJHVpTKiLigw8+iOnTp8e3v/3teO+99yIi4tlnn43ly5fvsuYAAAAAaJ52aKbU3//+9xg5cmR06NAh3nrrrbjggguiU6dOce+998bSpUvjV7/61a7uEwAAAIBmZIdmSk2YMCHOO++8eP3116OoqCg3/tnPfjYef/zxXdYcAAAAAM3TDoVSzzzzTFx00UXVxrt16xYrV67c6aYAAAAAaN52KJQqLCyMsrKyauOvvfZalJaW7nRTAAAAADRvOxRKnXLKKXHNNdfEpk2bIiIik8nE0qVL41vf+lZ8/vOf36UNAgAAAND87FAodf3118f69eujc+fO8eGHH8bw4cOjb9++0b59+7j22mt3dY8AAAAANDM79Ol7HTp0iHnz5sWTTz4ZL7zwQqxfvz4OPfTQGDly5K7uDwAAAIBmqM6hVKdOneK1116LvfbaK77yla/Ez372sxg2bFgMGzasIfsDAAAAoBmq8+17GzduzC1uftddd0VFRcVOn/zxxx+Pk08+OfbZZ5/IZDIxd+7c7R6zcOHCOPTQQ6OwsDD69u0bd9555073AQAAAEC66jxTasiQITFmzJgYPHhwJEkSX//616NNmzY11t5+++11eszy8vIYOHBgfOUrX4nTTz99u/WLFy+O0aNHx8UXXxwzZ86M+fPnxwUXXBB77713jBo1qq5PBQAAAIA8q3Mo9etf/zpuuOGGePPNNyMiYu3atTs9W+qkk06Kk046qc7106ZNi969e8f1118fEREHHHBAPPHEE3HDDTcIpQAAAACakDqHUl26dIkf/vCHERHRu3fvmDFjRuy5554N1lhNnn766WqLqY8aNSquuOKKWo+prKyMysrK3PbWWxCz2Wxks9kG6RMAAACgrj6eT2Sz2YgmnlfUNW/ZoYXOjz322GjduvUON7ejVq5cGV26dKky1qVLlygrK4sPP/ywxtsJp0yZEpMnT642vnr16l2yLhYAAADAzkg+2pD7fvXq1ZFpWZ7HbnbeunXr6lRX51Bq60Lne+21V9x1113xox/9KNq3b7/DDaZl4sSJMWHChNx2WVlZ9OjRI0pLS6OkpCSPnQEAAABEZDeVx7L//b60tDQKWhXntZ+dVVRUVKe6vC50Xl9du3aNVatWVRlbtWpVlJSU1NpLYWFhFBYWVhsvKCiIgoI6f/ggAAAAQMP4WD7RHPKKuva/QwudZzKZXbLQeX0NGTIkHn744Spj8+bNiyFDhqTaBwAAAAA7J68Lna9fvz7eeOON3PbixYvj+eefj06dOkXPnj1j4sSJsXz58vjVr34VEREXX3xx3HTTTXHVVVfFV77ylXj00Ufjt7/9bTz00EM71QcAAAAA6arXfLDPfvazsXbt2li8eHHsueee8cMf/jA++OCD3P533303DjzwwDo/3qJFi+KQQw6JQw45JCIiJkyYEIccckhcffXVERGxYsWKWLp0aa6+d+/e8dBDD8W8efNi4MCBcf3118f06dNj1KhR9XkaAAAAAI1S8tHu86FsmSRJkroWFxQUxMqVK6Nz584REVFSUhLPP/98fOpTn4qILes77bPPPrF58+aG6XYXKCsriw4dOsTatWstdA4AAADkTZLdHBvevD/Knr85KpY/nhsv6nFslBx8UbTtc2pkClrkscMdU9fspc6379WkHnkWAAAAAP8rW1kWqx4aGxVL51fbV7FsQVQsWxBFPY+PLqNnR0Fh85xU07SXcwcAAABoYpLs5loDqY+rWDo/Vj00NpJs470jbWfUK5TKZDKRyWSqjQEAAABQNxvevH+7gdRWFUvnx4b/eaCBO8qPet2+lyRJnHfeeVFYWBgRERUVFXHxxRdHcXFxRERUVlbu+g4BAAAAmpGyv99av/oXbo3ivqc1UDf5U69Qaty4cVW2v/zlL1erOffcc3euIwAAAIBmKvtRRVQsW1CvYyqWPRrZjyqioGVRA3WVH/UKpe64446G6gMAAACg2Us2rtvx45pZKGWhcwAAAICUZFq3T/W4xkwoBQAAAJCSgpZFUdTj2HodU9TjuGZ3616EUAoAAAAgVSUHX1S/+oH1q28qhFIAAAAAKWrb59Qo6nl8nWqLeh4fbT91SgN3lB9CKQAAAIAUZQpaRJfRs7cbTBX1PD66jJ4dmYIWKXWWLqEUAAAAQMoKCkui65gHo/PnZkdR9+FV9hX1OC46f252dB3zYBQUluSpw4bXMt8NAAAAAOyOMgUtorjvadGm14mxZGrHiIjoedGKaNFmzzx3lg4zpQAAAAAaiUwz/JS92gilAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1LXMdwMAAAAAu5PspvJatz+5LyKioFVxg/eUD0IpAAAAgBQtmdqx1n3Lbutebaz3FRsbsp28cfseAAAAAKkzUwoAAAAgRb3Gv5/vFhoFoRQAAABAiprrGlH15fY9AAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdY0ilJo6dWrsu+++UVRUFEcccUT89a9/rbX2zjvvjEwmU+WrqKgoxW4BAAAA2Fl5D6Vmz54dEyZMiEmTJsWzzz4bAwcOjFGjRsU777xT6zElJSWxYsWK3NeSJUtS7BgAAACAnZX3UOo//uM/4sILL4zzzz8/DjzwwJg2bVq0bds2br/99lqPyWQy0bVr19xXly5dUuwYAAAAgJ2V11Bq48aN8be//S1GjhyZGysoKIiRI0fG008/Xetx69evj169ekWPHj3i1FNPjZdffjmNdgEAAADYRVrm8+Rr1qyJzZs3V5vp1KVLl3jllVdqPGb//feP22+/PQ4++OBYu3Zt/PSnP42hQ4fGyy+/HN27d69WX1lZGZWVlbntsrKyiIjIZrORzWZ34bMBAAAAoK55S15DqR0xZMiQGDJkSG576NChccABB8Stt94a3//+96vVT5kyJSZPnlxtfPXq1VFRUdGgvQIAAADsbtatW1enuryGUnvttVe0aNEiVq1aVWV81apV0bVr1zo9RqtWreKQQw6JN954o8b9EydOjAkTJuS2y8rKokePHlFaWholJSU73jwAAAAA1RQVFdWpLq+hVOvWrWPw4MExf/78GDNmTERsmeI1f/78uPTSS+v0GJs3b44XX3wxPvvZz9a4v7CwMAoLC6uNFxQUREFB3td5BwAAAGhW6pq35P32vQkTJsS4cePisMMOi8MPPzxuvPHGKC8vj/PPPz8iIs4999zo1q1bTJkyJSIirrnmmjjyyCOjb9++8cEHH8RPfvKTWLJkSVxwwQX5fBoAAAAA1EPeQ6mxY8fG6tWr4+qrr46VK1fGoEGD4ve//31u8fOlS5dWSdjef//9uPDCC2PlypXRsWPHGDx4cDz11FNx4IEH5uspAAAAAFBPmSRJknw3kaaysrLo0KFDrF271ppSAAAAALtYXbMXiyoBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpaxSh1NSpU2PfffeNoqKiOOKII+Kvf/3rNuvnzJkT/fr1i6KiohgwYEA8/PDDKXUKAAAAwK6Q91Bq9uzZMWHChJg0aVI8++yzMXDgwBg1alS88847NdY/9dRTcdZZZ8VXv/rVeO6552LMmDExZsyYeOmll1LuHAAAAIAdlUmSJMlnA0cccUR85jOfiZtuuikiIrLZbPTo0SMuu+yy+Ld/+7dq9WPHjo3y8vJ48MEHc2NHHnlkDBo0KKZNm7bd85WVlUWHDh1i9erVUVJSUm1/QUFBtGzZMre9cePGWh8rk8lEq1atdqh206ZNUdtL31C1ERGtW7feodqPPvoostnsLqlt1apVZDKZBq3dvHlzbN68eZfUtmzZMgoKChpNbTabjY8++qjW2hYtWkSLFi0aTW2SJLFp06ZdUvvxv8+Gqo3Y9t+ya0TNta4RrhGuEfWvdY3YsVrXiJ2rbQx/964RrhGfrHWNcI1wjah/bWO/RpSVlUVpaWmsXbu2xuxlq5a17knBxo0b429/+1tMnDgxN1ZQUBAjR46Mp59+usZjnn766ZgwYUKVsVGjRsXcuXNrrK+srIzKysrcdllZWURE/PSnP43CwsJq9X379o0vfelLue0f//jHtf6C9erVK84777zc9g033BAbNmyosXbvvfeOr33ta7ntm266KT744IMaa0tLS+Nf//Vfc9u33nprrF69usbaPfbYIy6//PLc9i9/+ctYsWJFjbVt27aN//f//l9ue8aMGbFkyZIaa1u1ahXf/va3c9u/+c1v4o033qixNiJi0qRJue/vueee+O///u9aaydOnJh7Y3nggQfihRdeqLX2yiuvjOLi4oiIeOSRR2LRokW11l5++eWxxx57RETEvHnzav0dioi45JJLonPnzhER8dhjj8Vjjz1Wa+0FF1wQ3bp1i4gtM/X+9Kc/1Vo7bty42HfffSMi4plnnolHHnmk1tqzzjorPv3pT0dExAsvvBD3339/rbVnnHFGHHTQQRER8fLLL8c999xTa+2pp54agwYNioiI1157LX7zm9/UWnvSSSfF4YcfHhERb731Vtx111211o4cOTKGDRsWERHLly+P6dOn11o7fPjwGDFiREREvPPOO3HLLbfUWjtkyJA48cQTIyLigw8+iJ/97Ge11h522GExevToiIgoLy+Pn/70p7XWDhw4MMaMGRMRW641U6ZMqbX2gAMOiDPPPDO3fe2119Za6xqxhWvE/3GN2MI1YgvXiC1cI/6Pa8QWrhFbuEZs4Rrxf1wjtnCN2KI5XSM+nsNsS15DqTVr1sTmzZujS5cuVca7dOkSr7zySo3HrFy5ssb6lStX1lg/ZcqUmDx5crXx8vLyGtPXsrKyKrcOrl+/vtaUdt26ddVqP/zwwzrVrlu3LsrLy2usLSoqqnNtixYt6lybzWbrXNuyZcsqtWVlZbXWRkS9a7e+Uaxdu3abtatXr87tr0vt1nT4gw8+2GbtmjVrct/XpXZr+vv+++9vs/bdd9+Ntm3b1rl26+v23nvvbbP2vffe26Had999d5u177///g7VrlmzZpu1H3zwwQ7Vbu9nvHbt2lzthg0b6ly7cePGbdZ+8u++PrWuEVu4RrhGuEbUXOsasYVrhGuEa0TNta4RW7hGuEa4RtRc25SvEXUNpfJ6+97bb78d3bp1i6eeeiqGDBmSG7/qqqvisccei7/85S/VjmndunXcddddcdZZZ+XGbr755pg8eXKsWrWqWn1NM6V69OgRq1atcvueKbV1qjWldudqTal1jahvrWvEztU2hr971wjXiE/Wuka4RrhG1L/WNWLHal0jdq62Mfzdu0Y0j2tEWVlZdOnSpXHfvrfXXntFixYtqoVJq1atiq5du9Z4TNeuXetVX1hYWONtekVFRVFUVLTdHutSsyO1NfXUmGs//kbQFGoLCgqq/HE0t9qPX6gae21E5N4ImkptQ/3du0Y0ntrG8rfsGrFFY/i7d41o2NrG8HfvGvF/tfn+u3eNaPjaxvB37xrRdGvz/XfvGtHwtWn83W8rJPu4vH76XuvWrWPw4MExf/783Fg2m4358+dXmTn1cUOGDKlSH7Hlnt7a6gEAAABofPI6UyoiYsKECTFu3Lg47LDD4vDDD48bb7wxysvL4/zzz4+IiHPPPTe6deuWWzjs8ssvj+HDh8f1118fo0ePjlmzZsWiRYvitttuy+fTAAAAAKAe8h5KjR07NlavXh1XX311rFy5MgYNGhS///3vc4uZL126NHd/a0TE0KFD4+67745///d/j29/+9ux3377xdy5c6N///75egoAAAAA1FNeFzrPh7KysujQocN2F9sCAAAAoP7qmr3kdU0pAAAAAHZPQikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUtcy3w2kLUmSiIgoKyvLcycAAAAAzc/WzGVrBlOb3S6UWrduXURE9OjRI8+dAAAAADRf69atiw4dOtS6P5NsL7ZqZrLZbLz99tvRvn37yGQy+W5np5SVlUWPHj1i2bJlUVJSku92AIAG4j0fAJq/5vR+nyRJrFu3LvbZZ58oKKh95ajdbqZUQUFBdO/ePd9t7FIlJSVN/hcWANg+7/kA0Pw1l/f7bc2Q2spC5wAAAACkTigFAAAAQOqEUk1YYWFhTJo0KQoLC/PdCgDQgLznA0Dztzu+3+92C50DAAAAkH9mSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSjVSU6ZMic985jPRvn376Ny5c4wZMyZeffXV7R43Z86c6NevXxQVFcWAAQPi4YcfTqFbAGBH3XLLLXHwwQdHSUlJlJSUxJAhQ+KRRx7Z5jHe7wGgafvhD38YmUwmrrjiim3WNff3fKFUI/XYY4/F+PHj47/+679i3rx5sWnTpjjxxBOjvLy81mOeeuqpOOuss+KrX/1qPPfcczFmzJgYM2ZMvPTSSyl2DgDUR/fu3eOHP/xh/O1vf4tFixbFcccdF6eeemq8/PLLNdZ7vweApu2ZZ56JW2+9NQ4++OBt1u0O7/mZJEmSfDfB9q1evTo6d+4cjz32WBxzzDE11owdOzbKy8vjwQcfzI0deeSRMWjQoJg2bVparQIAO6lTp07xk5/8JL761a9W2+f9HgCarvXr18ehhx4aN998c/zgBz+IQYMGxY033lhj7e7wnm+mVBOxdu3aiNjyj9TaPP300zFy5MgqY6NGjYqnn366QXsDAHaNzZs3x6xZs6K8vDyGDBlSY433ewBousaPHx+jR4+u9l5ek93hPb9lvhtg+7LZbFxxxRUxbNiw6N+/f611K1eujC5dulQZ69KlS6xcubKhWwQAdsKLL74YQ4YMiYqKimjXrl3cd999ceCBB9ZY6/0eAJqmWbNmxbPPPhvPPPNMnep3h/d8oVQTMH78+HjppZfiiSeeyHcrAEAD2H///eP555+PtWvXxj333BPjxo2Lxx57rNZgCgBoWpYtWxaXX355zJs3L4qKivLdTqMhlGrkLr300njwwQfj8ccfj+7du2+ztmvXrrFq1aoqY6tWrYquXbs2ZIsAwE5q3bp19O3bNyIiBg8eHM8880z87Gc/i1tvvbVarfd7AGh6/va3v8U777wThx56aG5s8+bN8fjjj8dNN90UlZWV0aJFiyrH7A7v+daUaqSSJIlLL7007rvvvnj00Uejd+/e2z1myJAhMX/+/Cpj8+bNq3VNCgCgccpms1FZWVnjPu/3AND0HH/88fHiiy/G888/n/s67LDD4ktf+lI8//zz1QKpiN3jPd9MqUZq/Pjxcffdd8f9998f7du3z90z2qFDh2jTpk1ERJx77rnRrVu3mDJlSkREXH755TF8+PC4/vrrY/To0TFr1qxYtGhR3HbbbXl7HgDAtk2cODFOOumk6NmzZ6xbty7uvvvuWLhwYfzhD3+ICO/3ANActG/fvtoa0cXFxbHnnnvmxnfH93wzpRqpW265JdauXRsjRoyIvffeO/c1e/bsXM3SpUtjxYoVue2hQ4fG3XffHbfddlsMHDgw7rnnnpg7d+42F0cHAPLrnXfeiXPPPTf233//OP744+OZZ56JP/zhD3HCCSdEhPd7ANhd7I7v+ZkkSZJ8NwEAAADA7sVMKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHX/H6jmq/c11Zp+AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "aggregated = dml_obj.aggregate(\"group\")\n", - "print(aggregated)" + "print(aggregated)\n", + "fig, ax = aggregated.plot_effects()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -670,28 +368,47 @@ "\n", "------------------ Overall Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.480608 0.035103 42.178921 0.0 1.411807 1.549409\n", + "1.480476 0.035087 42.194475 0.0 1.411707 1.549245\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "2 0.920659 0.064105 14.361647 0.0 0.795014 1.046303\n", - "3 1.549048 0.051383 30.147284 0.0 1.448340 1.649757\n", - "4 1.972117 0.046579 42.339413 0.0 1.880824 2.063410\n", + "2 0.920815 0.063970 14.394375 0.0 0.795435 1.046195\n", + "3 1.546038 0.051435 30.058040 0.0 1.445227 1.646848\n", + "4 1.974576 0.046775 42.214387 0.0 1.882899 2.066254\n", "------------------ Additional Information ------------------\n", "Control Group: never_treated\n", "Anticipation Periods: 0\n", "Score: observational\n", "\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "aggregated_time = dml_obj.aggregate(\"time\")\n", - "print(aggregated_time)" + "print(aggregated_time)\n", + "fig, ax = aggregated_time.plot_effects()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -703,25 +420,44 @@ "\n", "------------------ Overall Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.990196 0.038709 51.414618 0.0 1.914328 2.066064\n", + "1.990824 0.038762 51.360161 0.0 1.914852 2.066796\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "-2.0 0.004233 0.068274 0.062002 0.950562 -0.129581 0.138048\n", - "-1.0 0.010997 0.040478 0.271675 0.785872 -0.068339 0.090333\n", - "0.0 0.992875 0.030721 32.319092 0.000000 0.932663 1.053087\n", - "1.0 2.022591 0.045690 44.267485 0.000000 1.933040 2.112143\n", - "2.0 2.955122 0.063113 46.822594 0.000000 2.831422 3.078821\n", + "-2.0 0.000656 0.068395 0.009596 0.992344 -0.133395 0.134707\n", + "-1.0 0.010468 0.040478 0.258610 0.795937 -0.068867 0.089803\n", + "0.0 0.992004 0.030775 32.233848 0.000000 0.931686 1.052322\n", + "1.0 2.024128 0.045782 44.212165 0.000000 1.934397 2.113860\n", + "2.0 2.956340 0.063267 46.728168 0.000000 2.832340 3.080341\n", "------------------ Additional Information ------------------\n", "Control Group: never_treated\n", "Anticipation Periods: 0\n", "Score: observational\n", "\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "aggregated_eventstudy = dml_obj.aggregate(\"eventstudy\")\n", - "print(aggregated_eventstudy)" + "print(aggregated_eventstudy)\n", + "fig, ax = aggregated_eventstudy.plot_effects()" ] }, { From cd24a6379bc16e57860e816ececd27e502f39781 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 19 Mar 2025 08:55:49 +0000 Subject: [PATCH 029/140] add dgp and nbs --- doc/api/datasets.rst | 18 ++++++++++++++++-- doc/examples/index.rst | 1 + 2 files changed, 17 insertions(+), 2 deletions(-) diff --git a/doc/api/datasets.rst b/doc/api/datasets.rst index 5e1497d9..7f7afb84 100644 --- a/doc/api/datasets.rst +++ b/doc/api/datasets.rst @@ -28,10 +28,24 @@ Dataset Generators datasets.make_iivm_data datasets.make_plr_turrell2018 datasets.make_pliv_multiway_cluster_CKMS2021 - datasets.make_did_SZ2020 + datasets.make_ssm_data datasets.make_confounded_plr_data datasets.make_confounded_irm_data datasets.make_heterogeneous_data datasets.make_irm_data_discrete_treatments - rdd.datasets.make_simple_rdd_data \ No newline at end of file + rdd.datasets.make_simple_rdd_data + + +.. _api_datasets_did: + +Difference-in-Differences (DiD) Datasets +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. currentmodule:: doubleml.did + +.. autosummary:: + :toctree: generated/ + + datasets.make_did_SZ2020 + datasets.make_did_CS2021 \ No newline at end of file diff --git a/doc/examples/index.rst b/doc/examples/index.rst index 17dbae22..ac9a1559 100644 --- a/doc/examples/index.rst +++ b/doc/examples/index.rst @@ -28,6 +28,7 @@ General Examples py_double_ml_ssm.ipynb py_double_ml_sensitivity_booking.ipynb py_double_ml_panel.ipynb + py_double_ml_panel_simple.ipynb py_double_ml_did.ipynb py_double_ml_did_pretest.ipynb py_double_ml_basic_iv.ipynb From 8e03430d0065d732ddecac8964bc59fb3c7bb931 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 19 Mar 2025 16:52:54 +0000 Subject: [PATCH 030/140] fix import from did dataset --- doc/guide/models.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/guide/models.rst b/doc/guide/models.rst index 3977c6d4..40d50b78 100644 --- a/doc/guide/models.rst +++ b/doc/guide/models.rst @@ -341,7 +341,7 @@ Estimation is conducted via its ``fit()`` method: import numpy as np import doubleml as dml - from doubleml.datasets import make_did_SZ2020 + from doubleml.did.datasets import make_did_SZ2020 from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier ml_g = RandomForestRegressor(n_estimators=100, max_depth=5, min_samples_leaf=5) @@ -381,7 +381,7 @@ Estimation is conducted via its ``fit()`` method: import numpy as np import doubleml as dml - from doubleml.datasets import make_did_SZ2020 + from doubleml.did.datasets import make_did_SZ2020 from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier ml_g = RandomForestRegressor(n_estimators=100, max_depth=5, min_samples_leaf=5) From 93c9fb608885f74355a108c18e941364ef1de4e2 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 19 Mar 2025 17:06:27 +0000 Subject: [PATCH 031/140] add titles to panel examples --- doc/examples/py_double_ml_panel.ipynb | 7 +++++++ doc/examples/py_double_ml_panel_simple.ipynb | 7 +++++++ 2 files changed, 14 insertions(+) diff --git a/doc/examples/py_double_ml_panel.ipynb b/doc/examples/py_double_ml_panel.ipynb index 0e311d10..db6bcdbb 100644 --- a/doc/examples/py_double_ml_panel.ipynb +++ b/doc/examples/py_double_ml_panel.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python: Panel Data" + ] + }, { "cell_type": "code", "execution_count": 1, diff --git a/doc/examples/py_double_ml_panel_simple.ipynb b/doc/examples/py_double_ml_panel_simple.ipynb index f1957c88..7d56e074 100644 --- a/doc/examples/py_double_ml_panel_simple.ipynb +++ b/doc/examples/py_double_ml_panel_simple.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python: Simple Panel Data Example" + ] + }, { "cell_type": "code", "execution_count": 1, From fda619c265863d07c630d43cb9d602a8fec41c80 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 4 Apr 2025 10:50:07 +0000 Subject: [PATCH 032/140] add did multi example to models --- doc/guide/models.rst | 41 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/doc/guide/models.rst b/doc/guide/models.rst index 40d50b78..6c264fc1 100644 --- a/doc/guide/models.rst +++ b/doc/guide/models.rst @@ -325,6 +325,47 @@ Difference-in-Differences Models (DID) Panel data ********** +Multi Period +^^^^^^^^^^^^ + +test + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + :okwarning: + + import numpy as np + import doubleml as dml + from doubleml.did.datasets import make_did_CS2021 + from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier + + np.random.seed(42) + df = make_did_CS2021(n_obs=500) + dml_data = dml.data.DoubleMLPanelData( + df, + y_col="y", + d_cols="d", + id_col="id", + t_col="t", + x_cols=["Z1", "Z2", "Z3", "Z4"], + datetime_unit="M" + ) + dml_did_obj = dml.did.DoubleMLDIDMulti( + obj_dml_data=dml_data, + ml_g=ml_g, + ml_m=ml_m, + gt_combinations="standard", + control_group="never_treated", + ) + print(dml_did_obj.fit()) + +Single Period +^^^^^^^^^^^^^ + If panel data are available, the observations are assumed to be iid. of form :math:`(Y_{i0}, Y_{i1}, D_i, X_i)`. Remark that the difference :math:`\Delta Y_i= Y_{i1}-Y_{i0}` has to be defined as the outcome ``y`` in the ``DoubleMLData`` object. From 6d90613e5616c417b275d579c1ec23a729999135 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 8 Apr 2025 07:24:04 +0000 Subject: [PATCH 033/140] add did literature section --- doc/literature/literature.rst | 52 +++++++++++++++++++++++------------ 1 file changed, 34 insertions(+), 18 deletions(-) diff --git a/doc/literature/literature.rst b/doc/literature/literature.rst index 03c18ec1..e9fb61a1 100644 --- a/doc/literature/literature.rst +++ b/doc/literature/literature.rst @@ -104,12 +104,6 @@ Double Machine Learning Literature * Journal of Business & Economic Statistics, 1-12., 2023* |br| :octicon:`link` :bdg-link-dark:`URL ` |hr| - - - Neng-Chieh Chang |br| - **Double/debiased machine learning for difference-in-differences models** |br| - *The Econometrics Journal, 23(2), Pages 177–191, 2020* |br| - :octicon:`link` :bdg-link-dark:`URL ` - |hr| - Chernozhukov, Victor and Demirer, Mert and Duflo, Esther and Fernández-Val, Iván |br| **Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India** |br| @@ -150,12 +144,6 @@ Double Machine Learning Literature :bdg-link-dark:`arXiv ` |hr| - - Pedro HC Sant'Anna, Jun Zhao |br| - **Doubly robust difference-in-differences estimators** |br| - *Journal of Econometrics, 219(1), Pages 101-122, 2020* |br| - :octicon:`link` :bdg-link-dark:`URL ` - |hr| - - Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, Vasilis Syrgkanis |br| **Long Story Short: Omitted Variable Bias in Causal Machine Learning** |br| *No. w30302. National Bureau of Economic Research, 2022* |br| @@ -174,12 +162,6 @@ Double Machine Learning Literature :octicon:`link` :bdg-link-dark:`arXiv ` |hr| - - Michael Zimmert |br| - **Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding** |br| - *arXiv preprint arXiv:1809.01643 [econ.EM], 2018* |br| - :octicon:`link` :bdg-link-dark:`arXiv ` - |hr| - - Claudia Noack, Tomasz Olma, Christoph Rothe |br| **Flexible Covariate Adjustments in Regression Discontinuity Designs** |br| *arXiv preprint arXiv:2107.07942v3 [econ.EM], 2024* |br| @@ -239,6 +221,40 @@ Double Machine Learning Literature :octicon:`link` :bdg-link-dark:`URL ` |hr| + .. dropdown:: Difference-in-Differences + :class-title: sd-bg-primary sd-font-weight-bold + + - Brantly Callaway, Pedro HC Sant'Anna |br| + **Difference-in-Differences with multiple time periods** |br| + *Journal of Econometrics, 225(2), Pages 200-230, 2021* |br| + :octicon:`link` :bdg-link-dark:`URL ` + |hr| + + - Neng-Chieh Chang |br| + **Double/debiased machine learning for difference-in-differences models** |br| + *The Econometrics Journal, 23(2), Pages 177–191, 2020* |br| + :octicon:`link` :bdg-link-dark:`URL ` + |hr| + + - Jonathan Roth, Pedro HC Sant'Anna, Alyssa Bilinski, John Poe |br| + **What’s trending in difference-in-differences? A synthesis of the recent econometrics literature** |br| + *Journal of Econometrics, 235(2), Pages 2218-2244, 2023* |br| + :octicon:`link` :bdg-link-dark:`URL ` + |hr| + + - Pedro HC Sant'Anna, Jun Zhao |br| + **Doubly robust difference-in-differences estimators** |br| + *Journal of Econometrics, 219(1), Pages 101-122, 2020* |br| + :octicon:`link` :bdg-link-dark:`URL ` + |hr| + + - Michael Zimmert |br| + **Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding** |br| + *arXiv preprint arXiv:1809.01643 [econ.EM], 2018* |br| + :octicon:`link` :bdg-link-dark:`arXiv ` + |hr| + + .. grid:: 1 .. grid-item-card:: Want to add or update a reference in the literature overview? From ec7cf9ee84f69a77bb54b5798ab22cc1ac3d3f24 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 8 Apr 2025 07:24:31 +0000 Subject: [PATCH 034/140] add new section for did multiple periods --- doc/guide/models.rst | 2 ++ doc/shared/models/did.rst | 24 ------------------------ doc/shared/models/did_binary.rst | 24 ++++++++++++++++++++++++ 3 files changed, 26 insertions(+), 24 deletions(-) create mode 100644 doc/shared/models/did_binary.rst diff --git a/doc/guide/models.rst b/doc/guide/models.rst index 6c264fc1..b6962bab 100644 --- a/doc/guide/models.rst +++ b/doc/guide/models.rst @@ -366,6 +366,8 @@ test Single Period ^^^^^^^^^^^^^ +.. include:: ../shared/models/did_binary.rst + If panel data are available, the observations are assumed to be iid. of form :math:`(Y_{i0}, Y_{i1}, D_i, X_i)`. Remark that the difference :math:`\Delta Y_i= Y_{i1}-Y_{i0}` has to be defined as the outcome ``y`` in the ``DoubleMLData`` object. diff --git a/doc/shared/models/did.rst b/doc/shared/models/did.rst index 38cea0f9..e69de29b 100644 --- a/doc/shared/models/did.rst +++ b/doc/shared/models/did.rst @@ -1,24 +0,0 @@ -**Difference-in-Differences Models (DID)** implemented in the package focus on the the binary treatment case with -with two treatment periods. - -Adopting the notation from `Sant'Anna and Zhao (2020) `_, -let :math:`Y_{it}` be the outcome of interest for unit :math:`i` at time :math:`t`. Further, let :math:`D_{it}=1` indicate -if unit :math:`i` is treated before time :math:`t` (otherwise :math:`D_{it}=0`). Since all units start as untreated (:math:`D_{i0}=0`), define -:math:`D_{i}=D_{i1}.` Relying on the potential outcome notation, denote :math:`Y_{it}(0)` as the outcome of unit :math:`i` at time :math:`t` if the unit did not receive -treatment up until time :math:`t` and analogously for :math:`Y_{it}(1)` with treatment. Consequently, the observed outcome -for unit is :math:`i` at time :math:`t` is :math:`Y_{it}=D_{it} Y_{it}(1) + (1-D_{it}) Y_{it}(0)`. Further, let -:math:`X_i` be a vector of pre-treatment covariates. - -Target parameter of interest is the average treatment effect on the treated (ATTE) - -.. math:: - - \theta_0 = \mathbb{E}[Y_{i1}(1)- Y_{i1}(0)|D_i=1]. - -The corresponding identifying assumptions are - -- **(Cond.) Parallel Trends:** :math:`\mathbb{E}[Y_{i1}(0) - Y_{i0}(0)|X_i, D_i=1] = \mathbb{E}[Y_{i1}(0) - Y_{i0}(0)|X_i, D_i=0]\quad a.s.` -- **Overlap:** :math:`\exists\epsilon > 0`: :math:`P(D_i=1) > \epsilon` and :math:`P(D_i=1|X_i) \le 1-\epsilon\quad a.s.` - -.. note:: - For a more detailed introduction and recent developments of the difference-in-differences literature see e.g. `Roth et al. (2022) `_. diff --git a/doc/shared/models/did_binary.rst b/doc/shared/models/did_binary.rst new file mode 100644 index 00000000..38cea0f9 --- /dev/null +++ b/doc/shared/models/did_binary.rst @@ -0,0 +1,24 @@ +**Difference-in-Differences Models (DID)** implemented in the package focus on the the binary treatment case with +with two treatment periods. + +Adopting the notation from `Sant'Anna and Zhao (2020) `_, +let :math:`Y_{it}` be the outcome of interest for unit :math:`i` at time :math:`t`. Further, let :math:`D_{it}=1` indicate +if unit :math:`i` is treated before time :math:`t` (otherwise :math:`D_{it}=0`). Since all units start as untreated (:math:`D_{i0}=0`), define +:math:`D_{i}=D_{i1}.` Relying on the potential outcome notation, denote :math:`Y_{it}(0)` as the outcome of unit :math:`i` at time :math:`t` if the unit did not receive +treatment up until time :math:`t` and analogously for :math:`Y_{it}(1)` with treatment. Consequently, the observed outcome +for unit is :math:`i` at time :math:`t` is :math:`Y_{it}=D_{it} Y_{it}(1) + (1-D_{it}) Y_{it}(0)`. Further, let +:math:`X_i` be a vector of pre-treatment covariates. + +Target parameter of interest is the average treatment effect on the treated (ATTE) + +.. math:: + + \theta_0 = \mathbb{E}[Y_{i1}(1)- Y_{i1}(0)|D_i=1]. + +The corresponding identifying assumptions are + +- **(Cond.) Parallel Trends:** :math:`\mathbb{E}[Y_{i1}(0) - Y_{i0}(0)|X_i, D_i=1] = \mathbb{E}[Y_{i1}(0) - Y_{i0}(0)|X_i, D_i=0]\quad a.s.` +- **Overlap:** :math:`\exists\epsilon > 0`: :math:`P(D_i=1) > \epsilon` and :math:`P(D_i=1|X_i) \le 1-\epsilon\quad a.s.` + +.. note:: + For a more detailed introduction and recent developments of the difference-in-differences literature see e.g. `Roth et al. (2022) `_. From 36e9b2bcb49c59375168a2e58facca59cedb5528 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 9 Apr 2025 11:11:09 +0000 Subject: [PATCH 035/140] restructure guide model section --- doc/guide/models.rst | 536 +------------------------------------------ 1 file changed, 12 insertions(+), 524 deletions(-) diff --git a/doc/guide/models.rst b/doc/guide/models.rst index b6962bab..0b5b7e32 100644 --- a/doc/guide/models.rst +++ b/doc/guide/models.rst @@ -3,555 +3,43 @@ Models ---------- -The :ref:`DoubleML ` includes the following models. +The :ref:`DoubleML `-package includes the following models. + +.. _plm-models: Partially linear models (PLM) +++++++++++++++++++++++++++++ -The partially linear models (PLM) take the form - -.. math:: - - Y = D \theta_0 + g_0(X) + \zeta, - -where treatment effects are additive with some sort of linear form. - -.. _plr-model: - -Partially linear regression model (PLR) -*************************************** - -.. include:: ../shared/models/plr.rst - -.. include:: ../shared/causal_graphs/plr_irm_causal_graph.rst - -``DoubleMLPLR`` implements PLR models. -Estimation is conducted via its ``fit()`` method: - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - - import numpy as np - import doubleml as dml - from doubleml.datasets import make_plr_CCDDHNR2018 - from sklearn.ensemble import RandomForestRegressor - from sklearn.base import clone - - learner = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2) - ml_l = clone(learner) - ml_m = clone(learner) - np.random.seed(1111) - data = make_plr_CCDDHNR2018(alpha=0.5, n_obs=500, dim_x=20, return_type='DataFrame') - obj_dml_data = dml.DoubleMLData(data, 'y', 'd') - dml_plr_obj = dml.DoubleMLPLR(obj_dml_data, ml_l, ml_m) - print(dml_plr_obj.fit()) - - .. tab-item:: R - :sync: r - - .. jupyter-execute:: - - library(DoubleML) - library(mlr3) - library(mlr3learners) - library(data.table) - lgr::get_logger("mlr3")$set_threshold("warn") - - learner = lrn("regr.ranger", num.trees = 100, mtry = 20, min.node.size = 2, max.depth = 5) - ml_l = learner$clone() - ml_m = learner$clone() - set.seed(1111) - data = make_plr_CCDDHNR2018(alpha=0.5, n_obs=500, dim_x=20, return_type='data.table') - obj_dml_data = DoubleMLData$new(data, y_col="y", d_cols="d") - dml_plr_obj = DoubleMLPLR$new(obj_dml_data, ml_l, ml_m) - dml_plr_obj$fit() - print(dml_plr_obj) +.. include:: models/plm/plm_models.rst -.. _pliv-model: - -Partially linear IV regression model (PLIV) -******************************************* - -.. include:: ../shared/models/pliv.rst - -.. include:: ../shared/causal_graphs/pliv_iivm_causal_graph.rst - -``DoubleMLPLIV`` implements PLIV models. -Estimation is conducted via its ``fit()`` method: - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - :okwarning: - - import numpy as np - import doubleml as dml - from doubleml.datasets import make_pliv_CHS2015 - from sklearn.ensemble import RandomForestRegressor - from sklearn.base import clone - - learner = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2) - ml_l = clone(learner) - ml_m = clone(learner) - ml_r = clone(learner) - np.random.seed(2222) - data = make_pliv_CHS2015(alpha=0.5, n_obs=500, dim_x=20, dim_z=1, return_type='DataFrame') - obj_dml_data = dml.DoubleMLData(data, 'y', 'd', z_cols='Z1') - dml_pliv_obj = dml.DoubleMLPLIV(obj_dml_data, ml_l, ml_m, ml_r) - print(dml_pliv_obj.fit()) - - .. tab-item:: R - :sync: r - - .. jupyter-execute:: - - library(DoubleML) - library(mlr3) - library(mlr3learners) - library(data.table) - - learner = lrn("regr.ranger", num.trees = 100, mtry = 20, min.node.size = 2, max.depth = 5) - ml_l = learner$clone() - ml_m = learner$clone() - ml_r = learner$clone() - set.seed(2222) - data = make_pliv_CHS2015(alpha=0.5, n_obs=500, dim_x=20, dim_z=1, return_type="data.table") - obj_dml_data = DoubleMLData$new(data, y_col="y", d_col = "d", z_cols= "Z1") - dml_pliv_obj = DoubleMLPLIV$new(obj_dml_data, ml_l, ml_m, ml_r) - dml_pliv_obj$fit() - print(dml_pliv_obj) - +.. _irm-models: Interactive regression models (IRM) ++++++++++++++++++++++++++++++++++++ -The interactive regression model (IRM) take the form - -.. math:: - - Y = g_0(D, X) + U, - -where treatment effects are fully heterogeneous. - -.. _irm-model: - -Binary Interactive Regression Model (IRM) -***************************************** - -.. include:: ../shared/models/irm.rst - -.. include:: ../shared/causal_graphs/plr_irm_causal_graph.rst - -``DoubleMLIRM`` implements IRM models. -Estimation is conducted via its ``fit()`` method: - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - - import numpy as np - import doubleml as dml - from doubleml.datasets import make_irm_data - from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier - - ml_g = RandomForestRegressor(n_estimators=100, max_features=10, max_depth=5, min_samples_leaf=2) - ml_m = RandomForestClassifier(n_estimators=100, max_features=10, max_depth=5, min_samples_leaf=2) - np.random.seed(3333) - data = make_irm_data(theta=0.5, n_obs=500, dim_x=10, return_type='DataFrame') - obj_dml_data = dml.DoubleMLData(data, 'y', 'd') - dml_irm_obj = dml.DoubleMLIRM(obj_dml_data, ml_g, ml_m) - print(dml_irm_obj.fit()) - - .. tab-item:: R - :sync: r - - .. jupyter-execute:: - - library(DoubleML) - library(mlr3) - library(mlr3learners) - library(data.table) - - set.seed(3333) - ml_g = lrn("regr.ranger", num.trees = 100, mtry = 10, min.node.size = 2, max.depth = 5) - ml_m = lrn("classif.ranger", num.trees = 100, mtry = 10, min.node.size = 2, max.depth = 5) - data = make_irm_data(theta=0.5, n_obs=500, dim_x=10, return_type="data.table") - obj_dml_data = DoubleMLData$new(data, y_col="y", d_cols="d") - dml_irm_obj = DoubleMLIRM$new(obj_dml_data, ml_g, ml_m) - dml_irm_obj$fit() - print(dml_irm_obj) - -.. _irm-apo-model: - -Average Potential Outcomes (APOs) -********************************* - -.. include:: ../shared/models/apo.rst - -``DoubleMLAPO`` implements the estimation of average potential outcomes. -Estimation is conducted via its ``fit()`` method: - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - - import numpy as np - import doubleml as dml - from doubleml.datasets import make_irm_data - from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier - - ml_g = RandomForestRegressor(n_estimators=100, max_features=10, max_depth=5, min_samples_leaf=2) - ml_m = RandomForestClassifier(n_estimators=100, max_features=10, max_depth=5, min_samples_leaf=2) - np.random.seed(3333) - data = make_irm_data(theta=0.5, n_obs=500, dim_x=10, return_type='DataFrame') - obj_dml_data = dml.DoubleMLData(data, 'y', 'd') - dml_apo_obj = dml.DoubleMLAPO(obj_dml_data, ml_g, ml_m, treatment_level=0) - print(dml_apo_obj.fit()) - - -.. _irm-apos-model: - -Average Potential Outcomes (APOs) for Multiple Treatment Levels -*************************************************************** - -.. include:: ../shared/models/apos.rst - -``DoubleMLAPOS`` implements the estimation of average potential outcomes for multiple treatment levels. -Estimation is conducted via its ``fit()`` method. The ``causal_contrast()`` method allows to estimate causal contrasts between treatment levels: - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - - import numpy as np - import doubleml as dml - from doubleml.datasets import make_irm_data - from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier - - ml_g = RandomForestRegressor(n_estimators=100, max_features=10, max_depth=5, min_samples_leaf=2) - ml_m = RandomForestClassifier(n_estimators=100, max_features=10, max_depth=5, min_samples_leaf=2) - np.random.seed(3333) - data = make_irm_data(theta=0.5, n_obs=500, dim_x=10, return_type='DataFrame') - obj_dml_data = dml.DoubleMLData(data, 'y', 'd') - dml_apos_obj = dml.DoubleMLAPOS(obj_dml_data, ml_g, ml_m, treatment_levels=[0, 1]) - print(dml_apos_obj.fit()) - - causal_contrast_model = dml_apos_obj.causal_contrast(reference_levels=0) - print(causal_contrast_model.summary) - - -.. _iivm-model: - -Interactive IV model (IIVM) -*************************** +.. include:: models/irm/irm_models.rst -.. include:: ../shared/models/iivm.rst -.. include:: ../shared/causal_graphs/pliv_iivm_causal_graph.rst - -``DoubleMLIIVM`` implements IIVM models. -Estimation is conducted via its ``fit()`` method: - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - :okwarning: - - import numpy as np - import doubleml as dml - from doubleml.datasets import make_iivm_data - from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier - - ml_g = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2) - ml_m = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2) - ml_r = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2) - np.random.seed(4444) - data = make_iivm_data(theta=0.5, n_obs=1000, dim_x=20, alpha_x=1.0, return_type='DataFrame') - obj_dml_data = dml.DoubleMLData(data, 'y', 'd', z_cols='z') - dml_iivm_obj = dml.DoubleMLIIVM(obj_dml_data, ml_g, ml_m, ml_r) - print(dml_iivm_obj.fit()) - - .. tab-item:: R - :sync: r - - .. jupyter-execute:: - - library(DoubleML) - library(mlr3) - library(mlr3learners) - library(data.table) - - set.seed(4444) - ml_g = lrn("regr.ranger", num.trees = 100, mtry = 20, min.node.size = 2, max.depth = 5) - ml_m = lrn("classif.ranger", num.trees = 100, mtry = 20, min.node.size = 2, max.depth = 5) - ml_r = ml_m$clone() - data = make_iivm_data(theta=0.5, n_obs=1000, dim_x=20, alpha_x=1, return_type="data.table") - obj_dml_data = DoubleMLData$new(data, y_col="y", d_cols="d", z_cols="z") - dml_iivm_obj = DoubleMLIIVM$new(obj_dml_data, ml_g, ml_m, ml_r) - dml_iivm_obj$fit() - print(dml_iivm_obj) - - -.. _did-model: +.. _did-models: Difference-in-Differences Models (DID) ++++++++++++++++++++++++++++++++++++++ -.. include:: ../shared/models/did.rst - - -.. _did-pa-model: - -Panel data -********** - -Multi Period -^^^^^^^^^^^^ - -test - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - :okwarning: - - import numpy as np - import doubleml as dml - from doubleml.did.datasets import make_did_CS2021 - from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier - - np.random.seed(42) - df = make_did_CS2021(n_obs=500) - dml_data = dml.data.DoubleMLPanelData( - df, - y_col="y", - d_cols="d", - id_col="id", - t_col="t", - x_cols=["Z1", "Z2", "Z3", "Z4"], - datetime_unit="M" - ) - dml_did_obj = dml.did.DoubleMLDIDMulti( - obj_dml_data=dml_data, - ml_g=ml_g, - ml_m=ml_m, - gt_combinations="standard", - control_group="never_treated", - ) - print(dml_did_obj.fit()) - -Single Period -^^^^^^^^^^^^^ - -.. include:: ../shared/models/did_binary.rst - -If panel data are available, the observations are assumed to be iid. of form :math:`(Y_{i0}, Y_{i1}, D_i, X_i)`. -Remark that the difference :math:`\Delta Y_i= Y_{i1}-Y_{i0}` has to be defined as the outcome ``y`` in the ``DoubleMLData`` object. - -``DoubleMLIDID`` implements difference-in-differences models for panel data. -Estimation is conducted via its ``fit()`` method: - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - :okwarning: - - import numpy as np - import doubleml as dml - from doubleml.did.datasets import make_did_SZ2020 - from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier - - ml_g = RandomForestRegressor(n_estimators=100, max_depth=5, min_samples_leaf=5) - ml_m = RandomForestClassifier(n_estimators=100, max_depth=5, min_samples_leaf=5) - np.random.seed(42) - data = make_did_SZ2020(n_obs=500, return_type='DataFrame') - # y is already defined as the difference of observed outcomes - obj_dml_data = dml.DoubleMLData(data, 'y', 'd') - dml_did_obj = dml.DoubleMLDID(obj_dml_data, ml_g, ml_m) - print(dml_did_obj.fit()) - -.. _did-cs-model: - -Repeated cross-sections -*********************** - -For repeated cross-sections, the observations are assumed to be iid. of form :math:`(Y_{i}, D_i, X_i, T_i)`, -where :math:`T_i` is a dummy variable if unit :math:`i` is observed pre- or post-treatment period, such -that the observed outcome can be defined as - -.. math:: - - Y_i = T_i Y_{i1} + (1-T_i) Y_{i0}. +.. include:: models/did/did_models.rst -Further, treatment and covariates are assumed to be stationary, such that the joint distribution of :math:`(D,X)` is invariant to :math:`T`. -``DoubleMLIDIDCS`` implements difference-in-differences models for repeated cross-sections. -Estimation is conducted via its ``fit()`` method: - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - :okwarning: - - import numpy as np - import doubleml as dml - from doubleml.did.datasets import make_did_SZ2020 - from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier - - ml_g = RandomForestRegressor(n_estimators=100, max_depth=5, min_samples_leaf=5) - ml_m = RandomForestClassifier(n_estimators=100, max_depth=5, min_samples_leaf=5) - np.random.seed(42) - data = make_did_SZ2020(n_obs=500, cross_sectional_data=True, return_type='DataFrame') - obj_dml_data = dml.DoubleMLData(data, 'y', 'd', t_col='t') - dml_did_obj = dml.DoubleMLDIDCS(obj_dml_data, ml_g, ml_m) - print(dml_did_obj.fit()) - -.. _ssm-model: +.. _ssm-models: Sample Selection Models (SSM) ++++++++++++++++++++++++++++++++++++++ -.. include:: ../shared/models/ssm.rst - -.. _ssm-mar-model: - -Missingness at Random -********************* - -Consider the following two additional assumptions for the sample selection model: - -- **Cond. Independence of Selection:** :math:`Y_i(d) \perp S_i|D_i=d, X_i\quad a.s.` for :math:`d=0,1` -- **Common Support:** :math:`P(D_i=1|X_i)>0` and :math:`P(S_i=1|D_i=d, X_i)>0` for :math:`d=0,1` - -such that outcomes are missing at random (for the score see :ref:`Scores `). - -``DoubleMLSSM`` implements sample selection models. The score ``score='missing-at-random'`` refers to the correponding score -relying on the assumptions above. The ``DoubleMLData`` object has to be defined with the additional argument ``s_col`` for the selection indicator. -Estimation is conducted via its ``fit()`` method: - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - :okwarning: - - import numpy as np - from sklearn.linear_model import LassoCV, LogisticRegressionCV - from doubleml.datasets import make_ssm_data - import doubleml as dml - - np.random.seed(42) - n_obs = 2000 - df = make_ssm_data(n_obs=n_obs, mar=True, return_type='DataFrame') - dml_data = dml.DoubleMLData(df, 'y', 'd', s_col='s') - - ml_g = LassoCV() - ml_m = LogisticRegressionCV(penalty='l1', solver='liblinear') - ml_pi = LogisticRegressionCV(penalty='l1', solver='liblinear') - - dml_ssm = dml.DoubleMLSSM(dml_data, ml_g, ml_m, ml_pi, score='missing-at-random') - dml_ssm.fit() - print(dml_ssm) - - -.. _ssm-nr-model: - -Nonignorable Nonresponse -************************ - -When sample selection or outcome attriction is realated to unobservables, identification generally requires an instrument for the selection indicator :math:`S_i`. -Consider the following additional assumptions for the instrumental variable: - -- **Cond. Correlation:** :math:`\exists Z: \mathbb{E}[Z\cdot S|D,X] \neq 0` -- **Cond. Independence:** :math:`Y_i(d,z)=Y_i(d)` and :math:`Y_i \perp Z_i|D_i=d, X_i\quad a.s.` for :math:`d=0,1` - -This requires the instrumental variable :math:`Z_i`, which must not affect :math:`Y_i` or be associated -with unobservables affecting :math:`Y_i` conditional on :math:`D_i` and :math:`X_i`. Further, the selection is determined via -a (unknown) threshold model: - -- **Threshold:** :math:`S_i = 1\{V_i \le \xi(D,X,Z)\}` where :math:`\xi` is a general function and :math:`V_i` is a scalar with strictly monotonic cumulative distribution function conditional on :math:`X_i`. -- **Cond. Independence:** :math:`Y_i \perp (Z_i, D_i)|X_i`. - -Let :math:`\Pi_i := P(S_i=1|D_i, X_i, Z_i)` denote the selection probability. -Additionally, the following assumptions are required: - -- **Common Support for Treatment:** :math:`P(D_i=1|X_i, \Pi)>0` -- **Cond. Effect Homogeneity:** :math:`\mathbb{E}[Y_i(1)-Y_i(0)|S_i=1, X_i=x, V_i=v] = \mathbb{E}[Y_i(1)-Y_i(0)|X_i=x, V_i=v]` -- **Common Support for Selection:** :math:`P(S_i=1|D_i=d, X_i=x, Z_i=z)>0\quad a.s.` for :math:`d=0,1` - -For further details, see `Bia, Huber and Lafférs (2023) `_. - -.. figure:: figures/py_ssm.svg - :width: 400 - :alt: DAG - :align: center - - Causal paths under nonignorable nonresponse - - -``DoubleMLSSM`` implements sample selection models. The score ``score='nonignorable'`` refers to the correponding score -relying on the assumptions above. The ``DoubleMLData`` object has to be defined with the additional argument ``s_col`` for the selection indicator -and ``z_cols`` for the instrument. -Estimation is conducted via its ``fit()`` method: - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - :okwarning: - - import numpy as np - from sklearn.linear_model import LassoCV, LogisticRegressionCV - from doubleml.datasets import make_ssm_data - import doubleml as dml +.. include:: models/ssm/ssm_models.rst - np.random.seed(42) - n_obs = 2000 - df = make_ssm_data(n_obs=n_obs, mar=False, return_type='DataFrame') - dml_data = dml.DoubleMLData(df, 'y', 'd', z_cols='z', s_col='s') - ml_g = LassoCV() - ml_m = LogisticRegressionCV(penalty='l1', solver='liblinear') - ml_pi = LogisticRegressionCV(penalty='l1', solver='liblinear') - - dml_ssm = dml.DoubleMLSSM(dml_data, ml_g, ml_m, ml_pi, score='nonignorable') - dml_ssm.fit() - print(dml_ssm) +.. _rdd-models: Regression Discontinuity Designs (RDD) ++++++++++++++++++++++++++++++++++++++ -.. include:: ../shared/models/rdd.rst +.. include:: models/rdd/rdd_models.rst From 858077a1f8adffb987c909e7911638b630bf4b8b Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 9 Apr 2025 11:11:25 +0000 Subject: [PATCH 036/140] restructure guide models --- .../models => guide/models/did}/did.rst | 0 .../models/did}/did_binary.rst | 0 doc/guide/models/did/did_models.rst | 116 ++++++++++++ .../models => guide/models/irm}/apo.rst | 0 .../models => guide/models/irm}/apos.rst | 0 .../models => guide/models/irm}/iivm.rst | 0 .../models => guide/models/irm}/irm.rst | 0 doc/guide/models/irm/irm_models.rst | 177 ++++++++++++++++++ .../models => guide/models/plm}/pliv.rst | 0 doc/guide/models/plm/plm_models.rst | 120 ++++++++++++ .../models => guide/models/plm}/plr.rst | 0 .../models/rdd/rdd_models.rst} | 0 .../models => guide/models/ssm}/ssm.rst | 0 doc/guide/models/ssm/ssm_models.rst | 110 +++++++++++ 14 files changed, 523 insertions(+) rename doc/{shared/models => guide/models/did}/did.rst (100%) rename doc/{shared/models => guide/models/did}/did_binary.rst (100%) create mode 100644 doc/guide/models/did/did_models.rst rename doc/{shared/models => guide/models/irm}/apo.rst (100%) rename doc/{shared/models => guide/models/irm}/apos.rst (100%) rename doc/{shared/models => guide/models/irm}/iivm.rst (100%) rename doc/{shared/models => guide/models/irm}/irm.rst (100%) create mode 100644 doc/guide/models/irm/irm_models.rst rename doc/{shared/models => guide/models/plm}/pliv.rst (100%) create mode 100644 doc/guide/models/plm/plm_models.rst rename doc/{shared/models => guide/models/plm}/plr.rst (100%) rename doc/{shared/models/rdd.rst => guide/models/rdd/rdd_models.rst} (100%) rename doc/{shared/models => guide/models/ssm}/ssm.rst (100%) create mode 100644 doc/guide/models/ssm/ssm_models.rst diff --git a/doc/shared/models/did.rst b/doc/guide/models/did/did.rst similarity index 100% rename from doc/shared/models/did.rst rename to doc/guide/models/did/did.rst diff --git a/doc/shared/models/did_binary.rst b/doc/guide/models/did/did_binary.rst similarity index 100% rename from doc/shared/models/did_binary.rst rename to doc/guide/models/did/did_binary.rst diff --git a/doc/guide/models/did/did_models.rst b/doc/guide/models/did/did_models.rst new file mode 100644 index 00000000..41aeb4db --- /dev/null +++ b/doc/guide/models/did/did_models.rst @@ -0,0 +1,116 @@ +.. include:: /guide/models/did/did.rst + +.. _did-pa-model: + +Panel data +********** + +Multi Period +^^^^^^^^^^^^ + +test + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + :okwarning: + + import numpy as np + import doubleml as dml + from doubleml.did.datasets import make_did_CS2021 + from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier + + np.random.seed(42) + df = make_did_CS2021(n_obs=500) + dml_data = dml.data.DoubleMLPanelData( + df, + y_col="y", + d_cols="d", + id_col="id", + t_col="t", + x_cols=["Z1", "Z2", "Z3", "Z4"], + datetime_unit="M" + ) + dml_did_obj = dml.did.DoubleMLDIDMulti( + obj_dml_data=dml_data, + ml_g=ml_g, + ml_m=ml_m, + gt_combinations="standard", + control_group="never_treated", + ) + print(dml_did_obj.fit()) + +Single Period +^^^^^^^^^^^^^ + +.. include:: /guide/models/did/did_binary.rst + +If panel data are available, the observations are assumed to be iid. of form :math:`(Y_{i0}, Y_{i1}, D_i, X_i)`. +Remark that the difference :math:`\Delta Y_i= Y_{i1}-Y_{i0}` has to be defined as the outcome ``y`` in the ``DoubleMLData`` object. + +``DoubleMLIDID`` implements difference-in-differences models for panel data. +Estimation is conducted via its ``fit()`` method: + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + :okwarning: + + import numpy as np + import doubleml as dml + from doubleml.did.datasets import make_did_SZ2020 + from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier + + ml_g = RandomForestRegressor(n_estimators=100, max_depth=5, min_samples_leaf=5) + ml_m = RandomForestClassifier(n_estimators=100, max_depth=5, min_samples_leaf=5) + np.random.seed(42) + data = make_did_SZ2020(n_obs=500, return_type='DataFrame') + # y is already defined as the difference of observed outcomes + obj_dml_data = dml.DoubleMLData(data, 'y', 'd') + dml_did_obj = dml.DoubleMLDID(obj_dml_data, ml_g, ml_m) + print(dml_did_obj.fit()) + +.. _did-cs-model: + +Repeated cross-sections +*********************** + +For repeated cross-sections, the observations are assumed to be iid. of form :math:`(Y_{i}, D_i, X_i, T_i)`, +where :math:`T_i` is a dummy variable if unit :math:`i` is observed pre- or post-treatment period, such +that the observed outcome can be defined as + +.. math:: + + Y_i = T_i Y_{i1} + (1-T_i) Y_{i0}. + +Further, treatment and covariates are assumed to be stationary, such that the joint distribution of :math:`(D,X)` is invariant to :math:`T`. + +``DoubleMLIDIDCS`` implements difference-in-differences models for repeated cross-sections. +Estimation is conducted via its ``fit()`` method: + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + :okwarning: + + import numpy as np + import doubleml as dml + from doubleml.did.datasets import make_did_SZ2020 + from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier + + ml_g = RandomForestRegressor(n_estimators=100, max_depth=5, min_samples_leaf=5) + ml_m = RandomForestClassifier(n_estimators=100, max_depth=5, min_samples_leaf=5) + np.random.seed(42) + data = make_did_SZ2020(n_obs=500, cross_sectional_data=True, return_type='DataFrame') + obj_dml_data = dml.DoubleMLData(data, 'y', 'd', t_col='t') + dml_did_obj = dml.DoubleMLDIDCS(obj_dml_data, ml_g, ml_m) + print(dml_did_obj.fit()) diff --git a/doc/shared/models/apo.rst b/doc/guide/models/irm/apo.rst similarity index 100% rename from doc/shared/models/apo.rst rename to doc/guide/models/irm/apo.rst diff --git a/doc/shared/models/apos.rst b/doc/guide/models/irm/apos.rst similarity index 100% rename from doc/shared/models/apos.rst rename to doc/guide/models/irm/apos.rst diff --git a/doc/shared/models/iivm.rst b/doc/guide/models/irm/iivm.rst similarity index 100% rename from doc/shared/models/iivm.rst rename to doc/guide/models/irm/iivm.rst diff --git a/doc/shared/models/irm.rst b/doc/guide/models/irm/irm.rst similarity index 100% rename from doc/shared/models/irm.rst rename to doc/guide/models/irm/irm.rst diff --git a/doc/guide/models/irm/irm_models.rst b/doc/guide/models/irm/irm_models.rst new file mode 100644 index 00000000..b61526a6 --- /dev/null +++ b/doc/guide/models/irm/irm_models.rst @@ -0,0 +1,177 @@ +The interactive regression model (IRM) take the form + +.. math:: + + Y = g_0(D, X) + U, + +where treatment effects are fully heterogeneous. + +.. _irm-model: + +Binary Interactive Regression Model (IRM) +***************************************** + +.. include:: /guide/models/irm/irm.rst + +.. include:: /shared/causal_graphs/plr_irm_causal_graph.rst + +``DoubleMLIRM`` implements IRM models. +Estimation is conducted via its ``fit()`` method: + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + + import numpy as np + import doubleml as dml + from doubleml.datasets import make_irm_data + from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier + + ml_g = RandomForestRegressor(n_estimators=100, max_features=10, max_depth=5, min_samples_leaf=2) + ml_m = RandomForestClassifier(n_estimators=100, max_features=10, max_depth=5, min_samples_leaf=2) + np.random.seed(3333) + data = make_irm_data(theta=0.5, n_obs=500, dim_x=10, return_type='DataFrame') + obj_dml_data = dml.DoubleMLData(data, 'y', 'd') + dml_irm_obj = dml.DoubleMLIRM(obj_dml_data, ml_g, ml_m) + print(dml_irm_obj.fit()) + + .. tab-item:: R + :sync: r + + .. jupyter-execute:: + + library(DoubleML) + library(mlr3) + library(mlr3learners) + library(data.table) + + set.seed(3333) + ml_g = lrn("regr.ranger", num.trees = 100, mtry = 10, min.node.size = 2, max.depth = 5) + ml_m = lrn("classif.ranger", num.trees = 100, mtry = 10, min.node.size = 2, max.depth = 5) + data = make_irm_data(theta=0.5, n_obs=500, dim_x=10, return_type="data.table") + obj_dml_data = DoubleMLData$new(data, y_col="y", d_cols="d") + dml_irm_obj = DoubleMLIRM$new(obj_dml_data, ml_g, ml_m) + dml_irm_obj$fit() + print(dml_irm_obj) + +.. _irm-apo-model: + +Average Potential Outcomes (APOs) +********************************* + +.. include:: /guide/models/irm/apo.rst + +``DoubleMLAPO`` implements the estimation of average potential outcomes. +Estimation is conducted via its ``fit()`` method: + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + + import numpy as np + import doubleml as dml + from doubleml.datasets import make_irm_data + from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier + + ml_g = RandomForestRegressor(n_estimators=100, max_features=10, max_depth=5, min_samples_leaf=2) + ml_m = RandomForestClassifier(n_estimators=100, max_features=10, max_depth=5, min_samples_leaf=2) + np.random.seed(3333) + data = make_irm_data(theta=0.5, n_obs=500, dim_x=10, return_type='DataFrame') + obj_dml_data = dml.DoubleMLData(data, 'y', 'd') + dml_apo_obj = dml.DoubleMLAPO(obj_dml_data, ml_g, ml_m, treatment_level=0) + print(dml_apo_obj.fit()) + + +.. _irm-apos-model: + +Average Potential Outcomes (APOs) for Multiple Treatment Levels +*************************************************************** + +.. include:: /guide/models/irm/apos.rst + +``DoubleMLAPOS`` implements the estimation of average potential outcomes for multiple treatment levels. +Estimation is conducted via its ``fit()`` method. The ``causal_contrast()`` method allows to estimate causal contrasts between treatment levels: + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + + import numpy as np + import doubleml as dml + from doubleml.datasets import make_irm_data + from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier + + ml_g = RandomForestRegressor(n_estimators=100, max_features=10, max_depth=5, min_samples_leaf=2) + ml_m = RandomForestClassifier(n_estimators=100, max_features=10, max_depth=5, min_samples_leaf=2) + np.random.seed(3333) + data = make_irm_data(theta=0.5, n_obs=500, dim_x=10, return_type='DataFrame') + obj_dml_data = dml.DoubleMLData(data, 'y', 'd') + dml_apos_obj = dml.DoubleMLAPOS(obj_dml_data, ml_g, ml_m, treatment_levels=[0, 1]) + print(dml_apos_obj.fit()) + + causal_contrast_model = dml_apos_obj.causal_contrast(reference_levels=0) + print(causal_contrast_model.summary) + + +.. _iivm-model: + +Interactive IV model (IIVM) +*************************** + +.. include:: /guide/models/irm/iivm.rst + +.. include:: /shared/causal_graphs/pliv_iivm_causal_graph.rst + +``DoubleMLIIVM`` implements IIVM models. +Estimation is conducted via its ``fit()`` method: + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + :okwarning: + + import numpy as np + import doubleml as dml + from doubleml.datasets import make_iivm_data + from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier + + ml_g = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2) + ml_m = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2) + ml_r = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2) + np.random.seed(4444) + data = make_iivm_data(theta=0.5, n_obs=1000, dim_x=20, alpha_x=1.0, return_type='DataFrame') + obj_dml_data = dml.DoubleMLData(data, 'y', 'd', z_cols='z') + dml_iivm_obj = dml.DoubleMLIIVM(obj_dml_data, ml_g, ml_m, ml_r) + print(dml_iivm_obj.fit()) + + .. tab-item:: R + :sync: r + + .. jupyter-execute:: + + library(DoubleML) + library(mlr3) + library(mlr3learners) + library(data.table) + + set.seed(4444) + ml_g = lrn("regr.ranger", num.trees = 100, mtry = 20, min.node.size = 2, max.depth = 5) + ml_m = lrn("classif.ranger", num.trees = 100, mtry = 20, min.node.size = 2, max.depth = 5) + ml_r = ml_m$clone() + data = make_iivm_data(theta=0.5, n_obs=1000, dim_x=20, alpha_x=1, return_type="data.table") + obj_dml_data = DoubleMLData$new(data, y_col="y", d_cols="d", z_cols="z") + dml_iivm_obj = DoubleMLIIVM$new(obj_dml_data, ml_g, ml_m, ml_r) + dml_iivm_obj$fit() + print(dml_iivm_obj) \ No newline at end of file diff --git a/doc/shared/models/pliv.rst b/doc/guide/models/plm/pliv.rst similarity index 100% rename from doc/shared/models/pliv.rst rename to doc/guide/models/plm/pliv.rst diff --git a/doc/guide/models/plm/plm_models.rst b/doc/guide/models/plm/plm_models.rst new file mode 100644 index 00000000..6bd097d8 --- /dev/null +++ b/doc/guide/models/plm/plm_models.rst @@ -0,0 +1,120 @@ +The partially linear models (PLM) take the form + +.. math:: + + Y = D \theta_0 + g_0(X) + \zeta, + +where treatment effects are additive with some sort of linear form. + +.. _plr-model: + +Partially linear regression model (PLR) +*************************************** + +.. include:: /guide/models/plm/plr.rst + +.. include:: /shared/causal_graphs/plr_irm_causal_graph.rst + +``DoubleMLPLR`` implements PLR models. +Estimation is conducted via its ``fit()`` method: + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + + import numpy as np + import doubleml as dml + from doubleml.datasets import make_plr_CCDDHNR2018 + from sklearn.ensemble import RandomForestRegressor + from sklearn.base import clone + + learner = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2) + ml_l = clone(learner) + ml_m = clone(learner) + np.random.seed(1111) + data = make_plr_CCDDHNR2018(alpha=0.5, n_obs=500, dim_x=20, return_type='DataFrame') + obj_dml_data = dml.DoubleMLData(data, 'y', 'd') + dml_plr_obj = dml.DoubleMLPLR(obj_dml_data, ml_l, ml_m) + print(dml_plr_obj.fit()) + + .. tab-item:: R + :sync: r + + .. jupyter-execute:: + + library(DoubleML) + library(mlr3) + library(mlr3learners) + library(data.table) + lgr::get_logger("mlr3")$set_threshold("warn") + + learner = lrn("regr.ranger", num.trees = 100, mtry = 20, min.node.size = 2, max.depth = 5) + ml_l = learner$clone() + ml_m = learner$clone() + set.seed(1111) + data = make_plr_CCDDHNR2018(alpha=0.5, n_obs=500, dim_x=20, return_type='data.table') + obj_dml_data = DoubleMLData$new(data, y_col="y", d_cols="d") + dml_plr_obj = DoubleMLPLR$new(obj_dml_data, ml_l, ml_m) + dml_plr_obj$fit() + print(dml_plr_obj) + + +.. _pliv-model: + +Partially linear IV regression model (PLIV) +******************************************* + +.. include:: /guide/models/plm/pliv.rst + +.. include:: /shared/causal_graphs/pliv_iivm_causal_graph.rst + +``DoubleMLPLIV`` implements PLIV models. +Estimation is conducted via its ``fit()`` method: + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + :okwarning: + + import numpy as np + import doubleml as dml + from doubleml.datasets import make_pliv_CHS2015 + from sklearn.ensemble import RandomForestRegressor + from sklearn.base import clone + + learner = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2) + ml_l = clone(learner) + ml_m = clone(learner) + ml_r = clone(learner) + np.random.seed(2222) + data = make_pliv_CHS2015(alpha=0.5, n_obs=500, dim_x=20, dim_z=1, return_type='DataFrame') + obj_dml_data = dml.DoubleMLData(data, 'y', 'd', z_cols='Z1') + dml_pliv_obj = dml.DoubleMLPLIV(obj_dml_data, ml_l, ml_m, ml_r) + print(dml_pliv_obj.fit()) + + .. tab-item:: R + :sync: r + + .. jupyter-execute:: + + library(DoubleML) + library(mlr3) + library(mlr3learners) + library(data.table) + + learner = lrn("regr.ranger", num.trees = 100, mtry = 20, min.node.size = 2, max.depth = 5) + ml_l = learner$clone() + ml_m = learner$clone() + ml_r = learner$clone() + set.seed(2222) + data = make_pliv_CHS2015(alpha=0.5, n_obs=500, dim_x=20, dim_z=1, return_type="data.table") + obj_dml_data = DoubleMLData$new(data, y_col="y", d_col = "d", z_cols= "Z1") + dml_pliv_obj = DoubleMLPLIV$new(obj_dml_data, ml_l, ml_m, ml_r) + dml_pliv_obj$fit() + print(dml_pliv_obj) \ No newline at end of file diff --git a/doc/shared/models/plr.rst b/doc/guide/models/plm/plr.rst similarity index 100% rename from doc/shared/models/plr.rst rename to doc/guide/models/plm/plr.rst diff --git a/doc/shared/models/rdd.rst b/doc/guide/models/rdd/rdd_models.rst similarity index 100% rename from doc/shared/models/rdd.rst rename to doc/guide/models/rdd/rdd_models.rst diff --git a/doc/shared/models/ssm.rst b/doc/guide/models/ssm/ssm.rst similarity index 100% rename from doc/shared/models/ssm.rst rename to doc/guide/models/ssm/ssm.rst diff --git a/doc/guide/models/ssm/ssm_models.rst b/doc/guide/models/ssm/ssm_models.rst new file mode 100644 index 00000000..b5b1e3fd --- /dev/null +++ b/doc/guide/models/ssm/ssm_models.rst @@ -0,0 +1,110 @@ +.. include:: /guide/models/ssm/ssm.rst + +.. _ssm-mar-model: + +Missingness at Random +********************* + +Consider the following two additional assumptions for the sample selection model: + +- **Cond. Independence of Selection:** :math:`Y_i(d) \perp S_i|D_i=d, X_i\quad a.s.` for :math:`d=0,1` +- **Common Support:** :math:`P(D_i=1|X_i)>0` and :math:`P(S_i=1|D_i=d, X_i)>0` for :math:`d=0,1` + +such that outcomes are missing at random (for the score see :ref:`Scores `). + +``DoubleMLSSM`` implements sample selection models. The score ``score='missing-at-random'`` refers to the correponding score +relying on the assumptions above. The ``DoubleMLData`` object has to be defined with the additional argument ``s_col`` for the selection indicator. +Estimation is conducted via its ``fit()`` method: + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + :okwarning: + + import numpy as np + from sklearn.linear_model import LassoCV, LogisticRegressionCV + from doubleml.datasets import make_ssm_data + import doubleml as dml + + np.random.seed(42) + n_obs = 2000 + df = make_ssm_data(n_obs=n_obs, mar=True, return_type='DataFrame') + dml_data = dml.DoubleMLData(df, 'y', 'd', s_col='s') + + ml_g = LassoCV() + ml_m = LogisticRegressionCV(penalty='l1', solver='liblinear') + ml_pi = LogisticRegressionCV(penalty='l1', solver='liblinear') + + dml_ssm = dml.DoubleMLSSM(dml_data, ml_g, ml_m, ml_pi, score='missing-at-random') + dml_ssm.fit() + print(dml_ssm) + + +.. _ssm-nr-model: + +Nonignorable Nonresponse +************************ + +When sample selection or outcome attriction is realated to unobservables, identification generally requires an instrument for the selection indicator :math:`S_i`. +Consider the following additional assumptions for the instrumental variable: + +- **Cond. Correlation:** :math:`\exists Z: \mathbb{E}[Z\cdot S|D,X] \neq 0` +- **Cond. Independence:** :math:`Y_i(d,z)=Y_i(d)` and :math:`Y_i \perp Z_i|D_i=d, X_i\quad a.s.` for :math:`d=0,1` + +This requires the instrumental variable :math:`Z_i`, which must not affect :math:`Y_i` or be associated +with unobservables affecting :math:`Y_i` conditional on :math:`D_i` and :math:`X_i`. Further, the selection is determined via +a (unknown) threshold model: + +- **Threshold:** :math:`S_i = 1\{V_i \le \xi(D,X,Z)\}` where :math:`\xi` is a general function and :math:`V_i` is a scalar with strictly monotonic cumulative distribution function conditional on :math:`X_i`. +- **Cond. Independence:** :math:`Y_i \perp (Z_i, D_i)|X_i`. + +Let :math:`\Pi_i := P(S_i=1|D_i, X_i, Z_i)` denote the selection probability. +Additionally, the following assumptions are required: + +- **Common Support for Treatment:** :math:`P(D_i=1|X_i, \Pi)>0` +- **Cond. Effect Homogeneity:** :math:`\mathbb{E}[Y_i(1)-Y_i(0)|S_i=1, X_i=x, V_i=v] = \mathbb{E}[Y_i(1)-Y_i(0)|X_i=x, V_i=v]` +- **Common Support for Selection:** :math:`P(S_i=1|D_i=d, X_i=x, Z_i=z)>0\quad a.s.` for :math:`d=0,1` + +For further details, see `Bia, Huber and Lafférs (2023) `_. + +.. figure:: /guide/figures/py_ssm.svg + :width: 400 + :alt: DAG + :align: center + + Causal paths under nonignorable nonresponse + + +``DoubleMLSSM`` implements sample selection models. The score ``score='nonignorable'`` refers to the correponding score +relying on the assumptions above. The ``DoubleMLData`` object has to be defined with the additional argument ``s_col`` for the selection indicator +and ``z_cols`` for the instrument. +Estimation is conducted via its ``fit()`` method: + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + :okwarning: + + import numpy as np + from sklearn.linear_model import LassoCV, LogisticRegressionCV + from doubleml.datasets import make_ssm_data + import doubleml as dml + + np.random.seed(42) + n_obs = 2000 + df = make_ssm_data(n_obs=n_obs, mar=False, return_type='DataFrame') + dml_data = dml.DoubleMLData(df, 'y', 'd', z_cols='z', s_col='s') + + ml_g = LassoCV() + ml_m = LogisticRegressionCV(penalty='l1', solver='liblinear') + ml_pi = LogisticRegressionCV(penalty='l1', solver='liblinear') + + dml_ssm = dml.DoubleMLSSM(dml_data, ml_g, ml_m, ml_pi, score='nonignorable') + dml_ssm.fit() + print(dml_ssm) From 2a381a0f1b48f41cde3b376a707ea427c1e7c9c8 Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Wed, 9 Apr 2025 14:38:16 +0200 Subject: [PATCH 037/140] start with SSM example in R --- doc/conf.py | 1 + doc/examples/R_double_ml_ssm.ipynb | 161 +++++++++++++++++++++++++++++ doc/examples/index.rst | 1 + 3 files changed, 163 insertions(+) create mode 100644 doc/examples/R_double_ml_ssm.ipynb diff --git a/doc/conf.py b/doc/conf.py index f5b41458..8ca6d1b0 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -168,6 +168,7 @@ 'examples/py_double_ml_basic_iv': '_static/basic_iv_example_nb.png', 'examples/R_double_ml_basic_iv': '_static/basic_iv_example_nb.png', 'examples/py_double_ml_ssm': '_static/ssm_example_nb.svg', + 'examples/R_double_ml_ssm': '_static/ssm_example_nb.svg', 'examples/py_double_ml_sensitivity_booking': '_static/dag_usecase_revised.png', } diff --git a/doc/examples/R_double_ml_ssm.ipynb b/doc/examples/R_double_ml_ssm.ipynb new file mode 100644 index 00000000..767519ad --- /dev/null +++ b/doc/examples/R_double_ml_ssm.ipynb @@ -0,0 +1,161 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f03c754a", + "metadata": {}, + "source": [ + "# R: Sample Selection Models\n", + "\n", + "In this example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate the average treatment effect (ATE) under sample selection or outcome attrition. The estimation is based on a simulated DGP from Appendix E of [Bia, Huber and Lafférs (2023)](https://doi.org/10.1080/07350015.2023.2271071). \n", + "\n", + "Consider the following DGP:\n", + "$$\n", + "\\begin{align*}\n", + "Y_i &= \\theta_0 D_i + X_i'\\beta_0 + \\varepsilon_i,\\\\\n", + "S_i &= \\mathbb{1}\\{D_i + \\gamma_0 Z_i + X_i'\\beta_0 + \\upsilon_i > 0\\}, \\\\\n", + "D_i &= \\mathbb{1}\\{X_i'\\beta_0 + \\xi_i > 0\\}\n", + "\\end{align*}\n", + "$$\n", + "where $Y_i$ is observed if $S_i=1$\n", + "with\n", + "$$X_i \\sim N(0, \\sigma^2_X), \\quad Z_i \\sim N(0, 1), \\quad (\\varepsilon,_i \\nu_i) \\sim N(0, \\sigma^2_{\\varepsilon, \\nu}), \\quad \\xi_i \\sim N(0, 1).$$\n", + "\n", + "Let $D_i\\in\\{0,1\\}$ denote the treatment status of unit $i$ and let $Y_{i}$ be the outcome of interest of unit $i$.\n", + "Using the potential outcome notation, we can write $Y_{i}(d)$ for the potential outcome of unit $i$ and treatment status $d$. Further, let $X_i$ denote a vector of pre-treatment covariates. \n", + "\n", + "## Outcome missing at random (MAR) \n", + "Now consider the first setting, in which the outcomes are missing at random (MAR), according to assumptions in [Bia, Huber and Lafférs (2023)](https://doi.org/10.1080/07350015.2023.2271071). \n", + "Let the covariance matrix $\\sigma^2_X$ be such that $a_{ij} = 0.5^{|i - j|}$, $\\gamma_0 = 0$, $\\sigma^2_{\\varepsilon, \\upsilon} = \\begin{pmatrix} 1 & 0 \\\\ 0 & 1 \\end{pmatrix}$ and finally, let the vector of coefficients $\\beta_0$ resemble a quadratic decay of coefficients importance; $\\beta_{0,j} = 0.4/j^2$ for $j = 1, \\ldots, p$. \n" + ] + }, + { + "cell_type": "markdown", + "id": "25181797", + "metadata": {}, + "source": [ + "### Data\n", + "\n", + "We will use the implemented data generating process `make_ssm_data` to generate data according to the simulation in Appendix E of [Bia, Huber and Lafférs (2023)](https://doi.org/10.1080/07350015.2023.2271071). The true ATE in this DGP is equal to $\\theta_0=1$ (it can be changed by setting the parameter `theta`). \n", + "\n", + "The data generating process `make_ssm_data` by default settings already returns a `DoubleMLData` object (however, it can return a pandas DataFrame or a NumPy array if `return_type` is specified accordingly). In this first setting, we are estimating the ATE under missingness at random, so we set `mar=True`.\n", + "The selection indicator `S` can be set via `s_col`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de1803b2", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "#remotes::install_github(\"DoubleML/doubleml-for-r\", ref = \"p-ssm_branch\", force = TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d35090ed", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "library(DoubleML)\n", + "library(mlr3)\n", + "\n", + "set.seed(1234)\n", + "n_obs = 2000\n", + "df = make_ssm_data(n_obs=n_obs, mar=TRUE, return_type=\"data.table\")\n", + "\n", + "dml_data = DoubleMLData$new(df, y_col=\"y\", d_cols=\"d\", s_col=\"s\")\n", + "dml_data\n" + ] + }, + { + "cell_type": "markdown", + "id": "79ee9309", + "metadata": {}, + "source": [ + "### Estimation\n", + "\n", + "To estimate the ATE under sample selection, we will use the `DoubleMLSSM` class. \n", + "\n", + "As for all `DoubleML` classes, we have to specify learners, which have to be initialized first.\n", + "Given the simulated quadratic decay of coefficients importance, Lasso regression should be a suitable option (as for propensity scores, this will be a $\\mathcal{l}_1$-penalized Logistic Regression). \n", + "\n", + "The learner `ml_g` is used to fit conditional expectations of the outcome $\\mathbb{E}[Y_i|D_i, S_i, X_i]$, whereas the learners `ml_m` and `ml_pi` will be used to estimate the treatment and selection propensity scores $P(D_i=1|X_i)$ and $P(S_i=1|D_i, X_i)$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a905df9", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "ml_g = lrn(\"regr.cv_glmnet\", nfolds = 5, s = \"lambda.min\")\n", + "ml_m = lrn(\"classif.cv_glmnet\", nfolds = 5, s = \"lambda.min\")\n", + "ml_pi = lrn(\"classif.cv_glmnet\", nfolds = 5, s = \"lambda.min\")" + ] + }, + { + "cell_type": "markdown", + "id": "d90a87ea", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "source": [ + "The `DoubleMLSSM` class can be used as any other `DoubleML` class. \n", + "\n", + "The score is set to `score='missing-at-random'`, since the parameters of the DGP were set to satisfy the assumptions of outcomes missing at random. Further, since the simulation in [Bia, Huber and Lafférs (2023)](https://doi.org/10.1080/07350015.2023.2271071) uses normalization of inverse probability weights, we will apply the same setting by `normalize_ipw=True`.\n", + "\n", + "After initialization, we have to call the `fit()` method to estimate the nuisance elements." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "761ab3d5", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "dml_ssm = DoubleMLSSM$new(dml_data, ml_g, ml_m, ml_pi, score=\"missing-at-random\",\n", + " normalize_ipw = TRUE)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/examples/index.rst b/doc/examples/index.rst index d200adee..f735378d 100644 --- a/doc/examples/index.rst +++ b/doc/examples/index.rst @@ -63,6 +63,7 @@ These are case studies with the R package :ref:`DoubleML `. R_double_ml_pension.ipynb R_double_ml_did.ipynb R_double_ml_multiway_cluster.ipynb + R_double_ml_ssm.ipynb R_double_ml_basic_iv.ipynb Sandbox From 4c112c10f58445d37b52fdc6e41234f9ae613f90 Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Wed, 9 Apr 2025 15:54:21 +0200 Subject: [PATCH 038/140] continue example, prepare remaining code blocks --- doc/examples/R_double_ml_ssm.ipynb | 355 ++++++++++++++++++++++++++++- 1 file changed, 348 insertions(+), 7 deletions(-) diff --git a/doc/examples/R_double_ml_ssm.ipynb b/doc/examples/R_double_ml_ssm.ipynb index 767519ad..5f0285e5 100644 --- a/doc/examples/R_double_ml_ssm.ipynb +++ b/doc/examples/R_double_ml_ssm.ipynb @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "de1803b2", "metadata": { "vscode": { @@ -59,7 +59,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d35090ed", + "id": "a1f2f984", "metadata": { "vscode": { "languageId": "r" @@ -69,8 +69,43 @@ "source": [ "library(DoubleML)\n", "library(mlr3)\n", + "library(ggplot2)\n", "\n", - "set.seed(1234)\n", + "# suppress messages during fitting\n", + "lgr::get_logger(\"mlr3\")$set_threshold(\"warn\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d35090ed", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "================= DoubleMLData Object ==================\n", + "\n", + "\n", + "------------------ Data summary ------------------\n", + "Outcome variable: y\n", + "Treatment variable(s): d\n", + "Covariates: X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26, X27, X28, X29, X30, X31, X32, X33, X34, X35, X36, X37, X38, X39, X40, X41, X42, X43, X44, X45, X46, X47, X48, X49, X50, X51, X52, X53, X54, X55, X56, X57, X58, X59, X60, X61, X62, X63, X64, X65, X66, X67, X68, X69, X70, X71, X72, X73, X74, X75, X76, X77, X78, X79, X80, X81, X82, X83, X84, X85, X86, X87, X88, X89, X90, X91, X92, X93, X94, X95, X96, X97, X98, X99, X100\n", + "Instrument(s): \n", + "Selection variable: s\n", + "No. Observations: 2000" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "set.seed(3141)\n", "n_obs = 2000\n", "df = make_ssm_data(n_obs=n_obs, mar=TRUE, return_type=\"data.table\")\n", "\n", @@ -95,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "4a905df9", "metadata": { "vscode": { @@ -127,17 +162,323 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "761ab3d5", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================= DoubleMLSSM Object ==================\n", + "\n", + "\n", + "------------------ Data summary ------------------\n", + "Outcome variable: y\n", + "Treatment variable(s): d\n", + "Covariates: X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26, X27, X28, X29, X30, X31, X32, X33, X34, X35, X36, X37, X38, X39, X40, X41, X42, X43, X44, X45, X46, X47, X48, X49, X50, X51, X52, X53, X54, X55, X56, X57, X58, X59, X60, X61, X62, X63, X64, X65, X66, X67, X68, X69, X70, X71, X72, X73, X74, X75, X76, X77, X78, X79, X80, X81, X82, X83, X84, X85, X86, X87, X88, X89, X90, X91, X92, X93, X94, X95, X96, X97, X98, X99, X100\n", + "Instrument(s): \n", + "Selection variable: s\n", + "No. Observations: 2000\n", + "\n", + "------------------ Score & algorithm ------------------\n", + "Score function: missing-at-random\n", + "DML algorithm: dml2\n", + "\n", + "------------------ Machine learner ------------------\n", + "ml_g: regr.cv_glmnet\n", + "ml_pi: classif.cv_glmnet\n", + "ml_m: classif.cv_glmnet\n", + "\n", + "------------------ Resampling ------------------\n", + "No. folds: 5\n", + "No. repeated sample splits: 1\n", + "Apply cross-fitting: TRUE\n", + "\n", + "------------------ Fit summary ------------------\n", + " Estimates and significance testing of the effect of target variables\n", + " Estimate. Std. Error t value Pr(>|t|) \n", + "d 0.94473 0.03045 31.03 <2e-16 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "\n" + ] + } + ], "source": [ "dml_ssm = DoubleMLSSM$new(dml_data, ml_g, ml_m, ml_pi, score=\"missing-at-random\",\n", - " normalize_ipw = TRUE)" + " normalize_ipw = TRUE)\n", + "dml_ssm$fit()\n", + "\n", + "print(dml_ssm)" + ] + }, + { + "cell_type": "markdown", + "id": "781e5376", + "metadata": {}, + "source": [ + "Confidence intervals at different levels can be obtained via" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1297e329", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 5 % 95 %\n", + "d 0.8946549 0.9948104\n" + ] + } + ], + "source": [ + "print(dml_ssm$confint(level = 0.9))" + ] + }, + { + "cell_type": "markdown", + "id": "bfe05e01", + "metadata": {}, + "source": [ + "### ATE estimates distribution\n", + "\n", + "Here, we add a small simulation where we generate multiple datasets, estimate the ATE and collect the results (this may take some time). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb8d5fcd", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "80a10b47", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "n_rep = 100\n", + "ATE_estimates = rep(NA, n_rep)\n", + "ATE_estimates[1] = dml_ssm$coef" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8956cb51", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "n_rep = 200\n", + "ATE = 1.0\n", + "\n", + "ATE_estimates = rep(NA, n_rep)\n", + "\n", + "set.seed(42)\n", + "for (i_rep in seq_len(n_rep)) {\n", + " if (i_rep %% (n_rep %/% 10) == 0) {\n", + " print(paste0(\"Iteration: \", i_rep, \"/\", n_rep))\n", + " }\n", + " dml_data = make_ssm_data(n_obs=n_obs, mar=TRUE)\n", + " dml_ssm = DoubleMLSSM$new(dml_data, ml_g, ml_m, ml_pi, score='missing-at-random', normalize_ipw=TRUE)\n", + " dml_ssm$fit()\n", + " ATE_estimates[i_rep] = dml_ssm$coef\n", + "}\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "2e8a6094", + "metadata": {}, + "source": [ + "The distribution of the estimates takes the following form" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7cd8060", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# Plot histogram of ATE_estimates with ggplot\n", + "hist_data = data.frame(ATE_estimates = ATE_estimates)\n", + "\n", + "ggplot(hist_data, aes(x = ATE_estimates)) +\n", + " geom_histogram(binwidth = 0.1, fill = \"blue\", color = \"black\", alpha = 0.7) +\n", + " geom_vline(aes(xintercept = ATE), color = \"red\", linetype = \"dashed\", size = 1) +\n", + " labs(title = \"Histogram of ATE Estimates\",\n", + " x = \"ATE Estimates\",\n", + " y = \"Frequency\") +\n", + " theme_minimal()" + ] + }, + { + "cell_type": "markdown", + "id": "645df4f8", + "metadata": {}, + "source": [ + "## Outcome missing under nonignorable nonresponse\n", + "Now consider a different setting, in which the outcomes are missing under nonignorable nonresponse assumptions in [Bia, Huber and Lafférs (2023)](https://doi.org/10.1080/07350015.2023.2271071). \n", + "Let the covariance matrix $\\sigma^2_X$ again be such that $a_{ij} = 0.5^{|i - j|}$, but now $\\gamma_0 = 1$ and $\\sigma^2_{\\varepsilon, \\upsilon} = \\begin{pmatrix} 1 & 0.8 \\\\ 0.8 & 1 \\end{pmatrix}$ to show a strong correlation between $\\varepsilon$ and $\\upsilon$. Let the vector of coefficients $\\beta$ again resemble a quadratic decay of coefficients importance; $\\beta_{0,j} = 0.4/j^2$ for $j = 1, \\ldots, p$.\n", + "\n", + "The directed acyclic graph (DAG) shows the the structure of the causal model." + ] + }, + { + "cell_type": "markdown", + "id": "d7121a72", + "metadata": {}, + "source": [ + "### Data\n", + "\n", + "We will again use the implemented data generating process `make_ssm_data` to generate data according to the simulation in Appendix E of [Bia, Huber and Lafférs (2023)](https://doi.org/10.1080/07350015.2023.2271071). We will again leave the default ATE equal to $\\theta_0=1$.\n", + "\n", + "In this setting, we are estimating the ATE under nonignorable nonresponse, so we set `mar=False`. Again, the selection indicator `S` can be set via `s_col`. Further, we need to specify an intrument via `z_col`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6f4f61f", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# TODO" + ] + }, + { + "cell_type": "markdown", + "id": "d7af8539", + "metadata": {}, + "source": [ + "### Estimation\n", + "\n", + "We will again use the `DoubleMLSSM` class. \n", + "\n", + "Further, will leave he learners for all nuisance functions to be the same as in the first setting, as the simulated quadratic decay of coefficients importance still holds.\n", + "\n", + "Now the learner `ml_g` is used to fit conditional expectations of the outcome $\\mathbb{E}[Y_i|D_i, S_i, X_i, \\Pi_i]$, whereas the learners `ml_m` and `ml_pi` will be used to estimate the treatment and selection propensity scores $P(D_i=1|X_i, \\Pi_i)$ and $P(S_i=1|D_i, X_i, Z_i)$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0ccb8f7", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# TODO" + ] + }, + { + "cell_type": "markdown", + "id": "5d9bea41", + "metadata": {}, + "source": [ + "The score is now set to `'nonignorable'`, since the parameters of the DGP were set to satisfy the assumptions of outcomes missing under nonignorable nonresponse." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42a55e3f", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# TODO" + ] + }, + { + "cell_type": "markdown", + "id": "fb9ad184", + "metadata": {}, + "source": [ + "### ATE estimates distribution\n", + "\n", + "Here we again add a small simulation where we generate multiple datasets, estimate the ATE and collect the results (this may take some time). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f7a9eae4", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# TODO" + ] + }, + { + "cell_type": "markdown", + "id": "797f5d2e", + "metadata": {}, + "source": [ + "And plot the estimates distribution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcaebd62", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# TODO" ] } ], From 52f79fb4fe9b5f8b209a745b10ccf0bda9a6d373 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 9 Apr 2025 15:07:50 +0000 Subject: [PATCH 039/140] change intermediate model suffix --- doc/guide/models.rst | 10 +- doc/guide/models/did/did_models.inc | 35 ++++++ doc/guide/models/did/did_models.rst | 116 ------------------ .../irm/{irm_models.rst => irm_models.inc} | 0 .../plm/{plm_models.rst => plm_models.inc} | 0 .../rdd/{rdd_models.rst => rdd_models.inc} | 0 .../ssm/{ssm_models.rst => ssm_models.inc} | 0 7 files changed, 40 insertions(+), 121 deletions(-) create mode 100644 doc/guide/models/did/did_models.inc delete mode 100644 doc/guide/models/did/did_models.rst rename doc/guide/models/irm/{irm_models.rst => irm_models.inc} (100%) rename doc/guide/models/plm/{plm_models.rst => plm_models.inc} (100%) rename doc/guide/models/rdd/{rdd_models.rst => rdd_models.inc} (100%) rename doc/guide/models/ssm/{ssm_models.rst => ssm_models.inc} (100%) diff --git a/doc/guide/models.rst b/doc/guide/models.rst index 0b5b7e32..0496d95e 100644 --- a/doc/guide/models.rst +++ b/doc/guide/models.rst @@ -10,7 +10,7 @@ The :ref:`DoubleML `-package includes the following models. Partially linear models (PLM) +++++++++++++++++++++++++++++ -.. include:: models/plm/plm_models.rst +.. include:: models/plm/plm_models.inc .. _irm-models: @@ -18,7 +18,7 @@ Partially linear models (PLM) Interactive regression models (IRM) ++++++++++++++++++++++++++++++++++++ -.. include:: models/irm/irm_models.rst +.. include:: models/irm/irm_models.inc .. _did-models: @@ -26,7 +26,7 @@ Interactive regression models (IRM) Difference-in-Differences Models (DID) ++++++++++++++++++++++++++++++++++++++ -.. include:: models/did/did_models.rst +.. include:: models/did/did_models.inc .. _ssm-models: @@ -34,7 +34,7 @@ Difference-in-Differences Models (DID) Sample Selection Models (SSM) ++++++++++++++++++++++++++++++++++++++ -.. include:: models/ssm/ssm_models.rst +.. include:: models/ssm/ssm_models.inc .. _rdd-models: @@ -42,4 +42,4 @@ Sample Selection Models (SSM) Regression Discontinuity Designs (RDD) ++++++++++++++++++++++++++++++++++++++ -.. include:: models/rdd/rdd_models.rst +.. include:: models/rdd/rdd_models.inc diff --git a/doc/guide/models/did/did_models.inc b/doc/guide/models/did/did_models.inc new file mode 100644 index 00000000..f8184133 --- /dev/null +++ b/doc/guide/models/did/did_models.inc @@ -0,0 +1,35 @@ +.. include:: /guide/models/did/did_setup.rst + + +.. _did-pa-model: + +Panel data +********** + +.. include:: /guide/models/did/did_pa.rst + + +.. _did-cs-model: + +Repeated cross-sections +*********************** + +.. note:: + Will be implemented soon. + + +.. _did-aggregation: + +Effect Aggregation +****************** + + +.. _did-binary-model: + +Two treatment periods +********************* + +.. note:: + This documentation refers the deprecated implementation for two time periods and is deprecated. We recommend using the implementation :ref:`did-pa-model` and :ref:`did-cs-model`. + +.. include:: /guide/models/did/did_binary.rst diff --git a/doc/guide/models/did/did_models.rst b/doc/guide/models/did/did_models.rst deleted file mode 100644 index 41aeb4db..00000000 --- a/doc/guide/models/did/did_models.rst +++ /dev/null @@ -1,116 +0,0 @@ -.. include:: /guide/models/did/did.rst - -.. _did-pa-model: - -Panel data -********** - -Multi Period -^^^^^^^^^^^^ - -test - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - :okwarning: - - import numpy as np - import doubleml as dml - from doubleml.did.datasets import make_did_CS2021 - from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier - - np.random.seed(42) - df = make_did_CS2021(n_obs=500) - dml_data = dml.data.DoubleMLPanelData( - df, - y_col="y", - d_cols="d", - id_col="id", - t_col="t", - x_cols=["Z1", "Z2", "Z3", "Z4"], - datetime_unit="M" - ) - dml_did_obj = dml.did.DoubleMLDIDMulti( - obj_dml_data=dml_data, - ml_g=ml_g, - ml_m=ml_m, - gt_combinations="standard", - control_group="never_treated", - ) - print(dml_did_obj.fit()) - -Single Period -^^^^^^^^^^^^^ - -.. include:: /guide/models/did/did_binary.rst - -If panel data are available, the observations are assumed to be iid. of form :math:`(Y_{i0}, Y_{i1}, D_i, X_i)`. -Remark that the difference :math:`\Delta Y_i= Y_{i1}-Y_{i0}` has to be defined as the outcome ``y`` in the ``DoubleMLData`` object. - -``DoubleMLIDID`` implements difference-in-differences models for panel data. -Estimation is conducted via its ``fit()`` method: - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - :okwarning: - - import numpy as np - import doubleml as dml - from doubleml.did.datasets import make_did_SZ2020 - from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier - - ml_g = RandomForestRegressor(n_estimators=100, max_depth=5, min_samples_leaf=5) - ml_m = RandomForestClassifier(n_estimators=100, max_depth=5, min_samples_leaf=5) - np.random.seed(42) - data = make_did_SZ2020(n_obs=500, return_type='DataFrame') - # y is already defined as the difference of observed outcomes - obj_dml_data = dml.DoubleMLData(data, 'y', 'd') - dml_did_obj = dml.DoubleMLDID(obj_dml_data, ml_g, ml_m) - print(dml_did_obj.fit()) - -.. _did-cs-model: - -Repeated cross-sections -*********************** - -For repeated cross-sections, the observations are assumed to be iid. of form :math:`(Y_{i}, D_i, X_i, T_i)`, -where :math:`T_i` is a dummy variable if unit :math:`i` is observed pre- or post-treatment period, such -that the observed outcome can be defined as - -.. math:: - - Y_i = T_i Y_{i1} + (1-T_i) Y_{i0}. - -Further, treatment and covariates are assumed to be stationary, such that the joint distribution of :math:`(D,X)` is invariant to :math:`T`. - -``DoubleMLIDIDCS`` implements difference-in-differences models for repeated cross-sections. -Estimation is conducted via its ``fit()`` method: - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - :okwarning: - - import numpy as np - import doubleml as dml - from doubleml.did.datasets import make_did_SZ2020 - from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier - - ml_g = RandomForestRegressor(n_estimators=100, max_depth=5, min_samples_leaf=5) - ml_m = RandomForestClassifier(n_estimators=100, max_depth=5, min_samples_leaf=5) - np.random.seed(42) - data = make_did_SZ2020(n_obs=500, cross_sectional_data=True, return_type='DataFrame') - obj_dml_data = dml.DoubleMLData(data, 'y', 'd', t_col='t') - dml_did_obj = dml.DoubleMLDIDCS(obj_dml_data, ml_g, ml_m) - print(dml_did_obj.fit()) diff --git a/doc/guide/models/irm/irm_models.rst b/doc/guide/models/irm/irm_models.inc similarity index 100% rename from doc/guide/models/irm/irm_models.rst rename to doc/guide/models/irm/irm_models.inc diff --git a/doc/guide/models/plm/plm_models.rst b/doc/guide/models/plm/plm_models.inc similarity index 100% rename from doc/guide/models/plm/plm_models.rst rename to doc/guide/models/plm/plm_models.inc diff --git a/doc/guide/models/rdd/rdd_models.rst b/doc/guide/models/rdd/rdd_models.inc similarity index 100% rename from doc/guide/models/rdd/rdd_models.rst rename to doc/guide/models/rdd/rdd_models.inc diff --git a/doc/guide/models/ssm/ssm_models.rst b/doc/guide/models/ssm/ssm_models.inc similarity index 100% rename from doc/guide/models/ssm/ssm_models.rst rename to doc/guide/models/ssm/ssm_models.inc From c3ccd41fc815a3f779dbf7a33bf9613f1808f107 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 9 Apr 2025 15:07:58 +0000 Subject: [PATCH 040/140] update did guide --- doc/guide/models/did/did.rst | 0 doc/guide/models/did/did_binary.rst | 70 +++++++++++++++++++++++++++++ doc/guide/models/did/did_pa.rst | 33 ++++++++++++++ doc/guide/models/did/did_setup.rst | 51 +++++++++++++++++++++ 4 files changed, 154 insertions(+) delete mode 100644 doc/guide/models/did/did.rst create mode 100644 doc/guide/models/did/did_pa.rst create mode 100644 doc/guide/models/did/did_setup.rst diff --git a/doc/guide/models/did/did.rst b/doc/guide/models/did/did.rst deleted file mode 100644 index e69de29b..00000000 diff --git a/doc/guide/models/did/did_binary.rst b/doc/guide/models/did/did_binary.rst index 38cea0f9..90fda78d 100644 --- a/doc/guide/models/did/did_binary.rst +++ b/doc/guide/models/did/did_binary.rst @@ -22,3 +22,73 @@ The corresponding identifying assumptions are .. note:: For a more detailed introduction and recent developments of the difference-in-differences literature see e.g. `Roth et al. (2022) `_. + + +Panel Data +~~~~~~~~~~~ + +If panel data are available, the observations are assumed to be iid. of form :math:`(Y_{i0}, Y_{i1}, D_i, X_i)`. +Remark that the difference :math:`\Delta Y_i= Y_{i1}-Y_{i0}` has to be defined as the outcome ``y`` in the ``DoubleMLData`` object. + +``DoubleMLIDID`` implements difference-in-differences models for panel data. +Estimation is conducted via its ``fit()`` method: + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + :okwarning: + + import numpy as np + import doubleml as dml + from doubleml.did.datasets import make_did_SZ2020 + from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier + + ml_g = RandomForestRegressor(n_estimators=100, max_depth=5, min_samples_leaf=5) + ml_m = RandomForestClassifier(n_estimators=100, max_depth=5, min_samples_leaf=5) + np.random.seed(42) + data = make_did_SZ2020(n_obs=500, return_type='DataFrame') + # y is already defined as the difference of observed outcomes + obj_dml_data = dml.DoubleMLData(data, 'y', 'd') + dml_did_obj = dml.DoubleMLDID(obj_dml_data, ml_g, ml_m) + print(dml_did_obj.fit()) + + +Repeated cross-sections +~~~~~~~~~~~~~~~~~~~~~~~~~ + +For repeated cross-sections, the observations are assumed to be iid. of form :math:`(Y_{i}, D_i, X_i, T_i)`, +where :math:`T_i` is a dummy variable if unit :math:`i` is observed pre- or post-treatment period, such +that the observed outcome can be defined as + +.. math:: + + Y_i = T_i Y_{i1} + (1-T_i) Y_{i0}. + +Further, treatment and covariates are assumed to be stationary, such that the joint distribution of :math:`(D,X)` is invariant to :math:`T`. + +``DoubleMLIDIDCS`` implements difference-in-differences models for repeated cross-sections. +Estimation is conducted via its ``fit()`` method: + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + :okwarning: + + import numpy as np + import doubleml as dml + from doubleml.did.datasets import make_did_SZ2020 + from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier + + ml_g = RandomForestRegressor(n_estimators=100, max_depth=5, min_samples_leaf=5) + ml_m = RandomForestClassifier(n_estimators=100, max_depth=5, min_samples_leaf=5) + np.random.seed(42) + data = make_did_SZ2020(n_obs=500, cross_sectional_data=True, return_type='DataFrame') + obj_dml_data = dml.DoubleMLData(data, 'y', 'd', t_col='t') + dml_did_obj = dml.DoubleMLDIDCS(obj_dml_data, ml_g, ml_m) + print(dml_did_obj.fit()) diff --git a/doc/guide/models/did/did_pa.rst b/doc/guide/models/did/did_pa.rst new file mode 100644 index 00000000..d46d3131 --- /dev/null +++ b/doc/guide/models/did/did_pa.rst @@ -0,0 +1,33 @@ + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + :okwarning: + + import numpy as np + import doubleml as dml + from doubleml.did.datasets import make_did_CS2021 + from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier + + np.random.seed(42) + df = make_did_CS2021(n_obs=500) + dml_data = dml.data.DoubleMLPanelData( + df, + y_col="y", + d_cols="d", + id_col="id", + t_col="t", + x_cols=["Z1", "Z2", "Z3", "Z4"], + datetime_unit="M" + ) + dml_did_obj = dml.did.DoubleMLDIDMulti( + obj_dml_data=dml_data, + ml_g=ml_g, + ml_m=ml_m, + gt_combinations="standard", + control_group="never_treated", + ) + print(dml_did_obj.fit()) diff --git a/doc/guide/models/did/did_setup.rst b/doc/guide/models/did/did_setup.rst new file mode 100644 index 00000000..8c3e0fb2 --- /dev/null +++ b/doc/guide/models/did/did_setup.rst @@ -0,0 +1,51 @@ +**Difference-in-Differences Models (DID)** implemented in the package focus on the the binary treatment case with staggered adoption. + +.. note:: + The notation and identifying assumptions are based on `Callaway and Sant'Anna (2021) `_, but adjusted to better fit into the general documentation conventions. + The underlying score functions are based on `Sant'Anna and Zhao (2020) `_, `Zimmert (2018) `_ and `Chang (2020) `_. + For a more detailed introduction and recent developments of the difference-in-differences literature see e.g. `Roth et al. (2022) `_. + +We consider :math:`n` observed units at time periods :math:`t=1,\dots, \mathcal{T}`. +The treatment status for unit :math:`i` at time period :math:`t` is denoted by the binary variable :math:`D_{i,t}=1`. The package considers the staggered adoption setting, +where a unit stays treated after it has been treated once (*Irreversibility of Treatment*). + +Let :math:`G^{\mathrm{g}}_i` be an indicator variable that takes value one if unit :math:`i` is treated at time period :math:`t=\mathrm{g}`, :math:`G^{\mathrm{g}}_i=1\{G_i=\mathrm{g}\}` with :math:`G_i` refering to the first post-treatment period. +I units are never exposed to the treatment, define :math:`G_i=\infty`. + +The target parameters are defined in terms of differences in potential outcomes. The observed and potential outcome for each unit :math:`i` at time period :math:`t` are assumed to be of the form + +.. math:: + Y_{i,t} = Y_{i,t}(0) + \sum_{\mathrm{g}=2}^{\mathcal{T}} (Y_{i,t}(\mathrm{g}) - Y_{i,t}(0)) \cdot G^{\mathrm{g}}_i, + +such that we observe one consistent potential outcome for each unit at each time period. + +The corresponding target parameters are the average causal effects of the treatment + +.. math:: + ATT(\mathrm{g},t):= \mathbb{E}[Y_{i,t}(\mathrm{g}) - Y_{i,t}(0)|G^{\mathrm{g}}_i=1]. + +This target parameter quantifies the average change in potential outcomes for units that are treated the first time in period :math:`\mathrm{g}` with the difference in outcome being evaluated for time period :math:`t`. + + +The corresponding identifying assumptions are: + +1. **Irreversibility of Treatment:** + :math:`D_{i,1} = 0 \quad a.s.` + For all :math:`t=2,\dots,\mathcal{T}`, :math:`D_{i,t-1} = 1` implies :math:`D_{i,t} = 1 \quad a.s.` + +2. **Panel Data (Random Sampling):** + :math:`(Y_{i,1},\dots, Y_{i,\mathcal{T}}, X_i, D_{i,1}, \dots, D_{i,\mathcal{T}})_{i=1}^n` is independent and identically distributed. + +3. **Limited Treatment Anticipation:** + There is a known :math:`\delta\ge 0` such that + :math:`\mathbb{E}[Y_{i,t}(\mathrm{g})|X_i, G_i^{\mathrm{g}}=1] = \mathbb{E}[Y_{i,t}(0)|X_i, G_i^{\mathrm{g}}=1]\quad a.s.` for all :math:`\mathrm{g}\in\mathcal{G}, t\in\{1,\dots,\mathcal{T}\}` such that :math:`t< \mathrm{g}-\delta`.` + +4. **(Cond.) Parallel Trends:** + :math:`\mathbb{E}[Y_{i1}(0) - Y_{i0}(0)|X_i, D_i=1] = \mathbb{E}[Y_{i1}(0) - Y_{i0}(0)|X_i, D_i=0]\quad a.s.` + +5. **Overlap:** ´ + :math:`\exists\epsilon > 0`: :math:`P(D_i=1) > \epsilon` and :math:`P(D_i=1|X_i) \le 1-\epsilon\quad a.s.` + +.. note:: + For a detailed discussion of the assumptions see `Callaway and Sant'Anna (2021) `_. + Currently, the package automatically imposes "no-anticipation", e.g. :math:`\delta=0`. \ No newline at end of file From bf724caa2f30c2108a1e5a1109b53b4a4916f73e Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Wed, 9 Apr 2025 20:06:40 +0200 Subject: [PATCH 041/140] add code for SSM in R --- doc/guide/models.rst | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/doc/guide/models.rst b/doc/guide/models.rst index 3977c6d4..c35eb39b 100644 --- a/doc/guide/models.rst +++ b/doc/guide/models.rst @@ -441,6 +441,22 @@ Estimation is conducted via its ``fit()`` method: dml_ssm.fit() print(dml_ssm) + .. tab-item:: R + :sync: r + + .. jupyter-execute:: + + library(DoubleML) + library(mlr3) + library(data.table) + + set.seed(3141) + n_obs = 2000 + df = make_ssm_data(n_obs=n_obs, mar=TRUE, return_type="data.table") + dml_data = DoubleMLData$new(df, y_col="y", d_cols="d", s_col="s") + dml_ssm = DoubleMLSSM$new(dml_data, ml_g, ml_m, ml_pi, score="missing-at-random") + dml_ssm$fit() + print(dml_ssm) .. _ssm-nr-model: @@ -508,6 +524,24 @@ Estimation is conducted via its ``fit()`` method: dml_ssm.fit() print(dml_ssm) + .. tab-item:: R + :sync: r + + .. jupyter-execute:: + + library(DoubleML) + library(mlr3) + library(data.table) + + set.seed(3141) + n_obs = 2000 + df = make_ssm_data(n_obs=n_obs, mar=FALSE, return_type="data.table") + dml_data = DoubleMLData$new(df, y_col="y", d_cols="d", z_cols = "z", s_col="s") + dml_ssm = DoubleMLSSM$new(dml_data, ml_g, ml_m, ml_pi, score="nonignorable") + dml_ssm$fit() + print(dml_ssm) + + Regression Discontinuity Designs (RDD) ++++++++++++++++++++++++++++++++++++++ From a0476dc3a2a3c35b33e2878e508daf0ecb1fb0db Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Wed, 9 Apr 2025 20:46:37 +0200 Subject: [PATCH 042/140] add example for SSMs in R --- doc/examples/R_double_ml_ssm.ipynb | 222 ++++++++++++++++++++++------- 1 file changed, 168 insertions(+), 54 deletions(-) diff --git a/doc/examples/R_double_ml_ssm.ipynb b/doc/examples/R_double_ml_ssm.ipynb index 5f0285e5..ab5d75e5 100644 --- a/doc/examples/R_double_ml_ssm.ipynb +++ b/doc/examples/R_double_ml_ssm.ipynb @@ -45,20 +45,6 @@ { "cell_type": "code", "execution_count": 1, - "id": "de1803b2", - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "#remotes::install_github(\"DoubleML/doubleml-for-r\", ref = \"p-ssm_branch\", force = TRUE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, "id": "a1f2f984", "metadata": { "vscode": { @@ -77,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "d35090ed", "metadata": { "vscode": { @@ -130,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "4a905df9", "metadata": { "vscode": { @@ -162,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "761ab3d5", "metadata": { "vscode": { @@ -228,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "1297e329", "metadata": { "vscode": { @@ -261,19 +247,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "fb8d5fcd", - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "80a10b47", "metadata": { "vscode": { @@ -289,14 +263,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "8956cb51", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Iteration: 20/200\"\n", + "[1] \"Iteration: 40/200\"\n", + "[1] \"Iteration: 60/200\"\n", + "[1] \"Iteration: 80/200\"\n", + "[1] \"Iteration: 100/200\"\n", + "[1] \"Iteration: 120/200\"\n", + "[1] \"Iteration: 140/200\"\n", + "[1] \"Iteration: 160/200\"\n", + "[1] \"Iteration: 180/200\"\n", + "[1] \"Iteration: 200/200\"\n" + ] + } + ], "source": [ "n_rep = 200\n", "ATE = 1.0\n", @@ -305,15 +296,14 @@ "\n", "set.seed(42)\n", "for (i_rep in seq_len(n_rep)) {\n", - " if (i_rep %% (n_rep %/% 10) == 0) {\n", - " print(paste0(\"Iteration: \", i_rep, \"/\", n_rep))\n", - " }\n", - " dml_data = make_ssm_data(n_obs=n_obs, mar=TRUE)\n", - " dml_ssm = DoubleMLSSM$new(dml_data, ml_g, ml_m, ml_pi, score='missing-at-random', normalize_ipw=TRUE)\n", - " dml_ssm$fit()\n", - " ATE_estimates[i_rep] = dml_ssm$coef\n", - "}\n", - "\n" + " if (i_rep %% (n_rep %/% 10) == 0) {\n", + " print(paste0(\"Iteration: \", i_rep, \"/\", n_rep))\n", + " }\n", + " dml_data = make_ssm_data(n_obs=n_obs, mar=TRUE)\n", + " dml_ssm = DoubleMLSSM$new(dml_data, ml_g, ml_m, ml_pi, score='missing-at-random', normalize_ipw=TRUE)\n", + " dml_ssm$fit()\n", + " ATE_estimates[i_rep] = dml_ssm$coef\n", + "}\n" ] }, { @@ -333,9 +323,34 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"\u001b[1m\u001b[22mUsing `size` aesthetic for lines was deprecated in ggplot2 3.4.0.\n", + "\u001b[36mℹ\u001b[39m Please use `linewidth` instead.\"\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], "source": [ - "# Plot histogram of ATE_estimates with ggplot\n", + "# Plot density plot of ATE_estimates with ggplot\n", "hist_data = data.frame(ATE_estimates = ATE_estimates)\n", "\n", "ggplot(hist_data, aes(x = ATE_estimates)) +\n", @@ -359,6 +374,16 @@ "The directed acyclic graph (DAG) shows the the structure of the causal model." ] }, + { + "cell_type": "markdown", + "id": "bcfc2d49", + "metadata": {}, + "source": [ + "

\n", + " \"Graph\"\n", + "

" + ] + }, { "cell_type": "markdown", "id": "d7121a72", @@ -373,16 +398,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "b6f4f61f", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================= DoubleMLData Object ==================\n", + "\n", + "\n", + "------------------ Data summary ------------------\n", + "Outcome variable: y\n", + "Treatment variable(s): d\n", + "Covariates: X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26, X27, X28, X29, X30, X31, X32, X33, X34, X35, X36, X37, X38, X39, X40, X41, X42, X43, X44, X45, X46, X47, X48, X49, X50, X51, X52, X53, X54, X55, X56, X57, X58, X59, X60, X61, X62, X63, X64, X65, X66, X67, X68, X69, X70, X71, X72, X73, X74, X75, X76, X77, X78, X79, X80, X81, X82, X83, X84, X85, X86, X87, X88, X89, X90, X91, X92, X93, X94, X95, X96, X97, X98, X99, X100\n", + "Instrument(s): z\n", + "Selection variable: s\n", + "No. Observations: 8000\n" + ] + } + ], "source": [ - "# TODO" + "set.seed(3141)\n", + "n_obs = 8000\n", + "df = make_ssm_data(n_obs=n_obs, mar=FALSE, return_type=\"data.table\")\n", + "dml_data = DoubleMLData$new(df, y_col=\"y\", d_cols=\"d\", z_cols = \"z\", s_col=\"s\")\n", + "print(dml_data)" ] }, { @@ -401,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "d0ccb8f7", "metadata": { "vscode": { @@ -410,7 +456,9 @@ }, "outputs": [], "source": [ - "# TODO" + "ml_g = lrn(\"regr.cv_glmnet\", nfolds = 5, s = \"lambda.min\")\n", + "ml_m = lrn(\"classif.cv_glmnet\", nfolds = 5, s = \"lambda.min\")\n", + "ml_pi = lrn(\"classif.cv_glmnet\", nfolds = 5, s = \"lambda.min\")" ] }, { @@ -423,16 +471,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "42a55e3f", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================= DoubleMLSSM Object ==================\n", + "\n", + "\n", + "------------------ Data summary ------------------\n", + "Outcome variable: y\n", + "Treatment variable(s): d\n", + "Covariates: X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20, X21, X22, X23, X24, X25, X26, X27, X28, X29, X30, X31, X32, X33, X34, X35, X36, X37, X38, X39, X40, X41, X42, X43, X44, X45, X46, X47, X48, X49, X50, X51, X52, X53, X54, X55, X56, X57, X58, X59, X60, X61, X62, X63, X64, X65, X66, X67, X68, X69, X70, X71, X72, X73, X74, X75, X76, X77, X78, X79, X80, X81, X82, X83, X84, X85, X86, X87, X88, X89, X90, X91, X92, X93, X94, X95, X96, X97, X98, X99, X100\n", + "Instrument(s): z\n", + "Selection variable: s\n", + "No. Observations: 8000\n", + "\n", + "------------------ Score & algorithm ------------------\n", + "Score function: nonignorable\n", + "DML algorithm: dml2\n", + "\n", + "------------------ Machine learner ------------------\n", + "ml_g: regr.cv_glmnet\n", + "ml_pi: classif.cv_glmnet\n", + "ml_m: classif.cv_glmnet\n", + "\n", + "------------------ Resampling ------------------\n", + "No. folds: 5\n", + "No. repeated sample splits: 1\n", + "Apply cross-fitting: TRUE\n", + "\n", + "------------------ Fit summary ------------------\n", + " Estimates and significance testing of the effect of target variables\n", + " Estimate. Std. Error t value Pr(>|t|) \n", + "d 0.95305 0.03438 27.72 <2e-16 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "\n" + ] + } + ], "source": [ - "# TODO" + "dml_ssm = DoubleMLSSM$new(dml_data, ml_g, ml_m, ml_pi, score=\"nonignorable\")\n", + "dml_ssm$fit()\n", + "\n", + "print(dml_ssm)" ] }, { @@ -456,7 +547,20 @@ }, "outputs": [], "source": [ - "# TODO" + "n_rep = 100\n", + "ATE = 1.0\n", + "ATE_estimates = rep(NA, n_rep)\n", + "\n", + "set.seed(42)\n", + "for (i_rep in seq_len(n_rep)) {\n", + " if (i_rep %% (n_rep %/% 10) == 0) {\n", + " print(paste0(\"Iteration: \", i_rep, \"/\", n_rep))\n", + " }\n", + " dml_data = make_ssm_data(n_obs=n_obs, mar=FALSE)\n", + " dml_ssm = DoubleMLSSM$new(dml_data, ml_g, ml_m, ml_pi, score='nonignorable')\n", + " dml_ssm$fit()\n", + " ATE_estimates[i_rep] = dml_ssm$coef\n", + "}" ] }, { @@ -478,7 +582,17 @@ }, "outputs": [], "source": [ - "# TODO" + "# Plot histogram of ATE_estimates with ggplot\n", + "hist_data = data.frame(ATE_estimates = ATE_estimates)\n", + "\n", + "ggplot(hist_data, aes(x = ATE_estimates)) +\n", + " geom_histogram(binwidth = 0.1, fill = \"blue\", color = \"black\", alpha = 0.7) +\n", + " geom_vline(aes(xintercept = ATE), color = \"red\", linetype = \"dashed\", size = 1) +\n", + " labs(title = \"Histogram of ATE Estimates\",\n", + " x = \"ATE Estimates\",\n", + " y = \"Frequency\") +\n", + " theme_minimal()\n", + " " ] } ], From d29cb4081cf00782e0b0d420e7ea40f19483a4b1 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 9 Apr 2025 19:19:04 +0000 Subject: [PATCH 043/140] add contraol group --- doc/guide/models/did/did_setup.rst | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/doc/guide/models/did/did_setup.rst b/doc/guide/models/did/did_setup.rst index 8c3e0fb2..8acc4c28 100644 --- a/doc/guide/models/did/did_setup.rst +++ b/doc/guide/models/did/did_setup.rst @@ -25,6 +25,13 @@ The corresponding target parameters are the average causal effects of the treatm ATT(\mathrm{g},t):= \mathbb{E}[Y_{i,t}(\mathrm{g}) - Y_{i,t}(0)|G^{\mathrm{g}}_i=1]. This target parameter quantifies the average change in potential outcomes for units that are treated the first time in period :math:`\mathrm{g}` with the difference in outcome being evaluated for time period :math:`t`. +The corresponding control groups, defined by an indicator :math:`C`, can be typically set as either the *never treated* or *not yet treated* units. +Let + +.. math:: + C_{i}^{nev}&:= 1\{G_i=\infty\} \quad \text{(never treated)}, \\ + + C_{i,t,\mathrm{g}}^{nyt}&:= 1\{G_i < \mathrm{g}\} \quad \text{(not yet treated)}. The corresponding identifying assumptions are: @@ -38,7 +45,7 @@ The corresponding identifying assumptions are: 3. **Limited Treatment Anticipation:** There is a known :math:`\delta\ge 0` such that - :math:`\mathbb{E}[Y_{i,t}(\mathrm{g})|X_i, G_i^{\mathrm{g}}=1] = \mathbb{E}[Y_{i,t}(0)|X_i, G_i^{\mathrm{g}}=1]\quad a.s.` for all :math:`\mathrm{g}\in\mathcal{G}, t\in\{1,\dots,\mathcal{T}\}` such that :math:`t< \mathrm{g}-\delta`.` + :math:`\mathbb{E}[Y_{i,t}(\mathrm{g})|X_i, G_i^{\mathrm{g}}=1] = \mathbb{E}[Y_{i,t}(0)|X_i, G_i^{\mathrm{g}}=1]\quad a.s.` for all :math:`\mathrm{g}\in\mathcal{G}, t\in\{1,\dots,\mathcal{T}\}` such that :math:`t< \mathrm{g}-\delta`. 4. **(Cond.) Parallel Trends:** :math:`\mathbb{E}[Y_{i1}(0) - Y_{i0}(0)|X_i, D_i=1] = \mathbb{E}[Y_{i1}(0) - Y_{i0}(0)|X_i, D_i=0]\quad a.s.` From 9dbe6dc988b6667a3f062c4d79feda1cd54891e9 Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Thu, 10 Apr 2025 09:26:27 +0200 Subject: [PATCH 044/140] add learners --- doc/guide/models.rst | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/doc/guide/models.rst b/doc/guide/models.rst index c35eb39b..b34ae648 100644 --- a/doc/guide/models.rst +++ b/doc/guide/models.rst @@ -454,6 +454,11 @@ Estimation is conducted via its ``fit()`` method: n_obs = 2000 df = make_ssm_data(n_obs=n_obs, mar=TRUE, return_type="data.table") dml_data = DoubleMLData$new(df, y_col="y", d_cols="d", s_col="s") + + ml_g = lrn("regr.cv_glmnet", nfolds = 5, s = "lambda.min") + ml_m = lrn("classif.cv_glmnet", nfolds = 5, s = "lambda.min") + ml_pi = lrn("classif.cv_glmnet", nfolds = 5, s = "lambda.min") + dml_ssm = DoubleMLSSM$new(dml_data, ml_g, ml_m, ml_pi, score="missing-at-random") dml_ssm$fit() print(dml_ssm) @@ -537,6 +542,11 @@ Estimation is conducted via its ``fit()`` method: n_obs = 2000 df = make_ssm_data(n_obs=n_obs, mar=FALSE, return_type="data.table") dml_data = DoubleMLData$new(df, y_col="y", d_cols="d", z_cols = "z", s_col="s") + + ml_g = lrn("regr.cv_glmnet", nfolds = 5, s = "lambda.min") + ml_m = lrn("classif.cv_glmnet", nfolds = 5, s = "lambda.min") + ml_pi = lrn("classif.cv_glmnet", nfolds = 5, s = "lambda.min") + dml_ssm = DoubleMLSSM$new(dml_data, ml_g, ml_m, ml_pi, score="nonignorable") dml_ssm$fit() print(dml_ssm) From 67fb4e80714468f6633fba05ef32841838f2d6d2 Mon Sep 17 00:00:00 2001 From: Sven Klaassen <47529404+SvenKlaassen@users.noreply.github.com> Date: Thu, 10 Apr 2025 10:42:10 +0200 Subject: [PATCH 045/140] fix typo --- .devcontainer/devcontainer.json | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index b2900011..88c920a3 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -12,9 +12,9 @@ "njpwerner.autodocstring", // Optional: Auto-generate docstrings "ms-python.black-formatter", // Optional: Black formatter "streetsidesoftware.code-spell-checker", // Optional: Spell checker - "github.copilot" // Add GitHub Copilot extension + "github.copilot", // Add GitHub Copilot extension "GitHub.github-vscode-theme", // GitHub theme - "github.vscode-github-actions" // GitHub Actions extension + "github.vscode-github-actions", // GitHub Actions extension "ms-toolsai.jupyter", // Jupyter extension "charliermarsh.ruff" // Ruff extension ], @@ -28,7 +28,7 @@ "python.linting.flake8Enabled": false, // Disable Flake8 for linting "python.linting.ruffEnabled": true, // Enable Ruff for linting "python.formatting.provider": "black", // Use Black for formatting - "python.testing.pytestEnabled": false, // Enable Pytest for testing + "python.testing.pytestEnabled": true, // Enable Pytest for testing "python.testing.pytestArgs": [], "python.testing.unittestEnabled": false, "files.exclude": { From b9194aca8262b6e9966527ba807eea4cf0505072 Mon Sep 17 00:00:00 2001 From: Sven Klaassen <47529404+SvenKlaassen@users.noreply.github.com> Date: Thu, 10 Apr 2025 10:54:33 +0200 Subject: [PATCH 046/140] locate venv in workspace --- .devcontainer/Dockerfile.dev | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/.devcontainer/Dockerfile.dev b/.devcontainer/Dockerfile.dev index c5583ebb..2dc7d209 100644 --- a/.devcontainer/Dockerfile.dev +++ b/.devcontainer/Dockerfile.dev @@ -45,18 +45,16 @@ RUN wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | # Install Python packages COPY requirements.txt /tmp/requirements.txt -RUN python -m venv /opt/venv && \ - /opt/venv/bin/python -m pip install --upgrade pip && \ - /opt/venv/bin/pip install -r /tmp/requirements.txt +RUN python -m venv /workspace/.venv && \ + /workspace/.venv/bin/python -m pip install --upgrade pip && \ + /workspace/.venv/bin/pip install -r /tmp/requirements.txt # Set the virtual environment as the default Python environment -ENV PATH="/opt/venv/bin:$PATH" +ENV PATH="/workspace/.venv/bin:$PATH" -# Check out the repo containing the Python package DoubleML (dev) -RUN git clone https://github.com/DoubleML/doubleml-for-py.git /doubleml-for-py -WORKDIR /doubleml-for-py -RUN /opt/venv/bin/pip uninstall -y DoubleML && \ - /opt/venv/bin/pip install -e .[rdd] +# Install from main branch of DoubleML for Python +RUN pip uninstall -y DoubleML && \ + pip install git+https://github.com/DoubleML/doubleml-for-py.git@main#egg=DoubleML[rdd] # Create a directory for R user libraries and set the default R user library path RUN mkdir -p /usr/local/lib/R/site-library @@ -70,4 +68,3 @@ RUN Rscript -e "install.packages('remotes')" && \ # Set the working directory WORKDIR /workspace - From 5e2f08853f5a6a308b5cd81ba2ccce37029f7032 Mon Sep 17 00:00:00 2001 From: Sven Klaassen <47529404+SvenKlaassen@users.noreply.github.com> Date: Thu, 10 Apr 2025 10:55:49 +0200 Subject: [PATCH 047/140] add ipykernel --- requirements.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/requirements.txt b/requirements.txt index 3e6af923..38fd47c5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -21,3 +21,6 @@ seaborn xgboost lightgbm flaml + +# notebooks +ipykernel \ No newline at end of file From 3847a1f1231fedc3d4313be6bae1df9f912e8051 Mon Sep 17 00:00:00 2001 From: Sven Klaassen <47529404+SvenKlaassen@users.noreply.github.com> Date: Thu, 10 Apr 2025 10:58:30 +0200 Subject: [PATCH 048/140] update black formatter --- .devcontainer/devcontainer.json | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 88c920a3..724ac45e 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -27,7 +27,11 @@ "python.linting.enabled": true, // Enable linting "python.linting.flake8Enabled": false, // Disable Flake8 for linting "python.linting.ruffEnabled": true, // Enable Ruff for linting - "python.formatting.provider": "black", // Use Black for formatting + "python.formatting.provider": "none", + "[python]": { + "editor.defaultFormatter": "ms-python.black-formatter", + "editor.formatOnSave": true + }, "python.testing.pytestEnabled": true, // Enable Pytest for testing "python.testing.pytestArgs": [], "python.testing.unittestEnabled": false, From e301bd743985cb17dffb4546108b2b4f4a7ca133 Mon Sep 17 00:00:00 2001 From: Sven Klaassen <47529404+SvenKlaassen@users.noreply.github.com> Date: Thu, 10 Apr 2025 11:09:56 +0200 Subject: [PATCH 049/140] add non root user --- .devcontainer/Dockerfile.dev | 8 ++++++++ .devcontainer/devcontainer.json | 5 +++-- 2 files changed, 11 insertions(+), 2 deletions(-) diff --git a/.devcontainer/Dockerfile.dev b/.devcontainer/Dockerfile.dev index 2dc7d209..18f5463b 100644 --- a/.devcontainer/Dockerfile.dev +++ b/.devcontainer/Dockerfile.dev @@ -3,6 +3,11 @@ FROM ubuntu:24.04 # Set non-interactive mode to avoid prompts ENV DEBIAN_FRONTEND=noninteractive +# Add a non-root user and create a workspace directory +RUN useradd -m -s /bin/bash -N vscode && \ + mkdir -p /workspace && \ + chown -R vscode:vscode /workspace + # Update package list and install dependencies RUN apt-get update && \ apt-get install -y \ @@ -66,5 +71,8 @@ RUN Rscript -e "install.packages('remotes')" && \ Rscript -e "install.packages(c('ggplot2', 'IRkernel', 'xgboost', 'hdm', 'reshape2', 'gridExtra', 'igraph', 'mlr3filters', 'mlr3measures', 'did', dependencies=TRUE))" && \ Rscript -e "IRkernel::installspec()" +# ownership of all relevant directories for the non-root user +RUN chown -R vscode:vscode /workspace /workspace/.venv /usr/local/lib/R/site-library + # Set the working directory WORKDIR /workspace diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 724ac45e..9aff90ef 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -19,7 +19,7 @@ "charliermarsh.ruff" // Ruff extension ], "settings": { - "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python", // Poetry virtual environment path + "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python", "editor.formatOnSave": true, // Auto-format code when saving "editor.codeActionsOnSave": { "source.organizeImports": true // Auto-organize imports on save @@ -46,5 +46,6 @@ "mounts": [ "source=${localWorkspaceFolder},target=/workspace,type=bind" // Mount your local workspace into the container ], - "remoteUser": "root" // Set the user inside the container + "remoteUser": "vscode", // Set the user inside the container + "postCreateCommand": "id && ls -la /workspace && echo 'Container is ready!'" } \ No newline at end of file From be173039aee331577a2eb0e65b41eb585cc2ac20 Mon Sep 17 00:00:00 2001 From: Sven Klaassen <47529404+SvenKlaassen@users.noreply.github.com> Date: Thu, 10 Apr 2025 11:24:54 +0200 Subject: [PATCH 050/140] fix vscode user --- .devcontainer/Dockerfile.dev | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/Dockerfile.dev b/.devcontainer/Dockerfile.dev index 18f5463b..abc4362b 100644 --- a/.devcontainer/Dockerfile.dev +++ b/.devcontainer/Dockerfile.dev @@ -4,7 +4,7 @@ FROM ubuntu:24.04 ENV DEBIAN_FRONTEND=noninteractive # Add a non-root user and create a workspace directory -RUN useradd -m -s /bin/bash -N vscode && \ +RUN useradd -m -s /bin/bash vscode && \ mkdir -p /workspace && \ chown -R vscode:vscode /workspace From e8700b28dd90075d6cd5ca531b70ce4b5bfbb3b6 Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Thu, 10 Apr 2025 11:31:00 +0200 Subject: [PATCH 051/140] use density plot instead of histogram --- doc/examples/R_double_ml_ssm.ipynb | 83 ++++++++++++++++++------------ 1 file changed, 51 insertions(+), 32 deletions(-) diff --git a/doc/examples/R_double_ml_ssm.ipynb b/doc/examples/R_double_ml_ssm.ipynb index ab5d75e5..0e426b47 100644 --- a/doc/examples/R_double_ml_ssm.ipynb +++ b/doc/examples/R_double_ml_ssm.ipynb @@ -316,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "e7cd8060", "metadata": { "vscode": { @@ -324,18 +324,9 @@ } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message:\n", - "\"\u001b[1m\u001b[22mUsing `size` aesthetic for lines was deprecated in ggplot2 3.4.0.\n", - "\u001b[36mℹ\u001b[39m Please use `linewidth` instead.\"\n" - ] - }, { "data": { - "image/png": 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SB5EMkeKxiORBJEOkeCwieRDJECkei0geRDJEiscikkdsNXNiESkai0gesdXMiUWkaCwiecRWMycWkaKxiOQRW82cWESKxiKSR2w1c2IRKRqLSB6x1cyJRaRoLCJ5xFYzJxaRorGI5BFbzZxYRIrGIpJHbDVzYhEpGotIHrHVzIlFpGgsInnEVjMnFpGisYjkEVvNnFhEisYikoeLVg2R4rGI5EEkQ6R4LCJ5EMkQKR6LSB5EMkSKxyKSB5EMkeKxiORBJEOkeOzwof3525j+yWArUkpuTtsspmsPd21WFOkc16RiuUUybpHisYjkQSRDpHgsInkQyRApHotIHkQyRIrHIpIHkQyR4rGI5BFbzZxYRIrGIpJHbDVzYhEpGotIHrHVzIlFpGgsInnEVjMnFpGisYjkEVvNnFhEisYikkdsNXNiESkai0gesdXMiUWkaCwiecRWMycWkaKxiOQRW82cWESKxiKSR2w1c2IRKRqLSB6x1cyJRaRoLCJ5xFYzJxaRorGI5OGiVUOkeCwieRDJECkei0geRDJEiscikgeRDJHisYjkQSRDpHgsInkQyRApHotIHkQyRIrHIpIHkQyR4rGI5EEkQ6R4LCJ5EMkQKR6LSB5EMkSKxyKSB5EMkeKxiOQRW82cWESKxiKSR2w1c2IRKRqLSB6x1cyJRaRoLCJ5xFYzJxaRorGI5BFbzZxYRIrGIpJHbDVzYhEpGotIHrHVzIlFpGgsInnEVjMnFpGisYjkEVvNnFhEisYikkdsNXNiESkai0gesdXMiUWkaCwiecRWMycWkaKxiOTholVDpHgsInkQyRApHotIHkQyRIrHIpIHkQyR4rGI5EEkQ6R4LCJ5EMkQKR7rDu3m19OqVXYUOMwAiGSIFI91hzZN03z/uGqbEocZAJEMkeKx7tBef99tXZpuf79cqc/1YxHJECke+8mhPW7mrUs3690uFTjMAIhkiBSP/ezQXjbT7mbpGn2uH4tIhkjx2NNDe77b3Rw93U53V+hz/VhEMkSKx/pDe7w93Kub1jrsAocZQGw1c2IRKRrrT39P093z+7vmK/S5fqzYaubEIlI01p/+3jx//nEjFDjMAGKrmROLSNFYf/p71SJvFDjMAGKrmROLSNFYf2h3uzdMN/wcaWUQyX6SSJv9GYZpur9Sn+vHiq1mTiwiRWPdoc3T7lq759XO2EX7XD9WbDVzYhEpGntyrd3x81UocJgBxFYzJxaRorHu0O6m+9fWXjfrXdYQ7HP9WLHVzIlFpGisO7SXeXd10DSveRa8wGEGEFvNnFhEisb6Q3vd3EzTzWbNk3YVDjOA2GrmxCJSNJb/Q9Yjtpo5sYgUjUUkDxetGiLFY/2hbd4eJHHWbl0QqfNzRNpMEyJlgEidnyPSPD2sWmVHgcMMgEiGSPHYL34guyoFDjMAIhkixWNPfiCbcP13gcMMgEiGSPHYkx/I3q76I6QdBQ4zACIZIsVjT3+vHScbMkCkDiJdrM/1YxHJECkeyw9kPYhkiBSPRSQPIhkixWNPDu3hbnu37nbVX4FS4DADIJIhUjzW//KTm93jo2la849SFDjMAGKrmROLSNFYd2j306b/UPY3/2Pf2iCS/SSR+tm69/+u0uf6sWKrmROLSNFYRPKIrWZOLCJFYz+/a7f57NdxzZ2352l9rh8rtpo5sYgUjfUnG95/Z8MnFwrNi2chkwocZgCx1cyJRaRo7Mmh/dr9zobPLl1FpBEQyX6WSF8yL58jUhREMkTacXiI1NpSpD9/G9M/Soi1vfZw1+ZLkf7lotV3gbhF+hbcItlPukX6r6u/Eem7IJL9JJH2vNz++uLDEem7IJL9PJHa63RqEnftRuCi1c6PE+mzKxvmxX+IFAWROj9OpN+f/SHm9ysauLLhGyBS5+eIdDjXsLlSn+vHIpIhUjz2c5HmNT2qcJgBEMkQKR7L/2ruQSRDpHgsInkQyRApHvvlD2RX/JVcBQ4zACIZIsVjEcmDSIZI8diTv4/Uf4HQy+2qv7i4wGEGQCRDpHjsF79En19+sjKI1Pk5Ir3dnXvldzasDCJ1fo5It1O/U/dyO91dqc/1Y8VWMycWkaKx7tCe/+V3Nlykz/VjxVYzJxaRorH+0F43/Xc2fPV/UXyPAocZQGw1c2IRKRrLD2Q9YquZE4tI0VhE8oitZk4sIkVj+WsUHrHVzIlFpGgsf43CI7aaObGIFI3lr1F4xFYzJxaRorH8En2P2GrmxCJSNBaRPGKrmROLSNHYwF+juEif68eKrWZOLCJFYwN/jeIifa4fK7aaObGIFI0N/DWKb1PgMANw0aohUjyWH8h6EMkQKR7rr/5e87HROwUOMwAiGSLFY92hzRmHWuAwAyCSIVI81v9vFLebNU8z7ClwmAEQyRApHhv8sy7pfa4fi0iGSPFYRPIgkiFSPJazdh5EMkSKxyKSB5EMkeKxy0Nb9f7cggKHGQCRDJHisSciJdhU4DADIJIhUjwWkTyIZIgUj0Ukj9hq5sQiUjQWkTxiq5kTi0jRWETyiK1mTiwiRWMRySO2mjmxiBSNPRZp/b+NFO1z/Vix1cyJRaRoLCJ5xFYzJxaRorFc2eARW82cWESKxiKSR2w1c2IRKRqLSB6x1cyJRaRoLCJ5xFYzJxaRorGI5BFbzZxYRIrGIpJHbDVzYhEpGotIHi5aNUSKxyKSB5EMkeKxiORBJEOkeCwieRDJECkei0geRDJEiscikgeRDJHisYjkQSRDpHgsInkQyRApHotIHkQyRIrHIpIHkQyR4rGI5EEkQ6R4LCJ5EMkQKR6LSB6x1cyJRaRoLCJ5xFYzJxaRorGI5BFbzZxYRIrGIpJHbDVzYhEpGotIHrHVzIlFpGgsInnEVjMnFpGisYjkEVvNnFhEisYikkdsNXNiESkai0gesdXMiUWkaCwiecRWMycWkaKxiOQRW82cWESKxiKSR2w1c2IRKRqLSB4uWjVEiscikgeRDJHisYjkQSRDpHgsInkQyRApHotIHkQyRIrHIpIHkQyR4rGI5EEkQ6R4LCJ5EMkQKR6LSB5EMkSKxyKSB5EMkeKxw4f2529j+ieDrUgpuTlts5iuPdy1WVGkc1yTiuUWybhFiscikkdsNXNiESkai0gesdXMiUWkaCwiecRWMycWkaKxiOQRW82cWESKxiKSR2w1c2IRKRqLSB6x1cyJRaRoLCJ5xFYzJxaRorGI5BFbzZxYRIrGIpJHbDVzYhEpGotIHrHVzIlFpGgsInnEVjMnFpGisYjkEVvNnFhEisYikoeLVg2R4rGI5EEkQ6R4LCJ5EMkQKR6LSB5EMkSKxyKSB5EMkeKxiORBJEOkeCwieRDJECkei0geRDJEiscikgeRDJHisYjkQSRDpHgsInkQyRApHotIHkQyRIrHIpJHbDVzYhEpGotIHrHVzIlFpGgsInnEVjMnFpGisYjkEVvNnFhEisYikkdsNXNiESkai0gesdXMiUWkaCwiecRWMycWkaKxiOQRW82cWESKxiKSR2w1c2IRKRqLSB6x1cyJRaRoLCJ5xFYzJxaRorGI5BFbzZxYRIrGIpKHi1YNkeKxiORBJEOkeCwieRDJECkei0geRDJEiscikgeRDJHisYjkQSRDpHgsInkQyRApHotIHkQyRIrHIpIHkQyR4rGI5EEkQ6R4LCJ5EMkQKR6LSB5EMkSKxyKSR2w1c2IRKRqLSB6x1cyJRaRoLCJ5xFYzJxaRorGI5BFbzZxYRIrGIpJHbDVzYhEpGotIHrHVzIlFpGgsInnEVjMnFpGisYjkEVvNnFhEisYikkdsNXNiESkai0gesdXMiUWkaCwiecRWMycWkaKxiOQRW82cWESKxiKSh4tWDZHisYjkQSRDpHgsInkQyRApHotIHkQyRIrHIpIHkQyR4rGI5EEkQ6R4LCJ5EMkQKR6LSB5EMkSKxyKSB5EMkeKxiORBJEOkeCwieRDJECkei0geRDJEiscikkdsNXNiESkai0gesdXMiUWkaCwiecRWMycWkaKxiOQRW82cWESKxiKSR2w1c2IRKRqLSB6x1cyJRaRoLCJ5xFYzJxaRorGBQ5u3vD+fs/pcP1ZsNXNiESkae/6hze9PQhJ1ChxmALHVzIlFpGgsInnEVjMnFpGiscFDm7/hUYXDDCC2mjmxiBSN/YZIxw+R/vw3kxb/KCHW9oxtkeK7Ih3u2YVulsT+1UxJ5aLVDrdIb8wnL5z1NXKmgkiGSHvURJo/eemcr5EzFUQyRNojJtL88RSRgiBSB5E688ez2Jk7sWGnpCJSB5Ha29m6uX1c4XD+18iZCiIZIu2REun7iA07JRWROog0hNiwU1IRqYNIQ4gNOyUVkTqINITYsFNSEamDSEOIDTslFZE6iDSE2LBTUhGpg0hDiA07J5a2hkiDiA07J5a2hkiDiA07J5a2hkiDiA07J5a2hkiDiA07J5a2hkiDiA07J5a2hkiDiA07J5a2hkiDiA07J5a2hkiDiA07J5a2hkiDiA07J5a2hkiDiA07J5a2hkiDiA07J5a2hkiDiA07JZWLVjuINITYsFNSEamDSEOIDTslFZE6iDSE2LBTUhGpg0hDiA07JRWROog0hNiwU1IRqYNIQ4gNOyUVkTqINITYsFNSEamDSEOIDTslFZE6iDSE2LBTUhGpg0hDiA07JRWROog0hNiwU1IRqYNIQ4gNOyeWtoZIg4gNOyeWtoZIg4gNOyeWtoZIg4gNOyeWtoZIg4gNOyeWtoZIg4gNOyeWtoZIg4gNOyeWtoZIg4gNOyeWtoZIg4gNOyeWtoZIg4gNOyeWtoZIg4gNOyeWtoZIg4gNOyeWtoZIg4gNOyWVi1Y7iDSE2LBTUhGpg0hDiA07JRWROog0hNiwU1IRqYNIQ4gNOyUVkTqINITYsFNSEamDSEOIDTslFZE6iDSE2LBTUhGpg0hDiA07JRWROog0hNiwU1IRqYNIQ4gNOyUVkTqINITYsFNSEamDSEOIDTsnlraGSIOIDTsnlraGSIOIDTsnlraGSIOIDTsnlraGSIOIDTsnlraGSIOIDTsnlraGSIOIDTsnlraGSIOIDTsnlraGSIOIDTsnlraGSIOIDTsnlraGSIOIDTsnlraGSIOIDTsnlraGSIOIDTsllYtWO4g0hNiwU1IRqYNIQ4gNOyUVkTqINITYsFNSEamDSEOIDTslFZE6iDSE2LBTUhGpg0hDiA07JRWROog0hNiwU1IRqYNIQ4gNOyUVkTqINITYsFNSEamDSEOIDTslFZE6iDSE2LBTUhGpg0hDiA07J5a2hkj/+sX+m+kfJWibx3TGtkixokhnIPavZk4sbY1bpEHEhp0TS1tDpEHEhp0TS1tDpEHEhp0TS1tDpEHEhp0TS1tDpEHEhp0TS1tDpEHEhp0TS1tDpEHEhp0TS1tDpEHEhp0TS1tDpEHEhp0TS1tDpEHEhp2SykWrHUQaQmzYKamI1EGkIcSGnZKKSB1EGkJs2CmpiNRBpCHEhp2SikgdRBpCbNgpqYjUQaQhxIadkopIHUQaQmzYKamI1EGkIcSGnZKKSB1EGkJs2CmpiNRBpCHEhp2SikgdRBpCbNgpqYjUQaQhxIadE0tbQ6RBxIadE0tbQ6RBxIadE0tbQ6RBxIadE0tbQ6RBxIadE0tbQ6RBxIadE0tbQ6RBxIadE0tbQ6RBxIadE0tbQ6RBxIadE0tbQ6RBxIadE0tbQ6RBxIadE0tbQ6RBxIadE0tbQ6RBxIadkspFqx1EGkJs2CmpiNRBpCHEhp2SikgdRBpCbNgpqYjUQaQhxIadkopIHUQaQmzYKamI1EGkIcSGnZKKSB1EGkJs2CmpiNRBpCHEhp2SikgdRBpCbNgpqYjUQaQhxIadkopIHUQaQmzYKamI1EGkIcSGnRNLW0OkQcSGnRNLW0OkQcSGnRNLW0OkQcSGnRNLW0OkQcSGnRNLW0OkQcSGnRNLW0OkQcSGnRNLW0OkQcSGnRNLW0OkQcSGnRNLW0OkQcSGnRNLW0OkQcSGnRNLW0OkQcSGnRNL2y2TGJElT9Nn8TVypiK1mly02klqW+EGFJE8iGRi31tEGkNq2IjUQUKvgsIAAAUHSURBVKQhGDYi7UGkIRg2Iu1BpCEYNiLtQaQhGDYi7UGkIRg2Iu1BpCEYNiLtQaQhGDYi7UGkIRg2Iu1BpCEYNiLtQaQhGLbRdgciDcGwjbY7EGkIhm203YFIQzBso+0ORBqCYRttdyDSEAzbaLsDkYZg2EbbHYg0BMM22u5ApCEYttF2ByINwbCNtjsQaQiGbbTdgUhL5i2hT2DYRtsdiLRgPjw5+2vkHKbUsLlotYNICxDpOyBSB5EWINJ3QKQOIi1wIv35b679G5wrsBXp2hUgzH+u9ooinUGBvxVw9ditSCm5Ut+ErNgKdRHpMrGIlBhboS4iXSYWkRJjK9RFpMvEIlJibIW6iHSZWERKjK1Q9xJXNhQ4zKvHIlJibIW6l7jWrsBhXj0WkRJjK9RFpMvEIlJibIW6iESsfGyFuohErHxshbqIRKx8bIW6iESsfGyFuohErHxshbqIRKx8bIW6iESsfGyFuohErHxshbqIRKx8bIW6iESsfGyFuohErHxshbqIRKx8bIW6iHSZWC5aTYytUBeRLhOLSImxFeoi0mViESkxtkJdRLpMLCIlxlaoi0iXiUWkxNgKdRHpMrGIlBhboS4iXSYWkRJjK9RFpMvEIlJibIW6iHSZWERKjK1QF5EuE4tIibEV6iLSZWIRKTG2Ql1EukwsIiXGVqh7CZEA/noQCWAFEAlgBRAJYAUQCWAFEAlgBRAJYAUQCWAFEAlgBRAJYAUQCWAFBEVa/lX18F9YvwIfBbXaHr9clDLfXD2R5sOT45ersrD+8KQuy2Wsb32hby4iJTO3MrM+g3n5T3zxrq3UNxeR0ikz67OQvWt3+tpFQaR0ECkRRPo2XqTy9+QRKZH5y1cujLZI/VRN+XEjUiKI9G1O1rH6uBEpkTJ1tUXSWk2ttq1+13ZU8bptESkdREpk/uSlq6An0uFH2PPi5crMh6daba++m+dwKDrP1/3uCooEUA9EAlgBRAJYAUQCWAFEAlgBRAJYAUQCWAFEKsI0Te/P3zh++eSdBx7m3Xu+jn4Q+HmQPIhUg8etHI/9hbhIu1f+TaR/ex+sBN/jGtxPd9P9+yvvm39iwKdK/KcniHQB+B7XYJpeP/b9PJF+zdPNw/5Wav+e7X930117uZnuXrfvf7qbpnnz/gHt9X6a7l8XnwhrgkgleNzeHN3v79u1M0Xa7O7iPRyJtFVn+n2zfXK/v7O4ZfMu0tyf3yw+EdYEkUrQJXo83Lf7EMk9Ilq+YZpe2tM0Lx4jdX9+d3V+99dupt+tPb9L1n5t37516GHxibAiiFSC4xMGZ4k0T/ePx5/bDen3Ed8DXh5/3R5Eutm9aXvP7+MTYUUQqQJvd8Pe79udddfucXtf7ealHYm0fK3dfpz8ax8OLj4RVgSRKnD/tuVv9+3OPGv3fDPNT1+KdD/dPDy+fCLS4RNhRRCpAnO/P9Ze3x+4nH36++HIn2OR9qfq3F27o0+EFeH7WYCnt5ui+2l/O3GWSPP2g5/dyYZ29NpTe/14jLTpJxt+T7eLT4QVQaQCbN4EeuzL3j452fDZlQ37s9i/+hvnz0TaLC6Q2H7A6+709/S8+ERYEUQqwOF3Dby9cJZIbTNPc9fh4XOR+gOv26f+0u4D2svu9bb4RFgRRAJYAUQCWAFEAlgBRAJYAUQCWAFEAlgBRAJYAUQCWAFEAlgBRAJYAUQCWAFEAlgBRAJYgf8D5RDMRLPyapIAAAAASUVORK5CYII=", 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", "text/plain": [ "plot without title" ] @@ -351,14 +342,12 @@ ], "source": [ "# Plot density plot of ATE_estimates with ggplot\n", - "hist_data = data.frame(ATE_estimates = ATE_estimates)\n", - "\n", - "ggplot(hist_data, aes(x = ATE_estimates)) +\n", - " geom_histogram(binwidth = 0.1, fill = \"blue\", color = \"black\", alpha = 0.7) +\n", - " geom_vline(aes(xintercept = ATE), color = \"red\", linetype = \"dashed\", size = 1) +\n", - " labs(title = \"Histogram of ATE Estimates\",\n", + "ggplot(data.frame(ATE_estimates), aes(x = ATE_estimates)) +\n", + " geom_density(fill = \"blue\", alpha = 0.5) +\n", + " geom_vline(aes(xintercept = ATE), color = \"red\", linetype = \"dashed\") +\n", + " labs(title = \"Density Plot of ATE Estimates\",\n", " x = \"ATE Estimates\",\n", - " y = \"Frequency\") +\n", + " y = \"Density\") +\n", " theme_minimal()" ] }, @@ -538,14 +527,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "f7a9eae4", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Iteration: 10/100\"\n", + "[1] \"Iteration: 20/100\"\n", + "[1] \"Iteration: 30/100\"\n", + "[1] \"Iteration: 40/100\"\n", + "[1] \"Iteration: 50/100\"\n", + "[1] \"Iteration: 60/100\"\n", + "[1] \"Iteration: 70/100\"\n", + "[1] \"Iteration: 80/100\"\n", + "[1] \"Iteration: 90/100\"\n", + "[1] \"Iteration: 100/100\"\n" + ] + } + ], "source": [ "n_rep = 100\n", "ATE = 1.0\n", @@ -573,26 +579,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "bcaebd62", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], "source": [ - "# Plot histogram of ATE_estimates with ggplot\n", - "hist_data = data.frame(ATE_estimates = ATE_estimates)\n", - "\n", - "ggplot(hist_data, aes(x = ATE_estimates)) +\n", - " geom_histogram(binwidth = 0.1, fill = \"blue\", color = \"black\", alpha = 0.7) +\n", - " geom_vline(aes(xintercept = ATE), color = \"red\", linetype = \"dashed\", size = 1) +\n", - " labs(title = \"Histogram of ATE Estimates\",\n", + "# Plot density plot of ATE_estimates with ggplot\n", + "ggplot(data.frame(ATE_estimates), aes(x = ATE_estimates)) +\n", + " geom_density(fill = \"blue\", alpha = 0.5) +\n", + " geom_vline(aes(xintercept = ATE), color = \"red\", linetype = \"dashed\") +\n", + " labs(title = \"Density Plot of ATE Estimates\",\n", " x = \"ATE Estimates\",\n", - " y = \"Frequency\") +\n", - " theme_minimal()\n", - " " + " y = \"Density\") +\n", + " theme_minimal()" ] } ], From 4a8a81effca5697348081d54a7aa778decf39cc6 Mon Sep 17 00:00:00 2001 From: Sven Klaassen <47529404+SvenKlaassen@users.noreply.github.com> Date: Thu, 10 Apr 2025 13:21:29 +0200 Subject: [PATCH 052/140] update vscode user --- .devcontainer/Dockerfile.dev | 69 +++++++++++++++++---------------- .devcontainer/devcontainer.json | 2 +- 2 files changed, 37 insertions(+), 34 deletions(-) diff --git a/.devcontainer/Dockerfile.dev b/.devcontainer/Dockerfile.dev index abc4362b..16c447b1 100644 --- a/.devcontainer/Dockerfile.dev +++ b/.devcontainer/Dockerfile.dev @@ -3,11 +3,6 @@ FROM ubuntu:24.04 # Set non-interactive mode to avoid prompts ENV DEBIAN_FRONTEND=noninteractive -# Add a non-root user and create a workspace directory -RUN useradd -m -s /bin/bash vscode && \ - mkdir -p /workspace && \ - chown -R vscode:vscode /workspace - # Update package list and install dependencies RUN apt-get update && \ apt-get install -y \ @@ -19,13 +14,12 @@ RUN apt-get update && \ apt-transport-https \ ca-certificates \ git \ - cmake - -# Install locale packages -RUN apt-get update && \ - apt-get install -y locales && \ + cmake \ + locales && \ locale-gen en_US.UTF-8 && \ - update-locale LANG=en_US.UTF-8 + update-locale LANG=en_US.UTF-8 && \ + apt-get clean && \ + rm -rf /var/lib/apt/lists/* # Set environment variables for locale ENV LANG=en_US.UTF-8 @@ -36,43 +30,52 @@ ENV LC_ALL=en_US.UTF-8 RUN add-apt-repository ppa:deadsnakes/ppa && \ apt-get update && \ apt-get install -y python3.12 python3.12-venv python3.12-dev python3-pip python3-full && \ - apt-get clean && rm -rf /var/lib/apt/lists/* - -# Set Python 3.12 as the default for both python and python3 -RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.12 1 && \ - update-alternatives --install /usr/bin/python python /usr/bin/python3.12 1 + update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.12 1 && \ + update-alternatives --install /usr/bin/python python /usr/bin/python3.12 1 && \ + apt-get clean && \ + rm -rf /var/lib/apt/lists/* # Add R repository and install R RUN wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | tee /etc/apt/trusted.gpg.d/cran_ubuntu_key.asc && \ add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu noble-cran40/' && \ apt-get update && \ - apt-get install -y r-base r-base-dev zlib1g-dev libicu-dev pandoc make libcurl4-openssl-dev libssl-dev - -# Install Python packages -COPY requirements.txt /tmp/requirements.txt -RUN python -m venv /workspace/.venv && \ - /workspace/.venv/bin/python -m pip install --upgrade pip && \ - /workspace/.venv/bin/pip install -r /tmp/requirements.txt + apt-get install -y r-base r-base-dev zlib1g-dev libicu-dev pandoc make libcurl4-openssl-dev libssl-dev && \ + apt-get clean && \ + rm -rf /var/lib/apt/lists/* -# Set the virtual environment as the default Python environment -ENV PATH="/workspace/.venv/bin:$PATH" +# Create non-root user with explicit UID/GID matching common host values +ARG USERNAME=vscode +ARG USER_UID=1000 +ARG USER_GID=$USER_UID -# Install from main branch of DoubleML for Python -RUN pip uninstall -y DoubleML && \ - pip install git+https://github.com/DoubleML/doubleml-for-py.git@main#egg=DoubleML[rdd] +RUN groupadd --gid $USER_GID $USERNAME && \ + useradd --uid $USER_UID --gid $USER_GID -m -s /bin/bash $USERNAME && \ + mkdir -p /workspace && \ + chown $USERNAME:$USERNAME /workspace -# Create a directory for R user libraries and set the default R user library path -RUN mkdir -p /usr/local/lib/R/site-library +# Create a directory for R user libraries +RUN mkdir -p /usr/local/lib/R/site-library && \ + chown -R $USERNAME:$USERNAME /usr/local/lib/R/site-library ENV R_LIBS_USER=/usr/local/lib/R/site-library +# Switch to non-root user for remaining operations +USER $USERNAME + +# Install Python packages in the virtual environment +COPY --chown=$USERNAME:$USERNAME requirements.txt /tmp/requirements.txt +RUN python -m venv /home/$USERNAME/.venv && \ + /home/$USERNAME/.venv/bin/python -m pip install --upgrade pip && \ + /home/$USERNAME/.venv/bin/pip install --no-cache-dir -r /tmp/requirements.txt && \ + /home/$USERNAME/.venv/bin/pip install --no-cache-dir git+https://github.com/DoubleML/doubleml-for-py.git@main#egg=DoubleML[rdd] + +# Set the virtual environment as the default Python environment +ENV PATH="/home/$USERNAME/.venv/bin:$PATH" + # Install R packages and Jupyter kernel RUN Rscript -e "install.packages('remotes')" && \ Rscript -e "remotes::install_github('DoubleML/doubleml-for-r', dependencies = TRUE)" && \ Rscript -e "install.packages(c('ggplot2', 'IRkernel', 'xgboost', 'hdm', 'reshape2', 'gridExtra', 'igraph', 'mlr3filters', 'mlr3measures', 'did', dependencies=TRUE))" && \ Rscript -e "IRkernel::installspec()" -# ownership of all relevant directories for the non-root user -RUN chown -R vscode:vscode /workspace /workspace/.venv /usr/local/lib/R/site-library - # Set the working directory WORKDIR /workspace diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 9aff90ef..3cfbeb46 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -19,7 +19,7 @@ "charliermarsh.ruff" // Ruff extension ], "settings": { - "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python", + "python.defaultInterpreterPath": "/home/vscode/.venv/bin/python", "editor.formatOnSave": true, // Auto-format code when saving "editor.codeActionsOnSave": { "source.organizeImports": true // Auto-organize imports on save From db09aded55681c20dff60ce66af06c58052327af Mon Sep 17 00:00:00 2001 From: Sven Klaassen <47529404+SvenKlaassen@users.noreply.github.com> Date: Thu, 10 Apr 2025 13:36:55 +0200 Subject: [PATCH 053/140] update for existing USER_UID --- .devcontainer/Dockerfile.dev | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.devcontainer/Dockerfile.dev b/.devcontainer/Dockerfile.dev index 16c447b1..12c375aa 100644 --- a/.devcontainer/Dockerfile.dev +++ b/.devcontainer/Dockerfile.dev @@ -48,10 +48,10 @@ ARG USERNAME=vscode ARG USER_UID=1000 ARG USER_GID=$USER_UID -RUN groupadd --gid $USER_GID $USERNAME && \ - useradd --uid $USER_UID --gid $USER_GID -m -s /bin/bash $USERNAME && \ +RUN if ! getent group $USER_GID >/dev/null; then groupadd --gid $USER_GID $USERNAME; fi && \ + if ! id -u $USERNAME >/dev/null 2>&1; then useradd --uid $USER_UID --gid $USER_GID -m -s /bin/bash $USERNAME; fi && \ mkdir -p /workspace && \ - chown $USERNAME:$USERNAME /workspace + chown $USER_UID:$USER_GID /workspace # Create a directory for R user libraries RUN mkdir -p /usr/local/lib/R/site-library && \ From eb5e9d09eba8b4d91d5c3703635504f677140835 Mon Sep 17 00:00:00 2001 From: Sven Klaassen <47529404+SvenKlaassen@users.noreply.github.com> Date: Thu, 10 Apr 2025 13:41:51 +0200 Subject: [PATCH 054/140] fix vscode user --- .devcontainer/Dockerfile.dev | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/.devcontainer/Dockerfile.dev b/.devcontainer/Dockerfile.dev index 12c375aa..457e5402 100644 --- a/.devcontainer/Dockerfile.dev +++ b/.devcontainer/Dockerfile.dev @@ -48,10 +48,12 @@ ARG USERNAME=vscode ARG USER_UID=1000 ARG USER_GID=$USER_UID -RUN if ! getent group $USER_GID >/dev/null; then groupadd --gid $USER_GID $USERNAME; fi && \ - if ! id -u $USERNAME >/dev/null 2>&1; then useradd --uid $USER_UID --gid $USER_GID -m -s /bin/bash $USERNAME; fi && \ +RUN if ! id -u $USERNAME >/dev/null 2>&1; then \ + if ! getent group $USER_GID >/dev/null; then groupadd --gid $USER_GID $USERNAME; fi && \ + useradd --uid $USER_UID --gid $USER_GID -m -s /bin/bash $USERNAME; \ + fi && \ mkdir -p /workspace && \ - chown $USER_UID:$USER_GID /workspace + chown -R $USER_UID:$USER_GID /workspace # Create a directory for R user libraries RUN mkdir -p /usr/local/lib/R/site-library && \ From 3cb95c0886870db0b072dc7a520c7b04083582be Mon Sep 17 00:00:00 2001 From: Sven Klaassen <47529404+SvenKlaassen@users.noreply.github.com> Date: Thu, 10 Apr 2025 13:52:19 +0200 Subject: [PATCH 055/140] set user to ubuntu --- .devcontainer/Dockerfile.dev | 14 ++++---------- .devcontainer/devcontainer.json | 2 +- 2 files changed, 5 insertions(+), 11 deletions(-) diff --git a/.devcontainer/Dockerfile.dev b/.devcontainer/Dockerfile.dev index 457e5402..5b53392e 100644 --- a/.devcontainer/Dockerfile.dev +++ b/.devcontainer/Dockerfile.dev @@ -43,17 +43,11 @@ RUN wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | apt-get clean && \ rm -rf /var/lib/apt/lists/* -# Create non-root user with explicit UID/GID matching common host values -ARG USERNAME=vscode -ARG USER_UID=1000 -ARG USER_GID=$USER_UID +# Reuse existing 'ubuntu' user (UID 1000) +ARG USERNAME=ubuntu -RUN if ! id -u $USERNAME >/dev/null 2>&1; then \ - if ! getent group $USER_GID >/dev/null; then groupadd --gid $USER_GID $USERNAME; fi && \ - useradd --uid $USER_UID --gid $USER_GID -m -s /bin/bash $USERNAME; \ - fi && \ - mkdir -p /workspace && \ - chown -R $USER_UID:$USER_GID /workspace +RUN mkdir -p /workspace && \ + chown -R $USERNAME:$USERNAME /workspace # Create a directory for R user libraries RUN mkdir -p /usr/local/lib/R/site-library && \ diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 3cfbeb46..1455bb9f 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -46,6 +46,6 @@ "mounts": [ "source=${localWorkspaceFolder},target=/workspace,type=bind" // Mount your local workspace into the container ], - "remoteUser": "vscode", // Set the user inside the container + "remoteUser": "ubuntu", "postCreateCommand": "id && ls -la /workspace && echo 'Container is ready!'" } \ No newline at end of file From 9f269c36326cea990a2a385497a88c03f563db51 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Thu, 10 Apr 2025 13:22:46 +0000 Subject: [PATCH 056/140] update default interpreter path --- .devcontainer/devcontainer.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 1455bb9f..4fdd8d40 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -19,7 +19,7 @@ "charliermarsh.ruff" // Ruff extension ], "settings": { - "python.defaultInterpreterPath": "/home/vscode/.venv/bin/python", + "python.defaultInterpreterPath": "/home/ubuntu/.venv/bin/python", "editor.formatOnSave": true, // Auto-format code when saving "editor.codeActionsOnSave": { "source.organizeImports": true // Auto-organize imports on save From c4194e409400ca3311ee24447905ae68205e529a Mon Sep 17 00:00:00 2001 From: Sven Klaassen <47529404+SvenKlaassen@users.noreply.github.com> Date: Thu, 10 Apr 2025 15:24:13 +0200 Subject: [PATCH 057/140] update interpreter path --- .devcontainer/devcontainer.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 1455bb9f..4fdd8d40 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -19,7 +19,7 @@ "charliermarsh.ruff" // Ruff extension ], "settings": { - "python.defaultInterpreterPath": "/home/vscode/.venv/bin/python", + "python.defaultInterpreterPath": "/home/ubuntu/.venv/bin/python", "editor.formatOnSave": true, // Auto-format code when saving "editor.codeActionsOnSave": { "source.organizeImports": true // Auto-organize imports on save From 3169e17157a72e4acaa815981da897dc714fe607 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Thu, 10 Apr 2025 17:24:13 +0200 Subject: [PATCH 058/140] update mount configuration --- .devcontainer/devcontainer.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 4fdd8d40..a22627c5 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -44,7 +44,7 @@ } }, "mounts": [ - "source=${localWorkspaceFolder},target=/workspace,type=bind" // Mount your local workspace into the container + "source=${localWorkspaceFolder},target=/workspace,type=bind,consistency=cached,z" // Mount your local workspace into the container ], "remoteUser": "ubuntu", "postCreateCommand": "id && ls -la /workspace && echo 'Container is ready!'" From ec1ace09369776ca5782a9f90362443cee145667 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Thu, 10 Apr 2025 15:31:53 +0000 Subject: [PATCH 059/140] fix typo --- .devcontainer/devcontainer.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index a22627c5..8f299d30 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -44,7 +44,7 @@ } }, "mounts": [ - "source=${localWorkspaceFolder},target=/workspace,type=bind,consistency=cached,z" // Mount your local workspace into the container + "source=${localWorkspaceFolder},target=/workspace,type=bind,consistency=cached" // Mount your local workspace into the container ], "remoteUser": "ubuntu", "postCreateCommand": "id && ls -la /workspace && echo 'Container is ready!'" From 8b399e8618d6c1fe478b00c9218ecc025dc03e85 Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Thu, 10 Apr 2025 21:15:11 +0200 Subject: [PATCH 060/140] prep release notes --- doc/release/release.rst | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/doc/release/release.rst b/doc/release/release.rst index 5123ebb9..cf7e4af2 100644 --- a/doc/release/release.rst +++ b/doc/release/release.rst @@ -499,17 +499,25 @@ Release Notes .. tab-item:: R - .. dropdown:: DoubleML 1.0.1 + .. dropdown:: DoubleML 1.0.2 :class-title: sd-bg-primary sd-font-weight-bold :open: + - Add sample selection models, thanks to new contributor Petra Jasenakova `@petronelaj `_ + `R #213 `_ + - Maintenance including updates to GitHub workflows + `R #205 `_ + `R #220 `_ + + .. dropdown:: DoubleML 1.0.1 + :class-title: sd-bg-primary sd-font-weight-bold + - Maintenance (upcoming breaking changes from ``paradox`` package), thanks to new contributor Martin Binder `@mb706 `_ `R #195 `_ `R #198 `_ .. dropdown:: DoubleML 1.0.0 :class-title: sd-bg-primary sd-font-weight-bold - :open: - Update citation info to publication in Journal of Statistical Software, rename helper function and fix links and GH actions `R #191 `_ From 06fb8a444df12d0033f6ddd23f7bb39a643faf46 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 11 Apr 2025 06:32:36 +0000 Subject: [PATCH 061/140] update did setup --- doc/guide/models/did/did_setup.rst | 34 +++++++++++++++++++----------- 1 file changed, 22 insertions(+), 12 deletions(-) diff --git a/doc/guide/models/did/did_setup.rst b/doc/guide/models/did/did_setup.rst index 8acc4c28..c782f5df 100644 --- a/doc/guide/models/did/did_setup.rst +++ b/doc/guide/models/did/did_setup.rst @@ -29,30 +29,40 @@ The corresponding control groups, defined by an indicator :math:`C`, can be typi Let .. math:: - C_{i}^{nev}&:= 1\{G_i=\infty\} \quad \text{(never treated)}, \\ - - C_{i,t,\mathrm{g}}^{nyt}&:= 1\{G_i < \mathrm{g}\} \quad \text{(not yet treated)}. - + \begin{align} + C_{i}^{nev} &:= 1\{G_i=\infty\} \quad \text{(never treated)}, \\ + C_{i,t}^{nyt} &:= 1\{G_i > t\} \quad \text{(not yet treated)}. + \end{align} The corresponding identifying assumptions are: 1. **Irreversibility of Treatment:** - :math:`D_{i,1} = 0 \quad a.s.` - For all :math:`t=2,\dots,\mathcal{T}`, :math:`D_{i,t-1} = 1` implies :math:`D_{i,t} = 1 \quad a.s.` + :math:`D_{i,1} = 0 \quad a.s.` + For all :math:`t=2,\dots,\mathcal{T}`, :math:`D_{i,t-1} = 1` implies :math:`D_{i,t} = 1 \quad a.s.` 2. **Panel Data (Random Sampling):** - :math:`(Y_{i,1},\dots, Y_{i,\mathcal{T}}, X_i, D_{i,1}, \dots, D_{i,\mathcal{T}})_{i=1}^n` is independent and identically distributed. + :math:`(Y_{i,1},\dots, Y_{i,\mathcal{T}}, X_i, D_{i,1}, \dots, D_{i,\mathcal{T}})_{i=1}^n` is independent and identically distributed. 3. **Limited Treatment Anticipation:** There is a known :math:`\delta\ge 0` such that :math:`\mathbb{E}[Y_{i,t}(\mathrm{g})|X_i, G_i^{\mathrm{g}}=1] = \mathbb{E}[Y_{i,t}(0)|X_i, G_i^{\mathrm{g}}=1]\quad a.s.` for all :math:`\mathrm{g}\in\mathcal{G}, t\in\{1,\dots,\mathcal{T}\}` such that :math:`t< \mathrm{g}-\delta`. -4. **(Cond.) Parallel Trends:** - :math:`\mathbb{E}[Y_{i1}(0) - Y_{i0}(0)|X_i, D_i=1] = \mathbb{E}[Y_{i1}(0) - Y_{i0}(0)|X_i, D_i=0]\quad a.s.` +4. **Conditional Parallel Trends:** + Let :math:`\delta` be defined as in Assumption 3. + For each :math:`\mathrm{g}\in\mathcal{G}` and :math:`t\in\{2,\dots,\mathcal{T}\}` such that :math:`t\ge \mathrm{g}-\delta`: + + a. **Never Treated:** + :math:`\mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i, G_i^{\mathrm{g}}=1] = \mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i,C_{i}^{nev}=1] \quad a.s.` -5. **Overlap:** ´ - :math:`\exists\epsilon > 0`: :math:`P(D_i=1) > \epsilon` and :math:`P(D_i=1|X_i) \le 1-\epsilon\quad a.s.` + b. **Not Yet Treated:** + :math:`\mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i, G_i^{\mathrm{g}}=1] = \mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i,C_{i,t+\delta}^{nyt}=1] \quad a.s.` + +5. **Overlap:** + For each time period :math:`t=2,\dots,\mathcal{T}` and :math:`\mathrm{g}\in\mathcal{G}` there exists a :math:`\epsilon > 0` such that + :math:`P(G_i^{\mathrm{g}}=1) > \epsilon` and :math:`P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t}^{nyt}=1) < 1-\epsilon\quad a.s.