diff --git a/blog-posts/informative_priors_pymc/chapterN.ipynb b/blog-posts/informative_priors_pymc/chapterN.ipynb new file mode 100644 index 0000000..52c5475 --- /dev/null +++ b/blog-posts/informative_priors_pymc/chapterN.ipynb @@ -0,0 +1,5123 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "---\n", + "title: Informative Priors for MMM Calibration in PyMC-Marketing\n", + "author:\n", + " - name: Carlos Trujillo\n", + " email: carlos.trujillo@pymc-labs.com\n", + "contributor:\n", + " - name: Ulf Aslak\n", + " email: ulf.aslak@pymc-labs.com\n", + " - name: Will Dean\n", + " email: william.dean@pymc-labs.com\n", + " - name: Juan Orduz\n", + " email:\n", + " - name: Thomas Wiecki\n", + " email: thomas.wiecki@pymc-labs.com\n", + " - name: Benjamin J. Vincent\n", + " email: ben.vincent@pymc-labs.com\n", + "date: '2024-04-01'\n", + "toc: true\n", + "number-sections: true\n", + "format:\n", + " html:\n", + " code-fold: true\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The ability to accurately measure and estimate the impact of marketing activities is paramount. Traditional marketing mix modelling (MMM) provides a robust framework for quantifying the effectiveness of various marketing channels on sales or other key performance indicators. However, one of the enduring challenges in Bayesian MMM is the specification of prior beliefs or assumptions about the effects of marketing activities. We definitely want to use this ability to incorporate prior knowledge and update beliefs because that is where Bayesian methods shine. In this context, PyMC-Marketing, emerges as a powerful tool for building Bayesian MMMs that can seamlessly integrate complex prior information. However, *how to specify the right priors to help our model?*\n", + "\n", + "## Experiments: A guide for priors\n", + "\n", + "Let's talk about a puzzle that many marketers face—understanding the real impact of their campaigns. *Imagine this*: your company decides to dial back its advertising budget in **Venezuela** between March 28th and May 5th. Now, as a marketer, you're scratching your head, wondering: Did this decision affect sales? If so, how significantly?\n", + "\n", + "Here’s how you can solve this mystery: through the power of experiments 🔬.\n", + "\n", + "Think of the world as your laboratory. While your company cut down on spending in Venezuela, it continued its usual advertising in other countries. This scenario offers a unique opportunity to peek into an alternate universe—what would have happened if you hadn’t reduced the spend?\n", + "\n", + "Let's pick a country—say, **Estonia**—where your advertising efforts remained unchanged during the same period. *Estonia* will be your control group, providing a baseline to compare against.\n", + "\n", + "> Note: This methodology isn't limited to countries. It's just as applicable to regions or cities, as long as you have consistent data from an area that wasn’t affected by the changes in advertising.\n", + "\n", + "Imagine creating two realities for the target variable (*Venezuelan* sales): one, the real world where you cut advertising (the \"*Treatment*\" group) and second, a hypothetical scenario where everything stayed the same (the \"*Control*\" group, generated by *Estonia*). By comparing these two versions, you can directly measure what might have been lost or gained.\n", + "\n", + "By observing these differences, you gain insights into the impact of your decisions but also empower yourself to make more informed choices in the future. Isn’t that a compelling way to approach marketing strategy? If your answer is positive, you are in the right place, and therefore, it is time to introduce yourself to your new best friend to solve these riddles: `CausalPy`.\n", + "\n", + "I'm sure you're wondering, what is `CausalPy`? It's a library from the PyMC Ecosystem, designed to facilitate causal inference analyses. Using [CausalPy](https://causalpy.readthedocs.io/en/latest/) we can explore different quasi-experimental methods to causally measure the effect of our actions, in the absence of natural or randomized experiments.\n", + "\n", + "**Recommended readings**:\n", + "1. [Causal Inference for the Brave and True](https://matheusfacure.github.io/python-causality-handbook/landing-page.html)\n", + "2. [What if? Causal inference through counterfactual reasoning in PyMC](https://www.pymc-labs.com/blog-posts/causal-inference-in-pymc/)\n", + "3. [Causal analysis with PyMC: Answering \"What If?\" with the new do operator](https://www.pymc-labs.com/blog-posts/causal-analysis-with-pymc-answering-what-if-with-the-new-do-operator/)\n", + "\n", + "That being said, it's time to jump into action 🔥\n", + "\n", + "## Considerations\n", + "Wait, before I forget it, we have to go over a few things.\n", + "\n", + "### Prerequisite Knowledge\n", + "\n", + "The notebook assumes the reader knows the essential functionalities of PyMC-Marketing. If one is unfamiliar, the [\"MMM Example Notebook\"](https://www.pymc-marketing.io/en/stable/notebooks/mmm/mmm_example.html) serves as an excellent starting point, offering a comprehensive introduction to media mix models in this context.\n", + "\n", + "### Installing PyMC-Marketing\n", + "\n", + "Before delving into the specifics of experiments and priors, the initial step is to install the **PyMC-Marketing** and **CausalPy** libraries and ascertain its version. \n", + "\n", + "The following command can be run on your Jupyter Notebook:\n", + "> `conda install pymc-marketing causalpy`\n", + "\n", + "- Install instructions for PyMC-Marketing: [here](https://www.pymc-marketing.io/en/stable/installation.html)\n", + "- Install instructions for CausalPy: [here](https://causalpy.readthedocs.io/en/latest/installation.html)\n", + "\n", + "## Importing libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "import arviz as az\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from pymc_marketing.mmm.delayed_saturated_mmm import DelayedSaturatedMMM\n", + "\n", + "import causalpy as cp\n", + "import pymc as pm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook configuration ⚙️\n", + "The following code its an small configuration for the notebook, to make it more readable and to avoid warnings. Defining a predifined style." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "az.style.use(\"arviz-darkgrid\")\n", + "plt.rcParams[\"figure.figsize\"] = [12, 7]\n", + "plt.rcParams[\"figure.dpi\"] = 100\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "%config InlineBackend.figure_format = \"retina\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Seed specification\n", + "The following code generates a seed to ensure reproducibility of the results. This seed will be used throughout the notebook to maintain consistency in the analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "seed: int = sum(map(ord, \"mmm\"))\n", + "rng: np.random.Generator = np.random.default_rng(seed=seed)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding our dataset 👀\n", + "\n", + "The following dataset contains everything we need to train a media mix model, and conduct our experiment. \n", + "- The columns for `Venezuela` and `Estonia` represent sales in each country. \n", + "- The columns with the names of **digital channels** represent the impressions or advertising spend on each channel. \n", + "- The `ds` column represents the date of the data, currently at weekly granularity.\n", + "- The `trend` column shows the trend of the target variable." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 362 entries, 0 to 361\n", + "Data columns (total 7 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 ds 362 non-null datetime64[ns]\n", + " 1 venezuela 362 non-null float64 \n", + " 2 estonia 362 non-null float64 \n", + " 3 meta 362 non-null float64 \n", + " 4 google 362 non-null float64 \n", + " 5 pinterest 362 non-null int64 \n", + " 6 trend 362 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(5), int64(1)\n", + "memory usage: 22.6 KB\n" + ] + } + ], + "source": [ + "# date range\n", + "df = pd.read_csv(\"datasets/dataset_chapter_n.csv\", index_col=0).dropna()[[\"ds\", \"venezuela\", \"estonia\", \"meta\", \"google\", \"pinterest\", \"trend\"]]\n", + "df[\"ds\"] = pd.to_datetime(df[\"ds\"])\n", + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Observing the sales 💶\n", + "\n", + "Let's pause for a moment and give our dataset a quick once-over before we proceed. We need to assess how well our sales fared in all countries before we apply any changes to just one of them. \n", + "\n", + "Bear in mind that the more similar the countries behave, the more accurate our counterfactual estimate will be." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 711, + "width": 1011 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Experiment Dates\n", + "start_date = pd.to_datetime(\"2022-03-28\") #Action start date\n", + "end_date = pd.to_datetime(\"2022-05-20\") #Action end date\n", + "\n", + "fig, ax = plt.subplots(\n", + " nrows=2, ncols=1, figsize=(10, 7), sharex=True, sharey=True, layout=\"constrained\"\n", + ")\n", + "sns.lineplot(x=\"ds\", y=\"venezuela\", data=df, color=\"C0\", ax=ax[0])\n", + "sns.lineplot(x=\"ds\", y=\"estonia\", data=df, color=\"C1\", ax=ax[1])\n", + "ax[0].axvline(start_date, color=\"black\", linestyle=\"--\", alpha=0.5)\n", + "ax[0].axvline(end_date, color=\"black\", linestyle=\"--\", alpha=0.5)\n", + "ax[1].axvline(start_date, color=\"black\", linestyle=\"--\", alpha=0.5)\n", + "ax[1].axvline(end_date, color=\"black\", linestyle=\"--\", alpha=0.5)\n", + "ax[1].set(xlabel=\"date\")\n", + "fig.suptitle(\"Sales on Venezuela v/s Estonia over time\", fontsize=16)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how the sales from *Venezuela* and *Estonia* were in sync until just before the intervention period, marked by the vertical black lines, where *Venezuela* appeared to experience a decline.\n", + "\n", + "Taking a simple look, we can notice that something has happened. But how can we quantify it?\n", + "\n", + "## **CausalPy** to the rescue 🚀\n", + "\n", + "Let’s dive into the heart of the process using a nifty tool in `CausalPy`, the `pymc_experiments.InterruptedTimeSeries` class. Think of it as your personal time travel machine, ready to dissect exactly when and how your strategies altered the course of your sales trajectory. \n", + "\n", + "The `InterruptedTimeSeries` class requires the following parameters:\n", + "\n", + "- `data`: The data used to fit the model.\n", + "- `treatment_time`: The time at which the treatment was applied.\n", + "- `formula`: The formula used to fit the model.\n", + "- `model`: A PyMC Model.\n", + "\n", + "Once our model is humming and all parameters are in place, we can observe the **estimated effect**. The estimated effect is the difference between the observed variable and the counterfactual variable. It represents the impact of the action (treatment) on the target variable." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [beta, sigma]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [12000/12000 00:42<00:00 Sampling 4 chains, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 42 seconds.\n", + "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n", + "The effective sample size per chain is smaller than 100 for some parameters. A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n", + "Chain 0 reached the maximum tree depth. Increase `max_treedepth`, increase `target_accept` or reparameterize.\n", + "Chain 1 reached the maximum tree depth. Increase `max_treedepth`, increase `target_accept` or reparameterize.\n", + "Chain 2 reached the maximum tree depth. Increase `max_treedepth`, increase `target_accept` or reparameterize.\n", + "Chain 3 reached the maximum tree depth. Increase `max_treedepth`, increase `target_accept` or reparameterize.\n", + "Sampling: [beta, sigma, y_hat]\n", + "Sampling: [y_hat]\n", + "Sampling: [y_hat]\n", + "Sampling: [y_hat]\n", + "Sampling: [y_hat]\n" + ] + }, + { + "data": { + "image/png": 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9e1bDhw+3+wPa19dXPj4+ue4zd+5cTZo0qSSGZ3DlPXHmWapRo4a6detmlG02m10Q2BF79+413RMvLy/dcsstebZ35fW5XJ6hwti2bZuGDRuWZ+C4fPnyuS4YlZCQoFdffVVffPFFoY5bVq9pdHS03cKn11xzjUP75jbb/yIfHx9VqFBBHh4euW6fP3++Ro8ene8HkpUqVbKbyTtnzhyHxpbd33//rZMnTxrlihUrqm/fvnm2L6n37tGjRzVixIg8A8e+vr4KDAwssVQfxX0/81Pa31+Xku9YkpYvX24qe3t7m/5fKkhycrIeeeQR/fvvv5KyPuCZOXOmw79/AUBpw8xjAADKsJ07d5rKHh4edvky8/LMM88YuTS7dOmie++9V126dDG+Ih0VFaWVK1fa5XO02Wx69tln9ffff5vq27dvr6FDh6pTp06qVq2apKwZZvv379fixYs1Z84cpaSkSJLCwsI0duxYzZo1q9SvRP7kk08aXzW+9tprdc8996hjx47G11IjIyP122+/adKkSYqLizP2mz59um699VbVq1fPrs8RI0YYX/GdOXOmQkNDTdvq1q2b53jatGljV+fqe+LsszR48GBTru5Fixbp8ccfdyoXbs6A8w033JDnjC9XX5/L4RkqjLNnz+rRRx+1C3L16NFD99xzj7p06SJPT09lZmYqJCREixYt0nfffWcKAE6aNEn16tXLN/Cfm+K4pq4WHx+vp556yjReX19f3XXXXU71U7NmTV1//fXq0qWLGjVqpNq1a5sCqydOnNCmTZv0008/GV+3l6T169dr+vTpeuSRR/Ls+5577tGvv/5qlBctWqRnnnmmwA8cs/vpp59M5cGDB+cZkC3J9+4777xjyi/t6empoUOHql+/fmrSpIkph21iYqKOHDmi4OBg/fXXX9q0aZPD5++s4ryfeSnt76+cqXGcyXccHh6u2bNnm+ruuOMOh3NXp6am6rHHHtPmzZslZaXt+O6778rMug4Arkyl+68xAABQaBf/YMyuZcuWDv8Rf+TIEVksFr388su55kitWLFirkGLb775RmvWrDHKHh4eeuONN3THHXfYtbVarWrevLmaN2+uAQMG6OGHHzbyJm7fvl3fffedHnzwQYfG6yo7duyQu7u73nzzzVzPsUqVKhoxYoS6dOmiYcOGGcGHtLQ0zZkzRy+99JLdPr179zZeL1u2zBT469Wrlzp37uzUGF19T5x9lvr06aPy5csbQcdTp05p06ZN6tq1q0PHS01NNQWwpPxTVrj6+lwOz1BhvPzyy6ZFqywWi1577TX973//M7WzWq1q2LChnnvuOd1666164IEHTN+aeP3119WxY0cjCOiI4rimJS0jI0Px8fEKDQ3Vhg0b9NNPPykiIsLYbrFYNH78eIc/EGzXrp1uvPFGXXvttfm2q127tmrXrq3bb79dU6ZMMc3+njFjhu655548c7a2adNGzZs3N4KUFy5c0G+//abbb7/doTGGh4dr3bp1Rtlisejuu+/Os31JvXcjIiK0YcMG03G+//57tW3bNtf2Pj4+atWqlVq1aqV77rlHERERpsBtUSiJ+5mX0vz+io6OVnh4uKnO0eBxdHS0HnnkESUmJhp11apV05NPPunw8cePH2/6MKNHjx5asmRJgfs1bNhQ3bt3d/g4AFCSSFsBAEAZlJ6erldeeUUZGRmm+j59+jjVz8iRIwtcXCu7CxcuaPLkyaa6Dz74INc/LnNq3ry5Jk+ebJr5NXPmTKWmpjo+YBd57rnnCjzHJk2a6NFHHzXV/fHHH8U5LEml55448yx5eXnp5ptvNtU5k7pizZo1pqBljRo18gw8l5brU5qfocIIDg42zR6XpEcffdQucJxTkyZNNH36dNM1jY+P18yZM50ew+VwTbds2aLGjRvn+q9Zs2bq1KmT7rrrLn3++eemwHGtWrX05ZdfOjUje+DAgQUGGrOzWq167LHHNGjQIKMuPj5eixcvzne/nPfYmdQVv/zyi+n/rWuvvTbPfOcl+d7dv3+/bDabUb7xxhvzDBznpmrVqurZs6fD7R1RUvczL6X1/ZXbgpwFpa2w2WxatWqVBg8ebErFUb58eU2bNs2pPMVhYWGm8nfffacPP/ywwH/Lli1z+BgAUNIIHgMAUMacPHlSI0eONL4yeVHFihULXHQou0qVKunxxx936tg//fST6Wu9vXv3tgsC5qd169a69dZbjXJERIT++usvp8ZQ0ho0aKD77rvPoba33367KVhx5syZQi1G6IzScE8K8yzlnCn8559/Kj4+3qF9FyxYYCoPGjQoz0WaSsP1Ke3PUGHMmjXLVK5bt64efvhhh/Zt0aKFXQBy3rx5SkpKcvj4ZfGaSllpKp544gn98ccf6tGjR4kcc9SoUaZyzv9bcrrllltMX/EPDg52aNZtenq65s6da6rL7/+sknzv5ky9UqtWLYePU9o4ez9zU5rfXznzHUtZC6/+9NNPpn8//vijpk+frtdee0033HCDxowZo9OnTxv7NGzYULNnz1azZs2KbawAcLkgbQUAAJeRiIgIu3yQUtYf3dHR0dq1a5c2b95st2iQ1WrVW2+9ledCNrkZMGCAvLy8nBrf0qVLTeXhw4c7tb8k9e/f3zTLdMuWLbrxxhud7qek3HXXXQ7n4q1QoYLq169vWsn96NGjTq0C76zScE8K8yy1atVKjRo1Mq5VUlKSfv/9d91555357hcZGWma8WqxWPJNWVEark9pf4YKI3vqAUkaMmSIUwuJDR8+XN9//70x2/PChQvauXOnw6lLyuI1lbIWEpw4caKWLFmixx57zBT8LC5BQUHy8/MzPrzZtWtXvu29vLw0ePBgffPNN0bdnDlz9NZbb+W736pVq4w0EpJUvXp1XX/99Xm2L8n3bs6Zp3v27HH6WKWFs/czN6X5/ZXbzOO3337boX0tFouaNm2qIUOG5JtrOz85PzgrasXdPwDkhuAxAACXkdDQUL3xxhtO7ePp6alx48Y5nbLC2Zyo0dHROnz4sFH28vJSp06dnOpDyvo6cXY5F/0rbZw9x9q1a5v+iM45o60olZZ7Utj8uoMHD9b7779vlBcsWFBg8Hjx4sVKT083yh07dszza++l5fqU5meoMI4ePWpKGyJlLVjojNq1a6tZs2amGavbt293OHh8uVzTunXrasSIEXluT05OVnR0tA4ePKjNmzcbs69DQ0P17LPPat26dXrvvfcKvbBoamqqjh49qvDwcCUkJCgxMdEu3ZEkU/9nz55VZmZmnrP5JWnYsGGaOXOmMjMzJWUFel944YV8c+vm/GD0rrvuMi38ll1Jv3dbtmwpi8VifJixYcMGTZgwQWPGjClUgLG4FNf9zKk0v79ym3nsqGbNmunll19Whw4dinBEAHD5I3gMAEAZ1qlTJ73yyisF5vvLTaNGjZxqHxwcbMoJ6e/vr19++cXp414MNlyUfSZaaVSzZk2n2vv6+prKjqZiKIzSck+cfZYuuu222/TJJ58YM+m3b9+u0NBQ1a1bN899Fi1aZCrnt1BXabk+pfkZKoyDBw+ayhUqVMgzgJ+fFi1amILHOfvNz+VyTatWrepwOqG4uDjNmDFDM2bMMJ65JUuWyMPDQ++++67Dx4yJidGiRYu0bNky7d271/RhiyNsNpvi4uIUEBCQZ5vatWure/fuRhqIxMRELV68OM+c12FhYaYFXt3d3fP9oKik37sVK1bUjTfeqOXLlxt1U6dO1S+//KL+/furR48eateund1zVBJK4n7mVFrfX0lJSabFQZ21d+9ejRgxQl988UWR56gGgMsZwWMAAMoAd3d3+fn5qXz58goKClLr1q3Vp08fNWzYsNB9OvOHpCS7HIbnzp1zepZ0bmJjY+3qlixZYsp1mR9fX1/ddtttlzyOvPj7+zvVPudMupzBi6JUkvckP84+SxdVqlRJ3bt316pVq4y6hQsXauzYsbm2Dw4ONs1G9PX1zTd9RGm5PqX5GSqMnOfvbKDpopx5ZZ25rmXtmkpZ5/T000+rSpUqpq/hz58/X3379nUoB/K8efP00Ucf2c0Md1ZCQkKB7+v//e9/phzCc+bMyTN4PGfOHFMwuHfv3qpatWqefbvivfvaa69pz549OnXqlFF3/vx5zZo1S7NmzZK7u7saNWqkdu3aqVOnTuratatTC60VRknez+xK6/vrwIEDdn0vXbrU7nehtLQ0RUVFaceOHfr+++/177//mra9/PLLWrFiRb4z5QHgSkLwGACAy0inTp1KLN+dszOonA2YOSq3RbImTJhg+gM+PzVr1izW4LGjeR9doSTvSX4uZTbe4MGDTcHjRYsW6cknn8z1K9Y5F8rr169fvnm+S8v1Kc3PUGHk/Ep6Ye9/zgCVM/errF3T7O655x7NnTvXNBN7xowZBQaPp0yZos8//7xIxuBI8O+6667T1VdfrbCwMEnSoUOHtG3bNruUAKmpqXbv3YJmY7vivVulShXNnTtXr7/+ulasWGG3PT09Xfv27dO+ffv0ww8/yMPDQz169ND9999fLGkQSvp+Zlda3185U1Z4e3urfv36du08PDxUrVo13XTTTerbt69efvll0zMYFRWluXPn6v777y/2MQPA5YDgMQAAKBI5F+mD65WFe3L99dercuXKxkzDM2fOaOPGjbr22mtN7VJTU7Vs2TJTXX4pK6SycX0uB0UVaCqtAauSZrFYdOONN5qCx9u2bVNMTIwqVKiQ6z5bt261CzR6enrqhhtuUOfOndW4cWNVq1ZN/v7+8vLykoeHh6ltr169HP7ALvs4hw0bpvfee8+omzNnjl0gddmyZaaZs3Xr1lWXLl3y7dtV791KlSpp0qRJOnDggBYsWKC1a9fmmSYhLS1NK1eu1MqVK3XLLbfozTffLLKZrK64n5eDnMHjRo0a5Zk3+yKLxaJx48bpn3/+UXh4uFH/888/EzwGgP9H8BgAABSJnF957dq1q2bOnOmawUBS2bgn7u7uuu222/TNN98YdQsXLrQLHq9cudI0G7Fu3bpq165dvn2XhetTGuX8qn5h85vGxcXl2++VLOfX8G02m/bv35/ngoITJkwwlRs3bqypU6c6nFLE0TRBOQ0ePFgTJkwwZvQuX75cL7/8sipWrGi0mTNnjmmfoUOHFvhBgavfu02aNNHLL7+sl19+WREREdq2bZu2b9+uf//9V/v37zel4JCyUidERkbq22+/LTCY6QhX3c/Sbv/+/aZy06ZNHdqvXLlyuv/++025w48dO6bg4GC1atWqSMcIAJcjgscAAKBIZA8GSNKJEyeK7VirV68utr7LkpK8J8Vp8ODBpuDxihUrFBcXZ0prMH/+fNM+Bc06lsrO9Sltcs5+LewMx5MnT5rKhc2dXRblNoM1Kioq17bnz5/X9u3bjbKbm5smTZrkcKAxPT3dLpDvqPLly+uWW27R3LlzJf2XomLkyJGSshZB3LFjh9Hey8tLAwcOLLDf0vTerVq1qvr376/+/ftLysq//Pvvv+vbb781PfubN2/WggUL8l0I0BGuvJ+lWVpaminnvSQ1a9bM4f0HDhyojz76yDSr/Y8//iB4DACS7JPFAQAAFELOGT4nT540fQUUJa+s3JOGDRuqZcuWRjklJUW//fabUT579qz++ecfo+zm5qYBAwYU2G9ZuT6lTaNGjUzlmJiYQgX39uzZYyo3btz4ksZVluQ2mztnaoKLDh06ZMpp27p1a9WpU8fhY+3bt08ZGRnOD/L/3XPPPabyL7/8YszM/emnn0zb+vfvn2fqjexK83u3cuXKGj58uJYuXWoXeFyyZMkl9+/q+1lahYSEKDU11VTnTPA4ICBA3bp1M9WtXLmySMYGAJc7gscAAKBI1K5dW7Vr1zbV/fHHHy4aTdlxKavUl6V7MnjwYFM5++JGixYtMgVDrr32WlWrVq3APsvS9cnPpTxDhVG/fn0FBgaa6pwNwpw8edIuf2lBaUiuJIcOHbKrq1y5cq5tc85Ivuqqq5w61po1a5xqn1OTJk1M9y4sLEwbNmxQQkKCXTC1oIXyLroc3rs+Pj56/PHHTXW53Tdnufp+llY5f164ubnZfZBVkJ49e5rKYWFhOnLkyCWPDQAudwSPAQBAkenbt6+p/NVXX5XZ3IolxdfX11R2Nn9sWbknt9xyi7y8vIzyrl27FBISIikrB3J2OQPN+Skr1yc/l/oMFUb37t1N5Tlz5ji1yNkPP/xgyhtbvnx5tW3btsjGdzmz2Wxavny5qc7DwyPPQFnOGckXLlxw+Fjx8fF2OYkLI+fs4zlz5ujXX381vdeaNWum1q1bO9zn5fDerVWrlqmcmJh4yX2WhvtZGuUMHterV0/e3t5O9ZEzeCyRJgsAJILHAACgCD3wwAPy8fExyufOndOLL75ot3gQHFepUiVT+WLA1FFl5Z6UL19effr0MdUtWLBA27dv17Fjx4y6ChUqqFevXg73W1auT34u9RkqjJzBwtDQUE2fPt2hffft26dZs2aZ6u68806nA0Fl1XfffWeX27Vr16655kGWpOrVq5vK//77r8MfIIwfPz7PXMrOuPHGG1WlShWjvGbNGlMec0m6++67neqzJN+76enphdrv6NGjpnL2a1BYpeF+lkaFXSwvu+rVq6tJkyamutIWPB4+fLgaN25s/HPm/zsAKCyCxwAAoMhUqlRJDz/8sKnuzz//1OjRoxUdHe1wP6mpqVq8eLEGDRpUZv/QdVTOnI2LFi1yavZaWbonORfBW7x4sbEQ10W33nqrPD09He6zLF2fvFzqM1QYrVq10rXXXmuq++KLL/TLL7/ku9+hQ4c0atQoU7DO399f9913X7GM83ISFxenjz/+WB988IGp3mKx2KVHyK5Zs2amwHJCQoLeeOONfNOXpKen64033tDixYsvfeDKmi2bfaG49PR0hYWFGWU/Pz/dcsstTvVZku/dTz75RM8884x27drlcL/R0dGaMGGCqa5z584O75+X0nA/SxubzaYDBw6Y6pzJd5xdztnHu3bt0vnz5ws9NgAoC9xdPQAAAFC2PPzww9q3b5/pa9Vr1qxR7969dccdd6hnz55q1aqV6av0SUlJCgkJ0YEDB7RhwwatXbu21H392FV69Oghq9VqBAaOHTumfv36qXfv3qpTp44plYOUFZyoX7++qa6s3JOuXbuqRo0axsJYkZGRdikrcgaYHVFWrk9eiuIZKox3331Xt912m2JiYiRlBXhee+01rV69WsOHD1fHjh3l6ekpm82mkJAQLV68WDNnzrRb9OqNN95wKIf15SgiIsJu0bjsUlJSFB0drQMHDmjTpk1KTk62a/Pwww/bLcyWnbu7u+644w7NnDnTqPv1118VHh6uhx9+WJ06dTJmdUdGRmrt2rX66quvjBn9devWVUJCgiIjIwt5llnuvvtuTZ8+PddZvAMGDLBLr+KIknrvpqena+nSpVq6dKlq166tG264Qe3bt1fTpk1Vo0YNWa1Zc7IyMjIUFhamv/76S99++60iIiKMPtzc3DRs2DCnzzGn0nI/S5Pjx4/bzb4uzMxjKSt4PHXqVKOcmZmpNWvW6I477rikMV7Odu/ebbeA6UU7d+60q8vvZ9qtt96a57ckAJReBI8BAECRslgs+uCDD2SxWEwLGCUkJOi7777Td999J0ny9vaWt7e3EhISnMqFeqWpUaOGBg4caFog7syZM5o9e3au7d977z27wF9ZuSdWq1UDBw40/WGf/SvqzZo1K1TAoKxcn7wUxTNUGNWqVdPkyZM1evRoU17WNWvWaM2aNbJYLCpfvrwSEhLyTAvw6KOPOj0j9XISGhqqN954o1D7WiwWjRo1SmPHji2w7SOPPKIVK1bo1KlTRt22bdu0bds2Wa1WlS9fXklJSUpJSTHt5+fnp88//1xjxowp1Bizq1atmnr37m2Xr1lyPmXFRa547544cULffPONkXbDYrHIz89Pbm5u+fb/5JNPqmXLlpd07ItKw/0sTXLmO5YKHzxu1aqVKlWqZJptvHr16is6ePzXX39p0qRJDrfP72faddddR/AYuAyRtgIAABS5cuXK6fPPP9dLL72U5x8JycnJiomJyfcP+fr169vNirwSvfbaa+rdu/cl9VFW7sngwYNlsVjy3FZYZeX65KUonqHC6NChg2bPnp1rMNpmsyk2NjbXwLGvr6/efvttPfHEEyUxzMtOmzZtNHv2bD3zzDMOtQ8MDNSXX36pmjVr2m3LzMxUTEyMXaCxSpUq+vbbb+1ywF6KnLmwJal9+/Z5LvbniJJ47+b1M0fKeo7j4uLy7N/Hx0evv/66XYqNS1Fa7mdpkTPfcc2aNVWhQoVC9WWxWNSjRw9T3caNG+2uJwBcSZh5DAAAis2IESM0ePBg/fDDD1q2bJndIk85WSwWNW7cWN26ddNNN92k1q1bl9BISzcfHx9NmTJF27Zt0++//669e/fq+PHjSkhIyPVr7Pm53O9JnTp11KFDB23dutVU7+HhUSQzVC/365OXonyGnNWoUSMtXbpU8+bN048//miXmzS7KlWqqF+/fnrkkUfsFvq7Enl4eMjPz08BAQFq1KiRWrRooT59+igoKMjpvho2bKgFCxZo8uTJmjdvXp55rytUqKDbb79djzzyiMqXL3+pp2DSvn17+fj4mI49dOjQIum7ON+7zz77rLp3766//vpLW7du1eHDh5WRkZFv/9WqVdPNN9+sESNGFEvaldJwP0uLnDOPLzVA3rNnT9M3NZKSkrRx40a7fMgAcKWw2MrSUtIAAKBUO3/+vHbv3q3z588rOjpa6enp8vHxUUBAgOrWraugoCC+zljCuCf54/oUvYiICAUHB+vcuXOKiYmRj4+PKlasqKCgIDVp0iTfWZ4oGikpKdqxY4eOHj2qCxcuyGq1qmLFimrYsKFatGghNze3YjnumjVr9MgjjxjlihUrau3atU4tcumo4nzvJiQkKCQkRCdOnNC5c+eUlJRkpK+oUqWKGjdurDp16pTYs+yq+wkAuDIQPAYAAAAAFLuHH35Yf/31l1EeOXKknnvuOdcNCAAAFIicxwAAAACAYnX8+HGtW7fOKFut1kIvlAcAAEoOwWMAAAAAQLGaPHmyMjMzjXLPnj1Vu3ZtF44IAAA4guAxAAAAAKDY/PHHH1q8eLGp7qGHHnLRaAAAgDPcXT0AAAAAAEDZsHv3bu3Zs0eSFBUVpZ07d5rSVUjSDTfcoDZt2rhgdAAAwFkEjwEAAAAAReKvv/7SpEmT8tweEBCg1157rQRHBAAALgVpKwAAAAAAxa5ChQqaNm2aqlWr5uqhAAAABzHzGAAAAABQLHx8fFS3bl11795d9957rypVquTqIQEAACdYbDabzdWDAAAAAAAAAACULqStAAAAAAAAAADYIXgMAAAAAAAAALBD8BgAAAAAAAAAYIfgMQAAAAAAAADADsFjAAAAAAAAAIAdgscAAAAAAAAAADsEjwEAAAAAAAAAdggeAwAAAAAAAADsEDwGAAAAAAAAANhxd/UAAAAAANibMWOGPv74Y0mSr6+vVqxYoUqVKrl4VK6zefNm3XvvvUa5U6dOmjVrlgtHdPkLDw/Xrl27dPr0aSUnJ8vHx0e1a9dW27ZtVbFixUL1+frrr2vOnDmSpKuuukrLli1TuXLlinLYAACgBBE8BgAAAEqZs2fPasqUKUb5wQcfvKIDxyhaK1eu1PTp07Vr165ct1utVnXp0kVjxoxRx44dner78ccf15IlS5SYmKjTp09r2rRpGjt2bFEMGwAAuADBYwAAAKCU+eCDD5SYmChJqly5sh544AGXjmfBggU6deqUUR40aJBq1arlwhGhMBITE/Xiiy9q+fLl+bbLzMzUxo0btXHjRg0fPlwvvvii3N0d+9OxcuXKuv/++zV58mRJ0jfffKM77rhDtWvXvuTxAwCAkkfOYwAAAKAU2bt3r5YtW2aU77vvPpd/7X/hwoWaNGmS8S97IBmXh+TkZD300EO5Bo4tFov8/f1z3W/WrFl67rnnZLPZHD7WvffeKx8fH0lSamqqJk6cWLhBAwAAlyN4DAAAAJQin376qRGoK1++vIYNG+biEaEsePvtt7V161ZTXfv27fXVV19p586d2rZtm/7991999tlnatSokandsmXL9OWXXzp8rAoVKujuu+82ykuXLtXhw4cv7QQAAIBLEDwGAAAASong4GCtX7/eKA8ePFh+fn4uHBHKguDgYM2dO9dUN2jQIM2aNUvXXXedvL29JUl+fn7q37+/fv75Z11zzTWm9pMnT9aZM2ccPuY999wjqzXrz83MzExNmzbtEs8CAAC4AsFjAAAAoJT49ttvTeW77rrLRSNBWTJhwgRTuVGjRnrrrbfk5uaWa3sfHx999tlnqlKlilGXmppqWsSxIDVr1jQFoP/44w+Fh4c7N3AAAOByBI8BAACAUiA8PNyUj7Zt27YKCgpy4YhQFuzfv18bNmww1b388svy8PDId7+AgAA9+eSTprr58+crKirK