From c3c1e8a12c49e540b5c47ae884cb0a0049e4ce91 Mon Sep 17 00:00:00 2001 From: david26694 Date: Wed, 9 Jul 2025 11:32:36 +0200 Subject: [PATCH 1/2] add e2e swbck example --- docs/e2e_mde_switchback.ipynb | 1252 +++++++++++++++++++++++++++++++++ 1 file changed, 1252 insertions(+) create mode 100644 docs/e2e_mde_switchback.ipynb diff --git a/docs/e2e_mde_switchback.ipynb b/docs/e2e_mde_switchback.ipynb new file mode 100644 index 0000000..315d5b7 --- /dev/null +++ b/docs/e2e_mde_switchback.ipynb @@ -0,0 +1,1252 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# End-to-end example\n", + "\n", + "We'll show the different functionalities of cluster_experiments, which are:\n", + "* MDE calculation in different setups of switchback experiments\n", + "* MDE calculation with simple covariate adjustment\n", + "* MDE calculation with cupac (adjustment via ML models)\n", + "* Inference for all the cases above\n", + "\n", + "\n", + "## Data generation\n", + "\n", + "We create some pre-experimental data that we could use to run power analysis.\n", + "\n", + "We have a dataframe with orders and customers, each customer may have many orders, and the two target metrics are delivery time and order value.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/n0/x7n4w_fs4vz094fp0sjd9mk00000gp/T/ipykernel_11416/441254994.py:38: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", + " date_range = pd.date_range(start=\"2024-01-01\", end=\"2024-03-31\", freq='H')\n" + ] + }, + { + "data": { + "text/html": [ + "
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06113972.1991862024-02-21 08:00:00City_399.2686872024-02-21False8
17042381.0118312024-01-26 16:00:00City_988.4215022024-01-26False16
22435289.9792942024-03-25 11:00:00City_346.9355992024-03-25False11
38312365.2351922024-03-27 09:00:00City_9110.2864792024-03-27False9
45615891.1543832024-03-29 14:00:00City_448.1007062024-03-29False14
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99952804683.9158132024-01-31 10:00:00City_238.8244912024-01-31False10
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99976853076.8101202024-02-22 02:00:00City_388.9825642024-02-22False2
99981565877.0469162024-03-16 10:00:00City_4911.1407492024-03-16True10
99992383878.3913662024-01-06 14:00:00City_439.8241242024-01-06True14
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" + ], + "text/plain": [ + " customer_id customer_age order_value datetime city \\\n", + "0 611 39 72.199186 2024-02-21 08:00:00 City_39 \n", + "1 704 23 81.011831 2024-01-26 16:00:00 City_98 \n", + "2 243 52 89.979294 2024-03-25 11:00:00 City_34 \n", + "3 831 23 65.235192 2024-03-27 09:00:00 City_91 \n", + "4 561 58 91.154383 2024-03-29 14:00:00 City_44 \n", + "... ... ... ... ... ... \n", + "9995 280 46 83.915813 2024-01-31 10:00:00 City_23 \n", + "9996 538 27 51.848917 2024-01-05 14:00:00 City_44 \n", + "9997 685 30 76.810120 2024-02-22 02:00:00 City_38 \n", + "9998 156 58 77.046916 2024-03-16 10:00:00 City_49 \n", + "9999 238 38 78.391366 2024-01-06 14:00:00 City_43 \n", + "\n", + " delivery_time date is_weekend hour_of_day \n", + "0 9.268687 2024-02-21 False 8 \n", + "1 8.421502 2024-01-26 False 16 \n", + "2 6.935599 2024-03-25 False 11 \n", + "3 10.286479 2024-03-27 False 9 \n", + "4 8.100706 2024-03-29 False 14 \n", + "... ... ... ... ... \n", + "9995 8.824491 2024-01-31 False 10 \n", + "9996 6.947604 2024-01-05 False 14 \n", + "9997 8.982564 2024-02-22 False 2 \n", + "9998 11.140749 2024-03-16 True 10 \n", + "9999 9.824124 2024-01-06 True 14 \n", + "\n", + "[10000 rows x 9 columns]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import random\n", + "import datetime\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "\n", + "from datetime import timedelta\n", + "from cluster_experiments import NormalPowerAnalysis\n", + "\n", + "np.random.seed(42)\n", + "random.seed(42)\n", + "\n", + "# Constants\n", + "N = 10000 # Number of orders\n", + "NUM_CUSTOMERS = 1000 # Unique customers\n", + "\n", + "def generate_customers(num_customers):\n", + " \"\"\"Generate unique customers with a mean order value based on age.\"\"\"\n", + " customer_ids = np.arange(1, num_customers + 1)\n", + " customer_ages = np.random.randint(20, 60, size=num_customers)\n", + " mean_order_values = 50 + 0.8 * customer_ages + np.random.normal(0, 10, size=num_customers)\n", + "\n", + " return pd.DataFrame({\n", + " \"customer_id\": customer_ids,\n", + " \"customer_age\": customer_ages,\n", + " \"mean_order_value\": mean_order_values\n", + " })\n", + "\n", + "def sample_orders(customers, num_orders):\n", + " \"\"\"Sample customers and generate order-level data.\"\"\"\n", + " sampled_customers = np.random.choice(customers[\"customer_id\"], size=num_orders)\n", + " return pd.DataFrame({\"customer_id\": sampled_customers}).merge(customers, on=\"customer_id\", how=\"left\")\n", + "\n", + "def generate_orders(customers, num_orders):\n", + " \"\"\"Full order generation pipeline using .assign() for cleaner transformations.\"\"\"\n", + " date_range = pd.date_range(start=\"2024-01-01\", end=\"2024-03-31\", freq='H')\n", + " \n", + " # Generate 100 cities\n", + " cities = [f\"City_{i:02d}\" for i in range(1, 101)]\n", + " \n", + " def calculate_delivery_time(df):\n", + " # Base delivery time per customer\n", + " base_time = 8 + np.sin(df[\"customer_id\"] / 10)\n", + " \n", + " # Time of day effect (slower during rush hours: 8-9am, 5-7pm)\n", + " hour = df[\"datetime\"].dt.hour\n", + " time_effect = np.where(\n", + " ((hour >= 8) & (hour <= 9)) | ((hour >= 17) & (hour <= 19)),\n", + " 1.5, # 1.5 hours slower during rush hours\n", + " 0\n", + " )\n", + " \n", + " # Day of week effect (slower on weekends)\n", + " day_of_week = df[\"datetime\"].dt.dayofweek\n", + " weekend_effect = np.where(day_of_week >= 5, 0.8, 0) # 0.8 hours slower on weekends\n", + " \n", + " # City effect (some cities have systematically longer delivery times)\n", + " # Extract city number and create varying delivery times\n", + " city_numbers = df[\"city\"].str.extract(r'City_(\\d+)')[0].astype(int)\n", + " city_effect = 0.5 * np.sin(city_numbers / 20) + 0.3 * np.cos(city_numbers / 15)\n", + " \n", + " # Random noise\n", + " noise = np.random.normal(0, 0.5, size=len(df))\n", + " \n", + " return base_time + time_effect + 4 * weekend_effect + city_effect + noise\n", + "\n", + " return (\n", + " sample_orders(customers, num_orders)\n", + " .assign(\n", + " order_value=lambda df: df[\"mean_order_value\"] + np.random.normal(0, 5, size=len(df)),\n", + " datetime=lambda df: pd.to_datetime(np.random.choice(date_range, size=len(df))),\n", + " city=lambda df: np.random.choice(cities, size=len(df)),\n", + " )\n", + " .assign(\n", + " delivery_time=calculate_delivery_time,\n", + " date=lambda df: df[\"datetime\"].dt.date # Extract date for compatibility\n", + " )\n", + " .drop(columns=[\"mean_order_value\"]) # Remove intermediate column\n", + " )\n", + "\n", + "def plot_mdes(mdes, x_lim=40, y_value=3):\n", + " sns.lineplot(\n", + " data=pd.DataFrame(mdes),\n", + " x=\"experiment_length\",\n", + " y=\"mde\",\n", + " )\n", + "\n", + " sns.lineplot(\n", + " x=[0, x_lim],\n", + " y=[y_value, y_value],\n", + " color=\"red\",\n", + " linestyle=\"--\",\n", + " )\n", + "\n", + "\n", + "def get_length_print(mdes, mde_value):\n", + " if any(x[\"mde\"] < mde_value for x in mdes):\n", + " length = min(x[\"experiment_length\"] for x in mdes if x[\"mde\"] < mde_value)\n", + " print(f\"Minimum experiment length to detect MDE of {mde_value}: {length}\")\n", + " return\n", + " print(f\"No MDE below {mde_value} found in the provided data.