` .. note:: For a detailed discussion of the assumptions see `Callaway and Sant'Anna (2021) `_. - Currently, the package automatically imposes "no-anticipation", e.g. :math:`\delta=0`. \ No newline at end of file + Currently, the package automatically imposes "no-anticipation", e.g. :math:`\delta=0`, but can manually be adjusted via the considered combinations. + +Under the assumptions above (either Assumption 4.a or 4.b), the target parameter :math:`ATT(\mathrm{g},t)` is identified see Theorem 1. `Callaway and Sant'Anna (2021) `_. \ No newline at end of file From 67bd650f7b2afc83478b9323cc79a876df3d83d1 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 11 Apr 2025 06:34:09 +0000 Subject: [PATCH 062/140] update nb to np.inf --- doc/examples/py_double_ml_panel.ipynb | 460 +++++++++---------- doc/examples/py_double_ml_panel_simple.ipynb | 220 ++++----- 2 files changed, 341 insertions(+), 339 deletions(-) diff --git a/doc/examples/py_double_ml_panel.ipynb b/doc/examples/py_double_ml_panel.ipynb index db6bcdbb..f75ec979 100644 --- a/doc/examples/py_double_ml_panel.ipynb +++ b/doc/examples/py_double_ml_panel.ipynb @@ -75,96 +75,96 @@ " \n", " 0\n", " 0\n", - " 214.177554\n", - " 214.177554\n", - " 215.868822\n", - " NaT\n", + " 196.628345\n", + " 196.628345\n", + " 196.469772\n", + " 2025-07-01\n", " 2025-01-01\n", - " -0.480522\n", - " 0.943682\n", - " -0.218748\n", - " 0.91184\n", - " 1.691268\n", - " Never Treated\n", + " -1.378363\n", + " -1.563339\n", + " -1.755421\n", + " -1.267233\n", + " -0.158573\n", + " 2025-07\n", " \n", " \n", " 1\n", " 0\n", - " 218.358132\n", - " 218.358132\n", - " 218.343452\n", - " NaT\n", + " 184.014741\n", + " 184.014741\n", + " 184.639146\n", + " 2025-07-01\n", " 2025-02-01\n", - " -0.480522\n", - " 0.943682\n", - " -0.218748\n", - " 0.91184\n", - " -0.014681\n", - " Never Treated\n", + " -1.378363\n", + " -1.563339\n", + " -1.755421\n", + " -1.267233\n", + " 0.624406\n", + " 2025-07\n", " \n", " \n", " 2\n", " 0\n", - " 220.434292\n", - " 220.434292\n", - " 219.332694\n", - " NaT\n", + " 171.033521\n", + " 171.033521\n", + " 173.094369\n", + " 2025-07-01\n", " 2025-03-01\n", - " -0.480522\n", - " 0.943682\n", - " -0.218748\n", - " 0.91184\n", - " -1.101598\n", - " Never Treated\n", + " -1.378363\n", + " -1.563339\n", + " -1.755421\n", + " -1.267233\n", + " 2.060849\n", + " 2025-07\n", " \n", " \n", " 3\n", " 0\n", - " 221.413949\n", - " 221.413949\n", - " 223.756078\n", - " NaT\n", + " 160.310762\n", + " 160.310762\n", + " 160.414796\n", + " 2025-07-01\n", " 2025-04-01\n", - " -0.480522\n", - " 0.943682\n", - " -0.218748\n", - " 0.91184\n", - " 2.342129\n", - " Never Treated\n", + " -1.378363\n", + " -1.563339\n", + " -1.755421\n", + " -1.267233\n", + " 0.104035\n", + " 2025-07\n", " \n", " \n", " 4\n", " 0\n", - " 224.374383\n", - " 224.374383\n", - " 223.932041\n", - " NaT\n", + " 150.447513\n", + " 150.447513\n", + " 151.102154\n", + " 2025-07-01\n", " 2025-05-01\n", - " -0.480522\n", - " 0.943682\n", - " -0.218748\n", - " 0.91184\n", - " -0.442342\n", - " Never Treated\n", + " -1.378363\n", + " -1.563339\n", + " -1.755421\n", + " -1.267233\n", + " 0.654641\n", + " 2025-07\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id y y0 y1 d t Z1 Z2 \\\n", - "0 0 214.177554 214.177554 215.868822 NaT 2025-01-01 -0.480522 0.943682 \n", - "1 0 218.358132 218.358132 218.343452 NaT 2025-02-01 -0.480522 0.943682 \n", - "2 0 220.434292 220.434292 219.332694 NaT 2025-03-01 -0.480522 0.943682 \n", - "3 0 221.413949 221.413949 223.756078 NaT 2025-04-01 -0.480522 0.943682 \n", - "4 0 224.374383 224.374383 223.932041 NaT 2025-05-01 -0.480522 0.943682 \n", + " id y y0 y1 d t Z1 \\\n", + "0 0 196.628345 196.628345 196.469772 2025-07-01 2025-01-01 -1.378363 \n", + "1 0 184.014741 184.014741 184.639146 2025-07-01 2025-02-01 -1.378363 \n", + "2 0 171.033521 171.033521 173.094369 2025-07-01 2025-03-01 -1.378363 \n", + "3 0 160.310762 160.310762 160.414796 2025-07-01 2025-04-01 -1.378363 \n", + "4 0 150.447513 150.447513 151.102154 2025-07-01 2025-05-01 -1.378363 \n", "\n", - " Z3 Z4 ite First Treated \n", - "0 -0.218748 0.91184 1.691268 Never Treated \n", - "1 -0.218748 0.91184 -0.014681 Never Treated \n", - "2 -0.218748 0.91184 -1.101598 Never Treated \n", - "3 -0.218748 0.91184 2.342129 Never Treated \n", - "4 -0.218748 0.91184 -0.442342 Never Treated " + " Z2 Z3 Z4 ite First Treated \n", + "0 -1.563339 -1.755421 -1.267233 -0.158573 2025-07 \n", + "1 -1.563339 -1.755421 -1.267233 0.624406 2025-07 \n", + "2 -1.563339 -1.755421 -1.267233 2.060849 2025-07 \n", + "3 -1.563339 -1.755421 -1.267233 0.104035 2025-07 \n", + "4 -1.563339 -1.755421 -1.267233 0.654641 2025-07 " ] }, "execution_count": 2, @@ -219,56 +219,56 @@ " 0\n", " 2025-01-01\n", " 2025-05\n", - " 209.300753\n", - " 201.766880\n", - " 217.039317\n", - " -0.026614\n", - " -2.304513\n", - " 2.419752\n", + " 209.232972\n", + " 202.011675\n", + " 216.526857\n", + " 0.054854\n", + " -2.278644\n", + " 2.420543\n", " \n", " \n", " 1\n", " 2025-01-01\n", " 2025-06\n", - " 210.202290\n", - " 202.774666\n", - " 217.882044\n", - " -0.001342\n", - " -2.241443\n", - " 2.269269\n", + " 210.451564\n", + " 203.071081\n", + " 218.424421\n", + " -0.073761\n", + " -2.409091\n", + " 2.299715\n", " \n", " \n", " 2\n", " 2025-01-01\n", " 2025-07\n", - " 212.076382\n", - " 204.560539\n", - " 219.492164\n", - " 0.069318\n", - " -2.108613\n", - " 2.353309\n", + " 211.487736\n", + " 204.039630\n", + " 219.377341\n", + " 0.000180\n", + " -2.362993\n", + " 2.340590\n", " \n", " \n", " 3\n", " 2025-01-01\n", " 2025-08\n", - " 213.364195\n", - " 205.573922\n", - " 221.975874\n", - " -0.041680\n", - " -2.449703\n", - " 2.358228\n", + " 213.457445\n", + " 204.544638\n", + " 222.339047\n", + " -0.054813\n", + " -2.368209\n", + " 2.522341\n", " \n", " \n", " 4\n", " 2025-01-01\n", " Never Treated\n", - " 215.096598\n", - " 206.449830\n", - " 225.569888\n", - " -0.050251\n", - " -2.313271\n", - " 2.264483\n", + " 215.221319\n", + " 206.282226\n", + " 225.772989\n", + " 0.008458\n", + " -2.368282\n", + " 2.319752\n", " \n", " \n", "\n", @@ -276,18 +276,18 @@ ], "text/plain": [ " t First Treated y_mean y_lower_quantile y_upper_quantile \\\n", - "0 2025-01-01 2025-05 209.300753 201.766880 217.039317 \n", - "1 2025-01-01 2025-06 210.202290 202.774666 217.882044 \n", - "2 2025-01-01 2025-07 212.076382 204.560539 219.492164 \n", - "3 2025-01-01 2025-08 213.364195 205.573922 221.975874 \n", - "4 2025-01-01 Never Treated 215.096598 206.449830 225.569888 \n", + "0 2025-01-01 2025-05 209.232972 202.011675 216.526857 \n", + "1 2025-01-01 2025-06 210.451564 203.071081 218.424421 \n", + "2 2025-01-01 2025-07 211.487736 204.039630 219.377341 \n", + "3 2025-01-01 2025-08 213.457445 204.544638 222.339047 \n", + "4 2025-01-01 Never Treated 215.221319 206.282226 225.772989 \n", "\n", " ite_mean ite_lower_quantile ite_upper_quantile \n", - "0 -0.026614 -2.304513 2.419752 \n", - "1 -0.001342 -2.241443 2.269269 \n", - "2 0.069318 -2.108613 2.353309 \n", - "3 -0.041680 -2.449703 2.358228 \n", - "4 -0.050251 -2.313271 2.264483 " + "0 0.054854 -2.278644 2.420543 \n", + "1 -0.073761 -2.409091 2.299715 \n", + "2 0.000180 -2.362993 2.340590 \n", + "3 -0.054813 -2.368209 2.522341 \n", + "4 0.008458 -2.368282 2.319752 " ] }, "execution_count": 3, @@ -321,7 +321,7 @@ "outputs": [ { "data": { - "image/png": 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", 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UlBSp/cOHD6UFivPrO7+49lyaN2+e77loCy2GziU/jo6O6N69OwDdRdA3bdqEtLQ0VK5cGcHBwS903i+D9np88cUX+V6PJUuWANC9HqtXr4abmxu+//57+Pn5wcXFBR07dsS8efPw8OHDl5rzs7nfuHEj39wbNmyol3thVaxYscD4vXv3dOLVqlVDu3btcO/ePWzfvl2KL168GMDTxc8LI+8TCvPL/cyZMxBPZuQjPDy8wP7y+zzcv38fAODi4gJ7e3uDbSpXrqzTtqQ87/pmZmbmu/B+Xnmv1YMHDwp17Pj4eABPiqx5mdp346tWvnx5g3FbW9sCt2sLT3kXjn/Ra0NERC+H/v+GJCIiegHaJyVlZWXpFT2Apz8kQ0ND8fXXX8PMzAwA0KJFC1SuXBlRUVE4fvw4mjRpgvj4eOzevRuWlpbo06ePTj8ajQYAEBgYiNq1axeYU6NGjfRiVlZW+bY/e/Yshg4dCjMzM8ycORNdunRB+fLlYW1tDZlMhhUrVmDo0KEGZzgUJL8CgPZcevToARsbmwL7cHFxKdIx//Of/2D9+vXYsGED5s2bBysrK+kH7ZAhQ3RyelnnnR/teecXb9asmVSAyE/emSLNmzdHdHQ0du3ahSNHjuD48ePYt28f9uzZgy+//BLbtm3DW2+9VSK550ebu7u7u15h9ll5ixclxdDfZvTo0di3bx8WL16MHj164O7du9ixYwdsbW0xePDgQvddoUIFODs7IzExEWfOnEGzZs1eKNeCPoMvS35jrigKM/4rVqwIJycnPH78GCdPnpRm+OUnNzcXf//9NwDozbQzpe9GY5DLC/7/6M/bnteLXhsiIno5WJgiIqISExsbKz22/tGjRzh27Fi+bWNiYrB371506tQJwJOizeDBg/HFF1/gl19+QZMmTbB27Vrk5uaiV69ecHR01Nnf29sbANC0aVMsWrSoRM9j8+bNEEJg1KhR0v89z8vQ7XQuLi5QKpXIzs7G7du3UbNmTb020dHRBo/n7e2N69ev47PPPkP9+vVfOP+8WrVqhUqVKuHmzZv47bff0KhRI4SHh8PMzEzvNqninHdBLCwsAACpqakGt9++fdtgXPu37dq1Kz799NMiHdPKygo9evRAjx49ADyZ9fD5559jxYoVGDJkSL7HLCna3F1cXPRmuJSEW7duGfxBrR1bXl5eetvat2+PqlWr4vDhw7h06RLWr18PtVqN9957L98ZSYbI5XJ06tQJa9aswdq1azFmzJjinkaBPD09ATz5DklJSTGYo3bmi7atlrm5OVQqFVJTUw3eqvW8v/+tW7cMxrXX19LSslDFYblcji5dumD16tVYs2YNPv300wJnpu3YsQPJycnSNc7LlL4bSzteGyIi08Rb+YiIqMSEhoZCrVajUaNG0u08hl7aood2dpXW4MGDIZfLsWnTJmRkZOR7qwrwZF0R4MkPury3apSExMREAICPj4/etqysLGzdulUvbm5ujqCgIADA+vXrDfb766+/Goxrz0W77lNJkslk0q1AP//8s3RL39tvv633o744510Qbf83b95ETk6O3nbtWlLP0l4PbaHsRbi6umLWrFkAgDt37uDx48cv1J+22JZ3vaa8GjRogDJlyuDy5cu4dOnSCx3LkDVr1hiMa9cf0q6Pk5dMJsOoUaMAAN9//z1WrlwJABg5cmSRjz9x4kQoFAqcPXtWup2ypHl5eUkz5QwV94QQUrxVq1Y627Rj7sqVK3r7Xbx4EXfv3i3w2GvXrjUY117fZs2aGVz3zJDx48dDoVAgMjISCxcuzLddcnKy9J3Yu3dvg7METeW7sbTjtSEiMk0sTBERUYnRFj3yW6BZS7vA8O+//66zjoeXlxfatm2LlJQUTJ48Gf/88w/Kly+P1q1b6/VRp04dhISE4O7du+jevbvB2Ujp6elYt25dodd40apRowYAYNWqVTqzfbKysjB8+PB8Z1V8/PHHAICFCxfixIkTOtsWLFiAkydPGtxv/PjxcHR0xPfff4+5c+caLOLcunUr3x/NzzN48GCYmZnh0KFDWLFiBQDDi54X97zz4+PjA19fXyQlJWHmzJk62w4fPoz//e9/Bvfr2rUrGjRogFOnTuH99983uNbL48ePsWzZMqlAdPv2baxcudLg+lc7d+4EADg5ORVphpAh2hlJ169fh0ql0ttubm6OL7/8EkIIdOvWDUePHtVro1ar8eeff+qNkcLYtm0bNmzYoBPbsmULtm7dCoVCIRWgnjV48GA4ODjg559/Rnx8PFq1amVwVt/z1KxZE/PmzQPwpLA1efJkJCUl6bVTq9XFOj8t7Uy5b775BhcuXJDiQgh8++23OH/+PBwdHfHhhx/q7NemTRsAwFdffYXs7GwpHh0djUGDBj230Hn27FmpkKl19OhRaU2usWPHFvocatWqJY37cePGYdasWXoFzatXr6JNmza4ceMGKlSokO8sHlP5bizteG2IiEzUK3v+HxERvdYOHz4sAAilUikSExOf275u3boCgJgzZ45OfMOGDdIjxgGI//3vf/n2kZKSIt566y0BQFhYWIgGDRqIXr16iZ49e4oGDRoICwsLAUBcuXJF2kf7SPTg4OB8+338+LHw8fERAISLi4t49913RUhIiHBzcxN2dnZi9OjRAoAYNGiQ3r4fffSRACDMzMxEy5YtRd++fUWtWrWEmZmZGDt2bL6PnD9y5IgoU6aMACDc3NxE69atRf/+/UXnzp1F5cqVBQDRqFGj517X/GgfgQ5AuLq6ipycnBI774Ku6datW4VMJhMARGBgoOjZs6eoV6+ekMlk4n//+5+U07Pu378vAgMDBQBhY2MjmjRpIvr06SO6d+8uAgMDhZmZmQAgPUb+3LlzAoAwNzeXxkGvXr1EnTp1BAAhk8nEypUrC329tI+q//LLL/W21a9fXwAQ1apVE/379xf/+c9/xGeffabTZvz48dK5+fn5ia5du4o+ffqIli1bCkdHRwFALF26tND5aP8uY8aMEQBEgwYNRL9+/USjRo2k43z//fcF9qHdF4DYunVroY9tSGhoqLC1tZU+e0FBQaJnz56iX79+om3btsLZ2VnaNnnyZJ19C/MZ1Gg04r333hMAhEKhEG+99Zbo27evqFatmgAgrKysxO7du/X2u3nzpnR9y5cvL0JCQkSLFi2ElZWVaNOmjWjSpIkAIA4dOqSzX3BwsAAgPv74YyGXy4Wfn5/o27evCA4OFnK5XAAQo0ePLta1WrBggfRd5OLiIrp06SL69OkjGjVqJH026tWrJ+7evVtgP6bw3VgUt27dknK9detWvu0GDRokAIhffvlFJ17QZ7Cg/bS0f9Nn/9bFuTZERPRysTBFREQlQvsjskePHoVqP3/+fAFA1KhRQyeelZUl/aiVyWTi5s2bBfajVqvF+vXrRceOHUXZsmWFubm5cHFxEbVq1RLvv/++2LZtm04RprA/vhISEsTw4cNF5cqVhVKpFOXKlRMDBgwQ169fF7/88ku+hSmNRiN+/PFHUbduXWFpaSkcHR1Fu3btxF9//SVWr14tAIi+ffsaPOaDBw/EF198IerWrSvs7OyEhYWF8PLyEk2aNBFffvmluHjxYoE5F2Tr1q3Sj8Rx48aV6Hk/75ru2rVLNG3aVFhbWwsbGxvRuHFjsXHjRiGEyLcwJcSTsbBs2TLRqlUr4eLiIhQKhXBzcxOBgYFixIgRYt++fVLblJQUMX/+fNGtWzfh6+srbG1thY2NjahataoYOHCgOHPmTJGuV0E/im/fvi369esnPDw8hEKhEACEj4+PXrtjx46J/v37Cx8fH6FUKoWdnZ2oWrWqePfdd8XKlSsLVcDV0hambt26JTZt2iSCgoKkc2zevLnYuXPnc/vYs2ePACC8vb1Fbm5uoY+dn0ePHomZM2eK1q1bC3d3d2FhYSGsrKyEt7e36Nixo/j+++9FTEyM3n5FKYCsX79eKuaZm5sLb29vMXjwYHH16tV897l8+bLo3r27cHJyEkqlUlSrVk18++23IicnJ99iRd74wYMHxVtvvSUcHByElZWVqF+/vggNDS3q5dERHR0txo8fL2rXri0cHByEhYWFKFeunHjnnXfEunXrhFqtfm4fpvLdWFimWpgSoujXhoiIXi6ZECX0aB0iIiIq0JAhQ/DLL79g7ty5GDdunLHToTfMgAEDsG7dOkybNg2TJk0ydjompWXLljhy5AgOHTpkcJ0uIiIienm4xhQREVEJunTpEtLT03ViGo0GP/74I0JDQ2FpaYm+ffsaKTt6U0VGRmLjxo2wtbXF0KFDjZ0OERERkaRwjxUhIiKiQpk9ezY2bdqEOnXqwNPTE+np6bh8+TKio6NhZmaGJUuWwMPDw9hp0hvigw8+QHp6Ovbs2YPc3Fx8/vnncHZ2NnZaRERERBIWpoiIiEpQ7969kZKSgrNnz+L8+fPIzc2Fm5sbevfujTFjxqBx48bGTpHeID/99BPkcjm8vb3x6aefYsKECcZOiYiIiEgH15giIiIiIiIiIiKj4BpTRERERERERERkFCxMERERERERERGRUXCNKTx5WlJMTAzs7Owgk8mMnQ4RERERERERUaklhEBqairKlSsHubzgOVEsTAGIiYmBt7e3sdMgIiIiIiIiInpt3L17F15eXgW2YWEKgJ2dHYAnF8ze3t7I2bwYjUaDhIQEuLq6PrcqSfQ8HE9kCMcFlSSOJyoujh0qSRxPVFwcO1SSXqfxlJKSAm9vb6neUhAWpgDp9j17e/vXojCVlZUFe3v7Uj+Qyfg4nsgQjgsqSRxPVFwcO1SSOJ6ouDh2qCS9juOpMMslvR5nSkREREREREREpQ4LU0REREREREREZBQsTBERERERERERkVGwMEVEREREREREREbBwhQRERERERERERkFn8pXDGq1Gjk5OcZOwyCNRgOVSoXMzMzXZhV/ejEWFhYwMzMzdhpEREREREREeliYKgIhBO7fv4/ExERjp5IvIQSEEEhMTCzUYxnpzeDs7AxPT0+OCSIiIiIiIjIpLEwVgbYo5e7uDhsbG5OckSSEgEajgVwuZxGCoNFokJ6ejri4OACAl5eXkTMiIiIiIiIieoqFqUJSq9VSUcrNzc3Y6eSLhSl6lo2NDQAgLi4OHh4evK2PiIiIiIiITIbpTfkxUdo1pbQ/8olKE+24NdW10YiIiIiIiOjNxMJUEZni7XtEz8NxS0RERERERKaIv1aJiIiIiIiIiMgoWJh6DR0+fBhyuRyhoaHGTuWNFR0dDZlMhqlTpxo7FSIiIiIiIiKTxcJUKXP48GHIZLJ8XydOnHhpxz5//jymTp2K6OjoF84z76tChQovLef8hIaGYv78+a/8uERERERERET0FJ/KV0r17dsXHTt21ItXqVIFjo6OyMjIgIWFRYke8/z58/jqq6/QsmXL5xaTatSogTVr1ujEVqxYgfDwcMybNw9lypSR4ra2tiWaZ2GEhoYiOjoaY8aMeeXHJiIiIiIiIqInWJgqperWrYsBAwboxYUQ0Gg0sLS0hEwmK7APIQTS09NfSmGobNmyevkdOHAA4eHhePfdd59b2EpNTYWdnV2J50VEREREREREpsOkb+WbMWMGZDLZc2e1bN68GdWrV4elpSX8/f2xe/fuV5OgiTK0xpT21rrQ0FAsXrwYNWvWhKWlJebMmQMAuHTpEnr27AlPT08olUq4u7ujVatW2LVrFwBg6tSpeP/99wEArVq1km7DGzx48Avlmnctpo0bN6JevXqwsrLCqFGjpDYHDhxAu3bt4OjoCEtLSwQEBGDZsmV6ff3xxx/o3bs3KlWqBCsrKzg6OqJdu3Y4cuSITrsKFSrgyJEjuH37ts4thYcPH5baXL9+He+99x48PDxgYWGBChUqYPz48UhPT9c77tGjR9G0aVNYWVmhbNmyGDlyJNLS0l7ouhARERERERG9CUx2xtTp06exfPlyBAQEFNju+PHj6Nu3L6ZPn47OnTtj/fr1ePfdd/H333+jVq1aryjbVy8jIwMPHz7UiSmVyufOfpo/fz4ePXqEDz/8EO7u7vD29sajR4/QunVrAMB///tf+Pj44OHDhzhz5gxOnjyJTp06oXv37oiNjcWKFSswefJk1KhRAwBQuXLlEjmf7du3Y+HChRg2bBj++9//wt7eHsCT2//++9//onHjxpgyZQpsbGywf/9+DBs2DDdu3MDs2bOlPkJDQ5GYmIiBAwfCy8sL9+/fx8qVK/HWW2/h0KFDaN68uXQNJk2ahIcPH2LevHnS/tpzOnv2LFq3bg1HR0cMHToUnp6euHDhAhYuXIhjx47hyJEjMDc3BwCcPHkSbdq0gZ2dHT777DM4Ojpiw4YNGDhwYIlcFyIiIiIiIqLXmjBBqampwtfXV+zfv18EBweL0aNH59u2V69eolOnTjqxRo0aiaFDhxb6eMnJyQKASE5OzrdNRkaGuHDhgsjIyCh0vy/DoUOHBACDr969ewuNRiMOHDggAIhffvlFbz8nJyfx4MEDnT7DwsIEALFx48YCj/3LL78IAOLQoUPFyn3QoEECgLh165YUu3XrlgAgFAqFuHz5sk77mJgYoVQqRd++ffX6+vjjj4VcLhc3btyQYmlpaXrt4uLihIuLi+jQoYNOPDg4WPj4+BjMMyAgQFSrVk2kpKToxH/77Te96xoUFCTMzc3FtWvXpFh2drZo0KCBACC+/PJLg8d41Yo7ftVqtYiNjRVqtfolZUalEccFlSSOJyoujh0qSRxPVFwcO1SSXqfxVJg6i5ZJzpgaMWIEOnXqhDZt2uDbb78tsG1ERATGjRunE3v77bexffv2fPfJzs5Gdna29D4lJQUAoNFooNFoDO6j0WgghJBexqI99ocffoiePXvqbHN3d9drq22v/ed7770HV1dXnXPQzk7as2cP3n77bel9fsd+0WtgKK9OnTqhevXqOv1u3rwZ2dnZGDJkCBISEnT66Ny5MxYuXIj9+/fjo48+AgBYW1tL+6elpSE7OxtyuRyNGjXCiRMnDOb8bCwyMhIXL17E1KlTkZWVhaysLGlb06ZNYWNjgz/++AODBg1CfHw8IiIi0KNHD/j6+kp9mZubY8yYMejfv7/Rx4uWNo+Cxrgh2nFflH3o9cdxQSWJ44mKi2OHShLHExUXxw6VJI1GA7lc/lqMp6Kcg8kVpjZs2IC///4bp0+fLlT7uLg4lC1bVidWtmxZxMXF5bvP9OnT8dVXX+nFExISdAoRealUqmL9sC9p2mNXqVJFuv3u2e15iz7a9tp/+vr66uXfvHlzvPfeewgNDcW6detQv359vPXWW+jVqxdq1qwptdP2W9xrYGj/vOfzbJ9XrlwBALRt2zbfPuPi4qT9bty4gS+++AJ//PEHkpKSdNrJZDKd/vPmktfly5cBPFlTa+rUqQUe899//wUAVK1aVa+f6tWrS8cxhS8V7bhITEyUbkMs7H7JyckQQkAuN+kl6egV4rigksTxRMXFsUMlieOJiotjh0qCmZkZhLkS2Ro5ErPNkJ2SBaVMA1luNtRqtbHTK5bU1NRCtzWpwtTdu3cxevRo7N+/H5aWli/tOJMmTdKZZZWSkgJvb2+4urrmO1soMzMTiYmJkMvlRv3C0R5bJpPlm4f2aXx522j/aWNjY3C/VatWYfz48dizZw+OHj2KefPmYfr06Zg3bx5Gjhyp029xr4Gh/QvKS1s8WrVqFTw8PAz2WalSJcjlcqSlpaFVq1ZIT0/H6NGj4e/vDzs7O8jlcsyYMQN//vmnTv95czFk3LhxaN++vcFtTk5OOudg6G9RmL/TqySXyyGTyeDs7AwrK6tC76fRaCCTyeDq6moS50GmgeOCShLHExUXxw6VJI4nKi6OHSoJjzJU+PHEbfxxLR7JGdlwsFaiXTU3fNTYB27WhZ9YYEqKUtMxqcLU2bNnER8fj7p160oxtVqNv/76C4sWLUJ2djbMzMx09nF3d8eDBw90Yg8ePNC7rS0vpVIJpVKpFy+o4KL9Ya99GUveopOhPPLeNpa3zfP2AwB/f3/4+/tjwoQJSEpKQqNGjTBp0iSMHDlSp8DyotegsHlVrVoVAODq6lrgrCkA+PPPPxETE4Off/5Zenqg1hdffKFzrGePa+iYCoXiucesVKkSAODatWt6/Whnexl7vGhp8yhOUbG4+9HrjeOCShLHExUXxw6VJI4nKi6OHXoRadm5WHHiNrb/E4fsXA1iU3IgN1Ng+z9xkAH4uHkl2CpNqnRTKEX5PJjUJ+ett95CZGQkzp8/L73q16+P/v374/z583pFKQAICgrCwYMHdWL79+9HUFDQq0q71EtMTNS73czR0REVK1ZERkaGdHuj9ol/iYmJrySvXr16QalU4ssvv0RmZqbe9uTkZGmtMO3YeHY9pz/++AMnT57U29fW1haPHz/Wa1+nTh3UqlULy5Ytw82bN/X2y83Nlc6/bNmyaNy4McLCwhAVFSW1ycnJ0XnaHxEREREREZEhqdm52B+VgCyVGrcfZyBDpcGdpExohMAfUQlIzc41doovnUmV3ezs7FCrVi2dmI2NDVxcXKT4wIED4enpienTpwMARo8ejeDgYMydOxedOnXChg0bcObMGaxYseKV519arV69GvPmzUO3bt1QpUoVmJub48iRI9i3bx969eol3frVoEEDyOVyfPfdd3j8+DFsbGxQsWJFNGrU6KXk5eXlhaVLl+KDDz5AjRo18N5778HHxwcJCQmIjIzE9u3bcfnyZVSoUAHNmjWDu7s7PvnkE0RHR8PLywvnz5/HmjVr4O/vj8jISJ2+GzdujN9//x0jR45EkyZNYGZmhtatW8PNzQ1r1qxB69atERAQgCFDhsDPzw8ZGRn4999/8dtvv2H69OkYPHgwAOD7779Hy5Yt0bRpU4wYMQKOjo7YsGEDcnNf/y8PIiIiIiIiejFJmSokpOXgblImNP8/byJLpUF8ag7kMhmSs1TwsH95Sx2ZApMqTBXGnTt3dKaENWnSBOvXr8fnn3+OyZMnw9fXF9u3b9crcFH+WrZsiXPnzuH3339HbGwszMzMULFiRcyZM0daXwoAypcvj59//hkzZ87EsGHDoFKpMGjQoJdWmAKA999/H1WrVsWcOXOwfPlyJCUloUyZMqhWrRq++eYb6ZZNR0dH7Nu3DxMmTMAPP/yA3Nxc1KtXD7t378ZPP/2kV5gaO3Ysbt68iS1btmDZsmXQaDQ4dOgQ3NzcEBgYiHPnzmH69OnYsWMHli1bBjs7O1SoUAGDBw/GW2+9JfUTFBSE/fv3Y+LEiZgxYwYcHBzQo0cPDBs2DP7+/i/tuhAREREREVHpZ2luhseZKqkoBQBW5nK42lrAxsIMDpalc42popAJU3iWvZGlpKTAwcEBycnJBS5+fv36dfj6+hZp8ehXTfsUOO2aWERA8cevRqNBfHw83NzceM88STguqCRxPFFxcexQSeJ4ouLi2KEX8ef1h4hNycLx6ESsOnMPAGClkKG8kzXkchm61XIvtWtMFabOolX6zo6IiIiIiIiIqBT7/fIDfLM/Cu52SkxoVQUAsC8qHjZmMtgpFWhXzRUfNfYplUWponr9z5CIiIiIiIiIyERsOh+D2Yf/BQDEpGRh1qF/MaZFJUxpUxWJ6VlwtrGEvaXijShKASxMERERERERERG9dEII/HL6LpYej9aJN6ngjOBKLgAEzHNS4WJn/0bdGsrCFBERERERERHRSySEwA9Hb2HN2Xs68UH1vTGiaQXIZDJoNAJqtdpIGRoPC1NERERERERERC+JRiMw89C/+C0yVic+omlFDG7gbaSsTAcLU0REREREREREL0GuWoOpf0Rh37V4nfiEVlXQs3Y5I2VlWliYIiIiIiIiIiIqYTm5GkzcfQXhNx9JMblMhi/bVUXHGmWNmJlpYWGKiIiIiIiIiKgEZeTk4tOdl3H6bpIUMzeTY1qH6mhZpYzxEjNBLEwREREREREREZWQlCwVxmy/hMi4FClmqZBjThc/NPJxMmJmpomFKSIiIiIiIiKiEpCYkYNR2/5BVEKaFLO1UGDBu7UQUM7eiJmZLhamiIiIiIiIiIheUFxKFkb8Fok7SZlSzMnKHIu6+6Oqq60RMzNtLEwREREREREREb2AO48zMHxrJB6kZUsxN1slFnf3RwVnayNmZvpYmCIiIiIiIiIiKqbrCWkYuS0SiRkqKeblYIklIQHwsLc0Ymalg9zYCZDxnT59GiNHjoSfnx9sbGxQvnx59OrVC1FRUXptr1y5gvbt28PW1hbOzs547733kJCQoNPm6tWrmDBhAgIDA2FnZwcPDw906tQJZ86c0etv6tSpkMlkei9Ly8J/eDUaDWbNmoWKFSvC0tISAQEB+PXXX/XaDR482OCxqlevXuhjEREREREREWn9E5uCoVsu6hSlKrvY4MdetVmUKiTOmCLMnDkTx44dQ8+ePREQEIC4uDgsWrQIdevWxYkTJ1CrVi0AwL1799CiRQs4ODhg2rRpSEtLw5w5cxAZGYlTp07BwsICALBy5Ur89NNPCAkJwfDhw5GcnIzly5ejcePG2Lt3L9q0aaOXw9KlS2Fr+/SeWzMzs0LnP2XKFMyYMQMffvghGjRogLCwMPTr1w8ymQx9+vTRaatUKrFy5UqdmIODQ6GPRURERERERAQAZ+4mYdyOS8hUqaWYX1k7LOxWC/aW5kbMrHRhYYowbtw4rF+/XiosAUDv3r3h7++PGTNmYO3atQCAadOmIT09HWfPnkX58uUBAA0bNkTbtm0RGhqKjz76CADQt29fTJ06VafQNGTIENSoUQNTp041WJjq0aMHypQpU+Tc79+/j7lz52LEiBFYtGgRAOCDDz5AcHAwxo8fj549e+oUuRQKBQYMGFDk4xARERERERFp/XXjESbtvoIctUaK1fVywLx3/GBtwVJLUfBWPiNLy85FbEoWrjxIRWxKFtKyc195Dk2aNNEpSgGAr68v/Pz8cOXKFSm2detWdO7cWSpKAUCbNm1QtWpVbNq0SYrVq1dPpygFAC4uLmjevLlOf3kJIZCSkgIhRJFyDwsLg0qlwvDhw6WYTCbDsGHDcO/ePUREROjto1arkZKSUqTjEBEREREREQHAvmvxGP/7ZZ2iVNOKzlj4bi0WpYqBhSkjSkjLxoLwm+i79iwG/noOfdeexcLwm0jIs4q/sQgh8ODBA2kW0/379xEfH4/69evrtW3YsCHOnTv33D7j4uLynRVVqVIlODg4wM7ODgMGDMCDBw8Klee5c+dgY2ODGjVq6OWk3Z5XRkYG7O3t4eDgAGdnZ4wYMQJpaWmFOhYRERERERG92X6LjMUXe65Ck2dSRduqrpjTuSaUisIvSUNPsZT3AtKyc/Hvw/Ri7etkZY6fT93FlosxUiwjR411f99Hjlrg/QbeeJypKqAHw4QQqORsBXsri+c3LsC6detw//59fP311wCA2NhYAICHh4deWw8PDyQmJiI7OxtKpdJgf+Hh4YiIiMDnn3+uE3dycsLIkSMRFBQEpVKJ8PBwLF68GKdOncKZM2dgb29fYJ6xsbEoW7YsZDKZXk4AEBMToxObMGEC6tatC41Gg71792LJkiW4cOECDh8+DIWCHwciIiIiIiIybM3Zu1gYfksn1rWWOya39oVcLstnL3oe/hJ/Af8+TMeHmy8UeT87pQIzO9fAihO3kWrg1r3lEbfRqaYbPvv9isHtz7MsxB91vYpfmLp69SpGjBiBoKAgDBo0CACQmZkJAAYLT9on6GVmZhrcHh8fj379+qFixYqYMGGCzrbRo0frvA8JCUHDhg3Rv39/LFmyBBMnTiww1/yOmTcnrenTp+u06dOnD6pWrYopU6Zgy5YtegulExEREREREQkhsCziNn4+dUcn3r+uF0Y3r6g3UYKKhrfyGYGDpQKPM1T5Fp1Ss3ORlJELB8tXXzeMi4tDp06d4ODggC1btkgLh1tZWQEAsrP1bzPMysrSaZNXeno6OnfujNTUVISFhemtPWVIv3794O7ujgMHDujklfelLThZWVkVOae8xo4dC7lcrnMsIiIiIiIiIgDQaATmHL6hV5QaGuTDolQJYWHKCJKzcuFkbQ47peHCk51SAUdrBZKzXu1C6MnJyejQoQOSkpKwd+9elCtXTtqmvTVOe0tfXrGxsXB2dtabuZSTk4Pu3bvj4sWLCAsLQ61atQqdi7e3NxITE3WOn/e1ceNGKR4XF6e3aLo2z7znYIiVlRVcXFx0jkVERERERESk1gh8cyAKmy7E6MTHBVfGB418WJQqIbyV7wVUKWODH3vWLta+TlbmGBrkg83PDHAA6Fm7HNxslPj+Hb8i96tdY6qosrKy0KVLF0RFReHAgQOoWbOmznZPT0+4urrizJkzevueOnUKgYGBOjGNRoOBAwfi4MGD2LRpE4KDg4t0DtHR0ahTp44U279/v04bP78n1yYwMBArV67ElStXdHI+efKktL0gqampePjwIVxdXQudHxEREREREb3ecnI1+HzvVRz696EUk8tkmNLGF+/4uRsxs9cPC1MvwFapQKCnQ7H3H9G0AizMZPgjKgHpOWrYWJihXVVXfNTYB2VslfApRp9CCGg0muc3zEOtVqN3796IiIhAWFgYgoKCDLYLCQnBqlWrcPfuXXh7ewMADh48iKioKIwdO1an7ahRo7Bx40YsX74c3bt3z/fYCQkJekWhpUuXIiEhAe3bt5dibdq0Mbh/165dMXbsWCxZsgSLFi0C8P/3/y5bBk9PTzRp0gTAk8KbSqWCnZ2dzv7ffPMNhBA6xyIiIiIiIqI3V5ZKjfG/X8aJ24+lmEIuwzftq6NNVU5qKGksTBmRq60SHzevhPcblkdylgoOlk9u77PN5xa/l+WTTz7Bjh070KVLFyQmJmLt2rU62wcMGAAAmDx5MjZv3oxWrVph9OjRSEtLw+zZs+Hv74/3339faj9//nwsWbIEQUFBsLa21uuvW7dusLGxAQD4+Pigd+/e8Pf3h6WlJY4ePYoNGzYgMDAQQ4cOfW7uXl5eGDNmDGbPng2VSoUGDRpg+/btCA8Px7p166Q1suLi4lCnTh307dsX1atXBwDs27cPu3fvRvv27dG1a9fiX0AiIiIiIiJ6LaRl52Lsjks4fz9ZilmYyTG7S000qeBsxMxeXyxMGZnt/xeiPOwtjZbD+fPnAQA7d+7Ezp079bZrC1Pe3t44cuQIxo0bh4kTJ8LCwgKdOnXC3LlzddaX0vYXERGBiIgIvf5u3bolFab69++P48ePY+vWrcjKyoKPjw8mTJiAKVOmwNraulD5z5gxA05OTli+fDlCQ0Ph6+uLtWvXol+/flIbR0dHdO7cGfv378eqVaugVqtRpUoVTJs2DZ9++inkci63RkRERERE9CZLylRh1LZIXI1Pk2LW5maY19UPdb0cjZfYa04mnl01+g2UkpICBwcHJCcnw97e3mCbzMxMXL9+Hb6+vs990psxaW/lk8vlXIiNJMUdvxqNBvHx8XBzc2PxjiQcF1SSOJ6ouDh2qCRxPFFxcey8PuJTszFyWyRuJWZIMXulAj9080dNd7sC9iw5r9N4KkydRYszpoiIiIiIiIjojXU/ORPDt0YiJiVLipWxscDi7v6o5GJjxMzeDCxMEREREREREdEb6eajdIz4LRIP03OkWDl7SywJ8Yeng+neLfU6YWGKiIiIiIiIiN44Vx6kYtS2f5CcpZJiFZ2tsaibP9zslAXsSSWJhSkiIiIiIiIieqP8fS8JY8MuIUOllmLVXG3xQ7dacLK2MGJmbx4WpoiIiIiIiIjojXE8OhHjd15GjlojxWqXc8D8rn6wVbJM8qrxihMRERERERHRG+Hg9QR8vucqcjVCijX2ccLszjVhaW5mxMzeXCxMEREREREREdFrb8elOHx34Do04mlRqlWVMvi2fXVYKORGzOzNxsIUEREREREREb3WNpy7j7lHbujEOtUoiy/aVoWZXGakrAhgYYqIiIiIiIiIXlNCCPx06g6WR9zWifeqXQ6fBFeGnEUpo2NhioiIiIiIiIheO0IILDx6C2vP3tOJv9+gPIY18YFMxqKUKWBhioiIiIiIiIheKxqNwIxD/2JbZKxOfFSzihhY39tIWZEhLEwRERERERER0WsjV63B//Zdw/6oBCkmAzDxLV909/cwXmJkEJedJ5w+fRojR46En58fbGxsUL58efTq1QtRUVF6ba9cuYL27dvD1tYWzs7OeO+995CQkKDT5urVq5gwYQICAwNhZ2cHDw8PdOrUCWfOnNHrb+rUqZDJZHovS0vLQuev0Wgwa9YsVKxYEZaWlggICMCvv/6ab9ulS5ciMDAQVlZWcHFxQevWrXHhwoVCH4+IiIiIiIhMU3auGuN/v6xTlJLLZPi6fXUWpUwUZ0wRZs6ciWPHjqFnz54ICAhAXFwcFi1ahLp16+LEiROoVasWAODevXto0aIFHBwcMG3aNKSlpWHOnDmIjIzEqVOnYGFhAQBYuXIlfvrpJ4SEhGD48OFITk7G8uXL0bhxY+zduxdt2rTRy2Hp0qWwtbWV3puZmRU6/ylTpmDGjBn48MMP0aBBA4SFhaFfv36QyWTo06ePTtshQ4Zg3bp1GDhwIEaOHIn09HScO3cO8fHxxbl0REREREREZCIycnIxbsdlnL2XJMUszOSY3rEGWlR2MV5iVCAWpgjjxo3D+vXrpcISAPTu3Rv+/v6YMWMG1q5dCwCYNm0a0tPTcfbsWZQvXx4A0LBhQ7Rt2xahoaH46KOPAAB9+/bF1KlTdQpNQ4YMQY0aNTB16lSDhakePXqgTJkyRc79/v37mDt3LkaMGIFFixYBAD744AMEBwdj/Pjx6Nmzp1Tk2rRpE1atWoXffvsN3bp1K/KxiIiIiIiIyDSlZKnw8bZ/cOlBqhSzMjfDnC410bC8kxEzo+fhrXxGps5KKvD9q9CkSROdohQA+Pr6ws/PD1euXJFiW7duRefOnaWiFAC0adMGVatWxaZNm6RYvXr1dIpSAODi4oLmzZvr9JeXEAIpKSkQQhQp97CwMKhUKgwfPlyKyWQyDBs2DPfu3UNERIQU//7779GwYUN069YNGo0G6enpRToWERERERERmZ5H6TkYuuWiTlHKTqnA4u7+LEqVAixMGVFuWgxSLixBblqMwffGJITAgwcPpFlM9+/fR3x8POrXr6/XtmHDhjh37txz+4yLi8t3VlSlSpXg4OAAOzs7DBgwAA8ePChUnufOnYONjQ1q1Kihl5N2OwCkpKTg1KlTaNCgASZPngwHBwfY2tqiUqVKOkU1IiIiIiIiKj1iU7Lw4eYL+Pfh04kHztbmWN4jAP4e9kbMjAqLt/KVgNz0OKjT44q0j9zKBWmXQpFxcxdUSTfgUGckks8tgirhAoQ6B3b+/4Em81Gh+zOzcYfCxr2oqedr3bp1uH//Pr7++msAQGzsk0dsenjoLxbn4eGBxMREZGdnQ6lUGuwvPDwcERER+Pzzz3XiTk5OGDlyJIKCgqBUKhEeHo7Fixfj1KlTOHPmDOztC/4iiY2NRdmyZSGTyfRyAoCYmCdFvhs3bkAIgQ0bNkChUGDWrFlwcHDAggUL0KdPH9jb26N9+/aFuDJERERERERkCm4nZmDEb5F4kJYtxcraKrEkxB/lnayNmBkVBQtTJSDj5u9IjVxZpH3MXfzg2PAzqJKjoXp0CQ8PDAMAKBwrw7pCezw+PhWqR5cK3Z99wFDY1Xq/SDnk5+rVqxgxYgSCgoIwaNAgAEBmZiYAGCw8aZ+gl5mZaXB7fHw8+vXrh4oVK2LChAk620aPHq3zPiQkBA0bNkT//v2xZMkSTJw4scBc8ztm3pwAIC0tDQDw6NEjnDhxAo0aNQIAvPPOO6hYsSK+/fZbFqaIiIiIiIhKiaiENIz8LRKPM1VSrLyjFRZ394e7feGf8k7Gx1v5jET16BKEKh0OdUbqxB3qfIzMu38WqShVkuLi4tCpUyc4ODhgy5Yt0sLhVlZWAIDs7Gy9fbKysnTa5JWeno7OnTsjNTUVYWFhemtPGdKvXz+4u7vjwIEDOnnlfWkLTlZWVoXKSfvPihUrSkUpALC1tUWXLl1w6tQp5ObmPjc3IiIiIiIiMq6LMSkYuvmiTlHKt4wNfuxVm0WpUoiFKSMxd/GDzNwGyecW6cSTzy2ElXdrmLv4vfKckpOT0aFDByQlJWHv3r0oV66ctE17a5z2lr68YmNj4ezsrDdzKScnB927d8fFixcRFhaGWrVqFToXb29vJCYm6hw/72vjxo1SPC4uTm/RdG2e2nPQ/rNs2bJ6x3Jzc4NKpeJi6ERERERERCbu1J3HGLktEmk5TycW+LvbY1mPADhbWxSwJ5kq3spXAqwrdYayrP6i4AXRrjGlenQJ5i5+OmtMZUTvhVOTqUVeY+pFZGVloUuXLoiKisKBAwdQs2ZNne2enp5wdXXFmTNn9PY9deoUAgMDdWIajQYDBw7EwYMHsWnTJgQHBxc6FyEEoqOjUadOHSm2f/9+nTZ+fk8Kd4GBgVi5ciWuXLmik/PJkyel7cCTwpS7uzvu37+vd7yYmBhYWlrCzs6u0DkSERERERHRq3XkxkNM2n0VKrVGitX3dsTcLjVhbcHyRmnFv1wJUBRz4XG7Wv8BZGaw8xsMhW05ODf9BqmXQqX3sPN+CdnqU6vV6N27NyIiIhAWFoagoCCD7UJCQrBq1SrcvXsX3t5Pcjt48CCioqIwduxYnbajRo3Cxo0bsXz5cnTv3j3fYyckJMDV1VUntnTpUiQkJOis+dSmTRuD+3ft2hVjx47FkiVLsGjRk9lnQggsW7YMnp6eaNKkidS2d+/eWLBgAfbv34+2bdsCAB4+fIiwsDC0bt0acjknEBIREREREZmiPVceYOofUdDkuVumeSUXzOhYAxYK/pYrzViYMiKFbTnY1x4OM0tHg+9flU8++QQ7duxAly5dkJiYiLVr1+psHzBgAABg8uTJ2Lx5M1q1aoXRo0cjLS0Ns2fPhr+/P95//+nC6/Pnz8eSJUsQFBQEa2trvf66desGGxsbAICPjw969+4Nf39/WFpa4ujRo9iwYQMCAwMxdOjQ5+bu5eWFMWPGYPbs2VCpVGjQoAG2b9+O8PBwrFu3TlojCwAmTZqETZs2ISQkBOPGjYODgwOWLVsGlUqFadOmFfv6ERERERER0cuz5UIMZh36F3kXcHm7mhumtqsKhRmLUqUdC1NG9mwR6lUXpQDg/PnzAICdO3di586detu1hSlvb28cOXIE48aNw8SJE2FhYYFOnTph7ty5OutLafuLiIhARESEXn+3bt2SClP9+/fH8ePHsXXrVmRlZcHHxwcTJkzAlClTYG1duMd7zpgxA05OTli+fDlCQ0Ph6+uLtWvXol+/fjrtypYti6NHj+LTTz/FvHnzoFKpEBQUhLVr16J27dqFOhYRERERERG9OqtO38WiY7d0Yt38PTCxVRXI5TIjZUUlSSaeXTX6DZSSkgIHBwckJyfD3t7eYJvMzExcv34dvr6+Bp8+ZyqEENBoNJDL5ZDJ+CGlJ4o7fjUaDeLj4+Hm5sZbHUnCcUElieOJiotjh0oSxxMVF8fOyyOEwJLj0Qg9fVcn/l49L4xqVvG1/L37Oo2nwtRZtDhjioiIiIiIiIhMhkYjMOfIDWy+EKMTH9akAt5v4P1aFqXeZCxMEREREREREZFJUGsEvvrjGvZcjdeJf9qyMnoHehopK3qZTG5u2NKlSxEQEAB7e3vY29sjKCgIe/bsybd9aGgoZDKZzsvS0vIVZkxERERERERELyonV4OJu67oFKXkMhm+bFeNRanXmMnNmPLy8sKMGTPg6+sLIQRWrVqFrl274ty5c/Dz8zO4j729Pa5duya957Q+IiIiIiIiotIjU6XGpzsv49Sdx1JMIZfhuw410Nq3jBEzo5fN5ApTXbp00Xn/3XffYenSpThx4kS+hSmZTAZ3d/dXkR4RERERERERlaDUrFyM2fEPLsakSDGlQo7ZnWsiqIKzETOjV8HkbuXLS61WY8OGDUhPT0dQUFC+7dLS0uDj4wNvb2907doVly5deoVZEhEREREREVFxJGbk4L9bL+oUpWwszLComz+LUm8Ik5sxBQCRkZEICgpCVlYWbG1tsW3bNtSsWdNg22rVquHnn39GQEAAkpOTMWfOHDRp0gSXLl2Cl5eXwX2ys7ORnZ0tvU9JefIB0Gg00Gg0BvfRaDQQQkiv0qC05Ekvn3bcFjTGDdGO+6LsQ68/jgsqSRxPVFwcO1SSOJ6ouDh2Xkx8WjZG/PYP7jzOlGIOVgosfLcWqrvZvnHX9XUaT0U5B5MsTFWrVg3nz59HcnIytmzZgkGDBuHIkSMGi1NBQUE6s6maNGmCGjVqYPny5fjmm28M9j99+nR89dVXevGEhARkZWUZ3EelUhXrh70xmHp+9Oppv+ASExNhbm5epP2Sk5MhhIBcbtITLOkV4rigksTxRMXFsUMlieOJiotjp/jup2Rj0oHbiE9XSTEXK3NMa+kJZ2QgPj7DiNkZx+s0nlJTUwvd1iQLUxYWFqhSpQoAoF69ejh9+jQWLFiA5cuXP3dfc3Nz1KlTB//++2++bSZNmoRx48ZJ71NSUuDt7Q1XV1fY29sb3CczMxOJiYmQy+WlYoCUhhzp1ZHL5ZDJZHB2doaVlVWh99NoNJDJZHB1deWYIgnHBZUkjicqLo4dKkkcT1RcHDvFc+NROiYfvoXH2YC54sn/OC/nYInF3WqhnIOlkbMzntdpPFlaFv7vaJKFqWdpNBqdW+8KolarERkZiY4dO+bbRqlUQqlU6sULKjppf9hrX6Yq7+17ppwnvVracVucwmpx96PXG8cFlSSOJyoujh0qSRxPVFwcO0VzOS4Vo7ZFIiU7F/j/n6yVnK2xqLs/XG31f6e/aV6X8VSU/E2uMDVp0iR06NAB5cuXR2pqKtavX4/Dhw9j3759AICBAwfC09MT06dPBwB8/fXXaNy4MapUqYKkpCTMnj0bt2/fxgcffGDM0yAiIiIiIiKiPM7eS8K4sEvIUKmlWA03Wyzs5g9Hq8IvOUKvF5MrTMXHx2PgwIGIjY2Fg4MDAgICsG/fPrRt2xYAcOfOHZ3K2+PHj/Hhhx8iLi4OTk5OqFevHo4fP57vYulERERERERE9Godu5WICb9fRo766ZrIdTwdMO8dP9goTa40Qa+Qyc0N++mnnxAdHY3s7GzEx8fjwIEDUlEKAA4fPozQ0FDp/bx583D79m1kZ2cjLi4Ou3btQp06dYyQeel1+vRpjBw5En5+frCxsUH58uXRq1cvREVF6bW9cuUK2rdvD1tbWzg7O+O9995DQkKCTpurV69iwoQJCAwMhJ2dHTw8PNCpUyecOXNGr7+pU6fq3CKpfRXlflSNRoNZs2ahYsWKsLS0REBAAH799Ve9doaOo33lHWNERERERERUcv64Fo9PdlzSKUoFVXDGwndrsShFpjdjil69mTNn4tixY+jZsycCAgIQFxeHRYsWoW7dujhx4gRq1aoFALh37x5atGgBBwcHTJs2DWlpaZgzZw4iIyNx6tQpWFhYAABWrlyJn376CSEhIRg+fDiSk5OxfPlyNG7cGHv37kWbNm30cli6dClsbW2l92ZmZoXOf8qUKZgxYwY+/PBDNGjQAGFhYejXrx9kMhn69OkjtVuzZo3evmfOnMGCBQvQrl27Qh+PiIiIiIiICifsnzh8dyAKIk/sLV9XfNO+GszNTG6uDBkBC1OEcePGYf369VJhCQB69+4Nf39/zJgxA2vXrgUATJs2Denp6Th79izKly8PAGjYsCHatm2L0NBQfPTRRwCAvn37YurUqTqFpiFDhqBGjRqYOnWqwcJUjx49UKZMmSLnfv/+fcydOxcjRozAokWLAAAffPABgoODMX78ePTs2VMqcg0YMEBv/8OHD0Mmk6Fv375FPjYRERERERHlb/3f9zDvr5s6sS413fF5G1/I5XxYFz3B8iShSZMmOkUpAPD19YWfnx+uXLkixbZu3YrOnTtLRSkAaNOmDapWrYpNmzZJsXr16ukUpQDAxcUFzZs31+kvLyEEUlJSdJ4qWBhhYWFQqVQYPny4FJPJZBg2bBju3buHiIiIfPfNzs7G1q1bERwcDC8vryIdl4iIiIiIiAwTQmDFidt6Rak+gZ4sSpEeFqaMKDknEwlZaXqv5JxMY6cGIQQePHggzWK6f/8+4uPjUb9+fb22DRs2xLlz557bZ1xcXL6zoipVqgQHBwfY2dlhwIABePDgQaHyPHfuHGxsbFCjRg29nLTb87N7924kJSWhf//+hToWERERERERFUwIgXl/3cSPJ27rxD9oVB7jgiuxKEV6eCtfCYjLSEFcZiqAJ7N1ajuXk7bdT09GQlYaAMBMJoe/s4e0LSNXhU5//Ai10EAmk8HKzBxmMjm2tXkft1IfITknCwBgpTBHNQc3ab8bKQ+RqsoGANiaK1HFvui3wD3PunXrcP/+fXz99dcAgNjYWACAh4eHXlsPDw8kJiYiOzsbSqXSYH/h4eGIiIjA559/rhN3cnLCyJEjERQUBKVSifDwcCxevBinTp3CmTNnYG9vX2CesbGxKFu2LGQy3S83bZ4xMTEFnqNSqUSPHj0KPAYRERERERE9n0Yj8N3B69hxKU4nPqZFJfSvy7tUyDAWpkrAjruXsfLaCQCAudwMxzqPkrZtunUev958MmvH0cIKf7QfKm1Lz83BzdREqIUG5mZmqGJXBsCTpxT8ePUEDsReBwBUdXDF2uCns3rmX/oLJxPuAADquHhiedOeJXo+V69exYgRIxAUFIRBgwYBADIzn8ziMlR40j5BLzMz0+D2+Ph49OvXDxUrVsSECRN0to0ePVrnfUhICBo2bIj+/ftjyZIlmDhxYoG55nfMvDkZkpKSgl27dqFjx45wdHQs8BhERERERERUMJVag//tu4YDUU+f2i4DMOktX3Tz15/gQKTFW/lIR1xcHDp16gQHBwds2bJFWjjcysoKwJN1mZ6VlZWl0yav9PR0dO7cGampqQgLC9Nbe8qQfv36wd3dHQcOHNDJK+9LW3CysrIqck7Ak/WysrKyeBsfERERERHRC8rOVePTnZd1ilJmMhm+7VCdRSl6LhamSJKcnIwOHTogKSkJe/fuRblyT29J1N4ap72lL6/Y2Fg4OzvrzVzKyclB9+7dcfHiRYSFhaFWrVqFzsXb2xuJiYk6x8/72rhxoxSPi4vTWzRdm2fec8hr3bp1cHBwQOfOnQudExEREREREelKz87FqG3/4Hj0099vFmZyzHmnJtpVcytgT6IneCtfCXjHuyYalvEGAL21jnpVDEQrjyoAnqwxlZeNwgKV7JylNabMZDKpzYfVG6NXpUAAT9aYymuMXwudNaZKQlZWFrp06YKoqCgcOHAANWvW1Nnu6ekJV1dXnDlzRm/fU6dOITAwUCem0WgwcOBAHDx4EJs2bUJwcHChcxFCIDo6GnXq1JFi+/fv12nj5+cHAAgMDMTKlStx5coVnZxPnjwpbX9WbGwsDh06hMGDB+e7JhYREREREREVLDlThY+3/4PLD1KlmLW5Gea+44f63o7GS4xKFRamSoC7tT3crQ0v0u1p4wBPGweD26wV5tiXZ80pLQu5GSraueR7vMolvNi5Wq1G7969ERERgbCwMAQFBRlsFxISglWrVuHu3bvw9n5SiDt48CCioqIwduxYnbajRo3Cxo0bsXz5cnTv3j3fYyckJMDV1VUntnTpUiQkJKB9+/ZSrE2bNgb379q1K8aOHYslS5Zg0aJFAJ4UtpYtWwZPT080adJEb58NGzZAo9HwNj4iIiIiIqJiepiejRFbI3EzMUOK2SsVWNjNH37udkbMjEobFqaMyMHC8PpHr9onn3yCHTt2oEuXLkhMTMTatWt1tg8YMAAAMHnyZGzevBmtWrXC6NGjkZaWhtmzZ8Pf3x/vv/++1H7+/PlYsmQJgoKCYG1trddft27dYGNjAwDw8fFB79694e/vD0tLSxw9ehQbNmxAYGAghg7VL9o9y8vLC2PGjMHs2bOhUqnQoEEDbN++HeHh4Vi3bp20RlZe69atQ7ly5dCyZcuiXioiIiIiIqI3XkxyFkb8dhH3krOkmLO1ORZ3D0CVMjZGzIxKIxamCOfPnwcA7Ny5Ezt37tTbri1MeXt748iRIxg3bhwmTpwICwsLdOrUCXPnztW5JU7bX0REBCIiIvT6u3XrllSY6t+/P44fPy4tRu7j44MJEyZgypQpsLa2LlT+M2bMgJOTE5YvX47Q0FD4+vpi7dq16Nevn17ba9eu4ezZsxg3bhzkci6xRkREREREVBTRiRkY8Vsk4tOePoTKw84Si0P84e1oGpMvqHSRiWdXjX4DpaSkwMHBAcnJybC3N3xLXmZmJq5fvw5fX998n/RmCoQQ0Gg0kMvleutd0ZuruONXo9EgPj4ebm5uLOSRhOOCShLHExUXxw6VJI4nKq43bexcjU/FqG3/IClTJcXKO1phSUgAytpx/d4X9TqNp8LUWbQ4Y4qIiIiIiIiICnQhJhmjt/+D9By1FKvqaosfutWCs7WFETOj0o6FKSIiIiIiIiLK14nbjzF+5yVk5WqkmL+HPRZ0rQU7S5YV6MVwBBERERERERGRQYf+fYgpe65CpX5alGrg7Yi57/jBylz/YVNERcXCFBERERERERHp2XX5Ab7eHwVNnqWpgyu7YFqHGrBQlO41kMh0sDBFRERERERERDo2nY/B7MP/6sQ6VHfDl+2qwUzOB21RyWFhioiIiIiIiIgkv5y6gyXHo3ViIQEemNCyCuQsSlEJY2GKiIiIiIiIiCCEwKJj0Vh95q5OfFB9b4xoWgEyGYtSVPJYmCIiIiIiIiJ6w2k0AjMP/YvfImN14iOaVsTgBt5GyoreBCxMEREREREREb3BctUafLU/CnuvxuvEJ7Sqgp61yxkpK3pTsDBFRERERERE9IbKydVg0u4r+OvmIykml8nwZbuq6FijrBEzozcFC1NEREREREREb6CMnFx8uvMyTt9NkmLmZnJ816E6WlUpY7zE6I3CwhQRERERERHRGyYlS4UxYZcQGZsixSwVcszp4odGPk5GzIzeNCxMEREREREREb1BEjNyMGrbP4hKSJNithYKLHi3FgLK2RsxM3oTyY2dABnf6dOnMXLkSPj5+cHGxgbly5dHr169EBUVpdf2ypUraN++PWxtbeHs7Iz33nsPCQkJOm2uXr2KCRMmIDAwEHZ2dvDw8ECnTp1w5swZvf6mTp0KmUym97K0tCx0/hqNBrNmzULFihVhaWmJgIAA/Prrrwbbbtq0CY0bN4ajoyNcXFwQHByMXbt2FfpYREREREREpdmD1Gx8uOmCTlHKycocy3oEsChFRsEZU4SZM2fi2LFj6NmzJwICAhAXF4dFixahbt26OHHiBGrVqgUAuHfvHlq0aAEHBwdMmzYNaWlpmDNnDiIjI3Hq1ClYWFgAAFauXImffvoJISEhGD58OJKTk7F8+XI0btwYe/fuRZs2bfRyWLp0KWxtbaX3ZmZmhc5/ypQpmDFjBj788EM0aNAAYWFh6NevH2QyGfr06SO1++GHH/Dxxx+jU6dOmDFjBrKyshAaGorOnTtj69at6N69e3EvIRERERERkcm78zgDI36LRFxqthRzs1VicXd/VHC2NmJm9CaTCSGEsZMwtpSUFDg4OCA5ORn29oYrxJmZmbh+/Tp8fX1hZWX1ijMsPCEENBoN5HI5ZDJZofY5fvw46tevLxWWAOD69evw9/dHjx49sHbtWgDA8OHDERoaiqtXr6J8+fIAgAMHDqBt27ZYvnw5PvroIwDA2bNnUa1aNZ1C06NHj1CjRg1UrVoVR48eleJTp07FV199hYSEBJQpU/TF9e7fv4+KFSvio48+wqJFi6RrEBwcjFu3biE6OloqclWtWhWOjo44efKkdG1SUlLg6emJ1q1bIywsrMjHLy2KO341Gg3i4+Ph5uYGuZwTLOkJjgsqSRxPVFwcO1SSOJ6ouErT2LmekIaR2yKRmKGSYl4OlljcPQDlHAp/xwq9PKVpPD1PYeosWqX7TF8T6oxk5KYkQJ2RbJTjN2nSRKcoBQC+vr7w8/PDlStXpNjWrVvRuXNnqSgFAG3atEHVqlWxadMmKVavXj2dohQAuLi4oHnz5jr95SWEQEpKCopaJw0LC4NKpcLw4cOlmEwmw7Bhw3Dv3j1ERERI8ZSUFLi5uekU7Ozt7WFra2vSxUYiIiIiIqIX8U9sCv675aJOUaqyiw1+7FWbRSkyOhamTIDIzcHdH7pD5OYYOxWJEAIPHjyQZjHdv38f8fHxqF+/vl7bhg0b4ty5c8/tMy4uLt9ZUZUqVYKDgwPs7OwwYMAAPHjwoFB5njt3DjY2NqhRo4ZeTtrtWi1btsTevXvxww8/IDo6GlevXsWIESOQnJyM0aNHF+p4REREREREpcmZu0kY/lskUrJzpVjNsnZY0TMAZWyURsyM6AmuMVUCcpPikJsc9/yGcjNYevvrhHIe3IDMwhKarFQIdQ6ybl+AwskDCns3qY0mOwM5cfoLkT9L4eAOM4eyRc7fkHXr1uH+/fv4+uuvAQCxsbEAAA8PD722Hh4eSExMRHZ2NpRKw19s4eHhiIiIwOeff64Td3JywsiRIxEUFASlUonw8HAsXrwYp06dwpkzZ5475S82NhZly5bVu21Rm2dMTIwUW7hwIR4+fIiPP/4YH3/8MQCgTJkyOHjwIIKCggo8DhERERERUWnz141HmLT7CnLUGilWx9MB87v6wdqC5QAyDRyJJSD13A48Prziue3MrB3h89kBAE9u3xO5ORAaFXJibiEn4RayY65AYe8GTXY61BnJMLN2AACoHt1BzM8fPLd/p5YfwbHlhy92MoA0kygoKAiDBg0C8GSNIgAGC0/aJ+hlZmYa3B4fH49+/fqhYsWKmDBhgs62Z2cqhYSEoGHDhujfvz+WLFmCiRMnFphrfsfMm5OWtbU1qlWrBi8vL3Tu3BmpqamYN28eunfvjvDwcFSpUqXAYxEREREREZUW+67F48u916DOs1xK04rOmNmpBpSKwj9siuhlY2HKSLS376kSbkGdlQqoc3H3hxDIZGZQOHqgwqTDRskrLi4OnTp1goODA7Zs2SItHK5dgyk7O1tvn6ysLJ02eaWnp0tFoKNHj+qtPWVIv3798Mknn+DAgQNSYSouTndGmoODA6ysrGBlZVXonHr27AmFQoGdO3dKsa5du8LX1xdTpkzBxo0bn5sbERERERGRqfstMhYzDl5H3hV821Z1xVdvV4O5GVf0IdPCwpSRyBQW8B71G9Tpj6BKvIe7P4TAe9RWKOzdILe0g0xh8fxOSlhycjI6dOiApKQkhIeHo1y5ctI27a1x2lv68oqNjYWzs7PezKWcnBx0794dFy9exL59+1CrVq1C5+Lt7Y3ExES942v98ssvGDx4MDw8PHDo0CEIIXRu59PmqT2HmzdvYu/evVixQndmm7OzM5o1a4Zjx44VOjciIiIiIiJTtebsXSwMv6UT61rLHZNb+0IuL9yT24leJRamSoBdnXdgVanh8xvKn06X1N6mp8lMgbJcDVi4VoSyXA2oUx5CrrSRtgOAuUt5lBuy8rndKxzci578/8vKykKXLl0QFRWFAwcOoGbNmjrbPT094erqijNnzujte+rUKQQGBurENBoNBg4ciIMHD2LTpk0IDg4udC5CCERHR6NOnTpSbP/+/Tpt/Pz8AACBgYFYuXIlrly5opPzyZMnpe0ApMXU1Wq13vFUKhVyc3P14kRERERERKWFEALLI27jp1N3dOL96npiTPNKeuvyEpkKFqZKgMLRHQrH4hWFLMpWRm5KwpNZUmYWsPSprddGrrSGpU9gofoTQjy/0TPUajV69+6NiIgIhIWF5bsQeEhICFatWoW7d+/C29sbAHDw4EFERUVh7NixOm1HjRqFjRs3Yvny5ejevXu+x05ISICrq6tObOnSpUhISED79u2lWJs2bQzu37VrV4wdOxZLlizBokWLADy5BsuWLYOnpyeaNGkCAKhSpQrkcjk2btyIoUOHSl/K9+7dQ3h4OJo1a1bQJSIiIiIiIjJZGo3A93/dxMbz93XiHzX2wQeNyrMoRSaNhSkToL2tzxi37wHAJ598gh07dqBLly5ITEzE2rVrdbYPGDAAADB58mRs3rwZrVq1wujRo5GWlobZs2fD398f77//vtR+/vz5WLJkCYKCgmBtba3XX7du3WBjYwMA8PHxQe/eveHv7w9LS0scPXoUGzZsQGBgIIYOHfrc3L28vDBmzBjMnj0bKpUKDRo0wPbt2xEeHo5169ZJa2S5urpiyJAhWLlyJd566y10794dqampWLJkCTIzMzFp0qQXuoZERERERETGoNYIfHsgCr9ffqATH9uiEvrV9TJSVkSFx8KUCch7254xnD9/HgCwc+dOnYXBtbSFKW9vbxw5cgTjxo3DxIkTYWFhgU6dOmHu3Lk660tp+4uIiEBERIRef7du3ZIKU/3798fx48exdetWZGVlwcfHBxMmTMCUKVNgbW1dqPxnzJgBJycnLF++HKGhofD19cXatWvRr18/nXZLly5F7dq18dNPP0mFqAYNGmD16tVo0aJFoY5FRERERERkKlRqDT7fcxV//vtQisllMkxp44t3/Iq/1AvRqyQTxbn36zWTkpICBwcHJCcnw97e3mCbzMxMXL9+Hb6+vgafPmcqhBDQaDSQy+WcrkmS4o5fjUaD+Ph4uLm5QS7n0zvoCY4LKkkcT1RcHDtUkjieqLiMOXayVGpM2HUFEdFPHxqlkMvwTfvqaFPVtYA9yVS9Tt9FhamzaHHGFBEREREREVEpkpadi7E7LuH8/WQpZmEmx+wuNdGkgrMRMyMqOhamiIiIiIiIiEqJpEwVPt4WiSvxaVLM2twM87r6oa6Xo/ESIyomFqaIiIiIiIiISoH41GyM2haJm4kZUsxeqcAP3fxR093OiJkRFR8LU0REREREREQm7n5yJoZvjURMSpYUK2NjgcXd/VHJxcaImRG9GBamiIiIiIiIiEzYzUfpGPlbJBLSc6RYOXtLLO7uDy9H0304F1FhsDBFREREREREZKKuPEjFqG3/IDlLJcUqOFljcXd/uNkpjZgZUclgYYqIiIiIiIjIBJ27n4yxYf8gPUctxaq52uKHbrXgZG1hxMyISg4LU0REREREREQm5nh0Iib8fhnZuRopVrucA+Z39YOtkj/l6fXB0UxERERERERkQg5eT8Dne64iVyOkWKPyTpjTpSYszc2MmBlRyWNhioiIiIiIiMhE7LwUh28PXIdGPC1KtapSBt+2rw4LhdyImRG9HCxMEREREREREZmADefuY+6RGzqxTjXK4ou2VWEmlxkpK6KXi+VWolIuOjoaMpkMoaGhxk6FiIiIiIiKQQiBn07e0StK9axdDv9jUYpecyxMEUJDQyGTyWBpaYn79+/rbW/ZsiVq1aplhMxejEwmK9Tr8OHDrySfJUuWsHhEREREREQ6hBBYePQWlkVE68Tfb1Ae41tWhpxFKXrN8VY+kmRnZ2PGjBn44YcfjJ1KiVizZo3O+9WrV2P//v168Ro1arySfJYsWYIyZcpg8ODBr+R4RERERERk2jQagRmH/sW2yFid+KhmFTGwvreRsiJ6tViYIklgYCB+/PFHTJo0CeXKlTN2OoWWnp4OGxsbvfiAAQN03p84cQL79+/Xiz8rIyMD1tbWJZojERERERFRXrlqDb784xr+uJYgxWQAPmtdBSEBpef3GNGL4q18JkBoBHIzVBB5HgVqDJMnT4ZarcaMGTMK1X7t2rWoV68erKys4OzsjD59+uDu3bvS9pEjR8LW1hYZGRl6+/bt2xfu7u5Qq9VSbM+ePWjevDlsbGxgZ2eHTp064dKlSzr7DR48GLa2trhx4wY6duwIOzs79O/fv5hn/PQ2xbNnz6JFixawtrbG5MmTATyZQfbll1+iSpUqUCqV8Pb2xoQJE5Cdna3Txy+//ILWrVvDzc0NSqUSNWvWxNKlS3XaVKhQAZcuXcKRI0ekWwhbtmwpbU9KSsKYMWPg7e0NpVKJKlWqYObMmdBoNDr9JCUlYfDgwXBwcICjoyMGDRqEpKSkYp8/ERERERG9ejm5GkzYdUWnKCWXyfB1++osStEbx+QKU0uXLkVAQADs7e1hb2+PoKAg7Nmzp8B9Nm/ejOrVq8PS0hL+/v7YvXv3K8r2xQmNQHZiJu7v+xfZiZlGLU5VrFgRAwcOxI8//oiYmJgC23733XcYOHAgfH198f3332PMmDE4ePAgWrRoIRVKevfujfT0dOzatUtn34yMDOzcuRM9evSAmZkZgCe33XXq1Am2traYOXMmvvjiC1y+fBnNmjVDdHS0zv65ubl4++234ebmhjlz5iAkJOSFzvvRo0fo0KEDAgMDMX/+fLRq1QoajQbvvPMO5syZgy5duuCHH37Au+++i3nz5qF37946+y9duhQ+Pj6YPHky5s6dC29vbwwfPhyLFy+W2syfPx9eXl6oXr061qxZgzVr1mDKlCnS9QgODsbatWsxcOBALFy4EE2bNsWkSZMwbtw4qQ8hBLp27Yo1a9ZgwIAB+Pbbb3Hv3j0MGjTohc6fiIiIiIhenYycXHy8/R+E33wkxSzM5JjduSbaV3czYmZERiJMzI4dO8SuXbtEVFSUuHbtmpg8ebIwNzcX//zzj8H2x44dE2ZmZmLWrFni8uXL4vPPPxfm5uYiMjKy0MdMTk4WAERycnK+bTIyMsSFCxdERkaG3raclCyRfj+lyK/Mh+kiIy5VXPvprPjn+whxfdV5kZmQLjRqjch6lPG0XXyazvGyHqYb7C8nJUtoNBqRm5srNBpNoc//l19+EQDE6dOnxY0bN4RCoRAff/yxtD04OFj4+flJ76Ojo4WZmZn47rvvdPqJjIwUCoVCims0GuHp6SlCQkJ02m3atEkAEH/99ZcQQojU1FTh6OgoPvzwQ512cXFxwsHBQSc+aNAgAUBMnDix0OenNWLECPHskA8ODhYAxLJly3Tia9asEXK5XISHh+vEly1bJgCIY8eOSTFDY+Ltt98WlSpV0on5+fmJ4OBgvbbffPONsLGxEVFRUTrxiRMnCjMzM3Hnzh0hhBDbt28XAMSsWbOkNrm5uaJ58+YCgPjll1/yPfeCxm9B1Gq1iI2NFWq1ukj70euN44JKEscTFRfHDpUkjicqrqKOneTMHDH4179F/XlHpFfzRUfFyduJLzlTKg1ep++iwtRZtExujakuXbrovP/uu++wdOlSnDhxAn5+fnrtFyxYgPbt22P8+PEAgG+++Qb79+/HokWLsGzZsleSc9KVh3h4Sv9pdgWxreQEx+plcCfsKnIeZ0FmJofcXI57u6/Dq6Mv0u4kI+H4k9viLMtYo2Kfp0/Fe3D0LtLvJuv1WaahJ8o0eLFpn5UqVcJ7772HFStWYOLEifDw8NBr89tvv0Gj0aBXr154+PChFHd3d4evry8OHTqEyZMnQyaToWfPnli+fDnS0tJga2sLANi4cSM8PT3RrFkzAMD+/fuRlJSEvn376vRnZmaGRo0a4dChQ3o5DBs27IXOMy+lUon3339fJ7Z582bUqFED1atX18mpdevWAIBDhw6hSZMmAAArKytpe3JyMlQqFYKDg7Fv3z4kJyfDwcGhwONv3rwZzZs3h5OTk86x2rRpgxkzZuCvv/5C//79sXv3bigUCp1zNzMzw6hRoxAeHl78C0BERERERC/do/QcjNwWiX8fpksxO6UCC96tBX8PeyNmRmRcJleYykutVmPz5s1IT09HUFCQwTYRERE6tzsBwNtvv43t27fn2292drbOOkEpKSkAAI1Go7emj5ZGo4EQQnrpePb988hlcKnjjvt7/0XO4yydTaqUbDwIv42yzX2QcOIeoBH4/2k+eQ9ouN9n8tDLMx/adtpzmzJlCtasWYPp06djwYIFeu2ioqIghICvr6/B/szNzaW2vXr1wvz58xEWFoZ+/fohLS0Nu3fvxkcffST1GRUVBeBp0edZ9vb2OueiUCjg6elZ6PMzdJ55eXp66uQMANevX8eVK1fg6upqsK8HDx5I7Y8dO4apU6ciIiJCbz2tpKQk2Nvr/kvm2eNfv34dFy9efO6xbt++DQ8PD9jY2Oj0UbVqVanf/K6JdltBY9wQ7bgvyj70+uO4oJLE8UTFxbFDJYnjiYqrsGMnNiULI7f9g3tJT3//OVmbY+G7fqjqasuxRwBer++iopyDSRamIiMjERQUhKysLNja2mLbtm2oWbOmwbZxcXEoW7asTqxs2bKIi4vLt//p06fjq6++0osnJCQgKyvLwB6ASqXK94e9poCCgEFqgUfnYuEeXAG3k67+f3HqSR/m9kqUbV4eqbcfQ6j//zjPDMz8ChCaYhQetP0BTwtzFSpUQP/+/fHjjz9iwoQJOtu1/5TJZPj999+lNaLysrV9+sXasGFDVKhQAZs2bUKfPn2wY8cOZGZmomfPnlIb7QLoq1at0vtbAk8KUdq2QggolUqdfIrq2WtpZWWl/zfVaODv74/Zs2cb7MPb2xsajQY3btxAmzZtUL16dcyZMwdeXl6wsLDAnj17sGDBAuTm5uodz9Cx2rRpg08//dTgsapWrSp9QRk677zX5nmF1cTERJibmxtsk99+ycnJEEJALje5JenISDguqCRxPFFxcexQSeJ4ouIqzNi5l5KNSftvIyFDJcVcrc0xrVU5OIoMxMfrPyyK3kyv03dRampqoduaZGGqWrVqOH/+PJKTk7FlyxYMGjQIR44cybc4VVTPLiqdkpICb29vuLq66s1u0crMzERiYiLkcrneAHGq6Qpb74Jv1zJEbmmGyv38cff3KKhScmDhYAmvjr6wcLKEnUwGa3e7J+3MdY9Ztll5qLPVev2Z21lI7YoyiGUymbSPdr/PP/8c69atw+zZs3W2A0DlypUhhEDlypWl2ToF6dmzJxYuXIi0tDRs3rwZFSpUkG6DA4AqVao8Oa+yZdGuXbtC51pceffNr7/KlSvjwoULaNu2rdTGkF27diE7OxthYWEoX768FD9y5IjUr7Zv7dP4DB0rPT39uefu4+ODP//8ExkZGdJtkcCTGVfa/vO7LnK5HDKZDM7Ozjq3Hj6Ptgjp6upa6r8YqeRwXFBJ4nii4uLYoZLE8UTF9byxE5WQhsmHbiEpBzBXPPkfxN5OlljUrRbc7SxfdbpkwkROMoTCThpPstxUyCyKXmcwFZaWhR/fJlmYsrCwkIoV9erVw+nTp7FgwQIsX75cr627uzsePHigE3vw4AHc3d3z7V+pVEqzbvIyVHTKu01bWHi2UGFhbwkL++J9qQiNQPmu1aXb95TOVpDJZbB0sc53H8syNvn3l2cmVUEFlby07fKeW5UqVTBgwACsWLECPj4+UCgU0raQkBBMnjwZX3/9NdauXatzHO2sHBcXFynWp08fzJ49G6tXr8bevXsxevRonX3at28Pe3t7TJ8+Ha1bt9ab0ZOQkKB3m1thzy2/88xvm1avXr2we/durFy5UrrtUCszMxMajQY2NjZQKBR6fSQnJyM0NFSKaeM2NjZISkoyeKypU6fijz/+wNtvv62zLSkpCba2tlAoFOjUqRN+/PFHLFu2TFpTTa1WY9GiRXrHMnR+2sJVUf9jq7j70euN44JKEscTFRfHDpUkjicqrvzGzsWYFIwJ+wep2bnA//9num8ZGyzq7g9nawsjZEqmKjctBqmXQmHnNxjW1jZAVjxS/v+9wvbF1pE2lqJ8l5pkYepZGo1GZ02ovIKCgnDw4EGMGTNGiu3fvz/fNalMjUwug9LZCp5vV4GZpQIyedELLi+Ldq2pa9eu6Sw8X7lyZXz77beYNGkSoqOj8e6778LOzg63bt3Ctm3b8NFHH+nclla3bl1UqVIFU6ZMQXZ2Nnr37q1zHHt7eyxduhTvvfce6tatiz59+sDV1RV37tzBrl270LRpU6n48qq899572LRpE/773//i0KFDaNq0KdRqNa5evYpNmzZh3759qF+/Ptq1awcLCwt06dIFQ4cORVpaGn788Ue4ubkhNjZWp8969eph6dKl+Pbbb1GlShW4ubmhdevWGD9+PHbs2IHOnTtj8ODBqFevHtLT0xEZGYktW7YgOjoaZcqUQZcuXdC0aVNMnDgR0dHRqFmzJn777TckJ+svhE9ERERERMZz6s5jfLrzMjJVT+90qeVuhwXv1oK9ZeGX1qDXnzorCamXQpFxYwdUSTdgGzAMiReXQvXoEgDAvvZwmFk6GjfJl8zkClOTJk1Chw4dUL58eaSmpmL9+vU4fPgw9u3bBwAYOHAgPD09MX36dADA6NGjERwcjLlz56JTp07YsGEDzpw5gxUrVhjzNIpEJpdBYW16X07aWVOrVq3S2zZx4kRUrVoV8+bNk9br8vb2Rrt27fDOO+/ote/duze+++47VKlSBXXr1tXb3q9fP5QrVw4zZszA7NmzkZ2dDU9PTzRv3lzviXmvglwux/bt2zFv3jysXr0a27Ztg7W1NSpVqoTRo0dLtzBWq1YNW7Zsweeff45PP/0U7u7uGDZsGFxdXTFkyBCdPv/3v//h9u3bmDVrFlJTUxEcHIzWrVvD2toaR44cwbRp07B582asXr0a9vb2qFq1Kr766ivpqX5yuRw7duzAmDFjpJlq77zzDubOnYs6deq88mtERERERET6jtx4iEm7r0Klfrr+a31vR8ztUhPWFib3E5yMzMzSEXZ+g6F6dBlZ944gI+48zM3NYe7iBzu/wa99UQoAZKKojzZ7yf7zn//g4MGDiI2NhYODAwICAvDZZ5+hbdu2AICWLVuiQoUK0q1SALB582Z8/vnniI6Ohq+vL2bNmoWOHTsW+pgpKSlwcHBAcnJygWtMXb9+Hb6+vkVao+dV0y6Arb31kAgo/vjVaDSIj4+Hm5sbp7WThOOCShLHExUXxw6VJI4nKq5nx86eKw8w9Y8oaPL8zG5eyQUzOtaAhYJji/KXFXsKcVvfBizLwNzaBWXaLIXSrfROQChMnUXL5Mq1P/30U4HbDx8+rBfr2bMnevbs+ZIyIiIiIiIiIjJM+6TyrRdjMPPPf5F35ke7aq74ql01KMxYlKL85abFIOXicpjZeCA3LQbCwhrJ5xbBuek3pXaNqaLgp4OIiIiIiIioiNKycxGXmoM4lTn+fZiB9Bw1PPI8FKubvwe+ebs6i1JUIO0aU6pHl6AsF4Sy3X6HhVs9qB5dQuqlUKizkoyd4ktncjOmiIiIiIiIiExZQlo2Vpy4jT+uxePmw3TkChm6+7tjQqsqmHXoX7zlWwajmlXk8ipkUG7afWTd+wu21ftKa0wBgJ3fYKSrbeDc7FvpKX1vwhpTLEwRERERERERFVJadi5WnLiN7ZFxiEvNRmJmLmQyGVaduQcA+KJtVdTzcmBRigzKjj+HxPCJ0GQnQ25hB+tKnaGwLQf72sMhs7BHZnw87Nzc34in8WlxTiERERERERFRIaVm52L/tQTEpmbhcaZKZ9vRW4nwdLBkUYoMSv93Ox4dHAlNdjIAIOnUDGQnXAAAvSLUm1KUAjhjioiIiIiIiKjQHmeqcP1hOpKzcnXiHvZKWCjkSM5S6aw1RSQ0aqScW4i0axt14ubO1aGw9TJSVqaDhakiEkI8vxGRieG4JSIiIiJ6cblqDYQQ0Dzzn9eeDpawt1TAxsIMDpbmxkmOTJImJxWPj32BrNgTOnHriu3h2HAyZGYWRsrMdPBWvkJSKJ7U8HJzc5/Tksj0qFRPphhrxzERERERERWNSq3BpN1X8fe9ZHT3dwcAyGRAOTsL2Cuf/Hd2u6qusFPyv7npidyUO0j44z+6RSmZDPaBI+DY+EsWpf4fPzGFpFAoYGZmhqSkJNjb2xs7HaIiSU5OhpmZGQtTRERERETFkJOrwWe7LuPorUREJaRhQqsqkMmAE7cfIzc3FzYWZmhXzRUfNfaBLQtTBCA77jQSj06BJidFisnMreEU9BWsvJobMTPTw09MIclkMnh4eODevXuwtLSEra2tSS5oJ4SARqOBXC43yfzo1RJCIC0tDUlJSfDy8uKYICIiIiIqouxcNT7ZcRkn7zwGAMSkZGH+Xzcws3NNWCrMkJieBWebJ7fysShFAJAetQVJZ78HhEaKKWw94NxiNswdqxgxM9PET00RODk5ISMjAw8ePEBcXJyx0zFICAEhBGQyGYsQBOBJUdXZ2RlOTk7GToWIiIiIqFTJVKkxNuwSzt5LkmJW5maY9JYvKrnYQKPRwDwnFS529pDLuVLOm05ocpF89nukX/9NJ27hWhvOzWfAzJK/yQxhYaoIZDIZvLy84OHhgZycHGOnY5BGo0FiYiKcnZ35xUgAAAsLC5iZmRk7DSIiIiKiUiU9Oxejwy7hQkyyFLOxMMPCd/0RUO7p8i5qtdoY6ZEJ0mQlIvPuIZ2YdaXOcGzwGWRmXBQ/PyxMFYOZmRmsrKyMnYZBGo0G5ubmsLKyYmGKiIiIiIioGFKzcvHx9kj8E5cqxeyUCizq5o+a7nZGzIxMmZm1G5ybTcejP0dCCA0c6oyCTbU+vJvpOViYIiIiIiIiIvp/KVkqjPwtElfi06SYg6U5Fnf3RzU3WyNmRqWB0i0Qjo2mQK50hGW5IGOnUyqwMEVEREREREQE4HFGDkb8FonrD9OlmLO1ORZ3D0CVMjZGzIxMjRACWfePwtKzKWQy3buVrCt2MFJWpRPv9SIiIiIiIqI33qP0HPx3y0WdolQZGwssC6nNohTpEGoVkk/PQOJf45F68Udjp1PqccYUERERERERvdHiU7MxbOtF3EnKlGJutkosDfFHeSdrI2ZGpkadlYTHRychO/4cACD10i9QOFSEdYV2Rs6s9GJhioiIiIiIiN5YcSlZGLb1Iu4lZ0kxDztLLOsRgHIOlkbMjEyNKvkmEo98ity0mKdBmRxClZr/TvRcLEwRERERERHRG+l+ciaGbYlEbOrTopSXgyWWhgTA3Z5FKXoq6/4xJB7/AkKVIcXk5rZwavYdLD0aGTGz0o+FKSIiIiIiInrj3HmcgWFbIxGfli3FfJyssKR7ANzslEbMjEyJEALpV9cj+fwiQAgprrDzhnPwHJjb+xgxu9cDC1NERERERET0Rrn1KAPDtl7Eo4wcKVbJ2RpLQgLgYmNhxMzIlAh1DpJOz0LGzd914kr3BnBu+h3kSnsjZfZ6YWGKiIiIiIiI3hj/PkzH8K0X8ThTJcWqutpiUbdacLJmUYqeUGclIjF8EnISLujEbar2gEPdMZDJWU4pKbySRERERERE9Ea4Fp+GEb9FIjnraVGqhpstFnX3h72luREzI1Oienwdj/4aD3V63NOgTA7H+p/AxjfEeIm9pliYIiIiIiIiotfepbhUjNoWidTsXCnm726PBe/Wgp0lfxrTE0IIJJ2aplOUklvYw7nZNCjd6xsxs9eX3NgJEBEREREREb1MF2NSMHzrRZ2iVO1yDljUnUUp0iWTyeDU5GvILZ6sH6Ww94Hr2z+xKPUSsTBFREREREREr62/7yVh5LZIZKjUUqyelyN+6FYL1hYsSpE+hZ03nJtPh6VnM7i2WwmFnbexU3qt8VNIREREREREr6VTdx5j3I5LyM7VSLHGPk6Y06UmlAozI2ZGpkKdlQiZwhpyhaVOXFm2HpRl6xkpqzcLZ0wRERERERHRa+d4dCLGhukWpZpVdMbcLn4sShEAICfxGhL2vo+kk99CCGHsdN5YnDFFREREREREr5UjNx5i0u6rUKmfFqVaVSmD7zpUh7kZ52cQkHn3EB5HfAWRm4XM2w9g7lAJdrWGGDutNxI/kURERERERPTaOBCVgM9+v6JTlGpb1RXTWJQiPHnqXuo/PyMxfBJEbpYUz7xzEEKdY8TM3lycMUVERERERESvhb1X4/HlvmvQ5Lktq0N1N0xtVw1yucyImZEpELnZeHzyG2TePqATt/RsBqcmX0FmZmGkzN5sLEwRERERERFRqbfzUhy+2R+FvCsFvePnjilv+bIoRVBnJCAxfAJyHl3RidvWGAD7wOGQyTibzlhYmCIiIiIiIqJS7bfIWEw/eF0nFhLggQktq7AoRch5dBmJf30GdWaCFJPJzeHYcCKsK3UyYmYEsDBFREREREREpdim8zGYffhfnVjfOp4Y26ISZDIWpd50Gbf3I+nENzrrR8ktneHSfAYsXAOMmBlpsTBFREREREREpdLas/ewIPymTmxgfW+MbFqBRak3nBAapEauROo/P+vEzR2rwDl4NhQ2HkbKjJ7FwhQRERERERGVOj+fuoOlx6N1Yh80Ko+PGvuwKEVIu7xaryhl6RUMp6AvITe3NlJWZAhX9yIiIiIiIqJSQwiBFSdu6xWlhjWpgKFBnClFT9hU6Q6Fraf03s5vMJybT2dRygSxMEVERERERESlghACi49F48cTt3XiHzeviCENyxspKzJFcqU9nIPnQG7pDKegqbCv/V8+ec9E8VY+IiIiIiIiMnlCCMz76yZ+PXdfJ/5JcGX0qeOZz170phDqHMjMLHRi5g4VUfad3yBXWBopKyoMlguJiIiIiIjIpGk0ArMP39ArSk16y5dFqTecEBqknF+ChwdH6Dx5T4tFKdPHwhQRERERERGZLI1GYNqf17H5QowUkwH4om1VdPfnk9XeZBpVBhL/+gypl1cj52Ekkk5NhxDC2GlREbEwRURERERERCZJoxH4en8Uwv6Jk2JymQxT366Gd/zcjZgZGVtueiwe7v8QWffDpVjGrT3IeXjRiFlRcXCNKSIiIiIiIjI5ao3A//ZdxR/XEqSYXCbDtx2qo21VVyNmRsaWnXABieETocl6LMVkZko4Bf0PStfaRsyMioOFKSIiIiIiIjIpKrUGU/ZcxaF/H0oxhVyG6R1roGWVMkbMjIwt4+bvSDo1A0KTK8XMrN3g3GImLJxrGDEzKi4WpoiIiIiIiMhk5ORqMHH3FYTffCTFLMzkmNm5BppVdDFiZmRMTxY5X4y0K+t04hYuNeDcYjbMrFiwLK1YmCIiIiIiIiKTkJ2rxqc7L+PE7ae3aCkVcszp4ofGPk5GzIyMSaNKx+Nj/0NWzDGduJVPWzg1+hwyhdJImVFJYGGKiIiIiIiIjC5Tpca4HZdw5m6SFLMyN8O8rn6o5+VotLzIuHLT7iPxyKdQJd/SidvX/i9saw6CTCYzUmZUUliYIiIiIiIiIqPKyMnF6LBLOH8/WYpZm5thYbdaqF3OwYiZkTGpkm/i4YFh0GQ/HRcyhSWcgqbCyrul8RKjEiU3dgJERERERET05krLzsXIbf/oFKVsLRRY3N2fRak3nMLWCwr7CtJ7M+uycG27gkWp1wwLU0RERERERGQUKVkqjPgtEpGxKVLMXqnA0h7+qOVhb8TMyBTIzCzg3HwGzGzcYVHGH67tf4G5U1Vjp0UljLfyERERERER0SuXlPmkKBWVkCbFnKzMsbi7P3xdbY2YGZkSM0snlHlrCcysykBmZmHsdOgl4IwpIiIiIiIieqUSM3IwdPMFnaKUi7UFlvUIYFHqDZWbcgePDo+DOitJb5vCthyLUq8xFqaIiIiIiIjolUlIy8bQzRdxMzFDirnZKrGiZwAqudgYMTMyluy400j44wNkxRzH46OTINQqY6dEr9ALF6bS0tLw999/Izw8vCTyISIiIiIiotfUg9RsDN1yEdGPnxal3O2UWN4jAOWdrI2YGRlLetQWPDw0GpqcJ+uMZcefQ+o/K42cFb1KxS5MRUdHo2vXrnByckKDBg3QqlUraduxY8dQs2ZNHD58uCRyJCIiIiIiolIuJjkLH22+gLtJmVLM08ESK3rWhpejlREzI2MQmlwknZ6NpDNzAKGR4hautWFTrY8RM6NXrViFqTt37qBx48bYvXs3unbtiqCgIAghpO2NGjXCw4cP8euvv5ZYokRERERERFQ63U3KxEebLyAmJUuKlXe0wooeteFhb2nEzMgYNNkpeHR4LNKvb9WJW1fqjDKtF8HM0slImZExFKsw9eWXX+Lx48c4cuQItmzZgrZt2+psVygUaN68OY4dO1YiSRIREREREVHpFJ2YgY82X8CDtGwpVsnZGst7BsDNTmnEzMgYVMnRSPhjCLLjTj8NyuRwqPsxHBtNgczM3HjJkVEUqzC1b98+dOvWDU2aNMm3jY+PD+7fv1/kvqdPn44GDRrAzs4Obm5uePfdd3Ht2rUC9wkNDYVMJtN5WVqy6k5ERERERGRMNx6mY+iWC3iYniPFqpSxwdIeAShjw6LUmyYr5gQe/vEBclPvSTG5uQ1cgufAtno/yGQyI2ZHxqIozk6JiYmoUKFCgW2EEMjOzi6wjSFHjhzBiBEj0KBBA+Tm5mLy5Mlo164dLl++DBub/J/QYG9vr1PA4oAmIiIiIiIynqiENAzfGonkrKdPWKvuZotF3fzhYMVZMW8SIQTSr21E8rmFOutJKWzLwTl4DswdKhkxOzK2YhWmypYti+vXrxfYJjIyEuXLly9y33v37tV5HxoaCjc3N5w9exYtWrTIdz+ZTAZ3d/ciH4+IiIiIiIhK1uW4VIzaFomU7Fwp5lfWDj9084edZbF+hlIpJTRqJJ+ZhfR/w3TiSre6cG4+HXKlg5EyI1NRrG+Etm3bYs2aNbh48SICAgL0toeHh+PPP//EmDFjXjQ/JCcnAwCcnZ0LbJeWlgYfHx9oNBrUrVsX06ZNg5+fn8G22dnZOrO5UlKePJZSo9FAo9EY3Ke00Gg0EEKU+vMg08DxRIZwXFBJ4nii4uLYoZLE8VSyLsamYPT2S8jIUUuxgHJ2mN/VDzYW8tfqOnPsPJ8QgCY3SydmXbkr7Ot9AsgVvHZ5vE7jqSjnIBN5H6dXSNHR0QgMDAQAjB8/HlevXsX69evx+++/4/jx4/j+++9hY2ODCxcuwMPDo6jdSzQaDd555x0kJSXh6NGj+baLiIjA9evXERAQgOTkZMyZMwd//fUXLl26BC8vL732U6dOxVdffaUXj4qKgp2dXbHzNQUajQbJyclwcHCAXF6sJcSIJBxPZAjHBZUkjicqLo4dKkkcTyXn4oN0fHnoDjJVT3+UBpS1wdRW3rA2NzNiZi8Hx07hCHUOVKcnQpNyHYrqH8HMuzOX3zHgdRpPqampqFq1KpKTk2Fvb19g22IVpgDg5MmT6NOnD27fvg2ZTAYhhPTP8uXLY8uWLahfv36xTkBr2LBh2LNnD44ePWqwwJQflUqFGjVqoG/fvvjmm2/0thuaMeXt7Y3Hjx8/94KZOo1Gg4SEBLi6upb6gUzGx/FEhnBcUEnieKLi4tihksTxVDJO3UnCJzsvIyf3aVGqYXlHzO5cA5avYVEK4NgpCnXmI+Qm34TSvYGxUzFZr9N4SklJgZOTU6EKU8W+ubdRo0a4fv06du7ciZMnTyIxMRH29vZo1KgRunbtCgsLi+J2DQAYOXIkfv/9d/z1119FKkoBgLm5OerUqYN///3X4HalUgmlUv8JEHK5vNT/8YEn6229LudCxsfxRIZwXFBJ4nii4uLYoZLE8fRijkcnYvzOy8hRa4D/nwjTpIIzZneuCQvF631NOXae0i5yriwXBHN7H51tchtXmNu4Gimz0uN1GU9Fyf+FVp1TKBTo1q0bunXr9iLd6BBCYNSoUdi2bRsOHz6MihUrFrkPtVqNyMhIdOzYscTyIiIiIiIiIn1/3XiEibuvQKV+OlOqRSUXTO9Y47UvStFTQp2DpNOzkHHzdyiub4Vru58gV5buO5Lo1SjWt0Tr1q2xevXqAtusXbsWrVu3LnLfI0aMwNq1a7F+/XrY2dkhLi4OcXFxyMzMlNoMHDgQkyZNkt5//fXX+OOPP3Dz5k38/fffGDBgAG7/H3v3HR5F2XYB/MxsTU82lUBoofcWeq8iSFOsr3wgoqIiRYUXOzaKoGJBLBS7IkgRkd577zWEmt432c3Wme8PdMO+AUyWwGST87suL917Sw7wuGFPZp65dAlPPvlkib8+ERERERERFc/GcxmYuPKkWynVo3Y4pvdjKVWROC1ZyNj4PMwJKwEAjrwryNrxKmTZ+zfxpjvPoyOmNm/ejK5du97yMZcuXcKWLVtK/NpffPEFABR5/QULFmD48OEAgMuXL7sdFpadnY1Ro0YhJSUFISEhaNmyJXbu3IkGDRqU+OsTERERERHRv1tzJg1vrD4D6bpti/vWi8CbvetCJXJj64rCnn0OmVtfhtOUUjgURPjEdIMgsJykf3dbp/LdislkgkajKfHzirMX++bNm91uf/TRR/joo49K/LWIiIiIiIio5FaeTMU76866lVL3NYjCaz1rQ2QpVWEUXN2K7J1vQnYUnuEkagNh6Pg+dFG3dzE0qjiKXUxdvnzZ7XZOTk6RGXBtf6crV65gyZIlqF69+m0HJCIiIiIiorJj2fFkvL/+HK4/pGBI40qY1K0WS6kKQpZl5J/8HsajXwDXlZPqwGoI7TIT6oAYBdORtyl2MVW9enUIwrU3GUEQMHv2bMyePfumj5dlGR988MHtJyQiIiIiIqIyYdHhJHyw2f3q5w81q4wXu9R0fV6k8k122pCz932YL6x2m+uj2yGk/TsQtf4KJSNvVexiatiwYRAEAbIs47vvvkPTpk3RrFmzIo9TqVQwGAzo3r077rnnntLMSkRERERERAr56eBVfLQ1wW32eMsqGNOxBkupCsJZkImsbZNgyzjuNvev9zACm42BIKoUSkberNjF1MKFC13/vWXLFowYMQIvvPDCnchEREREREREZcjCfVfw+Y4LbrMnWlfFM+2qsZSqIGxZZ5C1dSKc5lTXTBDVCIqbCL/YAQomI2/n0ebnFy5c+PcHERERERERkVeTZRnf7LmMr3Zfcps/3a4anmxTTaFUpARr0k63UkrUBcPQaSp0Ec0VTEXlAa/dSEREREREREXIsowvdl4qUkqN6ViDpVQF5N/w/+BTtTsAQBNUA+F95rOUolJRrCOmunfvDkEQ8O2336JKlSro3r17sV5cEARs2LDhtgISERERERHR3SXLMmZvu4AfD151m4/vXBOPtqiiUCpSkiCICG77BlR+lRDQ6AmIGj+lI1E5UaxiavPmzRAEAWaz2XW7OHiuMRERERERkXeRZRkzN5/HoiNJbvNJ3WrhgabRCqWiu8lpTofTnAptWCO3uajWI6j5GIVSUXlVrGJKkqRb3iYiIiIiIiLvJ0kypm2Kx9Jjya6ZAODVnnUwsFGUcsHorrFlnkTW1kmQJRvC+8yH2r+y0pGonOMeU0RERERERARJkvHO+rNupZQoCHirT12WUhWE+dI6ZKx/Bs6CdEjWXGRteQmS3aR0LCrnPLoqHxEREREREZUfTknGm2vOYM2ZNNdMFAS8c09d9K4boWAyuhtkWULesXnIOz7P/Q5BBdluArifFN1BLKaIiIiIiIgqMIdTwqt/ncbG+AzXTC0KeK9vfXSvHaZgMrobJEcBcnZNQcGVzW5zfZUuCGn3JkSNryK5qOJgMUVERERERFRB2RwS/rvqFLYlZLpmGpWI6f3qo1PNUAWT0d3gNKUic+vLsGefdZsHNByOgCZPQRC4+w/deSymiIiIiIiIKiCrw4mXV57CrotZrplWJWLmfQ3QrrpBwWR0N9gyjiNz60RIlsI/f0GlRXDrV+Bb4x4Fk1FFw2KKiIiIiIiogrHYnZiw4gT2XclxzfRqER8OaIS4qsGK5aK7w3xhNXL2vg/ZaXPNVPpQGDpPhzaskYLJqCJiMUVERERERFSBmG0OjFt+AocSc10zX40KHw9qhOaVgxRMRneaLEvIO/ol8k586zbXhNRBaOcPoPKLVCgZVWQspoiIiIiIiCqIfKsDLyw7jmPJRtfMT6vCJ4Mao0l0oILJ6G6wpR4sUkr5xHRDcLs3IKp9FEpFFd1t7WS2dOlSPPjgg2jSpAlq1arlmp8+fRozZsxAYmLibQckIiIiIiKi22e02PHc78fcSqlAnRpzhjRhKVVB6KJaIaDBMNftgMZPIqTjeyylSFEeHTElSRIeeeQRLF68GADg4+ODgoIC1/0hISF49dVX4XQ6MXny5NJJSkRERERERB7JKbDj+d+P4Ux6vmsW7KPB50Mao064v4LJ6G4LaPoMHKYk+FTpCp9qPZWOQ+TZEVMfffQRfvvtNzz99NPIzs7GSy+95HZ/ZGQkOnXqhD///LNUQhIREREREZFnssw2jF5y1K2UMvhq8OUDTVhKlXOO/KQiM0EQYejwLkspKjM8KqYWLlyIuLg4zJkzB4GBgRAEochjatWqhQsXLtx2QCIiIiIiIvJMhsmKZxYfRXyGyTUL99Piqweaomaon4LJ6E6SZQm5hz5F2sqHYEs/qnQcolvyqJiKj49Hp06dbvmY0NBQZGZmehSKiIiIiIiIbk9anhVP/3YUF7LMrlmkvw5fDm2KagZfBZPRnSTZTcja8jLyT/0IWbIjc9t/4TClKB2L6KY8KqZ8fHyQm5t7y8dcunQJwcHBnrw8ERERERER3YakXAtG/XYEl3MK9wKODtTjq6FNERPMja7LK0d+IjLWPglL0g7XTLJkwZqyV8FURLfm0ebnzZs3x5o1a2CxWKDX64vcn5WVhdWrV6Nz5863HZCIiIiIiIiK72pOAUYvOYqUPKtrVjXYB3Pub4LIAJ2CyehOsqYdQta2/0KyFh5EIqj1CGn3JnxiuimYjOjWPDpi6oUXXsDVq1dx//334+rVq273nT9/HoMHD0Zubi5eeOGFUglJRERERERE/+5SlhlP/XbErZSqHuKLL4eylCrPTPHLkbnhebdSSuUbifBeX7GUojLPoyOmBg4ciEmTJmH69OmoVq0a/PyubZoXERGBzMxMyLKM119/Hd27dy/VsERERERERHRjCZkmjF5yFFlmu2sWG+qHOfc3hsFXq2AyulNkyQnjoU+Qf+ZXt7k2rBEMnaZD5ROqUDKi4vPoiCkAmDp1KtasWYP+/fvD19cXKpUKkiThnnvuwV9//YUpU6aUZk4iIiIiIiK6ibPp+Xh6sXspVSfcH3MfaMJSqpySbPnI2vJikVLKt8Y9COsxh6UUeQ2Pjpj6R69evdCrV6/SykJEREREREQldDIlD2OWHoPR6nDNGkQG4NPBjRCo1yiYjO4Uh/EyMre+DIfxUuFQEBDYdDT86z8OQRCUC0dUQrdVTBEREREREZFyjiUbMWbpMZhsTtescaVAfDKoEfx1/LhXHkmOAqSvfwaSJcs1EzS+CGk3BT5VOimYjMgzHr1TXb58udiPrVq1qidfgoiIiIiIiG7hUGIuxi07DrO9sJRqXjkIHw9sCF8tS6nySlT7ILDpM8jZ8z4AQO1fCYbOH0ATXEvhZESe8ejdqnr16sU6NFAQBDgcjn99HBERERERERXfvss5mLDiOCwOyTWLiwnGrAEN4aNRKZiM7ga/2AFw5CbAlnkKhk7ToNKHKB2JyGMeFVPDhg27YTGVm5uLI0eO4MKFC+jSpQuqV69+u/mIiIiIiIjoOrsuZuGlP07C5iwspdpVN+CD/vWhU7OUKm9kpw2CqugG9oHNxgCy84b3EXkTj4qphQsX3vQ+WZYxa9YszJgxA/PmzfM0FxEREREREf2PbQmZmPTnKdivK6U61QzFtHvrQ6v2+KLrVEbZjZeQteUlBDR5Cr7V3C88JogqACwiyfuV+juXIAh46aWX0LBhQ7z88sul/fJEREREREQV0qb4DExcedKtlOpWKwzT+7GUKo8syXuQsWYkHHlXkLP7HdgyTygdieiOuGPvXq1atcLGjRvv1MsTERERERFVGGvPpOG/f56CQ5Jds951wzH13vrQqFhKlSeyLCP/zK/I3Dwekj3/2sxpQ+7BTyDL8r88m8j73LFLNZw/f54bnxMREREREd2mVadSMWXtWUjXlRL96kfijV51IIr/flEq8h6y5EDu/pkwxS9zm+siWsDQaWqxLkJG5G1KtZiSJAmJiYlYuHAhli9fjh49epTmyxMREREREVUoK06k4N11Z3H9cTKDGkVhcvfaLKXKGcmai6xtk2FNO+g296s1CEEtX4Sg0iiUjOjO8qiYEkXxlk2tLMsICQnBrFmzPA5GRERERERUkS0+koTpm+LdZkObRuOlLrEspcoZe24Csra8BEd+UuFQEBHUYhz86gzlkVJUrnlUTHXu3PmG/2OIooiQkBDExcVhxIgRiIiIuO2AREREREREFc3PhxLx4ZbzbrPHWlTB2E41WFKUM5akncja8Rpku9k1EzX+COn4HvSV2iiYjOju8KiY2rx5cynHICIiIiIiIgD4bv8VfLr9gttsRFxVjG5fjaVUOSLLMkynf0Lu4c+A6/YPUwfEwNBlJjSB1RRMR3T33LHNz4mIiIiIiKhkvtlzCV/uuuQ2e6ptNTzZpipLqXLGeGg28k//4jbTRcXB0OE9iLpAhVIR3X28rigREREREZHCZFnGFzsvFimlnutQA6Pa8kip8kgf3QEQCj+S+9V5AKFdP2IpRRVOsY6Y6t69u0cvLggCNmzY4NFziYiIiIiIKgJZlvHp9gv4/sBVt/n4zjXxaIsqCqWiO00XFYfglhOQc+BDBLd6EX6171c6EpEiilVMebqnFFt9IiIiIiKim5NlGbO2JODXw4lu85e71sKDzaIVSkV3i1+dB6CNbAVNUHWloxAppljFlCRJdzoHERERERFRhSJJMqZtisfSY8mumQBgco/aGNy4knLBqFTJsoz8k98DkBDQcHiR+1lKUUXHzc+JiIiIiIjuMkmS8e76c/jjZIprJgoCXu9VB/0bRCqYjEqT7LQhZ+/7MF9YDQBQB1aHT0xXZUMRlTHc/JyIiIiIiOguckoy3lx7pkgp9fY9dVlKlSPOgkxkbHjWVUoBQPaut+A0pyuYiqjsua0jpiwWC/bt24ekpCRYrdYbPmbYsGG38yWIiIiIiIjKDYdTwmurz2DDucJyQiUIeP/e+uheO0zBZFSabFlnkLV1IpzmVNdMENUIajkBKt9wBZMRlT0eF1Off/45Xn/9deTm5t7wflmWIQgCiykiIiIiIiIANoeEV/46hS3nM10zjUrEtHvro3NsqILJqDQVXNmE7F1TIDssrpmoC4Kh0zToIpormIyobPLoVL7ff/8dY8aMQUxMDGbOnAlZljFw4EC8//77uOeeeyDLMu6//37Mnz+/tPMSERERERF5HZtDwssrT7qVUlqViJn3NWApVU7Isoy84wuQtW2yWymlCaqB8D4LWEoR3YRHxdTHH3+MiIgI7Nq1C+PHjwcANGvWDJMmTcKff/6JH374AcuWLUO1atVKNSwREREREZG3sdidGL/iBHZezHLNdGoRHw1siPbVDQomo9vhtOS4/lt22mBLO4SCxO1uj9FX7oiw3t9A7R99l9MReQ+PiqmjR49iwIAB8PX1dc2cTqfrvx999FF0794db7/99u0nJCIiIiIi8lJmmwNjlx/H3svZrpmPRoVPBjVC66ohCiaj2+HIT4LxyBxI5hTo1U44sk4i78QCBDYeBU1oQwCAf/3/wNB5BkSNn8Jpico2j/aYstvtCA8v3LDNx8cHOTk5bo9p2rQpvvrqq9sKR0RERERE5K1MVgdeWH4cR5OMrpmfVoVPBjVGk+hABZPR7XBacpB3YiHM51fAnnkKfvUfQ9b+mbCm7gcABNR/DLKjAL41+ymclMg7eFRMRUdHIzk52XW7WrVqOHTokNtjLl26BLX6ti76R0RERERE5JXyLA68sOwYjqfkuWYBOjU+G9wYDaICFExGt0ulD0ZAw+Gw55yHLe0gTH8+AgEydJGt4N/gcah0BmhCaikdk8hreHQqX1xcHA4ePOi6fc8992DHjh2YOnUqTpw4gS+//BK///474uLiSi0oERERERGRN8gtsOPZ34+6lVJBeg2+uL8JS6lyQu0fjaDmz0NQ6SD4RAEAglpPhCakDkspohLyqJgaOnQorFYrLl68CACYPHkyqlSpgtdeew1NmjTB6NGj4e/vjxkzZpT4tadOnYq4uDgEBAQgIiICgwYNwpkzZ/71eb/99hvq1asHvV6Pxo0bY9WqVSX+2kRERERERLcjy2zD6CVHcTot3zUz+Gow94EmqBvhr2AyKk2O/CTkHvrs2g21L1T+lZF/6hfIdrOywYi8ULGLqV9++QU2mw0AMHjwYJw6dQrVq1cHAISHh+Pw4cOYNm0annrqKbz//vs4fvw4GjduXOJAW7ZswXPPPYfdu3dj3bp1sNvt6N27N0wm002fs3PnTjzyyCMYOXIkDh06hEGDBmHQoEE4fvx4ib8+ERERERGRJzJMVoxefBTnMgo/u4T5aTH3/qaoFcYNsMsDe26Ca48pe+YJaEIbIqL3l9BVagt71knknVjodrU+ouLItRUg3ZKPTKsZCPBBptWMdEs+cm0FSke7KwRZluXiPFAURRgMBjz22GN44okn0LRp0zudDQCQnp6OiIgIbNmyBZ07d77hYx566CGYTCasXLnSNWvbti2aNWuGuXPn/uvXMBqNCAoKQm5uLgIDvXsTQkmSkJaWhoiICIiiRwfEEblwPdGNcF1QaeJ6Ik9x7VBpKo31lJZnxeglR3E5p/CDZIS/Dl/c3xhVQ3xv8UzyFqZzvyNn/wcIaf8OtKENkHdiIQIaDofJ6Qc/lcl1W+0frXRU8jLplnwMXr8ADtkJh90BtUYNtaDC0p4jEK73ziMtS9KzFPtd95FHHkFBQQE+/fRTtGjRAnFxcZg7dy5yc3NvO/Ct/PP6BoPhpo/ZtWsXevbs6Tbr06cPdu3adUezERERERERJRsteGrxEbdSKjpQj6+HNmUpVU7kn/4ZOftmALKM7B2vwZ4dj8Cmz0L0jUJBQQFE3ygENn2WpRR5xOZ0wmi3wCnL1/0jKR3rrin2ZfN+/PFHGI1G/Pjjj5g/fz4OHDiAgwcP4sUXX8SQIUMwcuRIdO3atVTDSZKEcePGoUOHDmjUqNFNH5eSkoLIyEi3WWRkJFJSUm74eKvVCqvV6rptNBpdX0+SvPsPX5IkyLLs9b8OKhu4nuhGuC6oNHE9kae4dqg03c56Ssy1YPSSY0jNK/x8UTlYjzlDGiEqQMs1Wg7kn1iIvGNfuc2s6Uegq9zRbe2I2kD+eVOJrUs8g2i/YFw15aKyb9C1kkYGZEEGZHjtmipJ7mIXUwAQGBiI0aNHY/To0Thx4gTmzZuHH3/8ET/++CN++ukn1KhRA0888QT+7//+D5UrVy5x8P/13HPP4fjx49i+ffttv9b1pk6diilTphSZp6enw2KxlOrXutskSUJubi5kWeZh7XTbuJ7oRrguqDRxPZGnuHaoNHm6nq4arfjvukvIMNtdsyqBOrzXpRLEAiPSCox3Ii7dJbIswxn/PRwJv7rN1bGPoqDSA7CkpfG9iG7LvuxETDm1CfO7PAqnJOFyfjYq6/whA5AFAU7JibS0NKVjeiQvL+/fH/S3EhVT12vYsCE+/PBDzJgxAytWrMD8+fOxZs0avPbaa3jzzTfRu3dvjBw5EkOGDPHo9Z9//nmsXLkSW7duRZUqVW752KioKKSmprrNUlNTERUVdcPHT548GRMmTHDdNhqNiImJQXh4eLnYY0oQBISHh/ONkW4b1xPdCNcFlSauJ/IU1w6VJk/WU0KmGa9suoBcG6BRawAANUJ9MWdIIxh8tXcyLt0Fsiwj7/CnMF35HRqNxjUPaDoa/vUfd93mexHdjj5hoVifcxkQAI2ogq9GA71GA61aA5UoQiWqEBoRoXRMj+j1+mI/1uNiyvUCajWGDBmCIUOGICUlBd999x3mz5+Pv/76C2vWrIHD4SjR68myjDFjxmDp0qXYvHkzatSo8a/PadeuHTZs2IBx48a5ZuvWrUO7du1u+HidTgedTldkLopiuXgzEQSh3PxaSHlcT3QjXBdUmrieyFNcO1SaSrKezqXn47nfjyG7wA4I12Z1wv3x2eBGCGEp5fVkWYLxwCyYzv3uNg9qOQH+dR8s8ni+F1FJOCUJqr/XilYUMTWuH05kp2DvwHHwV+sgSU6oRBUgAFpR5bXrqiS5b7uYul52djbS0tKQk5MD4FrJVFLPPfccfvrpJyxfvhwBAQGufaKCgoLg4+MDABg2bBgqV66MqVOnAgDGjh2LLl26YNasWejXrx9++eUX7N+/H1999dVNvw4REREREVFJnU7Lw3NLjsFoLfwBfIPIAHw6uBEC9ZpbPJO8gSxLyNnzHswJfxYOBQHBcZPgV2uQYrmofMi2mvHi3hXoH9MQQ6o3BgD4qrVoHFIJerXGdYXQ0Ap2xdnbLqby8/Px888/Y/78+di7dy9kWYavry+GDRuGkSNHlvj1vvjiCwAospH6ggULMHz4cADA5cuX3f6Q2rdvj59++gmvvfYaXnnlFdSuXRvLli275YbpREREREREJXE82YgxS48j31ZYSjWOCsQngxvBX1eqP/MnBciSA9m73kLBpfWFQ0FESNvX4Vujr3LBqFy4asrBC7uX4aopBydzUhGu90OnqJoAAL26YpfaHr97btmyBfPnz8eSJUtQUFAAWZYRFxeHkSNH4pFHHkFAQIBHr1uco6w2b95cZDZ06FAMHTrUo69JRERERER0K4cTczF22XGY7U7XrFnlIMwe2BC+WpZS3k522pC143VYrm5xzQRBhZCO78InppuCyai82JqSgKumHACAJMuYeXwz2kZUg0ZUKRusDCjRO2hiYiIWLlyIhQsXIiEhAbIsIzQ0FKNGjcLIkSN5hBIREREREZU7B67mYPzyEyi4rpRqFROMDwc0hI+GHyrLA4fxIqwpe1y3BZUWho5Toa/cQcFUVJ48UrM5rphysOTiUVTxC8bsNoNYSv2t2MVU3759sX79ejidTgiCgJ49e2LkyJEYNGgQtFpu8EdEREREROXPnkvZePGPE7A6JNesbbUQzLyvAXRqfqgsLzQhdRDa+QNkbnkRgIDQLjOhi4pTOhZ5uZ2pF9E2oipEQYQgCHi5cVcEanR4uGZzhOh8lY5XZhS7mFqzZg2qVq2KESNGYMSIEahateqdzEVERERERKSoHReyMHHlSdichaVUxxoGTO/XAFp1xdmYuKLQRcXB0HEqBI0vdBHNlY5DXkySJcw5tRPfxe/HY7EtMLZhZwCAKIgYXZ9H4f2vEhVTPXv2hCAIdzIPERERERGR4jbHZ2DyqlNwSIV74HarFYb3+taDRsVSytvJThsEVdEzf3jqHpWGqUc2Yvnl4wCAH88fRIRPAB6pybLzZor9jtqrVy+WUkREREREVO6tP5uO//7pXkr1qhOO91lKlQtOSxbS1zyB/NO/KB2Fyql2EdVwfX1il5w3fzAVv5giIiIiIiIqr1Sqa/tF/XUqFa/+dRrO664W3rdeBN69px7ULKW8ntOchoz1o2HPiUfuwY9hil+qdCQqh7pH18bYBp2hEVV4p2VfDKvVSulIZRrfWYmIiIiIqMLKtzqQkmdDil2DhKwCpJtsiArQue4f0DAKb/WuC1Hk2SPezmFKRsb6Z+AwXnLN8k58C8lRoGAqKg/O5aZj0r6VsDjsrtmjsS3wS9fH0adyXQWTeYdi7zFFRERERERUnqTnW/HV7ktYdyYdl7JMsDiBIY2jMLFbLczYFI921UMwsWstllLlgCPvCjI2PA+nOdU1U/tXQmj3zyGqfRRMRt5ub/plTNq3EiaHDQ5Zwgdx/SEK144BivEPVjacl2AxRUREREREFU6+1YGvdl/CsuMpyDLbkGayQxAEfLv/KgBgUvdaaFcthPvslgP23ARkbnwBzoIM10wdWBVh3T+DyjdCwWTk7WxOB6YcWguTwwYA2JaSgG/j92NE7dYKJ/MuPJWPiIiIiIgqnDyrA2vPpCMt34rUPJvbfZvPZ6CGwZelVDlgzz6LjPXPupVSmuBYhPWcy1KKbptWpcaMuP7Qqa4d8xMXHoMHqzdVOJX3KZViKisrC1euXCmNlyIiIiIiIrrjMk02nErLR6bJ7jYP89PCV6tGrsV+k2eSt7BlnkDGhucgWXNcM42hLsJ6zIFKb1AuGHk1pyQhrSDPdbthSBTeb3kv7qvaAB+3GQQ/je4Wz6Yb8biYys3NxdixYxEZGYnw8HDUqFHDdd+ePXtw77334sCBA6USkoiIiIiIqLScTDHC5pSg+p8josL9tQj318JPq0KQXqNQOioN1rRDyNg4BpKtsEDQhjVGWPfPIOqCFExG3qzAYcfL+/7AkzsWIcNics07RdXE6816QyOqFEznvTwqprKystCmTRt8+umniImJQf369SFfdznVJk2aYMeOHfjxxx9LLSgREREREdHtkGUZS44mYeSiIziRkochjaMAAKIAVArQIMxXCwDoXSccATpux+utrCn7kLl5PGS72TXTRbRAaLfZELUBCiYjb5ZlNeOZnYuxPfUCUsx5GLdnGcwO278/kf6VR8XUW2+9hbNnz+KXX37B/v37MXToULf7fXx80KVLF2zcuLFUQhIREREREd0Oi92Jt9aexbSN8XBIMhYfTcajLapgZJuqaBgVAH+tCn5aFQY3isJTbavBn8WUV5LsZmRtfxWyw+Ka6Su1haHrhxA1vgomI2+nFkQUOAtP8b2cn414Y8YtnkHF5dG77YoVK9C/f388+OCDN31M9erVsXPnTo+DERERERERlYarOQWY9OcpnE3Pd82SjBbsuJiFKX3qwGyTkGWywOCnR6BezVLKi4kaXxg6vofMLS9Cdtqgr9IZhg7vQlBplY5GXi5Qq8fsNoMwYtsvkAF81GYAGgRHKR2rXPDoHTc5ORkPP/zwLR+j0+lgMplu+RgiIiIiIqI7afuFTLyx+gzyrA7XTBQEjO1UA480rwxBEBCok6Cx5SE0IBCiyAuXeztdVBwMnaah4NJaBLd5DYLIopE8syk5Hn5qLVqHVwUAVPINxCdtB8NPrUVlP+5VVlo8+j80NDT0X6/Cd/r0aVSqVMmjUERERERERLdDkmR8vecSvtlz2W1u8NVg6r310aJKsNvc6XTexXR0p+mj20Mf3V7pGOTFFl04jFnHN8NXpcXXHR9ErcAwAECdoHCFk5U/Hv04oHPnzli+fDmuXr16w/tPnjyJ1atXo2fPnrcVjoiIiIiIqKRyC+wYu/x4kVKqSXQgfni0RZFSirxX/tnfkH/6Z6VjUDmzPuksZh7bDFkGTA4bxu5ehiyr+V+fR57xqJh69dVX4XQ60aFDB/z444/IyLi24depU6cwb948dO/eHTqdDi+//HKphiUiIiIiIrqVU6l5ePynQ9h9Kdtt/nCzyvjy/iYI99cplIxKW97J75G7fxZyD86GKX6p0nGoHOkSFYu48BjX7Z7RtRGs1SuYqHzz6FS+xo0b49dff8Xjjz+OYcOGAbh26dVGjRpBlmUEBARg0aJFqF27dqmGJSIiIiIiupkVJ1IwfWM8bE7JNdOrRbzWqw761I1QMBmVJlmWkXd8HvKOfeOa5eydDo2hAbSGugomI29mczqgVV2rSDSiCjNa9cfTOxfj3ir18WhsC4XTlW8e7wI3YMAAXLhwAd9++y327NmDrKwsBAYGok2bNhgxYgTCwsJKMycREREREdEN2RwSPtgcj2XHU9zmVYN9MKN/A8SG+SmUjEqbLMswHpmD/JPfu80Dmz7DUoo8lmw2YuyeZXikZnMMrtYYAOCn0WFBp4ehEVUKpyv/buvyBAaDAePHjy+tLERERERERCWSbLRg0sqTOJWW7zbvEhuKt3rXhb+OV2QrL2RZQu6Bj2A6+5vbPKjFWPjXe0ShVOTtzuSmufaQmn50IyL0/ugQWQMAWErdJXyXJiIiIiIir7T7UjZeXXUKRqvDNRMFAc+2r45hrapAEAQF01FpkmUJOXunwXx+hds8OG4i/GoPUSgVlQd70i+7NjaXZBmfnNyGdhHVIAoebclNHvComPruu+/+9TGiKCIwMBB169ZF3bo8pJKIiIiIiEqHJMlYuP8K5u68CPm6ebCPBu/1rYfWVUMUy0alT5acyNnzDswXVhcOBREhbV6Fb81+ygWjcuHx2JZINOVi6aVjqBMUjo/bDGIpdZd5VEwNHz68RD99qFevHj799FN0797dky9HREREREQEAMizOPDm2jPYlpDpNm8YGYDp/RsgMoBX3StPZKcd2TtfR8GVzYVDQURI+ynwrdZLqVjkxWRZxqbkeHStFAtRECEIAiY27oZwH388WrM5fNVapSNWOB4VUwsWLMDvv/+OP/74A71790aHDh0QGRmJ1NRU7NixA2vXrsWAAQPQuXNnHDx4EL/++ivuvfdebNu2DXFxcaX9ayAiIiIiogrgXHo+Jq48iau5Frf5/U0qYULnWGjVPMqhPJGdNmRtfwWWxO2umSBqENLxPfhU6axgMvJWDsmJaUc3YsXlE3i8VkuMadAJAKASRTxZp43C6Souj4qpoKAgrF27Fhs2bEC3bt2K3L9582bce++9eOKJJzBhwgSMGjUKPXr0wLRp07BkyZLbDk1ERERERBXLqlOpeH/DOVgdkmumVYl4pUdt9GsQqWAyulOyd77hXkqptDB0mg59dDsFU5G3kmUZk/evwpaU8wCA7+MPINInAA/WaKZsMIJHP1J4//338eCDD96wlAKArl27YujQoXj33XcBAF26dME999yD7du33/DxREREREREN2J3SpixKR5vrjnjVkpVDtJjwcPNWEqVY351hkJQXTutSlD7ILTrRyylyGOCIKBX5Tqu26IgQK/SKJiI/uFRMXXixAlUqVLllo+pUqUKTpw44brdoEED5OTkePLliIiIiIioAkrLs+Lp347ityNJbvMONQz47pHmqBPur1Ayuht0kS1h6DQdoj4EYd1mQxfZUulI5OV6V66LFxp2gq9ag4/aDMSAqg2VjkTw8FQ+f39/bNu27ZaP2bZtG/z9C79RmEwmBAQEePLliIiIiIiogtl/JQev/nUKWWa7ayYAeLpddYyIi4EoFv9iTOS99NHtEHnfEogaX6WjkBc6lJmI7+L3Y1qrftCprtUfj9Vsgd7RdRDhw36irPDoiKmBAwdix44dePbZZ5Genu52X0ZGBp577jns2LEDAwcOdM0PHz6M2NjY20tLRERERETlmizL+G7/FTz3+zG3UipQp8bsQY0wsk1VllLlkLMgA+aElTe8j6UUeWJ90lmM2fU7dqRewOsH/4IkXzsVWBAEllJljEdHTE2dOhU7duzA3LlzsWDBAtSqVQsRERFIS0tDfHw8rFYr6tWrh6lTpwIAUlJSUFBQgOHDh5dmdiIiIiIiKkdMVgemrDuLTfEZbvN6Ef6Y3q8BooP0CiWjO8lpSkXGxufhyLsC2WGBX50HlI5EXs7ssOGDY5thk5wAgM3J57HowhE8XLO5ssHohjwqpkJDQ7F3715MmzYNP/74I06cOOHaT6p69ep47LHHMGnSJNepfFFRUTh48GDppSYiIiIionIlIdOEiStP4lJ2gdt8QMMoTOpWC1q1Ryd7UBnnyE9Exobn4DSlAABy9s+EoAuCb7VeCicjb+ar1mJm3H14dtcSWJ0O9IyugyHVGisdi27Co2IKAPz8/PDOO+/gnXfeQV5eHoxGIwIDA7mPFBERERERlci6s+l4Z91ZFNidrplWJeLlbrEY1KiSgsnoTrLnXkTmxjFwFhRuD6MOqAJtGAsEKjmb04FMqxmVfAMBAI0NlfBuy744mpWE5+p3gCiw3C6rPC6mrhcQEMBCioiIiIiISsThlPDJ9gv4+VCi2zwqQIfp/RqgQRQ/Y5RX9px4ZGx8AZIlyzXTBNVAaPdPofIJUzAZeaNcWwFe3PsH0i35WNDpYRh01/Yl6xIViy5R3Ou6rGNlSEREREREd12myYbRvx8rUkq1qRqC7x9twVKqHLNlnULG+mfdS6mQ2gjtMYelFJVYstmIkdsX4WhWEpLNRozbswxmh03pWFQCHhdTV65cwdNPP43Y2Fj4+PhApVIV+UetLpUDsoiIiIiIqBw5nJiL//x0EIcTc93mT7Suik8GNUKwj0ahZHSn2dKPInPD85BsRtdMG9oAYd0/h0ofomAy8lZ+ai2uv05nkikXV025N308lT0eNUcJCQlo06YNsrOz0bBhQ1itVlSrVg16vR4JCQmw2+1o2rQpgoODSzkuERERERF5K1mW8cvhJMzemgCnLLvm/lo1pvSpi86xoQqmozvNmnoQmVtehOwo3OBeG9EMoV1mQdT4KZiMvFmgVo/ZbQfhiW2/QqdS45O2g1DN36B0LCoBj46YmjJlCnJzc7FhwwYcOXIEADBixAicOnUKFy9exIABA2AymbB48eJSDUtERERERN7JbHPg1b9O48Mt591Kqdphfvju0eYspco5S9JuZG4e51ZK6aLiENr1I5ZSVGLLLh3H/owrrtvRvkH4tO1gLOj0MEspL+RRMbV+/Xrce++96NKli2sm//3NpVKlSvj1118BAK+88kopRCQiIiIiIm92OduMEb8cxrqz6W7zvvUiMP+hZogJ9lEoGd0NBVe3IWvry5Cdhfv+6KM7XDtSSs0/eyo+WZYx9/ROvH9kPSbu+wPnjRmu+2oHhbs2PSfv4lExlZGRgXr16rluq9VqmM1m122dTodevXph5cqVt5+QiIiIiIi81qb4DDz+0yEkZBV+XlCLAiZ2q4UpfepCr1EpmI7uBlHjBwiFuwD5xHSFodM0CCqtgqnIGy2/fALzz+4FAOTbbXhh9zIYbRaFU9Ht8qiYCgsLg8lkcrt98eJFt8eo1Wrk5OTcTjYiIiIiIvJSTknGp9svYOLKkzDbna55hL8OXw1tiqFNoyEIwi1egcoLXWQLGDrNgCBq4Fu9D0I6vAdBxQ3uqeT6xdRHq7AY1+37qzdBgEanYCIqDR5tfl67dm2cP3/edbt169ZYs2YNEhISULNmTaSnp2Px4sWIjY0ttaBEREREROQdssw2vPrXaey/kuM2b1klGO/fWw8GXx4pU9Hoo9sirPfX0ITUgSB4fHF4qoAKHHb4qK8VmRpRhRlx/fHsriV4uEZz3BtTX+F0VBo8ekfo27cvNm3a5Doiaty4ccjLy0OTJk0QFxeHOnXqICUlBWPGjCnNrEREREREVMYdTzbi8Z8OFSmlHm9ZBZ8PacxSqgKwZZ2+4VxrqMdSikokIS8TD276Dn9cPuGa+Wt0WNjpYZZS5YhH7wqjR4/G5s2boVJdOx+8a9eu+OWXX1CtWjUcP34ckZGR+OSTTzBq1KhSDUtERERERGWTLMtYcjQJo347grR8q2vuq1Fher8GeKFTTahEnrpX3uWdWIj01cNhOssrtNPtOZhxFaO2L0JqQR7eP7Ieu9Iuuu4TWXCWKx6dyhcYGIg2bdq4zYYOHYqhQ4eWSigiIiIiIvIeFrsTUzfGY9WpVLd5TYMvZvRvgGoGXimrvJNlGXlHv0TeiYUAgJz9MwGVDn6x9ykbjLzWoaxE5NmvldxOWcY3Z/egbXg17k1XDnlUM3bv3h2vv/56aWchIiIiIiIvczWnACMXHSlSSvWqE44FDzdjKVUByLIM46HZrlLqH5I1R5E8VD48Ubs1BlRtCABoEVoFH7cZyFKqnPLoiKk9e/agbdu2pZ2FiIiIiIi8yLaETLy55gzyrA7XTBQEjO1UA480r8wPkRWALEvI3fcBTPFL3eZBrV6Efx2eUUPFJ8kS/rp6Gn2r1IMoiBAEAf9t0h3V/A14qEZTaFUe1RfkBTz6k61Xrx4uXbpU2lmIiIiIiMgLSJKMr3Zfwry9l93mob5aTO1XH80rBymUjO4mWZaQs/tdmC+sKhwKAoLj/gu/WgOVC0Zex+Kw47WDf2FrSgIu5mXhuQYdAQBqUYXHa7VUOB3daR6dyjdmzBgsX74cJ0+eLO08RERERERUhuUW2DF2+fEipVTT6CD88FhzllIVhCw5kL3zjf8ppUSEtH2TpRSViCzLGLtnGbamJAAAvo3fj98uHFE4Fd1NHh0xVbNmTXTt2hVt27bF008/jbi4OERGRt7wUN3OnTvfdkgiIiIiIlLeqdQ8TFp5Csl5Frf5I80r44WONaBW8UpZFYHstCFrx2uwXN3qmgmCCiEd34VPTDcFk5E3EgQBQ6o3waHMRACATqVGhI+/wqnobvKomOratSsEQYAsy5g1a9Ytzx13Op0ehyMiIiIiorJh+fEUzNgUD5tTcs30ahGv96qD3nUjFExGd5PksCB7239hSd7tmgkqLQwdp0JfuYOCycjbyLLs6hL6VK6L1II8/BB/AB+2GYBGIZUUTkd3k0fF1BtvvMGNDImIiIiIKgCbQ8KMzfFYfjzFbV412Acf3NcANUP9FEpGd5tkNyNry0uwph10zQSVDqFdZkIXFadgMvI2W1PO47cLRzCr9QDXpuaPx7ZE/5gGMOh4Jc+KxqNi6q233irlGEREREREVNYkGy2YtPIkTqXlu827xobhrd514KfjVbIqEofxAmyZJ1y3BY0vQrvMgi6iuYKpyNssuXgUHxzbBEmW8cbB1Xi/1b2uq/CxlKqYeBI4EREREREVsetiFv7z40G3UkoUBIzpWAMz+tdnKVUBaUMbwtB5BgRRA1EbgLDun7KUohLJtRXgi1M7IMkyAGBjcjxWXjmlcCpS2m19Nzl06BB+/vlnnD59GmazGevXrwcAXLp0CXv27EHPnj1hMBhKJSgREREREd15kiRjwb4r+HLXRcjXzUN8NHivb33EVQ1WKhqVAfpKbWDoNA0q33BoQuooHYe8TJDWB7PaDMRzO5fAJjlxf/Um6B9TX+lYpDCPj5iaOHEiWrVqhZkzZ2LlypXYtGmT6z5ZlvHoo4/i+++/L/Hrbt26Fffddx+io6MhCAKWLVt2y8dv3rwZgiAU+SclJeWWzyMiIiIiInd5Fgde/OME5v5PKdUoKgDfP9qCpVQFIzkKbjjXV+7AUoqKLd9uRZI513W7qSEab7e4B8/V74CJjbtBFHgiV0Xn0QpYsGABZs6cif79++Po0aOYPHmy2/3Vq1dH69atsWLFihK/tslkQtOmTfH555+X6HlnzpxBcnKy65+ICF4ZhIiIiIiouM6m52PYzwex/UKW2/yBJtH48oGmiAzQKZSMlODIT0L6qseQf/Y3paOQF0styMOT2xfhuV2/I8tqds27R9fG/9WO40XVCICHp/LNmTMH9evXx5IlS6BWq6HVaos8pl69eq5T+0qib9++6Nu3b4mfFxERgeDg4BI/j4iIiIioolt1KhXvbzgHq0NyzbQqEa/0qI1+DSIVTEZKcBgvI2PjGDjNqcjdPwuCSge/2AFKxyIvcyEvE8/vWop0y7V96sbvWY657R+Aj1qjcDIqazwqpk6ePIlRo0ZBrb750yMjI5GWluZxsJJq1qwZrFYrGjVqhLfeegsdOnS46WOtViusVqvrttFoBABIkgRJkm72NK8gSRJkWfb6XweVDVxPdCNcF1SauJ7IU1w7pcPulPDR1gtYcjTZbR4dpMf0fvVQJ9y/Qvwecz0VsucmIGvTC5AshUfO5Z/6GfqqfSCoWCj8L66dmwvS6KFXqSH/fWJwpsWEtII8xPgFKxusDCtP66kkvwaPiim1Wg2bzXbLxyQlJcHf39+Tly+RSpUqYe7cuWjVqhWsViu++eYbdO3aFXv27EGLFi1u+JypU6diypQpRebp6emwWCx3OvIdJUkScnNzIcsyRJHn6tLt4XqiG+G6oNLE9USe4tq5fRkmO97dehWnM8xu89aVA/Byh8oIkM1ISzPf5NnlC9fTNZIxAfYDr0K2GV0zMaA6xMZvIj0zW8FkZRfXzq29FtsBE46uRojWB2/X7QadyYY00907gMXblKf1lJeXV+zHelRMNW7cGBs3boTT6YRKpSpy/z9X6GvZsqUnL18idevWRd26dV2327dvj/Pnz+Ojjz666ebrkydPxoQJE1y3jUYjYmJiEB4ejsDAwDue+U6SJAmCICA8PNzrFzIpj+uJboTrgkoT1xN5imvn9uy/koNX119AjtkOzd+n1QgC8FTbqhjeKgaiWLH2feF6AmyZJ5F15A2o5QJAc21NaELqwtD1Y4i6IIXTlV1cO4VkWcb35w+gUUgUWoRWAQBEIAJfBT+MKJ8A+GmKbgFE7srTetLr9cV+rEfF1BNPPIEnn3wSzzzzDD777DO3+4xGI5588kmkpKRg9uzZnrz8bWvdujW2b99+0/t1Oh10uqKbN4qi6PV/+AAgCEK5+bWQ8rie6Ea4Lqg0cT2Rp7h2Sk6WZXx/4Co+33ERkiwDf/dPgTo13u1bD+2qG5QNqKCKvJ6saYeQteVFyPbCI+S0YY0R2vUjiNo7fxaMt6vIa+cfTknCByc24/eLxxCg0eHrjg+iZkAoAKB2cLjC6bxLeVlPJcnvcTG1fv16zJs3D7/++qtr0/HWrVvj1KlTMJlMGD58OB544AFPXv62HT58GJUqVVLkaxMRERERlUUmqwNT1p3FpvgMt3m9CH9M79cA0UHF/+k2lR/WlH3I3PoyZEfhlia6iBYwdJkJUeOrYDLyJr9eOIzfLx4DAOTZrRi7exl+7fY4fNU8Sor+nUfFFAD89NNP6NatGz777DMcP34csixj//79qF+/Pl544QU8/fTTHr1ufn4+4uPjXbcvXLiAw4cPw2AwoGrVqpg8eTISExPx3XffAQA+/vhj1KhRAw0bNoTFYsE333yDjRs3Yu3atZ7+0oiIiIiIypWETBNe/uMkLucUuM0HNIzCpG61oFV790/myTOWxB3I2j4ZsrNw/2B9dDsYOk6DoC56hgnRzTxQvQm2pJzHocxEiIKAEbVbs5SiYvO4mAKAUaNGYdSoUSgoKEB2djYCAwNve8Pz/fv3o1u3bq7b/+wF9X//939YuHAhkpOTcfnyZdf9NpsNL774IhITE+Hr64smTZpg/fr1bq9BRERERFRRrT2ThnfXn0OB3emaaVUiXu4Wi0GNeJZBRVVwZROyd7wOWXK4ZvoqnWHo8C4EFQsF+nd5dgsCNNeOtNSq1JjZ+j6M2bUUI+u0QaeomgqnI28iyLIsl/RJ+fn5d+WKe3eL0WhEUFAQcnNzy8Xm52lpaYiIiPD6c1JJeVxPdCNcF1SauJ7IU1w7/87hlPDJ9gv4+VCi2zwqQIfp/RqgQVSAQsnKnoq2niSrEakrBkOym1wzn2o9EdLuLQjibR27UOFUtLXzj6NZSXhp7x94oWEn9I9p4JpLsgRRqDi/D6WtPK2nkvQsHv1KIyMj8dhjj2H16tWQJMmjkEREREREdGdkmKx4ZsmxIqVUm6oh+P7RFiylKjhRFwhDp2muI6N8a/ZDSPu3WUpRsWxKjsezO5cgx1aA9w6vw570S677WEqRJzxaNbGxsfj555/Rr18/REdHY/z48Thw4EBpZyMiIiIiohI6nJiL//x4CEeSct3mT7Suik8GNUKwj0ahZFSW6KLiYOj4PvzqDEVwm1chsFCgYjqbmw6bdO3UYKcs46fzh+DBiVhELh69+xw9ehSHDx/G+PHjoVKpMHv2bLRu3RoNGjTA1KlT3faAIiIiIiKiO0+WZfx8KBHPLD6KTHPhZtb+WjVm3dcQo9tXhygKCiakskZfuSOCW73IUopK5Km6bV2n73WrVAsz4vpDEPjeQp7z+B2oSZMmmDlzJq5evYo1a9bgsccew9WrV/Hqq6+iZs2a6Nq1K+bNm1eaWYmIiIiI6AbMNgde/es0PtxyHs7rjlyoHeaH7x5tjs6xoQqmIyXJsgzjsXnIP7NI6SjkpWxOB36/eMx1VJQgCHilaQ9MatIdU1vdC52Kp4DS7bntFSQIAnr16oVevXqhoKAAS5cuxffff4/169dj+/btGDlyZGnkJCIiIiKiG7iUZcbElSeRkGV2m/etF4FXetSGXqNSKBkpTZZlGI/MQf7J7wEAgkoLv1qDlA1FXsVos+DlfX/gUGYiUi15GF2vPQBALapwf/UmCqej8qJUq02HwwGr1Qqr1cpN0YmIiIiI7rBN8Rl4a80ZmO1O10wtCpjQJRYPNKnE02sqMFmWkHvgI5jO/uaa5eybAW1Ec2gCqymYjLyFJEt4dtcSnM1NBwAsOLsXUT4BGFytscLJqLy57WLK6XRi1apV+OGHH7By5UpYLBaIoojevXvj8ccfL42MRERERER0HackY87Oi/hu/xW3eYS/DtP61UfjSre+NDeVb7IsIWfvNJjPr3CbB8e9zFKKik0URDxaswXeOrQGABCg0aGaX4jCqag88riY2r17N3744QcsWrQImZmZkGUZzZo1w+OPP45HH30UkZGRpZmTiIiIiIgAZJlteGXVaRy4muM2b1klGO/fWw8GX60ywahMkCUncva8A/OF1YVDQURIm1fhW7OfcsHIa0iyBPHvDfHvjamPlII8LLt8DLPbDEKNAO5XR6XPo2Kqdu3aSEhIgCzLqFy5Ml5++WU8/vjjaNiwYWnnIyIiIiKivx1LNuK/f55CWr7Vbf54yyp4rkMNqHjVvQpNdtqRvfN1FFzZ7JoJggoh7afAp1pPxXKR9/jj8gn8dfU0Pm4zENq/NzUfUTsOQ2s0QYBGr3A6Kq88KqZSUlIwbNgwPP744+jWrdtNz123Wq3Q6XS3FZCIiIiIqKKTZRlLjiZj1pbzcEiFV93z1ajwZu+66F47TMF0VBbIThuytk2GJWmHayaIGoR0fA8+VTormIy8gSzLmHduL746vQsA8NahtXi35T0QBRGCILCUojvKo2IqLS0NPj4+N73/4MGDmDdvHn755RdkZmZ6HI6IiIiIqKKz2J2YujEeq06lus1rGnwxo38DVDP4KpSMygrJUYCsrRNhTdnnmgkqLQydZkAf3VbBZOQtMqwm/HL+oOv2+qSz6FopFr0r11UwFVUUHhVTNyqlcnJy8MMPP2DevHk4evQoZFm+ZXlFRERERES3djWnABNXnsS5DJPbvFedcLzWszZ8taV6kW3yQpLdhMwtL8KWdtg1E9Q+CO0yC7rIFsoFI68SrvfHzNYD8Pyu32GTnHiyblv0iq6jdCyqIG77O9n69esxb948LF++HFarFbIso127dhgxYgQeeuih0shIRERERFThbEvIxBurzyDf5nDNREHA2E418EjzyjfdToMqDlmWkbV1klspJWr8Edr1Q2jDmygXjLxCpsUEi9OByn5BAIBmoZUxpcU9MDlsGFCV+0fT3eNRMXXlyhUsWLAACxYswOXLl12boCcmJmL48OGYP39+aeckIiIiIqoQJEnGV7svYd7ey27zUF8tpvarj+aVgxRKRmWNIAgIaPh/sGUchey0QdQFIbTbbGgN9ZSORmXcxbwsjN2zDCpBxLyODyJEd+2U4B7RtRVORhWRWNwH2u12/Pbbb7jnnntQs2ZNvPXWW8jIyMBjjz2GtWvX4tKlSwAAtZqHExMREREReSK3wI6xy48XKaWaRgfhh8eas5SiInRRcTB0mgaVbyTCesxhKUX/6mROCp7c/iuSzUZcNeXgxb0rYHHYlY5FFVixW6To6GhkZWVBEAR069YNw4YNw5AhQ+Dn53cn8xERERERVQinUvMwaeUpJOdZ3OaPNK+MFzrWgFpV7J8pUwWjj26PyPt+g6DSKh2FvEAln0AEan1gtFsBAHl2K4x2C/RqjcLJqKIqdjGVmZkJURQxfvx4TJw4EeHh4XcyFxERERFRhbH8eApmbIqHzSm5Znq1iNd71UHvuhEKJqOyxJF3Fda0g/CLHVDkPpZSVFwhOl/MbjMII7f/iuoBBsyMuw+BWr3SsagCK/aPXYYPHw4fHx98+OGHqFKlCgYMGIDffvsNNpvtTuYjIiIiIiq3bA4J764/i3fXn3UrpaoG++DbR5qzlCIXe+5FZKwfjZw978MUv1TpOORFJFnCpye34XBmomsW4x+MLzs8gM/aDmYpRYordjE1f/58JCcn48svv0SLFi2wcuVKPPzww4iMjMTTTz+N7du338mcRERERETlSrLRgicXHcby4ylu866xYfjukeaoGcotM+gae/Y5ZGwYDWdBOgAgZ98MFFzZrGgm8g42pwOv7F+F7+MP4KW9K3AxL8t1X42AUGhV3COalFeiE9X9/f3x5JNPYteuXThx4gTGjRsHrVaLr7/+Gl26dIEgCDhz5oxrI3QiIiIiIipq18Us/OfHgziVlu+aiYKAMR1rYEb/+vDT8cMiXWPLPImMDc9BsmS7ZprgWtCGN1MuFHmN784fwMbkeACA0W7FuD3LYXM6FE5F5M7jHRTr16+PWbNmITExEYsWLULv3r0hCAK2bduG2NhY9OjRA99//31pZiUiIiIi8mqSJGPenssYu+w4jNbCD4chPhp8NrgxhrWKgSAICiakssSWfhSZG8dAshldM21oQ4R1/xwqfbBywchrDIttieahlQEAWlGFFxp05FFSVObc9qU91Go1HnjgAfz111+4ePEipkyZgmrVqmHTpk0YPnx4KUQkIiIiIvJ+eRYHXvzjBObuugj5unmjqAB8/2gLxFUNVioalUHW1IPI2DQWkt3kmukimiO0+6cQdYEKJqOyLttqdv23VqXGB3H3oakhGnPa34/u0bUVTEZ0Y6V6zdkqVarg9ddfx/nz57Fu3To8/PDDpfnyRERERERe6Wx6Pob9fBDbL2S5zR9oEo0vH2iKyACdQsmoLLIk7Ubm5nGQHQWumS6qNQxdP4Ko8VUwGZV1O1MvYvCGBVh15ZRrFqjV46sOQ9HEEK1gMqKbu2PH8PXo0QM9evS4Uy9PREREROQVVp1KxfsbzsHqKLzqnlYl4pUetdGvQaSCyagsKri6FdnbX4Us2V0zfeWOMHR8H4JKq2AyKuuWXzqOqUc3QJJlvHtkHcL1fogLrwoAPEWYyjSeXEpEREREdAfYnRI+3JKAxUeT3OaVg/SY0b8B6oT7K5SMyirzpXXI2fkWZNnpmvnEdENI+7chqDQKJiNvkGTOhSRfO1HYIUlYdvm4q5giKstYTBERERERlbK0PCv+++cpHEsxus071DDg7T51EahnyUDuzBf+QvbudwC58Mg63xr3ILjN6xBElYLJyFs8U689Ugry8NfV0xhUrREmNe6udCSiYmExRURERERUivZfycErq04hu6DwVCwBwNPtqmNEXAxEkafUUFEqn3AIohqy0wYA8Ks1EEFxkyAIpbotMJUjJrsVK66cxMM1mkEQBAiCgNea9UKb8GroW6UeT98jr8FiioiIiIioFMiyjO8PXMXnOy66TqcBgECdGu/2rYd21Q0KpqOyThfVCoZO05C1dSL8ag9BYIvxLBboptIt+Ri3ZznO5aYjz27FU3XbAgA0ogr3xtRXOB1RybCYIiIiIiK6TSarA1PWncWm+Ay3eb0If0zv1wDRQXqFkpE30Ue3R/g930IdVJOlFN2UzenAUzt+Q6IpFwDwzZndiPIJwICqDRVORuQZHhdKRERERHQbEjJNGPbzoSKl1ICGUZj3YDOWUlSELMuwph+54X2a4FiWUnRLWpUaI+u0cd0O0/uhXlCEgomIbg+PmCIiIiIi8tDaM2l4d/05FNgLr6KmVYl4uVssBjWqpGAyKqtkWYbx0Gzkn/4FwXHXTtsjKg5JliD+vedY/5gGSC3Iw9rEM/ik7WBE+gQonI7IcyymiIiIiIhKyOGU8Mn2C/j5UKLbPCpAh+n9GqBBFD8kUlGyLCF33wcwxS8FAOTsmwFBpYNvzX4KJ6OyTJZl/HD+IHalXcTHbQZCq7r2Mf6J2q3xaM0W8FHzKp/k3VhMERERERGVQIbJiv/+eRpHknLd5m2qhuDdvvUQ7MMPiVSULDmRs+c9mC+sKhwKAmTZefMnUYUnyRI+PL4Fiy5cO/Xz7cPr8HaLPhAFEYIgsJSicoF7TBERERERFdPhxFz858dDRUqpJ1pXxSeDGrGUohuSJQeyd77xP6WUiJB2b8EvdoBywajMSynIw19XT7tur008g91plxVMRFT6WEwREREREf0LWZbx86FEPLP4KDLNNtfcX6vGrPsaYnT76hBFblhNRclOG7K2TUbB5Q2umSCqYej4Hnyr91EwGXmDaN8gfBB3HzSiCqIg4OXG3dA+srrSsYhKFU/lIyIiIiK6BbPNgXfXn8O6s+lu89phfpjevwFign0USkZlneSwIGvbJFiT97hmgkoLQ6dp0Ee3VzAZlWWJplwIwrVSCgBahFXBm817Q6dSo0tUrMLpiEofiykiIiIiopu4lGXGxJUnkZBldpv3rReBV3rUhl6jUigZlXWS3YysLS/BmnbQNRPUeoR2/gC6qDgFk1FZdjInBeP3rIC/Wov5nR5CkPZa8d27cl2FkxHdOTyVj4iIiIjoBjbFZ2DYz4fcSim1KGBit1qY0qcuSym6KcmWh8xNY91LKY0vQrt+zFKKbupAxlU8s2Mxsq1mXDHlYMLeFbA6HUrHIrrjeMQUEREREdF1nJKMOTsv4rv9V9zmEf46TOtXH40rBSqUjLyBZM1FxqYXYM8645qJ2kCEdvsY2tAGCiajsq5GgAEGnR+SzNcuruCUJFicduhU/NhO5RuPmCIiIiIi+luW2Ybnfj9WpJRqWSUY3z/anKUU/Su78SIcuRdct0VdMMJ6fMZSiv6VQeeLT9oOQpBWj05RNTG3/QOuU/mIyjMWU0REREREAI4lG/H4T4dw4GqO2/zxllXw+ZDGMPhqlQlGXkUX3hSGTtMhiGqofMIQ1nMONCF1lI5FZZBdcuK9w+txNCvJNavqH4J5HR/CjFb9oVdrFExHdPfwmEAiIiIiqtBkWcaSo8mYteU8HJLsmvtqVHizd110rx2mYDryRvrodjB0mg51YFWoA2KUjkNlUL7dikn7V2Jf+hVsTonHvI4Poap/CAC4/k1UUfCIKSIiIiKqsCx2J95aexbTN8W7lVI1Db747pHmLKXoX0m2/BvO9ZU7sJSim1pwbi/2pV87ZTjXZsGLe1fAKUkKpyJSBospIiIiIqqQruYU4IlfD2PVqVS3ea864VjwcDNUM/gqlIy8hT03AWkrH4Lp3BKlo5CXeapuOzQ2VAIA+Km1mNi4G1QiP55TxcRT+YiIiIiowtmWkIk3Vp9Bvq3wUuwqQcDYzjXxcLNoCIKgYDryBrasM8jcNBaSNQc5+z6AoNLBt2Z/pWNRGZZWkIcInwAAgE6lxoetB+CVA6swvmEX1Ark0ZlUcbGSJSIiIqIKQ5JkzN15ERNWnHArpUJ9tfjigSZ4pHllllL0r2wZx5G58XlI1hzXzHRuKWSZp2LRjf119RQGb1iItYlnXLMgrQ8+b3c/Symq8HjEFBERERFVCLkFdry2+jR2X8p2mzeNDsK0fvUQ5qdTKBl5E2vaIWRueRGy3eyaacObILTLhxAE/tyfilp4bh/mnNoBAJhyaC1CdX5oGVZF4VREZQeLKSIiIiIq906l5mHSylNIzrO4zR9pXhkvdKwBtYqFAv07a8o+ZG55CbLT6prpIlvB0HkGRA33JKMbM9kL14tdcmJj8jkWU0TXYTFFREREROXa8uMpmLEpHjZn4WlWPhoVXutZG73rRiiYjLyJJXE7sra/Atlpc8300e1g6DgNgppH29HNja7fHikFeViTeAYjasfhmXrtlY5EVKawmCIiIiKicsnmkDBjczyWH09xm1cN9sEH9zVAzVA/hZKRtym4vBHZO16HLDtdM5+Yrghp/zYElVbBZFQWZVnNWHnlJB6PbQlBECAKIl5v1gs9K9dBl6hYpeMRlTkspoiIiIio3Ek2WjBp5UmcSst3m3eNDcNbvevAT8e/BlPxmC/8hezd7wDXbWzuU60XQtq9CUHkOiJ3l/OzMXbPMiSacmGTnHiyThsAgFalZilFdBN8JyUiIiKicmXXxSy89tdpGK2FV90TBQHPdaiOx1tW4VX3qNhM8cuQs286IMuumW/N/ghu8wo3OqciChx2PL1zMTItJgDAV6d3IdonEPfG1Fc4GVHZxndTIiIiIioXJEnGvD2XMXbZcbdSKsRHg88GN8awVjEspajYnAUZyD34sVsp5Vf7fpZSdFM+ag1GX7d/VIxfMJoYKimYiMg78IgpIiIiIvJ6eRYH3lhzGtsvZLnNG0UFYHq/BogI4ObUVDIqnzAYOk5D1taXIUt2+Nd7FIHNx7DcJABArq0ANskJyIDs74NMqxkQgC5RNZFSty32pF/CrLgBCNb5KB2VqMxjMUVEREREXkmlUgEAzqbnY9LKk7iaa3G7/4Em0RjfuSa0ah7dQp7RR7dFSMf3Yc8+g4BGI1lKkYtNcmLw+gVIMueiwGFHVf8QaEQVlvYcgVF12mB4rVbQqvhxm6g4+H8KEREREXmVfKsDRosDmXYNcrMKsO9yDqTCs62gU4t4pUdt3Fs/UrmQ5DWclhwI2kC32yp9sOu2T5VO8KnSSYFkVKbJQKI5FxkWE2RZxlVzLqr6BQMABEFgKUUekS1GhOiv/Ru+wUrHuWvK3I+Ptm7divvuuw/R0dEQBAHLli371+ds3rwZLVq0gE6nQ61atbBw4cI7npOIiIiI7r70fCtmb0vAIz/sR5+vdqPTZzuw82IWJnarhehAPSoH6TH/oWYspahYHPlJMB6ZA8mcDG3OHjhyzsF4ZA4c+UlKR6MyziFLyLEVHqVptFlgdtgUTETlgeyw4dLHgyBXsLVU5oopk8mEpk2b4vPPPy/W4y9cuIB+/fqhW7duOHz4MMaNG4cnn3wSa9asucNJiYiIiOhuyrc68NXuS1h8NBknU/ORa3Eiz+rAt/uv4qeDV/Fch+r47pHmqBPur3RU8gJOSw7yTiyE+fwKZGx4DjqtGpkbnkP+ye+Rd2IhnJYcpSNSGaYWRcT4BV07vVMAon0D4avWKh2LvJgsOSFLTkByKh3lritzxxf27dsXffv2Lfbj586dixo1amDWrFkAgPr162P79u346KOP0KdPnzsVk4iIiIjusjyrA3+eTMXFLDPsTtntvq0JmXi1Zx0E6jUKpSNvo9IHI6Dh/8GavBuWK5tRcHkTBEGALrIV/Ov/x+10PiIAkGQJFqfDVUAFavSo5hcMm8OBEJ0PVLxaI3nAac6F7LBBdlhhSzkL2WGDIzcVwLU97QS1FirfIGVD3mFlrpgqqV27dqFnz55usz59+mDcuHE3fY7VaoXVanXdNhqNAABJkiBJ0h3JebdIkgRZlr3+10FlA9cT3QjXBZUmricqibQ8K06k5sMpyQCuFVOiAFQO0kOvViHXYkdUAI9YoOKR7CYUXNmMwCbPwHJ1i2se0uFtqPyr8H2J3EiyhJnHt+B4Tgo+azMYGkGFpT1GQAYgSU6IogoCAI2g4tqhEpEdVlz59H5AcsKemwopPwuJ3zwBUa0BRBVixizxyjVVksxeX0ylpKQgMtJ9D4HIyEgYjUYUFBTAx6fo5TmnTp2KKVOmFJmnp6fDYrEUmXsTSZKQm5sLWZYhimzs6fZwPdGNcF1QaeJ6ouI6nmZGcEgIfNQi8qwOAIBaBKoEaqERZWhFCf4aEenp6ZBl+V9ejSo62ZoD4cIPCKrTD9nbX4MsX6s6BZ8oZB/6EiEdqsDk9IPdblc6KpUBkixjTsJe/JlyFgAwasvPeL9BTwRodK7vY0FBQRBFEU4A3v2Jku62IB8Vop/7DQDgyEnBpVn9EfV/X0BtqAIAsMsCctPSlIzokby8vGI/1uuLKU9MnjwZEyZMcN02Go2IiYlBeHg4AgMDb/HMsk+SJAiCgPDwcP4Fn24b1xPdCNcFlSauJyqODecy8MbmRDzbXoP7m1TCt/uvQq8RUclPDZ1WAwEC7m0YhWBfLfy04UrHpTLOkZ+ErL2vIbDpMzCd/gm2tAPQV2qDoLZvIf/4PNizTqDgzA8IbjoagjZC6bhUBmRYTDiQnwq15trH58vWfKRonIiNiOD3MfKY6fRm2NMSoOv8hGsmiiLUIZWgDasKlX+Yax4RoETC26PX64v9WK8vpqKiopCamuo2S01NRWBg4A2PlgIAnU4HnU5XZC6KYrl4MxEEodz8Wkh5XE90I1wXVJq4nuhWlhxNwvSN8ZABLD6ajIndakGnEbH/cg6MBVb4a9XoXTccT7WthgDuL0X/wp59FlmbxsNpyUT+6Z8R2HgUIIgIajEeZoTA0PEd5J1YiICGw6HShygdl8qICN8AzO3wAJ7ZuQTZVjPebnEPOkXVdN3P72NUErIsw7jrJ2Su/RiQZWiCKyGgWT8AgAQAf58W6u3rqST5vb6YateuHVatWuU2W7duHdq1a6dQIiIiIiK6XbIs45s9l/HV7kuuWZLRgt2XsvFW77rItzqRZbLA4KdHoF4Nf53X/7WW7jBr2iFkbXkJkt0EALBnnoD5/HIEt34Fok84CtLSEBARhcCmz3Ljc4IkS8iyFiBM7wcAqOZvwNz29+O8MRPdo2srnI68lSw5kfnnDBj3L3HNMla8A21Ubeii6kBQa1Ft3DIIFewKj2WugsvPz8fhw4dx+PBhAMCFCxdw+PBhXL58GcC10/CGDRvmevwzzzyDhIQETJw4EadPn8acOXOwaNEijB8/Xon4RERERHSbJEnGB5vPu5VSAPBQs8oY37kmAvUaRAVoEam5ttk5Syn6NwVXtiBz01hXKQUAGkM9BMW9DLWf+361LKVIkiVMP7oJw7b+hMv52a55NX8DSynymGQ1IfWn8W6lFAAEtn4Q2ohaAABBH4hsy7V/VyRlrpjav38/mjdvjubNmwMAJkyYgObNm+ONN94AACQnJ7tKKgCoUaMG/vzzT6xbtw5NmzbFrFmz8M0336BPnz6K5CciIiIiz9mdEl5bfRq/HUlym49uXx0vdqkJURRcM6fTebfjkReSbHnI2fMeZKfNNdNFxSGsxxyo9AYFk1FZ9E8ptfTSMWRYTHhm52K3corIE46cFCTNGwnzuZ2FQ0FE6L0TEXrPBAheftre7SpzP17q2rXrLa+ksnDhwhs+59ChQ3cwFRERERHdaWabAy+vPIW9lws/BIqCgMk9amFQo0oKJiNvJmoDYOg0FZmbxkGW7PCp2gMh7d6EoKpYp8pQ8VicDpzISXHdzrKakZCXiar+3HOMPGNNOoWUH8fBmZ/pmolaX0QMnQrfOh0UTFZ2VOxajoiIiIjKhGyzDaOXHHMrpbQqEdP61WcpRbdNF9kSIR3ehl+doQjp8A5LKbopX7UWc9rdjzpB4RAFAe+1vBddK9VSOhZ5KdOZbUiaP8qtlFIHRqDSyG9YSl2nzB0xRUREREQVS7LRgjFLj+FSdoFr5qtR4cOBDdGySrBywcgryZITEEQIguA294npBp+YbgqlorJMkiVczM9GzYBQAECgVo857e7H8ewUtI+srmw48mqizg+QHK7b2kp1EfXoR1AHRiiYquzhEVNEREREpJiETBOeXHTErZQy+Grw1dCmLKWoxCS7GZlbJiD/1A9KRyEvIckSph7ZiP/b+jP2phfuZRyo1bOUotvmU70FwgZe2y/bt25nRI/4mqXUDfCIKSIiIiJSxNEkI8YvPw6jtfCnyZWD9PhscGNUCfZRMBl5I6clB1lbJsCWeRLW5D0QdcHwi71P6VhUhv1TSi2/fBwA8OLeFZjVegBah1dVOBl5K8lqhqBSQ1AXni4c0PReqHyD4RPbtsJvcn4z/F0hIiIiortux4UsPPv7UbdSqk64P755sClLKSoxhykZGeufgi3zpGtmPPQpJFuegqmorJNkGUa7xXXbITmRb7cqmIi8mSM3FUnzRiJ92dtFLujmW7s9S6lb4O8MEREREd1Vq06lYsKKE7A6JNeseeUgzL2/CcL8dAomI29kz01Axrqn4TAWnoYl6g0I7f4pRG2AgsmorFOLKrzXsi+6VaoF1d8bnXePrq10LPJC1qTTSPz6/2BLPYf8Y6uRs/lrpSN5FZ7KR0RERER3zU8Hr+KjrQlusy6xoXivbz3o1CqFUpG3sqUfReaWlyDZjK6Z2j8aod1mQx0Qo2AyKqskWcKBjKuI+/t0vX/KqRM5qWhqiFY4HXkj0+mtSFv8CuTrjr7LO7gMQe0ehaj3VzCZ9+ARU0RERER0x8myjM+2XyhSSg1oGIXp/RqwlKISsyTuQMbGMW6llCakNsJ6fc1Sim5IkiW8d2Q9ntv1O36/eMw1V4sqllLkMdPJ9W6llLZSXUSPWshSqgR4xBQRERER3VFOScZ768/hj5MpbvPhcTF4tn11CIKgUDLyVuaEP5G95z1ALjwdVBfRAobOMyBq+WGQivqnlPrj8rV9yKYd3QAAGFK9sZKxqBwIH/AaHNlJsFw+DN+6nRFx/7sQdb5Kx/IqLKaIiIiI6I6xOpyYvOo0tiVkus3Hd66JR1tUUSgVebO8k9/DePhzt5lPTFeEtH8bgkp7k2dRRSdAQNh1R7CoBAEGHS+0QCUny7LbD1QEtRaRD8+Ecf8SBHcawU3OPcDfMSIiIiK6I/IsDjy/9LhbKaUSBEzpU5elFJWYLEvIPfRpkVLKr9YghHR8n6UU3ZIgCHimbjuMqB0HlSBgaqt+6FqpltKxyMs4jGlI+no4Ci4dcpur/IIR0mUkSykP8XeNiIiIiEpdhsmKpxcfweHEXNdMpxbx4cCGuLd+pILJyFs5TSkwxy9zmwU0egJBcZMgCPxYQ0VJsoRVV05BlmUAf5dT9drjx67/YSlFJWZNOo3Er4bBmngCqT+/CHvm5X9/EhUL38GJiIiIqFRdySnAyF+P4FyGyTUL1KkxZ0gTtK9uUDAZeTO1fzQMnT+4dmSUICCo1YsIbPIU9yijG5JkCe8cXoe3Dq3BzOOb3cqpmgGhCqcjb2M6vRVJ85+EMy8DACAVGJG+4j2FU5Uf3GOKiIiIiErNmbR8vLDsGLLMdtcswl+HTwc3Qs1QPwWTUXmgi2yBkPZTIEsO+FbrpXQcKsPeO7Ief145BQD47cIRqAUR4xt1UTgVeRtZlmHc/Qsy13wI/F1uAoA2qg4i7n9HwWTlC4spIiIiIioVB67mYMLyEzDbna5ZtRAffDa4MaIC9QomI28kWY0QtAFFjojyiemmUCLyJq3Dq+HPK6cgyTLUooiWYdzXjkpGlpzI/GsWjHsXuc1963RExAPv88p7pYin8hERERHRbdt4LgNjlh53K6UaRAbgmwebsZSiErPnXkDaX48j/+R3SkchL9Wncl1MaXEPtKIK01r1Q+eoWKUjkReRrGak/jShSCkV2OYhRD4yi6VUKeMRU0RERER0W34/lozpG+MhXXeaQ+uqIfigf334avnXTSoZW8ZxZG6eAMlmhPHIFxD1IfCLHaB0LCrjJFnCTwmHMLR6U+hU1953+lSui1ahVRCq52nEVHyS3Yqk+U/ClnK2cCiICL1nAoLaPqxcsHKMR0wRERERkUdkWcb8vZcxdcM5t1KqV51wfDywIUspKjFL0i5kbHweks3ompnO/gZZciiYiso6SZYw5dBafHJiG17auwJWZ+F6YSlFJSVqdPCt08l1W9D6IPLhmSyl7iAWU0RERERUYpIkY9aWBHyx86LbfGjTaLx7Tz1oVPxrJpWM+cJqZG15CbLD4pppw5sirMccCCJLTrq5D45txl9XTwMA9qRfxisHVrmuwkfkiZDuz8C/cR+oAsIR/cQ38KvXWelI5Rrf4YmIiIioROxOCW+tPYO1Z9Ld5s+0q44nWscU2aya6N/kn/4ZuQdnu830lTvB0OFdCGqdQqnIW9xbpT5WXz0Nk8MGjajCoKqN+D5EJWJNPAld5Qau24IgIHzQm3Cac6AOjFAwWcXAH2URERERUbGZbQ6MX37CrZQSAEzuURsj21Tlh0EqEVmWYTw8p0gp5VuzPwydprGUomJpbKiET9oORrDOB9Na9UOnqJpKRyIvIUsSMlZ9gMSvhiH/6Gq3+wS1lqXUXcIjpoiIiIioWHIK7Bi37DhOpOa5ZhqViHfvqYfutcMUTEbeSJacyNk7FeaElW7zgAbDENB0NEtOuimnJGHu6Z14qGZzhP29h1RjQyUs6zECvmqtwunIW0hWM9IWvwrz2W0AgPRlU6AOioK+WjNlg1VAPGKKiIiIiP5VitGCUYuOuJVSvhoVPhnUiKUUlZjssCJr23+LlFJBLcYisNmzLKXoppyShLcOrcG38fvxzM7FyLCYXPexlKLichjTkTT/SVcpBQCy5IAtPUHBVBUXiykiIiIiuqWETBNGLjqCi9lm1yzER4MvhzZBq5hg5YKRV5KsRmRsegGWxMIPhIKgQki7t+Bf7xEFk5E3mHN6B9YkngEAXM7Pxtg9yyDJksKpyNsIKjUka2Gpee3Ke7MQ2GqIgqkqLhZTRERERHRTx5KNGLXoCNLyra5ZdKAe3zzYFPUiAhRMRt7KWZAOR06867ag1sPQZSZ8a9yjYCryFg/WaIZo3yAAgEZU4dl67SEK/FhLJaPyC0HUY7Mh6gN45b0ygP8HExEREdEN7byYhWeXHIXR6nDNaoX54ZsHm6JqiK+CycibaYJjYegyE4JKC1EbiLDun0Ef3U7pWOQlIn0C8GWHB1AjwIAP4u5Dh8gaSkciLyFZ8t1ua8OrI+qx2aj81LfQVaqrUCoCuPk5EREREd3A6tNpeGvNGThl2TVrVjkIH97XEAF6/hWSbo8uojkMnaZB5VcJmiAWC3RzTknCjGObMKR6Y9QNunaFtEifAPzU5T9QiTzOgv6dLEnIXD0LBed3I/rJBVD5BLru01dtomAy+gf/TyYiIiIiNz8fSsTrq0+7lVKdaobis8GNWEpRidlzL0K+bi39Qx/dnqUU3ZJTkvDGodVYeukYntu5BGdy01z3sZSi4pCsZqT+/CKMe36FPeMSUn95GbLDrnQs+h/8v5mIiIiIAACyLGPOjov4cMt5t3n/BpH4oH8D6NQqhZKRtzJfWof0v/6D/JPfKh2FvNC38fuxLvEsAMBot2L8nuWwOh3/8iyiaxzGNCQtGOV25T3LpYMouLhfwVR0IyymiIiIiAhOScZ7G85hwb7LbvPHW1bBG73qQCUKCiUjb5V/9jdk73wDsuSA8chcmOKXKR2JvMzDNZuheWhlAIBWVOH1Zr2gU/GoTfp31pSzSPp6OGzJZ1wzQaNH5MMz4VuLe9qVNSymiIiIiCo4m0PCf/88heXHU9zmYzvVxAudakIQWEpR8cmyDOPRr5C7fxZw3Sl8toxjNzylj+hmfNVafNRmINqEV8UHre9Du4jqSkciL2A+ux1J80bCYbzu1M+AMEQ/8TX86nVRMBndDOtmIiIiogos3+rAhBUncCgx1zUTBQFv9KqDfg0iFUxG3kiWJeTum1Hk6Cj/eo8isPnzLDnplpyShCmH16JbpVroVqkWgGvl1CdtB3PtULHk7lmEzL9mArLkmmkjayHqsdlQB/F7WlnFI6aIiIiIKqhMkw1PLz7qVkrp1CJmDWjAUopKTHbakL39lSKlVGCz5xDU4gUIAj960M05JQmvHfwLq6+exiv7/8Sm5HjXfSylqDjyDv+JzFUz3Eop39rtET1yHkupMo7fHYiIiIgqoKs5BRi56DDOpue7ZgE6NeYMaYKONUIVTEbeSLLlI3PTOBRc2Vw4FESEtH0NAQ0eVyoWeZHll09gQ9I5AIBTljHl0BoYbRaFU5E38WvYE7oqjV23A+MeQOSjH0HU+SmYioqDxRQRERFRBXM2PR8jFx1GYm7hh75wPy2+HtoUTaIDFUxG3shZkImMDaNhTTvomgkqHUI7z4Bvzf4KJiNvMqhaQ9xXtQGAaxudT2vVH4FavcKpyJuIGh2iHpkFjaEKQu+ZgNB+kyCIvJqsN+AeU0REREQVyMGrOZiw4gRMNqdrVjXYB58NaYxKgfwQSCXjyLuKzE0vwJGf5JqJ2gCEdpkFbXgTBZORN5BkCeLfp3iKgohXm/aETlSjc1Qs2kZUUzgdlXXWlLOQ7VboYwqPklL5G1D52V8hanQKJqOSYjFFREREVEFsjs/Aq3+dhs1ZuP9Gg8gAfDywIUJ8tQomI29kzz6LjE3jIFmyXDOVTzhCu30MTXCsgsnIGzgkJ1498BfqBkXgiTqtAVwrpyY26a5wMvIG5rM7kPbbZAhqLaJHfQuNobLrPpZS3oen8hERERFVAMuPp2DSn6fcSqm4mGB8cX9jllLkGUEEJLvrpjqwKsJ6f8VSiv7VP6XUpuR4zD29EwvO7VU6EnkR497fkPLTeEg2M5zmHKT8+AKcBUalY9FtYDFFREREVI7JsoyF+67g3fVnIcmya96zTjg+HtgIvloeQE+e0QTXgqHzBxBUWmhD6yOs51dQ+1VSOhZ5gZ1pF92uujfvzB4km1ks0L/L2jgXGX9Od7vyniakMveS8nIspoiIiIjKKUmS8dHWBHy+44Lb/IEm0XjvnnrQqvlXQbo9uohmCO32CUK7fw6VPljpOOQlOkfFYlzDzgAAnUqND9sMRCVfXniB/p1PtRbAdSVUYKv7eeW9coA/IiMiIiIqhxxOCVPWncXq02lu81Ftq2FUm6oQBEGhZOSNZFmGNXk3dJXaFlk7uohmyoQir+KQnBAgQCVeK8QfjW0BURBQMyAUrcOrKpyOvIVPbGuE3/cK0le8i9De4xDY7lF+PysHWEwRERERlTMFdicm/XkKuy4WbkotAJjYrRYeaBqtXDDySrIsIffAhzCdXYzAJk8hoNETSkciL2OXnHj1wCroRDWmtOjjuhLfwzWbK5yMyjpbxiVogqMhqDWuWUCLgdBVaQxtRE0Fk1FpYjFFREREVI4YLXaMW3YCx1IK92tRiwLeuaceetYJVzAZeSPZaUP2rikouLwBAGA8+hVEfQj8ag1WOBl5i39Kqc3J512z68spopsxn9uJtEX/hV/Dnggb+LrbkVEspcoXvhsQERERlRNpeVaMWnTErZTy1agwe1AjllJUYpLdjMwtL7pKKQDXrsTHjxBUAmdz07Ej9aLr9uaU80jIy7r5E4jw95X3fhwHyWZG3qEVyN3+rdKR6A7idxUiIiKicuBilhlP/HoYCVlm1yzYR4Mv7m+C1lVDFExG3shpyULGhmdhTdnnmgkqLQwdp8Kv1kAFk5G3aRgShRlx/aERVdCp1PiozUDUCgxTOhaVUbIkIXP1h0WuvGe5dBCyJN3imeTNeCofERERkZc7kZKHscuOI9did80qBejx2ZBGqBriq2Ay8kaO/CRkbhoLR94V10zU+MPQ5QPoIrgnEP07u+SEzemAn0YHAOgQWQMz4vpDp1KjVViMwumorJJsBUhb/CrMZ7a6zQNaDUFYv0kQRB5XU16xmCIiIiLyYrsvZWPiypMosDtds9hQP3w6uBHC/XUKJiNvZM8+h8zN4+EsyHDNVPpQhHb7GJqQ2gomI29hl5z47/4/kW0149O2g93KKaKbcRjTkfrTeFiTTxcOBQGGXmMR1P4xXnmvnGPlSEREROSl1pxJw/jlx91KqSbRgfhqaBOWUlRi1rRDyFg/2q2UUgdUQVjvr1lKUbH8U0ptS0nA8ewUjNm9FCa7VelYVMZZU84h6ev/cyulBLUOkQ/NQHCH/7CUqgB4xBQRERGRF/r1cCJmbT4P+bpZhxoGTLu3PvQalWK5yDsVXN2K7B2vQXbaXDONoS5Cu34Eld6gYDLyJqkFeTiWney6HW/MwIX8LDQKqaRgKirLZElC+u+vw2FMc81UfgZEPfYxdJUbKJiM7iYeMUVERETkRWRZxtydFzHzf0qpfvUjMbN/A5ZSVGLmi2uQte2/bqWULrIVwnrMYSlFJVLFLxhftLsfwTof6FVqfNxmEEspuiVBFBHxwPsQdX4AAG1ELKKf+palVAXDI6aIiIiIvIQkyZi2KR5LjyW7zf/Tsgpe6FiDpzuQRzTBtSBq/CDZ8gAAPlW7I6TdWxBUWoWTkTewOR3IthUg0icAABAbGIYv2t2PXJsFLcKqKJyOvIE2oiYiHpoB4+6fEXH/uxD1/kpHoruMR0wREREReQGbQ8J/V50qUkqN6VgDYzvVZClFHtMEx8LQZSYElRZ+te9HSId3WUpRsdicDvx3/58Yuf1XJJpyXfPYwDCWUnRDkq0Amas/hLMgz23uG9sGkY9+xFKqguIRU0RERERlnMnqwIt/nMSBqzmumSgIeLVnbQxoGKVcMCo3dOFNEd73e6gDqrLkpGL5p5TannoBAPDMzsWY2/4BVPYLUjgZlVWOvIxrV95LOgVbajyi/vMJBFVhJcH3noqLR0wRERERlWFZZhueXnzUrZTSqkR80L8BSykqMcluhunsYsiyXOQ+TWA1fjCkYitw2pF83VEvRrsF6ZZ8BRNRWWZLjb925b2kUwCAgoS9yFw9S+FUVFawmCIiIiIqo5JyLXhy0RGcSS/8sOevVePzIY3ROTZUwWTkjZyWHGRufB45+2ci7/g8peOQlwvS+mBOuyGIDQyDj1qDj9sMQrPQykrHojLIYUxD0jdPwJGb6pqp/AwIaHafgqmoLCmzxdTnn3+O6tWrQ6/Xo02bNti7d+9NH7tw4UIIguD2j16vv4tpiYiIiErXufR8PPHrYVzJKXDNwvy0+GpoEzSrzFNlqGQcpmRkrH8KtsyTAIC8Y9/AFL9U4VTkbWxOB87mprtuh+h8MafdEHzWdgias5Sim1AHRiAg7gHXbW14TV55j9yUyT2mfv31V0yYMAFz585FmzZt8PHHH6NPnz44c+YMIiIibvicwMBAnDlzxnWbhyETERGRtzqUmIsJy08g3+ZwzWKCffDZ4MaIDuIP36hk7LkJyNw0Dk5zmmsm6g3QGOormIq8jc3pwMR9K3EoKxEftxnkKqJCdL4I0fkqnI7KOkPP5+HIvgrJakLkg9O5yTm5KZNHTH344YcYNWoURowYgQYNGmDu3Lnw9fXF/Pnzb/ocQRAQFRXl+icyMvIuJiYiIiIqHVvPZ+L534+5lVL1IvzxzYNNWUpRidnSjyJj3TNupZTavxLCe30JraGegsnIm/xTSu1Mu4gChx3j9izDocxEpWNRGSXZLDCd2uw2E0QR4UPeQdRjs1lKURFlrpiy2Ww4cOAAevbs6ZqJooiePXti165dN31efn4+qlWrhpiYGAwcOBAnTpy4G3GJiIiISs2KEyl4eeVJ2JySa9YqJhhz728Cg69WwWTkjSyJO5CxcQwkm9E104TURlivr6EOiFEwGXkj8bozUoT/uU30D0d+JpIXPIXUX16C6eRGt/tEjc7tKnxE/yhzqyIjIwNOp7PIEU+RkZE4ffr0DZ9Tt25dzJ8/H02aNEFubi5mzpyJ9u3b48SJE6hSpUqRx1utVlitVtdto/HaN2tJkiBJUpHHexNJkiDLstf/Oqhs4HqiG+G6oNLE9XSNLMv44WAiPtt+0W3erXYo3u5dF1q1WOF/j/4X186tmS+sQu7e9wG58PdHG94MIZ1mQND68/ftf3A93ZpaEPF+i76YfGAVDmYm4qPWA9E4OIq/X+DauZ4t7TxSfxoH59+bnKctfg1RI76ErnJDhZN5j/K0nkryayhzxZQn2rVrh3bt2rlut2/fHvXr18eXX36Jd955p8jjp06diilTphSZp6enw2Kx3NGsd5okScjNzYUsyxDFMndAHHkZrie6Ea4LKk1cT4Aky5h3MBVLTma6ze+tHYLnWxqQk5WhULKyjWvn5hwXFsNxdoHbTBXZHo4GLyMjxwzArEywMozrqSib5MSR3BTEhRRuaj6+ahyuhtdFJYcaaWlpt3h2xcG1c4390n4U/PUuZFvhBTsEjR8yM7Og1nCtFFd5Wk95eXnFfmyZK6bCwsKgUqmQmprqNk9NTUVUVFSxXkOj0aB58+aIj4+/4f2TJ0/GhAkTXLeNRiNiYmIQHh6OwMBAz8OXAZIkQRAEhIeHe/1CJuVxPdGNcF1Qaaro68nhlPDuhnj8ddYIjVrjmo9sE4NRbaryYi63UNHXzo3IsoS8I3NguvATNJrC9eQbOwCBrSZCEPj7dDNcT+5sTgcm7v8Tu9Mv4dUmPXBf1cIjXqqgkoLJyh6uHSBv/+8w/zUDalkC/n7v0YTXQMQjH0ETEq1wOu9SntaTXl/8fTHLXDGl1WrRsmVLbNiwAYMGDQJw7Q9nw4YNeP7554v1Gk6nE8eOHcO99957w/t1Oh10Ol2RuSiKXv+HD1zbCL68/FpIeVxPdCNcF1SaKup6stid+O+q09hxIevahi249q+XutbCg834F/niqKhr52ZyD82B6fRPbrOAhiMQ0OQplpzFwPV0jUNyYtKBVdidfgkA8P6xDVCrVOgX00DhZGVXRV07siQha90nyN35g9vcp2ZrRDw4HSqfAIWSebfysp5Kkr9M/konTJiAr7/+Gt9++y1OnTqF0aNHw2QyYcSIEQCAYcOGYfLkya7Hv/3221i7di0SEhJw8OBB/Oc//8GlS5fw5JNPKvVLICIiIropo8WOZ38/dq2U+ptaFPBu33ospchjvjXvhaj9++h/QUBQywkIbPo0SykqEZUgIjYg1HXbR6VBjF+wcoGoTJJsFqQtmlSklApoMQhR//mEpRSVSJk7YgoAHnroIaSnp+ONN95ASkoKmjVrhtWrV7s2RL98+bJb+5adnY1Ro0YhJSUFISEhaNmyJXbu3IkGDdjqExERUdmSlmfFmKXHkJBVuM+Pj0aFGf0boG21EAWTkbfTBNVEaJeZyNw8AUGtJ8G3Wi+lI5EXEgQBYxp0BAD8fukoPmk7GE0MLMzJnTXpJEynt7jNDL1eQFCHx1mGU4kJsizLSodQmtFoRFBQEHJzc8vFHlNpaWmIiIjw+kP/SHlcT3QjXBdUmiraerqUZcbzS48hJa/w6sBBeg1mD2qEhlH86XJJVLS1UxKSLQ+iluupJCr6erI6HVh19RQGVW3kKhVkWUZygRHRvkEKpyvbKvLaMe5bjIyV0yCotQgf8g78G/ZQOpLXK0/rqSQ9S5k8YoqIiIiovDmZkoexy48jp8DumkUF6PDp4MaobvBVMBl5I3vuBRRcXIOAJkVP1WMpRSVhdTrw4t4V2Jt+GZfzs/FCg04QBAGCILCUolsKjHsATlMOfGq1hb5KI6XjkBdjMUVERER0h+25lI2XV55Egd3pmtU0+OLTwY0REVD0gixEt2LLOI7MzRMg2YyAICKwyVNKRyIvJcsyXt73B/amXwYA/Hj+IHQqNZ6p117hZFTWGPcvhS31HELvfdmtDA/pyn2d6faxmCIiIiK6g9adTccbq0/DIRXuntC4UiA+HtgQgXqNgsnIG1mSdiFr+2TIDgsAIO/4fKgDYuBbo6/CycgbCYKAXtF1sCf9EmQZ8FNr0SGihtKxqAyRJQlZ6z9D7o7vAADq4GgEd/iPwqmovGExRURERHSH/HYkCR9sisf1G3q2q27A9H714aNRKZaLvJP5wmrk7H4Hslx45J02vCn00R0UTEXe7r6qDQEAH5/YitltB6FRSCWFE1FZIdksSF/6BkwnN7pmWetmw6d6C+gq80JjVHpYTBERERGVMlmW8fWey/h69yW3ed96EXijVx2oVd69oSndffmnf0buwdluM33lTjB0eBeCmqeDUvFZHHZ8d/4AhtdqBa3q2sfB+6o2RJeoWARq9Qqno7IkZ8s3bqUUABh6Pg9tdH2FElF5xWKKiIiIqBRJkowZm+Ox5Giy2/yR5pUxrlNNiCIvo03FJ8syjEfmIP/k925z35r9Edx6MgSRR95R8Vkcdry4bwX2pV/BqZxUTG/Vz1VOsZSi/xXcZSQKzu+GNfk0r7xHdxR/XEdERERUSmwOCa/8dbpIKfV8hxoY35mlFJWMLDmRs+e9IqWUf4PHEdzmVZZSVGKvH1yNfelXAAA7Ui/gncPrFE5EZZmo9UHkox9BF90AlYZ/yVKK7hgeMUVERERUCsw2B1764yT2XclxzURBwCs9amNgoyjlgpFXkh1WZO14DZbEbW7zoBZj4V/vEYVSkbd7LLYF9mZcRoHDDn+NFg/XbK50JCpD8g4uhzayttv+UerAcEQ/9a3blfiIShuPmCIiIiK6TVlmG55ZfNStlNKqRMzoX5+lFJWYZDUiY9MLbqWUIKgQ0u5NllJ0W5qFVsbsNoMQrvfHp22HoGEI35/o7yvvrfsU6cvfQcpP4+HISXG7n6UU3WkspoiIiIhuQ1KuBaMWHcGptHzXzE+rwqeDG6FLbJiCychbZe+aAlv6EddtQa2HoctM+Nboq2Aq8kYWhx3Tjm5AttXsmjULrYylPYazlCIAgGS3Iu23ycjZ/i0AwJmfiZQfx0KyWRRORhUJiykiIiIiD8VnmDBy0WFczilwzQy+Gnw1tClaVAlWLhh5tcDmz0PUBgIARG0gwrp/Cn10O4VTkbcpcNgxfu9y/H7xGEbvXOJWTv2z4TlVbM78LCQvfBqmkxvc5v5N+kLQ8GqfdPewmCIiIiLywJGkXDz12xFkmGyuWZUgPeY/1Ax1wv0VTEbeThNUA6FdP4Q6sBrCen0JbVhjpSORF5p2dAMOZFwFACTkZWLS/j8hy7LCqaissKUlIPHr4bBePe6aCWotIoZORXCn4Tx9j+4qVuVEREREJbQtIRP//fMUbE7JNasT7o9PBzeCwVerYDIqL7RhjRDR72cIAn+OTJ55ul47HMxMRGpBHgI0Ooxr2IllAwEACs7vReqiiZAshaegq/xCEPnwLOirNlEwGVVU/E5HREREVAIrT6bipT9OupVSLaoE4csHmrCUohIzX1qHnH0zbngkC0spuh3RvkGY2/4B1A4Kx6ftBqNBMPeUIsB4YBmSfxjjVkppwqoj+smFLKVIMTxiioiIiKiYvj9wBZ9su+A26xobhvf61oNWzRKBSib/7G/IPfAhIMsQtYEIbPqM0pHIixU47JhyaA2erNsWtQKvXXihsl8Qvu/8CESWnPQ3e9p5QHK6bvvUiEPEQ9Oh8glUMBVVdHyHIiIiIvoXsizjk20JRUqpQY2iML1ffZZSVCKyLMN49Cvk7p8F/H2kVN6JhbAk7VI4GXmrAocd4/Ysw8bkeDy7cwnOGzNc97GUousZ+oyHb93OAICA5gMQ9Z9PWEqR4njEFBEREdEtOCUZ764/i5UnU93mT7SuimfaVeOeLVQisiwhd98MmOKXuc396z0KXaU2yoQirzf39E4cykwEAOTYCjBp/59Y1O1xllIE2WGDoC48zVwQRUTc/y7yj69FQIuB/B5GZQLfqYiIiIhuwmJ34qU/ThQppV7sEovR7avzL/RUIrLThuztrxQppQKbPYegFi9wTyny2FN126KxoRIAIFCjw7st+rKUItjSEnDls6Ewnd7iNhd1vghsOYjfw6jM4LsVERER0Q0YLXY8v/Q4tl/Ics1UgoB37qmHh5tXVjAZeSPJlo/MTWNRcGVz4VAQEdL2NQQ0eFypWOTFrt8w30+jwydtBqFDZA181u5+1AuOUDAZlQUF5/ciad4TcGQnIm3xq7AmnVI6EtFNsZgiIiIi+h/p+VY8vfgojiTlumZ6tYgPBzbEPfX4gY9KxlmQiYwNo2FNO+SaCSotDJ2mw7dmfwWTkbcyO2x4fvfv2J5auO+dn0aHj9oMZClFyDu4wu3Ke7Ldgqy1sxVORXRzLKaIiIiIrnM524wnFx1BfIbJNQvUqfHF/U3QvrpBwWTkjRx5V5GxbhTs2edcM1EbgNDun8KnSicFk5G3MjtsGLdnOfalX8GkfSvdyikiyWpG9qa5/3PlvVaIeGiGgqmIbo3FFBEREdHfTqXm4clFR5BktLhmEf46fPNgMzSqxKsWUcnYss4gfd1TcOQnuWYqn3CE9ZwLXXhTBZORN1t04QgO/73RuV1y4r0j62F1OhRORWWFqPNF5GMfQ9D6APjnynuf8sp7VKbxqnxEREREAPZdzsFLf5yA2V74U+bqIb74bEhjRAboFExG3kiWJeTsfhuSpXCPMnVADEK7fwK1XyUFk5G3G1arJRLyMrH66mkEanSY3WYQdCp+rKNCuqg6iBw6FdaUswjuNIKbnFOZxyOmiIiIqMLbcC4dY5cfdyulGkUF4JsHm7KUIo8IgoiQDu9B1AUBALSh9RHW6yuWUuQRpyS5/lsURLzVvDeG1miKOe0fQJ2gcAWTkdJs6ReQ8vNLkKxmt7lvnY4I6fwESynyCiymiIiIqEJbfCQJk/88Bbuz8INf22ohmHN/EwT5aBRMRt5OE1QdoV0/hE9MV4R2/xwqfYjSkcgLmR02PLNzMX48f9A1EwURLzfuxlKqgitI2Iekb0bAfHoz0n6bDPm6faWIvAmP+SQiIqIKSZZlfLPnMr7afclt3qduBN7sXQcaFX9+R8UnyzIAGYLgvm60oQ1h6DRNmVDk9cwOG17YvQxHs5JwJOvaXmWPxbZQOBWVBXkHVyD9j/dcm5ybz+1AzraFCOkyUuFkRCXHv3ERERFRhSNJMj7YfL5IKfVQs8p4u09dllJUIrIsIffALOTsef/vgoqodGxOPo+jWYWb5y88txc51gIFE5HSZElC1vrPkb78bbcr7+mrt0Rg66EKJiPyHI+YIiIiogrF5pDw1tozWHc23W0+un11jIiL4X4cVCKy09IkgpEAAHpPSURBVIbsXVNQcHkDAEClNyCw2bMKp6Ly4t6Y+kiz5GPOqR0I0urxebv7EazzUToWKUSyW5G+9C2YTqxzmwc0uw9h970CQc3Tz8k7sZgiIiKiCsNsc+ClP05i35Uc10wUBEzuUQuDGnFTaioZyW5G1rZJsKbsc83yTv0An+p9oAmOVTAZebMChx06lQri36eFDq8dB62oQquwGO4pVYE5TdlI/flFWK4cdZuH9HiWV94jr8diioiIiCqEbLMN45afwMnUPNdMqxLxbt966FYrTMFk5I2clixkbp4Ae9Zp10xQaRHS/h2WUuQxk92KF/YsQ3X/ELzatKernHqU+0pVaPasRCR/OxqOnMLTOgW1FuGD3oJ/494KJiMqHSymiIiIqNxLNlowZukxXMou3JvFV6PChwMbomWVYOWCkVdy5Cchc9NYOPKuuGaixh+GLh9AF9FcwWTkzf4ppY5lJeNYVjIAuJVTVHGp/EIg6v0Lb/sGI/KRWdBXbapgKqLSw3c5IiIiKtcSMk14ctERt1LK4KvBV0ObspSiErNnn0PG2lFupZRKH4qwnl+wlKLbcjInFSezU1y3t6VcQGpBvoKJqKwQdb6IeuxjqALCoQmrhuhRC1lKUbnCYoqIiIjKraNJRoxadARp+VbXrHKQHvMebIa6Ef63eCZRUda0Q8hYPxpOS6Zrpg6ogrDeX0MTUlvBZFQexIVXxdRW/aASBARrfTCn/f2o5BuodCxSgCxJcBjT3GbqwAhUGvY5op9cAI2hikLJiO4MnspHRERE5dKOC1mY9OdJWB2Sa1Yn3B+zBzVEmJ9OwWTkjQqubkX2jtcgO22umcZQF6FdP4JKb1AwGXkzk90KQRDgq9YCALpWqoXpcf1R2TcIsYHc+64ikuxWpC+bAsulQ6j81LdQB0a47tNG1FQwGdGdwyOmiIiIqNxZdSoVE1accCulmlcOwpcPNGEpRSVmOr8CWdv+61ZK6SJbIazHHJZS5LF8uxVjdi/FuD3LYXYUrq3OUbEspSoopykbKd+Ohun4Wjjz0pHy4zhIVrPSsYjuOBZTREREVK78ePAq3lxzBpIsu2ZdYkPx2eDG8NfxYHEqGclhQf6JBYBcWHL6VO2O0K4fQtT4KZiMvFm+3YoXdi/F8ewUHM5MLFJOUcVjS7+IxK+H4//bu+8wSapy8ePfU1Wde3Ke3dm8bCDHhQVhgSVnBcWEICKIil7Un9d7DZdrvip6Lwa8cgXEhKgEQUEkKVEyuwsb2byTc+euqvP7o3qqu2dmYXdZmJ3l/TzPPNNdp/Kc6a566z3nZDa/5E/Ld68nu+3lCdwrId4aEpgSQgghxF5Ba811j67nB39/tWz62fs28+0zFhK05LJH7DzDClO35L8xQtUAxOa+i5qjv4YygxO7Y2JS68ok2JTo999vSPTRnZGOzt/O8r2bsAe2+e+NaBUtH/oJkZmHTeBeCfHWkCs0IYQQQkx6jqv56v1r+MUzm8umX3x4G19cOhfTUBO0Z2JvYFVOo27JtVQeeAVVh30WpeQSWrwxsyrq+PHi86kMhKgORbh+8flMj0uz0Lez2PxjqTvlagACddOY8pGbCE8/aGJ3Soi3iOSzCyGEEGJSy9oOX/jzSv7xam/Z9H85dhbvO0RGLhI7x7XTKCOAMsovk4N1CwnWLZygvRJ7g+F8hpSdpylSAcA+VQ38ePH5WIbBrIq6Cd478VbTWoPWKKMY6K488kIwTOIHnIoZkREZxduHBKaEEEIIMWkNZ2yu/tMKXtg66E8zleIrJ+/DaQuaJnDPxGTkZAboe+RqrMoZVB/5JZSSTDuxewznM3ziidsZzGW4fvG7aI56QYd9qhomeM/ERNB2jq7b/wMrXkfdaZ/xpyulqFr07gncMyEmhgSmhBBCCDEp9SSzXHX7ctb0JP1pIcvgv85cyOIZ0iRG7Bw72U7vQ5/CHtpErvdljHAtVQd/YqJ3S+wFRoJSrwx0AnDF438oC06Jtxcn2U/nbz7jd3Ju1U6latF7JnivhJhY0kBeCCGEEJPO5oE0l976YllQqjJk8eN3HiBBKbHT8oOv0nP/5dhDm/xpqfV/xsn0TeBeib1F3nXLRtxLO3nSTn4C90hMlFzPxjEj7/X99X+wE72vsZQQez8JTAkhhBBiUlnVleAjv3uBbUMZf1pjPMTP3n0gB7RKBoLYObnul+i5/wqcVJc/zYq30nDSTzHDEuQUb1xtKFro3LyGmlCUnyx+FzOlT6m3nfT6Z9l2wyXY/Vv9aUakkuaLfogVl/og3t6kKZ8QQgghJo1nNg/wmbtWkMo7/rTpNRF+eN7+NFeGJ3DPxGSU2foYfY/+G9rJ+tMCNXOpW/IDzIjcKIpdN5TLsDU1yIJqr6+7unCMnyw+n0Q+y4wKCXi+3Qy/cA89d30V7dj+tEDdNJrf/wMCddMmcM+E2DNIYEoIIYQQk8KDa3r44r0ryTuuP21hUwX/fe5+VEcCE7hnYiLozBA1Ye830eqdXj716j30P/V10MX6FGo8mNpjv4MRjO/GPRVvN0O5DJ988nY2Jvq47sh3sn9tCwD14Rj14dgE7514K2mt6X/opww8ckPZ9PD0g2m68LuY0aoJ2jMh9izSlE8IIYQQe7w/LmvnC39+pSwodcS0Gn7yrv0lKPU2pe0cG39wLrqk754dNfzyLfQ/+dWyoFSkbQl1x/+3BKXEGzKc94JSrwx0krLzXPXk7Szvb5/o3RITpO/ea8cEpeIHnk7LRT+SoJQQJSQwJYQQQog9ltaan/9zE998YA2u1v70k/Zp4Afn7Es0KMnfezutNW42iZPsH1uWHsLNZ0ivf4Zc16s4yQG0646zlpF1uQw+fx1DL/yobHpszjnUHPMNlBnc7fsv3l5ChkVVsNisOGhaxK3QBO6RmEjxA09HBYr1oWbJR2k47xqUJZ81QpSSqzkhhBBC7JFcV/O9R9bxuxe3lU2/4MBWPnvcbAxDTdCeid0pvf4Zcp1rcVIDuKmBst8jr7VjE5l9FI3nf83PkLIHO3GHu8l1rMaqbMTNJsj3bcHu30rVUe/115/v28rws3/EiFaT2fpXsr3PYwQsVMACy6Ryvw9TccBHUUrqk3jjgqbFdw8/i88+/SdWD3Zz/eLzpU+pt7FQ6wIaz/86Xbd9gfqzv0jFgadP9C4JsUeSwNReYjCXJuc6oIGKCL3ZFCgIGiZVwchE754QQgixU/KOy3/8dRV/XdVdNv2Ko2bw4SPaJIiwh9B2Dic1iJMawIxWYVU2+mX5/m30P/Dj8oBTeojpX3gYZRST9oefvZ3Esvted1tuehBt59h83TvBddCuA3aWzde9C6VMMC1mfP5v6Hy2bLlc1zoGHr0Z7drYQxvBzftlZrwFt+NOhp/4B2a8lvpzvoxVUe+X2wMduPk0ZrwOI1wh9U6MayiX4cW+bbyjeRZQDE51Z5JMiUlzrbeTzOZlBJvmYJTcf8XmH0fbv/xJRt4T4jVIYGovkXMdzvvbjdjaoTedIGgFCBkB/nLKZcjXoRBCiD2RkxlABSvL3pvhalI5m/939ys8tanYdEsB/3riXN65f8sE7Onbg3ZddD6NESrvnHnw8V9hD3XhpPq9AFOymMnk5lL+fDVLPkrN8R8tLujaJJbdO2Y7bma4rG8VYwc7LneS/SgrSNsn/wiAPdjBq187hmmf/IMfEHOSA5jxmlHL9QGgDAuros0LTmkHM96KEazEHurCHury5hnVvGbg8VsYeupWr8y0MGO1xA84ndqTPlF23pIr/oYZryv81EoQ621kKJfh40/8kTVD3fzHwadw6tT5gBeckqDU28PIQAx270bab/wokTlH0XThd8sC8BKUEuK1SWBqL+JoF9vVtKcTKKUwlUHaLj4VTNk5Pv3UndQGI9SFY5w+dQH71jQDYLsOfdkUNaEoAcOcqEMQQgjxNmEntjG84iYq9r2YSCSGm+pgeMVNxBZ+iO89kSgLSgVMg6+dOp8T5ta/xhpFKa01biZR1iQuPHV/zFi1P0/ylYcYfOyWYpO5zDDhqfvT+pGfl61r8KnfYg+8fufNTmqg7P32Ak4j2VUjzMJ8yrQwotWYhR8jWoMZG3ldjRmvH9VZsMZqnENoyr44Q904iV6cZC/hqSeUnws7h7JCaDuLMoNYFW1o7WAEygNwyrQwwhXl+5roLa7HsbGHunDzmbJ53MwwXb//t1HrCmDGazFjxWCVGa8jvv+pBBtnFdfpuqCUBLEmqZSd4+NP/JFVg15g8z+evw/LMFjaus8E75l4K2k7x4avH8v0q36P1i6pVX+n768/oO7Uqyd614SYNCQwtZexdXmHn0bJhU5PJskLvVv99wfWtvqBqY2Jft778C8BqAyE+MZhZ3BEwzQAOlJD/HXbaupCUWpDUfavaSEekE4c3w601lTHqtAlHQ4LIcQb4doZ3Ew/Qy/9lOSqW8l2PkvFQVfR++IPSfe8Qncix8mzLuaul735Y5biu2fM4tAZb9+nzVprdC6NkxpABUJlT96dRB99D13vZzL5gaj0ILhO2XqaP3gd0TlH+e/dTILM5pfK5hkdXAIwozW7FpgKxQk2zMKIVhWDS9FqjGC0bL6qo95P1VHvQwWjOx+gMQMowyQ87YAxRXZiG2a0kapF76byiAu8c5joLQSw+rCHe3CSff40tB6zfSfRN3aT8dpR8/SOmUc7eezBTuzBzrLp4bYDygJTyRX3033HNZixWu+nop5gw6yyjCwAe6gLIxhFhWISxNqDRMwAB9S2+IGpmlCUfSobJnivxBulHdsL7GcTuJlh3PRw4bX3Pti6gMiMQ4rz2zncYa/ZuTJMNPgDMZRmTQkhtk8CU3sZ291+YKo3myorqytJ1S8tG8pnCZvFqrF2uJcfvvyo//4Xx76P+dVeyvzjnRv40crHqA/FqA1F+OTCd1Ab8i44+7IpBnNp6kIxKgIhuZCaZLSryfdn2PrweqYsmYlRG0VJR8NCiB3gZPrIdS/DSbbjpDr933ayAzfTT6BuXyr3v4zstifIbH6Y9KaH0IDduBhznw9y/WNetlRNJMD/nFJHxePvZNuTBoYVRQViGIEoKhDHCMRQgZFpscI0r8yKtRBqPrxsv7wgu0apib1R8PplKmQpJYsZTZVHXFD2Xdl77/dJrrgfJzXgd/hdddT7qTv1X4rr0i7Dz/xxh7brpgbL3puxmjHzjBuYilVjhCvKgktmtBojVprdVE2gZmrZcsowmPqJ373ufhmh6OvOMx5lBZn+6TvGHd0q17Oc3kc+Q7j1KKqP/DJKGahQFCMUJVDXtsPbqDv1X8j3bysLaIWm7Fs2z3iBqe0xY2ODWtrOYQ92YA92eNOGe8Ys137TFeR7N6GsoJeBFastNh0seR1qnkugbtoO748o2pWHcUopPrvfEgAebF/LTxefz7T42P8r8dbSo4LM+b6tZLeuKAsumdFqqha/v2y5rdd/gFzvRnQu/Zrrrzr6IoKNs4sDMSR60NrBHuqi5eLrMUJxzHidBKWE2AkSmNqLmMogHggwN14Lptccr7RZXtAwObyhjb5smt5skrqSC8GeTLJsXfXhkqDVqLK6cHG5belB1gx2swbvKcGnFh7rl923dRXfX/6Ivx/3n3o50cLF4xNdG3ihbxt1oSh1oRgntMyRwNUE046Lk7Gx07b3dN522XTXKjJ9KTb2Z5l29jysWAAjaGEEDPl7CfE2pLWLm+7BSXZgFwJOwdqFhJoPK5sv1/0Sff/41+2uJ9/7MslQHeYpvyKULt6EG9EG+jMWKzq20loZ5rrz9qOFrXQBaBc3n4B8Ame7ay4KNR8+NjBlp2i/7URUIIoRiHu/rdg4wa5YYR4v2GVG6gk1Hjz+OXFsnPSgH2AKNswsy6hJr3+GgUdvLmtSt72bnoqDzkSVfDe7+bTf99GI0YEjcwf7ZxpvWauqhejcxcVMpsLv0Zre94M98wYrVIF2QhAqD0xltj1J36P/irYzpNbfixGqpeqQq3ZtE60LCLUueM15wtMOZOon/+AHrvwgVqKvLKDlJHoxR/UzM15Qa/Q8UAxWaTuHPdC+3Qy26mM/TO2JV/rv7cFOOn/3eT8bayQzq/LQc2W4+hI78zBuKJfh3q0ruWDGgahCM8zP7reED889grpwbNxlxI7TWqPzGdC6LGjtpIdILv8rbnoYJ1OeweT/FDKbmj9wHZGZh/rLpl99ip4/faNsO8GmuWMCU66dfd2gFICbS5YNxODaNjg2m687n0DtFMxYrd8XnhBix0hgai8RNExuX3oJaHBcB9Mw/VH5Ruxb08yPjnrXuMsfWNvKFw86ib5sir5ssiybanBUXwo1JaNM9GaKmVamUlQGi038+koCWoZSRMyA//7Jro385tXnAagIhDixda5f9j8r/sGD7WupC0eZFa/j3w9a6petHuwm7zp+s8KgKVX4teQTOexkDidtg6GITyv2zdH52CbS7QmctI2dzuPmvFu9+KwaqufXs+nOlWT7vQuD5OYhXv31MqadM5+BlT0kNw5iRQPE2ippOWGmv86hdX3gaMxIgEBViGClNPkUYjLRTg4n1eUFnkqznZIdhaynLrRr42qwUVhoKhdcSKj5MHKOTVcmge26pHSYkDYIKy+Ld0BbrNURbBQ2iiPqpxEI1XLm7deQyqbACuE6OULBGPe+8xree3ArHzi0jYZ4iGxX8nX2enyj+w8C0Plk4XcKJ58aW6412A7adtB52/9tBBuoPvzTVB5xQdm8a7/QjJvOgjK8LCxlUHHECYSnL/SzuLLbNpJc8RcolCtloqzxR8t1UgNlN2LjBYncUcElZVoEGmZ6fRqNzmgald1kVZV3HB9smk3zB/5nu+fQ38YeGJTaXiAhteE+Bp74T7Quhi9zvSvQTg5ljg3EaK29EY29N3hJdd40TfG1sgwMyzsP2nGx07Y/vxUNEKyfDvXTcfMO+eFcybpG1uv95FKQSw0Xp1ceSuTAmBfcTA3hpIZwgwtwbdffXqZniGymBfDeW2otSnk77eh6HN2ANzyAIj3YQv+yTv84cj2bGdpoAcNAAtgIKHIc5dVJwAiYuFt+Tnrtk5jxOvLuPBwaMYIxAhVhahZW+JlZQxs0uSEHXNAjJ6n0WCk5h95btDvy2iuLTqmg/tBW/2+w5S9rsVN50JqKWTXUHVKspxv/+ApO1vHX5/+9dPF1cZr2N0tJWePiNmr28zL984kc637xoj9fywkzqZpXR7Yvzaa7VpNY309m8zDTzp3P8Kv9pLYOY1iG//fPK5cHutbRnU9yV8Mgi5qnY1gmKuCV91kJKmbVEIh7dc3J2GR7U97yAZNAZahYjwo7sbc96NOui86lvOBRZrgQPCoJIOVS1Bz74bJleu+9ltSqvxeWSYDrUP2OS6hd+nF/HjeToOfub+3QPrjZRNl7IxR/3Xm2N9+4608nxgzEsP67pzHjX+4iUOtlZErgV4idI3f1e4mqQrDIdV26urqoa2zE2IkLySmxqu2OHHLRnMO4cOZB9GZT9OdSWCXBrunxGpa0zPabAholzSNKmwfWhsr7jegbVVZqa2qQbYWf0s7bAa5f+TiPdq4HYEF1Ezcf+16/7Oer/8lALkVtKMY+lQ0sbprhl7naLdu3yUa7Gidr46RtnHQeOzPy2sbJ5LEL042AydTTi0G+zn9sZHid1yQmVB8tC0zl+tKkO0Z9KRuKuoOb2XrvWnL95QHJXH+Gjkc2MOXUOSQ2DJAfzuJk7LJ5up/cSq7fe9JUObeWKafM8cs2370a7WisqIUZDWBFAljRAGYk4E2LBLAiFsqcvH8nIXbG6KYG/dkUOdch7zpEzEDZk/dlfe2knTy269IUiTO7stgJ+F2bVpB1bGztsrC6iQNrvRs+V7v88OXHsLVD3nU5rnk2R9TU4yTbSRPgmtUvYrsutnZ4z8yDOMLtpPeRz9DjWnzRnu0Hka4wt3CkMQTAq26YT9n7MtJo/D+sVzk26TU/WjHQyeWP3VY4Noev6wjzdALHCPOCOZVv5hpxsHCw+M28dxK20ySzWe4469+YGmlmQ7qTRff8kExumCsPryUUDbE1OchDvQPUzrmUFsOhzchj2UncfBKdT+HaSXQugbaTuLmkl1E10teiFcXNJIpN5lIDZLtXjunzI7eli3zPADrvgD1+LpayBhjSt5UFpnByoG1wvc/Bkfvh7LbncN0NxdmGkjiFcwSAESBQPXvMNuzBV+n68yUE6lpQhSwuu78Pq7kFM1KFCtdgRWswq1sZXvN3L5vLjGJVzqTpA7cUb9Jd7d+gRxqLdSg3mCHTk0XrQZSCWFvx+yDdmSA/mB21jpEb/sLNvqvLyynZlguBiqB/8w/Q+3wHucEMaAjVRag9oMkv63x0E/nh7Jj99baBH8gZsy+F8ujUCqrm1rL1/vWk+xJsvmctLcdNp//l9Qw8v55I9X5Eal4EINh8HH2rz6d33UvjB6B20JTT5lA528uEy/Sk2HDby37ZtHPm+ecz3ZFg052rdni93qX4fsW3JmQ6oTFrYxRubJObhshXXgaOjXZtauY/j5v2MrESPa1kkvsycnD2ugqGtm30V+dmhsm77yjfpGHS83Qx4yoQDxIz27EHtmEPbCNtt5LXLUAGQ20lt+zX/ry56EfQsUPKVmcPdXkduBsWyrDAtFBWaLsBTTNUPshOpivp1QcgVFd+TZjtTeNky681dpZ2it1cKFUSKAOseIBsX5otf15Dfsjbh2x/hk13rGTaOfNxsg6JV73rKK01GxL9NDiKBuI4A0Os3bKhrAUCQLg+6gemUu3DbLlnjV828z37EW7w5h9a08e2+1/FsBTKMjFM5QfAXuu3Mg2MQPF99cIGP7srN5TFTuYwLBMjYBCsDhfPwzh9qI17vsaZL7ny794onCVZShWHn+8FZAuGX7iH3r981wv4vM7/VvXiD6Ks4gNrJzVAvm9L2TxuZrjsvRHesaDRuMtGygc1UIEwyhr78DR+wGlEZhyKEanACFdghOLe61Dcex+pwAxXjBN00phVLQRqp2JJH2NC7JI9NjD1ox/9iO985zt0dHRw4IEHct1113HEEUdsd/7bbruNL33pS2zYsIG5c+fy7W9/m9NPP/0t3OO9W9C0aIlW0hKtLJt+6tT5/rC4o71/9iEc3TST3mwSS5VfhLhoLMPAdt0xganSgNboL/vSsvpR6dJ/27aatUNeqvsJLXPKAlPvffiX9Bf6uzp5yj5cMrdYlx5uX0vUClIfjtEUjhN7Czp2d22v2ZwVDfgXE+mOBMPrB3AyXrBpJJPJCz7t2EWZGSr/lzbDJV/66fJ1mJEAY7ia3uc7aD5uBpsGChlTBcGaMM3HzaD3+Q4oXNSNXoeTLgYSR5eltg7j5l+/AY4ZKgSuohZWJFB4HcCMWMSmVJZdZIm31mAuTc51vPufioj3/1jIzKwKjp8B8lbJObYfuMm7Dk2R4lDtXelhOtLD2K6Lo10Obyj2v/LKQCfrhnuxXRdTKc6aVuw75uH2tbwy0ImtXWJWiA/vU/zc+PW653ipbxt57dIaqeQz+y/xy7790oO80LcN23VYWN3ENYec6pd97PHfs6yvHVu7HN00k+8dcbZfdtljt7Ep4d0AnTZ1ftlyn3/mbr/J9QUzD+Rz+x/vl127/GFShSD+B2csZIHux0l2kEts46ZlKwo3s3lCK9pp05sAUHMv4B8dxc/l45pnY1TVeTfswGZd/BxMaW++kewRp5icQH/GZdnmDh57YA2rhzrZMJTCcTW2q/la6j3gtJEmStLsZ3N4GQCGoWjP19BkR3j37BNpcet49bePMu3MI7hk2iJMK8qwjhECXlm9kbtXrMbQkDKzfP+4k2hVCic1wKrlm9jS006ldok6Di1T9yM692C0Y6OdDLnkEKu+djVelomXTaK1Jrb/VWAYaCdHuKoba+Av5NuXgw6TC55eOFaFZT+J6XoPQlxVzWDPwei7V/uBEtdOkzEuxw05JdswyA1GMXJhwEBrBY7GiXtZVZHQrYSqFFWHX0U2WUvfKxFQFhgmsRlfQ9NHvm+QzNB8El2LxqvqBRu8v6MyCNQMjym1hzeDtmk54mG/363BjTNJbG4AZWAEFTPO1n5WV++zLkNrsuNmFO2oSHO8LDA1/Gof6Xbv4UdsWlVZYCq5eZBs7+s3lxlPfFYN8bYqNt25Ctf2vovyQxk2/GE5087ZBzsxH7trOQDRWWdSecjn6Xn5eYpRqV1Uuvjom/uyVe+e7JfS+3plBTAjxeuv+jP/DSPo/V/2PLON7ie3oF3HC5Iaoy7tlYkRiqNdL4iqHQc1avRlrUc3KSw72PLVBUPlhztmWU+gbnp5s9TMME5qEGVYZDZuYujpF/0sLDefKQRDjLEBjd1xOrf3tzMUwYoQ7Q+t94NSI0Y/jMP1gjXVwQgdhWsdyzCpGCe4MZIRBaCd8uMxAiVltgta4+Y15N0daqI8nup9i4GQgWVd9D63Da1dzHCQeR8tNmfb/Md/MLQ2gVIOkEfpPLhZ0DlwM+Ck0U4aq7KWykPOKAuG9d33B3R2GKWGCRje52J41hG4NKBdjWEZODljTEBoe9xsAtMq9sU1ejRMGCe4VJLNZASjxeDROAGkYGN58D/cdiBtV91emD+O2k6Li6pF796h/R+XDAwlxBuyRwambr31Vq6++mquv/56Fi1axA9+8ANOOeUUVq1aRWNj45j5H3/8cd773vfyzW9+kzPPPJNf//rXnHvuuTz33HPst99+42xh7+RkBlDByrL3Zrh6wvZndmV92VP9Ul8/9HS01gzns2Sc8qyoY5tnMTVWRW8mycLCqIEjerPFJh21wdcIaI0KWvVkkgznswxk0wzUFzs91VrzpefuJet4QZsPzTmMjy88BvCyDT77zz9RE4pQH4rxjuZZ7FfT4pel7DyxwhMTN+sU+mcqBpJGXtcd3IIV84I0Q2t6aX9wgx+gmXPxQf5TtUx3kt5nt233fO4IJ2ujXe0Hu6xI8V/cSefLnoKFG6LYqSov+BOxMCOFQFDYIlAdYtb7D2DL3SvJDWYIVIZoO2M+ruMSa60gVBPGSdlEW4sXEiN9VI2wosXAlJt3digoNXIMTtYm1z+2rPn4GX5gKjeUZf1vlmMWAlgNR00lNsWr/07GJrllCKskqGUEzb0uXf6t4GqXvmyajJPHUAbveuBGQOPaDpgGacfm7pM+wqOd6zmkbqofvO5KD3P35lewtYvjOpw1bV+mFoapf3W4l1vWPkPedbFdh08sPMYve7ZnCz9e+Rh518F2Xb53xNn+Ou/dspLvLnsIW7vYrstdJ13qB7Z/v+ElfrDi7/5+P3rGJ/ymvndsWsENq54EvCbHT5z1KX+++7au4tfrngO8EUlLA1OPd23gjo3eDW5LtLIsMLW8v4MH29cCMGfU59y21BDrCkHy6lEBu7zresE9xg5WUdon4OgRVkvL8iUjreX7V+MOrCPv5NGuTd/A3+leXvI5kjvQz27Kk4WR1aQ60bRguxrH1azqGubhZAUtqRyDrkveHGk2A6vTIdx8C11uDRtVDT2RAEE3BAT4Tm4JlUONGB0baMsnON6ME3UVEW3SlKsj7riESGChyRpzCBIgqk0q79hKdn6eTy48nvW/WUGmy2bjb1fw72e/g6wO0JPMUR8LYTzRx8e2hAk6ebL2KtyVv2Br4f/YyX6YBte7UXGAvp4cwxu8wNtALkMqn6HSPhJDKcySjFndPg0j4H2OVC6YRriykXzndbg6gmMXM0uMSAZLWaAdXGrI2G3eDerIelwHHT0E5drFm2cFWoFjay9zS7tec6dCNQhOnUK0rZnqoz/I8Kv99K0uZFJojValTXp2MIiynUxgbWfR2ibT+bx/H57rORIn42W6uJkc/U/c4M+f7DqW/NB+BGvmjQm62Il2cHOgzLImi6PfO6kMud6XC313xcoDCmOCDbv4WVya1TuQxoqHC8fTS24oSMcjG5ly6gFsu/MO4gs/SOWBV44JDOyykmMYE5cq3cTuSvp9re2NM7syTBgVcAKvY/nRnctr1/VXqhQoUxHf72TyzfNwkr3kt1bjJFy0m0NRHrCxIgF0NFRYVqGdHPn2flB+u0VAE25YgBHy6oFSimzHVtz+1YAmt2kLPVuf8teZd5aidRRlmiTTSTih2BdQxcwast2bwbVRwYgXkAhFUJaFQhXOt/IORyl/e4VYNApFuCRz0AgY1B8xxZ9fK2h6x3S2/HkNuYEMRsTCMAyCVSFaT5pNujtJ5exatOPi5l2iTgX5AYsNAz0cUt1CGBPXdtG262diKWtU8Kn071RS5o4q85fR2htRUzve38p10Nr7jeuiQhF/ZEtlGeS719P5u8/jZoZJDh1Azva+x6Kz9y9bb3brauzBcR5KEij8eNd09nAY9+XusjlyuUVoN4+ptvqBKTczTPfDG/zs+1Bl+YPDhH0hWldBIf8WbJSyAYfNd7+KGavw+iy1DPLDB6KbqzFCQcxgECMUwq1uIt2V9DM/lWHQ+OF7MCMxzKB33Tr6gez2jPd/sDupYJSZ/3I/ytrV8KIQYo8MTF177bVcdtllXHLJJQBcf/313HPPPfz85z/nX/91bGeq//3f/82pp57K5z73OQC++tWvcv/99/PDH/6Q66+//i3d94liJ7YxvOImKva9mHA4jJvq8N9b8dbXX8EEUEpRGQxTSfkX2UVzDtvOEvCDRefSnUnQm00xJVpshuBql5pgBMd1GMpny7Kwco7NcL54YVUatEo7eT8oBVBbUjaYSrN5XQe9tslW22RWs0V9LIuTthlOpHhkwxpijkWlG6A5XEFF4UmJo12Gclksw8BSBoEZFdQWRj5SllEWoHHSth+YMsO79u9ohgtfzmGvKZy2XVThSWrlvDoirRVYhXlK1R7YTO2BzeOtEvBS3dvO3IfNdz1B21kHEKqPoQzlB3/GMBT7fPRQnFQeO53HihWfvGtHUzG7BjuVx0nZ2Kn8DgeqSpUGu5zCOtxBx2uCUnLzke1NsfXetWXLKtPwgm9R7zyN15xw5LUZtibFCIROIQOotK+15f3tpOw8aSdPa6SSuVXek1StNdevfJxMIbPo2ObZHNPk9Q+WsnN89LHbSNt5Mo7NFfOP8gM0nekE5/zt55hK8dOjL2Aol+GWRWfTYAXosnMsfejXDOTSfO2F+/n6oaf7QaSebJLrVz7u79ch9VP94FNPJsk9m1/xyz4w51C/bDifYVlfsYlJuiRw7WiXoZL/ZbskQGONajKSdx3/vARKypxCE6GRAIBVcmM/OhhUVjY6iKTAdB0M7UI2hbZzfmq/ZRiYrk1VZpioqch1ri1kMzgESu4wbe2Q7VjtDSfvOszetpzwcDfKydPQu5zODXfhpPtwMn3MSobZ0rQ/ZrTJ35eBx24h37uWE159nJyrMNCEGirIxFwc18V2NfskOji861UMVzPDTdDhZsANkXz2fj5kTiOoAwQIEtYr+XPN+cxTl5LTMc4xDfYZ7qYx28Fg5mksbTKLDUytuIRjg15z4XbL4eYKly+t9voAqYhcQMCaUTg6BQyi8EaB00B/oJaUGcMyDKYvbKJmQSOb7nyFfF8aQ2tyfWl6/ryemRfuT2PhwXhOO0QBpTWGKg8uu6V/L6W8m7aCtJ1jMJ8lqjVKa8ySJsL96WGGs2mChsGajvUsnbMP8QNOg2Ad6eUNhRt8i5pDr6Rm30aMWDVOLsSrvy423QIvEBCo2YHRz7T2biq1S+3RXyfSFCmeohLRGadjhfrRdgq3PYbRGy4s5xQDXKNtt4n6yLlRjIQxRvokGnfjSns3+eMEjLSTRju58Q4MhQblApqs3U33fXf4pWblDVixIEoVM2gHX/gR9sBa3OR+GMrLWlCmlzGmzEDhffG3YQXBDHjTDC/4kO5M0HLiTDbf9RzayaPsIZQVIVyvaT1pNkMv/4Pq/Y6m6qBCs0sDr88iNRK8oOT1SHCmJJAxcg6MYuAGVR7cCFSEaF06y19XuL4YgA7VRrwm9aUBEraz7cKmitsvlKHKvu8q96nzmgoW5inNyKk9oInqhQ3l+67K11l6HGXzlTnQfzVyZaBdFzc9hJN8N85wD06yD6u2jfDUYgA/37uZbTfei5PoKzajBWa8+4qyplc9d9/FUM/vx9kuRMy/+a8tq7wftJYTZ7Lthq+S2fwSgJ9VVHvyp6k++gP+fPZQF8PP3VUYobAeM17rj1ZolGSyGAGThiOmlG1Du5qpp89l890r0domWBWh7cz5hGoj5BsC/NFawxXzF/sPCKYDh9s5f0Cf0vW4eQdlFs9vbHoVrSdUk+1Yh5tOM/z0MnRuGKuykciss2lYNKUQ2NL0/+MWnHQK7RqAhcYq/tbF96YxBQqBKcMywDDJd68v7ENx28oY9ZlhhEvO4GtwxwmYGSY4eS/AZJh+Bl9pcM2qqCK2+AN+VlLu6RqcjAWGF8hWI7+VQbrbge6Bkg1Uez8pvB+ATjDj/X5gSruazX8qNlNtXNzm90fmZGzW3PSCl+FVaOroZ3yZpc0hC80mLQNlKa9/MEv55bG2Kv+63M055AYyXv9hpoEVC4zb3YR2NfmEYuvD67z+7oJ6UlxDCrGn2eMCU7lcjmeffZYvfOEL/jTDMFi6dClPPPHEuMs88cQTXH311WXTTjnlFO644443c1f3GE5mgOEVN5Fadxf5/tXE9rmA7me/T7bzGZxkB7E55zHwz29Coe2/MkxQhX4ACtOsyhnUHPWVMevuf/wrhQuZ8vnHvjf990YgRnTm2GaU2c5n0a5TmM8cdz1KlaxzZL1myL+ImlNZPyY7Aby+rX57/AcB74bUKXypaq3JZ20+Me1IEsMpkskMc9xiQKt7eJh3bWghahtEbZPmOgWzvLKewSHe+2oxqFczmKM/4I2OlHHyNGS8Ly4bp+wSP+c4dKSH/Pd2dxdHTPUCUxvyg2xN9GMqA8swiA8O0lboayAX0Ni4hGIhL2BSCDb5mUwjWU1hrz8mI2xiWhrtZNH5rPeEPJ8l373KS4u3sxihOPFp5U/MBv5xE24mgbaz3ugj+QzVx1xMsHGWP09ixQP0P/IzGt/9X0w5eSbK6GfzdRcVOhMtfOEWboimfPQXXlq0UphBk6EnbiL58gPFp+pAdMESpp5W7OjSzWdpv/lTaMK4bgjXCeK6YfTIayeI6wbRThAVmwrKO0dWJEC2fRUDj95MNlFDvt+7QVaGgRUpZkfaaRsn0ecN4+v/cRRluXkjF+tWCLMkyAne6EdGwKXx4B7MgItSBmbz8eSGgl6GVjSAFewku/F5/xi9C65Ccx6lCsevyGoXd8EJZBwbhaItXk1m00vYw128MthNXzZJ1nWJN87mhP1P9Pfhxy89SGLDs2RclzlVDZw34wB/W1949i9sSw9ha80JrfvwscXvwSz0n/DpJ+8glxqkMjvM2dP3Z/rcI7ysBivILeue9YMa9eE4R0YrvBsJx2Zb13q0UmgUieFunPQQoAjls5iuDSODFyhoCARZ8Z1T2e+zfyHu5CA1QDybwC4J8lrKpCKbIJZLYWgXd+vLZFL9aNch2N/OnN71GNpFaY297p9w2DlAMTNoQdca4rkEuaejDIZjaNchHCvvtyHXt5neZfehXYfG3q2c2b4aQ7sY2qUv3U5CGaAdZvRupapuHwYj3t/Z1i6Z5+9h6Nk/sm9/B5cPdWO6Dtuqp8LpxY5Ww2aAq5/4P8J2lpBSrH/q536A6fRMgmPzOZTy9nkwEqD6WO9hyoG1rTQMdXLMY/9LwDDZ8s9b/HWedvH/cUBlNW7fSpqza+j47bVkN65CO3nOczWuFx7AQtOvXL9p3XujcRa3zGJbxRKSAxb/+dfVHPHADYTTec4mBoRBRRjSz7O+yrtJtrILuWr4EILpI9EqAiqMg4lWipBSHKZLPrkURNIGrvJGH2sDqtwIEUJou9jkytZ5rEIzvwrHe8auFJiGwjRyXmaSf48/cpPtzdNaEcaKVWJaBg2HTmHL3S+T7Rrw163tLOmtg2y5K8GMC70RXudWNZDoGcDRLmFzdHNR7WUoobzvlJIgzUhW2WC4goAZoCJe7X3HWCZb00MM5TNoDRuHEpw95zyic47Cydo8/urfSDhZwqbLK0aEC5oKzUGSOYy6MEHLLARIRv7PAaMYaPA+GymWGxSDAYYi3NRCoJD1GawKU3twsz9vzQGX+wH9WF+a6JyBsm2gXXCzaJ1FO1lws179q57llZcEHBIrH0fbaQKxw8FO4tpJolYnwap7wE4VgklF0dqniTVtpOn0d485ns4/fwc31YGfDVMIRL1e0tPUU+eO6cMl37OcbNfzRCJP+Flk43IKP4V4WOMZvyVQNcMrygyQ2vQgbWfsy5a7X0EbMUxrkKlnzMMZvJ36w5eU9eGlDEXj4rYxm3gjzLBF1fzxM8KtSICKWTXjlu3y9kLbzwwxgqbfrG93U4aBGavGjFVDyTVCqUBdG9M/e28hiDXoj0SoQuWZ62a8nmDzPoURCvvLglhl88Vqx0xzkn1j5xv1vZ3rXk//Q+M/jDZCMa/ZYKHpYN1pn8GqLLbAcFJ9mGFF2+lz2XzXE0w9/QACFQaD+QxXPvEH1g710Nm7hatNF5VN+v0sDRc683b8Dr69360fudEP3lmRAAw+Q+KRa4FizCU0dX+mHPU+Ik3F4F32mSexs+2vm3VXdcB7qT31DLSjvf6z8sVrzpDxLAG1EgjQcPjRZctFG/PYHc96ASorirKiYEbADIMKgxEEFQIzjFUR8jPBXNslUDsVUFTMOoKpZ36p2FR+2Uv++q14NXUnfdp/37XieXT5lddO25nsM227ONvJQttRbWfP8wNT6c4km+5c6ZfNOH8hkWbv75XYMMDWv66jYnYNlbNr2XTnKjL9abLbkkw7Zx6p9mHS7Un/O8D7bvC+E5RS/udsywkz/WBXYsMAyc1D3me5adB41FR/28nNg2S6UjBqeQrfR/76FWCOfEdR/L4yvGv08Kh+B7XtgqG8jvnjxUCr3wJCjb898ebSWlMdq/IHSHi72OMCUz09PTiOQ1NTU9n0pqYmVq5cOe4yHR0d487f0dEx7vzZbJZstvjUfWjI+0B3XRd3vKcEezgVrKRi34vJ9a8j1fU8iTW3o4BQ82HE57+PoWU/w80NveY69DjHrrUmteG+nd4fI1xHePqpY6b3PfZl3MzYfgheT/N7HvM/BEc6Ae998EvkBrrAjeI6UbQbwXUiuE4Y7YRx7bAX3LCDoE0OLtwmmbFm6qrC/rG2xCo5OVuN7eSwNVS1L2do2XNgWAynHMLUYeP1qWLYKVw3C0qRc8qfOJnKKHTcmidvZzBcB6U1ecMm2rWOxMptaMemZ+tmVpo92CqDQ4a2VBTX9eruk6qDH9bew5KNTxIDzmiejakdHK3pPPcaXujbSF0gSq0VZeZ9Pya98mFer7lHeObhNF/0o7JpA4/+AjdTXh+i+56MVT/DO8eZIYJNc2g469/Id65i048uZNrHf0vjO68BwEkO0Pnbz/jNDFzXLXu6lh9oJ9exumz9gaa5ZfXLtXNkNz8zZn8VXkuj0kvs+lO+Qmz/d3idu4csMq92kFz+Vxxdh+VuRusYWNUYkVP8beSTOX/kl9fjdWY5ErDwOp5WQ11oNIOP/ARVuIHr3r+FzPqAl7WgFAccuY2B+38AQE/mABw9H1cnUSpFddBFkUKpFN3ZXr538DaSlk1bXR03LLmQgUdvJrXqERKJfmw7hwmsWngyS/Y93t+v5Vte5owHvNGy4oEQ7YWMIoAzhnvJFYJAVcEw6anzic5dDEDItJjVvYZzXvkrNS9E2FJ4qhmon0Fo9gnkXe+zL5XP0X7zx8h3r0dr+MxgFyP1qf7pW9hYyB50tebUeDP3Ljip8DdSGFrTPNRBVXaY/3nhd5jr/sa/JHpwFh6D687zzitw5KZnOHrzcygg+vI9bCs8tQ46eS5JDfnBi0DvatxDzgK8UT+PbpzB8cvvpKZvC7S/RG8hWNV63Ee5cOZBWIZJwDAIJgcYfOJXADQ5NmfYeT8JIbuih3zhc6PVdfjGcZdhNc3BUob3vznURXbLcqZql1alUaZBqL61rJ5+YsHRbKps8P9ftF3MGmkIx2koSfZ0nBz5RAdOsoN3GT1kQ3306hQQArxz6bgGB9iNzExkMVc8gm0HGOiuA/dItBHGNCIYKuwFbFWEVOE1KoyhorQsq8Ckj+urTPpNxayUQzOtuOGTR3JieE7X8pw+kAHqmOHWcbqbodYob5KxfSOZNV6gyXINLCNMZdjCMhSmoaitNTGtMKZSzIlYnP+h/en+VtzLZHFc8q6BFyrKosiByqHwmgNFm+uIzGzECJjkhhJMOX0+m+96mVx/knz/FgK1UwnW1TH17H0xLBvXdWk9dTbJlzdjD/RixQ8iWHOsP9Jck45jRCoZUpBxHVrjVf5F9B+XP8SL/b1sywxzfMscrjnkOP8ov/DgTWxNeZlcp06Z7//NVcDg5n3b2VIY9e7Uivm8q1BmRCz+teFZOtLDtEQqOGvavnyoJLt35WAXDaHYmIE+xjOyvUB1iIaSG4/RZTUHNY1ZdkfFZly13TKtXXQ+5XUYn0+iR360ixkf28wn3HIAbrq10NF8smS5FNv9DlIWrrJQo64rnNyO9T8zhhkp/p2ClURaj2Ro+S+YesY5dDyylubj5pHruI34wvdjVs6clNdyewMVqcKKVGE1zPRHIRxRdeyHqSqMxKZdFzc1gJPs834K2VhOsh8zXjfm72cnxgamVLSmbD57aPufc242iZtNku/1mvrWnPqZsmUHHvslw/+8ldYrfs2Uk2ZgqF4GnnyQzxsx1g51o4HnNq9g5Qu30TyqE+3xOOnB8mMIjm1C5maGxhznjo4I5+TS3rk1vOChVlFi+59a0s9SDCNcQXRmQ9k2ms65kOZz37vTo8WNDDwwkh1V+rdtPnEmbtbGtV2seLBse9UHNuFm7LIAl7Zdv1mka7tecG10mV3yuWIqf512zh7TbHakzBldtqtK15kvX6cuKbOzNtGplVTMrGHD7a94AwZp7Q2M8MdXmH7OfLJ9GX8Aou1pOn6G/zmZ3DZM7wvevathKeoXFR+OD73aT/9LXW/o0MKNUWZcsNB/3/7gepJbvM/k6JQKpp07zy/beOdKLxA2jrIHMkZ5EKz0AU3V/HrqDy8ew6bbV3qtGwplI32jaVez9S9rywN3Iw92SgJr5dtTfhbtyLT4rGpCtd4TDydjM7S2zw/aRdsqy0bKTG0bLq53JHg4+v24x1YsK21dMTKIh3/8b4DW5SPOUhOe1MHAnfk+3uMCU2+Fb37zm1xzzTVjpnd3d5PJZMZZYs9mmiaBWB0suJwNKz9EEwqFS90hV5PY+ADpzhdfN+Lq5Gy6uso/8LRrk8/v/JMOZTlj1gWQy6bQo9antYl2Axhm8bznknNxc/W4ThgjmMTsLl5s9P1lM/ZADp04GO2O17SgdOVee3ZvpwofHKE6Bts7yT+7HG1nwc5hpBIE8poAoJc/Refqe8HVVLgupr4GTcC79k652PYw5AcJuElaAgG0TjEQjxE88N1Eo2Hcrb/jyZ6XaNz4NLhJTBzUn1KMjDMSwWKOLl4Q5O/9PZuXNRM46N9Zn7EIZFPs1+41ccr2riaXHwYUf40rfpUrtLFHcevWv+MObQEUAy4Mu2AqsJSiKVzpPf0CkgM9bNmwmqpMJ9GhlRiGSS7Vg8546x3J7uld8xCD+UGUClDXspBN3zsNu2+zV+7YbP6fdwEaZYWZ/vkHyNuO32ymu6cHFSx+eaWHh8bUm2QqiVtSJ3Q2ucN1q723l9zWDWRdm4ZgDKOvj3w+j623kXY24mrIGXE29l9IRcILfCyzOkhZv6V6uBdFjJpgHYoYmigZN0TWDaGIYag4YTcGhY6je3Np+nIpWh0bTRpbFQPYW3r6IeNdXKYDLjOHB/1jcNxKULUYqtbrS8MphtaChssH19YXXpusWP4U7uCBkKsnZAyirEFcnSQ4VFf2f2Nlbf//1nGd8vNVclFoOw79AwMkCssq28VxHLTW2E5xOTebI4qBi0nIsHDSGbKZLE6hPF4YHcdAEUCVbe+ommlccdT5VJkmf1n8LpzBDrR2sIe6WPovt2MZJtMSffQQ848hpjUfmX4Yud71/npG1mkC08LFJqGGXfzMqAU+P+NIEhW3ek1DHJd8YUSlaM7hfQ3FwRZy7S/76zSAeEmnv45tlzVYaHVMTNsr7+nuIZtMlR2jBuxMZsxnVzbvoHMGGi/IrV0LtImrA2htoXUAZb9C+tGvs3XNT7DtGG7fGWg7gMkncVJ/om+4B9vVGKqJ9M1en1VT1Bnefjs2RnD7TSt0IeXP1hZJHUWjMW2bvIaMCpLVLjmCXnhAKVa7+7Hc9s5RrQsOBnkVLGbAjPwUspmMkmltFSFMy8IMGhhBE51JQyZJfds7UYaBMi2cQCuqohYVMFBBg9TQANaRF3udd7sWyvT6HlNmFFTcbxaHYWDWt6EqI2ggA2BlaDt3Plv/shZ7eAvhpmqmnDmfXMhhcCCNN7Q9sM+hwKGUfKIXFZqYhYDeoeLN66VTD4CpB+BqTdYt/37bN1JHjQrQkUlQ5Rh+mas1m4f6cAqZHDGbccvW57J09veWlV30xK9xtEvAMLlk+sGc17rAL3u0dxPNoZjX5NsK7kEXmAqIez+Fj6yhcb67mXnZmEkGXoALJwt2Em2nwE6Dk/JeO1m6u8cGCvLBqeiYKs5np9F2crvZMyN6BlKoRHHfIpEYoeZDSbz8Taac+kWGnv8GFQdeTlrVkx7vGMSeSVVCvBLiM/xJNoz5HA6996foVD86NYCb6ken+hgyq/3vPYBs1zbytv26o8GhFL3JPCrtLVsVMYkfcCqxBUvId65h04/ew7SP30ps3jv4fiDKmvQgH3nqHoKhOJVGcIeuXfraNxOoKGaY5TPl3+EqFMN1jTHH6cw6DqP1YFQojgrFUMFY8XUoDsGYt6xhjr3OPuYTfqLhiPRwGoZ3bZCBHaag2CNHnuGuYl+wTDGA4MjV5g53v6a1BkejHU3O0P6xascl/o76QjBLkw7nyI18DmccgvvEwSkEu0Z+bNdfl7a9DDPt6JJp5Z89/UMDJCzvnGV6Etglo4P3DfRhud73UrpviKaDW9jyl7Xk+tKF/QaF1yy9/eENTD1tDgNrustGgRytq7urmOk6PORvT1FePxJDw2X7siuy2WzZOlOptL/OTDZdVpZJZ97w9ui1cLuK12XDWwf8bjfcakWuodCXpaMZWNvzxrYFVOomwrYX3M33Z+l/oDjKY9VxLYRaCy1UutIMPPDG+vQFqDt7GmahD+H0mkGGnykeQ/07Z2AURh9NrRoktby/MEZKSXazHxArTq+YUU31jDo23rmKXCpDfihD2xnzyAXtsqSayWR4eMcfSO1xgan6+npM06Szs7NsemdnJ83N4/eH09zcvFPzf+ELXyhr+jc0NERbWxsNDQ1UVm6n/5w9WDrvkhjcwquPfZ/9zvgxkUgN+UwPG/95HVOO+xpuaAYdHesJmS5BwyWoXAIlvw1tY1VOp3JUx/LaydFTP6/Q6WJhNBfXBm0XR3dxnUIAqNDKyw2iVBMVbrQ4qlzGxknbpLuOw80aXkZTIbNJu0GsSC/V027zt5samEcuPR00mNZWAsv/gM5n0PksRv80SMTQ2TRoFxUe1b4/nfU6iiwdzSYUQAW8qm5ZAQKZFLm7v1QsTx2DchWQBOcVXKd4c1O54DbMKCgzgzJy5Ld0kttSuJgJzkRZIWa2HUXTcd4TiAEny0npDQwZPTi4OFphKuW3JrM0RJWLjcIFQthYKkdNTTW53jQ66DVbNJSBpTRuIfg2nO5m5Cu9Chsz1+ldzAM5HSSLCdpr/mMqFyPgfVCuS/XyleV/wc3005rexPWBlZDZAjmbXiNIxrAwlSK34U4WDt2DRmG0/pZpn/0LOpfBHupiy/XvZ+rHfoVV2QRoyGUIxrOFm00La811mJFKHDPKq24QjAFoaqU6EPZGOFQW1NdzZ/cKckaQjJPnjKYZVM87GrRmOJ/h2Z7NuK6D4zocXNtKXTACaHrSCb61+TnWprwnTjcc/W7mNE2B1nl0ZxIs79uG0i5pZdJcGaWxos47J7leVqt+ppqdKKC5KkPQ8Eb7SaeH6Uj0odAoV3PogvfRdPLpOGmb1WtXcN+6jVy4/BFAY0UC/t8uaoVJFy4ekgGXyoo4TuE8m06c0uuO0htPpUvSnpXCVCbajeGqViyzCaOQkVWfy5YN7nDa1jnURr6CJoliFYHA035ZrXs4jnZRKklAZamK1hKr94aL/taRZ6OjAaz2F/wmowCBSIQ/nfrRsv+XrU/+lHzhGNoC2296cnDrTGqCQTb8z3lox8G184TMAO0/ugClTJRpMePzD9AariJQVzyG/uoaBgPjdbRazrTMMQNbONEY2VHLVsajVJXMl3X6sCvrwTBKmgib/u+RaY4bJ27VY+TCmGGLcH2U5PSFJAZOINk/E9cJoR2DnB0lffdy8nmDnBvHydoY7mdwAy5a24SdAXThytq/91FgG5A1X8BO2ig3Tzzr9Xmhgy79sdn8wnoPrjKJukHOyHvNPXXh/zlnhXEtAwcLBxMby3/tYBYCU6rQZM1AATVBC6sqxN+nfJc5Dhy9KeE1pbMMzlo8hfc1x6mJBIgP52B1H8GIhRk0MYIWRiHoZIbMQhOgQlnhvTLVzgdOTr1y5+YvobWm7cz5bLmrn6lnziNYF0EpRUXF62cl7KqvNp417vSsY3PZwqPYlhqiPTXEIVNm+vWyIz2Msgyswt9tftNUv6yzpEwDzbV1ZWXfebrY19oXD1jq9982mEvz5y0rmRKtpDlSyfR4DaHtjBS11yjp1HqE1hrtZPzMLTefQucT3mvbex1tmVZWL91UBz0v/ojstidIbX6YQLSOxPIbqD36q1Q0br/vRDFZjR34aIyTPoI+8cNeJlaikIGV6Cu87i+870XbOZqai/1YuYkeXv3OqbjpQbR2wM6z+YfnY8XqsGqnMO/jv2N2TSPfPXApzku/B2X4fSj5GUrh4mhwRjhGdJ9DCJZ8V7lVp+AuOBIVimOEYl7T//Gc/NHxp4uiltcom/ZahePzPn90IVPLxQpbftM6O15DbUOdn+FVMa3abzab1jGyAxmmnDybzXetItufwXUdDGUQrA7TunQWiY0DVLTVgOs9TBwZ0XUkAw0oa/FjVNu4FVm01hiB8usiN5Yib72xIGM4GilbZybUh7a8e7hoLFpWlgx1g/XGMk8rKirK1tlvbmKkB4GKykrqC2Wu7dJvbX5D2wKoqa2motFrCpwhybBVjA3U1tYQa/RaR6RyQySsHc0k3776hgY/C6u/E9LWoF/W0NSIWagrvZscMu5gsevHMtr/XTG7huppdWy8fSW5/gxG1MRNOmy791XazphDZUPlHvRga8eFwzs+mvoedwUUDAY59NBDeeCBBzj33HMBLwXsgQce4BOf+MS4yxx11FE88MADfPrTn/an3X///Rx11FHjzh8KhQiFxhne1TAwtvdlsQcz7H76XrqR/vblxOrmoFN5QrV1bNq6Dr3sd0w78ioufWoNw9mSZ80jd1VKYRmKWNCi9pmHadF9xAybuGETMx2C4fNwGw6iJu8S04qo1sRSDxDO9xEkhxqoxhlsQDkm2ra8D1ut6V12D0akCqui2AeD6xxHvn/DqL3XmA37UfuOQ/xgV6JjNQx5Za4dZvAfN/pz2/ZpuHpOYeQ5EzNUXejIWKNMGyfXDU4/6CSKJEonCdXWEmqqQplp6o89C2doPV2rinsQdn7rPbnVI6PJFJlWB2bpqFpl6ZmFLKx8tlhvtIOhIGCC5WpQ5eurUA4Vpc+zXO/my7CCnDVtFvOVpnLzU7hmgIA7gD2cBcMgrY2RzVGjbKzaSoxwEAyDp3Qzr6g4ecOkybBZdNgZRNqORQVC/O+KR1HpJKCpKOQaxA7eB4Cv5uewXHtZWAeqBN9gHaDpf/rr9B1+Dac88BMeOfES1mhNrKKB9z38c/5w9he46IGfMdi8mCwGHzLbOb/T6/ttWJtcnt/POy9V07jS3MIZpteJdWf3Rn7a14MV9W4Y9q1p4fQP/Zh8/xq2PPZNvhst9J+kDL7aUs2MmlpUIMa2vGLNmlchnwRlkBzcQKhpJlMuv4n2gR5+/MQf/VP5Dtfx/w7RQIjb9j/bL7v1+A8yoxC0WrH5Zf7r+b/6ZXefdCnhSBRqQGejvDgwzIvHecMrP3DaDVQEwmiteXTtM9y48ilqCBNTAT5y5HupOuLdoDUbHn6Bjq19RPImUdukNlCNnXFAaxwnTwsKQ3lNPpUCq7oFtIupvSCqUjBlbm3Z58+cWCu5Ji9QXjnrSBoO+Vihjrnk/jSIky1+mW15SKEeeRYzEiAasTDMg2Df72MEwQxqzBBYsTDZ7pTfwbthGdSe9CncbMLLVij874L2grvokWgzgcbZKCvEjKvuAMDJJnEyNoblFjuV1S5WRV3ZMVQcdCaR6QeXB4yUOTaAZAbKltNa03Th93FzDm4eXFvj5hVu3mFoZS9uzsHJObjZOMHDf+bNly1MyzlEp1TQvGQGqZxDfzpP7+9epuvPPTj2NlI1Sfoa1+KmOjAyA7T0xrAcq5CxkQM2kyFCsdGx9/RN4dJqjv/UJx+pwY56X7zaKD7RcswAQ+FWVke8ToUjroZ+7/+/nSle0El5dUObBtoyoBA4skImkbBFKBwgHAkQjQWIxYJUxoOcNbOm2Dmr7eLmHYyAWdYhsm9ew9hpe5hgpWLGhe/AsBxM883pK2dHRIwgl89fPG5ZRTDEvx94Eu2pQbalhtinusGvs+3pYYqPH2BKrHq7ZS2xKr9sfaKf/375H37ZD496J0c0eJ2pvzrcy92bXqY5WsGUaBUH100Z09HyXsWMQTD2+vNR7FfTSXYQmXUm8QM+RuKln5DvXcHwipuoPPDKCR2RWEwgw8CsrCewnRGhx6OtEDO++A+062APbGPjT97HtCt/g1UzFWUFMa0Qv13yQQyl0P/2d1QwstM3h0YkDpEda6YnJsB2vnaC8RDB+Nj7RYBYSwWxlgq0q5nx7n3ZfM8a0n0JIrVx2s6YS6g2Qnxa1bjLbk/joqk0Lpo6blnL8TNpWTLTy5gfCXAVAl64xWCXdx9WKCs0LxuZX1nl97mNR7d5fUm5GjNafh3WcMQUnLRdyNDHW/eo9TNq+6X7hdZEWyrKrwln1XgZUxrCNZFimdJEW+JjAnel74vbo+Q4dWHwW+916X18aR+XAIZpbrdsV5ml69SUrdM0S/ZF8frbKxlxNt8/0orIezBpD2fp/McmppwyBzP6+g979zQ7E1vZ4wJTAFdffTUf+tCHOOywwzjiiCP4wQ9+QDKZ9Efpu+iii5gyZQrf/OY3AfjUpz7Fcccdx/e+9z3OOOMMfvvb3/LMM8/wv//7vxN5GG+ZjmyY4LyL2Ld+X7Cr2Hz3P5h23jHsN+Nskq+8wsZlH+PK9h7snENImwQJYBHgkYb3MhRsI6I1UddmZmYrs1MbMVSEwdSv0TpJV6iVp1q+zMlJFwcYBjqG7qA267XTDwePIBZaAqWdGxYyhLJOgLSdxlAKwwATRdxVIw//vVmBZDbCptDBVIQCxIImgap+0j0ZFBkMVZ5GGjRexmITysxgRYJMvex//eFilaHY9vOPktn4XNkytYd/kupjPuS/z+ryvsesivIvAWUF/Z+GU75BsGmOnyWWWvUPEi/dhzIDmPFGjGCUQKFvJoD4gg8QmX4ykdanvdGCTKOQzVH4bRbaKJumNzR4oUNbM9rEPvEG9qk6Hvbz+hlKvXo3ma2Pol2Hf9U2n3Zs+m2HjBMi2rzID+RFUiEitkXGVQRUlvCMgwlPPQiATaufhXQSNFSr8kYwAyX//jUl5znfuwLHdRjWhvdAp9A8yglXowyToVyKdCFrIFtywxUa9SggW5K0Hcb1AhIFmUL/SE6mH3NwLW5+QXG/trzE8LYBAHI6jJ0vtnnvePwaOo0hIjNOJjz/irLtJRMd9Dz/dYxADMMJM9W2iZhBIlaA3Kb7ScXrMAJxptl53jdlOpFAjEgwRrjkRvi45lnMrqgjbFlEzABR07sRVEpx0dzDuWju4YznhFPG3sxqV+NkvBEInXS+8Nt7X5xWLA/WFLM1tdY4aQcj6AU7Qk2thKdN9dereRpjnGslJ+2tyzP2Jq/7+eKoYs3HTadmf6+DaSdj0/3klsJIhRax6dUEK0P+voycg5Ht25kwW+97nLazFxOojZe1p9dao/MuTs5BG40Qr0Xbmmhb8fiG1/WR3DqMm3VQAYOWJcWOibc98CpDq3rHTX13XO39aI1deO39HhmFTuG4mg2bB/jl8q3EnG4u5TraBk/Fshu8vvfya6lO3+mv09D7gy7PFjPHNhhDY+BiYJTUcwcLG5MtzGYFlfRTxwC1HBNqxA7GMEJBBuv356yGGDXRADVhi4pEnngsSGVFkOqKEDVVYSKR8Uf62RFGYdShyUwFo/QOdI3JmtuTVATCnDt9v3HL5lTW84NF57ItPUh7aoiZFcXOm7eVDIQBlI0m2z6qrDVa/B95ZaCTX6571n9/+4mX+IGpxzs3cM+Wl2mNVNISreLMtgVlI3Pu7cxwNRX7XgxAxb4Xk3Ri1B79VX8kYglKiRGO6+Ki/UE1co7Nk90bSdo5EvkcB9dNYU5lPX2GyTv/diO/OPRUVjmaaLSODz1xB78/8WIaSkYWVCUjPgsB3nV8qDZC2xlz/D6BQrWR3T4qX9kIn7vp+U20ZfuZyZWzxw5C8EZNPW3uuNONgMmM8xeOW7YzSrutCTfG2OeyQwoBLfxmdQCRljiz3rf/mMBdMSBGSeBrnEBc4X3ZOlsrvEE2CuspvaaLNMWpPai5GEgrDSIWtofWpNqHaTlxFpvvWkVuIOO3fQ1Uhmh6x/RdHr19Mtkjj/A973kP3d3dfPnLX6ajo4ODDjqIe++910933LRpU1n0bfHixfz617/mi1/8Iv/2b//G3LlzueOOO9hvv/EvIvc2zYEc2Xg1odCpbLh9BalXE6z/7XKmnfNZrOpOhla2M9VYB+HyT7JjcjVkStpWR9wqAqbXSZ2hojg6iaXzpEZ9trpG8YNM63E6xiuMImU7Lsmclx2QVZBWLnPzW9A6havTaJ3G1SnuScV59FfPoQs3vsf2vMiJ/XdiFgJa2gyhzSBYYQgMYVhZVCCMCgV5dlM/saBZ+LGItR1LsHYfQpEo4VCEQChMZPpBZbsXqJ9B60duRAVCGIGwty4rVPgJbj/FGqg85AIqD7lgu+XB2vlQO5/I1GO3O8+Ois46k+isM8umTRlnvvHzCD3/duCJdKYT9GaGaQpFaGmY6ge0Yo/fTiQ5hO06tE2dQ8PshV7zTBWgTxm4Tp72XJr9P3cvXdkE+WQ32nWIhOJkctWgHexohGCkHjefwMilMGzDH8I9MyYwZRC1AoTMAOZIkMNOEsPhCGOIMC4hXJpGhmEC6snzcXOLV6Zc5iqvvhmBOPOrGrlr6YcJmwEiVgAGVtPT4TV3mwNcV3oiXoSR7idrgdK/YL7jKFjyfQAaIxU0Ripw80kGnvwaQ8E4RiCGCoz8jpW8j5ZMj6Os8qeoyvCG+7Z24OlGaVq3NwEaj5nmB6+iLcULY3+klDeodISnfCJH//Ji3wJTTw/6galMV5KNf1yJFQ1QMaeGWFsVm/+0ktSWNK/euoJpZ88nsWmQxPoBP2tpdB8fyjKYf0Wxo+jk1mH6X+rEcTVu0KRrnxr6Unn603mCWweJD2aw3WLwyXUdcPMYfihopNGbjYlNEIcMEQYLzT1cXPKORYoY9XShVLEPO6XLs05cYwi0CSqLVjm0kcVRDs+pKWQVZL1h9whGArRZAYxwFBWvJ1hVT0VFlKqKIDOjQQ6JBqiNBKmOBAhO8kCR2DmVwTCLm2aMW7a0ZS7zqxrZVsi0airJmuhMJ/zXhlI0lXSqvDU1NKqsuNzygQ7u37raLztrWvGC/tZXX+Bv21bTGq1kSqyay/ZZVAwqaz0pmwGMx4q3UnnglahgJemuLioamyVTai80kE0zlM+QsLPErRDT4sWHCDeteZrBXJpEPsfhDW2cPMV7iOVql7Pu/zkJO0vaznPlgqO5uPBQKec6fPaff/LX8Zn9lvgjPee0jcbLxnfRbEi8dqfVQoxQhiJQE2bKKbMJRIO7PSgldszoa/DtjmQaMP1O0neXSGOMSOP4Wb+xaVXEdjB7TruamRfu52fgBSpDTD197psS7NwT7ZGBKYBPfOIT22269/DDD4+ZdsEFF3DBBdsPGOzNLEOR6+5n5R9fINeXwlJ1pLcNsP7WF5h+3r44KZfMts1+wAi8+8a6iEU+GMR1vQ5ajVwAw1GgIRKIk3d6iRkOhCxIFIMFebOGlBknbwQwDcjpbeTJk8Ump2yyyiGjXDYGW3m6uomUAlcp0JojsMkbFeRVLbYKkDcCbA5X+EEpgCdql/LPmiXkVRBHWa+d//jw2lETphV+PIZSxF/oJxb8J9GgSTRoEg9ZXhArCLFgnlhQEw/miAXTZUGueMj7HQ1404xJ+IGwX00L+22n66Bbl34UrTXDea/pUbCQnZNL9RHJpIiT452P3EJFqIJKexDlZND5BOdPPwjXDBK2Auxf00JDQ/F8X9+9CUspwtqm3tTUGC5uPombS/BERRuBymll+2CEqmmc9g6+kUt4Iz3lErj5MDrvoJ0sceVwujl2JEcViBE0LZpLMgyy9q61vVeBsV8kbnaQ9OaHdnJFilDjodSf+MOyyVq7DD79X8WglhXFCMYL773fpQEvZYZQhqL2gPFH5LKiAeZdfih22sZJ5bFHsrFS5dlZdtqb5mTHD2SVpgMXs6wKZZGSTsTTNtpxCTVEibRUsPZ3K3AGM2BVku5Ksfa2l5n1zgXkh3MMru0rz2TSxde33PMy/RmbgXSeOR0pDhosdByv4L9+96K/vVOTaZZkEljYhAuBJ2Pchvku2sihVRZX5bCVwRrVRFZBr+n9r+YIkyaKGXkMwgFQOVwjianANuOkrQaGqjajIzmMaAuB2AwilS1UVE/loqp6qscEmsZvHi7E9oStAHMq6/2b31If3ucIzp9xAO3pIbozST+rA8B2HcKmRcaxaQjHsUrKOkqCVvXhWNlyqwa7eLFvGy/2baMxEuej8470y77x4gP8s2cTrdFKFlY38cmF7/DLEvksUSuAoSZPUNUMV5eN9iNBqT2D1pq865Rl8S3vb6cjPUwin6MqGOb4ljl+2feXP8KaoR4SdpaDa6fwL/sd55d96B+/ob1Q38+atpAvHXSyX/abV5+nP+s9rAqZph+YMpTBcD7jZ2Yn88Xm1VErQOFyFICEXSxTWtGezXDA5/5CRy5DQ3jHmpYKAV5QZCA5SGNsz838FXu+tyoDb0+1xwamxM6I0L8sgxVqwajPke9NYQTC5BPQ8eg2pp46m8Fla/HajqlCXz6KSDiGCgQxAkahz5kYOFGsSIDqBf9FuCGGCkY4J9JApjeFEzDJmJDSi0jlvWyoZM4mmXPI5hwShdcj0+ycw9ysTapk2gvWUnLOa3emlzdC5Bm/TffOcrVmKGsztJ2b850RCZjl2Vllv0uDWWYhmFUMbpXOsydlUyilqAyWd0oXjNZSicnUSBzCNYXmS3HI9KMCcT62/wnbXd8RDdO2WzaeUOPBhBoPHrdMO3m0nSoMS57whzV3cwmsqhljj8UMEWo8BHek09zCctp97b+9MU5gSucT48z5OrQeN4iq7TTJtXfs8GqUMlGBGIHqOdQv/fGY8qFlN6AMyw9mWYE4wfoYKhBFWTFcsxrHjHn5RK4ml3PIpXLkkza5VM4LZqVsVqZzOJv6yTsad+sQAa0h643mt3pTP9nOYfKuS3jzME3pPNMOambTvWuxBzLkXe0Fs5UiMJBh60MbaDttNs/8c4s/4krJGcDEYcOqfxIxephCHzPsGNXGFDLKYL2aiqG1F7wG1gUMTlL3gcriqCy28oJP2sgVspq8H7D9NsGmAowIaxqXUhMN0hQNcEk4QG00QGP3u4gETCKVrVRUtVJVO5VQRStGQJpkiIlXGQxTGQwzb9TD1CsXHM3H5i+mP5dmIFcedK8JRWiLVdORHi5rGgiwNVXsfHV02aZkP+2Fjt3dUVmNn/3nn1jW305zpIJjm2fxqX2LWb/rh3upCkao2YW+dcTkk7bzXiaSnSPr2OxbU+xM/h8dr7Ksv51EPkvItMrqybdeeoC/bV1Nws4xp7KeXx73fr/spyuf4KlurwuIA2tbywJTKwY6eanPGyGrJlj+uRwPFK8FE/nykZjjVtAPTCXs8rJYIOQHpkrLDGUQNYMkC9NSJWVKKd75+B8IKZP6SGzvH4hACLFHejtn4Mmn7l7AigRpPm4W2btXYw+CbaRQlkGoNkrrSbPI9KZpOuFQrIiFGbYwI14/MmbE8jtBfj2x1kInzLthf/OOWwhWjQ1mJXMOiWzxden01Kh5UvntD7H+ZkjnHdJ5h57k68/7WgKmMU6Aq5CdFTSJbzfgVTrNJBIw37SbhJAV5K6zPo+L12ePaSgMNCHrret0T5kBlFmFEdqx9Ndg/X7jBnK0kxsVrCr9nSBQOWPc9QWq5+LkE7i5BE7O6yBcU+hDiUK/4OjCb+99X8pk+doebFeTd1zyroZMN7NS+THzlr8vXSegE/T2R7j7Dy95TdocTd51ydsuHxu8jpGh1f1lR9ZTsv8OJhnCZIkwQA0/41/KD3AZLOLvKDRZwmSIkDEjoCMMPt5JRoXJEqbRNjlAK6ZsGWDKCTPYODCEGnTIOoqgpQhWKaYsnUHv+q10hzai9CBBNUhE9RMx+oipXlBpLlHd+EOShMENwwDN3MSXy3ZrTTBEIvwSFSqFZShMwxugwSwM1OCNPhfBijUTjLcQqWwhEG/BijVzwswDUGMyPj7/elVHiD2SUoraUJTaUf3afHLhO/jkwnfgapfUqOG8969pwVCKbakh2mLVZWWlfVq1RMu/zbelB8m7DpuTA/SPCoR96qk76EgNEzYt3jPzID6+8Bi/7LHO9dSFYrRGK6kIhCRwNYFs1/H69Cx8Bnamh1k52EUinyWRz/HOGfv72XUPblvDnZtWkLCzJPM5frPkA/7f7v9WP8Uv1j4DeBlGD5/+cX8bT3Zv5Lb1XoZrTShaFpjKOQ5DheykMUGk0gCTPTbAVCzLbrcsOWq5ymCYilyIWCBIZaD8YeZ50/cn79hEA0H2rS4fpfHn73gPYTNAzAoSDxTXbyqDmRU12HkbyzQxJ1H2oBBi7/J2zcCTwNReYCTtb/qZ+7D5T6+Q6+wlPr2ZtrMWeKNCtO3cqBBvtoBpUBUxqIq8sSCH62rS+dEBrmIwayR4NV7AK1UyTzLn4OjRWR5vnrzjMpB2GRjVfGpnKRgTzIqOk71VGtDymjEWmyiOBLzMUdH4XM7ghqc6eGBVNwOpLNXREEvnNfCRRdNhFwaG0oUmXflCwKY02OL9Lk73y0eCO05JWWE52y1MH2f50uVKA0S2v10T242Td2PknfrCfE/56yzuw2WlR0CAPCHShMkQIkOYdOGn+L5noJGX1r5cduy1dHMZMX9ZxY7VtS05g6eTA2XTAuTIs2MBWROHGEliJLe7zZO4mxgl2WEKGHVfaVsWWDHqmr5Jtn8t0885ig13r8XIxzADCaacOY++vnupa61nfvx75EZlTLmAqRQhQ2EaZjHQZCgarTTfWDyfmmiQmkiAmmiAqnCA/gcPwc0OYMZaMGPN/o8Va8aMtWCEa8cJQAnx9mIoo+yGH+ATJUEjPep77exp+7ElOcC21BDzqooXu47r0lXS31VrSaaV7Tp+WcaxCZRkkTiuy2f+eZeffXX5/KO4dJ9FftltG16kNVpJa7SKqdEqwm/hg43JxnFdBnJpErYXRJoWr6Yi4GUzb00Ocs+WV0jmsyTsLJfus8j/Gz3auZ6vvnA/qUJ20x9OuJi2eDUAT3Zt5Osv/s3fxtLWudQVmqd1ZRI80bXBL0s7eb9z/VhJsCZl53G16we7ygJF+VFBpLLlRgefivU0OSr4NK+qgaxrE7OCzCqMnjvikrlHcP6MLDErSOOoUe1+fsx7thsILW3COtrMUdsACBomty+9BDQ4roNpmKC86UIIId4aEpjaS/htUs+cR8ffQzQfO2Ovb5NqGIpYyCIWsmh8A03/tNbkCh21jwSqUnmHZHb7Aa8xAa6c12SxtDP5N5sGEjmbRM6m63Xnfm0hy8viigctLj68jYfW9vDr57d6wyO7Lu1Jm5e7k/Qm85w0r4E7l3f4QSC7EPgpBpa8wNHogNHkpsgTJE+QBDsX6O2jgW/ztcI7TZBsIZDlBbNC4wS6wqTpY2yfNEEyZAkRIjum7LVkCY87PURm3OmlLGxMZ4jB5TdSuf9lJDf+jqlnvpeOR16l5bh5dG29B2PGWQyu/B/qokEMpbDM8gyn0YxwbSHI1Mz+c6pRZnm0c7zMNyHEzhl90/6RQtBoNEe7fP6AE2hPDbEtNcR+JRkmXZlEWbO/lpIO2jszw2VljSWjl3Vmhrl2+SP++y8ddBJnTdsXgN5Mkl+te46WaCVTolXsV9M8pkn5ZOFqF1drvw+wvOvwdPdmP8B0YG0Lswv9i/VmknzjpQdI5nMk7CxXzj/a7zR/zVAPF/391/56f7DoXL+sMz3MDaue9MvOmLrQD0wZKL85G5RnHI0OWibsLHWF0VrHlOWzfmCqNIgEXqbSSJAsFggRNi1igRBxK4jjupiFAWMOq2/zmspZQapH/T0/Mm8RH5xzKPHCcqU+tuDosSe24MjG6dst253ZeVVBryNk13Xp6uqirrFxp4Y4F0II8cZJYGovogxFoDZC88lvvzapb4RSipBlErJMaqO7kA5UwnF1WfPD8YJZxWneaz+ra9Q8b2UoJ2u7ZG0vY6i5MsRvnt9GIutl5ngjOXkBt189t5X3HNzKyq4Ew7uh3663H4WtIigzimso8qZBxlBYhkHA9AI5lmkQKPw+1FAETEXANLwyo4F/mr8mYGhCKktEZwirDCGdJkja+60zBHSKoE4T0BksJ0VFqI7/mrUQy/DWFTAVFjbxBwKggt74iQoUipERiVXJewB34GX6X72fqqmHs/nl65lx6ifY8OwPqVhwAS8n4hxsD1IdCaCUiRlrxIyOZDq1FLKdWjCjTZix5jGBKCHExAmaFudN33/csppglB8sOpetqUHaU0MsrC4OyrCtpBN2KO/TamtycLtl6xN9/HLds/77nyw+n0PrpwLwykAnv1r3HK3RSlqilZzYMvdNDVoN5zMMF5q6Ra0AU0uaP/5y3bMMZL0spkPr2jhpyj6AF4w65283krSzJO0cl89bzIf3OQLwAlOffuoOfx1X73ecH5gCr4+mEd2ZYpZaabYRvHaAqbRJ2+jlXqustHldUzjOAbWtxK0gsUCwrNP7Ixra+I+DTyEeCBK3QoTNYqbbB2cfykVzDmM8xzbP5tjm2eOWlY44KYQQQoxHAlN7mbdrm9Q9hWkoKsMBKsNvrMmC1sVmiqXBqpFg1khW1/b64yoNiO1MtlJV2KI/ld9u0Gk4azOQsqkKW3tEYMooZOWUBm8C2wnyjJ5vJEjjT/eXM7BMVViufLq/3sJrP8hT2K5lFtc73vKWofao0R31B5/0+tqyk+hcoYN5O4VbMkriSJ9cRqiaYNNhbHvki/RsW0/XugcxlE0g383Spd/CnPYtlDIwog3SzE6IvUTECviZO6MdUNPCr457P+3pIbamhsqaYXWkh8vmLe3TanRAq7WkbM1QD3/dusp/f2TDdD8w9cC2Ndy6/gWmRCu5cNbB1ASjXsvjiggd6WEcNLbrsCk5wHElAZLrXv4Hqwe7Sdg5DqhpKRv17aK//8YPop3ZtpAvH1wc9e2Wtc/62UimMvzA1MiobyP9e5U2TYuYAQyl/EyyRFmgaHQGU0mZNTqDqTzAZCpFPBAiagXLMoWaIxVcMPPAQoApVHYu969p4TdLPuBnKUVKmlIe3jCNw7czWMn0eC3T47XjlkkfYkIIId4sEpgSYg+klCIatIgGLRre4Lpytvu6wayRgJfjalorwzRVBBnM2LiuxtVgGgYKqAibNFYEmVkbY2p1ZFRwpzzIY5mjgzWF90Z5QKc0yDN6+dcMHu1hQZ7JSBkmKlSJEaqE1xkZ28kMMPTijwnkuliwzyLiB11J4oUfY/ctJ/XKzVQeeKUM1y7E20jQtJhb1cDcqrHfUme2LeS45tl+plVpM7+UnSNkWmQdG1Mpmkr6DiodVdBUqmy5NUPdvNC7lWV923jn9AM474Gf42gXO2/TnkuStW0ePP1j/Grtc2WBqWX9HbzQuxVgTDOy0oDQeB1v+6O+jepPKWaFSgJT5SO7xawgw4X5S/thChom+9U0E7ECxK0QU0qCSBWBEP9+4FIviBQo72upJVLJ42deNW5QqDFSwef2P37MdICoFSzL1hJCCCH2ZBKYEmIvF7QMglaQmujrzwuQyNpcesQ0bl/eARrydp6AFQAF5+3XzOy6GNees++bu9Nij2OGq6nY92IAKva9mLSuoOEdX2V4xU1U7HuxBKWEED6lFJXBMJXBMAtKmv8BXDjrYN4z8yD6c2m6M4myZmRR02tO15EeoiEc9/svAmgvybQayUpyCj8AdmG00p0Z2a20udvosopAiKgVIGaFxmQ7nT1tX7KOTSwQLGveCPDToy/w+mGygmXLKaX4+TsuHH2qAO/hzznT9xu3TLKUhBBCvB1IYEoIUSYesrjsSK/D0b+u6mbAzhMLmpw8r4GPHjmdeEg+Nt6urHgrlQdeiQpWkuzqItbYLJlSQoidppSiNhSlNlT+xORDcw/nQ3MPx9UuQ7nyANPMijoOqZtKZ2YYa1TH1EbJcKKjA1NzKutJ2DniVpCZFeVN1D44+zDOmbbfuKO+3fiOC7cbFLp8/lHbPbY5kqUkhBBC7DS5wxRCjNEQD3HVO2Zx8eHT6EtmqI2FqQxbEpQSmOFqXNctey+EELuToQyqQ5GyaRfPPZyL5x4OQHc6UVZWF47REInRGK7gt0s+WFZ25WuM+ra9/rNAMpWEEEKIt5LcZQohxhUPWUQDBoHcMHUVlTJ0shBCiD2D8jok12i0UlimiaVMTEMRtWTUTyGEEGKykcCUEOI1OY4z0bsghBBC+IKGye1LLwENjutgGiYob7oQQgghJh8JTAkhhBBCiEmjKug183Ndl66uLuoaGyWrVwghhJjE5FtcCCGEEEIIIYQQQkwICUwJIYQQQgghhBBCiAkhgSkhhBBCCCGEEEIIMSEkMCWEEEIIIYQQQgghJoQEpoQQQgghhBBCCCHEhJDAlBBCCCGEEEIIIYSYEBKYEkIIIYQQQgghhBATQgJTQgghhBBCCCGEEGJCSGBKCCGEEEIIIYQQQkwICUwJIYQQQgghhBBCiAkhgSkhhBBCCCGEEEIIMSEkMCWEEEIIIYQQQgghJoQEpoQQQgghhBBCCCHEhJDAlBBCCCGEEEIIIYSYEBKYEkIIIYQQQgghhBATQgJTQgghhBBCCCGEEGJCSGBKCCGEEEIIIYQQQkwICUwJIYQQQgghhBBCiAkhgSkhhBBCCCGEEEIIMSEkMCWEEEIIIYQQQgghJoQEpoQQQgghhBBCCCHEhLAmegf2BFprAIaGhiZ4T94413UZHh4mHA5jGBJ3FG+M1CcxHqkXYneS+iR2ldQdsTtJfRK7SuqO2J32pvo0El8Zibe8FglMAcPDwwC0tbVN8J4IIYQQQgghhBBC7B2Gh4epqqp6zXmU3pHw1V7OdV22bdtGRUUFSqmJ3p03ZGhoiLa2NjZv3kxlZeVE746Y5KQ+ifFIvRC7k9Qnsauk7ojdSeqT2FVSd8TutDfVJ601w8PDtLa2vm72l2RMAYZhMHXq1Inejd2qsrJy0ldkseeQ+iTGI/VC7E5Sn8SukrojdiepT2JXSd0Ru9PeUp9eL1NqxORutCiEEEIIIYQQQgghJi0JTAkhhBBCCCGEEEKICSGBqb1MKBTiK1/5CqFQaKJ3RewFpD6J8Ui9ELuT1Cexq6TuiN1J6pPYVVJ3xO70dq1P0vm5EEIIIYQQQgghhJgQkjElhBBCCCGEEEIIISaEBKaEEEIIIYQQQgghxISQwJQQQgghhBBCCCGEmBASmHoLfPOb3+Twww+noqKCxsZGzj33XFatWlU2TyaT4eMf/zh1dXXE43He9a530dnZ6Ze/+OKLvPe976WtrY1IJMKCBQv47//+77J1PPzwwyilxvx0dHS85v5prfnyl79MS0sLkUiEpUuXsmbNmrJ5vv71r7N48WKi0SjV1dVv7ISIN2Sy16cNGzZw6aWXMnPmTCKRCLNnz+YrX/kKuVxuN5ydt6/JXi8Azj77bKZNm0Y4HKalpYUPfvCDbNu27Q2eGbEr9ob6NCKbzXLQQQehlOKFF17YtRMidtjeUHdmzJgxZr3f+ta33uCZEbtib6hPAPfccw+LFi0iEolQU1PDueeeu+snReywyV5/trdepRRPP/30bjhDYmdM9voEsHr1as455xzq6+uprKzkmGOO4aGHHnqDZ2Y30eJNd8opp+gbb7xRL1++XL/wwgv69NNP19OmTdOJRMKf54orrtBtbW36gQce0M8884w+8sgj9eLFi/3y//u//9NXXXWVfvjhh/W6dev0LbfcoiORiL7uuuv8eR566CEN6FWrVun29nb/x3Gc19y/b33rW7qqqkrfcccd+sUXX9Rnn322njlzpk6n0/48X/7yl/W1116rr776al1VVbX7To7YaZO9Pv3lL3/RF198sb7vvvv0unXr9J133qkbGxv1Zz7zmd18pt5eJnu90Frra6+9Vj/xxBN6w4YN+rHHHtNHHXWUPuqoo3bjWRI7am+oTyOuuuoqfdppp2lAP//882/85IjXtDfUnenTp+v//M//LFtv6f6Lt87eUJ9+//vf65qaGv2Tn/xEr1q1Sq9YsULfeuutu/Esie2Z7PUnm82Wra+9vV1/5CMf0TNnztSu6+7msyVez2SvT1prPXfuXH366afrF198Ua9evVpfeeWVOhqN6vb29t14pnaNBKYmQFdXlwb0I488orXWemBgQAcCAX3bbbf587zyyisa0E888cR213PllVfq448/3n8/Uon7+/t3eF9c19XNzc36O9/5jj9tYGBAh0Ih/Zvf/GbM/DfeeKMEpvYwk7k+jfiv//ovPXPmzB3ejnh9e0O9uPPOO7VSSudyuR3elnhzTNb69Oc//1nPnz9fr1ixQgJTE2Qy1p3p06fr73//+zu8XvHWmWz1KZ/P6ylTpugbbrhhh9cr3jyTrf6MlsvldENDg/7P//zPHd6OePNMtvrU3d2tAf33v//dn2doaEgD+v7779/hbb1ZpCnfBBgcHASgtrYWgGeffZZ8Ps/SpUv9eebPn8+0adN44oknXnM9I+soddBBB9HS0sJJJ53EY4899pr7sn79ejo6Osq2XVVVxaJFi15z22LPsTfUp+1tW+y6yV4v+vr6+NWvfsXixYsJBAKvuX7x5puM9amzs5PLLruMW265hWg0umMHKna7yVh3AL71rW9RV1fHwQcfzHe+8x1s2379gxVvuslWn5577jm2bt2KYRgcfPDBtLS0cNppp7F8+fIdP2ix20y2+jPaXXfdRW9vL5dccslrrlu8NSZbfaqrq2PevHn84he/IJlMYts2P/3pT2lsbOTQQw/d8QN/k1gTvQNvN67r8ulPf5qjjz6a/fbbD4COjg6CweCYvpuampq225b08ccf59Zbb+Wee+7xp7W0tHD99ddz2GGHkc1mueGGG1iyZAlPPfUUhxxyyLjrGVl/U1PTDm9b7Dn2hvq0du1arrvuOr773e/u0DGL1zeZ68XnP/95fvjDH5JKpTjyyCO5++67d+rYxe43GeuT1pqLL76YK664gsMOO4wNGzbsyqGLN2gy1h2Aq666ikMOOYTa2loef/xxvvCFL9De3s6111670+dA7D6TsT69+uqrAPzHf/wH1157LTNmzOB73/seS5YsYfXq1fJQ7i00GevPaP/3f//HKaecwtSpU3fomMWbZzLWJ6UUf/vb3zj33HOpqKjAMAwaGxu59957qamp2aXzsDtJYOot9vGPf5zly5fz6KOP7vI6li9fzjnnnMNXvvIVTj75ZH/6vHnzmDdvnv9+8eLFrFu3ju9///vccsst/OpXv+Lyyy/3y//yl79gmuYu74eYeJO9Pm3dupVTTz2VCy64gMsuu2yXj0GUm8z14nOf+xyXXnopGzdu5JprruGiiy7i7rvvRim1y8ci3pjJWJ+uu+46hoeH+cIXvrDL+yzeuMlYdwCuvvpq//UBBxxAMBjk8ssv55vf/CahUGiXj0W8MZOxPrmuC8C///u/8653vQuAG2+8kalTp3LbbbeVrVO8uSZj/Sm1ZcsW7rvvPn73u9/t8v6L3Wcy1ietNR//+MdpbGzkH//4B5FIhBtuuIGzzjqLp59+mpaWll0+lt1BAlNvoU984hPcfffd/P3vfy+LdDc3N5PL5RgYGCiLsHZ2dtLc3Fy2jpdffpkTTzyRj370o3zxi1983W0eccQR/j/M2WefzaJFi/yyKVOm0N7e7m+rtDJ2dnZy0EEH7cphirfIZK9P27Zt4/jjj2fx4sX87//+7w4ft3htk71e1NfXU19fzz777MOCBQtoa2vjySef5KijjtrhcyB2n8lanx588EGeeOKJMUGEww47jPe///3cfPPNO3YCxC6brHVnPIsWLcK2bTZs2FB2syDeOpO1Po1MX7hwoV8eCoWYNWsWmzZt2sGjF2/UZK0/pW688Ubq6uo4++yzd+iYxZtnstanBx98kLvvvpv+/n4qKysB+PGPf8z999/PzTffzL/+67/u3InY3Sa6k6u3A9d19cc//nHd2tqqV69ePaZ8pKO03//+9/60lStXjukobfny5bqxsVF/7nOf2+FtL126VJ933nmvuW/Nzc36u9/9rj9tcHBQOj/fg+0N9WnLli167ty5+sILL9S2be/w9sX27Q31YrSNGzdqQD/00EM7vC9i95js9Wnjxo162bJl/s99992nAf373/9eb968eYf3Rey8yV53xvPLX/5SG4ah+/r6dnhfxO4x2evTyPvSzs9zuZxubGzUP/3pT3d4X8Sumez1p3TemTNnygjWE2yy16e77rpLG4ahh4eHy5bdZ5999Ne//vUd3pc3iwSm3gIf+9jHdFVVlX744YfLhnxMpVL+PFdccYWeNm2afvDBB/UzzzwzZpj0ZcuW6YaGBv2BD3ygbB1dXV3+PN///vf1HXfcodesWaOXLVumP/WpT2nDMPTf/va319y/b33rW7q6ulrfeeed+qWXXtLnnHPOmKElN27cqJ9//nl9zTXX6Hg8rp9//nn9/PPPj6nY4s032evTli1b9Jw5c/SJJ56ot2zZUrZ9sesme7148skn9XXXXaeff/55vWHDBv3AAw/oxYsX69mzZ+tMJrObz5Z4PZO9Po22fv16GZXvLTLZ687jjz+uv//97+sXXnhBr1u3Tv/yl7/UDQ0N+qKLLtrNZ0rsiMlen7TW+lOf+pSeMmWKvu+++/TKlSv1pZdeqhsbGyXQ+RbYG+qP1lr/7W9/04B+5ZVXdtOZEbtisten7u5uXVdXp9/5znfqF154Qa9atUp/9rOf1YFAQL/wwgu7+WztPAlMvQWAcX9uvPFGf550Oq2vvPJKXVNTo6PRqD7vvPPKbtS/8pWvjLuO6dOn+/N8+9vf1rNnz9bhcFjX1tbqJUuW6AcffPB19891Xf2lL31JNzU16VAopE888US9atWqsnk+9KEPjbt9yWR46032+nTjjTdu9xjErpvs9eKll17Sxx9/vK6trdWhUEjPmDFDX3HFFXrLli275fyInTPZ69NoEph660z2uvPss8/qRYsW6aqqKh0Oh/WCBQv0N77xDQmQT5DJXp+09jKkPvOZz+jGxkZdUVGhly5dqpcvX/6Gz414fXtD/dFa6/e+97168eLFb+hciDdub6hPTz/9tD755JN1bW2trqio0EceeaT+85///IbPze6gtNZ6bAM/IYQQQgghhBBCCCHeXMZE74AQQgghhBBCCCGEeHuSwJQQQgghhBBCCCGEmBASmBJCCCGEEEIIIYQQE0ICU0IIIYQQQgghhBBiQkhgSgghhBBCCCGEEEJMCAlMCSGEEEIIIYQQQogJIYEpIYQQQgghhBBCCDEhJDAlhBBCCCGEEEIIISaEBKaEEEIIISbYkiVLUEpN9G4IIYQQQrzlrIneASGEEEKIvcnOBpi01m/SngghhBBC7PkkMCWEEEIIsRt95StfGTPtBz/4AYODg+OWAfziF78glUq92bsmhBBCCLHHUVoe0wkhhBBCvKlmzJjBxo0bJTtKCCGEEGIU6WNKCCGEEGKCjdfH1E033YRSiptuuok//elPLFq0iGg0ypQpU/jSl76E67oA3HzzzRx44IFEIhGmTZvGd77znXG3obXm5z//OUcffTSVlZVEo1EOO+wwfv7zn7/pxyeEEEIIsT3SlE8IIYQQYg92++2389e//pVzzz2Xo48+mnvuuYevfe1raK2pqqria1/7Gueccw5LlizhD3/4A//v//0/mpqauOiii/x1aK15//vfz29+8xvmzp3L+973PoLBIPfffz+XXnopL7/8Mt/97ncn8CiFEEII8XYlTfmEEEIIId5kr9eUb8mSJTzyyCNl5TfddBOXXHIJgUCAxx57jMMPPxyA4eFh5syZQyKRoLKykscee4xZs2YBsHnzZubMmcO8efN46aWX/HX97Gc/46Mf/SiXXHIJP/3pTwkEAgDkcjnOP/98/vSnP/HMM89w6KGHvlmnQAghhBBiXNKUTwghhBBiD/aBD3zAD0oBVFRUcOaZZ5JKpfjYxz7mB6UA2traOOaYY3j55Zexbduf/sMf/pBYLMaPfvQjPygFEAwG+frXvw7Ab37zm7fgaIQQQgghyklTPiGEEEKIPdhBBx00ZlpLS8trljmOQ2dnJ1OmTCGVSrFs2TJaW1v59re/PWb+fD4PwMqVK3frfgshhBBC7AgJTAkhhBBC7MEqKyvHTLMs63XLRgJO/f39aK3ZunUr11xzzXa3k0wmd8fuCiGEEELsFAlMCSGEEELsxUaCV4ceeijPPPPMBO+NEEIIIUQ56WNKCCGEEGIvVlFRwYIFC3jllVcYGBiY6N0RQgghhCgjgSkhhBBCiL3cVVddRSqV4rLLLhu3yd769evZsGHDW79jQgghhHjbk6Z8QgghhBB7ucsvv5wnn3ySm2++mccee4ylS5fS2tpKZ2cnK1eu5KmnnuLXv/41M2bMmOhdFUIIIcTbjASmhBBCCCH2ckopbrrpJk4//XR+9rOfcffdd5NIJGhsbGTu3Ll897vfZenSpRO9m0IIIYR4G1Jaaz3ROyGEEEIIIYQQQggh3n6kjykhhBBCCCGEEEIIMSEkMCWEEEIIIYQQQgghJoQEpoQQQgghhBBCCCHEhJDAlBBCCCGEEEIIIYSYEBKYEkIIIYQQQgghhBATQgJTQgghhBBCCCGEEGJCSGBKCCGEEEIIIYQQQkwICUwJIYQQQgghhBBCiAkhgSkhhBBCCCGEEEIIMSEkMCWEEEIIIYQQQgghJoQEpoQQQgghhBBCCCHEhJDAlBBCCCGEEEIIIYSYEBKYEkIIIYQQQgghhBAT4v8DtzxbiHSl03gAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -452,58 +452,58 @@ "Learner ml_m: LogisticRegression()\n", "Out-of-sample Performance:\n", "Regression:\n", - "Learner ml_g0 RMSE: [[1.40819196 1.44505628 1.41728035 1.379876 1.39455406 1.37787884\n", - " 1.41904666 1.43565448 1.40797579 1.38017852 1.40451927 1.38072431\n", - " 1.43039705 1.37694706 1.34678838 1.34816616 1.37326228 1.37028313\n", - " 1.42504859 1.40519018 1.41838418 1.38843531 1.40401396 1.44212802\n", - " 1.4162496 1.38217708 1.39642643 1.37807542 1.4217658 1.44046735\n", - " 1.41240661 1.38467958 1.40290054 1.38110169 1.43555287 1.37725986\n", - " 1.41760164 1.34963967 1.34923315 1.36906865 1.36790292 1.37882656\n", - " 1.42638524 1.40408103 1.42022176 1.38230774 1.45470794 1.4070382\n", - " 1.44664494 1.41549701 1.38641625 1.40214879 1.3782188 1.41428144\n", - " 1.43695538 1.40921613 1.38186793 1.40181613 1.38614742 1.43599508\n", - " 1.37406388 1.41734029 1.3449007 1.35422125 1.37337241 1.36970938\n", - " 1.37931347 1.38973654 1.4245557 1.40297224 1.42138834 1.38065336\n", - " 1.45184981 1.45946997 1.40136093 1.44723397 1.41798682 1.38054239\n", - " 1.39672286 1.37835191 1.42119314 1.4368779 1.40631144 1.3798683\n", - " 1.40614563 1.38467314 1.4307338 1.37330371 1.41948619 1.34386942\n", - " 1.35453612 1.36734589 1.36875058 1.37659987 1.38817831 1.42855774\n", - " 1.4040472 1.42513802 1.3833212 1.45272282 1.4632959 1.36559363]]\n", - "Learner ml_g1 RMSE: [[1.47605877 1.43614981 1.43189302 1.41568368 1.45445651 1.46455328\n", - " 1.40513321 1.45989737 1.48957798 1.45307455 1.44203462 1.4623187\n", - " 1.46718391 1.43394086 1.38222077 1.42826691 1.464996 1.43138203\n", - " 1.44008356 1.45884963 1.46468759 1.43461302 1.4260418 1.37827039\n", - " 1.41634728 1.3602931 1.31745734 1.40214917 1.40656252 1.34977232\n", - " 1.40854993 1.37663541 1.40770326 1.39217878 1.43824775 1.39905778\n", - " 1.38000488 1.42700417 1.39766271 1.4244126 1.45105544 1.42307302\n", - " 1.43541971 1.42357496 1.43008494 1.4558015 1.36171327 1.44502331\n", - " 1.37754443 1.39154287 1.34879869 1.42305005 1.37082086 1.41723063\n", - " 1.53079569 1.4165877 1.40730615 1.41443007 1.43728109 1.4068946\n", - " 1.40884084 1.47446069 1.3632237 1.44705699 1.46048099 1.3975671\n", - " 1.41686714 1.38395203 1.4054631 1.51285144 1.4687299 1.40211314\n", - " 1.45000125 1.44030773 1.41158239 1.43214102 1.41042458 1.45950582\n", - " 1.41411585 1.38231938 1.43923629 1.47058911 1.44001136 1.37001004\n", - " 1.42089026 1.39028541 1.43794863 1.38073535 1.34795159 1.43850685\n", - " 1.4121238 1.37989255 1.39914325 1.4266645 1.38634561 1.45871682\n", - " 1.48979386 1.44037473 1.43479917 1.42681476 1.41880263 1.41376113]]\n", + "Learner ml_g0 RMSE: [[1.40636468 1.44015593 1.42710964 1.42719583 1.43995196 1.4435586\n", + " 1.39726542 1.42461424 1.43808497 1.44409564 1.40017327 1.39597072\n", + " 1.396656 1.43945749 1.40855224 1.37310968 1.4149736 1.40179805\n", + " 1.40867837 1.39912807 1.42085823 1.41535716 1.40426326 1.43647614\n", + " 1.43173649 1.42466185 1.43807652 1.44161035 1.39454981 1.42545671\n", + " 1.44018884 1.44159814 1.39295159 1.3933023 1.40527355 1.43729703\n", + " 1.44331899 1.40234896 1.37454139 1.41121844 1.41071582 1.42088789\n", + " 1.41167641 1.39742438 1.42029853 1.41766014 1.4181153 1.40648856\n", + " 1.44481289 1.42855508 1.42892803 1.43780527 1.43887904 1.40065218\n", + " 1.42290642 1.44203138 1.4426855 1.39935117 1.39471043 1.39955737\n", + " 1.44077749 1.44389316 1.40959275 1.37430287 1.41090721 1.39743489\n", + " 1.41798082 1.41512627 1.41168861 1.40389039 1.42310191 1.41282563\n", + " 1.4170658 1.493958 1.40166329 1.44236241 1.43090214 1.42508737\n", + " 1.44273197 1.439739 1.40341833 1.42703483 1.4432334 1.44745272\n", + " 1.39288688 1.39367199 1.39807572 1.43814946 1.44517488 1.41045434\n", + " 1.37506985 1.4152223 1.39963339 1.42097323 1.41427081 1.40806781\n", + " 1.40210414 1.422739 1.41561647 1.42284515 1.49904823 1.41586234]]\n", + "Learner ml_g1 RMSE: [[1.4185991 1.44547181 1.42515753 1.40101595 1.46872265 1.3818256\n", + " 1.4628924 1.4504521 1.42959483 1.45698996 1.36900108 1.40896171\n", + " 1.40513689 1.41310338 1.42166587 1.40811586 1.42525445 1.40842281\n", + " 1.45723778 1.43017939 1.4513467 1.47499349 1.39292093 1.46578427\n", + " 1.42443042 1.42285428 1.35717672 1.39697897 1.44784068 1.40726902\n", + " 1.42258459 1.40616465 1.4338824 1.42228726 1.47067428 1.44792118\n", + " 1.42312839 1.42381611 1.41728479 1.42996156 1.40903218 1.39283485\n", + " 1.45808644 1.40390273 1.43347473 1.44079198 1.44865711 1.47497189\n", + " 1.47580551 1.41607056 1.44257842 1.43859062 1.47297706 1.41696804\n", + " 1.41433485 1.46491518 1.41314016 1.44937681 1.41615882 1.46261754\n", + " 1.44746991 1.44444092 1.41598614 1.41675391 1.39886877 1.4423026\n", + " 1.4229864 1.39109575 1.48997319 1.44430582 1.46419359 1.39074961\n", + " 1.41294712 1.42137573 1.40659136 1.38673754 1.44535711 1.38052206\n", + " 1.4419186 1.4312496 1.38136619 1.43504706 1.420354 1.4179437\n", + " 1.41486017 1.41712425 1.3813208 1.40152805 1.35039453 1.37532784\n", + " 1.43802062 1.36895555 1.39766652 1.41516996 1.38555265 1.34793372\n", + " 1.43417418 1.40695701 1.4119966 1.40334784 1.40896759 1.4418237 ]]\n", "Classification:\n", - "Learner ml_m Log Loss: [[0.6402197 0.63989553 0.63958696 0.63878686 0.6386713 0.63947377\n", - " 0.63932819 0.63832396 0.63823896 0.63978397 0.64174643 0.6394592\n", - " 0.63814901 0.64072084 0.63928224 0.64275765 0.63857932 0.63827519\n", - " 0.6387454 0.63841224 0.63902581 0.63875985 0.65284347 0.65432609\n", - " 0.65229215 0.6525085 0.65261234 0.65388986 0.65270304 0.65188196\n", - " 0.65434595 0.65251652 0.65305233 0.65333667 0.65322817 0.65326683\n", - " 0.65282586 0.65269046 0.65267178 0.65268175 0.65331524 0.65393183\n", - " 0.65381077 0.65325546 0.65522282 0.65399914 0.65208396 0.67407657\n", - " 0.67359855 0.67321106 0.67375347 0.67369195 0.67411811 0.6757009\n", - " 0.67405671 0.67316274 0.67355969 0.67423525 0.67583733 0.67492287\n", - " 0.67259788 0.67359116 0.67277904 0.67354769 0.67383592 0.67425601\n", - " 0.67452952 0.67374397 0.67489846 0.67551743 0.67355545 0.67291432\n", - " 0.67516199 0.67478959 0.68637751 0.68705498 0.68633705 0.68699719\n", - " 0.68670008 0.68653345 0.68628361 0.68719356 0.68669908 0.68615908\n", - " 0.68598557 0.68701368 0.68647181 0.68662731 0.68724774 0.68610139\n", - " 0.68681593 0.68848259 0.68637051 0.68603932 0.68657004 0.68700917\n", - " 0.68752109 0.6881993 0.68638602 0.6884052 0.68700503 0.68560155]]\n", + "Learner ml_m Log Loss: [[0.6281689 0.62718229 0.62873721 0.62841144 0.62809364 0.63074713\n", + " 0.62710709 0.62983572 0.62847637 0.62913753 0.62705281 0.62844468\n", + " 0.62715822 0.62784034 0.62836604 0.62792282 0.627238 0.62753574\n", + " 0.6274565 0.62710326 0.62747932 0.62959891 0.65639651 0.65794761\n", + " 0.65661799 0.65557165 0.65477983 0.65566148 0.65534259 0.65569133\n", + " 0.65737074 0.65678175 0.65508098 0.65638901 0.65716924 0.65591208\n", + " 0.65594263 0.656025 0.65480669 0.65585401 0.65479674 0.65562862\n", + " 0.65599127 0.65610907 0.6557743 0.65579024 0.65817522 0.67274836\n", + " 0.67443247 0.67400074 0.6742192 0.67528908 0.67340597 0.67313809\n", + " 0.67383485 0.67294685 0.67417486 0.67351913 0.6743079 0.67351434\n", + " 0.67408451 0.67448127 0.67407152 0.67450092 0.67339408 0.67345134\n", + " 0.67515484 0.67353295 0.67369005 0.67508843 0.67431827 0.67338339\n", + " 0.67505614 0.67503072 0.69235596 0.69309191 0.69144638 0.69032821\n", + " 0.69158422 0.691708 0.6908099 0.69011545 0.69133415 0.69056207\n", + " 0.69119019 0.69140544 0.6904356 0.69144917 0.69116221 0.69156948\n", + " 0.69129438 0.69128426 0.69303902 0.69332413 0.69114589 0.69177384\n", + " 0.69100445 0.69126338 0.69067909 0.68984658 0.69151658 0.69150933]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 5\n", @@ -511,30 +511,30 @@ "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| \\\n", - "ATT(2025-05,2025-01,2025-02) 0.102392 0.070229 1.457976 0.144847 \n", - "ATT(2025-05,2025-01,2025-03) -0.025312 0.068922 -0.367252 0.713431 \n", - "ATT(2025-05,2025-02,2025-03) -0.131312 0.068425 -1.919061 0.054977 \n", - "ATT(2025-05,2025-01,2025-04) -0.003033 0.068161 -0.044492 0.964512 \n", - "ATT(2025-05,2025-02,2025-04) -0.105774 0.071330 -1.482872 0.138108 \n", + "ATT(2025-05,2025-01,2025-02) 0.119351 0.073840 1.616338 0.106021 \n", + "ATT(2025-05,2025-01,2025-03) 0.036582 0.080165 0.456336 0.648148 \n", + "ATT(2025-05,2025-02,2025-03) -0.077922 0.075045 -1.038341 0.299111 \n", + "ATT(2025-05,2025-01,2025-04) 0.046674 0.078244 0.596520 0.550828 \n", + "ATT(2025-05,2025-02,2025-04) -0.083015 0.076543 -1.084549 0.278121 \n", "... ... ... ... ... \n", - "ATT(2025-08,2025-03,2025-08) 1.019466 0.064371 15.837414 0.000000 \n", - "ATT(2025-08,2025-04,2025-08) 1.034559 0.064665 15.998823 0.000000 \n", - "ATT(2025-08,2025-05,2025-08) 1.027387 0.065064 15.790351 0.000000 \n", - "ATT(2025-08,2025-06,2025-08) 1.039214 0.066547 15.616158 0.000000 \n", - "ATT(2025-08,2025-07,2025-08) 1.045628 0.063143 16.559774 0.000000 \n", + "ATT(2025-08,2025-03,2025-08) 0.905554 0.064075 14.132681 0.000000 \n", + "ATT(2025-08,2025-04,2025-08) 0.944158 0.064087 14.732450 0.000000 \n", + "ATT(2025-08,2025-05,2025-08) 0.955429 0.064264 14.867283 0.000000 \n", + "ATT(2025-08,2025-06,2025-08) 0.943040 0.065569 14.382510 0.000000 \n", + "ATT(2025-08,2025-07,2025-08) 1.036910 0.064226 16.144725 0.000000 \n", "\n", " 2.5 % 97.5 % \n", - "ATT(2025-05,2025-01,2025-02) -0.035254 0.240037 \n", - "ATT(2025-05,2025-01,2025-03) -0.160397 0.109773 \n", - "ATT(2025-05,2025-02,2025-03) -0.265422 0.002799 \n", - "ATT(2025-05,2025-01,2025-04) -0.136625 0.130560 \n", - "ATT(2025-05,2025-02,2025-04) -0.245579 0.034031 \n", + "ATT(2025-05,2025-01,2025-02) -0.025373 0.264075 \n", + "ATT(2025-05,2025-01,2025-03) -0.120538 0.193702 \n", + "ATT(2025-05,2025-02,2025-03) -0.225008 0.069163 \n", + "ATT(2025-05,2025-01,2025-04) -0.106681 0.200029 \n", + "ATT(2025-05,2025-02,2025-04) -0.233037 0.067007 \n", "... ... ... \n", - "ATT(2025-08,2025-03,2025-08) 0.893302 1.145631 \n", - "ATT(2025-08,2025-04,2025-08) 0.907819 1.161300 \n", - "ATT(2025-08,2025-05,2025-08) 0.899863 1.154910 \n", - "ATT(2025-08,2025-06,2025-08) 0.908784 1.169644 \n", - "ATT(2025-08,2025-07,2025-08) 0.921870 1.169385 \n", + "ATT(2025-08,2025-03,2025-08) 0.779969 1.031139 \n", + "ATT(2025-08,2025-04,2025-08) 0.818550 1.069766 \n", + "ATT(2025-08,2025-05,2025-08) 0.829474 1.081384 \n", + "ATT(2025-08,2025-06,2025-08) 0.814528 1.071552 \n", + "ATT(2025-08,2025-07,2025-08) 0.911029 1.162790 \n", "\n", "[102 rows x 6 columns]\n" ] @@ -567,7 +567,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -580,13 +580,13 @@ " ])" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -617,46 +617,46 @@ "\n", "------------------ Bounds with CI ------------------\n", " CI lower theta lower theta theta upper \\\n", - "ATT(2025-05,2025-01,2025-02) -0.109525 0.006676 0.102392 0.198107 \n", - "ATT(2025-05,2025-01,2025-03) -0.235913 -0.122100 -0.025312 0.071477 \n", - "ATT(2025-05,2025-02,2025-03) -0.338899 -0.226328 -0.131312 -0.036296 \n", - "ATT(2025-05,2025-01,2025-04) -0.209205 -0.096719 -0.003033 0.090654 \n", - "ATT(2025-05,2025-02,2025-04) -0.317787 -0.200853 -0.105774 -0.010694 \n", + "ATT(2025-05,2025-01,2025-02) -0.089584 0.030287 0.119351 0.208415 \n", + "ATT(2025-05,2025-01,2025-03) -0.187237 -0.056760 0.036582 0.129924 \n", + "ATT(2025-05,2025-02,2025-03) -0.290854 -0.167463 -0.077922 0.011618 \n", + "ATT(2025-05,2025-01,2025-04) -0.175551 -0.046868 0.046674 0.140216 \n", + "ATT(2025-05,2025-02,2025-04) -0.305745 -0.178785 -0.083015 0.012755 \n", "... ... ... ... ... \n", - "ATT(2025-08,2025-03,2025-08) 0.825698 0.931629 1.019466 1.107304 \n", - "ATT(2025-08,2025-04,2025-08) 0.841346 0.947790 1.034559 1.121329 \n", - "ATT(2025-08,2025-05,2025-08) 0.831890 0.939009 1.027387 1.115764 \n", - "ATT(2025-08,2025-06,2025-08) 0.840852 0.950433 1.039214 1.127995 \n", - "ATT(2025-08,2025-07,2025-08) 0.856187 0.960048 1.045628 1.131208 \n", + "ATT(2025-08,2025-03,2025-08) 0.713646 0.819071 0.905554 0.992036 \n", + "ATT(2025-08,2025-04,2025-08) 0.752304 0.857735 0.944158 1.030581 \n", + "ATT(2025-08,2025-05,2025-08) 0.763137 0.868861 0.955429 1.041997 \n", + "ATT(2025-08,2025-06,2025-08) 0.746202 0.854195 0.943040 1.031886 \n", + "ATT(2025-08,2025-07,2025-08) 0.844033 0.949600 1.036910 1.124219 \n", "\n", " CI upper \n", - "ATT(2025-05,2025-01,2025-02) 0.313291 \n", - "ATT(2025-05,2025-01,2025-03) 0.184722 \n", - "ATT(2025-05,2025-02,2025-03) 0.076587 \n", - "ATT(2025-05,2025-01,2025-04) 0.202716 \n", - "ATT(2025-05,2025-02,2025-04) 0.107376 \n", + "ATT(2025-05,2025-01,2025-02) 0.333164 \n", + "ATT(2025-05,2025-01,2025-03) 0.264359 \n", + "ATT(2025-05,2025-02,2025-03) 0.137035 \n", + "ATT(2025-05,2025-01,2025-04) 0.269579 \n", + "ATT(2025-05,2025-02,2025-04) 0.138324 \n", "... ... \n", - "ATT(2025-08,2025-03,2025-08) 1.213296 \n", - "ATT(2025-08,2025-04,2025-08) 1.227770 \n", - "ATT(2025-08,2025-05,2025-08) 1.222845 \n", - "ATT(2025-08,2025-06,2025-08) 1.237489 \n", - "ATT(2025-08,2025-07,2025-08) 1.235229 \n", + "ATT(2025-08,2025-03,2025-08) 1.097551 \n", + "ATT(2025-08,2025-04,2025-08) 1.136124 \n", + "ATT(2025-08,2025-05,2025-08) 1.147826 \n", + "ATT(2025-08,2025-06,2025-08) 1.139749 \n", + "ATT(2025-08,2025-07,2025-08) 1.230090 \n", "\n", "[102 rows x 5 columns]\n", "\n", "------------------ Robustness Values ------------------\n", " H_0 RV (%) RVa (%)\n", - "ATT(2025-05,2025-01,2025-02) 0.0 3.205910 0.000443\n", - "ATT(2025-05,2025-01,2025-03) 0.0 0.793576 0.000335\n", - "ATT(2025-05,2025-02,2025-03) 0.0 4.121994 0.598492\n", - "ATT(2025-05,2025-01,2025-04) 0.0 0.098408 0.000605\n", - "ATT(2025-05,2025-02,2025-04) 0.0 3.331779 0.000561\n", + "ATT(2025-05,2025-01,2025-02) 0.0 3.999442 0.000597\n", + "ATT(2025-05,2025-01,2025-03) 0.0 1.186813 0.000356\n", + "ATT(2025-05,2025-02,2025-03) 0.0 2.615974 0.000581\n", + "ATT(2025-05,2025-01,2025-04) 0.0 1.508472 0.000537\n", + "ATT(2025-05,2025-02,2025-04) 0.0 2.605807 0.000455\n", "... ... ... ...\n", - "ATT(2025-08,2025-03,2025-08) 0.0 29.652040 26.857627\n", - "ATT(2025-08,2025-04,2025-08) 0.0 30.317103 27.492452\n", - "ATT(2025-08,2025-05,2025-08) 0.0 29.691389 26.881212\n", - "ATT(2025-08,2025-06,2025-08) 0.0 29.860784 27.007606\n", - "ATT(2025-08,2025-07,2025-08) 0.0 30.930192 28.159719\n", + "ATT(2025-08,2025-03,2025-08) 0.0 27.211550 24.335827\n", + "ATT(2025-08,2025-04,2025-08) 0.0 28.198087 25.355072\n", + "ATT(2025-08,2025-05,2025-08) 0.0 28.439172 25.602283\n", + "ATT(2025-08,2025-06,2025-08) 0.0 27.524975 24.644550\n", + "ATT(2025-08,2025-07,2025-08) 0.0 30.219202 27.467267\n", "\n", "[102 rows x 3 columns]\n" ] @@ -681,13 +681,13 @@ "\n", "------------------ Overall Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.745067 0.026549 65.731185 0.0 1.693033 1.797101\n", + "1.743996 0.027255 63.987523 0.0 1.690577 1.797416\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "2025-05 2.401745 0.035213 68.206319 0.0 2.332729 2.470761\n", - "2025-06 1.969894 0.034375 57.305166 0.0 1.902519 2.037268\n", - "2025-07 1.507049 0.038340 39.307637 0.0 1.431904 1.582194\n", - "2025-08 1.013121 0.049838 20.328317 0.0 0.915440 1.110801\n", + "2025-05 2.504777 0.038855 64.465171 0.0 2.428623 2.580931\n", + "2025-06 1.996162 0.034710 57.509321 0.0 1.928131 2.064193\n", + "2025-07 1.473812 0.038550 38.230791 0.0 1.398255 1.549370\n", + "2025-08 0.952538 0.048625 19.589422 0.0 0.857235 1.047842\n", "------------------ Additional Information ------------------\n", "Control Group: never_treated\n", "Anticipation Periods: 0\n", @@ -699,7 +699,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", " warnings.warn(\n" ] }, @@ -716,7 +716,7 @@ }, { "data": { - "image/png": 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+/vWvV1r39ttvx/vvvx/3339/nHPOObnxir61rSq3kW1trxERbdu2rbGrlGqr1+qq6u9Jt27dIpvNxr/+9a8yt59+2Zb20apVqzjvvPPivPPOi1WrVsVRRx0V119/vVAKgJ2GZ0oBQD20Zs2aePTRR+OEE06I008/vdzr0ksvjZUrV8aTTz4ZERGDBw+O9evXx//9v/83t49sNlsu0Npjjz2iZ8+e8fvf/z5WrVqVG3/++efj7bffrlJvbdu2jQEDBsTdd98dixYtKrd+6dKluT8PHjw4Xn755Zg1a1ZubNmyZfHggw+W2WbzlTabr9SJiFi3bl3ceeed5fbfrFmzCm9hO+OMM2LhwoVlzsFma9asiZKSkoiIOO644yIi4o477ihTc/vtt5fbriadeuqp0aBBgxgzZkyZ9xmx6X1//vnnEVHxuUiSJH71q1+V22ezZs0iYtOVZjVp8ODBUVhYGDfddFOsX7++3Pov/oyranNYWtO9VldVf0+GDh0aeXl5ccMNN5S7Ou2LP5tmzZpV+J42/zw322WXXaJ79+6xdu3aGngXAFA/uFIKAOqhJ598MlauXBknnXRShesPP/zwaNOmTTz44INx5plnxtChQ+Owww6L73//+/HBBx9Ejx494sknn4xly5ZFRNmrOW666aY4+eSTo1+/fnHeeefF8uXL4ze/+U307NmzTFC1JePGjYsjjjgi9ttvv7jgggvia1/7WixZsiRefvnl+Pjjj+Ott96KiIhrrrkmHnjggfjmN78Zl112WTRr1iwmTJgQnTt3jmXLluX66tu3b7Rs2TKGDx8el19+eWQymZg4cWK58CYiolevXjFp0qQYOXJkHHroobHLLrvEiSeeGGeffXb86U9/iosvvjimT58e/fr1i40bN8a7774bf/rTn+Ivf/lLHHLIIXHggQfGsGHD4s4774yioqLo27dvTJ06tcpXim2tbt26xU9/+tMYNWpUfPTRRzF06NBo3rx5zJ07Nx577LG48MIL4+qrr44ePXpEt27d4uqrr46FCxdGYWFhPPLIIxU+n6lXr14Rsemh7YMHD44GDRrEt7/97W3utbCwMO666644++yz4+CDD45vf/vb0aZNm5g/f348/fTT0a9fv/jNb35TrX02adIk9tlnn5g0aVJ8/etfj1atWkXPnj2jZ8+eW9zub3/7W5SWlpYb33///bfq+UxV/T3p3r17/PjHP46f/OQnceSRR8app54a+fn58eqrr8Yee+wRY8eOjYhNP4O77rorfvrTn0b37t2jbdu2cfTRR8c+++wTAwYMiF69ekWrVq3itddei4cffjguvfTSavcMAPVW3XzpHwCwLU488cSkoKAgKSkpqbTm3HPPTRo1apR89tlnSZIkydKlS5Ozzjorad68edKiRYvk3HPPTV566aUkIpKHHnqozLYPPfRQ0qNHjyQ/Pz/p2bNn8uSTTyannXZa0qNHj1zN3Llzk4hIbr755gqP/+GHHybnnHNO0r59+6RRo0ZJhw4dkhNOOCF5+OGHy9S9+eabyZFHHpnk5+cnHTt2TMaOHZvccccdSUQkixcvztW99NJLyeGHH540adIk2WOPPZJrrrkm+ctf/pJERDJ9+vRc3apVq5Kzzjor2XXXXZOISLp06ZJbt27duuTnP/95su+++yb5+flJy5Ytk169eiVjxoxJioqKcnVr1qxJLr/88qR169ZJs2bNkhNPPDFZsGBBEhHJ6NGjKz3nXzZ58uRy/Y0ePTqJiGTp0qUVbvPII48kRxxxRNKsWbOkWbNmSY8ePZIRI0Yk7733Xq7mX//6VzJo0KBkl112SXbbbbfkggsuSN56660kIpJ77703V7dhw4bksssuS9q0aZNkMplk8z/9KvvZTZ8+PYmIZPLkyWXG77333iQikldffbVc/eDBg5MWLVokBQUFSbdu3ZJzzz03ee2113I1w4cPT5o1a1bufW4+D180c+bMpFevXknjxo2/8lxv7rWy1xe37d+/f7LvvvuW20eXLl2SIUOGlBuv6u9JkiTJPffckxx00EG5uv79+ydTpkzJrV+8eHEyZMiQpHnz5klEJP3790+SJEl++tOfJocddliy6667Jk2aNEl69OiR3Hjjjcm6desqfc8AsKPJJEkF/8UIAOwUHn/88TjllFPixRdfjH79+m2x9sADD4w2bdpU+OyimnbllVfG3XffHatWrapXD8kGAKDqPFMKAHYSa9asKbO8cePG+PWvfx2FhYVx8MEH58bXr18fGzZsKFM7Y8aMeOutt2LAgAG13tfnn38eEydOjCOOOEIgBQCwA/NMKQDYSVx22WWxZs2a6NOnT6xduzYeffTRmDlzZtx0003RpEmTXN3ChQtj0KBB8Z3vfCf22GOPePfdd2P8+PHRvn37uPjii2u8rz59+sSAAQNi7733jiVLlsTvfve7KC4ujmuvvbbGjwUAwPZDKAUAO4mjjz46br311njqqaeitLQ0unfvHr/+9a/LPVi5ZcuW0atXr5gwYUIsXbo0mjVrFkOGDImf/exn0bp16xrv6/jjj4+HH344fvvb30Ymk4mDDz44fve738VRRx1V48cCAGD74ZlSAAAAAKTOM6UAAAAASJ1QCgAAAIDU7XTPlMpms/HJJ59E8+bNI5PJ1HU7AAAAADuUJEli5cqVsccee0ReXuXXQ+10odQnn3wSnTp1qus2AAAAAHZoCxYsiI4dO1a6fqcLpZo3bx4Rm05MYWFhHXcDAAAAsGMpLi6OTp065TKYyux0odTmW/YKCwuFUgAAAAC15Ksem+RB5wAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkrk5DqbFjx8ahhx4azZs3j7Zt28bQoUPjvffe2+I29913X2QymTKvgoKClDoGAAAAoCbUaSj1/PPPx4gRI+Lvf/97TJkyJdavXx/HHntslJSUbHG7wsLCWLRoUe41b968lDoGAAAAoCY0rMuDP/vss2WW77vvvmjbtm28/vrrcdRRR1W6XSaTifbt29d2ewAAAADUkjoNpb6sqKgoIiJatWq1xbpVq1ZFly5dIpvNxsEHHxw33XRT7LvvvhXWrl27NtauXZtbLi4ujoiIbDYb2Wy2hjoHAAAAICKqnLdkkiRJarmXKslms3HSSSfFihUr4sUXX6y07uWXX445c+bE/vvvH0VFRXHLLbfECy+8ELNnz46OHTuWq7/++utjzJgx5cbff//9aN68eY2+h9qUbFhdrfpMw6a11AkAAABA5VauXBlf//rXo6ioKAoLCyut225CqUsuuSSeeeaZePHFFysMlyqzfv362HvvvWPYsGHxk5/8pNz6iq6U6tSpUyxfvnyLJ2Z7M++O6j3MvcvlpbXUCQAAAEDliouLo2XLll8ZSm0Xt+9deuml8dRTT8ULL7xQrUAqIqJRo0Zx0EEHxQcffFDh+vz8/MjPzy83npeXF3l5dfqc91q1I783AAAAYPtV1UyiTkOpJEnisssui8ceeyxmzJgRXbt2rfY+Nm7cGG+//XYcf/zxtdDh9qPLiOVllrPrS2LBbzcFeJ0u/DjyGjWri7YAAAAAtkqdhlIjRoyIP/zhD/HEE09E8+bNY/HixRER0aJFi2jSpElERJxzzjnRoUOHGDt2bERE3HDDDXH44YdH9+7dY8WKFXHzzTfHvHnz4vzzz6+z95GGLYVOeY2aCaUAAACAeqVOQ6m77rorIiIGDBhQZvzee++Nc889NyIi5s+fX+ayr+XLl8cFF1wQixcvjpYtW0avXr1i5syZsc8++6TVNgAAAADbaLt50HlaiouLo0WLFl/5sK3tXXZ9Scwb1zIiNt3a50opAAAAYHtQ1ezF07ABAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QageQbCit6xYAAAAAqkUoVc8k2Y1RMufRWPL4ybmx+XfvHoseGRwlcx6NJLuxDrsDAAAAqJqGdd0AVZddWxxLnj4zSudPLbeudMH0KF0wPQo6HxPthkyKvPzCOugQAAAAoGpcKVVPJNmNlQZSX1Q6f2osefpMV0wBAAAA2zWhVD2x+sMnvjKQ2qx0/tRY/Z8na7kjAAAAgK0nlKoniv95d/Xq36pePQAAAECahFL1QHZDaZQumF6tbUoXTIusb+UDAAAAtlNCqXogWbcy1e0AAAAAaptQqh7ING6e6nYAAAAAtU0oVQ/kNSyIgk4Dq7VNQaejI69hQS11BAAAALBthFL1ROH+F1Wv/oDq1QMAAACkSShVTzTtdnIUdD6mSrUFnY+Jpl87qZY7AgAAANh6Qql6IpPXINoNmfSVwVRB52Oi3ZBJkclrkFJnAAAAANUnlKpH8vILo/3Qp6LtCZOioGP/MusKOh0dbU+YFO2HPhV5+YV11CEAAABA1TSs6waonkxeg2jW/ZRo0uXYmDeuZUREdL5oUTRo0rqOOwMAAACoOldK7QAyvmUPAAAAqGeEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOoa1nUDVE12fUmly19eFxGR16hZrfcEAAAAsLWEUvXEvHEtK1234Lcdy411vXJdbbYDAAAAsE3cvgcAAABA6lwpVU90GbG8rlsAAAAAqDFCqXrCM6IAAACAHYnb9wAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQ1rOsGACAt2fUl1arPa9SsljoBAACEUgDsNOaNa1mt+q5XrqulTgAAALfvAQAAAJA6V0oBsNPoMmJ5meXs+pJY8NuOERHR6cKP3a4HAAApEkoBsNPYUuiU16iZUAoAAFLk9j0AAAAAUieUAgAAACB1QikAAAAAUlenodTYsWPj0EMPjebNm0fbtm1j6NCh8d57733ldpMnT44ePXpEQUFB7LfffvHnP/85hW4BAAAAqCl1Gko9//zzMWLEiPj73/8eU6ZMifXr18exxx4bJSUllW4zc+bMGDZsWHz3u9+NN998M4YOHRpDhw6Nd955J8XOAQAAANgWmSRJkrpuYrOlS5dG27Zt4/nnn4+jjjqqwpozzzwzSkpK4qmnnsqNHX744XHggQfG+PHjv/IYxcXF0aJFiygqKorCwsIa6x2A+ie7viTmjWsZERFdRiz37XsAAFADqpq9bFfPlCoqKoqIiFatWlVa8/LLL8egQYPKjA0ePDhefvnlWu0NAAAAgJrTsK4b2CybzcaVV14Z/fr1i549e1Zat3jx4mjXrl2ZsXbt2sXixYsrrF+7dm2sXbs2t1xcXJw7XjabrYHOAaivvvj3wMZ1qyMaNKnDbgAAYMdQ1bxluwmlRowYEe+88068+OKLNbrfsWPHxpgxY8qNL126NEpLS2v0WADUD0l2Y2QX/iU2vH9fbuzj/9sh8tr2jYZ7nRN5HQZHJq9B3TUIAAD12MqVK6tUt12EUpdeemk89dRT8cILL0THjh23WNu+fftYsmRJmbElS5ZE+/btK6wfNWpUjBw5MrdcXFwcnTp1ijZt2nimFMBOKLu2OJb++duxbsG08us+nRnrPp0ZBZ2Ojt2Ofyjy8v09AQAA1VVQUFClujoNpZIkicsuuywee+yxmDFjRnTt2vUrt+nTp09MnTo1rrzyytzYlClTok+fPhXW5+fnR35+frnxvLy8yMvbrh6pBUAtS7IbY+kzw6K0gkDqi0oXTIulzwyL9kOfcsUUAABUU1XzljpNZUaMGBEPPPBA/OEPf4jmzZvH4sWLY/HixbFmzZpczTnnnBOjRo3KLV9xxRXx7LPPxq233hrvvvtuXH/99fHaa6/FpZdeWhdvAYB6ZPWHT0Tp/KlVqi2dPzVW/+fJWu4IAAB2XnUaSt11111RVFQUAwYMiN133z33mjRpUq5m/vz5sWjRotxy37594w9/+EP89re/jQMOOCAefvjhePzxx7f4cHQAiIgo/ufd1at/q3r1AABA1WWSJEnquok0FRcXR4sWLaKoqMgzpQB2ItkNpTHvN9Wf97tcWhx5Dat2TzwAAFD17MVDlQDYKSTrqvYNIDW1HQAAsGVCKQB2CpnGzVPdDgAA2DKhFAA7hbyGBVHQaWC1tinodLRb9wAAoJYIpQDYaRTuf1H16g+oXj0AAFB1QikAdhpNu50cBZ2PqVJtQedjounXTqrljgAAYOcllAJgp5HJaxDthkz6ymCqoPMx0W7IpMjkNUipMwAA2PkIpQDYqeTlF0b7oU9F2xMmRUHH/mXWFXQ6OtqeMCnaD30q8vIr/+paAABg2zWs6wYAIG2ZvAbRrPsp0aTLsTFvXMuIiOh80aJo0KR1HXcGAAA7D1dKAUBEZHzLHgAApEooBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqGtZ1AwCQluz6kkqXv7wuIiKvUbNa7wkAAHZWQikAdhrzxrWsdN2C33YsN9b1ynW12Q4AAOzU3L4HAAAAQOpcKQXATqPLiOV13QIAAPD/E0oBsNPwjCgAANh+uH0PAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQ1rOsGAACA+iO7vqRa9XmNmtVSJwDUd0IpAACgyuaNa1mt+q5XrqulTgCo79y+BwAAAEDqXCkFAABUWZcRy8ssZ9eXxILfdoyIiE4Xfux2PQCqTCgFAABU2ZZCp7xGzYRSAFSZ2/cAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAIAakWworesWAKhHhFIAAEC1JdmNUTLn0Vjy+Mm5sfl37x6LHhkcJXMejSS7sQ67A6A+aFjXDQAAAPVLdm1xLHn6zCidP7XcutIF06N0wfQo6HxMtBsyKfLyC+ugQwDqA1dKAQAAVZZkN1YaSH1R6fypseTpM10xBUCl6jSUeuGFF+LEE0+MPfbYIzKZTDz++ONbrJ8xY0ZkMplyr8WLF6fTMAAA7ORWf/jEVwZSm5XOnxqr//NkLXcEQH1Vp6FUSUlJHHDAATFu3Lhqbffee+/FokWLcq+2bdvWUocAAMAXFf/z7urVv1W9egB2HnX6TKnjjjsujjvuuGpv17Zt29h1111rviEAAKBS2Q2lUbpgerW2KV0wLbIbSiOvYUEtdQVAfVUvH3R+4IEHxtq1a6Nnz55x/fXXR79+/SqtXbt2baxduza3XFxcHBER2Ww2stlsrfcKAAA7io2lRVu/XdPGNdwNANurquYt9SqU2n333WP8+PFxyCGHxNq1a2PChAkxYMCAeOWVV+Lggw+ucJuxY8fGmDFjyo0vXbo0SktLa7tlAADYYSQbt+7fz58VrYnMqk9ruBsAtlcrV66sUl0mSZKklnupkkwmE4899lgMHTq0Wtv1798/OnfuHBMnTqxwfUVXSnXq1CmWL18ehYW+nhYAAKpjyaP/K0o/nlHl+oJOA6PdKc/UXkMAbHeKi4ujZcuWUVRUtMXspV5dKVWRww47LF588cVK1+fn50d+fn658by8vMjLq9PnvAMAQL1TeMDF1QqlCg+42L+7AXYyVZ336/3fDrNmzYrdd9+9rtsAAICdQtNuJ0dB52OqVFvQ+Zho+rWTarkjAOqrOr1SatWqVfHBBx/klufOnRuzZs2KVq1aRefOnWPUqFGxcOHC+P3vfx8REbfffnt07do19t133ygtLY0JEybEtGnT4q9//WtdvQUAANipZPIaRLshk2LJ02dG6fypldYVdD4m2g2ZFJm8Bil2B0B9Uqeh1GuvvRYDBw7MLY8cOTIiIoYPHx733XdfLFq0KObPn59bv27duvj+978fCxcujKZNm8b+++8fzz33XJl9AAAAtSsvvzDaD30qVv/nySiedWeUfvx8bl1Bp6Oj8ICLounXThJIAbBF282DztNSXFwcLVq0+MqHbQEAAF8tu74k5o1rGRERnS9aFA2atK7jjgCoa1XNXur9M6UAAIDtQ6ZhQV23AEA9IpQCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHUN67oBAACg/siuL6l0+cvrIiLyGjWr9Z4AqJ+EUgAAQJXNG9ey0nULftux3FjXK9fVZjsA1GNu3wMAAAAgda6UAgAAqqzLiOV13QIAOwihFAAAUGWeEQVATXH7HgAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkLqtCqUaNGgQn376abnxzz//PBo0aLDNTQEAAACwY9uqUCpJkgrH165dG40bN96mhgAAAADY8TWsTvEdd9wRERGZTCYmTJgQu+yyS27dxo0b44UXXogePXrUbIcAAAAA7HCqFUrddtttEbHpSqnx48eXuVWvcePGseeee8b48eNrtkMAAAAAdjjVCqXmzp0bEREDBw6MRx99NFq2bFkrTQEAAACwY6tWKLXZ9OnTa7oPAAAAAHYiW/Wg89NOOy1+/vOflxv/xS9+Ed/61re2uSkAAAAAdmxbFUq98MILcfzxx5cbP+644+KFF17Y5qYAAAAA2LFtVSi1atWqaNy4cbnxRo0aRXFx8TY3BQAAAMCObatCqf322y8mTZpUbvyhhx6KffbZZ5ubAgAAAGDHtlUPOr/22mvj1FNPjQ8//DCOPvroiIiYOnVq/PGPf4zJkyfXaIMAAAAA7Hi2KpQ68cQT4/HHH4+bbropHn744WjSpEnsv//+8dxzz0X//v1rukcAAAAAdjCZJEmSum4iTcXFxdGiRYsoKiqKwsLCum4HAAAAYIdS1exlq54pFRGxYsWKmDBhQvzoRz+KZcuWRUTEG2+8EQsXLtzaXQIAAACwk9iq2/f++c9/xqBBg6JFixbx0Ucfxfnnnx+tWrWKRx99NObPnx+///3va7pPAAAAAHYgW3Wl1MiRI+Pcc8+NOXPmREFBQW78+OOPjxdeeKHGmgMAAABgx7RVodSrr74aF110UbnxDh06xOLFi7e5KQAAAAB2bFsVSuXn50dxcXG58ffffz/atGmzzU0BAAAAsGPbqlDqpJNOihtuuCHWr18fERGZTCbmz58fP/jBD+K0006r0QYBAAAA2PFsVSh16623xqpVq6Jt27axZs2a6N+/f3Tv3j2aN28eN954Y033CAAAAMAOZqu+fa9FixYxZcqUeOmll+Ktt96KVatWxcEHHxyDBg2q6f4AAAAA2AFVOZRq1apVvP/++7HbbrvF//7f/zt+9atfRb9+/aJfv3612R8AAAAAO6Aq3763bt263MPN77///igtLa21pgAAAADYsVX5Sqk+ffrE0KFDo1evXpEkSVx++eXRpEmTCmvvueeeGmsQAAAAgB1PlUOpBx54IG677bb48MMPIyKiqKjI1VIAAAAAbJVMkiRJdTfq2rVrvPbaa9G6deva6KlWFRcXR4sWLaKoqCgKCwvruh0AAACAHUpVs5cqP1OqVatW8dlnn0VExMCBA6Nx48bb3iUAAAAAOyUPOgcAAAAgdR50DgAAAEDqtupB55lMxoPOAQAAANhqHnQOAAAAQI2p8QedR0Qcf/zxUVRUFHPnzo3WrVvHz372s1ixYkVu/eeffx777LPPVjcNAAAAwM6hWqHUs88+G2vXrs0t33TTTbFs2bLc8oYNG+K9996rue4AAAAA2CFVK5T6sq248w8AAAAAti2UAgAAAICtUa1QKpPJRCaTKTcGAAAAANXRsDrFSZLEueeeG/n5+RERUVpaGhdffHE0a9YsIqLM86YAAAAAKC+7vqRa9XmNmtVSJ3WrWqHU8OHDyyx/5zvfKVdzzjnnbFtHAAAAADuweeNaVqu+65XraqmTulWtUOree++trT4AAAAA2IlUK5QCAAAAYNt0GbG8zHJ2fUks+G3HiIjodOHHO+ztel8mlAIAAABI0ZZCp7xGzXaaUKpa374HAAAAADVBKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAANuJZENpXbeQGqEUAAAAQB1IshujZM6jseTxk3Nj8+/ePRY9MjhK5jwaSXZjHXZX+xrWdQMAAAAAO5vs2uJY8vSZUTp/arl1pQumR+mC6VHQ+ZhoN2RS5OUX1kGHtc+VUgAAAAApSrIbKw2kvqh0/tRY8vSZO+wVU0IpAAAAgBSt/vCJrwykNiudPzVW/+fJWu6obtRpKPXCCy/EiSeeGHvssUdkMpl4/PHHv3KbGTNmxMEHHxz5+fnRvXv3uO+++2q9TwAAAICaUvzPu6tX/1b16uuLOg2lSkpK4oADDohx48ZVqX7u3LkxZMiQGDhwYMyaNSuuvPLKOP/88+Mvf/lLLXcKAAAAsO2yG0qjdMH0am1TumBaZHfAb+Wr0wedH3fccXHcccdVuX78+PHRtWvXuPXWWyMiYu+9944XX3wxbrvtthg8eHBttQkAAABQI5J1K7d+u4YFNdxN3apX37738ssvx6BBg8qMDR48OK688spKt1m7dm2sXbs2t1xcXBwREdlsNrLZbK30CQAAAFChhs22erv6kmNUtc96FUotXrw42rVrV2asXbt2UVxcHGvWrIkmTZqU22bs2LExZsyYcuNLly6N0tId79I3AAAAYPuW17ZvZD+dWfX6dv1i6bLiiCiuvaZq0MqVVbsarF6FUltj1KhRMXLkyNxycXFxdOrUKdq0aROFhYV12BkAAACwMyo55LL47M9VD6Va97osmrZtW4sd1ayCgqrdZlivQqn27dvHkiVLyowtWbIkCgsLK7xKKiIiPz8/8vPzy43n5eVFXl6dPucdAAAA2Ant0n1orOp8TJTOn/qVtQWdj4lm3U+OTD3KMKqat9SfdxQRffr0ialTy/7ApkyZEn369KmjjgAAAACqJ5PXINoNmRQFnY/ZYl1B52Oi3ZBJkclrkFJn6arTUGrVqlUxa9asmDVrVkREzJ07N2bNmhXz58+PiE233p1zzjm5+osvvjj+85//xDXXXBPvvvtu3HnnnfGnP/0prrrqqrpoHwAAAGCr5OUXRvuhT0XbEyZFQcf+ZdYVdDo62p4wKdoPfSry8nfcRw/V6e17r732WgwcODC3vPnZT8OHD4/77rsvFi1alAuoIiK6du0aTz/9dFx11VXxq1/9Kjp27BgTJkyIwYMHp947AAAAwLbI5DWIZt1PiSZdjo1541pGRETnixZFgyat67izdGSSJEnquok0FRcXR4sWLaKoqMiDzgEAAIA6l11fkguluoxYHnmNmtVxR9umqtlLvXqmFAAAAAA7BqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKlrWNcNAAAAAOxMsutLKl3+8rqIiLxGzWq9p7oglAIAAABI0bxxLStdt+C3HcuNdb1yXW22U2fcvgcAAABA6lwpBQAAAJCiLiOW13UL2wWhFAAAAECKdtRnRFWX2/cAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASN12EUqNGzcu9txzzygoKIjevXvHP/7xj0pr77vvvshkMmVeBQUFKXYLAAAAwLaq81Bq0qRJMXLkyBg9enS88cYbccABB8TgwYPj008/rXSbwsLCWLRoUe41b968FDsGAAAAYFvVeSj1y1/+Mi644II477zzYp999onx48dH06ZN45577ql0m0wmE+3bt8+92rVrl2LHAAAAAGyrOg2l1q1bF6+//noMGjQoN5aXlxeDBg2Kl19+udLtVq1aFV26dIlOnTrFySefHLNnz06jXQAAAABqSMO6PPhnn30WGzduLHelU7t27eLdd9+tcJtvfOMbcc8998T+++8fRUVFccstt0Tfvn1j9uzZ0bFjx3L1a9eujbVr1+aWi4uLIyIim81GNputwXcDAAAAQFXzljoNpbZGnz59ok+fPrnlvn37xt577x133313/OQnPylXP3bs2BgzZky58aVLl0ZpaWmt9goAAACws1m5cmWV6uo0lNptt92iQYMGsWTJkjLjS5Ysifbt21dpH40aNYqDDjooPvjggwrXjxo1KkaOHJlbLi4ujk6dOkWbNm2isLBw65sHAAAAoJyCgoIq1dVpKNW4cePo1atXTJ06NYYOHRoRmy7xmjp1alx66aVV2sfGjRvj7bffjuOPP77C9fn5+ZGfn19uPC8vL/Ly6vw57wAAAAA7lKrmLXV++97IkSNj+PDhccghh8Rhhx0Wt99+e5SUlMR5550XERHnnHNOdOjQIcaOHRsRETfccEMcfvjh0b1791ixYkXcfPPNMW/evDj//PPr8m0AAAAAUA11HkqdeeaZsXTp0rjuuuti8eLFceCBB8azzz6be/j5/PnzyyRsy5cvjwsuuCAWL14cLVu2jF69esXMmTNjn332qau3AAAAAEA1ZZIkSeq6iTQVFxdHixYtoqioyDOlAAAAAGpYVbMXD1UCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHXbRSg1bty42HPPPaOgoCB69+4d//jHP7ZYP3ny5OjRo0cUFBTEfvvtF3/+859T6hQAAACAmlDnodSkSZNi5MiRMXr06HjjjTfigAMOiMGDB8enn35aYf3MmTNj2LBh8d3vfjfefPPNGDp0aAwdOjTeeeedlDsHAAAAYGtlkiRJ6rKB3r17x6GHHhq/+c1vIiIim81Gp06d4rLLLosf/vCH5erPPPPMKCkpiaeeeio3dvjhh8eBBx4Y48eP/8rjFRcXR4sWLWLp0qVRWFhYbn1eXl40bNgwt7xu3bpK95XJZKJRo0ZbVbt+/fqo7NTXVm1EROPGjbeqdsOGDZHNZmuktlGjRpHJZGq1duPGjbFx48YaqW3YsGHk5eVtN7XZbDY2bNhQaW2DBg2iQYMG201tkiSxfv36Gqn94ueztmojtvxZNkdUXGuOMEeYI6pfa47YulpzxLbVbg+fe3OEOeLLteYIc4Q5ovq12/scUVxcHG3atImioqIKs5fNGla6JgXr1q2L119/PUaNGpUby8vLi0GDBsXLL79c4TYvv/xyjBw5sszY4MGD4/HHH6+wfu3atbF27drccnFxcURE3HLLLZGfn1+uvnv37vFf//VfueVf/OIXlf6CdenSJc4999zc8m233RarV6+usHb33XePCy+8MLf8m9/8JlasWFFhbZs2beJ73/tebvnuu++OpUuXVli76667xhVXXJFb/t3vfheLFi2qsLZp06bxf/7P/8ktT5w4MebNm1dhbaNGjeJHP/pRbvmPf/xjfPDBBxXWRkSMHj069+eHH344/v3vf1daO2rUqNxfLE8++WS89dZbldZeffXV0axZs4iIeOaZZ+K1116rtPaKK66IXXfdNSIipkyZUunvUETEJZdcEm3bto2IiOeffz6ef/75SmvPP//86NChQ0RsulLvueeeq7R2+PDhseeee0ZExKuvvhrPPPNMpbXDhg2Lr3/96xER8dZbb8UTTzxRae3pp58e++67b0REzJ49Ox5++OFKa08++eQ48MADIyLi/fffjz/+8Y+V1h533HFx2GGHRUTERx99FPfff3+ltYMGDYp+/fpFRMTChQtjwoQJldb2798/BgwYEBERn376adx1112V1vbp0yeOPfbYiIhYsWJF/OpXv6q09pBDDokhQ4ZERERJSUnccsstldYecMABMXTo0IjYNNeMHTu20tq99947zjjjjNzyjTfeWGmtOWITc8T/MEdsYo7YxByxiTnif5gjNjFHbGKO2MQc8T/MEZuYIzbZkeaIL+YwW1KnodRnn30WGzdujHbt2pUZb9euXbz77rsVbrN48eIK6xcvXlxh/dixY2PMmDHlxktKSipMX4uLi8vcOrhq1apKU9qVK1eWq12zZk2ValeuXBklJSUV1hYUFFS5tkGDBlWuzWazVa5t2LBhmdri4uJKayOi2rWb/6IoKiraYu3SpUtz66tSuzkdXrFixRZrP/vss9yfq1K7Of1dvnz5Fms///zzaNq0aZVrN5+3ZcuWbbF22bJlW1X7+eefb7F2+fLlW1X72WefbbF2xYoVW1X7VT/joqKiXO3q1aurXLtu3bot1n75c1+dWnPEJuYIc4Q5ouJac8Qm5ghzhDmi4lpzxCbmCHOEOaLi2vo8R1Q1lKrT2/c++eST6NChQ8ycOTP69OmTG7/mmmvi+eefj1deeaXcNo0bN477778/hg0blhu78847Y8yYMbFkyZJy9RVdKdWpU6dYsmSJ2/dcUlulWpfUblutS2rNEdWtNUdsW+328Lk3R5gjvlxrjjBHmCOqX2uO2Lpac8S21W4Pn3tzxI4xRxQXF0e7du2279v3dtttt2jQoEG5MGnJkiXRvn37Crdp3759terz8/MrvE2voKAgCgoKvrLHqtRsTW1FPW3PtV/8i6A+1Obl5ZX5cOxotV+cqLb32ojI/UVQX2pr63Nvjth+areXz7I5YpPt4XNvjqjd2u3hc2+O+J/auv7cmyNqv3Z7+NybI+pvbV1/7s0RtV+bxud+SyHZF9Xpt+81btw4evXqFVOnTs2NZbPZmDp1apkrp76oT58+ZeojNt3TW1k9AAAAANufOr1SKiJi5MiRMXz48DjkkEPisMMOi9tvvz1KSkrivPPOi4iIc845Jzp06JB7cNgVV1wR/fv3j1tvvTWGDBkSDz30ULz22mvx29/+ti7fBgAAAADVUOeh1JlnnhlLly6N6667LhYvXhwHHnhgPPvss7mHmc+fPz93f2tERN++feMPf/hD/Pd//3f86Ec/ir322isef/zx6NmzZ129BQAAAACqqU4fdF4XiouLo0WLFl/5sC0AAAAAqq+q2UudPlMKAAAAgJ2TUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1DWs6wbSliRJREQUFxfXcScAAAAAO57NmcvmDKYyO10otXLlyoiI6NSpUx13AgAAALDjWrlyZbRo0aLS9Znkq2KrHUw2m41PPvkkmjdvHplMpq7b2SbFxcXRqVOnWLBgQRQWFtZ1OwD1krkUYNuZSwG23Y40lyZJEitXrow99tgj8vIqf3LUTnelVF5eXnTs2LGu26hRhYWF9f4XFqCumUsBtp25FGDb7Shz6ZaukNrMg84BAAAASJ1QCgAAAIDUCaXqsfz8/Bg9enTk5+fXdSsA9Za5FGDbmUsBtt3OOJfudA86BwAAAKDuuVIKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVCqho0dOzYOPfTQaN68ebRt2zaGDh0a7733Xpma0tLSGDFiRLRu3Tp22WWXOO2002LJkiW59W+99VYMGzYsOnXqFE2aNIm99947fvWrX5XZx4wZMyKTyZR7LV68eIv9JUkS1113Xey+++7RpEmTGDRoUMyZM6dMzZ577lluvz/72c+28cwAVN2OMJdGRDz99NPRu3fvaNKkSbRs2TKGDh269ScFoJrq+1xa2X4zmUy8+uqrNXCGAL5afZ9LIyLef//9OPnkk2O33XaLwsLCOOKII2L69OnbeGZqhlCqhj3//PMxYsSI+Pvf/x5TpkyJ9evXx7HHHhslJSW5mquuuir+3//7fzF58uR4/vnn45NPPolTTz01t/7111+Ptm3bxgMPPBCzZ8+OH//4xzFq1Kj4zW9+U+547733XixatCj3atu27Rb7+8UvfhF33HFHjB8/Pl555ZVo1qxZDB48OEpLS8vU3XDDDWX2e9lll23jmQGouh1hLn3kkUfi7LPPjvPOOy/eeuuteOmll+Kss86qgbMDUDX1fS7t27dvmf0tWrQozj///OjatWsccsghNXSWALasvs+lEREnnHBCbNiwIaZNmxavv/56HHDAAXHCCSd8ZeCVioRa9emnnyYRkTz//PNJkiTJihUrkkaNGiWTJ0/O1fz73/9OIiJ5+eWXK93P9773vWTgwIG55enTpycRkSxfvrzKvWSz2aR9+/bJzTffnBtbsWJFkp+fn/zxj3/MjXXp0iW57bbbqrxfgNpW3+bS9evXJx06dEgmTJhQ5f0C1Lb6Npd+2bp165I2bdokN9xwQ5WPA1DT6ttcunTp0iQikhdeeCFXU1xcnEREMmXKlCofq7a4UqqWFRUVRUREq1atImJTQrp+/foYNGhQrqZHjx7RuXPnePnll7e4n837+KIDDzwwdt999/jmN78ZL7300hZ7mTt3bixevLjMsVu0aBG9e/cud+yf/exn0bp16zjooIPi5ptvjg0bNnz1mwWoJfVtLn3jjTdi4cKFkZeXFwcddFDsvvvucdxxx8U777xT9TcNUMPq21z6ZU8++WR8/vnncd55521x3wC1qb7Npa1bt45vfOMb8fvf/z5KSkpiw4YNcffdd0fbtm2jV69eVX/jtaRhXTewI8tms3HllVdGv379omfPnhERsXjx4mjcuHHsuuuuZWrbtWtX6aVzM2fOjEmTJsXTTz+dG9t9991j/Pjxccghh8TatWtjwoQJMWDAgHjllVfi4IMPrnA/m/ffrl27LR778ssvj4MPPjhatWoVM2fOjFGjRsWiRYvil7/8ZbXPAcC2qo9z6X/+85+IiLj++uvjl7/8Zey5555x6623xoABA+L999+v8B8gALWpPs6lX/a73/0uBg8eHB07dqzSewaoafVxLs1kMvHcc8/F0KFDo3nz5pGXlxdt27aNZ599Nlq2bLlV56EmCaVq0YgRI+Kdd96JF198cav38c4778TJJ58co0ePjmOPPTY3/o1vfCO+8Y1v5Jb79u0bH374Ydx2220xceLEePDBB+Oiiy7KrX/mmWeiQYMGVTrmyJEjc3/ef//9o3HjxnHRRRfF2LFjIz8/f6vfC8DWqI9zaTabjYiIH//4x3HaaadFRMS9994bHTt2jMmTJ5fZJ0Aa6uNc+kUff/xx/OUvf4k//elPW90/wLaqj3NpkiQxYsSIaNu2bfztb3+LJk2axIQJE+LEE0+MV199NXbfffetfi81QShVSy699NJ46qmn4oUXXijzvznt27ePdevWxYoVK8okqUuWLIn27duX2ce//vWvOOaYY+LCCy+M//7v//7KYx522GG5D8dJJ50UvXv3zq3r0KFDLFq0KHesL/7iLVmyJA488MBK99u7d+/YsGFDfPTRR2U+JAC1rb7OpZvH99lnn9z6/Pz8+NrXvhbz58+v4rsHqBn1dS79onvvvTdat24dJ510UpXeM0BNq69z6bRp0+Kpp56K5cuXR2FhYURE3HnnnTFlypS4//7744c//GH1TkQN80ypGpYkSVx66aXx2GOPxbRp06Jr165l1vfq1SsaNWoUU6dOzY299957MX/+/OjTp09ubPbs2TFw4MAYPnx43HjjjVU69qxZs3K/iM2bN4/u3bvnXk2aNImuXbtG+/btyxy7uLg4XnnllTLHrmi/my/xA0hDfZ9Le/XqFfn5+WW+Lnj9+vXx0UcfRZcuXap/QgC2Qn2fS7/4Pu69994455xzolGjRtU+DwDbor7PpatXr46IiLy8svFPXl5e7ur+OlWXT1nfEV1yySVJixYtkhkzZiSLFi3KvVavXp2rufjii5POnTsn06ZNS1577bWkT58+SZ8+fXLr33777aRNmzbJd77znTL7+PTTT3M1t912W/L4448nc+bMSd5+++3kiiuuSPLy8pLnnntui/397Gc/S3bdddfkiSeeSP75z38mJ598ctK1a9dkzZo1SZIkycyZM5PbbrstmTVrVvLhhx8mDzzwQNKmTZvknHPOqeEzBVC5+j6XJkmSXHHFFUmHDh2Sv/zlL8m7776bfPe7303atm2bLFu2rAbPFEDldoS5NEmS5LnnnksiIvn3v/9dQ2cGoOrq+1y6dOnSpHXr1smpp56azJo1K3nvvfeSq6++OmnUqFEya9asGj5b1SeUqmERUeHr3nvvzdWsWbMm+d73vpe0bNkyadq0aXLKKackixYtyq0fPXp0hfvo0qVLrubnP/950q1bt6SgoCBp1apVMmDAgGTatGlf2V82m02uvfbapF27dkl+fn5yzDHHJO+9915u/euvv5707t07adGiRVJQUJDsvffeyU033ZSUlpbWyPkBqIr6PpcmyaavLv/+97+ftG3bNmnevHkyaNCg5J133tnmcwNQVTvCXJokSTJs2LCkb9++23QuALbWjjCXvvrqq8mxxx6btGrVKmnevHly+OGHJ3/+85+3+dzUhEySJEmtXIIFAAAAAJXwTCkAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1/x8bCE4vOD+pIgAAAABJRU5ErkJggg==", + "image/png": 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" ] @@ -744,20 +744,20 @@ " Event Study Aggregation \n", "\n", "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "2.448411 0.029846 82.033602 0.0 2.389914 2.506909\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "2.474609 0.031322 79.004292 0.0 2.413218 2.536\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "-6 months 0.118584 0.063783 1.859198 0.062999 -0.006427 0.243596\n", - "-5 months 0.021101 0.043800 0.481754 0.629981 -0.064746 0.106947\n", - "-4 months -0.041159 0.033809 -1.217373 0.223462 -0.107424 0.025107\n", - "-3 months -0.006282 0.029213 -0.215052 0.829727 -0.063539 0.050974\n", - "-2 months -0.013714 0.027415 -0.500233 0.616911 -0.067447 0.040019\n", - "-1 months -0.002954 0.026243 -0.112548 0.910389 -0.054390 0.048482\n", - "0 months 0.984152 0.025663 38.349080 0.000000 0.933853 1.034450\n", - "1 months 1.944027 0.031349 62.012347 0.000000 1.882584 2.005470\n", - "2 months 2.956757 0.038780 76.243869 0.000000 2.880750 3.032765\n", - "3 months 3.908710 0.055238 70.761776 0.000000 3.800446 4.016974\n", + "-6 months 0.050784 0.063342 0.801737 0.422705 -0.073365 0.174932\n", + "-5 months 0.041111 0.043754 0.939584 0.347431 -0.044646 0.126868\n", + "-4 months 0.002663 0.035115 0.075850 0.939538 -0.066160 0.071487\n", + "-3 months 0.005673 0.030395 0.186639 0.851944 -0.053900 0.065246\n", + "-2 months -0.002458 0.027898 -0.088089 0.929806 -0.057137 0.052222\n", + "-1 months -0.032274 0.026593 -1.213649 0.224882 -0.084395 0.019847\n", + "0 months 0.996740 0.026678 37.361703 0.000000 0.944452 1.049028\n", + "1 months 2.006814 0.031848 63.011564 0.000000 1.944393 2.069236\n", + "2 months 2.959074 0.040557 72.959962 0.000000 2.879582 3.038565\n", + "3 months 3.935807 0.057718 68.190550 0.000000 3.822683 4.048932\n", "------------------ Additional Information ------------------\n", "Control Group: never_treated\n", "Anticipation Periods: 0\n", @@ -769,7 +769,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", " warnings.warn(\n" ] }, @@ -786,7 +786,7 @@ }, { "data": { - "image/png": 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tY5tttqm17qWXXorXX389brrppjjhhBPyy2v61ra6nEa2vr1GRGyyySYbbJZSQ/VaX3X9Pendu3dUVlbGyy+/XOX0009b2zY6deoU3/zmN+Ob3/xmLF68OPbZZ5+44IILhFIAfO65phQANGJLly6Nu+++O77yla/EkUceWe3n9NNPj0WLFsX9998fERFDhw6NlStXxjXXXJPfRmVlZbVAa7PNNou+ffvGH/7wh1i8eHF++cMPPxwvvfRSnXrbZJNNYvDgwXHVVVfFnDlzqt0/b968/L+HDh0aTz75ZEydOjW/7IMPPohbb721yjprZtqsmakTEbFixYr4/e9/X237paWlNZ7CdvTRR8c777xT5TlYY+nSpbFkyZKIiDjooIMiIuJ3v/tdlZrLLrus2nob0hFHHBHFxcUxevToKo8zYvXjXrBgQUTU/FyklOK3v/1ttW2WlpZGxOqZZhvS0KFDo6ysLC6++OJYuXJltfs/OcZ1tSYs3dC91lddf0+GDx8eRUVFceGFF1abnfbJsSktLa3xMa0ZzzXatWsXffr0ieXLl2+ARwEATZuZUgDQiN1///2xaNGiOPTQQ2u8/0tf+lJ06dIlbr311jjmmGNi+PDhsdtuu8X//u//xvTp02O77baL+++/Pz744IOIqDqb4+KLL47DDjss9txzz/jmN78ZH374YVxxxRXRt2/fKkHV2owdOzb22muv6NevX5x88smx1VZbxXvvvRdPPvlkvP322/HCCy9ERMQPf/jDuOWWW+KAAw6IM844I0pLS+Paa6+NLbbYIj744IN8X3vssUd07NgxRowYEd/97ncjl8vFzTffXC28iYgYMGBA3HHHHXHWWWfFrrvuGu3atYtDDjkkjj/++PjTn/4Up556akyaNCn23HPPqKioiFdffTX+9Kc/xUMPPRRf/OIXY+edd45jjz02fv/738fChQtjjz32iAkTJtR5ptj66t27d/zsZz+Lc889N2bOnBnDhw+P9u3bx4wZM+Kee+6Jb3/723H22WfHdtttF717946zzz473nnnnSgrK4s///nPNV6facCAARGx+qLtQ4cOjeLi4vja1772mXstKyuLcePGxfHHHx9f+MIX4mtf+1p06dIl3nrrrXjggQdizz33jCuuuKJe22zTpk3ssMMOcccdd8Q222wTnTp1ir59+0bfvn3Xut6jjz4ay5Ytq7Z8p512Wq/rM9X196RPnz7x4x//OH7605/G3nvvHUcccUS0bt06nnnmmdhss81izJgxEbF6DMaNGxc/+9nPok+fPrHJJpvEfvvtFzvssEMMHjw4BgwYEJ06dYopU6bEXXfdFaeffnq9ewaAZqcwX/oHANTFIYcckkpKStKSJUtqrTnxxBNTy5Yt0/z581NKKc2bNy8dd9xxqX379qlDhw7pxBNPTI8//niKiHT77bdXWff2229P2223XWrdunXq27dvuv/++9NXv/rVtN122+VrZsyYkSIiXXLJJTXu/80330wnnHBC6tatW2rZsmXafPPN01e+8pV01113Val7/vnn0957751at26dunfvnsaMGZN+97vfpYhIc+fOzdc9/vjj6Utf+lJq06ZN2myzzdIPf/jD9NBDD6WISJMmTcrXLV68OB133HFpo402ShGRevbsmb9vxYoV6Re/+EXacccdU+vWrVPHjh3TgAED0ujRo9PChQvzdUuXLk3f/e53U+fOnVNpaWk65JBD0uzZs1NEpFGjRtX6nH/anXfeWa2/UaNGpYhI8+bNq3GdP//5z2mvvfZKpaWlqbS0NG233XbptNNOS6+99lq+5uWXX05DhgxJ7dq1SxtvvHE6+eST0wsvvJAiIt1www35ulWrVqUzzjgjdenSJeVyubTmLV5tYzdp0qQUEenOO++ssvyGG25IEZGeeeaZavVDhw5NHTp0SCUlJal3797pxBNPTFOmTMnXjBgxIpWWllZ7nGueh0964okn0oABA1KrVq3W+Vyv6bW2n0+uO2jQoLTjjjtW20bPnj3TsGHDqi2v6+9JSildf/31aZdddsnXDRo0KI0fPz5//9y5c9OwYcNS+/btU0SkQYMGpZRS+tnPfpZ22223tNFGG6U2bdqk7bbbLl100UVpxYoVtT5mAPi8yKVUw58eAYBm5d57743DDz88Hnvssdhzzz3XWrvzzjtHly5darx20YZ25plnxlVXXRWLFy9uUhfJBgDgs3NNKQBoZpYuXVrldkVFRVx++eVRVlYWX/jCF/LLV65cGatWrapSO3ny5HjhhRdi8ODBDd7XggUL4uabb4699tpLIAUA8DnkmlIA0MycccYZsXTp0hg4cGAsX7487r777njiiSfi4osvjjZt2uTr3nnnnRgyZEh84xvfiM022yxeffXVuPLKK6Nbt25x6qmnbvC+Bg4cGIMHD47tt98+3nvvvbjuuuuivLw8zjvvvA2+LwAAGj+hFAA0M/vtt1/8+te/jr/+9a+xbNmy6NOnT1x++eXVLqzcsWPHGDBgQFx77bUxb968KC0tjWHDhsXPf/7z6Ny58wbv6+CDD4677rorrr766sjlcvGFL3whrrvuuthnn302+L4AAGj8XFMKAAAAgMy5phQAAAAAmRNKAQAAAJC5Jn1NqcrKynj33Xejffv2kcvlCt0OAAAAwOdeSikWLVoUm222WRQV1T4fqkmHUu+++2706NGj0G0AAAAA8CmzZ8+O7t2713p/kw6l2rdvHxGrH2RZWVmBuwEAAACgvLw8evTokc9tatOkQ6k1p+yVlZUJpQAAAAAakXVdasmFzgEAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADIXItCNwAAAADweVK5ckm96otaljZQJ4UllAIAAADI0KyxHetVv+WZKxqok8Jy+h4AAAAAmTNTCgAAACBDPU/7sMrtypVLYvbV3SMiose33262p+t9WqOZKfXzn/88crlcnHnmmYVuBQAAAKDBFLUsrfZTl/uam0YRSj3zzDNx1VVXxU477VToVgAAAADIQMFDqcWLF8fXv/71uOaaa6Jjx/pd6AsAAACApqngodRpp50Ww4YNiyFDhhS6FQAAAAAyUtALnd9+++3x3HPPxTPPPFOn+uXLl8fy5cvzt8vLyyMiorKyMiorKxukRwAAAICG9MlMo7KyMqKJZxx1zWgKFkrNnj07vve978X48eOjpKSkTuuMGTMmRo8eXW35vHnzYtmyZRu6RQAAAIAGl1Z9nP/3vHnzItdiSQG7+ewWLVpUp7pcSik1cC81uvfee+Pwww+P4uLi/LKKiorI5XJRVFQUy5cvr3JfRM0zpXr06BEffvhhlJWVZdY7AAAAwIZSuXJJzB7XOSIieoxc0OS/ca+8vDw6duwYCxcuXGteU7CZUvvvv3+89NJLVZZ985vfjO222y7OOeecaoFURETr1q2jdevW1ZYXFRVFUVHBL48FAAAAUH+fyDSaQ8ZR1/4LFkq1b98++vbtW2VZaWlpdO7cudpyAAAAgM+DtGpZRBOfKVVXTTt6AwAAAGiiUmVFLHnj7njv3sPyy966atOY8+ehseSNuyNVVhSwu4ZXsGtKbQjl5eXRoUOHdZ6jCAAAANCYVC4vj/ceOCaWvTWh1pqSLfaPrsPuiKLWTSvzqGteY6YUAAAAQIZSZcU6A6mIiGVvTYj3Hjim2c6YEkoBAAAAZOjjN+9bZyC1xrK3JsTH/7m/gTsqDKEUAAAAQIbKX7yqfvUv1K++qRBKAQAAAGSkctWyWDZ7Ur3WWTZ7YlSuWtZAHRWOUAoAAAAgI2nFokzXa8yEUgAAAAAZybVqn+l6jZlQCgAAACAjRS1KoqTHvvVap6THflHUoqSBOiocoRQAAABAhsp2OqV+9f3rV99UCKUAAAAAMtS292FRssX+daot2WL/aLvVoQ3cUWEIpQAAAAAylCsqjq7D7lhnMFWyxf7RddgdkSsqzqizbAmlAAAAADJW1Losug3/a2zylTuipPugKveV9NgvNvnKHdFt+F+jqHVZgTpseC0K3QAAAADA51GuqDhK+xwebXoeGLPGdoyIiC1OmRPFbToXuLNsmCkFAAAA0EjkmuG37NVGKAUAAABA5oRSAAAAAGROKAUAAABA5oRSAAAAAGROKAUAAABA5oRSAAAAAGSuRaEbAAAAAPg8qVy5pNbbn74vIqKoZWmD91QIQikAAACADM0a27HW+2Zf3b3asi3PXNGQ7RSM0/cAAAAAyJyZUgAAAAAZ6nnah4VuoVEQSgEAAABkqLleI6q+nL4HAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkTigFAAAAQOaEUgAAAABkrkWhGwAAAADWrXLlknrVF7UsbaBOYMMQSgEAAEATMGtsx3rVb3nmigbqBDYMp+8BAAAAkDkzpQAAAKAJ6Hnah1VuV65cErOv7h4RET2+/bbT9WhyhFIAAADQBKwtdCpqWSqUoskp6Ol748aNi5122inKysqirKwsBg4cGA8++GAhWwIAAAAgAwUNpbp37x4///nP49lnn40pU6bEfvvtF4cddlhMmzatkG0BAAAA0MAKevreIYccUuX2RRddFOPGjYunnnoqdtxxxwJ1BQAAAE1LWrUswul7NDGN5ppSFRUVceedd8aSJUti4MCBNdYsX748li9fnr9dXl4eERGVlZVRWVmZSZ8AAABQSKmyIj7+z/2x6IXf55e9ddWmUdJ9cLTb6ZRou9WhkSsqLmCHfN7VNaMpeCj10ksvxcCBA2PZsmXRrl27uOeee2KHHXaosXbMmDExevToasvnzZsXy5Yta+hWAQAAoKDSykWx4rFTovK9x6rdt+ztybHs7clR1HWvaLXXVZFr2b4AHULEokWL6lSXSymlBu5lrVasWBFvvfVWLFy4MO6666649tpr4+GHH64xmKppplSPHj3iww8/jLKysizbBgAAgEylyop4/75DYtnsieusLemxX2xy2F/MmKIgysvLo2PHjrFw4cK15jUFD6U+bciQIdG7d++46qqr1llbXl4eHTp0WOeDBAAAgKZuyRt3x/sPfK3O9Zt85Y4o7XN4A3YENatrXlPQb9+rSWVlZZXZUAAAAEBE+YvrnrxRpf6F+tVD1gp6Talzzz03DjrooNhiiy1i0aJF8cc//jEmT54cDz30UCHbAgAAgEalctWyWDZ7Ur3WWTZ7YlSuWhZFLUoaqCv4bAoaSr3//vtxwgknxJw5c6JDhw6x0047xUMPPRQHHHBAIdsCAACARiWtqNuFo2tcTyhFI1XQUOq6664r5O4BAACgSci1Wr9v0lvf9SALje6aUgAAAEBVRS1KoqTHvvVap6THfk7do1ETSgEAAEATULbTKfWr71+/esiaUAoAAACagLa9D4uSLfavU23JFvtH260ObeCO4LMRSgEAAEATkCsqjq7D7lhnMFWyxf7RddgdkSsqzqgzWD9CKQAAAGgiilqXRbfhf41NvnJHlHQfVOW+kh77xSZfuSO6Df9rFLUuK1CHUHcF/fY9AAAAoH5yRcVR2ufwaNPzwJg1tmNERGxxypwobtO5wJ1B/ZgpBQAAAE1czrfs0QQJpQAAAADInNP3AAAAoAmoXLmk1tufvi8ioqhlaYP3BJ+FUAoAAACagDXXj6rJ7Ku7V1u25ZkrGrId+MycvgcAAABA5syUAgAAgCag52kfFroF2KCEUgAAANAEuEYUzY3T9wAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADI3HqFUsXFxfH+++9XW75gwYIoLi7+zE0BAAAA0LytVyiVUqpx+fLly6NVq1afqSEAAAAAmr8W9Sn+3e9+FxERuVwurr322mjXrl3+voqKinjkkUdiu+2227AdAgAAANDs1CuUuvTSSyNi9UypK6+8ssqpeq1atYpevXrFlVdeuWE7BAAAAKDZqVcoNWPGjIiI2HfffePuu++Ojh07NkhTAAAAADRv9Qql1pg0adKG7gMAAACAz5H1utD5V7/61fjFL35Rbfkvf/nLOOqooz5zUwAAAAA0b+sVSj3yyCNx8MEHV1t+0EEHxSOPPPKZmwIAAACgeVuv0/cWL14crVq1qra8ZcuWUV5e/pmbAgAAYMOpXLmkXvVFLUsbqBOA/1qvUKpfv35xxx13xPnnn19l+e233x477LBDnbczZsyYuPvuu+PVV1+NNm3axB577BG/+MUvYtttt12ftgAAAKjBrLH1+5KqLc9c0UCdAPzXeoVS5513XhxxxBHx5ptvxn777RcRERMmTIjbbrst7rzzzjpv5+GHH47TTjstdt1111i1alX86Ec/igMPPDBefvnlKC2VzAMAAAA0V7mUUlqfFR944IG4+OKLY+rUqdGmTZvYaaedYtSoUTFo0KD1bmbevHmxySabxMMPPxz77LPPOuvLy8ujQ4cOsXDhwigrK1vv/QIAADRnnz59r3Llkph9dfeIiOjx7berna7n9D3gs6hrXrNeM6UiIoYNGxbDhg1b39VrtHDhwoiI6NSpU433L1++PJYvX56/veb6VZWVlVFZWblBewEAAGg2ittUvf2Jz08p5ard7/MV8FnU9Riy3qHURx99FHfddVf85z//ibPPPjs6deoUzz33XHTt2jU233zzem+vsrIyzjzzzNhzzz2jb9++NdaMGTMmRo8eXW35vHnzYtmyZfXeJwAAwOdJqqyIynceilWv35hf9vY1m0fRJntEi61PiKLNh0auqLhwDQLNwqJFi+pUt16n77344osxZMiQ6NChQ8ycOTNee+212GqrreInP/lJvPXWW/GHP/yh3g2PHDkyHnzwwXjssceie/fuNdbUNFOqR48e8eGHHzp9DwAAYC0ql5fHvL99LZbNnlhrTUmP/aLLwbdHUWufr4D1V15eHh07dmyY0/fOOuusOPHEE+OXv/xltG/fPr/84IMPjuOOO67e2zv99NPjr3/9azzyyCO1BlIREa1bt47WrVtXW15UVBRFRUX13i8AAMDnQaqsiHkPHrvWQCoiYtnsiTHvwWOj2/C/mjEFrLe6ZjTrleQ888wzccopp1Rbvvnmm8fcuXPrvJ2UUpx++ulxzz33xMSJE2PLLbdcn3YAAABYi4/fvC+WvTWhTrXL3poQH//n/gbuCGA9Q6nWrVvnLzL+Sa+//np06dKlzts57bTT4pZbbok//vGP0b59+5g7d27MnTs3li5duj5tAQAAUIPyF6+qX/0L9asHWB/rFUodeuihceGFF8bKlSsjIiKXy8Vbb70V55xzTnz1q1+t83bGjRsXCxcujMGDB8emm26a/7njjjvWpy0AAAA+pXLVslg2e1K91lk2e2JUrvJlUkDDWq9Q6te//nUsXrw4Ntlkk1i6dGkMGjQo+vTpE+3bt4+LLrqozttJKdX4c+KJJ65PWwAAAHxKWlG3b8HaUOsB1NV6Xei8Q4cOMX78+Hj88cfjhRdeiMWLF8cXvvCFGDJkyIbuDwAAgM8g16r9uos24HoAdVXnUKpTp07x+uuvx8Ybbxz/8z//E7/97W9jzz33jD333LMh+wMAAOAzKGpREiU99q3XKXwlPfaLohYlDdgVQD1O31uxYkX+4uY33XRTLFvm/GIAAICmoGyn6t+evtb6/vWrB1gfdZ4pNXDgwBg+fHgMGDAgUkrx3e9+N9q0aVNj7fXXX7/BGgQAAOCzadv7sCjZYv9Y9taEddaWbLF/tN3q0Ay6Aj7v6jxT6pZbbomDDz44Fi9eHBERCxcujA8//LDGHwAAABqPXFFxdB12R5Rssf9a60q22D+6DrsjckXFGXUGfJ7lUkqpvittueWWMWXKlOjcuXND9FRn5eXl0aFDh1i4cGGUlZUVtBcAAIDGLlVWxMf/uT/Kp/4+lr39cH55SY/9oqz/KdF2q0MFUsBnVte8Zr0udL7vvvtGq1atNkijAAAAZCNXVBylfQ6PNj0PjFljO0ZExBanzIniNoWdcAB8PrnQOQAAQDNXuXJJtZ81Uqqo9T6AhuRC5wAAAM3cmllRNZl9dfdqy7Y8c0VDtgMQEfUIpW655Za49NJL480334xcLhcLFy40WwoAAACA9eJC5wAAAM1cfU/JK2pZ2kCdAJ8Hdc1r6nxNqYiIgw8+OBYuXBgzZsyIzp07x89//vP46KOP8vcvWLAgdthhh/VuGgAAgA2vqGVpvX4AslCvUOrvf/97LF++PH/74osvjg8++CB/e9WqVfHaa69tuO4AAAAAaJbqFUp92nqc+QcAAAAAny2UAgAAAID1Ua9QKpfLRS6Xq7YMAAAAAOqjRX2KU0px4oknRuvWrSMiYtmyZXHqqadGaenqC+F98npTAAAAAFCbeoVSI0aMqHL7G9/4RrWaE0444bN1BAAAAECzV69Q6oYbbmioPgAAAAD4HHGhcwAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHNCKQAAAAAyJ5QCAAAAIHMtCt0AAADQeFWuXFKv+qKWpQ3UCQDNjVAKAACo1ayxHetVv+WZKxqoEwCaG6fvAQAAAJC5gs6UeuSRR+KSSy6JZ599NubMmRP33HNPDB8+vJAtAQAAn9DztA+r3K5cuSRmX909IiK6/8/0KG7TuRBtAdAMFHSm1JIlS6J///4xduzYQrYBAADUoqhlaRS1LI1ccUksnflQzHvguPx9b1/fJ967/4hYOvOhyBWXuJ4UAPVS0JlSBx10UBx00EGFbAEAAFiHyuXl8d4Dx8SytyZUu2/Z7EmxbPakKNli/+g67I4oal1WgA4BaIpcUwoAAKhVqqyoNZD6pGVvTYj3HjgmUmVFRp0B0NQ1qW/fW758eSxfvjx/u7y8PCIiKisro7KyslBtAQBAs7Vk+r3rDKTWWPbWhFgy/b5o22d4wzYFQKNW14ymSYVSY8aMidGjR1dbPm/evFi2bFkBOgIAgOZt+ZTL61W/4NnLY3HZHg3UDQBNwaJFi+pU16RCqXPPPTfOOuus/O3y8vLo0aNHdOnSJcrKnLsOAAAbUlq1LN56/4l6rVP53uPRpVNZ5FqUNFBXADR2JSV1+z+gSYVSrVu3jtatW1dbXlRUFEVFLo8FAAAbUsWqJeu34qolUdSq7YZtBoAmo64ZTUFDqcWLF8f06dPzt2fMmBFTp06NTp06xRZbbFHAzgAAgFyr9pmuB8DnS0GnF02ZMiV22WWX2GWXXSIi4qyzzopddtklzj///EK2BQAARERRi5Io6bFvvdYp6bFfFDl1D4A6KOhMqcGDB0dKqZAtAAAAa1G20ymxbPakutf3P6UBuwGgOXEhJgAAoFZtex8WJVvsX6faki32j7ZbHdrAHQHQXAilAACAWqWKZdFl6PVR0n3wWutKug+OLkOvj1xRcTaNAdDkNalv3wMAALI1a2zHOtUte3tyzL6mZ2x55ooG7giA5sJMKQAAAAAyZ6YUAABQq56nfVjoFgBopoRSAABArYpalha6BQCaKafvAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmRNKAQAAAJA5oRQAAAAAmWtR6AY+r5YsX1Wv+tLWhgoAAABoPiQdBdL+xw/Wq77yV4c0UCcAAAAA2XP6HgAAAACZM1OqQBZddFCV20tWrIpuo8dHRMTcUQdEaStDAwAAADRfko8CWds1okpbtXANKQAAAKBZc/oeAAAAAJkTSgEAAACQOaEUAAAAAJkTSgEAAACQOaEUAAAAAJkTSgEAAACQOaEUAAAAAJkTSgEAAACQOaEUAAAAAJkTSgEAAACQOaFUI7RsZUWhWwAAAABoUEKpAquoTPHnF9+Nr1z3dH5Zlwv+EUOufDL+/OK7UVGZCtgdAAAAQMNoUegGPs/Kl62MI2+aEv98Y361+yZOnx8Tp8+PIVtvHHeN+GKUlbQsQIcAAAAADcNMqQKpqEy1BlKf9M835seRN00xY6qRWrJ8Vb1+AAAAgNXMlCqQe/89Z52B1Br/fGN+3DdtbhzRb9MG7or6av/jB+tVX/mrQxqoEwAAAGhazJQqkHFPzKpn/cyGaQQAAACgAMyUKoBlKyti4vS6zZJaY8Ib82PZyoooaVncQF2xPhZddFCV20tWrIpuo8dHRMTcUQdEaSsvMQAAAKiJT8wFsGg9ry20aPkqoVQjU9q69pdQaasWa70fAAAAPs+cvlcA7dczqFjf9QAAAAAaG6FUAZS0LI79+mxcr3X233pjs6QAAACAZsPUmwIZuUfPel1XauQevRquGYBmYkk9T492ii0AABSOd+MFMrzvpjFk643jn2+sO5gasvXGcdiO3TLoig1p2coKH3ghY+1//GC96it/dUgDdQIIiQGAdfG/f4EUF+XirhFfjCNvmrLWYGrI1hvHXSO+GMVFuQy7o74qKlPc++85ccVjM/PLulzwj9ivz8Yxco+eMbzvpsYQoI6EGc2DkBgAWBfv4gqorKRlPHjyl+K+aXPjisdmxOQ3F+Tv23/rjWPkHr3isB27CTMaufJlK2sNFydOnx8Tp8/Ph4tlJS0L0CHr4gNw87HoooOq3F6yYlV0Gz0+IiLmjjogSlsZu6ZAmAEA8Png3XmBFRfl4oh+m8bQbbrk34TPu+DA6NyudYE7oy4qKtM6Z7tFRPzzjflx5E1T4sGTvyRkbIR8AG4+1hYYFudyAkXI0NpC4pk/3i86t/VeBwA+77w7b4R8y17Tce+/59TpumARq4Op+6bNjSP6bdrAXQFOqW3ahBnNw5oQuKbXY6+LJno9AgCNI5QaO3ZsXHLJJTF37tzo379/XH755bHbbrsVui1Yp3FPzKpn/UyhVCPklK/mxSm1TZ8wo/nwegQA1qao0A3ccccdcdZZZ8WoUaPiueeei/79+8fQoUPj/fffL3RrsFbLVlbExOl1myW1xoQ35seylRUN1BHrq7R1i6o/nwihSlu1qHY/jVd9T6mtqEwZdUZ9lS9bGQdd81Qc9Ydn4+H/LKhy38Tp8+OoPzwbB13zVJQvW1mgDlkXr0cAYF0K/unqN7/5TZx88snxzW9+MyIirrzyynjggQfi+uuvj//7v/+r0zZWrFgRK1asqLa8qKgoWrRoUaWuNrlcLlq2bLletStXroyUan4jVdfaFSurX2h5bduNiGjVqtV61a5atSoqKys3SG3Lli0jl8s1aG1FRUVUVNQe5NSntkWLFlFUVLRBahcsrv13ZG0WLl0RJS3bREREZWVlrFpV+0W2i4uLo7i4uNHUppRi5craPwDWp/aTr8+Gqo1Y+2u5ttpPvh7LP14aLXOtC36MqG9txOfzGHHPv+fW65TaP099O4b37ZpftiGPERuitjG87gtxjKioTPHVG5+NCdMX1Hj/Gv98Y34cceMz8cD/7BqtW7WsUw8b4hhRE8eI6rX1PcX97hfficN22KTWmub2PmJD1DaG1733EY4Rn65tyu8jPmutY8Rnq3WMaF7HiLU9hk8qaCi1YsWKePbZZ+Pcc8/NLysqKoohQ4bEk08+Wa1++fLlsXz58vzt8vLyiIj41a9+Fa1bV7++RJ8+feLrX/96/vYvf/nLWn/BevbsGSeeeGL+9qWXXhoff/xxjbWbbrppfPvb387fvuKKK+Kjjz6qsbZLly7xne98J3/7qquuinnz5lWrW5GKIuILERFRmSqjsrIyrrvuupgzZ06N223btm384Ac/yN+++eabY9asmk8la9myZfzoRz/K377tttti+vTpNdZGRIwaNSr/77vuuiteeeWVWmvPPffc/C/j/fffHy+88EKttWeffXaUlpZGRMSDDz4YU6ZMqbX2e9/7Xmy00UYRETF+/Pgafx/WGDlyZGyyyeo3sQ8//HA8/PDDtdaedNJJsfnmm0dExBNPPBH//Oc/a60dMWJE9OrVKyIinnnmmXjwwaoXw16VchExoNb1azP7P69Hl536RUTEtGnT4q677qq19rDDDoudd945IiJef/31uO2222qtPeigg/Knvc6cOTNuuummWmuHDBkSe+65Z0REvPPOO3HttdfWWjto0KAYPHhwRES8//77MW7cuFprBw4cGAceeGBERHz00Ufx29/+ttbaL37xizFs2LCIiFiyZEn86le/qrW2f//+MXz48IhYfdwYM2ZMrbXbb799HH300fnbF110Ua21nz5G/PwXv4x/LyuNZ1ZuEhFlERGx+UWTo1dReRy0WYrLTj82f6pQ1seIiIiNNtoovve97+VvO0ZsFBFVjxE3L90m1oxdXZx7+yMxrc3r+dsb8hjxSccee2xss802ERHxwgsvxH333Vdr7ZFHHhk77rhjRHx+jxGvrNooJizvU+t2/ivFxOkL4ryb/ho//9ZhEdGwx4jG/D4iovEdI37/xMxaa2rys788G/++57la729u7yM+yTFitab+PsIxYrWm/D6iJo4RqzlGrOYYsVpdjhGfzG7WpqCh1Pz586OioiK6du1aZXnXrl3j1VdfrVY/ZsyYGD16dLXlS5YsqTF9LS8vr3Ia4OLFi2tNaRctWlStdunSpXWqXbRoUSxZsqTG2pKSkjrVrvzEmZTz3p8XS1oVr3W7lZWVde6hRYsWVWrLy8trrY2Ieteu+Y9i4cKFa62dN29e/v661K5JVj/66KO11s6f/9+/wtaldk36++GHH661dsGCBdG2bdu11vaID2N2dKx1G5+2RXwYSxaW5Z/jDz74YK09fPDBB/naBQsWrLX2ww8/XK/a+fPnr7X2o48+Wq/adY3xwoUL87Uff/xxnWtXrFix1tpPv+7rWrtoeUXcUN4zZqWNqtXNrCyLcW9H/HvcY3HtoX2ifevizI8REav/OlTX2s/jMWJV5GJmPQKpiIgZlWWxcMnH0SJW/2VnQx8jPlm7Pq/7z+sx4l/Ru9ZtVLU6JH7g7co4q4GPERGN+31EROM6RixbVRmT1jHT7dNeWlgU+8Z/X4+f1hzfR3yy1jGiab+PiHCMWKOpvo+ojWNE5Ht0jHCMWKMux4i6hlK5tLb5WA3s3Xffjc033zyeeOKJGDhwYH75D3/4w3j44YfjX//6V5X6mmZK9ejRI957770oK6v+QaQpTZdbsqIiulw4MSIiFv5saJS2amFKbROYUnvvtPfiuNterHUbn/bHY3eKI/tvbkptNK4ptRWVKQ6+7umYUIfTTPbfeuP427d2i4pVtffQVKbU1lbbVI8R85asiJ5jav/rZW1mnTsoupSufkym3X+22g1xjFi2siI6jZ5Y6zZqs/iiL0dJy2LT7hvJMWLe4uWx6YW1zxCozSdfj5/WHN9HfNbaxvC69z7CMeLTtU31fcSGqHWM+Gy1jhHN6xhRXl4eXbt2jYULF9aY16xR0JlSG2+8cRQXF8d7771XZfl7770X3bp1q1bfunXrGk/TKykpiZKSknXury4161NbU0/1ra3I/feFWpQriqKiog2y3Zp88penKdQWFRVVeXE0ptqjdtkirp/ybp2umTFk643jqF22qPJNUZ8+UK2rh0LXRkT+P4KmUluX1/I9/363ToFUxOqL1f/llffr9S2KDfVadoxYbc3rs0tx3V7Pn9alrDRKWlb/nWosx55Cv+6zPkYsWlW3v6p92pKVldG2dct699BQ7w2yfh+RZW1dXp8d2tT9NfxJtb0e16eHNRrLa9kxYrXG8N7AMaJhaxvDewPHiP/WFvp17xjR8LWN4XVfU21drylV0G/fa9WqVQwYMCAmTJiQX1ZZWRkTJkyoMnMKGqviolzcNeKLMWTrjddat+brrn11eeM07omar5FQe/3MhmmEz6SkZXHs12ftr8VP23/rjev0AZjstF/Pb7hc3/VoGF6PAEBdFDSUiog466yz4pprrombbropXnnllRg5cmQsWbIk/2180NiVlbSMB0/+Utw14osxuHfnKvft///DqAdP/lKUlazfLA4a1rKVFTFxet1mSa0x4Y35sWxl7dOmKZyRe/SsZ32vhmmE9SbMaD68HgGAdSn4nxWPOeaYmDdvXpx//vkxd+7c2HnnnePvf/97tYufNzdLllc9r3bJilU1/nuNUn8BbtSKi3JxRL9NY+g2XaL9j1d/c8a8Cw6Mzu3qPuWRwli0vPZz3Ne1ng/Bjc/wvpvGkK03rvMptYftWP1UcQpv5B496xUWCzMaJ69HAGBdCnqh88+qvLw8OnTosM4LZzVGRWf/pV71lb86pIE6YUNasnxVPpRadNFBwsQmYNnKimh77t/qvd7HYw4WSjVS5ctWxpE3TVnrB+E1p9Sawdg4VVSmOOiap+ocZjx48pecHt1IeT0CwOdTXfOagp++B1BIThVqfpxS2/S5Xl/zsGT5qijO5eKuE74Ytxy3S+y9Zacq9w/u3TluOW6XuOuEL0ZxzhgCwOeRaRwFsuiigwrdAvD/OVWo+fjkqdFDt+kSe/XqGN1Gj4+IiJk/3i86t119Su2aa4KZzdg4fTLM+Osr78VVT86KR2d8kL9/cO/OcdLuW8RXtu8qzGjE1swcrs3kNxfE5DcX5G+bFQ4Anz/ejReID0LNg2uDNQ+ue9J8rO1DcK+LJlZb5kNw4yTMAAD4fPAJGT6DtX1wWjM745N8cGqc1pwqVNfrnjhVCGDdzAoHANZFKAUQ/70O0X3T5sYVj82oMgtj/603jpF79IrDduwmkGrkfAhuHoxj82B2MACwLr59Dz6DT5++ty7eoDcNn/wWxXkXHBid27UucEcAAABNR13zGp+Q4TMQMjUPa7s2WEVK1e437gAAAJ+dT1bA555rgwEAAGSvqNANAAAAAPD5Y6YU8LnnosoAAADZE0oBn3uuEQUAAJA9p+8BAAAAkDmhFAAAAACZE0oBAAAAkDmhFAAAAACZE0oBAAAAkDmhFAAAAACZE0oBAAAAkDmhFAAAAACZE0oBAAAAkDmhFAAAAACZE0oBAAAAkDmhFAAAAACZE0oBAAAAkDmhFAAAAACZE0oBAAAAkDmhFAAAAACZE0oBAAAAkDmhFAAAAACZE0oBAAAAkDmhFAAAAACZa1HoBj6LlFJERJSXlxe4EwAAAAAi/pvTrMltatOkQ6lFixZFRESPHj0K3AkAAAAAn7Ro0aLo0KFDrffn0rpiq0assrIy3n333Wjfvn3kcrlCt/OZlZeXR48ePWL27NlRVlZW6HZYT8axeTCOzYexbB6MY/NgHJsPY9k8GMfmwTg2H81pLFNKsWjRothss82iqKj2K0c16ZlSRUVF0b1790K3scGVlZU1+V9AjGNzYRybD2PZPBjH5sE4Nh/Gsnkwjs2DcWw+mstYrm2G1BoudA4AAABA5oRSAAAAAGROKNWItG7dOkaNGhWtW7cudCt8BsaxeTCOzYexbB6MY/NgHJsPY9k8GMfmwTg2H5/HsWzSFzoHAAAAoGkyUwoAAACAzAmlAAAAAMicUAoAAACAzAmlmrEbb7wxNtpoo0K3wQZwwQUXxM4771zoNviMjGPzYSybB+PYPHi/07QZv+Zh8uTJkcvl4qOPPip0K3xGXpPNQ1MaR6HUWrzyyitx6KGHRocOHaK0tDR23XXXeOuttwrdVo169eoVl112WaHbaJROPPHEyOVyVX6+/OUvF7qtWuVyubj33nsL3Uajduqpp0Yul2vUv/PGsXYXXHBBbLfddlFaWhodO3aMIUOGxL/+9a9Ct1UrY1ndypUr45xzzol+/fpFaWlpbLbZZnHCCSfEu+++W+jWamUca3b33XfHgQceGJ07d45cLhdTp04tdEtr5f1OdWPHjo1evXpFSUlJ7L777vH0008XuqVaGb+aPfLII3HIIYfEZptt1iSOVYMHD44zzzyz0G00SmPGjIldd9012rdvH5tsskkMHz48XnvttUK3VSuvyZqNGzcudtpppygrK4uysrIYOHBgPPjgg4Vuq1ZNfRyFUrV48803Y6+99ortttsuJk+eHC+++GKcd955UVJSUujWWA9f/vKXY86cOfmf2267rdAtsZ7uueeeeOqpp2KzzTYrdCusp2222SauuOKKeOmll+Kxxx6LXr16xYEHHhjz5s0rdGvU0ccffxzPPfdcnHfeefHcc8/F3XffHa+99loceuihhW6NelqyZEnstdde8Ytf/KLQrbAe7rjjjjjrrLNi1KhR8dxzz0X//v1j6NCh8f777xe6NephyZIl0b9//xg7dmyhW+Ezevjhh+O0006Lp556KsaPHx8rV66MAw88MJYsWVLo1qiH7t27x89//vN49tlnY8qUKbHffvvFYYcdFtOmTSt0a81TokbHHHNM+sY3vlGvdUaMGJEOO+ywdNFFF6VNNtkkdejQIY0ePTqtXLkynX322aljx45p8803T9dff32V9V588cW07777ppKSktSpU6d08sknp0WLFlXb7iWXXJK6deuWOnXqlL7zne+kFStWpJRSGjRoUIqIKj8ppXTDDTekDh06pL///e9pu+22S6WlpWno0KHp3XffzW970qRJadddd01t27ZNHTp0SHvssUeaOXPm+j5tjdKa568+Bg0alE4//fT0ve99L2200UZpk002SVdffXVavHhxOvHEE1O7du1S796909/+9rcq602ePDntuuuuqVWrVqlbt27pnHPOSStXrqyy3TPOOCP94Ac/SB07dkxdu3ZNo0aNyt/fs2fPKuPYs2fPlFJKo0aNSv37909/+MMfUs+ePVNZWVk65phjUnl5eX7dO++8M/Xt2zf/e7T//vunxYsX1/v5aszefvvttPnmm6d///vfqWfPnunSSy9da71xbBoWLlyYIiL985//rLXGWDZ+Tz/9dIqINGvWrFprjGPjNWPGjBQR6fnnn19nrfc7jcduu+2WTjvttPztioqKtNlmm6UxY8bUuo7xa9wiIt1zzz3rrFtz/LruuutSjx49UmlpaRo5cmRatWpV+sUvfpG6du2aunTpkn72s59VWW/WrFnp0EMPTaWlpal9+/bpqKOOSnPnzq223dqOiyNGjKg2jjNmzEiTJk3K/18+YMCA1KZNmzRw4MD06quv5rc9derUNHjw4NSuXbvUvn379IUvfCE988wzG+aJa6Tef//9FBHp4YcfrrXGa7Jp6NixY7r22mtrvd84rj+hVA0qKipSu3bt0oUXXpgOPPDA1KVLl7Tbbrut8z+IESNGpPbt26fTTjstvfrqq+m6665LEZGGDh2aLrroovT666+nn/70p6lly5Zp9uzZKaWUFi9enDbddNN0xBFHpJdeeilNmDAhbbnllmnEiBFVtltWVpZOPfXU9Morr6S//OUvqW3btunqq69OKaW0YMGC1L1793ThhRemOXPmpDlz5qSUVv8itmzZMg0ZMiQ988wz6dlnn03bb799Ou6441JKKa1cuTJ16NAhnX322Wn69Onp5ZdfTjfeeONaP1A0RSNGjEgdOnRIXbp0Sdtss0069dRT0/z589e6zqBBg1L79u3TT3/60/y4FRcXp4MOOihdffXV6fXXX08jR45MnTt3TkuWLEkprQ5M2rZtm77zne+kV155Jd1zzz1p4403rvLBaNCgQamsrCxdcMEF6fXXX0833XRTyuVy6R//+EdK6b//cd1www1pzpw56f33308prX6D0K5du/zvySOPPJK6deuWfvSjH6WUUnr33XdTixYt0m9+85s0Y8aM9OKLL6axY8dWOaA1dRUVFWnfffdNl112WUop1TmUMo6N2/Lly9Mll1ySOnTokObNm1drnbFs/MaPH59yuVxauHBhrTXGsfGqbyjl/U7hLV++PBUXF1d7f3rCCSekQw89tNb1jF/jVp9Qql27dunII49M06ZNS/fff39q1apVGjp0aDrjjDPSq6++mq6//voUEempp55KKa1+L7XzzjunvfbaK02ZMiU99dRTacCAAWnQoEHVtlvbcfGjjz5KAwcOTCeffHJ+HFetWpUPpXbfffc0efLkNG3atLT33nunPfbYI7/tHXfcMX3jG99Ir7zySnr99dfTn/70pzR16tQN+vw1Nm+88UaKiPTSSy/VWuM12bitWrUq3XbbbalVq1Zp2rRptdYZx/UnlKrBnDlzUkSktm3bpt/85jfp+eefT2PGjEm5XC5Nnjy51vVGjBiRevbsmSoqKvLLtt1227T33nvnb69atSqVlpam2267LaWU0tVXX506duxY5a+uDzzwQCoqKsr/1WLNdletWpWvOeqoo9IxxxyTv13TB/QbbrghRUSaPn16ftnYsWNT165dU0qrf4EjYq2PqTm47bbb0n333ZdefPHFdM8996Ttt98+7brrrlWez08bNGhQ2muvvfK314zb8ccfn1+25vfkySefTCml9KMf/Shtu+22qbKyMl8zduzY1K5du/zvxKe3m1JKu+66azrnnHPyt2t6MzJq1KjUtm3bKn+9/8EPfpB23333lFJKzz77bIqIZv0XiosvvjgdcMAB+ee3rqGUcWyc/vKXv6TS0tKUy+XSZpttlp5++um11hvLxm3p0qXpC1/4Qv6NTm2MY+NV31DK+53Ce+edd1JEpCeeeKLK8h/84Adpt912q3U949e41SeU+vTxa+jQoalXr17VxnbNzLl//OMfqbi4OL311lv5+6dNm5YiIv//8LqOiymtPuZ+73vfq9LPJ2dKrfHAAw+kiEhLly5NKaXUvn37dOONN9bhWWgeKioq0rBhw9Kee+651jqvycbpxRdfTKWlpam4uDh16NAhPfDAA2utN47r73N/Talbb7012rVrl/959NFHo7KyMiIiDjvssPj+978fO++8c/zf//1ffOUrX4krr7xyrdvbcccdo6jov09r165do1+/fvnbxcXF0blz5/y5/q+88kr0798/SktL8zV77rlnVFZWVrko3o477hjFxcX525tuummdrhfQtm3b6N27d43rderUKU488cQYOnRoHHLIIfHb3/425syZs85tNlY1jWVExNe+9rU49NBDo1+/fjF8+PD461//Gs8880xMnjx5rdvbaaed8v9eM26fHMuuXbtGRFQZy4EDB0Yul8vX7LnnnrF48eJ4++23a9xuRN3HslevXtG+ffsa1+vfv3/sv//+0a9fvzjqqKPimmuuiQ8//HCd22yMahrHZ599Nn7729/GjTfeWOX5rQvjWDi1vSYjIvbdd9+YOnVqPPHEE/HlL385jj766HU+f8ayMNY2jhGrL3p+9NFHR0opxo0bt87tGcfCWNc41pf3O02b8WsePn386tq1a+ywww7VxvaT49ijR4/o0aNH/v4ddtghNtpoo3jllVdq3W5dxzGi6rF40003jYj/Hs/POuusOOmkk2LIkCHx85//PN588836PNwm57TTTot///vfcfvtt6+z1muy8dl2221j6tSp8a9//StGjhwZI0aMiJdffnmt6xjH9fO5D6UOPfTQmDp1av7ni1/8Ymy88cbRokWL2GGHHarUbr/99uv89r2WLVtWuZ3L5Wpctib4qqv13UZN66WU8rdvuOGGePLJJ2OPPfaIO+64I7bZZpt46qmn6tVbY1HTWNZkq622io033jimT5++1u2tayzXfEAq5FiuWa+4uDjGjx8fDz74YOywww5x+eWXx7bbbhszZsyoV2+NQU3j+Oijj8b7778fW2yxRbRo0SJatGgRs2bNiv/93/+NXr16rXV7xrFw1vaaLC0tjT59+sSXvvSluO6666JFixZx3XXXrXV7xrIw1jaOawKpWbNmxfjx46OsrGyd2zOOhVHX/yPryvudwtt4442juLg43nvvvSrL33vvvejWrdta1zV+zUNjG8dPr/vp4/kFF1wQ06ZNi2HDhsXEiRNjhx12iHvuuadevTUVp59+evz1r3+NSZMmRffu3ddZ39jG0msyolWrVtGnT58YMGBAjBkzJvr37x+//e1v17qOcVw/n/tQqn379tGnT5/8T5s2baJVq1ax6667Vvv6ztdffz169uy5Qfe//fbbxwsvvFDlGxkef/zxKCoqim233bbO22nVqlVUVFSsVw+77LJLnHvuufHEE09E3759449//ON6bafQahrLmrz99tuxYMGC/F9vNpTtt98+nnzyySov9Mcffzzat29fp/+M1mjZsuV6jWUul4s999wzRo8eHc8//3y0atWqSf5HX9M4Hn/88fHiiy9W+UC12WabxQ9+8IN46KGHNuj+jeOGU9fXZMTqN6zLly/foPs3lhtGbeO4JpB644034p///Gd07ty5QfZvHDeM+rweG4L3Oxteq1atYsCAATFhwoT8ssrKypgwYUIMHDhwg+7L+DUP22+/fcyePTtmz56dX/byyy/HRx99VO2P8WvzWcZxm222ie9///vxj3/8I4444oi44YYb1ms7jVVKKU4//fS45557YuLEibHllls2yH68JrPXUO9VjaNQqlY/+MEP4o477ohrrrkmpk+fHldccUX85S9/ie985zsbdD9f//rXo6SkJEaMGBH//ve/Y9KkSXHGGWfE8ccfnz+FoS569eoVjzzySLzzzjsxf/78Oq0zY8aMOPfcc+PJJ5+MWbNmxT/+8Y944403Yvvtt1/fh9PoLF68OH7wgx/EU089FTNnzowJEybEYYcdFn369ImhQ4du0H195zvfidmzZ8cZZ5wRr776atx3330xatSoOOuss6pM41yXXr16xYQJE2Lu3Ll1Pk3kX//6V1x88cUxZcqUeOutt+Luu++OefPmNZux7Ny5c/Tt27fKT8uWLaNbt271OmDXhXFsWEuWLIkf/ehH8dRTT8WsWbPi2Wefjf/5n/+Jd955J4466qgNui9j2XBWrlwZRx55ZEyZMiVuvfXWqKioiLlz58bcuXNjxYoVG3RfxrFhffDBBzF16tT8KQmvvfZaTJ06NebOnbtB9+P9TsM466yz4pprrombbropXnnllRg5cmQsWbIkvvnNb27Q/Ri/hrV48eL8H90iVj/mqVOnrvMMjfoaMmRI9OvXL77+9a/Hc889F08//XSccMIJMWjQoHrNnuzVq1f861//ipkzZ8b8+fPrNGNj6dKlcfrpp8fkyZNj1qxZ8fjjj8czzzzTrMYxYvUpe7fcckv88Y9/jPbt2+f/b1y6dOkG3Y/XZMM699xz45FHHomZM2fGSy+9FOeee25Mnjw5vv71r2/Q/RjH1YRStTj88MPjyiuvjF/+8pfRr1+/uPbaa+PPf/5z7LXXXht0P23bto2HHnooPvjgg9h1113jyCOPjP333z+uuOKKem3nwgsvjJkzZ0bv3r2jS5cudd73q6++Gl/96ldjm222iW9/+9tx2mmnxSmnnLI+D6VRKi4ujhdffDEOPfTQ2GabbeJb3/pWDBgwIB599NFo3br1Bt3X5ptvHn/729/i6aefjv79+8epp54a3/rWt+InP/lJvbbz61//OsaPHx89evSIXXbZpU7rlJWVxSOPPBIHH3xwbLPNNvGTn/wkfv3rX8dBBx20Pg/lc804Nqzi4uIqx51DDjkkFixYEI8++mjsuOOOG3RfxrLhvPPOO3H//ffH22+/HTvvvHNsuumm+Z8nnnhig+7LODas+++/P3bZZZcYNmxYRKy+DuMuu+yyzmto1pf3Ow3jmGOOiV/96ldx/vnnx8477xxTp06Nv//97/X6MFMXxq9hTZkyJXbZZZf8semss86KXXbZJc4///wNup9cLhf33XdfdOzYMfbZZ58YMmRIbLXVVnHHHXfUaztnn312FBcXxw477BBdunSpU3hWXFwcCxYsiBNOOCG22WabOProo+Oggw6K0aNHr+/DaZTGjRsXCxcujMGDB1f5v7G+z/G6eE02rPfffz9OOOGE2HbbbWP//fePZ555Jh566KE44IADNuh+jONqufTJ+fAAAAAAkAEzpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMwJpQAAAADInFAKAAAAgMz9PydHspHrxiW/AAAAAElFTkSuQmCC", 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", 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" ] @@ -811,7 +811,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv", + "display_name": ".venv", "language": "python", "name": "python3" }, diff --git a/doc/examples/py_double_ml_panel_simple.ipynb b/doc/examples/py_double_ml_panel_simple.ipynb index 7d56e074..98aa5112 100644 --- a/doc/examples/py_double_ml_panel_simple.ipynb +++ b/doc/examples/py_double_ml_panel_simple.