From 627634b4affb27d17732e2657b9fc6bbd1f263e7 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Tue, 4 Jul 2023 13:31:16 -0700 Subject: [PATCH 01/32] Added Tutorial part 1 --- .../Tutorial-I-Components.ipynb | 1181 +++++++ docs/tutorials/Introduction.ipynb | 3046 ----------------- 2 files changed, 1181 insertions(+), 3046 deletions(-) create mode 100644 docs/tutorials/Basic Tutorial/Tutorial-I-Components.ipynb delete mode 100644 docs/tutorials/Introduction.ipynb diff --git a/docs/tutorials/Basic Tutorial/Tutorial-I-Components.ipynb b/docs/tutorials/Basic Tutorial/Tutorial-I-Components.ipynb new file mode 100644 index 000000000..b6c9a6452 --- /dev/null +++ b/docs/tutorials/Basic Tutorial/Tutorial-I-Components.ipynb @@ -0,0 +1,1181 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "_q7iLq3GUYMz" + }, + "source": [ + "# Introduction to AutoRA\n", + "## Basic Tutorial I: Components" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + }, + "id": "5mfUKtGTUYM1" + }, + "source": [ + "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", + "\n", + "This notebook is the first of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", + "\n", + "[AutoRA Basic Tutorial I: Components](www.addlink.com)
\n", + "[AutoRA Basic Tutorial II: Loop Constructs](www.addlink.com)
\n", + "[AutoRA Basic Tutorial III: Workflow](www.addlink.com)
\n", + "[AutoRA Basic Tutorial IV: Customization](www.addlink.com)
\n", + "\n", + "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", + "\n", + "**How to use this notebook** *You can progress through the notebook section by section or directly navigate to specific sections. If you choose the latter, it is recommended to execute all cells in the notebook initially, allowing you to easily rerun the cells in each section later without issues.*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BWl9iqpgUYM2" + }, + "source": [ + "## Installation\n", + "\n", + "The AutoRA ecosystem is a comprehensive collection of packages that together establish a framework for closed-loop empirical research. At the core of this framework is the ``autora`` package, which serves as the parent package and is essential for end users to install. It provides functionalities for automating workflows in empirical research and includes vetted modules with minimal dependencies.\n", + "\n", + "However, the flexibility of autora extends further with the inclusion of *optional* modules as additional dependencies. Users have the freedom to selectively install these modules based on their specific needs and preferences.\n", + "\n", + "\"AutoRA\n", + "\n", + "*Optional dependencies enable users to customize their autora environment without worrying about conflicts with other packages within the broader autora ecosystem. To install an optional module, simply use the command ``pip install autora[dependency-name]``, where ``dependency-name`` corresponds to the name of the desired module (see example below).*\n", + "\n", + "To begin, we will install all the relevant optional dependencies. Our main focus will be on two experimentalists: ``experimentalist-falsification`` and ``experimentalist-sampler-novelty``, along with a Bayesian Machine Scientist (BMS) implemented in the ``theorist-bms`` package. It's important to note that installing a module will automatically include the main autora package, as well as any required dependencies for workflow management and running synthetic experiments.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "id": "2S9mfSxVUYM3" + }, + "outputs": [], + "source": [ + "!pip install -q \"autora[experimentalist-falsification]\"\n", + "!pip install -q \"autora[experimentalist-sampler-novelty]\"\n", + "!pip install -q \"autora[experimentalist-sampler-model-disagreement]\"\n", + "!pip install -q \"autora[theorist-bms]\"" + ] + }, + { + "cell_type": "markdown", + "source": [ + "To make all simulations in this notebook replicable, we will set some seeds." + ], + "metadata": { + "id": "B4DahNFBVNo3" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import torch\n", + "\n", + "np.random.seed(42)\n", + "torch.manual_seed(42)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tJNNbhskVMNq", + "outputId": "a54371dd-79ab-4849-abdc-4862bd116e94" + }, + "execution_count": 93, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 93 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bSGRphhDUYM4" + }, + "source": [ + "# Automated Empirical Research Components\n", + "\n", + "The goal of this section is to set up all ``autora`` components to enable a closed-loop discovery workflow with synthetic data. This involves specifying (1) the experiment runner, (2) a theorist for model discovery, (3) an experimentalist for identifying novel experiment conditions.