\")\n", + "\n", + "# Run the pipeline\n", + "customers = generate_customers(NUM_CUSTOMERS)\n", + "experiment_data = generate_orders(customers, N).assign(\n", + " is_weekend=lambda df: df[\"datetime\"].dt.dayofweek >= 5,\n", + " hour_of_day=lambda df: df[\"datetime\"].dt.hour,\n", + ")\n", + "\n", + "experiment_data\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Power analysis\n", + "\n", + "### Day-level split\n", + "\n", + "Assume we run an switchback test randomizing at day level" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# we want to detect an effect of 0.1 on delivery_time, so we set the mde_value to 0.1\n", + "MDE_VALUE = 0.1\n", + "EXPERIMENT_LENGTHS = (7, 14, 21, 28, 35, 42, 49, 56)\n", + "\n", + "mde_time_split = NormalPowerAnalysis.from_dict({\n", + " \"analysis\": \"clustered_ols\",\n", + " \"cluster_cols\": [\"datetime\"],\n", + " \"splitter\": \"switchback\",\n", + " \"switch_frequency\": \"24h\",\n", + " \"target_col\": \"delivery_time\",\n", + " \"time_col\": \"datetime\",\n", + "})\n", + "\n", + "mdes = mde_time_split.mde_time_line(\n", + " experiment_data,\n", + " powers=[0.8],\n", + " experiment_length=EXPERIMENT_LENGTHS,\n", + " n_simulations=10\n", + ")\n", + "\n", + "plot_mdes(mdes, y_value=MDE_VALUE)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No MDE below 0.1 found in the provided data.\n" + ] + } + ], + "source": [ + "get_length_print(mdes, MDE_VALUE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4h-level split\n", + "\n", + "Assume we randomize the treatment at 4h level, we can use the same data as above, but we need to change the switch frequency to 4h." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mde_time_split = NormalPowerAnalysis.from_dict({\n", + " \"analysis\": \"clustered_ols\",\n", + " \"cluster_cols\": [\"datetime\"],\n", + " \"splitter\": \"switchback\",\n", + " \"switch_frequency\": \"4h\",\n", + " \"target_col\": \"delivery_time\",\n", + " \"time_col\": \"datetime\",\n", + " \"washover\": \"constant_washover\",\n", + " \"washover_time_delta\": timedelta(minutes=30),\n", + "})\n", + "\n", + "mdes = mde_time_split.mde_time_line(\n", + " experiment_data,\n", + " powers=[0.8],\n", + " experiment_length=EXPERIMENT_LENGTHS,\n", + " n_simulations=10\n", + ")\n", + "\n", + "plot_mdes(mdes, y_value=MDE_VALUE)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No MDE below 0.1 found in the provided data.\n" + ] + } + ], + "source": [ + "get_length_print(mdes, MDE_VALUE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### City + 4h-level split\n", + "\n", + "Assume we randomize the treatment at city + 4h level, we can use the same data as above, but we need to add city as a cluster column." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mde_time_split = NormalPowerAnalysis.from_dict({\n", + " \"analysis\": \"clustered_ols\",\n", + " \"cluster_cols\": [\"datetime\", \"city\"],\n", + " \"splitter\": \"switchback\",\n", + " \"switch_frequency\": \"4h\",\n", + " \"target_col\": \"delivery_time\",\n", + " \"time_col\": \"datetime\",\n", + " \"washover\": \"constant_washover\",\n", + " \"washover_time_delta\": timedelta(minutes=30),\n", + "})\n", + "\n", + "mdes = mde_time_split.mde_time_line(\n", + " experiment_data,\n", + " powers=[0.8],\n", + " experiment_length=EXPERIMENT_LENGTHS,\n", + " n_simulations=10\n", + ")\n", + "\n", + "plot_mdes(mdes, y_value=MDE_VALUE)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No MDE below 0.1 found in the provided data.\n" + ] + } + ], + "source": [ + "get_length_print(mdes, MDE_VALUE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "is_weekend is a good predictor of delivery time that is not impacted by the treatment. We can use this covariate to adjust our analysis and decrease mde. We see that mde is smaller in this case, because we are using a covariate that is not impacted by the treatment." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "mde_variance_reduction = NormalPowerAnalysis.from_dict({\n", + " \"analysis\": \"clustered_ols\",\n", + " \"cluster_cols\": [\"datetime\", \"city\"],\n", + " \"splitter\": \"switchback\",\n", + " \"switch_frequency\": \"4h\",\n", + " \"target_col\": \"delivery_time\",\n", + " \"time_col\": \"datetime\",\n", + " \"covariates\": [\"is_weekend\"],\n", + " \"washover\": \"constant_washover\",\n", + " \"washover_time_delta\": timedelta(minutes=30),\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "mdes = mde_variance_reduction.mde_time_line(\n", + " experiment_data,\n", + " powers=[0.8],\n", + " experiment_length=EXPERIMENT_LENGTHS,\n", + " n_simulations=10\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_mdes(mdes, y_value=MDE_VALUE)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimum experiment length to detect MDE of 0.1: 42\n" + ] + } + ], + "source": [ + "get_length_print(mdes, MDE_VALUE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the last example of power analysis.\n", + "\n", + "We assume data from Feb onwards is used to run the power analysis (it's the data we take as experimental, though it is actually pre-experimental data). Data from Jan is taken as pre-experimental data. We simulate that the experiment happened after Feb. We do this because we need pre-experimental data to train the Cupac Model, which we didn't do before.\n", + "\n", + "We use cupac model with customer id and datetime features (delivery-time has a non-linear relationship with customer id, that's why we don't add it a single covariate). We use the same data as before, but we train the model with pre-experimental data. We see that mde is smaller in this case, because we are using a **better** covariate that is not impacted by the treatment.\n", + "\n", + "In this case we cannot init by dict because we're using cupac, but happy to review a PR that includes this :) In this case we need to create splitter and analysis classes." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "cutoff_date = datetime.date(2024, 2, 1)\n", + "\n", + "pre_experiment_df = experiment_data[experiment_data[\"date\"] < cutoff_date]\n", + "experiment_df = experiment_data[experiment_data[\"date\"] >= cutoff_date]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from cluster_experiments import SwitchbackSplitter, ClusteredOLSAnalysis, ConstantWashover\n", + "from sklearn.ensemble import HistGradientBoostingRegressor\n", + "\n", + "\n", + "splitter = SwitchbackSplitter(\n", + " cluster_cols=[\"datetime\", \"city\"],\n", + " switch_frequency=\"4h\",\n", + " time_col=\"datetime\",\n", + " washover=ConstantWashover(\n", + " washover_time_delta=timedelta(minutes=30)\n", + " )\n", + ")\n", + "ols = ClusteredOLSAnalysis(\n", + " target_col=\"delivery_time\",\n", + " covariates=[\"estimate_delivery_time\"],\n", + " cluster_cols=[\"datetime\", \"city\"],\n", + ")\n", + "\n", + "pwr = NormalPowerAnalysis(\n", + " splitter=splitter,\n", + " analysis=ols,\n", + " cupac_model=HistGradientBoostingRegressor(),\n", + " time_col=\"datetime\",\n", + " target_col=\"delivery_time\",\n", + " features_cupac_model=[\"customer_id\", \"is_weekend\", \"hour_of_day\"],\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mde_cupac = pwr.mde_time_line(\n", + " experiment_df,\n", + " pre_experiment_df,\n", + " powers=[0.8],\n", + " experiment_length=EXPERIMENT_LENGTHS,\n", + " n_simulations=10,\n", + ")\n", + "\n", + "plot_mdes(mde_cupac, y_value=MDE_VALUE)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimum experiment length to detect MDE of 0.1: 14\n" + ] + } + ], + "source": [ + "get_length_print(mde_cupac, MDE_VALUE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis\n", + "\n", + "Now we run analysis assuming that the experiment run after 2024-03-01 for 3 weeks. We simulate some fake effects (0.0 in order value and 0.1 in delivery time). We use functionalities in cluster_experiments to simulate the experiment." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "cutoff_date = datetime.date(2024, 3, 1)\n", + "\n", + "real_experiment_data = experiment_data[experiment_data[\"date\"] >= cutoff_date]\n", + "real_pre_experiment_data = experiment_data[experiment_data[\"date\"] < cutoff_date]\n", + "\n", + "from cluster_experiments import ConstantPerturbator\n", + "\n", + "\n", + "# Add effect on the order value\n", + "splitter = SwitchbackSplitter(\n", + " cluster_cols=[\"datetime\", \"city\"],\n", + " switch_frequency=\"4h\",\n", + " time_col=\"datetime\",\n", + " washover=ConstantWashover(\n", + " washover_time_delta=timedelta(minutes=30)\n", + " )\n", + ")\n", + "\n", + "perturbator = ConstantPerturbator(\n", + " target_col=\"delivery_time\",\n", + ")\n", + "\n", + "real_experiment_data = splitter.assign_treatment_df(real_experiment_data)\n", + "real_experiment_data = perturbator.perturbate(real_experiment_data, average_effect=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We run the analysis with and without covariate adjustment.\n", + "\n", + "We see that the effect is closer to the true effect when we use covariate adjustment." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from cluster_experiments import AnalysisPlan, HypothesisTest, Variant, SimpleMetric\n", + "\n", + "plan = AnalysisPlan.from_metrics_dict({\n", + " \"metrics\": [\n", + " {\"alias\": \"AOV\", \"name\": \"order_value\"},\n", + " {\"alias\": \"delivery_time\", \"name\": \"delivery_time\"},\n", + " ],\n", + " \"variants\": [\n", + " {\"name\": \"A\", \"is_control\": True},\n", + " {\"name\": \"B\", \"is_control\": False},\n", + " ],\n", + " \"variant_col\": \"treatment\",\n", + " \"alpha\": 0.05,\n", + " \"analysis_type\": \"clustered_ols\",\n", + " \"analysis_config\": {\"cluster_cols\": [\"city\", \"datetime\"]},\n", + "})\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that CIs contain the true effect, and CIs are pretty big." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " metric_alias control_variant_name treatment_variant_name \\\n", + "0 AOV A B \n", + "1 delivery_time A B \n", + "\n", + " control_variant_mean treatment_variant_mean analysis_type ate \\\n", + "0 82.007654 82.172876 clustered_ols 0.165222 \n", + "1 9.299918 9.452242 clustered_ols 0.152324 \n", + "\n", + " ate_ci_lower ate_ci_upper p_value std_error dimension_name \\\n", + "0 -0.880170 1.210615 0.756737 0.533373 __total_dimension \n", + "1 0.013381 0.291268 0.031657 0.070891 __total_dimension \n", + "\n", + " dimension_value alpha \n", + "0 total 0.05 \n", + "1 total 0.05 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run the analysis plan\n", + "plan.analyze(real_experiment_data).to_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we use covariate adjustment with is weekend." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "plan_covariates = AnalysisPlan.from_metrics_dict({\n", + " \"metrics\": [\n", + " {\"alias\": \"AOV\", \"name\": \"order_value\"},\n", + " {\"alias\": \"delivery_time\", \"name\": \"delivery_time\"},\n", + " ],\n", + " \"variants\": [\n", + " {\"name\": \"A\", \"is_control\": True},\n", + " {\"name\": \"B\", \"is_control\": False},\n", + " ],\n", + " \"variant_col\": \"treatment\",\n", + " \"alpha\": 0.05,\n", + " \"analysis_type\": \"clustered_ols\",\n", + " \"analysis_config\": {\"cluster_cols\": [\"city\", \"datetime\"], \"covariates\": [\"is_weekend\"]},\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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metric_aliascontrol_variant_nametreatment_variant_namecontrol_variant_meantreatment_variant_meananalysis_typeateate_ci_lowerate_ci_upperp_valuestd_errordimension_namedimension_valuealpha
0AOVAB82.00765482.172876clustered_ols0.167288-0.8785991.2131750.7539070.533626__total_dimensiontotal0.05
1delivery_timeAB9.2999189.452242clustered_ols0.0951470.0147370.1755570.0203850.041026__total_dimensiontotal0.05
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" + ], + "text/plain": [ + " metric_alias control_variant_name treatment_variant_name \\\n", + "0 AOV A B \n", + "1 delivery_time A B \n", + "\n", + " control_variant_mean treatment_variant_mean analysis_type ate \\\n", + "0 82.007654 82.172876 clustered_ols 0.167288 \n", + "1 9.299918 9.452242 clustered_ols 0.095147 \n", + "\n", + " ate_ci_lower ate_ci_upper p_value std_error dimension_name \\\n", + "0 -0.878599 1.213175 0.753907 0.533626 __total_dimension \n", + "1 0.014737 0.175557 0.020385 0.041026 __total_dimension \n", + "\n", + " dimension_value alpha \n", + "0 total 0.05 \n", + "1 total 0.05 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run the analysis plan\n", + "plan_covariates.analyze(real_experiment_data).to_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we run analysis using cupac model. We see that the effect in both metrics is closer to the true effect, and the CIs are smaller." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "plan_cupac = AnalysisPlan(\n", + " tests=[\n", + " HypothesisTest(\n", + " metric=SimpleMetric(alias=\"AOV\", name=\"order_value\"),\n", + " analysis_type=\"clustered_ols\",\n", + " analysis_config={\n", + " \"cluster_cols\": [\"city\", \"datetime\"],\n", + " \"covariates\": [\"estimate_order_value\"],\n", + " },\n", + " cupac_config={\n", + " \"cupac_model\": HistGradientBoostingRegressor(),\n", + " \"features_cupac_model\": [\"customer_id\", \"customer_age\"],\n", + " \"target_col\": \"order_value\",\n", + " },\n", + " ),\n", + " HypothesisTest(\n", + " metric=SimpleMetric(alias=\"delivery_time\", name=\"delivery_time\"),\n", + " analysis_type=\"clustered_ols\",\n", + " analysis_config={\n", + " \"cluster_cols\": [\"city\", \"datetime\"],\n", + " \"covariates\": [\"estimate_delivery_time\"],\n", + " },\n", + " cupac_config={\n", + " \"cupac_model\": HistGradientBoostingRegressor(),\n", + " \"features_cupac_model\": [\"customer_id\", \"is_weekend\", \"hour_of_day\"],\n", + " \"target_col\": \"delivery_time\",\n", + " },\n", + " ),\n", + " ],\n", + " variants=[\n", + " Variant(name=\"A\", is_control=True),\n", + " Variant(name=\"B\", is_control=False),\n", + " ],\n", + " variant_col=\"treatment\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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metric_aliascontrol_variant_nametreatment_variant_namecontrol_variant_meantreatment_variant_meananalysis_typeateate_ci_lowerate_ci_upperp_valuestd_errordimension_namedimension_valuealpha
0AOVAB82.00765482.172876clustered_ols0.322643-0.2704590.9157450.2863310.302609__total_dimensiontotal0.05
1delivery_timeAB9.2999189.452242clustered_ols0.1074050.0626090.1522010.0000030.022856__total_dimensiontotal0.05
\n", + "
" + ], + "text/plain": [ + " metric_alias control_variant_name treatment_variant_name \\\n", + "0 AOV A B \n", + "1 delivery_time A B \n", + "\n", + " control_variant_mean treatment_variant_mean analysis_type ate \\\n", + "0 82.007654 82.172876 clustered_ols 0.322643 \n", + "1 9.299918 9.452242 clustered_ols 0.107405 \n", + "\n", + " ate_ci_lower ate_ci_upper p_value std_error dimension_name \\\n", + "0 -0.270459 0.915745 0.286331 0.302609 __total_dimension \n", + "1 0.062609 0.152201 0.000003 0.022856 __total_dimension \n", + "\n", + " dimension_value alpha \n", + "0 total 0.05 \n", + "1 total 0.05 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plan_cupac.analyze(\n", + " real_experiment_data,\n", + " real_pre_experiment_data,\n", + ").to_dataframe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cluster-experiments", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 988718177ca07e5420c4c23df1ce36e824b02db1 Mon Sep 17 00:00:00 2001 From: david26694 Date: Wed, 9 Jul 2025 15:55:24 +0200 Subject: [PATCH 2/2] add calendars --- docs/city_hour.png | Bin 0 -> 39409 bytes docs/daily.png | Bin 0 -> 28856 bytes docs/e2e_mde_switchback.ipynb | 35 +++++++++++++++++++++++++++------- docs/hourly.png | Bin 0 -> 36493 bytes 4 files changed, 28 insertions(+), 7 deletions(-) create mode 100644 docs/city_hour.png create mode 100644 docs/daily.png create mode 100644 docs/hourly.png diff --git a/docs/city_hour.png b/docs/city_hour.png new file mode 100644 index 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z&g$DuN6)}?8*2Vq^kTjIV(ba zStw_lBuoopmEA0>Z>UX5l08jz{<&4Qc>l#s(UoS0hC9|vKE7uFk4|7}*z|NPtkJhcBgtN+Z1`v0qqrsI>(ZWD3{M$RfAKdDYbO;`1evQ6-R E0hPM5d;kCd literal 0 HcmV?d00001 diff --git a/docs/e2e_mde_switchback.ipynb b/docs/e2e_mde_switchback.ipynb index 315d5b7..6448398 100644 --- a/docs/e2e_mde_switchback.ipynb +++ b/docs/e2e_mde_switchback.ipynb @@ -30,7 +30,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/n0/x7n4w_fs4vz094fp0sjd9mk00000gp/T/ipykernel_11416/441254994.py:38: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", + "/var/folders/n0/x7n4w_fs4vz094fp0sjd9mk00000gp/T/ipykernel_25340/3103132035.py:38: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", " date_range = pd.date_range(start=\"2024-01-01\", end=\"2024-03-31\", freq='H')\n" ] }, @@ -322,7 +322,7 @@ " .drop(columns=[\"mean_order_value\"]) # Remove intermediate column\n", " )\n", "\n", - "def plot_mdes(mdes, x_lim=40, y_value=3):\n", + "def plot_mdes(mdes, x_lim=60, y_value=3):\n", " sns.lineplot(\n", " data=pd.DataFrame(mdes),\n", " x=\"experiment_length\",\n", @@ -365,6 +365,13 @@ "Assume we run an switchback test randomizing at day level" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](daily.png)" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -372,7 +379,7 @@ "outputs": [ { "data": { - "image/png": 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/7CIvjOyob3+wOVme+myX5BeVGF01AC6EcAPA6dbCmTK4nfzzNz3Ex8tDvtl7Rn77/o9yMa/Q6KoBcBGEGwBOaVSPFvLRw32lsZ+3bD9+QX41L0FOXsgzuloAXADhBoDTGhjXVJY+MUCaB/tLUnqO3Pv2Ftl3KsvoagFwcoQbAE6tY/NgWT51oHSIbCznLhXIuHcSZOPhc0ZXC4ATI9wAcHpRIQGyZMoA6d82THILS/R+VJ/vPGl0tQA4KcINAJcQEuAjHz3SV34ZHy3FpRb53dKf5F9rjojFwlRxAPYINwBchp+3l8we10MmD26rb7+x+rC8uHyfFJeUGl01AE6EcAPApXh6esiMkZ30ejgeHiKfbUuRyR/vlLzCYqOrBsBJEG4AuKSJA1vL3Ad6i5+3p6w5mC7j390qGTkFRlcLgLuHm40bN8o999wj0dHReuGuFStWOCy/fv16Xe7a48yZMw1WZwDO486uzWXhY/0kNNBHfjqZpXcVT87INbpaANw53OTm5kp8fLzMmTOnWo87dOiQpKWl2Y6IiIh6qyMA59Y7NkzvKt6ySYCcyMyTMW9vlvc3JdNNBbgxbyN/+MiRI/VRXSrMhIaG1kudALieds0aybInB8qkBTtk76ks+evX++WttUfk4UFtZOKA1hIS6GN0FQE0IJccc9OjRw+JioqS22+/XTZv3mx0dQA4gYjG/vL5EwNk5phuEhseKBfyiuTN1Ydl4Kw1MvObA5KenW90FQE0EJcKNyrQzJs3T7744gt9xMTEyJAhQ2TXrl2VPqagoECys7PtDgDmnSo+vm8rWTN9sN54s2PzxnrRv3c2HpNbXlsnf1q+V1Iy2Z8KMDsPi5OsgKUGBi9fvlxGjx5drccNHjxYWrVqJR9//HGF97/yyivy6quvXnc+KytLgoODa1xfAM5P/XlbezBd5qxLkl0pF/U5L08Puad7lDwxJE46NG9sdBUBVJFqnAgJCanS57dLtdxUpG/fvpKUlFTp/TNmzNAXwnqkpqY2aP0AGPufpmGdIvWA40WP95fbbmomJaUWWbH7tIyYvVEe+/cOSUy5YHQ1AZhpQHFd2L17t+6uqoyfn58+ALh3yOnfNlwfe09mydvrk2TVz2dk9f6z+hjYLlymDo3TX1VZAK7N0HCTk5Nj1+qSnJysw0pYWJjualKtLqdOnZJ///vf+v7Zs2dLmzZtpEuXLpKfny/z58+XtWvXynfffWfgqwDgSrq1DJG5D/aWpPQcmbfhqKxIPCVbjmbqI75liDw5NE5u7xSpV0IG4JoMDTc7duyQoUOH2m5Pnz5df504caIsWLBAr2GTkpJiu7+wsFCef/55HXgCAwOle/fu8v3339s9BwBURVxEI/n72Hh57vab5L2Nx/Q2DmohQLWVQ/uIRvLEkHZyT3y0+Hi5fO894HacZkCxMw5IAuA+1NYNH2xKlo8TTsilgrIFANXCgJNvaytj+8SIv4+X0VUE3Fp2NT6/CTcAUE52fpEOOCroZOYW6nNNG/nJo7e2kQf6tZLG/iwICBiBcOMA4QZAVVwuLJElO1Ll3Y3H5NTFy/pcsL+33rBTrXwcFuRrdBUBt5JNuKkc4QZAdRSVlMqXu0/L3PVJcvRc2aacAT5e8pu+MfLYrW0lOjTA6CoCbiGbcFM5wg2Amigttch3+8/InHVH9f5Vio+Xh4zp2VImD24rbZs1MrqKgKllE24qR7gBUBvqT+YPRzL0Wjlbj53X59TSOHd1i5Inh7STLtEhRlcRMCXCjQOEGwB1ZeeJ8/L2uqOy5mC67dyQDs30goA3tw4ztG6A2RBuHCDcAKhrB9KyZe76o/L1ntNSeuUvat/WYfLE0HYy5KZmrHoM1AHCjQOEGwD15XhGrt6B/IudJ6WwpFSf6xwVLE8ObScju0bpTTsB1AzhxgHCDYD6djY7X+b/cEw+/TFF8gpL9Lk2TYNkyuC2cm/PluLrzarHQHURbhwg3ABoKBdyC+WjhOPy4ebjknW5SJ+LCvHXU8jVVPJAX5ffuxhoMIQbBwg3ABpaTkGxfPZjirz3wzFJv1SgzzUJ9JFHBrWRCQNaS0ggqx4DN0K4cYBwA8AoBcUl8sXOU3o38pTzefpcIz9veaB/K5l0SxuJaOxvdBUBp0W4cYBwA8BoxSWlsnJvmp