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -32,12 +32,14 @@ "import numpy as np\n", "dta = pd.read_csv(\"https://raw.githubusercontent.com/d2cml-ai/csdid/main/data/sim_data.csv\")\n", "dta.head()\n", - "dta.loc[dta[\"G\"] == 0, \"G\"] = np.nan" + "# set dtype for G to float\n", + "dta[\"G\"] = dta[\"G\"].astype(float)\n", + "dta.loc[dta[\"G\"] == 0, \"G\"] = np.inf" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -46,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -81,84 +83,84 @@ " \n", " \n", " ATT(2.0,1,2)\n", - " 0.920815\n", - " 0.063970\n", - " 14.394375\n", + " 0.922607\n", + " 0.064144\n", + " 14.383278\n", " 0.000000\n", - " 0.795435\n", - " 1.046195\n", + " 0.796886\n", + " 1.048328\n", " \n", " \n", " ATT(2.0,1,3)\n", - " 1.983448\n", - " 0.064744\n", - " 30.635238\n", + " 1.990200\n", + " 0.064701\n", + " 30.760062\n", " 0.000000\n", - " 1.856552\n", - " 2.110344\n", + " 1.863388\n", + " 2.117011\n", " \n", " \n", " ATT(2.0,1,4)\n", - " 2.956340\n", - " 0.063267\n", - " 46.728168\n", + " 2.955379\n", + " 0.063298\n", + " 46.689908\n", " 0.000000\n", - " 2.832340\n", - " 3.080341\n", + " 2.831317\n", + " 3.079441\n", " \n", " \n", " ATT(3.0,1,2)\n", - " -0.044035\n", - " 0.065853\n", - " -0.668684\n", - " 0.503697\n", - " -0.173105\n", - " 0.085035\n", + " -0.041535\n", + " 0.065788\n", + " -0.631352\n", + " 0.527810\n", + " -0.170477\n", + " 0.087406\n", " \n", " \n", " ATT(3.0,2,3)\n", - " 1.105935\n", - " 0.065468\n", - " 16.892674\n", + " 1.107889\n", + " 0.065385\n", + " 16.944147\n", " 0.000000\n", - " 0.977620\n", - " 1.234251\n", + " 0.979737\n", + " 1.236041\n", " \n", " \n", " ATT(3.0,2,4)\n", - " 2.065059\n", - " 0.065618\n", - " 31.471099\n", + " 2.060141\n", + " 0.065261\n", + " 31.567865\n", " 0.000000\n", - " 1.936451\n", - " 2.193667\n", + " 1.932232\n", + " 2.188049\n", " \n", " \n", " ATT(4.0,1,2)\n", - " 0.000656\n", - " 0.068395\n", - " 0.009596\n", - " 0.992344\n", - " -0.133395\n", - " 0.134707\n", + " 0.001911\n", + " 0.068411\n", + " 0.027932\n", + " 0.977716\n", + " -0.132171\n", + " 0.135993\n", " \n", " \n", " ATT(4.0,2,3)\n", - " 0.062161\n", - " 0.066457\n", - " 0.935357\n", - " 0.349604\n", - " -0.068092\n", - " 0.192414\n", + " 0.058708\n", + " 0.066517\n", + " 0.882598\n", + " 0.377454\n", + " -0.071663\n", + " 0.189079\n", " \n", " \n", " ATT(4.0,3,4)\n", - " 0.951881\n", - " 0.067878\n", - " 14.023318\n", + " 0.949944\n", + " 0.067551\n", + " 14.062713\n", " 0.000000\n", - " 0.818842\n", - " 1.084920\n", + " 0.817547\n", + " 1.082340\n", " \n", " \n", "\n", @@ -166,18 +168,18 @@ ], "text/plain": [ " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.920815 0.063970 14.394375 0.000000 0.795435 1.046195\n", - "ATT(2.0,1,3) 1.983448 0.064744 30.635238 0.000000 1.856552 2.110344\n", - "ATT(2.0,1,4) 2.956340 0.063267 46.728168 0.000000 2.832340 3.080341\n", - "ATT(3.0,1,2) -0.044035 0.065853 -0.668684 0.503697 -0.173105 0.085035\n", - "ATT(3.0,2,3) 1.105935 0.065468 16.892674 0.000000 0.977620 1.234251\n", - "ATT(3.0,2,4) 2.065059 0.065618 31.471099 0.000000 1.936451 2.193667\n", - "ATT(4.0,1,2) 0.000656 0.068395 0.009596 0.992344 -0.133395 0.134707\n", - "ATT(4.0,2,3) 0.062161 0.066457 0.935357 0.349604 -0.068092 0.192414\n", - "ATT(4.0,3,4) 0.951881 0.067878 14.023318 0.000000 0.818842 1.084920" + "ATT(2.0,1,2) 0.922607 0.064144 14.383278 0.000000 0.796886 1.048328\n", + "ATT(2.0,1,3) 1.990200 0.064701 30.760062 0.000000 1.863388 2.117011\n", + "ATT(2.0,1,4) 2.955379 0.063298 46.689908 0.000000 2.831317 3.079441\n", + "ATT(3.0,1,2) -0.041535 0.065788 -0.631352 0.527810 -0.170477 0.087406\n", + "ATT(3.0,2,3) 1.107889 0.065385 16.944147 0.000000 0.979737 1.236041\n", + "ATT(3.0,2,4) 2.060141 0.065261 31.567865 0.000000 1.932232 2.188049\n", + "ATT(4.0,1,2) 0.001911 0.068411 0.027932 0.977716 -0.132171 0.135993\n", + "ATT(4.0,2,3) 0.058708 0.066517 0.882598 0.377454 -0.071663 0.189079\n", + "ATT(4.0,3,4) 0.949944 0.067551 14.062713 0.000000 0.817547 1.082340" ] }, - "execution_count": 4, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -206,24 +208,24 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/opt/venv/lib/python3.12/site-packages/doubleml/did/did_multi.py:1298: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_multi.py:1298: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", " warnings.warn(\n", - "/opt/venv/lib/python3.12/site-packages/matplotlib/cbook.py:1709: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", " return math.isfinite(val)\n", - "/opt/venv/lib/python3.12/site-packages/matplotlib/cbook.py:1709: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", " return math.isfinite(val)\n" ] }, { "data": { - "image/png": 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hw5ozZ45at27tOZVTkm699VY999xzGjBggG677TalpqZq/vz5Ovfccz2Lr5+oXbt2uvjii3XXXXfJ5XJ5Gm8PPPBApZ4jAADwI79+9x8AAKi0RYsWmZLK/Vm0aJFpmqa5YsUKs3///mZcXJwZHh5utmzZ0hw3bpx56NAhr/t79dVXzTPOOMMMCQkxJZnr1q0zTdM0e/XqZfbq1csTt27dOlOSmZSUVGY+W7Zs8Rp/9NFHTUlmWlqaZ+y9994zO3fubDqdTrN169bmrFmzzNdff92UZO7Zs8cTl5KSYg4ePNiMjo42JXnlkZ2dbU6ePNls166dGR4ebjZq1Mi86KKLzGeeecZ0u92euCNHjpg33XSTGRMTY9atW9e86aabzG3btnn9jspy7733mpLM3bt3lxvz2GOPmZLM7777zjOWlJTk9fsrz8lx//vf/8xbb73VbNu2rel0Os0GDRqYffr0MdeuXVvh/ZhmcY3OPfdc85tvvjETEhJMp9NptmrVynzppZfKvc2gQYNMSeaXX355yvs/UV5enjl79mwzISHBjImJMUNDQ80mTZqYV155pbl06VKzsLDQE1vea6XE22+/bZ533nmmw+EwGzRoYN54443m77//XiruzTffNM844wwzPDzc7Nq1q7lmzRpz9OjRZqtWrTwxe/bsMSWZTz/9tPnss8+a8fHxpsPhMC+55BKv+gAAgNrLZpqVnHcNAACAgHXttdfqhx9+0K+//urvVE6LvXv3qk2bNnr66ac1adIkf6cDAACqgTWlAAAAgtyhQ4f04Ycf6qabbvJ3KgAAAB6sKQUAABCk9uzZoy+++EILFy5UWFiYxo0b5++UAAAAPJgpBQAAEKQ2bNigm266SXv27NGSJUvUpEkTf6cEAADgwZpSAAAAAAAA8DlmSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAAAAAAMDnaEoBAAAAAADA52hKAQAAAAAAwOdoSgEAgKC1d+9e2Ww2LV682N+pBLXWrVtrzJgx/k4DAAAEGJpSAAAgYC1evFg2m63Mn4ceeqhGHnPGjBlatWrVKeN69+5dbm4n/jz22GM1kufJPvroI589VlX88ssveuCBB9S1a1dFR0eradOmGjx4sL755ptK34fL5dKDDz6oZs2aKSIiQj169FBycnINZg0AAE6HUH8nAAAA8Gc9/vjjatOmjddYx44d1apVK+Xl5SksLOy0PdaMGTM0bNgwDRkypMK4f/7znxo7dqzn8pYtW/TCCy9oypQpOvvssz3jnTt3Pm25VeSjjz7S3Llza11jauHChXrttdd03XXX6e6771ZmZqYWLFigv/zlL/r444/Vt2/fU97HmDFjtGLFCk2YMEHt27fX4sWLNWjQIK1bt04XX3yxD54FAACoDppSAAAg4A0cOFAXXHBBmdc5nc5T3j43N1dRUVGnNad+/fqVyuOFF15Qv3791Lt3b5/mUpuNHDlSjz32mOrUqeMZu/XWW3X22WfrscceO2VT6uuvv9by5cv19NNPa9KkSZKkm2++WR07dtQDDzygL7/8skbzBwAA1cfpewAAIGiVtabUmDFjVKdOHe3evVuDBg1SdHS0brzxRknSrl27dN1116lJkyZyOp1q0aKFRowYoczMTEmSzWZTbm6ulixZ4jn97s+spfTYY4/JZrPpv//9r2644QbVr1/fa2bPm2++qW7duikiIkINGjTQiBEj9Ntvv3ndx//7f/9Pw4cPV8uWLeVwOBQfH6+//e1vysvL83rOc+fO9TyHkp8ShmFo9uzZOvfcc+V0OtW4cWONGzdOx44d83os0zQ1ffp0tWjRQpGRkerTp49++umnMp/b7t27tXv37lP+Drp16+bVkJKkhg0b6pJLLtHPP/98ytuvWLFCISEhuuOOOzxjTqdTt912mzZt2lTq9wUAAGoPZkoBAICAl5mZqfT0dK+xRo0alRtfWFioAQMG6OKLL9YzzzyjyMhIud1uDRgwQC6XS/fee6+aNGmiAwcO6IMPPlBGRobq1q2rN954Q2PHjlX37t09TZC2bdv+6fyHDx+u9u3ba8aMGTJNU5L05JNP6pFHHtH111+vsWPHKi0tTS+++KIuvfRSbdu2TfXq1ZMkJSUl6fjx47rrrrvUsGFDff3113rxxRf1+++/KykpSZI0btw4HTx4UMnJyXrjjTdKPf64ceO0ePFi3XLLLbrvvvu0Z88evfTSS9q2bZu++OILz+mPU6dO1fTp0zVo0CANGjRI3377rfr37y+3213qPi+//HJJxY3B6khJSamwhiW2bdumDh06KCYmxmu8e/fukqTt27crPj6+WjkAAICaRVMKAAAEvLJO8Spp7pTF5XJp+PDhSkxM9Ixt375de/bsUVJSkoYNG+YZnzp1qmd71KhRuvPOO3XGGWdo1KhRpyl7qUuXLnrrrbc8l/ft26dHH31U06dP15QpUzzjQ4cO1XnnnaeXX37ZMz5r1ixFRER4Yu644w61a9dOU6ZM0f79+9WyZUslJCSoQ4cOSk5OLpX3559/roULF2rp0qW64YYbPON9+vTRFVdcoaSkJN1www1KS0vTU089pcGDB+v999/3zLT65z//qRkzZpy234VUPPtr06ZNevjhh08Ze+jQITVt2rTUeMnYwYMHT2tuAADg9OH0PQAAEPDmzp2r5ORkr59Tueuuu7wu161bV5K0Zs0aHT9+vEbyLM+dd97pdfmdd96RYRi6/vrrlZ6e7vlp0qSJ2rdvr3Xr1nliT2xI5ebmKj09XRdddJFM09S2bdtO+dhJSUmqW7eu+vXr5/VYJafVlTzW2rVr5Xa7de+993qd+jdhwoQy73fv3r3VmiWVmpqqG264QW3atNEDDzxwyvi8vDw5HI5S4yVriZ14GiMAAKhdmCkFAAACXvfu3ctd6LwsoaGhatGihddYmzZtNHHiRD333HNaunSpLrnkEl199dUaNWqUp2FVU07+5sBdu3bJNE21b9++zPgTv01w//79mjp1qt57771Sa0CVrIVVkV27dikzM1NxcXFlXp+amiqpePaWpFI5xcbGqn79+qd8nMrIzc3VlVdeqezsbH3++eel1poqS0REhFwuV6nx/Px8z/UAAKB2oikFAAAsx+FwyG4vPWH82Wef1ZgxY/Tuu+/qk08+0X333afExER99dVXpZpYp9PJjRPDMGSz2bR69WqFhISUii9p1hQVFalfv346evSoHnzwQZ111lmKiorSgQMHNGbMGBmGccrHNgxDcXFxWrp0aZnXx8bGVuMZVZ3b7dbQoUP1/fffa82aNerYsWOlbte0aVMdOHCg1PihQ4ckSc2aNTuteQIAgNOHphQAAMAJOnXqpE6dOunhhx/Wl19+qZ49e2r+/PmaPn26JHmdulZT2rZtK9M01aZNG3Xo0KHcuB9++EE7d+7UkiVLdPPNN3vGyzp9sby827Ztq7Vr16pnz54Vzipq1aqVpOKZVWeccYZnPC0trdQMraoyDEM333yzPv30U/373/9Wr169Kn3brl27at26dcrKyvJa7Hzz5s2e6wEAQO3EmlIAAACSsrKyVFhY6DXWqVMn2e12r9PDoqKilJGRUaO5DB06VCEhIZo2bVqpBdtN09SRI0ckyTOL6sQY0zQ1Z86cUvcZFRUlSaVyv/7661VUVKQnnnii1G0KCws98X379lVYWJhefPFFr8ebPXt2mc9h9+7d2r17d8VP9A/33nuv3n77bb388ssaOnRouXHp6en65ZdfvNb8GjZsmIqKivTKK694xlwulxYtWqQePXrwzXsAANRizJQCAACQ9Nlnn2n8+PEaPny4OnTooMLCQr3xxhsKCQnRdddd54nr1q2b1q5dq+eee07NmjVTmzZt1KNHj9OaS9u2bTV9+nRNnjxZe/fu1ZAhQxQdHa09e/Zo5cqVuuOOOzRp0iSdddZZatu2rSZNmqQDBw4oJiZG//nPf8qcudStWzdJ0n333acBAwYoJCREI0aMUK9evTRu3DglJiZq+/bt6t+/v8LCwrRr1y4lJSVpzpw5GjZsmGJjYzVp0iQlJibqyiuv1KBBg7Rt2zatXr1ajRo1KvV4l19+uSSdcrHz2bNn6+WXX1ZCQoIiIyP15ptvel1/7bXXehpqL730kqZNm6Z169apd+/ekqQePXpo+PDhmjx5slJTU9WuXTstWbJEe/fu1WuvvVbVXz0AAPAhmlIAAACSunTpogEDBuj999/XgQMHFBkZqS5dumj16tX6y1/+4ol77rnndMcdd+jhhx9WXl6eRo8efdqbUpL00EMPqUOHDnr++ec1bdo0SVJ8fLz69++vq6++WlLxgufvv/++Z+0rp9Opa6+9VuPHj1eXLl287m/o0KG69957tXz5cr355psyTVMjRoyQJM2fP1/dunXTggULNGXKFIWGhqp169YaNWqUevbs6bmP6dOny+l0av78+Vq3bp169OihTz7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", 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XXpG8vcu7Nw7hVEWpjz/+WBMnTtT06dO1c+dOtW3bVuHh4YqNjc03/pdfftHQoUP1wAMPaNeuXRowYIAGDBigffv22WLmz5+vRYsWafHixdq6dav8/f0VHh6utLTKP/9C7i2kn/7qd/36Z6KknFtIP/3V7xr9yW4KUwAAOAE/Lw8tHdxW829rpQ51q0qSOtStqhdva6Wlg9vKz6vyn/oPAACck8UYk//MmBVQp06ddP311+uf//ynJMlqtap+/fp69NFHNXny5Dzxd999t1JSUvT111/b2m688Ua1a9dOixcvljFGderU0ZNPPqlJkyZJkhITExUSEqLly5dryJAhRepXUlKSqlatqri4OAUGBuZZ7ubmJo+LrgXNyMgocF0Wi0Wenp4lis3MzFRB6cwv9p2txzTlm4NKzMj7niAfD82/rZVGd2pQ6HolycvLq0h9uDQ2KytL1kImbytOrKenpyz/Pb2xrGKzs7OVnZ3/3Y2KG+vh4SE3N7dyjXVzc1N8fLxq1aolKednURB3d3e5//eWpFartdxjjTHKzMwsldiL98+yipUK35cdNUakp6fr9OnTqlWrlu3/SUGxxR1PGCMq5hhhtVoVGxtry3lJ11sR9nvGiKLF7vwzUZ3f/Le2PXaTOl5VrUy/RzBGVIwxIjMzU6dOncp3bL80tqzGnoqw37vKGHHpuF5YbH4c+bsGY0TpjBG5Oa9bt67t/6Uz/K7BGJGjJPu91WrV6dOnVa1atXzH9UvXK1WM3zUu3T+TkpIUHBysxMTEfOskuZzmT2cZGRnasWOHpkyZYmtzc3NTz549FRkZme97IiMjNXHiRLu28PBwrV27VpIUHR2tmJgY9ezZ07a8atWq6tSpkyIjIwssSqWnpys9Pd32OikpSZK0YMECeedzil3Tpk01bNgw2+v58+cX+J+xQYMGGjVqlO31q6++qtTU1Hxja9eurQcffND2+p///KcSEhLyjQ0ODtbDDz9se71kyRK9un6Xzl/VTfKtan+9qjFKycjW6qiTuv/6+nrnnXd06tSpfNfr5+enp556yvb6/fff1/Hjx/ON9fT01NSpU22vP/roIx05ciTfWEmaPn267fmnn36q/fv3Fxg7ZcoU24Hlyy+/1O7duwuMnTRpkvz9/SVJ3377rX799dcCYx9//HEFBQVJkjZu3Fjg/zVJGjdunK3As2XLFm3ZsqXA2NGjR6tu3ZzLKX755Rd9//33BcaOHDlSDRs2lCRt375d3377bYGxQ4cOVfPmzSVJu3fv1hdffFFg7MCBA1WrVi1ZrVbt379fn376aYGx/fv3V7t27SRJhw4d0kcffVRgbJ8+fXTDDTdIko4dO6YVK1YUGNuzZ0916dJFknTy5EktXbq0wNiuXbuqW7dukqTY2Fi99dZbBcaGhYWpV69ekqSEhAS99tprBcZ27NhR/fr1kySlpKRowYIFBca2bdtWAwYMkJQzJs2dO7fA2JYtW2rw4MG21y+88EKBsY4cI/766y/5+/vbvgDlym+MiIuLy3e9QUFBevzxx22vGSOCJFXMMcIYo5SUFFvOizNG3HXXXbrmmmskSb/99htjhJxjjDDBjSVVlzFWWa3WMv0ewRhRccaIL7/8Mt+xXSq77xGMETkcPUZcOq5LFfd3DcaI0hkjcnP+5JNPKjQ0VJJz/K7BGJGj2GNE374y8+fLKylJc7y9ZSlgovOK+rvGxWPExTWTwjhNUSo+Pl7Z2dkKCQmxaw8JCdGBAwfyfU9MTEy+8TExMbbluW0FxeRn7ty5mjFjRp72lJSUfKuvSUlJdpcYJicnF1ilPX/+fJ7YCxcuFCn2/PnzSklJyTfWx8cnT6yXNUPGJyDvBGoWi7KtRlWykhUbG1voenMr90Xpg4eHh11sUlJSgbGSih2be6BITEwsNDYuLs62vCixudXhhISEQmPj4+Ntz4sSm1tVPnfuXKGxZ86ckZ+fX5Fjc39uZ8+eLTT27Nmz8vLykjGmSLG56z1z5kyhsefOnStRbHx8fKGxCQkJJYq9XI4TExNtsampqUWOzcjIKDT20v2+OLFlNUYkJyfbLk2+9BeX/MaIgvrs7u5e5FjGiP8pjzHCGGOX8+KOESWJZYwo3zHC+KVIqq6zZ88p1juzTL9HMEZUjDEiISGhwLFdKtvvEYwRjh8jLh3X84utKL9rMEaUzhiRm/P4+HjbWTPO8rsGY0QJxoiYGAX9+KOUkaGUdu1kuehMpItVxN81Lt0/i1qUcprL9/766y/VrVtXv/zyi8LCwmztTz/9tLZs2aKtW7fmeY+Xl5dWrFihoUOH2trefPNNzZgxQ6dPn9Yvv/yiLl266K+//lLt2rVtMYMHD5bFYtHHH3+cb1/yO1Oqfv36On36tFNdvvf3pf/WlmNJysrnLZ5uFnVrUl3fjrmRU2or8KU5VxLr5uamM2fOKDg4WBKX75VlrFQxTqlNT09XbGysgoODuXzPRcYIq9WquLg4W8457b74sc42Ruw8maib3tqmrY92Vof6XL5X1FhnHiMyMzMVExOT79h+aSyX5jj/GHHpuF5YbH64fK9kseV9+V5cXJzq1KnD5XsOii3XMUKS+f57nUtIkN/f/y63i/6fFrReqWL8rpHf5XshISGV5/K9mjVryt3dXadPn7ZrP336tO00xkuFhoYWGp/77+nTp+2KUqdPn7adPpgfb2/vfC/T8/HxkY+Pz2W3pSgxJYnNr0+Fxd7dsaF2nPpdCWl5d84qXu4a1K6u3Nzcir3eovIqYAerqLFubm52O52zx1qtVlksFrm5ueUZqC633vKOlWQ7EDhLbFnt98UdI7y9veXj41Pg9ekXxxZnvWURWxH2e2cfI6xWa4E5L+56y3u/Z4woWqyXZ85fMy2WnLG9LL9HlHdsRdjvK8IY4enpWeSxvSzHnvLe711ljChsXL80tjjrLYqKsN+74hiRm3N3d3e7QmRF+85xudjy3u+daYyw9u6trNhY+VSpctlxPVdF+V3jYoUVtC7mNHff8/LyUocOHbRp0yZbm9Vq1aZNm+zOnLpYWFiYXbyUc61ubnyjRo0UGhpqF5OUlKStW7cWuM7KhFtIAwAAAACA8uI0Z0pJ0sSJEzVy5Eh17NhRN9xwgxYuXKiUlBTdd999kqQRI0aobt26tkkDH3/8cXXt2lUvv/yy+vXrp1WrVunXX3/V//3f/0nKOd1swoQJmj17tpo1a6ZGjRrpueeeU506dWyTEFZmubeQ/nDXSb3z7+Pa+p9EdahbVQ/c2ED3XFeXW0gDAAAAAOAoxkgnTsgtPl767zQrlZ1TVR3uvvtuxcXFadq0aYqJiVG7du20fv1620TlJ06csDu9rXPnzvrwww/17LPPaurUqWrWrJnWrl2ra6+91hbz9NNPKyUlRQ8++KASEhJ00003af369cU6Tc2Z+Xl5aHSnBmpXO1A3LPpJr9/RWh2vqlbe3QIAAAAAwLWkp8syfrwCMzKktWul/05CX5k5VVFKksaPH6/x48fnuywiIiJP26BBgzRo0KAC12exWDRz5kzNnDmztLoIAAAAAABQfIGBMv+9y6YrcLqiFAAAAAAAQKXj4yPzwQdKjI1VLRe5estpJjoHAAAAAABA5UFRCgAAAAAAAA5HUQoAAAAAAKC8ZWRIL78sv7feynnuAphTCgAAAAAAoLxZrbJs2SKvjAzJai3v3jgERSkAAAAAAIDy5uEh88ADupCYKC8P1yjXuMZWAgAAVFKnktJ0Kinn1tEHYpNt/7q5WSRJtQN9VDvQNe7gAwCAU/PwkPr3V3psbM5zF+AaWwkAAFBJLYk8rpkbD9m1jVgVZXs+7dbmej68hYN7BQAAcHkUpQAAAJzY2LAGuv2aEEmS1Wp09txZVa9W3e5MKQAA4ASMkWJj5RYfLwUHl3dvHIKiFAAAgBO7+PI8q9WqWK901apVVW5u3GQZAACnkp4uy+jRCszIkNaulfz8yrtHZY6iFAAAAAAAQEXg7V3ePXAoilIAAAAAAADlzcdHZvVqJcTGqpaPa1x+z3ndAAAAAAAAcDjOlHJh3EIaAAAAAACUF4pSLoxbSAMAAAAAUEFkZkpvvSW/8+elSZNcYn4pilIujFtIAwAAAABQQWRny7Jhg7wyMqQnnijv3jgERSkXxi2kAQAAAACoIDw8ZIYPV1pSkrw8XKNc4xpbCQAAAAAAUJF5eEiDBystNlaBLlKUcppTYs6ePathw4YpMDBQQUFBeuCBB5ScnFxo/KOPPqoWLVrI19dXV111lR577DElJibaxVksljyPVatWlfXmAAAAAAAAuDSnKb0NGzZMp06d0saNG5WZman77rtPDz74oD788MN84//66y/99ddfWrBggVq1aqXjx4/roYce0l9//aVPP/3ULnbZsmXq3bu37XVQUFBZbgoAAAAAAIA9Y6TERFmSkqTg4PLujUM4RVFq//79Wr9+vbZv366OHTtKkl5//XX17dtXCxYsUJ06dfK859prr9Vnn31me92kSRO98MILGj58uLKysuRx0alwQUFBCg0NLfsNAQAAAAAAyE96uiz33quqGRnS2rWSn19596jMOUVRKjIyUkFBQbaClCT17NlTbm5u2rp1q+64444irScxMVGBgYF2BSlJeuSRRzR69Gg1btxYDz30kO677z5ZLJYC15Oenq709HS79UpSQkKCrFZrcTatwrBarUpKSpKXlxcTnbsIcu56yLnrIeeuh5y7HnLuWsi36yHnLiYtTcrMVEZmpjwSEuSWkVHePSqxpKQkSZIxptA4pyhKxcTEqFatWnZtHh4eql69umJiYoq0jvj4eM2aNUsPPvigXfvMmTN1yy23yM/PTxs2bNDDDz+s5ORkPfbYYwWua+7cuZoxY0ae9gYNGhSpLwAAAAAAAAWqW7e8e1Aqzp8/r6pVqxa4vFyLUpMnT9aLL75YaMz+/fuv+HOSkpLUr18/tWrVSs8//7zdsueee872/LrrrlNKSopeeumlQot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" ] @@ -238,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -251,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -279,13 +281,13 @@ "Learner ml_m: LogisticRegression()\n", "Out-of-sample Performance:\n", "Regression:\n", - "Learner ml_g0 RMSE: [[1.42395531 1.41102464 1.40193447 1.42414489 1.40480083 1.42114621\n", - " 1.42658311 1.4065011 1.42432406]]\n", - "Learner ml_g1 RMSE: [[1.40260415 1.44143708 1.40004539 1.41289911 1.4243203 1.38294772\n", - " 1.45655191 1.41725691 1.40904307]]\n", + "Learner ml_g0 RMSE: [[1.428271 1.40887549 1.40168244 1.42532747 1.4039708 1.41869578\n", + " 1.42626481 1.40583671 1.422799 ]]\n", + "Learner ml_g1 RMSE: [[1.4041563 1.43664137 1.39962992 1.41564601 1.42921804 1.38423683\n", + " 1.45633209 1.41489659 1.40683575]]\n", "Classification:\n", - "Learner ml_m Log Loss: [[0.69042286 0.69154849 0.69040404 0.68002834 0.67987763 0.679984\n", - " 0.66215109 0.66199744 0.66241892]]\n", + "Learner ml_m Log Loss: [[0.69055048 0.69162981 0.69073153 0.67993783 0.67912701 0.67981987\n", + " 0.66247836 0.6630701 0.66225537]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 5\n", @@ -293,15 +295,15 @@ "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.920815 0.063970 14.394375 0.000000 0.795435 1.046195\n", - "ATT(2.0,1,3) 1.983448 0.064744 30.635238 0.000000 1.856552 2.110344\n", - "ATT(2.0,1,4) 2.956340 0.063267 46.728168 0.000000 2.832340 3.080341\n", - "ATT(3.0,1,2) -0.044035 0.065853 -0.668684 0.503697 -0.173105 0.085035\n", - "ATT(3.0,2,3) 1.105935 0.065468 16.892674 0.000000 0.977620 1.234251\n", - "ATT(3.0,2,4) 2.065059 0.065618 31.471099 0.000000 1.936451 2.193667\n", - "ATT(4.0,1,2) 0.000656 0.068395 0.009596 0.992344 -0.133395 0.134707\n", - "ATT(4.0,2,3) 0.062161 0.066457 0.935357 0.349604 -0.068092 0.192414\n", - "ATT(4.0,3,4) 0.951881 0.067878 14.023318 0.000000 0.818842 1.084920\n" + "ATT(2.0,1,2) 0.922607 0.064144 14.383278 0.000000 0.796886 1.048328\n", + "ATT(2.0,1,3) 1.990200 0.064701 30.760062 0.000000 1.863388 2.117011\n", + "ATT(2.0,1,4) 2.955379 0.063298 46.689908 0.000000 2.831317 3.079441\n", + "ATT(3.0,1,2) -0.041535 0.065788 -0.631352 0.527810 -0.170477 0.087406\n", + "ATT(3.0,2,3) 1.107889 0.065385 16.944147 0.000000 0.979737 1.236041\n", + "ATT(3.0,2,4) 2.060141 0.065261 31.567865 0.000000 1.932232 2.188049\n", + "ATT(4.0,1,2) 0.001911 0.068411 0.027932 0.977716 -0.132171 0.135993\n", + "ATT(4.0,2,3) 0.058708 0.066517 0.882598 0.377454 -0.071663 0.189079\n", + "ATT(4.0,3,4) 0.949944 0.067551 14.062713 0.000000 0.817547 1.082340\n" ] } ], @@ -311,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -322,13 +324,13 @@ " Group Aggregation \n", "\n", "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.488207 0.034341 43.336421 0.0 1.4209 1.555514\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1.487887 0.034226 43.472843 0.0 1.420805 1.554968\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "2.0 1.953534 0.052242 37.393827 0.0 1.851142 2.055927\n", - "3.0 1.585497 0.056338 28.142811 0.0 1.475077 1.695917\n", - "4.0 0.951881 0.067878 14.023318 0.0 0.818842 1.084920\n", + "2.0 1.956062 0.052292 37.406316 0.0 1.853571 2.058553\n", + "3.0 1.584015 0.056203 28.183929 0.0 1.473859 1.694170\n", + "4.0 0.949944 0.067551 14.062713 0.0 0.817547 1.082340\n", "------------------ Additional Information ------------------\n", "Control Group: never_treated\n", "Anticipation Periods: 0\n", @@ -340,13 +342,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -363,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -375,12 +377,12 @@ "\n", "------------------ Overall Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.480476 0.035087 42.194475 0.0 1.411707 1.549245\n", + "1.481664 0.035122 42.186396 0.0 1.412826 1.550501\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "2 0.920815 0.063970 14.394375 0.0 0.795435 1.046195\n", - "3 1.546038 0.051435 30.058040 0.0 1.445227 1.646848\n", - "4 1.974576 0.046775 42.214387 0.0 1.882899 2.066254\n", + "2 0.922607 0.064144 14.383278 0.0 0.796886 1.048328\n", + "3 1.550398 0.051377 30.177088 0.0 1.449701 1.651094\n", + "4 1.971986 0.046573 42.341923 0.0 1.880705 2.063267\n", "------------------ Additional Information ------------------\n", "Control Group: never_treated\n", "Anticipation Periods: 0\n", @@ -392,13 +394,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -415,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -427,14 +429,14 @@ "\n", "------------------ Overall Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.990824 0.038762 51.360161 0.0 1.914852 2.066796\n", + "1.991002 0.038754 51.376042 0.0 1.915046 2.066957\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "-2.0 0.000656 0.068395 0.009596 0.992344 -0.133395 0.134707\n", - "-1.0 0.010468 0.040478 0.258610 0.795937 -0.068867 0.089803\n", - "0.0 0.992004 0.030775 32.233848 0.000000 0.931686 1.052322\n", - "1.0 2.024128 0.045782 44.212165 0.000000 1.934397 2.113860\n", - "2.0 2.956340 0.063267 46.728168 0.000000 2.832340 3.080341\n", + "-2.0 0.001911 0.068411 0.027932 0.977716 -0.132171 0.135993\n", + "-1.0 0.009913 0.040508 0.244708 0.806682 -0.069481 0.089306\n", + "0.0 0.992564 0.030753 32.274917 0.000000 0.932289 1.052840\n", + "1.0 2.025063 0.045671 44.340590 0.000000 1.935550 2.114576\n", + "2.0 2.955379 0.063298 46.689908 0.000000 2.831317 3.079441\n", "------------------ Additional Information ------------------\n", "Control Group: never_treated\n", "Anticipation Periods: 0\n", @@ -446,13 +448,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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", 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31/tcNtVuu+0WSZLELrvsEnvssUetdW+88Ua88847ce+998bw4cNz4zV9altdbiPb2F4jItq3b7/ZrlJqqF7rq64/J7vttltks9n45z//WeX2069a3z7atm0bZ511Vpx11lmxfPnyOOyww+LKK68USgGwzTCnFABsgT7//PN45JFH4rjjjotTTjml2teFF14Yy5Yti8cffzwiIoYMGRKrV6+O//u//8vtI5vNVgu0OnXqFL169Yrf/va3sXz58tz4Cy+8EG+88Uademvfvn0cfvjhcfvtt8e8efOqrV+4cGHu30OGDInp06fHzJkzc2OLFy+O+++/v8o26660WXelTkTEqlWr4tZbb622/+Li4hpvYTv11FPj448/rvIarPP5559HRUVFREQcc8wxERHx61//ukrNjTfeWG27zenkk0+OJk2axNixY6s8z4gvnvdnn30WETW/FkmSxK9+9atq+ywuLo6IL64025yGDBkSJSUlcc0118Tq1aurrf/y97iu1oWlm7vX+qrrz0lpaWkUFBTEVVddVe3qtC9/b4qLi2t8Tuu+n+u0bNkyevToEZWVlZvhWQDAlsGVUgCwBXr88cdj2bJlccIJJ9S4/uCDD4527drF/fffH6eddlqUlpbGgQceGD/4wQ/ivffei549e8bjjz8eixcvjoiqV3Ncc801ceKJJ8bAgQPjrLPOiiVLlsTNN98cvXr1qhJUrc8tt9wShxxySPTu3TvOPffc2HXXXWPBggUxffr0+Oijj+L111+PiIjLLrssfve738XXv/71uOiii6K4uDjuvPPO6NatWyxevDjX14ABA6JNmzYxYsSI+N73vheZTCbuu+++auFNRES/fv3iwQcfjFGjRsUBBxwQLVu2jOOPPz6+/e1vx0MPPRTnn39+TJ06NQYOHBhr166Nt956Kx566KF49tlnY//994++ffvGsGHD4tZbb42ysrIYMGBATJkypc5Xim2s3XbbLX72s5/F6NGj44MPPojS0tJo1apVzJ49Ox599NH47ne/G5deemn07Nkzdtttt7j00kvj448/jpKSkvjDH/5Q4/xM/fr1i4gvJm0fMmRINGnSJE4//fRN7rWkpCRuu+22+Pa3vx377bdfnH766dGuXbuYO3duPPnkkzFw4MC4+eab67XPFi1axF577RUPPvhg7LHHHtG2bdvo1atX9OrVa73b/elPf4qVK1dWG99nn302an6muv6c9OjRI370ox/FT3/60zj00EPj5JNPjsLCwnjllVeiU6dOMW7cuIj44ntw2223xc9+9rPo0aNHtG/fPo488sjYa6+94vDDD49+/fpF27ZtY8aMGfHwww/HhRdeWO+eAWCLlZ8P/QMANsXxxx+fFBUVJRUVFbXWnHnmmUmzZs2SRYsWJUmSJAsXLkzOOOOMpFWrVknr1q2TM888M/nzn/+cREQyYcKEKttOmDAh6dmzZ1JYWJj06tUrefzxx5NvfOMbSc+ePXM1s2fPTiIiufbaa2t8/Pfffz8ZPnx40rFjx6RZs2ZJ586dk+OOOy55+OGHq9S99tpryaGHHpoUFhYmXbp0ScaNG5f8+te/TiIimT9/fq7uz3/+c3LwwQcnLVq0SDp16pRcdtllybPPPptERDJ16tRc3fLly5Mzzjgj2X777ZOISLp3755bt2rVquQXv/hFsvfeeyeFhYVJmzZtkn79+iVjx45NysrKcnWff/558r3vfS/ZYYcdkuLi4uT4449PPvzwwyQikjFjxtT6mn/VxIkTq/U3ZsyYJCKShQsX1rjNH/7wh+SQQw5JiouLk+Li4qRnz57JyJEjk7fffjtX889//jMZPHhw0rJly2THHXdMzj333OT1119PIiK5++67c3Vr1qxJLrrooqRdu3ZJJpNJ1v3qV9v3burUqUlEJBMnTqwyfvfddycRkbzyyivV6ocMGZK0bt06KSoqSnbbbbfkzDPPTGbMmJGrGTFiRFJcXFztea57Hb7s5ZdfTvr165c0b958g6/1ul5r+/rytoMGDUr23nvvavvo3r17MnTo0Grjdf05SZIkueuuu5J99903Vzdo0KBk8uTJufXz589Phg4dmrRq1SqJiGTQoEFJkiTJz372s+TAAw9Mtt9++6RFixZJz549k6uvvjpZtWpVrc8ZALY2mSSp4b8YAYBtwqRJk+Kkk06Kl156KQYOHLje2r59+0a7du1qnLtoc7vkkkvi9ttvj+XLl29Rk2QDAFB35pQCgG3E559/XmV57dq1cdNNN0VJSUnst99+ufHVq1fHmjVrqtROmzYtXn/99Tj88MMbvK/PPvss7rvvvjjkkEMEUgAAWzFzSgHANuKiiy6Kzz//PPr37x+VlZXxyCOPxMsvvxzXXHNNtGjRIlf38ccfx+DBg+Nb3/pWdOrUKd56660YP358dOzYMc4///zN3lf//v3j8MMPjz333DMWLFgQv/nNb6K8vDx+8pOfbPbHAgCg8RBKAcA24sgjj4zrr78+nnjiiVi5cmX06NEjbrrppmoTK7dp0yb69esXd955ZyxcuDCKi4tj6NCh8fOf/zx22GGHzd7XscceGw8//HDccccdkclkYr/99ovf/OY3cdhhh232xwIAoPEwpxQAAAAAqTOnFAAAAACpE0oBAAAAkLptbk6pbDYbn3zySbRq1SoymUy+2wEAAADYqiRJEsuWLYtOnTpFQUHt10Ntc6HUJ598El27ds13GwAAAABbtQ8//DC6dOlS6/ptLpRq1apVRHzxwpSUlOS5GwAAAICtS3l5eXTt2jWXwdRmmwul1t2yV1JSIpQCAAAAaCAbmjbJROcAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApK5pvhsAAAAA2JZkV1fUq76gWXEDdZJfQikAAACAFM25pU296ne5ZFUDdZJfbt8DAAAAIHWulAIAAABIUfeRS6osZ1dXxId3dImIiK7f/WirvV3vq4RSAAAAAClaX+hU0Kx4mwml3L4HAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOryGkrddtttsc8++0RJSUmUlJRE//794+mnn17vNhMnToyePXtGUVFR9O7dO5566qmUugUAAABgc8lrKNWlS5f4+c9/Hn//+99jxowZceSRR8aJJ54Ys2bNqrH+5ZdfjmHDhsXZZ58dr732WpSWlkZpaWm8+eabKXcOAAAAwKbIJEmS5LuJL2vbtm1ce+21cfbZZ1dbd9ppp0VFRUU88cQTubGDDz44+vbtG+PHj6/T/svLy6N169ZRVlYWJSUlm61vAAAAgI2RXV0Rc25pExER3UcuiYJmxXnuaNPUNXtpmmJP67V27dqYOHFiVFRURP/+/WusmT59eowaNarK2JAhQ2LSpEm17reysjIqKytzy+Xl5RERkc1mI5vNbnrjAAAAAJvgy/lENpuN2MLzirrmLXkPpd54443o379/rFy5Mlq2bBmPPvpo7LXXXjXWzp8/Pzp06FBlrEOHDjF//vxa9z9u3LgYO3ZstfGFCxfGypUrN615AAAAgE2UrFmR+/en8z+KgsI2eexm0y1btqxOdXkPpb72ta/FzJkzo6ysLB5++OEYMWJEvPDCC7UGU/U1evToKldXlZeXR9euXaNdu3Zu3wMAAADyJsmujRX/fjyWvX5rbqzy0T5R1OXwaLnPebHdridEpqBJHjvcOEVFRXWqy3so1bx58+jRo0dERPTr1y9eeeWV+NWvfhW33357tdqOHTvGggULqowtWLAgOnbsWOv+CwsLo7CwsNp4QUFBFBTkdZ53AAAAYBuVrSyPT588LVbOnVJt3cqPpsXKj6ZFUbejosPQB6OgcMu6qKaueUujS2Wy2WyVOaC+rH///jFlStVv1uTJk2udgwoAAACgsUmya2NBLYHUl62cOyUWPHlaJNm1KXWWrrxeKTV69Og45phjolu3brFs2bJ44IEHYtq0afHss89GRMTw4cOjc+fOMW7cuIiIuPjii2PQoEFx/fXXx9ChQ2PChAkxY8aMuOOOO/L5NAAAAADqbMX7j20wkFpn5dwpseLfj0dxj5MauKv05TWU+vTTT2P48OExb968aN26deyzzz7x7LPPxte//vWIiJg7d26VS74GDBgQDzzwQPz4xz+OH/7wh7H77rvHpEmTolevXvl6CgAAAAD1Uv6P6lMWrbf+9du3ylAqkyRJku8m0lReXh6tW7eOsrIyE50DAAAAqcquWRlzbq5/HtH9wvIoaFq3CcTzra7ZS6ObUwoAAABga5WsWpbqdo2ZUAoAAAAgJZnmrVLdrjETSgEAAACkpKBpURR1PaJe2xR1PXKLuXWvPoRSAAAAACkq2ee8+tX3qV/9lkIoBQAAAJCi7XY7MYq6HVWn2qJuR8V2u57QwB3lh1AKAAAAIEWZgibRYeiDGwymirodFR2GPhiZgiYpdZYuoRQAAABAygoKS6Jj6RPR/rgHo6jLoCrriroeGe2PezA6lj4RBYUleeqw4TXNdwMAAAAA26JMQZMo7nFStOh+dMy5pU1ERHQ7b140abFDnjtLhyulAAAAABqJzFb4KXu1EUoBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpa5rvBgAAAAC2JdnVFbUuf3VdRERBs+IG7ykfhFIAAAAAKZpzS5ta1314R5dqY7tcsqoh28kbt+8BAAAAkDpXSgEAAACkqPvIJfluoVEQSgEAAACkaGudI6q+3L4HAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOryGkqNGzcuDjjggGjVqlW0b98+SktL4+23317vNvfcc09kMpkqX0VFRSl1DAAAAMDmkNdQ6oUXXoiRI0fGX/7yl5g8eXKsXr06jj766KioqFjvdiUlJTFv3rzc15w5c1LqGAAAAIDNoWk+H/yZZ56psnzPPfdE+/bt4+9//3scdthhtW6XyWSiY8eODd0eAAAAAA2kUc0pVVZWFhERbdu2XW/d8uXLo3v37tG1a9c48cQTY9asWWm0BwAAAMBmktcrpb4sm83GJZdcEgMHDoxevXrVWve1r30t7rrrrthnn32irKwsrrvuuhgwYEDMmjUrunTpUq2+srIyKisrc8vl5eW5x8tms5v/iQAAAABsw+qat2SSJEkauJc6ueCCC+Lpp5+Ol156qcZwqTarV6+OPffcM4YNGxY//elPq62/8sorY+zYsdXG33nnnWjVqtUm9QwAAABAVcuWLYs99tgjysrKoqSkpNa6RhFKXXjhhfHYY4/Fiy++GLvssku9t//mN78ZTZs2jd///vfV1tV0pVTXrl1jyZIl631hAAAAAKi/8vLyaNOmzQZDqbzevpckSVx00UXx6KOPxrRp0zYqkFq7dm288cYbceyxx9a4vrCwMAoLC6uNFxQUREFBo5pSCwAAAGCLV9e8Ja+h1MiRI+OBBx6Ixx57LFq1ahXz58+PiIjWrVtHixYtIiJi+PDh0blz5xg3blxERFx11VVx8MEHR48ePWLp0qVx7bXXxpw5c+Kcc87J2/MAAAAAoH7yGkrddtttERFx+OGHVxm/++6748wzz4yIiLlz51ZJ2JYsWRLnnntuzJ8/P9q0aRP9+vWLl19+Ofbaa6+02gYAAABgEzWKOaXSVF5eHq1bt97gfY0AAAAA1F9dsxeTKgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQuqb5bgAAAGBbll1dUa/6gmbFDdQJQLqEUgAAAHk055Y29arf5ZJVDdQJQLrcvgcAAABA6lwpBQAAkEfdRy6pspxdXREf3tElIiK6fvcjt+sBWy2hFAAAQB6tL3QqaFYslAK2Wm7fAwAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAaKSSNSvz3QJAgxFKAQAANAJJdm1UvPtILJh0Ym5s7u07xbw/DImKdx+JJLs2j90BbH5N890AAADAti5bWR4LnjwtVs6dUm3dyg+nxsoPp0ZRt6Oiw9AHo6CwJA8dAmx+rpQCAADIoyS7ttZA6stWzp0SC548zRVTwFZDKAUAAJBHK95/bIOB1Dor506JFf9+vIE7AkiHUAoAACCPyv9xe/3qX69fPUBjlddQaty4cXHAAQdEq1aton379lFaWhpvv/32BrebOHFi9OzZM4qKiqJ3797x1FNPpdAtAADA5pVdszJWfji1Xtus/PD5yPpUPmArkNdQ6oUXXoiRI0fGX/7yl5g8eXKsXr06jj766KioqKh1m5dffjmGDRsWZ599drz22mtRWloapaWl8eabb6bYOQAAwKZLVi1LdTuAxiSTJEmS7ybWWbhwYbRv3z5eeOGFOOyww2qsOe2006KioiKeeOKJ3NjBBx8cffv2jfHjx2/wMcrLy6N169ZRVlYWJSU+tQIAAMif7JqVMefm+v9d0v3C8ihoWtQAHQFsurpmL41qTqmysrKIiGjbtm2tNdOnT4/BgwdXGRsyZEhMnz69QXsDAADY3AqaFkVR1yPqtU1R1yMFUsBWoWm+G1gnm83GJZdcEgMHDoxevXrVWjd//vzo0KFDlbEOHTrE/Pnza6yvrKyMysrK3HJ5eXnu8bLZ7GboHAAAYOO17P3des0r1ar3d/0tAzRqdT1HNZpQauTIkfHmm2/GSy+9tFn3O27cuBg7dmy18YULF8bKlSYHBAAA8itpeXAUdDgksgs2/LdQQYdDYlnLg2L5p5+m0BnAxlm2rG7z3jWKUOrCCy+MJ554Il588cXo0qXLems7duwYCxYsqDK2YMGC6NixY431o0ePjlGjRuWWy8vLo2vXrtGuXTtzSgEAAI1CtvSRWPjU6bHyw+drrSnqemS0O3ZCFBT6OwZo3IqK6naLcV5DqSRJ4qKLLopHH300pk2bFrvssssGt+nfv39MmTIlLrnkktzY5MmTo3///jXWFxYWRmFhYbXxgoKCKChoVFNqAQAA26iCFttHx5OejBX/fjzKZ94aKz96IbeuqOuRUdLnvNhu1xMiU9Akj10C1E1d85a8hlIjR46MBx54IB577LFo1apVbl6o1q1bR4sWLSIiYvjw4dG5c+cYN25cRERcfPHFMWjQoLj++utj6NChMWHChJgxY0bccccdeXseAAAAmypT0CSKe5wULbofHXNuaRMREd3OmxdNWuyQ584AGkZeLxW67bbboqysLA4//PDYaaedcl8PPvhgrmbu3Lkxb9683PKAAQPigQceiDvuuCP69OkTDz/8cEyaNGm9k6MDAABsiTI+ZQ/YimWSJEny3USaysvLo3Xr1lFWVmZOKQAAoNHJrq7IXSnVfeSSKGhWnOeOAOqnrtmLSZUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUNc13AwAAANuy7OqKWpe/ui4iTHwObDWEUgAAAHm07pP2avLhHV2qje1yyaqGbAcgNW7fAwAAACB1rpQCAADIo+4jl+S7BYC8EEoBAADkkTmigG2V2/cAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUbVQo1aRJk/j000+rjX/22WfRpEmTTW4KAAAAgK3bRoVSSZLUOF5ZWRnNmzffpIYAAAAA2Po1rU/xr3/964iIyGQyceedd0bLli1z69auXRsvvvhi9OzZc/N2CAAAAMBWp16h1A033BARX1wpNX78+Cq36jVv3jx23nnnGD9+/ObtEAAAAICtTr1CqdmzZ0dExBFHHBGPPPJItGnTpkGaAgAAAGDrVq9Qap2pU6du7j4AAAAA2IZs1ETn3/jGN+IXv/hFtfFf/vKX8c1vfnOTmwIAAABg67ZRodSLL74Yxx57bLXxY445Jl588cVNbgoAAACArdtGhVLLly+P5s2bVxtv1qxZlJeXb3JTAAAAAGzdNiqU6t27dzz44IPVxidMmBB77bVXnffz4osvxvHHHx+dOnWKTCYTkyZNWm/9tGnTIpPJVPuaP39+fZ8CAAAAAHm0UROd/+QnP4mTTz453n///TjyyCMjImLKlCnx+9//PiZOnFjn/VRUVESfPn3iO9/5Tpx88sl13u7tt9+OkpKS3HL79u3r3jwAAAAAebdRodTxxx8fkyZNimuuuSYefvjhaNGiReyzzz7x3HPPxaBBg+q8n2OOOSaOOeaYej9++/btY/vtt6/3dgAAAAA0DhsVSkVEDB06NIYOHbo5e6mzvn37RmVlZfTq1SuuvPLKGDhwYK21lZWVUVlZmVteN+dVNpuNbDbb4L0CAAAAbEvqmrdsdCi1dOnSePjhh+Pf//53XHrppdG2bdt49dVXo0OHDtG5c+eN3e167bTTTjF+/PjYf//9o7KyMu688844/PDD469//Wvst99+NW4zbty4GDt2bLXxhQsXxsqVKxukTwAAAIBt1bJly+pUl0mSJKnvzv/xj3/E4MGDo3Xr1vHBBx/E22+/Hbvuumv8+Mc/jrlz58Zvf/vbejecyWTi0UcfjdLS0nptN2jQoOjWrVvcd999Na6v6Uqprl27xpIlS6rMSwUAAADApisvL482bdpEWVnZerOXjbpSatSoUXHmmWfGL3/5y2jVqlVu/Nhjj40zzjhjY3a50Q488MB46aWXal1fWFgYhYWF1cYLCgqioGCjPnwQAAAAgFrUNW/ZqFTmlVdeifPOO6/aeOfOnWP+/Pkbs8uNNnPmzNhpp51SfUwAAAAANs1GXSlVWFiYmzD8y955551o165dnfezfPnyeO+993LLs2fPjpkzZ0bbtm2jW7duMXr06Pj4449ztwPeeOONscsuu8Tee+8dK1eujDvvvDOef/75+OMf/7gxTwMAAACAPNmoUOqEE06Iq666Kh566KGI+GI+qLlz58bll18e3/jGN+q8nxkzZsQRRxyRWx41alRERIwYMSLuueeemDdvXsydOze3ftWqVfGDH/wgPv7449huu+1in332ieeee67KPgAAAABo/DZqovOysrI45ZRTYsaMGbFs2bLo1KlTzJ8/P/r37x9PPfVUFBcXN0Svm0V5eXm0bt16g5NtAQAAAFB/dc1eNupKqdatW8fkyZPjz3/+c7z++uuxfPny2G+//WLw4MEb3TAAAAAA2446h1Jt27aNd955J3bcccf4zne+E7/61a9i4MCBMXDgwIbsDwAAAICtUJ0/fW/VqlW5yc3vvffeWLlyZYM1BQAAAMDWrc5XSvXv3z9KS0ujX79+kSRJfO9734sWLVrUWHvXXXdttgYBAAAA2PrUOZT63e9+FzfccEO8//77EfHFZOeulgIAAABgY2zUp+/tsssuMWPGjNhhhx0aoqcG5dP3AAAAABpOXbOXOs8p1bZt21i0aFFERBxxxBHRvHnzTe8SAAAAgG2Sic4BAAAASJ2JzgEAAABI3UZNdJ7JZEx0DgAAAMBGM9E5AAAAAJvNZp/oPCLi2GOPjbKyspg9e3bssMMO8fOf/zyWLl2aW//ZZ5/FXnvttdFNAwAAALBtqFco9cwzz0RlZWVu+ZprronFixfnltesWRNvv/325usOAAAAgK1SvUKpr9qIO/8AAAAAYNNCKQAAAADYGPUKpTKZTGQymWpjAAAAAFAfTetTnCRJnHnmmVFYWBgREStXrozzzz8/iouLIyKqzDcFAAAAALWpVyg1YsSIKsvf+ta3qtUMHz580zoCAAAAYKtXr1Dq7rvvbqg+AAAAANiGmOgcAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQ1zXcDAAD8R3Z1Rb3qC5oVN1AnAAANSygFANCIzLmlTb3qd7lkVQN1AgDQsNy+BwAAAEDqXCkFANCIdB+5pMpydnVFfHhHl4iI6Prdj9yuBwBsNYRSAACNyPpCp4JmxUIpAGCr4fY9AAAAAFInlAIA2EIka1bmuwUAgM0mr6HUiy++GMcff3x06tQpMplMTJo0aYPbTJs2Lfbbb78oLCyMHj16xD333NPgfQIApC3Jro2Kdx+JBZNOzI3NvX2nmPeHIVHx7iORZNfmsTsAgE2X11CqoqIi+vTpE7fcckud6mfPnh1Dhw6NI444ImbOnBmXXHJJnHPOOfHss882cKcAAOnJVpbH/EnHxadPnh4rP36xyrqVH06NT588PeZPOi6yleV56hAAYNNlkiRJ8t1EREQmk4lHH300SktLa625/PLL48knn4w333wzN3b66afH0qVL45lnnqnT45SXl0fr1q2jrKwsSkpKNrVtAIDNKsmujfmTjouVc6dssLao21HRsfSJyBQ0SaEzAIC6qWv2skXNKTV9+vQYPHhwlbEhQ4bE9OnT89QRAMDmteL9x+oUSEVErJw7JVb8+/EG7ggAoGE0zXcD9TF//vzo0KFDlbEOHTpEeXl5fP7559GiRYtq21RWVkZlZWVuubz8i8vcs9lsZLPZhm0YAKCeyl8fX+/6FrueuOFCAICU1DVv2aJCqY0xbty4GDt2bLXxhQsXxsqVPsEGAGg8krUrY+VH0+q1zcoPp8aCeXMj06SoYZoCAKinZcuW1aluiwqlOnbsGAsWLKgytmDBgigpKanxKqmIiNGjR8eoUaNyy+Xl5dG1a9do166dOaUAgEZl7YqF8dFGbLdj6xbRZLt2m70fAICNUVRUt/8s26JCqf79+8dTTz1VZWzy5MnRv3//WrcpLCyMwsLCauMFBQVRULBFTakFAGztilpv1GZNilr7vQYAaDTq+ntJXn97Wb58ecycOTNmzpwZERGzZ8+OmTNnxty5cyPii6uchg8fnqs///zz49///ndcdtll8dZbb8Wtt94aDz30UHz/+9/PR/sAAJtVQdOiKOp6RL22Kep6ZBQ0deseALDlyWsoNWPGjNh3331j3333jYiIUaNGxb777htXXHFFRETMmzcvF1BFROyyyy7x5JNPxuTJk6NPnz5x/fXXx5133hlDhgzJS/8AAJtbyT7n1a++T/3qAQAai0ySJEm+m0hTeXl5tG7dOsrKyswpBQA0Okl2bcyfdFysnDtlg7VF3Y6KjqVPRKagSQqdAQDUTV2zF5MPAAA0IpmCJtFh6INR1O2o9dYVdTsqOgx9UCAFAGyxhFIAAI1MQWFJdCx9Itof92AUdRlUZV1R1yOj/XEPRsfSJ6Kg0FXfAMCWa4v69D0AgG1FpqBJFPc4KVp0Pzrm3NImIiK6nTcvmrTYIc+dAQBsHq6UAgDYQmR8yh4AsBVxpRQAQCOSXV1R6/JX10VEFDQrbvCeAAAaglAKAKARWXerXk0+vKNLtbFdLlnVkO0AADQYt+8BAAAAkDpXSgEANCLdRy7JdwsAAKkQSgEANCLmiAIAthVu3wMAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdY0ilLrlllti5513jqKiojjooIPib3/7W62199xzT2QymSpfRUVFKXYLAAAAwKbKeyj14IMPxqhRo2LMmDHx6quvRp8+fWLIkCHx6aef1rpNSUlJzJs3L/c1Z86cFDsGAAAAYFPlPZT63//93zj33HPjrLPOir322ivGjx8f2223Xdx11121bpPJZKJjx465rw4dOqTYMQAAAACbKq+h1KpVq+Lvf/97DB48ODdWUFAQgwcPjunTp9e63fLly6N79+7RtWvXOPHEE2PWrFlptAsAAADAZtI0nw++aNGiWLt2bbUrnTp06BBvvfVWjdt87Wtfi7vuuiv22WefKCsri+uuuy4GDBgQs2bNii5dulSrr6ysjMrKytxyeXl5RERks9nIZrOb8dkAAAAAUNe8Ja+h1Mbo379/9O/fP7c8YMCA2HPPPeP222+Pn/70p9Xqx40bF2PHjq02vnDhwli5cmWD9goAAACwrVm2bFmd6vIaSu24447RpEmTWLBgQZXxBQsWRMeOHeu0j2bNmsW+++4b7733Xo3rR48eHaNGjcotl5eXR9euXaNdu3ZRUlKy8c0DAAAAUE1RUVGd6vIaSjVv3jz69esXU6ZMidLS0oj44hKvKVOmxIUXXlinfaxduzbeeOONOPbYY2tcX1hYGIWFhdXGCwoKoqAg7/O8AwAAAGxV6pq35P32vVGjRsWIESNi//33jwMPPDBuvPHGqKioiLPOOisiIoYPHx6dO3eOcePGRUTEVVddFQcffHD06NEjli5dGtdee23MmTMnzjnnnHw+DQAAAADqIe+h1GmnnRYLFy6MK664IubPnx99+/aNZ555Jjf5+dy5c6skbEuWLIlzzz035s+fH23atIl+/frFyy+/HHvttVe+ngIAAAAA9ZRJkiTJdxNpKi8vj9atW0dZWZk5pQAAAAA2s7pmLyZVAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1TfPdAHVTUbmmXvXFhb61AAAAQOMludhCtPrR0/Wqz153fAN1AgAAALDp3L4HAAAAQOpcKbWFWHb1MVWWK1atiY5jJ0dExPwxX4/i5r6VAAAAwJZDkrGFWN8cUcXNm5pDCgAAANiiuH0PAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABIndmxARqpiso19ar3gQcAAMCWxF8wAI1Uqx89Xa/67HXHN1AnAAAAm5/b9wAAAABInSulABqpZVcfU2W5YtWa6Dh2ckREzB/z9Shu7hQOAABsufxFA9BIrW+OqOLmTc0hBQAAbNHcvgcAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAFuglavX5rsFAACATSKU2gr44xS2bmuzSfzhH5/Ecb/5W26s3ZV/jMHjp8cf/vFJrM0meewOAABg4zTNdwPUz9psEpPenBc3v/RBbqzdlX+MI3vsGBcM6B6lvXaKJgWZ/DUIbFblK1fHKffOiOfeXVRt3fPvLYrn31sUg3ffMR4esX+UFDXLQ4cAAAAbJ5MkyTb1X+zl5eXRunXrKCsri5KSkny3Uy/r++N0HX+cwtZjbTaJY/7vL+s95tcZvPuO8fS5BwulAQCAvKtr9tIobt+75ZZbYuedd46ioqI46KCD4m9/+9t66ydOnBg9e/aMoqKi6N27dzz11FMpdZo/a7PJBgOpiIjn3l0Up9w7w+08sBWY9Oa8OgVSEV8c+4/Nmt/AHQEAAGw+eQ+lHnzwwRg1alSMGTMmXn311ejTp08MGTIkPv300xrrX3755Rg2bFicffbZ8dprr0VpaWmUlpbGm2++mXLn6fLHKWx7bnt5Tj3rP2iYRgAAABpA3m/fO+igg+KAAw6Im2++OSIistlsdO3aNS666KL4n//5n2r1p512WlRUVMQTTzyRGzv44IOjb9++MX78+A0+3rpLyBYuXFjjJWQFBQXRtOl/ptpatWpVrfvKZDLRrFmzjapdvXp11PbS11Q75P/+FtP+vbjW/X/VEbu1jafPPqDaePPmzevUw1dr16xZE9lsdrPUNmvWLDKZTIPWrl27NtaurX0C+PrUNm3aNAoKChpNbTabjTVr1tRa26RJk2jSpEmjqU2SJFavXr1Zar98fDZUbcT6j+W0zhHLVqyM1ldMrrW+NkvHDo6iZk1q3W99jnvnCOcI54jqtRGN4xxR398jNkdthHPExtQ6R2xarXOEc0R9a50jNq22MRz3zhFbxzmivLw82rVrt8Hb9/I60fmqVavi73//e4wePTo3VlBQEIMHD47p06fXuM306dNj1KhRVcaGDBkSkyZNqrG+srIyKisrc8vl5eUREXHddddFYWFhtfoePXrEf/3Xf+WWf/nLX9b6A9a9e/c488wzc8s33HBDrFixosbanXbaKb773e/mlm+++eZYunRpjbXt2rWL//7v//5P7W23x7S53Wusrc3U9xfH2J9dE00z//nh2W677eL/+//+v9zyfffdF3Pm1HwlRrNmzeKHP/xhbvn3v/99vPfee7U+3pgxY3L/fvjhh+Nf//pXrbWjR4/OvbE8/vjj8frrr9dae+mll0ZxcXFERDz99NMxY8aMWmsvvvji2H777SMiYvLkybX+DEVEXHDBBdG+ffuIiHjhhRfihRdeqLX2nHPOic6dO0fEF1fqPffcc7XWjhgxInbeeeeIiHjllVfi6aefrrV22LBhsccee0RExOuvvx6PPfZYrbWnnHJK7L333hERMWvWrHj44YdrrT3xxBOjb9++ERHxzjvvxO9///taa4855pg48MADIyLigw8+iHvvvbfW2sGDB8fAgQMjIuLjjz+OO++8s9baQYMGxeGHHx4REZ9++mncdttttdb2798/jj766IiIWLp0afzqV7+qtXb//fePoUOHRkRERUVFXHfddbXW9unTJ0pLSyPii3PNuHHjaq3dc88949RTT80tX3311bXWpnWOuPHW2yNi11r7qM1Vv7guijP/eRPffvvt4+KLL84t/+Y3v4l58+bVuK1zxH84R3zBOeILjfEcUZ/fI26//fZYuHBhjbXOEf/hHPEF54gvOEd8wTniP5wjvuAc8QXniC+s7xzx5RxmffIaSi1atCjWrl0bHTp0qDLeoUOHeOutt2rcZv78+TXWz59f8+1q48aNi7Fjx1Ybr6ioqDF9LS8vr3Lr4PLly2tNaZctW1at9vPPP69T7bJly6KioqLG2qKioiq1ny2r+QdrQ5asqIzt4j8/wNlsts49NG3atEpteXl5rbURUe/adW8UZWVl661duHBhbn1datelw0uXLl1v7aJF/7kVsi6169LfJUuWrLf2s88+i+22267Otetet8WLF6+3dvHixRtV+9lnn623dsmSJRtVu2jRovXWLl26dKNqN/Q9Lisry9WuWLGizrWrVq1ab+1Xj/v61DbUOWJVRXmtPazPmhXlURH/CaObNGlS5+PeOeI/nCMi16NzROM8R9Tn94j11TpHVK11jnCOqKnWOcI5Yl2tc4RzRE21zhE119Y1lMrr7XuffPJJdO7cOV5++eXo379/bvyyyy6LF154If76179W26Z58+Zx7733xrBhw3Jjt956a4wdOzYWLFhQrb6mK6W6du0aCxYs2GJu31u2YmW0uXJKrfuuzeIxR1a5jSfCJbUbU+uS2k2rdUntxp8jjvnNjPrdtrtr23jyO/02uF+X3TtHOEdsHecIt+Y4RzhHOEfUVusc4RzhHFH/WueIzVtbXl4eHTp0aNy37+24447RpEmTamHSggULomPHjjVu07Fjx3rVFxYW1nibXlFRURQVFW2wx7rUbExtTT3VpnXL7eLIHjvG8+/VbaLziIijdt8xtm9VvNl6+PIbwZZQW1BQUOXg2Npqv3yiauy1EZF7I9hSahvquK/vOWLkIbvUK5QaeeiuG3yM+hz3zhFbbm2+j3vniIavbahj2TniC43lWHaO+EJjOO6dIxq2tjEc984R/6nN93HvHNHwtWkc9+sLyb4sr5++17x58+jXr19MmfKfq4Cy2WxMmTKlypVTX9a/f/8q9RFf3NNbW/3W4oIB9ZtT6oIBOzdMI0BqSnvtFIN337FOtYN33zFO3LvmcB4AAKAxymsoFRExatSo+L//+7+4995741//+ldccMEFUVFREWeddVZERAwfPrzKROgXX3xxPPPMM3H99dfHW2+9FVdeeWXMmDEjLrzwwnw9hVT44xS2PU0KMvHwiP03eOwP3n3HeHjE/tGkIJNSZwAAAJsur7fvRUScdtppsXDhwrjiiiti/vz50bdv33jmmWdyk5nPnTs3d39rRMSAAQPigQceiB//+Mfxwx/+MHbfffeYNGlS9OrVK19PIRXr/jg95d4Z8dy7td/G549T2LqUFDWLp889OB6bNT9ufml2THv/s9y6o3bfMS4YsHOcuHdHxzwAALDFyetE5/lQXl4erVu33uBkW43V2mzij1PYRlVUrolWP/rio38XXnl07NCy7vd3AwAApKWu2Uver5SifpoUZOLk3jvFkD3a+eMUtmFf/WRNAACALU3e55Ri0/njFAAAANjSCKUAAAAASJ1QCgAAAIDUmVMKoJGqqFxTdXnVmhr/vU5xoVM6AACw5fAXDEAjte7DDGrScezkamPZ645vyHYAAAA2K7fvAQAAAJA6V0oBNFLLrj4m3y0AAAA0GKEUQCNljigAAGBr5vY9AAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdT5vfAtRUbmm6vKqNTX+ex0fJQ8AAAA0ZpKLLUSrHz1d67qOYydXG8ted3xDtgMAAACwSdy+BwAAAEDqXCm1hVh29TH5bgEAAABgsxFKbSHMEQUAAABsTdy+BwAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApK5pvhtIW5IkERFRXl6e504AAAAAtj7rMpd1GUxttrlQatmyZRER0bVr1zx3AgAAALD1WrZsWbRu3brW9ZlkQ7HVViabzcYnn3wSrVq1ikwmk+92Nkl5eXl07do1PvzwwygpKcl3O0AKHPewbXLsw7bHcQ/bnq3puE+SJJYtWxadOnWKgoLaZ47a5q6UKigoiC5duuS7jc2qpKRki/+BBerHcQ/bJsc+bHsc97Dt2VqO+/VdIbWOic4BAAAASJ1QCgAAAIDUCaW2YIWFhTFmzJgoLCzMdytAShz3sG1y7MO2x3EP255t8bjf5iY6BwAAACD/XCkFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTijViK1evTouv/zy6N27dxQXF0enTp1i+PDh8cknn2xw21tuuSV23nnnKCoqioMOOij+9re/pdAxsLk88sgjcfTRR8cOO+wQmUwmZs6cWaftJk6cGD179oyioqLo3bt3PPXUUw3bKLDZ1Pe92/EOW64XX3wxjj/++OjUqVNkMpmYNGnSBreZNm1a7LffflFYWBg9evSIe+65p8H7BDafcePGxQEHHBCtWrWK9u3bR2lpabz99tsb3G5rf78XSjViK1asiFdffTV+8pOfxKuvvhqPPPJIvP3223HCCSesd7sHH3wwRo0aFWPGjIlXX301+vTpE0OGDIlPP/00pc6BTVVRURGHHHJI/OIXv6jzNi+//HIMGzYszj777HjttdeitLQ0SktL480332zAToHNob7v3Y532LJVVFREnz594pZbbqlT/ezZs2Po0KFxxBFHxMyZM+OSSy6Jc845J5599tkG7hTYXF544YUYOXJk/OUvf4nJkyfH6tWr4+ijj46Kiopat9kW3u8zSZIk+W6CunvllVfiwAMPjDlz5kS3bt1qrDnooIPigAMOiJtvvjkiIrLZbHTt2jUuuuii+J//+Z802wU20QcffBC77LJLvPbaa9G3b9/11p522mlRUVERTzzxRG7s4IMPjr59+8b48eMbuFNgU9T3vdvxDluPTCYTjz76aJSWltZac/nll8eTTz5Z5Q/R008/PZYuXRrPPPNMCl0Cm9vChQujffv28cILL8Rhhx1WY8228H7vSqktTFlZWWQymdh+++1rXL9q1ar4+9//HoMHD86NFRQUxODBg2P69OkpdQnkw/Tp06sc+xERQ4YMcexDI7cx792Od9i2OOZh61NWVhYREW3btq21Zls49oVSW5CVK1fG5ZdfHsOGDYuSkpIaaxYtWhRr166NDh06VBnv0KFDzJ8/P402gTyZP3++Yx+2QBvz3u14h21Lbcd8eXl5fP7553nqCthY2Ww2Lrnkkhg4cGD06tWr1rpt4f1eKNWI3H///dGyZcvc15/+9KfcutWrV8epp54aSZLEbbfdlscugc1tfcc+AACwdRk5cmS8+eabMWHChHy3kndN890A/3HCCSfEQQcdlFvu3LlzRPwnkJozZ048//zztV4lFRGx4447RpMmTWLBggVVxhcsWBAdO3ZsmMaBTVLbsV9fHTt2dOzDFmhj3rsd77Btqe2YLykpiRYtWuSpK2BjXHjhhfHEE0/Eiy++GF26dFlv7bbwfu9KqUakVatW0aNHj9xXixYtcoHUu+++G88991zssMMO691H8+bNo1+/fjFlypTcWDabjSlTpkT//v0b+ikAG6GmY39j9O/fv8qxHxExefJkxz40chvz3u14h22LYx62fEmSxIUXXhiPPvpoPP/887HLLrtscJtt4dh3pVQjtnr16jjllFPi1VdfjSeeeCLWrl2bu3e0bdu20bx584iIOOqoo+Kkk06KCy+8MCIiRo0aFSNGjIj9998/DjzwwLjxxhujoqIizjrrrLw9F6B+Fi9eHHPnzo1PPvkkIiLefvvtiPjif0vW/c/I8OHDo3PnzjFu3LiIiLj44otj0KBBcf3118fQoUNjwoQJMWPGjLjjjjvy8ySAOtvQe7fjHbYuy5cvj/feey+3PHv27Jg5c2a0bds2unXrFqNHj46PP/44fvvb30ZExPnnnx8333xzXHbZZfGd73wnnn/++XjooYfiySefzNdTAOpp5MiR8cADD8Rjjz0WrVq1yv1t37p169x/Sm+T7/cJjdbs2bOTiKjxa+rUqbm67t27J2PGjKmy7U033ZR069Ytad68eXLggQcmf/nLX9JtHtgkd999d43H/peP9UGDBiUjRoyost1DDz2U7LHHHknz5s2TvffeO3nyySfTbRzYaOt773a8w9Zl6tSpNb7PrzvOR4wYkQwaNKjaNn379k2aN2+e7Lrrrsndd9+det/Axqvtb/svH8vb4vt9JkmSJM0QDAAAAADMKQUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKTu/wdw4/osf8dAxgAAAABJRU5ErkJggg==", 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" ] @@ -477,7 +479,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv", + "display_name": ".venv", "language": "python", "name": "python3" }, From 3cb4125ef878b9a468cc2f01217d344f98d380b7 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 11 Apr 2025 06:42:34 +0000 Subject: [PATCH 063/140] restructure plm scores --- doc/guide/scores.rst | 13 ++++--------- doc/guide/scores/{ => plm}/pliv_score.rst | 0 doc/guide/scores/plm/plm_scores.inc | 13 +++++++++++++ doc/guide/scores/{ => plm}/plr_score.rst | 0 4 files changed, 17 insertions(+), 9 deletions(-) rename doc/guide/scores/{ => plm}/pliv_score.rst (100%) create mode 100644 doc/guide/scores/plm/plm_scores.inc rename doc/guide/scores/{ => plm}/plr_score.rst (100%) diff --git a/doc/guide/scores.rst b/doc/guide/scores.rst index 5cb36838..de5680bb 100644 --- a/doc/guide/scores.rst +++ b/doc/guide/scores.rst @@ -141,20 +141,15 @@ In the attribute ``psi`` the values of the score function :math:`\psi(W_i; \tild Implemented Neyman orthogonal score functions +++++++++++++++++++++++++++++++++++++++++++++ +.. _plm-scores: + Partially linear models (PLM) ***************************** -.. _plr-score: - -Partially linear regression model (PLR) -======================================= +.. include:: scores/plm/plm_scores.inc -.. include:: ./scores/plr_score.rst - -Partially linear IV regression model (PLIV) -=========================================== +.. _plr-score: -.. include:: ./scores/pliv_score.rst Interactive regression models (IRM) *********************************** diff --git a/doc/guide/scores/pliv_score.rst b/doc/guide/scores/plm/pliv_score.rst similarity index 100% rename from doc/guide/scores/pliv_score.rst rename to doc/guide/scores/plm/pliv_score.rst diff --git a/doc/guide/scores/plm/plm_scores.inc b/doc/guide/scores/plm/plm_scores.inc new file mode 100644 index 00000000..8fe5dd0a --- /dev/null +++ b/doc/guide/scores/plm/plm_scores.inc @@ -0,0 +1,13 @@ +The following scores for partially linear models are implemented. + +.. _plr-score: + +Partially linear regression model (PLR) +======================================= + +.. include:: /guide/scores/plm/plr_score.rst + +Partially linear IV regression model (PLIV) +=========================================== + +.. include:: /guide/scores/plm/pliv_score.rst diff --git a/doc/guide/scores/plr_score.rst b/doc/guide/scores/plm/plr_score.rst similarity index 100% rename from doc/guide/scores/plr_score.rst rename to doc/guide/scores/plm/plr_score.rst From d15e8f4ae0e5c67c5e16bfd9bdd3fb646742fcd5 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 11 Apr 2025 07:19:28 +0000 Subject: [PATCH 064/140] restructure irm scores --- doc/guide/scores.rst | 31 ++------------ doc/guide/scores/irm/apo_score.rst | 39 ++++++++++++++++++ doc/guide/scores/irm/cvar_score.rst | 17 ++++++++ doc/guide/scores/irm/iivm_score.rst | 21 ++++++++++ doc/guide/scores/irm/irm_score.rst | 52 ++++++++++++++++++++++++ doc/guide/scores/irm/irm_scores copy.inc | 48 ++++++++++++++++++++++ doc/guide/scores/irm/irm_scores.inc | 48 ++++++++++++++++++++++ doc/guide/scores/irm/lpq_score.rst | 30 ++++++++++++++ doc/guide/scores/irm/pq_score.rst | 18 ++++++++ doc/guide/scores/plm/plm_scores.inc | 3 ++ 10 files changed, 279 insertions(+), 28 deletions(-) create mode 100644 doc/guide/scores/irm/apo_score.rst create mode 100644 doc/guide/scores/irm/cvar_score.rst create mode 100644 doc/guide/scores/irm/iivm_score.rst create mode 100644 doc/guide/scores/irm/irm_score.rst create mode 100644 doc/guide/scores/irm/irm_scores copy.inc create mode 100644 doc/guide/scores/irm/irm_scores.inc create mode 100644 doc/guide/scores/irm/lpq_score.rst create mode 100644 doc/guide/scores/irm/pq_score.rst diff --git a/doc/guide/scores.rst b/doc/guide/scores.rst index de5680bb..709d7312 100644 --- a/doc/guide/scores.rst +++ b/doc/guide/scores.rst @@ -148,41 +148,16 @@ Partially linear models (PLM) .. include:: scores/plm/plm_scores.inc -.. _plr-score: +.. _irm-scores: Interactive regression models (IRM) *********************************** -Binary Interactive Regression Model (IRM) -========================================== +.. include:: scores/irm/irm_scores.inc -.. include:: ./scores/irm_score.rst -Average Potential Outcomes (APOs) -================================= - -.. include:: ./scores/apo_score.rst - -Interactive IV model (IIVM) -=========================== - -.. include:: ./scores/iivm_score.rst - -Potential quantiles (PQs) -========================= - -.. include:: ./scores/pq_score.rst - -Local potential quantiles (LPQs) -================================ - -.. include:: ./scores/lpq_score.rst - -Conditional value at risk (CVaR) -================================ - -.. include:: ./scores/cvar_score.rst +.. _did-scores: Difference-in-Differences Models ******************************** diff --git a/doc/guide/scores/irm/apo_score.rst b/doc/guide/scores/irm/apo_score.rst new file mode 100644 index 00000000..89ecdb6b --- /dev/null +++ b/doc/guide/scores/irm/apo_score.rst @@ -0,0 +1,39 @@ +For the average potential outcomes (APO) models implemented in ``DoubleMLAPO`` and ``DoubleMLAPOS`` +the ``score='APO'`` is implemented. Furthermore, weights :math:`\omega(Y,D,X)` and + +.. math:: + + \bar{\omega}(X) = \mathbb{E}[\omega(Y,D,X)|X] + +can be specified. For a given treatment level :math:`d` the general score function takes the form + +.. math:: + + \psi(W; \theta, \eta) :=\; &\omega(Y,D,X) \cdot g(d,X) + \bar{\omega}(X)\cdot \frac{1\lbrace D = d\rbrace }{m(X)}(Y - g(d,X)) - \theta + + =& \psi_a(W; \eta) \theta + \psi_b(W; \eta) + +with :math:`\eta=(g,m)`, where the true nuisance elements are + +.. math:: + + g_0(D, X) &= \mathbb{E}[Y | D, X], + + m_{0,d}(X) &= \mathbb{E}[1\lbrace D = d\rbrace | X] = P(D=d|X). + +The components of the linear score are + +.. math:: + + \psi_a(W; \eta) =& - 1, + + \psi_b(W; \eta) =\; &\omega(Y,D,X) \cdot g(d,X) + \bar{\omega}(X)\cdot \frac{1\lbrace D = d\rbrace }{m(X)}(Y - g(d,X)). + + +If no weights are specified, the weights are set to + +.. math:: + + \omega(Y,D,X) &= 1 + + \bar{\omega}(X) &= 1. diff --git a/doc/guide/scores/irm/cvar_score.rst b/doc/guide/scores/irm/cvar_score.rst new file mode 100644 index 00000000..db46bd06 --- /dev/null +++ b/doc/guide/scores/irm/cvar_score.rst @@ -0,0 +1,17 @@ +For ``DoubleMLCVAR`` the only valid option is ``score='CVaR'``. For ``treatment=d`` with :math:`d\in\{0,1\}` and +a quantile :math:`\tau\in (0,1)` this implements the score function: + +.. math:: + + \psi(W; \theta, \eta) := g_{d}(X, \gamma) + \frac{1\{D=d\}}{m(X)}(\max(\gamma, (1 - \tau)^{-1}(Y - \tau \gamma)) - g_d(X, \gamma)) - \theta + +where :math:`\eta=(g_d,m,\gamma)` with true values + +.. math:: + + g_{d,0}(X, \gamma_0) &= \mathbb{E}[\max(\gamma_0, (1 - \tau)^{-1}(Y - \tau \gamma_0))|X, D=d] + + m_0(X) &= P(D=d|X) + +and :math:`\gamma_0` being the potential quantile of :math:`Y(d)`. As for potential quantiles, the estimate :math:`g_d` is constructed via +a preliminary estimate of :math:`\gamma_0`. For further details, see `Kallus et al. (2019) `_. diff --git a/doc/guide/scores/irm/iivm_score.rst b/doc/guide/scores/irm/iivm_score.rst new file mode 100644 index 00000000..41d8c1f3 --- /dev/null +++ b/doc/guide/scores/irm/iivm_score.rst @@ -0,0 +1,21 @@ +For the IIVM model implemented in ``DoubleMLIIVM`` +we employ for ``score='LATE'`` the score function: + +``score='LATE'`` implements the score function: + +.. math:: + + \psi(W; \theta, \eta) :=\; &g(1,X) - g(0,X) + + \frac{Z (Y - g(1,X))}{m(X)} - \frac{(1 - Z)(Y - g(0,X))}{1 - m(X)} + + &- \bigg(r(1,X) - r(0,X) + \frac{Z (D - r(1,X))}{m(X)} - \frac{(1 - Z)(D - r(0,X))}{1 - m(X)} \bigg) \theta + + =\; &\psi_a(W; \eta) \theta + \psi_b(W; \eta) + +with :math:`\eta=(g, m, r)` and where the components of the linear score are + +.. math:: + + \psi_a(W; \eta) &= - \bigg(r(1,X) - r(0,X) + \frac{Z (D - r(1,X))}{m(X)} - \frac{(1 - Z)(D - r(0,X))}{1 - m(X)} \bigg), + + \psi_b(W; \eta) &= g(1,X) - g(0,X) + \frac{Z (Y - g(1,X))}{m(X)} - \frac{(1 - Z)(Y - g(0,X))}{1 - m(X)}. diff --git a/doc/guide/scores/irm/irm_score.rst b/doc/guide/scores/irm/irm_score.rst new file mode 100644 index 00000000..65522001 --- /dev/null +++ b/doc/guide/scores/irm/irm_score.rst @@ -0,0 +1,52 @@ +For the IRM model implemented in ``DoubleMLIRM`` one can choose between +``score='ATE'`` and ``score='ATTE'``. Furthermore, weights :math:`\omega(Y,D,X)` and + +.. math:: + + \bar{\omega}(X) = \mathbb{E}[\omega(Y,D,X)|X] + +can be specified. The general score function takes the form + +.. math:: + + \psi(W; \theta, \eta) :=\; &\omega(Y,D,X) \cdot (g(1,X) - g(0,X)) + + & + \bar{\omega}(X)\cdot \bigg(\frac{D (Y - g(1,X))}{m(X)} - \frac{(1 - D)(Y - g(0,X))}{1 - m(X)}\bigg) - \theta + + =& \psi_a(W; \eta) \theta + \psi_b(W; \eta) + +with :math:`\eta=(g,m)` and where the components of the linear score are + +.. math:: + + \psi_a(W; \eta) =& - 1, + + \psi_b(W; \eta) =\; &\omega(Y,D,X) \cdot (g(1,X) - g(0,X)) + + & + \bar{\omega}(X)\cdot \bigg(\frac{D (Y - g(1,X))}{m(X)} - \frac{(1 - D)(Y - g(0,X))}{1 - m(X)}\bigg). + +If no weights are specified, ``score='ATE'`` sets the weights + +.. math:: + + \omega(Y,D,X) &= 1 + + \bar{\omega}(X) &= 1 + +whereas ``score='ATTE'`` changes weights to: + +.. math:: + + \omega(Y,D,X) &= \frac{D}{\mathbb{E}_n[D]} + + \bar{\omega}(Y,D,X) &= \frac{m(X)}{\mathbb{E}_n[D]}. + +This score is identical to the original presentation in Section 5.1. of Chernozhukov et al. (2018) + +.. math:: + + \psi_a(W; \eta) &= -\frac{D}{\mathbb{E}_n[D]} + + \psi_b(W; \eta) &= \frac{D(Y-g(0,X))}{\mathbb{E}_n[D]} - \frac{m(X)(1-D)(Y-g(0,X))}{\mathbb{E}_n[D](1-m(X))}. + +For more details on other weight specifications, see :ref:`weighted_cates`. diff --git a/doc/guide/scores/irm/irm_scores copy.inc b/doc/guide/scores/irm/irm_scores copy.inc new file mode 100644 index 00000000..9841dcb6 --- /dev/null +++ b/doc/guide/scores/irm/irm_scores copy.inc @@ -0,0 +1,48 @@ +The following scores for nonparametric regression models are implemented. + +.. _irm-score: + +Binary Interactive Regression Model (IRM) +========================================== + +.. include:: /guide/scores/irm/irm_score.rst + + +.. _apo-score: + +Average Potential Outcomes (APOs) +================================= + +.. include:: /guide/scores/irm/apo_score.rst + + +.. _iivm-score: + +Interactive IV model (IIVM) +=========================== + +.. include:: /guide/scores/irm/iivm_score.rst + + +.. _pq-score: + +Potential quantiles (PQs) +========================= + +.. include:: /guide/scores/irm/pq_score.rst + + +.. _lpq-score: + +Local potential quantiles (LPQs) +================================ + +.. include:: /guide/scores/irm/lpq_score.rst + + +.. _cvar-score: + +Conditional value at risk (CVaR) +================================ + +.. include:: /guide/scores/irm/cvar_score.rst \ No newline at end of file diff --git a/doc/guide/scores/irm/irm_scores.inc b/doc/guide/scores/irm/irm_scores.inc new file mode 100644 index 00000000..9841dcb6 --- /dev/null +++ b/doc/guide/scores/irm/irm_scores.inc @@ -0,0 +1,48 @@ +The following scores for nonparametric regression models are implemented. + +.. _irm-score: + +Binary Interactive Regression Model (IRM) +========================================== + +.. include:: /guide/scores/irm/irm_score.rst + + +.. _apo-score: + +Average Potential Outcomes (APOs) +================================= + +.. include:: /guide/scores/irm/apo_score.rst + + +.. _iivm-score: + +Interactive IV model (IIVM) +=========================== + +.. include:: /guide/scores/irm/iivm_score.rst + + +.. _pq-score: + +Potential quantiles (PQs) +========================= + +.. include:: /guide/scores/irm/pq_score.rst + + +.. _lpq-score: + +Local potential quantiles (LPQs) +================================ + +.. include:: /guide/scores/irm/lpq_score.rst + + +.. _cvar-score: + +Conditional value at risk (CVaR) +================================ + +.. include:: /guide/scores/irm/cvar_score.rst \ No newline at end of file diff --git a/doc/guide/scores/irm/lpq_score.rst b/doc/guide/scores/irm/lpq_score.rst new file mode 100644 index 00000000..62d57029 --- /dev/null +++ b/doc/guide/scores/irm/lpq_score.rst @@ -0,0 +1,30 @@ +For ``DoubleMLLPQ`` the only valid option is ``score='LPQ'``. For ``treatment=d`` with :math:`d\in\{0,1\}`, instrument :math:`Z` and +a quantile :math:`\tau\in (0,1)` this implements the nonlinear score function: + +.. math:: + + \psi(W; \theta, \eta) :=& \Big(g_{d, Z=1}(X, \tilde{\theta}) - g_{d, Z=0}(X, \tilde{\theta}) + \frac{Z}{m(X)}(1\{D=d\} \cdot 1\{Y\le \theta\} - g_{d, Z=1}(X, \tilde{\theta})) + + &\quad - \frac{1-Z}{1-m(X)}(1\{D=d\} \cdot 1\{Y\le \theta\} - g_{d, Z=0}(X, \tilde{\theta}))\Big) \cdot \frac{2d -1}{\gamma} - \tau + + +where :math:`\eta=(g_{d,Z=1}, g_{d,Z=0}, m, \gamma)` with true values + +.. math:: + + g_{d,Z=z,0}(X, \theta_0) &= \mathbb{E}[1\{D=d\} \cdot 1\{Y\le \theta_0\}|X, Z=z],\quad z\in\{0,1\} + + m_{Z=z,0}(X) &= P(D=d|X, Z=z),\quad z\in\{0,1\} + + m_0(X) &= P(Z=1|X) + + \gamma_0 &= \mathbb{E}[P(D=d|X, Z=1) - P(D=d|X, Z=0)]. + +Further, the compliance probability :math:`\gamma_0` is estimated with the two additional nuisance components + +.. math:: + + m_{Z=z,0}(X) = P(D=d|X, Z=z),\quad z\in\{0,1\}. + +Remark that :math:`g_{d,Z=z,0}(X, \theta_0)` depends on the target parameter :math:`\theta_0`, such that +the score is estimated with a preliminary estimate :math:`\tilde{\theta}`. For further details, see `Kallus et al. (2019) `_. diff --git a/doc/guide/scores/irm/pq_score.rst b/doc/guide/scores/irm/pq_score.rst new file mode 100644 index 00000000..8b8663ee --- /dev/null +++ b/doc/guide/scores/irm/pq_score.rst @@ -0,0 +1,18 @@ +For ``DoubleMLPQ`` the only valid option is ``score='PQ'``. For ``treatment=d`` with :math:`d\in\{0,1\}` and +a quantile :math:`\tau\in (0,1)` this implements the nonlinear score function: + +.. math:: + + \psi(W; \theta, \eta) := g_{d}(X, \tilde{\theta}) + \frac{1\{D=d\}}{m(X)}(1\{Y\le \theta\} - g_d(X, \tilde{\theta})) - \tau + + +where :math:`\eta=(g_d,m)` with true values + +.. math:: + + g_{d,0}(X, \theta_0) &= \mathbb{E}[1\{Y\le \theta_0\}|X, D=d] + + m_0(X) &= P(D=d|X). + +Remark that :math:`g_{d,0}(X,\theta_0)` depends on the target parameter :math:`\theta_0`, such that +the score is estimated with a preliminary estimate :math:`\tilde{\theta}`. For further details, see `Kallus et al. (2019) `_. diff --git a/doc/guide/scores/plm/plm_scores.inc b/doc/guide/scores/plm/plm_scores.inc index 8fe5dd0a..b70269aa 100644 --- a/doc/guide/scores/plm/plm_scores.inc +++ b/doc/guide/scores/plm/plm_scores.inc @@ -7,6 +7,9 @@ Partially linear regression model (PLR) .. include:: /guide/scores/plm/plr_score.rst + +.. _pliv-score: + Partially linear IV regression model (PLIV) =========================================== From 12604149be2592d0a54b2ef8295200dfc7c26e1e Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 11 Apr 2025 07:22:46 +0000 Subject: [PATCH 065/140] restructure did scores --- doc/guide/scores.rst | 9 +-------- doc/guide/scores/{ => did}/did_score.rst | 0 doc/guide/scores/did/did_scores.inc | 17 +++++++++++++++++ doc/guide/scores/{ => did}/didcs_score.rst | 0 4 files changed, 18 insertions(+), 8 deletions(-) rename doc/guide/scores/{ => did}/did_score.rst (100%) create mode 100644 doc/guide/scores/did/did_scores.inc rename doc/guide/scores/{ => did}/didcs_score.rst (100%) diff --git a/doc/guide/scores.rst b/doc/guide/scores.rst index 709d7312..8f6dbe94 100644 --- a/doc/guide/scores.rst +++ b/doc/guide/scores.rst @@ -162,15 +162,8 @@ Interactive regression models (IRM) Difference-in-Differences Models ******************************** -Panel Data -========== +.. include:: scores/did/did_scores.inc -.. include:: ./scores/did_score.rst - -Repeated Cross-Sectional Data -============================= - -.. include:: ./scores/didcs_score.rst Sample Selection Models ************************ diff --git a/doc/guide/scores/did_score.rst b/doc/guide/scores/did/did_score.rst similarity index 100% rename from doc/guide/scores/did_score.rst rename to doc/guide/scores/did/did_score.rst diff --git a/doc/guide/scores/did/did_scores.inc b/doc/guide/scores/did/did_scores.inc new file mode 100644 index 00000000..dd81c3c6 --- /dev/null +++ b/doc/guide/scores/did/did_scores.inc @@ -0,0 +1,17 @@ +The following scores for difference-in-differences models are implemented. + + +.. _did_pa-score: + +Panel Data +========== + +.. include:: /guide/scores/did/did_score.rst + + +.. _did_cs-score: + +Repeated Cross-Sectional Data +============================= + +.. include:: /guide/scores/did/didcs_score.rst diff --git a/doc/guide/scores/didcs_score.rst b/doc/guide/scores/did/didcs_score.rst similarity index 100% rename from doc/guide/scores/didcs_score.rst rename to doc/guide/scores/did/didcs_score.rst From 54da4b4d77d323cee61c6cdcc5e654d949411b03 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 11 Apr 2025 07:58:35 +0000 Subject: [PATCH 066/140] restructure ssm scores --- doc/guide/scores.rst | 15 ++----- doc/guide/scores/apo_score.rst | 39 ------------------ doc/guide/scores/cvar_score.rst | 17 -------- doc/guide/scores/iivm_score.rst | 21 ---------- doc/guide/scores/irm_score.rst | 52 ------------------------ doc/guide/scores/lpq_score.rst | 30 -------------- doc/guide/scores/pq_score.rst | 18 -------- doc/guide/scores/{ => ssm}/mar_score.rst | 0 doc/guide/scores/{ => ssm}/nr_score.rst | 0 doc/guide/scores/ssm/ssm_scores.inc | 16 ++++++++ 10 files changed, 19 insertions(+), 189 deletions(-) delete mode 100644 doc/guide/scores/apo_score.rst delete mode 100644 doc/guide/scores/cvar_score.rst delete mode 100644 doc/guide/scores/iivm_score.rst delete mode 100644 doc/guide/scores/irm_score.rst delete mode 100644 doc/guide/scores/lpq_score.rst delete mode 100644 doc/guide/scores/pq_score.rst rename doc/guide/scores/{ => ssm}/mar_score.rst (100%) rename doc/guide/scores/{ => ssm}/nr_score.rst (100%) create mode 100644 doc/guide/scores/ssm/ssm_scores.inc diff --git a/doc/guide/scores.rst b/doc/guide/scores.rst index 8f6dbe94..8a2679ec 100644 --- a/doc/guide/scores.rst +++ b/doc/guide/scores.rst @@ -165,22 +165,13 @@ Difference-in-Differences Models .. include:: scores/did/did_scores.inc +.. _ssm-scores: + Sample Selection Models ************************ -.. _ssm-mar-score: - -Missingness at Random -====================== - -.. include:: ./scores/mar_score.rst - -.. _ssm-nr-score: - -Nonignorable Nonresponse -========================= +.. include:: scores/ssm/ssm_scores.inc -.. include:: ./scores/nr_score.rst Specifying alternative score functions via callables ++++++++++++++++++++++++++++++++++++++++++++++++++++ diff --git a/doc/guide/scores/apo_score.rst b/doc/guide/scores/apo_score.rst deleted file mode 100644 index 89ecdb6b..00000000 --- a/doc/guide/scores/apo_score.rst +++ /dev/null @@ -1,39 +0,0 @@ -For the average potential outcomes (APO) models implemented in ``DoubleMLAPO`` and ``DoubleMLAPOS`` -the ``score='APO'`` is implemented. Furthermore, weights :math:`\omega(Y,D,X)` and - -.. math:: - - \bar{\omega}(X) = \mathbb{E}[\omega(Y,D,X)|X] - -can be specified. For a given treatment level :math:`d` the general score function takes the form - -.. math:: - - \psi(W; \theta, \eta) :=\; &\omega(Y,D,X) \cdot g(d,X) + \bar{\omega}(X)\cdot \frac{1\lbrace D = d\rbrace }{m(X)}(Y - g(d,X)) - \theta - - =& \psi_a(W; \eta) \theta + \psi_b(W; \eta) - -with :math:`\eta=(g,m)`, where the true nuisance elements are - -.. math:: - - g_0(D, X) &= \mathbb{E}[Y | D, X], - - m_{0,d}(X) &= \mathbb{E}[1\lbrace D = d\rbrace | X] = P(D=d|X). - -The components of the linear score are - -.. math:: - - \psi_a(W; \eta) =& - 1, - - \psi_b(W; \eta) =\; &\omega(Y,D,X) \cdot g(d,X) + \bar{\omega}(X)\cdot \frac{1\lbrace D = d\rbrace }{m(X)}(Y - g(d,X)). - - -If no weights are specified, the weights are set to - -.. math:: - - \omega(Y,D,X) &= 1 - - \bar{\omega}(X) &= 1. diff --git a/doc/guide/scores/cvar_score.rst b/doc/guide/scores/cvar_score.rst deleted file mode 100644 index db46bd06..00000000 --- a/doc/guide/scores/cvar_score.rst +++ /dev/null @@ -1,17 +0,0 @@ -For ``DoubleMLCVAR`` the only valid option is ``score='CVaR'``. For ``treatment=d`` with :math:`d\in\{0,1\}` and -a quantile :math:`\tau\in (0,1)` this implements the score function: - -.. math:: - - \psi(W; \theta, \eta) := g_{d}(X, \gamma) + \frac{1\{D=d\}}{m(X)}(\max(\gamma, (1 - \tau)^{-1}(Y - \tau \gamma)) - g_d(X, \gamma)) - \theta - -where :math:`\eta=(g_d,m,\gamma)` with true values - -.. math:: - - g_{d,0}(X, \gamma_0) &= \mathbb{E}[\max(\gamma_0, (1 - \tau)^{-1}(Y - \tau \gamma_0))|X, D=d] - - m_0(X) &= P(D=d|X) - -and :math:`\gamma_0` being the potential quantile of :math:`Y(d)`. As for potential quantiles, the estimate :math:`g_d` is constructed via -a preliminary estimate of :math:`\gamma_0`. For further details, see `Kallus et al. (2019) `_. diff --git a/doc/guide/scores/iivm_score.rst b/doc/guide/scores/iivm_score.rst deleted file mode 100644 index 41d8c1f3..00000000 --- a/doc/guide/scores/iivm_score.rst +++ /dev/null @@ -1,21 +0,0 @@ -For the IIVM model implemented in ``DoubleMLIIVM`` -we employ for ``score='LATE'`` the score function: - -``score='LATE'`` implements the score function: - -.. math:: - - \psi(W; \theta, \eta) :=\; &g(1,X) - g(0,X) - + \frac{Z (Y - g(1,X))}{m(X)} - \frac{(1 - Z)(Y - g(0,X))}{1 - m(X)} - - &- \bigg(r(1,X) - r(0,X) + \frac{Z (D - r(1,X))}{m(X)} - \frac{(1 - Z)(D - r(0,X))}{1 - m(X)} \bigg) \theta - - =\; &\psi_a(W; \eta) \theta + \psi_b(W; \eta) - -with :math:`\eta=(g, m, r)` and where the components of the linear score are - -.. math:: - - \psi_a(W; \eta) &= - \bigg(r(1,X) - r(0,X) + \frac{Z (D - r(1,X))}{m(X)} - \frac{(1 - Z)(D - r(0,X))}{1 - m(X)} \bigg), - - \psi_b(W; \eta) &= g(1,X) - g(0,X) + \frac{Z (Y - g(1,X))}{m(X)} - \frac{(1 - Z)(Y - g(0,X))}{1 - m(X)}. diff --git a/doc/guide/scores/irm_score.rst b/doc/guide/scores/irm_score.rst deleted file mode 100644 index 65522001..00000000 --- a/doc/guide/scores/irm_score.rst +++ /dev/null @@ -1,52 +0,0 @@ -For the IRM model implemented in ``DoubleMLIRM`` one can choose between -``score='ATE'`` and ``score='ATTE'``. Furthermore, weights :math:`\omega(Y,D,X)` and - -.. math:: - - \bar{\omega}(X) = \mathbb{E}[\omega(Y,D,X)|X] - -can be specified. The general score function takes the form - -.. math:: - - \psi(W; \theta, \eta) :=\; &\omega(Y,D,X) \cdot (g(1,X) - g(0,X)) - - & + \bar{\omega}(X)\cdot \bigg(\frac{D (Y - g(1,X))}{m(X)} - \frac{(1 - D)(Y - g(0,X))}{1 - m(X)}\bigg) - \theta - - =& \psi_a(W; \eta) \theta + \psi_b(W; \eta) - -with :math:`\eta=(g,m)` and where the components of the linear score are - -.. math:: - - \psi_a(W; \eta) =& - 1, - - \psi_b(W; \eta) =\; &\omega(Y,D,X) \cdot (g(1,X) - g(0,X)) - - & + \bar{\omega}(X)\cdot \bigg(\frac{D (Y - g(1,X))}{m(X)} - \frac{(1 - D)(Y - g(0,X))}{1 - m(X)}\bigg). - -If no weights are specified, ``score='ATE'`` sets the weights - -.. math:: - - \omega(Y,D,X) &= 1 - - \bar{\omega}(X) &= 1 - -whereas ``score='ATTE'`` changes weights to: - -.. math:: - - \omega(Y,D,X) &= \frac{D}{\mathbb{E}_n[D]} - - \bar{\omega}(Y,D,X) &= \frac{m(X)}{\mathbb{E}_n[D]}. - -This score is identical to the original presentation in Section 5.1. of Chernozhukov et al. (2018) - -.. math:: - - \psi_a(W; \eta) &= -\frac{D}{\mathbb{E}_n[D]} - - \psi_b(W; \eta) &= \frac{D(Y-g(0,X))}{\mathbb{E}_n[D]} - \frac{m(X)(1-D)(Y-g(0,X))}{\mathbb{E}_n[D](1-m(X))}. - -For more details on other weight specifications, see :ref:`weighted_cates`. diff --git a/doc/guide/scores/lpq_score.rst b/doc/guide/scores/lpq_score.rst deleted file mode 100644 index 62d57029..00000000 --- a/doc/guide/scores/lpq_score.rst +++ /dev/null @@ -1,30 +0,0 @@ -For ``DoubleMLLPQ`` the only valid option is ``score='LPQ'``. For ``treatment=d`` with :math:`d\in\{0,1\}`, instrument :math:`Z` and -a quantile :math:`\tau\in (0,1)` this implements the nonlinear score function: - -.. math:: - - \psi(W; \theta, \eta) :=& \Big(g_{d, Z=1}(X, \tilde{\theta}) - g_{d, Z=0}(X, \tilde{\theta}) + \frac{Z}{m(X)}(1\{D=d\} \cdot 1\{Y\le \theta\} - g_{d, Z=1}(X, \tilde{\theta})) - - &\quad - \frac{1-Z}{1-m(X)}(1\{D=d\} \cdot 1\{Y\le \theta\} - g_{d, Z=0}(X, \tilde{\theta}))\Big) \cdot \frac{2d -1}{\gamma} - \tau - - -where :math:`\eta=(g_{d,Z=1}, g_{d,Z=0}, m, \gamma)` with true values - -.. math:: - - g_{d,Z=z,0}(X, \theta_0) &= \mathbb{E}[1\{D=d\} \cdot 1\{Y\le \theta_0\}|X, Z=z],\quad z\in\{0,1\} - - m_{Z=z,0}(X) &= P(D=d|X, Z=z),\quad z\in\{0,1\} - - m_0(X) &= P(Z=1|X) - - \gamma_0 &= \mathbb{E}[P(D=d|X, Z=1) - P(D=d|X, Z=0)]. - -Further, the compliance probability :math:`\gamma_0` is estimated with the two additional nuisance components - -.. math:: - - m_{Z=z,0}(X) = P(D=d|X, Z=z),\quad z\in\{0,1\}. - -Remark that :math:`g_{d,Z=z,0}(X, \theta_0)` depends on the target parameter :math:`\theta_0`, such that -the score is estimated with a preliminary estimate :math:`\tilde{\theta}`. For further details, see `Kallus et al. (2019) `_. diff --git a/doc/guide/scores/pq_score.rst b/doc/guide/scores/pq_score.rst deleted file mode 100644 index 8b8663ee..00000000 --- a/doc/guide/scores/pq_score.rst +++ /dev/null @@ -1,18 +0,0 @@ -For ``DoubleMLPQ`` the only valid option is ``score='PQ'``. For ``treatment=d`` with :math:`d\in\{0,1\}` and -a quantile :math:`\tau\in (0,1)` this implements the nonlinear score function: - -.. math:: - - \psi(W; \theta, \eta) := g_{d}(X, \tilde{\theta}) + \frac{1\{D=d\}}{m(X)}(1\{Y\le \theta\} - g_d(X, \tilde{\theta})) - \tau - - -where :math:`\eta=(g_d,m)` with true values - -.. math:: - - g_{d,0}(X, \theta_0) &= \mathbb{E}[1\{Y\le \theta_0\}|X, D=d] - - m_0(X) &= P(D=d|X). - -Remark that :math:`g_{d,0}(X,\theta_0)` depends on the target parameter :math:`\theta_0`, such that -the score is estimated with a preliminary estimate :math:`\tilde{\theta}`. For further details, see `Kallus et al. (2019) `_. diff --git a/doc/guide/scores/mar_score.rst b/doc/guide/scores/ssm/mar_score.rst similarity index 100% rename from doc/guide/scores/mar_score.rst rename to doc/guide/scores/ssm/mar_score.rst diff --git a/doc/guide/scores/nr_score.rst b/doc/guide/scores/ssm/nr_score.rst similarity index 100% rename from doc/guide/scores/nr_score.rst rename to doc/guide/scores/ssm/nr_score.rst diff --git a/doc/guide/scores/ssm/ssm_scores.inc b/doc/guide/scores/ssm/ssm_scores.inc new file mode 100644 index 00000000..b1ebc1ed --- /dev/null +++ b/doc/guide/scores/ssm/ssm_scores.inc @@ -0,0 +1,16 @@ +The following scores for sample selection models are implemented. + +.. _ssm-mar-score: + +Missingness at Random +====================== + +.. include:: /guide/scores/ssm/mar_score.rst + + +.. _ssm-nr-score: + +Nonignorable Nonresponse +========================= + +.. include:: /guide/scores/ssm/nr_score.rst From 8f22f5f2217b80f1e9c6111a74b4338fdfdf2e2b Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 11 Apr 2025 08:18:05 +0000 Subject: [PATCH 067/140] update warning for two period did --- doc/guide/models/did/did_models.inc | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/doc/guide/models/did/did_models.inc b/doc/guide/models/did/did_models.inc index f8184133..fa431bfe 100644 --- a/doc/guide/models/did/did_models.inc +++ b/doc/guide/models/did/did_models.inc @@ -29,7 +29,11 @@ Effect Aggregation Two treatment periods ********************* +.. warning:: + This documentation refers to the deprecated implementation for two time periods. + This functionality will be removed in a future version. + .. note:: - This documentation refers the deprecated implementation for two time periods and is deprecated. We recommend using the implementation :ref:`did-pa-model` and :ref:`did-cs-model`. + We recommend using the implementation :ref:`did-pa-model` and :ref:`did-cs-model`. .. include:: /guide/models/did/did_binary.rst From bd3e1a4f43f612ecf82dbb374b863d6cc44793b3 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 11 Apr 2025 08:18:16 +0000 Subject: [PATCH 068/140] add notation warning --- doc/guide/models/did/did_setup.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/guide/models/did/did_setup.rst b/doc/guide/models/did/did_setup.rst index c782f5df..3f4d67ed 100644 --- a/doc/guide/models/did/did_setup.rst +++ b/doc/guide/models/did/did_setup.rst @@ -1,7 +1,7 @@ **Difference-in-Differences Models (DID)** implemented in the package focus on the the binary treatment case with staggered adoption. .. note:: - The notation and identifying assumptions are based on `Callaway and Sant'Anna (2021) `_, but adjusted to better fit into the general documentation conventions. + The notation and identifying assumptions are based on `Callaway and Sant'Anna (2021) `_, but adjusted to better fit into the general package documentation conventions, sometimes slightly abusing notation. The underlying score functions are based on `Sant'Anna and Zhao (2020) `_, `Zimmert (2018) `_ and `Chang (2020) `_. For a more detailed introduction and recent developments of the difference-in-differences literature see e.g. `Roth et al. (2022) `_. @@ -48,7 +48,7 @@ The corresponding identifying assumptions are: :math:`\mathbb{E}[Y_{i,t}(\mathrm{g})|X_i, G_i^{\mathrm{g}}=1] = \mathbb{E}[Y_{i,t}(0)|X_i, G_i^{\mathrm{g}}=1]\quad a.s.` for all :math:`\mathrm{g}\in\mathcal{G}, t\in\{1,\dots,\mathcal{T}\}` such that :math:`t< \mathrm{g}-\delta`. 4. **Conditional Parallel Trends:** - Let :math:`\delta` be defined as in Assumption 3. + Let :math:`\delta` be defined as in Assumption 3.\\ For each :math:`\mathrm{g}\in\mathcal{G}` and :math:`t\in\{2,\dots,\mathcal{T}\}` such that :math:`t\ge \mathrm{g}-\delta`: a. **Never Treated:** From 207bdb6ab68cd3ed4b57688e8fa18e008425b51f Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 11 Apr 2025 08:28:52 +0000 Subject: [PATCH 069/140] restructure did score --- doc/guide/models/did/did_cs.rst | 2 ++ doc/guide/models/did/did_models.inc | 3 +- ...idcs_score.rst => did_cs_binary_score.rst} | 0 doc/guide/scores/did/did_cs_score.rst | 2 ++ ...{did_score.rst => did_pa_binary_score.rst} | 0 doc/guide/scores/did/did_pa_score.rst | 1 + doc/guide/scores/did/did_scores.inc | 29 ++++++++++++++++--- 7 files changed, 31 insertions(+), 6 deletions(-) create mode 100644 doc/guide/models/did/did_cs.rst rename doc/guide/scores/did/{didcs_score.rst => did_cs_binary_score.rst} (100%) create mode 100644 doc/guide/scores/did/did_cs_score.rst rename doc/guide/scores/did/{did_score.rst => did_pa_binary_score.rst} (100%) create mode 100644 doc/guide/scores/did/did_pa_score.rst diff --git a/doc/guide/models/did/did_cs.rst b/doc/guide/models/did/did_cs.rst new file mode 100644 index 00000000..dee07ecb --- /dev/null +++ b/doc/guide/models/did/did_cs.rst @@ -0,0 +1,2 @@ +.. note:: + Will be implemented soon. \ No newline at end of file diff --git a/doc/guide/models/did/did_models.inc b/doc/guide/models/did/did_models.inc index fa431bfe..433b8341 100644 --- a/doc/guide/models/did/did_models.inc +++ b/doc/guide/models/did/did_models.inc @@ -14,8 +14,7 @@ Panel data Repeated cross-sections *********************** -.. note:: - Will be implemented soon. +.. include:: /guide/models/did/did_cs.rst .. _did-aggregation: diff --git a/doc/guide/scores/did/didcs_score.rst b/doc/guide/scores/did/did_cs_binary_score.rst similarity index 100% rename from doc/guide/scores/did/didcs_score.rst rename to doc/guide/scores/did/did_cs_binary_score.rst diff --git a/doc/guide/scores/did/did_cs_score.rst b/doc/guide/scores/did/did_cs_score.rst new file mode 100644 index 00000000..dee07ecb --- /dev/null +++ b/doc/guide/scores/did/did_cs_score.rst @@ -0,0 +1,2 @@ +.. note:: + Will be implemented soon. \ No newline at end of file diff --git a/doc/guide/scores/did/did_score.rst b/doc/guide/scores/did/did_pa_binary_score.rst similarity index 100% rename from doc/guide/scores/did/did_score.rst rename to doc/guide/scores/did/did_pa_binary_score.rst diff --git a/doc/guide/scores/did/did_pa_score.rst b/doc/guide/scores/did/did_pa_score.rst new file mode 100644 index 00000000..30d74d25 --- /dev/null +++ b/doc/guide/scores/did/did_pa_score.rst @@ -0,0 +1 @@ +test \ No newline at end of file diff --git a/doc/guide/scores/did/did_scores.inc b/doc/guide/scores/did/did_scores.inc index dd81c3c6..01aa09df 100644 --- a/doc/guide/scores/did/did_scores.inc +++ b/doc/guide/scores/did/did_scores.inc @@ -1,17 +1,38 @@ The following scores for difference-in-differences models are implemented. -.. _did_pa-score: +.. _did-pa-score: Panel Data ========== -.. include:: /guide/scores/did/did_score.rst +.. include:: /guide/scores/did/did_pa_score.rst -.. _did_cs-score: +.. _did-cs-score: Repeated Cross-Sectional Data ============================= -.. include:: /guide/scores/did/didcs_score.rst +.. include:: /guide/scores/did/did_cs_score.rst + + +Two treatment periods +===================== + +.. warning:: + This documentation refers to the deprecated implementation for two time periods. + This functionality will be removed in a future version. The generalized version are :ref:`did-pa-score` and :ref:`did-pa-score`. + + +Panel Data +~~~~~~~~~~~ + +.. include:: /guide/scores/did/did_pa_binary_score.rst + + +Repeated Cross-Sectional Data +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +.. include:: /guide/scores/did/did_cs_binary_score.rst \ No newline at end of file From ba58660a727dc025a11451b95c52c420e918bbac Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 11 Apr 2025 14:05:12 +0000 Subject: [PATCH 070/140] update did model section --- doc/guide/models/did/did_pa.rst | 41 +++++++++++++ doc/guide/models/did/did_setup.rst | 10 ++-- doc/guide/scores/did/did_pa_score.rst | 83 ++++++++++++++++++++++++++- 3 files changed, 128 insertions(+), 6 deletions(-) diff --git a/doc/guide/models/did/did_pa.rst b/doc/guide/models/did/did_pa.rst index d46d3131..0ff87493 100644 --- a/doc/guide/models/did/did_pa.rst +++ b/doc/guide/models/did/did_pa.rst @@ -1,3 +1,40 @@ +For the estimation of the target parameters :math:`ATT(\mathrm{g},t)` the following nuisance functions are required: + +.. math:: + \begin{align} + g_{0, \mathrm{g}, t_{pre}, t_{eval}}(X_i) &:= \mathbb{E}[Y_{i,t} - Y_{i,\mathrm{g} - \delta - 1}|X_i, C_{i,t}^{(\cdot)} = 1], \\ + m_{0, \mathrm{g}, t + \delta}(X_i) &:= P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t + \delta}^{(\cdot)}=1). + \end{align} + +where :math:`g_{0, \mathrm{g}, t, \delta}(\cdot)` denotes the population outcome regression function and :math:`m_{0, \mathrm{g}, t + \delta}(\cdot)` the generalized propensity score. +Remark that the nuisance functions depend on the control group used for the estimation of the target parameter. +By slight abuse of notation we use the same notation for both control groups :math:`C_{i,t}^{(nev)}` and :math:`C_{i,t}^{(nyt)}`. + +Under these assumptions the target parameter :math:`ATT(\mathrm{g},t)` can be estimated by choosing a suitable combination of :math:`(\mathrm{g}, t, \delta)`. + +.. note:: + The package does not support a direct choice of the parameter :math:`\delta` but require the user to specify the tuple :math:`(\mathrm{g}, t_{pre}, t_{eval})` with the following interpretation + + - :math:`\mathrm{g}` is the first post-treatment period of interest, i.e. the treatment group. + - :math:`t_{pre}` is the pre-treatment period, i.e. the time period from which the conditional parallel trends are assumed. + - :math:`t_{eval}` is the time period of interest or evaluation period, i.e. the time period where the treatment effect is evaluated. + + The tuple :math:`(\mathrm{g}, t_{pre}, t_{eval})` is used to implicitly define the corresponding tuple :math:`(g, t, \delta)` with :math:`t=t_{eval}` and :math:`\delta=t_{eval}-t_{pre}-1`. + +For a given tuple :math:`(\mathrm{g}, t_{pre}, t_{eval})` the target parameter :math:`ATT(\mathrm{g},t)` is estimated by solving the empirical version of the the following linear moment condition: + +.. math:: + ATT(\mathrm{g}, t_{pre}, t_{eval}):= -\frac{\mathbb{E}[\psi_b(W,\eta_0)]}{\mathbb{E}[\psi_a(W,\eta_0)]} + +with nuisance elements :math:`\eta_0=(g_{0, \mathrm{g}, t_{eval}, t_{eval}-t_{pre}-1}, m_{0, \mathrm{g}, 2t_{eval}-t_{pre}-1})` and score function :math:`\psi(W,\theta, \eta)` being defined in section :ref:`did-pa-score`. +Under the identifying assumptions above + +.. math:: + ATT(\mathrm{g}, t_{pre}, t_{eval}) = ATT(\mathrm{g},t). + +``DoubleMLDIDMulti`` implements the estimation of :math:`ATT(\mathrm{g}, t_{pre}, t_{eval})` for multiple time periods. +Setting ``gt_combinations='standard'`` will estimate the target parameter for all combinations of :math:`(\mathrm{g}, t_{pre}, t_{eval})` with :math:`\mathrm{g}\in\{2,\dots,\mathcal{T}\}` and :math:`t_{pre}\in\{1,\dots,\mathrm{g}-1\}` and :math:`t_{eval}\in\{\mathrm{g},\dots,\mathcal{T}\}`. +Estimation is conducted via its ``fit()`` method: .. tab-set:: @@ -31,3 +68,7 @@ control_group="never_treated", ) print(dml_did_obj.fit()) + + +.. note:: + A more detailed example is available in the :ref:`Example Gallery `. diff --git a/doc/guide/models/did/did_setup.rst b/doc/guide/models/did/did_setup.rst index 3f4d67ed..7094c59c 100644 --- a/doc/guide/models/did/did_setup.rst +++ b/doc/guide/models/did/did_setup.rst @@ -30,8 +30,8 @@ Let .. math:: \begin{align} - C_{i}^{nev} &:= 1\{G_i=\infty\} \quad \text{(never treated)}, \\ - C_{i,t}^{nyt} &:= 1\{G_i > t\} \quad \text{(not yet treated)}. + C_{i,t}^{(nev)} \equiv C_{i}^{(nev)} &:= 1\{G_i=\infty\} \quad \text{(never treated)}, \\ + C_{i,t}^{(nyt)} &:= 1\{G_i > t\} \quad \text{(not yet treated)}. \end{align} The corresponding identifying assumptions are: @@ -52,14 +52,14 @@ The corresponding identifying assumptions are: For each :math:`\mathrm{g}\in\mathcal{G}` and :math:`t\in\{2,\dots,\mathcal{T}\}` such that :math:`t\ge \mathrm{g}-\delta`: a. **Never Treated:** - :math:`\mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i, G_i^{\mathrm{g}}=1] = \mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i,C_{i}^{nev}=1] \quad a.s.` + :math:`\mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i, G_i^{\mathrm{g}}=1] = \mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i,C_{i}^{(nev)}=1] \quad a.s.` b. **Not Yet Treated:** - :math:`\mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i, G_i^{\mathrm{g}}=1] = \mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i,C_{i,t+\delta}^{nyt}=1] \quad a.s.` + :math:`\mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i, G_i^{\mathrm{g}}=1] = \mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i,C_{i,t+\delta}^{(nyt)}=1] \quad a.s.` 5. **Overlap:** For each time period :math:`t=2,\dots,\mathcal{T}` and :math:`\mathrm{g}\in\mathcal{G}` there exists a :math:`\epsilon > 0` such that - :math:`P(G_i^{\mathrm{g}}=1) > \epsilon` and :math:`P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t}^{nyt}=1) < 1-\epsilon\quad a.s.` + :math:`P(G_i^{\mathrm{g}}=1) > \epsilon` and :math:`P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t}^{(nyt)}=1) < 1-\epsilon\quad a.s.` .. note:: For a detailed discussion of the assumptions see `Callaway and Sant'Anna (2021) `_. diff --git a/doc/guide/scores/did/did_pa_score.rst b/doc/guide/scores/did/did_pa_score.rst index 30d74d25..1b750d65 100644 --- a/doc/guide/scores/did/did_pa_score.rst +++ b/doc/guide/scores/did/did_pa_score.rst @@ -1 +1,82 @@ -test \ No newline at end of file +For the difference-in-differences model implemented in ``DoubleMLDIDMulti`` one can choose between +``score='observational'`` and ``score='experimental'``. + +``score='observational'`` implements the score function (dropping the unit index :math:`i`): + +.. math:: + + \psi(W,\theta, \eta) + :&= -\frac{D}{\mathbb{E}_n[D]}\theta + \left(\frac{D}{\mathbb{E}_n[D]} - \frac{\frac{m(X) (1-D)}{1-m(X)}}{\mathbb{E}_n\left[\frac{m(X) (1-D)}{1-m(X)}\right]}\right) \left(Y_1 - Y_0 - g(0,X)\right) + + &= \psi_a(W; \eta) \theta + \psi_b(W; \eta) + +where the components of the linear score are + +.. math:: + + \psi_a(W; \eta) &= - \frac{D}{\mathbb{E}_n[D]}, + + \psi_b(W; \eta) &= \left(\frac{D}{\mathbb{E}_n[D]} - \frac{\frac{m(X) (1-D)}{1-m(X)}}{\mathbb{E}_n\left[\frac{m(X) (1-D)}{1-m(X)}\right]}\right) \left(Y_1 - Y_0 - g(0,X)\right) + +and the nuisance elements :math:`\eta=(g, m)` are defined as + +.. math:: + + g_{0}(0, X) &= \mathbb{E}[Y_1 - Y_0|D=0, X] + + m_0(X) &= P(D=1|X). + +If ``in_sample_normalization='False'``, the score is set to + +.. math:: + + \psi(W,\theta,\eta) &= - \frac{D}{p}\theta + \frac{D - m(X)}{p(1-m(X))}\left(Y_1 - Y_0 -g(0,X)\right) + + &= \psi_a(W; \eta) \theta + \psi_b(W; \eta) + +with :math:`\eta=(g, m, p)`, where :math:`p_0 = \mathbb{E}[D]` is estimated on the cross-fitting folds. +Remark that this will result in the same score, but just uses slightly different normalization. + +``score='experimental'`` assumes that the treatment probability is independent of the covariates :math:`X` and +implements the score function: + +.. math:: + + \psi(W,\theta, \eta) + :=\; &-\theta + \left(\frac{D}{\mathbb{E}_n[D]} - \frac{1-D}{\mathbb{E}_n[1-D]}\right)\left(Y_1 - Y_0 -g(0,X)\right) + + &+ \left(1 - \frac{D}{\mathbb{E}_n[D]}\right) \left(g(1,X) - g(0,X)\right) + + =\; &\psi_a(W; \eta) \theta + \psi_b(W; \eta) + +where the components of the linear score are + +.. math:: + + \psi_a(W; \eta) \;= &- 1, + + \psi_b(W; \eta) \;= &\left(\frac{D}{\mathbb{E}_n[D]} - \frac{1-D}{\mathbb{E}_n[1-D]}\right)\left(Y_1 - Y_0 -g(0,X)\right) + + &+ \left(1 - \frac{D}{\mathbb{E}_n[D]}\right) \left(g(1,X) - g(0,X)\right) + +and the nuisance elements :math:`\eta=(g)` are defined as + +.. math:: + + g_{0}(0, X) &= \mathbb{E}[Y_1 - Y_0|D=0, X] + + g_{0}(1, X) &= \mathbb{E}[Y_1 - Y_0|D=1, X] + +Analogously, if ``in_sample_normalization='False'``, the score is set to + +.. math:: + + \psi(W,\theta, \eta) + :=\; &-\theta + \frac{D - p}{p(1-p)}\left(Y_1 - Y_0 -g(0,X)\right) + + &+ \left(1 - \frac{D}{p}\right) \left(g(1,X) - g(0,X)\right) + + =\; &\psi_a(W; \eta) \theta + \psi_b(W; \eta) + +with :math:`\eta=(g, p)`, where :math:`p_0 = \mathbb{E}[D]` is estimated on the cross-fitting folds. +Remark that this will result in the same score, but just uses slightly different normalization. From 91f2f8e5174bff7949740673a236427faee0722e Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 15 Apr 2025 14:27:04 +0000 Subject: [PATCH 071/140] adjust for anticipation --- doc/guide/models/did/did_setup.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/doc/guide/models/did/did_setup.rst b/doc/guide/models/did/did_setup.rst index 7094c59c..9fd67b05 100644 --- a/doc/guide/models/did/did_setup.rst +++ b/doc/guide/models/did/did_setup.rst @@ -63,6 +63,5 @@ The corresponding identifying assumptions are: .. note:: For a detailed discussion of the assumptions see `Callaway and Sant'Anna (2021) `_. - Currently, the package automatically imposes "no-anticipation", e.g. :math:`\delta=0`, but can manually be adjusted via the considered combinations. Under the assumptions above (either Assumption 4.a or 4.b), the target parameter :math:`ATT(\mathrm{g},t)` is identified see Theorem 1. `Callaway and Sant'Anna (2021) `_. \ No newline at end of file From 231ed3a44502e737e65078aa104406a51b944933 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 15 Apr 2025 14:58:55 +0000 Subject: [PATCH 072/140] did pa documentation --- doc/guide/models/did/did_pa.rst | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/doc/guide/models/did/did_pa.rst b/doc/guide/models/did/did_pa.rst index 0ff87493..94b05efb 100644 --- a/doc/guide/models/did/did_pa.rst +++ b/doc/guide/models/did/did_pa.rst @@ -2,25 +2,27 @@ For the estimation of the target parameters :math:`ATT(\mathrm{g},t)` the follow .. math:: \begin{align} - g_{0, \mathrm{g}, t_{pre}, t_{eval}}(X_i) &:= \mathbb{E}[Y_{i,t} - Y_{i,\mathrm{g} - \delta - 1}|X_i, C_{i,t}^{(\cdot)} = 1], \\ - m_{0, \mathrm{g}, t + \delta}(X_i) &:= P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t + \delta}^{(\cdot)}=1). + g_{0, \mathrm{g}, t_{pre}, t_{eval}, \delta}(X_i) &:= \mathbb{E}[Y_{i,t_{eval}} - Y_{i,t_{pre}}|X_i, C_{i,t_{eval} + \delta}^{(\cdot)} = 1], \\ + m_{0, \mathrm{g}, t_{eval} + \delta}(X_i) &:= P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t_{eval} + \delta}^{(\cdot)}=1). \end{align} -where :math:`g_{0, \mathrm{g}, t, \delta}(\cdot)` denotes the population outcome regression function and :math:`m_{0, \mathrm{g}, t + \delta}(\cdot)` the generalized propensity score. -Remark that the nuisance functions depend on the control group used for the estimation of the target parameter. -By slight abuse of notation we use the same notation for both control groups :math:`C_{i,t}^{(nev)}` and :math:`C_{i,t}^{(nyt)}`. - -Under these assumptions the target parameter :math:`ATT(\mathrm{g},t)` can be estimated by choosing a suitable combination of :math:`(\mathrm{g}, t, \delta)`. - -.. note:: - The package does not support a direct choice of the parameter :math:`\delta` but require the user to specify the tuple :math:`(\mathrm{g}, t_{pre}, t_{eval})` with the following interpretation - +where :math:`g_{0, \mathrm{g}, t_{pre}, t_{eval}}(\cdot)` denotes the population outcome regression function and :math:`m_{0, \mathrm{g}, t_{eval} + \delta}(\cdot)` the generalized propensity score. +The interpretation of the parameters is as follows: - :math:`\mathrm{g}` is the first post-treatment period of interest, i.e. the treatment group. - :math:`t_{pre}` is the pre-treatment period, i.e. the time period from which the conditional parallel trends are assumed. - :math:`t_{eval}` is the time period of interest or evaluation period, i.e. the time period where the treatment effect is evaluated. + - :math:`\delta` is number of anticipation periods, i.e. the number of time periods for which units are assumed to anticipate the treatment. - The tuple :math:`(\mathrm{g}, t_{pre}, t_{eval})` is used to implicitly define the corresponding tuple :math:`(g, t, \delta)` with :math:`t=t_{eval}` and :math:`\delta=t_{eval}-t_{pre}-1`. +.. note:: + Remark that the nuisance functions depend on the control group used for the estimation of the target parameter. + By slight abuse of notation we use the same notation for both control groups :math:`C_{i,t}^{(nev)}` and :math:`C_{i,t}^{(nyt)}`. More specifically, the + control group only depends on :math:`\delta` for *not yet treated* units. + +Under these assumptions the target parameter :math:`ATT(\mathrm{g},t_{eval})` can be estimated by choosing a suitable combination +of :math:`(\mathrm{g}, t_{pre}, t_{eval}, \delta)` if :math:`t_{eval} - t_{pre} \ge 1 + \delta`, i.e. the parallel trends are assumed to hold at least one period more than the anticipation period. +.. note:: + The tuple :math:`(\mathrm{g}, t_{pre}, t_{eval})` is used to implicitly define the corresponding tuple :math:`(g, t, \delta)` with :math:`t=t_{eval}` and :math:`\delta=t_{eval}-t_{pre}-1`. For a given tuple :math:`(\mathrm{g}, t_{pre}, t_{eval})` the target parameter :math:`ATT(\mathrm{g},t)` is estimated by solving the empirical version of the the following linear moment condition: .. math:: From 160d80c39876230d2954c448b1e9a7b2ba7033ff Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 15 Apr 2025 21:25:15 +0000 Subject: [PATCH 073/140] update data backend section --- doc/guide/data/base_data.rst | 173 +++++++++++++++++++++++++++++++++ doc/guide/data/panel_data.rst | 10 ++ doc/guide/data_backend.rst | 175 +++------------------------------- doc/guide/guide.rst | 2 +- doc/workflow/workflow.rst | 6 +- 5 files changed, 202 insertions(+), 164 deletions(-) create mode 100644 doc/guide/data/base_data.rst create mode 100644 doc/guide/data/panel_data.rst diff --git a/doc/guide/data/base_data.rst b/doc/guide/data/base_data.rst new file mode 100644 index 00000000..1c848720 --- /dev/null +++ b/doc/guide/data/base_data.rst @@ -0,0 +1,173 @@ +The usage of both interfaces is demonstrated in the following. +We download the Bonus data set from the Pennsylvania Reemployment Bonus experiment. + +.. note:: + - In Python we use :py:class:`pandas.DataFrame` and :py:class:`numpy.ndarray`. + The data can be fetched via :py:func:`doubleml.datasets.fetch_bonus`. + - In R we use `data.table::data.table() `_, `data.frame() `_, and `matrix() `_. + The data can be fetched via `DoubleML::fetch_bonus() `_ + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + + from doubleml.datasets import fetch_bonus + + # Load data + df_bonus = fetch_bonus('DataFrame') + df_bonus.head(5) + + .. tab-item:: R + :sync: r + + .. jupyter-execute:: + + library(DoubleML) + + # Load data as data.table + dt_bonus = fetch_bonus(return_type = "data.table") + head(dt_bonus) + + # Load data as data.frame + df_bonus = fetch_bonus(return_type = "data.frame") + head(df_bonus) + + +DoubleMLData from dataframes +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The ``DoubleMLData`` class serves as data-backend and can be initialized from a dataframe by +specifying the column ``y_col='inuidur1'`` serving as outcome variable :math:`Y`, the column(s) ``d_cols = 'tg'`` +serving as treatment variable :math:`D` and the columns ``x_cols`` specifying the confounders. + +.. note:: + * In Python we use :py:class:`pandas.DataFrame` + and the API reference can be found here :py:class:`doubleml.DoubleMLData`. + * In R we use `data.table::data.table() `_ and the API reference can be found here `DoubleML::DoubleMLData `_. + * For initialization from the R base class `data.frame() `_ the API reference can be found here `DoubleML::double_ml_data_from_data_frame() `_. + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + + from doubleml import DoubleMLData + + # Specify the data and the variables for the causal model + obj_dml_data_bonus = DoubleMLData(df_bonus, + y_col='inuidur1', + d_cols='tg', + x_cols=['female', 'black', 'othrace', 'dep1', 'dep2', + 'q2', 'q3', 'q4', 'q5', 'q6', 'agelt35', 'agegt54', + 'durable', 'lusd', 'husd'], + use_other_treat_as_covariate=True) + print(obj_dml_data_bonus) + + .. tab-item:: R + :sync: r + + .. jupyter-execute:: + + # Specify the data and the variables for the causal model + + # From data.table object + obj_dml_data_bonus = DoubleMLData$new(dt_bonus, + y_col = "inuidur1", + d_cols = "tg", + x_cols = c("female", "black", "othrace", "dep1", "dep2", + "q2", "q3", "q4", "q5", "q6", "agelt35", "agegt54", + "durable", "lusd", "husd"), + use_other_treat_as_covariate=TRUE) + obj_dml_data_bonus + + # From dat.frame object + obj_dml_data_bonus_df = double_ml_data_from_data_frame(df_bonus, + y_col = "inuidur1", + d_cols = "tg", + x_cols = c("female", "black", "othrace", "dep1", "dep2", + "q2", "q3", "q4", "q5", "q6", "agelt35", "agegt54", + "durable", "lusd", "husd"), + use_other_treat_as_covariate=TRUE) + obj_dml_data_bonus_df + +Comments on detailed specifications: + +* If ``x_cols`` is not specified, all variables (columns of the dataframe) which are neither specified as outcome + variable ``y_col``, nor treatment variables ``d_cols``, nor instrumental variables ``z_cols`` are used as covariates. +* In case of multiple treatment variables, the boolean ``use_other_treat_as_covariate`` indicates whether the other + treatment variables should be added as covariates in each treatment-variable-specific learning task. +* Instrumental variables for IV models have to be provided as ``z_cols``. + + +DoubleMLData from arrays and matrices +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +To introduce the array interface we generate a data set consisting of confounding variables ``X``, an outcome +variable ``y`` and a treatment variable ``d`` + +.. note:: + * In python we use :py:class:`numpy.ndarray`. + and the API reference can be found here :py:func:`doubleml.DoubleMLData.from_arrays`. + * In R we use the R base class `matrix() `_ + and the API reference can be found here `DoubleML::double_ml_data_from_matrix() `_. + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + + import numpy as np + + # Generate data + np.random.seed(3141) + n_obs = 500 + n_vars = 100 + theta = 3 + X = np.random.normal(size=(n_obs, n_vars)) + d = np.dot(X[:, :3], np.array([5, 5, 5])) + np.random.standard_normal(size=(n_obs,)) + y = theta * d + np.dot(X[:, :3], np.array([5, 5, 5])) + np.random.standard_normal(size=(n_obs,)) + + .. tab-item:: R + :sync: r + + .. jupyter-execute:: + + # Generate data + set.seed(3141) + n_obs = 500 + n_vars = 100 + theta = 3 + X = matrix(stats::rnorm(n_obs * n_vars), nrow = n_obs, ncol = n_vars) + d = X[, 1:3, drop = FALSE] %*% c(5, 5, 5) + stats::rnorm(n_obs) + y = theta * d + X[, 1:3, drop = FALSE] %*% c(5, 5, 5) + stats::rnorm(n_obs) + +To specify the data and the variables for the causal model from arrays we call + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + + from doubleml import DoubleMLData + + obj_dml_data_sim = DoubleMLData.from_arrays(X, y, d) + print(obj_dml_data_sim) + + .. tab-item:: R + :sync: r + + .. jupyter-execute:: + + library(DoubleML) + + obj_dml_data_sim = double_ml_data_from_matrix(X = X, y = y, d = d) + obj_dml_data_sim diff --git a/doc/guide/data/panel_data.rst b/doc/guide/data/panel_data.rst new file mode 100644 index 00000000..e1766119 --- /dev/null +++ b/doc/guide/data/panel_data.rst @@ -0,0 +1,10 @@ +The ``DoubleMLPanelData`` class serves as data-backend for :ref:`DiD models ` and can be initialized from a dataframe. +The class is a subclass of :ref:`DoubleMLData ` and inherits all methods and attributes. +Furthermore, it provides additional methods and attributes to handle panel data () + +* ``id_col``: column to identify the individual units +* ``t_col``: column to specify the time periods +* ``datetime_unit``: unit of the time periods (e.g. 'Y', 'M', 'D', 'h', 'm', 's') + +.. note:: + The ``t_col`` can contain either \ No newline at end of file diff --git a/doc/guide/data_backend.rst b/doc/guide/data_backend.rst index 7a5684ab..8849f264 100644 --- a/doc/guide/data_backend.rst +++ b/doc/guide/data_backend.rst @@ -1,175 +1,30 @@ .. _data_backend: -The data-backend DoubleMLData ------------------------------ +Data Backend +------------ -:ref:`DoubleML ` provides interfaces to dataframes as well as arrays. The usage of both interfaces is -demonstrated in the following. We download the Bonus data set from the Pennsylvania Reemployment Bonus experiment. +:ref:`DoubleML ` generally provides interfaces to dataframes as well as arrays. -.. note:: - - In Python we use :py:class:`pandas.DataFrame` and :py:class:`numpy.ndarray`. - The data can be fetched via :py:func:`doubleml.datasets.fetch_bonus`. - - In R we use `data.table::data.table() `_, `data.frame() `_, and `matrix() `_. - The data can be fetched via `DoubleML::fetch_bonus() `_ +.. _dml_data: -.. tab-set:: +DoubleMLData +~~~~~~~~~~~~ - .. tab-item:: Python - :sync: py +.. include:: data/base_data.rst - .. ipython:: python - from doubleml.datasets import fetch_bonus +.. _dml_data_types: - # Load data - df_bonus = fetch_bonus('DataFrame') - df_bonus.head(5) +Special Data Types +~~~~~~~~~~~~~~~~~~ - .. tab-item:: R - :sync: r +The :ref:`DoubleMLData ` class is extended by the following classes to support special data types or allow for additional parameters. - .. jupyter-execute:: - library(DoubleML) +.. _dml_panel_data: - # Load data as data.table - dt_bonus = fetch_bonus(return_type = "data.table") - head(dt_bonus) +DoubleMLPanelData +^^^^^^^^^^^^^^^^^ - # Load data as data.frame - df_bonus = fetch_bonus(return_type = "data.frame") - head(df_bonus) +.. include:: data/panel_data.rst - -DoubleMLData from dataframes -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The ``DoubleMLData`` class serves as data-backend and can be initialized from a dataframe by -specifying the column ``y_col='inuidur1'`` serving as outcome variable :math:`Y`, the column(s) ``d_cols = 'tg'`` -serving as treatment variable :math:`D` and the columns ``x_cols`` specifying the confounders. - -.. note:: - * In Python we use :py:class:`pandas.DataFrame` - and the API reference can be found here :py:class:`doubleml.DoubleMLData`. - * In R we use `data.table::data.table() `_ and the API reference can be found here `DoubleML::DoubleMLData `_. - * For initialization from the R base class `data.frame() `_ the API reference can be found here `DoubleML::double_ml_data_from_data_frame() `_. - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - - from doubleml import DoubleMLData - - # Specify the data and the variables for the causal model - obj_dml_data_bonus = DoubleMLData(df_bonus, - y_col='inuidur1', - d_cols='tg', - x_cols=['female', 'black', 'othrace', 'dep1', 'dep2', - 'q2', 'q3', 'q4', 'q5', 'q6', 'agelt35', 'agegt54', - 'durable', 'lusd', 'husd'], - use_other_treat_as_covariate=True) - print(obj_dml_data_bonus) - - .. tab-item:: R - :sync: r - - .. jupyter-execute:: - - # Specify the data and the variables for the causal model - - # From data.table object - obj_dml_data_bonus = DoubleMLData$new(dt_bonus, - y_col = "inuidur1", - d_cols = "tg", - x_cols = c("female", "black", "othrace", "dep1", "dep2", - "q2", "q3", "q4", "q5", "q6", "agelt35", "agegt54", - "durable", "lusd", "husd"), - use_other_treat_as_covariate=TRUE) - obj_dml_data_bonus - - # From dat.frame object - obj_dml_data_bonus_df = double_ml_data_from_data_frame(df_bonus, - y_col = "inuidur1", - d_cols = "tg", - x_cols = c("female", "black", "othrace", "dep1", "dep2", - "q2", "q3", "q4", "q5", "q6", "agelt35", "agegt54", - "durable", "lusd", "husd"), - use_other_treat_as_covariate=TRUE) - obj_dml_data_bonus_df - -Comments on detailed specifications: - -* If ``x_cols`` is not specified, all variables (columns of the dataframe) which are neither specified as outcome - variable ``y_col``, nor treatment variables ``d_cols``, nor instrumental variables ``z_cols`` are used as covariates. -* In case of multiple treatment variables, the boolean ``use_other_treat_as_covariate`` indicates whether the other - treatment variables should be added as covariates in each treatment-variable-specific learning task. -* Instrumental variables for IV models have to be provided as ``z_cols``. - -DoubleMLData from arrays and matrices -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -To introduce the array interface we generate a data set consisting of confounding variables ``X``, an outcome -variable ``y`` and a treatment variable ``d`` - -.. note:: - * In python we use :py:class:`numpy.ndarray`. - and the API reference can be found here :py:func:`doubleml.DoubleMLData.from_arrays`. - * In R we use the R base class `matrix() `_ - and the API reference can be found here `DoubleML::double_ml_data_from_matrix() `_. - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - - import numpy as np - - # Generate data - np.random.seed(3141) - n_obs = 500 - n_vars = 100 - theta = 3 - X = np.random.normal(size=(n_obs, n_vars)) - d = np.dot(X[:, :3], np.array([5, 5, 5])) + np.random.standard_normal(size=(n_obs,)) - y = theta * d + np.dot(X[:, :3], np.array([5, 5, 5])) + np.random.standard_normal(size=(n_obs,)) - - .. tab-item:: R - :sync: r - - .. jupyter-execute:: - - # Generate data - set.seed(3141) - n_obs = 500 - n_vars = 100 - theta = 3 - X = matrix(stats::rnorm(n_obs * n_vars), nrow = n_obs, ncol = n_vars) - d = X[, 1:3, drop = FALSE] %*% c(5, 5, 5) + stats::rnorm(n_obs) - y = theta * d + X[, 1:3, drop = FALSE] %*% c(5, 5, 5) + stats::rnorm(n_obs) - -To specify the data and the variables for the causal model from arrays we call - -.. tab-set:: - - .. tab-item:: Python - :sync: py - - .. ipython:: python - - from doubleml import DoubleMLData - - obj_dml_data_sim = DoubleMLData.from_arrays(X, y, d) - print(obj_dml_data_sim) - - .. tab-item:: R - :sync: r - - .. jupyter-execute:: - - obj_dml_data_sim = double_ml_data_from_matrix(X = X, y = y, d = d) - obj_dml_data_sim \ No newline at end of file diff --git a/doc/guide/guide.rst b/doc/guide/guide.rst index 2c2566cb..dcaecb9c 100644 --- a/doc/guide/guide.rst +++ b/doc/guide/guide.rst @@ -10,7 +10,7 @@ User Guide :numbered: The basics of double/debiased machine learning - The data-backend DoubleMLData + Data Backend Models Heterogeneous Treatment Effects Score functions diff --git a/doc/workflow/workflow.rst b/doc/workflow/workflow.rst index d0615b42..8e201e02 100644 --- a/doc/workflow/workflow.rst +++ b/doc/workflow/workflow.rst @@ -62,15 +62,15 @@ transform the confounding variables in the regression model. However, machine learning techniques offer greater flexibility in terms of a more data-driven specification of the main regression equation and the first stage. -1. Data-Backend +1. Data Backend --------------- -In Step 1., we initialize the data-backend and thereby declare the role of the outcome, the treatment, and the confounding variables. +In Step 1., we initialize the data backend and thereby declare the role of the outcome, the treatment, and the confounding variables. We use data from the 1991 Survey of Income and Program Participation which is available via the function `fetch_401K (Python) `_ or `fetch_401k (R) `_. -The data-backend can be initialized from various data frame objects in Python and R. To estimate the intent-to-treat effect in the +The data backend can be initialized from various data frame objects in Python and R. To estimate the intent-to-treat effect in the 401(k) example, we use eligibility (``e401``) as the treatment variable of interest. The outcome variable is ``net_tfa`` and we control for confounding variables ``['age', 'inc', 'educ', 'fsize', 'marr', 'twoearn', 'db', 'pira', 'hown']``. From e84a0b448f32c8e2e1e6b82749aac0c4776c4ad2 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 16 Apr 2025 00:09:51 +0000 Subject: [PATCH 074/140] add score version --- doc/guide/scores/did/did_pa_score.rst | 91 ++++++++++++++++----------- 1 file changed, 55 insertions(+), 36 deletions(-) diff --git a/doc/guide/scores/did/did_pa_score.rst b/doc/guide/scores/did/did_pa_score.rst index 1b750d65..8d6602e5 100644 --- a/doc/guide/scores/did/did_pa_score.rst +++ b/doc/guide/scores/did/did_pa_score.rst @@ -1,82 +1,101 @@ -For the difference-in-differences model implemented in ``DoubleMLDIDMulti`` one can choose between -``score='observational'`` and ``score='experimental'``. - -``score='observational'`` implements the score function (dropping the unit index :math:`i`): +As in the description of the :ref:`DiD model `, the required nuisance elements are .. math:: + \begin{align} + g_{0, \mathrm{g}, t_\text{pre}, t_\text{eval}, \delta}(X_i) &:= \mathbb{E}[Y_{i,t_\text{eval}} - Y_{i,t_\text{pre}}|X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)} = 1], \\ + m_{0, \mathrm{g}, t_\text{eval} + \delta}(X_i) &:= P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t_\text{eval} + \delta}^{(\cdot)}=1). + \end{align} - \psi(W,\theta, \eta) - :&= -\frac{D}{\mathbb{E}_n[D]}\theta + \left(\frac{D}{\mathbb{E}_n[D]} - \frac{\frac{m(X) (1-D)}{1-m(X)}}{\mathbb{E}_n\left[\frac{m(X) (1-D)}{1-m(X)}\right]}\right) \left(Y_1 - Y_0 - g(0,X)\right) +for a certain choice of :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval})` and :math:`\delta` and control group :math:`C_{i,t_\text{eval} + \delta}^{(\cdot)}`. - &= \psi_a(W; \eta) \theta + \psi_b(W; \eta) +For notational purposes, we will omit the subscripts :math:`\mathrm{g}, t_\text{pre}, t_\text{eval}, \delta` in the following and use the notation -where the components of the linear score are +* :math:`g_0(0, X_i)\equiv g_{0, \mathrm{g}, t_\text{pre}, t_\text{eval}, \delta}(X_i)` (population outcome regression function of the control group) +* :math:`m_0(X_i)\equiv m_{0, \mathrm{g}, t_\text{eval} + \delta}(X_i)` (generalized propensity score) + +All scores in the multi-period setting have the form .. math:: - \psi_a(W; \eta) &= - \frac{D}{\mathbb{E}_n[D]}, + \psi(W_i,\theta, \eta) := + \begin{cases} + \tilde{\psi}(W_i,\theta, \eta) & \text{for } G_i^{\mathrm{g}} \vee C_{i,t_\text{eval} + \delta}^{(\cdot)}=1 \\ + 0 & \text{otherwise} + \end{cases} - \psi_b(W; \eta) &= \left(\frac{D}{\mathbb{E}_n[D]} - \frac{\frac{m(X) (1-D)}{1-m(X)}}{\mathbb{E}_n\left[\frac{m(X) (1-D)}{1-m(X)}\right]}\right) \left(Y_1 - Y_0 - g(0,X)\right) +i.e. the score is only non-zero for units in the corresponding treatment group :math:`\mathrm{g}` and control group :math:`C_{i,t_\text{eval} + \delta}^{(\cdot)}`. -and the nuisance elements :math:`\eta=(g, m)` are defined as +For the difference-in-differences model implemented in ``DoubleMLDIDMulti`` one can choose between +``score='observational'`` and ``score='experimental'``. + +``score='observational'`` implements the score function (dropping the unit index :math:`i`): .. math:: - g_{0}(0, X) &= \mathbb{E}[Y_1 - Y_0|D=0, X] + \tilde{\psi}(W,\theta, \eta) + :&= -\frac{G^{\mathrm{g}}}{\mathbb{E}_n[G^{\mathrm{g}}]}\theta + \left(\frac{G^{\mathrm{g}}}{\mathbb{E}_n[G^{\mathrm{g}}]} - \frac{\frac{m(X) (1-G^{\mathrm{g}})}{1-m(X)}}{\mathbb{E}_n\left[\frac{m(X) (1-G^{\mathrm{g}})}{1-m(X)}\right]}\right) \left(Y_{t_\text{eval}} - Y_{t_\text{pre}} - g(0,X)\right) - m_0(X) &= P(D=1|X). + &= \tilde{\psi}_a(W; \eta) \theta + \tilde{\psi}_b(W; \eta) -If ``in_sample_normalization='False'``, the score is set to +where the components of the final linear score :math:`\psi` are .. math:: + \psi_a(W; \eta) &= \tilde{\psi}_a(W; \eta) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}), - \psi(W,\theta,\eta) &= - \frac{D}{p}\theta + \frac{D - m(X)}{p(1-m(X))}\left(Y_1 - Y_0 -g(0,X)\right) + \psi_b(W; \eta) &= \tilde{\psi}_b(W; \eta) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}) - &= \psi_a(W; \eta) \theta + \psi_b(W; \eta) +and the nuisance elements :math:`\eta=(g, m)`. -with :math:`\eta=(g, m, p)`, where :math:`p_0 = \mathbb{E}[D]` is estimated on the cross-fitting folds. -Remark that this will result in the same score, but just uses slightly different normalization. +.. note:: + Remark that :math:`1-G^{\mathrm{g}}=C^{(\cdot)}` if :math:`G^{\mathrm{g}} \vee C_{t_\text{eval} + \delta}^{(\cdot)}=1`. -``score='experimental'`` assumes that the treatment probability is independent of the covariates :math:`X` and -implements the score function: +If ``in_sample_normalization='False'``, the score is set to .. math:: - \psi(W,\theta, \eta) - :=\; &-\theta + \left(\frac{D}{\mathbb{E}_n[D]} - \frac{1-D}{\mathbb{E}_n[1-D]}\right)\left(Y_1 - Y_0 -g(0,X)\right) + \tilde{\psi}(W,\theta,\eta) &= - \frac{G^{\mathrm{g}}}{\mathbb{E}_n[G^{\mathrm{g}}]}\theta + \frac{G^{\mathrm{g}} - m(X)}{\mathbb{E}_n[G^{\mathrm{g}}](1-m(X))}\left(Y_{t_\text{eval}} - Y_{t_\text{pre}} - g(0,X)\right) + + &= \tilde{\psi}_a(W; \eta) \theta + \tilde{\psi}_b(W; \eta) + +with :math:`\eta=(g, m)`. +Remark that this will result in the same score, but just uses slightly different normalization. - &+ \left(1 - \frac{D}{\mathbb{E}_n[D]}\right) \left(g(1,X) - g(0,X)\right) +``score='experimental'`` assumes that the treatment probability is independent of the covariates :math:`X` and does not rely on the propensity score. Instead define +the population outcome regression for treated and control group as - =\; &\psi_a(W; \eta) \theta + \psi_b(W; \eta) +* :math:`g_0(0, X_i)\equiv \mathbb{E}[Y_{i,t_\text{eval}} - Y_{i,t_\text{pre}}|X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)} = 1]` (control group) +* :math:`g_0(1, X_i)\equiv \mathbb{E}[Y_{i,t_\text{eval}} - Y_{i,t_\text{pre}}|X_i, G_i^{\mathrm{g}} = 1]` (treated group) -where the components of the linear score are +``score='experimental'`` implements the score function: .. math:: - \psi_a(W; \eta) \;= &- 1, + \tilde{\psi}(W,\theta, \eta) + :=\; &-\theta + \left(\frac{G^{\mathrm{g}}}{\mathbb{E}_n[G^{\mathrm{g}}]} - \frac{1-G^{\mathrm{g}}}{\mathbb{E}_n[1-G^{\mathrm{g}}]}\right)\left(Y_{t_\text{eval}} - Y_{t_\text{pre}} - g(0,X)\right) - \psi_b(W; \eta) \;= &\left(\frac{D}{\mathbb{E}_n[D]} - \frac{1-D}{\mathbb{E}_n[1-D]}\right)\left(Y_1 - Y_0 -g(0,X)\right) + &+ \left(1 - \frac{G^{\mathrm{g}}}{\mathbb{E}_n[G^{\mathrm{g}}]}\right) \left(g(1,X) - g(0,X)\right) - &+ \left(1 - \frac{D}{\mathbb{E}_n[D]}\right) \left(g(1,X) - g(0,X)\right) + =\; &\tilde{\psi}_a(W; \eta) \theta + \tilde{\psi}_b(W; \eta) -and the nuisance elements :math:`\eta=(g)` are defined as +where the components of the final linear score :math:`\psi` are .. math:: + \psi_a(W; \eta) &= \tilde{\psi}_a(W; \eta) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}), - g_{0}(0, X) &= \mathbb{E}[Y_1 - Y_0|D=0, X] + \psi_b(W; \eta) &= \tilde{\psi}_b(W; \eta) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}) - g_{0}(1, X) &= \mathbb{E}[Y_1 - Y_0|D=1, X] +and the nuisance elements :math:`\eta=(g)`. Analogously, if ``in_sample_normalization='False'``, the score is set to .. math:: - \psi(W,\theta, \eta) - :=\; &-\theta + \frac{D - p}{p(1-p)}\left(Y_1 - Y_0 -g(0,X)\right) + \tilde{\psi}(W,\theta, \eta) + :=\; &-\theta + \frac{G^{\mathrm{g}} - \mathbb{E}_n[G^{\mathrm{g}}]}{\mathbb{E}_n[G^{\mathrm{g}}](1-\mathbb{E}_n[G^{\mathrm{g}}])}\left(Y_{t_\text{eval}} - Y_{t_\text{pre}} - g(0,X)\right) - &+ \left(1 - \frac{D}{p}\right) \left(g(1,X) - g(0,X)\right) + &+ \left(1 - \frac{G^{\mathrm{g}}}{\mathbb{E}_n[G^{\mathrm{g}}]}\right) \left(g(1,X) - g(0,X)\right) - =\; &\psi_a(W; \eta) \theta + \psi_b(W; \eta) + =\; &\tilde{\psi}_a(W; \eta) \theta + \tilde{\psi}_b(W; \eta) -with :math:`\eta=(g, p)`, where :math:`p_0 = \mathbb{E}[D]` is estimated on the cross-fitting folds. +with :math:`\eta=(g)`. Remark that this will result in the same score, but just uses slightly different normalization. From bd44ca25592078c9ed44c0b081961fda3d1d75da Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 16 Apr 2025 00:16:36 +0000 Subject: [PATCH 075/140] update pa doc --- doc/guide/models/did/did_pa.rst | 36 ++++++++++++++++++++++++--------- 1 file changed, 27 insertions(+), 9 deletions(-) diff --git a/doc/guide/models/did/did_pa.rst b/doc/guide/models/did/did_pa.rst index 94b05efb..00b40d55 100644 --- a/doc/guide/models/did/did_pa.rst +++ b/doc/guide/models/did/did_pa.rst @@ -6,12 +6,13 @@ For the estimation of the target parameters :math:`ATT(\mathrm{g},t)` the follow m_{0, \mathrm{g}, t_{eval} + \delta}(X_i) &:= P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t_{eval} + \delta}^{(\cdot)}=1). \end{align} -where :math:`g_{0, \mathrm{g}, t_{pre}, t_{eval}}(\cdot)` denotes the population outcome regression function and :math:`m_{0, \mathrm{g}, t_{eval} + \delta}(\cdot)` the generalized propensity score. +where :math:`g_{0, \mathrm{g}, t_{pre}, t_{eval},\delta}(\cdot)` denotes the population outcome regression function and :math:`m_{0, \mathrm{g}, t_{eval} + \delta}(\cdot)` the generalized propensity score. The interpretation of the parameters is as follows: - - :math:`\mathrm{g}` is the first post-treatment period of interest, i.e. the treatment group. - - :math:`t_{pre}` is the pre-treatment period, i.e. the time period from which the conditional parallel trends are assumed. - - :math:`t_{eval}` is the time period of interest or evaluation period, i.e. the time period where the treatment effect is evaluated. - - :math:`\delta` is number of anticipation periods, i.e. the number of time periods for which units are assumed to anticipate the treatment. + +* :math:`\mathrm{g}` is the first post-treatment period of interest, i.e. the treatment group. +* :math:`t_{pre}` is the pre-treatment period, i.e. the time period from which the conditional parallel trends are assumed. +* :math:`t_{eval}` is the time period of interest or evaluation period, i.e. the time period where the treatment effect is evaluated. +* :math:`\delta` is number of anticipation periods, i.e. the number of time periods for which units are assumed to anticipate the treatment. .. note:: Remark that the nuisance functions depend on the control group used for the estimation of the target parameter. @@ -22,20 +23,26 @@ Under these assumptions the target parameter :math:`ATT(\mathrm{g},t_{eval})` ca of :math:`(\mathrm{g}, t_{pre}, t_{eval}, \delta)` if :math:`t_{eval} - t_{pre} \ge 1 + \delta`, i.e. the parallel trends are assumed to hold at least one period more than the anticipation period. .. note:: - The tuple :math:`(\mathrm{g}, t_{pre}, t_{eval})` is used to implicitly define the corresponding tuple :math:`(g, t, \delta)` with :math:`t=t_{eval}` and :math:`\delta=t_{eval}-t_{pre}-1`. + The choice :math:`t_{pre}= \min(\mathrm{g},t_\text{eval}) -\delta-1` corresponds to the definition of :math:`ATT_{dr}(\mathrm{g},t_\text{eval};\delta)` from `Callaway and Sant'Anna (2021) `_. + +In the following, we will omit the subscript :math:`\delta` in the notation of the nuisance functions and the control group (implicitly assuming :math:`\delta=0`). + For a given tuple :math:`(\mathrm{g}, t_{pre}, t_{eval})` the target parameter :math:`ATT(\mathrm{g},t)` is estimated by solving the empirical version of the the following linear moment condition: .. math:: ATT(\mathrm{g}, t_{pre}, t_{eval}):= -\frac{\mathbb{E}[\psi_b(W,\eta_0)]}{\mathbb{E}[\psi_a(W,\eta_0)]} -with nuisance elements :math:`\eta_0=(g_{0, \mathrm{g}, t_{eval}, t_{eval}-t_{pre}-1}, m_{0, \mathrm{g}, 2t_{eval}-t_{pre}-1})` and score function :math:`\psi(W,\theta, \eta)` being defined in section :ref:`did-pa-score`. +with nuisance elements :math:`\eta_0=(g_{0, \mathrm{g}, t_{pre}, t_{eval}}, m_{0, \mathrm{g}, t_{eval}})` and score function :math:`\psi(W,\theta, \eta)` being defined in section :ref:`did-pa-score`. Under the identifying assumptions above .. math:: ATT(\mathrm{g}, t_{pre}, t_{eval}) = ATT(\mathrm{g},t). -``DoubleMLDIDMulti`` implements the estimation of :math:`ATT(\mathrm{g}, t_{pre}, t_{eval})` for multiple time periods. -Setting ``gt_combinations='standard'`` will estimate the target parameter for all combinations of :math:`(\mathrm{g}, t_{pre}, t_{eval})` with :math:`\mathrm{g}\in\{2,\dots,\mathcal{T}\}` and :math:`t_{pre}\in\{1,\dots,\mathrm{g}-1\}` and :math:`t_{eval}\in\{\mathrm{g},\dots,\mathcal{T}\}`. +``DoubleMLDIDMulti`` implements the estimation of :math:`ATT(\mathrm{g}, t_{pre}, t_{eval})` for multiple time periods and requires :ref:`DoubleMLPanelData ` as input. +Setting ``gt_combinations='standard'`` will estimate the target parameter for all (possible) combinations of :math:`(\mathrm{g}, t_{pre}, t_{eval})` with :math:`\mathrm{g}\in\{2,\dots,\mathcal{T}\}` and :math:`(t_{pre}, t_{eval})` with :math:`t_{eval}\in\{2,\dots,\mathcal{T}\}` and +:math:`t_{pre}= \min(\mathrm{g},t_\text{eval}) -\delta-1`. +This corresponds to the setting where all trends are set as short as possible, but still respecting the anticipation period. + Estimation is conducted via its ``fit()`` method: .. tab-set:: @@ -71,6 +78,17 @@ Estimation is conducted via its ``fit()`` method: ) print(dml_did_obj.fit()) +.. note:: + Remark that the output contains two different outcome regressions :math:`g(0,X)` and :math:`g(1,X)`. As in the :ref:`IRM model ` + the outcome regression :math:`g(0,X)` refers to the control group, whereas :math:`g(1,X)` refers to the outcome regression for the treatment group, i.e. + + .. math:: + \begin{align} + g(0,X) &\approx g_{0, \mathrm{g}, t_{pre}, t_{eval}, \delta}(X_i) = \mathbb{E}[Y_{i,t_{eval}} - Y_{i,t_{pre}}|X_i, C_{i,t_{eval} + \delta}^{(\cdot)} = 1],\\ + g(1,X) &\approx \mathbb{E}[Y_{i,t_{eval}} - Y_{i,t_{pre}}|X_i, G_i^{\mathrm{g}} = 1]. + \end{align} + + Further, :math:`g(1,X)` is only required for :ref:`Sensitivity Analysis ` and is not used for the estimation of the target parameter. .. note:: A more detailed example is available in the :ref:`Example Gallery `. From c62320a77ede5e5a820d39e9a2d2745b62de9aca Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 22 Apr 2025 06:34:26 +0000 Subject: [PATCH 076/140] remove copy --- doc/guide/scores/irm/irm_scores copy.inc | 48 ------------------------ 1 file changed, 48 deletions(-) delete mode 100644 doc/guide/scores/irm/irm_scores copy.inc diff --git a/doc/guide/scores/irm/irm_scores copy.inc b/doc/guide/scores/irm/irm_scores copy.inc deleted file mode 100644 index 9841dcb6..00000000 --- a/doc/guide/scores/irm/irm_scores copy.inc +++ /dev/null @@ -1,48 +0,0 @@ -The following scores for nonparametric regression models are implemented. - -.. _irm-score: - -Binary Interactive Regression Model (IRM) -========================================== - -.. include:: /guide/scores/irm/irm_score.rst - - -.. _apo-score: - -Average Potential Outcomes (APOs) -================================= - -.. include:: /guide/scores/irm/apo_score.rst - - -.. _iivm-score: - -Interactive IV model (IIVM) -=========================== - -.. include:: /guide/scores/irm/iivm_score.rst - - -.. _pq-score: - -Potential quantiles (PQs) -========================= - -.. include:: /guide/scores/irm/pq_score.rst - - -.. _lpq-score: - -Local potential quantiles (LPQs) -================================ - -.. include:: /guide/scores/irm/lpq_score.rst - - -.. _cvar-score: - -Conditional value at risk (CVaR) -================================ - -.. include:: /guide/scores/irm/cvar_score.rst \ No newline at end of file From 0f1f3f64a797461228882cc68a7facae2457c14c Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 22 Apr 2025 06:41:09 +0000 Subject: [PATCH 077/140] restructure sensitivity irm and plm --- doc/guide/sensitivity.rst | 21 +++++++------------ .../sensitivity/{ => irm}/apo_sensitivity.rst | 0 doc/guide/sensitivity/irm/irm_sensitivity.inc | 18 ++++++++++++++++ .../sensitivity/{ => irm}/irm_sensitivity.rst | 0 doc/guide/sensitivity/plm/plm_sensitivity.inc | 8 +++++++ .../sensitivity/{ => plm}/plr_sensitivity.rst | 0 6 files changed, 34 insertions(+), 13 deletions(-) rename doc/guide/sensitivity/{ => irm}/apo_sensitivity.rst (100%) create mode 100644 doc/guide/sensitivity/irm/irm_sensitivity.inc rename doc/guide/sensitivity/{ => irm}/irm_sensitivity.rst (100%) create mode 100644 doc/guide/sensitivity/plm/plm_sensitivity.inc rename doc/guide/sensitivity/{ => plm}/plr_sensitivity.rst (100%) diff --git a/doc/guide/sensitivity.rst b/doc/guide/sensitivity.rst index df9d49bc..ecbb2ec0 100644 --- a/doc/guide/sensitivity.rst +++ b/doc/guide/sensitivity.rst @@ -42,26 +42,21 @@ Model-specific implementations This section contains the implementation details for each specific model and model specific interpretations. -.. _sensitivity_plr: +.. _plm-sensitivity: -Partially linear regression model (PLR) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Partially linear models (PLM) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. include:: ./sensitivity/plr_sensitivity.rst +.. include:: sensitivity/plm/plm_sensitivity.inc -.. _sensitivity_irm: -Interactive regression model (IRM) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. _irm-sensitivity: -.. include:: ./sensitivity/irm_sensitivity.rst +Interactive regression models (IRM) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. _sensitivity_apo: +.. include:: sensitivity/irm/irm_sensitivity.inc -Average Potential Outcomes (APOs) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. include:: ./sensitivity/apo_sensitivity.rst .. _sensitivity_did: diff --git a/doc/guide/sensitivity/apo_sensitivity.rst b/doc/guide/sensitivity/irm/apo_sensitivity.rst similarity index 100% rename from doc/guide/sensitivity/apo_sensitivity.rst rename to doc/guide/sensitivity/irm/apo_sensitivity.rst diff --git a/doc/guide/sensitivity/irm/irm_sensitivity.inc b/doc/guide/sensitivity/irm/irm_sensitivity.inc new file mode 100644 index 00000000..a461f52a --- /dev/null +++ b/doc/guide/sensitivity/irm/irm_sensitivity.inc @@ -0,0 +1,18 @@ +The following nonparametric regression models implemented. + + +.. _sensitivity_irm: + +Interactive regression model (IRM) +======================================= + +.. include:: /guide/sensitivity/irm/irm_sensitivity.rst + + +.. _sensitivity_apo: + +Average Potential Outcomes (APOs) +======================================= + +.. include:: /guide/sensitivity/irm/apo_sensitivity.rst + diff --git a/doc/guide/sensitivity/irm_sensitivity.rst b/doc/guide/sensitivity/irm/irm_sensitivity.rst similarity index 100% rename from doc/guide/sensitivity/irm_sensitivity.rst rename to doc/guide/sensitivity/irm/irm_sensitivity.rst diff --git a/doc/guide/sensitivity/plm/plm_sensitivity.inc b/doc/guide/sensitivity/plm/plm_sensitivity.inc new file mode 100644 index 00000000..347da4d2 --- /dev/null +++ b/doc/guide/sensitivity/plm/plm_sensitivity.inc @@ -0,0 +1,8 @@ +The following partially linear models are implemented. + +.. _sensitivity_plr: + +Partially linear regression model (PLR) +======================================= + +.. include:: /guide/sensitivity/plm/plr_sensitivity.rst diff --git a/doc/guide/sensitivity/plr_sensitivity.rst b/doc/guide/sensitivity/plm/plr_sensitivity.rst similarity index 100% rename from doc/guide/sensitivity/plr_sensitivity.rst rename to doc/guide/sensitivity/plm/plr_sensitivity.rst From 2c9d2d0aa46d1f98df7adc5e8070faf87808754f Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 22 Apr 2025 06:46:04 +0000 Subject: [PATCH 078/140] restructure did sensitivity --- doc/guide/sensitivity.rst | 15 ++++----------- .../sensitivity/{ => did}/did_cs_sensitivity.rst | 0 doc/guide/sensitivity/did/did_sensitivity.inc | 16 ++++++++++++++++ .../sensitivity/{ => did}/did_sensitivity.rst | 0 4 files changed, 20 insertions(+), 11 deletions(-) rename doc/guide/sensitivity/{ => did}/did_cs_sensitivity.rst (100%) create mode 100644 doc/guide/sensitivity/did/did_sensitivity.inc rename doc/guide/sensitivity/{ => did}/did_sensitivity.rst (100%) diff --git a/doc/guide/sensitivity.rst b/doc/guide/sensitivity.rst index ecbb2ec0..4782f780 100644 --- a/doc/guide/sensitivity.rst +++ b/doc/guide/sensitivity.rst @@ -58,16 +58,9 @@ Interactive regression models (IRM) .. include:: sensitivity/irm/irm_sensitivity.inc -.. _sensitivity_did: +.. _did-sensitivity: -Difference-in-Differences for Panel Data -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Difference-in-Differences Models +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. include:: ./sensitivity/did_sensitivity.rst - -.. _sensitivity_did_cs: - -Difference-in-Differences for repeated cross-sections -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. include:: ./sensitivity/did_cs_sensitivity.rst \ No newline at end of file +.. include:: sensitivity/did/did_sensitivity.inc diff --git a/doc/guide/sensitivity/did_cs_sensitivity.rst b/doc/guide/sensitivity/did/did_cs_sensitivity.rst similarity index 100% rename from doc/guide/sensitivity/did_cs_sensitivity.rst rename to doc/guide/sensitivity/did/did_cs_sensitivity.rst diff --git a/doc/guide/sensitivity/did/did_sensitivity.inc b/doc/guide/sensitivity/did/did_sensitivity.inc new file mode 100644 index 00000000..92c183ee --- /dev/null +++ b/doc/guide/sensitivity/did/did_sensitivity.inc @@ -0,0 +1,16 @@ +The following difference-in-differences models implemented. + +.. _sensitivity_did: + +Difference-in-Differences for Panel Data +======================================== + +.. include:: /guide/sensitivity/did/did_sensitivity.rst + + +.. _sensitivity_did_cs: + +Difference-in-Differences for repeated cross-sections +===================================================== + +.. include:: /guide/sensitivity/did/did_cs_sensitivity.rst diff --git a/doc/guide/sensitivity/did_sensitivity.rst b/doc/guide/sensitivity/did/did_sensitivity.rst similarity index 100% rename from doc/guide/sensitivity/did_sensitivity.rst rename to doc/guide/sensitivity/did/did_sensitivity.rst From 7d7438a26c1e62cfffb6af81a6686ad8014b9c2f Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Tue, 22 Apr 2025 10:59:53 +0200 Subject: [PATCH 079/140] fix typo in cond. independence for SSM (nonignorable), spotted by L. Laffers --- doc/guide/models.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/guide/models.rst b/doc/guide/models.rst index b34ae648..3b031287 100644 --- a/doc/guide/models.rst +++ b/doc/guide/models.rst @@ -479,7 +479,7 @@ with unobservables affecting :math:`Y_i` conditional on :math:`D_i` and :math:`X a (unknown) threshold model: - **Threshold:** :math:`S_i = 1\{V_i \le \xi(D,X,Z)\}` where :math:`\xi` is a general function and :math:`V_i` is a scalar with strictly monotonic cumulative distribution function conditional on :math:`X_i`. -- **Cond. Independence:** :math:`Y_i \perp (Z_i, D_i)|X_i`. +- **Cond. Independence:** :math:`V_i \perp (Z_i, D_i)|X_i`. Let :math:`\Pi_i := P(S_i=1|D_i, X_i, Z_i)` denote the selection probability. Additionally, the following assumptions are required: From 4cf7ca1c17192583197e2f674e3a7375623960be Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 22 Apr 2025 10:57:32 +0000 Subject: [PATCH 080/140] fix score note --- doc/guide/scores/did/did_scores.inc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/guide/scores/did/did_scores.inc b/doc/guide/scores/did/did_scores.inc index 01aa09df..14498bde 100644 --- a/doc/guide/scores/did/did_scores.inc +++ b/doc/guide/scores/did/did_scores.inc @@ -22,7 +22,7 @@ Two treatment periods .. warning:: This documentation refers to the deprecated implementation for two time periods. - This functionality will be removed in a future version. The generalized version are :ref:`did-pa-score` and :ref:`did-pa-score`. + This functionality will be removed in a future version. The generalized version are :ref:`did-pa-score` and :ref:`did-cs-score`. Panel Data From 89a18abcb33ac347eca45d87126bc17310aa4cfc Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 22 Apr 2025 11:06:06 +0000 Subject: [PATCH 081/140] update sensitivity did guide --- .../did/did_cs_binary_sensitivity.rst | 34 +++++++++++++++++ .../sensitivity/did/did_cs_sensitivity.rst | 36 +----------------- ...vity.rst => did_pa_binary_sensitivity.rst} | 0 .../sensitivity/did/did_pa_sensitivity.rst | 28 ++++++++++++++ doc/guide/sensitivity/did/did_sensitivity.inc | 38 +++++++++++++++++-- 5 files changed, 99 insertions(+), 37 deletions(-) create mode 100644 doc/guide/sensitivity/did/did_cs_binary_sensitivity.rst rename doc/guide/sensitivity/did/{did_sensitivity.rst => did_pa_binary_sensitivity.rst} (100%) create mode 100644 doc/guide/sensitivity/did/did_pa_sensitivity.rst diff --git a/doc/guide/sensitivity/did/did_cs_binary_sensitivity.rst b/doc/guide/sensitivity/did/did_cs_binary_sensitivity.rst new file mode 100644 index 00000000..3b278f3d --- /dev/null +++ b/doc/guide/sensitivity/did/did_cs_binary_sensitivity.rst @@ -0,0 +1,34 @@ +In the :ref:`did-cs-model` with ``score='observational'`` and ``in_sample_normalization=True`` the score function implies the following representations + +.. math:: + + m(W,g) &= \Big(\big(g(1,1,X) - g(1,0,X)\big) - \big(g(0,1,X) - g(0,0,X)\big)\Big) \frac{D}{\mathbb{E}[D]} + + \alpha(W) &= \frac{DT}{\mathbb{E}[DT]} - \frac{D(1-T)}{\mathbb{E}[D(1-T)]} + + &\quad - \frac{m(X)(1-D)T}{1-m(X)}\mathbb{E}\left[\frac{m(X)(1-D)T}{1-m(X)}\right]^{-1} + + &\quad + \frac{m(X)(1-D)(1-T)}{1-m(X)}\mathbb{E}\left[\frac{m(X)(1-D)(1-T)}{1-m(X)}\right]^{-1}. + +If instead ``in_sample_normalization=False``, the Riesz representer (after simplifications) changes to + +.. math:: + + \alpha(W) = \left(\frac{T}{\mathbb{E}[D]\mathbb{E}[T]} + \frac{1-T}{\mathbb{E}[D](1-\mathbb{E}[T])}\right)\left(D - (1-D)\frac{m(X)}{1-m(X)}\right). + +For ``score='experimental'`` and ``in_sample_normalization=True`` implies the score function implies the following representations + +.. math:: + + m(W,g) &= \big(g(1,1,X) - g(1,0,X)\big) - \big(g(0,1,X) - g(0,0,X)\big) + + \alpha(W) &= \frac{DT}{\mathbb{E}[DT]} - \frac{D(1-T)}{\mathbb{E}[D(1-T)]} - \frac{(1-D)T}{\mathbb{E}[(1-D)T]} + \frac{(1-D)(1-T)}{\mathbb{E}[(1-D)(1-T)]}. + +And again, if instead ``in_sample_normalization=False``, the Riesz representer (after simplifications) changes to + +.. math:: + + \alpha(W) = \frac{DT}{\mathbb{E}[D]\mathbb{E}[T]} - \frac{D(1-T)}{\mathbb{E}[D](1-\mathbb{E}[T])} - \frac{(1-D)T}{(1-\mathbb{E}[D])\mathbb{E}[T]} + \frac{(1-D)(1-T)}{(1-\mathbb{E}[D])(1-\mathbb{E}[T])}. + + +The ``nuisance_elements`` are then computed with plug-in versions according to the general :ref:`sensitivity_implementation`. \ No newline at end of file diff --git a/doc/guide/sensitivity/did/did_cs_sensitivity.rst b/doc/guide/sensitivity/did/did_cs_sensitivity.rst index 3b278f3d..dee07ecb 100644 --- a/doc/guide/sensitivity/did/did_cs_sensitivity.rst +++ b/doc/guide/sensitivity/did/did_cs_sensitivity.rst @@ -1,34 +1,2 @@ -In the :ref:`did-cs-model` with ``score='observational'`` and ``in_sample_normalization=True`` the score function implies the following representations - -.. math:: - - m(W,g) &= \Big(\big(g(1,1,X) - g(1,0,X)\big) - \big(g(0,1,X) - g(0,0,X)\big)\Big) \frac{D}{\mathbb{E}[D]} - - \alpha(W) &= \frac{DT}{\mathbb{E}[DT]} - \frac{D(1-T)}{\mathbb{E}[D(1-T)]} - - &\quad - \frac{m(X)(1-D)T}{1-m(X)}\mathbb{E}\left[\frac{m(X)(1-D)T}{1-m(X)}\right]^{-1} - - &\quad + \frac{m(X)(1-D)(1-T)}{1-m(X)}\mathbb{E}\left[\frac{m(X)(1-D)(1-T)}{1-m(X)}\right]^{-1}. - -If instead ``in_sample_normalization=False``, the Riesz representer (after simplifications) changes to - -.. math:: - - \alpha(W) = \left(\frac{T}{\mathbb{E}[D]\mathbb{E}[T]} + \frac{1-T}{\mathbb{E}[D](1-\mathbb{E}[T])}\right)\left(D - (1-D)\frac{m(X)}{1-m(X)}\right). - -For ``score='experimental'`` and ``in_sample_normalization=True`` implies the score function implies the following representations - -.. math:: - - m(W,g) &= \big(g(1,1,X) - g(1,0,X)\big) - \big(g(0,1,X) - g(0,0,X)\big) - - \alpha(W) &= \frac{DT}{\mathbb{E}[DT]} - \frac{D(1-T)}{\mathbb{E}[D(1-T)]} - \frac{(1-D)T}{\mathbb{E}[(1-D)T]} + \frac{(1-D)(1-T)}{\mathbb{E}[(1-D)(1-T)]}. - -And again, if instead ``in_sample_normalization=False``, the Riesz representer (after simplifications) changes to - -.. math:: - - \alpha(W) = \frac{DT}{\mathbb{E}[D]\mathbb{E}[T]} - \frac{D(1-T)}{\mathbb{E}[D](1-\mathbb{E}[T])} - \frac{(1-D)T}{(1-\mathbb{E}[D])\mathbb{E}[T]} + \frac{(1-D)(1-T)}{(1-\mathbb{E}[D])(1-\mathbb{E}[T])}. - - -The ``nuisance_elements`` are then computed with plug-in versions according to the general :ref:`sensitivity_implementation`. \ No newline at end of file +.. note:: + Will be implemented soon. \ No newline at end of file diff --git a/doc/guide/sensitivity/did/did_sensitivity.rst b/doc/guide/sensitivity/did/did_pa_binary_sensitivity.rst similarity index 100% rename from doc/guide/sensitivity/did/did_sensitivity.rst rename to doc/guide/sensitivity/did/did_pa_binary_sensitivity.rst diff --git a/doc/guide/sensitivity/did/did_pa_sensitivity.rst b/doc/guide/sensitivity/did/did_pa_sensitivity.rst new file mode 100644 index 00000000..c2fa7bf8 --- /dev/null +++ b/doc/guide/sensitivity/did/did_pa_sensitivity.rst @@ -0,0 +1,28 @@ +For a detailed description of the scores and nuisance elements, see :ref:`did-pa-score`. + +In the :ref:`did-pa-model` with ``score='observational'`` and ``in_sample_normalization=True`` the score function implies the following representations + +.. math:: + + m(W,g) &= \big(g(1,X) - g(0,X)\big)\cdot \frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]} + + \alpha(W) &= \frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]} - \frac{\frac{m(X)(1-G^{\mathrm{g}})}{1-m(X)}}{\mathbb{E}\left[\frac{m(X)(1-G^{\mathrm{g}})}{1-m(X)}\right]}. + +If instead ``in_sample_normalization=False``, the Riesz representer changes to + +.. math:: + + \alpha(W) = \frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]} - \frac{m(X)(1-G^{\mathrm{g}})}{\mathbb{E}[G^{\mathrm{g}}](1-m(X))}. + +For ``score='experimental'`` implies the score function implies the following representations + +.. math:: + + m(W,g) &= \big(g(1,X) - g(0,X)\big)\cdot \max(G^{\mathrm{g}}, C^{(\cdot)}) + + \alpha(W) &= \frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]} - \frac{1-G^{\mathrm{g}}}{1-\mathbb{E}[G^{\mathrm{g}}]}. + +The ``nuisance_elements`` are then computed with plug-in versions according to the general :ref:`sensitivity_implementation`. + +.. note:: + Remark that the elements are only non-zero for units in the corresponding treatment group :math:`\mathrm{g}` and control group :math:`C_{t_\text{eval} + \delta}^{(\cdot)}`. \ No newline at end of file diff --git a/doc/guide/sensitivity/did/did_sensitivity.inc b/doc/guide/sensitivity/did/did_sensitivity.inc index 92c183ee..9ae884a8 100644 --- a/doc/guide/sensitivity/did/did_sensitivity.inc +++ b/doc/guide/sensitivity/did/did_sensitivity.inc @@ -1,16 +1,48 @@ The following difference-in-differences models implemented. -.. _sensitivity_did: +.. note:: + Remark that :ref:`sensitivity_benchmark` is only relevant for ``score='observational'``, since no effect of :math:`X` on treatment assignment is assumed. + Generally, we recommend ``score='observational'``, if unobserved confounding seems plausible. + + +.. _sensitivity-did-pa: Difference-in-Differences for Panel Data ======================================== -.. include:: /guide/sensitivity/did/did_sensitivity.rst +.. include:: /guide/sensitivity/did/did_pa_sensitivity.rst -.. _sensitivity_did_cs: +.. _sensitivity-did-cs: Difference-in-Differences for repeated cross-sections ===================================================== .. include:: /guide/sensitivity/did/did_cs_sensitivity.rst + + +.. _sensitivity-did-binary: + +Two treatment periods +====================== + + +.. warning:: + This documentation refers to the deprecated implementation for two time periods. + This functionality will be removed in a future version. The generalized version are :ref:`sensitivity-did-pa` and :ref:`sensitivity-did-cs`. + + +.. _sensitivity-did-pa-binary: + +Panel Data +"""""""""" + +.. include:: /guide/sensitivity/did/did_pa_binary_sensitivity.rst + + +.. _sensitivity-did-cs-binary: + +Repeated Cross-Sectional Data +""""""""""""""""""""""""""""" + +.. include:: /guide/sensitivity/did/did_cs_binary_sensitivity.rst \ No newline at end of file From f635fb4a953a5427ec34a171cfcf06d938dc84ff Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 22 Apr 2025 11:08:02 +0000 Subject: [PATCH 082/140] simplify note --- doc/guide/sensitivity/did/did_pa_sensitivity.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/guide/sensitivity/did/did_pa_sensitivity.rst b/doc/guide/sensitivity/did/did_pa_sensitivity.rst index c2fa7bf8..533cf3c3 100644 --- a/doc/guide/sensitivity/did/did_pa_sensitivity.rst +++ b/doc/guide/sensitivity/did/did_pa_sensitivity.rst @@ -25,4 +25,4 @@ For ``score='experimental'`` implies the score function implies the following re The ``nuisance_elements`` are then computed with plug-in versions according to the general :ref:`sensitivity_implementation`. .. note:: - Remark that the elements are only non-zero for units in the corresponding treatment group :math:`\mathrm{g}` and control group :math:`C_{t_\text{eval} + \delta}^{(\cdot)}`. \ No newline at end of file + Remark that the elements are only non-zero for units in the corresponding treatment group :math:`\mathrm{g}` and control group. \ No newline at end of file From 49eeabede340d4ff7de82e5ef73bbce1bf000f25 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 22 Apr 2025 11:12:48 +0000 Subject: [PATCH 083/140] clarify sensitivity elements --- doc/guide/sensitivity/did/did_pa_sensitivity.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/guide/sensitivity/did/did_pa_sensitivity.rst b/doc/guide/sensitivity/did/did_pa_sensitivity.rst index 533cf3c3..7a9a665d 100644 --- a/doc/guide/sensitivity/did/did_pa_sensitivity.rst +++ b/doc/guide/sensitivity/did/did_pa_sensitivity.rst @@ -6,13 +6,13 @@ In the :ref:`did-pa-model` with ``score='observational'`` and ``in_sample_normal m(W,g) &= \big(g(1,X) - g(0,X)\big)\cdot \frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]} - \alpha(W) &= \frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]} - \frac{\frac{m(X)(1-G^{\mathrm{g}})}{1-m(X)}}{\mathbb{E}\left[\frac{m(X)(1-G^{\mathrm{g}})}{1-m(X)}\right]}. + \alpha(W) &= \left(\frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]} - \frac{\frac{m(X)(1-G^{\mathrm{g}})}{1-m(X)}}{\mathbb{E}\left[\frac{m(X)(1-G^{\mathrm{g}})}{1-m(X)}\right]}\right) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}). If instead ``in_sample_normalization=False``, the Riesz representer changes to .. math:: - \alpha(W) = \frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]} - \frac{m(X)(1-G^{\mathrm{g}})}{\mathbb{E}[G^{\mathrm{g}}](1-m(X))}. + \alpha(W) = \left(\frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]} - \frac{m(X)(1-G^{\mathrm{g}})}{\mathbb{E}[G^{\mathrm{g}}](1-m(X))}\right) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}). For ``score='experimental'`` implies the score function implies the following representations @@ -20,9 +20,9 @@ For ``score='experimental'`` implies the score function implies the following re m(W,g) &= \big(g(1,X) - g(0,X)\big)\cdot \max(G^{\mathrm{g}}, C^{(\cdot)}) - \alpha(W) &= \frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]} - \frac{1-G^{\mathrm{g}}}{1-\mathbb{E}[G^{\mathrm{g}}]}. + \alpha(W) &= \left(\frac{G^{\mathrm{g}}}{\mathbb{E}[G^{\mathrm{g}}]} - \frac{1-G^{\mathrm{g}}}{1-\mathbb{E}[G^{\mathrm{g}}]}\right) \cdot \max(G^{\mathrm{g}}, C^{(\cdot)}). The ``nuisance_elements`` are then computed with plug-in versions according to the general :ref:`sensitivity_implementation`. .. note:: - Remark that the elements are only non-zero for units in the corresponding treatment group :math:`\mathrm{g}` and control group. \ No newline at end of file + Remark that the elements are only non-zero for units in the corresponding treatment group :math:`\mathrm{g}` and control group :math:`C^{(\cdot)}`, as :math:`1-G^{\mathrm{g}}=C^{(\cdot)}` if :math:`G^{\mathrm{g}} \vee C_{t_\text{eval} + \delta}^{(\cdot)}=1`. From 943a04b7a18fa87e147528dd97e8b99ceda49e6f Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 22 Apr 2025 11:19:47 +0000 Subject: [PATCH 084/140] fix reference --- doc/guide/models/did/did_pa.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/guide/models/did/did_pa.rst b/doc/guide/models/did/did_pa.rst index 00b40d55..61cfc6aa 100644 --- a/doc/guide/models/did/did_pa.rst +++ b/doc/guide/models/did/did_pa.rst @@ -88,7 +88,7 @@ Estimation is conducted via its ``fit()`` method: g(1,X) &\approx \mathbb{E}[Y_{i,t_{eval}} - Y_{i,t_{pre}}|X_i, G_i^{\mathrm{g}} = 1]. \end{align} - Further, :math:`g(1,X)` is only required for :ref:`Sensitivity Analysis ` and is not used for the estimation of the target parameter. + Further, :math:`g(1,X)` is only required for :ref:`Sensitivity Analysis ` and is not used for the estimation of the target parameter. .. note:: A more detailed example is available in the :ref:`Example Gallery `. From 08a6e495c367cdc7a4105ecd340388a043b2423d Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 22 Apr 2025 14:57:41 +0000 Subject: [PATCH 085/140] update panel data description --- doc/guide/data/panel_data.rst | 28 +++++++++++++++++++++++++--- 1 file changed, 25 insertions(+), 3 deletions(-) diff --git a/doc/guide/data/panel_data.rst b/doc/guide/data/panel_data.rst index e1766119..c1ed1a43 100644 --- a/doc/guide/data/panel_data.rst +++ b/doc/guide/data/panel_data.rst @@ -2,9 +2,31 @@ The ``DoubleMLPanelData`` class serves as data-backend for :ref:`DiD models ` and inherits all methods and attributes. Furthermore, it provides additional methods and attributes to handle panel data () -* ``id_col``: column to identify the individual units -* ``t_col``: column to specify the time periods +* ``id_col``: column to with unique identifiers for each unit +* ``t_col``: column to specify the time periods of the observation * ``datetime_unit``: unit of the time periods (e.g. 'Y', 'M', 'D', 'h', 'm', 's') .. note:: - The ``t_col`` can contain either \ No newline at end of file + The ``t_col`` can contain ``float``, ``int`` or ``datetime`` values. + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + + from doubleml.did.datasets import make_did_CS2021 + + np.random.seed(42) + df = make_did_CS2021(n_obs=500) + dml_data = dml.data.DoubleMLPanelData( + df, + y_col="y", + d_cols="d", + id_col="id", + t_col="t", + x_cols=["Z1", "Z2", "Z3", "Z4"], + datetime_unit="M" + ) + print(dml_data) From 36710bf6c4c585a3ac2c348583ee801ccd855d1f Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 22 Apr 2025 14:57:53 +0000 Subject: [PATCH 086/140] update aggregation --- doc/guide/models/did/did_aggregation.rst | 57 ++++++++++++++++++++++++ doc/guide/models/did/did_models.inc | 2 + 2 files changed, 59 insertions(+) create mode 100644 doc/guide/models/did/did_aggregation.rst diff --git a/doc/guide/models/did/did_aggregation.rst b/doc/guide/models/did/did_aggregation.rst new file mode 100644 index 00000000..df09c380 --- /dev/null +++ b/doc/guide/models/did/did_aggregation.rst @@ -0,0 +1,57 @@ +The following section considers the aggregation of different :math:`ATT(\mathrm{g},t)` to summary measures based on `Callaway and Sant'Anna (2021) `_. +All implemented aggregation schemes take the form of a weighted average of the :math:`ATT(\mathrm{g},t)` estimates + +.. math:: + \theta = \sum_{\mathrm{g}\in \mathcal{G}} \sum_{t=2}^{\mathcal{T}} \omega(\mathrm{g},t) \cdot ATT(\mathrm{g},t) + +where :math:`\omega(\mathrm{g},t)` is a weight function based on the treatment group :math:`\mathrm{g}` and time period :math:`t`. +The aggragation schemes are implmented via the ``aggregate()`` method of the ``DoubleMLDIDMulti`` class. + + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + :okwarning: + + import numpy as np + import doubleml as dml + from doubleml.did.datasets import make_did_CS2021 + from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier + + np.random.seed(42) + df = make_did_CS2021(n_obs=500) + dml_data = dml.data.DoubleMLPanelData( + df, + y_col="y", + d_cols="d", + id_col="id", + t_col="t", + x_cols=["Z1", "Z2", "Z3", "Z4"], + datetime_unit="M" + ) + dml_did_obj = dml.did.DoubleMLDIDMulti( + obj_dml_data=dml_data, + ml_g=ml_g, + ml_m=ml_m, + gt_combinations="standard", + control_group="never_treated", + ) + dml_did_obj.fit() + + agg_did_obj = dml_did_obj.aggregate(aggregation="group") + agg_did_obj.aggregated_frameworks.bootstrap() + print(agg_did_obj) + +The method ``aggregate()`` requires the ``aggregation`` argument to be set to one of the following values: + +* ``'group'``: aggregates :math:`ATT(\mathrm{g},t)` estimates by the treatment group :math:`\mathrm{g}`. +* ``'time'``: aggregates :math:`ATT(\mathrm{g},t)` estimates by the time period :math:`t` (based on group size). +* ``'eventstudy'``: aggregates :math:`ATT(\mathrm{g},t)` estimates based on time difference to first treatment assignment like an event study (based on group size). +* ``dictionary``: a dictionary with values containing the aggregation weights (as ``numpy.ma.MaskedArray``). + +.. note:: + A more detailed example on effect aggregation is available in the :ref:`example gallery `. + For a detailed discussion on different aggregation schemes, we refer to of `Callaway and Sant'Anna (2021) `_. diff --git a/doc/guide/models/did/did_models.inc b/doc/guide/models/did/did_models.inc index 433b8341..88575949 100644 --- a/doc/guide/models/did/did_models.inc +++ b/doc/guide/models/did/did_models.inc @@ -22,6 +22,8 @@ Repeated cross-sections Effect Aggregation ****************** +.. include:: /guide/models/did/did_aggregation.rst + .. _did-binary-model: From 29599b759ce2840daa16af0dd4bff97223345f96 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 23 Apr 2025 06:19:24 +0000 Subject: [PATCH 087/140] update simple notebook --- doc/examples/py_double_ml_panel_simple.ipynb | 584 +++++++++++++------ 1 file changed, 411 insertions(+), 173 deletions(-) diff --git a/doc/examples/py_double_ml_panel_simple.ipynb b/doc/examples/py_double_ml_panel_simple.ipynb index 98aa5112..4ebe20f5 100644 --- a/doc/examples/py_double_ml_panel_simple.ipynb +++ b/doc/examples/py_double_ml_panel_simple.ipynb @@ -4,12 +4,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Python: Simple Panel Data Example" + "# Python: Panel Data Introduction\n", + "\n", + "In this example, we replicate the results from the guide [Getting Started with the did Package](https://bcallaway11.github.io/did/articles/did-basics.html) of the [did-R-package](https://bcallaway11.github.io/did/index.html).\n", + "\n", + "The notebook requires the following packages:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -23,32 +27,19 @@ ] }, { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "dta = pd.read_csv(\"https://raw.githubusercontent.com/d2cml-ai/csdid/main/data/sim_data.csv\")\n", - "dta.head()\n", - "# set dtype for G to float\n", - "dta[\"G\"] = dta[\"G\"].astype(float)\n", - "dta.loc[dta[\"G\"] == 0, \"G\"] = np.inf" - ] - }, - { - "cell_type": "code", - "execution_count": 7, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "dml_data = DoubleMLPanelData(dta, y_col=\"Y\", d_cols=\"G\", id_col=\"id\", t_col=\"period\", x_cols=[\"X\"])" + "## Data\n", + "\n", + "The data we will use is simulated and part of the [CSDID-Python-Package](https://d2cml-ai.github.io/csdid/index.html).\n", + "\n", + "A description of the data generating process can be found at the [CSDID-documentation](https://d2cml-ai.github.io/csdid/examples/csdid_basic.html#Examples-with-simulated-data).\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -72,188 +63,267 @@ " \n", " \n", " \n", - " coef\n", - " std err\n", - " t\n", - " P>|t|\n", - " 2.5 %\n", - " 97.5 %\n", + " G\n", + " X\n", + " id\n", + " cluster\n", + " period\n", + " Y\n", + " treat\n", " \n", " \n", " \n", " \n", - " ATT(2.0,1,2)\n", - " 0.922607\n", - " 0.064144\n", - " 14.383278\n", - " 0.000000\n", - " 0.796886\n", - " 1.048328\n", + " 0\n", + " 3\n", + " -0.876233\n", + " 1\n", + " 5\n", + " 1\n", + " 5.562556\n", + " 1\n", " \n", " \n", - " ATT(2.0,1,3)\n", - " 1.990200\n", - " 0.064701\n", - " 30.760062\n", - " 0.000000\n", - " 1.863388\n", - " 2.117011\n", + " 1\n", + " 3\n", + " -0.876233\n", + " 1\n", + " 5\n", + " 2\n", + " 4.349213\n", + " 1\n", " \n", " \n", - " ATT(2.0,1,4)\n", - " 2.955379\n", - " 0.063298\n", - " 46.689908\n", - " 0.000000\n", - " 2.831317\n", - " 3.079441\n", + " 2\n", + " 3\n", + " -0.876233\n", + " 1\n", + " 5\n", + " 3\n", + " 7.134037\n", + " 1\n", " \n", " \n", - " ATT(3.0,1,2)\n", - " -0.041535\n", - " 0.065788\n", - " -0.631352\n", - " 0.527810\n", - " -0.170477\n", - " 0.087406\n", + " 3\n", + " 3\n", + " -0.876233\n", + " 1\n", + " 5\n", + " 4\n", + " 6.243056\n", + " 1\n", " \n", " \n", - " ATT(3.0,2,3)\n", - " 1.107889\n", - " 0.065385\n", - " 16.944147\n", - " 0.000000\n", - " 0.979737\n", - " 1.236041\n", + " 4\n", + " 2\n", + " -0.873848\n", + " 2\n", + " 36\n", + " 1\n", + " -3.659387\n", + " 1\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " G X id cluster period Y treat\n", + "0 3 -0.876233 1 5 1 5.562556 1\n", + "1 3 -0.876233 1 5 2 4.349213 1\n", + "2 3 -0.876233 1 5 3 7.134037 1\n", + "3 3 -0.876233 1 5 4 6.243056 1\n", + "4 2 -0.873848 2 36 1 -3.659387 1" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dta = pd.read_csv(\"https://raw.githubusercontent.com/d2cml-ai/csdid/main/data/sim_data.csv\")\n", + "dta.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To work with the [DoubleML-package](https://docs.doubleml.org/stable/index.html), we initialize a ``DoubleMLPanelData`` object.\n", + "\n", + "Therefore, we set the *never-treated* units in group column `G` to `np.inf` (we have to change the datatype to `float`)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" ], "text/plain": [ - " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.922607 0.064144 14.383278 0.000000 0.796886 1.048328\n", - "ATT(2.0,1,3) 1.990200 0.064701 30.760062 0.000000 1.863388 2.117011\n", - "ATT(2.0,1,4) 2.955379 0.063298 46.689908 0.000000 2.831317 3.079441\n", - "ATT(3.0,1,2) -0.041535 0.065788 -0.631352 0.527810 -0.170477 0.087406\n", - "ATT(3.0,2,3) 1.107889 0.065385 16.944147 0.000000 0.979737 1.236041\n", - "ATT(3.0,2,4) 2.060141 0.065261 31.567865 0.000000 1.932232 2.188049\n", - "ATT(4.0,1,2) 0.001911 0.068411 0.027932 0.977716 -0.132171 0.135993\n", - "ATT(4.0,2,3) 0.058708 0.066517 0.882598 0.377454 -0.071663 0.189079\n", - "ATT(4.0,3,4) 0.949944 0.067551 14.062713 0.000000 0.817547 1.082340" + " G X id cluster period Y treat\n", + "0 3.0 -0.876233 1 5 1 5.562556 1\n", + "1 3.0 -0.876233 1 5 2 4.349213 1\n", + "2 3.0 -0.876233 1 5 3 7.134037 1\n", + "3 3.0 -0.876233 1 5 4 6.243056 1\n", + "4 2.0 -0.873848 2 36 1 -3.659387 1" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "control_group = \"not_yet_treated\"\n", - "control_group = \"never_treated\"\n", - "\n", - "ml_g=LinearRegression()\n", - "ml_m=LogisticRegression()\n", - "\n", - "# ml_g = LGBMRegressor()\n", - "# ml_m = LGBMClassifier()\n", - "\n", - "dml_obj = DoubleMLDIDMulti(\n", - " obj_dml_data=dml_data,\n", - " ml_g=ml_g,\n", - " ml_m=ml_m,\n", - " gt_combinations=\"standard\",\n", - " control_group=control_group,\n", - ")\n", + "# set dtype for G to float\n", + "dta[\"G\"] = dta[\"G\"].astype(float)\n", + "dta.loc[dta[\"G\"] == 0, \"G\"] = np.inf\n", + "dta.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can initialize the ``DoubleMLPanelData`` object, specifying\n", "\n", - "dml_obj.fit()\n", - "dml_obj.summary" + " - `y_col` : the outcome\n", + " - `d_cols`: the group variable indicating the first treated period for each unit\n", + " - `id_col`: the unique identification column for each unit\n", + " - `t_col` : the time column\n", + " - `x_cols`: the additional pre-treatment controls\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_multi.py:1298: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", - " warnings.warn(\n", - "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n", - "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n" + "================== DoubleMLPanelData Object ==================\n", + "\n", + "------------------ Data summary ------------------\n", + "Outcome variable: Y\n", + "Treatment variable(s): ['G']\n", + "Covariates: ['X']\n", + "Instrument variable(s): None\n", + "Time variable: period\n", + "Id variable: id\n", + "No. Observations: 3979\n", + "\n", + "------------------ DataFrame info ------------------\n", + "\n", + "RangeIndex: 15916 entries, 0 to 15915\n", + "Columns: 7 entries, G to treat\n", + "dtypes: float64(3), int64(4)\n", + "memory usage: 870.5 KB\n", + "\n" ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "fig, ax = dml_obj.plot_effects()" + "dml_data = DoubleMLPanelData(dta, y_col=\"Y\", d_cols=\"G\", id_col=\"id\", t_col=\"period\", x_cols=[\"X\"])\n", + "print(dml_data)" ] }, { - "cell_type": "code", - "execution_count": 10, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "level = 0.95\n", - "\n", - "ci = dml_obj.confint(level=level)\n", - "dml_obj.bootstrap(n_rep_boot=5000)\n", - "ci_joint = dml_obj.confint(level=level, joint=True)" + "## ATT estimation" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -281,13 +351,13 @@ "Learner ml_m: LogisticRegression()\n", "Out-of-sample Performance:\n", "Regression:\n", - "Learner ml_g0 RMSE: [[1.428271 1.40887549 1.40168244 1.42532747 1.4039708 1.41869578\n", - " 1.42626481 1.40583671 1.422799 ]]\n", - "Learner ml_g1 RMSE: [[1.4041563 1.43664137 1.39962992 1.41564601 1.42921804 1.38423683\n", - " 1.45633209 1.41489659 1.40683575]]\n", + "Learner ml_g0 RMSE: [[1.42723659 1.40976728 1.39815026 1.42717567 1.40453859 1.42290288\n", + " 1.42334363 1.40484296 1.42488798]]\n", + "Learner ml_g1 RMSE: [[1.40243017 1.43571358 1.39765614 1.41559579 1.43028918 1.38457862\n", + " 1.4575677 1.41732542 1.40766147]]\n", "Classification:\n", - "Learner ml_m Log Loss: [[0.69055048 0.69162981 0.69073153 0.67993783 0.67912701 0.67981987\n", - " 0.66247836 0.6630701 0.66225537]]\n", + "Learner ml_m Log Loss: [[0.69194005 0.69043888 0.69081606 0.67933534 0.67918078 0.67926994\n", + " 0.66255063 0.66234746 0.66203825]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 5\n", @@ -295,25 +365,193 @@ "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.922607 0.064144 14.383278 0.000000 0.796886 1.048328\n", - "ATT(2.0,1,3) 1.990200 0.064701 30.760062 0.000000 1.863388 2.117011\n", - "ATT(2.0,1,4) 2.955379 0.063298 46.689908 0.000000 2.831317 3.079441\n", - "ATT(3.0,1,2) -0.041535 0.065788 -0.631352 0.527810 -0.170477 0.087406\n", - "ATT(3.0,2,3) 1.107889 0.065385 16.944147 0.000000 0.979737 1.236041\n", - "ATT(3.0,2,4) 2.060141 0.065261 31.567865 0.000000 1.932232 2.188049\n", - "ATT(4.0,1,2) 0.001911 0.068411 0.027932 0.977716 -0.132171 0.135993\n", - "ATT(4.0,2,3) 0.058708 0.066517 0.882598 0.377454 -0.071663 0.189079\n", - "ATT(4.0,3,4) 0.949944 0.067551 14.062713 0.000000 0.817547 1.082340\n" + "ATT(2.0,1,2) 0.918086 0.064134 14.315195 0.000000 0.792387 1.043786\n", + "ATT(2.0,1,3) 1.988341 0.064616 30.771560 0.000000 1.861696 2.114986\n", + "ATT(2.0,1,4) 2.956276 0.063196 46.779647 0.000000 2.832415 3.080138\n", + "ATT(3.0,1,2) -0.039725 0.066021 -0.601700 0.547374 -0.169125 0.089675\n", + "ATT(3.0,2,3) 1.107672 0.065377 16.942897 0.000000 0.979536 1.235808\n", + "ATT(3.0,2,4) 2.059259 0.065442 31.466923 0.000000 1.930995 2.187523\n", + "ATT(4.0,1,2) 0.000070 0.068208 0.001028 0.999180 -0.133615 0.133756\n", + "ATT(4.0,2,3) 0.060247 0.066492 0.906086 0.364890 -0.070074 0.190569\n", + "ATT(4.0,3,4) 0.950055 0.067429 14.089722 0.000000 0.817897 1.082213\n" ] } ], "source": [ + "dml_obj = DoubleMLDIDMulti(\n", + " obj_dml_data=dml_data,\n", + " ml_g=LinearRegression(),\n", + " ml_m=LogisticRegression(),\n", + " control_group=\"never_treated\",\n", + ")\n", + "\n", + "dml_obj.fit()\n", "print(dml_obj)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual for the DoubleML-package, you can obtain joint confidence intervals via bootstrap." + ] + }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2.5 %97.5 %