\n", + "\n", + "\n", + "* **Experiment Runner:** The experiment runner collects observations reflecting experimental conditions.\n", + "* **Theorist:** The theorist automates the construction of models from data. These can take many forms, for example linear regression and the bayesian machine scientist.\n", + "* **Experimentalist:** Each experimentalist identifies experimental conditions that yield scientific merit.\n", + "\n", + "\"AutoRA\n", + "\n", + "Each of these components automates a process of the scientific method that is generally conducted manually. The experiment runner parallels a *research assistant* that collects data from participants. The theorist takes the place of a *computational scientist* that applies modelling techniques to discover how to best describe the data. The experimentalist acts as a *research design expert* to determine the next iteration of experimentation. Each of these steps in the scientific method can be arduous and time consuming to conduct manually, and so ``autora`` allows for the automation of these steps and thus quickens the scientific method by leveraging data-driven techniques." + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Toy Example of the Components\n", + "Before jumping into each component in detail, we will present a toy example to provide you with an overview on how these components work together within a closed-loop. After some setup, you will see steps 1-3, which uses the three componens - namely, the EXPERIMENTALIST to propose new conditions, the EXPERIMENT RUNNER to retrieve new observations from those conditions, and the THEORIST to model the new data. We then finish this example by plotting our data and findings." + ], + "metadata": { + "id": "F_lZhwcg8wW8" + } + }, + { + "cell_type": "code", + "source": [ + "#Setup: Import modules\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autora.theorist.bms import BMSRegressor\n", + "from autora.experimentalist.sampler.random_sampler import random_sample #Note that this sampler is embedded within the autora-core module and so does not need to be explicitly installed\n", + "\n", + "#Step 0: Defining variables\n", + "ground_truth = lambda x: np.sin(x) #Define a ground truth model that we will attempt to recover - here a sine wave\n", + "initial_X = np.linspace(0, 4 * np.pi, 200) #Define initial data\n", + "\n", + "#Step 1: EXPERIMENTALIST: Sample using the experimentalist\n", + "new_conditions = random_sample(initial_X, n = 20)\n", + "new_conditions = np.array(new_conditions).reshape(-1,1) #Turn variable into a 2D array\n", + "\n", + "#Step 2: EXPERIMENT RUNNER: Define and then obtain observations using the experiment runner\n", + "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape) #Define the runner, which here is simply the ground truth with noise\n", + "new_observations = run_experiment(new_conditions) #Obtain observations from the runner for the conditions proposed by the experimentalist\n", + "new_observations = new_observations.reshape(-1,1) #Turn variable into a 2D array\n", + "\n", + "#Step 3: THEORIST: Initiate and fit a model using the theorist\n", + "theorist_bms = BMSRegressor(epochs=100) #Initiate the BMS theorist\n", + "theorist_bms.fit(new_conditions, new_observations) #Fit a model to the data\n", + "\n", + "#Wrap-Up: Plot data and model\n", + "sort_index = np.argsort(new_conditions, axis=0)[:,0] #We will first sort our data\n", + "new_conditions = new_conditions[sort_index,:]\n", + "new_observations = new_observations[sort_index,:]\n", + "\n", + "plt.plot(initial_X, ground_truth(initial_X), label='Ground Truth')\n", + "plt.plot(new_conditions, new_observations, 'o', label='Sampled Conditions')\n", + "plt.plot(new_conditions, theorist_bms.predict(new_conditions), label=f'Bayesian Machine Scientist ({theorist_bms.repr()})')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Sine Function')\n", + "plt.legend()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "8P3iMrqN-pOU", + "outputId": "92dede11-65c8-423d-b8a5-ac0b6191404c" + }, + "execution_count": 125, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:14<00:00, 6.96it/s]\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 125 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "***WARNING:*** *Do not stop here! We have now shown you the three components and how they work together. At this point, it may be tempting to start working on your own project, but we urge you to continue through the tutorials. ``autora`` has a lot of embedded functionality that you are going to want to use, and this toy example has stripped those away. So, keep going and see how much ``autora`` has to offer!