5GfujsJX1OjcMZ1ydGHr+trcSEBRpdRcDpEG4cINwAcKZVj9ceTJc565MkMeWiPqdmVI2Kj5YnhrST9pGNja4i4DQINw4QbgA4G/VnWK12rFY9VqsfW93ROVKeHBonPWJCDa0f4AwINw4QbgA4sz0nL+ruqm/3nxHrX+dBceEydUicDGgXzoKAcFvZhJvKEW4AuIKk9Esyd/0x+XL3KSm+suyxasFR+1cN7xQpniwICDeTTbipHOEGgCs5eSFP3tt4TBZtT5WC4rJVj8ODfGXwTc1kcIdm+mtooK/R1QTqHeHGAcINAFd07lKBfLg5WT7ZekKy84tt51UDTs9WTWRoh2YypEOEdIkOpusKpkS4cYBwA8CVFZWUyq4TF2TdoXOy/lC6HDxTNpXcqlljP71Z59COEXJL+6YS7M8CgTAHwo0DhBsAZnL64mVZf+icrDuULpuTMmx7WSnenh7SO7aJDjpDO0TITZGNaNWByyLcOEC4AWDmFZB3HL8g6w6m67Bz9Fyu3f1qXyvVdaW6sAbFNZUgP/a2gusg3DhAuAHgLlIy82T94XQddhKOZUp+UdmAZMXXy1P6tgmTIVfG6rRrFkSrDpwa4cYBwg0Ad5RfVCJbj2XqLiy1KrJ1byurmLAA3XWljv5twyXA18uwugIVIdw4QLgB4O7Un/3kjFzboOQfj52XwpKrrTp+3p56wUAVdFTLTmx4kKH1BRTCjQOEGwCwl1tQLAlHM/U4HdWyc+riZbv72zYNKhur07GZ7sry86ZVBw2PcOMA4QYAKqc+Eo6k5+hxOirobD9+3rZCshLg46UHI6sWHTULq0VogKH1hfvIJtxUjnADAFV3Kb9ITzFfd7Bsunn6pQK7+9X08rLuqwjp07qJ+Hh5GlZXmFs24aZyhBsAqBn1cbE/LVu36KixOjtPXJByjTrSyM9bbolrqruvVNiJDPY3srowGcKNA4QbAKgbF/MK5YcjGbpFZ8Ohc5KZW2h3f+eoYB10VMuO2vTTm1Yd1ALhxgHCDQDUvdJSi+w9lWVbLfmnkxel/KdLsL+33Ka2hegQoTf8bNrIz8jqwgURbhwg3ABA/cvMKZCNR87psTobDp+TrMtFtvvUWoHdW4Toris1MLl7y1DxUjuAAg4Qbhwg3ABAwyoptcju1Au2Vp19p7Lt7g8L8pXBN6lxOs3ktvbNpEmQr2F1hfMi3DhAuAEAY6Vn58v6w2WDkn84nCGXCopt96kGnPiYUD0wWU0579kqlHV1oBFuHCDcAIDzKCoplV0nLthWSz545pLd/f4+ag+scLklLlyHnU7Ng8WTLiy3lE24qRzhBgCc1+mLl2VTUoZeW0cdGTmF13Vhqa0hVMuOOmLCAg2rKxoW4cYBwg0AuAb18XT4bI4t7Px4LFNyC0uu2/DT2oU1sF1THX5gToQbBwg3AOC6XVg/pV60hZ3ElIt2W0NY19a5pX1Z2Lm5dRMJ9PU2rL6oW4QbBwg3AGAOOQXFsj35vC3sXDtex8fLQ3q1alLWstO+qZ5+zkKCrotw4wDhBgDM6dylAtly1DpeJ/O63c0b+3lLv7Zlg5NV6067Zo3EQy26A5fQYOGmsLBQkpOTpV27duLt7RpNf4QbADA/9dF2IjPP1qqz5Wim3UKCSkRjP9t4HXU0D2EvLLcON3l5efL000/LRx99pG8fPnxY2rZtq8+1aNFCXnjhBXFWhBsAcM+FBPefzraFne3Hz0tBcaldmXbNgmxhp3+7cAn29zGsvjAg3EybNk02b94ss2fPljvvvFP27Nmjw82XX34pr7zyiiQmJoqzItwAAPKLSvT6OjrsHM2UvScv2u1wrpbSUdtCWMNOr1gWEzR9uImNjZXFixdL//79pXHjxvLTTz/pcJOUlCS9evXSFXBWhBsAwLWy8ook4Vhm2Xidoxly7FzudYsJ3tw6zBZ21KwsFhN03s/vGg2UOXfunERERFx3Pjc3l8FZAACXExLoI3d2ba4P62KC1rE6qnVHDVb+4UiGPpQmgT56XR0VdFTgaRXOYoLOpEbhpk+fPrJy5Uo9xkaxBpr58+fLgAED6raGAAA0sOjQABnbJ0YfqoPjSHqObDqiwk6GbD12Xi7kFcnKvWn6UFo2Kb+YYLiEN/Iz+iW4tRp1S23atElGjhwpDz74oCxYsEAmT54s+/fvly1btsiGDRukd+/e4qzolgIA1HYxwT0nL8qmI5m6Cysx5YIUldh/lHZSiwle2Q+rb5swFhN0langR48elVmzZunxNjk5OXqszR//+Efp1q2bODPCDQCgLuUWFMu24+dlS1KGbErKlANp2dctJtjTuphgXLgeqOzDYoLVxiJ+DhBuAAD1KSNHLSaYqcOOGqNz7WKCjfy8pWerUOnbOkz6tA7T3/v7MBPLkHBTnRlQzhwaCDcAgIaiPmJTzpctJrglqawb62Je0XUtO11bhNjCTp/YJtKEDUAbJtx4enpWeSZUSYn9rq3OhHADADBKaalF74G148R52ZZ8Xi8meDa74Lpy7SMa6aDTt00T6RMbpgcsu/ts5Oz6CDdqoLDV8ePH9SrEDz30kG12VEJCgl6xeObMmTJx4kRxVoQbAICzUB/BJy9c1iGn7LggSek515WLCvEvCzutm+ivHSIbu906O9n1PeZm2LBh8uijj8r48ePtzi9cuFDeffddWb9+vTgrwg0AwJmdzy2UHcfPy44TF3Trzr5TWVJcfvlktQmov7fuvipr3QmTbi1CTD9uJ7u+w01gYKCeJdW+fXu782qPqR49eui9p5wV4QYA4EouF5ZIYuoF2XH8gm7dUdtG5BbaD//w9fKU+JiQK607YdIrtomEBJhrb6x6X6E4JiZG3nvvPXnttdfszqtF/NR9AACgbgT4eunVkNWhFJeUyoG0SzrolI3duaBnaKkuLXXMlaOihueoriu1ZUSf1k10605USIC4ixq13HzzzTdy3333SVxcnPTr10+f27Ztm265WbZsmdx1113irGi5AQCYifoYP5GZp9faUd1Z249fkOQM+72xlBahATrk6LDTOkzaNWvkUuN2GmSdm9TUVJk3b54cOHBA3+7UqZNMmTLF6VtuCDcAALM7d6nAFnRUC8/Pp7Psdj1XQgN99LidstadsnE7vt6e7h1u8vPzZc+ePZKeni6lpaV29/3yl78UZ0W4AQC4m5yCYtmdctHWupOYclEuF9mP2/Hz9pQeMaFXWnfCpFerUGns7+M+4WbVqlUyYcIEyczM1M1hdk/o4cE6NwAAOPn+WD+fzpbtV9baUTOz1Cyt8lSPldojS7XslB1NJCLY37zhRs2SuuOOO+Sll16SyMhIcSWEGwAA7KkocPRcrm7VKWvduaBXVr5WbHigXlRQLy7YOkzaNg1qsMUF6z3cqCdNTEyUdu3aiash3AAAcGNnsvL1bKyy1p0LcuBMtlybGMKDfPUAZWvrTufo4HrbFLTew80jjzwigwYNkkmTJomrIdwAAFB92flFeo0d1aqjWnd2p16UwmL7MbeBvl56I1A1bX3q0DjXCjdqkb6xY8dKs2bNpFu3buLjYz/g6JlnnhFnRbgBAKD2CopL9OrJekZWctm4nazLZZuCqsHIy54cJC4Vbt5//3097dvf31/Cw8Pt+tvU98eOHRNnRbgBAKB+NgVNOpejt4xQ20OM6tHCtcJN8+bNdeuM2jxT7RbuSgg3AAC4nup8ftcomRQWFsq4ceNcLtgAAADzq1E6mThxoixevLjuawMAAFBLNdo4Uy3SpzbN/Pbbb6V79+7XDSh+8803a1svAACAhgs3e/fulZ49e+rv9+3bZ3dfQy3mAwAAUGfhZt26dTV5GAAAQL0zdETwxo0b5Z577pHo6Gjd4rNixYobPmb9+vXSq1cv8fPzk7i4OFmwYEGD1BUAALgGQ8NNbm6uxMfHy5w5c6pUPjk5We6++24ZOnSo7N69W5599ll59NFH9dgfAACAGndL1ZWRI0fqo6rmzZsnbdq0kTfeeEPf7tSpk2zatEn+8Y9/yIgRI+qxpgAAwFW41EI1CQkJMnz4cLtzKtSo8wAAAIa33FTXmTNnJDIy0u6cuq1WLbx8+bIEBARc95iCggJ9WKmyAADAvFyq5aYmZs6cqZdrth4xMTFGVwkAANQjlwo3ak+rs2fP2p1Tt9UeExW12igzZszQ+1BYj9TU1AaqLQAAMIJLdUsNGDBAvvnmG7tzq1ev1ucro6aMqwMAALgHQ1tucnJy9JRudVineqvvU1JSbK0uEyZMsJWfMmWKHDt2TP7whz/IwYMH5e2335YlS5bIc889Z9hrAAAAzsXQcLNjxw69jYN1K4fp06fr71966SV9Oy0tzRZ0FDUNfOXKlbq1Rq2Po6aEz58/n2ngAADAxsNisVjEjajZUmpgsRp/o8bqAAAAc31+u9SAYgAAgBsh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFNxinAzZ84cad26tfj7+0u/fv1k27ZtlZZdsGCBeHh42B3qcQAAAE4RbhYvXizTp0+Xl19+WXbt2iXx8fEyYsQISU9Pr/QxwcHBkpaWZjtOnDjRoHUGAADOy/Bw8+abb8pjjz0mDz/8sHTu3FnmzZsngYGB8sEHH1T6GNVa07x5c9sRGRnZoHUGAADOy9BwU1hYKDt37pThw4dfrZCnp76dkJBQ6eNycnIkNjZWYmJiZNSoUfLzzz83UI0BAICzMzTcZGRkSElJyXUtL+r2mTNnKnxMhw4ddKvOl19+KZ988omUlpbKwIED5eTJkxWWLygokOzsbLsDAACYl+HdUtU1YMAAmTBhgvTo0UMGDx4sy5Ytk2bNmsk777xTYfmZM2dKSEiI7VCtPQAAwLwMDTdNmzYVLy8vOXv2rN15dVuNpakKHx8f6dmzpyQlJVV4/4wZMyQrK8t2pKam1kndAQCAczI03Pj6+krv3r1lzZo1tnOqm0ndVi00VaG6tfbu3StRUVEV3u/n56dnV5U/AACAeXkbXQE1DXzixInSp08f6du3r8yePVtyc3P17ClFdUG1aNFCdy8pf/nLX6R///4SFxcnFy9elNdff11PBX/00UcNfiUAAMAZGB5uxo0bJ+fOnZOXXnpJDyJWY2lWrVplG2SckpKiZ1BZXbhwQU8dV2WbNGmiW362bNmip5EDAAB4WCwWi7gRNVtKDSxW42/oogIAwHyf3y43WwoAAMARwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVb6MrYDq5uZXf5+Ul4u9ftbKeniIBATUrm5cnYrFUXNbDQyQwsGZlL18WKS2tvB5BQTUrm58vUlJSN2VVfVW9lYICkeLiuimrrq+6zkphoUhRUd2UVe8H9b6obllVTpWvjJ+fiLd39cuqa6CuRWV8fUV8fKpfVv2bqX+7yqhyqnx1y6r3mHqv1UVZdQ3UtVDU74T63aiLstX5vTfL34jyv7OAESxuJisrS/2W6q/1ouzPQMXHXXfZlw0MrLzs4MH2ZZs2rbxsnz72ZWNjKy/bubN9WXW7srLqecpTP6eysqp+5an6V1ZWve7y1HVxdN3K+9WvHJfNybladuJEx2XT06+WffJJx2WTk6+W/d3vHJfdt+9q2Zdfdlx227arZV97zXHZdeuuln3rLcdlv/76atkPP3RcdsmSq2XV947KqueyUj/DUVlVRytVd0dl1Wu3UtfEUVl1Ta3UtXZUVv1bWal/Q0dl1XvASr03HJVV7y0r9Z5zVFa9Z8tzh78RgMGf33RLAQAAU/FQCUfcSHZ2toSEhEhWVpYEBwfX/Q9whybnytAtVf2ydEuVoVvKXH8j6JaCwZ/fhBsAAGCqz2+6pQAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKk4RbiZM2eOtG7dWvz9/aVfv36ybds2h+WXLl0qHTt21OW7desm33zzTYPVFQAAODfDw83ixYtl+vTp8vLLL8uuXbskPj5eRowYIenp6RWW37Jli4wfP14mTZokiYmJMnr0aH3s27evwesOAACcj+F7S6mWmptvvlneeustfbu0tFRiYmLk6aeflhdeeOG68uPGjZPc3Fz5+uuvbef69+8vPXr0kHnz5t3w57G3FAAArsdl9pYqLCyUnTt3yvDhw69WyNNT305ISKjwMep8+fKKaumprHxBQYG+IOUPAABgXt5G/vCMjAwpKSmRyMhIu/Pq9sGDByt8zJkzZyosr85XZObMmfLqq69ed56QAwCA67B+blelw8nQcNMQZsyYocf0WJ06dUo6d+6su74AAIBruXTpku6ectpw07RpU/Hy8pKzZ8/anVe3mzdvXuFj1PnqlPfz89OHVaNGjSQ1NVUaN24sHh4eUtepUoUm9fyM56k+rl/tcQ1rh+tXe1zD2uH6VU612KhgEx0dLTdiaLjx9fWV3r17y5o1a/SMJ+uAYnX7qaeeqvAxAwYM0Pc/++yztnOrV6/W56tCjelp2bKl1Cf1huRNWXNcv9rjGtYO16/2uIa1w/Wr2I1abJymW0p1GU2cOFH69Okjffv2ldmzZ+vZUA8//LC+f8KECdKiRQs9dkaZNm2aDB48WN544w25++67ZdGiRbJjxw559913DX4lAADAGRgebtTU7nPnzslLL72kBwWrKd2rVq2yDRpOSUnRrS1WAwcOlIULF8p///d/y4svvijt27eXFStWSNeuXQ18FQAAwFkYHm4U1QVVWTfU+vXrrzs3duxYfTgbNbZHLUZYfowPqo7rV3tcw9rh+tUe17B2uH4mWcQPAADAVNsvAAAA1CXCDQAAMBXCDQAAMBXCTR2ZM2eOtG7dWvz9/fVmoNu2bTO6Sk5r48aNcs899+iFmNRCimq2W3lqGJiaPRcVFSUBAQF6L7EjR44YVl9no5ZFUJvNqoUoIyIi9BpRhw4dsiuTn58vU6dOlfDwcL1w5X333Xfd4pfubO7cudK9e3fbWiJqnaz//Oc/tvu5ftUza9Ys/btcfv0xrqFjr7zyir5m5Y+OHTva7uf61Q7hpg4sXrxYr9ejRrjv2rVL4uPj9Wae6enpRlfNKal1jNQ1UoGwIq+99pr87//+r97l/ccff5SgoCB9PdUvO0Q2bNig/+ht3bpVL2BZVFQkd9xxh76uVs8995x89dVXsnTpUl3+9OnTMmbMGEPr7UzUQp7qA1lt3KvWyfrFL34ho0aNkp9//lnfz/Wruu3bt8s777yjw2J5XMMb69Kli6SlpdmOTZs22e7j+tWSmi2F2unbt69l6tSpttslJSWW6Ohoy8yZMw2tlytQb8Hly5fbbpeWllqaN29uef31123nLl68aPHz87N89tlnBtXSuaWnp+vruGHDBtv18vHxsSxdutRW5sCBA7pMQkKCgTV1bk2aNLHMnz+f61cNly5dsrRv396yevVqy+DBgy3Tpk3T57mGN/byyy9b4uPjK7yP61d7tNzUUmFhof7fn+o6sVKLDqrbCQkJhtbNFSUnJ+vFHMtfT7Xcturq43pWLCsrS38NCwvTX9X7UbXmlL+Gqrm7VatWXMMKlJSU6JXOVcuX6p7i+lWdakFUK8WXv1YK17BqVHe76p5v27atPPDAA3rRWoXrZ5JF/FxZRkaG/uNoXVHZSt0+ePCgYfVyVSrYKBVdT+t9uErtxabGOQwaNMi2Sre6TmrfttDQULuyXEN7e/fu1WFGdXeqMQ3Lly+Xzp07y+7du7l+VaACoeqGV91S1+I9eGPqP2wLFiyQDh066C6pV199VW699VbZt28f168OEG4AF/+fs/pjWL6vHlWjPlRUkFEtX59//rne406NbcCNqR2r1T5/asyXmkSB6hs5cqTtezVeSYWd2NhYWbJkiZ5IgdqhW6qWmjZtKl5eXteNYle3mzdvbli9XJX1mnE9b0xtWfL111/LunXr7Ha6V9dJdZdevHjRrjzX0J76n3FcXJz07t1bz0BTg9z/+c9/cv2qQHWbqAkTvXr1Em9vb32oYKgmAqjvVQsD17B6VCvNTTfdJElJSbwH6wDhpg7+QKo/jmvWrLHrKlC3VZM3qqdNmzb6l7f89czOztazprieZdQ4bBVsVDfK2rVr9TUrT70ffXx87K6hmiqu+vO5hpVTv7cFBQVcvyoYNmyY7tZTLV/Wo0+fPnrciPV7rmH15OTkyNGjR/USGLwH60AdDEp2e4sWLdKzeRYsWGDZv3+/5fHHH7eEhoZazpw5Y3TVnHaGRWJioj7UW/DNN9/U3584cULfP2vWLH39vvzyS8uePXsso0aNsrRp08Zy+fJlo6vuFJ544glLSEiIZf369Za0tDTbkZeXZyszZcoUS6tWrSxr16617NixwzJgwAB9oMwLL7ygZ5clJyfr95i67eHhYfnuu+/0/Vy/6is/W0rhGjr2/PPP699h9R7cvHmzZfjw4ZamTZvq2Y8K1692CDd15F//+pd+I/r6+uqp4Vu3bjW6Sk5r3bp1OtRce0ycONE2HfzPf/6zJTIyUofGYcOGWQ4dOmR0tZ1GRddOHR9++KGtjAqCTz75pJ7eHBgYaLn33nt1AEKZRx55xBIbG6t/X5s1a6bfY9Zgo3D9ah9uuIaOjRs3zhIVFaXfgy1atNC3k5KSbPdz/WqHXcEBAICpMOYGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGQIM5fvy4eHh46P2HXJGq+4oVK8QZvPLKK9KjRw+jqwE4JcINgAYTExMjaWlp0rVrV6Or4lLhwJlCFeAKvI2uAAD3UFhYKL6+vnrXdwCoT7TcACZUWloqM2fOlDZt2khAQIDEx8fL559/rjbKleHDh8uIESP098r58+elZcuW8tJLL+nb69ev1y0FK1eulO7du4u/v7/0799f9u3bZ/czNm3aJLfeeqt+ftUi88wzz0hubq7t/tatW8tf//pXmTBhggQHB8vjjz9+XbeU9Wd9++230rNnT/1cv/jFLyQ9PV3+85//SKdOnfRj77//fsnLy7vh67OyPu+aNWukT58+EhgYKAMHDpRDhw7p+xcsWCCvvvqq/PTTT7qcOtS56kpNTZVf//rXEhoaKmFhYTJq1Cj9Gq0eeughGT16tPz973+XqKgoCQ8Pl6lTp0pRUZGtjGrJuvvuu/XrUK9n4cKF+trNnj3bdh2Ve++9V9fTetvq448/1udCQkLkN7/5jVy6dKnarwMwnVpuvAnACf3tb3+zdOzY0bJq1SrL0aNH9Y7haof19evXW06ePKl3Gp49e7YuO3bsWL2TfVFRkd2u7Z06ddI7Ze/Zs8fyX//1X5bWrVtbCgsLdRm1e3FQUJDlH//4h+Xw4cOWzZs3W3r27Gl56KGHbHVQu24HBwdb/v73v+vy6khOTtbPnZiYaPez+vfvb9m0aZNl165dlri4OL3D9B133KFvb9y40RIeHm6ZNWtWlV5f+eft16+fPvfzzz9bbr31VsvAgQP1/Xl5eZbnn3/e0qVLF73TsjrUuRtRz7l8+XL9vboW6hqpHcbVNdq/f7/l/vvvt3To0MFSUFCgy6id7tU1mDJliuXAgQOWr776Su/w/O6779qec/jw4ZYePXpYtm7datm5c6d+7QEBAfraKunp6bZd31U91W3l5ZdftjRq1MgyZswYy969e/V1at68ueXFF1+s5bsHcH2EG8Bk8vPz9Qfoli1b7M5PmjTJMn78eP39kiVLLP7+/pYXXnhBhxQVUKyswWDRokW2c5mZmfoDd/Hixbbnevzxx+2e/4cffrB4enpaLl++bAs3o0ePtitTWbj5/vvvbWVmzpypz6nQYjV58mTLiBEjqvz6KnrelStX6nPW+qlwEB8fX61rWz7cfPzxxzrIlJaW2u5XoUZdp2+//dYWbtR1KC4utpVRYXLcuHH6exV41HNu377ddv+RI0f0OWu4ufbnWqn6q+uQnZ1tO/f73/9eBzrA3THmBjCZpKQk3YVz++23XzfmRXX9KGPHjpXly5fLrFmzZO7cudK+ffvrnmfAgAG271WXS4cOHeTAgQP6turO2bNnj3z66ae2MuozWHUXJScn6+4kRXUJVYXq/rKKjIzU3Uht27a1O7dt27Yqv76Knld1Cymqy6tVq1ZSW+oaqLo0btzY7nx+fr4cPXrUdrtLly7i5eVlV4+9e/fq71U3mbe3t/Tq1ct2f1xcnDRp0qRKdVDdUeV/vnpu9foAd0e4AUwmJydHf1VjZlq0aGF3n5+fn/6qwsHOnTv1h+6RI0dq9DMmT56sx9lcq3xwCAoKqtLz+fj42L5X40rK37aeU8HJ+rNv9Poqe17F+jy1perRu3dvu4Bn1axZswrrcO1rqa36fG7AlRFuAJPp3Lmz/pBPSUmRwYMHV1jm+eefF09PTz1o96677tIDWtVA3vK2bt1qCyoXLlyQw4cP21pkVEvD/v37dSuDM76+qlAzt0pKSmr8eHUNFi9eLBEREXrQc02o1rDi4mJJTEzUQUlRrUHqel8bYmpTV8DdEG4Ak1HdFL/73e/kueee0/+Lv+WWWyQrK0s2b96sP4SbNm0qH3zwgSQkJOgP6N///vcyceJE3c1UvjvkL3/5i57do7qE/vSnP+nHqZk/yh//+Ec9g+qpp56SRx99VLfQqLCzevVqeeuttwx9feq1VLVLR3WhqZlbaraYet5rW34ceeCBB+T111/XM6TUtVLPceLECVm2bJn84Q9/0LdvpGPHjnr2mppJproHVYhRwVPNnLK2NFnrqmZ+DRo0SNexqt1WgLtiKjhgQmoK9p///Gc9XVq1ttx55526G0d9SE6aNEkvYGcd56GmRKsAM2XKFLvnUONxpk2bplsUzpw5I1999ZVu7bCOZdmwYYNuzVHTwdVYFzWVPDo62tDXp6ZSV9V9992nHzd06FDdjfTZZ59Vqw5qXNDGjRt169aYMWN0PdS1VWNuqtOS8+9//1tf/9tuu01P937sscd00FJT8K3eeOMNHRzVlPtrxxUBuJ6HGlVcwXkAbkqtEaM+8FXXiFq/BQ3r5MmTOsR8//33MmzYMKOrA7gkuqUAwEBr167Vg5O7deumF/RTXVqqhU215ACoGbqlAEBEz3pq1KhRhYeazl1f1GrFL774ov4ZqltKdZGp1rNrZ0IBqDq6pQBARG9bcPbs2QrvU0EjNja2wesEoGYINwAAwFTolgIAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAGIm/x8HmSb3xYXabAAAAABJRU5ErkJggg==", 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", 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" ] @@ -431,6 +438,13 @@ "Assume we randomize the treatment at 4h level, we can use the same data as above, but we need to change the switch frequency to 4h." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](hourly.png)" + ] + }, { "cell_type": "code", "execution_count": 4, @@ -438,7 +452,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -495,6 +509,13 @@ "Assume we randomize the treatment at city + 4h level, we can use the same data as above, but we need to add city as a cluster column." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](city_hour.png)" + ] + }, { "cell_type": "code", "execution_count": 6, @@ -502,7 +523,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", 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lcW0/yc7Nl5/2xRldHQCAnSIAwem6wYruEA8AQHEIQHA6BQFo47GLkpiaZXR1AAB2iAAEpxMZ4itt6wVKXr5Vftwba3R1AAB2iAAEp1TYDbaLbjAAwM0IQHBK97StIy4uIttOXZZzVzKMrg4AwM4QgOCUwgK9pHNkDX28kMHQAIAbEIDg9Ftj0A0GALgRAQhOa1DrcLG4usiB2GQ5mpBqdHUAAHaEAASnFezrIb2ahOhj1gQCABRFAIIpusHUOCCr1Wp0dQAAdoIABKd2V8sw8bS4yonENNl3Ltno6gAA7AQBCE7Nz9Mi/VqE6mN2iAcAFCAAwekNubYo4qI9sZKfTzcYAIAABBO4vVkt8fe0SGxSpmw9ecno6gAA7AABCE7Py91N+rcO08fMBgMAKAQgmGpvMLU5ak5evtHVAQAYjAAEU+h+W00J8fOQy+k5sv5ootHVAQAYjAAEU7C4ucqgNuH6eCFbYwCA6RGAYLpusJ/2x0lmTp7R1QEAGIgABNNoXz9Y6gZ5S1p2nqw6lGB0dQAABiIAwTRcXV3knqir3WDsEA8A5kYAgim7wVbFJEhyZo7R1QEAGIQABFNpGR4gt9XylezcfFm2P97o6gAADEIAgqm4uLjI0Ki6+phFEQHAvAhAMJ2h0Ve7wTYcTZSLqVlGVwcAYAACEEynYYivtKkbKHn5Vr0yNADAfAhAMPVgaLrBAMCcCEAwJTUd3sVFZOvJy3LuSobR1QEAVDMCEEwpPNBbOkXW0MeLaAUCANMhAMG06AYDAPMiAMG01OaoFlcX2X8+WY5dSDW6OgCAakQAgmnV8PWQnk1C9DFbYwCAuRCAYGoF3WALd58Xq9VqdHUAANWEAARTu7tVmHhaXOV4YpruCgMAmIPhAWjOnDkSGRkpXl5e0qVLF9myZUuJZffv3y/Dhw/X5dWWBjNnzqzwc8Lc/Dwt0rdFbX3MYGgAMA9DA9D8+fNl4sSJMmXKFNmxY4dERUVJ//79JSEhodjy6enp0qhRI5k2bZqEhYVVynMCRbvB8vPpBgMAM3CxGjjwQbXOdOrUSWbPnq3v5+fnS0REhIwfP14mTZpU6teqFp5nn31W3yrrOQskJydLYGCgJCUlSUBAgFS6tLSSH3NzE/HyKltZV1cRb+9bK5ueLlLSf71aIdDH59bKZmSoi15yPXx9b61sZqZIXl7llFX1VfVWsrIkMyNLev9jtaRk5cpnT3SWjtfWByqurOTmlvy86vqq66xkZ4vk5FROWfXzoH4uyltWlVPlS+LpKWKxlL+sugbqWpTEw0PE3b38ZdX/mfq/K4kqp8qXt6z6GVM/a5VRVl0DdS0U9Tuhfjcqo2x5fu95j6j294hSf+95j6jYe0QlK9fnt9UgWVlZVjc3N+uCBQtszo8aNco6dOjQX/36Bg0aWP/5z39WynNmZmZak5KSCm9nzpxRv8n6uEpcfaso/jZokG1ZH5+Sy/bpY1s2JKTksh072pZt0KDksi1b2pZV90sqq56nKPV9Siqr6leUqn9JZdXrLkpdl9KuW1EPPFB62dTU62VHjy69bELC9bJ//GPpZU+cuF72T38qvey+fdfLTplSetktW66XffPN0suuXn297OzZpZddtOh62U8+Kb3s119fL6uOSyurnquA+h6llVV1LKDqXlpZ9doLqGtSWll1TQuoa11aWfV/VUD9H5ZWVv0MFFA/G6WVVT9bBdTPXGll1c9sUaWV5T3i6o33COd4j6hk6nO7rJ/fhnWBJSYmSl5enoSGhtqcV/fj4uKq9TmnTp2qE2PBTbUYAQAA52VYF9j58+elbt26snHjRunWrVvh+RdffFHWrl0rv/zyS7m7wG71ObOysvStaBOaCkF0gZmneTs3L1/6TF8tl9Jy5P3ftpfeTa8OjKZ5u5iydIFdPVa/E3SBmeY9olLK8h5hV11g12pb/UJCQsTNzU3i4+Ntzqv7JQ1wrqrn9PT01LdqU/QX0aiyRd+QKrNs0TfQyixb9A2/Msuq/3dPT/2LcGeHRvLZ5lPy/ZEk6d2uYYlly0T9ghd8qBpVVr3BlPVNpjxl1ZtcwRtdZZZVb8pl/RkuT1n1IVIVZdWHXlWUVeyhLO8R5f+95z2i/L/3BjKsC8zDw0M6dOggK1euLDynBiyr+0Vbb4x+TpjHkGuzwZbtj5fMnFL+OgQAODxDI5qarj569Gjp2LGjdO7cWa/rk5aWJo8//rh+fNSoUbpLS43RUbKzs+XAgQOFx+fOnZNdu3aJn5+fNG7cuEzPCZSkY4NgCQ/0ktikTFkTkyADWocbXSUAgDMGoBEjRsiFCxfk1Vdf1YOUo6OjZenSpYWDmE+fPi2uBX2g18b4tGvXrvD+jBkz9K1Pnz6yZs2aMj0nUBJXVxfdCvTBuuPy7fazBCAAcGKGrgNkr6p8HSDYrZi4FBkwa50ezznr4Wi5N7qu0VUCAFTB57fhW2EA9qRZmL+Mv7OJPn7pu71yMrGUWTMAAIdFAAJu8MydjaVzZA1Jy86TcV/tkKxcBkQDgLMhAAE3sLi5yqyR0RLk4y77ziXLP5bEGF0lAIA9BSA1EysmJkZyS1v4CXBA4YHeMuOBKH388YYTsuKA7dpSAAATBiC1K/sTTzwhPj4+0qpVKz1bS1Ebjqqd2gFn0K9lqPyux9UFEf/07W6JTSplhWAAgPMHoMmTJ8vu3bv11HOvIitq9uvXT+bPn1+Z9QMM9eeBzaR13QC5kp4jE77apbfMAACYNAB9//33Mnv2bOnZs6e4FOyBIqJbg44dO1aZ9QMM5Wlxk9kj24uvh5tsOXlJ3ll11OgqAQCMCkBqocHata9tFlmEWnG5aCACnEFkiK/8/f42+vjdVUdk47FEo6sEADAiAKltJhYvXlx4vyD0fPTRR+y5BaekFkR8qGM9vUDis/N2ycXUUnY6BgA451YYf//732XgwIF6Xy41A2zWrFn6eOPGjbJ27drKryVgB14b2kp2nL4iRxNS5flvdsvHozvp7TMAACZpAVJjf9QmpCr8tGnTRpYtW6a7xDZt2qR3YweckY+HRWY/0k48LK6yJuaC/Hv9CaOrBAC4RewFVgz2AkNpPt98Sv7y/T6xuLrIt2O7S3REkNFVAgBI+T6/LeV50rIiNMCZPdqlvh4I/ePeOBn/1Q5Z/EwvCfByN7paAIByKHMACgoKKvMMr7w89k6C81K/B1Pvbyt7zibJmUsZMvm7vTJ7ZDtmQAKAMwag1atXFx6fPHlSJk2aJI899ljhrC81/ufTTz+VqVOnVk1NATsS6O0u745sJw++t0kW74mVno1DZGTn+kZXCwBQlWOA+vbtK7///e9l5MiRNue//PJL+eCDD/QK0Y6MMUAoq/fXHpOpSw6Jp8VVfhjXU5qF+RtdJQAwreRyfH7f0iww1dqj1gK6kTq3ZcuWW3lKwCE92auR9G5aS7Jy82XclzskI5vuXwBwBLcUgCIiIuTDDz+86bxaCFE9BpiFWgfo7YeipJa/pxxJSJXXF+43ukoAgKpaCPGf//ynDB8+XJYsWSJdunTR51TLz+HDh+W77767lacEHFaIn6fMHBEtv/n3LzJv6xnp3jhEhkbVMbpaAIDKbgEaNGiQDjtDhw6VS5cu6duQIUPkyJEj+jHAbHo0DpFxdzTWxy99t1dOXUwzukoAgKpYCDEzM1P27NkjCQkJkp+fb/OYCkaOjEHQuBW5efky8sPNsvXkZWlbL1C+HdNdrxoNAHDghRCLWrp0qYwaNUouXrwoN+YntRYK6wDBjCxurjLr4XYycNbPeo2gN5cekr/c09LoagEAinFLf56OHz9eHnzwQTl//rxu/Sl6I/zAzOoEecuMB6P08UfrT8iqQ/FGVwkAUFkBKD4+XiZOnCihoaG38uWAU7urZag81j1SHz//9W6JS8o0ukoAgMoIQA888IDDL3YIVKXJg5pLqzoBcjk9RybM2yl5+ew5DAAOPwg6PT1dd4HVqlVL2rRpI+7uthtBPvPMM+LIGASNynAiMU3ueednScvOk2f7NZFn+zU1ukoA4NSSy/H5fUsB6N///reMGTNGvLy8pGbNmjabQKrj48ePiyMjAKGyLNh5Vp6bv1tcXUS+fLKrdG1U0+gqAYDTqvIAFBYWplt51Iaorq7ON82XAITK9Kdvdsu3289KaICnLJnQW2r4ehhdJQBwSlW+F1h2draMGDHCKcMPUNleH9pKGtXylfjkLB2GbnHpLQBAJbqlBDN69GiZP39+ZdYDcFq+nhaZPbK9XhRx1aEE+ff6E0ZXCQBM75YWQlRr/bz55pvy008/Sdu2bW8aBP32229XVv0Ap9CyToC8MriFvPK//fKPpYekc8Ma0rZekNHVAgDTuqUAtHfvXmnXrp0+3rdvn81jRQdEA7juN10byIajF2Xp/jgZ9+VOWfxMT/H3sv3jAQBg53uBOTMGQaOqJKXnyKB3fpZzVzJkSFQdeefhaP5oAABHGQQN4NYE+rjLOyPbiZuriyzcfV6+3nbG6CoBgCkRgIBq1qFBsDx/99VFEaf8sF8Ox6cYXSUAMB0CEGCAMb1vk15NQiQzJ1/GfblDMnPYRBgAqhMBCDCAq6uLvP1QtIT4ecrh+FR5feEBo6sEAKZCAAIMUsvfU2aOUIOgRb7acloW7TlvdJUAwDQIQICBejYJkT/efps+nvzfvXL6YrrRVQIAUyAAAQZ7rl9TPTA6JStXxs/bKdm5+UZXCQCcHgEIMJjFzVVPjQ/wssjuM1dkxrIYo6sEAE6PAATYgbpB3jL9wSh9/MG647I6JsHoKgGAUyMAAXaif6swGd2tgT5+/uvdEp+caXSVAMBpEYAAOzJ5UAtpGR4gl9Ky5dl5uyQvn51qAKAqEIAAO+Ll7ibvPtJOfDzcZNPxizJn9VGjqwQATokABNiZ22r5yV/vba2PZ644LFtOXDK6SgDgdAhAgB0a3qGe3N++rqgesAnzdsrltGyjqwQAToUABNgp1QrUKMRXYpMy5YVvd4vVynggAKgsBCDATvl6WvR4IA83V1lxMEE+2XDS6CoBgNMgAAF2rFWdQHl5cAt9PHXJQdl7NsnoKgGAUyAAAXZuVLcGcnfLUMnJs8r4r3ZIalau0VUCAIdHAALsnIuLi7z5QFu9WvTJi+ny8oK9jAcCgAoiAAEOIMjHQ2Y9HC1uri7yv13n5ZvtZ42uEgA4NAIQ4CA6RtaQiXc11cdT/rdfjiakGF0lAHBYBCDAgYztc5v0bBwiGTl5Mu7LnZKZk2d0lQDAIRGAAAfi6uoib4+IkhA/DzkUlyJvLD5gdJUAwCERgAAHU9vfS95+KFoff775tCzZG2t0lQDA4RCAAAfUu2ktGXv7bfr4xf/ukTOX0o2uEgA4FAIQ4KDUgOj29YMkJTNXnpm3U3Ly8o2uEgA4DAIQ4KDc3Vxl1sPtJMDLIjtPX5G3lh02ukoA4DAIQIADi6jhoxdJVN5be0zWHr5gdJUAwCEQgAAHN6B1uPy2awN9PHH+LklIzjS6SgBg9wwPQHPmzJHIyEjx8vKSLl26yJYtW0ot/80330jz5s11+TZt2siPP/5o83hqaqqMGzdO6tWrJ97e3tKyZUt57733qvhVAMZSG6Y2D/OXi2nZ8tzXuyQvn60yAMBuA9D8+fNl4sSJMmXKFNmxY4dERUVJ//79JSEhodjyGzdulJEjR8oTTzwhO3fulGHDhunbvn37Csuo51u6dKl8/vnncvDgQXn22Wd1IPrhhx+q8ZUB1cvL3U1mP9JevN3dZMPRi7o7DABQMhergbsqqhafTp06yezZs/X9/Px8iYiIkPHjx8ukSZNuKj9ixAhJS0uTRYsWFZ7r2rWrREdHF7bytG7dWpd75ZVXCst06NBBBg4cKG+88UaZ6pWcnCyBgYGSlJQkAQEBlfBKgerxzbYz8sK3e/SeYfOf6qq3zwAAs0gux+e3YS1A2dnZsn37dunXr9/1yri66vubNm0q9mvU+aLlFdViVLR89+7ddWvPuXPn9I7Zq1evlsOHD8vdd99dha8GsA8PdKgn97Wrq7vAnvlqp1xJzza6SgBglwwLQImJiZKXlyehoaE259X9uLi4Yr9Gnf+18u+++64e96PGAHl4eMiAAQP0OKPevXuXWJesrCydGoveAEfk4uIifx3WWhqG+Mr5pEx58ds9+g8BAICdDYKubCoAbd68WbcCqRamt956S55++mlZsWJFiV8zdepU3WRWcFPdcICj8vO0yLsj24mHm6ssOxAv/9l0yugqAYDdMSwAhYSEiJubm8THx9ucV/fDwsKK/Rp1vrTyGRkZ8tJLL8nbb78tQ4YMkbZt2+oB0GpM0IwZM0qsy+TJk3V/YcHtzJkzlfIaAaO0rhsokwc118d/W3xQ9p1LMrpKAGBXDAtAqntKDU5euXJl4Tk1CFrd79atW7Ffo84XLa8sX768sHxOTo6+qbFERamgpZ67JJ6ennqwVNEb4Oge6x4p/VqESnZevoz/aqckZ+YYXSUAsBuGdoGpKesffvihfPrpp3rK+tixY/Usr8cff1w/PmrUKN06U2DChAl6irvq1jp06JC89tprsm3bNt3Ko6jg0qdPH3nhhRdkzZo1cuLECZk7d6785z//kfvuu8+w1wkYNR5o+gNtJTzQS04kpsk976yXXWeuGF0tALALhgaggq6pV199VU9l37Vrlw44BQOdT58+LbGxsTYzvL788kv54IMP9JpB3377rXz//fd66nuBefPm6an1jz76qB4MPW3aNPnb3/4mY8aMMeQ1AkYK9vWQD0d1lLpB3nL6Uro88K+N8q81xySfhRIBmJyh6wDZK9YBgrNJysiRlxbslcV7rv5B0bNxiLz9UJTUDvAyumoAYK51gABUn0Bvd5k9sp28ObytXi16/dFEGTDrZ1l9qPhV1wHA2RGAABONCXqoU4QsHN9TWoYHyKW0bHl87lZ5feF+ycrNM7p6AFCtCECAyTSu7ScLnu4uv+vRUN//ZMNJuW/ORjmakGp01QCg2hCAABPytLjJq0NaysePdZQavh5yIDZZhry7XuZvPc3K0QBMgQAEmNidzUNl6YRe0qNxTcnIyZM//3evXjNIDZoGAGdGAAJMTs0E++x3XeTPA5qLxdVFFu2JlUGzfpbtpy4ZXTUAqDIEIADi6uoiY2+/Tb4d213q1/CRc1cy5KH3N8vsVUf0zvIA4GwIQAAKRUcEyeJnesqw6Do6+MxYdlge/WizxCZlGF01AKhUBCAANvy93GXmw+30Qom+Hm6y+fglGTjrZ1m2P87oqgFApSEAASjW/e3ryaJnekmbuoFyJT1Hnvpsu7zy/T7JzGHNIACOjwAEoEQNQ3zlv2O7y1O9G+n7n20+JffO3iCH41OMrhoAVAgBCECpPCyu8tKgFvLp7zpLiJ+nxMSn6DWDvvjlFGsGAXBYBCAAZdKnaS1ZMqGX/jcrN19eXrBPxn6+Q66kZxtdNQAoNwIQgDKr5e8pnzzWSf4yuIW4u7nI0v1xeoD0L8cvGl01ACgXAhCAcq8Z9PtejWTBH3voMUKxSZky8sPN8s/lhyU3L9/o6gFAmRCAANyS1nUDZdH4nvJAh3qi1kqctfKIPPzBZjl7Od3oqgHAryIAAbhlvp4WmfFglMx6OFr8PC2y7dRlvY3Gkr2xRlcNAEpFAAJQYfdG15Ufn+mlV5JOzsyVsV/skMnf7ZWMbNYMAmCfCEAAKkX9mj7yzZhu8sfbbxMXF5GvtpyWIbPXy8HYZKOrBgA3IQABqDTubq7y4oDm8vkTXaS2v6ccTUiVe+dskE83nmTNIAB2hQAEoNL1aByi1wy6s3ltyc7Nlyk/7Jcn/7NNLqWxZhAA+0AAAlAlavp5yr9Hd5TXhrQUDzdXWXEwQQbOWicbjyUaXTUAIAABqDouLi7yWI+G8v3TPeS2Wr4Sn5wlj370i0z/6ZDksGYQAAMRgABUuZZ1AmTh+J4ysnOEqKFAc1Yfk4fe3yRnLrFmEABjEIAAVAsfD4tMvb+tzHmkvfh7WWTn6St6zaAfdp83umoATIgABKBaDW4brgdId2gQLClZufLMVzvlhW92S1pWrtFVA2AiBCAA1a5esI/Mf6qrPNO3ibi6iHyz/awMeXe97DuXZHTVAJgEAQiAISxurjLxrqby5ZNdJTzQS44npsl9/2+DfPTzcdYMAlDlCEAADNW1UU3dJda/Vajk5FnljcUH5fG5WyUxNcvoqgFwYgQgAIYL8vGQ937TQd4Y1lo8La6yJuaCDJj5s/x85ILRVQPgpAhAAOxmzaDfdG0gP4zrKU1D/XQL0G//vUWm/nhQryYNAJWJAATArjQL89ch6Ddd6+v77687Lg+8t1FOJqYZXTUAToQABMDueLm7yRvD2sj7v+0ggd7usudskgx+52dZsPOs0VUD4CQIQADsVv9WYXqAdOeGNSQtO0+em79bJs7fJamsGQSggghAAOxanSBv+erJrnrKvFoz6Lud53Rr0K4zV4yuGgAHRgACYPfcXF30oolf/6Gb1A3yllMX0/WaQWoF6fjkTKOrB8ABEYAAOIyOkTXkx2d6ybDoOnpTVbWC9O3T18jMFYclPZtuMQBl52JlydWbJCcnS2BgoCQlJUlAQIDR1QFQjO2nLssbiw/oTVWV0ABP+dPdzWR4+3riqvrKAJhOcjk+vwlAxSAAAY5BvX0t3hsr05YckrOXM/S5VnUC5OXBLaT7bSFGVw9ANSMAVRABCHAsmTl58unGkzJ71VG9w7zSr0WoTB7UXG6r5Wd09QBUEwJQBRGAAMd0MTVLZq08Il/8clry8q1icb26urQaQF3D18Po6gGoYgSgCiIAAY7taEKqTFtyUFYcTND3/b0sMv7OxjK6e6R4WtyMrh6AKkIAqiACEOAcNh5N1LvLH4hN1vcjanjLpAEtZFCbML33GADnQgCqIAIQ4DxUV9h/d5yVGT/FSEJKlj7XoUGw/GVwC2lXP9jo6gGoRASgCiIAAc5HrRP0/trj8sG645KRk6fPDYmqIy/2byYRNXyMrh6ASkAAqiACEOC84pIy5a1lMfLtjrN6MUUPi6v8rkdD+eMdt0mAl7vR1QNQAQSgCiIAAc5v//kkeWPRQdl0/KK+X9PXQ569q6mM7BQhFjcWyQccEQGogghAgDmot7+VBxPk7z8elOOJafpc49p+8vKgFnJ7s1oMlAYcDAGogghAgLnk5OXLl7+c1nuKXU7P0ed6Ng7RK0q3COc9AHAUBKAKIgAB5pSUkSNzVh+VuRtOSnZevqgGoIc6RMjzdzeV2gFeRlcPwK8gAFUQAQgwt9MX0+UfSw/pfcYUHw83GdPnNnmyVyPx9mAhRcBeEYAqiAAEQNl+6pL8ddFB2XXm6o7zYQFe8kL/ZnJfu7rsOA/YIQJQBRGAABRQb5EL98TKP5YcknNXru4437pugLw8qKV0u62m0dUDUAQBqIIIQACK23H+kw0n5f+tvr7j/F0tQ2XywObSiB3nAbtAAKogAhCA0nacn7niiHy5xXbH+Ql9m0gwO84DhiIAVRABCMCvOZqQIn//8ZCsOnR1x/kAL4s807eJ/LZbA3acBwxCAKogAhCAslp/RO04f0AOxaXo+/Vr+OhusQGt2XEeqG4EoAoiAAEo947z28/K9GUxcuHajvOdIoPl5cEtJToiyOjqAaaRTACqGAIQgFuRlpUr769TO84fk8ycfH3u3ug68uKA5lI3yNvo6gFOL5kAVDEEIAAVEZuUITN+Oizf7by+4/zvezaUsbffJv7sOA9UGQJQBRGAAFSGfeeS9Pigzccv6fshfh7y3F1NZURHdpwHqgIBqIIIQAAqi3qLXXFtx/kT13acb6J2nB+sdpyvbXT1AKdCAKogAhCAypadmy9f/HJKZq08Ileu7Tjfq8nVHeebh/E+A1T357fhbbBz5syRyMhI8fLyki5dusiWLVtKLf/NN99I8+bNdfk2bdrIjz/+eFOZgwcPytChQ/VF8PX1lU6dOsnp06er8FUAQOnUOKDHezSUtX+6Q57s1VDc3Vzk5yOJMmjWzzL5uz2SkJJpdBUBUzE0AM2fP18mTpwoU6ZMkR07dkhUVJT0799fEhKuLix2o40bN8rIkSPliSeekJ07d8qwYcP0bd++fYVljh07Jj179tQhac2aNbJnzx555ZVXdGACAKMF+rjr6fErJvaRQW3CJN8q8tWWM3LH9DXyyvf7ZMuJS5KvTgKoUoZ2gakWH9U6M3v2bH0/Pz9fIiIiZPz48TJp0qSbyo8YMULS0tJk0aJFhee6du0q0dHR8t577+n7Dz/8sLi7u8tnn312y/WiCwxAddl68pK8sfig7L6247xSJ9BL7omqI0Oj6kirOgEsqAg4UxdYdna2bN++Xfr163e9Mq6u+v6mTZuK/Rp1vmh5RbUYFZRXAWrx4sXStGlTfb527do6ZH3//fel1iUrK0tftKI3AKgOnSJryIKx3eU/v+ssw9vXE39Pi5xPypQP1h2Xe95dL33fWitvLz8sRxNSja4q4FQMC0CJiYmSl5cnoaGhNufV/bi4uGK/Rp0vrbzqOktNTZVp06bJgAEDZNmyZXLffffJ/fffL2vXri2xLlOnTtWJseCmWqEAoLq4urpI76a15K2HomTrX/rJe7/pIIPbhIunxVWOJ6bJOyuPSL+31+rxQu+tPSZnL6cbXWXA4VnEiagWIOXee++V5557Th+r7jE1dkh1kfXp06fYr5s8ebIei1RAtQARggAYwcvdTe8jpm6pWbmy/ECcLNwdK+sOX5ADscn6Nm3JIenQIFh3kQ1qEy61/D2NrjbgcAwLQCEhIeLm5ibx8fE259X9sLCwYr9GnS+tvHpOi8UiLVu2tCnTokULWb9+fYl18fT01DcAsCd+nha5r109fbucli1L9sXJD7vPyS8nLsn2U5f17fWF+6VH4xAZ0raO9G8dJoHerDQN2HUXmIeHh3To0EFWrlxp04Kj7nfr1q3Yr1Hni5ZXli9fXlhePacaVB0TE2NT5vDhw9KgQYMqeR0AUB2CfT3kkS71Zd5T3WTz5L7yyj0tJSoiSM8iU9PpX/zvHun0xgp58j/bZOHu85KenWt0lQG7ZmgXmOp2Gj16tHTs2FE6d+4sM2fO1LO8Hn/8cf34qFGjpG7dunqMjjJhwgTdjfXWW2/J4MGDZd68ebJt2zb54IMPCp/zhRde0LPFevfuLXfccYcsXbpUFi5cqKfEA4AzCA3wkid6NtS3UxfTZNGeWPlh13mJiU+R5Qfi9c3Hw036tQjV3WRqfJFahwiAHa0ErabAT58+XQ9kVuN13nnnHT1zS7n99tv1Iolz5861WQjxL3/5i5w8eVKaNGkib775pgwaNMjmOT/++GMdms6ePSvNmjWT119/XY8LKiumwQNwRDFxKbqLTI0ZOn3p+kDpAC+LDGwdLkOj60jXRjXFzZVp9XBObIVRQQQgAI5Mva3vPpukW4UW7TkvCSlZhY+F+HnKPW3DZUhUHWlfP4g1huBUCEAVRAAC4Czy8q16dekfdp+XJftiC/chU+oFe+sgpLrJmof5E4bg8AhAFUQAAuCsG7KuP3pBd5Et2x8nadl5hY81ru2ng5C6RYb4GlpP4FYRgCqIAATA2WVk58mqQwl6zNDqmAs6HBVoWy9QT6u/JypcwgO9Da0nUB4EoAoiAAEwk+TMHFm2P153k204mqi7zRTVI6a26lCtQgNbh0lNP9ZLg30jAFUQAQiAWV1MzZIf98XJwl3nZcvJS4Xn1cyxno1DdBi6u1Wo+Hux4CLsDwGogghAACBy/kqGnkWmxgztPZdUeF6tKXRns9p6Wv2dzWvr7TsAe0AAqiACEADYOn4hVQchNWbo2IU0m+067m4ZqmeT9WwSIu5uLLgI4xCAKogABADFUx8ZB2PVgouqZei8nLuSUfhYsI+7DGwTrrvJOkfW0LvcA9WJAFRBBCAA+HXq42PH6Ss6CKntOBJTry+4GBrgKYPbXO0i69QwWDwtdJOh6hGAKogABADlk5uXL5uPX9JhSC24mJx5fTNWtS9Z99tC5PZmtfStXrCPoXWF8yIAVRABCABuXVZunqw7nCjLD8TJmpgLNltxKE1q++kgdEez2tIxsgYbtaLSEIAqiAAEAJVDfcQciE3WQWhNTILuMitYZ0jx9XCTHo1V61BtHYrqBLHwIm4dAaiCCEAAUDWS0nPk56MqDF29FR03pDQL9b/WVaZah4KZVYZyIQBVEAEIAKpefn5B61CC3o5j5+nLUqRxSE+x79G4pu4qU4EoLNDLyOrCARCAKogABADV70p6tqw7kqgD0dqYC3IxLdvmcbVjfUFXWYcGtA7hZgSgCiIAAYDxrUP7zifpbrLVMQmy68wVKfpp5e9p0QsvFnSXhQbQOgQhAFUUAQgA7MultGz5+cjVcUNrD1/Q94tqER4gd1wLQ+3rB4mF1iFTSiYAVQwBCADsl5pFpvYmKxg7tOfsDa1DXhbp3aSW9FGBqGktqU3rkGkkE4AqhgAEAI61g/26a61D6w5fkMvpOTaPt6oTULjuUHQErUPOLJkAVDEEIABw3Nah3WevFK47tOfs9V3slQAvi/RqejUM9WlaS2r5expWV1Q+AlAFEYAAwDlcSMnSrUJrDl9tHUrKsG0dalM3sHCLjuiIYHFjA1eHRgCqIAIQADjnfmXXW4cu6HFERQX5uEuvJqp1qJb0blpLQvxoHXI0BKAKIgABgPNLSMnUe5apafY/H75gs4Gri0tB69DVdYei6gXROuQACEAVRAACAPO1Du08o1qHEnTr0P7zyTaPB/u461YhtW9ZxwbB0jDEV1xUSoJdIQBVEAEIAMwtITlTjxtSgejnI4mSUqR1SKnp66FXo+4UWUPvWdaqTiC72tsBAlAFEYAAAAVyVOvQ6autQ1tPXpLdZ5IkOy/fpoynxVVPsVdhqGNkDWlfP1gCvd0Nq7NZJROAKoYABAAoSVZunuw7lyRbT16WbScvybZTl+XKDWsPqd4xtbO9CkSqlUi1FtUN8qbbrIoRgCqIAAQAKM++ZccTU2XbyctXQ9GpS3LqYvpN5cIDvWy6zZqHBTCwupIRgCqIAAQAqOgMs+06DF1tJdp3Plkv0liUn6dF2tUPko4NakinyGCJrh8kPh4Ww+rsDAhAFUQAAgBUpvTsXL2j/bZroWjHqcuSmmU7sFq1BqltOwoCUYfIYKntzz5m5UEAqiACEACgKqnWoJi4FN1dVjCWKDYp86ZyDWr6FHabqVDUKMRPXOk2KxEBqIIIQACA6nbuSsbVQdV6LNEliYlPsdnlvmC1arUOUYdrrURt6gWKp8XNqCrbHQJQBRGAAABGS87M0V1l209dDUSqCy0zx3b6vVp7qG3dQD31XnebNQiWIB8PMatkAlDFEIAAAPa4HpFaobqglUh1nyWmZt9Urkltv6vrEelWohoSUcM80++TCUAVQwACANg79fF98mK6TSA6diHtpnK1/D2vtQ5dbSVqGR4gFjfnXLWaAFRBBCAAgCO6mJqlu8wKus3Ujvc5ebYf8z4ebtdWrb4aiNrVD9ZT8p0BAaiCCEAAAGeQmZMne86qVasv6VCkWouK7nqv/K5HQ3l1SEsx2+e3c0Q+AABwEy93N+ncsIa+FaxaffRCqg5EBd1mnRsGixkRgAAAMAm1hlDTUH99e7RLA33OrB1BzjkKCgAAlImLSWaI3YgABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATMdidAXskdVq1f8mJycbXRUAAFBGBZ/bBZ/jpSEAFSMlJUX/GxERYXRVAADALXyOBwYGllrGxVqWmGQy+fn5cv78efH39xcXF5dKT6cqWJ05c0YCAgIq9bmdDdeq7LhWZce1KjuuVdlxrezjWqlIo8JPnTp1xNW19FE+tAAVQ120evXqVen3UP/p/JKUDdeq7LhWZce1KjuuVdlxrYy/Vr/W8lOAQdAAAMB0CEAAAMB0CEDVzNPTU6ZMmaL/Rem4VmXHtSo7rlXZca3KjmvleNeKQdAAAMB0aAECAACmQwACAACmQwACAACmQwCqRnPmzJHIyEjx8vKSLl26yJYtW8Ts1q1bJ0OGDNGLVqlFJ7///nubx9UQtVdffVXCw8PF29tb+vXrJ0eOHBEzmjp1qnTq1Ekv0Fm7dm0ZNmyYxMTE2JTJzMyUp59+WmrWrCl+fn4yfPhwiY+PF7P517/+JW3bti1cZ6Rbt26yZMmSwse5TiWbNm2a/l189tlnC89xva577bXX9PUpemvevHnh41wrW+fOnZPf/OY3+nqo9/A2bdrItm3b7OI9ngBUTebPny8TJ07UI9937NghUVFR0r9/f0lISBAzS0tL09dChcPivPnmm/LOO+/Ie++9J7/88ov4+vrq66beZMxm7dq1+o118+bNsnz5csnJyZG7775bX8MCzz33nCxcuFC++eYbXV6taH7//feL2aiFTNUH+fbt2/Wb7Z133in33nuv7N+/Xz/OdSre1q1b5f3339fhsSiul61WrVpJbGxs4W39+vWFj3Gtrrt8+bL06NFD3N3d9R8gBw4ckLfeekuCg4Pt4z1ezQJD1evcubP16aefLryfl5dnrVOnjnXq1KmG1sueqB/HBQsWFN7Pz8+3hoWFWadPn1547sqVK1ZPT0/rV199ZTW7hIQEfc3Wrl1beG3c3d2t33zzTWGZgwcP6jKbNm2yml1wcLD1o48+4jqVICUlxdqkSRPr8uXLrX369LFOmDBBn+d62ZoyZYo1Kiqq2Me4Vrb+/Oc/W3v27GktidHv8bQAVYPs7Gz9l6hq2iu63Ya6v2nTJkPrZs9OnDghcXFxNtdNLXGuug+5biJJSUn63xo1auh/1c+YahUqer1U03z9+vVNfb3y8vJk3rx5uqVMdYVxnYqnWhcHDx5sc10UrtfNVBeN6rZv1KiRPProo3L69Gl9nmtl64c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