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" + ], + "text/plain": [ + " 2.5 % 97.5 %\n", + "ATT(2.0,1,2) 0.742383 1.093789\n", + "ATT(2.0,1,3) 1.811316 2.165366\n", + "ATT(2.0,1,4) 2.783143 3.129410\n", + "ATT(3.0,1,2) -0.220600 0.141150\n", + "ATT(3.0,2,3) 0.928564 1.286781\n", + "ATT(3.0,2,4) 1.879972 2.238546\n", + "ATT(4.0,1,2) -0.186795 0.186935\n", + "ATT(4.0,2,3) -0.121916 0.242411\n", + "ATT(4.0,3,4) 0.765324 1.134785" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "level = 0.95\n", + "\n", + "ci = dml_obj.confint(level=level)\n", + "dml_obj.bootstrap(n_rep_boot=5000)\n", + "ci_joint = dml_obj.confint(level=level, joint=True)\n", + "ci_joint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A visualization of the effects can be obtained via the `plot_effects()` method.\n", + "\n", + "Remark that the plot used joint confidence intervals per default. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = dml_obj.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Effect Aggregation" + ] + }, + { + "cell_type": "code", + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -325,16 +563,16 @@ "\n", "------------------ Overall Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.487887 0.034226 43.472843 0.0 1.420805 1.554968\n", + "1.487145 0.034209 43.472838 0.0 1.420097 1.554192\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "2.0 1.956062 0.052292 37.406316 0.0 1.853571 2.058553\n", - "3.0 1.584015 0.056203 28.183929 0.0 1.473859 1.694170\n", - "4.0 0.949944 0.067551 14.062713 0.0 0.817547 1.082340\n", + "2.0 1.954235 0.052280 37.380505 0.0 1.851769 2.056701\n", + "3.0 1.583466 0.056236 28.157478 0.0 1.473245 1.693686\n", + "4.0 0.950055 0.067429 14.089722 0.0 0.817897 1.082213\n", "------------------ Additional Information ------------------\n", - "Control Group: never_treated\n", - "Anticipation Periods: 0\n", - "Score: observational\n", + "Score function: observational\n", + "Control group: never_treated\n", + "Anticipation periods: 0\n", "\n" ] }, @@ -348,7 +586,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -360,7 +598,7 @@ "source": [ "aggregated = dml_obj.aggregate(\"group\")\n", "print(aggregated)\n", - "fig, ax = aggregated.plot_effects()" + "_ = aggregated.plot_effects()" ] }, { From 1b2b462ffe122060172c351e85d0dabd338609c2 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 23 Apr 2025 06:19:45 +0000 Subject: [PATCH 088/140] update did notebook --- doc/examples/py_double_ml_panel.ipynb | 482 +++++++++++++------------- 1 file changed, 236 insertions(+), 246 deletions(-) diff --git a/doc/examples/py_double_ml_panel.ipynb b/doc/examples/py_double_ml_panel.ipynb index f75ec979..e5357635 100644 --- a/doc/examples/py_double_ml_panel.ipynb +++ b/doc/examples/py_double_ml_panel.ipynb @@ -27,7 +27,7 @@ "\n", "# simulate data\n", "n_obs = 5000\n", - "df = make_did_CS2021(n_obs, dgp_type=1, n_periods=8, n_pre_treat_periods=4, time_type=\"datetime\")\n", + "df = make_did_CS2021(n_obs, dgp_type=1, n_periods=8, n_pre_treat_periods=4, time_type=\"datetime\", anticipation_periods=1)\n", "df[\"ite\"] = df[\"y1\"] - df[\"y0\"]" ] }, @@ -73,79 +73,79 @@ " \n", " \n", " \n", - " 0\n", + " 1\n", " 0\n", - " 196.628345\n", - " 196.628345\n", - " 196.469772\n", - " 2025-07-01\n", + " 220.164100\n", + " 220.164100\n", + " 219.334836\n", + " 2025-06-01\n", " 2025-01-01\n", - " -1.378363\n", - " -1.563339\n", - " -1.755421\n", - " -1.267233\n", - " -0.158573\n", - " 2025-07\n", + " 0.685912\n", + " 0.330527\n", + " -0.212164\n", + " 0.818772\n", + " -0.829265\n", + " 2025-06\n", " \n", " \n", - " 1\n", + " 2\n", " 0\n", - " 184.014741\n", - " 184.014741\n", - " 184.639146\n", - " 2025-07-01\n", + " 222.561289\n", + " 222.561289\n", + " 222.890774\n", + " 2025-06-01\n", " 2025-02-01\n", - " -1.378363\n", - " -1.563339\n", - " -1.755421\n", - " -1.267233\n", - " 0.624406\n", - " 2025-07\n", + " 0.685912\n", + " 0.330527\n", + " -0.212164\n", + " 0.818772\n", + " 0.329485\n", + " 2025-06\n", " \n", " \n", - " 2\n", + " 3\n", " 0\n", - " 171.033521\n", - " 171.033521\n", - " 173.094369\n", - " 2025-07-01\n", + " 229.011563\n", + " 229.011563\n", + " 228.624548\n", + " 2025-06-01\n", " 2025-03-01\n", - " -1.378363\n", - " -1.563339\n", - " -1.755421\n", - " -1.267233\n", - " 2.060849\n", - " 2025-07\n", + " 0.685912\n", + " 0.330527\n", + " -0.212164\n", + " 0.818772\n", + " -0.387014\n", + " 2025-06\n", " \n", " \n", - " 3\n", + " 4\n", " 0\n", - " 160.310762\n", - " 160.310762\n", - " 160.414796\n", - " 2025-07-01\n", + " 232.255967\n", + " 232.255967\n", + " 231.807130\n", + " 2025-06-01\n", " 2025-04-01\n", - " -1.378363\n", - " -1.563339\n", - " -1.755421\n", - " -1.267233\n", - " 0.104035\n", - " 2025-07\n", + " 0.685912\n", + " 0.330527\n", + " -0.212164\n", + " 0.818772\n", + " -0.448837\n", + " 2025-06\n", " \n", " \n", - " 4\n", + " 5\n", " 0\n", - " 150.447513\n", - " 150.447513\n", - " 151.102154\n", - " 2025-07-01\n", + " 235.314414\n", + " 237.134471\n", + " 235.314414\n", + " 2025-06-01\n", " 2025-05-01\n", - " -1.378363\n", - " -1.563339\n", - " -1.755421\n", - " -1.267233\n", - " 0.654641\n", - " 2025-07\n", + " 0.685912\n", + " 0.330527\n", + " -0.212164\n", + " 0.818772\n", + " -1.820057\n", + " 2025-06\n", " \n", " \n", "\n", @@ -153,18 +153,18 @@ ], "text/plain": [ " id y y0 y1 d t Z1 \\\n", - "0 0 196.628345 196.628345 196.469772 2025-07-01 2025-01-01 -1.378363 \n", - "1 0 184.014741 184.014741 184.639146 2025-07-01 2025-02-01 -1.378363 \n", - "2 0 171.033521 171.033521 173.094369 2025-07-01 2025-03-01 -1.378363 \n", - "3 0 160.310762 160.310762 160.414796 2025-07-01 2025-04-01 -1.378363 \n", - "4 0 150.447513 150.447513 151.102154 2025-07-01 2025-05-01 -1.378363 \n", + "1 0 220.164100 220.164100 219.334836 2025-06-01 2025-01-01 0.685912 \n", + "2 0 222.561289 222.561289 222.890774 2025-06-01 2025-02-01 0.685912 \n", + "3 0 229.011563 229.011563 228.624548 2025-06-01 2025-03-01 0.685912 \n", + "4 0 232.255967 232.255967 231.807130 2025-06-01 2025-04-01 0.685912 \n", + "5 0 235.314414 237.134471 235.314414 2025-06-01 2025-05-01 0.685912 \n", "\n", " Z2 Z3 Z4 ite First Treated \n", - "0 -1.563339 -1.755421 -1.267233 -0.158573 2025-07 \n", - "1 -1.563339 -1.755421 -1.267233 0.624406 2025-07 \n", - "2 -1.563339 -1.755421 -1.267233 2.060849 2025-07 \n", - "3 -1.563339 -1.755421 -1.267233 0.104035 2025-07 \n", - "4 -1.563339 -1.755421 -1.267233 0.654641 2025-07 " + "1 0.330527 -0.212164 0.818772 -0.829265 2025-06 \n", + "2 0.330527 -0.212164 0.818772 0.329485 2025-06 \n", + "3 0.330527 -0.212164 0.818772 -0.387014 2025-06 \n", + "4 0.330527 -0.212164 0.818772 -0.448837 2025-06 \n", + "5 0.330527 -0.212164 0.818772 -1.820057 2025-06 " ] }, "execution_count": 2, @@ -219,56 +219,56 @@ " 0\n", " 2025-01-01\n", " 2025-05\n", - " 209.232972\n", - " 202.011675\n", - " 216.526857\n", - " 0.054854\n", - " -2.278644\n", - " 2.420543\n", + " 209.552980\n", + " 197.520569\n", + " 222.082885\n", + " -0.040413\n", + " -2.317893\n", + " 2.385829\n", " \n", " \n", " 1\n", " 2025-01-01\n", " 2025-06\n", - " 210.451564\n", - " 203.071081\n", - " 218.424421\n", - " -0.073761\n", - " -2.409091\n", - " 2.299715\n", + " 210.554072\n", + " 198.302910\n", + " 222.867786\n", + " 0.019272\n", + " -2.274532\n", + " 2.359568\n", " \n", " \n", " 2\n", " 2025-01-01\n", " 2025-07\n", - " 211.487736\n", - " 204.039630\n", - " 219.377341\n", - " 0.000180\n", - " -2.362993\n", - " 2.340590\n", + " 212.095170\n", + " 198.467939\n", + " 225.239903\n", + " 0.002804\n", + " -2.335157\n", + " 2.237084\n", " \n", " \n", " 3\n", " 2025-01-01\n", " 2025-08\n", - " 213.457445\n", - " 204.544638\n", - " 222.339047\n", - " -0.054813\n", - " -2.368209\n", - " 2.522341\n", + " 213.806216\n", + " 200.665427\n", + " 227.372799\n", + " -0.016073\n", + " -2.496059\n", + " 2.250476\n", " \n", " \n", " 4\n", " 2025-01-01\n", " Never Treated\n", - " 215.221319\n", - " 206.282226\n", - " 225.772989\n", - " 0.008458\n", - " -2.368282\n", - " 2.319752\n", + " 218.582217\n", + " 203.797220\n", + " 237.916535\n", + " -0.011793\n", + " -2.241407\n", + " 2.430760\n", " \n", " \n", "\n", @@ -276,18 +276,18 @@ ], "text/plain": [ " t First Treated y_mean y_lower_quantile y_upper_quantile \\\n", - "0 2025-01-01 2025-05 209.232972 202.011675 216.526857 \n", - "1 2025-01-01 2025-06 210.451564 203.071081 218.424421 \n", - "2 2025-01-01 2025-07 211.487736 204.039630 219.377341 \n", - "3 2025-01-01 2025-08 213.457445 204.544638 222.339047 \n", - "4 2025-01-01 Never Treated 215.221319 206.282226 225.772989 \n", + "0 2025-01-01 2025-05 209.552980 197.520569 222.082885 \n", + "1 2025-01-01 2025-06 210.554072 198.302910 222.867786 \n", + "2 2025-01-01 2025-07 212.095170 198.467939 225.239903 \n", + "3 2025-01-01 2025-08 213.806216 200.665427 227.372799 \n", + "4 2025-01-01 Never Treated 218.582217 203.797220 237.916535 \n", "\n", " ite_mean ite_lower_quantile ite_upper_quantile \n", - "0 0.054854 -2.278644 2.420543 \n", - "1 -0.073761 -2.409091 2.299715 \n", - "2 0.000180 -2.362993 2.340590 \n", - "3 -0.054813 -2.368209 2.522341 \n", - "4 0.008458 -2.368282 2.319752 " + "0 -0.040413 -2.317893 2.385829 \n", + "1 0.019272 -2.274532 2.359568 \n", + "2 0.002804 -2.335157 2.237084 \n", + "3 -0.016073 -2.496059 2.250476 \n", + "4 -0.011793 -2.241407 2.430760 " ] }, "execution_count": 3, @@ -321,7 +321,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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" ] @@ -405,14 +405,14 @@ "Instrument variable(s): None\n", "Time variable: t\n", "Id variable: id\n", - "No. Observations: 5000\n", + "No. Observations: 4166\n", "\n", "------------------ DataFrame info ------------------\n", "\n", - "RangeIndex: 40000 entries, 0 to 39999\n", + "Index: 33328 entries, 1 to 44999\n", "Columns: 12 entries, id to First Treated\n", - "dtypes: datetime64[s](2), float64(8), int64(1), object(1)\n", - "memory usage: 3.7+ MB\n", + "dtypes: datetime64[ns](1), datetime64[s](1), float64(8), int64(1), object(1)\n", + "memory usage: 3.3+ MB\n", "\n" ] } @@ -440,70 +440,58 @@ "Instrument variable(s): None\n", "Time variable: t\n", "Id variable: id\n", - "No. Observations: 5000\n", + "No. Observations: 4166\n", "\n", "------------------ Score & algorithm ------------------\n", "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", + "Control group: not_yet_treated\n", + "Anticipation periods: 1\n", "\n", "------------------ Machine learner ------------------\n", "Learner ml_g: LinearRegression()\n", "Learner ml_m: LogisticRegression()\n", "Out-of-sample Performance:\n", "Regression:\n", - "Learner ml_g0 RMSE: [[1.40636468 1.44015593 1.42710964 1.42719583 1.43995196 1.4435586\n", - " 1.39726542 1.42461424 1.43808497 1.44409564 1.40017327 1.39597072\n", - " 1.396656 1.43945749 1.40855224 1.37310968 1.4149736 1.40179805\n", - " 1.40867837 1.39912807 1.42085823 1.41535716 1.40426326 1.43647614\n", - " 1.43173649 1.42466185 1.43807652 1.44161035 1.39454981 1.42545671\n", - " 1.44018884 1.44159814 1.39295159 1.3933023 1.40527355 1.43729703\n", - " 1.44331899 1.40234896 1.37454139 1.41121844 1.41071582 1.42088789\n", - " 1.41167641 1.39742438 1.42029853 1.41766014 1.4181153 1.40648856\n", - " 1.44481289 1.42855508 1.42892803 1.43780527 1.43887904 1.40065218\n", - " 1.42290642 1.44203138 1.4426855 1.39935117 1.39471043 1.39955737\n", - " 1.44077749 1.44389316 1.40959275 1.37430287 1.41090721 1.39743489\n", - " 1.41798082 1.41512627 1.41168861 1.40389039 1.42310191 1.41282563\n", - " 1.4170658 1.493958 1.40166329 1.44236241 1.43090214 1.42508737\n", - " 1.44273197 1.439739 1.40341833 1.42703483 1.4432334 1.44745272\n", - " 1.39288688 1.39367199 1.39807572 1.43814946 1.44517488 1.41045434\n", - " 1.37506985 1.4152223 1.39963339 1.42097323 1.41427081 1.40806781\n", - " 1.40210414 1.422739 1.41561647 1.42284515 1.49904823 1.41586234]]\n", - "Learner ml_g1 RMSE: [[1.4185991 1.44547181 1.42515753 1.40101595 1.46872265 1.3818256\n", - " 1.4628924 1.4504521 1.42959483 1.45698996 1.36900108 1.40896171\n", - " 1.40513689 1.41310338 1.42166587 1.40811586 1.42525445 1.40842281\n", - " 1.45723778 1.43017939 1.4513467 1.47499349 1.39292093 1.46578427\n", - " 1.42443042 1.42285428 1.35717672 1.39697897 1.44784068 1.40726902\n", - " 1.42258459 1.40616465 1.4338824 1.42228726 1.47067428 1.44792118\n", - " 1.42312839 1.42381611 1.41728479 1.42996156 1.40903218 1.39283485\n", - " 1.45808644 1.40390273 1.43347473 1.44079198 1.44865711 1.47497189\n", - " 1.47580551 1.41607056 1.44257842 1.43859062 1.47297706 1.41696804\n", - " 1.41433485 1.46491518 1.41314016 1.44937681 1.41615882 1.46261754\n", - " 1.44746991 1.44444092 1.41598614 1.41675391 1.39886877 1.4423026\n", - " 1.4229864 1.39109575 1.48997319 1.44430582 1.46419359 1.39074961\n", - " 1.41294712 1.42137573 1.40659136 1.38673754 1.44535711 1.38052206\n", - " 1.4419186 1.4312496 1.38136619 1.43504706 1.420354 1.4179437\n", - " 1.41486017 1.41712425 1.3813208 1.40152805 1.35039453 1.37532784\n", - " 1.43802062 1.36895555 1.39766652 1.41516996 1.38555265 1.34793372\n", - " 1.43417418 1.40695701 1.4119966 1.40334784 1.40896759 1.4418237 ]]\n", + "Learner ml_g0 RMSE: [[1.39102995 1.39785296 1.40925291 1.40633464 1.44375731 1.41471744\n", + " 1.41210696 1.42784954 1.38852508 1.49782591 1.42356244 1.40503666\n", + " 1.46535437 1.40179023 1.39414503 1.39890822 1.40112555 1.40870043\n", + " 1.40924143 1.44293372 1.41553378 1.41140838 1.43293957 1.38991131\n", + " 1.40808286 1.49778755 1.42976231 1.41080943 1.44231313 1.46708232\n", + " 1.40100231 1.39235662 1.39948933 1.41544625 1.41283421 1.4041461\n", + " 1.41680001 1.44845168 1.40899921 1.41031489 1.4289529 1.38966733\n", + " 1.40784501 1.49459982 1.43004707 1.40615406 1.4459733 1.42017204\n", + " 1.46798444 1.40265149 1.39965208 1.39484507 1.37154232 1.40236951\n", + " 1.38886096 1.39472112 1.38153943 1.42034343 1.39615562 1.40887446\n", + " 1.40556836 1.38194898 1.36463509 1.50146752 1.42594774 1.40779292\n", + " 1.44461683 1.42026359 1.46359276 1.40271775 1.39207198 1.39156808\n", + " 1.37210025 1.43837414]]\n", + "Learner ml_g1 RMSE: [[1.44108768 1.43051681 1.42283966 1.40311933 1.40759614 1.4135038\n", + " 1.43322294 1.43044446 1.44691302 1.42010109 1.41696436 1.46442698\n", + " 1.42632506 1.42666941 1.46975518 1.40460735 1.39007963 1.40666422\n", + " 1.42469757 1.40228892 1.44287032 1.39403734 1.41211762 1.44506159\n", + " 1.44287631 1.41304506 1.47817095 1.45456335 1.42679013 1.39575439\n", + " 1.40985718 1.37789346 1.35837811 1.33592772 1.35879952 1.42428147\n", + " 1.38880731 1.43699938 1.43766851 1.36334639 1.38922835 1.37988807\n", + " 1.39233539 1.38240226 1.43151334 1.36401159 1.37956067 1.39221812\n", + " 1.35133584 1.42987349 1.38536043 1.38011349 1.36677398 1.39589914\n", + " 1.43215836 1.4486525 1.45965801 1.49626254 1.45608327 1.42396588\n", + " 1.46031411 1.39798025 1.45693325 1.44373815 1.40192536 1.44116015\n", + " 1.399939 1.47543899 1.42452738 1.44875899 1.46017325 1.43829177\n", + " 1.40531652 1.41977931]]\n", "Classification:\n", - "Learner ml_m Log Loss: [[0.6281689 0.62718229 0.62873721 0.62841144 0.62809364 0.63074713\n", - " 0.62710709 0.62983572 0.62847637 0.62913753 0.62705281 0.62844468\n", - " 0.62715822 0.62784034 0.62836604 0.62792282 0.627238 0.62753574\n", - " 0.6274565 0.62710326 0.62747932 0.62959891 0.65639651 0.65794761\n", - " 0.65661799 0.65557165 0.65477983 0.65566148 0.65534259 0.65569133\n", - " 0.65737074 0.65678175 0.65508098 0.65638901 0.65716924 0.65591208\n", - " 0.65594263 0.656025 0.65480669 0.65585401 0.65479674 0.65562862\n", - " 0.65599127 0.65610907 0.6557743 0.65579024 0.65817522 0.67274836\n", - " 0.67443247 0.67400074 0.6742192 0.67528908 0.67340597 0.67313809\n", - " 0.67383485 0.67294685 0.67417486 0.67351913 0.6743079 0.67351434\n", - " 0.67408451 0.67448127 0.67407152 0.67450092 0.67339408 0.67345134\n", - " 0.67515484 0.67353295 0.67369005 0.67508843 0.67431827 0.67338339\n", - " 0.67505614 0.67503072 0.69235596 0.69309191 0.69144638 0.69032821\n", - " 0.69158422 0.691708 0.6908099 0.69011545 0.69133415 0.69056207\n", - " 0.69119019 0.69140544 0.6904356 0.69144917 0.69116221 0.69156948\n", - " 0.69129438 0.69128426 0.69303902 0.69332413 0.69114589 0.69177384\n", - " 0.69100445 0.69126338 0.69067909 0.68984658 0.69151658 0.69150933]]\n", + "Learner ml_m Log Loss: [[0.47788849 0.4779702 0.47789215 0.53296563 0.53262834 0.53304946\n", + " 0.59193082 0.59043865 0.59218525 0.618091 0.62053889 0.61774019\n", + " 0.61758939 0.61840344 0.61784413 0.49861564 0.54987001 0.55008169\n", + " 0.54982671 0.55061567 0.55032813 0.60809804 0.60929183 0.60853521\n", + " 0.6079423 0.63423704 0.63303062 0.6315783 0.63329716 0.63398162\n", + " 0.63291562 0.63158606 0.63375026 0.49244109 0.54762749 0.54809141\n", + " 0.6141926 0.61379044 0.61482435 0.61323514 0.61316049 0.61338617\n", + " 0.61394327 0.65208595 0.6524183 0.65117553 0.65328153 0.65376124\n", + " 0.65276944 0.65090737 0.65134004 0.65345162 0.65289515 0.50609693\n", + " 0.56570004 0.56500714 0.63515806 0.63620603 0.6365891 0.67069242\n", + " 0.6693576 0.67076087 0.67016105 0.66893999 0.67061238 0.67368678\n", + " 0.66932447 0.67012514 0.67010419 0.6700244 0.67003583 0.670244\n", + " 0.67080285 0.66882085]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 5\n", @@ -511,42 +499,44 @@ "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| \\\n", - "ATT(2025-05,2025-01,2025-02) 0.119351 0.073840 1.616338 0.106021 \n", - "ATT(2025-05,2025-01,2025-03) 0.036582 0.080165 0.456336 0.648148 \n", - "ATT(2025-05,2025-02,2025-03) -0.077922 0.075045 -1.038341 0.299111 \n", - "ATT(2025-05,2025-01,2025-04) 0.046674 0.078244 0.596520 0.550828 \n", - "ATT(2025-05,2025-02,2025-04) -0.083015 0.076543 -1.084549 0.278121 \n", + "ATT(2025-05,2025-01,2025-03) -0.079444 0.056807 -1.398489 0.161966 \n", + "ATT(2025-05,2025-01,2025-04) 0.932054 0.057050 16.337614 0.000000 \n", + "ATT(2025-05,2025-02,2025-04) 0.941147 0.056759 16.581426 0.000000 \n", + "ATT(2025-05,2025-01,2025-05) 1.853857 0.059092 31.372646 0.000000 \n", + "ATT(2025-05,2025-02,2025-05) 1.898532 0.059837 31.728281 0.000000 \n", "... ... ... ... ... \n", - "ATT(2025-08,2025-03,2025-08) 0.905554 0.064075 14.132681 0.000000 \n", - "ATT(2025-08,2025-04,2025-08) 0.944158 0.064087 14.732450 0.000000 \n", - "ATT(2025-08,2025-05,2025-08) 0.955429 0.064264 14.867283 0.000000 \n", - "ATT(2025-08,2025-06,2025-08) 0.943040 0.065569 14.382510 0.000000 \n", - "ATT(2025-08,2025-07,2025-08) 1.036910 0.064226 16.144725 0.000000 \n", + "ATT(2025-08,2025-02,2025-08) 1.933147 0.072072 26.822485 0.000000 \n", + "ATT(2025-08,2025-03,2025-08) 1.851633 0.071174 26.015547 0.000000 \n", + "ATT(2025-08,2025-04,2025-08) 1.983352 0.070664 28.067562 0.000000 \n", + "ATT(2025-08,2025-05,2025-08) 2.025502 0.070682 28.656639 0.000000 \n", + "ATT(2025-08,2025-06,2025-08) 1.872791 0.073207 25.581973 0.000000 \n", "\n", " 2.5 % 97.5 % \n", - "ATT(2025-05,2025-01,2025-02) -0.025373 0.264075 \n", - "ATT(2025-05,2025-01,2025-03) -0.120538 0.193702 \n", - "ATT(2025-05,2025-02,2025-03) -0.225008 0.069163 \n", - "ATT(2025-05,2025-01,2025-04) -0.106681 0.200029 \n", - "ATT(2025-05,2025-02,2025-04) -0.233037 0.067007 \n", + "ATT(2025-05,2025-01,2025-03) -0.190784 0.031896 \n", + "ATT(2025-05,2025-01,2025-04) 0.820239 1.043869 \n", + "ATT(2025-05,2025-02,2025-04) 0.829901 1.052392 \n", + "ATT(2025-05,2025-01,2025-05) 1.738040 1.969674 \n", + "ATT(2025-05,2025-02,2025-05) 1.781253 2.015810 \n", "... ... ... \n", - "ATT(2025-08,2025-03,2025-08) 0.779969 1.031139 \n", - "ATT(2025-08,2025-04,2025-08) 0.818550 1.069766 \n", - "ATT(2025-08,2025-05,2025-08) 0.829474 1.081384 \n", - "ATT(2025-08,2025-06,2025-08) 0.814528 1.071552 \n", - "ATT(2025-08,2025-07,2025-08) 0.911029 1.162790 \n", + "ATT(2025-08,2025-02,2025-08) 1.791889 2.074406 \n", + "ATT(2025-08,2025-03,2025-08) 1.712134 1.991131 \n", + "ATT(2025-08,2025-04,2025-08) 1.844854 2.121850 \n", + "ATT(2025-08,2025-05,2025-08) 1.886968 2.164035 \n", + "ATT(2025-08,2025-06,2025-08) 1.729307 2.016275 \n", "\n", - "[102 rows x 6 columns]\n" + "[74 rows x 6 columns]\n" ] } ], "source": [ - "# control_group = \"not_yet_treated\"\n", - "control_group = \"never_treated\"\n", + "control_group = \"not_yet_treated\"\n", + "# control_group = \"never_treated\"\n", "\n", "gt_combinations = \"all\"\n", "#gt_combinations = \"standard\"\n", "\n", + "anticipation_periods = 1\n", + "\n", "ml_g = LGBMRegressor(n_estimators=500, learning_rate=0.01, verbose=-1)\n", "ml_m = LGBMClassifier(n_estimators=500, learning_rate=0.01, verbose=-1)\n", "\n", @@ -559,6 +549,7 @@ " ml_m=ml_m,\n", " gt_combinations=gt_combinations,\n", " control_group=control_group,\n", + " anticipation_periods=anticipation_periods,\n", ")\n", "\n", "dml_obj.fit()\n", @@ -586,7 +577,7 @@ }, { "data": { - "image/png": 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Fk8kkF95efvllh/aRiIiIKlGlPvuPiIiIHLZmzRoBoNivNWvWCCGE2Lx5s+jatasICQkRGo1G1K5dW4wYMUIkJCTYrO/9998X9erVEyqVSgAQe/bsEUII0aFDB9GhQwc5bs+ePQKAiIuLKzKfgwcP2rTPmDFDABDJycly21dffSWaN28uPD09RZ06dcSCBQvEhx9+KACI8+fPy3GJiYmiZ8+eQq/XCwA2eWRlZYnJkyeL+vXrC41GI4KCgkTbtm3F22+/LcxmsxyXmpoqnnnmGeHr6yv8/PzEM888I44ePWpzjIoybtw4AUCcO3eu2JiZM2cKAOKPP/6Q2+Li4myOX3Fujvvnn3/E0KFDRWRkpPD09BSBgYGiU6dOYteuXSWuR4j871GTJk3EoUOHRExMjPD09BQRERHi3XffLfY9PXr0EADEL7/8Uur6C8vJyRFLliwRMTExwtfXV3h4eIjQ0FDxyCOPiI0bNwqLxSLHFjdWCnz66aeiRYsWQqvVisDAQPHUU0+JK1eu2MVt2LBB1KtXT2g0GhEdHS127NghhgwZIiIiIuSY8+fPCwDirbfeEgsXLhTh4eFCq9WK9u3b23x/iIiIyHUphHBw3jURERERua3HH38cf/75J86ePVvZqZSLCxcuoG7dunjrrbcwadKkyk6HiIiIbgHvKUVERERUxSUkJODbb7/FM888U9mpEBEREcl4TykiIiKiKur8+fPYv38/Vq9eDbVajREjRlR2SkREREQyzpQiIiIiqqJ++uknPPPMMzh//jzWrVuH0NDQyk6JiIiISMZ7ShERERERERERkdNxphQRERERERERETkdi1JEREREREREROR0LEoREREREREREZHTsShFREREREREREROx6IUERERERERERE5HYtSRERERERERETkdCxKERERERERERGR07EoRURERERERERETseiFBEREREREREROR2LUkRERERERERE5HQsShERERERERERkdOxKEVERERERERERE7HohQRERERERERETkdi1JEREREREREROR0LEoREREREREREZHTsShFREREREREREROx6IUERERERERERE5HYtSRERERERERETkdCxKERERERERERGR07EoRURERERERERETseiFBEREREREREROR2LUkRERERERERE5HQsShERERERERERkdOxKEVERERERERERE7HohQRERERERERETkdi1JEREREREREROR0LEoREREREREREZHTsShFREREREREREROx6IUERERERERERE5HYtSRERERERERETkdCxKERERERERERGR07EoRURERERERERETseiFBEREREREREROR2LUkRERERERERE5HQsShERERERERERkdOxKEVERERERERERE7HohQRERERERERETkdi1JEREREREREROR0LEoREREREREREZHTsShFREREREREREROx6IUERERERERERE5HYtSRERERERERETkdCxKERERERERERGR07EoRURERERERERETseiFBEREREREREROR2LUkRERFShLly4AIVCgbVr11Z2KlVanTp1EBsbW9lpEBERETmMRSkiIiK6LWvXroVCoSjy69VXX62Qbc6dOxdbtmwpNa5jx47F5lb4a+bMmRWS582+++47p22rLE6dOoWXX34Z0dHR0Ov1CAsLQ8+ePXHo0KEi469evYr+/fvD398fvr6+eOyxx/DPP//YxFy+fBmzZs1C69atERAQgKCgIHTs2BG7du2yW19JYygxMdHh/fjggw/QuHFjeHp6okGDBli2bJldzMyZM4vcjqenp8PbISIiovLhUdkJEBERUdXw+uuvo27dujZtTZs2RUREBHJycqBWq8ttW3PnzkXfvn3Ru3fvEuOmTp2KYcOGycsHDx7EO++8gylTpqBx48Zye/Pmzcstt5J89913eO+991yuMLV69Wp88MEH6NOnD0aPHo2MjAysWrUK9913H7Zv347OnTvLsQaDAZ06dUJGRgamTJkCtVqNxYsXo0OHDjh27BiqVasGANi6dSsWLFiA3r17Y8iQIbBYLPjoo4/QpUsXfPjhh3j22Wft8ihqDPn7+zu0D6tWrcLIkSPRp08fTJw4Efv27cP48eORnZ2NV155xS5+xYoV8PHxkZdVKpVD2yEiIqLyw6IUERERlYvu3bujVatWRfY5MgvFaDRCp9OVa05dunSxy+Odd95Bly5d0LFjR6fm4soGDhyImTNn2hRphg4disaNG2PmzJk2Ranly5fjzJkz+P3333HvvfcCyP/eN23aFAsXLsTcuXMBAJ06dcKlS5cQFBQkv3fkyJGIjo7G9OnTiyxKlTSGSpKTk4OpU6eiZ8+e2Lx5MwBg+PDhkCQJb7zxBp5//nkEBATYvKdv3742uREREZHz8fI9IiIiqlBF3VMqNjYWPj4+OHfuHHr06AG9Xo+nnnoKAHDmzBn06dMHoaGh8PT0RK1atTBgwABkZGQAABQKBYxGI9atWydfenU791IquJzr5MmTGDRoEAICAnD//ffL/Rs2bEDLli3h5eWFwMBADBgwAJcvX7ZZx759+9CvXz/Url0bWq0W4eHh+N///oecnBybfX7vvffkfSj4KiBJEpYsWYImTZrA09MT1atXx4gRI3Djxg2bbQkhMHv2bNSqVQve3t7o1KkT/vrrryL37dy5czh37lypx6Bly5Y2BSkAqFatGtq3b4+///7bpn3z5s2499575YIUADRq1AgPPfQQPvvsM7mtSZMmdkUfrVaLHj164MqVK8jKyioyl6ysLFit1lJzLmzPnj1ITU3F6NGjbdrHjBkDo9GIb7/91u49QghkZmZCCFGmbREREVH54UwpIiIiKhcZGRlISUmxaStpJorFYkG3bt1w//334+2334a3tzfMZjO6desGk8mEcePGITQ0FFevXsU333yD9PR0+Pn5Yf369Rg2bBhat26N559/HgAQGRl52/n369cPDRo0wNy5c+VCxZw5c/Daa6+hf//+GDZsGJKTk7Fs2TI88MADOHr0qHxpWVxcHLKzszFq1ChUq1YNv//+O5YtW4YrV64gLi4OADBixAhcu3YNO3fuxPr16+22P2LECKxduxbPPvssxo8fj/Pnz+Pdd9/F0aNHsX//fvnyx+nTp2P27Nno0aMHevTogSNHjqBr164wm81263zooYcA5BcGb0ViYqLN91CSJBw/fhxDhw61i23dujW+//57ZGVlQa/Xl7hOb29veHt72/V16tQJBoMBGo0G3bp1w8KFC9GgQYNS8zx69CgA2M2yatmyJZRKJY4ePYqnn37apq9evXowGAzQ6XTo3bs3Fi5ciOrVq5e6LSIiIio/LEoRERFRuSh8iVeBkmahmEwm9OvXD/PmzZPbjh07hvPnzyMuLg59+/aV26dPny6/fvrppzFy5EjUq1fPrtBwO+6++258/PHH8vLFixcxY8YMzJ49G1OmTJHbn3jiCbRo0QLLly+X2xcsWAAvLy855vnnn0f9+vUxZcoUXLp0CbVr10ZMTAwaNmyInTt32uX9888/Y/Xq1di4cSMGDRokt3fq1AkPP/ww4uLiMGjQICQnJ+PNN99Ez5498fXXX8szraZOnSpfNlde9u3bh19//RXTpk2T29LS0mAymRAWFmYXX9B27do1REVFFbnOs2fP4osvvkC/fv1s7uHk7e2N2NhYdOrUCb6+vjh8+DAWLVqEtm3b4siRIwgPDy8x14SEBKhUKoSEhNi0azQaVKtWDdeuXZPbAgICMHbsWMTExECr1WLfvn1477338Pvvv+PQoUPw9fUt/eAQERFRuWBRioiIiMrFe++9h4YNG5bpPaNGjbJZ9vPzAwDs2LEDPXr0KHI2TUUZOXKkzfIXX3wBSZLQv39/mxlgoaGhaNCgAfbs2SMXpQoXpIxGI3JyctC2bVsIIXD06FHUrl27xG3HxcXBz88PXbp0sdlWwWV1e/bswaBBg7Br1y6YzWaMGzfO5tK/F154ocii1K3OkEpKSsKgQYNQt25dvPzyy3J7weWIWq3W7j0F9w0rfMliYdnZ2ejXrx+8vLwwf/58m77+/fujf//+8nLv3r3RrVs3PPDAA5gzZw5WrlxZYr45OTnQaDRF9nl6etrkNGHCBJv+Pn36oHXr1njqqaewfPnyCntiJBEREdljUYqIiIjKRevWrct0k2oPDw/UqlXLpq1u3bqYOHEiFi1ahI0bN6J9+/Z49NFH8fTTT8sFq4py81Pfzpw5AyFEsZePFX6a4KVLlzB9+nR89dVXdveAKrgXVknOnDmDjIwMu5k+BZKSkgDkz94CYJdTcHCw3Y28b5XRaMQjjzyCrKws/Pzzzzb3mioovplMJrv35ebm2sQUZrVaMWDAAJw8eRLbtm1DjRo1Ss3j/vvvR5s2bbBr1y65LTk52eZ+Uz4+PvDx8YGXl1eRly8W5FVUToUNGjQIL774Inbt2sWiFBERkROxKEVERESVQqvVQqm0f+bKwoULERsbi61bt+L777/H+PHjMW/ePPz22292RazydHPhQpIkKBQKbNu2zeZSswIFxRqr1YouXbogLS0Nr7zyCho1agSdToerV68iNjYWkiSVum1JkhASEoKNGzcW2R8cHHwLe1R2ZrMZTzzxBI4fP44dO3agadOmNv2BgYHQarVISEiwe29BW1EFp+HDh+Obb77Bxo0b8eCDDzqcT3h4OOLj4+Xle++9Vy7MAcCMGTMwc+ZMhIWFwWq1IikpyaawZzabkZqa6lARLDw8HGlpaQ7nRkRERLePRSkiIiJyOc2aNUOzZs0wbdo0/PLLL2jXrh1WrlyJ2bNnA4DNpWsVJTIyEkII1K1bt8TLEv/880+cPn0a69atw+DBg+X2nTt32sUWl3dkZCR27dqFdu3alTirJyIiAkD+zKp69erJ7cnJyXYztMpKkiQMHjwYP/zwAz777DN06NDBLkapVKJZs2Y4dOiQXd+BAwdQr149u5ucv/TSS1izZg2WLFmCgQMHlimnf/75x6Ygt3HjRptL8QqOQXR0NADg0KFD6NGjh9x/6NAhSJIk9xdHCIELFy6gRYsWZcqPiIiIbo/9f08SERERVZLMzExYLBabtmbNmkGpVNpcMqbT6ZCenl6huTzxxBNQqVSYNWuW3Q3bhRBITU0FAHkWVeEYIQSWLl1qt06dTgcAdrn3798fVqsVb7zxht17LBaLHN+5c2eo1WosW7bMZntLliwpch/OnTuHc+fOlbyj/xo3bhw+/fRTLF++HE888USxcX379sXBgwdtClPx8fHYvXs3+vXrZxP71ltv4e2338aUKVPs7uVUWHJysl3bd999h8OHD+Phhx+W29q1a4fOnTvLXwVFqQcffBCBgYFYsWKFzTpWrFgBb29v9OzZs8RtrVixAsnJyTbbIiIioorHmVJERETkMnbv3o2xY8eiX79+aNiwISwWC9avXw+VSoU+ffrIcS1btsSuXbuwaNEi1KhRA3Xr1kWbNm3KNZfIyEjMnj0bkydPxoULF9C7d2/o9XqcP38eX375JZ5//nlMmjQJjRo1QmRkJCZNmoSrV6/C19cXn3/+eZEzl1q2bAkAGD9+PLp16waVSoUBAwagQ4cOGDFiBObNm4djx46ha9euUKvVOHPmDOLi4rB06VL07dsXwcHBmDRpEubNm4dHHnkEPXr0wNGjR7Ft2zYEBQXZbe+hhx4CUPoNz5csWYLly5cjJiYG3t7e2LBhg03/448/LhfURo8ejffffx89e/bEpEmToFarsWjRIlSvXh0vvvii/J4vv/wSL7/8Mho0aIDGjRvbrbNLly6oXr06AKBt27Zo0aIFWrVqBT8/Pxw5cgQffvghwsPDbZ58WBwvLy+88cYbGDNmDPr164du3bph37592LBhA+bMmYPAwEA5NiIiAk8++SSaNWsGT09P/Pzzz9i0aROio6MxYsSIUrdFRERE5UgQERER3YY1a9YIAOLgwYNF9p8/f14AEGvWrJHbhgwZInQ6nV3sP//8I4YOHSoiIyOFp6enCAwMFJ06dRK7du2yiTt16pR44IEHhJeXlwAghgwZ4lCucXFxAoDYs2eP3DZjxgwBQCQnJxf5ns8//1zcf//9QqfTCZ1OJxo1aiTGjBkj4uPj5ZiTJ0+Kzp07Cx8fHxEUFCSGDx8u/vjjD7v9tlgsYty4cSI4OFgoFApx80ex//u//xMtW7YUXl5eQq/Xi2bNmomXX35ZXLt2TY6xWq1i1qxZIiwsTHh5eYmOHTuKEydOiIiICLvjEBERISIiIko9LkOGDBEAiv06f/68Tfzly5dF3759ha+vr/Dx8RGPPPKIOHPmjE1MwXEt7qvw92Dq1KkiOjpa+Pn5CbVaLWrXri1GjRolEhMTS8395uMXFRUlNBqNiIyMFIsXLxaSJNnEDBs2TNx1111Cr9cLtVot6tevL1555RWRmZlZpm0RERHR7VMIcdN8dCIiIiIiIiIiogrGe0oREREREREREZHTsShFREREREREREROx6IUERERERERERE5HYtSRERERERERETkdCxKERERERERERGR07EoRURERERERERETudR2Qk4myRJuHbtGvR6PRQKRWWnQ0RERERERERUpQghkJWVhRo1akCpLH4+1B1XlLp27RrCw8MrOw0iIiIiIiIioirt8uXLqFWrVrH9d1xRSq/XA8g/ML6+vpWcze2RJAnJyckIDg4usfJIdLs41shdcKySy8nNhXjmGZjy8qD5+GMovb0rOyOiYvEcSq6I45LcBceqrczMTISHh8s1mOLccUWpgkv2fH19q0RRKjc3F76+vhz0VKE41shdcKySy/HygvToo4DBAH1AAJRabWVnRFQsnkPJFXFckrvgWC1aabdNuuOKUkREREROo1YDo0YhJykJerW6srMhIiIiciksShERERERERHRHcdiTIDVmFhsv0oXCg9dmBMzuvOwKEVEREREREREd5ys4+8j/cDsYvv920xDQMz0Mq+3LMWuO70w5tZFqfnz52Py5MmYMGEClixZUtnpEBEREdnKzYXiySfhbzIBX3wB8EbnRERELkPffDi8I3sBAMxpp5CyfQiCHl4HTWAjAPkFoVtRlmJXRRXG3IXbFqUOHjyIVatWoXnz5pWdChEREVHxrFZAkio7CyIiIrqJhy4MSo0fjPGbkHniAwBA5rF34dv0OeiiBkCp/u8/k0qb0aTwCgGgAlC2Ype++XB4RnRG7oXvYTi3BZbUk/Codhd8InvDs05XqP3qlvduuxS3LEoZDAY89dRTeP/99zF7dvEVRSIiIqJKpdVCrFmDjORkBPPJe0RERC5FystGys4RyLmwA1JeFgDAfP0I0tLOIOfSHgR1WSUXpkqb0eTXeipQbwSAshW7lBo/ZB1bYZODJS0emVnLkZd+DkFdVlXIvrsKtyxKjRkzBj179kTnzp1LLUqZTCaYTCZ5OTMzE0D+4xolN/9fS0mSIIRw+/0g18exRu6CY5VckRQQAMligSQEZ0yRS+M5lFwRxyVVpKxTm5B94XsIc/p/jcIKyZyO7IvfI+vUJuibxAIAdE2fgzb8IeRc3Insf7bCkvoXPKo1gXe9x+AV0QUqfW2YsvPHqpSXjdRdo5FzcQdEXn4Nwnz9CFLTziL74o+o1nn5f8WuMuTgThz9mXW7otSmTZtw5MgRHDx40KH4efPmYdasWXbtycnJyM3NLe/0nEqSJGRkZEAIAaVSWdnpUBXGsUbugmOVXBHHJbkLjlVyRRyXVJHSzvwOjSUPSgCKQu0CgNWSh6tnfkdgcA8AgGSxIPPYVkjXdkOb+w+UAMw3/kHWX99AeSMb+rtHIdNggBACpss/IOfqSXiZb0D8u24hrID5BlKvnUT2sS/hFdGlzDm4k6ysLIfi3KoodfnyZUyYMAE7d+6Ep6enQ++ZPHkyJk6cKC9nZmYiPDwcwcHB8PX1rahUnUKSJCgUCgQHB/METRWKY43cBccquRyLBWLrVnhlZkL/1FNQajSVnRFRsXgOJVfEcUkVKc/wGyyS0a5dAUBlNUJv+A0hISEAgKy/1sJ8aR2E+YYcp5Ry4JV9HIrLl+FduxGUwd0RHByMpN8+AXJOyusq/K9Xzt/QXv0EIfc+VeYc3ImjNRu3KkodPnwYSUlJuOeee+Q2q9WKvXv34t1334XJZIJKpbJ5j1arhbaIezgolcoqcVJTKBRVZl/ItXGskbvgWCWXIkkQ69bBy2yGcuBAjktyeTyHkiviuKSK4uETBsuN04Cw2HcqVfDwCZPHXfaZOIi8omf/iLws5JzdDGVIDyiVSkiGK4CwFr1RyQLJcEVeb1lycCeO5uxWRamHHnoIf/75p03bs88+i0aNGuGVV16xK0gRERERVSqVCuKhh2DOyoKGn1OIiIhcik/DfjAnHoZU+H5O/1KqfeDTsJ+8bDVcLaHQZIXVcA0FZRiVT03k3ThbbKFJ5VPzlnKoityqKKXX69G0aVObNp1Oh2rVqtm1ExEREVU6tRqYMAHZSUnwUasrOxsiIiIqRBc1ADmX9iDn4g5I5qz8opNCBaVGD6+IbtBFDZBjSy801ZAXy1JoKksOVZH7zQEjIiIiIiIiIrpNkjkD+uhR8L17NDwCowAAHoFR8L17NPTRoyCZM+RYn4b9oFT7FLkepdoHugZ95WVd1AB41ekGpdYfUPw7U1qhglLrb1doUqq9EdRlFQLbz4emev6tijTV70Fg+/kI6rJKfkpfVeVWM6WK8uOPP1Z2CkRERERERETkZrKOv4/0A7Nt2iypJ5GeehL4fS7820xDQMx0AA7MaGrYHzk3DAD+K3ap/SNhOLcFltST8AiMgk9kb3jW6QrJnCEXmyzGBFiNidCEtIBv9FikbB8C3+ix0AQ2Qt6NeKh0ofDQhTn3wDiR2xeliIiIiFxWbi4UQ4bAz2QCPv4Y8K7a/9tJRETkTvTNh8M7slex/SpdqPy6YEaTMX4TMk98AHPiQWiq3wPfps/lz3xSeQLIL0qVpdhVVGzK9iHy68KxVRGLUkREREQVyWiEwmyu7CyIiIjoJh66MIdnIZU2o0nhFQIg/1K9shS7yhJbFbEoRURERFRRtFqIlSuRmZKCIK22srMhIiKiW1TajCa/1lOBeiMAlK3YVZbYqohFKSIiIqKKolAANWpA8vDIf01ERERuqbQZTQqvEKQZnZhQFcGiFBERERERERFRCUqb0SRJEmBMcmJGVYOyshMgIiIiqrIsFuDbb6HdtSv/NRERERHJOFOKiIiIqKJYLFCsWgUvsxl44glAo6nsjIiIiIhcBotSRERERBVFqYRo1w55BgM0Sk5QJyIiIiqMRSkiIiKiiqLRAK+8AmNSEnScJUVERERkg0UpIiIiIiIiKhcWYwKsxsRi+1W60BJvFk1EdxYWpYiIiIiIiKhcZB1/H+kHZhfb799mGgJipt/SustS8GJxjMg9sChFREREVFFMJiiGD4dfbi6wbh3g5VXZGRERVSh98+HwjOiM3Avfw3BuCyypJ+FR7S74RPaGZ52uUPvVlWMdKRwpvarLy2UpeFVkcYyIyg+LUkREREQVRQggLQ0Kszn/NRFRFafU+CHr2ArkXNgBKS8LAGBJi0dm1nLkpZ9DUJdVcqwjhSO/NtPk5bIUvMoSS0SVh0UpIiIiooqi0UAsWYKs1FRU443OiegOYIzflF+QMqf/1yiskMzpyLm4A8b4TdA3HQogv3DkHdkLAGBOO4WU7UMQ9PA6aAIbAcifKVVYWQpeZYklosrDZxMTERERVRSlEqhXD9aIiPzXRERVnOF0HKQ8Q5F9Up4BhtNx8rKHLgzqgCiYk44i89i7AIDMY+/CnHQU6oAou3s+2RS8hDW/8aaC163EElHl4acjIiIiIiIiKhdWw9X/ikA3k6z5/QWLedlI2TkCaXtfhfn6EQCA+foRpO19FSk7R0DKy7Z5e1kKXmWJJaLKw6IUERERUUWxWIAffoBm377810REVZzKpyagUBXdqVTl9/+rrLOZylLwKkssEVUeFqWIiIiIKorFAsXSpfB+/30WpYjojuDTsB+Uap8i+5RqH/g07Ccvl3U2U1kKXmWJJaLKw6IUERERUUVRKiFatULe3XfznlJEdEfQRQ2AV51uUGr9/ysKKVRQav3hFdENuqgBcmxZZzOVpeBVllgiqjz8dERERERUUTQaYPp0GF98Mf81EVEVJ5kzoI8eBd+7R8MjMAoA4BEYBd+7R0MfPQqSOUOOLetsprIUvMoSS0SVx6OyEyAiIiIiIqKqIev4+0g/MNumzZJ6EumpJ4Hf58K/zTQExEwHkD+byZx4OP+eUjcpajaTUu2NoC6rYIzfhMwTH8CceBCa6vfAt+lz0EUNgFLtfUuxRFR5WJQiIiIiIiKicqFvPhzekb2K7VfpQuXXuqgByLm0BzkXd0AyZ+VfyqdQQanRFzmbyWJMgNWYCE1IC/hGj0XK9iHwjR4LTWAj5N2Ih0oXCg9dWJljiajysChFREREVFFMJijGjoVvTg7w/vuAl1dlZ0REVKE8dGEOF3sKLvVT+0fCcG4LLKkn4REYBZ/I3vCs0xWSOQNKL085vqhZWCnbh8ivC8/CKkssEVUepxalVCoVEhISEBISYtOempqKkJAQWK3F3OSOiIiIyB0JASQkQGk2578mIiKZI5f6+bWZJveVZRZWWWKJqPI4tSglivkwZjKZoOHNP4mIiKiq0WggFixAVmoqqvGzDhGRjbIWjsoyC6sssURUeZxSlHrnnXcAAAqFAqtXr4aPz3+P5rRardi7dy8aNWrkjFSIiIiInEepBBo3hjUpKf81ERHJHCkcSZLkpGyIqDI4pSi1ePFiAPkzpVauXAmV6r/Hfmo0GtSpUwcrV650RipEREREREREROQCnFKUOn/+PACgU6dO+OKLLxAQEOCMzRIRERFVLqsV2L8f6hs3gB49OFuKiIiIqBCn3lNqz549ztwcERERUeXKy4PizTehM5uBLl0AtbqyMyIiIiJyGU7977o+ffpgwYIFdu1vvvkm+vXr58xUiIiIiCqeUgk0bQpLVBRnSRERERHdxKkzpfbu3YuZM2fatXfv3h0LFy50ZipEREREFU+jgZg7F4akJHjz6XtUzizGBFiNicX2q3ShfPoYERG5NKcWpQwGAzRFfCBTq9XIzMx0ZipERERERG4t6/j7SD8wu9h+/zbTEBAz/ZbWXZaCF4tjRER0q5xalGrWrBk+/fRTTJ9u+8tx06ZNuOuuu5yZChERERGRW9M3Hw7PiM7IvfA9DOe2wJJ6Eh7V7oJPZG941ukKtV9dm/jSikcKrxAA+U/JLkvBqyKLY0REVLU5tSj12muv4YknnsC5c+fw4IMPAgB++OEHfPLJJ4iLi3NmKkREREQVz2yG4sUXoc/JAd59F/D0rOyMqApRavyQdWwFci7sgJSXBQCwpMUjM2s58tLPIajLKpv40opHfq2nAvVGAChbwausxTEiIqICTi1K9erVC1u2bMHcuXOxefNmeHl5oXnz5ti1axc6dOjg0DrmzZuHL774AqdOnYKXlxfatm2LBQsWICoqqoKzJyIiIiojSQLOn4fKbM5/TVSOjPGb8gtS5vT/GoUVkjkdORd3wBi/CfqmQ+UuffPh8I7sBQAwp51CyvYhCHp4HTSBjQDkz5RKM+bHlqXgVdbiGBERUQGnPwamZ8+e2L9/P4xGI1JSUrB7926HC1IA8NNPP2HMmDH47bffsHPnTuTl5aFr164wGo0VmDURERHRLdBoIF5/HYaXXwZ4o3MqZ4bTcZDyDEX2SXkGGE7bXongoQuDOiAK5qSjyDz2LgAg89i7MCcdhTogyua+TzYFL2HNb7yp4HUrsURERIU5daYUAKSnp2Pz5s34559/MGnSJAQGBuLIkSOoXr06atasWer7t2/fbrO8du1ahISE4PDhw3jggQcqKm0iIiKislMqgehoWJKS8l8TlSOr4ep/RaCbSdb8/sJNedlI2TnCZkaT+foRpKWdQc6lPQh8aIUc60jBq2AWVlliiYiICnNqUer48ePo3Lkz/Pz8cOHCBQwbNgyBgYH44osvcOnSJXz00UdlXmdGRgYAIDAwsMh+k8kEk8kkLxc85U+SJEhuPo1ekiQIIdx+P8j1cayRu+BYJVfEcUkVRelTC7hxFhCWIjo9oPSpZTPusk5tQvaF7yGKuNwv++L3UMd/BhHcHZIkwWJIKLHgZTEkyOsuSyxRWfEcSu6CY9WWo8fBqUWpiRMnIjY2Fm+++Sb0er3c3qNHDwwaNKjM65MkCS+88ALatWuHpk2bFhkzb948zJo1y649OTkZubm5Zd6mK5EkCRkZGRBCQMn/faUKxLFG7oJjlVyO1QrVH38g12BAUkwMlGp1ZWdEVYip5kDkpCTBK/s4BAAFIP+b49UYqDkQSUlJcnzamd+hseRB+W9MAQHAaslDwtmDUKhjIIRAls990KRfglIy2MVKKh1yfO6T112WWKKy4u92chccq7aysrIcinNqUergwYNYtcr+Roc1a9ZEYmLxj6ctzpgxY3DixAn8/PPPxcZMnjwZEydOlJczMzMRHh6O4OBg+Pr6lnmbrkSSJCgUCgQHB3PQU4XiWCN3wbFKLic3F1i+HHqTCR49ekDp7V3ZGVEVIgU8jtSUnci5dBkwZwHCAoXCAwqNHtVq3IVq0Y9Dqf5vzOUZfoNFsr8PqwKAymqEj+EA1P7+CA4Ohq5Ba9xI/BLCbB/r4aFGcIPW0IeEAAC8yhBLVFb83U7ugmPVlqeDTxx2alFKq9XKl88Vdvr0aQQHB5dpXWPHjsU333yDvXv3olatWiVuU6vV2rUrlcoqMVAUCkWV2RdybRxr5C44VsmleHhANGgAa3Y2NB4eHJdUrpRaHwR3XQlj/CZknvgA5sSD0FRvAd+mz0EXNcCmIAUAHj5hsNw4Xczlfip4+ITK51B9owEwXd6DnIs7IJmz8i/PU6ig1OjhFdEV+kYD5PFclliiW8Hf7eQuOFb/4+gxcOqRevTRR/H6668jLy8PQP437NKlS3jllVfQp08fh9YhhMDYsWPx5ZdfYvfu3ahbt25FpkxERER06zQaiIULkTVrFp++R+XOYkxA3o14aEJawDd6LADAN3osNCEtkHcjHhZjgk28T8N+UKp9ilyXUu0DXYO+8rJkzoA+ehR87x4Nj8AoAIBHYBR87x4NffQoSOaMW4olIiIqzKkzpRYuXIi+ffsiJCQEOTk56NChAxITExETE4M5c+Y4tI4xY8bg448/xtatW6HX6+XL/vz8/ODl5VWR6RMRERERuYys4+8j/cBsm7aU7UPk1/5tpiEgZrq8rIsagJxLxc1o6gZdw/7IuWEodt2W1JNITz0J/D7XZt1liSUiIirMqUUpPz8/7Ny5E/v378cff/wBg8GAe+65B507d3Z4HStW5D+qtmPHjjbta9asQWxsbDlmS0RERETkuvTNh8M7slex/SpdqM1ywYwmtX8kDOe2wJJ6Eh6BUfCJ7A3POl3/ndGkKvO6y5oHERFRgQovSgUGBuL06dMICgrC0KFDsXTpUrRr1w7t2rW7pfUJIco5QyIiIqIKYjZDMXUqfLKzgYULAQdv+knkCA9dGDx0YQ7Hlzajya/1VKDeiDKvu6x5EBERFajwopTZbEZmZiaCgoKwbt06LFiwAHq9vqI3S0RERFT5JAn4+294mM35r4kqUWkzmhReIUizfzgfERFRhanwolRMTAx69+6Nli1bQgiB8ePHF3vvpw8//LCi0yEiIiJyHrUaYsoUGG/cgEatruxs6A5X2owmSZIAY5ITMyIiojtdhRelNmzYgMWLF+PcuXMAgIyMDOTm5lb0ZomIiIgqn0oF3Hcf8pKS8l8TERERkazCi1LVq1fH/PnzAQB169bF+vXrUa1atYreLBERERERERERuTBlRW8gMDAQKSkpAIBOnTpBo9FU9CaJiIiIXIMkAX/+CY+//+Y9pYiIiIhuwhudExERERViMSbAakwstl+lC3X8SWMFT98zm4E2bQCPCv/oRUREROQ2eKNzIiIiokKyjr+P9AOzi+33bzMNATHTHVuZQgGEh0PKycl/XQblWhwjIiIickFOvdG5QqHgjc6JiIjIpembD4d3ZC8AgDntFFK2D0HQw+ugCWwEIL8Y5DCtFuK995CZlARPrbZMhaZyLY4RERERuSDe6JyIiIioEA9dmN0MJE1gI2hDWtjFOlJkUnpVl5fLUmgq1+IYERERkQtyyo0NevTogU8++QTnz58HAMyfPx8jR46Ev78/ACA1NRXt27fHyZMnnZEOERERUYmkvGwY4zch88QHAICU3WPh2/Q56KIGQKn2luMcKTL5tZkmL5el0OShC4NS42eTR+axd4vMg4iIiMgdOaUotX37dphMJnl57ty56N+/v1yUslgsiI+Pd0YqRERERCWS8rKRsnMEci7sgJSXBQAwXz+CtLQzyLm0B0FdVskFoVKLTOpqUEyfDh+jEZg3r0yFprLkQUREROSOKuURMEKIytgsERERUamM8ZvyC0Hm9P8ahRWSOR05F3fAGL8J+qZDAThwqV9uLsSxY/AwmwFJKlOhqSx5EBEREbkjZWUnQERERORKDKfjIOUZiuyT8gwwnI67qS0bWSc+RMrusQDyL/XLOvEhpLxsQK2GmDgR2SNHAmq1baFJWPNXcFOh6VbzICIiInI3TpkppVAooLjpMcg3LxMRERG5Aqvh6n8Fo5tJ1vz+gkUHZj6hY0eYk5IAlcqhQlPB7Key5EFERETkjpxSlBJCIDY2FlqtFgCQm5uLkSNHQqfTAYDN/aaIiIiIKpPKpybybpwFhMW+U6mCyqemvOjIJXa6u2LlrrIUmsqSBxEREZE7ckpRasiQITbLTz/9tF3M4MGDnZEKERERUYl8GvaDOfGwbaHpX0q1D3wa9pOXS535dOoz6NTtoEpNBYKCylRoKkseRERERO7IKUWpNWvWOGMzRERERLdNFzUAOZf2IOfiDkjmrPyZTQoVlBo9vCK6QRc1QI4tdeZT+hUoXnwRerMZ2LKlTIWmsuRBRERE5I54o3MiIiKiQpRqbwR1WYXA9vOhqX4PAEBT/R4Etp9v83Q8IP8SOyhUxaxIBZW+JhASAikoCFAooIsaAK863aDU+v/3PoUKSq2/XaGpLHkQERERuSMWpYiIiIgKsRgTkHcjHpqQFvCNzn+inm/0WGhCWiDvRjwsxgQ51qdhPyjVPkWuR6n2gU+TJyFWr0bmokWAVlvmQpNS7Q1906EIevBdAEDQg+9C33QoC1JERERUJTjl8j0iIiIid5F1/H2kH5ht05ay/b/7Y/q3mYaAmOkAyn6JncWYAKsxUS54pWwfkl/wCmyEvBvxUOlC4aELs4kFAHPaKZt/AdjEEhEREbkjFqWIiIiICtE3Hw7vyF7F9qt0ofLrgplPxvhNyDzxAcyJB6Gpfg98mz4HXdQAKNXekCRJji9LwasssURERETuiEUpIiIiokI8dGEOz0AqdeaTuhqU766DzmAAZs0qU8GrLLFERERE7ohFKSIiIqJbVOpsppavwv/AEajNZkCSylTwKkssERERkTtiUYqIiIjoFpU6m0kbBDHmBLLT06Hx4McuIiIiosL46YiIiIjoFjkym0nqVhPmpCSARSkiIiIiG8rKToCIiIiIiIiIiO48LEoRERERVRQhgEuXoLxyJf81EREREck4j5yIiIioophMUIwdC1+zGdiyBfD2ruyMiIiIiFwGi1JEREREFcnXFyI3t7KzICIiInI5LEq5mYTMXCRk5n+wlSSBtBtGBJozoFQqAABhvp4I8/WszBSJiIiogKcnxIYNyEhKQognfz8TERERFcailJtZ9etFvL7zdLH907s0xMxuUU7MiFxR4eJlUVi8JFfBsUpEREREdOdiUcrNjIiJQNeGQdgWn4StfybixHUDmlb3wWPNQtE9KgR1q+kqO0VyASxekrvgWCUiIiIiunOxKOVm/Dw98O7+C9h+KglZJgsA4O8kA678fAHnUrKxuv/dlZwhuYIRMRF4tEl1AMCpJAOe/vgoNgxqgUYhPgDAmSfkMlxlrHLGFlUYsxlYuhTeBgMweTLAS/iIiIiIZCxKuZmPj17F9lNJSM+1yG1WAaTnWrD9VBI+PnoVw9pEVGKG5ArCfD3h5+mBj49exerfLgEA3tl3HsPuq41BLWrCW+OcH33+oU+lKY+xWh732uOMLaowkgTFTz9BYzYDklTZ2RARERG5FLctSr333nt46623kJiYiLvvvhvLli1D69atKzutCvfZsWswmK1F9hnMVnx27BqLUoRsswXDPvvDZkbd4SvpOP21AbvPpGB1/7udUpjiH/pUmvIYq+UxzgpfGr3lz0T8dd2AJtV90JuXRtPt8vCAeO455GRkQOPhth+7iIiIiCqEW346+vTTTzFx4kSsXLkSbdq0wZIlS9CtWzfEx8cjJCSkstOrUFczcmGVRJF9VkngagYfOU2uM6POVS7NItdVHmO18Dg7mZiFwZuO4aMB0bgrVA/AsXFW1KXRp5IMeI+XRtPt8vAAHnsMpqSk/NdEREREJHPLT0eLFi3C8OHD8eyzzwIAVq5ciW+//RYffvghXn31VYfWYTabYTab7dqVSiU8Cn1oLCqmgEKhgFqtvqXYvLw8CFF0camk2DCdCmcUgKWIt6qUCoT5eJSYh0ajcSiHm2MtFgukEi47KEusWq2GQqGo0Fir1QqrtegZZWWN9fDwgFKpdJlYSZJgsViKjVWpVCXPqDNZsOnQRQxuEQaVSgWVSlXqeiVJssnPkRxUKhXCfD3hq1Vh/cELWPP7FQDA0h/P4NnWtTAguia8NUpYrVY5ByEE8vLyil1v4Z/PiooFSv5ZdvVzxO3EAs4/R2w6dBEGU9FjyWC2YtPhSxjcIqzY9arVavkSwPUHL+LDAxcBAMv2nsGzrcPlcWY2m0v8uf/o94vYdjIBGeb/9lcujv2diI9+98XQ1kUXx9zxHOHIz72zYkv7+SxLrKueIyRJkj93FHyfAJ4jbjWWnyNuL7Yift9XZOydcI4oDs8R+W5u5zmCnyNc9RxR3O/7O/UcUVI+hbldUcpsNuPw4cOYPHmy3KZUKtG5c2f8+uuvdvEmkwkmk0lezszMBAC8/fbb0Gq1dvH169fHU089JS+/+eabxQ7GiIgIxMbGysuLFy9GdnZ2kbFhYWF4/vnn5eV3330X6enpRcYGBwdj9OjR8vKqVauQnJwMAPC+mgGPFC0s9e8HhAD+PdkBgK9GhRqX9mHOnF1Frtfb2xsvvfSSvLx+/XpcvHixyFi1Wo0pU6bIy5988gnOnj1bZCwAzJgxQ369efNm/P3338XGTp48Wf4l9NVXX+GPP/4oNnbSpEnQ6fIvm9m2bRsOHTpUbOyECRPg7+8PANi5c2eR46HAqFGj5Fl1P/30E3766adiY4cNG4aaNWsCAH755Rfs2lX08QWAIUOGoE6dOgCAgwcPYtu2bcXGDhw4EA0bNgQA/PHHH9i6dWuxsX379kWTJk0AAH/99Rc2b95cbOxjjz2GhIycYmfUWaxWHN/yAeYk/YDu3bvLl71euHAB69atK/I9Qgi0bNkSPXv2BABcvXoVq1evLjaHDh06oGPHjsg2WzDkgx+xbecPMNW6G/DU49A/Cfhz73a8p7yBXndVxwP3t0PXrl0BAOnp6Vi6dGmx623VqpWcg9FoxNtvv11s7N13343evXsDyD9vzJs3r9jYxo0bo3///vLynDlzio119XPEzfz9/TFhwgR5+YMPPkBCQkKRsZVxjjj+ywVYmj0OKFV277FKAmcO7MGc68X/zE2aNAkKtRYjNh/H13v2w+gV/O84S8Sfe3fI40ytUpZ4jthw+AqyancEvPxszqsAkGXMxlvz5yOhZa0ic3DHc0R0dDQA4PTp0/jkk0+KjXX0HAEAnTt3Rrt27QA4fo4AgKSkJKxYsaLY2JiYGPc+RyxYAHN6OrKzs+EdFARFoQ+pPEf8h58j8lX2OUIIgfbt26N69fzZpzxH9AbAzxGVfY4QQmDUqFFywYjniDoA+DnCFc8RQggYjUbodDq5KAncueeIwnWYkrhdUSolJQVWq1X+ZVmgevXqOHXqlF38vHnzMGvWLLt2o9FYZPU1MzMTSUlJ8rLBYCi2SpuVlWUXm5OT41BsVlYWjEZjkbGenp7FxtbTKxF9IxnnL/6MtNDmyNP6wtsDaOCrxEN19ah2UYXUlKLXK0mSwzl4eHjYxGZmZhYbC6DMsQW/KDIyMkqMTU5OlvsdiS2oxqanp5cYm5KSIr92JLag+nvjxo0SY1NTU+Ht7e1wbMFxS0tLKzE2LS2tTLExIR64mAYYLAJAoT+whYDGlIlwnQJGoxE3btyQ15uamlrseoUQMBgMSEpKglKpREpKSok5pKenIykpCd/HJyE+MR25BUVUAELrg9z69yPl4s/4+0oKmv8bC5T+Pc7IyJBjs7OzHY41m80lxt78c1+WWFc7R9xMpVI5HFsZ54hwnQKZpkyYPf3tikE6NRDlp4TRmFnsepOTk/HzZQP+unQdhoB6xY6zyCBdiecIjWSG8NTb5QAAkkoDjVT8GHLHc4QjP/cFOd5KrKPniLLGuuU5IiMDHl9+CbUkwfjYY1AU+t9EniP+w88R+Sr7HCGEkMeaUqnkOYKfI1ziHCGEQHp6OoQQUCqVPEfwc4TLniOEEMjNzb+dTuGi1J16jnC0KKUQJc2pdEHXrl1DzZo18csvvyAmJkZuf/nll/HTTz/hwIEDNvFFzZQKDw/H9evX4evra7d+d5gul2224LM/rmHtwcv4/Vo27gv3xdA2ERgQXQNqheC0e3BK7brDV/HKN3/jRq59XIAGmNM9CrH31i7T5XtpaWkIDQ2FUql0eEpt9/d/w55zqbAU8W3zUAAd6vjiu+fbctp9EbFVadp9SbFrD17ClG3xSC/i0AR6emDOww0wpFXRM5SA/J/lHqsPlDrOvn6uTYk/970+OICfLmQWeWm0hxLoEJG/jqK44zmC0+6deI7IzASefRYmkwnqDRug/PePCYDniFuN5eeI24utiN/3pa33dmKr/DmCnyMAlH75XkZGBoKDg6FUKnmO4OcIlz1HSJKE5ORkeawWFQvcOeeIzMxMVK9eHRkZGUXWXgq43UypoKAgqFQqXL9+3ab9+vXrCA0NtYvXarVFXqbn6ekJT8/Sb37rSMytxBaVkyOx+Y8+t6B13RD4eHlh8KZjGN2uHu4K1eN0SrZDjz6/lRwK/9Jwh1ilUmnzw1HVYgufqIoy6J5a2H02Nf+mzWYLrBKgUgK+Gg90axSCwW3qwfOmJ5qVtF5JkuRfVgVfpeUAAFcyTLAW87vdKoBrRsluvwt+aTiiomIr6ufeGeeIyoq91Z/lwW3qYe/FrCLHatdGIXimdR27sXozR8bZzcf+5p+5J1vVweGEkzY3XC+g13jgyVZ1HPr+ucs5wpViAdf4ua+wc4S/P6TPP0d6UhL0/v42H1JvZ72u8HPPzxHuG1vev+9LW+/txAKu8XPPzxEVG1vSz6ckScjMzJTHJM8RFR9b2T/37nqOkCQJWq0Wnp6e5fb73p3PEY7eU6r4I+WiNBoNWrZsiR9++EFukyQJP/zwg83Mqapq1a8X0WrJPrRasg+DNx0DAAzedExuW/Vr0ddt053FW+OB1f3vxpu97kLLmv4AgJY1/bGg111Y3f9ueJfyR355qennCZXS/nIoIP/G/DX9+PS9O115jNXyGGeDWtTEw41C4O/pAZWy4L1AgGd+IXdQi5oO7xMRERERETnG7WZKAcDEiRMxZMgQtGrVCq1bt8aSJUtgNBrlp/FVZYUffS5JAmk30hAYEAjlv3+QOTpLiqq2/Bl1ubinph/Gt6+Lpz8+ivHt66JRiA9OJRnKNKPudvSProFDl9OLmX2iQv/oGhWeA7m28hir5THOMnItGNuuDiKDvLHlz0T8dd2ARsE+6N0sFN2jQpCRa3FaMZeIiIiI6E7hlp+wn3zySSQnJ2P69OlITExEdHQ0tm/fbnfz86qo8B9okiQhSWNCSIhfidMD6c6z6teLeH3naZu2pz8+Kr+e3qUhZnaLqvA8BrWoid1nUoq9jJCzT6g8xmrhcWYwW2CR8u8DpS/DOCsqj7+uG/DX9bOYs+us035mqArKywNWrIB3VhYwaRJQhinwRERERFWd293o/HZlZmbCz8+v1JttuYOCJ1yEhISwKEU2CmafFKesM6VudawlZObifKoR2+KT5NknTar/N/ukbjUdZ/fd4cpjrBYeZ1v/TMSJ6wY0re6Dx8owzsr7Z4ZIlpsL0bcvTGYzNFu22NzonMjV8LMluSKOS3IXHKu2HK29uOVMKSIqmav8Ac3ZJ1Sa8hirRY2zE9cNOFGGceYqPzNUBXl4QDz9NHIzM6Epw01biYiIiO4E/HRERBWm8D3QisIiAJUH3muPXJqHB9C/P3KTkuDLohQRERGRDX46IqIKw9kn5Ay81x4RERERkXviJ3YiIiKiiiIEkJEBRWZm/msiIiIiknGmFBEREVFFMZmgeOYZ+JnNwJYtAG90TkRERCS744pSBQ8bzMzMrORMbp8kScjKyoKnpycvU6EKxbFG7oJjlVxObi5EXh5MFgs0mZlQWiyVnRFRsXgOJVfEcUnugmPVVkHNRZQyU/yOK0plZWUBAMLDwys5EyIiIrqjhIVVdgZERERETpWVlQU/P79i+xWitLJVFSNJEq5duwa9Xg+FQlHZ6dyWzMxMhIeH4/Lly/D19a3sdKgK41gjd8GxSq6I45LcBccquSKOS3IXHKu2hBDIyspCjRo1Spw5dsfNlFIqlahVq1Zlp1GufH19OejJKTjWyF1wrJIr4rgkd8GxSq6I45LcBcfqf0qaIVWAFzoSEREREREREZHTsShFREREREREREROx6KUG9NqtZgxYwa0Wm1lp0JVHMcauQuOVXJFHJfkLjhWyRVxXJK74Fi9NXfcjc6JiIiIiIiIiKjycaYUERERERERERE5HYtSRERERERERETkdCxKERERERERERGR07EoVc7mzZuHe++9F3q9HiEhIejduzfi4+NtYnJzczFmzBhUq1YNPj4+6NOnD65fvy73//HHHxg4cCDCw8Ph5eWFxo0bY+nSpTbr+PHHH6FQKOy+EhMTS8xPCIHp06cjLCwMXl5e6Ny5M86cOSP3X7hwAc899xzq1q0LLy8vREZGYsaMGTCbzeVwdKg8uftYA4BHH30UtWvXhqenJ8LCwvDMM8/g2rVrt3lkyNVUhbFawGQyITo6GgqFAseOHbu1A0IuoSqMyzp16titd/78+bd5ZMjVVIWxCgDffvst2rRpAy8vLwQEBKB37963flCo0rn7uCxuvQqFAgcPHiyHI0SuxN3HKwCcPn0ajz32GIKCguDr64v7778fe/bsuc0j4yIElatu3bqJNWvWiBMnTohjx46JHj16iNq1awuDwSDHjBw5UoSHh4sffvhBHDp0SNx3332ibdu2cv8HH3wgxo8fL3788Udx7tw5sX79euHl5SWWLVsmx+zZs0cAEPHx8SIhIUH+slqtJeY3f/584efnJ7Zs2SL++OMP8eijj4q6deuKnJwcIYQQ27ZtE7GxsWLHjh3i3LlzYuvWrSIkJES8+OKL5Xyk6Ha5+1gTQohFixaJX3/9VVy4cEHs379fxMTEiJiYmHI8SuQKqsJYLTB+/HjRvXt3AUAcPXr09g8OVZqqMC4jIiLE66+/brPewvlT1VAVxurmzZtFQECAWLFihYiPjxd//fWX+PTTT8vxKJGzufu4NJlMNutLSEgQw4YNE3Xr1hWSJJXz0aLK5u7jVQghGjRoIHr06CH++OMPcfr0aTF69Gjh7e0tEhISyvFIVQ4WpSpYUlKSACB++uknIYQQ6enpQq1Wi7i4ODnm77//FgDEr7/+Wux6Ro8eLTp16iQvFwz4GzduOJyLJEkiNDRUvPXWW3Jbenq60Gq14pNPPin2fW+++aaoW7euw9uhylEVxtrWrVuFQqEQZrPZ4W2R+3HXsfrdd9+JRo0aib/++otFqSrIHcdlRESEWLx4scPrparB3cZqXl6eqFmzpli9erXD6yX3427j8mZms1kEBweL119/3eHtkPtyt/GanJwsAIi9e/fKMZmZmQKA2Llzp8PbclW8fK+CZWRkAAACAwMBAIcPH0ZeXh46d+4sxzRq1Ai1a9fGr7/+WuJ6CtZRWHR0NMLCwtClSxfs37+/xFzOnz+PxMREm237+fmhTZs2t7Rtci3uPtbS0tKwceNGtG3bFmq1usT1k3tzx7F6/fp1DB8+HOvXr4e3t7djO0puxR3HJQDMnz8f1apVQ4sWLfDWW2/BYrGUvrPk1txtrB45cgRXr16FUqlEixYtEBYWhu7du+PEiROO7zS5PHcblzf76quvkJqaimeffbbEdVPV4G7jtVq1aoiKisJHH30Eo9EIi8WCVatWISQkBC1btnR8x10Ui1IVSJIkvPDCC2jXrh2aNm0KAEhMTIRGo4G/v79NbPXq1Yu91vSXX37Bp59+iueff15uCwsLw8qVK/H555/j888/R3h4ODp27IgjR44Um0/B+qtXr+7wts+ePYtly5ZhxIgRpe4vVR53HmuvvPIKdDodqlWrhkuXLmHr1q0O7ze5H3ccq0IIxMbGYuTIkWjVqlWZ95lcnzuOSwAYP348Nm3ahD179mDEiBGYO3cuXn755TLtO7kXdxyr//zzDwBg5syZmDZtGr755hsEBASgY8eOSEtLK9sBIJfkjuPyZh988AG6deuGWrVqlbq/5N7ccbwqFArs2rULR48ehV6vh6enJxYtWoTt27cjICCgzMfA1XhUdgJV2ZgxY3DixAn8/PPPt7yOEydO4LHHHsOMGTPQtWtXuT0qKgpRUVHyctu2bXHu3DksXrwY69evx8aNG20KSdu2bYNKpSrTtq9evYqHH34Y/fr1w/Dhw295H6jiufNYe+mll/Dcc8/h4sWLmDVrFgYPHoxvvvkGCoXilveFXJc7jtVly5YhKysLkydPvuWcybW547gEgIkTJ8qvmzdvDo1GgxEjRmDevHnQarW3vC/kutxxrEqSBACYOnUq+vTpAwBYs2YNatWqhbi4OP7HZxXgjuOysCtXrmDHjh347LPPbjl/ch/uOF6FEBgzZgxCQkKwb98+eHl5YfXq1ejVqxcOHjyIsLCwW94XV8CiVAUZO3YsvvnmG+zdu9em4h4aGgqz2Yz09HSbSuz169cRGhpqs46TJ0/ioYcewvPPP49p06aVus3WrVvLP1yPPvoo2rRpI/fVrFkTCQkJ8rYKD9zr168jOjraZl3Xrl1Dp06d0LZtW/zf//2fw/tNzufuYy0oKAhBQUFo2LAhGjdujPDwcPz222+IiYlx+BiQe3DXsbp79278+uuvdn/kt2rVCk899RTWrVvn2AEgl+Su47Iobdq0gcViwYULF2w+FFPV4K5jtaD9rrvukvu1Wi3q1auHS5cuObj35KrcdVwWtmbNGlSrVg2PPvqoQ/tM7stdx+vu3bvxzTff4MaNG/D19QUALF++HDt37sS6devw6quvlu1AuJrKvqlVVSNJkhgzZoyoUaOGOH36tF1/wU3UNm/eLLedOnXK7iZqJ06cECEhIeKll15yeNudO3cWjz/+eIm5hYaGirfffltuy8jIsLvp35UrV0SDBg3EgAEDhMVicXj75FxVYazd7OLFiwKA2LNnj8O5kOtz97F68eJF8eeff8pfO3bsEADE5s2bxeXLlx3OhVyLu4/LomzYsEEolUqRlpbmcC7k+tx9rBYsF77RudlsFiEhIWLVqlUO50Kuxd3HZeHYunXr8knjVZy7j9evvvpKKJVKkZWVZfPehg0bijlz5jici6tiUaqcjRo1Svj5+Ykff/zR5jGQ2dnZcszIkSNF7dq1xe7du8WhQ4dETEyMiImJkfv//PNPERwcLJ5++mmbdSQlJckxixcvFlu2bBFnzpwRf/75p5gwYYJQKpVi165dJeY3f/584e/vL7Zu3SqOHz8uHnvsMZvHTV65ckXUr19fPPTQQ+LKlSs22yfX4u5j7bfffhPLli0TR48eFRcuXBA//PCDaNu2rYiMjBS5ubnlfLSoMrn7WL3Z+fPn+fS9KsDdx+Uvv/wiFi9eLI4dOybOnTsnNmzYIIKDg8XgwYPL+UhRZXP3sSqEEBMmTBA1a9YUO3bsEKdOnRLPPfecCAkJYQHVjVWFcSmEELt27RIAxN9//11OR4ZckbuP1+TkZFGtWjXxxBNPiGPHjon4+HgxadIkoVarxbFjx8r5aDkfi1LlDECRX2vWrJFjcnJyxOjRo0VAQIDw9vYWjz/+uE3RZ8aMGUWuIyIiQo5ZsGCBiIyMFJ6eniIwMFB07NhR7N69u9T8JEkSr732mqhevbrQarXioYceEvHx8XL/mjVrit0Hci3uPtaOHz8uOnXqJAIDA4VWqxV16tQRI0eOFFeuXCmX40Ouw93H6s1YlKoa3H1cHj58WLRp00b4+fkJT09P0bhxYzF37lwW9asgdx+rQuTPjHrxxRdFSEiI0Ov1onPnzuLEiRO3fWyo8lSFcSmEEAMHDhRt27a9rWNBrq8qjNeDBw+Krl27isDAQKHX68V9990nvvvuu9s+Nq5AIYQQDlzlR0REREREREREVG6UlZ0AERERERERERHdeViUIiIiIiIiIiIip2NRioiIiIiIiIiInI5FKSIiIiIiIiIicjoWpYiIiIiIiIiIyOlYlCIiIiIiIiIiIqdjUYqIiIiIiIiIiJyORSkiIiIiIiIiInI6FqWIiIiIiIiIiMjpWJQiIiIiIiIiIiKnY1GKiIiIiIiIiIicjkUpIiIiIiIiIiJyOhaliIiIiIiIiIjI6ViUIiIiIiIiIiIip2NRioiIiIiIiIiInI5FKSIiIiIiIiIicjoWpYiIiIiIiIiIyOlYlCIiIqIKdeHCBSgUCqxdu7ayU6nS6tSpg9jY2MpOg4iIiMhhLEoRERHRbVm7di0UCkWRX6+++mqFbHPu3LnYsmVLqXEdO3YsNrfCXzNnzqyQPG/23XffOW1bZXHq1Cm8/PLLiI6Ohl6vR1hYGHr27IlDhw4VGX/16lX0798f/v7+8PX1xWOPPYZ//vnHJuby5cuYNWsWWrdujYCAAAQFBaFjx47YtWuX3fpKGkOJiYkO78cHH3yAxo0bw9PTEw0aNMCyZcuKjf30008RExMDnU4Hf39/tG3bFrt373Z4W0RERHT7PCo7ASIiIqoaXn/9ddStW9emrWnTpoiIiEBOTg7UanW5bWvu3Lno27cvevfuXWLc1KlTMWzYMHn54MGDeOeddzBlyhQ0btxYbm/evHm55VaS7777Du+9957LFaZWr16NDz74AH369MHo0aORkZGBVatW4b777sP27dvRuXNnOdZgMKBTp07IyMjAlClToFarsXjxYnTo0AHHjh1DtWrVAABbt27FggUL0Lt3bwwZMgQWiwUfffQRunTpgg8//BDPPvusXR5FjSF/f3+H9mHVqlUYOXIk+vTpg4kTJ2Lfvn0YP348srOz8corr9jEzpw5E6+//jr69u2L2NhY5OXl4cSJE7h69WoZjxwRERHdDhaliIiIqFx0794drVq1KrLP09Oz1PcbjUbodLpyzalLly52ebzzzjvo0qULOnbs6NRcXNnAgQMxc+ZM+Pj4yG1Dhw5F48aNMXPmTJui1PLly3HmzBn8/vvvuPfeewHkf++bNm2KhQsXYu7cuQCATp064dKlSwgKCpLfO3LkSERHR2P69OlFFqVKGkMlycnJwdSpU9GzZ09s3rwZADB8+HBIkoQ33ngDzz//PAICAgAAv/32G15//XUsXLgQ//vf/8q8LSIiIio/vHyPiIiIKlRR95SKjY2Fj48Pzp07hx49ekCv1+Opp54CAJw5cwZ9+vRBaGgoPD09UatWLQwYMAAZGRkAAIVCAaPRiHXr1smXeN3OvZRmzpwJhUKBkydPYtCgQQgICMD9998v92/YsAEtW7aEl5cXAgMDMWDAAFy+fNlmHfv27UO/fv1Qu3ZtaLVahIeH43//+x9ycnJs9vm9996T96Hgq4AkSViyZAmaNGkCT09PVK9eHSNGjMCNGzdstiWEwOzZs1GrVi14e3ujU6dO+Ouvv4rct3PnzuHcuXOlHoOWLVvaFKQAoFq1amjfvj3+/vtvm/bNmzfj3nvvlQtSANCoUSM89NBD+Oyzz+S2Jk2a2BSkAECr1aJHjx64cuUKsrKyiswlKysLVqu11JwL27NnD1JTUzF69Gib9jFjxsBoNOLbb7+V25YsWYLQ0FBMmDABQggYDIYybYuIiIjKD2dKERERUbnIyMhASkqKTdvNRYnCLBYLunXrhvvvvx9vv/02vL29YTab0a1bN5hMJowbNw6hoaG4evUqvvnmG6Snp8PPzw/r16/HsGHD0Lp1azz//PMAgMjIyNvOv1+/fmjQoAHmzp0LIQQAYM6cOXjttdfQv39/DBs2DMnJyVi2bBkeeOABHD16VL60LC4uDtnZ2Rg1ahSqVauG33//HcuWLcOVK1cQFxcHABgxYgSuXbuGnTt3Yv369XbbHzFiBNauXYtnn30W48ePx/nz5/Huu+/i6NGj2L9/v3z54/Tp0zF79mz06NEDPXr0wJEjR9C1a1eYzWa7dT700EMA8guDtyIxMdHmeyhJEo4fP46hQ4faxbZu3Rrff/89srKyoNfrS1ynt7c3vL297fo6deoEg8EAjUaDbt26YeHChWjQoEGpeR49ehQA7GZZtWzZEkqlEkePHsXTTz8NAPjhhx/Qtm1bvPPOO5g9ezZSU1MRGhqKqVOnYuzYsaVui4iIiMoPi1JERERULgpf4lWgoLhTFJPJhH79+mHevHly27Fjx3D+/HnExcWhb9++cvv06dPl108//TRGjhyJevXqyYWG8nD33Xfj448/lpcvXryIGTNmYPbs2ZgyZYrc/sQTT6BFixZYvny53L5gwQJ4eXnJMc8//zzq16+PKVOm4NKlS6hduzZiYmLQsGFD7Ny50y7vn3/+GatXr8bGjRsxaNAgub1Tp054+OGHERcXh0GDBiE5ORlvvvkmevbsia+//lqeaTV16lT5srnysm/fPvz666+YNm2a3JaWlgaTyYSwsDC7+IK2a9euISoqqsh1nj17Fl988QX69esHlUolt3t7eyM2NhadOnWCr68vDh8+jEWLFqFt27Y4cuQIwsPDS8w1ISEBKpUKISEhNu0ajQbVqlXDtWvXAAA3btxASkoK9u/fj927d2PGjBmoXbs21qxZg3HjxkGtVmPEiBGOHSAiIiK6bSxKERERUbl477330LBhwzK9Z9SoUTbLfn5+AIAdO3agR48eRc6mqSgjR460Wf7iiy8gSRL69+9vMwMsNDQUDRo0wJ49e+SiVOGClNFoRE5ODtq2bQshBI4ePYratWuXuO24uDj4+fmhS5cuNtsquKxuz549GDRoEHbt2gWz2Yxx48bZXPr3wgsvFFmUutUZUklJSRg0aBDq1q2Ll19+WW4vuBxRq9XavafgvmGFL1ksLDs7G/369YOXlxfmz59v09e/f3/0799fXu7duze6deuGBx54AHPmzMHKlStLzDcnJwcajabIPk9PTzmngkv1UlNTsWnTJjz55JMAgL59+6JZs2aYPXs2i1JEREROxKIUERERlYvWrVuX6SbVHh4eqFWrlk1b3bp1MXHiRCxatAgbN25E+/bt8eijj+Lpp5+WC1YV5eanvp05cwZCiGIvHyv8NMFLly5h+vTp+Oqrr+zuAVVwL6ySnDlzBhkZGXYzfQokJSUByJ+9BcAup+DgYPlG3rfLaDTikUceQVZWFn7++Webe00VFN9MJpPd+3Jzc21iCrNarRgwYABOnjyJbdu2oUaNGqXmcf/996NNmzbYtWuX3JacnGxzvykfHx/4+PjAy8uryMsXC/IqyKngX7VabTMTT6lU4sknn8SMGTPkmW1ERERU8ViUIiIiokqh1WqhVNo/c2XhwoWIjY3F1q1b8f3332P8+PGYN28efvvtN7siVnm6uZgiSRIUCgW2bdtmc6lZgYJijdVqRZcuXZCWloZXXnkFjRo1gk6nw9WrVxEbGwtJkkrdtiRJCAkJwcaNG4vsDw4OvoU9Kjuz2YwnnngCx48fx44dO9C0aVOb/sDAQGi1WiQkJNi9t6CtqILT8OHD8c0332Djxo148MEHHc4nPDwc8fHx8vK9994rF+YAYMaMGZg5cybCwsJgtVqRlJRkU9gzm81ITU2VcwoMDISnpyf8/f3tvqcF77tx4waLUkRERE7CohQRERG5nGbNmqFZs2aYNm0afvnlF7Rr1w4rV67E7NmzAcDm0rWKEhkZCSEE6tatW+JliX/++SdOnz6NdevWYfDgwXL7zp077WKLyzsyMhK7du1Cu3btipxpVCAiIgJA/syqevXqye3Jycl2M7TKSpIkDB48GD/88AM+++wzdOjQwS5GqVSiWbNmOHTokF3fgQMHUK9ePbubnL/00ktYs2YNlixZgoEDB5Ypp3/++cemILdx40abywMLjkF0dDQA4NChQ+jRo4fcf+jQIUiSJPcrlUpER0fj4MGDMJvNNpf8Fdx3ylkFQCIiIgLs/3uSiIiIqJJkZmbCYrHYtDVr1gxKpdLmkjGdTof09PQKzeWJJ56ASqXCrFmz7G7YLoRAamoqAMgzbgrHCCGwdOlSu3XqdDoAsMu9f//+sFqteOONN+zeY7FY5PjOnTtDrVZj2bJlNttbsmRJkftw7tw5nDt3ruQd/de4cePw6aefYvny5XjiiSeKjevbty8OHjxoU5iKj4/H7t270a9fP5vYt956C2+//TamTJmCCRMmFLvO5ORku7bvvvsOhw8fxsMPPyy3tWvXDp07d5a/CopSDz74IAIDA7FixQqbdaxYsQLe3t7o2bOn3Pbkk0/CarVi3bp1cltubi42btyIu+66y6FLC4mIiKh8cKYUERERuYzdu3dj7Nix6NevHxo2bAiLxYL169dDpVKhT58+clzLli2xa9cuLFq0CDVq1EDdunXRpk2bcs0lMjISs2fPxuTJk3HhwgX07t0ber0e58+fx5dffonnn38ekyZNQqNGjRAZGYlJkybh6tWr8PX1xeeff17kzKWWLVsCAMaPH49u3bpBpVJhwIAB6NChA0aMGIF58+bh2LFj6Nq1K9RqNc6cOYO4uDgsXboUffv2RXBwMCZNmoR58+bhkUceQY8ePXD06FFs27YNQUFBdtt76KGHAJR+w/MlS5Zg+fLliImJgbe3NzZs2GDT//jjj8sFtdGjR+P9999Hz549MWnSJKjVaixatAjVq1fHiy++KL/nyy+/xMsvv4wGDRqgcePGduvs0qULqlevDgBo27YtWrRogVatWsHPzw9HjhzBhx9+iPDwcJsnHxbHy8sLb7zxBsaMGYN+/fqhW7du2LdvHzZs2IA5c+YgMDBQjh0xYgRWr16NMWPG4PTp06hduzbWr1+Pixcv4uuvvy51W0RERFSOBBEREdFtWLNmjQAgDh48WGT/+fPnBQCxZs0auW3IkCFCp9PZxf7zzz9i6NChIjIyUnh6eorAwEDRqVMnsWvXLpu4U6dOiQceeEB4eXkJAGLIkCEO5RoXFycAiD179shtM2bMEABEcnJyke/5/PPPxf333y90Op3Q6XSiUaNGYsyYMSI+Pl6OOXnypOjcubPw8fERQUFBYvjw4eKPP/6w22+LxSLGjRsngoODhUKhEDd/FPu///s/0bJlS+Hl5SX0er1o1qyZePnll8W1a9fkGKvVKmbNmiXCwsKEl5eX6Nixozhx4oSIiIiwOw4REREiIiKi1OMyZMgQAaDYr/Pnz9vEX758WfTt21f4+voKHx8f8cgjj4gzZ87YxBQc1+K+Cn8Ppk6dKqKjo4Wfn59Qq9Widu3aYtSoUSIxMbHU3G8+flFRUUKj0YjIyEixePFiIUmSXdz169fFkCFDRGBgoNBqtaJNmzZi+/btZdoWERER3T6FEDfNRyciIiIiIiIiIqpgvKcUERERERERERE5HYtSRERERERERETkdCxKERERERERERGR07EoRURERERERERETseiFBEREREREREROR2LUkRERERERERE5HQsShERERERERERkdN5VHYCziZJEq5duwa9Xg+FQlHZ6RARERERERERVSlCCGRlZaFGjRpQKoufD3XHFaWuXbuG8PDwyk6DiIiIiIiIiKhKu3z5MmrVqlVs/x1XlNLr9QDyD4yvr28lZ3N7JElCcnIygoODS6w8Et0ujjVyFxyr5Io4Lskt5OVBWrMGWUYj9KNHQ6nVVnZGRAB4DiX3wbFqKzMzE+Hh4XINpjh3XFGq4JI9X1/fKlGUys3Nha+vLwc9VSiONXIXHKvkijguyV1IEyciNykJvtWqcaySy+A5lNwFx2rRSrttEo8UERERERERERE53R03U4qIiIiIiIiIyBksxgRYjYnFBygUgBDFdqt0ofDQhVVAZq6BRSkiIiIiojtdbi4UTz4Jf5MJ+OILwNu7sjMiIqpwt1swKtwvSRKktDSYEChfvqfShSLr+PtIPzC72FVoa7aH6eq+Yvv920xDQMz0knfEjbEoRUREREREgNUKSFJlZ0FE5DS3WzAqqr9wicu/zTTomw+Hd2QvAIA57RRStg9B0MProAlslB9UqLBVVL9KF3oLe+Y+3K4otWLFCqxYsQIXLlwAADRp0gTTp09H9+7dKzcxIiIiIiJ3pdVCrFmDjORkBPPJe0R0h7jdglHhflPK30j9PhbVuq6FNqgxgP8uvbv58jtNYCNoQ1oUm1dp/VWJ2xWlatWqhfnz56NBgwYQQmDdunV47LHHcPToUTRp0qSy0yMiIiIicj8KBVCtGoTVmv+aiOgO4KELg1LjB2P8JmSe+AAAkHnsXfg2fQ66qAFQqu0vZS6uYCT9O9NUHRhl1y/lZdtsI2X3WJttlNZflbldUapXr142y3PmzMGKFSvw22+/sShFRERERERERA6R8rKRsnMEci7sgJSXBQAwXz+CtLQzyLm0B0FdVgGAQwWljBMfAgBSf5wAv6ZDbfpL2kZgx8VI+/F/JeZQlQtTbleUKsxqtSIuLg5GoxExMTFFxphMJphMJnk5MzMTwL83IXPza+YlSYIQwu33g1wfxxq5C45VckUcl+QWLBaIrVuhzcyE9NRTgEZT2RkRAeA5lCpW1qlNyL7wPYQ5/b9GYYVkTkf2xe+R+ddHMF39BTkXd0Dk5dcSzNePIDXtLLIv/oiADgtx46cX/+3PLyjlXT+C1LQzyL74I6p1Xg7j6c9K3Ab2vlJif9apTdA3ia3oQ1HuHP2Zdcui1J9//omYmBjk5ubCx8cHX375Je66664iY+fNm4dZs2bZtScnJyM3N7eiU61QkiQhIyMDQgj57v5EFYFjjdwFxyq5Io5Lcgu5ufD/v/+Dh8WCpE6doOTT98hF8BxKFSntzO/QWPKgBFD4wmUBwGrJw+WTP0BlOA8v8w2If2OEsALmG0i9dhLpexfBevXkTf0WuT/72JfIuVryNhKvXYJ3Cf1Xz/yOwOAeFXocKkJWVpZDcQohSnq+oWsym824dOkSMjIysHnzZqxevRo//fRTkYWpomZKhYeH48aNG/D19XVm2uVOkiQkJycjODiYJ2iqUBxr5C44VskVcVySW8jLg3j3XRiMRuheeglK3uycXATPoVSRrm64B5a0v5FfArqZAvDQAdZcQFjsu5VqKNS+EOaMYvu1tTrAarhW4jaEUguFZCq23yOwMWo+faQsu+USMjMzERAQgIyMjBJrL245U0qj0aB+/foAgJYtW+LgwYNYunQpVq1aZRer1WqhLeKXqlKprBInNYVCUWX2hVwbxxq5C45VckUcl+TytFpI//sfcpKSoNdqOVbJpfAcShXFwycMlhuniykqqaCAlD8zqiiSJf+SvRL6JcMVePjULHEbKrUXJLOl2H4PnzC3HPuO5ux+e1YESZJsZkMREREREREREZXEp2E/KNU+RfYp1T7w8K0NKFRFv1mpglKtK7Ff5VOz1G141+tZYr9Pw36l7oc7c7ui1OTJk7F3715cuHABf/75JyZPnowff/wRTz31VGWnRkRERERERERuQhc1AF51ukGp9f+vuKRQQan1h1dEN+jvHn3bBaXSthHQ/s0S+3VRA8p1n12N212+l5SUhMGDByMhIQF+fn5o3rw5duzYgS5dulR2akRERERE7ik3F4ohQ+BnMgEffwzwRudEdAdQqr0R1GUVjPGbkHniA5gTD0JT/R74Nn1OLgaZru5HzsUdkMz/XqqnUEGp0csFJWHN+7ffkH8JnsIDSo2PXFAqbRuO9FdlbleU+uCDDyo7BSIiIiKiqsdohMJsruwsiIicSqn2hr7pUGhCWuDax20Q9OC70Ia0kPsdLShlnPgQeYm/Q139Hvg1HWpTUCptG6X1V2VuV5QiIiIiIqJyptVCrFyJzJQUBPHJe0R0h7AYE2A1JgIAzGmnbP4FACgUgBDQhLSAb/RYpGwfAt/osdAENkLejXiodKHw0IVB33QoPILuRuKmGFTruBReoS3LvI3i+gu2UVWxKEVEREREdKdTKIAaNSB5eOS/JiK6A2Qdfx/pB2bbtKVsHyK/1tZsD9PVfcX2+7aYAJ/GgwAAeWnx8r8FT55T6UJvexv+baYhIGZ6mffNXbAoRURERERERER3HH3z4fCO7FV8QKFZTEUxnPoE1z5uY9OW+n2s/Nq/zbTb3oZKF1r8e6sAFqWIiIiIiO50FguwbRu0GRlA//6ARlPZGRERVTgPXdhtXRqn0oXCp9FAAIAkSbiRloaAwECbmVK3u42qjkUpIiIiIqI7ncUCxapV8DKbgSeeYFGKiMgBhQtOkiRBiSRoQ0LkohSVjkUpIiIiIqI7nVIJ0a4d8gwGaPjHFBEROQmLUkREREREdzqNBnjlFRiTkqDjLCkiInIS/jcIERERERERERE5HYtSRERERERERETkdCxKERERERHd6UwmKGJj4Td+PGAyVXY2RER0h+A9pYiIiIiI7nRCAGlpUJjN+a+JiIicgEUpIiIiIqI7nUYDsWQJslJTUY03OiciIidhUYqIiIiI6E6nVAL16sHq45P/moiIyAlYlCIiIiIiIqIqxWJMgNWYWGy/ShcKD12YEzMioqKwKEVEREREdKezWIA9e6BJTwceewzgJXx0ixwpBgEoMQYKhXxvM0mSIKWlwYRAKAtm8RXqL24bWcffR/qB2cXG+LeZhoCY6SXsCRE5A4tSRERERER3OosFiqVL4W02Az17sihFt8yRYhCAEmO0NdvDdHWfTVtiKf03b0PffDi8I3sBAMxpp5CyfQiCHl4HTWAjAP8Vx4iocrEoRURERER0p1MqIVq1Qp7RCA3vKUW3wdFiUEkxhWdCmVL+Rur3sajWdS20QY3t+ovbhocuzO7yPE1gI2hDWlTczhNRmbEoRURERER0p9NogOnTYUxKgo6zpOg2eOjCoNT4wRi/CZknPgAAZB57F75Nn4MuagCUam8AcCgGyL98DwDUgVE2BSUpL7vU998ck7J7bJHbIKLK43ZFqXnz5uGLL77AqVOn4OXlhbZt22LBggWIioqq7NSIiIiIiIjuaFJeNlJ2jkDOhR2Q8rIAAObrR5CWdgY5l/YgqMsqAHAoxhi/CRknPgQApP44AX5Nh0IXNcDh95cWw8IUUeVzu6LUTz/9hDFjxuDee++FxWLBlClT0LVrV5w8eRI6na6y0yMiIiIiIrpjGeM35ReCzOn/NQorJHM6ci7ugDF+EwCUGGM4+RFyr+63KSjlXT+MtLTTyLm0B9qa7W57G8b4TdA3HVohx4CIHOd2Rant27fbLK9duxYhISE4fPgwHnjggUrKioiIiIjIjZlMUIwdC9+cHOD99wEvr8rOiNyU4XQcpDxDkX1SngGG03Hy6+JiMv5YDsmQWGxByZzyx21vw3A6jkUpIhfg1KKUSqVCQkICQkJCbNpTU1MREhICq9Va5nVmZGQAAAIDA4vsN5lMMJlM8nJmZiaAfx8t+u/1ye5KkiQIIdx+P8j1cayRu+BYJVfEcUluwWoFEhKgNJkgWa0AxyvdIoshARDF/F0nWfP7gZJjMq8A1tyiu/OMkDKv3P42DAk8L1O54u97W44eB6cWpcS/T0i4mclkguYWbqgoSRJeeOEFtGvXDk2bNi0yZt68eZg1a5Zde3JyMnJziz7RuQtJkpCRkQEhBJR8SgpVII41chccq+SKOC7JLUgSlC+9hKysLHilp0NpKHqGCVFpsnzugyb9EpSSAYpC7QKApNIhx+c+ACgxxujZHHrDbzZ9coxkRZZ3c+iyj9/WNnJ87kNSUlI57DFRPv6+t5WVleVQnFOKUu+88w4AQKFQYPXq1fDx8ZH7rFYr9u7di0aNGpV5vWPGjMGJEyfw888/FxszefJkTJw4UV7OzMxEeHg4goOD4evrW+ZtuhJJkqBQKBAcHMxBTxWKY43cBccquSKOS3IXUkgIrMnJHKt0W7watMaNxC8hzLbtCgAeHmoEN2gNACXGBGrSYVEoAWE/00KhVCFAkw7JrL6tbQQ3aA39TVfwEN0O/r635enp6VCcU4pSixcvBpA/U2rlypVQqVRyn0ajQZ06dbBy5coyrXPs2LH45ptvsHfvXtSqVavYOK1WC61Wa9euVCqrxEBRKBRVZl/ItXGskbvgWCVXxHFJ7oJjlW6XvtEAmC7vQc7FHZDMWfmX0ClUUGr08IroCn2j/KfnlRSjrdkO6fun295T6l9KtQ7+d4/MvxH6bWxD32gAxzmVO55D/+PoMXBKUer8+fMAgE6dOuGLL75AQEDALa9LCIFx48bhyy+/xI8//oi6deuWV5pERERERHcmqxXYvx/qGzeAHj0A/kFFt0ip9kZQl1Uwxm9C5okPYE48CE31e+Db9DnoogZAqfYGgBJjAMAkF50MgLAACg8oNT7wiugGn7sGw+euwbe1jYIYIqpcClHcjZ5c1OjRo/Hxxx9j69atiIqKktv9/Pzg5cBTQjIzM+Hn54eMjIwqcfleUlISQkJCWImlCsWxRu6CY5VcEccluYXcXIi+fWEym6HZsgVKb/7BTrfPlHQU1z5ugxqDDkAb0qJMMVJeNozxm5Bx4kPkJf4OdWhr+DUdaldQup1tEJUn/r635Wjtxak3Ou/Tpw9at26NV155xab9zTffxMGDBxEXF1fqOlasWAEA6Nixo037mjVrEBsbW16pEhERERHdOZRKoGlTWIxGaPjHFN0GizEBVmMiAMCcdsrmXwBQ6UIBoNQYD10Y9E2HwiPobiRuikG1jkvhFdqy3LdBRJXLqUWpvXv3YubMmXbt3bt3x8KFCx1ah5tN7CIiIiIicn0aDcTcuTAkJcH7Fp6KTVQg6/j7SD8w26YtZfsQ+bV/m2kAUGKMb4sJ8Gk8CACQlxYv/1sw+8Rw6hNkHllyW9vwbzMNATHTHd8xIqoQTi1KGQwGaIr4JadWq5GZmenMVIiIiIiIiKic6ZsPh3dkr2L7C2YxlRRjOPUJrn3cxqYt9ftY+bVviwmoMejAbW2jIIaIKpdTi1LNmjXDp59+iunTbSvSmzZtwl133eXMVIiIiIiIiKiceejCHLosrqQYlS4UPo0GAsi/T8+NtDQEBAbKM6UcvfSOl+cRuT6nFqVee+01PPHEEzh37hwefPBBAMAPP/yATz75xKH7SRERERERUQUwm6F48UXoc3KAd98FPD0rOyO6gxUubEmSBCWSoOXNo4mqJKcWpXr16oUtW7Zg7ty52Lx5M7y8vNC8eXPs2rULHTp0cGYqRERERERUQJKA8+ehMpvzXxMRETmBU4tSANCzZ0/07NnT2ZslIiIiIqLiaDQQr78OQ1oaAnmjcyIichKnz39MT0/H6tWrMWXKFKSlpQEAjhw5gqtXrzo7FSIiIiIiAgClEoiOhqVp0/zXRERETuDUmVLHjx9H586d4efnhwsXLmDYsGEIDAzEF198gUuXLuGjjz5yZjpERERERERERFRJnPrfIBMnTkRsbCzOnDkDz0I3T+zRowf27t3rzFSIiIiIiKiA1QocPAiPY8fyXxMRETmBU2dKHTx4EKtWrbJrr1mzJhITE52ZChERERERFcjLg+KNN+BjNgMPPACo1ZWdERER3QGcWpTSarXIzMy0az99+jSCg4OdmQoRERERERVQKoEGDWDNzuY9pYiIyGmc+hvn0Ucfxeuvv468vDwAgEKhwKVLl/DKK6+gT58+zkyFiIiIiIgKaDQQCxcia9YsgE/fIyIiJ3FqUWrhwoUwGAwICQlBTk4OOnTogPr160Ov12POnDnOTIWIiIiIiIiIiCqRUy/f8/Pzw86dO7F//3788ccfMBgMuOeee9C5c2dnpkFERERERERERJWswotSgYGBOH36NIKCgjB06FAsXboU7dq1Q7t27Sp600RERERE5AizGYqpU+GTnQ0sXAgUelJ2aSzGBFiNxT+0SKULhYcurDyyJCKiKqbCi1JmsxmZmZkICgrCunXrsGDBAuj1+oreLBERERFRlVauxSBJAv7+Gx5mc/7rMmzHcOoTZB5ZUmy/f5tpCIiZ7lgeRER0R6nwolRMTAx69+6Nli1bQgiB8ePHw8vLq8jYDz/8sKLTISIiIiKqErKOv4/0A7OL7fdvMw365sNLLChBoQCEAKxWSBP6IiszE/r0E1Aa1ADyC1ulbce3xQTUGHQAAGBOO4WU7UMQ9PA6aAIbyesgIiIqSoUXpTZs2IDFixfj3LlzAICMjAzk5uZW9GaJiIiIiKo0ffPh8I7sBaD4YlBpBSVtzfYwXd1n02a8/N/rgsJWadu5eUaWJrARtCEtbnsfiYioaqvwolT16tUxf/58AEDdunWxfv16VKtWraI3S0RERERUpXnowkotBpVWUJJnSgEwpfyN1O9jUa3rWmiDGgP4r+Ck1PjBGL8JmSc+AABkHnsXvk2fgy5qAJRqb0h52Tb9KbvH2vQTEREVRVnRGwgMDERKSgoAoFOnTtBoNLe1vr1796JXr16oUaMGFAoFtmzZUg5ZEhERERG5HykvG1knPkTK7rEA8otBWSc+hJSXDSC/cKUOiII56Sgyj70LIL+gZE46CnVAFLTB0dCGtIA26G6o01QAALV/g/y2kBbw0IVBystGys4RSNv7KszXjwAAzNePIG3vq0jZOQKW7JQS+wtyISIiulmFF6UKbnQOAOvWrbvtS/eMRiPuvvtuvPfee+WRHhERERGRWyqtWCTlZTsck3X0fVheHwUASP1hgk1hyxi/CTkXdkAypwPCmr9xYYVkTkfOxR24se/lEvuN8ZuceViIiMiNuN2Nzrt3747u3buXd5pERERERG7FplhUoIhiUEkxhpMfIffqfuSc2w48YAAA5KUeRdreV5FzaQ+CuqyC4XQcpDxDkTlIeQZk//Ntif2G03HQNx1aHrtMRERVjFNvdK5QKJx+o3OTyQSTySQvF8zakiQJ0k2Pu3U3kiRBCOH2+0Guj2ON3AXHKrkijkuqKFmnN5dQDDIi6/Tmf18XH5P+x0pIhgQIawbgU9CTX7TKvvg9sk5tgsWQ8N8MKLuVWGHNy4GihH6LIYHjn24Zz6HkLjhWbTl6HKr8jc7nzZuHWbNm2bUnJye7/VMAJUlCRkYGhBBQKiv8Sky6g3GskbvgWCVXxHFJFSUtVwdvIUFRRJ+QrEjL1QFAiTE3zP7QWS5BCdjECABWSx6unvkd8LkPmvRLUEoGuxhJpUO2riW8DYeL7c/xuQ9JSUm3t7N0x+I5lNwFx6qtrKwsh+IqvCgFAD169MAnn3yC8+fPAwDmz5+PkSNHwt/fHwCQmpqK9u3b4+TJk+W+7cmTJ2PixInycmZmJsLDwxEcHAxfX99y354zSZIEhUKB4OBgDnqqUBxr5C44VskVcVxSRZE8jTAplICw/99ohVKFQE8jAJQY45t7HJCM9n0AVFYj9Ibf4NtiPG4kfglhto/x8FAjtEZtZP9zvNj+4AatoQ8JucW9pDsdz6HkLjhWbXl6ejoU55Si1Pbt220uoZs7dy769+8vF6UsFgvi4+MrZNtarRZardauXalUVomBolAoqsy+kGvjWCN3wbFKrojjkiqCvmFf5CUesr1f1L+Uah30DfsCQIkxKl0o8m6cBYTFfgNKFTx8wqBvNACmy3uQc3EHJHNW/qV8ChWUGj28Iroi8IEFgGQutl/faADHPt0WnkPJXXCs/sfRY1ApR0oIURmbJSIiIiKqMnRRA+BVpxuUWn9AocpvVKig1PrDK6IbdFEDSo3R3z0aSvW/N5O66SO6Uu0Dn4b9oFR7I6jLKgS2nw9N9XsAAJrq9yCw/XwEdVkFD++gEvuVau+KPxhEROSWnDJTqjwZDAacPXtWXj5//jyOHTuGwMBA1K5duxIzIyIiIiJynoJikTF+EzJPfABz4kFoqt8D36bPQRc1QC4GlRQDAKar+5FzYTuEMQtCLRWa5dRNjlGqvaFvOhSakBa49nEbBD34LrQhLWxyKamfiIioKE4pSikUCigUCru2W3Ho0CF06tRJXi64X9SQIUOwdu3aW86RiIiIiMjdOFIMKi2moGiVceJD5CX+DnX1lvBrOlQubFmMCbAaEwEA5rRTNv8CABQK4N8rIYrqV+lC4aELq5D9JyIi9+aUopQQArGxsfK9nXJzczFy5EjodPlPBCl8v6nSdOzYkZf/EREREdEdr7RikUoXCgAOFZQ0IS3g23w0UhN/h2/z0dAENUbejXiodKHIOv4+0g/Mttl2yvYh8mttzfYwXd1XbL9/m2kIiJl+u7tLRERVkFOKUkOGDLFZfvrpp+1iBg8e7IxUiIiIiIiqhNKKRf5tpgFAmQtKqd/H2qxD33w4vCN7FZ9IoZlSRSkojhEREd3MKUWpNWvWOGMzRERERER3jNKKRQXFIIcKSiYTxMsvIy8vDx6LFkLp6SWvw0MXxsvviIioQrjdjc6JiIiIiAgOF4scKiiZTBBe9SEpcqENbgGll1c5ZEhERFQyFqWIiIiIiO50Wi3E6tXITEqC57/3gSUiIqpoyspOgIiIiIiIiIiI7jwsShERERERERERkdOxKEVEREREdKczm4E5c6BbvDj/NRERkRPwnlJERERERHc6SYLiwAGozWZAkio7GyIiukOwKEVEREREdKfz8IAYMwbZ6enQePBPBCIicg7+xiEiIiIiutN5eADdusGclJT/moiIyAl4TykiIiIiIiIiInI6FqWIiIiIiO50QgCXLkF55Ur+ayIiIifg3FwiIiIiojudyQTF2P9n777DmyrbP4B/kzSju3RSoBRElmxBSkEFZAm8IrJ+qLyAMgVERURFZSnDBSii8AoCAipDBUUBWQoqKKNsKMhepYvukSbn+f1RGhuStEmbJjnl+7muXibnPHnOfQ53niS3zzlnHAL0emDDBsDHx90RERHRXYBFKSIiIiIiAgICIPLy3B0FERHdRViUIqqEbmTk4UaG7S+VkQE6RAbo7po4iIiIqBQ6HcSqVUhPTES4jp/NRETkGixKyUzxH/mSJJB6KxvB+nQolQoA/JFPhRbvvYQZ287YXD+lSz1M61b/romDPJczCpccF8nZHMlLe9pG+GkqPA4iIiIiOWJRSmb4I5/sMSo2Gr0aRQAATidmYdBXcVj1VAs0CPcDAJf9iPGUOMhzOWNMc0Yf/PFPxTmSU/a0ndKlrum5I7nGz3wiIiKq7FiUkpniP/JPJmRi8DeH8eXA5rivqj8A/sinQtZ+QDcI98P9NYLuyjjIczmjcOmMcZE//qk4R/LS0Rx2JNdY2CeX0uuBjz6CT1YW8PrrAE/hIyIiF5BtUWrhwoV4//33kZCQgGbNmmHBggVo3bq1u8OqcMV/5EtS4e16+SOfrMnRG/BV3DUs2XcZADDm22MY3qYmnmpRHT4a1731PSUO8kyRAToE6rzMcuTjPRccyhFnjIv88U/FOVJQt6etJEmmx47kGgv75FKSBMVvv0Gj1wPFcpaIiKgiyfIX4Zo1azBhwgQsWrQIMTExmD9/Prp164b4+HiEh4e7O7wKV/Qjf+m+SwCA578/hmFtovkjn0xy9AYMX3sEW04nIjPfAAA4eDUNZ37Mws6zyVgyoJlLcsVT4iDP5awcKe+46IziGFUujhTUHWnraKGJhX1yGS8viGHDkJueDo0Xc4uIiFxDIYQQ7g7CUTExMXjggQfwySefACj8P5BRUVF4/vnn8dprr5X42oyMDAQGBiI9PR0BAQGuCNep7vwBZxSASgH4a73waINw/sgnAMCSvy5h0o8nkZZnsFgXpPPCe4/dh+Ex0Xb3J0kSEhMTER4eDqVS6bY4qPJxRo44Y1zk2ErFOZIP9rTVeSnNxtDihaa/r6ShdVSQ1UIT85Jcrayf90QViXlJcsFcNWdv7UV232T0ej0OHjyI119/3bRMqVSic+fO2Lt3r0P96PV6i+VKpRJexf7vkLU2RRQKBdRqdZnaFhQUwFY9sKS2X/59CZtP3kC6/t/XGgWQlmfAltOJ+HL/RTz7QE2bcWg0/94BqKQY7mxrMBjMTj8oT1u1Wg2FQlGhbY1GI4xGo1Paenl5mQYVT2grSRIMBssf8EVUKhXWHr6OLL31/rLyDfjmwCUMbhEJlUoFlUpVar+SJJnFZ08M/8ZhvV1RHM+0qmGKQQiBgoICm/0Wf39WVFug5Peyp48R5WkLuH6M+ObAJWTl28gRvRHfHLyMwS0ibfarVqvxVdw1bDmdaFbYMo2LJ2/gy78D8Gzr6BLf9yWOracSTH1YI8cxwp73vavalvb+dKSts8aIUj9r/75gygd72g5tFWX63pFnkDBq/VH8ciYZWQWFrymaHbjt1A0s7tfUVGgqse+TtvOyMo0R1vB7RPnaVsTnfUW29cQxoqS2AL9HlKVtSe/PO5dzjOD3CE8dIyRJMn3eFy9K3a1jREnxFCe7olRycjKMRiMiIiLMlkdEROD06dMW7fPz85Gfn296npGRAQD44IMPoNVqLdrfe++9ePrpp03P33vvPZvJGB0djaFDh5qez5s3Dzk5OVbbRkZGYuTIkabnn3zyCdLS0qy2DQsLw5gxY0zPFy9ejKSkJADAqoNXkVmzA+AdCNwe6Ipk641Y8NnnuPGL9Wuf+Pj44JVXXjE9X7lyJS5dumS1rVqtxuTJk03Pv/76a/zzzz9W2wLA1KlTTY/Xr1+PU6dO2Wz7+uuvmz6EfvjhBxw5csRm24kTJ8LX1xcAsHnzZhw4cMBm2xdeeAFBQUEAgG3btpVYpHzuuedMp3r+9ttv+O2332y2HT58OKpXrw4A+PPPP7F9+3abbYcMGYJatWoBAPbv34/NmzfbbPvkk0+iXr16AIAjR45g48aNNtv269cPjRo1AgCcOHEC69evt9n28ccfx430XBgl6wOHwWjE0Q1LMTNxB7p37266FtvFixexYsUKq68RQqBly5bo2bMnAODatWtYsmSJzRjat2+PDh064EZ6LgySAKCwaFMUx7Y6OejatSsAIC0tDR999JHNflu1amWKITs7Gx988IHNts2aNUPv3r0BFA6Is2fPttm2YcOGGDBggOn5zJkzbbb19DHiTkFBQXjhhRdMz5cuXYobN25YbeuOMeLonxdhaPIEoFRZvMYoCZz9axdm3rT9nps4cSLWHb5WWPwUwnxcFAIZabfw/pw5uNGyRoljRElja2Z2jqkPa+Q4RjRv3hwAcObMGXz99dc229o7RgBA586d0a5dOwD2jxEAkJiYiM8++8xm29jYWJePEaV91hbPh9Lafvj+B7jeLALZ2dnw9fXF4esZ2J6sRd69D5pytqjQtGnTTxj8+zdoUT2w1L4z0tNs5mVlGiOs4feIQk4fI4SAyM5G+zZtEHH7vcExojcAfo9w9xghhMBzzz1nKhhxjKgFgN8jPHGMEEKYPu8VxT6379YxongdpiSyK0o5avbs2Zg+fbrF8uzsbKvV14yMDCQmJpqeZ2Vl2azSZmZmWrTNzc21q21mZiays7OtttXpdDbbaiQ9hM7f4sspUPgDTmvMR3a29Sp40XRCe2Lw8vIya5uRkWGzLQCH2xZ9UKSnp5fYNikpybTenrZF1di0tLQS2yYnJ5se29O2qPp769atEtumpKTAx8fH7rZFxy01NbXEtqmpqQ61jQ33wqVUIMtwR0FICGjyMxDlq0B2djZu3bpl6jclJcVmv0IIZGVlITExEUqlEsnJySXGkJaWhsTERMSGK3ExWUK2UWlRLCiKo6gtUPq/cXp6uqltTk6O3W31en2Jbe983zvS1tPGiDupVCq727pjjIjyVSAjPwN6XZDFuOarBuoHKpGdnWGz36SkJPgZsgqvyXvnuKhQQOgCoJEK//1LGiNKGlsllcbUhzVyHCPsed8XxViWtvaOEY62ddUYUdpnbfF8KL1tPrKzs5GXlwcAuJacBqn6I5ZFVADGqvVw7eoO1AvyKrVvofW1mZeVaYyw1ZbfIypgjDAYoPvhB+h++w2JLVtC6ePDMYLfIzxijBBCIC0tDUIIKJVKjhH8HuGxY4QQwvR5X7wodbeOEfYWpWR3TSm9Xg8fHx+sX7/eVJkECqvGaWlpFhVgazOloqKicPPmTavnNXr6dLnHlv6F3y5mwGDlpWqlAg/XCsCPz9q+CyGn3TveVo5TalccvIZXN53CLSvX6amiAWZ2r4+hD9R06PS91NRUVK1aFUql0u4ptV/8fRmTNp1EWp7lvhXFMaxNbU67t9K2Mk27L6nt8v2XMXlzPNKsHJpgnRdmPloXQ1pZn6EEFL6Xeyz5C7vOpcBgZfNeCqB9rQD8OCymxPd9SWOrlxJoH13YhzVyHCM47b7ktqV+1kb7m/LBnrYbn3kASUlJCAsLwwPz9+B0qh7W3lkKAA2CNTg4oX2pfZeUl5VpjLCG3yPK19bm+zMvD3jmGRj0eujWrIHSx4djBL9HeMQYIUkS0tPTERYWBqVSyTGC3yM8doyQJMn0ec/T9wprLxEREZXvmlIajQYtW7bEjh07TEUpSZKwY8cOjBs3zqK9Vqu1epqeTqeDTlf6Lb7taVOWttZisqft/7WqhYM3rF8U2E+jwv+1jLY7DkdiKP6hIYe2SqXS7M1R2doWH6iseer+Gtj5T0rhxXH1BhglQKUEAjRe6NYgHINj7oHujovjltSvJEmmD6uiv9JisDcOtdq8n6IPDXtUVNuKet+7YoxwV9uyvpcHx9yD3ZcyreZI1wbh+G/rWha5eqf+zatj/5V0q+Oiv9YL/9eqlsWxv/M9V9LY6q+x3oc1chkjPKkt4Bnv++JtS/2sLZYP9rbVarXQ6XSoHhKAs2kpt09tviMGpQLVQwLs6tuRvJTzGOGutp7yXnbpGKHTQfr2W6QlJsLHx8ehz/sS+y1nW8DzxojS8HuE421Len9KkoSMjAxTTnKMqPi27n7fy3WMkCTJ9Hlf0oXO75Yxwt5rSsnykvATJkzA559/jhUrVuDUqVN47rnnkJ2djWeeecbdoVW4p1pUx6MNwhGk84LX7X89LyVQRVf4I/+pFtXdGyB5BB+NF5YMaIb3HrsPLasHAQBaVg/Cu4/d59K7NXlKHOS5nJEjzhgXi/ehut2HimPrXcuRfHA0dwY0rwY/jfUvuf4aFQY0r1bmvomIiIjkRnan7xX55JNP8P777yMhIQHNmzfHxx9/jJgY66dWFGfvbQk9WdGtpJfuu4S/rqQjJioQw9pEW9xKmggADl1NQ6v5e3DgxYdwf42gMvXhjNubOiMOqtzKkyPOGBeL+liy7zL+vpKG1lFBGN6mJsfWu5Qj+VBa2+JjaJ5BwvC1R2zOIL2zGMu8JFfi7czJEzEvSS6Yq+bsrb3I9tvMuHHjrJ6uV9ndyMjDjYw83F89ELp2tfHXN4cxtl1t3FfVH6cTsxAZoENkgP3T9qhyKsoTADidmGX2XwAuyxNPiYM8lzNyxFnjoo/GC8NjonF/9UC0mr8Hn/ZtwgLqXcyRfHC07ZIBzcwKTS2r2y40MS/JZQoKgM8+g09mJjBxIuDA6RpERERlJdui1N1q8d5LmLHtjNmywd8cNj2e0qUepnWr7+KoyNNYy5NBX8WZHrsqTzwlDvJczsgRZ4yLLKBScY7kgz1tI/zMr1Nib6GJeUkuZTRC8csv0Oj1wEsvuTsaIiK6S8j29L2ykvvpe8W/oEqSQOqtVARXCYZSWXh3B35BJcA8T6xxNE/KOhXV2XFQ5eOMHHHGuDhta7xFYas4FlDvLo7kgz1tp3SpaxpDb2bpzQpNg76Kw6qnWqBBuB8A83xlXpJLGQyQ1q9HRkYGAoYOhdKBiz4TVSSeEkVywVw1Z2/thUUpGWPSk6sw10guWEAlZ3AkH+xpG+GnMeXljG1n7S40MS/J1fh5T56IeUlywVw1V+mvKUVEROQs/HFPxTmSD/a0lSTJ9HhUbDR6NYoosb+yxEFEREQkRyxKEREREbkIC03ksYQA0tOhyMgAwsLcHQ0REd0lWJQiIiIiIrrb5edD8d//IlCvBzZsAHx83B0RERHdBe66olTRJbQyMjLcHEn5SZKEzMxM6HQ6nrNKFYq5RnLBXCVPxLwkWcjLgygoQL7BAE1GBpQGg7sjIgLAMZTkg7lqrqjmUtplzO+6olRmZiYAICoqys2REBERERF5oMhId0dARESVRGZmJgIDA22uv+vuvidJEq5fvw5/f38oFAp3h1MuGRkZiIqKwpUrV2R/J0HybMw1kgvmKnki5iXJBXOVPBHzkuSCuWpOCIHMzExUq1atxJljd91MKaVSiRo1arg7DKcKCAhg0pNLMNdILpir5ImYlyQXzFXyRMxLkgvm6r9KmiFVhCc6EhERERERERGRy7EoRURERERERERELseilIxptVpMnToVWq3W3aFQJcdcI7lgrpInYl6SXDBXyRMxL0kumKtlc9dd6JyIiIiIiIiIiNyPM6WIiIiIiIiIiMjlWJQiIiIiIiIiIiKXY1GKiIiIiIiIiIhcjkUpIiIiIiIiIiJyORalnGz27Nl44IEH4O/vj/DwcPTu3Rvx8fFmbfLy8jB27FiEhITAz88Pffv2xc2bN03rjxw5gieffBJRUVHw9vZGw4YN8dFHH5n18euvv0KhUFj8JSQklBifEAJTpkxBZGQkvL290blzZ5w9e9a0/uLFixg2bBhq164Nb29v1KlTB1OnToVer3fC0SFnknuuAUCvXr1Qs2ZN6HQ6REZG4r///S+uX79eziNDnqYy5GqR/Px8NG/eHAqFAocPHy7bASGPUBnyslatWhb9zpkzp5xHhjxNZchVAPjpp58QExMDb29vVKlSBb179y77QSG3k3te2upXoVBg//79TjhC5Enknq8AcObMGTz++OMIDQ1FQEAAHnzwQezataucR8ZDCHKqbt26iWXLlonjx4+Lw4cPix49eoiaNWuKrKwsU5vRo0eLqKgosWPHDnHgwAHRpk0b0bZtW9P6pUuXivHjx4tff/1VnDt3TqxcuVJ4e3uLBQsWmNrs2rVLABDx8fHixo0bpj+j0VhifHPmzBGBgYFiw4YN4siRI6JXr16idu3aIjc3VwghxObNm8XQoUPF1q1bxblz58TGjRtFeHi4ePnll518pKi85J5rQggxd+5csXfvXnHx4kXxxx9/iNjYWBEbG+vEo0SeoDLkapHx48eL7t27CwAiLi6u/AeH3KYy5GV0dLSYMWOGWb/F46fKoTLk6vr160WVKlXEZ599JuLj48WJEyfEmjVrnHiUyNXknpf5+flm/d24cUMMHz5c1K5dW0iS5OSjRe4m93wVQoi6deuKHj16iCNHjogzZ86IMWPGCB8fH3Hjxg0nHin3YFGqgiUmJgoA4rfffhNCCJGWlibUarVYt26dqc2pU6cEALF3716b/YwZM0Z07NjR9Lwo4W/dumV3LJIkiapVq4r333/ftCwtLU1otVrx9ddf23zde++9J2rXrm33dsg9KkOubdy4USgUCqHX6+3eFsmPXHP1559/Fg0aNBAnTpxgUaoSkmNeRkdHi3nz5tndL1UOcsvVgoICUb16dbFkyRK7+yX5kVte3kmv14uwsDAxY8YMu7dD8iW3fE1KShIAxO7du01tMjIyBACxbds2u7flqXj6XgVLT08HAAQHBwMADh48iIKCAnTu3NnUpkGDBqhZsyb27t1bYj9FfRTXvHlzREZGokuXLvjjjz9KjOXChQtISEgw23ZgYCBiYmLKtG3yLHLPtdTUVKxevRpt27aFWq0usX+SNznm6s2bNzFixAisXLkSPj4+9u0oyYoc8xIA5syZg5CQELRo0QLvv/8+DAZD6TtLsia3XD106BCuXbsGpVKJFi1aIDIyEt27d8fx48ft32nyeHLLyzv98MMPSElJwTPPPFNi31Q5yC1fQ0JCUL9+fXz55ZfIzs6GwWDA4sWLER4ejpYtW9q/4x6KRakKJEkSXnzxRbRr1w6NGzcGACQkJECj0SAoKMisbUREhM1zTf/880+sWbMGI0eONC2LjIzEokWL8O233+Lbb79FVFQUOnTogEOHDtmMp6j/iIgIu7f9zz//YMGCBRg1alSp+0vuI+dce/XVV+Hr64uQkBBcvnwZGzdutHu/SX7kmKtCCAwdOhSjR49Gq1atHN5n8nxyzEsAGD9+PL755hvs2rULo0aNwqxZszBp0iSH9p3kRY65ev78eQDAtGnT8Oabb2LTpk2oUqUKOnTogNTUVMcOAHkkOeblnZYuXYpu3bqhRo0ape4vyZsc81WhUGD79u2Ii4uDv78/dDod5s6diy1btqBKlSoOHwNP4+XuACqzsWPH4vjx4/j999/L3Mfx48fx+OOPY+rUqejatatpef369VG/fn3T87Zt2+LcuXOYN28eVq5cidWrV5sVkjZv3gyVSuXQtq9du4ZHH30U/fv3x4gRI8q8D1Tx5Jxrr7zyCoYNG4ZLly5h+vTpGDx4MDZt2gSFQlHmfSHPJcdcXbBgATIzM/H666+XOWbybHLMSwCYMGGC6XHTpk2h0WgwatQozJ49G1qttsz7Qp5LjrkqSRIA4I033kDfvn0BAMuWLUONGjWwbt06/o/PSkCOeVnc1atXsXXrVqxdu7bM8ZN8yDFfhRAYO3YswsPDsWfPHnh7e2PJkiV47LHHsH//fkRGRpZ5XzwBi1IVZNy4cdi0aRN2795tVnGvWrUq9Ho90tLSzCqxN2/eRNWqVc36OHnyJDp16oSRI0fizTffLHWbrVu3Nr25evXqhZiYGNO66tWr48aNG6ZtFU/cmzdvonnz5mZ9Xb9+HR07dkTbtm3xv//9z+79JteTe66FhoYiNDQU9erVQ8OGDREVFYV9+/YhNjbW7mNA8iDXXN25cyf27t1r8SO/VatWePrpp7FixQr7DgB5JLnmpTUxMTEwGAy4ePGi2ZdiqhzkmqtFy++77z7Teq1Wi3vuuQeXL1+2c+/JU8k1L4tbtmwZQkJC0KtXL7v2meRLrvm6c+dObNq0Cbdu3UJAQAAA4NNPP8W2bduwYsUKvPbaa44dCE/j7otaVTaSJImxY8eKatWqiTNnzlisL7qI2vr1603LTp8+bXERtePHj4vw8HDxyiuv2L3tzp07iyeeeKLE2KpWrSo++OAD07L09HSLi/5dvXpV1K1bVwwcOFAYDAa7t0+uVRly7U6XLl0SAMSuXbvsjoU8n9xz9dKlS+LYsWOmv61btwoAYv369eLKlSt2x0KeRe55ac2qVauEUqkUqampdsdCnk/uuVr0vPiFzvV6vQgPDxeLFy+2OxbyLHLPy+Jta9euzTuNV3Jyz9cffvhBKJVKkZmZafbaevXqiZkzZ9odi6diUcrJnnvuOREYGCh+/fVXs9tA5uTkmNqMHj1a1KxZU+zcuVMcOHBAxMbGitjYWNP6Y8eOibCwMDFo0CCzPhITE01t5s2bJzZs2CDOnj0rjh07Jl544QWhVCrF9u3bS4xvzpw5IigoSGzcuFEcPXpUPP7442a3m7x69aq49957RadOncTVq1fNtk+eRe65tm/fPrFgwQIRFxcnLl68KHbs2CHatm0r6tSpI/Ly8px8tMid5J6rd7pw4QLvvlcJyD0v//zzTzFv3jxx+PBhce7cObFq1SoRFhYmBg8e7OQjRe4m91wVQogXXnhBVK9eXWzdulWcPn1aDBs2TISHh7OAKmOVIS+FEGL79u0CgDh16pSTjgx5Irnna1JSkggJCRF9+vQRhw8fFvHx8WLixIlCrVaLw4cPO/louR6LUk4GwOrfsmXLTG1yc3PFmDFjRJUqVYSPj4944oknzIo+U6dOtdpHdHS0qc27774r6tSpI3Q6nQgODhYdOnQQO3fuLDU+SZLEW2+9JSIiIoRWqxWdOnUS8fHxpvXLli2zuQ/kWeSea0ePHhUdO3YUwcHBQqvVilq1aonRo0eLq1evOuX4kOeQe67eiUWpykHueXnw4EERExMjAgMDhU6nEw0bNhSzZs1iUb8SknuuClE4M+rll18W4eHhwt/fX3Tu3FkcP3683MeG3Kcy5KUQQjz55JOibdu25ToW5PkqQ77u379fdO3aVQQHBwt/f3/Rpk0b8fPPP5f72HgChRBC2HGWHxERERERERERkdMo3R0AERERERERERHdfViUIiIiIiIiIiIil2NRioiIiIiIiIiIXI5FKSIiIiIiIiIicjkWpYiIiIiIiIiIyOVYlCIiIiIiIiIiIpdjUYqIiIiIiIiIiFyORSkiIiIiIiIiInI5FqWIiIiIiIiIiMjlWJQiIiIiIiIiIiKXY1GKiIiIiIiIiIhcjkUpIiIiIiIiIiJyORaliIiIiIiIiIjI5ViUIiIiIiIiIiIil2NRioiIiIiIiIiIXI5FKSIiIiIiIiIicjkWpYiIiIiIiIiIyOVYlCIiIqIKdfHiRSgUCixfvtzdoVRqtWrVwtChQ90dBhEREZHdWJQiIiKiclm+fDkUCoXVv9dee61Ctjlr1ixs2LCh1HYdOnSwGVvxv2nTplVInHf6+eefXbYtR5w+fRqTJk1C8+bN4e/vj8jISPTs2RMHDhyw2v7atWsYMGAAgoKCEBAQgMcffxznz583a3PlyhVMnz4drVu3RpUqVRAaGooOHTpg+/btFv2VlEMJCQl278fSpUvRsGFD6HQ61K1bFwsWLLBoU6tWLZvbqlu3rt3bIiIiovLzcncAREREVDnMmDEDtWvXNlvWuHFjREdHIzc3F2q12mnbmjVrFvr164fevXuX2O6NN97A8OHDTc/379+Pjz/+GJMnT0bDhg1Ny5s2beq02Ery888/Y+HChR5XmFqyZAmWLl2Kvn37YsyYMUhPT8fixYvRpk0bbNmyBZ07dza1zcrKQseOHZGeno7JkydDrVZj3rx5aN++PQ4fPoyQkBAAwMaNG/Huu++id+/eGDJkCAwGA7788kt06dIFX3zxBZ555hmLOKzlUFBQkF37sHjxYowePRp9+/bFhAkTsGfPHowfPx45OTl49dVXTe3mz5+PrKwss9deunQJb775Jrp27WrvISMiIiInYFGKiIiInKJ79+5o1aqV1XU6na7U12dnZ8PX19epMXXp0sUijo8//hhdunRBhw4dXBqLJ3vyyScxbdo0+Pn5mZY9++yzaNiwIaZNm2ZWlPr0009x9uxZ/P3333jggQcAFP7bN27cGB9++CFmzZoFAOjYsSMuX76M0NBQ02tHjx6N5s2bY8qUKVaLUiXlUElyc3PxxhtvoGfPnli/fj0AYMSIEZAkCW+//TZGjhyJKlWqAIDVQuY777wDAHj66acd3jYRERGVHU/fIyIiogpl7ZpSQ4cOhZ+fH86dO4cePXrA39/fVBA4e/Ys+vbti6pVq0Kn06FGjRoYOHAg0tPTAQAKhQLZ2dlYsWKF6bSr8lxLadq0aVAoFDh58iSeeuopVKlSBQ8++KBp/apVq9CyZUt4e3sjODgYAwcOxJUrV8z62LNnD/r374+aNWtCq9UiKioKL730EnJzc832eeHChaZ9KPorIkkS5s+fj0aNGkGn0yEiIgKjRo3CrVu3zLYlhMA777yDGjVqwMfHBx07dsSJEyes7tu5c+dw7ty5Uo9By5YtzQpSABASEoKHHnoIp06dMlu+fv16PPDAA6aCFAA0aNAAnTp1wtq1a03LGjVqZFaQAgCtVosePXrg6tWryMzMtBpLZmYmjEZjqTEXt2vXLqSkpGDMmDFmy8eOHYvs7Gz89NNPJb7+q6++Qu3atdG2bVuHtktERETlw5lSRERE5BTp6elITk42W3ZnUaI4g8GAbt264cEHH8QHH3wAHx8f6PV6dOvWDfn5+Xj++edRtWpVXLt2DZs2bUJaWhoCAwOxcuVKDB8+HK1bt8bIkSMBAHXq1Cl3/P3790fdunUxa9YsCCEAADNnzsRbb72FAQMGYPjw4UhKSsKCBQvw8MMPIy4uznRq2bp165CTk4PnnnsOISEh+Pvvv7FgwQJcvXoV69atAwCMGjUK169fx7Zt27By5UqL7Y8aNQrLly/HM888g/Hjx+PChQv45JNPEBcXhz/++MN0+uOUKVPwzjvvoEePHujRowcOHTqErl27Qq/XW/TZqVMnAIWFwbJISEgw+zeUJAlHjx7Fs88+a9G2devW+OWXX5CZmQl/f/8S+/Tx8YGPj4/Fuo4dOyIrKwsajQbdunXDhx9+aNd1nuLi4gDAYpZVy5YtoVQqERcXh0GDBtl87alTp/DGG2+Uuh0iIiJyLhaliIiIyCmKn+JVpKi4Y01+fj769++P2bNnm5YdPnwYFy5cwLp169CvXz/T8ilTppgeDxo0CKNHj8Y999xjs9BQFs2aNcNXX31len7p0iVMnToV77zzDiZPnmxa3qdPH7Ro0QKffvqpafm7774Lb29vU5uRI0fi3nvvxeTJk3H58mXUrFkTsbGxqFevHrZt22YR9++//44lS5Zg9erVeOqpp0zLO3bsiEcffRTr1q3DU089haSkJLz33nvo2bMnfvzxR9NMqzfeeMN02pyz7NmzB3v37sWbb75pWpaamor8/HxERkZatC9adv36ddSvX99qn//88w++++479O/fHyqVyrTcx8cHQ4cORceOHREQEICDBw9i7ty5aNu2LQ4dOoSoqKgSY71x4wZUKhXCw8PNlms0GoSEhOD69es2X7t69WoAPHWPiIjIHViUIiIiIqdYuHAh6tWr59BrnnvuObPngYGBAICtW7eiR48eVmfTVJTRo0ebPf/uu+8gSRIGDBhgNgOsatWqqFu3Lnbt2mUqShUvSGVnZyM3Nxdt27aFEAJxcXGoWbNmidtet24dAgMD0aVLF7NtFZ1Wt2vXLjz11FPYvn079Ho9nn/+ebNT/1588UWrRamyzpBKTEzEU089hdq1a2PSpEmm5UWnI2q1WovXFF03rPgpi8Xl5OSgf//+8Pb2xpw5c8zWDRgwAAMGDDA97927N7p164aHH34YM2fOxKJFi0qMNzc3FxqNxuo6nU5nMyZJkvDNN9+gRYsWZhe+JyIiItdgUYqIiIiconXr1g5dpNrLyws1atQwW1a7dm1MmDABc+fOxerVq/HQQw+hV69eGDRokKlgVVHuvOvb2bNnIYSwefpY8bsJXr58GVOmTMEPP/xgcQ2oomthleTs2bNIT0+3mOlTJDExEUDh7C0AFjGFhYWZLuRdXtnZ2fjPf/6DzMxM/P7772bXmioqvuXn51u8Li8vz6xNcUajEQMHDsTJkyexefNmVKtWrdQ4HnzwQcTExGD79u2mZUlJSWbXm/Lz84Ofnx+8vb2tnr5YFJe1mADgt99+w7Vr1/DSSy+VGg8RERE5H4tSRERE5BZarRZKpeU9Vz788EMMHToUGzduxC+//ILx48dj9uzZ2Ldvn0URy5nuLFxIkgSFQoHNmzebnWpWpKhYYzQa0aVLF6SmpuLVV19FgwYN4Ovri2vXrmHo0KGQJKnUbUuShPDwcNOpZHcKCwsrwx45Tq/Xo0+fPjh69Ci2bt2Kxo0bm60PDg6GVqvFjRs3LF5btMxawWnEiBHYtGkTVq9ejUceecTueKKiohAfH296/sADD5gKcwAwdepUTJs2DZGRkTAajUhMTDQr7On1eqSkpNgsgq1evRpKpRJPPvmk3TERERGR87AoRURERB6nSZMmaNKkCd588038+eefaNeuHRYtWoR33nkHAMxOXasoderUgRACtWvXLvG0xGPHjuHMmTNYsWIFBg8ebFq+bds2i7a24q5Tpw62b9+Odu3a2ZzVAwDR0dEACmdW3XPPPablSUlJFjO0HCVJEgYPHowdO3Zg7dq1aN++vUUbpVKJJk2a4MCBAxbr/vrrL9xzzz0WFzl/5ZVXsGzZMsyfP9/h4s/58+fNCnKrV682OxWv6Bg0b94cAHDgwAH06NHDtP7AgQOQJMm0vrj8/Hx8++236NChg10zt4iIiMj5LP/3JBEREZGbZGRkwGAwmC1r0qQJlEql2Sljvr6+SEtLq9BY+vTpA5VKhenTp1tcsF0IgZSUFAAwzaIq3kYIgY8++siiT19fXwCwiH3AgAEwGo14++23LV5jMBhM7Tt37gy1Wo0FCxaYbW/+/PlW9+HcuXM4d+5cyTt62/PPP481a9bg008/RZ8+fWy269evH/bv329WmIqPj8fOnTvRv39/s7bvv/8+PvjgA0yePBkvvPCCzT6TkpIslv388884ePAgHn30UdOydu3aoXPnzqa/oqLUI488guDgYHz22WdmfXz22Wfw8fFBz549rfaflpbGC5wTERG5EWdKERERkcfYuXMnxo0bh/79+6NevXowGAxYuXIlVCoV+vbta2rXsmVLbN++HXPnzkW1atVQu3ZtxMTEODWWOnXq4J133sHrr7+Oixcvonfv3vD398eFCxfw/fffY+TIkZg4cSIaNGiAOnXqYOLEibh27RoCAgLw7bffWp251LJlSwDA+PHj0a1bN6hUKgwcOBDt27fHqFGjMHv2bBw+fBhdu3aFWq3G2bNnsW7dOnz00Ufo168fwsLCMHHiRMyePRv/+c9/0KNHD8TFxWHz5s0IDQ212F6nTp0AlH7B8/nz5+PTTz9FbGwsfHx8sGrVKrP1TzzxhKmgNmbMGHz++efo2bMnJk6cCLVajblz5yIiIgIvv/yy6TXff/89Jk2ahLp166Jhw4YWfXbp0gUREREAgLZt26JFixZo1aoVAgMDcejQIXzxxReIiooyu/OhLd7e3nj77bcxduxY9O/fH926dcOePXuwatUqzJw5E8HBwRavWb16NbRarVleERERkYsJIiIionJYtmyZACD2799vdf2FCxcEALFs2TLTsiFDhghfX1+LtufPnxfPPvusqFOnjtDpdCI4OFh07NhRbN++3azd6dOnxcMPPyy8vb0FADFkyBC7Yl23bp0AIHbt2mVaNnXqVAFAJCUlWX3Nt99+Kx588EHh6+srfH19RYMGDcTYsWNFfHy8qc3JkydF586dhZ+fnwgNDRUjRowQR44csdhvg8Egnn/+eREWFiYUCoW486vY//73P9GyZUvh7e0t/P39RZMmTcSkSZPE9evXTW2MRqOYPn26iIyMFN7e3qJDhw7i+PHjIjo62uI4REdHi+jo6FKPy5AhQwQAm38XLlwwa3/lyhXRr18/ERAQIPz8/MR//vMfcfbsWbM2RcfV1l/xf4M33nhDNG/eXAQGBgq1Wi1q1qwpnnvuOZGQkFBq7Hcev/r16wuNRiPq1Kkj5s2bJyRJsmiXnp4udDqd6NOnj0P9ExERkXMphLhjPjoREREREREREVEF4zWliIiIiIiIiIjI5ViUIiIiIiIiIiIil2NRioiIiIiIiIiIXI5FKSIiIiIiIiIicjkWpYiIiIiIiIiIyOVYlCIiIiIiIiIiIpfzcncAriZJEq5fvw5/f38oFAp3h0NEREREREREVKkIIZCZmYlq1apBqbQ9H+quK0pdv34dUVFR7g6DiIiIiIiIiKhSu3LlCmrUqGFz/V1XlPL39wdQeGACAgLcHE35SJKEpKQkhIWFlVh5JCov5hrJBXOVPBHzkuSCuUoeJy8P4r//RX5BATRffQWlj4+7IyKyiWOouYyMDERFRZlqMLbcdUWpolP2AgICKkVRKi8vDwEBAUx6qlDMNZIL5ip5IuYlyQVzlTyOtzekXr2ArCz4V6kCpVbr7oiIbOIYal1pl02664pSREREREREJANqNfDcc8hNTIS/Wu3uaIioArAoRURERERERER3HUP2DRizE2yuV/lWhZdvpAsjuvuwKEVEREREREREd53Mo58j7a93bK4PinkTVWKn2N2fyL2J/MRrNk/fY5HLEotSRERERERE5Hny8qD4v/9DUH4+8N13AC90Tk7m33QEfOo8BgDQp55G8pYhCH10BVTeoTDmJkPlHYL8xDiz1xhzkwEIqLzDzJZLkoSCYwuQcP4rm9vzazwMAU1H2tVfkcpeyGJRioiIiIiIiDyT0QhIkrujoErKyzfSouCjCW6AnHM/ljiDqiR+jZ5FQLNRZkWuvKt7kHV8CbKOL0XW8aUO9efobC25YVGKiIiIiIiIPI9WC7FsGdKTkhDGO++RC9maQaUJbmA2s6n4Oq+geriVmorAqPug8a9u6ksT3ADeUR0Q0HSEQ/1pghsAKJwpVZmxKEVERERERESeR6EAQkIgjMbCx0QuYmsGlTa8hdX2muAGUIc2gxKJ8PINd0p/ttZVNtavvuXBZs+ejQceeAD+/v4IDw9H7969ER8f7+6wiIiIiIiIiEhmpIIcZB7/Ask7xwEAkneOQ+bxLyAV5Lg5sruD7IpSv/32G8aOHYt9+/Zh27ZtKCgoQNeuXZGdne3u0IiIiIiIiMhZDAbgu++g/emnwsdETiYV5CB52yik7n4N+puHAAD6m4eQuvs1JG8bVabCFItcjpHd6Xtbtmwxe758+XKEh4fj4MGDePjhh90UFRERERERETmVwQDF8uXw1uuBJ58ENBp3R0SVTHb8N8i9uBWSPu3fhcIISZ+GnItbkLJzHPS3Cs/MSt45DgGNh8G3/kDTazNuX7Q8eec4+DUaBmPAg0jZPgl5l7ZAKsgEcLvIlXoWORe3QRfVHlmnVtrVX9E6pbpy33VSdkWpO6WnpwMAgoOD3RwJEREREREROY1KBdGpE/SZmdCoVO6OhiqhrDPrIBVkWV0n9BnIOv0NAAHAvLgEBZB3aYdZ4elW6j/IDu0Kn+StgLUi1/kfkHPxZ8Cot6u/1NSzyL28C6FdFlfqwpSsi1KSJOHFF19Eu3bt0LhxY6tt8vPzkZ+fb3qekZFheq0k81uLSpIEIYTs94M8H3ON5IK5Sp6IeUlywVwlj6NSQXr+eWQnJcFHpQKYm+RkhqwbgDBaXScgoBDFThs1zaDafPvFOWbrhP4WREocoM+03p9UAIVUYHd/kj4NOZd+Qebpb+DfaGgZ9s697P0skXVRauzYsTh+/Dh+//13m21mz56N6dOnWyxPSkpCXl5eRYZX4SRJQnp6OoQQUCpld3kwkhHmGskFc5U8EfOS5IK5Sp6IeUkVKdOvDTRpl6GUslD8/o6Fc6MUhYWpO5YX1loElIDFOk1BIgQk2LpXpLDympL6MxoKcO3s3wgO6+H4zrlZZqb14tydZFuUGjduHDZt2oTdu3ejRo0aNtu9/vrrmDBhgul5RkYGoqKiEBYWhoCAAFeEWmEkSYJCoUBYWBgHaKpQzDWSC+YqeSLmJckFc5U8EfOSKpJ33da4lfA9hN58uQKwKEgVLVdKOabHd67zktKtbkcUa+NIfypjNvyz9iE8PLyUPfE8Op3OrnayK0oJIfD888/j+++/x6+//oratWuX2F6r1UKr1VosVyqVlWJQUygUlWZfyLMx10gumKvkiZiXJBfMVfIoeXlQDBmCoPx8KL/6CkqfyntdHXIP/wYDkX9lF3IvbYWkzyw8lU+hAqCAQhjxbznpX4rbJStrJKUPlEIPiII7XmNbSf1BqYKXX6Qsx2R7Y5bdno0dOxarVq3CV199BX9/fyQkJCAhIQG5ubnuDo2IiIiIiIicKTsbipyc0tsRlYFS7YPQLosR/NAcaCLuBwBoIu6HX4OBUGhsnFnlpQO8vK2u0uvugSa8OZTaoNvFLRT+18sbUKod7k+p9oNfvf6O7JLsyG6m1GeffQYA6NChg9nyZcuWYejQoa4PiIiIiIiIiJxPq4VYtAgZyckItXL2C5EzKNU+8G/8LDThLXD9qxiEPvIJ1FXqQxgLLGZQKTX+0EV1Krxb3uUdZusUmgAoq3VCeIdXkH/xR2QcXwp9wn5oIu6Hf8P/IvfqbxavKak/pcYf3tHd4Ft/oLsPUYWSXVFKCBvT2oiIiIiIiKjyUCiAatUgeXkVPiZykaIZVNnx35gVlwIaDzMVie5c59doGLKrdIKXTwg0dxS5tOEt4HffYIf6K1qnVFfu01ZlV5QiIiIiIiIiIqpI1mZQacNbmNZbzK4KbYbcxESn9Vd8XWUmu2tKERERERER0V3AYAB++gna7dsLHxNRpcOZUkREREREROR5DAYoFi+Gt14P9OkDaDTujogqGUP2DRizEwAA+tTTZv8FAJVvVbfEdTdhUYqIiIiIiIg8j1IJ0a4dCrKyoLHz9vJEjsg8+jnS/nrHbFnyliGmx0Exb8KnzmN29ydybyI/8RqUSqVZkcuYmwxjbjJU3iEw5qaYlgOAMTcZgIDKO8xmYczLN7JM+ycHLEoRERERERGR59FogFdfRXZiInw5S4oqgH/TEVaLTsWLSHcWimwVkSRJQsGxBUg4/5VZX8WLXPYsv3NdUMybqBI7xaH9khMWpYiIiIiIiIjoruPlG2l1FtKtvTNKnEF1p+Lr/Bo9i4Bmo8zWFy9yqbzDLNYVFbmsqeynELIoRURERERERER0m60ZVIDtIpIkSbiVmorAqPug8a/ugigrBxaliIiIiIiIyPPk50MxYgQC8/KAFSsAb293R0R3CVszqEoiSRKUSISXb3gFRVU5sShFREREREREnkcIIDUVCr2+8DERVTosShEREREREZHn0Wgg5s9HZkoKQnihc6JKiUUpIiIiIiIi8jxKJXDPPTD6+RU+JqJKh+9sIiIiIiIiIiJyORaliIiIiIiIyPMYDMCOHdDs2VP4mIgqHZ6+R0RERERERJ7HYIDio4/go9cDPXsCvK4UUaXDohQRERERERF5HqUSolUrFGRnQ8NrShFVSixKERERERERkefRaIApU5CdmAhfzpIiqpRYbiYiIiIiIiIiIpdjUYqIiIiIiIiIiFyORSkiIiIiIiLyPPn5UIwahYCJE4H8fHdHQ0QVwKVFKZVKhcTERIvlKSkpUKlUrgyFiIiIiIiIPJkQwI0bUCYmFj4mokrHpRc6FzYGkvz8fGh44ToiIiIiIiIqotFAvPsuMlNSEMLfi0SVkkuKUh9//DEAQKFQYMmSJfDz8zOtMxqN2L17Nxo0aOCKUIiIiIiIiEgOlEqgYUMYExMLHxNRpeOSotS8efMAFM6UWrRokdmpehqNBrVq1cKiRYtcEQoREREREREREXkAlxSlLly4AADo2LEjvvvuO1SpUsUVmyUiIiIiIiK5MhqBP/6A+tYtoEcPzpYiqoRcek2pXbt2uXJzRERERERE5EKG7BswZifYXK/yrQov30j7OisogOK99+Cr1wNdugBqtZOiJCJP4dKiVN++fdG6dWu8+uqrZsvfe+897N+/H+vWrXNlOEREREREROREmUc/R9pf79hcHxTzJqrETrGvM6UShqa1kJufDJFyFMosrUUTh4pcRORxXFqU2r17N6ZNm2axvHv37vjwww9dGQoRERERERE5mX/TEfCp8xgAQJ96GslbhiD00RXQBDeAMTcZgEB+YpzF64rWqbzDzJandzQi+8R3wLffWd2eX+NhCGg60u7+irCYReQZXFqUysrKgsbKrTzVajUyMjJcGQoRERERERE5mZdvpEWxRxPcANrwFri1d0aJs6hK4tfoWeii2psVuTKOfo6s40uQdXypw/05NGOLiCqMS4tSTZo0wZo1azBlivmb/5tvvsF9993nylCIiIiIiIjIheyZRaXyDjNb5xVUD7dSUxEYdR9EbiKAf4tcVWLfQkDTEQ71pwluAKBwphQRuZ9Li1JvvfUW+vTpg3PnzuGRRx4BAOzYsQNff/01rydFRERERERUiZU0i8oajX8daGYuR2BuLrw++QQFt4tSZe6vhHVE5B4uLUo99thj2LBhA2bNmoX169fD29sbTZs2xfbt29G+fXtXhkJEREREREQVQCrIQXb8N8i4fVpd8s5xCGg8DL71B0Kp9nGgIwm4cAEqvb7wMRFVOi4tSgFAz5490bNnzzK/fvfu3Xj//fdx8OBB3LhxA99//z169+7tvACJiIiIiIioTKSCHCRvG4Xci1shFWQCAPQ3DyE19SxyL+9CaJfF9hem1GqIGTOQkZQA7dmvkHVqGYByFLmIyOMoXb3BtLQ0LFmyBJMnT0ZqaioA4NChQ7h27Zpdr8/OzkazZs2wcOHCigyTiIiIiIiIHJQd/01hQUqfBghj4UJhhKRPQ+6lrcg6+SUyj3+B5J3jABQWmDKPfwGpIAdSQY75ul/HI0P5N1L1O3Drjzegv3kIwO0i1+7XkLxtFAw5yfb3V2wdEXkGl86UOnr0KDp37ozAwEBcvHgRw4cPR3BwML777jtcvnwZX375Zal9dO/eHd27d3dBtEREREREROSIrDPrIBVkWV0n6bNw66+ZgCHfYhZVzsVtgALIu7TDbJ0++QSMXjUg9Lf+7eh2kSvn4hYUbDwHw61/7O6vTDO2iKjCuLQoNWHCBAwdOhTvvfce/P39Tct79OiBp556qkK2mZ+fj/z8fNPzjIwMAIAkSZBkfl6yJEkQQsh+P8jzMddILpir5ImYlyQXzFVyBkPWjX9nSN1JGGDMSYYCxdabCkybb3eQY7YOhhx4558HVFa6K8hCfuIRKESB3f1J+jTkXPoFmae/gX+joWXaRyJrOIaas/c4uLQotX//fixevNhiefXq1ZGQkFAh25w9ezamT59usTwpKQl5eXkVsk1XkSQJ6enpEEJAqXT5mZh0F2GukVwwV8kTMS9JLpir5AyZfm2gSbsMpZQFRbHlAoCk9AEgoJRyLddJhY+UgNk6SIBCabC6LSEkSAoNlCiwuz8BwGgowLWzfyM4rEfZdpLICo6h5jIzM+1q59KilFarNc1UKu7MmTMICwurkG2+/vrrmDBhgul5RkYGoqKiEBYWhoCAgArZpqtIkgSFQoGwsDAmPVUo5hrJBXOVPBHzkuSCuUrO4F23NW4lfA+hN1+uAKCU8s1nSZmtyzE9NqMEIAAhAIXFSgGlyLV4TUn9KQCojNnwz9qH8PBw+3aKyA4cQ83pdDq72rm0KNWrVy/MmDEDa9euBQAoFApcvnwZr776Kvr27Vsh29RqtdBqtRbLlUplpUgUhUJRafaFPBtzjeSCuUqeiHlJcsFcpfLybzAQ+Vd2IffSVkj6zMJT8BQqKDX+gEoLKTcFEJYznxRQoHAekyWjQgMV9BbLFQovQEgALE8TKqk/KFXw8otknpPTcQz9l73HwKVH6sMPP0RWVhbCw8ORm5uL9u3b495774W/vz9mzpzpylCIiIiIiIjIyZRqH4R2WYzgh+ZAE3E/AEATcT+CH5qDoJg3oFT7WX+hlw7w8ra6Kl9XC/DyARS3LyylUEGpDYImvDkUGn+rrympP6XaD371+juyW0RUQVw6UyowMBDbtm3DH3/8gSNHjiArKwv3338/OnfubHcfWVlZ+Oeff0zPL1y4gMOHDyM4OBg1a9asiLCJiIiIiIjITkq1D/wbPwtNeAtc/yoGoY98Am14C0gFOci/9ofVWVS6qE6Fd8u7vMNsnUITAFX1bgiKqouc0yuhT9gPTcT9CGg8DN739ELqry851J9S4w/v6G7wrT/Q3YeJiOCColRwcDDOnDmD0NBQPPvss/joo4/Qrl07tGvXrkz9HThwAB07djQ9L7pe1JAhQ7B8+XJnhExEREREREROVjSLKjv+G2QcX2pWYCoqEpmtC2mB0B2BKDiaDZ95Q+FTLcasyAXAsf6KrVOqfdx2HIjoXxV++p5erzdd3HzFihXlvuNdhw4dIISw+GNBioiIiIiIyLMVzaIKfeQTAEDoI5/Av/GzUKp9LNe1nwtNkgpe584X3U6vfP0VW0dEnqHCZ0rFxsaid+/eaNmyJYQQGD9+PLy9rZ/b+8UXX1R0OERERERERFRBDNk3YMxOAADoU0+b/RcAVL5V4eUbaV9nXl4Qkycj+9YtaNRqp8dKRO5X4UWpVatWYd68eTh37hwAID09vdyzpYiIiIiIiMjzZB79HGl/vWO2LHnLENPjoJg3USV2in2dqVQwNIlG/pVs5KcchSHtDIByFLmIyONUeFEqIiICc+bMAQDUrl0bK1euREhISEVvloiIiIiIiFzMv+kI+NR5zOo6Y24yAIH8xDi7Z1FlHVuC/L9nIqHYsuJFLr/GwxDQdKRFf0XbUnmHlX/GFhFVGJde6Lxjx47QaDQVvUkiIiIiIiJyAy/fSJvFnlt7Z5Q4i8qiwJR8Cpq8qtBFvQr/5m2h9q9q9tqMo58j6/gSZB1farW/O5V5xhYRVZgKL0oVXeg8NDQUK1aswLvvvgt/f/+K3iwRERERERF5kJJmUVktMP0y1PRYG/Yq/B5+2+w1VWLfQkDTEVb7Kz5TyhqVb1Wry4nItXihcyIiIiIiIqpwJc2islpg0ushPvgA+fn58B70rEP9EZE8uPRC5wqFghc6JyIiIiIiIjO2CkzS3DVIS0yEf3C4G6IioorGC50TEREREREREZHLKV2xkR49eiA9PR0XLlxASEgI5syZg7S0NNP6lJQU3Hfffa4IhYiIiIiIiIiIPIBLilJbtmxBfn6+6fmsWbOQmppqem4wGBAfH++KUIiIiIiIiEgO9HoopkyB37vvAnq9u6MhogpQ4afvWSOEcMdmiYiIiIiISC4kCTh8GF56feFjIqp03FKUIiIiIiIiIiqRWg0xYQJy0tKgUavdHQ0RVQCXFKUUCgUUCoXFMiIiIiIiIiKrVCqgQwfoExMLHxNRpeOSopQQAkOHDoVWqwUA5OXlYfTo0fD19QUAs+tNERERERERERFR5eeSotSQIUPMng8aNMiizeDBg10RChEREREREcmBJAFnz0KVkgKEhgJKl9yni4hcyCVFqWXLlrliM0RERERERFRZ6PVQvPwy/PV6YMMGwIuXRCaqbPiuJiIiIiIiIs+jUADh4ZDy8gofE1Glw6IUEREREREReR6tFmLJEmQkJkJ3+/rERFS58KRcIiIiIiIiIiJyORaliIiIiIiIiIjI5ViUIiIiIiIiIs+j1wMzZ8J33rzCx0RU6fCaUkSV0I2MPNzIyLO5PjJAh8gAnQsjIiIiIiJykCRB8ddfUOv1gCS5OxoiqgAsSslM8WKDJAmk3spGsD4dSmXh3ShYbCAAWLz3EmZsO2Nz/ZQu9TCtW30XRkRkHQuo5ImYlyQXzFWq9Ly8IMaORU5aGjRe/OlKVBnxnS0zLDaQPUbFRqNXowgAwOnELAz6Kg6rnmqBBuF+AMAvqOQxOKaRJ/KUvGTBgUrjqlwtLReVCgUkIWyuZ65SmXl5Ad26QZ+YWPiYiCodvrNlpnix4WRCJgZ/cxhfDmyO+6r6A2CxgQpZ+/LXINwP99cIcmkc/EFFpWEBlTyRI3lpzzgX4adxqH1R/55SHCPPZU+uOlJQsjULv7RcfPieYOw+n2pzPXOViIhsYVFKZop/WZWkwi8Q7ig2ENmDP6ioNM4ooDrjtGYWUKk4R/LSnnFuSpe6DrUvGhdZtKXS2JOr07bGl7ugVFouFi9sMVfJqYQALl+GMjkZCAtzdzREVAFYlCKiCsMfVOQKzih+soBKZeXoOOdIe0+Z9Ury5khBydYs/LLkInOVnCI/H4px4xCg1wMbNgA+Pu6OiIicjEUpIqow/EFFruCM05pZQKWysmeck4rdMYrjIrmaIznHWfjkkQICIPJsz2YmInljUUqGcvQGfBV3DUv3XQIAPP/9MQxrE42nWlSHj4b/pFSoKE+W7LsMABjz7TEMb1OTeUKVjjNOa2ahgO7EMZTkgrlKlZpOB7FqFdITExGu4/8gIqqM+EklMzl6A4avPYItpxORmW8AABy4mo74H09i59lkLBnQjF9AyGqeHLyahjM/ZjFPyOPwBxV5Gk8aQ/n+oJK4MleZi0REVBGU7g6grBYuXIhatWpBp9MhJiYGf//9t7tDcomv4q5hy+lEpOUZYLx9512jANLyDNhyOhFfxV1zb4DkETwpT3L0Biz56xLGfHsMQOGX2CV/XUKO3uCyGMhzFf2gmvTjSRy8mgag8AfVpB9PYvjaI8wTcgtPGUP5/qDS2JurpX0WF61//vvC9c9/b7m+tFzk5z0REZWFLItSa9aswYQJEzB16lQcOnQIzZo1Q7du3ZCYmOju0Crc2sPXkaU3Wl2XpTdi7eHrLo6IPJGn5Al/UFFpnPXjv7QfVI70wR9U5OgY6mju2NveU4pj5LnsydXSPouTs/JN6w9cTQdQOAu/+Gd1abm4bP8Vft5TxdDrgQ8/hM9nnxU+JqJKR5ZzbefOnYsRI0bgmWeeAQAsWrQIP/30E7744gu89tprdvWh1+uhtzKwKZVKeHl5mbWzRaFQQK1Wl6ltQUEBxO07nTjS9mpKJoyS9dcZJYGrqVklxqHRaOyK4c62BoPB7EKt5WmrVquhUCgqtK3RaITRaP1LmqNtvby8oFQqPaatJEkwGGx/uVOpVLiWnldynqRkQq/XQ6VSQaVSldqvJElm8dkTg0qlwldx17D5dCLS8/5ta/oSe/IGvvw7ACNi7zHFIIRAQUGBzX6Lvz8rqi1Q8nvZ08eI8rQFXD9GfHPgErLyredSlt6Ibw5exuAWkTb7VavVyC0wYvjaI9h8MgFZBYX9HriajtMbj2HbqRtY3K8pfDReJb7vc/QGjFp/FL+cSUZWQeE+F50Csz3+Jhb1aWzz9BQ5jhH2vO9d1ba096cjbZ01RpT6WXt7DAVKzp2dZ5Pxae+G0HkpTd878gySzfbF8xUo7f1hwDcHLll9f1SmMcIafo/4t609n/df/n0Bm0/dRHr+v30V/yx+URiw+VQS0vXCcv2pm/jy7wtYf+RGiWP1p39cxPX0XJvbuPPzXu5jREltAX6PKEtbm+/PvDyIXbugMRiA2+s5RvB7hKeOEZIkmT7vi47nnW2Bu2eMKCkesz5ESSOFB9Lr9fDx8cH69evRu3dv0/IhQ4YgLS0NGzduNGufn5+P/Px80/OMjAxERUXh1VdfhVartej/3nvvxdNPP216PmvWLJvJGB0djaFDh5qev//++8jJybHaNjIyEiNHjjQ9/+ijj5CWlma1bVhYGMaMGWN6/umnnyIpKQkAsOrgVVyq2QGSdyBwe6ArolYqcO/1PehXx/pFAH18fPDKK6+Yni9fvhyXLl2y2latVmPy5Mmm56tXr8Y///xjtS0ATJ061fR47dq1OHXqlM22r7/+uulDaMOGDThy5IjNthMnToSvry8A4KeffsKBAwdstn3hhRcQFBQEAPjll1+wd+9em22fe+45hIeHAwB+/fVX/PbbbzbbDh8+HNWrVwcA/PHHH9i+fbvNtkOGDEGtWrUAAH///Tc2b95ss+2TTz6JevXqAQAOHz5skbvF9evXD40aNQIAnDhxAuvXr7fZ9vHHH8fr+/Ow61wKDJIAUCxPhIAyNw3Rl3/DoJY10L17d7Ru3RoAcPHiRaxYscJqn0IItGzZEj179oRSqcS1a9ewZMkSmzG0b98eHTp0QPfP92HX2SQYhMI8X4vFsXBcf3Tt2hUAkJaWho8++shmv61atULPnj0BANnZ2fjggw9stm3WrJlpjNDr9Zg9e7bNtg0bNsSAAQNMz6dPn26zraePEXcKCgrCCy+8YHr+v//9Dzdu3LDa1h1jxGd/XkRykycApcriNQoANZLiMLSW7S9REydOxJoTKXh10yncyjMAQhTm2u3/6v75HZ1D89GiemCJY0TctXRsT9Yi794H/+3jNr+Mq+ggxaNF9UCrMchxjGjevDkA4MyZM/j6669ttrV3jACAzp07o127dgBg9xgBAImJifjss89sto2NjXX5GFHaZ22Nk99jUMsaAErOnWCdFx5K3oEm4T7Izs6Gr68vDl/PsNne++Lf6BSUacq1kt4fkIwIPfY9nmtby2JVZRojrOH3iEJPPvkkXvgttdTPewC4Uq8nDEq1eQe322iPb0V+40et5ruXVICoMz8hM99Q4ljtq1YiT6+HASqbn/fLXx1aacaIO/F7RKEKGSMkCeLMGbz46KMIePJJKDUajhH8HuGxY4QQwvR5ryg2Ft6tY0R+fj7effddpKenIyAgwOrrARnOlEpOTobRaERERITZ8oiICJw+fdqi/ezZs63+o2dnZ1utvmZkZJidBpiVlWWzSpuZmWnRNjc31662mZmZyM7OttpWp9PZbNs0VIPspONIjLb8MntfkALNJa3NfiVJsjsGLy8vs7YZGRk22wJwuG3RB0V6enqJbZOSkkzr7WlbVI1NS0srsW1ycrLpsT1ti6q/t27dKrFtSkoKfHx87G5bdNxSU1NLbJuamupQ26cbhONmyi0cTZUsfqSHJR0vzKPsbNy6dcvUb0pKis1+hRDIyspCYmIilEolkpOTS4whLS0NiYmJ8DNkQYLC4ksuFAoIXQA0kt7UFij93zg9Pd3UNicnx+62er2+xLZ3vu8daetpY8SdVCqV3W3dMUZE+SqQkZ8BvS7IIk981UD9QCWyszNs9puUlIS/T19GgcFgPibeznmpeiNcu7YT9YK8ShwjriWnQar+iMW4CgBGv1BcO/8X6gVZ/8iU4xhhz/u+KMaytLV3jHC0ravGiNI+a2vdHkOBknNHbzTgalIa7vEVyLt9O/MSc61qPVy7usOUayW9P7QFWYjyVVjdx8o0Rthqy+8RhW2fbuBf6uf96cQsGJVWxq/bn8XBGgnXdf6Wn9UAJKUXNJIeUb4qG7ko4KtWoFmQwN4kFcwKY8W2oZH0lWqMuBO/RxSqqDFC1KiBm23aIC81FUqlkmMEv0d47BghxL+f98WLUnfrGFF8clBJZDdT6vr166hevTr+/PNPxMbGmpZPmjQJv/32G/766y+z9rZmSt28edNqtc7Tp8vl6A0Y890xbDuTjMwCwCAALyUQoPFCtwZh+OTxhvBWW/k/qrdx2r3jbeU4pTbfKDBq/VFsOXUTGXoJRgGoFECAGuhSLxSf9mkCH42XQ6fvpaamomrVqlAqlXZPqe3++b7b/wfXyj4pgPa1AvDzyLacdm+lbaWZdl9K2+X7L2Py5nikWTk0wTovzHy0Loa0qmGzX7VajeYf/oaTidmwFqkCQINgDQ5OaF/i+77l3N9wOlVfah/WyHGM4LT7ktsW/6zNKCg8Dan4Z+2CXg1Mp9jZkzv7X3wISUlJCAsLwwPz99idayW9P6poFJjZvR6GPlDTsp9KNEZYw+8R/7YtOh20pM/7/1t5EL9dyrD5WRyoFkgvUMBg5Z/SSwm0jw5A36aRJY7VkQFaxCdn2/15L/cxoqS2AL9HlKVtSe9PSZKQnp6OsLAwKJVKjhH8HuGxY4QkSabPe56+V1h7iYiIqHwzpUJDQ6FSqXDz5k2z5Tdv3kTVqlUt2mu1Wqun6el0Ouh01k9zu7OdvRxpay0me9rqdMAXTz2Ar+KuYem+S/jrSjpaVg/EsDbRDt+S15EYin9oyKGtUqk0e3NUtrbFBypr1Grg8wHNTbdu/vtKGlrWCCrx1s0l9StJkunDquivtBgAoH/z6th/JR1peZYfKv5aL/xfq1oW+130oWGPimpbUe97V4wR7mpb1vfy4Jh7sPtSZuHtzPUGGCVAdfvHf9cG4fhv61rQlTKuRQZ6Iz455/bpK+ZUSgWqhwRYHPs733PVQwJwNi3FoT6skcsY4UltAc943xdvW/yztmgMvb+69THU3tzRarXQ6XQO5VpJ749uDcIxOOaeUt8fgLzHCHe19ZT3cmlt/bxK/7z/v1a1cPDGSZufxT0aReDHEzetr7/9+qdaVC9xrH6wdjDe3Hza7s97uY8RpeH3CMfb2nx/CgHp5k1kpaRAGR4OpVLJMcIFbd393UCuY4QkSabP++JFqTvdLWOEvdeUkt3d9zQaDVq2bIkdO3aYlkmShB07dpjNnKrMfDReGB4TjQVPNAEALHiiCYbHRDtUkKLKryhPPu1bmCef9nV9njzVojoebRCOIJ0XVLdHG5USqKIr/EH1VIvqLouFPJOPxgtLBjTDe4/dh5bVgwAALasH4d3H7sOSAc3sytcBzavBT2P9S4O/RoUBzau5pA+qXOwdQx3NHUfaO+P9QZVfabla2mfxh481Mq33ur3e647P6tJycegDUfy8p4qRnw/F8OEImDABsPNUICKSF9kVpQBgwoQJ+Pzzz7FixQqcOnUKzz33HLKzs0134yMiz8AfVGSP8hZQi//gsvWDypE++IOKHOFo7jja3hP+BwPJW2mfxaF+2mLrA2+vD7T4rC4pF/l5TxVKqwUcmMVERPIiy0+I//u//0NSUhKmTJmChIQENG/eHFu2bLG4+DkRuV/Rl9j7qwei1fw9+LRvE9xfI8jdYVElUvRjqDynNRfvw3QKjI3TtYiKsyd3il+jhLlG7lDaZ3HR+uaRAWj98e9Y8EQTtKpZxanbICoTnQ5i3TqkJSYi3IFTk4hIPmT7zWfcuHEYN26cu8NwuRsZebiRUXhF/9OJWab/KpWFF9KLDNAhMoAD9t3OVp4UYZ5QZcMfVORMjo6hjuYOc42chZ/3REQkd7ItSt2tFu+9hBnbzpgtG/zNYdPjKV3qYVq3+i6OijyNtTwZ9FWc6THzhDwFf1CRJ+IYSnLhqlzlWE1ERBWFRSmZGRUbjV6NCk9TlCSB1FupCK4SbDZTiqh4nljjqjzhl1gqDX/8kyfiGEpyYU+ulpZHSoUC0u3bd9uahV/aWP3Sw/fg6fur29wGc5XKrKAA+Owz+GRmAhMnFl5fiogqFRalZKb4h7okSUjU5CM8PLDEW07S3cdTvvyx4EClccaPf2ec1swf/1ScI//e9uROhJ/GofZF2+YYSqWxJ1enbY0vMY8evicYu8+nmq2/cxZ+aWP1V4euodX8PTa3wVylMjMaofjlF2j0euCll9wdDRFVAIUQt//XyF0iIyMDgYGBSE9PR0BAgLvDKRdJkpCYmIjw8HAWpahClTXXiv/4soY/9MkZrP3gKs6eH0PO6IPuTvbkzpQudU1j6IxtZ+3ONY6h5Ayl5VHxmVK2ZuE7Uti3hrlKZWYwQFq/HhkZGQgYOhRK3oWPPBh/n5uzt/bCopSMMenJVZhr5MmK/xjiDypyNXtyJ8JPYxpDb2bpmWvksfh5T56IeUlywVw1Z2/thafvERGRrDnjtGYWAqis7MkdSZIcak9ERER0t2D5joiIiIiIiDyPEEB6OhQZGYWPiajS4UwpIiIiIiIi8jz5+VD8978I1OuBDRsAHx93R0RETnbXFaWKLqGVkZHh5kjKT5IkZGZmQqfT8ZxVqlDMNZIL5ip5IuYlyQVzlTxOXh5EQQHyDQZoMjKgNBjcHRGRTRxDzRXVXEq7jPldV5TKzMwEAERFRbk5EiIiIiIiIrJLZKS7IyCiMsjMzERgYKDN9Xfd3fckScL169fh7+8PhULh7nDKJSMjA1FRUbhy5Yrs7yRIno25RnLBXCVPxLwkuWCukidiXpJcMFfNCSGQmZmJatWqlThz7K6bKaVUKlGjRg13h+FUAQEBTHpyCeYayQVzlTwR85LkgrlKnoh5SXLBXP1XSTOkivBERyIiIiIiIiIicjkWpYiIiIiIiIiIyOVYlJIxrVaLqVOnQqvVujsUquSYayQXzFXyRMxLkgvmKnki5iXJBXO1bO66C50TEREREREREZH7caYUERERERERERG5HItSRERERERERETkcixKERERERERERGRy7Eo5WSzZ8/GAw88AH9/f4SHh6N3796Ij483a5OXl4exY8ciJCQEfn5+6Nu3L27evGlaf+TIETz55JOIioqCt7c3GjZsiI8++sisj19//RUKhcLiLyEhocT4hBCYMmUKIiMj4e3tjc6dO+Ps2bOm9RcvXsSwYcNQu3ZteHt7o06dOpg6dSr0er0Tjg45k9xzDQB69eqFmjVrQqfTITIyEv/9739x/fr1ch4Z8jSVIVeL5Ofno3nz5lAoFDh8+HDZDgh5hMqQl7Vq1bLod86cOeU8MuRpKkOuAsBPP/2EmJgYeHt7o0qVKujdu3fZDwq5ndzz0la/CoUC+/fvd8IRIk8i93wFgDNnzuDxxx9HaGgoAgIC8OCDD2LXrl3lPDIeQpBTdevWTSxbtkwcP35cHD58WPTo0UPUrFlTZGVlmdqMHj1aREVFiR07dogDBw6INm3aiLZt25rWL126VIwfP178+uuv4ty5c2LlypXC29tbLFiwwNRm165dAoCIj48XN27cMP0ZjcYS45szZ44IDAwUGzZsEEeOHBG9evUStWvXFrm5uUIIITZv3iyGDh0qtm7dKs6dOyc2btwowsPDxcsvv+zkI0XlJfdcE0KIuXPnir1794qLFy+KP/74Q8TGxorY2FgnHiXyBJUhV4uMHz9edO/eXQAQcXFx5T845DaVIS+jo6PFjBkzzPotHj9VDpUhV9evXy+qVKkiPvvsMxEfHy9OnDgh1qxZ48SjRK4m97zMz8836+/GjRti+PDhonbt2kKSJCcfLXI3ueerEELUrVtX9OjRQxw5ckScOXNGjBkzRvj4+IgbN2448Ui5B4tSFSwxMVEAEL/99psQQoi0tDShVqvFunXrTG1OnTolAIi9e/fa7GfMmDGiY8eOpudFCX/r1i27Y5EkSVStWlW8//77pmVpaWlCq9WKr7/+2ubr3nvvPVG7dm27t0PuURlybePGjUKhUAi9Xm/3tkh+5JqrP//8s2jQoIE4ceIEi1KVkBzzMjo6WsybN8/ufqlykFuuFhQUiOrVq4slS5bY3S/Jj9zy8k56vV6EhYWJGTNm2L0dki+55WtSUpIAIHbv3m1qk5GRIQCIbdu22b0tT8XT9ypYeno6ACA4OBgAcPDgQRQUFKBz586mNg0aNEDNmjWxd+/eEvsp6qO45s2bIzIyEl26dMEff/xRYiwXLlxAQkKC2bYDAwMRExNTpm2TZ5F7rqWmpmL16tVo27Yt1Gp1if2TvMkxV2/evIkRI0Zg5cqV8PHxsW9HSVbkmJcAMGfOHISEhKBFixZ4//33YTAYSt9ZkjW55eqhQ4dw7do1KJVKtGjRApGRkejevTuOHz9u/06Tx5NbXt7phx9+QEpKCp555pkS+6bKQW75GhISgvr16+PLL79EdnY2DAYDFi9ejPDwcLRs2dL+HfdQLEpVIEmS8OKLL6Jdu3Zo3LgxACAhIQEajQZBQUFmbSMiImyea/rnn39izZo1GDlypGlZZGQkFi1ahG+//RbffvstoqKi0KFDBxw6dMhmPEX9R0RE2L3tf/75BwsWLMCoUaNK3V9yHznn2quvvgpfX1+EhITg8uXL2Lhxo937TfIjx1wVQmDo0KEYPXo0WrVq5fA+k+eTY14CwPjx4/HNN99g165dGDVqFGbNmoVJkyY5tO8kL3LM1fPnzwMApk2bhjfffBObNm1ClSpV0KFDB6Smpjp2AMgjyTEv77R06VJ069YNNWrUKHV/Sd7kmK8KhQLbt29HXFwc/P39odPpMHfuXGzZsgVVqlRx+Bh4Gi93B1CZjR07FsePH8fvv/9e5j6OHz+Oxx9/HFOnTkXXrl1Ny+vXr4/69eubnrdt2xbnzp3DvHnzsHLlSqxevdqskLR582aoVCqHtn3t2jU8+uij6N+/P0aMGFHmfaCKJ+dce+WVVzBs2DBcunQJ06dPx+DBg7Fp0yYoFIoy7wt5Ljnm6oIFC5CZmYnXX3+9zDGTZ5NjXgLAhAkTTI+bNm0KjUaDUaNGYfbs2dBqtWXeF/JccsxVSZIAAG+88Qb69u0LAFi2bBlq1KiBdevW8X98VgJyzMvirl69iq1bt2Lt2rVljp/kQ475KoTA2LFjER4ejj179sDb2xtLlizBY489hv379yMyMrLM++IJWJSqIOPGjcOmTZuwe/dus4p71apVodfrkZaWZlaJvXnzJqpWrWrWx8mTJ9GpUyeMHDkSb775ZqnbbN26tenN1atXL8TExJjWVa9eHTdu3DBtq3ji3rx5E82bNzfr6/r16+jYsSPatm2L//3vf3bvN7me3HMtNDQUoaGhqFevHho2bIioqCjs27cPsbGxdh8Dkge55urOnTuxd+9eix/5rVq1wtNPP40VK1bYdwDII8k1L62JiYmBwWDAxYsXzb4UU+Ug11wtWn7fffeZ1mu1Wtxzzz24fPmynXtPnkqueVncsmXLEBISgl69etm1zyRfcs3XnTt3YtOmTbh16xYCAgIAAJ9++im2bduGFStW4LXXXnPsQHgad1/UqrKRJEmMHTtWVKtWTZw5c8ZifdFF1NavX29advr0aYuLqB0/flyEh4eLV155xe5td+7cWTzxxBMlxla1alXxwQcfmJalp6dbXPTv6tWrom7dumLgwIHCYDDYvX1yrcqQa3e6dOmSACB27dpldyzk+eSeq5cuXRLHjh0z/W3dulUAEOvXrxdXrlyxOxbyLHLPS2tWrVollEqlSE1NtTsW8nxyz9Wi58UvdK7X60V4eLhYvHix3bGQZ5F7XhZvW7t2bd5pvJKTe77+8MMPQqlUiszMTLPX1qtXT8ycOdPuWDwVi1JO9txzz4nAwEDx66+/mt0GMicnx9Rm9OjRombNmmLnzp3iwIEDIjY2VsTGxprWHzt2TISFhYlBgwaZ9ZGYmGhqM2/ePLFhwwZx9uxZcezYMfHCCy8IpVIptm/fXmJ8c+bMEUFBQWLjxo3i6NGj4vHHHze73eTVq1fFvffeKzp16iSuXr1qtn3yLHLPtX379okFCxaIuLg4cfHiRbFjxw7Rtm1bUadOHZGXl+fko0XuJPdcvdOFCxd4971KQO55+eeff4p58+aJw4cPi3PnzolVq1aJsLAwMXjwYCcfKXI3ueeqEEK88MILonr16mLr1q3i9OnTYtiwYSI8PJwFVBmrDHkphBDbt28XAMSpU6ecdGTIE8k9X5OSkkRISIjo06ePOHz4sIiPjxcTJ04UarVaHD582MlHy/VYlHIyAFb/li1bZmqTm5srxowZI6pUqSJ8fHzEE088YVb0mTp1qtU+oqOjTW3effddUadOHaHT6URwcLDo0KGD2LlzZ6nxSZIk3nrrLRERESG0Wq3o1KmTiI+PN61ftmyZzX0gzyL3XDt69Kjo2LGjCA4OFlqtVtSqVUuMHj1aXL161SnHhzyH3HP1TixKVQ5yz8uDBw+KmJgYERgYKHQ6nWjYsKGYNWsWi/qVkNxzVYjCmVEvv/yyCA8PF/7+/qJz587i+PHj5T425D6VIS+FEOLJJ58Ubdu2LdexIM9XGfJ1//79omvXriI4OFj4+/uLNm3aiJ9//rncx8YTKIQQwo6z/IiIiIiIiIiIiJxG6e4AiIiIiIiIiIjo7sOiFBERERERERERuRyLUkRERERERERE5HIsShERERERERERkcuxKEVERERERERERC7HohQREREREREREbkci1JERERERERERORyLEoREREREREREZHLsShFREREREREREQux6IUERERERERERG5HItSRERERERERETkcixKERERERERERGRy7EoRURERERERERELseiFBERERERERERuRyLUkRERERERERE5HIsShERERERERERkcuxKEVERERERERERC7HohQRERFVqIsXL0KhUGD58uXuDqVSq1WrFoYOHeruMIiIiIjsxqIUERERlcvy5cuhUCis/r322msVss1Zs2Zhw4YNpbbr0KGDzdiK/02bNq1C4rzTzz//7LJtOeL06dOYNGkSmjdvDn9/f0RGRqJnz544cOCA1fbXrl3DgAEDEBQUhICAADz++OM4f/68WZsrV65g+vTpaN26NapUqYLQ0FB06NAB27dvt+ivpBxKSEiwez+WLl2Khg0bQqfToW7duliwYIHVdtu3b0fHjh0RGhqKoKAgtG7dGitXrrR7O0REROQcXu4OgIiIiCqHGTNmoHbt2mbLGjdujOjoaOTm5kKtVjttW7NmzUK/fv3Qu3fvEtu98cYbGD58uOn5/v378fHHH2Py5Mlo2LChaXnTpk2dFltJfv75ZyxcuNDjClNLlizB0qVL0bdvX4wZMwbp6elYvHgx2rRpgy1btqBz586mtllZWejYsSPS09MxefJkqNVqzJs3D+3bt8fhw4cREhICANi4cSPeffdd9O7dG0OGDIHBYMCXX36JLl264IsvvsAzzzxjEYe1HAoKCrJrHxYvXozRo0ejb9++mDBhAvbs2YPx48cjJycHr776qqndDz/8gN69eyM2NhbTpk2DQqHA2rVrMXjwYCQnJ+Oll14qwxEkIiKislAIIYS7gyAiIiL5Wr58OZ555hns378frVq1KnM/2dnZ8PX1tautn58f+vXr5/ApgevXr0f//v2xa9cudOjQwSmxOGLcuHFYuHAhKuLrV61atdChQ4cynSZ58OBB1K9fH35+fqZlKSkpaNiwIerVq4fff//dtPy9997Dq6++ir///hsPPPAAgMKZVo0bN8akSZMwa9YsAMCJEycQERGB0NBQ02vz8/PRvHlzZGVl4cqVK6bl5c2h3NxcREVFoU2bNti0aZNp+aBBg7BhwwZcuXIFVapUAQB07doVJ06cwPnz56HVagEABoMBDRo0gK+vL44cOeLw9omIiKhsePoeERERVShr15QaOnQo/Pz8cO7cOfTo0QP+/v54+umnAQBnz55F3759UbVqVeh0OtSoUQMDBw5Eeno6AEChUCA7OxsrVqwwneJVnmspFc2WOXnyJJ566ilUqVIFDz74oGn9qlWr0LJlS3h7eyM4OBgDBw40K6gAwJ49e9C/f3/UrFkTWq0WUVFReOmll5Cbm2u2zwsXLjTtQ9FfEUmSMH/+fDRq1Ag6nQ4REREYNWoUbt26ZbYtIQTeeecd1KhRAz4+PujYsSNOnDhhdd/OnTuHc+fOlXoMWrZsaVaQAoCQkBA89NBDOHXqlNny9evX44EHHjAVpACgQYMG6NSpE9auXWta1qhRI7OCFABotVr06NEDV69eRWZmptVYMjMzYTQaS425uF27diElJQVjxowxWz527FhkZ2fjp59+Mi3LyMhAlSpVTAUpAPDy8kJoaCi8vb0d2i4RERGVD0/fIyIiIqdIT09HcnKy2bI7ixLFGQwGdOvWDQ8++CA++OAD+Pj4QK/Xo1u3bsjPz8fzzz+PqlWr4tq1a9i0aRPS0tIQGBiIlStXYvjw4WjdujVGjhwJAKhTp0654+/fvz/q1q2LWbNmmWYyzZw5E2+99RYGDBiA4cOHIykpCQsWLMDDDz+MuLg406ll69atQ05ODp577jmEhITg77//xoIFC3D16lWsW7cOADBq1Chcv34d27Zts3r9olGjRplmDI0fPx4XLlzAJ598gri4OPzxxx+m0x+nTJmCd955Bz169ECPHj1w6NAhdO3aFXq93qLPTp06ASgsDJZFQkKC2b+hJEk4evQonn32WYu2rVu3xi+//ILMzEz4+/uX2KePjw98fHws1nXs2BFZWVnQaDTo1q0bPvzwQ9StW7fUOOPi4gDAYpZVy5YtoVQqERcXh0GDBgEovM7Yu+++i7feegtDhgyBQqHAV199hQMHDpgV1YiIiMgFBBEREVE5LFu2TACw+ieEEBcuXBAAxLJly0yvGTJkiAAgXnvtNbO+4uLiBACxbt26Erfp6+srhgwZ4nCs69atEwDErl27TMumTp0qAIgnn3zSrO3FixeFSqUSM2fONFt+7Ngx4eXlZbY8JyfHYluzZ88WCoVCXLp0ybRs7NixwtrXrz179ggAYvXq1WbLt2zZYrY8MTFRaDQa0bNnTyFJkqnd5MmTBQCLYxIdHS2io6OtH4xS7N69WygUCvHWW2+ZliUlJQkAYsaMGRbtFy5cKACI06dP2+zz7NmzQqfTif/+979my9esWSOGDh0qVqxYIb7//nvx5ptvCh8fHxEaGiouX75caqxjx44VKpXK6rqwsDAxcOBA0/OsrCwxYMAAoVAoTHnq4+MjNmzYUOp2iIiIyLl4+h4RERE5xcKFC7Ft2zazv9I899xzZs8DAwMBAFu3bkVOTk6FxGnL6NGjzZ5/9913kCQJAwYMQHJysumvatWqqFu3Lnbt2mVqW/y0r+zsbCQnJ6Nt27YQQphm8ZRk3bp1CAwMRJcuXcy2VXRaXdG2tm/fDr1ej+eff97s1L8XX3zRar8XL14s0yypxMREPPXUU6hduzYmTZpkWl50OmLxU9+K6HQ6szZ3ysnJQf/+/eHt7Y05c+aYrRswYACWLVuGwYMHo3fv3nj77bexdetWpKSkYObMmaXGm5ubC41GY3WdTqczi0mr1aJevXro168fvv76a6xatQqtWrXCoEGDsG/fvlK3RURERM7D0/eIiIjIKVq3bu3QRaq9vLxQo0YNs2W1a9fGhAkTMHfuXKxevRoPPfQQevXqhUGDBpkKVhXlzru+nT17FkIIm6ePFb+b4OXLlzFlyhT88MMPFteAKroWVknOnj2L9PR0hIeHW12fmJgIALh06RIAWMQUFhZmupB3eWVnZ+M///kPMjMz8fvvv5tda6qo+Jafn2/xury8PLM2xRmNRgwcOBAnT57E5s2bUa1atVLjePDBBxETE4Pt27ebliUlJZldb8rPzw9+fn7w9va2evpiUVzFYxo3bhz27duHQ4cOQaks/P+zAwYMQKNGjfDCCy/gr7/+KjU2IiIicg4WpYiIiMgttFqtqShQ3IcffoihQ4di48aN+OWXXzB+/HjMnj0b+/btsyhiOdOdxRRJkqBQKLB582aoVCqL9kXFGqPRiC5duiA1NRWvvvqq6S5u165dw9ChQyFJUqnbliQJ4eHhWL16tdX1YWFhZdgjx+n1evTp0wdHjx7F1q1b0bhxY7P1wcHB0Gq1uHHjhsVri5ZZKziNGDECmzZtwurVq/HII4/YHU9UVBTi4+NNzx944AFTYQ4Apk6dimnTpiEyMhJGoxGJiYlmhT29Xo+UlBRTTHq9HkuXLsWkSZPMck+tVqN79+745JNPoNfrbc66IiIiIudiUYqIiIg8TpMmTdCkSRO8+eab+PPPP9GuXTssWrQI77zzDgCYnbpWUerUqQMhBGrXro169erZbHfs2DGcOXMGK1aswODBg03LrZ2+aCvuOnXqYPv27WjXrl2Jd4CLjo4GUDiz6p577jEtT0pKspih5ShJkjB48GDs2LEDa9euRfv27S3aKJVKNGnSBAcOHLBY99dff+Gee+6xuMj5K6+8gmXLlmH+/Pl48sknHYrp/PnzZgW51atXm52KV3QMmjdvDgA4cOAAevToYVp/4MABSJJkWp+SkgKDwWD17n4FBQWQJMnhO/8RERFR2fGaUkREROQxMjIyYDAYzJY1adIESqXS7JQxX19fpKWlVWgsffr0gUqlwvTp00134ysihEBKSgoAmGZRFW8jhMBHH31k0aevry8AWMQ+YMAAGI1GvP322xavMRgMpvadO3eGWq3GggULzLY3f/58q/tw7tw5nDt3ruQdve3555/HmjVr8Omnn6JPnz422/Xr1w/79+83K0zFx8dj586d6N+/v1nb999/Hx988AEmT56MF154wWafSUlJFst+/vlnHDx4EI8++qhpWbt27dC5c2fTX1FR6pFHHkFwcDA+++wzsz4+++wz+Pj4oGfPngCA8PBwBAUF4fvvvzc73S8rKws//vgjGjRoUGJRkIiIiJyLM6WIiIjIY+zcuRPjxo1D//79Ua9ePRgMBqxcuRIqlQp9+/Y1tWvZsiW2b9+OuXPnolq1aqhduzZiYmKcGkudOnXwzjvv4PXXX8fFixfRu3dv+Pv748KFC/j+++8xcuRITJw4EQ0aNECdOnUwceJEXLt2DQEBAfj222+tzlxq2bIlAGD8+PHo1q0bVCoVBg4ciPbt22PUqFGYPXs2Dh8+jK5du0KtVuPs2bNYt24dPvroI/Tr1w9hYWGYOHEiZs+ejf/85z/o0aMH4uLisHnzZoSGhlpsr1OnTgBQ6sXO58+fj08//RSxsbHw8fHBqlWrzNY/8cQTpoLamDFj8Pnnn6Nnz56YOHEi1Go15s6di4iICLz88sum13z//feYNGkS6tati4YNG1r02aVLF0RERAAA2rZtixYtWqBVq1YIDAzEoUOH8MUXXyAqKgqTJ08u5V+q8NTLt99+G2PHjkX//v3RrVs37NmzB6tWrcLMmTMRHBwMoLCAOHHiRLz55pto06YNBg8eDKPRiKVLl+Lq1asWMRIREVEFc9+N/4iIiKgyWLZsmQAg9u/fb3X9hQsXBACxbNky07IhQ4YIX19fi7bnz58Xzz77rKhTp47Q6XQiODhYdOzYUWzfvt2s3enTp8XDDz8svL29BQAxZMgQu2Jdt26dACB27dplWjZ16lQBQCQlJVl9zbfffisefPBB4evrK3x9fUWDBg3E2LFjRXx8vKnNyZMnRefOnYWfn58IDQ0VI0aMEEeOHLHYb4PBIJ5//nkRFhYmFAqFuPOr2P/+9z/RsmVL4e3tLfz9/UWTJk3EpEmTxPXr101tjEajmD59uoiMjBTe3t6iQ4cO4vjx4yI6OtriOERHR4vo6OhSj8uQIUMEAJt/Fy5cMGt/5coV0a9fPxEQECD8/PzEf/7zH3H27FmzNkXH1dZf8X+DN954QzRv3lwEBgYKtVotatasKZ577jmRkJBQaux3Hr/69esLjUYj6tSpI+bNmyckSbJot3r1atG6dWsRFBQkvL29RUxMjFi/fr1D2yIiIqLyUwhxx3x0IiIiIiIiIiKiCsZrShERERERERERkcuxKEVERERERERERC7HohQREREREREREbkci1JERERERERERORyLEoREREREREREZHLsShFREREREREREQux6IUERERERERERG5nJe7A3A1SZJw/fp1+Pv7Q6FQuDscIiIiIiIiIqJKRQiBzMxMVKtWDUql7flQd11R6vr164iKinJ3GEREREREREREldqVK1dQo0YNm+vvuqKUv78/gMIDExAQ4OZoykeSJCQlJSEsLKzEyiNReTHXSC6Yq+SJmJckF8xV8kTMS5KFggJIy5YhMzsb/mPGQKnVujsit8vIyEBUVJSpBmPLXVeUKjplLyAgoFIUpfLy8hAQEMABmioUc43kgrlKnoh5SXLBXCVPxLwkuZAmTEBeYiICQkKYq8WUdtkkHikiIiIiIiIiInK5u26mFBERERERERGRJzBk34AxO8HmepVvVXj5RrowItdiUYqIiIiIiIiIqKzy8qD4v/9DUH4+8N13gI+P3S/NPPo50v56x+b6oJg3USV2ijOi9EgsShERERERERERlYfRCEiSwy/zbzoCPnUeAwDoU08jecsQhD66AprgBgAKZ0pVZixKERERERERERGVlVYLsWwZ0pOSEObgnfe8fCMtTs/TBDeANryFMyP0WCxKERERERERERGVlUIBhIRAGI2Fj8luLEoRERERERER0V2noi4ybqtfY24yjLnJUHmHQOUdZnX93YZFKSIiIiIiIiK665T3IuOm4pPBAOnXX6HKykJ+z57IOrUcWceXOByPX+PhDr9G7liUIiIiIiIiIqK7jrWLjAd3+AhKXRAAQOUdgvzEOLPXFJ89Za2olbVutumxX+Nh0NV42HTxcpV3qGmmlDE3xeKi5sbc5DIVs+SMRSkiIiIiIiIiuutYu8i4PvlYiYUhv8bDENB0JABAWy0WoY+uAIwGGH75AWmKjQjptATaiCYACgtYRafx3Xnx8qJiV/HldxbA7gYsShERERERERERAfCt+wQCmo4A8O/sqdBHVyDv6h5kHV+CrONLkXV8qeXrGg8DTgDqsEZmxaeSrllFLEoREREREREREQEAVN6hZkUloHA2k3dUB6vFqqJT7wqyE5F9wrJYRSVjUYqIiIiIiIiIqATWTvUrfuqdlHCwzH1LBTnIjv8GGbdnYCXvHIeAxsPgW38glGqfsgctAyxKERERERERERGVVV4eFJMnA00B6PMdeqlkyEXytlHIvbgVUkEmAEB/8xBSU88i9/IuhHZZXKkLU0p3bnz37t147LHHUK1aNSgUCmzYsKHU1/z666+4//77odVqce+992L58uUVHicRERERERERkU25uWV6Wd7FXwoLUvo0QBgLFwojJH0aci9tRXb8N86L0QO5tSiVnZ2NZs2aYeHChXa1v3DhAnr27ImOHTvi8OHDePHFFzF8+HBs3bq1giMlIiIiIiIiIrJCq4WYPLnwsVrj0EtzLm+DVJBldZ1UkIWsM+vKG51Hc+vpe927d0f37t3tbr9o0SLUrl0bH374IQCgYcOG+P333zFv3jx069atosIkIiIiIiIiIrJOoQDCwv597AApJ+nfGVIWK40wZl0rZ3CeTVbXlNq7dy86d+5stqxbt2548cUXbb4mPz8f+fn/ntOZkZEBAJAkCZIkVUicriJJEoQQst8P8nzMNZIL5ip5IuYlyQVzlTwR85IqmlSQg+wza5F54gsAQNLO5+Hf6Fn41htgyrs76wfWloti/5UkqcR+lWof0+sUPuFA5lVAGCyDU3pB6VdDlvlvb8yyKkolJCQgIiLCbFlERAQyMjKQm5sLb29vi9fMnj0b06dPt1ielJSEvLy8CovVFSRJQnp6OoQQUCrdeiYmVXLMNZIL5ip5IuYlyQVzlTwR85IqkmTIR8bhhZCu74Q27yyUAPITjyEj41MoL52GX3QHAMCt1FQokfjv61JTTcthuIL8S7ug/GUJUAW4tGsOtPf8B/rk45Bu/GbZ78UTUIc2hv7ST9ABSM/3BnR14ZN7CgKAAjD9N9e7IVD9SSQmJkJuMjMz7Wonq6JUWbz++uuYMGGC6XlGRgaioqIQFhaGgIAAN0ZWfpIkQaFQICwsjAM0VSjmGskFc5U8EfOS5IK5Sp6IeUkVKfPEcugvr4DQ3zItU0q58M45Clz8B1LiRgCAdHQ6fIvNcspHMBIABAX4IPPIW1Cc3wKFMg0AoEv6Gbi1AxrJAIWkt+hXXDgNxSUv6IyFZ3T5pP0OoVBBKDVQCCMgjFAovKDQ+COk2n0Iaf6ELO++p9Pp7Gonq6JU1apVcfPmTbNlN2/eREBAgNVZUgCg1Wqh1WotliuVykoxqCkUikqzL+TZmGskF8xV8kTMS5IL5ip5IuYlVZScs+sgCmzM6DHkwJhxAQBQcPMg0lLPIO/SNuii2iPr1EoAQPIvz8CYcxMw5sJomvNiBAw5sHVlKYWkB4oVqyCMhcUoL294+VSDIeMiNBEtENB4GHzrD5RlQQqA3e9XWRWlYmNj8fPPP5st27ZtG2JjY90UERERERERERHJkTHrmu2LjBcnjJD0acg5/wNyLv4MGAuLSsaMi/+2cez65pakAih9woCMiwh95BNow1uUs0N5cGupOSsrC4cPH8bhw4cBABcuXMDhw4dx+fJlAIWn3g0ePNjUfvTo0Th//jwmTZqE06dP49NPP8XatWvx0ksvuSN8IiIiIiIiIpIplV91QKGy/wVSAWDIta+Q5SjJWHgnvruMW4tSBw4cQIsWLdCiRWEFcMKECWjRogWmTJkCALhx44apQAUAtWvXxk8//YRt27ahWbNm+PDDD7FkyRJ069bNLfETERERERERkTz51esPpdrP3WEUUqoKZ0rdZdx6+l6HDh0ghLC5fvny5VZfExcXV4FREREREREREVFl51t/IHIv70Lupa2Q9JnlmwFVdMu8Iko1oPQqPNVPGAtnZKk0gGQonHF1B6XaDz41u0CfsL/sMcgQrxRHRERERERERHcdpdoHoV0WI/ihOdBE3A8AUAXUArys30jNJglQpRdNuFFBqQ2Czz29EPzQu6Z+NRH3I+Shd+FTpxeU2qB/TxtUFLb3ju4GXa2uTtkvOZHVhc6JiIiIiIiIiJxFqfaBf+NnoQlvgetfxSDs0eXIPPyZ+eypUmY5wUsHpV8ojLgKdfj9CGz6753zdJExuP5VjOni5X73DUZ2/DfIOL4U+oT90ETcb7rTXsGteNcfADfjTCkiIiIiIiIiIgBKL2+L2VOlzXLyvfdxBA9cCQAIeeRj+Dd+Fkq1j/X+bxfBQh/5BAAQ+sgnJbav7DhTioiIiIiIiIjotjtnT9kzyyk/5VSZtmXMTUZ+YuF1s/Wpp83+CwAq36rw8o0s/055KBaliIiIiIiIiIhKYatYBYMB+PvvwkZGxy6Wnn32e2QdX2K2LHnLENPjoJg3USV2Srlj91QsShERERERERERlZXBAMXXXwMxAIwGh17qW/cJBDQdYXO9yrdqOYPzbCxKERERERERERGVlVIJcd99APYBCscu3a3yDi2cbXWX4oXOiYiIiIiIiIjKSqMBRtye7aRWuzcWmeFMKSIiIiIiIiK66xiyb8CYnQDA+kXGjbnJbonrbsKiFBERERERERHddTKPfo60v94xW1b8IuN+jYeX+Prid84rSI03/Vfkp8KYmwyVdwiMuSkALO+oR4VYlCIiIiIiIiKiu45/0xHwqfOYzfXG3GSLO+MVZ+3OeSm/DLXa1rzYNQy6Gg8DsCxWeflG2hN6pVGmopRKpcKNGzcQHh5utjwlJQXh4eEwOngLRCIiIiIiIiIiV/LyjbQoAhU/pa/o9D1bs5xMd87Lz4eYNAkFBQXwmvshhMg2zZRSeYeZ2mcc/RxZx5cg6/hSZB1fCsC8WBUU8yaqxE5x/o56sDIVpYQQVpfn5+dDo9GUKyAiIiIiIiIiInco/ZS+f2c5FRaeQgG1GspXZiAvS4mQaq2h9LJeaqkS+1ZhEcuGu/G0PoeKUh9//DEAQKFQYMmSJfDz8zOtMxqN2L17Nxo0aODcCImIiIiIiIiIXMDWKX2lzXIKbP0GjPVGAUqlzb6tzcy62zlUlJo3bx6AwplSixYtgkqlMq3TaDSoVasWFi1a5NwIiYiIiIiIiIhcwFbhqLRZTgrvcKRmV2RklZNDRakLFy4AADp27IjvvvsOVapUqZCgiIiIiIiIiIg8RYmznIxGSH/8AfWtW0CPHiXOliJzZbqm1K5du5wdBxERERERERGR/BQUQPHee/DV64EuXQC12t0RyUaZynd9+/bFu+++a7H8vffeQ//+/csdFBERERERERGRLCiVQOPGMNSvz1lSDirT0dq9ezd69Ohhsbx79+7YvXt3uYMiIiIiIiIiIpIFjQZi1ixkvfEGoNG4OxpZKVNRKisrCxorB1qtViMjI6PcQRERERERERERUeVWpqJUkyZNsGbNGovl33zzDe67775yB0VERERERERERJVbmS50/tZbb6FPnz44d+4cHnnkEQDAjh078PXXX2PdunUO97dw4UK8//77SEhIQLNmzbBgwQK0bt3aatvly5fjmWeeMVum1WqRl5fn+I4QEREREREREZWHXg/Fyy/DPzcX+OQTQKdzd0SyUaai1GOPPYYNGzZg1qxZWL9+Pby9vdG0aVNs374d7du3d6ivNWvWYMKECVi0aBFiYmIwf/58dOvWDfHx8QgPD7f6moCAAMTHx5ueKxSKsuwGEREREREREVH5SBJw4QJUen3hY7JbmYpSANCzZ0/07Nmz3AHMnTsXI0aMMM1+WrRoEX766Sd88cUXeO2116y+RqFQoGrVquXeNhERERERERFRuWg0EDNmICs1FcG80LlDylyUSktLw/r163H+/HlMnDgRwcHBOHToECIiIlC9enW7+tDr9Th48CBef/110zKlUonOnTtj7969Nl+XlZWF6OhoSJKE+++/H7NmzUKjRo2sts3Pz0d+fr7pedGF2CVJgiTzCqYkSRBCyH4/yPMx10gumKvkiZiXJBfMVfJEzEuSC6lpUxQkJUECOFsKsPs9W6ai1NGjR9G5c2cEBgbi4sWLGD58OIKDg/Hdd9/h8uXL+PLLL+3qJzk5GUajEREREWbLIyIicPr0aauvqV+/Pr744gs0bdoU6enp+OCDD9C2bVucOHECNWrUsGg/e/ZsTJ8+3WJ5UlKS7K9DJUkS0tPTIYSAUlmma9YT2YW5RnLBXCVPxLwkuWCukidiXpJcMFfNZWZm2tWuTEWpCRMmYOjQoXjvvffg7+9vWt6jRw889dRTZenSblom8OIAAEmxSURBVLGxsYiNjTU9b9u2LRo2bIjFixfj7bfftmj/+uuvY8KECabnGRkZiIqKQlhYGAICAio01oomSRIUCgXCwsKY9FShmGskF8xV8kTMS5IL5ip5IuYlyYLRCOnAAWjS0hD0yCNQqtXujsjtdHZe7L1MRan9+/dj8eLFFsurV6+OhIQEu/sJDQ2FSqXCzZs3zZbfvHnT7mtGqdVqtGjRAv/884/V9VqtFlqt1mK5UqmsFIOaQqGoNPtCno25RnLBXCVPxLwkuWCukidiXpLH0+uhmDkTfno9lO3bQ2mlBnG3sff9WqZ3tVarNV2bqbgzZ84gLCzM7n40Gg1atmyJHTt2mJZJkoQdO3aYzYYqidFoxLFjxxAZGWn3domIiIiIiIiInEKpBOrWhbF27cLHZLcyzZTq1asXZsyYgbVr1wIorFxfvnwZr776Kvr27etQXxMmTMCQIUPQqlUrtG7dGvPnz0d2drbpbnyDBw9G9erVMXv2bADAjBkz0KZNG9x7771IS0vD+++/j0uXLmH48OFl2RUiIiIiIiIiorLTaCA+/BCZiYnw5t33HFKmotSHH36Ifv36ITw8HLm5uWjfvj0SEhIQGxuLmTNnOtTX//3f/yEpKQlTpkxBQkICmjdvji1btpgufn758mWzaV+3bt3CiBEjkJCQgCpVqqBly5b4888/cd9995VlV4iIiIiIiIiIyA0UQghR1hf/8ccfOHLkCLKysnD//fejc+fOzoytQmRkZCAwMBDp6emV4kLniYmJCA8P5/nVVKGYayQXzFXyRMxLkgvmKnki5iXJBXPVnL21F7tnSgUHB+PMmTMIDQ3Fs88+i48++gjt2rVDu3btnBIwEREREREREZHs6PVQvPEG/HJygA8/BOy88xw5cKFzvV5vurj5ihUrkJeXV2FBERERERERERHJgiQBp07B6+zZwsdkN7tnSsXGxqJ3795o2bIlhBAYP348vL29rbb94osvnBYgEREREREREZHHUqshJk9G9q1b0KjV7o5GVuwuSq1atQrz5s3DuXPnAADp6emcLUVEREREREREdzeVCmjTBgWJiYWPyW52F6UiIiIwZ84cAEDt2rWxcuVKhISEVFhgRERERERERERUedl9Tang4GAkJycDADp27AiNRlNhQRERERERERERyYIkAceOwevUKV5TykG80DkRERERERERUVkV3X1v9mxAr3d3NLLCC50TVUI3MvJwI8N24TgyQIfIAN6mlIiIiIiIqNwUCiAqClJubuFjsluZLnSuUCh4oXM3KV5skCSB1FvZCNanQ6ksTHwWGwgAFu+9hBnbzthcP6VLPUzrVt+FEREREREREVVSWi3EwoXISEyETqt1dzSywgudywyLDWSPUbHR6NUoAgBwOjELg76Kw6qnWqBBuB8AsHBJREREREREbmd3UQoAevToga+//hoXLlwAAMyZMwejR49GUFAQACAlJQUPPfQQTp486fRAqVDxYsPJhEwM/uYwvhzYHPdV9QfAYgMVsjZjrkG4H+6vEeTSOHgaIZWGOUKeiHlJcuGqXC1tO0qFApIQFR4HERFVPg4VpbZs2YL8/HzT81mzZmHAgAGmopTBYEB8fLxTAyRzxT/UJanww98dxQYie3BmH5WGOUKeyFPyksUxKo2rcrW07Tx8TzB2n0+t8DiIiDyWXg/FjBnwy84GZs8GdPx8tpdDRak7iRL+jwgREU8jpNI4I0d4rT1yNk8ZuzylOEaey55cdUZxs7TtFJ8pxc97IrorSRJw+DC89PrCx2S3chWliIhK4imnEZLnckaOOOOHO2ekUHGeMnZ5SnGMPJc9uTpta3y5x8iyvCf4eU9EdxW1GmLCBOSkpUGjVrs7GllxqCilUCiguOP2hnc+JyIiciVnXGuPM1KorOwpaEb4aRxqX5SznlIcI3mzZ5bToatpADjblIiozFQqoEMH6BMTCx+T3RwqSgkhMHToUGhv3+IwLy8Po0ePhq+vLwCYXW+KiIjIFZxxrT3OSKGysqegOaVLXYfaswBKzlRacdMZM6mIiIjKyqGi1JAhQ8yeDxo0yKLN4MGDyxcRERGRi3FGCpWVowVNFkDJ0/DOzuRszj4l/kZGHk7cNJ/BVyQ5Ww8ACPXVWHspZ/qR60gScPYsVCkpQGgooFS6OyLZcKgotWzZsoqKg4iIiEh27CloSsUueMoCKHka3tmZnM3ZM0L/t+8y3t5+tkyxjIipiVGx0VbXsWBFTqXXQ/Hyy/DX64ENGwAvXr7bXjxSMpSjN+CruGtYuu8SAOD5749hWJtoPNWiOnw0/CelQkV5smTfZQDAmG+PYXibmswTIiIiIqowJc0ILZrZVHQdsztZKxSNbFMT7SLVCK4SjDPJ2Vb7C/XVWGxr8d5L+Pyvy/j8r8tWt8VTU8mpFAogPBxSXl7hY7Ibf5nKTI7egOFrj2DL6URk5hsAAAeupiP+x5PYeTYZSwY0Y8GBrObJwatpOPNjFvOEiIiIiCpMSTNCS7uGmbWZTUUz+CIDtKbT90qazWfaVjedqS+eLk0VTquFWLIEGYmJ0N2+BjfZh79KZearuGvYcjoRaXkG0zKjANLyDNhyOhFfxV3D8BjrU1Tp7sE8ITnhrD4iorLjGEpyUtIsqtJmNr3VuQCPN65q97Z4ujSRPPCTSmbWHr6OLL3R6rosvRFrD19nsYE8Kk/4ZZlKwll95Kk4dpEcuHIMLe09wfcM2aPEWVQ2ZjbVC/VF6q1UNIqOxM0svTvCJqIK5BGXhF+4cCFq1aoFnU6HmJgY/P333yW2X7duHRo0aACdTocmTZrg559/dlGk7nctPQ/G21NY72SUBK6l277TBd09PCVPir4sT/rxJA7evnbAwatpmPTjSQxfewQ5ekPJHVClV3xWn/F2yt45q4/I1Th2kVy4agwt7T2RnJXP9wyVW2SADvfXCML9NYJMp9gVFqwC0TTCl6fbkWfT64GZM+E7b17hY7Kb24tSa9aswYQJEzB16lQcOnQIzZo1Q7du3ZCYmGi1/Z9//oknn3wSw4YNQ1xcHHr37o3evXvj+PHjLo7cPaoH6qBSWr9wmkqpQPVADtbkOXnCggOVxp5ZffbI0Ruw5K9LeP77YwAKbwCx5K9L/CFEZeJJY1dRbo/5tjC3x3zL3KZ/2TuGljePSntPvPzjCY95zxARuYUkQfHXX1DHxQHF7rpLpXP7XNq5c+dixIgReOaZZwAAixYtwk8//YQvvvgCr732mkX7jz76CI8++iheeeUVAMDbb7+Nbdu24ZNPPsGiRYvs3q5er4feSgVTqVTCq9jtG621KaJQKKBWq8vUtqCgAEJYn8lSUts+jUKx/1IK0q1syl+jQp/GYSXGodFo7IrhzrYGg8HsltblaatWq6G4fUeCimprNBphNFr/kuZoW6//b+/O42O61z+Af2ayTPZFJBJBYgmidloS7RW3NLQlVLkNLXpRa2lVb8vtLdWFlpZev9vettxU0dKWotaidmqPNQspIRqyiWxkkszz+yOdI5PMTCZkm/i8X695Sc555nu+c3znmTNPvuccW1uo1epaE6vT6VBYaPog0sbGBkM7NsSxq5kG15TSc7EtHkdarRY2NjawsbEpt12dTmfQP0v6YGNj8+fBsvG4nPxCrDqWiBe7NlL6ICIoKCgw2W7J92dVxQLm38u1PUfcTyxQ/TkiKT3b7Ky+pIxcs/vNzs4OtwuKMOb7U9hy/jpyCorbPZZ0C7Hrz2B7TDK+eLY9nOxty33f52kLsSr6GqKOJAG4e+rJ39r7QmNj+i4q1pgjLHnfV1dsee/PisRWVo5YdSwROfkmcpe2CKuOJWJEJ78y6woKyj5Hq9VCp9Mpxx36/ycAZfZR6bGepy3EuB9P45f4NOQUFL9P9Kdm7Yi5jv8+287oaVF1KUcYw+OIu7HlzYxOSs9GZk4eJqw9h23xaQan+MWtv6XkSAB/5r+rAO7e2fm5Dn6wV5f/nth0PqVCn/fWniPMxQI8jigZq8+LBQWFZvtf+v1Z3vOMLdc/p2T39O2aaq+u5wgeR1RjjtDpoHvpJWTeugUPnQ7qEuPsQc0R5vpTUo0WpbRaLY4fP44ZM2Yoy9RqNXr37o1Dhw4Zfc6hQ4cwbdo0g2Xh4eFYt26d0fj8/Hzk5+crv2dlZQEAFixYAI2Rq+K3aNECw4cPV37/6KOPTA7GgIAAjBo1Svl94cKFyMvLMxrr5+eHl156Sfn9//7v/5CZmWk01tvbGxMnTlR+/+KLL5CamgoAKCjSoVHsDeTrPKFt1B46BzfYqgE3e1uEt/aGNvoXvP/LDaPtOjk5KcU8AFi+fDkSExONxtrZ2WHmzJnK79999x0uXrxoNBYAZs2apfz8448/IiYmxmTsjBkzlA+sDRs24NSpUyZjp0+fDmdnZwDAli1bcOzYMZOxU6dOhYeHBwBg+/btJscQAEyYMAE+Pj4AgD179mDPnj0mY8eMGQN/f38AxTP1duzYYTJ25MiRCAwMBAAcPXoUW7ZsMRkbGRmJli1bAgBOnTqF9evXm4x99tln8dBDDwEAzp07hx9//NFkbEREBJ7r0Ba7LqRi09FYZBepIA5uUN3JgibpNPzVN3FF3QDvb1ejX79+eOSRRwAAly9fxrJly4y2KSLo0qULnnrqKQDAtWvXsGTJEpN96NmzJ8LCwpB86zYKdQKg7Bf6wqIinF63FNub5+GJJ54AAGRmZuLTTz812W7Xrl2VPuTm5mLBggUmYzt06ICBAwcCKM41c+fONRkbHByMoUOHKr+///77JmNre44ozcPDA1OnTlV+X7p0KZKTk43G1kSOyD+eBFWTMIije5nb59qqVdBcOYb3399nst3p06dj9bl0bItNwS2trvhIVKVCkU5wSwv8vO4n3N6/Cp383c3miIIiHX4+fwMJOk/kN+oAOLjieFImLvycg1UbtuIRVRLsbIxPLrbGHNGxY0cAQHx8PL777juTsZbmCADo3bs3evToAcDyHAEAKSkp+Pzzz03GhoSEVHuOOH3wMgrbDQLUNmWeV6QTnF63FO+n7FSWFRTpcPZ6Nk7cVANNe2HSmtMY3a0JnuvYEIsWfAStVovc3Fw4OztDpVIp8efEG/Bsr8Qn7/wWhdq7xywnr93CjjQN7rR49O7Y/nP2yc+btyBv/3fo5O9epo91KUcYw+OIYpGRkWjkrsGFtJyyn7UiUN3ORH7sHoz4fTP2OnZClqaesrpkjsze+x2uZt4ulf9uIf7n81i/9xja3YrG6cNXzL4n8rQF5X7eH2yvqjM5ojQeRxQzliN++z0VQBssXboUfjZ3+25nZ4e+IyYBKL67XukckVzkpDzPV52LCRMmQKfTKXfiK91eyedoC7RKcUmfI0q2V/J5dT1H8DiiGnPEhx9CRIo/7y9cUIqSwIObI0rWYcyp0aJUWloaioqK0KBBA4PlDRo0QGxsrNHnXL9+3Wj89evXjcbPnTsX77zzTpnlubm5RquvWVlZBqcO5uTkmKzSZmdnl4m9ffu2RbHZ2dnIzc01Guvg4GA29vEAF7S4mYXY1COIa9wbTzd1xMA2PujZzAvfr8o12a5Op7O4D7a2tgaxWVlZJmMBVDhWfzB569Yts7GpqanKekti9dXYzMxMs7FpaWnKz5bE6qu/N2/eNBubnp4OJycni2P1+y0jI8NsbEZGRoViczIz8EFPP3TAH4j6+VfENX4cLVOPorVHPgI8XaC9cxvaP/uobzc9Pd1kuyKCnJwcpKSkQK1WIy0tzWwfMjMzkZKSghAfNS6n6ZBbpDYsOIjAPj8LjZ1VSixQ/v/xrVu3lNi8vDyLY/VfCE0p/b6vSGxtzBEl2djYWBxbEzmifX175KaeRUrA3S/dem08VPiLmwNyM9PKtKeXmpqKI7FXUFBYaPh8lQoQgc7/IVy79itaetiazREJablItfHEnWaPKn9iLRLg5p1CJOYXwTU7Dc3rOxvtgzXmCEve9/o+3kuspTmiorHVlSMaO6uQlZ8FrYNHmWKps13xen18YZEOR65k4ne1NzIaFs84OfPHLfzfr+cQeykJqqxs6IoKcedO8bX8inSixGf6tzCIdzt3FZ0bOML2zwLotbRM6Pz/Wua9AQBFDVrg2tUdaOlR9lCuLuUIU7E8jiiOHd7aFTfSb+J0xt2ivP5f79SzaF/fHlfSMlHYwtXwySVy5KVL23HTuaFB/iv8M/8lFOTC9kaamfeEwNlOhYe91Dh8Q4s8nY3Jz/u6lCNK43FEsZI5Ir+gCIfir2H/pXSgcRvsu5CM1o75CPAsznG2trbIuJkBAMi4mVHmfX/7zwLn7Tu3kSu5yMzMhIgg4+btu8txNw8n3ryN2NsaoHEbzNt8EoPb+aFnMy9lTBi0h7vbqes5gscR1ZsjRET5vC9ZlHpQc4SlRSmVmJt/WcX++OMP+Pv74+DBgwgJCVGW/+Mf/8CePXtw+PDhMs+xt7fHsmXLEBkZqSz77LPP8M477+DGjbIzhIzNlGrcuDFu3LgBNze3MvHWNF3uRFImHv3vURx+ORRdGnuW2y7Aaff3EmvtU2qPXslAj88O48DEbujU0M1srLnT9zIyMuDr6wu1Wm1xH/535Ar+sfE8Mu+UfW2e9sD7/VphdPemnHZvJLa2TLuv6hyRpy3ExLVnsD0+DVkFxYUgGxXgrime/fmfgW3gYGv68od2dnbo+PEenE/JhbGeqgC0rmeP49N6mn3f9196GHsuZ6HQSCO2aqBngBt+Ht3NaB+sPUfUdGytmXZfIvbro1cwc0scMo28Zes52OK98OYY9XATi2NHdGmE1NRUeHt745vjSSbj9XlR33aXT/YgNkNb7tgus64O5QhjeBxxN/ZOoQ7jfjyNrTE3kKXVKTnUzQ7o07I+PnumHR77vwNmx5GTugj5YmM2/w1u72d2nD/9kA82nL2OzPyy+9fY57215whzsQCPI/SnHm85fx3ZBaKMS1c74Ik/x6WTvS3OptzGI//ejyNTHkV7X2eD9+fJP7KU49cOvi5IScvAjmsFWHrkKg5fvYWHGzrjxYcboX9wA7y28Tx+iU9DdonjCDeNLfq29saCJ1th/blkRB1NwtE/cpXnDe3QsMyp/XUxR/A4ohpzhFYLXWIi0jMy4NWhA9Q2NkZjgQcnR2RlZaFBgwa4deuW0dqLXo3OlKpfvz5sbGzKFJNu3LgBX19fo8/x9fWtULxGozF6mp6DgwMcHMq/2LMlMfcSa6xPFY21ty9erlKplSRSGe0a35Z9+UG1KFatVhu8OepabMlEVV6s5s/9prG3NztGzbWr0+mUDyv9w5I+DOvcCL9eTC++VbW2EEU6wEY53dQHI7o1g52dYTs2NmVPDTClqmKr6n1f3TmiOmPv9b3s4AD8b9jDym3Ej1zNRJdGHhW6jbifuyPi0vL+PHXEkI1aBX8vtzL7vvR77o9cnXJx3tKKdMXrLfn/s8YcUdOxQO1435eMHdGtGfYmZhvNXU/8mbsc/hyba8+nI6dQBRj5yp+lLcLa8+l4qUcLaDQaODg4mI3PLlRh7fl0jH+s+HQLfy83XMhMr9DYNsaac0RNxdaW93J5sS62wFdDO5rNoeWNI7GxR1GB8S+r+vxX3ntiQf+20BbB4s97a88R5XnQjyNWnUrGlthUZGrvjrkiATK1wC8XM7E2Jh1jugVArS6eUaJWq8q8PzX2d/781x46tS3+78BlRJ3LRtaf1zY7kZyLC9sSEHXiOuJTcw2ut6uf6bwlNhUX0/NwITVXuZ6a/nl7E7OxZGgHg//Xupgj7jW2po8NrDJHqFSQ6dPhrdXCft06qM28Xx+UHGHpNaVq9O579vb26NKlC3buvHtdBp1Oh507dxrMnCopJCTEIB4oPqfXVDwR1Rwne1ssGdoBH/Vvgy7+HgCALv4e+LB/GywZ2sGiggPVfU72thjTLQCfDW4HAPhscDuM6RZg8fgY2rEhXOyNHzS42hdf+L88teWOlVQ7VCR3lXeh6Wu37hgsq0h8ZYxtqvvKy6HljaMAT8dy819574n6Lhp+3pOivLtCfnfimsm7QRq7U+Tkn85gx6VbuGnk7o4nr91SCk6lZeUXr+ddIanauLlBXFxquhdWp8Y/IaZNm4aRI0eia9eueOSRR7Bo0SLk5uYqd+MbMWIE/P39lYsLTp06FT179sTHH3+Mp556CqtWrcKxY8fw5Zdf1uTLICIT9AfLnf3d0XXRPnw2uB06N/Ko6W5RHTKskz9+vZCGrbHFd38q1BWfcuL651/oh3XyL7cNc3es5Jf/B5Olucvf3QEX0nJNz2YqVdCsSHzJsW1s9oklY5uovHH0aNN6eGtLbLn5r7z3BD/vSc9c8b1QJziedMugmKS/q+gvcSlQQYXt8akG605ey4SdiZvgFpo+2w46KX4Yk6MtwvfRf2BMtwCLXxeRWQ4OkBUrcCslBT4VmLFEtaAo9be//Q2pqal4++23cf36dXTs2BFbt25VLmZ+5coVg9snh4aG4ttvv8Vbb72FmTNnIigoCOvWrUPbtm1r6iUQEVEN0v8F/9uT17D0t0QcvnoLXfzdMbp7gMWnAPLLP92rihY0KxJfcmwrp2b5V+z0VqLyxhEA7L+UcV+FfaKSzBXf1Sogr6DQoJikn720+XwKoALyCnQG64oEKITxuzveK2MzWYmoZtSKo5nJkydj8uTJRtft3r27zLIhQ4ZgyJAhVdwrIiKyFvq/0Hf0c8Mj/96PxYPaoWsTzwo9n1/+6V5UtKBZ0XjOPqHKUN44ut/CPlFJ5orvapXp2Uu3C3VGL8hvjlpV/DA2Y8pWbXq2FE/NJ6o9avSaUkRERLXF/V7bih5MFb12Hq+1R7WRPv8tHlSc/xYPYv6jezeskz/6tvaBh4MtbP78tmmjBjwdbOFkZ2OyKGWuIGVqnZvGFp383Y1uq5O/O1w1xscwT82nSqfVAh9/DKfPPy/+mSzGohQRERHRfahoQZMFUCKqy8wV37s08oCtiQvrmzs5z8lGBTs1yhSe+rb2wabR3Yxua9PobuhnojjGU1Op0ul0UO3ZA/tDhwCdmYudURk8+rEyyVl3kJxVfP5zbEqO8q/6z+Tu5+YAPzdORX3QmRonehwnRERERFRVzJ0yqr8jXmmOtuoy15TSC3JTo0MzX8Sm5Bk9xd7UtnhqPlUbW1vI6NG4fesW7G05tiqCe8vKfHEoEXO2xxssG7EqWvn57T4tMTu8VTX3imobY+Pk+W9PKj9znBARERFRdTN3Xb3HW9a/e/e9Euvc7W3xeDNXzB7UDvFpeRW6vh6vy0fVxtYWiIhAfkpK8c9kMe4tKzMuJAADHiq+M6FOJ8i4mYF6nvUMZkoRlRwnxnCcUG3BWX1UG3FckrXgWCVrY8ndIMus69YYffztOLOJqI7iO9vKlDy40Ol0SLHPh4+PO9RqXh6M7qotB6E8WKbycFYf1UYcl2Qtqmuslvd5rlapoBMxuZ6f91RSebOXSq/r2NANKSkpNddhIkuIACkpUKelAd7eNd0bq8KiFBFVGX6xo/JwVh/VRhyXZC2qa6yW93n+l2b1sPf3DJPr+XlPRHVefj5UY8bATasF1q0DnJxqukdWg0UpIqoy/GJH5amMv57zBhBU2WrLmOFsUyqPJWOgMsZReZ/nJWdKmeonEVGdp9HUdA+sEotSRFRl+IWJqkNl3ACCX/6pNuJsU6oM5Y2jV//SDMM7F1/Lx1xhnzmQiMgMBwfIDz8gMyUFPg7MlxXBohQREVm1yrgBBL/8072ypKDZwMW+QvH6McvZplQZyhtH3564hq6L9hks452diYiourAoRUREVq0ybgDBL/90rywpaL7dJ6hC8foCAGenUGUobxz5uTlg2J8zpXhnZ6oM1Tn7mDOdiawfi1JERPTA40Er3auKFjRZAKXahnd2pspW2bOPk7Pu4NyNXNTT3kJ8Wi6Au4WnLw4l4qvDV0xua2y3JhgXEsCCFVW9ggLg88/hlJ0NTJ/O60tVAItSRERERPfIki81Op2uQvFERNbMXPE9LVcLADiRlGlxoejL367g3R0XDJYZKzyVpi9YlSxa8dR8qjJFRVD98gvstVrg1VdrujdWhUUpIiIiIiIiqhTmiu+zt8WZnUVlbGZTSIAnFj/ZFM396sPHtWy7prY3O9zBaLGq5POIKo2tLeT553EnKwv2tiyzVAT3FhEREREREVU5c7OoypvZ9K/etninb2uLt8WZqVStbG2BoUNxJyUFbixKVQj3FhEREREREVU5s7OoTMxs0l+A/6EAv6ruHhHVABaliIiIiIiIqEaZKlgpF+DnrCeqzUSAW7egysoCvL1rujdWhUUpIiIiIiIiIqJ7lZ8P1QsvwF2rBdatA5ycarpHVuOBK0qJCAAgKyurhnty/3Q6HbKzs+Hg4MDb9lKV4lgja8GxSrURxyVZC45Vqo04Lskq3LkDKShAfmEh7LOyoC4srOke1Th9zUVfgzHlgStKZWdnAwAaN25cwz0hIiIiIiIiojrFj9c/Kyk7Oxvu7u4m16ukvLJVHaPT6fDHH3/A1dUVKpWqprtzX7KystC4cWNcvXoVbm5uNd0dqsM41shacKxSbcRxSdaCY5VqI45LshYcq4ZEBNnZ2WjYsKHZWY4P3EwptVqNRo0a1XQ3KpWbmxsHPVULjjWyFhyrVBtxXJK14Fil2ojjkqwFx+pd5mZI6fGkXCIiIiIiIiIiqnYsShERERERERERUbVjUcqKaTQazJo1CxqNpqa7QnUcxxpZC45Vqo04LslacKxSbcRxSdaCY/XePHAXOiciIiIiIiIioprHmVJERERERERERFTtWJQiIiIiIiIiIqJqx6IUERERERERERFVOxaliIiIiIiIiIio2rEoVcnmzp2Lhx9+GK6urvDx8cHAgQMRFxdnEHPnzh1MmjQJXl5ecHFxweDBg3Hjxg1l/alTpxAZGYnGjRvD0dERwcHB+PTTTw3a2L17N1QqVZnH9evXzfZPRPD222/Dz88Pjo6O6N27Ny5cuKCsv3z5MkaPHo2mTZvC0dERzZs3x6xZs6DVaith71BlsvaxBgADBgxAkyZN4ODgAD8/P7zwwgv4448/7nPPUG1TF8aqXn5+Pjp27AiVSoXo6Oh72yFUK9SFcRkYGFim3Xnz5t3nnqHapi6MVQDYtGkTunXrBkdHR3h6emLgwIH3vlOoxln7uDTVrkqlwtGjRythD1FtYu3jFQDi4+MRERGB+vXrw83NDY8++ih27dp1n3umlhCqVOHh4RIVFSVnz56V6OhoefLJJ6VJkyaSk5OjxIwfP14aN24sO3fulGPHjkn37t0lNDRUWb906VKZMmWK7N69WxISEmT58uXi6OgoixcvVmJ27dolACQuLk6Sk5OVR1FRkdn+zZs3T9zd3WXdunVy6tQpGTBggDRt2lRu374tIiJbtmyRUaNGybZt2yQhIUHWr18vPj4+8tprr1XynqL7Ze1jTUTkk08+kUOHDsnly5flwIEDEhISIiEhIZW4l6g2qAtjVW/KlCnSr18/ASAnT568/51DNaYujMuAgACZM2eOQbsl+091Q10Yqz/++KN4enrK559/LnFxcXLu3DlZvXp1Je4lqm7WPi7z8/MN2ktOTpYxY8ZI06ZNRafTVfLeoppm7eNVRCQoKEiefPJJOXXqlMTHx8vEiRPFyclJkpOTK3FP1QwWpapYSkqKAJA9e/aIiEhmZqbY2dnJDz/8oMTExMQIADl06JDJdiZOnCi9evVSftcP+Js3b1rcF51OJ76+vjJ//nxlWWZmpmg0Gvnuu+9MPu+jjz6Spk2bWrwdqhl1YaytX79eVCqVaLVai7dF1sdax+rmzZuldevWcu7cORal6iBrHJcBAQGycOFCi9ulusHaxmpBQYH4+/vLkiVLLG6XrI+1jcvStFqteHt7y5w5cyzeDlkvaxuvqampAkD27t2rxGRlZQkA2b59u8Xbqq14+l4Vu3XrFgCgXr16AIDjx4+joKAAvXv3VmJat26NJk2a4NChQ2bb0bdRUseOHeHn54c+ffrgwIEDZvty6dIlXL9+3WDb7u7u6Nat2z1tm2oXax9rGRkZWLlyJUJDQ2FnZ2e2fbJu1jhWb9y4gbFjx2L58uVwcnKy7IWSVbHGcQkA8+bNg5eXFzp16oT58+ejsLCw/BdLVs3axuqJEydw7do1qNVqdOrUCX5+fujXrx/Onj1r+YumWs/axmVpGzZsQHp6Ol588UWzbVPdYG3j1cvLC61atcI333yD3NxcFBYW4osvvoCPjw+6dOli+QuvpViUqkI6nQ6vvPIKevTogbZt2wIArl+/Dnt7e3h4eBjENmjQwOS5pgcPHsTq1avx0ksvKcv8/Pzw3//+F2vWrMGaNWvQuHFjhIWF4cSJEyb7o2+/QYMGFm/74sWLWLx4McaNG1fu66WaY81j7Y033oCzszO8vLxw5coVrF+/3uLXTdbHGseqiGDUqFEYP348unbtWuHXTLWfNY5LAJgyZQpWrVqFXbt2Ydy4cfjggw/wj3/8o0KvnayLNY7V33//HQAwe/ZsvPXWW9i4cSM8PT0RFhaGjIyMiu0AqpWscVyWtnTpUoSHh6NRo0blvl6ybtY4XlUqFXbs2IGTJ0/C1dUVDg4O+OSTT7B161Z4enpWeB/UNrY13YG6bNKkSTh79iz2799/z22cPXsWERERmDVrFp544glleatWrdCqVSvl99DQUCQkJGDhwoVYvnw5Vq5caVBI2rJlC2xsbCq07WvXrqFv374YMmQIxo4de8+vgaqeNY+1119/HaNHj0ZiYiLeeecdjBgxAhs3boRKpbrn10K1lzWO1cWLFyM7OxszZsy45z5T7WaN4xIApk2bpvzcvn172NvbY9y4cZg7dy40Gs09vxaqvaxxrOp0OgDAP//5TwwePBgAEBUVhUaNGuGHH37gHz7rAGsclyUlJSVh27Zt+P777++5/2Q9rHG8iggmTZoEHx8f7Nu3D46OjliyZAn69++Po0ePws/P755fS23AolQVmTx5MjZu3Ii9e/caVNx9fX2h1WqRmZlpUIm9ceMGfH19Ddo4f/48Hn/8cbz00kt46623yt3mI488ory5BgwYgG7duinr/P39kZycrGyr5MC9ceMGOnbsaNDWH3/8gV69eiE0NBRffvmlxa+bqp+1j7X69eujfv36aNmyJYKDg9G4cWP89ttvCAkJsXgfkHWw1rH666+/4tChQ2W+5Hft2hXDhw/HsmXLLNsBVCtZ67g0plu3bigsLMTly5cNDoqpbrDWsapf3qZNG2W9RqNBs2bNcOXKFQtfPdVW1jouS4qKioKXlxcGDBhg0Wsm62Wt4/XXX3/Fxo0bcfPmTbi5uQEAPvvsM2zfvh3Lli3Dm2++WbEdUdvU9EWt6hqdTieTJk2Shg0bSnx8fJn1+ouo/fjjj8qy2NjYMhdRO3v2rPj4+Mjrr79u8bZ79+4tgwYNMts3X19fWbBggbLs1q1bZS76l5SUJEFBQfLcc89JYWGhxdun6lUXxlppiYmJAkB27dplcV+o9rP2sZqYmChnzpxRHtu2bRMA8uOPP8rVq1ct7gvVLtY+Lo1ZsWKFqNVqycjIsLgvVPtZ+1jV/17yQudarVZ8fHzkiy++sLgvVLtY+7gsGdu0aVPeabyOs/bxumHDBlGr1ZKdnW3w3JYtW8r7779vcV9qKxalKtmECRPE3d1ddu/ebXAbyLy8PCVm/Pjx0qRJE/n111/l2LFjEhISIiEhIcr6M2fOiLe3tzz//PMGbaSkpCgxCxculHXr1smFCxfkzJkzMnXqVFGr1bJjxw6z/Zs3b554eHjI+vXr5fTp0xIREWFwu8mkpCRp0aKFPP7445KUlGSwfapdrH2s/fbbb7J48WI5efKkXL58WXbu3CmhoaHSvHlzuXPnTiXvLapJ1j5WS7t06RLvvlcHWPu4PHjwoCxcuFCio6MlISFBVqxYId7e3jJixIhK3lNU06x9rIqITJ06Vfz9/WXbtm0SGxsro0ePFh8fHxZQrVhdGJciIjt27BAAEhMTU0l7hmojax+vqamp4uXlJc8884xER0dLXFycTJ8+Xezs7CQ6OrqS91b1Y1GqkgEw+oiKilJibt++LRMnThRPT09xcnKSQYMGGRR9Zs2aZbSNgIAAJebDDz+U5s2bi4ODg9SrV0/CwsLk119/Lbd/Op1O/vWvf0mDBg1Eo9HI448/LnFxccr6qKgok6+BahdrH2unT5+WXr16Sb169USj0UhgYKCMHz9ekpKSKmX/UO1h7WO1NBal6gZrH5fHjx+Xbt26ibu7uzg4OEhwcLB88MEHLOrXQdY+VkWKZ0a99tpr4uPjI66urtK7d285e/bsfe8bqjl1YVyKiERGRkpoaOh97Quq/erCeD169Kg88cQTUq9ePXF1dZXu3bvL5s2b73vf1AYqERELzvIjIiIiIiIiIiKqNOqa7gARERERERERET14WJQiIiIiIiIiIqJqx6IUERERERERERFVOxaliIiIiIiIiIio2rEoRURERERERERE1Y5FKSIiIiIiIiIiqnYsShERERERERERUbVjUYqIiIiIiIiIiKodi1JEREREJly+fBkqlQrR0dFVvq2vv/4aHh4eVb6dqhIWFoZXXnnlvtqw9n1AREREFcOiFBEREVmlUaNGQaVSlXn07du3prtWrsDAQCxatMhg2d/+9jfEx8dX+bbDwsKUfeXg4IA2bdrgs88+u+92165di3fffbcSekhEREQPCtua7gARERHRverbty+ioqIMlmk0mhrqzf1xdHSEo6NjtWxr7NixmDNnDvLy8vDNN99g0qRJ8PT0RGRkZIXb0mq1sLe3R7169aqgp0RERFSXcaYUERERWS2NRgNfX1+Dh6enJwBg2LBh+Nvf/mYQX1BQgPr16+Obb74BAGzduhWPPvooPDw84OXlhaeffhoJCQkmt2fs9LJ169ZBpVIpvyckJCAiIgINGjSAi4sLHn74YezYsUNZHxYWhsTERLz66qvKjCVTbX/++edo3rw57O3t0apVKyxfvtxgvUqlwpIlSzBo0CA4OTkhKCgIGzZsKHe/OTk5wdfXF82aNcPs2bMNnpeZmYkxY8bA29sbbm5u+Otf/4pTp04pz509ezY6duyIJUuWoGnTpnBwcFBeV8nT927evIkRI0bA09MTTk5O6NevHy5cuFBmfzZp0gROTk4YNGgQ0tPTy+07ERER1R0sShEREVGdNHz4cPz888/IyclRlm3btg15eXkYNGgQACA3NxfTpk3DsWPHsHPnTqjVagwaNAg6ne6et5uTk4Mnn3wSO3fuxMmTJ9G3b1/0798fV65cAVB8mlujRo0wZ84cJCcnIzk52Wg7P/30E6ZOnYrXXnsNZ8+exbhx4/Diiy9i165dBnHvvPMOhg4ditOnT+PJJ5/E8OHDkZGRUaE+Ozo6QqvVAgCGDBmClJQUbNmyBcePH0fnzp3x+OOPG7R58eJFrFmzBmvXrjV5va1Ro0bh2LFj2LBhAw4dOgQRwZNPPomCggIAwOHDhzF69GhMnjwZ0dHR6NWrF957770K9ZuIiIisG4tSREREZLU2btwIFxcXg8cHH3wAAAgPD4ezszN++uknJf7bb7/FgAED4OrqCgAYPHgwnnnmGbRo0QIdO3bE//73P5w5cwbnz5+/5z516NAB48aNQ9u2bREUFIR3330XzZs3V2Yi1atXDzY2NnB1dVVmdxmzYMECjBo1ChMnTkTLli0xbdo0PPPMM1iwYIFB3KhRoxAZGYkWLVrggw8+QE5ODo4cOWJRX4uKirBixQqcPn0af/3rX7F//34cOXIEP/zwA7p27YqgoCAsWLAAHh4e+PHHH5XnabVafPPNN+jUqRPat29fpt0LFy5gw4YNWLJkCR577DF06NABK1euxLVr17Bu3ToAwKeffoq+ffviH//4B1q2bIkpU6YgPDzcon4TERFR3cCiFBEREVmtXr16ITo62uAxfvx4AICtrS2GDh2KlStXAiieFbV+/XoMHz5cef6FCxcQGRmJZs2awc3NDYGBgQCgzGq6Fzk5OZg+fTqCg4Ph4eEBFxcXxMTEVLjNmJgY9OjRw2BZjx49EBMTY7CsZFHI2dkZbm5uSElJMdv2Z599BhcXFzg6OmLs2LF49dVXMWHCBJw6dQo5OTnw8vIyKPRdunTJ4LTGgIAAeHt7m+27ra0tunXrpizz8vJCq1atlP7HxMQYrAeAkJAQs/0mIiKiuoUXOiciIiKr5ezsjBYtWphcP3z4cPTs2RMpKSnYvn07HB0dDe7O179/fwQEBOCrr75Cw4YNodPp0LZtW+VUttLUajVExGCZ/nQ0venTp2P79u1YsGABWrRoAUdHRzz77LMm27xfdnZ2Br+rVKpyTz8cPnw4/vnPf8LR0RF+fn5Qq4v/TpmTkwM/Pz/s3r27zHNKXu/K2dn5vvtNRERExKIUERER1VmhoaFo3LgxVq9ejS1btmDIkCFKESc9PR1xcXH46quv8NhjjwEA9u/fb7Y9b29vZGdnIzc3VynMlL6m0oEDBzBq1CjlulU5OTm4fPmyQYy9vT2KiorMbis4OBgHDhzAyJEjDdpu06ZNua+7PO7u7kaLeZ07d8b169dha2urzBq7F8HBwSgsLMThw4cRGhoK4O7+1vc/ODgYhw8fNnjeb7/9ds/bJCIiIuvDohQRERFZrfz8fFy/ft1gma2tLerXr6/8PmzYMPz3v/9FfHy8wUXCPT094eXlhS+//BJ+fn64cuUK3nzzTbPb69atG5ycnDBz5kxMmTIFhw8fxtdff20QExQUhLVr16J///5QqVT417/+VWbmUmBgIPbu3YvnnnsOGo3GoL96r7/+OoYOHYpOnTqhd+/e+Pnnn7F27VqDO/lVtt69eyMkJAQDBw7ERx99hJYtW+KPP/7Apk2bMGjQIHTt2tWidoKCghAREYGxY8fiiy++gKurK9588034+/sjIiICADBlyhT06NEDCxYsQEREBLZt24atW7dW2WsjIiKi2ofXlCIiIiKrtXXrVvj5+Rk8Hn30UYOY4cOH4/z58/D39ze4RpNarcaqVatw/PhxtG3bFq+++irmz59vdnv16tXDihUrsHnzZrRr1w7fffcdZs+ebRDzySefwNPTE6Ghoejfvz/Cw8PRuXNng5g5c+bg8uXLaN68uclrMw0cOBCffvopFixYgIceeghffPEFoqKiEBYWZvkOqiCVSoXNmzfjL3/5C1588UW0bNkSzz33HBITE9GgQYMKtRUVFYUuXbrg6aefRkhICEQEmzdvVmaqde/eHV999RU+/fRTdOjQAb/88gveeuutqnhZREREVEuppPSFEYiIiIiIiIiIiKoYZ0oREREREREREVG1Y1GKiIiIiIiIiIiqHYtSRERERERERERU7ViUIiIiIiIiIiKiaseiFBERERERERERVTsWpYiIiIiIiIiIqNqxKEVERERERERERNWORSkiIiIiIiIiIqp2LEoREREREREREVG1Y1GKiIiIiIiIiIiqHYtSRERERERERERU7ViUIiIiIiIiIiKiaseiFBERERERERERVTsWpYiIiIiIiIiIqNqxKEVERERERERERNXOtqY7UJOKiopQUFBQ090gIiIiIiIiIqoT7OzsYGNjY1HsA1mUEhFcv34dmZmZNd0VIiIiIiIiIqI6xcPDA76+vlCpVGbjHsiilL4g5ePjAycnp3J3EhERERERERERmSciyMvLQ0pKCgDAz8/PbPwDV5QqKipSClJeXl413R0iIiIiIiIiojrD0dERAJCSkgIfHx+zp/I9cBc6119DysnJqYZ7QkRERERERERU9+hrLuVdx/uBK0rp8ZQ9IiIiIiIiIqLKZ2nN5YEtShERERERERERUc1hUYqIiKiWycvLw+DBg+Hm5gaVSoXMzEyjy4iIiIiIrBmLUlZCpVKZfcyePbtKtjtq1CgMHDiwStq+F19//TU8PDxqvI2SZs+ejY4dO1Zae0RUeXbv3m02d/bq1aumu2jUsmXLsG/fPhw8eBDJyclwd3c3uux+hYWF4ZVXXrn/DhNRlRk1apSSs+zt7dGiRQvMmTMHhYWFlbqd6s4HdfEYk4iqXlXlRP0xoyV/9AsMDMSiRYvua3sVUduO1yr7++8Dd/c9a5WcnKz8vHr1arz99tuIi4tTlrm4uCg/iwiKiopga8v/3qqk389EVHuFhoYa5E+9DRs2YPz48Zg4ceI9t63VamFvb38/3TMpISEBwcHBaNu2rdllRFT9LqTm4H9HryIxIw8B9Zzw94cbI8jbpfwn3oe+ffsiKioK+fn52Lx5MyZNmgQ7OzvMmDHDIK4q8xLAY0wiKqvg5gVkn/sahVmJsHULgOtDo2DnGVSl27Q0J9akoqIiqFQqqNWcB1QuecDcvn1bzp8/L7dv367prtyzqKgocXd3V37ftWuXAJDNmzdL586dxc7OTnbt2iVFRUXywQcfSGBgoDg4OEj79u3lhx9+UJ5XWFgof//735X1LVu2lEWLFinrZ82aJQAMHrt27ZJLly4JAFm9erU8+uij4uDgIF27dpW4uDg5cuSIdOnSRZydnaVv376SkpJi0PevvvpKWrduLRqNRlq1aiX/+c9/lHX6dtesWSNhYWHi6Ogo7du3l4MHDxq8zpKPWbNmGd1H0dHREhYWJi4uLuLq6iqdO3eWo0ePmm3jm2++kS5duoiLi4s0aNBAIiMj5caNG2b3c1RUVJn2oqKi7vF/loiqw/nz58XV1VX++c9/Giw/c+aM9O3bV5ydncXHx0eef/55SU1NVdb37NlTJk2aJFOnThUvLy8JCwsTEZHdu3fLww8/LPb29uLr6ytvvPGGFBQUmO3Dvn37lPzZqFEjefnllyUnJ0fZTsmc0rNnT6PLRETu3Lkjr732mjRs2FCcnJzkkUcekV27dhlsa//+/dKzZ09xdHQUDw8PeeKJJyQjI0NGjhxZJn9dunTp/nYuUR33v8OJYjN9g9i+/rPBv1FHrlTZNkeOHCkREREGy/r06SPdu3dX1r333nvi5+cngYGBIiJy5coVGTJkiLi7u4unp6cMGDDA7PvbVD7gMSYRmZN19mv5fZFGfl/kYPBv1tllVbZNczkxIyNDXnjhBfHw8BBHR0fp27evxMfHK3GXL1+Wp59+Wjw8PMTJyUnatGkjmzZtUnJEycfIkSONbr/0MZm+pKL/jr5+/XoJDg4WGxsbuXTpUrnHamlpafLcc89Jw4YNxdHRUdq2bSvffvutwes1l5+3bt0qHTt2FAcHB+nVq5fcuHFDNm/eLK1btxZXV1eJjIyU3Nxcpb3y8re+3R07dkiXLl3E0dFRQkJCJDY2Vnmdln7/tbT2wqKU4crih053d1lBQfEyrdby2Px8y2LvkamiVPv27eWXX36RixcvSnp6urz33nvSunVr2bp1qyQkJEhUVJRoNBrZvXu3iIhotVp5++235ejRo/L777/LihUrxMnJSVavXi0iItnZ2TJ06FDp27evJCcnS3JysuTn5ytvWn3b58+fl+7du0uXLl0kLCxM9u/fLydOnJAWLVrI+PHjlX6uWLFC/Pz8ZM2aNfL777/LmjVrpF69evL111+LiBi0u3HjRomLi5Nnn31WAgICpKCgQPLz82XRokXi5uam9Cc7O9voPnrooYfk+eefl5iYGImPj5fvv/9eoqOjzbaxdOlS2bx5syQkJMihQ4ckJCRE+vXrZ3Y/JyUlyWuvvSYPPfSQ0l5eXt49/98SWav8/HyTj9IFGnOx2lK51ljM/bh586YEBQVJ//79RVciJ9+8eVO8vb1lxowZEhMTIydOnJA+ffpIr169lJiePXuKi4uLvP766xIbGyuxsbGSlJQkTk5OMnHiRImJiZGffvpJ6tevb/bLzMWLF8XZ2VkWLlwo8fHxcuDAAenUqZOMGjVKRETS09Nl7NixEhISIsnJyZKenm50mYjImDFjJDQ0VPbu3SsXL16U+fPni0ajUQ7ATp48KRqNRiZMmCDR0dFy9uxZWbx4saSmpkpmZqaEhITI2LFjlfxVWFh4X/uXqC6LT8kWm+kbRPVa2YfN9A1yITWnSrZr7AvYgAEDpHPnzjJy5EhxcXGRF154Qc6ePStnz54VrVYrwcHB8ve//11Onz4t58+fl2HDhkmrVq1M5lBT+YDHmERkijYjvrgQtdCu7GORRrQ3L1TJds3lxAEDBkhwcLDs3btXoqOjJTw8XFq0aKEcXz711FPSp08fOX36tCQkJMjPP/8se/bskcLCQlmzZo0AkLi4OElOTpbMzEyj209PT5dGjRrJnDlzlHwhUvwd3c7OTkJDQ+XAgQMSGxsrubm55R6rJSUlyfz58+XkyZOSkJAg//73v8XGxkYOHz4sIuXn5+7duxvkxp49e8oTTzwhJ06ckL1794qXl5fMmzdP6X95+Vvfbrdu3WT37t1y7tw5eeyxxyQ0NFRERPLy8iz+/suilAlmd8zTTxc/Sg7A1auLl/3734axgwcXLy8xk0bWrSteNn++YeywYcXLExPvLtu69Z5fg6mi1Lp165Rld+7cEScnJ+UvQHqjR4+WyMhIk21PmjRJBg8erPxu7E2v/2BfsmSJsuy7774TALJz505l2dy5c6VVq1bK782bNzeo+oqIvPvuuxISEmKy3XPnzgkAiYmJMfraTXF1dVUOREqztI2jR48KAOWgxNh+Fin+a1+HDh3KbY+oLps1a5bJx4oVKwxi33vvPZOxpf/S8uGHH5aJuVdFRUXSr18/CQ4OlqysLIN17777rjzxxBMGy65evaocnIgUF6U6depkEDNz5kxp1aqVQYHrP//5j7i4uEhRUZHRfowePVpeeuklg2X79u0TtVqtfDZNnTpVmQ2lV3pZYmKi2NjYyLVr1wziHn/8cZkxY4aIiERGRkqPHj2M9kP/mqZOnWpyPRHd9eam82L7+s9Gi1K2r/8sb246XyXbLXksptPpZPv27aLRaGT69OkycuRIadCggUGxafny5WXyUn5+vjg6Osq2bdtMbsdYPuAxJhGZkr5vZvHMKKNFKQdJ3zezSrZrKicOHDhQAMiBAweU2LS0NHF0dJTvv/9eRETatWsns2fPNtquPt/dvHmz3D4EBATIwoULDZbpZxBFR0cryyw5VjPmqaeektdee0353Vx+3rFjh7Js7ty5AkASEhKUZePGjZPw8HARsSx/G2t306ZNAkA5TrX0+6+lRSmeEF6HdO3aVfn54sWLyMvLQ58+fQxitFotOnXqpPz+n//8B//73/9w5coV3L59G1qt1uKLlrVv3175uUGDBgCAdu3aGSxLSUkBAOTm5iIhIQGjR4/G2LFjlZjCwsIyF+st2a6fnx8AICUlBa1bt7aoXwAwbdo0jBkzBsuXL0fv3r0xZMgQNG/e3Oxzjh8/jtmzZ+PUqVO4efMmdDodAODKlSto06aNEldyPxOR9Zg5cyYOHTqEI0eOwNXV1WDdqVOnsGvXLoPr8+klJCSgZcuWAIAuXboYrIuJiUFISAhUKpWyrEePHsjJyUFSUhKaNGlSpr1Tp07h9OnTWLlypbJMRKDT6XDp0iUEBwdb9HrOnDmDoqIipW96+fn58PLyAgBER0djyJAhFrVHROYlZuRBRIyuExEkZuRV2bY3btwIFxcXFBQUQKfTYdiwYZg9ezYmTZqEdu3aGVxH6tSpU7h48WKZPHfnzh0kJCRg37596Nevn7L8iy++wPDhw81un8eYRFRaYVYiis/eMkb+XF81jOXEZ555Bhs3bkS3bt2UOC8vL7Rq1QoxMTEAgClTpmDChAn45Zdf0Lt3bwwePNggL5S2cuVKjBs3Tvl9y5YteOyxx0zG29vbG7RnybFaUVERPvjgA3z//fe4du0atFot8vPz4eTkZNG+KJ0vnZyc0KxZM4NlR44cAWB5/i7dbsl8aezY9n6xKFXSDz8U/6vR3F32zDPAgAGAjY1h7IoVZWOfegoIDwdKX8xs6dKysY8/Xjl9LsHZ2Vn5OScnBwCwadMm+Pv7G8Rp/uzHqlWrMH36dHz88ccICQmBq6sr5s+fj8OHD1u0PTs7O+Vn/Rey0sv0hR19f7766iuDRAEANqX2rbF29e1Yavbs2Rg2bBg2bdqELVu2YNasWVi1ahUGDRpkND43Nxfh4eEIDw/HypUr4e3tjStXriA8PBxardYgtuR+JqJiM2fONLmu9AUeX3/9dZOxJYs7ACrtTiOrVq3CggULsGnTJgQFlb34Zk5ODvr3748PP/ywzDr9BzFQOe//nJwcjBs3DlOmTCmzriIf9Dk5ObCxscHx48fL5FF9cc3R0fH+OktEioB6TsU5ykhhSqVSIaCeZV8g7kWvXr3w+eefw97eHg0bNjS40HjpvJSTk4MuXboYFL71vL29YW9vj+joaGWZvuhjDo8xiag0W7cAACoTa1V/rq8axnLihg0byn3emDFjEB4ejk2bNuGXX37B3Llz8fHHH+Pll182Gj9gwACDvFI655Xm6OhocCxrybHa/Pnz8emnn2LRokVo164dnJ2d8corr5T5DmpK6bxW8nf9stL50lz+NtUuUHX5kkWpkhwcyi6ztS1+VEVsFWrTpg00Gg2uXLmCnj17Go05cOAAQkNDDe4+lZCQYBBjb29fKXeYa9CgARo2bIjff/+93L/GmVOR/rRs2RItW7bEq6++isjISERFRWHQoEFG24iNjUV6ejrmzZuHxo0bAwCOHTtW6X0iqqsqcrenqoo1JTo6GqNHj8a8efMQHh5uNKZz585Ys2YNAgMDK3RXqeDgYKxZswYionxgHzhwAK6urmjUqJHJbZ0/fx4tWrSo+IspoVOnTigqKkJKSorJv9q1b98eO3fuxDvvvGN0PfMXkeX+/nBjzN910eg6EcHoRyr/r8d6zs7OFueMzp07Y/Xq1fDx8YGbm5vRGGNtWZoPeIxJRADg+tAo3Dr+sYm1Ate2L1bZto3lxODgYBQWFuLw4cMIDQ0FAKSnpyMuLs7grJfGjRtj/PjxGD9+PGbMmIGvvvoKL7/8snLMWTIPuLq6lpl1ClieLyw5Vjtw4AAiIiLw/PPPAygu/MTHxxv0ubLykyX52xKVnS95f8I6ytXVFdOnT8err76KZcuWISEhASdOnMDixYuxbNkyAEBQUBCOHTuGbdu2IT4+Hv/6179w9OhRg3YCAwNx+vRpxMXFIS0tDQUFBffcp3feeQdz587Fv//9b8THx+PMmTOIiorCJ598YnEbgYGByMnJwc6dO5GWloa8vLJT5W/fvo3Jkydj9+7dSExMxIEDB3D06FHllBhjbTRp0gT29vZYvHgxfv/9d2zYsAHvvvuuxX26dOkSoqOjkZaWhvz8fItfDxFVrbS0NAwcOBBhYWF4/vnncf36dYNHamoqAGDSpEnIyMhAZGQkjh49ioSEBGzbtg0vvvii2Q/diRMn4urVq3j55ZcRGxuL9evXY9asWZg2bZrJWwC/8cYbOHjwICZPnozo6GhcuHAB69evx+TJkyv02lq2bInhw4djxIgRWLt2LS5duoQjR45g7ty52LRpEwBgxowZOHr0KCZOnIjTp08jNjYWn3/+OdLS0gAU56/Dhw/j8uXLSEtL44wBIjOCvF2wZGhHqFWAjVpl8O+SoR3Ron7tmEk9fPhw1K9fHxEREdi3bx8uXbqE3bt3Y8qUKUhKSjL5PEvzwYN8jElEd9l5BqF+7y8BlRpQ2Rj8W7/3l7DzuL8/vlVUUFAQIiIiMHbsWOzfvx+nTp3C888/D39/f0RERAAonoG/bds2XLp0CSdOnMCuXbuU74gBAQFQqVTYuHEjUlNTlVlFxgQGBmLv3r24du2ackxljCXHakFBQdi+fTsOHjyImJgYjBs3Djdu3Cizvco4XrMkf1ui0r//lnt1qjrG0ott1WamLnRe+qJsOp1OFi1aJK1atRI7Ozvx9vaW8PBw2bNnj4gUX+hs1KhR4u7uLh4eHjJhwgR58803DS5alpKSIn369BEXF5cyt+s9efKk2T4Yu2DkypUrpWPHjmJvby+enp7yl7/8RdauXSsiYrTdmzdvKtvVGz9+vHh5eZm8XW9+fr4899xz0rhxY7G3t5eGDRvK5MmTDf7PjbXx7bffSmBgoGg0GgkJCZENGzYY9MfUfr5z544MHjxYPDw8zN4Sk4iq39dff13mtrUlHwEBAUpsfHy8DBo0SLmNcOvWreWVV15RLhZs6qLgu3fvlocffljs7e3F19dX3njjjTJ3HCztyJEjSm51dnaW9u3by/vvv6+st+RC5yJ373AVGBgodnZ24ufnJ4MGDZLTp08b9C80NFQ0Go14eHhIeHi4ksfi4uKke/fu4ujoqNximIjMu5CaI29uOi+Ry4/Jm5vOV9ld9/SMXRC8vHXJyckyYsQIqV+/vmg0GmnWrJmMHTtWbt26ZXI7xvIBjzGJqDzamxckfd9MubFpuKTvm1lld93TM5cTMzIy5IUXXhB3d3dxdHSU8PBw5S53IiKTJ0+W5s2bi0ajEW9vb3nhhRckLS1NWT9nzhzx9fUVlUolI0eONNmHQ4cOSfv27UWj0Yi+pGLqZgnlHaulp6dLRESEuLi4iI+Pj7z11lsyYsQIg9doaX421ofSFyUvL38ba/fkyZMGx4mWfv+1tPaiEjFxxcY66s6dO7h06RKaNm0KB2On1RERERERERER0T2ztPbC0/eIiIiIiIiIiKjasShFRERERERERETVjkUpIiIiIiIiIiKqdixKERERERERERFRtXtgi1IP2PXdiYiIiIiIiIiqhaU1lweuKGVnZwcAyMvLq+GeEBERERERERHVPfqai74GY4ptdXSmNrGxsYGHhwdSUlIAAE5OTlCpVDXcKyIiIiIiIiIi6yYiyMvLQ0pKCjw8PGBjY2M2XiUP4HlsIoLr168jMzOzprtCRERERERERFSneHh4wNfXt9xJQA9kUUqvqKgIBQUFNd0NIiIiIiIiIqI6wc7OrtwZUnoPdFGKiIiIiIiIiIhqxgN3oXMiIiIiIiIiIqp5LEoREREREREREVG1Y1GKiIiIiIiIiIiqHYtSRERERERERERU7ViUIiIiIiIiIiKiaseiFBERERERERERVTsWpYiIiIiIiIiIqNr9Px+jBJnIDA41AAAAAElFTkSuQmCC", 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", "text/plain": [ "
" ] @@ -617,48 +608,48 @@ "\n", "------------------ Bounds with CI ------------------\n", " CI lower theta lower theta theta upper \\\n", - "ATT(2025-05,2025-01,2025-02) -0.089584 0.030287 0.119351 0.208415 \n", - "ATT(2025-05,2025-01,2025-03) -0.187237 -0.056760 0.036582 0.129924 \n", - "ATT(2025-05,2025-02,2025-03) -0.290854 -0.167463 -0.077922 0.011618 \n", - "ATT(2025-05,2025-01,2025-04) -0.175551 -0.046868 0.046674 0.140216 \n", - "ATT(2025-05,2025-02,2025-04) -0.305745 -0.178785 -0.083015 0.012755 \n", + "ATT(2025-05,2025-01,2025-03) -0.282814 -0.189270 -0.079444 0.030382 \n", + "ATT(2025-05,2025-01,2025-04) 0.728060 0.821977 0.932054 1.042130 \n", + "ATT(2025-05,2025-02,2025-04) 0.737026 0.830494 0.941147 1.051800 \n", + "ATT(2025-05,2025-01,2025-05) 1.653944 1.751299 1.853857 1.956414 \n", + "ATT(2025-05,2025-02,2025-05) 1.695394 1.793787 1.898532 2.003276 \n", "... ... ... ... ... \n", - "ATT(2025-08,2025-03,2025-08) 0.713646 0.819071 0.905554 0.992036 \n", - "ATT(2025-08,2025-04,2025-08) 0.752304 0.857735 0.944158 1.030581 \n", - "ATT(2025-08,2025-05,2025-08) 0.763137 0.868861 0.955429 1.041997 \n", - "ATT(2025-08,2025-06,2025-08) 0.746202 0.854195 0.943040 1.031886 \n", - "ATT(2025-08,2025-07,2025-08) 0.844033 0.949600 1.036910 1.124219 \n", + "ATT(2025-08,2025-02,2025-08) 1.723780 1.842463 1.933147 2.023832 \n", + "ATT(2025-08,2025-03,2025-08) 1.644001 1.760944 1.851633 1.942321 \n", + "ATT(2025-08,2025-04,2025-08) 1.776974 1.893331 1.983352 2.073373 \n", + "ATT(2025-08,2025-05,2025-08) 1.821187 1.937298 2.025502 2.113706 \n", + "ATT(2025-08,2025-06,2025-08) 1.661052 1.781514 1.872791 1.964069 \n", "\n", " CI upper \n", - "ATT(2025-05,2025-01,2025-02) 0.333164 \n", - "ATT(2025-05,2025-01,2025-03) 0.264359 \n", - "ATT(2025-05,2025-02,2025-03) 0.137035 \n", - "ATT(2025-05,2025-01,2025-04) 0.269579 \n", - "ATT(2025-05,2025-02,2025-04) 0.138324 \n", + "ATT(2025-05,2025-01,2025-03) 0.123905 \n", + "ATT(2025-05,2025-01,2025-04) 1.136078 \n", + "ATT(2025-05,2025-02,2025-04) 1.145245 \n", + "ATT(2025-05,2025-01,2025-05) 2.053635 \n", + "ATT(2025-05,2025-02,2025-05) 2.101914 \n", "... ... \n", - "ATT(2025-08,2025-03,2025-08) 1.097551 \n", - "ATT(2025-08,2025-04,2025-08) 1.136124 \n", - "ATT(2025-08,2025-05,2025-08) 1.147826 \n", - "ATT(2025-08,2025-06,2025-08) 1.139749 \n", - "ATT(2025-08,2025-07,2025-08) 1.230090 \n", + "ATT(2025-08,2025-02,2025-08) 2.142442 \n", + "ATT(2025-08,2025-03,2025-08) 2.059731 \n", + "ATT(2025-08,2025-04,2025-08) 2.189680 \n", + "ATT(2025-08,2025-05,2025-08) 2.230310 \n", + "ATT(2025-08,2025-06,2025-08) 2.084632 \n", "\n", - "[102 rows x 5 columns]\n", + "[74 rows x 5 columns]\n", "\n", "------------------ Robustness Values ------------------\n", " H_0 RV (%) RVa (%)\n", - "ATT(2025-05,2025-01,2025-02) 0.0 3.999442 0.000597\n", - "ATT(2025-05,2025-01,2025-03) 0.0 1.186813 0.000356\n", - "ATT(2025-05,2025-02,2025-03) 0.0 2.615974 0.000581\n", - "ATT(2025-05,2025-01,2025-04) 0.0 1.508472 0.000537\n", - "ATT(2025-05,2025-02,2025-04) 0.0 2.605807 0.000455\n", + "ATT(2025-05,2025-01,2025-03) 0.0 2.179344 0.000659\n", + "ATT(2025-05,2025-01,2025-04) 0.0 22.679195 20.548048\n", + "ATT(2025-05,2025-02,2025-04) 0.0 22.768020 20.653810\n", + "ATT(2025-05,2025-01,2025-05) 0.0 41.950925 39.894601\n", + "ATT(2025-05,2025-02,2025-05) 0.0 42.034609 40.038228\n", "... ... ... ...\n", - "ATT(2025-08,2025-03,2025-08) 0.0 27.211550 24.335827\n", - "ATT(2025-08,2025-04,2025-08) 0.0 28.198087 25.355072\n", - "ATT(2025-08,2025-05,2025-08) 0.0 28.439172 25.602283\n", - "ATT(2025-08,2025-06,2025-08) 0.0 27.524975 24.644550\n", - "ATT(2025-08,2025-07,2025-08) 0.0 30.219202 27.467267\n", + "ATT(2025-08,2025-02,2025-08) 0.0 47.188378 44.541290\n", + "ATT(2025-08,2025-03,2025-08) 0.0 45.790412 43.174465\n", + "ATT(2025-08,2025-04,2025-08) 0.0 48.268995 45.660364\n", + "ATT(2025-08,2025-05,2025-08) 0.0 49.639416 47.105105\n", + "ATT(2025-08,2025-06,2025-08) 0.0 45.947902 43.287908\n", "\n", - "[102 rows x 3 columns]\n" + "[74 rows x 3 columns]\n" ] } ], @@ -681,17 +672,17 @@ "\n", "------------------ Overall Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.743996 0.027255 63.987523 0.0 1.690577 1.797416\n", + "2.648287 0.032251 82.115458 0.0 2.585077 2.711497\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "2025-05 2.504777 0.038855 64.465171 0.0 2.428623 2.580931\n", - "2025-06 1.996162 0.034710 57.509321 0.0 1.928131 2.064193\n", - "2025-07 1.473812 0.038550 38.230791 0.0 1.398255 1.549370\n", - "2025-08 0.952538 0.048625 19.589422 0.0 0.857235 1.047842\n", + "2025-05 3.374550 0.039381 85.689722 0.0 3.297364 3.451735\n", + "2025-06 2.888170 0.039673 72.799586 0.0 2.810412 2.965927\n", + "2025-07 2.433550 0.044432 54.769702 0.0 2.346464 2.520636\n", + "2025-08 1.932632 0.054882 35.214097 0.0 1.825064 2.040199\n", "------------------ Additional Information ------------------\n", - "Control Group: never_treated\n", - "Anticipation Periods: 0\n", - "Score: observational\n", + "Score function: observational\n", + "Control group: not_yet_treated\n", + "Anticipation periods: 1\n", "\n" ] }, @@ -716,7 +707,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -745,23 +736,22 @@ "\n", "------------------ Overall Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "2.474609 0.031322 79.004292 0.0 2.413218 2.536\n", + "3.377737 0.037099 91.046038 0.0 3.305024 3.45045\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "-6 months 0.050784 0.063342 0.801737 0.422705 -0.073365 0.174932\n", - "-5 months 0.041111 0.043754 0.939584 0.347431 -0.044646 0.126868\n", - "-4 months 0.002663 0.035115 0.075850 0.939538 -0.066160 0.071487\n", - "-3 months 0.005673 0.030395 0.186639 0.851944 -0.053900 0.065246\n", - "-2 months -0.002458 0.027898 -0.088089 0.929806 -0.057137 0.052222\n", - "-1 months -0.032274 0.026593 -1.213649 0.224882 -0.084395 0.019847\n", - "0 months 0.996740 0.026678 37.361703 0.000000 0.944452 1.049028\n", - "1 months 2.006814 0.031848 63.011564 0.000000 1.944393 2.069236\n", - "2 months 2.959074 0.040557 72.959962 0.000000 2.879582 3.038565\n", - "3 months 3.935807 0.057718 68.190550 0.000000 3.822683 4.048932\n", + "-5 months 0.067237 0.053476 1.257338 0.208631 -0.037573 0.172048\n", + "-4 months -0.033589 0.036016 -0.932617 0.351018 -0.104178 0.037000\n", + "-3 months -0.025337 0.028967 -0.874694 0.381740 -0.082111 0.031437\n", + "-2 months 0.016692 0.029598 0.563938 0.572796 -0.041320 0.074703\n", + "-1 months 0.970043 0.027939 34.719967 0.000000 0.915283 1.024803\n", + "0 months 1.931918 0.029499 65.491446 0.000000 1.874102 1.989735\n", + "1 months 2.917106 0.036888 79.081093 0.000000 2.844808 2.989405\n", + "2 months 3.829247 0.048285 79.305763 0.000000 3.734611 3.923883\n", + "3 months 4.832676 0.066051 73.165730 0.000000 4.703218 4.962134\n", "------------------ Additional Information ------------------\n", - "Control Group: never_treated\n", - "Anticipation Periods: 0\n", - "Score: observational\n", + "Score function: observational\n", + "Control group: not_yet_treated\n", + "Anticipation periods: 1\n", "\n" ] }, @@ -786,7 +776,7 @@ }, { "data": { - "image/png": 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", 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" ] From 15d6fdeabefdffa6c69d82a51456fbc5fbc18624 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 23 Apr 2025 11:26:32 +0000 Subject: [PATCH 089/140] restructure model api --- doc/api/api.rst | 1 - doc/api/dml_models.rst | 59 ++++++++++++++++++++++++++++++++++++---- doc/api/other_models.rst | 11 -------- 3 files changed, 54 insertions(+), 17 deletions(-) delete mode 100644 doc/api/other_models.rst diff --git a/doc/api/api.rst b/doc/api/api.rst index 3a428cca..474c938e 100644 --- a/doc/api/api.rst +++ b/doc/api/api.rst @@ -11,7 +11,6 @@ API Reference DoubleML Data Class DoubleML Models - Other Models Datasets Utility Classes and Functions Score Mixin Classes for DoubleML Models diff --git a/doc/api/dml_models.rst b/doc/api/dml_models.rst index 84f5b8cb..11481640 100644 --- a/doc/api/dml_models.rst +++ b/doc/api/dml_models.rst @@ -3,7 +3,13 @@ DoubleML Models ------------------------------ -.. currentmodule:: doubleml + +.. _api_plm_models: + +doubleml.plm +~~~~~~~~~~~~~~~ + +.. currentmodule:: doubleml.plm .. autosummary:: :toctree: generated/ @@ -11,14 +17,57 @@ DoubleML Models DoubleMLPLR DoubleMLPLIV + + +.. _api_irm_models: + +doubleml.irm +~~~~~~~~~~~~~~~ + +.. currentmodule:: doubleml.irm + +.. autosummary:: + :toctree: generated/ + :template: class.rst + DoubleMLIRM DoubleMLAPO DoubleMLAPOS DoubleMLIIVM - DoubleMLDID - DoubleMLDIDCS - DoubleMLSSM DoubleMLPQ DoubleMLLPQ DoubleMLCVAR - DoubleMLQTE \ No newline at end of file + DoubleMLQTE + DoubleMLSSM + + +.. _api_did_models: + +doubleml.did +~~~~~~~~~~~~~~~ + +.. currentmodule:: doubleml.did + +.. autosummary:: + :toctree: generated/ + :template: class.rst + + DoubleMLDIDMulti + DoubleMLDIDAggregation + DoubleMLDIDBinary + DoubleMLDID + DoubleMLDIDCS + + +.. _api_rdd_models: + +doubleml.rdd +~~~~~~~~~~~~~ + +.. currentmodule:: doubleml.rdd + +.. autosummary:: + :toctree: generated/ + :template: class.rst + + RDFlex \ No newline at end of file diff --git a/doc/api/other_models.rst b/doc/api/other_models.rst deleted file mode 100644 index 2595dd30..00000000 --- a/doc/api/other_models.rst +++ /dev/null @@ -1,11 +0,0 @@ -.. _api_other_models: - -Other models ------------------------------- -.. currentmodule:: doubleml - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - rdd.RDFlex From 5619b96e785a77f5bb453d99afe63529268e0f23 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 23 Apr 2025 12:01:53 +0000 Subject: [PATCH 090/140] update data pi --- doc/api/data_class.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/api/data_class.rst b/doc/api/data_class.rst index 312bc2f4..366bdf7f 100644 --- a/doc/api/data_class.rst +++ b/doc/api/data_class.rst @@ -3,7 +3,7 @@ DoubleML Data Class ---------------------------------- -.. currentmodule:: doubleml +.. currentmodule:: doubleml.data .. autosummary:: :toctree: generated/ @@ -11,3 +11,4 @@ DoubleML Data Class DoubleMLData DoubleMLClusterData + DoubleMLPanelData From dc3d1ec202b637efe7988d8b78b50b2cdc7dd198 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 23 Apr 2025 12:28:03 +0000 Subject: [PATCH 091/140] fix index did model --- doc/guide/models/did/did_pa.rst | 36 +++++++++++++++--------------- doc/guide/models/did/did_setup.rst | 10 ++++----- 2 files changed, 23 insertions(+), 23 deletions(-) diff --git a/doc/guide/models/did/did_pa.rst b/doc/guide/models/did/did_pa.rst index 61cfc6aa..4365875b 100644 --- a/doc/guide/models/did/did_pa.rst +++ b/doc/guide/models/did/did_pa.rst @@ -2,45 +2,45 @@ For the estimation of the target parameters :math:`ATT(\mathrm{g},t)` the follow .. math:: \begin{align} - g_{0, \mathrm{g}, t_{pre}, t_{eval}, \delta}(X_i) &:= \mathbb{E}[Y_{i,t_{eval}} - Y_{i,t_{pre}}|X_i, C_{i,t_{eval} + \delta}^{(\cdot)} = 1], \\ - m_{0, \mathrm{g}, t_{eval} + \delta}(X_i) &:= P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t_{eval} + \delta}^{(\cdot)}=1). + g_{0, \mathrm{g}, t_\text{pre}, t_\text{eval}, \delta}(X_i) &:= \mathbb{E}[Y_{i,t_\text{eval}} - Y_{i,t_\text{pre}}|X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)} = 1], \\ + m_{0, \mathrm{g}, t_\text{eval} + \delta}(X_i) &:= P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t_\text{eval} + \delta}^{(\cdot)}=1). \end{align} -where :math:`g_{0, \mathrm{g}, t_{pre}, t_{eval},\delta}(\cdot)` denotes the population outcome regression function and :math:`m_{0, \mathrm{g}, t_{eval} + \delta}(\cdot)` the generalized propensity score. +where :math:`g_{0, \mathrm{g}, t_\text{pre}, t_\text{eval},\delta}(\cdot)` denotes the population outcome regression function and :math:`m_{0, \mathrm{g}, t_\text{eval} + \delta}(\cdot)` the generalized propensity score. The interpretation of the parameters is as follows: * :math:`\mathrm{g}` is the first post-treatment period of interest, i.e. the treatment group. -* :math:`t_{pre}` is the pre-treatment period, i.e. the time period from which the conditional parallel trends are assumed. -* :math:`t_{eval}` is the time period of interest or evaluation period, i.e. the time period where the treatment effect is evaluated. +* :math:`t_\text{pre}` is the pre-treatment period, i.e. the time period from which the conditional parallel trends are assumed. +* :math:`t_\text{eval}` is the time period of interest or evaluation period, i.e. the time period where the treatment effect is evaluated. * :math:`\delta` is number of anticipation periods, i.e. the number of time periods for which units are assumed to anticipate the treatment. .. note:: Remark that the nuisance functions depend on the control group used for the estimation of the target parameter. - By slight abuse of notation we use the same notation for both control groups :math:`C_{i,t}^{(nev)}` and :math:`C_{i,t}^{(nyt)}`. More specifically, the + By slight abuse of notation we use the same notation for both control groups :math:`C_{i,t}^{(\text{nev})}` and :math:`C_{i,t}^{(\text{nyt})}`. More specifically, the control group only depends on :math:`\delta` for *not yet treated* units. -Under these assumptions the target parameter :math:`ATT(\mathrm{g},t_{eval})` can be estimated by choosing a suitable combination -of :math:`(\mathrm{g}, t_{pre}, t_{eval}, \delta)` if :math:`t_{eval} - t_{pre} \ge 1 + \delta`, i.e. the parallel trends are assumed to hold at least one period more than the anticipation period. +Under these assumptions the target parameter :math:`ATT(\mathrm{g},t_\text{eval})` can be estimated by choosing a suitable combination +of :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval}, \delta)` if :math:`t_\text{eval} - t_\text{pre} \ge 1 + \delta`, i.e. the parallel trends are assumed to hold at least one period more than the anticipation period. .. note:: - The choice :math:`t_{pre}= \min(\mathrm{g},t_\text{eval}) -\delta-1` corresponds to the definition of :math:`ATT_{dr}(\mathrm{g},t_\text{eval};\delta)` from `Callaway and Sant'Anna (2021) `_. + The choice :math:`t_\text{pre}= \min(\mathrm{g},t_\text{eval}) -\delta-1` corresponds to the definition of :math:`ATT_{dr}(\mathrm{g},t_\text{eval};\delta)` from `Callaway and Sant'Anna (2021) `_. In the following, we will omit the subscript :math:`\delta` in the notation of the nuisance functions and the control group (implicitly assuming :math:`\delta=0`). -For a given tuple :math:`(\mathrm{g}, t_{pre}, t_{eval})` the target parameter :math:`ATT(\mathrm{g},t)` is estimated by solving the empirical version of the the following linear moment condition: +For a given tuple :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval})` the target parameter :math:`ATT(\mathrm{g},t)` is estimated by solving the empirical version of the the following linear moment condition: .. math:: - ATT(\mathrm{g}, t_{pre}, t_{eval}):= -\frac{\mathbb{E}[\psi_b(W,\eta_0)]}{\mathbb{E}[\psi_a(W,\eta_0)]} + ATT(\mathrm{g}, t_\text{pre}, t_\text{eval}):= -\frac{\mathbb{E}[\psi_b(W,\eta_0)]}{\mathbb{E}[\psi_a(W,\eta_0)]} -with nuisance elements :math:`\eta_0=(g_{0, \mathrm{g}, t_{pre}, t_{eval}}, m_{0, \mathrm{g}, t_{eval}})` and score function :math:`\psi(W,\theta, \eta)` being defined in section :ref:`did-pa-score`. +with nuisance elements :math:`\eta_0=(g_{0, \mathrm{g}, t_\text{pre}, t_\text{eval}}, m_{0, \mathrm{g}, t_\text{eval}})` and score function :math:`\psi(W,\theta, \eta)` being defined in section :ref:`did-pa-score`. Under the identifying assumptions above .. math:: - ATT(\mathrm{g}, t_{pre}, t_{eval}) = ATT(\mathrm{g},t). + ATT(\mathrm{g}, t_\text{pre}, t_\text{eval}) = ATT(\mathrm{g},t). -``DoubleMLDIDMulti`` implements the estimation of :math:`ATT(\mathrm{g}, t_{pre}, t_{eval})` for multiple time periods and requires :ref:`DoubleMLPanelData ` as input. -Setting ``gt_combinations='standard'`` will estimate the target parameter for all (possible) combinations of :math:`(\mathrm{g}, t_{pre}, t_{eval})` with :math:`\mathrm{g}\in\{2,\dots,\mathcal{T}\}` and :math:`(t_{pre}, t_{eval})` with :math:`t_{eval}\in\{2,\dots,\mathcal{T}\}` and -:math:`t_{pre}= \min(\mathrm{g},t_\text{eval}) -\delta-1`. +``DoubleMLDIDMulti`` implements the estimation of :math:`ATT(\mathrm{g}, t_\text{pre}, t_\text{eval})` for multiple time periods and requires :ref:`DoubleMLPanelData ` as input. +Setting ``gt_combinations='standard'`` will estimate the target parameter for all (possible) combinations of :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval})` with :math:`\mathrm{g}\in\{2,\dots,\mathcal{T}\}` and :math:`(t_\text{pre}, t_\text{eval})` with :math:`t_\text{eval}\in\{2,\dots,\mathcal{T}\}` and +:math:`t_\text{pre}= \min(\mathrm{g},t_\text{eval}) -\delta-1`. This corresponds to the setting where all trends are set as short as possible, but still respecting the anticipation period. Estimation is conducted via its ``fit()`` method: @@ -84,8 +84,8 @@ Estimation is conducted via its ``fit()`` method: .. math:: \begin{align} - g(0,X) &\approx g_{0, \mathrm{g}, t_{pre}, t_{eval}, \delta}(X_i) = \mathbb{E}[Y_{i,t_{eval}} - Y_{i,t_{pre}}|X_i, C_{i,t_{eval} + \delta}^{(\cdot)} = 1],\\ - g(1,X) &\approx \mathbb{E}[Y_{i,t_{eval}} - Y_{i,t_{pre}}|X_i, G_i^{\mathrm{g}} = 1]. + g(0,X) &\approx g_{0, \mathrm{g}, t_\text{pre}, t_\text{eval}, \delta}(X_i) = \mathbb{E}[Y_{i,t_\text{eval}} - Y_{i,t_\text{pre}}|X_i, C_{i,t_\text{eval} + \delta}^{(\cdot)} = 1],\\ + g(1,X) &\approx \mathbb{E}[Y_{i,t_\text{eval}} - Y_{i,t_\text{pre}}|X_i, G_i^{\mathrm{g}} = 1]. \end{align} Further, :math:`g(1,X)` is only required for :ref:`Sensitivity Analysis ` and is not used for the estimation of the target parameter. diff --git a/doc/guide/models/did/did_setup.rst b/doc/guide/models/did/did_setup.rst index 9fd67b05..73fbb732 100644 --- a/doc/guide/models/did/did_setup.rst +++ b/doc/guide/models/did/did_setup.rst @@ -30,8 +30,8 @@ Let .. math:: \begin{align} - C_{i,t}^{(nev)} \equiv C_{i}^{(nev)} &:= 1\{G_i=\infty\} \quad \text{(never treated)}, \\ - C_{i,t}^{(nyt)} &:= 1\{G_i > t\} \quad \text{(not yet treated)}. + C_{i,t}^{(\text{nev})} \equiv C_{i}^{(\text{nev})} &:= 1\{G_i=\infty\} \quad \text{(never treated)}, \\ + C_{i,t}^{(\text{nyt})} &:= 1\{G_i > t\} \quad \text{(not yet treated)}. \end{align} The corresponding identifying assumptions are: @@ -52,14 +52,14 @@ The corresponding identifying assumptions are: For each :math:`\mathrm{g}\in\mathcal{G}` and :math:`t\in\{2,\dots,\mathcal{T}\}` such that :math:`t\ge \mathrm{g}-\delta`: a. **Never Treated:** - :math:`\mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i, G_i^{\mathrm{g}}=1] = \mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i,C_{i}^{(nev)}=1] \quad a.s.` + :math:`\mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i, G_i^{\mathrm{g}}=1] = \mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i,C_{i}^{(\text{nev})}=1] \quad a.s.` b. **Not Yet Treated:** - :math:`\mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i, G_i^{\mathrm{g}}=1] = \mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i,C_{i,t+\delta}^{(nyt)}=1] \quad a.s.` + :math:`\mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i, G_i^{\mathrm{g}}=1] = \mathbb{E}[Y_{i,t}(0) - Y_{i,t-1}(0)|X_i,C_{i,t+\delta}^{(\text{nyt})}=1] \quad a.s.` 5. **Overlap:** For each time period :math:`t=2,\dots,\mathcal{T}` and :math:`\mathrm{g}\in\mathcal{G}` there exists a :math:`\epsilon > 0` such that - :math:`P(G_i^{\mathrm{g}}=1) > \epsilon` and :math:`P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t}^{(nyt)}=1) < 1-\epsilon\quad a.s.` + :math:`P(G_i^{\mathrm{g}}=1) > \epsilon` and :math:`P(G_i^{\mathrm{g}}=1|X_i, G_i^{\mathrm{g}} + C_{i,t}^{(\text{nyt})}=1) < 1-\epsilon\quad a.s.` .. note:: For a detailed discussion of the assumptions see `Callaway and Sant'Anna (2021) `_. From 444d243337ff8cdf0401babee07be5f4efa1dd45 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 23 Apr 2025 12:45:07 +0000 Subject: [PATCH 092/140] update datasets api --- doc/api/datasets.rst | 18 +++--------------- 1 file changed, 3 insertions(+), 15 deletions(-) diff --git a/doc/api/datasets.rst b/doc/api/datasets.rst index 7f7afb84..38116c42 100644 --- a/doc/api/datasets.rst +++ b/doc/api/datasets.rst @@ -34,18 +34,6 @@ Dataset Generators datasets.make_confounded_irm_data datasets.make_heterogeneous_data datasets.make_irm_data_discrete_treatments - rdd.datasets.make_simple_rdd_data - - -.. _api_datasets_did: - -Difference-in-Differences (DiD) Datasets -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. currentmodule:: doubleml.did - -.. autosummary:: - :toctree: generated/ - - datasets.make_did_SZ2020 - datasets.make_did_CS2021 \ No newline at end of file + did.datasets.make_did_SZ2020 + did.datasets.make_did_CS2021 + rdd.datasets.make_simple_rdd_data \ No newline at end of file From 25ad188975fbad20afd386db36e62cfb5caf7a0c Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 23 Apr 2025 12:52:05 +0000 Subject: [PATCH 093/140] restructure example gallery (separate did section) --- .../py_did.ipynb} | 0 .../py_did_pretest.ipynb} | 0 .../py_panel.ipynb} | 0 doc/examples/did/py_panel_simple.ipynb | 739 ++++++++++++++++++ doc/examples/index.rst | 18 +- doc/examples/py_double_ml_panel_simple.ipynb | 739 ------------------ 6 files changed, 753 insertions(+), 743 deletions(-) rename doc/examples/{py_double_ml_did.ipynb => did/py_did.ipynb} (100%) rename doc/examples/{py_double_ml_did_pretest.ipynb => did/py_did_pretest.ipynb} (100%) rename doc/examples/{py_double_ml_panel.ipynb => did/py_panel.ipynb} (100%) create mode 100644 doc/examples/did/py_panel_simple.ipynb delete mode 100644 doc/examples/py_double_ml_panel_simple.ipynb diff --git a/doc/examples/py_double_ml_did.ipynb b/doc/examples/did/py_did.ipynb similarity index 100% rename from doc/examples/py_double_ml_did.ipynb rename to doc/examples/did/py_did.ipynb diff --git a/doc/examples/py_double_ml_did_pretest.ipynb b/doc/examples/did/py_did_pretest.ipynb similarity index 100% rename from doc/examples/py_double_ml_did_pretest.ipynb rename to doc/examples/did/py_did_pretest.ipynb diff --git a/doc/examples/py_double_ml_panel.ipynb b/doc/examples/did/py_panel.ipynb similarity index 100% rename from doc/examples/py_double_ml_panel.ipynb rename to doc/examples/did/py_panel.ipynb diff --git a/doc/examples/did/py_panel_simple.ipynb b/doc/examples/did/py_panel_simple.ipynb new file mode 100644 index 00000000..e1027e55 --- /dev/null +++ b/doc/examples/did/py_panel_simple.ipynb @@ -0,0 +1,739 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python: Panel Data Introduction\n", + "\n", + "In this example, we replicate the results from the guide [Getting Started with the did Package](https://bcallaway11.github.io/did/articles/did-basics.html) of the [did-R-package](https://bcallaway11.github.io/did/index.html).\n", + "\n", + "The notebook requires the following packages:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "\n", + "from doubleml.data import DoubleMLPanelData\n", + "from doubleml.did import DoubleMLDIDMulti" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "\n", + "The data we will use is simulated and part of the [CSDID-Python-Package](https://d2cml-ai.github.io/csdid/index.html).\n", + "\n", + "A description of the data generating process can be found at the [CSDID-documentation](https://d2cml-ai.github.io/csdid/examples/csdid_basic.html#Examples-with-simulated-data).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " G X id cluster period Y treat\n", + "0 3.0 -0.876233 1 5 1 5.562556 1\n", + "1 3.0 -0.876233 1 5 2 4.349213 1\n", + "2 3.0 -0.876233 1 5 3 7.134037 1\n", + "3 3.0 -0.876233 1 5 4 6.243056 1\n", + "4 2.0 -0.873848 2 36 1 -3.659387 1" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# set dtype for G to float\n", + "dta[\"G\"] = dta[\"G\"].astype(float)\n", + "dta.loc[dta[\"G\"] == 0, \"G\"] = np.inf\n", + "dta.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can initialize the ``DoubleMLPanelData`` object, specifying\n", + "\n", + " - `y_col` : the outcome\n", + " - `d_cols`: the group variable indicating the first treated period for each unit\n", + " - `id_col`: the unique identification column for each unit\n", + " - `t_col` : the time column\n", + " - `x_cols`: the additional pre-treatment controls\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLPanelData Object ==================\n", + "\n", + "------------------ Data summary ------------------\n", + "Outcome variable: Y\n", + "Treatment variable(s): ['G']\n", + "Covariates: ['X']\n", + "Instrument variable(s): None\n", + "Time variable: period\n", + "Id variable: id\n", + "No. Observations: 3979\n", + "\n", + "------------------ DataFrame info ------------------\n", + "\n", + "RangeIndex: 15916 entries, 0 to 15915\n", + "Columns: 7 entries, G to treat\n", + "dtypes: float64(3), int64(4)\n", + "memory usage: 870.5 KB\n", + "\n" + ] + } + ], + "source": [ + "dml_data = DoubleMLPanelData(dta, y_col=\"Y\", d_cols=\"G\", id_col=\"id\", t_col=\"period\", x_cols=[\"X\"])\n", + "print(dml_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ATT estimation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDMulti Object ==================\n", + "\n", + "------------------ Data summary ------------------\n", + "Outcome variable: Y\n", + "Treatment variable(s): ['G']\n", + "Covariates: ['X']\n", + "Instrument variable(s): None\n", + "Time variable: period\n", + "Id variable: id\n", + "No. Observations: 3979\n", + "\n", + "------------------ Score & algorithm ------------------\n", + "Score function: observational\n", + "Control group: never_treated\n", + "Anticipation periods: 0\n", + "\n", + "------------------ Machine learner ------------------\n", + "Learner ml_g: LinearRegression()\n", + "Learner ml_m: LogisticRegression()\n", + "Out-of-sample Performance:\n", + "Regression:\n", + "Learner ml_g0 RMSE: [[1.42396544 1.41158324 1.3978899 1.42395052 1.40376056 1.42007484\n", + " 1.4266361 1.4063818 1.42214803]]\n", + "Learner ml_g1 RMSE: [[1.40306056 1.43905986 1.39682481 1.41540689 1.42449545 1.38343701\n", + " 1.45842903 1.41567674 1.40890656]]\n", + "Classification:\n", + "Learner ml_m Log Loss: [[0.69074217 0.69095477 0.69118935 0.67934316 0.67920411 0.67956728\n", + " 0.66202776 0.6625894 0.66211293]]\n", + "\n", + "------------------ Resampling ------------------\n", + "No. folds: 5\n", + "No. repeated sample splits: 1\n", + "\n", + "------------------ Fit summary ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "ATT(2.0,1,2) 0.919867 0.063932 14.388135 0.000000 0.794562 1.045172\n", + "ATT(2.0,1,3) 1.985312 0.064527 30.767368 0.000000 1.858842 2.111782\n", + "ATT(2.0,1,4) 2.952502 0.063172 46.737645 0.000000 2.828688 3.076317\n", + "ATT(3.0,1,2) -0.044075 0.065837 -0.669460 0.503202 -0.173113 0.084963\n", + "ATT(3.0,2,3) 1.107183 0.065402 16.929006 0.000000 0.978998 1.235368\n", + "ATT(3.0,2,4) 2.059324 0.065538 31.421799 0.000000 1.930871 2.187776\n", + "ATT(4.0,1,2) 0.002899 0.068528 0.042310 0.966251 -0.131414 0.137213\n", + "ATT(4.0,2,3) 0.060997 0.066718 0.914254 0.360583 -0.069768 0.191763\n", + "ATT(4.0,3,4) 0.951764 0.067450 14.110600 0.000000 0.819564 1.083965\n" + ] + } + ], + "source": [ + "dml_obj = DoubleMLDIDMulti(\n", + " obj_dml_data=dml_data,\n", + " ml_g=LinearRegression(),\n", + " ml_m=LogisticRegression(),\n", + " control_group=\"never_treated\",\n", + ")\n", + "\n", + "dml_obj.fit()\n", + "print(dml_obj)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual for the DoubleML-package, you can obtain joint confidence intervals via bootstrap." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 2.5 % 97.5 %\n", + "ATT(2.0,1,2) 0.745889 1.093845\n", + "ATT(2.0,1,3) 1.809717 2.160907\n", + "ATT(2.0,1,4) 2.780594 3.124410\n", + "ATT(3.0,1,2) -0.223236 0.135086\n", + "ATT(3.0,2,3) 0.929207 1.285159\n", + "ATT(3.0,2,4) 1.880976 2.237671\n", + "ATT(4.0,1,2) -0.183586 0.189385\n", + "ATT(4.0,2,3) -0.120562 0.242556\n", + "ATT(4.0,3,4) 0.768213 1.135316" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "level = 0.95\n", + "\n", + "ci = dml_obj.confint(level=level)\n", + "dml_obj.bootstrap(n_rep_boot=5000)\n", + "ci_joint = dml_obj.confint(level=level, joint=True)\n", + "ci_joint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A visualization of the effects can be obtained via the `plot_effects()` method.\n", + "\n", + "Remark that the plot used joint confidence intervals per default. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n" + ] + }, + { + "data": { + "image/png": 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j/3bRz325Vz//kSIp73bRz325Vw9/vIvCFAAAFVSAj5cWDWyjOXdco/Z1q0iS2tetopfvuEaLBrZRgE/lP8UfAABULi7/9rJixQqNGzdOCxYsUMeOHTVv3jxFRUXpwIEDqlWrVoH4H3/8UYMHD9asWbN0++2366OPPlK/fv20Y8cOXXfddZKkOXPmaP78+Vq6dKkaNWqkF198UVFRUdq7d6/8/Io/30JWVpaysrIKtHt4eMjrgms7HcXks1gs8vb2LlVsdna2jHE8oWlhsR9s+13f7D2ulKz/ve/i20UPb1en0PVKko+PT7H6cHFsTk6OrEVMxlaSWG9vb1n+e7piWcXm5uYqN9fxXYxKGuvl5SUPD49yE2u1Wm3/f/PbLuTp6SnP/95i1Gq1Kien8GKlM2KNMcrOzr4isRfun2UVKxW9L7tqjLgw756enpc9nhSGMaLksWW53+fLzc0tcp+7eIxw9X7PGFG6McJLVg27vrauDfVX57e36rU7Wqh9vaqSrMrOzi6T/Z4xwnGsK8YIq9Vq17+y/B7h6v2eMcI+trDvdc7+XeNyYyXGiOLGXvi9zsfHp9z9ruHq/d4dxojCfpcrz/WICxX1WXbrMkWNAk7QsWNH3XDDDfr73/8uKe8/V/369fXEE09owoQJBeLvvfdepaen66uvvrK13XTTTWrbtq0WLFggY4zq1KmjZ555RuPHj5ckpaSkKCwsTEuWLNGgQYMKrDMzM1OZmZm216mpqapfv76ef/55+To4Za5JkyYaMmSI7fXMmTML/Q/WoEEDjRgxwvb6lVde0blz5xzG1q5dW4888ojt9RtvvKHk5GSHsaGhoXrsscdsr99++20lJSXpw+1/6PerusrqX6XANajeHhZ1bVxdd5ndOn78uMP1BgQE6Nlnn7W9XrJkiX7//XeHsd7e3po0aZLt9bJly3T48GGHsZI0ZcoU2/OPP/5Y+/btKzR24sSJtgPL6tWrtWvXrkJjx48fr8DAQEnSmjVr9PPPPxca+9RTT6lq1aqSpPXr1ysmJqbQ2NGjR9sKo9HR0dqyZUuhsQ8//LDq1s27bOKHH37Qxo0bC40dPny4GjZsKEnatm2bvvnmm0JjBw8erGbNmkmSYmNj9fnnnxcae8899+jaa6+VJO3Zs0cffPCBAgMDbQe7C/Xt21dt27aVJB08eFD/+te/Cl1vr169dOONN0qSjhw5oqVLlxYaGxkZqc6dO0uSjh07pkWLFhUa26VLF3Xt2lWSlJiYqHfeeafQ2IiICPXo0UOSlJycrDfeeKPQ2A4dOqhPnz6SpPT0dM2dO7fQ2DZt2qhfv36S8gbNWbNmFRrbsmVLDRw40PZ66tSphca6aowwxig9PV2BgYGqVauWwzHCkapVq+qpp56yvf7HP/7BGKGKM0Y0adJESUlJOnbsmL788stCYy8cI3799Vd98sknhcYyRuQpz2PEn7n+ei/jWj3k96vqeJ6XdGW+RzjCGPE/rh4jjDG6/fbb1a5dO3l4eJTZ9wjGiPI1Rvztb39TcnKyw+91zv5dwxHGiP+5kmPEhd/rRo4cWa5+12CMKMMxIitLZs4cvbBunby7dpXFwUTn5bkecaHMzEy9/PLLSklJUUhIiMP1SC4+UyorK0vbt2/XxIkTbW0eHh6KjIwsdAeOiYnRuHHj7NqioqK0evVqSVJ8fLwSEhIUGRlpW16lShV17NhRMTExDotSs2bNcnhgSE9Pd1h9TU1NVWJiou11WlpaoVXas2fPFog9f/58sWLPnj2r9PR0h7F+fn4OY32sWTJ+wQ4nRcu1GgXlpOnsucLXa7Vai90HLy8vu9jU1NRCYyWVODb/QJGSklJkbFJSkm15cWLzK7bJyclFxp48edL2vDix+ZXiM2fOFBl76tQpBQQEFDs2/+d2+vTpImNPnz5tF5uRkXdrcEdFqQtjT506VeR6z5w5U6rYkydPFhmbnJxcqthL5TglJcUWe+7cuWLHZmVlFRl78X5fklhnjRHGGFveSzKeeHp6FjuWMeJ/yssYERISopSUlBKPEcWNZYwon2PEeSPJIp0/f17plnSHsaX5HuEIY4R9rCvHCGOM7f+Ph4dHmX6PYIwoX2NEYd/rnP27hiOMEfaxV2qMuPB7XXn8XYMxoozGiIwMVf3uO+nYMaWnpclywdlIBWL/qzzVIy504Yk/RXHpmVJ//vmn6tatqx9//FERERG29ueee05btmzR1q1bC7zHx8dHS5cu1eDBg21tb7/9tqZOnaoTJ07oxx9/VOfOnfXnn3+qdu3atpiBAwfKYrFoxYoVBdZZ2JlSJ06ccFjRK8+ny93x3lZtOZKqHAdvyz9T6osR7TmlVhXn0pzSxObk5Oj48eMKDQ3l8r1yctp9Ya705XtJSUkKDQ3l8r0rEFtRxggp70tw9erVi8wFp93nqSxjxI4/knXzgp/0/aM3qF29qg5juTSn8o0RVqtVZ86cUVhYmDw8PLg0x03GiIyMDNvxncv33GeMuPB7HZfvXV5shRojcnJkNm5U4qlTqtK3rzwu+H/taL1S+apHXCg1NVVhYWHl+0yp8sLX19fhZXp+fn7FmoOqJPNUlSTWUZ8uFXtvh4bafnyvkjMK7qBBPp4a0LZuqdZbHD4OdpjyHOvh4WG3I1WmWC8vL/n6+srPz89hUeri9V44qLkiVpLtQFBRYstqv7+cMcJqtRaa97La7xkjXB9rtVptXwwutb9fuF5X7/eMEZcX6+Pja/u3sPeXh/2eMeLKxlqtVnl6esrDw8P2KKs+uHq/Z4ywjy3u97qy/l3DlbHlYb935hhR2Pe68vL9xNX7faUdI3x8ZO3ZU5bERPkFBRXru115qkdcqLhzSrn07ns1a9aUp6enTpw4Ydd+4sQJhYeHO3xPeHh4kfH5/5ZknZUJt4sGAAAAAAAVgUuLUj4+Pmrfvr02bdpka7Nardq0aZPd5XwXioiIsIuXpA0bNtjiGzVqpPDwcLuY1NRUbd26tdB1VibcLhoAAAAAgArIGOnoUXn88Ufeczfg8grFuHHjNHz4cHXo0EE33nij5s2bp/T0dD3wwAOSpGHDhqlu3bq2meqfeuopdenSRa+++qr69Omj5cuX6+eff9Y//vEPSXnXNo4dO1YzZsxQ06ZN1ahRI7344ouqU6eObeb7yi7Ax0sPd2ygtrVDdOP87/XmXa3U4apqru4WAAAAAAAoTGamLGPGKCQrS1q9WvrvpPWVmcuLUvfee6+SkpI0efJkJSQkqG3btlq7dq3CwsIkSUePHrW7jrJTp0766KOP9MILL2jSpElq2rSpVq9ereuuu84W89xzzyk9PV2PPPKIkpOTdfPNN2vt2rUlun4SAAAAAADAqUJCZP5750V34PKilCSNGTNGY8aMcbgsOjq6QNuAAQM0YMCAQtdnsVg0bdo0TZs27Up1EQAAAAAAoOz4+cl8+KFSEhNVy01OqnHpnFIAAAAAAABwTxSlAAAAAAAA4HQUpQAAAAAAAFwtK0t69VUFvPNO3nM3UC7mlAIAAAAAAHBrVqssW7bIJytLslpd3RunoCgFAAAAAADgal5eMg89pPMpKfLxco9yjXtsJQAAQAV3PDVDx1PzbhG9PzHN9q+Hh0WSVDvET7VD3ONOPQAAVEpeXlLfvspMTMx77gbcYysBAAAquIUxv2vahoN2bcOWx9qeT76tmV6Kau7kXgEAAJQeRSkAAIAKYFREA915bZgkyWo1On3mtKpXq253phQAAKjAjJESE+Vx8qQUGurq3jgFRSkAAIAK4MLL86xWqxJ9MlWrVhV5eHAzZQAAKoXMTFkeflghWVnS6tVSQICre1TmKEoBAAAAAACUB76+ru6BU1GUAgAAAAAAcDU/P5mVK5WcmKhafu5xWT7newMAAAAAAMDpOFOqkuF20QAAAAAAoCKgKFXJcLtoAAAAAAAqoOxs6Z13FHD2rDR+vFvML0VRqpLhdtEAAAAAAFRAubmyrF8vn6ws6emnXd0bp6AoVclwu2gAAAAAACogLy+ZoUOVkZoqHy/3KNe4x1YCAAAAAACUZ15e0sCBykhMVIibFKU4fQYAAAAAAABOR1EKAAAAAADA1YyRUlJkSU3Ne+4G3ON8MAAAAAAAgPIsM1OW++9XlawsafVqKSDA1T0qcxSlHDD/rUimpqa6uCeXx2q16uzZs/Lz82OiczdD7t0TeXdP5N09kXf3RN7dE3l3T+TdTWVkyGRnKzMnRz6pqfLIyXF1j0otv55iLnHGF0UpB86ePStJql+/vot7AgAAAAAA3E7t2q7uwRVx9uxZValSpdDlFnOpspUbslqt+vPPPxUcHCyLxeLq7pRaamqq6tevr//85z8KCQlxdXfgROTePZF390Te3RN5d0/k3T2Rd/dE3t1XZcm9MUZnz55VnTp1ijzbjzOlHPDw8FC9evVc3Y0rJiQkpEL/Z0bpkXv3RN7dE3l3T+TdPZF390Te3RN5d1+VIfdFnSGVj4tTAQAAAAAA4HQUpQAAAAAAAOB0FKUqMV9fX02ZMkW+vr6u7gqcjNy7J/Lunsi7eyLv7om8uyfy7p7Iu/tyt9wz0TkAAAAAAACcjjOlAAAAAAAA4HQUpQAAAAAAAOB0FKUAAAAAAADgdBSlAAAAAAAA4HQUpQAAAAAAAOB0FKUqiFmzZumGG25QcHCwatWqpX79+unAgQOXfN/KlSvVokUL+fn5qVWrVvr666/tlhtjNHnyZNWuXVv+/v6KjIzUoUOHymozUEKlyfu7776rW265RdWqVVO1atUUGRmpbdu22cWMGDFCFovF7tGzZ8+y3BSUQGnyvmTJkgI59fPzs4thfy//SpP7rl27Fsi9xWJRnz59bDHs8+XbO++8o9atWyskJEQhISGKiIjQN998U+R7OL5XfCXNO8f3yqGkeef4XjmUNO8c2yun2bNny2KxaOzYsUXGudsxnqJUBbFlyxY9/vjj+ve//60NGzYoOztbPXr0UHp6eqHv+fHHHzV48GA99NBD2rlzp/r166d+/frpl19+scXMmTNH8+fP14IFC7R161YFBgYqKipKGRkZztgsXEJp8h4dHa3Bgwdr8+bNiomJUf369dWjRw8dO3bMLq5nz546fvy47fGvf/2rrDcHxVSavEtSSEiIXU5///13u+Xs7+VfaXK/atUqu7z/8ssv8vT01IABA+zi2OfLr3r16mn27Nnavn27fv75Z916663q27evfv31V4fxHN8rh5LmneN75VDSvEsc3yuDkuadY3vl89NPP2nhwoVq3bp1kXFueYw3qJASExONJLNly5ZCYwYOHGj69Olj19axY0czatQoY4wxVqvVhIeHm1deecW2PDk52fj6+pp//etfZdNxXJbi5P1iOTk5Jjg42CxdutTWNnz4cNO3b98y6CHKQnHyvnjxYlOlSpVCl7O/V0yl2edff/11ExwcbNLS0mxt7PMVT7Vq1cyiRYscLuP4XnkVlfeLcXyvPIrKO8f3yqsk+zvH9ort7NmzpmnTpmbDhg2mS5cu5qmnnio01h2P8ZwpVUGlpKRIkqpXr15oTExMjCIjI+3aoqKiFBMTI0mKj49XQkKCXUyVKlXUsWNHWwzKl+Lk/WLnzp1TdnZ2gfdER0erVq1aat68uUaPHq1Tp05d0b7iyilu3tPS0tSgQQPVr1+/wF/f2N8rptLs8++9954GDRqkwMBAu3b2+YohNzdXy5cvV3p6uiIiIhzGcHyvfIqT94txfK/4ipt3ju+VS2n2d47tFdvjjz+uPn36FDh2O+KOx3gvV3cAJWe1WjV27Fh17txZ1113XaFxCQkJCgsLs2sLCwtTQkKCbXl+W2ExKD+Km/eLPf/886pTp47dwNWzZ0/1799fjRo1UlxcnCZNmqRevXopJiZGnp6eZdF9lFJx8968eXO9//77at26tVJSUjR37lx16tRJv/76q+rVq8f+XgGVZp/ftm2bfvnlF7333nt27ezz5d+ePXsUERGhjIwMBQUF6bPPPtM111zjMJbje+VRkrxfjON7xVWSvHN8rzxKu79zbK/Yli9frh07duinn34qVrw7HuMpSlVAjz/+uH755Rd9//33ru4KnKg0eZ89e7aWL1+u6Ohou0kxBw0aZHveqlUrtW7dWo0bN1Z0dLS6d+9+RfuNy1PcvEdERNj9ta1Tp05q2bKlFi5cqOnTp5d1N1EGSrPPv/fee2rVqpVuvPFGu3b2+fKvefPmio2NVUpKij755BMNHz5cW7ZsKXaBAhVTafPO8b1iK0neOb5XHqXd3zm2V1z/+c9/9NRTT2nDhg0FblCA/+HyvQpmzJgx+uqrr7R582bVq1evyNjw8HCdOHHCru3EiRMKDw+3Lc9vKywG5UNJ8p5v7ty5mj17ttavX3/JCfWuvvpq1axZU4cPH74S3cUVUpq85/P29tb1119vyyn7e8VSmtynp6dr+fLleuihhy4Zyz5f/vj4+KhJkyZq3769Zs2apTZt2uiNN95wGMvxvfIoSd7zcXyv+EqT93wc3yuu0uSdY3vFtn37diUmJqpdu3by8vKSl5eXtmzZovnz58vLy0u5ubkF3uOOx3iKUhWEMUZjxozRZ599pv/7v/9To0aNLvmeiIgIbdq0ya5tw4YNtr+2NGrUSOHh4XYxqamp2rp1a7Gvb0bZKk3epbw7MkyfPl1r165Vhw4dLhn/xx9/6NSpU6pdu/bldhlXQGnzfqHc3Fzt2bPHllP294rhcnK/cuVKZWZmaujQoZeMZZ8v/6xWqzIzMx0u4/heeRWVd4nje2V1qbxfiON75VGcvHNsr9i6d++uPXv2KDY21vbo0KGDhgwZotjYWIeXWbrlMd6186yjuEaPHm2qVKlioqOjzfHjx22Pc+fO2WLuv/9+M2HCBNvrH374wXh5eZm5c+eaffv2mSlTphhvb2+zZ88eW8zs2bNN1apVzeeff252795t+vbtaxo1amTOnz/v1O2DY6XJ++zZs42Pj4/55JNP7N5z9uxZY0ze3R/Gjx9vYmJiTHx8vNm4caNp166dadq0qcnIyHD6NqKg0uR96tSpZt26dSYuLs5s377dDBo0yPj5+Zlff/3VFsP+Xv6VJvf5br75ZnPvvfcWaGefL/8mTJhgtmzZYuLj483u3bvNhAkTjMViMevXrzfGcHyvrEqad47vlUNJ887xvXIoad7zcWyvfC6++x7HeGMoSlUQkhw+Fi9ebIvp0qWLGT58uN37Pv74Y9OsWTPj4+Njrr32WrNmzRq75Var1bz44osmLCzM+Pr6mu7du5sDBw44YYtQHKXJe4MGDRy+Z8qUKcYYY86dO2d69OhhQkNDjbe3t2nQoIEZOXKkSUhIcO7GoVClyfvYsWPNVVddZXx8fExYWJjp3bu32bFjh9162d/Lv9KO9fv37zeSbF9uL8Q+X/49+OCDpkGDBsbHx8eEhoaa7t272+WS43vlVNK8c3yvHEqad47vlUNpxnmO7ZXTxUUpjvHGWIwxpmzPxQIAAAAAAADsMacUAAAAAAAAnI6iFAAAAAAAAJyOohQAAAAAAACcjqIUAAAAAAAAnI6iFAAAAAAAAJyOohQAAAAAAACcjqIUAAAAAAAAnI6iFAAAQDEdOXJEFotFsbGxZf5ZS5YsUdWqVcv8c8pK165dNXbs2MtaR0X/GQAAgKJRlAIAAJXCiBEjZLFYCjx69uzp6q5dUsOGDTVv3jy7tnvvvVcHDx4s88/u2rWr7Wfl5+ena665Rm+//fZlr3fVqlWaPn36FeghAACorLxc3QEAAIArpWfPnlq8eLFdm6+vr4t6c3n8/f3l7+/vlM8aOXKkpk2bpnPnzumDDz7Q448/rmrVqmnw4MElXldWVpZ8fHxUvXr1MugpAACoTDhTCgAAVBq+vr4KDw+3e1SrVk2SdN999+nee++1i8/OzlbNmjX1wQcfSJLWrl2rm2++WVWrVlWNGjV0++23Ky4urtDPc3R52erVq2WxWGyv4+Li1LdvX4WFhSkoKEg33HCDNm7caFvetWtX/f7773r66adtZywVtu533nlHjRs3lo+Pj5o3b65//vOfdsstFosWLVqku+66SwEBAWratKm++OKLS/7cAgICFB4erquvvlovvfSS3fuSk5P18MMPKzQ0VCEhIbr11lu1a9cu23tfeukltW3bVosWLVKjRo3k5+dn264LL987c+aMhg0bpmrVqikgIEC9evXSoUOHCvw8r7rqKgUEBOiuu+7SqVOnLtl3AABQcVGUAgAAbmHIkCH68ssvlZaWZmtbt26dzp07p7vuukuSlJ6ernHjxunnn3/Wpk2b5OHhobvuuktWq7XUn5uWlqbevXtr06ZN2rlzp3r27Kk77rhDR48elZR3mVu9evU0bdo0HT9+XMePH3e4ns8++0xPPfWUnnnmGf3yyy8aNWqUHnjgAW3evNkuburUqRo4cKB2796t3r17a8iQITp9+nSJ+uzv76+srCxJ0oABA5SYmKhvvvlG27dvV7t27dS9e3e7dR4+fFiffvqpVq1aVeh8WyNGjNDPP/+sL774QjExMTLGqHfv3srOzpYkbd26VQ899JDGjBmj2NhYdevWTTNmzChRvwEAQMVCUQoAAFQaX331lYKCguweM2fOlCRFRUUpMDBQn332mS3+o48+0p133qng4GBJ0t13363+/furSZMmatu2rd5//33t2bNHe/fuLXWf2rRpo1GjRum6665T06ZNNX36dDVu3Nh2JlL16tXl6emp4OBg29ldjsydO1cjRozQY489pmbNmmncuHHq37+/5s6daxc3YsQIDR48WE2aNNHMmTOVlpambdu2Fauvubm5+vDDD7V7927deuut+v7777Vt2zatXLlSHTp0UNOmTTV37lxVrVpVn3zyie19WVlZ+uCDD3T99derdevWBdZ76NAhffHFF1q0aJFuueUWtWnTRsuWLdOxY8e0evVqSdIbb7yhnj176rnnnlOzZs305JNPKioqqlj9BgAAFRNFKQAAUGl069ZNsbGxdo9HH31UkuTl5aWBAwdq2bJlkvLOivr88881ZMgQ2/sPHTqkwYMH6+qrr1ZISIgaNmwoSbazmkojLS1N48ePV8uWLVW1alUFBQVp3759JV7nvn371LlzZ7u2zp07a9++fXZtFxaFAgMDFRISosTExCLX/fbbbysoKEj+/v4aOXKknn76aY0ePVq7du1SWlqaatSoYVfoi4+Pt7ussUGDBgoNDS2y715eXurYsaOtrUaNGmrevLmt//v27bNbLkkRERFF9hsAAFRsTHQOAAAqjcDAQDVp0qTQ5UOGDFGXLl2UmJioDRs2yN/f3+7ufHfccYcaNGigd999V3Xq1JHVatV1111nu5TtYh4eHjLG2LXlX46Wb/z48dqwYYPmzp2rJk2ayN/fX/fcc0+h67xc3t7edq8tFsslLz8cMmSI/vrXv8rf31+1a9eWh0fe3y3T0tJUu3ZtRUdHF3jPhfNdBQYGXna/AQCA+6EoBQAA3EanTp1Uv359rVixQt98840GDBhgK+KcOnVKBw4c0LvvvqtbbrlFkvT9998Xub7Q0FCdPXtW6enptsLMxXMq/fDDDxoxYoRt3qq0tDQdOXLELsbHx0e5ublFflbLli31ww8/aPjw4Xbrvuaaay653ZdSpUoVh8W8du3aKSEhQV5eXrazxkqjZcuWysnJ0datW9WpUydJ//t55/e/ZcuW2rp1q937/v3vf5f6MwEAQPlHUQoAAFQamZmZSkhIsGvz8vJSzZo1ba/vu+8+LViwQAcPHrSbJLxatWqqUaOG/vGPf6h27do6evSoJkyYUOTndezYUQEBAZo0aZKefPJJbd26VUuWLLGLadq0qVatWqU77rhDFotFL774YoEzlxo2bKhvv/1WgwYNkq+vr11/8z377LMaOHCgrr/+ekVGRurLL7/UqlWr7O7kd6VFRkYqIiJC/fr105w5c9SsWTP9+eefWrNmje666y516NChWOt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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = dml_obj.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Effect Aggregation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDAggregation Object ==================\n", + " Group Aggregation \n", + "\n", + "------------------ Overall Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1.487114 0.034206 43.475739 0.0 1.420072 1.554156\n", + "------------------ Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "2.0 1.952560 0.052177 37.422088 0.0 1.850296 2.054825\n", + "3.0 1.583253 0.056277 28.133003 0.0 1.472952 1.693555\n", + "4.0 0.951764 0.067450 14.110600 0.0 0.819564 1.083965\n", + "------------------ Additional Information ------------------\n", + "Score function: observational\n", + "Control group: never_treated\n", + "Anticipation periods: 0\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "aggregated = dml_obj.aggregate(\"group\")\n", + "print(aggregated)\n", + "_ = aggregated.plot_effects()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDAggregation Object ==================\n", + " Time Aggregation \n", + "\n", + "------------------ Overall Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1.479621 0.035052 42.212631 0.0 1.410921 1.54832\n", + "------------------ Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "2 0.919867 0.063932 14.388135 0.0 0.794562 1.045172\n", + "3 1.547594 0.051301 30.166942 0.0 1.447046 1.648142\n", + "4 1.971400 0.046602 42.302563 0.0 1.880061 2.062739\n", + "------------------ Additional Information ------------------\n", + "Score function: observational\n", + "Control group: never_treated\n", + "Anticipation periods: 0\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "aggregated_time = dml_obj.aggregate(\"time\")\n", + "print(aggregated_time)\n", + "fig, ax = aggregated_time.plot_effects()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDAggregation Object ==================\n", + " Event Study Aggregation \n", + "\n", + "------------------ Overall Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1.988922 0.038713 51.376571 0.0 1.913047 2.064797\n", + "------------------ Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "-2.0 0.002899 0.068528 0.042310 0.966251 -0.131414 0.137213\n", + "-1.0 0.009851 0.040546 0.242963 0.808034 -0.069618 0.089320\n", + "0.0 0.992060 0.030664 32.352735 0.000000 0.931960 1.052160\n", + "1.0 2.022204 0.045705 44.245145 0.000000 1.932625 2.111784\n", + "2.0 2.952502 0.063172 46.737645 0.000000 2.828688 3.076317\n", + "------------------ Additional Information ------------------\n", + "Score function: observational\n", + "Control group: never_treated\n", + "Anticipation periods: 0\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "aggregated_eventstudy = dml_obj.aggregate(\"eventstudy\")\n", + "print(aggregated_eventstudy)\n", + "fig, ax = aggregated_eventstudy.plot_effects()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/examples/index.rst b/doc/examples/index.rst index ac9a1559..7416c129 100644 --- a/doc/examples/index.rst +++ b/doc/examples/index.rst @@ -27,10 +27,6 @@ General Examples py_double_ml_multiway_cluster.ipynb py_double_ml_ssm.ipynb py_double_ml_sensitivity_booking.ipynb - py_double_ml_panel.ipynb - py_double_ml_panel_simple.ipynb - py_double_ml_did.ipynb - py_double_ml_did_pretest.ipynb py_double_ml_basic_iv.ipynb py_double_ml_plm_irm_hetfx.ipynb py_double_ml_meets_flaml.ipynb @@ -53,6 +49,20 @@ Effect Heterogeneity py_double_ml_pq.ipynb py_double_ml_cvar.ipynb + +.. _did_examplegallery: + +Difference-in-Differences ++++++++++++++++++++++++++ + +.. nbgallery:: + :name: case-studies-py-did + + did/py_panel_simple.ipynb + did/py_panel.ipynb + did/py_did.ipynb + did/py_did_pretest.ipynb + R: Case studies --------------- diff --git a/doc/examples/py_double_ml_panel_simple.ipynb b/doc/examples/py_double_ml_panel_simple.ipynb deleted file mode 100644 index 4ebe20f5..00000000 --- a/doc/examples/py_double_ml_panel_simple.ipynb +++ /dev/null @@ -1,739 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Python: Panel Data Introduction\n", - "\n", - "In this example, we replicate the results from the guide [Getting Started with the did Package](https://bcallaway11.github.io/did/articles/did-basics.html) of the [did-R-package](https://bcallaway11.github.io/did/index.html).\n", - "\n", - "The notebook requires the following packages:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "from sklearn.linear_model import LinearRegression, LogisticRegression\n", - "\n", - "from doubleml.data import DoubleMLPanelData\n", - "from doubleml.did import DoubleMLDIDMulti" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data\n", - "\n", - "The data we will use is simulated and part of the [CSDID-Python-Package](https://d2cml-ai.github.io/csdid/index.html).\n", - "\n", - "A description of the data generating process can be found at the [CSDID-documentation](https://d2cml-ai.github.io/csdid/examples/csdid_basic.html#Examples-with-simulated-data).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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GXidclusterperiodYtreat