4WPfeeedxuv09HTNnj3b4X0BAEDpQPAYAAAAKAUWLVqkjIwMo3zTTTe5cDQoK/78809TuW7duuratatD+95yyy2mtCnp6elas2aNw8fu0aOHsXCeZP+MAwCA0s/d1QMAAAAAIC1evNhU7tu3b6H7yszM1LFjx3Tw4EGdP39eCQkJslqt8vb2VuXKlVWrVi0FBQXJ19f3Uod9yUJCQnTgwAGdPXtW6enpCgwMVJMmTdSiRQtZLBZXD++yt2rVKlPZmQ8lypUrp+uvv15Lly419Xf77bc7tL+3t7d69Oih33//XZIUGRmpDRs2qHv37g6PAQAAuBbBYwAAAMDFDh48qGPHjhnlBg0aqEaNGk73ExUVpRkzZujXX39VZGRkvm2tVqsaNmyo66+/XoMGDVK9evVM24cPH64tW7bkuu+9996bb9/vvfeeBg8enG+b3377TdOmTdOhQ4dy3V6tWjXdf//9Gj58uNzd+bOlMKKjo3Xw4EFTXbt27Zzqo127dqbg8aZNm5za/7rrrjOCx5K0fPlygscAAFxG+C0MAAAAcLF169aZyp07d3a6j7/++kvPP/+8YmNjHWqfmZmpgwcP6uDBg0pKStIrr7zi9DELIykpSWPHji0w/cHZs2f1/vvv648//tDUqVNLZGxlTUhIiF1d69atneojZ/uEhASdOXNG1atXd2j/nM/y2rVrZbPZmFUOAMBlgpzHAAAAgIvlXNCsffv2Tu2/detWPfbYY7kGji0Wi/z8/FShQoUCF0krbklJSXr44YfzDBz7+PjIy8vLVLdz50499NBDSklJKYkhlilHjx41lf39/VWhQgWn+qhdu3aB/eanVq1aqlatmlGOjIy0mw0NAABKL2YeAwAAAC5ks9m0e/duU13jxo2d2n/cuHFKS0sz6vz9/TV8+HDdcMMNatCggTw9PY1tFy5c0KFDh7Rz506tXbtW27Zty7XfESNGqH///pKkmTNnKjQ01LStbt26eY6pTZs2uda///772rx5s6nu6quv1ujRo9WzZ08jsHn27Fn98ccfmjZtmqKiorR7925NmjQpn6tQtI4ePWo3zuLUokULtWzZssj7zZ4KRVKhUqEEBATIx8dHiYmJpn67devmcB9NmjTR2bNnjfKuXbvUpEkTp8cCAABKHsFjAAAAwIVCQ0MVHx9vlD08PPINzOYUHBxsmgkaEBCgX375Jc8+ypcvrw4dOqhDhw4aOXKkjh8/rlOnTtm16927t/F62bJlpuBxr169nE6tsXXrVv3888+mul69eumzzz6Tt7e3qb5atWq67777dOutt2rkyJHau3evdu3a5dTxLsXOnTv1xhtvlNjxHnvssWIJHueciV6lSpVC9VOlShWFhYXl2W9BGjdurLVr1xrl3bt3a8iQIYUaCwAAKFmkrQAAAABcKGcKgOrVqzu1QNzevXtN5SFDhjgVfK5Tp466du3qcPvCmjp1qmw2m1Fu1KiRJkyYYBc4zq5ixYr66quvVKlSpWIfX1mUfbawpHyvdX5yphLJ2W9BatasaSrnlosZAACUTgSPAQAAABfKOevX0YXILrpw4YKpXKtWrUseU1E7fvy4Nm7caKp79dVX7YKSualYsaLGjh1bXEMr05KSkkxlR653bnIGnXP2W5Ccz3RuM90BAEDpRNoKAAAAwIUiIiJM5YoVKzq1v7+/v6m8Z8+eUpcSYM2aNaZZxw0aNHAq7cVtt92mDz/80C5QXlwGDx6swYMHl8ixilNycrKpXNgFE7PnzM6t34LkfKYjIyNls9lksVgKNR4AAFBymHkMAAAAuNClphZo1aqVqTx//nzNnj1bmZmZlzy2ohIcHGwqZ8+n7AgvLy9de+21RTmkK0LOmcbZF1V0Rmpqar79FqRcuXKmcmZmptMBaAAA4BoEjwEAAAAXyhlEczYw17JlS7Vo0cIoZ2RkaPz48erTp48++eQTbd261S74V9IOHDhgKjdr1szpPgqzz5XOx8fHVE5JSSlUPzmf0Zz9FiS3D0ScTX0BAABcg7QVAAAAgAvlXBwvPT3d6T4++OADDRs2TLGxsUbdqVOnNH36dE2fPl2enp5q1qyZ2rVrp86dO6tz5852s0GLU0xMjKl81VVXOd1HzkXXULCcQd7CzvbNGXR2Nnic24znwqbQAAAAJYuZxwAAAIAL5QziFmZ2aIMGDTR37lx16tQp1+2pqanauXOnvvnmGz388MPq1q2bnnvuOR08eLBQY3ZWzlzFfn5+TveRM7czChYQEGAqR0ZGFqqfnPvl7LcguQWtS/LDCwAAUHjMPAYAAABcqEKFCqZyXFxcofq5+uqrNWvWLG3btk2LFy/W33//rfDw8FzbJiYmasmSJfr1119177336rnnnivRmaCFWSgt+4J7xe3o0aPavHlziR2vRYsWatmyZZH3W7duXVM5r+chP7GxsXZ5uXP2W5D4+HhT2dfX127GPQAAKJ34HxsAAABwoZwpHM6ePXtJ/XXo0EEdOnSQJJ08eVLbtm3T9u3btW3bNoWEhJja2mw2fffdd4qJidGHH354ScfNT/ny5XXu3DmjXJgAec4AZHHauXOn3njjjRI73mOPPVYsweP69eubynFxcYqJibH7wCI/J0+eLLDfguR8pklBAgDA5YO0FQAAAIAL1a5d21Q+c+ZMkfVdq1YtDRw4UOPHj9eyZcu0evVqjR07VoGBgaZ2ixcv1j///FNkx80pZ7Dy9OnTTvdx6tSpIhrNlSMoKMiubteuXU71sXPnTlPZx8dH1atXd6qPnM90zmceAACUXgSPAQAAABdq3LixrNb/fi2PjY01zdItSjVr1tQjjzyipUuXqlatWqZtS5YsKZZjSlnnmN2+ffuc7qMw+1zpKlasqEaNGpnqtm/f7lQfOdt36dLF6bQjR44cMZWbNWvm1P4AAMB1SFsBAAAAuJCvr6/q1atnSilx8OBBVa5cudiOWblyZY0cOdKUmiG/xfPc3NxM5czMTKeO17p1a/32229GedWqVXr66acd3j8lJUXr16936piXYvDgwRo8eHCJHa849e7dW4cOHTLKf/zxh8aOHevQvklJSfrrr7/s+nNWzmerOFJ0AACA4sHMYwAAAMDFOnbsaCrv2bOn2I+Zc+ZxzkXRsvP19TWVnc0/fP3115tmqx45ckRbtmxxeP8lS5bowoULTh0TWW644QZTOTQ01OEUJb/99pvpXru7u6tnz55OHT85Odk089jd3V1t27Z1qg8AAOA6BI8BAAAAF+vevbupvHnzZof3TU9PL9Qxjx49aipXqVIlz7aVKlUylXMuvFeQq6++Wl26dDHVvfXWW0pNTS1w36ioKH322WdOHQ//ad68ubp27Wqqe++995SWlpbvfhcuXNCECRNMdYMGDbJ7FgqyY8cO07HatGmj8uXLO9UHAABwHYLHAAAAgIt17dpV3t7eRnn79u0OBVYl6YUXXtC4cePs8srm5+TJk5oxY4aprnPnznm2z5mjdtGiRfnOVM7N6NGjTeVDhw7pySefVHJycp77REVFaeTIkTp//rxTx4JZzjQVBw8e1Lhx45SRkZFr+8TERI0dO1aRkZFGnaenp8aMGeP0sTdt2mQqOztzGQAAuBY5jwEAAAAX8/HxUZ8+fbR06VJJWblmN2zY4FCgLSkpSUuXLtXPP/+sRo0aqU+fPmrTpo2aNGmiqlWrGuki0tLSFBISopUrV+q7774zpYHw8fHR7bffnucxevToIavVauQ6PnbsmPr166fevXurTp068vLyMrXv3Lmz6tevb1d311136ZdffjHqVq9erQEDBmj06NHq2bOnAgICJElnz57V8uXLNXXqVEVFRUnKmrG6c+fOAq8H7LVu3VqDBw/WggULjLoFCxYoLCxMY8aMUceOHeXl5aWEhAStW7dOU6ZMMeVJlqRHHnlEV111ldPHXrFihfHaarXq1ltvLfyJAACAEkfwGAAAACgFBg4caASPJWn58uVOz9I8dOiQKehntVrl7+8vKStPcW4zTa1Wq9544w3VqFEjz35r1KihgQMHmoKPZ86c0ezZs3Nt/95779kFjyXppZde0rFjx7R161ajLjQ0VC+88IKkrNzKGRkZdrORW7ZsqUcffVSjRo3Kc4zI37hx4xQaGqrt27cbdf/++68efPBBWSwW+fn5KS4uLtd9+/btazdz3BEhISGmFCddunRRtWrVnB88AABwGdJWAAAAAKXANddcozp16hjlFStWKCkpqcD9si9El1NmZqZiY2MVGxuba+A4MDBQEydO1IABAwo8zmuvvabevXsX2C4/Pj4++vLLL3X99dfnuj0hIcEucNy2bVtNnz7dbnYznFOuXDl99dVXuvHGG+222Wy2PAPHw4YN0yeffCKr1fk/HZcsWWIqDx061Ok+AACAaxE8BgAAAEoBq9Wq++67zyjHx8dr2bJlBe738ccfa/LkybrrrrtUv379fIPJF9WtW1ePP/6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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 811, + "width": 711 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "experiment = cp.pymc_experiments.InterruptedTimeSeries(\n", + " data=df.query(f\"ds<='{end_date}'\").set_index(\"ds\"),\n", + " treatment_time=start_date,\n", + " formula=\"venezuela ~ estonia\",\n", + " model=cp.pymc_models.LinearRegression(sample_kwargs={\"random_seed\": seed, \"target_accept\": 0.95, \"tune\": 2000}),\n", + ")\n", + "\n", + "fig, ax = experiment.plot()\n", + "plt.xticks(rotation=45, fontsize=8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look about to the output. We can see a plot with three figures:\n", + "\n", + "1. The initial image depicts the fit of our model before and during the intervention. It is evident from the image that prior to the intervention, the model was accurately predicting the sales in Venezuela based on the sales in Estonia. However, after the intervention, there was a noticeable divergence, leading to the appearance of a bluish delta in the figure.\n", + "\n", + "2. The subsequent photo provides us with another angle of the same data. We can discern that the difference between predictions and actual sales before the intervention was zero, but after the intervention, it increased up to `-1.33`. This implies that the relationship between variables underwent a significant change after the intervention.\n", + "\n", + "3. the third image portrays how the daily differences that we observed in the second photo keep accumulating over time, leading to a more pronounced effect.\n", + "\n", + "As you can see, these three images allow us to reveal and quantify the impact of our actions. But I'm sure you're wondering, how can we know that these differences are actually the causal effect of our actions and not simply an error in the model estimations?\n", + "\n", + "### **Quasi-Experiments considerations** ⚠️\n", + "\n", + "To pinpoint the cause of any changes observed in a time series, it's super important to make sure that the model effectively controls all the factors that may impact it. The dataset used in this case was created synthetically, which made the job easier by using only \"Estonia\" as a predictor to capture the effect. But in real-world situations, you gotta include additional variables that account for all the different conditions that might arise during an experiment. If you don't, you might mistakenly attribute an effect to an action where there's no connection whatsoever.\n", + "\n", + "Additionally, when we are conducting a **quasi-experiment**, in addition to making sure you control for the right variables, it's important to maintain the assumption that there couldn't be any other explanation for the results found. If you don't do that, the experiment's reliability goes down. We will never be able to control everything, and if there is some other reason for my change to occur, how can I be sure that my action was the cause?\n", + "\n", + "To determine if your causal model is well calibrated before the experiment, here is some recommended reading.:\n", + "1. [Bayesian Power Analysis in CausalPy]()\n", + "2. [A/A Test with PyMC](https://juanitorduz.github.io/time_based_regression_pymc/)\n", + "\n", + "### Estimating the total causal effect 🔬\n", + "\n", + "We have estimated our causal effect with our `CausalPy` model. However, we still have to process the resulting data a bit more.\n", + "\n", + "The initial step is to extract the maximum point of our effect, which is typically found at the end of our period. This will enable us to gauge the delta lost by our actions and understand the distance covered by our curve after we reduced advertising spending. We could estimate the daily difference of our effect compared to the previous day and calculate its total, in order to find this number." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Detected Cumulative effect: -1.33\n" + ] + } + ], + "source": [ + "detected_effect = (\n", + " pd.DataFrame(experiment.post_impact.mean(dim=[\"chain\", \"draw\"]).values) \n", + " - pd.DataFrame(experiment.post_impact.mean(dim=[\"chain\", \"draw\"]).values).shift(1)\n", + ").sum().item()\n", + "\n", + "print(f\"Detected Cumulative effect: {detected_effect:.2f}\")\n", + "# Detected Cumulative effect: -1.33" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have observed a total decrease of `-1.33` in the meta channel post-treatment. This means that we experienced a `1.33` decline in sales (`Venezuela`) after change our spend from Xt to Xt1 amount.\n", + "\n", + "This point is the lost delta contribution over the period from our marketing activities in 'meta'. However, this delta was caused by the decreased delta in advertising spending in the platform. Therefore, we are going to estimate it by calculating the absolute difference in spending during the period." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spend before treatment: 3.0229571428571425\n", + "Spend after treatment: 0.3145857142857143\n", + "Delta | Change: 2.708371428571428\n" + ] + } + ], + "source": [ + "meta_start = df.loc[df[\"ds\"] == start_date, \"meta\"].values[0]\n", + "meta_end = df.loc[df[\"ds\"] == end_date, \"meta\"].values[0]\n", + "\n", + "delta_x = abs(meta_start - meta_end)\n", + "\n", + "print(\"Spend before treatment:\", meta_start)\n", + "print(\"Spend after treatment:\", meta_end)\n", + "print(\"Delta | Change:\", delta_x)\n", + "\n", + "# Spend before treatment: 3.0229571428571425\n", + "# Spend after treatment: 0.3145857142857143\n", + "# Delta | Change: 2.708371428571428" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This means our negative change of `2.7` units in the `meta` channel during the intervention is responsible for a decrease of `1.33` sales in *Venezuela*.\n", + "\n", + "These two points represent the change on the $X$ and $Y$ axis respectively. **Our assumption is that marketing effects saturate**, and if that's the case, we can assume these points are one coordinate pair in a bigger non-linear function. Additionally, we assume that said non-linear saturation function is represented by the Michaelis Menten equation.\n", + "\n", + "Since we are looking at the extra value on the $X$ axis giving an extra value on the $Y$ axis, it is natural to think about estimating the derivative. If we estimate the derivative correctly, we can then obtain the prior values that our function should have to match our experiment.\n", + "\n", + "We will use `sympy` to estimate the derivative of our objective function." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - \\frac{\\alpha x}{\\left(\\lambda + x\\right)^{2}} + \\frac{\\alpha}{\\lambda + x}$" + ], + "text/plain": [ + "-alpha*x/(lambda + x)**2 + alpha/(lambda + x)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sympy import symbols, diff\n", + "\n", + "# Define symbols\n", + "x, alpha, lam = symbols(\"x alpha lambda\")\n", + "\n", + "# Define the Michaelis-Menten equation\n", + "equation = alpha * x / (lam + x)\n", + "\n", + "# Find its derivative with respect to x\n", + "derivative = diff(equation, x)\n", + "\n", + "derivative" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the derivative of the Michaelis-Menten function based on the sympy expression.\n", + "def derivative_michaelis_menten(x, alpha, lam):\n", + " return -alpha * x / (lam + x)**2 + alpha / (lam + x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's remember that the derivative will give us the rate of change on the $Y$ axis associate to the absolute values on $X$. However, now we have absolute values for both axes, how can we convert use it then?" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average rate of change: 0.4903217844128708\n", + "X midpoint: 1.6687714285714284\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 511, + "width": 1011 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Average rate of change as an estimate of the derivative\n", + "average_rate_of_change = abs(detected_effect) / delta_x\n", + "x_midpoint = (meta_end + meta_start) / 2\n", + "print(\"Average rate of change:\", average_rate_of_change)\n", + "print(\"X midpoint:\", x_midpoint)\n", + "\n", + "# Average rate of change: 0.4903217843787133\n", + "# X midpoint: 1.6687714285714284\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.scatter(x_midpoint, average_rate_of_change, color=\"green\", label=\"Experiment\", zorder=5)\n", + "plt.xlabel(\"Spend/Impressions (x)\")\n", + "plt.ylabel(\"Rate of Change (dy/dx)\")\n", + "plt.title('Experiment coordinates')\n", + "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The previous calculation converts our coordinates to exist in the same two-dimensional space in which our derivative exists, we can construct a function that evaluates the best set of `alpha`, and `lam` values that match the given point." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Objective function: Sum of squared differences between observed and predicted dy/dx\n", + "from scipy.optimize import minimize\n", + "def objective_function(params, x_data, y_data):\n", + " alpha, lam = params\n", + " y_pred = derivative_michaelis_menten(x_data, alpha, lam)\n", + " error = np.sum((y_data - y_pred) ** 2)\n", + " return error" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function is evaluated using the `scipy.optimize.minimize` function, if you want ot read more about it, you can check the [documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Alpha for Single Point: 3.3254882418143215\n", + "Best Lambda for Single Point: 2.1482888290689073\n" + ] + } + ], + "source": [ + "# Single fake data point: (x, dy/dx)\n", + "x_range = np.linspace(0, 5, 100)\n", + "initial_guess = [1, 1]\n", + "experiment_data = np.array([(\n", + " x_midpoint, average_rate_of_change\n", + ")])\n", + "\n", + "# Perform optimization with a single point\n", + "result_single = minimize(objective_function, initial_guess, args=(experiment_data[:, 0], experiment_data[:, 1]), method='L-BFGS-B', bounds=[(0, None), (0, None)])\n", + "\n", + "# Extract the best alpha and lambda for the single point\n", + "best_alpha_single, best_lam_single = result_single.x\n", + "\n", + "# Plot the observed point and the best fit line for the single point\n", + "best_fit_y_single = derivative_michaelis_menten(x_range, best_alpha_single, best_lam_single)\n", + "\n", + "print(\"Best Alpha for Single Point:\", best_alpha_single)\n", + "print(\"Best Lambda for Single Point:\", best_lam_single)\n", + "\n", + "# Best Alpha for Single Point: 3.325\n", + "# Best Lambda for Single Point: 2.148" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great!\n", + "\n", + "We have the best guess `alpha` and `lambda` parameters for our Michaelis-Menten function.\n", + "\n", + "Let's observe the estimated fit for the found `lam` and `alpha`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 511, + "width": 1011 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "plt.plot(x_range, best_fit_y_single, label=\"Best Fit Derivative\", color=\"grey\", linestyle=\"--\")\n", + "plt.scatter(experiment_data[:, 0], experiment_data[:, 1], label=\"Observed Experiment\", color=\"blue\")\n", + "plt.xlabel(\"Spend/Impressions (x)\")\n", + "plt.ylabel(\"Rate of Change (dy/dx)\")\n", + "plt.title(\"Best Fit for Michaelis-Menten Derivative (Single Point)\")\n", + "plt.legend(loc=\"upper left\", bbox_to_anchor=(1, 1))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are getting closer to the interesting part. Let's do a little review of what has happened so far:\n", + "\n", + "1. We created a model in CausalPy to estimate the change in our spending in Venezuela after reducing goal spending.\n", + "2. We calculated the maximum value of the drop in sales given the drop in advertising spending at goal.\n", + "3. We determined our best bet for the parameters of our Michaelis Menten function based on our experiment.\n", + "\n", + "### Let's Model\n", + "\n", + "We have our prior `lam` and `alpha` points given the experiment, it is time to start modelling. First, we must separate our regressors and target in two variables." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "X = df[[\"ds\", \"meta\", \"google\", \"trend\"]].copy()\n", + "y = df[\"venezuela\"].copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we will create a model in PyMC-Marketing choosing the transformations we want according to our assumptions. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "generic_mmm = DelayedSaturatedMMM(\n", + " date_column = \"ds\", \n", + " channel_columns= [\"meta\",\"google\"], \n", + " control_columns=[\"trend\"], \n", + " saturation=\"michaelis_menten\", \n", + " adstock=\"weibull_pdf\", \n", + " adstock_max_lag = 4, \n", + " yearly_seasonality = 4\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we have the model created, let's examine the priors that are given to the model by default." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'intercept': {'dist': 'Normal', 'kwargs': {'mu': 0, 'sigma': 2}},\n", + " 'likelihood': {'dist': 'Normal',\n", + " 'kwargs': {'sigma': {'dist': 'HalfNormal', 'kwargs': {'sigma': 2}}}},\n", + " 'gamma_control': {'dist': 'Normal', 'kwargs': {'mu': 0, 'sigma': 2}},\n", + " 'gamma_fourier': {'dist': 'Laplace', 'kwargs': {'mu': 0, 'b': 1}},\n", + " 'adstock_lambda': {'dist': 'Beta', 'kwargs': {'alpha': 1, 'beta': 3}},\n", + " 'adstock_shape': {'dist': 'Beta', 'kwargs': {'alpha': 1, 'beta': 3}},\n", + " 'saturation_alpha': {'dist': 'HalfNormal', 'kwargs': {'sigma': 2}},\n", + " 'saturation_lambda': {'dist': 'Gamma', 'kwargs': {'mu': 1, 'sigma': 2}}}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "generic_mmm.default_model_config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can notice that alpha and lam are quite wide. Let's quickly visualise using `Preliz` the distributions given for these parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "import preliz as pz\n", + "\n", + "prior_alpha = pz.HalfNormal(sigma=1)\n", + "prior_lam = pz.Gamma(mu=2, sigma=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gamma(mean=2.0, median=1.84, std=1.0, lower=0.58, upper=4.25)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 582, + "width": 1317 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "print(prior_lam.summary())\n", + "prior_lam.plot_pdf()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HalfNormal(mean=0.8, median=0.67, std=0.6, lower=0.04, upper=2.17)\n" + ] + }, + { + "data": { + "image/png": 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3NdLjSZINGzZ0P54wYULmzJkz6DHBYNTbHBuMc845p1d73bp1tRkI1DF1DOqXD40YrR5//PG8+93vzs6dO7vXvfGNbxzWD5fUM8aTWsyxwXBexliydOnSvOpVr+pu7969OzfddNOg+1XHYJ/hmmeDoZZRb3bv3p1//Md/7G7/xV/8RWbPnl27AUUtAwBgbJlQ6wGMNieddFKv9tq1a/t97ObNm7svOZ4kS5YsGfR45s+fnylTpmT37t1HPJ6yLHudoJxwwgm9vnUItVBvc2ww9j/hr+W3eKFeqWNQ384555w88cQT3e1169Zl8eLFtRsQHMaqVavyx3/8x2lpaele98pXvjKf+tSnhvV51TPGi1rNscFwXsZYc/HFF+ff//3fu9uPPfbYoPtUx6C34Zhng6GWUW/27NnT64sdV111Va666qpDHlOWZa/2j370o/z4xz/ubr/uda/Lpz/96QGPSS0DAGAscWW+I3TMMcdkxowZ3e1HHnmk38c+/PDDvdpDETRqaGjo9YHqqlWr+n358CeffDJtbW1DOh4YrHqbY4Oxa9euXu1a3fIX6pk6BvXNh0aMJhs3bszb3/72PPvss93rLrvssvzP//k/09AwvKe+6hnjQS3n2GA4L2OsmTt3bq/2jh07Bt2nOga9Dcc8Gwy1jHrX0dFx2KVSqfQ6pizLQ24/UmoZAABjSf2+21rHzj///O7Hzz33XFatWtWv4+65555e7eXLlw/5eDo6OvLAAw/067h77713WMYDg1Vvc2ygVq9e3atd61sNQL1Sx6B++dCI0WLLli15+9vf3uvqCytWrMgXv/jFEbuignrGWFYPc2ygnJcx1uwfKpo5c+aQ9KuOwT7DNc8GSi2D/lHLAAAYK4T5BuCKK67o1f75z3/er+Ouvfba7seTJ0/OJZdcUtPx7L/f/v1ArdTbHBuIXbt25c477+xuT5gwIUuXLq3ZeKCeqWNQv3xoxGiwY8eOvOtd78pTTz3VvW7ZsmX58pe/nClTpozYONQzxqp6mWMD4byMsWj/uxIcd9xxQ9KvOgb7DNc8Gwi1jHo0c+bMPPbYY0e0fPvb3+7Vx5VXXtlr+2c/+9lBj0stAwBgrBDmG4DLL7+81zfPv//976e9vf2Qx9x22215+umnu9uXXXZZmpqahmQ8K1as6PXB6k9/+tNs27btkMesXLkyv/71r7vbZ555ZhYuXDgk44HBqrc5NhDf+ta3en2L9+yzz+51+2BgH3UM6pMPjRgNdu/enfe973156KGHutedcsop+drXvpbp06eP6FjUM8aieppjA+G8jLFm9+7d+clPftJr3cUXXzwkfatjUDWc82wg1DLoP7UMAICxQphvAObOnZs3velN3e1Vq1blK1/5ykH3b2try9/+7d92t4uiyPvf//6D7r9mzZqceuqp3cvll19+yPFMnDgx73rXu7rb27dvP+S3mCqVSj7xiU+kUql0r/vABz5wyOeAkVRvc+yxxx47gtEnt9xyS770pS/1Wvf2t7/9iPqA0Uwdg7HBh0bUu7179+ZP//RPe4VOFy9enG984xuZNWvWoPtXzxjv6m2OOS9jLNmzZ08effTRIzqmq26sW7eue93ZZ5+dJUuW9Lm/OsZ4V4/zTC2DI6OWAQAwXgnzDdB73/veTJs2rbt99dVX5xvf+EavP/qTZMuWLXnXu96VJ554onvdq171qpxxxhlDOp63vOUtvS73/6//+q/53Oc+lz179vTab8eOHfnIRz6S2267rXvdueeem5e+9KVDOh4YrHqaY29961vznve8J9ddd13a2toOut+2bdvyxS9+Me9973t7XUlwxYoVefnLXz5k44GxSB2DI+NDI8a7sizzsY99LDfeeGP3uvnz5+eb3/xm5s6dW7NxqWeMFfU4x5yXMZbs3r07r3vd6/Lf/tt/yw033HBAndjf/fffnz/8wz/Mj370o+51DQ0N+au/+qshHZc6xlhSj/NMLYPhp5YBADAWFGVZlrUexGh144035v3vf3+vcNHixYvzwhe+MM3NzVm5cmVuuOGG7N69u3v7ySefnH/5l3855O1o1qxZkyuuuKK7PX/+/Fx//fWHHc9DDz2Ut7zlLdm1a1f3umOOOSaXXXZZ5syZkw0bNuT666/vdVnxo48+Oj/4wQ8yb968fv+7YaTUyxy74IILsn379iTJ1KlTc+qpp+bkk09Oc3NzpkyZku3bt+eJJ57IPffc02v+JcmJJ56Ya665Js3NzUf6z4dhtXbt2rzsZS/rc1tHR0evdmNjY5/7ffOb38yKFSsOWK+OweFdfvnlWbt2bZL+z5GejnSeLV++POeee27e+MY35sUvfnEmT57c537btm3LN7/5zfyf//N/snfv3u71K1asyHe+850jGiMMp7Vr1x4QYi2KIg0NR/Z9tfnz5+e6667rc5t6xnhWj3PMeRljybZt27J8+fLu9tSpU3Paaafl5JNPzqxZszJ16tS0trZmw4YNefDBB7N69epexxdFkb/7u7/LG97whoM+hzrGeFeP80wtYzy7/fbb84d/+Ifd7SuvvPKQV81L1DIAAMavCbUewGj2kpe8JJ/5zGfy13/9190nBc8880yeeeaZPvc//fTT86UvfemQIaPBOPPMM3P11Vfnz//8z9PS0pIk2bRpU77//e/3uf+CBQty9dVXOzmhbtXbHEuSXbt25b777st999132H1f8YpX5FOf+pQ32ahLZVkeENo7mIPtN9TfB1DHGGv6G5pdu3btQa8oe7DQ7JEqyzI33XRTbrrppgF9aHT11VcPegwwlPqqQUdS27oc6f79oZ4xFtTzHEuclzH27Nq1K/fee2/uvffew+577LHH5pOf/GR+53d+Z1jGoo4xVtXTPOsaj1oGw0MtAwBgtBPmG6TXve51WbZsWT7/+c/npptu6nXZ+y5HH3103vSmN+V973tfJk2aNKzjufTSS/OTn/wkn//853Pttdce8GFsksyaNSuvfe1r86EPfWhYQ08wFOphjr33ve/NzTffnAcffDA7d+485L6TJk3KpZdemre97W256KKLhnwsMNapY4wl9RiaTXxoBCNBPYOh57yMsWTatGn53Oc+l5tvvjl33nlnNm7ceNhjzjjjjFx55ZV5/etfP+x1Qx1jLKjHeaaWwchRywAAGM3cZncIbd26Nffcc082bNiQ1tbWzJ07NwsXLsx555130FsUDqfW1tbcddddWb9+fZ5//vnMnj078+fPzwUXXDDsoUIYDrWeY5VKJStXrszKlSuzYcOGbN++Pe3t7WlqasqsWbNy4okn5owzzjC/YIioY4x2+98OZiC+/e1v58ILLzxs34e71cxXv/pVHxpBjahnMLSclzEWbdq0KU8++WTWrFmT559/Pm1tbWlqasr06dOzYMGCnHnmmZk5c2ZNxqaOMVbU0zxTy2BkqWUAAIw2wnwAADBO+NAIAAAAAAAA6pcwHwAAAAAAAAAAANRYQ60HAAAAAAAAAAAAAOOdMB8AAAAAAAAAAADUmDAfAAAAAAAAAAAA1JgwHwAAAAAAAAAAANSYMB8AAAAAAAAAAADUmDAfAAAAAAAAAAAA1JgwHwAAAAAAAAAAANSYMB8AAAAAAAAAAADUmDAfAAAAAAAAAAAA1JgwHwAAAAAAAAAAANSYMB8AAAAAAAAAAADUmDAfAAAAAAAAAAAA1JgwHwAAAAAAAAAAANSYMB8AAAAAAAAAAADUmDAfAAAAAAAAAAAA1JgwHwAAAAAAAAAAANSYMB8AAAAAAAAAAADUmDAfAAAAAAAAAAAA1JgwHwAAAAAAAAAAANSYMB8AAAAAAAAAAADUmDAfAAAAAAAAAAAA1JgwHwAAAAAAAAAAANSYMB8AAAAAAAAAAADUmDAfAAAAAAAAAAAA1JgwHwAAAAAAAAAAANTY/w9XIK/y9CMPegAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 582, + "width": 1273 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "print(prior_alpha.summary())\n", + "prior_alpha.plot_pdf()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we sample the prior parameters given to our generic model using the sample prior predictive function, we can then get an idea of our *Prior Michaelis Menten curve* and analyze how far our experiment is from its mean, given the prior distributions." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [saturation_alpha, saturation_lambda]\n" + ] + } + ], + "source": [ + "generic_mmm.build_model(X,y)\n", + "\n", + "with generic_mmm.model:\n", + " prior_predictive = pm.sample_prior_predictive(\n", + " random_seed=seed,\n", + " var_names=[\"saturation_alpha\", \"saturation_lambda\"]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "prior_alpha_mean = prior_predictive.prior[\"saturation_alpha\"].mean().item()\n", + "prior_lambda_mean = prior_predictive.prior[\"saturation_lambda\"].mean().item()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 511, + "width": 1511 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "x_values = np.linspace(0, 5, 500)\n", + "prior_mm_mean_derivative = derivative_michaelis_menten(\n", + " x_values, \n", + " alpha=(prior_alpha_mean * df.venezuela.max()), \n", + " lam=(prior_lambda_mean * df.meta.max())\n", + ")\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "plt.plot(\n", + " x_values, prior_mm_mean_derivative, \n", + " color=\"Blue\", label=\"Prior Median Derivative\", linestyle='--'\n", + ")\n", + "\n", + "plt.scatter(\n", + " x_midpoint, abs(average_rate_of_change), \n", + " color=\"green\", label=\"Experiment\", zorder=5\n", + ")\n", + "plt.xlabel(\"Spend/Impressions (x)\")\n", + "plt.ylabel(\"Rate of Change (dy/dx)\")\n", + "plt.title(\"Prior Derivative of Michaelis-Menten Function \\n\")\n", + "plt.legend(loc=\"upper left\", bbox_to_anchor=(1, 1))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The previous Michaelis-Menten (Saturation) curve's derivative, given by the mean of prior distributions for each parameter, is far from our experiment, as expected.\n", + "\n", + "Let's create our own prior distributions based on the estimated `alpha` and `lam` given by the experiment.\n", + "\n", + "In order to proceed, it is necessary to scale the values. It's important to remember that **our model internally scales the values using Max Abs Scaler**. This means that the values are divided by their maximum. If `alpha` is on the Y-axis and `lam` is on the X-axis, we need to scale them accordingly before passing them on to the model.\n", + "\n", + "> **Note**: For this reason we multiply the values prior given by the model because these are being using as scaled." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Alpha prior Original Scale: 3.33, Lambda prior Original Scale: 2.15\n", + "Alpha prior: 0.48, Lambda prior: 0.70\n" + ] + } + ], + "source": [ + "scaled_alpha_prior = best_alpha_single / df.venezuela.max()\n", + "scaled_lam_prior = best_lam_single / df.meta.max()\n", + "print(f\"Alpha prior Original Scale: {best_alpha_single:.2f}, Lambda prior Original Scale: {best_lam_single:.2f}\")\n", + "print(f\"Alpha prior: {scaled_alpha_prior:.2f}, Lambda prior: {scaled_lam_prior:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we are operating in a scaled space, we have noticed that both the values of `alpha` and `lam` lie within a range of 0 to 1. Therefore, we can make use of a Beta distribution, which is limited to positive values and has a range between 0 and 1.\n", + "\n", + "The beta distribution can be characterised by two parameters, namely `alpha` and `beta`. To obtain a distribution with a mean around the scaled saturation values of `lam` and `alpha`, we need to identify a suitable combination of these parameters. This same logic can be applied with any type of distribution. \n", + "\n", + "You can easily find these values using the PyMC function `find_constrained_prior`. Let's take an example: we want to set the prior for the alpha parameter in Michaelis Menten's function to around 0.48. Using this function, we can look for a set of values that will give us a distribution where most of its mass is between `0.3` and `0.5`.\n", + "\n", + "Let's check the following code!" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Custom prior parameter for Alpha Saturation (Beta distribution): {'alpha': 36.03088851373635, 'beta': 55.87769954065099}\n", + "Custom prior parameter for Lambda Saturation (Beta distribution): {'alpha': 21.88328996404308, 'beta': 6.78635411045391}\n" + ] + } + ], + "source": [ + "alpha_custom_prior = pm.find_constrained_prior(\n", + " pm.Beta, lower=0.3, upper=0.5, init_guess={\"alpha\": 2, \"beta\": 3}\n", + ")\n", + "\n", + "lam_custom_prior = pm.find_constrained_prior(\n", + " pm.Beta, lower=0.6, upper=0.9, init_guess={\"alpha\": 1, \"beta\": 3}\n", + ")\n", + "\n", + "print(f\"Custom prior parameter for Alpha Saturation (Beta distribution): {alpha_custom_prior}\")\n", + "print(f\"Custom prior parameter for Lambda Saturation (Beta distribution): {lam_custom_prior}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual let's see the new distributions with Preliz." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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YtSudOeMOxas85d7X74w7AACNKdv9dMz42i9FNrwzmY8ueluMXPtRQc86Nr7g2hh55YeSWTayJ9q/9YGI0X0FbQUAAAAA1UvYE6CObaho9TxhVsTcs4rZBTiguzv9Det+zZ4AADSioV3R/vVfjqbdW5Lx2PxXxfDrPhmR+U+X9W70sl+M0Z43J7PmrQPRdutvR+T+UBwAAAAAHMx/MQWoYwOD6bujIyLTigKFq2z23LAhYmzMb2QCANBAxoZjxr+8L5q3DiTj8bOWxdCb/iiiuVTQYkyrLIvh1/12jM9Oz5CU+r4VpYf+saClAAAAAKA6CXsC1LHBwTQ81uWEO1SF7q70PTIa8cgjxewCAADTLi9H2y2/Ec2b70/G5VPnxf63/++I0syCFqMQpZkx9JY/ibz1hGTc+v1PR9OTqwpaCgAAAACqj7AnQB0bqDjj3tmp1ROqwUknZXH22emsf+DQnwsAAPWm9e4/jVL/TcmsPGtO7P/Jv4qYcWpBW1Gk/NR5MXTDHySzrDwa7d/6YGR7txa0FQAAAABUF2FPgDq1a1ceTz+dzjo7itkFmKjylHtfvzPuAADUv5b134zWH/3vZJa3zoqht/9l5CefW9BWVIPxjuti5PJfSmZNe5+Jtpt/PSIvF7QVAAAAAFQPYU+AOjVY0erZWoqYd0ExuwATdXelTbt9/QUtAgAA06TpiRXRduvHk1meNcfQmz4f5TMWFrQV1WTk6vfH2PlXJbOWR++K0oq/LWgjAAAAAKgewp4AdWpgMH3Pnx/R0uKMO1SLymbPjQ9HjI5q9wQAoD5lOx6LGd98X2Tjo8l8+NqPxvi8VxS0FVWnqSWG3vDZKM86Ixm33vlH0fTU2oKWAgAAAIDqIOwJUKc2bEhDY064Q3Xp6kzfo6MRDz9SzC4AADClhnbFjG/858iGdiTjkYt/LsaWv7uYnaheM0+L4Z/4TORx4A+sZuXRaP+3D0eM7C1wMQAAAAAolrAnQJ2qbPbs7NTqCdXkxBOzOPecdOaUOwAAdac8Fu3f/lA0PfdwMh6bf02MXPNrBS1FtRs//8oYveKXk1nTjsei7fb/UdBGAAAAAFA8YU+AOjQ8nMdjj6WzyhZBoHjdFafc+/qdcQcAoL603vlH0fLoXclsfE53DL3xsxFNzQVtRS0Yedl7Y/ysZcmstP4b0dL7rYI2AgAAAIBiCXsC1KGHH4kYLx94Z1nEgguL2wc4tJ7utHG3f6CgRQAAYAq09P5rtD7w/yaz8sw5MfS2v4honVXQVtSM5lIMvfGzkbeekIzbvvs7ke14vKClAAAAAKA4wp4AdajyhPu550bMnOmMO1Sb7q70/fDDzzfzAgBArWt6el20fedjySxvKsXQW/4k8pPOLmgrak1+8rkx/NrfTmbZyN5ov+U3IsrjxSwFAAAAAAUR9gSoQ4ODaVisq6OgRYAXVRn2HBt7vpkXAABqWbZvW7T/y3+JbHw4mQ9f+7Eon31xQVtRq8Z63hiji9+ezJqfWBGlVV8saCMAAAAAKIawJ0AdGtyQvjs7tXpCNZo1K4vzz0tn/f3F7AIAAJNifDTav/XBaNq9JRmPLvuZGFv2zoKWotYNv+ajUT753GTWeucfRbZtY0EbAQAAAMD0E/YEqDPj43lsfDiddWr2hKrV052++/qdcQcAoHa1/uB/RvMTDySz8XMujeHX/GZBG1EXWmfF0Ot/L/I48IdZs/GRaL/5NyLKYwUuBgAAAADTR9gToM48vjliaCiddXUWswvw0rq70+bdPs2eAADUqOaBm6O14rR2+YSzYuhNn49obi1mKepG+bwrYvSSn09mzU+vidL9f13QRgAAAAAwvYQ9AepM5Qn3OXMiTj3VGXeoVpXNno9sihge1u4JAEBtyZ57JNpv+Wgyy5tbY+gtfxr5rDkFbUW9Gbn6g1E+dX4ya73nL6Lpmd6CNgIAAACA6SPsCVBnBgfTkJgT7lDdOjsisoPy2OPjERs2FrcPAAActdF90f6t90c2ui8ZD1/7sSiftaSgpahLpfYYuuFTkWcH/rN2Vh6Ntpt/I2JspMDFAAAAAGDqCXsC1JmBwfQt7AnVbebMLC44P5055Q4AQM3I82i77XeieVt6ZmJ08dtjbMk7ClqKelaeuzxGL/+lZNa8tT9a7/vLgjYCAAAAgOkh7AlQR/I8j8GKsGdXpxPuUO26K0659w844w4AQG1oWfPVKPX+SzIbn9MVw9d+PK2wh0k0ctWvxvic9P9Ile77y8gqQscAAAAAUE+EPQHqyLPPRuzclc40e0L16+lOfxO8X7MnAAA1oOnpddH2vf+RzPLWWTH05s9HlGYUsxSNoaU1hm/4/ciz5h+PsvJotN/6WxF5ucDFAAAAAGDqCHsC1JGBigKLWbMi5s4tZhfgyPVUNHs+siliaEi7JwAAVWxoZ7R/6wORjY+m49f/XuSnzi9oKRpJ+YxFMXrZLySz5idXRctDXypoIwAAAACYWsKeAHWk8oR7Z0dEU5OzeVDtnv9ePfAulyM2bCxuHwAAeFF5Hu23fDSadj2RjEcu+fcx3nV9QUvRiEauem+UTz4/mbXd+bnIdj9V0EYAAAAAMHWEPQHqyOCGtAnQCXeoDe3tWcy7IJ31OeUOAECVKj30j9Gy8bvJbPzsi2PklR8uaCMaVqk9hl/3O8koG9kbbd/9ZETuWgIAAAAA9UXYE6COTGz21OoJtaLylHtfv9+YBACg+jQ92x+td3wmmeUzTo2hN34uorlU0FY0svHzr4rRxT+ZzFoe/l40D95S0EYAAAAAMDWEPQHqxK7deWypuFLW2VnMLsDR6+5Kw9maPQEAqDqj+6L92x+ObHwkGQ9d/6nITzyroKUgYvhVH4nyzNnJrO3234sY2lnQRgAAAAAw+YQ9AerEhg3pu1SKCWehgerVXdHs+dhjEfv2afcEAKB6tH3/D6LpuY3JbOSSn4/xC68paCP4v2acEiOv+Wgyatq3NVrv+uOCFgIAAACAySfsCVAnBivCnvPnRZRKzrhDrejsiGg+6Fdm5fLE72sAAChK88DNUVrz1WQ2fsbCGHnFhwvaCFJjXTfE2IWvTmalh74UTU+vK2YhAAAAAJhkwp4AdWJgMG0AdMIdaktbWxbz5qezfqfcAQCoAtnOJ6L91t9KZnlpZgy98bMRLa0FbQUVsiyGr/1Y5C3tB0aRR9t3PxmRlwtcDAAAAAAmh7AnQJ2oPOPe1aHVE2pNT8Up9/4BZ9wBACjY+Gi0/9uHIxvenYyHr/t45KfOP8xfBMXITzonRq78lWTW/NTqaFnzTwVtBAAAAACTR9gToA4MD+exaVM66+goZBXgOPR0pyHtPs2eAAAUrPXeP4/mLQ8ls9GFb46xRW8rZiF4CaOX/kKUT52XzNru/FzE/u3FLAQAAAAAk0TYE6AOPPJIxPhBF8myLKJT2BNqTmWz52OPR+zbp90TAIBiND2xIkr3/VUyK598fgxf94mCNoIj0NIaw9d+LBllQzuj7YefK2ghAAAAAJgcwp4AdWCw4oT7OedEzJzpjDvUmgvnRzQ3H3jneUT/QHH7AADQwIb3RPvNvx5ZfuBPFuZNpRh642cjWmcVuBi8tPELro7RrhuSWWntP0XTkw8WsxAAAAAATAJhT4A6MLAhbf7T6gm1qa0tiwUXpjNhTwAAitD2/U9F087NyWzk5f8lymctKWgjODoj1/xa5KWZyazt9k9GlMcL2ggAAAAAjo+wJ0AdGBxM312dWj2hVnVXnHLv7XPGHQCA6dW84bYorftaMhs/59IYvew/FrQRHL38xLNi5KpfTWbNz/RGy9p/LmgjAAAAADg+wp4ANW58PI8NG9OZZk+oXT3daVi7v7+gRQAAaEjZ3mej/dbfSmZ566wYuuHTEU3NBW0Fx2b0kp+P8dkLklnrXX8cMby7oI0AAAAA4NgJewLUuCeeiBgaSmddncXsAhy/hT3pe/MTEbt2a/cEAGAa5Hm0fefjke3fnoyHX/PRyE8+p6Cl4Dg0l2Lk1b+ZjJr2Pxet9/7vghYCAAAAgGMn7AlQ4wYqTrjPPi3itNOccYdaNX9eRGspnQ0MFLIKAAANpmXNV6LlkTuS2VjH62Js0duKWQgmwfgFL4+xBdcms9KqL0a2fVMxCwEAAADAMRL2BKhxAxvSxr9OrZ5Q00qlLBZ0pLM+p9wBAJhi2fZN0fb9Tyez8qw5MfS634nI/IFCatvwqz4SedOBP1WXlUej7Qf/s8CNAAAAAODoCXsC1LgNG9K3sCfUvp7u9N3X74w7AABTqDwe7Tf/emRj+5Px8Ot/L2LGqQUtBZMnP3VejF78c8msZePt0fzo3QVtBAAAAABHT9gToIbleT7hjHtnh8YVqHU93en3cV9fQYsAANAQSiu+EM1bHkpmo8vfFePzX1XQRjD5Rq76lSjPOC2ZtX7/DyLKYwVtBAAAAABHR9gToIZt3RqxY0c66+o45KcCNaSy2fOppyO279DuCQDA5Mu2bYzWu/80mZVPuSCGX/WRgjaCKdJ2Yoxc/YFk1LxtMFrWfLWghQAAAADg6Ah7AtSwwYoT7jNnRpx9djG7AJPngvMj2tvTWX9/MbsAAFDHymPRfstvRjY+8uNRHlkM3fCpiNLMAheDqTG25Kdi/PSeZNZ2159EDO0qaCMAAAAAOHLCngA1rPKEe8eCiKYmZ9yh1rW0ZNFZ0dLbJ+wJAMAkKz3whWh+anUyG730P0T57IsL2gimWFNzjLz6N5JRNrQjWu//64IWAgAAAIAjJ+wJUMMGN6Rnnbs6C1oEmHSVp9z7+51xBwBg8mTbNkTrPRXn20+dFyMvf39BG8H0GD/vihjreG0yK638u8h2byloIwAAAAA4MsKeADWs8ox7Z6dWT6gXPd3p97NmTwAAJk15LNpv+Whk46M/HuWRxdD1vx9Rai9wMZgew6/4UORZ84/f2fhwtN79py/yVwAAAABA8YQ9AWrUnj15PPlkOuvqOPTnArWnstnz2a0RW7dp9wQA4Pg5306jy0+bH2PLfjqZtaz7RjQ960/ZAQAAAFC9hD0BalRlq2dLS8S8eYWsAkyB886LmDEjnWn3BADgeB36fPt859tpOCNXvTfy0swfv7PIo/WHny1wIwAAAAB4ccKeADWqMuw5f15EqeSMO9SLpqYsurvSWX+/Zk8AAI5DeSzab/7N9Hx71hRD1/+e8+00nHzWnBi5/BeTWcumH0bzo3cXtBEAAAAAvDhhT4AaNTiYhr46OwtaBJgyC3vSt2ZPAACOR+mBL0Tz02uS2eglzrfTuEYv/Q9RnjUnmbX+8A8j8nJBGwEAAADA4Ql7AtSogYpmz84OrZ5Qb3q60+/rvv6IPNfuCQDA0cu2bTzM+fb/UtBGUAVKM2PkZen3QPMzvdHS9+2CFgIAAACAwxP2BKhBIyN5bNqUzro0e0Ld6elO39u3RzzzbDG7AABQw/JytN/68UOcb/9959tpeGNLfjLKpy1IZq13fT5ibKSYhQAAAADgMIQ9AWrQI5sixsfTWceCQ34qUMPOPjvihBPSmVPuAAAcrZbVX47mJ1cls+fPt19UzEJQTZpaYviVH05Hu56M0pqvFrQQAAAAAByasCdADRocTN/nnB0xa5Yz7lBvsv+fvfuOjqrc2gD+nGmpECChSCAEQjqgIL0IUsRrQ7CgICKiwEUQCyiKFRsXC0pRUQQEFVG6iBRBkWroJKQSaigJkAKkTTvfH3wZ8k4yk0kykzNJnt9arJV3zyk7h5xJmT17S1KJ7p6JiRzjTkRERESOk65dhMeOT4WY2S8I+u4TFMqIyP2YWvWBKfB2Iab992vAkKdQRkRERERERERERCWx2JOIqBpKThGLvUI5wp2oxipR7MnOnkRERETkKFmGx9bpkPS5QrhwwLuA1kuhpIjckCShsMcLQkiVdxnaQz8qkw8REREREREREVEpWOxJRFQNpRwX12Gh7OpJVFNFRoj3d2ISIMvs7klEREREZVOnbILmxF9CzBA9BKagrgplROS+zM06whjcS4jp9i0ACq4qlBEREREREREREZGIxZ5ERNWMySTjuFWxJzt7EtVc4VadPa9dA85fUCYXIiIiIqpG8rPhse19IWT29kfhHVMUSojI/el7TBLWUuFV6A4sVCgbIiIiIiIiIiIiEYs9iYiqmXPngfwCMRbWWplciMj1GjcC6tUTYxzlTkRERERl8fjnY6jyrggx/Z3TAK96yiREVA2YG0fDGDpQiGkPLoVkdS8REREREREREREpgcWeRETVTEqKuPZvAPj7c4w7UU0lSRIirLp7JiVxjDsRERER2aY+sxfaY6uEmLHVnTCG3a1QRkTVR2H3iZClm382lwx50MZ8o2BGREREREREREREN7DYk4iomklOEYu8WrOrJ1GNZ13smZCoTB5EREREVA0YCuCx5W0hJOt8UNjvLUDiGwWJyiL7h8AYNUiIaY8sg3T1vEIZERERERERERER3cBiTyKiaibluLgOC1UmDyKqOhHh4ovyScmA2czunkRERERUkm7vPKhyzgixwp4vQa7TRKGMiKoffdfnIKu0lrVkMkC390sFMyIiIiIiIiIiImKxJxFRtSLLMpKtxriHhrIzC1FNZ93ZMy8PSEtTJhciIiIicl+qjHho9y8SYqam7WG89TGFMiKqnmS/QBjaPSrENPFrIWWfVSgjIiIiIiIiIiIiQKN0AkRE5LjLl4HsbDEWxjHuRDVeQICEgAAZly/fjCUmAUFByuVERERERG7GbILHlncgySZLSFZpUTBgOiDx/d5E5WXoMhbauJWQjAUAAMlshC5mPgrvel/hzIiIiIioqu3evRujRo0CAERHR2PlypWQpKpvxrJq1Sq89tprlvXWrVvRrFmzKs+jyJw5czB37lzLOikpSbFcagKz2YwzZ87g9OnTSE9Px9WrV6HX6+Ht7Y169eohIiICoaGhUKvVTj3vhQsXEBsbi/PnzyMvLw8eHh4ICAhAy5YtERERAZ1O59Tz2ZOdnY2DBw/i4sWLuH79Oho1aoRmzZqhffv2Ff68lbqupeE9Q7WBwWBAcnIykpKSkJOTg8LCQvj6+qJhw4Zo06YNAgMD3er4U6dOxerVqy0fF32/d2cs9iQiqkasu3p6ewNNmyqTCxFVrYhwYKdQ7CnjrgHs7EtEREREN2iOLoc6PVaI6buMhezPdwgSVYTs0xCGWx+D7sBiS0wTvxb6zmMh12uuXGJERERU41gX8NkjSRI8PT3h5eWFunXrokWLFmjZsiW6d++Orl27wsPDw8XZ1j4GgwHvvfeeZT158mRFCj2pZsrMzMR3332HgwcPIiEhAfn5+Xa39/PzwwMPPIDRo0fjlltuqfB5zWYz1q1bhyVLluDYsWM2t9NqtWjfvj3GjBmDXr16Vfh8ZTl58iQ+/fRT/P333zAYDCUeb9iwIYYOHYqxY8c6VHyq1HUl6tu3L86dO1ehfTdv3owWLVq4xTkqIj09Hd9++y3Wrl2Lq1ev2twuNDQUw4YNw9ChQ8tVZO2q4z///PP4/fffodfrMXfuXNx///0ICAhwOC8lsNiTiKgaSTkurkNbAyoVf6Ekqg0iwiXs3CVb1ol8sx8RERER/T/pegY8ds4SYub6LWHo9KxCGRHVDIaOT0N75Gd29yQiIiK3Icsy8vPzkZ+fj8zMTJw6dQrbt2/H4sWLUadOHYwaNQqjR4+Gp6en0qlaXL16Fd9//32pj0VGRqJ///5VnFH5/PTTTzhx4gQAoHPnzujevbvCGVFNcu7cOSxYsMDh7XNycrB06VKsXLkSb775JoYMGVLuc6alpeHll1/G4cOHy9zWYDAgJiYGt956q8uKPdeuXYt33nkHeXl5Nre5dOkS5s6di23btmHOnDlldrRV4rrWBgkJCfjzzz8t65EjR6Ju3boKZkTu4s8//8Trr7+OnJycMrdNSUnBu+++i9WrV2PevHlo1KiRosdv2rQphg4diqVLl+L69ev44osvhDd5uCMWexIRVSPJKbKwDg1VKBEiqnLh4eI6OQUwGmVoNCz4JiIiIqrtdNv/B0l/XYgV9n8H0FTdqDWimojdPYmIiKg6uXbtGmbPno0dO3Zg/vz58PPzUzolADeKPYuPLS5u8ODBbl3smZeXh/nz51vWY8aMUTAbqg0CAgIQFhaGFi1awM/PD2q1GtnZ2UhISMDhw4dhNpsB3PjafO2116DX6/HYY485fPyUlBSMGjUKly5dssQkSUK7du0QEREBf39/FBYWWka7nz171umfY3Hbt2/H1KlTLZ8XAAQHB6NLly6oV68ezpw5g7/++gsFBTfegBcfH4+xY8di+fLl8PX1dfg8rr6utUVCQoLwfD548GAWe9ohSRJUKlW5tnfHc5Rl586deOGFF4SuvBqNBl27dkVYWBi8vLyQlZWFQ4cOISEhwbLN0aNHMXLkSPz6669272dXHx8ARo0ahWXLlsFoNGLVqlV49tlnERQUVJHLUSVY7ElEVI1Yd/YMa80iL6LaIsKq2LOgADhzBmjVSpl8iIiIiMg9qE/tgjZpgxAzRA2CqXlnhTIiqlnY3ZOIiIiqm0OHDmH69On49NNPlU6l2vvxxx9x5coVAEBYWJhLx1hT7aRWq9GpUycMHDgQPXr0QCs7L/qcO3cO06dPx99//22Jffjhh+jevbtDRUmZmZl45plnhELP/v3747XXXrPZKTM5ORmrVq1CnTp1HP+kHJSRkYEXX3zRUmgpSRJeffVVjBw5Uihey8zMxKRJkxATEwMAOH78ON566y189tlnNo9dldeVyJYHH3wQM2bMqPbnsKegoABvvvmmUIjZqVMnzJw5E02bNi2x/Z49ezBlyhTL89CJEycwe/ZsvP7664ocv0hgYCDuvvturF+/HkajEV9++aWi17Usjpf3EhGRoq5dk3HhghgLba1MLkRU9erXk9CksRjjKHciIiKiWs5QAI+t04WQ7OEH/R1TFEqIqOYp6u5ZnCZ+LaRs13a4ISIiIgIAlUqFgIAA4Z9OV3YH//Xr1yM5ObkKMqy5DAYDli5dalmzyx+5QlRUFH744QeMGDHCbkEicKMY6csvv0SPHj0sscLCQvz4448OneuDDz7AxYsXLetx48Zh3rx5dkeih4WFYerUqRg7dqxD5yiPr776Crm5uZb1xIkTMWrUqBJdChs0aIAFCxYgJCTEEtuwYQPi4+NtHrsqrytRbbZt2zacP3/esg4ODsY333xTaiEmAHTr1g0LFiyARnOzN+WKFSug1+sVOX5xQ4cOtXy8fv16ZGRklLmPUljsSURUTVh39dRogOBgRVIhIoVYd/dMTJKVSYSIiIiI3IJu37dQ5ZwRYoW9XoLs7a9QRkQ1k6Hj05A1npZ1UXdPIiIiIle75ZZbsGvXLuHf0aNHsWHDBtx555129926dWsVZVkzbdy4Eenp6QAADw8P3H///QpnRHSjY+XLL78sxHbs2FHmfrt378b69est6379+uHFF190en6Ounz5Mn799VfLOigoCGPGjLG5vYeHB958803LWpZlfPXVV07Lp6LXlai227Nnj7B+6qmn4O3tbXefiIgI9OvXz7LOzc1FbGysIscvrnPnzmjRogWAG2/4WLZsWZn7KIXFnkRE1YR1sWerloBWyzHuRLVJeLh4z7OzJxEREVHtJWWehHbft0LMdMttMLZ9WKGMiGoudvckIiIidyJJEkJCQjB79my0bNnS5napqalVmFXNs3LlSsvHPXr0QN26dRXMhuim6OhoodjpgvVoyFJ88803lo+1Wi1ee+01l+TmqK1btwpjmR999FFotVq7+3Tr1k3o0rl9+3bk5eU5LaeKXFei2q7oTRFFbrvtNof269Chg7C21UXT1ce3dvfdd1s+XrNmDWTZPRsvacrehIiI3EFyiviNJDRUoUSISDHWnT2PHweMRhkaDQu/iYiIiGoVWYbH1umQTDdfGJElNQr7vwNIfG83kSsYOj4N7ZGfIRkLANzs7ll41/sKZ0ZERES1lU6nQ9euXXHy5MlSH8/Kyir3MfV6PY4ePYq0tDTk5OQgPz8fderUQf369REdHW3peKU0WZZx/vx5pKWlIT09HVlZWSgsLITJZIK3tzd8fX0RGBiI8PBw1K9fv9zHT09Px7///mtZ33XXXRXONTMzE8nJyThz5gxycnJgNptRt25dNGrUCO3bt0eDBg0qfOzKiI+Px/Hjx5Geng5PT0/ccsst6Nq1K3x9fZ12jlOnTiE+Ph4XLlyAJEkICAhAx44dbY7fLYu7Xksl+Pj4WAodyypGOnv2LPbu3WtZ9+7dG82bN3dpfmXZtm2bsC5eYGXPwIEDLR09CwsLsWvXLgwYMMBpeZXnurqCq+7LzMxMHDx4EJcuXUJOTg58fX0REBCA22+/HQ0bNnRS9uXPifdz9Wc2m4W1p6enjS1FXl5ewlqSSn+t29XHtzZgwADMn39jksv58+cRExODLl26OLRvVWKxJxFRNZGSIq7DWrO4i6i2Cbcq9tQbgBMngTAWfxMRERHVKpqE36A5u1eIGTqMhLlhuI09iKiyirp76g4stsQ08Wuh7zwWcj1lXyglIiIiKk29evUc2k6WZWzbtg0//PAD9u/fD71eb3PbJk2aYPDgwRg5cqTdIso5c+Zg7ty5ZZ579erVWL16damPDR48GDNmzLCsjx07hnXr1uHw4cNITk52uKNf69atMWjQIDz66KMOX5M///xTKDDp3r27Q/sViY2NxYYNG7Bjxw6kWL/AZ6V9+/Z49tlnhZGzzmD9f5CUdGNU2MaNGzF37txS89LpdLjvvvswZcqUShVaHThwAJ988gkOHjxY6uNdunTB66+/joiIiDKP5Q7X0t3k5+cjOzvbsi6rcHPDhg1C4eK9997rqtQcduDAAcvHAQEBDheftm/fXljv27fPacWe5b2uzuSq+3Lr1q2YP38+jh49arN49bbbbsPEiRPRs2dPm8cJt36B8v/Zu9eWLFlSokiO93PN06xZM2F94cIFu13Hi5w7d05Y23oziauPb61NmzaoV6+e5blgy5Ytblnsybf6ExFVA4WFMk6fFmPs7ElU+9StIyHQ6g2vHOVOREREVMsU5EC3/X9CyFznFui7jVcoIaLaw9Dxaciam10kJLMRun+/VjAjIiIiqs30er3QfdJacHBwmcc4ceIEBg8ejPHjx2P37t12Cz0B4OLFi/jqq6/Qt29f/Pbbb+VNuVI2b96MxYsX4/Dhw+Ua3Xz8+HF8+umnuO+++/D33387tM+OHTssHwcHB6Nx48YOn+/q1at4+OGHsXDhwjKLmQDg0KFDGD9+PF5//fUyr39lffjhh5g0aZLNvPR6PVatWoX77rsPiYmJFTrHwoULMWLECJuFngDw77//4vHHH8eePXvsHsudr6WSNm7cKIxA79Onj93tDx8+LKw7duzogqwcl56ejmvXrlnWkZGRDu8bFRUlrFNTU52WV3mvq7O44r68evUqRo8ejfHjx+PIkSN2u5QePnwYo0ePxttvvw2j0Vihz8HRnHg/1zy9evUS1n/88UeZ+5hMJmzatMmyLurErcTxrUmShE6dOlnW//zzj0P7VTV29iQiqgZOnARMxTpUSxLQOkS5fIhIORERwLnzN9eJSTIeuI+dfomIiIhqC4+ds6DKzxRihX3fAHQ+CmVEVHuU2t0zYR303cZDrhuoXGJERERUq8iyjJMnT+LTTz/FiRMnSt1GpVLhwQcftHuc7du344UXXihX4WSRvLw8TJ48GSkpKXjppZfKvb8SLl26hAkTJmDBggXo2rWr3W2LFyq2adOmwudUqVRo3bo1QkJC0LhxY/j4+MBkMuHy5cuIi4sTCrdWrlwJb29vvPHGGxU+nz0//vgjvv/+ewA3xtv27NkTwcHBMJlMSElJwd69ey2FbleuXMFTTz2FX375BUFBQQ6fY/Xq1fjf/268OdHX1xfdunVDUFAQtFotzpw5g+3btyM3NxfAja+hl19+Gb///rvdLrFF3OlaKikxMdFyjYEbHXxHjhxpd5+4uDjLx40bN0ajRo0AAKdPn8aqVauwc+dOXLhwAfn5+ahfvz5atmyJHj16YNCgQfD393f652D9vNW0aVMbW5YUEBAArVZr+Vo9efKkU3KqyHV1Blfcl5mZmXjiiSeEQlitVosOHTogLCwMdevWRV5eHhITE7Fv3z5LgefPP/+MgoIC4ToUUavVAG58/yne9bgoXhp7Y7Nry/2cmJiIF198EfHx8bh8+TKAG19bzZo1Q6dOndC/f3+HOhwrfQ57+vTpg/DwcEv36JUrV6Jv37648847S91elmXMnDkTp06dssSee+45qFSl96p09fFL07ZtW2zZsgXAjefJ9PT0cr3poyqw2JOIqBpIOS6umzUDvL1Z3EVUG0WES9i67eY78NjZk4iIiKj2UF2MheboL0LMGNIPppC+CmVEVPsYOj4N7ZGfIRkLANzo7qndvwj6vtXvhSciIiJyfxcuXECPHj2E2NWrV8vscjZixAi7I4jj4+MrXOhZ3Pz58xEYGIihQ4dW6jhVxWAw4JVXXsGff/4JnU5X6jZnzpxBTk6OZe1oN7AikiShW7dueOihh9CrVy+7o+OTkpLw9ttv49ChQwCApUuX4v7778ett95arnM6YsaMGQCAAQMGYPr06SXGQZ89exavvPKKpdA1KysL06ZNw5IlS+wWbRX35ptvAgCefPJJTJo0Cb6+vsLjmZmZmDRpEmJiYgDcKF5bsmQJJk2aVOrxquJaDhgwoMS4X2caP348JkyYUOH9ZVnG9evXkZycjI0bN+Lnn3+23P/e3t6YPXs2AgICbO5/7do1ZGRkWNbNmzeH0WjE119/ja+++qpEJ8e8vDycO3cOO3fuxJw5c/DMM89g/PjxDn8NOCI9PV1YN2nSxOF9JUlC48aNkZaWVuqxHFXZ6+oszr4vZVnGlClTLIWekiThsccew8SJE0st3D179iymTZtm6RK9Zs0adOvWrcSbBeLj4wEAq1atwmuvvWaJb968ucSYbVtq4/2ckJCAhIQEIXb9+nWkpaVh7969mDNnDu644w688cYbDo8ZV+Ic9qjVasyaNQvDhg1DdnY2TCYTxo8fj6FDh+LBBx9EWFgYvLy8kJWVhcOHD2Px4sVCV/LHHnsMDz30kGLHL4319/3Y2FgWexIRUfklp4it1cNaK5QIESkuwurvSidOAIWFMjw8WABOREREVKPJZnhsfQ8Sbv5+KGu8UHjn6womRVT7yD4NYWj7CHSHllpi2tgVMHQZB9nH9S8GEhERFWcyycjJsT2alWzz85OgVrv/31TNZrOlU5cjJEnC448/jqlTp9rcxmQyYfLkyXYLPXv27Ino6Gg0aNAAaWlp2LZtm80Cmg8++AB9+vQRCiG8vb0thVImkwlZWVml7uvh4YE6deqU+ph1oWARtVqNkJAQtG7dGk2bNoWfnx+8vLygUqmQl5eH9PR0HDlyBEePHi11//T0dKxduxaPPPJIqY8nJycL6/IWx9SpUweLFy92aNvw8HAsWbIEjz32GI4dOwYA+P777/HZZ5+V65yO0Ov16NOnD7744otSu/E1b94cCxYswPDhwy2FQzExMdiyZQvuuusuh85hMBgwceJEm8VQDRo0wNy5czFw4EDL18TatWttFntWxbU0mUwwmUwOnaMi7I3PLs2JEydw3333WdZms7nUY/Tu3RuvvfYaWrZsafd42dnZwjogIACvv/461q5dW2YueXl5mD17NhITEzFr1ixoNM4pL7J+7vH29i7X/j4+NyebGI1GFBYWwsPDw+4+zr6uzuLs+3L16tXYuXOnZT1t2jSMGDHC5vmbN2+O7777DqNGjcK+ffsAAHPmzMH9999vt2tnRdTG+9kR//zzDx566CF88skn6NOnj9OPXxXnCAkJwa+//opp06YhJiYGZrMZy5Ytw7Jly2zuExAQgOeff96hN4u4+vjWgoODhXVSUhL69+9f7uO4Eos9iYiqgZQUcd26tfv/AYKIXCMsFJAkoOj3CaMROHESiHRdB34iIiIicgOauJVQp8cKMX3X/0Ku6/i4MyJyDkPHUdAeWQbJfKMLjmQqhPbgEuh7VY8RpkREVDNs2lyIDz7KxZVMFntWhH8DCdNe88HAu+wXCFU348ePx/jx4+2OK123bp0w3rc4f39/zJ8/H23bthXiU6ZMwYwZM/DTTz+V2KewsBBfffUV3nnnHUts9OjRGD16NAAgLS0N/fr1K/V899xzj6WzXVnat2+P2bNno2fPnkKxly27du3Cs88+W2rhz/bt220WexZ1DCxSnq6DFaHT6TBmzBhLweP27dthNpvLNXLWEV5eXpg+fbrdAi4fHx+89957ePjhhy2xZcuWOVzsGRkZifHjx9vdxs/PD4MHD8bChQsBAOfOnUNGRoZltHhlVNW1dCVZlu0Wq6lUKgwfPhzPPPOMQ1+b165dE9a7du2yxDw9PfHUU0/hnnvuQYsWLWAwGJCUlIRffvkF69atsxS2bd68GZ999hleeeWVSnxmN1kXe5ZVqGnNevvc3Nwyj+Hs6+oszrwvZVnGggULLOs77rjDbqFnEa1Wi3fffRf33nsvZFlGWloaduzY4bLCQ0dV5/u5cePG6NevH7p3747w8HD4+/tDp9MhOzsbCQkJ+PPPP7F69WpLN9lr167h+eefx9KlSx3u7FwV5yiPoKAgLF26FKtWrcKMGTOEDtnWoqKi8M4775QrD1cfvzjrLp6u7BZbUSz2JCJycyaTjNQTYiwsVJlciEh5Pj4SgprLOH3mZiwxicWeRERERDVafjY8dojdC8z1g2G4faRCCRHVbnKdW2CMfADaY6ssMe2Rn6DvNBrw9FMwMyIiqk3enp6La9dY6FlRVzJlvD09t8YVe86bNw+bN2/Gxx9/jMjIyFK3Wblypc39Z8+eXaLQE7hRXPXmm28iJiYGx48fL/H4hg0b8NZbb7m0CKe8hUc9evRA27Ztcfjw4RKPFY1ELk3xkdcASoxVdoWwsDDLx9evX8eJEyfQurVzx/wNHDjQoTG0bdu2RYcOHSzXaM+ePbh27ZrNDqzFDRs2zKGvgc6dO1uKPQEgNTXVKcWeQPmv5bZt25xy3qpiNpuxdOlS/Pzzz3jiiSfw0ksvQafT2dzeurCyqNCzXr16WLx4sfA84enpiY4dO6Jjx47o2bMnXn31VZjNZgDAwoUL8cADDyAiovIvRhUWFgprrVZbrv2tP1/r41VEea+rszjzvoyNjRUK+R0p9CwSEhKCsLAwJCUlAQD+/fdfxYs9gep5P3/wwQfo1KlTqZ1wGzZsiIYNG+KOO+7A6NGjMWHCBEs36cLCQrz44ovYuHFjmV97VXGO8kpNTcUHH3yAXbt2lbltfHw8Hn30UfTu3RvTp093qMDa1ccvzsvLCz4+PsjNzQVwoyO4u2GxJxGRmzubBhQUiDEWexLVbhHhEIo9ExJlDB7Ejr9ERERENZXH7i8gFWQLscI73wDUrn/hgYhKp+/8LDTxayDJN178lPS50B7+CYau/1U4MyIiIqrtUlJSMHz4cPz0008lCrNyc3NLLX4EgNDQUHTs2NHmcVUqFXr16lVqsWdOTg7i4uLQrl27SuXuCJPJhIMHD2L79u1ISUnBqVOnkJOTg7y8PIeLvq5cuQKDwVBqkZl1cZynp2eFc83OzsbGjRuxf/9+JCcnIzMzE9evX7d0WrPl4sWLTi/2vPPOOx3etm/fvpaiMlmWERsbi+7du5e5X6dOnRw6fmBgoLC+evVqmfu407V0pZCQEEvBHXBjzHdRp76NGzfit99+g8FggMFgwKJFi5CcnIyvv/7aZuGWrfj06dNtFoQDwAMPPIC4uDh8//33AG58HSxcuBAzZ86sxGd3g3UXToPBUK79rf/PHekM6uzr6izOvC9jYmIsH0uShNtvv71cuQQHB1uuUdHIeFepyfdzt27dHNquRYsWWLx4MR5++GGcP38ewI0Okr/++iuGDx+u+DnKY9euXXjuueeQn58P4EYB9yOPPIJ7770XYWFh8PLyQnZ2No4ePYqff/4Z//zzD4Ab3VoHDx6MH374ASEhIYodvzSenp6WYk/rnwvcAYs9iYjcXLLVCPeAAKB+fRZ1EdVmERESNm25+a79Yr+fEhEREVENo0o/Bs2R5ULM2HoATME9FMqIiABArh8MY9hAaJP+sMR0B5fc6Lir9VYwMyIiqi3efcuHY9wroWiMu7sLDAws0anMYDAgIyMDO3bswJw5c3D58uUS++Xm5mLKlClYs2aNMB74+PHjNgurUlJSEB4eXuFcT5486fJiz7Vr1+KLL75wykjVq1evwt/fv0TcutiovF0HASA/Px/z5s3D4sWLy13IVpSbs5WnI6N1EeDx48cdKvZ0pEMhcKNrWnH2Cmnc8VpWJZ1Oh0aNGqFRo0bo3bs3Ro4ciXHjxuHChQsAbhRBzZ07Fy+99FKp+/v4lHyeCwkJwcCBA8s895gxY/DTTz9Zrvvff//tlDHa3t7i70vl7cxpvX1pn2NZKntdncWZ92XxAk1Zlh0uvi5S1MUVuFGM6Qq1/X625u/vj8mTJwtfZxs2bHBqIaarz3HmzBlMmDDBUohZt25dLFiwoMQI9YYNG6Jfv37o168fli1bhnfeeQcAkJmZif/+979Yu3Ztie8NVXF8W4oXejuje7CzsdiTiMjNpaSIf6gJc+83qxBRFYiw+nvbyVNAQYEMT08WghMRERHVKLIZHtveg4SbvxfKGk8U9pmqYFJEVMTQeYxQ7CkVZEMb+ysMHUYqmBUREdUWA+/yQP9+OuTksNizIvz8JKjV1fPvqVqtFoGBgXjsscfQvXt3DBo0qNRiueTkZPz111/o37+/JZaZmemyvLKyslx2bAB477338MMPPzjteAXWY/X+n3Unv/IWJOXl5eHZZ5/F/v37K5ybKwpLAgICKrytowVW1kV8tkiSeO/JcunPY+56LZUUERGBb7/9FoMHD7Z8bX7//fcYNWoU6tevX2L70gohHe0mGRAQgHbt2uHAgQMAbnTwPX78uDBauyKsv06Kuuc5qvj2Go3Goc6eZSnvdXUWZ96X1s/BJpOpwnldu3atwvvawvu5dAMHDoSvry+uX78OADh8+DDy8/PLVZio5Dk++eQT4WeQ6dOnlyjEtPb4448jKSkJy5YtAwCcPn0aP/30E0aPHl3lx7el+Bs/nPEc42ws9iQicnPWnT1DOcKdqNYLbQ2oVEDRm+zMZiDlONC2jbJ5EREREZFzaY6tgfrCESGm7zIWct2mCmVERMWZG0bA2LI3NCe3W2La/YtgaPc4oHHtuD8iIiIAUKslNGhQPQsWyTmCgoJw//33Y/ny5aU+vnXrVqHY0xUFPEWKikhcYc2aNU4t9ARsFxhaF6LZKgq1ZebMmUIxk4+PDx544AF069YNLVu2ROPGjeHp6SkUj6SlpaFfv35l5lYZ5Snqsd62vMV4zuKu11JpoaGhuOeee7B27VoAN75Gt2/fjgcffLDEtv7+/tBqtULRcmg5XmwOCwuzFHsCQHp6eqWLPa07wKanpzu8ryzLwvaOdpN1RHmuq7M48750ZtdLV9w3vJ9Lp9Fo0LZtW+zZswcAYDQakZGRgRYtWrj9Oa5du4Y///zTsg4KCsLdd9/t0L5jxoyxFGMCNzp3Wxdjuvr49hR1EgUcfyNDVWKxJxGRG5NlGcePi7GwUP7hhqi28/SU0DJYRuqJm7HEJBZ7EhEREdUoBVeh2/GpEDLXC4Lh9qcVSoiISqPvMlYo9lRdT4cmYS2MbR9RMCsiIiKqTeyNXk9JETuK1K1b12V5uLIIZ968eTYfa9u2LcaNG4fbbrsN9evXF8bWjxgxAjExMeU6V6NGjYT1lStX0Lx5c4f2zcjIwC+//GJZh4SEYNGiRWUWpNkbY+4s+fn58PX1dXjb4ioyJruyqupaDhgwAOfOnatQjo4YP348JkyY4PTjdu/e3VKUCABJSUmlbqfVahEUFITU1FRLzM/Pz+HzWG+bk5NTzkxLCgkJEdbluf6XL18WCldbtWpV6XyKc/S6Oosz78vixaCNGzfGP//8U/kEnYT3s33+/v7COisry6nFnq46R2xsrNBBtlOnTiU6N9vStGlTNGvWDGlpaQBu/LxSWFgoFPu6+vi25OfnC1971j8XuAMWexIRubGMS0CO1Ztw2NmTiAAgPBxWxZ4yABaDExEREdUUut1zoMoXRywW3jmN3QKJ3Iy5aXsYm3WGJu1mEYEuZgGM0YMBFf/8TkRERK6nUqlsPmbdybNBgwY2tx04cCBmz57ttLycJTU1FWfOnCn1sZYtW+Knn34qMXq9SEU63TVr1kxYl6fr4Pbt24XClHfeecehzoNXrlxxPMEKunLlisNFZZcvXxbWriwStqWqrqXJZKrUuOuyuKoI2nqkt73Ouq1btxaKPYuPJy6L9bbOGGfcqFEj1KlTx/L8lJCQ4PC+8fHxwtrZxZ7lua7O4Mz7svi4+YyMDIcL26oC72f7rAt5XfH/5opzWP//NGzYsFz7N2zY0FKMaTabkZ2dLXxduPr4tlh/3w8MDCzXeauC7Z/8iIhIcVZvuISvD3BLE2VyISL3EhEuFnYmJiqUCBERERE5nepSIrRHfhJixpB+MLW8Q6GMiMgeQ5cxwlqVcwaa5E0KZUNERES1jXX3zuKsO7+FhIRAq9WWuu2+ffuEjnnOYq8LlyOFM2fPnrX5WN++fe0WehYvcHOU9YjqU6dOObzvyZMnLR97e3ujU6dODu0XGxvr8DkqqjwFdYlWLzi0bt3a2emUyZ2vpTuwLkK0V5DbuXNnYV2eAuaLFy8K6+IFhZVx++23Wz6+cuWKzYJuawcPHhTWjn5dOKo819UZnHlfFu+YKssy9u3bV7nknIj3s33W3+fsvTHDnc5hXTBaUFBQrv2tC1Ctx6W7+vi2FP96BYCIiIhynbcq8K3FRERuLNnq9/PWre3/UkxEtUek1c+VZ84CubkyfHz4HEFERERUrckyPLa9D0k23wypPVDY5zUFkyIie0xB3WFq3Abq9DhLTPvvfBjD7wH4dxwiIiJyofT0dPz22282H7cePerj44P27duXOto8MzMTS5YswejRox0+f0FBAVauXAlPT0889NBDpW7j6elpc/+MjIwyz2HdnbS4rKwsm4998803FSpeDQoKgp+fn2VcdXJyssP7Fs/V19fX4df0Nm7cWL4kK+Dvv//G3Xff7dC227Zts3wsSRLatm3rqrRsqqprWfxzrU6sO1zecsstNrft378/3n//fUtx9cGDB/Hkk0+WeQ5ZlnH48GHLWq1WO63oqV+/fvj7778t640bN2LMmDG2d/h/mzbdfFOdh4cHevTo4ZR8ipTnujqDM+/Lbt264csvv7Ss169fj549ezon0f+n0YglZmaz2caWIt7Ptl24cEF404a/v7/Tx4a76hzWBaPleYOFwWAQirx1Oh3q1KlTpce3xfr7vhLfA8vCzp5ERG4sJUV8R2MYR7gT0f8LaQUU/51KlksWiBMRERFR9aNJWAf1uQNCTN/5Wch+7jcyiIj+nyRB32WsEFJfSYH65D8KJUREREQ1XX5+PrZs2YInnnjC7qjyrl27logNHjzY5vaffvopFi1aZHcMrizLOHDgAN5//3306dMH06dPx/nz521uX6dOHZuj5mNiYrBjxw67HT79/PxsPrZ58+ZSO28uW7YMCxYssLlfWTp27Gj5uDyd5Yp3Us3KyirRVaw027dvx7Fjx8qXYAVs2rTJoeLauLg4oXtit27dHC6QcSZ3vpZKKygoKFHk3b17d5vbN2nSBB06dLCst23bhszMzDLPs3PnTuHebteuncMjx8vSt29focvwr7/+WmZx9p49e4SOe71793a4U58jyntdncGZ92WHDh2EcdO//fZbuYrVHWHdLbqoKL48+/F+Fn355ZfC98AePXo4vfmXq84RFRUl3McxMTG4dOmSQ/tu3boVeXl5lvVtt91W5ce3pfj3/aCgIIdGv1c1FnsSEbmx5OPiOjSU3SCI6AadTkKrVmIsMUmZXIiIiIjISQqvQffPJ0LI7Ncchk7PKJQQETnKFNIXJv8QIabdv1ChbIiIiKimuHDhAnr06CH869KlCzp06IAJEybYHX3s6elZase4QYMGCeN+izOZTJgxYwb69OmDadOm4dtvv8WPP/6Ir7/+Gh999BGeeuopdO7cGcOGDcPSpUvtdtYsotPp0LJly1IfMxqNeOaZZ3Drrbeie/fuwuep1+sBANHR0TaLUq5fv47BgwfjrbfewqJFizBv3jw8+uijeOeddxwaEW9Lr169LB+fPn3a4bHXxccqGwwGrFq1yu72586dw5tvvlmxJMspLy8Pb7/9tt1OfHl5eXjrrbeEa/f4449XRXoluPO1dBa9Xl9iNHdZzGYz3n77baEI89Zbb0Ur6xeMrEycONHycWFhId577z272+fm5uKDDz4QYiNGjLC5fd++fREeHm75l5aWZvf4AQEBePTRRy3rM2fO4JtvvrG5fWFhId5//33LWpIk/Pe//y1126q8rpXlzPtSo9Fg7Nibb0I0Go2YMGGCw89fRfbv3295/rVWvJgUcLwYvrbczydOnCjXPitXrsQvv/xiWUuSZLfrblWcA3D8fvb29kaXLl0sa+v71JasrCzMnDmzxDmr+vilkWUZ+/bts6zvuOMOh/araiz2JCJyUzk5Mqx/9mJnTyIqLiJcXCcmVfyPV0RERESkPN3eL6HKuyzECu98DdB4KJQRETlMUsFw+9NCSJMWA9VFxztBEREREVkzm824fPmy8C87O9uh0bnjxo1DkyZNSsTVajU+/vhjux3xMjIysGLFCnzyySeYPn06Zs2ahcWLF2PPnj12O4naUlaxRGFhIa5cuSJ8nkWFTf7+/na76+Xl5WH58uWYMWMGZs+ejSNHjgAAVCoV6tevX+5cgRtFIcW7ke7evduh/Xr37i10IZs5cyY2b95c6ra7du3CE088gfT0dKd2J7RFp9Nh27ZteOGFF0ot0j137hyeffZZoZNe586dMWDAAJfnVhp3vpbOUlBQgAcffBDPP/88/vrrL5sFdkWOHDmCJ598EmvWrLHEVCoVpk2bVua5unXrhj59+ljWGzZswKuvviqM1y5y5swZjBo1Suii2bZtW/znP/8p+5Mqh7FjxwodH+fMmYNFixaVeH7LzMzEM888g+PHb3ZJuueeexAVFVXqcavyus6ZM0coipszZ06Z+xTn7Pvy4YcfFkbbnz59GkOGDMG6devsdmzOycnBihUrMGzYMAwfPhwFBQWlbte6dWuho+hXX32F7du329y+SG25n++77z689NJL2LlzJ4xGo81tL126hHfffRevv/66EH/wwQftjgyvinOU13PPPSesN27ciOeff95mB86jR49i2LBhOHfunCUWEBCAoUOHKnJ8a3FxccjOzras+/fv79B+VU1T9iZERKSEFKuunjot0CJImVyIyD1FhEtY99vNAs8kdvYkIiIiqrakzJPQHvpBiBlb9YGp1Z0KZURE5WWMuA/mXZ9DlXvzRQft/oUovG+WglkRERFRbfTEE09g3LhxNh+Pjo7G7Nmz8fzzzwtjTl2Zz/Llyyt8rpdffhn79u0rs2iruPHjxyMmJgYxMTHlPl/jxo3RtWtXS5Hn5s2bMXjw4DL3a9iwIYYNG4bvv/8ewI3CnIkTJyIqKgodO3ZEnTp1kJWVhX379iElJQXAjeLbqVOn4q233ip3nuUxdepUTJ8+HZs2bcI///yDXr16ITg4GCaTCSkpKdizZ48wRrt+/fr44IMPnD5O2FHufC2dSZZlbNq0CZs2bYKXlxciIiLQunVr+Pn5wcvLC7m5ubh48SJiY2Nx9uxZYV9JkvD+++/j1ltvdehcM2fOxNChQy1FnGvWrMHWrVvRq1cvBAUFwWAwIDk5GXv37hW+Fho0aIDZs2cLBdDO0LhxY3z22Wf473//C7PZDFmWMWPGDPz888/o2rUr6tWrh9OnT+Ovv/4Siglbt26N6dOn2z12VV7XynD2falWqzFr1iyMGDECSf//ouHly5cxZcoUfPjhh+jcuTMCAwPh7e2N3NxcXLlyBYmJiUhNTbVbDFpEo9FgyJAhlvsyIyMDY8aMAXCjm3Txr5Fvv/0WHTt2BFB77meTyYTff/8dv//+O3x9fREZGYlWrVrBz88PWq0WV69eRWJiIg4fPiz8vwJAx44dy/y6rqpzlEeHDh0wbtw4fP3115bYpk2b8Ndff6Fz584ICwuDt7c3srOzcfjwYcTFxQn7a7VafPLJJzYLe119fGtbtmyxfNy4cWOhs6g7YbEnEZGbsi72bNUK0Gg4xp2IbrLu7Jl2Drh6TUbdOnyuICIiIqpuPP7+CJL55jvyZbUWhb2nKpgREZWbRgdD+xHw2PnZzVDKZuizz0Ku11zBxIiIiKi2aNWqFSZPnox+/fqVuW2vXr2wZs0avPLKKzh8+HCFzxkcHFxml7BmzZrh888/x+TJkyvUGTQ6Ohr/+9//8OqrrzpU8Pnkk09i4sSJdsdOl+Xhhx+2FHvu2rUL165dEzra2TJ58mQkJydjz549llh8fDzi4+NLbKvVavH+++9bCqJcafjw4Th16hSWLFmC/Px8m131gBvFfYsWLUJQkLJdaNz1WrpKfn4+Dh06hEOHDpW5bePGjfHuu+/izjsdf4Oon58fFixYgOeff97SKfLatWvYsGGDzX1atWqF+fPno2nTpg6fpzz69OmDjz76CO+88w7y8/MBAKdOncKpU6dK3T4yMhJz586Fr6+vw+dw9XWtDFfcl35+fvj555/x5ptvYv369ZZ4VlYWNm3aVGZOGo3GbmHvCy+8gLi4OBw4cECIW3f3tC4erW338/Xr17Fv3z5hJLgtjz/+OKZOnQqdTud253DEiy++CK1Wiy+//NLy/67X67Fz507s3LnT5n4NGjTAjBkz0K1bN0WPX1zxe2Tw4MFOL3J3FvfMioiIkJIijmMO5Qh3IrLSquWNrr/FsbsnERERUfWjPrEdmlM7hJihw1OQ67dQKCMiqihDu6GQdTdHEUqyGdoDi5VLiIiIiGokDw8P1K9fHy1btkT//v0xYcIErFixAn/88YdDhZ5FWrRogeXLl2PRokXo16+fQ52vtFotOnTogPHjx2PFihXYtGmTMB7alt69e2Pjxo2YPHkyevTogUaNGsHT09PhXO+55x4sW7YMnTt3trlNZGQkPv/8c4fGL5dl4MCBaNKkCYAbY+bXrVvn0H46nQ4LFizAxIkTUbdu3VK30Wq16Nu3L3799Vc8+OCDlc7VUdOmTcOsWbPQqlWrUh/X6XQYMmQIfv/9d0RERFRZXra487V0Bh8fH/zvf//Dfffdh8aNGzu0T1RUFKZNm4YNGzZUqCCxWbNmWL58OV566SUEBgba3K5Ro0aYMmUKVq9e7fKi3wcffBCrVq1C//79hVHfxTVs2BDPPfccfvnlFzRr1szu8aryulqPko6OjnZ43yKuuC+9vb3x6aefYvny5ejfv3+Zz7VarRadO3fG1KlTsX37drvFtN7e3li6dClmzZqF//znPwgODoaPj0+ZRXE1/X729PTEuHHj0L59e4cKKr29vfHAAw9gxYoVeOeddxz6flgV56ioop9DHnjgAXh4eNjd1t/fH2PHjsX69evRu3dvtzg+AOzbt89SaK7VajFs2DCH961qkizLctmbOS4rK8uZh3M6SZJQr149AEB2djac/OkTkYN4L5btiafMKP6mpZdflDB4ELv1kfPxfqzexvzXjPiEm+uxz0oYMZzPFdUV70ci98B7kch91Ir70aSH95JBUGWdsoTMPg2RN+oPoFjBGJGSasW96ES67TOhO7DIspY1nsh9dhvgVV/BrKim4P1I5B7q1+dzOtVMJpMJycnJOHHiBLKzs3Ht2jWoVCr4+PjA398frVq1QnBwsEs6g5XH2bNnceDAAVy6dAlqtRqNGzdGaGgowsLCnHqe7777DjNnzgQAhIWF4bfffivX/gUFBTh48CBSU1ORm5uLevXqoVGjRujQoYPl+7mrzJkzB3PnzrWsk6w6RcTFxSE1NRUZGRnQ6XRo2rQpunXrVq6OiVVJyWtZVTIyMpCamoq0tDTk5OSgsLAQ3t7e8PX1RbNmzRAdHW2zSK4iZFlGbGwsTp48iUuXLkGSJDRo0ACRkZGKFftmZWXh4MGDuHjxInJzcxEQEIDmzZujQ4cOUKvVFTqmK69r//79LWPg27dvj59//rlCxyniqvtSr9fjyJEjOHv2LLKzsy3XoEGDBggODkZoaKhLCwGt1fT72Wg04uTJkzhz5ozla9loNKJOnTqoW7cuQkNDER4eXuGv6ao6R0Xp9XrEx8cjNTUVV69eRUFBAXx8fFC/fn1ERUWhVatWkKSKv5btquNPnjzZ8n3+gQcewMcff1zhHF2NxZ78QwiRIngv2ldQIOOue2SYzTdjX8+T0CaaBVzkfLwfq7fPPjdj1Zqb6z53AO9PZ/P26or3I5F74L1I5D5qw/2o3b8IHv/MFGIFd38EY9SDyiREVIracC86k3TtAry/uwuS2WiJFXabAEO35xTMimoK3o9E7oHFnkS1Q35+Pvr374/Lly8DABYsWIBevXopnJVjyir2JKLKOXv2LPr3729ZL1261G7nYSJyXxcuXMCAAQNgMBigVquxYcMGBAcHK52WTawEICJyQ6knIBR6ShIQUnrndiKq5SLCxSLwBP69hoiIiKjakHIvQ7f3SyFmatIOxsgHFMqIiJxBrnMLjOH3CDHd4R8BQ75CGRERERFRRXh5eWHs2LGW9TfffKNgNkTkTnbv3m35uFevXiz0JKrGFi5cCIPBAAAYPHiwWxd6Aiz2JCJySykp4jqoOeDlxa6eRFSS9SSN9HQgM5NdPYiIiIiqA92uzyHprwuxwjtfByT+yY6oujN0fFpYS/lZ0MSvUSYZIiIiIqqwxx9/HCEhIQCAmJgY7NmzR+GMiMgdFBV7SpKEl156SeFsiKiiLly4gOXLlwMAfHx88MILLyibkAP4l2MiIjeUfFws1AoNVSgRInJ7LYIAL08xlpCoTC5ERERE5DhV+jFo4lYJMUPUIJhvuVWhjIjImcwNw2EMFkd86vYvBswmZRIiIiIiogrRarV44403LOuPP/4YssyGC0S1mSzL+PfffwEA99xzD6KiohTOiIgqavbs2SgsLAQATJgwAQ0bNlQ4o7JplE6AiIhKsu7sGRbKrp5EVDq1WkJ4uIzDR27GEpNk9OjO5w0iIiIityXL8PjrQ0i4+QKhrPWGvic7QRDVJIaOT0Nzaodlrco5A/XxrTCF3aVgVkRERERUXt27d0dSUpLSaRCRm5AkCXv37lU6DSJygo8++ggfffSR0mmUCzt7EhG5GaNRRuoJMRbaWplciKh6iLQa5R6foEweREREROQYTdIGqM8fFGL6LuMg+zZSKCMicgVT8y4wNRI7vOj2fwewExQREREREREREVUAiz2JiNzMmbOAXi/GwjjGnYjsiIwUu3gmJoJjZIiIiIjclSEPun8+FkJmvyAYOoxUKCEichlJgqHj00JIffEoVOcOKJQQERERERERERFVZyz2JCJyM9Yj3Bs1Avz8OI6ZiGyLDBfXOVeB8xeUyYWIiIiI7NPFLIDqeroQK+z9CqDRKZQREbmSMWwgzHUDhZju4BKFsiEiIiKi2mLixIlISkqy/CMiIqKagcWeRERuJvm42I2PXT2JqCxNmgD16omxxERFUiEiIiIiO6Scc9DuXyjEjEHdYArpq1BGRORyKg0MHUYIIXXqVkg5aQolRERERERERERE1RWLPYmI3Ix1Z8/Q1srkQUTVhyRJiIwQY/GJHONORERE5G48/vkYkqnQspYlNfR9XgMkTnMgqskM0Q9B1vlY1pJshvbQUgUzIiIiIiIiIiKi6ojFnkREbkSWZSSXKPbki35EVLYIq1Hu7OxJRERE5F5UafugSdkkxAy3Pg5zAMc5ENV4Hr4wtHlYCGnjVgKF1xVKiIiIiIiIiIiIqiMWexIRuZGLF4HrVn/n5xh3InJEVKRYGJ6UDBiN7O5JRERE5BZkMzy2/08MefpB3+05hRIioqpmaP8EZOnmn+MlfS60cSsUzIiIiIiIiIiIiKobFnsSEbmR5OPiuk4doHFjZXIhouolwmqMe0EBcPqMMrkQERERkUiTsA7q9GNCTN99IuBVT5mEiKjKyX7NYArpJ8S0h34AzCaFMiIiIiIiIiIiouqGxZ5ERG7k+HGxC19YKCBJHONORGWrX0/CLU3EWAJHuRMREREpz5AH3c7PhZC5QSsY2j6qTD5EpBj97SOFterqOaiPb1UoGyIiIiIiIiIiqm5Y7ElE5EaSU8R1aGtl8iCi6sm6u2dCAse4ExERESlNu38RVNfThVjhHa8Aaq1CGRGRUsxNO8DUuI0Q0x1aolA2RERERERERERU3bDYk4jIjaRYFXuGhbKrJxE5LjJCfM5ISFIoESIiIiICAEjX0qHb950QM7boDlPLOxTKiIgUJUkwdBC7e6rPHYDqYpxCCRERERERERERUXXCYk8iIjeRnS0j45IYCw1VJhciqp4irTp7pqYChYXs7klERESkFN2uzyEZ8y1rWVJBf8ergMQ39hHVVsawgTD7NhZi2oPfK5QNERERERERERFVJyz2JCJyEynHxbWHBxDUXJlciKh6Cg8DVMV+ujOZSj63EBEREVHVUKUfgzZ+jRAztnkY5oZhyiRERO5BrYXhtuFCSJO8EdK1dIUSIiIiIiIiIiKi6oLFnkREbiIpWVy3agWo1ez2QkSO8/aW0CJIjCUkKpMLERERUa0my/DY/j8xpPOBvvtEhRIiIndiaPsIZI2XZS2ZjdAe/lHBjIiIiIiIiIiIqDpgsScRkZtIThFHLYez2QsRVUBkpLhOTOQYdyIiIqKqpj7+J9Rp+4SYvvNYyD4BCmVERG7Fqx6M0YOEkPboL4AhT6GEiIiIiIiIiIioOmCxJxGRm0i26uwZHsqunkRUfpER4nNHPDt7EhEREVUtkx4e/3wihMx1m8LQ4UmFEiIid6RvLz4nSIU50CT8plA2RERERERERERUHbDYk4jIDVy/LiPtnBgLY2dPIqqAyHBxffYscO0au3sSERERVRXt4Z+gyjkjxPS9XgY0HgplRETuSG7QEsaWvYWY9tAPgMzf34iIiIiIiIiIqHQs9iQicgMpx8W1RgO0DFYkFSKq5kJCAK1WjCUmKZMLERERUa2TnwXd3i+FkOmW22AM+49CCRGROzO0f0JYq68ch/psjELZEBERERERERGRu2OxJxGRG0hOEdetWgI6Hce4E1H5abUSWrcWYyz2JCIiIqoauj3zIBVeE2KFvV8FJP5+R0QlmVp0h7l+sBDTHv5RmWSIiIiIiIiIiMjtsdiTiMgNJCeLI7rCQhVKhIhqhKgIcZ2QyDGARERERK4mZZ6A9sjPQswQfi/MTW9TJiEicn+SCobbhgshdepWSFfPK5QQERERERERERG5MxZ7EhG5gSSrzp5hYez6QkQVFxEhPockJCiUCBEREVEt4rF9JiTZZFnLag/oe72oYEZEVB0Yoh6ErPW2rCXZXKJwnIiIiIiIiIiICGCxJxGR4vLzZZw5I8bY2ZOIKsO6s+ely8Dly+zuSUREROQq6tO7oDm5XYgZbn8Kct1AhTIiomrDwxeG6MFCSBv7K2AoUCghIiIiIiIiIiJyVyz2JCJSWOoJwGy+uVapgNYhyuVDRNVf8+aAj48YS0hUJhciIiKiGs9sgm77TDHkHQB952cVSoiIqhvDbcOEtVSQDU3SBoWyISIiIiIiIiIid8ViTyIihSUli+sWQYCnJ8e4E1HFqVQSIsLFWEIiO3sSERERuYImbiXUl8Vf7PQ9ngd0Pjb2ICISyQ1awdiiuxDTHv4BkPl7HBERERERERER3cRiTyIihSWniH+4DwtTKBEiqlFKFnsqkwcRERFRjabPhW73HCFkCgiDMXqIQgkRUXVluO0JYa3OSIDq/CGFsiEiIiIiIiIiInfEYk8iIoUlW3X2DA9lV08iqryoSPG5JCERkNkVhoiIiMipdPu+gyrvshDT934VUKkVyoiIqitTyztg9msmxLSHf1AoGyIiIiIiIiIickcapRMgIqrN9HoZJ06KMXb2JCJniIgQ19evA2nngObNSt+eiIiIiMpHup4B7YHFQszYsjdMVqOYiYgcolLDcOswePwz0xLSpGyB/noGZN9GCiZGRERERLt378aoUaMAANHR0Vi5ciUkqeqbt6xatQqvvfaaZb1161Y0a6bcH/3nzJmDuXPnWtZJSUmK5VJTZGdnIzk5GadPn0Z2djZkWYafnx+aNm2K2267DXXq1FE6RZcwGAxITk5GUlIScnJyUFhYCF9fXzRs2BBt2rRBYGCgWx/fEbxfarcLFy4gNjYW58+fR15eHjw8PBAQEICWLVsiIiICOp1O6RQrxWw2IzY2FqmpqcjMzIRGo0GjRo0QFhaG1q1bl+tYU6dOxerVqy0fF33/pZtY7ElEpKATJwGTSYyFlu97HRFRqRo1BBrUBzKzbsYSElnsSUREROQsut2zIRnzLWtZUqHwjskKZkRE1Z2hzZD/f24pAABIZiO0R5dD332iwpkRERGRK1kX8NkjSRI8PT3h5eWFunXrokWLFmjZsiW6d++Orl27wsPDw8XZ1j4GgwHvvfeeZT158mRFCj2pZjKbzdi/fz+2bNmCvXv3Itl6JGQxkiShW7dueOqpp9C7d+8KnS83Nxfx8fE4evQojh49itjYWJw7d87yeGBgILZt21ahY1dEeno6vv32W6xduxZXr161uV1oaCiGDRuGoUOHQq12fJqKq49PZI/ZbMa6deuwZMkSHDt2zOZ2Wq0W7du3x5gxY9CrV69KnXPEiBGIiYmp1DEAYMKECZg4sey/ReTk5GDhwoVYtmwZcnJySt0mPDwcI0eOxEMPPeTQuZ9//nn8/vvv0Ov1mDt3Lu6//34EBASUK/+ajsWeREQKSk4R182aAT4+/AWRiCpPkiRERsrYtftmLCFBxl39+RxDREREVFmqy8nQHFstxIxtHoLsz3fvEVElePrBGHk/tLG/WkKao79A32UsoK7eXT6IiIjIOWRZRn5+PvLz85GZmYlTp05h+/btWLx4MerUqYNRo0Zh9OjR8PT0VDpVi6tXr+L7778v9bHIyEj079+/ijMqn59++gknTpwAAHTu3Bndu3OaAznP3XffjdOnTzu0rSzL2L17N3bv3o17770X06dPh6+vr0P7Llq0CKtWrcLx48dhNpsrk7LT/Pnnn3j99ddtFogVl5KSgnfffRerV6/GvHnz0KhR2dMPXH382iYhIQF//vmnZT1y5EjUrVtXwYzcW1paGl5++WUcPny4zG0NBgNiYmJw6623VrrY01m8vb3L3Obo0aMYP348Ll26ZHe7pKQkvP7669iyZQs++eSTMp+3mjZtiqFDh2Lp0qW4fv06vvjiC+FNF8RiTyIiRSUny8I6PFShRIioRoqMkLBr983nmUROhCAiIiJyCt0/n0CSb744Imu9oe82QcGMiKimMNz2hFDsqcq7DE3yJhgj71cwKyIiIqoOrl27htmzZ2PHjh2YP38+/Pz8lE4JwI1iz+Kji4sbPHiwWxd75uXlYf78+Zb1mDFjFMyGaqLMzMwSseDgYLRr1w4BAQHw8PDAxYsXsWfPHly8eNGyze+//46MjAx89913DnX03bdvn92uoVVt586deOGFF2AwGCwxjUaDrl27IiwsDF5eXsjKysKhQ4eQkJBg2ebo0aMYOXIkfv31V7sFY64+fm2UkJAgPJcPHjyYxZ42pKSkYNSoUUIRpCRJaNeuHSIiIuDv74/CwkLLaPezZ8867dwqlapC3WlNxcbRSpKEu+66y+72R48exRNPPIHCwkJLzNvbGz169EDLli1hNpuRmpqK3bt3W7b566+/MGnSJMyfPx8ajf1yxVGjRmHZsmUwGo1YtWoVnn32WQQFBZX786qpWOxJRKSgJKvOnmFh7LhHRM4TGSGuk5IBo1GGRsPnGiIiIqKKUp/eDc2pHULMcPsoyL7s+kBElWduGAZTs05Qp+2zxLSHfmCxJxERETns0KFDmD59Oj799FOlU6n2fvzxR1y5cgUAEBYW5jYd16jmCQwMxCOPPILBgwejSZMmJR43mUz45Zdf8NFHH1kKp/bt24fPP/8cr776aoXO6e3tjejoaBw7dgx5eXmVyr88CgoK8OabbwqFmJ06dcLMmTPRtGnTEtvv2bMHU6ZMsRTOnThxArNnz8brr7+uyPGJ7MnMzMQzzzwjFHr2798fr732Gpo1a1bqPsnJyVi1ahXq1KlT6fPb6qRtz5YtWzBhws03sXfs2BHNmze3uf3169cxadIkodDzzjvvxAcffAB/f39h2wsXLuDVV1/Fv//+C+BGIfYXX3yBl19+2W5OgYGBuPvuu7F+/XoYjUZ8+eWXmDFjRrk/t5pKpXQCRES1ldEoI/W4GAtjZ08icqKIcHGt1wMnTyqTCxEREVGNIJuh++cTIWT2CYC+4yiFEiKimkh/2xPCWn3xKFQXYxXKhoiIiJSkUqkQEBAg/NPpdGXut379erfq4lcdGQwGLF261LJ+7LHHFMyGaqqmTZviww8/xJYtW/Df//631EJPAFCr1Xj88ccxZ84cqFQ3y3yWLl2K9PT0Ms/j4eGBdu3aYfjw4fjoo4+wfv16HDhwAD/88APq16/vtM/HEdu2bcP58+ct6+DgYHzzzTelFmICQLdu3bBgwQKhE+CKFSug1+sVOT6RPR988IHQhXfcuHGYN2+ezUJP4MabCaZOnYqxY8dWRYolrFq1SlgPHjzY7vY//PCDcI917doVc+fOLVHoCQC33HILvv32W0RHR1ti33//vXCNbBk6dKjl4/Xr1yMjI6PMfWoLFnsSESnk9BlAbxBjLPYkImfy85MQaPW7awJHuRMRERFVmCZhHdSXEoSYvttEQOejUEZEVBOZWveFuc4tQkx7+EeFsiEiIiIl3XLLLdi1a5fw7+jRo9iwYQPuvPNOu/tu3bq1irKsmTZu3GgpovPw8MD997PTOjnfqlWr8NBDDzk8drl379649957LWuDweDQvT5r1iz8+uuveOuttzBkyBCEhoYKRaNVac+ePcL6qaeegre3t919IiIi0K9fP8s6NzcXsbGlvyHO1ccnsmX37t1Yv369Zd2vXz+8+OKLCmZUtszMTOzYcXOCkbe3N+6++267+/z4482/T0iShHfeecfuWHYPDw+8/fbblnVhYSG++eabMnPr3LkzWrRoAeDGc92yZcvK3Ke2YLEnEZFCrN9Q2aTxjcIsIiJnirAa5Z6QICuTCBEREVF1ZyiAbtdsIWTyD4GxzRCFEiKiGkulgeFWsXOUJukPID9LoYSIiIjInUiShJCQEMyePRstW7a0uV1qamoVZlXzrFy50vJxjx49ULduXQWzoZrKXoGULcWLPQFUu6JE606kt912m0P7dejQQVjb6vLn6uMT2VK8gFGr1eK1115TMBvH/PbbbzAYbnYou+uuu+DjY/tN7SkpKcK90bFjR7s/ixS59dZbERYWZllv3LgRZrO5zP2KF56uWbMGsszXuQGg/N85iIjIKZJSxG9Exb63ERE5TVSkhK3bbj7fJCQqmAwRERFRNaY9tBSqaxeEmL7XZEDFP68RkfMZ2zwE3e65kMw3XnSRTHpoj62BoeMohTMjIiIid6HT6dC1a1ecPHmy1Mezssr/RhG9Xo+jR48iLS0NOTk5yM/PR506dVC/fn1ER0dbOmwpTZZlnD9/HmlpaUhPT0dWVhYKCwthMpng7e0NX19fBAYGIjw8vEIjqtPT0/Hvv/9a1nfddVeFc83MzERycjLOnDmDnJwcmM1m1K1bF40aNUL79u3RoEGDCh+7MuLj43H8+HGkp6fD09MTt9xyC7p27QpfX1+nnePUqVOIj4/HhQsXIEkSAgIC0LFjR5vjtMvirteyqgUFBQnry5cvK5RJxVgXeHl6ejq0n5eXl7CWpNKbKLn6+K7iqnsyMzMTBw8exKVLl5CTkwNfX18EBATg9ttvR8OGDZ2Ufflzqmn38tmzZ7F3717Lunfv3mjevLmCGTlm9erVwrqsEe7Hjh0T1u3bt3f4XO3bt0fy/3dEu3LlCg4ePIiOHTva3WfAgAGYP38+AOD8+fOIiYlBly5dHD5nTcW/RhMRKcS6s2dYKLt6EpHzRYSL65MngYICGZ6efM4hIiIiclheJnQx4nghY/MuMLXsrVBCRFTTyd7+MIbeBW3S75aY9sjPMNw+EpA4sIuIiIjKVq9ePYe2k2UZ27Ztww8//ID9+/dDr9fb3LZJkyYYPHgwRo4cabeIcs6cOZg7d26Z5169enWJQpMigwcPxowZMyzrY8eOYd26dTh8+DCSk5ORl5dX5vEBoHXr1hg0aBAeffRRh6/Jn3/+KRSMde/e3aH9isTGxmLDhg3YsWMHUlJS7G7bvn17PPvss8IIaWew/j9ISkoCcKOb2ty5c0vNS6fT4b777sOUKVMqVWx14MABfPLJJzh48GCpj3fp0gWvv/46IqxHk5XCHa6lu8nNzRXWFekOqqRmzZoJ6wsXLjjUGfDcuXPC2lbxuauP72yuuie3bt2K+fPn4+jRoza7Id52222YOHEievbsafM44eHhpcbt3WdLliwpUZBX0+/lDRs2CNfZugOvO0pKSkJCQoJlHRgYWGYhZXZ2trBu3Lixw+ez3nbfvn1lFnu2adMG9erVs5x3y5YtLPYEx7gTESnCbJZh/TNMODt7EpELhIUC6mI/8ZnMQLL936GIiIiIyIpu71eQ9NeFmP6OKUAVd3kgotrFcNvjwlqVcwbq03sUyoaIiIjcjV6vF7pPWgsODi7zGCdOnMDgwYMxfvx47N69226hJwBcvHgRX331Ffr27YvffvutvClXyubNm7F48WIcPnzY4UJPADh+/Dg+/fRT3Hffffj7778d2mfHjh2Wj4ODg8tVzHL16lU8/PDDWLhwYZkFTQBw6NAhjB8/Hq+//nqZ17+yPvzwQ0yaNMlmXnq9HqtWrcJ9992HxMSKjQlbuHAhRowYYbPQEwD+/fdfPP7449izx/7Ptu58LZVUVLhbpEmTJgplUjG9evUS1n/88UeZ+5hMJmzatMmyLurcq8TxnckV9+TVq1cxevRojB8/HkeOHLE79vrw4cMYPXo03n77bRiNxgp9Do7mVNPv5cOHDwvrsooY3YH1my0GDRpUZkfbgoICYa3T6Rw+n4eHh7A+fvx4mftIkoROnTpZ1v/884/D56vJqleJPxFRDZGWBuSL3wcRFqpMLkRUs3l5SQhuKSM19WYsIRFo11a5nIiIiIiqEynrFLRHfxZihoj7YW4crVBGRFRbmJt2gMk/FOorN18M0x5ZBlNwDwWzIiIiIqXJsoyTJ0/i008/xYkTJ0rdRqVS4cEHH7R7nO3bt+OFF14oV+Fkkby8PEyePBkpKSl46aWXyr2/Ei5duoQJEyZgwYIF6Nq1q91tixcqtmnTpsLnVKlUaN26NUJCQtC4cWP4+PjAZDLh8uXLiIuLE4q3Vq5cCW9vb7zxxhsVPp89P/74I77//nsAN8ZV9+zZE8HBwTCZTEhJScHevXthMBgA3Biv+9RTT+GXX34pMTLcntWrV+N///sfAMDX1xfdunVDUFAQtFotzpw5g+3bt1u6Uubl5eHll1/G77//brdLbBF3upZKW7dunbAu6+vZ3fTp0wfh4eGWotWVK1eib9++uPPOO0vdXpZlzJw5E6dOnbLEnnvuOahUpfe2c/XxncUV92RmZiaeeOIJpBZ7UVCr1aJDhw4ICwtD3bp1kZeXh8TEROzbt89S4Pnzzz+joKDAcv8Wp1arAdy4TsU7HhfFS2OvYLCm3stxcXGWjxs3boxGjRoBAE6fPo1Vq1Zh586duHDhAvLz81G/fn20bNkSPXr0wKBBg+Dv71/l+RqNxhJv2ihrhDsA1KlTR1hfvXrV4XNab2vrZxhrbdu2xZYtWwDcuJ7p6enlehNGTcRiTyIiBSRZvWHF3x/w92dHGCJyjchwWBV7ygD4nENERETkCI+dsyCZb3Y3kNU66HtOUjAjIqo1JAmGWx+Dett7lpD6xF+Qrl2EXKd6de8hIiLXkM0mID9L6TSqJ6/6kFS2C1XcxYULF9Cjh/hGj6tXr5bZ6WzEiBFo3ry5zcfj4+MrXOhZ3Pz58xEYGIihQ4dW6jhVxWAw4JVXXsGff/5psxvZmTNnkJOTY1mXt7ufJEno1q0bHnroIfTq1cvu6PikpCS8/fbbOHToEABg6dKluP/++3HrrbeW65yOmDFjBgBgwIABmD59eomR0GfPnsUrr7xiKXTNysrCtGnTsGTJkjI7vRV58803AQBPPvkkJk2aBF9fX+HxzMxMTJo0CTExMQBuFLAtWbIEkyaV/jt2VVzLAQMGlBjf7Uzjx4/HhAkTnHa8mJgYy/UDbhRe2RvB7Y7UajVmzZqFYcOGITs7GyaTCePHj8fQoUPx4IMPIiwsDF5eXsjKysLhw4exePFioYvxY489hoceekix4zuLs+9JWZYxZcoUS6GnJEl47LHHMHHixFKLCc+ePYtp06ZZPvc1a9agW7duJd4oEB8fDwBYtWoVXnvtNUt88+bNaNasmUOfa02/l69du4aMjAzLunnz5jAajfj666/x1VdfleiampeXh3PnzmHnzp2YM2cOnnnmGYwfP97h51pn2LFjBy5fvmxZd+zY0aHifusiS0e6cxZJTk4W1hcuXHBoP+vvw7GxsSz2VDoBIqLaKDlZbJcezq6eRORCkZES1m+4+byTkKBgMkRERETViOr8IWhSNgsxQ/snINcNVCgjIqptjJEPQN7xKSTDjUIMSTZDG/sr9N0nKpwZEREpzRy3Dqb1rwO5l8vemEryCYD6vg+havOA0pnYZTabhWKMskiShMcffxxTp061uY3JZMLkyZPtFnr27NkT0dHRaNCgAdLS0rBt2zabRTQffPAB+vTpIxReeHt7IyAgwHK+rKzSi5I9PDxKdAkrYl0oWEStViMkJAStW7dG06ZN4efnBy8vL6hUKuTl5SE9PR1HjhzB0aNHS90/PT0da9euxSOPPFLq49bFKC1atCh1O1vq1KmDxYsXO7RteHg4lixZgsceewzHjh0DAHz//ff47LPPynVOR+j1evTp0wdffPFFqR35mjdvjgULFmD48OFI+P8XEWJiYrBlyxbcddddDp3DYDBg4sSJNguiGjRogLlz52LgwIGWr4m1a9faLPasimtpMplgMpkcOkdF2BuhXV75+fmWgtoio0aNgo+Pj9POUVVCQkLw66+/Ytq0aYiJiYHZbMayZcuwbNkym/sEBATg+eefd6i43NXHdwZn35OrV6/Gzp07Letp06ZhxIgRNs/fvHlzfPfddxg1ahT27dsHAJgzZw7uv/9+u107K6Km38vZ2dnCOiAgAK+//jrWrl1b5nHz8vIwe/ZsJCYmYtasWdBoqqaMz3qEuyNdPQGgffv2wnrnzp0wGo1l5p2bmysUqgNw+A0nwcHBwjopKQn9+/d3aN+ayrV9h4mIqFTJVp09w8KUyYOIaofICHF97jyQk+O8PzAQERER1UiyDI/tM8WQpx/0nccqlBAR1UoevjBG3i+ENLG/AiaDQgkREZG7MK2dzELPysi9fOMa1jDjx4/HtGnT7I4fXrdunTDitzh/f3+sWLEC3333HV566SU89dRTeOONN/DHH39g2LBhpe5TWFiIr776SoiNHj0au3btwq5du7BixQqbudxzzz2W7az/WY/tbd++PWbPno19+/bht99+w6xZszBlyhSMGTMGI0aMwPDhw/Hss8/ijTfewK+//oqFCxfaLFjavn27zZzS0tKEdZMmru2ortPpMGbMGMt6+/btwqhkZ/Hy8sL06dPtFnH5+PjgvffeE2L2iuOsRUZGYvz48Xa38fPzE4qKzp07J3TEq4yqupZKeffdd4VR461atcIzzzyjXEKVFBQUhKVLl+Kjjz6Cn5+f3W2joqLw5ZdflqsQ09XHryxn3pOyLGPBggWW9R133GG30LOIVqvFu+++a+komZaWhh07djj6KbhMdbuXr127Jqx37dplKfT09PTEuHHjsG7dOhw5cgT79+/Hjz/+iEGDBgmdPDdv3uySQv/S5OTk4K+//rKsvby8cPfddzu0r7+/v9Bl9dKlS3a/zxdZsmQJcnNzhVhhYaFDBbrWXTxd2cG1umBnTyKiKibLMpLENwUiNJTjlInIdVq1BDw8gMLCm7H4RKBbF+VyIiIiInJ36pQtUF84LMT0Xf8LeNZVJiEiqrUMtz4G7dHllrUq9xLUqdtgChuoYFZERETkjubNm4fNmzfj448/RmRkZKnbrFy50ub+s2fPRtu2bUvEPTw88OabbyImJqbUka0bNmzAW2+9ZbfItLL69OlTru179OiBtm3b4vDhwyUeKxqLXBrrwkPr0cquEFasK8z169dx4sQJtG7d2qnnGDhwoENjb9u2bYsOHTpYrtGePXtw7do1mx1Yixs2bJhDXwOdO3fGwoULLevU1FQ0atSozP0cUd5ruW3bNqec19W+//57oROfTqfDxx9/DA8PDwWzqpzU1FR88MEH2LVrV5nbxsfH49FHH0Xv3r0xffp0h4qwXX38ynLmPRkbGysU8TtS6FkkJCQEYWFhSEpKAgD8+++/5X6+dYXqdC9bd6gsKv6sV68eFi9eLHw/9vT0RMeOHdGxY0f07NkTr776qqWQdeHChXjggQcQEWHVxcfJ1q9fD71eb1nfddddNrtpl+bpp58WOjLPnDkTYWFh6NChQ6nb79ixA/PmzSv1sYKCgjK7E3t5ecHHx8dSLJqenu5wrjUViz2JiKrYhYvA9etijGPciciVNBoJ4WEyjsbejMXHy+jWhYXmRERERKUy6eGx81MhZPZrDsOtjyuUEBHVZuaGETA1bQ/1+UOWmPbIzyz2JCKq5dSDPuEY98r4/zHuNVFKSgqGDx+On376qUTBSG5ubqnFjwAQGhqKjh072jyuSqVCr169Si32zMnJQVxcHNq1a1ep3B1hMplw8OBBbN++HSkpKTh16hRycnKQl5eHwuIdD+y4cuUKDAYDtFpticesi3Y8PT0rnGt2djY2btyI/fv3Izk5GZmZmbh+/bpQZFOaixcvOr3Y884773R42759+1oKy2RZRmxsLLp3717mfp06dXLo+IGBgcL66tWrZe7jTteyqm3cuBEzZswQYtOnT0ebNm0Uyqjydu3aheeeew75+fkAbnSYfOSRR3DvvfciLCwMXl5eyM7OxtGjR/Hzzz/jn3/+AXCjw+PgwYPxww8/ICQkRLHjO4Mz78ni47ElScLtt99erlyCg4MtxZ5FI+NdpSbeyzqdrtT49OnTbb7xAgAeeOABxMXF4fvvvwdw4/924cKFmDlzps19nGHNmjXC2tER7kUGDhyIPn364O+//wZw42eLkSNHYsSIERg0aBBatmwJWZaRmpqKVatWYdmyZTAajQAAb29vy/dZSZLg5eXl0Dk9PT0txZ6Ojn+vyVjsSURUxZKtunrWrQs48KYdIqJKiYqEWOzp2t/ViIiIiKo17dFfoMo+I8T0PV8E1KX/8ZaIyNUM7R4Tij01Z/dCyjwJuUFLBbMiIiIlqdo8ACnqXiA/S+lUqiev+pBUtkfnuovAwMAS3coMBgMyMjKwY8cOzJkzB5cvlyz4zc3NxZQpU7BmzRphRPDx48dhMBhKPVdKSgrCw8MrnOvJkyddXuy5du1afPHFF04Z4Xr16lX4+/uXiFsXHJVWEFqW/Px8zJs3D4sXL7Z5vcvKzdnK0ynOujjp+PHjDhV7OtKlEECJ4h57hTvueC2r0p49ezBlyhRhhPXLL79c7uIsd3LmzBlMmDDBUohZt25dLFiwQBgNDQANGzZEv3790K9fPyxbtgzvvPMOACAzMxP//e9/sXbt2lILxVx9fGdx5j1ZvEBTlmWHC6+LFP/6ys7OLte+jqrJ93JpnSlDQkIwcGDZb9AcM2YMfvrpJ8s1+fvvv2E2m13WKTs1NRVHjx61rAMDA9G1a9dyHUOSJPzvf//D008/jWPHjgG48b3zu+++w3fffWdzv+effx6///67pQutr6+vw59n8YJaR9/YUZOx2JOIqIolpcjCOiz0xjdEIiJXioqSANx8/klIvPELH59/iIiIiKwUXoNujzhayNSkHYxhdyuUEBERYAwbCPnvjyAVZFti2qM/Q9/nNeWSIiIixUkqNeAToHQaVMW0Wi0CAwPx2GOPoXv37hg0aFCpxXLJycn466+/0L9/f0ssMzPTZXllZbm28Pi9997DDz/84LTjFRQUlBq37tBW3qKkvLw8PPvss9i/f3+Fc3NFIUtAgOPPFdbbOlpk5e3t7dB21q9LyLJc6nbuei2rytGjRzF+/HihAHn06NEYM2aMgllV3ieffCI8Z02fPr1EIaa1xx9/HElJSVi2bBkA4PTp0/jpp58wevToKj++szjznrR+/jWZTBXOq2gEuTPV9Hu5tGJPRzu3BgQEoF27djhw4ACAG52yjx8/Loyxd6bVq1cL60GDBlXoteJ69erhxx9/xPvvv4/Vq1fb/Zrz8vLClClTMHz4cCxevNgSr1u3rsPnK/486OHhUe58axoWexIRVTHrzp4u+j5NRCSIspoScPUqkHYOaN5MmXyIiIiI3JUu5luhmAoACnu/AvBNMkSkJI0HDG2GQLd/oSWkPbYG+h4vAFrXdZshIiIi9xYUFIT7778fy5cvL/XxrVu3CsWerijiKXL9+nWXHXvNmjVOLfQEbBcYWhcs2ioKtWXmzJlCQZOPjw8eeOABdOvWDS1btkTjxo3h6ekpFKukpaWhX79+ZeZWGeXpUGi9bdHo3KrmrteyKiQnJ+PZZ58VihYfeeQRvPLKKwpmVXnXrl3Dn3/+aVkHBQXh7rsde3PtmDFjLMWYwI1Ov9bFmK4+vjM58550ZtdLV9wzNf1e9vf3h1arFd4cEBoa6vD+YWFhlmJPAEhPT3dJsafZbMa6deuEWGW6BHt5eeGDDz7A6NGjsXbtWuzduxdpaWnIyclBnTp1EBgYiDvvvBNDhgzBLbfcgpycHOFrtXXr1g6fq6hTL+D4GwtqMhZ7EhFVIVmWkWRV7BkeyhcMicj1GjcC/BsAV4q9cTs+nsWeRERERMVJ1y5Ae3CJEDOG9IM58HaFMiIiusnQbii0+xdB+v+pDVLhVWiSNsDY5iGFMyMiIiIl2Ru9npKSIqzL00WrvFxZiDNv3jybj7Vt2xbjxo3Dbbfdhvr16wtj60eMGIGYmJhynatRo0bC+sqVK2jevLlD+2ZkZOCXX36xrENCQrBo0aIyx5vbG2PuLPn5+fD19XV42+JK61rnalV1LQcMGIBz585VKEdHjB8/HhMmTCjXPmfOnMHTTz8tjNO+++67MX36dCdnV/ViY2OFDoCdOnVyuKtg06ZN0axZM6SlpQG48fxWWFgoFAi6+vjO5Mx7sngxaOPGjfHPP/9UPkEnqQ33slarRVBQkGU8OQD4+fk5fGzrbXNyciqWZBl27dqF9PR0y/r2229HUFBQpY/bqlUrvPjii2VuFxsbK6zbtm3r0PHz8/OFrwfr79O1EYs9iYiq0OXLQLGfywGwsycRVQ1JkhAVKWPHrpux+AQZA+9iwTkRERFREd3uOZBMN8dCyZIahb1eVjAjIqKb5HpBMAX3hObUDktMe+RnFnsSERHVciqVyuZj1p08GzRoYHPbgQMHYvbs2U7Ly1lSU1Nx5syZUh9r2bIlfvrppxKj14tUpNtds2Zih4TihTFl2b59u1Bo9s4775RZ0ATcKCh1tStXrjhcWHb58mVh7coiYVuq6lqaTKZKjbwuS3mLoNPT0/HUU0/h0qVLlljv3r3xySef2L3Xqwvr/5+GDRuWa/+GDRtaijHNZjOys7OFrwtXH9+ZnHlP1q9f3/JxRkaGS4tUy6u23MutW7cWij2Ljx0vi/W2rvq/W7NmjbCuTFfPijh48KCwbteunUP7WX8fDgwMdFpO1VX1/25ARFSNJIlvoIS3NxDYVJlciKj2iYoSCzvjExRKhIiIiMgNqS4lQ3NsjRAztnsEcoOWyiRERFQKw62PCWt1ehxUF+MUyoaIiIjcgXX3zuKsu7+FhIRAq9WWuu2+ffuEEbTOYq+rniOFcGfPnrX5WN++fe0WehYvvHGU9ejcU6dOObzvyZMnLR97e3ujU6dODu1n3e3MFRISHH9BIDExUViXZ9Sus7jztXSVzMxMPPXUU0J3ws6dO2P27Nk279vqxrqIraCgoFz7W3e4tB7n7OrjO5Mz78mQkBDLx7IsY9++fZVLzolqy73cuXNnYV2eNwpcvHhRWBcv3nWW69ev488//7Ssvby88J///Mfp57Hn999/t3zs7++P7t27O7Rf8a8hAIiIiHBqXtURO3sSEVWhZKsR7mGhgErFrnpEVDWiIsV1ynGgsFCGhwefh4iIiIh0u2ZZRiMDgKz1hr7rcwpmRERUkqllb5jr3ALVtQuWmPbIMhQ2+UDBrIiIiEgp6enp+O2332w+bj3q1MfHB+3bty91tHlmZiaWLFmC0aNHO3z+goICrFy5Ep6ennjoodK7jXt6etrcPyMjo8xzWHcnLS4rK8vmY998802FileDgoLg5+dnGaObbP3inh3Fc/X19XV4fPTGjRvLl2QF/P3337j77rsd2nbbtm2WjyVJcnjUrjNV1bUs/rkq6fr163jmmWdw4sQJS6xdu3b46quv7N5D1Y11d+HyFGQbDAahy69Op0OdOnWq9PjO5Mx7slu3bvjyyy8t6/Xr16Nnz57OSfT/aTRieZnZbHZov9pyL/fv3x/vv/++5U0MBw8exJNPPlnmfrIs4/Dhw5a1Wq12STHjhg0bhOLnAQMGONxZ1hl27twpvHli0KBBDhexW38fVuJ7krthZ08ioiqUnCK+QzEsVKFEiKhWiggHiv8OZTTeKPgkIiIiqu1UafugOfG3EDPcPgqyT4AyCRER2aJSw9D2ESGkSdoAFJR/RCkRERFVX/n5+diyZQueeOIJu6PKu3btWiJmb2zrp59+ikWLFtkdhSvLMg4cOID3338fffr0wfTp03H+/Hmb29epU8fm+OmYmBjs2LHDbodPPz8/m49t3ry51M6by5Ytw4IFC2zuV5aOHTtaPi5Pd7ninVSzsrJKdAkszfbt23Hs2LHyJVgBmzZtcqi4Ni4uThi1261bN5cWvNniztfS2QoKCjBu3Dgh97CwMCxYsKBKi7GqQlRUlFDgFRMTI4yst2fr1q3Iy8uzrG+77bYqP74zOfOe7NChgzDa+rfffitXobojrDtFFxXEl2e/mnwvN2nSBB06dLCst23bhszMzDL327lzp/A9tF27di6571evXi2shwwZ4vRz2KLX6/HRRx9Z1l5eXnjiiScc3r/49+GgoCA0btzYqflVRyz2JCKqQiU6e4axmx4RVR0fHwnBLcQYR7kTERFRrSfL8PjnEyFk9vaHvuMohRIiIrLP2PZhyKqbXVUkYwG0ibY7ehEREVH1deHCBfTo0UP416VLF3To0AETJkwQutBZ8/T0LLVr3KBBg4SRv8WZTCbMmDEDffr0wbRp0/Dtt9/ixx9/xNdff42PPvoITz31FDp37oxhw4Zh6dKldjtrFtHpdGjZsmWpjxmNRjzzzDO49dZb0b17d+Hz1Ov1AIDo6GibneCuX7+OwYMH46233sKiRYswb948PProo3jnnXccGhFvS69evSwfnz592uFxvMVHKxsMBqxatcru9ufOncObb75ZsSTLKS8vD2+//bbdbnx5eXl46623hGv3+OOPV0V6JbjztXQmo9GISZMmCWO3g4ODsWjRIruFzu6mb9++CA8Pt/xLS0srdTtvb2906dLFsi4sLMT7779f5vGzsrIwc+bMEues6uM7kzPvSY1Gg7Fjx1rWRqMREyZMKNcocQDYv3+/5bnXWvFiUsDxQvjaci8DwMSJEy0fFxYW4r333rO7fW5uLj74QJzSMWLECLv7OHqvFXfmzBmhYLhp06alvhnEFUwmEyZPnozjx292H5o4cWKJrydbZFkWnh/vuOMOp+dYHbHYk4ioimRlyciweuMQO3sSUVWLihLX8QkV/4MXERERUU2gPr4F6otHhZih638BnY+NPYiIlCX7NIQpRHzhUXP0F6ASBQ1ERETknsxmMy5fviz8y87Odmh87rhx49CkSZMScbVajY8//hje3t42983IyMCKFSvwySefYPr06Zg1axYWL16MPXv22O0kaktZxRmFhYW4cuWK8HkWFTf5+/uje/fuNvfNy8vD8uXLMWPGDMyePRtHjhwBAKhUKtSvX7/cuQI3immKdyPdvXu3Q/v17t1b6Co4c+ZMbN68udRtd+3ahSeeeALp6el2/y+cRafTYdu2bXjhhRdKLdI9d+4cnn32WaGbXufOnTFgwACX51Yad76WziLLMqZOnYq///7bEgsMDMTixYsREFBzJ40899xzwnrjxo14/vnnbXbgPHr0KIYNG4Zz585ZYgEBARg6dKgixy8yZ84coehuzpw5dre35ux78uGHH0aPHj0s69OnT2PIkCFYt26d3W7NOTk5WLFiBYYNG4bhw4cLo76La926tdBR9KuvvsL27dttbl+kNtzLRbp164Y+ffpY1hs2bMCrr74qjLIvcubMGYwaNQonT560xNq2bYv//Oc/Ts/LusB20KBBNt9E4ajp06dj3rx5dotN4+PjMXz4cGzatMkSa9++PUaOHOnweeLi4pCdnW1Z9+/fv0L51jSasjchIiJnSE4R1x4eQFBzZXIhotorKlLC7xtuvgDIzp5ERERUq5kM8Ng5SwiZ/YJKjEgmInI3hraPQpNy8wUy9eVkqC4ehfmWWxXMioiIiNzFE088gXHjxtl8PDo6GrNnz8bzzz8vjC12ZT7Lly+v8Llefvll7Nu3z2bHudKMHz8eMTExiImJKff5GjdujK5du1qKPDdv3ozBgweXuV/Dhg0xbNgwfP/99wBujOeeOHEioqKi0LFjR9SpUwdZWVnYt28fUlJuvHCoVqsxdepUvPXWW+XOszymTp2K6dOnY9OmTfjnn3/Qq1cvBAcHw2QyISUlBXv27IHBYLBsX79+fXzwwQeVLgiqKHe+ls5y/vx5/PbbbyVi/fr1K9dxAgMDsWXLFrvbnDt3zmaRYPFCwHPnziHKumvI/1u8eDE6d+5crtxK06FDB4wbNw5ff/21JbZp0yb89ddf6Ny5M8LCwuDt7Y3s7GwcPnwYcXFxwv5arRaffPKJzWJAVx/fWZx9T6rVasyaNQsjRoxAUlISAODy5cuYMmUKPvzwQ3Tu3BmBgYHw9vZGbm4urly5gsTERKSmptotBi2i0WgwZMgQyz2ZkZGBMWPGALjRSbp4gfy3336Ljh07Aqgd93JxM2fOxNChQy1FnGvWrMHWrVvRq1cvBAUFwWAwIDk5GXv37hX+fxs0aIDZs2cL19EZZFnGunXrhJgj38/KcvnyZfz444+YPXs2QkNDER0djUaNGkGlUuHy5cs4cuSI5f+zSHh4OL788ktoNI6XKhZ/bmvcuLHQubc2Y7EnEVEVsS72bB0CaDQc405EVSsqUlyfPw9kZcuoX4/PR0RERFT7aI6tgirrlBDT93wBUOsUyYeIyFGmFt1grhsI1dWb3We0R39BIYs9iYiIarVWrVph8uTJDhWL9erVC2vWrMErr7yCw4cPV/icwcHBaNu2rd1tmjVrhs8//xyTJ0+uUGfQ6Oho/O9//8Orr77qUMHnk08+iYkTJ5Y5Dteehx9+2FLsuWvXLly7dk3oamfL5MmTkZycjD179lhi8fHxiI+PL7GtVqvF+++/bymKcqXhw4fj1KlTWLJkCfLz82121gNuFB0tWrQIQUFBLs/LHne9ls4il9KZX5ZlhwrvinNk+/Ic19Z2peVbUS+++CK0Wi2+/PJLy/n0ej127tyJnTt32tyvQYMGmDFjBrp166bo8Z3BFfekn58ffv75Z7z55ptYv369JZ6VlSV0V7RFo9HYLTZ84YUXEBcXhwMHDghx6+6e1l9DNf1eLs7Pzw8LFizA888/b+nKeu3aNWzYsMHmPq1atcL8+fPRtGlTp+fz77//Cl1rb7/9drRo0cKp50hJSSlR2GntrrvuwocffujQ99Hiin/dDh482OnFsNUVrwIRURVJShJ/AA7lCHciUkDLYMDTU4wlsLsnERER1UaGPOj2zBVCpsZtYAwbqFBCRETlIKlKdCHWJP0BFF5XKCEiIiKqSh4eHqhfvz5atmyJ/v37Y8KECVixYgX++OOPcnUFbNGiBZYvX45FixahX79+DnWy02q16NChA8aPH48VK1Zg06ZNwthaW3r37o2NGzdi8uTJ6NGjBxo1agRP6z9W23HPPfdg2bJldjsLRkZG4vPPP8e0adMcPq4tAwcORJMmTQDcGDNv3RnNFp1OhwULFmDixImoW7duqdtotVr07dsXv/76Kx588MFK5+qoadOmYdasWWjVqlWpj+t0OgwZMgS///47IiIiqiwvW9z5WlLlFT1vPfDAA/Dw8LC7rb+/P8aOHYv169ejd+/ebnF867Hw0dHRDu1XnCvuSW9vb3z66adYvnw5+vfvX+bzrFarRefOnTF16lRs374dvr6+do+9dOlSzJo1C//5z38QHBwMHx+fMgvwatu93KxZMyxfvhwvvfQSAgMDbW7XqFEjTJkyBatXr3ZZcf2aNWuEtTO6egJAv3790LlzZ+h0tt8wL0kSunTpgm+++QZz5swpd6Hnvn37cOrUKQA3vj6GDRtWmZRrFEl2Zvk9blSEuzNJklCvXj0AQHZ2tlPffUBEjquN9+Ijj5lx4eLN9dRXJNx3DzvpkfJq4/1Y202YZMbhIzfXTz0JPPM03wPkDng/ErkH3otE7sOV96N271fw2D1biOU/vAimoK5OOwdRTcHvje5Jup4B72/7QpJvdk0p6Pc2jLc+pmBW5Gq8H4ncQ/369ZVOgcglTCYTkpOTceLECWRnZ+PatWtQqVTw8fGBv78/WrVqheDgYLvFHVXh7NmzOHDgAC5dugS1Wo3GjRsjNDQUYWFhTj3Pd999h5kzZwIAwsLCSozcLktBQQEOHjyI1NRU5Obmol69emjUqBE6dOhg+X7uKnPmzMHcuTff4Fg01rlIXFwcUlNTkZGRAZ1Oh6ZNm6Jbt252C72UpOS1JNfT6/WIj49Hamoqrl69ioKCAvj4+KB+/fqIiopCq1atbI4vV+r4/fv3x9mzZwEA7du3x88//1zh/ADX3ZN6vR5HjhzB2bNnkZ2djcLCQnh7e6NBgwYIDg5GaGhouQrvK6u23cuyLCM2NhYnT57EpUuXIEkSGjRogMjISLcoqq+swsJCxMbG4vTp07hy5QqMRiO8vb0RFBSE2267DQ0aNKjwsSdPnmz5vvvAAw/g448/dlba1R6LPfmHECJF1LZ7MSdHxr2DxM9x0QIJoa1Z7EnKq233IwFffm3GT8V+5+zcCfjsYxZ7ugPej0Tugfcikftw2f2YlwmfhXdB0udaQsbgXigY8o1zjk9Uw/B7o/vyXDcRmuN/WtamhpHIf2IlUIkXQsm98X4kcg8s9iSqHfLz89G/f39cvnwZALBgwQL06tVL4awcU1axJxFV3NmzZ9G/f3/LeunSpXa7DhNR+Vy4cAEDBgyAwWCAWq3Ghg0bEBwcrHRaboOv6hMRVYGkZHGt090YpUxEpISoSPFFv/gEwGzmi0NERERUe+j+/Voo9JQhQd/rJQUzIiKqGEPbR4W1+lICVOnHFMqGiIiIqGbx8vLC2LFjLetvvuEbBIkI2L17t+XjXr16sdCTyMkWLlwIg8EA4MboeRZ6iljsSURUBayLPVuHABoNOywQkTKiIsX19etAWpoyuRARERFVNSn7LLRHxNFaxsj7YG5Y/UcnEVHtY2rRHea6TYWYNvYXhbIhIiIiqnkef/xxhISEAABiYmKwZ88ehTMiIqUVFXtKkoSXXuKbh4mc6cKFC1i+fDkAwMfHBy+88IKyCbkhFnsSEVWBxCSxY154uEKJEBEBaNRIQkCAGDuWoEwuRERERFVNt3s2JLPBspbVWui7T1IwIyKiSlCpYWjzsBDSJP4OFOteTEREREQVp9Vq8cYbb1jWH3/8MWSZk7KIaitZlvHvv/8CAO655x5ERUUpnBFRzTJ79mwUFhYCACZMmICGDRsqnJH70SidABFRbZCUJK4jwtnVk4iUFRUJ/LPj5jo+QcZ/BvK5iYiIiGo2VUY8tInrhZjh1mGQ/QIVyoiIqPKM0UOg2zMXkmwGAEiGPGgSf4ex3aNl7ElEREREjujevTuSrF/sI6JaSZIk7N27V+k0iGqsjz76CB999JHSabg1dvYkInKx7GwZF9PFWHiYMrkQERWJihQLO+PjFUqEiIiIqArpdnwqrGWdL/RdxiqUDRGRc8h1GsPUqo8Q4yh3IiIiIiIiIqKah8WeREQulpQsrnU6ILiFMrkQERWJihTXx1OBwkKOniEiIqKaS316FzSndwsxfednAa/6CmVEROQ8hraPCGt1+jGo0o8plA0REREREREREbkCiz2JiFzMutgztDWg0XBUMhEpKyIcUBX7SdBkApJTlMuHiIiIyKVkM3Q7PhNCZp+GMLQfoVBCRETOZQruBbNvEyGmjf1VoWyIiIiISGkTJ05EUlKS5R8RERHVDCz2JCJyscQksVNeRLhCiRARFePtLaFlsBiLT1AkFSIiIiKX0yT9AXVGvBDTd5sAaL0UyoiIyMlUahjaPiyENInrAX2uQgkREREREREREZGzsdiTiMjFrDt7hoexqycRuQfrUe7x8RzjTkRERDWQSQ/drs+FkLlBKxjbDFEmHyIiFzG2GQJZuvknf0mfC03SHwpmREREREREREREzsRiTyIiF8rKlpGeLsbC2dmTiNxEVKRYfM7OnkRERFQTaY/+AlVOmhAr7PkioNIolBERkWvIdW6BqeUdQoyj3ImIiIiIiIiIag4WexIRuVBSkrj28ABaBCmTCxGRtagocX3hIpCVxe6eREREVIMUXodu75dCyNS0PUwh/RRKiIjItQxtHxXW6otHocrgO/uIiIiIiIiIiGoCFnsSEbmQ9Qj30NaARsMx7kTkHoJbAF5eYuwYXwMkIiKiGkS3fyGk/CwhVtjrZUDi72VEVDOZWvaC2bexENOwuycRERERERERUY3AYk8iIhdKTBI75EVwhDsRuRG1WirxvBQfz86eREREVDNIuZegPbBYiBlD+sIceLsyCRERVQWVBsboIUJIm/AbYMhTKCEiIiIiIiIiInIWFnsSEbmQdWfP8DB2jyEi9xIVKa7j2dmTiIiIagjdnnmQjPmWtSypUNjzRQUzIiKqGoa2D0HGzb9BSfrr0CRtVDAjIiIiIiIiIiJyBhZ7EhG5SFaWjIwMMRbOzp5E5GaiosQi9IREwGxmd08iIiKq3qSsk9DErhBixujBkP1bK5QREVHVkesGwhTcU4hp41bY2JqIiIiIiIiIiKoLFnsSEblIolVXT09PoEWQMrkQEdkSbdXZMzcXOHNWmVyIiIiInMVj5xeQZJNlLas9oO82QcGMiIiqlqHtI8Jaff4QpCupCmVDRERERERERETOwGJPIiIXSUoS16GtAbWaY9yJyL0EBEho1FCMxccrkwsRERGRM6guHIEmZZMQM3QYAblOE4UyIiKqeqZWfWD29hdi2rhVCmVDRERERERERETOwGJPIiIXSUoSxyBzhDsRuasoq+6ex+I5xp2IiIiqKVmGxz+fiCEPP+g7PatQQkREClFrYYwcJIQ0CWsBk0GhhIiIiIiIiIiIqLJY7ElE5CJJVmPcw8PY1ZOI3FNUlPj8FMfOnkRERFRNqU9uh/rcfiGm7zIW8KyrUEZERMoxtH1IWKvyrkB94m9lkiEiIiIiIiIiokpjsScRkQtkZsrIuCTGItjZk4jcVJtocX3yJJCXx+6eREREVM2YTdDt+EwM1bkFhtuGKZQQEZGy5AatYGraXohp41YqlA0REREREREREVUWiz2JiFzAuqunlycQ1FyZXIiIyhIeBqjVN9dmM5CQqFw+RERERBWhSfgN6ispQkzf43lA46FQRkREyjO0eVhYq0/tgHQtXaFsiIiIiIiIiIioMljsSUTkAtbFnqGhgFrNMe5E5J48PCSEhYqxuGPK5EJERERUIcZC6HbPFkKmgDAYI+5XKCEiIvdgDBsIWettWUuyGZr4NcolREREREREREREFcZiTyIiF0hMEscfh4cplAgRkYOirUa5HzvGMe5ERERUfWgP/wjVtQtCTN/rZUCltrEHEVEtofOBMfweIaSNWwXIZoUSIiIiIiIiIiKiimKxJxGRCyQlieuIcHb1JCL3Fh0lPk8diwdkmQWfREREVA0U5ED37zdCyNisM0zBvRRKiIjIvRjaPCSsVTlnoD67T6FsiIiIiIiIiIiooljsSUTkZFeuyLh0WYyFhyuTCxGRo9pEieucq0DaOWVyISIiIioPXcy3kApzhJi+18uAxDfdEREBgPmWW2HyDxFimriVCmVDREREREREREQVxWJPIiInS0oW116eQPNmyuRCROSoJk0A/wZiLO6YMrkQEREROUq6dgHaQ0uFmCHsbphvaadQRkREbkiSYGzzsBDSpGwGCq4qlBAREREREREREVUEiz2JiJws0WqEe1gYoFazowwRuTdJkhAdLcaOHeMYdyIiInJvut1zIZn0lrUsqaHvMUnBjIiI3JMh8gHIKq1lLZkKoUlcr2BGRERERERERERUXiz2JCJysqRksTgqPEyhRIiIyik6SixMZ2dPIiIicmeqyynQxK8RYsZ2j0CuH6xIPkREbs27AUwhdwohLUe5ExERERERERFVKyz2JCJyMusx7hHh7OpJRNVDG6vOnidOAnl57O5JRERE7km363NIstmylrXe0Hcdr2BGRETuzWA1yl2dEQ9VRoJC2RARERERERERUXmx2JOIyIkuX5Fx+bIYY2dPIqouwsMAtfrm2mwGEpOUy4eIiIjIFtW5A9CkbhNihg4jIfs0VCgjIiL3Z2rRHeY6twgxDbt7EhERERERERFVGyz2JCJyoiSroigvL6B5c2VyISIqL09PCa1DxBhHuRMREZHbkWV47PhMCJm9GkDf8WmFEiIiqiZUahijHhRC2oT1gLFQmXyIiIiIiIiIiKhcWOxJROREiUniuOPwMECl4hh3Iqo+rEe5H4vnGHciIiJyL+oTf0F9/qAQM3QZB3j4KpQREVH1YWgzRFhLhTnQHP9ToWyIiIiIiIiIiKg8WOxJROREScnimiPciai6iY4WC9SPHQNkmQWfRERE5CbMRuh2WnX19GsGQ7uhCiVERFS9yH7NYAzqKsQ0sSsUyoaIiIiIiIiIiMqDxZ5ERE5UotgznF09iah6iY4S19k5wLlzyuRCREREZE0TvxbqK6lCTN99EqDRKZQREVH1Y2zzsLDWnN0LKfusQtkQEREREREREZGjWOxJROQkly/LuHJFjEWwsycRVTNNbwHq1xdjcfHK5EJEREQkMBRAt3uuEDI1jIQx4h6FEiIiqp6MrftD9vATYtpjqxTKhoiIiIiIiIiIHMViTyIiJ0lIFNfe3kCzZsrkQkRUUZIkoY1Vd89j8RzjTkRERMrTHv4RqusXhZi+10uAxD9vERGVi8YDhsj7xNCx1YDZpFBCRERERERERETkCP41nIjISRISxWKoiHBApeIYdyKqfqKjxeeuY8cUSoSIiIjo/8n52dD+O1+IGZt3halFD4UyIiKq3oxtxVHuquvpUJ/Zo1A2RERERERERETkCBZ7EhE5SWKSuI6IUCYPIqLKirbq7JmaCuTns7snERERKcf8zxxIhVeF2I2unnyDHRFRRZgbRsDUSPzlT8NR7kREREREREREbo3FnkRETiDLcokx7pHhfNGRiKqniHBAXeynRJO5ZEE7ERERUVWRc87DvPc7IWYIuxvmJm0VyoiIqGYwRg8R1prjW4GCHIWyISIiIiIiIiKisrDYk4jICc6dA65dE2OR7OxJRNWUl5eEkNZiLI6j3ImIiEghpr8+AYwFlrWs0kDfY5KCGRER1QyGiHshq7WWtWTSQ5P4u4IZERERERERERGRPSz2JCJyggSrjnf16gGNGyuSChGRU1iPcj8WzzHuREREVPWkK8chH/xZiBnbPgy5frAyCRER1SRe9WAM6SeEtMdWK5QMERERERERERGVhcWeREROkJgoFkFFRQCSxDHuRFR9tYkSn8PijgGyzIJPIiIiqlq6HbMA2WxZyxov6LuOVzAjIqKaxRg9WFir0+OgupysUDZERERERERERGQPiz2JiJwgIVFcR0Sw0JOIqrc20eI6Oxs4f0GRVIiIiKiWUp0/BE3qViFmuP0pyD4NFcqIiKjmMbXoDrPV86qG3T2JiIiIiIiIiNwSiz2JiCrJaJSRnCLGIiOUyYWIyFmaNgXq+YmxY8eUyYWIiIhqIVmGx45PxZBXfeg7Pq1QQkRENZRKA2PUICGkSfgNMBkUSoiIiIiIiIiIiGxhsScRUSWdPgMUFIixCBZ7ElE1J0kSoq26e8Yd4xh3IiIiqhrqE39Dfe6AENN3/S/g4atQRkRENZcheoiwVuVdgfrUDoWyISIiIiIiIiIiW1jsSURUSdYj3Js0BurX4xh3Iqr+2kSLz2Vx8QolQkRERLWL2QTdzs/EWL3mMLZ7TJl8iIhqOLlBS5huuU2IaeNWKZMMERERERERERHZxGJPIqJKSkwUO92xqycR1RTRUeI69TiQn8/unkRERORamoR1UF85LsTU/acCGp1CGRER1XyG6MHCWn1yO6S8KwplQ0REREREREREpWGxJxFRJVl39oyMYFdPIqoZIsIBVbGfFk1mIDFJuXyIiIioFjAUQLdrthhrEg2p7eDStyciIqcwht8DWeNpWUtmIzQJvymYERERERERERERWWOxJxFRJej1MlJPiLFIdvYkohrC21tC6xAxFhunTC5ERERUO2iP/ATV9YtCTH3XNEgq/gmLiMilPHxhDB0ghDTHVgMypzsQEREREREREbkL/qWciKgSjqcCRuPNtSQB4WHK5UNE5Gxt24jruDi+0EdEREQuUnAVun+/EUKm5l0gtb5ToYSIiGoXo/Uo98vJUGXEK5QNERERERERERFZY7EnEVElWI9wD2oO+PhwjDsR1Rxt2ojPabHHALOZBZ9ERETkfLp9CyAV5ggx/R2TIUn8HYuIqCqYmneBuW5TIaaJW6VQNkREREREREREZI3FnkRElZCYKBY8RUYqlAgRkYtYd/a8dg04fUaZXIiIiKjmkq6lQ3twiRAzhN0Nc5O2CmVERFQLSSoYox4UQtrE3wFjoTL5EBERERERERGRgMWeRESVkJAkriPD2XGGiGqWJo0lNGooxuLilMmFiIiIai7dnrmQTDeLiWRJDX2PSQpmRERUOxmsRrlLhTlQn/hLoWyIiIiIiIiIiKg4FnsSEVVQXp6M06fFWESEMrkQEblSG6vunrFxHONOREREziNlnoDmmDgm2Nj2Ycj1g5VJiIioFpP9msHYrLMQ03KUOxERERERERGRW2CxJxFRBSUmAXKxeie1Gmgdolw+RESu0raN2LU49phCiRAREVGN5LFzFiTZbFnLGi/ou45XMCMiotrN2Ebs7qk+vQvStXSFsiEiIiIiIiIioiIs9iQiqqBEqxHuISGAhwfHuBNRzdPWqrPn2bNAVja7exIREVHlqc4fgub4n0LMcPtIyL6NFMqIiIiMoXdB1npb1pJshiZhnYIZERERERERERERwGJPIqIKS0gUC50iwxVKhIjIxVqHAJ6eYiwuTplciIiIqAaRZXjs+FQMedaDvuNohRIiIiIAgNYbxrC7xdCxVeKIGyIiIiIiIiIiqnIs9iQiqqDERHEdGcGunkRUM2k0EqIixVjsMb7IR0RERJWjPrkd6nMHhJi+6zjAw1ehjIiIqIihzRBhrco6BdWFw8okQ0REREREREREAFjsSURUIVnZMi5cFGMREcrkQkRUFaxHucfGKpMHERER1RBmE3Q7PhNDdZvC0O5xhRIiIqLizE07wFyvhRDTxq1SKBsiIiIiIiIiIgJY7ElEVCGJSeLawwMIblH6tkRENUGbNmL34qQkQK9nd08iIiKqGE3Cb1BfSRFi+u6TAI1OoYyIiEggSTBEDxZCmuQ/AEOeQgkRERERERERERGLPYmIKsB6hHtY6I0xx0RENVWbKHGtNwDJKaVvS0RERGSXsRC63bOFkKlhBIyR9ymUEBERlcYYNQiydPMlBEmfC03KnwpmRERERERERERUu7HYk4ioAhISxW52kRzhTkQ1XJ06EloGi7GjHOVOREREFaA9sgyqaxeEmL7ni4DEP1MREbkTuU4TmIK6CzHNsdUKZUNERERERERERPwrOhFROcmyXKKzZ2Qku3oSUc3Xtq24jjvGMe5ERERUTgVXofv3ayFkbNYZpuBeCiVERET2GNtYjXI/uxdSzjmFsiEiIiIiIiIiqt1Y7ElEVE4Zl4DMLDEWGa5MLkREValtG7GwPTbuRgE8ERERkaN0+7+DVJAjxPS9XgYkvoGOiMgdGUP6QfaoK8Q08WuUSYaIiIiIiIiIqJZjsScRUTlZd/WsUwcIDFQmFyKiqtQ2WlxnZQHn2NCFiIiIHCRdz4D24BIhZgwdCPMt7RTKiIiIyqTxgDHiHiGkPbYakM0KJUREREREREREVHux2JOIqJwSEsUudhHhgMQuNERUCwQGAvXri7HYY8rkQkRERNWPbs88SMYCy1qW1CjsOUnBjIiIyBGG6CHCWnX1HNRp+xXKhoiIiIiIiIio9mKxJxFROSVYdfaMiFAmDyKiqiZJEtq2EWOxcRzjTkRERGWTMk9AE7dSiP0fe/cZZ2dZ54//e58yJY3QQu81CWkEQu9FivRid3XXsva1rq666lrWtevquq6uZbFiowrSIXRIJwkt9A4hkJBMOeX+P+D3T7gmgJTM3HNm3u9n12cGX58nzpnJ+Zzrrk86NfL1tyuoEQAvVXOT3aKx4Y5J5lHuAAAAAAPP2BPgZWg287j1tjQbv4tbPYHhY7eJ6c+8BQsKKgIAtJT2q78TWd5Yfc4rndG793sLbATAS5ZlUZ9wYhJVbv9rRG1VMX0AAAAAhiljT4CX4d77IlauTLMJE4rpAlCEyZPS8933RKxY4XZPAOCFlR6aG5U7L06y2vS3Rj5qXEGNAHi56uOPizxb83ZCVlsVlTsufpH/AgAAAIB1zdgT4GVYtDg9jxsXsdGGbvYEho+dd4poq6bZLYuK6QIAtIA8j/aZ30yjjrHRu8c/FFQIgFciHzUuGtvsl2Qe5Q4AAAAwsIw9AV6GRYvS2+smjC+oCEBB2tqy2GWXNFuwwM2eAMDzK999ZZQfvDnJevd6d0T76IIaAfBK1SeckJzL990Q2YqHC2oDAAAAMPwYewK8DH1v9pww3q2ewPAzqc+j3G9ZWEwPAGCQazairc+tns0xm0dtyhsLKgTAq1Hf4bDInzPWzyKPyqJzCmwEAAAAMLwYewK8RN3dedx1V5pNnFBMF4AiTZqYDt0XLY6o193uCQCkKovOivLSO5Osd98PRlTaCmoEwKtS7Yj6zkel0aKzInJ/DwIAAAAMBGNPgJfottsjGs0153IpYpedi+sDUJTddkvP3d0Rdy4ppgsAMEjVuqLtmu8lUWPj8VEff1xBhQBYF2oTTkzOpWX3ROnhuYV0AQAAABhujD0BXqKFi9Lz9ttHdHR4jDsw/Kw/NouttkqzBbcU0wUAGJyqc86I0srHkqz3wI9GZP4pCqCVNTefFs2xWydZddHZBbUBAAAAGF78CzvAS7RocfpIqvHjCyoCMAhMmpieF9zisX0AwP/TtSzabvxxEtW32Tca2+xXUCEA1pksW+t2z8ptF0TUe4rpAwAAADCMGHsCvESLFqfnCePd6gkMX5N2S38Gzl8QkecGnwBARNv1P4ys95nV5zyy6D3gowU2AmBdqk84PjlnPcujsuSygtoAAAAADB/GngAvwRNL83gsfQJhTHCzJzCMTZ6Unp94IuLhR4rpAgAMHtlT90V13m+TrD7+uGiOm1BQIwDWtXzMFlHfaq8kqyw6q5gyAAAAAMOIsSfAS7C4z62eI0ZEbLN1MV0ABoOtt44Yu16azZtfTBcAYPBou+Y7kTVrq895uRq9+32wwEYA9Id6n0e5l++5JrKVjxdTBgAAAGCYMPYEeAkWLU4fTTx+14hy2WPcgeEry7KY1Od2z/nzPcYdAIaz0iMLonrbBUlWm/qWyMdsUVAjAPpLfacjIq+OWH3O8kZUFp9XYCMAAACAoc/YE+AlWNTnZk+PcAeImDI5Hb3PX1BQEQCgeHke7Vd9PY3ax0TvjHcWVAiAftU2Muo7HZFElUVnReQ+BAgAAADQX4w9Af6GRiOPxbem2YTxbvUEmDI5Pd97X8Syp7yxBwDDUfnuK6P8wE1J1rvXuyM6xxZTCIB+t9aj3J+4PUqPL37+bwYAAADgVTP2BPgb7r0vYtWqNHOzJ0DETjtGdHakmds9AWAYajaibeY302j0ZlGb+qaCCgEwEBpbzYjm6M2SrLLwrGLKAAAAAAwDxp4Af0PfR7hvsknEhhu62ROgUsliwoQ0mz/fzZ4AMNxUFp0V5aV3Jlnvfh+KqLQX1AiAAZGVoj7h+CSq3npeRKO3oEIAAAAAQ5uxJ8DfsGhxOlxyqyfAGlMmp+P3efMLKgIAFKPWFW3X/mcSNTbeNerjjyuoEAADqTbhhOScdS2L8t0zC2oDAAAAMLQZewL8DYsWpefxu7rVE+D/N2Vyer7jjohVq9zuCQDDRXXOGVF65tEk6z3gYxGZf3ICGA7y9beLxubTkqy66KxiygAAAAAMcf7lHeBFdHXlcdfdaTZxwvN/L8BwNGF8RLm85txoRixc9MLfDwAMIV3Lou3GHydRfZt9o7HtfgUVAqAItQknJufyXVdGdC0rpgwAAADAEGbsCfAibrs9otlccy6XInbZubg+AINNZ2cWO/f5uTh/gZs9AWA4aLv+h5H1PrP6nEcWvQd8tMBGABShvvNRkZfbVp+zZi2qt/6lwEYAAAAAQ5OxJ8CLWLQ4PW+/fURHh8e4AzzXlEnped78YnoAAAMne+r+qM77bZLVx782muM8CgFg2OkYE/UdD0uiike5AwAAAKxzxp4AL2LR4vR2ugnjCyoCMIhNmZyO4BcuiqjX3e4JAENZ2zXfiaxZW33Oy9Xo3e9DBTYCoEj1vo9yf/SWyJbeWUwZAAAAgCHK2BPgRfS92XPCBLd6AvQ1uc/Nnj09EbfdXkwXAKD/lR5ZENXb0sfz1qa+OfIxWxTUCICiNbbZN5ojN0qy6sKziikDAAAAMEQZewK8gCeW5vHYY2nmZk+Ata23XhbbbptmHuUOAENUnkfbVd9Io/Yx0TvjXQUVAmBQKFWivuvxSVRZfG5Es1FQIQAAAIChx9gT4AUsWpSeR46M2GbrYroADHZ9b/ecv8Bj3AFgKCrffVVUHrgxyXr3endE59hiCgEwaNQnnpCcSysfi/J91xXUBgAAAGDoMfYEeAGLFqdDpV13iSiVPMYd4PlMmZT+fJy/IKLZNPgEgCGl2Yi2md9Mo9GbRW3qmwoqBMBg0txo52iMm5BkFY9yBwAAAFhnjD0BXsAtC9OzR7gDvLApk9Pz8uUR995XTBcAoH9UFp0d5aV3JFnvfh+KqLQX1AiAwaY+4cTkXLnzkoieFcWUAQAAABhijD0Bnke9nsett6XZxIlu9QR4IZtumsW4cWk2b34xXQCAflDrirZrv5dEjY13jfr44woqBMBgVNv12MhLldXnrNETldsuKLARAAAAwNBh7AnwPJbcFdHdnWYTJzz/9wLwrMmT0vP8+R7jDgBDRXXOGVF65tEk6z3gYxGZf1oC4DlGbBCN7Q5KouqiswsqAwAAADC0+Bd5gOexcFF63nKLiPXHutkT4MVMmZz+nJy3oKAiAMC6terJaLvxx0lU32bfaGy7X0GFABjMahNPTM7lh2ZHtuzeYsoAAAAADCHGngDPY+HC9Da6iRMLKgLQQqb0udnz0UcjHnnU7Z4A0OrarvtBZL3PJFnvAR8tqA0Ag11juwMj7xibZG73BAAAAHj1jD0BnsctC9PzbhPd6gnwt2y7bcTo0Wk23+2eANDSsifvjur83yVZbfxx0Rw3oaBGAAx65bao7fraJKosPjsibxZUCAAAAGBoMPYE6GPZsjwefCjNdvM+JsDfVCplMbnP7Z7z57vZEwBaWfvMb0aWN1af83J79O7/4QIbAdAK6n0e5V5a/lCUH7i5mDIAAAAAQ4SxJ0AfCxel586OiO22K6YLQKuZMjm9CXnuvIKKAACvWun+G6Oy5NIkq01/W+SjNyuoEQCtojluQjQ23DHJKovOKqYMAAAAwBBh7AnQxy0L01voxo+PqFQ8xh3gpZgyOT3fc++zNyYDAC0mb0b7VV9LombnBtG75zsKKgRAS8myqE84MYkqt/81oraqmD4AAAAAQ4CxJ0AftyxMzxMnFtMDoBXtsvOzNyI/l9s9AaD1VG49P8qPpn8c9e77/oj2UQU1AqDV1McfF3m25i2IrLYqKndcXGAjAAAAgNZm7AnwHPV6Hrfelma7TXCrJ8BLValkMWlSms2d52ZPAGgpte5ou/rbSdTcYPuoTzqtoEIAtKJ81LhobLNfknmUOwAAAMArZ+wJ8BxL7oro7k6ziROK6QLQqqZOSUfybvYEgNZSnXNGlFY8nGQ9B34solQpqBEArao+4YTkXL7vhsiWP1RQGwAAAIDWZuwJ8Bx9H+G+5RYRY8e62RPg5Zg6JT0vuSvi6afd7gkALWHVk9F244+SqL7VXtHY7uBC6gDQ2uo7HBZ5++jV5yzyqCw+p8BGAAAAAK3L2BPgORYuTMdIEycWVASghY3fNaK9Pc3mzS+mCwDw8rRd94PIelcmWe+BH4/IfAgOgFeg2hH1nY9Oo4VnReQ+EAgAAADwchl7AjzHLYvS824TvaEJ8HJVq1ns1mcsP3eeN/IAYLDLnrw7qvN/l2S18cdHcxOfggPglatNPDE5l566N0oPzy2kCwAAAEArM/YE+H+WLcvjoYfSbLcJxXQBaHXTpqZj+Tlzi+kBALx07TO/GVneWH3Oy+3Ru/8/FVcIgCGhudnUaI7dJsmqi84uqA0AAABA6zL2BPh/blmYnjs7IrbbrpguAK1u6pT0fOeSiOUr3O4JAINV6f4bo7Lk0iSrTX9b5KM3K6gRAENGlkVtwglJVLntgoh6T0GFAAAAAFqTsSfA/3PLwnSENH58RKXiMe4Ar8T4XSPaqmvOeR4xf0FxfQCAF5E3o/2qryVRs3OD6N3zHQUVAmCoqU84PjlnPcujsuSygtoAAAAAtCZjT4D/Z+Gi9DxxYjE9AIaC9vYsJkxIs7lz3ewJAINR5dbzo/xo+qiD3n3fH9E+qqBGAAw1+Zgtor7VXklWWfjngtoAAAAAtCZjT4CIqNfzWHxrmu02wa2eAK/GtKnpee68QmoAAC+m1h1tV387iZobbB/1SacVVAiAoao+8cTkXL73msieeayYMgAAAAAtyNgTICKW3BXR05NmEyc8//cC8NJMnZKO5m+/I2LlSrd7AsBgUp1zRpRWPJxkPQd+LKJUKagRAENVfccjIq+OWH3O8mZUbj2vwEYAAAAArcXYEyAibkmfWBhbbhkxdqybPQFejYkTIirP2Yk0mxHzbymuDwDQx6ono+3GHyVRfau9orHdwYXUAWCIaxsZ9Z2OTKLKwrMich8KBAAAAHgpjD0BImLhwvQflXebWFARgCGkoyOL8bum2dy53sQDgMGi7bofRNa7Msl6D/x4ROaDbwD0j/qEE5NzeekdUXpsUTFlAAAAAFqMsSdARNzS59+Ud5vgzU2AdWHq1PQ8d14hNQCAPrIn747q/N8lWW388dHcxCffAOg/ja32jOaYzZOssujsgtoAAAAAtBZjT2DYW7Ysj4ceSrOJ3t8EWCemTUnH87feFtHV5XZPACha+8xvRJY3Vp/zcnv07v9PxRUCYHjISlEff3wSVRefG9HoLagQAAAAQOsw9gSGvVsWpufOzojttyumC8BQs9vEiPJzfuNsNNb+uQsADKzyfTdEZcllSVab/rbIR29WUCMAhpNan0e5Z91PRfnuq4opAwAAANBCjD2BYW/BwvSGuQnjI8plj3EHWBdGjMhil13SbM5cN3sCQGGajWi78qtp1LlB9O75joIKATDc5OtvE43Nd0+y6sKziikDAAAA0EKMPYFhb8GC9LybR7gDrFPTpqbnOXOLaAEARERUFv45yo/fmmS9+34gon1UQY0AGI5qE05IzuW7r4roWlZQGwAAAIDWYOwJDGs9PXnceluaTdrNrZ4A69LUqenP1cW3RnR3u90TAAZc78pou+a7SdTYcKeoTzq1oEIADFf1XY6OvNy++pw1a1G99fwCGwEAAAAMfsaewLB22+0Rtdqac5a52RNgXZu8W0TpOb911usRCxcV1wcAhqu2G/8nSqueSLLeg/85olQpqBEAw1b76KjveFgSVRadVUwXAAAAgBZh7AkMawtuSc/bbx8xapSbPQHWpZEjs9h5pzSbM9fNngAwkLKnH4zqrJ8nWX27g6KxzX7FFAJg2KtPODE5lx9dGKUnbi+mDAAAAEALMPYEhrX5C9Kx0aTdCioCMMRNm5qeZ88ppAYADFttM78RWaN39TkvVaLnoE8U2AiA4a6xzb7RHDkuySoLzy6oDQAAAMDgZ+wJDFt5nsctfW72nLybWz0B+sPu09Kfr4sWR3R1ud0TAAZC6cHZUb39wiSrTXlD5BtsX1AjAIiIUjnq449Losqt50Q06wUVAgAAABjcjD2BYeu++yKeXp5mkycV0wVgqJsyOaL8nN886/WIBbe88PcDAOtI3oz2K76aRu3rRe/e7y2oEACsUZt4YnIurXwiyvdeW0wZAAAAgEHO2BMYtvqOjDbeKGKTTYrpAjDUjRiRxfjxaTZrtps9AaC/VRafF+VHFyRZ7z7vjegcW0whAHiOfMMdo7HJbklWWXRWMWUAAAAABjljT2DYmr8gHRlNmhSRZR7jDtBfpk1Lz7PnFlIDAIaP2qpou/pbSdRcf7uoTXlDQYUAYG31CScm58qdl0Z0L3/+bwYAAAAYxow9gWFrfp+bPSdPMvQE6E/Tp6U/Z2+7LeKZZ9zuCQD9pXrzz6L0zKNJ1nPQxyPK1YIaAcDaarseE3lpzWtT1uiNyu0XFNgIAAAAYHAy9gSGpWXL8njggTSbtNvzfy8A68ak3SKqz9mWNJsR8+YX1wcAhrJsxSPRdtP/Jll9632isd3BhfQBgBfUuX40tj84iaoLzyqkCgAAAMBgZuwJDEt9b/Xs7IjYYftiugAMF+3tWew2Mc1mz3GzJwD0h7arvx1ZvWv1Oc9K0XvQJyMyTzQAYPCpTTwxOZcfnhvZsruLKQMAAAAwSBl7AsPSggXpuGjChIhKxZueAP1t9z6Pcp81u6AiADCElR5ZENXF5yRZfdJp0dx454IaAcCLa2x7QDQ7N0iy6qKzC2oDAAAAMDgZewLD0oI+N3tOnlRMD4DhZvdp6fnOJRFPP+12TwBYZ/I82q/4ahq1jYrefT9QUCEAeAnK1ajvemwSVRadE5E3CyoEAAAAMPgYewLDTk9PHrfdnmaTJ7nVE2AgTBgf0dGRZnPmFlIFAIak8h1/jfJD6dXZvXu9O/IRGxbUCABemnqfR7mXVjwc5ftvLKYMAAAAwCBk7AkMO4tvjajX15xLpYiJE4rrAzCcVKvZWrcpz5rjZk8AWCfqPdF+1TeSqLneVlGb9taCCgHAS9fceHw0Nto5ySoL/1xQGwAAAIDBx9gTGHb6PsJ9h+0jRoxwsyfAQNl9Wvozd86cgooAwBBTnf1/UVr+YJL1HPCxiEpbQY0A4GXIsqhPPCmJKndcHNG7sqBCAAAAAIOLsScw7CxYkN4g1/eGOQD61/Td0/M990Y8sdTtngDwamQrH4+2G3+UZI0t9ojGTkcU1AgAXr76rq+NPCuvPmf1rqjccVGBjQAAAAAGD2NPYFhpNvNYsDDNJu3mVk+AgbTTjhEjR6bZnLmFVAGAIaPt6m9H9pybz/LIoufgT0Zk/t4BoHXkIzeKxrb7J1ll4VnFlAEAAAAYZIw9gWHlnnsjVqxIs0lu9gQYUJVKFlOnpNns2W72BIBXqvTIgqgu/HOS1SeeGM1NJhbUCABeudrEE5Nz5YEbI3v6wWLKAAAAAAwixp7AsLLglvS8ySYRm4xz0w3AQJs+Lf3ZO2tOQUUAoNXlzWi//Mtp1DYyevf/cEGFAODVaWx/SOTtY5KssvjsgtoAAAAADB7GnsCwsuCW9Oa4SbsVVARgmJs2LT0/9FDEI4+43RMAXq7K4nOj/PC8JOvd6z2Rj9y4oEYA8CpV2qO+y9FJVF14dkTub0YAAABgeDP2BIaV+QvS86Td3OoJUIQdto9YL72oJWa73RMAXp7eldE285tJ1By7ddSmvaWgQgCwbtQmnpScS0/fF6WHZhfUBgAAAGBwMPYEho2lS/N46KE0m+xmT4BClErZWrd7zprjlhYAeDnabvhRlFY+nmQ9B38yotJWUCMAWDeam06O5vrbJVl14VnFlAEAAAAYJIw9gWGj762eI0ZEbL99MV0AiJi+e3q78pw5EbnH8gHAS5I9dV9UZ/88yerbHhCN7Q4uog4ArFtZFrWJJyZR5fYLI2rdxfQBAAAAGASMPYFhY978dEC028SIctlj3AGKsvvU9PzY4xEPPFhIFQBoOe1Xfi2yRm31OS9Vnr3VM/M3DgBDQ3388ZHHmte1rPeZqCy5tMBGAAAAAMUy9gSGjXnz0/PUKd4EBSjS1ltHbLhhms2aVUwXAGgl5XuvWWvsUpv25sg38OgCAIaOfPSm0dh67ySrLPxzQW0AAAAAimfsCQwLK1bkceeSNJsyuZguADwry7LYY/c0u2mWx7gDwItq1KL98n9PombnBtG713sKKgQA/ac+4cTkXL7vushWPFpMGQAAAICCGXsCw8KCWyLy5+yH2qoR43ctrg8Az9pjenrL8qzZEY2GwScAvJDqvN9G6cn0k2y9+/9TRMeYYgoBQD+q73R45G0jV5+zvBmVxecU2AgAAACgOMaewLAwd346HJowIaKtzWPcAYq2x/T0/MwzEbfdXkwXABj0Vj0Zbdd9P4ka4yZEfeLJBRUCgH5WHRH1nV6TRovOTj/VDQAAADBMGHsCw8K8eenZI9wBBoeNN85i223S7Kabi+kCAINd+7Xfi6xneZL1HPLpiFK5oEYA0P9qE09MzqUnl0Tp0VuKKQMAAABQIGNPYMjr7s7j1tvSbOoUt3oCDBZ77JGeb57lhhYA6Kv02OKozD8zyWq7vjaaW+xeUCMAGBjNLaZHc70tk6yy8KxiygAAAAAUyNgTGPIWLopoNNacy6WIiROK6wNAas/p6QB/wS0RXV0GnwCwWp5H++VfjizWvD7mlc7oPeCjBZYCgAGSlaI+/oQkqt56fkS9t6BCAAAAAMUw9gSGvLnz0sHQzjtHjBjhZk+AwWLa1Ijyc54+W69HzJtfWB0AGHQqt18Y5QdnJVnvjHdGPnrTghoBwMCqTUjHnlnP01G+64piygAAAAAUxNgTGPL6DoamTC6mBwDPb8SIbK0bl2/yKHcAeFZtVbRd9fUkao7ZImrT315QIQAYePnYraKxxR5JVl10VjFlAAAAAApi7AkMabVaHgsXpdnUKW71BBhs9ujzKPebby6oCAAMMm03/E+UVjycZD0HfSKi2lFQIwAoRm3iicm5fM/MyFYtLaYMAAAAQAGMPYEh7bbbI3p60mzypGK