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" - ], - "text/plain": [ - " G X id cluster period Y treat\n", - "0 3 -0.876233 1 5 1 5.562556 1\n", - "1 3 -0.876233 1 5 2 4.349213 1\n", - "2 3 -0.876233 1 5 3 7.134037 1\n", - "3 3 -0.876233 1 5 4 6.243056 1\n", - "4 2 -0.873848 2 36 1 -3.659387 1" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dta = pd.read_csv(\"https://raw.githubusercontent.com/d2cml-ai/csdid/main/data/sim_data.csv\")\n", - "dta.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To work with the [DoubleML-package](https://docs.doubleml.org/stable/index.html), we initialize a ``DoubleMLPanelData`` object.\n", - "\n", - "Therefore, we set the *never-treated* units in group column `G` to `np.inf` (we have to change the datatype to `float`)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " G X id cluster period Y treat\n", - "0 3.0 -0.876233 1 5 1 5.562556 1\n", - "1 3.0 -0.876233 1 5 2 4.349213 1\n", - "2 3.0 -0.876233 1 5 3 7.134037 1\n", - "3 3.0 -0.876233 1 5 4 6.243056 1\n", - "4 2.0 -0.873848 2 36 1 -3.659387 1" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set dtype for G to float\n", - "dta[\"G\"] = dta[\"G\"].astype(float)\n", - "dta.loc[dta[\"G\"] == 0, \"G\"] = np.inf\n", - "dta.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can initialize the ``DoubleMLPanelData`` object, specifying\n", - "\n", - " - `y_col` : the outcome\n", - " - `d_cols`: the group variable indicating the first treated period for each unit\n", - " - `id_col`: the unique identification column for each unit\n", - " - `t_col` : the time column\n", - " - `x_cols`: the additional pre-treatment controls\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLPanelData Object ==================\n", - "\n", - "------------------ Data summary ------------------\n", - "Outcome variable: Y\n", - "Treatment variable(s): ['G']\n", - "Covariates: ['X']\n", - "Instrument variable(s): None\n", - "Time variable: period\n", - "Id variable: id\n", - "No. Observations: 3979\n", - "\n", - "------------------ DataFrame info ------------------\n", - "\n", - "RangeIndex: 15916 entries, 0 to 15915\n", - "Columns: 7 entries, G to treat\n", - "dtypes: float64(3), int64(4)\n", - "memory usage: 870.5 KB\n", - "\n" - ] - } - ], - "source": [ - "dml_data = DoubleMLPanelData(dta, y_col=\"Y\", d_cols=\"G\", id_col=\"id\", t_col=\"period\", x_cols=[\"X\"])\n", - "print(dml_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ATT estimation" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDMulti Object ==================\n", - "\n", - "------------------ Data summary ------------------\n", - "Outcome variable: Y\n", - "Treatment variable(s): ['G']\n", - "Covariates: ['X']\n", - "Instrument variable(s): None\n", - "Time variable: period\n", - "Id variable: id\n", - "No. Observations: 3979\n", - "\n", - "------------------ Score & algorithm ------------------\n", - "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", - "\n", - "------------------ Machine learner ------------------\n", - "Learner ml_g: LinearRegression()\n", - "Learner ml_m: LogisticRegression()\n", - "Out-of-sample Performance:\n", - "Regression:\n", - "Learner ml_g0 RMSE: [[1.42723659 1.40976728 1.39815026 1.42717567 1.40453859 1.42290288\n", - " 1.42334363 1.40484296 1.42488798]]\n", - "Learner ml_g1 RMSE: [[1.40243017 1.43571358 1.39765614 1.41559579 1.43028918 1.38457862\n", - " 1.4575677 1.41732542 1.40766147]]\n", - "Classification:\n", - "Learner ml_m Log Loss: [[0.69194005 0.69043888 0.69081606 0.67933534 0.67918078 0.67926994\n", - " 0.66255063 0.66234746 0.66203825]]\n", - "\n", - "------------------ Resampling ------------------\n", - "No. folds: 5\n", - "No. repeated sample splits: 1\n", - "\n", - "------------------ Fit summary ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.918086 0.064134 14.315195 0.000000 0.792387 1.043786\n", - "ATT(2.0,1,3) 1.988341 0.064616 30.771560 0.000000 1.861696 2.114986\n", - "ATT(2.0,1,4) 2.956276 0.063196 46.779647 0.000000 2.832415 3.080138\n", - "ATT(3.0,1,2) -0.039725 0.066021 -0.601700 0.547374 -0.169125 0.089675\n", - "ATT(3.0,2,3) 1.107672 0.065377 16.942897 0.000000 0.979536 1.235808\n", - "ATT(3.0,2,4) 2.059259 0.065442 31.466923 0.000000 1.930995 2.187523\n", - "ATT(4.0,1,2) 0.000070 0.068208 0.001028 0.999180 -0.133615 0.133756\n", - "ATT(4.0,2,3) 0.060247 0.066492 0.906086 0.364890 -0.070074 0.190569\n", - "ATT(4.0,3,4) 0.950055 0.067429 14.089722 0.000000 0.817897 1.082213\n" - ] - } - ], - "source": [ - "dml_obj = DoubleMLDIDMulti(\n", - " obj_dml_data=dml_data,\n", - " ml_g=LinearRegression(),\n", - " ml_m=LogisticRegression(),\n", - " control_group=\"never_treated\",\n", - ")\n", - "\n", - "dml_obj.fit()\n", - "print(dml_obj)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As usual for the DoubleML-package, you can obtain joint confidence intervals via bootstrap." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ATT(2.0,1,2)0.7423831.093789
ATT(2.0,1,3)1.8113162.165366
ATT(2.0,1,4)2.7831433.129410
ATT(3.0,1,2)-0.2206000.141150
ATT(3.0,2,3)0.9285641.286781
ATT(3.0,2,4)1.8799722.238546
ATT(4.0,1,2)-0.1867950.186935
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" - ], - "text/plain": [ - " 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.742383 1.093789\n", - "ATT(2.0,1,3) 1.811316 2.165366\n", - "ATT(2.0,1,4) 2.783143 3.129410\n", - "ATT(3.0,1,2) -0.220600 0.141150\n", - "ATT(3.0,2,3) 0.928564 1.286781\n", - "ATT(3.0,2,4) 1.879972 2.238546\n", - "ATT(4.0,1,2) -0.186795 0.186935\n", - "ATT(4.0,2,3) -0.121916 0.242411\n", - "ATT(4.0,3,4) 0.765324 1.134785" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "level = 0.95\n", - "\n", - "ci = dml_obj.confint(level=level)\n", - "dml_obj.bootstrap(n_rep_boot=5000)\n", - "ci_joint = dml_obj.confint(level=level, joint=True)\n", - "ci_joint" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A visualization of the effects can be obtained via the `plot_effects()` method.\n", - "\n", - "Remark that the plot used joint confidence intervals per default. " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n", - "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "_ = dml_obj.plot_effects()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Effect Aggregation" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Group Aggregation \n", - "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.487145 0.034209 43.472838 0.0 1.420097 1.554192\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "2.0 1.954235 0.052280 37.380505 0.0 1.851769 2.056701\n", - "3.0 1.583466 0.056236 28.157478 0.0 1.473245 1.693686\n", - "4.0 0.950055 0.067429 14.089722 0.0 0.817897 1.082213\n", - "------------------ Additional Information ------------------\n", - "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "aggregated = dml_obj.aggregate(\"group\")\n", - "print(aggregated)\n", - "_ = aggregated.plot_effects()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Time Aggregation \n", - "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.481664 0.035122 42.186396 0.0 1.412826 1.550501\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "2 0.922607 0.064144 14.383278 0.0 0.796886 1.048328\n", - "3 1.550398 0.051377 30.177088 0.0 1.449701 1.651094\n", - "4 1.971986 0.046573 42.341923 0.0 1.880705 2.063267\n", - "------------------ Additional Information ------------------\n", - "Control Group: never_treated\n", - "Anticipation Periods: 0\n", - "Score: observational\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "aggregated_time = dml_obj.aggregate(\"time\")\n", - "print(aggregated_time)\n", - "fig, ax = aggregated_time.plot_effects()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Event Study Aggregation \n", - "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.991002 0.038754 51.376042 0.0 1.915046 2.066957\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "-2.0 0.001911 0.068411 0.027932 0.977716 -0.132171 0.135993\n", - "-1.0 0.009913 0.040508 0.244708 0.806682 -0.069481 0.089306\n", - "0.0 0.992564 0.030753 32.274917 0.000000 0.932289 1.052840\n", - "1.0 2.025063 0.045671 44.340590 0.000000 1.935550 2.114576\n", - "2.0 2.955379 0.063298 46.689908 0.000000 2.831317 3.079441\n", - "------------------ Additional Information ------------------\n", - "Control Group: never_treated\n", - "Anticipation Periods: 0\n", - "Score: observational\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "aggregated_eventstudy = dml_obj.aggregate(\"eventstudy\")\n", - "print(aggregated_eventstudy)\n", - "fig, ax = aggregated_eventstudy.plot_effects()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "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": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 8b8ff1aa474ac187ed5f4f7bbc8f1201a028a820 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 23 Apr 2025 13:07:45 +0000 Subject: [PATCH 094/140] update gallery links --- doc/guide/models/did/did_aggregation.rst | 2 +- doc/guide/models/did/did_pa.rst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/guide/models/did/did_aggregation.rst b/doc/guide/models/did/did_aggregation.rst index df09c380..63025aa4 100644 --- a/doc/guide/models/did/did_aggregation.rst +++ b/doc/guide/models/did/did_aggregation.rst @@ -53,5 +53,5 @@ The method ``aggregate()`` requires the ``aggregation`` argument to be set to on * ``dictionary``: a dictionary with values containing the aggregation weights (as ``numpy.ma.MaskedArray``). .. note:: - A more detailed example on effect aggregation is available in the :ref:`example gallery `. + A more detailed example on effect aggregation is available in the :ref:`example gallery `. For a detailed discussion on different aggregation schemes, we refer to of `Callaway and Sant'Anna (2021) `_. diff --git a/doc/guide/models/did/did_pa.rst b/doc/guide/models/did/did_pa.rst index 4365875b..e0f9d620 100644 --- a/doc/guide/models/did/did_pa.rst +++ b/doc/guide/models/did/did_pa.rst @@ -91,4 +91,4 @@ Estimation is conducted via its ``fit()`` method: Further, :math:`g(1,X)` is only required for :ref:`Sensitivity Analysis ` and is not used for the estimation of the target parameter. .. note:: - A more detailed example is available in the :ref:`Example Gallery `. + A more detailed example is available in the :ref:`Example Gallery `. From f1f055af9456131bd336ec66dafd86fd562f5f24 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 23 Apr 2025 14:39:04 +0000 Subject: [PATCH 095/140] add learners --- doc/guide/models/did/did_aggregation.rst | 4 ++-- doc/guide/models/did/did_pa.rst | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/guide/models/did/did_aggregation.rst b/doc/guide/models/did/did_aggregation.rst index 63025aa4..d1750089 100644 --- a/doc/guide/models/did/did_aggregation.rst +++ b/doc/guide/models/did/did_aggregation.rst @@ -34,8 +34,8 @@ The aggragation schemes are implmented via the ``aggregate()`` method of the ``D ) dml_did_obj = dml.did.DoubleMLDIDMulti( obj_dml_data=dml_data, - ml_g=ml_g, - ml_m=ml_m, + ml_g=RandomForestRegressor(min_samples_split=10), + ml_m=RandomForestClassifier(min_samples_split=10), gt_combinations="standard", control_group="never_treated", ) diff --git a/doc/guide/models/did/did_pa.rst b/doc/guide/models/did/did_pa.rst index e0f9d620..2e024af5 100644 --- a/doc/guide/models/did/did_pa.rst +++ b/doc/guide/models/did/did_pa.rst @@ -71,8 +71,8 @@ Estimation is conducted via its ``fit()`` method: ) dml_did_obj = dml.did.DoubleMLDIDMulti( obj_dml_data=dml_data, - ml_g=ml_g, - ml_m=ml_m, + ml_g=RandomForestRegressor(min_samples_split=10), + ml_m=RandomForestClassifier(min_samples_split=10), gt_combinations="standard", control_group="never_treated", ) From cb6df59865a315c7dae2a0832a9cd71df1b51323 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 23 Apr 2025 14:39:13 +0000 Subject: [PATCH 096/140] update notebook --- doc/examples/did/py_panel_simple.ipynb | 367 +++++++++++++++++++------ 1 file changed, 283 insertions(+), 84 deletions(-) diff --git a/doc/examples/did/py_panel_simple.ipynb b/doc/examples/did/py_panel_simple.ipynb index e1027e55..ec0ed4ad 100644 --- a/doc/examples/did/py_panel_simple.ipynb +++ b/doc/examples/did/py_panel_simple.ipynb @@ -8,12 +8,14 @@ "\n", "In this example, we replicate the results from the guide [Getting Started with the did Package](https://bcallaway11.github.io/did/articles/did-basics.html) of the [did-R-package](https://bcallaway11.github.io/did/index.html).\n", "\n", + "As the [did-R-package](https://bcallaway11.github.io/did/index.html) the implementation of [DoubleML](https://docs.doubleml.org/stable/index.html) is based on [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001).\n", + "\n", "The notebook requires the following packages:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -39,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -136,7 +138,7 @@ "4 2 -0.873848 2 36 1 -3.659387 1" ] }, - "execution_count": 2, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -157,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -254,7 +256,7 @@ "4 2.0 -0.873848 2 36 1 -3.659387 1" ] }, - "execution_count": 3, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -281,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -310,7 +312,14 @@ } ], "source": [ - "dml_data = DoubleMLPanelData(dta, y_col=\"Y\", d_cols=\"G\", id_col=\"id\", t_col=\"period\", x_cols=[\"X\"])\n", + "dml_data = DoubleMLPanelData(\n", + " data=dta,\n", + " y_col=\"Y\",\n", + " d_cols=\"G\",\n", + " id_col=\"id\",\n", + " t_col=\"period\",\n", + " x_cols=[\"X\"]\n", + ")\n", "print(dml_data)" ] }, @@ -318,12 +327,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## ATT estimation" + "## ATT estimation\n", + "\n", + "The [DoubleML-package](https://docs.doubleml.org/stable/index.html) implements estimation of group-time average treatment effect via the `DoubleMLDIDMulti` class (see [model documentation](https://docs.doubleml.org/stable/guide/models.html#difference-in-differences-models-did)).\n", + "\n", + "The class basically behaves like other `DoubleML` classes and requires the specification of two learners (for more details on the regression elements, see [score documentation](https://docs.doubleml.org/stable/guide/scores.html#difference-in-differences-models)). The model will be estimated using the `fit()` method." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -351,13 +364,13 @@ "Learner ml_m: LogisticRegression()\n", "Out-of-sample Performance:\n", "Regression:\n", - "Learner ml_g0 RMSE: [[1.42396544 1.41158324 1.3978899 1.42395052 1.40376056 1.42007484\n", - " 1.4266361 1.4063818 1.42214803]]\n", - "Learner ml_g1 RMSE: [[1.40306056 1.43905986 1.39682481 1.41540689 1.42449545 1.38343701\n", - " 1.45842903 1.41567674 1.40890656]]\n", + "Learner ml_g0 RMSE: [[1.42617854 1.4109985 1.3977513 1.42696223 1.40564857 1.41934546\n", + " 1.42376336 1.40757926 1.42390621]]\n", + "Learner ml_g1 RMSE: [[1.4030393 1.43646172 1.39614341 1.4195028 1.42759927 1.38453389\n", + " 1.45829099 1.41782049 1.41006229]]\n", "Classification:\n", - "Learner ml_m Log Loss: [[0.69074217 0.69095477 0.69118935 0.67934316 0.67920411 0.67956728\n", - " 0.66202776 0.6625894 0.66211293]]\n", + "Learner ml_m Log Loss: [[0.69136666 0.69104942 0.69043707 0.68049621 0.67961183 0.67915833\n", + " 0.66231844 0.66275239 0.66321942]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 5\n", @@ -365,15 +378,15 @@ "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.919867 0.063932 14.388135 0.000000 0.794562 1.045172\n", - "ATT(2.0,1,3) 1.985312 0.064527 30.767368 0.000000 1.858842 2.111782\n", - "ATT(2.0,1,4) 2.952502 0.063172 46.737645 0.000000 2.828688 3.076317\n", - "ATT(3.0,1,2) -0.044075 0.065837 -0.669460 0.503202 -0.173113 0.084963\n", - "ATT(3.0,2,3) 1.107183 0.065402 16.929006 0.000000 0.978998 1.235368\n", - "ATT(3.0,2,4) 2.059324 0.065538 31.421799 0.000000 1.930871 2.187776\n", - "ATT(4.0,1,2) 0.002899 0.068528 0.042310 0.966251 -0.131414 0.137213\n", - "ATT(4.0,2,3) 0.060997 0.066718 0.914254 0.360583 -0.069768 0.191763\n", - "ATT(4.0,3,4) 0.951764 0.067450 14.110600 0.000000 0.819564 1.083965\n" + "ATT(2.0,1,2) 0.925650 0.064143 14.430953 0.000000 0.799932 1.051369\n", + "ATT(2.0,1,3) 1.987835 0.064616 30.763940 0.000000 1.861190 2.114479\n", + "ATT(2.0,1,4) 2.955752 0.063206 46.764006 0.000000 2.831871 3.079633\n", + "ATT(3.0,1,2) -0.039327 0.066091 -0.595052 0.551809 -0.168862 0.090208\n", + "ATT(3.0,2,3) 1.103572 0.065372 16.881417 0.000000 0.975446 1.231699\n", + "ATT(3.0,2,4) 2.059269 0.065414 31.480585 0.000000 1.931060 2.187478\n", + "ATT(4.0,1,2) -0.000186 0.068382 -0.002723 0.997827 -0.134213 0.133841\n", + "ATT(4.0,2,3) 0.063073 0.066448 0.949209 0.342514 -0.067163 0.193309\n", + "ATT(4.0,3,4) 0.959826 0.067745 14.168265 0.000000 0.827048 1.092603\n" ] } ], @@ -389,6 +402,20 @@ "print(dml_obj)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The summary estimates the $ATT(g,t_\\text{eval})$ effects for different combinations of $(g,t_\\text{eval})$ via $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$, where\n", + " - $\\mathrm{g}$ specifies the group\n", + " - $t_\\text{pre}$ specifies the corresponding pre-treatment period\n", + " - $t_\\text{eval}$ specifies the evaluation period\n", + "\n", + "This corresponds to the estimates given in `att_gt` function in the [did-R-package](https://bcallaway11.github.io/did/index.html), where the standard choice is $t_\\text{pre} = \\min(\\mathrm{g}, t_\\text{eval}) - 1$ (without anticipation).\n", + "\n", + "Remark that this includes pre-tests effects if $\\mathrm{g} > t_{eval}$, e.g. $ATT(4,2)$." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -398,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -429,48 +456,48 @@ " \n", " \n", " ATT(2.0,1,2)\n", - " 0.745889\n", - " 1.093845\n", + " 0.754473\n", + " 1.096828\n", " \n", " \n", " ATT(2.0,1,3)\n", - " 1.809717\n", - " 2.160907\n", + " 1.815397\n", + " 2.160272\n", " \n", " \n", " ATT(2.0,1,4)\n", - " 2.780594\n", - " 3.124410\n", + " 2.787077\n", + " 3.124427\n", " \n", " \n", " ATT(3.0,1,2)\n", - " -0.223236\n", - " 0.135086\n", + " -0.215701\n", + " 0.137046\n", " \n", " \n", " ATT(3.0,2,3)\n", - " 0.929207\n", - " 1.285159\n", + " 0.929116\n", + " 1.278028\n", " \n", " \n", " ATT(3.0,2,4)\n", - " 1.880976\n", - " 2.237671\n", + " 1.884702\n", + " 2.233837\n", " \n", " \n", " ATT(4.0,1,2)\n", - " -0.183586\n", - " 0.189385\n", + " -0.182676\n", + " 0.182304\n", " \n", " \n", " ATT(4.0,2,3)\n", - " -0.120562\n", - " 0.242556\n", + " -0.114254\n", + " 0.240401\n", " \n", " \n", " ATT(4.0,3,4)\n", - " 0.768213\n", - " 1.135316\n", + " 0.779038\n", + " 1.140614\n", " \n", " \n", "\n", @@ -478,18 +505,18 @@ ], "text/plain": [ " 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.745889 1.093845\n", - "ATT(2.0,1,3) 1.809717 2.160907\n", - "ATT(2.0,1,4) 2.780594 3.124410\n", - "ATT(3.0,1,2) -0.223236 0.135086\n", - "ATT(3.0,2,3) 0.929207 1.285159\n", - "ATT(3.0,2,4) 1.880976 2.237671\n", - "ATT(4.0,1,2) -0.183586 0.189385\n", - "ATT(4.0,2,3) -0.120562 0.242556\n", - "ATT(4.0,3,4) 0.768213 1.135316" + "ATT(2.0,1,2) 0.754473 1.096828\n", + "ATT(2.0,1,3) 1.815397 2.160272\n", + "ATT(2.0,1,4) 2.787077 3.124427\n", + "ATT(3.0,1,2) -0.215701 0.137046\n", + "ATT(3.0,2,3) 0.929116 1.278028\n", + "ATT(3.0,2,4) 1.884702 2.233837\n", + "ATT(4.0,1,2) -0.182676 0.182304\n", + "ATT(4.0,2,3) -0.114254 0.240401\n", + "ATT(4.0,3,4) 0.779038 1.140614" ] }, - "execution_count": 6, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -514,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -529,7 +556,7 @@ }, { "data": { - "image/png": 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" ] @@ -546,12 +573,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Effect Aggregation" + "## Effect Aggregation\n", + "\n", + "As the [did-R-package](https://bcallaway11.github.io/did/index.html), the $ATT$'s can be aggregated to summarize multiple effects.\n", + "For details on different aggregations and details on their interpretations see [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001).\n", + "\n", + "The aggregations are implemented via the `aggregate()` method." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Group Aggregation\n", + "\n", + "To obtain group-specific effects it is possible to aggregate several $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$ values based on the group $\\mathrm{g}$ by setting the `aggregation=\"group\"` argument." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -563,12 +604,12 @@ "\n", "------------------ Overall Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.487114 0.034206 43.475739 0.0 1.420072 1.554156\n", + "1.490559 0.034343 43.402684 0.0 1.423248 1.557869\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "2.0 1.952560 0.052177 37.422088 0.0 1.850296 2.054825\n", - "3.0 1.583253 0.056277 28.133003 0.0 1.472952 1.693555\n", - "4.0 0.951764 0.067450 14.110600 0.0 0.819564 1.083965\n", + "2.0 1.956413 0.052253 37.441069 0.0 1.853998 2.058827\n", + "3.0 1.581421 0.056230 28.124109 0.0 1.471212 1.691630\n", + "4.0 0.959826 0.067745 14.168265 0.0 0.827048 1.092603\n", "------------------ Additional Information ------------------\n", "Score function: observational\n", "Control group: never_treated\n", @@ -580,13 +621,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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", 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" ] @@ -596,14 +637,32 @@ } ], "source": [ - "aggregated = dml_obj.aggregate(\"group\")\n", + "aggregated = dml_obj.aggregate(aggregation=\"group\")\n", "print(aggregated)\n", "_ = aggregated.plot_effects()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output is a `DoubleMLDIDAggregation` object which includes an overall aggregation summary based on group size." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Time Aggregation\n", + "\n", + "This aggregates $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$, based on $t_\\text{eval}$, but weighted with respect to group size. Corresponds to *Calendar Time Effects* from the [did-R-package](https://bcallaway11.github.io/did/index.html).\n", + "\n", + "For calendar time effects set `aggregation=\"group\"`." + ] + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -614,13 +673,13 @@ " Time Aggregation \n", "\n", "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.479621 0.035052 42.212631 0.0 1.410921 1.54832\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1.482646 0.035122 42.214692 0.0 1.413809 1.551483\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "2 0.919867 0.063932 14.388135 0.0 0.794562 1.045172\n", - "3 1.547594 0.051301 30.166942 0.0 1.447046 1.648142\n", - "4 1.971400 0.046602 42.302563 0.0 1.880061 2.062739\n", + "2 0.925650 0.064143 14.430953 0.0 0.799932 1.051369\n", + "3 1.547060 0.051295 30.160244 0.0 1.446524 1.647596\n", + "4 1.975228 0.046695 42.300363 0.0 1.883707 2.066749\n", "------------------ Additional Information ------------------\n", "Score function: observational\n", "Control group: never_treated\n", @@ -632,13 +691,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -653,9 +712,18 @@ "fig, ax = aggregated_time.plot_effects()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Event Study Aggregation\n", + "\n", + "Finally, `aggregation=\"eventstudy\"` aggregates $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$ based on exposure time $e = t_\\text{eval} - \\mathrm{g}$ (respecting group size)." + ] + }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -667,14 +735,14 @@ "\n", "------------------ Overall Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.988922 0.038713 51.376571 0.0 1.913047 2.064797\n", + "1.991584 0.038736 51.414242 0.0 1.915663 2.067506\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "-2.0 0.002899 0.068528 0.042310 0.966251 -0.131414 0.137213\n", - "-1.0 0.009851 0.040546 0.242963 0.808034 -0.069618 0.089320\n", - "0.0 0.992060 0.030664 32.352735 0.000000 0.931960 1.052160\n", - "1.0 2.022204 0.045705 44.245145 0.000000 1.932625 2.111784\n", - "2.0 2.952502 0.063172 46.737645 0.000000 2.828688 3.076317\n", + "-2.0 -0.000186 0.068382 -0.002723 0.997827 -0.134213 0.133841\n", + "-1.0 0.013228 0.040589 0.325893 0.744505 -0.066325 0.092781\n", + "0.0 0.995559 0.030849 32.272276 0.000000 0.935096 1.056021\n", + "1.0 2.023443 0.045615 44.359392 0.000000 1.934039 2.112846\n", + "2.0 2.955752 0.063206 46.764006 0.000000 2.831871 3.079633\n", "------------------ Additional Information ------------------\n", "Score function: observational\n", "Control group: never_treated\n", @@ -686,13 +754,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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", 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333xzk89lc/Xu3TuSJIlevXrFHnvsUWvdq6++Gm+99VbcfffdMWrUqOx4TZ/aVpfLyBraa0REx44dt9gspcbqtb7q+nPSu3fvyGQy8Y9//KPK5aeft6n7aN++fZx55plx5plnxurVq+Pwww+PK664QigFwHbDmlIAsBX65JNP4qGHHoqvfvWrcfLJJ1f7uuCCC2LVqlXx6KOPRkTE8OHDY926dfGrX/0qex+ZTKZaoNWlS5fo169f/OY3v4nVq1dnx5999tl49dVX69Rbx44d44gjjohbb701Fi1aVO32pUuXZv89fPjwmDNnTsybNy87tnz58rj33nur7LNxps3GmToREWvXro2bb7652v0XFRXVeAnbKaecEh988EGV12CjTz75JMrLyyMi4thjj42IiF/+8pdVam644YZq+21JJ510UjRr1iwmTJhQ5XlGfPq8P/7444io+bVIkiR+8YtfVLvPoqKiiPh0ptmWNHz48CguLo6rr7461q1bV+32z36P62pjWLqle62vuv6clJSURH5+flx55ZXVZqd99ntTVFRU43Pa+P3cqHXr1tGnT5+orKzcAs8CALYOZkoBwFbo0UcfjVWrVsXxxx9f4+0HH3xwdOjQIe6999449dRTo6SkJA488MD43ve+F++880707ds3Hn300Vi+fHlEVJ3NcfXVV8cJJ5wQhxxySJx55pmxYsWKuOmmm6Jfv35VgqpNmTRpUhx66KHRv3//OOecc2K33XaLJUuWxJw5c+Lf//53vPLKKxERcemll8Zvf/vb+PKXvxwXXnhhFBUVxe233x49evSI5cuXZ/saMmRItGvXLkaPHh3f+c53Ii8vL+65555q4U1ExKBBg+L++++PsWPHxgEHHBCtW7eO4447Lr75zW/GAw88EOedd17MmjUrDjnkkNiwYUO88cYb8cADD8TTTz8d+++/fwwcODBGjhwZN998c5SWlsaQIUNixowZdZ4p1lC9e/eOn/zkJzFu3Lh47733oqSkJNq0aRPz58+Phx9+OL797W/HJZdcEn379o3evXvHJZdcEh988EEUFxfH73//+xrXZxo0aFBEfLpo+/Dhw6NZs2Zx2mmnbXavxcXFccstt8Q3v/nN2G+//eK0006LDh06xMKFC+Pxxx+PQw45JG666aZ63WerVq1ir732ivvvvz/22GOPaN++ffTr1y/69eu3yf3++Mc/RkVFRbXxffbZp0HrM9X156RPnz7xgx/8IH784x/HYYcdFieddFIUFBTEiy++GF26dImJEydGxKffg1tuuSV+8pOfRJ8+faJjx45x1FFHxV577RVHHHFEDBo0KNq3bx9z586NBx98MC644IJ69wwAW63cfOgfALA5jjvuuKSwsDApLy+vteaMM85IWrRokSxbtixJkiRZunRpcvrppydt2rRJ2rZtm5xxxhnJn/70pyQikilTplTZd8qUKUnfvn2TgoKCpF+/fsmjjz6afO1rX0v69u2brZk/f34SEck111xT4+O/++67yahRo5LOnTsnLVq0SLp27Zp89atfTR588MEqdS+//HJy2GGHJQUFBUm3bt2SiRMnJr/85S+TiEgWL16crfvTn/6UHHzwwUmrVq2SLl26JJdeemny9NNPJxGRzJo1K1u3evXq5PTTT0923HHHJCKSnj17Zm9bu3Zt8rOf/SzZe++9k4KCgqRdu3bJoEGDkgkTJiSlpaXZuk8++ST5zne+k+y0005JUVFRctxxxyXvv/9+EhHJ+PHja33NP2/q1KnV+hs/fnwSEcnSpUtr3Of3v/99cuihhyZFRUVJUVFR0rdv32TMmDHJm2++ma35xz/+kQwbNixp3bp1svPOOyfnnHNO8sorryQRkdx5553ZuvXr1ycXXnhh0qFDhyQvLy/Z+Ktfbd+7WbNmJRGRTJ06tcr4nXfemURE8uKLL1arHz58eNK2bduksLAw6d27d3LGGWckc+fOzdaMHj06KSoqqvY8N74On/XCCy8kgwYNSlq2bPmFr/XGXmv7+uy+Q4cOTfbee+9q99GzZ89kxIgR1cbr+nOSJElyxx13JPvuu2+2bujQocn06dOzty9evDgZMWJE0qZNmyQikqFDhyZJkiQ/+clPkgMPPDDZcccdk1atWiV9+/ZNrrrqqmTt2rW1PmcA2NbkJUkN/8UIAGwXpk2bFieeeGI8//zzccghh2yyduDAgdGhQ4ca1y7a0i6++OK49dZbY/Xq1VvVItkAANSdNaUAYDvxySefVNnesGFD3HjjjVFcXBz77bdfdnzdunWxfv36KrWzZ8+OV155JY444ohG7+vjjz+Oe+65Jw499FCBFADANsyaUgCwnbjwwgvjk08+icGDB0dlZWU89NBD8cILL8TVV18drVq1ytZ98MEHMWzYsPjGN74RXbp0iTfeeCMmT54cnTt3jvPOO2+L9zV48OA44ogjYs8994wlS5bEr3/96ygrK4sf/ehHW/yxAABoOoRSALCdOOqoo+K6666Lxx57LCoqKqJPnz5x4403VltYuV27djFo0KC4/fbbY+nSpVFUVBQjRoyIn/70p7HTTjtt8b6+8pWvxIMPPhi33XZb5OXlxX777Re//vWv4/DDD9/ijwUAQNNhTSkAAAAAUmdNKQAAAABSJ5QCAAAAIHXb3ZpSmUwmPvzww2jTpk3k5eXluh0AAACAbUqSJLFq1aro0qVL5OfXPh9quwulPvzww+jevXuu2wAAAADYpr3//vvRrVu3Wm/f7kKpNm3aRMSnL0xxcXGOuwEAAADYtpSVlUX37t2zGUxttrtQauMle8XFxUIpAAAAgEbyRcsmWegcAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQ1z3UDAAAAANuTzLryetXntyhqpE5ySygFAAAAkKIFk9rVq77XxWsbqZPccvkeAAAAAKkzUwoAAAAgRT3HrKiynVlXHu/f1i0iIrp/+9/b7OV6nyeUAgAAAEjRpkKn/BZF200o5fI9AAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFKX01DqlltuiX322SeKi4ujuLg4Bg8eHE8++eQm95k6dWr07ds3CgsLo3///vHEE0+k1C0AAAAAW0pOQ6lu3brFT3/60/jb3/4Wc+fOjaOOOipOOOGEeP3112usf+GFF2LkyJFx1llnxcsvvxwlJSVRUlISr732WsqdAwAAALA58pIkSXLdxGe1b98+rrnmmjjrrLOq3XbqqadGeXl5PPbYY9mxgw8+OAYOHBiTJ0+u0/2XlZVF27Zto7S0NIqLi7dY3wAAAAANkVlXHgsmtYuIiJ5jVkR+i6Icd7R56pq9NJk1pTZs2BBTpkyJ8vLyGDx4cI01c+bMiWHDhlUZGz58eMyZMyeNFgEAAADYQprnuoFXX301Bg8eHBUVFdG6det4+OGHY6+99qqxdvHixdGpU6cqY506dYrFixfXev+VlZVRWVmZ3S4rK4uIiEwmE5lMZgs8AwAAAICG+2w+sWHtmohmrXLYzeara96S81DqS1/6UsybNy9KS0vjwQcfjNGjR8ezzz5bazBVXxMnTowJEyZUG1+6dGlUVFRskccAAAAAqK8ksyEyHzwd69+6Kzv27191jfyOQ6L57qMiv+vwyMtvlrsGG2jVqlV1qst5KNWyZcvo06dPREQMGjQoXnzxxfjFL34Rt956a7Xazp07x5IlS6qMLVmyJDp37lzr/Y8bNy7Gjh2b3S4rK4vu3btHhw4drCkFAAAA5ESmsiyWPnFarH1/ZvXbPnoh1n70QhR2Pyp2/sqUyC/YuvKLwsLCOtXlPJT6vEwmU+Vyu88aPHhwzJgxIy6++OLs2PTp02tdgyoioqCgIAoKCqqN5+fnR35+k1lSCwAAANhOJJkNsfTJkVFRQyD1WRXvz4ylT46MziWPbVUzpuqat+Q0lBo3blwce+yx0aNHj1i1alXcd999MXv27Hj66acjImLUqFHRtWvXmDhxYkREXHTRRTF06NC47rrrYsSIETFlypSYO3du3Hbbbbl8GgAAAAB1tubdR6Ji4Yw61VYsnBFr/vVoFPU5sZG7Sl9OQ6mPPvooRo0aFYsWLYq2bdvGPvvsE08//XR8+ctfjoiIhQsXVknXhgwZEvfdd1/88Ic/jO9///ux++67x7Rp06Jfv365egoAAAAA9VL29+pLFm2y/pVbt8lQKi9JkiTXTaSprKws2rZtG6WlpdaUAgAAAFKVWV8RC26qfx7R84KyyG9et7Wacq2u2YtFlQAAAABSkqyt2yfTban9mjKhFAAAAEBK8lq2SXW/pkwoBQAAAJCS/OaFUdj9yHrtU9j9qK3m0r36EEoBAAAApKh4n3PrVz+gfvVbC6EUAAAAQIp26H1CFPY4uk61hT2Ojh12O76RO8oNoRQAAABAivLym0WnEfd/YTBV2OPo6DTi/sjLb5ZSZ+kSSgEAAACkLL+gODqXPBYdv3p/FHYbWuW2wu5HRcev3h+dSx6L/ILiHHXY+JrnugEAAACA7VFefrMo6nNitOp5TCyY1C4iInqcuyiatdopx52lw0wpAAAAgCYibxv8lL3aCKUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASF3zXDcAAAAAsD3JrCuvdfvzt0VE5LcoavSeckEoBQAAAJCiBZPa1Xrb+7d1qzbW6+K1jdlOzrh8DwAAAIDUmSkFAAAAkKKeY1bkuoUmQSgFAAAAkKJtdY2o+nL5HgAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpy2koNXHixDjggAOiTZs20bFjxygpKYk333xzk/vcddddkZeXV+WrsLAwpY4BAAAA2BJyGko9++yzMWbMmPjzn/8c06dPj3Xr1sUxxxwT5eXlm9yvuLg4Fi1alP1asGBBSh0DAAAAsCU0z+WDP/XUU1W277rrrujYsWP87W9/i8MPP7zW/fLy8qJz586N3R4AAAAAjSSnodTnlZaWRkRE+/btN1m3evXq6NmzZ2Qymdhvv/3i6quvjr333rvG2srKyqisrMxul5WVRUREJpOJTCazhToHAAAAICLqnLfkJUmSNHIvdZLJZOL444+PlStXxvPPP19r3Zw5c+Ltt9+OffbZJ0pLS+Paa6+N5557Ll5//fXo1q1btforrrgiJkyYUG38rbfeijZt2mzR5wAAAACwvVu1alXsscceUVpaGsXFxbXWNZlQ6vzzz48nn3wynn/++RrDpdqsW7cu9txzzxg5cmT8+Mc/rnZ7TTOlunfvHitWrNjkCwMAAABA/ZWVlUW7du2+MJRqEpfvXXDBBfHYY4/Fc889V69AKiKiRYsWse+++8Y777xT4+0FBQVRUFBQbTw/Pz/y83O6zjsAAADANqeueUtOU5kkSeKCCy6Ihx9+OGbOnBm9evWq931s2LAhXn311dhll10aoUMAAAAAGkNOZ0qNGTMm7rvvvnjkkUeiTZs2sXjx4oiIaNu2bbRq1SoiIkaNGhVdu3aNiRMnRkTElVdeGQcffHD06dMnVq5cGddcc00sWLAgzj777Jw9DwAAAADqJ6eh1C233BIREUcccUSV8TvvvDPOOOOMiIhYuHBhlWlfK1asiHPOOScWL14c7dq1i0GDBsULL7wQe+21V1ptAwAAALCZmsxC52kpKyuLtm3bfuFiWwAAAADUX12zFyt9AwAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqWue6wYAAAC2Z5l15fWqz29R1EidAKRLKAUAAJBDCya1q1d9r4vXNlInAOly+R4AAAAAqTNTCgAAIId6jllRZTuzrjzev61bRER0//a/Xa4HbLOEUgAAADm0qdApv0WRUArYZrl8DwAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAoIlK1lfkugWARiOUAgAAaAKSzIYof/uhWDLthOzYwlt3iUW/Hx7lbz8USWZDDrsD2PKa57oBAACA7V2msiyWPH5qVCycUe22ivdnRcX7s6Kwx9HRacT9kV9QnIMOAbY8M6UAAAByKMlsqDWQ+qyKhTNiyeOnmjEFbDOEUgAAADm05t1HvjCQ2qhi4YxY869HG7kjgHQIpQAAAHKo7O+31q/+lfrVAzRVOQ2lJk6cGAcccEC0adMmOnbsGCUlJfHmm29+4X5Tp06Nvn37RmFhYfTv3z+eeOKJFLoFAADYsjLrK6Li/Vn12qfi/ZmR8al8wDYgp6HUs88+G2PGjIk///nPMX369Fi3bl0cc8wxUV5eXus+L7zwQowcOTLOOuusePnll6OkpCRKSkritddeS7FzAACAzZesXZXqfgBNSV6SJEmum9ho6dKl0bFjx3j22Wfj8MMPr7Hm1FNPjfLy8njssceyYwcffHAMHDgwJk+e/IWPUVZWFm3bto3S0tIoLvapFQAAQO5k1lfEgpvq/3dJzwvKIr95YSN0BLD56pq9NKk1pUpLSyMion379rXWzJkzJ4YNG1ZlbPjw4TFnzpxG7Q0AAGBLy29eGIXdj6zXPoXdjxJIAduE5rluYKNMJhMXX3xxHHLIIdGvX79a6xYvXhydOnWqMtapU6dYvHhxjfWVlZVRWVmZ3S4rK8s+XiaT2QKdAwAANFzr/t+u17pSbfp/298yQJNW13NUkwmlxowZE6+99lo8//zzW/R+J06cGBMmTKg2vnTp0qiosDggAACQW0nrgyO/06GRWfLFfwvldzo0VrU+KFZ/9FEKnQE0zKpVdVv3rkmEUhdccEE89thj8dxzz0W3bt02Wdu5c+dYsmRJlbElS5ZE586da6wfN25cjB07NrtdVlYW3bt3jw4dOlhTCgAAaBIyJQ/F0idOi4r3Z9ZaU9j9qOjwlSmRX+DvGKBpKyys2yXGOQ2lkiSJCy+8MB5++OGYPXt29OrV6wv3GTx4cMyYMSMuvvji7Nj06dNj8ODBNdYXFBREQUFBtfH8/PzIz29SS2oBAADbqfxWO0bnEx+PNf96NMrm3RwV/342e1th96OieMC5scNux0defrMcdglQN3XNW3IaSo0ZMybuu+++eOSRR6JNmzbZdaHatm0brVq1ioiIUaNGRdeuXWPixIkREXHRRRfF0KFD47rrrosRI0bElClTYu7cuXHbbbfl7HkAAABsrrz8ZlHU58Ro1fOYWDCpXURE9Dh3UTRrtVOOOwNoHDmdKnTLLbdEaWlpHHHEEbHLLrtkv+6///5szcKFC2PRokXZ7SFDhsR9990Xt912WwwYMCAefPDBmDZt2iYXRwcAANga5fmUPWAblpckSZLrJtJUVlYWbdu2jdLSUmtKAQAATU5mXXl2plTPMSsiv0VRjjsCqJ+6Zi8WVQIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdc1z3QAAAMD2LLOuvNbtz98WET6ND9hmCKUAAAByaMGkdrXe9v5t3aqN9bp4bWO2A5Aal+8BAAAAkDozpQAAAHKo55gVuW4BICeEUgAAADlkjShge+XyPQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABS16BQqlmzZvHRRx9VG//444+jWbNmm90UAAAAANu2BoVSSZLUOF5ZWRktW7bcrIYAAAAA2PY1r0/xL3/5y4iIyMvLi9tvvz1at26dvW3Dhg3x3HPPRd++fbdshwAAAABsc+oVSl1//fUR8elMqcmTJ1e5VK9ly5ax6667xuTJk7dshwAAAABsc+oVSs2fPz8iIo488sh46KGHol27do3SFAAAAADbtnqFUhvNmjVrS/cBAAAAwHakQQudf+1rX4uf/exn1cZ//vOfx9e//vXNbgoAAACAbVuDQqnnnnsuvvKVr1QbP/bYY+O5557b7KYAAAAA2LY1KJRavXp1tGzZstp4ixYtoqysbLObAgAAAGDb1qBQqn///nH//fdXG58yZUrstddedb6f5557Lo477rjo0qVL5OXlxbRp0zZZP3v27MjLy6v2tXjx4vo+BQAAAAByqEELnf/oRz+Kk046Kd5999046qijIiJixowZ8bvf/S6mTp1a5/spLy+PAQMGxLe+9a046aST6rzfm2++GcXFxdntjh071r15AAAAAHKuQaHUcccdF9OmTYurr746HnzwwWjVqlXss88+8cwzz8TQoUPrfD/HHntsHHvssfV+/I4dO8aOO+5Y7/0AAAAAaBoaFEpFRIwYMSJGjBixJXups4EDB0ZlZWX069cvrrjiijjkkENqra2srIzKysrs9sY1rzKZTGQymUbvFQAAAGB7Ute8pcGh1MqVK+PBBx+Mf/3rX3HJJZdE+/bt46WXXopOnTpF165dG3q3m7TLLrvE5MmTY//994/Kysq4/fbb44gjjoi//OUvsd9++9W4z8SJE2PChAnVxpcuXRoVFRWN0icAAADA9mrVqlV1qstLkiSp753//e9/j2HDhkXbtm3jvffeizfffDN22223+OEPfxgLFy6M3/zmN/VuOC8vLx5++OEoKSmp135Dhw6NHj16xD333FPj7TXNlOrevXusWLGiyrpUAAAAAGy+srKyaNeuXZSWlm4ye2nQTKmxY8fGGWecET//+c+jTZs22fGvfOUrcfrppzfkLhvswAMPjOeff77W2wsKCqKgoKDaeH5+fuTnN+jDBwEAAACoRV3zlgalMi+++GKce+651ca7du0aixcvbshdNti8efNil112SfUxAQAAANg8DZopVVBQkF0w/LPeeuut6NChQ53vZ/Xq1fHOO+9kt+fPnx/z5s2L9u3bR48ePWLcuHHxwQcfZC8HvOGGG6JXr16x9957R0VFRdx+++0xc+bM+MMf/tCQpwEAAABAjjQolDr++OPjyiuvjAceeCAiPl0PauHChXHZZZfF1772tTrfz9y5c+PII4/Mbo8dOzYiIkaPHh133XVXLFq0KBYuXJi9fe3atfG9730vPvjgg9hhhx1in332iWeeeabKfQAAAADQ9DVoofPS0tI4+eSTY+7cubFq1aro0qVLLF68OAYPHhxPPPFEFBUVNUavW0RZWVm0bdv2CxfbAgAAAKD+6pq9NGimVNu2bWP69Onxpz/9KV555ZVYvXp17LfffjFs2LAGNwwAAADA9qPOoVT79u3jrbfeip133jm+9a1vxS9+8Ys45JBD4pBDDmnM/gAAAADYBtX50/fWrl2bXdz87rvvjoqKikZrCgAAAIBtW51nSg0ePDhKSkpi0KBBkSRJfOc734lWrVrVWHvHHXdssQYBAAAA2PbUOZT67W9/G9dff328++67EfHpYudmSwEAAADQEA369L1evXrF3LlzY6eddmqMnhqVT98DAAAAaDx1zV7qvKZU+/btY9myZRERceSRR0bLli03v0sAAAAAtksWOgcAAAAgdRY6BwAAACB1DVroPC8vz0LnAAAAADSYhc4BAAAA2GK2+ELnERFf+cpXorS0NObPnx877bRT/PSnP42VK1dmb//4449jr732anDTAAAAAGwf6hVKPfXUU1FZWZndvvrqq2P58uXZ7fXr18ebb7655boDAAAAYJtUr1Dq8xpw5R8AAAAAbF4oBQAAAAANUa9QKi8vL/Ly8qqNAQAAAEB9NK9PcZIkccYZZ0RBQUFERFRUVMR5550XRUVFERFV1psCAAAAgNrUK5QaPXp0le1vfOMb1WpGjRq1eR0BAAAAsM2rVyh15513NlYfAAAAAGxHLHQOAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkrnmuGwAA4D8y68rrVZ/foqiROgEAaFxCKQCAJmTBpHb1qu918dpG6gQAoHG5fA8AAACA1JkpBQDQhPQcs6LKdmZdebx/W7eIiOj+7X+7XA8A2GYIpQAAmpBNhU75LYqEUgDANsPlewAAAACkTigFALCVSNZX5LoFAIAtRigFANAEJZkNUf72Q7Fk2gnZsYW37hKLfj88yt9+KJLMhhx2BwCw+XIaSj333HNx3HHHRZcuXSIvLy+mTZv2hfvMnj079ttvvygoKIg+ffrEXXfd1eh9AgCkKVNZFounfTU+evy0qPjguSq3Vbw/Kz56/LRYPO2rkaksy1GHAACbL6ehVHl5eQwYMCAmTZpUp/r58+fHiBEj4sgjj4x58+bFxRdfHGeffXY8/fTTjdwpAEA6ksyGWPL4qVGxcMYm6yoWzoglj59qxhQAsNXK6afvHXvssXHsscfWuX7y5MnRq1evuO666yIiYs8994znn38+rr/++hg+fHhjtQkAkJo17z7yhYHURhULZ8Safz0aRX1ObOSuAAC2vK1qTak5c+bEsGHDqowNHz485syZk6OOAAC2rLK/31q/+lfqVw8A0FTkdKZUfS1evDg6depUZaxTp05RVlYWn3zySbRq1araPpWVlVFZWZndLiv7dO2FTCYTmUymcRsGAKiHZH1FVLw/q177VLw/MzasXRN5zQsbqSsAgPqpa96yVYVSDTFx4sSYMGFCtfGlS5dGRYWPVQYAmo6k4uMG7ffRh/Mjr3CnLdwNAEDDrFq1qk51W1Uo1blz51iyZEmVsSVLlkRxcXGNs6QiIsaNGxdjx47NbpeVlUX37t2jQ4cOUVxc3Kj9AgDUR7K+OBY2YL+OXXqZKQUANBmFhXX7vWSrCqUGDx4cTzzxRJWx6dOnx+DBg2vdp6CgIAoKCqqN5+fnR37+VrWkFgCwrWu5QxR2P7Jel/AVdj8qmrXcoRGbAgCon7rmLTlNZVavXh3z5s2LefPmRUTE/PnzY968ebFw4af/Rzhu3LgYNWpUtv68886Lf/3rX3HppZfGG2+8ETfffHM88MAD8d3vfjcX7QMAbHHF+5xbv/oB9asHAGgqchpKzZ07N/bdd9/Yd999IyJi7Nixse+++8bll18eERGLFi3KBlQREb169YrHH388pk+fHgMGDIjrrrsubr/99hg+fHhO+gcA2NJ26H1CFPY4uk61hT2Ojh12O76ROwIAaBx5SZIkuW4iTWVlZdG2bdsoLS21phQA0CRlKstiyeOnRsXCGbXWFPY4OjqNuD/yC/w+AwA0LXXNXiyqBADQxOQXFEfnksei41fvj8JuQ6vcVtj9qOj41fujc8ljAikAYKu2VS10DgCwvcjLbxZFfU6MVj2PiQWT2kVERI9zF0WzVjvluDMAgC3DTCkAgK1EXvO6fbwyAMDWQCgFAAAAQOpcvgcA0IRk1pXXuv352yIi8lsUNXpPAACNQSgFANCEbFw/qibv39at2livi9c2ZjsAAI3G5XsAAAAApM5MKQCAJqTnmBW5bgEAIBVCKQCAJsQaUQDA9sLlewAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqaRCg1adKk2HXXXaOwsDAOOuig+Otf/1pr7V133RV5eXlVvgoLC1PsFgAAAIDNlfNQ6v7774+xY8fG+PHj46WXXooBAwbE8OHD46OPPqp1n+Li4li0aFH2a8GCBSl2DAAAAMDmynko9b//+79xzjnnxJlnnhl77bVXTJ48OXbYYYe44447at0nLy8vOnfunP3q1KlTih0DAAAAsLlyGkqtXbs2/va3v8WwYcOyY/n5+TFs2LCYM2dOrfutXr06evbsGd27d48TTjghXn/99TTaBQAAAGALaZ7LB1+2bFls2LCh2kynTp06xRtvvFHjPl/60pfijjvuiH322SdKS0vj2muvjSFDhsTrr78e3bp1q1ZfWVkZlZWV2e2ysrKIiMhkMpHJZLbgswEAAACgrnlLTkOphhg8eHAMHjw4uz1kyJDYc88949Zbb40f//jH1eonTpwYEyZMqDa+dOnSqKioaNReAQAAALY3q1atqlNdTkOpnXfeOZo1axZLliypMr5kyZLo3Llzne6jRYsWse+++8Y777xT4+3jxo2LsWPHZrfLysqie/fu0aFDhyguLm548wAAAABUU1hYWKe6nIZSLVu2jEGDBsWMGTOipKQkIj6d4jVjxoy44IIL6nQfGzZsiFdffTW+8pWv1Hh7QUFBFBQUVBvPz8+P/Pycr/MOAAAAsE2pa96S88v3xo4dG6NHj479998/DjzwwLjhhhuivLw8zjzzzIiIGDVqVHTt2jUmTpwYERFXXnllHHzwwdGnT59YuXJlXHPNNbFgwYI4++yzc/k0AAAAAKiHnIdSp556aixdujQuv/zyWLx4cQwcODCeeuqp7OLnCxcurJKwrVixIs4555xYvHhxtGvXLgYNGhQvvPBC7LXXXrl6CgAAAADUU16SJEmum0hTWVlZtG3bNkpLS60pBQAAALCF1TV7sagSAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKlrnusGqJvyyvX1qi8q8K0FAAAAmi7JxVaizQ+erFd95trjGqkTAAAAgM3n8j0AAAAAUmem1FZi1VXHVtkuX7s+Ok+YHhERi8d/OYpa+lYCAAAAWw9JxlZiU2tEFbVsbg0pAAAAYKvi8j0AAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1VscGaKLKK9fXq94HHgAAAFsTf8EANFFtfvBkveoz1x7XSJ0AAABseS7fAwAAACB1ZkoBNFGrrjq2ynb52vXRecL0iIhYPP7LUdTSKRwAANh6+YsGoIna1BpRRS2bW0MKAADYqrl8DwAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCmArVLFuQ65bAAAA2CxCKYAmbkMmid///cP46q//mh3rcMUfYtjkOfH7v38YGzJJDrsDAABoGJ8nvg2oWLfBR8PDNqqsYl2cfPfceObtZdVum/nOspj5zrIYtvvO8eDo/aO4sEUOOgQAAGgYM6W2MmZMwPZjQyapNZD6rGfeXhYn3z3X8Q8AAGxVTK/ZipgxAduXaa8t+sJAaqNn3l4Wj7y+OE7qv0sjdwUAALBlNImZUpMmTYpdd901CgsL46CDDoq//vWvm6yfOnVq9O3bNwoLC6N///7xxBNPpNRp7pgxAdufW15YUM/69xqnEQAAgEaQ81Dq/vvvj7Fjx8b48ePjpZdeigEDBsTw4cPjo48+qrH+hRdeiJEjR8ZZZ50VL7/8cpSUlERJSUm89tprKXeerobMmAC2XhXrNsTMd+p2zG804+1lPpUPAADYauQlSZLTKTUHHXRQHHDAAXHTTTdFREQmk4nu3bvHhRdeGP/zP/9Trf7UU0+N8vLyeOyxx7JjBx98cAwcODAmT578hY9XVlYWbdu2jaVLl0ZxcXG12/Pz86N58/9c1bh27dpa7ysvLy9atGjRoNp169ZFbS99TbXDf/XXmP2v5bXe/+cd2bt9PHnWAdXGW7ZsWacePl+7fv36yGQyW6S2RYsWkZeX16i1GzZsiA0bav/jvD61zZs3j/z8/CZTm8lkYv369bXWNmvWLJo1a9ZkapMkiXXr1m2R2s8en41VG7HpYzmtc8SHK8qj21Uza62vzfvfPzI6tP7P8Vifc0+Ec0RDap0jNq/WOSKd3yO2RG2Ec0RDap0jNq/WOcI5or61zhGbV9sUjnvniG3jHFFWVhYdOnSI0tLSGrOXjXK6ptTatWvjb3/7W4wbNy47lp+fH8OGDYs5c+bUuM+cOXNi7NixVcaGDx8e06ZNq7G+srIyKisrs9tlZWUREXHttddGQUFBtfo+ffrEf/3Xf2W3f/7zn9f6A9azZ88444wzstvXX399rFmzpsbaXXbZJb797W9nt2+66aZYuXJljbUdOnSI//7v//5P7S23xuyFPWusrc2sd5fHhJ9cHc3z/vPDs8MOO8T//b//N7t9zz33xIIFNV8e1KJFi/j+97+f3f7d734X77zzTq2PN378+Oy/H3zwwfjnP/9Za+24ceOybyyPPvpovPLKK7XWXnLJJVFUVBQREU8++WTMnTu31tqLLroodtxxx4iImD59eq0/QxER559/fnTs2DEiIp599tl49tlna609++yzo2vXrhHx6Uy9Z555ptba0aNHx6677hoRES+++GI8+eSTtdaOHDky9thjj4iIeOWVV+KRRx6ptfbkk0+OvffeOyIiXn/99XjwwQdrrT3hhBNi4MCBERHx1ltvxe9+97taa4899tg48MADIyLivffei7vvvrvW2mHDhsUhhxwSEREffPBB3H777bXWDh06NI444oiIiPjoo4/illtuqbV28ODBccwxx0RExMqVK+MXv/hFrbX7779/jBgxIiIiysvL49prr621dsCAAVFSUhIRn55rJk6cWGvtnnvuGaecckp2+6qrrqq1Nq1zxG/v/FVE9K61j9rcfMO1VY77HXfcMS666KLs9q9//etYtGhRjfs6R/yHc8SnnCM+1RTPEfX5PeLWW2+NpUuX1ljrHPEfzhGfco74lHPEp5wj/sM54lPOEZ9yjvjUps4Rn81hNiWnodSyZctiw4YN0alTpyrjnTp1ijfeeKPGfRYvXlxj/eLFNV+uNnHixJgwYUK18fLy8hrT17KysiqXDq5evbrWlHbVqlXVaj/55JM61a5atSrKy8trrC0sLKxS+/Gqmn+wvsiKNZWxQ/znBziTydS5h+bNm1epLSsrq7U2Iupdu/GNorS0dJO1S5cuzd5el9qN6fDKlSs3Wbts2X8ui6pL7cb0d8WKFZus/fjjj2OHHXaoc+3G12358uWbrF2+fHmDaj/++ONN1q5YsaJBtcuWLdtk7cqVKxtU+0Xf49LS0mztmjVr6ly7du3aTdZ+/rivT21jnSMqVpdF91gR70e7Wnv5vB6xIirXrI7Pnv6bNWtW5+PeOeI/nCMi26NzRNM8R9Tn94hN1TpHVK11jnCOqKnWOcI5YmOtc4RzRE21zhE119Y1lMrp5XsffvhhdO3aNV544YUYPHhwdvzSSy+NZ599Nv7yl79U26dly5Zx9913x8iRI7NjN998c0yYMCGWLFlSrb6mmVLdu3ePJUuWbDWX761aUxHtrphR633XZvn4o6KwRbMqY6bU1r/WlNrNqzWltuHniIdfWxyn/+7vte7zefeN3CdK9q4a2pt2X3Otc4RzxLZwjnBpjnOEc4RzRG21zhHOEc4R9a91jtiytWVlZdGpU6emffnezjvvHM2aNasWJi1ZsiQ6d+5c4z6dO3euV31BQUGNl+kVFhZGYWHhF/ZYl5qG1NbUU23att4hjuqzc70WPT56951jxzZFW6yHz74RbA21+fn5VQ6Oba32syeqpl4bEdk3gq2ltrGO+/qeI76+b4+4Y+6HdfqQg2G77xxf37dHNMvP+8L7rU8PddUUjnvniP/U5vq4d45o/NrGOpadIz7VVI5l54hPNYXj3jmicWubwnHvHPGf2lwf984RjV+bxnG/qZDss3L66XstW7aMQYMGxYwZ/5kFlMlkYsaMGVVmTn3W4MGDq9RHfHpNb23124rzh9RvTanzh+zaOI0AqWmWnxcPjt4/hu2+8ybrhu2+czw4ev8vDKQAAACakpyGUhERY8eOjV/96ldx9913xz//+c84//zzo7y8PM4888yIiBg1alSVhdAvuuiieOqpp+K6666LN954I6644oqYO3duXHDBBbl6Cqko6bfLF/5hutGw3XeOE/aueeYYsHUpLmwRT55zcDw4ev84ovdOVW47+v+HUU+ec3AUF9btf78AAACaipxevhcRceqpp8bSpUvj8ssvj8WLF8fAgQPjqaeeyi5mvnDhwuz1rRERQ4YMifvuuy9++MMfxve///3YfffdY9q0adGvX79cPYVUbJwxcfLdczd5KY8ZE7DtaZafFyf13yWG79Eh2vzg009ZWXrFMbFT67pPpQUAAGhqcrrQeS6UlZVF27Ztv3CxraZqQyaJR15fHDc9Pz9mv/txdvzo3XeO84fsGifs3VkgBduo8sr12VBq1VXHRlFBzv9fAQAAoJq6Zi/+otnKmDEBAAAAbAtyvqYUm6+wRd1X+gcAAABoCoRSAAAAAKROKAUAAABA6qwpBdBElVeur7q9dn2N/97IwucAAMDWxF8wAE3Uxg8zqEnnCdOrjWWuPa4x2wEAANiiXL4HAAAAQOrMlAJoolZddWyuWwAAAGg0QimAJsoaUQAAwLbM5XsAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApK55rhugbsor11fdXru+xn9vVFTgWwsAAAA0XZKLrUSbHzxZ622dJ0yvNpa59rjGbAcAAABgs7h8DwAAAIDUmSm1lVh11bG5bgEAAABgixFKbSWsEQUAAABsS1y+BwAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApK55rhtIW5IkERFRVlaW404AAAAAtj0bM5eNGUxttrtQatWqVRER0b179xx3AgAAALDtWrVqVbRt27bW2/OSL4qttjGZTCY+/PDDaNOmTeTl5eW6nc1SVlYW3bt3j/fffz+Ki4tz3Q6QAsc9bJ8c+7D9cdzD9mdbOu6TJIlVq1ZFly5dIj+/9pWjtruZUvn5+dGtW7dct7FFFRcXb/U/sED9OO5h++TYh+2P4x62P9vKcb+pGVIbWegcAAAAgNQJpQAAAABInVBqK1ZQUBDjx4+PgoKCXLcCpMRxD9snxz5sfxz3sP3ZHo/77W6hcwAAAAByz0wpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUKpJmzdunVx2WWXRf/+/aOoqCi6dOkSo0aNig8//PAL9500aVLsuuuuUVhYGAcddFD89a9/TaFjYEt56KGH4phjjomddtop8vLyYt68eXXab+rUqdG3b98oLCyM/v37xxNPPNG4jQJbTH3fux3vsPV67rnn4rjjjosuXbpEXl5eTJs27Qv3mT17duy3335RUFAQffr0ibvuuqvR+wS2nIkTJ8YBBxwQbdq0iY4dO0ZJSUm8+eabX7jftv5+L5RqwtasWRMvvfRS/OhHP4qXXnopHnrooXjzzTfj+OOP3+R+999/f4wdOzbGjx8fL730UgwYMCCGDx8eH330UUqdA5urvLw8Dj300PjZz35W531eeOGFGDlyZJx11lnx8ssvR0lJSZSUlMRrr73WiJ0CW0J937sd77B1Ky8vjwEDBsSkSZPqVD9//vwYMWJEHHnkkTFv3ry4+OKL4+yzz46nn366kTsFtpRnn302xowZE3/+859j+vTpsW7dujjmmGOivLy81n22h/f7vCRJklw3Qd29+OKLceCBB8aCBQuiR48eNdYcdNBBccABB8RNN90UERGZTCa6d+8eF154YfzP//xPmu0Cm+m9996LXr16xcsvvxwDBw7cZO2pp54a5eXl8dh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" ] @@ -707,12 +775,143 @@ "fig, ax = aggregated_eventstudy.plot_effects()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Aggregation Details\n", + "\n", + "The `DoubleMLDIDAggregation` objects include several `DoubleMLFrameworks` which support methods like `bootstrap()` or `confint()`.\n", + "Further, the weights can be accessed via the properties\n", + "\n", + " - ``overall_aggregation_weights``: weights for the overall aggregation\n", + " - ``aggregation_weights``: weights for the aggregation\n", + "\n", + "To clarify, e.g. for the eventstudy aggregation" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDAggregation Object ==================\n", + " Event Study Aggregation \n", + "\n", + "------------------ Overall Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1.991584 0.038736 51.414242 0.0 1.915663 2.067506\n", + "------------------ Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "-2.0 -0.000186 0.068382 -0.002723 0.997827 -0.134213 0.133841\n", + "-1.0 0.013228 0.040589 0.325893 0.744505 -0.066325 0.092781\n", + "0.0 0.995559 0.030849 32.272276 0.000000 0.935096 1.056021\n", + "1.0 2.023443 0.045615 44.359392 0.000000 1.934039 2.112846\n", + "2.0 2.955752 0.063206 46.764006 0.000000 2.831871 3.079633\n", + "------------------ Additional Information ------------------\n", + "Score function: observational\n", + "Control group: never_treated\n", + "Anticipation periods: 0\n", + "\n" + ] + } + ], + "source": [ + "print(aggregated_eventstudy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, the overall effect aggregation aggregates each effect with positive exposure" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0. 0.33333333 0.33333333 0.33333333]\n" + ] + } + ], + "source": [ + "print(aggregated_eventstudy.overall_aggregation_weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If one would like to consider how the aggregated effect with $e=0$ is computed, one would have to look at the third set of weights within the ``aggregation_weights`` property" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.32875335, 0. , 0. , 0. , 0.32674263,\n", + " 0. , 0. , 0. , 0.34450402])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aggregated_eventstudy.aggregation_weights[2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking a look at the original `dml_obj`, one can see that this combines the following estimates:\n", + "\n", + " - $\\widehat{ATT}(2,1,2)$\n", + " - $\\widehat{ATT}(3,2,3)$\n", + " - $\\widehat{ATT}(4,3,4)$" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "ATT(2.0,1,2) 0.925650 0.064143 14.430953 0.000000 0.799932 1.051369\n", + "ATT(2.0,1,3) 1.987835 0.064616 30.763940 0.000000 1.861190 2.114479\n", + "ATT(2.0,1,4) 2.955752 0.063206 46.764006 0.000000 2.831871 3.079633\n", + "ATT(3.0,1,2) -0.039327 0.066091 -0.595052 0.551809 -0.168862 0.090208\n", + "ATT(3.0,2,3) 1.103572 0.065372 16.881417 0.000000 0.975446 1.231699\n", + "ATT(3.0,2,4) 2.059269 0.065414 31.480585 0.000000 1.931060 2.187478\n", + "ATT(4.0,1,2) -0.000186 0.068382 -0.002723 0.997827 -0.134213 0.133841\n", + "ATT(4.0,2,3) 0.063073 0.066448 0.949209 0.342514 -0.067163 0.193309\n", + "ATT(4.0,3,4) 0.959826 0.067745 14.168265 0.000000 0.827048 1.092603\n" + ] + } + ], + "source": [ + "print(dml_obj.summary)" + ] } ], "metadata": { From b1999766521ba4032c60c16747ec7df345eebf03 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Thu, 24 Apr 2025 14:44:05 +0000 Subject: [PATCH 097/140] fix typos and add thumbnail cell tag --- doc/examples/did/py_panel_simple.ipynb | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/doc/examples/did/py_panel_simple.ipynb b/doc/examples/did/py_panel_simple.ipynb index ec0ed4ad..465bb213 100644 --- a/doc/examples/did/py_panel_simple.ipynb +++ b/doc/examples/did/py_panel_simple.ipynb @@ -327,7 +327,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## ATT estimation\n", + "## ATT Estimation\n", "\n", "The [DoubleML-package](https://docs.doubleml.org/stable/index.html) implements estimation of group-time average treatment effect via the `DoubleMLDIDMulti` class (see [model documentation](https://docs.doubleml.org/stable/guide/models.html#difference-in-differences-models-did)).\n", "\n", @@ -406,7 +406,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The summary estimates the $ATT(g,t_\\text{eval})$ effects for different combinations of $(g,t_\\text{eval})$ via $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$, where\n", + "The summary displays estimates of the $ATT(g,t_\\text{eval})$ effects for different combinations of $(g,t_\\text{eval})$ via $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$, where\n", " - $\\mathrm{g}$ specifies the group\n", " - $t_\\text{pre}$ specifies the corresponding pre-treatment period\n", " - $t_\\text{eval}$ specifies the evaluation period\n", @@ -542,7 +542,11 @@ { "cell_type": "code", "execution_count": 13, - "metadata": {}, + "metadata": { + "tags": [ + "nbsphinx-thumbnail" + ] + }, "outputs": [ { "name": "stderr", @@ -657,7 +661,7 @@ "\n", "This aggregates $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$, based on $t_\\text{eval}$, but weighted with respect to group size. Corresponds to *Calendar Time Effects* from the [did-R-package](https://bcallaway11.github.io/did/index.html).\n", "\n", - "For calendar time effects set `aggregation=\"group\"`." + "For calendar time effects set `aggregation=\"time\"`." ] }, { From d1258e12f6cae8656ae7836d7746230afc9b7103 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Thu, 24 Apr 2025 14:44:21 +0000 Subject: [PATCH 098/140] create extended example nb --- doc/examples/did/py_panel.ipynb | 2089 ++++++++++++++++++++++++------- 1 file changed, 1647 insertions(+), 442 deletions(-) diff --git a/doc/examples/did/py_panel.ipynb b/doc/examples/did/py_panel.ipynb index e5357635..3c648801 100644 --- a/doc/examples/did/py_panel.ipynb +++ b/doc/examples/did/py_panel.ipynb @@ -4,18 +4,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Python: Panel Data" + "# Python: Panel Data with Multiple Time Periods\n", + "\n", + "In this example, a detailed guide on Difference-in-Differences with multiple time periods using the [DoubleML-package](https://docs.doubleml.org/stable/index.html). The implementation is based on [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001).\n", + "\n", + "The notebook requires the following packages:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", + "import numpy as np\n", "\n", "from lightgbm import LGBMRegressor, LGBMClassifier\n", "from sklearn.linear_model import LinearRegression, LogisticRegression\n", @@ -23,164 +28,192 @@ "from doubleml.did import DoubleMLDIDMulti\n", "from doubleml.data import DoubleMLPanelData\n", "\n", - "from doubleml.did.datasets import make_did_CS2021\n", + "from doubleml.did.datasets import make_did_CS2021" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "\n", + "We will rely on the `make_did_CS2021` DGP, which is inspired by [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001) (Appendix SC) and [Sant'Anna and Zhao (2020)](https://doi.org/10.1016/j.jeconom.2020.06.003).\n", "\n", - "# simulate data\n", + "We will observe `n_obs` units over `n_periods`. Remark that the dataframe includes observations of the potential outcomes `y0` and `y1`, such that we can use oracle estimates as comparisons. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "n_obs = 5000\n", - "df = make_did_CS2021(n_obs, dgp_type=1, n_periods=8, n_pre_treat_periods=4, time_type=\"datetime\", anticipation_periods=1)\n", - "df[\"ite\"] = df[\"y1\"] - df[\"y0\"]" + "n_periods = 6\n", + "\n", + "df = make_did_CS2021(n_obs, dgp_type=4, n_periods=n_periods, n_pre_treat_periods=3, time_type=\"datetime\")\n", + "df[\"ite\"] = df[\"y1\"] - df[\"y0\"]\n", + "\n", + "print(df.shape)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Details\n", + "\n", + "*Here, we slightly abuse the definition of the potential outcomes. $Y_{i,t}(1)$ corresponds to the (potential) outcome if unit $i$ would have received treatment at time period $\\mathrm{g}$ (where the group $\\mathrm{g}$ is drawn with probabilities based on $Z$).*\n", + "\n", + "More specifically\n", + "\n", + "$$\n", + "\\begin{align*}\n", + "Y_{i,t}(0)&:= f_t(Z) + \\delta_t + \\eta_i + \\varepsilon_{i,t,0}\\\\\n", + "Y_{i,t}(1)&:= Y_{i,t}(0) + \\theta_{i,t,\\mathrm{g}} + \\epsilon_{i,t,1} - \\epsilon_{i,t,0}\n", + "\\end{align*}\n", + "$$\n", + "\n", + "where\n", + " - $f_t(Z)$ depends on pre-treatment observable covariates $Z_1,\\dots, Z_4$ and time $t$\n", + " - $\\delta_t$ is a time fixed effect\n", + " - $\\eta_i$ is a unit fixed effect\n", + " - $\\epsilon_{i,t,\\cdot}$ are time varying unobservables (iid. $N(0,1)$)\n", + " - $\\theta_{i,t,\\mathrm{g}}$ correponds to the exposure effect of unit $i$ based on group $\\mathrm{g}$ at time $t$\n", + "\n", + "For the pre-treatment periods the exposure effect is set to\n", + "$$\n", + "\\theta_{i,t,\\mathrm{g}}:= 0 \\text{ for } t<\\mathrm{g}\n", + "$$\n", + "such that \n", + "\n", + "$$\n", + "\\mathbb{E}[Y_{i,t}(1) - Y_{i,t}(0)] = \\mathbb{E}[\\epsilon_{i,t,1} - \\epsilon_{i,t,0}]=0 \\text{ for } t<\\mathrm{g}\n", + "$$\n", + "\n", + "The [DoubleML Coverage Repository](https://docs.doubleml.org/doubleml-coverage/) includes coverage simulations based on this DGP." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Description" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data is a balanced panel where each unit is observed over `n_periods` starting Janary 2025." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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idyy0y1dtZ1Z2Z3Z4iteFirst Treated
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" - ], "text/plain": [ - " id y y0 y1 d t Z1 \\\n", - "1 0 220.164100 220.164100 219.334836 2025-06-01 2025-01-01 0.685912 \n", - "2 0 222.561289 222.561289 222.890774 2025-06-01 2025-02-01 0.685912 \n", - "3 0 229.011563 229.011563 228.624548 2025-06-01 2025-03-01 0.685912 \n", - "4 0 232.255967 232.255967 231.807130 2025-06-01 2025-04-01 0.685912 \n", - "5 0 235.314414 237.134471 235.314414 2025-06-01 2025-05-01 0.685912 \n", - "\n", - " Z2 Z3 Z4 ite First Treated \n", - "1 0.330527 -0.212164 0.818772 -0.829265 2025-06 \n", - "2 0.330527 -0.212164 0.818772 0.329485 2025-06 \n", - "3 0.330527 -0.212164 0.818772 -0.387014 2025-06 \n", - "4 0.330527 -0.212164 0.818772 -0.448837 2025-06 \n", - "5 0.330527 -0.212164 0.818772 -1.820057 2025-06 " + "t\n", + "2025-01-01 4000\n", + "2025-02-01 4000\n", + "2025-03-01 4000\n", + "2025-04-01 4000\n", + "2025-05-01 4000\n", + "2025-06-01 4000\n", + "dtype: int64" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# rename for plotting\n", - "df[\"First Treated\"] = df[\"d\"].dt.strftime(\"%Y-%m\").fillna(\"Never Treated\")\n", - "df.head()" + "df.groupby(\"t\").size()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The treatment column `d` indicates first treatment period of the corresponding unit, whereas `NaT` units are never treated.\n", + "\n", + "*Generally, never treated units should take either on the value `np.inf` or `pd.NaT` depending on the data type (`float` or `datetime`).*\n", + "\n", + "The individual units are roughly uniformly divided between the groups, where treatment assignment depends on the pre-treatment covariates `Z1` to `Z4`." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "d\n", + "2025-04-01 6120\n", + "2025-05-01 5958\n", + "2025-06-01 5646\n", + "NaT 6276\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby(\"d\", dropna=False).size()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, the group indicates the first treated period and `NaT` units are never treated. To simplify plotting and pands" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "d\n", + "2025-04-01 6120\n", + "2025-05-01 5958\n", + "2025-06-01 5646\n", + "NaT 6276\n", + "dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby(\"d\", dropna=False).size()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get a better understanding of the underlying data and true effects, we will compare the unconditional averages and the true effects based on the oracle values of individual effects `ite`." + ] + }, + { + "cell_type": "code", + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -218,57 +251,57 @@ " \n", " 0\n", " 2025-01-01\n", - " 2025-05\n", - " 209.552980\n", - " 197.520569\n", - " 222.082885\n", - " -0.040413\n", - " -2.317893\n", - " 2.385829\n", + " 2025-04\n", + " 208.695846\n", + " 191.856539\n", + " 225.468568\n", + " -0.046168\n", + " -2.387576\n", + " 2.419717\n", " \n", " \n", " 1\n", " 2025-01-01\n", - " 2025-06\n", - " 210.554072\n", - " 198.302910\n", - " 222.867786\n", - " 0.019272\n", - " -2.274532\n", - " 2.359568\n", + " 2025-05\n", + " 209.973551\n", + " 194.550897\n", + " 226.127392\n", + " 0.016787\n", + " -2.264166\n", + " 2.417971\n", " \n", " \n", " 2\n", " 2025-01-01\n", - " 2025-07\n", - " 212.095170\n", - " 198.467939\n", - " 225.239903\n", - " 0.002804\n", - " -2.335157\n", - " 2.237084\n", + " 2025-06\n", + " 212.783391\n", + " 196.613952\n", + " 229.704736\n", + " -0.042261\n", + " -2.236899\n", + " 2.128698\n", " \n", " \n", " 3\n", " 2025-01-01\n", - " 2025-08\n", - " 213.806216\n", - " 200.665427\n", - " 227.372799\n", - " -0.016073\n", - " -2.496059\n", - " 2.250476\n", + " Never Treated\n", + " 216.797150\n", + " 199.947937\n", + " 232.832769\n", + " 0.049817\n", + " -2.356475\n", + " 2.538946\n", " \n", " \n", " 4\n", - " 2025-01-01\n", - " Never Treated\n", - " 218.582217\n", - " 203.797220\n", - " 237.916535\n", - " -0.011793\n", - " -2.241407\n", - " 2.430760\n", + " 2025-02-01\n", + " 2025-04\n", + " 208.534462\n", + " 182.657322\n", + " 233.434507\n", + " -0.032835\n", + " -2.303948\n", + " 2.404574\n", " \n", " \n", "\n", @@ -276,26 +309,28 @@ ], "text/plain": [ " t First Treated y_mean y_lower_quantile y_upper_quantile \\\n", - "0 2025-01-01 2025-05 209.552980 197.520569 222.082885 \n", - "1 2025-01-01 2025-06 210.554072 198.302910 222.867786 \n", - "2 2025-01-01 2025-07 212.095170 198.467939 225.239903 \n", - "3 2025-01-01 2025-08 213.806216 200.665427 227.372799 \n", - "4 2025-01-01 Never Treated 218.582217 203.797220 237.916535 \n", + "0 2025-01-01 2025-04 208.695846 191.856539 225.468568 \n", + "1 2025-01-01 2025-05 209.973551 194.550897 226.127392 \n", + "2 2025-01-01 2025-06 212.783391 196.613952 229.704736 \n", + "3 2025-01-01 Never Treated 216.797150 199.947937 232.832769 \n", + "4 2025-02-01 2025-04 208.534462 182.657322 233.434507 \n", "\n", " ite_mean ite_lower_quantile ite_upper_quantile \n", - "0 -0.040413 -2.317893 2.385829 \n", - "1 0.019272 -2.274532 2.359568 \n", - "2 0.002804 -2.335157 2.237084 \n", - "3 -0.016073 -2.496059 2.250476 \n", - "4 -0.011793 -2.241407 2.430760 " + "0 -0.046168 -2.387576 2.419717 \n", + "1 0.016787 -2.264166 2.417971 \n", + "2 -0.042261 -2.236899 2.128698 \n", + "3 0.049817 -2.356475 2.538946 \n", + "4 -0.032835 -2.303948 2.404574 " ] }, - "execution_count": 3, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# rename for plotting\n", + "df[\"First Treated\"] = df[\"d\"].dt.strftime(\"%Y-%m\").fillna(\"Never Treated\")\n", "\n", "# Create aggregation dictionary for means\n", "def agg_dict(col_name):\n", @@ -307,41 +342,18 @@ "\n", "# Calculate means and confidence intervals\n", "agg_dictionary = agg_dict(\"y\") | agg_dict(\"ite\")\n", - "# convert \"d\" to month period\n", "\n", - "# fill NaT values since they are not supported by groupby\n", "agg_df = df.groupby([\"t\", \"First Treated\"]).agg(**agg_dictionary).reset_index()\n", "agg_df.head()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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uK2fPnsWaNWs8XseOHfPafsSIEV6X79ixAwAwevRoqNVqj/XDhg1DQEAArFYr9u3bV2f1+/v7Y8iQIR7LQ0ND0a9fP7fa6poQwuvyiRMnQpIkrFu3DiUlJW7rVq1aBcD9Z6XT6ZQfWlDVz4Sbb74Zer0e+/fv9zo3X1Vfl4YyYMAAj2VJSUkAKiaQ79u3b5Xrz58/Ly+ri2tDRET1h8EUERE1iE8//RRZWVmIiorymLQ5LCwMAwYMkCcIr8z1wezSCXpdH9zuvfdet+UnTpwAUDGZumuC3Utfs2fPBgDk5OR41Oltot7KXn/9dcTGxmLChAlYtGgRVq5cKX9Id4UhFotFbn/x4kX5Q1BV+65quetcevToUeW5uIIWb+dSFX9/fwwbNgyA+yTo69evR2FhIRISEpCamvqnzvtqcF2Pf/zjH1Vej2XLlgFwvx5vvfUWQkND8cILL6BVq1YICgrCgAED8OKLL+LixYs1qiEgIAB33303SkpK3CbsX7ZsGYQQuPfee+Hn51fjc4uLi7viunPnzrktf+ihhwD8EUQBwIEDB/DNN98gLCysRiFEcHAwgKr70aBBgyAqRt5DCIHbbrvtsvurqk+7Jhqv6nwlSZLX1XRS8ivVU9Vk+lVd36q4rtWFCxeq1b7yRN0hISHy/8fHxyM1NRVmsxkfffSRvHz//v345ZdfEBkZ6RbM5Obmyt9jMTExXvu/QqFAYWEhnE6n10nvr/Tzrb41adLEY5lerwcAREREeA2gXX9wqBwu1cW1ISKi+uP5052IiKgeuD7El5aWeoQewB8fQlevXo2nnnoKSqUSANCzZ08kJCQgPT0du3fvRrdu3ZCdnY0tW7ZAo9Fg9OjRbvtxOp0AgJSUFNx0002Xralz584ey7RabZXt9+3bh8mTJ0OpVOLZZ5/F4MGD0aRJE/j5+UGSJLz22muYPHlylSMkqlLVB2bXuYwYMQI6ne6y+wgKCqrRMf/yl7/g3Xffxfvvv48XX3wRWq3WbZRG5Zqu1nlXxXXeVS3v3r37FR9BX3kUXY8ePXDq1Cl8+umn2LlzJ3bv3o2tW7fis88+wxNPPIGPPvroikFLZQ899BBWrFiB5cuXY+bMmSgtLZWflDdt2rRq76emLr2+Xbp0QadOnbB3717s3LkTqampckh1//33Q6VSVXvf7du3x9tvv42ffvoJTqcTCsWf+1vm5b6Proaq+kxNVLf/dujQAWvXrsVPP/2E8vJyr+FJZa5RiUFBQR7B0KRJk7Bjxw6sXr1aHjnp+j6855575J+DgPs5Tpgw4Yp1ehuRVt9flyu5XD+rSR+si2tDRET1h8EUERHVu8zMTGzZsgVAxV+2v/322yrbnj9/Hp9//jkGDhwIoCK0mThxIv7xj39g1apV6NatG9auXYvy8nKMGjXK41HtMTExAIBbbrkFS5curdPz+OCDDyCEwIMPPiiPuqrM2+10QUFBUKvVsNlsOH36NFq2bOnR5tSpU16PFxMTg6NHj+Lvf/87br755j9df2W33nor4uPjceLECfz3v/9F586dsWvXLiiVSo8PdrU578txBSZWq9Xr+tOnT3td7vra3nnnnfjb3/5Wo2NqtVqMGDFCHkWUk5ODxx9/HK+99homTZpU5TG9admyJfr06YPt27fjs88+w/nz51FQUID+/ftfMTCrysmTJ6tc5+of0dHRHuseeughjB8/HkuXLsVNN92Ed955Bz4+PnjggQdqdPxBgwZh1qxZyM/Px5YtWzBo0KAabV9dUVFRAP4Y/eaN61q42gK17zMuVX2PVV7n7fp6M3jwYMyaNQtmsxkbN27E8OHDq2wrhJBvtRw0aJBHCD18+HBMnz4dX375Jc6ePYuwsDC8++67ADxHgwYHB0Or1aKkpATPP/+8PHKLeG2IiK41vJWPiIjq3erVq+FwONC5c2e324EufblCj8q3SAEVc7EoFAqsX78excXFVd7GBwD9+/cHAGzatKnO5xHJy8sDAMTGxnqsKy0txYYNGzyW+/r6omvXrgAgf+C81Hvvved1uetcXPM+1SVJkuTbJN988035lr477rjDLRAAanfel1M5nCgrK/NY75pL6lKu6+EKyv6MkJAQPPfccwCAM2fOuM0XVR0PP/wwAGDp0qV1MqfTgQMHcODAAY/lv/32G3766Se3+bMqGzVqFCIiIvDxxx/j6aefRlFREYYOHYrIyMgaHT8xMVGem2fmzJkwm821O5ErcM3vtm7dOq/fnx999BHy8/NhMBjQoUMHebmrzxw6dMjrfqvqMy4FBQXYvHmzx/KcnBx8/vnnbrVdSUJCAkaNGgUAePTRR1FQUFBl22XLluHAgQPw8fHBo48+6rHez88PaWlp8jx6mzdvRm5uLm655RYkJye7tVUqlbj99tsBXJ2fCdcyXhsiomtMfc60TkREJISQH++9bNmyy7b79ddfBQDh6+srsrOz3da5nnj28MMPCwCiSZMmwuFweN2P6xHw/fv3FydPnvRYX1hYKNauXSuysrLkZdV5UtwLL7wgAIh27dq5PfGvpKRE3HvvvfKTpC59UpXrKVMGg8Hj0fCup295O/bZs2eFv7+/UCqV4vnnnxc2m82jphMnToi33367ypov59y5c0KpVApJkkRQUJAA4PGY9T9z3pe7pklJSQKAeOqppzy2CQgI8PpUPofDIT/BcMKECR59RAgh8vLyxPLly+Wn9p06dUq8/vrrwmw2e7R1PSEuICBAlJeXV3mdvHE4HHK/BiASEhJq9GREl8pP5evWrZvIy8uT1xUUFIgePXoIAGLkyJFV7uOpp56S9wFA7Ny5s8Z1CFFx7Vzn1LJlS7Fjxw6v7U6ePCmaNWsmAIhVq1a5rXM9/e2rr77yum1paalo0qSJACCmTJni9nTFEydOiLi4OAFAzJ071227M2fOCIVCIRQKhVtdTqdTvPTSS1V+D1V+Kl9SUpLbkw1LS0vFyJEjBQDRqVOnalyhP+Tm5oqmTZvK3xeVnwwqhBB2u10sXrxYKJVKAUAsXry4yn3t2bNHABCJiYnyE0NXrlzpte2+ffuESqUSfn5+YvXq1V5/Bh48eNDj+/hKX5eaqqun8tV0OyGq/rlS22tDRET1j8EUERHVqx07dggAQq1Wu33orkr79u29PvL7/fffd/vw/c9//rPKfVgsFnHbbbcJAEKlUomOHTuKUaNGiZEjR4qOHTsKlUolAIhDhw7J21QnmMrPzxexsbECgAgKChJ33XWXGD58uAgNDRUGg0EOzbx9WLv//vsFAKFUKkWvXr3EmDFjROvWrYVSqRQzZswQAMTtt9/usd3OnTtFcHCwACBCQ0NF7969xbhx48SgQYNEQkKCACA6d+58xetaFdcHYQAiJCRElJWV1dl5X+6abtiwQUiSJACIlJQUMXLkSNGhQwchSZL45z//WeUH14yMDJGSkiIACJ1OJ7p16yZGjx4thg0bJlJSUuQgoKSkRAghxP79++Ww09UPRo0aJdq1aycACEmSqgwBrqRyqHi54OFyXMHUkCFDRHx8vPD39xdDhw4Vw4YNE4GBgXKgcuHChSr3ceHCBaFWqwUA0bZt21rVUXlfru8dACI6OloMGjRIjB8/XgwfPly0bdtW/rq1adNGHDx40G376gQge/fulc8tNjZWpKWliQEDBgiNRiMAiDvuuMNrCOvqZ67voWHDhomEhATh6+sr5syZc9lgqmvXrqJz587Cz89PDBo0SIwaNUpERkbK31eHDx+u8bU6d+6cuPnmm+V+1LFjRzF69GgxZMgQERISIv/8WbJkyRX31aJFC/ma63Q6YbVaq2y7fv164efnJ399+vbtK8aNGyf69+8voqOjBQCRlpbmts2NEEwJUbtrQ0RE9Y/BFBER1au7775bABAjRoyoVnvXh/0WLVq4LS8tLZU/zEqSJE6cOHHZ/TgcDvHuu++KAQMGiLCwMOHr6yuCgoJE69atxb333is++ugjtxCmOsGUEELk5OSIqVOnioSEBKFWq0VkZKQYP368OHr0qBwyePuw5nQ6xeuvvy7at28vNBqN8Pf3F3379hVff/21eOuttwQAMWbMGK/HvHDhgvjHP/4h2rdvLwwGg1CpVCI6Olp069ZNPPHEE+LAgQOXrflyNmzYIH9InDlzZp2e95Wu6aeffipuueUW4efnJ3Q6nejSpYtYt26dEOLyH1xLS0vFq6++Km699VYRFBQkfHx8RGhoqEhJSRHTpk0TW7duldtaLBaxZMkSMXToUJGUlCT0er3Q6XQiOTlZ3HPPPeLHH3+s2QWr5NChQwKA8PPzE/n5+bXaR+Vrl52dLSZPniyio6OFSqUSMTEx4qGHHhK5ublX3E/nzp0FALFixYpa1XGp7du3i0mTJolmzZoJo9EofHx8REBAgGjfvr2YPHmy2LZtm9cRKdUNQM6cOSOmTZsm4uPjhUqlEgaDQXTt2tVttNulnE6nWLx4sWjRooVQqVQiMDBQDB48WOzbt6/KvlZ5eWFhoXj00UdFXFycUKlUIiwsTEycOFGcOXOmtpdJOBwO8d5774k777xTREZGCpVKJYxGo2jTpo2YNWuW1xGb3jz33HNVjjz05uTJk2LGjBmidevWQqfTCY1GI2JjY0WvXr3EwoULxbFjx9za3yjBlGv7mlwbIiKqf5IQdfTIHCIiIqoTkyZNwqpVq7B48WLMnDmzocuhanr88cfx9NNP4/7778eKFSsarI709HQ0b94cJpMJGRkZ8PPza7BaGpsdO3bg1ltvRWpqKnbs2NHQ5RARERE4+TkREVGD+O2331BUVOS2zOl04vXXX8fq1auh0WgwZsyYBqqOaiozMxOvvPIKFAoFHnnkkQat5Z///CeEEJgyZQpDKSIiImr0fBq6ACIiohvRokWLsH79erRr1w5RUVEoKirC77//jlOnTkGpVGLZsmWIiIho6DLpCubMmYOMjAxs374dBQUFeOCBB9CiRYt6r2PTpk3YuHEjfvvtN3z//fcIDw+Xn2pJRERE1JgxmCIiImoAaWlpsFgs2LdvH37++WeUl5cjNDQUaWlpeOSRR9ClS5eGLpGq4f3338eZM2cQHh6ORx55BAsXLmyQOn766Se8+eabMBgM6NOnD1544QX4+/s3SC1ERERENcE5poiIiIiIiIiIqEFwjikiIiIiIiIiImoQDKaIiIiIiIiIiKhBcI6pKjidTpw/fx4GgwGSJDV0OURERERERERE1wQhBKxWKyIjI6FQXH5MFIOpKpw/fx4xMTENXQYRERERERER0TXp7NmziI6OvmwbBlNVMBgMACouotFobOBqas/pdCInJwchISFXTCmJroT9iS7FPkF1hX2Jaot9h+oK+xLVFvsO1aXrpT9ZLBbExMTI2crlMJiqguv2PaPReM0HU6WlpTAajdd0p6bGgf2JLsU+QXWFfYlqi32H6gr7EtUW+w7VpeutP1VnaqRr/yyJiIiIiIiIiOiaxGCKiIiIiIiIiIgaBIMpIiIiIiIiIiJqEAymiIiIiIiIiIioQTCYIiIiIiIiIiKiBsGn8tUBh8OBsrKyhi7DK6fTCbvdjpKSkutiRn+qGyqVCkqlsqHLICIiIiIiohscg6k/QQiBjIwM5OXlNXQpVRJCQAiBvLy8aj2mkW4cgYGBiIqKYr8gIiIiIiKiBsNg6k9whVLh4eHQ6XSNckSSEAJOpxMKhYIBBAGoGEVXVFSErKwsAEB0dHQDV0REREREREQ3KgZTteRwOORQKjQ0tKHLqRKDKfJGp9MBALKyshAREcHb+oiIiIiIiKhBNL4hPtcI15xSrg/4RNcaV99trPOjERERERER0fWv0QVTzzzzDDp27AiDwYDQ0FDcddddOHLkiFubyZMnIyEhAVqtFiEhIbjzzjtx+PBhtzaSJHm83n///TqvtzHevkdUHey7RERERERE1NAa3SfTnTt3Ytq0afjuu++wbds22O129O3bF0VFRXKbDh06YNWqVTh06BC2bt0KIQT69u0Lh8Phtq9Vq1YhMzNTft111131fDZERERERERERFSVRhdMff7555g4cSJatWqFm266CatXr8aZM2ewb98+uc3999+Pnj17omnTpmjfvj3+/e9/4+zZszh16pTbvvz9/REeHi6/NBpNPZ9N47Fjxw5IkoTVq1c3dCk3rFOnTkGSJMyfP7+hSyEiIiIiIiJqFBpdMHUps9kMoOLR9t4UFRVh1apViIuLQ0xMjNu6adOmITg4GJ06dcKbb74JIcRVr7chuEInby+FQoHvvvvuqh37559/xvz58z1CwZrWeemradOmV63mqqxevRpLliyp9+MSERERERER3aga9VP5nE4nHnnkEdxyyy1o3bq127ply5Zh9uzZKCoqQrNmzbBt2zaoVCp5/VNPPYXevXvDz88PX3zxBaZOnYrCwkI89NBDXo9ls9lgs9nk9xaLRa7B6XR6rU0IIb8akuv4Y8aMQf/+/T3WJyYmIjg4GMXFxfD19a3Tevfv348nn3wSqampiI2NvWzb5s2b46233nJb9vrrr2PXrl144YUXEBwcLC/X6/X1fl1Xr16NU6dO4eGHH74q+3edT2PoM5XrqKqPe+Pq99VtT9c/9gmqK+xLVFvsO1RX2Jeotth3qC5dL/2pJvU36mBq2rRp+PXXX/HNN994rBs3bhxuv/12ZGZm4vnnn8eoUaPw7bffyrfr/eMf/5DbtmvXDkVFRVi0aFGVwdQzzzyDJ5980mN5Tk4OSktLPZbb7fYaf6i/WlzHT0lJwdixY6tc7wruLlevEAJFRUXQ6/XVOrYrYKnOdQgJCfGob/v27di1axeGDBniMUrq0v1ZrVYYDIZq1VUblc/lanDtt7H8kHH9wMvLy4Ovr2+1tzGbzRBCcPJ0AsA+QXWHfYlqi32H6gr7EtUW+w7VpeulP1mt1mq3bbTB1PTp0/HJJ5/g66+/RnR0tMd6k8kEk8mEpKQkdOnSBQEBAfjoo48wZswYr/vr3Lkz/vWvf8Fms0GtVnusf+yxxzBz5kz5vcViQUxMDEJCQmA0Gj3al5SUIC8vDwqFosE7i+v4rlv3vPn666/Ru3dvvPnmm5g4cSKAilvrXMuKioqwbNkyHD9+HHPmzMH8+fPx22+/4cknn8Tu3btx8eJFBAQEoEWLFpg1axYGDhyI+fPn46mnngIA9OnTRz7WhAkTsGrVqmrVLkmSfA6u2k+dOoX4+Hj885//RIsWLbBo0SL8/vvvSEtLk/e7fft2LFq0CHv37kVpaSmSk5MxZcoUPPDAA277/+KLL/Dmm2/ihx9+QGZmJtRqNTp16oS5c+ciNTVVbhcXF4fTp08DAHx8/vi2+L//+z/06tULAHD06FH861//wvbt25Gbm4vIyEiMGDEC8+fPh06nczvuN998gzlz5uCnn36C0WjEiBEj5Nou93WqTwqFApIkITAwEFqttlrbOJ1OSJKEkJCQRnEO1PDYJ6iusC9RbbHvUF1hX6LaYt+hunS99KeazPHd6IIpIQQefPBBfPTRR9ixYwfi4uKqtY0Qwu1WvEv9/PPPCAgI8BpKAYBarfa6rqrgyfWh3vVqSK7jl5SUIDc3122dSqVyC00q1+v670svvYTc3Fzcd999CA8PR0xMDPLy8nDbbbcBAB544AHExsbi4sWL+PHHH7F3714MGjQIw4cPR1ZWFl577TXMnTsXLVq0AAAkJCTU+Jp4q2vjxo14+eWX5cDJaDRCkiS89tpreOCBB9ClSxfMmzcPOp0O27Ztw9SpU3HixAksWrRI3u+aNWuQl5eHe+65B9HR0cjIyMDKlSvRp08ffPXVV+jRowcAYMmSJXjsscdw8eJFvPjii/L2LVu2hCRJ2LdvH3r37g1/f39MnjwZUVFR+OWXX/Dyyy9j9+7d2Llzpzzq6Pvvv8ftt98Og8GAv//97/D398f777+PCRMmeJxrQ6o8D1lNfuDVZhu6vrFPUF1hX6LaYt+husK+RLXFvkN16XroTzWpvdEFU9OmTcO7776LjRs3wmAwICsrC0DFCCmtVosTJ05g3bp16Nu3L0JCQnDu3DksXLgQWq0WAwYMAABs3rwZFy5cQJcuXaDRaLBt2zYsWLAAf/vb3xry1K66J554Ak888YTbsrS0NLzzzjuX3e7MmTM4fPgwQkND5WWbNm1CdnY21q1bh1GjRnndrm3btujatStee+013H777fLIorry22+/4cCBA3LgBQCZmZl46KGHMHr0aLz77rvy8qlTp+Lhhx/GCy+8gClTpiA+Ph5AxRxWl45meuCBB9CqVSs888wzcjB11113YcmSJSgpKcH48eM9apk0aRIiIiLwww8/uN1OeNttt2HYsGF455135JFoM2bMgNPpxLfffovk5GS5vu7du9fNhSEiIiIiIiK6TjS6+G358uUwm83o1asXIiIi5Ne6desAVAwH27VrFwYMGIDExESkpaXBYDBg9+7dcrDi6+uLV155BV27dkVKSgpWrFiBF154wSO0ud7cf//92LZtm9tr3rx5V9zunnvucQulgIogEAA+++wzeSL4+jZw4EC3UAoAPvzwQ9hsNvzlL3/BxYsX3V6DBw+G0+nE9u3b5faVQ6nCwkLk5uZCqVSic+fO+P7776tVx8GDB3HgwAGMHTsWNpvN7Zjdu3eHTqfDF198AQDIzs7Gnj17cOedd8qhFFAxcm3GjBl/5nIQERERERERXXca3YipKz2tLDIyElu2bLlsm379+qFfv351WdY1ISkpyW2uJ6B6E21XDlBcUlNTcc8992D16tV455130LFjR/Tp0wdpaWlo2bJlndZdk7oOHToEAB7nWdmFCxfk/z9+/DjmzZuHrVu3oqCgwK1ddW+ncx3T24i0S4954sQJABVPILxUfV03IiIiIiIiujYJIeCvMzWKJ7nXl0YXTFH98/Pz87p8zZo1ePTRR/HZZ59h165dWLx4MZ5++mksWbIE06dPb5C6XN+cb731FiIiIrxu57qNr7CwED179kRRUREeeeQRtGnTBgaDAQqFAs888wz+7//+r1p1uI45a9asKgPPgICAau2LiIiIiIiIyBvhFLDnlyJjx0lE9YqDItAPkqLh5ye+2hhM0WW1bt0arVu3xqOPPoqCggJ07twZc+bMwbRp0xpkEu+kpCQAQHBw8GVHTQHAl19+ifPnz+PNN9/Evffe67bu8ccf92hf1bm4jqlUKq94TNdk/YcPH/ZY9/vvv192WyIiIiIiIroxCaeALa8YZz89ipK8Ipz99BhiBiZBHai97sOpRjfHFDUOeXl5HrcA+vv7Iy4uDsXFxSgtLQUA6PV6uX19GDVqFNRqNZ544gmUlJR4rDebzfLTGZVKJQDP20O/+OILr/NL6fV65Ofne7Rv164dWrdujVdffVW+Va+y8vJy+fzDwsLQpUsXbNy4Eenp6XKbsrIyt6f9ERERERERETnLnSjKtKI4w4Ljaw/CllvxOdduseHclqOw5ZVAOK/v2/o4Yoq8euutt/Diiy9i6NChSExMhK+vL3bu3ImtW7di1KhR0Gq1AICOHTtCoVDg6aefRn5+PnQ6HeLi4tC5c+erUld0dDSWL1+Ov/71r2jRogXuvvtuxMbGIicnBwcPHsTHH3+M33//HU2bNkX37t0RHh6OWbNm4dSpU4iOjsbPP/+Mt99+G23atMHBgwfd9t2lSxd88sknmD59Orp16walUonevXsjNDQUb7/9Nnr37o22bdti0qRJaNWqFYqLi3Hs2DH897//xTPPPCM/le+FF15Ar169cMstt2DatGnw9/fH+++/j/Ly8qtyTYiIiIiIiOja4Sx3ouh0ASzH8lB4xoyYQcnI2H4CZXklUGp9AE3FGCK7xYYLu04j6o5E+Pj5NnDVVw+DKfKqV69e2L9/Pz755BNkZmZCqVQiLi4Ozz//vNv8Uk2aNMGbb76JZ599FlOmTIHdbseECROuWjAFAPfeey+Sk5Px/PPPY8WKFSgoKEBwcDCaNWuGf/3rXwgPDwdQMcJr69atmD17Nl5++WWUl5ejQ4cO2LJlC9544w2PYGrGjBk4ceIEPvzwQ7z66qtwOp346quvEBoaipSUFOzfvx/PPPMMNm3ahFdffRUGgwFNmzbFxIkTcdttt8n76dq1K7Zt24Y5c+Zg4cKFMJlMGDFiBKZMmYI2bdpctetCREREREREjZOz3ImiM2ZYjuah8FQBnHaHvC53fxbCU5viTMFhlJltUP4vmPI1qhHWIxZKzfUd3UjiRprqvQYsFgtMJhPMZjOMRqPH+pKSEhw9ehRJSUny6KHGyPVUPoVCUe/zQVHjVps+7HQ6kZ2djdDQUCgUvBOY2Ceo7rAvUW2x71BdYV+i2mLfoSspyrDg3CdH3cKoSxkSA+HfKgQZnx+DrbAU2kD9NT3H1JUylcqu79iNiIiIiIiIiKieuEZG+epV0ITqAACaID84y50ebSWlAvqmJhgTAqFv6g/JR4HYoc3lp/Jdq6FUTTGYIiIiIiIiIiL6E4QQyPy/k7Aey4fT7oCpeTAi+8QDAJQaH+hijCg6Y64Io2JNMCZWhFEKldJtP74BGkTdkQBfP9UNEUoBDKaIiIiIiIiIiGrEWe5EWV6JPCpKkiQ4isvl2/WsJ/LhLHdC4VNxe2dQu3CYmgfD4CWMqkySJBQUmRGqC736J9FIMJgiIiIiIiIiIroCZ7kTRWfNsB7Lg/VEAQAg6S/t5PDJmBSIwtMVy4VDwJZbDG2YHgCgizE1RMnXBAZTREREREREREReuIVRJwvgLHOfwLzorBmGuAAAgD7OH4aEABjiA2GIu/zIKPoDgykiIiIiIiIiov+5UhhVmfVYnhxMKdU+iO6fVF9lXjcYTBERERERERHRDe2PMCof1pP5lw2jJIUEXZP/TWAe519/RV6nGEwRERERERER0Q3nz4RRSjXjlLrCK0lEREREREREN5ysr07CfCS3yvUMo+oHryoRERERERERXbeEw4misxZYjuchvEesPCm5Pi7AI5hiGFX/eIWJiIiIiIiI6LpUml2E0xsPw2mruE1PF2OCKTkIAKCPNUHyUQBOwTCqAfFqExEREREREdE1TzicKDpngUKlhF+EAQCgCtQCzj/aWI/lycGUwleJJoOToQ72YxjVgHjliYiIiIiIiOia5AqjLMfyYD2RD6fNAX1Tf/gNqgimFD4K6Jv6w3I0F5JCAiRACAFJkgAAflHGhiyfACgaugBqnH744QdMnz4drVq1gk6nQ5MmTTBq1Cikp6d7tD106BD69esHvV6PwMBA3H333cjJyXFrc/jwYcyePRspKSkwGAyIiIjAwIED8eOPP3rsb/78+ZAkyeOl0WiqXb/T6cRzzz2HuLg4aDQatG3bFu+9955Hu4kTJ3o9VvPmzat9LCIiIiIiIqo/wuFE4ekCnP/yBI6++TPObk6H+dBF+Xa9ojNmOCo9YS+gbSgibotH0qR2iO6fJIdS1DhwxBR59eyzz+Lbb7/FyJEj0bZtW2RlZWHp0qVo3749vvvuO7Ru3RoAcO7cOfTs2RMmkwkLFixAYWEhnn/+eRw8eBB79+6FSqUCAKxcuRJvvPEGhg8fjqlTp8JsNmPFihXo0qULPv/8c/Tp08ejhuXLl0Ov18vvlUplteufN28eFi5ciPvuuw8dO3bExo0bMXbsWEiShNGjR7u1VavVWLlypdsyk8lU7WMRERERERHR1VV5ZFThiQI4bOVVtxVA6YVC6GIqPtf5RRjkW/uo8WEwRV7NnDkT7777rhwsAUBaWhratGmDhQsXYu3atQCABQsWoKioCPv27UOTJk0AAJ06dcLtt9+O1atX4/777wcAjBkzBvPnz3cLmiZNmoQWLVpg/vz5XoOpESNGIDg4uMa1Z2RkYPHixZg2bRqWLl0KAPjrX/+K1NRUPProoxg5cqRbyOXj44Px48fX+DhERERERER09VSEUVZYjuVeMYyCJEEXY4QxKRCGuAAoNYw7rhX8SjUyhbZyWG3lKCixw1/rC4PaB/oGmIStW7duHsuSkpLQqlUrHDp0SF62YcMGDBo0SA6lAKBPnz5ITk7G+vXr5WCqQ4cOHvsLCgpCjx49sGPHDq81CCFgsVhgMBhqNNRy48aNsNvtmDp1qrxMkiRMmTIFY8eOxZ49e9C9e3e3bRwOB4qKimA08v5iIiIiIiKihiKcAkVnLbD+b86oaoVRiYEwxDOMulbxq9aI5BTa8Np3p7EtPQdFZQ7oVEr0TQ7BfV1iEaJXN3R5EELgwoULaNWqFYCKkUnZ2dm4+eabPdp26tQJW7ZsueI+s7KyqhwVFR8fj8LCQuh0Otx1111YvHgxwsLCrrjP/fv3Q6fToUWLFh41udZXDqaKi4thNBpRXFyMgIAAjBkzBs8++6zb6C4iIiIiIiKqB0Lg/BfHqw6kGEZdd/gVrGOFtnIcu1hU4+0CtL54c+9ZfHjgvLysuMyBd37KQJlD4N6OMcgvsdd4v0IIxAdqYdSqrtz4Ct555x1kZGTgqaeeAgBkZmYCACIiIjzaRkREIC8vDzabDWq191Bt165d2LNnDx5//HG35QEBAZg+fTq6du0KtVqNXbt24ZVXXsHevXvx448/XnFUU2ZmJsLCwjxGWbnqPH/+vNuy2bNno3379nA6nfj888+xbNky/PLLL9ixYwd8fPgtQkREREREVNeEw4miDCusx/LgLHMgql8iAEBSKmCID0DBoUoP1GIYdV3jV7OOHbtYhPs++KVG2xjUPnh2UAu89t1pWL2kwiv2nMbAlqH4+yeHvK6/kleHt0H76D8XTB0+fBjTpk1D165dMWHCBABASUkJAHgNnlxP0CspKfG6Pjs7G2PHjkVcXBxmz57ttu7hhx92ez98+HB06tQJ48aNw7JlyzBnzpzL1lrVMSvX5PLMM8+4tRk9ejSSk5Mxb948fPjhhx4TpRMREREREdGfd+Hbs8g/cKHijSQhrMQOH60vAMCQFIiCwxcZRt0gFA1dAAEmjQ/yi+1Vhk5WWzkKisthaqBvxKysLAwcOBAmkwkffvihPHG4VqsFANhsNo9tSktL3dpUVlRUhEGDBsFqtWLjxo3VumVu7NixCA8Px/bt293qqvxyBU5arbbGNVU2Y8YMKBQKt2MRERERERFRzVXMGWVG5lcnUV70x11Ahjj/So0ErMfz5be6aCOS/9IOTYY0g3/LEIZS1zl+dRsBc2k5AvwqJjr3Fk4Z1D7w9/OBubTmo6X+dG1mM/r374+CggLs2rULkZGR8jrXrXGuW/oqy8zMRGBgoMfIpbKyMgwbNgwHDhzA1q1b0bp162rXEhMTg7y8PI/ju6xatQoTJ05EREQEvvrqKwgh3G7nc9VZ+Ry80Wq1CAoKcjsWERERERERVY9wChRnWGA5lg/r8Tw4/vdZVhPsh4A2FfMG+0UZodT4wGFzQBdthK/hj7t8JIXEMOoGwq90HUsM1uH1kTfVeLsArS8md43FB7+c91g38qZIhOrUeGFIqxrv1zXHVG2UlpZi8ODBSE9Px/bt29GyZUu39VFRUQgJCcGPP/7ose3evXuRkpLitszpdOKee+7Bl19+ifXr1yM1NbVG53Hq1Cm0a9dOXrZt2za3Nq5J2VNSUrBy5UocOnTIrebvv/9eXn85VqsVFy9eREhISLXrIyIiIiIiupFVFUZVZjmWJwdTkkJC9IAkqAI08i18dGNiMFXH9GofpESZarXttFuaQqWU8MUlT+W7v0ssgvVqxNZin0IIOJ3OGm/ncDiQlpaGPXv2YOPGjejatavXdsOHD8eaNWtw9uxZxMTEAAC+/PJLpKenY8aMGW5tH3zwQaxbtw4rVqzAsGHDqjx2Tk6ORyi0fPly5OTkoF+/fvKyPn36eN3+zjvvxIwZM7Bs2TIsXboUQMV1ePXVVxEVFYVu3boBqAje7HY7DAaD2/b/+te/IIRwOxYRERERERG5cwujTuTDcbkHdkkSJKUCwikgKSrubPGLNFTdnm4YDKYakRC9Gg/1iMe9nZrAXGqHSVNxe59eXf9fplmzZmHTpk0YPHgw8vLysHbtWrf148ePBwDMnTsXH3zwAW699VY8/PDDKCwsxKJFi9CmTRvce++9cvslS5Zg2bJl6Nq1K/z8/Dz2N3ToUOh0OgBAbGws0tLS0KZNG2g0GnzzzTd4//33kZKSgsmTJ1+x9ujoaDzyyCNYtGgR7HY7OnbsiI8//hi7du3CO++8I8+RlZWVhXbt2mHMmDFo3rw5AGDr1q3YsmUL+vXrhzvvvLP2F5CIiIiIiOg6VNMwShdlgCGpYgJzjowibxhMNTL6/wVREUZNg9bx888/AwA2b96MzZs3e6x3BVMxMTHYuXMnZs6ciTlz5kClUmHgwIFYvHix2/xSrv3t2bMHe/bs8djfyZMn5WBq3Lhx2L17NzZs2IDS0lLExsZi9uzZmDdvHvz8/KpV/8KFCxEQEIAVK1Zg9erVSEpKwtq1azF27Fi5jb+/PwYNGoRt27ZhzZo1cDgcSExMxIIFC/C3v/0NCgWfDUBERERERAQAtvwS5P1yAdbj1QyjEgNhSGAYRVcmCSFEQxfRGFksFphMJpjNZhiNRo/1JSUlOHr0KJKSkq74lLeG5LqVT6FQuE0ETlSbPux0OpGdnY3Q0FAGdwSAfYLqDvsS1Rb7DtUV9iWqreu17wingHAKKHwqzqk404rTGw55b8wwqs5cL/3pSplKZRwxRUREREREREQVt+mdt8J6LA+W4/kIah+OoHYVT0PXhunho1OhvKisonHlMCo+AD5+DKOodhhMEREREREREREgAZlfnoTdagMAWI7mycGUpJBgTA5EaU4xjAyjqA4xmCIiIiIiIiK6gQinQHFmxcgou8WGmMHNAACSJMGQGIC8/VkAgNLsIpRZbFAZK+YPDu0WwyliqM4xmCIiIiIiIiK6zgmnQEmmFZZj+bAcy3ObwLzMXAqVqeIBXMbEQOT9fAF+kQYYEwOgVCvldgyl6GpgMEVERERERER0HaocRlmP56G82PvT9KzH8hHUoeKWPU2oDkn3pvA2Pao3DKaIiIiIiIiIrhPVDaNc/KIM8PVXy+8lSWIoRfWKwRQRERERERHRNUw4BUqyCmE5mle9MCrSAGNSIAzxgfDRMYSihsVgioiIiIiIiOgalftTJvJ+zqpWGGVIDIQxgWEUNS4MpoiIiIiIiIiuAcIpUJpTBG2YXl7mtDuqDKX+CKMC4KNT1VeZRDXCYIqIiIiIiIioESuz2JD3cxasx/NRXlSG+LFtoA7UAgCMSUG4+MN5uS3DKLrWMJgiIiIiIiIiakSEU0A4nFD4KivelzuRf+CCvN5yNBchnaMBAOpALUzNgqAJ0zOMomsSgykiIiIiIiKiBiacAiUXCmE9lgfLsXyYmgcjtOsf4ZM6UAtbXgkAwHo8Xw6mACDy9oQGqZmoLigaugBqnH744QdMnz4drVq1gk6nQ5MmTTBq1Cikp6d7tD106BD69esHvV6PwMBA3H333cjJyXFrc/jwYcyePRspKSkwGAyIiIjAwIED8eOPP3rsb/78+ZAkyeOl0WiqXb/T6cRzzz2HuLg4aDQatG3bFu+9916VbZcvX46UlBRotVoEBQWhd+/e+OWXX6p9PCIiIiIiopoSToHiTCsu7DqNY2t+wekNh5D3ywWUF5XBejwPQgi5rTE5CNoIA8J6NEHMkGYNWDVR3eKIKfLq2WefxbfffouRI0eibdu2yMrKwtKlS9G+fXt89913aN26NQDg3Llz6NmzJ0wmExYsWIDCwkI8//zzOHjwIPbu3QuVqmIY6cqVK/HGG29g+PDhmDp1KsxmM1asWIEuXbrg888/R58+fTxqWL58OfT6Pyb1UyqV1a5/3rx5WLhwIe677z507NgRGzduxNixYyFJEkaPHu3WdtKkSXjnnXdwzz33YPr06SgqKsL+/fuRnZ1dm0tHRERERERUpT9GRuXDciwP5UVlXtuVFZTCdrEEmhA/AEBQhwgE3xxZn6US1QsGU+TVzJkz8e6778rBEgCkpaWhTZs2WLhwIdauXQsAWLBgAYqKirBv3z40adIEANCpUyfcfvvtWL16Ne6//34AwJgxYzB//ny3oGnSpElo0aIF5s+f7zWYGjFiBIKDg2tce0ZGBhYvXoxp06Zh6dKlAIC//vWvSE1NxaOPPoqRI0fKIdf69euxZs0a/Pe//8XQoUNrfCwiIiIiIqIrqW4Y5aKN0MOYEAgfva+8TJKkq10mUYPgrXyNjKO04LLv60u3bt3cQikASEpKQqtWrXDo0CF52YYNGzBo0CA5lAKAPn36IDk5GevXr5eXdejQwS2UAoCgoCD06NHDbX+VCSFgsVjchq9Wx8aNG2G32zF16lR5mSRJmDJlCs6dO4c9e/bIy1944QV06tQJQ4cOhdPpRFFRUY2ORURERERE5M0ft+mdqXSbXlaVoZQ2Qo+w7k2QODEFTYe3RGBKOHy0vl7bEl1PGEw1IuWF52H5ZRnKC897fd/QhBC4cOGCPIopIyMD2dnZuPnmmz3adurUCfv377/iPrOysqocFRUfHw+TyQSDwYDx48fjwoULXttdav/+/dDpdGjRooVHTa71AGCxWLB371507NgRc+fOhclkgl6vR3x8vFuoRkREREREVBUhBPx1Jrc/qDvLHDj+VjXCqHDPMMpXz6fq0Y2Ft/JdJeVFWXAUZVW7vUIbBOvBN1By4hPYC47D1G46zPuXwp77GyAc0LeaCGdJbo1qUOrCofQLq2npVXrnnXeQkZGBp556CgCQmZkJAIiIiPBoGxERgby8PNhsNqjVaq/727VrF/bs2YPHH3/cbXlAQACmT5+Orl27Qq1WY9euXXjllVewd+9e/PjjjzAajZetMzMzE2FhYR5DXV11nj9fEfQdP34cQgi8//778PHxwXPPPQeTyYSXXnoJo0ePhtFoRL9+/apxZYiIiIiI6EYknAL2/FJkfHkCEalx0IbqICkkKFRKKP18YS/0DKS04XoYEwNhSAiAr8H7ZyWiGwmDqauk+MQnsB5cWe32vkGtYGxzH+x5h2DP/Q0Xt0+Rl+uapSF/9/yKkKoGjG0nQ99qYo22qcrhw4cxbdo0dO3aFRMmTAAAlJRUPKrUW/DkeoJeSUmJ1/XZ2dkYO3Ys4uLiMHv2bLd1Dz/8sNv74cOHo1OnThg3bhyWLVuGOXPmXLbWqo5ZuSYAKCwsBADk5ubiu+++Q+fOnQEAQ4YMQVxcHP79738zmCIiIiIiIq+EU6AkqxAnP/gNtovFKM0qQsK4tlAHaiEpJBiTAlGaXTFVCMMooqrxVr5Gwp77G0rO/h9M7R5yW25qNx3CXljjUKouZWVlYeDAgTCZTPjwww/licO1Wi0AwGazeWxTWlrq1qayoqIiDBo0CFarFRs3bvSYe8qbsWPHIjw8HNu3b3erq/LLFThptdpq1eT6b1xcnBxKAYBer8fgwYOxd+9elJeXX7E2IiIiIiK6MZQX2yGEgHAK2PJKcGbzEdhyKz6H2HJLcGbTYdjySiCcAsbEwIrb9CbchKYj/nebHkMpIg8MphoJ36BW0Mb0hnn/f9yWm/cvheSrh29Qqwapy2w2o3///igoKMDnn3+OyMg/Hk/qujXOdUtfZZmZmQgMDPQYuVRWVoZhw4bhwIED2LhxI1q3bl3tWmJiYpCXl+d2/MqvdevWycuzsrI8Jk131ek6B9d/w8I8b3cMDQ2F3W7nZOhERERERDc4p90B8+GLOLPpCI6u+hmlF4rgKC3HhV2n4Sx1oPIEIraLJbiw6zQcpeXwNagZRhFVA2/lu0r84gdBHeY5KXhVXHNMlRcch2/ITW5zTBUdWYeAbvNrNcfUn1FaWorBgwcjPT0d27dvR8uWLd3WR0VFISQkBD/++KPHtnv37kVKSorbMqfTiXvuuQdffvkl1q9fj9TU1GrXIoTAqVOn0K5dO3nZtm3b3Nq0alUR3qWkpGDlypU4dOiQW83ff/+9vB6oCKbCw8ORkZHhcbzz589Do9HAYDBUu0YiIiIiIro+CKdA0TkLLEcuwnoiH067U15nPpKLsB5NENYjFue2HEV5cRmcEPD1U0EdoEVYj1goNfyoTVRd/G65Snx04fCpYTBkbHs/JKUKhlYT4aOPROAt/4L1t9XyexhialzHpaOGqsvhcCAtLQ179uzBxo0b0bVrV6/thg8fjjVr1uDs2bOIiamo78svv0R6ejpmzJjh1vbBBx/EunXrsGLFCgwbNqzKY+fk5CAkJMRt2fLly5GTk+M251OfPn28bn/nnXdixowZWLZsGZYuXQqg4jq8+uqriIqKQrdu3eS2aWlpeOmll7Bt2zbcfvvtAICLFy9i48aN6N27NxQKDiokIiIiIroRCCFgu1gM8+FcWI7morzY7rWd5WguwrrHQB2oRfSAJJz99ChK8gqh8q9475pjioiqh8FUI+Kjj4TxpqlQavy9vq9Ps2bNwqZNmzB48GDk5eVh7dq1buvHjx8PAJg7dy4++OAD3HrrrXj44YdRWFiIRYsWoU2bNrj33nvl9kuWLMGyZcvQtWtX+Pn5eexv6NCh0Ol0AIDY2FikpaWhTZs20Gg0+Oabb/D+++8jJSUFkydPvmLt0dHReOSRR7Bo0SLY7XZ07NgRH3/8MXbt2oV33nlHniMLAB577DGsX78ew4cPx8yZM2EymfDqq6/CbrdjwYIFtb5+RERERER0bbBbbTCn58JyJBe2vJIq2yl8lTAkBsCUHAxIEiSFBHWgFjEDE5Gx4ySiesUxlCKqBQZTjcylIVRDhFIA8PPPPwMANm/ejM2bN3usdwVTMTEx2LlzJ2bOnIk5c+ZApVJh4MCBWLx4sdv8Uq797dmzB3v27PHY38mTJ+Vgaty4cdi9ezc2bNiA0tJSxMbGYvbs2Zg3bx78/PyqVf/ChQsREBCAFStWYPXq1UhKSsLatWsxduxYt3ZhYWH45ptv8Le//Q0vvvgi7HY7unbtirVr1+Kmm26q1rGIiIiIiOja4rCVw3o8H+YjF1GcYa26oSRBH2uCKTkI+vgAKHzc76iQFBJ8AzSIuiMBvn4qhlJEtSCJ2t7rdZ2zWCwwmUwwm80wGo0e60tKSnD06FEkJSV5ffJcYyGEgNPphEKhgCTxhyT9oTZ92Ol0Ijs7G6GhobzNkQCwT1DdYV+i2mLfobrCvnRjKDOXInvPORSeLIBwOKtspw3Tw9gsCMakQPhofS+7T/YdqkvXS3+6UqZSGUdMERERERER0XVJCAFR7oTCt2I6D4WPEtbj+YCX8Rm+RjVMzYJgahYMlb+mvkslumExmCIiIiIiIqLrSllBqTxvlF+0ERG3NgUA+Oh8oYsxouiMGQCgVPvAmBQIY7MgaMP1vMuEqAEwmCIiIiIiIqLrSuZXp1CcYQEAOErLEdajiTw/lH+LYCh8lTA1D4K+iQmS8tq9XYroesDvQCIiIiIiIromOcudsBzNRcYXx93mjDI1C5L/32Erl0dIAYAxKQjR/RNhiAtgKEXUCHDEFBEREREREV0zhFOg+LwV5iMXYT2eD2eZAwAqnpzX1B8AYEgIQNbO0/K8UZpQXQNWTESXw2CKiIiIiIiIGj1bXgnMhy/CfCQX5UVlHuvNRy7KwZRS7YP4cW3ga1Bx3iiiRo7BFBERERERETVK5UVlMKfnwXzkImwXi6tsJ/kooFAp3ZapjOqrXR4R1QEGU0RERERERNRoOMscsJ7Ih/nIRRSdswJCeG8oSdDFGGFqFgRDXIBHMEVE1wYGU0RERERERNSghFOg6KwZ5iO5sJ7Ihyh3VtlWE6KDqVkQjElB8NH51mOVRHQ1MJgiIiIiIiKiBmU5mofz245Xud5Xr4KxWRBMzYKhDtTWY2VEdLUxmCIiIiIiIqJ6U2axwZKeC224HrpoIwDAEOcPyUfhNlJKoVLCmBgIU7MgaCMMkBScxJzoesRgioiIiIiIiK46IQTObkpH0VkzAMCQECgHUwqVEob4AFiP5UEX6w9TsyDom/pD4aNoyJKJqB7wu5y8+uGHHzB9+nS0atUKOp0OTZo0wahRo5Cenu7R9tChQ+jXrx/0ej0CAwNx9913Iycnx63N4cOHMXv2bKSkpMBgMCAiIgIDBw7Ejz/+6LG/+fPnQ5Ikj5dGo6l2/U6nE8899xzi4uKg0WjQtm1bvPfeex7tvB3H9br99turfTwiIiIiInLnLHei5EKh/F6SJCjUf0xQXniqAA5bufw+tEs0kia1Q8zAJBgTAxlKEd0gOGKKvHr22Wfx7bffYuTIkWjbti2ysrKwdOlStG/fHt999x1at24NADh37hx69uwJk8mEBQsWoLCwEM8//zwOHjyIvXv3QqVSAQBWrlyJN954A8OHD8fUqVNhNpuxYsUKdOnSBZ9//jn69OnjUcPy5cuh1+vl90pl9Z+yMW/ePCxcuBD33XcfOnbsiI0bN2Ls2LGQJAmjR4+W27399tse2/7444946aWX0Ldv32ofj4iIiIiIKkZFlWQVVkxifjQPTocTyZPayU/MMzULgvVYHoCKeaPsZhuUoRUfS32N6garm4gaDoMp8mrmzJl499135WAJANLS0tCmTRssXLgQa9euBQAsWLAARUVF2LdvH5o0aQIA6NSpE26//XasXr0a999/PwBgzJgxmD9/vlvQNGnSJLRo0QLz58/3GkyNGDECwcHBNa49IyMDixcvxrRp07B06VIAwF//+lekpqbi0UcfxciRI+WQa/z48R7b79ixA5IkYcyYMTU+NhERERHRjaisoBTmIxdhPpILu8Xmts56Ih+m5hW/1+ubmBCYEg5jYiA0YTpIEueNIrrRcWwkedWtWze3UAoAkpKS0KpVKxw6dEhetmHDBgwaNEgOpQCgT58+SE5Oxvr16+VlHTp0cAulACAoKAg9evRw219lQghYLBYIIWpU+8aNG2G32zF16lR5mSRJmDJlCs6dO4c9e/ZUua3NZsOGDRuQmpqK6OjoGh2XiIiIiOhGUl5sR96BCzi5/jccX3sAF3847xFKAYA5PVf+f0mpQFj3JtCG6xlKEREAjphqVMxlJShzOjyWqxRKmFQN/0hUIQQuXLiAVq1aAagYmZSdnY2bb77Zo22nTp2wZcuWK+4zKyurylFR8fHxKCwshE6nw1133YXFixcjLCzsivvcv38/dDodWrRo4VGTa3337t29brtlyxYUFBRg3LhxVzwOEREREdGNxml3oPBkAcxHclF4xgxc5o/IflFGmJoFwZAQUI8VEtG1hsHUVZJVbEFWiRVAxWidmwIj5XUZRWbklFZMAqiUFGgTGAEAKHM6MPCL12FzlMvb6X3U+KjPvQCAk9ZcmMtKAQBaH180M4XK+zxuuQirveKvE3pfNRKNNb8F7kreeecdZGRk4KmnngIAZGZmAgAiIiI82kZERCAvLw82mw1qtfd7xXft2oU9e/bg8ccfd1seEBCA6dOno2vXrlCr1di1axdeeeUV7N27Fz/++COMRuNl68zMzERYWJjHX2BcdZ4/f/6y56hWqzFixIjLHoOIiIiI6EYhnALFGVaYj1yE9Xg+nHbPP6a7qAO1MDULhjE5EL4GzhlFRFfGYOoq2XT2d6w88h0AwFehxLeDHpTXrT/5M947sR8A4K/S4ot+k+V12SWFyLUVV2ynVKKZMURet+LwHvxf5jEAQLIpBGtT/xjVs+S3r/F9zhkAQLugKKy4ZWSdns/hw4cxbdo0dO3aFRMmTAAAlJSUAIDX4Mn1BL2SkhKv67OzszF27FjExcVh9uzZbusefvhht/fDhw9Hp06dMG7cOCxbtgxz5sy5bK1VHbNyTd5YLBZ8+umnGDBgAPz9/S97DCIiIiKiG4ElPRcXvj2L8qKyKtv4+PnCmBwEU7NgqIO1vEWPiGqEc0zRFWVlZWHgwIEwmUz48MMP5YnDtdqK2wttNs/7yEtLS93aVFZUVIRBgwbBarVi48aNHnNPeTN27FiEh4dj+/btbnVVfrkCJ61WW+OagIr5skpLS3kbHxERERHdsOyFZXCW/TEiSqFWeg2lFL4KmJoFo8mQZkicmIKw7k2gCfFjKEVENcYRU3RZZrMZ/fv3R0FBAXbt2oXIyD9uSXTdGue6pa+yzMxMBAYGeoxcKisrw7Bhw3DgwAFs3boVrVu3rnYtMTExyMvL8zi+y6pVqzBx4kRERETgq6++ghDC7R9GV52Vz6Gyd955ByaTCYMGDap2TURERERE1zpnuROWo7mwHMlF0TkLInrHwb9lxZ0buhgTlFpfOErsgCRBF2OEqVkwDPH+UPgqG7hyIroeMJi6SobEtESn4BgA8Pirwai4FNwakQigYo6pykK1ephUGnm7yusnN++K0fHtAFTMMVXZI616us0xVRdKS0sxePBgpKenY/v27WjZsqXb+qioKISEhODHH3/02Hbv3r1ISUlxW+Z0OnHPPffgyy+/xPr165GamlrtWoQQOHXqFNq1aycv27Ztm1sb16TsKSkpWLlyJQ4dOuRW8/fffy+vv1RmZia++uorTJw4sco5sYiIiIiIrkeSBGR/exaO0oq5bs1HLsrBlKSQENyh4g/CxuQg+Pj5VrkfIqLaYDB1lYT7GRHu532S7iidCVE6k8dylUKJT/ve53U5AMQZgqo8XkIdT3bucDiQlpaGPXv2YOPGjejatavXdsOHD8eaNWtw9uxZxMRUBHFffvkl0tPTMWPGDLe2Dz74INatW4cVK1Zg2LBhVR47JycHISEhbsuWL1+OnJwc9OvXT17Wp08fr9vfeeedmDFjBpYtW4alS5cCqAi2Xn31VURFRaFbt24e27z//vtwOp28jY+IiIiIrltCCJRmF8F8JBeO0nJE9U0AAEhKBYxJgcg/mA0AKM6wwm61yZOXB6aEN1jNRHT9YzDViJhU3uc+agizZs3Cpk2bMHjwYOTl5WHt2rVu68ePHw8AmDt3Lj744APceuutePjhh1FYWIhFixahTZs2uPfee+X2S5YswbJly9C1a1f4+fl57G/o0KHQ6XQAgNjYWKSlpaFNmzbQaDT45ptv8P777yMlJQWTJ0/GlURHR+ORRx7BokWLYLfb0bFjR3z88cfYtWsX3nnnHXmOrMreeecdREZGolevXjW9VEREREREjVqZxQbLkYswH8lFWUHFvKuQJITdEgMfnQpAxWgoc3oujIlBMDULkpcTEV1tDKbIq59//hkAsHnzZmzevNljvSuYiomJwc6dOzFz5kzMmTMHKpUKAwcOxOLFi91uiXPtb8+ePdizZ4/H/k6ePCkHU+PGjcPu3bvlychjY2Mxe/ZszJs3D35+ftWqf+HChQgICMCKFSuwevVqJCUlYe3atRg7dqxH2yNHjmDfvn2YOXMmFAo+D4CIiIiIrn2O0nJYjuXBfOQiSjILPRsIAXN6HoLaVYyG0obrkXRvOyh8+PswEdUvSQghGrqIxshiscBkMsFsNsNo9Lwlr6SkBEePHkVSUlKVT3lrDIQQcDqdUCgUfEIGualNH3Y6ncjOzkZoaChDPALAPkF1h32Jaot9h+rK9dCXnOVOFJ4ugOVILgpPFUA4q/6op40wICglDIaEwHqs8Pp0PfQdajyul/50pUylMo6YIiIiIiIiukYJp0BJViHMRy7CciwPTpujyrYqfw1MzYJhTA6EyqSpxyqJiKrGYIqIiIiIiOgaY8svgflwLizpubBbbVW2U2p9YUwKhKlZEDShOt5FQUSNDoMpIiIiIiKia0z+wWzkH7jgdZ2kVMAQ7w9Ts2DoYoyQlNfu7UBEdP1jMEVERERERNRIOe0OWE8UwHzkIiJubQpfQ8UDhkzNgjyCKV20EcZmwTAkBECp8nwSNRFRY8RgioiIiIiIqBEqM5fi5Pu/wWmvmDfKkp6HoA4RAABNqA4qfw0kpQKmZkEwJgfBV69qyHKJiGqFwRQREREREVEDE0LAdrEEzrJy+EVVPMHK16iGUusjB1PmIxcR2D4ckiRBkiQ0HdESSg0/0hHRtY0/xYiIiIiIiBqIvbAMliO5MB+5CFteCTTBfogb3RoAIEkSTM2CcPGH81D4KqEJ00E4BCSfignMGUoR0fWAP8mIiIiIiIjqkcNWDuvxfJiP5KI4w+K2rvRiMWx5JVAHagEAphYhUAdooY/zh8KX80YR0fWHwRQREREREdFVJhxOFJ4xw3IkF9aTBRAOZ5Vti85Z5GBKZVRDZVTXV5lERPWOwRQREREREdFVIIRA6YUimI/kwnI0F47S8irb+hrVMDULgqlZMFT+mnqskoioYTGYIiIiIiIiqkNlBaUwp+fCciQXZebSKtsp1T4wJAXC1CwI2nA9JEmqxyqJiBoHBlNERERERER15Py24zAfya1yvaSQoI/zh6lZMPSxJkhKRT1WR0TU+DCYIiIiIiIiqgVnuRPF5yzQN/WXl1V1G55fpAHGZkEwJgTyaXpERJU0unj+mWeeQceOHWEwGBAaGoq77roLR44ccWszefJkJCQkQKvVIiQkBHfeeScOHz7s1ubMmTMYOHAg/Pz8EBoaikcffRTl5VXf003ufvjhB0yfPh2tWrWCTqdDkyZNMGrUKKSnp3u0PXToEPr16we9Xo/AwEDcfffdyMnJcWtz+PBhzJ49GykpKTAYDIiIiMDAgQPx448/euxv/vz5kCTJ46XRVP9ee6fTieeeew5xcXHQaDRo27Yt3nvvPa9t169fjy5dusDf3x9BQUFITU3Fp59+Wu1jEREREdGNpcxciswvT+Lom/tx9pN0lOYUy+uMyUHy/6sCNAjpEo3Ee25C7LAWCGgVylCKiOgSje6n4s6dOzFt2jR07NgR5eXlmDt3Lvr27Yvff/8dOp0OANChQweMGzcOTZo0QV5eHubPn4++ffvi5MmTUCqVcDgcGDhwIMLDw7F7925kZmbinnvuga+vLxYsWNDAZ3htePbZZ/Htt99i5MiRaNu2LbKysrB06VK0b98e3333HVq3bg0AOHfuHHr27AmTyYQFCxagsLAQzz//PA4ePIi9e/dCpVIBAFauXIk33ngDw4cPx9SpU2E2m7FixQp06dIFn3/+Ofr06eNRw/Lly6HX6+X3SmX1H487b948LFy4EPfddx86duyIjRs3YuzYsZAkCaNHj5bbvfzyy3jooYcwcOBALFy4EKWlpVi9ejUGDRqEDRs2YNiwYbW9hERERER0HRFOAUkhyf9fcOiPP8Sa0y9CE9IEAKAyaRDWvQn8Ig1Qh/hx3igioiuQhBCioYu4nJycHISGhmLnzp3o2bOn1zYHDhzATTfdhGPHjiEhIQGfffYZBg0ahPPnzyMsLAwA8Oqrr+Lvf/87cnJy5LDkciwWC0wmE8xmM4xGo8f6kpISHD16FElJSdBqtX/uJK8iIQScTicUCkWN/lHcvXs3br75ZrdrdfToUbRp0wYjRozA2rVrAQBTp07F6tWrcfjwYTRpUvGP8fbt23H77bdjxYoVuP/++wEA+/btQ7NmzdyCptzcXLRo0QLJycn45ptv5OXz58/Hk08+iZycHAQHB9f4nDMyMhAXF4f7778fS5cula9DamoqTp48iVOnTskhV3JyMvz9/fH999/L18disSAqKgq9e/fGxo0ba3z8a0Vt+rDT6UR2djZCQ0OhUDS6AZfUANgnqK6wL1Ftse9QXXE4HLAXl8HXTyX/rlheVAZzeh4sRy5CF+uP0K7RcvuT639DaXYRgIrb9+LHtWEIdYPizyGqS9dLf7pSplJZoxsxdSmz2QwACAwM9Lq+qKgIq1atQlxcHGJiYgAAe/bsQZs2beRQCgDuuOMOTJkyBb/99hvatWvnsR+bzQabzSa/t1gsACo6hdPp9GjvdDohhJBfdclZYoEoL4Pko4JCe/kvYE3UpM6uXbt6bJOYmIhWrVrh0KFD8vINGzZg0KBBiImJkZfddtttSE5Oxvr163HfffcBANq3b++xv8DAQPTo0QM7duxwW+76f6fTCbPZDIPBUKN/5D/++GPY7XZMmTLFbb8PPPAAxo0bh927d6N79+4AKr7OycnJbsc1GAzQ6/XQarV1/rVtTFx9t6o+7o2r31e3PV3/2CeorrAvUW2x71BdEELAnl+KjB0nEZkah/LScuTty0TxOQtcvw6Wl5YjqGOEPGrK2CwICpUSxmZBMMT5X5XPBXRt4M8hqkvXS3+qSf2NOphyOp145JFHcMstt8i3jrksW7YMs2fPRlFREZo1a4Zt27bJo3uysrLcQikA8vusrCyvx3rmmWfw5JNPeizPyclBaannI17tdnuNP9RXlyi34cx/hqPJQxvqZN91VZ8QAhcuXEDLli3hdDqRkZGB7OxstG/f3uMYHTt2xGeffXbFY2dmZiI4ONitnesf9ISEBBQWFkKn0+HOO+/EokWLPL6u3uzfvx86nQ7NmjVz2+/NN98MAPjpp5/QrVs3AEBqaio2bNiA//znPxg0aBBKS0vxyiuvwGw2Y/r06df8D4PLcf3Ay8vLg6+vb7W3MZvNEEJc0+k91R32Caor7EtUW+w79Gep1WqoypQ4/dERlGQXouSsBU2GNIdQCtjtdrldeb4dGb+fgSr0fyPNwwB1mD9scMBWUPVT+Oj6x59DVJeul/5ktVqr3bZRB1PTpk3Dr7/+6nabl8u4ceNw++23IzMzE88//zxGjRqFb7/9tkYTZFf22GOPYebMmfJ7i8WCmJgYhISEVHkrX15eHhQKhdfOUl6QhXKz9xDMjUIJTUwb922d5RA2K0S5HfZzBwEh4BMQCR9jqNzGaStGWZbnROSV+ZjC4eMfXnGYOujQa9euRUZGBp588kkoFApcuHABABAZGemx/4iICOTl5cFut0OtVnvd365du/Ddd99h3rx5btsHBgZi2rRp6Nq1K9RqNXbt2oVly5bhhx9+wA8//HDFYYCuYPLSOamioqIAVIRhruP95z//QW5uLh555BE88sgjAIDg4GBs375dHjV2vXLd3hkYGFijW/kkSUJISMg1/UOS6g77BNUV9iWqLfYdqi1nmQOlucVAIXBqw++w5ZVAEgJl+Tac2XQEsXc2h1LpA+vxfPjqfWFMDoJ/kxD4Gr3/bks3Lv4corp0vfSnmmQzjTaYmj59Oj755BN8/fXXiI6O9lhvMplgMpmQlJSELl26ICAgAB999BHGjBmD8PBw7N271629K0QJDw/3ejy1Wu01QKkqeHJ9qHe9LlX482bk73jtiuep9PNH7N+3w1FshigvAwCUZR1DWc5J2DIPyWGUs8QCyfTHaKHyvLPIXHXfZfcd0Ot++Pf6o82fuef98OHDmD59Orp27YqJEydCkiR5JJlGo/HYtyvoKC0t9dohs7OzMW7cOMTFxeHvf/+72/augMhlxIgR6Ny5M8aNG4fly5djzpw5l621pKQEarX6sjW51rlGVkVHR2PQoEGwWq148cUXMXz4cOzatQuJiYnVuDrXJlffraqPX267mm5D1zf2Caor7EtUW+w7VF2O0nJYT+bDejwfRRlWNBmSjIwvjqEsr8StnT2/FFlfn0bM4GQE3hQGvwiDfAsfkTf8OUR16XroTzWpvdEFU0IIPPjgg/joo4+wY8cOxMXFVWsbIYQ8R1TXrl3x9NNPyxOGAcC2bdtgNBrRsmXLq1p/bYnyMpx9eRjgdKAs5yTgKMfZl4dDkpSA0gfxT3zfYLVlZWVh4MCBMJlM+PDDD+WRSK6gp/LcXC6u0MrbSJyioiI5BPrmm2/cJkSvytixYzFr1ixs375dDqYuvS3TZDJBq9VCq9VWu6aRI0fCx8cHmzdvlpfdeeedSEpKwrx587Bu3bor1kZEREREjZtwCpzdnI6icxag0jxQufuzEJ7aFGcKDsOWX/G7olKlhCZch5hBSVAHaBlIERFdZY0umJo2bRreffddbNy4EQaDQQ4fXKHDiRMnsG7dOvTt2xchISE4d+4cFi5cCK1WiwEDBgAA+vbti5YtW+Luu+/Gc889h6ysLDz++OOYNm1albeVNTTJR4WYB/8LALCdP4QzSwYj5sEN8ogp4ShvkLrMZjP69++PgoIC7Nq1C5GRkfK6iIgIABW3xl0qMzMTgYGBHte7rKwMw4YNw4EDB7B161aPucMuJyYmBnl5eR7Hd1m1ahUmTpyIiIgIfPXVVxBCuI2actXpOocTJ07g888/x2uvuY9sCwwMRPfu3fHtt99WuzYiIiIiajzKCkpRZi6FPtYfACApJAincAulAKDwRD4kpYTYES2R8fkx2Kyl0AbpETMwCepAhlJERPWh0QVTy5cvBwD06tXLbbkrdNBoNNi1axeWLFmC/Px8hIWFoWfPnti9e7c8OkqpVOKTTz7BlClT0LVrV+h0OkyYMAFPPfVUvZ2Hod0QaOM7XbmhomL0kdLPJC8STjtUIXFQR7aAw3IRgHBbDwC+QU0QOWnlZXftY/J+22J1lZaWYvDgwUhPT8f27ds9RptFRUUhJCQEP/74o8e2e/fuRUpKitsyp9OJe+65B19++SXWr1+P1NTUatcihMCpU6fcnqi4bds2tzatWrUCAKSkpGDlypU4dOiQW83ff/+9vB744/ZOh8PhcTy73Y7y8oYJA4mIiIiodszpucjddx623BIoNT5ImtRODpcMCQEozqh48rZCrYShqT8MCYHQxRghKRWIHdocGTtOIqpXHEMpIqJ61OiCqSs9YjUyMhJbtmy54n5iY2Or1e5q8fH/Y+LxmpIUvlBoDJCUKmhib/LaRqH2gyY25Yr7qu0jax0OB9LS0rBnzx5s3LixyonAhw8fjjVr1uDs2bOIiYkBAHz55ZdIT0/HjBkz3No++OCDWLduHVasWIFhw4ZVeeycnByEhIS4LVu+fDlycnLQr18/eVmfPn28bn/nnXdixowZWLZsGZYuXQqg4jq8+uqriIqKkp/Il5iYCIVCgXXr1mHy5Mny6Kpz585h165d6N69++UuERERERE1IOEUKM0ugjrYDwqfirlMhN0JW27FfFGO0nIUn7dCF13x4BxDfABseSUwxgfAL8oASek+/4lvgAZRdyTA10/FUIqIqB41umCK/ritT/JRNVgNs2bNwqZNmzB48GDk5eVh7dq1buvHjx8PAJg7dy4++OAD3HrrrXj44YdRWFiIRYsWoU2bNrj33nvl9kuWLMGyZcvQtWtX+Pn5eexv6NCh0Ol0ACpCxbS0NLRp0wYajQbffPMN3n//faSkpGDy5MlXrD06OhqPPPIIFi1aBLvdjo4dO+Ljjz/Grl278M4778hzZIWEhGDSpElYuXIlbrvtNgwbNgxWqxXLli1DSUkJHnvssT91DYmIiIiobgmHE8XnrbCeqJjAvLzYjpjByfIte/o4f2CHJN+yV3TWIgdTvnoVIno1rXLfkiShoMiMUF1olW2IiKjuMZhqhC69ba8h/PzzzwCAzZs3u00M7uIKpmJiYrBz507MnDkTc+bMgUqlwsCBA7F48WK3+aVc+9uzZw/27Nnjsb+TJ0/KwdS4ceOwe/dubNiwAaWlpYiNjcXs2bMxb948+Pn5Vav+hQsXIiAgACtWrMDq1auRlJSEtWvXYuzYsW7tli9fjptuuglvvPGGHER17NgRb731Fnr27FmtYxERERHR1eMsd6LorBnW4/koPFkAh819ugXriXw5mPLx84WpeRB89SoY4gOhDvZ8EA8RETUukqjtvV7XOYvFApPJBLPZDKPR6LG+pKQER48eRVJSktcnzzUWQgg4nU4oFAq3icCJatOHnU6n/LTLa/nRpVR32CeorrAvUW2x71yfHGUOFJ4qgPV4PorOFMBpd1bZVqn1RdK9KX/69jv2Jaot9h2qS9dLf7pSplIZR0wREREREVGDKy+xo/BEAawn8lB01lLxFL2qSBL8IvUwJATCEOfPOaGIiK5hDKaIiIiIiKhBlBeVwXIsD9bj+SjOLJTnhvJGUkjQxZhgSAiAPs4fPlrfeqyUiIiuFgZTRERERERUb4QQ8hQTRRlWXNh1psq2Cl8F9LH+MMQHQNfUH0qVsr7KJCKiesJgioiIiIiIrhohBGw5xbAcz0fhyXwEd4qCMTEQAKBvWnEbXuXb9pRqH+jj/GFICIAuxgSFz7U7xwoREV0ZgykiIiIiIrpqRLkTp/57CKK8YgJz6/F8OZhSqpTQxRhRerEEhvgAGOID4Bdl4JxRREQ3EAZTRERERET0pwmHE0XnrLCeyAcARNzaFACg8FVC38QkLy88VQBnuVMeCRV5ewIUKiXDKCKiGxSDKSIiIiIiqhWn3YGiM2ZYT+TDerIAzjIHAEDyUSCsewwUvhVzQhniA2A9kQ91sB8M8QHut+5p+JGEiOhGxn8FiIiIiIio2hy2chSeLID1RD4Kz5jlW/QqE+VOFJ0xw5BQccueIT4ACXe3hcqkqe9yiYiokWMwRUREREREl1VeZIf1ZD6sx/NRdM4CCFF1Y0mCLtoARaUn6ClUSqj4RD0iIvKCwRQREREREXkos9hgPZ4P64k8lGQWXratpFRA18QEY0IA9E39eXseERFVG//FICIiIiIiN6c/Pozic5bLtlGolNA39YchIQD6JiZ5PikiIqKaYDBFdJ05deoU4uLisGrVKkycOLGhyyEiIqJGTAiB0uwi2HJL4N8yRF7uq/P12l6p9YUhriKM8osyyk/WIyIiqi3+S0IeVq9eDUmSoNFokJGR4bG+V69eaN26dQNU9udIklSt144dO+qlnmXLlmH16tX1ciwiIiKiS1nSc3FszS849cHvyPzqFByl5fI616TlAOCjVyHwpjDEDm2OpHtTENE7DvpYf4ZSRERUJzhiiqpks9mwcOFCvPzyyw1dSp14++233d6/9dZb2LZtm8fyFi1a1Es9y5YtQ3BwMEc1ERER0VXnLHei+JwF2nC9PP+TQq1EeWFZRQMhUHiqAKbmwQAAXYwRQR0iYYj3hyZUB0mSGqp0IiK6zjGYoiqlpKTg9ddfx2OPPYbIyMiGLqfaioqKoNPpPJaPHz/e7f13332Hbdu2eSy/VHFxMfz8/Oq0RiIiIqKrzVnmQOEZM6zH81B4ygyn3YGIW+Pg36rilj2/KCMUKiWcZQ4AQOnFYpj+t63CV4nQrtENVDkREd1IOP62ERJOgfJiO4TzMo/hrQdz586Fw+HAwoULq9V+7dq16NChA7RaLQIDAzF69GicPXtWXj99+nTo9XoUFxd7bDtmzBiEh4fD4XDIyz777DP06NEDOp0OBoMBAwcOxG+//ea23cSJE6HX63H8+HEMGDAABoMB48aNq+UZ/3Gb4r59+9CzZ0/4+flh7ty5ACpGkD3xxBNITEyEWq1GTEwMZs+eDZvN5raPVatWoXfv3ggNDYVarUbLli2xfPlytzZNmzbFb7/9hp07d8q3EPbq1UteX1BQgEceeQQxMTFQq9VITEzEs88+C6fT6bafgoICTJw4ESaTCf7+/pgwYQIKCgpqff5ERER0bXOUlqPgUA7OfpKO9Df2I+PzY7AczYPTXvE7luV4ntxW4aNAUPsIhHVvgsR7bkJY9yYNVTYREd3AOGKqkRFOAVteCS7sOo2wHrFQB2ohKRpm6HRcXBzuuecevP7665gzZ85lR009/fTT+Mc//oFRo0bhr3/9K3JycvDyyy+jZ8+e2L9/P/z9/ZGWloZXXnkFn376KUaOHClvW1xcjM2bN2PixIlQKiue5vL2229jwoQJuOOOO/Dss8+iuLgYy5cvR/fu3bF//340bdpU3r68vBx33HEHunfvjueff/5Pj27Kzc1F//79MXr0aIwfPx5hYWFwOp0YMmQIvvnmG9x///1o0aIFDh48iBdffBHp6en4+OOP5e2XL1+OVq1aYciQIfDx8cHmzZsxdepUOJ1OTJs2DQCwZMkSPPjgg9Dr9Zg3bx4AICwsTL4eqampyMjIwOTJk9GkSRPs3r0bjz32GDIzM7FkyRIAFZOV3nnnnfjmm2/wwAMPoEWLFvjoo48wYcKEP3X+REREdG2xF5bBeiIfhSfyUZRhBUTVf9wsybTCWe6U54cKvvnaGRVPRETXJwZTV4ndaoPdWlajbRRqJUS5E2c/TYfdbIMtvxRNhjSTwylbXok8KaXCVwFNyB+3q9lyi+GwOdz252tQwUev+lPnMW/ePLz11lt49tln8dJLL3ltc/r0aTzxxBP497//LY8uAoBhw4ahXbt2WLZsGebOnYvu3bsjKioK69atcwumPv30UxQVFSEtLQ0AUFhYiIceegh//etf8dprr8ntJkyYgGbNmmHBggVuy202G0aOHIlnnnnmT52rS1ZWFl599VVMnjxZXrZ27Vps374dO3fuRPfu3eXlrVu3xgMPPIDdu3ejW7duAICdO3dCq9XKbaZPn45+/frhhRdekIOpu+66C48//jiCg4M9biV84YUXcPz4cezfvx9JSUkAgMmTJyMyMhKLFi3CrFmzEBMTg02bNuHrr7/Gc889h0cffRQAMGXKFNx66611ch2IiIio8SorKIX1RD6sJ/JRklV42baSjwL6WBMM8QHQN+Wk5URE1LgwmLpKCg5dxMW9nk+0q4o+PgD+zYNxZuNhlOWXAgDsljKc23IU0QOSoA7UIuf7DFj/N/xaE+yHuNF/PBnvwjdnUXTW7LbP4E5RCO745/4KFh8fj7vvvhuvvfYa5syZg4iICI82//3vf+F0OjFq1ChcvHhRXh4eHo6kpCR89dVXmDt3LiRJwsiRI7FixQoUFhZCr9cDANatW4eoqCg58Nm2bRsKCgowZswYt/0plUp07twZX331lUcNU6ZM+VPnWZlarca9997rtuyDDz5AixYt0Lx5c7eaevfuDQD46quv5GCqcihlNptht9uRmpqKrVu3wmw2w2Qy4XI++OAD9OjRAwEBAW7H6tOnDxYuXIivv/4a48aNw5YtW+Dj4+N27kqlEg8++CB27dpV+wtAREREjZLdakPBoYuwHs+DLbfksm0VaiUMTf1hSAiELsYIha+ynqokIiKqGQZTjYFCQlC7cGR8fkwOpVzsFhsu7DqNqDsSgQZ6GMrjjz+Ot99+GwsXLvQ6auro0aMQQsijey7l6+sr/39aWhqWLFmCTZs2YezYsSgsLMSWLVswefJk+WkvR48eBfBH6HMpo9Ho9t7HxwfR0XU3OWdUVBRUKveRZkePHsWhQ4cQEhLidZvs7Gz5/7/99ls88cQT2LNnj8d8WtUJpo4ePYoDBw5c8VinT59GRESEHPC5NGvW7LL7JyIiomuDcAoIp5BHONmtZZf9w6dS6wtDQgAM8QHQRRkgKTkyioiIGj8GU42BUyB3fxbCU5viTMFht3DK16hGWI/Yisf6NtBc6PHx8Rg/frw8aupSTqcTkiThs88+k+eIqqxycNKlSxc0bdoU69evx9ixY7F582aUlJTIt/G59gdUzDMVHh7usT8fH/duq1aroVDU3S9elUc8Va6pTZs2eOGFF7xuExMTAwA4fvw4brvtNjRv3hwvvPACYmJioFKpsGXLFrz44osek5d743Q6cfvtt2P27Nle1ycnJ9fgbIiIiOhaIpwCxRmW/92mV4CgduEITKn4fUgbrodS6wtHiV1u72tUwxAfAENCALRh+gabm5SIiKi2GExdJf4tgqGLNl65YSUKtRLxY9vIc0z5mjTybXySQkJI5ygE3lQxQbbC1z2ICeseA4fN/bY9X8Ofm1+qsscffxxr167Fs88+67EuISEBQgjExcVVKzQZNWoUXnrpJVgsFqxbtw5NmzZFly5d3PYHAKGhoejTp0+dncOfkZCQgF9++QW33XabPLLLm82bN8Nms2HTpk1o0uSPJ9t4u/2wqv0kJCSgsLDwiuceGxuLL7/80u22SAA4cuTIlU6HiIiIGhEhxB+/F0jA+e0nUV5UMVep9US+HExJCgmG+ACUZFr/NzIqEOpg7WV/NyEiImrsOL73KvE1qOEXaajRSxPkB02IDk2GNIe+qb/bxOcAoA7U/tG20sTnAKAO8vPYn69BXWfnk5CQgPHjx2PFihXIyspyWzds2DAolUo8+eSTEJc8BUYIgdzcXLdlaWlpsNlsWLNmDT7//HOMGjXKbf0dd9wBo9GIBQsWwG6341I5OTl1dFbVN2rUKGRkZOD111/3WFdSUoKioiIAkEeMVb4OZrMZq1at8thOp9OhoKDA67H27NmDrVu3eqwrKChAeXnFBPgDBgxAeXk5li9fLq93OBx4+eWXa3ZyREREVO8cZQ6Y03Nx7rNjOLPxjz8qSZIEQ0KA/L74vBXlxX/8PhTWownix7ZBSOdoaEL8GEoREdE1jyOmGhlJIUEdqEXUHYlQanwa1XDsefPm4e2338aRI0fQqlUreXlCQgL+/e9/47HHHsOpU6dw1113wWAw4OTJk/joo49w//33429/+5vcvn379khMTMS8efNgs9ncbuMDKuaQWr58Oe6++260b98eo0ePRkhICM6cOYNPP/0Ut9xyC5YuXVpv5w0Ad999N9avX48HHngAX331FW655RY4HA4cPnwY69evx9atW3HzzTejb9++UKlUGDx4MCZPnozCwkK8/vrrCA0NRWZmpts+O3TogOXLl+Pf//43EhMTERoait69e+PRRx/Fpk2bMGjQIEycOBEdOnRAUVERDh48iA8//BCnTp1CcHAwBg8ejFtuuQVz5szBqVOn0LJlS/z3v/+F2Wyu4iyIiIioIZWX2FF4ogDWE3koOmuBcP7xhyy71Sb/UdGQEID8g9nwizTAkBAASfnH74N8oh4REV1vGEw1QpJCgo+f75Ub1rPExESMHz8ea9as8Vg3Z84cJCcn48UXX8STTz4JoGLepb59+2LIkCEe7dPS0vD0008jMTER7du391g/duxYREZGYuHChVi0aBFsNhuioqLQo0cPjyfm1QeFQoGPP/4YL774It566y189NFH8PPzQ3x8PB5++GH5FsZmzZrhww8/xOOPP46//e1vCA8Px5QpUxASEoJJkya57fOf//wnTp8+jeeeew5WqxWpqano3bs3/Pz8sHPnTixYsAAffPAB3nrrLRiNRiQnJ+PJJ5+UJ09XKBTYtGkTHnnkEaxduxaSJGHIkCFYvHgx2rVrV+/XiIiIiDzZrbaK+aKO56M4sxAQ3icNtZ4sQGDbiikb/CIMSJqUAh9t4/t9kIiIqK5J4tJ7rwgAYLFYYDKZYDabPZ4CB1TcvnX06FEkJSV5nSy7sRBCwOl0QqFQcKg3ualNH3Y6ncjOzkZoaGidTjhP1y72Caor7EtUW42x79jyS2A9ng/riXyUZhddtq3CVwF9rD/8W4fWeH5SqluNsS/RtYF9h+rS9dKfrpSpVMYRU0REREREf4IQAracYliO56PwZD5seSWXba9U+0Af7w9DfAB0MSbenkdERDc0BlNERERERH/C2U1HUHTWctk2PjoVDPEBMMQHwC/K0KjmESUiImpIDKaIiIiIiKpBOJwoOmdFSaYVIV2i5eWaUJ3XYEpl0lSEUQkB0ITqGEYRERF5wWCKiIiIiOgKrMfzcP7Lk3CWOQAAxmZBUAdUzNFoiA9A7r6Kp++qg/3kkVHqIC3n+CQiIroCBlNERERERJU4SstReKoA2nA9VP4aAICvSSOHUgBgPZ4P9c0VwZQmVIewnrHQx5qgMmkapGYiIqJrFYMpIiIiIrrhlRfZYT2ZD+vxfBSdswBCILhTFEI6RQEA1EFaqEwalJlLAUlCebFd3laSJAS2DWuo0omIiK5pDKb+JCFEQ5dAVCvsu0REdKMrs9hgPZ4P64k8lGQWeqy3Hs+TgylJkhDcMRIAoG/qD6WGv0YTERHVBf6LWks+PhWXrry8vIErIaodu73iL72uvkxERHS9E0KgLL+0Iow6nofSi8WXbW+3lqG8xA4frS8AwNQ8uD7KJCIiuqHwE2kt+fj4QKlUoqCgAEajsaHLIaoxs9kMpVLJYIqIiK5rQgiUZhf9b2RUPsoKSi/bXqnxkZ+k5xdlhMJHUU+VEhER3Zj4ibSWJElCREQEzp07B41GA71e3yifuiKEgNPphEKhaJT1Uf0TQqCwsBAFBQWIjo5mvyAiomuaEAL+OpPHLep2qw25+7NgPZGP8sKyy+7DR6+CMaHiSXraCAMkBf9tJCIiqi8Mpv6EgIAAFBcX48KFC8jKymrocrwSQkAIAUmSGECQTJIkBAYGIiAgoKFLISIiqjXhFLDnlyJjx0lEpcYBRjWU6opfb4UA8g9cqHJblb8Ghv+FUZpQHX9PIiIiaiAMpv4ESZIQHR2NiIgIlJVd/i9xDcXpdCIvLw+BgYFQKDgUnSqoVCoolcqGLoOIiKjWhFPAlleC0x8dRnFWIUrPFaLpiJbwi6wY8aQyqqEO9oOt0jxSmhCdHEapA7UNWD0RERG5MJiqA0qlElpt4/zlxul0wtfXF1qtlsEUERERXRdcodS5LUdRklkIYXdUhFQbDiF+XBuoA7WQFBKMiYEoUilhiA+APj4AKqO6oUsnIiKiSzCYIiIiIqJGTTgFSrIKYTmai9KLxYjun4QLu07DbrFBqfGB0+4AANhyi5G14xSiByTBx88XQR0iEHxzZANXT0RERJfDYIqIiIiIGh0hBGy5JbCk58KSngt7pQnMHbZyhPWIxbktR+F0OIFCCUq1EppQHcJTY6HUVPyKy3mjiIiIGj8GU0RERETUaJSZS2E5mgdLei5seSVe2+QfzEZY9yaIHpCEs58ehcPhgDZIj5iBSfJtfERERHRtYDBFRERERA2qvMgOy7FcWI7moSSr8LJt/aIM8AvXQ1JIUAdqETMwseKpfL3iGEoRERFdgxhMEREREVG9c9jKYT2eD8vRXBSdswJCVNlWE6KDMTkIxqRA+OpV8nJJIcE3QIOoOxLg66diKEVERHQNYjBFRERERPWm6JwF+QcvoPCUGcLhrLKdyl8jh1HqgKqffixJEgqKzAjVhV6NcomIiOgqYzBFRERERFeNcFaMhHKNZrJdLIb1eL7Xtj46FYxJgTAmB0ET4sfJy4mIiG4ADKaIiIiIqE4JIVCSVVgxifnRPET3S4BflBEAYEwKxIVvz8q37inUShgTK8IovwgDb8cjIiK6wTCYIiIiIqI65Si24/SGQ/J7c3qeHEz56FTQx5qg8FXAmBwEXYwJCh9FQ5VKREREDYzBFBERERHVmt1ig+VoHiABQe0jAFSET37RRhSfswAArMfyEN6zCSRlRQAVPTCJt+kRERERAAZTRERERFRD5SV2WI/lwZyei5LMQgCAUuODwJvC5PDJlByE4nMW+EUaYEwKhBCAK4piKEVEREQuDKaIiIiI6IqcZQ5YT+TDnJ6LorMWeY4oF0dpOYrOWaCP9QcAGBMDoYsxwtegboBqiYiI6FrBYIqIiIiIvHKWO1F0xgxLei6sJwsgHM4q2/oa1XCW/7FeoVJCoVLWR5lERER0DWMwRUREREQy4RQozrDAkp4Hy4k8OG2OKtsqtb4wJVc8UU8TquMtekRERFRjDKaIiIiICCUXCivCqKO5KC+2V9lOoVLCkBAAU3IQ/KKMkBQMo4iIiKj2GEwREREREXK+z0DRGbPXdZJSAX1Tf5iSA6GL9YfCR1HP1REREdH1isEUERER0Q3EbrXBcjQPhacKEDOkmRwymZKD3IMpSYIuxghTchD08QFQcr4oIiIiugoYTBERERHdIApPFeDsJ+ny+6IzZhjiAwAAhvgASD4KaIL9YEwOgjExED5+vg1VKhEREd0gGEwRERERXYecdgesJwugMqqhDdcDALSRBkhKhfx0PUt6rhxMKVRKJE1MgVLDXw+JiIio/vA3DyIiIqLrhHA4UXjGDEt6Hqwn8yHKnTAmBSIqPBEAoFQpoW/qD+vxPPga1FAFat22ZyhFRERE9Y2/fRARERFdw4RToCTTCnN6HqzH8uCwlbutt54sgLPMAcX/5ogKvjkCgSlh0IbrIUl8oh4RERE1LAZTRERERNcYIQRsOcUwp+fCcjQP5UVlVbaVJAmlucXwizAAADQhuvoqk4iIiOiKGEwRERERXSPKCkorwqj0XJQVlFbZTlJI0Df1hzE5CPqm/vKT94iIiIgaGwZTRERERI2YvbAM1mN5MKfnojS7qOqGkgRdlAHG5CAYEgKgVPPXPCIiImr8+BsLERERUSMlHE6cePcgnGWOKttoQnUwJQfBmBQIH52qHqsjIiIi+vMYTBERERE1Ak67A4UnC2C3liGoQwQAQFIqoG/qD0t6rltbVYAGpuRgGJMCofLXNES5RERERHWCwRQRERFRA8s/eAHZu8/CaXdCUkjwbx0i34pnSg6CJT0XPnoVTElBMCYHQR2s5RP1iIiI6LrAYIqIiIioHgmnQMmFQqiD/KBUKQEAPnoVnHanvN56PB/+LUMAALoYI2KHtYA2XA9JwTCKiIiIri8MpoiIiIiuMiEEbLklsPzviXr2wjJE3BYP/xbBAAB9ExOUah84bOVQ+CrgKCmXt5WUCvhFGhqqdCIiIqKrisEUERER0VVSZi6F5WgeLOm5sOWVuK2zpOfKwZSkVCCkSxSUah/o4/yh8FU2RLlERERE9Y7BFBEREVEdKi+yw3IsF5ajeSjJKqyyXXGGBY7Scig1Fb+OBbQJq68SiYiIiBoNBlNEREREf5LDVg7riXxY0nNRdM4KCFFlW02IDsbkIBiTAuVQioiIiOhGxd+GiIiIiGrBWe5E4ekCWNJzUXjKDOFwVtlW5a+BMTkIpuQgqPw19VglERERUePGYIqIiIiohmx5JTj14e9wljmqbOOjU8GYFAhjchA0IX6QJD5Rj4iIiOhSDKaIiIiILkMIIc8V5RdR8XQ8lb8GklIBwD2YUqiVMCZWhFF+EQZICoZRRERERJfDYIqIiIioCrk/ZSL/12zYLTb4RRoQO6wFAEBSSDAmBSL/wAVIPgoY4vxhTA6CLsYEhY+igasmIiIiunYwmCIiIiL6n/JiO3z8fOX3dmsZ7BYbAKD4vBV2qw2+BjUAIKB1KLRhOhjiAqBQKRukXiIiIqJrHf+kR0RERDe08mI78g5cwKkPf8fRVT/DXlgmrzMmB7q1LT5vlf9fHaiFqVkwQykiIiKiP4EjpoiIiOiG4yhzoPBEPszpuSg6awGEkNdZj+UhMCUcAKAN00Mf6w+/KAOMSYHyaCn6f/buOzyO6l4f+Duzs73vqnfbktwLxYBtAqYZQkmAVCAQSggBBy5plx9JuAkhgSQ3JPemJzeUm0sCJPRA6IRqBwjNDVuSi3rd3ndnZ35/rDzSWpLLWs3S+3keP9LMnBmdgdFKevec7yEiIiIaHwymiIiIaFZQZAWxthBCTT5EdwehZpVR24WafFowJYgCqs9rnMxuEhEREc0qDKaIiIhoxlIVFfHOMEJNPkR2BqCks2O21Zn1cDZ64GjwTmIPiYiIiGY3BlNEREQ0o6iqimRfDOEmP8LNPsjxzJhtRYMO9nluOBu9sFQ6IIjCJPaUiIiIiBhMERER0YwRbQ2i59VWZEKpMdsIOhG2OS44Gzyw1rogSlwLhoiIiGiqMJgiIiKiI1YmkoIgiZDMegCAziSNHkoJAqzVDjgbvbDNdUPHlfSIiIiIpgUGU0RERHREURUVwW39CDf5EO+KoHhVFYqOqQAAmEqsMLhMSAeTAABzmQ2ORi8c9R5IFv1UdpuIiIiIRsFgioiIiKY9VVUhCIP1nwTA9163NjIq3OTTgilBEOBZUYZsUoaj0QuDwzhVXSYiIiKig8BgioiIiKacqqpwWZ1QVXVoX1ZBtC2EcJMPmUgadZ9cBCAXPjkbvRh4uwsAkPIlkPInYPSYAQDuJSWTfwNEREREVJCCgqlFixbh6quvxmWXXQavl0sqExERUeFURUUmkETny7tRuXYO0gIQ3NSHSEsA2ZSstRsePjkavAhs6Yej3gNHowcGt2mquk9EREREh6GgZWja2trw9a9/HVVVVbjooovw0ksvjXe/iIiIaBZQFRUpfwJtT+xAZIcPO/+0GXIoDTkh54VSABDa4dM+N3rMaLhiBcpOroWl3D40zY+IiIiIjigFBVM9PT349a9/jSVLluDBBx/EGWecgfr6evzwhz9ET0/PePeRiIiIZqBMNIV4dwS7/rQZ0T0hKMksUr4E2h7fDteCItjmugEAgijAPtcNS4U973xBZBhFREREdKQrKJiy2Wy45ppr8Pbbb+ODDz7Addddh0AggG9+85uoqanBhRdeiKeffjqvTgQRERGRHMvA/34Pdj+0DZlIGl3P7UTKF89rkw4k0fPKHhSvrED5qXPQcNVRqDq7AbZa5xT1moiIiIgmSkHB1HBLly7FL37xC3R1deH//u//cOKJJ+Lxxx/Hueeei9raWtx6663o7Owcj74SERHRESqyO4C2x7ej+d730ft6G5I9Ufje60HZyXV59aFESYS50o7qcxth9FrgWlQMnZFrtRARERHNVIcdTO1lNBpx5pln4uyzz0ZZWRlUVUVHRwduvfVWzJ07F+vXr0c8Hj/whYiIiOiIp8hK3sjp1EACsfYwMGxfdFcAwe0DqL1gISwVNuicBlhrXaj7xCIYvRZO1SMiIiKaBcYlmHruuefw6U9/GlVVVbjpppsgCAJuueUWtLS04C9/+QuOPvpo/Pa3v8X69evH48sRERHRNKQqKmLtIXS9uAvNd7+H1EBCO+Zo9OS1lawGeI4qQ/HKCliqHaj75CLY6lyoPqceRo+ZoRQRERHRLFHw2PjOzk7cfffduOeee9Da2goAWLduHa655hqcd9550Ol0AIC5c+fik5/8JM477zw8/vjj49NrIiIimnYy4RTaHt+hbYebfDAVWwAABqcJtlonJJsBjkZvbiW9YeGT3m1C5ZnzoLcYGEoRERERzSIFjZg699xzUVdXh+985ztIJBK46aabsHPnTjz99NM4//zztVBquNWrVyMUCh3w2nfccQdWrlwJu92OkpISnH/++dixY+iXXL/fj+uvvx7z58+H2WxGTU0NbrjhhhHXFgRhxL8HHnigkNslIiKifaSDSfS/1Qn/+0Or8RpcJphKrNp2qMkHVRmauld93nyUnzIH1krHiPBJEAQEYyEIAkMpIiIiotmkoBFTTz/9NE455RRcc801uOCCCyBJB77Meeedh4qKigO2e+WVV7B+/XqsXLkSsizjm9/8JtatW4dt27bBarWiq6sLXV1d+MlPfoJFixahtbUVX/rSl9DV1YWHHnoo71r33HMPzjrrLG3b5XId8r0SERFRTiaaRqTFj9AOH5L9MQCA3maAe1mpFjQ5G71I9sdhrXbA2egdrCnFsImIiIiIRldQMLVjxw7U19cf0jlLlizBkiVLDtjumWeeydu+9957UVJSgnfeeQcnnXQSlixZgocfflg7Pm/ePPzgBz/A5z73OciynBeSuVwulJWVHVI/iYiIaEg2JSOyM4BQkw/xjvCI45loGomeKCwVdgCAc2ERHI1eSBb9ZHeViIiIiI5ABU3lO9RQ6nDsnaLn8Xj228bhcIwYubV+/XoUFRXhuOOOw9133523OhARERGNTpEVhFv86Ph7M5rveg/dL+0eNZQCAKPHDCWT1bZ1RomhFBEREREdtIKLn08GRVFw4403Ys2aNWOOthoYGMBtt92GL37xi3n7v/e97+HUU0+FxWLBc889h+uuuw7RaBQ33HDDqNdJpVJIpVLadjgc1vqgKMo43dHkU5Tcct1H8j3Q9MHnifbFZ2LmUBUV8Y4wws1+RHYFoKTH/n+qt+lhb/DC0eiB0WuGIAiH/QzwWaJC8dmh8cJniQrFZ4fG00x5ng6l/9M6mFq/fj22bNmC119/fdTj4XAY55xzDhYtWoTvfve7ecduueUW7fOjjjoKsVgM//mf/zlmMHXHHXfg1ltvHbG/v78fyWSy8JuYYoqiIBQKQVVViGJBA+SINHyeaF98Jo58GV8SyT1RpNqiUJLZMdsJBh1M1VYY62zQF5sAQUBYiQL90XHpB58lKhSfHRovfJaoUHx2aDzNlOcpEokcdFtBnabz27785S/j8ccfx6uvvoo5c+aMOB6JRHDmmWfCYrHgySefhMlk2u/1nnrqKZx77rlIJpMwGo0jjo82Yqq6uhqBQAAOh+Pwb2iKKIqC/v5+FBcXH9EPNU0PfJ5oX3wmjnytj2xHonv0cEmUBNjmuOFo9MBa7YCgm7j/x3yWqFB8dmi88FmiQvHZofE0U56ncDgMt9utlV7an2k3YkpVVVx//fV49NFH8fLLL48aSoXDYZx55pkwGo144oknDhhKAcD7778Pt9s9aigFAEajcdRjoige0Q8DkFuCeybcB00PfJ5oX3wmjgyZaBrhZh9i7WFUn9uoraLnmu9FsmdYMCUIsNU44Gj0wj7HDdGgm7Q+8lmiQvHZofHCZ4kKxWeHxtNMeJ4Ope/TLphav349/vznP+Pxxx+H3W5HT08PAMDpdMJsNiMcDmPdunWIx+O47777EA6HtXpQxcXF0Ol0+Nvf/obe3l6ccMIJMJlMeP7553H77bfj61//+lTeGhER0ZQI7/Sj8+kWbTveGYG1OvfOlb3eg97X2mAqtcLZ6IW93gPJzOLlRERERDQ5pl0w9Zvf/AYAsHbt2rz999xzDy6//HK8++67ePPNNwGMXB1w9+7dqKurg16vx69+9St85StfgaqqqK+vx09/+lNcffXVk3IPREREU0XJZBHdE4TBbYapyAIAsJTbAUEABmfvh5t8WjAlmfWov2IFwygiIiIimhKHFUw9+uijuP/++7F9+3bE43G0tOTejd2+fTueeOIJXHLJJaisrDykax6o5NXatWsP2Oass87CWWeddUhfl4iI6EilKipi7WGEm3y5FfUyWbgWF6P8lNx0eMmih7XagVhbCHqbAXpn/tR1hlJERERENFUKCqYURcFFF12Ehx56CABgNpuRSCS04263G9/61reQzWZx8803j09PiYiISKOqKhI9UYSb/Ai3+JFNZPKOR1oCKP1ILUQpN7+/+LhKFB1bAXOZTasvRUREREQ01QqqpPWzn/0Mf/3rX3HNNdcgEAiMqN1UWlqKj3zkI3jqqafGpZNERESUk/In0LexAzv/bxNaH/4Qgc29I0IpAFAVBSn/0JtG5jIbLBV2hlJERERENK0UNGLq3nvvxcqVK/HrX/8aQK5i/L7q6+sZTBEREY2DTCSFcLMfoSYfUgPxsRsKAmy1zsEV9VwQ9ZO3oh4RERERUSEKCqZaWlqwfv36/bbxer3w+XwFdYqIiGi2yyZlhFv8CDf5EO+K7LetpcIOR6MXjnoPdKZpt64JEREREdGYCvrt1Ww2IxQK7bdNa2srXC5XIZcnIiKa1ZRMFi3/+z6UjDJmG2ORBc5GLxwNHujtxjHbERERERFNZwUFU0cddRSeffZZJJNJmEymEcf9fj+eeeYZnHTSSYfdQSIioplMzSqIdYSRiaThXlICABD1OlgqHYjuCea11TuMcDR44ZzvhdFjnoLeEhERERGNr4KCqRtuuAEXXHABPvGJT+B3v/td3rGdO3fiyiuvRCgUwg033DAunSQiIpqJAlv60P/PDmSTMkS9Ds4FRdoqes5GL6J7gtCZJDgavHA0enIr6o1S15GIiIiI6EhVUDD18Y9/HDfddBN+9KMfoba2FlarFQBQUlICn88HVVVxyy234NRTTx3XzhIRER3JUv4E9A6jFj6Jeh2ySRlAbvpedE8QjnoPAMA2x4Xq8xphrXJA0BW0iC4RERER0bRX8G+6d9xxB5599lmce+65sFgs0Ol0UBQFZ511Fp5++mnceuut49lPIiKiI1ImnILvnW7sun8Ldv15M6KtQe2YfY4LwmBIJYgC0sGkdkzU62CrdTGUIiIiIqIZ7bCW7jnjjDNwxhlnjFdfiIiIZgQ5kUGkxY9Qkx+J7vwV9cJNPjjm5UZFiQYdio6tgM4swTGPK+oRERER0ezD34CJiIjGgZLOIrI7iHCTD9G2EKCqo7aL7glBSWchGnQAgKJjKyazm0RERERE00pBwVRbW9tBt62pqSnkSxAREU17alZBrD2M0A4fIrsDUGVlzLZ6hxHORi8cjV4tlCIiIiIimu0KCqbq6uoOalUgQRAgy3IhX4KIiGhaUhUViZ4oQk0+RFr8WvHy0ejMejgaPHA2emEqtXJFPSIiIiKifRQUTF122WWj/nIdCoXwwQcfYPfu3Tj55JNRV1d3uP0jIiKaNlL+BNqf2IFMND1mG1Gvg32eG45Gb25FPZFhFBERERHRWAoKpu69994xj6mqijvvvBM//vGPcddddxXaLyIioimXDqegygqMHjOA3HS8bDo7op0gCrDVueBo9MJW54IocSU9IiIiIqKDMe7FzwVBwNe//nU89dRT+MY3voGHH354vL8EERHRhApu60dwWz8SPVHY6lyoPrcRACBKIuxz3QhtHwAAWKoccDZ6YZ/nhs7I9USIiIiIiA7VhP0Wfeyxx+IPf/jDRF2eiIho3KhZBYJuaJRTvDOMRE8UABBtDSGblKEz5X5kupeUwOi1wNHggd5mmJL+EhERERHNFBM212Dnzp0sfE5ERNOWmlUQ2R1A57MtaLr7vbwi5o7GomENVUT3BLVNc5kN3qPKGEoREREREY2DcR0xpSgKOjs7ce+99+Lxxx/HaaedNp6XJyIiOiyqoiLRHRlcUS+AbGoojIrsDMC1uBgAYK12wFhkgbXSAUejB6YS61R1mYiIiIhoRisomBJFcb9LXquqCrfbjTvvvLPgjhEREY0HVVWRGogj1ORDuMkPOTb6inqhJp8WTAmigLmfXTKZ3SQiIiIimpUKCqZOOumkUYMpURThdruxcuVKXHHFFSgpKTnsDhIRERUiHUoi3ORHqMmHdCAxZjtBFGCb44Kz0TuJvSMiIiIiIqDAYOrll18e524QEREdPjmeQbjFj/AOHxK90bEbCgKslXY4uKIeEREREdGU4m/iRER0xIt3RzDwVhdiHWFAVcdsZyqxwtnohaPBA8nK4uVERERERFONwRQRER1xFFkBVBWiXgcAULMqYu2hUdsaXCY4Gr1wNnphcJkms5tERERERHQABxVMnXrqqQVdXBAEvPjiiwWdS0RENJyqqIh3RRBu8iG804/i46vgWVYKALBU2CFZDVphc8mih6PRC0ejF6Ziy34X7CAiIiIioqlzUMFUoTWl+IcAERGNp85ndyKbyAAAwk0+LZgSRAGuxcWQI2k4Gr2wVNohiPwZREREREQ03R1UMKUoykT3g4iISJMOJhFq8iETTqHi9LkAcuGTs9ED/we9AIBETxTpcAoGhxEAUHxc5ZT1l4iIiIiICsMaU0RENC3IsTTCzX6EmnxI9sW0/cXHVUI/GD45Gr3wb+qDtcoB53wvJBN/jBERERERHcn4Gz0REU2ZbEpGZGcA4SYfYp2RUVfUCzf74T2mHEBuVb2GK1ZAsugnu6tERERERDQBDiuYSiaTePvtt9HV1YVUKjVqm8suu+xwvgQREc0wiqwg2hpEuMmH6J4Q1OzY08UNbhN05qEfVYIgMJQiIiIiIppBCg6mfvWrX+GWW25BKDT68tyqqkIQBAZTRESUW1GvM4xQkw+RnQEo6eyYbSWrAc7BFfWMRWYupEFERERENIMVFEw98sgjuP7667F06VLccsst+NrXvobzzz8fxx9/PF599VU8/fTT+MQnPoFzzz13vPtLRERHkGR/HKHtAwg3+yDHM2O20xkl2Os9cDZ6YC7ninpERERERLOFWMhJ//Vf/4WSkhJs3LgRX/nKVwAAK1aswE033YSnnnoK9913Hx577DHU1taOa2eJiGh6UVUVLqsT6ii1oQAg3OSD/4OeUUMpQRLhaPCg6pwGNFy5AuWn1MFS6WAoRUREREQ0ixQUTG3atAkf+9jHYLFYtH3Z7NC0jIsvvhinnnoqvve97x1+D4mIaFpSFRWZQBKdz+5E2p9EeFcAux/cinQwqbVxNHrzTxIEWGucqDh9LhqvPAqVZ9bDPscNQVfQjyMiIiIiIjrCFTSVL5PJoLi4WNs2m80IBoN5bZYvX47f//73h9U5IiKanlRFRcqfQPtTLYj3R7CrfRNqPr4Akt2AcLMPRSsrAQDGIjOMHjNEgw6ORi8c9R4WLyciIiIiIk1BwVRFRQW6u7u17draWrz33nt5bVpbWyFJh7XoHxERTTOKrCDRH4MoCOh8bicy4RQEnYBMOI22x7ej5uMLEG0PaQtgCIKAuk8tgqjXTXXXiYiIiIhoGipo7sTKlSvx7rvvattnnXUW3njjDdxxxx3YunUrfve73+GRRx7BypUrx62jREQ0NVRFRawzjO6XdqP5f9+HAGih1F6iSYd0IIme11rhWVoKNTtUc4qhFBERERERjaWgYOpTn/oUUqkU9uzZAwC4+eabUVVVhW9/+9tYtmwZrr32WthsNvz4xz8ez74SEdEkSg7E0fdGO1r++AHaHt2O4LZ+KAkZvvd6UHpiDXTDpuTpTBKsdU5Un9MIg8sEUWLNKCIiIiIiOrCDnmv3wAMP4MILL4TBYMAFF1yACy64QDtWXFyM999/H3/4wx+wa9cu1NbW4tJLL0VlZeWEdJqIiCZGJpJCuMmPUNMAUr7EqG2iuwIQRAFVH61H53O7IPszMLjMqD6nAUaPmavqERERERHRQTvoYOriiy+Gx+PBJZdcgiuvvBLLly/PO+52u/GNb3xj3DtIREQTK5uUEd7pR3iHD/GuyNgNBQHWKjscjUWwz3NDlERUn1OPzpd3o3LtHIZSRERERER0yA46mLrooovw2GOP4Re/+AV++ctf4uijj8ZVV12Fiy66CE6ncyL7SERE40yRFURbgwjv8CG6JwhVUcdsayq2wjnfC0eDB5LVkHdM7zah8sx50FsMDKWIiIiIiOiQHXQw9ac//QnhcBh/+tOfcPfdd+Odd97Bu+++i6997Wu48MILcdVVV2Ht2rUT2FUiIhovbY9vR6I7OuZxvcMIZ6MXjvleGN3mMdsJgoBgLIQSa8lEdJOIiIiIiGa4Q6pO63A4cO211+Ltt9/G5s2b8W//9m+w2Wz405/+hNNOOw319fW4/fbb0dnZOVH9JSKiQ6CqKpL9cQz8qwuqOjQqyl7nHtFWZ5LgXlqC2k8sxLxLl6H4hKr9hlJERERERESHq+BlkxYvXoyf/vSn6OzsxEMPPYSPfvSjaG1txbe//W3U1dXhnHPOwSOPPDKefSUiokMQ745g9/1bsPvBLej/ZweSfTHtmKPRAwAQJBGOBg+qz21EwxUrUHZyHSzldggCp+UREREREdHEO+ipfGNeQJJw4YUX4sILL0RPTw/++Mc/4u6778bTTz+NZ599FrIsj0c/iYjoALJJGaqiQrLoAQCS1YCUf2hlvdAOH8ylNgCA3m5E9bmNsFTYIRp0U9JfIiIiIiKiww6mhgsEAujr60MwGASAvGkjREQ0/hRZQXRPEOGmXBFz97JSlJ5YAwAwOIwwl9u0WlKZUCrvXFuda7K7S0RERERElOewg6loNIr7778fd999N9566y2oqgqLxYLLLrsMV1111Xj0kYiIhlEVFfHOCEJNA4jsDEBJZ7Vj4SYfSlZXayvkeZaVIlXlgKNx/0XMiYiIiIiIpkLBwdQrr7yCu+++Gw8//DASiQRUVcXKlStx1VVX4aKLLoLdbh/PfhIRzWqqqiI1kECoaQDhJj/kWHrUdnI8g3hnGNZqJwDA0eCdzG4SEREREREdkkMKpjo7O3Hvvffi3nvvxa5du6CqKrxeL66++mpcddVVWLJkyUT1k4hoVsqEUwg1+RBu8uXVi9qXIImwz3XD2eiFpYJvDBARERER0ZHhoIOpj370o3jhhReQzWYhCAJOP/10XHXVVTj//PNhMBgmso9ERLNKNikj3OJHaIcPie7I2A0FAdZqB5yNXtjnulnEnIiIiIiIjjgHHUw9++yzqKmpwRVXXIErrrgCNTU1E9kvIqJZR1VUdD7TguieIFRl7MUjTCVWOOd74WjwaivwERERERERHYkOKZg6/fTTIQjCRPaHiGjWUBUVmXAKBpcJACCIArIpedRQyuA0wTHfC2ejV2tPRERERER0pDvoYOqMM86YyH4QEc0aKX8CwW39CDf5oKpAwxUrtFX0nI1FiHfmpu/pzHo4GjxwzvfCVGLlGwNERERERDTjFLwqHxERFSbeFYH//R5tO9Yegq3WBQCw17sR7wrD0eiFtcoBQSdOUS+JiIiIiIgmHv/iISKaINmkjMDmXux5eBti7SFtv6PeAwwb/RTa4dM+1xklVJwxD7ZaF0MpIiIiIqJZRk2G4TblPs4WHDFFRDSOlEwW0d1BhJp8iLaGADVXLyq0wwdrtRMAoDNJsNU6kU3IcMz35oIqIiIiIiKa9VQ5jdb/Oh9zv/rEVHdl0jCYIiI6TKqiIt4RRqjJh8jOAJRMdkSbyM4AlJOzEPU6AEDlWfUQJY6IIiIiIiIiQA71Ir7zTZjnHQcoI/+emMkYTBERFUBVVST74wjv8CHc7IMcz4zZdm8Rc0VWtGCKoRQRERER0eylyhkkW99DvGUDLPWrIRhMMBTXITPQClVRIId6AeTKfwiSATqLc2o7PIHGJZjy+/2IxWKorq4ej8sREU1b6VAS4SY/Qk0DSAeSY7YT9SLsc91wNBbBWu3QVt0jIiIiIiLKxoPo/uN1gKiDddGp2POj04GsDMFgAaxedN39BQiiDhB1qL7+kanu7oQqOJgKhUL4j//4DzzwwAMYGBiAIAiQZRkA8Oabb+LWW2/FbbfdhmOOOWbcOktENBWUdBahHQMI7fAh0RMdu6EgwFbjgGN+EexzXNroKCIiIiIimn2UdALJPe8i3rIBgqiD96yvasckRzEMpQ1I97UgGwui7qYXAADZqA8df/o6Kq78AyRnGYDciKmZrKBgyu/3Y/Xq1WhqasLRRx+N4uJifPjhh9rxZcuW4Y033sCf/vQnBlNEdMRTVRW9r7dDzSqjHjeX2eBozBUxlyz6Se4dERERERFNJ6nObfC/+CskW9+DKqcBAKLRCs8ZN0DQDcUw5vpVSPe1IPjaPbA0rIa5fjWMlYshiCIkZykkR/FU3cKkKiiY+u53v4umpiY88MAD+PSnP41bb70V3/ve97TjZrMZJ598Ml566aVx6ygR0URTFRWxjjDCO3wQJBHlp9QBAHRGCbY6JyI7A1pbg8sER6MXzkYvDC7TFPWYiIiIiIimkpKKQUnF80IkQTIisfPNEe2SHZthrj1K2+da/Tm4Tvx8Xv0oOdwHiLNr5kVBwdQTTzyBc889F5/+9KfHbFNXV4cNGzYU3DEiosnW/eJuhHYMAAAESUTpmmqIhtwPBef8IsS7onA2euBo9MJUYoUgsG4UEREREdFsoioK0r3NSDRvQLxlI5LtH8B+1MdQ/LFvaW30JXMhOUpyIRMAiDqYqpcDqpp3LZ3NM+L6gmRA7Y2Pzfjpe8MVFEx1d3fjs5/97H7bGI1GxGKxgjpFRDTR0qEkws1+eJaXarWgbHNcWjClygoiuwJwLijKHatzoeGKFSxiTkREREQ0y2TjISR2vol48xtItGxENubPO55o2QBVVbU3rgVBgG3FucjGg7DUr4J5zkqIJttBfS3B5EAgnESJwzHu9zFdFRRMeb1etLe377fN9u3bUV5eXlCniIgmgpzIINzsR7hpqIi5wWGEo9ELIBc+iXodFFmBrcYJyT70LgUDKSIiIiKi2SW69UWENvwfUp1bR4x2Gk4O9SLTvxuGkrnaPs9p101GF2eEgoKpk046CY8//jg6OjpQVVU14vi2bdvwzDPP4IorrjjsDhIRHQ4lk0VkVxDhpgFE28IjfqCEmnxaMCVKIqrOboCxyAzJzCLmRERERESzhRz1QZSMeSOb1FQMqY4tY56j99bAXL8KlvrVkNyVk9HNGamgYOpb3/oWHn/8caxZswa33347BgZyU18+/PBDbNiwAd/61rdgNBrxjW98Y1w7S0R0MFRFRaw9hHCTD5FdASiZ0VfTA4BMOAU1q0DQiQAAa/XsGTJLRERERDRbqVkZyfZNg7WiNiDd04Si874Fx7EXaG3MDavzzhH0JpjnrMyFUQ2rofeMHKhDh66gYGrp0qV48MEHcemll+Kyyy4DkFtOfcmSJVBVFXa7HX/5y1/Q0NAwrp0lIhqLqqpI9sUQ2uFDuNmPbCIzZlvJooejwQvHfC9MxRYWMSciIiIimmV6H/g64k2v5+1LtGzIC6YkexGsi06D5CqHpX41TLUrZlVR8slSUDAFAB/72Mewe/du/O///i/efPNN+P1+OBwOHH/88bjiiitQVFQ0nv0kIhpVOphEqMmHcJMP6WByzHaiXgf7PDecjV5YqhysGUVERERENMOpchqJ1veQaN4A99qr86bpmeqOGRlM7XwTalaGoBuKSko/86NJ6+9sVXAwBQAejwdf+cpXxqsvRESHJNYeRtvj28duIAiw1TrhbPTCNselrb5HREREREQzj6qqkP3tiLdsRKJ5AxK7/wVVTgEATDXLYV10qtbWUr8a/uf+GwBgLF8Ac8NqWOpXA4I4JX2fzQ4rmCIimiy5IuYBSBaDVgfKXG6Dzighm5Lz2prLbHDO98Je72ERcyIiIiKiGUxJxZHY/S8kdm5EvHkD5EDnqO3iLRvzgil9yVyUfPJ2mOccC53NM1ndpVEUFEz98Y9/PGAbURThcDgwf/58zJ8/v5AvQ0QEAOh5pRWh7f1QMgqsNU4tmBIlEfZ6D4Jb+2Bwm+CcXwRHoxcGh3GKe0xERERERBNNVVW0/+JCZCMD+28oiFAS4fxdggDb0nUT2Ds6WAUFU5dffvkhFQtesGABfvGLX+DUU089cGMimtVUVUU6mITRbdb2KXJWW1kv1h6GHMtAsuZGQnmPKoN7cTGMLGJORERERDQjZRMRJHe/jVT3dnhOu07bLwgCTLVHI7bluRHn6OxFsNSvhrl+FczzjofOzNW3p6uCgql77rkHjzzyCP72t79h3bp1WLNmDUpLS9Hb24s33ngDzz33HD72sY/hpJNOwrvvvosHH3wQZ599Nl577TWsXLlyvO+BiGYArYj5Dh/S4RQarlyhTcNzzi9C6MPcuyCiJCLpi8NmdQIADC7TlPWZiIiIiIgmRjYRQfjtvyLRvAHJ9k2Amnuj2n7MBdC7yrV2lvpVuWBK1MFUswKWhtUw16+GobSeb1wfIQoKppxOJ5577jm8+OKLOOWUU0Ycf/nll3H22WfjyiuvxFe/+lVcffXVOO200/DDH/4QDz/88GF3mohmBjmeQbjZh9AOH5J9sbxj4WY/PMtKAQCWCjucC4pgq3XCNscNUWJBQiIiIiKimWTf1fAEUYfgy/8DNZvJa5do2Qj9sRdq25bGE1F60Z0wzzkWotE6af2l8VNQMHX77bfj05/+9KihFACsXbsWn/rUp/D9738fH/vYx3DyySfjrLPOwuuvvz5qeyKaPZR0roh5qMmHWHsYUNVR24WbfFowJYgCKk6fO5ndJCIiIiKiCaQqWaQ6tiLesgGJ5g0Q9CZUXPl77bhotMBUexQSu97S9gmSAdmoL+86Oqsb1gUnT1q/afwVFExt3boVZ5xxxn7bVFVV4aGHHtK2Fy1ahOeff76QL0dERzg1qyDWHkaoyYfIrgBUWRmzrcFthnO+F45G7yT2kIiIiIiIJpoc7keiZWMujNr5JpRkZOigICKbiEBntmu7zPWrIId7tVpRptqjIRpYymOmKSiYstlseO211/bb5rXXXoPNZtO2Y7EY7Hb7fs4goplEVVUke2MI7fAh3OJHNpEZs61k0cPR6IVzvhfGIhYxJyIiIiKaKTL+DoT/9QgSLRuR7m0eu6GqILn7bVgXDS2a5lx1CVxrLp2EXtJUKiiY+vjHP4677roL1113HW699VYUFxdrxwYGBvCd73wHb7zxBq666ipt//vvv4958+Ydfo+JaNqL7A6g9/U2ZEKpMduIBh3s89xwNnphqXRAEBlGEREREREd6VQlC0HUadvZWAChN/6433MMpQ0w16+Cvqgub78gsrbsbFBQMHXHHXfgjTfewG9/+1vcc889qK+vR0lJCfr6+tDS0oJUKoUFCxbgjjvuAAD09PQgkUjg8ssvH8++E9E0IcczEHQCdMbcS4rOKI0aSgmiAGutC875XtjqXCxiTkRERER0hFMyKST3vKNN0bM0rIH3rK9qx42ViyGaHVASYW2faLLDPO/4wSl6J0BylExF12maKCiY8nq9eOutt/DDH/4Qf/rTn7B161Zs3boVAFBXV4dLLrkEN910kzaVr6ysDO++++749ZqIpoSqqnBZnVBVFWpWQbjZj9AOH2IdYZSuqYZnRRkAwFxmg95hRCacC6csFXY4Gr1w1HugMxX0skNERERERNNM6M2/wP/cf0GV09q+uCBieLVYQRRhnncCZH8HzPWrYGlYDWPVkrxRVTS7FfwXotVqxW233YbbbrsNkUgE4XAYDoeDdaSIZihVUZEJJNH58m5Urp0Dg9uMvo0dkGO5H0KhJp8WTAmiAO9RZcimsnA2eqF3GKey60REREREdBiUVByJ3f+CoWQe9J5Kbb/kKs8LpQAg078bmWA39K5ybV/JJ77PaXk0pnEZumC32xlIEc1QqqIi6Y9DSWbR9th2ZDIZtD/VgupzGlB8QiW6X9wNAEj2xZAKJGB0mwEA7qWlU9ltIiIiIiIqkKqqSPe2INGyEYmWDUi2vQ81K8N92nVwn3Sl1s4851gIOj3UbAYQRJiql8JcvxqilP/GNEMp2h/OqSGiEVRFRaI7kltNL6PAMdeNtse3IxVIQufQIxNOoePvzag8sx6ORi8EnQDn/CIYnFy6lYiIiIjoSJRNhJHY+eZgraiNyEb6R7RJNG/IC6ZEgxnu066D5KqAee5K6MyOyewyzRAFB1Pt7e34/ve/jxdeeAFdXV1Ip9Mj2giCAFmWD6uDRDQ5VEVFoieKcLMfkZ1+yPEMIAqovWABOp9pQTqQzLVLK4ARyIRT6NvQhsp19ZCs+inuPRERERERFar/b7cj8s5jgKrst12q60Mo6SREw9Ab0q41l05w72imKyiY2rVrF44//ngEAgEsXrwYqVQKtbW1MJlM2LVrFzKZDJYvXw6XyzXO3SWi8aQqKhK9e8OogFYvSqOo8L3Xg7KT69AW3A45mgF0AgBA7zCi9CO10Jk58JKIiIiI6EiQjfoR3/lPWBedBlE/NN1OshWNGUpJrgpYGtfAPG8VzHOOzQuliMZDQX9R3nrrrQiFQnjxxRdx8sknQxRFXHHFFfiP//gPdHd349prr8W2bdvwwgsvjHd/iegw7Q2jIi0BhFv8I8OofSgpGdAB8z63DO1/b0HCH4XeYUTV2Q0weswQRGGSek5ERERERIdCVVWk2j5AvGUDEi0bker6EACgs3pgqT9Ba2euX4XAy78HAAiSAeY5x8JcvxqWhtWQPNUQBP7OTxOnoGDqhRdewNlnn42TTz5Z26eqKgCgvLwcDz74IJYuXYpvfvOb+N3vfjc+PSWiw+Z/vwe+93sgR/cfRlkq7LDXe+CY59Gm6amKiupz6rVV+RhKERERERFNb4IgoP+xW5Hxt+ftT7RsyAumjJWL4VxzGcxzj4Op9qi80VREE62gYGpgYAALFiwYuogkIR6Pa9tGoxFnnHEGHnvsscPuIBEVRlVVpAbiMBZZtHc4sunsmKGUudwOR4MH9rlu6G2GEccFUYDebULlmfOgtxgYShERERERTQOqnEay7QPEWzYi1bEF5Zf/Nm8VPHP9KmTeyg+mkq3v5W0Logjvuhsmpb9E+yoomCoqKkIsFsvb3rNnT/6FJQnBYPBw+kZEBchEUghs6kN4px+ZcApzPrsEpiILAMBR78HAW51aW3OZDY56D+z1nlHDqH0JgoBgLIQSa8mE9Z+IiIiIiPYv4+9EomUD4i0bkdj9NtR0QjuW7tkBY8VCbdvSsBrht/4CQ1kjLPWrYW5YDVPV0qnoNtGoCgqmGhoasHPnTm37uOOOw7PPPotdu3Zh7ty56O/vx0MPPYR58+aNW0eJaHSqqkLNqhCl3LsiiqzA9163djzS4teCKaPHDOd8L0zFVtjnuaG3c4guEREREdF0p8ppJHa9rdWKyvjaxmwbb9mYF0yZ5x6Hmq89DclRPBldJTpk4oGbjPTRj34U//jHP7QRUTfeeCMikQiWLVuGlStXorGxET09Pbj++uvHs69ENEhVVST74+jb2I6d923CwNtDo6CMbjOMg0EUAIRb/HnnVpwxD54VZQyliIiIiIiOEEo6gZ4/34jwmw+OHUoJAoxVSyDZ8wMoQTIwlKJpraARU9deey3Wrl0LnU4HAFi7di0eeOABfPe738WWLVtQW1uL73//+7j66qvHtbNEs5mqqkj5Egg3+xHZ6Uc6mNSOhVv8KD6hSqsl5aj3ICIK2jQ9IiIiIiKa3pRUTBsVBSWL4o/foh3TWZwwVi5GqmNL3jk6qwfm+lWwNKyGee7x0Fldk9xrosNXUDDlcDhw/PHH5+371Kc+hU996lPj0ikiylFVFWl/AuEWP8LN+WHUcJlQCqmBOEzFVgCA9+hyFB1bMZldJSIiIiKiAqS6tsP3zE+RbP8AULIAcqOcvGf/e97qeJb61Uh1boOpZvlgGLUGhtKGvELnREeigoKpU089FWvWrMFtt9023v0hIgCp4WFUILHftqYiC+wNHkiWoeLlXDGPiIiIiGj6ySbCUFIx6F3l2j7RZEey9d28dqqcRrL1XVjqV2n7HMd/Bs5VF0M02Satv0SToaBg6s0338QJJ5ww3n0hmtVSgcFpei1+pPz7D6OMRRY46j1w1HtgcJkmqYdERERERHQoVEVBqmsbEs25FfRSnVthW3YWSi78ntZG76mE3luj1Y4SdHqYao+CoNPnXUtncU5q34kmS0HB1IIFC9Da2jrefSGalRRZwZ6/bkPKF99vO6PXDEe9F/Z6N4xu8yT1joiIiIiI9kdNhuE25T7C4oIc9SHR8k8kWjYgvvOfUOKhvPaJlo1QFSVvCp59xXmQI/2wNKyGqe4YiAb+vk+zR0HB1PXXX48vf/nL2LZtGxYtWjTefSKa0dLBJDLhFKw1uXc8REmEqB99XrjRY4Z9cGSU0cMfTkRERERE040qp9H6X+ej7vq/ovv/rke668P9ts/GAkh3b4excuhvaddJV0x0N4mmrYKCqblz52Lt2rU44YQTcM0112DlypUoLS3VVgQb7qSTTjqka99xxx145JFHsH37dpjNZqxevRo/+tGPMH/+fACA3+/Hd77zHTz33HNoa2tDcXExzj//fNx2221wOoeGNra1teHaa6/FP/7xD9hsNnz+85/HHXfcAUkq6JaJDltohw/+97qRHIhDshpQ//nlWi0oR70HiZ4oAMDgNsHR4GUYRUREREQ0TcmhXgh609D0OiULQEWmbycg6gBVAVQ17xxD8VyYG1bDUr8ahtL6ye800TRVUEqzdu1aCIIAVVVx5513jhpI7ZXNZg/p2q+88grWr1+PlStXQpZlfPOb38S6deuwbds2WK1WdHV1oaurCz/5yU+waNEitLa24ktf+hK6urrw0EMPaV/znHPOQVlZGTZs2IDu7m5cdtll0Ov1uP322wu5ZaJDlg6noLcZtPApm8wgOZCbrifH0kj0RmEptwMA7PUeZFNyrmaUx7zf7ykiIiIiIppcuWLk7yO+cyMSTRvgPn099N4aqHIacqgXqqIgGwug/PLfAgCysSD6H/kPmOcdB/O8VbDUr4LkKpviuyCangoKpv7jP/5jwv5wfuaZZ/K27733XpSUlOCdd97BSSedhCVLluDhhx/Wjs+bNw8/+MEP8LnPfQ6yLEOSJDz33HPYtm0bXnjhBZSWlmLFihW47bbbcNNNN+G73/0uDAbDvl+WaFxkwqncanotfiT7Yqi9YAEslQ4AgH2eB72vtWltY+1hLZjS2wwoPr5qSvpMRERERET71/fwtxHb9lJuQ9RBZ3Vhzx2nQO8uh6pkAUVG9z1fRDbcD0gG1N30ImpvegGCjjN2iA6koO+S7373u+PcjbGFQrlCcR6PZ79tHA6HNk1v48aNWLp0KUpLS7U2Z555Jq699lps3boVRx111MR2mmaVTCSFcEsAkRY/Er3RvGPhloAWTOltBjjneyHZDHDUe2Es4jQ9IiIiIqLpQsmkkNzzDhItG+H6yBXQ2Yb+BjXPWTkUTKkKsrEgar7yBCRnKeRQL1p/dzkqrvwDJGduVJQgGRhKER2kaf2doigKbrzxRqxZswZLliwZtc3AwABuu+02fPGLX9T29fT05IVSALTtnp6eUa+TSqWQSqW07XA4rPVBUZTDuo+ppCgKVFU9ou9hOspE04jsHAyjemJjtgu3+FC8pkqbzld22hztmKqqUPeZdz7d8XmiffGZoPHCZ4kKxWeHxgufpdlHVVXI/nYkWjYg0bIRyd3vQM2mAQD6svmwLT9ba2ucd8LgZwIM5QuR6toG07xVECwe6AAIogidsxSizaudw2eJCjFTXosOpf+HFUy99957uP/++7F9+3bE43G88MILAIDW1la8+eabOP300/c70ulA1q9fjy1btuD1118f9Xg4HMY555yDRYsWHfYorjvuuAO33nrriP39/f1IJpOHde2ppCgKQqEQVFWFKI6+8hsdnGxcRqo9hlRbFJmB/T8TOqsEY40Nxmor+vr7ZkzNKD5PtC8+EzRe+CxRofjs0HjhszQ7qJkE5I4PILe+A7n1bSih7lHbDWx6AfHyY4ft0UO/7mboKpZANDuRBpAGgIEBuI1AVgWUrAJfX98k3AXNZDPltSgSiRx024KDqX//93/HnXfeqY34GP6Ht6qquPjii3HnnXfi3/7t3wq6/pe//GU8+eSTePXVV1FVNbL2TiQSwVlnnQW73Y5HH30Uer1eO1ZWVoa33norr31vb692bDQ333wzvvrVr2rb4XAY1dXVKC4uhsPhKOgepgNFUSAIAoqLi4/oh3qqZGJpRHcGEGkJIN49NE1PkvQj2uptetjrPbDXe2AqscyYMGo4Pk+0Lz4TNF74LFGh+OzQeOGzNPOpqorOn58POZgLo3QAdPqRv9dDEGHWiygpKcnfX3LBqNdVEmHM/erfoDOYUOI8cv92pOlhprwWmUymg25bUDB1zz334Cc/+QnOO+88/OAHP8D999+PH/7wh9rxuro6HHfccXjiiScOOZhSVRXXX389Hn30Ubz88suYM2fOiDbhcBhnnnkmjEYjnnjiiRE3vGrVKvzgBz9AX1+f9mLy/PPPw+FwYNGiRaN+XaPRCKPROGK/KIpH9MMA5ELDmXAfky2yO4COp5q17dFyJslqgKPeA0eDB6ZS64wMo/bF54n2xWeCxgufJSoUnx0aL3yWZgYlFUNi11tItm+G54zr835HN9cdi8j7fxtxjs5eDEv9Kpgb1sA8dyV05kMImMwODESSKHE6+OzQuJgJr0WH0veCgqlf//rXWLhwIR5++GFIkjTqKncLFizQpvYdivXr1+PPf/4zHn/8cdjtdq0mlNPphNlsRjgcxrp16xCPx3HfffchHA5r9aCKi4uh0+mwbt06LFq0CJdeeil+/OMfo6enB9/+9rexfv36UcMnIjmeQWSnH6ZiK8xlNgDIrZgnCMA+daByYZQb9noPzKU2rX4UERERERFNDSUVR/itvyDevAHJ9g8AJQsAsB/1MRiK67R25obVuWBK1MFUswKWhtUw16+GobR+VrzJTDQdFRRMbdu2DVdffbW2Ct5oSktL0VfA/Nrf/OY3AIC1a9fm7b/nnntw+eWX491338Wbb74JAKivr89rs3v3btTV1UGn0+HJJ5/Etddei1WrVsFqteLzn/88vve97x1yf2hmUxUV7U82IdYeBlQVzoVFWjClM0mwVjsQawtBsuSm6TnqPTCXMYwiIiIiIppKqpKFIOq0bUEnIfDqXVDTibx2iZYN+cHUvBNQ+tmfwDx3JUSjdbK6S0T7UVAwJUkS0un0ftt0dXXBZrMd8rUPtErZ2rVrD2ols9raWvz9738/5K9PM5ucyCDlT8BamRuaqwVMg89UZFcA6loFgi437LBoZQWKjimHudzOMIqIiIiIaIqoioJ093bEWzYi0fwGVCWLyi/+r3ZckAwwz1mJ+I5Xh/bp9MjGAnnX0ZntsC5cO1ndJqKDUFAwtXTpUrz00kvIZrPQ6XQjju9doe+YY4457A4SHa5sUkZkVwDhZj9iHWGIehENVx4FUcqFT456D2JtIQC5ZV7ToRSMHjOAwel8REREREQ06bKxAOIt/0Ri50YkWjaOCJmyUT90tqFV4C0Nq5Hu2wlLwxpYGlbDVHcMRIN5srtNRIeooGDqyiuvxBe+8AV86Utfwi9/+cu8Y+FwGF/4whfQ09OD//7v/x6XThIdKi2MavFr0/T2UtJZxNpDsM9xAwDsc91I9MXgqPfAUsGRUUREREREU0UO9SLy7mOIN72BVPeHI+q9Dhff+U/Yl5+tbduPuRCOlZ+cjG4S0TgqOJh64YUXcNddd+HBBx+Ey+UCABx33HH48MMPEYvFcPnll+OTn+SLAk2ebEpGZFcQkRYfYu1hqMrYP8Sie4JaMKUzSShfWzdJvSQiIiIior1URYEwbPWubCKMwMv/s99z9EV1sDTkCpYPJxzBK5gRzWYFBVMA8Oc//xmnnHIKfvnLX2LLli1QVRX/+te/sHDhQtxwww245pprxrOfRKPKpmREdwdzI6PaQvsNo3RGCfZ57tzIqEpO0SMiIiIimmxqVkayYzMSzRsQb9kAU+USFJ13s3bcUFoPnb0I2ciAtk8wmGGesxKWhjUw16+C3l0xFV0noglScDAFAFdffTWuvvpqJBIJBAIBOByOggqeEx2KbDqL6O7BmlEHEUbZ5rrgqPfCWmXXipoTEREREdHkirz/FHx//zGUVEzbpyTCUFUVgpArpyEIAiz1q5Hq3Apzw2pY6lfDVLMcgmSYqm4T0QQrKJiKRqN5AZTZbIbZzKJyNDl6XtqNcIt/zOOiQQf7XDccDR5YqxwMo4iIiIiIJpEqZ5Bsex+Sqxx6T5W2X3KU5IVSACAHu5EZ2AND8RxtX9F534SgO6wxFER0BCnou720tBTnn38+Lr30Uqxbtw4i5/LSBFDSWUT3BBHZFUD5aXMg6nMrQNrnuUcEU6JBB/ucXBhlqXJoK+4REREREdHEywS6kGjZgHjzBiR2vw01nYDrpCvhOe06rY2pZgUEgxlqOgEAMJTPh6VhDUSDJe9aDKWIZpeCvuPnzZuH+++/Hw888ACKi4tx0UUX4XOf+xyOOeaY8e4fzVLx7gjaHt8BVVYA5MIoR4MXAGCrc0GQRAiCAPtcF+z1HlirnQyjiIiIiIgmiSqnkdjzDhItGxFvfgOZgdYRbRLNG4BhwZQg6eFe+0XorG6Y61dBsnkns8tENE0VFExt2rQJmzZtwh//+Efcf//9+O///m/8/Oc/x/z583HppZfikksuQU1NzXj3lWYoJZNFtC0EyayHpSJXlNxUlP+uSbjFrwVTol6H2gsWwOi1MIwiIiIiIppk/hd+hdDGP0OVU/ttl+ppQjYRhs7s0Pa51lw60d0joiNMwX/VL1u2DD/5yU/Q0dGBZ599Fpdccgk6OjrwrW99C3PnzsXatWtx1113jWdfaQZRZAWRnX50PtuC5rvfQ+fTLfC/36MdF/U62Oe4Bj8XIRp0UNWhIufmUhtDKSIiIiKiCaSkE4g3vQ4lFc/bL5rsY4ZSOqsbtuVno+QT30ftN57NC6WIiEZz2JN3BUHAGWecgTPOOAOJRAKPPvoo/u///g8vvPACXn/9dVx11VXj0U+aARRZQawthHCLH9HdQSiZbN7xaGsI2XQWOkOulpRneRns9R7YapxafSkiIiIiIpo46f7dSDRvQLxlA5Kt70GV0yj97E9gXbhWa2NpWA3/8z/PbQgiTNVLYa5fDUvDahjK5kNgDWIiOgTjWlVOlmWkUimkUikoijKel6YjlCIriLWHEG4ePYzKIwCp/hgslbl3VcxlNnCtRyIiIiKiydP315uR7m3J2xdv2ZAXTOlL5sF5wkUw1iyHee5xHBVFRIflsIOpbDaLv//977jvvvvw5JNPIplMQhRFrFu3DpdeyvnDs9HeMCrS4kdkdxBKeuwwStCJsNU54aj3wFbrgmjgyCgiIiIioomiqirSvS1ItGxEcs87KL3ozrxV8Mz1q0cEU8ldb+VtC4IA70e/Nin9JaKZr+Bg6p///Cfuu+8+/OUvf4HP54OqqlixYgUuvfRSXHzxxSgtLR3PftI0pyqqNk0vsitw4DCqdjCMqmMYRUREREQ0kbKJCBK73hycorcR2Ui/dizZsRnm2qO0bUvDaoTe+CMkZynMDWtgqV8F89zjpqLbRDRLFBRMNTQ0YNeuXVBVFZWVlfjGN76BSy+9FIsXLx7v/tGRQlXR+fxOKKnRAylBFGCtdcFR74F9DsMoIiIiIqKJoioK0j07EG/ZiETzG0i2bwbU0UutJJo35AVTpurlqFr/F+iL50AQhMnqMhHNYgUFUz09Pbjssstw6aWX4pRTThnzBSuVSsFoNB5WB2l6UbMKYp0RRJr9UOQsKs+sB5AbBWWf60bowwGtrSAKsNY44WjwwDbHrRU1JyIiIiKiiaNmkuj6wxVQs/J+20muCogme94+QdLDUDJ3IrtHRJSnoGCqr68PZvPYZanfffdd3HXXXXjggQfg8/kK7hxNP72vtyOwuTe3IQgoPSkDyawHADjqPQjv8OXCqHoPbHNc0BnHtb4+EREREREhNyoq1bkViZYNyMaDKDrnJu2YaLTAVHMUErvfzjtHkAwwzzlWW0FP8lRzVBQRTbmCUoPRQqlgMIj77rsPd911FzZt2gRVVfcbXtH0pioq4p0RhFv8KD6+EpIlFz7Z5riGgilVRWRXAO7FJQAAa5UDDVcdxTCKiIiIiGiCZHzt8L/0GyR2/hNKIpzbKergOf3LEI1WrZ25fhUSu9+GvqgWlvrVMDeshqn2aIh6zmghounlsBOEF154AXfddRcef/xxpFIpqKqKVatW4YorrsBnPvOZ8egjTRJVURHvzk3TC+8MIJvIAABMxRa4lwyGT5V26EwSsqksrNUO6G1DP9gEnQidTpySvhMRERERzTSqkoUSD0Fn82j7BL0JsS3P5TdUskjsegvWhadou+xHnQfr4tOhd1dMVneJiApSUDDV3t6Oe+65B/fccw/a2tq0IuidnZ24/PLLcffdd493P2mCqIqKRHcE4ZYAwi1+LYwaLtzi14IpQSei6uwGGD1m6EwcGUVERERENJ7kcB8SLRsRb34DiV1vwTz3OJR+5sfacclRDENpA9K9zdo+Q/FcqEp+cXOd1Q2d1T1p/SYiKtRBJwuZTAaPPfYY7rrrLrz44ovIZrOwWq245JJLcNlll+HUU0+FJEmQJIYV04mqqnBZnVBVdWifoiLRE0W4xY9Iix9yfGQYpREECKIAVVEhiLn555YK+9jtiYiIiIjooKlyBsn2DxBv3oBEywake1vyjid2vgk1K0PQDf2dZV1yBvTeapjnrcrVinKWTna3iYjGzUGnSBUVFfD7/RAEAaeccgouu+wyXHjhhbBarQc+maaEqqjIBJLofHk3KtfOQUYUENraj3BLAHIsPfaJggBLpR2Oeg/sc91afSkiIiIiIhofidb3ENpwH5K73oaSjo/ZTknFkGzfBHPd0do+90lXTkYXiWgShdIJpJUsoAKwm+FLxQEBMIg6OA0zu373QQdTPp8PoijiK1/5Cv793/8dxcXFE9kvOkyqoiLlT6DtiSbEusNIdkZRc958pCPpMUMpLYya52EYRUREREQ0TlQ5DTWbyStOriQiiG9/ZcxzRJMd5nknwNKwGoaSeZPRTSKaAkk5A5OkR1rJ4oIX7kFakaHIWUh6CZKgw6OnXzHVXZxwBx1MXX755fjrX/+Kn/70p/j5z3+OM888E5deeik+/vGPw2AwTGQf6RDtDaU6/t6MdDAJNZlFKpVA2+PbUfPxBQCA6K4AgNy0PHu9B455HkhWhlFEREREROMh42tDvGUjEs0bkNj9L7hP/gJcJw39gWmecywEnQQ1K+d2CAKM5QthblgNS/1qGKsWQxB1U9R7IppIG/v24JmO7dgc6EEqK+OpdV8AAGRVBVlVhaKqEFQVApQDXGlmOOhg6u6778bPf/5zPPDAA7jrrrvw5JNP4qmnnoLD4cCnP/1pXHrppRPZTzoE2aSM3tdakQmnIOpFQBQAFUgHkuh5ZQ+qzm2EtdoB+xw39DaGikRERERE42ngqR8j/NZf8vbFWzbmBVOi0QLLwlMhiDqY61fDUn8Ci5UTzSCqqqI7EcaWQA+2BnqwfuEaGAZrxe2K+PF0x3atbV8iAkEQpqqrU048lMY2mw1f+MIXsHHjRmzduhU33ngjDAYD/ud//gcnn3wyBEHAjh070NraOlH9pYOgM0ko/Ugt9A4jAAGCQYSoF2GutKPq3EaYvBZ4lpYylCIiIiIiKpCqqkj370Zo458hh/vyjhkrFo5on2x7H0oymrev9FO3o+QTt8G+/KMMpYiOcNFMCkl5aGGxf3S34PwX7sG333ka9+96D9tDQ68TS9xleeduDfRonwuYfQFVwUvoLVy4EHfeeSd+9KMfaav1Pf/883jttdcwb948nHzyybj88ss5kmoKCKIAo8eMqrMb0P5UM2Q5A7PHhupzGmD0mLXV9YiIiIiI6OApqTgSu95CvGUDEi0bIQe7AQCC3gTHsRdq7cz1q7TPdTYvLPWrc/t0LJ1BNJMEUnH8Ytvr2BLsQWvUjx8cczZOr2gEACxy5a+WuSXQg2WeCgDAAmcJvCYrFjhLsNhdhrkOLwBAJ4hQRRUQBOgEATrhkMYSHbEKDqa0C0gSPvnJT+KTn/wkOjo6cM899+Dee+/FP/7xD7z88ssMpqbI3nCq+px6bVU+hlJERERERKNTk2G4TbmPsLhy+1QV6d4WJFo2It78BpJt7wNKdsS5iZaNecGUZC9C0XnfgrFqMQylDbN6ig7RTNCXiGDz4JS8Re5SLXyySgY807kdspKrBbUl0KMdKzXb4TFa4E/FoRd1CKUT2vWMOgl/P+MLea8NoXQiV+hcBbJKFjpRp63KN9MddjA1XFVVFW655RbccsstePHFF3H33XeP5+XpEAmiAL3bhMoz50FvMTCUIiIiIiIagyqn0fpf52PuV5/Q9nXffXUujDqAzEArVFXN+yPTcewFE9FNIppgcTmNQCqBSqtT23f5aw9gIBkDAJxe0aiFTwadhAZHMT4M9gLIBVN7CYKAby0/HR6jBQ2OIq2+1PDjwzkNZgCAoijo6+uDt6QEosgRU4fltNNOw2mnnTZRl6eDJAgCgrEQSqwlU90VIiIiIqJpQ8mkIAe7IAc6kQl2w1x7NCCn8toYyhpGDaYEnR6m2qNyRcsbVkNfPIejooiOcH9s+Ree6diOXREfFrvLcNeJn9GOLXGX4eXunQCALYNTePc6obgGToMJi11lWOGtyDv2kbK5E9/xGWDCgikiIiIiIqKpoioKstEBZAKdgKrCXHf00DFVResPT0XJp+6AzuqC0exAemA3lHQScqgXGCw+7DjhYoTffghQVUiuClga18A8bxXMc46FaLRM0Z0RUaGCqQQ+CHRha6AHHbEQbj/2bO1YfyKKlvAAAGBHsA8ZJQv94DS6Je5yvNy9EzpBgFNvRkLOwCzlasZdu3DN5N/IDMNgioiIiIiIjkhKKo5MoBPy4L/c5x25j8FuqHIaAGCsWoLKq+/VzhMEAZK7AjqrC3t+dDqQlaGqWQhmN7ru/gIEUQeIOlRf/zC8Z34NlsbVkDzVHBVFdARJZWVsD/VhkatUC5j+1r4Nv9j2mtbmK8mTUGyyAQAWu8uA3bn9aSWL5nA/Frlyq+etq2jEck8FFjhLYNQxRhlv/C9KRERERETTkqookMO9g8FTF2zLzoIgGbTjvuf+C5F/PXLA68iBzhH79K4KZGNB1N30Qq5NuB8dd12Niiv/AMmZ+2NUkAxwrvrsON0NEU2GD4O9uP2DF9ES7kdWVXHPRz6bC52Qm5I33JZAD04prwcALPOUY2VxNZa4yrDYXYZaq1trV2ZxoMzimLybmGUYTBERERER0ZRRklEomSQke5G2L5uIoOv3l0EOdUPNytp+Y/VSGIrnaNt6d+VBfY1sLAAlFc+bfuc44bNQEhEIogTJXQnJWQbR5oXkLIXkKB6HOyOiiZJRsnizvw1bAt3YGujBZ+YehRNLc68NToMJO0J9WtvNgW4tmFroLIE4OPJxrt0LVVW1dhUWJ3616hOTeBe0F4MpIiIiIiKaMGpWhhzqHZxq1zFsyl3uo5IIw7r4DJR++g7tHNFkgxzuywulgNzIp+HBlLRPMCWaHdC7KyG5K4d9rILkroCgN+W1tdSvyr92uA+YBcuyEx1p0lkZTeF+GEQJjc5caKyoKr7x1hPIDgZLjc5iLZgqNzvgMpoRTCUAAFuHrZRnkvS4+yOfQZ3NA8uw0Zc0tRhMERERERFRwVRVhZIIQw52w1ixIO9Y36PfRfSDvwOqst9r7DvVThAE6N2VSPfvytuf8XfkbZtqVqDk0z8aDKEqoDMXPtVGkAyovfGxvKmCRDS1vvbWE/hnXysyShZnVDbiB8fkipUbdRLqHcXayKgtw8InQRBwfs0SZFUFi11lWOopz7vm3rpRNH0wmCIiIiIiov1SVTWv8Lcc6oXvmTuR8edGQCmpGACg7luvQTSYtXaiwXzAUAoAMoGOEftsR50HJRkZHPGUGwGlc5TktZHsRbAtPq3Q28ojmBwIhJMocbCODNFk2uzvxj/7W7E10AO9qMN/Hnde3vGMkgWQHz4BuXpRTeE+1Nk8qHcU5R27jivlHVEYTBERERERzXKqqkKJBZDxd4y6yp1t2UfhOeN6rb0gGRDb9tKI68iBLhhK52nbkmtkDShBMkByVQxNtfPkgqd9wy/XmkvH+S6JaCrJShYtYR9aIgM4t3qRtv+Zzu346+4PAORGQslKFtLgtNrFrjK81pMbOdkdDyOQisM9WCvuC43HY/3CNbDpjZN8JzTeGEwREREREc0CqpyBIOnz9g089WMk97yDTKATaiY55rkZf3vetmhxQTCYoaYT+e0CnXnBlLnuaLjXfhGSu2Jo1JOtCIIojsMdEdF0tbeo+N6w+e/tH+L2D15AenD00+qSOngGA6bFrjL8FblgKpWVsTPiw3xnbnTkCSU1CKUTWOwuwxJ3GVzDRmR6TdZJux+aWAymiIiIiIhmmEygC9H3n8wrNg4li9p/fz6vnRzsQrpv50Fcb2QNKEv9KqhZWRv5JLkrYapaktfOWLkIxspFIKKZz5eM4fG2rdga7MGWQA/+6/iPY6GrFABQarZroRQAbAl046SyXIi9ZHDFvGqrC0vcZZCEoeB6kauMNaFmAQZTRERERERHCCUVhxzsGlrVbrDGk2352bAtPXOoXTyIwMu/H+X8GETj0CiDfVe10wgCJEcJJFdulJO+ZN6IJqWf+fHh3xARHXEUVcHOsA9bgj1Y6CzFAldudFNczuC32zdo7bYEerRgaqGrBKIgQBkcSbU91K8FU9VWF14460twGEyg2YnBFBERERHRNBV49W5k+nYOBlFdyMb8o7bTF9XlBVOSp2rUdplAJ4xljdq2qWopspH+3DQ7V8VQzSdnGVenIyIAQDorw6DLRQcZJYszn/0dopk0AODz9cdqwVSV1QmnwYRQOjcteEugG5+asxwAYJEM+OL8VSgz27HEXYZqq0u7viAIDKVmOQZTRERERESTTI4MINn2QV6RcdFgRuln/zOvXWzzswVNtRNNdogWJ0SDNRc0DRYb11lcee1sy86CbdlZh30/RDSzPNq6GW/2tWJzoAfHFFXhe0fnXif0og7lFieaQ/0AgM3DVsoTBAGLXWXYHfVjibsMxxfX5l3zysbjJu8G6IjCYIqIiIiIaBypWRlyqHcwcMpNtbMuPh3GioVam1T7JvT95aa880SzY8S1JHflfoMp0ezITbXz1uTtFwQBtd94nkXGiWhMiqpgTzSALYEehDNJfG7eMdqx13p24fXe3QByU/KGW+Iq04Kp9lgwb0XN/zzuPOgHV9QjOlgMpoiIiIiICqAqCmJbX9BGPGkfQz2AquS11dmL84Kp0Wo7KYkwsokwdMMCKn1RHfT9u7UV7bSpdu5KSK4K6Mz2MfvHUIqIhgumEnAZh1a1+9mWV/Hg7vcBACadhIvmHAXd4OvGYneZFkx1xIIIpRNwDq6Id071QiwZXCWv1ubWQikADKWoIAymiIiIiIj2ocppyKEerbh4JtAJndUD14mXDTUSBAw88X0o6fgBryfvM9VOPyyY0lndWvCkyum8dt51N8C77obDuxkimrW2BXvwp53vYkugB93xMJ4984twGy0AgPmDtaEAIJmVsTPiQ6OzGEBupbwSsw1LXGVY7C6HgKHwaZmnAss8FZN7IzSjMZgiIiIiollHVVUosUAueAp2wTR3JSSbVzsefuuv8D37s7xzDGWNecGUIAiQPFVI9zTt92sJkmFE4CSabKi67gFIrgqIg38kEhEVQlVVtMeC2BLowdZgDz5Vtxx1dg8AIJpJ4/nOodeorcFenFg6B0BuSt5eZkmPnkRYC6aOK6rBk2d8YRLvgmYzBlNEREREdMRSk2G4TbmP2Kewt6pkkRlo1abZ7TvlTs0ktbaln/0JpIVrte3RptrJgc68WipAbuRTuqcJOnvR0FQ7d1Xe1DudzTvqtDpDaf3h/wcgolknlE5AUVVt5FN7LIhPvvS/2vG5dq8WTC12lUIQAFXNHdsS6NaCqRqbC99afjoWu8sw1+6BKAy9Tg1/nSOaaAymiIiIiOiIpCoKlEwSe/7zLMz5yuNIdW6DpWG1dlxJxdDxq08f1LX2nWq3bzAl6E2QnGVQ0wkIw0Y4FZ33TRR/4vsQ9cbDuBMiov1TVRXfe/85bPJ3oz0WxBUNK3HtwjUAgGqrCw69EeFMCgCwNdCDT9QtAwBY9UYc462GRdJjibscq0vqtGuKgoiP1y6Z9Hsh2heDKSIiIiKatpRMCnKwC3p3JQTJoO0PvHoPQq//L8ou/TkUXyuURAjhdx7LC6Z0ZgdEkx1KMnLAr5PZtwaUpxrFF96WW/HOUwXR6h51BIHO6j6MuyMiytcdD2NzoBtbAz1wGsy4svE4ALkRTB8G+9AeCwIAtgSHVsoTBAGL3GX4Z18rDKIO2X0WX/j16k9MWv+JCsFgioiIiIimjKqqUOLB3BQ7f8ewYuO5j3K4DwBQee2fYSxrBABk4yFYF54C85xjIIf7oKpZyOE+uE+6HHK4H4JkgM7iBJCbapfq3g4AEA0WSJ6qYVPuhq1y5yzL65doMMG+/KOT+F+CiGabaCaF9lgQC12l2r4fbnoJG/v2AABqbG4tmAKApe5y7Ir4AADbAr1QVEWbfvelBatw3YI1qHd4IXFlPDrCMJgiIiIiogmlZmXIoV4IOgmSc+gPsHT/HnT9/rKDW9XO36EFU6qcRvvPL0RmYA9UNQtkZbT/4hMQdXoYKhai+vpHtPO8Z38Dgk6fKzJucbJuChFNuSfbt+GPLf9Ca9QPs06Plz56rRYwLXaXacFUWzSAcDoJh8EEAFhZXI2BVAyL3WVY6s4P0xe58reJjiQMpoiIiIho3IXffgixbS/lRkCFegBVgeP4z6Lo7K9rbSR70UGFUgCQ8XdonwuSATVf/zvUVAxyZABtv7sctevvh95dAUDIm/Jnqlk+bvdERHSwwukk3h5ox9ZAD7YEe/CfK8+F02AGACiqij0RPwAgLmewO+LHPEcRAGiBk17UodFZjEA6rgVT6yrnY13l/Cm4G6KJxWCKiIiIiA5IVRRkowO5oMnfoU212zv1ruqGR6AzO7T2GV8bErveyruGHOjI2xZNNogWJ5R4aGinIEBylEByDU6x81RB766CsXKx1kRncUIH5+A17BBNNujdFZAcJRNw50REowulE0grWaiqiozVgL5kDDpRgEHUYXuoDzf/6ymt7dZAL1aX1gEAluwz2mlLoEcLppZ7KnDPRz6LBkcRDDr+uU6zA590IiIiIgKQq/c0fKpburcF/hd+mQufgl1Q5fSY58r+DugqF2nbkrtqRJt9C4wDgPsjVwI6CXp31WDdp4q8EU8HhfVUiGgKJLMyjn7sp4jJaaiqinKLA0UmKx49/QosGlY3CsgVK98bTNXZ3PhI2Vw0OIqwxF2OZe5yrZ1FMmCxm9PyaHZhMEVEREQ0C8WbNyDVuS038snfgUygE641l8K56uL8dk2vH9T1Mv4OGIcFU4aSuTBWLc2NehosMK731Iw4z7n6ksO6D0EyoPbGxw49zCIiOkjvDnTgg0AXtgR6cIy3ChfPOxoAIEJAIptBVlWgqiqichpuNTddz643odbmRkcsiAZnMdyD0/gAQBRE3Hncx6bkXoimIwZTRERERDOImpUhB7vzp9qF+1DyyR/kjYaKvPsYYtteyjs342/P25ZcFWN/IVEHvatiaFU7d35b85xjUXn1PYd/QwcgmBwIhJMocTgO3JiIaD9UVUVXPIxAOo4lw0Yx3bHpRbRGAwCAjJLVgikIgFnSI5POAgCS2Uze9X52/MdRbLLByCl5RPvF7xAiIiKiI1TG14bYh//Q6jwNLzS+L+Xsf4fO6tK2R51q59+nBpTRAvPc4yCa7EMjn9y5uk+SsxQCp9AR0Qzx+x3/xMN7NiGQimOO3YMHT7lMO7bMU64FU1sC3VBURVtFz6E3QhJEGCDCZjTlXbNq2GsuEY2NwRQRERHRNCSH+5DxtecCp0AHMv4OOI65AOa5K7U26YFW+J//xUFdLxPoyAum9O7KoULj7iroPfkFxvcq//yvD/teiIimA18yhrcH2rE50I1dER9+tepCLWDKqgoCqdwqoXuifkQzKdj0RgDAEnc5/ta2DeUWB5a5yxGXM9oxr9EKpyELOSND0umgG7weER08BlNEREREU0BJJyEHu5Dxd0BJhmFfcW7e8d77v4ZU14d5+4zlC/KCKb1n5KinfQl6E/TuyhGFy20rzoX9qPNYm4mIZqRUVsa2YC8WOEtglvQAgFd6duGHm17U2rRFg6izewAgrwC5quZWyjuhpBYAcEZFI04umweP0ZL3NQyiDo+efgWgAlklC52oA4TcfiI6eAymiIiIiCZINhFBpn93fr2nQK7QeDYyoLUT9CbYlp+TVwNKcleOCKYygfypdpKrAhAE6Czu3FQ7T5U21U7vroTkqYLO6sm77l7i4Lv9REQzye6ID9997zk0hfqQVVX8ctWFOK44t/DC0n1Wu9sU6NaCqaXucqyrnI+l7nIs9ZShwVGstbON8XrpHCxorigK+vr64C0pgShyxBTRoWIwRURERFQgVc5ADnVrNZ7MjSdC7xp61z266Wn4/v7jA18nk0Q25odk82r79MNqQAk6CZKrAjpTfoFvUW9E3Tdfg2jIr2tCRDSTKaqCzYEebPJ3Y0ugG2dWzsepFQ0AAI/Rgg+DvVrbD/xdWjA1z+GFRdJDVhQsdJXCOmzEqMNgwveP+ejk3ggRAWAwRURERLRf2UQYSjwEvbc6b3/n7y5FqntHXqHxErMzL5g6mKl2e8n+zrxgyrbiXJjrT4DkroTkKIUwxrvwDKWIaCZTVRW9iQgS2Qzm2L2D+4Ab/vkoEnJuFTyXwawFU06DGdVWF9pjQQC5KXl7iYKIez9yESqtTug53Y5o2mAwRURERLOaqiiQw71DU+38ual2e6fcKYkw9EV1qL7+ofwTBXHE6ndyoDNvOy+YEgRIjtLB1e2GptppU+7M+aOhDMV1QHHdON4pEdGR5WdbXsHzXU0YSMawuqQO/3XC+QAAnShikasU7wzkpjdvCnTnnXduzSKE08nBaXnlecf2Tt0joumDwRQRERHNeEo6ATnQBclTlVdbKfj6/yLw0m+gZuX9ni8Hu6AqSt6oJb2nCqnOrXntMqH8P44kZznKPvfzXBjlLIcwWICXiIhyBpIxvO/vxGZ/N4LpBG49+iztWDCdwEAyBgDYEuiGqqpazbxl7nK8M9CBErMN8+xeKKqirbB3RcNxk38jRFQwBlNEREQ046iKgv7Hvpsb/eTvQDbmBwBUfOEemKqXau1Eo+2AoRQAqHIa2egAJEeJts+66DTovbVawXG9pwqi1Z13niDpYWlYPU53RUR0ZEtnZewI9WOJu0wLmP6y+33c2/w2AEAUBNy07FRYBms/LfWU4+mO7QCAcCaFtlgAtbbciKdPzVmBT9QtQ4nZPgV3QkTjicEUERERTTk1GYbblPsIi2v0NsMKjQ9f3U72d0BfVIvSzwwVGRdEEYmdbyIb9eVdQw50AsOCKWmUGlCCTg/JXZGbYjcYOEnuKohGW14766JTYV106mHcNRHR7LDJ34X/3vYatgf7kFGyeOjUz6PGlgvylwxbKU9RVWwJ9GjFyo/2VuHU8nos81RgqbscFRan1rbIZJ3cmyCiCcNgioiIiKacKqfR+l/nY+5Xn8jbH/7XI4hteS4XRIV7cxVvRzs/mxmxT++uHBFMZfwdeduGknlwnXTlUAjlroTOUTJmoXEiIhpdVlHwYah3cKW8Hlw09yitvpNRJ2Gzf2iq8+ZAtxZMLXXn2kiiiPnOEqgYep2fa/fihyvPncS7IKKpwGCKiIiIJp2STiLy3hOQQ92QQ71wrbkU2d5mqPsWEw92I7H7Xwe83mg1oIw1yyFIxrwC48byhXnnSfYieE67bnxuiohoFhlIxpBVFZQOTqULZZK48rUHteMNziItmKq3F8Es6bVV9DYHunFO9SIAgNtowb0nfRb19iIYdPzzlGg24nc+ERERjRs1K0MODa5wF+yCHOyCHOiEoawRrhM/r7UTRBGSqwzGigUAgEygE6qSgRzohCDklvAWJAMkd+WYX0swmKF3V0FyV0LvqYIqpyAYzNpx77p/m6C7JCKavW57/zm8PdCOnngEn6xbhn9flpvS7DFaUGl1ojMWAgBsCfRo5+hEEedULYQkiljiLscKT0XeNRe5ykBEsxeDKSIiIipY5L2/Idn6Xq7WU7ALcqgX2GfUEwCYY4H8YEoyQGf1Ys8PTwGyMlQ1C2RldPzmc5BsbkDUofr6R2AongtTzQotfJLclXmFxvcWzyUiovHjT8Xxgb8LWwLd0Akirlu4RjvWEQuhJx4BAGwK5K9EutRdjs5YCB6jBR6DJe/Y3gCLiMaWTQYhGBx52zqTa+o6NEkYTBEREdEISiqGeMtGyIEubfRTNtSLyuseyJsul9j1FqKbnj7g9TKBzhH71EwSdTe9CEHUQY4OoO3Xn0PVtX+C3lMNIBdemWqWoeKqP4zfjRERUR5ZyaItFsRcu1fb99Mtr+C5zh0AAJfRjGsXrNbeCFjqLsd7vtxrekt4AAk5A7OkBwB8ofF4fGnBKpSbHXzjgOgQydEuRLbeC/viy2E2W6HEe7RtyVZx4AscwRhMERERzTJKKg452IVMYHCqXbALxuplsC0+XWuTjfrR95f/N+LcbKQfkrNU25Zc+/9FSdBJkJxl0HtroKpq3h8qxoqFEAxmCIIAXbgPOmdpblSUo3gc7pKIiPZnQ+8e3NvyNj4M9iKjZPGPj16nBUxL3eVaMBVMJdAZD6HK6gIArCyuRms0gKWecix1l0M/7M2KvQXNiejgKKkwMsEWCHorIlvuRnTb/yI9sBn2o78G/6bfIOPbCgBwLL9uRo+cYjBFREQ0g0U3P4t0T1MuhBqcbpeNB0e0syejecGU5CwDBGHEKniZQGdeMKX3VkNylEByV0JyVQyubleR+9xVsd8V7kSjZZ8dusJvlIiIRsgqCprC/dgcyK2Ud+2C1Si35KYJpRQZ7/uGRrN+GOzF0UVVAIClnlzNJ1EQ0OgsRjiT1NodX1yL44trJ/EuiGaebNKP/mcuRzbeBwDQexfDsfRqpHvfQar7LSSeuwZ6vR5672LYF18+o0MpgMEUERHREUlJxZDq2q6NeMoEOqHKGZR++o68duG3/opk2/sHvJ4c7MrbFiQ9JEcJ5FAvdBYXJFcFJHclxGHFxQHAvuJc2Fcc/lLegmRA7Y2PQZAMh30tIqLZyp+KQycIcA6+Vu8I9+HyVx/Qjq8uqdOCqWXu8rxzNwW6tWCq0VGM367+JBa5SmEaHEVFRAdn7yioTLAZmUAzBFGC67j8Ueii0QUlE9W2M76tSLS/BOcxX0X/81dDMORGmDuP+vKMn8YHMJgiIiKadlRVhRILaAXFM4FOmOuOhalmmdYm1bEV3X+8Lv9EQYSqZCEMG3kkuSuAAwRTotEKQW8asb/88t9DZ3WPHNk0AQSTA4FwEiUOx4EbExGRJqNk8f33n8emQDc6YyF8edGJuKz+WABAg6MYBlGHtJIFAGwOdOOsqtxqqF6TFSeWzkGZ2Y4l7nIcOxhKAYAk6rSQiohGpypZyJE2ZIItkIMtyARyYdTeUVB7iXobnCtvyitnIAgi9M55SA9sBpAbMWWuOR2Bjd+DIpqRVQWoWRXBd38J74m3zfhwisEUERHRFIptfwUZX7s28kkOdCET7II6bNoEAGDtF/OCKcldOfJiqgI53Ae9a+hdcL27EoJkGJxml5tit3f0k949OArKZB+1SK3eM8rXICKiKRFIxbUpeUUmKz49ZwUAQC/q8K6vE72J3Ep5m/1DK+XpRR0Wukrxgb8LLoMZhn2mTP/0+I9PWv+JZoJY88NI+z6EHNqJTHAn1Gz6gOcomSiy8R5I1vxRipZ5H4Op6iPQuxqgd81DcNP/oLOvA3AfB8/R12Pg3V8Ae96B5LoHrhXrZ/R0PgZTREREE0CV07kRT4EubeSTaDDDfco1ee18f/8x5FDvAa+X2WeqneQsBQQRUBVAEHMFxt0VUOX8X5BcH7kCrpOvHrPOExERTT+KqqA3EdWm3QHA19/+mxY6LXSVasEUACzzlOP5zlwwtSnQnbfYxNeWrIVVMqDK6uRKeUQHoKoK5HAb5PAemKvXjjgea34UmWDLQV9PNLqgdzdAlRMjjlnnnQcA8MfTiKdl6OZcAkMoCcP8y/CTdxL42urbkNr+R8hzL0FKtGHix69PHQZTREREBVCzMuRwn1ZQ3FR3bN4Io+imZ9D/+PfyzpFc5SOCKclVeVDBlJKM5m0LOgkVV/4PdPZiSI4SCLrRf6SzZhMR0ZHjuc4deLR1M7YFe2HW6fH0uqu1MGmZu1wLpppCfUhlZRgHX/uPL65FQs5gibscyzzlUKFCQO68Ba6SqbkZomlOSYWRCe1EJtCETKB5xCiosgufGTFKSe+uHz2YEkToHbWQ3A2DI6DqoXc3QDR5te9hVVXRE0lhe18UO/qi2N4fRVNfFElZwY/OXYjvPtuLr6/+PL739z681RaAP16Kr6y6Ar98LYRbziiDxTBz45uZe2dERESHSVUUpDo2ayOf9tZ7koNduTBJVbS2xed/Jy+YktwjawHIoV6oWTkvRJLcFUDruxDNDuhdFbkV7dyVuc/3brsqIOqNI65nqlk+zndMREQTTVEV7Az7sDnQg82BLvy/ZadpAdNAMoZ3BjoAAAk5g+5EGBUWJwBg6WCxclEQMMfuRX8yiiqrCwDwsZrF+FjN4sm/GaIjwN5RUJlgM+TgTmQCzcgEW5CN7/+NQTnYAl3ZsXn79K4GiMaNufDJXQ+9qx6SqwF65xwIuqE3AxVFRVswgR1t/VoQtaMvinBKHvF1qpwmBOIZvLLTh1d2+rT9b7UF8WVfHAAQSmZQ7hhZD3SmYDBFRESzkqqqUOKhXNg0WNtJZ/Pss8Kciq67r84LoMay76p2kis/mBL0JuhdFVASYehsHm2/94wbUPTRr0M02Q7rfoiIaHoKp5PQizqYB1e3e7l7J/7fv57Sjp9fuxTLPbmfGcs8+TVoNgd6tGDq2KJq/HLVhVjiLoOFo2GJDlrgjW8j0fbSIZ+XCTbDuE8wZZ3/WVgXXJw3LTaTVdDki2NHvx/b+6LY3hdFy0AMiUz2oL5OKCnDbdHDbpSQlLMwSSL0ImAx5GrCWQ06OE0ze3VMBlNERDRjKan44CinLphqV0BnHqrVEXz1bgRe+k1ee1Pt0XnBlCDqIDlLIQe7cSCZwMgaUCWfvH1w9FM5RKt71Noew0MqIiKaGYKpBH6+7TVsCnSjLRrA94/5KNZVzgcALHGX5bXd5O/Wgqn5zhIscJVgobMUS9xlONo7NBLXYTDhuOKaybsJomlMqwUVbEFm779AMzwfuR0Gb/7oQck5F8D+gynR6Bw2CqoBkqseeuecEe2SWaC5P5IbBdWfGwW10xeDrKiH1H8BQK3bgvklNswvsaLIYsD6NXV4fGsPoAIZOQO9lAum1jUWw26c2dHNzL47IiKa0VQlCznQOTi9rnvw8y6t7lM2HtTaln/+NzDPXaltS87SEdeTg50j9kmuilwwJQiQ7MWQ3JWDK9xVatPs9O5K6OzFeecJog62pevG72aJiGjaCaeT2BLowaZAFxodxTi1ogEAYJH0eLZzBzJKbsTElkC3FkyVmO0oMdvQl4jCoTcirQxN7dGLOvzxpIsn/0aIprGhWlDNg9PxWsZcES8TaBkRTOld9UMbe2tBDdaA2lsPSjQXjXgDMZzMYEdnENv7I9jRF8OOvihaA3EcWgQFSKKAeV5rLoQqtmFBiQ31RZYRNaOuWVULUQCe29GPoJyB1aDDuvnF+OIJtbAxmCIiIpoaqqIgG+4bnGrXCclVDvOcoSHVcrgP7T+/8KCudaCpdkCuoLmqKHkr2BWdcxMEnR6SswyCNLOHURMR0dgUVUEwnYTHOLQ21sWv3Ie+RG5xilPK67VgyqCTsMBVohUr3+TPH3n73aPORJHRihqbC6LAVVOJ9pXs2ohY88PIBJoPWAtquEywecQ+Q/EyuE/49uAoqLl5taCAXHmHgVgaO/qjg/WgYmjqj6IrnDzkfpv1OtQXWbGgJBdAzS+2Ya7XAr3uwN/nxTYjbvjIXFy+sgb+WBIeqwkOkzTjQymAwRQREU0hVVWhxALIBDphrFwEQdRpx3zP/Rzhf/4ZanbonWTb8rPzginJUQqIOkA58Bz+fafaGUrmwfvRrw+NfnKVQzSOXIjXUDK3kFsjIqIjRCidQFrJAioAuxm+VBwQAIOog9NgxhNtW/F85w5sCfSgzu7BPR/5rHbuElcZXkrkVujaHOiGqqraqIvji2thlQxY4i7HCk/+myHHFlVP2v0RTUfDR0EZihaPGOWkpIJIdr5+cBcbNgrKULRsxGGdyQPL3FypBlVV0RlKaAHU9v4odvRF4I9nDvkeHEZpcCreUAhV4zJDFEeWbjhYNqMEi16EPh2B1+6AKM6O4JrBFBERFUxNhuE25T7C4hq1TTYRya1it3ea3d7PBwuOq3IKAFD9b49B76nSzhMN5rxQCgDkQP5UO0EUITnLtP2CZIDkKh8MmoZWtdO7KqD35tfl0FmccJ7wWRAR0eyWVrI4/4W7kZAziKSTyEJFhcWJR0+/AgCwMzyAN/vbAABNoX6kszIMg6voLfVU4KXuXDBl1xsRk9OwDa6i+sX5J0zB3RBNL6qqQI60Qx5cCW/vv2ysR2tjX3TZ/qffDZNfC6p+zFFQAJBVVLQG4lpB8u19UTT3xxBNj1wZ70CKrQZtKt7eIKrMbhy1fuh4yGYPrnD6TMFgioiICqbKabT+7OOY85VHkerYCslVnjfCKNn6Prru/sJBXUsOdOUFU5K7cmSb0Mih3MXnfQuC3gjJVQGdzZs3DY+IiGg0CTkDg6iDbvBnxkAyhq54WBvxNHy63hJ3OYD3AAAZJYvtoT4sGxwBdWp5PebaPVjiLoNdP3OXcic6GIqcQMa/fZ9aULugZlP7PS8TbBmxT3LU5YInZx30rsbBelDzIJqLRw2D0rKCloGYNh2vqT+Kpv4Y0tkDr6y8ryqnCY2DtaD2hlAeC1fCnEgMpoiI6IBUOTOivtLA03fCtvRMZHubkI0MoPfBb8C55jJ4Tv2S1kZyle97qTFlgl0wD9s2ViyC66Sr8oqMj1aw3DzvuEO+HyIimn164mH8aee72BToxo5QH+468TNYPLhCnkmX/zMukR2a1rPcU446uwdL3WVY6q5AldWlHSu3OFBucYBoNlFVBWomBtFgz9svB5ox8MK1h3y90WpICTo9Ss6+b9T28bSMHf25YuR7Q6hdvjiy6qGVJRcFAXM8FjQWD9WEaiy2zYqaTtMN/4sTERGAwRXugt3I+NqG/WtFxteObHQAdd96DYKoQzYegiqnYT/6Y8gMtEJVs5DDfSi//LcQ9GZk4yHoLE4AgM5eDEEn5U3JE80O6Aen2EnuysHPK2EsX5DXH0NxHTynHfovN0RENLvJShbN4QFs8ndjibtMC58UqHhw9/tauw/8XdoxsyRBEASoqgpREJBVh0ZZlJjt+Mspl03qPRBNF0o6kpt+NzgVTw42IxPcBVPlifCc+IO8ttIY0+80ggjJUQO9q35wOt7eFfGKxzwlEE9rIdSO/ih29EXRFkwc8n0YdCLqi/aujGfFghI76ossMEq6A59ME47BFBHRLJbs2Irgq3ch42uDHOgYUdNpODnQBb23GqqcRvsvLsytlhfqAbIy2n/xCQiCDqLZjrnf/Zd2jiCKKDr3Zohmp1brSTTZJuPWiIhollBURVvZTlayWPfs7xDN5JaR/9y8Y7TwqdzsQJHJioFkDACwyd+Fi+cdDQCQBB2qrU6ICmA1GiEJ/GOVZhetFpQWQu1EJticVwtquNFWvxP1Fki2CsjRLogGhxY8Sa76XE0ox1wIknGMr6+iL5rOjYLSVseLoi+6/2mAo7HodWgssWHBsHpQdW4zpINYGY+mBoMpIqIZKtW1Hem+ndqop4yvFa4TPw/b0jO1Nmo2jfiOVw/qehlfG/TeagiSAdXXPwJVTiM9sBvtv70UtV/+CyR3BQRRgiDlz8G3H/3xcb0vIiKiF7qa8GZfGzYFutDoKMZtx3wUACCJOlRb3fgwmJsa9MGwFVkFQcDKomp0xsNY7inHyqLcohgGUZcrdK4CWSULnajTVuUjmukUOQHfi9cdVC2o4eRIBxQ5CVHKr63mPvEH0Jm8Y9aCAgBFUdExuDLe9r4odvTH0NQfRTBx6Cvjucx6zN+nHlSlw3RYK+PR5GMwRUR0hFJScWT87cj42pCN+kasMOd79mdI7nknb1+6b2fe9r4r1Q0nmuzQe2ugL6qF3lMDabAw+d5pegAgSHqItiLoi2ohOUoO95aIiIjypLIytgV70R0P4+zqhdr+Zzt24JWe3M+04fWggFxNqL3BVG8ikjei6rtHnTnij2WnIVfhUFEU9PX1wVtSMmuWaKeZTVUVZCMdyASbkQm0QM0m4Dz6xrw2omSGHOs9qFBKNDhy0/DcDWNO2zN4FuZty1kFu/1xLYDKrYwXRTxz6KvOldmNaNw7CmowjCq2GSZsZTyaPAymiIimMVXOIBPoHKr35G9HZqA1F0ZF+ocaCgIcx16YN1pJX1Q7IpjKDLTmbeusHhirlkJylORCKG8N9N5a6L01EC3Og/tBz3eUiYhoAvyx5V/47fYNkBUFBlGHMyoboR/8mbPUU64FUz3xCPoSEZSYc4WYz6pagAXOUizzlKNyn59l/AOWZiqtFtTgVDw52IJMaBdUOam1EXQGOFZcD2Gf3930rnlI9fiHduynFtT+voeSmSxaBmKDIVRuKt5OX/yQV8YTAFS7zNoIqL1FyV1m/QHPpSMTgykioimmKgrkUA8yvjYYSubmjTyKbnkO/Y9+5yAuoiLj74ChZK62S+8ZHA0l6qB3V0LvrYWpelneaYIgoPLqewruuyAZUHvjYyOm7xERER2M/mQUL3fvxOZANz7wd+HuEz8Dr8kKACgx2SAruT9o00oWTaF+rV7UCk8FLJIei91lWOaugE4YGuG0yFWGRa6yyb8ZokmU6HgNGd+2wTBq7FpQw6nZNORIG/TOOXn7zTWnQu+ce1C1oPaKJGU0DUS1lfF29EWxJ5CAcogr4+kEAfOKrJhfbMP8ktzHxmIrLAZGFbMJ/28TEU2RgSd/iOSed5Hxt0MdnIZQdN634Dj2Aq3N/qba5REEyKGevGDKvvxsWBecPFj7aWJGNQkmBwLhJEocXCqbiIj2L5pJYXOgG/PsXm10U3s0iP/c/A+tzaZAN04pz00RWuYpzzt/x7Bgaom7DC999Fptih7RTJQbBbUThuLlI0YqRbf9L9IDWw7+YoIIyV4NNR0Zcchaf8EoJwzxx9NaPajtfVE09UfRGUru95zRGCURjUU2NJZYczWhim2Y57XCIPH7eLZjMEVENI6yibBWaDw3/a4Nsq8NSiaJ6usfymub8Xcg3b9rn31tedv6otq8bZ3VM2zK3dDUO8lTBVGf/86WzuaBzuYZx7sjIiI6dKF0Al/a8DB2RQagqsDXl67Fp+esAAAscpVCJwjIDo6y2OTv0oKpcrMDVzQeh/mOYizzVKBocCQVAAZSNKMM1YLauyJe/iioso8/Dp21NO8cvat+zGBqqBZUPSRXA/SuedA75x1wFJSqqugOp4am4g1OxxuIpQ/5nmwGCfNLcqOg9q6OV+e2sCg5jYrBFBHRYUi2vo/wu49BHgyhsvHgmG2VdALiYIFVIDcaKrHzn3ltRtSAMjtQ8snbofdU5eo+mWzj2n8iIqLxstnfjff9Xdjk78JSTzkuqz8WAODQm+BLxbB3hs8mf7cWTJkkPRqdJeiOh7HUU45GZ7F2PUEQcO2C1ZN9G0QTSklHteBJDrYgE2hBJrQzrxbUvjLBllGDqb2joHI1oBq0wuQHqgUF5FbGaw0ksKM/qgVRTX1RhFPyId+T12LIhVDFQzWhyh1G1nSjg8ZgiohoDBlfG9IDrZB9bUj7WiH72uE+5RqYaldobeRwL6LvP3lw1/O3w1jWqG2bapYjGx2A3lOdG/XkrYbBWzviPNvSdYd9L0REROPJn4qjNxHBQtfQH8s/2vwSmkK5hTnCmaQWTAmCgOXuCq1Y+eZAd961fn7C+XDoTfwjlo5I2WQQgsGRt60zuUZtG3rnp4ju+Mshf41MsBmmyjV5+8xzz4Fl7nkHHAUFAGlZwS5/DDv6YloQ1dwfRVI+tKLkAFDhMGlFyffWhSqyHrgPRPsz7YKpO+64A4888gi2b98Os9mM1atX40c/+hHmz5+vtfn973+PP//5z3j33XcRiUQQCATgcrnyrlNXV4fW1tYR1/5//+//TcZtENERQFUUZMN9yPjaoKSisC46Ne94z59uRMaXP7XOuujUvGBqvzWgBHGw6Hhuyp1osOQdti09E7alZx72fRAREU2WP+98F3/d8wE6YyFUWp149LQrtGPLPRVaMLU10IOMktVW0VtXOR9z7R4s91RiiTu/MLlz2GhioiOJHO1CZOu9sC26DCY1AjkYQmTL3bDN/wwEgx0Gd0Nee9FcPMaVhskbBTW4Kp534YhmojT6900ik0Vzf0yrB7WjP4pdvhhk5dCKkouCgDq3GY3DQqjGYiscJq6MR+Nv2gVTr7zyCtavX4+VK1dClmV885vfxLp167Bt2zZYrbl55fF4HGeddRbOOuss3HzzzWNe63vf+x6uvvpqbdtut094/4loelFVFUosoNV7yvhakfG3IzOQ+6jKuTnzOqtnRDCl99aMCKb23dZ7ayA5SnIfvTUweGsh7a395KqAIPGHNxERHVnichpbAj34wN+F7aE+/PjYc6ETczWdklkZnbEQAKAzFoI/FYfHmHvjZam7HH/d/QFKzXYs85QjmknBPXjsjMpGnFHZOPoXJDqCKJkYMoEmCJIFkc13IbrtHiTbXoLjmK/A9/rPkOr9F1Q5DseKL484V+/aJ6gyOHL1n9wNuRXxXPUHVQtqr3AykxdANfXF0BqI49AiKECvEzHPa8mbjtdQZIVJPzGL5xDta9oFU88880ze9r333ouSkhK88847OOmkkwAAN954IwDg5Zdf3u+17HY7ysq4VCzRbKAko8j42kbUYQptuA/+5/77gOdnY35kExHozEMB9vDRUDqLKxc+OfNfU0SjFTVf+/s43AEREdHkU1UV3Ykw3AYLzINvpvyjuwW3vvec1qY5PIAFrhIAwApPRd75m/xdWDtYrPyksrl48oyrtBX3iGYCOdKOZOdrSPu3I+PfDjnSDqgq9N7FcCy9Gum+d5Hq/Rf6nroYgiDAWHosbAsuQbJrAwye+XnX0nsWwLH8S1o9KNFSclBTWFVVxUAsrQVQ2/tyIVR35NBXxjPrdWgoHipIvqDEhjkeC/Q6LihAU2faBVP7CoVy78h4PIe+stQPf/hD3HbbbaipqcHFF1+Mr3zlK5Ck0W85lUohlUpp2+FwGACgKAoU5dDn3k4XiqLkRowcwfdA08d0e558T/0Y6d5mZPxtUGIBAEDJxT+DpWFoDr7OWT7W6SOkB1phrFykbduOuRCWRadD8tZAZx6qHTBd7n86mG7PBB25+CxRofjsFKYvEcVPt76CzYFuDKRi+Mmx5+EjZXMBAEtcZVCHjbn4wN+JRkcRAGCBsxgnls7BElcZlnnKschVqv23N4kSTEbpiP1/wWdpdlMycUBJQzS68vanBrYh9O7PR7TP+LYi0f4SnMd8FX1/v2hwrwrnsV9DJtgMiIYRz5JgcMC68DJtW1VVqGr++CZVVdEZSmJ7fwxNg6vi7eiPIRDPHPI9OcwS5hdbB2tB2TC/2Ipqp3nUlfH43E8fM+W16FD6P62DKUVRcOONN2LNmjVYsmTJIZ17ww034Oijj4bH48GGDRtw8803o7u7Gz/96U9HbX/HHXfg1ltvHbG/v78fyeShJ9HThaIoCIVCUFUVosgUnA7PZDxPqpKFEu6BEuwc+hfogKA3w3Lud/LaRndsQNafX0vOt2szos6hYdJZWJHJDPtBrtNDdJZD56qE6K6C6KrU/gUlD4S+vmFXMwEGExBJ5v7RCHyNofHCZ4kKxWdn/4KZJLZH+rEt3I9FjmKc4KkGACSyGbzYsQPK4B/FG9qbMF/MjTg2qCqskBDKJFFldiAWjqBv2M/Hf689PvdJFgj7AghP7i1NGD5Ls4cqJ6BGdkIJtUANt0AJN0ONd0KqPR/S/C/ktVWU4vzfJQcZi5bAVHM6Ahu/B9XggSroIUhmhLY9APea2xDLWvO+b0aTVVS0h1No8SWxM5BEiz+Jnf4k4pnsId9TkUWPeo8J8zymwY9mFFukYSOyVCATxcBA9JCvTZNrprwWRSKRg247rYOp9evXY8uWLXj99dcP+dyvfvWr2ufLli2DwWDANddcgzvuuANG48g5uzfffHPeOeFwGNXV1SguLobD4RjR/kihKAoEQUBxcfER/VDT9DARz1Nyz7uI73gVGX8bMgOtkINdgJL/w1gAIJrsKC7eZ+nbykbEI115bS1yCN6SkqE+u52wnvf/BmtA1UJylkHg98K44WsMjRc+S1QoPjtDFFVBJJPSiomrqorPPfc/CGUSAICkXsDHSo7R2i/wlKEpnCtWvisTRcmwn5+/PPGTqLA44JpFhcn5LM1MSiYOOdiMjP9DZAI7kPHvQDbcCkCFgMHfMwFAkmBId+T9HgkAqlqM3nddEHQm6D0Lofc0Qu9eAINnASJb/oBsoh+m6lNgW74e0Q9+Bdm/FYkd98G1/FoIhqFrpWUFLb4YdvRFtdFQzQMxZOR9K0KJ0Ev7f/6qXCZtBNT84lxdKLeFdU1nipnyWmQymQ667bQNpr785S/jySefxKuvvoqqqqrDvt7xxx8PWZaxZ8+evBX+9jIajaMGVqIoHtEPA5Bboncm3AdNPTUZhscsQEhHIVpcB2yfjYcGi4wPFh4faIXn9C9D763W2qS7tyP8zz8f8FpKMgIkwxCtbm2fqWoJlEQY+qJa6D3V0BfVwlDakPesi0YzXKsuGu2SNE74GkPjhc8SFWq2PzuPt27BS90t2BLoxnxnCX69+hPasaWecrzRuxsAsDnYk/ff6OTyetTY3FjmqcBRnsq8Y0s8Bz8VfiaZ7c/STJFN+hF+7+dI+7dDDrcC6sGVA88EmyAIgCDk//8v+9ijEI0jBytYFn4eoUQGcv3n8PtNaVy29FsQWu6DZeHnsSdqwFvt3bl6UP1R7PLFtRGKefZTYkoUBMz1WtBYvHdlvFwQZTVO2z/jaZzMhNeiQ+n7tHuiVVX9/+3dd3wcZ50/8M/MzmzvklbVVZZlpzndjlNwiC8kOS6FAL9AuJAQ0gtc4I6Dg0vg4ICDS4AACSTgQCC0HCEhgTRCCmmkucZNsuUuq+1qtX3K8/tjV6Nd7dqWZNkq/rxfL78kTXl2ZjOxpI+f7/fBLbfcgkceeQTPP/885syZMy7jrly5ErIsl/xLEBGNjtBz2PadizH3tsesbWY2Ba13O4Seg3PmcUPHCoHtd70fIpcuGcN73PklwVRxg/FKZKcvHzxVzYQwSqdRB8+8EsEzrzyIOyIiIpo6OlNxrI7uQUciimtbl1jb343txatdHQCAdbFOGKZpraJ3XCGYkiUJXsWBjK7BWWhyXjwG0cRD0/kAAGtQSURBVFRj6mlo0U0QuQScjaeX7JMVD1IdTwPiwD1ubM4qqOEFUKsWQg215kOsYWFRpVAqkdXxuw0GFjZege/8tRevbO3F33cG8a9LP4bfvZVGrc/E3X/bOuL7sdtktFR7ML/QkHxBjRfN1W44FK6MR9PfpAumbrrpJjz00EN49NFH4fP50NnZCQAIBAJwufJTiTs7O9HZ2Ym2tjYAwJo1a+Dz+TBz5kyEw2G8+uqreP3113H22WfD5/Ph1Vdfxb/8y7/gYx/7GEKh0D5fm4gqM3Np6NE9kBQVItELIx1H1yN3QOvpgDHQDQgBe30rmq7/pXWOJElQwzOQ69xUMpbWu73ka7VqJiTFYYVP1p9w/qPsDoxotRIiIqLpRDcNyJIEuTBz48G2t3D3uy9Z+z88exGCjvzPxseF6/HItjUAgLSulayi977GVhwTrMPRoTq4Ffthvgui8TEYQmmFlfG0vg3Q4tsAYcLmqUPdsGBKUhxQA3OhxdpKtsvOMOzhhfkgKpwvx7O5a0Z0DfGMhvbeFNp7ktjRn8Z751XjS09uQCytA8j/o+wL7b14ob0XPoeCx64+BT6HgoGsXjaWx26zZkHlZ0J5MTvshq1CU3KiI8GkC6buueceAMCyZctKtq9YsQJXXnklAODee+8taVR+1llnlRzjcDjw61//GnfccQey2SzmzJmDf/mXfynpIUVEQ4Rpwkj0Qo/ugr1hIWR1qKw1u2cDhJ4DAOg9XTAG9kLr6UD4vdcDAIxkDHt//Zn8rCkhSkIktWrWUDAl26CGmyCppSWzavUszP6Pl9j3iYiIjnidqTge2bYGK/t2493YXqw48zLMK6yG1xoo/eV5dXQ3zqprBgAcF2pAkyeIReF6HBtqQK3Lax3X4A6gwR04fDdBdJBMPQM9ugm5vg35vlBFIVQlRrITRiYGmzNYst1RvwQ2d6QQQi2EPdwK2VVzwH/wzOoGtval0NaTQntv0gqjuhJDK7g3BZw4utZnhVLDDWR1xFI6Ak4Fqk0qKsXLf2zwOyuujEd0pJp0wdTw5TIrueOOO3DHHXfsc/+JJ56I1157bRyvimjqE6ZZEv4IQ8feX/8rtOhO6NFdVvjUeN2DcDQstI6TVCe2fu0swNAhhAEYOnbcfSkkyQbYFMz+3LOAJEPk0jAGeqD4h35wDiz5CHwn/BPU8AwooQZIcvlUZClfyH8I75yIiGhyEUJgWyKK1dE9WBiIoKUQOqUNDSs2v2Edt6pvtxVMHROqgyxJMIWAXbZhb3poZa0Z3iB+f86Vh/UeiA6FnuduRXbvmyMqwSumxzbDVndKybbACTfv9xzDFNgZS6OtED619STR3pPEzv5M5V5QRfozOkJutWxGlGqT4FRkVHvsaK52474PLUKVx87Z/0QHMOmCKSIaGyEEjGQf9L6d0KK7Ch/zoZMW3QVHw1Go++id1vGSTUFm52qYqf6ScfLHFgVTki0fPgHQ413Y/r0PYMatj0ANNgA2GyCA2g9/A2p4JmzecMlYxT2niIiIjlTFM4pzho73P/sTxLL5HoyfmH+qFUzN8obgVx2Ia/mZGav7duPS2fnvpW7Fji8e/w+Y5QmhNVADu40/xtPUYs2EKqyMFzzl3yDZSstLJZvjgKGU7AzBHl4ANZTvC2UPL4Ds2nc5nhAC3Ykc2nqTaOvJ/9nSm8LWvhRyxugCsKExga5EDv98UhN+v2YP7IoMG0w4VRWQgEuOqUPE64CXTcqJRoT/pxBNIULPATa15F9dYi89gMSqP0GL7oLQs/s8V+/bWbZNDTYiOyyY0qO7Sr6WVCcy21ZBCTfB0XAUlEgLnE3HQPEPLSTgqGsZ6y0RERFNSy92tuOtnp1Y2bcbCwIRfH7ROQAAu01B2O62gqnVfXusc2RJxqKqRnSmB3BcqB6nRWaVjPn+GUcdvhsgOghCz0KLDZbjFf70by0JnTzzL4U9vLDkPDW8AJldQ73USkKoQk8o2R3Z5wykeEbLz3wanAHVm8KW3mTFPk8jYbfJmB12o7nKjXnVHsyr9qC5yoOINz8L6tQZQQRdCp7e2I1YKgOP3YZzW2tw7ZJZDKWIRoH/txBNUmYmgf7Xfm3NetKju6DHuzDr356FzRO0jjNSMeS6txxwPC26s6wHlKt5MZRgHZRQE9RwE5RQIxy1pSGT4q9B8Ix/BpCfMQWbOj43SERENA0Ypom2gR50pgfwnkLPJwB4uGM1XuvaBgBIFsrlBy0KN2DLQC8AYGuiD6YwrSbn3zrl/dbnRFNBaQi1Md8XalgIVYnWt6EsmHI2LAFMrRBCLdxnCJXRBvtAFXpA9ebL8LqTubJjR0ICMCPoQnO1xwqhmqs8mBF07bcheY3XgVvPnIsrT5mJvmQGYY8TfqfCUIpolPh/DNFhJvQctNiefIld307o0XzpnbPpWATPvHLoQElG9K/3lp2vRXeVBFNquKnyC0kylEAt1FA+cFJDjYChAUUr8oSX3zSqa5cUO2Z9+g+QuKoPERERft72Jn6y6XWkdQ0exY6/nH+9FSodG6q3gqntiShi2bS1it75TQswP1CD40L1aPZXlQRRDKVoqomv+gESG387qnNkRwBCz5Rtt1cdDXvV0dbXhimwIzoYQCXR1pOfAbUjlsaBOxNXFvE6MLfKjeaqwRlQbswJu+FUy3uhjoTXocCtylBzA6jy+SFzQR+iUWMwRXSIDbz9GDLbV+ZDqFh+1hMqNVQ0NKAomJIdbtg8IRjJaMlhenQX0DT0DdteOx+eo95rhU9KqCn/MVAHSRnf2U2S049oPIOI3z+u4xIREU1WfdkUXunqwJq+PVgd3YMfn/5B+FQnACCgOpHWNQD5WVFbBvqsZuWLwg1QZBkLAhEsCjfAKJo9cnxVI46vajz8N0M0CsLIQYtthta7Pj8bKroRvmOvgavprJLj1PCC/Y4j2/35GVBVC/Mr5IVaYfPUl8yEEkKgK5ErCqDyM6E6DqIPlNeuoLnag3nVpSGU33loZv8bhnFIxiU6EjCYIhojM5tCZseqoWbj0V3QB7rR8MkVJd9oU22vILnu2QOOpw3r7QQAangGJMUxFDqFm2CvnVdyjHPmcXDO/J+DvyEiIqIjXM7Q8W5sL2Z6Qwg73ACAjf1d+Mo7T1vHrOnrxNLa2QCA48L1JeevjXZawdSJVY14/vwb2aScpgQrhOrbYPWF0mNb8isyF9F61+03mLJCqHC+J5QaXlAWQsUzGtp29aOtJ1VYES+JLT0pJHJj7wM1J+wuKcObV+VBjZer4RFNFfxOSVSBEAJmOl4otdsFLboTjvoFcLcstY7R+zvR+eAtZecayT4o3irrazW0/38RlewuqKEmqDVzyvbVf+J+SJwOTEREdEj1ZVP47N8fw4b+Luimif9YtBwXzToGAHBMqA6SNDTZeVV0txVMzfKG8OE5i7AgWItFoQY0eQLWmIo8trIgosPBSHUjs+sl5PrW7zOEqiTXt6Fsm+KfjfAZ/10WQmU0A5t6U2jbtrekDK9njH2gZElCU8BZmAWVD5+aq9xoOkAfKCKa/BhM0REv3fE2tO6thVlPO60wyswmS47zn3xpSTClBBsqjqf37SoJppTwDCj+yFCJXbjwMdQINdwE2R3c57/mMJQiIiIaP23xHqzs24U1fXtwTKgeH5qzCAAQtDuxdaAPupkvGVod3W0FUz7ViTneKuxJx3F0sA4zivo8ypKMzx579mG/D6KREkYOppaEzRkq2a4ndiL2xshn3Mt2H9TwAjgiJ5btM4WETs8StO1Oor1nG9p6k9jSm8LOg+wDZc2AqvKguTrfB8qhMPAlmo4YTNG0ZqTjQ6V2fTthahmEz7mh5Ji+p7+L7K51BxxLi+4s+Vq2O2HzVcMY6IGkOq3QSRo2Zd9/0sXwn3TxQd8LERERjVxSy6Irk8Ac39A/Fn3+zSewLZHv3diXTVnBlCzJODY81Kx8Vd+ekrG+t+RiVDk8sPEfjGgSy5fjtUGLbkSutzATqn8LnDPORvj0/yo5Vg21omQqYJHBEEoNtVp9oWye/D/I7h3IYtXWXrQXyvC29KawtS8FbYx9oHyOQh+owuyn5kPcB4qIJicGUzSlCUOH3r8XenQXlFBDyQp1qbZXy0rtJMWO0NnXlcxEUkKNIwqmRC5dtq3+n78PmycM2RNiDTsREdFh1J9LI2cagADgc6E3mwIkIJ7L4D/ffhJtAz2Y7Q3jN2dfYZ1zXLjeCqbWRjthCtNaBW95w3zM8YZxXLgBx4bqSl4r4vIdtvsiGglh5KD1t0Mr9IPK9W2AHmuHMMv7NGkVyu9k1Q3FNwNmus/qBTXYF8rmbUR/Rs/3f+pJoW1zEm09q7ClN4lkbmwNvu022VoJr7m6MAuKfaCIqIDBFE16Rnqg0OepUGpXmP2k9e2E3t8JFFa5CS+/GcGiVe0qldoJPQcj0QvFX2NtGwyzJMUONdRUVmqnhBqhBBsgq46y8YY3IiciIqLxIYRA2tAQz2XQr2XQ5A7AU/he3DHQB0OYOPfJe5EzTUTsbqiqAkWy4efv+Si2DPRCCGDrQB8GtIy1it5xoQb8cfu78KsOHBduwICWRcDuAgBcOPPofV4L0WQgjBz637qzEEK1VQyhKtEHdsDMJSDbvSXbq5f/CFnZh46+NNp6k2jrSKLtzT5s6d2J3tTY+0DNCDoLAVR+JtS8ajeaAi7I7ANFRPvAYIomnDCN/Kynvp0Qpl7SxwkAdt59KYxk3wHHGb6qnRqsL5uibPNVw0hFS4KpwOLL4D/1w7B5q/gvNkRERIeAZhpWwBSyuxAqrHgXy6bxYPubiOey6NfSuLz5JCwK5/9haV2sE5946TfWGHefdgkW18wCAPRmU1BlGb3ZNAxhwm+zQ1ZskGDCPqzpePEqemfXz8PxVQ2YyZnONEkJQ4PWvwVGam/Z6neQVaR3PA8zGzvgOLLqhRputWZDGUJGR28SbT1JtPem0NaTxJbeJHb1Zw66D9S8wkp4zVUezAm7YVdY8kpEo8Ngig4LM5sEJBtku9Paltz4Evqe/F9osT2AmZ8WrFbNhLvl9yXnquGmEQVT+rBgSlLsqH7/F2DzVuVnPgUbSl5/kM0bHsstERERHXFMYSKh5RDLpeG0KVaJmylM/Gjja4jnMohrGby3vgXnNLQAyJfWLX/yXmuMzx67DB+eczwAIGfqeLDtLWvfGbVzrWDKr5Z+z+7PZazP/aoDaUOzvk6bOtzIz6ZSZRtOCDfh2HAdjg03YFG4fug8uxP+Cj8LEE0EYerQ+7dY/aC06AZo0TYIU4Nkc8D5oecgFQWtkiTBXrUQmd2vlowjqx4rgFJDrYjZ56AtHUR7XwZtnUlsWZdER/TtMfeB8hf6QDUXZj81F8rwfE7+KklE44N/m9C4EKYJI95VUm43uLqdHt0FIxVD5INfg/fY91nnSDYVWl9pQ3E9thvCNMt6QGHHautrmydsldhZpXahRqhVM8uuy3/yJYfgbomIiKa2wTK5wbCn3u239v1my0rsSsXQn8vgqGAd/t/c46195z99P6LZFADgsrnH47ZjlgHINw9/qP1tZI18aVGDy28FU17VDlmSYBZmMMe1rDXeYBndoOLwKbiffQG7E9mMDtVmgwpbyexoWZLww9MvHd0bQnSIWSFUoSeU1rceWqwdwqhcMieMLPR4B9Rgc8l2e80JEHoWangBsp552ImZ2JQMoL0vjfaNKbT3JpHS9gDYU3Hc/XEoMuaG3UUhVL4RebWHfaCI6NBiMEUjZmZTMLPJkjI4YRrY+f0P5QMlY/917mWldqHGsmOEocOId0EJDjUd9Z/yIXiOXm71fZKH/aBKRER0JMsZOuJaFllDR6MnYG1/etdGbOzvQiyXRrXTixsWDJXKX/fKw1jZm/++fFpkNr675GJr32M71mFzfzcAIGPoJcGUV7FbwVRxUATkZzh1G4n8Pm1onyzJ8KkO6/h40WIiDpuCkMMNh80Gv+qEr6ifo1e149NHn4WA3YmA3YUWf7W1L+LyQZIktPproAsDuqbDJkmwSSwhoskn+tpXkd729D5DqH3R+jZADTYjldOxtS9dKMM7HW39x6N9SxJ9KQ1AX+HPyMmShJlBl7UC3mAAxT5QRDRRGExNcyITR8iZ/wh3cFTnptpfR+KdxwqzoHbBSEbhal6M+it+YB0jyTaYufQBQykA0IfNjlICdfAee15+tlOoMd90PNwEm7e65DjnzONGdd1ERERTkSlMxHNZxLUMZnpD1va/d2/HWz07ENeyMIXA5xedY+3771XP4g/b1gIAGj0BPHLOVda+v+5pw192bwYANPurS4Ipj2K3Po9rpQFToKiEbnj4FLA7sSOZ/zw2bF+N0wNDmAjYnVYPqUE3LDgdsiQhYHdizrAS+qfed23F90OWZHy0+cSK+wDALtvwyPKrAAEYpgGbbAMklPWYIjqUhmZCbYTWtwG+Y6+GzVn6jEuycsBQSlLdUILzkXA2Y7c0E5tyM7B2jQ9bXvg7dvVn9nvu/tQW+kANBlDzqj2YHWIfKCKaXBhMTXNCz2Hbdy7G3NseAwCYuXTRCndD5XZadCdqLvkynE3HWOfq0d1IrHmqZLzhpXdAfuaTMdBdsk2yKVCCDUOhU6gJjqbS1W4kRUXkg18dr1slIiKaFIQQSOk59GsZRJxeKIWgZFN/N17obLf6MH1h0XI4bPkfxR5sewvfX/8ShMiv2/Hq+2+FXJj981bPDqzY/AaAfOjy78e91yqrcdlU63UHKoRIg+L72Tc8fAo5XPCrDgTsLtS6SlfxumzuCYhrGfhVJ5qKZmcBwANnfWSf78kHZh+7z31jNVgGaJomurq6UBWJQJb5yzaNjZGJQbL7S762OYMlx+QX7Ckux9sALba5JHRyNpwGW+MZJeep4QUlX0uKG5p3HnqVWdghZuLdbAPW9PuxdX0GujlYlqoDiI74+gf7QA2GT81V+V5QXgd/3SOiyY9/U01zQgiYsd0wEr3Ydd9VMBI9gDBLejEM0nq3lwRTlUrt9NgeCNMoacToPe48OGefaJXaqaEm2PyRkj5RREREU1HO0BHLpeFXnXAq+RCoMxXHk7s2FlaZS+MTLYutEroXO9vx728+Ad3MNxl++L0ft2Y/bYp3476Nr1lj37jwdNQWmoe7FdX61iwEMKBlreDFX1TCnjMNZA3dupbiHk0DehamMK1AK6A64VZU+O1O1DhLA6ZzG1rRGoggoDpR7fSU7Pvqiefvs5/MuY2tI33riKYMPbEbA+segO/oK+FyeWCmOjGw7gF4F14OPb4d2c7XC83JNx1w5lOubwOcRcFUXyqHrXoz4t7zsdVowupkHVZ2+ZHcPfxn8TRGwqHIVvPx5mo35hU+VrnZB4qIpi4GU9OQkeqH0PPfNI34XpjpGLToLtR++Ov5bckY9v76M2XhVFmpXbgJanhG0aynfLNxmCZQFEz5T/ngIb4jIiKig2OYJuJaBnbZBk+hj1FKz+GRbWsRz6XRr2VwQdNCHFdYEW7LQC+ufPFXyBRK1f/31AtxZt1cAEBXJoEfrn/ZGvt9jQusYMplU61QCgBiuTRmIh9MBSqsMjcYTAXs5fsGQ6eA3QlVtsFvdyKgOpE2NCuYOrVmBhT5DARUJwJ2Z/5be+F30+sXLMUNC0+v+H4srZ2NpZhdcR9/uaUjiZGJYWDdA0i1PwYt1g7vcTeib/UPkd31IozkHribL0Jy08MHHMcUAlnhwNYdXXi9tx1tvUls6R3sAwUAZxcdXf4PxMPJkoRZoaI+UFUeNFd70Oh3sg8UEU07DKamIaHnsOPuDwCmkZ/dBGDH3ZdCkmyATcHszz0LSDIgDMguv1Vqp0bmloyjhhow41OPTMxNEBHREaE/l0bONPK/p/lc6M2mrD5Bw1dsA/IzgZN6DgICvqKg5+GOVejLphHPpXFKzUy8py6/klXO0HH+0/dhoLAS3KeOPguXF/oWaaaB76570RqjxV9jBVMexW6FUkBpM+/hq8UV92ja3ypzAbsTsiRZTb51YVj75vtrcP2CpQjYnfCrToSLejRd0LQA/9i0sGJgdEyoHseE6su2AwyYiIYTwoSR7IQWa4Mea4MW3QxAgvfojyMXbUeqay3iT18PRZbhrD0F3gWXI77mvtIxAOR0E2lhR69tJjrMmXg3XY/V6Tr0oBaiRwawq9LL71OdzzG0El5VflU89oEioiMJg6lpSFLsmHHL7wEAen8ntn7rPMy88VdQQg35EjxJRuM1P4MSaoDN5T/AaERERIeGKUxkDR0f+MsD1spqaWHAgMAT//BJPL+nHRfNGioxv/yFX6I93gNTCFwy69iSJuD3rn8F8UL4ZJNkK5iy2xRkiwKm4hXhfKoDkjQ0gbgkRKowu8naV5jd5FHs8NudkDAUANW5fLh6/mIrYGoNDK1ke0yoDq+8/xar1K7YTG8In5h/asX3qdLxRLR/Zi4xFEDF2qDF2qH1t0NoqfKDI4uBhddhy8ZPwDAEbDYJx5z1KSS2PYvYntVIGgo6pZlo15uwNlWPHWhCN+ogMLr/NwNOtaj8zoN5VR7MrXKzDxQRHfH4t+A0ZHMXNyMVsAUb4GhYAMUfmbBrIiKiI8uGWBc6En2I5tJw2hRcMmuo+fXtbz+JV7o6kNSzWHHmR2AIE4YQMIRAVzYJzTCQ1HN4sXNLSTAlhIBZSJH6c6X9WPx2lxVM9Q9bZc5vd6InkyzbJ0syfIoDGUNHwO6CUtQb0amouKrlFHhUB4J2F44J1Q2NpzrxyvtvsZqaD3+t6xacVvE9YcBEdGhp/R2Ir/wBtNhmGMnOEZ2jhI+G4m3E5hf/FzndhCmAnCmw6aW7MP+9t2O7+/34wl+jowqhnIqMuYU+UPOq3YVm5B6E3SpnMhIRVcBg6kjAZZOJiGiMYtk0dqf7EctmkNCzJc2vf7t1JZ7cuaEQPqn41bKPWfse2vI2nty5AUB+NlBxMJUxdPTnMrBJkhU0DbJJEgY7spSFT0WzmAZDqEEBuxO7UxL8dicccumPN5fNPQG6aSJod2Gev6pk35/OvQZ2W+Ufh/bVn0mSJCgSv7cSHW5GJmrNgJIdQbjnnF+yX7LZkdn10n7HyBkmMpqJrG4iKtfg5Pdch73rfoPunSsRrF+EyCm3ouuN7yG2ZxU61zyEk5fcCq8jiYGsXjaWLEmYbfWByodQzVUeNLAPFBHRqDCYmuYkxY5Zn/4DJMU+0ZdCREQTSAiBhJ5FNJtGLJfGwmAt1MI/XLzZswN/3L4OsVwG0VwK95/+YSus+W3HKtxfWElOliQsb2ixZv50pxNYG83PSnAOC3dCRb2WYtnS0pniHk3lwZRsvZar0OB70PlNC3BidRMCqhMzPMGSffecdikcNqXibIQr5p28z/dlX6EUEU0cYeSgxzvy5XeFIEqPtsHI9FrH2GsWlQVTNk89JNUNoaXyzch1ExndRMJ0YpfZgE3ZWuw067EHjdiLBkQCQfy7Pg/++ddihumEfcEV+M5badxyxn/Bt+HnUFuvwKaYgoBTgdeuoLkQPM0rNCSfxT5QRETjgj+NTXOS049oPIOIn72kiIimE1OY6M9l4FHsVriyO9WPJ3asRyyXD59uWng6Ggrl3U/v3oQvvfVn6/zfn3Mlmgrhzu5UHH8uzG4C8ivJRQqrxYWGhUgJLQd/ocdSsKhBd8bQkdGHVosrbgKeMXSYwrQCrVNrZsJhsyFkd8OnOmCTZAgICElCk9sPRbah1uXD3ad9oOSei8v6hnMOC7GIaGoQpo5s599LAig9vg2iaHGASvRYG0Qh2N4Tz2JTdwKbepIIpo5CLJXG5mwt9qARnWhEP0IAykPr/owOWZbw2af68NmlH8dX/tSFt3ZGsbWvCl848yrc90oMXzmvEb/46InwsA8UEdEhw79hiYiIJgEhhDXbJ61reGnvFkSzaURzKby3vgXzC020twz04vqXH0Zcy8AUAt9ZfDGW1s4GAOxNJ3BfYXYTAHxg1nFWMBW0lzbzjmbTVjA1fF8slxkKphylq8xFcykrmJrjDWNpZDaCDhdCdhfMoiXQL551DM5tnI+Q3QW3Yi+ZyXROQwvOaWgBkC/Xe2T5VYAADNOATbZZq/IR0fRh6hmYqS4o/pll+/pe/ByEqVU4q5QQQEY3kNVNxFMGvvabl7C2z4ZkrjjE+vCIrkeWJFR77BjIGjiu3o8b/7gdDkXG3JATPYkcbntyJy45pg7VHjtDKSKiQ4x/yxIRER1CG2Jd6MumEM2lMMcXxlHBfBPtnKHjhlf/r1Bal8K1rafhsrknAADShoYvFs1uijh9VjDlsqmIFfVeKv48XCFEGhSyu/e5r8bpRZMniFAhYFKLmoCfEG7EdxZfbO2rcXqtfUtrZ1uh2HBhhxthh7vivmKDM6tM00RXVxeqIhHIMktjiKYqIUwYyT3QooUZULHN0GLt0BM7YXNWoe6Sx0uOl2QFSmA2tOjmku26KZDRDKQNGV1SA7ZqddiYqcVuNKITDUjCDyQAYP8zqwDAa1fQUuNBS7UH82u8aKn2oLnaDYeSD8CPrfPB57Dh6Y3diKUMeOw2nNtag2uXzOKKeUREhwH/piUiIhqBpJZFX6E/k8OmWEERANy59nlsS8QQy6Vxeu0cXNu6xNp3wysPI6nnAAAfaz7JCqZU2YYNsS5oZv6XqmhRwBRQh89gGtpXXFoHAH3DwidZyjcAD9ldVr8mAGhw+/GZY5Yh5HAhaHehtej6FwZr8ftzrqx431VOD5Y6Pft/c4joiGTmEoXwqa1QitcOrb8dQktVPN5I98DIRGFzhqxtOd1EvzoDufS7iEsh7DAbsClTi616PTrRgB7UwsTIZlBKAJqCLsyr9mB+jQfzq71oqfGgzufY72p4NV4Hbj1zLq48ZSb6khmEPU74nQpDKSKiw4R/2xIR0RHHFCYGtCzSuoY691APvid2vIsNsS5Ec2nUu3y46agzrH03vPp/2BDrAgCcWTcX/3vqhda+17u3Y+tAHwCgsVA6NyjocFnBVPEsJUmSELA70ZNJAsivfjfIJsvwqw7EtSwUWUbWHFoNyqmo+MjcE+ArhE+Lwg3WPp/qwCvvv8Xq5VTMqzrw/+YeP/I3iYhoP3qevRHZrrdHdY5uCry9/m2sN+ZjU3cSm7sT6Iim4RWLoeFMpDHyENyt2tA8GEDVeDGvsCqe2z62X2+8DgVuVYaaG0CVz8+Zm0REhxGDKSIimvIM04St6JeIddFOrIt1IppNwxQmblh4urXv66v+gke3r4UpBGb7wvjt2VdY+57f044XOtsBAPMDNbgJQ8FUcSlc8QymwX1b0VdxX9Duwq5kP4B8X6diy+qakdRzCDncJQETADz4nsvhUx3wDOvPBAD/csx7Kr4PkiRBqtDgl4hopIxMtGgGVH4WVPU5P4Sslpbmyo59L6wjkJ8JldENxKQIdpoN2JiNoD1Xhy0vZJHG1pLj4whVHqigwe+0ZkG1VHsxv8aDBr8Tsjz+f98ZxoFLA4mIaHwxmCIiokmrJ5PE5nh3oQ9TGh+eswhKoSn2o9vW4udtbyKWSyNtaHj5H2+xApzn9mzGg21vAQBciloSTKmyDLOwklNsWFBU3Oh7eIhU3CB8+L4WfzU0YRRK5CIl+/712GX5se3usjK8fzvuvfu893o3V1MlokNHGDno8Y58+BRtg9bfDj3aBiPTW3as3r8F9urSVTGV4Dxgx/MwTIGMbiIlXNgrNWKLVof16Qh2mvXYiwbk4Cwbb18ciozmKg9aisrwWqo9LKkjIprm+Lc8ERGNWn8ujZxp5P9Z3OdCbzZlraQWGBa+AIBmGuhKJxDNpRDNprEo3GCt7La6bzd+uunviOby4dM9Sy+1VpJ7ee9WfG3Vs9Y45za2orrQ7yhr6tiRjFn7BrSsNWawaHZTWteQNXQ4bPlveaGihtz9Wn5G1WDpW9jhRsDuRNDuQm1hVbpBl8w6Fktr5yBkd6FqWFPvzxTCp0oGe0oREU209Pa/IL3j+fyMqPg2QJgjOk+LtUEJH41t0TQ29ySwqTuJ3t01kGLnYlOuFp1oQj+CwChmbEa8jqJZUPlyvJlB1yGZBUVERJMbgykiIhoVIQQyho5/euZ+CABCN6CoCgAJv3zP5bhr7YvozSZxTesSHFcoT1vTtwfXv/KwNca9Sz+IE6ubAAApXcMrXR3Wvmg2bQVTweGrzGVTVjAVHBaAxXJpK5ganPlkkyQE7C4ktKwVTJ1cPQNmq7CagAsB63ep6xcsxfULlla87+OrGkf3RhERHWamnoYea4cWa4OzYSls7tIZnLne9Uhve+bA44j8LKgkfOhEE159uRvP/vll5IziICsEYPkBx1JtMuaG3WWleAGXOsq7IyKi6YrBFBHREU4IAUOYVomcYZp4bMe6QvlcCksjc7AkMgsA0JdN4aJnf4IfnHYpNvX3IOLywiMpkISAKUwk9Rye2rUBhhA4v2mhFUyFHOUh0qDhAVN0PyvQFe+b7Q3j/KYFCNpdCDpc8KoOa9/y+hacVTsXPrV8JaZF4Yayfk5ERFOJECaMxG6rD9RgTyg9sQsolCqHln4Z7tnvKzlPDTaXjZUzTGQMG/psDdhhNGBjphbrMxF0ogFJFJcUH3h2VditWsHT4CyoWSEXFBsbiRMR0b4xmCIimsbWx/aiO5NANJtGvduPU2tmWvuu+dtvsTsVRyyXxmVzj8ctR50JAJAlCf+z+jkYhV9uPIrDCqb8qgO6OfTLiW6aGFzFWxm2ElysJGAqLX0rDpiqnR4cHapDyO5CyOFCuCjEmuevxgNnXZYPn+wuuGxD/8I+P1CDL594XsX7dioqnOC/xhPR1GdqSWjRTYUAqt1qSC709H7P02JtAIaCqVROR3uuDoYRRCcasEWrx9pkDbYZdehGLcTgX+YjYJMkzA67rR5Q82vyYVTYbR/rbRIR0RGMwRQR0RSQ1jWrPxMAHB0a6lv0ow2v4t3YXvRlUzgqWIvPLzrH2nf7O0+hYyC/Wtw/NM4vCaa6Mgl0ZxIASkMkSZIQdLjRm0mW7VNkG3xFM5P0ov4ksiTBJsmY4Q0hoDpLZkL57Q58+cT35RuAO1xoKGrsXe30YMWZl1W8b7diZ48mIjoiCNOAMHOQldKZoultTyP292+OaizNEOjYvgF/z2zD5p4kNncnsLM/U9j7hVGN5XcoaKkpnQU1J+yGXeEsKCIiGh8MpoiIJkBG1xDXMogUNdh+sbMdq/p2oy+bgtOm4nNFK7Z98e0/46XOLQCABcEIfn7WR619a6N78Hr3dgCw+igNCtld6Ch8XhwwDe7bk4oDAPqGr05nd6E3k0TA7oRtWCncJ+YvRsDuxExvEDZJhs0UsBVCqYjLi9+efUXZ/cqSjPObFo7gnSEimv6MTF9h9tPm/Ip4sTbo8Q74jr4SvmM+UXKsGigvvxskBJA1TMRttehEfkW8NYkabNXrEO2tgtiybcTXJEsSZgSdaKnxFgKo/Mp4NV57WUk0ERHReGIwRUQ0zrYnolgT3YNoLo3+bBo3Ljzd+qH+p5v+jp+1vYG0rsGj2PHXC260znu9ezt+t3UVgPzKccXBVLioFK4vmyp5vXDRCnHRYfuKezRFh4VPZ9U1ozUQQdDhwjxfdcm+H53+Qbhtdtjk8n8Rv2zuCejPpfGX828ABGCYBmyyzVqVj4iI8oSRgx7vyJffFQIoLdYGM9NX8XgturlsmxKcBwDQTYE03Oi1NWGH0YAN6QjWJCPYgzpocJSdtz8euw3zhvWCaq5yw6ny73AiIjr8GEwREe1DSs9hRzKGaDaNvmwKZ9bNgU/Nr/r2alcH7tv4GvoKDcJ/f85VVkD0SlcH7lz7gjXOx1tOsRpzK7KMtK4BAJJ6DjlDh70wy6m49K0/l4YpTMiFvk3FzcNj2TSEEFbYNddXhWPD9QjZXWgsrGY36PoFS/GJ+YsRdrgRLKxYN+gT80/d570P3ue+BArXapomurq6UBWJQK4QYhERHYm0WDuiL38RWnwbIA7cNHzovDbkdBMd0RQ2dSexqTuBzT1JaLFrsTVXhTiCsJYRHaGmQOksqJZqL+r95QtDEBERTRQGU0R0RNBNA9FcGtFsGnUuH/yFkGZ3qh8rNr2R79+US+OzxyzDwmAtAGBl7258+vU/WGM8cNZlVr+jrKFjbbTT2hfNpqxgqngGU35f2gqmhq8yF8ulrXK+sMMNt6Ii5HAjZHchY+hwK/lGsu+tb8Fsbxhhh7tshbuPt5yCj7ecUvG+Z/vCI3+TiIjogEwtBb1/izX7yeaKwHd0aQmz7AxB6996wLEMUyAl+9Frm4EdRj3Wd9fhqR+8bC0+MWTeAcdyqTbMq/JgXo0H8wuzoOZVu+G288d9IiKa3PidioimJMM0YQjTmm2UM3T8ftsaRHNpxLJpnNPQYjX63p3qx8XPrrDO/cqJ5+G8pgUAgIyh49Hta619nekBK5gaHgAVl8KFhoVPfdkUBruAFIdPLkVFQs9aXy8IRHDFvJMRdLgQtrvhUYZWMPrArGNx6ezjKt7vgmAEC4KR/b8pREQ0boQwYSR2Wavg6YUgSk/syjd3KlBD88uCKZszDNkZtkr2BICcaUNcbUInGtGWq8OaRDU2ZiNIwo9Sw0OpcnU+x7BZUB40BVyQZc6CIiKiqYfBFBFNOu/07kJPJoloLoXZ3rAVMAkh8JHnf4GeTAJxLYtPti7Bta1LrPOKy+ca3H7rvMCwsrRoURPw4TOYSsKn4fuKzqt1erE0Mhshhwthhxs1Tq+179hQPR5d/gmE7C44FbVkjJZADVoCNRXvm2UVREQTRxgaku1/yAdQ0TZo/Vsg9PQBz9P7t0KYBqRCj714RsOm7iT67WehO5fF+nQEKweqsceshsDoejjZbTKaq9xWP6j5NR7Mq/bA71QPfDIREdEUwWCKiA6ZeC6DnmwSsWwaiizjuHCDte+utS9gQ38Xork0TqpqKmn0ffs7T6IzNQAAuHDm0VbAJEkSork04lp+BlKsqNG33abAq9qR0HIASkMkt2KHXbYhZxpl5wXsTtS5fQio+YApXDRLqsrpwZ2LL0LI7kLI4UK1w2Ptq3P78Z0lF1e8b6eiol7hLw1ERJONMHXoA9thpHrgrB/WZ0+2Ib7qHggtVfnkCnJwIK7MxBMvrcG6mILN3Ul0JQZnyS4d1bXVeOyYV+MtlOHle0HNDLlg4ywoIiKa5hhMEdEB9efSyBkGTAgYXid6sylrBbbXu7djVd9uRLNpBOzOkoDp8289gTe6dwAAjq9qxI9P/5C1793YXqzq2w0AiBTNNgKAkN1tBVPFAVN+n8taea58nxtZw0DQ7oKjaHU4SZJw69FnwiErCDlcmOursvbJkozHll9d8b5V2YYzaueM7E0iIqLDzsjEINn9JV/bnMHC533Qom3QY5utflB6fweEqUFWPaj74LMlM1UlSYYaaEauZ03Z65gCSNvr0SM3YbtRj/XpCN6OV2GvEYKADOyMj/iaFVnC3Kr8zKfBMryWag9CbvuBTyYiIpqGGEwRHWGEEEjqOcS1DOpdfuuH8tV9u/F693YMaBkk9Ry+dPy51jm92RROfew70E0TsiRhfqAaimTDI8uvwt+7t+Ox7esAAPXu0j4ZYftQH6ZotvRfoIsbhFcKnwbFhu07t7EVsVwKQbsb84eVxP3iPZfDaVMqlsR9eM7x+3xPiIho6tETuzGw7gH4jvo4nEhD7x/AwNqfwNt6GQbW/QzZPa/u81xTS8JIdULx1JdsVwLzkOzbgn77LHSKBmwu9IJak6yCBseorzHkUgsBlNcKoWaH3VBtXMWUiIhoEIMpoilKCIGMoSOuZRCyu6wm4B0DfXhuTxviWgb9uTQ+ddRZCBbK0/6wbS2+sfovMAtNW/9y/vXwFfovrezbjfs2vmaN/7lj32uNCQB6odm4LgQMISAhv/x1cYi03/Bp2L75gRoMaFmEHC7M9IRK9t12zHtwG96DoN0Fn1r6i8An5g8rvSjiYvkcEdG0JYQJScoHOkYmhoF1DyDV/hgyu/4G//E3ofelu5Dd+yaEloS7+aL9BlMAkOzZhO1xDzb3JLGpO4FN3Ul0dC/GgL4EwOjK52RJwuyQq7AinhcthX5QVW47+wcSEREdAIMpokkgZ+iI5dLwqg64C6u09WVT+OP2dYWAKYOPzD0Bzf5qAMBbPTvxqdcesXomrTjzMhwdqgMAdCT6cO+GV6yxP9Z8khVMuRTVCqUAIJ7LWsFUcFij77iWRXUhmLIV/1AtAEMIKIVNVU4Pgg5Xvg+T3Q3dNKAUyujOb1qIReEGhIb1bgKAq+cvxtXzF1d8P2Z6QxW3ExHRkcHI9EGPtQ+tiNffDq1/K2ovfAQ2Zwg2ZxC+o69ELtqO1LankX7io5AAOOpOhnfB5Yivua9kPF0NoV+dhT1oQFu2FqsT1Vj9iAEdK4e98oFnMvkcStksqOYqD+wKZ0ERERGNBYMponGkmwbiWhaKJMNvzwc+OUPHbztWIZ7LB0znNLRYzbz3pgfwwed+hqyhAwC+fOL7cH7TQgD5ErYfrH/ZGntpZLYVTLkUxQqlAKA/l7E+DwwLmIr3+YfNPurX0mhEwNonSxL8qhM+1WFdE5DvtVTj9OQXsDZFyY/tl809AZfNPaHi+7EgGMGCYGSf7xcRER3ZTC0FvX/LUPgUa4fWvwVmJlrxeL1/C2zOkwAAaaUGWHgdujc8A58wIUlA1Ym3IdG1Fjv0WrS5jsO7qRq83V+Fzpyr4nj7IwGYEXShpSiAml/jRcTLWVBERETjicEUUQWmMDGgZWEKgVBROdrDHavQnUkinsvguHC9FSIJIXDuUz+yQqCr5y/GdQtOA5Cf3v+9dS9ZYzS4/VYw5VXsJQFQcYg0fAZTv7a/fUN9mAKFQMynOuC3O2FiaIbUbG8Yn2xdgoDqhN/uQL1rqCfUWXVz8cr7b4Eslf+LryrbUOvyQRcGdE2HIsuwVTiOiIhoJAbefRCptt9DT+wZ1XlarA2O2pOQyupI9e9Cxyt3IWE4kUYVskJFz6sPYs45X8dexz/h209tHPG4btVWWoZX7UFztQcu1Xbgk4mIiOigMJiiac0UJhJaDjnTQLXTY21/ZtcmbE30IZ5Lo94dwOXNJ1r7PvHSr7Eu1gkhgH9onI+vnXSBte/BtrewJ5VfeSdn6lYwJUlSSaATLwqRFNkGj2JHUs+V7XMrdtgkCUahvK44mBrsreRWVPjtzpIgKGR346r5pyKgOhGwO7Eo3GDtm+0N4bV/urViwFTv9uPa1iUV36tKxw+yy/lG5xCAYRqwyTZrVT4iIqJBQggYqb2FWVBt0GPt8C78GNRQS+lxRnbEoZSwB5Gwz0SX1Ih321x49921+Mp7a7F35U/QvXMlgvUnInLKreh643uI7VmF3tU/xdIlt8LnUDCQ1cvGa/A7Cz2gvIVZUB7U+5yQZc6CIiIimggMpmhKEEIgbWhIaFlEXD5r+9+7t2NNdA/iuQzssg03HXWGte/2t5/EU7s2whQCrYEIHnzPR619j21fi9e7twMAFoUbSoIpmyRjsA1TvCgoAvKzkQaDqf5h+4J2p9Xge/g+v90JQ5jwqU44bEMNuiVJwidbl8AuKwjYnVgYGCp7U2UbXn7/LVArhD8uRcUNC5ZWfK/2FzCN1WB5oGma6OrqQlUkAlnmjCkioiOZmY1D628vBFBboPW3Q49tgaklSo6zR04oC6bUYHP5gDYHMq5Z6JMbscOox8ZsBCvjVdjSVVqG3hRIYWPMBn/rFZghAPuCK/Cdt9K45Yz/gm/Dz6G2XoFNMQU1XjtmBl1oqfGgpXqoHM/j4I+/REREkwm/M9Nhl9E19GsZ1Dg9VoiyPrYXr3VvQzyXQVzL4IuL/sHq33DfxtewYvPfoZsm3IqK5y+4yRrrhc52/G7rKgBAyOEuCaYUWbYafRfPUgJg9X8CSkvkgKFSuIr7VCdU2Qa/3QlPoUn5oA/OXoSUnoPf7sRcX1XJvoff+/GKAROAfTYAB7DPc4iIiA63XN96pLc9W2hK3g4j3T2i87RYe8nXQgjE1NnoDSzFbtGItmwEaxLVWNPthiYOPGupP6NDliV89qk+fHbpx/GVP3Xh7Z0xdETD+MKZV+G+V2P46nlN+NXlJ3EWFBER0RTAYIrGLGfoiGtZ+FUH7IXV23YmY3huT5sVMF3XehqqCiV0T+7cgK+ufMZq2v3nc6+x9q3u24N71g+tJHfb0e+Bp1DKZpdt0E0TAJDSNWimYQU2fnUoRIrn0hBCWIFWcR+m/txQD6bBfbIkwW93IlA0BgCc29iKo4J1CNidqCuanQUAdy6+CIokV2x6+qE5i/b5XjFgIiKiyU4IE8bATmj9W6D4mqAG55Xs1/s7kFj/y9EN6qzG7iTw7KrdaOtNoq0nhfaeJBI5HcDFo75Gj92G2WE3spqJ4xv8uPnxHXAoMuaEHOgeyOFf/rwTlxxTh5BbZShFREQ0RTCYmqb6c+l8ACQA+FzozaasnkDDV20zTBNxLQOHTYG7MAuoP5fG4zvWI55Lo1/L4AOzjsP8QA0AYE3fHtz82u+R1jUAwL1LP4gTq5sAANsTMXz/3b9ZY1886xgrfHLYhq0kp2WsfX778NXiMlYwNfx647mh8wJ2JyQJ8CkOBOwuZA0dTiVfKre4ZhacNhUBuxMBu6sktPr00Wfhs8csqxgwndvYus/3lQETERFNdUIImJm+kpXw8h+3QBhZAID3qH9G4PjSYKpi+Z2104OMczZ6bY3YrtdjQyaClf0hbOtSgO0A0Daqa7RJEmaFXJhX48G8Kg/mVef/1Pkc1vfu1ogXHrsNT2/sRiylw2O34dzWGly7ZBa8LNcjIiKaMvhde5rKmQYueXYFdNNATzoByBIkSPjrBTdaQU80m8Klzz2AhJZvyv2FRctx8axjAAAJLYfvrnvRGu/4cKMVTLkV1QqlgNIyuaCjdPZRPJe1Ph8+M6m4D1NAzV+TR7HDb3eWrFR3VLAWn5h/KgJ2FwKqEy5lqEfTB2cfhw/PWVSxr9KpNTOt1e+GY8BERERHAqFnocU2QYttsUIoLdYOMxvb73n6sPI7AFD8syHZnNBdDYgqM7BbNGBztharBqrwbrcDuqgw0AjUeh1oLgRP86rcmFftweywG6pt/70Ma7wO3HrmXFx5ykz0JTMIe5zwOxWGUkRERFMMv3NPY4YwYUCgM5uABAk2SS6ZseRR7FYoBZQGTMNnMJXu23fANFha51JU+FWn1eMJAJo8AVzVcgr8dhcCdidmeoLWvsU1M/HK+2+BUiEwmh+osUKx4SodT0REdKQRpg6hJSE7AiXbtehGdD9z7ajH0/q3YCCjo703ibaeJDb3JNHWm8TWnq9iQDPHdI0euy0fQBXNgGqucsPvVA988j54HQrcqgw1N4Aqn58LcxAREU1BDKaOADbIMJEPiERRUGS3KXAVzX4q7sPkVRyQJQmKJMNvd0IuKnkL2l24Yt7JVn+mE6oarX0Nbj/+9o83Wz2nikVcPtyw8PTK18gfJImIiA5ICAEj1Wk1IM+vhNcOPb4NzsYzET7z6yXHK8P6RFUiOUJIu2ahR2rENr0O69MRrOwKYue9rxzw3EpskoTZYTeaq937LMMbb4ZhHPggIiIimpQYTB0BZEmCCVEIf0p/ILxi3smQJQkB1YWFwYi1XZIkvHDBTXBUCJhU2Yabi1a/K30tGfYDTL0nIiKiAzOzcWj97dBibVYQpfdvgaklKx6v9ZeX38mqG4q3HnpiDyTFBd09E1GlCTuNBmzO1mDVQDXWd6swxNjq8IaX4bXUeDEr5DpgGR4RERHRIAZT05hNkiEgMMcThF1VoUi2shK9q+cv3uf5lUIpIiIiOrRMLYWux/8fjHT3qM7TB3ZCGDlINrtVhre5J4lu2zXYJClY3e3eRxnegUOp4WV4LdUeNFd54HPyZwUiIiI6OPxpYpqyyzY8svwqQACGacAm26xV+YiIiOjwE8KEMbCzMAuqvdBgXCB85jdKjpNVN4QYWWma7Iog5ZyJbqkR2/R6vP7YWmzq1bA3kS06ylv4eODeUMVleC3VXswrlOPVHsIyPCIiIjqyMZiapgZX3jNNE11dXaiKRNgQlIiI6DAQQsDM9BZK74ZWwtP7t0IY2ZJjJZsdwjQgDfuHIzXYjGxnn/W1bPdBc89Gn60RO416bMxGsGqgCpu2S8PK8BIjvs7BMryW6qE+UCzDIyIiosONwRQRERHROIivuge57jXQ+tthZvtHdI4wctAHdkANzLa2DWR07PEvQ7c2H23ZWqxJVmPtXgcSWqVZVCMrw5tXNPtpHsvwiIiIaBLhTyREREREByBMHXp8W37mU3wbfMd+sqy0Lbv3beR61oxsQEmC7GlEyjETL7R1YX1aoK0nifaeZKEMr6bwZ9CBS/tskoQ5VW40WyvhsQyPiIiIJj8GU0REREQFQggYqU5rFbzBFfH0ge0Qpm4d527+JyieupJz1WBzxWBKdoaguWej19aEHUY9NmYiWBUPoW2rWSjDywDYMarrrPM5SpqRswyPiIiIpioGU0RERHTEyvWtR65n7VAQ1d8OoaUOeJ4eay8PpqqOgtSzCTG1CbtFA9qzEaxOVmNdpw3J3PAZTyNrbu61K/kAimV4RERENE3xpxoiIiKa1oSehRbfAsU/G7LiKtmX3PgbpLY+OboBJRm5xF7s6E6grSeJtt4UNvck0d5Ti67ElRVOOHAIpciF1fCKyvBaqr2IeO0swyMiIqJpjcEUERERTQtCmNAHdkDv35LvBRVry39M7AKEier3fh+OupNLzlECzfsd0+auRc49Cz1yvgxvfboGq/sD2PKkDkO8PabrLC7Da6nJf5zJMjwiIiI6QjGYIiIioilFCAEz3ZMPnfqHSvD0/q0QRm6f52mxtrJgSg3OAwDIdj+Ebw5iygzsEg3YnKnBqoEqbNwtKpThaSO6zuIyvJbqoTI8r4M/fhEREREN4k9GRERENKGMTAyS3V/ytc0ZBJAPoYaXsiXe/Tniq+4Z9eto/VvyHw0THX2p/Cp4XUHscPwP1vbZ0bVzeKillw9SQXEZ3mAANa/awzI8IiIiohFgMEVEREQTRk/sxsC6FfAe9XE4pSz0/nYMrP0pvK2XIbHhl3A2nA7PvItKzlH8Mw88sCRB8TYi65qNbqkR2416rNtbjzU/fxPbounCanjF9j3Tqlidz2EFT/OqWYZHREREdLAYTBEREdEBCWFC6BkIPQWhpyH0NEw9XdiWLvmjhlrLSuZyfRvR/9adRcdlEDjpNiTbfo/Euw8is/2v8J/0L+h98S5k974JoSXgbr4Iue5VZdeiDusLZXNWwfTORlRpwi6zAZuyEawaCGPzDr1CGd6BV9wD8mV48wr9n+ZVu1mGR0RERHSI8KcrIiKiaUSYOsxcIh8gGYXQSCuESEYGQisES0amJGByNb0HzsYzSsbK7Poboq99tTDWyGYUAYC39cNlwRSMbFnINLD+F/Afew1yve8i0/kG0k98FBIAR93J8C64HPE190FWnCXn5HQTHZkg9tZdjS25CNYma/Bunw1d27PDrmL415UpsoQ5YbfVjJxleERERESHF4MpIiKiCWakuqAndpXMPjL1VOHrwW2DQVMGZiFc8h71z3A1nVUyVqr9UcTe+Naor8Hmri0LpgAJZjY26rFMrXxWkqS6y7ZpveuQ3P4XeE+8DfE/XgYhAEkCqk68Dam9q5BVI+hUWvDY37fn+0H1JIvK8FoLo+gYaS+oep8TzYXZT/Oq8/2gZgZdUFiGR0RERDRhGEwRERFVIIQJYWQh9DRk1QvJZi/Zn+tdBy26yQqOzGHlbJW3ZxA67T/LAqDU1j8hvureUV+jkews2yYprlGPAwBCz5SPVSFMGtFYRvlYsj0AZ/0SSIoTkuKCpLhgemZBCh2F7c//B+JmGLqQoEFB32sPYvY5X8capxu3P7URQMeoXt/nUIpmQLnRUu1Fc5UbHpbhEREREU06/AmNiIjGZH8rqR0uQgjA1PKziIp6Hwk9A7XqqLIysNS2Z6D1rssHRlphRlJJeVsapjVDaShcqV5+DxyRE0rGSm97FokNvxr1NZtasmybZHOMehxgH2HSKIKpwZBIVpyQVU/Zfpu7Ft4Fl0GyOSEp7qLjXVa4VBw0yYVjIKsVxqpBeNld6E7ksKkngY6+FC5scmHba9/Ftp2bEaw/DXWn3IquN76Hvt2r4F31Uyxdcit8DgUD2cozooaX4bUUekLVsAyPiIiIaMpgMEVERKOWX0ntAfiOvhIulwdmqtP6WvE2lB2fb5xdCI20fEmaWeh9JIzB2UUpuOf+U9nMpPia+6H1rC3MPCotbzP1FCDMitcY+ceHIAfmlmzL7n4Vqa1/GvX9HmwAVDpWhTK3/YwlyWqFACj/0eatLzteDbUieOrnINlckFR3/libA5LqHgqTbM78eNL+S9gUbwMCJ3561PcIAKYpsC2axsbuBDZ1J7CxO4nN3QlE0xoAoCngREv1PPjnX4EZJmBfcAW+81Yat5zxX/Bt+DnU1iuwKaYg4MwHU4NleC3VXqsZOcvwiIiIiKY+BlNERFRGCFEIkRIwtWT+Yy7/0eafhcS6FUhs/A2ynW/Af+p/oG/1PdB61wEA/ItuRP8b30Cub8NQGDXCxtnOpmWwuapKtml9G5HZ89ro70FPl20bz9I0qXg2liTlAyNbIUBS3YXwxwVZLcw0Ksw6Gr6iHAA4G89A9fJ7yoInSXFBkkf3rVrxNkCZd8mo7/FgpDUDbT1JK4Da1JVAW28SWb1yaAgA/Rkdsizhs0/14bNLP46v/KkLf98excauEG5fdiV++FIUXz2/AV89bwFmh1mGR0RERDRd8ac8IqJpRggTQkvC1BL5j7lEydcQJjzzP1h2Xs9zt8JI7ICZyx+7r5lIatXR8B97DbJ7/o7Mjr8iE9sGVbVDrToavqOvhM0ZhJHpq9j/6IDXrqcAlAZTkjJ+ZW6y6oPsCEJSnMPK0YZmJJVsL4RLaqi1bCxPywfhnnshZMUF2A6udMzmqobNVT3m8w+nvlQOG7sS2NSdtGZDbY+mIUY5zkBWx45oGgsjXnzikQ44FBlzQg70ZzR85qlduOSYOtT5HPAykCIiIiKa1vjTHhHRJCJMvRAkJQpBUiofEkGCq+nMsuN7X/w3mJmoNbNp8Jz9kVVPxWDKSHVCT+w54DVqveuQ3vEcAifdhq4/fRQQ+UgicMLNVhnf2Mvcymc5qYG5MGoW5Wce2RyF0MhthUnlAVN+e6WZSf5F18G/6LoxXdtwsuoGxjgDayowTYGd/WkrgBosxetJjmz2WzFFltBc5cH8Gi/m13jQGvGipdoDr0PBmXOrEHareHpjN2KpDLx2Bee21uDaJbMYShEREREdAfgTHxHROBBCAEZuaJaSlgAkGfbwgtLjTB2xv3+jtEROS0IUZikJI1txfMVbXzGYyvWsgZmJjupaTT0FIcyy/kKy6h3R+WrV0XDNeC+iL38JkGxAYa5M/zvfR/j0/4LibYCzfgkUT11h1lEhLFLzM5BkxTXU78jmgKS4h8Ile/k1+I75BHzHfGJU90ijk9PNfClez9BMqLbuJFKaMeqxvHYFLTUeLIh4rSBqTtgNdR+9oGq8Dtx65lxcecpM9CUzCHuc8DsVhlJERERERwj+1EdER7zifkoAYHNHSvabWgqJdx8shE6DgVIhSMoNBUzCLF05TA23InLez0pfTLLlm2/vo0xuX8xc+UpuACArbpgYXTAFISC0VFkI5Gw8A0qwGbLqhaR6IaseSKoHst1b2ObJf1RciK/5MUxtAO55F8N73A1IFHpMDax7AP5FN8K74COjuyY6bPrTWkkZ3qbuJLb2pWCK0RbjAXU+B1pqvGit8aC1Jh9E1fsdoy5r9DoUuFUZam4AVT4/ZJkNzYmIiIiOFAymiGhKE8IETL1sJTczG0d629MlIVI+SEqWzGrKf0xaQZGj7hRUv/fuYS9iYmDditFfWy5Rtk2SJMiqF2YuPqqxTC0BIUTZL/z2yAlQ/DOHQiPVC9nuLfs6HzINBkzuig21RzMryXf0VQAk+I6+EknDg/Dp/2WtymdzBkd1b3RoCCGwJ54tWRVvU1cCexOVZ+XtjyxJmFvlRkt1vgxvfrUXrREP/E51XK/ZMEY/Q4uIiIiIpjYGU0Q0IkYmBsnuL/l6vAMIMxtHtuud8llJWiLfd0lPFfVfGuqn5Jq1HOHTv1o6lpZA7M1vj/oahFY+M0lS3YAkWb2URnw/FcYCADXYDFNLFAVHxaGRZyhEKgmYPBXHCi354qiuabwo3gb4F90Iye5HuqsLvkgd/ItuZCg1QTTDxNa+VElT8s3dSSRy+oFPHsat2jCvMAOqtVCK11zlgV3hLCYiIiIiGn8MpojogPTEbms2jMvlgZnqtL6WnSHo/Vtg5pLlfZMGA6VhX5taEu7Z5yJw4qeHvc5O9L30uVFfX6UwaaT9koYztUqznGTIjlBh3Hxpm6R4yoKj0kAp/7GS6uX3jOnaJhubMwjTNEu+pkMvkdWtEryNhY9bepPQzdGX4tV47IVSvKGm5I1+J2R57CsMEhERERGNBoMpoilksGxNGDkIMwcYGoSZgzA1CCMHGDkIa78GmBqUwByogTkl42j9W5DueNo6ZvB4YeQKXw+N6zvq40hu+h0S63+BXNc78J38OfQV+gkBgHfh5eh+6upR34uR6SvbNuYwqVLJXNFqaZLiyodHdg9kxQNpWGnbUMDkgeysqvga9R/405iujWishBDoSuTys6AGm5J3JbA7nhn1WBKAWSE35keGekHNr/Eg7LYf8FwiIiIiokOJwRTRPgjTKA1/isIe63NDswIde/UxsLlKQ41s1zvI7n2rEBgNhUYwc4XwRysJggbH9h17DVwzlpWMldz8f4i98a1R34d/0fVlwZQe34GBdQ+M6Py4ocF/7DXIda9GtvNNpJ++DqqqQq06Gr6jr4QwRv9LMgCICs28y1Zkk6R8kDQ4G2l4E+5CwGTzNpaPJSuov/RpSKoHkmwb0zUSHS6GKbAtmi/F29idtGZE9We0UY/lUGS0VHtKmpI3V3vgUvn/ARERERFNPgymprHD0RNoPAgh8oFMyYydwoygotk/wtAAMwdH7SmQFEfJGOkdz0OPd+SPHRyjbDy9EDQNfR467XaogbklY8Xe/DaSm38/6lXTwmd9C66mM0u2Zfe+jYE194/6PTGzFVZZk8b2v6swcuVD2UY+S0LrXYf0jucQOOk29Dx7vbU9cMLNULwN0Po7Kp4nSbaimUmesn5KanBe2TmyI4jIBb+0QidJcUGSxt7XRnb4D3wQ0WGWyulo60mVrIrX1pNEzhjd3zkAEHSpmD8YQBWaks8KuViKR0RERERTBoOpaWr/PYGq8jN2Ssq/tKLZQToAAUftSWXjJtsfg5mNFUKi4gCpeNaPXlYaVn3u/WUBQ89fbkaue2Xh9Uau9qI/QFHqSralO55Eesfzo3yXKq+aBkijDqUAAGb5zAZJHtuKVQcbJh14LEfhjx2QVUg2OyRZza/UZrNDkgtf2+xQAs1wzngPoi9/CZLissbof+f7CJ/+X1A8Dag6+3tFDbzzHyXb6JeMlyQZarB5TPdJNBn1JnPY2J0olOPlV8XbEUtj9N2ggBlBl7Uq3mBPqGqPfdT/nxERERERTSYMpqYhIxPDwLoHkGp/DFqsHZ75H0L3W/+LbOcbMJJ74G6+CNGX97+Sl2z3of6Dz5RtT6z7GfTErlFfkzBykBTn8I2jDqUAABWCFow1ABLlS5OPb5ikQpJshbCnEADZ1HwYVPg6//ng9vxHxTejbCw13JpfBc2mArJSOCc/bvH4+X2DYZMdsiNYNpaj9kQ0/L8XRnRfRiaG+KofwkjthaPxDHiPuwGJQo+pgXUPwL/oRjjrTx31+0U0nZimwI5Y2mpGPjgbqi81+lI81SajucpdmAmVD6Baqj3wOPgtm4iIiIimH/6UOw3ZnEH4jr4SWqwdWu86dD3xe0gQcNSeDO+CyxFfc98Bx6gUsgBjn7UDIwcMD6bGHCaVh1mDYVJZUFMS/qjDZggpkO2+srEc9Uvyq6lVOsdmByTF+lySFStMsrlry8bytF4G74KPjOk+h1MDc8vKDg+HwecJAHxHX4mk4UH49P+yZuBNxvJQokMpqxto70lhw7BSvLRWHnQfiN+hoKUQPi2I5JuSzw65oNjGXsJKRERERDSVMJiaphRvAwIn3IyeZ29Afj0mgcBJtyG94zlrNbX9ERXK0gAMhUmSPBQClZSCVQ6FUKHUxD37fbBXH5ufVTTsvJIZRiWvoUDxNpWNFVzyRQSXfGlcSlqc9aeO2wyg6VJio3gb8rO17H6ku7rgi9TBv+hGhlI07cXSGjZ1JwrleEls7k6gI5qGKUZfjFfvc2J+xDPUE6rGi1rf6EteiYiIiIimEwZT05Se2I3+d74PAJDsQciyhPjanyJ8xlfhqD0ZQhvIz/SR1aESsqLwBzY7hBBlvzDVnHt/vlTsIBpSD3LP/ceDHmPQeFwP7Z/NGYRpmiVfE00XpimwO54pKcPb1J1EVyI76rFskoS5Ve6hVfEiXrRUe+B3jm2WKBERERHRdMZgahoa7DGl9a6DWnU0Qmd/3+oJlNz8+4Oa6TLmUj4iokkip5vY2pcqaUq+uTuBZG70pXhu1Yb5ES/mFzUlnxN2w64wLCciIiIiGgkGU9MQewIREeXFMxo29yTzAVR3Epu6E9jSm4IxhlK8iNeBlkIJ3mBT8ga/E7LMUjwiIiIiorFiMDVNsScQER1JhBDYO5DFpu6k1ZR8c3cSu+OZUY8lSxJmh1wlTclbqj0IuTljlIiIiIhovDGYmsbYE4iIpiPdMNERTZc1JY9ny1fsPBCnIqOl2lvUlNyL5io3nKrtEFw5ERERERENx2CKiIgmrVROL5Tg5ZuSb+5OoL03hZxhHvjkYcJuFfNrvCWr4s0IuliKR0REREQ0gSZdMPX1r38dv//977Fhwwa4XC4sXboU3/zmN9Ha2mod8+Mf/xgPPfQQ3n77bQwMDCAajSIYDJaM09fXh1tuuQV//OMfIcsyLr30Unz3u9+F1+s9zHdEREQHIoRATzJnBVAbuxLY3JPEjlh61GNJAGYEXSVNyefXeFDtcYz/hRMRERER0UGZdMHUCy+8gJtuugmnnHIKdF3HF77wBZx77rl499134fF4AACpVArnnXcezjvvPHz+85+vOM7ll1+OPXv24JlnnoGmabjqqqtw7bXX4qGHHjqct0NENO3ZbKMrezNNgW3RNDb1JEqakkfT2qhf226TMa/aU9KUfF61G277pPv2RkREREREFUy6n9yffPLJkq8feOABRCIRvPXWWzjrrLMAAJ/+9KcBAM8//3zFMdavX48nn3wSb7zxBk4++WQAwN13340LLrgA3/72t9HQ0HDIrp+I6EiRyOqIZ3T0aiq0gRz8TgVeR+m3lYxmoK2nMAuqO98LanNPEll99KV4fodSmP001JR8VsgNG0vxiIiIiIimrEkXTA3X398PAAiHwyM+59VXX0UwGLRCKQBYvnw5ZFnG66+/jksuuWTcr5OI6EjSncjix69twzMbuxFLZRB0O3HO/BpccXIT3toRw5s7+7G5O4ntsTRMIUY9foPfme8FFfFgfrUXrREvIl47JIkhFBERERHRdDKpgynTNPHpT38ap59+Oo455pgRn9fZ2YlIJFKyTVEUhMNhdHZ2Vjwnm80im81aX8fjcesaile2m2pM04QQYkrfA00efJ6mB9MUyBkmcoYJzRAlH0u3mcgVfa4ZApphYn6NF79ZtRu/fmcXTAFkcjo6Yjm8ubMfnfEMFs8K4ZmN3SO6FptNwtywG/NrPJhfnV8Zr6XaA5+z/NuTEAJiDCEXTQ38+4XGis8OjRc+SzRWfHZoPE2X52k01z+pg6mbbroJa9euxd/+9rdD/lpf//rX8eUvf7lse3d3NzKZzCF//UPFNE309/dDCAFZlif6cmiKE0IgmUwCAGeujJApBHRTFMKe/Oc5wyx8FNBMAd0QyFkfCwFQ4Rzro2GWbBs8f3A8zRi+TUArGmvwNTRDjGkG0yCfQ8H/XnwcHvj7DgxkdQCAgICE/PPwf6v34OOnzIDTJqz9g9yqDc1hJ5pDTswtfJwZsEO1Ff/dlEU6nkU6PuZLpCmK369orPjs0Hjhs0RjxWeHxtN0eZ4GBgZGfOykDaZuvvlmPP7443jxxRfR1NQ0qnPr6urQ1dVVsk3XdfT19aGurq7iOZ///Odx2223WV/H43HMmDEDNTU18Pv9o7+BScI0TUiShJqamin9UNPES+YMDGR1pE0n3G4nvA4FHvvoml4faqIkBNrH7B/dhGbmP9cGj9HzIU5+X/6YofBo2LHDPi+ZbTR4ftE2w5gMM3ykwh/AZgMO5r9atdeJWFpDImcUwkkBCAn5nFJCImegP62jtc4Pn0MtzILKNyav9zsYaNI+8fsVjRWfHRovfJZorPjs0HiaLs+T0+kc8bGTLpgSQuCWW27BI488gueffx5z5swZ9RinnXYaYrEY3nrrLZx00kkAgOeeew6maWLx4sUVz3E4HHA4ypcSl2V5Sj8MQH5my3S4D5o4lfoJndtag2uWzILPoVghUE4vD232VxpWGvQMnZvbV1A07NzhgVHOmITTXadZDtOf1RFyq/A5FCRyOiRIsMmAy67AqcqocttxVJ0X97eeMNGXSlMQv1/RWPHZofHCZ4nGis8Ojafp8DyN5tonXTB100034aGHHsKjjz4Kn89n9YQKBAJwuVwA8j2kOjs70dbWBgBYs2YNfD4fZs6ciXA4jIULF+K8887DNddcg3vvvReapuHmm2/GZZddxhX5aFop7hWU1Yc+Fn8+fFtOF8johvV51jDyH3Vz6HPDRFY3oOkCFx9bh79s7sHP39wBUxRqhaNZvLmzH7v68/2E7v7b1ol+K6Y9WZJgt0mw22SoNrnwUYJdyX+ulOwr/XzweOucoo92RYYiS6X7FRmqLMOuVHg9mwxDCNx8xmz8YW0nIABN16AqKiABlxxTh4BTnei3i4iIiIiIpohJF0zdc889AIBly5aVbF+xYgWuvPJKAMC9995b0g/qrLPOKjvml7/8JW6++Wacc845kGUZl156Kb73ve8d8uunI49p5oOcnL6PIGgEYVGl/TlDIKMZ1myirG4MOyY/W+hQ8jkU1Psd+O3K3dAKJWlCDE0C+v2aTlx56gz4HEpZP6GpTAKGwhxFKoQ0MlS5ENqUBDyDoY9UFuCUnFMW9JQGRoo8GDJVGMcmQ5Yn19Sra5fMggTg6Y3diOkaPHYbzm2twbVLZsHrmHTfWoiIiIiIaJKadL89jGTFpTvuuAN33HHHfo8Jh8N46KGHxumqpjabbXL1AToUDDMf3Az2EMoOhjeFzwdDI+vzCttyhomMPvR5SQi0nwBJNydDD6FDI+BUEE1p+wydBrI6YikdAefBBVP2sqBHKoQ/lT/f3+yfEQVGw1+vKDBSZQk2WWIvpAOo8Tpw65lzceUpM9GXzCDsccLvVBhKERERERHRqPA3iGkskdURz+jo1VRoA7lD/kujbpTO5hma4TMUGhVvyxkmMpphfV4cJFUuQRv6mCn6PKubMLiE/KjJkgRHoQzMocglnw+GQCG3ioaAE41+B5KaAQkShDBgs9kgSxJ8dhtmh1246tQZACqFPsUzh0pnHw0GRgpDoCnL61DgVmWouQFU+fxTugaeiIiIiIgmBoOpaao7kcW9r3bgT+u7EEtm4XfbcV5rLT5x6gzsiWcQz+ojLCkrDZA065jBoGno2INZgv5IZRsMhwqhkLPoc7siwzH4sThAqrRNGZodlP/aVvjcVmHbYE+ikYUIiayOj58yA4/so59Qvd+JlhrvIX6naDIzDGOiL4GIiIiIiKYoBlPTUCKr48evbcMPXu5AImtACAEpnsO6zgR6klk2qx5msPFzxaBnWABUcf/gvuLQqNK2os8HQyPbJOsbVInXoeCaJbMAsJ8QERERERERjS/+RjkNDWR1PLOpu2J51GRtVq0Wz/ax2WBXhmb7qDYZTtVmrTQ2vOxsJNsqHlMIiiZbU+nJiP2EiIiIiIiI6FDgb5XTUCytIZkzUKlQa3/NqgcDHLsiwanYysrJhj6X4LDZCuGOVBQkDYVKowmQJuOKY1SO/YSIiIiIiIhovDGYmoaCLhUeuw0htwqvQ4FpGlCVfLNqr92G1ogXP/zAcVCLAiRVZjhEI8N+QkRERERERDReGExNQz6HgnPn1+CRtZ1wKYCmC6iKAkjAhUfXIVwIrIiIiIiIiIiIJhLTiWmIzaqJiIiIiIiIaCpgQjFNsVk1EREREREREU12TCmmMTarJiIiIiIiIqLJjEnFEYDNqomIiIiIiIhoMmIwRUREREREREREE4LBFBERERERERERTQgGU0RERERERERENCEYTBERERERERER0YRgMEVERERERERERBOCwRQREREREREREU0IBlNERERERERERDQhGEwREREREREREdGEYDBFREREREREREQTgsEUERERERERERFNCAZTREREREREREQ0IRhMERERERERERHRhGAwRUREREREREREE4LBFBERERERERERTQgGU0RERERERERENCEYTBERERERERER0YRgMEVERERERERERBOCwRQREREREREREU0IBlNERERERERERDQhlIm+gMlKCAEAiMfjE3wlB8c0TQwMDMDpdEKWmUPSweHzRMPxmaDxwmeJxorPDo0XPks0Vnx2aDxNl+dpMEsZzFb2h8HUPgwMDAAAZsyYMcFXQkREREREREQ09QwMDCAQCOz3GEmMJL46Apmmid27d8Pn80GSpIm+nDGLx+OYMWMGduzYAb/fP9GXQ1Mcnycajs8EjRc+SzRWfHZovPBZorHis0Pjabo8T0IIDAwMoKGh4YAzvzhjah9kWUZTU9NEX8a48fv9U/qhpsmFzxMNx2eCxgufJRorPjs0Xvgs0Vjx2aHxNB2epwPNlBo0dQsWiYiIiIiIiIhoSmMwRUREREREREREE4LB1DTncDhw++23w+FwTPSl0DTA54mG4zNB44XPEo0Vnx0aL3yWaKz47NB4OhKfJzY/JyIiIiIiIiKiCcEZU0RERERERERENCEYTBERERERERER0YRgMEVERERERERERBOCwdQE+PrXv45TTjkFPp8PkUgEF198MTZu3FhyTCaTwU033YSqqip4vV5ceuml2Lt3r7V/1apV+MhHPoIZM2bA5XJh4cKF+O53v1syxvPPPw9Jksr+dHZ27vf6hBD4z//8T9TX18PlcmH58uXYvHlzyTFf+9rXsHTpUrjdbgSDwYN7Q+igTPXnqaOjA1dffTXmzJkDl8uF5uZm3H777cjlcuPw7hyZpvozAQAXXnghZs6cCafTifr6evzzP/8zdu/efZDvDI3FdHieBmWzWRx//PGQJAkrV64c2xtCIzIdnpvZs2eXjfuNb3zjIN8ZGovp8DwBwBNPPIHFixfD5XIhFArh4osvHvubQgc01Z+bfY0rSRLeeOONcXiHaDSm+vMEAJs2bcJFF12E6upq+P1+nHHGGfjrX/96kO/MOBF02L3vfe8TK1asEGvXrhUrV64UF1xwgZg5c6ZIJBLWMddff72YMWOG+Mtf/iLefPNNsWTJErF06VJr/09+8hNx6623iueff160t7eLBx98ULhcLnH33Xdbx/z1r38VAMTGjRvFnj17rD+GYez3+r7xjW+IQCAg/vCHP4hVq1aJCy+8UMyZM0ek02nrmP/8z/8Ud955p7jttttEIBAYvzeHRm2qP09//vOfxZVXXimeeuop0d7eLh599FERiUTEZz7zmXF+p44cU/2ZEEKIO++8U7z66quio6NDvPzyy+K0004Tp5122ji+SzRS0+F5GnTrrbeK888/XwAQ77zzzsG/ObRP0+G5mTVrlvjKV75SMm7x9dPhMx2ep4cffliEQiFxzz33iI0bN4p169aJ3/zmN+P4LtFwU/25yWazJePt2bNHfPKTnxRz5swRpmmO87tFBzLVnychhGhpaREXXHCBWLVqldi0aZO48cYbhdvtFnv27BnHd2psGExNAl1dXQKAeOGFF4QQQsRiMaGqqvjd735nHbN+/XoBQLz66qv7HOfGG28UZ599tvX14EMdjUZHfC2maYq6ujrxrW99y9oWi8WEw+EQv/rVr8qOX7FiBYOpSWYqP0+D/ud//kfMmTNnxK9D+zcdnolHH31USJIkcrnciF+LDo2p+jz96U9/EgsWLBDr1q1jMDUBpuJzM2vWLHHXXXeNeFw6fKba86RpmmhsbBT333//iMel8TfVnpvhcrmcqKmpEV/5yldG/Dp06Ey156m7u1sAEC+++KJ1TDweFwDEM888M+LXOlRYyjcJ9Pf3AwDC4TAA4K233oKmaVi+fLl1zIIFCzBz5ky8+uqr+x1ncIxixx9/POrr6/EP//APePnll/d7LVu3bkVnZ2fJawcCASxevHi/r02Tx3R4nvb12jQ2U/2Z6Ovrwy9/+UssXboUqqrud3w69Kbi87R3715cc801ePDBB+F2u0d2ozSupuJzAwDf+MY3UFVVhRNOOAHf+ta3oOv6gW+WDrmp9jy9/fbb2LVrF2RZxgknnID6+nqcf/75WLt27chvmg7aVHtuhnvsscfQ29uLq666ar9j0+Ex1Z6nqqoqtLa24uc//zmSySR0XcePfvQjRCIRnHTSSSO/8UNEmegLONKZpolPf/rTOP3003HMMccAADo7O2G328t6N9XW1u6ztvSVV17Bb37zGzzxxBPWtvr6etx77704+eSTkc1mcf/992PZsmV4/fXXceKJJ1YcZ3D82traEb82TR7T4Xlqa2vD3XffjW9/+9sjumfav6n8THzuc5/D97//faRSKSxZsgSPP/74qO6dxt9UfJ6EELjyyitx/fXX4+STT0ZHR8dYbp0OwlR8bgDg1ltvxYknnohwOIxXXnkFn//857Fnzx7ceeedo34PaPxMxedpy5YtAIA77rgDd955J2bPno3//d//xbJly7Bp0yb+Y9xhMBWfm+F+8pOf4H3vex+amppGdM906EzF50mSJDz77LO4+OKL4fP5IMsyIpEInnzySYRCoTG9D+OJwdQEu+mmm7B27Vr87W9/G/MYa9euxUUXXYTbb78d5557rrW9tbUVra2t1tdLly5Fe3s77rrrLjz44IP45S9/ieuuu87a/+c//xk2m23M10ETb6o/T7t27cJ5552HD33oQ7jmmmvGfA80ZCo/E//6r/+Kq6++Gtu2bcOXv/xlXHHFFXj88cchSdKY74UOzlR8nu6++24MDAzg85///JivmQ7OVHxuAOC2226zPj/uuONgt9tx3XXX4etf/zocDseY74UOzlR8nkzTBAD8x3/8By699FIAwIoVK9DU1ITf/e53JWPSoTEVn5tiO3fuxFNPPYXf/va3Y75+Gj9T8XkSQuCmm25CJBLBSy+9BJfLhfvvvx//9E//hDfeeAP19fVjvpfxwGBqAt188814/PHH8eKLL5Yk33V1dcjlcojFYiWJ6969e1FXV1cyxrvvvotzzjkH1157Lb74xS8e8DVPPfVU63+gCy+8EIsXL7b2NTY2Ys+ePdZrFT+ce/fuxfHHHz+W26TDZKo/T7t378bZZ5+NpUuX4sc//vGI75v2bao/E9XV1aiursb8+fOxcOFCzJgxA6+99hpOO+20Eb8HNH6m6vP03HPP4dVXXy0LEk4++WRcfvnl+NnPfjayN4DGZKo+N5UsXrwYuq6jo6Oj5JcGOnym6vM0uP2oo46y9jscDsydOxfbt28f4d3TWE3V56bYihUrUFVVhQsvvHBE90yHzlR9np577jk8/vjjiEaj8Pv9AIAf/vCHeOaZZ/Czn/0M//7v/z66N2K8TXSTqyORaZripptuEg0NDWLTpk1l+wcbpz388MPWtg0bNpQ1Tlu7dq2IRCLiX//1X0f82suXLxeXXHLJfq+trq5OfPvb37a29ff3s/n5JDYdnqedO3eKlpYWcdlllwld10f8+lTZdHgmhtu2bZsAIP7617+O+FpofEz152nbtm1izZo11p+nnnpKABAPP/yw2LFjx4ivhUZnqj83lfziF78QsiyLvr6+EV8LjY+p/jwNfl3c/DyXy4lIJCJ+9KMfjfhaaHSm+nNTfOycOXO4YvUEm+rP02OPPSZkWRYDAwMl586fP1987WtfG/G1HCoMpibADTfcIAKBgHj++edLloBMpVLWMddff72YOXOmeO6558Sbb75ZtlT6mjVrRE1NjfjYxz5WMkZXV5d1zF133SX+8Ic/iM2bN4s1a9aIT33qU0KWZfHss8/u9/q+8Y1viGAwKB599FGxevVqcdFFF5UtNblt2zbxzjvviC9/+cvC6/WKd955R7zzzjtlDzodelP9edq5c6eYN2+eOOecc8TOnTtLXp/GZqo/E6+99pq4++67xTvvvCM6OjrEX/7yF7F06VLR3NwsMpnMOL9bdCBT/XkabuvWrVyV7zCY6s/NK6+8Iu666y6xcuVK0d7eLn7xi1+ImpoaccUVV4zzO0UjMdWfJyGE+NSnPiUaGxvFU089JTZs2CCuvvpqEYlEGHQeQtPhuRFCiGeffVYAEOvXrx+nd4bGYqo/T93d3aKqqkp84AMfECtXrhQbN24Un/3sZ4WqqmLlypXj/G6NHoOpCQCg4p8VK1ZYx6TTaXHjjTeKUCgk3G63uOSSS0p+Ub/99tsrjjFr1izrmG9+85uiublZOJ1OEQ6HxbJly8Rzzz13wOszTVN86UtfErW1tcLhcIhzzjlHbNy4seSYj3/84xVfn7MZDr+p/jytWLFin/dAYzPVn4nVq1eLs88+W4TDYeFwOMTs2bPF9ddfL3bu3Dku7w+NzlR/noZjMHV4TPXn5q233hKLFy8WgUBAOJ1OsXDhQvHf//3fDMcnyFR/noTIz5D6zGc+IyKRiPD5fGL58uVi7dq1B/3e0L5Nh+dGCCE+8pGPiKVLlx7Ue0EHbzo8T2+88YY499xzRTgcFj6fTyxZskT86U9/Ouj3ZjxIQghRXuBHRERERERERER0aMkTfQFERERERERERHRkYjBFREREREREREQTgsEUERERERERERFNCAZTREREREREREQ0IRhMERERERERERHRhGAwRUREREREREREE4LBFBERERERERERTQgGU0RERERERERENCEYTBERERFNEsuWLYMkSRN9GURERESHjTLRF0BEREQ0HY02YBJCHKIrISIiIpq8GEwRERERHQK333572bbvfOc76O/vr7gPAH7+858jlUod6ksjIiIimjQkwX+eIyIiIjosZs+ejW3btnF2FBEREVEBe0wRERERTRKVekw98MADkCQJDzzwAP74xz9i8eLFcLvdaGxsxJe+9CWYpgkA+NnPfoZFixbB5XJh5syZ+Na3vlXxNYQQ+OlPf4rTTz8dfr8fbrcbJ598Mn76058e8vsjIiIiGo6lfERERERTwCOPPIKnn34aF198MU4//XQ88cQT+OpXvwohBAKBAL761a/ioosuwrJly/B///d/+Ld/+zfU1tbiiiuusMYQQuDyyy/Hr371K7S0tOCjH/0o7HY7nnnmGVx99dV499138e1vf3sC75KIiIiONCzlIyIiIjpMDlTKt2zZMrzwwgsl+x944AFcddVVUFUVL7/8Mk455RQAwMDAAObNm4dEIgG/34+XX34Zc+fOBQDs2LED8+bNQ2trK1avXm2Ndd999+Haa6/FVVddhR/96EdQVRUAkMvl8MEPfhB//OMf8eabb+Kkk046VG8BERERUQmW8hERERFNAR/72MesUAoAfD4f3v/+9yOVSuGGG26wQikAmDFjBs444wy8++670HXd2v79738fHo8HP/jBD6xQCgDsdju+9rWvAQB+9atfHYa7ISIiIspjKR8RERHRFHD88ceXbauvr9/vPsMwsHfvXjQ2NiKVSmHNmjVoaGjAN7/5zbLjNU0DAGzYsGFcr5uIiIhofxhMEREREU0Bfr+/bJuiKAfcNxg4RaNRCCGwa9cufPnLX97n6ySTyfG4XCIiIqIRYTBFREREdAQYDK9OOukkvPnmmxN8NURERER57DFFREREdATw+XxYuHAh1q9fj1gsNtGXQ0RERASAwRQRERHREePWW29FKpXCNddcU7Fkb+vWrejo6Dj8F0ZERERHLJbyERERER0hrrvuOrz22mv42c9+hpdffhnLly9HQ0MD9u7diw0bNuD111/HQw89hNmzZ0/0pRIREdERgsEUERER0RFCkiQ88MADuOCCC3Dffffh8ccfRyKRQCQSQUtLC7797W9j+fLlE32ZREREdASRhBBioi+CiIiIiIiIiIiOPOwxRUREREREREREE4LBFBERERERERERTQgGU0RERERERERENCEYTBERERERERER0YRgMEVERERERERERBOCwRQREREREREREU0IBlNERERERERERDQhGEwREREREREREdGEYDBFREREREREREQTgsEUERERERERERFNCAZTREREREREREQ0IRhMERERERERERHRhGAwRUREREREREREE+L/A7KO6uS0pW14AAAAAElFTkSuQmCC", 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hQ90CxQXtu6C49lxatGhR4LloCy35nUtB7Ozs0KNHDwDyRdC3b9+OtLQ0VK1aFa1atSrWeb8M2uvxySefFHg9Vq1aBUB+PTZu3AhnZ2csXboUfn5+cHR0RKdOnbBs2TI8fPjwpeb8dO43b94sMPfGjRvr5V5UlStXLjR+//59Wbx69ero0KED7t+/j99//10X/+abbwD8u/h5UeR9Q2FBuZ8/fx7iyax7HD9+vND9FfR5iI6OBgA4OjrCxsYm3z5Vq1aV9S0pz7q+mZmZBS68n1fea/XgwYMiHTs+Ph7AkyJrXqXtu/FV8/T0zDduZWVVaLu28JR34fjiXhsiInr19P+JkoiI6Bm0b0rKysrSK3oA/95Irl+/Hp999hmUSiUAoGXLlqhatSoiIiJw6tQpNGvWDPHx8di3bx/MzMzQt29f2X40Gg0AICAgAHXr1i00pyZNmujFzM3NC+x/4cIFjBgxAkqlEgsWLEDXrl3h6ekJCwsLSJKE7777DiNGjMh3hkNhCioAaM+lV69esLS0LHQfjo6Oz3XM9957Dz/99BO2bt2KZcuWwdzcXHdDO3ToUFlOL+u8C6I974LizZs31xUgCpJ3pkiLFi0QFRWFvXv3IjQ0FKdOncLBgwexf/9+zJo1C7/99hvatm1bIrkXRJu7i4uLXmH2aXmLFyUlv7/NuHHjcPDgQXzzzTfo1asX7t27h927d8PKygqDBw8u8r69vb3h4OCAxMREnD9/Hs2bNy9WroV9Bl+Wgsbc8yjK+K9cuTLs7e3x+PFjnDlzRjfDryC5ubn43//+BwB6M+1K03ejISgUhf9b+bPa8yrutSEiolePhSkiInousbGxutfWP3r0CCdPniywb0xMDA4cOIDOnTsDeFK0GTx4MD755BP8+OOPaNasGTZv3ozc3Fz06dMHdnZ2su09PDwAAG+88QZWrlxZoufxyy+/QAiBDz/8UPev53nl9zido6MjTE1NkZ2djTt37qBWrVp6faKiovI9noeHByIjI/Hxxx+jYcOGxc4/rzZt2qBKlSq4desWfv31VzRp0gTHjx+HUqnUe0zqRc67MCYmJgCA1NTUfNvv3LmTb1z7t3377bfx0UcfPdcxzc3N0atXL/Tq1QvAk1kPM2fOxHfffYehQ4cWeMySos3d0dFRb4ZLSbh9+3a+N9TasVWpUiW9to4dO8LX1xfHjh3D1atX8dNPP0GtVuPdd98tcEZSfhQKBTp37oxNmzZh8+bNGD9+/IueRqHc3d0BPPkOSUlJyTdH7cwXbV8tY2NjqFQqpKam5vuo1rP+/rdv3843rr2+ZmZmRSoOKxQKdO3aFRs3bsSmTZvw0UcfFTozbffu3UhOTtZd47xK03djWcdrQ0RU9vBRPiIiei7r16+HWq1GkyZNdI/z5PejLXpoZ1dpDR48GAqFAtu3b0dGRkaBj6oAT9YVAZ7c0OV9VKMkJCYmAgC8vLz02rKysrBz5069uLGxMQIDAwEAP/30U777/fnnn/ONa89Fu+5TSZIkSfco0A8//KB7pO/NN9/Uu6l/kfMujHb/t27dQk5Ojl67di2pp2mvh7ZQVhxOTk5YuHAhAODu3bt4/PhxsfanLbblXa8pr0aNGqFChQq4du0arl69Wqxj5WfTpk35xrXrD2nXx8lLkiR8+OGHAIClS5di7dq1AIAxY8Y89/GnTp0KIyMjXLhwQfc4ZUmrVKmSbqZcfsU9IYQu3qZNG1mbdsyFh4frbXf58mXcu3ev0GNv3rw537j2+jZv3jzfdc/yM3nyZBgZGeHKlSv46quvCuyXnJys+04MDg7Od5ZgafluLOt4bYiIyh4WpoiI6Lloix4FLdCspV1g+D//+Y9sHY9KlSqhffv2SElJwfTp0/H333/D09MTQUFBevuoV68eevbsiXv37qFHjx75zkZKT0/Hli1birzGi1bNmjUBABs2bJDN9snKysKoUaMKnFUxduxYAMBXX32F06dPy9pWrFiBM2fO5Lvd5MmTYWdnh6VLl2LJkiX5FnFu375d4E3zswwePBhKpRJHjx7Fd999ByD/Rc9f9LwL4uXlBR8fHyQlJWHBggWytmPHjuHTTz/Nd7u3334bjRo1wtmzZzFkyJB813p5/Pgxvv32W12B6M6dO1i7dm2+61/t2bMHAGBvb/9cM4Tyo52RFBkZCZVKpddubGyMWbNmQQiB7t2748SJE3p91Go1/vvf/+qNkaL47bffsHXrVllsx44d2LlzJ4yMjHQFqKcNHjwYtra2+OGHHxAfH482bdrkO6vvWWrVqoVly5YBeFLYmj59OpKSkvT6qdXqFzo/Le1Muc8//xyXLl3SxYUQ+OKLL/DXX3/Bzs4Ow4YNk23Xrl07AMCcOXOQnZ2ti0dFRWHQoEHPLHReuHBBV8jUOnHihG5NrgkTJhT5HGrXrq0b9xMnTsTChQv1CprXr19Hu3btcPPmTXh7exc4i6e0fDeWdbw2RERl0Ct7/x8REZV5x44dEwCEqampSExMfGb/+vXrCwBi8eLFsvjWrVt1rxgHID799NMC95GSkiLatm0rAAgTExPRqFEj0adPH9G7d2/RqFEjYWJiIgCI8PBw3TbaV6K3atWqwP0+fvxYeHl5CQDC0dFRvPPOO6Jnz57C2dlZWFtbi3HjxgkAYtCgQXrbDh8+XAAQSqVStG7dWvTr10/Url1bKJVKMWHChAJfOR8aGioqVKggAAhnZ2cRFBQk+vfvL7p06SKqVq0qAIgmTZo887oWRPsKdADCyclJ5OTklNh5F3ZNd+7cKSRJEgBEQECA6N27t2jQoIGQJEl8+umnupyeFh0dLQICAgQAYWlpKZo1ayb69u0revToIQICAoRSqRQAdK+Rv3jxogAgjI2NdeOgT58+ol69egKAkCRJrF27tsjXS/uq+lmzZum1NWzYUAAQ1atXF/379xfvvfee+Pjjj2V9Jk+erDs3Pz8/8fbbb4u+ffuK1q1bCzs7OwFArF69usj5aP8u48ePFwBEo0aNREhIiGjSpInuOEuXLi10H9ptAYidO3cW+dj5Wb9+vbCystJ99gIDA0Xv3r1FSEiIaN++vXBwcNC1TZ8+XbZtUT6DGo1GvPvuuwKAMDIyEm3bthX9+vUT1atXFwCEubm52Ldvn952t27d0l1fT09P0bNnT9GyZUthbm4u2rVrJ5o1ayYAiKNHj8q2a9WqlQAgxo4dKxQKhfDz8xP9+vUTrVq1EgqFQgAQ48aNe6FrtWLFCt13kaOjo+jatavo27evaNKkie6z0aBBA3Hv3r1C91Mavhufx+3bt3W53r59u8B+gwYNEgDEjz/+KIsX9hksbDst7d/06b/1i1wbIiIyHBamiIioyLQ3kb169SpS/+XLlwsAombNmrJ4VlaW7qZWkiRx69atQvejVqvFTz/9JDp16iQqVqwojI2NhaOjo6hdu7YYMmSI+O2332RFmKLefCUkJIhRo0aJqlWrClNTU+Hm5iYGDBggIiMjxY8//lhgYUqj0Yjvv/9e1K9fX5iZmQk7OzvRoUMH8eeff4qNGzcKAKJfv375HvPBgwfik08+EfXr1xfW1tbCxMREVKpUSTRr1kzMmjVLXL58udCcC7Nz507dTeLEiRNL9LyfdU337t0r3njjDWFhYSEsLS1F06ZNxbZt24QQosDClBBPxsK3334r2rRpIxwdHYWRkZFwdnYWAQEBYvTo0eLgwYO6vikpKWL58uWie/fuwsfHR1hZWQlLS0vh6+srBg4cKM6fP/9c16uwm+I7d+6IkJAQ4erqKoyMjAQA4eXlpdfv5MmTon///sLLy0uYmpoKa2tr4evrK9555x2xdu3aIhVwtbSFqdu3b4vt27eLwMBA3Tm2aNFC7Nmz55n72L9/vwAgPDw8RG5ubpGPXZBHjx6JBQsWiKCgIOHi4iJMTEyEubm58PDwEJ06dRJLly4VMTExets9TwHkp59+0hXzjI2NhYeHhxg8eLC4fv16gdtcu3ZN9OjRQ9jb2wtTU1NRvXp18cUXX4icnJwCixV540eOHBFt27YVtra2wtzcXDRs2FCsX7/+eS+PTFRUlJg8ebKoW7eusLW1FSYmJsLNzU1069ZNbNmyRajV6mfuo7R8NxZVaS1MCfH814aIiAxHEqKEXrtDREREGDp0KH788UcsWbIEEydONHQ69JoZMGAAtmzZgrlz52LatGmGTqdUad26NUJDQ3H06NF81+kiIiIiw+AaU0RERM/p6tWrSE9Pl8U0Gg2+//57rF+/HmZmZujXr5+BsqPX1ZUrV7Bt2zZYWVlhxIgRhk6HiIiIqEiK9soRIiIi0lm0aBG2b9+OevXqwd3dHenp6bh27RqioqKgVCqxatUquLq6GjpNek28//77SE9Px/79+5Gbm4uZM2fCwcHB0GkRERERFQkLU0RERM8pODgYKSkpuHDhAv766y/k5ubC2dkZwcHBGD9+PJo2bWroFOk1sm7dOigUCnh4eOCjjz7ClClTDJ0SERERUZFxjSkiIiIiIiIiIjIIrjFFREREREREREQGwcIUEREREREREREZRLlbY0qj0SAmJgbW1taQJMnQ6RARERERERERvVaEEEhNTYWbmxsUisLnRJW7wlRMTAw8PDwMnQYRERERERER0Wvt3r17qFSpUqF9yl1hytraGsCTk7exsTFwNsWj0WiQkJAAJyenZ1YYiYqK44oKwrFBLwPHFRUXxxC9DBxXVFwcQ/QylKdxlZKSAg8PD12NpjDlrjClfXzPxsamXBSmsrKyYGNjU+YHJZUeHFdUEI4Nehk4rqi4OIboZeC4ouLiGKKXoTyOq6IssVQ+zpSIiIiIiIiIiMocFqaIiIiIiIiIiMggWJgiIiIiIiIiIiKDYGGKiIiIiIiIiIgMgoUpIiIiIiIiIiIyiHL3Vr7nIYRAbm4ucnNzDZ1KvjQaDVQqFTIzM8vNivxUPCYmJlAqlYZOg4iIiIiIiKhEvJaFKSEEEhMTERcXB7Vabeh0CiSE0OValFcs0uvBwcEB7u7uHBNERERERERU5r2Whano6GgkJibCzs4OdnZ2MDIyKpU3+UIIaDQaKBSKUpkfvVoajQbp6emIi4sDAFSqVMnAGREREREREREVz2tXmMrNzcXjx4/h4uICZ2dnQ6dTKBam6GmWlpYAgLi4OLi6uvKxPiIiIiIiIirTXruFi1QqFYQQsLKyMnQqRC9EW5zKyckxcCZERERERERExfPaFaa0OAOJyiouhE9ERERERETlBe9wiYiIiIiIiIjIIFiYKuWOHTsGhUKB9evXGzqV11ZUVBQkScLs2bMNnQoRERERERFRucLClAEdO3YMkiQV+HP69OmXduy//voLs2fPRlRUVLHzzPvj7e390nIuyPr167F8+fJXflwiIiIiIiIiKp7X7q18pVG/fv3QqVMnvXi1atVgZ2eHjIwMmJiYlOgx//rrL8yZMwetW7d+ZjGpZs2a2LRpkyz23Xff4fjx41i2bBkqVKigixtiUfn169cjKioK48ePf+XHJiIiIiIiIqIXx8JUKVC/fn0MGDBALy6EgEajgZmZ2TMXaxdCID09/aUUhipWrKiX3+HDh3H8+HG88847zyxspaamwtrausTzIiIiIiIiIqKyrdQ9yjd79my9x8Nq1Khh6LQMJr81prSP1q1fvx7ffPMNatWqBTMzMyxevBgAcPXqVfTu3Rvu7u4wNTWFi4sL2rRpg7179wJ4co2HDBkCAGjTpo3uOg8ePLhYueZdi2nbtm1o0KABzM3N8eGHH+r6HD58GB06dICdnR3MzMzg7++Pb7/9Vm9ff/zxB4KDg1GlShWYm5vDzs4OHTp0QGhoqKyft7c3QkNDcefOHdmYOXbsmK5PZGQk3n33Xbi6usLExATe3t6YPHky0tPT9Y574sQJvPHGGzA3N0fFihUxZswYpKWlFeu6EBEREREREVH+SuWMKT8/Pxw+fFj3u5FRqUyzxGRkZODhw4eymKmp6TNnPy1fvhyPHj3CsGHD4OLiAg8PDzx69AhBQUEAgA8++ABeXl54+PAhzp8/jzNnzqBz587o0aMHYmNj8d1332H69OmoWbMmAKBq1aolcj6///47vvrqK4wcORIffPABbGxsADx5/O+DDz5A06ZNMWPGDFhaWuLQoUMYOXIkbt68iUWLFun2sX79eiQmJmLgwIGoVKkSoqOjsXbtWrRt2xZHjx5FixYtdNdg2rRpePjwIZYtW6bbXntOFy5cQFBQEOzs7DBixAi4u7vj0qVL+Oqrr3Dy5EmEhobC2NgYAHDmzBm0a9cO1tbW+Pjjj2FnZ4etW7di4MCBJXJdiIiIiIiIiEiuVFZ8jIyM4OLiYug0XplZs2Zh1qxZslhwcDB+/vnnQre7e/curl+/DmdnZ11s9+7diI+Px7Zt29CnT598t/P390dgYCC+++47tG/fHq1bty72OeR19epVXL58WVccAoDY2FiMHTsWffv2xU8//aSLjxo1CuPGjcPSpUsxcuRIVKlSBQDw/fffw9LSUrbfDz74AH5+fpg3b56uMPXOO+9g+fLlyMzMzPdxyKFDh8LV1RXnzp2TPU7Ytm1b9OjRA1u2bNHNFJswYQI0Gg1OnjwJX19fXX7NmzcvmQtDRERERERERDKl7lE+4MmjV25ubqhSpQr69++Pu3fvGjqll2r48OE4dOiQ7GfmzJnP3G7gwIGyohQA2NraAgD279+PlJSUl5Lvs3Tu3FlWlAKAHTt2IDs7G++99x4ePnwo++natSs0Go1sllzeolRaWhoePXoEpVKJJk2a4MyZM0XK48qVK7h8+TJCQkKQnZ0tO2bz5s1haWmJP/74AwAQHx+PsLAwvP3227qiFACYmJhgwoQJxbkcREREREREREWiVCoNncIrV+pmTDVp0gTr169H9erVERsbizlz5qBFixb4+++/811AOzs7G9nZ2brftcUYjUYDjUaj11+j0UAIofsxJO3xq1WrhrZt2z6zr7a/9r8+Pj5659CyZUsMHDgQ69evx5YtW9CoUSO0bdsWwcHBqFWrlt6xi3sdiprXtWvXAADt2rUrcF9xcXG67W7evImZM2fi4MGDSEpKkvWTJCnfnAs6Zn4z0rQePHgAIQRu3rwJAKhevbrefrRFttIwZvLmUdAYfxbtZ+BFtqXyjWODXgaOKyoujiF6GTiuqLg4hqikpeeokZqdi0c5RlClZsPa1AiWJmW3SPU8n41SV5h66623dP/b398fTZo0gZeXF7Zv34733ntPr/+8efMwZ84cvXhCQgKysrL04iqVqlg39SVJe/zCvtDyFn20fbT/NTc3z3e7H374ARMnTsSBAwdw4sQJLF26FHPnzsXSpUsxevRo2X5f9Drkt31heWn7r1+/vsDHNKtUqQKNRoO0tDS0atUK6enpGDt2LOrUqQMrKysoFAosWLAAR48ele0/by55aX+fMGEC3nzzzXyPaW9vLzuH/P4WRfk7vUra/xNMTEzUrY/1vNsnJydDCAGFolROmiQD4digl4HjioqLY4heBo4rKi6OISopSqUSucYW+P7MXRy8/gApmTmwMTdBxxoV8X4TTxipMqBWqw2d5nNLTU0tct9SV5h6mp2dHXx9fXHjxo1826dNm4aJEyfqfk9JSYGHhwecnJx0i27nlZmZicTERCgUCoN/gWiPL0lSgblIkqTXpyjb+fv7w9/fH1OmTEFSUhKaNm2K6dOnY8yYMXr7epHroM0r7/aF5eXj4wMAcHJyQocOHQrd99GjRxETE4N169bp3h6opZ35lHf/Tx9fS/tInpGR0TOPqV34PSIiQm8/169fL/C8DEGhUECSJDg4OMDc3Py5t9doNJAkCU5OTqXifKj04Nigl4HjioqLY4heBo4rKi6OISop6TlqfHXiNnZfi0d8mgpKAKamEnZfS4BCqcTY5pXL5MwpMzOzIvct9YWptLQ03Lx5E++++26+7aampjA1NdWLF1Rw0d7Ua38MKW/RKb9c8j42lrdPYdslJibCzs5Odu729vaoXLkyIiMjkZ2dDXNzc91jkY8fPy7WdShqXsHBwZgxYwZmz56NNm3a6BVUkpOTYWZmBlNTU9lbGPPu548//tCtL5U3bmVlhcePH+vF69evj9q1a2PNmjX44IMPdAura+Xm5iIlJQUODg5wcXFB06ZNsWvXLkRGRuqKWjk5OVi+fHmB52UI2jyKU1wt7vZUfnFs0MvAcUXFxTFELwPHFRUXxxCVhLScHBy8Ho/7yVlIy1YDELAwNYaFiRKHIhIwtLEnrM2e/0kZQ3uez0WpK0x99NFH6Nq1K7y8vBATE4NZs2ZBqVSiX79+hk6tTNi4cSOWLVuG7t27o1q1ajA2NkZoaCgOHjyIPn366ApCjRo1gkKhwJdffonHjx/D0tISlStXRpMmTV5KXpUqVcLq1avx/vvvo2bNmnj33Xfh5eWFhIQEXLlyBb///juuXbsGb29vNG/eHC4uLpg0aRKioqJQqVIl/PXXX9i0aRPq1KmDK1euyPbdtGlT/Oc//8GYMWPQrFkzKJVKBAUFwdnZGZs2bUJQUBD8/f0xdOhQ+Pn5ISMjAzdu3MCvv/6KefPm6d7Kt3TpUrRu3RpvvPEGRo8eDTs7O2zduhW5ubkv5ZoQERERERHR6+1heg6uPkhDdq52CRngfnIWqlWwQHqOGslZKrjaFH32UVlU6gpT9+/fR79+/fDo0SM4OTmhefPmOH36NJycnAydWpnQunVrXLx4Ef/5z38QGxsLpVKJypUrY/HixRgzZoyun6enJ3744QcsWLAAI0eOhEqlwqBBg15aYQoAhgwZAl9fXyxevBhr1qxBUlISKlSogOrVq+Pzzz/XrT1lZ2eHgwcPYsqUKfj666+Rm5uLBg0aYN++fVi3bp1eYWrChAm4desWduzYgW+//RYajQZHjx6Fs7MzAgICcPHiRcybNw+7d+/Gt99+C2tra3h7e2Pw4MGyRecDAwNx6NAhTJ06FfPnz4etrS169eqFkSNHok6dOi/tuhAREREREdHr58K9JACAiVKhK0xJEuBibQqFJMHSRAnbMjhb6nlJojS8ZqwEpaSkwNbWFsnJyQWuMRUZGQkfH58XWp/nVdIutq19/JAIKP4Y1mg0iI+Ph7OzM6cdkwzHBr0MHFdUXBxD9DJwXFFxcQxRcQgh8MulWCwJvYkxb3jj9J3H2HD+PoyUElwtjWFlZgJIQPfaLhjbogqsTEvdnKJnelZtJq+yd3ZERERERERERGWQSq3BomM38duVWADAjsuxmNKmGkyMFDh39zHSsnJgaaJEh+pOGN7Uq0wWpZ5X+T9DIiIiIiIiIiIDS8pUYcp/ruFidLIuFpOShQv3kzC7Q3Wk56iRmJ4FB0sz2JgZvRZFKYCFKSIiIiIiIiKil+rGw3RM2n0VMSlZuphCkjC+ZRX0DXCDJEmwNlXCOCcVjtY2r9UjoixMERERERERERG9JKE3H+LTA/8gQ6XWxaxNjTC3U0009bKX9VWr1U9vXu6xMEVEREREREREVMKEEPjx3D2sPhUli3vZm2NpNz942lsYJrFShoUpIiIiIiIiIqISlKVS47NDETgUkSCLB3o74MuONWBtxnKMFq8EEREREREREVEJiU/Nxkd7riI8Pk0WD6nvjnHNq0ChkAyUWenEwhQRERERERERUQn4OzYFH+25hkcZObqYsVKBqUHV0M3PxYCZlV4sTBERERERERERFdO+8Af48nAkctQaXczBwhiLuvjB383GgJmVbixMERERERERERG9II1GYOXJ29h04b4s7utkhSVda8HFxsxAmZUNLEwREREREREREb2A9OxczDhwHSdvJ8ribX2cMKuDL8yNlQbKrOxgYYqIiIiIiIiI6DndS8rEpN1XcTsxQxYf3tQL7zfxhCRxkfOiYGGKiIiIiIiIiOg5nL37GNP2hiMlO1cXMzNSYM6bNRDkU8GAmZU9CkMnQCXr3LlzGDNmDPz8/GBpaQlPT0/06dMHERERen3Dw8PRsWNHWFlZwcHBAe+++y4SEhJkfa5fv44pU6YgICAA1tbWcHV1RefOnXH+/Hm9/c2ePRuSJOn9mJkV/XlajUaDhQsXonLlyjAzM4O/vz9+/vlnvX6DBw/O91g1atQo8rGIiIiIiIiInocQAtv/isGHv/0tK0q5WJtiXXAAi1IvgDOmypkFCxbg5MmT6N27N/z9/REXF4eVK1eifv36OH36NGrXrg0AuH//Plq2bAlbW1vMnTsXaWlpWLx4Ma5cuYKzZ8/CxMQEALB27VqsW7cOPXv2xKhRo5CcnIw1a9agadOmOHDgANq1a6eXw+rVq2FlZaX7Xaks+jO1M2bMwPz58zFs2DA0atQIu3btQkhICCRJQt++fWV9TU1NsXbtWlnM1ta2yMciIiIiIiIiKiqVWoNFx27ityuxsri/mw0WdakFBwsTA2VWtrEwVc5MnDgRP/30k66wBADBwcGoU6cO5s+fj82bNwMA5s6di/T0dFy4cAGenp4AgMaNG6N9+/ZYv349hg8fDgDo168fZs+eLSs0DR06FDVr1sTs2bPzLUz16tULFSo8f5U4OjoaS5YswejRo7Fy5UoAwPvvv49WrVph8uTJ6N27t6zIZWRkhAEDBjz3cYiIiIiIiIieR1KmClP+cw0Xo5Nl8a61XDA1qBpMjPhA2ovilStBadm5iE3JQviDVMSmZCEtz7S+V6VZs2ayohQA+Pj4wM/PD+Hh4brYzp070aVLF11RCgDatWsHX19fbN++XRdr0KCBrCgFAI6OjmjRooVsf3kJIZCSkgIhxHPlvmvXLqhUKowaNUoXkyQJI0eOxP379xEWFqa3jVqtRkpKynMdh4iIiIiIiKiobjxMx6CfL8qKUgpJwsRWVfFJex8WpYqJV6+EJKRlY8XxW+i3+QIG/nwR/TZfwFfHbyEhLdvQqUEIgQcPHuhmMUVHRyM+Ph4NGzbU69u4cWNcvHjxmfuMi4srcFZUlSpVYGtrC2trawwYMAAPHjwoUp4XL16EpaUlatasqZeTtj2vjIwM2NjYwNbWFg4ODhg9ejTS0tKKdCwiIiIiIiKiZwm9+RDvbfsLMSlZupi1qRFWvFMb/eq58817JYCP8v2/tOxc3HiY/kLb2psb44ez97DjcowulpGjxpb/RSNHLTCkkQceZ6qee79VHS1gYVz82uGWLVsQHR2Nzz77DAAQG/vkeVhXV1e9vq6urkhMTER2djZMTU3z3d/x48cRFhaGmTNnyuL29vYYM2YMAgMDYWpqiuPHj+Obb77B2bNncf78edjY2BSaZ2xsLCpWrKj3wdbmGRMTI4tNmTIF9evXh0ajwYEDB7Bq1SpcunQJx44dg5ERhzYRERERERG9GCEEfjx3D6tPRcniXvbmWNrND572FoZJrBzi3fv/u/EwHcN+ufTc21mbGmFBl5r47vQdpObz6N6asDvoXMsZH/8nPN/2wnzXyx/+rtbPnVNe169fx+jRoxEYGIhBgwYBADIzMwEg38KT9g16mZmZ+bbHx8cjJCQElStXxpQpU2Rt48aNk/3es2dPNG7cGP3798eqVaswderUQnMt6Jh5c9KaN2+erE/fvn3h6+uLGTNmYMeOHXoLpRMREREREREVRZZKjc8OReBQhPyt9YHeDviyYw1Ym7GUUpL4KF8x2ZoZ4XGGqsCiU2p2LpIycmFrgIEbFxeHzp07w9bWFjt27NAtHG5ubg4AyM7Wf8wwKytL1iev9PR0dOnSBampqdi1a5fe2lP5CQkJgYuLCw4fPizLK++PtuBkbm7+3DnlNWHCBCgUCtmxiIiIiIiIiIoqPjUbw3+5pFeUCqnvjuXd/FiUeglYmCqm5Kxc2FsYw9o0/8FpbWoEOwsjJGe92oXQk5OT8dZbbyEpKQkHDhyAm5ubrk37aJz2kb68YmNj4eDgoDdzKScnBz169MDly5exa9cu1K5du8i5eHh4IDExUXb8vD/btm3TxePi4vQWTdfmmfcc8mNubg5HR0fZsYiIiIiIiIiK4kpsCgb+fBHh8f+uXWysVODT9r6Y0LIqFAquJ/UysNT3/6pVsMT3veu+0Lb25sYYEeiFXy7F6LX1rusGZ0tTLO3m99z7rer4Ys+sZmVloWvXroiIiMDhw4dRq1YtWbu7uzucnJxw/vx5vW3Pnj2LgIAAWUyj0WDgwIE4cuQItm/fjlatWhU5FyEEoqKiUK9ePV3s0KFDsj5+fk+uTUBAANauXYvw8HBZzmfOnNG1FyY1NRUPHz6Ek5NTkfMjIiIiIiIi2hf+AF8ejkSOWqOLOVgYY1EXP/i7Fb5eMhUPC1P/z8rUCAHuti+8/eg3vGGilPBHRALSc9SwNFGig68Thjf1QgUrU3i9wD6FENBoNM/umIdarUZwcDDCwsKwa9cuBAYG5tuvZ8+e2LBhA+7duwcPDw8AwJEjRxAREYEJEybI+n744YfYtm0b1qxZgx49ehR47ISEBL2i0OrVq5GQkICOHTvqYu3atct3+7fffhsTJkzAqlWrsHLlSgBPrsG3334Ld3d3NGvWDMCTwptKpYK1tXz9rc8//xxCCNmxiIiIiIiIiAqi0QisPHkbmy7cl8V9naywpGstuNiYGSiz1wcLUyXEycoUY1tUwZDGnkjOUsHW7MnjfVYFPOL3skyaNAm7d+9G165dkZiYiM2bN8vaBwwYAACYPn06fvnlF7Rp0wbjxo1DWloaFi1ahDp16mDIkCG6/suXL8eqVasQGBgICwsLvf11794dlpaWAAAvLy8EBwejTp06MDMzw4kTJ7B161YEBARgxIgRz8y9UqVKGD9+PBYtWgSVSoVGjRrh999/x/Hjx7FlyxbdGllxcXGoV68e+vXrhxo1agAADh48iH379qFjx454++23X/wCEhERERER0WshPTsXMw5cx8nb8uVg2vo4YVYHX5gbKw2U2euFhakSZPX/hShXA1ZU//rrLwDAnj17sGfPHr12bWHKw8MDoaGhmDhxIqZOnQoTExN07twZS5Yska0vpd1fWFgYwsLC9PZ3+/ZtXWGqf//+OHXqFHbu3ImsrCx4eXlhypQpmDFjBiwsivZY4vz582Fvb481a9Zg/fr18PHxwebNmxESEqLrY2dnhy5duuDQoUPYsGED1Go1qlWrhrlz5+Kjjz6CQsGl04iIiIiIiKhg95IyMWn3VdxOzJDFhzf1wvtNPCFJXE/qVZHE0ytNl3EpKSmwtbVFcnIybGz0nwPNzMxEZGQkfHx8nvmWN0PTPsqnUCj4oSCd4o5hjUaD+Ph4ODs7s4hHMhwb9DJwXFFxcQzRy8BxRcXFMVS2nb37GNP2hiMl+9+XlJkZKTDnzRoI8qlgsLzK07h6Vm0mL86YIiIiIiIiIqJyTwiBXy7FYknoTWjyzNFxsTbFkm5+8HWyMmB2ry8WpoiIiIiIiIioXFOpNVh07CZ+uxIri9d1s8XCLjXhYGFioMyIhSkiIiIiIiIiKrceZ+Tg473huBidLIt3reWCqUHVYGJUth+bK+tYmCIiIiIiIiKicunGw3RM2n0VMSlZuphCkjC+ZRX0DXDjes6lAAtTRERERERERFTuhN58iE8P/IMMlVoXszY1wtxONdHUy96AmVFeLEwRERERERERUbkhhMCP5+5h9akoWdzL3hxLu/nB097CMIlRvliYIiIiIiIiIqJyIUulxmeHInAoIkEWD/R2wJcda8DajGWQ0oZ/ESIiIiIiIiIq8+JTs/HRnqsIj0+TxUPqu2Nc8ypQKLieVGnEwhQRERERERERlWlXYlMwec81PMrI0cWMlQpMC6qGrn4uBsyMnoWFKSIiIiIiIiIqs/aFP8CXhyORo9boYg4WxljUxQ/+bjYGzIyKgoUpIiIiIiIiIipzNBqBlSdvY9OF+7K4r5MVlnStBRcbMwNlRs+DhSkiIiIiIiIiKlPSs3Mx48B1nLydKIu39XHCrA6+MDdWGigzel4KQydAJevcuXMYM2YM/Pz8YGlpCU9PT/Tp0wcRERF6fcPDw9GxY0dYWVnBwcEB7777LhIS5G8uuH79OqZMmYKAgABYW1vD1dUVnTt3xvnz5/X2N3v2bEiSpPdjZlb0KrVGo8HChQtRuXJlmJmZwd/fHz///HOBfVevXo2AgACYm5vD0dERQUFBuHTpUpGPR0RERERERGXLvaRMDNn2l15RanhTL8zrVINFqTKGM6bKmQULFuDkyZPo3bs3/P39ERcXh5UrV6J+/fo4ffo0ateuDQC4f/8+WrZsCVtbW8ydOxdpaWlYvHgxrly5grNnz8LExAQAsHbtWqxbtw49e/bEqFGjkJycjDVr1qBp06Y4cOAA2rVrp5fD6tWrYWVlpftdqSz6l8KMGTMwf/58DBs2DI0aNcKuXbsQEhICSZLQt29fWd+hQ4diy5YtGDhwIMaMGYP09HRcvHgR8fHxL3LpiIiIiIiIqJQ7e/cxpu0NR0p2ri5mZqTAnDdrIMinggEzoxfFwlQ5M3HiRPz000+6whIABAcHo06dOpg/fz42b94MAJg7dy7S09Nx4cIFeHp6AgAaN26M9u3bY/369Rg+fDgAoF+/fpg9e7as0DR06FDUrFkTs2fPzrcw1atXL1So8PxfCNHR0ViyZAlGjx6NlStXAgDef/99tGrVCpMnT0bv3r11Ra7t27djw4YN+PXXX9G9e/fnPhYRERERERGVHUII/HIpFktCb0IjhC7uYm2KJd384OtkVcjWVJrxUb4SpM5KKvT3V6FZs2ayohQA+Pj4wM/PD+Hh4brYzp070aVLF11RCgDatWsHX19fbN++XRdr0KCBrCgFAI6OjmjRooVsf3kJIZCSkgKR58uiKHbt2gWVSoVRo0bpYpIkYeTIkbh//z7CwsJ08aVLl6Jx48bo3r07NBoN0tPTn+tYREREREREVDao1BrM++8NLDp2Q1aUqutmiw396rEoVcaxMFVCctNikHJpFXLTYvL93ZCEEHjw4IFuFlN0dDTi4+PRsGFDvb6NGzfGxYsXn7nPuLi4AmdFValSBba2trC2tsaAAQPw4MGDIuV58eJFWFpaombNmno5adsBICUlBWfPnkWjRo0wffp02NrawsrKClWqVJEV1YiIiIiIiKhse5yRg9G/XsFvV2Jl8a61XLCqRx04WJgUsCWVFXyU7ym56XFQp8c91zYKc0ekXV2PjFt7oUq6Cdt6Y5B8cSVUCZcg1DmwrvMeNJmPirw/paULjCxdnjf1Am3ZsgXR0dH47LPPAACxsU8+0K6urnp9XV1dkZiYiOzsbJiamua7v+PHjyMsLAwzZ86Uxe3t7TFmzBgEBgbC1NQUx48fxzfffIOzZ8/i/PnzsLGxKTTP2NhYVKxYEZIk6eUEADExT4p8N2/ehBACW7duhZGRERYuXAhbW1usWLECffv2hY2NDTp27FiEK0NERERERESlVWRCGj7acw0xKVm6mEKSML5lFfQNcNO7d6SyiYWpp2Tc+g9Sr6x9rm2MHf1g1/hjqJKjoHp0FQ8PjwQAGNlVhYV3Rzw+NRuqR1eLvD8b/xGwrj3kuXIoyPXr1zF69GgEBgZi0KBBAIDMzEwAyLfwpH2DXmZmZr7t8fHxCAkJQeXKlTFlyhRZ27hx42S/9+zZE40bN0b//v2xatUqTJ06tdBcCzpm3pwAIC0tDQDw6NEjnD59Gk2aNAEAdOvWDZUrV8YXX3zBwhQREREREVEZFnrzIT458A8yVWpdzNrUCHM71URTL3sDZkYljY/ylQDVo6sQqnTY1hsji9vWG4vMe/99rqJUSYqLi0Pnzp1ha2uLHTt26BYONzc3BwBkZ2frbZOVlSXrk1d6ejq6dOmC1NRU7Nq1S2/tqfyEhITAxcUFhw8fluWV90dbcDI3Ny9STtr/Vq5cWVeUAgArKyt07doVZ8+eRW5urt5+iIiIiIiIqHQTQuCHs3fx0Z5rsqKUl7051vcNYFGqHGJhqgQYO/pBMrZE8sWVsnjyxa9g7hEEY0e/V55TcnIy3nrrLSQlJeHAgQNwc3PTtWkfjdM+0pdXbGwsHBwc9GYu5eTkoEePHrh8+TJ27dqF2rVrFzkXDw8PJCYmyo6f92fbtm26eFxcnN6i6do8teeg/W/FihX1juXs7AyVSsXF0ImIiIiIiMqYLJUaM/Zfx+pTUbJ4oLcDfgyuB097C8MkRi8VH+V7ikWVLjCtqL8oeGG0a0ypHl2FsaOfbI2pjKgDsG82+7nXmCqOrKwsdO3aFRERETh8+DBq1aola3d3d4eTkxPOnz+vt+3Zs2cREBAgi2k0GgwcOBBHjhzB9u3b0apVqyLnIoRAVFQU6tWrp4sdOnRI1sfP70nhLiAgAGvXrkV4eLgs5zNnzujagSeFKRcXF0RHR+sdLyYmBmZmZrC2ti5yjkRERERERGRY8anZ+GjPVYTHp8niIfXdMa55FSgUXE+qvGJh6ilGL7jwuHXt9wBJCWu/wTCycoPDG58j9ep63e+w9ngJ2epTq9UIDg5GWFgYdu3ahcDAwHz79ezZExs2bMC9e/fg4fEktyNHjiAiIgITJkyQ9f3www+xbds2rFmzBj169Cjw2AkJCXBycpLFVq9ejYSEBNmaT+3atct3+7fffhsTJkzAqlWrsHLlk9lnQgh8++23cHd3R7NmzXR9g4ODsWLFChw6dAjt27cHADx8+BC7du1CUFAQFApOBiQiIiIiIioLrsSmYPKea3iUkaOLGSsVmBZUDV39Su7FYFQ6sTBVQoys3GBTdxSUZnb5/v6qTJo0Cbt370bXrl2RmJiIzZs3y9oHDBgAAJg+fTp++eUXtGnTBuPGjUNaWhoWLVqEOnXqYMiQfxdeX758OVatWoXAwEBYWFjo7a979+6wtLQEAHh5eSE4OBh16tSBmZkZTpw4ga1btyIgIAAjRox4Zu6VKlXC+PHjsWjRIqhUKjRq1Ai///47jh8/ji1btujWyAKAadOmYfv27ejZsycmTpwIW1tbfPvtt1CpVJg7d+4LXz8iIiIiIiJ6dfaFP8CXhyORo9boYg4WxljUxQ/+boW/2Z3KBxamStDTRahXXZQCgL/++gsAsGfPHuzZs0evXVuY8vDwQGhoKCZOnIipU6fCxMQEnTt3xpIlS2TrS2n3FxYWhrCwML393b59W1eY6t+/P06dOoWdO3ciKysLXl5emDJlCmbMmAELi6I9Czx//nzY29tjzZo1WL9+PXx8fLB582aEhITI+lWsWBEnTpzARx99hGXLlkGlUiEwMBCbN29G3bp1i3QsIiIiIiIiMgyNRmDlydvYdOG+LO7rZIUlXWvBxcbMQJnRqyaJp1eaLuNSUlJga2uL5ORk2NjoV1czMzMRGRkJHx+ffN88V5oIIaDRaKBQKCBJfJ6WnijuGNZoNIiPj4ezszMfeSQZjg16GTiuqLg4huhl4Lii4uIYKp607FzM2H8dp6ISZfG2Pk6Y1cEX5sbKArYs38rTuHpWbSYvzpgiIiIiIiIiolfiXlImJu2+ituJGbL48KZeeL+JJydlvIZYmCIiIiIiIiKil+7s3ceYtjccKdm5upiZkQJz3qyBIJ8KBsyMDImFKSIiIiIiIiJ6aYQQ+OVSLJaE3oQmz2pCLtamWNLND75OVgbMjgyNhSkiIiIiIiIieilUag0WHbuJ367EyuJ13WyxsEtNOFiYGCgzKi1YmCIiIiIiIiKiEvc4Iwcf7w3HxehkWbxrLRdMDaoGE6OyvcA3lQwWpoiIiIiIiIioREUmpOGjPdcQk5KliykkCeNbVkHfADcuck46LEwRERERERERUYk5duMhPj34DzJVal3M2tQIczvVRFMvewNmRqURC1NEREREREREVGxCCPx47h5Wn4qSxb3szbG0mx887S0MkxiVaixMEREREREREVGxZKnU+OxQBA5FJMjigd4O+LJjDVibsfxA+ePIICIiIiIiIqIXFp+ajY/2XEV4fJosHlLfHeOaV4FCwfWkqGAsTBERERERERHRC7kSm4LJe67hUUaOLmasVGBaUDV09XMxYGZUVrAwRURERERERETPbV/4A3x5OBI5ao0u5mBhjEVd/ODvZmPAzKgsURg6ASpZ586dw5gxY+Dn5wdLS0t4enqiT58+iIiI0OsbHh6Ojh07wsrKCg4ODnj33XeRkCB/Hvj69euYMmUKAgICYG1tDVdXV3Tu3Bnnz5/X29/s2bMhSZLej5mZWZHz12g0WLhwISpXrgwzMzP4+/vj559/1uuX33G0P+3bty/y8YiIiIiIiOj5aDQCXx2/hVkH/5EVpXydrLChbz0Wpei5cMZUObNgwQKcPHkSvXv3hr+/P+Li4rBy5UrUr18fp0+fRu3atQEA9+/fR8uWLWFra4u5c+ciLS0NixcvxpUrV3D27FmYmJgAANauXYt169ahZ8+eGDVqFJKTk7FmzRo0bdoUBw4cQLt27fRyWL16NaysrHS/K5XKIuc/Y8YMzJ8/H8OGDUOjRo2wa9cuhISEQJIk9O3bV9dv06ZNetueP38eK1asQIcOHYp8PCIiIiIiIiq6tOxczNh/HaeiEmXxtj5OmNXBF+bGRb//IwJYmCp3Jk6ciJ9++klXWAKA4OBg1KlTB/Pnz8fmzZsBAHPnzkV6ejouXLgAT09PAEDjxo3Rvn17rF+/HsOHDwcA9OvXD7Nnz5YVmoYOHYqaNWti9uzZ+RamevXqhQoVKjx37tHR0ViyZAlGjx6NlStXAgDef/99tGrVCpMnT0bv3r11Ra4BAwbobX/s2DFIkoR+/fo997GJiIiIiIiocPeSMjFp91XcTsyQxYc39cL7TTwhSVzknJ4fH+UrZ5o1ayYrSgGAj48P/Pz8EB4erovt3LkTXbp00RWlAKBdu3bw9fXF9u3bdbEGDRrIilIA4OjoiBYtWsj2l5cQAikpKRBCPFfuu3btgkqlwqhRo3QxSZIwcuRI3L9/H2FhYQVum52djZ07d6JVq1aoVKnScx2XiIiIiIiICnf27mMM/vmirChlZqTAgs61MKypF4tS9MJYmCohyTmZSMhK0/tJzsk0dGoQQuDBgwe6WUzR0dGIj49Hw4YN9fo2btwYFy9efOY+4+LiCpwVVaVKFdja2sLa2hoDBgzAgwcPipTnxYsXYWlpiZo1a+rlpG0vyL59+5CUlIT+/fsX6VhERERERET0bEIIbP8rBh/+9jdSsnN1cRdrU6wLDkCQz/M/LUOUFx/le0pcRgriMlMBPJmtU9fBTdcWnZ6MhKw0AIBSUqCOg6uuLSNXhc5/fA+10ECSJJgrjaGUFPit3RDcTn2E5JwsAIC5kTGq2zrrtruZ8hCpqmwAgJWxKarZlPyHesuWLYiOjsZnn30GAIiNjQUAuLq66vV1dXVFYmIisrOzYWpqmu/+jh8/jrCwMMycOVMWt7e3x5gxYxAYGAhTU1McP34c33zzDc6ePYvz58/DxqbwBfBiY2NRsWJFvUq7Ns+YmJhCz9HU1BS9evUq9BhERERERERUNCq1BouO3cRvV2Jl8bputljYpSYcLEwK2JKo6FiYesrue9ew9p/TAABjhRInu3yoa9t++y/8fOvJrB07E3P80XGEri09Nwe3UhOhFhoYK5WoZl0BwJO3E3x//TQOx0YCAHxtnbC51b+zepZf/RNnEu4CAOo5umPNG71L9HyuX7+O0aNHIzAwEIMGDQIAZGY+mcWVX+FJ+wa9zMzMfNvj4+MREhKCypUrY8qUKbK2cePGyX7v2bMnGjdujP79+2PVqlWYOnVqobkWdMy8OeUnJSUFe/fuRadOnWBnZ1foMYiIiIiIiOjZHmfk4OO94bgYnSyLd/NzwdSgajBW8gEsKhkcSeVYXFwcOnfuDFtbW+zYsUO3cLi5uTmAJ+syPS0rK0vWJ6/09HR06dIFqamp2LVrl97aU/kJCQmBi4sLDh8+LMsr74+24GRubv7cOQFP1svKysriY3xEREREREQlIDIhDYO3/iUrSikkCRNbVcXMdj4sSlGJ4mgqp5KTk/HWW28hKSkJBw4cgJvbv48kah+N0z7Sl1dsbCwcHBz0Zi7l5OSgR48euHz5Mnbt2oXatWsXORcPDw8kJv77KlFXV1fZz7Zt23TxuLg4vUXTtXnmPYe8tmzZAltbW3Tp0qXIOREREREREZG+Yzce4r3tlxCTkqWLWZsaYcU7tdGvnjsXOacSx0f5ntLNoxYaV/AAAL0PXJ/KAWjjWg3AkzWm8rI0MkEVawfdGlNKSdL1GVajKfpUCQDwZI2pvMb7tZStMVUSsrKy0LVrV0RERODw4cOoVauWrN3d3R1OTk44f/683rZnz55FQECALKbRaDBw4EAcOXIE27dvR6tWrYqcixACUVFRqFevni526NAhWR8/Pz8AQEBAANauXYvw8HBZzmfOnNG1Py02NhZHjx7F4MGDC1wTi4iIiIiIiAonhMAPZ+/h27AoWdzL3hxLu/nB097CMIlRucfC1FNcLGzgYpH/It3ulrZwt7TNt83CyBgH86w5pWWiUKKytWOBx6tawoudq9VqBAcHIywsDLt27UJgYGC+/Xr27IkNGzbg3r178PB4Uog7cuQIIiIiMGHCBFnfDz/8ENu2bcOaNWvQo0ePAo+dkJAAJycnWWz16tVISEhAx44ddbF27drlu/3bb7+NCRMmYNWqVVi5ciWAJ1+O3377Ldzd3dGsWTO9bbZu3QqNRsPH+IiIiIiIiF5QlkqNzw5F4FBEgiwe6O2ALzvWgLUZSwf08nB0lRBbk/zXP3rVJk2ahN27d6Nr165ITEzE5s2bZe0DBgwAAEyfPh2//PIL2rRpg3HjxiEtLQ2LFi1CnTp1MGTIEF3/5cuXY9WqVQgMDISFhYXe/rp37w5LS0sAgJeXF4KDg1GnTh2YmZnhxIkT2Lp1KwICAjBihH7R7mmVKlXC+PHjsWjRIqhUKjRq1Ai///47jh8/ji1btujWyMpry5YtcHNzQ+vWrZ/3UhEREREREb324lOz8dGeqwiPT5PFQ+q7Y1zzKlAo+OgevVwsTJUzf/31FwBgz5492LNnj167tjDl4eGB0NBQTJw4EVOnToWJiQk6d+6MJUuWyB6J0+4vLCwMYWFhevu7ffu2rjDVv39/nDp1SrcYuZeXF6ZMmYIZM2bAwqJo0z7nz58Pe3t7rFmzBuvXr4ePjw82b96MkJAQvb7//PMPLly4gIkTJ0Kh4HJpREREREREz+NKbAom77mGRxk5upixUoFpQdXQ1c/FgJnR60QST680XcalpKTA1tYWycnJsLHRfyQvMzMTkZGR8PHxKfAtb6WFEAIajQYKhYILzJFOccewRqNBfHw8nJ2dWdAjGY4Nehk4rqi4OIboZeC4ouIqD2NoX/gDfHk4EjlqjS7mYGGMRV384O+W//I29HKVh3Gl9azaTF6cMUVERERERET0mtBoBFaevI1NF+7L4r5OVljStRZcbMwMlBm9rliYIiIiIiIiInoNpGXnYsb+6zgVlSiLt/VxwqwOvjA31l/Xl+hlY2GKiIiIiIiIqJy7l5SJSbuv4nZihiw+vKkX3m/iyeVjyGBYmCIiIiIiIiIqx87efYxpe8ORkp2ri5kZKTDnzRoI8qlgwMyIWJgiIiIiIiIiKpeEEPjlUiyWhN6EJs97z1ysTbGkmx98nawMmB3REyxMEREREREREZUzKrUGi47dxG9XYmXxum62WNilJhwsTAyUGZEcC1NERERERERE5cjjjBx8vDccF6OTZfFufi6YGlQNxkqFgTIj0sfCFBEREREREVE5EZmQho/2XENMSpYuppAkjG9ZBX0D3LjIOZU6LEwRERERERERlQPHbjzEpwf/QaZKrYtZmxphbqeaaOplb8DMiArGwhQRERERERFRGSaEwA9n7+HbsChZ3MveHEu7+cHT3sIwiREVAQtTRERERERERGVUlkqNzw5F4FBEgiwe6O2ALzvWgLUZb/updOMIJSIiIiIiIiqD4lOz8dGeqwiPT5PFQ+q7Y1zzKlAouJ4UlX4sTBERERERERGVMVdiUzB5zzU8ysjRxYyVCkwLqoaufi4GzIzo+fAdkeXMuXPnMGbMGPj5+cHS0hKenp7o06cPIiIi9PqGh4ejY8eOsLKygoODA959910kJMinf16/fh1TpkxBQEAArK2t4erqis6dO+P8+fN6+5s9ezYkSdL7MTMzK3L+Go0GCxcuROXKlWFmZgZ/f3/8/PPP+fbdvn07mjZtCjs7Ozg6OqJVq1bYu3dvkY9FRERERERUFu0Lf4APdlyWFaUcLIzxbU9/FqWozOGMqXJmwYIFOHnyJHr37g1/f3/ExcVh5cqVqF+/Pk6fPo3atWsDAO7fv4+WLVvC1tYWc+fORVpaGhYvXowrV67g7NmzMDExAQCsXbsW69atQ8+ePTFq1CgkJydjzZo1aNq0KQ4cOIB27drp5bB69WpYWVnpflcqlUXOf8aMGZg/fz6GDRuGRo0aYdeuXQgJCYEkSejbt6+u39dff42xY8eic+fOmD9/PrKysrB+/Xp06dIFO3fuRI8ePV70EhIREREREZVKGo3AypO3senCfVnc18kKS7rWgotN0ScFEJUWkhBCGDqJkpSSkgJbW1skJyfDxsZGrz0zMxORkZHw8fGBubm5ATIsOiEENBoNFAoFJKlozwafOnUKDRs21BWWACAyMhJ16tRBr169sHnzZgDAqFGjsH79ely/fh2enp4AgMOHD6N9+/ZYs2YNhg8fDgC4cOECqlevLis0PXr0CDVr1oSvry9OnDihi8+ePRtz5sxBQkICKlSo8NznGx0djcqVK2P48OFYuXKl7hq0atUKt2/fRlRUlK7I5evrCzs7O5w5c0Z3bVJSUuDu7o6goCDs2rXruY9fVhR3DGs0GsTHx8PZ2RkKBSdN0r84Nuhl4Lii4uIYopeB44qKyxBjKC07FzP2X8epqERZvK2PE2Z18IW5cdEnBFDpVJ6+m55Vm8mrbJ9pKaTOSEZuSgLUGckGOX6zZs1kRSkA8PHxgZ+fH8LDw3WxnTt3okuXLrqiFAC0a9cOvr6+2L59uy7WoEEDWVEKABwdHdGiRQvZ/vISQiAlJQXPW/PctWsXVCoVRo0apYtJkoSRI0fi/v37CAsL08VTUlLg7OwsK9jZ2NjAysqq1BcciYiIiIiInse9pEwM3faXXlFqeFMvzOtUg0UpKtNYmCphIjcH977uAZGb8+zOr4gQAg8ePNDNYoqOjkZ8fDwaNmyo17dx48a4ePHiM/cZFxdX4KyoKlWqwNbWFtbW1hgwYAAePHhQpDwvXrwIS0tL1KxZUy8nbbtW69atceDAAXz99deIiorC9evXMXr0aCQnJ2PcuHFFOh4REREREVFpd/buYwz++SJuJ2boYmZGCizoXAvDmnoV+ekaotKqVK8xNX/+fEybNg3jxo3D8uXLX8kxc5PikJsc9+yOCiXMPOrIQjkPbkIyMYMmKxVCnYOsO5dgZO8KIxtnXR9NdgZy4vQXIs/LyNYFRnYlt2Ddli1bEB0djc8++wwAEBsbCwBwdXXV6+vq6orExERkZ2fD1NQ03/0dP34cYWFhmDlzpixub2+PMWPGIDAwEKampjh+/Di++eYbnD17FufPn3/m9L3Y2FhUrFhR74tVm2dMTIwu9tVXX+Hhw4cYO3Ysxo4dCwCoUKECjhw5gsDAwEKPQ0REREREVNoJIbD9UgyWht6CJs/TKC7WpljSzQ++TlaFbE1UdpTawtS5c+ewZs0a+Pv7v9Ljpl7cjcfHvntmP6WFHbw+PgzgyeN7IjcHQqNCTsxt5CTcRnZMOIxsnKHJToc6IxlKC1sAgOrRXcT88H6h+7ZvPRz2bYYX/2QA3UyiwMBADBo0CMCTNYoA5Ft40r5BLzMzM9/2+Ph4hISEoHLlypgyZYqs7emZSj179kTjxo3Rv39/rFq1ClOnTi0014KOmTcnLQsLC1SvXh2VKlVCly5dkJqaimXLlqFHjx44fvw4qlWrVuixiIiIiIiISiuVWoOFR2/g97/lkybqutliYZeacLAwKWBLorKnVBam0tLS0L9/f3z//ff44osvDJ3OM2kf31Ml3IY6KxVQ5+Le1z0hSUoY2bnCe9oxg+QVFxeHzp07w9bWFjt27NAtHK5dgyk7O1tvm6ysLFmfvNLT03VFoBMnTuitPZWfkJAQTJo0CYcPH9YVpuLi5F+utra2MDc3h7m5eZFz6t27N4yMjLBnzx5d7O2334aPjw9mzJiBbdu2PTM3IiIiIiKi0uZxRg4+3huOi9HydYu7+blgalA1GCu5Ig+VL6WyMDV69Gh07twZ7dq1KxOFKcnIBB4f/gp1+iOoEu/j3tc94fHhThjZOENhZg3J6NVXs5OTk/HWW28hKSkJx48fh5ubm65N+2ic9pG+vGJjY+Hg4KA3cyknJwc9evTA5cuXcfDgQdSuXbvIuXh4eCAx8d9F+p5+hPDHH3/E4MGD4erqiqNHj0IIIXucT5un9hxu3bqFAwcO4Lvv5DPbHBwc0Lx5c5w8ebLIuREREREREZUWkQlp+GjPNcSkZOliCknC+JZV0DfAjetJUblU6gpTW7duxf/+9z+cO3euSP2zs7Nls2xSUlIAPHnNokaj0euv0WgghND9PM0qoCvMKjd69oEVSt32CvMnayepM5Jh6loTJk6VYepaE7mpCZBMLKAwt9H1NXLwgOuQ7wvdtZGti15uz/OGu6ysLHTt2hURERE4dOgQatasKdvezc0NTk5OOH/+vN5+z549i4CAAFlco9Fg4MCBOHLkCLZt24aWLVsWOR8hBKKiolCvXj3dNn/88Yesj5+fH4QQqFu3LtauXYtr166hVq1auvbTp08DAOrWrQshhG7GVW5url4eKpUq33h5oh27BY3xZ9F+Bl5kWyrfODboZeC4ouLiGKKXgeOKiutljKFjNx9h1sF/kKX6d59Wpkp8+VYNNPWyL/AelsqP8vTd9DznUKoKU/fu3cO4ceNw6NAh3bpCzzJv3jzMmTNHL56QkKB7BCwvlUpV6E29wsYZJnkWKy/M09sbOVWGSH8EydQakpExTCrV0e9nbAYTj2evm6Xd5nkHpFqtRnBwMMLCwvDrr7+iSZMm+e6jR48e2LhxI+7cuQMPDw8AwJEjRxAREYFx48bJtvnwww+xbds2rF69Gu+8806BOSUkJMDJyUkWW716NRISEtChQwfddkFBQfmeb9euXTFx4kSsWrUKX331FYAnRZhvv/0W7u7uaNq0KTQaDapUqQKFQoFt27Zh2LBhun81uH//Po4fP4433nijXHyQC6L9skpMTISxsfELbZ+cnAwhBBQKTgOmf3Fs0MvAcUXFxTFELwPHFRVXSY4hIQR+/vshNv4VL4tXsjHF7DaVUMlchfj4+AK2pvKkPH03paamFrmvJEpRyfX3339H9+7ddWshAU8KLZIkQaFQIDs7W9YG5D9jysPDA48fP873LXCZmZm4ceMGfHx88l1Hqbg0mSkQuTmQjEx0M6mKtT+N5rkG5Pjx4/HVV1+ha9eu6N27t177gAEDADwpAtavXx92dnYYO3Ys0tLSsHjxYlSqVAlnz57VPcq3fPlyTJw4EYGBgRg5cqTe/rp37w5LS0sAgKWlJYKDg1G7dm2YmZnh5MmT2Lp1K+rWrYsTJ07AwsLimflPmTIFixcvxrBhw9CoUSPs2rULe/fuxebNmxESEqLrN2zYMKxbtw5t2rRB9+7dkZqaitWrVyM2NhZHjhxBy5Yti3zNyprMzExERkaiWrVqLzSGNRqNrohY1r/sqGRxbNDLwHFFxcUxRC8DxxUVV0mNoSyVGp8fjsThiIeyeKC3PT5/szqszUrVXBJ6ycrTd1NKSgrs7e2RnJycb20mr1I1ytu2bYsrV67IYkOGDEGNGjXw8ccf6xWlgCdvlsvvTW4KhSLfP6RCoYAkSbqfkqZ9+15JyFszLGquly5dAgDs2bNHtjC41rvvvgsA8PT0RGhoKCZOnIhp06bBxMQEnTt3xpIlS2Sz1bT7CwsLQ1hYmN7+bt++rVsEvX///jh16hR27tyJrKwseHl5YcqUKZgxY4auePUsCxYsgIODA9asWYMNGzbAx8cHmzdvRv/+/WX9vv32WwQEBGDdunWYPn06AKBRo0bYuHEjWrVqVaRjlVXasVvQGC/qPoqzPZVfHBv0MnBcUXFxDNHLwHFFxVXcMRSfmo2P9lxFeHwakOd2L6S+O8Y1rwKFgutJvY7Ky3fT8+RfqmZM5ad169YICAjA8uXLi9Q/JSUFtra2BVbltLNNXtaMqZKkfeRQW0wjAoo/hjUaDeLj4+Hs7Fzmv+yoZHFs0MvAcUXFxTFELwPHFRVXccfQldgUfLTnKhIzVLqYsVKBaUHV0NXPpSRTpTKkPH03Pas2k1epmjFFREREREREVJ7tvfYAXx6JhEr977q4DhbGWNTFD/5uxV8OhqisKfWFqWPHjhk6BSIiIiIiIqJi0WgEVp68jU0X7svi1Z2ssKSbHypa6y9RQ/Q6KPWFKSIiIiIiIqKyLC07FzP2X8epqERZvK2PE2Z18IW5sf56ykSvCxamiIiIiIiIiF6Se0mZmLT7Km4nZsjiw5t64f0mnlxPmF57LEwRERERERERvQRn7z7GtL3hSMnO1cXMjBSY82YNBPlUMGBmRKUHC1NEREREREREJUgIge2XYrA09BY0QujiLtamWNLND75OVgbMjqh0YWGKiIiIiIiIqISo1BosPHoDv/8dJ4vXdbPFwi414WBhYqDMiEonFqaIiIiIiIiISsDjjBxM2RuOv6KTZfFufi6YGlQNxkqFgTIjKr1YmCIiIiIiIiIqpsiENEzafQ2xqVm6mEKSMKFlFQQHuHGRc6ICsDBFREREREREVAzHbjzEpwf/QaZKrYtZmxphbqeaaOplb8DMqKxQZyVBMrGR/a40szNcQq8Q5xESERERERERPSelUgkhBNaduYvJ/7kmK0p52Ztjfd8AFqWoSHLTYpByaRU0GXEwNzeHJiMOKZdWITctxtCpvRIsTBGVIlFRUZAkCevXrzd0KkRERERElI+07FzEpeYgNscI4QnpMFZKcLMx07UHejvgx+B68LS3MGCWVFaos5KQ+vc6pF5ajcTj02CcEYnEk58g4+ZupF5dD3VWkqFTfOlYmCpn1q9fD0mSYGZmhujoaL321q1bo3bt2gbIrHgkSSrSz7Fjx15JPqtWrWLxiIiIiIjoNZOQlo0Vx2+h98ZzaL06DO2/PY0zdx5jSptqcLMxQ0h9dyzv5gdrM66aQ88mhIDq0VWYe70JY/vqyLxzCPF/jIDq0VUYO/rB2m/wa/E4Hz8t5VR2djbmz5+Pr7/+2tCplIhNmzbJft+4cSMOHTqkF69Zs+YryWfVqlWoUKECBg8e/EqOR0REREREhpWWnYvvTt/Blv9FIzo5C7lqgRx1Ljacvw9JAr7sVAO1XWyevSMiAOqMeCSdnY+smFOwqNoNtg0mIn5fCJCTDBhXgG29MTCycjN0mq8EC1PlVEBAAL7//ntMmzYNbm5lZzCnp6fD0tJSLz5gwADZ76dPn8ahQ4f04k/LyMiAhQWn0BIRERERUfEkZ6mw+cJ93EvKksWVCgnn7iVhhoWJgTKjskQIgYybu5Fy8StoVOkwdvSDuUcQHp+cCUlpCmFkDgBIvrgSDm98/loUp/goXwkTGoHcDBWERhg0j+nTp0OtVmP+/PlF6r9582Y0aNAA5ubmcHBwQN++fXHv3j1d+5gxY2BlZYWMjAy9bfv16wcXFxeo1f8u9rd//360aNEClpaWsLa2RufOnXH16lXZdoMHD4aVlRVu3ryJTp06wdraGv3793/BM/73McULFy6gZcuWsLCwwPTp0wE8mUE2a9YsVKtWDaampvDw8MCUKVOQnZ0t28ePP/6IoKAgODs7w9TUFLVq1cLq1atlfby9vXH16lWEhobqHiFs3bq1rj0pKQnjx4+Hh4cHTE1NUa1aNSxYsAAajUa2n6SkJAwePBi2traws7PDoEGDkJSU9MLnT0REREREL8fVuFT8E5+mV5QyM1KgsoMFNOJJ4YqoMLlpMXh0dCySzs6DRpUOALCq0Q9p139CbsodmFfuAueOP8LY0Q+qR1dfmzWmOGOqBAmNQHZiJh4cv4OKLbxg6mAOSSEZJJfKlStj4MCB+P777zF16tRCZ019+eWX+OSTT9CnTx+8//77SEhIwNdff42WLVvi4sWLsLOzQ3BwML755hvs3bsXvXv31m2bkZGBPXv2YPDgwVAqlQCePHY3aNAgvPnmm1iwYAEyMjKwevVqNG/eHBcvXoS3t7du+9zcXLz55pto3rw5Fi9eXOzZTY8ePcJbb72Fvn37YsCAAahYsSI0Gg26deuGEydOYPjw4ahZsyauXLmCZcuWISIiAr///rtu+9WrV8PPzw/dunWDkZER9uzZg1GjRkGj0WD06NEAgOXLl+PDDz+ElZUVZsyYAQCoWLGi7nq0atUK0dHRGDFiBDw9PXHq1ClMmzYNsbGxWL58OYAnVfK3334bJ06cwAcffICaNWvit99+w6BBg4p1/kREREREVHKyc9X45mQU9l57gPldasLa1Aip2bkAAFszJVxtzKFQSLA0UcLWzNjA2VJpJoQGj46OQ27qPVk8O/YMbBtOgtLSBdZ+g5GutoTDG58j9ep6rjH1ulKlZkOVmvPc2ylMlRC5GtzbGwFVcg5UqZGo1MkHpg7myEnKgjrryZeXwlgBM6d/H1XLfpQBdbZati9jaxMYW5sW70QAzJgxAxs3bsSCBQuwYsWKfPvcuXMHs2bNwhdffKGbXQQAPXr0QL169bBq1SpMnz4dzZs3h7u7O7Zt2yYrTO3duxfp6ekIDg4GAKSlpWHs2LF4//338d133+n6DRo0CNWrV8fcuXNl8ezsbPTu3Rvz5s0r9vkCQFxcHL799luMGDFCF9u8eTMOHz6M0NBQNG/eXBevXbs2PvjgA5w6dQrNmjUDAISGhsLc3FzXZ8yYMejYsSOWLl2qK0y98847mDlzJipUqKD3KOHSpUtx8+ZNXLx4ET4+PgCAESNGwM3NDYsWLcKkSZPg4eGB3bt3488//8TChQsxefJkAMDIkSPRpk2bErkORERERERUPFdiUzD74D+4m5QJALj+IA096rhgy8VouFqbwlQhoJCeTETo4OsEa1PeXlPBJEkBm3pjkfjnk/s/SWkCa//hsKoR8qSt7ihIJjbIjI+HtbMLbOqOei2KUgALU3qSwh/i4Vn9t9kVxqqKPexqVMDdXdeR8zgLklIBhbEC9/c9KU6l3U1GwqknVVGzChao3Pfft+I9OHEP6feSZfur0NgdTo3di30uVapUwbvvvovvvvsOU6dOhaurq16fX3/9FRqNBn369MHDhw91cRcXF/j4+ODo0aOYPn06JElC7969sWbNGqSlpcHKygoAsG3bNri7u+sKPocOHUJSUhL69esn259SqUSTJk1w9OhRvRxGjhxZ7HPVMjU1xZAhQ2SxX375BTVr1kSNGjVkOQUFBQEAjh49qitM5S1KJScnQ6VSoVWrVjh48CCSk5Nha2tb6PF/+eUXtGjRAvb29rJjtWvXDvPnz8eff/6J/v37Y9++fTAyMpKdu1KpxIcffojjx4+/+AUgIiIiIqJiycnVYM3pO9h84T404t8lWnZcjsWXb9WAq40ZDkckICkjC5YmSnSo7oThTb1gxcIUPYN5pRYw9+4AdXoc7JvMhJGNp65NaWYnW/7ldSlKASxMFZ9CgmM9F0QfuIGcx/LnjVUp2brH+hJO3wcMsO7UzJkzsWnTJsyfPz/fWVORkZEQQuhm9zzN2Pjf6ajBwcFYvnw5du/ejZCQEKSlpWHfvn0YMWIEpP//l4LIyEgA/xZ9nmZjI39LhZGRESpVqvRC55Yfd3d3mJjIFx2MjIxEeHg4nJyc8t0mPj5e979PnjyJWbNmISwsTG89raIUpiIjI3H58uVnHuvOnTtwdXXVFfi0qlevXuj+iYiIiIjo5Ql/kIrZB//BrUT5vUAFSxNMblMVtV1t4O1ggaGNPZGYngUHSzPYmBmxKEUyuSl3kRbxC2wbTIAkyZf2tms8DZLSVC/+OuOnp7g0Ao8uxsGllTfuJl2XFaeMbUxRsYUX0u4mG6QoBTyZNTVgwADdrKmnaTQaSJKE/fv369aIyitv4aRp06bw9vbG9u3bERISgj179iAzM1P3GJ92f8CTdaZcXFz09mdkJB9ypqamUChK7gOZd8ZT3pzq1KmDpUuX5ruNh4cHAODmzZto27YtatSogaVLl8LDwwMmJibYt28fli1bprd4eX40Gg3at2+PKVOm5Nvu6+v7HGdDRERERESvgkqtwbozd/HjuXuyWVIA8FYNZ3zUuips/n8NKStTI1gYK2CckwpHa5sSvZ+hsk0IDdKu/4TUy99BqHNgZOUGqxr9ZH0URvr3rK87FqaeYlezAiwr2Ty741MUpkpUCamjW2PK2MZUt8aUpJBg4fKkwKMwln9pVWzuAXW2fGFyY+uSfc3ozJkzsXnzZixYsECvrWrVqhBCoHLlykUqmvTp0wcrVqxASkoKtm3bBm9vbzRt2lS2PwBwdnZGu3btSu4kiqFq1aq4dOkS2rZtq5vZlZ89e/YgOzsbu3fvhqfnv1Mq83v8sKD9VK1aFWlpac88dy8vLxw5ckT2WCQA/PPPP886HSIiIiIiKkERCWmY80cEIhLSZHEHC2NMC/JB62oV8t0u71vJiVTJt5B0+gvkPLqmi6Vc+hZm7s1hZO1hwMxKP5Z2n2JsbQoLN+vn/jFztICZkyU8u9WAlbetrChl6mD+b788C58DgKmjhd6+SmLh87yqVq2KAQMGYM2aNYiLi5O19ejRA0qlEnPmzIF46l8GhBB49OiRLBYcHIzs7Gxs2LABBw4cQJ8+fWTtb775JmxsbDB37lyoVPqvS01ISCihsyq6Pn36IDo6Gt9//71eW2ZmJtLTn7ymUztjLO91SE5Oxo8//qi3naWlJZKSkvI9VlhYGA4ePKjXlpSUhNzcJ4vgd+rUCbm5uVi9erWuXa1W4+uvv36+kyMiIiIiohei1gisO3MXg36+qFeUauvjhG3vNiywKEWkJTRqpF5dj4T9g2RFKQAwcaoLKPi2xmfhjKkSpC1Cub9ZDUozI0iKgmfnvGozZszApk2b8M8//8DPz08Xr1q1Kr744gtMmzYNUVFReOedd2BtbY3bt2/jt99+w/Dhw/HRRx/p+tevXx/VqlXDjBkzkJ2dLXuMD3iyhtTq1avx7rvvon79+ujbty+cnJxw9+5d7N27F2+88QZWrlz5ys4bAN59911s374dH3zwAY4ePYo33ngDarUa169fx/bt23Hw4EE0bNgQHTp0gImJCbp27YoRI0YgLS0N33//PZydnREbGyvbZ4MGDbB69Wp88cUXqFatGpydnREUFITJkydj9+7d6NKlCwYPHowGDRogPT0dV65cwY4dOxAVFYUKFSqga9eueOONNzB16lRERUWhVq1a+PXXX5GcnFzAWRARERERUUm59Sgdc/6IwLUHqbK4rZkxPg6qhva++a8ZS5SX6nEkHp/5AqpE+ZMvCmNL2NQbC4uq3Qp9aoeeYGGqhEkKCUYWpa8iWq1aNQwYMAAbNmzQa5s6dSp8fX2xbNkyzJkzB8CTdZc6dOiAbt266fUPDg7Gl19+iWrVqqF+/fp67SEhIXBzc8P8+fOxaNEiZGdnw93dHS1atNB7Y96roFAo8Pvvv2PZsmXYuHEjfvvtN1hYWKBKlSoYN26c7hHG6tWrY8eOHZg5cyY++ugjuLi4YOTIkXBycsLQoUNl+/z0009x584dLFy4EKmpqWjVqhWCgoJgYWGB0NBQzJ07F7/88gs2btwIGxsb+Pr6Ys6cObrF0xUKBXbv3o3x48dj8+bNkCQJ3bp1w5IlS1CvXr1Xfo2IiIiIiF4HGo3A5v/dx5qwO8hRy9eQbVnFETPa+cDBomSXVqHyR6hVSL22AWl//wgh5I90mrk1g13jqVBaOBsou7JHEk8/v1XGpaSkwNbWFsnJyXpvgAOePLoVGRkJHx+ffBfKLk2EENBoNFAoFKyykk5xx7BGo0F8fDycnZ25UCPJcGzQy8BxRcXFMUQvA8fV6+nu4wzM/iMCV2JTZHFrUyN81Loq3qrhXOT7Lo6h11dO4nUknf4CqqQbsrjCxBq2DSbA3PutF75/L0/j6lm1mbw4Y4qIiIiIiIjKLY1GYNulGHxz8jayc+WzpAK9HTCzrQ+cS3idXyp/hBBIvfwdUq9tAIR8HJlVagm7RlOgNOeaZC+ChSkiIiIiIiIql6KTMzHnjwhcjJav5WphrMTEVlXRza8in06hIpEkCZqcZFlRSmFqC9sGk2Du1Z7jqBhYmCIiIiIiIqJyRQiBnZdj8dWJ28hUydcAauRhh0/b+8LFxsxA2VFZZRMwBlkxp6BOj4O5RxvYNpoMpZmDodMq81iYIiIiIiIionIjLiULnx2KwLl7SbK4ubESY5tXRk9/V85uoWdSZyRAaSF/O6PC2AL2TT+BJjsZ5p5BBsqs/GFhioiIiIiIiMo8IQR2X32ApaE3kfHULKl67raY1cEX7ral+wVYZHia3EykXl6D9Mhf4dRhHYztfWTtphUbGCiz8ouFKSIiIiIiIirTEtKy8cXhSJyKSpTFTY0UGP1GZQTXdYNCwVlSVLjs+ItIOv0FctOiAQCPz3wBpw7rIClYOnmZXturK4QwdApEL4Rjl4iIiIjoCSEE9l+Px+JjN5GanStrq+Nqg9kdfOFpb2Gg7Kis0KgykPLXN0iP3CmLqxL/QVZMGMwrtTBQZq+H164wZWT05JRzcnJgYcEvKCp7VCoVgH/HMhERERHR6ygxIwdfHo7En7ceyeImSgVGBHphQP1KnCVFz5Qddw6Pz3wJdXqcLK60cIZd42kwcws0UGavj9fuztbY2BhmZmZ4/PgxbG1tuegdlTnJyclQKpUsTBERERHRa+tQRAIW/PcGkrNUsnititaY1cEXVRwtDZQZlRWanDSk/PU10m/s0muzrPY2bAI+hMLEygCZvX5eyztbZ2dn3L17F1FRUbC3t4eJiUmpLFAJIaDRaKBQKEplfvRqCSGQlpaGpKQkVKpUiWOCiIiIiF47SZkqLDh6A4cjEmRxI4WE95t4YXAjDyg5S4qeISsmDEln50GdES+LKy1dYN9kBkxdGhkos9fTa1mYsrOzAwDEx8fj7t27hk2mEEIICCEgSRKLEAQAkCQJDg4OsLe3N3QqRERERESv1LEbDzHvv5FIzJDPkvJ1ssKsDr7wdeLsFiqcJjsFyf9bjozb+/TaLH16wiZgNBTGXPLnVXstC1PAk+KUnZ0dVCoVcnNzn72BAWg0GiQmJsLBwQEKhcLQ6VApYGJiAqVSaeg0iIiIiIhemZQsFRYfu4n91+WzWxSShCGNPPBeE08YK3m/RM+WeHIGsuPOyWJGVm6wazITphXrGygrem0LU1rGxsYwNjY2dBr50mg0MDY2hrm5OQtTRERERET02jl5OxFfHI7Aw/QcWbyKgwVmdaiOWi7WBsqMyiIb/+FIeHABEBpAkmDl2wfWdT+Awsjc0Km91l77whQRERERERGVLmnZuVgaegt7rsnflKaQJAxoUAkjmnrBxIj/eE/Px6RCHVhV74us6OOwazoTpk51DZ0SgYUpIiIiIiIiKkXO3n2Mz/6IwIO0bFnc084cs9+sjjquNgbKjMoKdVYi0q5thI3/SEhGprI2G/8RsPEfoRcnw2FhioiIiIiIiAwuIycXK47fxq9XYmVxCUDfeu4Y/YY3TI243ioVTAiBzDuHkHxhCTTZyYBkBNt6Y2R9WJAqfViYIiIiIiIiIoP63/0kzPkjAjEpWbK4u60ZZnWojnrutgbKjMoKdeZDJJ1biKz7f+piadd/grlnG5g4+hkwM3oWFqaIiIiIiIjIILJUaqw8GYVtf0XrtfXyd8OHzb1hYcLbViqYEAKZUfuRfGE5NDkpsjZj2yqQFKXzZWf0L37CiYiIiIiI6JW7HJOCOX/8g7tJmbK4i7UpPmnvi8ae9gbKjMoKdUY8ks7OR1bMKVlckpSwqj0E1rUGQVKyMFXasTBFREREREREr0xOrgbfhkVhy/+ioRFC1vZ2bRdMaFEFlqa8VaWCCSGQcWsPUv63AhpVuqzN2KE67JvMhLG9j4Gyo+fFTzsRERERERG9EtfiUjH7j39wOzFDFneyNMHM9r5o5u1goMyorMhNj0XSmXnIjjsri0sKY1jXeQ9WNQdAUrDUUZbwr0VEREREREQvlUqtwfen72LD+Xt6s6Q61ayISa2qwMaMj1xR4dJv7ELyxRUQKnlh08SxFuyazoSxbRUDZUbFwcIUERERERERvTQRCWmYffAfRD6UP3LlYGGMGW190bKqo4Eyo7JGk/VIVpSSlCawrjMMVjVCICmUBsyMioOFKSIiIiIiIipxuWoNfjx3D+vO3IX6qVlS7X2d8HGbarA15ywpKjqrWgORee8oVI8jYVKhzpNZUjZehk6LiomFKSIiIiIiIipRNx+mY84f/yA8Pk0WtzM3xtSgamjr42SgzKisUGc9htJM/mZGSWEE+6afIPvBBVhWD4YkKQyUHZUkFqaIiIiIiIioRKg1Apsu3Md3p+9ApdbI2lpXrYBpbavBwcLEQNlRWSCEBmnXf0Lq5e/h2GoJTF0aytqN7X1hbO9roOzoZWBhioiIiIiIiIrtTmIGZv/xD/6OS5XFbUyNMLlNNbxZ3QmSJBkoOyoLVMm3kHT6S+Q8ugoASDr7JZze2gKFsYWBM6OXiYUpIiIiIiIiemEajcDWv6Lxzcko5Dw1S+qNyg6Y0dYHTlamBsqOygKhUSMtfDNSr6yF0Kh08dy0WGTc+g+sqvcxYHb0srEwRURERERERC/kflImZv8RgUsxybK4pYkSE1tVRddaFTlLigqlSrqBx6e/gCrxuiyuMLaETb2xsKjazUCZ0avCwhQRERERERE9F41GYMflWHx94haycuWzpJp42uOT9r6oaM1ZUlQwoclF6tUNSLv6I4QmV9Zm5hYIu0ZTobSsaKDs6FViYYqIiIiIiIiKLCY5C58fjsD5e0myuLmxEuNbVkH32i6cJUWFykn8B0lnvoDqcaQsrjCxhm398TCv3Ilj6DXCwhQRERERERE9kxACv/8dh+V/3kKGSi1rq1/JFrPaV4ebrZmBsqOyQKhzkPr3D0i9thEQ8pl2Zu4tYNdoCpQWTgbKjgyFhSkiIiIiIiIqVHxqNj4/HIHTdx7L4qZGCnzYvDJ6+7tBoeAMFypc0rmFyLj1H1lMYWID24aTYO7VgbOkXlMsTBEREREREVG+hBDYGx6PJcduIi1Hvg5QHVcbzO7gC097CwNlR2WNda2ByLzzB4Q6BwBg7tEato2mQGnmYODMyJBYmCIiIiIiIiI9j9Jz8OWRSBy/9UgWN1EqMLKZN0LquXOWFD0XIxtP2PiPQGr4Ztg1nAxzzyBDp0SlAAtTREREREREpCOEwKGIBCz47w2kZMtnSdWqaI3ZHaqjsiNnSVHBNLlZSLu6HlY1QqAwtZG1WdboB4sqXaAwtTVQdlTasDBFREREREREAIDHGTmY998bOHrjoSxupJAwrKkXBjX0gJKzpKgQ2fEXkXT6C+SmRUOdEQf7wNmydklSQGJRivJgYYqIiIiIiIjw38iHmP/fSDzOVMni1Z2sMPvN6qhWwdJAmVFZoFFlIOXSKqRH7NDFMm4fgLlne5i5v2HAzKi0Y2GKiIiIiIjoNZaSpcKCozfwxz8JsrhSkjC0iSeGNvKAkVJhoOyoLMiOO4fHZ76EOj1OFldaOAMKYwNlRWUFC1NERERERESvqeO3HuHLw5F4lJEji1d1tMTsN31Rw9naQJlRWaDJSUPKX18j/cYuvTaLqt1gW28sFCZWBsiMyhIWpoiIiIiIiF4zqVm5WPrnTfzn2gNZXCFJGNiwEoY18YKJEWdJUcGyYsKQdHYe1BnxsrjS0gV2jafDzLWxgTKjsoaFKSIiIiIiotdIWFQivjgcifi0bFncy94csztUR21XmwK2JAI02SlIvrgCGbf26rVZ+vSETcBoKIz51kYqOhamiIiIiIiIXgMZOblYfvw2frsSK4tLAELqV8LIZl4wNVIaJjkqE7JiTiHpzFyoM596a6OVG+yazIRpxfoGyozKMhamiIiIiIiIyrnz95Lw+aEIxKRkyeKVbM0wq0N1BLjbGigzKkvUGQnyopQkwap6MKz9R0BhZG64xKhMY2GKiIiIiIionMpUqfH1idv45VKMXlvvum74sHllmBtzlhQVjUXVbsi8exjZcedgZO0Bu6YzYepU19BpURnHwhQREREREVE59Fd0Mj47FIF7SZmyuJuNGT5p74uGHnaGSYzKBE1OKiRjK0iSpItJkgS7xtOQcXMXrP2GQjIyNWCGVF6wMEVERERERFSOZOeqsepUFH7+XzTEU23d67hifIvKsDDhrSDlTwiBzLuHkXx+CWwbTISFdwdZu5GVG2zqjjRQdlQe8duIiIiIiIionPg7NgVz/ohA1OMMWdzZyhQz2/kg0NvBQJlRWaDOfIikc4uQdT8UAJB8YQlMXRpCacZxQy8PC1NERERERERlXE6uBt+fuYON5+9DI+TzpLrUqoiJLavC2oy3f5Q/IQQyo/Yj+cJyaHJSdHFNdjLSwrfAtt6HBsyOyjt+MxEREREREZVh1+NTMftgBG4+SpfFHS1MMKOdD1pUcTRQZlQWqDPikXR2PrJiTsnikqSEVe0hsK41yECZ0euChSkiIiIiIqIySKXW4Mdz9/DDmbtQPzVL6s3qzpjSpipszIwNlB2VdkIIZNzag5T/rYBGJS9qGjtUh32TmTC29zFQdvQ6YWGKiIiIiIiojLnxMB2zDv6DiIQ0Wdze3BhTg3wQ5FPBQJlRWZCbHoukM/OQHXdWFpcUxrCu8x6sag6ApGC5gF4NjjQiIiIiIqIyQq0R2HD+Hr4/fQe5GvksqTbVKmBaUDXYW5gYKDsq7YTQIOPGb0j+6xsIlXyBfBPHWrBrOhPGtlUMlB29ropdmEpLS0NERATS09PRokWLksiJiIiIiIiInnL7UQbm/PEPrj5IlcVtTI3wcVA1tPd1giRJBsqOyoLUv39E6pXvZTFJaQJr/+Gwqt4PkkJpoMzodaZ40Q2joqLw9ttvw97eHo0aNUKbNm10bSdPnkStWrVw7NixksiRiIiIiIjotaXRCGy6cA8DfvqfXlGqRRVHbB/YEB2qO7MoRc9k6dMdChMb3e8mTv5wfmszrGsOYFGKDOaFZkzdvXsXTZs2xaNHj/D2228jLi4OYWFhuvYmTZrg4cOH+Pnnn9G6deuSypWIiIiIiOi1cvdxBuYcisDlmBRZ3MrECJNaV0XnmixIUdEpzRxg22Aiks7Og03dkbCs3geS9MLzVYhKxAsVpmbNmoXHjx8jNDQUzZo1w5w5c2SFKSMjI7Ro0QInT54ssUSJiIiIiIheFxqNwC+XY/D1idvIztXI2pp62eOTdr5wtjY1UHZU2gmhQfo/W2FWqTWMrNxkbebeb8K0YgMoLZwMlB2R3AsVpg4ePIju3bujWbNmBfbx8vLCf//73xdOjIiIiIiI6HUUk5yFOYf+wf/uJ8viFsZKjG9ZBe/UduEsKSqQKvkWkk5/iZxHV5EVcxqObVbIxoskSSxKUanyQoWpxMREeHt7F9pHCIHs7OwX2T0REREREdFrRwiB3/6Ow4o/byFDpZa1NfSww6ftfeFqY2ag7Ki0Exo10sI3I/XKWgiNCgCQHXcWGTd3w7La2wbOjqhgL1SYqlixIiIjIwvtc+XKFXh6er5QUkRERERERK+TB6nZ+OxQBM7efSyLmxkp8GHzKujl7wqFgrOkKH+qx5F4fOYLqBL/kcUVxpaQlCYGyoqoaF6oMNW+fXts2rQJly9fhr+/v1778ePH8d///hfjx48vbn5ERERERETllhACe649wNLQm0jPkc+Squtmi1kdfOFhZ26g7Ki0E2oVUq9tQNrV9RCaXFmbmVsz2DWeCqWFs4GyIyqaFypMzZw5Ezt27EDLli0xefJk3LhxAwCwf/9+nDp1CkuXLkWFChUwefLkEk2WiIiIiIiovEhIy8bcI5E4cTtRFjdRKjD6DW/0DXDnLCkqUE7idSSd/gKqpBuyuMLEGrYNJsDc+y2uRUZlwgsVpry9vXHw4EH07dsXn3zyCSRJghACXbp0gRACnp6e2LFjB1xdXUs6XyIiIiIiojJNCIED1+Ox+NhNpGTLZ7nUdrHG7A7V4eVgYaDsqLQT6hyk/v0DUq9tBIT8jY1mlVrCrtEUKM0rGCg7ouf3QoUpAGjSpAkiIyOxZ88enDlzBomJibCxsUGTJk3w9ttvw8SEz7ESERERERHllZiRg3lHbuDYzYeyuLFSgeFNvfBug0pQcpYUFSDn0TUknf4cquTbsrjC1Ba2DSbB3Ks9Z0lRmfPChSkAMDIyQvfu3dG9e/eSyoeIiIiIiKhcOhyRgAVHbyApUyWL13C2wuwO1VG1gqWBMqOyQpOVqFeUMvdoA9tGk6E0czBQVkTFo3iRjYKCgrBx48ZC+2zevBlBQUEvlBQREREREVF5kZypwvR94Zi2L1xWlFJKEoY39cKPwQEsSlGRmLk3h0XljgAAhZk9HJrPhUOLeSxKUZn2QjOmjh07htatWxfa586dOwgNDX2R3RMREREREZULf958hC+PRCAxQz5LyqeCJWa/WR2+TlYGyoxKO01uFiSlCSRJPp/Etv5ESEYWsK4zHEozO8MkR1SCivUoX2HS09NhbGz8snZPRERERERUaqVkqbD42E3svx4viyskCYMaemBYU08YK1/oARZ6DWTHX0TS6S9g6dMDVjX7y9oUpjawazTFQJkRlbwiF6bu3r0r+z0pKUkvBgBqtRr37t3Dzp074e3tXewEiYiIiIiIypJTUYn44lAEEtJzZPHKDhaY3aE6arlYGygzKu00qgykXFqF9IgdAICUy2tg5t4CRjaeBs6M6OUpcmHK29tbt7q/JElYsWIFVqxYUWB/IQQWLVpU/AyJiIiIiIjKgPTsXCw7fgu7/o6TxSUAAxpUwgeB3jAx4iwpyl923DkknZ2L3LRYXUyoc5By+Vs4NJ9rwMyIXq4iF6YGDhwISZIghMDGjRtRt25dBAQE6PVTKpVwcHBAUFAQOnbsWJK5EhERERERlUpn7z7G54ciEJeaLYt72pljVofq8HezMVBmVNppctKQ8tfXSL+xS6/NstrbsAn40ABZEb06RS5MrV+/Xve/Q0NDMWTIEIwdO/Zl5ERERERERFQmZOTk4usTUdhxOUavLTjAHWPe8IaZsdIAmVFZkBUThqSz86DOkK9FprR0gX2TGTB1aWSgzIhenRda/Pz27dslnQcREREREVGZ8r/7SfjsUASik7NkcTcbM8zq4Iv6lewMkxiVeprsFCRfXIGMW3v12ix9esImYDQUxhYGyIzo1St1DzivXr0a/v7+sLGxgY2NDQIDA7F//35Dp0X/x959x1dV338cf527s3cIEPbeS0BB9hJtcVsVB26tdVWt4gRFUVtr+7O1akVxILZurQoIiiACskH2XgGyd3Jz7z3n9wc2eAsqhMST8X4+Hr9HPZ/vTXjnx+GOT75DREREREQA8AdD/Pmr7dz4ztqjmlLndWvMzMt6qyklP6ps30IyP730qKaUK7oJySOeI77v3WpKSYNyXDOmhg8fjmEYvPrqq6SnpzN8+PDj+uaGYTBv3rwTCpSens4TTzxBu3btsCyLV199lbPPPptVq1bRpUuXE/peIiIiIiIi1WndgUImzd7MnvyysHqjaC8PjmpP/xYJNiWTuqB0xyfkLXk0vGgYRLe/iJgeN+JwRdgTTMRGx9WYmj9/PoZhUFpaWnl9PP57it+J+PWvfx12/dhjj/GPf/yDJUuWqDElIiIiIiK2qAiaPL94FzNW7se0rLCxX3dO4/dDWhPtrdJOKdKA+JoNw/XdS5Un77limhF/6gN4U3rYnEzEPsf1zGma5k9e15RQKMTbb79NSUkJp5122i/yZ4qIiIiIiPzQxkNFTJq9mR25pWH15CgP949sx+mtkmxKJnWNwx1JfL/7yP7yNqI7XkJst+sxXF67Y4nYqla29NetW8dpp51GeXk50dHRvP/++3Tu3PmYj/X7/fj9R45kLSwsBA43z36pBlpNMU0Ty7Lq/M8htYvuK/kxujekJui+kpOle0hqwvHeV4GQycvf7uWV5Xux/uehZ3RM4c4hrYn1uXV/NkA/dw9ZlkX5nrm4YlviTmgXNuZO7UPKWf/GFd0EC7B0/8j36tNr3on8DLWyMdWhQwdWr15NQUEB77zzDldeeSVfffXVMZtTU6dOZfLkyUfVs7KyKC8vP6pel5imSUFBAZZl4XDUun3qpY7SfSU/RveG1ATdV3KydA9JTTie+2p7bjlPf7OfHXnhnynifS5u7d+YAc1jKS/Mo7zwl0gstc1P3UOWP5fghucIZS7GEdMa96nPYDj+96O3C0ozf7nAUifUp9e8oqKi436sYVn/s0C6Fho5ciRt2rThhRdeOGrsWDOmmjVrRl5eHrGxsb9kzGpnmiZZWVmkpKTU+ZtSag/dV/JjdG9ITdB9JSdL95DUhJ+6r4Ihk9dW7OOlb/cSCoV/VBreLpl7hrUhPsL9S8aVWuhY95BlWZTtmkXhqr9gVRz5UB7d7TpiulxlV1SpQ+rTa15hYSEJCQkUFBT8bG+mVs6Y+l+maYY1n37I6/Xi9R69JtfhcNT5v0g4vIF8fflZpPbQfSU/RveG1ATdV3KydA9JTTjWfbUjp4RJszezMbP4+wcd/p84n5t7hrdlVPsUG5JKbfXDeyhUmkn+t09QnvHN/zzGicPh1POXHLf68pp3IvlrXWNq4sSJjB07lubNm1NUVMSbb77J/PnzmT17tt3RRERERESkHjJNizdW7uP5xbsJhML3RRncOon7R7YjMdJjUzqpbULl+RiewzNALMuiIm8rhSv/iv/Q8rDHuRM7kND/gaP2mBL5XwUVZVSYIbCAmAhy/KVggMfhJM4TYXe8GlfrGlOZmZlcccUVHDhwgLi4OLp3787s2bMZNWqU3dFERERERKSecDqdAOzJK2XSnC2sOxC+WVSM18VdQ9swtmMqhmHYEVFqoWBxBkXrpxPTZQI+Z4hgzjqKvptGdKfLMINlBHLWYzjcxHS7huhOlx1jbymRo1WYIcZ9Pg0Li2AgiMvtwmU4eX9kw1gCWuv+lUybNs3uCCIiIiIiUk8V+4MUlgfJqXCRl1PKsj355JRUhD3mtJaJPDCiHakxR28ZIg1XqDyfovXTKd3+ERWZq4nuMoHcFX+unCkV3fESSja9SfypD+COa21zWqlrTMuiNBTAZYFhWRjU/ZP5jleta0yJiIiIiIjUhKxiPy8u2c0nGw6xJasYt9PJed3S+MOwtjz15TbyywL8fkgbxnVppFlSchSnL56YzldQfmAJZbvnULprNoZh4G10CtGdLiNUnkvyqH9iOJx2R5U6wh8K4nUebss4DAOnYRC0Qg2uUXNSP+/777/PzJkz2bRpE6WlpWzbtg2ATZs28dFHHzF+/HiaNm1aLUFFRERERESqqtgf5MUlu3ll2V4OFfkxTYvyoMWry/cBcOOAFvRqEkdarM/mpFKbWcEy4rrfSOa+BZW1uH734Ipviy8iycZkUtd8m7WHSatm80CPUbSLSwbA7XASJGhzsl9elbZ5N02T3/zmN1xwwQW8++677Nixg507d1aOJyQkcP/99/Paa69VW1AREREREZGqyi8L8PKyPRws9GNZR+oOAxbtzKWnmlJyHAx3FIXrXvrvFc7IVIo3vQWhY58iL/K/LMvi2Q0L+d3i98guL+HRNZ9jWhZOw4HTMPA5XDgNA6dRt0/lOxFV+kmfeeYZ3n77bW644Qby8vK46667wsYbNWrEoEGD+OSTT6olpIiIiIiISFV9vTOHjZnFHCoM30sq0u2gVVIkbpeDgvKATemkrvjvHlOhkgx8zYbS6LxZeJsOIpCz/nC9PN/uiFIHGIZBwAxVXueWl5BTXsL7I6/i45HX8p8x1/HxyGt5f+RVeBrIstAqNaamT59O3759ee6554iNjT3m+uu2bduGzaISERERERH5JRWWB7j/s408NGsz8REuYryHdzIxDEiOctE8IQKP00GUx0mcz21zWqltTH8h5fu/rrx2+uKJ6TKByDbjSB7xHIHoTiQOfJTINuOI6TIBpy/evrBSq1mWRfAHzajfdTqdtrGHl++d17I7rWOSSPFFk+SNhKIykryRpPiiifNE2BX5F1WlPaa2bdvGzTff/JOPSUpKIicnp0qhRERERERETsaS3Xk8MmczWd+fuLfpUDHndUvjX2syaBLrw2GFMDj8C/bR7VMqm1YiAIG8LeQuvIdQaRbJI57Dk9IdAFd0E2J7/BbDE0tZZiYxqWnE9vitmlLyoworynlszVziPD7u6zESAI/TxZQ+Y9lXks/gtDY2J7RflZ59IyIiKCgo+MnH7N69m/j4+Kp8exERERERkSopC4T4v4U7eWdtRlj93XUHeOKsTjSJ8/H55izyS0NEeZyM7pDC9ae2IFqNKfle6c5Z5H87Fev7faNyv76PlDNexfn95uZOXzymaVY+Xk0p+TGrcvbzwIrPyCovBuC01JYMa9wWgNYxSbSO0Yb5UMXGVK9evZg9ezbl5eX4fEdvEJibm8usWbMYPHjwSQcUERERERE5HusOFDJp9mb25JeF1ZvHRzB5TAc6psZw26AIrurbnNySchKjfMT6XGpKCQCWGaRw1bMUb/5XWN1wRWAGiisbUyLHy7Qssv3FlddPrv2Cgakt8Tj1nPNDVdpj6tZbb2Xfvn2cf/757Nu3L2xs+/btnHvuuRQUFHDrrbdWS0gREREREZEfEwiZPLdoF9f+e81RTakLezRhxvjedG0cC0C010VajIdG7gBpMR41pQSAUHkuOV/cclRTytd0ECljXsYd28KmZFLXmNaR2XR9ktO5sm1fAJpHJ/DXU89RU+oYqvT/kbPPPpt77rmHJ598khYtWhAVFQVAamoqOTk5WJbFgw8+yPDhw6s1rIiIiIiIyA/tyCnhoVmb2ZxVHFZPjfby0Kj29G+RcMyvC4VCx6xLw1ORvY7chfcRKss6UjQMYrpeS0zXqzCMKs3nkAbGsiz+s3cDb+5YxUsDLyTK7QXg+g6nEuFyc3GrXkS4dMjCsVS5VTd16lSGDx/O3/72N5YuXUp5eTmmaXLGGWdw6623MmbMmOrMKSIiIiIiUsk0Ld5ctZ9/fLOLipAZNja2Yyp3DW1DrE7ak59gWRal2z+gYPnTWGawsu5wR5MwYDK+pgNtTCd1SXHAz9S18/h8/xYAnlr3JZN7nwGAy+Hkqnb97IxX653UHLJRo0YxatSo6soiIiIiIiLyszIKypk0ZzOr9ocfyBTrdXHfyHaMaJdiUzKpK6xQBfnL/0jp9o/D6u641iQOfhJXTDObkkldZADr8w5VXn+2bxNjmnZkQKOWtmWqS7S4UURERERE6gTLsvh4wyGenr+d0kD4UrwBLRN5cFQ7kqO8NqWTuiJUcojcr++lImdjWD2ixUji+92Hwx1pUzKpSyzLwjAMAKLcXh7tfQbXLfo3BnBjxwGcmtrc3oB1SJUaU3v27DnuxzZvrr8MERERERE5ObmlFUyZu5WFO3LC6hFuJ3cMbs05XdMqPySK/JRQeTaBvG1HCoaDuF6/I6rDJbqH5LjsLyngoVWzuKnjAE5JPjy7rltiY/7QbTgd41PoHJ9mc8K6pUqNqZYtWx7XP1jDMAgGgz/7OBERERERkR/z5bZsps7bSl5ZIKzevUksj4zpQNO4CJuSSV3kSepC3Cl3kv/tEzi88SSe/hjeRn3sjiV1xKd7N/LUui8oDQZ4eOVs3hw6njjP4eeg81p2szld3VSlxtQVV1xxzMZUQUEBa9asYefOnQwZMoSWLVuebD4REREREWmgiv1B/jR/O59sPBRWdzsd3HhaCy7rnY7DoRkucuKi2p6DWVFERItRuKI0u0WO35rcDEqDh5vkWeXFPLN+AZN66fC3k1GlxtT06dN/dMyyLJ5++mmeeuoppk2bVtVcIiIiIiLSgC3bk8/kOZs5VOwPq7dLjmLymA60S4m2KZnUJcGifZTvX0h0x0uOGovpfLkNiaSuu6PLYFbm7GN3cR69kppyY4fT7I5U51X75ueGYXDXXXfxySefcPfdd/Puu+9W9x8hIiIiIiL1lD8Y4u+LdjFz1f6wusMwuLxPOtef2gKPy2FTOqlLyjO+Ie+bhzErinD4kohsOdruSFLHhEyTl7d+S3HAzx1dhwDgc7mZ0mcsiw7tYkK7U3AYej46WTV2Kt8pp5zCSy+9VFPfXkRERERE6pkNB4t4ePZmduWVhtWbxvmYPKYDPZrE2ZRM6hLLMileP53Cdf8EywIgf+ljuONa4U5oZ3M6qSsOlBby4MpZrM3NAKBfSnMGNmoFQIe4VDrEpdoZr16pscbU9u3btfG5iIiIiIj8rGDI5JVle5m2dA+h7xsJ/3Vut8bcPqgVkZ4a++gi9YhZUUze4smU718YVncndcLhS7QpldRFu4vzKptSAJNXz+G94ROIdnttTFU/Veuzu2ma7N+/n+nTp/Phhx8yYsSI6vz2IiIiIiJSz+zOLeWh2ZvZcKgorJ4U6eHBUe0Z2ErNBDk+gYId5C64h2DR3rB6dIeLiO11K4ZDzU05fqemtuCS1r2YuWMVUS4Pd3YdqqZUDanSv0yHw3HMU/n+y7IsEhISePrpp6scTERERERE6i/TtHh7bQb/t3AnFSEzbGxEuxQmDm9LXITbpnRS15Tt+YK8JY9iBcsqa4bTQ3y/iUS2GmtjMqkrvss7wD83L2XqKWcS6fIAcHOngZQGA1zVvi9NIrWUuKZUqTE1ePDgYzamHA4HCQkJ9O3bl6uuuorUVK25FBERERGRcIeK/Eyes5lle/PD6jFeF38Y1pYxHVJ+8hfhIv9lmSEK1/yD4o1vhNVd0Y1JOP0JPIkdbEomdYVpmUzfupwXNy/GtCz+/N1XPNBzFAAep4v7e460OWH9V6XG1Pz586s5hoiIiIiI1HeWZfHZpkz++OV2iivC96Pt1zyBh0e1JzVGS2Xk+ITK88n75kH8B5eF1b2N+5M44BEcXs1wkZ9XEqzgvd1rMb/f3+6jPesZlNaaIWltbE7WcGiRrYiIiIiI1Lj8sgCPz9vKl9uyw+pel4PbBrXmgu6NNUtKjlsgbys5C+4mVHIwrB7T+QpietyIYThsSiZ1TYzbxyO9zuCmxe9gWTC6aQf6JKXbHatBUWNKRERERERq1MIdOUyZu4Xc0kBYvWtaDJPHdKB5QqRNyaSuMjzR4ftJuSNJOPVBIpoNszGV1AWlwQqe+W4BZzXrRM+kpgD0Tk7nhg4DSIuIYWx6RzXJf2HH1ZgaPnx4lb65YRjMmzevSl8rIiIiIiJ1W2lFkD8v2MGH34XPanEaBtef1oIrT2mG06EPgHLiXFGNSRw4hewvb8MV3ZTEwU/ijmttdyyp5TbkH+TBFbPYW5LP0uzdzBgynhi3D4Cr2/ezOV3DdVyNqaruKaUuo4iIiIhIw7RqfwGTZm8mo7A8rN46MZLJZ3SgY2qMTcmkvvCm9SXx9Kl4G/XB4Ym2O47UAe/uWsveknwADpYW8dTaL3m0j05ttNtxNaZM0/z5B4mIiIiISINXETR5fvEu3lixD+sHdQO4pHdTbh7QCo9L+//I8avIWkvpzk+J6/uHo/aOimg2xKZUUhfd2XUoK3P2s7+kgNSIaM5r2d3uSIL2mBIRERERkWqyJauYh2ZtZntOSVi9cYyPh8e0p096vD3BpE6yLIvSbe9RsOIZLDOIM7IRMV2vsjuW1CFfHtjGjqIcrmnfH4BIl4cpvccyc8cq/tBtGLEen80JBdSYEhERERGRk2SaFq+t2McLi3cRNK2wsV93TuPOIa2J8uqjhxw/K1RB/rKnKN3xn8pa4boX8SR3w5t2io3JpC4oCwb4y/oFvL97HQDdEhrTL6U5AF0S0pii5Xu1ykm9OpSXl7Ns2TIyMjLw+/3HfMwVV1xxMn+EiIiIiIjUYnvzy3h49mbWHSgMqydEuLl/ZDuGtEm2KZnUVcGSg+QuvJdA7qawekSLUbiTu9iUSuqSVTn7K5tSAJNWzWbm0MuI80TYmEp+TJUbU3//+9958MEHKSgoOOa4ZVkYhqHGlIiIiIhIPWRZFu+tO8BfFuygPBi+J+2QNkncN6IdiZEem9JJXeU/uJzcRQ9g+vOPFA0Hcb1uIarDxTpgS47LgEYtOb9ld97dtRbDgLPSOxHp0vNRbVWlxtR7773HLbfcQrdu3XjwwQe58847Oeecc+jfvz8LFizgs88+4/zzz+dXv/pVdecVERERERGbZRX7eXTuVhbvyg2rR7qd3D2sLWd1SlUDQU6IZVkUb5pB4ernwDrS6HT4Ekk8/TG8qb1sTCe1XXZ5CdO2LOX2LoPxOg+3OW7rPIh9Jflc2a4vpyQ3szmh/JQqNab+8pe/kJqayuLFi4mMjOTOO++kZ8+e3HPPPdxzzz28+eabXHnlldx8883VnVdERERERGz0+ZYsnpi3lUJ/MKzeJz2eh0e3p3GsNhOWE2MGSslf+hhle+aF1T1JnUkc9ATOyFSbkkldsODgdqasnkt+RRkOw+DubsMA8LncPHvaeTank+NRpXNa165dy7hx44iMjKyshUKhyv++9NJLGT58OI888sjJJxQREREREdsVlge4/7ON3PfpxrCmlMfp4I7BrXnuvG5qSskJCxbuIXvOtUc1paLank3yyOfVlJKfVB4MMHXtF+RXlAHw9s41LDy4w+ZUcqKq1JgKBAKkpKRUXkdERJCfnx/2mB49erBy5cqTCiciIiIiIvZbsjuPi19fwZzNWWH1TqnRvH5pLy7tnY7DoaV7cmIqsr8ja/bVBAqONBIMh5v4fvcS328ihlN7AslP87ncTOo1uvK6fVwKzaPj7QskVVKlpXxNmjThwIEDldctWrRg1apVYY/ZvXs3LpeOhBURERERqavKAiH+b+FO3lmbEVZ3GAZX92vGNf2a43JW6XfdIrjiWuGISMQMFAPgjEwl8fTH8SR3tTmZ1FaWZfHWztX0SGxM5/g0APqntODytn2wLLix42l4nOpD1DVV+hvr27dv2GyoM844g7/+9a9MnTqVcePG8fXXX/Pee+8xcuTIagsqIiIiIiK/nHUHCpk0ezN78svC6s3jI3jkjI50SYuxKZnUFw53FImDniRrzjV4EjqQcPpjOH2JdseSWirXX8rkVXNYnLmLZlHxvD7k0sqT9n7X6XQduFCHVenXGxdeeCF+v59du3YBMHHiRNLT03nggQfo3r07N910E9HR0Tz11FPVmVVERERERGpYIGTy3KJdXPvvNUc1pS7q0YQZ43urKSVVYlnWUTV3XCtSRj5P0vBn1ZSSn/TCpsUsztwFwN6SfJ7+bn7lmJpSddtxz5h66623OO+88/B4PJx77rmce+65lWMpKSmsXr2al156iR07dtCiRQsuv/xymjZtWiOhRURERESk+u3IKeGhWZvZnFUcVk+N9vLw6Pb0a55gUzKp68p2z6V46zskD/0rhssbNuZOaG9TKqlLftd5IN9k7uJQWREeh5NO8Y2wLEtNqXrguBtTl156KYmJiYwfP56rr76aHj16hI0nJCRw9913V3tAERERERGpWaZp8eaq/fzjm11UhMywsbEdU7l7aFtifNq3RU6cZYYoXPMcxRtnAJC/bCrxpz6sZoL8rO2F2azJPcB5LbsBEOP28WjvM3j6u6+Y3HsMrWOSbE4o1eW4l/JdcskllJWV8eyzz9K7d2/69u3L888/T0FBQU3mExERERGRGpRRUM6N767lrwt3hDWl4nxunjirE4+c0VFNKamSUHkeOV/eVtmUAijdOYvyfV/ZmEpqO8uy+PfO1Vy5YCZPrfuCVTn7K8d6JjXl1cEXqylVzxx3Y2rGjBkcOHCAv//97/Tu3ZsVK1Zw880306RJEy6//HLmz59fgzFFRERERKQ6WZbFR+sPcskbK1i1P/yXzQNbJfLW5b0Z0S7FpnRS11XkbiRr9lX4Dy0Pq8d0mYAvfbBNqaQuWJWznz+tm0+FGcK0LB5aOYuiQHnluMPQSaD1zQn9jcbGxnLTTTexbNky1q1bx2233UZ0dDQzZsxgxIgRtG3blscff5z9+/f//DcTERERERFb5JZWcOfHG3j08y2UBkKV9Qi3k/tGtOOZcV1IjvL+xHcQ+XGlO/5D9uc3ECo5WFkz3JEkDnqS2B43YqixID+hd3I6v27eufK6TUwSIfPojfOl/qjyM0KXLl3485//zP79+3nnnXcYO3Ysu3fv5oEHHqBly5acddZZvPfee9WZVURERERETtKX27L5zesrWLgjJ6zeo0kcMy/rzbndGmv/H6kSKxQgf9lT5C2ZghWqqKy7YluQMvplIpoNsTGd1FYVoSCvb1tBRShYWbuz61DaxCZzV7ehPNP/bOK9ETYmlJp20ovFXS4X5513Hueddx4HDx7ktdde4+WXX+azzz5j9uzZBIPBn/8mIiIiIiJSo4r9Qf40fzufbDwUVnc7Hdx4Wgsu652Ow6GGlFRNqDSL3K/voyJ7XVg9otlQ4k99CIc70qZkUpvtLMrhgZWz2FqQRa6/hNu6HF7mGeny8MbgS3E6NLuuIajWXQzz8vLIzMwkPz8fOLxuXURERERE7LVsTz6T52zmULE/rN4uOYrJYzrQLiXapmRSH/gzV5P39f2Eyn8wC88wiO1+I9Gdr9AMPDmmoBnitqUfcLC0CIAZ21dyamoL+qe0AFBTqgE56cZUcXExM2fO5OWXX+bbb7/FsiwiIyO54ooruOaaa6ojo4iIiIiIVIE/GOJvX+/irdXhe8A6DIMrTknnuv4t8Lj04U+qzp+1hpx5N2NZR/Yqc3hiSRj4KL7G/W1MJrWdy+Hk7q7DuPPbjwCI90aguS0NU5UbU1999RUvv/wy7777LmVlZViWRd++fbnmmmu45JJLiImJqc6cIiIiIiJyAjYcLOLh2ZvZlVcaVm8a52PymA70aBJnUzKpTzxJXfGk9sR/aAUA7oR2JA56Eld0E5uTSW30bdYe4j0RtI87fOLnoLTWXNiqB/tKCnio5yiSfFE2JxQ7nFBjav/+/UyfPp3p06ezY8cOLMsiKSmJ6667jmuuuYauXbvWVE4RERERETkOwZDJK8v2Mm3pHkL/M/3g3G6NuX1QKyI91bqjhzRghsNJwsApZM2agLdRL+L63ovD5bM7ltQyATPE85u+4Y3tK2gRnchrgy7B53IDcHuXwTgNA4dOa2ywjvsVaezYscydO5dQKIRhGIwcOZJrrrmGc845B4/HU5MZRURERETkOOzOLeWh2ZvZcKgorJ4c5eHBUe0Z0DLRpmRSX5jBMhyu8BPSnL4EUs54BYc3QftJyTE9v+kbXt92eFbdrqJc/rphIfd0Hw6A2+G0M5rUAsfdmJo9ezbNmzfnqquu4qqrrqJ58+Y1mUtERERERI6TaVq8vTaD/1u4k4qQGTY2sn0K9w5rS1yE26Z0Uh9YlkXxxtcp2foeKWNexukLb3L+77XID13e5hQ+3beJnPKSypppmZolJcAJNqZGjhypDriIiIiISC1yqMjP5DmbWbY3P6we43Vxz/C2jG6fovfwclLMQCn5Sx6hbO98AHK/vo/k4X/DcGhJqBxbYUU5K3P2MbRxW+DwxuaTe43hgZWfcX+PkQxJa2NzQqlNjvuZZNSoUTWZQ0REREREToBlWXy2KZM/frmd4opg2Fj/5gk8NKo9qTFem9JJfREs3EPuwnsIFOysrFVkrqZ873wiWoy0L5jUWiuz9/Hgylnk+Et46fSL6JrQGIB+Kc35cMTVRLg0e1PCqcUtIiIiIlLH5JcFeHzeVr7clh1W97oc3DaoNRd0b6xZUnLSyvYtJH/xJMzAkeVXhsNNXN+71ZSSY9pSkMVNi9/hv+cuPLBiFm8OHU+k6/C+1GpKybGoMSUiIiIiUocs3JHDlLlbyC0NhNW7psUweUwHmidE2pRM6gvLMila9xJF370cVndGppI46Ak8SZ1tSia1XbvYZEY16cCc/ZsBcBoG2eUlNI/WgWny49SYEhERERGpA0orgvx5wQ4+/O5gWN1pGFx/WguuPKUZTodmScnJMf2F5C2eRHnGN2F1b2pvEk6fok3OJYxlWSw4tIOBqS1xOZwYhsG93YezNi+DU1NacEeXIZolJT9LjSkRERERkVpu1f4CJs3eTEZheVi9dWIkj5zRkQ6p0TYlk/okkL+N3AX3ECzeH1aP7ngpsT1vxnA4bUomtVFRoJwn1n7B5/u3cFW7vtzUaSAA0W4vbwweT6zHZ3NCqSvUmBIRERERqaUqgibPL97FGyv2Yf2gbgCX9k7ntwNa4nHpuHU5eaW7Pyd/6WNYwSPNT8PlI77//US20EFYEs60TG785l22FmQBMH3bMvqntKB3cjqAmlJyQqrlVSw3N5e9e/dWx7cSERERERFgS1YxV8xcxev/05RqHOPj+Qu6c/vg1mpKSbWoyFpL3qIHw5pSruimpIx+SU0pOSaH4eDqdv2OXGOwqzjPxkRSl1X5laygoIDbbruNRo0akZKSQqtWrSrHli5dyplnnsmKFSuqJaSIiIiISENhmhavfLuHK2euYntOSdjYrzunMfOy3vROj7cnnNRL7uRuRLb+VeW1r8kAUsa8gju+rY2ppLbJKC1gzw+aTyOatGNc8y6kR8UzbdBvOK9lNxvTSV1WpaV8ubm5DBgwgC1bttC7d29SUlLYuHFj5Xj37t1ZtGgRM2bMoE+fPtUWVkRERESkPtubX8bDszez7kBhWD0x0s39I9ozuE2STcmkPjMMg/i+fyBYsANvkwHEdL0aw9BsPDni070beWrdFzSNiueV03+Dx3m4lXBn16FYWES6dOqeVF2Vnm0mTZrEli1beOutt1i+fDkXXnhh2HhERARDhgzhiy++qJaQIiIiIiL1mWVZvLs2g0vfWHFUU2pImyTeuqyPmlJSbYLFGUfVDKeH5JEvENvtWjWlJMyb21cyadVsSoMBthZk8feNiyrHIlxuNaXkpFXpGeejjz7iV7/6FRdddNGPPqZly5bs27evysFERERERBqCrGI/t324nie+2EZ50KysR3mcPDy6A3/8VWcSIvXBT06eFaog/9snyPx0PIH87UeNG063DamktjsjvSPx3ojK600FmQTMkI2JpL6pUmPqwIEDdO7c+Scf4/V6KSkp+cnHiIiIiIg0ZHM2Z3Lx6ytYvCs3rN4nPZ6Zl/XhV50bYRiGTemkPgmVZpI99yZKtn2AFSwjd+E9mBVFdseSWihkmqzLPVB5neiN5OGeo3EaBjd0PI1/DDgft8NpY0Kpb6q0x1RSUtLPnsK3adMmGjduXKVQIiIiIiL1WWF5gCe/3MaczVlhdY/Twc0DW3Jxz6Y4HGpISfXwZ64i9+v7McuPNECDxRn4Dy0notkwG5NJbXOgtJAHV85iQ/5BXhl0MR3iUgEY2KgV7424isaRsTYnlPqoSjOmBg8ezIcffvijS/U2bNjArFmzGDly5EmFExERERGpb5bszuPi11cc1ZTqlBrN65f24tLe6WpKSbWwLIvizf8iZ97vwppSDm8cycP+qqaUhNlfUsD4r95gbW4GQdPkgRWfUR4MVI6rKSU1pUqNqfvvv59QKMTAgQOZMWMG2dnZAGzcuJFp06YxfPhwvF4vd999d7WGFRERERGpq8oCIZ78Yhu3vL+OrJKKyrrDMLju1Ba8/JuetE6KsjGh1CdmsJz8xZMoWPEMlnVkPyB3YgdSxryCN62vjemkNmoSGUvf5OaV19nlJWwtzLYxkTQUVVrK161bN/71r39x+eWXc8UVVwCHu/Fdu3bFsixiYmL497//Tbt27ao1rIiIiIhIXbTuQCEPz97M3vyysHrz+AgeOaMjXdJibEom9VGwOIPchfcQyNsaVo9sdSbxfe/BcHltSia1zdaCLNrEJuEwHBiGwf09RvJd3kHSImN4tPcZNImMszuiNABVakwBjBs3jp07d/Lqq6+ydOlScnNziY2NpX///lx11VUkJydXZ04RERERkTonEDL555I9vLp8L6ZlhY39pmdTfjewJT63NhGW6lOesYS8bx7CrCisrBmGk7g+dxDZ7nxtpi8AmJbJ9K3L+efmxVzb4VSuad8fgFiPj+cHXECTyFicjiotsBI5YVVuTAEkJiZyxx13VFcWEREREZF6Y3t2CQ/N3syWrOKwemq0l4dHt6df8wSbkkl9ZFkWxRteo3Dt8/CDJqjTl0TCoMfxpvSwMZ3UNncv+w8LD+4A4J+bl9AvuTndEg8fXtYsOt7GZNIQnVRjSkREREREwpmmxZur9vPcN7sIhMywsbEdU7l7aFtifHobLtUrkLeZwrUvhDWlPMndSDz9cZyRKTYmk9poZJN2lY0p07L48sC2ysaUyC+tSq+Ir7322s8+xuFwEBsbS4cOHejQoUNV/hgRERERkTolo6CcSXM2s2p/QVg9zudm4oi2jGinBoHUDE9iR2K730jhmucAiGp3PnG9b8dwum1OJrVBabCC4oCf1IjD+9mNTe/E4szdLDi4nbu7DePM9E42J5SGrEqNqQkTJpzQ2uSOHTvy7LPPMnz48Kr8cSIiIiIitZplWXy84RBPz99OaSAUNjawVSIPjmxPUpTHpnTSUER3vpxgwXa8aX2JbP0ru+NILbEx/xAPrPiMaLeXaadfhMtxeF+7e7oN4/oOp5IeFW9vQGnwqtSYeuWVV3jvvff4+OOPGT16NAMHDqRRo0YcOnSIRYsWMWfOHMaNG8fgwYNZuXIl//rXvzjzzDNZuHAhffvqWFIRERERqT9ySyuYMncrC3fkhNUj3U7uGNKas7ukacNpqVaWZVKRtQZvaq+wumEYJAyYbFMqqY3mZmzhoZWzCJqHlxW/uHkJv+00EIAot5cot05oFPtVqTEVFxfHnDlzmDdvHsOGDTtqfP78+Zx55plcffXV/P73v+e6665jxIgRPPHEE7z77rsnHVpEREREpDb4cls2j8/bSn5ZIKzeo0kck8e0p2lchE3JpL4y/YXkffMQ5QeXkjTkGXxNTrU7ktRi3RMaE+l0U2j6AZi1fxMT2vUl0qUZnFJ7VOn8x8cff5yLLrromE0pgKFDh3LhhRcyZcoUAIYMGcIZZ5zB119/XfWkIiIiIiK1RLE/yMOzN/OH/2wIa0q5nQ5uOb0VL17QXU0pqXaBvC1kzb6S8gNLwLLI++YhgsX77Y4ltczB0sLK/06NiOH+nqMAGNGkHW8MHq+mlNQ6VWpMrV+/nvT09J98THp6OuvXr6+87ty5M/n5+VX540REREREao1le/K5+PUVfLrxUFi9fUo0r13SiytOaYbDoaV7Ur1Kd84ia851BIsPVNYsM0CwcI+NqaQ2KQ8GmLpmHhd88So7io4sLR7WuC3/PP0iHu9zJrEen40JRY6tSkv5oqOjWbhw4U8+ZuHChURHR1del5SUEBMTU5U/TkRERETEdv5giL99vYu3VofPUHEYBlecks51/VvgcVXp974iP8oygxSuepbizf8Kq7ti0kkc9CTu+DY2JZPaJNdfyg2L3mZ3cR4A96/4jFcHXYzHefgjf4/EJnbGE/lJVXrlPPvss1m0aBG//e1vycrKChvLzs7m5ptvZtGiRZx99tmV9dWrV9OmjZ40RURERKTu2XCwiMtmrDqqKZUe5+OfF/bg5oGt1JSSahcqzyXni1uOakr5mp5OyphX1JSSSgmeCJpExlVe7yjKZknWbhsTiRy/Ks2Ymjp1KosWLeL555/nlVdeoW3btqSmppKZmcm2bdvw+/107NiRqVOnAnDw4EHKysqYMGFCdWYXEREREalRwZDJy8v2Mm3pHkzLChs7r1tjbhvUikhPld5Si/ykiux15C68j1DZDyYCGAYxXa8lputVGIYaoQ1dnr+UOI8Ph+HAMAwe7jWaS+a/gdMwmNxrDH1TmtsdUeS4VOlVNCkpiW+//ZYnnniCGTNmsH79+sr9pFq2bMn48eO55557KpfypaWlsXLlyupLLSIiIiJSw3bllvLw7M1sOFQUVk+O8vDgqPYMaJloUzKp70q2vU/B8j9jmUc21ne4o0kYMBlf04E2JpPaYsHB7UxZPZfL2vbhiranAJDojeSZ/uNoGhlHnEeHL0jdUeVf70RFRfHoo4/y6KOPUlRURGFhIbGxsdpHSkRERETqNNO0eHttBv+3cCcVITNsbGT7FO4d1pa4CLdN6aQ+s0IV5C//E6XbPwqru+Nakzj4SVwxzWxKJrXJsxsW8vq2FQA8v+kb+iU3p2N8KgCd49PsjCZSJdUy7zgmJkYNKRERERGp8w4V+Zk8ZzPL9uaH1WO8Lu4d3pbRHVLtCSYNQrDkAGW7Pw+rRbQYSXy/+3C4I21KJbVN86iEyv8OmiavbVvO46ecaWMikZOjhckiIiIi0uBZlsWnGw9x8esrjmpK9W+ewFuX9VFTSmqcO7YFCac+cPjCcBDX6xYSBjyqplQDZ1kWRYHyyutxzbswrHFbAMa36c2kXqPtiiZSLao8Y2rv3r1MmTKFuXPnkpGRQUVFxVGPMQyDYDB4UgFFRERERGpSflmAx+dt5ctt2WF1r8vBbYNac0H3xhiGYVM6aWgimo8gtsdePEld8aadYnccsVmuv5TJq+ZQEqzghQEX4HQc3uj8/h4jOb9ld/ppg3OpB6rUmNqxYwf9+/cnLy+PLl264Pf7adGiBT6fjx07dhAIBOjRowfx8fHVHFdEREREpPos3JHDlLlbyC0NhNW7pcUyaUx7midoporUDDNYRtmu2US2OfuoxmdMlwn2hJJaZXXOfu5Z/gl5/lIAXt76Ldd1OBWAWI9PTSmpN6q0lG/y5MkUFBQwb9481qxZA8BVV13Fxo0b2bVrF+PGjaOkpIR33nmnWsOKiIiIiFSH0oogU+Zu4fcfrQ9rSjkNg5sGtOSfF/VQU0pqTLBoH9lzriX/2yco3fa+3XGklkqNiKYidGQF0oztKyioKLMxkUjNqFJjau7cuZx55pkMGTKksmZZFgCNGzfmX//6FwD33XdfNUQUEREREak+q/YXcMkbK/nwu4Nh9daJkbx6SS+u7tccp0NL96RmlGcsJmv2VQTytwNQsOLPVGSttTmV1BZlwSON8iaRcdzbYwQAbWOTeXnQxcR5IuyKJlJjqrSULzs7m44dOx75Ji4XpaWlldder5dRo0bxwQcfnHRAEREREZHqUBE0eX7xLt5YsQ/rB3UDGN8nnZtOa4nHpbOBpGZYlknx+ukUrvsnWEfuQIc3HrSHWYNnWRbv7FrLPzcv4aXTL6J59OGT98Y07YBpmYxo3A6Ps8pbRIvUalW6s5OTkykpKQm73rVrV/g3drnIz88/mWwiIiIiItViS1YxD83azPackrB64xgfk8a0p3d6vD3BpEEwK4rJW/II5fsWhNU9Kd1JPP1xnBHJNiWT2qA0WMGDK2ex8OAOAB5Y+RnTTv8NbocTgLHpneyMJ1LjqtSYateuHdu3b6+87tevH7Nnz2bHjh20bt2arKws3nnnHdq0aVNtQUVERERETlTItHht+V5eXLKboGmFjY3rksbvB7cmyqtZCFJzAgU7yF1wD8GivWH1qPYXENfrNgyn26ZkUlv4nC7KQ0eW8G3Kz+SzfZsY17yLjalEfjlVmqs8duxYvvzyy8oZUbfffjtFRUV0796dvn370r59ew4ePMgtt9xSnVlFRERERI7b3vwyrnt7Dc99syusKZUY6ebpX3fhwVHt1ZSSGlW290uy5lwb1pQynB4STnuI+FPuUlOqAQuYocp9mh2Gg4d7jibO48PjcHJn16H8ullnmxOK/HKq9Ep80003MXToUJzOw1MLhw4dyltvvcWkSZP47rvvaNGiBVOmTOG6666r1rAiIiIiIj/HsizeW3eAvyzYQXnQDBsb2iaZ+0a0JSHSY1M6aQgsy6RwzT8o3vB6WN0ZlUbioCfwJHb8ka+UhmBXUS73r/yMs5t34aJWPQFIjYjhsT5nkuiNpG2slnZKw1KlxlRsbCz9+/cPq1144YVceOGF1RJKRERERKQqsor9PDp3K4t35YbVozxO7h7aljM7pWJoo2mpQaa/gNxFD+I/+G1Y3ZvWl8SBU3B442xKJrXBR3vW88d1X+IPBfm/9Qvpk5ROm+8bUf1SmtucTsQeVVrKN3z4cB588MHqziIiIiIiUmVzNmdy8esrjmpK9UmP563L+nBW50ZqSkmNs6wQwcJdYbXozpeTNOyvakoJ5aEA/lAQgAozxJ++m29vIJFaoEqNqaVLlxIKhao7CwBTp06lb9++xMTEkJqayjnnnMPmzZtr5M8SERERkbqvsDzA/Z9t5P7PNlHoD1bWPU4Hvx/ShufO60ZarM/GhNKQOH2JJA6aiuFwY7gjSRw0lbieN2MYVfroJfVAyDyypPjClj04vVErAPqnNGdK77F2xRKpNaq0lK9jx47s3r27urMA8NVXX3HzzTfTt29fgsEg9913H6NHj2bDhg1ERUXVyJ8pIiIiInXT4l25PPr5FrJKKsLqnVKjmTymI62SIm1KJg2ZJ6kLCac9jCu+Ne641nbHEZsEzBAvbFrMxoJDPHvquTgMB4Zh8GDPUXyesYULWnbHoYalSNUaU7fccgu/+93v2LBhA507V+9pAbNmzQq7nj59OqmpqaxYsYLBgwdX658lIiIiInVTWSDE/y3cyTtrM8LqDsPgmv7NubpvM1xOfeCTmhUqy6F44wxie96M4XCGjUW0GGlTKqkN9pXkc9+KT9mUnwnAG9tXckXbUwBI8EZWbnouIlVsTLVu3ZqhQ4dy6qmncsMNN9C3b18aNTr2mv2TbSYVFBQAkJiYeFLfR0RERETqh7UZhUyas5m9+WVh9RYJETwypiOd02JsSiYNSUXWWnK/vo9QWTZgEdf7NrsjSS1iWha7i/Mqr5/f9A2jm7QnLTLWxlQitVOVGlNDhw7FMAwsy+Lpp5/+yU0kT2YvKtM0uf322xk4cCBdu3Y95mP8fj9+v7/yurCwsPJrTdM85tfUFaZpYllWnf85pHbRfSU/RveG1ATdV3KyTNPE4XBgmiaBkMlLS/fw6op9WP9zS13UszE3D2iJz+3U/SY/62SemyzLonTb+xSu/AtYh/c0K940E1dCRyJajKrmpFJbHeseMi2zcmleemQcv+88mClr5xLnjuD+HiNI9UXr+Ul+Un1633QiP0OVGlMPPfTQL3Kiyc0338x3333H119//aOPmTp1KpMnTz6qnpWVRXl5eU3Gq3GmaVJQUIBlWTgcmoou1UP3lfwY3RtSE3RfSVU5nU4stxe/6SDX76Qkv4yV+wr4ZMMBKioClY9LjnRz54Am9GocTWFeDoU2Zpa6o6rPTVaoguDG5wjt/zys7ohpSaGVSlFmZnVHlVrqf++hdQWH+NuOpUzuNJw0XzQA/bzJXNakGyNTWpPkiCRT94f8jPr0vqmoqOi4H2tYlmXVYJYq+93vfseHH37IggULaNWq1Y8+7lgzppo1a0ZeXh6xsXV7mqRpmmRlZZGSklLnb0qpPXRfyY/RvSE1QfeVVFVOaYAXl+xmzuZMduWUELAMzu2axqW903nqi21kFJZzRscU7hrShhhflX7XKg1YVZ6bQiUHyft6IoG88BPDfc1HEtdvIg5XRE1ElVrqv/dQYlIS07Z9y6vblmNi0S2hMS+cdgFOveZJFdSn902FhYUkJCRQUFDws72ZWvcqblkWt9xyC++//z7z58//yaYUgNfrxev1HlV3OBx1/i8SwDCMevOzSO2h+0p+jO4NqQm6r+REFfuDvLhkN2+vOUBGQRklFSEMw+DV5fsAuKR3U1KjvQxvl2xzUqnLTuS5yX9wObmLHsD05//gGziI63ULUR0u/kVWk0jtUFBRRoUZAguM2EgKQhUMb9KeromNufvbj/ku7yBv717LpW162x1V6qj68r7pRPKfVGNq1apVzJw5k02bNlFaWsrcuXMB2L17N0uXLmXkyJEnvGn5zTffzJtvvsmHH35ITEwMBw8eBCAuLo6ICP0WQkRERKS+K/IHeX/dAXbmlhIywyf3z96SycQR7UiP1/tCqXmWZVG8aQaFq5/jhxubOXwJJA58DG8jNR8amgozxLlzXyFohQgGgrjcLiwLpg36DQ7D4FfNunBui252xxSpU6rcmPrDH/7A008/zX9XAv7wtwSWZXHppZfy9NNPc9ttJ3Y6xT/+8Q/g8AbrP/TKK68wYcKEqsYVERERkTogZFrszitlU2ZJWN1hQKMYL/ERboorgjalk4bEDJSSv/QxyvbMC6t7kjqTOOgJnJGpNiUTO5mWxZ6SPLxON9GGC8OycBkO4twRPN7nTIY1aWd3RKnDrPJCEnyH/5fIeLvj/GKqNDfslVde4U9/+hO/+tWvWLt2LRMnTgwbb9myJf369eOjjz464e9tWdYx/09NKREREZH6LavYz50frcfpMIjxHvn9qc9l0CoxkvgIN1EeJ3E+t40ppSEIlWaSPefao5pSkW3GkTzyeTWlGqglmbvJLi+hsMLPobKiw0v6vudzudSUkpNmBSvY/ZdzsIIVdkf5RVWpMfXcc8/RqVMn3n33Xbp27YrH4znqMR07dmTr1q0nHVBERERE6r+lu/MYP2Mli3blsulQMed1SwMgIcJNeqwXj/Pw29bR7VPCmlYiNcHhiQPnkQao4XAT3+9eEvrfh+E8+rOPNAy7i3MJfb+k07IsDvmLbU4k9UnJ5gWH/+MHDc+Gokqv6hs2bOC6667D5frxL2/UqJGOwxQRERGRn2SaFi8u2c3L3+7hv7tJvbP2ABNHtCUxys3C7bnkl5YT5XEyukMK15/agmg1pqSGGS4viYOeIGvWBAynl8TTH8OTrH2DGiLTMnEYhxvjF7bqwdbCbFyGA4/TRaonAqdh4DTq9ibVYq9QaQFWsAJ3QlMqDm3HMk2CBYeAw9slGS4Pzsg4e0PWsCq9qrtcLioqfnpqWUZGBtHR0VUKJSIiIiL1X05JBfd/tokV+/LD6rE+Fy0SIrlnWDtuOi1Ibkk5iVE+Yn0uNaXkF+OKakzSkKdxRjfB6TuxA52k7guZJq9sW8a63AM8038cDsOBw3CQ5I1k6bjbiXF7ME0Tp8MJBngcTrsjSx1lBSvY++x5EAriz9wBEXFkvHwthsMJDifNbnnP7og1rkqv7N26deOLL74gFArhdB79D/C/J/T16dPnpAOKiIiISP2zbE8+D8zaSG5pIKx+Qfcm3DG4NR7X4RkIkW4H7ooikmJi6/zR2VI7BQp2ULj6ORJOm4TDE/6LdU9yV5tSiZ32leTz4MpZrM87fEL8v3eu4eLWvQBI9kWT7IvGNE0yMzNJSk3Vc5OcMCtYQem2xUR1HILh8lQ2nwJ5+9j9fxfS5OqXcMUdXtJuuOr/8uEq/Qu6+uqr2bJlCzfeeCN+vz9srLCwkAkTJnDw4EGuu+66agkpIiIiIvWDaVr8c8lubn5vbVhTKtLt5LGxHblneNvKptR/hUINb78NqTmh8vzK//YfWk6wYDeh8jzyljyC9f3+QdKwmZbFtsLsyuu/bfiazLIiGxNJfVKRuYP9/5zAoZl3UrptMc7IOFyxKbhiU3AnNMURlYArrlFlrb4v44OTaExdfPHFTJs2jZSUFKZNmwZAv379aNq0Ke+88w5XXnklF1xwQbWGFREREZG6K7e0gt+9v44Xl+yu3E8KoH1KNK9f2ovRHXTSmdSsYHEGhWuewyw5gKdwBYZlUrLtPWK7XUeoLIeSzf+2O6LYJGQeaUo2j07gti6DAIhxe3mw12hSI2Lsiib1hGVZFCz9F/tfuIyKg1sAyHp/EqGS/PAHNsBloVVepP/mm28ybNgw/va3v/Hdd99hWRbLly+nU6dO3Hrrrdxwww3VmVNERERE6rCV+/K5/7NNZJeE71N6brfG3DmkNV5Xw3sjLr+sUHk+ReunU7r1PcozviG2x03kLXoA/6HlAMT2uAFPQkebU4odvsjYyrMbv+Yfp51PWmQsAOe36E52eQnntuhGIzWl5CQFi7LJ+mAyZdsWhw9YJoHcvTij4oHDy/Za3P5Bg1i+90MntXvkddddx3XXXUdZWRl5eXnExsZqw3MRERERqWSaFtOX7+WFxbsxrSPzpCLcTu4b0Y4zOmqWlPwyHN44otpfQPne+fj3f03mvoUYhoG30SnE9rgJd2JHDJ2u1qCUBwM8ue4LPtm7EYBJq+bw3IDzcBgODMPgxo4DbE4o9UHJxvlkffQoZmlBWD2y3UBSznkYZ/SRwxUMXyx5heWkxsb+0jFtVaXGVHFxcVgDKiIigoiIiGoLJSIiIiJ1X15pBQ/N3syS3Xlh9TZJUTxxVidaJkbalEwammDRXvK/fQJndFPiet9O5qeXHh4wDBIGPoonqbO9AcUWboeTPT9YRrUyZx//2buRcc272BdK6g3TX0rOrD9TtPKDsLrh8pA05g5i+l6AYRj2hKtlqvQrgUaNGjF+/HhmzZqFaWqDQBEREREJt3p/AZe9ueqoptS4LmlMv7inmlLyi7DMEEUbXifz0/GYwXIimg2nYMWfDw86vbhiW1K47iWCxRn2BpVfTMAMYX0/e9PpcDC51xgiXG4chsFV7foyNl3LOaV6HJp551FNKU/jDjS9cQax/S5UU+oHqtSYatOmDTNnzuSss86iSZMm3HHHHaxYsaK6s4mIiIhIHWOaFq8u28sN76wls/jI6c0+l4NJozvw4Kj2+NzaT0pqXiBvC1lzrqZw9d+xQhVEd7yE4k0z8GeuwNd8FI3O/RRPam8COespWj897LQ+qZ+2FmRx5YKZfLx3Q2UtPSqeB3qM4oWBF3JTp4G4G+DG01Iz4odcA/9tPhkG8adfSdNrp+NJaWVvsFqoSo2ptWvXsnr1au644w6cTid//etf6devH507d2bq1Kns2bOnunOKiIiISC1XUBbgjo/W87dFO8P2k2qdGMmrl/TirM6NbEwnDU2w5BCB3M2V18WbZhLT9Wpi+9xB8vC/EohsR+LAR4lsM46YLhNw+uLtCys1buaOVUxY+BbbCrP583fzyfjBfj+jmranR2ITG9NJfWBZVth1RKtTiBtwOa64RjS+8nkSR92C4XLblK52q/Luft27d+dPf/oT+/btY/bs2YwfP559+/Zx//3307p1a4YOHcq0adOqM6uIiIiI1FJrMwq5dMZKvtmVG1Y/q1Mjpl/Si9ZJUTYlk4YqIn0QES1GAuBwRxPV9mw8KT2J63krjsg0ysrKcESmEdvjt7ii1ZSo7wJmiIAZAqA0GOCZ7xbYnEjqC8uyKFr9Hw5MuwYz4A8bSxx+E01vmklEqz42pasbTupUPgDDMBg1ahSjRo2irKyM999/n9dff525c+fy9ddfc80111RHThERERGphSzLYsbK/fzt652EfvDbYq/LwR+GtWVclzQb00lDYVYUY7gjjzpVL67P7zEcHmJ7/hZnRDIATl982D65milVP1mWRYUZwus8/JH3sja9+frQTlbn7GdQWmsm9hhhc0KpD0JlhWR//Dgl6+cCkDf3bySNvbNy3HC5cWqW1M866cbUDwWDQfx+P36/X5uii4iIiNRzheUBJs3ZwsIdOWH1lgmRPHFWJ9oka5aU1LyyvV9RsPyPRHcaT3THS8LGnL5EEk57yKZkYpeCijKmrpmH3wzx537jMAwDh+FgUq/RLMvay7jmXbTxtJy0sh3LyHr/YYKFmZW1giUzieoyEl/zHjYmq3tOujEVCoX49NNPeeONN/jPf/5DeXk5DoeD0aNHc/nll1dHRhERERGpZb47UMh9n27iQFF5WP2MjqlMHN6WSE+1/v5T5CihshwKlv+Rsr3zAShc+wK+9CFaltfArczex30rPiXXXwrA+7u/47yW3QBoEhnH2S3i7Iwn9Uggd29YUwqHk8ThN+FN72ZfqDqqyu8YlixZwhtvvMG///1vcnJysCyLnj17cvnll3PppZfSqJE2txQRERGpbyzLYuaq/Tz79U6C5pGlex6ng7uHteHsLmmaiSA1yrIsSnd8TOGqZzErio7Ug+UUrX+FhP7325hO7Bbn8VH8g31+/r7xa85I70Cky2NjKqmPYvqcS+mWryndvAB3cgtSz5+Ct0knu2PVSVVqTLVr144dO3ZgWRZNmzbl7rvv5vLLL6dLly7VnU9EREREaomi8iCPfL6F+duzw+rN4yN44qxOtEuJtimZNBTBon3kf/sE/kPLwwcMB9GdxhPb9Vp7gomtSoMVlY2nNrHJ/LbTQP6yfgGtYhJ5pPcZakrJSbNMk9LNC4jsOKTyly+GYZAy7gHyv3mDhKHX4/D4bE5Zd1WpMXXw4EGuuOIKLr/8coYNG/ajvxXz+/14vd6TCigiIiIi9ttwsIiJn24kozB86d6o9ik8MLKdlu5JjbLMEMWb3qRo3UtYofBTr9yJHYjvdx+exA42pRO7BM0QL29dxru71vLGkEtJ8R1ujl/cuicuh4Ozm3et3PxcpKqChVlkfTCZsu1LSP71fcSecl7lmDM6kaTRt9qYrn6o0r/SzMxMIiIifnR85cqVTJs2jbfeeoucnJwffZyIiIiI1G6WZfHvNRn8ZcGOo5bu/X5Ia87r1lhL96RGBfK2kLf0cQK5m8LqhtNDTLfriO54KYbDaVM6sUtOeQm///YjNuYfAuDR1Z/z1/7nVG50flGrnvYGlHqhZOOXZH00BbO0AICcz57G17IPnuQWNierX6rUmDpWUyo/P5833niDadOmsXbtWizL+snmlYiIiIjUbsX+II9+voUvtoUv3UuP8/HEWZ3pkKqle1JzrKCfou+mUbTxDbDCT/z2pvYmvv9EXDHNbEondovz+LA40ixfkrmbJVm7OS21pX2hpN4w/aXkzHqaopUfHjUWyNyuxlQ1O+l5jXPnzmXatGl8+OGH+P1+LMvitNNO46qrruI3v/lNdWQUERERkV/YpswiJn6ykX0F4Uv3hrdN5sFR7Yn2anmM1Kyija9TtOG1sJrDHU1sr1uIbDNOM/UaoMKKcmK/38fH5XAyudcYLv/qTbxOF/d0H66mlFSL8n3fkfXuAwRy94XVvY07knL+FDwpLe0JVo9V6R3F3r17eeWVV3jllVfYs2dP5Sbo+/fvZ8KECbz88svVnVNEREREfgGWZfHu2gP8ecEOAqEjs1TcTge3D2rNhT20dE9+GdEdL6V0+8eESg8v1YpoNpS4PnfijEyxOZnYYV7GVqaumcvd3Yczpunh/cRaxSTx2Cln0ikuldSIGJsTSl1nmSHyF7xM3vx/hs/SNAziT59AwtDrMVxu+wLWY8fdmAoEAnzwwQdMmzaNefPmEQqFiIqKYvz48VxxxRUMHz4cl8uFy6XfnomIiIjURSX+II/N28rnW7LC6k1ifUw9sxOd0/TBT345Dnck8f3uIX/JY8T1vYuIZsPsjiQ2sCyLKWs+5+M9GwB4au0X9ExsQqPvG1FD0trYGU/qkexPnqRo+XthNVdcGinnPUJEy942pWoYjruL1KRJE3JzczEMg2HDhnHFFVdw3nnnERUVVZP5REREROQXsCWrmImfbGRPfllYfWibZB4a1Z4Yn375KDUjVJZDwcpniOl8Be6E9mFjviYDSB33Lg6XjmFvqAzDIC0itvK6KODntW3LububGpVSveJOvZTi1Z9gBQ+f/Bnd7QySzroHp2bj1bjjfoeRk5ODw+Hgjjvu4A9/+AMpKZpCKyIiIlLXWZbFB98d5E/zt1Pxg6V7LofBrYNac3HPJlq6JzXCsixKd/yHwlXPYlYUEizaR8roaUedsKemVMNTEQpSYYaIdnsBuLpdP77J3MXG/ENc2a4v17bvb3NCqQ8sywp7ffOktCTpjN+TO/dZks+6l+juZ9iYrmFxHO8DJ0yYQEREBH/+859JT09n3LhxvP3221RUVNRkPhERERGpIaUVQR6avZnH520Na0o1jvHx0kU9uaRXUzWlpEYEi/eT8+Wt5C99DLOiEIBA7iaKN8+0OZnYbUtBFlcsmMmjqz/Hsg6fuud0OHik1xm8OPBCbuo4APf/NC9FTlTZ9m/JePEKQsW5YfWYU84j/ZZ31ZT6hR13Y+rll1/mwIEDvPDCC/Tu3Zv//Oc/XHzxxTRq1IgbbriBr7/+uiZzioiIiEg12pZdwhUzVzFrU2ZYfVDrJN4Y34su2k9KaoBlhijeOIPMTy7Ff3BZ2Jg7oR3eRn1sSia1wcd71jNh4Ux2FOXw5YFtfLpvY+VYs+h4uic2sTGd1AdWsIKcWc9w4LXf4s/YSNZHUyoboHB46agrOsnGhA3TcTemAKKjo7n22mtZvHgx69ev5/bbb8fj8fDPf/6TIUOGYBgGmzdvZvfu3TWVV0RERERO0kfrDzLhrVXszjuyn5TTMLh9cGue/nVnYn06dUiqXyBvC9mfX0vBqmexQv7KuuH0ENvzt6SMeQVPYicbE4rd2seFbxfz8pZvCZnmjzxa5MRUHNrO/hevoGDxjMpa6eYFFK/9zMZUAifYmPqhTp068fTTT7N//37+/e9/M3r0aAzDYOHChbRp04YRI0bw+uuvV2dWERERETkJZYEQk2Zv5tHPt+APHvmw1yjay4sX9mB873Qt3ZNqZ4UqKFzzPFmzrqIiZ2PYmDe1F6lj3yCm8xUYDm2w39BYlkVmWVHldYe4VK7rcCpw+LS9l06/CKejyh9ZRcIEizKpOLQtrBbb70KiOg23KZH810k/+7tcLi644AIuuOAC9u3bxyuvvML06dP58ssvmT9/Ppdffnl15BQRERGRk7Ajp4SJn2xkR25pWH1Ay0QeGdOBuAjNkpLq589cRf7SxwkW7Q2rO9zRxPb6HZFtxmEYajw0RHn+Uqauncea3APMHHoZid5IAK5sewqtY5IY3Ki1GuVSrSLbnkZs/4spXPoWzqhEUs55iMj2p9sdS6iGxtQPpaen8+CDD/Lggw8yb948Xn755er89iIiIiJSBZ9sOMQTX2yl/AezpByGwc0DW3JZ73QcDn34k+pXtOE1Clc/d1Q9otlQ4vrciTNSp3w3VNsLs/nt4vfI8x9ulD++Zi5/7PtrDMPAYTgYktbG5oRSH5RuW0xEyz4YLk9lLXHULYBFwuBrcEYn2hdOwtTYfNkRI0YwYsSImvr2IiIiIvIzygMhnvpyOx9vOBhWT4328tjYjvRsGmdTMmkIvKm9wDDgvyer+ZKI63sXEc2G2ZxM7NYsKp5Eb2RlY+qbzF1sL8qhbWyyzcmkPjD9peR89ieKVn1E3MArSBp9a+WYw+0l+cy7bUwnx6J5syIiIiL10K7cUia8tfqoptSpLRJ449JeakpJjfMkdyO6/UUARLb5NalnzVRTqgHbW5xf+d8ep4tHeo3B7XDSOiaJ6YMuUVNKqkX53nXsf/5SilZ9BEDBN69TtnOFzank52iHQREREZF6ZtamTB6ft5WyQKiy5jAMbjitBRNOaaale1KtLMuibM9cIpoOxnB5w8Ziut+AL30I3ka9bUondguaIf65ZSmvbl3G433OZHiTdgC0i0vh/049l24JaXic+lgqJ8cyQ+R/NY28r14CK/wkR3/GRiJa9bEpmRwPPQOIiIiI1BP+YIg/zd/OB9+Fz5JKjvLw2NiO9E6PtyeY1FvB4v3kf/sE/oPLCHS6jLhevwsbd7gj1ZRqwIJmiGu//jcb8g8BMHXtPLonNiHZFwVAn+R0O+NJPRHI3Ufmuw/i37curO6KSyPlvEeIaKnnoNpOjSkRERGRemBPXikTP93ElqzisHq/5gk8ekYHEiM9P/KVIifOskxKNr9F4doXsYLlABRvepOIFiPxJHa0OZ3UFi6Hk34pzSsbUwUV5czZv5lL26hRICfPsiyKV31Mzmd/wqwIP3E2uvtYks78A86IGJvSyYlQY0pERESkjpuzOZPH5m6l9AdL9wzg+tNacHXf5lq6J9UqkLeV/G8fpyJnY1jdcLgIFuxQY6qByywrIsLlJsbtA+C6DqfyTeYuDpYWcm+PEYxs0t7mhFJfFHwzg9w5fwmrOXzRJP9qItHdxtgTSqpEjSkRERGROqoiaPLnBdt5d+2BsHpipJspZ3Sib/N4e4JJvWSFKij67mWKN7yOZYXCxrypvYjvNxFXbHOb0kltMGf/Zp5a+wUDGrXikd5nAOB2OHm8z5lEuNyk+KJtTij1SUzPX1HwzeuEinMA8LXoTep5j+CKT7M5mZwoNaZERERE6qB9+WXc+8lGNv/P0r0+6fE8NrYjSVFauifVx5+5ivxvpxIs3BNWd7ijiO31OyLbnI1h6MDvhuy5jYuYvnUZALP2beL0Rq0Y3bQDAM2jE+yMJvWEZVkYxpEZwM6oeFLOeZhDM+8kYfiNxA24HMOh56G6SH9rIiIiInXMvK1ZXPbmyrCmlAFc2785z53XTU0pqTZmoIT8ZU+RPfemo5pSvvTBpJ41k6i256opJQxOa43jB02D/+zdgGVZNiaS+qTi0DYy/jmBiqydYfXIdgNodvuHxJ9+pZpSdZhmTImIiIjUERVBk78s3MHbazLC6gkRbh49oyP9W2hWglSf8v2LyF/2JKHSzLC6w5dI/Cl34Ws2LGz2gjQs/lCQ/aUFtI5JAqBrQmOuatePV7Z+y9Xt+3N1u766P+SkWaZJ4dK3yP38WaxQgMx3H6DptdMxXO7Kx7hiU21MKNVBjSkRERGROiCjoJyJn25kw6GisHqvpnE8NrYjKdFem5JJfRXI33pUUyqy9a+I63UrDm+sTamkNthckMlDK2dRGPDz1tDLiPNEAHBN+34MbdyGDnFqFMjJCxZmkfXBJMq2L62sVRzYTP7CV0gYdr2NyaS6qTElIiIiUst9uS2bR+ZsobgiGFa/qm9zbjitBU6duic1ILrTZZTt/pxA/nZc0U2I7zcRb1pfu2OJzZZm7eaOpR8SNE0Anlz7JY/1GYthGLgcTjWlpNqYZYWU714VVovsMJjYvhfYlEhqihpTIiIiIrVUIGTy7Nc7mblqf1g9PsLNI2M6cFrLRJuSSX1j+gtweOPCaobDRXz/+ynbPZeY7tfjcPlsSie1SY+EJjSNjGN3cR4AK3P2keMvJdkXZXMyqW88jdqQOPIWcmY9jeHykjT2TmL6nKslovWQGlMiIiIitdCBwnImfrKR9f+zdK9HkzgeH9uR1Bgt3ZOTZ1kmJZv/ReHaF0ka/NRRM6I8SZ3xJHW2KZ3UBpZlsTbvAD0SmwDgc7mZ3HsM1yz8F4PT2jCx+wjivRE2p5T6oHzvOtzJLXBGHFkqHNv/NwQLDhBzyvl4klvYmE5qkhpTIiIiIrXMgu05TJ6zmUJ/+NK9K05pxk2ntcDl1MlDcvIC+dvIX/o4FTkbAMj/diopZ76pmVFSKddfymNr5rLw4A6e6X82Axu1AqBzfBpvDBlP65gkzV6Rk2aZIfK/mkbeVy8R1WkYqRc9UXlfGQ4HSWf83uaEUtP0rkZERESklgiGTP66cAd3frw+rCkV63XxzNlduOX0VmpKyUmzQhUUrnmBrM+urGxKAQSLMyjd8bGNyaQ2KQ74uWT+Gyw8uAOAR1Z/Tr6/rHK8TWyymlJy0gI5e8mYdg15818Ey6RkwzyKV//H7ljyC9M7GxEREZFa4FCRn+vfWcsbK/aF1bs1jmXG+N6c3irJpmRSn/iz1pD52eUUrX8FywpV1h3uKOL73UNUu/NtTCe1SbTby9j0jpXXxQE/a/MybEwk9YllWRSt/Ih9z1+Kf993YWP/ey31n5byiYiIiNhs0c5cHpq16aile+N7p/O7gS01S0pOmhkooXD1c5RsffeoMV/TQcT3vRtnpE5Ta+jW5R6gTWwSkS4PAL/tOIDFmbtwGA4e6TWGdnEpNieU+iBUWkD2x49RsuGLsLrDF03yr+4juttom5KJXdSYEhEREbFJyLR47ptdvLZ8b1g9xuti0ugODG6jWVJy8sr3LyJ/2ZOESjPD6g5fIvGn3Imv2XAtyWrgAmaIlzYv4dVty/lVs8480HMUAB6ni7/2P4dEbyQepz46yskr3b6UrPcfJlSUHVb3texD6rmTccWn2ZRM7KRnFxEREREbZBb5ue+zTazJKAird2kUw+NndqJJnDaglpMTKs+lYMWfKds996ixyNZnEdfrNhze2GN8pTQ0D6z4jC8PbAPgoz3rGZTWmiFpbQBIi9Q9ItWjZNNXHJp5Z1jNcLpIGP5b4gZchuHQ7OCGSo0pERERkV/Y4l25PDR7M/llgbD6Jb2acsvprXBr6Z5Ug4JlT1G2d35YzRXdmLi+E/E17mdLJqmdLm7di/kHt2FZh69XZu+rbEyJVJfItqfhadSWikOHm6DulFaknj8Fb+MONicTu6kxJSIiIvILCZkWLyzezSvL9oTVoz0uHhrdnmFtk21KJvVRbI/fUp7xDVaoAgwH0R1+Q0z363G4IuyOJjY7VFZEjr+Ezt8vm+qV1JTL2vThoz3rmdh9BMObtLM5odQHlmWFLRM2XB5Sz5/C/hevIKb3OSSOuhWHR7ODRY0pERERkV9Edomf+z7dxKr94Uv3OqVGM/WsTjSNU7NAqpcrtjkxXa+lbPds4vvfhyepi92RpBaYtW8TT637giiXl5lDLyPa7QXghg6ncWnr3iT5omxOKPVBsDCTrA8mEz/wSiLaHJmh6WnUlma3vo8rrpGN6aS20TxxERERkRr27Z48xs9YeVRT6sIeTXjpop5qSslJCeRvI2f+nZj+gqPGojuNJ2XMdDWlBIAPdn/HQytnURyo4FBZEX/6bn7lmMfpUlNKqkXx+nnse+5iyrYvJeuDSYTKCsPG1ZSS/6XGlIiIiEgNMU2LFxbv4nfvrSO39Mh+UlEeJ0+c1Yk/DGuLx6W3Y1I1VqiCwrUvkvXZlZRnLKJg5V+OeozhcGI43b98OKmVRjdtT5PIuMrr3cV5lAUDP/EVIsfP9JeS9f5kMv99D+b3zahgYSY5n/3J5mRS2+mdkIiIiEgNyCmp4Ob31/HS0j1YP6h3SInm9Ut7M6Jdim3ZpO6ryFpL5qwrKPruZSwrBEDpzs8oP7DU5mRSm5QHA3yRsbXyOtLl4ZHeY/A4nFzf8TReGngRES41LqV6mIEySrcsDKt5m3QmYci1NiWSukJ7TImIiIhUs+V783ngs03klFaE1c/v3pjfD26jWVJSZWaglMI1z1Gy9V0qj1D7nq/pINxxrWxKJrXNxvxDPLRyFruL8/jbaefRL6U5AN0Tm/DByKtJ1rI9qWau6CSSz36IQzN/D4aD+EETSBh6PYZTbQf5abpDRERERKqJaVq8vGwP/1yyB/MHTYNIt5P7R7ZjdIdUG9NJXVee8Q353z5JqPRQWN3hSyCuz++JaD4y7AQsabgySgu45ut/ETRNACavmsPMoZcR+/0JaGpKSXUI5OwFw4E7sWllLarjYBKGXk9E6374WvS0L5zUKWpMiYiIiFSD3NIKHpy1mW/35IXV2yVH8cRZnWieEGlTMqnrQuV5FKx8hrJdc44ai2x1JnG9b8PhjTvGV0pD1SQyjnOad+WdXWsBMLHIKC2sbEyJnAzLsihe9RHZn/0JT0prmlwzLWxWVMKw621MJ3WRGlMiIiIiJ2nlvnzu/2wT2SXhS/fO7daYO4e0xuty2pRM6jLLsijbNYuClX856sQ9V3Rj4vrei69xf5vSSW1iWRaf7dvE8MZt8X2/Z9QtnQexNGsP7eNSuLf7cOI8Ov1TTl6oJJ/sjx+jZOOXAPj3ryd/wTQSht1gczKpy9SYEhEREaki07R4dflenl+8O2zpXoTbycThbRnbSUdiS9XlffMQZbs/Dy8aDqI7XERM9xtwuNRoEMgpL2HKmrksOrST9fkHubvbMAAiXG5eHvQbYt0+LfGUalG6bQlZH0wiVJQdVi/fswbLNDEc2j9RqkaNKREREZEqyC8L8NDszSzelRtWb5N0eOley0Qt3ZOT403rG9aYcse1Jv7U+/EkdbExldQmlmVx29IP2FKQBcDbO9cwMLUVAxq1BNAsKakWZsBP7ufPUrj0rbC64XSRMPy3xA24TE0pOSlqTImIiIicoDUZBdz36SYyi/1h9V93TuMPw9rgc2vpnpy8yNa/pmzXHCqy1hDT9WqiO12G4XTbHUtqEcMwuLXzIH63+D0APA4nmeXFNqeS+sR/cAtZ7zxARdaOsLo7pRWpFzyGN629TcmkPlFjSkREROQ4mabFGyv38fdFu8KW7vlcDu4d3o6zOmvpnpw4K1RB2Z55RLQ8I2zJlWEYxPe/Dyvkxx3XysaEUpusztmP2+GkS0IaAP1SmnNx656syNnPI73G0CY22eaEUl/4D24h48UrsUKBsHps/9+QOOpWHG6vTcmkvlFjSkREROQ4FJQFeHjOZhbtDF+61yoxkifO6kTrJB2/LieuImst+d9OJVCwEwwHkS3HhI27opvYlExqm4pQkBc3L+H17ctpEhnHjCHjiXR5APhdp9MB8Dj18U6qj6dROyJa96V06zcAOKMSSTl3EpHtBticTOobLQQVERER+RnrDhQyfsbKo5pSZ3ZqxKuX9FJTSk6YGSglf/nTZM294XBTCihY8Qyh8nx7g0mtNW3rt7y2bTmWBftLCvjr+oWVYx6nS00pqXaGYZB89kM4I+OJ7DCY9Jv/paaU1Ag9e4mIiIj8CMuymLFyP3/7eiehHyzd8zgd3DO8Lb/u3EinXckJK89YTP6yJwmVHAwfMAyCRbtx+uJtySW12+Vt+vDp3o0cKisCIGiFMC0Th6G5BnLyTH8JOZ/+CV+LnsT0Pruy7opJpsn1r+GKb6zXO6kxakyJiIiIHENheYBJc7awcEdOWL1FQgRPnNWZtsmaJSUnJlSeR+HKv1C6a/ZRY5GtxhLX+3Yc3jgbkkltdLC0kG+z9zKu+eFTGKPdXib1GsN9Kz5lYvfhDG3c1uaEUl+U71lL5rsPEMzPoHjDXHwteuNOalY57k7QkmKpWWpMiYiIiPyP9QeLmPjJRg4UlYfVx3RI5b4RbYn06C2UHD/LsijbNZuClX/B9OeHjTmj0ojvey++JqfaE05qHcuy+GzfJv647ktKQxU0j4qnZ1JTAPokp/PhiKvwuXQ6o5w8KxQk76uXyF/wMljm4VpFGVnvP0zja6ZphpT8YvSuSkREROR7lmXx1uoM/m/hDoJm+NK9O4e24dyuaXqjLickWHKAgmVPUZ6xOHzAMIju8Btiul2Pwx1pTzipldbnH2TSqiOz6h5eNZs3h4wn6vsT0NSUkupihYKUrJ9b2ZQCcMU3IXH0bXqtk1+UGlMiIiIiQFF5kEfnbuHLbdlh9ebxEUw9qxPtU6JtSiZ1kWVZlGx5m8I1/8AKloWNueNaE9//PjzJXW1KJ7VZ14TG/Lp5Zz7eswGAZF8UJcGKysaUSHVxeHyknj+FjJcmYIWCRPc4i+Sz/oDDq6Xq8stSY0pEREQavI2Hirj3k41kFIYv3RvVPoX7R7Qjyqu3THJiDMOgImt1WFPKcLiJ6XoV0Z0ux3Bq1oscVh4M8MaOlVzRpk/lyXq/7zKE1TkZnNWsM1e2PQWnQxucy8kLleQTLMrEm9a+suZt0pHEMb/HGZVAdNdRNqaThkzvskRERKTBsiyLt9cc4C8LdxAIHVnK4HY6+P3g1pzfXacQSdXFnXIX/oPLMSsK8SR3I77/RNxxre2OJbXI+ryDPLRyFntL8ikO+Lm9y2AAotxeZg69rLJRJXKySrd+Q9YHkzFcHtJvmonDd2QWcFz/i2xMJqLGlIiIiDRQxf4gU+ZuZd7WrLB60zgfT5zViY6pMTYlk7ooVJ6P0xcfVnP6Eok75U7MikKi2p2PYWjWixxREQryh2X/Iau8GIA3t69kYGpL+qY0B1BTSqqFGfCT+/n/Ubj0X5W17E+fIvW8R2xMJRJOr44iIiLS4GzOLObyN1ce1ZQa1jaZNy7traaUHDczUErBij9z6OPzCBZnHDUe2XIM0e0vVFNKjuJxuri3+/DK6xRfNA7dJ1KN/Ae3kPHC5WFNKYCKjE2Y3zdERWoDteFFRESkwbAsi/fWHeDPX+2g4gdL91wOgzsGt+HCHlq6J8evPGMx+cueJFRyEID8ZU+SNPQvuofkmCzL4r3d6+gUn0rn+DQABqW15twW3SgO+rmn23BiPT6bU0p9YJkmBYtnkDfvOaxQIGwstv/FJI66BYc205daRI0pERERaRBKK4I8Nm8rczaHz5JqEutj6pmd6JymWVJyfELl+RSu+gulO2eF1f0HllKRtRpvai+bkkltlV1ewqOrP2dx5i5aRCfwxpDxeL9fqveHbsO0ublUm2BhJlnvPUzZzmVhdWd0EinnTiKy7Wk2JRP5cWpMiYiISL23NauYez/ZyJ78srD6kDZJPDSqPbE+nZAmP8+yLMp2z6FgxTOY/vywMWdUGvF971FTSo7phU2LWZy5C4DdxXn8bcPX3NltKICaUlJtQsW57HvuYsyywrB6ZMehpIx7AGdUvD3BRH6GGlMiIiJSb1mWxYfrD/LHL7eHLd1zGga3DmrFJb2aatmVHJdgyUEKlj1FecY34QOGQXT7C4npfiMOd6Q94aTWu6Xz6XyduZOc8hI8DidNo+LsjiT1kDM6keguoyhc/i4AhttH0ti7iOl9tl7rpFZTY0pERETqpdKKIE98sY3PNmWG1dNivEw9sxNdG8falEzqEssyKdn6LoVr/oEVKA0bc8e1Ir7/fXiSu9mUTmqrldn72FqYzW9a9wQg1uPjoZ6j+Memb5jUawytY5LsDSj1VuKYOyjbtRyHN5rU8x/FndTc7kgiP0uNKREREal3tmeXcO8nG9mVF95IGNQ6iUmjtXRPjk+gYAf5S6dSkb0urG44XER3mUBM5ysxnLqX5IiKUJDnNy1mxo4VGBh0SWhE14TGAJyW2pL+Kc118p5UCysUJG/+PzEcDhKG3VBZd3h8NL787zhjkjGc+rgvdYPuVBEREalXPl5/kCe/3IY/eGTpnsMw+N3pLbmsd7qWM8hxsSyT3AV/IFi0L6zuSe5GfP+JuONa25RMarMlWbt5Y/sKACwsHl45mzeGjCfCdbiBqaaUVIdAzh4y33kAf8YGMBxEtO6Pr0XPynHX96c+itQVemYUERGReqEsEGLS7M088vmWsKZUarSXf17Yg8v7NFNTSo6bYTiI633HkWtXBHF9fk/yqBfUlJIfNTitDWOadqi87pOcjoVlYyKpTyzLonD5++z7x6WHm1IAlknmew9hBvz2hhM5CZoxJSIiInXejpwSJn6ykR254Uv3BrRM5JExHYiL0HIrOXG+pgOJaDEKK1BMXN97cEVpFoKEO1BayGvblvP7rkNwO5wA3N1tGLtL8ri2fX8Gp7WxOaHUK5ZFyXdzsALllSVHRCyJo2/F4fbaGEzk5KgxJSIiInXapxsPMXXeVsr/Z+nebwe05PI+6TgcmiUlP608Ywml2z8kYeAUjO+bC/+V0P8BcHo0207CWJbFJ/s28qd1X1IaDBDr9nJTp4HA4Y3OXx10ie4ZqXaGw0HKeZPZ99zFmGWFRLTuR8q5k3DFptodTeSkqDElIiIidZI/GOKpL7fz0fqDYfWUKA+PndmJXk11HLv8NNNfQMHKZyjdOQsAz+a3iO40PuwxhkuzEORoRQE/f12/gNJgAIBXty1nYKNWdE9sAqCmlFQLM+Cn4tA2fOldKmuu2FRSxj1AMP8AsadeguHQ7jxS96kxJSIiInXO7txS7v10I9uyS8Lqp7ZI4JExHUiI9NiUTOoCy7Io2zOXghV/xizPq6wXrn0RX/oQXDHpNqaTuiDW42Ni9xHcu/wTALokpJHkjbI5ldQn/oNbyHrnAYKFh2h600zcCU0qx6I6D7cxmUj1U2NKRERE6pRZmzJ5fN5WygKhyprDMLjhtBZMOKWZlu7JTwqVHCJ/+R8p3/91+IBhENlmHA5foj3BpFYrCwb428avOad5V9rFpQAwvEk7zmnRlSaRcVzRto9O3JNqYZkmBYtnkDfv71ihIABZ7z9M4wkvaHaU1FtqTImIiEidUBE0+dNX23l/3YGwelKkh8fO7Eif9Hh7gkmdYFkmpVvfo2DNc1iB8E3yXXEtSeh3H56U7jalk9rsu7wDPLxyNntL8lmZs59XB12Mx3n4Y9TE7iO0bE+qTbDgEFnvP0zZzuVh9UDOHoJ5+3EnNbMpmUjNUmNKREREar09eaVM/HQTW7KKw+p9m8UzZWxHErV0T35CoGAX+d8+TkXW2rC64XAR3WUCMZ2vwHDqHpJje33bCvaW5AOwvTCb5zct5tYugwDtJSXVp3jdHLL/MxWzvCisHtVpGMm/vh9nVLw9wUR+AWpMiYiISK32+ZYspny+hdIfLN0zgOtObcE1/Zpr6Z78KCsUoHjjGxR99zKWGQgb8yR3Jb7/fbjjWtuUTuqKe7oPZ1XufvL9ZaT4ojkttYXdkaQeMf0lZH/yJMVrPg2rG54IksfeRXSvcWqASr2nxpSIiIjUShVBk2cW7OCdtRlh9cRIN1PO6ETf5vH2BJM6wQpVkDXnGgJ5W8PqhiuC2B43EtX+QgztCST/w7Is3t61hoBpMr5NbwASvZE80GMkn+/fwh+6DyPG7bM5pdQXVjDA/hevIJC9O6zuTe9K6nmPaumeNBhqTImIiEitsy+/jImfbmRTZvjSvd7pcTw+thNJUVp2JT/NcHrwpvYKa0z5Gp9KXN8/4Ipu8hNfKQ1VVnkxj67+nCWZu3E5HJySnE6HuFQABqe1YXBaG5sTSn1juNzE9D6H3Dl//b7gIGHItcQPvhrDqY/q0nDobhcREZFa5Yut2Tzy+WZKKsKX7l3drznXn9pCS/fkuMV0v5GyfQuwgmXE9b6diJZnaEmM/KhlWXtZknl45krQNHlo5SxeH3xp5UbnIjUh7rTxlG1dRCD/AKnnPYqvuQ5hkIZHz7IiIiJSK1QETf7v6538a/X+sHpChJtHzujIqS0SbEomtZ3pL8B/aAURzYeH1R3uSBIHPYEzMhWnL9GmdFJXjE3vyFcHt/PlgW14HE7Ob9kdt8NpdyypJyzLomjFB/gzNpAy7v7KuuFwkHrB4xhuHw5vpI0JRexT6xpTCxYs4I9//CMrVqzgwIEDvP/++5xzzjl2xxIREZEalFFQzsRPN7LhUPhpRL2axvHY2I6kRHttSia1mWVZlO2ZS8GKP2P6C0iJfgVPYoewx3gSO9qUTmqjgooyKswQWFAR5WFbUQ4Jngg8Tidxnggmdh9BhRnits6DaBmjZqZUj1BJHlkfPkrp5gUARLTsQ3T3MyrHndG616Rhq3WNqZKSEnr06MHVV1/NeeedZ3ccERERqWFfbc9m8pwtFPmDYfUJfZtx42ktcWrpnhxDqDST/GVPUb7/68pa/rePkzL6ZQzNcpEfUWGGOGfuyxwoLSSnvBTDMGgUEcPXv/odAPHeCJ7pf7bNKaU+Kd2yiKwPJhMqya2sZf9nKr6WfXDFptiYTKT2qHWNqbFjxzJ27Fi7Y4iIiEgNC4RMnv16JzNXhS/di/O5eeSMDgxoqd8gy9Esy6R02/sUrP47VqA0fCzkJ1SWhSsqzaZ0UheUh4Lk+EsJWSYGBofKiwiYoZ//QpEqKNkwL6wpZTjdJAy7AWd0ko2pRGqXWteYEhERkfrvQGE59326ke8Ohi/d694klqljO5Eao6V7AqHyfAxPbOV1sPgARRtepXTbB2GPMxwuojtfSUyXKzGcOrFRfprH4SQtIpb9JfkAxHt8uAyHvaGk3koaexflu1cSyN2HJ7UNKedPwZvWzu5YIrVKnW9M+f1+/H5/5XVhYSEApmlimqZdsaqFaZpYllXnfw6pXXRfyY/RvSE14Vj31cKduYeX7pWHL927rE9TbjqtBS6nQ/ehYJYepGj9dKI7X4nPG0EwbzOFa/5BdMdLCeRtJZCzHgB3Umfi+k7EHd8GC7B078j/yK8o4+nvvuK69v2JcnuxsIh1eylx+4jxRhDn8WJg6HlHTsixXt8s08S//zt8zX5wsp7bR9I5kyndMJeEETdjuDy61+RH1af34yfyM9T5xtTUqVOZPHnyUfWsrCzKy8ttSFR9TNOkoKAAy7JwOPRbHKkeuq/kx+jekJpgWRYlJSUAhCyYvjqTd9Znhz0mxuPkroFN6Z8eRW5O9rG+jTQwcZEGJd9No3jrB5RnriOm6wSylz2F/9ByLMsiqsPFZH/9MM52VxJq/mvyKhyQmWl3bKmFVuUf4Omti8ipKGN77iGeGXAeVtDEtEyS3RE4DSdW0CRkhsjUPSQn4H/fN5lFmZTN+SPBjO+IuvAvuNJ+cBCDtxH0Gk9Wbr5teaVuqE/vx4uKin7+Qd+r842piRMn8vvf/77yurCwkGbNmpGSkkJsbOxPfGXtZ5omhmGQkpJS529KqT10X8mP0b0h1a2kIkSRP0iZ6cPr87Imo5BlB/y4Xe7Kx3RtHMNjYzuQFuOzManURu5uVxPMXk357s8o3/UphgG+tL7EdBpPecY3NB73b1zRTeyOKbWYZVl8vGMRBVYAl9vFjvJ8CoJ+/nPG9ViAaYZwOJwYgNvhJDY1xu7IUof88H1T2Ya5ZP/nCQx/MW6XE/OrZ0i+/g0c3ki7Y0odU5/ej/t8x//ers43prxeL17v0ftQOByOOv8XCWAYRr35WaT20H0lP0b3hlSXrGI/Ly7Zzeebs9hfUEpZ0OLsLmn8YXhbnvpyGxmF5Vzauym/G9gKt1P3mxxDRDKx3W+gfM+87wsGcafcheGOIq7PHRiGTmuUnzep12gunf8GhQE/rWIScRgGKRHRmKZJZmYmyampes2TKrHKC0n0QfDABrLeexAsq3LMLM4lkLmViBa9bEwodVV9eT9+IvlrXWOquLiYbdu2VV7v3LmT1atXk5iYSPPmzW1MJiIiIsej2B/kxSW7eX/dQbKK/WSXVGAYBq8u3wfAxb2a0iTWy5A2yTYnldrMLM+heMPrldeGJ4bizW+RePrjakrJMVmWxcd7N2BaFue06ApAakQMD/QcxZKs3dzeeTC+H8zYFDkZZnkJOyedQsu7Z4PhAOvwyY7e9G6knv8o7sR0mxOK1B21rjG1fPlyhg0bVnn932V6V155JdOnT7cplYiIiByvIn+Qj9YfZGduKf5g+MaXn246xL0j2tEsPsKmdFIXhMrzKVo/nUD+ViJajCKm120Ur3+FQM56itZPJ7bHb3H64u2OKbVIYUU5T6z9grkZW/A4nHRLSKNN7OHm99DGbRnauK3NCaW+MP2l5H31T6I6DcOqOLynseFwYhkOEgZdRfyQazAcTptTitQtta4xNXToUKwfTIMUERGRuqPUH2RnbinrDxYfNZYY6SYp0ktJRfAYXykNmT9zFcUbZxyeDeX04PTFE9NlAgAxXSZQEooiceCjFK2fTkyXCWpKyVG+OLCNuRlbAKgwQ9y/4jNeG3wJHmet+7gjdViotIBg/gGiOg0jWJiJZYUIFmbS+Op/4oyMx+GNVlNKpAr0TC0iIiLVYsH2HP6+aCd3DWtDjNdFkf9wA8rpgMaxPmJ9LqI8TuJ8Wkojh1lmkKJ1L1G04VWwLArX/IO43rcB4IpuQmyP32J4YinLzCQmNU0zpeRHnd28C18d3M6iQztxGgZnNuuEq47vzyK1jxWsYN8/LiWQvQvLCkEoSMZLV+NOaAJON81uec/uiCJ1khpTIiIiclJySyv44/ztzN2SBcCmQ8Wc1y2NV5fvI87nIjHCgc99+C3H6PYpxHj19kMgWLiHvMUPU5GzsbJWvGkmvqan423UBwCnLx7TPLIcVE0p+a+M0gLe3rmGWzqfjsNwYBgGD/UcxT3LP+H2LoPoHJ9md0SpByzTJFSahys6CQDD5aHF3bMJleYRzN3H3um/pfktb+OKS6scF5ETp3eGIiIiUiWWZfHxhkP8dcEOCv1Hlue9s/YAD4xsR3KUh6+255BfWk6Ux8noDilcf2oLotWYatAsy6J0x8cUrHgGK1gWNhbZZhzuxE42JZO6Yta+TTy59gtKghUkeCO5ou0pACR4I3lx4IU2p5P6oiJrJ9kfPYZZXkTTG2dgOF04I+OAww0oR0QcDl8MrrhGuGJTbE4rUrfpnaGIiIicsH35ZTw2byvL9+aH1R2GwfB2yXRrHEunRjHccFpLckvKSYw6vJRPTamGzfQXkP/tVMr2zg+rOzyxxPefSESzYcf8OpH/yikvYeraeZQFAwD8Y+MiTklO1wwpqTZWsIL8ha+Qv/AVrNDhX7oUfPMG8YMmVD7GGRmHVegH7SclUi307lBERESOW8i0mLFyHy8s3k1FKPzEvfYp0Twwsh2dGsVU1iLdDtwVRSTFxOLQfi8Nmv/gMvIWP0KoLCus7k3rS8KpD+KMTLUpmdQlSb4o7uw6lCmrPwega0JjEjyRNqeS+sIyTTKmXYs/Y0NYveCbN4jt/xscniMnyhouDy1u/0DL90SqgRpTIiIiclw2ZRYx5fOtbM4KP3HP43Rw/aktGN+7KS7n0c2nUCj0S0WUWsgKVVC49kWKN82AH5y8bDhcxPa4iaiOl2AYalrKsZmWyevbVjCqaXuafL+M6tfNOrMsaw8tYhK5qm1fnGp6SzUxHA6ie5wZ1piKbDeApLPuDWtKARi+WPIKy0mNjf2lY4rUO2pMiYiIyE8qD4R4ccluZqzcj/mDxgJAn/R47h/ZjmbxET/y1dKQBQp2kffNQwTytoTVXbEtSBjwCJ7EDjYlk7rgUFkRD62cxaqc/Sw4tIMXB1yI03F4o/NHep+BYRh2R5Q6zrIsrEB5WNMptt9FFK/5lGDBQZLG3kVU11G610RqmBpTIiIi8qO+3ZPH4/O2sr+gPKwe43Vx++DW/LpzI71hlx9Vtvvzo5pSUe3OI7bXrThcPptSSV3x5NovWJWzH4B1uQeYtvVbru9wKoCed+SkBfIyyPnkCTAcNLr0mcp7ynA4SL1wKo6IGJwRmg0l8ktQY0pERESOUlge4JkFO/jPhkNHjY1sn8JdQ9qQFKV9NeSnxXS9Cv+BxVTkbMDhjSe+//1EpA+yO5bUEXd1G8rq3P0UByqIdLlJ/34pn8jJsMwQhUveIveLf2AFDv/SpWTDPKK7jKx8jDuxqV3xRBokNaZERESkkmVZfL4li6e/2k5uaSBsLDXayz3D2jK4TZJN6aSuMRwuEgZMpnD134k75W6cEbp35Mdtys8ko7SA4U3aAdAkMo57u49g5o5VPNr7DNKj4u0NKHWeP2Mj2R89hv/AprB67uy/ENVxKIZTH49F7KB/eSIiIgLAwcJynvhyG4t25h41dkH3JvxuYEuivHrrIEezQhUUrv47vmZD8ab2ChtzxTQjcdATNiWTusC0TGZsX8U/Ni3CZThoE5tEi+hEAEY37cDIJu1waIN8OQmmv5S8L5+nYMlbYIWfKOtr3pPkcferKSViI/3rExERaeBM0+KdtQf4+6KdlAbCT9BrlRjJ/SPb0aOJltDIsQXyt5G36CECBTso2zuf1DPfwOGJsTuW1CFzM7by7IaFAAQxeWDFLF4e9BvcDieAmlJyUkq3LCL7P1MJFhwMqzt80SSOuo2Y3mdj6GRHEVupMSUiItKA7cgpYcrcraw7UBhWdzkMrurXnAmnNMPj0ht2OZplWZRs+TeFq/+OFaoAIFR6iPxlT5I4cIrN6aQuGdmkHR/u+Y5lWXsBaBIZS0UoWNmYEqmqkk1fcWjmnUfVo7qMImnsnbhikm1IJSL/S40pERGRBqgiaPLKsj1MX7aXoGmFjXVLi+WBUe1onRRlUzqp7UJlOeQvnUJ5xuKwusOXSGSrs2xKJXWFPxTk473rOb9FdwzDwGE4mNRrDFcv/BfXtO/P2c276NQ9qRaR7QbiSWtPxcHDp4O64hqRdNa9RHXQIQwitYkaUyIiIg3MmowCHpu7lZ25pWH1SLeTmwe24oLujXE49KFQjq1s30Lylz6G6c8Pq/uank58//tw+hLtCSZ1wrbCbO5f8Sk7i3IJmRa/ad0TgBRfNO+NmKBZUnJSgoWZuGJTK68Np4uUcQ+QMe1qYvtdRMKwG3F4I21MKCLHosaUiIhIA1HiD/K3Rbt4Z23GUWMDWyUycXg7GsV4bUgmdYEZLKdw1f9RsvW9sLrh9BDX+zYi256nWS7yk0oCfm5Y9DZFAT8Az25YSO+kprSLSwFQU0qqzApWkL9wOvkLXyH1oieJ6ji4cszbtDPNbv8orGElIrWLGlMiIiINwILtOTz55TYyi/1h9YQIN3cNbcOo9ilqKsiPqsjdTN43DxEs3B1Wdye0I2HAI7jjWtmUTOqSKLeXGzsO4I/rvgQg2u2tbFKJVFXZrpVkf/w4gexdAOR8+iQRrU4JmxmlppRI7abGlIiISD2WU1LBH+dvZ97WrKPGftW5EbcPak1chNuGZFIXWJZJ8aY3KVrzPJYZDBuL7jSe2O43YDg9NqWTumBJ5m5axySSGnH4pMYLWnZnSdZuLMviwZ6jSNCyKjkJRWs+Jeu9h8JqwYJDFK/9lNi+F9iUSkROlBpTIiIi9ZBlWXy84RB/WbCDIn94Q6FJrI/7R7ajX/MEm9JJXREqOUDR2hfDmlLOiBQSTnsIb1pfG5NJbVcRCvLcpm94c/tK+iSn8/fTzsNhODAMgym9x+JzujRLU05aZPvTcUYlEirJBcAZlUDS2LuI6jra5mQiciJ0/rOIiEg9sze/jN++t45HP98S1pRyGAaX90nnX5f3UVNKjosruilxvW+rvI5oNpTUM99QU0p+1pPrvuDN7SsBWJG9j9e2ragci3C51ZSSKjH94Yd2OCNiSTrzbgBiep9N+i3vEt1tjO4vkTpGM6ZERETqiZBpMWPlPl5YvJuKkBk21iElmvtHtqNToxib0kldFdn2PPyZq/Cm9SOy9a/1gU+Oy5Vt+zI3YytlwQAAef/TUBA5EZYZonDpv8ib/yKNr3oRb1r7yrGoLiNpmtwirCYidYsaUyIiIvXApswipny+lc1ZxWF1j9PBDae1YHzvdJwONRTkx1XkbKB8/yJiu18XVjcMg8SBU2xKJXVFYUU52wqz6Z2cDkDz6ATu6jqUZzd8zYM9RzEorbXNCaWu8h/YTPZHU/BnbAQg+8MpNLluOobj8OIfwzDUlBKp49SYEhERqcPKAyFeWLKbN1fux7SssLFTmsVz34h2NIuPsCmd1AWWZVK84fXDe0lZIdzxrYloPsLuWFKHrMjex0MrZ1ES9DNjyGU0jYoD4FfNOjMkrQ2xHp/NCaUuMivKyPvyBQoWvwnWkVnA/owNlKz/nOhuY2xMJyLVSY0pERGROurbPXk8Pm8r+wvKw+qxXhe3DW7Nrzs30rIr+UnBkoPkL56MP3NVZS3/2yfwJHXFGdXIxmRSVyzP3svNi9/lv33xB1d+xj8HXoTTcXijczWlpCpKt35D9n+mEsw/EFZ3eKNIHH0bUV1G2ZRMRGqCGlMiIiJ1TEFZgGcW7OCTjYeOGhvZPoW7hrQhKcpjQzKpS0p3f07Bt09iBsKXf3ob98dwaZadHJ/eSU3pkdiU1Tn7ASgM+Mn2l9AoQvvZyYkLFeeSM+tpitfNPmosqvMIksbehSs2xYZkIlKT1JgSERGpIyzL4vMtWfxp/nbyygJhY6nRXu4Z1pbBbZJsSid1hRkooWD5nyjd+VlY3XBHEn/K3US0PEMz7eRHWZbF8uy9nJLcDMMwcBgOHuk1hvFfzWBEk3bc0WUIES633TGlDrJMk4yXryWQsyes7opNJemse4nqONimZCJS09SYEhERqQMOFpbzxJfbWLQzN6xuABf0aMLNA1oS5dXLuvy0iux15H3zMMHijLC6J7kbCQMm4YpualMyqQuKA36eWPsFc/ZvZmKPEZzbohsAaZGx/GvYFST7omxOKHWZ4XAQP/gast5/+PuCQVz/i0kYfhMOb6S94USkRukdrIiISC1mmhZvr83guUW7KA2EwsZaJUZy/8h29GgSZ1M6qSssM0TR+ukUfTctbBNhDAcxXa8mpstVGA6nfQGl1guYISYsfIs9xXkA/Pm7r+iV2JSWMYkAakrJCbOCFYTKi3BFH5npG93jTIrXfEKoNJ/kX9+PL72LjQlF5JeixpSIiEgttSOnhCmfb2XdwcKwusthcFW/5kw4pRkel8OmdFJXBIszyPvm4f9v777j5KjrP46/pmy93kty6Z2SkARCQif0bi+AggVQEBVFxYYoCDbUH1hQFLBhV3rvQiiB9N4u/XrfPjPf3x+zt3tzd0kuBTYXPs/HI7nb+c7Ofnfvu7sz7/l+v0OyZZlnuZlfQ8ncm/BXHJmjmonhxKcbvGf0Efx8xYuAG1Qt72jIBFNC7I345sU0PzT8a54AAHxfSURBVHQLRriYmsvuQtPd7zJN06h8//fRg/lohhyqCvFuIe92IYQQ4iCTtBzueWML976xFctRnrIjagr55mkTGVcmvRPE0HQt+r8BoVR47FkUzfoyuj8/R7USw8HOaBcBw6Q0PYzqI+Nm8GpTPVsjndw86ywOL6nJcQ3FcGPHuml7+g66F/4bgBTQ/dYDFM5+T2YdI684N5UTQuSMBFNCCCHEQWTJjk5ufmod9e1Rz/Kwz+Dq48by/iNr0HWZmFoMXdGsL5FofAsn2YXuy6fo6K8QHnNGrqslDnJPbl/DbUuf4YiSWn465wJ0TXcnOp95FgHDJGzKlT/F3lFWku2//ihWx07P8s5X/kjBzAszvaaEEO8+EkwJIYQQB4FIwuKOlzfxr6U7B5QdP7aUr506kaqCQA5qJoY7I1xB8Zyv07Pmr5TMvREzT3q5iN373drXuGv1AgAWNNXzt01L+Mi4owAokUmoxT7STD8FM86j/fnfZpYVHHUBpWd8XkIpId7lJJgSQgghcuzFDa384Ln1NPUkPMtLwz6+dNJ4Tp9UgaZJLymxe8qx6Fn5B8LjL8QIlXnKQnUnExx5IpomB39iz06pmcC9694gYVsAvNiwgQ+NnY4u7UfsBeU4qFTcc0W94hMup2f5k6AU5ed/ndDY2TmsoRDiYCHBlBBCCJEjrZEkP3p+A8+sax5Qdv60ar5w4lgKg74c1EwMN1bXFtoX3EiydRXJ5qWUnnz7gBBKQimxK45y2NLTkZnIfFxBGV887CR+uOxZPjFpDp+ceIyEUmKvJBrW0vLAzfgqxlL53psyyzXTT/VHf4ZZVIUmw0GFEGkSTAkhhBDvMKUUD61s5GcvbqQ7YXnKRhQF+fr8iRwzqiRHtRPDiVKK6MaH6XzzdpQVAyC+81Uia/9B/uQP5bh2YjhoinXznUVPsraziT+ffAlVoQIA3jP6cKaX1jC+sDzHNRTDiZOM0/78b+h85U+gHBI7VlIw/VxC44/JrOMrq8thDYUQByMJpoQQQoh30NaOGLc8vY43t3V4luuaxsUzR3DFsaMJ+ozcVE4MK06ii47Xv09s6/Oe5bq/ECNcmZM6ieFlQ1cLV778D7pS7jDib7/1OL+a9z50TUfTNAmlxF6Jrl9Ay0O3YnXs8Cxve+r/qB33RxmSLoTYJQmmhBBCiHeAZTv8+a3t/ObVzSRtx1M2uSKfb54+kSmVBTmqnRhuEg0LaX/1u9jRJs/yQNUsSubeKMGUGJLR+SWMzCtmZUcjAKs7m1jf1cqkoooc10wMJ3ZPG62P307PsscHlOVNO5Wys6+XUEoIsVsSTAkhhBBvs1WN3dz89DrWNvd4lvsNnSvnjubimSMxdNlpF3um7BRdS++iZ/WfQanMck03KTjySvKnXixzSYnd2hHtpCZUiKZpmLrBzbPO5pIX/szo/FK+N/MsRuXLMGIxNEopehY9ROuTP8OJdXnKzMJKys79KnlTTspR7YQQw4kEU0IIIcTbJJayuWvBZu5ftB2nT4gAcHRdMV+fP5GRxaEc1U4MN6nOetpf+Tap9rWe5WbhKErmfRd/6ZQc1UwMB45y+OvGxfxi1ct8Y/ppnFM3FYCRecX8at77mVBYjk+XYcRi6LoX/puWh2/1LtQ0Co/5IKXzP4seyMtNxYQQw44EU0IIIcTb4LXN7Xz/mXXs6Ip7lhcGTL5w4jjOm1YlQxvEkCiliG74L51v/gxlJzxleRMuonDm59FNCTjFrimluP6Nh3mpYSMAP1z2LEeW1jAyrxiAqcVVOaydGK7yp59Lx//uy8wp5a+aSPkF3yA48vAc10wIMdxIX28hhBDiAOqMpbjxiTVc859lA0Kp0ydV8I+Pz+b8w6ollBJDlmxaRMfrP/CEUnqgiNITf0jxMV+TUErskaZpHFZcnbkdtVI8tGVFDmskhqNUu3dSc90fpPz8G9DMAKWnf44RV/5RQikhxD6RHlNCCCHEAaCU4sm1zfzk+Q20x1Kessr8AF89ZQInji/LUe3EcBaomkl47FlEN7kTCwdq5lBy7LcwQnLFNLFrSdsi4VgU+IIAXDZxNq81b2ZlRyNfPOwk3jNaAgQxNE68h7an76Rr4b+pvfw3BEfPyJSFJ8xl1BcfwsgvzV0FhRDDngRTQgghxH5q6Ipz67PreaW+zbNcA94/vZar540hLyBfuWLfFc2+nmTrKvImXETe5A/JBOditzZ2t/LNNx+jNlzIj44+H03T0DWd7808ix4rybgCCcnFnimliK56jpZHf4jd3QJA80O3MPKqP6OZ/sx6EkoJIfaX7CULIYQQ+8hxFP9YuoNfvFxPLGV7ysaWhvnmaZM4srYwR7UTw1GqcyNW12ZCdad4luu+PCrP+TOaLrtuYvee3L6G7y56kqRjs76rhX9vXsb7xhwJQGWogMoc108MH50v/5G2p/7PsyzVvInoulfIm3pybiolhDgkyd6NEEIIsQ82tES4+em1LG/o9iw3dY3LjxnFZbPr8JvSq0UMjVKKyNp/0LX4TtB0zKJx+ApHe9aRUEoMxZj8UvpeA/QP6xdywajD5Ip7Yq/lH3k2HS/+DicRAcBXWkf5eTcQGn9MjmsmhDjUyB6zEEIIsReSlsOvX6nnkr+8NSCUOqKmkD9fPJMrjh0toZQYMjveRtsL19H55u0oO4my4rS/ciPKTu35zkIA3anshRYmFVVwzbTjAZhbOYZ7TviwhFJiSOxYl+e2WVhB6enXgm5QfOInGPHZv0ooJYR4W8ipNyGEEGKIFm/v5Jan11HfHvUsD/sMrjl+LO87ogZdl6vtiaGLb3+Z9tduxom3e5YbwTKUHUczfDmqmRgOUo7Nr1e/wsNbV/Hnky6mPJgHwIfHzqAqmM8pNRPkCqBij5xknPbnf0P3G/9kxGfux1c6IlNWMOs9BMfOxl8+ejdbEEKI/SPBlBBCCLEHPQmLO1/exL+W7hxQdsK4Mr56ygSqCgI5qJkYrpSVoHPxHUTW/tOzXDP8FM38POEJ75VAQexWc7yHL73+IKs7mgC4adET/PzYi9A1HU3TOLV2Yo5rKIaD6PpXaXno+1gdOwBoefhWqi+9I/P5o+m6hFJCiLedBFNCCCHEbrywoYUfPreBpp6EZ3lp2Mf1J09g/sRyCRDEXkm1r6X9lRtJdW7yLPeVTKRk3k34isblqGZiOCnyBbFVdjap15q3sLBlG8dUjMphrcRwYUfaaX3ip/QsedSzPLbhVeL1bxIaOztHNRNCvBtJMCWEEEIMojWS5IfPrefZ9S0Dys6fVs0XThxLYVCGWYmhU8ohsuavdC3+Fcrxzh+VP+WjFE6/Cs3w7+LeQkDcShE03c8dv2Fyy8yzufTFvxDQDb511BkSSok9UkrRs+QRWh+/HaffnFJGQQXl535FQikhxDtOgikhhBCiD6UUD65o5OcvbaQ7YXnKRhYFuWH+RI4ZVZKj2onhyo420/7q90g0vO5ZboTKKT722wRrZEJhsXuLWrfz7bce5/OHncBptZMAGFNQyq2zz2VSYTmVoYIc11Ac7FKtW2h56FZim97wFmgahUd/gNLTrkYP5OWmckKIdzUJpoQQQoi0Le1Rvv/Met7c1uFZrmsaF88cwRXHjibok6tbib2jlKL1hetIta/zLA+OPIniY27ACBbnpmJi2PjNmlf5/drXcJTi+0ue5rDiamrChQAcXzU2x7UTw4GTjLPj7k9gRzs8y/2V4ym/4JsE647ITcWEEAKQa1kLIYR417Nsh3vf2MpH/vTWgFBqSmU+f/jIUVx7wjgJpcQ+0TSNoplfgN7JhM0gxcd8jdITbpNQSgyJoxyc9HxSPakkd61ZkOMaieFG9wcpPvETmdua6af0tGsYcdWfJZQSQuSc9JgSQgjxrrayoZtbnlnH2uYez/KAqXPl3NF89KiRGLpMbi72T6BqFvlTLibRuNCd4LxQrnIlds9ybEzdDcM/PelY3mjZyrK2nZw/ahpfOvzk3FZOHPSceA/KSmLkl2aWFc75ED1LH0MPFlB+/tfxlY7MYQ2FECJLgikhhBDvSrGUza8X1PPXRTsyPRF6HV1XzNfnT2RkcShHtRPDlVIOsU2PEhpzNpru7WFXeOSVwJVohkyaL3Ytkkrww2XPYSmHm2eejaZpGLrO92aexYr2Rk4fMSnXVRQHMaUU0VXP0fLoDwnUTqPqIz/JXDlW0w2qL70TPVQoV5MVQhxUJJgSQgjxrvPq5nZufWYdO7rinuWFAZMvnDiO86ZVyU672Gt2pJH2BTeRaHoLK9JA4RGf8pRLICX2ZGVHAzcsfJSdUfdqaXMrx3Be3TQAasNF1IaLclk9cZCzuppoeeQHRFe/AEB0zYtEVz1H3rRTM+sY0oaEEAchCaaEEEK8a3TGUtz+4kYeXdU4oOz0SRV8+eTxlIb9OaiZGO5iW56h4/XbcJLdAHQv/z3B6mPwVxyZ45qJ4aYp1p35/fblz3NK9XjyfIEc1kgMB3a0k22/+CBO3DssvfPV+wlPPUVOtgghDmoSTAkhhDjkKaV4Yk0zt7+wgfZYylNWmR/ga6dO4IRxZTmqnRjOnFSUzjd/QnTjI57lmhnEjrfmqFZiOHGUg6651yOaVlzNVVPm8YtVL1MTLuR7M8+SUEoMiREuIv/Ic+h6/e/uAt2geN6lFJ/0KQmlhBAHPQmmhBBCHNJ2dsW59dn1LKhv8yzXgPdPr+Wa48YQ9svXodh7yZZltL9yI1bPDs9yf9lhlMy7CbNAJhYWu/fMjnX8Zs0Cfj3v/ZQEwgBcOmEWCnj/mCPJl1BK7IKTSoBS6P5gZlnpaVcTXf08RmEl5ed/g0D1xBzWUAghhk72xIUQQhySHEfx9yU7+OUr9cRStqdsXGmYb5w2iSNrC3NUOzGcKcemZ+V9dC27G5STLdB0Cg67jILDP4Gmyy6W2LWYleIny5/nwS0rALh5ydP8+Ojz0TQNXdO5bOLROa6hOJjFNrxOy8PfJzz5RMrOui6zXA/kUXP5bzCLa9F0PYc1FEKIvSN7TUIIIQ45G1oi3Pz0WpY3dHuW+wydy4+u4+Oz6/CbstMu9p7Vs4P2Bd8h2bzUs9zIq6Zk7ncIVM7ITcXEsKJrGsvad2Zuv9Swkad2rOWMEZNzWCtxsLMj7bQ+8VN6ljwKQOerfyX/iLMIjJiWWcdXKj01hRDDjwRTQgghDhlJy+H3r2/h3je2YivlKTuytpBvzp/E2LJwjmonhrvopsfpXPgjnFTEszw85kyKZl+P7s/PUc3EcKCUysz1EzBMbp55Npe/9FdSyuayCUdzas2EHNdQHKyUUvQseZTWJ27HiXb2KXBof+4uqi/5ee4qJ4QQB4AEU0IIIQ4Ji7Z3csvTa9ncHvMsD/sMPnf8WN57RA26LhPAin0T3fQo7Qu+61mm+/Iomn094bFn5ahWYrhoiUe4adETfGDsdE6sHg/AxKIKvjZ9PrWhQmaWSy8XMbhU61ZaHr6V2MbXvQWaRuHR76d0/tW5qZgQQhxAEkwJIYQY1noSFnf8bxP/XrZzQNkJ48r42ikTqCyQCYTF/gmNOo2elX8i1bkRAH/FkZTM/Q5mfm2OayYOdi82bODmxU/TkYyxurOJv5xcRUXQ7V13Xt20PdxbvFsp26Lz5T/S/sJvUVbSU+avGEf5Bd8kOOrIHNVOCCEOLAmmhBBCDFsvbGjhB8+upzni3WkvDfu4/uQJzJ9YLpfJFgeEZvgpmXcTzU9dQcG0j5E/7WNoupHraolhYEnbDjqSbk/OzmSc25Y+y0+OuSDHtRIHu/YX7qbjhbs9yzTTT/FJn6J43qVopi9HNRNCiANPgikhhBDDTkskwY+e28Cz61sGlF1wWDWfP2EshUHZaRf7xureirIT+Iq9c/74SiZSdcF/MILFuamYGJaumjKPN1q2srqjiclFlVw77fhcV0kMA0VzP0r3wn9hR9oBCI09mvLzb8BXNirHNRNCiANPgikhhBDDhlKKB1Y08PMXN9GTtDxlI4uCfH3+JI4eVZybyolhTylFdOPDdL55O0a4goqz7kM3Q551JJQSu6OU4h/1S9jQ1coN0+cD4NMNbp55Ng9tWcEVU+bik552YhDJ5nr8FWMyt41QIWVnf5mWR35A2VnXkT/9XOkBLIQ4ZEkwJYQQYljY0h7l5qfXsWh7p2e5rmlcMmskVxw7ioApB3xi3ziJLjreuI3YlmcBsLq20PXWzyk+5ms5rpkYLtoTUb67+ClebtwEwKzykZwxYjIAo/JLuFp6SolBWF1NtDzyQ6JrX2LkVX/GX5XtqZl3+BmEJszFCBXmsIZCCPH2k2BKCCHEQc2yHf745jbufm0LSdvxlE2pzOebp01icmV+jmonDgWJxrdoX/Ad7GiTZ7nVtQVlJ9EMf45qJoaTrZEOFjTVZ27fuuQZZpaNpDyYl7tKiYOWchy63vgn7U/fiZOMAtD84M3UfvL3aLoOgKZpEkoJId4VJJgSQghx0FrZ0M3NT69lXUvEszxg6lw1dwwfOWoEhi5DG8S+UXaKrmW/oWfVn0CpzHJNMyiYfiX5Uy9B0/Qc1lAMJ0eW1vKJSXO4e82r+HWDz049jrJAONfVEgeptid+Suer93uWJbavJLFtuVxtTwjxriPBlBBCiINOLGXz6wX1/HXRDpw+gQHA0XXFfOO0iYwoCu3i3kLsWaprM+2vfJtU2xrPcrOgjpLjvou/dGqOaiaGi/ruNu5Y9T9unHEGhf4gAJ+ceAxNsW4+Mu4oxheW57iG4mBWeMwH6Fr4L5TlXlU2UDuN8gu/SaB6Uo5rJoQQ7zwJpoQQQhxUFtS3cduz69nRFfcsLwyYfPGk8Zw7tVImgBX7TClFdMMDdL71M5TlbWN5Ey6icObnB0x4LkRfSin+u2U5ty9/gYRt8f0lT3PrbHdiakPX+eaM03NdRXEQsnvaMPJLM7d9ZaMoPulTdL50LyXzP0vhMR/MDOETQoh3GwmmhBBCHBQ6Yiluf2EDj61uGlB2+qQKvnzyeErDMteP2Hd2vIOO128lvu0Fz3I9UETxMV8nVHdSjmomhpOkY/On9W+SsN0rgz67cz2PbFvFeXXTclwzcTCyIx20PvkzomtepO6af3rCqeJ5l1Iw/VzMoqoc1lAIIXJPgikhhBA5pZTiiTXN/OSFDXTEUp6yqvwAX5s/gePHluWoduJQoawEzU9chh1p8CwPVB9DybHfwghX5KhmYrgJGCY3zzqbT/7vb1iOw5yKUcytGJ3raomDjFKKnqWP0fr4T3Ci7tVkWx77MVUf+H5mHc30SSglhBBIMCWEECKHdnbFufXZ9Syob/Ms14APTK/l6uPGEPbLV5XYf5oZIG/Ce+la8kv3tu6jcMZnyZv8IZngXOyW5dj8Zs2rHFU2grmVYwCYWlzFNVOPRwEfGTcDXdqQ6CPVto2Wh75PbOPrnuWRFU+RPOlT+CvH5ahmQghxcJK9fSGEEO84x1H8bckOfvVKPbGU7SkbVxrmm6dP4ogauUS2OLDyp11CYucCnEQHJfNuwlcikwyL3dva08E333qMVR2NPLBlBfeffAml6SvtfXT8zBzXThxslG3R+cqfaH/+N5lJzXv5K8ZRfsE3JJQSQohBSDAlhBDiHbW+JcLNT61lRWO3Z7nP0PnEMXV8fHYdPkN6H4h9p5Qi2byYQOVRnuWaplNy/C3oZh6aGchR7cRw8u/NS1nV0QhAeyLKdxc9yU/nXCgXYBADxLctp+XBm0k2rvcs10w/xSd9iuJ5l6KZvhzVTgghDm4STAkhhHhHJC2H372+hfve2IqtlKfsyNpCvjl/EmPLwjmqnThU2PE2Ol69mfiOVyg7+acEa+d6yo1g6S7uKcRAn5kyjwVNm9nY3UqhL8AFow6TUEoMYPe0sfOeKwb0kgqOmUXFBd/AVzYqRzUTQojhQYIpIYQQb7tF2zu55em1bG6PeZaHfQafO34s7z2iBl2Xgz2xf+I7XqH91Ztx4u6cZe2vfo/Kc/6MESzJcc3EcLG0bQevNm/hisnHAuA3TG6ZdTb/t/J/fGP6fCpDBTmuoTgYGfmlFM27hI4Xfw+AHiqk7IwvkH/U+RJkCiHEEEgwJYQQ4m3Tk7D4v/9t4j/Ldg4oO2FcGV87ZQKVBTKkSuwfZSXoXHwHkbX/9C5P9ZBqXYkx4rgc1UwMF7bj8Lt1r/P7ta/hKMXEwnJOqZkAwPjCcn5+7EW5raA4qFhdTWi+IEYoOxdiyUmfIrLiaQIjDqPszC9i5EvvTCGEGCoJpoQQQrwtnlvfwo+eW09zxDu0oTTs4yunTODUCeVyJlnst1T7WtpfuZFU5ybPcl/xBEqO+y6+IploWOzZqs5Gfrf2VXpHGd+y+CkOK66SHlLCQzkO3Qv/RdvTd5I3bT4VF307U6aZfkZc8Qf0YH4OayiEEMOTBFNCCCEOqOaeBD96fgPPrW8ZUHbBYdV8/oSxFAZlAlixf5RyiKz5G12Lf4lyUp6y/CkfpnD6Z9EMf45qJ4abw0tq+Nj42dy3fiEAJ1SPI98nvTlFVrJxAy0P3UJ861IAuhc9SP6RZxMad3RmHQmlhBBi30gwJYQQ4oBwHMUDKxr4v5c20ZO0PGV1xSG+Pn8is+uKc1M5cUixo820v/o9Eg2ve5YbwTKK536bYM2cHNVMDBdRK8nv1r7GJyYeQ146gLpyylxWdjZywajDOXPE5BzXUBxMUm3b2H7XxSjb+93W9eZ/PMGUEEKIfSPBlBBCiP22pT3KzU+vY9H2Ts9yXdO4dNZIPn3sKAKmkaPaiUNJbOsLdLz+fZyEt60FR55I8TFfxwgW56ZiYthY2dHAN998nG2RDlriEW6aeRYApm5w57HvlSHGYgBf6Ujyps2nZ9kTAGj+EKWnfpbCOR/Kcc2EEOLQIMGUEEKIfWbZDn94cxu/e20LSdvxlE2tzOebp09iUoUMbRAHRs/qv9D51v95lmlGgKJZXyA8/iIJFMQeOcrhW+lQCuCxbas5tnI0Z4+cCiBtSABgRzvR/WE0MzvsvOysLxFd9wrBUTMoP/ermMXVOayhEEIcWiSYEkIIsU9WNnRz89NrWdcS8SwPmDpXzR3DR44agaHLQZ44cIIjT6Jr2d2oVBQAX+lkSubehK9oTG4rJoYNXdO58agzuOLlf+AoRVWogJo+V1YT725KKXqWPk7b4z+hcO5HKTnxE5kyI7+UEZ/5C2ZRtQSYQghxgEkwJYQQYq/EUja/eqWevy3egdN7Cau0Y0aV8PX5ExhRFMpR7cShzMwfQfHsL9P+6vfIn3IxhUdeiWbIRPpi957fuZ5CX5CZ5SMBOLK0lk9PPpYNXa3cMP1UCnzBHNdQHAxSbdtpefhWYhteBaDjhbvJP+w0fGWjMuv4imtyVT0hhDikSTAlhBBiyBbUt3Hbs+vZ0RX3LC8MmFx30njOmVopZ5LFAWFHGtHMEHrA25slNOZsfCWT8BVPyFHNxHARt1L8dMWL/GfzMiqC+dx/8iUU+t0Q6vKJR6OhyefVu5iKd1ESBCfWSdeih2h/7i6UlciWW0k6/vcHKi78Zg5rKYQQ7w56risghBDi4NcRS/Htx1dz7X+XDwilzpxcyT8+Pptzp1XJQZ44IGJbnqHpsUvpeOM2VL9eeZqmSSglhuTe9W/wn83LAGiO93DLkqcz7UnXdPm8epdTVpLNPz4bu3Mnbc/8whNKaYaPklOuovzcr+SwhkII8e4hPaaEEELsklKKx1c3cfuLG+mIpTxlVfkBbpg/kePGluaoduJQ46SidL55O9GNDwMQ2/IswdpHCY87N8c1E8PRxycczdM71rGlpx1Ng7q8YhQKDQmkDmVWVxN2TytOKo5K/zNLRhConpRZx451YUfasVvqUbaFphsoAOUQHHUU5Rd8A3/56Jw9ByGEeLeRYEoIIQTghlAlecWZHgU7u+Lc+ux6FtS3edbTgA/OGMFn540m7JevEXFgJFuW0/7KjVg92z3Lo/WPExp7jvRuEXvUlojyYsNGLhp9OAAh08cts87mK288zDenn8bRFaP2sAXxTkg0rEUlojipGCoVx0nGCY07GrOgPLNOfPNiupc+mgmWetdTqXjmfu7vccbc8AKanh0E0vbkz+lZ9oTnMQvnfJjAOV/GjnairCRK2aTatqJ0HauriZrLfg2aju7Pw1cxRj5vhBDiHSZHFEIIIYglLJzOBNtfqGfESWNoMuBrj6xiY1vUs9640jDfPH0SR9TIVazEgaGUQ8+K++ha9ltQTrZA0ymY9nEKjvikHCSKPXq5cRM3LX6SjkSMymA+86rGADC5qJJ/n3oZhi6zV/SnlAKlPKGOk4iSbFib7m0Uw0nGwU5RMPMCz307X/s7ie3L+4VGMU8vJScVJzT6KKov+T/PfXfecyVOvNuzrPrSOzzBVKp1C90L/z2055GKowXCmdvaIJPZq5Q7BF1ZSbbe8V6wU5jVkxjz1VdofeIW4qtfxCipYdTnH5TPGyGEyAEJpoR4F7HjHWj+Qs9tI1icuwqJA8I9uABNd3emle0Q3dGNk7Sxkw6hqjwCpe5V8qxYisaXtuAk7cw/szJM/uhiNv57FYm2GJ2bOhj3vqncXFfG4w1RGlI2CUPn2EnlzD+8mgAadtzCCMpXiNg/VmQn7a98h2TzEs9yI6+akrk3Eqg8Kkc1E8NJfXcb173+AL3Tkd20+EnuP/kSStNhxXAMpZRSYKdANz3BUap9B6nWLX0CoRhmYQXhScd77t/8n5twkhFPT6O+vY16A6SyM79I0dyPZu6XbNrAjt9/akB98o863xPYxDe/RWTF03t8Hk4iOmCZ5gtCv2BKJWPedfxDv7KrSsVhiMGUZvqp+9y/wfCR6rLZ9uCrjLzw5/gu1sFOopn+IT+uEEKIA0eOKoR4l7B6dtC94l4KDruMUCgPJ9qQuW3m1+a6eu9qqe4EdsL2hEV2qu9tBztp4SQd93a6zE5kf684diTls92/o3IUm/+7BkcpbKUwZ1aTGFtMT8Im0h2naHEDtlI4SjFiWgXFo4rZ8K9VJNpjKAWJ9hgb/7WKCe+dymkdceqXNVGTFyCwI0LDjg0A1J4+nqLJZQAkO+Jsf3IDZsiHETYpm1FNoMw9SLCTNqmuBGbYhxE0M+GZePfovfKVindBuDizPFr/JJ1v/AAnFfGsHxp9OsVHfwXdX/AO11QcrHbVhnqNKSjlw2OP4v6NiwAoD+YRSSUzwdQBr086NFJWEj2Y7ymLb1mKHWnLhj/JGMGxszzzGyUbN9D+/F2DhEbeXkcoxcjP/csz11HP4odof/63nscMTzxuQDAVWfkMTnJgKNSfk/JezGKwUAfcnkaaL7DH9QZuPzZgme4LYu+hHnqwALOoCs0XQvcF0dL/dH9o4O/9wqTCWe8lPPE4zzp6sBA7bmHF/Shl4kQVWx5YR3RzhM0Prmf0hVMx8wrRDAOllPSaEkKId5gEU0IcZJRS4FgoJwUodF9ev3KHVMtylJNC2Sl3PSeFspPZ39O3UTbKThGonUtk1V+IbX2WVMcG8o/8DG1Lf0WqdQUqFSF/6sUkmt5CMwJouh/N8Lu/p3+i+9DMgKdcD1W8K3fclFIoy0n3RsoGR5mwqLcXUthH8bSKzP12PreJeFMUJ2kTHlFAzaljM2VbHlhDsiM9zECBQmE74KTDI9tROIpMmOSky+z077ZSPLZgMy8v305P0iKSsPhiUyozve+LL9Xz8pvuGXdTKa7vdg8JDJ/G7Nkj2PTIWuLt3oOHRHuM7c9tYux5k1A7etD6XRnNDGe/PlKRJPGmbLhQPDX7vGM7e9j60JrMbSPkwwyZGGH3pxn2ucvCPox+t3Vz+PVyEAMpK8nmn13EuOseBMBJ9tD55o+Jbnrcs57mC1M8+yuEx56Vi2qKg1j/NqSU4tFtqzi1ZiIh0wfA1VOPY2HrNmaXjuCzk47BtFM48R5PcKSsJNF1r3h6DKlUnMJjPoAeyH7Xdi96iMiKpwYJjbI9jlAKs7iWUV980FPX1sd+TGLHSs+ysnOu9wRTTrybyMpnh/bchxAcOcmB4Y/mC8IQgqn+29d3FUyl4tAnmDILq/CVj0b3ZYMiTxDkC6L5Q5iFVQO2VXHRjSjlZNbRfEGMPO9FNMITjmXUdY/ssf6prgTxtiR2rA3NNMgfXYS/ajz+qvHseGYj8cYIVqwDO94CSpE/roTiKeVseWA1ibYYigKiW7vZ+JdljLpwCh2rW4hs7sx8T+WPKqLi2JGZx4ts7QRdwwz5MPN8GAE5lBJCiANBPk3Fu5JSjnu200mhnCSa7htwdt5JRUi1ruwTACXdwMhOZsOfdFnvbZwUZtFY8ia8x7MtO95G+/++mbkPmeDIyoRLykll6tTLX34EFWd4z4yiFM1PXbFXzze+81UKp1+FFW0k1bqCpievxOfz4Ss7jPCEi+hY+BNSrSv2apu1H/4faN6PkNbnryPVsSEdaLn/yIRZATTD1yfsyoZf+VM+iu73nnVOtq7ASXS56+s+dzt9A7P0/TF8aNqeAwzlKE9vnWRnnGRHHCdhoxxF0ZTs3BYdK5vp2dLp6cGU7cXkQL+QJvMYveGRo1BlIVL5PiJJi56kTXh9K762OI6jaOuK8UY8Tk/SpidhcfK2LorjNk46gNoXbV1xtthG9vlpEEhvK0B2oxbQbEBKA5/fYMv6FkpOHkNLe5xIexQFGGjkl4SoOGUMTaua8YVMrJjled5GyJf53Y54r9ZnhvuU9buSnx1LucvaBh5I9af7DIywiRnyMerCyeg+9/nFmyMkO+LpcMuXGaYockdZSVJt27CjHTjRDneC4VScorkfcVdw3DC04+X7aHvhVpxkd7/2VE5wxEy6X3+W7tefxSwZQdkZn/c8RsujP8LuafUsK5zzIUKjs8P9EttX0vHyHwdWcJAQvfzcr2KEizK3u5c8Rmzd/zzrBEYeQdGxH/Ysa/rXtwZ/Efo9RsH08wiNPyZzO9lcT8dLv/feRTepuOjbnmWdC+4n0bCG/vpfSc5XOZ7i4y7xLGt55IcDgoaieZfgrxyXuR3bvIiexQ8P9gQGLCk/72toRvZzvvutB4lvX+5ZJzRmFvlHnJm57SSitD3580G2P1DBrIsI1E7N3E7sXEP3W//tUyUNPVRE4az3ZNtQIsZDv7+K7sZ1vBDM4/D8kkxPo+8lY2Al2J5uW4XHfIDyc7+arVsyRuNfvzygHnnT5nuCqVTbVqLrXtlj/fu/1rD7YWSZdfZ2mJpn+wPvO1g9/FUTsGOdnuCoNwjq2wMpOGqG535mURU1n/itt5eSL4ge9O4jlc7/DKXzPzPk59FXcPSMAcuUUjhJGyuWwo5Z7s+o5b0dS2EETUacOSFzv53P1xPZ0glAqDqf/NHZ93SqM0Gi73eNrlF2VDXbH19Psj0OmoZmBkDTSLbHaXihnhFnTaCnvoNUT5JUTxJ/sffvufO5elJdCQCKppZTOz/73tr+5AY0XcMMD36yRXoNCyHErkkwdZAa7nMBeXr99OnBo/sLBwQQdqSRZPvadEjTJ/TpE/gox4J0j6DebQZqjyM08kTPtpLNS+l86+f9Hjc1SE8ix3O/vAkXUXzM1zzLrO4ttDz7ub1+7sERJwwIplAOiaa39npbykkOXKjp7gHQLsKRwaRaV5DY8QpFR11DyzPXgOFuo+ioa4hte3GvQym3DsaAxXasBTvauHfbAvImvg/wtovu5fcS3/4SAMoxcaw8lONP//OhHD9O+jaEUCqEIkiw9gzQCzND3uyETaqrEScepfaEt9BNNyjrqq+hq77M3TE1IJXqIK5M4o5Jz6og1hYfth7AxsBxFLZK92BybJTjYCsNW5Fe7pb1/ZM0d0S5u7Uzc/sD3TYTku4K25MpnkolMmVHJG3y7cH/nrYGCc0NmrI/tcztRPp2Q78/x58LDaz0+rG++8Gaxt3F7kd/Zb6fo8eV8NDaZq649HAW/m0lLU09VFblc+SHpvH3Ta2cN7uWmlPHoRyFnbDcA4RoCn9R9sy5ETLJH1OcOXgwQtmvFivqDab2hpOycTptrO4kWp/eU11r22hdtBNww6vJV87KlLW+tZPIti73bHemJ5YPMx1w9fbU0gzpjTUYZVs4sS7saIcnZHLSt+2Y+3v+jPPJP2x+5n6JHavZ8btPuDc0jaoP/wQjr5hUZyN2VxPKcbA6GwmNn0tt3f1YXY1s/80HQCn0UDkKP7H1r2e256+ZPKBusXWvkGrb6lkWnnwS9Lmiu9XdQmTFU0N6rmVnfsFzO9mwZsCVvJTjDAimepY+NqTtB0Yc7gmm7EgbPUse9ayjmf4BwVRs0xtE17y4x+2Hxh87IJjqWfooTrzHsyzviDM9wVSqZTPdbz0wpOdQfu5XvHWrXzjwOeimJ5hSVoKuhf8a0vaD4472BFOptm10vf4PTxtCN7A6GzNtKBGMcv65X8LqbuK1336C1nARBX168vSlkkMbpjbU4WwDtj9IIKT79xxM6cECAjVTdt3TqM8ys7jGc9+8w+YTHHl4pqeRu/7AsKrm478c0nPoTzP9nrB3fzgpG83UM72rY409RLd3Y0X7hk3Zn8p29rBFMPO8w/bMvidJYpanzOhzksStkKJ1UQPVJ41hS8dqEu3Zv4u/JEj1SWNoXdRA3zNEfbevlPJ8p3nKHEXXurbd75tpGkbQTAdX3tCq9/dgRdhzckcIId4tDtpg6he/+AU/+tGPaGhoYPr06dxxxx0cc8wxe77jIWCocwH17/XT25tHOSmMvBp007tzlGpfS6pr85B6/bjbsVBOkvzJH8ZfNs2zrciGh4is+Vv6cVMo28rWo1+vn76Kj74+HUJkxRtep+O1W/b6ddIDJQOCKScVJbm3IQu4wVc/mr5vOwaDhUmavo+TadoDX0dN09B0n/s3HAJN9+GrmI4z6aO0p6KEz/0nIa2ImOqk3U4SmPRRfC3LSbUsG3K1NMM/6DC+3ueulOYJkNyfgext5d4OFKzB8Lm9omKNPe6k3OleSXml2Y+nZHQU3Q27GN6j3P96dwW741FszeozDA78yQim3c2GJU9iawkcR6FFjsaMHJ/Zh9z59E9Bc3eK/dHjCMSOpYVKEnh3+AvpoIBOzzKF5vkHGsV08RkeI4UPCx/t5kWsoZqEBm2G+9qNpJ6JrKInVMzKYICEphHXNGKaTlwziGoGSc3ESm/D3ZZJSssnEAyT7zfI85vkBQxG+E0m+w3yAyb5fpP8gEFe+nae3yDfbw64resa0aTFCH8H3Tsf59gPn8Xapzcz6bTRtDc+xgXj51CQnuBc6x26MEgPpby6IvLqihhMwYRS/MXB7IFI1D3rbUVTWDELO5rCTgx8//VlhHye9uY5MOi3Ax9vjmTOnu92mwGz39nsbJAVKAkSHnFoXHVQ2Sn3sujt20juXJsJmJxoJ0ZhBYXHvB9IHwwqhy0/Ph8nGUPTDfr3oFGOlQn1zZJKAiPHZu7nWG0oO+6+H3UDI1xE/W3zMfLy0IOl6Ci2330ZVtsWlAOjv/IEGAHMcBWaKT3eRD+ajpFXTP0P5oPS0MvHgOaw5e5Pgq5hte+g7osPousGttp1mNF/fiPN9A96YkdZCc9tX3EtwVEzdtvTqHdZf2XnfIVSK+ld3/QGZ76SWkZc9ae9fFFcZn4ZZn7ZPt13fzmWk+716g2ViqdVZIa0da1rpWnBNqxoCmU5TPr0zExZdHs3Ta9s3d1D7JEdtzxzQHlOhPTroRuudk94maF076X0CQtfkZ9xFx/JtkfXEWuLECwOU3fORBzbIa+2gEBJMNNbK1iRnadMpRyUlW1vnt7BCWvPJwyVyvQaTrQOvkrt6eMomuz24E52xNn26LrMd1TZzNpMfeyERbIjnvkOk6HvQojh7qAMpv72t79x3XXX8etf/5o5c+bws5/9jDPPPJM1a9ZQWVmZ6+q9rex4B90r7iW68RFSnZvIm3IJzQt/QKLxTexoE3kTLqJ9wXcG7fXTV/npdxGomO5ZFt34CD1r/rbXdQqNPAn6BVNOooNUx/q93tZgYcq+B0CDhEnGPgZA6SDNUQ5Kga5pmTApqTQS6Di4AUQRVmbERrcy6NZ8KM2HMvyMMrNzQjXGummJR9ydZjtFbfXRaLo7lK3e0ml2TGzNwG/6ODo/7D6e7mN5wmJnSoFmUhAq4ow+1XypYSPbo50kJ32BMn+I02vGuEPadB8PN26hIR7DRmdUfinnjT4CNANN07h35Qscl0zws6XP8cUx89n50mJGnFDHb+uf4tPTz+LByg+g117M1PxSTq2uQ9kJlJXgocWrsRMpDMuhptzPjNo8sBPYKcVL/34dkg66DQX4yVMmybZLsBMWEQv3EtQofChMFOCglCKhQFPuqxkKRcgz17K+LUXXzhip+jZUugeSSkXJdxLu3EqxHrQ+O4MK9yp0g+0CNnQlsPG+N0qxCKHoiRsowx0O4lNxjL4bUD7Q3IMTpXfhGM00aiW0amG3V5Lu9j6axCYmaEtAS6L0BIokSktmb2tJIAWaYlSfzf81dDRLGUE4HQqN8xvMcXYwL/ooekBD1zUMzW17uqZh6H1+10iXu7eLjrqagmmXep5jbNtLdC78EVoqPczR7B0u2Xe+MPd3R/fTnV4WGnMm5sY/s3XJv0ke3sDU8z/BjmW/I7LsV9Qe8UF8h3+cyNZVuC+4+3d0XyQFykn/LRSg8BWNJ1A921Mvw9cDsYcxNYUZUgRCyg3Vwf0MUwrlKJyUhpXQcBI6dtIAvQqz4AisWCozhM9JdtO15FdE6kdiR9wDDsuI0P7qg5n6RTaNw+rp2/tOoel+jHCFp152wiLRUp9Zp8/qBEubKZm4PP0Zq2hecTROyoduJghX9TD6I1/IrN69qR3d1Olacis4zehGAi39aZF5bfo+X9zXqvfAquqC/9BfyzNXk2pf57mPQqFsGycWQ6UsVCrl/WlZhEafi0rEcWKdVH7ohwSqJ9H20leI71hAckcLyc0NnsfRC8JEt93tWZbqXINKuEOSNcN7MO3EmnESbujXtejXJDqyc+uolIXVmX49NQ070srorzyBZijsWDc777uW6kt+hhEqAqXjJGPkTTrDDcAGOZjz9ZnsObOsYuyAIMAIeQNEPZCHv89cPm7lBj9Y1HTvLpCRX4a/crxnmVk0cH4cf8W4AcsGM6BuviC+8jHeOgxyBTCzoAJf2agBywesVzhwf8gsrkX1m1dI7xeK6P4wZskI7x0HeY0UCsux6f2GthybLsNPKr8MpRQBw8SnG+ihQnpSCV5r3kLSsbCjnUzOLyOQHgKYsC0aYt3uUC0UtaFCgul5obbEI/z+9QdJOQ4px+YreeH089JIxroY/bXniDk27bFuVv/qEg6/+i8YhVWUoejobGLs9HMozSvZZa8jX2md5zlpmsbIq/+OZgb6TJwd8Fz1DiD/yLPIP3Lf5jrzlY7c80oHCeWozEkDO5Ya0IPJiqbcCcPTw+qcVP8py115dUXZuZYUmaFu4PZi6i3rOwR8b3hPJJgoW6GZ7o5Y8WEV5I8pzgyV66t0RjWlg20w/dzrzpvI9uc3MeLksQRKw2i6Rt5uTkpops74S450T6xEUwTL+4RWlkOoOj9T1jfA2ht9w65UTzI9FNENWEuOyH4exRoinvkb9YDhnjwK+zLD3N3f0z20ensMy/yNQoiD1EEZTN1+++18+tOf5vLLLwfg17/+NY888gi///3v+drXvraHew9vRrAY7cirMSZdjM/wY2iF+E+9yw10/Pl0v/INlOV2Pe5QJnF0bDR8OFRq2TNFWyOdJH0NOEqRZ/oZW1AG6QBotRMmgoEDlGAxQc+eUXzNKXS3qTRqtART9WgmTHpwywridgpbKeqiSbJTN8Pf7UpS6d4iR2g9TNfdoQSWgnvsWmw0FHB6Vxcnpe/Tk0rw4+XPk+ioJ2KN5my9hSN1dwLlFuXjTnskDhqOgo/4Wjncl0LTfWxTYW5PVqBv7cDo/gtfPvxkjix1e5KtTNh82zgepWk46HynKsykUBDN8PFazOHWhk53m2jcNXEso0P5YPh5OqZzy4M/yzyfh0//JBV51VSccTf/3LmVn65fmp4/ROPZ0z5Gnt8Nku7ZsIjfrnoNXYGOxkvvuTazjX+tWcR/1i1FRyPf8PPnE3+QOQh/efVrPNewDl1BWSDMsaMvcI+rHcXr219jUfM2dAWlBYozZmRf55ffXMHWhhZ0NMpL4NzDskMgVj/xFh1t3WhKw8yL01Bfknm8SH0HxhEpvlF3Npv+vQqrPU5T80auu/ACut/soPKNGAE7wc5JKYwPHgm4XdaNl5enu7RrbDzcz9xjTsBxFNF4iua/PulOBA5EfSEKzTC2KsFxFDtSbZnjnJAexE8g3XvJoZv0JaIV/Kv9XLaYl8Ffl1Nsx7m8O9sWHzSOJmrMwSRJoW1zplaU6b+Rp3T8gIbC1hStukVSt0loFraySeInoUFET9DiayGgtWLq3YzVqyknhI8k24MdLMlbgKMlcLQUF6YUvbujK8Obeb0oyuvodKkSKpPjCBo+8gMGSm1ltWrBwB0ReYGzA186MFqv57FFK8bUwKcpTtfb0iGTxhXTJ9BdWoOha4RMH3MqRtO9YgVdS0JsUwHalYmBIqA5jNGywwtalUkSHR1FAEWxZmUCg7iVcueE0jTsZDd2tIm9pQdLMaZfw8jJl6DsFB2pBsJTziFv6vkYgQLaX/46qdble94Q7pDY/sGUHWuma8mv9rpewREnUHbcOZ5lyk4QWfdvgkE/gaowjh0GbKIb+wwfTRyHoVfi2CEcO4xy/OhGEPAGUwBOqmfAMgAnvolE48LMbat7Bo5jAn5Mf7tn3R1PbsRJ2aTaj0Ap95NOM2LoRizzUzei7u9m+nc9im7G0c3s3zmy5iWSjetwoh10L/0fdncjZlkRvsqSzDp2JE5s6a5PCMRSr2XCFrunLb3Ufddo5sChtyo1SLhvGqiEe+GFAWW+AJrmRzMNNH+/A0zTIDipDs1nopkG3evuoeioK2l/7RZK5t2G3bMNM7+UzuV3Ujr3Rsz8WsKf+csun8tgqj96+x7XCY2dxci93G6v4uMupfi4S/e43shr/r5P2w+MmEbd5/45aFlPKkHctkg6Fr7TP0ddnwm7V7Q30JWKk3JsyoN5TCuuzpQ9uGUFHckYKcdmclElx/d57j9c+iwRK0mqeQfH6wHOqXO/L/IOP52r21tJOTYpx+bD447ifWOOzNTjzCd+Qyo9n9P129fygbHuSa7WRJSPavkw+WwAvnPUmZltbuxu5YaF2Ymqf3TpLzmp2g35lrXt5Ev/y54U++W89zGp3A2MXm3azIuvZsPZ6LQPM+1L7lDBZ3esozyYx+mP3cWCUy8G3UAvrOLihY/xj1Mvo6RsNDWT5g39D5Dmrxi755WGKeUo7LiFZmiZMMhJ2rQu2okVdcOnksMryRvl9nBNdsTZ+Jeh95belb5zCfYfPmfFUpl5mnovmqH7jUwvpkyP1aD3whiZC2UEdz/0OlASIlCy970uNV3DVxJkxJnj8YX9Q5r7SdM1/MXBAfNOAfgKAox5f/Ykbu98WX17C3t6DKf/HlY0hZPMBn6e+RujQ5+/0UnYJBN25kIqu6P7jExoNeqCvvM3Rkl2xTNDC/1FQxvSKoQQB8JBF0wlk0nefPNNbrjhhswyXdc57bTTWLBgQQ5r9s5JaQZ3rl3EV0afxLb/baDi+FFcv/pRvnzEiRS1rcqs9392Ha857qH0FC3KT3zrMmW3rVvK0pg7p9GcilHcMfe9mZ5Jv7BHslG5X+LH6Z18Xa8HTUfTffzWHk2Dcg/5z/TFOKIgkhlmcefK/9GZiKEB7ystYcrIM9DwozQfD22O4CjQ0cmvKOCEkWWgmySVwcKFm9HQ0dGYomXPgieSKdau2IzmaGjOSTgjphAqGwkYhBIJWLkAn9LQ0QjUzSBQVI1SimBPF7VrX0fvAX2nTU9NhN5TYk6yjFktM9EBXWkE5syjeLT7mOFlqzj3zaXoSkNXYFsTaTYDKEdR1tPJJ9vq0JSGDjS3rKVTN0BBXSSPL3ZPQUuHT84ZBeh+9yxZ2TqL65e7Z88dDdRF2e7l1RsUn1njPrauaWzakT24PzwGIxLuGWtTN9i6c22m7Iiowaike1Ys4p12iVE7fExqdLt498S9O1Gjmv1MaisGIK/DpKPTDSmUgrkjRlFeXsTaf69AtSfxoWO3JNjynzVMfM9hjG0fwdZVO2hsi/N/L23MTMo92bbwpzt5vLGhg7t/+QqRpNuN/vOWjZ4+dm1PpehW6avKobD07EFt3HawlJ0ucyC9f2lrCp/K7mxGdcWyYBdx3SGu2TRqJfSQHq5gdLOpbANxzSauORSlJmA6xSQ16DA62RzOvrajYkWEHXcnq9uIsT20Kd3rSON43xeoChaT5zdoUDt5OfJmuhcSHD/264TDAfJNh56O9by5fSk+Ayr0Lp4+61hKgu774HeLGvjV+mJ3HjUcvjL1/WhOEmUnebq5nb+0RUEpgprDB2sUykmi7ARP90T47yb3wK08mMejZ3walA2axt+sKp513ABipJbgLt/qzPP5pV3Hq+n3+SQtyk996zI9A7/w2gO81boNgBl+ixv7tIfrUxPYpEIYKOboXVxnbsmUfSM1nmZ86MD8bfVcPMrgvS/8lURnPTtUAAfw+wt48eyr8Pf5zLnFGoODho7iBL2DE/UO92+p3M8jf1OE/CVPM792InMq3LYfsx3utmrRAF1THKd1MCkdhncrg0edcvT0AMhj9U5GpnutdVgOz9cvdXuNoTG3cjRl6ZClS3dYqoFhRtCBw5VBgea2sVT5q9SrUGabE1SSvKJpFM/5CV1dUVo7u1AxGy3h4Fu6Fuwwyg5hWWEsJwRKI24kCCvQNXdYqu0EMzmNMuKZ3k5Oyu7Tg0ADx0n3/vJjKx+ownSPqXTvKcd9F6BAzwuCphG5601qThlDbPFDRFY+i6NCxKNTwa7G59MxSuPopvt6ab6B4dKuONGOdLX0we9rGoOGVb7acrAdiuddT6D6SIxQEUa4GD1cROfCHxJZP/jcRJqmYZZlh3PaPeuI73iRoqPSE5jrJmhQNP0qz5D0t5tSCls5JB0by3Eo8AUyn9FtiSiNsW5S6bKZ5dmeLqs7mljb1YzlOGgavGf0EZmyZ3esY0nbDpKOTZ7p55ppx2fK7l33Bq83byHl2FSHCvjerLMzZTe+9TgLmjaTdCymFVfzy3nZYe2ff+2/LGtz502bVzmGnx17Uabs5iVPs6GrBYDTR0zillnZwPbuta/SEHXD/veMPoLjq7Khy5Pb19CVnsuuKpQPuCGSpmms7WzCTp89aE1ke1j5dCMTSgGe3326t714yrTdlPW7X9LOlvkHbLPPMKl0LyYr3eNQS69rK/dv0tvr6lCmnPSE4L2BRqZnU5+fvb2a0j8BKuaOpHxW9n3W8saOzO/h2oJMMNV3GNz+6Du3k78wQNHU8nTw5MOXn+0VmDeykMlXzT5oeuxomkZHpJPKvAM/GkP3G/j9xpDCHcdysKMpT4gHYOb5KJxQmh76fmDnb0x22mjdmmf+xs61LbQtcnvW9p+/sW1xgzt/Y56391UmTAz70P3Gu/JqzUIcaEopivOK0sca7x4HXTDV0tKCbdtUVXm7z1dVVbF69eoB6ycSCRKJbJfhrq4uABzHwXH2rRttrvnQ+ETVbLY/uo7ONetINEW56axTsXcksYNfRaUiKAxmdkcoT1joaFSaflLll4DSAZ15m3qYGI+54UyPD2eOQ3jSBwiOOZv5f3uD+V0OOhpmTYjqD52ZuarZB+58GDPqoAMV/jza80po36JQ6g2u7hiJnX5NSwJhmoLZ3jqf62zKzPJjFxaSN/UEAPwJi0/8OzuZrF5ZmPm7qITF++uzk3qWtBo0+DsAd6f27O7sjkKgo5tGn/slbNkpjuvJ9iJwupPZv3XM5sj27JVjrJiVLYvbjOvOdru2GqNEdXebZsqiIp7debK6k5mdYCOpCNrZL27btrPb7PMFrClo7o7hoGHZip6URbj3YFapdKDjHt8mUjZ2enJNpRzaoslMWTRlk3LcGz1xm+89tYaUrUjZDsVdcUbbOqBo6ba47P5FpGyHlKOY1RVnlOXOC5NKWUSjPSgFmqFx6ntn0vDERuLt2R5JCoi1xWh4YTNTz5zA5lU7aWlP8uzCbZl1RiiLEid9hjOp6NF7dz41tvhigCKuOWgqH90JZiba3hraSUxziOs2QauGgF1BQoOobrM6byUx3cZCUZ2YTHF6k92a4s7y+uxjx4uo0HTyAya2L8AietwQSYc5xaVMLqgh32/SaDfzpx1r0NPD3b534hRmlNWQ5zd4s30zNy7JbvNnJx/GmHw3xXxkK2xYkh3icsGRo6hI91LYtilCoCUbFvp0LdtuwzVogfr0TFIaBYddll1v1f8wEm8CEDADlJx4VaaMpc+icMNCHXd7edMuIzz14wTfegzfjjWgFOG8QiqOcYfrKjuJf/kCzLZGUIpQXh5Fkz6Er/xwHMfB7h1KB/j8hYTHX5QJwlI7EiRS7rA5KxB0J4xNb7OxO4+djglK0amFUY5NMtKIrRwi6fwknozi6Ca+smmZ+cdec4roPaQco2XbkoXG004pencSY/MyRueVcHSZ2yMiaqX4j9PbW0ljhJlkkpYETafbCfAHJ3vwVGNCnRkBNJo0P7ctfSb7tzvmIkqLitBDFWyzg/wwmv2O+El+C9NMCzSNdakgN0Wynw93FXVQUlpOoCLMG4nN3LTjyUzZfVMbKTfcHoEPxvz8usOkNOkANr+pnE1QB5TJ2kg7sR1t5FsGnVs3EP7Nx3GiHRSd/i2aYj20JaKgQuTZUJGM9Hld3NCOdFvx935eaNBmFBJRoLU18eCGRj4ecg8UlSokZpyGoyuSySCLA61cOskENBJtfhpW9mCrCIoeDCKE9Ri6kcTwW2zzh4j4FMmQia+rk7Mdh9DY8/BXzuT+HTtJ1nXi+MOMLy7lxNIy9ypvmsY/m1tIKbdv1ZRp+cwoKCRYexx6oJAHtiyHaA862xlfeTJjq9y5HhUaL7a3oaVD33HhfGpD7jBmy4Hl8RSjqg+jJxWl3Agw5tuLcTSdDidBqquZdd3tJB2bmWUjMu+7hlg3j21bTcqxSTo27xt9BDVhN5Rd1dHInza8lSn76hGnZMpeatjInatfzvQAuuf4D1MedOvyj/ol/Hj585m/ybNnfoY8n/tZ/9/6ZfxqTfbKa6+ee23moOqp7Wv4wwa311zY8HNh3WGZ9d5o3sI/Ny8FoCKYz2enZHvtbOxq4Y0WNwQek1/q2Q/pTiVoTw+z60klPGU+Tc+8l5OO7Skz+5bZVr/7GZmyhJ3y3k/vW2YNKLPScxim+pQZ9J2xz3u/3ZWZmuYts1KeMp9u4NN1/LrpDulLl+WZPqYWV+HXDXy6QUAzMmXVwQJ0XafI76c5leTwLz9KcyrpDo9XDNt9PHCHuqUibtik+w3yRmaHj+18eiPxllhmLqd9OS6xItn9ImW4+wGO5W4oFc2WaT5336b/Y+imhtF3Xqag27vGCPkwg+6wOiOc/V33Zf9uRr6P6lPGeLaX3WcineEfHH87x3E87TFndPd1M/J9mXoBBGvyqanxnqHsLcufUIKvJJgJtOw+8zb2DSx3137MkPt+7D34tSKpzPpGyPS8LtGd3XRv6tjt09AMzRNUue2nN8Qy8RcFCVbm7XYbw81B04bEIUMpRao9nhlmTElwWAe+e/PeOOiCqb116623ctNNNw1Y3tzcTDy+5+6sB5uSggC+iMHaB5ZR4+RhECCxM0L7P1cx5j2H0RoromNFJ2Az3dE5TJkoTUMZBl2J7JdXTSzJ6EgPhnJItm2l/r4nQTfwH3kBBREfeeljynhbN1v/+wP3bLZuUNVehmm5PUOCcZtEPAXpgxct/eEL4Ng2qUQUHAvQ8NkpHBRK04nFEzQ1uQfgtuVgKJU+AIJkNEZjYyOaptETieHTegMfzb3ijuXuKNvKIaCbmaFbynZIkkIpRdJxCGpu01Vo7Gzq4bnl9ViOom1nO4XpGTEUsHBtM4ubEqQcRaqxgzEqe4a1pTuJhvslbGFjKH9m3qLtHTG3Tkq58xUpX2aX+0O/f4M4OilHMT0WZ37CR+9wmTN+/UomrDoxkeSEpD9Tz82JvqGQBpp7VswBGvqEqwoDIz3htubAvxZng6IPRPMwU+72fQ4s3taWKTsqXoRtu9tzNIigUO7LTsvKFqpPHUNrWxex9ljvjE8ES0NUnzaWph09vOXPp95xSFnZs3AP+ouwUcQ1RVzTMn8fgL8ECt3XRFPoysBIn9VWKCythqBPJ2hqBPPDhH0BKnw6ATNEnT6ToKnhNzTGhEuoDRUQ9umgW6yK+fEbYOpwXPlIRqQPPFsSEZ5qcns+2EpxemU1tSG3vW+NFmIZk7GUg6MU4wIaYasHZUEomWJOYW3mftH2TpqibhKmInEmBktwUNhK0dnajvK5B43JnhjlRtCdQB1FW0srPemgMtLdg+GQnnBXy7R1gM6uLqz08ChL6d6ynu5smZHylEWiUaz0fBS2rdMWCwJu+4ipMDYBd2feLKMnb7Y73USsyb1fepsp8kmMviCzzVT3Q6h0rxmtYgxq0gmZMnvhv9ESboCi543ESfXgpKJoRhBNBd0AC3fi7IJjbqSjsxvQUAsfQEt/Bpgjz0MfMc3tkWNbaAv/jULDSln0dHVnnl9bMh+tIHtpb2PCpRhV6Xl8op1oi7LzFOlTLkdPz62T7G7B6nP1s66ODloIoc39LU7HTljxdHabR1wFBW5PQqt1C6x+AcOxsXUD/YirSYWKaGpqIrHiJY7bsIBwKkY4FUOVjKArFUXFuzmscyff6XFno60vHoE655/Yhvt5sb1oGdVrryGQjFCJRmRzuotm2ybemJ3HC9vqybMMjuhs48LGzSjCKMIkbT+WCqJreehaGPr0DrE0Pwllg5ViXaSNqGGQSqWwfUVYuo8UENdMVgfKidecBkA81UNH3jLiysHRdIKmn2C4GAc3BOuOdpKIWxCH0EsBVix5HT2Yhx6cQk+7STMVREyLxqmlzCo8FgAn5fCHhn/S7iRAg/fW1jKhehY9nXEgzi1vPZnpWXPJqOlcXOcO+YrbFt/YcH/m+VwxdjbvyXf/rm12jC+u+Be/LR3PyY/cSbnmUFVQSbynkaST5I/nfpWvv/EgSVtx09RTOCY9J8/KribuXJ69Gt00sxAjPcfTxvYdPL5lZabsQ5WTMfLc7/iG1hbWt2ffT9ubGnDSl7aPdGXfdwA7mhooSl9xLdYT8ZY1NmR698Qj2fdW1HY879d4NJYpixDzlCVi8UxZdyzqKbPiiez9+pclktn7RXo8ZXafss4eb5mTSmXv16+sQg/gMxV+3SCQtD1lxxRUYymFqWlUOD5P2Qerp+HTdExdZ6wWyn6fK4evjj/ODZo0g9FGQabMUQ6/PuJcTE3Hpxvkm/5MWT7wr9kfoK/esiLgB5NOyRYkVKasEKAgQIUvj6sXPo5yHDRdx9B0bMf7fHJFKYWyFCpu4yTS/+Lpfwkblb6th00K52RPtrU/s4NUk7tP4K8KUXxqNqDvbOjAak0MeKy90dXaBX3bkE+hNIUeMIgko6g+ZaFZJWh+HT1goAcNtICBZmqDHgjZkJ7DMen+cz8mhi3Hcejs7EQpha4fHL249koo/c+dXAAT98Cu95SbclSmTap4n7aZ/ofPu5/S3daV2c/TNKNfWbdnH3BQFqQSSegYvDgwMo+iE7JDkduf2YFKOughg8CIMKGJ2V63yeYYuqmjh9Jt8iA9MB/2bUgMoJQCW6Fst8e73mfuOqsriRO1UU56HUe5VxO13WPVzPJ0Web39CW8la0wi/3kz8hewKLjhZ1Y7UmUrSidVU3x2DK2PrKGWFuU1INx6s6dTNJveTriDCfd3d1DXvegC6bKy8sxDIPGRu8l5xsbG6murh6w/g033MB1112Xud3V1UVdXR0VFRUUFg6/qyrZsSTNL62nxslD2Ra9E/pa3Q4NL21j5FlH0bVqC8q28aPwabjraDpmn27tVeFCVLQVzbHRU1God4dBFh11FjMqRmJp7pA8f74fe8l/3Il1FdRal6QHwABWHDuRbUzVpWNRupk5m5LoacaId7hl6WrY/ny22ZX8Y12UpO1gWRanpg/2FGD970XWvvA6Dho2YSqCn4X03FQ9vhJazfx0zyGFaUNByg1ekmgkAYVOi78Sh4A7dA5Y97+XILUGWzPI04qYyhFuOAM0vvYaHVoMWzPQjDocbZI7x5TmTp1UnGxEacqtj+bOPdVpFpLStfQ2NHRl43OSOJqGhY5KRNB0AwODJsPHCyEdBw2lafhN9yfABkw6TJWpZ29g5G7XQPXWI12m0uU22fVtze2t0+uhAsVDfdb39fmi/nf2+9xjZFGQkaMK6PY7HHbpTHY+uharM45ZFKTmnElstJM0FwZYV5nH9q44Rb7s1dzy+1zFLXNFN5/7053E270qXH7AyFwJLt9v4jMG36ndk/MZeIl4gEpgWt3g84JUUsmsMRMHL6us5KTxhw1adl5lJedNmTlo2SWVlVxyxNxByz5XeSqfm3nqoGVfKTuDLzqnYSsHB0Vxn4mary86natSJ2IrB13TqMzPTsl6behULk1GsZUiaJhUlmQPYK4yTqQlHsFBUeQPUlmRLbt82lya4z3YSlEbLvRcHOKDE2fSmnC3Oamw3FN2zpjD6UhGcZRietlIdF8+RrAEgiWEoxGU6UOzk/h8QQJ5pVSlP0qnbx6d7qUF46rHUDXC/ZtErSTjiqvcMqWoLcs+nh6PUBYuwMHBdhSVpaWZsp5uA9OX/RoqLynJlDX67EyZ4diUBaBYdWFHOygm6b2fmUI9dQt2tIOxbdv5VstmAlaS751yLeVl5VTmuz2oKhqWcnp9dki4L96W2ZH0OalMm823ElRXVmUmbw535BELhClMRdE0DZ/P/awtDGrooTwaulNAiqA/Qrj9rcz22yyLLY5D1BdCDxZz/vgTwVcCRhEvWwlWd3SSZxnYhT7qTv4CnP8lutd3sOXBt4gm3bn99IJ45jVpbwTLHyKV7umiGT7P576mZd93hq5jODpEFSpqMaE7nzGO2x7XFIQy22x5fTvXrhpPUrPpNi16JudnyiJbOpnTXkaPzyJq2FSQT2l+CUbQJOakPH+DkqKizP20eA+aYYJmYCuNs8bP5bpJJ/HrjS/z2MbX3IsyGCamrsgvyrbbKp/j2WZ+cVGmvVdocU9ZYXFx5n1SYXd5y0pKqCxw318ViRZPWVFpKZXpULssssP7HMrLCKcnIy/tLMZsyJZVVFRkh2l3lFLeWYBfNyj0BT3vrSN66ujSbHy6QUUwz1N2emIao0sr8ekGlf3KLmMu58cj+A2T0kCYyvJs2XfnnEvCtvDrJgW+QKb+AH+Z/3EMTcPQ9AGfuffN3/V8WT+qfO8uy75Uefouy95bNXA/rFfNLkv2XVcqwcNnXeF+Jzo2um6g4Q4PLKws2NPd94ljOZ6hZrGGHqI7e9weTFELO+7tkdLbE2l3zBLT8/dOlXTR3eYGij7N5ylLlnTS07n7M8yaRnZ+ppDZ53e3l0qgLESoMttOKi6v2PV38iF+UaHdcRwHTdOoqKiQUAEou6A0MxeWpmuE+7zHnNoYcdOf6ZGl7D23+/4Kyrz7KZ3R7dhxB6fHIVgT8JStfeAtnGS6Z1//9t53CGGfHlm95bubk+xAkzZ0YCnHDXQ0XcvM+2YnLFLdSZTloGxFsCov8xmdaI8R29GDY7mhkLuOe/VMx3ZDI9X703J/OlafZbbbXXzcxdnh+o0vbqZ9WTPgDqedcNn0TNnOZZvoWb2LS2oOken37jfEtFacVIqC8aUU1Zaw4/GNOBEbwzBwIjY7Ht9I3bkTKKwoPGgD2t0JBoc+V91BF0z5/X5mzZrFM888w0UXXQS4b/pnnnmGa665ZsD6gUCAQCAwYLmu68PyA8ImTtn0IO3rG4m3dmFSgEol8JeGqD6+ltZFW0mmou7cNPRe3compVtsS6TcMAOwlWJEqgHDjmM7zbR0RVEo/u/J9ZSoPPJTDhYK1b6TI5qyEwCH/MvQtbA7F1DvlaWU+/vjeVXEjVCmZHpiOeMjK+iNVpRy2MQkHthRQVOTO6TSsFMc1fN3txwHx+nAUb2DgbqxUr/L3PepyotYXHBsJsgZFd3KR1ru9NQlqfv5waQ7PK/ZxdsWMLFnRZ8lD/ROY8SsPkvX5R/On0Z6ry74jbW34HO8Vwq8d9R11IenZG7P7nid8xr+nLk9eFwBjqazaNKdqHRvriafxqy2vzIpsgwHHUczsDWDpYXH8kpp9lp7QTvCe3beg60ZOJqR7r1mukMJDdOdzFg30QyTzZXHESkYhc/Q8Ok6xYkGajqXoxsmhuFDN02UP59I7THuOoZOnk9ntL2d63/3FkdPHst1Z89mywOvMPLsI/jxy8t5dVMDd31wJvddNIZwKA8zlO1m3dtDbjh+EOZCUPezq4/f8lA+5aH8QcvGF5XvcpuzKup2WXb6yMGDPICPTpi1y7IvHH6i53ZzvAd/XhUOMCpkoJsGOhD2BT2fo/ec+OFBt5fvD/Kv+ZcNWlYZLuDps7NDGpWVwo60oawkE0pqeeOCL+Kke7t1PncXLa/+ESfaQVmknb9F2nGiHTjJKMZb97Mj3Q7HzbuUf596uRt2KUVVMkrjejdwKlSKyaF8UPDrGWdQk1eUeQ6jK0bTmVdC74Ako0+7zjP91IbddLcqlEfANNHTPTqPqxpL68jD8XVsxw4WEK4chxEuJlA2mrkFleT5AjjKIehYjDzj0+6cTKFC1u1cT2PbDhylKPQFqJl6XObxyjcuorjdvUDFjHABht9tOcVTKtjk5LGuJYIvpTGuMruzGywL0z7aINKTwp+EPM3AMI3MPFembuCk+3aaut53pDGGpuFoOnHDJuj3ZbZpx93vEtPR8Dk6pm5kyrrXtzN/Z7ZtVuxMsOG1Je5RQkDnUx2jiJg2UdOmRCVo62jADPlIGClqokFUj8X7Rh/FtSPnseXxdVxx0hyUSk/G7vajxSJ7ljlg+tDIDvvSdC1TVhQIMbGwIl1mEPL5M2W1ecWcM3IqPt3ArxsU+AOZssNKa/jctBMyZYWBbJs+qWYCowtK8esmPl0nZGa3+eFxM7hw9OH4dQMzvT/R+zl45ZR5XNln+F5fl0yYzSUTZg9adv7owwddDnB89a6v9DehaODE/b1C+j5eiXaYKA64Yapt26SiSXxBP4Yx9LnWID1/T7zPEKf01edKDq/MTPrcubqF5te3Y8dSOCnHMw9SdFs3La9v3+X2h/L16MQsz2epL+zP3K9/Wd6IQndy6r5D6ULeA3Hdbwxpsm6xZ5qmDdtjhgNND/vxhQf/TKk9JXtyUCm3J1ZmCGH6vWVF+k7unp30XdluwOTLy37GKtvBSdiZ90HfMidlo1KO573lXjnSYuC1vQcyAu57pXLeSArGuiem7LhF98b2zNUdAyUhdP/efZYMRilFSX5xph0NZ0opT2Djhjvu770T4oP79+nZ3JkpD9cWZC5AYEVTtL61M3N/5Tg4lhokLHK3mwmJegOl9DQnYz94WGbYZ8/2HrY/nr3wy/hLjsRMz8WWaIzS+MLm/Xvi/f52us/Itj3b2xNON40hfebvltN/mzqaoVF2VDU7n9mE1Z2gdyQOaFjdCRpf2sKIMycMuLjEcLA374uDLpgCuO666/j4xz/O7NmzOeaYY/jZz35GJBLJXKXvUGY5CrOqjCmXn8WWh1YSWbeCvPEjGXXBYeCPUhSEP7y2nGQqhaFsDGx0ZdOmVfC86T1I/ZDzJvl2J4ay0c1aDGWzM+Hj5TyF29VKY5rdwWQ9gI6NoWxiyVcGrxiwIPR+4n3OQlR1bmZkapFnnSaKaDKz71hDs0nZm3axRYXtNGdu9egO3Ub2vhYWqt9XkK0N/BIxlD1g2WBs3Pv6DB2f7oY2ft0dyqBp6QMlDcaXF1JaWoRP1/EZGhN35FHYYbrraO7BlJYeItf3to7GV+dPwW8ameCo6Dk//k2RPvfROH56AV86dbZbD0ND72mm7RcbMo8/QG8uZ0HV8eeQN2VG9jVbup2mf/3Zs7qvdCR1H/YGCPW/+Cpf7VhB14sK7aQHGHXWODStlWOevZLTAhrJu320AAUzL6Tiwm9l7qeSMeq/f2J6cnwDdANNN6i94g/4+1zOvXPB/XS9/vdMufvTBD19v3QPCT1USNUHb/PUrf3F35NqqU+v794/PPUUwuPnZNaxulvoXvhvt1zT3Z+G6b2dvn944jyMvOwcQ6n2HaRaN6NpvUGfgR4swF/pPRBMtbuTw2a3l16/723ddIe1HmJBnV83+O/pn6LASqLZKZTho9v0D5iYuD9lJbGjnTixTuxoB3a0AyfaSXjS8ZhF2TmgepY/RdtTd2RCJgB/zWRGXuW2XV3T0TWIrnyGVIt3B0NPl3uWJXqoyy/O3HaS2bnjDE0jlB6CNyEYyvR6Aigqrsb2B9CDBRjh4vSk3u7PonBRn2Ulnsc8vKQGPvPHQV+DucDcyjGDlp05YjJnjhg8PPzwuKMGXQ7wmT6TafeVN7KQj350YG+W3olzx/S7AlTmsu/RFFNjIayohRkyGXd49sygHUsxuagSBQTKQoyekg3vrWiKSUUVmV6yem+7V+7QpWN9tW7EZ4O5RaN567Z0seJb9hEULotx65T5LHpoGZH2CNHOGF86ex7xxV38pu14QpOKGVk1KvN4vieb+JtzWvY99kKEelaCphHW4FZtllsG8FwrWzT3rGWepvEpbRxoGqVHVJIXKsjUP/BKO/O1EjTNvdR6KN0jKtWdgIVtjNFAw52frFnbmv4M7v2Ah3j6957090Tmwxz387/4sIrMznqiLUbP5s7M53jRtAqM9IFPoi1GdGd39rND6/N533/b6YfP/N77U9fIq+sz1KUjjhVJuvc3NEJV2eA72ZXASViZ56Jp/bZH9vE0z3Pqs56hZ+oPZCf67/3OfId6JSjHO9+GVhLCiVvZkCnaZyLwePZ2b/vve8WzvgrGleAvMrKP0ZUdJmHHLfT0pN3mPk4QbgRMz1XnensBAJQcUUnBhNJM6NRX2cy3o++ZEAeOpmkYQfeKiezhiohKKVTKwYqmMkEwuO+54sMqM0FW30nfrT6T6e8LO2FhJ7zbSHbG2fls9nik7vzJ5I92P09jTREaX9qCGTbTc2P5MkFMb5Blhn2e+vc+h76fTXppeJ8DY6WyQ72cdIjTGxI5g/xu5vkI12R7tLUtbcSOpVCWIlgRpnBSdqjY9ic3uPPV9d4/07OoNxjKbn9Xqo4fRekMt8esk3Q8QVHNqWMzwZSTtGlb3LBPr0FfTp+6aIb3NVW7KdsnSnk+n/U+321Ov9dEN/f8eJquoRk6Wjpw0k33RJt7WydQ5n3PFIwrIVAWJtERp+bUsex4eiPJzuz3ka8wQNUJo9332yHuoHyGH/rQh2hububb3/42DQ0NzJgxg8cff3zAhOiHovqoySf+uojjxpTynXOnUv/fdkacfzjXPLeRfy3fyT8unc3CsZeyrXPPg/r/NuIze1xnZcEsVhb06VmhVCak0pX7U8PBUDYJ3ftG+l/Z2SwpnJsJx3Tl0G0WeYKfgKbz8tjL8Wk2Pk3hx8bUHHyajYlK/3QwsZk68nDGlNVg6hp+Q6eoK44vfGI6gHMwsCnyhbjxtMmZ4MdnaOQ/PwGzMeEOW1QWmrLRHBtNWemf7lmXmdNGc92HTvCEChu/Exww6+d3zj6MYF32wK1zwVJaG4bQDVE3mHWk92pTDQt9RPudkSkuDFNakj2QTiU0OoYYdPReCr6Xcgb58tYHvq2rzv4CVb4gjqNINq6j/s4PMeaav3Hi9X9E1zWcSAeNf/2SZw4cd/t27y/pMdQpFAN7UNnRdlJtW/dYfyOvdMCy2PpXiW9+y7PMLK71BFN2VxPtz/9mj9sHqP30fZ5gKrLyWdqe/JlnneCoGdR+8m7Psh2/+wR2d8set198wuWUnnZ15naqfQfbf3MpWjp8Ix1kjbjyTxih7E5Dx//uI7LqeU/AN+CnZmAW11B2xrWex+x48R7saLvbcy69/bxppxKomeypR3Tt//a4fU03CI6Zhe7Ptum8WBfBSBt6qIhNPzmPsdc9SFG0wxPe2T1tNP/3O9iR3gAqGzL1V/WR2z3BFEphdezwrJO5alwfRriYFHs+89X/vpovSHD0TPRgvidsMsIlnvWKjv0IRfMuRRvmZzX7000dvTCAr3Bg7+H++l/hpeTwSsI1Be4Vn4I+zD6fAU7SxtD07Im7PjTAv4ueK5qmUTGpkqIp5Sx/YCnRSBQNjWhHlC3/Xc2k9xyOFnfwWz7PldUSzdHM2dJ9VTCm2FP/rnXZefjyxxQTqnbDGztm0bFi/+coKhhfkgmm4k0Rml7e4inrDXai27to2M+zugOukrWkgfZl7nMw8/xMvHxGpqzltW10rtm/4QZ5dYWMujDbe3jzv1YRb3Hf8wXjSxl5dnbuuA1/XEoyHezsKgTrDdgGDcE0jdLpVZkDH2U7bLx/OeFRRRSMKmLbkxtIRuJsfWQ9I88aT8ubO+nZ2L5fz8+KpjJXTDP7nYW2o6nM1eR6gyPdb2Qnc84MoevzM+xOEu7+3P1wokBZmD2/W4UY/jRNQ0tfobAv3WdQ02+S/F5m2MeY90/r0/PKDZy9PbRSOIndn5g2+4S+/a9i2Pc9n+pKENu553lwdJ+eCa3yxxYTqspn60NriLfHqG+NM+Z90wiUhtB0jfblTUS3d6FshRHyeZ5rwwv1mR5HmSAqPZ/RUBWML/EEU+1LGkmmjw2LppR7gqnI1i7s2L5fxRHwDN1824OiAY/n/Sx1rD6P1/8Kn5qGbqaDIUNHMzX03t8NNxzS0z81PVvuvvZu3fNGF7mf4aY+4AqiJUdWUTixzN1WOnzS+/zedwjiUPV+74Ebdo48ZyJbH1mH1ZbCVxhg5DkTM+3qUHdQBlMA11xzzaBD9w51xSEfAZ/Bo6ub+fK8KurOmUSPkeCfy3YS9hkUh02iKZuwz8j0uPEbOmZvDyBDw0wv7w1u+q7nS6/rN/qUZe6rYxp9yvrc329omHrf7WS323c9Ux+sN8ngZ//3bBxwxh7XYtyPd1usHAccd7hK/7rVXfsfdy4vx0LZFsqx8Fd4e9KEJx2PUVjlruPYkF6v//0G+0IJjTsGI1To3i+9rr/KOx+SZpgERh6e3e5gj5FephnenWd3HjIGbG/AskA+9beeDLaF49j4nBQ7fvEB9HSvoDFffTrbK6ovZxdf+v17rg3xiguD1Y1BwrX+66ld1WOwx+gfOqhB6jZYT6ChPkb/8M5O4UQ7B9ajX1tLtW0jsW3ZHjfvr54EeIOp7sUPkWrd4lnmKx/tCaaSTRtoffSHe9w+QN0XHkT395ls9/W/0/Xq/dRc9mvslk3YkTZ6lj9J+dlfzt7JMImu23WPyr76B0d6eOAEaHakY8Ays2QEvki7J1xyfxalh8elezUVeudE0TSN2k/sObjUzEN72NNQ9G+XeXVFnl44fY1+31T3TPeAnlj9DhJ6597pPUOtu13Stz2+nnBcZ2JhBQr3Ihip1hgNL9Qz8qwJdPcLFg7IVZF3t9/W57m/Ixdg7vt4b8Nz2902D8zj9Tv42HWRG3iq3ivNZtfem2rYfXo2KQX+0hD5IwvZ/J9VOCkHfO4B5LZH11N9kttjd3/CKTue/e7xFwcpmlqeCZ36DpcoGFvsGdonhHh76aaeOYmwO47lZHpc9X4/Zb+jUpj52e98O+rd1zTDfSa0jg4ttHFSDk4qQaA8TKAkRP0/VpBoj4NSxHb2sO3RdZkQIdbYkzkx4ivyxtBWzPL00NwXqt/cdn0Dof49n3RTZ+h70YPz9GDq91noCZFMHSNgZkKg3gBH38Xv7m1vsKObOv4+r1mwIkzdeZMyQVOgTy+9/NHFTPrUzOz2DkB4E64p8IR+ffmLgu5VO94mmq4RKA1Rd+6ETE+8d0soBQdxMPVuVRAwOWNSBf9etpPtHTEqQj6aO2NMLA/zniNqmFKRzxNX7GqWIzEYTddhFz0kfOmrQe2Or2wUvrJRe1xvMEVzPrjHdcyiKkZ8+t592n7BjPPImzbfE5Bp2sDnauSVMP67bq8kq7OBLb+6hFFX/THTq0U5DhXnfwNf+RjP/TRfgNL5V7vBkLLTAZuNHvJ+YPtrp1Aw43yUcst71/P8VA56aOCnuVFUjS/W1Wc9Gz3Qb4dEKTTDl9nObg3o9TVI8DVIMDXU8GvI4d1Q1+u//UH+foM95yHXY7DH6BP82dFOCqafQ96Uk7C6mlDKxupqouCoC7C6mtFMP0a4yP2baPqeX3/A7hdM+UpGUHDUBX16MhWhh4tRyhsWV773u0N+DuLt1/dMd2/Pkt1RtpMZXqWUYuRZE9j22HqsSArLSmGYPnzV+Yw4bRyxph6C5d7LhheOL0kHHGR7TimVDVl6yxTeEKT3d0dh9B12ld7B612v73wimq7hKwhkHs/zWL2P03uZ1nTQonp/71unPvuK/XujeV+cA5AU9d8v7bPJAeeDDkgytevH211dDsxjKcqOqmb74+tJtsc9B5ipniQNL9Qz4qwJ9NR3uFcyIT30MNOjqfcy9f1u9+nl1HdYjr84SO38wef50gx9t3mnECI3dFNHLwjgK9hz/8OiKWXkjS7KhFZ9h9CaeT7y6grdEy7pixrs8jM0feKl97MpQ7nBeeNLmxlx5gR0X3Zfrn+IpB+AXkX9w6feYWK9AU1fwao8fAX+wXsNGbrboyjTy6hfeJQOi/qGQZqhMf6SI7Pb6vNZaoZ8TPr04BcW2ldmyEd+n97Qfemm7l7K+xCi6Rq+kiAjzhzvzkf4LgmlADS12z2p4aerq4uioiI6OzuH5VX5AJp7Evz21c08taaZrliCwlCA0ydXcMWxoynPl87fYv9YXU1s/PG5jPvyI5iFw+9qPMpx3HBEOekwzg2/UA7KsTDCJWh9hgdZPa3YXc3ZkEzZ6IE8AtWTPNuNrHoeZSXTvd+cTFCmbO/t4KjpBEdlr9BhdbfQ+fIfM6GZcixwbMrPu8ETAHUuuJ/Yxtc9Ad9gP30VY6n6wPc9ddt+18dIdWx3e6Y5NsqxqHjv98g/bH5mnZ4Vz9D0968O6TUcdf0TmPluN2+rq5lNNx+P3d2MUjbKSqGZPoxgIb6KMdR97t+Yhe7kyw1/+jzoRr/hcsXovT2aem8HCw654XJi7ylHkWiLsfWRdcTaegiV5lN37qHbJb33akLuDeU5e+tYjjtHUzrcyux6ZUKwPr8PGpa5N4MV2WHgyc64e6ZfuTuyfXsYxJujWNFkv8dKb6/3d0f1e9x0QfrxzHwf+aOLM9vsXNOaGQ7iLw56DhTaljbiJOyBz2svgr6CscWZbTqWQ/uKRoKlYbY8tBY7ZuHoDqbpw18UoPb0caCDprRM6KT5Bl6ZUIjdcRyHpqYmKisrh/3E1eLAUY7Cjlue3ldW1Mr0EPYVBghV5bH5v6tJtsbck2y6TsH4kkyPqdZFO+la04pm6pghH3XnZ/c5O1Y2E2vo2XUQZKSHn/X9vW+QlA6DzLzsvm7/E31ieDuUPpv2JpuRYOog1ZOw6IpbtEXilOYFKQya5Aekg5vYf1ZXExtvv4Bx1z04LIMpMTjl2KhUfLehV28w568YlwnN7GinO1eUY2N1NrD5V5cw+rN/xiwegaYbmR5TQuwLN5yK9umSvu+Tw4p3n3dbuCneWYfSwZ94Z2U/m9YSa4vIZ5M4oA6lz6a9yWYk6ThI5QdMwj4dX7KbsoLCYd8oxcFDM/2M/sJ/Zb6dQ4ymG2iBvD2v2I87f5MbPGmmHz1YgK+0TkJLcUC8m7uki/33bp9vQwhxcMp+Nk2UzyYhDhBJOw5ytr2/09UJ4aUFC2mPuz+FGGCwieGF2A+aptER6ZRhBmKfeMLNkqAc+AkhDgry2STEgSXBlBBCCEB60wkhDk4SbgohDkby2STEgSPBlBBCCEB60wkhhBBCCCHeeRJMCSGEEEIIIYQQQoickGBKCCGEEEIIIYQQQuSEBFNCCCGEEEIIIYQQIickmBJCCCGEEEIIIYQQOSHBlBBCCCGEEEIIIYTICQmmhBBCCCGEEEIIIUROSDAlhBBCCCGEEEIIIXJCgikhhBBCCCGEEEIIkRMSTAkhhBBCCCGEEEKInJBgSgghhBBCCCGEEELkhARTQgghhBBCCCGEECInJJgSQgghhBBCCCGEEDkhwZQQQgghhBBCCCGEyAkJpoQQQgghhBBCCCFETkgwJYQQQgghhBBCCCFyQoIpIYQQQgghhBBCCJETEkwJIYQQQgghhBBCiJyQYEoIIYQQQgghhBBC5IQEU0IIIYQQQgghhBAiJySYEkIIIYQQQgghhBA5Yea6AgeaUgqArq6uHNdk/zmOQ3d3N8FgEF2XDFEcGNKuxK5I2xBvB2lXYn9JGxJvB2lXYn9JGxJvh0OpXfVmMr0Zze4ccsFUd3c3AHV1dTmuiRBCCCGEEEIIIcS7V3d3N0VFRbtdR1NDia+GEcdx2LFjBwUFBWialuvq7Jeuri7q6urYunUrhYWFua6OOERIuxK7Im1DvB2kXYn9JW1IvB2kXYn9JW1IvB0OpXallKK7u5va2to99v465HpM6brOyJEjc12NA6qwsHDYN0px8JF2JXZF2oZ4O0i7EvtL2pB4O0i7EvtL2pB4Oxwq7WpPPaV6De9Bi0IIIYQQQgghhBBi2JJgSgghhBBCCCGEEELkhARTB7FAIMCNN95IIBDIdVXEIUTaldgVaRvi7SDtSuwvaUPi7SDtSuwvaUPi7fBubVeH3OTnQgghhBBCCCGEEGJ4kB5TQgghhBBCCCGEECInJJgSQgghhBBCCCGEEDkhwZQQQgghhBBCCCGEyAkJpvbBrbfeytFHH01BQQGVlZVcdNFFrFmzxrNOPB7n6quvpqysjPz8fN73vvfR2NiYKV+yZAkf+chHqKurIxQKMXXqVH7+8597tvH888+jadqAfw0NDbutn1KKb3/729TU1BAKhTjttNNYt26dZ51bbrmFefPmEQ6HKS4u3r8XROy34d6m6uvr+eQnP8nYsWMJhUKMHz+eG2+8kWQyeQBeHTHc2wfABRdcwKhRowgGg9TU1HDppZeyY8eO/XxlxP44FNpVr0QiwYwZM9A0jcWLF+/bCyL22qHQhsaMGTNgu7fddtt+vjJiXx0KbQrgkUceYc6cOYRCIUpKSrjooov2/UURe2W4t6FdbVfTNN54440D8AqJfTHc2xXA2rVrufDCCykvL6ewsJDjjz+e5557bj9fmQNIib125plnqnvuuUctX75cLV68WJ1zzjlq1KhRqqenJ7POVVddperq6tQzzzyjFi5cqI499lg1b968TPnvfvc7de2116rnn39ebdiwQf3xj39UoVBI3XHHHZl1nnvuOQWoNWvWqJ07d2b+2ba92/rddtttqqioSP33v/9VS5YsURdccIEaO3asisVimXW+/e1vq9tvv11dd911qqio6MC9OGKfDPc29dhjj6nLLrtMPfHEE2rDhg3qgQceUJWVlepLX/rSAX6l3p2Ge/tQSqnbb79dLViwQNXX16uXX35ZzZ07V82dO/cAvkpibx0K7arXtddeq84++2wFqEWLFu3/iyOG5FBoQ6NHj1bf/e53PdvtW3/xzjoU2tQ///lPVVJSon71q1+pNWvWqBUrVqi//e1vB/BVErsz3NtQIpHwbG/nzp3qU5/6lBo7dqxyHOcAv1piqIZ7u1JKqYkTJ6pzzjlHLVmyRK1du1Z99rOfVeFwWO3cufMAvlL7ToKpA6CpqUkB6oUXXlBKKdXR0aF8Pp/6xz/+kVln1apVClALFizY5XY++9nPqlNOOSVzu7dhtre3D7kujuOo6upq9aMf/SizrKOjQwUCAXX//fcPWP+ee+6RYOogNJzbVK8f/vCHauzYsUN+HDF0h0L7eOCBB5SmaSqZTA75scTba7i2q0cffVRNmTJFrVixQoKpHBuObWj06NHqpz/96ZC3K95Zw61NpVIpNWLECHX33XcPebvi7TXc2lB/yWRSVVRUqO9+97tDfhzx9htu7aq5uVkB6sUXX8ys09XVpQD11FNPDfmx3k4ylO8A6OzsBKC0tBSAN998k1QqxWmnnZZZZ8qUKYwaNYoFCxbsdju92+hrxowZ1NTUcPrpp/Pyyy/vti6bNm2ioaHB89hFRUXMmTNnt48tDi6HQpva1WOL/Tfc20dbWxt//vOfmTdvHj6fb7fbF++c4diuGhsb+fSnP80f//hHwuHw0J6oeNsMxzYEcNttt1FWVsZRRx3Fj370IyzL2vOTFe+I4dam3nrrLbZv346u6xx11FHU1NRw9tlns3z58qE/aXFADbc21N+DDz5Ia2srl19++W63Ld5Zw61dlZWVMXnyZP7whz8QiUSwLIu77rqLyspKZs2aNfQn/jYyc12B4c5xHL7whS9w3HHHcfjhhwPQ0NCA3+8fMHdTVVXVLseHvvLKK/ztb3/jkUceySyrqanh17/+NbNnzyaRSHD33Xdz8skn89prrzFz5sxBt9O7/aqqqiE/tji4HAptav369dxxxx38+Mc/HtJzFkM3nNvHV7/6Ve68806i0SjHHnssDz/88F49d/H2GY7tSinFZZddxlVXXcXs2bOpr6/fl6cuDpDh2IYArr32WmbOnElpaSmvvPIKN9xwAzt37uT222/f69dAHFjDsU1t3LgRgO985zvcfvvtjBkzhp/85CecfPLJrF27Vk7YvcOGYxvq73e/+x1nnnkmI0eOHNJzFm+/4diuNE3j6aef5qKLLqKgoABd16msrOTxxx+npKRkn16HA02Cqf109dVXs3z5cv73v//t8zaWL1/OhRdeyI033sgZZ5yRWT558mQmT56cuT1v3jw2bNjAT3/6U/74xz/y5z//mSuvvDJT/thjj2EYxj7XQxwchnub2r59O2eddRYf+MAH+PSnP73Pz0EMbji3j+uvv55PfvKTbN68mZtuuomPfexjPPzww2iats/PRRwYw7Fd3XHHHXR3d3PDDTfsc53FgTMc2xDAddddl/n9yCOPxO/3c+WVV3LrrbcSCAT2+bmI/Tcc25TjOAB84xvf4H3vex8A99xzDyNHjuQf//iHZ5vi7Tcc21Bf27Zt44knnuDvf//7PtdfHHjDsV0ppbj66quprKzkpZdeIhQKcffdd3P++efzxhtvUFNTs8/P5UCRYGo/XHPNNTz88MO8+OKLnhS7urqaZDJJR0eHJzVtbGykurras42VK1cyf/58rrjiCr75zW/u8TGPOeaYzJvgggsuYM6cOZmyESNGsHPnzsxj9W1gjY2NzJgxY1+epngHDfc2tWPHDk455RTmzZvHb37zmyE/bzE0w719lJeXU15ezqRJk5g6dSp1dXW8+uqrzJ07d8ivgTjwhmu7evbZZ1mwYMGA8GD27NlcfPHF3HfffUN7AcR+G65taDBz5szBsizq6+s9BwfinTVc21Tv8mnTpmXKA4EA48aNY8uWLUN89uJAGK5tqK977rmHsrIyLrjggiE9Z/H2G67t6tlnn+Xhhx+mvb2dwsJCAH75y1/y1FNPcd999/G1r31t716It0OuJ7kajhzHUVdffbWqra1Va9euHVDeO/nZP//5z8yy1atXD5j8bPny5aqyslJdf/31Q37s0047Tb3nPe/Zbd2qq6vVj3/848yyzs5Omfz8IHcotKlt27apiRMnqg9/+MPKsqwhP77Ys0OhffS3efNmBajnnntuyHURB9Zwb1ebN29Wy5Yty/x74oknFKD++c9/qq1btw65LmLfDfc2NJg//elPStd11dbWNuS6iANnuLep3tt9Jz9PJpOqsrJS3XXXXUOui9h3w70N9V137NixcoXrg8Rwb1cPPvig0nVddXd3e+47adIkdcsttwy5Lm8nCab2wWc+8xlVVFSknn/+ec9lHKPRaGadq666So0aNUo9++yzauHChQMujb5s2TJVUVGhLrnkEs82mpqaMuv89Kc/Vf/973/VunXr1LJly9TnP/95peu6evrpp3dbv9tuu00VFxerBx54QC1dulRdeOGFAy4XuXnzZrVo0SJ10003qfz8fLVo0SK1aNGiAY1VvDOGe5vatm2bmjBhgpo/f77atm2b5/HF/hvu7ePVV19Vd9xxh1q0aJGqr69XzzzzjJo3b54aP368isfjB/jVEkM13NtVf5s2bZKr8r3DhnsbeuWVV9RPf/pTtXjxYrVhwwb1pz/9SVVUVKiPfexjB/iVEkM13NuUUkp9/vOfVyNGjFBPPPGEWr16tfrkJz+pKisrJex8hxwKbUgppZ5++mkFqFWrVh2gV0bsj+Herpqbm1VZWZl673vfqxYvXqzWrFmjvvzlLyufz6cWL158gF+tfSPB1D4ABv13zz33ZNaJxWLqs5/9rCopKVHhcFi95z3v8Ryk33jjjYNuY/To0Zl1fvCDH6jx48erYDCoSktL1cknn6yeffbZPdbPcRz1rW99S1VVValAIKDmz5+v1qxZ41nn4x//+KCPL70XcmO4t6l77rlnl89B7L/h3j6WLl2qTjnlFFVaWqoCgYAaM2aMuuqqq9S2bdsOyOsj9s1wb1f9STD1zhvubejNN99Uc+bMUUVFRSoYDKqpU6eq73//+xKY59Bwb1NKuT2kvvSlL6nKykpVUFCgTjvtNLV8+fL9fm3E0BwKbUgppT7ykY+oefPm7ddrIQ6cQ6FdvfHGG+qMM85QpaWlqqCgQB177LHq0Ucf3e/X5kDRlFJq4AA/IYQQQgghhBBCCCHeXnquKyCEEEIIIYQQQggh3p0kmBJCCCGEEEIIIYQQOSHBlBBCCCGEEEIIIYTICQmmhBBCCCGEEEIIIUROSDAlhBBCCCGEEEIIIXJCgikhhBBCCCGEEEIIkRMSTAkhhBBCCCGEEEKInJBgSgghhBBCCCGEEELkhARTQgghhBDvkJNPPhlN03JdDSGEEEKIg4aZ6woIIYQQQgxHexswKaXeppoIIYQQQgxfEkwJIYQQQuyDG2+8ccCyn/3sZ3R2dg5aBvCHP/yBaDT6dldNCCGEEGLY0JScvhNCCCGEOCDGjBnD5s2bpXeUEEIIIcQQyRxTQgghhBDvkMHmmLr33nvRNI17772Xhx56iDlz5hAOhxkxYgTf+ta3cBwHgPvuu4/p06cTCoUYNWoUP/rRjwZ9DKUUv//97znuuOMoLCwkHA4ze/Zsfv/737/tz08IIYQQYm/JUD4hhBBCiIPAf/7zH5588kkuuugijjvuOB555BFuvvlmlFIUFRVx8803c+GFF3LyySfzr3/9i6985StUVVXxsY99LLMNpRQXX3wx999/PxMnTuSjH/0ofr+fp556ik9+8pOsXLmSH//4xzl8lkIIIYQQXjKUTwghhBDiANnTUL6TTz6ZF154wVN+7733cvnll+Pz+Xj55Zc5+uijAeju7mbChAn09PRQWFjIyy+/zLhx4wDYunUrEyZMYPLkySxdujSzrd/+9rdcccUVXH755dx11134fD4Akskk73//+3nooYdYuHAhs2bNerteAiGEEEKIvSJD+YQQQgghDgKXXHJJJpQCKCgo4LzzziMajfKZz3wmE0oB1NXVcfzxx7Ny5Uosy8osv/POO8nLy+MXv/hFJpQC8Pv93HLLLQDcf//978CzEUIIIYQYGhnKJ4QQQghxEJgxY8aAZTU1Nbsts22bxsZGRowYQTQaZdmyZdTW1vKDH/xgwPqpVAqA1atXH9B6CyGEEELsDwmmhBBCCCEOAoWFhQOWmaa5x7LewKm9vR2lFNu3b+emm27a5eNEIpEDUV0hhBBCiANCgikhhBBCiENAb3g1a9YsFi5cmOPaCCGEEEIMjcwxJYQQQghxCCgoKGDq1KmsWrWKjo6OXFdHCCGEEGJIJJgSQgghhDhEXHvttUSjUT796U8POmRv06ZN1NfXv/MVE0IIIYTYBRnKJ4QQQghxiLjyyit59dVXue+++3j55Zc57bTTqK2tpbGxkdWrV/Paa6/xl7/8hTFjxuS6qkIIIYQQgARTQgghhBCHDE3TuPfeeznnnHP47W9/y8MPP0xPTw+VlZVMnDiRH//4x5x22mm5rqYQQgghRIamlFK5roQQQgghhBBCCCGEePeROaaEEEIIIYQQQgghRE5IMCWEEEIIIYQQQgghckKCKSGEEEIIIYQQQgiRExJMCSGEEEIIIYQQQoickGBKCCGEEEIIIYQQQuSEBFNCCCGEEEIIIYQQIickmBJCCCGEEEIIIYQQOSHBlBBCCCGEEEIIIYTICQmmhBBCCCGEEEIIIUROSDAlhBBCCCGEEEIIIXJCgikhhBBCCCGEEEIIkRMSTAkhhBBCCCGEEEKInJBgSgghhBBCCCGEEELkxP8DkuzeZ3fzs1YAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "def plot_data_seaborn_improved(df, col_name='y'):\n", + "def plot_data(df, col_name='y'):\n", " \"\"\"\n", " Create an improved plot with colorblind-friendly features\n", " \n", @@ -380,16 +392,92 @@ " plt.grid(alpha=0.3, linestyle='-')\n", " plt.tight_layout()\n", "\n", - " plt.show()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So let us take a look at the average values over time" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_data(agg_df, col_name='y')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead the true average treatment treatment effects can be obtained by averaging (usually unobserved) the `ite` values.\n", + "\n", + "The true effect just equals the exposure time (in months):\n", "\n", - "# Call the function with your dataframes\n", - "plot_data_seaborn_improved(agg_df)\n", - "plot_data_seaborn_improved(agg_df, col_name='ite')" + "$$\n", + "ATT(\\mathrm{g}, t) = \\min(\\mathrm{t} - \\mathrm{g} + 1, 0) =: e\n", + "$$\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_data(agg_df, col_name='ite')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DoubleMLPanelData\n", + "\n", + "Finally, we can construct our `DoubleMLPanelData`, specifying\n", + "\n", + " - `y_col` : the outcome\n", + " - `d_cols`: the group variable indicating the first treated period for each unit\n", + " - `id_col`: the unique identification column for each unit\n", + " - `t_col` : the time column\n", + " - `x_cols`: the additional pre-treatment controls\n", + " - `datetime_unit`: unit required for `datetime` columns and plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -405,30 +493,90 @@ "Instrument variable(s): None\n", "Time variable: t\n", "Id variable: id\n", - "No. Observations: 4166\n", + "No. Observations: 4000\n", "\n", "------------------ DataFrame info ------------------\n", "\n", - "Index: 33328 entries, 1 to 44999\n", + "RangeIndex: 24000 entries, 0 to 23999\n", "Columns: 12 entries, id to First Treated\n", - "dtypes: datetime64[ns](1), datetime64[s](1), float64(8), int64(1), object(1)\n", - "memory usage: 3.3+ MB\n", + "dtypes: datetime64[s](2), float64(8), int64(1), object(1)\n", + "memory usage: 2.2+ MB\n", "\n" ] } ], "source": [ - "dml_data = DoubleMLPanelData(df, y_col=\"y\", d_cols=\"d\", id_col=\"id\", t_col=\"t\", x_cols=[\"Z1\", \"Z2\", \"Z3\", \"Z4\"], datetime_unit=\"M\")\n", + "dml_data = DoubleMLPanelData(\n", + " data=df,\n", + " y_col=\"y\",\n", + " d_cols=\"d\",\n", + " id_col=\"id\",\n", + " t_col=\"t\",\n", + " x_cols=[\"Z1\", \"Z2\", \"Z3\", \"Z4\"],\n", + " datetime_unit=\"M\"\n", + ")\n", "print(dml_data)" ] }, { - "cell_type": "code", - "execution_count": 6, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", + "source": [ + "## ATT Estimation\n", + "\n", + "The [DoubleML-package](https://docs.doubleml.org/stable/index.html) implements estimation of group-time average treatment effect via the `DoubleMLDIDMulti` class (see [model documentation](https://docs.doubleml.org/stable/guide/models.html#difference-in-differences-models-did))." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Basics\n", + "\n", + "The class basically behaves like other `DoubleML` classes and requires the specification of two learners (for more details on the regression elements, see [score documentation](https://docs.doubleml.org/stable/guide/scores.html#difference-in-differences-models)).\n", + "\n", + "The basic arguments of a `DoubleMLDIDMulti` object include\n", + "\n", + " - `ml_g` \"outcome\" regression learner\n", + " - `ml_m` propensity Score learner\n", + " - `control_group` the control group for the parallel trend assumption\n", + " - `gt_combinations` combinations of $(\\mathrm{g},t_\\text{pre}, t_\\text{eval})$\n", + " - `anticipation_periods` number of anticipation periods\n", + "\n", + "We will construct a `dict` with \"default\" arguments." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "default_args = {\n", + " \"ml_g\": LGBMRegressor(n_estimators=500, learning_rate=0.01, verbose=-1, random_state=123),\n", + " \"ml_m\": LGBMClassifier(n_estimators=500, learning_rate=0.01, verbose=-1, random_state=123),\n", + " \"control_group\": \"never_treated\",\n", + " \"gt_combinations\": \"standard\",\n", + " \"anticipation_periods\": 0,\n", + " \"n_folds\": 5,\n", + " \"n_rep\": 1,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " The model will be estimated using the `fit()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", "output_type": "stream", "text": [ "================== DoubleMLDIDMulti Object ==================\n", @@ -440,146 +588,287 @@ "Instrument variable(s): None\n", "Time variable: t\n", "Id variable: id\n", - "No. Observations: 4166\n", + "No. Observations: 4000\n", "\n", "------------------ Score & algorithm ------------------\n", "Score function: observational\n", - "Control group: not_yet_treated\n", - "Anticipation periods: 1\n", + "Control group: never_treated\n", + "Anticipation periods: 0\n", "\n", "------------------ Machine learner ------------------\n", - "Learner ml_g: LinearRegression()\n", - "Learner ml_m: LogisticRegression()\n", + "Learner ml_g: LGBMRegressor(learning_rate=0.01, n_estimators=500, random_state=123,\n", + " verbose=-1)\n", + "Learner ml_m: LGBMClassifier(learning_rate=0.01, n_estimators=500, random_state=123,\n", + " verbose=-1)\n", "Out-of-sample Performance:\n", "Regression:\n", - "Learner ml_g0 RMSE: [[1.39102995 1.39785296 1.40925291 1.40633464 1.44375731 1.41471744\n", - " 1.41210696 1.42784954 1.38852508 1.49782591 1.42356244 1.40503666\n", - " 1.46535437 1.40179023 1.39414503 1.39890822 1.40112555 1.40870043\n", - " 1.40924143 1.44293372 1.41553378 1.41140838 1.43293957 1.38991131\n", - " 1.40808286 1.49778755 1.42976231 1.41080943 1.44231313 1.46708232\n", - " 1.40100231 1.39235662 1.39948933 1.41544625 1.41283421 1.4041461\n", - " 1.41680001 1.44845168 1.40899921 1.41031489 1.4289529 1.38966733\n", - " 1.40784501 1.49459982 1.43004707 1.40615406 1.4459733 1.42017204\n", - " 1.46798444 1.40265149 1.39965208 1.39484507 1.37154232 1.40236951\n", - " 1.38886096 1.39472112 1.38153943 1.42034343 1.39615562 1.40887446\n", - " 1.40556836 1.38194898 1.36463509 1.50146752 1.42594774 1.40779292\n", - " 1.44461683 1.42026359 1.46359276 1.40271775 1.39207198 1.39156808\n", - " 1.37210025 1.43837414]]\n", - "Learner ml_g1 RMSE: [[1.44108768 1.43051681 1.42283966 1.40311933 1.40759614 1.4135038\n", - " 1.43322294 1.43044446 1.44691302 1.42010109 1.41696436 1.46442698\n", - " 1.42632506 1.42666941 1.46975518 1.40460735 1.39007963 1.40666422\n", - " 1.42469757 1.40228892 1.44287032 1.39403734 1.41211762 1.44506159\n", - " 1.44287631 1.41304506 1.47817095 1.45456335 1.42679013 1.39575439\n", - " 1.40985718 1.37789346 1.35837811 1.33592772 1.35879952 1.42428147\n", - " 1.38880731 1.43699938 1.43766851 1.36334639 1.38922835 1.37988807\n", - " 1.39233539 1.38240226 1.43151334 1.36401159 1.37956067 1.39221812\n", - " 1.35133584 1.42987349 1.38536043 1.38011349 1.36677398 1.39589914\n", - " 1.43215836 1.4486525 1.45965801 1.49626254 1.45608327 1.42396588\n", - " 1.46031411 1.39798025 1.45693325 1.44373815 1.40192536 1.44116015\n", - " 1.399939 1.47543899 1.42452738 1.44875899 1.46017325 1.43829177\n", - " 1.40531652 1.41977931]]\n", + "Learner ml_g0 RMSE: [[2.00900186 2.00832765 2.00147128 3.04799845 4.22606599 2.03866158\n", + " 1.97307679 1.97176622 2.0941594 3.14777236 2.01165794 2.06105985\n", + " 2.0159968 2.07337162 1.95812371]]\n", + "Learner ml_g1 RMSE: [[2.05650513 1.92499257 2.03184487 2.98708533 4.06173642 1.94313614\n", + " 2.07179882 2.03096348 1.98713429 3.05754135 2.05766274 2.06959237\n", + " 2.00498525 2.05151027 2.07028147]]\n", "Classification:\n", - "Learner ml_m Log Loss: [[0.47788849 0.4779702 0.47789215 0.53296563 0.53262834 0.53304946\n", - " 0.59193082 0.59043865 0.59218525 0.618091 0.62053889 0.61774019\n", - " 0.61758939 0.61840344 0.61784413 0.49861564 0.54987001 0.55008169\n", - " 0.54982671 0.55061567 0.55032813 0.60809804 0.60929183 0.60853521\n", - " 0.6079423 0.63423704 0.63303062 0.6315783 0.63329716 0.63398162\n", - " 0.63291562 0.63158606 0.63375026 0.49244109 0.54762749 0.54809141\n", - " 0.6141926 0.61379044 0.61482435 0.61323514 0.61316049 0.61338617\n", - " 0.61394327 0.65208595 0.6524183 0.65117553 0.65328153 0.65376124\n", - " 0.65276944 0.65090737 0.65134004 0.65345162 0.65289515 0.50609693\n", - " 0.56570004 0.56500714 0.63515806 0.63620603 0.6365891 0.67069242\n", - " 0.6693576 0.67076087 0.67016105 0.66893999 0.67061238 0.67368678\n", - " 0.66932447 0.67012514 0.67010419 0.6700244 0.67003583 0.670244\n", - " 0.67080285 0.66882085]]\n", + "Learner ml_m Log Loss: [[0.68315785 0.68798742 0.6814065 0.67731183 0.67871757 0.71707894\n", + " 0.71569694 0.72396693 0.73503987 0.72433498 0.73399346 0.73499374\n", + " 0.73633645 0.74302782 0.73076103]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 5\n", "No. repeated sample splits: 1\n", "\n", "------------------ Fit summary ------------------\n", - " coef std err t P>|t| \\\n", - "ATT(2025-05,2025-01,2025-03) -0.079444 0.056807 -1.398489 0.161966 \n", - "ATT(2025-05,2025-01,2025-04) 0.932054 0.057050 16.337614 0.000000 \n", - "ATT(2025-05,2025-02,2025-04) 0.941147 0.056759 16.581426 0.000000 \n", - "ATT(2025-05,2025-01,2025-05) 1.853857 0.059092 31.372646 0.000000 \n", - "ATT(2025-05,2025-02,2025-05) 1.898532 0.059837 31.728281 0.000000 \n", - "... ... ... ... ... \n", - "ATT(2025-08,2025-02,2025-08) 1.933147 0.072072 26.822485 0.000000 \n", - "ATT(2025-08,2025-03,2025-08) 1.851633 0.071174 26.015547 0.000000 \n", - "ATT(2025-08,2025-04,2025-08) 1.983352 0.070664 28.067562 0.000000 \n", - "ATT(2025-08,2025-05,2025-08) 2.025502 0.070682 28.656639 0.000000 \n", - "ATT(2025-08,2025-06,2025-08) 1.872791 0.073207 25.581973 0.000000 \n", + " coef std err t P>|t| \\\n", + "ATT(2025-04,2025-01,2025-02) -0.186576 0.127491 -1.463442 1.433464e-01 \n", + "ATT(2025-04,2025-02,2025-03) 0.165520 0.130817 1.265276 2.057726e-01 \n", + "ATT(2025-04,2025-03,2025-04) 0.885967 0.137076 6.463310 1.024374e-10 \n", + "ATT(2025-04,2025-03,2025-05) 1.887206 0.182686 10.330322 0.000000e+00 \n", + "ATT(2025-04,2025-03,2025-06) 2.521738 0.275739 9.145392 0.000000e+00 \n", + "ATT(2025-05,2025-01,2025-02) -0.020345 0.115761 -0.175750 8.604906e-01 \n", + "ATT(2025-05,2025-02,2025-03) 0.135493 0.114989 1.178310 2.386732e-01 \n", + "ATT(2025-05,2025-03,2025-04) 0.068758 0.106044 0.648392 5.167313e-01 \n", + "ATT(2025-05,2025-04,2025-05) 1.173640 0.121208 9.682895 0.000000e+00 \n", + "ATT(2025-05,2025-04,2025-06) 2.002293 0.174835 11.452444 0.000000e+00 \n", + "ATT(2025-06,2025-01,2025-02) -0.062934 0.102768 -0.612393 5.402778e-01 \n", + "ATT(2025-06,2025-02,2025-03) 0.127089 0.109588 1.159701 2.461705e-01 \n", + "ATT(2025-06,2025-03,2025-04) 0.005414 0.099999 0.054136 9.568266e-01 \n", + "ATT(2025-06,2025-04,2025-05) 0.007135 0.104696 0.068147 9.456687e-01 \n", + "ATT(2025-06,2025-05,2025-06) 1.182735 0.103009 11.481824 0.000000e+00 \n", "\n", " 2.5 % 97.5 % \n", - "ATT(2025-05,2025-01,2025-03) -0.190784 0.031896 \n", - "ATT(2025-05,2025-01,2025-04) 0.820239 1.043869 \n", - "ATT(2025-05,2025-02,2025-04) 0.829901 1.052392 \n", - "ATT(2025-05,2025-01,2025-05) 1.738040 1.969674 \n", - "ATT(2025-05,2025-02,2025-05) 1.781253 2.015810 \n", - "... ... ... \n", - "ATT(2025-08,2025-02,2025-08) 1.791889 2.074406 \n", - "ATT(2025-08,2025-03,2025-08) 1.712134 1.991131 \n", - "ATT(2025-08,2025-04,2025-08) 1.844854 2.121850 \n", - "ATT(2025-08,2025-05,2025-08) 1.886968 2.164035 \n", - "ATT(2025-08,2025-06,2025-08) 1.729307 2.016275 \n", - "\n", - "[74 rows x 6 columns]\n" + "ATT(2025-04,2025-01,2025-02) -0.436453 0.063302 \n", + "ATT(2025-04,2025-02,2025-03) -0.090877 0.421917 \n", + "ATT(2025-04,2025-03,2025-04) 0.617303 1.154632 \n", + "ATT(2025-04,2025-03,2025-05) 1.529148 2.245264 \n", + "ATT(2025-04,2025-03,2025-06) 1.981300 3.062176 \n", + "ATT(2025-05,2025-01,2025-02) -0.247233 0.206543 \n", + "ATT(2025-05,2025-02,2025-03) -0.089882 0.360868 \n", + "ATT(2025-05,2025-03,2025-04) -0.139084 0.276601 \n", + "ATT(2025-05,2025-04,2025-05) 0.936077 1.411202 \n", + "ATT(2025-05,2025-04,2025-06) 1.659622 2.344964 \n", + "ATT(2025-06,2025-01,2025-02) -0.264355 0.138487 \n", + "ATT(2025-06,2025-02,2025-03) -0.087699 0.341878 \n", + "ATT(2025-06,2025-03,2025-04) -0.190580 0.201408 \n", + "ATT(2025-06,2025-04,2025-05) -0.198065 0.212334 \n", + "ATT(2025-06,2025-05,2025-06) 0.980841 1.384630 \n" ] } ], "source": [ - "control_group = \"not_yet_treated\"\n", - "# control_group = \"never_treated\"\n", - "\n", - "gt_combinations = \"all\"\n", - "#gt_combinations = \"standard\"\n", - "\n", - "anticipation_periods = 1\n", + "dml_obj = DoubleMLDIDMulti(dml_data, **default_args)\n", + "dml_obj.fit()\n", + "print(dml_obj)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The summary displays estimates of the $ATT(g,t_\\text{eval})$ effects for different combinations of $(g,t_\\text{eval})$ via $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$, where\n", + " - $\\mathrm{g}$ specifies the group\n", + " - $t_\\text{pre}$ specifies the corresponding pre-treatment period\n", + " - $t_\\text{eval}$ specifies the evaluation period\n", "\n", - "ml_g = LGBMRegressor(n_estimators=500, learning_rate=0.01, verbose=-1)\n", - "ml_m = LGBMClassifier(n_estimators=500, learning_rate=0.01, verbose=-1)\n", + "The choice `gt_combinations=\"standard\"`, used estimates all possible combinations of $ATT(g,t_\\text{eval})$ via $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$,\n", + "where the standard choice is $t_\\text{pre} = \\min(\\mathrm{g}, t_\\text{eval}) - 1$ (without anticipation).\n", "\n", - "ml_g = LinearRegression()\n", - "ml_m = LogisticRegression()\n", + "Remark that this includes pre-tests effects if $\\mathrm{g} > t_{eval}$, e.g. $\\widehat{ATT}(g=\\text{2025-04}, t_{\\text{pre}}=\\text{2025-01}, t_{\\text{eval}}=\\text{2025-02})$ which estimates the pre-trend from January to February even if the actual treatment occured in April." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual for the DoubleML-package, you can obtain joint confidence intervals via bootstrap." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 2.5 % 97.5 %\n", + "ATT(2025-04,2025-01,2025-02) -0.559296 0.186145\n", + "ATT(2025-04,2025-02,2025-03) -0.216925 0.547966\n", + "ATT(2025-04,2025-03,2025-04) 0.485223 1.286711\n", + "ATT(2025-04,2025-03,2025-05) 1.353122 2.421290\n", + "ATT(2025-04,2025-03,2025-06) 1.715614 3.327862\n", + "ATT(2025-05,2025-01,2025-02) -0.358774 0.318084\n", + "ATT(2025-05,2025-02,2025-03) -0.200679 0.471665\n", + "ATT(2025-05,2025-03,2025-04) -0.241263 0.378779\n", + "ATT(2025-05,2025-04,2025-05) 0.819289 1.527991\n", + "ATT(2025-05,2025-04,2025-06) 1.491160 2.513426\n", + "ATT(2025-06,2025-01,2025-02) -0.363376 0.237508\n", + "ATT(2025-06,2025-02,2025-03) -0.193292 0.447470\n", + "ATT(2025-06,2025-03,2025-04) -0.286934 0.297761\n", + "ATT(2025-06,2025-04,2025-05) -0.298943 0.313213\n", + "ATT(2025-06,2025-05,2025-06) 0.881587 1.483884" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "level = 0.95\n", "\n", - "dml_obj = DoubleMLDIDMulti(\n", - " obj_dml_data=dml_data,\n", - " ml_g=ml_g,\n", - " ml_m=ml_m,\n", - " gt_combinations=gt_combinations,\n", - " control_group=control_group,\n", - " anticipation_periods=anticipation_periods,\n", - ")\n", + "ci = dml_obj.confint(level=level)\n", + "dml_obj.bootstrap(n_rep_boot=5000)\n", + "ci_joint = dml_obj.confint(level=level, joint=True)\n", + "ci_joint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A visualization of the effects can be obtained via the `plot_effects()` method.\n", "\n", - "dml_obj.fit()\n", - "print(dml_obj)" + "Remark that the plot used joint confidence intervals per default. " ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": 18, + "metadata": { + "tags": [ + "nbsphinx-thumbnail" + ] + }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n" + ] + }, { "data": { "text/plain": [ - "(
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,\n", + " [,\n", + " ,\n", + " ])" ] }, - "execution_count": 7, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -587,13 +876,21 @@ } ], "source": [ - "dml_obj.bootstrap()\n", - "dml_obj.plot_effects(default_jitter=0.1)" + "dml_obj.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sensitivity Analysis\n", + "\n", + "As descripted in the [Sensitivity Guide](https://docs.doubleml.org/stable/guide/sensitivity.html), robustness checks on omitted confounding/parallel trend violations are available, via the standard `sensitivity_analysis()` method." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -608,48 +905,56 @@ "\n", "------------------ Bounds with CI ------------------\n", " CI lower theta lower theta theta upper \\\n", - "ATT(2025-05,2025-01,2025-03) -0.282814 -0.189270 -0.079444 0.030382 \n", - "ATT(2025-05,2025-01,2025-04) 0.728060 0.821977 0.932054 1.042130 \n", - "ATT(2025-05,2025-02,2025-04) 0.737026 0.830494 0.941147 1.051800 \n", - "ATT(2025-05,2025-01,2025-05) 1.653944 1.751299 1.853857 1.956414 \n", - "ATT(2025-05,2025-02,2025-05) 1.695394 1.793787 1.898532 2.003276 \n", - "... ... ... ... ... \n", - "ATT(2025-08,2025-02,2025-08) 1.723780 1.842463 1.933147 2.023832 \n", - "ATT(2025-08,2025-03,2025-08) 1.644001 1.760944 1.851633 1.942321 \n", - "ATT(2025-08,2025-04,2025-08) 1.776974 1.893331 1.983352 2.073373 \n", - "ATT(2025-08,2025-05,2025-08) 1.821187 1.937298 2.025502 2.113706 \n", - "ATT(2025-08,2025-06,2025-08) 1.661052 1.781514 1.872791 1.964069 \n", + "ATT(2025-04,2025-01,2025-02) -0.512575 -0.305211 -0.186576 -0.067940 \n", + "ATT(2025-04,2025-02,2025-03) -0.161367 0.050859 0.165520 0.280181 \n", + "ATT(2025-04,2025-03,2025-04) 0.549459 0.772720 0.885967 0.999215 \n", + "ATT(2025-04,2025-03,2025-05) 1.416909 1.711872 1.887206 2.062539 \n", + "ATT(2025-04,2025-03,2025-06) 1.836706 2.290795 2.521738 2.752681 \n", + "ATT(2025-05,2025-01,2025-02) -0.314133 -0.124444 -0.020345 0.083754 \n", + "ATT(2025-05,2025-02,2025-03) -0.162868 0.024233 0.135493 0.246753 \n", + "ATT(2025-05,2025-03,2025-04) -0.203224 -0.030277 0.068758 0.167793 \n", + "ATT(2025-05,2025-04,2025-05) 0.876336 1.072862 1.173640 1.274418 \n", + "ATT(2025-05,2025-04,2025-06) 1.554392 1.839144 2.002293 2.165443 \n", + "ATT(2025-06,2025-01,2025-02) -0.337896 -0.167851 -0.062934 0.041982 \n", + "ATT(2025-06,2025-02,2025-03) -0.162546 0.018039 0.127089 0.236140 \n", + "ATT(2025-06,2025-03,2025-04) -0.264030 -0.099474 0.005414 0.110301 \n", + "ATT(2025-06,2025-04,2025-05) -0.269157 -0.096285 0.007135 0.110554 \n", + "ATT(2025-06,2025-05,2025-06) 0.906228 1.075461 1.182735 1.290010 \n", "\n", " CI upper \n", - "ATT(2025-05,2025-01,2025-03) 0.123905 \n", - "ATT(2025-05,2025-01,2025-04) 1.136078 \n", - "ATT(2025-05,2025-02,2025-04) 1.145245 \n", - "ATT(2025-05,2025-01,2025-05) 2.053635 \n", - "ATT(2025-05,2025-02,2025-05) 2.101914 \n", - "... ... \n", - "ATT(2025-08,2025-02,2025-08) 2.142442 \n", - "ATT(2025-08,2025-03,2025-08) 2.059731 \n", - "ATT(2025-08,2025-04,2025-08) 2.189680 \n", - "ATT(2025-08,2025-05,2025-08) 2.230310 \n", - "ATT(2025-08,2025-06,2025-08) 2.084632 \n", - "\n", - "[74 rows x 5 columns]\n", + "ATT(2025-04,2025-01,2025-02) 0.145371 \n", + "ATT(2025-04,2025-02,2025-03) 0.499391 \n", + "ATT(2025-04,2025-03,2025-04) 1.228118 \n", + "ATT(2025-04,2025-03,2025-05) 2.370601 \n", + "ATT(2025-04,2025-03,2025-06) 3.209748 \n", + "ATT(2025-05,2025-01,2025-02) 0.275703 \n", + "ATT(2025-05,2025-02,2025-03) 0.438621 \n", + "ATT(2025-05,2025-03,2025-04) 0.344842 \n", + "ATT(2025-05,2025-04,2025-05) 1.477449 \n", + "ATT(2025-05,2025-04,2025-06) 2.457223 \n", + "ATT(2025-06,2025-01,2025-02) 0.210598 \n", + "ATT(2025-06,2025-02,2025-03) 0.416560 \n", + "ATT(2025-06,2025-03,2025-04) 0.275214 \n", + "ATT(2025-06,2025-04,2025-05) 0.282757 \n", + "ATT(2025-06,2025-05,2025-06) 1.460213 \n", "\n", "------------------ Robustness Values ------------------\n", " H_0 RV (%) RVa (%)\n", - "ATT(2025-05,2025-01,2025-03) 0.0 2.179344 0.000659\n", - "ATT(2025-05,2025-01,2025-04) 0.0 22.679195 20.548048\n", - "ATT(2025-05,2025-02,2025-04) 0.0 22.768020 20.653810\n", - "ATT(2025-05,2025-01,2025-05) 0.0 41.950925 39.894601\n", - "ATT(2025-05,2025-02,2025-05) 0.0 42.034609 40.038228\n", - "... ... ... ...\n", - "ATT(2025-08,2025-02,2025-08) 0.0 47.188378 44.541290\n", - "ATT(2025-08,2025-03,2025-08) 0.0 45.790412 43.174465\n", - "ATT(2025-08,2025-04,2025-08) 0.0 48.268995 45.660364\n", - "ATT(2025-08,2025-05,2025-08) 0.0 49.639416 47.105105\n", - "ATT(2025-08,2025-06,2025-08) 0.0 45.947902 43.287908\n", - "\n", - "[74 rows x 3 columns]\n" + "ATT(2025-04,2025-01,2025-02) 0.0 4.677112 0.000542\n", + "ATT(2025-04,2025-02,2025-03) 0.0 4.301563 0.000595\n", + "ATT(2025-04,2025-03,2025-04) 0.0 21.159281 16.152326\n", + "ATT(2025-04,2025-03,2025-05) 0.0 27.849112 23.674655\n", + "ATT(2025-04,2025-03,2025-06) 0.0 28.186201 22.631893\n", + "ATT(2025-05,2025-01,2025-02) 0.0 0.593707 0.000555\n", + "ATT(2025-05,2025-02,2025-03) 0.0 3.641365 0.000433\n", + "ATT(2025-05,2025-03,2025-04) 0.0 2.092666 0.000599\n", + "ATT(2025-05,2025-04,2025-05) 0.0 29.735287 25.235671\n", + "ATT(2025-05,2025-04,2025-06) 0.0 31.043061 26.805368\n", + "ATT(2025-06,2025-01,2025-02) 0.0 1.810658 0.000377\n", + "ATT(2025-06,2025-02,2025-03) 0.0 3.487516 0.000645\n", + "ATT(2025-06,2025-03,2025-04) 0.0 0.156944 0.000541\n", + "ATT(2025-06,2025-04,2025-05) 0.0 0.209767 0.000545\n", + "ATT(2025-06,2025-05,2025-06) 0.0 28.414619 24.530328\n" ] } ], @@ -658,58 +963,60 @@ "print(dml_obj.sensitivity_summary)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example one can clearly, distinguish the robustness of the non-zero effects vs. the pre-treatment periods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Control Groups\n", + "\n", + "The current implementation support the following control groups\n", + "\n", + " - ``\"never_treated\"``\n", + " - ``\"not_yet_treated\"``\n", + "\n", + "Remark that the ``\"not_yet_treated\" depends on anticipation.\n", + "\n", + "For differences and recommendations, we refer to [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001)." + ] + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 55, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Group Aggregation \n", - "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "2.648287 0.032251 82.115458 0.0 2.585077 2.711497\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "2025-05 3.374550 0.039381 85.689722 0.0 3.297364 3.451735\n", - "2025-06 2.888170 0.039673 72.799586 0.0 2.810412 2.965927\n", - "2025-07 2.433550 0.044432 54.769702 0.0 2.346464 2.520636\n", - "2025-08 1.932632 0.054882 35.214097 0.0 1.825064 2.040199\n", - "------------------ Additional Information ------------------\n", - "Score function: observational\n", - "Control group: not_yet_treated\n", - "Anticipation periods: 1\n", - "\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:328: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", - " warnings.warn(\n" + "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n" ] }, { "data": { "text/plain": [ - "(