6APDC9kwvaIkld0UsXep2TwCGt2zZPVGd9dMkq2+1dzR2PKKgRgBQnPpOr4m8subDDlmzHpVbzyuwEQAAAMDAMvYEhrS589Lz9ttFjBnjZk+AwWb8rhEjRqTZrNnFdAGAQSHPo/3yr0TWqK2JSpXoOfQzEZm/aQAYhtpHRb3PBx4qC88uqAwAAADAwDP2BIa0efPTW+GmTCmoCAAvqlLJYvdpaXbTzW72BGD4Ki+5LCr3zEyy2rS3Rr7hDgU1AoDi1SeekJzLjy+O0uO3FtQGAAAAYGAZewJDVqORx/wFaTZ1shtwAAarPaenP6NvnhWR5wafAAxDte5ov+Lfk6g5cuPo3ee9BRUCgMGhsdXe0Ry1SZJVFp5VTBkAAACAAWbsCQxZd90VsXJlmk2ZXEwXAP62Paan58efiLj3vmK6AECR2m76SZSWP5hkvQd+IqJtZEGNAGCQKJWjPiG9X3oQDwAAnDlJREFU3bNy63kRjVpBhQAAAAAGjrEnMGTNnZ+et9wiYqON3OwJMFhtvXXEuI3T7OZZxXQBgKJkT90f1Zt+nGSNLfaI+q7HFtQIAAaX2oQTk3Np1dIo33t1MWUAAAAABpCxJzBkzZuXPvp3sls9AQa1LMvWut3zpps9xh2A4aX9yq9G1uhdfc6zcvQc+pmIzAfXACAiIt9gu2hsmv5DX3Xh2QW1AQAAABg4xp7AkJTn+Vo3e06d7M1RgMFujz3Sn9Vz5kbU6wafAAwP5buujMqSy5KsNvWN0dx4l4IaAcDgVJt4YnIu33VZRNdThXQBAAAAGCjGnsCQdN99EU89lWZTphRSBYCXYY/d0/OqVRGLFhfTBQAGVL032q/4ShI1R2wYvfu8v6BCADB41Xc+OvJydfU5a9SicttfCmwEAAAA0P+MPYEhac689LzRRhGbb1ZMFwBeug02yGKHHdLMo9wBGA6qs34WpafuS7LeAz4a0TGmoEYAMIh1jo3G9ocmUXWRR7kDAAAAQ5uxJzAkzZmbDoOmTY3IMo9xB2gFe0xPzzfPKqYHAAyUbPlD0XbDfydZY7OpUZ9wQkGNAGDwW+tR7o/Mj2zpncWUAQAAABgAxp7AkJPnecyZk2bTphp6ArSKPaenP7MXLYpYudLtngAMXe1X/kdk9e7V5zyy6Dn0MxGZf7YBgBfS2Hb/aI7cKMmqt/y5oDYAAAAA/c+7BsCQc999EU8uS7PdpxZSBYBXYOqUiGp1zbnRjJg1u7g+ANCfyvdeE5U7Lkqy+pTXRXOTiQU1AoAWUapEffzxSVRZfE5Eo1ZQIQAAAID+ZewJDDmz56bnjTeK2GKLQqoA8Ap0dGQxeVKa3XiTmz0BGIIavdF+2ZeTKO8YGz37fqigQgDQWmoTT07OpVVPRPmemQW1AQAAAOhfxp7AkDNnbjoImjY1Iss8xh2glew1I/25fcONEXlu8AnA0FKd/X9RWnZ3kvXs/+GIzrHFFAKAFpNvuEM0NpuSZNVb/lRQGwAAAID+ZewJDCl5nsecuWm2+zRDT4BWM2PP9PzwIxH3319MFwDoD9nyh6Ltuv9KssYmu0V9t1MKagQAranv7Z7lu6+MbNXSgtoAAAAA9B9jT2BIuefeiGXL0mza1EKqAPAq7LB9xIYbptkNNxXTBQD6Q/vlX4ms3rX6nEcWPYd+NqJULrAVALSe+i5HR17pWH3OmvWoLD63wEYAAAAA/cPYExhS+t7qOW5cxOabF1IFgFchy7LYa0aa3XCjx7gDMDSU77o8KksuTbL65NOjudnkghoBQAtrHx31nY5Iosotf4zI/Q0JAAAADC3GnsCQMmdu+o+406Y+OxgCoPXstWf683vO3IieHm/WAdDial3RftmXk6jZuUH07P/hggoBQOurTzwlOZeX3hmlR28pqA0AAABA/zD2BIaMPM/Xutlz2lRDT4BWtcf0iNJzflvt6YmYv6C4PgCwLrTd8N9RWv5gkvUe9PGIjvUKagQAra+x1Z7RHLNFklUX/qmgNgAAAAD9w9gTGDLuvifiqafSbPdpRTQBYF1Yb70sxu+aZtd7lDsALSxbuiSqN/8syRpb7hn18ScU1AgAhoisFLWJJyVR5da/RNS6CyoEAAAAsO4ZewJDRt9bPTfZJGKzTQupAsA6MmPP9HzDjcX0AIBXLc+j/dJ/i6xZWxOVKtF92L9GZJ5IAACvVn3iiZHHmtfUrGd5VJZcWmAjAAAAgHXL2BMYMubMSW97mzY1IvOmKUBL22tG+nP8nnsiHn3M7Z4AtJ7K4nOi8kD6qYXa9LdHvuGOBTUCgKElH7NFNLbeK8kqHuUOAAAADCHGnsCQ0GzmMXdemu0+1dAToNWN3zVi9Og0u9HtngC0mu6no+3KryVRc8zm0bv3ewoqBABDU33iKcm5fO91kS1/qKA2AAAAAOuWsScwJNxzT8RTT6fZtKlFNAFgXSqXs9hzjzS74UY3ewLQWtqv/naUup5Msp5DPhNR7SyoEQAMTfUdD4+8fc0nBrPIo7LorOIKAQAAAKxDxp7AkDB7bnredJOIzTZzsyfAUND3Ue43z4qo1w0+AWgNpYfnR2X+mUlW3+GwaOxwSEGNAGAIq3ZEfZdj0mjhWRF5s5g+AAAAAOuQsScwJMyZm45+dp9WUBEA1rkZfW72fGZlxKLFxXQBgJelWY/2Sz8fWaz5eyWvdEbPIf9SYCkAGNpqE09OzqWn74/yAzcX1AYAAABg3TH2BFpes5nH3LlpNm2qWz0BhoqNN85ih+3TzKPcAWgF1bm/ifJj6ScUevd5b+RjNi+oEQAMfc1NJ0Vjwx2SrLLwTwW1AQAAAFh3jD2BlnfX3RFPL0+zaVMLqQJAP9lrRnq+8aZiegDAS5U981i0XfvdJGtsuGPUdv+7ghoBwDCRZVGfeEoSVW7/a0TPMwUVAgAAAFg3jD2Bljd7dnrebNOITTd1syfAUDJjz/Tn+q23RSx7yu2eAAxebVd+NbLelUnWc9jnIsrVghoBwPBRH39c5Fl59Tmrd0fl9gsKbAQAAADw6hl7Ai3v5lnp2Gf67gUVAaDfTJ4U0dGx5pznETfeWFwfAHgx5Xuuiept6aCkNvGkaG65R0GNAGB4yUduFI3tD06y6sI/F1MGAAAAYB0x9gRaWr2ex5x5aTZ9uls9AYaatrYs9pieZtde72ZPAAahWle0X/r5JMrb14ueAz5WTB8AGKZqE09KzuWH5kT25F0FtQEAAAB49Yw9gZa2+NaIrq40mz6tmC4A9K+990rH/Dfc+OzoHwAGk7br/ytKTz+QZD0HfCRixAYFNQKA4amx3YHRHLFhkrndEwAAAGhlxp5AS5s1Oz3vsH3EBhu42RNgKNpn7/T8zDMRCxcV0wUAnk/p8VujevPPkqyxxfSoTzq1oEYAMIyVq1Eff3wSVRadFdGsF9MHAAAA4FUy9gRa2s2z0hvdpu9eUBEA+t0m47LYYYc0u86j3AEYLJqNaL/4c5HljdVRXqpG9+FfiMj88wsAFKHe51HupZVPRPmeqwtqAwAAAPDqeLcBaFldXXncsjDNpu/uVk+AoWzfPrd7Xnd9MT0AoK/qvN9E+ZH5SVab8a7IN9zhBf4LAKC/NTfaKRqbTk4yj3IHAAAAWpWxJ9Cy5i+IqD/nqUvlUsS0qYXVAWAA7L1XOupfclfEI4+63ROAYmUrHo62q7+dZM31t4veGe8qqBEA8P+r9bnds7zk8oiuZQW1AQAAAHjljD2BljVrdjrumTAhYsQIN3sCDGUTJ0SMHp1m199QTBcAiIiIPI/2y74UWW1VEncf/vmISlsxnQCA1eq7HBN5uX31OWvWorr43AIbAQAAALwyxp5Ay7p5VnqevnsxPQAYOJVKFnvNSLPrrnezJwDFKd95cVSWXJZktd1OieZWM17gvwAABlTHmKjvdEQSVW75U0Tub0kAAACgtRh7Ai3p6afzuOPONJu+u1s9AYaDffdOf97Pmh3R0+NNOgAK0LMi2i/7UhI1R2wYPQd8rKBCAMDzqfd9lPsTt0Xp0YUFtQEAAAB4ZYw9gZY0e0764fuOjmcf7QvA0Ddjz4jsOXvP7u6IufOK6wPA8NV29bejtPLxJOs9+FMRnWOLKQQAPK/G1ntHc8wWSVa95Q8FtQEAAAB4ZYw9gZY0a3Z6g9vkSRFtbW72BBgOxo7N1hr4e5Q7AAOt9NCcqM77bZLVtz0g6rscU1AjAOAFZaWo7XZyElVuPS+itqqgQgAAAAAvn7En0JJunpWe95hu6AkwnOzT51Hu114fkecGnwAMkEZvtF/8uchizWtPXumMnsP+Nb1+GgAYNOoTT4o8W/OWSNa7Miq3/7XARgAAAAAvj7En0HIeeTSPBx5Ms+m7F9MFgGLsu3d6fuihiPvvL6YLAMNP9eafRXnpHUnWu+8HIl9vy4IaAQB/Sz56s2hsu3+SVRd4lDsAAADQOow9gZYza3Z6HjMmYqcdi+kCQDF23DFio43S7Nrri+kCwPCSPXl3tF3/X0nWGDc+aru/paBGAMBLVdvt1ORcfmh2ZEuXFNQGAAAA4OUx9gRazqxZ6WN6d58WUSp5VCLAcJJlWeyzV5pdd73HuAPQz/JmdFz82cgavWuirBQ9h/9bRKlSYDEA4KVobH9wNEdsmGTVW/5YUBsAAACAl8fYE2gpeZ6vdbPnHrsbegIMR/vsnf78nzsvYuVKg08A+k913m+i/OCsJKtNe0s0N92toEYAwMtSrkZ9wolJVF10VsRzPsgBAAAAMFgZewIt5e57IpY+mWbTpxdSBYCC7TE9olpdc240Im64qbg+AAxt2fIHo23mt5Ksud5W0bvfhwpqBAC8ErVJpyTnrGtZlJdcXlAbAAAAgJfO2BNoKTffnJ7HjYvYcotiugBQrBEjspg2Nc2uucbNngD0gzyP9os/F1ltVRL3HPHFiGpnQaUAgFciX3+7aGyxR5JVF/yhoDYAAAAAL52xJ9BSbrgpHfHM2CMiyzzGHWC42n+/9DXg2usj6nWDTwDWrcqis6Ny7zVJVpt0WjS23qugRgDAq1GbdGpyLt97TWTLHyyoDQAAAMBLY+wJtIyenjzmzkuzGTMMPQGGs/32Tc8rVkQsuKWYLgAMTdnKx6P9iq8mWXPkuOg58OMFNQIAXq36TkdG3j569TmLPCoLzyquEAAAAMBLYOwJtIz5CyJ6etacS6WIPXYvrg8AxdtkXBY775RmV3uUOwDrUPtlX4qs5+kk6zn88xHPGYgAAC2m2hm1XV+bRrf8MaLZKKgQAAAAwN9m7Am0jJtuTsc7u+4SMWaMmz0Bhru+t3tefU1Enht8AvDqlW+/KCp3XJRktV2OjcYOhxTUCABYV+p9HuVeWvFwlO+7rqA2AAAAAH+bsSfQMm64KT3P2LOYHgAMLgfsnw7/H3wo4p57CyoDwNDR9VS0X/bFJMo714+eQ/6loEIAwLrUHDchGuMmJFl1wR8KagMAAADwtxl7Ai3hiaV5LFmSZjP2dKsnABE77RgxbuM0u/qaYroAMHS0X/kfUVr1RJL1HPLpiBEbFNQIAFjXan1u9ywvuSyyVUsLagMAAADw4ow9gZZw083peeTIiAnji+kCwOCSZVnst1+aXXOtx7gD8MqV77k6qovOSrL69gdHfZdjiikEAPSL+i7HRl7pWH3OmrWoLDqnwEYAAAAAL8zYE2gJN92Ujnam7x5RqbjZE4Bn7b9v+pqwcFHEk08afALwCvSujPaL/zWJ8rZR0XPY5yIyf4MAwJDSMSbqO78miaq3/CEi9/ckAAAAMPgYewKDXrOZx02z0mzGHt5kBWCNaVMjRoxYc87ziGuvK6wOAC2sbeY3o7Ti4STrOfDjkY/etKBGAEB/qu12SnIuPXlXlB6aU1AbAAAAgBdm7AkMencuiVi2LM1mzCimCwCDU1tbFnv1eW242qPcAXiZyvddF23zfpNk9a32ivqk0wpqBAD0t+YWe0Rz7DZJVr3lDwW1AQAAAHhhxp7AoHfjTel5yy0iNt/MzZ4ApPo+yv2mmyO6uw0+AXiJep6J9r9+OonySmf0HPFvHt8OAENZlkVt0qlJVLntwoieFQUVAgAAAHh+xp7AoHfjTelQZ8aeBRUBYFDbZ++I8nN+u+3pibh5VnF9AGgt7Vd9fa3Ht/ce8NHIx25dUCMAYKDUJ5wQeamy+pzVu6Jy6/kFNgIAAABYm7EnMKh1deUxf0GazdjTrToArG3MmCwmTUqzq69xsycAf1v5nmuiuuDMJKtvOSNqU99QUCMAYCDlIzeOxvYHJ1l1wZkRub8pAQAAgMHD2BMY1ObMjajX15zL5YjdpxVWB4BBbv/90g8EXHNdRLPpzTkAXkTPimi/6DNJlFdHRM9rvhyR+WcTABguapNOS87lxxZH6dFbCmoDAAAAsDbvWgCD2o03pwOdSbtFjBjhZk8Ant8B+6XnZcsibllYTBcAWkP7lf8RpWceSbKeAz8e+XpbFtQIAChCY5v9ojlm8ySrzv9dQW0AAAAA1mbsCQxqN96Ynvfcw9ATgBe2xRZZbLdtml01082eADy/8t1XRfWWPyZZfeu9oz75dQU1AgAKUyqvdbtn5da/RPSsKKgQAAAAQMrYExi0Hnk0j/vuT7O99iymCwCt48AD0vOVMyPy3OATgD66n472iz6bRHl1RPQc+aWIzIfMAGA4qk88OfJSZfU5q3dFZfG5BTYCAAAAWMPYExi0rr8hPa83JmKnnYrpAkDrOOjAdKDz8MMRd95ZUBkABq32K74apZWPJVnPQf8c+ZgtCmoEABQtHzUuGtsfkmTV+WdG+AAhAAAAMAgYewKD1vU3pP+IuueeEeWyG3YAeHE77Rix2aZpdtXV3pgDYI3yksujuuisJKtvs1/U+zy6FQAYfmqTX5ecy0/cFqWH5xXUBgAAAGANY09gUOrtzePmWWm2796GngD8bVmWxYEHptmVVxXTBYBBqOupaL/kc0mUt42KniO/6PHtAEA0ttknmuttlWTVBWcW1AYAAABgDWNPYFCaNz+iu3vNOcsiZuxZXB8AWstBB6Rjnbvujrjvfrd7AhDRfsVXorTy8STrOfhTkY/erKBGAMCgkpWi1ue278ptF0R0Ly+oEAAAAMCzjD2BQem669NBzoTxEWPHumUHgJdmt4kRG26QZlfNLKYLAINH+fa/RnXxuUlW3+7AqE88qaBGAMBgVJ94UuSlyupzVu+O6uJzCmwEAAAAYOwJDFLX3ZCe9/EIdwBehlIpiwP2T7MrZ7rZE2A4y555LDr6Pr69fXT0HP5vHt8OACTykRtFfcfDk6wy/3cRub8rAQAAgOIYewKDzgMP5HH//Wm2917FdAGgdR10YDrcWbw44tHHvDEHMCzlebRf9JnIup9O4p5DPhP56E0KKgUADGb1yacn5/LSO6P00JyC2gAAAAAYewKDUN9bPTdYP2LnnYrpAkDrmjY1YtSoNJt5dSFVAChYZf5vo3LPzCSr7XxU1McfV1AjAGCwa2y1VzTHbp1k1fm/K6gNAAAAgLEnMAhdd31669reez/7OF4AeDkqlSz23y/NrrzKzZ4Aw0227O5ov/JrSdYcuXH0HPavHt8OALywrBS1SentnpXbL4zoeqqYPgAAAMCwZ+wJDCpdXXnMnZtm++zlDVgAXpmDDkhfQ+bNj1j2lMEnwLDRrEfHBZ+MrN6dxD2v+UpE5/oFlQIAWkVt4kmRl6urz1mjN6qLzy6wEQAAADCcGXsCg8rsORG9tTXncilizz2K6wNAa5uxZ0RHx5pzsxlxzTXF9QFgYFVv+FGUH5mfZL1T3hiNbfcvqBEA0FJGbBD1HY9Iour8MyNyHyIEAAAABp6xJzCoXHdD+g+lkyZFjBrlZk8AXpn29iz23ivNrpzpTTmA4aD0yIJou/6HSdZcf9voPfBjBTUCAFpRffLrknPpybui9ODNBbUBAAAAhjNjT2DQyPM8rrs+zfbZ29ATgFfnwD6Pcr/p5ohnnjH4BBjSal3RccEnIssbq6M8K0f3Uf8RUe0ssBgA0GoaW+4ZzfW3S7Lq/DMLagMAAAAMZ8aewKBx9z0Rjz6aZvvs9bzfCgAv2b57R1Qqa871esQ11xbXB4D+13bVN6K07J4k6937PdHcbHIxhQCA1pVlUZt8ehJV7vhrRNeyggoBAAAAw5WxJzBoXH9Deh43LmK77Z7/ewHgpRo1KosZe6bZZVe42RNgqCrfPTPa5v06yRqbTo7ajHcV1AgAaHW1CSdEXq6uPmeNWlQXnlVcIQAAAGBYMvYEBo3rrk+HN/vsFZFlHuMOwKt36CHp68mNN0WsWGHwCTDkdC2L9os+nUR5pSO6j/5qxHMGGgAAL0vn+lHf6TVJVF1wZkTu70oAAABg4Bh7AoPCM8/kMX9Bmu2zt6EnAOvG/vtGVJ+z8anVIq6+prg+APSDPI+OSz4fpZWPJ3HPgR+PfH2PDAAAXp2+j3IvLbsnyvdfX1AbAAAAYDgy9gQGhRtvjmg01pyr1YjdpxXXB4ChZdSoLPaakWYe5Q4wtFQW/D4qd1yUZPVt9o/6lDcU1AgAGEqaW+wRjQ13SLLq3F8X1AYAAAAYjow9gUHhmmvSwc3UKREjRrjZE4B159CD136U+3KPcgcYErIn74r2K/49yfKOsdHzmi9HZP6uAADWgSxb60Mk5SWXRbbikYIKAQAAAMONsSdQuHo9j2v7PPFo//28IQvAurXfvhFtz3mUe6MRMfPq4voAsI7Ue6Pj/I9FVu9O4u7XfDnyUeMKKgUADEW18SdEXh2x+pzlzajO/12BjQAAAIDhxNgTKNyCWyJWrEiz/fYtpgsAQ9fIkVnstVeaXXa5mz0BWl3b1d+K8uOLk6x3yhuiscOhBTUCAIas9lFRn3B8ElUW/CGi0VtQIQAAAGA4MfYECnd1n0e477xTxKabuNkTgHXvsEPS15ebZ0U8/bTBJ0CrKt89M9pm/yLJGhvuEL0HfqKgRgDAUFebnD7KvbTqiajccXFBbQAAAIDhxNgTKFSe5zGzz9jTrZ4A9Jd994loa1tz9ih3gNaVrXwi2v/6qSTLy23Rc8y3IqodBbUCAIa65sY7R2OL6UlWnfebgtoAAAAAw4mxJ1CoJXc14sEH02z//dzqCUD/GDEii333TrPLrnCzJ0DLyfNo/+u/RGnV0iTuPfAT0dx454JKAQDDRW3qG5Nz+cFZUXr89oLaAAAAAMOFsSdQqMuvqCXnjTd69jHuANBfDunzKPdZsyKWPWXwCdBKqnPOiMo9M5Osvt1Baw0vAAD6Q33Hw6M5YqMkq877dUFtAAAAgOHC2BMo1BVX9ibn/faLyDI3ewLQf/bdO6K9fc250Yy4auYLfz8Ag0vp8VujbeY3kqw5cqPofs1XIvwtAQAMhHJb1CedlkSVxedG9KwoqBAAAAAwHBh7AoV5Ymkz5s2vJ9n++3pzFoD+1dmZxb77pNlll7vZE6Al1Lqi4/yPRdZInxDQ85qvRozYoKBSAMBwVJt8euRZefU5q62K6qJzCmwEAAAADHXGnkBhrrqqN/LnbGs6OyN2n1ZcHwCGj8P6PMp9ztyIJ580+AQY7Nqv/I8oPbkkyXqn/300tt2voEYAwHCVj940GjsckmTVeb+J5B88AQAAANYhY0+gMJf3eYT7XjMi2trc7AlA/9t7r4jOjjXnZjPisisKqwPAS1C+/a9Rnf+7JGuMmxC9+3+ooEYAwHBXm/rG5Fx6ckmU77+xoDYAAADAUGfsCRSiuzuPa69LH724/36GngAMjI6OLA7YP80uvsTtKwCDVfbUfdFx0WeSLK90Rvcx34gotxXUCgAY7hpb7R3N9bdLsuq83xTUBgAAABjqjD2BQtw8K6K7e825VIrYZ6/i+gAw/BxxePohg4WLIh580OATYNCp90bH+R+JrPeZJO459NORb7DdC/xHAAADIMuiNuUNSVS+85LIVjxaUCEAAABgKDP2BApx9TXpmGbypIj11nOzJwADZ889Isaul2YXX1pMFwBeWNvMb0T50YVJVht/XNQnnlxQIwCANWoTToi80rn6nOWNqC74fYGNAAAAgKHK2BMYcM1mHtdcl44999vX0BOAgVWpZHHIIWl20cV55LnbPQEGi/IdF0fbnDOSrLn+dtFz2OciMn9DAACDQMeYqI8/LokqC86MaNQKKgQAAAAMVcaewIBbtDjiySfTbP/9iukCwPB2ZJ9Hud93f8TtdxRUBoBE9vSD0XHRZ5IsL7dH92u/HdE2sqBWAABrq015fXIurXw8KndeXFAbAAAAYKgy9gQG3FUz0xvTttk6Yqst3coDwMDbbWLEZpum2UWXuNkToHCN3ug4/yOR9SxP4p5D/iWaG+9SUCkAgOfXHDc+GptPS7LqnF8V1AYAAAAYqow9gQGV53lceVWaHXSgoScAxciyLA4/PM0uvSyi0TD4BChS29XfifIj85OstssxUZ90WkGNAABeXG3qm5Jz+aHZUXp0YUFtAAAAgKHI2BMYUEvuinjwoTQz9gSgSH0f5f7EExFz5xVUBoAo33V5tM36WZI1x24dPYd/ISLztwMAMDjVdzoymiPHJVl1zhkFtQEAAACGImNPYEBdeVV6U9rmm5dil50LKgMAEbHdtlnstGOaXexR7gCFyJY/FB0XfirJ8nI1ul/77Yj2UQW1AgB4CcrVqE19QxJVbvtLZCufKKgQAAAAMNQYewIDqu8j3A8/tC0yt/MAULAj+tzuecWVET09Bp8AA6rRGx3nfTiy7qeTuPegf47muAkFlQIAeOlqk06PvNy2+pw1alGZ/7sCGwEAAABDibEnMGDuuz+Pu+5Os8MPa3v+bwaAAXT4oemTgZ9ZGXH9DcX1ARiO2q78WpQfmZ9k9Z1eE7UpbyyoEQDAyzRig6jvemwSVef/NqLRW1AhAAAAYCgx9gQGzFUz0/NGG2UxbWqlmDIA8BzjxmUxZXKaeZQ7wMCpLD4v2ub+Ksma620d3Uf8W7rGBwAY5GrT3pycSyufiMrtFxXUBgAAABhKjD2BAXPlVelo5rBD2qJU8sYtAIPDkUekr0nXXhexYoXBJ0B/y5beGe0X/2uS5eX26D7+uxEdYwpqBQDwyjTHTYjGFtOTrDrnjILaAAAAAEOJsScwIB55NI/Ft6aZR7gDMJgcfFBE5TkXTvfWIi67orA6AMND78roPPdDkdW7krjn8M9Fc+NdCyoFAPDq9E57a3IuPzI/Sg/PK6gNAAAAMFQYewIDYubV6Xn06Ig996gWUwYAnseY0Vnst2+aXXChmz0B+k2eR/tFn4nSk3clcW23U6M+8aSCSgEAvHqNHQ+N5ujNkqw655cFtQEAAACGCmNPYED0fYT7/vtlUa16hDsAg8vRR6WvTbcsjLjvfoNPgP5QnfPLqN5+YZI1xk2InkM/U1AjAIB1pFSJ2pQ3JFHl9gsje+axggoBAAAAQ4GxJ9Dvli3LY/6CNDvoQENPAAafvWdEjB2bZhf81dgTYF0rPTQn2q76WpLl7WOi+7jvRlTaC2oFALDu1CadFnmlY/U5a9ajOu+3BTYCAAAAWp2xJ9Dvrro6otlcc+7siJixR3F9AOCFVCpZvOaINPvrXyMaDYNPgHUlW7U0Os77cGTNepJ3H/3VyNfbsqBWAADrWOfYqI8/Lokq838XUe8pqBAAAADQ6ow9gX532eXpQGbvvSPa293sCcDg1PdR7o89HjF7TkFlAIaaZj3az/9YlJ55NIl7Z7w7GtsfUlApAID+UZv65uRc6noyKrddUFAbAAAAoNUZewL9atmyPObMTbNDDzb0BGDw2nGHLHbeKc0uuNDNngDrQtvMb0bl/uuTrL7V3tG77wcKagQA0H+aG+8c9a32TrLqnP+LyP2NCQAAALx8xp5Av7ryqrUf4b7P3i/8/QAwGBz1mvSDCVfOjFi50ptxAK9GZfF50Tbr50nWHDkueo79RkSpXEwpAIB+VpuW3u5ZfmxxlB6aXVAbAAAAoJUZewL96tI+j3Dfd9+Ijg43ewIwuB1xeET5Obujnp6Iy68orA5Ayys9tijaL/5skuXlanQf/73IR2xYUCsAgP7X2P7gaK63ZZK1zf6/gtoAAAAArczYE+g3S5fmMXdemh16iKEnAIPf+mOz2HefNPuLR7kDvDJdy6LjnA9EVu9O4p5D/zWam00pqBQAwAAplaM29U1JVL7zksiefqCgQgAAAECrMvYE+s0VV0Xkz9nFdHZG7D2juD4A8HIc3edR7vMXRDzwgMEnwMvSrEfH+R+J0vKHkrg25fVRn3RqQaUAAAZWbbdTIq+OWH3O8mZU3e4JAAAAvEzGnkC/uazPI9wP2C+ivd3NngC0hn32jhi7XppdeJGxJ8DL0TbzW1G57/oka2y+e/Qc/KmCGgEAFKB9dNT6fNClessfI7qXF1QIAAAAaEXGnkC/ePzxPOYvSDOPcAeglVSrWRxxeJpd8NeIZtPgE+ClqCw+L9pm/SzJmiPHRfdx34kotxVTCgCgILVpb408W/OWTFZbFdUFZxbYCAAAAGg1xp5Av+j7CPeRIyNm7FlcHwB4JY45Kv2gwqOPRsyaXVAZgBZSemxxtF/82STLy9XoPu67kY/cuKBWAADFydfbIuo7HZlk1Tm/jGjUCmoEAAAAtBpjT6BfrPUI9/0j2trc7AlAa9lppyx23CHNzjnPzZ4AL2rVk9Fxzgciq3cncc+hn43m5lOL6QQAMAjUpr8tOZeeeTQqt19YTBkAAACg5Rh7Auvco4/lseCWNDv0YENPAFrTca9NX8NmXh2xbJnBJ8DzavRG57kfjNLyB5O4Nvl1UZ90WkGlAAAGh+ZmU6Kx+e5JVp318/QRSQAAAAAvwNgTWOcuvyI9jxoVsecehVQBgFftyMMj2tvXnOv1iAv+WlwfgEErz6P90n+L8oOzkrix2dToOeRfCioFADC49Pa53bP82KIoP3BTMWUAAACAlmLsCaxzl1yafhL9wAMiqlU3ewLQmkaPzuKQg9Ps3PPzyN28ApCozjkjqrf8McmaozaN7uO/F1FuK6gVAMDg0tjh0Giut3WSVWf9rKA2AAAAQCsx9gTWqfvuz+PW29LssEMMPQFobccdm76W3X9/xNx5BZUBGITKd8+Mtiv/I8nySmd0n/iDyEduXFArAIBBqFSO2u5vTaLKXVdE9uTdBRUCAAAAWoWxJ7BOXXxJesvZ+utHTN+9oDIAsI5MnhSx7TZpdu55bvYEiIjInrwrOs7/SGR5M8m7j/r3aI6bUFArAIDBq7bbSZG3r5dkbbN/UVAbAAAAoFUYewLrTJ7ncdHFaXbYoRGVips9AWhtWZbFa/vc7nnFlRHLlxt8AsNc11PRedZ7Iut9Jol79nl/NHZ+TUGlAAAGueqIqE0+PYkqC8+KWPVkMX0AAACAlmDsCawzixZHPPhQmh15uKEnAEPDUUdGVKtrzr21iAsvKq4PQOEateg4/8NReuq+JK7tfFTU9n5vQaUAAFpDbdqbIy+t+SMza/REdd5vCmwEAAAADHbGnsA60/cR7ltuETF+14LKAMA6NnZsFgcekGbnnpdHnrvdExie2q74alTuuz7JGptMjJ7XfCUi86EvAIAXk48aF/Vdj0my6txfR9S6C2oEAAAADHbGnsA6Ua/ncenlaXbE4c8+9hYAhorjX5u+rt19T8TCRcV0AShSdc6vom3er5OsOXLj6D7+BxHVzoJaAQC0ltr0tyXnUteTUVn4p2LKAAAAAIOesSewTsyaHbFsWZodeYShJwBDy7SpEVtsnmbnnOtmT2B4Kd91ebRd8ZUky8vt0X389yMfvUlBrQAAWk9z412jvs3+SdY262cRzXpBjQAAAIDBzNgTWCcu6vMI9/G7Rmy1pbEnAENLqZTFa49NX98uvTxixQqDT2B4KD22KDrO/1hkeTPJe478UjQ3m1xQKwCA1lXb8x3JufT0A1G5/a8FtQEAAAAGM2NP4FXr7s7jqplpdsThhp4ADE3HHBVRLq859/REXHhRcX0ABkq24pHo+PN7IqutSvKefd4f9fGvLagVAEBra2w1IxqbTEqy6k3/G5H7UCEAAACQMvYEXrWrr4no6lpzLpUiDjukuD4A0J823DCLA9Kn7MWfz8oj90YcMJT1royOP/9jlFY+lsS18cdHbe/3FlQKAGAIyLLo7XO7Z/nxxVG+99qCCgEAAACDlbEn8KpddHE6bpm++7NDGAAYqk4+MX2du+/+iJtnFVQGoL8169Fx3oej/MRtSdzYcs/oOeKLEZnf/QEAXo3GjodFc+w2SVa96ScFtQEAAAAGK2NP4FV58sk8brgxzY70CHcAhrhpUyO23TbN/nSWmz2BISjPo/3yL0flnplJ3Fx/2+g67nsRlbaCigEADCGlcvTu+Q9JVLn/+ig9sqCgQgAAAMBgZOwJvCoXXRLRaK45d3ZEHHRgcX0AYCBkWRYnnZB+uOGaayMeedTgExhaqrN/EdV5v02yvHP96DrpRxGdY4spBQAwBNXHnxDNkRsnWZvbPQEAAIDnMPYEXrE8z+MvF6SjloMOihgxws2eAAx9Rx0Z0dm55txsRpx9rrEnMHSU77g42q78WpLl5bboOv77kY/duqBWAABDVKUtaru/NYnKd1wc2bK7CyoEAAAADDbGnsArdvsdEXf1+bfGY44y9ARgeBg5MoujXpNm550f0dtr8Am0vtKDs6LjLx+PLNKfaT1H/Xs0t9i9oFYAAENbbfLrIm8btfqcRR5tN/+swEYAAADAYGLsCbxiF1yYvvG76SYRU6cUVAYACtD3Ue7LlkVccVVBZQDWkWzpndF51nsja/Qkec/+H476LscU1AoAYBhoHx21Ka9PosqisyJ75rGCCgEAAACDibEn8Ir09uZx0SVpdvRREaWSmz0BGD623y5b64MOfz7LzZ5A68pWPBKdf3pXZD3Lk7y22ylR2/OdBbUCABg+atPeEnm5uvqcNWpRnXNGgY0AAACAwcLYE3hFrrs+Ynn6/m8cdaShJwDDz8knpa9/C26JuP0Og0+gBXUvj44/vStKKx5O4vr2B0fP4Z+PyPy+DwDQ3/JR46I+4cQkq877TUT308UUAgAAAAYNY0/gFbngr+mIZeqUiC228OYvAMPPgftHbLhhmv3+D8aeQIup90TnOe+P8tI7krix6eToPvabEaVKQcUAAIaf3j3+PvJY82+tWe/KqM75ZYGNAAAAgMHA2BN42Z58Mo/rrkuzo48y9ARgeKpUsjjphPR18JLLIpYuNfgEWkSzER0X/HOUH7gpjdffNrpO+u+I6oiCigEADE/5+ttGfZejk6xt9hkRvSsLagQAAAAMBsaewMt28aURjeaac0dHxCEHFdcHAIp2wnERbdU151ot4qxzjD2BFpDn0XbFV6Nyx1+TuDlyo+g6+ScRnesXVAwAYHirzXh3cs56no7q3F8X1AYAAAAYDIw9gZclz/P4ywXpeOWgAyNGjHCzJwDD1/rrZ3HEEWl21jkRPT0Gn8DgVr3pJ9E2N30kaN42MrpP+p/I19uioFYAADQ33jnqOxyWZNVZP4+odRVTCAAAACicsSfwsiy+NWLJXWl2jEe4A0Ccdkr6erhs2bOPcwcYrCrzz4z2q7+VZHmpGt3H/2c0x40vqBUAAP+/3r3fk5xLXU9GZcHvC2oDAAAAFM3YE3hZzj0vvaFss80ipk0tpgsADCY77pDF9N3T7Mw/5JHnbvcEBp/y7RdG+yWfXyvvOerfo7H1PgNfCACAtTQ3mRj1bQ9IsupNP4m81l1QIwAAAKBIxp7AS7ZqVR6XXJpmrz0mi1LJzZ4AEBFx+qnpa+KSJRFz5hbTBeCFlO+eGR1/+URkkY7Rew7656jvemxBrQAAeD5r3e75zGPRnPO7gtoAAAAARTL2BF6yiy+N6HrOh8bLpYhjjy6uDwAMNvvsHbHlFml25h/c7AkMHqUHZ0fHuR+MrFlL8t693h216W8rphQAAC+oufm0qG+1V5rN/M/IG7UX+C8AAACAocrYE3jJ+j7CfZ99IjbayK2eAPD/K5WyOO2U9LXxmmsjHnjA4BMoXunxW6PzrH+MrJ4+9rN3yhujd98PFdQKAIC/pbbXP6bBUw9EPu8PxZQBAAAACmPsCbwkd9yRx623pdnxrzX0BIC+jj4qYtTINec8j/id2z2BgmXL7omOP74jsp4VSV7b9bXRe+inIzK/2wMADFaNrfaKxubT0uzK70U0GwU1AgAAAIpg7Am8JOecn45Uxm0csdeMgsoAwCA2YkQWx702zc7/S8Sypww+gWJkKx6Jzj/8fZRWLU3y+vYHR89rvhKR+acBAIBBLcuit+/tnk/eHeXbLiimDwAAAFAI7+gAf1N3dx4XX5xmxx4TUS67/QcAns9pp2RRLq859/ZG/OnPxp5AAVY9GZ1//IcorXg4iRtb7BHdx347olwtqBgAAC9HY9sDojFuQpK13fDfbvcEAACAYcTYE/ibLr8i4pmVa85ZFnHs0YaeAPBCxo3L4sjD0+yPf47o6jL4BAZQ17Jnb/R88q4kboybEF0n/jCi2lFQMQAAXrbnud2ztPTOqNx+YUGFAAAAgIFm7An8Teeclw5TZuwZsemmxp4A8GLe8Lr0tXL58ojzPWEPGCjdy6Pzj++I8hO3JXFzg+2j6+QfR7SPKqgYAACvVGPHw6K50c5J1nbdD9zuCQAAAMOEsSfwopbclceCW9Ls+NcaegLA37L99lnsu3ea/fZ3edTrbvcE+lnvyuj80zuj/NiiJG6O2Ty6TvlJxIgNCioGAMCrkpWid9/3J1Fp2d1RufX8ggoBAAAAA8nYE3hRf/pzOkjZYP2I/fYtqAwAtJg3viH9gMQjj0ZcfmVBZYDhobYqOv/87ig/Mj+Jm6M2ja7TfhH56M0KKgYAwLrQ2PGIiM0mJVnb9T+IaNYLagQAAAAMFGNP4AU980weF12cZscfF1GpuNkTAF6KKZMjJoxPs1//Jo88d7sn0A9q3dFx1vui/OCsJG6O3Di6TvtZ5OttWVAxAADWmSyL8qEfT6LSU/dFZdE5BRUCAAAABoqxJ/CCLvhrRFf3mnO5FHHCcYaeAPBSZVkWb+pzu+cdd0bcdHNBhYChq94THed8ICr3X5/EzREbRtepP4t8/W2L6QUAwDqX7XJEZFtMSbK26/8rolErqBEAAAAwEIw9geeV53n8+az01rED9o/YeGNjTwB4OfbfL2LLPpfpnfErN3sC61CjNzrO+6eo3Ht1EucdY6P7lJ9GvuEOBRUDAKA/ZFkWpUM/kWSl5Q9GZeGfC2oEAAAADARjT+B53Twr4r770+zkkww9AeDlKpezeOPr09fQOXMj5s03+ATWgUZvdJz3kajcdUUS5+1jouvU/43mxjsX0wsAgH6V7XRoNDbrc7vnDf8dUe8tqBEAAADQ34w9gef1pz63em67bcS0qYVUAYCWd9SRERtvlGa/OMPYE3iV6r3Rce6HorLk0iTO20ZG18k/jua4CQUVAwCgv2VZFr37fSjJSisejsrCPxbUCAAAAOhvxp7AWh55NI9rrk2zk0/MIsvc7AkAr0RbWxZvfEP6OnrjTRGLFht8Aq9QvSc6znn/2jd6Vkc8O/TcbHIxvQAAGDDNrfeJxhbTk6zthh9F1HsKagQAAAD0J2NPYC1nn5NHs7nmPGLEszeSAQCv3HHHRqy/fpr93y+NPYFXoNYdHWe/Lyr3zEzivDoiuk76UTQ3n1ZQMQAABlSWRe++H0yi0jOPRnX+mQUVAgAAAPqTsSeQ6O3N49zz0+yoIyNGjHCrJwC8Gh0dWbzhdenr6dXXRNxxp8En8DLUuqLj7PdE5d5rknj1o9u33KOgYgAAFKGx1Yyob7VXklVv+p+I2qqCGgEAAAD9xdgTSFx6WcRTT6XZyScZegLAunDi8RFjxqSZ2z2Bl6y2Kjr+/I9Rue/6JM7bRkXXyT+J5ha7F1QMAIAi9e77geRcWvlEVOf8qqA2AAAAQH8x9gRWy/M8fvv7dHAyffeIbbcx9gSAdWHEiCxOPzV9Xb3iyoh77jX4BP6G3pXR+ad3R+WBG5M4bx8TXaf+NJqbTy2mFwAAhWtuMT3q2+yXZG03/SSi++mCGgEAAAD9wdgTWG3W7IglS9Ks7yAFAHh1Tj05YtTINec8j/jFGcaewIvoXh6df3pnlB+8OYnz9vWeHXpuOqmgYgAADBa9+384OWc9y6Ptxh8X1AYAAADoD8aewGpn9rnVc6utIvbZu6AyADBEjRqVxSknp9kll0bcfY/BJ/A8Vj0Znb9/W5QfmpPEecfY6DrtZ9HcZGJBxQAAGEyam0yM2i5HJ1l1zi8jW/FoQY0AAACAdc3YE4iIiHvvzePa69Ps9FOzKJXc7AkA69rpp2YxYsSac55H/PTnxp5AKlvxSIw48y1Rfnxxkued60fXaT+P5rjxBTUDAGAw6t33g5Fn5dXnrNETbdf/V4GNAAAAgHXJ2BOIiIgz/5AOTMaMiTjqyILKAMAQt956WZx+appdfkXE7XcYfALPypbdG52/e1OUnrwryZsjN46u034RzY13KagZAACDVb7+tlGflP6xWbnlj5Etu7ugRgAAAMC6ZOwJxFNP5XHhRWl2wvERnZ1u9QSA/vK607IYNSrNfvLTZjFlgEGl9MTt0Xnmm6O0/KEkb47ZIrpe98tobrRTQc0AABjsevd+b+SVjtXnLG9E2zXfK7ARAAAAsK4YewJx9rkRPT1rzpVKxCknGnoCQH8aPTqLN74+fb295tqI+QtqBTUCBoPSIwui88y3RmnlE0ne3GD76HrdLyMfu3VBzQAAaAX5qHFRm/aWJKvefmGUHl1YUCMAAABgXTH2hGGutzePP/05fWTsYYdGbLSRsScA9LdTT44Yu16a/ecPuoopAxSufP+N0fn7t0XW/XSSN8aNj1WnnxH56E0LagYAQCvp3fMfIm8fk2RtM79ZUBsAAABgXTH2hGHukksjlj6ZZq87zdATAAbCiBFZvOmN6evutdfVYtZst3vCcFO+89Lo+NO7IqutSvLG5rtH12m/iBixQUHNAABoOR3rRe+MdyZR5b7ronzP1QUVAgAAANYFY08YxprNPH71m/RWz2lTI3beydgTAAbKSSdEbNhnw/W976+KPM+f/z8AhpzK/DOj49wPRtboSfL6NvtF1yk/jmgfXVAzAABaVW3qm6I5clyStV35tYhmo6BGAAAAwKtl7AnD2MxrIu69L83e+HpDTwAYSB0dWbz1zenr782z6nHd9QUVAgZOnkf1uh9ExyWfiyxvJl+q73hEdJ/wXxHVEQWVAwCgpVU7o3e/DyVReekdUVn4p4IKAQAAAK+WsScMU3mexy9/ld4YtsMOEXvvVVAhABjGjnttxCabpNkPf9SMRsPtnjBkNRvRfukXov2676/1pdrEk6P7td+KqLQVUAwAgKGiPuGEaGy8a5K1Xfu9iN6VBTUCAAAAXg1jTximZs2OWHxrmr35jVlkmZs9AWCgtbVl8Y6/T1+D77o74sKLCioE9K9ad3Sc+6Gozv/dWl/q3esfo+fIL0WUKgUUAwBgSCmVo/fAj6fRyiei7eafFlQIAAAAeDWMPWGYOqPPrZ6bbx5xyEEFlQEA4sjDI3bcIc3+96d59PS43ROGlK6novOP/xCVJZcmcR5ZdB/62WcftekDWAAArCONbfaN+rYHJFn15p9GtuLRghoBAAAAr5SxJwxDi2/NY9bsNHvj67OoVLypDABFKZezeM+701/PH3s84vd/LKgQsM5lKx6OzjPfHOWH0l/G83JbdB/3nahPfWNBzQAAGMp6D/xY5Nmavzezevezj3MHAAAAWoqxJwxDfW/13HCDiKNfU1AZAGC1vWZE7L1XNcl++as8nn7a7Z7Q6kqPLozOX78uykuXJHnePjq6TvlJNHY6sqBmAAAMdc2Ndo76bqckWWXhn6P0+K0FNQIAAABeCWNPGGbuuTePq2am2emnZdHe7lZPAChalmXxkX8akWTPrIz4v18ae0IrKy+5PDp/95YorXw8yZsjx0XX6b+M5pZ7FtQMAIDhonffD0ReXfP3ZhZ5tF35HxG5vzcBAACgVRh7wjDT91bPUaMiTjy+oDIAwFomTqjEMUe3Jdmfzop48EFvwEHLyfOozv6/6Dj7fZHVu5IvNTfYPrre8OtobrxzQeUAABhO8pEbR++e/5Bklfuuj/KSSwtqBAAAALxcxp4wjNx3Xx4XX5JmJ58YMXKkWz0BYDD50PtHRKWy5lyrRfzgv409oaU069F2+Zej/Yp/jyzS///Wt9orVr3+15GP2aKgcgAADEe16W+L5qhNkqz9yv+IqPcU1AgAAAB4OYw9YRj5+f/l0WyuOXd2Rpx+qqEnAAw2W25ZjlNOSl+jr5oZMWu2wSe0hN6V0XH2+6Jt7q/W+lJt4knRffL/RHSsV0AxAACGteqI6D3gY0lUevqBqM76eTF9AAAAgJfF2BOGiXvvzeOSy9Ls1JMjxo419gSAwejtf5fF2D5bsO99P4963eATBrNsxaPR+bu3ROXuq9b6Ws9+H4qeI78cUW4roBkAAETUdz02GpvvnmRtN/5PZCseLagRAAAA8FIZe8Iw8fMz1r7V8/WnG3oCwGA1enQW7/iH9LV6yV0R551fUCHgbyo9PC86f31alB9fnOR5uRrdx3wjanv9Y0Tmd3AAAAqUZdFzyL9EHmt+L81qq6Jt5jcLLAUAAAC8FMaeMAzcc28el1yaZqeeErHeet5oBoDB7LhjI3bYIc1+/L95LF/hdk8YbCqLzorOM98apZWPJ3neMTa6Tv1Z1Hc9tqBmAACQam4yMeq7nZJk1VvPjdJDcwpqBAAAALwUxp4wDPzsF3nkz9mEjBgR8frTDD0BYLArl7P40PvT1+ynl0f8/BfGnjBoNBvRduXXouPCT0XW6E2/NHabWPWG30Zzi+kFlQMAgOfXu/8/Rd4+OsnaL/9yRN58gf8CAAAAKJqxJwxxd9+Tx2WXp9mpJ7vVEwBaxe7TsjjowDT745+ffY0HCta9PDrOek+0zfrZWl+qb7V3rHrDbyJff5sCigEAwIvLR2wYvXu/N8nKjy6Myi1/KqgRAAAA8LcYe8IQ99Ofp7d6jhwZ8frTDT0BoJW89x+zaKuuOTcaEd/6Th55bvAJRcmevDtG/OZ1Ubln5lpf6532lug+5ccRnesX0AwAAF6a2tQ3RXOD7ZOsfeY3I7qWFdQIAAAAeDHGnjCE3XprHpdfkWannRIxZoyxJwC0ki02z+J1r0uzOXMjLr6kkDow7JXvnhkjfvO6KC27J8nzUjW6j/hi9B7yLxGlSjHlAADgpSpXo+fgTyVR1v1UtF/97YIKAQAAAC/G2BOGsB/+T3rb16hREaefZugJAK3o796cxaabpNl//lceK1a43RMGTN6M6g3/HR1/fndkPSuSLzVHbBhdp/086pNOLagcAAC8fI1t94/6jkckWXXB76P00NxiCgEAAAAvyNgThqibbs5j1uw0e8ubshgz2tgTAFpRR0cW//TB9HV82bKIH/+vsScMiO7l0XH2+6P9mu9GFun/7xrjxkfXG8+M5ha7F1QOAABeuZ5DPhV5dUSStV/6hYhmvaBGAAAAwPMx9oQhqNnM44c/St+A3nijiFNPLqgQALBO7L9fFvvvl2Z/Pjvi1tsMPqE/lR6/NUb86tSo3HX5Wl+r7XxUdL3uV5GP2byAZgAA8OrlozeL3n3el2Tlx2+N6txfF9QIAAAAeD7GnjAEXXZ5xO13pNnfvy2L9na3egJAq/vQ+7Nob19zzvOIb347j0bD4BP6Q2XhWdH569dH6en7kzzPStGz/0ei59hvRVQ7C2oHAADrRm3aW6Kx4U5J1nbt9yJ75rGCGgEAAAB9GXvCEFOr5fE/fR7nus3WEUcfVVAhAGCd2myzLN721vQDHItvjTjr7IIKwRCV13uice4/R/uFn4ys0ZN8rdm5QXSf8r9Rm/HOiMwHqgAAGALK1eg5/HNJlPWujLYr/r2gQgAAAEBfxp4wxJxzXsRDD6XZu96ZRaXiTWgAGCpef/qzH+Z4rv/+cR6PPOJ2T1gXsuUPReN/T4rmjb9Y62uNTSdH15v/GI2t9y6gGQAA9J/mFtOjNvHkJKvefmGU77m6oEYAAADAcxl7whDyzDN5/OwX6chj4oSIA/cvqBAA0C+q1Sw++uH0gxxdXRFf/1YeeW7wCa9G+c5Lo/OMkyJ/YPZaX+ud8oboOv2MyEdvWkAzAADofz0HfizyjvWSrP2Sz0fUVhVTCAAAAFjN2BOGkF+ckcdTT6XZe96dRebRkgAw5Ow+LYvjXptmN9wY8deLi+kDLa/eG22XfyU6z3l/ZN1PJ1/KKx3RfdR/RO9h/xpRaSuoIAAADIDO9aNn/48kUWn5g9F2zfcKKgQAAAD8/4w9YYi4/4E8fv/HNNt/v4ipUww9AWCoeu+7s9hoozT73vfzePJJt3vCy5Etuzc6f/uGaJtzxlpfa47dOrre8NuoTzi+gGYAADDw6pNOjcYW05OsOueMKD08v6BGAAAAQISxJwwZP/hhHvX6mnOlEvG+9xh6AsBQNnp0Fh/r8zj35csjvvOfxp7wUlVuPT9G/OqUKD+2aK2vZbsdH11v/mM0N96lgGYAAFCQrBTdR34x8vKaW+2zvBntF30motFbYDEAAAAY3ow9YQi46eY8rr4mzU47JWKrLY09AWCo23+/LA47JM0uuzziypkGn/Cial3RftFno+MvH4usd2XypbzSHuXjvx7l038U0T66oIIAAFCcfP3tonef9yVZeekdUb3pJwU1AgAAAIw9ocXV63l87/vpmGPs2Ii/e4uhJwAMF//0wSzGjEmzr38zj2XLDD7h+ZQeWxQjfnVaVG/5w1pfa26wfXS98cwo7fmWyDK/UwMAMHzVpr89GhuPT7K2G/47sqVLCmoEAAAAw5uxJ7S4c86LuPueNHvXO7IYNcob0wAwXKy/fhYfen/62v/UUxFf+0YeeW7wCas1G1G96SfR+evXR+nJtd+grk08KVa96feRe2w7AABElKvRc+QXI8/WvJWUNWrRcfFnI/JmgcUAAABgeDL2hBb29NN5/O9P0wHHjjtEHHt0QYUAgMIceUTEAful2cxrIi64sJg+MNhkyx+Kzj+8PdpnfjOyZi35Wl4dEd1HfTV6XvOViOqIghoCAMDg09xkYtSmvy3Jyg/NieqcXxZTCAAAAIYxY09oYT/8UR5PL0+zD30gi3LZrZ4AMNxkWRaf+FgWY8em+Xf+M4+HH3a7J8NbZfF5MeKME6P8wE1rfa0xbkKsetMfoj7hhAKaAQDA4Ne7z/ujud7WSdY281uRPXl3QY0AAABgeDL2hBY1f0Ee5/0lzQ4+KGLaVENPABiu1l8/i3/+WPq7wKpVEV/+ah7NpsEnw1D38mg//2PRccHHI+tZkXwpjyx6Z7wrut7wm8g32K6gggAA0AKqndFz5BeTKGv0RMeFn4xo1gsqBQAAAMOPsSe0oHo9j298Kx1sdHZGfOj9hp4AMNwdsH8WxxydZnPnRfzu98X0gaKU77k6RvzfCVG97fy1vtYcs3l0nf5/0bv/hyPKbQW0AwCA1tLYakb0Tn1zkpUfmR/Vm39aUCMAAAAYfow9oQWd+YeIu/o8Ieedf5/FxhsbewIAz34AZNNN0uxHP87j1lvd7skw0LMi2i/6bHT+6Z1ReuaRtb5cG398rHrLWdHcco8CygEAQOvqPeAj0Ry7TZK1Xfv9KD1+a0GNAAAAYHgx9oQW88gjefz05+lQY6cdI04+qaBCAMCgM3JkFp/+VBbZcz4HUq9HfO7f8li50uCToat8z9Ux4hfHR/WWP6z1tbx9THQf+83oOfo/ItpHF9AOAABaXLUzuo/6auTZmreWsmYt2i/4ZESjt8BiAAAAMDwYe0KL+e5/5tHdveacZREf+0gWlYpbPQGANaZNzeLNb0yzBx+K+Pq38shzg0+GmL9xm2d9671j1VvPjvouxxRQDgAAho7m5lOjtuc7kqz8xG3Rdt1/FdQIAAAAhg9jT2ghl1+Rx8xr0uz410ZMnGDoCQCs7R/ensVuE9Pskksjzr+gmD7QH170Ns/qiOg+/PPRfcpPIx+9aQHtAABg6Ond+33R2GiXJKve9OMoPTSnoEYAAAAwPBh7Qot46qk8vvmd9BausWMj3v0uQ08A4PlVKll87jNZjBqV5t/+bh733Ot2T1pc17Jov/BTf+M2z3OiPvl1z16HDwAArBuVtug5+quRl6qroyxvRsdfPh7Rs6LAYgAAADC0GXtCi/jOf+bx1FNp9sH3ZzFmtDeuAYAXttlmWXzy4+nvCz09EZ/9XB5dXQaftKA8j8qis2Pkz46J6qKz1v7yc2/zXG+Lge8HAADDQHPjXaN3n/cmWWn5g9F+yecjcn9rAgAAQH8w9oQWMPPqPC65NM323y/iiMOK6QMAtJaDD8rixBPS7O57Ir769Txyb8LRQrKn7ouOP/5DdFz4yci6n1rr6/Wt94lVf+c2TwAAGAi1Pd8ZjS2mJ1n1tr9E5Xk+lAUAAAC8esaeMMgtX57HN76VjjBGjYr42EeyyLyBDQC8RB94bxY77JBml14W8fs/FtMHXpZGLao3/jhG/OL4qNx33VpfzttG/b/bPP838jFu8wQAgAFRKkf30V+LvH1MErdf9qXIlt1dUCkAAAAYuow9YZD73g/yWPpkmn3o/VlstKGhJwDw0rW3Z/HlL2QxamSa/+CHecyb73ZPBq/SQ3Oj81enRfvV34qs0bPW1+s7HRmr3na+2zwBAKAA+ZjNo/uILyZZVlsVHX/5eESjt6BWAAAAMDQZe8IgdvU1eVz41zTbZ++Io15TTB8AoLVtuWUWn/l0OoZrNCL+9fN5PLHU4JPBJVu1NNr/+ukY8ds3RPmJ29b6enPUptF1wg+i+7jvRj5qXAENAQCAiIjGzkdGbdJpSVZ+dGG0Xf3dghoBAADA0GTsCYPUE0vz+OrX0tHFyJERH/f4dgDgVdh/3yz+7i1ptvTJiM9+Lo/eXoNPBoFmPapzfhUjfnZMVBf+aa0v55FF77S3xKq3nReNHQ4toCAAANBXz8GfjOYG2ydZ26yfRvmuK4opBAAAAEOQsScMQs1mHl/5ah5PPZ3mH3hfFuPGGXoCAK/O378tixl7ptmCWyK+8a088tzgk+KUHpz97CPbL/9SZD3L1/p6Y6NdousNv43eQ/4lom1kAQ0BAIDnVR0R3cd+M/JyNYk7LvhkZE8/WFApAAAAGFqMPWEQ+sOfIm68Kc0OPCDi2KOL6QMADC3lchaf+0wWm22a5n+5MOI3vyumE8NbtvKJaL/wkzHid2+K8uO3rvX1vDoieg78RHS96ffR3GxyAQ0BAIC/pbnxrtF7wMeTLOt5OjrO+1BEvaegVgAAADB0GHvCIHPnkjx++KP0Rq2NNor45495fDsAsO6st14WX/lSFp0daf7DH+Vx9TVu92SA1HuieuOPY8TPjorqorOf91tqu742Vr39gqjt8faIPrcEAQAAg0tt2pujtvNRSVZ+dGG0X/6VghoBAADA0GHsCYNIT08eX/hiHrVamn/6k1mst56hJwCwbu20Yxaf/XQWz/08SZ5HfOGLedy5xOCTfpTnUbntghjx82Oj/epvRda7cq1vaWy4U6w67RfRc8zXIx81roCSAADAy5Zl0XPEF6O5/nZJXF1wZlQWnVVMJwAAABgijD1hEPne9/O4+540e/3pEXvuYegJAPSPAw/I4t3vTH/X6OqO+MSn8njiCYNP1r3Sw/Oj83dvio7zPxKl5Q+u9fW8bVT0HPzJ6HrzH6O51YwCGgIAAK9K+6joPu67kVc60/iSL0Tp8dsLKgUAAACtz9gTBokLL8rj7HPTbKcdI971DkNPAKB/vekNEUe9Js0eeyzio/+cxzPPGHyybmQrHo72v3wiRvzmdVF+aM7zfk9twgmx6u1/idruf+eR7QAA0MKaG+0UPUd8Icmyend0nPuBiO6nC2oFAAAArc3YEwaBu+7K4+vfTIcUHR0Rn/tsFm1txp4AQP/Ksiw+8dEsJu2W5kuWRPzLZ/Po7TX45FXofjrarvpGjPjp0VG99dzn/ZbGFtNj1Rt/Hz1HfTXykRsPcEEAAKA/1McfF7Upr0+y0lP3Rcf5H41o1gtqBQAAAK3L2BMKtmpVHp/+XB49PWn+iY9mse02hp4AwMBoa8viK1/KYsst03z2nIgv/XsezabBJy9TrSuqN/44Rv7vkdF28/9G1uhZ61ua620VXcd9N7pOPyOam+72PP8jAABAK+s56FPR2GRSklXuvSbarvp6QY0AAACgdRl7QoHyPI+vfj2P++9P8xOPjzjyCENPAGBgrT82i299PYsN1k/zyy6P+P5/5ZHnBp+8BI1aVOb/Lkb89DXRfvW3IutZvta35O2jo+fAT8SqvzsvGjsdGZH53RcAAIakSlt0H//daI7YKInbZv9fVG75Y0GlAAAAoDUZe0KBfv+HZ8cTz7XLzhEfeJ83uwGAYmy+WRbf+FoWnZ1pfuYfIn5xRjGdaBF5HuXbL4wRvzguOi75fJRWPr72t2Tl6J3yhlj59gujtsfbIyptBRQFAAAGUj56s+g+/nuRl6tJ3n7JF6L04OyCWgEAAEDrMfaEgtxwYx7f/2F6O9bo0RFf/EIW7e3GngBAcXbeKYuvfDGLcjnNf/LTPH57pts96SPPo3znJdH5y1Oi87wPR+mpe5/322q7HB2r3nZe9B72rxEjNhjgkgAAQJGam0+LnsO/kGRZsxYd534wsuUPFdQKAAAAWouxJxTg3nvz+NwX8mg20/wzn8pi880MPQGA4u25Rxaf/uTav5d8/7/y+PPZBp/EsyPPJZdF569Oic5zPhDlxxc/77fVt9kvVr3pD9Fz7LciX3/bge0IAAAMGvWJJ0Xv9LclWWnV0ug4+30RPc8UUwoAAABaSKXoAjDcLF+exz//Sx7PrEzzv39bFvvta+gJAAweRx6RxTMrI771nXTc+c1v59HRHnH0UX53GZbyPMp3XRFt130/yo8tesFva2wyKXoP+Eg0tt57AMsBAACDWe8BH4vS0iVRuWfm6qz8+K3Rcd6HovvE/47o86h3AAAAYA03e8IAqtfz+Ozn83jgwTQ/9JCIt/9dMZ0AAF7MySdm8b73rD3q/Pev5XHRxW74HFbyZpSXXB6dvz49Os9+7wsOPZsbbB9dr/1udL3xd4aeAABAqlSO7mO+Ec31t0viyr3XRvvF/xqR+zsTAAAAXoibPWGA5Hke3/leHrNmp/nOO0f8yz9nkWVuxgIABqc3vC6L7u6I//3Zmjfdms2IL34lj1o94tij/R4zpDXrUbntgqje+OMoL73jhb9t/e2id5/3RX3noyJK5QEsCAAAtJSOMdF10n9H52/eEKWuJ1fH1UVnRT56s+jd74MFlgMAAIDBy9gTBsgvzog465w023CDiK9+KYuODgMJAGBwe9tbI7q7I371mzVZnkf8+3/kUatFnHi832eGnFp3VBb+Kdpu/mmUlj/4gt/WXH/b6N37vVHf5RgjTwAA4CXJx24d3Sf+MDp//3eR1btX5203/DCaozeN+uTTC2wHAAAAg5OxJwyAc8/L4yc/TR8/01aN+MqXshg3zjACABj8siyLf3xXRL2Rx+/OTL/2jW/lUa9HnHqy32uGhJ4VUZ33m6jO/r8orVr6gt/WHLvNsyPPXY+JKPnTEgAAeHmam02O7mO/FR3nvD+yvLk6b7/03yIfNS4a2x9cXDkAAAAYhLwjB/3s6mvz+Pq30qFnlkV85tNZTJxgEAEAtI4sy+L974loq+Zxxq/Sr33ne3msWhXxljc9+320nuyZx6I655dRnfebyHqfecHva2y4Q9T2fGfUdz3WyBMAAHhVGjscEj2H/Wt0XPL51VmWN6Lj3H+KrpP/J5pbzSiuHAAAAAwy3pmDfnTLwjw+94U8ms00/9AHsjj0YCMIAKD1ZFkW73pHRLUa8dOfpx9o+Z+f5PHksogPvi+iVPK7TqsoPbowqrN/EZXbLoysWXvB72tsOjl6Z7wrGjscEpGVBrAhAAAwlNUnvy56lz8cbTf+aHWWNXqi86z3RNepP43mZlMKbAcAAACDh7En9JPb78jjY/+cR09Pmr/lTR5xCgC0tizL4u/f9uzg80c/Tgeff/hjxLIn8/j0pyLa2vzOM2g1G1Fecmm0zf6/KD8460W/tb7NflGb8a5obLnns1fUAwAArGO9+30osmcejeqis1ZnWW1VdP7p3dF1+i+iufEuxZUDAACAQcLYE/rBkrvy+PBH83imz9Mvj35NxLve4Q1yAGBoeMubsmhvj/je99PB56WXRzz1dB5f+WLEyJF+9xlUelZE9ZY/RnXOL6O0/MEX/LY8smjsdGT0znhnNDeZOIAFAQCAYSnLoufIL0bWuzIqd168Ju55Ojr++A/RdfoZkW+wXYEFAQAAoHievQfr2L335vFPH83j6eVpvvdeEf/88SwytyEBAEPI6adm8bnPZlHp8zGyWbMj3vuBPB55NH/+/5ABlS29M9ou+1KM/J+Do/3K/3jBoWdebo/apNNj1dvPj+7jvmPoCQAADJxSJbqP+UbUtz0gjVctjc4/vD2ypx8oqBgAAAAMDsaesA7d/0AeH/xIHsuWpfnu0yK+9IUsKhVDTwBg6DnisCy+/tUsOjvTfMldEe/6xzwWLjL4LES9Nyq3nh+dv3tLjPzFcdE291eR1VY977c2R46Lnv0/HCvfdXn0HPGFyNd3Yw4AAFCASlt0H/fdaGyxRxKXnnk0Os98a2RP3VdQMQAAACiesSesI/fem8cH/ymPpUvTfPKkiP/4ShYdHYaeAMDQteceWfznd7IYOzbNn1wW8YEP5XHJpQafAyV7+oFom/nNGPHjQ6LjLx+L8oM3v+D3NjbZLbqP/nqsesfFUZvxrojO9QewKQAAwPOodkbXSf8djU0nJ3FpxcPx/7V332FSlecbx+8zZTuwu2yT3nuVInZBowQ1amwh6k8NKhprjIkmlmCKmhiNMRESC2BLYkxiCygqKLGgKEWkC0iHpewuy/aZOef3x8C4s4VtszPvLN/Pde218542z6L37Ow5z7wn+aUrZBV8HaPCAAAAAACILZo9gQhY/5WjG291tHdf+PLBg6Tf/9ZScjKNngAAoO0b0N/SX5+w1L1b+PIqnzTtV46enmnLtmn6bBW2X+6NC5T0n+uU8syZSvjsabnKC+rc1LFc8vc9S2WXvqjy7/9T/oHnSO6EKBcMAAAAAEeQkKryC/6qQFb/sMWu0j1K/uf/ybVvfYwKAwAAAAAgdmj2BFpoxZfBGT2LisKXD+gfbPRMSaHREwAAHD06d7b0lycsjRlde93s56Sf/sxRcTENn5Fi7d+ohP89rJSnxiv5tRvl2fyBLNX972undFTV2OtUNuVtVZz7mOzOx0oW71UBAAAAGCo5XeUXz1IgZ1DYYlfZPiW/fJVce9bEqDAAAAAAAGKDZk+gBRZ/5uj2nzgqKQ1fPmig9OjDltq14+I5AAA4+rRrZ+nhhyxdcH7tdZ98Kk25ztH6r2j4bLaKYnm++IeS/3apUp89Rwmfz5SrdF+9m/u7jFXF2Y+o7NoFqjrpR3Lad45isQAAAADQAskZKr9oVq1bulvlhUp++Wq5dn0Ro8IAAAAAAIg+T6wLAOLVvLcdPfg7R35/+PJjR0oP/YYZPQEAwNHN47H049ss9ejm6PE/OwrY36zbtVu6/oeObr9NOnuSZDG7ZMPsgNzbPpFn5SvybHhXVqDyiJs7ie3kG3S+fMMuldOxd5SKBAAAAIBWkNRe5Rc+o+RXpsq9c2losVV5QMkvX62Kcx5VoNdpsasPAAAAAIAoodkTaCLHcfTs89LTM2vPRnXiCdIvf2EpMZGGBQAAAEm68LuWeveW7pvmqKDwm+VVPumhhx19tkT6ye1SWhrvn2pxHLn2rpFnzRx51s2Vq2R3g7sE8obJN+xS+ft/W/ImR6FIAAAAAIiCxDSVf/dJJb36Q3m2Lw4ttvzlSnrtJlWeMU3+oRfFsEAAAAAAAFofzZ5AE/h8jh5+xNHct2qvO+N06Z6fWfJ4aFQAAACobsRwSzOfku6d5ujLleHr5i+QVq929It7pSGDeR8lSVbhFnnWzZF37Ry5CjY1uL2dmiX/wO/IN/gCOR37RKFCAAAAAIiBhFRVXPAXJb1xqzybPwgttpyAkt65V5Wle+Q77gaJu0cAAAAAANoomj2BRioudnTvNEdLltZed9GF0s0/tOR2cxIJAACgLllZlh7/g/TEXxz969/h63btlm682dHVV0mXf19H5YdnrJI98qx/U561c+XevaLB7R2XV4Fep8k35AIFepwsufjTDgAAAMBRwJusivOeUOI798q7+rWwVYkf/0mukj2qnHAPfyMBAAAAANok/toFGuGrDY5+fq+jXbvCl1uWdMtNli6+8OhrSAAAAGgqr9fSbTdbGjHM0UMPOyop+WZdwJaenunow4+kn98l9erZ9t9fWQfz5dnwjjxfvS3XjiWyHLvBfQLZA+QffIF8A8+VkjOiUCUAAAAAGMbtVeVZD8pJzVHCZ0+FrfKueEnWgW2qOPtRKalDjAoEAAAAAKB10OwJNODtdxz99veOKivDlyclSdPutXTSiW2/EQEAACCSTjvV0oAB0q9+4+iLGpNYrl0nTbnO0dVXSt//Xtub5dM6sF2er96W56u35d71RaP2sdt3kn/AOfL3P1t2dr9WrhAAAAAA4oBlqerk2+Wk5Srhvd/IkhNa5dnysVL+donKz5sup2PvGBYJAAAAAEBk0ewJ1MPvd/TEDEcv/7v2uo6Z0m8fsDRgQNtqPgAAAIiWvFxLf3xUeu4FafZzjuxqk1r6fNKTTzta+D/p7rukXr3i+D2X48gq2CTPhnfl+Wqe3HvWNGo3OzlT/n4T5R94juxjRgSnlAcAAAAAhPGNvEx2araS3vyJrEBVaLmraKtS/n6pKib9XoFep8WuQAAAAAAAIohmT6AOO3Y4mvZrR2vquBY/ZLD06/stZWVxwR0AAKAlPB5LP7hKGjtGeuAhR1u3ha9ft166+lpHl17i6KorLKWkxMn7r0CV3DuWyL3pfXk2vi/Xga2N2s3xpsjf5wz5B5yjQLdxktvbyoUCAAAAQPwL9DtT5WnZSnrjFrlK94WWW1WlSnr1h6o66UfyjbmGD9EBAAAAAOKe5TiO0/BmjVdYWBjJw0WcZVlKT0+XJBUVFSnCPz7agHlvO3rkMUdlZbXXnf8d6dabLXm9nBRqKbIImIM8AuY4mvNYWenomdmO/vGSwmb5PCwnW7rlJkunnhL8dzJOeaE8Xy+Ue9NCeTZ/KKuqpFG7OYnt5O81Xv6+ZyrQ/UTJm9TKhaIxjuYsAqYhj4AZyCJgDvJYP+vgbiW9fpPc+atqrfP3Gq+Ksx6QktOjXxjapIyMjFiXAAAAAOAoRLMnJ0JwSGmpo0cfczTvndrrErzS7T+ydM4kAxsL4hRZBMxBHgFzkEdp1WpHDzzkaEs9k2GOHSP96FZLXbvE+H2Z48i1b53cX38gz6b35Nq5XJYa99/LSUqXv8/p8vc9S4Fux0nuhFYuFk1FFgFzkEfADGQRMAd5bICvQolv3yPvujm1VtntjlHF2Y/I7jQyBoWhraHZEwAAAEAscBt3QNInnzr63e8d7dlbe12XLtL991nq349GTwAAgNY2eJClmU9Js5939Pd/SH5/+PrFn0lXXOXo/POCt3ZPT4/eezSrZI/cWz+We8vHcm9ZJFfZvoZ3OsROzZG/z+kK9D1TgS6jJRd/igEAAABAxHmTVDnpYdnZ/ZXw4R/CPpTnOrhLyS9doaqTbpNv9A8kyxXDQgEAAAAAaDquMOKoVnzQ0Z+fcDT3rbrXT/q2dNvNllJSaPQEAACIlsRES1OvsTTxW44e/aOjJUvD1/v90r/+Lb35lqPLvy9dclFwn4jzlcu9Y4ncWz4KNnjuW9+k3QO5Q+TvdZoCvU6TnTNIMvH28wAAAADQ1liWfGOvlZ0zUIlv3ilXecE3q5yAEj94RO5ti1V55q/lpOXEsFAAAAAAAJqG27hzi5OjkuM4+t8H0qN/dLR/f+31qanST39s6fQJXJBvLWQRMAd5BMxBHmtzHEcL3pP+NN3Rvnom0szJlqb8wNJZ35I8nha8fwtUybV7pdzbP5N72ydy71gqK1DV+Fo9yQp0PyHY4NnzFC4axjGyCJiDPAJmIIuAOchj01gle5Q49yfybF9ca52T2EGVE+6Wf8A5fDgPTcZt3AEAAADEAs2enAg56mzd5uixxx0t/qzu9aOOle76iaVjjuHkTmsii4A5yCNgDvJYv7IyR7Ofc/TyvyWfr+5tOneS/u+KJjR9+qvk2v1FsLlz+2dy71wuy1/RpLrsDl3k73GyAr3GK9B1rORJbNL+MBNZBMxBHgEzkEXAHOSxGeyAEj6ZLu8nM8Ju636Yv88ZqjxjmpyUjjEoDvGKZk8AAAAAsUCzJydCjhrl5Y6ee8HR318K3vqzppQU6aYfWjr37OD/J2hdZBEwB3kEzEEeG7Zrl6OnnnH09rv1b9Opk/R/l1uaeGaNpk9fmdy7vpR7+2dybf9M7l1fyApUNun5nYQ0BbqNk7/7CQp0P1FOerdm/iQwGVkEzEEeATOQRcAc5LH53FsXKfHNn8pVWvu2EU5yhion3CN/v28zyycahWZPAAAAALFAsycnQto8v9/R3LekZ2bVfct2STpurPTTOyzl5nASJ1rIImAO8giYgzw23tp1jqb/xdHSZfVt4Wh415266vQvdFynZUrcs1yuvetkOYEmPY9juWUfM0z+7icq0P0E2XlDJZenxfXDbGQRMAd5BMxAFgFzkMcWKi9U4oLfyLtuTp2r/d1PVOWEe+Rk9IhuXYg7NHsCAAAAiAWuUqLNchxHH34k/eVJR1u21r1NVpZ08w8tTRjPbJ4AAADxZEB/S398VPp0sfTMbEcb1lVpYIfVGp65XMMylmt45nLlJO2VChT8aoJAVn8FuoxRoOtYBboeJyW1b5WfAQAAAAAQZckZqjz79/L3/ZaS5t8vqzx8AhPPlo/kfu478o25VlVjrpW8STEqFAAAAACA2mj2RJvjOI4+WSw9+5yjlavq3sbtli69WLrq/yylpNDkCQAAEFcCVXLt3yDX7pU6tXiVxp+4Ula/9XI5/iYfypElO3uAAl3HBBs8O4+SkpmdAwAAAADaskC/s1TWeZQS598vz4Z3w9ZZAZ8SPpkuz5o3VHnaXQr0Gs+t3QEAAAAARqDZE22GbQdn8nz2eUfr1te/3dgx0i03WerRnZMzAAAAxrP9cu3fKFf+SrnzV8qVv1quvWtlBaqadTi/7db64v76qmqMUgaN0ZBJo9QuKz2yNQMAAAAAjOekZqni3Mfl/mqeEt97UK7SPWHrXQe2Kfm1GxXoPEqVp/xE9jHDY1QpAAAAAABBNHsi7vl8jt5bKL34N0cbN9W/Xb9+0g+nWho9iiZPAAAAI1UUy7Vvvdz71sm1d71ce9fKtW+9LH9Fsw9ZVNVBXxSM0IrCEfqiYIRWFg1RRSAluPITKeEFafxptiaeaenYkZLbzXtFAAAAADhqWJYC/SaqrMfJSlj0Z3mXPi/LCYRt4t6xRCl//558/Saq6sTb5GR0j1GxAAAAAICjHc2eiFuFhY5ee0N65TVH+/fXv90xx0jXTrF0xgTJ5eLiPQAAQMzZAVlFW+Xeu1auw42d+9bJVbyzRYd1ZMnu2Ed2p5EKdBqhQKcR2lXSXe//W5r7hVRZWXufqipp3tvSvLcdZWVJ3zrd0VlnWurTm/eNAAAAAHDUSEhV1al3yj/oAiXOv1/unUtrbeJd/5Y8G96Vf9D5qhp7rZz0bjEoFAAAAABwNLMcx3EiecDCwsJIHi7iLMtSenq6JKmoqEgR/vHRyhzH0Zq10iuvOnp3geTz1b9tl87SFZdbOutbksfDxXrTkEXAHOQRMEeby2PAJ+vAtuBt2As2yVWwMfS4JbN1Hmand1Mgd4js3MGyc4cokDNISkyrc9viYkdvzpNefd3Rtm0NH7t3b+n08ZZOPVnq3p33kkebNpdFII6RR8AMZBEwB3mMAseWZ+1cJXz0h3o/lOhYbvkHnK2qsdfJ6dg7ygXCBBkZGbEuAQAAAMBRiGZPToTEhf37Hc17R5r7lqPNm4+8bY8e0v9dbmnCaTR5mowsAuYgj4A54jaPvnK5CjcHGzr3b5SrYKOsgo1yFW6VZR/h0zlNYHfoEmzszBkkO+9QY2dShyYfx3EcLVsebPpc+D8pEGhwF/XoLp1ysnTqKZb69Q3+d0LbFrdZBNog8giYgSwC5iCPUeSvkveLvynhk7/IqjxQ5yaOLPn7nSXfsVfK7jQiuvUhpmj2BAAAABALNHtyIsRY5eWOFn0avKXmJ59IAfvI2w8fJl18oaVTTuZ27fGALALmII+AOYzOo69crqKtsoq2yFW4Ra6irXIVbZFVtFWukvyIPY3j8sru2Ft2Vj/Z2f1lZ/VXIHeQlBz5iygFBY7emR98v7n+q8btk5crjRsnjRtr6diRUkoK7zvbIqOzCBxlyCNgBrIImIM8xkDFASUsfkreZS/IClTWu1kgd6h8Iy+Xv99EyZMQxQIRCzR7AgAAAIgFmj05EWKUww2e773vaNEnUkUDd/f0eqVvnS5ddKGlfn250B5PyCJgDvIImCOmeXQcqaJIrgM7ZBXvCDVzuoq2yircIlfpnog/pZ2aLTurv+zsfrKzBwQfZ/aQ3NG/KLbpa0fz3nb0zrvSnr2N28fjkYYNlcYdZ2nMaKl3Lz501FbwuxEwB3kEzEAWAXOQx9ixSvfK+/lseVf8Q5avrN7t7JQs+YdeLN+Q78rp0CWKFSKaaPYEAAAAEAs0e3IiJOby9zj65FPpk08dffZ5ww2eknTMMdI5kyx95xwpI4ML6vGILALmII+AOVo1j44jlRfKVXyomfNwU2fxDlnFO+Uq3nnEi1UtYadkyc7sJadjL9mZfWR37KVAVn8pJbNVnq8lbNvR8i+k9xc6WviBtH9/4/dt314aPlQaMcLS8GFS3z6S28171XjE70bAHOQRMANZBMxBHg1QXqiEpc8FZ/qsKjnipv6ux8k/+Hz5+54peVOiVCCigWZPAAAAALFAsycnQqLO53O0arW06JNgk+fGTY3bLylJGn+qNOnbwYvnzJoU38giYA7yCJij2Xk83MhZsltWSb6sg/lyleQHH5fky3UwX1bxTln+8tYrXpLdvpPszN6yM3sFb8V+6LGS01v1eVuLbTtavUb63weO3v+ftHNn0/ZPTZWGDpGGDbU0cIA0oL/Urh3vYeMBvxsBc5BHwAxkETAHeTRIRbG8X/5T3uV/k+vgriNu6nhT5O97pvz9zlKg2wnc5r0NoNkTAAAAQCzQ7MmJkFZXWRm8SL78C2n5F45WrpIqKxu3r9sljRwpnTHB0oTxUkoKF8fbCrIImIM8AuaolUfbliqLZZXtl6t0n6yyfbJK98o6uEdWye6whk4r4ItKjXZaruz07nLSu8lO7yY7o7uc9O6y07u26VlKHMfRxk3SJ59Kny52tOJLKRBo+nG6dpUGDZAGDgw2gPbuJSUl8R7XNPxuBMxBHgEzkEXAHOTRQLZf7o3vybvsBXm2L25wcychTf7e4+Xve6YC3U+SvElRKBKRRrMnAAAAgFig2ZMTIRHlOI5275ZWr5XWrg02ea5ZI1U1offgcIPnhPGWTjlJSk/n4ndbRBYBc5BHIMocR6oqOdS4ue9QI+d+WWV7ZZUVKKGqSCrZq0BxfnCbKDVxhsqz3HLa5clu31lORnfZ6d1lp3er1tCZHNV6TFVW5mjpMumTxY4+XSztOvIkLvWyLKlzZ6lPL6lXL0u9e0u9e0qdOjGTfSzxuxEwB3kEzEAWAXOQR7O59q6TZ8VL8q6dI6uyuMHtHXeiAl3HKtDjJPl7niwnvUfwD0UYj2ZPAAAAALFAsycnQprN73e0Y4e08Wtp0yZH69YHGzuLDjT9WO3aSWPHSMePs3TcWCmDBs82jywC5iCPQAs4jlR5UFZ5oayKouD38qLwx4fWqfo62x+7kl2eb5o523eW3b6TnA6dD407yUnLlVyemNUXr3bnO/ri0Ez2y76Qtm9v2fGSk6Tu3YMzgXbtYqlb18OPme0+GvjdCJiDPAJmIIuAOchjnPBXyrNxgTyrXpF7y0eyHLtRu9kduijQdZwCXUYp0HmMnPadaP40FM2eAAAAAGKBZk9OhDSostLRjp3Sjh3Slq3S118Hb2G5Zavka8FEU337SOPGSccfZ2nQQMnj4YTF0YQsAuYgjzjqBapkVR4M3i694qCsyoPB2TcOfQ+NK6qPD0oVRcHmTacZ9/JuJY7lkpOSJaddnpy0HNlpwe9OuzzZ7fLkdOgiJzVHcrljXWqbt29/sPlzxZeOVq+VNmxo2Xvn6jp2DDZ9HpMn5eVJeXmW8nKlvFwpJ0fyenlf3VL8bgTMQR4BM5BFwBzkMf5YJXvkWTdXnq/ekXvn0ibta7c7RoHOo2TnDVMgd7DsnAGSN6WVKkVT0OwJAAAAIBZo9uREiPx+RwUF0p69wa+dO6UdOxxt3xFs8Nyzt+XP4XJJffpII4dLI4ZbGj5Mat+ei9BHM7IImIM8Ii75qyRfqayqUllVZaHHqiqV5Sv75nHNZYf3qSwJNndWHpTlr4j1T9MoTkKanNQsOSkdDzVx5spplxvW0OmkZjErp6F8PkcbN0qr10pr1jhaszb44alIv+RalpTVMdgEmp0tdcyUOna01LFjcPnh7+3aBV//UTd+NwLmII+AGcgiYA7yGN+skj3ybHhH7vVvy73j80bP+HmYY7lkZ/aUnTNYdu5g2Vl9ZWf2lpOazQygUUazJwAAAIBYoNmzDZ8Iqax0VHRAKioKfhUWSYWF0t69jvbslfYe+tq/Xwo07XxCgzq0lwYMkAYOkAYNtDRsqJSWxokGfONoyiJgOvKIiHIcKeCTApXBJkpfhSx/+aHvFZKvXPLXXhb+vfr6Q9v7KiRfmSxfmVRVJsuO0BSJMeYkpMpJ6SgnNTv4PSVLiR27yErLVqmVKjslMzhTZ0pHyZsU63IRYRUVjjZvljZskjZtCs6ev3GjVHSg9Z87wStlZkoZmcH37h06HP5uVXv8zVe7NCkh4ehpEOV3I2AO8giYgSwC5iCPbUjFAbm3LpLn6w/k3vyhXKV7mn0oJyFNdmavb77Su8lp31l2h85SUjqNoK2AZk8AAAAAscC0P4arrHR08KBUUiqVlEilh76XlB5+7AS/l0oHDx5q7DzU4FlWFp0aO3aUeveSevWUBg6wNGCA1OmYo+dCMAAAUWUHJNsfbKi0fbJs/6Fx8Ltl+2qM/ZLtC41DywK+4O3LA1VSoEryV1Z7XG15oFKW//Djurb3he1rBapi/S8UM447UU5yhpzk9OBXUrqc5AwpOSP0OLju0DglU/Imhx3DsiylHLpoF+CiXZuXlBR87zxggCQF3zs7TnDW/Y2bpG3bpG3bHW3dJm3bLu3eHbmZQKt80u784Fe4+p/A45HSUh2lpqrWV1qqlJIqpaVaSk2RkpKl5CQpMVFKTg5+T0o69HXocWIifzMAAAAAOIoldVCg30QF+k2UHEeufevk3vap3Ns/l3v757Iqihp9KKuqRO7dK+TevaLWOichVXZ6N/mGXCT/8Mk0fgIAAABAHIt4s2dVVfMv8LtcLnk8dZcUb8f1+XzNvjjvcrkkufWbhxwteE8KBJpdYkR1zHTUqZPUo7ujnj0d9ezhqEeP4Kw/CQkJde7j9/tl282fNjTax7VtW36/v9nH9Xg8h/77cdy6jit9k7nq630+X6P+e/Ia0fBxW5INy7Lk9XojflyJ14jDTM1yfXmM5WtEczQmy1ZJvry7lgebHh1bsgPB22U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Yk8fqCgsLddddd+n9999v9D6TJk3SH/7wh9YrCmhlJmQxUs2el112me67774IVATEhgl5PGzZsmW69dZblZ+f3+h9evbsqSeeeEK9e/dulZqAaDEpi5s3b9b111+vr7/+ulHbjxw5Un/84x+Vm5vbKvUAAAAAAIDmM7LZc+HChbr++uvDZtHs0aOHjjvuOKWnp2vr1q167733VFFREVrfp08fvfTSS0pLS4tYHeedd57Wrl0btqxTp04aMWKEcnJylJKSon379mnx4sXavHlz2Ha9e/fWiy++qIyMjIjVA0SbKVms2ezZoUMH9e/fXz179lSHDh2UkJCg4uJiffXVV/r8889rfVJ+6tSpuv322yNWDxALpuSxMaZMmaIPP/yw1nKaPdEWmJTFms2eTZl55ZlnnmnWBykAk5iUx8Py8/N15ZVX1rqQ3q9fPw0bNkxZWVkKBALas2ePVq5cqY0bN0qi2RPxzZQs/vznP9err77a5P0CgUDY+Nlnn9W4ceMiVBUQXabkUQreJvqyyy4LuzuLZVkaNWqUBg8erHbt2unAgQNauXKlli1bFrZvdna2/vnPf6pTp04RrQmIFpOyuH37dl100UUqLCwMLfN6vRo3bpz69esnj8ejLVu26IMPPlBpaWlomwEDBujFF1+M+jklAAAAAABwZMY1e+7Zs0cTJ04MnViwLEt33nmnrrzySrlcrtB2BQUFuvXWW8MucJ999tl69NFHI1bL4WbPrKwsXXDBBbrwwgvVs2fPWts5jqN58+bpvvvu04EDB0LLzzrrLD3++OMRqweIJpOyWFhYqClTpmjixIk65ZRT1L9/f1mWVee2BQUFevjhh/Wf//wntMyyLL3wwgsaPXp0xGoCosmkPDbk1Vdf1Z133ikpeIGu+q3BaPZEvDMti9WbPckXjjam5VGSKisrdckll4R9YHD06NG677771L9//zr32bZtm1577TUVFRXpnnvuiXhNQGszMYtNsXr1al1wwQWhcefOnTV//vx6/94ETGZSHh3H0SWXXKIVK1aElvXr10+PPPKI+vXrV2v71atX6/bbbw/7sMQZZ5yhJ554ImI1AdFiUhYDgYAuvvhirVq1KrRs5MiRevjhh9W1a9ewbYuKijRt2jS9+eabrVYPAAAAAABoOVfDm0TXjBkzwj5BevPNN+vqq68OOxEiSZmZmXr66afDbukzd+5crV69OmK1ZGZm6q677tJ7772nO+64o85GTyl4wmbixImaNWuWkpOTQ8vnzZsXdlITiCcmZTEjI0P/+c9/dN1112nAgAFHvPCWmZmpBx98UBdeeGFomeM4mj17dsTqAaLNpDweSUFBgR566CFJwd+NP/vZz6LyvEC0xEsWgaOBiXmcPn16WKPnd77zHb3wwgv1NnpKUteuXXXTTTfR6Im4ZWIWm6L6hwQl6fzzz6fRE3HLpDyuWLEi7Jxohw4dNHPmzDobPaXgLayfffZZtWvXLrRs/vz5Tbr9O2AKk7I4d+7csEbP3r17a+bMmbUaPSUpPT1djz76qMaPHx9aNmfOHK5vAAAAAABgGKOaPfft26eXX345NO7WrZuuu+66erdPTEzUvffeGxo7jqMZM2ZErJ6nnnpKV199tRISEhq1/eDBg3XllVeGLZs3b17E6gGixbQsNscdd9wRdjvbum4pDcSDeMrjAw88ELot2KWXXqrhw4dH5XmBaIinLAJtnYl53LBhg5555pnQePDgwXrwwQdpGkObZmIWm8Ln82nOnDmhsWVZYbN8AvHEtDwuWrQobHzJJZcoOzv7iPvk5ubq4osvDqvp008/jVhNQDSYlsUXX3wxbHzPPfcoJSWl3u1dLpemTZsmj8cTWsYMuwAAAAAAmMWoZs/58+fL5/OFxpdccom8Xu8R9zn++OPVq1ev0HjhwoUqKyuLSD3VT2o01tlnnx025pOviEemZbE5MjMzw+opLy8PNaEB8SRe8vjBBx/ojTfekBS8ffuPf/zjVn0+INriJYvA0cDEPM6aNSusprvvvrtZf08C8cTELDbFwoULVVBQEBqPHj26zpnOgHhgWh5rzsg5YsSIRu137LHHho337NkTkXqAaDEpiyUlJWHXJjp37qwTTjihwf3y8vJ08sknh8YfffSRiouLW1wPAAAAAACIDKOaPRcsWBA2njhxYqP2O+uss0KPKysr9dFHH0W0rqbo3r172Hj//v0xqgRovraQRUlKS0sLGzuOE6NKgOaLhzyWlZXpF7/4RWj8s5/9TO3bt2+15wNiIR6yCBwtTMtjaWmp5s6dGxoPHDhQo0aNisixAZOZlsWmeuWVV8LGzOqJeGZaHm3bDhsnJSU1ar/k5OSwMTNkI96YlMW1a9cqEAiExo1tupbCG699Pp/ee++9FtcDAAAAAAAiw6hmzyVLloQeZ2VlNXpGhZEjR4aNP/vss4jW1RSlpaVhY2ZzQTxqC1mUpJ07d4Yep6WlKT09PXbFAM0UD3l87LHHtGPHDknSSSedVGuWa6AtiIcsAkcL0/L43nvvhc2+xO9BHC1My2JTFBYWauHChaFxcnJyWKMNEG9My2OXLl3Cxrt27WrUfof/rjys5ofqAdOZlMWadzjKzc1t9L45OTkRrwcAAAAAAESGMc2e+fn5OnjwYGg8cODARu87aNCgsPHGjRsjVldTrVu3Lmycl5cXo0qA5mkrWVy8eHHYbcNOPfVUuVzGvOQBjRIPeVyxYoVeeOEFSVJiYmLYDJ9AWxEPWQSOFibmcfny5WHj0aNHR+S4gMlMzGJT/Pe//w27ze6ZZ55Z684QQLwwMY/VbwEtSW+++Waj9qs+U3ZKSoqOO+64iNQDRINpWayoqAgbJyQkNHrfxMTEsPGGDRtaXA8AAAAAAIgMY6ad3LRpU9i4U6dOjd43KytLXq83dKL+66+/jmhtTfH666+HjceNGxejSoDmaQtZ3LVrl+6+++7Q2Ov16oYbbohJLUBLmJ5Hv9+ve++9N3RbsBtuuEHdunWL+PMAsWZ6FqXgbNZ33XWXvvzyS+3du1c+n08ZGRnKzc3V6NGjddppp3FbabQJJuZx5cqVoccejyd0YT8/P1+vvfaaFixYoO3bt6ukpEQZGRnq0qWLjj/+eJ177rmNnu0JMI2JWWwKbuGOtsTEPA4YMECnnXaa3n//fUnSRx99pL///e+aPHlyvfs8++yz+uSTT0Ljq6++Wu3atYtIPUA0mJbF9u3bh42Li4sbvW/NbWv+bAAAAAAAIHaMafasPgOf1LQZMS3LUm5urrZv317nsaJl8+bNeuONN0Jjt9utb33rWzGpBWiueM1iaWmpNm3apAULFuj5558PfZLe4/HooYceUt++faNWCxAppufx6aef1tq1ayVJvXv31pQpUyL+HIAJTM+iJG3fvj30HIeVlZVpx44dWrp0qZ588kmNHDlSd999t4YOHdoqNQDRYGIeq8+8lJ2draSkJP3tb3/Tww8/HHZ7d0kqLy/Xzp07tXjxYk2fPl3f+9739NOf/rRJMy0BJjAxi421fv16rVq1KjTu3LkzH9RFXDM1j7/61a/0/e9/X9u2bZMkTZs2TZ999pkuvfRSDR48WKmpqSopKdHKlSv1t7/9TW+//XZo3/Hjx/OhXcQd07JY81bsTZmd86uvvgobHzhwQGVlZUpJSWlxXQAAAAAAoGWMafaseRGsqScOUlNTQ4/9fr8qKytr3W6kNdm2rXvuuSfsNmDnn38+M7Ug7sRDFquqqjRixIjQ2HEc2bZda7tjjz1WP/vZzzRs2LCIPj8QLSbncfPmzZo+fbqk4EWJ+++/n0YVtFkmZ7Epli1bpsmTJ+sXv/iFLr744qg/PxAJpuXRtu2w23VmZ2frscce04wZMxrc1+fz6fnnn9eqVav01FNPcQtpxBXTstgUNWf1PO+882RZVlSeG2gNpuYxJydHL730kqZNmxZq5JwzZ47mzJlT7z5paWm65pprdN1118ntdre4BiCaTMtiv379lJaWppKSEknS8uXLVVhYqIyMjCPu5ziOFi5cWGs5zZ4AAAAAAJjBFesCDqt5MqSpJzJqbl9aWtrimpriiSee0GeffRYaZ2Zm6o477ohqDUAkxEMWHcdRIBAIfdXV6HnOOefogQceoNETcc3UPDqOo3vvvVeVlZWSpO9+97saM2ZMRI4NmMjULEpSenq6vvvd7+oPf/iD5s6dqyVLlmjVqlVatGiRnnvuOf3gBz8IayDz+Xy677779M4770SsBiCaTMvjwYMH5ThOaPz111+HGj09Ho8uu+wyvfzyy1q6dKmWLVumf//737riiivk8XzzuculS5fq7rvvblEdQLSZlsXGCgQCYXdkkbiFO+KfyXns2LGj/vSnP+nJJ59scJbDbt266bHHHtMNN9xAoyfikmlZdLvdOuWUU0LjqqoqzZw5s8H93njjDW3durXW8po/HwAAAAAAiA1jmj0PN4wc5vV6m7R/zdnEah6vNb377rt64oknQmPLsvSb3/xGmZmZUasBiJR4zmJ1//3vfzVp0iTdeeedYbMtAfHE1Dz+61//0uLFiyVJGRkZ+slPfhKR4wKmMjWLN998s/73v//pwQcf1KRJk9S7d2+lpaXJ4/EoMzNTxx13nO68807NmzdPY8eODe1n27buuusuFRYWRqQOIJpMy2PNi96H33cmJibqySef1H333adhw4YpNTVVKSkpGjJkiO655x49/fTTSkpKCu331ltvacGCBS2qBYgm07LYWB9++KH27t0bGo8ePVrdunWLynMDrcXkPObn5+u2227T1KlTtXv37iNuu3XrVl1zzTX63ve+16TbTQOmMDGLP/jBD8LGM2fOPOIH/1atWqVf//rXda6rqKhocT0AAAAAAKDljGn2rPnJ1eq3Q2+MqqqqIx6vtSxZskQ//vGPw2ZzuemmmzRhwoSoPD8QafGQxcTERK1bty70tWrVKn388ceaPXu2rrjiitAthWzb1quvvqrLL79cBw4ciHgdQGszMY979+7V7373u9D4zjvvbPAWYEC8MzGLkjR27NhGHSsrK0tPPvmkBg0aFFpWUlKip556KiJ1ANFkWh7r2/+2227TiSeeWO9+xx9/vG6//fawZc8880yLagGiybQsNlbNW7gzqyfaAlPzuHbtWp133nl688035TiOLMvSueeeq1mzZmnRokVauXKlFi1apGeeeUbnnHOOLMuSJC1btkwXXXRR2B2UgHhgYhaHDh2qyZMnh8Z+v1+33HKL7r33Xq1YsUIVFRXy+XzauHGjHnvsMV122WWhc6jVbysvNf229AAAAAAAoHUY0+xZ82RBUz+5WnP7micjWsO6det0/fXXh32qdfLkybrpppta/bmB1hKPWfR4POrYsaOOP/543XPPPXrjjTc0YMCA0Pq1a9fql7/8ZavXAUSaiXn81a9+peLiYknBRjMukONoYGIWmyo5OVm/+MUvwpbNnTs36nUALWVaHuu66N2hQwdddtllDe47efLksLtBLFmyREVFRS2qB4gW07LYGMXFxWEz6CYnJ2vixImt/rxAazMxjwcOHNC1114bmkne6/VqxowZ+v3vf68TTjhBmZmZ8nq9yszM1EknnaRHHnlE06dPD82EWF5erptuuilsJl7AdCZmUZJ+/vOf6+STTw6NbdvWP//5T1188cUaPny4hgwZokmTJmnGjBkqLy+XJF144YUaNWpU2HHat28fkXoAAAAAAEDLGNvsWVpa2qT9q2/v8XhafVaIrVu3asqUKaGGF0maNGmS7rvvvlZ9XqC1xVsW69KlSxc988wz6tChQ2jZf//7X3311VdRrwVoCdPyOH/+fM2bN09S8GLd/fff36LjAfHCtCw214gRI9SnT5/QeNeuXdq8eXNMagGay7Q8JiUlyePxhC074YQTGnXchIQEnXTSSaGx4zhatmxZi+oBosW0LDbGnDlzwhppzjzzTKWlpbX68wKtzcQ8/uUvf9GePXtC49tuu03jx48/4j4TJkzQrbfeGhoXFRVpxowZLa4FiBYTsygF33P+9a9/1bXXXlvrVvE1eTwe3XjjjfrlL38Z1mztcrn4nQkAAAAAgCGMafbMzc0NG+fn5zd6X8dxwraveaxIy8/P11VXXRV2wuPkk0/W7373O7lcxvyTAs0ST1k8kqysrFozKr399tsxqgZoHtPy+NBDD4UeX3fdderVq1eLjwnEA9Oy2BIjRowIG+/cuTM2hQDNZGIeax6nb9++jd63X79+YeOm/DxALJmYxYa8+uqrYWNmqEdbYVoeHccJy1tKSoouv/zyRu17xRVXhDXMvfHGG7Jtu8U1AdFgWharc7vduuOOO/TOO+/oRz/6kcaOHaucnBwlJCSoQ4cOGjBggK699lq9/vrruuWWW+R2u7Vjx47Q/r169eK6BwAAAAAAhvA0vEl09O7dO2xc/WRCQ/bt2yefzxcat2bzSUFBga6++uqw+kaPHq0///nPoVsNAfEsXrLYGCeeeKKmT58eGq9bty6G1QBNZ1oeD9+CTwrO1PKXv/ylSfsvXrxYgwYNCo3HjBmjZ599tsV1Aa3NtCy2RMeOHcPG1XMNxAMT89i7d++wOqrPLt+QmtseOHAgIjUBrc3ELB7Jpk2btHz58tC4U6dOGjduXKs/LxANpuVx69atKigoCI2HDRumpKSkRu2blJSkoUOH6tNPP5UkFRcXa8uWLerZs2eL6wJam2lZrEteXp6uv/56XX/99UfcbvPmzWF3NBs6dGir1AMAAAAAAJrOmI9j5uTkqF27dqHxmjVrGr3v6tWrw8atdTKkpKRE11xzjTZu3BhaNnjwYP31r39t9ElLwHTxkMXGqtnQUlJSEqNKgOYxOY+BQKBRX0fajxlaEC9MzmJTlZeXh41jdUt5oLlMzGOfPn3CxlVVVY3et+a2ZBLxwsQsHknNWT3PO+88WZbV6s8LRINpedy/f3/YOCsrq0n7Z2dnh435cBLihWlZbImlS5eGjYcNGxajSgAAAAAAQE3GNHtK0qhRo0KP9+/fr61btzZqv5onH8aMGRPRuiSpoqJCU6dO1apVq0LL+vTpo6efflppaWkRfz4glkzOYlPUbO5s3759jCoBmq+t5BGId20li9u2bQsbZ2ZmxqgSoPlMy+PYsWPDxk25Zefu3bvDxhkZGRGpCYgG07JYH9u29frrr4ct4xbuaGtMymPNDy5UVlY2af+aH06qflt3wHQmZbEl/vvf/4Yee71eTZw4MYbVAAAAAACA6oxq9jz99NPDxm+99Vaj9ps3b17ocWJiok488cSI1uXz+XTLLbfo888/Dy3r1q2bZs6cyQVytEmmZrGpan4q/phjjolRJUDzmZTHzz//XOvWrWv01/z588P2Hzt2bNj6559/vsU1AdFiUhabq7y8XJ999llo7PF41Ldv35jVAzSXaXk88cQTwxpRal6sP5Jly5aFjQcNGhSRmoBoMC2L9Vm0aJF27doVGo8aNUrdu3dv1ecEos2kPNa8y0r1OyQ1Rs3tOfeKeGJSFptr48aN+uSTT0LjCRMmkEMAAAAAAAxiVLPnhAkT5PV6Q+OXX35ZPp/viPssWrRIX3/9dWh86qmnRvQT37Zt684779TChQtDy/Ly8jRr1izl5uZG7HkAk5iYxaZyHEf/+te/wpadcMIJMaoGaL62kEegLWgLWXz22WfDZr0ePnx42G0GgXhhWh4TEhJ0xhlnhMYrVqzQ+vXrG9xv48aNWrJkSWick5NDAzbiimlZrM8rr7wSNmZWT7RFJuUxLy9POTk5ofGmTZu0du3aRu375ZdfavPmzaFx586dw44FmM6kLDbXb37zG9m2LUlyuVyaMmVKzGoBAAAAAAC1GdXsmZWVpUsuuSQ03rp1q5588sl6t6+srNSvf/3r0NiyLN1www31br99+3b1798/9DVhwoQGa5o2bZrmzJkTGnfs2FGzZs1Sly5dGtwXiFemZXHFihVNqD7oj3/8Y9h+xxxzjI477rgmHweINdPyCBytTMviunXrmlC99OGHH+rPf/5z2LKrrrqqSccATGFaHiXphz/8oTweT2g8bdq0I17Y9/v9mjZtmhzHCS274oorGnwewCQmZrGmkpISvfvuu6FxcnKyvv3tbzf5OIDpTMvj+PHjw8bTpk1TVVXVEfeprKzU/fffH7aMv08Rb0zLYlM9+OCD+uijj0LjyZMna/jw4RF9DgAAAAAA0DJGNXtK0tSpU5Wamhoa/+lPf9KsWbNCnyY9rKCgQNdcc402bNgQWjZp0qSI3vbu0Ucf1UsvvRQad+jQQTNnzlSvXr0i9hyAqUzK4pQpU/SDH/xAc+fOVXl5+RG33bBhg2666SbNmDEjbPldd92lhISEiNUERJNJeQSOZiZl8fLLL9d1112nd955R5WVlfVuV1xcrMcff1xTp04NazwbO3aszjzzzIjVA0SbSXmUpJ49e+rSSy8NjZcsWaIbbrhBe/bsqbXt3r17deONN2rx4sWhZZ07d9b3v//9iNYERINpWazpzTffDPsb8lvf+pbS0tJa9TmBWDEpj9ddd13Y7IbLli3TlClTtHXr1jq337Rpk6688kp9+eWXoWWJiYm65pprIlYTEC0mZVGSpk+frt/+9rdhz1PT1q1bdf3112v27NmhZT169NDtt98e0VoAAAAAAEDLWU71qUQM8f777+uGG24IOwHSo0cPjRs3Tunp6dqyZYvee+89VVRUhNb36dNHL7300hFP2m/fvl2nn356aNy5c2ctWLCg3u379+8fNrYsSy5X0/tjV69e3eR9ABOYksXRo0fr4MGDkoK3yezbt6/69++v9PR0paamqqysTHv37tXq1avrPHF5880366abbmrSzw6YxpQ8NkXNY48dO1bPP/98RI4NxIopWaz+uzE5OVn9+/dXnz59lJ6erqSkJB08eFAbNmzQ0qVLa31QomfPnvrHP/6h9PT0pv74gFFMyeNhVVVVuuqqq8JuzZ6UlKQTTzxRvXv3lhRsZvnoo4/CcpmcnKwXX3xRgwcPbtwPDhjGtCxWd9lll+nzzz8PjWfPnq3jjz++SccA4olJeXzppZd03333hS1zu90aNWqUBg0apLS0NB08eFCrVq3S0qVLazXC/fa3v9X555/fmB8bMI5JWfztb3+rmTNnSpK6deumYcOGKS8vTwkJCdq/f79Wr14d1mgtBe+Q9Nxzz6lbt25N+rkBAAAAAEDr8zS8SfSddtppevDBBzVt2rTQRbDNmzdr8+bNdW4/cOBA/fnPf2712Rkcx1EgEGjV5wBMYmIWq6qqtGrVKq1atarBbdu3b68777xTF110UavVA0SLiXkEjkYmZrG8vFzLly/X8uXLG9z2rLPO0i9/+UsaPdEmmJbHhIQETZ8+Xbfffnvo9pcVFRWaP3++5s+fX+c+2dnZmjFjBo2eiGumZfGwbdu2hTVfd+rUSePGjWvV5wRizaQ8Hp7x+oEHHgg1tAUCAS1evDhsduuaUlJSdO+999LoibhmUhar27p1a70z7B42ZswYPfLII8rNzW3VWgAAAAAAQPMYdxv3w84//3z95z//0RlnnBF225/qsrOzdeONN+qf//ynunTpEuUKgaODCVl88MEHdeGFFzb60+Q9evTQj370I7311ls0eqJNMSGPAMzI4tSpU3XccccpJSWlwW0TEhJ0+umna/bs2Xr88cdp9ESbYkIeq0tPT9czzzyj+++/X3369DnidlOnTtWcOXM0dOjQVq0JiAbTsihJr776qqrfzOa8886TZVmt/rxArJmUx0svvVSvv/66Jk+eHHZb67qkpaXpsssu0+uvv67vfve7rVYTEC2mZHHcuHE66aSTGvzbcciQIfr973+v559/nkZPAAAAAAAMZuRt3GsqLCzU0qVLtXv3bpWWliorK0tdu3bVscceK7fbHevygKOGCVksLCzUhg0btH37dhUWFqqiokJJSUlKS0tTbm6uhgwZoo4dO0alFiCWTMgjgNhn0bZtbdm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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 582, + "width": 1341 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "custom_prior_alpha = pz.Beta(**alpha_custom_prior)\n", + "custom_prior_lam = pz.Beta(**lam_custom_prior)\n", + "\n", + "custom_prior_alpha.plot_pdf()\n", + "custom_prior_lam.plot_pdf()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The new distributions look much better now! Since we know the actual values of the function parameters, it's easy to compare and see how similar the custom and generic prior distributions are to the actual values." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 511, + "width": 1211 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "n_samples = 100\n", + "# default prior original scale\n", + "default_alpha_prior_orignal_scale = prior_alpha.rvs(n_samples) * df.venezuela.max()\n", + "default_lam_prior_orignal_scale = prior_lam.rvs(n_samples) * df.meta.max()\n", + "# custom prior original scale\n", + "custom_alpha_prior_original_scale = custom_prior_alpha.rvs(n_samples) * df.venezuela.max()\n", + "custom_lam_prior_original_scale = custom_prior_lam.rvs(n_samples) * df.meta.max()\n", + "\n", + "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(12, 5), sharex=True, sharey=True)\n", + "sns.violinplot(data=default_alpha_prior_orignal_scale, color=\"blue\", orient=\"h\", ax=ax[0][0], label=\"prior Alpha defult\")\n", + "sns.violinplot(data=default_lam_prior_orignal_scale, color=\"blue\", orient=\"h\", ax=ax[0][1], label=\"prior Lam default\")\n", + "\n", + "sns.violinplot(data=custom_alpha_prior_original_scale, color=\"red\", orient=\"h\", ax=ax[1][0], label=\"prior Alpha custom\")\n", + "sns.violinplot(data=custom_lam_prior_original_scale, color=\"red\", orient=\"h\", ax=ax[1][1], label=\"prior Lam custom\")\n", + "\n", + "# Vertical lines for the true values\n", + "ax[0][0].axvline(2.75, color=\"black\", linestyle=\"--\")\n", + "ax[0][1].axvline(2.50, color=\"black\", linestyle=\"--\")\n", + "\n", + "ax[1][0].axvline(2.75, color=\"black\", linestyle=\"--\")\n", + "ax[1][1].axvline(2.50, color=\"black\", linestyle=\"--\")\n", + "\n", + "ax[0][0].set(title=\"Alpha Prior (Original Scale)\",)\n", + "ax[0][1].set(title=\"Lambda Prior (Original Scale)\",)\n", + "\n", + "ax[1][0].set(xlabel=\"Prior Value\")\n", + "ax[1][1].set(xlabel=\"Prior Value\")\n", + "\n", + "#add name on the Y axis column row 0\n", + "ax[0][0].set(ylabel=\"Default\")\n", + "ax[1][0].set(ylabel=\"Custom\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our new distributions are now more informative than before. We will pass this information to our model using the **model config** parameter, which receives a dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "custom_model_config = {\n", + " \"saturation_alpha\": {\n", + " \"dist\": \"Beta\",\n", + " \"kwargs\":{\n", + " \"alpha\":np.array([alpha_custom_prior[\"alpha\"], 1.8]), \n", + " \"beta\": np.array([alpha_custom_prior[\"beta\"], 2])\n", + " },\n", + " },\n", + " \"saturation_lambda\": {\n", + " \"dist\": \"Beta\",\n", + " \"kwargs\":{\n", + " \"alpha\":np.array([lam_custom_prior[\"alpha\"], 1.8]), \n", + " \"beta\": np.array([lam_custom_prior[\"beta\"], 2])\n", + " },\n", + " },\n", + "}\n", + "\n", + "model_config = {**generic_mmm.default_model_config, **custom_model_config}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our priors in place to be used by the model.\n", + "\n", + "Even with just one single point of experimentation, our model can now utilise greater knowledge to converge to the actual causal relationship between our target and the marketing actions. \n", + "\n", + "Let's create a new model!" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "mmm = DelayedSaturatedMMM(\n", + " date_column = \"ds\", \n", + " channel_columns= [\"meta\",\"google\"], \n", + " control_columns=[\"trend\"], \n", + " saturation=\"michaelis_menten\", \n", + " adstock=\"weibull_pdf\", \n", + " adstock_max_lag = 4, \n", + " yearly_seasonality = 4,\n", + " model_config=custom_model_config\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take one final look at the custom priors we've derived before we proceed with model training. We can compare our derivative of the Michaelis Menten function with the new priors against the generic ones provided by the model to see how they measure up." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [saturation_alpha, saturation_lambda]\n" + ] + } + ], + "source": [ + "mmm.build_model(X,y)\n", + "\n", + "with mmm.model:\n", + " custom_prior_predictive = pm.sample_prior_predictive(\n", + " random_seed=seed,\n", + " var_names=[\"saturation_alpha\", \"saturation_lambda\"]\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before we have to sample using the `sample_prior_predictive` function. Now using the new model." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 511, + "width": 1511 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "custom_prior_alpha_mean = custom_prior_predictive.prior[\"saturation_alpha\"].mean().item()\n", + "custom_prior_lambda_mean = custom_prior_predictive.prior[\"saturation_lambda\"].mean().item()\n", + "\n", + "# x_values = np.linspace(0, 5, 500)\n", + "custom_prior_mm_mean_derivative = derivative_michaelis_menten(\n", + " x_values, \n", + " alpha=(custom_prior_alpha_mean * df.venezuela.max()), \n", + " lam=(custom_prior_lambda_mean * df.meta.max())\n", + ")\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "plt.plot(\n", + " x_values, custom_prior_mm_mean_derivative, \n", + " color=\"purple\", label=\"Custom Prior Median Derivative\", linestyle=\"--\"\n", + ")\n", + "\n", + "plt.plot(\n", + " x_values, prior_mm_mean_derivative, \n", + " color=\"blue\", label=\"Prior Median Derivative\", linestyle=\"--\"\n", + ")\n", + "\n", + "plt.scatter(\n", + " x_midpoint, abs(average_rate_of_change), \n", + " color=\"black\", label=\"Experiment\", zorder=5\n", + ")\n", + "plt.xlabel(\"Spend/Impressions (x)\")\n", + "plt.ylabel(\"Rate of Change (dy/dx)\")\n", + "plt.title(\"Prior Derivative of Michaelis-Menten Function \\n\")\n", + "plt.legend(loc=\"upper left\", bbox_to_anchor=(1, 1))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the new derivative is much closer to our experiment, compared to the generic model. This means that we can proceed with training our media mix model 🔥" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (6 chains in 4 jobs)\n", + "CompoundStep\n", + ">NUTS: [intercept, saturation_lambda, saturation_alpha, gamma_control, gamma_fourier, likelihood_sigma]\n", + ">CompoundStep\n", + ">>Metropolis: [adstock_lambda]\n", + ">>Metropolis: [adstock_shape]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [12000/12000 02:05<00:00 Sampling 6 chains, 0 divergences]\n", + "
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      <xarray.Dataset> Size: 262MB\n",
+              "Dimensions:                          (chain: 6, draw: 1000, control: 1,\n",
+              "                                      fourier_mode: 8, channel: 2, date: 362)\n",
+              "Coordinates:\n",
+              "  * chain                            (chain) int64 48B 0 1 2 3 4 5\n",
+              "  * draw                             (draw) int64 8kB 0 1 2 3 ... 997 998 999\n",
+              "  * control                          (control) <U5 20B 'trend'\n",
+              "  * fourier_mode                     (fourier_mode) <U11 352B 'sin_order_1' ....\n",
+              "  * channel                          (channel) <U6 48B 'meta' 'google'\n",
+              "  * date                             (date) datetime64[ns] 3kB 2021-07-01 ......\n",
+              "Data variables: (12/14)\n",
+              "    intercept                        (chain, draw) float64 48kB 0.1537 ... 0....\n",
+              "    gamma_control                    (chain, draw, control) float64 48kB 0.13...\n",
+              "    gamma_fourier                    (chain, draw, fourier_mode) float64 384kB ...\n",
+              "    adstock_lambda                   (chain, draw, channel) float64 96kB 0.22...\n",
+              "    adstock_shape                    (chain, draw, channel) float64 96kB 0.22...\n",
+              "    saturation_lambda                (chain, draw, channel) float64 96kB 0.78...\n",
+              "    ...                               ...\n",
+              "    channel_adstock                  (chain, draw, date, channel) float64 35MB ...\n",
+              "    channel_contributions            (chain, draw, date, channel) float64 35MB ...\n",
+              "    control_contributions            (chain, draw, date, control) float64 17MB ...\n",
+              "    fourier_contributions            (chain, draw, date, fourier_mode) float64 139MB ...\n",
+              "    yearly_seasonality_contribution  (chain, draw, date) float64 17MB 0.1131 ...\n",
+              "    mu                               (chain, draw, date) float64 17MB 0.4938 ...\n",
+              "Attributes:\n",
+              "    created_at:                 2024-04-18T21:50:41.882633\n",
+              "    arviz_version:              0.17.0\n",
+              "    inference_library:          pymc\n",
+              "    inference_library_version:  5.10.4\n",
+              "    sampling_time:              125.97286200523376\n",
+              "    tuning_steps:               1000

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+              "  * accept_dim_0           (accept_dim_0) int64 16B 0 1\n",
+              "  * scaling_dim_0          (scaling_dim_0) int64 16B 0 1\n",
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+              "Data variables: (12/20)\n",
+              "    n_steps                (chain, draw) float64 48kB 255.0 63.0 ... 255.0 255.0\n",
+              "    tree_depth             (chain, draw) int64 48kB 8 6 7 8 8 9 ... 8 8 8 8 8 8\n",
+              "    accept                 (chain, draw, accept_dim_0) float64 96kB 0.3241 .....\n",
+              "    scaling                (chain, draw, scaling_dim_0) float64 96kB 1.772 .....\n",
+              "    diverging              (chain, draw) bool 6kB False False ... False False\n",
+              "    step_size              (chain, draw) float64 48kB 0.01064 ... 0.01339\n",
+              "    ...                     ...\n",
+              "    energy_error           (chain, draw) float64 48kB -0.0451 ... -0.0001007\n",
+              "    max_energy_error       (chain, draw) float64 48kB -0.09181 ... -0.008014\n",
+              "    accepted               (chain, draw, accepted_dim_0) float64 96kB 0.0 ......\n",
+              "    index_in_trajectory    (chain, draw) int64 48kB -163 -29 55 ... 209 146 239\n",
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+              "Attributes:\n",
+              "    created_at:                 2024-04-18T21:50:41.906811\n",
+              "    arviz_version:              0.17.0\n",
+              "    inference_library:          pymc\n",
+              "    inference_library_version:  5.10.4\n",
+              "    sampling_time:              125.97286200523376\n",
+              "    tuning_steps:               1000

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+              "  * date     (date) datetime64[ns] 3kB 2021-07-01 2021-07-02 ... 2022-07-05\n",
+              "Data variables:\n",
+              "    y        (date) float64 3kB 0.4913 0.5362 0.5451 ... 0.9679 0.9305 0.9425\n",
+              "Attributes:\n",
+              "    created_at:                 2024-04-18T21:50:41.910762\n",
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+              "  * date          (date) datetime64[ns] 3kB 2021-07-01 2021-07-02 ... 2022-07-05\n",
+              "  * channel       (channel) <U6 48B 'meta' 'google'\n",
+              "  * control       (control) <U5 20B 'trend'\n",
+              "  * fourier_mode  (fourier_mode) <U11 352B 'sin_order_1' ... 'cos_order_4'\n",
+              "Data variables:\n",
+              "    channel_data  (date, channel) float64 6kB 0.3083 0.009071 ... 0.3888 0.8934\n",
+              "    target        (date) float64 3kB 0.4913 0.5362 0.5451 ... 0.9305 0.9425\n",
+              "    control_data  (date, control) float64 3kB 0.7783 0.7844 ... 1.782 1.783\n",
+              "    fourier_data  (date, fourier_mode) float64 23kB 0.01075 -0.9999 ... 0.9732\n",
+              "Attributes:\n",
+              "    created_at:                 2024-04-18T21:50:41.914995\n",
+              "    arviz_version:              0.17.0\n",
+              "    inference_library:          pymc\n",
+              "    inference_library_version:  5.10.4

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+              "Data variables:\n",
+              "    ds       (index) datetime64[ns] 3kB 2021-07-01 2021-07-02 ... 2022-07-05\n",
+              "    meta     (index) float64 3kB 0.945 0.9327 0.9377 ... 1.197 1.195 1.192\n",
+              "    google   (index) float64 3kB 0.0239 0.1459 0.181 ... 2.333 2.338 2.354\n",
+              "    trend    (index) float64 3kB 0.7783 0.7844 0.7905 ... 1.78 1.782 1.783\n",
+              "    y        (index) float64 3kB 3.369 3.677 3.738 3.815 ... 6.638 6.381 6.463

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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
saturation_lambda[meta]0.790.060.670.910.000.004024.913959.531.00
saturation_lambda[google]0.680.130.450.940.020.0142.09316.491.10
saturation_alpha[meta]0.410.020.390.450.000.003019.363532.431.00
saturation_alpha[google]0.450.050.350.540.010.0094.20212.771.05
\n", + "
" + ], + "text/plain": [ + " mean sd hdi_3% hdi_97% mcse_mean mcse_sd \\\n", + "saturation_lambda[meta] 0.79 0.06 0.67 0.91 0.00 0.00 \n", + "saturation_lambda[google] 0.68 0.13 0.45 0.94 0.02 0.01 \n", + "saturation_alpha[meta] 0.41 0.02 0.39 0.45 0.00 0.00 \n", + "saturation_alpha[google] 0.45 0.05 0.35 0.54 0.01 0.00 \n", + "\n", + " ess_bulk ess_tail r_hat \n", + "saturation_lambda[meta] 4024.91 3959.53 1.00 \n", + "saturation_lambda[google] 42.09 316.49 1.10 \n", + "saturation_alpha[meta] 3019.36 3532.43 1.00 \n", + "saturation_alpha[google] 94.20 212.77 1.05 " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "az.summary(mmm.fit_result, var_names=[\"saturation_lambda\", \"saturation_alpha\"], round_to=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model seems to face some convergence issues related to the `lambda` parameter as it is attempting to fit the data with given prior information. Despite these challenges, the recovered data is very close to the actual data." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Posterior Alpha and Lambda values: 2.85, 2.42\n", + "True Alpha and Lambda values: 2.75, 2.50\n" + ] + } + ], + "source": [ + "print(\"Posterior Alpha and Lambda values: {:.2f}, {:.2f}\".format(\n", + " mmm.idata.posterior[\"saturation_alpha\"].sel(channel=\"meta\").mean().item() * df.venezuela.max(), \n", + " mmm.idata.posterior[\"saturation_lambda\"].sel(channel=\"meta\").mean().item() * df.meta.max()\n", + " )\n", + ")\n", + "\n", + "print(\n", + " \"True Alpha and Lambda values: {:.2f}, {:.2f}\".format(2.75, 2.50)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It appears that the estimation of Facebook was more accurate than that of Google (As expected), with the values being more aligned with the reality. If we wanted to go even deeper we could play with Google, set up an experiment and estimate informative priors that help the model find the real values.\n", + "\n", + "Wait for a new article, if you want to see how we make it!\n", + "\n", + "## Final considerations\n", + "\n", + "Please keep in mind that we estimate the values of lambda and alpha with a single experiment. However, it is important to note that our current PyMC models do not consider time as a factor in our regression. Therefore, the contribution estimate is average. \n", + "\n", + "It is essential to understand that every experiment is inherently connected to the time in which it is executed. That means your model and experiments may not agree entirely. After running multiple experiments, the average contribution detected in total (considering each experiment) should align with the model. However, a single experiment may not. The nature of time variation was ignored in this example to simplify the case. However, in real life, it should be considered.\n", + "\n", + "## Bonus\n", + "\n", + "You can tweak the code mentioned earlier to find the best *alpha* and *lam* values for all the experiments you've run. This will give you more insights than just using a single point to determine the values. Keep in mind that for one point, there can be an endless number of curves that can be adjusted to fit it." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Alpha for Single Point: 3.9960464433851666\n", + "Best Lambda for Single Point: 1.4044831825669613\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 511, + "width": 1211 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# fake data point: (x, dy/dx)\n", + "x_range = np.linspace(0, 5, 100)\n", + "initial_guess = [5, 2]\n", + "experiment_data = np.array([\n", + " (x_midpoint, average_rate_of_change), # Experiment 1.\n", + " (1,1), # Experiment 2.\n", + " (.5, 1.5), # Experiment 3.\n", + " (3, .5), # Experiment 4.\n", + " (5, .1), # Experiment 5.\n", + " (.1, 2.5) # Experiment 6.\n", + "])\n", + "\n", + "# Perform optimization with a single point\n", + "result_single = minimize(objective_function, initial_guess, args=(experiment_data[:, 0], experiment_data[:, 1]), method='L-BFGS-B', bounds=[(0, None), (0, None)])\n", + "\n", + "# Extract the best alpha and lambda for the single point\n", + "best_alpha_single, best_lam_single = result_single.x\n", + "\n", + "# Plot the observed point and the best fit line for the single point\n", + "best_fit_y_single = derivative_michaelis_menten(x_range, best_alpha_single, best_lam_single)\n", + "\n", + "print(\"Best Alpha for Single Point:\", best_alpha_single)\n", + "print(\"Best Lambda for Single Point:\", best_lam_single)\n", + "\n", + "plt.figure(figsize=(12, 5))\n", + "plt.plot(x_range, best_fit_y_single, label=\"Best Fit Derivative\", color=\"grey\", linestyle=\"--\")\n", + "plt.scatter(experiment_data[:, 0], experiment_data[:, 1], label=\"Observed Experiments\", color=\"lightgreen\")\n", + "plt.xlabel(\"Spend/Impressions (x)\")\n", + "plt.ylabel(\"Rate of Change (dy/dx)\")\n", + "plt.title(\"Best Fit for Michaelis-Menten Derivative (Multiple Experiments)\")\n", + "plt.legend(loc=\"upper left\", bbox_to_anchor=(1, 1))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We just showed how to come up with useful priors for media mix models using **PyMC-Marketing** and experiments. By using causal analysis with `CausalPy`, we can figure out \"what if\" scenarios and estimate how marketing activities contribute to sales. This can help us get better priors in Bayesian MMMs, which means we can decompose more accurately and understand the impact of our marketing efforts better.\n", + "\n", + "We've given you a step-by-step guide to using Bayesian methods and causal inference in your marketing analysis. This should help you make more informed decisions and optimize your marketing strategies more efficiently.\n", + "\n", + "Now it's your turn to put this knowledge into practice and give us your feedback. We're always looking to improve!\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/blog-posts/informative_priors_pymc/datasets/dataset_chapter_n.csv b/blog-posts/informative_priors_pymc/datasets/dataset_chapter_n.csv new file mode 100644 index 0000000..f2a611f --- /dev/null +++ b/blog-posts/informative_priors_pymc/datasets/dataset_chapter_n.csv @@ -0,0 +1,363 @@ 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