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OevXVV7f6WrZXnz59IkmS6N27d3zuc5+rte6ll16K1157Le64444YM2ZMbrymT22ry2Nk29prRESnTp0a7C6lxuq1vur6c9KnT5/IZrPxz3/+s8rjp5+0tXO0b98+zjzzzDjzzDNj3bp1cfTRR8dPfvIToRQAuwxrSgFAE/Thhx/GAw88ECeddFJ86UtfqvZ1/vnnx9q1a+Phhx+OiIjhw4fHxo0b43//7/+dO0c2m60WaO2xxx7Rv3//+N3vfhfr1q3Ljc+bNy9eeumlOvXWqVOnGDp0aNx8882xfPnyavtXrVqV+/Pw4cPjmWeeiYULF+bGPvjgg7j77rurHLPlTpstd+pERGzYsCFuvPHGaudv3bp1jY+wfeUrX4m33367ynuwxYcffhjl5eUREXHiiSdGRMT1119fpea6666rdlxDOu2006JZs2YxadKkKq8z4qPX/f7770dEze9FkiTxq1/9qto5W7duHREf3WnWkIYPHx7FxcVxxRVXxMaNG6vt//j3uK62hKUN3Wt91fXnZNSoUVFQUBCXXXZZtbvTPv69ad26dY2vacv3c4s2bdpE3759o7KysgFeBQA0De6UAoAm6OGHH461a9fGKaecUuP+ww8/PDp27Bh33313nH766TFq1Kg49NBD47vf/W688cYb0a9fv3j44Yfjgw8+iIiqd3NcccUVMXLkyDjiiCPizDPPjNWrV8dvfvOb6N+/f5WgamtuuOGGOPLII2P//fePs88+Oz772c/GypUr45lnnon//Oc/8eKLL0ZExPe+972466674vjjj48LLrggWrduHVOnTo2ePXvGBx98kOtr8ODB0a5duxg7dmxceOGFkclk4s4776wW3kREDBw4MKZNmxbjx4+Pz3/+89GmTZs4+eST4xvf+Eb84Q9/iG9961sxZ86cOOKII2Lz5s3xyiuvxB/+8Id4/PHH45BDDokDDzwwRo8eHTfeeGOUlpbG4MGDY9asWXW+U2xb9enTJ372s5/FhAkT4q233opRo0ZF27ZtY/HixfHggw/GOeecE5dcckn069cv+vTpE5dcckm8/fbbUVxcHPfff3+N6zMNHDgwIj5atH348OHRrFmz+OpXv7rdvRYXF8dNN90U3/jGN+Lggw+Or371q9GxY8dYunRpPProo3HEEUfEb37zm3qds1WrVrHvvvvGtGnT4nOf+1y0b98++vfvH/3799/qcX/5y1+ioqKi2viAAQO2aX2muv6c9O3bN374wx/GT3/60zjqqKPitNNOi8LCwnj22Wdjjz32iMmTJ0fER9+Dm266KX72s59F3759o1OnTnHsscfGvvvuG0OHDo2BAwdG+/btY8GCBXHffffF+eefX++eAaDJys+H/gEA2+Pkk09OioqKkvLy8lprzjjjjKRFixbJe++9lyRJkqxatSr52te+lrRt2zYpKSlJzjjjjOTpp59OIiK59957qxx77733Jv369UsKCwuT/v37Jw8//HDyxS9+MenXr1+uZvHixUlEJFdddVWN13/zzTeTMWPGJF26dElatGiRdOvWLTnppJOS++67r0rdCy+8kBx11FFJYWFh0r1792Ty5MnJ9ddfn0REsmLFilzd008/nRx++OFJq1atkj322CP53ve+lzz++ONJRCRz5szJ1a1bty752te+luy+++5JRCS9evXK7duwYUPy85//PNlvv/2SwsLCpF27dsnAgQOTSZMmJaWlpbm6Dz/8MLnwwguTDh06JK1bt05OPvnkZNmyZUlEJBMnTqz1Pf+k6dOnV+tv4sSJSUQkq1atqvGY+++/PznyyCOT1q1bJ61bt0769euXjBs3Lnn11VdzNf/85z+TYcOGJW3atEk+85nPJGeffXby4osvJhGR3Hbbbbm6TZs2JRdccEHSsWPHJJPJJFv+6Vfb927OnDlJRCTTp0+vMn7bbbclEZE8++yz1eqHDx+elJSUJEVFRUmfPn2SM844I1mwYEGuZuzYsUnr1q2rvc4t78PHzZ8/Pxk4cGDSsmXLT32vt/Ra29fHjx0yZEiy3377VTtHr169khEjRlQbr+vPSZIkya233pocdNBBubohQ4YkM2fOzO1fsWJFMmLEiKRt27ZJRCRDhgxJkiRJfvaznyWHHnposvvuuyetWrVK+vXrl1x++eXJhg0ban3NALCzySRJDf/FCADsEmbMmBGnnnpqPPXUU3HEEUdstfbAAw+Mjh071rh2UUO7+OKL4+abb45169Y1qUWyAQCoO2tKAcAu4sMPP6yyvXnz5vj1r38dxcXFcfDBB+fGN27cGJs2bapSO3fu3HjxxRdj6NChjd7X+++/H3feeWcceeSRAikAgJ2YNaUAYBdxwQUXxIcffhiDBg2KysrKeOCBB2L+/PlxxRVXRKtWrXJ1b7/9dgwbNiy+/vWvxx577BGvvPJKTJkyJbp06RLf+ta3GryvQYMGxdChQ2OfffaJlStXxm9/+9soKyuLH//4xw1+LQAAdhxCKQDYRRx77LFxzTXXxCOPPBIVFRXRt2/f+PWvf11tYeV27drFwIEDY+rUqbFq1apo3bp1jBgxIq688sro0KFDg/f1hS98Ie6777645ZZbIpPJxMEHHxy//e1v4+ijj27wawEAsOOwphQAAAAAqbOmFAAAAACpE0oBAAAAkLpdbk2pbDYb77zzTrRt2zYymUy+2wEAAADYqSRJEmvXro099tgjCgpqvx9qlwul3nnnnejRo0e+2wAAAADYqS1btiy6d+9e6/5dLpRq27ZtRHz0xhQXF+e5GwAAAICdS1lZWfTo0SOXwdRmlwultjyyV1xcLJQCAAAAaCSftmyShc4BAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASF1eQ6mbbropBgwYEMXFxVFcXByDBg2Kxx57rNb622+/PTKZTJWvoqKiFDsGAAAAoCE0z+fFu3fvHldeeWXstddekSRJ3HHHHTFy5Mh44YUXYr/99qvxmOLi4nj11Vdz25lMJq12AQAAAGggeQ2lTj755Crbl19+edx0003x17/+tdZQKpPJRJcuXdJoDwAAAIBGktdQ6uM2b94c06dPj/Ly8hg0aFCtdevWrYtevXpFNpuNgw8+OK644opaA6yIiMrKyqisrMxtl5WVRURENpuNbDbbcC8AAAAAgDrnLXkPpV566aUYNGhQVFRURJs2beLBBx+Mfffdt8bavffeO2699dYYMGBAlJaWxtVXXx2DBw+ORYsWRffu3Ws8ZvLkyTFp0qRq46tWrYqKiooGfS0AAAAAu7q1a9fWqS6TJEnSyL1s1YYNG2Lp0qVRWloa9913X0ydOjXmzZtXazD1cRs3box99tknRo8eHT/96U9rrKnpTqkePXrE6tWro7i4uMFeBwAAAAAfZS/t2rWL0tLSrWYveb9TqmXLltG3b9+IiBg4cGA8++yz8atf/SpuvvnmTz22RYsWcdBBB8Ubb7xRa01hYWEUFhZWGy8oKIiCgrx++GC9ZDeW16u+oEXrRuoEAAAAoHZ1zVvyHkp9UjabrXJn09Zs3rw5XnrppfjCF77QyF3l35Ib2tWrvvfFGxqpEwAAAIDtl9dQasKECXHiiSdGz549Y+3atXHPPffE3Llz4/HHH4+IiDFjxkS3bt1i8uTJERFx2WWXxeGHHx59+/aNNWvWxFVXXRVLliyJs846K58vAwAAAIB6ymso9e6778aYMWNi+fLlUVJSEgMGDIjHH388jj/++IiIWLp0aZVbvlavXh1nn312rFixItq1axcDBw6M+fPn12n9qaau17jVVbazG8tj2S0fLe7e45z/eFwPAAAAaFLyvtB52srKyqKkpORTF9va0WU3luce6es1brVQCgAAANgh1DV7aTorfQMAAACw0xBKAQAAAJA6oRQAAAAAqRNK7QSSTRX5bgEAAACgXoRSTUyS3Rzlrz8QK2eMzI0tvblrLL9/eJS//kAk2c157A4AAACgbprnuwHqLltZFisfPT0qls6qtq9i2ZyoWDYninoeF51HTIuCwqb7yYIAAADAzs+dUk1Ekt1cayD1cRVLZ8XKR093xxQAAACwQxNKNRHr33zoUwOpLSqWzor1/364kTsCAAAA2HZCqSai7B8316/+xfrVAwAAAKRJKNUEZDdVRMWyOfU6pmLZ7Mj6VD4AAABgByWUagKSDWtTPQ4AAACgsQmlmoBMy7apHgcAAADQ2IRSTUBB86Io6nFMvY4p6nFsFDQvaqSOAAAAALaPUKqJKB5wbv3qD6hfPQAAAECahFJNxG59RkZRz+PqVFvU87jY7bOnNHJHAAAAANtOKNVEZAqaRecR0z41mCrqeVx0HjEtMgXNUuoMAAAAoP6EUk1IQWFxdBn1SHQ6aVoUdR9SZV9Rj2Oj00nTosuoR6KgsDhPHQIAAADUTfN8N0D9ZAqaReu+p0arXifEkhvaRUREz3OXR7NWHfLcGQAAAEDduVNqJ5DxKXsAAABAEyOUAgAAACB1QikAAAAAUieUAgAAACB1FjpvIrIby2vd/uS+iIiCFq0bvScAAACAbSWUaiK2fNJeTZbd0r3aWO+LNzRmOwAAAADbxeN7AAAAAKTOnVJNRK9xq/PdAgAAAECDEUo1EdaIAgAAAHYmHt8DAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSl9dQ6qabbooBAwZEcXFxFBcXx6BBg+Kxxx7b6jHTp0+Pfv36RVFRUey///7xxz/+MaVuAQAAAGgoeQ2lunfvHldeeWU899xzsWDBgjj22GNj5MiRsWjRohrr58+fH6NHj45vfvOb8cILL8SoUaNi1KhR8fLLL6fcOQAAAADbI5MkSZLvJj6uffv2cdVVV8U3v/nNavtOP/30KC8vj0ceeSQ3dvjhh8eBBx4YU6ZMqdP5y8rKoqSkJEpLS6O4uLjB+gYAAACg7tnLDrOm1ObNm+Pee++N8vLyGDRoUI01zzzzTAwbNqzK2PDhw+OZZ55Jo0UAAAAAGkjzfDfw0ksvxaBBg6KioiLatGkTDz74YOy777411q5YsSI6d+5cZaxz586xYsWKWs9fWVkZlZWVue2ysrKIiMhms5HNZhvgFQAAAACwRV3zlryHUnvvvXcsXLgwSktL47777ouxY8fGvHnzag2m6mvy5MkxadKkauOrVq2KioqKBrkGAE1Dsml9veozzXdrpE4AAGDntXbt2jrV5T2UatmyZfTt2zciIgYOHBjPPvts/OpXv4qbb765Wm2XLl1i5cqVVcZWrlwZXbp0qfX8EyZMiPHjx+e2y8rKokePHtGxY0drSgHsYpZcX1Sv+l4X+s8LAACor6Kiuv27O++h1Cdls9kqj9t93KBBg2LWrFlx8cUX58ZmzpxZ6xpUERGFhYVRWFhYbbygoCAKCnaYJbUA2AH5ewIAAOqvrv+OzmsoNWHChDjxxBOjZ8+esXbt2rjnnnti7ty58fjjj0dExJgxY6Jbt24xefLkiIi46KKLYsiQIXHNNdfEiBEj4t57740FCxbELbfcks+XAUAT0Wvc6irb2Y3lseyW7hER0eOc/0RBi9b5aAsAAHZJeQ2l3n333RgzZkwsX748SkpKYsCAAfH444/H8ccfHxERS5curZKuDR48OO6555740Y9+FD/4wQ9ir732ihkzZkT//v3z9RIAaEK2FjoVtGgtlAIAgBRlkiRJ8t1EmsrKyqKkpCRKS0utKQWwi8tuLI8lN7SLiI/uohJKAQDA9qtr9mKxDAAAAABSJ5QCAAAAIHVCKQCIiGRTRb5bAACAXYpQCoBdTpLdHOWvPxArZ4zMjS29uWssv394lL/+QCTZzXnsDgAAdg15/fQ9AEhbtrIsVj56elQsnVVtX8WyOVGxbE4U9TwuOo+YFgWFPhADAAAaizulANhlJNnNtQZSH1exdFasfPR0d0wBAEAjEkoBsMtY/+ZDnxpIbVGxdFas//fDjdwRAADsuoRSAOwyyv5xc/3qX6xfPQAAUHdCKQB2CdlNFVGxbE69jqlYNjuyPpUPAAAahVAKgF1CsmFtqscBAABbJ5QCYJeQadk21eMAAICtE0oBsEsoaF4URT2OqdcxRT2OjYLmRY3UEQAA7NqEUgDsMooHnFu/+gPqVw8AANSdUAqAXcZufUZGUc/j6lRb1PO42O2zpzRyRwAAsOsSSgGwy8gUNIvOI6Z9ajBV1PO46DxiWmQKmqXUGQAA7HqEUgDsUgoKi6PLqEei00nToqj7kCr7inocG51OmhZdRj0SBYXFeeoQAAB2Dc3z3QAApC1T0Cxa9z01WvU6IZbc0C4iInqeuzyateqQ584AAGDX4U4pAIiIjE/ZAwCAVAmlAAAAAEidUAoAAACA1AmlAAAAAEidhc4B2GVkN5bXuv3JfRERBS1aN3pPAACwqxJKAbDL2PJJezVZdkv3amO9L97QmO0AAMAuzeN7AAAAAKTOnVIA7DJ6jVud7xYAAID/RygFwC7DGlEAALDj8PgeAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKlrnu8GAACApiO7sbxe9QUtWjdSJwA0dUIpAACgzpbc0K5e9b0v3tBInQDQ1Hl8DwAAAIDU5fVOqcmTJ8cDDzwQr7zySrRq1SoGDx4cP//5z2Pvvfeu9Zjbb789zjzzzCpjhYWFUVFR0djtAgDALq/XuNVVtrMby2PZLd0jIqLHOf/xuB4AdZbXUGrevHkxbty4+PznPx+bNm2KH/zgB3HCCSfEP//5z2jduva/zIqLi+PVV1/NbWcymTTaBQCAXd7WQqeCFq2FUgDUWV5DqT/96U9Vtm+//fbo1KlTPPfcc3H00UfXelwmk4kuXbo0dnsAAAAANJIdaqHz0tLSiIho3779VuvWrVsXvXr1imw2GwcffHBcccUVsd9++9VYW1lZGZWVlbntsrKyiIjIZrORzWYbqHMAANg1ffzf1NlsNsK/sQF2eXXNW3aYUCqbzcbFF18cRxxxRPTv37/Wur333jtuvfXWGDBgQJSWlsbVV18dgwcPjkWLFkX37t2r1U+ePDkmTZpUbXzVqlXWoQIAgO2UbFqf+/OqVasi07w8j90AsCNYu3ZtneoySZIkjdxLnZx33nnx2GOPxVNPPVVjuFSbjRs3xj777BOjR4+On/70p9X213SnVI8ePWL16tVRXFzcIL0DAMCuKruxPJbd1CEiInqc9741pQCIsrKyaNeuXZSWlm41e9kh7pQ6//zz45FHHoknn3yyXoFURESLFi3ioIMOijfeeKPG/YWFhVFYWFhtvKCgIAoKCrapXwAA4P/52L+p/RsbgIio898Fef0bI0mSOP/88+PBBx+M2bNnR+/evet9js2bN8dLL70UXbt2bYQOAQCAuko2WR4DgLrLayg1bty4uOuuu+Kee+6Jtm3bxooVK2LFihXx4Ycf5mrGjBkTEyZMyG1fdtll8ec//zn+/e9/x/PPPx9f//rXY8mSJXHWWWfl4yUAAMAuKclujvLXH4iVM0bmxpbe3DWW3z88yl9/IJLs5jx2B0BTkNfH92666aaIiBg6dGiV8dtuuy3OOOOMiIhYunRpldu+Vq9eHWeffXasWLEi2rVrFwMHDoz58+fHvvvum1bbAACwS8tWlsXKR0+PiqWzqu2rWDYnKpbNiaKex0XnEdOioNA6rgDUbIdZ6DwtZWVlUVJS8qmLbQEAANUl2c2xYsZJNQZSn1TU87joMuqRyBQ0S6EzAHYUdc1erEIIAADU2fo3H6pTIBURUbF0Vqz/98ON3BEATZVQCgAAqLOyf9xcv/oX61cPwK5DKAUAANRJdlNFVCybU69jKpbNjqxP5QOgBkIpAACgTpINa1M9DoCdm1AKAACok0zLtqkeB8DOTSgFAADUSUHzoijqcUy9jinqcWwUNC9qpI4AaMqEUgAAQJ0VDzi3fvUH1K8egF2HUAoAAKiz3fqMjKKex9WptqjncbHbZ09p5I4AaKqEUgAAQJ1lCppF5xHTPjWYKup5XHQeMS0yBc1S6gyApkYoBQAA1EtBYXF0GfVIdDppWhR1H1JlX1GPY6PTSdOiy6hHoqCwOE8dAtAUNM93AwAAQNOTKWgWrfueGq16nRBLbmgXERE9z10ezVp1yHNnADQV7pQCAAAaRMan7AFQD0IpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdT59DwAAqLPsxvJatz+5LyKioEXrRu8JgKZJKAUAANTZkhva1bpv2S3dq431vnhDY7YDQBPm8T0AAAAAUudOKQAAoM56jVud7xYA2EkIpQAAgDqzRhQADcXjewAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqa57sBAAAAgF1JdmN5veoLWrRupE7ySygFAAAAkKIlN7SrV33vizc0Uif55fE9AAAAAFLnTikAAACAFPUat7rKdnZjeSy7pXtERPQ45z877eN6nySUAgAAAEjR1kKnghatd5lQyuN7AAAAAKROKAUAAABA6vIaSk2ePDk+//nPR9u2baNTp04xatSoePXVVz/1uOnTp0e/fv2iqKgo9t9///jjH/+YQrcAAAAANJS8hlLz5s2LcePGxV//+teYOXNmbNy4MU444YQoLy+v9Zj58+fH6NGj45vf/Ga88MILMWrUqBg1alS8/PLLKXYOAAAAwPbIJEmS5LuJLVatWhWdOnWKefPmxdFHH11jzemnnx7l5eXxyCOP5MYOP/zwOPDAA2PKlCmfeo2ysrIoKSmJ0tLSKC4ubrDeAQAAALZFdmN5LLmhXUR89Ml8TX2h87pmLzvUp++VlpZGRET79u1rrXnmmWdi/PjxVcaGDx8eM2bMqLG+srIyKisrc9tlZWUREZHNZiObzW5nxwAAAADb5+P5RDabjWjieUVd85YdJpTKZrNx8cUXxxFHHBH9+/evtW7FihXRuXPnKmOdO3eOFStW1Fg/efLkmDRpUrXxVatWRUVFxfY1DQAAALCdkk3rc39etWpVZJrXvqxRU7B27do61e0wodS4cePi5ZdfjqeeeqpBzzthwoQqd1aVlZVFjx49omPHjh7fAwAAAPIuu7E8lv2/P3fs2LHJP75XVFRUp7odIpQ6//zz45FHHoknn3wyunfvvtXaLl26xMqVK6uMrVy5Mrp06VJjfWFhYRQWFlYbLygoiIKCvK7zDgAAABDxsXwik90QBQVt89jM9qtr3pLXVCZJkjj//PPjwQcfjNmzZ0fv3r0/9ZhBgwbFrFmzqozNnDkzBg0a1FhtAgAAADS4JLs5yl9/IFbOGJkbW3pz11h+//Aof/2BSLKb89hd48vrnVLjxo2Le+65Jx566KFo27Ztbl2okpKSaNWqVUREjBkzJrp16xaTJ0+OiIiLLroohgwZEtdcc02MGDEi7r333liwYEHccssteXsdAAAAAPWRrSyLlY+eHhVLZ1XbV7FsTlQsmxNFPY+LziOmRUHhzrn8UF7vlLrpppuitLQ0hg4dGl27ds19TZs2LVezdOnSWL58eW578ODBcc8998Qtt9wSBxxwQNx3330xY8aMrS6ODgAAALCjSLKbaw2kPq5i6axY+ejpO+0dU5kkSZJ8N5GmsrKyKCkpidLSUgudAwAAAKkrf/2BePfRr9a5vtNJ06J131MbsaOGVdfsxUrfAAAAACkq+8fN9at/sX71TYVQCgAAACAl2U0VUbFsTr2OqVg2O7KbKhqpo/wRSgEAAACkJNmwNtXjdmRCKQAAAICUZFq2TfW4HZlQCgAAACAlBc2LoqjHMfU6pqjHsVHQvKiROsofoRQAAABAiooHnFu/+gPqV99UCKUAAAAAUrRbn5FR1PO4OtUW9TwudvvsKY3cUX4IpQAAAABSlCloFp1HTPvUYKqo53HRecS0yBQ0S6mzdAmlAAAAAFJWUFgcXUY9Ep1OmhZF3YdU2VfU49jodNK06DLqkSgoLM5Th42veb4bAAAAANgVZQqaReu+p0arXifEkhvaRUREz3OXR7NWHfLcWTrcKQUAAACwg8jshJ+yVxuhFAAAAACp26ZQqlmzZvHuu+9WG3///fejWbOdc/EtAAAAABrONoVSSZLUOF5ZWRktW7bcroYAAAAA2PnVa6Hz66+/PiIiMplMTJ06Ndq0aZPbt3nz5njyySejX79+DdshAAAAADudeoVS1157bUR8dKfUlClTqjyq17Jly9hzzz1jypQpDdshAAAAADudeoVSixcvjoiIY445Jh544IFo165dozQFAAAAwM6tXqHUFnPmzGnoPgAAAADYhWzTQudf/OIX4+c//3m18V/84hfx5S9/ebubAgAAAGDntk2h1JNPPhlf+MIXqo2feOKJ8eSTT253UwAAAAA7q+zG8mpfddm3s9mmx/fWrVsXLVu2rDbeokWLKCsr2+6mAAAAAHZWS26ofY3uZbd0rzbW++INjdlO3mzTnVL7779/TJs2rdr4vffeG/vuu+92NwUAAADAzm2b7pT68Y9/HKeddlq8+eabceyxx0ZExKxZs+L3v/99TJ8+vUEbBAAAANiZ9Bq3Ot8t7BC2KZQ6+eSTY8aMGXHFFVfEfffdF61atYoBAwbEE088EUOGDGnoHgEAAAB2GgUtWue7hR1CJkmSJN9NpKmsrCxKSkqitLQ0iouL890OAAAAwE6lrtnLNq0pFRGxZs2amDp1avzgBz+IDz74ICIinn/++Xj77be39ZQAAAAA7CK26fG9f/zjHzFs2LAoKSmJt956K84666xo3759PPDAA7F06dL43e9+19B9AgAAALAT2aY7pcaPHx9nnHFGvP7661FUVJQb/8IXvhBPPvlkgzUHAAAAwM5pm0KpZ599Ns4999xq4926dYsVK1Zsd1MAAAAA7Ny2KZQqLCyMsrKyauOvvfZadOzYcbubAgAAAGDntk2h1CmnnBKXXXZZbNy4MSIiMplMLF26NL7//e/HF7/4xQZtEAAAAICdzzaFUtdcc02sW7cuOnXqFB9++GEMGTIk+vbtG23bto3LL7+8oXsEAAAAYCezTZ++V1JSEjNnzoynn346XnzxxVi3bl0cfPDBMWzYsIbuDwAAAICdUJ1Dqfbt28drr70Wn/nMZ+J//s//Gb/61a/iiCOOiCOOOKIx+wMAAABgJ1Tnx/c2bNiQW9z8jjvuiIqKikZrCgAAAICdW53vlBo0aFCMGjUqBg4cGEmSxIUXXhitWrWqsfbWW29tsAYBAAAA2PnUOZS666674tprr40333wzIiJKS0vdLQUAAADANskkSZLU96DevXvHggULokOHDo3RU6MqKyuLkpKSKC0tjeLi4ny3AwAAALBTqWv2Uuc1pdq3bx/vvfdeREQcc8wx0bJly+3vEgAAAIBdkoXOAQAAAEidhc4BAAAASN02LXSeyWQsdA4AAADANrPQOQAAAAANpsEXOo+I+MIXvhClpaWxePHi6NChQ1x55ZWxZs2a3P73338/9t13321uGgAAAIBdQ71CqT/96U9RWVmZ277iiivigw8+yG1v2rQpXn311YbrDgAAAICdUr1CqU/ahif/AAAAAGD7QikAAAAA2Bb1CqUymUxkMplqYwAAAABQH83rU5wkSZxxxhlRWFgYEREVFRXxrW99K1q3bh0RUWW9KQAAAACoTb1CqbFjx1bZ/vrXv16tZsyYMdvXEQAAAAA7vXqFUrfddltj9QEAAADALsRC5wAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOryGko9+eSTcfLJJ8cee+wRmUwmZsyYsdX6uXPnRiaTqfa1YsWKdBoGAAAAoEHkNZQqLy+PAw44IG644YZ6Hffqq6/G8uXLc1+dOnVqpA4BAAAAaAzN83nxE088MU488cR6H9epU6fYfffdG74hAAAAAFLRJNeUOvDAA6Nr165x/PHHx9NPP53vdgAAAACop7zeKVVfXbt2jSlTpsQhhxwSlZWVMXXq1Bg6dGj87W9/i4MPPrjGYyorK6OysjK3XVZWFhER2Ww2stlsKn0DAAAA7Crqmrc0qVBq7733jr333ju3PXjw4HjzzTfj2muvjTvvvLPGYyZPnhyTJk2qNr5q1aqoqKhotF4BAAAAdkVr166tU12TCqVqcuihh8ZTTz1V6/4JEybE+PHjc9tlZWXRo0eP6NixYxQXF6fRIgAAAMAuo6ioqE51TT6UWrhwYXTt2rXW/YWFhVFYWFhtvKCgIAoKmuSSWgAAAAA7rLrmLXkNpdatWxdvvPFGbnvx4sWxcOHCaN++ffTs2TMmTJgQb7/9dvzud7+LiIjrrrsuevfuHfvtt19UVFTE1KlTY/bs2fHnP/85Xy8BAAAAgG2Q11BqwYIFccwxx+S2tzxmN3bs2Lj99ttj+fLlsXTp0tz+DRs2xHe/+914++23Y7fddosBAwbEE088UeUcAAAAAOz4MkmSJPluIk1lZWVRUlISpaWl1pQCAAAAaGB1zV4sqgQAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKQur6HUk08+GSeffHLssccekclkYsaMGZ96zNy5c+Pggw+OwsLC6Nu3b9x+++2N3icAAAAADSuvoVR5eXkccMABccMNN9SpfvHixTFixIg45phjYuHChXHxxRfHWWedFY8//ngjdwoAAABAQ2qez4ufeOKJceKJJ9a5fsqUKdG7d++45pprIiJin332iaeeeiquvfbaGD58eGO1CQAAAEADy2soVV/PPPNMDBs2rMrY8OHD4+KLL671mMrKyqisrMxtl5WVRURENpuNbDbbKH0CAAAA7Krqmrc0qVBqxYoV0blz5ypjnTt3jrKysvjwww+jVatW1Y6ZPHlyTJo0qdr4qlWroqKiotF6BQAAANgVrV27tk51TSqU2hYTJkyI8ePH57bLysqiR48e0bFjxyguLs5jZwAAAAA7n6KiojrVNalQqkuXLrFy5coqYytXrozi4uIa75KKiCgsLIzCwsJq4wUFBVFQkNd13gEAAAB2OnXNW5pUKjNo0KCYNWtWlbGZM2fGoEGD8tQRAAAAANsir6HUunXrYuHChbFw4cKIiFi8eHEsXLgwli5dGhEfPXo3ZsyYXP23vvWt+Pe//x3f+9734pVXXokbb7wx/vCHP8R3vvOdfLQPAAAAwDbKayi1YMGCOOigg+Kggw6KiIjx48fHQQcdFJdeemlERCxfvjwXUEVE9O7dOx599NGYOXNmHHDAAXHNNdfE1KlTY/jw4XnpHwAAAIBtk0mSJMl3E2kqKyuLkpKSKC0ttdA5AAAAQAOra/bSpNaUAgAAAGDnIJQCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSt0OEUjfccEPsueeeUVRUFIcddlj8/e9/r7X29ttvj0wmU+WrqKgoxW4BAAAA2F55D6WmTZsW48ePj4kTJ8bzzz8fBxxwQAwfPjzefffdWo8pLi6O5cuX576WLFmSYscAAAAAbK+8h1K//OUv4+yzz44zzzwz9t1335gyZUrstttuceutt9Z6TCaTiS5duuS+OnfunGLHAAAAAGyvvIZSGzZsiOeeey6GDRuWGysoKIhhw4bFM888U+tx69ati169ekWPHj1i5MiRsWjRojTaBQAAAKCBNM/nxd97773YvHlztTudOnfuHK+88kqNx+y9995x6623xoABA6K0tDSuvvrqGDx4cCxatCi6d+9erb6ysjIqKytz22VlZRERkc1mI5vNNuCrAQAAAKCueUteQ6ltMWjQoBg0aFBue/DgwbHPPvvEzTffHD/96U+r1U+ePDkmTZpUbXzVqlVRUVHRqL0CAAAA7GrWrl1bp7q8hlKf+cxnolmzZrFy5coq4ytXrowuXbrU6RwtWrSIgw46KN54440a90+YMCHGjx+f2y4rK4sePXpEx44do7i4eNubBwAAAKCaoqKiOtXlNZRq2bJlDBw4MGbNmhWjRo2KiI9u8Zo1a1acf/75dTrH5s2b46WXXoovfOELNe4vLCyMwsLCauMFBQVRUJD3dd4BAAAAdip1zVvy/vje+PHjY+zYsXHIIYfEoYceGtddd12Ul5fHmWeeGRERY8aMiW7dusXkyZMjIuKyyy6Lww8/PPr27Rtr1qyJq666KpYsWRJnnXVWPl8GAAAAAPWQ91Dq9NNPj1WrVsWll14aK1asiAMPPDD+9Kc/5RY/X7p0aZWEbfXq1XH22WfHihUrol27djFw4MCYP39+7Lvvvvl6CQAAAADUUyZJkiTfTaSprKwsSkpKorS01JpSAAAAAA2srtmLRZUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDU7RCh1A033BB77rlnFBUVxWGHHRZ///vft1o/ffr06NevXxQVFcX+++8ff/zjH1PqFAAAAICGkPdQatq0aTF+/PiYOHFiPP/883HAAQfE8OHD4913362xfv78+TF69Oj45je/GS+88EKMGjUqRo0aFS+//HLKnQMAAACwrTJJkiT5bOCwww6Lz3/+8/Gb3/wmIiKy2Wz06NEjLrjggvjv//7vavWnn356lJeXxyOPPJIbO/zww+PAAw+MKVOmfOr1ysrKoqSkJFatWhXFxcXV9hcUFETz5s1z2xs2bKj1XJlMJlq0aLFNtRs3boza3vrGqo2IaNmy5TbVbtq0KbLZbIPUtmjRIjKZTKPWbt68OTZv3twgtc2bN4+CgoIdpjabzcamTZtqrW3WrFk0a9Zsh6lNkiQ2btzYILUf//1srNqIrf8umyNqrjVHmCPMEfWvNUdsW605Yvtqd4Tfe3OEOeKTteYIc4Q5ov61O/ocUVZWFh07dozS0tIas5ctmte6JwUbNmyI5557LiZMmJAbKygoiGHDhsUzzzxT4zHPPPNMjB8/vsrY8OHDY8aMGTXWV1ZWRmVlZW67rKwsIiKuvvrqKCwsrFbft2/f+K//+q/c9i9+8Ytaf8B69eoVZ5xxRm772muvjfXr19dY27Vr1zjnnHNy27/5zW9izZo1NdZ27Ngxvv3tb+e2b7755li1alWNtbvvvntcdNFFue3f/va3sXz58hprd9ttt/hf/+t/5bbvvPPOWLJkSY21LVq0iB/84Ae57d///vfxxhtv1FgbETFx4sTcn++7777417/+VWvthAkTcn+xPPzww/Hiiy/WWnvJJZdE69atIyLiscceiwULFtRae9FFF8Xuu+8eEREzZ86s9WcoIuK8886LTp06RUTEvHnzYt68ebXWnnXWWdGtW7eI+OhOvSeeeKLW2rFjx8aee+4ZERHPPvtsPPbYY7XWjh49Oj73uc9FRMSLL74YDz30UK21X/rSl2K//faLiIhFixbFfffdV2vtyJEj48ADD4yIiNdeey1+//vf11p74oknxqGHHhoREW+99VbccccdtdYOGzYsjjjiiIiIePvtt2Pq1Km11g4ZMiSGDh0aERHvvvtu3HTTTbXWDho0KE444YSIiFizZk386le/qrX2kEMOiREjRkRERHl5eVx99dW11h5wwAExatSoiPhorpk8eXKttfvss0985StfyW1ffvnltdaaIz5ijvj/zBEfMUd8xBzxEXPE/2eO+Ig54iPmiI+YI/4/c8RHzBEf2ZnmiI/nMFuT11Dqvffei82bN0fnzp2rjHfu3DleeeWVGo9ZsWJFjfUrVqyosX7y5MkxadKkauPl5eU1pq9lZWVVHh1ct25drSnt2rVrq9V++OGHdapdu3ZtlJeX11hbVFRU59pmzZrVuTabzda5tnnz5lVqy8rKaq2NiHrXbvmLorS0dKu1q1atyu2vS+2WdHjNmjVbrX3vvfdyf65L7Zb0d/Xq1Vutff/992O33Xarc+2W9+2DDz7Yau0HH3ywTbXvv//+VmtXr169TbXvvffeVmvXrFmzTbWf9j0uLS3N1a5fv77OtRs2bNhq7Sd/7+tTa474iDnCHGGOqLnWHPERc4Q5whxRc6054iPmCHOEOaLm2qY8R9Q1lMrr43vvvPNOdOvWLebPnx+DBg3KjX/ve9+LefPmxd/+9rdqx7Rs2TLuuOOOGD16dG7sxhtvjEmTJsXKlSur1dd0p1SPHj1i5cqVHt9zS22dat1Su321bqk1R9S31hyxfbU7wu+9OcIc8clac4Q5whxR/1pzxLbVmiO2r3ZH+L03R+wcc0RZWVl07tx5x3587zOf+Uw0a9asWpi0cuXK6NKlS43HdOnSpV71hYWFNT6mV1RUFEVFRZ/aY11qtqW2pp525NqP/0XQFGoLCgqq/HLsbLUfn6h29NqIyP1F0FRqG+v33hyx49TuKL/L5oiP7Ai/9+aIxq3dEX7vzRH/vzbfv/fmiMav3RF+780RTbc237/35ojGr03j935rIdnH5fXT91q2bBkDBw6MWbNm5cay2WzMmjWryp1THzdo0KAq9REfPdNbWz0AAAAAO5683ikVETF+/PgYO3ZsHHLIIXHooYfGddddF+Xl5XHmmWdGRMSYMWOiW7duuYXDLrroohgyZEhcc801MWLEiLj33ntjwYIFccstt+TzZQAAAABQD3kPpU4//fRYtWpVXHrppbFixYo48MAD409/+lNuMfOlS5fmnm+NiBg8eHDcc8898aMf/Sh+8IMfxF577RUzZsyI/v375+slAAAAAFBPeV3oPB/KysqipKTkUxfbAgAAAKD+6pq95HVNKQAAAAB2TUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFLXPN8NpC1JkoiIKCsry3MnAAAAADufLZnLlgymNrtcKLV27dqIiOjRo0eeOwEAAADYea1duzZKSkpq3Z9JPi222slks9l45513om3btpHJZPLdznYpKyuLHj16xLJly6K4uDjf7QA0SeZSgO1nLgXYfjvTXJokSaxduzb22GOPKCiofeWoXe5OqYKCgujevXu+22hQxcXFTf4HFiDfzKUA289cCrD9dpa5dGt3SG1hoXMAAAAAUieUAgAAACB1QqkmrLCwMCZOnBiFhYX5bgWgyTKXAmw/cynA9tsV59JdbqFzAAAAAPLPnVIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFINbPLkyfH5z38+2rZtG506dYpRo0bFq6++WqWmoqIixo0bFx06dIg2bdrEF7/4xVi5cmVu/4svvhijR4+OHj16RKtWrWKfffaJX/3qV1XOMXfu3MhkMtW+VqxYsdX+kiSJSy+9NLp27RqtWrWKYcOGxeuvv16lZs8996x23iuvvHI73xmAutsZ5tKIiEcffTQOO+ywaNWqVbRr1y5GjRq17W8KQD019bm0tvNmMpl49tlnG+AdAvh0TX0ujYh47bXXYuTIkfGZz3wmiouL48gjj4w5c+Zs5zvTMIRSDWzevHkxbty4+Otf/xozZ86MjRs3xgknnBDl5eW5mu985zvxf/7P/4np06fHvHnz4p133onTTjstt/+5556LTp06xV133RWLFi2KH/7whzFhwoT4zW9+U+16r776aixfvjz31alTp63294tf/CKuv/76mDJlSvztb3+L1q1bx/Dhw6OioqJK3WWXXVblvBdccMF2vjMAdbczzKX3339/fOMb34gzzzwzXnzxxXj66afja1/7WgO8OwB109Tn0sGDB1c53/Lly+Oss86K3r17xyGHHNJA7xLA1jX1uTQi4qSTTopNmzbF7Nmz47nnnosDDjggTjrppE8NvFKR0KjefffdJCKSefPmJUmSJGvWrElatGiRTJ8+PVfzr3/9K4mI5Jlnnqn1PN/+9reTY445Jrc9Z86cJCKS1atX17mXbDabdOnSJbnqqqtyY2vWrEkKCwuT3//+97mxXr16Jddee22dzwvQ2JraXLpx48akW7duydSpU+t8XoDG1tTm0k/asGFD0rFjx+Syyy6r83UAGlpTm0tXrVqVRETy5JNP5mrKysqSiEhmzpxZ52s1FndKNbLS0tKIiGjfvn1EfJSQbty4MYYNG5ar6devX/Ts2TOeeeaZrZ5nyzk+7sADD4yuXbvG8ccfH08//fRWe1m8eHGsWLGiyrVLSkrisMMOq3btK6+8Mjp06BAHHXRQXHXVVbFp06ZPf7EAjaSpzaXPP/98vP3221FQUBAHHXRQdO3aNU488cR4+eWX6/6iARpYU5tLP+nhhx+O999/P84888ytnhugMTW1ubRDhw6x9957x+9+97soLy+PTZs2xc033xydOnWKgQMH1v2FN5Lm+W5gZ5bNZuPiiy+OI444Ivr37x8REStWrIiWLVvG7rvvXqW2c+fOtd46N3/+/Jg2bVo8+uijubGuXbvGlClT4pBDDonKysqYOnVqDB06NP72t7/FwQcfXON5tpy/c+fOW732hRdeGAcffHC0b98+5s+fHxMmTIjly5fHL3/5y3q/BwDbqynOpf/+978jIuInP/lJ/PKXv4w999wzrrnmmhg6dGi89tprNf4DBKAxNcW59JN++9vfxvDhw6N79+51es0ADa0pzqWZTCaeeOKJGDVqVLRt2zYKCgqiU6dO8ac//SnatWu3Te9DQxJKNaJx48bFyy+/HE899dQ2n+Pll1+OkSNHxsSJE+OEE07Ije+9996x995757YHDx4cb775Zlx77bVx5513xt133x3nnntubv9jjz0WzZo1q9M1x48fn/vzgAEDomXLlnHuuefG5MmTo7CwcJtfC8C2aIpzaTabjYiIH/7wh/HFL34xIiJuu+226N69e0yfPr3KOQHS0BTn0o/7z3/+E48//nj84Q9/2Ob+AbZXU5xLkySJcePGRadOneIvf/lLtGrVKqZOnRonn3xyPPvss9G1a9dtfi0NQSjVSM4///x45JFH4sknn6zyvzldunSJDRs2xJo1a6okqStXrowuXbpUOcc///nPOO644+Kcc86JH/3oR596zUMPPTT3y3HKKafEYYcdltvXrVu3WL58ee5aH//BW7lyZRx44IG1nvewww6LTZs2xVtvvVXllwSgsTXVuXTL+L777pvbX1hYGJ/97Gdj6dKldXz1AA2jqc6lH3fbbbdFhw4d4pRTTqnTawZoaE11Lp09e3Y88sgjsXr16iguLo6IiBtvvDFmzpwZd9xxR/z3f/93/d6IBmZNqQaWJEmcf/758eCDD8bs2bOjd+/eVfYPHDgwWrRoEbNmzcqNvfrqq7F06dIYNGhQbmzRokVxzDHHxNixY+Pyyy+v07UXLlyY+0Fs27Zt9O3bN/fVqlWr6N27d3Tp0qXKtcvKyuJvf/tblWvXdN4tt/gBpKGpz6UDBw6MwsLCKh8XvHHjxnjrrbeiV69e9X9DALZBU59LP/46brvtthgzZky0aNGi3u8DwPZo6nPp+vXrIyKioKBq/FNQUJC7uz+v8rnK+s7ovPPOS0pKSpK5c+cmy5cvz32tX78+V/Otb30r6dmzZzJ79uxkwYIFyaBBg5JBgwbl9r/00ktJx44dk69//etVzvHuu+/maq699tpkxowZyeuvv5689NJLyUUXXZQUFBQkTzzxxFb7u/LKK5Pdd989eeihh5J//OMfyciRI5PevXsnH374YZIkSTJ//vzk2muvTRYuXJi8+eabyV133ZV07NgxGTNmTAO/UwC1a+pzaZIkyUUXXZR069Ytefzxx5NXXnkl+eY3v5l06tQp+eCDDxrwnQKo3c4wlyZJkjzxxBNJRCT/+te/GuidAai7pj6Xrlq1KunQoUNy2mmnJQsXLkxeffXV5JJLLklatGiRLFy4sIHfrfoTSjWwiKjx67bbbsvVfPjhh8m3v/3tpF27dsluu+2WnHrqqcny5ctz+ydOnFjjOXr16pWr+fnPf5706dMnKSoqStq3b58MHTo0mT179qf2l81mkx//+MdJ586dk8LCwuS4445LXn311dz+5557LjnssMOSkpKSpKioKNlnn32SK664IqmoqGiQ9wegLpr6XJokH310+Xe/+92kU6dOSdu2bZNhw4YlL7/88na/NwB1tTPMpUmSJKNHj04GDx68Xe8FwLbaGebSZ599NjnhhBOS9u3bJ23btk0OP/zw5I9//ON2vzcNIZMkSdIot2ABAAAAQC2sKQUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKTu/wK2eAC1hgEt9AAAAABJRU5ErkJggg==", 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,\n", - " )" + "(
,\n", + " [,\n", + " ,\n", + " ])" ] }, - "execution_count": 10, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] }, "metadata": {}, @@ -786,17 +1080,928 @@ } ], "source": [ - "aggregated_eventstudy = dml_obj.aggregate(\"eventstudy\")\n", - "print(aggregated_eventstudy)\n", - "aggregated_eventstudy.plot_effects()" + "linear_learners = {\n", + " \"ml_g\": LinearRegression(),\n", + " \"ml_m\": LogisticRegression(),\n", + "}\n", + "\n", + "dml_obj_linear = DoubleMLDIDMulti(dml_data, **(default_args | linear_learners))\n", + "dml_obj_linear.fit()\n", + "dml_obj_linear.bootstrap(n_rep_boot=5000)\n", + "dml_obj_linear.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aggregated Effects\n", + "As the [did-R-package](https://bcallaway11.github.io/did/index.html), the $ATT$'s can be aggregated to summarize multiple effects.\n", + "For details on different aggregations and details on their interpretations see [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001).\n", + "\n", + "The aggregations are implemented via the `aggregate()` method." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Group Aggregation\n", + "\n", + "\n", + "To obtain group-specific effects one can would like to average $ATT(\\mathrm{g}, t_\\text{eval})$ over $t_\\text{eval}$.\n", + "As a sample oracle we will combine all `ite`'s based on group $\\mathrm{g}$." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "d\n", + "2025-04-01 1.917883\n", + "2025-05-01 1.484009\n", + "2025-06-01 1.045526\n", + "Name: ite, dtype: float64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_post_treatment = df[df[\"t\"] >= df[\"d\"]]\n", + "df_post_treatment.groupby(\"d\")[\"ite\"].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To obtain group-specific effects it is possible to aggregate several $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$ values based on the group $\\mathrm{g}$ by setting the `aggregation=\"group\"` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDAggregation Object ==================\n", + " Group Aggregation \n", + "\n", + "------------------ Overall Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1.519998 0.104835 14.498974 0.0 1.314526 1.725471\n", + "------------------ Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "2025-04 1.764970 0.173168 10.192264 0.0 1.425568 2.104373\n", + "2025-05 1.587966 0.133331 11.909997 0.0 1.326643 1.849290\n", + "2025-06 1.182735 0.103009 11.481824 0.0 0.980841 1.384630\n", + "------------------ Additional Information ------------------\n", + "Score function: observational\n", + "Control group: never_treated\n", + "Anticipation periods: 0\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "aggregated_group = dml_obj.aggregate(aggregation=\"group\")\n", + "print(aggregated_group)\n", + "_ = aggregated_group.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output is a `DoubleMLDIDAggregation` object which includes an overall aggregation summary based on group size." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Time Aggregation\n", + "\n", + "To obtain time-specific effects one can would like to average $ATT(\\mathrm{g}, t_\\text{eval})$ over $\\mathrm{g}$ (respecting group size).\n", + "As a sample oracle we will combine all `ite`'s based on group $\\mathrm{g}$. As oracle values, we obtain" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "t\n", + "2025-04-01 0.900632\n", + "2025-05-01 1.437580\n", + "2025-06-01 2.026848\n", + "Name: ite, dtype: float64" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_post_treatment.groupby(\"t\")[\"ite\"].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To aggregate $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$, based on $t_\\text{eval}$, but weighted with respect to group size. Corresponds to *Calendar Time Effects* from the [did-R-package](https://bcallaway11.github.io/did/index.html).\n", + "\n", + "For calendar time effects set `aggregation=\"time\"`." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDAggregation Object ==================\n", + " Time Aggregation \n", + "\n", + "------------------ Overall Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1.447253 0.115185 12.564566 0.0 1.221494 1.673012\n", + "------------------ Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "2025-04 0.885967 0.137076 6.463310 1.024374e-10 0.617303 1.154632\n", + "2025-05 1.535208 0.129116 11.890192 0.000000e+00 1.282146 1.788270\n", + "2025-06 1.920584 0.148870 12.901079 0.000000e+00 1.628804 2.212363\n", + "------------------ Additional Information ------------------\n", + "Score function: observational\n", + "Control group: never_treated\n", + "Anticipation periods: 0\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "aggregated_time = dml_obj.aggregate(\"time\")\n", + "print(aggregated_time)\n", + "fig, ax = aggregated_time.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Event Study Aggregation\n", + "\n", + "To obtain event-study-type effects one can would like to aggregate $ATT(\\mathrm{g}, t_\\text{eval})$ over $e = t_\\text{eval} - \\mathrm{g}$ (respecting group size).\n", + "As a sample oracle we will combine all `ite`'s based on group $\\mathrm{g}$. As oracle values, we obtain" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "e\n", + "-122 days -0.007736\n", + "-92 days -0.014805\n", + "-61 days 0.000861\n", + "-31 days 0.031158\n", + "0 days 0.962324\n", + "31 days 1.967536\n", + "59 days 2.937695\n", + "Name: ite, dtype: float64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"e\"] = pd.to_datetime(df[\"t\"]).values.astype(\"datetime64[M]\") - \\\n", + " pd.to_datetime(df[\"d\"]).values.astype(\"datetime64[M]\")\n", + "df.groupby(\"e\")[\"ite\"].mean()[1:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Analogously, `aggregation=\"eventstudy\"` aggregates $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$ based on exposure time $e = t_\\text{eval} - \\mathrm{g}$ (respecting group size)." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDAggregation Object ==================\n", + " Event Study Aggregation \n", + "\n", + "------------------ Overall Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1.84764 0.152535 12.112918 0.0 1.548678 2.146603\n", + "------------------ Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "-4 months -0.062934 0.102768 -0.612393 0.540278 -0.264355 0.138487\n", + "-3 months 0.051390 0.079708 0.644732 0.519101 -0.104834 0.207614\n", + "-2 months -0.017152 0.071696 -0.239239 0.810920 -0.157674 0.123369\n", + "-1 months 0.082539 0.072499 1.138493 0.254915 -0.059556 0.224634\n", + "0 months 1.077206 0.079043 13.628169 0.000000 0.922285 1.232126\n", + "1 months 1.943978 0.148708 13.072411 0.000000 1.652514 2.235441\n", + "2 months 2.521738 0.275739 9.145392 0.000000 1.981300 3.062176\n", + "------------------ Additional Information ------------------\n", + "Score function: observational\n", + "Control group: never_treated\n", + "Anticipation periods: 0\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "aggregated_eventstudy = dml_obj.aggregate(\"eventstudy\")\n", + "print(aggregated_eventstudy)\n", + "aggregated_eventstudy.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Aggregation Details\n", + "\n", + "The `DoubleMLDIDAggregation` objects include several `DoubleMLFrameworks` which support methods like `bootstrap()` or `confint()`.\n", + "Further, the weights can be accessed via the properties\n", + "\n", + " - ``overall_aggregation_weights``: weights for the overall aggregation\n", + " - ``aggregation_weights``: weights for the aggregation\n", + "\n", + "To clarify, e.g. for the eventstudy aggregation" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDAggregation Object ==================\n", + " Event Study Aggregation \n", + "\n", + "------------------ Overall Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "1.84764 0.152535 12.112918 0.0 1.548678 2.146603\n", + "------------------ Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "-4 months -0.062934 0.102768 -0.612393 0.540278 -0.264355 0.138487\n", + "-3 months 0.051390 0.079708 0.644732 0.519101 -0.104834 0.207614\n", + "-2 months -0.017152 0.071696 -0.239239 0.810920 -0.157674 0.123369\n", + "-1 months 0.082539 0.072499 1.138493 0.254915 -0.059556 0.224634\n", + "0 months 1.077206 0.079043 13.628169 0.000000 0.922285 1.232126\n", + "1 months 1.943978 0.148708 13.072411 0.000000 1.652514 2.235441\n", + "2 months 2.521738 0.275739 9.145392 0.000000 1.981300 3.062176\n", + "------------------ Additional Information ------------------\n", + "Score function: observational\n", + "Control group: never_treated\n", + "Anticipation periods: 0\n", + "\n" + ] + } + ], + "source": [ + "print(aggregated_eventstudy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, the overall effect aggregation aggregates each effect with positive exposure" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0. 0. 0. 0.33333333 0.33333333\n", + " 0.33333333]\n" + ] + } + ], + "source": [ + "print(aggregated_eventstudy.overall_aggregation_weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If one would like to consider how the aggregated effect with $e=0$ is computed, one would have to look at the corresponding set of weights within the ``aggregation_weights`` property" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0. , 0.34529452, 0. , 0. ,\n", + " 0. , 0. , 0. , 0.33615437, 0. ,\n", + " 0. , 0. , 0. , 0. , 0.31855112])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the weights for e=0 correspond to the fifth element of the aggregation weights\n", + "aggregated_eventstudy.aggregation_weights[4]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking a look at the original `dml_obj`, one can see that this combines the following estimates (only show month):\n", + "\n", + " - $\\widehat{ATT}(04,03,04)$\n", + " - $\\widehat{ATT}(05,04,05)$\n", + " - $\\widehat{ATT}(06,05,06)$" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ATT(2025-04,2025-01,2025-02) -0.186576\n", + "ATT(2025-04,2025-02,2025-03) 0.165520\n", + "ATT(2025-04,2025-03,2025-04) 0.885967\n", + "ATT(2025-04,2025-03,2025-05) 1.887206\n", + "ATT(2025-04,2025-03,2025-06) 2.521738\n", + "ATT(2025-05,2025-01,2025-02) -0.020345\n", + "ATT(2025-05,2025-02,2025-03) 0.135493\n", + "ATT(2025-05,2025-03,2025-04) 0.068758\n", + "ATT(2025-05,2025-04,2025-05) 1.173640\n", + "ATT(2025-05,2025-04,2025-06) 2.002293\n", + "ATT(2025-06,2025-01,2025-02) -0.062934\n", + "ATT(2025-06,2025-02,2025-03) 0.127089\n", + "ATT(2025-06,2025-03,2025-04) 0.005414\n", + "ATT(2025-06,2025-04,2025-05) 0.007135\n", + "ATT(2025-06,2025-05,2025-06) 1.182735\n", + "Name: coef, dtype: float64\n" + ] + } + ], + "source": [ + "print(dml_obj.summary[\"coef\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Anticipation\n", + "\n", + "As described in the [Model Guide](https://docs.doubleml.org/stable/guide/models.html#difference-in-differences-models-did), one can include anticipation periods $\\delta>0$ by setting the `anticipation_periods` parameter." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data with Anticipation\n", + "\n", + "The DGP allows to include anticipation periods via the `anticipation_periods` parameter.\n", + "In this case the observations will be \"shifted\" such that units anticipate the effect earlier and the exposure effect is increased by the number of periods where the effect is anticipated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(19080, 10)\n" + ] + } + ], + "source": [ + "n_obs = 4000\n", + "n_periods = 6\n", + "\n", + "df_anticipation = make_did_CS2021(n_obs, dgp_type=4, n_periods=n_periods, n_pre_treat_periods=3, time_type=\"datetime\", anticipation_periods=1)\n", + "\n", + "print(df_anticipation.shape)\n", + "df_anticipation.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To visualize the anticipation, we will again plot the \"oracle\" values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tFirst Treatedy_meany_lower_quantiley_upper_quantileite_meanite_lower_quantileite_upper_quantile
02025-01-012025-04208.533893191.327132227.3412320.006281-2.3122762.348005
12025-01-012025-05210.612290193.917897226.878333-0.120014-2.5042682.276403
22025-01-012025-06212.816950195.161619229.957651-0.049635-2.2979252.360860
32025-01-01Never Treated217.348892201.317126233.4235070.027843-2.1678242.376174
42025-02-012025-04208.258789181.903694236.3585670.052753-2.1391592.236515
\n", + "
" + ], + "text/plain": [ + " t First Treated y_mean y_lower_quantile y_upper_quantile \\\n", + "0 2025-01-01 2025-04 208.533893 191.327132 227.341232 \n", + "1 2025-01-01 2025-05 210.612290 193.917897 226.878333 \n", + "2 2025-01-01 2025-06 212.816950 195.161619 229.957651 \n", + "3 2025-01-01 Never Treated 217.348892 201.317126 233.423507 \n", + "4 2025-02-01 2025-04 208.258789 181.903694 236.358567 \n", + "\n", + " ite_mean ite_lower_quantile ite_upper_quantile \n", + "0 0.006281 -2.312276 2.348005 \n", + "1 -0.120014 -2.504268 2.276403 \n", + "2 -0.049635 -2.297925 2.360860 \n", + "3 0.027843 -2.167824 2.376174 \n", + "4 0.052753 -2.139159 2.236515 " + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_anticipation[\"ite\"] = df_anticipation[\"y1\"] - df_anticipation[\"y0\"]\n", + "df_anticipation[\"First Treated\"] = df_anticipation[\"d\"].dt.strftime(\"%Y-%m\").fillna(\"Never Treated\")\n", + "agg_df_anticipation = df_anticipation.groupby([\"t\", \"First Treated\"]).agg(**agg_dictionary).reset_index()\n", + "agg_df_anticipation.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can see that the effect is already anticipated one period before the actual treatment assignment." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_data(agg_df_anticipation, col_name='ite')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize a corresponding `DoubleMLPanelData` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dml_data_anticipation = DoubleMLPanelData(\n", + " data=df_anticipation,\n", + " y_col=\"y\",\n", + " d_cols=\"d\",\n", + " id_col=\"id\",\n", + " t_col=\"t\",\n", + " x_cols=[\"Z1\", \"Z2\", \"Z3\", \"Z4\"],\n", + " datetime_unit=\"M\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ATT Estimation\n", + "\n", + "Let us take a look at the estimation without anticipation." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n" + ] + }, + { + "data": { + "text/plain": [ + "(
,\n", + " [,\n", + " ,\n", + " ])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dml_obj_anticipation = DoubleMLDIDMulti(dml_data_anticipation, **default_args)\n", + "dml_obj_anticipation.fit()\n", + "dml_obj_anticipation.bootstrap(n_rep_boot=5000)\n", + "dml_obj_anticipation.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The effects are obviously biased. To include anticipation periods, one can adjust the `anticipation_periods` parameter. Correspondingly, the outcome regression (and not yet treated units) are adjusted." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n" + ] + }, + { + "data": { + "text/plain": [ + "(
,\n", + " [,\n", + " ,\n", + " ])" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dml_obj_anticipation = DoubleMLDIDMulti(dml_data_anticipation, **(default_args| {\"anticipation_periods\": 1}))\n", + "dml_obj_anticipation.fit()\n", + "dml_obj_anticipation.bootstrap(n_rep_boot=5000)\n", + "dml_obj_anticipation.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Group-Time Combinations\n", + "\n", + "The default option `gt_combinations=\"standard\"` includes all group time values with the specific choice of $t_\\text{pre} = \\min(\\mathrm{g}, t_\\text{eval}) - 1$ (without anticipation) which is the weakest possible parallel trend assumption.\n", + "\n", + "Other options are possible or only specific combinations of $(\\mathrm{g},t_\\text{pre},t_\\text{eval})$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### All combinations\n", + "\n", + "The option `gt_combinations=\"all\"` includes all relevant group time values with $t_\\text{pre} < \\min(\\mathrm{g}, t_\\text{eval})$, including longer parallel trend assumptions.\n", + "This can result in multiple estimates for the same $ATT(\\mathrm{g},t)$, which have slightly different assumptions (length of parallel trends)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
,\n", + " [,\n", + " ,\n", + " ])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dml_obj_all = DoubleMLDIDMulti(dml_data, **(default_args| {\"gt_combinations\": \"all\"}))\n", + "dml_obj_all.fit()\n", + "dml_obj_all.bootstrap(n_rep_boot=5000)\n", + "dml_obj_all.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Selected Combinations\n", + "\n", + "Instead it is also possible to just submit a list of tuples containing $(\\mathrm{g}, t_\\text{pre}, t_\\text{eval})$ combinations. E.g. only two combinations" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
,\n", + " [])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gt_dict = {\n", + " \"gt_combinations\": [\n", + " (np.datetime64('2025-04'),\n", + " np.datetime64('2025-01'),\n", + " np.datetime64('2025-02')),\n", + " (np.datetime64('2025-04'),\n", + " np.datetime64('2025-02'),\n", + " np.datetime64('2025-03')),\n", + " ]\n", + "}\n", + "\n", + "dml_obj_all = DoubleMLDIDMulti(dml_data, **(default_args| gt_dict))\n", + "dml_obj_all.fit()\n", + "dml_obj_all.bootstrap(n_rep_boot=5000)\n", + "dml_obj_all.plot_effects()" + ] } ], "metadata": { From c87a617ff90028a4fd83fd2ac652e72c2b9c0f85 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Thu, 24 Apr 2025 14:57:47 +0000 Subject: [PATCH 099/140] add R example to ssm models --- doc/guide/models/ssm/ssm_models.inc | 44 +++++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) diff --git a/doc/guide/models/ssm/ssm_models.inc b/doc/guide/models/ssm/ssm_models.inc index b5b1e3fd..77d12709 100644 --- a/doc/guide/models/ssm/ssm_models.inc +++ b/doc/guide/models/ssm/ssm_models.inc @@ -42,6 +42,28 @@ Estimation is conducted via its ``fit()`` method: dml_ssm.fit() print(dml_ssm) + .. tab-item:: R + :sync: r + + .. jupyter-execute:: + + library(DoubleML) + library(mlr3) + library(data.table) + + set.seed(3141) + n_obs = 2000 + df = make_ssm_data(n_obs=n_obs, mar=TRUE, return_type="data.table") + dml_data = DoubleMLData$new(df, y_col="y", d_cols="d", s_col="s") + + ml_g = lrn("regr.cv_glmnet", nfolds = 5, s = "lambda.min") + ml_m = lrn("classif.cv_glmnet", nfolds = 5, s = "lambda.min") + ml_pi = lrn("classif.cv_glmnet", nfolds = 5, s = "lambda.min") + + dml_ssm = DoubleMLSSM$new(dml_data, ml_g, ml_m, ml_pi, score="missing-at-random") + dml_ssm$fit() + print(dml_ssm) + .. _ssm-nr-model: @@ -108,3 +130,25 @@ Estimation is conducted via its ``fit()`` method: dml_ssm = dml.DoubleMLSSM(dml_data, ml_g, ml_m, ml_pi, score='nonignorable') dml_ssm.fit() print(dml_ssm) + + .. tab-item:: R + :sync: r + + .. jupyter-execute:: + + library(DoubleML) + library(mlr3) + library(data.table) + + set.seed(3141) + n_obs = 2000 + df = make_ssm_data(n_obs=n_obs, mar=FALSE, return_type="data.table") + dml_data = DoubleMLData$new(df, y_col="y", d_cols="d", z_cols = "z", s_col="s") + + ml_g = lrn("regr.cv_glmnet", nfolds = 5, s = "lambda.min") + ml_m = lrn("classif.cv_glmnet", nfolds = 5, s = "lambda.min") + ml_pi = lrn("classif.cv_glmnet", nfolds = 5, s = "lambda.min") + + dml_ssm = DoubleMLSSM$new(dml_data, ml_g, ml_m, ml_pi, score="nonignorable") + dml_ssm$fit() + print(dml_ssm) \ No newline at end of file From 1672265110b6328b50943503ca604283438339c6 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Thu, 24 Apr 2025 16:47:25 +0000 Subject: [PATCH 100/140] clean nbs --- doc/examples/did/py_panel.ipynb | 1248 ++---------------------- doc/examples/did/py_panel_simple.ipynb | 629 +----------- 2 files changed, 111 insertions(+), 1766 deletions(-) diff --git a/doc/examples/did/py_panel.ipynb b/doc/examples/did/py_panel.ipynb index 3c648801..64d84be8 100644 --- a/doc/examples/did/py_panel.ipynb +++ b/doc/examples/did/py_panel.ipynb @@ -111,27 +111,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "t\n", - "2025-01-01 4000\n", - "2025-02-01 4000\n", - "2025-03-01 4000\n", - "2025-04-01 4000\n", - "2025-05-01 4000\n", - "2025-06-01 4000\n", - "dtype: int64" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df.groupby(\"t\").size()" ] @@ -149,25 +131,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "d\n", - "2025-04-01 6120\n", - "2025-05-01 5958\n", - "2025-06-01 5646\n", - "NaT 6276\n", - "dtype: int64" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df.groupby(\"d\", dropna=False).size()" ] @@ -181,25 +147,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "d\n", - "2025-04-01 6120\n", - "2025-05-01 5958\n", - "2025-06-01 5646\n", - "NaT 6276\n", - "dtype: int64" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df.groupby(\"d\", dropna=False).size()" ] @@ -213,121 +163,9 @@ }, { "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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tFirst Treatedy_meany_lower_quantiley_upper_quantileite_meanite_lower_quantileite_upper_quantile
02025-01-012025-04208.695846191.856539225.468568-0.046168-2.3875762.419717
12025-01-012025-05209.973551194.550897226.1273920.016787-2.2641662.417971
22025-01-012025-06212.783391196.613952229.704736-0.042261-2.2368992.128698
32025-01-01Never Treated216.797150199.947937232.8327690.049817-2.3564752.538946
42025-02-012025-04208.534462182.657322233.434507-0.032835-2.3039482.404574
\n", - "
" - ], - "text/plain": [ - " t First Treated y_mean y_lower_quantile y_upper_quantile \\\n", - "0 2025-01-01 2025-04 208.695846 191.856539 225.468568 \n", - "1 2025-01-01 2025-05 209.973551 194.550897 226.127392 \n", - "2 2025-01-01 2025-06 212.783391 196.613952 229.704736 \n", - "3 2025-01-01 Never Treated 216.797150 199.947937 232.832769 \n", - "4 2025-02-01 2025-04 208.534462 182.657322 233.434507 \n", - "\n", - " ite_mean ite_lower_quantile ite_upper_quantile \n", - "0 -0.046168 -2.387576 2.419717 \n", - "1 0.016787 -2.264166 2.417971 \n", - "2 -0.042261 -2.236899 2.128698 \n", - "3 0.049817 -2.356475 2.538946 \n", - "4 -0.032835 -2.303948 2.404574 " - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# rename for plotting\n", "df[\"First Treated\"] = df[\"d\"].dt.strftime(\"%Y-%m\").fillna(\"Never Treated\")\n", @@ -349,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -404,20 +242,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plot_data(agg_df, col_name='y')" ] @@ -437,24 +264,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "jupyter": { "source_hidden": true } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_data(agg_df, col_name='ite')" ] @@ -477,34 +293,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLPanelData Object ==================\n", - "\n", - "------------------ Data summary ------------------\n", - "Outcome variable: y\n", - "Treatment variable(s): ['d']\n", - "Covariates: ['Z1', 'Z2', 'Z3', 'Z4']\n", - "Instrument variable(s): None\n", - "Time variable: t\n", - "Id variable: id\n", - "No. Observations: 4000\n", - "\n", - "------------------ DataFrame info ------------------\n", - "\n", - "RangeIndex: 24000 entries, 0 to 23999\n", - "Columns: 12 entries, id to First Treated\n", - "dtypes: datetime64[s](2), float64(8), int64(1), object(1)\n", - "memory usage: 2.2+ MB\n", - "\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dml_data = DoubleMLPanelData(\n", " data=df,\n", @@ -548,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -572,88 +363,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDMulti Object ==================\n", - "\n", - "------------------ Data summary ------------------\n", - "Outcome variable: y\n", - "Treatment variable(s): ['d']\n", - "Covariates: ['Z1', 'Z2', 'Z3', 'Z4']\n", - "Instrument variable(s): None\n", - "Time variable: t\n", - "Id variable: id\n", - "No. Observations: 4000\n", - "\n", - "------------------ Score & algorithm ------------------\n", - "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", - "\n", - "------------------ Machine learner ------------------\n", - "Learner ml_g: LGBMRegressor(learning_rate=0.01, n_estimators=500, random_state=123,\n", - " verbose=-1)\n", - "Learner ml_m: LGBMClassifier(learning_rate=0.01, n_estimators=500, random_state=123,\n", - " verbose=-1)\n", - "Out-of-sample Performance:\n", - "Regression:\n", - "Learner ml_g0 RMSE: [[2.00900186 2.00832765 2.00147128 3.04799845 4.22606599 2.03866158\n", - " 1.97307679 1.97176622 2.0941594 3.14777236 2.01165794 2.06105985\n", - " 2.0159968 2.07337162 1.95812371]]\n", - "Learner ml_g1 RMSE: [[2.05650513 1.92499257 2.03184487 2.98708533 4.06173642 1.94313614\n", - " 2.07179882 2.03096348 1.98713429 3.05754135 2.05766274 2.06959237\n", - " 2.00498525 2.05151027 2.07028147]]\n", - "Classification:\n", - "Learner ml_m Log Loss: [[0.68315785 0.68798742 0.6814065 0.67731183 0.67871757 0.71707894\n", - " 0.71569694 0.72396693 0.73503987 0.72433498 0.73399346 0.73499374\n", - " 0.73633645 0.74302782 0.73076103]]\n", - "\n", - "------------------ Resampling ------------------\n", - "No. folds: 5\n", - "No. repeated sample splits: 1\n", - "\n", - "------------------ Fit summary ------------------\n", - " coef std err t P>|t| \\\n", - "ATT(2025-04,2025-01,2025-02) -0.186576 0.127491 -1.463442 1.433464e-01 \n", - "ATT(2025-04,2025-02,2025-03) 0.165520 0.130817 1.265276 2.057726e-01 \n", - "ATT(2025-04,2025-03,2025-04) 0.885967 0.137076 6.463310 1.024374e-10 \n", - "ATT(2025-04,2025-03,2025-05) 1.887206 0.182686 10.330322 0.000000e+00 \n", - "ATT(2025-04,2025-03,2025-06) 2.521738 0.275739 9.145392 0.000000e+00 \n", - "ATT(2025-05,2025-01,2025-02) -0.020345 0.115761 -0.175750 8.604906e-01 \n", - "ATT(2025-05,2025-02,2025-03) 0.135493 0.114989 1.178310 2.386732e-01 \n", - "ATT(2025-05,2025-03,2025-04) 0.068758 0.106044 0.648392 5.167313e-01 \n", - "ATT(2025-05,2025-04,2025-05) 1.173640 0.121208 9.682895 0.000000e+00 \n", - "ATT(2025-05,2025-04,2025-06) 2.002293 0.174835 11.452444 0.000000e+00 \n", - "ATT(2025-06,2025-01,2025-02) -0.062934 0.102768 -0.612393 5.402778e-01 \n", - "ATT(2025-06,2025-02,2025-03) 0.127089 0.109588 1.159701 2.461705e-01 \n", - "ATT(2025-06,2025-03,2025-04) 0.005414 0.099999 0.054136 9.568266e-01 \n", - "ATT(2025-06,2025-04,2025-05) 0.007135 0.104696 0.068147 9.456687e-01 \n", - "ATT(2025-06,2025-05,2025-06) 1.182735 0.103009 11.481824 0.000000e+00 \n", - "\n", - " 2.5 % 97.5 % \n", - "ATT(2025-04,2025-01,2025-02) -0.436453 0.063302 \n", - "ATT(2025-04,2025-02,2025-03) -0.090877 0.421917 \n", - "ATT(2025-04,2025-03,2025-04) 0.617303 1.154632 \n", - "ATT(2025-04,2025-03,2025-05) 1.529148 2.245264 \n", - "ATT(2025-04,2025-03,2025-06) 1.981300 3.062176 \n", - "ATT(2025-05,2025-01,2025-02) -0.247233 0.206543 \n", - "ATT(2025-05,2025-02,2025-03) -0.089882 0.360868 \n", - "ATT(2025-05,2025-03,2025-04) -0.139084 0.276601 \n", - "ATT(2025-05,2025-04,2025-05) 0.936077 1.411202 \n", - "ATT(2025-05,2025-04,2025-06) 1.659622 2.344964 \n", - "ATT(2025-06,2025-01,2025-02) -0.264355 0.138487 \n", - "ATT(2025-06,2025-02,2025-03) -0.087699 0.341878 \n", - "ATT(2025-06,2025-03,2025-04) -0.190580 0.201408 \n", - "ATT(2025-06,2025-04,2025-05) -0.198065 0.212334 \n", - "ATT(2025-06,2025-05,2025-06) 0.980841 1.384630 \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dml_obj = DoubleMLDIDMulti(dml_data, **default_args)\n", "dml_obj.fit()\n", @@ -684,138 +396,9 @@ }, { "cell_type": "code", - 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ATT(2025-04,2025-02,2025-03)-0.2169250.547966
ATT(2025-04,2025-03,2025-04)0.4852231.286711
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ATT(2025-06,2025-01,2025-02)-0.3633760.237508
ATT(2025-06,2025-02,2025-03)-0.1932920.447470
ATT(2025-06,2025-03,2025-04)-0.2869340.297761
ATT(2025-06,2025-04,2025-05)-0.2989430.313213
ATT(2025-06,2025-05,2025-06)0.8815871.483884
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dml_obj.plot_effects()" ] @@ -890,74 +441,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== Sensitivity Analysis ==================\n", - "\n", - "------------------ Scenario ------------------\n", - "Significance Level: level=0.95\n", - "Sensitivity parameters: cf_y=0.03; cf_d=0.03, rho=1.0\n", - "\n", - "------------------ Bounds with CI ------------------\n", - " CI lower theta lower theta theta upper \\\n", - "ATT(2025-04,2025-01,2025-02) -0.512575 -0.305211 -0.186576 -0.067940 \n", - "ATT(2025-04,2025-02,2025-03) -0.161367 0.050859 0.165520 0.280181 \n", - "ATT(2025-04,2025-03,2025-04) 0.549459 0.772720 0.885967 0.999215 \n", - "ATT(2025-04,2025-03,2025-05) 1.416909 1.711872 1.887206 2.062539 \n", - "ATT(2025-04,2025-03,2025-06) 1.836706 2.290795 2.521738 2.752681 \n", - "ATT(2025-05,2025-01,2025-02) -0.314133 -0.124444 -0.020345 0.083754 \n", - "ATT(2025-05,2025-02,2025-03) -0.162868 0.024233 0.135493 0.246753 \n", - "ATT(2025-05,2025-03,2025-04) -0.203224 -0.030277 0.068758 0.167793 \n", - "ATT(2025-05,2025-04,2025-05) 0.876336 1.072862 1.173640 1.274418 \n", - "ATT(2025-05,2025-04,2025-06) 1.554392 1.839144 2.002293 2.165443 \n", - "ATT(2025-06,2025-01,2025-02) -0.337896 -0.167851 -0.062934 0.041982 \n", - "ATT(2025-06,2025-02,2025-03) -0.162546 0.018039 0.127089 0.236140 \n", - "ATT(2025-06,2025-03,2025-04) -0.264030 -0.099474 0.005414 0.110301 \n", - "ATT(2025-06,2025-04,2025-05) -0.269157 -0.096285 0.007135 0.110554 \n", - "ATT(2025-06,2025-05,2025-06) 0.906228 1.075461 1.182735 1.290010 \n", - "\n", - " CI upper \n", - "ATT(2025-04,2025-01,2025-02) 0.145371 \n", - "ATT(2025-04,2025-02,2025-03) 0.499391 \n", - "ATT(2025-04,2025-03,2025-04) 1.228118 \n", - "ATT(2025-04,2025-03,2025-05) 2.370601 \n", - "ATT(2025-04,2025-03,2025-06) 3.209748 \n", - "ATT(2025-05,2025-01,2025-02) 0.275703 \n", - "ATT(2025-05,2025-02,2025-03) 0.438621 \n", - "ATT(2025-05,2025-03,2025-04) 0.344842 \n", - "ATT(2025-05,2025-04,2025-05) 1.477449 \n", - "ATT(2025-05,2025-04,2025-06) 2.457223 \n", - "ATT(2025-06,2025-01,2025-02) 0.210598 \n", - "ATT(2025-06,2025-02,2025-03) 0.416560 \n", - "ATT(2025-06,2025-03,2025-04) 0.275214 \n", - "ATT(2025-06,2025-04,2025-05) 0.282757 \n", - "ATT(2025-06,2025-05,2025-06) 1.460213 \n", - "\n", - "------------------ Robustness Values ------------------\n", - " H_0 RV (%) RVa (%)\n", - "ATT(2025-04,2025-01,2025-02) 0.0 4.677112 0.000542\n", - "ATT(2025-04,2025-02,2025-03) 0.0 4.301563 0.000595\n", - "ATT(2025-04,2025-03,2025-04) 0.0 21.159281 16.152326\n", - "ATT(2025-04,2025-03,2025-05) 0.0 27.849112 23.674655\n", - "ATT(2025-04,2025-03,2025-06) 0.0 28.186201 22.631893\n", - "ATT(2025-05,2025-01,2025-02) 0.0 0.593707 0.000555\n", - "ATT(2025-05,2025-02,2025-03) 0.0 3.641365 0.000433\n", - "ATT(2025-05,2025-03,2025-04) 0.0 2.092666 0.000599\n", - "ATT(2025-05,2025-04,2025-05) 0.0 29.735287 25.235671\n", - "ATT(2025-05,2025-04,2025-06) 0.0 31.043061 26.805368\n", - "ATT(2025-06,2025-01,2025-02) 0.0 1.810658 0.000377\n", - "ATT(2025-06,2025-02,2025-03) 0.0 3.487516 0.000645\n", - "ATT(2025-06,2025-03,2025-04) 0.0 0.156944 0.000541\n", - "ATT(2025-06,2025-04,2025-05) 0.0 0.209767 0.000545\n", - "ATT(2025-06,2025-05,2025-06) 0.0 28.414619 24.530328\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dml_obj.sensitivity_analysis()\n", "print(dml_obj.sensitivity_summary)" @@ -988,41 +474,9 @@ }, { "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n" - ] - }, - { - "data": { - "text/plain": [ - "(
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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dml_obj_nyt = DoubleMLDIDMulti(dml_data, **(default_args | {\"control_group\": \"not_yet_treated\"}))\n", "dml_obj_nyt.fit()\n", @@ -1044,41 +498,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n" - ] - }, - { - "data": { - "text/plain": [ - "(
,\n", - " [,\n", - " ,\n", - " ])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "linear_learners = {\n", " \"ml_g\": LinearRegression(),\n", @@ -1115,24 +537,9 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "d\n", - "2025-04-01 1.917883\n", - "2025-05-01 1.484009\n", - "2025-06-01 1.045526\n", - "Name: ite, dtype: float64" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df_post_treatment = df[df[\"t\"] >= df[\"d\"]]\n", "df_post_treatment.groupby(\"d\")[\"ite\"].mean()" @@ -1147,50 +554,9 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Group Aggregation \n", - "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.519998 0.104835 14.498974 0.0 1.314526 1.725471\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "2025-04 1.764970 0.173168 10.192264 0.0 1.425568 2.104373\n", - "2025-05 1.587966 0.133331 11.909997 0.0 1.326643 1.849290\n", - "2025-06 1.182735 0.103009 11.481824 0.0 0.980841 1.384630\n", - "------------------ Additional Information ------------------\n", - "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "aggregated_group = dml_obj.aggregate(aggregation=\"group\")\n", "print(aggregated_group)\n", @@ -1216,24 +582,9 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "t\n", - "2025-04-01 0.900632\n", - "2025-05-01 1.437580\n", - "2025-06-01 2.026848\n", - "Name: ite, dtype: float64" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df_post_treatment.groupby(\"t\")[\"ite\"].mean()" ] @@ -1249,50 +600,9 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Time Aggregation \n", - "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.447253 0.115185 12.564566 0.0 1.221494 1.673012\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "2025-04 0.885967 0.137076 6.463310 1.024374e-10 0.617303 1.154632\n", - "2025-05 1.535208 0.129116 11.890192 0.000000e+00 1.282146 1.788270\n", - "2025-06 1.920584 0.148870 12.901079 0.000000e+00 1.628804 2.212363\n", - "------------------ Additional Information ------------------\n", - "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "aggregated_time = dml_obj.aggregate(\"time\")\n", "print(aggregated_time)\n", @@ -1311,28 +621,9 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "e\n", - "-122 days -0.007736\n", - "-92 days -0.014805\n", - "-61 days 0.000861\n", - "-31 days 0.031158\n", - "0 days 0.962324\n", - "31 days 1.967536\n", - "59 days 2.937695\n", - "Name: ite, dtype: float64" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df[\"e\"] = pd.to_datetime(df[\"t\"]).values.astype(\"datetime64[M]\") - \\\n", " pd.to_datetime(df[\"d\"]).values.astype(\"datetime64[M]\")\n", @@ -1348,65 +639,9 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Event Study Aggregation \n", - "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.84764 0.152535 12.112918 0.0 1.548678 2.146603\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "-4 months -0.062934 0.102768 -0.612393 0.540278 -0.264355 0.138487\n", - "-3 months 0.051390 0.079708 0.644732 0.519101 -0.104834 0.207614\n", - "-2 months -0.017152 0.071696 -0.239239 0.810920 -0.157674 0.123369\n", - "-1 months 0.082539 0.072499 1.138493 0.254915 -0.059556 0.224634\n", - "0 months 1.077206 0.079043 13.628169 0.000000 0.922285 1.232126\n", - "1 months 1.943978 0.148708 13.072411 0.000000 1.652514 2.235441\n", - "2 months 2.521738 0.275739 9.145392 0.000000 1.981300 3.062176\n", - "------------------ Additional Information ------------------\n", - "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "(
,\n", - " )" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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ctlSvXr0iSZLYddddY/fdd6+17uWXX4433ngjbrzxxhg7dmxuvKZPbavLbWSb22tERKdOnRrsKqXG6rW+6nqc9OrVK7LZbPzzn/+scvvpZ21sG+3bt49vfOMb8Y1vfCNWrVoVhxxySFxwwQVCKQC2G+aUAoAm6JNPPok777wzvvjFL8Zxxx1X7evMM8+MlStXxr333hsREaNGjYq1a9fGH//4x9w2stlstUCra9eu0a9fv/if//mfWLVqVW78sccei5dffrlOvXXq1ClGjBgRf/jDH2LRokXVHl+2bFnu+1GjRsXMmTNj9uzZubEPPvggbr755irrrL/SZv2VOhERa9asid///vfVtl9SUlLjLWwnnHBCvPvuu1Veg/U++eSTKC8vj4iII488MiIifvOb31SpueKKK6qt15COPfbYKCwsjEmTJlV5nhGfPu/3338/Imp+LZIkiV//+tfVtllSUhIRn15p1pBGjRoVpaWlcfHFF8fatWurPb7hz7iu1oelDd1rfdX1OBkzZkwUFBTEhRdeWO3qtA1/NiUlJTU+p/U/z/Vat24dvXv3joqKigZ4FgDQNLhSCgCaoHvvvTdWrlwZRx99dI2PH3jggdGxY8e4+eab48QTT4wxY8bE/vvvH9///vfjzTffjD59+sS9994bH3zwQURUvZrj4osvjmOOOSaGDRsW3/jGN+LDDz+M3/3ud9GvX78qQdXGTJkyJQ466KDo379/nHbaafG5z30ulixZEjNnzox33nknXnzxxYiI+OEPfxh/+tOf4vOf/3ycddZZUVJSEtdcc03ssssu8cEHH+T6Gjp0aLRr1y7GjRsX3/3udyOTycRNN91ULbyJiBg8eHDcdtttMWHChNhvv/2idevWcdRRR8XJJ58cf/nLX+L000+P6dOnx7Bhw6KysjJee+21+Mtf/hIPPfRQ7LvvvjFw4MA46aST4ve//32sWLEihg4dGtOmTavzlWKbq1evXvGLX/wizjvvvHj77bdjzJgx0aZNm5g3b17cdddd8a1vfSvOPffc6NOnT/Tq1SvOPffcePfdd6O0tDT++te/1jg/0+DBgyPi00nbR40aFYWFhfGVr3xli3stLS2NK6+8Mk4++eTYZ5994itf+Up07NgxFixYEPfff38MGzYsfve739Vrmy1btoy+ffvGbbfdFrvvvnu0b98++vXrF/369dvoen//+99j9erV1cb33nvvzZqfqa7HSe/eveMnP/lJ/PznP4+DDz44jj322CgqKopnn302unbtGpMnT46IT38GV155ZfziF7+I3r17R6dOneKwww6Lvn37xogRI2Lw4MHRvn37mDVrVtxxxx1x5pln1rtnAGiy8vOhfwDAljjqqKOS4uLipLy8vNaaU045JWnevHmyfPnyJEmSZNmyZclXv/rVpE2bNknbtm2TU045JXnyySeTiEhuvfXWKuveeuutSZ8+fZKioqKkX79+yb333pt8+ctfTvr06ZOrmTdvXhIRySWXXFLj/t96661k7NixSZcuXZLmzZsnO++8c/LFL34xueOOO6rUvfDCC8nBBx+cFBUVJd26dUsmT56c/OY3v0kiIlm8eHGu7sknn0wOPPDApGXLlknXrl2TH/7wh8lDDz2UREQyffr0XN2qVauSr371q8kOO+yQRETSo0eP3GNr1qxJfvWrXyV77bVXUlRUlLRr1y4ZPHhwMmnSpGTFihW5uk8++ST57ne/m3To0CEpKSlJjjrqqGThwoVJRCQTJ06s9TX/rNtvv71afxMnTkwiIlm2bFmN6/z1r39NDjrooKSkpCQpKSlJ+vTpk4wfPz55/fXXczX//Oc/k5EjRyatW7dOdtxxx+S0005LXnzxxSQikuuvvz5Xt27duuSss85KOnbsmGQymWT9W7/afnbTp09PIiK5/fbbq4xff/31SUQkzz77bLX6UaNGJW3btk2Ki4uTXr16Jaecckoya9asXM24ceOSkpKSas9z/euwoaeeeioZPHhw0qJFi02+1ut7re1rw3WHDx+e7LXXXtW20aNHj2T06NHVxut6nCRJklx33XXJoEGDcnXDhw9Ppk6dmnt88eLFyejRo5M2bdokEZEMHz48SZIk+cUvfpHsv//+yQ477JC0bNky6dOnT3LRRRcla9asqfU5A8C2JpMkNfwXIwCwXbj77rvjS1/6UjzxxBMxbNiwjdYOHDgwOnbsWOPcRQ3tnHPOiT/84Q+xatWqJjVJNgAAdWdOKQDYTnzyySdVlisrK+O3v/1tlJaWxj777JMbX7t2baxbt65K7YwZM+LFF1+MESNGNHpf77//ftx0001x0EEHCaQAALZh5pQCgO3EWWedFZ988kkMGTIkKioq4s4774ynnnoqLr744mjZsmWu7t13342RI0fG17/+9ejatWu89tprcdVVV0WXLl3i9NNPb/C+hgwZEiNGjIg999wzlixZEtdee22UlZXFz372swbfFwAAWw+hFABsJw477LC47LLL4r777ovVq1dH796947e//W21iZXbtWsXgwcPjmuuuSaWLVsWJSUlMXr06PjlL38ZHTp0aPC+vvCFL8Qdd9wRV199dWQymdhnn33i2muvjUMOOaTB9wUAwNbDnFIAAAAApM6cUgAAAACkTigFAAAAQOq2uzmlstlsvPfee9GmTZvIZDL5bgcAAABgm5IkSaxcuTK6du0aBQW1Xw+13YVS7733XnTv3j3fbQAAAABs0xYuXBjdunWr9fHtLpRq06ZNRHz6wpSWlua5GwAAAIBtS1lZWXTv3j2XwdRmuwul1t+yV1paKpQCAAAAaCSbmjbJROcAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApE4oBQAAAEDqhFIAAAAApK5ZvhsAAAAA2J5k15bXq76geUkjdZJfQikAAACAFM2f0q5e9bues6aROskvt+8BAAAAkDpXSgEAAACkqMf4D6ssZ9eWx8Kru0VERPdvvbPN3q73WUIpAAAAgBRtLHQqaF6y3YRSbt8DAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHVCKQAAAABSJ5QCAAAAIHV5DaWuvPLK2HvvvaO0tDRKS0tjyJAh8cADD2x0ndtvvz369OkTxcXF0b9///jb3/6WUrcAAAAANJS8hlLdunWLX/7yl/Hcc8/FrFmz4rDDDotjjjkm5syZU2P9U089FSeddFJ885vfjBdeeCHGjBkTY8aMiVdeeSXlzgEAAADYEpkkSZJ8N7Gh9u3bxyWXXBLf/OY3qz124oknRnl5edx33325sQMPPDAGDhwYV111VZ22X1ZWFm3bto0VK1ZEaWlpg/UNAAAAsDmya8tj/pR2ERHRY/yHUdC8JM8dbZm6Zi9bzZxSlZWVceutt0Z5eXkMGTKkxpqZM2fGyJEjq4yNGjUqZs6cmUaLAAAAADSQZvlu4OWXX44hQ4bE6tWro3Xr1nHXXXdF3759a6xdvHhxdO7cucpY586dY/HixbVuv6KiIioqKnLLZWVlERGRzWYjm802wDMAAAAA2Hwb5hPZbDaiiecVdc1b8h5K7bHHHjF79uxYsWJF3HHHHTFu3Lh47LHHag2m6mvy5MkxadKkauPLli2L1atXN8g+AAAAADZXsu7j3PfLli2LTLPyPHaz5VauXFmnuryHUi1atIjevXtHRMTgwYPj2WefjV//+tfxhz/8oVptly5dYsmSJVXGlixZEl26dKl1++edd15MmDAht1xWVhbdu3ePjh07mlMKAAAAyLvs2vJY+H/fd+zYscnPKVVcXFynuryHUp+VzWar3G63oSFDhsS0adPinHPOyY1NnTq11jmoIiKKioqiqKio2nhBQUEUFGw1U2oBAAAA26sN8oltIa+oa/95DaXOO++8OPLII2OXXXaJlStXxi233BIzZsyIhx56KCIixo4dGzvvvHNMnjw5IiLOPvvsGD58eFx22WUxevTouPXWW2PWrFlx9dVX5/NpAAAAAFBPeQ2lli5dGmPHjo1FixZF27ZtY++9946HHnooPv/5z0dExIIFC6qka0OHDo1bbrklfvrTn8aPf/zj2G233eLuu++Ofv365espAAAAALAZMkmSJPluIk1lZWXRtm3bWLFihTmlAAAAgLzLri2P+VPaRUREj/EfNvk5peqavTTtmxQBAAAAaJKEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAABbiWTd6ny3kBqhFAAAAEAeJNnKKJ97Zyy5+5jc2II/7BSL/joqyufeGUm2Mo/dNb5m+W4AAAAAYHuTrSiLJfefGKsXTKv22OqF02P1wulRvMvh0Xn0bVFQVJqHDhufK6UAAAAAUpRkK2sNpDa0esG0WHL/idvsFVNCKQAAAIAUffzWPZsMpNZbvWBafPyvexu5o/wQSgEAAACkqOylP9Sv/sX61TcVeQ2lJk+eHPvtt1+0adMmOnXqFGPGjInXX399o+vccMMNkclkqnwVFxen1DEAAADA5suuWx2rF06v1zqrFz4a2W3wU/nyGko99thjMX78+Hj66adj6tSpsXbt2jjiiCOivLx8o+uVlpbGokWLcl/z589PqWMAAACAzZesWZnqeluzvH763oMPPlhl+YYbbohOnTrFc889F4ccckit62UymejSpUtjtwcAAADQoDIt2qS63tYsr6HUZ61YsSIiItq3b7/RulWrVkWPHj0im83GPvvsExdffHHstddeNdZWVFRERUVFbrmsrCwiIrLZbGSz2QbqHAAAAKAOClpEcbcRsfqdGXVepbj7oREFLZpMjlHXPreaUCqbzcY555wTw4YNi379+tVat8cee8R1110Xe++9d6xYsSIuvfTSGDp0aMyZMye6detWrX7y5MkxadKkauPLli2L1au3vfsxAQAAgK1btsdXIuoRSmV3+UosXbq08RpqYCtX1u1Ww0ySJEkj91InZ5xxRjzwwAPxxBNP1Bgu1Wbt2rWx5557xkknnRQ///nPqz1e05VS3bt3jw8//DBKS0sbpHcAAACAukqylbH0nqNi9cJHN1lb3P2w6HTM/0amoDCFzhpGWVlZtGvXLlasWLHR7GWruFLqzDPPjPvuuy8ef/zxegVSERHNmzePQYMGxZtvvlnj40VFRVFUVFRtvKCgIAoK8jrPOwAAALA9KiiIzl/8Syy5/8RYvWBarWXFuxwenUffFgXNmqfY3Jara96S11QmSZI488wz46677opHH300dt1113pvo7KyMl5++eXYaaedGqFDAAAAgIZXUFQaXcbcF52+eFsUdxte5bHi7odFpy/eFl3G3BcFRdvuXV55vVJq/Pjxccstt8Q999wTbdq0icWLF0dERNu2baNly5YRETF27NjYeeedY/LkyRERceGFF8aBBx4YvXv3jo8++iguueSSmD9/fpx66ql5ex4AAAAA9ZUpKIyS3l+Klj2OiPlT2kVExC7fXhSFLTvkubN05DWUuvLKKyMiYsSIEVXGr7/++jjllFMiImLBggVVLvv68MMP47TTTovFixdHu3btYvDgwfHUU09F375902obAAAAoFFkmhXnu4XUbDUTnaelrKws2rZtu8nJtgAAAADSkF1bnrtSqsf4D6OgeUmeO9oydc1ezPQNAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkrlm+GwAAAICGlF1bXq/6guYljdQJsDFCKQAAALYp86e0q1f9ruesaaROgI1x+x4AAAAAqXOlFAAAANuUHuM/rLKcXVseC6/uFhER3b/1jtv1YCshlAIAAGCbsrHQqaB5iVAKthJu3wMAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdXkNpSZPnhz77bdftGnTJjp16hRjxoyJ119/fZPr3X777dGnT58oLi6O/v37x9/+9rcUugUAAACgoeQ1lHrsscdi/Pjx8fTTT8fUqVNj7dq1ccQRR0R5eXmt6zz11FNx0kknxTe/+c144YUXYsyYMTFmzJh45ZVXUuwcAAAAgC2RSZIkyXcT6y1btiw6deoUjz32WBxyyCE11px44olRXl4e9913X27swAMPjIEDB8ZVV121yX2UlZVF27ZtY8WKFVFaWtpgvQMAALB1yq4tj/lT2kVERI/xH0ZB85I8dwRVbWvHaF2zl61qTqkVK1ZERET79u1rrZk5c2aMHDmyytioUaNi5syZjdobAAAAAA2nWb4bWC+bzcY555wTw4YNi379+tVat3jx4ujcuXOVsc6dO8fixYtrrK+oqIiKiorccllZWW5/2Wy2AToHAABga7bhv/2y2WyEfwuyldnWjtG65i1bTSg1fvz4eOWVV+KJJ55o0O1Onjw5Jk2aVG182bJlsXr16gbdFwAAAFufZN3Hue+XLn4nCora5bEbqG7DY3TZsmWRaVb7XNtNwcqVK+tUt1WEUmeeeWbcd9998fjjj0e3bt02WtulS5dYsmRJlbElS5ZEly5daqw/77zzYsKECbnlsrKy6N69e3Ts2NGcUgAAANuwJFsZH//r3lj54u9zYxV3DYjibiOi9d7fjlafOzoyBYV57BA+lV1bHgv/7/uOHTs2+TmliouL61SX11AqSZI466yz4q677ooZM2bErrvuusl1hgwZEtOmTYtzzjknNzZ16tQYMmRIjfVFRUVRVFRUbbygoCAKCraqKbUAAABoINmKslh6/4mxesG0ao+tfmdGrH5nRhTvcnh0Hn1bFBS5YIE82yCf2Bbyirr2n9dnOX78+PjTn/4Ut9xyS7Rp0yYWL14cixcvjk8++SRXM3bs2DjvvPNyy2effXY8+OCDcdlll8Vrr70WF1xwQcyaNSvOPPPMfDwFAAAAtjJJtjKW1BJIbWj1gmmx5P4TI8lWptQZsKG8hlJXXnllrFixIkaMGBE77bRT7uu2227L1SxYsCAWLVqUWx46dGjccsstcfXVV8eAAQPijjvuiLvvvnujk6MDAACw/fj4rXs2GUitt3rBtPj4X/c2ckdATfJ++96mzJgxo9rY8ccfH8cff3wjdAQAAEBTV/bSH+pX/+IfoqT3lxqpG6A2TfsmRQAAANhAdt3qWL1wer3WWb3w0ciu8+nskDahFAAAANuMZE3dPoq+odYDNp9QCgAAgG1GpkWbVNcDNp9QCgAAgG1GQbPiKO5+aL3WKe5+WBQ0K26kjoDaCKUAAADYppTu/e361Q+oXz3QMIRSAAAAbFNa9Tominc5vE61xbscHq0+d3QjdwTURCgFAADANiVTUBidR9+2yWCqeJfDo/Po2yJTUJhSZ8CGhFIAAABscwqKSqPLmPui0xdvi+Juw6s8Vtz9sOj0xduiy5j7oqCoNE8dAs3y3QAAAAA0hkxBYZT0/lK07HFEzJ/SLiIidvn2oihs2SHPnQERrpQCAABgO5LxKXuw1RBKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqfPpewAAAAApyq4tr3X5s49FRBQ0L2n0nvJBKAUAAACQovlT2tX62MKru1Ub2/WcNY3ZTt64fQ8AAACA1LlSCgAAACBFPcZ/mO8WtgpCKQAAAIAUbatzRNWX2/cAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUNct3AwAAANCQsmvLa13+7GMREQXNSxq9J6A6oRQAAADblPlT2tX62MKru1Ub2/WcNY3ZDlCLzbp9r7CwMJYuXVpt/P3334/CwsItbgoAAACAbdtmXSmVJEmN4xUVFdGiRYstaggAAAC2RI/xH+a7BaAO6hVK/eY3v4mIiEwmE9dcc020bt0691hlZWU8/vjj0adPn4btEAAAAOrBHFHQNNQrlLr88ssj4tMrpa666qoqt+q1aNEievbsGVdddVXDdggAAADANqdeodS8efMiIuLQQw+NO++8M9q1q33yOAAAAACozWbNKTV9+vSG7gMAAACA7chmffrel7/85fjVr35Vbfy//uu/4vjjj6/zdh5//PE46qijomvXrpHJZOLuu+/eaP2MGTMik8lU+1q8eHF9nwIAAAAAebRZodTjjz8eX/jCF6qNH3nkkfH444/XeTvl5eUxYMCAmDJlSr32//rrr8eiRYtyX506darX+gAAAADk12bdvrdq1apo0aJFtfHmzZtHWVlZnbdz5JFHxpFHHlnv/Xfq1Cl22GGHeq8HAAAAwNZhs0Kp/v37x2233Rbnn39+lfFbb701+vbt2yCNbczAgQOjoqIi+vXrFxdccEEMGzas1tqKioqoqKjILa8PzbLZbGSz2UbvFQAAAGB7Ute8ZbNCqZ/97Gdx7LHHxltvvRWHHXZYRERMmzYt/vznP8ftt9++OZusk5122imuuuqq2HfffaOioiKuueaaGDFiRPzjH/+IffbZp8Z1Jk+eHJMmTao2vmzZsli9enWj9QoAAACwPVq5cmWd6jJJkiSbs4P7778/Lr744pg9e3a0bNky9t5775g4cWIMHz58czYXmUwm7rrrrhgzZky91hs+fHjssssucdNNN9X4eE1XSnXv3j0+/PDDKC0t3axeAQAAAKhZWVlZtGvXLlasWLHR7GWzrpSKiBg9enSMHj16c1dvMPvvv3888cQTtT5eVFQURUVF1cYLCgqioGCz5nkHAAAAoBZ1zVs2O5X56KOP4pprrokf//jH8cEHH0RExPPPPx/vvvvu5m5ys8yePTt22mmnVPcJAAAAwJbZrCulXnrppRg5cmS0bds23n777Tj11FOjffv2ceedd8aCBQvif/7nf+q0nVWrVsWbb76ZW543b17Mnj072rdvH7vsskucd9558e677+a2d8UVV8Suu+4ae+21V6xevTquueaaePTRR+Phhx/enKcBAAAAQJ5s1pVSEyZMiFNOOSXmzp0bxcXFufEvfOEL8fjjj9d5O7NmzYpBgwbFoEGDctsdNGhQ7lP9Fi1aFAsWLMjVr1mzJr7//e9H//79Y/jw4fHiiy/GI488EocffvjmPA0AAAAA8mSzJjpv27ZtPP/889GrV69o06ZNvPjii/G5z30u5s+fH3vsscdW/al2ZWVl0bZt201OtgUAAABA/dU1e9msK6WKioqirKys2vgbb7wRHTt23JxNAgAAALAd2axQ6uijj44LL7ww1q5dGxERmUwmFixYED/60Y/iy1/+coM2CAAAAMC2Z7NCqcsuuyxWrVoVnTp1ik8++SSGDx8evXv3jjZt2sRFF13U0D0CAAAAsI3ZrE/fa9u2bUydOjWefPLJePHFF2PVqlWxzz77xMiRIxu6PwAAAAC2QXUOpdq3bx9vvPFG7LjjjvGf//mf8etf/zqGDRsWw4YNa8z+AAAAANgG1fn2vTVr1uQmN7/xxhu36k/YAwAAAGDrVucrpYYMGRJjxoyJwYMHR5Ik8d3vfjdatmxZY+11113XYA0CAAAAsO2pcyj1pz/9KS6//PJ46623IiJixYoVrpYCAAAAYLNkkiRJ6rvSrrvuGrNmzYoOHTo0Rk+NqqysLNq2bRsrVqyI0tLSfLcDAAAAsE2pa/ZS5zml2rdvH8uXL4+IiEMPPTRatGix5V0CAAAAsF0y0TkAAAAAqTPROQAAAACp26yJzjOZjInOAQAAANhsJjoHAAAAoME0+ETnERFf+MIXYsWKFTFv3rzo0KFD/PKXv4yPPvoo9/j7778fffv23eymAQAAANg+1CuUevDBB6OioiK3fPHFF8cHH3yQW163bl28/vrrDdcdAAAAANukeoVSn7UZd/4BAAAAwJaFUgAAAACwOeoVSmUymchkMtXGAAAAAKA+mtWnOEmSOOWUU6KoqCgiIlavXh2nn356lJSURERUmW8KAAAAAGpTr1Bq3LhxVZa//vWvV6sZO3bslnUEAAAAwDavXqHU9ddf31h9AAAATUB2bXm96gualzRSJwA0dfUKpQAAgO3b/Cnt6lW/6zlrGqkTAJo6n74HAAAAQOpcKQUAANRZj/EfVlnOri2PhVd3i4iI7t96x+16ANSZUAoAAKizjYVOBc1LhFIA1Jnb9wAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAABoEMm61fluAYAmRCgFAADUW5KtjPK5d8aSu4/JjS34w06x6K+jonzunZFkK/PYHQBNQbN8NwAAADQt2YqyWHL/ibF6wbRqj61eOD1WL5wexbscHp1H3xYFRaV56BCApsCVUgAAQJ0l2cpaA6kNrV4wLZbcf6IrpgColVAKAACos4/fumeTgdR6qxdMi4//dW8jdwRAUyWUAgAA6qzspT/Ur/7F+tUDsP0QSgEAAHWSXbc6Vi+cXq91Vi98NLI+lQ+AGgilAACAOknWrEx1PQC2bUIpAACgTjIt2qS6HgDbNqEUAABQJwXNiqO4+6H1Wqe4+2FR0Ky4kToCoCkTSgEAAHVWuve361c/oH71AGw/hFIAAECdtep1TBTvcnidaot3OTxafe7oRu4IgKZKKAUAANRZpqAwOo++bZPBVPEuh0fn0bdFpqAwpc4AaGryGko9/vjjcdRRR0XXrl0jk8nE3Xffvcl1ZsyYEfvss08UFRVF796944Ybbmj0PgEAgH8rKCqNLmPui05fvC2Kuw2v8lhx98Oi0xdviy5j7ouCotI8dQhAU5DXUKq8vDwGDBgQU6ZMqVP9vHnzYvTo0XHooYfG7Nmz45xzzolTTz01HnrooUbuFAAA2FCmoDBKen8pOh9zd25sl28vip2+/GCU9P6SK6QA2KRm+dz5kUceGUceeWSd66+66qrYdddd47LLLouIiD333DOeeOKJuPzyy2PUqFGN1SYAAFAHGZ+yB0A95DWUqq+ZM2fGyJEjq4yNGjUqzjnnnFrXqaioiIqKitxyWVlZRERks9nIZrON0icAAGwvNnxPnc1mI7zHBtju1TVvaVKh1OLFi6Nz585Vxjp37hxlZWXxySefRMuWLautM3ny5Jg0aVK18WXLlsXq1asbrVcAANgeJOs+zn2/bNmyyDQrz2M3AGwNVq5cWae6JhVKbY7zzjsvJkyYkFsuKyuL7t27R8eOHaO01MSLAACwJbJry2Ph/33fsWPHKGhektd+AMi/4uK63c7dpEKpLl26xJIlS6qMLVmyJEpLS2u8SioioqioKIqKiqqNFxQUREFBXud5BwCApm+D99TeYwMQEXX+W9Ck/mIMGTIkpk2bVmVs6tSpMWTIkDx1BAAAAMDmyGsotWrVqpg9e3bMnj07IiLmzZsXs2fPjgULFkTEp7fejR07Nld/+umnx7/+9a/44Q9/GK+99lr8/ve/j7/85S/xve99Lx/tAwAAALCZ8hpKzZo1KwYNGhSDBg2KiIgJEybEoEGD4vzzz4+IiEWLFuUCqoiIXXfdNe6///6YOnVqDBgwIC677LK45pprYtSoUXnpHwAAAIDNk0mSJMl3E2kqKyuLtm3bxooVK0x0DgAAWyi7tjzmT2kXERE9xn9oonMA6py9NKk5pQAAAADYNgilAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEhds3w3AAAANB3ZteW1Ln/2sYiIguYljd4TAE2TUAoAAKiz+VPa1frYwqu7VRvb9Zw1jdkOAE2Y2/cAAAAASJ0rpQAAgDrrMf7DfLcAwDZCKAUAANSZOaIAaChu3wMAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdVtFKDVlypTo2bNnFBcXxwEHHBDPPPNMrbU33HBDZDKZKl/FxcUpdgsAAADAlsp7KHXbbbfFhAkTYuLEifH888/HgAEDYtSoUbF06dJa1yktLY1FixblvubPn59ixwAAAABsqbyHUv/93/8dp512WnzjG9+Ivn37xlVXXRWtWrWK6667rtZ1MplMdOnSJffVuXPnFDsGAAAAYEvlNZRas2ZNPPfcczFy5MjcWEFBQYwcOTJmzpxZ63qrVq2KHj16RPfu3eOYY46JOXPmpNEuAAAAAA2kWT53vnz58qisrKx2pVPnzp3jtddeq3GdPfbYI6677rrYe++9Y8WKFXHppZfG0KFDY86cOdGtW7dq9RUVFVFRUZFbLisri4iIbDYb2Wy2AZ8NAAAAAHXNW/IaSm2OIUOGxJAhQ3LLQ4cOjT333DP+8Ic/xM9//vNq9ZMnT45JkyZVG1+2bFmsXr26UXsFAAAA2N6sXLmyTnV5DaV23HHHKCwsjCVLllQZX7JkSXTp0qVO22jevHkMGjQo3nzzzRofP++882LChAm55bKysujevXt07NgxSktLN795AAAAAKopLi6uU11eQ6kWLVrE4MGDY9q0aTFmzJiI+PQSr2nTpsWZZ55Zp21UVlbGyy+/HF/4whdqfLyoqCiKioqqjRcUFERBQd7neQcAAADYptQ1b8n77XsTJkyIcePGxb777hv7779/XHHFFVFeXh7f+MY3IiJi7NixsfPOO8fkyZMjIuLCCy+MAw88MHr37h0fffRRXHLJJTF//vw49dRT8/k0AAAAAKiHvIdSJ554YixbtizOP//8WLx4cQwcODAefPDB3OTnCxYsqJKwffjhh3HaaafF4sWLo127djF48OB46qmnom/fvvl6CgAAAADUUyZJkiTfTaSprKws2rZtGytWrDCnFAAAAEADq2v2YlIlAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdc3y3QAAAP9WXrGuXvUlRd7OAQBNk3cxQIPxDymALdfmJw/Uqz576VGN1AkAQOPyL0KgwfiHFAAAAHUllAIA2IqsvOjIKsvla9ZFl0lTIyJi8cTPR0kLb98AgG2DdzVAg/EPKYAtt7Fbm0taNHPrMwCwzfCuBmgw/iEFAABAXRXkuwEAAAAAtj9CKQAAAABSJ5QCAAAAIHUmeGkiyivW1ave3D0AAADA1kxy0US0+ckD9arPXnpUI3UCAAAAsOXcvgcAAABA6lwp1USsvOjIKsvla9ZFl0lTIyJi8cTPR0kLP0oAAACg6ZBkNBEbmyOqpEUzc0gB1IH5+QAAYOvh3TYA2w3z8wEAwNbDnFIAAAAApM6VUgBsN8zPBwAAWw/vvgHYbpifDwAAth5u3wMAAAAgdUIpAIAmYvXayny3AADQYIRSAABbocpsEn996b344rXP5MY6XvBwjLxqZvz1pfeiMpvksTsAgC1n8gwAgK1M2eq1cdyNs+KRucurPfbom8vj0TeXx8jddow7xu0bpcXN89AhAMCWc6UUAMBWpDKb1BpIbeiRucvjuBtnuWIKAGiyhFJAKsyDAlA3d7+yaJOB1HqPzF0e98xZ3MgdAQA0DqEU0ODMgwKw+a58an49699unEYAABqZOaWABmUeFIDNt3ptZTz6Zt2uklpv2tzlsXptZRQ3L2ykrgAAGocrpYAGYx4UgC2zsmJdqusBAOSTUApoMOZBAdgybYo27yL2zV0PACCfhFJAgzEPCsCWKW5eGIf13rFe6xy+245u3QMAmiShFNAgtmQeFAD+7YyhPepZ37NxGgEAaGRCKaBBmAcFoGGM6bdTjNytbldLjdxtxzhmry6N3BEAQOMQSgENwjwoAA2jsCATd4zbd5PB1PpPMi0syKTUGQBAwxJKAQ3CPCgADae0uHk8cNqBcce4fWNErw5VHjv8/8KoB047MEqLm+epQwCALbdVhFJTpkyJnj17RnFxcRxwwAHxzDPPbLT+9ttvjz59+kRxcXH0798//va3v6XUKbAx5kGhKTO/GVubwoJMHNt/p/jf/9w/N7bsgiNi6reHxLH9d3KFFADQ5OU9lLrttttiwoQJMXHixHj++edjwIABMWrUqFi6dGmN9U899VScdNJJ8c1vfjNeeOGFGDNmTIwZMyZeeeWVlDsHPss8KDQVldkk/vrSe/HFa//9nyAdL3g4Rl41M/760ntRmU3y2B3UztWlAMC2JJMkSV7feR9wwAGx3377xe9+97uIiMhms9G9e/c466yz4v/9v/9Xrf7EE0+M8vLyuO+++3JjBx54YAwcODCuuuqqTe6vrKws2rZtG8uWLYvS0tJqjxcUFESzZv+e42bNmjW1biuTyUTz5s03q3bt2rVR20tfl9ryNeuiwwXTIiJi5UVHRsn/zcuzse1GRLRo0aJOPXy2dt26dZHNZhuktnnz5pHJZBq1trKyMiora7/qoT61zZo1i4KCgq2mNpvNxrp1tU8OXlhYGIWFhXmrLVu9Lk66ZXZMe/P9Wrd1WO8OcetXB0ZpcbMq202SJNauXVvrehv+fjZWbcTGf5ebyjlic2ojto9zRNnqtfHlG57d6DF6+G47xl/H7Rulxc23it/7bekc8dnaTf1+OkfU/DffOcL7COeI+tduq+eImmqdI5wjnCPqX+sc0bC1ZWVl0bFjx1ixYkWN2ct6eZ1heM2aNfHcc8/FeeedlxsrKCiIkSNHxsyZM2tcZ+bMmTFhwoQqY6NGjYq77767xvqKioqoqKjILZeVlUVExKWXXhpFRUXV6nv37h1f+9rXcsv/9V//VesB1qNHjzjllFNyy5dffnl8/PHHNdbutNNO8a1vfSu3/Lvf/S4++uijGms7duwY3/nOd3LLf/jDH2LZsmVVatYkBRGxT0REZJNs7gR67bXXxqJFi2rcbqtWreIHP/hBbvmmm26K+fPn11jbvHnz+PGPf5xb/vOf/xxvvvlmjbURERMnTsx9f8cdd8Srr75aa+15552X+8Ny7733xosvvlhr7bnnnhslJSUREfHAAw/ErFmzaq09++yzY4cddoiIiKlTp9Z6DEVEnHHGGdGpU6eIiHjsscfiscceq7X21FNPjZ133jkiPr1S75FHHqm1dty4cdGzZ8+IiHj22WfjgQceqLX2pJNOit133z0iIl588cW45557aq097rjjYq+99oqIiDlz5sQdd9xRa+0xxxwTAwcOjIiIN954I/785z/XWnvkkUfG/vt/elvI22+/HTfeeGOttSNHjoxhw4ZFRMS7774b11xzTa21PzzkkDh9yD7xuyffjsf+9UFufNeCshjcfGns8d6s+O1lD0VExJAhQ+KII46IiIiPPvoofv3rX9e63X333TdGjx4dERHl5eVx6aWX1lo7YMCAGDNmTER8eq6ZPHlyrbV77rlnnHDCCbnliy66qNbapnKOWG+HHXaIs88+O7e8vZ8j2pS2jS/fOOv/AqkkImq6/SmJaXOXx5dvnBV/++b+8ffHnSMiGvYcMXz48BgxYkRERCxdujSuvPLKWmudI2r+m+8c4X2Ec8SnnCM+5X3EvzlHfMo54lPOEZ/KxzliwxxmY/IaSi1fvjwqKyujc+fOVcY7d+4cr732Wo3rLF68uMb6xYsX11g/efLkmDRpUrXx8vLyGtPXsrKyKrcOrlq1qtaUduXKldVqP/nkkzrVrly5MsrLy2usLS4u3mTt2g3uvFy2dFmUtyjc5Haz2Wyde2jWrFmV2rKyslprI6Letev/UKxYsWKjtcuWLcs9Xpfa9enwRx99tNHa5cuX576vS+369PfDDz/caO37778frVq1qnPt+tftgw8+2GjtBx98sFm177///kZrP/zww82qXb58+UZrV65YEcP2KoxBX+wZvX/zaSh1RjwRLbOVERURG/6WfPTRR7ntbupnvGLFilztxx9/XOfaNWvWbLT2s7/39andWs8R6xUWFta5dns4R9z1yuKYNnd51B5IRW582tzlcdNTr8cOzhH1rt3UOWLD3/v61G6v54ia/uY7R3gf4RxRvXZ7PUfUVOsc4RzhHFG91jki3XNEXUOpvN6+995778XOO+8cTz31VAwZMiQ3/sMf/jAee+yx+Mc//lFtnRYtWsSNN94YJ510Um7s97//fUyaNCmWLFlSrb6mK6W6d+8eS5YsaeK371VGxwsfjYiIFb8YFSUt3L7nktpPbU2X1JavWRdtf/rpFVHLzj8sSlpUnwvFJbU117rsvnHOEUf88R8xfSO37X3WYb07xAPf3C/vv/fb6jkiwmX3dbt9r/rffOcI7yOcI+pfu62eI2qqdY5wjnCOqH+tc0TD1paVlUXnzp237tv3dtxxxygsLKwWJi1ZsiS6dKl5AuQuXbrUq76oqKjG2/SKi4ujuLh4kz3WpWZzamvqqT61lZl//5KuWZdEm+KCBtlubTb8Q9AUagsKCqr8cmxrtRueqLbm2oLMv/93v7ioKIqLNr3O+j8addFYtY31e5/mOSLt2q3h935TtavXVtYrkIqIePTN96MyCqK4OP+/99viOWK9reH3fms+R2z4N78gUxAFBQVbxe/9tnaO2NDW8rvsHPGpreH3fms+R9Rka/i9d45ourX5/r13jmj82jR+7zcWkm0or5++16JFixg8eHBMmzYtN5bNZmPatGlVrpza0JAhQ6rUR3x6T29t9dsanxgFsHlWVtT+P26NsR4AALBxeb1SKiJiwoQJMW7cuNh3331j//33jyuuuCLKy8vjG9/4RkREjB07NnbeeefcxGFnn312DB8+PC677LIYPXp03HrrrTFr1qy4+uqr8/k0UlG2em0cd+OseGTu8mqPPfrm8nj0zeUxcrcd447/+8QoAP6tTR2u1GvI9QAAgI3L65VSEREnnnhiXHrppXH++efHwIEDY/bs2fHggw/mJjNfsGBBlU94GDp0aNxyyy1x9dVXx4ABA+KOO+6Iu+++O/r165evp5CKymxSayC1oUfmLo/jbpzliimAzyhuXhiH9d6xXuscvtuOUdy87pdjAwAAdbdV/PfvmWeeGWeeeWaNj82YMaPa2PHHHx/HH398I3e1dbn7lUWbDKTWe2Tu8rhnzuI4tv9OjdwVQNNyxtAe8eibdTuXflrfs/GaAQCA7Vzer5Sibq58an49699unEYAmrAx/XaKkbvV7WqpkbvtGMfsVfOHaAAAAFtOKNUErF5bWa//2Y+ImDZ3eaxeW/vHfgJsjwoLMnHHuH03GUytn5+vsCCTUmcAALD92Spu32PjtuQTo8yFAlBVaXHzeOC0A+OeOYvjd0/MixlvvZ977PDddowzhvaMY/bqIpACAIBGJpRqAnxiFEDDKizIxLH9d4pRu3eMNj95ICIill1wRHRoXZTnzgAAYPvh9r0mwCdGATQ+50wAAEiXS2maCJ8YBQDbh/LP3LZfvmZdjd+vV+LKaACgifIupolY/4lRj8zddDDlE6MAoOlaf0tpTbpMmlptLHvpUY3ZDgBAo3H7XhPhE6MAAACAbYkrpZoQnxgFANu+lRcdme8WAABSIZRqYnxiFABs28wRBQBsL9y+tw3wiVEAAABAU+O/4oAG4xOjAAAAqCv/IgQajE+MAgAAoK7cvgcAAABA6lwpBTQYnxgFAABAXQmlgAZjjigAAADqyu17AAAAAKROKAUAAABA6txrA8B2o7xiXdXlNetq/H49t6QCAEDj8W4bgO1Gm588UOtjXSZNrTaWvfSoxmwHAAC2a27fAwAAACB1rpQCYLux8qIj890CAADwf4RSAGw3zBEFAABbD7fvAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJC6ZvlugLopr1hXdXnNuhq/X6+kyI8WAAAA2HpJLpqINj95oNbHukyaWm0se+lRjdkOAAAAwBZx+x4AAAAAqXOlVBOx8qIj890CAAAAQIMRSjUR5ogCAAAAtiVu3wMAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFLXLN8NpC1JkoiIKCsry3MnAAAAANue9ZnL+gymNttdKLVy5cqIiOjevXueOwEAAADYdq1cuTLatm1b6+OZZFOx1TYmm83Ge++9F23atIlMJpPvdrZIWVlZdO/ePRYuXBilpaX5bgdq5Dhla+cYpSlwnLK1c4zSFDhO2dptS8dokiSxcuXK6Nq1axQU1D5z1HZ3pVRBQUF069Yt3200qNLS0iZ/wLLtc5yytXOM0hQ4TtnaOUZpChynbO22lWN0Y1dIrWeicwAAAABSJ5QCAAAAIHVCqSasqKgoJk6cGEVFRfluBWrlOGVr5xilKXCcsrVzjNIUOE7Z2m2Px+h2N9E5AAAAAPnnSikAAAAAUieUAgAAACB1QikAAAAAUieUoooLLrggBg4cmO82YKMcp2ztHKM0BTfccEPssMMO+W4DqnFssrWbMWNGZDKZ+Oijj/LdCtSqqZxLhVKN5PTTT49MJhNXXHFFvlupVSaTibvvvjvfbZCyCy64IPr06RMlJSXRrl27GDlyZPzjH//Id1u1cpxuX9auXRs/+tGPon///lFSUhJdu3aNsWPHxnvvvZfv1mrlGN0+3XnnnXHEEUdEhw4dIpPJxOzZs/Pd0kb17Nlzq35PQsOaMmVK9OzZM4qLi+OAAw6IZ555Jt8t1cqxuf15/PHH46ijjoquXbs2ib+hI0aMiHPOOSffbZCyyZMnx3777Rdt2rSJTp06xZgxY+L111/Pd1u1asrnUqFUI7jrrrvi6aefjq5du+a7Fahm9913j9/97nfx8ssvxxNPPBE9e/aMI444IpYtW5bv1iA+/vjjeP755+NnP/tZPP/883HnnXfG66+/HkcffXS+W4MqysvL46CDDopf/epX+W4FqrjttttiwoQJMXHixHj++edjwIABMWrUqFi6dGm+W4OI+PT8OWDAgJgyZUq+W4FaPfbYYzF+/Ph4+umnY+rUqbF27do44ogjory8PN+tbXsSGtQ777yT7Lzzzskrr7yS9OjRI7n88ss3Wj98+PDkzDPPTM4+++xkhx12SDp16pRcffXVyapVq5JTTjklad26ddKrV6/kb3/7W5X1ZsyYkey3335JixYtki5duiQ/+tGPkrVr11bZ7llnnZX84Ac/SNq1a5d07tw5mThxYu7xHj16JBGR++rRo0eSJEkyceLEZMCAAcn//M//JD169EhKS0uTE088MSkrK8ute/vttyf9+vVLiouLk/bt2yeHH354smrVqi1+7ciPFStWJBGRPPLII7XWOE7Jp2eeeSaJiGT+/Pm11jhGyZd58+YlEZG88MILm6wdN25ccswxxyQXXXRR0qlTp6Rt27bJpEmTkrVr1ybnnntu0q5du2TnnXdOrrvuuirrvfTSS8mhhx6aO1ZOO+20ZOXKldW2e8kllyRdunRJ2rdvn3znO99J1qxZkyTJp8fxhsfp+rd/119/fdK2bdvkwQcfTPr06ZOUlJQko0aNSt57773ctqdPn57st99+SatWrZK2bdsmQ4cOTd5+++0GeOVoLPvvv38yfvz43HJlZWXStWvXZPLkybWu49gkXyIiueuuuzZZt/7v6rXXXpt07949KSkpSc4444xk3bp1ya9+9aukc+fOSceOHZNf/OIXVdabP39+cvTRRyclJSVJmzZtkuOPPz5ZvHhxte3W9vd63Lhx1Y7RefPmJdOnT8+9fx48eHDSsmXLZMiQIclrr72W2/bs2bOTESNGJK1bt07atGmT7LPPPsmzzz7bMC8cqVu6dGkSEcljjz1Wa41z6eYRSjWgysrK5NBDD02uuOKKJEmSOodSbdq0SX7+858nb7zxRvLzn/88KSwsTI488sjk6quvTt54443kjDPOSDp06JCUl5cnSfJp8NWqVavkO9/5TvLqq68md911V7LjjjtW+YfS8OHDk9LS0uSCCy5I3njjjeTGG29MMplM8vDDDydJ8u9fquuvvz5ZtGhRsnTp0iRJPj0xt27dOjn22GOTl19+OXn88ceTLl26JD/+8Y+TJEmS9957L2nWrFny3//938m8efOSl156KZkyZUqVXxqajoqKiuSSSy5J2rZtmyxbtqzWOscp+TR16tQkk8kkK1asqLXGMUq+1DeUatOmTTJ+/PjktddeS6699tokIpJRo0YlF110Ue7Ybd68ebJw4cIkSZJk1apVyU477ZQ7lqZNm5bsuuuuybhx46pst7S0NDn99NOTV199Nfnf//3fpFWrVsnVV1+dJEmSvP/++0m3bt2SCy+8MFm0aFGyaNGiJEk+fbPavHnzZOTIkcmzzz6bPPfcc8mee+6ZfPWrX02SJEnWrl2btG3bNjn33HOTN998M/nnP/+Z3HDDDRsNiMmvioqKpLCwsNo/8seOHZscffTRta7n2CRf6hNKtW7dOjnuuOOSOXPmJPfee2/SokWLZNSoUclZZ52VvPbaa8l1112XRETy9NNPJ0ny6b/NBg4cmBx00EHJrFmzkqeffjoZPHhwMnz48Grbre3v9UcffZQMGTIkOe2003LH6Lp163Kh1AEHHJDMmDEjmTNnTnLwwQcnQ4cOzW17r732Sr7+9a8nr776avLGG28kf/nLX5LZs2c36OtHeubOnZtERPLyyy/XWuNcunmEUg3o4osvTj7/+c8n2Ww2SZK6h1IHHXRQbnndunVJSUlJcvLJJ+fGFi1alEREMnPmzCRJkuTHP/5xsscee+T2kyRJMmXKlKR169ZJZWVljdtNkiTZb7/9kh/96Ee55Zr+CEycODFp1apVlf/N/8EPfpAccMABSZIkyXPPPZdEhP+JauL+93//NykpKUkymUzStWvX5JlnntloveOUfPnkk0+SffbZJ/fHszaOUfKlvqFUjx49csdXkiTJHnvskRx88MG55fXH7p///OckSZLk6quvTtq1a1flKrr7778/KSgoyP1v//rtrlu3Lldz/PHHJyeeeGJuuab3JNdff30SEcmbb76ZG5syZUrSuXPnJEk+fZMbEcmMGTPq8EqwNXj33XeTiEieeuqpKuM/+MEPkv3337/W9Ryb5Et9QqnP/l0dNWpU0rNnz2rH7fqrAh9++OGksLAwWbBgQe7xOXPmJBGRe++7qb/XSfLpe4Gzzz67Sj8bXim13v33359ERPLJJ58kSZIkbdq0SW644YY6vAps7SorK5PRo0cnw4YN22idc+nmMafUZrj55pujdevWua+///3v8dxzz8Wvf/3ruOGGGyKTydRre3vvvXfu+8LCwujQoUP0798/N9a5c+eIiNxcAK+++moMGTKkyn6GDRsWq1atinfeeafG7UZE7LTTTnWaT6Bnz57Rpk2bGtcbMGBAHH744dG/f/84/vjj449//GN8+OGH9Xm6pKSm43S9Qw89NGbPnh1PPfVU/Md//EeccMIJmzw2HKc0tI0doxGfTnp+wgknRJIkceWVV25ye45RGsOmjtP62muvvaKg4N9vvzp37lzlOF1/7G54nA4YMCBKSkpyNcOGDYtsNltlwtW99torCgsLc8t1PU5btWoVvXr1qnG99u3bxymnnBKjRo2Ko446Kn7961/HokWLNuNZ0xQ4NtnaffbvaufOnaNv377VjtsNj9Hu3btH9+7dc4/37ds3dthhh3j11Vdr3W5dj9GIqu8Rdtppp4j49/uMCRMmxKmnnhojR46MX/7yl/HWW2/V5+myFRk/fny88sorceutt26y1rm0/oRSm+Hoo4+O2bNn57723Xff+Pvf/x5Lly6NXXbZJZo1axbNmjWL+fPnx/e///3o2bPnRrfXvHnzKsuZTKbK2Pp/MGWz2Xr1WdN267KNja1XWFgYU6dOjQceeCD69u0bv/3tb2OPPfaIefPm1as3Gl9Nx+l6JSUl0bt37zjwwAPj2muvjWbNmsW111670e05TmloGztG1wdS8+fPj6lTp0Zpaekmt+cYpTFs7DjdHJs6TteP5fM4TZIkt3z99dfHzJkzY+jQoXHbbbfF7rvvHk8//XS9eiM9O+64YxQWFsaSJUuqjC9ZsiS6dOmy0XUdm2zttrZj9LPrfvZ9xgUXXBBz5syJ0aNHx6OPPhp9+/aNu+66q169kX9nnnlm3HfffTF9+vTo1q3bJuu3tuO0KZxLhVKboU2bNtG7d+/cV8uWLePkk0+Ol156qcob165du8YPfvCDeOihhxp0/3vuuWfMnDmzysH05JNPRps2ber0i7Je8+bNo7Kyst77z2QyMWzYsJg0aVK88MIL0aJFCyfYrVBNx2ltstlsVFRUNOj+HadsSm3H6PpAau7cufHII49Ehw4dGmX/jlHqoj7n0saw5557xosvvljl036efPLJKCgoiD322KPO22nRosVmHacREYMGDYrzzjsvnnrqqejXr1/ccsstm7UdGl+LFi1i8ODBMW3atNxYNpuNadOmxZAhQxp0X45NtnZ77rlnLFy4MBYuXJgb++c//xkfffRR9O3bt87b2ZJjdPfdd4/vfe978fDDD8exxx4b119//WZth/QlSRJnnnlm3HXXXfHoo4/Grrvu2ij7cS4VSjWYDh06RL9+/ap8NW/ePLp06VKvg6kuvvOd78TChQvjrLPOitdeey3uueeemDhxYkyYMKHKpYKb0rNnz5g2bVosXry4zreN/OMf/4iLL744Zs2aFQsWLIg777wzli1bFnvuuefmPh1SVF5eHj/+8Y/j6aefjvnz58dzzz0X//mf/xnvvvtuHH/88Q26L8cpm2Pt2rVx3HHHxaxZs+Lmm2+OysrKWLx4cSxevDjWrFnToPtyjLIlPvjgg5g9e3b885//jIiI119/PWbPnh2LFy9u0P187Wtfi+Li4hg3bly88sorMX369DjrrLPi5JNPzt2SWhc9e/aMxx9/PN59991Yvnx5ndaZN29enHfeeTFz5syYP39+PPz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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "aggregated_eventstudy = dml_obj.aggregate(\"eventstudy\")\n", "print(aggregated_eventstudy)\n", @@ -1430,36 +665,9 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Event Study Aggregation \n", - "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.84764 0.152535 12.112918 0.0 1.548678 2.146603\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "-4 months -0.062934 0.102768 -0.612393 0.540278 -0.264355 0.138487\n", - "-3 months 0.051390 0.079708 0.644732 0.519101 -0.104834 0.207614\n", - "-2 months -0.017152 0.071696 -0.239239 0.810920 -0.157674 0.123369\n", - "-1 months 0.082539 0.072499 1.138493 0.254915 -0.059556 0.224634\n", - "0 months 1.077206 0.079043 13.628169 0.000000 0.922285 1.232126\n", - "1 months 1.943978 0.148708 13.072411 0.000000 1.652514 2.235441\n", - "2 months 2.521738 0.275739 9.145392 0.000000 1.981300 3.062176\n", - "------------------ Additional Information ------------------\n", - "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", - "\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(aggregated_eventstudy)" ] @@ -1473,18 +681,9 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0. 0. 0. 0. 0.33333333 0.33333333\n", - " 0.33333333]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(aggregated_eventstudy.overall_aggregation_weights)" ] @@ -1498,22 +697,9 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0. , 0. , 0.34529452, 0. , 0. ,\n", - " 0. , 0. , 0. , 0.33615437, 0. ,\n", - " 0. , 0. , 0. , 0. , 0.31855112])" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# the weights for e=0 correspond to the fifth element of the aggregation weights\n", "aggregated_eventstudy.aggregation_weights[4]" @@ -1532,32 +718,9 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ATT(2025-04,2025-01,2025-02) -0.186576\n", - "ATT(2025-04,2025-02,2025-03) 0.165520\n", - "ATT(2025-04,2025-03,2025-04) 0.885967\n", - "ATT(2025-04,2025-03,2025-05) 1.887206\n", - "ATT(2025-04,2025-03,2025-06) 2.521738\n", - "ATT(2025-05,2025-01,2025-02) -0.020345\n", - "ATT(2025-05,2025-02,2025-03) 0.135493\n", - "ATT(2025-05,2025-03,2025-04) 0.068758\n", - "ATT(2025-05,2025-04,2025-05) 1.173640\n", - "ATT(2025-05,2025-04,2025-06) 2.002293\n", - "ATT(2025-06,2025-01,2025-02) -0.062934\n", - "ATT(2025-06,2025-02,2025-03) 0.127089\n", - "ATT(2025-06,2025-03,2025-04) 0.005414\n", - "ATT(2025-06,2025-04,2025-05) 0.007135\n", - "ATT(2025-06,2025-05,2025-06) 1.182735\n", - "Name: coef, dtype: float64\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(dml_obj.summary[\"coef\"])" ] @@ -1585,15 +748,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(19080, 10)\n" - ] - } - ], + "outputs": [], "source": [ "n_obs = 4000\n", "n_periods = 6\n", @@ -1615,119 +770,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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tFirst Treatedy_meany_lower_quantiley_upper_quantileite_meanite_lower_quantileite_upper_quantile
02025-01-012025-04208.533893191.327132227.3412320.006281-2.3122762.348005
12025-01-012025-05210.612290193.917897226.878333-0.120014-2.5042682.276403
22025-01-012025-06212.816950195.161619229.957651-0.049635-2.2979252.360860
32025-01-01Never Treated217.348892201.317126233.4235070.027843-2.1678242.376174
42025-02-012025-04208.258789181.903694236.3585670.052753-2.1391592.236515
\n", - "
" - ], - "text/plain": [ - " t First Treated y_mean y_lower_quantile y_upper_quantile \\\n", - "0 2025-01-01 2025-04 208.533893 191.327132 227.341232 \n", - "1 2025-01-01 2025-05 210.612290 193.917897 226.878333 \n", - "2 2025-01-01 2025-06 212.816950 195.161619 229.957651 \n", - "3 2025-01-01 Never Treated 217.348892 201.317126 233.423507 \n", - "4 2025-02-01 2025-04 208.258789 181.903694 236.358567 \n", - "\n", - " ite_mean ite_lower_quantile ite_upper_quantile \n", - "0 0.006281 -2.312276 2.348005 \n", - "1 -0.120014 -2.504268 2.276403 \n", - "2 -0.049635 -2.297925 2.360860 \n", - "3 0.027843 -2.167824 2.376174 \n", - "4 0.052753 -2.139159 2.236515 " - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df_anticipation[\"ite\"] = df_anticipation[\"y1\"] - df_anticipation[\"y0\"]\n", "df_anticipation[\"First Treated\"] = df_anticipation[\"d\"].dt.strftime(\"%Y-%m\").fillna(\"Never Treated\")\n", @@ -1744,20 +787,9 @@ }, { "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plot_data(agg_df_anticipation, col_name='ite')" ] @@ -1797,41 +829,9 @@ }, { "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n" - ] - }, - { - "data": { - "text/plain": [ - "(
,\n", - " [,\n", - " ,\n", - " ])" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dml_obj_anticipation = DoubleMLDIDMulti(dml_data_anticipation, **default_args)\n", "dml_obj_anticipation.fit()\n", @@ -1848,43 +848,9 @@ }, { "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n", - "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n" - ] - }, - { - "data": { - "text/plain": [ - "(
,\n", - " [,\n", - " ,\n", - " ])" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dml_obj_anticipation = DoubleMLDIDMulti(dml_data_anticipation, **(default_args| {\"anticipation_periods\": 1}))\n", "dml_obj_anticipation.fit()\n", @@ -1917,31 +883,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
,\n", - " [,\n", - " ,\n", - " ])" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Llxe5PSYm5hzeUfnZbDYNGDBAu3bt0oYNG9SqVSuX7VFRUbJarTp+/Hih1+aXFZVwGj16tD788EMtX75cPXr0KHM89evX1969e53PL7/8cmdiTpIee+wxTZs2TXFxccrNzVViYqJLYs9ms+nUqVNlSoLVr19fSUlJZY4NAAC4B0kpAABQqbRu3VqtW7fWo48+qm+++UZdunTRwoULNWPGDElymbpWUZo2bSrDMNS4ceMSpyX+9NNP2rdvn1577TXdfvvtzvKNGzcWqltc3E2bNtWmTZvUpUuXEkf1NGzYUFLeyKomTZo4y0+cOFFohFZ5ORwO3X777fr000/19ttvq2vXroXqmM1mtW7dWtu2bSu07fvvv1eTJk0KLXL+wAMPaMmSJZo7d65uvvnmcsX0+++/uyTkli9f7jIVL/8zaNu2rSRp27Zt6tu3r3P7tm3b5HA4nNuLYxiGDh06pHbt2pUrPgAAcP4K/4kSAADAC1JTU2W3213KWrduLbPZ7DJlLCwsTMnJyRUay4ABAxQQEKDp06cXWrDdMAydOnVKkpyjqArWMQxD8+bNK9RmWFiYJBWKfciQIcrNzdUTTzxR6DV2u91Zv2fPngoKCtL8+fNd9jd37twi38OBAwd04MCBkt/o3yZMmKC33npLL730kgYMGFBsvUGDBmnr1q0uiam9e/fqs88+0+DBg13q/uc//9EzzzyjKVOmFFrLqaATJ04UKvv444+1fft2XXvttc6yLl26qGfPns6f/KRUjx49FBUVpQULFri0sWDBAoWGhqpfv34l7mvBggU6ceKEy74AAIBnMFIKAABUCp999pnGjx+vwYMHq1mzZrLb7XrjjTcUEBCggQMHOuu1b99emzZt0nPPPac6deqocePG6tixo1tjadq0qWbMmKHJkyfr0KFD6t+/vyIiInTw4EGtWbNGd955pyZNmqQWLVqoadOmmjRpko4eParIyEi9++67RY5cat++vSTpnnvuUe/evRUQEKChQ4eqa9euGjNmjGbOnKmdO3fqmmuuUVBQkPbv369Vq1Zp3rx5GjRokGJiYjRp0iTNnDlT1113nfr27av4+HitW7dO0dHRhfZ39dVXSyp9wfO5c+fqpZdeUqdOnRQaGqply5a5bL/pppucCbWxY8fq5ZdfVr9+/TRp0iQFBQXpueeeU61atfSvf/3L+Zo1a9bowQcf1IUXXqiWLVsWarNXr16qVauWJKlz585q166dLrvsMlWrVk07duzQ4sWLVb9+fZc7HxYnJCRETzzxhMaNG6fBgwerd+/e+vLLL7Vs2TI9+eSTioqKctZt2LCh/vGPf6h169YKDg7WV199pZUrV6pt27YaM2ZMqfsCAABuZgAAAJyjJUuWGJKMrVu3Frn94MGDhiRjyZIlzrLhw4cbYWFhher+/vvvxsiRI42mTZsawcHBRlRUlNG9e3dj06ZNLvX27NljXHXVVUZISIghyRg+fHiZYl21apUhydi8ebOz7LHHHjMkGSdOnCjyNe+++65xxRVXGGFhYUZYWJjRokULY9y4ccbevXuddX755RejZ8+eRnh4uBEdHW2MHj3a+PHHHwu9b7vdbkyYMMGIiYkxTCaTcfbXsP/+979G+/btjZCQECMiIsJo3bq18eCDDxrHjh1z1snNzTWmT59uxMXFGSEhIUa3bt2M3bt3Gw0bNiz0OTRs2NBo2LBhqZ/L8OHDDUnF/hw8eNCl/h9//GEMGjTIiIyMNMLDw43rrrvO2L9/v0ud/M+1uJ+CffDII48Ybdu2NapVq2YEBQUZDRo0MO6++24jISGh1NjP/vyaN29uWCwWo2nTpsacOXMMh8PhUmfUqFHGRRddZERERBhBQUHGBRdcYDz00ENGampqufYFAADcw2QYZ41JBwAAAAAAACoYa0oBAAAAAADA40hKAQAAAAAAwONISgEAAAAAAMDjSEoBAAAAAADA43w2KTVr1iyZTCbdd9993g4FAAAAAAAA5RTo7QDOxdatW7Vo0SK1adOmXK9zOBw6duyYIiIiZDKZKig6AAAAAACAqsswDKWlpalOnToym4sfD+VzSan09HTdcsstevnllzVjxoxyvfbYsWOqX79+BUUGAAAAAACAfH/88Yfq1atX7HafS0qNGzdO/fr1U8+ePUtNSmVnZys7O9v53DAMSdLhw4cVGRlZoXFWNIfDoZMnTyo6OrrErCN8A/3pP+hL/0J/orw4ZvwL/QmnrCxp+HDZbDYFLl8uc2iotyPCeeL8RnlxzJRPamqqGjZsqIiIiBLr+VRSauXKldqxY4e2bt1apvozZ87U9OnTC5VnZ2crKyvL3eF5lMPhUG5urrKysjgh/AD96T/oS/9Cf6K8OGb8C/0JJ7tdgSNHKjMjQ0F2u8w+/rsEOL9Rfhwz5ZM/QKi0pZN8Jin1xx9/6N5779XGjRsVHBxcptdMnjxZEydOdD5PTU1V/fr1FRMT4xcjpUwmk2JiYjgh/AD96T/oS/9Cf6K8OGb8C/2JghwDByrnxAmOBz/B+Y3y4pgpn7LmbXwmKbV9+3YlJibq0ksvdZbl5uZqy5YteuGFF5Sdna2AgACX11itVlmt1kJtmc1mvziITCaT37wX0J/+hL70L/Qnyotjxr/QnyiI48G/0J8oL46ZsivrZ+QzSamrr75aP/30k0vZHXfcoRYtWuihhx4qlJACAAAAALdwOKTff1fAqVNSdLTEL6QA4BY+k5SKiIhQq1atXMrCwsJUs2bNQuUAAAAA4DY2m0z33acIm01au1YK9JlfowCgUuNqCgAAAAAlMZmkqCgZWVl5jwH4PHvGceVmJBS7PSCstgLD4jwYUdXk00mpzz//3NshAAAAAPB3VquMpUuVkpio2CLWrAXge9J2vazk72cUu716x0dVo9NUD0ZUNfl0UgoAAAAAAKC8ItqMVmjT6yVJtqQ9Orl+uKKvfU2WqBaS8kZKoeKRlAIAAAAAAFVKYFhcoel5lqgWssa2K3MbTAE8fySlAAAAAKAkNpv07LMKS0+X/v1vKTjY2xEBqASYAnj+SEoBAAAAQEkcDpm+/lpBNpvkcHg7GgCVBFMAzx9JKQAAAAAoSWCgjDFjlJmSIksgv0IByOOOKYBVHVdUAAAAAChJYKDUr5+yExPzHgMA3MLs7QAAAAAAAABQ9ZCUAgAAAICSGIZ07JjMCQl5jwEAbsHYUwAAAAAoSXa2THfdpUibTVq7VgoN9XZEAOAXSEoBAAAAQGnCwmSwnhQAuBVXVQAAAAAoSXCwjDffVEpiomKDg70dDQD4DdaUAgAAAAAAgMeRlAIAAAAAAIDHkZQCAAAAgJLk5Ejz5in0v//NewwAcAvWlAIAAADgc+wZx5WbkVDs9oCw2goMi3PPznJzZfr0U1lsNmnSJPe0CeC8ePQagApDUgoAAACAz0nb9bKSv59R7PbqHR9VjU5T3bOzwEAZI0YoMyVFFu7AB1QKHr0GoMJwRQUAAADgcyLajFZo0+slSbakPTq5friir31NlqgWkvJGSbhNYKA0YICyExPzHgPwOo9eA1BhuKICAAAA8DmBYXGFpuZYolrIGtvOSxEB8CSuAf6Bhc4BAAAAoCSGIZ06JVNSUt5jAIBbMFIKAAAAAEqSnS3THXeoms0mrV0rhYZ6OyIA8AuMlAIAAACA0gQESGZ+fQIAd2KkFAAAAACUJDhYxpo1Sk5MVGxwsLejAQC/4TOp/gULFqhNmzaKjIxUZGSkOnXqpHXr1nk7LAAAAAAAAJwDn0lK1atXT7NmzdL27du1bds29ejRQzfeeKN+/vlnb4cGAAAAAACAcvKZ6XvXX3+9y/Mnn3xSCxYs0HfffaeLL764yNdkZ2crOzvb+Tw1NVWS5HA45HA4Ki5YD3A4HDIMw+ffB/LQn/6DvvQv9CfKi2PGv9CfviO/jyrse35OjoyXX1ZIeroc994rWa3u3wc8ivPbv5zvNaAsry/tmKnw65CPKetn4DNJqYJyc3O1atUqZWRkqFOnTsXWmzlzpqZPn16o/MSJE8rKyqrIECucw+FQSkqKDMOQmQUXfR796T/oS/9Cf6K8OGb8C/3pOxxJSZKk00lJMivR/TvIylL1996T2W5X4pAhMnP3PZ/H+e1fzvcaUJbXl3bMVPh1yMekpaWVqZ5PJaV++uknderUSVlZWQoPD9eaNWt00UUXFVt/8uTJmjhxovN5amqq6tevr5iYGEVGRnoi5ArjcDhkMpkUExPDRdQP0J/+g770L/Qnyotjxr/Qn74jW1FKkFQjKkrW2Fj378Bul3H77cpMTVVsnToyWyzu3wc8ivPbv5zvNaAsry/tmKnw65CPCS7jTSF8KinVvHlz7dy5UykpKXrnnXc0fPhwffHFF8UmpqxWq6xFDK01m81+ceExmUx+815Af/oT+tK/0J8oL44Z/0J/+ob8/qmwvrJY5LjlFmUnJqqaxcLx4Cc4v/3H+V4Dyvr6ko6ZCr8O+ZiyfgY+lZSyWCy64IILJEnt27fX1q1bNW/ePC1atMjLkQEAAAAAAKA8fDp953A4XBYyBwAAAAC3MwwpI0OmM2fyHgMA3MJnRkpNnjxZffr0UYMGDZSWlqYVK1bo888/14YNG7wdGgAAAAB/lp0t0803q5rNJq1dK7HQOQC4hc8kpRITE3X77bfr+PHjqlatmtq0aaMNGzaoV69e3g4NAAAAAAAA5eQzSalXX33V2yEAAAAAqIqsVhmrVys5MVGxRdxICYBvcuScUcbelUrdnZdvOPnZeEW2+qfCmg+VOYgRkZ7gM0kpAAAAAPAKk0kKDMz7MZm8HQ0AN3DknNHJjWOUeWiDHDlpkiTbXzuUlLRfmUc2K7rXIhJTHuDTC50DAAAAAACUV8belXkJKVuyZOTmFRq5ctiSlXl4gzL2rixTO46cM0rbvVgnPxsvKW+0VdruxXLknKmgyP0LSSkAAAAAKIndLi1erJA338x7DMDnpe9bJUdOepHbHDnpSt+3qtQ28kdbJW15WLa/dkj6e7TVlod1cuMYElNlQFIKAAAAAEpit8u0dq2s69aRlAL8RG760f+NkDqbIzdveyncNdqqKiMpBQAAAAAlCQyU0b+/svv0yVtXCoDPCwivK5kCit5oDsjbXgp3jLaq6riiAgAAAEBJAgOlkSOVmZioCJJSgF8IbzZYtoTteaOczmIOCld4s8GltuGO0VZVHSOlAAAAAABAlRLWfKhCGvWW2Vr9fyOmTAEyW6srpGFvhTUfWmob7hhtVdWRlAIAAACAkhhG3lpSdnveYwA+zxwUquheixR15SxZal0qSbLUulRRV85SdK9FMgeFltpGeLPBMgeFF9N+2UZbVXUkpQAAAACgJNnZMg0YoOojR0rZ2d6OBoCbmINCFdFqpKJ7vCBJiu7xgiJajSxTQkpyz2irqo6kFAAAAAAAQDm5Y7RVVccqfQAAAABQEqtVxptvKuXECcVYrd6OBkAlkj/ayhLbTsdWdFR0jxdkjW3n7bB8BiOlAAAAAKAkJpMUFiYjNDTvMQDALRgpBQAAAKBKsmccV25GQrHbA8JqKzAszoMRAahqqvp1iKQUAAAAgCopbdfLSv5+RrHbq3d8VDU6Tc27697KlQpOTZVGjZIsFg9GCcCflfk65KdISgEAAACokiLajFZo0+slSbakPTq5friir31NlqgWkvJGKEiS7HaZVq5UsM0mjRhBUgqA25T5OuSnSEoBAAAAqJICw+IKTYuxRLUovEhxQICMPn2UnZ4uS0CAByME4O/KfB3yUySlAAAAAKAkQUHS3XcrMzFREUFB3o4GAPwGd98DAAAAAACAxzFSCgAAAADOUVW/cxYAnA+SUgAAAABQkqwsmf7xD1XPzpZWr5ZCQ52bqvqdswDgfJCUAgAAAIDS5OZKDkeh4qp+5ywAOB8+k5SaOXOmVq9erT179igkJESdO3fW7Nmz1bx5c2+HBgAAAMCfWa0ylixRyokTirFaXTZV9TtnAcD58JmFzr/44guNGzdO3333nTZu3KicnBxdc801ysjI8HZoAAAAAPyZySTVrCkjKirvMQDALXxmpNT69etdni9dulSxsbHavn27rrrqqiJfk52drezsbOfz1NRUSZLD4ZCjiKG3vsThcMgwDJ9/H8hDf/oP+tK/0J8oL44Z/0J/+o78Pjqf7/mltVGW48EdccAzOL/9y/mee2V5fWnHjCeuQ76krPH7TFLqbCkpKZKkqKioYuvMnDlT06dPL1R+4sQJZWVlVVhsnuBwOJSSkiLDMGQ2+8yANxSD/vQf9KV/oT9RXhwz/oX+9B2OpCRJ0umkJJmV6P427HZZ1q9X7pkzSuzfX2aLpcLigGdwfvsHhz1b2Ue3KOvgewqWdOjzmQpufKOsda+SOdBa6uud7ZTh3C3tmKnw65CPSUtLK1M9n0xKORwO3XffferSpYtatWpVbL3Jkydr4sSJzuepqamqX7++YmJiFBkZ6YlQK4zD4ZDJZFJMTAwXUT9Af/oP+tK/0J8oL44Z/0J/+o5sRSlBUo2oKFljY93fRlaWtHatQrOzFThypMwF7r7n7jjgGZzfvs+Rc0anNj0o0+ENCs7JmxUV/NdHMiV9qYCTvVWz50syBxV9rp6tLOduacdMhV+HfExwcHCZ6p1TUiogIEDHjx9X7Fkf0qlTpxQbG6vc3NxzabbMxo0bp927d+urr74qsZ7VapXVWjg7ajab/eLCYzKZ/Oa9gP70J/Slf6E/UV4cM/6F/vQN+f1zPn1VYhtBQXJcfbVsaWmyBAUVuw93xAHP4fz2bRn731bW4fUybMn/KzRyZdhOK+vIemXuf1sRrUaWqa2ynrslHTMVfh3yMWWN/5zepWEYRZZnZ2fLUsxQVncZP368PvzwQ23evFn16tWr0H0BAAAAqLwcOWeUtnuxTn42XpJ08rPxStu9WI6cM+7dUVCQdO+9OnPnnXmPAXhd+r5VcuSkF7nNkZOu9H2rPBwRzkW5Rko9//zzkvKyg6+88orCw8Od23Jzc7Vlyxa1aNHCvRH+zTAMTZgwQWvWrNHnn3+uxo0bV8h+AAAAAFR+jpwzOrlxjDIPbZAjJ2/tEttfO5SUtF+ZRzYruteiMk/dAeB7ctOPSkYxs7QcuXnbUemVKyk1Z84cSXkJooULFyogIMC5zWKxqFGjRlq4cKF7I/zbuHHjtGLFCr333nuKiIhQQkKCJKlatWoKCQmpkH0CAAAAqJwy9q7MS0idNXXHYUtW5uENyti7ssxTdwD4noDwuso5/Ztk2AtvNAcoILyu54M6R46cM8rYu1Kpu1+VlDfqM7LVPxXWfKjfJ9fLlZQ6ePCgJKl79+5avXq1atSoUSFBFWXBggWSpG7durmUL1myRCNGjPBYHAAAAAC8ryxTd9yWlMrKkmn4cFXLzpZWrJCKWegcgOeENxssW8J218T038xB4QpvNtjzQZ2Dqj7q85wWOt+8ebO74yhVcetYAQAAAKh6PD51JyNDJpvNvW0COGdhzYcq88hmZR7eIIctLe96YAqQ2RKhkIa9FdZ8qLdDLJOqPurznBY6HzhwoGbPnl2o/Omnn9bgwb6RjQQAAADguwLC60qmgKI3unvqjtUqY+FCpT79tFTE3b0BeJ45KFTRvRYp6spZstS6VJJkqXWpoq6c5VOji6r6gu3nlJTasmWL+vbtW6i8T58+2rJly3kHBQAAAAAlCW82WOag8CK3uX3qjskk1akjR+3aeY8BVArmoFBFtBqp6B4vSJKie7ygiFYjfSYhJbFg+zklpdLT02WxWAqVBwUFKTU19byDAgAAAICShDUfqpBGvWW2Vv/fiClTgMzW6h6fuuPIOaO03Yt18rPxkvIWKU7bvViOnDMeiwGAb/LoqM9K6JySUq1bt9Zbb71VqHzlypW66KKLzjsoAAAAACiJR6fu2O3SRx/JumlT3uMC8hcpTtrysGx/7ZD09yLFWx7WyY1jSEwBKJFHR31WQue00Pm///1vDRgwQAcOHFCPHj0kSZ9++qnefPNNrVrl3/MdAQAAAFQO+VN3LLHtdGxFR0X3eEHW2HblaqNMt2K322VatEghNps0YIBUYNZIVV+kGMD58ZcF28/VOSWlrr/+eq1du1ZPPfWU3nnnHYWEhKhNmzbatGmTunbt6u4YAQAAAMDtynwrdrNZRpcuyklPl8XsOtmkLIsUk5QCUJz8UZ/5yXFbwlZZal1aODnup84pKSVJ/fr1U79+/dwZCwAAAAB4TJlHOVks0kMPKSMxUWFnra1b1RcpBnD+3DHq01ed05pSkpScnKxXXnlFU6ZMUVJSkiRpx44dOnqUiy4AAACAys8dt2Kv6osUA77KnnFc2Ynxyk6Mly1pjyTJlrTHWWbPOO7lCKuGcxoptWvXLvXs2VPVqlXToUOHNGrUKEVFRWn16tU6cuSIXn/9dXfHCQAAAABu5Y5RTuHNBsuWsN11tNXfqsIixYCvStv1spK/n+FSdnL9cOfj6h0fVY1OUz0dVpVzTkmpiRMnasSIEXr66acVERHhLO/bt6+GDRvmtuAAAAAAoKIEhNdVzunfJMNeeGPBUU7Z2TKNHq1qWVnSa69JISHOalV9kWLAV0W0Ga3QptcXuz0grHapbdgzjis3I0GSXEZbFWwjMCzuPCP1b+eUlNq6dasWLVpUqLxu3bpKSEg476AAAAAAoKKVeZSTYUhJSTLZbHmPXepV7UWKAV8VGBZ33gkjRludv3NKSlmtVqWmphYq37dvn2JiYs47KAAAAACoaGUe5WSxyJg7V2mnTqnmWQudS1V7kWKgKnPHaKuq7pySUjfccIMef/xxvf3225Ikk8mkI0eO6KGHHtLAgQPdGiAAAMC5Op6apeOpWcVuj4sMVlxksAcjAlCZlHmUk9ksNWmi3PDwvMduVnAKUFGYAgRUTu4YbVXVnVNS6tlnn9WgQYMUGxurzMxMde3aVQkJCerUqZOefPJJd8cIAABwThZ9e1iPb9xX7PapvZppWu/mHozo3JBcAypOZRjlVNQUoIKYAgTAX51TUqpatWrauHGjvv76a/34449KT0/XpZdeqp49e7o7PgAAgHM2plND3XBxLUnSnsR03boiXsuGtVOL2HBJ8plEjr8k1wCfZbdLmzfLkpws3XijVMQUvvNRcAqQLWmPTq4fruhrX5MlqoUkpgAB8F9lTkpFRUVp3759io6O1siRIzVv3jx16dJFXbp0qcj4AAAAzllRI4haxIbr0nrVvRPQOfKX5Brgs+x2mebNU6jNJvXr5/akVFFTgCxRLViXCoDfK/OEaJvN5lzc/LXXXlNWVvFDyAEAAOA+cZHBurRedV1ar7ozEZWfXLu0XnWSUkBFM5tlXHaZci65pELWlAKAqqrMI6U6deqk/v37q3379jIMQ/fcc49CQkKKrLt48WK3BQgAAFCZFVzvyeEwlHQ6Q1G2FJnNJkms9wT4BYtFmjpVGYmJCnPzKCkAqMrKnJRatmyZ5syZowMHDkiSUlJSGC0FAACqPNZ7AgAAODdlTkrVqlVLs2bNkiQ1btxYb7zxhmrWrFlhgQEAAPiCgus9/ZKQpttX7tTrQ9vqotoRkljvCQAAoDjntNB59+7dZWHYKgAAgMv0PIfDkOSbi6kDKMH/s3ff4U1V/x/A30nbJN0D2lJKKXsrZWgtqIACRRQBGSKioIIgqCgiivhliYCKguKAnyioiCii4GAIguAAFGhBdkEoq6WF7pkm9/z+iIkNTdqkTZPm5v16nj69uffk3JOc0eTTc88tLYXiyScRVFwMfPghYGUZEyIie+kK06AvTAdguPtm+d+A4e6b198IQU7caqHz3bt3Y8CAAWjYsCEUCgU2bNjg9DIQEREREZGHEQJIS4MyI8OwTUTkIPmHP8TlNfG4vCYeV7eMBgBc3TLatC//8IcuLmHtcquFzgsLC9GxY0c8+uijuO+++2rlHERERERERGZUKojXXkP+tWuoxytGiMiBAm8cB7/mA6we9/Jv4MTSOF+1FjpXKBQuWej8rrvuwl133WVz+tLSUpSWlpoeG2d6SZIESZIcXj5nkiQJQgi3fx1kwPqUD9alvLA+5cV4aZ0k1V6dCiGZftfWOZzxOsiAY4D7MNZRTT7nV5WH1Lo1dJmZkAyJHF4OqawIhae+Qv5Rwz/4M3c8hcD2j8K/1XAoffzsyouqxv4tL44YA2w5R220GaVvJJS+kVWe293YWmZZL3S+YMECzJkzp8L+zMxMt79zoCRJyM3NhRACSqXNV2FSHcX6lA/WpbywPuUlK7vw399ZyFCVVpG6mufIKvj3dzYy1GW1cw4nvA4y4BjgPqSsLABAdlYWlMiolTystQdRfAWi2JBeyjsNALh27k8o/81P4RsBRVVfOHWlyEt+D9LlHVCXpEAJoDTjb+TlvQ9l6gkExU2C0ltdrddFlrF/y4sjxoAqz8E2Y5f8/Hyb0tkclAKA/v3744svvsDZs2cBAAsXLsSECRMQEhICALh27Rpuu+02HDt2zL7S1pLp06djypQppsd5eXmIiYlBeHg4goKCXFiympMkCQqFAuHh4ewQMsD6lA/WpbywPuUlTJtr+B0ahoiI4No5R6mP4XdYKCIiQh2ad5FWh7XJl/HRvksAgFm/XMJj8Y0xIq4h/FR2faQjG3EMcB+lCEM6gNCwMKgjIhyfh14P6fffocrJQchdd0Hp42M6lLN3OXL/fNUsedneyabt4JtnIOSW/1V67vyjq6A9/wmENtu0TykVw7foMBQXLsA/tg0C24+p1usiy9i/5cURY0BV2Gbso9HYdvdhuz7BbNmyxexyuPnz52P48OGmoJROp8PJkyftybJWqdVqqNUV/6OgVCpl0YgUCoVsXguxPuWEdSkvrE/5UCoVpt+1VZ8KhdL025HnKNLq8PjXf2PLiQzkl+oAAPsv5uJk5nHsOH0NK4Z3ZGCqlnAMcA/G+qlJXVWah1YLxaJFCNBqoezbF8py3zGCOj4O/xb3Ws3Xy79BlWUqSlkHUWZ5VoEoy0dRyjoE3/Coja+EbMX+LR+OGANswTZjO1vfoxp9ehG88wQRERFRrVuTdAlbTmQgp0Rn2qcXQE6JDltOZGBN0iWMjY91YQmJZE6pBDp0gK6wEKrrvmh5+0fV+Hbt+oJLgNBbPijpDceJiGSI4T0iIiKiOu6r5Mso0Fr+wlqg1eOr5MtOLhGRh1GpIObPR8GMGUAt3H3PKyAaUHhZPqj0MhwnIpIhu2ZKKRQKKBSKCvucpaCgAKdPnzY9Pnv2LJKTkxEWFobGjRs7rRxEREREznQptwR6yfIMdb0kcCnXvW/gQuTpAloNgzb9ACRtToVjSp8ABLQa5vxCERE5gV1BKSEExowZY1qnqaSkBBMmTIC/vz8AmK03VRv279+PXr16mR4bFzEfPXo0Vq1aVavnJiIiInKV6GANUq4WQmchMOWlVCA62LbFRImobvJvPQLF53eiOHUrJG2+4VI+hReUqkD4xibCv/UIVxeRiKhW2BWUGj16tNnjUaNGVUjz8MMP16xElejZsyfXsSIiIiKPMzyuIfZfyDFbU8ooUOWF4XENXVAqIg+i1ULx3HMILC4G3n0XsPGuUrZS+vihfp/lKDy5FnlHPoI2/S+oIjsjqMNj8G89AkofP4eej4iorrArKLVy5craKgcRUa1IyytBWp71y1qigjSICuIMAyKq20Z2isaOlKuGu+9pddBLgJcSCFJ5I7FNBEZ24nozRLVKkoCzZ+Gl1Rq2a4HSxw+BHR6FKqITLq+JR/073oU6olOtnIuIqK7gvYOJSNaW70nF3G2nrB6f2acVZie2dmKJiEiOirQ6rEm6hI/2pgIAnvr2bzx2SyxGdoqGn6rmH7f8VN5YMbwj1iRdwoq95/HnhRx0iQ7B2FsaO+wcRFQJlQpi7lwUZGUhrBYWOici8lT8BENEsjY+IRb3to8EAJzIKMCoNUlYPbIT2kQEAABnSRFRjRVpdRj71SHDLKZSw+V1+y/m4uT3x7Aj5SpWDO/osMDU2PhYdI4ORtclv+L9ITegc6OQGudLRDZQKoG4OOgyMgzbRETkEAxKEZGsWbo8r01EAL/IEZHDrEm6hC0nMszWe9ILIKdEhy0nMrAm6RLGxse6sIREZI2uMA36wnQAgDbrhNlvAPDybwBv/yiXlI2IyBMwKEVERERUA18lX0aBVm/xWIFWj6+SLzMoRVRH5R/+EDn75pntu7rlv5s7hcS/jNCEmYBeD+zfD+/sbODOOzlbiojIQRiUIiIiIlkzrve0Yu95AMDE9X87dC2mS7kl0EuW7w6slwQu5Vq/2QIRuVbgjePg13yA1eNe/g0MG2VlULzyCgK0WuD22wEfHyeVkIhI3hiUIiIiItmytN7TgYs5OPV9gcPWe4oO1iDlaiF0FgJTXkoFooO5dh1RXeXtH2Xb5XlKJdCyJfRFRZwlRUTkQBxRiYiISLbKr/ek/zdmdP16TzU1PK4hAlReFo8FqrwwPK5hjc9BRC6mUkG8+Sby58wBePc9IiKHYVCKiIiIZMuW9Z5qamSnaPRrE4EQjTe8//1k5a0EQjXeSGwTgZGdomt8DiKSN11hGkozklCakWS24Lpxn64wzcUlJCKqHbx8j4iIiGTLGes9+am8sWJ4R6xJuoSP9qZi34VcdIkOxmO3xDps3SoikjebF1wnIhPePVMe+CmJiIiIZMtZ6z35qbwxNj4WcVFBuPmd37B08A3o2jjUIXkTUR2g1UIxYwYCioqAN98ENI5dK87mBdeJyITBXHlgUIqIiIhka3hcQ+y/kIOcEl2FY+603lNaXgnS8gyzuk5kFJj9BoCoIA2igrigOlGtkSTg+HF4a7WGbQezecF1IjJhMFceGJQiIiIi2RrZKRo7Uq4a7r6n1UEvAV5KIEjlXus9Ld+TirnbTpntG7UmybQ9s08rzE5s7exiEXkOHx+Il15CYXY2VD4+ri4NEYHBXLlgUMqNlP8vqSQJZGUXIkybC6VSAYD/JSUiIrpe+fWeVuw9jz8v5KBLdAjG3tLYrdZ7Gp8Qi3vbR1o9zr//RLXMywu45RaUZWQYtomIyCHc45MYAbD8X9Ly+F9SIiKiiozrPXWODkbXJb/i/SE3oHOjEFcXyy78xxMRERHJEYNSbqT8f0mPpefj4bXJ+HREHNo1CATA/5ISyV352ZKW8EsrERFRLZEk4O+/4Z2VBdSvDyiVri4REZEsMCjlRsp/4ZT+vYtQm4gAt/tvLxFVD2dLEhER/cept4M33n1PqwXi4wFvfo0iInIEjqZERG6i/GzJExkFGLUmCatHdkKbiAAAnC1JRESexam3g1cogJgYSMXFhm0iInIIBqWIiNyEpcvzOFuSiIg8lVNvB69WQ7z3HvIyMqBRqx2XLxGRh2NQioiIiIiI3A5vB09E5P64Qh8RERERERERETmd2wWl3nvvPTRp0gQajQbx8fH4888/XV0kIiIiIiKSM60WipkzEfDaa4BW6+rSEBHJhlsFpb788ktMmTIFs2bNwsGDB9GxY0ckJiYiIyPD1UUjIiIiIiK5kiQgORneR48atomIyCHcak2pt956C+PGjcMjjzwCAFi2bBl+/PFHfPzxx3jxxRdtzker1UJr4T8cSqUS3uVu72opjZFCoYCPj0+10paVlUEIUaO0ZbqyivsqyRcAVCpVtdLqdDpIlfzxtSetj48PFP/esaS20ur1euj1eoek9fb2hlKprPW0ZWVlpnZp3G8prSRJ0Ol0VvP18vKCl5dXnUkrhEBZWcW2Wp205ftnddOWlelMv4191p5+b0taSZKg1WpRVlYGdbmFUGtrjNDpdFbztidfgGOEtbTW+ibgvDHC1rR1od/X5THCOAaUfz/tyRew3JfT8kqRnl8KpVKBk5lFAAx3yDSep0GgGlFB5gsju/pzhL1pAc8cI4xjevkxoC5+juAY4YTPEUJAeuop5OTkIEQIKGvpc4QRx4jqpbWn3xvbjr35uuN3DY4RjhkjjH8TdDqdqV064nNEddK6wxhRWZnKc5uglFarxYEDBzB9+nTTPqVSid69e2PPnj0Wn1NaWorS0lLT47y8PADAokWLzL4sGrVo0QIPPvig6fHrr79utYHFxsZizJgxpseLFy9GUVGRxbRRUVF4/PHHTY/fffdd5OTkWEwbHh6OiRMnmh4vX74cmZmZpsdleglH0vORlK0Amt6Bp789jEfjYzEiriFWr/oYaWlpFvP18/PD888/b3r82WefITU11WJaHx8fvPTSS6bHX3zxBU6fPm0xLQDMmjXLtP3111/j+PHjVtNOnz7d1IG/++47HDp0yGraqVOnwt/fHwCwefNm7N+/32rayZMnIyQkBACwbds2q20CAJ544glEREQAAHbt2oVdu3ZZTTt27FhER0cDAP744w9s377datrRo0ejSZMmAIC//voLmzdvtpr2gQceQKtWrQAAhw4dwoYNG1BYWAh/f3/THzGjoUOHon379gCAo0eP4uuvv7aa78CBAxEXFwcAOHXqFL744gurae+66y7cfPPNAIBz587hk08+sZq2d+/e6N69OwDg0qVLWLFihdW0PXr0QM+ePQEAGRkZ+OCDD6ymTUhIQN++fQEAOTk5ePvtt62m7dq1K+6++24AQGFhIRYtWmQ1bceOHTFo0CAAhrFj7rxXcSQ9HwezlUDTXhgydT46h0ro0CAQN3Zoj+HDh5ue++qrr1rN15YxQgiBwsJCtGvXzhRABxw/RkiSYcBfv/4b/Jh3yWLakJAQTJ482fT4o48+4hgB+8aI3bt3Y9OmTRb7JuC8MWLjxo1W03KMMLBljEjT+wFoh127dqHLqEEADGPEggULrObbtm3bKseIXdqG2F3W0Gzfw2uTTdu3+1xGD9Vls+Ou+hxRHseI/1gbI4xjevkxoC5+juAYUbufI4xjhKk9HD1qag+2jBFG7vBdozy5jxFTpkyBEAKSJMn+uwbHCMeMEcYxoFu3bhg8eDAAx3yOMJLbGFE+FlMZtwlKXb16FXq9HpGRkWb7IyMjceLECYvPWbBgAebMmVNhf2FhocXoa15entmlgAUFBVajtPn5+RXSFhcX25Q2Pz8fhYWFFtNqNBqraXV6CX+ez8E/ynBkNbwRAHD4ch7e3XEUJ85eRL2cXKv5SpJkcxm8vb3N0ubl5VlNC8DutMY/FLm51ssLAJmZmabjtqQ1RmJzcnIqTXv16lXTti1pjdHf7OzsStNeu3YNfn5+Nqc1vm9ZWVkoLCxESUkJAFT44puVlVUhrTXl0167dq3StNnZ2dVKe/Xq1UrT5uTkVCttVXWcm5trSltUVGRz2vzCYvxy/JJZv8lo2AV/ph/G1exLiG4YbdaGK8vXljFCCIGSkhKLaR0xRnj7qLD61yPYcMzwx2HLkfNoJq4hNtQX3l7mM3m8vLxszpdjxH/KjxHZ2dlW+6YxrbPGCGs4RsB0vKoxohiGOiwoKDCl1Wq1dvV7S2nbIBUxSEN0w4bo1esOFBTkIyAgEF+s/QJ6vR7+ZVoUlpn/t9AVnyOuxzHCPK2lMcI4pgP/jQF19XOENRwjYDpenc8R5ccIS+3BljHCqK5/17ieJ4wRxpkdnvBdwxqOETAdr2qMMI4B1sYISzx5jLA1KKUQlc2rrEMuX76M6Oho/PHHH0hISDDtnzZtGnbt2oV9+/ZVeI6lmVIxMTG4cuUKgoKCKqSv69PlVv11Hi9tPokcC6cK03hjXr8WGNM1xmo53G1KraddvpeZmYnw8HBevmdFdafdf7QvFS98f8RivwlVAfP7t8W4bs1M+xxx+V5mZiYiIiIcfvlekVaHSd8cwU+ns5FXqoNeAF4KINAH6NuqPt6/7wb4qbztzteIY0TFtGVlZUhPT7fYNwFOu69pWmePEUmX89D9/X3YMykBN8WG2Z0vUPUYoVQqTeN5Ze+DO0y7v54njhHGMb38GFAXP0dwjHDC5XuSBCklBVnZ2Qjr2hXKf4978qU513O3McLLywtXr15FeHg4JEmS9XcNjhGOu3wvMzMTkZGRvHzPhrR5eXmIjIxEbm6uxfiLkdvMlKpfvz68vLxw5coVs/1XrlxBgwYNLD5HrVZbvExPo9FAo9FUeU5b0lQnraUy2ZL2m2PXUKBTAKjYKPK0enxz9Com3NrS4WUo/4fAHdIqlUqzzuEOab28vKBWq6HRaCx+8S2ftvxAVVW+rk4LmF+v74q06w6nW+03+ToF1h/NxPhbW5j21bTfS5JkGnvK16UjxojVh1Kx9XQ2ckr++0OtF0COFvjpdA6+OX4NY+Nj7c63pmnrQr+vrTHCx8fHpr5pb761mdbV/b4ujxFqlWGGg4+Pt1l92pNvVX1ZkiQoFAoolco69znCVWnrQr+v7hhhHNOtjQF1pd9zjHBC2pISiBkzUF+rhWrDBiit9Nna6vccIxyftvx4bU+brCt9mWOEgTPHCOPfBJVKVWufI5yR1ln93tY1pdzm7nsqlQpdunTBzz//bNonSRJ+/vlns5lTcnYptwR6yXKUUi8JXMotcXKJiOo+OfWbr5Ivo0Br+T9dBVo9vkq+bPEYERER1ZBCAUREQKpf37BNREQO4TYzpQDDYnSjR49G165dcfPNN2PJkiUoLCw0W0xYzqKDNUi5WgidhS/YXkoFooNtj44SeQo59Rs5BdiIiIjciloNsWIF8jIyoLFj5gAREVXOrYJS999/PzIzMzFz5kykp6cjLi4OW7ZsqbD4uVwNj2uI/RdyzC7dMQpUeWF4XEMLzyLybHLqN3IKsBEREREREbnN5XtGTz75JFJTU1FaWop9+/YhPj7e1UVympGdotGvTQRCNN7w/rfmvJVAqMYbiW0iMLJTtGsLSFQHle83xpvTeblpvxke1xABKsvXrLtbgI2IiIiIiMjtglKezE/ljRXDO+L1Ae3QJToYANAlOhivDWiHFcM7mt11i4gMzPtNCACgS3SIW/YbOQXYiIiI3IpWC7z6KvwXLzZsExGRQ7jPtzECYPiCPTY+FnFRQbj5nd+wdPAN6No41NXFIqrTjP2mc3Qwui75Fe8PuQGdG4W4ulh2MwbY1iRdwoq95/HnhRx0iQ7B2FsaY2SnaLcKsBEREbkVSYJi3z74aLWAJLm6NEREssFvMEREbkQuATYiIiK34u0NMWkSinJyoLLjdvVERFQ5jqhEREQkW2l5JUjLM9yZ8kRGgdlvAIgK0iAqiDcJIKIqeHsDiYnQZmQYtomIyCE4ohIREZFsLd+TirnbTpntG7UmybQ9s08rzE5s7exiEREREREYlCIiIiIZG58Qi3vbR1o9zllSRGQTIYDz56G8ehUID3d1aYiIZINBKSIiIpItXp5HRA5RWgrFk08iSKsFNmwA/PxcXSIiIllgUIqIiIiIiKgqQUEQJSWuLgURkawwKEVERERERFQZjQZi9WrkZmQgQsPZl0REjsKglBuxdgchpVIBgJcoEMkd7yJGRERERERywqCUG7F0B6GH1yabtnkHISJ5413EiIiIiIhIThiUciPl7yAkSQJZ2VkICw0zmylFRPLFu4gRERG5iFYLvP02/AoKgOnTAV7CR0TkEAxKuZHyl+ZIkoQMVSkiIoKhVCpdXDIicgZenkdEROQikgTFrl1QabWAJLm6NEREssGgFBERERERUWW8vSEeewzFublQefMrFBGRo3BEJSIiIiIiqoy3NzBwIEozMgzbRETkELzui4iIiIiIiIiInI5BKSIiIiIiosoIAWRkQHn1qmGbiIgcgnNPiYiIiIiIKlNaCsXYsQjSaoENGwA/P1eXiIhIFhiUIiIiIiIiqopa7eoSEBHJDoNSREREREREldFoINatQ05GBiI0GleXhohINrimFBEREREREREROZ3bzJR69dVX8eOPPyI5ORkqlQo5OTmuLhIRuYG0vBKk5ZUAAE5kFJj9BoCoIA2igvgfTyIiIiIiImdzm6CUVqvFsGHDkJCQgI8++sjVxSEiN7F8Tyrmbjtltm/UmiTT9sw+rTA7sbWzi0VERETupKwM+OAD+OXnA1Oncn0pIiIHcZug1Jw5cwAAq1atcm1BiMitjE+Ixb3tI60e5ywpIiIiqpJeD8VPP0Gl1QLPPuvq0hARyYbbBKWqo7S0FKWlpabHeXl5AABJkiBJkquK5RCSJEEI4favgwxYn7UnMkCFyABVpWkc+b6zLuWF9Un2YpuRF9YnmSiVEA8+iOK8PHgrlQDbhNtj/yZ7sc3Yx9b3SdZBqQULFphmWJWXmZmJkpISF5TIcSRJQm5uLoQQUCq5Xr27Y33KB+tSXlifZC+2GXlhfVJ5Uo8eyM3NRXFWFtuDDLB/k73YZuyTn59vUzqXBqVefPFFvPbaa5WmOX78ONq0aVOt/KdPn44pU6aYHufl5SEmJgbh4eEICgqqVp51hSRJUCgUCA8PZ4eQAdanfLAu5YX1SfZim5EX1ieVx/YgL6xPshfbjH00GtuWSXFpUOq5557DmDFjKk3TrFmzauevVquhtrAIoVKplEUjUigUsnktxPqUE9alvLA+yV5sM/LC+iQAgBBAfj6U+flQRkSwPcgE+zfZi23Gdra+Ry4NSoWHhyM8PNyVRSAiIiIiIqpcaSkUDz2EYK0W2LAB8PNzdYmIiGTBbdaUOn/+PLKysnD+/Hno9XokJycDAFq0aIGAgACb8hBCAPhvwXN3JkkS8vPzodFoGKWVAdanfLAu5YX1SfZim5EX1ieZlJRAlJWhVKeDKi8PSp3O1SWiGmL/JnuxzdjHGHcxxmGsUYiqUtQRY8aMwSeffFJh/86dO9GzZ0+b8rh48SJiYmIcXDIiIiIiIiIiIrrehQsX0KhRI6vH3SYo5QiSJOHy5csIDAyEQqFwdXFqxLho+4ULF9x+0XZifcoJ61JeWJ9kL7YZeWF9UnlsD/LC+iR7sc3YRwiB/Px8NGzYsNKZZW5z+Z4jKJXKSiN07igoKIgdQkZYn/LBupQX1ifZi21GXlifVB7bg7ywPslebDO2Cw4OrjINL4QkIiIiIiIiIiKnY1CKiIiIiIiIiIicjkEpN6VWqzFr1iyo1WpXF4UcgPUpH6xLeWF9kr3YZuSF9UnlsT3IC+uT7MU2Uzs8aqFzIiIiIiIiIiKqGzhTioiIiIiIiIiInI5BKSIiIiIiIiIicjoGpYiIiIiIiIiIyOkYlCIiIiIiIiIiIqdjUIqIiIiIiIiIiJyOQakqLFiwADfddBMCAwMRERGBQYMG4eTJk2ZpSkpKMGnSJNSrVw8BAQEYMmQIrly5Yjp+6NAhPPDAA4iJiYGvry/atm2Lt99+2yyPX375BQqFosJPenp6peUTQmDmzJmIioqCr68vevfujZSUFNPxc+fO4bHHHkPTpk3h6+uL5s2bY9asWdBqtVW+9l9++QWdO3eGWq1GixYtsGrVKrPju3fvxoABA9CwYUMoFAps2LChyjxdzVPrMy0tDSNHjkSrVq2gVCrxzDPPVEizatWqCuXVaDSV5utq7l6fAHDvvfeicePG0Gg0iIqKwkMPPYTLly9X+drl1j89tS7l2jedQQ5txqi0tBRxcXFQKBRITk6u8rXLrf8DnlufHAMsk0N7aNKkSYV8Fy5cWOVrZ/+WT32yf1efHNoMAPz444+Ij4+Hr68vQkNDMWjQoCpf++HDh3HbbbdBo9EgJiYGr7/+utnxo0ePYsiQIaY2uWTJkirzrOsYlKrCrl27MGnSJOzduxfbtm1DWVkZ+vbti8LCQlOaZ599Ft9//z3WrVuHXbt24fLly7jvvvtMxw8cOICIiAisXr0aR48exYwZMzB9+nS8++67Fc538uRJpKWlmX4iIiIqLd/rr7+Od955B8uWLcO+ffvg7++PxMRElJSUAABOnDgBSZKwfPlyHD16FIsXL8ayZcvw0ksvVZrv2bNncffdd6NXr15ITk7GM888g7Fjx2Lr1q2mNIWFhejYsSPee+89m97LusBT67O0tBTh4eF4+eWX0bFjR6vpgoKCzMqbmppaab6u5u71CQC9evXCV199hZMnT2L9+vU4c+YMhg4dWmm+cuyfnlqXcu2bziCHNmM0bdo0NGzY0KbXLcf+D3hufXIMsEwu7WHu3Llm+T711FOV5sv+La/6ZP+uPjm0mfXr1+Ohhx7CI488gkOHDuH333/HyJEjK803Ly8Pffv2RWxsLA4cOIA33ngDs2fPxv/93/+Z0hQVFaFZs2ZYuHAhGjRoUOV76RYE2SUjI0MAELt27RJCCJGTkyN8fHzEunXrTGmOHz8uAIg9e/ZYzWfixImiV69epsc7d+4UAER2drbNZZEkSTRo0EC88cYbpn05OTlCrVaLL774wurzXn/9ddG0adNK8542bZpo37692b77779fJCYmWkwPQHz77bc2l72u8JT6LK9Hjx5i8uTJFfavXLlSBAcH25xPXSSH+ty4caNQKBRCq9VaTeMJ/dNT6rI8OfdNZ3DXNrNp0ybRpk0bcfToUQFAJCUlVZq3J/R/ITynPsvjGGCdO7aH2NhYsXjxYpvzFYL9W271WR77d824W5spKysT0dHRYsWKFTbnK4QQ77//vggNDRWlpaWmfS+88IJo3bq1xfQ1bZd1BWdK2Sk3NxcAEBYWBsAQgS0rK0Pv3r1Nadq0aYPGjRtjz549leZjzKO8uLg4REVFoU+fPvj9998rLcvZs2eRnp5udu7g4GDEx8dX69zl7dmzxyxfAEhMTKw0X3fkKfVpq4KCAsTGxiImJgYDBw7E0aNHHZKvs7h7fWZlZeHzzz9Ht27d4OPjYzVvT+ifnlKXtnL3vukM7thmrly5gnHjxuGzzz6Dn5+fTa/TE/o/4Dn1aStPHwPcsT0AwMKFC1GvXj106tQJb7zxBnQ6XaV5s3/Lqz5t5en92xbu1mYOHjyIS5cuQalUolOnToiKisJdd92FI0eOVJr3nj17cPvtt0OlUpn2JSYm4uTJk8jOzq70ue6MQSk7SJKEZ555Bt27d0eHDh0AAOnp6VCpVAgJCTFLGxkZafVa1D/++ANffvklHn/8cdO+qKgoLFu2DOvXr8f69esRExODnj174uDBg1bLY8w/MjLS5nOfPn0aS5cuxfjx4yt9renp6RbzzcvLQ3FxcaXPdReeVJ+2aN26NT7++GNs3LgRq1evhiRJ6NatGy5evFjjvJ3BnevzhRdegL+/P+rVq4fz589j48aNlb5WufdPT6pLW7h733QGd2wzQgiMGTMGEyZMQNeuXW1+rXLv/4Bn1actPH0McMf2AABPP/001q5di507d2L8+PGYP38+pk2bVulrZf8OMUvr7vVpC0/v37Zwxzbzzz//AABmz56Nl19+GT/88ANCQ0PRs2dPZGVlVZq3pXzLn1eOvF1dAHcyadIkHDlyBL/99lu18zhy5AgGDhyIWbNmoW/fvqb9rVu3RuvWrU2Pu3XrhjNnzmDx4sX47LPP8Pnnn5sFHjZv3gwvLy+7zn3p0iX069cPw4YNw7hx40z7AwICTNujRo3CsmXLqvPS3A7r01xCQgISEhLMyty2bVssX74cr7zyil1lcwV3rs/nn38ejz32GFJTUzFnzhw8/PDD+OGHH6BQKDyyf7Iuzbl733QGd2wzS5cuRX5+PqZPn241jSf2f4D1eT1PHwPcsT0AwJQpU0zbN954I1QqFcaPH48FCxZArVazf7M+AbB/28Id24wkSQCAGTNmYMiQIQCAlStXolGjRli3bh3Gjx+P9u3bm9YPu+2227B58+Zqvz53x6CUjZ588kn88MMP2L17Nxo1amTa36BBA2i1WuTk5JhFaq9cuVJh4bFjx47hzjvvxOOPP46XX365ynPefPPNps537733Ij4+3nQsOjoaaWlppnNFRUWZnTsuLs4sr8uXL6NXr17o1q2b2UJpAMzuDBMUFGR6XeXvXmDMNygoCL6+vlWWva7ztPqsDh8fH3Tq1AmnT5+udh7O4u71Wb9+fdSvXx+tWrVC27ZtERMTg7179yIhIcHj+qen1WV1uFPfdAZ3bTM7duzAnj17oFarzfLu2rUrHnzwQXzyySce1/8Bz6vP6vCkMcBd24Ml8fHx0Ol0OHfuHFq3bs3+7QH1WR2e1L9t4a5txri/Xbt2puNqtRrNmjXD+fPnAQCbNm1CWVkZAJj6t7UxwHhMtly9qFVdJ0mSmDRpkmjYsKE4depUhePGRda+/vpr074TJ05UWGTtyJEjIiIiQjz//PM2n7t3795i8ODBlZatQYMGYtGiRaZ9ubm5FRbmu3jxomjZsqUYMWKE0Ol0Np172rRpokOHDmb7HnjgAbdfaNFT67M8awstXk+n04nWrVuLZ5991u5zOIsc6vN6qampAoDYuXOn1TRy7J+eWpflyalvOoO7t5nU1FTx999/m362bt0qAIivv/5aXLhwwWrecuz/QnhufZbHMeA/7t4eLFm9erVQKpUiKyvLahr2b3nVZ3ns3/Zx9zZjfFx+oXOtVisiIiLE8uXLreZtXOi8/E1ypk+fLvuFzhmUqsITTzwhgoODxS+//CLS0tJMP0VFRaY0EyZMEI0bNxY7duwQ+/fvFwkJCSIhIcF0/O+//xbh4eFi1KhRZnlkZGSY0ixevFhs2LBBpKSkiL///ltMnjxZKJVKsX379krLt3DhQhESEiI2btwoDh8+LAYOHCiaNm0qiouLhRCGAEaLFi3EnXfeKS5evGh2/sr8888/ws/PTzz//PPi+PHj4r333hNeXl5iy5YtpjT5+fkiKSlJJCUlCQDirbfeEklJSSI1NdWu99iZPLU+hRCmuurSpYsYOXKkSEpKEkePHjUdnzNnjti6das4c+aMOHDggBgxYoTQaDRmaeoad6/PvXv3iqVLl4qkpCRx7tw58fPPP4tu3bqJ5s2bi5KSEqv5yrF/empdCiHPvukM7t5mrnf27Fmb7tYmx/4vhOfWpxAcAyxx9/bwxx9/iMWLF4vk5GRx5swZsXr1ahEeHi4efvjhSvNl/5ZXfQrB/l1d7t5mhBBi8uTJIjo6WmzdulWcOHFCPPbYYyIiIqLSQGZOTo6IjIwUDz30kDhy5IhYu3at8PPzMwtklZaWmtpVVFSUmDp1qkhKShIpKSl2vcd1CYNSVQBg8WflypWmNMXFxWLixIkiNDRU+Pn5icGDB5sFCWbNmmUxj9jYWFOa1157TTRv3lxoNBoRFhYmevbsKXbs2FFl+SRJEv/73/9EZGSkUKvV4s477xQnT540HV+5cqXV11CVnTt3iri4OKFSqUSzZs3MXrPxuKV8R48eXWXeruLJ9VlVmZ955hnRuHFjoVKpRGRkpOjfv784ePBglfm6krvX5+HDh0WvXr1EWFiYUKvVokmTJmLChAni4sWLVeYtt/7pyXUpx77pDO7eZq5nTxBDbv1fCM+uT44BFbl7ezhw4ICIj48XwcHBQqPRiLZt24r58+dX+U8KIdi/5Vaf7N/V4+5tRgjDzKjnnntOREREiMDAQNG7d29x5MiRKvM+dOiQuPXWW4VarRbR0dFi4cKFZseNf1+u/+nRo0eVeddVCiGEABERERERERERkRMpXV0AIiIiIiIiIiLyPAxKERERERERERGR0zEoRURERERERERETsegFBEREREREREROR2DUkRERERERERE5HQMShERERERERERkdMxKEVERERERERERE7HoBQRERERERERETkdg1JEREREREREROR0DEoREREREREREZHTMShFREREREREREROx6AUERERERERERE5HYNSRERERERERETkdAxKERERERERERGR0zEoRURERERERERETsegFBEREREREREROR2DUkRERERERERE5HQMShEREVGtOXfuHBQKBVatWuXqoshakyZNMGbMGFcXg4iIiMguDEoRERFRta1atQoKhcLiz4svvlgr55w/fz42bNhQZbqePXtaLVv5n9mzZ9dKOa+3adMmp53LHidOnMC0adMQFxeHwMBAREVF4e6778b+/fstpr906RKGDx+OkJAQBAUFYeDAgfjnn3/M0ly4cAFz5szBzTffjNDQUNSvXx89e/bE9u3bK+RXWRtKT0+3+XV89NFHaNu2LTQaDVq2bImlS5daTfvll18iISEB/v7+CAkJQbdu3bBjxw6bz0VERESO4e3qAhAREZH7mzt3Lpo2bWq2r0OHDoiNjUVxcTF8fHwcdq758+dj6NChGDRoUKXpZsyYgbFjx5oe//XXX3jnnXfw0ksvoW3btqb9N954o8PKVplNmzbhvffeq3OBqRUrVuCjjz7CkCFDMHHiROTm5mL58uW45ZZbsGXLFvTu3duUtqCgAL169UJubi5eeukl+Pj4YPHixejRoweSk5NRr149AMDGjRvx2muvYdCgQRg9ejR0Oh0+/fRT9OnTBx9//DEeeeSRCuWw1IZCQkJseg3Lly/HhAkTMGTIEEyZMgW//vornn76aRQVFeGFF14wSzt79mzMnTsXQ4cOxZgxY1BWVoYjR47g0qVLdr5zREREVFMMShEREVGN3XXXXejatavFYxqNpsrnFxYWwt/f36Fl6tOnT4VyvPPOO+jTpw969uzp1LLUZQ888ABmz56NgIAA075HH30Ubdu2xezZs82CUu+//z5SUlLw559/4qabbgJgqPsOHTrgzTffxPz58wEAvXr1wvnz51G/fn3TcydMmIC4uDjMnDnTYlCqsjZUmeLiYsyYMQN33303vv76awDAuHHjIEkSXnnlFTz++OMIDQ0FAOzduxdz587Fm2++iWeffdbucxEREZFj8fI9IiIiqjWW1pQaM2YMAgICcObMGfTv3x+BgYF48MEHAQApKSkYMmQIGjRoAI1Gg0aNGmHEiBHIzc0FACgUChQWFuKTTz4xXeJVk7WUZs+eDYVCgWPHjmHkyJEIDQ3Frbfeajq+evVqdOnSBb6+vggLC8OIESNw4cIFszx+/fVXDBs2DI0bN4ZarUZMTAyeffZZFBcXm73m9957z/QajD9GkiRhyZIlaN++PTQaDSIjIzF+/HhkZ2ebnUsIgXnz5qFRo0bw8/NDr169cPToUYuv7cyZMzhz5kyV70GXLl3MAlIAUK9ePdx22204fvy42f6vv/4aN910kykgBQBt2rTBnXfeia+++sq0r3379mYBKQBQq9Xo378/Ll68iPz8fItlyc/Ph16vr7LM5e3cuRPXrl3DxIkTzfZPmjQJhYWF+PHHH037lixZggYNGmDy5MkQQqCgoMCucxEREZFjcaYUERER1Vhubi6uXr1qtu/6oER5Op0OiYmJuPXWW7Fo0SL4+flBq9UiMTERpaWleOqpp9CgQQNcunQJP/zwA3JychAcHIzPPvsMY8eOxc0334zHH38cANC8efMal3/YsGFo2bIl5s+fDyEEAODVV1/F//73PwwfPhxjx45FZmYmli5dittvvx1JSUmmS8vWrVuHoqIiPPHEE6hXrx7+/PNPLF26FBcvXsS6desAAOPHj8fly5exbds2fPbZZxXOP378eKxatQqPPPIInn76aZw9exbvvvsukpKS8Pvvv5suf5w5cybmzZuH/v37o3///jh48CD69u0LrVZbIc8777wTgCEwWB3p6elmdShJEg4fPoxHH320Qtqbb74ZP/30E/Lz8xEYGFhpnn5+fvDz86twrFevXigoKIBKpUJiYiLefPNNtGzZsspyJiUlAUCFWVZdunSBUqlEUlISRo0aBQD4+eef0a1bN7zzzjuYN28erl27hgYNGmDGjBl48sknqzwXERERORaDUkRERFRj5S/xMjIGdywpLS3FsGHDsGDBAtO+5ORknD17FuvWrcPQoUNN+2fOnGnaHjVqFCZMmIBmzZqZAg2O0LFjR6xZs8b0ODU1FbNmzcK8efPw0ksvmfbfd9996NSpE95//33T/tdeew2+vr6mNI8//jhatGiBl156CefPn0fjxo2RkJCAVq1aYdu2bRXK/dtvv2HFihX4/PPPMXLkSNP+Xr16oV+/fli3bh1GjhyJzMxMvP7667j77rvx/fffm2ZazZgxw3TZnKP8+uuv2LNnD15++WXTvqysLJSWliIqKqpCeuO+y5cvo3Xr1hbzPH36NL755hsMGzYMXl5epv1+fn4YM2YMevXqhaCgIBw4cABvvfUWunXrhoMHDyImJqbSsqalpcHLywsRERFm+1UqFerVq4fLly8DALKzs3H16lX8/vvv2LFjB2bNmoXGjRtj5cqVeOqpp+Dj44Px48fb9gYRERGRQzAoRURERDX23nvvoVWrVnY954knnjB7HBwcDADYunUr+vfvb3E2TW2ZMGGC2eNvvvkGkiRh+PDhZjPAGjRogJYtW2Lnzp2moFT5gFRhYSGKi4vRrVs3CCGQlJSExo0bV3rudevWITg4GH369DE7l/Gyup07d2LkyJHYvn07tFotnnrqKbNL/5555hmLQanqzpDKyMjAyJEj0bRpU0ybNs2033g5olqtrvAc47ph5S9ZLK+oqAjDhg2Dr68vFi5caHZs+PDhGD58uOnxoEGDkJiYiNtvvx2vvvoqli1bVml5i4uLoVKpLB7TaDSmMhkv1bt27RrWrl2L+++/HwAwdOhQ3HDDDZg3bx6DUkRERE7GoBQRERHV2M0332zXItXe3t5o1KiR2b6mTZtiypQpeOutt/D555/jtttuw7333otRo0aZAla15fq7vqWkpEAIYfXysfJ3Ezx//jxmzpyJ7777rsIaUMa1sCqTkpKC3NzcCjN9jDIyMgAYZm8BqFCm8PBw00LeNVVYWIh77rkH+fn5+O2338zWmjIG30pLSys8r6SkxCxNeXq9HiNGjMCxY8ewefNmNGzYsMpy3HrrrYiPj8f27dtN+zIzM83WmwoICEBAQAB8fX0tXr5oLJexTMbfPj4+ZjPxlEol7r//fsyaNcs0s42IiIicg0EpIiIicjq1Wg2lsuL9Vt58802MGTMGGzduxE8//YSnn34aCxYswN69eysEsRzp+mCKJElQKBTYvHmz2aVmRsZgjV6vR58+fZCVlYUXXngBbdq0gb+/Py5duoQxY8ZAkqQqzy1JEiIiIvD5559bPB4eHl6NV2Q/rVaL++67D4cPH8bWrVvRoUMHs+NhYWFQq9VIS0ur8FzjPksBp3HjxuGHH37A559/jjvuuMPm8sTExODkyZOmxzfddJMpMAcAs2bNwuzZsxEVFQW9Xo+MjAyzwJ5Wq8W1a9dMZQoLC4NGo0FISEiFOjU+Lzs7m0EpIiIiJ2JQioiIiOqUG264ATfccANefvll/PHHH+jevTuWLVuGefPmAYDZpWu1pXnz5hBCoGnTppVelvj333/j1KlT+OSTT/Dwww+b9m/btq1CWmvlbt68ObZv347u3btbnGlkFBsbC8Aws6pZs2am/ZmZmRVmaNlLkiQ8/PDD+Pnnn/HVV1+hR48eFdIolUrccMMN2L9/f4Vj+/btQ7NmzSoscv78889j5cqVWLJkCR544AG7yvTPP/+YBeQ+//xzs8sDje9BXFwcAGD//v3o37+/6fj+/fshSZLpuFKpRFxcHP766y9otVqzS/6M6045KwBIREREBhX/RUlERETkAnl5edDpdGb7brjhBiiVSrNLxvz9/ZGTk1OrZbnvvvvg5eWFOXPmVFiwXQiBa9euAYBpxk35NEIIvP322xXy9Pf3B4AKZR8+fDj0ej1eeeWVCs/R6XSm9L1794aPjw+WLl1qdr4lS5ZYfA1nzpzBmTNnKn+h/3rqqafw5Zdf4v3338d9991nNd3QoUPx119/mQWmTp48iR07dmDYsGFmad944w0sWrQIL730EiZPnmw1z8zMzAr7Nm3ahAMHDqBfv36mfd27d0fv3r1NP8ag1B133IGwsDB88MEHZnl88MEH8PPzw913323ad//990Ov1+OTTz4x7SspKcHnn3+Odu3a2XRpIRERETkOZ0oRERFRnbBjxw48+eSTGDZsGFq1agWdTofPPvsMXl5eGDJkiCldly5dsH37drz11lto2LAhmjZtivj4eIeWpXnz5pg3bx6mT5+Oc+fOYdCgQQgMDMTZs2fx7bff4vHHH8fUqVPRpk0bNG/eHFOnTsWlS5cQFBSE9evXW5y51KVLFwDA008/jcTERHh5eWHEiBHo0aMHxo8fjwULFiA5ORl9+/aFj48PUlJSsG7dOrz99tsYOnQowsPDMXXqVCxYsAD33HMP+vfvj6SkJGzevBn169evcL4777wTQNULni9ZsgTvv/8+EhIS4Ofnh9WrV5sdHzx4sCmgNnHiRHz44Ye4++67MXXqVPj4+OCtt95CZGQknnvuOdNzvv32W0ybNg0tW7ZE27ZtK+TZp08fREZGAgC6deuGTp06oWvXrggODsbBgwfx8ccfIyYmxuzOh9b4+vrilVdewaRJkzBs2DAkJibi119/xerVq/Hqq68iLCzMlHb8+PFYsWIFJk2ahFOnTqFx48b47LPPkJqaiu+//77KcxEREZGDCSIiIqJqWrlypQAg/vrrL4vHz549KwCIlStXmvaNHj1a+Pv7V0j7zz//iEcffVQ0b95caDQaERYWJnr16iW2b99ulu7EiRPi9ttvF76+vgKAGD16tE1lXbdunQAgdu7cado3a9YsAUBkZmZafM769evFrbfeKvz9/YW/v79o06aNmDRpkjh58qQpzbFjx0Tv3r1FQECAqF+/vhg3bpw4dOhQhdet0+nEU089JcLDw4VCoRDXfwz7v//7P9GlSxfh6+srAgMDxQ033CCmTZsmLl++bEqj1+vFnDlzRFRUlPD19RU9e/YUR44cEbGxsRXeh9jYWBEbG1vl+zJ69GgBwOrP2bNnzdJfuHBBDB06VAQFBYmAgABxzz33iJSUFLM0xvfV2k/5OpgxY4aIi4sTwcHBwsfHRzRu3Fg88cQTIj09vcqyX//+tW7dWqhUKtG8eXOxePFiIUlShXRXrlwRo0ePFmFhYUKtVov4+HixZcsWu85FREREjqEQ4ro56URERERERERERLWMa0oREREREREREZHTMShFREREREREREROx6AUERERERERERE5HYNSRERERERERETkdAxKERERERERERGR03m7ugDOJEkSLl++jMDAQCgUClcXh4iIiIiIiIhIdoQQyM/PR8OGDaFUWp8P5VFBqcuXLyMmJsbVxSAiIiIiIiIikr0LFy6gUaNGVo97VFAqMDAQgOFNCQoKcnFpakaSJGRmZiI8PLzSqCO5B9anfLAu5YX1SfZim5EX1ieVx/YgL6xPsktJCcRDD6G0rAyqNWug9PNzdYnqvLy8PMTExJjiMNZ4VFDKeMleUFCQLIJSJSUlCAoK4iAqA6xP+WBdygvrk+zFNiMvrE8qj+1BXlifZBc/P0hTp0Kfk4OgsDAoVSpXl8htVLV0kkcFpYiIiIiIiIiI7OLtDdx5J7QZGYZtchiGhImIiIiIiIiIyOkYlCIiIiIiIiIiskaSgH/+gVdqqmGbHIbzzoiIiIiIiIiIrNFqoXjmGQRqtcCGDbyEz4H4ThIRERERERERWaNQAGFhECUlhm0rdIVp0BemWz3u5d8A3v5RtVFCt8WgFBERERERERGRNWo1xKpVyM3IQIRabTVZ/uEPkbNvntXjIfEvIzRhZm2U0G0xKEVEREREREREVEOBN46DX/MBAABt1glc3TIa9ft9AlVYGwCGmVJkjkEpIiIiIiIiIqIa8vaPqnB5niqsDdQRnVxUorqPd98jIiIiIiIiIrJGqwVeew3+S5catslhOFOKiIiIiIiIiNyKUxcVlyQofv8dPlotIEmOyZMAMChFRERERERERG7GqYuKe3tDjB+P4txcqLwZRnEkvptERERERERE5Facsah4+dlY0k0NUZylgSbrbyiVStM5HDYby0MxKEVEREREREREbsUZi4pbmo1V/oJBh87G8lAMShERERERERERXcc0G0sIlJ7eg2t/PYt6fVZCHd4OgGNmY3k6BqWIiIiIiIiIiK5jmo1VUgLx7jNAPOAT2NShs7E8ndLVBSAiIiIiIiIiqtN8fV1dAlliUIqIiIiIiIiIyBqNBmL+fMO2Su3assgMg1JEREREREREROR0DEoREREREREREZHTMShFRERERERERGRNWRnwxReGbZ3OtWWRGQaliIiIiIiIiIis0euh+PNPw7akd21ZZIZBKSIiIiIiIiIia7y9IQYMMGx7ebu2LDLDoBQRERERERERkTXe3sAddxi2vbxcWxaZYYiPiIiIiIiIiGRNV5gGfWG61eNe/g3g7R/lxBIRwKAUEREREREREclc/uEPkbNvntXjIfEvIzRhpuWDQgC5uf9tk8MwKEVEREREREREshZ44zj4NTesC6XNOoGrW0ajfr9PoAprA8AwU8qq0lIoZs8G4gGUaWu/sB6EQSkiIiIiIiIikjVv/6gKl+epwtpAHdHJtgyUXJK7NvBdJSIiIiIiIiKyRqOBePNNw7ZK7dqyyIzbBKUWLFiAm266CYGBgYiIiMCgQYNw8uRJVxeLiIiIiIiIiIiqwW2CUrt27cKkSZOwd+9ebNu2DWVlZejbty8KCwtdXTQiIiIiIiIiIrKT26wptWXLFrPHq1atQkREBA4cOIDbb7/d4nNKS0tRWlpqepyXlwcAkCQJkiTVXmGdQJIkCCHc/nWQAetTPliX8sL6JHuxzcgL65PKY3uQF9anvBjr0dbv+vamR1kZsG4dAEBotbVzDpmx9TW7TVDqern/3o4xLCzMapoFCxZgzpw5FfZnZmaipKSk1srmDJIkITc3F0IIKLngmttjfcoH61JeWJ9kL7YZeWF9UnlsD/LC+pQXKSsLAJCdlQUlMhyeHiUl8P/lF+A2IOvqVXirrD9H0pWi9NJulJzdCA2Ac78sgKbpQKijb4fS23PWo8rPz7cpnVsGpSRJwjPPPIPu3bujQ4cOVtNNnz4dU6ZMMT3Oy8tDTEwMwsPDERQU5Iyi1hpJkqBQKBAeHs5BVAZYn/LBupQX1ifZi21GXlifVB7bg7ywPuWlFGFIBxAaFgZ1RITD00OnQ+k99wC5BxEWHg6NledIZUW4tn0aFKlboSkzXKmlufIjFFm/wutqIur1fh9KHz87Xpn70mg0NqVzy6DUpEmTcOTIEfz222+VplOr1VCrK0YilUqlLAYehUIhm9dCrE85YV3KC+uT7MU2Iy+sTyqP7UFeWJ/yYaxDW+vT3vRQqYC77gLWzoXCx8fqcwpTvkJJ6hYIbc5/O4UeQpuNkvNbUJzyFQI7PFr1+WTA1n7ldr3vySefxA8//ICdO3eiUaNGri4OEREREREREREKTq2DVFZg8ZhUVoCCU+ucXKK6z21mSgkh8NRTT+Hbb7/FL7/8gqZNm7q6SEREREREREQkd0IAxcX/bVuhL7gECL3lg5LecJzMuM1MqUmTJmH16tVYs2YNAgMDkZ6ejvT0dBQbGwYRERERERERkaOVlkLx0kuG7TKt1WReAdGAwsvyQaWX4TiZcZug1AcffIDc3Fz07NkTUVFRpp8vv/zS1UUjIiIiIiIiIg8X0GoYlD4BFo8pfQIQ0GqYk0tU97nV5XtERERERERERE6lVkMsWgSsuxXwUVlN5t96BIrP70Rx6lZI2nzDpXwKLyhVgfCNTYR/6xFOLLR7cJuZUkRERERERERETqdQAF5e/21bofTxQ/0+yxF220KoIjsDAFSRnRF220LU77McSh8/Z5TWrbjNTCkiIiIiIiIiorpM6eOHwA6PQhXRCZfXxKP+He9CHdHJ1cWqszhTioiIiIiIiIjIAqmsCPnJH6LgdcOld9d+fhr5Rz6GVFbk4pLJA2dKERERERERERFdRyorwtVt41F8ZgsUwbkAgLLMg8janYLi8zt5SZ4DMChFRERERERuJy2vBGl5JVaPRwVpEBWkcWKJiEhuCk+uRfG5rZD0uUCQca8ekjYHxalbUXhyLQI7POrKIro9BqWIiIiIiMjtLN+TirnbTlk9PrNPK8xObO3EEhGR3BScWgeprMDw4Lr1zaWyAhScWsegVA0xKEVERERERG5nfEIs7m0fCQA4kVGAUWuSsHpkJ7SJCAAAzpIiohrTF1wChN7yQUlvOE41wqAUERERERG5HUuX57WJCEDnRiGuKRARyY5XQDTKsk8DQlfxoNILXgHRzi+UzPDue0RERERERERE1wloNQxKH8PsSwjzY0qfAAS0Gub8QskMZ0oREREREZFH4mLpRFQZ/9YjUHx+J4r/2QLFtVzogwHAC0p1IHxjE+HfeoSri+j2GJQiIiIiIiKPxMXSiagySh8/1O+zHIUnvkDewRXQZx+AT2RnBN/wGPxbj4DSx8/VRXR7DEoREREREZFH4mLpRFQVpY8fAm94DN7hcUhfm4B6vd6Bb4Muri6WbDAoRUREREREHomLpRMRuRYXOiciIiIiIiIiskanA7ZsMWzr9a4ti8wwKEVEREREREREZI1OB8XWrYZtvc61ZZEZBqWIiIiIiIiIiKzx8oLo3s2wrfRybVlkhmtKERERERERVVNaXgnS8kqsHre0bhURuRkfH2DoMGDtW4A3wyiOxHeTiIiIiIiompbvScXcbaesHp/ZpxVmJ7Z2YomIiNwHg1JERERERETVND4hFve2jwQAnMgowKg1SVg9shPaRAQAAGdJEVGldIVp0BemWz3u5d8A3v5RTiyRczEoRUREREREVE2WLs9rExGAzo1CXFMgInK8khIonnsOuAmAttShWecf/hA5++ZZPR4S/zJCE2Y69Jx1CYNSRERERERELsR1qYjcgCTVSraBN46DX/MBAABt1glc3TIa9ft9AlVYGwCGmVJyxqAUERERERGRC3FdKqLqkcqKUHhyLfKOfAQAuLrjSQR1eAz+rUdA6ePnuBOp1RCzZwOb+wE+KsflC8DbP6rC5XmqsDZQR3Ry6HnqKgaliIiIiIiIXIjrUhHZTyorwtVt41F8biuksnwAgPbKQWRlpaD4/E7U77O8QmCq2kEshQIIDv5vmxyGQSkiIiIiIiIX4rpURPYrPLnWEJDS5vy3U+ghaXNQnLoVhSfXIrDDo6ZD1QliUe1TuroA9ti9ezcGDBiAhg0bQqFQYMOGDa4uEhERERERERE5WcGpdZDKCiwek8oKUHBqndk+syCW0Bt2XhfEskqnA3bsMGzr9Q4oPRm5VVCqsLAQHTt2xHvvvefqohARERERERGRi+gLLv0XXLqepDccL8feIJYZnQ6K77//98Q668kK01CakYTSjCRos04AMCxebtynK0yzfg4PVa3L97y8vJCWloaIiAiz/deuXUNERAT0tRQ5vOuuu3DXXXfZnL60tBSlpf/drjEvLw8AIEkSpFpaOd9ZJEmCEMLtXwcZsD7lg3UpL6xPshfbjLywPt2HJAnT7+rWV1V52NIenFEOcgz2b/enDGgEZJ8GhIUgkdIbyoBGZvWrK0irNIilK0iz3h4UCoiuXQHshYDCarq8Q/+H3D9fNdt3dcto03bwzTMQcsv/rL4mY75yiVnYolpBKSGExf2lpaVQqRy7En1NLFiwAHPmzKmwPzMzEyUl1m+56g4kSUJubi6EEFAq3WrCG1nA+pQP1qW8sD7JXmwz8sL6dB9Z2YX//s5Chqq0itTVy8OW9uCMcpBjsH+7v9LoB1B8NQO+RYchACgA0+9i37ZA9APIyMgwpc8PuAWqnPNQSgUov1S5ACB5+aM44Baz9NfT9esHbHsXWfn58LaSTkQNgrpvN+tl9o2o9BxSVhYAIDsrC0pYT+cO8vPzbUpnV1DqnXfeAQAoFAqsWLECAQEBpmN6vR67d+9GmzZt7MmyVk2fPh1TpkwxPc7Ly0NMTAzCw8MRFBTkwpLVnCRJUCgUCA8P5yAqA6xP+WBdygvrk+zFNiMvrE/3EabNNfwODUNERHCt5GFLe3BGOcgx2L/dnxQ6GNeubkPx+QuANh8QOigU3lCoAlGvYTvUixtstnC5b8ubkZ3+LYTWPB8FAG9vH4S3vBmB110NVl6JFIorAMJCQ6Gxms76821RijCkAwgNC4O6krK4A43GtruG2hWUWrx4MQDDTKlly5bBy8vLdEylUqFJkyZYtmyZPVnWKrVaDbVaXWG/UqmUxcCjUChk81qI9SknrEt5YX2Svdhm5IX16R6USoXpd3XrypY8qmoPzioHOQb7t3tTqgMQ3ncZCk+uRd6Rj6BN/wuqyE4I6vAY/FuPqHAnvcA2I1B6YSeKU7dC0uYbLuVTeEGpCoRvbF8EthlRaVtQ/HtMUYttxpivHNqlreW3Kyh19uxZAECvXr3wzTffIDQ01P6SERERERERkZkirQ5rki5hxd7zAICJ6//G2FsaY2SnaPipqrXqCpHsKX38ENjhUagiOuHymnjUv+NdqCM6WU1bv8/y64JYna0GscyUlEDx0kvAjQC0vKzWkao1uu3cudPR5SAiIiIiIvJIRVodxn51CFtOZCC/1LBo84GLOTj1fQF2pFzFiuEdGZgicgB7glgVFBfXbuE8VLXmgw0ZMgSvvfZahf2vv/46hg0bVuNCWVNQUIDk5GQkJycDMMzcSk5Oxvnz52vtnERERESVScsrwcGLOf/+5OLwlUIcvJhr2peW5943VyGi2rcm6RK2nMhATokO+n/vKaUXQE6JDltOZGBN0qXKMyCi2qVWQ7z0kmHbp+7c3E0OqhVu3717N2bPnl1h/1133YU333yzpmWyav/+/ejVq5fpsXER89GjR2PVqlW1dl4iIiIia5bvScXcbaesHp/ZpxVmJ7Z2YomIyN18lXwZBVrLt6ov0OrxVfJljI2PdXKpiMhEoQDCw//bdjCprMh0WSEAXN3xpG2XFcpAtYJSBQUFUKkqRgd9fHyQl5dX40JZ07NnTwghai1/IiIiInuNT4jFve0jAQDH0vPx8NpkfDoiDu0aBAIAooJsu/sMEXmuS7kl0EuWv+foJYFLuZxxSSRXUlkRrm4bj+JzWyGV5QMAtFcOIisrBcXnd6J+n+WyDkxV6/K9G264AV9++WWF/WvXrkW7du1qXCgiIiIidxEVpEHnRiHo3CgEbSICAABtIgJM+xiUIqKqRAdr4KW0PPvCS6lAdDDHESKX0umA334zbOstz2qsrsKTaw0BKW2O4Y6AACD0kLQ5KE7disKTax16vrqmWjOl/ve//+G+++7DmTNncMcddwAAfv75Z3zxxRdYt26dQwtIRERERERUl9X0znnD4xpi/4Uc5JToKhwLVHlheFxDh5eZiOyg00Gxfj0QD0BfsZ/WRMGpdZDKCiwek8oKUHBqHQI7POrQc9Yl1QpKDRgwABs2bMD8+fPx9ddfw9fXFzfeeCO2b9+OHj16OLqMREREREREdZIj7pw3slM0dqRcNeSh1UEvAV5KIEjljcQ2ERjZKdoZL4WIrFEqITp2BLAXUFTrgjOr9AWX/pshdT1JbzguY9W+r+jdd9+Nu+++25FlISIiIiIicivl75xndP2d86papNxP5Y0VwzuaZlv9eSEHXaJD7JptRUSOpytMg74wHQBQdm8C8NNylOX/A2WGGgDg5d8A3v5RNTqHV0A0yrJPA8LCDCylF7wC5B2UrvbolpOTg6+//hr//PMPpk6dirCwMBw8eBCRkZGIjpb3m0ZERERERAQ47s55fipvjI2PRefoYHRd8iveH3IDOjcKcXBpicge+Yc/RM6+eWb7rv00xrQdEv8yQhNm1ugcAa2GQZt+wLCm1HWUPgEIaDWsRvnXddUKSh0+fBi9e/dGcHAwzp07h7FjxyIsLAzffPMNzp8/j08//dTR5SQiIiIiIqpzeOc8IvkKvHEc/JoPAABIkoTsrCyEhoVBqTRcwufl36DG5/BvPQLF53eiOHUrJG2+4VI+hReUqkD4xibCv/WIGp+jLqvWxZBTpkzBmDFjkJKSAo3mvztB9O/fH7t373ZY4YiIiIiIiOoy3jmPSL68/aOgjugEdXA7aKa9jdDZH0Id3M6wL6JTjS/dAwCljx/q91mOsNsWQhXZGQCgiuyMsNsWon6f5VD6+NX4HHVZtYJSf/31F8aPH19hf3R0NNLT02tcKCIiIiIiIncwPK4hAlReFo/xznlEMiEEkJUFRU6OYdvBlD5+COzwKOrf8S4AoP4d7yKww6OyD0gB1bx8T61WIy8vr8L+U6dOITw8vMaFIiIiIiIicge8cx6RB1CpIJYsQf61a6inUrm6NLJSrZlS9957L+bOnYuysjIAgEKhwPnz5/HCCy9gyJAhDi0gERERERGRJUVaHVbsS8XE9X8DACau/xsr9qWiSGvhLla1lIfxznmvD2iHLtEhAIAu0SF4bUA7rBjekXfOI5IDpRJo1gz62FjDNjlMtUbIN998E0OHDkVERASKi4vRo0cPpKenIyEhAa+++qqjy0hERERERGSmSKvD2K8OGWYolRoCSAcu5uDU9wXYkXLVpoCQI/IAeOc8IqLqqlZQKjg4GNu2bcPvv/+OQ4cOoaCgAJ07d0bv3r0dXT4iIiIiIqIK1iRdwpYTGcgp+W9Gk14AOSU6bDmRgTVJlzA2PrbW83CEtLwSpOUZ7tJ3IqPA7DcARAVpEBXEBdOJXEanA3buhConBxg4EOAlfA5jc1AqLCwMp06dQv369fHoo4/i7bffRvfu3dG9e/faLB8REREREVEFXyVfRoFWb/FYgVaPr5IvVxlQckQejrB8Tyrmbjtltm/UmiTT9sw+rTA7sXWtl4OIrNDpoHj7bfhptcDddzMo5UA2B6W0Wi3y8vJQv359fPLJJ3jttdcQGBhYm2UjIiIiIiKy6FJuCfSS5btg6SWBS7klTsnDEcYnxOLe9pFWj3OWFJGLKZUQXbuirLAQKq4p5VA2B6USEhIwaNAgdOnSBUIIPP300/D19bWY9uOPP3ZYAYmIiIiIiK4XHaxBytVC6CwElbyUCkQHVx3IcUQejsDL84jqOJUKmDkThRkZ8OcsKYeyOcS3evVq9O/fHwUFhmubc3NzkZ2dbfGHiIiIiIioNg2Pa4gAlZfFY4EqLwyPa+iUPIiIqPpsnikVGRmJhQsXAgCaNm2Kzz77DPXq1au1ghEREREREVkzslM0dqRcNdw5T6uDXgK8lECQyhuJbSIwslO0U/IgIqLqq9ZC57169YKKU9aIiIiojit/RytL3OWSGbm8DiJH8lN5Y8XwjliTdAkr9p7Hnxdy0CU6BGNvaYyRnaLhp6r6q44j8iAiD1BaCsWTTyKouBj48EPAylJGZD8udO5Gyn8glSSBrOxChGlzoVQqAPADKRER0fUs3dGqPHe5o5VcXgeRo/mpvDE2Phado4PRdcmveH/IDejcKMTpeRCRzAkBpKVBqdUath1IV5gGfWE6AECbdcLsNwB4+TeAt3+UQ89Zl3ChczfCD6RERET2KX9HqxMZBRi1JgmrR3ZCm4gAAO5zRyu5vA4iIiK3pFJBvPYa8q9dQz0HXzWWf/hD5OybZ7bv6pbRpu2Q+JcRmjDToeesS2wOSq1evRqLFy/GmTNnoFAokJubi5IS59wilQzKfyA9lp6Ph9cm49MRcWjXwDBjjR9IiSriJS9Ens1SH28TEeB2syDk8jqIiIjcklIJtG0LfUaGYduBAm8cB7/mA6we9/Jv4NDz1TVc6NyNlP9AKv1721p+ICWqHGcYEhERERFRXeXtHyXry/OqYtfKff3798cXX3yBs2fPAgAWLlyICRMmICQkBABw7do13HbbbTh27JjDC0pEVB1yuuSFs76IiIiIiFxArwd+/x0+2dlA//4Ony3lyewKSm3ZsgWlpaWmx/Pnz8fw4cNNQSmdToeTJ086tIDXe++99/DGG28gPT0dHTt2xNKlS3HzzTfX6jmJyH3J6ZIXzvoiIiIiInKBsjIoXn8d/lot0KcP4OPj6hLJRo3ucSocvOp8Vb788ktMmTIFy5YtQ3x8PJYsWYLExEScPHkSERERTi0LEZGzyWnWFxERERGR21AqgQ4doCsshIqzpByqRkEpZ3vrrbcwbtw4PPLIIwCAZcuW4ccff8THH3+MF1980eZ8tFottFpthf1KpRLe3t5m6axRKBTwKRcdtSdtWVmZ1YCerWnLdGUV91WSLwCoyt0lwJ60Op0OkiQ5JK2Pjw8UCkWtptXr9dDr9Q5J6+3tDeW/g05tpi0rKzO1S+V1g1z5tJIkQafTWc3Xy8sLXl5edSatEAJlZRXbanXSlu+f1U1bVqYz/Tb2WXv6vS1pJUmCVqtFWVkZ1Gq1Tfna2u/raZSo7+tvlrZFmAYdIvwsnseesQfgGGEtrbW+CThvjLA1bV3o93V5jDCOAeXfT3vyBaoeI8qrrc8Rxve3/FhmLa0jPnMYeeIYYRzTy48BdfFzBMeI/+7QbqlfAI4ZIyy1h+vHCEufNSyVAajb3zXsTQu43xhhbDv25uuO3zU8YYwo/1vxb3+pbDy5Pr0tY4Q0ezayMjKgUiphbJWO/hwhpzGisjKVZ1dQSqFQmDpY+X3OoNVqceDAAUyfPt20T6lUonfv3tizZ4/F55SWlppdbpiXlwcAWLRokdmXRaMWLVrgwQcfND1+/fXXrTaw2NhYjBkzxvR48eLFKCoqspg2KioKjz/+uOnxu+++i5ycHItpw8PDMXHiRNPj5cuXIzMzs0K6y3pfAO0hhGQaQD/66COkpaVZzNfPzw/PP/+86fFnn32G1NRUi2l9fHzw0ksvmR5/8cUXOH36tMW0ADBr1izT9tdff43jx49bTTt9+nTTH5bvvvsOhw4dspp26tSp8Pf3BwBs3rwZ+/fvt5p28uTJpstIt23bZrVNAMATTzxhmlm3a9cu7Nq1y2rasWPHIjo6GgDwxx9/YPv27VbTjh49Gk2aNAEA/PXXX9i8ebPVtA888ABatWoFADh06BA2bNiAwsJC+Pv7V+hTQ4cORfv27QEAR48exddff20134EDByIuLg4AcOrUKXzxxRdW0951112mS1/PnTuHTz75xGra3r17o3v37gCAS5cuYcWKFVbT9ujRAz179gQAZGRk4IMPPrCaNiEhAX379gUA5OTk4O2337aatmvXrrj77rsBAIWFhVi0aJHVtB07dsSgQYMAGMaOBQsWAADS9H4A2uGjjz5ClJehv7Zt2xbDhw83PffVV1+1mq8tY4QQAoWFhWjXrp0pgA44foww3uxg/fpv8GPeJYtpQ0JCMHnyZNNjjhEhAOwbI3bv3o1NmzZZ7JuA88aIjRs3Wk3LMcLAljHCOAbs2rULXUYNAmA+Rlhi7xjR5vb+AAAhpFr7HPHll18BiDEby4xs/RwBcIwoz9oYYRzTy48BdfFzBMcIIC/f8DnfUr8AHDNGWGoP148RH330Ea7/rGHkTt81APmPEVOmTIEQApIkyf67hieMEWFe6bg3yNBOs/SGu9VV9jni+vS2jBHGMaBbt24YPHgwAMd/jpDTGFE+FlMZu4JSQgiMGTPGFNApKSnBhAkTTH/wbT1pdVy9ehV6vR6RkZFm+yMjI3HixAmLz1mwYAHmzJlTYX9hYaHF6GteXh4yMjJMjwsKCqxGafPz8yukLS4utiltfn4+CgsLLabVaDQ2pS0WABRAVlY2MtRlVeYrSZLNZfD29jZLm5eXZzUtALvTGv9Q5ObmVpo2MzPTdNyWtMZIbE5OTqVpr169atq2Ja0x+pudnV1p2mvXrsHPz8/mtMb3LSsrC4WFhSgpMSxgff0X36ysrApprSmf9tq1a5Wmzc7Orlbaq1evVpo2JyenWmmrquPc3FxT2qKiIpvTarVaU9piGN7b4pJiFMKw7/p+X1m+towRQgiUlJRYTOvIMSIr23C8sKgQeitpvby8bM6XY8R/yo8R2dnZVvumMa2zxghrOEbAdLyqMcI4BhQUFFgcIyyxd4zIysoGYPj7XFufIwqL/h3Tyo1lRrZ+jgA4Rlyf1tIYYRzTgf/GgLr6OcIaTxkjsrMNfc9SvwAcM0ZYag/XjxHFJcVWy+FO3zUAzxgjjDM7POG7hjVyGSN8fYqBIKC4uBiFZYUV0l5fx9ent2WMMI4B1r5rWOLo7xpG7jBG2BofUgg7FoYq/1//yqxcudLWLG12+fJlREdH448//kBCQoJp/7Rp07Br1y7s27evwnMszZSKiYnBlStXEBQUVCG9O02XO3gxB7cu+wv7nuqGLjGhVeYLuN+UWk+7fC8zMxPh4eG8fM8KR1y+l3Q5D93f34ffJ8ajU8OgCmkBx1y+l5mZiYiICIdfvlc+7cGLubj5nd/wx8RbENcwsMb5AhwjLKUtKytDenq6xb4JcNp9TdM6e4wwjgF7JiXgptgwu/MFqh4jDqXlI37pH9j3VDfcEOlvNW1NPkf8ee4aur2/12wss5bWky/NcdTle9f/fa6LnyM4RgAHLuYg/p3fLfYLwDFjhKX2oFQqkVmkQ1qe4TvHkbQcPLruCD4e1gGtww1jQINANaIC1W71XcPetID7jRFeXl64evUqwsPDIUmSrL9reMIYUZaZjKvrb0f9IbvhEx5XaVpL6ascI7Ra4IUXUFxcDN933oEqIMB62nI8+fK9vLw8REZGIjc312L8xciumVK1EWyyVf369eHl5YUrV66Y7b9y5QoaNGhg8TlqtdriZXoajQYaTdULAtuSpjppLZXJ1rRFWh3WJF3CR3sN02Gf3nAUj90Si5GdouFXg3wrU/4PgTukVSqVZp3DHdJ6eXlBrVZDo9FY/OJbPm35gaqqfF2dFjC/Xt/VadWqkn9/q6z22Zr2e0mSTGNP+bp09BihVBo+7KhUPjbnXZOxpzJ1od/X1hjh4+NjU9+0N9/aTOvqfl+XxwjjGODj421Wn/bka62/FWl1WG3t77Oq6vfDnjFCpTK0h8rGMqPa6veeMkYYx3RrY0Bd6fccIwCvf+vHln5hLd+qxghr7eHDnWcr3B330XVHTNvW7o5bF79ruCKtq/q9JElQKBR2t8m60pc5RhgY+6fi3zanUquhttJfyvf7qtJbGiPEhQuAVguVt+M/RzgzrbP6fa2sKeVKKpUKXbp0wc8//2xaK0aSJPz888948sknXVs4JynS6jD2q0PYciID+aWG6PH+i7k4+f0x7Ei5ihXDO9r0wZeIiIgch3+fiTxb+bvjWsK74xLVDbrCNOgL0wEA2qwTZr8BwMu/Abz9oyw/WaWCmDsXBVlZCLMjOEpVc6tPSFOmTMHo0aPRtWtX3HzzzViyZAkKCwttvqzQ3a1JuoQtJzKQU/LfdEa9AHJKdNhyIgNrki5hbHysC0tIRETkeZz199k4W3rF3vMAgInr/8bYWxrbPBuLiGpHVJCGgSciN5B/+EPk7Jtntu/qltGm7ZD4lxGaMNPyk5VKIC4OuowMwzY5jFt9grn//vuRmZmJmTNnIj09HXFxcdiyZUuFxc/l6qvkyyjQWr4euECrx1fJlxmUIiIicjJn/H22NBvrwMUcnPq+gLOxiIiIbBB44zj4NR9g9biXv+Vlgah2ud2nlyeffNJjLte73qXcEugly4uM6SWBS7klTi4RETkbZ0oQ1T3O+PvM2dJEREQ14+0fZf3yvKro9cD+/fDOzgbuvJOzpRyI76QbiQ7WwEtZ8ZbkAOClVCA6mNOGieTMOFNi2vfHcOBiDgDDTIlp3x/D2K8OoUhr/U4lRFR7nPH32ZbZWERERFRLysqgeOUVBLz1FlDJ3fbIfgxKuZHhcQ0RoLK8sn+gygvD4xo6uURE5EzlZ0ro/52Ucf1MCSJyPmf8feZsaSIiIhdSKoGWLaFv2pSzpByM76YbGdkpGv3aRCBE4w3vf2vOWwmEaryR2CYCIztFu7aARFSrOFOCqG5yxt9nzpYmIiJyIZUK4s03kT9nDsC77zkUg1JuxE/ljRXDO+L1Ae3QJToYANAlOhivDWjHBU6JPABnShDVTc74+8zZ0kRERCRHDEq5GT+VN8bGx2Lp4BsAAEsH34Cx8bEMSBF5AM6UIKq7avvvc/nZWF7/fnrz4mxpIiIicnMMShERuQnOlCDyXOazsUIAAF2iQzhbmoiIyBm0WiheeAEBr7wCaLWuLo2sMChFROQmOFOCyLMZZ2O9P8QwG+v9IZwtTURE5BSSBBw/Du+UFMM2OQw/xRCR7BVpdViTdAkr9p4HAExc/zfG3tIYIztFu9WXOeNMCeNr+fNCDrpEh7jlayEiIiIichs+PhAvvYTC7GyofHxcXRpZ4TcYIpK1Iq0OY786hC0nMpBfqgMAHLiYg1PfF2BHylW3u+zFOFOic3Qwui75Fe8PuQGdG4W4ulhERERERPLl5QXccgvKMjIM2+Qw7vNNjIioGtYkXcKWExnIKdGZ9ukFkFOiw5YTGViTdAlj42NdWEIiIiIiIrKXrjAN+sJ0AIA264TZbwDw8m8Ab/8ol5SNbMegFBHJ2lfJl1Gg1Vs8VqDV46vkywxKERERERG5mfzDHyJn3zyzfVe3jDZth8S/jNCEmY45mSQBf/8N76wsoH59QMnluR2FQSkikrVLuSXQS8LiMb0kcCm3xMklIiIiIiKimgq8cRz8mg+wetzLv4HjTqbVQjFjBgK0WiA+HvBmKMVR+E66kbS8EqTlGb5An8goMP1WKhUAgKggDaKCNC4rH1FdFB2sQcrVQugsBKa8lApEB7PPEBERERG5G2//KOddnqdQADExkIqLDdvkMAxKuZHle1Ixd9sps30Pr002bc/s0wqzE1s7uVREddvwuIbYfyHHbE0po0CVF4bHNXRBqYjImeRyB04iIiJyEbUa4r33kJeRAY1a7erSyAo/ibmR8QmxuLd9JABAkgSysrMQFhpmNlOKiMyN7BSNHSlXDXff0+qglwAvJRCk8kZimwiM7BTt6iISUS2S2x04iYiIiOSEn8LcSPnL8yRJQoaqFBERwVBykTUiq/xU3lgxvKNplsSfF3LQJTrELWdJWLuE14iX8BJVxDtwElFl+LeViMi13OfbGBFRNfmpvDE2Phado4PRdcmveH/IDejcKMTVxbKbpUt4R61JMm3zEl6iingHTiKqDP+2EpFNtFoo5s5FQGEhsGABoGGw2lEYlCIichPlL+G1hP/JJapILnfg5GwOotrBv61EZBNJApKT4a3VGrbJYRiUIiJyE/zSSWQ/udyBk7M5iGoH/7YSkU18fCCmTEFRTg5UPj6uLo2sMChFREREsiWXO3ByNgcREZELeXkBPXtCm5Fh2CaHYVCKiIiIZEsud+DkbA4iIiKSI962jYiIiGTLeAfO1we0Q5foEABAl+gQvDagHVYM7+hWd+AkIiIiF5EkICUFXv/8wzWlHIyfxIiIiEjW5HIHTiIyxxsAEJHTaLVQPPccArVaYMMGwJuhFEfhO0lERERERG6HNwAgIqdRKICICEglJYZtchi3CUq9+uqr+PHHH5GcnAyVSoWcnBxXF4mIiIiIiFyENwAgIqdRqyFWrEBeRgY0arWrSyMrbhOU0mq1GDZsGBISEvDRRx+5ujhERERERORCvDyPiMj9uU1Qas6cOQCAVatW2fyc0tJSlJaWmh7n5eUBACRJguTmi5NJkgQhhNu/DjJgfTqHJAnT79p6r1mX8sL6lBdnjAFCSKbfbDfuj2MAlcf2IC+sT7IX24x9bH2f3CYoVR0LFiwwBbPKy8zMRElJiQtK5DiSJCE3NxdCCCiVvImiu2N9OkdWduG/v7OQoSqtInX1sC7lhfUpL84YA7KyCv79nY0MdVmtnIOch2MAlcf2IC+sT7KLVgu/d9+FV2kpMp59FkoNZ2lWJT8/36Z0sg5KTZ8+HVOmTDE9zsvLQ0xMDMLDwxEUFOTCktWcJElQKBQIDw/nICoDrE/nCNPmGn6HhiEiIrhWzsG6lBfWp7w4YwwIK/Ux/A4LRUREaK2cg5yHYwCVx/YgL6xPsktJCXDsGFSlpfCuXx9KPz9Xl6jO09gYuHNpUOrFF1/Ea6+9Vmma48ePo02bNtXKX61WQ21hETKlUimLgUehUMjmtRDr0xmUSoXpd22+z6xLeWF9yoczxgCFQmn6zTYjDxwDqDy2B3lhfZLNVCpIkyahKCcHISoV24wNbH2PXBqUeu655zBmzJhK0zRr1sw5hSEiIiIiIiIiup63N5CYCG1GhmGbHMal72Z4eDjCw8NdWQQiIiIiIiIiInIBtwnxnT9/HllZWTh//jz0ej2Sk5MBAC1atEBAQIBrC0dERERERERE8iQEcP48lFevApxY41BuE5SaOXMmPvnkE9PjTp06AQB27tyJnj17uqhURERERERERCRrpaVQPPkkgrRaYMMGgAudO4zbBKVWrVqFVatWuboYRORm0vJKkJZXAgA4kVFg9hsAooI0iAriLV2JqPqsjTPGhdU5zhAREclAUBBESYmrSyE7bhOUIiKqjuV7UjF32ymzfaPWJJm2Z/ZphdmJrZ1dLCKSEUvjzMNrk03bHGeIiIjcnEYDsXo1cjMyEKHhP5ociUEpIpK18QmxuLd9pNXjnL1ARDVVfpyRJIGs7CyEhYaZzZQiIiIioooYlCIiWeNlM0RU28qPM5IkIUNVioiIYCiVSheXjIiIiKhu46clIiIiIiIiIiJrtFrgzTfh98EHhm1yGM6UIiIiIiIiIiKyRpKg2LULKq0WkCRXl0ZWGJQiIiIiIiIiIrLG2xviscdQnJsLlTfDKI7Ed5OIiIiIiIiIyBpvb2DgQJRmZBi2yWG4phQRERERERERETkdg1JERERERERERNYIAWRkQHn1qmGbHIbzzoiIiIiIiIiIrCkthWLsWARptcCGDYCfn6tLJBsMShERERERERERVUatdnUJZIlBKSIiIiIiIiIiazQaiHXrkJORgQiNxtWlkRWuKUVERERERERERE7HmVJEREQkW2l5JUjLKwEAnMgoMPsNAFFBGkQF8T+eRERERK7AoBQRERHJ1vI9qZi77ZTZvlFrkkzbM/u0wuzE1s4uFhEREbmTsjLggw/gl58PTJ3K9aUciEEpIiIikq3xCbG4t32k1eOcJUVERERV0uuh+OknqLRa4NlnXV0aWWFQioiIiGSLl+cRERFRjXl7Q4wahZK8PKi8GUZxJL6bRERERERERETWeHsDw4ejJCMDQQxKORTvvkdERERERERERE7HoBQRERERERERkTVCALm5UOTlGbbJYTjvjIiIiIiIiIjImtJSKB56CMFaLbBhA+Dn5+oSyYZHBaXEvxHNvLw8F5ek5iRJQn5+PjQaDZRKTnhzd6xP+WBdygvrk+zFNiMvrE8qj+1BXlifZJeSEoiyMpTqdFDl5UGp07m6RHWeMe4iqphZ5lFBqfz8fABATEyMi0tCRERERERERG4nKsrVJXAr+fn5CA4OtnpcIaoKW8mIJEm4fPkyAgMDoVAoXF2cGsnLy0NMTAwuXLiAoKAgVxeHaoj1KR+sS3lhfZK92GbkhfVJ5bE9yAvrk+zFNmMfIQTy8/PRsGHDSmcjetRMKaVSiUaNGrm6GA4VFBTEDiEjrE/5YF3KC+uT7MU2Iy+sTyqP7UFeWJ9kL7YZ21U2Q8qIF88SEREREREREZHTMShFREREREREREROx6CUm1Kr1Zg1axbUarWri0IOwPqUD9alvLA+yV5sM/LC+qTy2B7khfVJ9mKbqR0etdA5ERERERERERHVDZwpRURERERERERETsegFBEREREREREROR2DUkRERERERERE5HQMShERERERERERkdMxKEVERERERERERE7HoFQVFixYgJtuugmBgYGIiIjAoEGDcPLkSbM0JSUlmDRpEurVq4eAgAAMGTIEV65cMR0/dOgQHnjgAcTExMDX1xdt27bF22+/bZbHL7/8AoVCUeEnPT290vIJITBz5kxERUXB19cXvXv3RkpKiun4uXPn8Nhjj6Fp06bw9fVF8+bNMWvWLGi12ipf+y+//ILOnTtDrVajRYsWWLVqldnx3bt3Y8CAAWjYsCEUCgU2bNhQZZ6u5qn1mZaWhpEjR6JVq1ZQKpV45plnKqRZtWpVhfJqNJpK83U1d69PALj33nvRuHFjaDQaREVF4aGHHsLly5erfO1y65+eWpdy7ZvOIIc2Y1RaWoq4uDgoFAokJydX+drl1v8Bz61PjgGWyaE9NGnSpEK+CxcurPK1s3/Lpz7Zv6tPDm0GAH788UfEx8fD19cXoaGhGDRoUJWv/fDhw7jtttug0WgQExOD119/3ez40aNHMWTIEFObXLJkSZV51nUMSlVh165dmDRpEvbu3Ytt27ahrKwMffv2RWFhoSnNs88+i++//x7r1q3Drl27cPnyZdx3332m4wcOHEBERARWr16No0ePYsaMGZg+fTrefffdCuc7efIk0tLSTD8RERGVlu/111/HO++8g2XLlmHfvn3w9/dHYmIiSkpKAAAnTpyAJElYvnw5jh49isWLF2PZsmV46aWXKs337NmzuPvuu9GrVy8kJyfjmWeewdixY7F161ZTmsLCQnTs2BHvvfeeTe9lXeCp9VlaWorw8HC8/PLL6Nixo9V0QUFBZuVNTU2tNF9Xc/f6BIBevXrhq6++wsmTJ7F+/XqcOXMGQ4cOrTRfOfZPT61LufZNZ5BDmzGaNm0aGjZsaNPrlmP/Bzy3PjkGWCaX9jB37lyzfJ966qlK82X/lld9sn9XnxzazPr16/HQQw/hkUcewaFDh/D7779j5MiRleabl5eHvn37IjY2FgcOHMAbb7yB2bNn4//+7/9MaYqKitCsWTMsXLgQDRo0qPK9dAuC7JKRkSEAiF27dgkhhMjJyRE+Pj5i3bp1pjTHjx8XAMSePXus5jNx4kTRq1cv0+OdO3cKACI7O9vmskiSJBo0aCDeeOMN076cnByhVqvFF198YfV5r7/+umjatGmleU+bNk20b9/ebN/9998vEhMTLaYHIL799luby15XeEp9ltejRw8xefLkCvtXrlwpgoODbc6nLpJDfW7cuFEoFAqh1WqtpvGE/ukpdVmenPumM7hrm9m0aZNo06aNOHr0qAAgkpKSKs3bE/q/EJ5Tn+VxDLDOHdtDbGysWLx4sc35CsH+Lbf6LI/9u2bcrc2UlZWJ6OhosWLFCpvzFUKI999/X4SGhorS0lLTvhdeeEG0bt3aYvqatsu6gjOl7JSbmwsACAsLA2CIwJaVlaF3796mNG3atEHjxo2xZ8+eSvMx5lFeXFwcoqKi0KdPH/z++++VluXs2bNIT083O3dwcDDi4+Orde7y9uzZY5YvACQmJlaarzvylPq0VUFBAWJjYxETE4OBAwfi6NGjDsnXWdy9PrOysvD555+jW7du8PHxsZq3J/RPT6lLW7l733QGd2wzV65cwbhx4/DZZ5/Bz8/PptfpCf0f8Jz6tJWnjwHu2B4AYOHChahXrx46deqEN954AzqdrtK82b/lVZ+28vT+bQt3azMHDx7EpUuXoFQq0alTJ0RFReGuu+7CkSNHKs17z549uP3226FSqUz7EhMTcfLkSWRnZ1f6XHfGoJQdJEnCM888g+7du6NDhw4AgPT0dKhUKoSEhJiljYyMtHot6h9//IEvv/wSjz/+uGlfVFQUli1bhvXr12P9+vWIiYlBz549cfDgQavlMeYfGRlp87lPnz6NpUuXYvz48ZW+1vT0dIv55uXlobi4uNLnugtPqk9btG7dGh9//DE2btyI1atXQ5IkdOvWDRcvXqxx3s7gzvX5wgsvwN/fH/Xq1cP58+excePGSl+r3PunJ9WlLdy9bzqDO7YZIQTGjBmDCRMmoGvXrja/Vrn3f8Cz6tMWnj4GuGN7AICnn34aa9euxc6dOzF+/HjMnz8f06ZNq/S1sn+HmKV19/q0haf3b1u4Y5v5559/AACzZ8/Gyy+/jB9++AGhoaHo2bMnsrKyKs3bUr7lzytH3q4ugDuZNGkSjhw5gt9++63aeRw5cgQDBw7ErFmz0LdvX9P+1q1bo3Xr1qbH3bp1w5kzZ7B48WJ89tln+Pzzz80CD5s3b4aXl5dd57506RL69euHYcOGYdy4cab9AQEBpu1Ro0Zh2bJl1Xlpbof1aS4hIQEJCQlmZW7bti2WL1+OV155xa6yuYI71+fzzz+Pxx57DKmpqZgzZw4efvhh/PDDD1AoFB7ZP1mX5ty9bzqDO7aZpUuXIj8/H9OnT7eaxhP7P8D6vJ6njwHu2B4AYMqUKabtG2+8ESqVCuPHj8eCBQugVqvZv1mfANi/beGObUaSJADAjBkzMGTIEADAypUr0ahRI6xbtw7jx49H+/btTeuH3Xbbbdi8eXO1X5+7Y1DKRk8++SR++OEH7N69G40aNTLtb9CgAbRaLXJycswitVeuXKmw8NixY8dw55134vHHH8fLL79c5TlvvvlmU+e79957ER8fbzoWHR2NtLQ007mioqLMzh0XF2eW1+XLl9GrVy9069bNbKE0AGZ3hgkKCjK9rvJ3LzDmGxQUBF9f3yrLXtd5Wn1Wh4+PDzp16oTTp09XOw9ncff6rF+/PurXr49WrVqhbdu2iImJwd69e5GQkOBx/dPT6rI63KlvOoO7tpkdO3Zgz549UKvVZnl37doVDz74ID755BOP6/+A59VndXjSGOCu7cGS+Ph46HQ6nDt3Dq1bt2b/9oD6rA5P6t+2cNc2Y9zfrl0703G1Wo1mzZrh/PnzAIBNmzahrKwMAEz929oYYDwmW65e1KqukyRJTJo0STRs2FCcOnWqwnHjImtff/21ad+JEycqLLJ25MgRERERIZ5//nmbz927d28xePDgSsvWoEEDsWjRItO+3NzcCgvzXbx4UbRs2VKMGDFC6HQ6m849bdo00aFDB7N9DzzwgNsvtOip9VmetYUWr6fT6UTr1q3Fs88+a/c5nEUO9Xm91NRUAUDs3LnTaho59k9Prcvy5NQ3ncHd20xqaqr4+++/TT9bt24VAMTXX38tLly4YDVvOfZ/ITy3PsvjGPAfd28PlqxevVoolUqRlZVlNQ37t7zqszz2b/u4e5sxPi6/0LlWqxURERFi+fLlVvM2LnRe/iY506dPl/1C5wxKVeGJJ54QwcHB4pdffhFpaWmmn6KiIlOaCRMmiMaNG4sdO3aI/fv3i4SEBJGQkGA6/vfff4vw8HAxatQoszwyMjJMaRYvXiw2bNggUlJSxN9//y0mT54slEql2L59e6XlW7hwoQgJCREbN24Uhw8fFgMHDhRNmzYVxcXFQghDAKNFixbizjvvFBcvXjQ7f2X++ecf4efnJ55//nlx/Phx8d577wkvLy+xZcsWU5r8/HyRlJQkkpKSBADx1ltviaSkJJGammrXe+xMnlqfQghTXXXp0kWMHDlSJCUliaNHj5qOz5kzR2zdulWcOXNGHDhwQIwYMUJoNBqzNHWNu9fn3r17xdKlS0VSUpI4d+6c+Pnnn0W3bt1E8+bNRUlJidV85dg/PbUuhZBn33QGd28z1zt79qxNd2uTY/8XwnPrUwiOAZa4e3v4448/xOLFi0VycrI4c+aMWL16tQgPDxcPP/xwpfmyf8urPoVg/64ud28zQggxefJkER0dLbZu3SpOnDghHnvsMREREVFpIDMnJ0dERkaKhx56SBw5ckSsXbtW+Pn5mQWySktLTe0qKipKTJ06VSQlJYmUlBS73uO6hEGpKgCw+LNy5UpTmuLiYjFx4kQRGhoq/Pz8xODBg82CBLNmzbKYR2xsrCnNa6+9Jpo3by40Go0ICwsTPXv2FDt27KiyfJIkif/9738iMjJSqNVqceedd4qTJ0+ajq9cudLqa6jKzp07RVxcnFCpVKJZs2Zmr9l43FK+o0ePrjJvV/Hk+qyqzM8884xo3LixUKlUIjIyUvTv318cPHiwynxdyd3r8/Dhw6JXr14iLCxMqNVq0aRJEzFhwgRx8eLFKvOWW//05LqUY990BndvM9ezJ4ght/4vhGfXJ8eAity9PRw4cEDEx8eL4OBgodFoRNu2bcX8+fOr/CeFEOzfcqtP9u/qcfc2I4RhZtRzzz0nIiIiRGBgoOjdu7c4cuRIlXkfOnRI3HrrrUKtVovo6GixcOFCs+PGvy/X//To0aPKvOsqhRBCgIiIiIiIiIiIyImUri4AERERERERERF5HgaliIiIiIiIiIjI6RiUIiIiIiIiIiIip2NQioiIiIiIiIiInI5BKSIiIiIiIiIicjoGpYiIiIiIiIiIyOkYlCIiIiIiIiIiIqdjUIqIiIjIgnPnzkGhUCA5ObnWz7Vq1SqEhITU+nlqS8+ePfHMM8/UKA93fw+IiIjIfgxKERERkdsZM2YMFApFhZ9+/fq5umhVatKkCZYsWWK27/7778epU6dq/dw9e/Y0vVcajQbt2rXD+++/X+N8v/nmG7zyyisOKCERERF5Em9XF4CIiIioOvr164eVK1ea7VOr1S4qTc34+vrC19fXKecaN24c5s6di6KiInz66aeYNGkSQkND8cADD9idl1arhUqlQlhYWC2UlIiIiOSOM6WIiIjILanVajRo0MDsJzQ0FAAwcuRI3H///Wbpy8rKUL9+fXz66acAgC1btuDWW29FSEgI6tWrh3vuuQdnzpyxej5Ll5dt2LABCoXC9PjMmTMYOHAgIiMjERAQgJtuugnbt283He/ZsydSU1Px7LPPmmYsWcv7gw8+QPPmzaFSqdC6dWt89tlnZscVCgVWrFiBwYMHw8/PDy1btsR3331X5fvm5+eHBg0aoFmzZpg9e7bZ83JycjB27FiEh4cjKCgId9xxBw4dOmR67uzZsxEXF4cVK1agadOm0Gg0ptdV/vK97OxsPPzwwwgNDYWfnx/uuusupKSkVHg/GzduDD8/PwwePBjXrl2rsuxEREQkLwxKERERkew8+OCD+P7771FQUGDat3XrVhQVFWHw4MEAgMLCQkyZMgX79+/Hzz//DKVSicGDB0OSpGqft6CgAP3798fPP/+MpKQk9OvXDwMGDMD58+cBGC5za9SoEebOnYu0tDSkpaVZzOfbb7/F5MmT8dxzz+HIkSMYP348HnnkEezcudMs3Zw5czB8+HAcPnwY/fv3x4MPPoisrCy7yuzr6wutVgsAGDZsGDIyMrB582YcOHAAnTt3xp133mmW5+nTp7F+/Xp88803VtfbGjNmDPbv34/vvvsOe/bsgRAC/fv3R1lZGQBg3759eOyxx/Dkk08iOTkZvXr1wrx58+wqNxEREbk/BqWIiIjILf3www8ICAgw+5k/fz4AIDExEf7+/vj2229N6desWYN7770XgYGBAIAhQ4bgvvvuQ4sWLRAXF4ePP/4Yf//9N44dO1btMnXs2BHjx49Hhw4d0LJlS7zyyito3ry5aSZSWFgYvLy8EBgYaJrdZcmiRYswZswYTJw4Ea1atcKUKVNw3333YdGiRWbpxowZgwceeAAtWrTA/PnzUVBQgD///NOmsur1eqxevRqHDx/GHXfcgd9++w1//vkn1q1bh65du6Jly5ZYtGgRQkJC8PXXX5uep9Vq8emnn6JTp0648cYbK+SbkpKC7777DitWrMBtt92Gjh074vPPP8elS5ewYcMGAMDbb7+Nfv36Ydq0aWjVqhWefvppJCYm2lRuIiIikg8GpYiIiMgt9erVC8nJyWY/EyZMAAB4e3tj+PDh+PzzzwEYZkVt3LgRDz74oOn5KSkpeOCBB9CsWTMEBQWhSZMmAGCa1VQdBQUFmDp1Ktq2bYuQkBAEBATg+PHjdud5/PhxdO/e3Wxf9+7dcfz4cbN95YNC/v7+CAoKQkZGRqV5v//++wgICICvry/GjRuHZ599Fk888QQOHTqEgoIC1KtXzyzQd/bsWbPLGmNjYxEeHl5p2b29vREfH2/aV69ePbRu3dpU/uPHj5sdB4CEhIRKy01ERETyw4XOiYiIyC35+/ujRYsWVo8/+OCD6NGjBzIyMrBt2zb4+vqa3Z1vwIABiI2NxYcffoiGDRtCkiR06NDBdCnb9ZRKJYQQZvuMl6MZTZ06Fdu2bcOiRYvQokUL+Pr6YujQoVbzrCkfHx+zxwqFosrLDx988EHMmDEDvr6+iIqKglJp+B9lQUEBoqKi8Msvv1R4Tvn1rvz9/WtcbiIiIiKAQSkiIiKSqW7duiEmJgZffvklNm/ejGHDhpmCONeuXcPJkyfx4Ycf4rbbbgMA/Pbbb5XmFx4ejvz8fBQWFpoCM9evqfT7779jzJgxpnWrCgoKcO7cObM0KpUKer2+0nO1bdsWv//+O0aPHm2Wd7t27ap83VUJDg62GMzr3Lkz0tPT4e3tbZo1Vh1t27aFTqfDvn370K1bNwD/vd/G8rdt2xb79u0ze97evXurfU4iIiJyTwxKERERkVsqLS1Fenq62T5vb2/Ur1/f9HjkyJFYtmwZTp06ZbZIeGhoKOrVq4f/+7//Q1RUFM6fP48XX3yx0vPFx8fDz88PL730Ep5++mns27cPq1atMkvTsmVLfPPNNxgwYAAUCgX+97//VZi51KRJE+zevRsjRoyAWq02K6/R888/j+HDh6NTp07o3bs3vv/+e3zzzTdmd/JztN69eyMhIQGDBg3C66+/jlatWuHy5cv48ccfMXjwYHTt2tWmfFq2bImBAwdi3LhxWL58OQIDA/Hii4we5HoAABmISURBVC8iOjoaAwcOBAA8/fTT6N69OxYtWoSBAwdi69at2LJlS629NiIiIqqbuKYUERERuaUtW7YgKirK7OfWW281S/Pggw/i2LFjiI6ONlujSalUYu3atThw4AA6dOiAZ599Fm+88Ual5wsLC8Pq1auxadMm3HDDDfjiiy8we/ZsszRvvfUWQkND0a1bNwwYMACJiYno3LmzWZq5c+fi3LlzaN68udW1mQYNGoS3334bixYtQvv27bF8+XKsXLkSPXv2tP0NspNCocCmTZtw++2345FHHkGrVq0wYsQIpKamIjIy0q68Vq5ciS5duuCee+5BQkIChBDYtGmTaabaLbfcgg8//BBvv/02OnbsiJ9++gkvv/xybbwsIiIiqsMU4vrFEYiIiIiIiIiIiGoZZ0oREREREREREZHTMShFREREREREREROx6AUERERERERERE5HYNSRERERERERETkdAxKERERERERERGR0zEoRURERERERERETsegFBEREREREREROR2DUkRERERERERE5HQMShERERERERERkdMxKEVERERERERERE7HoBQRERERERERETkdg1JEREREREREROR0DEoREREREREREZHTMShFREREREREREROx6AUERERERERERE5HYNSRERERERERETkdAxKERERERERERGR0zEoRURERERERERETsegFBEREREREREROZ23qwvgKnq9HmVlZa4uBhERERERERGRbPj4+MDLy8umtB4XlBJCID09HTk5Oa4uChERERERERGR7ISEhKBBgwZQKBSVpvO4oJQxIBUREQE/P78q3yAiIiIiIiIiIqqaEAJFRUXIyMgAAERFRVWa3qOCUnq93hSQqlevnquLQ0REREREREQkK76+vgCAjIwMREREVHopn0ctdG5cQ8rPz8/FJSEiIiIiIiIikidj3KWqtbw9KihlxEv2iIiIiIiIiIhqh61xF48MShERERERERERkWsxKEVERERERERERE7HoBQREVEdUlRUhCFDhiAoKAgKhQI5OTkW9xERERERuTsGpeo4hUJR6c/s2bNr5bxjxozBoEGDaiXv6li1ahVCQkJcnkd5s2fPRlxcnMPyIyLH+uWXXyodP3v16uXqIlr0ySef4Ndff8Uff/yBtLQ0BAcHW9xXUz179sQzzzxT8wITeZCUzAJM33QcI1cfwPRNx5GSWVDr5xwzZoxp3FKpVGjRogXmzp0LnU7n0PM4e0yQ42dNIk9Ulp2CrN9mIGPTKGT9NgNl2Sm1er7aGhONnxtt+cdfkyZNsGTJkhqdzx517TObo78HezssJ6oVaWlppu0vv/wSM2fOxMmTJ037AgICTNtCCOj1enh7s1prk/F9JqK6rVu3bmZjqNF3332HCRMmYOLEidXOW6vVQqVS1aR4Vp05cwZt27ZFhw4dKt1HRM618s/zGLfuEBQKBYQQUCgUeGPnaawYHocxN8XU6rn79euHlStXorS0FJs2bcKkSZPg4+OD6dOnm6WrzbEJ4GdNIjKXf/QTXN0+HoACgACgQO6BN1G/9/8hsP3DtXZeW8dEV9Lr9VAoFFAqOQ+oSsKDFBcXi2PHjoni4uLrDxh+JOm/fWVlhn1are1pS0ttS1tNK1euFMHBwabHO3fuFADEpk2bROfOnYWPj4/YuXOn0Ov1Yv78+aJJkyZCo9GIG2+8Uaxbt870PJ1OJx599FHT8VatWoklS5aYjs+aNUvAMKqYfnbu3CnOnj0rAIgvv/xS3HrrrUKj0YiuXbuKkydPij///FN06dJF+Pv7i379+omMjAyzsn/44YeiTZs2Qq1Wi9atW4v33nvPdMyY7/r160XPnj2Fr6+vuPHGG8Uff/xh9jrL/8yaNcvie5ScnCx69uwpAgICRGBgoOjcubP466+/Ks3j008/FV26dBEBAQEiMjJSPPDAA+LKlSuVvs8rV66skN/KlSurWbNE7q20tNTqT9l1Y15labXXjbeW0tTUsWPHRGBgoJgxY4bZ/r///lv069dP+Pv7i4iICDFq1CiRmZlpOt6jRw8xadIkMXnyZFGvXj3Rs2dPIYQQv/zyi7jpppuESqUSDRo0EC+88EKF13y9X3/91TSGNmrUSDz11FOioKDAdJ7y40qPHj0s7hNCiJKSEvHcc8+Jhg0bCj8/P3HzzTeLnTt3mp3rt99+Ez169BC+vr4iJCRE9O3bV2RlZYnRo0dXGMPOnj1bszeXSMZOZeQLr6nfCcVzFX+8pn4nUjILau3co0ePFgMHDjTb16dPH3HLLbeYjs2bN09ERUWJJk2aCCGEOH/+vBg2bJgIDg4WoaGh4t577620j1sbE/hZk4is0WadEv8sUYt/FvtU/FmiFtrslFo5b2VjYlZWlnjooYdESEiI8PX1Ff369ROnTp0ypTt37py45557REhIiPDz8xPt2rUTP/74o2mMKP8zevRoi+e//nOZMaRi/K6+ceNG0bZtW+Hl5SXOnj1b5ee1q1evihEjRoiGDRsKX19f0aFDB7FmzRqz11vZ+LxlyxYRFxcnNBqN6NWrl7hy5YrYtGmTaNOmjQgMDBQPPPCAKCwsNOVX1fhtzHf79u2iS5cuwtfXVyQkJIgTJ06YXqet34Otxl+uw6CUEELcc4/hJyfnv31ffmnY98475mmHDDHsLxe0EBs2GPa98YZ52pEjDftTU//bt2VLtctvLSh14403ip9++kmcPn1aXLt2TcybN0+0adNGbNmyRZw5c0asXLlSqNVq8csvvwghhNBqtWLmzJnir7/+Ev/8849YvXq18PPzE19++aUQQoj8/HwxfPhw0a9fP5GWlibS0tJEaWmpqbMa8z527Ji45ZZbRJcuXUTPnj3Fb7/9Jg4ePChatGghJkyYYCrn6tWrRVRUlFi/fr34559/xPr160VYWJhYtWqVEEKY5fvDDz+IkydPiqFDh4rY2FhRVlYmSktLxZIlS0RQUJCpPPn5+Rbfo/bt24tRo0aJ48ePi1OnTomvvvpKJCcnV5rHRx99JDZt2iTOnDkj9uzZIxISEsRdd91V6ft88eJF8dxzz4n27dub8isqKqp23RK5s1mzZln9Wb16tVnaefPmWU17/R+01157rUKamsjOzhYtW7YUAwYMEFK5fxZkZ2eL8PBwMX36dHH8+HFx8OBB0adPH9GrVy9Tmh49eoiAgADx/PPPixMnTogTJ06IixcvCj8/PzFx4kRx/Phx8e2334r69etXWs7Tp08Lf39/sXjxYnHq1Cnx+++/i06dOokxY8YIIYS4du2aGDdunEhISBBpaWni2rVrFvcJIcTYsWNFt27dxO7du8Xp06fFG2+8IdRqtenDV1JSklCr1eKJJ54QycnJ4siRI2Lp0qUiMzNT5OTkiISEBDFu3DjTGKbT6Wr0/hLJ2Ys/HhPez39vMSjl/fz34sUfj9XauS19Abv33ntF586dxejRo0VAQIB46KGHxJEjR8SRI0eEVqsVbdu2FY8++v/t3XtQU9faBvAng0nKJSUFUURRvICgn1hvVXAs7TlqxmEqRatTRdFWPd7A2+Cg9oLaClZrx6LV8egRnZaO9YyOWhmLLSNe0AG8AJ4qUCM11WoxaHsmIojwfn/Q7BJIIH5F7KfPbyZjstfK2mvH4c3ab/Ze620pKiqSS5cuyeTJk6V3794Ok/uOYgLHmkTkSMXJFXJ143MOklLPScXJFY9lv83FxLFjx0pISIicOHFCCgoKxGAwSK9evZQfPiMjI2XUqFFSVFQkRqNRvv76azl+/Lg8fPhQ9u3bJwCkpKREbt68Kb82zA00PO6KCunSpYusXr1aiRci9efqarVawsPDJScnR4qLi+XevXstjteuX78u69evlwsXLojRaJTU1FRxcXGR3NxcEWk5Pg8bNswmNkZERMjo0aPl/PnzcuLECfH29pa1a9cq/W8pflvbHTp0qGRnZ8v3338vI0aMkPDwcBERqaysdPo82NmkFK+9fQqsXr0ao0aNAgBUV1cjOTkZ3333HcLCwgAAPXr0wKlTp7Bt2zZERERArVZj1apVyvu7d++OM2fOYO/evZg4cSI8PDzg6uqK6upq+Pr6NtlfQkICDAYDAGDhwoWYNGkSsrKyMHz4cADAjBkzsGvXLqV+UlISNmzYgHHjxin7u3TpErZt24Zp06bZtBsZGQkAWLVqFfr27YsrV64gODgYnp6eUKlUdvvTkMlkwtKlSxEcHAwACAwMVMoctfH2228rz3v06IHU1FQMGTIEFovF5vbIhp8zUH/rZLt27VrsExE9eXV1dZg8eTLatWuH9PR0qFQqpWzz5s0YMGAAkpOTlW07d+6Ev78/SktLERQUBKA+nqxbt06p884778Df3x+bN2+GSqVCcHAwfv75ZyQmJuL999+3e7l2SkoKYmJilHkBAgMDkZqaioiICGzduhVeXl5wc3ODRqOxiS2Nt5lMJqSlpcFkMsHPzw9AfQz95ptvkJaWhuTkZKxbtw6DBw/Gli1blHb69u2rPNdoNHBzc2MMI3LCtTuVEBG7ZSKCa3cq26QfIoKsrCxkZmYiPj4et2/fhru7O3bs2KHctvfFF1+grq4OO3bsUGJdWloa9Ho9srOzMXr06Cbtenp6NhsTONYkosYe/vca6i+UsUd+L3+8GsbEMWPG4MCBA8jJyUF4eDgAID09Hf7+/jhw4AAmTJgAk8mE8ePHo1+/fgDqY5eVl5cXAKBDhw7Nzi/n5eUFFxcX6HS6JvGipqYGW7ZsQf/+/QE4N17r3LkzEhISlDbi4+ORmZmJvXv34qWXXmoxPn/44Yc2sXH58uUwGo3Ksb3xxhs4duwYEhMTnYrfVmvWrFFeL1u2DJGRkaiqqoKrq2urnwczKQUA//53/b9a7R/bxo0Dxo4FXFxs637xRdO6kZGAwQA0PgH517+a1v3731unzw0MHjxYeX7lyhVUVlbaJE+A+jkGBgwYoLz+7LPPsHPnTphMJty/fx8PHjxwerKy0NBQ5XnHjh0BQPnDtm4rLy8HANy7dw9GoxEzZszArFmzlDoPHz5sMlFvw3Y7deoEACgvL1cSTM5YsmQJZs6cic8//xwjR47EhAkT0LNnz2bfc+7cOaxcuRKFhYW4e/cu6urqANQHkT59+ij1Gn7ORPSHFStWOCxrnJhZunSpw7oNE0UAWnVCxxUrVuDMmTPIy8uDTqezKSssLMSxY8dsktBWRqNRSUoNGjTIpuzy5csICwuz6ffw4cNhsVhw/fp1dO3atUl7hYWFKCoqQnp6urJNRFBXV4eysjKEhIQ4dTwXL15EbW2t0jer6upqeHt7AwAKCgowYcIEp9ojouZ183Kr/1u3k5hSqVTo5uX2WPd/+PBheHh4oKamRkmyr1y5EvPnz0e/fv1s5pEqLCzElStXmsS6qqoqGI1GnDx5EmPGjFG2b9u2DTExMc3un2NNImqs3fPdUD+XlD2q38sfD3sxcdy4cTh8+DCGDh2q1PP29kbv3r1x+fJlAMCCBQswd+5cHD16FCNHjsT48eNt4kJj6enpmD17tvL6yJEjGDFihMP6Go3Gpj1nxmu1tbVITk7G3r17cePGDTx48ADV1dVwc3Pue6VxvHRzc7NJtnXs2BF5eXkAnI/fjdttGC/tjW//LCalAOC555pua9eu/vE46rYyd3d35bnFUr8KTEZGBjp37mxTT/t7cmzPnj1ISEjAhg0bEBYWBp1Oh/Xr1yM3N9ep/anVauW59WSs8TZrYsfan+3bt9sECABwaZTws9eutR1nrVy5EpMnT0ZGRgaOHDmCpKQk7NmzB9HR0Xbr37t3DwaDAQaDAenp6fDx8YHJZILBYMCDBw9s6jb8nInoD48yqe7jqtucPXv24OOPP0ZGRobN1ZNWFosFr732Gj766KMmZdYvYaB1YoDFYsHs2bOxYMGCJmWP8iVvsVjg4uKCc+fONYml1uSaq6vrn+ssESneHuKP9ceu2C0TEcx4qfUH6Q29+uqr2Lp1KzQaDfz8/GwmGm8cmywWCwYNGmST/Lby8fGBRqNBQUGBss2a9GkOx5pE1Jiu73T8dm6Dg1KB7n/eemz7thcTDx061OL7Zs6cCYPBgIyMDBw9ehQpKSnYsGED4uPj7dYfO3asTVxpHPMac3V1tfmx0pnx2vr16/Hpp59i48aN6NevH9zd3bFo0aIm56KONI5rDV9btzWOl83Fb0ftAo8vXjIp9ZTp06cPtFotTCaTzeV3DVkvaWy48pTRaLSpo9FoWmWFuY4dO8LPzw9Xr15t8Ve45jxKf4KCghAUFITFixdj0qRJSEtLQ3R0tN02iouLUVFRgbVr18Lfv37lnLNnz7Z6n4joySgoKMCMGTOwdu1a5VaQxgYOHIh9+/YhICDgkVaUCgkJwb59+5RVuID6+KrT6dClSxeH+7p06RJ69er16AfTwIABA1BbW4vy8nKHv9iFhoYiKyvL5haahhjDiJwX6OOBHRNfxMy9BTar74kIdkx8Eb3aP94frtzd3Z2OGwMHDsRXX32FDh064Pnnn7dbx15bzsYEjjWJCADULwSi/ch/wvzdP9Bw9T1A0H7kP6HW/7mxTnPsxcSQkBA8fPgQubm5yu17FRUVKCkpsbn7xd/fH3PmzMGcOXOwfPlybN++HfHx8cqPoQ3jgE6na3LVKeB8vHBmvJaTk4OoqChMmTIFQH3ip7S01KbPrRWfnInfzmjteMn1CZ8yOp0OCQkJWLx4MXbv3g2j0Yjz589j06ZN2L17N4D6OUzOnj2LzMxMlJaW4r333kN+fr5NOwEBASgqKkJJSQnMZjNqamr+z31atWoVUlJSkJqaitLSUly8eBFpaWn45JNPnG4jICAAFosFWVlZMJvNqKxsOnfD/fv3ERcXh+zsbFy7dg05OTnIz89Xboex10bXrl2h0WiwadMmXL16FYcOHcIHH3zgdJ/KyspQUFAAs9mM6upqp4+HiB4/s9mM119/Ha+88gqmTJmCW7du2Txu374NAJg/fz7u3LmDSZMmIT8/H0ajEZmZmXjrrbea/cKdN28efvrpJ8THx6O4uBgHDx5EUlISlixZ4nD538TERJw+fRpxcXEoKCjADz/8gIMHDyIuLu6Rji0oKAgxMTGIjY3F/v37UVZWhry8PKSkpCAjIwMAsHz5cuTn52PevHkoKipCcXExtm7dCrPZDKA+huXm5uLHH3+E2Wzm1QJELZg+xB/FiX9Dwis9MbG/HxJe6YnixL9h+hD/J901GzExMWjfvj2ioqJw8uRJlJWVITs7GwsWLMD169cdvs/ZmPAsjzWJyJaubyy6TPsPPActgXvgG/ActARdpv0Hur6xbd6XwMBAREVFYdasWTh16hQKCwsxZcoUdO7cGVFRUQDqp4bIzMxEWVkZzp8/j2PHjinnit26dYNKpcLhw4dx+/Zt5aoiewICAnDixAncuHFDGVfZ48x4LTAwEN9++y1Onz6Ny5cvY/bs2fjll1+a7K81xmzOxG9ntPp5cLPToD9lnJ39/a/K0ep7d+/etalXV1cnGzdulN69e4tarRYfHx8xGAxy/PhxEalfRnz69Oni6ekper1e5s6dK8uWLZP+/fsrbZSXl8uoUaPEw8OjyTK9Fy5caLYPjfspIpKeni4vvviiaDQaeeGFF+Tll1+W/fv3i4jYbffu3bvKfq3mzJkj3t7eDpfpra6uljfffFP8/f1Fo9GIn5+fxMXF2fx/22vjyy+/lICAANFqtRIWFiaHDh2y6Y+jz7mqqkrGjx8ver2+2aUwiejJ2LVrV5Mlaxs+unXrptQtLS2V6OhoZQnh4OBgWbRokbJKX0REhCxcuLDJPrKzs2XIkCGi0WjE19dXEhMTpaamptl+5eXlKfHV3d1dQkNDZc2aNUr5woULJSIiwuY99rZZV7cKCAgQtVotnTp1kujoaCkqKrLpX3h4uGi1WtHr9WIwGJRYVlJSIsOGDRNXV1dleWEi+uuxt9JUS2U3b96U2NhYad++vWi1WunRo4fMmjVLfvvtN4f7sRcTONYkor+a5mLinTt3ZOrUqeLp6Smurq5iMBiUVe5EROLi4qRnz56i1WrFx8dHpk6dKmazWSlfvXq1+Pr6ikqlkmnTpjnsw5kzZyQ0NFS0Wq1YUyr24pJIy+O1iooKiYqKEg8PD+nQoYO8++67Ehsba3OMzsZne31ISkqyib0txW977V64cMFmrOjsebCz+ReViIOlRJ5CVVVVKCsrQ/fu3fGcvfmeiIiIiIiIiIjoT3E2/8Lb94iIiIiIiIiIqM0xKUVERERERERERG2OSSkiIiIiIiIiImpzTEoREREREREREVGbeyaTUs/Q3O5ERERERERERG3K2bzLM5WUUqvVAIDKyson3BMiIiIiIiIioqeTNe9izcM40q4tOvNX4eLiAr1ej/LycgCAm5sbVCrVE+4VEREREREREdH/fyKCyspKlJeXQ6/Xw8XFpdn6KnnG7mUTEdy6dQu//vrrk+4KEREREREREdFTR6/Xw9fXt8ULgZ65pJRVbW0tampqnnQ3iIiIiIiIiIieGmq1usUrpKye2aQUERERERERERE9Oc/UROdERERERERERPTXwKQUERERERERERG1OSaliIiIiIiIiIiozTEpRUREREREREREbY5JKSIiIiIiIiIianNMShERERERERERUZtjUoqIiIiIiIiIiNock1JERERERERERNTm/hfJTe9r89PybwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dml_obj_all = DoubleMLDIDMulti(dml_data, **(default_args| {\"gt_combinations\": \"all\"}))\n", "dml_obj_all.fit()\n", @@ -1960,31 +902,9 @@ }, { "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
,\n", - " [])" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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GXidclusterperiodYtreat
03-0.8762331515.5625561
13-0.8762331524.3492131
23-0.8762331537.1340371
33-0.8762331546.2430561
42-0.8738482361-3.6593871
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" - ], - "text/plain": [ - " G X id cluster period Y treat\n", - "0 3 -0.876233 1 5 1 5.562556 1\n", - "1 3 -0.876233 1 5 2 4.349213 1\n", - "2 3 -0.876233 1 5 3 7.134037 1\n", - "3 3 -0.876233 1 5 4 6.243056 1\n", - "4 2 -0.873848 2 36 1 -3.659387 1" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dta = pd.read_csv(\"https://raw.githubusercontent.com/d2cml-ai/csdid/main/data/sim_data.csv\")\n", "dta.head()" @@ -159,108 +60,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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GXidclusterperiodYtreat
03.0-0.8762331515.5625561
13.0-0.8762331524.3492131
23.0-0.8762331537.1340371
33.0-0.8762331546.2430561
42.0-0.8738482361-3.6593871
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" - ], - "text/plain": [ - " G X id cluster period Y treat\n", - "0 3.0 -0.876233 1 5 1 5.562556 1\n", - "1 3.0 -0.876233 1 5 2 4.349213 1\n", - "2 3.0 -0.876233 1 5 3 7.134037 1\n", - "3 3.0 -0.876233 1 5 4 6.243056 1\n", - "4 2.0 -0.873848 2 36 1 -3.659387 1" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# set dtype for G to float\n", "dta[\"G\"] = dta[\"G\"].astype(float)\n", @@ -283,34 +85,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLPanelData Object ==================\n", - "\n", - "------------------ Data summary ------------------\n", - "Outcome variable: Y\n", - "Treatment variable(s): ['G']\n", - "Covariates: ['X']\n", - "Instrument variable(s): None\n", - "Time variable: period\n", - "Id variable: id\n", - "No. Observations: 3979\n", - "\n", - "------------------ DataFrame info ------------------\n", - "\n", - "RangeIndex: 15916 entries, 0 to 15915\n", - "Columns: 7 entries, G to treat\n", - "dtypes: float64(3), int64(4)\n", - "memory usage: 870.5 KB\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "dml_data = DoubleMLPanelData(\n", " data=dta,\n", @@ -336,60 +113,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDMulti Object ==================\n", - "\n", - "------------------ Data summary ------------------\n", - "Outcome variable: Y\n", - "Treatment variable(s): ['G']\n", - "Covariates: ['X']\n", - "Instrument variable(s): None\n", - "Time variable: period\n", - "Id variable: id\n", - "No. Observations: 3979\n", - "\n", - "------------------ Score & algorithm ------------------\n", - "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", - "\n", - "------------------ Machine learner ------------------\n", - "Learner ml_g: LinearRegression()\n", - "Learner ml_m: LogisticRegression()\n", - "Out-of-sample Performance:\n", - "Regression:\n", - "Learner ml_g0 RMSE: [[1.42617854 1.4109985 1.3977513 1.42696223 1.40564857 1.41934546\n", - " 1.42376336 1.40757926 1.42390621]]\n", - "Learner ml_g1 RMSE: [[1.4030393 1.43646172 1.39614341 1.4195028 1.42759927 1.38453389\n", - " 1.45829099 1.41782049 1.41006229]]\n", - "Classification:\n", - "Learner ml_m Log Loss: [[0.69136666 0.69104942 0.69043707 0.68049621 0.67961183 0.67915833\n", - " 0.66231844 0.66275239 0.66321942]]\n", - "\n", - "------------------ Resampling ------------------\n", - "No. folds: 5\n", - "No. repeated sample splits: 1\n", - "\n", - "------------------ Fit summary ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.925650 0.064143 14.430953 0.000000 0.799932 1.051369\n", - "ATT(2.0,1,3) 1.987835 0.064616 30.763940 0.000000 1.861190 2.114479\n", - "ATT(2.0,1,4) 2.955752 0.063206 46.764006 0.000000 2.831871 3.079633\n", - "ATT(3.0,1,2) -0.039327 0.066091 -0.595052 0.551809 -0.168862 0.090208\n", - "ATT(3.0,2,3) 1.103572 0.065372 16.881417 0.000000 0.975446 1.231699\n", - "ATT(3.0,2,4) 2.059269 0.065414 31.480585 0.000000 1.931060 2.187478\n", - "ATT(4.0,1,2) -0.000186 0.068382 -0.002723 0.997827 -0.134213 0.133841\n", - "ATT(4.0,2,3) 0.063073 0.066448 0.949209 0.342514 -0.067163 0.193309\n", - "ATT(4.0,3,4) 0.959826 0.067745 14.168265 0.000000 0.827048 1.092603\n" - ] - } - ], + "outputs": [], "source": [ "dml_obj = DoubleMLDIDMulti(\n", " obj_dml_data=dml_data,\n", @@ -425,102 +151,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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2.5 %97.5 %
ATT(2.0,1,2)0.7544731.096828
ATT(2.0,1,3)1.8153972.160272
ATT(2.0,1,4)2.7870773.124427
ATT(3.0,1,2)-0.2157010.137046
ATT(3.0,2,3)0.9291161.278028
ATT(3.0,2,4)1.8847022.233837
ATT(4.0,1,2)-0.1826760.182304
ATT(4.0,2,3)-0.1142540.240401
ATT(4.0,3,4)0.7790381.140614
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" - ], - "text/plain": [ - " 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.754473 1.096828\n", - "ATT(2.0,1,3) 1.815397 2.160272\n", - "ATT(2.0,1,4) 2.787077 3.124427\n", - "ATT(3.0,1,2) -0.215701 0.137046\n", - "ATT(3.0,2,3) 0.929116 1.278028\n", - "ATT(3.0,2,4) 1.884702 2.233837\n", - "ATT(4.0,1,2) -0.182676 0.182304\n", - "ATT(4.0,2,3) -0.114254 0.240401\n", - "ATT(4.0,3,4) 0.779038 1.140614" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "level = 0.95\n", "\n", @@ -541,34 +174,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n", - "/home/ubuntu/.venv/lib/python3.12/site-packages/matplotlib/cbook.py:1719: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = dml_obj.plot_effects()" ] @@ -596,50 +208,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Group Aggregation \n", - "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.490559 0.034343 43.402684 0.0 1.423248 1.557869\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "2.0 1.956413 0.052253 37.441069 0.0 1.853998 2.058827\n", - "3.0 1.581421 0.056230 28.124109 0.0 1.471212 1.691630\n", - "4.0 0.959826 0.067745 14.168265 0.0 0.827048 1.092603\n", - "------------------ Additional Information ------------------\n", - "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "aggregated = dml_obj.aggregate(aggregation=\"group\")\n", "print(aggregated)\n", @@ -666,50 +237,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Time Aggregation \n", - "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.482646 0.035122 42.214692 0.0 1.413809 1.551483\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "2 0.925650 0.064143 14.430953 0.0 0.799932 1.051369\n", - "3 1.547060 0.051295 30.160244 0.0 1.446524 1.647596\n", - "4 1.975228 0.046695 42.300363 0.0 1.883707 2.066749\n", - "------------------ Additional Information ------------------\n", - "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "aggregated_time = dml_obj.aggregate(\"time\")\n", "print(aggregated_time)\n", @@ -727,52 +257,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Event Study Aggregation \n", - "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.991584 0.038736 51.414242 0.0 1.915663 2.067506\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "-2.0 -0.000186 0.068382 -0.002723 0.997827 -0.134213 0.133841\n", - "-1.0 0.013228 0.040589 0.325893 0.744505 -0.066325 0.092781\n", - "0.0 0.995559 0.030849 32.272276 0.000000 0.935096 1.056021\n", - "1.0 2.023443 0.045615 44.359392 0.000000 1.934039 2.112846\n", - "2.0 2.955752 0.063206 46.764006 0.000000 2.831871 3.079633\n", - "------------------ Additional Information ------------------\n", - "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/.venv/lib/python3.12/site-packages/doubleml/did/did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "aggregated_eventstudy = dml_obj.aggregate(\"eventstudy\")\n", "print(aggregated_eventstudy)\n", @@ -796,34 +283,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Event Study Aggregation \n", - "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "1.991584 0.038736 51.414242 0.0 1.915663 2.067506\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "-2.0 -0.000186 0.068382 -0.002723 0.997827 -0.134213 0.133841\n", - "-1.0 0.013228 0.040589 0.325893 0.744505 -0.066325 0.092781\n", - "0.0 0.995559 0.030849 32.272276 0.000000 0.935096 1.056021\n", - "1.0 2.023443 0.045615 44.359392 0.000000 1.934039 2.112846\n", - "2.0 2.955752 0.063206 46.764006 0.000000 2.831871 3.079633\n", - "------------------ Additional Information ------------------\n", - "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(aggregated_eventstudy)" ] @@ -837,17 +299,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0. 0. 0.33333333 0.33333333 0.33333333]\n" - ] - } - ], + "outputs": [], "source": [ "print(aggregated_eventstudy.overall_aggregation_weights)" ] @@ -861,21 +315,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.32875335, 0. , 0. , 0. , 0.32674263,\n", - " 0. , 0. , 0. , 0.34450402])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "aggregated_eventstudy.aggregation_weights[2]" ] @@ -893,26 +335,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2.0,1,2) 0.925650 0.064143 14.430953 0.000000 0.799932 1.051369\n", - "ATT(2.0,1,3) 1.987835 0.064616 30.763940 0.000000 1.861190 2.114479\n", - "ATT(2.0,1,4) 2.955752 0.063206 46.764006 0.000000 2.831871 3.079633\n", - "ATT(3.0,1,2) -0.039327 0.066091 -0.595052 0.551809 -0.168862 0.090208\n", - "ATT(3.0,2,3) 1.103572 0.065372 16.881417 0.000000 0.975446 1.231699\n", - "ATT(3.0,2,4) 2.059269 0.065414 31.480585 0.000000 1.931060 2.187478\n", - "ATT(4.0,1,2) -0.000186 0.068382 -0.002723 0.997827 -0.134213 0.133841\n", - "ATT(4.0,2,3) 0.063073 0.066448 0.949209 0.342514 -0.067163 0.193309\n", - "ATT(4.0,3,4) 0.959826 0.067745 14.168265 0.000000 0.827048 1.092603\n" - ] - } - ], + "outputs": [], "source": [ "print(dml_obj.summary)" ] From 76c7a27fd9b49e1503196b65ec7a20d654bef8b3 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 25 Apr 2025 06:51:50 +0000 Subject: [PATCH 101/140] instead of dockerfile reference image --- .devcontainer/devcontainer.json | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 8f299d30..c480f797 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -1,7 +1,8 @@ { "name": "DoubleML Documentation Development", - "dockerFile": "Dockerfile.dev", // Path to your Dockerfile - "context": "..", // Context for the build (root of your project) + "image": "svenklaassen/doubleml-docs:latest", + // "dockerFile": "Dockerfile.dev", + // "context": "..", "workspaceFolder": "/workspace", // Folder inside the container for your project // Customizations for VS Code "customizations": { From b43b70a1b7768039362ded4287df22483a5ee1eb Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 25 Apr 2025 06:52:04 +0000 Subject: [PATCH 102/140] add guide to build and push image locally --- .devcontainer/build_image_guide.md | 133 +++++++++++++++++++++++++++++ 1 file changed, 133 insertions(+) create mode 100644 .devcontainer/build_image_guide.md diff --git a/.devcontainer/build_image_guide.md b/.devcontainer/build_image_guide.md new file mode 100644 index 00000000..942ffe44 --- /dev/null +++ b/.devcontainer/build_image_guide.md @@ -0,0 +1,133 @@ +# Building and Publishing the Docker Image + +This guide shows how to build the DoubleML documentation development container locally and publish it to Docker Hub. + +## Prerequisites + +- [Docker Desktop](https://www.docker.com/products/docker-desktop/) installed and running +- Access to the `svenklaassen` [Docker Hub](https://www.docker.com/products/docker-hub/) account +- [doubleml-docs](https://github.com/DoubleML/doubleml-docs) repository cloned to your local machine + +## Step 1: Login to Docker Hub + +Open a terminal and login to Docker Hub: + +```bash +docker login +``` + +Enter the Docker Hub username (`svenklaassen`) and password (or token) when prompted. + +## Step 2: Build the Docker Image + +Navigate to your project root directory and build the image (using the `latest`-tag): + +```bash +docker build -t svenklaassen/doubleml-docs:latest -f .devcontainer/Dockerfile.dev . +``` + +To force a complete rebuild without using cache: + +```bash +docker build --no-cache -t yourusername/doubleml-docs:latest -f .devcontainer/Dockerfile.dev . +``` + +## Step 3 (Optional): Verify the image + +### Open the repository in VS Code + +1. Ensure your `.devcontainer/devcontainer.json` is configured to use your local image: + + ```json + "image": "svenklaassen/doubleml-docs:latest" + ``` + +2. Open your repository in VS Code: + + ```bash + code /path/to/doubleml-docs + ``` + +3. Open the Command Palette (`Ctrl+Shift+P`) and select `Remote-Containers: Reopen in Container`. + VS Code will use your locally built image. + +### Build the documentation + +Once inside the container, verify that you can successfully build the documentation: + +1. Open a terminal in VS Code (`Terminal > New Terminal`) + +2. Build the documentation: + + ```bash + cd doc + make html + ``` + +3. Check the output for any errors or warnings + +4. View the built documentation by opening the output files: + + ```bash + # On Windows + explorer.exe _build/html + + # On Linux + xdg-open _build/html + + # On macOS + open _build/html + ``` + +If the documentation builds successfully and looks correct, your Docker image is working properly and ready to be pushed to Docker Hub. + +## Step 4: Push to Docker Hub + +Push your built image to Docker Hub: + +```bash +docker push svenklaassen/doubleml-docs:latest +``` + +## Step 5: Using the Published Image + +After publishing, there are two ways to use the image: + +### Option 1: Manual Container Management +Pull and run the container manually: + +```bash +docker pull svenklaassen/doubleml-docs:latest +# Then run commands to create a container from this image +``` + +### Option 2: VS Code Integration (Recommended) +Simply reference the image in your `devcontainer.json` file: + +```json +"image": "svenklaassen/doubleml-docs:latest" +``` + +VS Code will automatically pull the image when opening the project in a container - no separate `docker pull` command needed. + +## Troubleshooting + +### Clear Docker Cache + +If you're experiencing issues with cached layers: + +```bash +# Remove build cache +docker builder prune + +# For a more thorough cleanup +docker system prune -a +``` + +### Check Image Size + +To verify the image size before pushing: + +```bash +docker images svenklaassen/doubleml-docs +``` \ No newline at end of file From 2bc5e418295aca7ea86045f874a13d261355ceaa Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 25 Apr 2025 07:04:41 +0000 Subject: [PATCH 103/140] update guide to use docker container --- .devcontainer/{guide.md => docker_guide.md} | 34 +++++++++++++++------ 1 file changed, 24 insertions(+), 10 deletions(-) rename .devcontainer/{guide.md => docker_guide.md} (64%) diff --git a/.devcontainer/guide.md b/.devcontainer/docker_guide.md similarity index 64% rename from .devcontainer/guide.md rename to .devcontainer/docker_guide.md index d508dfd2..0d9d648d 100644 --- a/.devcontainer/guide.md +++ b/.devcontainer/docker_guide.md @@ -36,29 +36,43 @@ You can verify the installations in a terminal: - [Developing inside a Container](https://code.visualstudio.com/docs/devcontainers/containers) -## Build & Open the Development Container +## Open the Development Container (Using Pre-built Image) - - Open the project `doubleml-docs` in VS code: +For faster setup, we'll use a pre-built Docker image: + +1. Open the `doubleml-docs` repository in VS Code: ```bash - code . + code /path/to/doubleml-docs ``` - - Open the Command Palette (`Ctrl+Shift+P`). - - Type `Remote-Containers: Reopen Folder in Container`. - - VS Code will build the new container(this may take some time) and open the project in it. +2. Open the Command Palette (`Ctrl+Shift+P`). +3. Type `Remote-Containers: Reopen in Container`. + +VS Code will pull the `svenklaassen/doubleml-docs:latest` image (if needed) based on `.devcontainer.json` and open the project in the container.
+This approach is much faster than building the container from scratch. VS Code automatically downloads the image from Docker Hub if it's not already on your system. ## Build the documentation -You can build the documentation via +1. Open a terminal in VS Code (`Terminal > New Terminal`) + +2. Build the documentation: ```bash cd doc make html ``` - Open the directory in WSL with +3. View the built documentation by opening the output files: + ```bash - explorer.exe . - ``` \ No newline at end of file + # On Windows + explorer.exe _build/html + + # On Linux + xdg-open _build/html + + # On macOS + open _build/html + ``` From cc3814f025d228d3ce18b5e49815cb16d262b7ea Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 25 Apr 2025 07:12:54 +0000 Subject: [PATCH 104/140] update guides --- .devcontainer/build_image_guide.md | 7 ++++--- .devcontainer/docker_guide.md | 22 +++++++++++----------- 2 files changed, 15 insertions(+), 14 deletions(-) diff --git a/.devcontainer/build_image_guide.md b/.devcontainer/build_image_guide.md index 942ffe44..fcaf6e2c 100644 --- a/.devcontainer/build_image_guide.md +++ b/.devcontainer/build_image_guide.md @@ -29,7 +29,7 @@ docker build -t svenklaassen/doubleml-docs:latest -f .devcontainer/Dockerfile.de To force a complete rebuild without using cache: ```bash -docker build --no-cache -t yourusername/doubleml-docs:latest -f .devcontainer/Dockerfile.dev . +docker build --no-cache -t svenklaassen/doubleml-docs:latest -f .devcontainer/Dockerfile.dev . ``` ## Step 3 (Optional): Verify the image @@ -41,14 +41,15 @@ docker build --no-cache -t yourusername/doubleml-docs:latest -f .devcontainer/Do ```json "image": "svenklaassen/doubleml-docs:latest" ``` + Note: The `.devcontainer/devcontainer.json` file is configured to use the pre-built image. If you want to build the container from scratch, uncomment the `dockerFile` and `context` lines and comment out the `image` line. -2. Open your repository in VS Code: +2. Open the `doubleml-docs` repository in VS Code: ```bash code /path/to/doubleml-docs ``` -3. Open the Command Palette (`Ctrl+Shift+P`) and select `Remote-Containers: Reopen in Container`. +3. Open the Command Palette (`Ctrl+Shift+P`) and select `Dev Containers: Reopen in Container`. VS Code will use your locally built image. ### Build the documentation diff --git a/.devcontainer/docker_guide.md b/.devcontainer/docker_guide.md index 0d9d648d..7c9fae06 100644 --- a/.devcontainer/docker_guide.md +++ b/.devcontainer/docker_guide.md @@ -7,7 +7,7 @@ Requirements: - [WSL2](https://learn.microsoft.com/en-us/windows/wsl/install) - [Docker Desktop](https://docs.docker.com/desktop/setup/install/windows-install/) -## Verify installations & Setup +## Step 1: Verify installations & Setup You can verify the installations in a terminal: @@ -20,23 +20,23 @@ You can verify the installations in a terminal: ### Configure Docker to Use WSL2 See [Docker Desktop Documentation](https://docs.docker.com/desktop/features/wsl/#turn-on-docker-desktop-wsl-2). - - Open Docker Desktop. - - Go to **Settings > General** and make sure **Use the WSL 2 based engine** is checked. - - Under **Settings > Resources > WSL Integration**, ensure that your desired Linux distribution(s) are selected for integration with Docker. + 1. Open Docker Desktop. + 2. Go to **Settings > General** and make sure **Use the WSL 2 based engine** is checked. + 3. Under **Settings > Resources > WSL Integration**, ensure that your desired Linux distribution(s) are selected for integration with Docker. ### Install Extensions - - Open Visual Studio Code. - - Press `Ctrl+Shift+X` to open the Extensions view. - - Search and install (includes WSL and Dev Containers Extensions): + 1. Open Visual Studio Code. + 2. Press `Ctrl+Shift+X` to open the Extensions view. + 3. Search and install (includes WSL and Dev Containers Extensions): - [Remote Development Extension Pack](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.vscode-remote-extensionpack) - VS Code Documentations: + Helpful VS Code Documentations: - [Developing in WSL](https://code.visualstudio.com/docs/remote/wsl) - [Developing inside a Container](https://code.visualstudio.com/docs/devcontainers/containers) -## Open the Development Container (Using Pre-built Image) +## Step 2: Open the Development Container (Using Pre-built Image) For faster setup, we'll use a pre-built Docker image: @@ -47,13 +47,13 @@ For faster setup, we'll use a pre-built Docker image: ``` 2. Open the Command Palette (`Ctrl+Shift+P`). -3. Type `Remote-Containers: Reopen in Container`. +3. Type `Dev Containers: Reopen in Container`. VS Code will pull the `svenklaassen/doubleml-docs:latest` image (if needed) based on `.devcontainer.json` and open the project in the container.
This approach is much faster than building the container from scratch. VS Code automatically downloads the image from Docker Hub if it's not already on your system. -## Build the documentation +## Step 3: Build the documentation 1. Open a terminal in VS Code (`Terminal > New Terminal`) From 9f9b849159a4b934ac7290811ea2d066988712a3 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 25 Apr 2025 07:16:38 +0000 Subject: [PATCH 105/140] fix typo --- .devcontainer/docker_guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/docker_guide.md b/.devcontainer/docker_guide.md index 7c9fae06..ea8cf9dc 100644 --- a/.devcontainer/docker_guide.md +++ b/.devcontainer/docker_guide.md @@ -49,7 +49,7 @@ For faster setup, we'll use a pre-built Docker image: 2. Open the Command Palette (`Ctrl+Shift+P`). 3. Type `Dev Containers: Reopen in Container`. -VS Code will pull the `svenklaassen/doubleml-docs:latest` image (if needed) based on `.devcontainer.json` and open the project in the container.
+VS Code will pull the `svenklaassen/doubleml-docs:latest` image (if needed) based on `devcontainer.json` and open the project in the container.
This approach is much faster than building the container from scratch. VS Code automatically downloads the image from Docker Hub if it's not already on your system. From 3c7464840779a2e958ff792afa60f24f46e9df80 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 25 Apr 2025 08:41:27 +0000 Subject: [PATCH 106/140] use prebuild image to build documentation --- .github/workflows/test_build_docu_dev.yml | 68 +++++------------------ 1 file changed, 13 insertions(+), 55 deletions(-) diff --git a/.github/workflows/test_build_docu_dev.yml b/.github/workflows/test_build_docu_dev.yml index ef5989de..095686a6 100644 --- a/.github/workflows/test_build_docu_dev.yml +++ b/.github/workflows/test_build_docu_dev.yml @@ -30,8 +30,9 @@ on: jobs: build: - - runs-on: ubuntu-22.04 + runs-on: ubuntu-24.04 + container: + image: svenklaassen/doubleml-docs:latest env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} @@ -39,14 +40,14 @@ jobs: - name: Check out the repo containing the docu source uses: actions/checkout@v4 - - name: Check out the repo containing the python pkg DoubleML (dev) + - name: Check out the repo containing the python pkg DoubleML (main branch) if: ${{ github.event_name != 'workflow_dispatch' }} uses: actions/checkout@v4 with: repository: DoubleML/doubleml-for-py path: doubleml-for-py - - name: Check out the repo containing the python pkg DoubleML (dev) + - name: Check out the repo containing the python pkg DoubleML (custom branch) if: ${{ github.event_name == 'workflow_dispatch' }} uses: actions/checkout@v4 with: @@ -54,66 +55,23 @@ jobs: path: doubleml-for-py ref: ${{ github.event.inputs.doubleml-py-branch }} - - name: Install graphviz - run: sudo apt-get install graphviz - - - name: Install python - uses: actions/setup-python@v5 - with: - python-version: '3.12' - - name: Install dependencies and the python package + - name: Install Python package run: | - python -m pip install --upgrade pip - pip install -r requirements.txt pip uninstall -y DoubleML cd doubleml-for-py pip install -e .[rdd] - - name: Add R repository - run: | - sudo apt install dirmngr gnupg apt-transport-https ca-certificates software-properties-common - sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 - sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/' - - name: Install R - run: | - sudo apt-get update - sudo apt-get install r-base - sudo apt-get install r-base-dev - sudo apt-get install -y zlib1g-dev libicu-dev pandoc make libcurl4-openssl-dev libssl-dev - - - name: Get user library folder - run: | - mkdir ${GITHUB_WORKSPACE}/tmp_r_libs_user - echo R_LIBS_USER=${GITHUB_WORKSPACE}/tmp_r_libs_user >> $GITHUB_ENV - - - name: Query R version - run: | - writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") - shell: Rscript {0} - - - name: Cache R packages - uses: actions/cache@v4 - with: - path: ${{ env.R_LIBS_USER }} - key: doubleml-test-build-dev-${{ hashFiles('.github/R-version') }} - - - name: Install R kernel for Jupyter and the R package DoubleML (dev) + - name: Install R package DoubleML (main branch) if: ${{ github.event_name != 'workflow_dispatch' }} run: | - install.packages('remotes') - remotes::install_github('DoubleML/doubleml-for-r', dependencies = TRUE) - install.packages(c('ggplot2', 'IRkernel', 'xgboost', 'hdm', 'reshape2', 'gridExtra', "igraph", "mlr3filters", "mlr3measures", "did")) - IRkernel::installspec() - shell: Rscript {0} - - - name: Install R kernel for Jupyter and the R package DoubleML (dev) + Rscript -e "remotes::install_github('DoubleML/doubleml-for-r', dependencies = TRUE)" + shell: bash + + - name: Install R package DoubleML (custom branch) if: ${{ github.event_name == 'workflow_dispatch' }} run: | - install.packages('remotes') - remotes::install_github('DoubleML/doubleml-for-r@${{ github.event.inputs.doubleml-r-branch }}', dependencies = TRUE) - install.packages(c('ggplot2', 'IRkernel', 'xgboost', 'hdm', 'reshape2', 'gridExtra', "igraph", "mlr3filters", "mlr3measures", "did")) - IRkernel::installspec() - shell: Rscript {0} + Rscript -e "remotes::install_github('DoubleML/doubleml-for-r@${{ github.event.inputs.doubleml-r-branch }}', dependencies = TRUE)" + shell: bash - name: Build docu with sphinx run: | From fafadff20cabe67d869301225cc3c6a6a9231bf4 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 25 Apr 2025 11:45:02 +0200 Subject: [PATCH 107/140] Revert "use prebuild image to build documentation" This reverts commit 3c7464840779a2e958ff792afa60f24f46e9df80. --- .github/workflows/test_build_docu_dev.yml | 68 ++++++++++++++++++----- 1 file changed, 55 insertions(+), 13 deletions(-) diff --git a/.github/workflows/test_build_docu_dev.yml b/.github/workflows/test_build_docu_dev.yml index 095686a6..ef5989de 100644 --- a/.github/workflows/test_build_docu_dev.yml +++ b/.github/workflows/test_build_docu_dev.yml @@ -30,9 +30,8 @@ on: jobs: build: - runs-on: ubuntu-24.04 - container: - image: svenklaassen/doubleml-docs:latest + + runs-on: ubuntu-22.04 env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} @@ -40,14 +39,14 @@ jobs: - name: Check out the repo containing the docu source uses: actions/checkout@v4 - - name: Check out the repo containing the python pkg DoubleML (main branch) + - name: Check out the repo containing the python pkg DoubleML (dev) if: ${{ github.event_name != 'workflow_dispatch' }} uses: actions/checkout@v4 with: repository: DoubleML/doubleml-for-py path: doubleml-for-py - - name: Check out the repo containing the python pkg DoubleML (custom branch) + - name: Check out the repo containing the python pkg DoubleML (dev) if: ${{ github.event_name == 'workflow_dispatch' }} uses: actions/checkout@v4 with: @@ -55,23 +54,66 @@ jobs: path: doubleml-for-py ref: ${{ github.event.inputs.doubleml-py-branch }} - - name: Install Python package + - name: Install graphviz + run: sudo apt-get install graphviz + + - name: Install python + uses: actions/setup-python@v5 + with: + python-version: '3.12' + - name: Install dependencies and the python package run: | + python -m pip install --upgrade pip + pip install -r requirements.txt pip uninstall -y DoubleML cd doubleml-for-py pip install -e .[rdd] - - name: Install R package DoubleML (main branch) + - name: Add R repository + run: | + sudo apt install dirmngr gnupg apt-transport-https ca-certificates software-properties-common + sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 + sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/' + - name: Install R + run: | + sudo apt-get update + sudo apt-get install r-base + sudo apt-get install r-base-dev + sudo apt-get install -y zlib1g-dev libicu-dev pandoc make libcurl4-openssl-dev libssl-dev + + - name: Get user library folder + run: | + mkdir ${GITHUB_WORKSPACE}/tmp_r_libs_user + echo R_LIBS_USER=${GITHUB_WORKSPACE}/tmp_r_libs_user >> $GITHUB_ENV + + - name: Query R version + run: | + writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") + shell: Rscript {0} + + - name: Cache R packages + uses: actions/cache@v4 + with: + path: ${{ env.R_LIBS_USER }} + key: doubleml-test-build-dev-${{ hashFiles('.github/R-version') }} + + - name: Install R kernel for Jupyter and the R package DoubleML (dev) if: ${{ github.event_name != 'workflow_dispatch' }} run: | - Rscript -e "remotes::install_github('DoubleML/doubleml-for-r', dependencies = TRUE)" - shell: bash - - - name: Install R package DoubleML (custom branch) + install.packages('remotes') + remotes::install_github('DoubleML/doubleml-for-r', dependencies = TRUE) + install.packages(c('ggplot2', 'IRkernel', 'xgboost', 'hdm', 'reshape2', 'gridExtra', "igraph", "mlr3filters", "mlr3measures", "did")) + IRkernel::installspec() + shell: Rscript {0} + + - name: Install R kernel for Jupyter and the R package DoubleML (dev) if: ${{ github.event_name == 'workflow_dispatch' }} run: | - Rscript -e "remotes::install_github('DoubleML/doubleml-for-r@${{ github.event.inputs.doubleml-r-branch }}', dependencies = TRUE)" - shell: bash + install.packages('remotes') + remotes::install_github('DoubleML/doubleml-for-r@${{ github.event.inputs.doubleml-r-branch }}', dependencies = TRUE) + install.packages(c('ggplot2', 'IRkernel', 'xgboost', 'hdm', 'reshape2', 'gridExtra', "igraph", "mlr3filters", "mlr3measures", "did")) + IRkernel::installspec() + shell: Rscript {0} - name: Build docu with sphinx run: | From d4cab6287232dac4d4e5a16ff55c91f7b7f185fc Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 25 Apr 2025 13:26:48 +0000 Subject: [PATCH 108/140] add execute argument to guide --- .devcontainer/docker_guide.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.devcontainer/docker_guide.md b/.devcontainer/docker_guide.md index ea8cf9dc..18e318ef 100644 --- a/.devcontainer/docker_guide.md +++ b/.devcontainer/docker_guide.md @@ -64,6 +64,11 @@ This approach is much faster than building the container from scratch. VS Code a make html ``` + To build without notebook examples: + ```bash + make html NBSPHINX_EXECUTE=never + ``` + 3. View the built documentation by opening the output files: ```bash From ac2d34b2630749cd71a90ac89b0b2419043ed155 Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Fri, 25 Apr 2025 19:31:26 +0200 Subject: [PATCH 109/140] fix cond. independence under nonignorable nonresponse --- doc/guide/models/ssm/ssm_models.inc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/guide/models/ssm/ssm_models.inc b/doc/guide/models/ssm/ssm_models.inc index 77d12709..d218c815 100644 --- a/doc/guide/models/ssm/ssm_models.inc +++ b/doc/guide/models/ssm/ssm_models.inc @@ -81,7 +81,7 @@ with unobservables affecting :math:`Y_i` conditional on :math:`D_i` and :math:`X a (unknown) threshold model: - **Threshold:** :math:`S_i = 1\{V_i \le \xi(D,X,Z)\}` where :math:`\xi` is a general function and :math:`V_i` is a scalar with strictly monotonic cumulative distribution function conditional on :math:`X_i`. -- **Cond. Independence:** :math:`Y_i \perp (Z_i, D_i)|X_i`. +- **Cond. Independence:** :math:`V_i \perp (Z_i, D_i)|X_i`. Let :math:`\Pi_i := P(S_i=1|D_i, X_i, Z_i)` denote the selection probability. Additionally, the following assumptions are required: From 599d27f560f6ab7cbd621a765c71f043daa42adb Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 28 Apr 2025 07:16:29 +0000 Subject: [PATCH 110/140] fix broken links --- doc/examples/py_double_ml_apo.ipynb | 8 ++++---- doc/examples/py_double_ml_multiway_cluster.ipynb | 2 +- doc/examples/py_double_ml_pension.ipynb | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/doc/examples/py_double_ml_apo.ipynb b/doc/examples/py_double_ml_apo.ipynb index 68f353b0..50c7037f 100644 --- a/doc/examples/py_double_ml_apo.ipynb +++ b/doc/examples/py_double_ml_apo.ipynb @@ -247,7 +247,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Further, the [DoubleMLAPO](https://docs.doubleml.org/dev/api/generated/doubleml.DoubleMLAPO.html#doubleml.DoubleMLAPO) model requires a specification of the treatment level $a$ for which the APOs should be estimated. In this example, we will loop over all treatment levels." + "Further, the [DoubleMLAPO](https://docs.doubleml.org/dev/api/generated/doubleml.irm.DoubleMLAPO.html) model requires a specification of the treatment level $a$ for which the APOs should be estimated. In this example, we will loop over all treatment levels." ] }, { @@ -434,7 +434,7 @@ "source": [ "## Multiple Average Potential Outcome Models (APOS)\n", "\n", - "Instead of looping over different treatment levels, one can directly use the [DoubleMLAPOS](https://docs.doubleml.org/dev/api/generated/doubleml.DoubleMLAPOS.html#doubleml.DoubleMLAPOS) model which internally combines multiple [DoubleMLAPO](https://docs.doubleml.org/dev/api/generated/doubleml.DoubleMLAPO.html#doubleml.DoubleMLAPO) models. An advantage of this approach is that the model can be parallelized, create joint confidence intervals and allow for a comparison between the average potential outcome levels.\n", + "Instead of looping over different treatment levels, one can directly use the [DoubleMLAPOS](https://docs.doubleml.org/dev/api/generated/doubleml.irm.DoubleMLAPOS.html#doubleml.irm.DoubleMLAPOS) model which internally combines multiple [DoubleMLAPO](https://docs.doubleml.org/dev/api/generated/doubleml.irm.DoubleMLAPO.html) models. An advantage of this approach is that the model can be parallelized, create joint confidence intervals and allow for a comparison between the average potential outcome levels.\n", "\n", "### Average Potential Outcome (APOs)\n", "\n", @@ -869,7 +869,7 @@ "source": [ "### Causal Contrasts\n", "\n", - "The [DoubleMLAPOS](https://docs.doubleml.org/dev/api/generated/doubleml.DoubleMLAPOS.html#doubleml.DoubleMLAPOS) model also allows for the estimation of causal contrasts. \n", + "The [DoubleMLAPOS](https://docs.doubleml.org/dev/api/generated/doubleml.irm.DoubleMLAPOS.html#doubleml.irm.DoubleMLAPOS) model also allows for the estimation of causal contrasts. \n", "The contrast is defined as the difference in the average potential outcomes between the treatment levels $d_i$ and $d_j$ where\n", "\n", "$$ \\theta_{0,ij} = \\mathbb{E}[Y(d_i)] - \\mathbb{E}[Y(d_{j})]$$\n", @@ -12411,7 +12411,7 @@ ], "metadata": { "kernelspec": { - "display_name": "dml_dev", + "display_name": "Python 3", "language": "python", "name": "python3" }, diff --git a/doc/examples/py_double_ml_multiway_cluster.ipynb b/doc/examples/py_double_ml_multiway_cluster.ipynb index 0fdbc908..82db9b26 100644 --- a/doc/examples/py_double_ml_multiway_cluster.ipynb +++ b/doc/examples/py_double_ml_multiway_cluster.ipynb @@ -150,7 +150,7 @@ }, "source": [ "### Data-Backend for Cluster Data\n", - "The implementation of cluster robust double machine learning is based on a special data-backend called [DoubleMLClusterData](https://docs.doubleml.org/stable/api/generated/doubleml.DoubleMLClusterData.html#doubleml.DoubleMLClusterData). As compared to the standard data-backend [DoubleMLData](https://docs.doubleml.org/dev/api/generated/doubleml.DoubleMLData.html), users can specify the clustering variables during instantiation of a [DoubleMLClusterData](https://docs.doubleml.org/stable/api/generated/doubleml.DoubleMLClusterData.html#doubleml.DoubleMLClusterData) object. The estimation framework will subsequently account for the provided clustering options." + "The implementation of cluster robust double machine learning is based on a special data-backend called [DoubleMLClusterData](https://docs.doubleml.org/stable/api/generated/doubleml.DoubleMLClusterData.html#doubleml.DoubleMLClusterData). As compared to the standard data-backend [DoubleMLData](https://docs.doubleml.org/stable/api/generated/doubleml.DoubleMLData.html), users can specify the clustering variables during instantiation of a [DoubleMLClusterData](https://docs.doubleml.org/stable/api/generated/doubleml.DoubleMLClusterData.html#doubleml.DoubleMLClusterData) object. The estimation framework will subsequently account for the provided clustering options." ] }, { diff --git a/doc/examples/py_double_ml_pension.ipynb b/doc/examples/py_double_ml_pension.ipynb index 251baf7b..8dce90f4 100644 --- a/doc/examples/py_double_ml_pension.ipynb +++ b/doc/examples/py_double_ml_pension.ipynb @@ -294,7 +294,7 @@ "id": "2e1fe478", "metadata": {}, "source": [ - "To start our analysis, we initialize the data backend, i.e., a new instance of a [DoubleMLData](https://docs.doubleml.org/dev/api/generated/doubleml.DoubleMLData.html#doubleml.DoubleMLData) object. We implement the regression model by using scikit-learn's `PolynomialFeatures` class.\n", + "To start our analysis, we initialize the data backend, i.e., a new instance of a [DoubleMLData](https://docs.doubleml.org/dev/api/generated/doubleml.data.DoubleMLData.html#doubleml.data.DoubleMLData) object. We implement the regression model by using scikit-learn's `PolynomialFeatures` class.\n", "\n", "To implement both models (basic and flexible), we generate two data backends: `data_dml_base` and `data_dml_flex`." ] From 35d8d563ea91e59528d590a8f75afbd13dc6a74c Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 28 Apr 2025 08:49:36 +0000 Subject: [PATCH 111/140] fix apo api links --- doc/examples/py_double_ml_apo.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/examples/py_double_ml_apo.ipynb b/doc/examples/py_double_ml_apo.ipynb index 50c7037f..1f9925fc 100644 --- a/doc/examples/py_double_ml_apo.ipynb +++ b/doc/examples/py_double_ml_apo.ipynb @@ -708,7 +708,7 @@ "source": [ "## Sensitivity Analysis\n", "\n", - "For [DoubleMLAPO](https://docs.doubleml.org/dev/api/generated/doubleml.DoubleMLAPO.html#doubleml.DoubleMLAPO) and [DoubleMLAPOS](https://docs.doubleml.org/dev/api/generated/doubleml.DoubleMLAPOS.html#doubleml.DoubleMLAPOS) model all methods for sensitivity analysis are available." + "For [DoubleMLAPO](https://docs.doubleml.org/dev/api/generated/doubleml.irm.DoubleMLAPO.html) and [DoubleMLAPOS](https://docs.doubleml.org/dev/api/generated/doubleml.irm.DoubleMLAPOS.html#doubleml.irm.DoubleMLAPOS) model all methods for sensitivity analysis are available." ] }, { From 2562977bccc346afe33ecc9a168666a3c56a35ca Mon Sep 17 00:00:00 2001 From: Ezequiel Smucler Date: Wed, 7 May 2025 22:33:54 +0200 Subject: [PATCH 112/140] add nb --- doc/examples/py_double_ml_robust_iv.ipynb | 241 ++++++++++++++++++++++ 1 file changed, 241 insertions(+) create mode 100644 doc/examples/py_double_ml_robust_iv.ipynb diff --git a/doc/examples/py_double_ml_robust_iv.ipynb b/doc/examples/py_double_ml_robust_iv.ipynb new file mode 100644 index 00000000..79baae34 --- /dev/null +++ b/doc/examples/py_double_ml_robust_iv.ipynb @@ -0,0 +1,241 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python: Confidence intervals for instrumental variables models that are robust to weak instruments" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example we will show how to use the DoubleML package to obtain confidence sets for the treatment effects that are robust to weak instruments. Weak instruments are those that have a relatively weak correlation with the treatment. It is well known that in this case, standard methods to construct confidence intervals have poor properties and can have coverage much lower than the nominal value. We will assume that the reader of this notebook is already familiar with DoubleML and how it can be used to fit instrumental variable models.\n", + "\n", + "Throughout this example\n", + "\n", + "- Z is the instrument,\n", + "- X is a vector of covariates,\n", + "- D is treatment variable,\n", + "- Y is the outcome.\n", + "\n", + "Next, we will generate two synthetic data sets, one where the instrument is weak and another where it is not. Then, we will compare the output of the standard way to compute confidence intervals using the DoubleMLIIVM class, with the confidence sets computed using the uniform_confset method from the same class. We will see that using the uniform_confset method is an easy way to ensure the results of an analysis are robust to weak instruments." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "import doubleml as dml\n", + "\n", + "np.random.seed(1234)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating synthetic data" + ] + }, + { + "cell_type": "markdown", + "id": "774e45c7", + "metadata": {}, + "source": [ + "The following function generates data from an instrumental variables model. The true_effect argument is the estimand of interest, the true effect of the treatment on the outcome. The instrument_strength argument is a measure of the strength of the instrument. The higher it is, the stronger the correlation is between the instrument and the treatment. Notice that the instrument is fully randomized." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "82111204", + "metadata": {}, + "outputs": [], + "source": [ + "def generate_weakiv_data(n_samples, true_effect, instrument_strength):\n", + " u = np.random.normal(0, 2, size=n_samples)\n", + " X = np.random.normal(0, 1, size=n_samples)\n", + " Z = np.random.binomial(1, 0.5, size=n_samples)\n", + " D = instrument_strength * Z + u \n", + " D = np.array(D > 0, dtype=int)\n", + " Y = true_effect * D + np.sign(u)\n", + " return pd.DataFrame({\"Y\": Y, \"Z\": Z, \"D\": D, \"X\": X})" + ] + }, + { + "cell_type": "markdown", + "id": "8c938fd8", + "metadata": {}, + "source": [ + "We call the function two times to get two data sets, one where the instrument is weak, the other where the instrument is strong. In both cases the true effect is 1." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a58c545e", + "metadata": {}, + "outputs": [], + "source": [ + "data_weak = generate_weakiv_data(5000, 1, 0.003)\n", + "data_strong = generate_weakiv_data(5000, 1, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting the DoubleML model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we fit the DoubleML model. We begin by preparing the two data sets into the DoubleMLData format." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "600b8196", + "metadata": {}, + "outputs": [], + "source": [ + "dml_data_strong = dml.DoubleMLData(\n", + " data_strong, y_col='Y', d_cols='D', \n", + " z_cols='Z', x_cols='X'\n", + ")\n", + "dml_data_weak = dml.DoubleMLData(\n", + " data_weak, y_col='Y', d_cols='D', \n", + " z_cols='Z', x_cols='X'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "30ff957e", + "metadata": {}, + "source": [ + "Next, we define the nuisance estimators we will use. We will use a linear regression model for g, and a logistic regression for r. We will assume that we know the true m function, as is the case in a controlled experiment, such as an AB test." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "962900bc", + "metadata": {}, + "outputs": [], + "source": [ + "class TrueMFunction(LogisticRegression):\n", + " def predict(self, X):\n", + " return np.full(X.shape[0], 0.5)\n", + "\n", + "ml_g = LinearRegression()\n", + "ml_m = TrueMFunction()\n", + "ml_r = LogisticRegression(penalty=None)" + ] + }, + { + "cell_type": "markdown", + "id": "53fd3370", + "metadata": {}, + "source": [ + "Now, we fit the DoubleML model on the data set with a strong instrument and then print both the standard and robust results confidence sets. We see that the results are similar." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "549c98fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Standard confidence interval results\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "D 0.951444 0.144382 6.589761 4.405342e-11 0.66846 1.234428\n", + "Uniform confidence set results\n", + "[(np.float64(0.6317710537372012), np.float64(1.206690551996729))]\n" + ] + } + ], + "source": [ + "dml_iivm_strong = dml.DoubleMLIIVM(dml_data_strong, ml_g, ml_m, ml_r)\n", + "\n", + "print(\"Standard confidence interval results\")\n", + "print(dml_iivm_strong.fit().summary)\n", + "print(\"Uniform confidence set results\")\n", + "print(dml_iivm_strong.uniform_confset())" + ] + }, + { + "cell_type": "markdown", + "id": "91cd61dd", + "metadata": {}, + "source": [ + "We now repeat the process with the weak instruments data set. In this case, the standard method reports a confidence interval equal to [2.08, 3.46]. Thus, an analyst reading this would think that she can have high confidence that the true effect is roughly between 2 and 3.5. We know however that the true effect is equal to 1, and thus that the standard DoubleML estimator is badly biased in this case. On the other hand, the uniform confidence set method returns the whole real line as a confidence interval. This indicates that the data does not contain enough information to make any claims about the effect of the treatment of the outcome, because the instrument is too weak." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "25d98c5a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Standard confidence interval results\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "D 2.210317 3.689771 0.599039 0.549147 -5.021502 9.442136\n", + "Uniform confidence set results\n", + "[(-inf, inf)]\n" + ] + } + ], + "source": [ + "ml_g = LinearRegression()\n", + "ml_m = TrueMFunction()\n", + "ml_r = LogisticRegression(penalty=None)\n", + "dml_iivm_weak = dml.DoubleMLIIVM(dml_data_weak, ml_g, ml_m, ml_r)\n", + "\n", + "print(\"Standard confidence interval results\")\n", + "print(dml_iivm_weak.fit().summary)\n", + "print(\"Uniform confidence set results\")\n", + "print(dml_iivm_weak.uniform_confset())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From b426d9d5eaf7d88939415e421a5f0e2e000a21f0 Mon Sep 17 00:00:00 2001 From: Ezequiel Smucler Date: Thu, 8 May 2025 16:33:38 +0200 Subject: [PATCH 113/140] add refs --- doc/examples/py_double_ml_robust_iv.ipynb | 29 ++++++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/doc/examples/py_double_ml_robust_iv.ipynb b/doc/examples/py_double_ml_robust_iv.ipynb index 79baae34..22b1b0fe 100644 --- a/doc/examples/py_double_ml_robust_iv.ipynb +++ b/doc/examples/py_double_ml_robust_iv.ipynb @@ -188,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "25d98c5a", "metadata": {}, "outputs": [ @@ -215,6 +215,33 @@ "print(\"Uniform confidence set results\")\n", "print(dml_iivm_weak.uniform_confset())" ] + }, + { + "cell_type": "markdown", + "id": "fe5629c5", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "id": "946cbbcf", + "metadata": {}, + "source": [ + "- Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., and Hansen, C. (2018). Double/debiased machine learning for\n", + "treatment and structural parameters. The Econometrics Journal, 21(1):C1–C68.\n", + "- Ma, Y. (2023). Identification-robust inference for the late with high-dimensional covariates. arXiv preprint arXiv:2302.09756.\n", + "- Stock, J. H. and Wright, J. H. (2000). GMM with weak identification. Econometrica, 68(5):1055–1096.\n", + "- Takatsu, K., Levis, A. W., Kennedy, E., Kelz, R., and Keele, L. (2023). Doubly robust machine learning for an instrumental\n", + "variable study of surgical care for cholecystitis. arXiv preprint arXiv:2307.06269." + ] + }, + { + "cell_type": "markdown", + "id": "7ce927dd", + "metadata": {}, + "source": [] } ], "metadata": { From 84bfda65cdcba372715df15d28706ad3cca7152b Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Mon, 12 May 2025 16:20:13 +0200 Subject: [PATCH 114/140] remove malte from maintainers --- doc/index.rst | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/doc/index.rst b/doc/index.rst index c8ff3a7f..28b0be27 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -186,8 +186,7 @@ Source code and maintenance Documentation and website: `https://docs.doubleml.org/ `_ -DoubleML is currently maintained by -`@MalteKurz `_, `@PhilippBach `_ and +DoubleML is currently maintained by `@PhilippBach `_ and `@SvenKlaassen `_. 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"cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pyreadr\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "from sklearn.dummy import DummyRegressor, DummyClassifier\n", + "from sklearn.linear_model import LassoCV, LogisticRegressionCV\n", + "\n", + "from doubleml.data import DoubleMLPanelData\n", + "from doubleml.did import DoubleMLDIDMulti" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Causal Research Question\n", + "\n", + "[Callaway and Sant'Anna (2021)](https://doi.org/10.1016/j.jeconom.2020.12.001) study the causal effect of raising the minimum wage on teen employment in the US using county data over a period from 2001 to 2007. A county is defined as treated if the minimum wage in that county is above the federal minimum wage. We focus on a preprocessed balanced panel data set as provided by the [did-R-package](https://bcallaway11.github.io/did/index.html). The corresponding documentation for the `mpdta` data is available from the [did package website](https://bcallaway11.github.io/did/reference/mpdta.html). We use this data solely as a demonstration example to help readers understand differences in the `DoubleML` and `did` packages. An analogous notebook using the same data is available from the [did documentation](https://bcallaway11.github.io/did/articles/did-basics.html#an-example-with-real-data).\n", + "\n", + "We follow the original notebook and provide results under identification based on unconditional and conditional parallel trends. For the Double Machine Learning (DML) Difference-in-Differences estimator, we demonstrate two different specifications, one based on linear and logistic regression and one based on their $\\ell_1$ penalized variants Lasso and logistic regression with cross-validated penalty choice. The results for the former are expected to be very similar to those in the [did data example](https://bcallaway11.github.io/did/articles/did-basics.html#an-example-with-real-data). Minor differences might arise due to the use of sample-splitting in the DML estimation.\n", + "\n", + "\n", + "## Data\n", + "\n", + "We will download and read a preprocessed data file as provided by the [did-R-package](https://bcallaway11.github.io/did/index.html).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "year", + "rawType": "int32", + "type": "integer" + }, + { + "name": "countyreal", + "rawType": "float64", + "type": "float" + }, + { + "name": "lpop", + "rawType": "float64", + "type": "float" + }, + { + "name": "lemp", + "rawType": "float64", + "type": "float" + }, + { + "name": "first.treat", + "rawType": "float64", + "type": "float" + }, + { + "name": "treat", + "rawType": "float64", + "type": "float" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "5e2a60ba-8445-46e1-b56a-ecf010c51d7d", + "rows": [ + [ + "0", + "2003", + "8001.0", + "5.896760933305299", + "8.461469042643875", + "2007.0", + "1.0" + ], + [ + "1", + "2004", + "8001.0", + "5.896760933305299", + "8.336869637284956", + "2007.0", + "1.0" + ], + [ + "2", + "2005", + "8001.0", + "5.896760933305299", + "8.340217320947035", + "2007.0", + "1.0" + ], + [ + "3", + "2006", + "8001.0", + "5.896760933305299", + "8.37816098272068", + "2007.0", + "1.0" + ], + [ + "4", + "2007", + "8001.0", + "5.896760933305299", + "8.487352349405215", + "2007.0", + "1.0" + ] + ], + "shape": { + "columns": 6, + "rows": 5 + } + }, + "text/html": [ + "
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" + ], + "text/plain": [ + " year countyreal lpop lemp first.treat treat\n", + "0 2003 8001.0 5.896761 8.461469 2007.0 1.0\n", + "1 2004 8001.0 5.896761 8.336870 2007.0 1.0\n", + "2 2005 8001.0 5.896761 8.340217 2007.0 1.0\n", + "3 2006 8001.0 5.896761 8.378161 2007.0 1.0\n", + "4 2007 8001.0 5.896761 8.487352 2007.0 1.0" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# download file from did package for R\n", + "url = \"https://github.com/bcallaway11/did/raw/refs/heads/master/data/mpdta.rda\"\n", + "pyreadr.download_file(url, \"mpdta.rda\")\n", + "\n", + "mpdta = pyreadr.read_r(\"mpdta.rda\")[\"mpdta\"]\n", + "mpdta.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To work with [DoubleML](https://docs.doubleml.org/stable/index.html), we initialize a `DoubleMLPanelData` object. The input data has to satisfy some requirements, i.e., it should be in a *long* format with every row containing the information of one unit at one time period. Moreover, the data should contain a column on the unit identifier and a column on the time period. The requirements are virtually identical to those of the [did-R-package](https://bcallaway11.github.io/did/index.html), as listed in [their data example](https://bcallaway11.github.io/did/articles/did-basics.html#an-example-with-real-data). In line with the naming conventions of [DoubleML](https://docs.doubleml.org/stable/index.html), the treatment group indicator is passed to `DoubleMLPanelData` by the `d_cols` argument. To flexibly handle different formats for handling time periods, the time variable `t_col` can handle `float`, `int` and `datetime` formats. More information are available in the [user guide](https://docs.doubleml.org/stable/guide/data_backend.html#doublemlpaneldata). To indicate never treated units, we set their value for the treatment group variable to `np.inf`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can initialize the ``DoubleMLPanelData`` object, specifying\n", + "\n", + " - `y_col` : the outcome\n", + " - `d_cols`: the group variable indicating the first treated period for each unit\n", + " - `id_col`: the unique identification column for each unit\n", + " - `t_col` : the time column\n", + " - `x_cols`: the additional pre-treatment controls\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLPanelData Object ==================\n", + "\n", + "------------------ Data summary ------------------\n", + "Outcome variable: lemp\n", + "Treatment variable(s): ['first.treat']\n", + "Covariates: ['lpop']\n", + "Instrument variable(s): None\n", + "Time variable: year\n", + "Id variable: countyreal\n", + "No. Observations: 500\n", + "\n", + "------------------ DataFrame info ------------------\n", + "\n", + "RangeIndex: 2500 entries, 0 to 2499\n", + "Columns: 6 entries, year to treat\n", + "dtypes: float64(5), int32(1)\n", + "memory usage: 107.6 KB\n", + "\n" + ] + } + ], + "source": [ + "# Set values for treatment group indicator for never-treated to np.inf\n", + "mpdta.loc[mpdta['first.treat'] == 0, 'first.treat'] = np.inf\n", + "\n", + "dml_data = DoubleMLPanelData(\n", + " data=mpdta,\n", + " y_col=\"lemp\",\n", + " d_cols=\"first.treat\",\n", + " id_col=\"countyreal\",\n", + " t_col=\"year\",\n", + " x_cols=['lpop']\n", + ")\n", + "print(dml_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we specified a pre-treatment confounding variable `lpop` through the `x_cols` argument. To consider cases under unconditional parallel trends, we can use dummy learners to ignore the pre-treatment confounding variable. This is illustrated below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ATT Estimation: Unconditional Parallel Trends\n", + "\n", + "We start with identification under the unconditional parallel trends assumption. To do so, initialize a `DoubleMLDIDMulti` object (see [model documentation](https://docs.doubleml.org/stable/guide/models.html#difference-in-differences-models-did)), which takes the previously initialized `DoubleMLPanelData` object as input. We use scikit-learn's `DummyRegressor` (documentation [here](https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyRegressor.html)) and `DummyClassifier` (documentation [here](https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html)) to ignore the pre-treatment confounding variable. At this stage, we can also pass further options, for example specifying the number of folds and repetitions used for cross-fitting. \n", + "\n", + "When calling the `fit()` method, the model estimates standard combinations of $ATT(g,t)$ parameters, which corresponds to the defaults in the [did-R-package](https://bcallaway11.github.io/did/index.html). These combinations can also be customized through the `gt_combinations` argument, see [the user guide](https://docs.doubleml.org/stable/guide/models.html#panel-data)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "ATT(2004.0,2003,2004) -0.0105 0.0232 -0.4526 0.6508 -0.0560 0.0350\n", + "ATT(2004.0,2003,2005) -0.0704 0.0310 -2.2700 0.0232 -0.1312 -0.0096\n", + "ATT(2004.0,2003,2006) -0.1372 0.0363 -3.7763 0.0002 -0.2085 -0.0660\n", + "ATT(2004.0,2003,2007) -0.1008 0.0343 -2.9381 0.0033 -0.1680 -0.0335\n", + "ATT(2006.0,2003,2004) 0.0065 0.0232 0.2811 0.7786 -0.0390 0.0520\n", + "ATT(2006.0,2004,2005) -0.0027 0.0195 -0.1400 0.8886 -0.0410 0.0355\n", + "ATT(2006.0,2005,2006) -0.0046 0.0179 -0.2585 0.7960 -0.0397 0.0304\n", + "ATT(2006.0,2005,2007) -0.0412 0.0202 -2.0404 0.0413 -0.0807 -0.0016\n", + "ATT(2007.0,2003,2004) 0.0304 0.0151 2.0197 0.0434 0.0009 0.0600\n", + "ATT(2007.0,2004,2005) -0.0027 0.0165 -0.1645 0.8693 -0.0350 0.0296\n", + "ATT(2007.0,2005,2006) -0.0310 0.0179 -1.7348 0.0828 -0.0660 0.0040\n", + "ATT(2007.0,2006,2007) -0.0260 0.0167 -1.5628 0.1181 -0.0587 0.0066\n" + ] + } + ], + "source": [ + "dml_obj = DoubleMLDIDMulti(\n", + " obj_dml_data=dml_data,\n", + " ml_g=DummyRegressor(),\n", + " ml_m=DummyClassifier(),\n", + " control_group=\"never_treated\",\n", + " n_folds=10\n", + ")\n", + "\n", + "dml_obj.fit()\n", + "print(dml_obj.summary.round(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The summary displays estimates of the $ATT(g,t_\\text{eval})$ effects for different combinations of $(g,t_\\text{eval})$ via $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$, where\n", + " - $\\mathrm{g}$ specifies the group\n", + " - $t_\\text{pre}$ specifies the corresponding pre-treatment period\n", + " - $t_\\text{eval}$ specifies the evaluation period\n", + "\n", + "This corresponds to the estimates given in `att_gt` function in the [did-R-package](https://bcallaway11.github.io/did/index.html), where the standard choice is $t_\\text{pre} = \\min(\\mathrm{g}, t_\\text{eval}) - 1$ (without anticipation).\n", + "\n", + "Remark that this includes pre-tests effects if $\\mathrm{g} > t_{eval}$, e.g. $ATT(2007,2005)$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual for the DoubleML-package, you can obtain joint confidence intervals via bootstrap." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2.5 % 97.5 %\n", + "ATT(2004.0,2003,2004) -0.075498 0.054493\n", + "ATT(2004.0,2003,2005) -0.157304 0.016469\n", + "ATT(2004.0,2003,2006) -0.239029 -0.035446\n", + "ATT(2004.0,2003,2007) -0.196831 -0.004706\n", + "ATT(2006.0,2003,2004) -0.058475 0.071524\n", + "ATT(2006.0,2004,2005) -0.057417 0.051949\n", + "ATT(2006.0,2005,2006) -0.054715 0.045468\n", + "ATT(2006.0,2005,2007) -0.097724 0.015351\n", + "ATT(2007.0,2003,2004) -0.011777 0.072669\n", + "ATT(2007.0,2004,2005) -0.048822 0.043404\n", + "ATT(2007.0,2005,2006) -0.081068 0.019054\n", + "ATT(2007.0,2006,2007) -0.072675 0.020621\n" + ] + } + ], + "source": [ + "level = 0.95\n", + "\n", + "ci = dml_obj.confint(level=level)\n", + "dml_obj.bootstrap(n_rep_boot=5000)\n", + "ci_joint = dml_obj.confint(level=level, joint=True)\n", + "print(ci_joint)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A visualization of the effects can be obtained via the `plot_effects()` method.\n", + "\n", + "Remark that the plot used joint confidence intervals per default. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "tags": [ + "nbsphinx-thumbnail" + ] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\bachp\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\matplotlib\\cbook.py:1762: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n" + ] + }, + { + "data": { + "image/png": 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BAz388MPO9oLbEQMDAws9p2DdsIKYrKwsBQQEFHn8oKAgZ1zBrXqnTp3SwoULNWHCBA0YMECffPKJLr/8ck2dOvWcuZ4vp7NvoQQAAGWPmVIAAMDr2rZtW+xC50Wx2WyqXbu2S1uDBg00fvx4zZo1S0uXLtX111+vW265RYMGDXIWrNzlr58cuG/fPpmmqcaNGxcZf/anCR4+fFiTJk3Shx9+WGgNqIK1sM5l3759Sk1NLTSrqEBSUpIkx+wtSYVyioqKci4afrEyMzN10003KT09XV999ZXLWlMFxbecnJxCz8vOznaJCQ4OLvKWwoLYs+Mkx/fz7NlxVqtVt912m5566innbLOinC+nswuGAACg7FGUAgAA5U5gYKCs1sITvmfOnKkhQ4bov//9r9asWaOxY8dq+vTp+vrrrwsVscrSX4sXhmHIYrFo5cqV8vPzKxRfUKzJz89X165dlZycrEceeUTNmjVTaGiojhw5oiFDhsgwjPOe2zAMRUdHa+nSpUXuj4qKKsUVXTi73a4+ffro+++/1+rVq9WiRQuX/ZGRkQoMDHTeMne2graaNWtKctw6l5+fr6SkJJdim91u16lTp5xxkZGRCgoKUuXKlQt9nwued/r06WKLUgW37RWXU8F5AACAe1CUAgAAFUrLli3VsmVLPfHEE9q8ebPat2+v+fPnO2/lOvvWNXdp1KiRTNNUgwYN1KRJk2LjfvjhB+3du1dLlizR3Xff7Wwv6vbF4vJu1KiR1q1bp/bt259zZk+9evUkOWZWNWzY0Nl+4sSJQjO0LpRhGLr77rv12Wef6d1331XHjh0LxVitVrVs2VI7duwotG/r1q1q2LChwsPDJUmtWrWSJO3YsUM9e/Z0xu3YsUOGYTj3W61WtWrVStu3b5fdbne55a9g3alzFeVatGghm82mHTt2aMCAAc52u92uXbt2ubQBAICyx5pSAACgQkhLS1NeXp5LW8uWLWW1Wl1uzwoNDVVKSopbc+nTp4/8/Pw0ZcqUQgu2m6apU6dOSZJzds/ZMaZpas6cOYWOGRoaKkmFch8wYIDy8/P1zDPPFHpOXl6eMz4+Pl7+/v6aO3euy/lmz55d5DXs379f+/fvP/eF/mHMmDF65513NG/ePPXp06fYuH79+mn79u0uhak9e/Zo/fr16t+/v7PthhtuUGRkpF555RWX57/yyisKCQlRr169nG233Xab8vPztWTJEmdbdna2li5dqssvv9xlttPPP/+sw4cPOx9XqlRJ8fHxeuutt5Senu5sf/PNN5WRkeGSEwAAKHvMlAIAABXC+vXrNXr0aPXv319NmjRRXl6e3nzzTfn5+alv377OuNatW2vdunWaNWuWatasqQYNGig2NrZMc2nUqJGmTp2qxx57TAcPHlTv3r0VHh6uAwcO6IMPPtDw4cM1YcIENWvWTI0aNdKECRN05MgRRURE6D//+U+RM5dat24tSRo7dqy6d+8uPz8/DRw4UB07dtSIESM0ffp07dq1S926dZO/v7/27dunhIQEzZkzR/369VNUVJQmTJig6dOn66abblLPnj21c+dOrVy5UtWqVSt0vi5dukjSeRc7nz17tubNm6e4uDiFhITorbfectl/6623Ogtqo0aN0muvvaZevXppwoQJ8vf316xZs1S9enU99NBDzucEBwfrmWee0f3336/+/fure/fu+vLLL/XWW2/p2WefVWRkpDN2xIgRWrhwoe6//37t3btXdevW1ZtvvqlDhw7po48+csmlefPm6tixozZu3Ohse/bZZ9WuXTt17NhRw4cP1++//66ZM2eqW7duuvHGG8957QAA4CKZAAAAXrJo0SJTkrl9+/Yi9x84cMCUZC5atMjZNnjwYDM0NLRQ7K+//moOHTrUbNSokRkUFGRGRkaanTt3NtetW+cS9/PPP5v/93//ZwYHB5uSzMGDB5co14SEBFOSuWHDBmfbU089ZUoyT5w4UeRz/vOf/5gdOnQwQ0NDzdDQULNZs2bm/fffb+7Zs8cZ87///c+Mj483w8LCzGrVqpn33nuv+d133xW67ry8PHPMmDFmVFSUabFYzL++jXv11VfN1q1bm8HBwWZ4eLjZsmVL8+GHHzaPHj3qjMnPzzenTJli1qhRwwwODjY7depk/vjjj2a9evUKfR/q1atn1qtX77zfl8GDB5uSiv06cOCAS/xvv/1m9uvXz4yIiDDDwsLMm266ydy3b1+Rx3711VfNpk2bmgEBAWajRo3MF1980TQMo1Dc8ePHzcGDB5uRkZFmYGCgGRsba65atapQnCSzY8eOhdq//PJLs127dmZQUJAZFRVl3n///WZaWtp5rx0AAFwci2n+ZU45AAAAAAAA4GasKQUAAAAAAACPoygFAAAAAAAAj6MoBQAAAAAAAI+jKAUAAAAAAACPoygFAAAAAAAAj7N5O4GKwDAMHT16VOHh4bJYLN5OBwAAAAAAwGtM01R6erpq1qwpq7X4+VAUpcrA0aNHVadOHW+nAQAAAAAA4DN+++031a5du9j9FKXKQHh4uCTHNzsiIsLL2ZSeYRg6ceKEoqKizlnJBDyJfglfRL+Er6JvwudkZ8u86y7l5OYqYNkyWUNCvJ0R4MSYCV9UUfplWlqa6tSp46yXFIeiVBkouGUvIiKi3BelsrOzFRERUa47PyoW+iV8Ef0Svoq+CZ8TEiJjwgTlp6QoIjJS1oAAb2cEODFmwhdVtH55viWOKEoBAAAAcA+bTerSRfakJMc2AABnKf9lNwAAAAAAAJQ7FKUAAAAAuIdhSL/+Kr9DhxzbAACchTm0AAAAANzDbpflgQcUbrdLK1ZwCx8AwAW/FS5xx9KydSwtW5JkGKaST2cq0p4qq9WxGFmNiCDViAjyZooAAAAorywWKTJSZna2YxsAgLNQlLrELdhySE+v3Vvs/kldm2hy96YezAgAAAAVRmCgzMWLlZqUpOjAQG9nAwDwMRSlLnEj4urpliuqS5L+l5iuu5fv0r8HttLlMeGSxCwpAAAAAADgFhSlLnFn355nGKYkqVl0mK6pXdmLWQEAAAAAgIqOT98DAAAA4B52uzRjhkLnznVsAwBwFmZKAQAAAHAPw5Bl0yb52+2SYXg7GwCAj6EoBQAAAMA9bDaZI0YoKzVVATb+9AAAuOI3AwAAAAD3sNmkXr2Uk5Tk2AYA4CysKQUAAAAAAACPoygFAAAAwD1MUzp6VNbERMc2AABnYQ4tAAAAAPfIyZFl5EhF2O3SihVSSIi3MwIA+BCKUgAAAADcJzRUJutJAQCKwG8HAAAAAO4RFCTz7beVmpSk6KAgb2cDAPAxrCkFAAAAAAAAj6MoBQAAAAAAAI+jKAUAAADAPXJzpTlzFPLqq45tAADOQlEKAAAAgHvk58vy2WcK+OorKT/f29kAAHxMuStKvfzyy6pfv76CgoIUGxurbdu2nTM+ISFBzZo1U1BQkFq2bKlPP/3UZf+QIUNksVhcvm688UZ3XgIAAABwabDZZA4ZoqzbbpP4BD4AwF+Uq98M77zzjsaPH6/58+crNjZWs2fPVvfu3bVnzx5FR0cXit+8ebNuv/12TZ8+XTfddJOWLVum3r1769tvv1WLFi2ccTfeeKMWLVrkfBwYGOiR6wEAlB/H0rJ1LC1bkmQYppJPZyrSniqr1SJJqhERpBoRfLIUALiw2aQ+fZSTlERRCgBQSLn6zTBr1izde++9uueeeyRJ8+fP1yeffKI33nhDjz76aKH4OXPm6MYbb9Q//vEPSdIzzzyjtWvX6l//+pfmz5/vjAsMDFRMTEyJ88jJyVFOTo7zcVpamiTJMAwZhlGqa/MFpmk4/y3P14GKxTAMmaZJn4TXzd98UM+s21fs/ifjG+upbk08mBFQGGMmfBH9Er6KvglfVFH6ZUnzLzdFKbvdrm+++UaPPfaYs81qtSo+Pl5btmwp8jlbtmzR+PHjXdq6d++uFStWuLRt3LhR0dHRqlKlim644QZNnTpVVatWLTaX6dOna8qUKYXaT5w4oezs7Au4Kt+SnJzxx7+nlRTIQpTwDYZhKDU1VaZpymotd3ccowLpc1mI2te4XJK07+QZjVl5UHN71FfjaiGSpOqh/kpKSvJmigBjJnyPaco8dUrp6ekyDUNWPz9vZwQ4MWbCF1WUfpmenl6iuHJTlDp58qTy8/NVvXp1l/bq1avr559/LvI5iYmJRcYnJiY6H994443q06ePGjRooP3792vixInq0aOHtmzZIr9ifmk+9thjLsWutLQ01alTR1FRUYqIiCjtJXpdZI6/49/IKoqOruLlbAAHwzBksVgUFRVVrgdllH/R0VLLP7ar/HZa0kG1vaymWtdhvITvYMyEz8nOlu69V1VycmT74ANZQ0K8nRHgxJgJX1RR+mVQUMmWtSg3RSl3GThwoHO7ZcuWuvLKK9WoUSNt3LhRXbp0KfI5gYGBRa47ZbVay3WnsViszn/L83Wg4rFYLOX+9YWKhfESvowxEz7FapXp5yf5+dEv4ZMYM+GLKkK/LGnu5aYoVa1aNfn5+en48eMu7cePHy92PaiYmJgLipekhg0bqlq1avrll1+KLUoBAAAAKF5e5jHlZzruTjAWTNLp5GRVSdsta4bjjxS/0BjZQmt4M0UAgA8oN2W3gIAAtW7dWp999pmzzTAMffbZZ4qLiyvyOXFxcS7xkrR27dpi4yXp999/16lTp1SjBr8kAQAAgNJI//41HV0Wq6PLYpW4PE45a3opcXmcsy39+9e8nSIAwAeUm5lSkjR+/HgNHjxYbdq0Udu2bTV79mxlZmY6P43v7rvvVq1atTR9+nRJ0rhx49SxY0fNnDlTvXr10vLly7Vjxw69+uqrkqSMjAxNmTJFffv2VUxMjPbv36+HH35Yl112mbp37+616wQAAADKs/Ar71VIo5slSTknd+vUmiGq2m2xAqs1l+SYKQUAQLkqSt122206ceKEJk2apMTERLVq1UqrVq1yLmZ++PBhl/sW27Vrp2XLlumJJ57QxIkT1bhxY61YsUItWrSQJPn5+en777/XkiVLlJKSopo1a6pbt2565plnilwzCgAAAMD52UJrOG7Py82V8e47kiT/iEYKjL7ay5kBAHxJuSpKSdLo0aM1evToIvdt3LixUFv//v3Vv3//IuODg4O1evXqskwPAAAAQIH8fFk2bZZiJRn53s4GAOBjys2aUgAAAADKGZtNZsGyGH7l7v/DAQBuRlEKAAAAgHvYbNKNNzq2/fy8mwsAwOdQlAIAAAAAAIDHUZQCAAAA4B6mKWVl/bkNAMBZKEoBAAAAcI+cHFkmTnRs59q9mwsAwOdQlAIAAAAAAIDHUZQCAAAA4B6BgTJfeMGx7R/g3VwAAD6HohQAAAAA97BY/vzUPYvFu7kAAHwORSkAAAAAAAB4HEUpAAAAAO6Rlyf997+O7fx87+YCAPA5FKUAAAAAuEdeniwbNzq28/O8mgoAwPdQlAIAAADgHjabzE6dHNt+Nq+mAgDwPRSlAAAAALiHzSb97W+O7YIFzwEA+ANFKQAAAAAAAHgcRSkAAAAA7mGafy5wbprezQUA4HMoSgEAAABwj5wcWSZMcGzn2r2bCwDA51CUAgAAAAAAgMdRlAIAAADgHoGBMqdNc2z7B3g3FwCAz6EoBQAAAMA9LBYpOPjPbQAAzkJRCgAAAAAAAB5HUQoAAACAe+TlSatWObYLPoUPAIA/UJQCAAAA4B55ebKsXu3Yzs/zbi4AAJ9DUQoAAACAe/j5yWzfzrFt9fNuLgAAn2PzdgIAAAAAKih/f6lff2n5LMnGnx4AUJS8zGPKz0yUJBmGISM5WTmKlNXqmEfkFxojW2gNb6boNvxmAAAAAAAA8JL0719TytapLm2JZ21Xjn1CVeImeTYpD6EoBQAAAAAA4CXhV96rkEY3S5JyTu7WqTVDVLXbYgVWay7JMVOqoqIoBQAAAMA9srNleegh6VpJ9hxvZwMAPskWWsN5e55hGJIk/8imCoy+2ptpeQQLnUNn7HlauPWQxnzwgyRpzAc/aOHWQzpj5xNSAAAAcJH++AMLAIC/oih1iTtjz9Owd7/Twx/9Tzt+T5Uk7fg9VQ9/9D8Ne/c7ClMAAAAovcBAmZMnO7b9A7yaCgDA91CUusQt23lEq35OUkp2nvJNR1u+KaVk52nVz0latvOIdxMEAABA+WWxSJUq/bkNAMBZKEpd4t7ddVQZ9vwi92XY8/XurqMezggAAAAAAFwKKEpd4o6kZivfMIvcl2+YOpKa7eGMAAAAUGHk5Unr1zu284v+j1AAwKWLotQlrlalIPlZi55K7We1qFalIA9nBAAAgAojL0+Wjz5ybOezVikAwFWpilJ+fn5KSkoq1H7q1Cn5+flddFLwnAGtaiosoOifWXiAnwa0qunhjAAAAFBh+PnJbNvWsW3l7wQAgKtSFaVMs+jbvXJychQQ4N5P1Xj55ZdVv359BQUFKTY2Vtu2bTtnfEJCgpo1a6agoCC1bNlSn376qct+0zQ1adIk1ahRQ8HBwYqPj9e+ffvceQk+5Y6ra+nGZtGqHGST7Y/eYLNKVYJs6t4sWndcXcu7CQIAAKD88veXbr/dsW2zeTcXAIDPuaDfDC+99JIkyWKxaOHChQoLC3Puy8/P1xdffKFmzZqVbYZneeeddzR+/HjNnz9fsbGxmj17trp37649e/YoOjq6UPzmzZt1++23a/r06brpppu0bNky9e7dW99++61atGghSXr++ef10ksvacmSJWrQoIGefPJJde/eXf/73/8UFHRht67Z7XbZ7fZC7VarVbazfgkXFVPAYrHI39+/VLG5ubnFFgyLi7VJmte7uZbvitCibb9p+9FMXV0jTPe2a6g7rq6lkADbOY8ryaUQeSGxeXl5MgyjTGL9/f1l+eMTXdwVm5+fr/xzrIVwIbE2m01Wq9VnYg3DUF5e8VPq/fz8nLMgPR1rGIbsdrvy8/Od+Zqmqdzc3BId93yxZ78+3RUrnfu17MtjxMXGShVzjMjNc/zsTf15LYwR3hkjioq9lMeIv76+GCNKFsv7iIuLPd/r0zDyz9pmjLjQWIn3EaWJvdAx4lwYIy4u1hde9+VtjJCkvNzcYl+j5WGMONdzXY5jnutV/RcNGjSQJB06dEi1a9d2uVUvICBA9evX19NPP63Y2NiSHvKCxMbG6tprr9W//vUvSY6OVadOHY0ZM0aPPvpoofjbbrtNmZmZ+vjjj51t1113nVq1aqX58+fLNE3VrFlTDz30kCZMmCBJSk1NVfXq1bV48WINHDiwyDxycnKUk5PjfJyWlqY6derokUceUWBgYKH4yy67THfeeafz8bRp04rtjPXq1dOQIUOcj//5z3/qzJkzRcbWqFFDw4cPdz6eM2eOUlJSioyNiorSqFGjnI/nzZunEydOuMQczQ/W69lXaFy1Q5r58J+xr776qo4dO1bkcUNCQvSPf/zD+Xjx4sU6dOhQkbH+/v6aOHGi8/HSpUv1yy+/FBkrSU899ZRz+91339Xu3buLjX3sscecv1hWrFih7777rtjYCRMmKDQ0VJL0ySefaMeOHcXGjhs3TpUrV5YkrVmzRlu2bCk29r777nMWRzdu3KjPP/+82Nhhw4apVi3HLLRNmzZp3bp1xcYOHjxY9evXlyRt27ZNK1euLDb29ttvV5MmTSRJu3bt0n//+99iY/v166crrrhCkvTTTz/pvffeKzb2b3/7m1q1aiVJ2rt3r95+++1iY3v06KG2f0zTP3jwoJYsWVJsbHx8vNq3by9JOnLkiBYuXFgoxjRNZWZmqmfPnurcubMkKSkpSa+88kqxx42Li1O3bt0kSSkpKZozZ06xsW3atFGvXr0kSZmZmXrhhReKjb3qqqvUu3dvSY5Bdvr06cXGNm/eXAMGDHA+njJlSrGx5WWMKFC5cmWNGzfO+fhSGiNy8w39mJiunactOtLgBrWJsml4x+Ya2Kqmvtq4njFCnh8jCnTs2FGdOnWSdGmPEXXr1lXPnj0VFRUlq9XKGPEH3kfUl+S9MaJ35yaqvOseVR+wSYfTwhkjxPsIXxkjDMPQiRMntHHjRv3888/FxjJGOPA+wsHdY0R24jc6/m57fZg6RMlGjSJjy8MYkZOToxkzZig1NVURERHF5ndBM6UOHDggSercubPef/99ValS5UKeflHsdru++eYbPfbYY842q9Wq+Pj4Yl+8W7Zs0fjx413aunfvrhUrVkhyXE9iYqLi4+Od+ytVqqTY2Fht2bKl2KLU9OnTi/yhZ2ZmFll9TUtLc1mDKyMjo9gqbXp6eqHYrKysEsWmp6crMzOzyNigoKDzxmaZkixS1pkzJT6uYRgljrXZbC6xaWlpxcZKuuDYgl8Uqamp54w9ceKEc39JYgsqvCkpKeeMPXnypHO7JLEFVeXTp0+fM/bUqVMKCQkpcWzB9y05OfmcscnJyaWKPXXq1DljT58+XarYkydPFhlrmqays7NLFFsgJSXFGXu+n3Fqaqoz9syZMyWOtdvt54z96+v+QmJ9dYwo8Nd1BS+VMSIv39C2wyn61Rql5JpXSpJ+OmXXv9b/pJ8P/K6rjHO/jhgj5MyxLMeIAme/7i/lMSItLU0pKSkyTVNWq5Ux4gJieR/hpjEiL0/5L74odZaSExN1Kuvcrw3GCAfeRzi4e4wwDEOpqann/TkzRvwZy/sI948ReadPS5KysrKUmVd0fHkYI86eyHMuFzRTypuOHj2qWrVqafPmzYqLi3O2P/zww/r888+1devWQs8JCAjQkiVLdHvBfexyVO2nTJmi48ePa/PmzWrfvr2OHj2qGjX+rEAOGDBAFotF77zzTpG5FDdT6vjx40VWAMvLlNpvf09Rh/nb9dV9bXVdg6gSHVdi2n1pYplSW/LYgv/BiomJcfZhX5hSy7R735t27+4xYvH2w5q4co9SiviWRwbZNK1HE93duvh1+BgjPBd7KY8RpmkqJSXFOVOKMaJksbyPuLjYYn+H555R5o9vyzbpSaX0ypCt6rUKu/IeBTbsI6t/SJHHZYwoHCvxPqI0sSUZIwreZ55vsgVjxMXF+sJ7g/I0RhTMlIrss1GB0dec97iSb44RaWlpql69etnOlCrQt29ftW3bVo888ohL+/PPP6/t27crISGhNIctNwIDA4u8TS8oKKhE61BdyFpVFxJbVE4XEhsQ4GgL8A9wDiBlcdziXMii+L4Qa7VaXV50FS327IHKl2INw1BgYKD8/f1d+uWFfNKnL8S663XvyTHC07G+8Lo/O/b9/51SRp5FUuE3w2n2fL33Y5KGt29UouP6yuu+IowRxfGF1703xgjDMGSxWGS1WmW1WhkjfCjWV17LnhojjNwzOrl+nLIOrpLR0/G/7HnJ3ypt0z4FH/lc1bouKLYwda7jlkWs5Buve95HuDe2pK9Pi8WigADXv3/K4rgXGusrr2XeRzh483Vv+aMvBgQElvg16otjREnXlCrVp+998cUX6tmzZ6H2Hj166IsvvijNIc+rWrVq8vPz0/Hjx13ajx8/rpiYmCKfExMTc874gn8v5JgAgEvTkdRs5RtF/+9svmHqSGq2hzMCAN+VuWe5sg6ulmFPlSx/zKYw82XYU5R1aLUy9yz3boIAAJ9QqqJURkZGkVVff39/paWlXXRSRQkICFDr1q312WefOdsMw9Bnn33mcjvf2eLi4lziJWnt2rXO+AYNGigmJsYlJi0tTVu3bi32mACAS1OtSkHys1qK3OdntahWpQv7xFYAqMgy9ibIyM0ocp+Rm6GMvRX7zgoAQMmUqijVsmXLItdbWr58uS6//PKLTqo448eP12uvvaYlS5Zo9+7duu+++5SZmal77rlHknT33Xe7LIQ+btw4rVq1SjNnztTPP/+syZMna8eOHRo9erQkx1TNBx54QFOnTtWHH36oH374QXfffbdq1qzp/GQMAAAkaUCrmgoLKHrKdXiAnwa0qunhjADAd+VnHJHMYtabMfId+wEAl7xSrSn15JNPqk+fPtq/f79uuOEGSdJnn32mt99+263rSd122206ceKEJk2apMTERLVq1UqrVq1S9erVJUmHDx92uRe4Xbt2WrZsmZ544glNnDhRjRs31ooVK9SiRQtnzMMPP6zMzEwNHz5cKSkp6tChg1atWnVB904CACq+O66upfX7TmrVz0nKsOcpz5BsVik8wKbuzaJ1x9XFL3IOAJcav7Bayj39i2QWsbCw1U9+YYyZAICL+PS9Tz75RNOmTdOuXbsUHBysK6+8Uk899ZQ6duxY1jn6vLS0NFWqVOm8q8r7uh2HT6vtS19p29gOalP33J9AAXhKwUcBR0dHl3gBSsBdztjztGznEb3+9SFt/S1VsXUq6e/X1dMdV9dSSECp/p8HKFOMmfAV6T++oeQvHpVhT3F8PsRZdz9bAysr8vrnFN5iqLfSAyQxZsI3ZSV+o8TlcYoZuEXBMa29nU6plbROUup30L169VKvXr1K+3QAAMqdkACbhsXWU6saEWr70leae2tLivgAUITQpgOVdXiDsg6tlmHPcMyYsthkDQhTcL3uCm060NspAgB8QKnLwSkpKVq4cKEmTpyo5ORkSdK3336rI0e4PxwAAAC4lFn9Q1St6wJFXv+c/KtfI0nyr36NIq9/TtW6LpDVP8TLGQIAfEGpZkp9//33io+PV6VKlXTw4EENGzZMkZGRev/993X48GH9+9//Lus8AQAAAJQjVv8QhbcYKlu1q5S4PE5VO80p17eiAADKXqlmSo0fP15DhgzRvn37XBYE79mzp7744osySw4AAABAOZaTI8tTTzm27Xbv5gIA8DmlKkpt375dI0aMKNReq1YtJSYmXnRSAAAAACoA05TS0goeeDUVAIDvKVVRKjAwUGnOXy5/2rt3r6Kioi46KQAAAAAVQECAzAkTHNs2f+/mAgDwOaUqSt1yyy16+umnlZubK0myWCw6fPiwHnnkEfXt27dMEwQAAABQTlmtUq1af24DAHCWUv1mmDlzpjIyMhQdHa2srCx17NhRl112mcLDw/Xss8+WdY4AAAAAAACoYEr16XuVKlXS2rVrtWnTJn333XfKyMjQNddco/j4+LLODwAAAEB5lZcnbdvm2M7P924uAACfU+KiVGRkpPbu3atq1app6NChmjNnjtq3b6/27du7Mz8AAAAA5VVenixvvy3FSsrP83Y2AAAfU+Lb9+x2u3Nx8yVLlig7O9ttSQEAAACoAKxWmZdf7ti2sKYUAMBViWdKxcXFqXfv3mrdurVM09TYsWMVHBxcZOwbb7xRZgkCAAAAKKcCAqR775WWvyH58+l7AABXJS5KvfXWW3rxxRe1f/9+SVJqaiqzpQAAAAAAAFAqJS5KVa9eXc8995wkqUGDBnrzzTdVtWpVtyUGAAAAAACAiqvEN3ZHRkbq5MmTkqTOnTsrICDAbUkBAAAAqABycmR59lnHtt3u3VwAAD6Hhc4BAAAAuIdpSn/8x7ZkejUVAIDvYaFzAAAAAO4RECBz7Fhp69eSjYXOAQCuSrXQucViYaFzAAAAAOdmtUoNGkhb/9gGAOAsLHQOAAAAAAAAj7ug/67o2bOnUlNTdeDAAVWtWlXPPfecUlJSnPtPnTqlyy+/vKxzBAAAAFAe5edLu3Y5to18r6YCAPA9F1SUWrVqlXJycpyPp02bpuTkZOfjvLw87dmzp+yyAwAAAFB+5ebKsmSJYzsvz7u5AAB8zkXd2G2afIIGAAAAgGJYrVKjRo5tC2tKAQBc8ZsBAAAAgHsEBMgcPdqx7c+n7wEAXF1QUcpischisRRqAwAAAAAAAC5EiT99T3LcrjdkyBAFBgZKkrKzszVy5EiFhoZKkst6UwAAAAAAAEBxLqgoNXjwYJfHgwYNKhRz9913X1xGAAAAACoGu12Wf/5TqiMpN9fb2QCAzzJyzyhzz3Kl/viGJOnUxnGq1GKoQpsOlNU/xMvZuc8FFaUWLVrkrjwAAAAAVDSGIR096ihKmYa3swEAn2TkntHJtSOUdXC1jNx0SVLu8W+UnLxXWYc3qFrXBRW2MMVC5wAAAADcIyBA5siRjm0bC50DQFEy9yx3FKTsKZKZ72g082XYU5R1aLUy9yz3an7uRFEKAAAAgHtYrVLTpn9uAwAKydibICM3o8h9Rm6GMvYmeDgjz+E3AwAAAAAAgJfkZxz5c4bUXxn5jv0VFEUpAAAAAO6Rny/99JNj2yjmDy4AuMT5hdWSLH5F77T6OfZXUBSlAAAAALhHbq4sCxc6tvPyvJsLAPiosCb9ZfUPK3Kf1T9MYU36ezgjz6EoBQAAAMA9rFapbl3HtoU/PQCgKKFNByq4fndZAytLFpuj0WKTNbCygut1V2jTgV7Nz534zQAAAADAPQICZD74oGPbn0/fA4CiWP1DVK3rAkVe/5z8q18jSfKvfo0ir39O1boukNU/xMsZuk+5KUolJyfrzjvvVEREhCpXrqy///3vysgoenX6AtnZ2br//vtVtWpVhYWFqW/fvjp+/LhLjMViKfS1fHnF/bhFAAAAAADgW6z+IQpvMVRVO82RJFXtNEfhLYZW6IKUVI6KUnfeead++uknrV27Vh9//LG++OILDR8+/JzPefDBB/XRRx8pISFBn3/+uY4ePao+ffoUilu0aJGOHTvm/Ordu7ebrgIAAAAAAACSZPN2AiWxe/durVq1Stu3b1ebNm0kSXPnzlXPnj31wgsvqGbNmoWek5qaqtdff13Lli3TDTfcIMlRfGrevLm+/vprXXfddc7YypUrKyYmpsT55OTkKCcnx/k4LS1NkmQYhgzDKNU1+gLTNJz/lufrQMViGIZM06RPwqcwXsJXMWbC59jt0pw5UnXJtOfQN+FTGDPhi8w/+qNZzusLJc29XBSltmzZosqVKzsLUpIUHx8vq9WqrVu36tZbby30nG+++Ua5ubmKj493tjVr1kx169bVli1bXIpS999/v4YNG6aGDRtq5MiRuueee2SxWIrNZ/r06ZoyZUqh9hMnTig7O7u0l+l1yckZf/x7WkmBuV7OBnAwDEOpqakyTVP/3959h0dRrn0c/+2m9wIJCb1IUxAQBAP6ggoGQRFEOBQVVFAEREUsoAIigr0eERWJDUSxcFABRYoKclCUUJQuiJQQWiokm2Sf94+YPSwpBEh2N8n3c117MTtz78w9y7PPzt6ZecZqrTAnd6KSo7+Ep6LPhMfJylLQ9u1SDenYkaPy9k12d0aAA30mPFHu8eOSpGPHj8vbWnH7zPT09FLFVYiiVFJSkqKjo53meXt7KzIyUklJScW+xtfXV+Hh4U7za9So4fSaKVOm6KqrrlJgYKC+/fZbjRw5UhkZGRozZkyx+YwfP15jx451PE9LS1OdOnUUFRWl0NDQc9hDzxCZnT/4ZGRkhKKjI9ycDZDPbrfLYrEoKiqKgwV4DPpLeCr6THicvDxl3XmntGWEIqOj5X/aMT3gTvSZ8ERZ9ggdkhQZEVGh+0x/f/9Sxbm1KPXII4/omWeeKTFmy5Yt5ZrD448/7phu06aNMjMz9dxzz5VYlPLz85Ofn1+h+VartUJ3ZpZ/btNrsVTs/UDlY7FYKvznC5UL/SU8GX0mPIrVKkurVtIWyeLtTbuEx6HPhKex/NMWLRW8XZY2d7cWpR544AENHTq0xJiGDRsqJiZGycnOp63l5ubq2LFjxY4FFRMTI5vNppSUFKezpQ4dOlTi+FEdOnTQk08+qezs7CILTwAAAAAAADh/bi1KRUVFKSoq6oxxcXFxSklJ0a+//qq2bdtKkpYvXy673a4OHToU+Zq2bdvKx8dHy5YtU9++fSVJ27Zt0969exUXF1fsthITExUREUFBCgAAADhHuZkHlZeZJNntytnwnSQp5+gWx1/OvYJi5B0U684UAQAeoEKMKdW8eXN1795dw4cP18yZM5WTk6PRo0drwIABjjvv7d+/X1dffbXef/99tW/fXmFhYbrjjjs0duxYRUZGKjQ0VPfcc4/i4uIcg5x/+eWXOnTokC677DL5+/tr6dKlmjZtmsaNG+fO3QUAAAAqtPSNbytl7VSneUeX3u6YDu/wmCLiJro6LQCAh6kQRSlJmjNnjkaPHq2rr75aVqtVffv21auvvupYnpOTo23btunEiROOeS+99JIjNjs7W/Hx8ZoxY4ZjuY+Pj15//XXdf//9Msboggsu0Isvvqjhw4e7dN8AAJ7vYFqWDqbl32F1a3KG41+rNf9urbGh/ooNLd2AjgBQ2YVcPFyBja6XbDaZ559Xdna2fMePl/WfgW+9goofTgMAUHVYjDHG3UlUdGlpaQoLC1NqamqFu/veqT+y/khK163zEvX+gNa6MCZEEj+y4H52u13JycmKjo6u0AP9oeKb/M02TVm6vdjlE7s10eT4pi7MCCiMPhOeiHYJT0XbhCc6mfSrkubFKWbAGgXEtHV3OuestHWSCnOmFMrHm2v+KvQj69Z5iY5pfmQBQL674uqp10U1JEl2u9Gx48cUGRHpdKYUAAAAgNKjKFXF8SMLAErn1DNH7Xa7kn2zFR0dxl9WAQAAgHNEUaqK40cWAAAAyo3NJsuUKQrOzJSmT5f8+YMnAOB/KEoBAAAAKB92u5SYKG+bLX8aAIBTUJQCAAAAUD58fGTGjtWJlBT5+vi4OxsAgIehKAUAAACgfHh5SV26yJacnD8NAMApGDgIAAAAAAAALkdRCgAAAED5sNulHTvk9eefjCkFACiEy/cAAAAAlA+bTZYHHlCIzSYtWCB58/MDAPA/fCsAAAAAKB8WixQdLXtWVv40AACnoCgFAAAAoHz4+cnMmqW05GT5+/m5OxsAgIdhTCkAAAAAAAC4HEUpAAAAAAAAuBxFKQAAAADlw2aTnnpKQS+9lD8NAMApGFMKAAAAQPmw22VZu1Y+Nptkt7s7GwCAh6EoBQAAAKB8eHvLjBqlEykp8vXmpwcAwBnfDAAAAADKh7e3FB8vW3Jy/jQ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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = dml_obj.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Effect Aggregation\n", + "\n", + "As the [did-R-package](https://bcallaway11.github.io/did/index.html), the $ATT$'s can be aggregated to summarize multiple effects.\n", + "For details on different aggregations and details on their interpretations see [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001).\n", + "\n", + "The aggregations are implemented via the `aggregate()` method. We follow the structure of the [did package notebook](https://bcallaway11.github.io/did/articles/did-basics.html#an-example-with-real-data) and start with an aggregation relative to the treatment timing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Event Study Aggregation\n", + "\n", + "\n", + "We can aggregate the $ATT$s relative to the treatment timing. This is done by setting `aggregation=\"eventstudy\"` in the `aggregate()` method. \n", + " `aggregation=\"eventstudy\"` aggregates $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$ based on exposure time $e = t_\\text{eval} - \\mathrm{g}$ (respecting group size)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDAggregation Object ==================\n", + " Event Study Aggregation \n", + "\n", + "------------------ Overall Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "-0.077214 0.019951 -3.870174 0.000109 -0.116317 -0.038111\n", + "------------------ Aggregated Effects ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "-3.0 0.030446 0.015075 2.019662 0.043418 0.000900 0.059992\n", + "-2.0 -0.000549 0.013317 -0.041223 0.967118 -0.026650 0.025552\n", + "-1.0 -0.024393 0.014200 -1.717808 0.085832 -0.052226 0.003439\n", + "0.0 -0.019919 0.011816 -1.685694 0.091855 -0.043079 0.003241\n", + "1.0 -0.050930 0.016783 -3.034679 0.002408 -0.083824 -0.018037\n", + "2.0 -0.137238 0.036342 -3.776254 0.000159 -0.208467 -0.066008\n", + "3.0 -0.100768 0.034297 -2.938126 0.003302 -0.167989 -0.033548\n", + "------------------ Additional Information ------------------\n", + "Score function: observational\n", + "Control group: never_treated\n", + "Anticipation periods: 0\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\bachp\\Documents\\Promotion\\DissundPapers\\Software\\DoubleML\\doubleml-for-py\\doubleml\\did\\did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# rerun bootstrap for valid simultaneous inference (as values are not saved) \n", + "dml_obj.bootstrap(n_rep_boot=5000)\n", + "aggregated_eventstudy = dml_obj.aggregate(\"eventstudy\")\n", + "# run bootstrap to obtain simultaneous confidence intervals\n", + "print(aggregated_eventstudy)\n", + "fig, ax = aggregated_eventstudy.plot_effects()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, the $ATT$ could also be aggregated according to (calendar) time periods or treatment groups, see the [user guide](https://docs.doubleml.org/stable./guide/models.html#effect-aggregation)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Aggregation Details\n", + "\n", + "**TODO**: Keep or drop this?\n", + "\n", + "The `DoubleMLDIDAggregation` objects include several `DoubleMLFrameworks` which support methods like `bootstrap()` or `confint()`.\n", + "Further, the weights can be accessed via the properties\n", + "\n", + " - ``overall_aggregation_weights``: weights for the overall aggregation\n", + " - ``aggregation_weights``: weights for the aggregation\n", + "\n", + "To clarify, e.g. for the eventstudy aggregation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If one would like to consider how the aggregated effect with $e=0$ is computed, one would have to look at the third set of weights within the ``aggregation_weights`` property" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0. , 0. , 0. , 0. ,\n", + " 0.23391813, 0. , 0. , 0. , 0. ,\n", + " 0.76608187, 0. ])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aggregated_eventstudy.aggregation_weights[2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ATT Estimation: Conditional Parallel Trends\n", + "\n", + "We briefly demonstrate how to use the `DoubleMLDIDMulti` model with conditional parallel trends. As the rationale behind DML is to flexibly model nuisance components as prediction problems, the DML DiD estimator includes pre-treatment covariates by default. In DiD, the nuisance components are the outcome regression and the propensity score estimation for the treatment group variable. This is why we had to enforce dummy learners in the unconditional parallel trends case to ignore the pre-treatment covariates. Now, we can replicate the classical doubly robust DiD estimator as of [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001) by using linear and logistic regression for the nuisance components. This is done by setting `ml_g` to `LinearRegression()` and `ml_m` to `LogisticRegression()`. Similarly, we can also choose other learners, for example by setting `ml_g` and `ml_m` to `LassoCV()` and `LogisticRegressionCV()`. We present the results for the ATTs and their event-study aggregation in the corresponding effect plots.\n", + "\n", + "Please note that the example is meant to illustrate the usage of the `DoubleMLDIDMulti` model in combination with ML learners. In real-data applicatoins, careful choice and empirical evaluation of the learners are required. Default measures for the prediction of the nuisance components are printed in the model summary, as briefly illustrated below." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\bachp\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\matplotlib\\cbook.py:1762: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n" + ] + }, + { + "data": { + "text/plain": [ + "(
,\n", + " [,\n", + " ,\n", + " ])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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RZujQoWbz5s3mgw8+MIGBgeaNN94os/2Hd/Lmfrl7924jyXzzzTfm4MGDzofdbi+z/Yd3cle/XL58uZFktm3b5tLHcnNzi8yN8bJq8+a+yZhZdbnzd3mfPn1Mx44dzdKlS83u3bvNDz/8YFatWlVkbmdzjIDKx5v7pjHGSDLx8fEuY+bJkyfL9ocAr+OufpmcnOzSt/766y8TGRlpJk+eXGRuFek4k6JUJZCUlGQkme+++84Yk9dp/fz8zIIFC5wxW7ZsMZLMmjVrjDHGLFq0yFitVpOYmOiMef31101YWJjJyspy2f6///1v88gjj5jJkyef8cv/jBkzTLVq1Vy28cADD5imTZue626igvGmfpn/BWvDhg1ls3OosMqrX+Z/8T9+/HiJc2G8xKm8qW8yZiJfefXLxYsXm/DwcHP06NES53I2xwio/LypbxqTV5T69NNPz3GvUNGV9/effJ9++qmxWCxmz549ReZSkY4zuXyvEkhJSZEkRUZGSpJ+/vlnZWdnq3v37s6YZs2aqV69elqzZo0kac2aNWrVqpWio6OdMT169FBqaqp+++03Z1t8fLz++OMPTZ48uUS5rFmzRv/3f/8nm83mst1t27bp+PHjpd9JVDje1C/z9enTRzVr1tSll16qzz77rNT7hoqrPPulJLVp00axsbG68sortXr16mJzYbzEqbypb+ZjzER59cvPPvtM7du317PPPqvatWurSZMmmjBhgk6ePFlkLmfT31H5eVPfzDdmzBjVqFFDHTp00OzZs2WMKbP9RcVQ3r/L87399tvq3r276tevX2QuFek409fTCeDcOBwO3X333ercubNatmwpSUpMTJTNZlNERIRLbHR0tHMOicTERJeOn78+f50k7dixQw8++KC+//57+fqWrKskJiaqYcOGRW63WrVqZ7eDqJC8rV+GhITohRdeUOfOnWW1WvXxxx+rb9++Wrhwofr06XMuu4oKpDz7ZWxsrGbOnKn27dsrKytLs2bNUteuXbV27VpddNFFhebDeIl83tY3GTMhlW+//OOPP7Rq1SoFBATo008/1ZEjRzR69GgdPXpU8fHxheZTku2iavC2vilJU6ZM0eWXX66goCB9/fXXGj16tNLT0zVu3Liy2m14ufLsl6c6cOCAFi9erHnz5hWbT0U6zqQoVcGNGTNGmzdv1qpVq8p0u7m5uRoyZIgef/xxNWnSpEy3jcrP2/pljRo1NH78eOfziy++WAcOHNBzzz3HF6wqpLz6pSQ1bdpUTZs2dT7v1KmTdu3apenTp+u9994r8/dD5eJtfZMxE1L59kuHwyGLxaK5c+cqPDxckvTiiy/quuuu04wZMxQYGFjm74nKwxv75qOPPupcbtu2rTIyMvTcc89RlKpCyrNfnmrOnDmKiIhQ3759y/V93InL9yqwsWPH6osvvtDy5ctVp04dZ3tMTIzsdruSk5Nd4g8dOqSYmBhnzOmz/uc/j4mJUVpamtavX6+xY8fK19dXvr6+mjJlijZu3ChfX199++23heZ0pu2i8vPGflmYjh07aufOnaXcS1Q05dkvi9KhQ4di+xjjJSTv7JuFYcysWsq7X8bGxqp27drOL/2S1Lx5cxljtG/fvkJzYsyE5J19szAdO3bUvn37lJWVdVb7h4rJXb/LjTGaPXu2brzxRpfL8gpTkcZMilIVkDFGY8eO1aeffqpvv/22wGl57dq1k5+fn5YtW+Zs27Ztm/bu3au4uDhJUlxcnDZt2qSkpCRnzNKlSxUWFqYWLVooLCxMmzZtUkJCgvMxatQoNW3aVAkJCerYsWOhucXFxWnlypXKzs522W7Tpk296hRBlD1v7peFSUhIUGxs7DnuNbydO/plUc7UxxgvqzZv7ptl9RpUPO7ql507d9aBAweUnp7ujNm+fbusVqvLF7pTlba/o3Lw5r5ZmISEBFWrVk3+/v6l2l9UDO7+Xf7dd99p586dGj58+Blzq1DHmR6aYB3n4I477jDh4eFmxYoVLreGPHHihDNm1KhRpl69eubbb78169evN3FxcSYuLs65Pv/Wk1dddZVJSEgwS5YsMVFRUcXeVrewu5y9+uqr5vLLL3c+T05ONtHR0ebGG280mzdvNvPnzzdBQUFeeetJlC1v7pfvvPOOmTdvntmyZYvZsmWLeeqpp4zVajWzZ88uux8AvJK7+uX06dPNwoULzY4dO8ymTZvMXXfdZaxWq/nmm2+cMYyXOJU3903GzKrLXf0yLS3N1KlTx1x33XXmt99+M999951p3LixGTFihDPmk08+cblLVGmOEVB5eHPf/Oyzz8xbb71lNm3aZHbs2GFmzJhhgoKCzKRJk8r5pwJPc/f3nxtuuMF07Nix0Fwq8nEmRakKSFKhj/j4eGfMyZMnzejRo021atVMUFCQ6devnzl48KDLdvbs2WOuvvpqExgYaGrUqGHuvfdek52dXeT7Fvblf/LkyaZ+/foubRs3bjSXXnqp8ff3N7Vr1zZPP/30ue4yKgBv7pfvvPOOad68uQkKCjJhYWGmQ4cOLrdmReXlrn75zDPPmEaNGpmAgAATGRlpunbtar799luXbTBe4lTe3DcZM6sud/4u37Jli+nevbsJDAw0derUMePHj3f5IhcfH29O//v52R4joPLw5r65ePFi06ZNGxMSEmKCg4NN69atzcyZM01ubm75/DDgNdzZL5OTk01gYKB58803C82lIh9nWozhXpUAAAAAAABwL+aUAgAAAAAAgNtRlAIAAAAAAIDbUZQCAAAAAACA21GUAgAAAAAAgNtRlAIAAAAAAIDbUZQCAAAAAACA21GUAgAAAAAAgNtRlAIAADhHe/bskcViUUJCQrm/1zvvvKOIiIhyf5/y0rVrV919993ntI2K/jMAAAB5KEoBAIBKbdiwYbJYLAUePXv29HRqZ9SgQQO99NJLLm3//ve/tX379nJ/765duzp/VgEBAWrRooVmzJhxztv95JNP9MQTT5RBhgAAoKLz9XQCAAAA5a1nz56Kj493afP39/dQNucmMDBQgYGBbnmvkSNHasqUKTpx4oTeffddjRkzRtWqVdPgwYPPelt2u102m02RkZHlkCkAAKiIOFMKAABUev7+/oqJiXF5VKtWTZI0ZMgQ/fvf/3aJz87OVo0aNfTuu+9KkpYsWaJLL71UERERql69uv71r39p165dRb5fYZeXLVy4UBaLxfl8165duvbaaxUdHa2QkBBdfPHF+uabb5zru3btqj///FP33HOP84ylorb9+uuvq1GjRrLZbGratKnee+89l/UWi0WzZs1Sv379FBQUpMaNG+uzzz47488tKChIMTExOu+88/TYY4+5vC45OVkjRoxQVFSUwsLCdPnll2vjxo3O1z722GNq06aNZs2apYYNGyogIMC5X6devnf8+HHddNNNqlatmoKCgnT11Vdrx44dBX6e9erVU1BQkPr166ejR4+eMXcAAOD9KEoBAIAqbejQofr888+Vnp7ubPvqq6904sQJ9evXT5KUkZGh8ePHa/369Vq2bJmsVqv69esnh8NR6vdNT09Xr169tGzZMm3YsEE9e/bUNddco71790rKu8ytTp06mjJlig4ePKiDBw8Wup1PP/1Ud911l+69915t3rxZt99+u2655RYtX77cJe7xxx/XoEGD9Ouvv6pXr14aOnSojh07dlY5BwYGym63S5IGDhyopKQkLV68WD///LMuuugiXXHFFS7b3Llzpz7++GN98sknRc63NWzYMK1fv16fffaZ1qxZI2OMevXqpezsbEnS2rVrNXz4cI0dO1YJCQnq1q2bnnzyybPKGwAAeCeKUgAAoNL74osvFBIS4vKYOnWqJKlHjx4KDg7Wp59+6oyfN2+e+vTpo9DQUEnSgAED1L9/f51//vlq06aNZs+erU2bNun3338vdU6tW7fW7bffrpYtW6px48Z64okn1KhRI+eZSJGRkfLx8VFoaKjz7K7CPP/88xo2bJhGjx6tJk2aaPz48erfv7+ef/55l7hhw4Zp8ODBOv/88zV16lSlp6frp59+KlGuubm5ev/99/Xrr7/q8ssv16pVq/TTTz9pwYIFat++vRo3bqznn39eERER+u9//+t8nd1u17vvvqu2bdvqwgsvLLDdHTt26LPPPtOsWbN02WWXqXXr1po7d67279+vhQsXSpJefvll9ezZU/fff7+aNGmicePGqUePHiXKGwAAeDeKUgAAoNLr1q2bEhISXB6jRo2SJPn6+mrQoEGaO3eupLyzov73v/9p6NChztfv2LFDgwcP1nnnnaewsDA1aNBAkpxnNZVGenq6JkyYoObNmysiIkIhISHasmXLWW9zy5Yt6ty5s0tb586dtWXLFpe2U4tCwcHBCgsLU1JSUrHbnjFjhkJCQhQYGKiRI0fqnnvu0R133KGNGzcqPT1d1atXdyn07d692+Wyxvr16ysqKqrY3H19fdWxY0dnW/Xq1dW0aVNn/lu2bHFZL0lxcXHF5g0AACoGJjoHAACVXnBwsM4///wi1w8dOlRdunRRUlKSli5dqsDAQJe7811zzTWqX7++3nrrLdWqVUsOh0MtW7Z0Xsp2OqvVKmOMS1v+5Wj5JkyYoKVLl+r555/X+eefr8DAQF133XVFbvNc+fn5uTy3WCxnvPxw6NChevjhhxUYGKjY2FhZrXl/z0xPT1dsbKxWrFhR4DWnzncVHBx8znkDAIDKi6IUAACo8jp16qS6devqww8/1OLFizVw4EBnEefo0aPatm2b3nrrLV122WWSpFWrVhW7vaioKKWlpSkjI8NZmDl9TqXVq1dr2LBhznmr0tPTtWfPHpcYm82m3NzcYt+refPmWr16tW6++WaXbbdo0eKM+30m4eHhhRbzLrroIiUmJsrX19d51lhpNG/eXDk5OVq7dq06deok6Z+fd37+zZs319q1a11e9+OPP5b6PQEAgPegKAUAACq9rKwsJSYmurT5+vqqRo0azud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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dml_obj_linear_logistic = DoubleMLDIDMulti(\n", + " obj_dml_data=dml_data,\n", + " ml_g=LinearRegression(),\n", + " ml_m=LogisticRegression(penalty=None),\n", + " control_group=\"never_treated\",\n", + " n_folds=10\n", + ")\n", + "\n", + "dml_obj_linear_logistic.fit()\n", + "dml_obj_linear_logistic.bootstrap(n_rep_boot=5000)\n", + "dml_obj_linear_logistic.plot_effects(title=\"Estimated ATTs by Group, Linear and logistic Regression\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We briefly look at the model summary, which includes some standard diagnostics for the prediction of the nuisance components." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDMulti Object ==================\n", + "\n", + "------------------ Data summary ------------------\n", + "Outcome variable: lemp\n", + "Treatment variable(s): ['first.treat']\n", + "Covariates: ['lpop']\n", + "Instrument variable(s): None\n", + "Time variable: year\n", + "Id variable: countyreal\n", + "No. Observations: 500\n", + "\n", + "------------------ Score & algorithm ------------------\n", + "Score function: observational\n", + "Control group: never_treated\n", + "Anticipation periods: 0\n", + "\n", + "------------------ Machine learner ------------------\n", + "Learner ml_g: LinearRegression()\n", + "Learner ml_m: LogisticRegression(penalty=None)\n", + "Out-of-sample Performance:\n", + "Regression:\n", + "Learner ml_g0 RMSE: [[0.17197022 0.18219482 0.25977582 0.25762107 0.1726177 0.15159716\n", + " 0.20238368 0.20650302 0.17381994 0.15150495 0.20118645 0.16352671]]\n", + "Learner ml_g1 RMSE: [[0.10293317 0.12500233 0.13673155 0.1356723 0.13881924 0.1126528\n", + " 0.08473008 0.10271377 0.13282935 0.16430589 0.15938565 0.1613265 ]]\n", + "Classification:\n", + "Learner ml_m Log Loss: [[0.23186483 0.23061763 0.23153821 0.23146489 0.34981033 0.34925181\n", + " 0.35002383 0.34858068 0.60836257 0.60569912 0.60630968 0.60700137]]\n", + "\n", + "------------------ Resampling ------------------\n", + "No. folds: 10\n", + "No. repeated sample splits: 1\n", + "\n", + "------------------ Fit summary ------------------\n", + " coef std err t P>|t| 2.5 % \\\n", + "ATT(2004.0,2003,2004) -0.013506 0.022282 -0.606161 0.544408 -0.057178 \n", + "ATT(2004.0,2003,2005) -0.077125 0.028778 -2.680033 0.007361 -0.133528 \n", + "ATT(2004.0,2003,2006) -0.132188 0.035710 -3.701704 0.000214 -0.202178 \n", + "ATT(2004.0,2003,2007) -0.104989 0.033066 -3.175173 0.001497 -0.169797 \n", + "ATT(2006.0,2003,2004) -0.000609 0.022310 -0.027312 0.978211 -0.044336 \n", + "ATT(2006.0,2004,2005) -0.005410 0.018285 -0.295867 0.767332 -0.041248 \n", + "ATT(2006.0,2005,2006) 0.003109 0.020571 0.151158 0.879851 -0.037209 \n", + "ATT(2006.0,2005,2007) -0.041588 0.019824 -2.097880 0.035916 -0.080442 \n", + "ATT(2007.0,2003,2004) 0.027959 0.014092 1.983968 0.047259 0.000338 \n", + "ATT(2007.0,2004,2005) -0.004452 0.015648 -0.284504 0.776024 -0.035121 \n", + "ATT(2007.0,2005,2006) -0.028741 0.018211 -1.578214 0.114517 -0.064434 \n", + "ATT(2007.0,2006,2007) -0.027488 0.016248 -1.691824 0.090679 -0.059333 \n", + "\n", + " 97.5 % \n", + "ATT(2004.0,2003,2004) 0.030165 \n", + "ATT(2004.0,2003,2005) -0.020722 \n", + "ATT(2004.0,2003,2006) -0.062197 \n", + "ATT(2004.0,2003,2007) -0.040182 \n", + "ATT(2006.0,2003,2004) 0.043118 \n", + "ATT(2006.0,2004,2005) 0.030428 \n", + "ATT(2006.0,2005,2006) 0.043428 \n", + "ATT(2006.0,2005,2007) -0.002734 \n", + "ATT(2007.0,2003,2004) 0.055580 \n", + "ATT(2007.0,2004,2005) 0.026217 \n", + "ATT(2007.0,2005,2006) 0.006952 \n", + "ATT(2007.0,2006,2007) 0.004357 \n" + ] + } + ], + "source": [ + "print(dml_obj_linear_logistic)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\bachp\\Documents\\Promotion\\DissundPapers\\Software\\DoubleML\\doubleml-for-py\\doubleml\\did\\did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "es_linear_logistic = dml_obj_linear_logistic.aggregate(\"eventstudy\")\n", + "es_linear_logistic.plot_effects(title=\"Estimated ATTs by Group, Linear and logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\bachp\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\matplotlib\\cbook.py:1762: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + " return math.isfinite(val)\n" + ] + }, + { + "data": { + "text/plain": [ + "(
,\n", + " [,\n", + " ,\n", + " ])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dml_obj_lasso = DoubleMLDIDMulti(\n", + " obj_dml_data=dml_data,\n", + " ml_g=LassoCV(),\n", + " ml_m=LogisticRegressionCV(),\n", + " control_group=\"never_treated\",\n", + " n_folds=10\n", + ")\n", + "\n", + "dml_obj_lasso.fit()\n", + "dml_obj_lasso.bootstrap(n_rep_boot=5000)\n", + "dml_obj_lasso.plot_effects(title=\"Estimated ATTs by Group, LassoCV and LogisticRegressionCV()\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLDIDMulti Object ==================\n", + "\n", + "------------------ Data summary ------------------\n", + "Outcome variable: lemp\n", + "Treatment variable(s): ['first.treat']\n", + "Covariates: ['lpop']\n", + "Instrument variable(s): None\n", + "Time variable: year\n", + "Id variable: countyreal\n", + "No. Observations: 500\n", + "\n", + "------------------ Score & algorithm ------------------\n", + "Score function: observational\n", + "Control group: never_treated\n", + "Anticipation periods: 0\n", + "\n", + "------------------ Machine learner ------------------\n", + "Learner ml_g: LassoCV()\n", + "Learner ml_m: LogisticRegressionCV()\n", + "Out-of-sample Performance:\n", + "Regression:\n", + "Learner ml_g0 RMSE: [[0.17242002 0.18142511 0.25935844 0.25958681 0.17338302 0.15220244\n", + " 0.20145569 0.20566302 0.17235633 0.15194071 0.20103747 0.16455826]]\n", + "Learner ml_g1 RMSE: [[0.09911511 0.13069581 0.13900982 0.15099534 0.13918785 0.11262764\n", + " 0.08873154 0.1102721 0.131316 0.16060588 0.15919193 0.16009149]]\n", + "Classification:\n", + "Learner ml_m Log Loss: [[0.22914236 0.22913907 0.22913769 0.22913938 0.35596113 0.35595921\n", + " 0.35596421 0.35595231 0.60886635 0.60885348 0.60885687 0.60885362]]\n", + "\n", + "------------------ Resampling ------------------\n", + "No. folds: 10\n", + "No. repeated sample splits: 1\n", + "\n", + "------------------ Fit summary ------------------\n", + " coef std err t P>|t| 2.5 % \\\n", + "ATT(2004.0,2003,2004) -0.016264 0.023168 -0.702029 0.482661 -0.061672 \n", + "ATT(2004.0,2003,2005) -0.077256 0.028982 -2.665626 0.007685 -0.134060 \n", + "ATT(2004.0,2003,2006) -0.136480 0.035719 -3.820990 0.000133 -0.206487 \n", + "ATT(2004.0,2003,2007) -0.104836 0.033835 -3.098432 0.001945 -0.171152 \n", + "ATT(2006.0,2003,2004) -0.001649 0.023339 -0.070667 0.943663 -0.047394 \n", + "ATT(2006.0,2004,2005) -0.005104 0.019313 -0.264274 0.791569 -0.042956 \n", + "ATT(2006.0,2005,2006) -0.003129 0.017887 -0.174943 0.861124 -0.038187 \n", + "ATT(2006.0,2005,2007) -0.041235 0.020320 -2.029240 0.042434 -0.081062 \n", + "ATT(2007.0,2003,2004) 0.028086 0.015158 1.852874 0.063900 -0.001623 \n", + "ATT(2007.0,2004,2005) -0.004803 0.016425 -0.292438 0.769951 -0.036996 \n", + "ATT(2007.0,2005,2006) -0.030093 0.017879 -1.683182 0.092340 -0.065134 \n", + "ATT(2007.0,2006,2007) -0.028444 0.016791 -1.693985 0.090268 -0.061354 \n", + "\n", + " 97.5 % \n", + "ATT(2004.0,2003,2004) 0.029143 \n", + "ATT(2004.0,2003,2005) -0.020452 \n", + "ATT(2004.0,2003,2006) -0.066473 \n", + "ATT(2004.0,2003,2007) -0.038520 \n", + "ATT(2006.0,2003,2004) 0.044095 \n", + "ATT(2006.0,2004,2005) 0.032748 \n", + "ATT(2006.0,2005,2006) 0.031928 \n", + "ATT(2006.0,2005,2007) -0.001408 \n", + "ATT(2007.0,2003,2004) 0.057795 \n", + "ATT(2007.0,2004,2005) 0.027389 \n", + "ATT(2007.0,2005,2006) 0.004948 \n", + "ATT(2007.0,2006,2007) 0.004466 \n" + ] + } + ], + "source": [ + "# Model summary\n", + "print(dml_obj_lasso)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\bachp\\Documents\\Promotion\\DissundPapers\\Software\\DoubleML\\doubleml-for-py\\doubleml\\did\\did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "es_rf = dml_obj_lasso.aggregate(\"eventstudy\")\n", + "es_rf.plot_effects(title=\"Estimated ATTs by Group, LassoCV and LogisticRegressionCV()\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From d62e9263be8ca7b2a9cd6fbfa5ef3c110162dd42 Mon Sep 17 00:00:00 2001 From: Ezequiel Smucler Date: Wed, 14 May 2025 14:48:10 +0200 Subject: [PATCH 116/140] rename method, set all seeds --- doc/examples/py_double_ml_robust_iv.ipynb | 24 ++++++++++++----------- 1 file changed, 13 insertions(+), 11 deletions(-) diff --git a/doc/examples/py_double_ml_robust_iv.ipynb b/doc/examples/py_double_ml_robust_iv.ipynb index 22b1b0fe..1f9d83f6 100644 --- a/doc/examples/py_double_ml_robust_iv.ipynb +++ b/doc/examples/py_double_ml_robust_iv.ipynb @@ -25,16 +25,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", + "import random\n", "from sklearn.linear_model import LinearRegression, LogisticRegression\n", "import doubleml as dml\n", "\n", - "np.random.seed(1234)\n" + "np.random.seed(1234)\n", + "random.seed(1234)\n" ] }, { @@ -54,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 16, "id": "82111204", "metadata": {}, "outputs": [], @@ -79,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "id": "a58c545e", "metadata": {}, "outputs": [], @@ -104,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "id": "600b8196", "metadata": {}, "outputs": [], @@ -129,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "id": "962900bc", "metadata": {}, "outputs": [], @@ -153,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "id": "549c98fd", "metadata": {}, "outputs": [ @@ -165,7 +167,7 @@ " coef std err t P>|t| 2.5 % 97.5 %\n", "D 0.951444 0.144382 6.589761 4.405342e-11 0.66846 1.234428\n", "Uniform confidence set results\n", - "[(np.float64(0.6317710537372012), np.float64(1.206690551996729))]\n" + "[(np.float64(0.6317710537372013), np.float64(1.206690551996729))]\n" ] } ], @@ -175,7 +177,7 @@ "print(\"Standard confidence interval results\")\n", "print(dml_iivm_strong.fit().summary)\n", "print(\"Uniform confidence set results\")\n", - "print(dml_iivm_strong.uniform_confset())" + "print(dml_iivm_strong.robust_confset())" ] }, { @@ -188,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "25d98c5a", "metadata": {}, "outputs": [ @@ -213,7 +215,7 @@ "print(\"Standard confidence interval results\")\n", "print(dml_iivm_weak.fit().summary)\n", "print(\"Uniform confidence set results\")\n", - "print(dml_iivm_weak.uniform_confset())" + "print(dml_iivm_weak.robust_confset())" ] }, { From 2d2f521c29e492ae637f8edc8d46469341a21e8b Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 14 May 2025 15:10:35 +0200 Subject: [PATCH 117/140] fix broken links (currently refering to dev verison) --- doc/examples/did/py_panel_data_example.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/examples/did/py_panel_data_example.ipynb b/doc/examples/did/py_panel_data_example.ipynb index dfa83622..2bb15f06 100644 --- a/doc/examples/did/py_panel_data_example.ipynb +++ b/doc/examples/did/py_panel_data_example.ipynb @@ -250,7 +250,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To work with [DoubleML](https://docs.doubleml.org/stable/index.html), we initialize a `DoubleMLPanelData` object. The input data has to satisfy some requirements, i.e., it should be in a *long* format with every row containing the information of one unit at one time period. Moreover, the data should contain a column on the unit identifier and a column on the time period. The requirements are virtually identical to those of the [did-R-package](https://bcallaway11.github.io/did/index.html), as listed in [their data example](https://bcallaway11.github.io/did/articles/did-basics.html#an-example-with-real-data). In line with the naming conventions of [DoubleML](https://docs.doubleml.org/stable/index.html), the treatment group indicator is passed to `DoubleMLPanelData` by the `d_cols` argument. To flexibly handle different formats for handling time periods, the time variable `t_col` can handle `float`, `int` and `datetime` formats. More information are available in the [user guide](https://docs.doubleml.org/stable/guide/data_backend.html#doublemlpaneldata). To indicate never treated units, we set their value for the treatment group variable to `np.inf`." + "To work with [DoubleML](https://docs.doubleml.org/stable/index.html), we initialize a `DoubleMLPanelData` object. The input data has to satisfy some requirements, i.e., it should be in a *long* format with every row containing the information of one unit at one time period. Moreover, the data should contain a column on the unit identifier and a column on the time period. The requirements are virtually identical to those of the [did-R-package](https://bcallaway11.github.io/did/index.html), as listed in [their data example](https://bcallaway11.github.io/did/articles/did-basics.html#an-example-with-real-data). In line with the naming conventions of [DoubleML](https://docs.doubleml.org/stable/index.html), the treatment group indicator is passed to `DoubleMLPanelData` by the `d_cols` argument. To flexibly handle different formats for handling time periods, the time variable `t_col` can handle `float`, `int` and `datetime` formats. More information are available in the [user guide](https://docs.doubleml.org/dev/guide/data_backend.html#doublemlpaneldata). To indicate never treated units, we set their value for the treatment group variable to `np.inf`." ] }, { @@ -549,7 +549,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Alternatively, the $ATT$ could also be aggregated according to (calendar) time periods or treatment groups, see the [user guide](https://docs.doubleml.org/stable./guide/models.html#effect-aggregation)." + "Alternatively, the $ATT$ could also be aggregated according to (calendar) time periods or treatment groups, see the [user guide](https://docs.doubleml.org/dev/guide/models.html#effect-aggregation)." ] }, { From 2a809a21f76107a734daec9b193cf7cfc5a30a99 Mon Sep 17 00:00:00 2001 From: Ezequiel Smucler Date: Wed, 14 May 2025 16:11:12 +0200 Subject: [PATCH 118/140] change structure to report small simulation --- doc/examples/py_double_ml_robust_iv.ipynb | 176 ++++++++-------------- 1 file changed, 61 insertions(+), 115 deletions(-) diff --git a/doc/examples/py_double_ml_robust_iv.ipynb b/doc/examples/py_double_ml_robust_iv.ipynb index 1f9d83f6..55ac3138 100644 --- a/doc/examples/py_double_ml_robust_iv.ipynb +++ b/doc/examples/py_double_ml_robust_iv.ipynb @@ -20,12 +20,12 @@ "- D is treatment variable,\n", "- Y is the outcome.\n", "\n", - "Next, we will generate two synthetic data sets, one where the instrument is weak and another where it is not. Then, we will compare the output of the standard way to compute confidence intervals using the DoubleMLIIVM class, with the confidence sets computed using the uniform_confset method from the same class. We will see that using the uniform_confset method is an easy way to ensure the results of an analysis are robust to weak instruments." + "Next, we will run a simulation, where we will generate two synthetic data sets, one where the instrument is weak and another where it is not. Then, we will compare the output of the standard way to compute confidence intervals using the DoubleMLIIVM class, with the confidence sets computed using the robust_confset method from the same class. We will see that using the robust_confset method is an easy way to ensure the results of an analysis are robust to weak instruments." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "id": "82111204", "metadata": {}, "outputs": [], @@ -76,154 +76,106 @@ "id": "8c938fd8", "metadata": {}, "source": [ - "We call the function two times to get two data sets, one where the instrument is weak, the other where the instrument is strong. In both cases the true effect is 1." + "To fit the DML model, we need to decide on how we will estimate the nuisance functions. We will use a linear regression model for g, and a logistic regression for r. We will assume that we know the true m function, as is the case in a controlled experiment, such as an AB test. The following class defines defines this \"fake\" estimator." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 8, "id": "a58c545e", "metadata": {}, "outputs": [], - "source": [ - "data_weak = generate_weakiv_data(5000, 1, 0.003)\n", - "data_strong = generate_weakiv_data(5000, 1, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fitting the DoubleML model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we fit the DoubleML model. We begin by preparing the two data sets into the DoubleMLData format." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "600b8196", - "metadata": {}, - "outputs": [], - "source": [ - "dml_data_strong = dml.DoubleMLData(\n", - " data_strong, y_col='Y', d_cols='D', \n", - " z_cols='Z', x_cols='X'\n", - ")\n", - "dml_data_weak = dml.DoubleMLData(\n", - " data_weak, y_col='Y', d_cols='D', \n", - " z_cols='Z', x_cols='X'\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "30ff957e", - "metadata": {}, - "source": [ - "Next, we define the nuisance estimators we will use. We will use a linear regression model for g, and a logistic regression for r. We will assume that we know the true m function, as is the case in a controlled experiment, such as an AB test." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "962900bc", - "metadata": {}, - "outputs": [], "source": [ "class TrueMFunction(LogisticRegression):\n", " def predict(self, X):\n", - " return np.full(X.shape[0], 0.5)\n", - "\n", - "ml_g = LinearRegression()\n", - "ml_m = TrueMFunction()\n", - "ml_r = LogisticRegression(penalty=None)" + " return np.full(X.shape[0], 0.5)" ] }, { "cell_type": "markdown", - "id": "53fd3370", + "id": "becf84b0", "metadata": {}, "source": [ - "Now, we fit the DoubleML model on the data set with a strong instrument and then print both the standard and robust results confidence sets. We see that the results are similar." + "We will now run a loop, where for each of 100 replications we will generate data using the previously defined function. We will take a sample size of 5000, a true effect equal to 1, and take two possible values for the instrument strength: 0.003 and 1. In the latter case the instrument is strong, in the former it is weak. We will then compute both the robust and the standard confidence intervals, check whether they contain the true effect, and compute their length." ] }, { "cell_type": "code", - "execution_count": 20, - "id": "549c98fd", + "execution_count": 9, + "id": "600b8196", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Standard confidence interval results\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "D 0.951444 0.144382 6.589761 4.405342e-11 0.66846 1.234428\n", - "Uniform confidence set results\n", - "[(np.float64(0.6317710537372013), np.float64(1.206690551996729))]\n" - ] - } - ], + "outputs": [], "source": [ - "dml_iivm_strong = dml.DoubleMLIIVM(dml_data_strong, ml_g, ml_m, ml_r)\n", - "\n", - "print(\"Standard confidence interval results\")\n", - "print(dml_iivm_strong.fit().summary)\n", - "print(\"Uniform confidence set results\")\n", - "print(dml_iivm_strong.robust_confset())" + "n_samples = 5000\n", + "true_effect = 1\n", + "output_list = []\n", + "for _ in range(100):\n", + " for instrument_strength in [0.003, 1]:\n", + " dataset = generate_weakiv_data(n_samples = n_samples, true_effect = true_effect, instrument_strength = instrument_strength)\n", + " dml_data = dml.DoubleMLData(\n", + " dataset, y_col='Y', d_cols='D', \n", + " z_cols='Z', x_cols='X'\n", + " )\n", + " ml_g = LinearRegression()\n", + " ml_m = TrueMFunction()\n", + " ml_r = LogisticRegression(penalty=None)\n", + " dml_iivm = dml.DoubleMLIIVM(dml_data, ml_g, ml_m, ml_r)\n", + " dml_iivm.fit()\n", + " dml_standard_ci = dml_iivm.confint(joint=False)\n", + " dml_robust_confset = dml_iivm.robust_confset()\n", + " dml_covers = dml_standard_ci[\"2.5 %\"].iloc[0] <= true_effect <= dml_standard_ci[\"97.5 %\"].iloc[0]\n", + " robust_covers = any(interval[0] <= true_effect <= interval[1] for interval in dml_robust_confset)\n", + " dml_length = dml_standard_ci[\"97.5 %\"].iloc[0] - dml_standard_ci[\"2.5 %\"].iloc[0]\n", + " dml_robust_length = max(interval[1] - interval[0] for interval in dml_robust_confset)\n", + " output_list.append({\n", + " \"instrument_strength\": instrument_strength,\n", + " \"dml_covers\": dml_covers,\n", + " \"robust_covers\": robust_covers,\n", + " \"dml_length\": dml_length,\n", + " \"robust_length\": dml_robust_length\n", + " })\n", + "results_df = pd.DataFrame(output_list)" ] }, { "cell_type": "markdown", - "id": "91cd61dd", + "id": "3f1366d2", "metadata": {}, "source": [ - "We now repeat the process with the weak instruments data set. In this case, the standard method reports a confidence interval equal to [2.08, 3.46]. Thus, an analyst reading this would think that she can have high confidence that the true effect is roughly between 2 and 3.5. We know however that the true effect is equal to 1, and thus that the standard DoubleML estimator is badly biased in this case. On the other hand, the uniform confidence set method returns the whole real line as a confidence interval. This indicates that the data does not contain enough information to make any claims about the effect of the treatment of the outcome, because the instrument is too weak." + "Having stored the results of the simulation in the results_df dataframe, we will compute some summary statistics. We see in the table below that, when the instrument is strong, the standard DML confidence interval and the robust confidence set behave similarly, with coverage close to the nominal level and similar median lengths. On the other hand, when the instrument is strong, the coverage of the standard DML confidence interval is very low, whereas the coverage of the robust confidence set is again close to the nominal value. Note that in this case the robust confidence set has an infinite median length. When the robust confidence set has infinite length, the analyst should interpret the results as indicating that the data contains little information about the estimand of interest, possibly because the instrument is weak." ] }, { "cell_type": "code", - "execution_count": 21, - "id": "25d98c5a", + "execution_count": 10, + "id": "86c83edc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Standard confidence interval results\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "D 2.210317 3.689771 0.599039 0.549147 -5.021502 9.442136\n", - "Uniform confidence set results\n", - "[(-inf, inf)]\n" + " DML coverage Robust coverage DML median length \\\n", + "instrument_strength \n", + "0.003 0.16 0.91 0.482675 \n", + "1.000 0.94 0.93 0.572038 \n", + "\n", + " Robust median length \n", + "instrument_strength \n", + "0.003 inf \n", + "1.000 0.582104 \n" ] } ], "source": [ - "ml_g = LinearRegression()\n", - "ml_m = TrueMFunction()\n", - "ml_r = LogisticRegression(penalty=None)\n", - "dml_iivm_weak = dml.DoubleMLIIVM(dml_data_weak, ml_g, ml_m, ml_r)\n", - "\n", - "print(\"Standard confidence interval results\")\n", - "print(dml_iivm_weak.fit().summary)\n", - "print(\"Uniform confidence set results\")\n", - "print(dml_iivm_weak.robust_confset())" - ] - }, - { - "cell_type": "markdown", - "id": "fe5629c5", - "metadata": {}, - "source": [ - "## References" + "results_df = pd.DataFrame(output_list)\n", + "summary_df = results_df.groupby(\"instrument_strength\").agg(\n", + " **{\"DML coverage\": (\"dml_covers\", \"mean\"),\n", + " \"Robust coverage\": (\"robust_covers\", \"mean\"),\n", + " \"DML median length\": (\"dml_length\", \"median\"),\n", + " \"Robust median length\": (\"robust_length\", \"median\")}\n", + ")\n", + "print(summary_df)" ] }, { @@ -238,12 +190,6 @@ "- Takatsu, K., Levis, A. W., Kennedy, E., Kelz, R., and Keele, L. (2023). Doubly robust machine learning for an instrumental\n", "variable study of surgical care for cholecystitis. arXiv preprint arXiv:2307.06269." ] - }, - { - "cell_type": "markdown", - "id": "7ce927dd", - "metadata": {}, - "source": [] } ], "metadata": { From 027e222ae1a85918e6ac5ff854043623f4f0e9e3 Mon Sep 17 00:00:00 2001 From: Ezequiel Smucler Date: Wed, 14 May 2025 16:12:24 +0200 Subject: [PATCH 119/140] add refs section --- doc/examples/py_double_ml_robust_iv.ipynb | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/doc/examples/py_double_ml_robust_iv.ipynb b/doc/examples/py_double_ml_robust_iv.ipynb index 55ac3138..4ad89f5c 100644 --- a/doc/examples/py_double_ml_robust_iv.ipynb +++ b/doc/examples/py_double_ml_robust_iv.ipynb @@ -43,7 +43,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Generating synthetic data" + "# Running a small simulation" ] }, { @@ -178,6 +178,14 @@ "print(summary_df)" ] }, + { + "cell_type": "markdown", + "id": "f4fd3d05", + "metadata": {}, + "source": [ + "# References" + ] + }, { "cell_type": "markdown", "id": "946cbbcf", From 6da0c07a794382b481dd682c8cb1ece31b787209 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Thu, 15 May 2025 11:50:32 +0000 Subject: [PATCH 120/140] add example to gallery --- doc/examples/index.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/examples/index.rst b/doc/examples/index.rst index f363e19e..fe0db0c6 100644 --- a/doc/examples/index.rst +++ b/doc/examples/index.rst @@ -29,6 +29,7 @@ General Examples py_double_ml_did.ipynb py_double_ml_did_pretest.ipynb py_double_ml_basic_iv.ipynb + py_double_ml_robust_iv.ipynb py_double_ml_plm_irm_hetfx.ipynb py_double_ml_meets_flaml.ipynb py_double_ml_rdflex.ipynb From d7fab88208d8789e41dfb3011b6834dd2c1becb9 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Thu, 15 May 2025 12:12:33 +0000 Subject: [PATCH 121/140] update learner and small format changes --- doc/examples/py_double_ml_robust_iv.ipynb | 56 ++++++++++++++--------- 1 file changed, 34 insertions(+), 22 deletions(-) diff --git a/doc/examples/py_double_ml_robust_iv.ipynb b/doc/examples/py_double_ml_robust_iv.ipynb index 4ad89f5c..81b4aa57 100644 --- a/doc/examples/py_double_ml_robust_iv.ipynb +++ b/doc/examples/py_double_ml_robust_iv.ipynb @@ -15,28 +15,29 @@ "\n", "Throughout this example\n", "\n", - "- Z is the instrument,\n", - "- X is a vector of covariates,\n", - "- D is treatment variable,\n", - "- Y is the outcome.\n", + "- $Z$ is the instrument,\n", + "- $X$ is a vector of covariates,\n", + "- $D$ is treatment variable,\n", + "- $Y$ is the outcome.\n", "\n", - "Next, we will run a simulation, where we will generate two synthetic data sets, one where the instrument is weak and another where it is not. Then, we will compare the output of the standard way to compute confidence intervals using the DoubleMLIIVM class, with the confidence sets computed using the robust_confset method from the same class. We will see that using the robust_confset method is an easy way to ensure the results of an analysis are robust to weak instruments." + "Next, we will run a simulation, where we will generate two synthetic data sets, one where the instrument is weak and another where it is not. Then, we will compare the output of the standard way to compute confidence intervals using the ``DoubleMLIIVM`` class, with the confidence sets computed using the ``robust_confset()`` method from the same class. We will see that using the ``robust_confset()`` method is an easy way to ensure the results of an analysis are robust to weak instruments." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import random\n", + "from sklearn.base import BaseEstimator, ClassifierMixin\n", "from sklearn.linear_model import LinearRegression, LogisticRegression\n", "import doubleml as dml\n", "\n", "np.random.seed(1234)\n", - "random.seed(1234)\n" + "random.seed(1234)" ] }, { @@ -51,12 +52,12 @@ "id": "774e45c7", "metadata": {}, "source": [ - "The following function generates data from an instrumental variables model. The true_effect argument is the estimand of interest, the true effect of the treatment on the outcome. The instrument_strength argument is a measure of the strength of the instrument. The higher it is, the stronger the correlation is between the instrument and the treatment. Notice that the instrument is fully randomized." + "The following function generates data from an instrumental variables model. The ``true_effect`` argument is the estimand of interest, the true effect of the treatment on the outcome. The ``instrument_strength`` argument is a measure of the strength of the instrument. The higher it is, the stronger the correlation is between the instrument and the treatment. Notice that the instrument is fully randomized." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "id": "82111204", "metadata": {}, "outputs": [], @@ -76,19 +77,30 @@ "id": "8c938fd8", "metadata": {}, "source": [ - "To fit the DML model, we need to decide on how we will estimate the nuisance functions. We will use a linear regression model for g, and a logistic regression for r. We will assume that we know the true m function, as is the case in a controlled experiment, such as an AB test. The following class defines defines this \"fake\" estimator." + "To fit the DML model, we need to decide on how we will estimate the nuisance functions. We will use a linear regression model for $g$, and a logistic regression for $r$. We will assume that we know the true $m$ function, as is the case in a controlled experiment, such as an AB test. The following class defines defines this \"fake\" estimator." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "a58c545e", + "execution_count": 3, + "id": "9a347c25", "metadata": {}, "outputs": [], "source": [ - "class TrueMFunction(LogisticRegression):\n", + "class TrueMFunction(BaseEstimator, ClassifierMixin):\n", + " def __init__(self, prob_dist=(0.5, 0.5)):\n", + " self.prob_dist = prob_dist \n", + "\n", + " def fit(self, X, y):\n", + " self.prob_dist_ = np.array(self.prob_dist)\n", + " self.classes_ = np.array(sorted(set(y)))\n", + " return self\n", + "\n", + " def predict_proba(self, X):\n", + " return np.tile(self.prob_dist_, (len(X), 1))\n", + "\n", " def predict(self, X):\n", - " return np.full(X.shape[0], 0.5)" + " return np.full(len(X), self.classes_[np.argmax(self.prob_dist_)])" ] }, { @@ -96,12 +108,12 @@ "id": "becf84b0", "metadata": {}, "source": [ - "We will now run a loop, where for each of 100 replications we will generate data using the previously defined function. We will take a sample size of 5000, a true effect equal to 1, and take two possible values for the instrument strength: 0.003 and 1. In the latter case the instrument is strong, in the former it is weak. We will then compute both the robust and the standard confidence intervals, check whether they contain the true effect, and compute their length." + "We will now run a loop, where for each of $100$ replications we will generate data using the previously defined function. We will take a sample size of $5000$, a true effect equal to $1$, and take two possible values for the instrument strength: $0.003$ and $1$. In the latter case the instrument is strong, in the former it is weak. We will then compute both the robust and the standard confidence intervals, check whether they contain the true effect, and compute their length." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "id": "600b8196", "metadata": {}, "outputs": [], @@ -142,12 +154,12 @@ "id": "3f1366d2", "metadata": {}, "source": [ - "Having stored the results of the simulation in the results_df dataframe, we will compute some summary statistics. We see in the table below that, when the instrument is strong, the standard DML confidence interval and the robust confidence set behave similarly, with coverage close to the nominal level and similar median lengths. On the other hand, when the instrument is strong, the coverage of the standard DML confidence interval is very low, whereas the coverage of the robust confidence set is again close to the nominal value. Note that in this case the robust confidence set has an infinite median length. When the robust confidence set has infinite length, the analyst should interpret the results as indicating that the data contains little information about the estimand of interest, possibly because the instrument is weak." + "Having stored the results of the simulation in the ``results_df`` dataframe, we will compute some summary statistics. We see in the table below that, when the instrument is strong, the standard DML confidence interval and the robust confidence set behave similarly, with coverage close to the nominal level and similar median lengths. On the other hand, when the instrument is strong, the coverage of the standard DML confidence interval is very low, whereas the coverage of the robust confidence set is again close to the nominal value. Note that in this case the robust confidence set has an infinite median length. When the robust confidence set has infinite length, the analyst should interpret the results as indicating that the data contains little information about the estimand of interest, possibly because the instrument is weak." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "id": "86c83edc", "metadata": {}, "outputs": [ @@ -157,13 +169,13 @@ "text": [ " DML coverage Robust coverage DML median length \\\n", "instrument_strength \n", - "0.003 0.16 0.91 0.482675 \n", - "1.000 0.94 0.93 0.572038 \n", + "0.003 0.15 0.91 0.489567 \n", + "1.000 0.93 0.92 0.572717 \n", "\n", " Robust median length \n", "instrument_strength \n", "0.003 inf \n", - "1.000 0.582104 \n" + "1.000 0.582754 \n" ] } ], @@ -216,7 +228,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.1" + "version": "3.12.3" } }, "nbformat": 4, From ce64e5ccb69a9a71d0547fa5c131245ed8407f78 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Thu, 15 May 2025 15:07:05 +0000 Subject: [PATCH 122/140] add basic thumbnail of robust iv example --- doc/conf.py | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/conf.py b/doc/conf.py index be565248..473eb247 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -159,6 +159,7 @@ 'examples/py_double_ml_gate_sensitivity': '_static/sensitivity_example_nb.png', 'examples/py_double_ml_firststage': '_static/firststage_example_nb.png', 'examples/py_double_ml_basic_iv': '_static/basic_iv_example_nb.png', + 'examples/py_double_ml_robust_iv': '_static/basic_iv_example_nb.png', 'examples/R_double_ml_basic_iv': '_static/basic_iv_example_nb.png', 'examples/py_double_ml_ssm': '_static/ssm_example_nb.svg', 'examples/py_double_ml_sensitivity_booking': '_static/dag_usecase_revised.png', From 7ca556386cbbee3a04f96af99b27f1d5dada9b1d Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Thu, 15 May 2025 15:07:11 +0000 Subject: [PATCH 123/140] title case --- doc/examples/py_double_ml_robust_iv.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/examples/py_double_ml_robust_iv.ipynb b/doc/examples/py_double_ml_robust_iv.ipynb index 81b4aa57..7cb7121f 100644 --- a/doc/examples/py_double_ml_robust_iv.ipynb +++ b/doc/examples/py_double_ml_robust_iv.ipynb @@ -2,9 +2,10 @@ "cells": [ { "cell_type": "markdown", + "id": "255d0213", "metadata": {}, "source": [ - "# Python: Confidence intervals for instrumental variables models that are robust to weak instruments" + "# Python: Confidence Intervals for Instrumental Variables Models That Are Robust to Weak Instruments" ] }, { From d176d2d2bcffb68348a809594864f001d9edbb15 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 19 May 2025 10:54:59 +0000 Subject: [PATCH 124/140] add example to gallery --- doc/examples/index.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/examples/index.rst b/doc/examples/index.rst index dcbef213..15890754 100644 --- a/doc/examples/index.rst +++ b/doc/examples/index.rst @@ -60,6 +60,7 @@ Difference-in-Differences did/py_panel_simple.ipynb did/py_panel.ipynb + did/py_panel_data_example.ipynb did/py_did.ipynb did/py_did_pretest.ipynb From 5b2502f536efc078cc628ca7399069bce7f3f2a1 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 19 May 2025 10:56:41 +0000 Subject: [PATCH 125/140] move old examples to sandbox --- doc/examples/did/py_did.ipynb | 19 ++++++++++++++++--- doc/examples/did/py_did_pretest.ipynb | 17 +++++++++++++++-- doc/examples/index.rst | 7 ++++--- 3 files changed, 35 insertions(+), 8 deletions(-) diff --git a/doc/examples/did/py_did.ipynb b/doc/examples/did/py_did.ipynb index dd6587e1..58b9e8ac 100644 --- a/doc/examples/did/py_did.ipynb +++ b/doc/examples/did/py_did.ipynb @@ -5,8 +5,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Python: Difference-in-Differences\n", - "\n", + "# Python: Difference-in-Differences" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Remark:**\n", + "*This notebook is based on the deprecated version of the DiD implementation of DoubleML. Please check out the [DiD Section](https://docs.doubleml.org/dev/guide/models.html#difference-in-differences-models-did) to find out about the current implementation.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "In this example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate the average treatment effect on the treated (ATT) under the conditional parallel trend assumption. The estimation is based on [Chang (2020)](https://doi.org/10.1093/ectj/utaa001), [Sant'Anna and Zhao (2020)](https://doi.org/10.1016/j.jeconom.2020.06.003) and [Zimmert et al. (2018)](https://arxiv.org/abs/1809.01643).\n", "\n", "In this example, we will adopt the notation of [Sant'Anna and Zhao (2020)](https://doi.org/10.1016/j.jeconom.2020.06.003).\n", @@ -24,7 +37,7 @@ "\n", "- **Overlap:** For some $\\epsilon > 0$, $P(D_i=1) > \\epsilon$ and $P(D_i=1|X_i) \\le 1-\\epsilon$ a.s.\n", "\n", - "For a detailed explanation of the assumptions see e.g. [Sant'Anna and Zhao (2020)](https://doi.org/10.1016/j.jeconom.2020.06.003) or [Zimmert et al. (2018)](https://arxiv.org/abs/1809.01643).\n" + "For a detailed explanation of the assumptions see e.g. [Sant'Anna and Zhao (2020)](https://doi.org/10.1016/j.jeconom.2020.06.003) or [Zimmert et al. (2018)](https://arxiv.org/abs/1809.01643)." ] }, { diff --git a/doc/examples/did/py_did_pretest.ipynb b/doc/examples/did/py_did_pretest.ipynb index 03bed5c6..daa17b78 100644 --- a/doc/examples/did/py_did_pretest.ipynb +++ b/doc/examples/did/py_did_pretest.ipynb @@ -5,8 +5,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Python: Difference-in-Differences Pre-Testing\n", - "\n", + "# Python: Difference-in-Differences Pre-Testing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Remark:**\n", + "*This notebook is based on the deprecated version of the DiD implementation of DoubleML. Please check out the [DiD Section](https://docs.doubleml.org/dev/guide/models.html#difference-in-differences-models-did) to find out about the current implementation.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "This example illustrates how to use the Difference-in-Differences implmentation `DoubleMLDID` of the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to pre-test the parallel trends assumptions.\n", "The example is based on the great implmentation of the [did-package](https://cran.r-project.org/web/packages/did/vignettes/did-basics.html) in `R`. \n", "You can find further references and a detailed guide on pre-testing with the `did`-package at the [did-package pre-testing documentation](https://cran.r-project.org/web/packages/did/vignettes/pre-testing.html)." diff --git a/doc/examples/index.rst b/doc/examples/index.rst index 15890754..122568ee 100644 --- a/doc/examples/index.rst +++ b/doc/examples/index.rst @@ -61,8 +61,7 @@ Difference-in-Differences did/py_panel_simple.ipynb did/py_panel.ipynb did/py_panel_data_example.ipynb - did/py_did.ipynb - did/py_did_pretest.ipynb + R: Case studies --------------- @@ -79,7 +78,7 @@ These are case studies with the R package :ref:`DoubleML `. R_double_ml_ssm.ipynb R_double_ml_basic_iv.ipynb -Sandbox +Sandbox/Archive ---------- These are examples which are work-in-progress and/or not yet fully documented. @@ -90,3 +89,5 @@ These are examples which are work-in-progress and/or not yet fully documented. R_double_ml_pipeline.ipynb double_ml_bonus_data.ipynb + did/py_did.ipynb + did/py_did_pretest.ipynb From 0bda21f3714f224c57000c254e824b224a8dd5f1 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 19 May 2025 11:13:29 +0000 Subject: [PATCH 126/140] more detail on comparison to callaway and sant'anna --- doc/guide/models/did/did_pa.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/doc/guide/models/did/did_pa.rst b/doc/guide/models/did/did_pa.rst index 2e024af5..3b12d6d9 100644 --- a/doc/guide/models/did/did_pa.rst +++ b/doc/guide/models/did/did_pa.rst @@ -25,6 +25,9 @@ of :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval}, \delta)` if :math:`t_\text{e .. note:: The choice :math:`t_\text{pre}= \min(\mathrm{g},t_\text{eval}) -\delta-1` corresponds to the definition of :math:`ATT_{dr}(\mathrm{g},t_\text{eval};\delta)` from `Callaway and Sant'Anna (2021) `_. + As an example, if the target parameter is the effect on the group receiving treatment in :math:`2006` but evaluated in :math:`2007` with an anticipation period of :math:`\delta=1`, then the pre-treatment period is :math:`2004`. + The parallel trend assumption is slightly stronger with anticipation as the trends have to parallel for a longer periods, i.e. :math:`ATT_{dr}(2006,2007;1)=ATT(2006,2004;2006)`. + In the following, we will omit the subscript :math:`\delta` in the notation of the nuisance functions and the control group (implicitly assuming :math:`\delta=0`). For a given tuple :math:`(\mathrm{g}, t_\text{pre}, t_\text{eval})` the target parameter :math:`ATT(\mathrm{g},t)` is estimated by solving the empirical version of the the following linear moment condition: From 432cf5c01d2b54cde98a8681f990531ed9a765fa Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Mon, 19 May 2025 15:28:36 +0200 Subject: [PATCH 127/140] add thumbnails --- doc/_static/basic_iv_example_nb.png | Bin 9065 -> 16685 bytes doc/_static/robust_iv_example_nb.png | Bin 0 -> 16966 bytes doc/conf.py | 1 + 3 files changed, 1 insertion(+) create mode 100644 doc/_static/robust_iv_example_nb.png diff --git a/doc/_static/basic_iv_example_nb.png b/doc/_static/basic_iv_example_nb.png index d0bbf23bd26f6e58c47424442071e69febf69455..17155b786e222f2617037205eab81119aed59476 100644 GIT binary patch literal 16685 zcmYMcWmH^Uuq_G%cY?bIcXxMp8h3Yh0zrbiyF=sd4#6Fo;O=h0;qje&&wYQodyKvJ zs9I}P&5~KYBa{`Tkl^v)!N9NIu8C>YY z_^E6B`+%hhfmEd?B}EnXK}*I;29ibo!~h3)TDNEnX$WIrO3YzmK3D#Z!NfYxXGg;Z 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insertions(+), 7 deletions(-) diff --git a/doc/guide/resampling.rst b/doc/guide/resampling.rst index 5aac4ffd..55c0d233 100644 --- a/doc/guide/resampling.rst +++ b/doc/guide/resampling.rst @@ -202,10 +202,7 @@ Standard errors are obtained as described in :ref:`se_confint`. The aggregation of the estimates of the causal parameter and its standard errors is done using the median .. math:: - \tilde{\theta}_{0} &= \text{Median}\big((\tilde{\theta}_{0,m})_{m \in [M]}\big), - - \hat{\sigma} &= \sqrt{\text{Median}\big((\hat{\sigma}_m^2 + (\tilde{\theta}_{0,m} - \tilde{\theta}_{0})^2)_{m \in [M]}\big)}. - + \tilde{\theta}_{0} = \text{Median}\big((\tilde{\theta}_{0,m})_{m \in [M]}\big). The estimate of the causal parameter :math:`\tilde{\theta}_{0}` is stored in the ``coef`` attribute and the asymptotic standard error :math:`\hat{\sigma}/\sqrt{N}` in ``se``. @@ -214,6 +211,18 @@ and the asymptotic standard error :math:`\hat{\sigma}/\sqrt{N}` in ``se``. .. tab-item:: Python :sync: py + In python, the confidence intervals and p-values are based on the :py:class:`doubleml.DoubleMLFramework` object. + This class provides methods such as ``confint``, ``bootstrap`` or ``p_adjust``. For different repetitions, + the computations are done separately and combined via the median (based on Chernozhukov et al., 2018). + + The estimate of the asymptotic standard error :math:`\hat{\sigma}/\sqrt{N}` is then based on the median aggregated confidence intervals with crictial value :math:`1.96`, i.e., + + .. math:: + + \hat{\sigma}/\sqrt{N} = (\text{Median}\big((\tilde{\theta}_{0,m} + 1.96\cdot \tilde{\sigma}_{m}/\sqrt{N})_{m \in [M]}\big) - \text{Median}\big((\tilde{\theta}_{0,m})_{m \in [M]}\big)) / 1.96. + + Remark that methods such as methods such as ``confint``, ``bootstrap`` or ``p_adjust`` do not use the the estimate of the standard error. + .. ipython:: python print(dml_plr_obj.coef) @@ -222,6 +231,12 @@ and the asymptotic standard error :math:`\hat{\sigma}/\sqrt{N}` in ``se``. .. tab-item:: R :sync: r + The aggregation of the standard errors is done using the median + + .. math:: + + \hat{\sigma} = \sqrt{\text{Median}\big((\hat{\sigma}_m^2 + (\tilde{\theta}_{0,m} - \tilde{\theta}_{0})^2)_{m \in [M]}\big)}. + .. jupyter-execute:: print(dml_plr_obj$coef) @@ -249,9 +264,7 @@ The parameter estimates :math:`(\tilde{\theta}_{0,m})_{m \in [M]}` and asymptoti print(dml_plr_obj$all_coef) print(dml_plr_obj$all_se) -In python, the confidence intervals and p-values are based on the :py:class:`doubleml.DoubleMLFramework` object. -This class provides methods such as ``confint``, ``bootstrap`` or ``p_adjust``. For different repetitions, -the computations are done seperately and combined via the median (as based on Chernozhukov et al., 2018). + Externally provide a sample splitting / partition +++++++++++++++++++++++++++++++++++++++++++++++++ From c36d7974dda73e35dd9d52341097781f166ff6a2 Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Thu, 22 May 2025 09:18:32 +0200 Subject: [PATCH 129/140] fix typo --- doc/guide/resampling.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/guide/resampling.rst b/doc/guide/resampling.rst index 55c0d233..6eee60ac 100644 --- a/doc/guide/resampling.rst +++ b/doc/guide/resampling.rst @@ -221,7 +221,7 @@ and the asymptotic standard error :math:`\hat{\sigma}/\sqrt{N}` in ``se``. \hat{\sigma}/\sqrt{N} = (\text{Median}\big((\tilde{\theta}_{0,m} + 1.96\cdot \tilde{\sigma}_{m}/\sqrt{N})_{m \in [M]}\big) - \text{Median}\big((\tilde{\theta}_{0,m})_{m \in [M]}\big)) / 1.96. - Remark that methods such as methods such as ``confint``, ``bootstrap`` or ``p_adjust`` do not use the the estimate of the standard error. + Remark that methods such as methods such as ``confint``, ``bootstrap`` or ``p_adjust`` do not use the estimate of the standard error. .. ipython:: python From 17156f60868b8e066ede3a413a007452e4ba95fd Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Thu, 22 May 2025 10:23:00 +0200 Subject: [PATCH 130/140] add bootstrap call for aggregated effects --- doc/examples/did/py_panel_data_example.ipynb | 230 +++++++++---------- 1 file changed, 104 insertions(+), 126 deletions(-) diff --git a/doc/examples/did/py_panel_data_example.ipynb b/doc/examples/did/py_panel_data_example.ipynb index 2bb15f06..c570987b 100644 --- a/doc/examples/did/py_panel_data_example.ipynb +++ b/doc/examples/did/py_panel_data_example.ipynb @@ -92,8 +92,7 @@ "type": "float" } ], - "conversionMethod": "pd.DataFrame", - "ref": "5e2a60ba-8445-46e1-b56a-ecf010c51d7d", + "ref": "cfd60d8a-39c4-4501-8de6-4be80e47c406", "rows": [ [ "0", @@ -339,18 +338,18 @@ "output_type": "stream", "text": [ " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2004.0,2003,2004) -0.0105 0.0232 -0.4526 0.6508 -0.0560 0.0350\n", - "ATT(2004.0,2003,2005) -0.0704 0.0310 -2.2700 0.0232 -0.1312 -0.0096\n", - "ATT(2004.0,2003,2006) -0.1372 0.0363 -3.7763 0.0002 -0.2085 -0.0660\n", - "ATT(2004.0,2003,2007) -0.1008 0.0343 -2.9381 0.0033 -0.1680 -0.0335\n", - "ATT(2006.0,2003,2004) 0.0065 0.0232 0.2811 0.7786 -0.0390 0.0520\n", - "ATT(2006.0,2004,2005) -0.0027 0.0195 -0.1400 0.8886 -0.0410 0.0355\n", - "ATT(2006.0,2005,2006) -0.0046 0.0179 -0.2585 0.7960 -0.0397 0.0304\n", - "ATT(2006.0,2005,2007) -0.0412 0.0202 -2.0404 0.0413 -0.0807 -0.0016\n", - "ATT(2007.0,2003,2004) 0.0304 0.0151 2.0197 0.0434 0.0009 0.0600\n", - "ATT(2007.0,2004,2005) -0.0027 0.0165 -0.1645 0.8693 -0.0350 0.0296\n", - "ATT(2007.0,2005,2006) -0.0310 0.0179 -1.7348 0.0828 -0.0660 0.0040\n", - "ATT(2007.0,2006,2007) -0.0260 0.0167 -1.5628 0.1181 -0.0587 0.0066\n" + "ATT(2004.0,2003,2004) -0.0105 0.0234 -0.4491 0.6533 -0.0564 0.0353\n", + "ATT(2004.0,2003,2005) -0.0704 0.0309 -2.2811 0.0225 -0.1309 -0.0099\n", + "ATT(2004.0,2003,2006) -0.1373 0.0366 -3.7561 0.0002 -0.2089 -0.0656\n", + "ATT(2004.0,2003,2007) -0.1008 0.0345 -2.9255 0.0034 -0.1683 -0.0333\n", + "ATT(2006.0,2003,2004) 0.0065 0.0234 0.2777 0.7813 -0.0393 0.0523\n", + "ATT(2006.0,2004,2005) -0.0027 0.0195 -0.1401 0.8886 -0.0409 0.0355\n", + "ATT(2006.0,2005,2006) -0.0046 0.0177 -0.2584 0.7961 -0.0393 0.0302\n", + "ATT(2006.0,2005,2007) -0.0412 0.0203 -2.0283 0.0425 -0.0810 -0.0014\n", + "ATT(2007.0,2003,2004) 0.0305 0.0151 2.0169 0.0437 0.0009 0.0601\n", + "ATT(2007.0,2004,2005) -0.0027 0.0164 -0.1626 0.8708 -0.0348 0.0295\n", + "ATT(2007.0,2005,2006) -0.0311 0.0179 -1.7371 0.0824 -0.0663 0.0040\n", + "ATT(2007.0,2006,2007) -0.0262 0.0167 -1.5696 0.1165 -0.0588 0.0065\n" ] } ], @@ -398,18 +397,18 @@ "output_type": "stream", "text": [ " 2.5 % 97.5 %\n", - "ATT(2004.0,2003,2004) -0.075498 0.054493\n", - "ATT(2004.0,2003,2005) -0.157304 0.016469\n", - "ATT(2004.0,2003,2006) -0.239029 -0.035446\n", - "ATT(2004.0,2003,2007) -0.196831 -0.004706\n", - "ATT(2006.0,2003,2004) -0.058475 0.071524\n", - "ATT(2006.0,2004,2005) -0.057417 0.051949\n", - "ATT(2006.0,2005,2006) -0.054715 0.045468\n", - "ATT(2006.0,2005,2007) -0.097724 0.015351\n", - "ATT(2007.0,2003,2004) -0.011777 0.072669\n", - "ATT(2007.0,2004,2005) -0.048822 0.043404\n", - "ATT(2007.0,2005,2006) -0.081068 0.019054\n", - "ATT(2007.0,2006,2007) -0.072675 0.020621\n" + "ATT(2004.0,2003,2004) -0.077246 0.056229\n", + "ATT(2004.0,2003,2005) -0.158449 0.017634\n", + "ATT(2004.0,2003,2006) -0.241543 -0.033030\n", + "ATT(2004.0,2003,2007) -0.199055 -0.002518\n", + "ATT(2006.0,2003,2004) -0.060206 0.073193\n", + "ATT(2006.0,2004,2005) -0.058341 0.052880\n", + "ATT(2006.0,2005,2006) -0.055168 0.046003\n", + "ATT(2006.0,2005,2007) -0.099079 0.016728\n", + "ATT(2007.0,2003,2004) -0.012621 0.073559\n", + "ATT(2007.0,2004,2005) -0.049479 0.044141\n", + "ATT(2007.0,2005,2006) -0.082276 0.019993\n", + "ATT(2007.0,2006,2007) -0.073682 0.021375\n" ] } ], @@ -450,7 +449,7 @@ }, { "data": { - "image/png": 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MGYqLi9N9992nxMREzZ07V1deeaVz8fUzXXbZZWrbtq0eeOAB5eTkOAtvjz76aImuEQAAeJFXP/sPAACU2MKFCw1JxX4tXLjQMAzDeO+994zOnTsbUVFRhtVqNWrXrm0MGzbMOHbsmMvx3njjDaN+/fqGn5+fIcnYuHGjYRiG0a5dO6Ndu3bOuI0bNxqSjPj4+CLz2bZtm0v7hAkTDEnGiRMnnG0rV640rrrqKiMwMNCoW7euMW3aNGPBggWGJOPAgQPOuISEBKN79+5GWFiYIcklj/T0dOOJJ54wLrvsMsNqtRpVqlQxWrdubbz44ouGzWZzxiUlJRl33323ER4eblSoUMG4++67jR07drh8j4oycuRIQ5Kxf//+YmMmTpxoSDJ++OEHZ1t8fLzL9684/4z7/fffjUGDBhkNGjQwAgMDjYiICKNDhw7GunXrznocw3D8jK688kpj+/btRkxMjBEYGGjUqVPHeOWVV4p9Trdu3QxJxubNm895/DNlZWUZM2fONGJiYozw8HDDYrEY0dHRxs0332wsXbrUyMvLc8YW11cKvPPOO8Y111xjBAQEGBEREcZdd91l/Pnnn4Xi3nrrLaN+/fqG1Wo1mjdvbqxZs8YYMGCAUadOHWfMgQMHDEnG//3f/xnTp083atWqZQQEBBg33nijy88HAAD4LpNhlHDeNQAAAMqs2267Tbt27dJvv/3m7VRKxcGDB1WvXj393//9n8aOHevtdAAAwAVgTSkAAIBy7tixY/rkk0909913ezsVAAAAJ9aUAgAAKKcOHDigr7/+WvPnz5e/v7+GDRvm7ZQAAACcmCkFAABQTn3++ee6++67deDAAS1evFjR0dHeTgkAAMCJNaUAAAAAAADgccyUAgAAAAAAgMdRlAIAAAAAAIDHUZQCAAAAAACAx1GUAgAAAAAAgMdRlAIAAAAAAIDHUZQCAAAAAACAx1GUAgAAAAAAgMdRlAIAAAAAAIDHUZQCAAAAAACAx1GUAgAAAAAAgMdRlAIAAAAAAIDHUZQCAAAAAACAx1GUAgAAAAAAgMdRlAIAAAAAAIDHUZQCAAAAAACAx1GUAgAAAAAAgMdRlAIAAAAAAIDHUZQCAAAAAACAx1GUAgAAAAAAgMdRlAIAAAAAAIDHUZQCAAAAAACAx1GUAgAAAAAAgMdRlAIAAAAAAIDHUZQCAAAAAACAx1GUAgAAAAAAgMdRlAIAAAAAAIDHUZQCAAAAAACAx1GUAgAAAAAAgMdRlAIAAAAAAIDHUZQCAAAAAACAx1GUAgAAAAAAgMdRlAIAAAAAAIDHUZQCAAAAAACAx1GUAgAAAAAAgMdRlAIAAAAAAIDHUZQCAAAAAACAx1GUAgAAAAAAgMdRlAIAAAAAAIDHUZQCAAAAAACAx1GUAgAAAAAAgMdRlAIAAAAAAIDHUZQCAAAAAACAx1GUAgAAAAAAgMdRlAIAAAAAAIDHUZQCAAAAAACAx1GUAgAAPuvgwYMymUxatGiRt1Mp1+rWrauBAwd6Ow0AAHCJoSgFAAC8ZtGiRTKZTEV+Pf74424555QpU7RixYpzxrVv377Y3M78mjhxolvy/KdPP/3UY+c6H7/++qseffRRNW/eXGFhYapWrZq6d++u7du3Fxl/5MgR9e3bVxUrVlR4eLj+/e9/6/fffy8y9s0331STJk0UGBioyy+/XLNnzz5nPp06dZLJZNKIESNKfA2bN29W27ZtFRwcrOjoaI0aNUoZGRklfj4AALgwFm8nAAAA8Mwzz6hevXoubU2bNlWdOnWUlZUlf3//UjvXlClT1Lt3b/Xo0eOscU8++aQGDx7sfLxt2za9/PLLGjdunJo0aeJsv+qqq0ott7P59NNP9eqrr/pcYWr+/Pl688031atXLw0fPlypqamaN2+ebrjhBq1evVqxsbHO2IyMDHXo0EGpqakaN26c/P399dJLL6ldu3bauXOnKleu7IydN2+e7r//fvXq1UtjxozRl19+qVGjRun06dN67LHHiszlgw8+0JYtW84r/507d6pjx45q0qSJZsyYoT///FMvvvii9u3bp1WrVl3YNwUAAJQIRSkAAOB1Xbt21XXXXVfkvsDAwHM+PzMzUyEhIaWaU6dOnQrl8fLLL6tTp05q3769R3PxZXfccYcmTpyo0NBQZ9ugQYPUpEkTTZw40aUoNWfOHO3bt0/ffvutrr/+ekmOn33Tpk01ffp0TZkyRZKUlZWlJ598Ut27d9d7770nSRoyZIjsdrueffZZDR06VJUqVXLJIzs7W4888ogee+wxjR8/vsT5jxs3TpUqVdKmTZsUHh4uyXE745AhQ/TZZ5+pc+fOF/aNAQAA58TtewAAwGcVtabUwIEDFRoaqv3796tbt24KCwvTXXfdJUnat2+fevXqpejoaAUGBqpmzZrq16+fUlNTJUkmk0mZmZlavHix8/a7i1lLaeLEiTKZTPrll1905513qlKlSmrbtq1z/1tvvaUWLVooKChIERER6tevn/744w+XY3z55Zfq06ePateurYCAANWqVUsPP/ywsrKyXK751VdfdV5DwVcBu92umTNn6sorr1RgYKCqVq2qYcOG6dSpUy7nMgxDkydPVs2aNRUcHKwOHTro559/LvLa9u/fr/3795/ze9CiRQuXgpQkVa5cWTfeeKN2797t0v7ee+/p+uuvdxakJKlx48bq2LGj3n33XWfbxo0blZSUpOHDh7s8/8EHH1RmZqY++eSTQnm88MILstvtGjt27DlzLpCWlqa1a9eqf//+zoKUJN1zzz0KDQ11yQkAAJQ+ZkoBAACvS01N1cmTJ13aqlSpUmx8Xl6e4uLi1LZtW7344osKDg6WzWZTXFyccnJyNHLkSEVHR+vIkSP6+OOPlZKSogoVKmjJkiUaPHiwWrZsqaFDh0qSGjRocNH59+nTR5dffrmmTJkiwzAkSc8995yefvpp9e3bV4MHD9aJEyc0e/Zs/etf/9KOHTtUsWJFSVJ8fLxOnz6tBx54QJUrV9a3336r2bNn688//1R8fLwkadiwYTp69KjWrl2rJUuWFDr/sGHDtGjRIt17770aNWqUDhw4oFdeeUU7duzQ119/7bz9cfz48Zo8ebK6deumbt266fvvv1fnzp1ls9kKHbNjx46SHIXBC5GQkODyM7Tb7frxxx81aNCgQrEtW7bUZ599pvT0dIWFhWnHjh2SVGj2XIsWLWQ2m7Vjxw7179/f2X748GE9//zzWrBggYKCgkqc465du5SXl1foPFarVc2bN3fmAQAA3IOiFAAA8Lozb/EqUFDcKUpOTo769OmjqVOnOtt27typAwcOKD4+Xr1793a2n3krV//+/XX//ferfv36LkWNi3X11Vdr2bJlzseHDh3ShAkTNHnyZI0bN87Z3rNnT11zzTWaM2eOs33atGkuhZShQ4fqsssu07hx43T48GHVrl1bMTExatiwoXNWz5m++uorzZ8/X0uXLtWdd97pbO/QoYO6dOmi+Ph43XnnnTpx4oReeOEFde/eXR999JFzptWTTz7pvG2utHz55ZfasmWLnnrqKWdbcnKycnJyVK1atULxBW1Hjx5Vo0aNdOzYMfn5+SkqKsolzmq1qnLlyjp69KhL+yOPPKJrrrlG/fr1O688jx075nL+f+b05ZdfntfxAADA+eH2PQAA4HWvvvqq1q5d6/J1Lg888IDL4woVKkiS1qxZo9OnT7slz+Lcf//9Lo8/+OAD2e129e3bVydPnnR+RUdH6/LLL9fGjRudsWcWpDIzM3Xy5Em1bt1ahmGUaKZOfHy8KlSooE6dOrmcq+C2uoJzrVu3TjabTSNHjnS59e+hhx4q8rgHDx68oFlSiYmJuvPOO1WvXj09+uijzvaC2xEDAgIKPadg3bCCmKysLFmt1iKPHxgY6HJr48aNG/X+++9r5syZ553ruXI68zwAAKD0MVMKAAB4XcuWLYtd6LwoFotFNWvWdGmrV6+exowZoxkzZmjp0qW68cYbdeutt6p///7OgpW7/POTA/ft2yfDMHT55ZcXGX/mpwkePnxY48eP18qVKwutAVWwFtbZ7Nu3T6mpqYVmFRVITEyU5Ji9JalQTpGRkYUWDb9QmZmZuvnmm5Wenq6vvvrKZa2pguJbTk5OoedlZ2e7xAQFBRV5S2FBbEFcXl6eRo0apbvvvttlnaqSOldO53MrIAAAOH8UpQAAQJkTEBAgs7nwhO/p06dr4MCB+t///qfPPvtMo0aN0tSpU/XNN98UKmKVpn8WL+x2u0wmk1atWiU/P79C8QXFmvz8fHXq1EnJycl67LHH1LhxY4WEhOjIkSMaOHCg7Hb7Oc9tt9sVFRWlpUuXFrk/MjLyAq7o/NlsNvXs2VM//vij1qxZo6ZNm7rsj4iIUEBAgPOWuTMVtFWvXl2S49a5/Px8JSYmuhTbbDabkpKSnHH//e9/tWfPHs2bN6/QrK709HQdPHhQUVFRCg4OLjLngtv2isup4DwAAMA9KEoBAIBypVmzZmrWrJmeeuopbd68WW3atNHcuXM1efJkSXK5dc1dGjRoIMMwVK9ePTVs2LDYuF27dmnv3r1avHix7rnnHmd7UbcvFpd3gwYNtG7dOrVp0+asM3vq1KkjyTGzqn79+s72EydOFJqhdb7sdrvuuecerV+/Xu+++67atWtXKMZsNqtZs2bavn17oX1bt25V/fr1FRYWJklq3ry5JGn79u3q1q2bM2779u2y2+3O/YcPH1Zubq7atGlT6Jj//e9/9d///lcffvihevToUWTeTZs2lcVi0fbt29W3b19nu81m086dO13aAABA6WNNKQAAUC6kpaUpLy/Ppa1Zs2Yym80ut2eFhIQoJSXFrbn07NlTfn5+mjRpUqEF2w3DUFJSkiQ5Z1GdGWMYhmbNmlXomCEhIZJUKPe+ffsqPz9fzz77bKHn5OXlOeNjY2Pl7++v2bNnu5yvuLWY9u/fr/3795/9Qv8ycuRIvfPOO5o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", "text/plain": [ "
" ] @@ -500,16 +499,16 @@ "\n", "------------------ Overall Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "-0.077214 0.019951 -3.870174 0.000109 -0.116317 -0.038111\n", + "-0.077247 0.020031 -3.856478 0.000115 -0.116507 -0.037988\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", - "-3.0 0.030446 0.015075 2.019662 0.043418 0.000900 0.059992\n", - "-2.0 -0.000549 0.013317 -0.041223 0.967118 -0.026650 0.025552\n", - "-1.0 -0.024393 0.014200 -1.717808 0.085832 -0.052226 0.003439\n", - "0.0 -0.019919 0.011816 -1.685694 0.091855 -0.043079 0.003241\n", - "1.0 -0.050930 0.016783 -3.034679 0.002408 -0.083824 -0.018037\n", - "2.0 -0.137238 0.036342 -3.776254 0.000159 -0.208467 -0.066008\n", - "3.0 -0.100768 0.034297 -2.938126 0.003302 -0.167989 -0.033548\n", + "-3.0 0.030469 0.015107 2.016929 0.043703 0.000861 0.060078\n", + "-2.0 -0.000526 0.013300 -0.039529 0.968469 -0.026594 0.025542\n", + "-1.0 -0.024496 0.014236 -1.720682 0.085309 -0.052398 0.003406\n", + "0.0 -0.019998 0.011810 -1.693240 0.090410 -0.043145 0.003150\n", + "1.0 -0.050919 0.016811 -3.028909 0.002454 -0.083869 -0.017970\n", + "2.0 -0.137287 0.036551 -3.756054 0.000173 -0.208925 -0.065649\n", + "3.0 -0.100786 0.034451 -2.925461 0.003439 -0.168310 -0.033263\n", "------------------ Additional Information ------------------\n", "Score function: observational\n", "Control group: never_treated\n", @@ -517,17 +516,9 @@ "\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\bachp\\Documents\\Promotion\\DissundPapers\\Software\\DoubleML\\doubleml-for-py\\doubleml\\did\\did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", - " warnings.warn(\n" - ] - }, { "data": { - "image/png": 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vn9Uuu+wSSZLETjvtFJ/73OdqrHvrrbfiP//5T9x7771x1llnZcer+9S2ulxGtqW9RkR069Ztq81Sqq9eN1ddj5NddtklMplM/Otf/6p0+emn1baNrl27xtlnnx1nn312rFy5Mg499NC49tprhVIANHnuKQUAzdgnn3wSDz/8cBxzzDFx8sknV/m6+OKLY8WKFfHYY49FRMTQoUOjoqIi/ud//ie7jUwmUyXQ6tGjR+yxxx7x61//OlauXJkdf+655+Ktt96qU2/dunWLww47LH7+85/H/PnzqyxftGhR9vHQoUNj+vTp8cYbb2THli5dGpMmTaq0zoaZNhtm6kRErFmzJn72s59V2X5BQUG1l7CdcsopMXfu3Erfgw0++eSTKCsri4iIo446KiIibrvttko1P/nJT6qstzWdeOKJ0apVq7juuusq7WfE+v1esmRJRFT/vUiSJH76059W2WZBQUFErJ9ptjUNHTo0CgsL46abboqKiooqyzf+N66rDWHp1u51c9X1OBkxYkTk5ubG9ddfX2V22sb/NgUFBdXu04Z/zw06dOgQffr0ifLy8q2wFwDQsMyUAoBm7LHHHosVK1bEcccdV+3yAw88MIqKimLSpElx6qmnxogRI+KAAw6Ib3zjG/Huu+9G375947HHHoulS5dGROXZHDfddFMcf/zxcfDBB8fZZ58dH3/8cdxxxx2xxx57VAqqajNhwoQ45JBDYs8994zRo0fHzjvvHAsWLIjp06fHRx99FG+++WZERHzrW9+K3/zmN/GlL30pLrnkkigoKIhf/OIX0atXr1i6dGm2r4MOOii6dOkSI0eOjEsvvTRycnLivvvuqxLeRET0798/HnjggRgzZkx84QtfiA4dOsSxxx4bZ555Zvzud7+LCy64IJ555pk4+OCDY926dfHvf/87fve738Wf//zn2H///WOfffaJ008/PX72s5/F8uXL46CDDoqpU6fWeabYltpll13ie9/7XowdOzbef//9GDFiRHTs2DFmz54djzzySJx33nlxxRVXRN++fWOXXXaJK664IubOnRuFhYXx+9//vtr7M/Xv3z8i1t+0fejQodGqVas47bTTPnOvhYWFceedd8aZZ54Z++23X5x22mlRVFQUH3zwQTzxxBNx8MEHxx133LFZ22zXrl3069cvHnjggfjc5z4XXbt2jT322CP22GOPWtf761//GqtXr64yvtdee23R/Znqepz06dMnvv3tb8cNN9wQX/ziF+PEE0+MvLy8eOWVV6JHjx4xfvz4iFj/b3DnnXfG9773vejTp09069YtjjjiiOjXr18cdthh0b9//+jatWu8+uqr8dBDD8XFF1+82T0DQKPTMB/6BwCk4dhjj03y8/OTsrKyGmtGjRqVtGnTJlm8eHGSJEmyaNGi5Ctf+UrSsWPHpFOnTsmoUaOSF154IYmIZPLkyZXWnTx5ctK3b98kLy8v2WOPPZLHHnssOemkk5K+fftma2bPnp1ERPLDH/6w2td/7733krPOOispLi5O2rRpk2y//fbJMccckzz00EOV6mbMmJF88YtfTPLy8pIddtghGT9+fHLbbbclEZGUlJRk61544YXkwAMPTNq1a5f06NEj+da3vpX8+c9/TiIieeaZZ7J1K1euTL7yla8knTt3TiIi6d27d3bZmjVrkptvvjnZfffdk7y8vKRLly5J//79k+uuuy5Zvnx5tu6TTz5JLr300mSbbbZJCgoKkmOPPTb58MMPk4hIxo0bV+P3/NMefPDBKv2NGzcuiYhk0aJF1a7z+9//PjnkkEOSgoKCpKCgIOnbt29y0UUXJe+880625l//+lcyZMiQpEOHDsm2226bjB49OnnzzTeTiEjuueeebN3atWuTSy65JCkqKkpycnKSDb8i1vRv98wzzyQRkTz44IOVxu+5554kIpJXXnmlSv3QoUOTTp06Jfn5+ckuu+ySjBo1Knn11VezNSNHjkwKCgqq7OeG78PGXnzxxaR///5J27ZtN/m93tBrTV8brzto0KBk9913r7KN3r17J8OHD68yXtfjJEmS5Je//GWy7777ZusGDRqUTJkyJbu8pKQkGT58eNKxY8ckIpJBgwYlSZIk3/ve95IDDjgg6dy5c9KuXbukb9++yY033pisWbOmxn0GgKYiJ0mq+dMhAMBGHn300TjhhBPib3/7Wxx88MG11u6zzz5RVFRU7b2LtrbLLrssfv7zn8fKlSub1E2yAQBwTykA4FM++eSTSs/XrVsXt99+exQWFsZ+++2XHa+oqIi1a9dWqn322WfjzTffjMMOO6ze+1qyZEncd999ccghhwikAACaIPeUAgAqueSSS+KTTz6JgQMHRnl5eTz88MPx4osvxk033RTt2rXL1s2dOzeGDBkSX/3qV6NHjx7x73//OyZOnBjFxcVxwQUXbPW+Bg4cGIcddljstttusWDBgrj77rujtLQ0rrnmmq3+WgAA1D+hFABQyRFHHBE/+tGP4vHHH4/Vq1dHnz594vbbb69yY+UuXbpE//794xe/+EUsWrQoCgoKYvjw4fH9738/ttlmm63e19FHHx0PPfRQ3HXXXZGTkxP77bdf3H333XHooYdu9dcCAKD+uacUAAAAAKlzTykAAAAAUieUAgAAACB17im1FWQymZg3b1507NgxcnJyGrodAAAAgAaTJEmsWLEievToEbm5Nc+HEkptBfPmzYuePXs2dBsAAAAAjcaHH34YO+ywQ43LhVJbQceOHSNi/Te7sLCwgbsBAAAAaDilpaXRs2fPbF5SE6HUVrDhkr3CwkKhFAAAAEDEJm9x5EbnAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKSudUM3QMMoK1+7WfUFeQ4VAAAAYOuRNLRQHb/95GbVZ245tp46AQAAAFoil+8BAAAAkDozpVqoFTceVel52Zq1UXzdlIiIKBn3pSho69AAAAAA6o/koYWq7R5RBW1bu4cUAAAAUK9cvgcAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKSuyYVSEyZMiB133DHy8/NjwIAB8fLLL9da/+CDD0bfvn0jPz8/9txzz/jTn/5UafmoUaMiJyen0tewYcPqcxcAAAAAWrwmFUo98MADMWbMmBg3bly8/vrrsffee8fQoUNj4cKF1da/+OKLcfrpp8c555wTM2bMiBEjRsSIESPin//8Z6W6YcOGxfz587Nfv/3tb9PYHQAAAIAWq0mFUj/+8Y9j9OjRcfbZZ0e/fv1i4sSJ0b59+/jlL39Zbf1Pf/rTGDZsWHzzm9+M3XbbLW644YbYb7/94o477qhUl5eXF8XFxdmvLl26pLE7AAAAAC1Wkwml1qxZE6+99loMGTIkO5abmxtDhgyJ6dOnV7vO9OnTK9VHRAwdOrRK/bPPPhvdunWLz3/+83HhhRfGkiVLtv4OAAAAAJDVuqEbqKvFixfHunXronv37pXGu3fvHv/+97+rXaekpKTa+pKSkuzzYcOGxYknnhg77bRTvPfee3H11VfHUUcdFdOnT49WrVpVu93y8vIoLy/PPi8tLY2IiEwmE5lMZov2r6Flkkylx011PwAAAICGVddMocmEUvXltNNOyz7ec889Y6+99opddtklnn322Rg8eHC164wfPz6uu+66KuOLFi2K1atX11uv9WnVmnXZx4sWLoqyttUHcgAAAAC1WbFiRZ3qmkwote2220arVq1iwYIFlcYXLFgQxcXF1a5TXFy8WfURETvvvHNsu+228e6779YYSo0dOzbGjBmTfV5aWho9e/aMoqKiKCwsrOsuNSpla9ZmHxd1K4qCtk3m0AAAAAAakfz8/DrVNZnkoW3bttG/f/+YOnVqjBgxIiLWTwebOnVqXHzxxdWuM3DgwJg6dWpcdtll2bEpU6bEwIEDa3ydjz76KJYsWRLbbbddjTV5eXmRl5dXZTw3Nzdyc5vMbboqyc3JrfS4qe4HAAAA0LDqmik0qeRhzJgx8T//8z9x7733xsyZM+PCCy+MsrKyOPvssyMi4qyzzoqxY8dm67/+9a/HU089FT/60Y/i3//+d1x77bXx6quvZkOslStXxje/+c34+9//Hu+//35MnTo1jj/++OjTp08MHTq0QfYRAAAAoCVoMjOlIiJOPfXUWLRoUXz3u9+NkpKS2GeffeKpp57K3sz8gw8+qJTGHXTQQXH//ffHd77znbj66qtj1113jUcffTT22GOPiIho1apV/OMf/4h77703li1bFj169IgjjzwybrjhhmpnQgEAAACwdeQkSZI0dBNNXWlpaXTq1CmWL1/edO8pVb42On77yYiIWHHjUVGQ16TySgAAAKCRqGtO0qQu3wMAAACgeRBKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqWvd0A0AbFBWvnaz6gvynMIAAACaKu/ogEaj47ef3Kz6zC3H1lMnAAAA1DeX7wEAAACQOjOlgEZjxY1HVXpetmZtFF83JSIiSsZ9KQraOmUBAAA0F97hUcXqinXu1UODqO24K2jb2nEJAADQjLh8r4Vbl0ni9/+YF8fc/XJ2rOjav8SQidPj9/+YF+sySQN2BwAAADRXph20YKWrK+Lke1+Np2ctrrJs2ruLY9q7i2PIrtvGQyP3j8L8Ng3QIQAAANBcmSnVQq3LJDUGUht7etbiOPneV82YAgAAALYqoVQL9eg/528ykNrg6VmL4w9vl9RzRwAAAEBLIpRqoe58cc5m1r9fP40AAAAALZJQqgVaXbEupr1bt1lSG0ydtThWV6yrp44AAACAlkYo1QKtKF+b6noAAAAAnyaUaoE65m3Zhy5u6XoAAAAAnyaUaoHy27SKI/psu1nrDN5128hv06qeOgIAAABaGqFUC3XhQb03s37H+mkEAAAAaJGEUi3UiD22iyG71m221JBdt43jdy+u544AAACAlkQo1UK1ys2Jh0buv8lgasiu28ZDI/ePVrk5KXUGAAAAtATuXN2CFea3iSdHHxh/eLsk7vjb7Hj2vSXZZYN33TYuPGjHOH73YoEUAAAAsNUJpVq4Vrk5ceKe28XQzxVFx28/GRERi649MrbpkNfAnQEAAADNmcv3qMKn7AEAAAD1TSgFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkrnVDNwAAjVlZ+drNqi/I818rAADUhd+cAaAWHb/95GbVZ245tp46AQCA5sXlewAAAACkzkwpAKjFihuPqvS8bM3aKL5uSkRElIz7UhS09V8pDSNTUbZZ9bltCuqpEwCALeM3aQCoRW33iCpo29o9pGgwcyZ02az6nS5bU0+dAABsGZfvAQAAAJA6f94FAGiCel/0caXnmYqy+PCuHSIioud5H7lcDwBo9IRSAABNUG2hU26bAqEUANDouXwPAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKaBJWV6xr6BYAAADYippcKDVhwoTYcccdIz8/PwYMGBAvv/xyrfUPPvhg9O3bN/Lz82PPPfeMP/3pT5WWJ0kS3/3ud2O77baLdu3axZAhQ2LWrFn1uQvAJqzLJPH7f8yLY+7+78930bV/iSETp8fv/zEv1mWSBuwOAACAraFJhVIPPPBAjBkzJsaNGxevv/567L333jF06NBYuHBhtfUvvvhinH766XHOOefEjBkzYsSIETFixIj45z//ma35wQ9+ELfddltMnDgxXnrppSgoKIihQ4fG6tWr09otYCOlqyviqP/5e3z516/Fc/+7pNKyae8uji//+rU46n/+HqWrKxqoQwAAALaGJhVK/fjHP47Ro0fH2WefHf369YuJEydG+/bt45e//GW19T/96U9j2LBh8c1vfjN22223uOGGG2K//faLO+64IyLWz5L6yU9+Et/5znfi+OOPj7322it+/etfx7x58+LRRx9Ncc+AiPUzpE6+99V4etbiWuuenrU4Tr73VTOmAAAAmrDWDd1AXa1ZsyZee+21GDt2bHYsNzc3hgwZEtOnT692nenTp8eYMWMqjQ0dOjQbOM2ePTtKSkpiyJAh2eWdOnWKAQMGxPTp0+O0007b7B7XrFlTZTw3Nzdat25dqa4mOTk50aZNmy2qraioiCSp/k36pmrXVKz977K1FRF5rWus/bS2bdtuUe3atWsjk8lsldo2bdpETk5OvdauW7cu1q2r+b5Gm1PbunXryM3NbTS1mUwm1q5dW2Ntq1atolWrVvVe+9AbH20ykNrg6VmL4/dvfBQj9uhe63aTJImKippnVW3881lftRG1/yw3hXPEltZGNL9zxMbnyyT+uy/OEfV/jqhr7aZ+PpvrOSJTUXXcOaJutX6P+Gy1jeHn3jnC7xGfrnWOcI5wjtj82q15jqht3Y01mVBq8eLFsW7duujevfIb0O7du8e///3vatcpKSmptr6kpCS7fMNYTTXVKS8vj/Ly8uzz0tLSiIi45ZZbIi8vr0p9nz594owzzsg+/8EPflDjAda7d+8YNWpU9vmtt94aq1atqrZ2u+22i/POOy/7/I477ohly5ZVW1tUVBRf+9rXss9//vOfx6JFi7LP1yS5EbFfRERMnPjz+Nbll2aX3X333TF//vxqt9u+ffv45je/mX1+3333xZw5c6qtbdOmTVx99dXZ57/97W/j3XffrbY2ImLcuHHZxw899FDMnDmzxtqxY8dm/2N57LHH4s0336yx9oorroiCgoKIiHjyySfj1VdfrbH261//enTu3DkiIqZMmVJjABoRceGFF0a3bt0iIuK5556L5557rsbac889N7bffvuIWH+Z6dNPP11j7ciRI2PHHXeMiIhXXnklnnzyyRprTz/99Pjc5z4XERFvvvlm/OEPf6ix9uSTT47dd989IiLefvvteOihh2qsPf7442OfffaJiIj//Oc/8dvf/rbG2qOOOioOOOCAiIh4//334957762xdsiQIXHwwQdHRMTcuXNj7OS/RkRhjfWfNnby8/F2u/9UGR84cGAceeSRERGxbNmy+OlPf1rjNvbff/8YPnx4RESUlZXFLbfcUmPt3nvvHSNGjIiI9SfZ8ePH11i72267xSmnnJJ9fuONN9ZY2xTOERvr3LlzfP3rX88+b2nniI3Plx9/vCzad9smIpwj0jhH/OIXv6ixdtCgQXHYYYdFRMTChQvjzjvvrLG2uZ4jWsea+GqX9Y8zmUxEJuMc8X/8HrFjRDhHtPRzRITfIzbmHLGec8R6zhHrbc1zxMaZSW2aTCjVmIwfPz6uu+66KuNlZWXVpq+lpaWV7nu1cuXKGlPaFStWVKn95JNP6lS7YsWKKCsrq7Y2Pz+/1tqKja7kXLlyZZ23m8lk6lzbunXrSrWlpaU11kbEZtdu+I9i+fLltdYuWrQou7wutRsS3mXLltVau3jxf2f41KV2Q6r88ccf11q7ZMmSaN++fZ1rN3zfli5dWmvt0qVLt6h2yZIltdZ+/PHHW1Q7d8GieD9T90AqImJ2pjCWl62K1lH5L2bLli3LbndT/8bLly/P1q5atarOtWvWrKm19tM/95tT2xjPERtr1apViz5HbHy+XLx4cbSJ9X8ldI6o33PE4sWLa63d+Od+c2qb0zmidU5FxP+FUosWLYqc1mXOEZtR6/cI54jqapvTOSLC7xEbc45YzzliPeeI9bbmOaKuoVROUtv8x0ZkzZo10b59+3jooYey6WHE+mR32bJl1aa0vXr1ijFjxsRll12WHRs3blw8+uij8eabb8b//u//xi677BIzZszIJrMR61PSffbZp8bEs7qZUj179owFCxZEYWHVN9VNYUpt2Zp1UXT9tIiIWDzuiOhc0K5O240wpXZLak2prVq7oPST2P5702qsrcmcsYOiqKBtpTFTaquvNe1+65wjNj5fLrvhyOiQt/574Rxh2n1DnyMyFWWx4O7tIiKi54VLIrdNgXNEHWv9HvHZahvDz71zhN8jPl3rHOEc4Ryx+bVb8xxRWloa3bt3j+XLl1ebk2zQZGZKtW3bNvr37x9Tp07NhlKZTCamTp0aF198cbXrDBw4MKZOnVoplJoyZUoMHDgwIiJ22mmnKC4ujqlTp2ZDqdLS0njppZfiwgsvrLGXvLy8ai/Ty8/Pj/z8/E3uS11qtqS2up7qWrsu578/oHlt87InkM+63dps/B9BU6jNzc2t9EPX3Go3PlE1RG2Xgrof6xsrKiyI/Dataq3Z8J9GXdRXbX393Kd1jmiI2sbwc19d7cbny1a5rbLny8bys9xczxGbUxvROH7u0z5HZFr9981Bbm5u5ObmOkc0otrG8rPsHLFeY/i593tE/dY2hp9754j/1jb0z71zRP3Xfvrns9ndUyoiYsyYMTFy5MjYf//944ADDoif/OQnUVZWFmeffXZERJx11lmx/fbbZ6+9/PrXvx6DBg2KH/3oRzF8+PCYPHlyvPrqq3HXXXdFxPpk77LLLovvfe97seuuu8ZOO+0U11xzTfTo0aPSbCyg/uW3aRVH9Nk2pr1btxudR0QM3nXbTQZSAAAANE5NKpQ69dRTY9GiRfHd7343SkpKYp999omnnnoqe6PyDz74oNIMn4MOOijuv//++M53vhNXX3117LrrrvHoo4/GHnvska351re+FWVlZXHeeefFsmXL4pBDDomnnnpqsxJBYOu48KDemxVKXXjQjvXXDAAAAPWqydxTqjErLS2NTp06bfJaycasrHxtdPz2+k9aWHHjUVGQ16TySpqJdZkkjvqfv8fTszYdTA3Zddt4cvSB0So3J4XO4L+cL2msMhVlMWfC+jud977o48htU9DAHQEALVVdc5LcGpcApKxVbk48NHL/GLLrtrXWDdl123ho5P4CKQBoZDIVZZv1BUDL5s+7QKNSmN8mnhx9YPzh7ZK442+z49n3lmSXDd5127jwoB3j+N2LBVIA0AhtmK1XVztdVrcb4QLQPAmlgEanVW5OnLjndjH0c0XZy6QWXXtkbNOh7p+4AgAAQOMmlAKaBJ+yBwCNX++LPq70PFNRFh/etUNERPQ87yP3OgPYyOZextwcz6FCKQAAYKuo7Q1TbpuCZvmGCmBLueTZjc4BAAAAaABmSgEAAACkzCXPQikAAACA1Lnk2eV7AAAAADQAM6VaqLLytZWfr1lb7eMNCvIcKgAAAMDWI2looTp++8kalxVfN6XKWOaWY+uzHQAAAKCFcfkeAAAAAKkzU6qFWnHjUQ3dAgAAANCCCaVaKPeIAgAAABqSy/cAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAJqZZO3qhm4BAGCThFIAAE1YklkXZbMejgWPHp8d++Dn28X83w+NslkPR5JZ14DdAQDUrHVDNwAAwJbJlJfGgidOjdUfTK2ybPWHz8TqD5+J/F6Do/vwByI3r7ABOgQAqJmZUgAATVCSWVdjILWx1R9MjQVPnGrGFADQ6AilAACaoFXv/WGTgdQGqz+YGqv+97F67ggAYPMIpQAAmqDSf/x88+rf3Lx6AID6JpQCAGhiMmtXx+oPn9msdVZ/OC0yPpUPAGhEhFIAsIVWV7hHDw0jWbMi1fUAAOqDUAoA6mBdJonf/2NeHHP3y9mxomv/EkMmTo/f/2NerMskDdgdLU1O246prgcAUB9aN3QDANDYla6uiJPvfTWenrW4yrJp7y6Oae8ujiG7bhsPjdw/CvPbNECHtDS5rfMjv+fhm3UJX37PIyK3dX49dgUAsHnMlAKAWqzLJDUGUht7etbiOPneV82YIjWFe52/efV7b149AEB9E0oBQC0e/ef8TQZSGzw9a3H84e2Seu4I1mu/y/GR32twnWrzew2O9jsfV88dAQBsHqEUANTizhfnbGb9+/XTCHxKTm6r6D78gU0GU/m9Bkf34Q9ETm6rlDoDAKgboRQA1GB1xbqY9m7dZkltMHXWYp/KR2py8wqjeMTj0e2YByJ/h0GVluX3PCK6HfNAFI94PHLzChuoQwCAmrnROQDUYEX52i1eL7+NWSmkIye3VRT0OSHa9T4y5kzoEhERvc6fH63abdPAnQEA1M5MKQCoQce8LfvbzZauB1tLjk/ZAwCaAKEUANQgv02rOKLPtpu1zuBdtzVLCgAA6kAoBQC1uPCg3ptZv2P9NAIAAM2MUAoAajFij+1iyK51my01ZNdt4/jdi+u5IwAAaB6EUgBQi1a5OfHQyP03GUwN2XXbeGjk/tEqNyelzgAAoGlzJ1YA2ITC/Dbx5OgD4w9vl8Qdf5sdz763JLts8K7bxoUH7RjH714skAIAgM0glAKAOmiVmxMn7rldDP1cUXT89pMREbHo2iNjmw55DdwZAAA0TS7fA4At5FP2AABgywmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1LVu6AYANigrX1v5+Zq11T7eoCDPKQwAAKCp8o4OaDQ6fvvJGpcVXzelyljmlmPrsx0AAJqJTEXZZtXntimop06AjQmlAAAAaNbmTOiyWfU7XbamnjoBNiaUAhqNFTce1dAtAAAAkBKhFNBouEcUAAD1ofdFH1d6nqkoiw/v2iEiInqe95HL9aCBeAcIAABAs1Zb6JTbpkAoBQ0kt6EbAAAAAKDlEUoBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAD1Llm7uqFbAKCRaTKh1NKlS+OMM86IwsLC6Ny5c5xzzjmxcuXKWtdZvXp1XHTRRbHNNttEhw4d4qSTTooFCxZUqsnJyanyNXny5PrcFQAAaNaSzLoom/VwLHj0+OzYBz/fLub/fmiUzXo4ksy6BuwOgMaiyYRSZ5xxRrz99tsxZcqUePzxx+P555+P8847r9Z1Lr/88vjjH/8YDz74YDz33HMxb968OPHEE6vU3XPPPTF//vzs14gRI+ppLwAAoHnLlJdGyaPHxMInTovVc5+vtGz1h8/EwidOi5JHj4lMeWkDdQhAY9G6oRuoi5kzZ8ZTTz0Vr7zySuy///4REXH77bfH0UcfHbfcckv06NGjyjrLly+Pu+++O+6///444ogjImJ9+LTbbrvF3//+9zjwwAOztZ07d47i4uJ0dgYAAJqpJLMuFjxxaqz+YGqtdas/mBoLnjg1ikc8Hjm5rVLqDoDGpkmEUtOnT4/OnTtnA6mIiCFDhkRubm689NJLccIJJ1RZ57XXXouKiooYMmRIdqxv377Rq1evmD59eqVQ6qKLLopzzz03dt5557jgggvi7LPPjpycnBr7KS8vj/Ly8uzz0tL1f+XJZDKRyWQ+074C0Lhlkkylx877NBYbH4uZTCbCsUkDKHv30U0GUhus/mBqlL37h2jfZ0T9NgXVcM6kMWpOx2Vdf0duEqFUSUlJdOvWrdJY69ato2vXrlFSUlLjOm3bto3OnTtXGu/evXulda6//vo44ogjon379vGXv/wlvva1r8XKlSvj0ksvrbGf8ePHx3XXXVdlfNGiRbF6tRs4AjRnq9b89z4oixYuirK2/sJP45CsXZV9vGjRoshpXdaA3dBSlb96+2bVL3nt9lhZeFA9dQM1c86kMWpOx+WKFSvqVNegodRVV10VN998c601M2fOrNcerrnmmuzjfffdN8rKyuKHP/xhraHU2LFjY8yYMdnnpaWl0bNnzygqKorCwsJ67ReAhlW2Zm32cVG3oiho2yT+vkMLkKkoiw//73FRUVHktilo0H5oeZK1q+ODhS9u1jqZBS9EUdfCyGmdX09dQfWcM2mMmtNxmZ9ft/N6g/4m/Y1vfCNGjRpVa83OO+8cxcXFsXDhwkrja9eujaVLl9Z4L6ji4uJYs2ZNLFu2rNJsqQULFtR6/6gBAwbEDTfcEOXl5ZGXl1dtTV5eXrXLcnNzIze3ydw7HoAtkJuTW+mx8z4NJVPxqb+ervuk8uNPHZtN+RdbmoZ1a7fwL/pryyK3bfut2wxsykbnSO/jaDSa0XFZ194bNJQqKiqKoqKiTdYNHDgwli1bFq+99lr0798/IiKmTZsWmUwmBgwYUO06/fv3jzZt2sTUqVPjpJNOioiId955Jz744IMYOHBgja/1xhtvRJcuXWoMpAAAGoM5E7rUuOzDu3aoMrbTZWvqsx2InLYdU10PgKavSVxzsNtuu8WwYcNi9OjRMXHixKioqIiLL744TjvttOwn782dOzcGDx4cv/71r+OAAw6ITp06xTnnnBNjxoyJrl27RmFhYVxyySUxcODA7E3O//jHP8aCBQviwAMPjPz8/JgyZUrcdNNNccUVVzTk7gIAQJOT2zo/8nseHqs/fKbO6+T3PCJyXboH0GI1iVAqImLSpElx8cUXx+DBgyM3NzdOOumkuO2227LLKyoq4p133olVq/57Y7Bbb701W1teXh5Dhw6Nn/3sZ9nlbdq0iQkTJsTll18eSZJEnz594sc//nGMHj061X0DANhcvS/6uKFbgCoK9zp/s0Kpwr3Pr8duAGjscpIkSRq6iaautLQ0OnXqFMuXL3ejc4Bmrqx8bXT89pMREbHixqOiIK/J/H0HoN4lmXVR8ugxsfqDqZusze81OIpHPB45uT7FlPRlKsqyl0H3vuhj992jUWhOx2Vdc5Kme9csAACgUcnJbRXdhz8Q+b0G11qX32twdB/+gEAKoIUTSgEAAFtNbl5hFI94PLod80Dk7zCo0rL8nkdEt2MeiOIRj0dunisMAFo61xwAAABbVU5uqyjoc0K0631k9lKUXufPj1bttmngzgBoTMyUAgAA6l2OT9kD4FOEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOpaN3QDANCYlZWvrfx8zdpqH29QkOe/VgAAqAu/OQNALTp++8kalxVfN6XKWOaWY+uzHQAAaDZcvgcAAABA6syUAoBarLjxqIZuAQAAmiWhFADUwj2iAACgfrh8DwAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUbVEo1apVq1i4cGGV8SVLlkSrVq0+c1MAAAAANG9bFEolSVLteHl5ebRt2/YzNQQAAABA89d6c4pvu+22iIjIycmJX/ziF9GhQ4fssnXr1sXzzz8fffv23bodAgAAANDsbFYodeutt0bE+plSEydOrHSpXtu2bWPHHXeMiRMnbt0OAQAAAGh2NiuUmj17dkREHH744fHwww9Hly5d6qUpAAAAAJq3zQqlNnjmmWe2dh8AAAAAtCBbdKPzk046KW6++eYq4z/4wQ/iy1/+8mduCgAAAIDmbYtCqeeffz6OPvroKuNHHXVUPP/885+5KQAAAACaty0KpVauXBlt27atMt6mTZsoLS39zE0BAAAA0LxtUSi15557xgMPPFBlfPLkydGvX7/P3BQAAAAAzdsW3ej8mmuuiRNPPDHee++9OOKIIyIiYurUqfHb3/42Hnzwwa3aIAAAAADNzxaFUscee2w8+uijcdNNN8VDDz0U7dq1i7322iuefvrpGDRo0NbuEQAAAIBmZotCqYiI4cOHx/Dhw7dmLwAAAAC0EFt0T6mIiGXLlsUvfvGLuPrqq2Pp0qUREfH666/H3Llzt1pzAAAAADRPWzRT6h//+EcMGTIkOnXqFO+//36ce+650bVr13j44Yfjgw8+iF//+tdbu08AAAAAmpEtmik1ZsyYGDVqVMyaNSvy8/Oz40cffXQ8//zzW605AAAAAJqnLQqlXnnllTj//POrjG+//fZRUlLymZsCAAAAoHnbolAqLy8vSktLq4z/5z//iaKios/cFAAAAADN2xaFUscdd1xcf/31UVFREREROTk58cEHH8SVV14ZJ5100lZtEAAAAIDmZ4tCqR/96EexcuXK6NatW3zyyScxaNCg6NOnT3Ts2DFuvPHGrd0jAAAAAM3MFn36XqdOnWLKlCnxwgsvxJtvvhkrV66M/fbbL4YMGbK1+wMAAACgGapzKNW1a9f4z3/+E9tuu238v//3/+KnP/1pHHzwwXHwwQfXZ38AAAAANEN1vnxvzZo12Zub33vvvbF69ep6awoAAACA5q3OM6UGDhwYI0aMiP79+0eSJHHppZdGu3btqq395S9/udUaBAAAAKD5qXMo9Zvf/CZuvfXWeO+99yIiYvny5WZLAQAAALBF6hxKde/ePb7//e9HRMROO+0U9913X2yzzTb11hgAAAAAzVed7ynVtWvXWLx4cUREHH744dG2bdt6awoAAACA5s2NzgEAAABInRudAwAAAJC6LbrReU5OjhudAwAAALDF3OgcAAAAgNTV+Z5SERFHH310LF++PGbPnh3bbLNNfP/7349ly5Zlly9ZsiT69eu3tXsEAAAAoJnZrFDqqaeeivLy8uzzm266KZYuXZp9vnbt2njnnXe2XncAAAAANEubFUp9WpIkW6sPAAAAAFqQzxRKAQAAAMCW2KxQKicnJ3JycqqMAQAAAMDmqPOn70Wsv1xv1KhRkZeXFxERq1evjgsuuCAKCgoiIirdbwoAAAAAarJZodTIkSMrPf/qV79apeass876bB0BAABNUqairMbnn14WEZHbpqDeewKg8dqsUOqee+6prz4AAIAmbs6ELjUu+/CuHaqM7XTZmvpsB4BGzo3OAQAAABqRZO3qhm4hFZs1U6ohLV26NC655JL44x//GLm5uXHSSSfFT3/60+jQoUON69x1111x//33x+uvvx4rVqyIjz/+ODp37vyZtwsAAFTV+6KPG7oFgCYnyayLVe/9IUrf+Fl27IOfbxf5PQ+Pwr3Oj/a7HB85ua0asMP602RmSp1xxhnx9ttvx5QpU+Lxxx+P559/Ps4777xa11m1alUMGzYsrr766q26XQAAoKrcNgWb9QXQ0mXKS6Pk0WNi4ROnxeq5z1datvrDZ2LhE6dFyaPHRKa8tIE6rF85SZIkDd3EpsycOTP69esXr7zySuy///4REfHUU0/F0UcfHR999FH06NGj1vWfffbZOPzww6vMlPqs292gtLQ0OnXqFMuXL4/CwsIt20kAAABSkakoy94DrfdFHwtJaRBJZl2UPHpMrP5g6iZr83sNjuIRjzeZGVN1zUmaxOV706dPj86dO2eDo4iIIUOGRG5ubrz00ktxwgknpLrd8vLyKC8vzz4vLV2fWGYymchkMlvUCwAAAOnY+H1bJpOJ8D6OBlD27qN1CqQiIlZ/MDXK3v1DtO8zon6b2krqmo00iVCqpKQkunXrVmmsdevW0bVr1ygpKUl9u+PHj4/rrruuyviiRYti9eqWcTMyAACApipZuyr7eNGiRZHTuqwBu6GlKn/19s2qX/La7bGy8KB66mbrWrFiRZ3qGjSUuuqqq+Lmm2+utWbmzJkpdVN3Y8eOjTFjxmSfl5aWRs+ePaOoqMjlewAAAI1cpqIsPvy/x0VFRS7fI3XJ2tXxwcIXN2udzIIXoqhrYeS0zq+nrrae/Py69digodQ3vvGNGDVqVK01O++8cxQXF8fChQsrja9duzaWLl0axcXFW/z6W7rdvLy8yMvLqzKem5sbublN5t7xAAAALdNG79u8j6MhrFu7hbPz1pZFbtv2W7eZelDXn6kGDaWKioqiqKhok3UDBw6MZcuWxWuvvRb9+/ePiIhp06ZFJpOJAQMGbPHr19d2AQAAAGqS07Zjqus1Vk0iDt5tt91i2LBhMXr06Hj55ZfjhRdeiIsvvjhOO+207CfkzZ07N/r27Rsvv/xydr2SkpJ444034t13342IiLfeeiveeOONWLp0aZ23CwAAALA15bbOj/yeh2/WOvk9j4jcJnDp3uZoEqFURMSkSZOib9++MXjw4Dj66KPjkEMOibvuuiu7vKKiIt55551Yteq/N6ybOHFi7LvvvjF69OiIiDj00ENj3333jccee6zO2wUAAADY2gr3On/z6vfevPqmICdJkqShm2jqSktLo1OnTrF8+XI3OgcAAGjkMhVlMWdCl4iI6H3Rx250ToNIMuui5NFjYvUHUzdZm99rcBSPeDxyclul0NlnV9ecpMnMlAIAAABoLnJyW0X34Q9Efq/Btdbl9xoc3Yc/0GQCqc0hlAIAAABoALl5hVE84vHodswDkb/DoErL8nseEd2OeSCKRzweuXnN86qsBv30PQAAAICWLCe3VRT0OSHa9T4ye1lpr/PnR6t22zRwZ/XPTCkAAACARiSnmX3KXk2EUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAALRYydrVDd0CtFhCKQAAAFqEJLMuymY9HAsePT479sHPt4v5vx8aZbMejiSzrgG7g5andUM3AAAAAPUtU14aC544NVZ/MLXKstUfPhOrP3wm8nsNju7DH4jcvMIG6BBaHjOlAAAAaNaSzLoaA6mNrf5gaix44lQzpiAlQikAAACatVXv/WGTgdQGqz+YGqv+97F67giIEEoBAADQzJX+4+ebV//m5tUDW0YoBQAAQLOVWbs6Vn/4zGats/rDaZHxqXxQ74RSAAAANFvJmhWprgfUnVAKAACAZiunbcdU1wPqTigFAABAs5XbOj/yex6+Wevk9zwiclvn11NHwAZCKQAAAJq1wr3O37z6vTevHtgyQikAAACatfa7HB/5vQbXqTa/1+Bov/Nx9dwRECGUAgAAoJnLyW0V3Yc/sMlgKr/X4Og+/IHIyW2VUmfQsgmlAAAAaPZy8wqjeMTj0e2YByJ/h0GVluX3PCK6HfNAFI94PHLzChuoQ2h5Wjd0AwAAAJCGnNxWUdDnhGjX+8iYM6FLRET0On9+tGq3TQN3Bi2TmVIAAAC0WDk+ZQ8ajFAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABIXZMJpZYuXRpnnHFGFBYWRufOneOcc86JlStX1rrOXXfdFYcddlgUFhZGTk5OLFu2rErNjjvuGDk5OZW+vv/979fTXgAAAAAQ0YRCqTPOOCPefvvtmDJlSjz++OPx/PPPx3nnnVfrOqtWrYphw4bF1VdfXWvd9ddfH/Pnz89+XXLJJVuzdQAAAAA+pXVDN1AXM2fOjKeeeipeeeWV2H///SMi4vbbb4+jjz46brnllujRo0e161122WUREfHss8/Wuv2OHTtGcXHx1mwZAAAAgFo0iZlS06dPj86dO2cDqYiIIUOGRG5ubrz00kufefvf//73Y5tttol99903fvjDH8batWs/8zYBAAAAqFmTmClVUlIS3bp1qzTWunXr6Nq1a5SUlHymbV966aWx3377RdeuXePFF1+MsWPHxvz58+PHP/5xjeuUl5dHeXl59nlpaWlERGQymchkMp+pHwAAAOrXxu/bMplMhPdxNALN6bisazbSoKHUVVddFTfffHOtNTNnzqzXHsaMGZN9vNdee0Xbtm3j/PPPj/Hjx0deXl6164wfPz6uu+66KuOLFi2K1atX11uvAAAAfHbJ2lXZx4sWLYqc1mUN2A2s15yOyxUrVtSprkFDqW984xsxatSoWmt23nnnKC4ujoULF1YaX7t2bSxdunSr3wtqwIABsXbt2nj//ffj85//fLU1Y8eOrRRmlZaWRs+ePaOoqCgKCwu3aj8AAABsXZmKsvjw/x4XFRVFbpuCBu0HIprXcZmfn1+nugYNpYqKiqKoqGiTdQMHDoxly5bFa6+9Fv3794+IiGnTpkUmk4kBAwZs1Z7eeOONyM3NrXK54Mby8vKqnUWVm5sbublN4jZdAAAALddG79u8j6PRaEbHZV17bxL3lNptt91i2LBhMXr06Jg4cWJUVFTExRdfHKeddlr2k/fmzp0bgwcPjl//+tdxwAEHRMT6e1GVlJTEu+++GxERb731VnTs2DF69eoVXbt2jenTp8dLL70Uhx9+eHTs2DGmT58el19+eXz1q1+NLl26NNj+AgAAADR3TSZ2mzRpUvTt2zcGDx4cRx99dBxyyCFx1113ZZdXVFTEO++8E6tW/fcazIkTJ8a+++4bo0ePjoiIQw89NPbdd9947LHHImL9jKfJkyfHoEGDYvfdd48bb7wxLr/88krbBQAAAGDry0mSJGnoJpq60tLS6NSpUyxfvtw9pQAAABq5TEVZzJmw/uqY3hd93KTv3UPz0ZyOy7rmJE1mphQAAAAAzYdQCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUtW7oBgAAAABamkxFWY3PP70sIiK3TUG995Q2oRQAAABAyuZM6FLjsg/v2qHK2E6XranPdhqEy/cAAAAASJ2ZUgAAAAAp633Rxw3dQoMTSgEAAACkrDneI2pzuXwPAAAAgNQ1mVBq6dKlccYZZ0RhYWF07tw5zjnnnFi5cmWt9Zdcckl8/vOfj3bt2kWvXr3i0ksvjeXLl1eq++CDD2L48OHRvn376NatW3zzm9+MtWvX1vfuAAAAALRoTebyvTPOOCPmz58fU6ZMiYqKijj77LPjvPPOi/vvv7/a+nnz5sW8efPilltuiX79+sWcOXPiggsuiHnz5sVDDz0UERHr1q2L4cOHR3Fxcbz44osxf/78OOuss6JNmzZx0003pbl7AAAAAC1KTpIkSUM3sSkzZ86Mfv36xSuvvBL7779/REQ89dRTcfTRR8dHH30UPXr0qNN2HnzwwfjqV78aZWVl0bp163jyySfjmGOOiXnz5kX37t0jImLixIlx5ZVXxqJFi6Jt27Z12m5paWl06tQpli9fHoWFhVu2kwAAAKQiU1EWcyZ0iYj1N5t2bx/YuuqakzSJy/emT58enTt3zgZSERFDhgyJ3NzceOmll+q8nQ3fjNatW2e3u+eee2YDqYiIoUOHRmlpabz99ttbbwcAAAAAqKRJXL5XUlIS3bp1qzTWunXr6Nq1a5SUlNRpG4sXL44bbrghzjvvvErb3TiQiojs89q2W15eHuXl5dnnpaWlERGRyWQik8nUqR8AAAAaxsbv2zKZTIT3cbBV1TUbadBQ6qqrroqbb7651pqZM2d+5tcpLS2N4cOHR79+/eLaa6/9zNsbP358XHfddVXGFy1aFKtXr/7M2wcAAKD+JGtXZR8vWrQoclqXNWA30PysWLGiTnUNGkp94xvfiFGjRtVas/POO0dxcXEsXLiw0vjatWtj6dKlUVxcXOv6K1asiGHDhkXHjh3jkUceiTZt2mSXFRcXx8svv1ypfsGCBdllNRk7dmyMGTMm+7y0tDR69uwZRUVF7ikFAADQyGUqyuLD/3tcVFTknlKwleXn59eprkFDqaKioigqKtpk3cCBA2PZsmXx2muvRf/+/SMiYtq0aZHJZGLAgAE1rldaWhpDhw6NvLy8eOyxx6p8UwYOHBg33nhjLFy4MHt54JQpU6KwsDD69etX43bz8vIiLy+vynhubm7k5jaJ23QBAAC0XBu9b/M+Dra+uv5MNYmfvN122y2GDRsWo0ePjpdffjleeOGFuPjii+O0007LfvLe3Llzo2/fvtmZT6WlpXHkkUdGWVlZ3H333VFaWholJSVRUlIS69ati4iII488Mvr16xdnnnlmvPnmm/HnP/85vvOd78RFF11UbegEAAAAwNbRJG50HhExadKkuPjii2Pw4MGRm5sbJ510Utx2223Z5RUVFfHOO+/EqlXrrw1+/fXXs5/M16dPn0rbmj17duy4447RqlWrePzxx+PCCy+MgQMHRkFBQYwcOTKuv/769HYMAAAAoAXKSZIkaegmmrrS0tLo1KlTLF++3D2lAAAAGrlMRVnMmdAlIiJ6X/Sxe0rBVlbXnKRJXL4HAAAAQPMilAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgda0bugEAAACoT5mKshqff3pZRERum4J67wkQSgEAANDMzZnQpcZlH961Q5WxnS5bU5/tAP/H5XsAAAAApM5MKQAAAJq13hd93NAtANUQSgEAANCsuUcUNE4u3wMAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFLXZEKppUuXxhlnnBGFhYXRuXPnOOecc2LlypW11l9yySXx+c9/Ptq1axe9evWKSy+9NJYvX16pLicnp8rX5MmT63t3AAAAAFq01g3dQF2dccYZMX/+/JgyZUpUVFTE2WefHeedd17cf//91dbPmzcv5s2bF7fcckv069cv5syZExdccEHMmzcvHnrooUq199xzTwwbNiz7vHPnzvW5KwAAAAAtXk6SJElDN7EpM2fOjH79+sUrr7wS+++/f0REPPXUU3H00UfHRx99FD169KjTdh588MH46le/GmVlZdG69fo8LicnJx555JEYMWLEFvdXWloanTp1iuXLl0dhYeEWbwcAAACgqatrTtIkZkpNnz49OnfunA2kIiKGDBkSubm58dJLL8UJJ5xQp+1s+GZsCKQ2uOiii+Lcc8+NnXfeOS644II4++yzIycnp8btlJeXR3l5efZ5aWlpRERkMpnIZDKbs2sAAAAAzUpds5EmEUqVlJREt27dKo21bt06unbtGiUlJXXaxuLFi+OGG26I8847r9L49ddfH0cccUS0b98+/vKXv8TXvva1WLlyZVx66aU1bmv8+PFx3XXXVRlftGhRrF69uk79AAAAADRHK1asqFNdg4ZSV111Vdx888211sycOfMzv05paWkMHz48+vXrF9dee22lZddcc0328b777htlZWXxwx/+sNZQauzYsTFmzJhK2+/Zs2cUFRW5fA8AAABo0fLz8+tU16Ch1De+8Y0YNWpUrTU777xzFBcXx8KFCyuNr127NpYuXRrFxcW1rr9ixYoYNmxYdOzYMR555JFo06ZNrfUDBgyIG264IcrLyyMvL6/amry8vErLNtyWa+XKlZGb22Q+0BAAAABgq1u5cmVE/DcvqUmDhlJFRUVRVFS0ybqBAwfGsmXL4rXXXov+/ftHRMS0adMik8nEgAEDalyvtLQ0hg4dGnl5efHYY4/VKal74403okuXLjUGUtXZMC2tZ8+edV4HAAAAoDlbsWJFdOrUqcblTeLT9yIijjrqqFiwYEFMnDgxKioq4uyzz479998/7r///oiImDt3bgwePDh+/etfxwEHHBClpaVx5JFHxqpVq+KRRx6JgoKC7LaKioqiVatW8cc//jEWLFgQBx54YOTn58eUKVPiiiuuiCuuuKLae0bVJJPJxLx586Jjx4613iC9sdtwGeKHH37oMkQaDccljZHjksbKsUlj5LiksXJs0hg1l+MySZJYsWJF9OjRo9YryprEjc4jIiZNmhQXX3xxDB48OHJzc+Okk06K2267Lbu8oqIi3nnnnVi1alVERLz++uvx0ksvRUREnz59Km1r9uzZseOOO0abNm1iwoQJcfnll0eSJNGnT5/48Y9/HKNHj96s3nJzc2OHHXb4jHvYeBQWFjbpg5/myXFJY+S4pLFybNIYOS5prBybNEbN4bisbYbUBk0mlOratWt2VlR1dtxxx0rXKh522GGbvHZx2LBhMWzYsK3WIwAAAAB1467cAAAAAKROKEVWXl5ejBs3brNu8g71zXFJY+S4pLFybNIYOS5prBybNEYt7bhsMjc6BwAAAKD5MFMKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVCqhbn22mujb9++UVBQEF26dIkhQ4bESy+9tMn1JkyYEDvuuGPk5+fHgAED4uWXX06hW1qCioqKuPLKK2PPPfeMgoKC6NGjR5x11lkxb968Ta7ruKS+Pfzww3HkkUfGNttsEzk5OfHGG2/Uab0HH3ww+vbtG/n5+bHnnnvGn/70p/ptlBZlc899jkfq2/PPPx/HHnts9OjRI3JycuLRRx/d5DrPPvts7LfffpGXlxd9+vSJX/3qV/XeJy3L+PHj4wtf+EJ07NgxunXrFiNGjIh33nlnk+s5Z1Kf7rzzzthrr72isLAwCgsLY+DAgfHkk0/Wuk5zPyaFUi3M5z73ubjjjjvirbfeir/97W+x4447xpFHHhmLFi2qcZ0HHnggxowZE+PGjYvXX3899t577xg6dGgsXLgwxc5prlatWhWvv/56XHPNNfH666/Hww8/HO+8804cd9xxta7nuCQNZWVlccghh8TNN99c53VefPHFOP300+Occ86JGTNmxIgRI2LEiBHxz3/+sx47paXY3HOf45E0lJWVxd577x0TJkyoU/3s2bNj+PDhcfjhh8cbb7wRl112WZx77rnx5z//uZ47pSV57rnn4qKLLoq///3vMWXKlKioqIgjjzwyysrKalzHOZP6tsMOO8T3v//9eO211+LVV1+NI444Io4//vh4++23q61vCcdkTpIkSUM3QcMpLS2NTp06xdNPPx2DBw+utmbAgAHxhS98Ie64446IiMhkMtGzZ8+45JJL4qqrrkqzXVqIV155JQ444ICYM2dO9OrVq9oaxyVpev/992OnnXaKGTNmxD777FNr7amnnhplZWXx+OOPZ8cOPPDA2GeffWLixIn13CnN3eae+xyPpC0nJyceeeSRGDFiRI01V155ZTzxxBOV3lSddtppsWzZsnjqqadS6JKWaNGiRdGtW7d47rnn4tBDD622xjmThtC1a9f44Q9/GOecc06VZS3hmDRTqgVbs2ZN3HXXXdGpU6fYe++9a6x57bXXYsiQIdmx3NzcGDJkSEyfPj2tVmlhli9fHjk5OdG5c+dqlzsuacymT59e6diMiBg6dKhjk89sS859jkcaI8clDWH58uURsT4AqIljkzStW7cuJk+eHGVlZTFw4MBqa1rCMSmUaoEef/zx6NChQ+Tn58ett94aU6ZMiW233bba2sWLF8e6deuie/fulca7d+8eJSUlabRLC7N69eq48sor4/TTT4/CwsJqaxyXNGYlJSWOTerFlpz7HI80RjUdl6WlpfHJJ580UFc0Z5lMJi677LI4+OCDY4899qixzjmTNLz11lvRoUOHyMvLiwsuuCAeeeSR6NevX7W1LeGYFEo1Y5MmTYoOHTpkv/76179GRGSv33/xxRdj2LBhccopp7gPD6mp6biMWH/T81NOOSWSJIk777yzAbukJart2AQAmq6LLroo/vnPf8bkyZMbuhWIz3/+8/HGG2/ESy+9FBdeeGGMHDky/vWvfzV0Ww2mdUM3QP057rjjYsCAAdnn22+/fUREFBQURJ8+faJPnz5x4IEHxq677hp33313jB07tso2tt1222jVqlUsWLCg0viCBQuiuLi4fneAZqmm43JDIDVnzpyYNm1ajbOkIhyX1I+ajs3NVVxc7NikXmzJuc/xSGNU03FZWFgY7dq1a6CuaK4uvvjiePzxx+P555+PHXbYodZa50zS0LZt2+jTp09ERPTv3z9eeeWV+OlPfxo///nPq9S2hGPSTKlmrGPHjtnwqU+fPjX+J5/JZKK8vLzaZW3bto3+/fvH1KlTK9VPnTq1xuteoTbVHZcbAqlZs2bF008/Hdtss02t23BcUh/qes7clIEDB1Y6NiMipkyZ4tjkM9uSc5/jkcbIcUkakiSJiy++OB555JGYNm1a7LTTTptcx7FJQ6jt/XiLOCYTWoyVK1cmY8eOTaZPn568//77yauvvpqcffbZSV5eXvLPf/4zW3fEEUckt99+e/b55MmTk7y8vORXv/pV8q9//Ss577zzks6dOyclJSUNsRs0M2vWrEmOO+64ZIcddkjeeOONZP78+dmv8vLybJ3jkoawZMmSZMaMGckTTzyRREQyefLkZMaMGcn8+fOzNWeeeWZy1VVXZZ+/8MILSevWrZNbbrklmTlzZjJu3LikTZs2yVtvvdUQu0Azs6lzn+ORhrBixYpkxowZyYwZM5KISH784x8nM2bMSObMmZMkSZJcddVVyZlnnpmt/9///d+kffv2yTe/+c1k5syZyYQJE5JWrVolTz31VEPtAs3QhRdemHTq1Cl59tlnK/1+uWrVqmyNcyZpu+qqq5LnnnsumT17dvKPf/wjueqqq5KcnJzkL3/5S5IkLfOYFEq1IJ988klywgknJD169Ejatm2bbLfddslxxx2XvPzyy5XqevfunYwbN67S2O2335706tUradu2bXLAAQckf//731PsnOZs9uzZSURU+/XMM89k6xyXNIR77rmn2mNz42Nx0KBByciRIyut97vf/S753Oc+l7Rt2zbZfffdkyeeeCLdxmnWajv3OR5pCM8880y158oNx+LIkSOTQYMGVVlnn332Sdq2bZvsvPPOyT333JN63zRvNf1+ufGx5pxJ2v7f//t/Se/evZO2bdsmRUVFyeDBg7OBVJK0zGMyJ0mSJL15WQAAAADgnlIAAAAANAChFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACp+//K6ZFIisg46wAAAABJRU5ErkJggg==", + "image/png": 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", "text/plain": [ "
" ] @@ -541,6 +532,7 @@ "dml_obj.bootstrap(n_rep_boot=5000)\n", "aggregated_eventstudy = dml_obj.aggregate(\"eventstudy\")\n", "# run bootstrap to obtain simultaneous confidence intervals\n", + "aggregated_eventstudy.aggregated_frameworks.bootstrap()\n", "print(aggregated_eventstudy)\n", "fig, ax = aggregated_eventstudy.plot_effects()" ] @@ -637,7 +629,7 @@ }, { "data": { - "image/png": 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HD6tmzZpq27atGjZs6Dyr53Rff/21Fi9erGXLlum2225ztnfu3Fk9evTQypUrddtttyk+Pl7PPvusevXqpY8//th5ptVjjz3mnDZXXL766itt3bpVjz/+uLMtMTFRWVlZqlKlSr74vLajR4+qUaNGOnbsmHx8fBQVFeUS5+/vr4oVK+ro0aOSpJMnT+rEiRP65ptvtGnTJk2ZMkU1a9bUkiVLNGbMGPn5+WnEiBGF5nns2DGX/Z+Z01dffXX+Bw8AAIqM6XsAAMDjXn75ZW3YsMHl51zuu+8+l8flypWTJK1bt06nTp0qkTwLc++997o8/uCDD2S32zVgwACdOHHC+RMdHa0GDRpo8+bNztjTC1Lp6ek6ceKE2rVrJ2NMkc7UWblypcqVK6du3bq57CtvWl3evjZu3CibzaYxY8a4TP27//77C9zuwYMHL+gsqbi4ON12222qU6eOHn74YWd73nTEgICAfM/Ju25YXkxGRob8/f0L3H5gYKAzLm+qXkJCghYvXqwJEyZowIAB+r//+z81bdpU06dPP2uu58rp9CmUAACg+HGmFAAA8LjWrVsXeqHzgvj6+qp69eoubXXq1NH48eP1wgsvaNmyZbr66qt14403avDgwc6CVUk5886Be/fulTFGDRo0KDD+9LsJHj58WJMnT9ZHH32U7xpQedfCOpu9e/cqOTk531lFeeLi4iQ5zt6SlC+nyMhI50XD/6309HRdf/31Sk1N1ddff+1yram84ltWVla+52VmZrrEBAUFFTilMC/29DjJ8Xqefnac1WrVLbfcoilTpjjPNivIuXI6vWAIAACKH0UpAABQ6gQEBMhqzX/C9+zZszV06FB9+OGHWr9+vcaOHauZM2fqu+++y1fEKk5nFi/sdrssFovWrl0rHx+ffPF5xZrc3Fx169ZNiYmJeuSRR9S4cWOFhIToyJEjGjp0qOx2+zn3bbfbFRUVpWXLlhW4PjIy8gKO6PzZbDb16dNHv/zyi9atW6dmzZq5rI+IiFBAQIBzytzp8tqqVq0qyTF1Ljc3V3FxcS7FNpvNpoSEBGdcRESEAgMDVb58+Xyvc97zTp48WWhRKm/aXmE55e0HAACUDIpSAACgTGnevLmaN2+uxx9/XN9++63at2+vhQsXOqdynT51raTUq1dPxhjVqVNHDRs2LDRu165d+vPPP/Xmm2/qjjvucLYXNH2xsLzr1aunjRs3qn379mc9s6dWrVqSHGdW1a1b19keHx+f7wyt82W323XHHXfo888/13vvvaeOHTvmi7FarWrevLm2b9+eb922bdtUt25dhYWFSZJatGghSdq+fbt69uzpjNu+fbvsdrtzvdVqVYsWLfTDDz/IZrO5TPnLu+7U2YpyzZo1k6+vr7Zv364BAwY42202m3bu3OnSBgAAih/XlAIAAGVCSkqKcnJyXNqaN28uq9XqMj0rJCRESUlJJZpLnz595OPjo2nTpuW7YLsxRgkJCZLkPLvn9BhjjObNm5dvmyEhIZKUL/cBAwYoNzdXTz31VL7n5OTkOOO7du0qPz8/zZ8/32V/c+fOLfAY9u/fr/3795/9QP9nzJgxevfdd7VgwQL16dOn0Lh+/frphx9+cClM7dmzR5s2bVL//v2dbddee60iIiL0yiuvuDz/lVdeUXBwsHr16uVsu+WWW5Sbm6s333zT2ZaZmally5apadOmLmc7/fHHHzp8+LDzcbly5dS1a1e9/fbbSk1Ndba/9dZbSktLc8kJAAAUP86UAgAAZcKmTZs0evRo9e/fXw0bNlROTo7eeust+fj4qG/fvs64li1bauPGjXrhhRdUtWpV1alTR23atCnWXOrVq6fp06fr0Ucf1cGDB9W7d2+FhYXpwIEDWr16tYYPH64JEyaocePGqlevniZMmKAjR44oPDxc77//foFnLrVs2VKSNHbsWMXExMjHx0cDBw5Ux44dNWLECM2cOVM7d+5U9+7d5efnp71792rlypWaN2+e+vXrp8jISE2YMEEzZ87U9ddfr549e2rHjh1au3atKlWqlG9/Xbp0kaRzXux87ty5WrBggdq2bavg4GC9/fbbLutvvvlmZ0Ft5MiReu2119SrVy9NmDBBfn5+euGFF1S5cmU9+OCDzucEBQXpqaee0qhRo9S/f3/FxMToq6++0ttvv62nn35aERERztgRI0Zo8eLFGjVqlP7880/VrFlTb731lg4dOqSPP/7YJZcmTZqoY8eO2rJli7Pt6aefVrt27dSxY0cNHz5cf//9t2bPnq3u3burR48eZz12AADwLxkAAAAPWbJkiZFkfvjhhwLXHzhwwEgyS5YscbYNGTLEhISE5Iv966+/zLBhw0y9evVMYGCgiYiIMJ07dzYbN250ifvjjz/MNddcY4KCgowkM2TIkCLlunLlSiPJbN682dk2ZcoUI8nEx8cX+Jz333/fdOjQwYSEhJiQkBDTuHFjM2rUKLNnzx5nzO+//266du1qQkNDTaVKlcw999xjfv7553zHnZOTY8aMGWMiIyONxWIxZ36Ne/XVV03Lli1NUFCQCQsLM82bNzcPP/ywOXr0qDMmNzfXTJs2zVSpUsUEBQWZTp06mV9//dXUqlUr3+tQq1YtU6tWrXO+LkOGDDGSCv05cOCAS/x///tf069fPxMeHm5CQ0PN9ddfb/bu3Vvgtl999VXTqFEj4+/vb+rVq2fmzJlj7HZ7vrjjx4+bIUOGmIiICBMQEGDatGljPvvss3xxkkzHjh3ztX/11VemXbt2JjAw0ERGRppRo0aZlJSUcx47AAD4dyzGnHFOOQAAAAAAAFDCuKYUAAAAAAAA3I6iFAAAAAAAANyOohQAAAAAAADcjqIUAAAAAAAA3I6iFAAAAAAAANzO19MJlAV2u11Hjx5VWFiYLBaLp9MBAAAAAADwGGOMUlNTVbVqVVmthZ8PRVGqGBw9elQ1atTwdBoAAAAAAABe47///a+qV69e6HqKUsUgLCxMkuPFDg8P93A2F85utys+Pl6RkZFnrWQC7kS/hDeiX8Jb0TfhdTIzZW6/XVnZ2fJfvlzW4GBPZwQ4MWbCG5WVfpmSkqIaNWo46yWFoShVDPKm7IWHh5f6olRmZqbCw8NLdedH2UK/hDeiX8Jb0TfhdYKDZZ8wQblJSQqPiJDV39/TGQFOjJnwRmWtX57rEkcUpQAAAACUDF9fqUsX2eLiHMsAAJym9JfdAAAAAAAAUOpQlAIAAABQMux26a+/5HPokGMZAIDTcA4tAAAAgJJhs8ly//0Ks9mkNWuYwgcAcMFvhYvcsZRMHUvJlCTZ7UaJJ9MVYUuW1eq4GFmV8EBVCQ/0ZIoAAAAorSwWKSJCJjPTsQwAwGkoSl3kFm09pCc3/Fno+sndGmpqTCM3ZgQAAIDSLif9mHLTYyVJ9mfH6WRioiok/y5rquPqIT4h0fINqeLJFAEAXoCi1EVuRNtauvGSypKk32NTdceKnfrPwBZqGh0mSZwlBQAAgPOW+strSto23aUt9rTl8m0eV4W2k92bFADA61CUusidPj3PbjeSpMZRobqienkPZgUAAIDSLOzSexRc7wZJUtaJ3UpYP1QVuy9VQKUmkhxnSgEAQFEKAAAAQLHyDanimJ5ns8n+xhtSoOQXVlcBUZd7OjUAgBexejoBAAAAAGWU3S7Lzz87lo3ds7kAALwORSkAAAAAJcPXV6ZvX8eyD5M0AACuKEoBAAAAKBm+vlKHDo5lHx/P5gIA8DoUpQAAAAAAAOB2pa4o9fLLL6t27doKDAxUmzZt9P333581fuXKlWrcuLECAwPVvHlzffrppy7rhw4dKovF4vLTo0ePkjwEAAAA4OJgjBQf/88yAACnKVVFqXfffVfjx4/XlClT9NNPP+myyy5TTEyM4uLiCoz/9ttvdeutt+quu+7Sjh071Lt3b/Xu3Vu//vqrS1yPHj107Ngx588777zjjsMBAAAAyrasLFlmzHAsZ9s8mwsAwOuUqqLUCy+8oHvuuUd33nmnmjZtqoULFyo4OFhvvPFGgfHz5s1Tjx499NBDD6lJkyZ66qmndMUVV+ill15yiQsICFB0dLTzp0KFCu44HAAAAKDsCwrydAYAAC9Vam6BYbPZ9OOPP+rRRx91tlmtVnXt2lVbt24t8Dlbt27V+PHjXdpiYmK0Zs0al7YtW7YoKipKFSpU0LXXXqvp06erYsWKheaSlZWlrKws5+OUlBRJkt1ul91eem91a/53m15jSvdxoGyx2+0yxtAn4VXol/BW9E14HX9/2adPl95rL+PrR9+EV2HMhDcqK/2yqPmXmqLUiRMnlJubq8qVK7u0V65cWX/88UeBz4mNjS0wPjY21vm4R48e6tOnj+rUqaP9+/dr0qRJuu6667R161b5FHKHkJkzZ2ratGn52uPj45WZmXm+h+Y1EhPT/vfvScUFZHs4G8DBbrcrOTlZxhhZraXq5E6UYfRLeCv6JrxRzsmTkqTEkyflay34shuAJzBmwhuVlX6ZmppapLhSU5QqKQMHDnQuN2/eXJdeeqnq1aunLVu2qEuXLgU+59FHH3U5AyslJUU1atRQZGSkwsPDSzznkhKR5ef4N6KCoqKYwgjvYLfbZbFYFBkZWaoHZZQt9Et4K/omvFGmvYKOS4qoUEGBUVGeTgdwYsyENyor/TIwMLBIcaWmKFWpUiX5+Pjo+PHjLu3Hjx9XdHR0gc+Jjo4+r3hJqlu3ripVqqR9+/YVWpQKCAhQQEBAvnar1VqqO43FYnX+W5qPA2WPxWIp9Z8vlH7HUjJ1LMVxNqzdbpR48pQislNltVokSVXCA1UlvGi/fIGSxJgJr5KdLcu770oWyWK30y/hdRgz4Y3KQr8sau6lpijl7++vli1b6vPPP1fv3r0lOSqIn3/+uUaPHl3gc9q2bavPP/9c999/v7Ntw4YNatu2baH7+fvvv5WQkKAqVaoUZ/oAgFJu0dZDenLDn4Wun9ytoabGNHJjRgBQCuTmyvL991IbSfZcT2cDAPAypaYoJUnjx4/XkCFD1KpVK7Vu3Vpz585Venq67rzzTknSHXfcoWrVqmnmzJmSpHHjxqljx46aPXu2evXqpRUrVmj79u169dVXJUlpaWmaNm2a+vbtq+joaO3fv18PP/yw6tevr5iYGI8dJwDA+4xoW0s3XuK4TuHvsam6Y8VO/WdgCzWNDpMkzpICgIL4+srccIMU953kU6r+9AAAuEGp+s1wyy23KD4+XpMnT1ZsbKxatGihzz77zHkx88OHD7ucItauXTstX75cjz/+uCZNmqQGDRpozZo1atasmSTJx8dHv/zyi958800lJSWpatWq6t69u5566qkCp+cBAC5ep0/Ps9uNJKlxVKiuqF7eg1kBgJfz9ZWuvVZaIamQmwgBAC5epaooJUmjR48udLreli1b8rX1799f/fv3LzA+KChI69atK870AAAAAAAAUASl96pZAAAAALybMVJy8j/LAACchqIUAAAAgJKRlSXL1KmO5WybR1MBAHgfilIAAAAASk4pvqU5AKBk8RsCAAAAQMkIDJSZPdux7M+NhAAArihKAQAAAAAAwO0oSgEAAAAAAMDtKEoBAAAAKBnZ2dKqlY7lnBzP5gIA8DoUpQAAAACUjNxcWb751rFsz/VsLgAAr0NRCgAAAEDJ8PWViYlxLPv4ejYXAIDXoSgFAAAAoGT4+ko9ejiWfXw8mwsAwOtQlAIAAAAAAIDbUZQCAAAAUDKMkTIy/lkGAOA0FKUAAAAAlIysLFkmTXIsZ9s8mwsAwOtQlAIAAAAAAIDbUZQCAAAAUDICAmSef96x7Ofv2VwAAF6HohQAAACAkmGx/HPXPYvFs7kAALwORSkAAAAAAAC4HUUpAAAAACUjJ0f68EPHcm6uZ3MBAHgdilIAAAAASkZOjixbtjiWc3M8mgoAwPtQlAIAAABQMnx9ZTp1ciz7+Ho0FQCA96EoBQAAAKBk+PpKN93kWM674DkAAP9DUQoAAAAAAABuR1EKAAAAQMkw5p8LnBvj2VwAAF6HohQAAACAkpGVJcuECY7lbJtncwEAeB2KUgAAAAAAAHA7ilIAAAAASkZAgMyMGY5lP3/P5gIA8DrclxUAAABAybBYpKCgf5YBAPnkpB9TbnqsJMlut8uemKgsRchqdZxH5BMSLd+QKp5MscRQlAIAAAAAAPCQ1F9eU9K26S5tsactl2/zuCq0nezepNyEohQAAACAkpGTI332mWM57y58AAAXYZfeo+B6N0iSsk7sVsL6oarYfakCKjWR5DhTqqyiKAUAAACgZOTkyLJundRGUm6Op7MBAK/kG1LFOT3PbrdLkvwiGikg6nJPpuUWXOgcAAAAQMnw8ZFp386xbPXxbC4AAK9DUQoAAABAyfDzk/r1dyz7MkkDAOCKohQAAAAAAADcjqIUAAAAgGJnzz6l1F/fUMKWcZKkhC3jlPrrG7Jnn/JwZgAAb8E5tAAAAACKlT37lE5sGKGM/Z/JkpgslZOyY39UYuKfyji8WZW6LZLVL9jTaQIAPIwzpQAAAAAUq/Q9K5RxcJ3sucnKDc9rzZXdlqSMQ+uUvmeFJ9MDAHgJilIAAAAAilXanytlz05zPLC4rrNnpyntz5XuTwoA4HVKXVHq5ZdfVu3atRUYGKg2bdro+++/P2v8ypUr1bhxYwUGBqp58+b69NNPXdYbYzR58mRVqVJFQUFB6tq1q/bu3VuSh+B1TtlytHjbIY1ZvUuSNGb1Li3edkinbDkezgwAAAClUW7aEcnkFrzSnutYDwC46JWqa0q9++67Gj9+vBYuXKg2bdpo7ty5iomJ0Z49exQVFZUv/ttvv9Wtt96qmTNn6vrrr9fy5cvVu3dv/fTTT2rWrJkk6dlnn9WLL76oN998U3Xq1NETTzyhmJgY/f777woMDDyv/Gw2m2w2W752q9Uq39NugVtQTB6LxSI/P78Lis3OzpYx5rxiT9lyNGLVL1r/5wmlZTueu/3vZO35+Hdt2ntCiwdcJj+LKXS7kuTv71+kHM6MzcnJkd1uL5ZYPz8/WSyWEo3Nzc1Vbm4hX67OM9bX11dWq9VrYu12u3JyCi9C+vj4yMfHxyOxdrtdNptNubm5znyNMcrOzi7Sds8Ve/rns6RipbN/lr15jPi3sVLZHCOycxzvvdE/x8IY4ZkxoqDYi3mMOPPzxRhRtFi+R/y72II+n7kBVWQs+2Qx+T+3xuKj3IAqBfZPxoj8sRLfIy4k9nzHiLNhjPh3sd7w3aC0jRGSlJOdXehntDSMEWd7rst2zNk+1V6mTZs2uvLKK/XSSy9JcnSsGjVqaMyYMZo4cWK++FtuuUXp6en65JNPnG1XXXWVWrRooYULF8oYo6pVq+rBBx/UhAkTJEnJycmqXLmyli5dqoEDBxaYR1ZWlrKyspyPU1JSVKNGDT3yyCMKCAjIF1+/fn0NGjTI+XjGjBmFdsZatWpp6NChzsfPPfecTp0q+A4lVapU0fDhw52P582bp6SkpAJjIyMjNXLkSOfjBQsWKD4+XjuOJGvjiQBl1u8gGSNZLM5/IwJ99cz1TZSz8zMdO3aswO0GBwfroYcecj5eunSpDh06VGCsn5+fJk2a5Hy8bNky7du3r8BYSZoyZYpz+b333tPu3bsLjX300Uedv1jWrFmjn3/+udDYCRMmKCQkRJL0f//3f9q+fXuhsePGjVP58uUlSevXr9fWrVsLjb3vvvucxdEtW7boiy++KDT27rvvVrVq1SRJ33zzjTZu3Fho7JAhQ1S7dm1J0vfff6+1a9cWGnvrrbeqYcOGkqSdO3fqww8/LDS2X79+uuSSSyRJv/32m1atWlVo7E033aQWLVpIkv7880+98847hcZed911at26tSTp4MGDevPNNwuN7dq1q9q3by9JOnLkiBYvXpwvxhij9PR09ezZU507d5YkxcXF6ZVXXil0u23btlX37t0lSUlJSZo3b16hsa1atVKvXr0kSenp6Xr++ecLjb3sssvUu3dvSY5BdubMmYXGNmnSRAMGDHA+njZtWqGx3jxGFKR8+fIaN26c8/Grr7560Y0RR3OD9HrmJdo4tLk6Na0piTHCU2NEno4dO6pTp06SLu4xombNmurZs6ciIyNltVoZI/6H7xG1Jbl3jMiK26lqWZ/r+kttzq+YeT75xV9HArooIKpFvu0yRjjwPcKhpMcIu92u+Ph4bdmyRX/88UehsYwRDnyPcCjpMSIz9kcdf6+9PkoeqkR7lQJjS8MYkZWVpVmzZik5OVnh4eEFbMGh1JwpZbPZ9OOPP+rRRx91tlmtVnXt2rXQD+/WrVs1fvx4l7aYmBitWbNGknTgwAHFxsaqa9euzvXlypVTmzZttHXr1kKLUjNnzizwTU9PTy+w+pqSkqK4uDjn47S0tEKrtKmpqfliMzIyihSbmpqq9PT0AmMDAwMLjD1yIkn2atfK5dvC//615ebo+z8OqsFZtmu324ucg6+vr0tsSkpKobGSzjs27xdFcnLyWWPj4+Od64sSm1fhTUpKOmvsiRMnnMtFic2rKp88efKssQkJCQoODi5ybN7rlpiYeNbYxMTEC4pNSEg4a+zJkycvKPbEiRMFxhpjlJmZWaTYPElJSc7Yc73HycnJzthTp04VOdZms5019szP/fnEetMYURAfH58ix5bVMSLDSLLk9WHHWbWMEZ4ZI/Kc/rm/mMeIlJQUJSUlyRgjq9XKGHEesXyPKN4xwgTV0Z7ky5Tzy1/qVTNRfuWNci1B+ttWQ3syyymgfB3lFLB9xggHvkc4lPQYYbfblZycfM73mTHin1i+R5T8GJFz8qQkKSMjQ+k5BceXhjHi9BN5zuaCzpTy8fHRsWPH8k2ZS0hIUFRU1FlP47tQR48e1f+zd+9xMpb/H8ffM7s7ez5Y1u46S44RIlrqh1LENznEN3RQKCGVdFJRKjrr8E0q2VSkfCvfDiiJRBJlRTlHcljrtEd2Z3fn+v2x7WTswVq7M7O7r+fjMQ/3XPdn7vnc65pr7/nsfV937dq19cMPPyguLs7Zfv/99+u7777T2rVrC7zGZrNpzpw5Gjx4sLNtxowZevzxx3Xo0CH98MMP6ty5sw4cOKDY2H8qkIMGDZLFYtGHH35YaC5FnSl16NChQiuA3nxKbbsXv9PWY3YV9iqLpBY1g7X+rs5ecUotp91XzVNq8/+CFRMT4+zD3nBKLafde99p9+4cI37Zl6xLZ67Tj2Pj1L5epCTGCE67944xwhij5ORk55lSjBEli+U44txii/wdnn1CGb/Pl3nneWU03SffGhcrpNUt8j+vv6x+QYVulzGiYKzEcURpYksyRuQfZ1arVq3IOIkx4lxjveHYoCKNEflnSkX2XyH/mhedcbuSd44Rqampio6OLp8zpYoaCLKyslwGgsrK39+/0Mv0AgICSjQP1dnMVXU2sYXldKbY2tXDtCP5qHIcBf9PfawWxYYHlmq7JXE2fcUbYq1Wq8uHrrLFnjpQeVOsw+GQv7+//Pz8nL/YJDl/EZSEN8SW1+e+vMcIT8Z6w+f+9NgT9hzN27Bfb/+Yd+nAXf/7XcMvqa8hbWsryObnFZ/lqjZGFMUbPveeGCMcDocsFousVqusVitjhBfFestn2a1jRECAgjqO1sn6HZUxP041ur+iwJh2Jdpmsds9x1jJOz73HEeUb2xJP58Wi0U2m83lOLMstnu2sd7yWeY4Io8nP/eWv/uizeZf4s+oN44RJZ1T6qyKUq+88oqkvA/urFmzFBIS4lyXm5urlStXqlmzZmezyRKrUaOGfHx8dOjQIZf2Q4cOKSYmptDXxMTEFBuf/++hQ4dczpQ6dOiQ85rWym5Qm1pa/1eykjMLVo1DbT4a1KaWB7ICAO9zwp6jER9t1JKtSUrLyhszT78xRJCtwlwVDwAAAHjcWR09T58+XVLemVIzZ850qfLZbDY1aNBAM2fOLNsMT9l+u3bttGzZMucEgQ6HQ8uWLdPYsWMLfU1cXJyWLVumu+++29m2dOlS5+V/DRs2VExMjJYtW+YsQqWmpmrt2rW64447ymU/vM2QtrX17Y4jWrI1Sen2HOU4JF+rFGrzVY9mNTWkbW1PpwgAXmHehv1asjXJpYifa6TkzBwt2ZqkeRv2a0TH+h7MEAAAAKhYzqootXv3bklSt27d9Mknn5zx2tuyNn78eN18881q3769OnTooJdeekkZGRm65ZZbJEk33XSTateu7byTxV133aUuXbrohRdeUO/evTV//nytX79eb775pqS8M77uvvtuPfnkk2rcuLEaNmyoRx99VLVq1XIWviq7IJuvZg1q7bwcZe1fKWpXO/yUy1H4qz8ASNJHCQeUbi987oR0e64+SjhAUQoATpeZKcvEidKFkuwlm/QWAFB1lKrisHz58rLOo0T+/e9/6/Dhw5o0aZISExPVpk0bLVmyRNHR0ZKkvXv3ulwL3KlTJ82bN0+PPPKIJk6cqMaNG2vhwoVq2bKlM+b+++9XRkaGbrvtNiUnJ+vSSy/VkiVLzurayYouyOarER3rq01smDq8skqv9mul9vXcW3AEAG+3PyVTuYXMvydJuQ6j/SmZbs4IACqIIu7cBABAqYpSAwYMUIcOHfTAAw+4tD/77LNat26dFixYUCbJFWbs2LFFXq63YsWKAm0DBw7UwIEDi9yexWLRlClTNGXKlLJKEQBQCdUOD9COIxlF3hiidnjV+WMGAJSYv7/MxInSsj6SX+W/IRIA4OyU7BYDp1m5cqV69epVoP3qq6/WypUrzzkpAAC8zaA2tRRiK/yOKdwYAgCKYLFIUVH/LAMAcIpSFaXS09MLvb2ln5+fUlNTzzkpAAC8zZC2tdWzWU1FBPjK9+/fnr5WqVoAN4YAAAAASqNURalWrVrpww8/LNA+f/58tWjR4pyTAgDA2+TfGOLZa1qoXe1wSVK72uF65poWmjWoNTeGAIDC5ORIq1blLecWfrMIAEDVVaoj6EcffVT9+/fXrl27dPnll0uSli1bpg8++KBc55MCAMCTuDEEAJylnBxZPv5Y6igpN8fT2QAAvEypilLXXHONFi5cqKlTp+q///2vAgMDdeGFF+qbb75Rly5dyjpHAAAAABWR1SrTurWkHyVLqS7SAABUYqW+1qB3797q3bt3WeYCAAAAoDKx2aRhw6T5b0h+fp7OBgDgZUr954rk5GTNmjVLEydO1LFjxyRJv/zyi/bv319myQEAAAAAAKByKtWZUr/++qu6d++u8PBw7dmzRyNGjFBkZKQ++eQT7d27V++++25Z5wkAAAAAAIBKpFRnSo0fP17Dhg3Tjh07FBAQ4Gzv1auXVq5cWWbJAQAAAKjAsrJkmTw5b9lu92wuAACvU6qi1Lp163T77bcXaK9du7YSExPPOSkAAAAAlYAxUmpq/hOPpgIA8D6lKkr5+/sr1fnL5R/bt29XVFTUOScFAAAAoBKw2WQmTMhb9mWicwCAq1IVpfr06aMpU6YoOztbkmSxWLR371498MADGjBgQJkmCAAAAKCCslql2rX/WQYA4BSl+s3wwgsvKD09XTVr1tTJkyfVpUsXnX/++QoNDdVTTz1V1jkCAAAAAACgkinV3ffCw8O1dOlSrV69Whs3blR6erouuugide/evazzAwAAAFBR5eRIP/2Ut5yb69lcAABep8RFqcjISG3fvl01atTQrbfeqpdfflmdO3dW586dyzM/AAAAABVVTo4sH3wgdZSUm+PpbAAAXqbEl+/Z7Xbn5OZz5sxRZmZmuSUFAAAAoBKwWmVatMhbtjCnFADAVYnPlIqLi1Pfvn3Vrl07GWM0btw4BQYGFho7e/bsMksQAAAAQAVls0kjR0rzZ0t+3H0PAOCqxEWp999/X9OnT9euXbskSSkpKZwtBQAAAAAAgFIpcVEqOjpaTz/9tCSpYcOGeu+991S9evVySwwAAAAAAACVV4kv7I6MjNSRI0ckSd26dZPNZiu3pAAAAABUAllZsjz1VN6y3e7ZXAAAXoeJzgEAAACUD2Okv/+wLRmPpgIA8D5MdA4AAACgfNhsMuPGSWt/lHyZ6BwA4KpUE51bLBYmOgcAAABQPKtVathQWvv3MgAAp2CicwAAAAAAALjdWf25olevXkpJSdHu3btVvXp1Pf3000pOTnauP3r0qFq0aFHWOQIAAACoiHJzpYSEvGVHrkdTAQB4n7MqSi1ZskRZWVnO51OnTtWxY8ecz3NycrRt27ayyw4AAABAxZWdLcucOXnLOTmezQUA4HXO6cJuY7iDBgAAAIAiWK1So0Z5yxbmlAIAuOI3AwAAAIDyYbPJjB2bt+zH3fcAAK7OqihlsVhksVgKtAEAAAAAAKB0HNknlLZ5to6uuEuSdHTFXUrbPFuO7BMezqx8lfjue1Le5XrDhg2Tv7+/JCkzM1OjRo1ScHCwJLnMNwUAAAAAAIDiObJP6MjS23Vyz1dyZKdJkrIP/axjx7br5N7lqnHlG7L6BXk4y/JxVkWpm2++2eX5DTfcUCDmpptuOreMAAAAAFQOdrsszz0n1ZWUne3pbADAK2Vsm59XkLIn/9NocuWwJ+vkn18pY9t8hba81WP5laezKkrFx8eXVx4AAAAAKhuHQzpwIK8oZRyezgYAvFL69gVyZKcXus6Rna707QsqbVGKic4BAAAAlA+bTWbUqLxlXyY6B4DC5Kbvl0xu4SsduXnrKymKUgAAAADKh9UqNW36zzIAoACfkNqSxafwlVafvPWVFL8ZAAAAAAAAPCSkyUBZ/UIKXWf1C1FIk4Fuzsh9KEoBAAAAKB+5udJvv+UtO4q4NAUAqrjgptcrsEEPWf0jJMvfU39bfGX1j1Bg/R4Kbnq9R/MrTxSlAAAAAJSP7GxZZs3KW87J8WwuAOClrH5BqnHlG4q87Gn5RV8kSfKLvkiRlz2tGle+IatfkIczLD8Vpih17NgxDR06VGFhYYqIiNDw4cOVnl747PT5MjMzNWbMGFWvXl0hISEaMGCADh065BJjsVgKPObPn1+euwIAAABUDVarVK9e3rKlwnz1AAC3s/oFKbTlrare9WVJUvWuLyu05a2VuiAlVaCi1NChQ/Xbb79p6dKl+uKLL7Ry5Urddtttxb7mnnvu0eeff64FCxbou+++04EDB9S/f/8CcfHx8Tp48KDz0bdv33LaCwAAAKAKsdlk7rknb9mPu+8BAFz5ejqBktiyZYuWLFmidevWqX379pKkV199Vb169dLzzz+vWrVqFXhNSkqK3n77bc2bN0+XX365pLziU/PmzfXjjz/qkksuccZGREQoJiamxPlkZWUpKyvL+Tw1NVWS5HA45HA4SrWP3sAYh/PfirwfqFwcDoeMMfRJeBXGS3grxkx4I/N3fzQV/FgZlQ9jJrxRZRkzS5p7hShKrVmzRhEREc6ClCR1795dVqtVa9euVb9+/Qq85ueff1Z2dra6d+/ubGvWrJnq1aunNWvWuBSlxowZoxEjRui8887TqFGjdMstt8hisRSZz7Rp0/T4448XaD98+LAyMzNLu5sed+xY+t//HleSf7aHswHyOBwOpaSkyBgjK7eShpdgvIS3YsyEN8o5flySdOz4cflakzycDfAPxkx4o8oyZqalpZUorkIUpRITE1WzZk2XNl9fX0VGRioxMbHI19hsNkVERLi0R0dHu7xmypQpuvzyyxUUFKSvv/5ao0ePVnp6usaNG1dkPg899JDGjx/vfJ6amqq6desqKipKYWFhpdhD7xCZlXdKdWRkNdWsWc3D2QB5HA6HLBaLoqKiOFiA12C8hLdizITXsduV9fL7SoyWIkNDFHDaMT3gSYyZ8EaZjmo6JCmyWrUKPWYGBASUKM6jRakHH3xQzzzzTLExW7ZsKdccHn30Uedy27ZtlZGRoeeee67YopS/v7/8/f0LtFut1go9mFn+nnzSYqnY+4HKx2KxVPjPFyoXxkt4M8ZMeBvLnj1StGSR6JfwOoyZ8DaWv/uipYL3y5Lm7tGi1L333qthw4YVG3PeeecpJiZGSUmup63l5OTo2LFjRc4FFRMTI7vdruTkZJezpQ4dOlTs/FEdO3bUE088oaysrEILTwAAAABKyM9P5tZbpd9+lHwrxEUaAAA38uhvhqioKEVFRZ0xLi4uTsnJyfr555/Vrl07SdK3334rh8Ohjh07Fvqadu3ayc/PT8uWLdOAAQMkSdu2bdPevXsVFxdX5HslJCSoWrVqFKQAAACAUsrJOKjcjLwpM7Jr26TfpOzknbL65l3+7BMcI9/gWE+mCADwAhXizxXNmzdXz549NXLkSM2cOVPZ2dkaO3asrr/+eued9/bv368rrrhC7777rjp06KDw8HANHz5c48ePV2RkpMLCwnTnnXcqLi7OOcn5559/rkOHDumSSy5RQECAli5dqqlTp2rChAme3F0AAACgQkv79S0lr33Spe3o18OcyxEdH1G1uEluzgoA4G0qRFFKkubOnauxY8fqiiuukNVq1YABA/TKK68412dnZ2vbtm06ceKEs2369OnO2KysLPXo0UMzZsxwrvfz89Nrr72me+65R8YYnX/++XrxxRc1cuRIt+4bAMD7HUzN1MHUvDusbk1Kd/5rtebdrTU2LECxYSWb0BEAKrvQC0cqqNE1ksMhx86dSk1JUVjbtrL+fQmfT3DR02kAAKqOClOUioyM1Lx584pc36BBAxljXNoCAgL02muv6bXXXiv0NT179lTPnj3LNE8AQOX0xpo/NWXpdpe2m+YnOJcnXdlEj/Vo6uasAMA7+QbH5l2el5kp8/RkWex22RbeIGtQkKdTAwB4kQpTlAIAwJNuj6uvPhdES5IcDqNjx48pslqky5lSAIDTWCxS3bpynDyZtwwAwCkoSgEAUAKnXp7ncDiUZMtSzZrhFfpWvQBQ7vz9ZV57TalJSQrgRkIAgNNwJA0AAAAAAAC3oygFAAAAAAAAt+PyvSqOu0kBAACg3NjtskyZopCMDGnaNCm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", 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" ] @@ -697,13 +689,13 @@ "Learner ml_m: LogisticRegression(penalty=None)\n", "Out-of-sample Performance:\n", "Regression:\n", - "Learner ml_g0 RMSE: [[0.17197022 0.18219482 0.25977582 0.25762107 0.1726177 0.15159716\n", - " 0.20238368 0.20650302 0.17381994 0.15150495 0.20118645 0.16352671]]\n", - "Learner ml_g1 RMSE: [[0.10293317 0.12500233 0.13673155 0.1356723 0.13881924 0.1126528\n", - " 0.08473008 0.10271377 0.13282935 0.16430589 0.15938565 0.1613265 ]]\n", + "Learner ml_g0 RMSE: [[0.17225098 0.18180751 0.25875512 0.25787083 0.17217289 0.15149323\n", + " 0.2015511 0.20672252 0.17245042 0.15118131 0.20165754 0.16345075]]\n", + "Learner ml_g1 RMSE: [[0.10293871 0.1231148 0.13516937 0.1467575 0.13801965 0.11120553\n", + " 0.08784557 0.10496696 0.13719248 0.16202855 0.15922994 0.16103467]]\n", "Classification:\n", - "Learner ml_m Log Loss: [[0.23186483 0.23061763 0.23153821 0.23146489 0.34981033 0.34925181\n", - " 0.35002383 0.34858068 0.60836257 0.60569912 0.60630968 0.60700137]]\n", + "Learner ml_m Log Loss: [[0.23066697 0.23113821 0.23155331 0.23101858 0.34930842 0.35094054\n", + " 0.34738288 0.34716226 0.60658247 0.60615553 0.60872424 0.60938367]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 10\n", @@ -711,32 +703,32 @@ "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % \\\n", - "ATT(2004.0,2003,2004) -0.013506 0.022282 -0.606161 0.544408 -0.057178 \n", - "ATT(2004.0,2003,2005) -0.077125 0.028778 -2.680033 0.007361 -0.133528 \n", - "ATT(2004.0,2003,2006) -0.132188 0.035710 -3.701704 0.000214 -0.202178 \n", - "ATT(2004.0,2003,2007) -0.104989 0.033066 -3.175173 0.001497 -0.169797 \n", - "ATT(2006.0,2003,2004) -0.000609 0.022310 -0.027312 0.978211 -0.044336 \n", - "ATT(2006.0,2004,2005) -0.005410 0.018285 -0.295867 0.767332 -0.041248 \n", - "ATT(2006.0,2005,2006) 0.003109 0.020571 0.151158 0.879851 -0.037209 \n", - "ATT(2006.0,2005,2007) -0.041588 0.019824 -2.097880 0.035916 -0.080442 \n", - "ATT(2007.0,2003,2004) 0.027959 0.014092 1.983968 0.047259 0.000338 \n", - "ATT(2007.0,2004,2005) -0.004452 0.015648 -0.284504 0.776024 -0.035121 \n", - "ATT(2007.0,2005,2006) -0.028741 0.018211 -1.578214 0.114517 -0.064434 \n", - "ATT(2007.0,2006,2007) -0.027488 0.016248 -1.691824 0.090679 -0.059333 \n", + "ATT(2004.0,2003,2004) -0.013438 0.022263 -0.603612 0.546102 -0.057073 \n", + "ATT(2004.0,2003,2005) -0.079094 0.028471 -2.778025 0.005469 -0.134896 \n", + "ATT(2004.0,2003,2006) -0.139463 0.035008 -3.983730 0.000068 -0.208078 \n", + "ATT(2004.0,2003,2007) -0.108604 0.032494 -3.342312 0.000831 -0.172291 \n", + "ATT(2006.0,2003,2004) -0.000821 0.022370 -0.036698 0.970726 -0.044665 \n", + "ATT(2006.0,2004,2005) -0.005271 0.018474 -0.285323 0.775397 -0.041480 \n", + "ATT(2006.0,2005,2006) 0.000197 0.019361 0.010173 0.991883 -0.037750 \n", + "ATT(2006.0,2005,2007) -0.041419 0.019782 -2.093730 0.036284 -0.080192 \n", + "ATT(2007.0,2003,2004) 0.026013 0.014054 1.850934 0.064179 -0.001532 \n", + "ATT(2007.0,2004,2005) -0.004210 0.015648 -0.269053 0.787889 -0.034880 \n", + "ATT(2007.0,2005,2006) -0.029017 0.018184 -1.595756 0.110543 -0.064657 \n", + "ATT(2007.0,2006,2007) -0.029940 0.016430 -1.822237 0.068419 -0.062142 \n", "\n", " 97.5 % \n", - "ATT(2004.0,2003,2004) 0.030165 \n", - "ATT(2004.0,2003,2005) -0.020722 \n", - "ATT(2004.0,2003,2006) -0.062197 \n", - "ATT(2004.0,2003,2007) -0.040182 \n", - "ATT(2006.0,2003,2004) 0.043118 \n", - "ATT(2006.0,2004,2005) 0.030428 \n", - "ATT(2006.0,2005,2006) 0.043428 \n", - "ATT(2006.0,2005,2007) -0.002734 \n", - "ATT(2007.0,2003,2004) 0.055580 \n", - "ATT(2007.0,2004,2005) 0.026217 \n", - "ATT(2007.0,2005,2006) 0.006952 \n", - "ATT(2007.0,2006,2007) 0.004357 \n" + "ATT(2004.0,2003,2004) 0.030197 \n", + "ATT(2004.0,2003,2005) -0.023291 \n", + "ATT(2004.0,2003,2006) -0.070848 \n", + "ATT(2004.0,2003,2007) -0.044918 \n", + "ATT(2006.0,2003,2004) 0.043023 \n", + "ATT(2006.0,2004,2005) 0.030938 \n", + "ATT(2006.0,2005,2006) 0.038144 \n", + "ATT(2006.0,2005,2007) -0.002646 \n", + "ATT(2007.0,2003,2004) 0.053558 \n", + "ATT(2007.0,2004,2005) 0.026460 \n", + "ATT(2007.0,2005,2006) 0.006623 \n", + "ATT(2007.0,2006,2007) 0.002263 \n" ] } ], @@ -749,14 +741,6 @@ "execution_count": 11, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\bachp\\Documents\\Promotion\\DissundPapers\\Software\\DoubleML\\doubleml-for-py\\doubleml\\did\\did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", - " warnings.warn(\n" - ] - }, { "data": { "text/plain": [ @@ -770,7 +754,7 @@ }, { "data": { - "image/png": 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" ] @@ -781,6 +765,7 @@ ], "source": [ "es_linear_logistic = dml_obj_linear_logistic.aggregate(\"eventstudy\")\n", + "es_linear_logistic.aggregated_frameworks.bootstrap()\n", "es_linear_logistic.plot_effects(title=\"Estimated ATTs by Group, Linear and logistic Regression\")" ] }, @@ -812,7 +797,7 @@ }, { "data": { - "image/png": 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JSEgwmzdvNi+//LI5dOiQSU5ONvHx8Wb48OHOfionJ+es3l+gonvws9+N7b5PCi1K2e77xDz42e/lst3Cfpz16dPHnH/++eamm24yISEh5oYbbjCbN282mzdvNna73TRv3tzccsst5pdffjG///67GTx4sGnatGmRxfuiPvMcLwJV25Gvx5s/XggooigVYI58Pb5ctltcv9enTx/TvHlzs3btWpOQkGB69OhhGjVq5PwDZu/evc1ll11mfvnlF7Nr1y7zySefmDVr1picnBzz4YcfGklm27Zt5sCBAyb55N/wJ+/3kSOmdu3aZvLkyc4+wZi839S+vr6mU6dO5ttvvzVbt241GRkZpz3m+vvvv80zzzxjNm7caHbt2mVeeukl4+PjY9avX2+MOX0ffOGFF7r0f126dDGXX365+fnnn83atWtNtWrVzJNPPunM/3R9dP56O3bsaFavXm1+++03c/HFF5tOnToZY4w5fvx4iX+vlrQoxfm1KNLkyZN12WWXSZKysrI0depUffnll4qPj5cknXPOOfrmm280a9YsdenSRb6+vnrsscecr2/QoIHWrVun999/XwMHDlRISIgCAwOVlZWl2NjYAtsbN26cevToIUm66667NGjQIK1cuVKdO3eWJN1666168803nfETJ07Uc889p379+jm39/vvv2vWrFm66aabXNbbu3dvSdJjjz2mc889Vzt37lSzZs0UHh4ui8VSaD4n27t3r+677z41a9ZMktS4cWPnsqLWccsttzinzznnHL300ku64IILlJ6e7nJ55Mnvs5R36aTNZjttTgDcw+FwaPDgwbLZbJo/f74sFotz2X//+1+1bdtWU6dOdc6bM2eO6tSpo+3bt6tJkyaS8vqMp59+2hnz8MMPq06dOvrvf/8ri8WiZs2aaf/+/XrggQc0YcKEQk/1njZtmoYMGeIcU6Bx48Z66aWX1KVLF7366quKiopSUFCQ/Pz8XPqPU+ft3btXc+fO1d69e1WzZk1Jef3k8uXLNXfuXE2dOlVPP/202rdvrxkzZjjXc+655zqn/fz8FBQURD8F/OPPo8dljCl0mTFGfx49Xu45GGO0cuVKff7557rzzjt16NAhBQcHa/bs2c7L9t555x05HA7Nnj3b2ZfNnTtXERERWr16tS6//PIC6w0PDy/2M8/xIlA15aT+qbwTZQpj/llevk7u96644gotXrxY3377rTp16iRJmj9/vurUqaPFixdrwIAB2rt3r/r3769WrVpJyuuf8kVFRUmSYmJiih1DLioqSj4+PgoNDS3QJ2RnZ2vGjBlq3bq1pJIdc9WqVUvjxo1zruPOO+/U559/rvfff18dOnQ4bR88ZcoUl/7voYce0q5du5z7du2112rVqlV64IEHStRH53viiSeczx988EH17t1bmZmZCgwMLPPfqxSlytOiRXn/+vv/O69fP6lPH8nHxzX2nXcKxvbuLfXoIZ364+SNNwrGXnpp2eR8kvbt2zund+7cqePHj7sUT6S88Qnatm3rfP7KK69ozpw52rt3r06cOCG73V7iQdDOO+8853SNGjUkydlh5M9LSkqSJGVkZGjXrl269dZbNXz4cGdMTk5OgUF8T15vXFycJCkpKclZYCqJsWPHatiwYXr77bfVvXt3DRgwQA0bNiz2NT/99JMmTZqkTZs26dixY3I4HJLyOqcWLVo4405+n4GqZPz48UUuO7Uoc9999xUZe3KRSFKZDwQ5fvx4rVu3Tj/88INCQ0Ndlm3atEmrVq1yKTTn27Vrl7Mo1a5dO5dlW7ZsUXx8vEvunTt3Vnp6uv7++2/VrVu3wPo2bdqkX375RfPnz3fOM8bI4XBo9+7dat68eYn259dff1Vubq4zt3xZWVmqVq2aJCkhIUEDBgwo0foASPWigvI+z4UUpiwWi+pFBZXbtj/99FOFhIQoOzvbWUSfNGmSRo0apVatWrmMI7Vp0ybt3LmzQF+WmZmpXbt26euvv9YVV1zhnD9r1iwNGTKk2O1zvAhUTbawesobS6owln+Wl4/C+r1+/frp008/VceOHZ1x1apVU9OmTbVlyxZJ0pgxY3THHXfoiy++UPfu3dW/f3+Xz/6p5s+fr9tvv935fNmyZbr44ouLjPfz83NZX0mOuXJzczV16lS9//772rdvn+x2u7KyshQUVLLvjVP7xKCgIJdiW40aNfTDDz9IKnkffep6T+4TCztGPVsUpcpTQEDBeTZb3qM8YstYcHCwczo9Pe/uMZ999plq1arlEuf/T3Fs4cKFGjdunJ577jnFx8crNDRUzzzzjNavX1+i7fn6+jqn83+onTovv7CTn8/rr7/u0vFIks8pBb/C1pu/npKaNGmSBg8erM8++0zLli3TxIkTtXDhQl1zzTWFxmdkZKhHjx7q0aOH5s+fr+joaO3du1c9evSQ3W53iT35fQaqkjMZcLe8Yk9n4cKFevbZZ/XZZ5+5nCGZLz09XVdddZWeeuqpAsvyv8Clsvmcp6en6/bbb9eYMWMKLDuTA4T09HT5+Pjop59+KtBf5hfXAgMDvfBL1wAACFZJREFUzy5ZoIq55YI6embVzkKXGWN0a4eyP4jP161bN7366qvy8/NTzZo1XQYaP7XvSU9PV7t27VyK2/mio6Pl5+enhIQE57z8ok9xOF4EqqbQc4cq5afnilhqFNry5nLbdmH93pIlS077umHDhqlHjx767LPP9MUXX2jatGl67rnndOeddxYa36dPH5e+49R+7VSBgYEuf3AsyTHXM888oxdffFEvvPCCWrVqpeDgYN19990FfjMW5dS+6+Tn+fNO7ROL66OLWq9Ufn0iRSmUSIsWLeTv76+9e/e6nNZ3svxTJU++K9WuXbtcYvz8/MrkDnM1atRQzZo19ccff5z2L3jFOZN8mjRpoiZNmuiee+7RoEGDNHfuXF1zzTWFrmPr1q06cuSInnzySdWpk3fHnQ0bNpR5TgDKT0JCgm699VY9+eSTzktFTnX++efrww8/VP369c/ojlPNmzfXhx9+6LxDl5TXh4aGhqp27dpFbuv3339Xo0aNznxnTtK2bVvl5uYqKSmpyL/2nXfeeVq5cqXLJTYno58CXDWODtHsgW007P0El7vvGWM0e2AbNapefn+ACg4OLnG/cP755+u9995TTEyMwsLCCo0pbF0l/cxzvAhUHb6RjVW9+2s6/OVtOvnue5JR9e6vyTfi7I5XilNYv9e8eXPl5ORo/fr1zsv3jhw5om3btrlcpVKnTh2NGDFCI0aM0EMPPaTXX39dd955p/OPmid/1kNDQwucWSqVvE8oyTHXt99+q6uvvlrXX3+9pLzCz/bt211yLqs+qCR9dEmUdZ/I/QlRIqGhoRo3bpzuuecezZs3T7t27dLPP/+sl19+WfPmzZOUN77Jhg0b/r+9+wuJKu/jOP6ZrZlZy8WJNKZac0qsYMGCRaguUhZiLifp4aFQR7uQyqYMWbCgkIrwLqIuuqybuhSUupCIpn9GDqTZQmrUFBWVnpQgzD/gdy+iYWedsdkHn/Hh8f2CuZlz5ne+R5nv/M6Xc35fdXZ2anBwUCdOnFAsFksaJxAIqK+vTwMDA3IcR1NTU/9xTCdPnlRra6vOnz+vwcFBPXnyRJcuXdLZs2czHiMQCOjz58+6efOmHMfR2NjMNR++fPmiSCSiaDSqV69e6f79+4rFYolHZVKNsWbNGnk8Hl24cEEvXrxQR0eHTp8+nXFM8Xhcvb29chxHExMTGZ8PgLnhOI527typiooKVVdX6/3790mv4eFhSdLBgwc1MjKiPXv2KBaL6fnz5+rs7NTevXtn/bFuaGjQ69evdejQIfX396u9vV0tLS1qampK2zq4ublZXV1dikQi6u3t1bNnz9Te3q5IJPKPzm39+vWqqqpSOBxWW1ub4vG4uru71draquvXr0uSjh07plgspoaGBvX19am/v18XL16U4ziSvuaphw8f6uXLl3Ich7sJAEl1ZYXqb/5Nv1cU69+bVun3imL1N/+murLC+Q4toaqqSvn5+QqFQrp7967i8bii0agOHz6sN2/epP1cpt/5hTxfBBain34J6+faP5T3a5OWlvxLeb826efaP/TTL+Gsx1JSUqJQKKT6+nrdu3dPjx8/VnV1tVavXq1QKCTp6xIPnZ2disfjevTokW7dupW4pisqKpLL5dK1a9c0PDycuKsolUAgoDt37ujt27eJuVEqmcy5SkpKdOPGDXV1denp06fat2+fPnz4MON4czHvyiRHZ2LOr1dnXQYdGcl0Vfn/Vem6742OjibtNz09befOnbMNGzaY2+22goICCwaDdvv2bTP72mK8rq7O8vLyzOfz2YEDB+zo0aO2adOmxBhDQ0O2Y8cOy83NndHit6enZ9YY/h6nmdmVK1ds8+bN5vF4bNmyZbZ9+3Zra2szM0s57ujoaOK43+zfv9+WL1+etsXvxMSE7d692woLC83j8diqVassEokk/b9TjXH16lULBALm9Xpt69at1tHRkRRPur/z+Pi47dq1y3w+36wtNgH891y+fHlGu9u/voqKihL7Dg4OWmVlZaL98MaNG+3IkSOJLn3l5eXW2Ng44xjRaNTKysrM4/GY3++35uZmm5qamjWu7u7uRA5dunSplZaW2pkzZxLbGxsbrby8POkzqd771v0qEAiY2+22lStXWmVlpfX19SXFt23bNvN6vebz+SwYDCby1cDAgG3ZssVycnISrYkBZF+qLlTf2/bu3TsLh8OWn59vXq/X1q1bZ/X19fbp06e0x0n1nWe+CGA+zJb3RkZGrKamxvLy8iwnJ8eCwWCiy52ZWSQSseLiYvN6vVZQUGA1NTXmOE5i+6lTp8zv95vL5bLa2tq0MTx48MBKS0vN6/Xat5JKqtxj9v0518ePHy0UCllubq6tWLHCjh8/buFwOOkcM83BqWJoaWlJyq/fy9Gpxu3p6Uma72V6vZppncRllqZVCDI2Pj6ueDy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" ] @@ -865,13 +850,13 @@ "Learner ml_m: LogisticRegressionCV()\n", "Out-of-sample Performance:\n", "Regression:\n", - "Learner ml_g0 RMSE: [[0.17242002 0.18142511 0.25935844 0.25958681 0.17338302 0.15220244\n", - " 0.20145569 0.20566302 0.17235633 0.15194071 0.20103747 0.16455826]]\n", - "Learner ml_g1 RMSE: [[0.09911511 0.13069581 0.13900982 0.15099534 0.13918785 0.11262764\n", - " 0.08873154 0.1102721 0.131316 0.16060588 0.15919193 0.16009149]]\n", + "Learner ml_g0 RMSE: [[0.17280482 0.18273009 0.25965768 0.25946547 0.17286749 0.15159456\n", + " 0.20062163 0.20576772 0.17416292 0.15124097 0.20127059 0.16501317]]\n", + "Learner ml_g1 RMSE: [[0.10329207 0.12864046 0.14013825 0.14544455 0.14122605 0.11350921\n", + " 0.08570141 0.10355616 0.13376426 0.16146262 0.15928815 0.16044963]]\n", "Classification:\n", - "Learner ml_m Log Loss: [[0.22914236 0.22913907 0.22913769 0.22913938 0.35596113 0.35595921\n", - " 0.35596421 0.35595231 0.60886635 0.60885348 0.60885687 0.60885362]]\n", + "Learner ml_m Log Loss: [[0.22913674 0.22913857 0.22913404 0.22914337 0.3559686 0.35595763\n", + " 0.35596035 0.35595994 0.60884191 0.60884291 0.6088457 0.60884412]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 10\n", @@ -879,32 +864,32 @@ "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % \\\n", - "ATT(2004.0,2003,2004) -0.016264 0.023168 -0.702029 0.482661 -0.061672 \n", - "ATT(2004.0,2003,2005) -0.077256 0.028982 -2.665626 0.007685 -0.134060 \n", - "ATT(2004.0,2003,2006) -0.136480 0.035719 -3.820990 0.000133 -0.206487 \n", - "ATT(2004.0,2003,2007) -0.104836 0.033835 -3.098432 0.001945 -0.171152 \n", - "ATT(2006.0,2003,2004) -0.001649 0.023339 -0.070667 0.943663 -0.047394 \n", - "ATT(2006.0,2004,2005) -0.005104 0.019313 -0.264274 0.791569 -0.042956 \n", - "ATT(2006.0,2005,2006) -0.003129 0.017887 -0.174943 0.861124 -0.038187 \n", - "ATT(2006.0,2005,2007) -0.041235 0.020320 -2.029240 0.042434 -0.081062 \n", - "ATT(2007.0,2003,2004) 0.028086 0.015158 1.852874 0.063900 -0.001623 \n", - "ATT(2007.0,2004,2005) -0.004803 0.016425 -0.292438 0.769951 -0.036996 \n", - "ATT(2007.0,2005,2006) -0.030093 0.017879 -1.683182 0.092340 -0.065134 \n", - "ATT(2007.0,2006,2007) -0.028444 0.016791 -1.693985 0.090268 -0.061354 \n", + "ATT(2004.0,2003,2004) -0.016311 0.023291 -0.700298 0.483741 -0.061961 \n", + "ATT(2004.0,2003,2005) -0.079929 0.028965 -2.759481 0.005789 -0.136701 \n", + "ATT(2004.0,2003,2006) -0.138565 0.035080 -3.950017 0.000078 -0.207319 \n", + "ATT(2004.0,2003,2007) -0.102563 0.033298 -3.080115 0.002069 -0.167826 \n", + "ATT(2006.0,2003,2004) 0.000346 0.023067 0.015005 0.988028 -0.044865 \n", + "ATT(2006.0,2004,2005) -0.005166 0.019393 -0.266391 0.789938 -0.043175 \n", + "ATT(2006.0,2005,2006) -0.004599 0.017809 -0.258254 0.796211 -0.039504 \n", + "ATT(2006.0,2005,2007) -0.041240 0.020283 -2.033242 0.042028 -0.080995 \n", + "ATT(2007.0,2003,2004) 0.027119 0.015109 1.794824 0.072682 -0.002495 \n", + "ATT(2007.0,2004,2005) -0.004499 0.016376 -0.274728 0.783525 -0.036595 \n", + "ATT(2007.0,2005,2006) -0.030829 0.017972 -1.715418 0.086269 -0.066053 \n", + "ATT(2007.0,2006,2007) -0.028859 0.016941 -1.703485 0.088477 -0.062064 \n", "\n", " 97.5 % \n", - "ATT(2004.0,2003,2004) 0.029143 \n", - "ATT(2004.0,2003,2005) -0.020452 \n", - "ATT(2004.0,2003,2006) -0.066473 \n", - "ATT(2004.0,2003,2007) -0.038520 \n", - "ATT(2006.0,2003,2004) 0.044095 \n", - "ATT(2006.0,2004,2005) 0.032748 \n", - "ATT(2006.0,2005,2006) 0.031928 \n", - "ATT(2006.0,2005,2007) -0.001408 \n", - "ATT(2007.0,2003,2004) 0.057795 \n", - "ATT(2007.0,2004,2005) 0.027389 \n", - "ATT(2007.0,2005,2006) 0.004948 \n", - "ATT(2007.0,2006,2007) 0.004466 \n" + "ATT(2004.0,2003,2004) 0.029339 \n", + "ATT(2004.0,2003,2005) -0.023158 \n", + "ATT(2004.0,2003,2006) -0.069810 \n", + "ATT(2004.0,2003,2007) -0.037299 \n", + "ATT(2006.0,2003,2004) 0.045557 \n", + "ATT(2006.0,2004,2005) 0.032843 \n", + "ATT(2006.0,2005,2006) 0.030306 \n", + "ATT(2006.0,2005,2007) -0.001486 \n", + "ATT(2007.0,2003,2004) 0.056733 \n", + "ATT(2007.0,2004,2005) 0.027597 \n", + "ATT(2007.0,2005,2006) 0.004395 \n", + "ATT(2007.0,2006,2007) 0.004345 \n" ] } ], @@ -915,17 +900,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\bachp\\Documents\\Promotion\\DissundPapers\\Software\\DoubleML\\doubleml-for-py\\doubleml\\did\\did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", - " warnings.warn(\n" - ] - }, { "data": { "text/plain": [ @@ -933,13 +910,13 @@ " )" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -950,6 +927,7 @@ ], "source": [ "es_rf = dml_obj_lasso.aggregate(\"eventstudy\")\n", + "es_rf.aggregated_frameworks.bootstrap()\n", "es_rf.plot_effects(title=\"Estimated ATTs by Group, LassoCV and LogisticRegressionCV()\")" ] } From 49ad7f116ac16f9d8a4319a5e7c252b2f4fe7483 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Thu, 22 May 2025 11:10:24 +0000 Subject: [PATCH 131/140] clear output --- doc/examples/did/py_panel_data_example.ipynb | 616 +------------------ 1 file changed, 27 insertions(+), 589 deletions(-) diff --git a/doc/examples/did/py_panel_data_example.ipynb b/doc/examples/did/py_panel_data_example.ipynb index c570987b..7ea893ed 100644 --- a/doc/examples/did/py_panel_data_example.ipynb +++ b/doc/examples/did/py_panel_data_example.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -49,193 +49,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "year", - "rawType": "int32", - "type": "integer" - }, - { - "name": "countyreal", - "rawType": "float64", - "type": "float" - }, - { - "name": "lpop", - "rawType": "float64", - "type": "float" - }, - { - "name": "lemp", - "rawType": "float64", - "type": "float" - }, - { - "name": "first.treat", - "rawType": "float64", - "type": "float" - }, - { - "name": "treat", - "rawType": "float64", - "type": "float" - } - ], - "ref": "cfd60d8a-39c4-4501-8de6-4be80e47c406", - "rows": [ - [ - "0", - "2003", - "8001.0", - "5.896760933305299", - "8.461469042643875", - "2007.0", - "1.0" - ], - [ - "1", - "2004", - "8001.0", - "5.896760933305299", - "8.336869637284956", - "2007.0", - "1.0" - ], - [ - "2", - "2005", - "8001.0", - "5.896760933305299", - "8.340217320947035", - "2007.0", - "1.0" - ], - [ - "3", - "2006", - "8001.0", - "5.896760933305299", - "8.37816098272068", - "2007.0", - "1.0" - ], - [ - "4", - "2007", - "8001.0", - "5.896760933305299", - "8.487352349405215", - "2007.0", - "1.0" - ] - ], - "shape": { - "columns": 6, - "rows": 5 - } - }, - "text/html": [ - "
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" - ], - "text/plain": [ - " year countyreal lpop lemp first.treat treat\n", - "0 2003 8001.0 5.896761 8.461469 2007.0 1.0\n", - "1 2004 8001.0 5.896761 8.336870 2007.0 1.0\n", - "2 2005 8001.0 5.896761 8.340217 2007.0 1.0\n", - "3 2006 8001.0 5.896761 8.378161 2007.0 1.0\n", - "4 2007 8001.0 5.896761 8.487352 2007.0 1.0" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# download file from did package for R\n", "url = \"https://github.com/bcallaway11/did/raw/refs/heads/master/data/mpdta.rda\"\n", @@ -267,34 +83,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLPanelData Object ==================\n", - "\n", - "------------------ Data summary ------------------\n", - "Outcome variable: lemp\n", - "Treatment variable(s): ['first.treat']\n", - "Covariates: ['lpop']\n", - "Instrument variable(s): None\n", - "Time variable: year\n", - "Id variable: countyreal\n", - "No. Observations: 500\n", - "\n", - "------------------ DataFrame info ------------------\n", - "\n", - "RangeIndex: 2500 entries, 0 to 2499\n", - "Columns: 6 entries, year to treat\n", - "dtypes: float64(5), int32(1)\n", - "memory usage: 107.6 KB\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Set values for treatment group indicator for never-treated to np.inf\n", "mpdta.loc[mpdta['first.treat'] == 0, 'first.treat'] = np.inf\n", @@ -330,29 +121,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " coef std err t P>|t| 2.5 % 97.5 %\n", - "ATT(2004.0,2003,2004) -0.0105 0.0234 -0.4491 0.6533 -0.0564 0.0353\n", - "ATT(2004.0,2003,2005) -0.0704 0.0309 -2.2811 0.0225 -0.1309 -0.0099\n", - "ATT(2004.0,2003,2006) -0.1373 0.0366 -3.7561 0.0002 -0.2089 -0.0656\n", - "ATT(2004.0,2003,2007) -0.1008 0.0345 -2.9255 0.0034 -0.1683 -0.0333\n", - "ATT(2006.0,2003,2004) 0.0065 0.0234 0.2777 0.7813 -0.0393 0.0523\n", - "ATT(2006.0,2004,2005) -0.0027 0.0195 -0.1401 0.8886 -0.0409 0.0355\n", - "ATT(2006.0,2005,2006) -0.0046 0.0177 -0.2584 0.7961 -0.0393 0.0302\n", - "ATT(2006.0,2005,2007) -0.0412 0.0203 -2.0283 0.0425 -0.0810 -0.0014\n", - "ATT(2007.0,2003,2004) 0.0305 0.0151 2.0169 0.0437 0.0009 0.0601\n", - "ATT(2007.0,2004,2005) -0.0027 0.0164 -0.1626 0.8708 -0.0348 0.0295\n", - "ATT(2007.0,2005,2006) -0.0311 0.0179 -1.7371 0.0824 -0.0663 0.0040\n", - "ATT(2007.0,2006,2007) -0.0262 0.0167 -1.5696 0.1165 -0.0588 0.0065\n" - ] - } - ], + "outputs": [], "source": [ "dml_obj = DoubleMLDIDMulti(\n", " obj_dml_data=dml_data,\n", @@ -389,29 +160,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2.5 % 97.5 %\n", - "ATT(2004.0,2003,2004) -0.077246 0.056229\n", - "ATT(2004.0,2003,2005) -0.158449 0.017634\n", - "ATT(2004.0,2003,2006) -0.241543 -0.033030\n", - "ATT(2004.0,2003,2007) -0.199055 -0.002518\n", - "ATT(2006.0,2003,2004) -0.060206 0.073193\n", - "ATT(2006.0,2004,2005) -0.058341 0.052880\n", - "ATT(2006.0,2005,2006) -0.055168 0.046003\n", - "ATT(2006.0,2005,2007) -0.099079 0.016728\n", - "ATT(2007.0,2003,2004) -0.012621 0.073559\n", - "ATT(2007.0,2004,2005) -0.049479 0.044141\n", - "ATT(2007.0,2005,2006) -0.082276 0.019993\n", - "ATT(2007.0,2006,2007) -0.073682 0.021375\n" - ] - } - ], + "outputs": [], "source": [ "level = 0.95\n", "\n", @@ -432,32 +183,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\bachp\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\matplotlib\\cbook.py:1762: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = dml_obj.plot_effects()" ] @@ -487,46 +219,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDAggregation Object ==================\n", - " Event Study Aggregation \n", - "\n", - "------------------ Overall Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "-0.077247 0.020031 -3.856478 0.000115 -0.116507 -0.037988\n", - "------------------ Aggregated Effects ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "-3.0 0.030469 0.015107 2.016929 0.043703 0.000861 0.060078\n", - "-2.0 -0.000526 0.013300 -0.039529 0.968469 -0.026594 0.025542\n", - "-1.0 -0.024496 0.014236 -1.720682 0.085309 -0.052398 0.003406\n", - "0.0 -0.019998 0.011810 -1.693240 0.090410 -0.043145 0.003150\n", - "1.0 -0.050919 0.016811 -3.028909 0.002454 -0.083869 -0.017970\n", - "2.0 -0.137287 0.036551 -3.756054 0.000173 -0.208925 -0.065649\n", - "3.0 -0.100786 0.034451 -2.925461 0.003439 -0.168310 -0.033263\n", - "------------------ Additional Information ------------------\n", - "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", - "\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# rerun bootstrap for valid simultaneous inference (as values are not saved) \n", "dml_obj.bootstrap(n_rep_boot=5000)\n", @@ -570,22 +265,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0. , 0. , 0. , 0. , 0. ,\n", - " 0.23391813, 0. , 0. , 0. , 0. ,\n", - " 0.76608187, 0. ])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "aggregated_eventstudy.aggregation_weights[2]" ] @@ -603,41 +285,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\bachp\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\matplotlib\\cbook.py:1762: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n" - ] - }, - { - "data": { - "text/plain": [ - "(
,\n", - " [,\n", - " ,\n", - " ])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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LLbeYli1bmnXr1pn9+/ebb7/91nz99df55nY+nxFQ+nhz3zTGGElm8eLFLmNmenp60T4J8Dru6peJiYkufeuvv/4yERERZtKkSfnmVpI+Z1KUKgXi4uKMJPPFF18YYxyd1s/PzyxfvtwZs3v3biPJbN682RhjzGeffWasVquJjY11xixYsMCEh4ebjIwMl/3ffvvt5sknnzSTJk065x//8+fPN+XKlXPZx+OPP24aNGhwsaeJEsab+mXOH1g7duwompNDiVVc/TLnD/+TJ08WOhfGS5zJm/omYyZyFFe/XLVqlSlTpoyJj48vdC7n8xkBpZ839U1jHEWpFStWXORZoaQr7r9/cqxYscJYLBZz4MCBfHMpSZ8zWb5XCiQlJUmSIiIiJEnff/+9MjMz1bFjR2dMw4YNVb16dW3evFmStHnzZjVt2lQVK1Z0xsTExCg5OVm//PKLs23x4sX6888/NWnSpELlsnnzZt1www3y9/d32e+ePXt08uTJCz9JlDje1C9z3HLLLYqKitJ1112njz/++ILPDSVXcfZLSWrWrJkqVaqkTp066ZtvvikwF8ZLnMmb+mYOxkwUV7/8+OOPdfXVV+vZZ59VlSpVVL9+fY0dO1bp6en55nI+/R2lnzf1zRwPPPCAKlSooBYtWuj111+XMabIzhclQ3H/Ls/x2muvqWPHjqpRo0a+uZSkz5m+nk4AF8dut+uhhx5SmzZt1KRJE0lSbGys/P39VbZsWZfYihUrOq8hERsb69Lxc+7PuU+S9u7dq3Hjxumrr76Sr2/hukpsbKxq1aqV737LlSt3fieIEsnb+mVoaKhmzpypNm3ayGq16oMPPlCPHj20cuVK3XLLLRdzqihBirNfVqpUSQsXLtTVV1+tjIwMvfrqq2rXrp22bNmiq666Ks98GC+Rw9v6JmMmpOLtl3/++ae+/vprBQYGasWKFTpx4oRGjBih+Ph4LV68OM98CrNfXBq8rW9K0lNPPaUbb7xRwcHBWrt2rUaMGKHU1FSNHj26qE4bXq44++WZjhw5olWrVuntt98uMJ+S9DmTolQJ98ADD+jnn3/W119/XaT7zc7O1oABAzRlyhTVr1+/SPeN0s/b+mWFChU0ZswY5+1rrrlGR44c0XPPPccfWJeQ4uqXktSgQQM1aNDAebt169bat2+fZs+erTfffLPIj4fSxdv6JmMmpOLtl3a7XRaLRUuXLlWZMmUkSbNmzVLv3r01f/58BQUFFfkxUXp4Y9/8z3/+49y+8sorlZaWpueee46i1CWkOPvlmd544w2VLVtWPXr0KNbjuBPL90qwkSNH6tNPP9XGjRtVtWpVZ3t0dLRsNpsSExNd4o8dO6bo6GhnzNlX/c+5HR0drZSUFG3fvl0jR46Ur6+vfH199dRTT+nHH3+Ur6+vNmzYkGdO59ovSj9v7Jd5admypf74448LPEuUNMXZL/PTokWLAvsY4yUk7+ybeWHMvLQUd7+sVKmSqlSp4vyjX5IaNWokY4z+/vvvPHNizITknX0zLy1bttTff/+tjIyM8zo/lEzu+l1ujNHrr7+uO+64w2VZXl5K0phJUaoEMsZo5MiRWrFihTZs2JBrWl7z5s3l5+enzz//3Nm2Z88eHTp0SK1atZIktWrVSrt27VJcXJwzZt26dQoPD9dll12m8PBw7dq1Szt37nT+3HfffWrQoIF27typli1b5plbq1at9OWXXyozM9Nlvw0aNPCqKYIoet7cL/Oyc+dOVapU6SLPGt7OHf0yP+fqY4yXlzZv7ptF9RiUPO7ql23atNGRI0eUmprqjPn9999ltVpd/qA704X2d5QO3tw387Jz506VK1dOAQEBF3S+KBnc/bv8iy++0B9//KG77777nLmVqM+ZHrrAOi7C/fffb8qUKWM2bdrk8tWQp06dcsbcd999pnr16mbDhg1m+/btplWrVqZVq1bO+3O+erJz585m586dZvXq1SYyMrLAr9XN61vO5s2bZ2688Ubn7cTERFOxYkVzxx13mJ9//tksW7bMBAcHe+VXT6JoeXO/XLJkiXn77bfN7t27ze7du83TTz9trFaref3114vuCYBXcle/nD17tlm5cqXZu3ev2bVrl3nwwQeN1Wo169evd8YwXuJM3tw3GTMvXe7qlykpKaZq1aqmd+/e5pdffjFffPGFqVevnrnnnnucMR9++KHLt0RdyGcElB7e3Dc//vhj88orr5hdu3aZvXv3mvnz55vg4GAzceLEYn5W4Gnu/vtn0KBBpmXLlnnmUpI/Z1KUKoEk5fmzePFiZ0x6eroZMWKEKVeunAkODja33XabOXr0qMt+Dhw4YLp27WqCgoJMhQoVzCOPPGIyMzPzPW5ef/xPmjTJ1KhRw6Xtxx9/NNddd50JCAgwVapUMf/9738v9pRRAnhzv1yyZIlp1KiRCQ4ONuHh4aZFixYuX82K0std/XLGjBmmTp06JjAw0ERERJh27dqZDRs2uOyD8RJn8ua+yZh56XLn7/Ldu3ebjh07mqCgIFO1alUzZswYlz/kFi9ebM7+//Pz/YyA0sOb++aqVatMs2bNTGhoqAkJCTFXXHGFWbhwocnOzi6eJwNew539MjEx0QQFBZmXX345z1xK8udMizF8VyUAAAAAAADci2tKAQAAAAAAwO0oSgEAAAAAAMDtKEoBAAAAAADA7ShKAQAAAAAAwO0oSgEAAAAAAMDtKEoBAAAAAADA7ShKAQAAAAAAwO0oSgEAAAAAAMDtKEoBAAAAAADA7ShKAQAAAAAAwO0oSgEAAAAAAMDtKEoBAAAAAADA7ShKAQAAAAAAwO0oSgEAAAAAAMDtKEoBAAAAAADA7ShKAQAAAAAAwO0oSgEAAAAAAMDtKEoBAACvdeDAAVksFi1ZssTTqZRqNWvW1JAhQzydBgAAuMRQlAIAAB6zZMkSWSyWPH/GjRtXLMd85plntHLlynPGtWvXLt/czvyZPHlyseR5ts8++8xtxzofv/32mx577DE1a9ZMYWFhqlSpkrp3767t27fnGX/48GH17dtXZcuWVXh4uG699Vb9+eefeca+9tpratSokQIDA1WvXj3Nmzcv3zzeffddtWrVSiEhISpbtqxat26tDRs2FOocvv32W1133XUKDg5WdHS0Ro8erdTU1EI9FgAAXDhfTycAAADw1FNPqVatWi5tTZo0UY0aNZSeni4/P78iO9Yzzzyj3r17q0ePHgXGPfHEE7rnnnuct7dt26YXXnhBEyZMUKNGjZztl19+eZHlVpDPPvtML730ktcVpl599VW99tpr6tWrl0aMGKGkpCQtWrRI1157rVavXq2OHTs6Y1NTU9W+fXslJSVpwoQJ8vPz0+zZs9W2bVvt3LlT5cuXd8YuWrRI9913n3r16qUxY8boq6++0ujRo3Xq1Ck9/vjjLjlMnjxZTz31lHr37q0hQ4YoMzNTP//8sw4fPnzO/Hfu3KkOHTqoUaNGmjVrlv7++289//zz2rt3r1atWlV0TxQAAMiFohQAAPC4rl276uqrr87zvsDAwHM+Pi0tTSEhIUWaU6dOnXLl8cILL6hTp05q166dW3PxZv3799fkyZMVGhrqbBs6dKgaNWqkyZMnuxSl5s+fr71792rr1q265pprJDle+yZNmmjmzJl65plnJEnp6el64okn1L17d73//vuSpHvvvVd2u11Tp07VsGHDVK5cOUnSd999p6eeekozZ87Uww8/fN75T5gwQeXKldOmTZsUHh4uybGc8d5779XatWvVuXPnC3tiAADAObF8DwAAeK28rik1ZMgQhYaGat++ferWrZvCwsI0cOBASdLevXvVq1cvRUdHKzAwUFWrVlW/fv2UlJQkSbJYLEpLS9Mbb7zhXH53MddSmjx5siwWi3799VcNGDBA5cqV03XXXee8/6233lLz5s0VFBSkiIgI9evXT3/99ZfLPr766iv16dNH1atXV0BAgKpVq6aHH35Y6enpLuf80ksvOc8h5yeH3W7XnDlz1LhxYwUGBqpixYoaPny4Tp486XIsY4ymTZumqlWrKjg4WO3bt9cvv/yS57nt27dP+/btO+dz0Lx5c5eClCSVL19e119/vXbv3u3S/v777+uaa65xFqQkqWHDhurQoYPee+89Z9vGjRsVHx+vESNGuDz+gQceUFpamv73v/852+bMmaPo6Gg9+OCDMsac17K75ORkrVu3ToMGDXIWpCTpzjvvVGhoqEtOAACg6DFTCgAAeFxSUpJOnDjh0lahQoV847OyshQTE6PrrrtOzz//vIKDg2Wz2RQTE6OMjAyNGjVK0dHROnz4sD799FMlJiaqTJkyevPNN3XPPfeoRYsWGjZsmCSpTp06F51/nz59VK9ePT3zzDMyxkiSnn76af3nP/9R3759dc899+j48eOaN2+ebrjhBu3YsUNly5aVJC1fvlynTp3S/fffr/Lly2vr1q2aN2+e/v77by1fvlySNHz4cB05ckTr1q3Tm2++mev4w4cP15IlS3TXXXdp9OjR2r9/v1588UXt2LFD33zzjXP548SJEzVt2jR169ZN3bp10w8//KDOnTvLZrPl2meHDh0kOQqDFyI2NtblNbTb7frpp580dOjQXLEtWrTQ2rVrlZKSorCwMO3YsUOScs2ea968uaxWq3bs2KFBgwZJkj7//HO1bt1aL7zwgqZNm6b4+HhFR0friSee0MiRIwvMcdeuXcrKysp1HH9/fzVr1syZBwAAKB4UpQAAgMeducQrR05xJy8ZGRnq06ePpk+f7mzbuXOn9u/fr+XLl6t3797O9okTJzq3Bw0apPvuu0+1a9d2FjWKwhVXXKG3337befvgwYOaNGmSpk2bpgkTJjjbe/bsqSuvvFLz5893ts+YMUNBQUHOmGHDhqlu3bqaMGGCDh06pOrVq6tVq1aqX7++c1bPmb7++mu9+uqrWrp0qQYMGOBsb9++vbp06aLly5drwIABOn78uJ599ll1795dn3zyiXOm1RNPPOFcNldUvvrqK23evFlPPvmksy0hIUEZGRmqVKlSrvictiNHjqhBgwY6evSofHx8FBUV5RLn7++v8uXL68iRI5KkkydP6sSJE/rmm2+0YcMGTZo0SdWrV9fixYs1atQo+fn5afjw4fnmefToUZfjn53TV199df4nDwAACo3lewAAwONeeuklrVu3zuXnXO6//36X22XKlJEkrVmzRqdOnSqWPPNz3333udz+8MMPZbfb1bdvX504ccL5Ex0drXr16mnjxo3O2DMLUmlpaTpx4oRat24tY0yhZuosX75cZcqUUadOnVyOlbOsLudY69evl81m06hRo1yW/j300EN57vfAgQMXNEsqLi5OAwYMUK1atfTYY48523OWIwYEBOR6TM51w3Ji0tPT5e/vn+f+AwMDnXE5S/Xi4+P16quvauzYserbt6/+97//6bLLLtO0adMKzPVcOZ25hBIAABQ9ZkoBAACPa9GiRb4XOs+Lr6+vqlat6tJWq1YtjRkzRrNmzdLSpUt1/fXX65ZbbtGgQYOcBavicvY3B+7du1fGGNWrVy/P+DO/TfDQoUOaOHGiPv7441zXgMq5FlZB9u7dq6SkpFyzinLExcVJcszekpQrp8jISOdFwy9WWlqabrrpJqWkpOjrr792udZUTvEtIyMj1+NOnz7tEhMUFJTnksKc2DPjJMfzeebsOKvVqttvv12TJk1yzjbLy7lyOrNgCAAAih5FKQAAUOIEBATIas094XvmzJkaMmSIPvroI61du1ajR4/W9OnT9d133+UqYhWls4sXdrtdFotFq1atko+PT674nGJNdna2OnXqpISEBD3++ONq2LChQkJCdPjwYQ0ZMkR2u/2cx7bb7YqKitLSpUvzvD8yMvICzuj82Ww29ezZUz/99JPWrFmjJk2auNwfERGhgIAA55K5M+W0Va5cWZJj6Vx2drbi4uJcim02m03x8fHOuIiICAUGBqps2bK5nuecx508eTLfolTOsr38cso5DgAAKB4UpQAAQKnStGlTNW3aVE8++aS+/fZbtWnTRgsXLnQu5Tpz6VpxqVOnjowxqlWrlurXr59v3K5du/T777/rjTfe0J133ulsz2v5Yn5516lTR+vXr1ebNm0KnNlTo0YNSY6ZVbVr13a2Hz9+PNcMrfNlt9t155136vPPP9d7772ntm3b5oqxWq1q2rSptm/fnuu+LVu2qHbt2goLC5MkNWvWTJK0fft2devWzRm3fft22e125/1Wq1XNmjXTtm3bZLPZXJb85Vx3qqCiXJMmTeTr66vt27erb9++znabzaadO3e6tAEAgKLHNaUAAECpkJycrKysLJe2pk2bymq1uizPCgkJUWJiYrHm0rNnT/n4+GjKlCm5LthujFF8fLwkOWf3nBljjNHcuXNz7TMkJESScuXet29fZWdna+rUqbkek5WV5Yzv2LGj/Pz8NG/ePJfjzZkzJ89z2Ldvn/bt21fwif5j1KhRevfddzV//nz17Nkz37jevXtr27ZtLoWpPXv2aMOGDerTp4+z7cYbb1RERIQWLFjg8vgFCxYoODhY3bt3d7bdfvvtys7O1htvvOFsO336tJYuXarLLrvMZbbTb7/9pkOHDjlvlylTRh07dtRbb72llJQUZ/ubb76p1NRUl5wAAEDRY6YUAAAoFTZs2KCRI0eqT58+ql+/vrKysvTmm2/Kx8dHvXr1csY1b95c69ev16xZs1S5cmXVqlVLLVu2LNJc6tSpo2nTpmn8+PE6cOCAevToobCwMO3fv18rVqzQsGHDNHbsWDVs2FB16tTR2LFjdfjwYYWHh+uDDz7Ic+ZS8+bNJUmjR49WTEyMfHx81K9fP7Vt21bDhw/X9OnTtXPnTnXu3Fl+fn7au3evli9frrlz56p3796KjIzU2LFjNX36dN10003q1q2bduzYoVWrVqlChQq5jtehQwdJOufFzufMmaP58+erVatWCg4O1ltvveVy/2233eYsqI0YMUKvvPKKunfvrrFjx8rPz0+zZs1SxYoV9cgjjzgfExQUpKlTp+qBBx5Qnz59FBMTo6+++kpvvfWWnn76aUVERDhjhw8frldffVUPPPCAfv/9d1WvXl1vvvmmDh48qE8++cQll0aNGqlt27batGmTs+3pp59W69at1bZtWw0bNkx///23Zs6cqc6dO6tLly4FnjsAALhIBgAAwEMWL15sJJlt27blef/+/fuNJLN48WJn2+DBg01ISEiu2D///NMMHTrU1KlTxwQGBpqIiAjTvn17s379epe43377zdxwww0mKCjISDKDBw8uVK7Lly83kszGjRudbZMmTTKSzPHjx/N8zAcffGCuu+46ExISYkJCQkzDhg3NAw88YPbs2eOM+fXXX03Hjh1NaGioqVChgrn33nvNjz/+mOu8s7KyzKhRo0xkZKSxWCzm7I9xL7/8smnevLkJCgoyYWFhpmnTpuaxxx4zR44cccZkZ2ebKVOmmEqVKpmgoCDTrl078/PPP5saNWrkeh5q1KhhatSocc7nZfDgwUZSvj/79+93if/rr79M7969TXh4uAkNDTU33XST2bt3b577fvnll02DBg2Mv7+/qVOnjpk9e7ax2+254o4dO2YGDx5sIiIiTEBAgGnZsqVZvXp1rjhJpm3btrnav/rqK9O6dWsTGBhoIiMjzQMPPGCSk5PPee4AAODiWIw5a045AAAAAAAAUMy4phQAAAAAAADcjqIUAAAAAAAA3I6iFAAAAAAAANyOohQAAAAAAADcjqIUAAAAAAAA3M7X0wmUBna7XUeOHFFYWJgsFoun0wEAAAAAAPAYY4xSUlJUuXJlWa35z4eiKFUEjhw5omrVqnk6DQAAAAAAAK/x119/qWrVqvneT1GqCISFhUlyPNnh4eEezubC2e12HT9+XJGRkQVWMgF3ol/CG9Ev4a3om/A6p0/L3HGHMjIz5f/227IGB3s6I8CJMRPeqLT0y+TkZFWrVs1ZL8kPRakikLNkLzw8vMQXpU6fPq3w8PAS3flRutAv4Y3ol/BW9E14neBg2ceOVXZiosIjImT19/d0RoATYya8UWnrl+e6xBFFKQAAAADFw9dX6tBBtrg4xzYAAGco+WU3AAAAAAAAlDgUpQAAAAAUD7td+vNP+Rw86NgGAOAMzKEFAAAAUDxsNlkeekhhNpu0ciVL+AAALvitcIk7mnxaR5NPS5LsdqOEk2mKsCXJanVcjKxSeKAqhQd6MkUAAACUVBaLFBEhc/q0YxsAgDNQlLrELdp8UE+t+z3f+yd2qq/JMQ3cmBEAAABKjYAAmSVLlBQXp6iAAE9nAwDwMhSlLnHDW9XQLY0rSpJ+jU3Rnct26v/6NdNl0WGSxCwpAAAAAABQLChKXeLOXJ5ntxtJUsOoUF1VtawHswIAAAAAAKUd374HAAAAoHjYbNKMGQqZN8+xDQDAGZgpBQAAAKB42O2yfPON/Gw2yW73dDYAAC9DUQoAAABA8fD1lRk+XOlJSfL35U8PAIArfjMAAAAAKFJZaUeVnRYrSbJfU1npCYEKTNglq9Vx9RCfkGj5hlTyZIoAAC9AUQoAAABAkUr56RUlbpnm0hZ7xnbZlk+qXKuJ7k0KAOB1StyFzl966SXVrFlTgYGBatmypbZu3Vpg/PLly9WwYUMFBgaqadOm+uyzz1zuHzJkiCwWi8tPly5divMUAAAAgFIt7PJ7VXnAFlXu/53KXzNbklS+02JH24AtCrv8Xg9nCADwBiWqKPXuu+9qzJgxmjRpkn744QddccUViomJUVxcXJ7x3377rfr376+7775bO3bsUI8ePdSjRw/9/PPPLnFdunTR0aNHnT/vvPOOO04HAAAAKJV8QyopIOpKBZS5TP4vvitJ8gur5WiLupKlewAASSWsKDVr1izde++9uuuuu3TZZZdp4cKFCg4O1uuvv55n/Ny5c9WlSxc9+uijatSokaZOnaqrrrpKL774oktcQECAoqOjnT/lypVzx+kAAAAApV9QkKczAAB4qRJzTSmbzabvv/9e48ePd7ZZrVZ17NhRmzdvzvMxmzdv1pgxY1zaYmJitHLlSpe2TZs2KSoqSuXKldONN96oadOmqXz58vnmkpGRoYyMDOft5ORkSZLdbpe9BH/VrTF2578l+TxQutjtdhlj6JPwKvRLeCv6JryOv7/s06ZJ77WR8fWjb8KrMGbCG5WWflnY/EtMUerEiRPKzs5WxYoVXdorVqyo3377Lc/HxMbG5hkfG/vvZRa7dOminj17qlatWtq3b58mTJigrl27avPmzfLx8clzv9OnT9eUKVNytR8/flynT58+31PzGgkJqf/8e1JxAZkezgZwsNvtSkpKkjHG+Y09gKfRL+Gt6JvwRlknT0qSEk6elK8178tuAJ7AmAlvVFr6ZUpKSqHiSkxRqrj069fPud20aVNdfvnlqlOnjjZt2qQOHTrk+Zjx48e7zMBKTk5WtWrVFBkZqfDw8GLPubhEZPg5/o0op6goljDCO9jtdlksFkVGRpboQRmlC/0S3oq+CW902l5OxyRFlCunwKgoT6cDODFmwhuVln4ZGBhYqLgSU5SqUKGCfHx8dOzYMZf2Y8eOKTo6Os/HREdHn1e8JNWuXVsVKlTQH3/8kW9RKiAgQAEBAbnarVZrie40FovV+W9JPg+UPhaLpcS/v1DyHU0+raPJjtmwdrtRwslTishMkdVqkSRVCg9UpfDC/fIFihNjJrxKZqYs774rWSSL3U6/hNdhzIQ3Kg39srC5l5iilL+/v5o3b67PP/9cPXr0kOSoIH7++ecaOXJkno9p1aqVPv/8cz300EPOtnXr1qlVq1b5Hufvv/9WfHy8KlXiG0EAAP9atPmgnlr3e773T+xUX5NjGrgxIwAoAbKzZdm6VWopyZ7t6WwAAF6mxBSlJGnMmDEaPHiwrr76arVo0UJz5sxRWlqa7rrrLknSnXfeqSpVqmj69OmSpAcffFBt27bVzJkz1b17dy1btkzbt2/Xyy+/LElKTU3VlClT1KtXL0VHR2vfvn167LHHVLduXcXExHjsPAEA3md4qxq6pbHjOoW/xqbozmU79X/9mumy6DBJYpYUAOTF11fm5puluO8knxL1pwcAwA1K1G+G22+/XcePH9fEiRMVGxurZs2aafXq1c6LmR86dMhliljr1q319ttv68knn9SECRNUr149rVy5Uk2aNJEk+fj46KefftIbb7yhxMREVa5cWZ07d9bUqVPzXJ4HALh0nbk8z243kqSGUaG6qmpZD2YFAF7O11e68UZpmaR8vkQIAHDpKlFFKUkaOXJkvsv1Nm3alKutT58+6tOnT57xQUFBWrNmTVGmBwAAAAAAgEIouVfNAgAAAODdjJGSkv7dBgDgDBSlAAAAABSPjAxZJk92bGfaPJoKAMD7UJQCAAAAUHxK8FeaAwCKF78hAAAAABSPwECZmTMd2/58kRAAwBVFKQAAAAAAALgdRSkAAAAAAAC4HUUpAAAAAMUjM1N6f7ljOyvLs7kAALwORSkAAAAAxSM7W5ZvvnVs27M9mwsAwOtQlAIAAABQPHx9ZWJiHNs+vp7NBQDgdShKAQAAACgevr5Sly6ObR8fz+YCAPA6FKUAAAAAAADgdhSlAAAAABQPY6T09H+3AQA4A0UpAAAAAMUjI0OWCRMc25k2z+YCAPA6FKUAAAAAAADgdhSlAAAAABSPgACZ5593bPv5ezYXAIDXoSgFAAAAoHhYLP9+657F4tlcAABeh6IUAAAAAAAA3I6iFAAAAIDikZUlffSRYzs727O5AAC8DkUpAAAAAMUjK0uWTZsc29lZHk0FAOB9KEoBAAAAKB6+vjLt2jm2fXw9mgoAwPtQlAIAAABQPHx9pVtvdWznXPAcAIB/UJQCAAAAAACA21GUAgAAAFA8jPn3AufGeDYXAIDXoSgFAAAAoHhkZMgydqxjO9Pm2VwAAF6HohQAAAAAAADcjqIUAAAAgOIRECDzzDOObT9/z+YCAPA6fC8rAAAAgOJhsUhBQf9uAwByyUo7quy0WEmS3W6XPSFBGYqQ1eqYR+QTEi3fkEqeTLHYUJQCAAAAAADwkJSfXlHilmkubbFnbJdt+aTKtZro3qTchKIUAAAAgOKRlSWtXu3YzvkWPgCAi7DL71VwnZslSRkndit+7RCV77xEARUaSXLMlCqtKEoBAAAAKB5ZWbKsWSO1lJSd5elsAMAr+YZUci7Ps9vtkiS/iAYKiLrSk2m5BRc6BwAAAFA8fHxk2rR2bFt9PJsLAMDrUJQCAAAAUDz8/KTefRzbvizSAAC4oigFAAAAAAAAt6MoBQAAAAAAALejKAUAAACgeJw+Lcsjjzi2bRmezQUA4HUoSgEAAAAoPv98kxQAAGejKAUAAACgyNkzTyll71LFd3TMkIr/5lGl/Py67JmnPJwZAMBblLii1EsvvaSaNWsqMDBQLVu21NatWwuMX758uRo2bKjAwEA1bdpUn332mcv9xhhNnDhRlSpVUlBQkDp27Ki9e/cW5yl4nVO2LL265aBGrdglSRq1Ypde3XJQp2xZHs4MAAAAJZE985ROrBuuhK/Gy5b6kyQpM+4HJXw5TifWDacwBQCQVMKKUu+++67GjBmjSZMm6YcfftAVV1yhmJgYxcXF5Rn/7bffqn///rr77ru1Y8cO9ejRQz169NDPP//sjHn22Wf1wgsvaOHChdqyZYtCQkIUExOj06dPu+u0POqULUv3vPejHvvkV23/O0mStP3vJD32ya+6570fKUwBAADgvKXtWab0A2tktyVKJtvRaLJltyUq/eAape1Z5tH8AADewWKMMZ5OorBatmypa665Ri+++KIkyW63q1q1aho1apTGjRuXK/72229XWlqaPv30U2fbtddeq2bNmmnhwoUyxqhy5cp65JFHNHbsWElSUlKSKlasqCVLlqhfv36Fyis5OVllypTR8ePHFR4enut+q9UqX19f522bzZbvviwWi/z8/C4oNjMzU/m9nPnFvr71oMZ/tkdJttyPKxvoq2dvvkyDr6qc734lyd/fv1A5nB2blZUlewHXGDifWD8/P1kslmKNzc7OVnZ2dpHE+vr6ymq1ek2s3W5XVlb+BUgfHx/5+Ph4JNZutysuLk6VKlVy9mFjjDIzMwu133PFnvn+LK5YqeD3sjePERcbK5XOMeL7vxPVZv4WbRndRtdUj5DEGOGpMSKv2Et5jDDG6OTJk4qKipLVamWMKGQsnyMuLjav9+fRFTcrO/YrWUzu962x+Mon+npVuu2TXPcxRuSOlfgccSGxhRkjcj5nRkRE5BsnMUZcbKw3fDYoSWNEeuz3il3WShV6famAiledc7+Sd44RycnJioyMVFJSUp51khy++d7jZWw2m77//nuNHz/e2Wa1WtWxY0dt3rw5z8ds3rxZY8aMcWmLiYnRypUrJUn79+9XbGysOnbs6Ly/TJkyatmypTZv3pxvUSojI0MZGf9+e0hycrIk6fnnn1dAQECu+Lp162rgwIHO288++2y+nbFGjRoaMmSI8/bs2bN16lTe05srVaqkYcOGOW+/+OKLSkxMzDM2MjJSI0aMcN5etGiRjh8/rre+/1sp1dtJQWWkfwa5HGm2bC3feVhZO1bp6NGjee43ODhYjz76qPP2m2++qYMHD+YZ6+fnpwkTJjhvv/POO/rjjz/yjJWkSZMmObfff/997d69O9/Y8ePHO38Jffzxx/rxxx/zjR07dqxCQkIkSatWrdL27dvzjX3wwQdVtmxZSdK6devy7WuSdP/99ysqKkqS9MUXX+iLL77IN/aee+5RlSpVJDlm9K1fvz7f2MGDB6tmzZqSpG3btmnVqlX5xvbv31/169eXJP3444/66KOP8o3t3bu3GjduLEn65Zdf9P777+cbe+utt6pZs2aSpN9//13vvPNOvrFdu3ZVixYtJEkHDhzQG2+8kW9sx44d1aZNG0nS4cOH9eqrr+aKMcYoLS1N3bp1U/v27SVJcXFxWrBgQb77bdWqlTp37ixJSkxM1Ny5c/ONvfrqq9W9e3dJUlpamp5//vl8Y6+44gr16NFDkmNMmj59er6xjRo1Ut++fZ23n3766XxjvXmMyEvZsmX14IMPOm+/9tprl9wYcSQ7SFJjpSQny24vK4kxwlNjRI62bduqXbt2ki7tMaJ69erq1q2b848gxggHPkfUlOTeMSL5px814ros+frkfmx2VpbmLftR4b/m7veMEQ58jnAo7jHCbrfLGKMPPvhAv/32W76xjBEOfI5wKO4xwvzzO/z1119Xgj3v16MkjBFn1kwKckFFKR8fHx09etT5psgRHx+vqKioAiumF+rEiRPKzs5WxYoVXdorVqyY7wASGxubZ3xsbKzz/py2/GLyMn36dE2ZMiVXe1paWp7V1+TkZJclhqmpqflWaVNSUnLFpqenFyo2JSVFaWlpecYGBgbmGetvt8kEhuUqSElStt0oNCtVKafy32/O/y4UJgdfX1+X2OTk5HxjJZ13bM4viqSkpAJjjx8/7ry/MLE51eHExMQCY0+cOOHcLkxsTlX55MmTBcbGx8crODi40LE5z1tCQkKBsQkJCRcUGx8fX2DsyZMnLyj2xIkTecYaY3T69OlCxeZITEx0xp7rNU5KSnLGnjp1qtCxNputwNiz3/fnE+tNY0RefHx8Ch1bWseIdCPJktOHAyUxRnhqjMhx5vv+Uh4jkpOTlZiYKGOMrFYrY8R5xPI5omjHCJt/VSWkp6hCSKasZ3zMNEaKT/eTzb9qnvtnjHDgc4RDcY8RdrtdSUlJ53ydGSP+jeVzRPGPEVknT0qS0tPTlZaVd3xJGCMKW5S6oOV7VqtVsbGxuYpSR44cUZ06dfJN+mIc+X/27j1OxvL/4/h7ZndnzwfLsussORUhsi31RSmipEQOHVSUkEo6qZCKzsdfUslSkfKtfFVScqhIQlaUcySHtU57ZHd2d67fH9tOxh6stTszu/t6Ph7zcM99f+a+P/e45pp7Pnvf171/v+rUqaOffvpJcXFxzvkPPfSQvv/+e61evbrAa2w2m2bNmqWBAwc6502dOlVPPvmkDh48qJ9++kmdOnXS/v37FRMT44zp37+/LBaLPv7440JzKexMqXr16ungwYMV7vK9a95bre93pyqnkJf5WS3q0jhSC4a047R7cUqtJy/fO3TokKKjo7l8T5x2X9rYsuojjttz9MmG/Zq55m/9sv+4YuuG6o6LG2pAm9ry97HQR3hJbFXuI4wxSk5OVlRUFJfvcRzhttjCPp9pf7yvlJ8el09OcoHX5vpWU3jHpxR63i0FltFHFIyVOI4oTWxJL987dOiQqlWrVmScRB9xtrHecGxQkfqIzMR1OvhJJ0Vev1z+NSv25Xu1atUq28v3Xn/9defGp0+frpCQEOey3Nxc/fDDD2revPmZrLLEatSoIR8fHx08eNBl/sGDBxUdHV3oa6Kjo4uNz//34MGDLkWpgwcPOk8fLIy/v3+hl+kFBAQoICDgtPtSkpjSxBaW0+lib2zfUOsO/KHkzIIf0BCbj/q1qVOq9ZbEyV8aFSHWarW6fOgqW+zJHZU3xTocDvn7+8vPz8/5xSbJ+UVQEt4QW16f+/LuIzwZ6w2f+5Njj9tzNGrB71q0JUlpWXl95tp9adr65WYt3XFE0/u3VlAJ/z+85XNfGfqIonjD594TfYTD4ZDFYpHVapXVaqWP8KJYb/ksu6uPsLW6SSbxR5346xs57OmSyZEsvrLaQhTc4EpVb3WTrH7Ft0/6iH9xHHHmsSX9fFosFtlsNpfjzLJY75nGestnmeOIPJ783Fv+aYs2m3+JP6Pe2EcUV9A62RkVpV555RVJeRW+adOmubyhNptNDRs21LRp085klSVms9nUrl07LVmyxHkttsPh0JIlSzRq1KhCXxMXF6clS5bovvvuc85bvHix80yrRo0aKTo6WkuWLHEWoVJTU7V69Wrdfffd5bIf3mZQ2zpauv2wFm1JUro9RzkOydcqhdp81b15TQ1qW8fTKQKAV5izfp8WbUlyKeLnGik5M0eLtiRpzvp9GhrbwIMZAoD3sPoFqcYVbytj61ylbJqh7MRf5FfrQoW3vF3BzQbI6hfk6RQBAF7gjIpSu3btkiR17dpVn3322WlPcyxrY8aM0a233qr27durQ4cOevXVV5WRkaHbbrtNknTLLbeoTp06zkED7733XnXu3FkvvfSSevXqpblz52rt2rV65513JOVVxe+77z49/fTTatKkiRo1aqQnnnhCtWvXdha+Krsgm6+m92+tOev36b2f/9Lqv1PUrk647ri4gQa1raMgW4UZCx8AytUnCfuVbi/8NPV0e64+SdhPUQoATmL1C1LouYPk9/LHOnCBVL3j8wqs39HTaQEAvEipKg7Lli0r6zxK5MYbb9ShQ4c0fvx4JSYmqk2bNlq0aJFzoPI9e/a4nHbZsWNHzZkzR48//rjGjRunJk2aaP78+WrZsqUz5qGHHlJGRobuvPNOJScn65JLLtGiRYvO6DS1ii7I5quhsQ3UJiZMHV5foTeua6X29d1bcAQAb7cvJVO5jsLHsch1GO1LyXRzRgBQQZTDeLMAgMqhVEWpvn37qkOHDnr44Ydd5j///PNas2aN5s2bVybJFWbUqFFFXq63fPnyAvP69eunfv36Fbk+i8WiSZMmadKkSWWVIgCgEqoTHqDthzOUU0hhysdqUZ3wqvPHDAAoMX9/mXHjpCW9Jb+Sj68DAKgaSjaa2yl++OEH9ezZs8D8q666Sj/88MNZJwUAgLfp36a2QmyFD04ZavNR/za13ZwRAFQAFosUFfXvNAAAJylVUSo9Pb3QOwn4+fkpNTX1rJMCAMDbDGpbRz2a11REgK98//n29LVK1QK4MQQAAABQGqUqSrVq1Uoff/xxgflz587Veeedd9ZJAQDgbfJvDPH8NeepXZ1wSVK7OuF67przNL1/a24MAQCFycmRVqzIm84t/GYRAICqq1RH0E888YSuv/567dy5U5dddpkkacmSJfroo4/KdTwpAAA8iRtDAMAZysmR5dNPpVhJuTmezgYA4GVKVZS65pprNH/+fE2ePFn//e9/FRgYqAsuuEDfffedOnfuXNY5AgAAAKiIrFaZ1q0l/SxZSnWRBgCgEiv1tQa9evVSr169yjIXAAAAAJWJzSYNGSLNfVvy8/N0NgAAL1PqP1ckJydr+vTpGjdunI4ePSpJ+vXXX7Vv374ySw4AAAAAAACVU6nOlPrtt9/UrVs3hYeHa/fu3Ro6dKgiIyP12Wefac+ePXr//ffLOk8AAAAAAABUIqU6U2rMmDEaMmSItm/froCAAOf8nj176ocffiiz5AAAAABUYFlZskyYkDdtt3s2FwCA1ylVUWrNmjW66667CsyvU6eOEhMTzzopAAAAAJWAMVJqav4Tj6YCAPA+pSpK+fv7K9X55fKvbdu2KSoq6qyTAgAAAFAJ2GwyY8fmTfsy0DkAwFWpilK9e/fWpEmTlJ2dLUmyWCzas2ePHn74YfXt27dMEwQAAABQQVmtUp06/04DAHCSUn0zvPTSS0pPT1fNmjV14sQJde7cWeeee65CQ0P1zDPPlHWOAAAAAAAAqGRKdfe98PBwLV68WCtXrtSGDRuUnp6uCy+8UN26dSvr/AAAAABUVDk50i+/5E3n5no2FwCA1ylxUSoyMlLbtm1TjRo1dPvtt+u1115Tp06d1KlTp/LMDwAAAEBFlZMjy0cfSbGScnM8nQ0AwMuU+PI9u93uHNx81qxZyszMLLekAAAAAFQCVqvMeeflTVsYUwoA4KrEZ0rFxcWpT58+ateunYwxGj16tAIDAwuNnTFjRpklCAAAAKCCstmkYcOkuTMkP+6+BwBwVeKi1IcffqhXXnlFO3fulCSlpKRwthQAAAAAAABKpcRFqVq1aunZZ5+VJDVq1EgffPCBqlevXm6JAQAAAAAAoPIq8YXdkZGROnz4sCSpa9eustls5ZYUAAAAgEogK0uWZ57Jm7bbPZsLAMDrMNA5AAAAgPJhjPTPH7Yl49FUAADeh4HOAQAAAJQPm01m9Ghp9c+SLwOdAwBclWqgc4vFwkDnAAAAAIpntUqNGkmr/5kGAOAkDHQOAAAAAAAAtzujP1f07NlTKSkp2rVrl6pXr65nn31WycnJzuVHjhzReeedV9Y5AgAAAKiIcnOlhIS8aUeuR1MBAHifMypKLVq0SFlZWc7nkydP1tGjR53Pc3JytHXr1rLLDgAAAEDFlZ0ty6xZedM5OZ7NBQDgdc7qwm5juIMGAAAAgCJYrVLjxnnTFsaUAgC44psBAAAAQPmw2WRGjcqb9uPuewAAV2dUlLJYLLJYLAXmAQAAAAAAoHQc2ceVtmmGjiy/V5J0ZPm9Sts0Q47s4x7OrHyV+O57Ut7lekOGDJG/v78kKTMzU8OHD1dwcLAkuYw3BQAAAAAAgOI5so/r8OK7dGL3N3Jkp0mSsg+u09Gj23RizzLVuOJtWf2CPJxl+TijotStt97q8vymm24qEHPLLbecXUYAAAAAKge7XZYXXpDqScrO9nQ2AOCVMrbOzStI2ZP/nWly5bAn68Rf3yhj61yFtrzdY/mVpzMqSsXHx5dXHgAAAAAqG4dD2r8/ryhlHJ7OBgC8Uvq2eXJkpxe6zJGdrvRt8yptUYqBzgEAAACUD5tNZvjwvGlfBjoHgMLkpu+TTG7hCx25ecsrKYpSAAAAAMqH1So1a/bvNACgAJ+QOpLFp/CFVp+85ZUU3wwAAAAAAAAeEtK0n6x+IYUus/qFKKRpPzdn5D4UpQAAAACUj9xc6fff86YdRVyaAgBVXHCzAQps2F1W/wjJ8s/Q3xZfWf0jFNigu4KbDfBofuWJohQAAACA8pGdLcv06XnTOTmezQUAvJTVL0g1rnhbkZc+K79aF0qS/GpdqMhLn1WNK96W1S/IwxmWnwpTlDp69KgGDx6ssLAwRURE6I477lB6euGj0+fLzMzUyJEjVb16dYWEhKhv3746ePCgS4zFYinwmDt3bnnuCgAAAFA1WK1S/fp505YK89MDANzO6hek0Ja3q3qX1yRJ1bu8ptCWt1fqgpRUgYpSgwcP1u+//67Fixfryy+/1A8//KA777yz2Nfcf//9+uKLLzRv3jx9//332r9/v66//voCcfHx8Tpw4IDz0adPn3LaCwAAAKAKsdlk7r8/b9qPu+8BAFz5ejqBkti8ebMWLVqkNWvWqH379pKkN954Qz179tSLL76o2rVrF3hNSkqK3nvvPc2ZM0eXXXaZpLziU4sWLfTzzz/r4osvdsZGREQoOjq6xPlkZWUpKyvL+Tw1NVWS5HA45HA4SrWP3sAYh/PfirwfqFwcDoeMMbRJeBX6S3gr+kx4I/NPezQV/FgZlQ99JrxRZekzS5p7hShKrVq1ShEREc6ClCR169ZNVqtVq1ev1nXXXVfgNevWrVN2dra6devmnNe8eXPVr19fq1atcilKjRw5UkOHDtU555yj4cOH67bbbpPFYikynylTpujJJ58sMP/QoUPKzMws7W563NGj6f/8e0xJ/tkezgbI43A4lJKSImOMrNxKGl6C/hLeij4T3ijn2DFJ0tFjx+RrTfJwNsC/6DPhjSpLn5mWllaiuApRlEpMTFTNmjVd5vn6+ioyMlKJiYlFvsZmsykiIsJlfq1atVxeM2nSJF122WUKCgrSt99+qxEjRig9PV2jR48uMp9HH31UY8aMcT5PTU1VvXr1FBUVpbCwsFLsoXeIzMo7pToysppq1qzm4WyAPA6HQxaLRVFRURwswGvQX8Jb0WfC69jtynrtQyXWkiJDQxRwyjE94En0mfBGmY5qOigpslq1Ct1nBgQElCjOo0WpRx55RM8991yxMZs3by7XHJ544gnndNu2bZWRkaEXXnih2KKUv7+//P39C8y3Wq0VujOz/DP4pMVSsfcDlY/FYqnwny9ULvSX8Gb0mfA2lt27pVqSRaJdwuvQZ8LbWP5pi5YK3i5LmrtHi1IPPPCAhgwZUmzMOeeco+joaCUluZ62lpOTo6NHjxY5FlR0dLTsdruSk5NdzpY6ePBgseNHxcbG6qmnnlJWVlahhScAAAAAJeTnJ3P77dLvP0u+FeIiDQCAG3n0myEqKkpRUVGnjYuLi1NycrLWrVundu3aSZKWLl0qh8Oh2NjYQl/Trl07+fn5acmSJerbt68kaevWrdqzZ4/i4uKK3FZCQoKqVatGQQoAAAAopZyMA8rNyBsyI7uOTfpdyk7eIatv3uXPPsHR8g2O8WSKAAAvUCH+XNGiRQv16NFDw4YN07Rp05Sdna1Ro0ZpwIABzjvv7du3T5dffrnef/99dejQQeHh4brjjjs0ZswYRUZGKiwsTPfcc4/i4uKcg5x/8cUXOnjwoC6++GIFBARo8eLFmjx5ssaOHevJ3QUAAAAqtLTf3lXy6qdd5h35dohzOiL2cVWLG+/mrAAA3qZCFKUkafbs2Ro1apQuv/xyWa1W9e3bV6+//rpzeXZ2trZu3arjx487573yyivO2KysLHXv3l1Tp051Lvfz89Obb76p+++/X8YYnXvuuXr55Zc1bNgwt+4bAMD7HUjN1IHUvDusbklKd/5rtebdrTUmLEAxYSUb0BEAKrvQC4YpqPE1ksMhx44dSk1JUVjbtrL+cwmfT3DRw2kAAKqOClOUioyM1Jw5c4pc3rBhQxljXOYFBATozTff1Jtvvlnoa3r06KEePXqUaZ4AgMrp7VV/adLibS7zbpmb4Jwef0VTTezezM1ZAYB38g2Oybs8LzNT5tkJstjtss2/SdagIE+nBgDwIhWmKAUAgCfdFddAvc+vJUlyOIyOHjuqyGqRLmdKAQBOYbFI9erJceJE3jQAACehKAUAQAmcfHmew+FQki1LNWuGV+hb9QJAufP3l3nzTaUmJSmAGwkBAE7BkTQAAAAAAADcjqIUAAAAAAAA3I6iFAAAAIDyYbfLMn68Qp57TrLbPZ0NAMDLMKZUFcctzgEAAFBuHA4pIUG+dnveNAAAJ6EoVcVxi3MAAACUGz8/mTFjdDw5WTY/P09nAwDwMhSlqjhucQ4AAIBy4+Mjdekie1JS3jQAACehKFXFcYtzAAAAAADgCVQeAAAAAJQPh0Pavl0+f/7JmFIAgAI4UwoAAABA+bDbZXngAYXa7dL8+ZIvPz8AAP/iWwEAAABA+bBYpJo15cjMzJsGAOAkFKUAAAAAlA9/f5np05WalKQAf39PZwMA8DKMKQUAAAAAAAC3oygFAAAAAAAAt6MoBQAAAKB82O3SM88o+JVX8qYBADgJY0oBAAAAKB8OhyyrV8vPbpccDk9nAwDwMhSlAAAAAJQPX1+ZkSN1PDlZNl9+egAAXPHNAAAAAKB8+PpK3bvLnpSUNw0AwEkYUwoAAAAAAABuR1EKAAAAQPkwRtqzR9a9e/OmAQA4CefQAgAAACgfWVmyjBqlMLtdmj9fCgrydEYAAC9CUQoAAABA+QkLk8nM9HQWAAAvRFEKAAAAQPkICJD58EOlJCWpZkCAp7MBAHgZxpQCAAAAAACA21GUAgAAAAAAgNtRlAIAAABQPux26aWXFPTWW3nTAACchDGlAAAAAJQPh0OW77+XzW6XHA5PZwMA8DIUpQAAAACUD19fmTvu0ImUFNl8+ekBAHDFNwMAAACA8uHrK117rbKSkvKmAQA4CWNKAQAAAAAAwO0oSgEAAAAoH8ZISUmyHj6cNw0AwEk4hxYAAABA+cjKkmXoUIXZ7dL8+VJQkKczAgB4EYpSAAAAAMqPv7+nMwAAeCmKUgAAAADKR0CAzLx5Sk5KUs2AAE9nAwDwMowpBQAAAAAAALerMEWpo0ePavDgwQoLC1NERITuuOMOpaenF/uad955R126dFFYWJgsFouSk5PLZL0AAAAAAABlISfjgLKS1israb2yj26VJGUf3eqcl5NxwMMZlp8KU5QaPHiwfv/9dy1evFhffvmlfvjhB915553Fvub48ePq0aOHxo0bV6brBQAAAFAC2dnS//2fgt57L28aAFBA2m/vav+cWO2fE6sj3w6RJB35dohzXtpv73o2wXJkMcb77826efNmnXfeeVqzZo3at28vSVq0aJF69uypvXv3qnbt2sW+fvny5eratauOHTumiIiIMltvvtTUVIWHhyslJUVhYWGl20kv4HA4lJSUpJo1a8pqrTD1SlRytEt4I9olvBVtE14nM1PmhhuUZbfLNn++rNx9D16EPhPeIifjgHIzEiXltctjR4+qWmSks136BEfLNzjGkymesZLWSSrEQOerVq1SRESEs3AkSd26dZPVatXq1at13XXXuXW9WVlZysrKcj5PTU2VlNd4HA5HqXLxBg6HQ8aYCr0PqHxol/BGtEt4K9omvI7VKjN4sE6kpsrXapVom/Ai9JnwFtbAWrIG1pKU1y4t5pD8akS5FEsrWjstab4VoiiVmJiomjVruszz9fVVZGSkEhMT3b7eKVOm6Mknnyww/9ChQ8rMzCx1Pp7mcDiUkpIiYwx/KYDXoF3CG9Eu4a1om/BGjs6dlZKSohNHj9Iu4VXoM+GNKku7TEtLK1GcR4tSjzzyiJ577rliYzZv3uymbEru0Ucf1ZgxY5zPU1NTVa9ePUVFRVX4y/csFouioqIqdONH5UK7hDeiXcJb0TbhjWiX8Fa0TXijytIuAwICShTn0aLUAw88oCFDhhQbc8455yg6OlpJSUku83NycnT06FFFR0eXevulXa+/v7/8/f0LzLdarRW60UiSxWKpFPuByoV2CW9Eu4S3om3CqxgjpaXJmpYmK+P2wAvRZ8IbVYZ2WdLcPVqUioqKUlRU1Gnj4uLilJycrHXr1qldu3aSpKVLl8rhcCg2NrbU2y+v9QIAAACQlJUly803K9xul+bPlxjoHABwkgoxplSLFi3Uo0cPDRs2TNOmTVN2drZGjRqlAQMGOO+Qt2/fPl1++eV6//331aFDB0l5Y0YlJiZqx44dkqSNGzcqNDRU9evXV2RkZInWWxL5NzDMH/C8onI4HEpLS1NAQECFrsiicqFdwhvRLuGtaJvwOpmZMtnZysrJkS01VdacHE9nBDjRZ8IbVZZ2mV8fya+XFMlUEEeOHDEDBw40ISEhJiwszNx2220mLS3NuXzXrl1Gklm2bJlz3oQJE4ykAo/4+PgSr7ck/v7770K3w4MHDx48ePDgwYMHDx48ePDgUVUff//9d7H1FIsxpytb4XQcDof279+v0NBQWSwWT6dTavkDtv/9998VesB2VC60S3gj2iW8FW0T3oh2CW9F24Q3qizt0hijtLQ01a5du9gzvirE5Xvezmq1qm7dup5Oo8yEhYVV6MaPyol2CW9Eu4S3om3CG9Eu4a1om/BGlaFdhoeHnzam4l6gCAAAAAAAgAqLohQAAAAAAADcjqIUnPz9/TVhwgT5+/t7OhXAiXYJb0S7hLeibcIb0S7hrWib8EZVrV0y0DkAAAAAAADcjjOlAAAAAAAA4HYUpQAAAAAAAOB2FKUAAAAAAADgdhSlAAAAAAAA4HYUpQAAAAAAAOB2FKUqoClTpuiiiy5SaGioatasqT59+mjr1q0uMZmZmRo5cqSqV6+ukJAQ9e3bVwcPHnSJ2bNnj3r16qWgoCDVrFlTDz74oHJycgrd5sqVK+Xr66s2bdqcNr/ffvtNl156qQICAlSvXj09//zzpd5XVBze3C53794ti8VS4PHzzz+f1T7D+7mrXS5fvrzQNpaYmFhsfvSXVZc3t036zKrLnd/lWVlZeuyxx9SgQQP5+/urYcOGmjFjRrH5nckxAioXb2+bhfWZc+fOLZudh9dyV7scMmRIoW3s/PPPLza/CnOcaVDhdO/e3cTHx5tNmzaZhIQE07NnT1O/fn2Tnp7ujBk+fLipV6+eWbJkiVm7dq25+OKLTceOHZ3Lc3JyTMuWLU23bt3M+vXrzcKFC02NGjXMo48+WmB7x44dM+ecc4658sorTevWrYvNLSUlxdSqVcsMHjzYbNq0yXz00UcmMDDQvP3222W2//BO3twud+3aZSSZ7777zhw4cMD5sNvtZbb/8E7uapfLli0zkszWrVtd2lhubm6RudFfVm3e3DbpM6sud36X9+7d28TGxprFixebXbt2mZ9++smsWLGiyNzO5BgBlY83t01jjJFk4uPjXfrMEydOlO2bAK/jrnaZnJzs0rb+/vtvExkZaSZMmFBkbhXpOJOiVCWQlJRkJJnvv//eGJPXaP38/My8efOcMZs3bzaSzKpVq4wxxixcuNBYrVaTmJjojHnrrbdMWFiYycrKcln/jTfeaB5//HEzYcKE0/74nzp1qqlWrZrLOh5++GHTrFmzs91NVDDe1C7zf2CtX7++bHYOFVZ5tcv8H/7Hjh0rcS70lziZN7VN+kzkK692+fXXX5vw8HBz5MiREudyJscIqPy8qW0ak1eU+vzzz89yr1DRlffvn3yff/65sVgsZvfu3UXmUpGOM7l8rxJISUmRJEVGRkqS1q1bp+zsbHXr1s0Z07x5c9WvX1+rVq2SJK1atUqtWrVSrVq1nDHdu3dXamqqfv/9d+e8+Ph4/fnnn5owYUKJclm1apX+85//yGazuax369atOnbsWOl3EhWON7XLfL1791bNmjV1ySWXaMGCBaXeN1Rc5dkuJalNmzaKiYnRFVdcoZUrVxabC/0lTuZNbTMffSbKq10uWLBA7du31/PPP686deqoadOmGjt2rE6cOFFkLmfS3lH5eVPbzDdy5EjVqFFDHTp00IwZM2SMKbP9RcVQ3t/l+d577z1169ZNDRo0KDKXinSc6evpBHB2HA6H7rvvPnXq1EktW7aUJCUmJspmsykiIsIltlatWs4xJBITE10afv7y/GWStH37dj3yyCP68ccf5etbsqaSmJioRo0aFbneatWqndkOokLytnYZEhKil156SZ06dZLVatWnn36qPn36aP78+erdu/fZ7CoqkPJslzExMZo2bZrat2+vrKwsTZ8+XV26dNHq1at14YUXFpoP/SXyeVvbpM+EVL7t8s8//9SKFSsUEBCgzz//XIcPH9aIESN05MgRxcfHF5pPSdaLqsHb2qYkTZo0SZdddpmCgoL07bffasSIEUpPT9fo0aPLarfh5cqzXZ5s//79+vrrrzVnzpxi86lIx5kUpSq4kSNHatOmTVqxYkWZrjc3N1eDBg3Sk08+qaZNm5bpulH5eVu7rFGjhsaMGeN8ftFFF2n//v164YUX+IFVhZRXu5SkZs2aqVmzZs7nHTt21M6dO/XKK6/ogw8+KPPtoXLxtrZJnwmpfNulw+GQxWLR7NmzFR4eLkl6+eWXdcMNN2jq1KkKDAws822i8vDGtvnEE084p9u2bauMjAy98MILFKWqkPJslyebNWuWIiIi1KdPn3Ldjjtx+V4FNmrUKH355ZdatmyZ6tat65wfHR0tu92u5ORkl/iDBw8qOjraGXPqqP/5z6Ojo5WWlqa1a9dq1KhR8vX1la+vryZNmqQNGzbI19dXS5cuLTSn060XlZ83tsvCxMbGaseOHaXcS1Q05dkui9KhQ4di2xj9JSTvbJuFoc+sWsq7XcbExKhOnTrOH/2S1KJFCxljtHfv3kJzos+E5J1tszCxsbHau3evsrKyzmj/UDG567vcGKMZM2bo5ptvdrksrzAVqc+kKFUBGWM0atQoff7551q6dGmB0/LatWsnPz8/LVmyxDlv69at2rNnj+Li4iRJcXFx2rhxo5KSkpwxixcvVlhYmM477zyFhYVp48aNSkhIcD6GDx+uZs2aKSEhQbGxsYXmFhcXpx9++EHZ2dku623WrJlXnSKIsufN7bIwCQkJiomJOcu9hrdzR7ssyunaGP1l1ebNbbOsXoOKx13tslOnTtq/f7/S09OdMdu2bZPVanX5QXey0rZ3VA7e3DYLk5CQoGr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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dml_obj_linear_logistic = DoubleMLDIDMulti(\n", " obj_dml_data=dml_data,\n", @@ -661,108 +311,18 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDMulti Object ==================\n", - "\n", - "------------------ Data summary ------------------\n", - "Outcome variable: lemp\n", - "Treatment variable(s): ['first.treat']\n", - "Covariates: ['lpop']\n", - "Instrument variable(s): None\n", - "Time variable: year\n", - "Id variable: countyreal\n", - "No. Observations: 500\n", - "\n", - "------------------ Score & algorithm ------------------\n", - "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", - "\n", - "------------------ Machine learner ------------------\n", - "Learner ml_g: LinearRegression()\n", - "Learner ml_m: LogisticRegression(penalty=None)\n", - "Out-of-sample Performance:\n", - "Regression:\n", - "Learner ml_g0 RMSE: [[0.17225098 0.18180751 0.25875512 0.25787083 0.17217289 0.15149323\n", - " 0.2015511 0.20672252 0.17245042 0.15118131 0.20165754 0.16345075]]\n", - "Learner ml_g1 RMSE: [[0.10293871 0.1231148 0.13516937 0.1467575 0.13801965 0.11120553\n", - " 0.08784557 0.10496696 0.13719248 0.16202855 0.15922994 0.16103467]]\n", - "Classification:\n", - "Learner ml_m Log Loss: [[0.23066697 0.23113821 0.23155331 0.23101858 0.34930842 0.35094054\n", - " 0.34738288 0.34716226 0.60658247 0.60615553 0.60872424 0.60938367]]\n", - "\n", - "------------------ Resampling ------------------\n", - "No. folds: 10\n", - "No. repeated sample splits: 1\n", - "\n", - "------------------ Fit summary ------------------\n", - " coef std err t P>|t| 2.5 % \\\n", - "ATT(2004.0,2003,2004) -0.013438 0.022263 -0.603612 0.546102 -0.057073 \n", - "ATT(2004.0,2003,2005) -0.079094 0.028471 -2.778025 0.005469 -0.134896 \n", - "ATT(2004.0,2003,2006) -0.139463 0.035008 -3.983730 0.000068 -0.208078 \n", - "ATT(2004.0,2003,2007) -0.108604 0.032494 -3.342312 0.000831 -0.172291 \n", - "ATT(2006.0,2003,2004) -0.000821 0.022370 -0.036698 0.970726 -0.044665 \n", - "ATT(2006.0,2004,2005) -0.005271 0.018474 -0.285323 0.775397 -0.041480 \n", - "ATT(2006.0,2005,2006) 0.000197 0.019361 0.010173 0.991883 -0.037750 \n", - "ATT(2006.0,2005,2007) -0.041419 0.019782 -2.093730 0.036284 -0.080192 \n", - "ATT(2007.0,2003,2004) 0.026013 0.014054 1.850934 0.064179 -0.001532 \n", - "ATT(2007.0,2004,2005) -0.004210 0.015648 -0.269053 0.787889 -0.034880 \n", - "ATT(2007.0,2005,2006) -0.029017 0.018184 -1.595756 0.110543 -0.064657 \n", - "ATT(2007.0,2006,2007) -0.029940 0.016430 -1.822237 0.068419 -0.062142 \n", - "\n", - " 97.5 % \n", - "ATT(2004.0,2003,2004) 0.030197 \n", - "ATT(2004.0,2003,2005) -0.023291 \n", - "ATT(2004.0,2003,2006) -0.070848 \n", - "ATT(2004.0,2003,2007) -0.044918 \n", - "ATT(2006.0,2003,2004) 0.043023 \n", - "ATT(2006.0,2004,2005) 0.030938 \n", - "ATT(2006.0,2005,2006) 0.038144 \n", - "ATT(2006.0,2005,2007) -0.002646 \n", - "ATT(2007.0,2003,2004) 0.053558 \n", - "ATT(2007.0,2004,2005) 0.026460 \n", - "ATT(2007.0,2005,2006) 0.006623 \n", - "ATT(2007.0,2006,2007) 0.002263 \n" - ] - } - ], + "outputs": [], "source": [ "print(dml_obj_linear_logistic)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
,\n", - " )" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "es_linear_logistic = dml_obj_linear_logistic.aggregate(\"eventstudy\")\n", "es_linear_logistic.aggregated_frameworks.bootstrap()\n", @@ -771,41 +331,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\bachp\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\matplotlib\\cbook.py:1762: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " return math.isfinite(val)\n" - ] - }, - { - "data": { - "text/plain": [ - "(
,\n", - " [,\n", - " ,\n", - " ])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dml_obj_lasso = DoubleMLDIDMulti(\n", " obj_dml_data=dml_data,\n", @@ -822,77 +350,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================== DoubleMLDIDMulti Object ==================\n", - "\n", - "------------------ Data summary ------------------\n", - "Outcome variable: lemp\n", - "Treatment variable(s): ['first.treat']\n", - "Covariates: ['lpop']\n", - "Instrument variable(s): None\n", - "Time variable: year\n", - "Id variable: countyreal\n", - "No. Observations: 500\n", - "\n", - "------------------ Score & algorithm ------------------\n", - "Score function: observational\n", - "Control group: never_treated\n", - "Anticipation periods: 0\n", - "\n", - "------------------ Machine learner ------------------\n", - "Learner ml_g: LassoCV()\n", - "Learner ml_m: LogisticRegressionCV()\n", - "Out-of-sample Performance:\n", - "Regression:\n", - "Learner ml_g0 RMSE: [[0.17280482 0.18273009 0.25965768 0.25946547 0.17286749 0.15159456\n", - " 0.20062163 0.20576772 0.17416292 0.15124097 0.20127059 0.16501317]]\n", - "Learner ml_g1 RMSE: [[0.10329207 0.12864046 0.14013825 0.14544455 0.14122605 0.11350921\n", - " 0.08570141 0.10355616 0.13376426 0.16146262 0.15928815 0.16044963]]\n", - "Classification:\n", - "Learner ml_m Log Loss: [[0.22913674 0.22913857 0.22913404 0.22914337 0.3559686 0.35595763\n", - " 0.35596035 0.35595994 0.60884191 0.60884291 0.6088457 0.60884412]]\n", - "\n", - "------------------ Resampling ------------------\n", - "No. folds: 10\n", - "No. repeated sample splits: 1\n", - "\n", - "------------------ Fit summary ------------------\n", - " coef std err t P>|t| 2.5 % \\\n", - "ATT(2004.0,2003,2004) -0.016311 0.023291 -0.700298 0.483741 -0.061961 \n", - "ATT(2004.0,2003,2005) -0.079929 0.028965 -2.759481 0.005789 -0.136701 \n", - "ATT(2004.0,2003,2006) -0.138565 0.035080 -3.950017 0.000078 -0.207319 \n", - "ATT(2004.0,2003,2007) -0.102563 0.033298 -3.080115 0.002069 -0.167826 \n", - "ATT(2006.0,2003,2004) 0.000346 0.023067 0.015005 0.988028 -0.044865 \n", - "ATT(2006.0,2004,2005) -0.005166 0.019393 -0.266391 0.789938 -0.043175 \n", - "ATT(2006.0,2005,2006) -0.004599 0.017809 -0.258254 0.796211 -0.039504 \n", - "ATT(2006.0,2005,2007) -0.041240 0.020283 -2.033242 0.042028 -0.080995 \n", - "ATT(2007.0,2003,2004) 0.027119 0.015109 1.794824 0.072682 -0.002495 \n", - "ATT(2007.0,2004,2005) -0.004499 0.016376 -0.274728 0.783525 -0.036595 \n", - "ATT(2007.0,2005,2006) -0.030829 0.017972 -1.715418 0.086269 -0.066053 \n", - "ATT(2007.0,2006,2007) -0.028859 0.016941 -1.703485 0.088477 -0.062064 \n", - "\n", - " 97.5 % \n", - "ATT(2004.0,2003,2004) 0.029339 \n", - "ATT(2004.0,2003,2005) -0.023158 \n", - "ATT(2004.0,2003,2006) -0.069810 \n", - "ATT(2004.0,2003,2007) -0.037299 \n", - "ATT(2006.0,2003,2004) 0.045557 \n", - "ATT(2006.0,2004,2005) 0.032843 \n", - "ATT(2006.0,2005,2006) 0.030306 \n", - "ATT(2006.0,2005,2007) -0.001486 \n", - "ATT(2007.0,2003,2004) 0.056733 \n", - "ATT(2007.0,2004,2005) 0.027597 \n", - "ATT(2007.0,2005,2006) 0.004395 \n", - "ATT(2007.0,2006,2007) 0.004345 \n" - ] - } - ], + "outputs": [], "source": [ "# Model summary\n", "print(dml_obj_lasso)" @@ -900,31 +360,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
,\n", - " )" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "es_rf = dml_obj_lasso.aggregate(\"eventstudy\")\n", "es_rf.aggregated_frameworks.bootstrap()\n", From ffab9edb3315045f44b161821ea25972c6490aa0 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Thu, 22 May 2025 12:10:10 +0000 Subject: [PATCH 132/140] start release notes for 0.10.0 --- doc/release/release.rst | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/doc/release/release.rst b/doc/release/release.rst index cf7e4af2..fb726341 100644 --- a/doc/release/release.rst +++ b/doc/release/release.rst @@ -7,16 +7,26 @@ Release Notes .. tab-item:: Python - .. dropdown:: DoubleML 0.9.3 + .. dropdown:: DoubleML 0.10.0 :class-title: sd-bg-primary sd-font-weight-bold :open: + - **Release highlight:** + - Multi-Period Difference-in-Differences for Panel Data + - test + + - Maintenance package + + - Maintenance documentation + + .. dropdown:: DoubleML 0.9.3 + :class-title: sd-bg-primary sd-font-weight-bold + - Fix / adapted unit tests which failed in the release of 0.9.2 to conda-forge `Docs #208 `_ .. dropdown:: DoubleML 0.9.2 :class-title: sd-bg-primary sd-font-weight-bold - :open: - Make `rdrobust` optional for conda. Create `pyproject.toml` and remove `setup.py` for packaging `Py #285 `_ @@ -25,14 +35,13 @@ Release Notes - Maintenance package `Py #284 `_ - - Maintenance doccumentation + - Maintenance documentation `Docs #205 `_ `Docs #206 `_ `Docs #207 `_ .. dropdown:: DoubleML 0.9.1 :class-title: sd-bg-primary sd-font-weight-bold - :open: - **Release highlight:** Regression Discontinuity Designs with Flexible Covariate Adjustment via ``RDFlex`` class (in cooperation with `Claudia Noack `_ From 536e2ba67feca517a2efd610b916b10904a7ed52 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Thu, 22 May 2025 12:50:36 +0000 Subject: [PATCH 133/140] add pyreadr dependency --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 38fd47c5..4cabe8c9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,4 +23,5 @@ lightgbm flaml # notebooks -ipykernel \ No newline at end of file +ipykernel +pyreadr \ No newline at end of file From d30089f7b048b235fe5de2c90a8dfb9bde0ff52e Mon Sep 17 00:00:00 2001 From: PhilippBach Date: Thu, 22 May 2025 16:49:51 +0200 Subject: [PATCH 134/140] include updated DAGs in IV notebooks --- doc/examples/R_double_ml_basic_iv.ipynb | 7 +------ doc/examples/py_double_ml_basic_iv.ipynb | 7 +------ doc/examples/py_double_ml_robust_iv.ipynb | 3 +++ 3 files changed, 5 insertions(+), 12 deletions(-) diff --git a/doc/examples/R_double_ml_basic_iv.ipynb b/doc/examples/R_double_ml_basic_iv.ipynb index 979df915..1d0df3ae 100644 --- a/doc/examples/R_double_ml_basic_iv.ipynb +++ b/doc/examples/R_double_ml_basic_iv.ipynb @@ -49,16 +49,11 @@ ] }, { - "attachments": { - "basic_iv_example_nb.png": { - "image/png": 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" - } - }, "cell_type": "markdown", "id": "a3250ef4", "metadata": {}, "source": [ - "![basic_iv_example_nb.png](attachment:basic_iv_example_nb.png)" + "![basic_iv_example_nb.png](../_static/basic_iv_example_nb.png)" ] }, { diff --git a/doc/examples/py_double_ml_basic_iv.ipynb b/doc/examples/py_double_ml_basic_iv.ipynb index 98aa2548..003dd0b1 100644 --- a/doc/examples/py_double_ml_basic_iv.ipynb +++ b/doc/examples/py_double_ml_basic_iv.ipynb @@ -45,16 +45,11 @@ ] }, { - "attachments": { - "basic_iv_example_nb.png": { - "image/png": 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" - } - }, "cell_type": "markdown", "id": "14d56698", "metadata": {}, "source": [ - "![basic_iv_example_nb.png](attachment:basic_iv_example_nb.png)" + "![basic_iv_example_nb.png](../_static/basic_iv_example_nb.png)" ] }, { diff --git a/doc/examples/py_double_ml_robust_iv.ipynb b/doc/examples/py_double_ml_robust_iv.ipynb index 7cb7121f..40c47eb1 100644 --- a/doc/examples/py_double_ml_robust_iv.ipynb +++ b/doc/examples/py_double_ml_robust_iv.ipynb @@ -21,6 +21,9 @@ "- $D$ is treatment variable,\n", "- $Y$ is the outcome.\n", "\n", + "![robust_iv_example_nb.png](../_static/robust_iv_example_nb.png)\n", + "\n", + "\n", "Next, we will run a simulation, where we will generate two synthetic data sets, one where the instrument is weak and another where it is not. Then, we will compare the output of the standard way to compute confidence intervals using the ``DoubleMLIIVM`` class, with the confidence sets computed using the ``robust_confset()`` method from the same class. We will see that using the ``robust_confset()`` method is an easy way to ensure the results of an analysis are robust to weak instruments." ] }, From e41f79b0d4870afc010143ee83ffb96afb5f01fb Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 23 May 2025 11:29:34 +0000 Subject: [PATCH 135/140] fix sandbox header --- doc/examples/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/examples/index.rst b/doc/examples/index.rst index 142a2160..97fb0848 100644 --- a/doc/examples/index.rst +++ b/doc/examples/index.rst @@ -80,7 +80,7 @@ These are case studies with the R package :ref:`DoubleML `. R_double_ml_basic_iv.ipynb Sandbox/Archive ----------- +--------------- These are examples which are work-in-progress and/or not yet fully documented. From 7bc50fc31fc1adb8880597254223445cc14cce30 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 23 May 2025 12:45:15 +0000 Subject: [PATCH 136/140] add release notes --- doc/release/release.rst | 64 +++++++++++++++++++++++++++++++++++++++-- 1 file changed, 61 insertions(+), 3 deletions(-) diff --git a/doc/release/release.rst b/doc/release/release.rst index fb726341..a5febb76 100644 --- a/doc/release/release.rst +++ b/doc/release/release.rst @@ -11,13 +11,69 @@ Release Notes :class-title: sd-bg-primary sd-font-weight-bold :open: - - **Release highlight:** - - Multi-Period Difference-in-Differences for Panel Data - - test + - **Release highlight:** Multi-Period Difference-in-Differences for Panel Data + + - Implementation via ``DoubleMLDIDMulti`` class + `Py #292 `_ + `Py #315 `_ + - New ``doubleml.data`` submodule including ``DoubleMLData`` and ``DoubleMLPanelData`` classes + `Py #292 `_ + - Extended User Guide and Example Gallery + `Docs #224 `_ + `Docs #233 `_ + `Docs #237 `_ + + - Added Confidence sets which are robust to weak IVs: ``robust_confset()`` method for ``DoubleMLIIVM`` + (added by `Ezequiel Smucler `_ and `David Masip `_) + `Py #318 `_ + `Docs #234 `_ + + - Update sensitivity operations to improve sensitivity bounds + `Py #295 `_ + + - Improve ``DoubleMLAPO`` nuisance estimation and update weighted score elements. + Added example to compare ``DoubleMLIRM`` and ``DoubleMLAPO``. + `Py #295 `_ + `Py #297 `_ + `Docs #220 `_ + + - Updated variance aggregation over repetitions via confidence intervals + `Py #324 `_ + `Docs #236 `_ + + - Added a separate package citation using `CITATION.cff` + `Py #321 `_ + + - Update package formatting, linting and add pre-commit hooks + `Py #288 `_ + `Py #289 `_ + `Py #294 `_ + `Py #316 `_ - Maintenance package + `Py #287 `_ + `Py #288 `_ + `Py #291 `_ + `Py #319 `_ - Maintenance documentation + `Docs #211 `_ + `Docs #213 `_ + `Docs #214 `_ + `Docs #215 `_ + `Docs #216 `_ + `Docs #217 `_ + `Docs #218 `_ + `Docs #219 `_ + `Docs #221 `_ + `Docs #225 `_ + `Docs #227 `_ + `Docs #228 `_ + `Docs #229 `_ + `Docs #230 `_ + `Docs #232 `_ + `Docs #238 `_ + `Docs #239 `_ .. dropdown:: DoubleML 0.9.3 :class-title: sd-bg-primary sd-font-weight-bold @@ -514,9 +570,11 @@ Release Notes - Add sample selection models, thanks to new contributor Petra Jasenakova `@petronelaj `_ `R #213 `_ + `Docs #223 `_ - Maintenance including updates to GitHub workflows `R #205 `_ `R #220 `_ + `Docs #226 `_ .. dropdown:: DoubleML 1.0.1 :class-title: sd-bg-primary sd-font-weight-bold From 480ea593be765ebf926b1fa8521918dbfdf3e588 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Sun, 25 May 2025 10:02:49 +0000 Subject: [PATCH 137/140] remove todo --- doc/examples/did/py_panel_data_example.ipynb | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/doc/examples/did/py_panel_data_example.ipynb b/doc/examples/did/py_panel_data_example.ipynb index 7ea893ed..0f87e648 100644 --- a/doc/examples/did/py_panel_data_example.ipynb +++ b/doc/examples/did/py_panel_data_example.ipynb @@ -245,8 +245,6 @@ "source": [ "### Aggregation Details\n", "\n", - "**TODO**: Keep or drop this?\n", - "\n", "The `DoubleMLDIDAggregation` objects include several `DoubleMLFrameworks` which support methods like `bootstrap()` or `confint()`.\n", "Further, the weights can be accessed via the properties\n", "\n", @@ -372,7 +370,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -386,7 +384,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.3" } }, "nbformat": 4, From 993b8e63044a81d9c0f879ba85ed95ae5d5aa669 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Sun, 25 May 2025 10:03:02 +0000 Subject: [PATCH 138/140] add more explanation to plr --- doc/guide/models/plm/plm_models.inc | 7 +++++-- doc/guide/scores/plm/plr_score.rst | 20 ++++++++++++++++++-- 2 files changed, 23 insertions(+), 4 deletions(-) diff --git a/doc/guide/models/plm/plm_models.inc b/doc/guide/models/plm/plm_models.inc index 6bd097d8..086a3051 100644 --- a/doc/guide/models/plm/plm_models.inc +++ b/doc/guide/models/plm/plm_models.inc @@ -15,8 +15,11 @@ Partially linear regression model (PLR) .. include:: /shared/causal_graphs/plr_irm_causal_graph.rst -``DoubleMLPLR`` implements PLR models. -Estimation is conducted via its ``fit()`` method: +``DoubleMLPLR`` implements PLR models. Estimation is conducted via its ``fit()`` method. + +.. note:: + Remark that standard approach with ``score='partialling out'`` does not rely on a direct estimate of :math:`g_0(X)`, + but :math:`\ell_0(X) := \mathbb{E}[Y \mid X] = \theta_0 \mathbb{E}[D \mid X] + g(X)`. .. tab-set:: diff --git a/doc/guide/scores/plm/plr_score.rst b/doc/guide/scores/plm/plr_score.rst index 0b4f493e..0dba9733 100644 --- a/doc/guide/scores/plm/plr_score.rst +++ b/doc/guide/scores/plm/plr_score.rst @@ -11,7 +11,15 @@ For the PLR model implemented in ``DoubleMLPLR`` one can choose between &= \psi_a(W; \eta) \theta + \psi_b(W; \eta) -with :math:`\eta=(\ell,m)` and where the components of the linear score are +with :math:`\eta=(\ell,m)`, where + +.. math:: + + \ell_0(X) &:= \mathbb{E}[Y \mid X] = \theta_0\mathbb{E}[D \mid X] + g(X), + + m_0(X) &:= \mathbb{E}[D \mid X]. + +The components of the linear score are .. math:: @@ -29,7 +37,15 @@ with :math:`\eta=(\ell,m)` and where the components of the linear score are &= \psi_a(W; \eta) \theta + \psi_b(W; \eta) -with :math:`\eta=(g,m)` and where the components of the linear score are +with :math:`\eta=(g,m)`, where + +.. math:: + + g_0(X) &:= \mathbb{E}[Y - D \theta_0\mid X], + + m_0(X) &:= \mathbb{E}[D \mid X]. + +The components of the linear score are .. math:: From 5c2890c1d0dd5c1548a06ccaf89fa81747e9e5f9 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 26 May 2025 08:07:36 +0000 Subject: [PATCH 139/140] allow warnings --- doc/guide/algorithms.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/guide/algorithms.rst b/doc/guide/algorithms.rst index 8e52a076..6ffa56f0 100644 --- a/doc/guide/algorithms.rst +++ b/doc/guide/algorithms.rst @@ -83,6 +83,7 @@ The default version of the :class:`DoubleML` class is based on the DML2 algorith :sync: py .. ipython:: python + :okwarning: import doubleml as dml from doubleml.datasets import make_plr_CCDDHNR2018 From 291d6a23d73827fd822a05e0a78353f7800b427a Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 26 May 2025 09:08:53 +0000 Subject: [PATCH 140/140] add sklean<1.6 to disable warnings --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 4cabe8c9..543443a5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ DoubleML[rdd] +scikit-learn<1.6 # test pytest