*" + ], + "metadata": { + "id": "zGaP3IQNBff2" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KbUon6UcUYM5" + }, + "source": [ + "## Experiment Runners\n", + "\n", + "``autora`` provides support for experiment runners, which serve as interfaces for conducting both real-world and synthetic experiments. An experiment runner typically accepts experiment conditions as input (e.g., a 2-dimensional numpy array with columns representing different independent variables) and produces collected observations as output (e.g., a 2-dimensional numpy array with columns representing different dependent variables). These experiment runners can be combined with other ``autora`` components to facilitate closed-loop scientific discovery.\n", + "\n", + "\"AutoRA\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "### Types\n", + "\n", + "AutoRA offers two types of experiment runners: **real-world experiments** and **synthetic experiments**.\n", + "\n", + "For **real-world experiments**, experiment runners can include interfaces for various scenarios such as web-based experiments for behavioral data collection (e.g., using [Firebase and Prolific](https://autoresearch.github.io/autora/user-guide/experiment-runners/firebase-prolific/)) or experiments involving electrical circuits (e.g., using [Tinkerforge](https://en.wikipedia.org/wiki/Tinkerforge)). These runners often require external components such as databases to store collected observations or servers to host the experiments. You may refer to the respective tutorials for these interfaces on how to set up all required components.\n", + "\n", + "---\n", + "CHAD Suggestion: You mention to refer to the respective tutorials, but the tinkerforge link brings you to wikipedia. Is there any Autora page describing the tinkerforge interface that we could link instead? Even if it's just a description and not a tutorial. \n", + "\n", + "---\n", + "\n", + "\n", + "**Synthetic experiments** are conducted on synthetic experiment runners, which are functions that take experimental conditions as input and generate simulated observations as output. These experiments serve multiple purposes, including *testing autora components* before applying them to real-world experiments, *benchmarking methods for automated scientific discovery*, or *conducting computational metascientific experiments*.\n", + "\n", + "In this introductory tutorial, we primarily focus on simple synthetic experiments. For more complex synthetic experiments implementing various scientific models, you can utilize the [autora-synthetic](https://github.com/autoresearch/autora-synthetic/) module." + ], + "metadata": { + "id": "SL3Si-rIFqlQ" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_UWBS-P-UYM5" + }, + "source": [ + "### Usage\n", + "\n", + "To create a synthetic experiment runner, we begin with **defining a ground truth** from which to generate data. Here, we consider a simple sine function:\n", + "\n", + "$y = f(x) = \\sin(x)$\n", + "\n", + "In this case, $x$ corresponds to an *independent* variable (the variable we can manipulate in an experiment), $y$ corresponds to a *dependent* variable (the variable we can observe after conducting the experiment), and $f(x)$ is the *ground-truth function* (or \"mechanism\") that we seek to uncover via a combination of experimentation and model discovery.\n", + "\n", + "However, we assume that observations are obtained with a measurement error when running the experiment.\n", + "\n", + "$\\hat{y} = \\hat{f}(x) = f(x) + \\epsilon, \\quad \\epsilon \\sim \\mathcal{N}(0,0.1)$\n", + "\n", + "where $\\epsilon$ is the measurement error sampled from a normal distribution with zero mean and a standard deviation of $0.1$.\n", + "\n", + "The following code block defines the ground truth $f(x)$ and the experiment runner $\\hat{f}(x)$ as ``lambda`` functions." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "id": "DM9HkPNqUYM5" + }, + "outputs": [], + "source": [ + "ground_truth = lambda x: np.sin(x)\n", + "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hBbzNQjXUYM5" + }, + "source": [ + "Next, we generate a pool of all possible experimental conditions from the domain $[0, 2\\pi]$." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "id": "cjnQjoANUYM6" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "condition_pool = np.linspace(0, 2 * np.pi, 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ejj3Yd_FUYM6" + }, + "source": [ + "In order to run a simple synthetic experiment, we can first sample from the pool of possible experiment conditions (without replacement), and then pass these conditions to the synthetic experiment runner:" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 489 + }, + "id": "epUuwg8rUYM6", + "outputId": "590bf1d8-7c66-468e-982a-55a097dba006" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 97 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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RkYH333+/1jmnT5+OsWPHIigoCKGhodi0aRMuX76s7r2qCzXJ6uHhAYlEgt27d6Nfv34wNTWFhYVFnWXQJ+wRItIWUhng1QXwH6r6ySKIdEiTJk1w4cIF9OrVC/PmzUNAQACCgoLw7bffYvbs2fjkk0+qPH/w4MH4+uuv8cUXX6Bly5b47rvv8PPPP6N79+7qNj/99BNKSkrQrl07zJgxA59++mmtcw4fPhwffPAB5syZg3bt2iExMRGTJ0+u9fNUp7qsLi4u+OijjzB37lw4ODhg6tSpdZ5BX0iE2sxxFNmRI0fw+eefIyIiAqmpqdixYwcGDx5c5Tnh4eGYOXMmLl++DDc3N7z//vtl1luoTnZ2NqysrJCVlQW5XP50b4CoMtxHjOpAQUEB4uPj4eXlBRMTE7HjENW7qn7na/r9rVU9Qnl5eQgICMDKlStr1D4+Ph79+/dHjx49EBkZiRkzZmDixIn455+Gn0pKVCnuI0ZEJBqtGiPUt29f9O3bt8bt16xZAy8vLyxbtgyAapDbsWPH8NVXX6F374afRUFUzsN9xB7fQuPhPmIcDE1EVK+0qhCqrZMnT6JXr15ljvXu3Zs789ITycwvwrX0XGQ9KEZBsUJ9EwDYWxrD0coUjnIT2FkaQyatwYwZ7iNGRCQ6nS6E0tLS4OBQdsVdBwcHZGdn48GDBxWuTVFYWFhm4a7s7Ox6z0map7BEgROxd3Eq/i6upuXgSmoO0rJrtnKsTCpBUzsLdPS2QUdvW3TwsoGthXH5hrXZR8yry5O9ESIiqpJOF0JPYtGiRfjoo4/EjkEiyC8qweGrtxF2OQ0HYzKQU1hSro1rI1M0tjCGiaEUJoYymBjIIEBARk4h0rIKkJFTCIVSwNX0HFxNz8H6k4kAAF9HSwwMdMbQdq6wtywd0Md9xIiIRKfThZCjoyPS08t+iaSnp0Mul1fYGwQA8+bNw8yZM9X3s7Oz4ebmVq85SVxJ9/Lxw9E4bD13Cw+K/1uczN7SGD1b2KOlsxVaOFmiuYMlLE0Mq3gmQKEUkJFTgMibmTgVdxen4u7hanoOrqTl4ErYVSzbew09fe0xsoM7uprbo0YXvLiPGBFRvdHpQqhTp07Ys2dPmWP79u2rcl8YY2NjGBtXcBmDdM6l5CysPRKHv6JSoVCqxum42Ziibysn9G7piDZu1pDWZKzPI2RSCZysTOHkb4q+/qqVoO/mFuJATAZ+PXsT529mYm90OvZGp8OzkTH+NnGASUEGJBWOE5KoVo/mPmJERPVGqwqh3NxcxMbGqu/Hx8cjMjISNjY2cHd3x7x585CcnIxffvkFAPDGG29gxYoVmDNnDsaPH4+DBw9i69at+Ouvv8R6C6QBEu/m4ZPd0dgf898mhF2aNcYb3ZogpIltrbYGqAlbC2MMa++GYe3dcDUtB1vOJuH3C7eQcL8QM6QjscZoOQRIHiuGuI8YEVFD0KpC6Ny5c+jRo4f6/sNLWGPGjMG6deuQmpqKmzdvqh/38vLCX3/9hbfffhtff/01XF1d8cMPP3DqvJ4qKFZgVfgNrDl8A0UlSkglwIDWznitqzdauVg1SAYfR0t8+LwfZvdujp+PJ2BNuAHeKALmG/4CZ8m9/xrKnVVFEKfOExHVK61aWVoMXFlaNxyISceCPy8j6d4DAKoeoPnPt0RTe3H35rmfV4TVh2/glxNxCFRGw1GaiS5tWmHgwKHldpAmqg5Xlq4/EomkRrsZVGXs2LHIzMzEzp076yxXXVq3bh1mzJiBzMxMAMCCBQuwc+fOKjenTUhIgJeXFy5cuIDAwMAGyfkovVtZmqi2cgtLMHNLJCasP4ekew/gZGWCVaPa4pfxHUQvggCgkbkR3uvXAgdnPwOrFj2wsyQEs87KMWTNKVxNyxE7HukzpQKIPwpEbVf9VNbvLue3b9/G5MmT4e7uDmNjYzg6OqJ3797ldpTXdYIgYO3atQgODoaFhQWsra0RFBSE5cuXIz8/v0GzzJ49GwcOHFDfHzt2bLlC0M3NDampqWjVqlWDZqtLWnVpjKg2Lqdk4a3NFxB3Jw9SCTCpqzemPdMM5saa92vvbG2KNa+0w66LKfjwj8u4lJyNAd8exYxezTG5W5NaD9omeirRu1SLfT66zpXcGeizpN4u17744osoKirC+vXr4e3tjfT0dBw4cAB3796tl9fTVK+++ip+//13vP/++1ixYgXs7Oxw8eJFLF++HJ6enk/VI1VbFhYW1e5oL5PJ4Ojo2ECJ6gd7hEjnCIKAX04mYMiqE4i7kwcnKxNseb0T5vVtoZFF0EMSiQSDAl2w7+2u6NXCHsUKAZ//cxWvbYhATkGx2PFIXzzc9uXxxT4fbvtSD3vgZWZm4ujRo1iyZAl69OgBDw8PdOjQAfPmzcPAgf8VXl9++SX8/f1hbm4ONzc3TJkyBbm5uerH161bB2tra+zevRs+Pj4wMzPD0KFDkZ+fj/Xr18PT0xONGjXCtGnToFD818Pl6emJTz75BCNHjoS5uTlcXFyq3dMyKSkJw4YNg7W1NWxsbDBo0CAkJCSoH1coFJg5cyasra1ha2uLOXPmoLqRKFu3bsWmTZvwv//9D++99x7at28PT09PDBo0CAcPHlSPkVUqlfj444/h6uoKY2NjBAYGIiwsTP08CQkJkEgk+P3339GjRw+YmZkhICAAJ0+eLPN669atg7u7O8zMzDBkyJByReeCBQvUl7sWLFiA9evX448//oBEIoFEIkF4eLj6tR69fHb48GF06NABxsbGcHJywty5c1FS8t+6bN27d8e0adMwZ84c2NjYwNHREQsWLFA/LggCFixYoO4ddHZ2xrRp06r87J4GCyHSKflFJXhz83l8+MdlFJUo0auFPfZM64L2njZiR6sxe7kJvh8dhCUv+sPIQIr9MekYvPI44m7nVn8y0dOodtsXqLZ9qePLZA97Hnbu3FlmZf/HSaVSfPPNN7h8+TLWr1+PgwcPYs6cOWXa5Ofn45tvvsGvv/6KsLAwhIeHY8iQIdizZw/27NmDDRs24LvvvsP27dvLnPf5558jICAAFy5cwNy5czF9+nTs27evwhzFxcXo3bs3LC0tcfToURw/fhwWFhbo06cPioqKAADLli3DunXr8NNPP+HYsWO4d+8eduzYUeXnsGnTJvj4+GDQoEHlHpNIJLCyUk3q+Prrr7Fs2TJ88cUX+Pfff9G7d28MHDgQ169fL3PO//3f/2H27NmIjIxE8+bNMXLkSHVBcvr0aUyYMAFTp05FZGQkevTogU8//bTSbLNnz8awYcPQp08fpKamIjU1FSEh5Zf2SE5ORr9+/dC+fXtcvHgRq1evxo8//ljuudevXw9zc3OcPn0aS5cuxccff6z+vH/77Td89dVX+O6773D9+nXs3LkT/v7+VX52T0WgKmVlZQkAhKysLLGjUDUysguEgd8eFTze3S00fe8v4YejcYJSqRQ71lO5cPO+EPzZfsHj3d1Cq/lhwsGYdLEjkQZ78OCBEB0dLTx48ODJniDuiCDMl1d/iztSt8EFQdi+fbvQqFEjwcTERAgJCRHmzZsnXLx4scpztm3bJtja2qrv//zzzwIAITY2Vn3s9ddfF8zMzIScnBz1sd69ewuvv/66+r6Hh4fQp0+fMs89fPhwoW/fvur7AIQdO3YIgiAIGzZsEHx8fMr8+1JYWCiYmpoK//zzjyAIguDk5CQsXbpU/XhxcbHg6uoqDBo0qNL306JFC2HgwIFVvmdBEARnZ2fhs88+K3Osffv2wpQpUwRBEIT4+HgBgPDDDz+oH798+bIAQIiJiREEQRBGjhwp9OvXr9x7trKyUt+fP3++EBAQoL4/ZsyYcvkfvtaFCxcEQRCE9957r9xns3LlSsHCwkJQKBSCIAhCt27dhM6dO5fL/+677wqCIAjLli0TmjdvLhQVFVX7WVT1O1/T72/2CJFOiM3IxQurj+PirSw0MjPE/yZ1xITOXnW+JlBDC3Szxq63QhHk0Qg5BSUYv/4sfjgaJ3Ys0lUibvvy4osvIiUlBbt27UKfPn0QHh6Otm3bYt26deo2+/fvR8+ePeHi4gJLS0u8+uqruHv3bplBxGZmZmjSpIn6voODAzw9PcuMdXFwcEBGxn/riAEot9Bup06dEBMTU2HWixcvIjY2FpaWlureLBsbGxQUFODGjRvIyspCamoqgoOD1ecYGBggKCioys9AqMEk7uzsbKSkpCA0NLTM8dDQ0HJ5W7durf7fTk6qBV4fvu+YmJgy+YDyn8GTiImJQadOncr82xsaGorc3FzcunWrwmwP8z3M9tJLL+HBgwfw9vbGpEmTsGPHjjKX1uoaCyHSeqfj7uLF1SeQdO8B3G3M8NvkEARp0aWw6thbmmDzpI54OdgdggB8+lcMlu29WqN/NIlqpabbudTTti8mJiZ49tln8cEHH+DEiRMYO3Ys5s+fD0A17mXAgAFo3bo1fvvtN0RERKjH8Ty8HAWg3LITEomkwmNKpfKJc+bm5qJdu3aIjIwsc7t27RpefvnlJ37e5s2b48qVK098/uMefd8PC5Oned91qar/Jm5ubrh69SpWrVoFU1NTTJkyBV27dkVxcf2MlWQhRFot7FIqXv3xDLIeFCPQzRo7poTA2078afF1zchAioVD/DGnjw8A4NuDsfjoz2golSyGqA55hKhmh6GynlQJIHdpsG1f/Pz8kJeXBwCIiIiAUqnEsmXL0LFjRzRv3hwpKSnVPEPNnTp1qtz9Fi1aVNi2bdu2uH79Ouzt7dG0adMyNysrK1hZWcHJyQmnT59Wn1NSUoKIiIgqM7z88su4du0a/vjjj3KPCYKgXg/H2dm53LICx48fh5+fX03fLlq0aFEmH1D+M3ickZFRmUHmlT3vyZMny/yhdvz4cVhaWsLV1bXG+UxNTfH888/jm2++QXh4OE6ePImoqKgan18bLIRIa/0dlYo3N19AkUKJ3i0d8L9JHWFrodv7xE3p3hSfDGoJAFh3IgGzt19EiUIz/sIjHSCVqabIAyhfDNXfti93797FM888g40bN+Lff/9FfHw8tm3bhqVLl6oHDjdt2hTFxcX49ttvERcXhw0bNmDNmjV1luH48eNYunQprl27hpUrV2Lbtm2YPn16hW1HjRqFxo0bY9CgQTh69Cji4+MRHh6OadOmqS//TJ8+HYsXL8bOnTtx5coVTJkyRb1QYWWGDRuG4cOHY+TIkVi4cCHOnTuHxMRE7N69G7169cKhQ4cAAO+88w6WLFmCLVu24OrVq5g7dy4iIyMrzVuRadOmISwsDF988QWuX7+OFStWlJl5VhFPT0/8+++/uHr1Ku7cuVNhD82UKVOQlJSEt956C1euXMEff/yB+fPnY+bMmZBKa1ZyrFu3Dj/++CMuXbqEuLg4bNy4EaampvDw8Kjx+6sNFkKklcIupeKt/12AQilgSBsXrBrVDqZG+rEn16udPPHV8ADIpBL8fj4Zb24+j6ISFkNUR/wGAsN+AeROZY/LnVXH62EdIQsLCwQHB+Orr75C165d0apVK3zwwQeYNGkSVqxYAQAICAjAl19+iSVLlqBVq1bYtGkTFi1aVGcZZs2ahXPnzqFNmzb49NNP8eWXX1a6HZOZmRmOHDkCd3d3vPDCC2jRogUmTJiAgoIC9QrGs2bNwquvvooxY8agU6dOsLS0xJAhQ6rMIJFIsHnzZnz55ZfYuXMnunXrhtatW2PBggUYNGiQOs+0adMwc+ZMzJo1C/7+/ggLC8OuXbvQrFmzGr/fjh074vvvv8fXX3+NgIAA7N27F++//36V50yaNAk+Pj4ICgqCnZ1dhYtduri4YM+ePThz5gwCAgLwxhtvYMKECdU+96Osra3x/fffIzQ0FK1bt8b+/fvx559/wtbWtsbPURvcYqMa3GJD84RdSsPUzedRUloEffGSqijQN/ui09VFUP/WTvhmRBu9/BzoP3W6xYZSASSeUA2MtnBQXQ7T0Q2APT09MWPGDMyYMUPsKFRL3GKD9M6jRdDgQGe9LYIA4Fk/B3w/OgiGMgn++jcV/7cjigOoqe5IZYBXF8B/qOqnjhZBRCyESGscvX5bXQQNCnTGsmGBelsEPdStuR2+HtEGUgnw69kkLNwTw2KIiKgWNHe/AaJHXE7JwuSNqiKof2snLNPjnqDH9fN3wuIXWmPOb//i+6PxsDI1xNRnaj5WgEjfPbo1Bukf9giRxrt1Px/jfj6L3MISdPS2wZfDAmAg46/uo4a1d8MHA1RTZ7/Yew2/nEwQNxARkZbgtwlptKz8Yoz9+Swycgrh42CJ714NgrEBxypUZEJnL0zvqeoJWrDrMg5dzajmDNJVvDxK+qIuftdZCJHGKihWYNIv5xCbkQtHuQl+HtceVqaG1Z+ox2b0aoZhQa5QCsBbmy/gWnqO2JGoAT1crffRLSeIdNnD3/XHV6quDY4RIo0kCALmbP8XZxLuwdLYAOvGt4eztanYsTSeRCLBp4P9kXg3H6fj72HC+rPYOSVU5xeaJBWZTAZra2v1nk1mZmZav98eUUUEQUB+fj4yMjJgbW0NmezJrxSwECKN9MPReOy6mAIDqQTfvdoOvo5cw6mmjAykWPNKOwxedRyJd/Px+oYIbJoUzEuKesLR0REAym0qSqSLrK2t1b/zT4oLKlaDCyo2vGPX72D0T6ehFICPBrbEmBBPsSNppdiMHAxZdQI5BSV4oa0Llr0UwN4BPaJQKOptk0oiTWBoaFhlT1BNv7/ZI0QaJelePt7633koBWBoO1eM7lQ/e8vog6b2llj5cluMW3cWv59PRktnK0zo7CV2LGogMpnsqS4XEOkLDpYmjfGgSIHXN0Tgfn4xWrta4dPBrdiD8ZS6NrfD+/1VO2gv2hODiMT7IiciItIsLIRIIwiCgLm//4vo1Gw0tjDCmlfawcSQf83WhbEhnujf2gklSgFTN5/HvbwicQMpFUD8USBqu+qnUiFuHiLSa7w0Rhph4+mb+CNSNTh65cttOUOsDkkkEix5sTViUrIRdycPM7ZEYt3Y9pCKsTJ39C4g7F0gO+W/Y3JnoM+SetnVnIioOuwRItHFpGbjk93RAIC5fX0R7G0rciLdY2FsgFWvtIWJoRRHrt3GikOxDR8iehewdXTZIggAslNVx6N3NXwmItJ7LIRIVPlFJXjrfxdQVKLEM772HMxbj3wd5fh0sD8A4Kv913Ds+p2Ge3GlQtUThIomqZYeC5vLy2RE1OBYCJGoPv4zGrEZubC3NMbnQ1tzcHQ9G9rOFcOD3CAIwIwtF3Ant7BhXjjxRPmeoDIEIDtZ1Y6IqAGxECLR/HkxBb+eTYJEAiwfEcjVjxvIR4NaormDBe7kFmHub1ENsy9VbnrdtiMiqiMshEgUSffy8d7vUQCAN7s3RUiTxiIn0h8mhjIsH94GRjIp9sek49ezSfX/ohYOdduOiKiOsBCiBqdQCpj+6wXkFJagnUcjzOjVTOxIesfPWY7ZvZsDUF2ejL+TV78v6BGimh2Gyi59SgC5i6odEVEDYiFEDe77o3E4fzMTlsYG+HpEIAxk/DUUw8TO3ujkbYsHxQq8vSUSJQpl/b2YVKaaIg+gfDFUer/PYlU7IqIGxG8galDX0nPw5d5rAIAPnveDayMzkRPpL6lUgmXDAmBpYoDIpMz6n1LvNxAY9gsgdyp7XO6sOs51hIhIBFxQkRpMsUKJWVsvokihRE9fe7zUzlXsSHrP2doUnw5uhem/RuLbg7Ho1twObdwb1d8L+g0EfPurZoflpqvGBHmEsCeIiETDHiFqMKvDbyAqOQtWpoZY9II/p8priEGBLhgY4AyFUsA72/9FYUk9r+UjlQFeXQD/oaqfLIKISEQshKhBXE7JwjcHrgMAPh7UEvZyE5ET0aM+GtgSjS2MEJuRixUHRVh1mohIJCyEqN4VlaguiZUoBfRp6YiBAc5iR6LHNDI3wseDWgFQ9dxFp2SLnIiIqGGwEKJ6tyo8FlfScmBjboRPh7TiJTEN1c/fCX1aOqJEKWDObxfrdxYZEZGGYCFE9So2IxerDt0A8PDyC1eP1mQfD24JK1NDXErOxtqjcWLHISKqdyyEqN4olQLe+z0KRQolevjYYUBrp+pPIlHZW5rggwF+AIDl+6/jxu1ckRMREdUvFkJUb7aeS8KZhHswNZTh40G8JKYtXmzrgm7N7VBUosS72/+FUtkAe5EREYmEhRDVi9s5hVi4JwYAMOu55nCz4cKJ2kIikWDhC/4wN5LhXOJ9bItogL3IiIhEwkKI6sXHu6ORXVCCVi5yjA3xFDsO1ZKLtSnefla1F9miv6/gXl6RyImIiOoHCyGqc4euZuDPiymQSoBFQ1pzLzEtNTbEE76OlsjML8aSv6+IHYeIqF7wG4rqVEGxAh/svAQAGBfqBX9XK5ET0ZMykEnx6WDV2kJbziXhXMI9kRMREdU9FkJUp1aH38Ct+w/gZGWCmaWXVkh7BXnaYHiQGwDg/Z2XuLYQEekcFkJUZ27ezcfqw6o1g97v7wdzY+7pqwve7esLazNDXEnLwboTCWLHISKqUyyEqM588lc0ikqUCGlii37+jmLHoTpiY26EuX18AQBf7buG1KwHIiciIqo7LISoThy6moF90ekwkErw0cCWXDNIxwwLckNbd2vkFSnw2V8xYschIqozLIToqRWWKPDxn9EAgHGhnmjmYClyIqprUqkEnwxuBYkE2P1vKs7Ec+A0EekGFkL01H48Fo/4O3mwszTGtJ7NxI5D9aSlsxVGtHcHAHz052UouOI0EekAFkL0VFKzHuDbA7EAgPf6+cLSxFDkRFSfZj/XHJYmBricko3tXHGaiHQACyF6Kkv+voIHxQq092yEwYEuYsehemZrYYzppb1+n/9zFdkFxSInIiJ6OiyE6IlFJmViZ2QKJBJg/vMcIK0vRnfyhLedOe7kFmHFwVix4xARPRUWQvREBEHAp7tVA6RfbOuKVi5cQVpfGBlI8cEAPwDAz8fjEXc7V+RERERPjoUQPZG/L6XhXOJ9mBrKMPs5n/8eUCqA+KNA1HbVT6VCvJBUb3r42KOHjx2KFQKn0xORVuPSv1RrhSUKLPpb9eX3ejdvOFqZqB6I3gWEvQtkp/zXWO4M9FkC+A0UISnVp/cH+OHo9SM4cCUDx67fQedmjcWORERUa+wRolpbdzwBSfcewEFujNe6eqsORu8Cto4uWwQBQHaq6nj0roYPSvWqiZ0FXunoAQBY9HcMlJxOT0RaiIUQ1crd3EL1ANl3evvCzMhAdfkr7F0AFX0Rlh4Lm8vLZDpoWs9msDRWTaf/42Ky2HGIiGqNhRDVyvL915FTWIJWLnK80KZ0unziifI9QWUIQHayqh3pFBtzI0zu0QQA8MU/11BQzGKXiLQLCyGqsRu3c7H5zE0AwP/184NUWjpdPje9Zk9Q03akVcaHesHJygTJmQ+4Oz0RaR0WQlRjX/xzFQqlgF4t7NGpie1/D1g41OwJatqOtIqJoQyzSmcOrjwUi/t5RSInIiKqORZCVCMXbt7H35fSIJWoxgaV4RGimh2GyhZUlAByF1U70klD2righZMcOQUl+JaLLBKRFmEhRNUSBAFLwq4AAF5o6wofx8d2l5fKVFPkAZQvhkrv91msakc6SSaV4L1+qgJ5w6kEJN7NEzkREVHNsBCiah2+dhun4u7ByECKt59tXnEjv4HAsF8AuVPZ43Jn1XGuI6TzujSzQ5dmjVGsEPDF3mtixyEiqhEuqEhVUioFLAm7CgAY08kDLtamlTf2Gwj49lfNDstNV40J8ghhT5Aemde3BY5eP4o/L6bgjW7eaOnMrVeISLOxR4iqtOtiCmJSs2FpYoAp3ZtWf4JUBnh1AfyHqn6yCNIrfs5yPB/gDEA1uJ6ISNOxEKJKFZYo8MVe1ZfZG92aoJG5kciJSBvMfLY5ZFIJDl29jbMJ98SOQ0RUJRZCVKnNp2/i1v0HsLc0xvhQL7HjkJbwamyOYUFuAIDPw65CELj1BhFpLhZCVKH8ohKsPKSaBj29VzOYGvESF9XctJ5NYWQgxZmEewi/dlvsOERElWIhRBVafyIRd3KL4G5jpv7rnqimnKxMMaaTakPWL/65yg1ZiUhjaV0htHLlSnh6esLExATBwcE4c+ZMpW3XrVsHiURS5mZiYtKAabVTdkEx1hy+AQCY0asZDGVa92tCGmBy96awKN2Qdc+lVLHjEBFVSKu+4bZs2YKZM2di/vz5OH/+PAICAtC7d29kZGRUeo5cLkdqaqr6lpiY2ICJtdNPx+KR9aAYTezMMSjQRew4pKVszI0wsYtqbNmXe6+hRKEUORERUXlaVQh9+eWXmDRpEsaNGwc/Pz+sWbMGZmZm+Omnnyo9RyKRwNHRUX1zcOB+V1W5n1eEH4/GAwBmPusDmbSybTOIqjexizdszI0QdycPv19IFjsOEVE5WlMIFRUVISIiAr169VIfk0ql6NWrF06ePFnpebm5ufDw8ICbmxsGDRqEy5cvN0RcrbX2aBxyCkvQwkmOvq0cxY5DWs7C2ABvdPMGAHx78DqK2StERBpGawqhO3fuQKFQlOvRcXBwQFpaWoXn+Pj44KeffsIff/yBjRs3QqlUIiQkBLdu3ar0dQoLC5GdnV3mpi9u5xRi3fEEAMCsZ5tDyt4gqgOvdvREYwtjJN17gN8iKv//HhGRGLSmEHoSnTp1wujRoxEYGIhu3brh999/h52dHb777rtKz1m0aBGsrKzUNzc3/ZkxtTr8Bh4UKxDgZo2eLezFjkM6wtRI9kivUCyKStgrRESaQ2sKocaNG0MmkyE9Pb3M8fT0dDg61uwSjqGhIdq0aYPY2NhK28ybNw9ZWVnqW1JS0lPl1hZpWQXYeFo1kHz2c80hkbA3iOrOKx09YGdpjOTMB9gWoR//nyIi7aA1hZCRkRHatWuHAwcOqI8plUocOHAAnTp1qtFzKBQKREVFwcnJqdI2xsbGkMvlZW76YHW46i/19p6N0LlpY7HjkI4xMZThze5NAAArDsaisEQhciIiIhWtKYQAYObMmfj++++xfv16xMTEYPLkycjLy8O4ceMAAKNHj8a8efPU7T/++GPs3bsXcXFxOH/+PF555RUkJiZi4sSJYr0FjZSWVYD/nVX9lf52L/YGUf0Y0cEdjnITpGYVYMtZ9goRkWYwEDtAbQwfPhy3b9/Ghx9+iLS0NAQGBiIsLEw9gPrmzZuQSv+r7e7fv49JkyYhLS0NjRo1Qrt27XDixAn4+fmJ9RY00prDN9S9QZ2a2Iodh3SUiaEMbz7TFB/svISVh2IxLMgNJobcuoWIxCURuCNilbKzs2FlZYWsrCydvEyWnl2ALksPoahEiU0TgxHKy2JUjwpLFOjxeThSsgrw4QA/jO/MzXyJqH7U9Ptbqy6NUd1bHa7qDQryaIQQ9gZRPTM2kGHqM80AqHoiC4o5VoiIxMVCSI9lZBfgf2duAgBmcGwQNZCh7VzhYm2KjJxCbD3HsUJEJC4WQnps9eEbKCztDQptyt4gahhGBlK8UTqDbHX4Dc4gIyJRsRDSUxnZBdh8WtUbNL1XM/YGUYN6qZ0rHOTGSM0qwG8R3IOMiMTDQkhPrTkch8ISJdp5cN0gangmhjK83lXVK7QqPJZ7kBGRaFgI6aE7uYXYfEa1ivS0nuwNInGM7OCOxhbGuHX/AXZwZ3oiEgkLIT3007F4FBQrEeBqha7N2BtE4jA1kuG1rqrp86sOxaKEvUJEJAIWQnomK78Yv5xU9QZNfYa9QSSuUcEesDE3QsLdfPz5b4rYcYhID7EQ0jPrTiQgt7AEvo6W6OnLHeZJXObGBphQuqjiioOxUCi5visRNSwWQnokt7AEP5+IBwC82aMppFL2BlE9UyqA+KNA1HbVT2X5qfKjO3nAytQQN27n4e9LqSKEJCJ9plV7jdHT2XQqEZn5xfBubI5+/k5ixyFdF70LCHsXyH7kkpfcGeizBPAbqD5kaWKIcaGeWL7/OlYeuoH+/k68ZEtEDYY9QnqioFiB74+qeoOm9GgKGXuDqD5F7wK2ji5bBAFAdqrqePSuMofHhnjCzEiGmNRshF+93YBBiUjfsRDSE1vOJuFObiFcG5liUKCz2HFIlykVqp4gVDTep/RY2Nwyl8mszYzwSkcPAMCKQ7HgXtBE1FBYCOmBohIl1hy+AQB4o1sTGMr4n53qUeKJ8j1BZQhAdrKq3SMmdvaCkUyKiMT7OBN/r34zEhGV4jeiHth5IRmpWQWwtzTG0HauYschXZeb/kTt7OUmeClI9fu5MvxGXaciIqoQCyEdp1AK6t6g17p6w8RQJnIi0nkWDk/c7vWuTSCTSnDk2m1E3cqq42BEROWxENJxey+nIe5OHqxMDTGig7vYcUgfeISoZoehsgH5EkDuomr3GHdbMwwMUI1hWxUeW38ZiYhKsRDSYYIgYFXpJYYxIZ6wMOZqCdQApDLVFHkA5Yuh0vt9FqvaVWByd9VmrGGX0xCbkVM/GYmISrEQ0mHHYu8gKjkLpoYyjA3xFDsO6RO/gcCwXwD5Y+tVyZ1Vxx9ZR+hxzR0s8ZyfAwQBWB0eV89BiUjfsYtAh60u7Q0a0cENNuZGIqchveM3EPDtr5odlpuuGhPkEVJpT9Cj3uzRFHuj0/FHZDJmPtccLtamDRCYiPQRe4R0VGRSJk7cuAsDqQQTu3iLHYf0lVQGeHUB/IeqftagCAKAADdrhDSxRYlSwA9H2StERPWHhZCOWl060HRwGxf+NU1a6Y1uqrFCv55Jwv28IpHTEJGuYiGkg2IzcvDP5XRIJMAb3dgbRNqpS7PGaOksx4NiBX45mSh2HCLSUSyEdNCaw6pLCc/5OaCpvaXIaYiejEQiUfcKrTsRj/yiEpETEZEuYiGkY1KzHmDnhWQAwOTuTUVOQ/R0+rZyhLuNGe7nF2Pr2SSx4xCRDmIhpGN+OhaPEqWAjt42CHSzFjsO0VMxkEkxqavq8u73R+NRrFCKnIiIdA0LIR2S9aAYm0/fBAC8XnpJgUjbvdTOFY0tjJCc+QB//Zsqdhwi0jEshHTIptOJyCtSwMfBEt2b24kdh6hOmBjKMC7UCwCw5vANCIIgciIi0iUshHREQbECPx9PAAC83s0bEkll+zwRaZ9Xgj1gbiTDlbQchF+9LXYcItIhLIR0xI4LybidUwhnKxM8X7ppJZGusDIzxMvBqk2DvztyQ+Q0RKRLWAjpAIVSwPdHVFPmx3f2gqGM/1lJ94wL9YKBVIJTcfdwMSlT7DhEpCP4jakD9kWnI+5OHuQmBhjRwV3sOET1wtnaFANLezvXHuG2G0RUN1gIaTlBELDmsOpSwaudPGBhzH10SXc9nEr/96VU3LybL3IaItIFLIS03NmE+4hMyoSRgRRjQ7zEjkNUr1o4ydG1uR2UAvDDMfYKEdHTYyGk5R5eInixrSvsLI1FTkNU/14v7RXaei4J97gZKxE9JRZCWuzG7Vzsj1FtrjqxC3uDSD+ENLFFS2c5CoqV2MDNWInoKbEQ0mI/HFX1BvVq4YAmdhYipyFqGBKJBK+V9gr9cjIBBcUKkRMRkTZjIaSlbucU4rfzqs1VH34pEOmL/v5OcLE2xd28ImyPuFV5Q6UCiD8KRG1X/VSyaCKislgIaakNJxNQVKJEoJs1gjwaiR2HqEEZyKSY0Fl1OfiHo3FQKCvYdiN6F7C8FbB+APDbBNXP5a1Ux4mISrEQ0kIPihT45ZRqbMRrXbmdBumn4e3dIDcxQMLdfOyPSS/7YPQuYOtoIDul7PHsVNVxFkNEVIqFkBbaHpGEzPxiuNmYondLR7HjEInC3NgAr3T0AAD1yuoAVJe/wt4FUNHmrKXHwubyMhkRAWAhpHUUSgE/HIsHAEzs7A2ZlL1BpL/GhHjCUCbBucT7uHDzvupg4onyPUFlCEB2sqodEek9FkJaZl90GhLv5sPK1BAvBbmKHYdIVA5yEwwKdAEA/HBU9QcCctOrOOMRNW1HRDqNhZCWebiA4qsdPWBmxO00iB6uofX3pVQk3csHLBxqdmJN2xGRTmMhpEUiEu/h/M1MGMmkGB3iIXYcIo3g6/jfths/HosHPEIAuTOAyi4bSwC5i6odEek9FkJa5GHX/+A2zrC3NBE5DZHmmFTaK7T1XBKyCpRAnyWljzxeDJXe77MYkMoaLB8RaS4WQlri5t18/HM5DQAwsQsXUCR6VOemjeHraIn8IgU2nUkE/AYCw34B5E5lG8qdVcf9BooTlIg0DgeZaImfjsdDKQDdmtuhuYOl2HGINIpEIsGkLt6Yte0i1h1PwMTO3jDyGwj49lfNDstNV40J8ghhTxARlcEeIS2QlV+MreeSAHBzVaLKPB/gDAe5MTJyCrHrYun0eakM8OoC+A9V/WQRRESPYSGkBTafuYn8IgV8HS3RuWljseMQaSQjAynGhvy37YYgVLSgIhFRWSyENFxRiRLrTpQuoNiF22kQVeXlDu4wM5LhSloOTty4K3YcItICLIQ03O5/U5CeXQh7S2MMDHAWOw6RRrMyM8SwIDcAql4hIqLqsBDSYIIg4PvSKfNjQjxhZMD/XETVGRfqCYkEOHT1NmIzcsSOQ0Qajt+sGuzkjbuISc2GqaEMo4LdxY5DpBU8bM3xnJ9q1egfS/flIyKqDAshDfZwc9WXglxhbWYkchoi7fFwra3fzifjbm6hyGmISJOxENJQsRm5OHglAxIJMD6UU+aJaiPIoxECXK1QVKLEhlOJYschIg3GQkhD/XRc1RvUq4UDPBubi5yGSLtIJBJMKO0V2nAyEQXFCpETEZGmYiGkge7lFeG3iFsAgImd2RtE9CT6tnKEs5UJ7uYV4Y/IZLHjEJGGYiGkgTafTkRhiRL+Llbo4GUjdhwirWQok2Jc6MMFFuO5wCIRVYiFkIYpLFFg/UnVmIaJXby4gCLRUxjewQ3mRjJcz8jFket3xI5DRBqIhZCG+fNiKm7nFMJRboJ+/k7Vn0BElZKbGGJYey6wSESVYyGkQQRBUP9jPTbUE4Yy/uchelrjQrwglQBHr9/B1TQusEhEZfGbVoOcuHEXV9JyYGoow8j2XECRqC6425rhOT9HAMBPXGCRiB7DQkiDPFwFd1iQK6zMDEVOQ6Q7JnZRDZreEZmMO1xgkYgewUJIQzy6gOI4LqBIVKfaPbLA4kYusEhEj2AhpCG4gCJR/Xl0gcWNp7jAIhH9p9aF0JgxY3DkyJH6yKK37ucV4ffzXECRqD71beUIJysT3Mktwq7IFLHjEJGGqHUhlJWVhV69eqFZs2ZYuHAhkpO5YuvT2nzmJgqKlWjlIucCikT1xFAmxdgQTwCq8XhcYJGIgCcohHbu3Ink5GRMnjwZW7ZsgaenJ/r27Yvt27ejuLi4PjLqtKISJdafSAAATOjMBRSJ6tOIDu4wM5LhanoOjsVygUUiesIxQnZ2dpg5cyYuXryI06dPo2nTpnj11Vfh7OyMt99+G9evX6/rnDpr978pyMgphL2lMfr7O4sdh0inWZkaYljQwwUWOZWeiJ5ysHRqair27duHffv2QSaToV+/foiKioKfnx+++uqruspYxsqVK+Hp6QkTExMEBwfjzJkzVbbftm0bfH19YWJiAn9/f+zZs6decj0JQRDUU+bHhHjCyIBj14nq27hQT0gkwOFrtxGbwQUWifRdrb95i4uL8dtvv2HAgAHw8PDAtm3bMGPGDKSkpGD9+vXYv38/tm7dio8//rjOw27ZsgUzZ87E/Pnzcf78eQQEBKB3797IyMiosP2JEycwcuRITJgwARcuXMDgwYMxePBgXLp0qc6zPYlTcfdwOSUbJoZSjArmAopEDcHD1hzPtnAAAPx4LEHcMEQkOolQyxGDjRs3hlKpxMiRIzFp0iQEBgaWa5OZmYk2bdogPr5uu56Dg4PRvn17rFixAgCgVCrh5uaGt956C3Pnzi3Xfvjw4cjLy8Pu3bvVxzp27IjAwECsWbOmRq+ZnZ0NKysrZGVlQS6X180bKTVx/Tnsj0nHqGB3fDbEv06fm4gqdzruLoavPQVjAylOzusJG3MjsSMRUR2r6fd3rXuEvvrqK6SkpGDlypUVFkEAYG1tXedFUFFRESIiItCrVy/1MalUil69euHkyZMVnnPy5Mky7QGgd+/elbZvSPF38nDgSjoAYDynzBM1qA5eNmjlIkdhiRKbuMAikWiiU7IRdikNCqV4szhrXQi9+uqrMDExqY8sVbpz5w4UCgUcHBzKHHdwcEBaWlqF56SlpdWqPQAUFhYiOzu7zK0+/Hw8HoIAPONrjyZ2FvXyGkRUMYlEggmlf4D8cioRhSVcYJFIDCsOXccbGyOwNOyKaBk4OvcxixYtgpWVlfrm5uZWL6/jbmMGO0tj9T/GRNSw+vs7w0FujNs5hdh9MVXsOER6J+lePsIuqTomXmjrKloOrSmEGjduDJlMhvT09DLH09PT4ejoWOE5jo6OtWoPAPPmzUNWVpb6lpSU9PThKzCxizeOvdsDIU1s6+X5iahqRgZSjO7kCQD4gQssEjW4dScSoBSALs0aw8fRUrQcWlMIGRkZoV27djhw4ID6mFKpxIEDB9CpU6cKz+nUqVOZ9gCwb9++StsDgLGxMeRyeZlbfTE2kHEBRSIRjQp2h4mhFDGp2TgZd1fsOER6I6egGFvOqjoaxL4yojWFEADMnDkT33//PdavX4+YmBhMnjwZeXl5GDduHABg9OjRmDdvnrr99OnTERYWhmXLluHKlStYsGABzp07h6lTp4r1FohIg1ibGWFoO1WX/E/HuMAiUUPZeu4WcgtL0NTeAt2a24maxUDUV6+l4cOH4/bt2/jwww+RlpaGwMBAhIWFqQdE37x5E1Lpf7VdSEgINm/ejPfffx/vvfcemjVrhp07d6JVq1ZivQUi0jDjQr2w8dRN7I/JQNztXHhz8gJRvVIoBfx8XPWHx/hQ8beWqvU6QvqmPtcRIiLNMH7dWRy8koFXO3rgk8H8Q4moPv0dlYrJm86jkZkhTs7rCRNDWb28Tr2tI0REpGsmlo5R2B5xC5n5RSKnIdJtP5Rehn6lo0e9FUG1wUKIiPRepya28HW0xINiBTafuSl2HCKdFZmUiYjE+zCUSfBqRw+x4wBgIUREBIlEgoldvAEA608koKhEKXIiIt30cKPxgQEusJc3/OLMFWEhREQE4PkAJ9hZGiM9uxB7orjAIlFdS8l8oP7/lthT5h/FQoiICKp1vUaXdtX/cCyOCywS1bH1JxKgUAro5G0LP2fNmXzEQoiIqNSojh4wNpDiUnI2zsTfEzsOkc7ILSxRj7+b2EVzeoMAFkJERGo25kbqPY9+4AKLRHVm27kk5BSUwLuxOXr42IsdpwwWQkREj5jQ2RMAsD8mHQl38sQNQ6QDFEoBPz1cQLGzF6RSzdpaioUQEdEjmtpboruPHQQB6n+8iejJ7YtOQ9K9B7A2M8SLIu4yXxkWQkREj5nYWTWVftu5W8jKLxY5DZF2ezhl/pVgD5gaib+A4uNYCBERPSa0KRdYJKoLkUmZOJugWkBxdCfNWEDxcSyEiIgeI5FI1OucrDsRzwUWiZ7Qw96g5wOcNWYBxcexECIiqsDAQGcusEj0FJI1dAHFx7EQIiKqgLGBDGM6cYFFoif1cAHFkCa2aOlsJXacSrEQIiKqxMvBHjAxVC2weJoLLBLVWE5BMf53WjW+TpN7gwAWQkRElbIxN1JP9/3haJzIaYi0x9Zzt5BTWAJvO81bQPFxLISIiKrw8K/Z/TEZiLudK3IaIs1XolDip9JB0hM0cAHFx7EQIiKqgredBXq1UP1FywUWiaoXdjkNyZkPyvSoajIWQkRE1ZhQusDi9ohbuJ9XJHIaIs0lCAK+P1q6gGJHD5gYat4Cio9jIUREVI2O3jZo5SJHQbESm04nih2HSGNFJN7HxaRMGBlI8WpHzVxA8XEshIiIqiGRSNTbbqw7kYjCEoXIiYg00/elkwqGBLrAztJY5DQ1w0KIiKgG+rd2gpOVCe7kFuKPCylixyHSOAl38rA3Oh0AMLGLZk+ZfxQLISKiGjCUSTEu1BOA6q9eLrBIVNZPx+MhCEB3Hzs0c7AUO06NsRAiIqqhER3cYWFsgOsZuQi/dlvsOEQaIzO/CNvO3QIATOriLXKa2mEhRERUQ3ITQ4xo7waACywSPWrT6Zt4UKyAr6MlQprYih2nVlgIERHVwrjOXpBJJTgeexeXU7LEjkMkusISBdadSAAAvNbVGxKJZi+g+DgWQkREteBibYp+/k4AgB+OcoFFoj8upOB2TiEc5SYY0NpZ7Di1xkKIiKiWJpXOiPnzYgpSsx6InIZIPEqlgLWll4nHd/aEkYH2lRXal5iISGStXa0R7GWDEqWAdccTxI5DJJrD124jNiMXFsYGGNHBXew4T4SFEBHRE3itq2pmzObTN5FTUCxyGiJxrD2i6g0a2cENchNDkdM8GRZCRERPoIePPZrYmSOnsAS/nkkSOw5Rg4u6lYWTcXdhIJVgXKj2LKD4OBZCRERPQCqVqHuFfjoej2KFUuRERA3r4XYaA1o7wdnaVOQ0T46FEBHRExrcRrWfUmpWAf68yG03SH/cup+Pv6JSAQCTumrXAoqPYyFERPSEjA1kGBviCUA1VoLbbpC++OlYAhRKAZ2bNkZLZyux4zwVFkJERE/hlWAPmBnJcCUtB0eu3xE7DlG9y8ovxpazNwFo1+aqlWEhRET0FKzMDDGivWra8NojN0ROQ1T/Np5ORF6RajuNbs3txI7z1FgIERE9pfGdPdXbblxK5rYbpLsKihX4+bhqRfXXu2nfdhoVYSFERPSUXBuZYUBr1bYb3x3hZqyku34/n4w7uUVwsTbVyu00KsJCiIioDjycSr8nKhVJ9/JFTkNU9xRKQT1lfkJnLxjKdKOE0I13QUQkspbOVujctDEUSgE/HuNmrKR79kWnIf5OHqxMDTG8vZvYceoMCyEiojryRrcmAIBfz97EvbwikdMQ1R1BELD6sKo3aHQnD5gbG4icqO6wECIiqiOhTW3RykWOgmIl1p9IEDsOUZ05E38PF5MyYWQgxZjStbN0BQshIqI6IpFI1L1C608mIL+oRORERHXj4SSAl9q5orGFschp6hYLISKiOtS3lRM8bM2QmV+MLWe5GStpv2vpOTh4JQMSCTCpi3Zvp1ERFkJERHVI9shmrD8c5WaspP3WhKsWCu3T0hGejc1FTlP3WAgREdWxF9uqLh8kZz7gZqyk1W7dz8cfpb/Dk7s3ETlN/WAhRERUx0wMZRgX6gkA+O4wN2Ml7fX9kTj15qqtXa3FjlMvWAgREdWDVzp6wMLYAFfTc3DoaobYcYhq7U5uIX4tHec2RUd7gwAWQkRE9cLK1BCjglWbsa4O52aspH3WHU9AYYkSAa5W6NTEVuw49YaFEBFRPRnf2QtGMinOJtzH2YR7YschqrGcgmKsP5kAAJjcvalObK5aGRZCRET1xEFughfbuQIAVh2KFTkNUc1tPn0TOQUlaGJnjuf8HMSOU69YCBER1aM3unlDKgEOXb2NyylZYschqlZBsQI/lO6X90a3JpBKdbc3CGAhRERUrzxszfF8gDMAYNUhjhUizff7+WTczimEs5UJBgW6iB2n3rEQIiKqZw/XX9lzKRU3bueKnIaociUKJb47oirYJ3bxhpGB7pcJuv8OiYhE5usoR68WDhCE/1bpJdJEf0WlIvFuPhqZGWJEBzex4zQIFkJERA1gSg9Vr9COC8lIznwgchqi8pRKASsOqgb1T+jsBTMjA5ETNQwWQkREDaCteyOENLFFiVLA96U7eRNpkr3RabiekQtLEwOMDvEUO06DYSFERNRA3uzRFADwvzM3cSe3UOQ0RP8RBAErSpd4GBviCbmJociJGg4LISKiBhLSxBYBbtYoLFHix9LpyUSaIPzabVxKzoapoQzjQr3EjtOgWAgRETUQiUSCqaW9Qr+cSEBmfpHIiYhKe4NKxwa90tEdNuZGIidqWCyEiIgaUK8W9mjhJEdekQI/sVeINMCpuHuISLwPIwMpJnXxFjtOg2MhRETUgCQSCaY9o+oV+vlEArIeFIuciPTdikPXAQDDg9xgLzcROU3DYyFERNTAerd0RHMHC+QUlGD9iQSx45Aei0i8j+Oxd2EgleD1bvrXGwSwECIianBSqQRTn2kGAPjxWDxyC0tETkT66tuDqt6gIW1c4NrITOQ04mAhREQkgv7+TvC2M0fWg2L8cjJB7DikhyKTMhF+9TZkUol6aQd9xEKIiEgEMul/M8h+OBqP/CL2ClHD+uaAqjdocKALPBubi5xGPCyEiIhEMjDAGR62ZriXV4RNp26KHYf0yMWkTBy8kgGpBJj6jP72BgEshIiIRGMgk2JK6c703x2Jw4MihciJSF+oe4PauMBLj3uDABZCRESiGtLGFS7WpriTW4hNpxPFjkN6IOpWFg6U9ga9VTpoX5+xECIiEpGRgRTTeqouTaw5fINjhajefX3gGgDV2CB97w0CWAgREYnuhbaucLcxw53cImw4yV4hqj+XkrOwP4Zjgx6lNYXQvXv3MGrUKMjlclhbW2PChAnIzc2t8pzu3btDIpGUub3xxhsNlJiIqGYMZVK8Vfql9N2ROORxXSGqJ8v3q8YGDQp0gbedhchpNIPWFEKjRo3C5cuXsW/fPuzevRtHjhzBa6+9Vu15kyZNQmpqqvq2dOnSBkhLRFQ7Q9q4wLN0Btl6ritE9SDqVhb2x6SzN+gxWlEIxcTEICwsDD/88AOCg4PRuXNnfPvtt/j111+RkpJS5blmZmZwdHRU3+RyeQOlJiKqOQOZFNN6qgaurj0Sh5wC7kFGdWvZvqsAVL1BTdgbpKYVhdDJkydhbW2NoKAg9bFevXpBKpXi9OnTVZ67adMmNG7cGK1atcK8efOQn59fZfvCwkJkZ2eXuRERNYSBAc7wtjNHZn4x9yCjOnUu4Z56FekZvThT7FFaUQilpaXB3t6+zDEDAwPY2NggLS2t0vNefvllbNy4EYcOHcK8efOwYcMGvPLKK1W+1qJFi2BlZaW+ubm51cl7ICKqjoFMiumP9Apls1eI6siyvaqZYsOCXOFhy5lijxK1EJo7d265wcyP365cufLEz//aa6+hd+/e8Pf3x6hRo/DLL79gx44duHHjRqXnzJs3D1lZWepbUlLSE78+EVFtDWjtjGb2FsguKMGPR+PFjkM64ETsHZyMuwsjmVS92S/9x0DMF581axbGjh1bZRtvb284OjoiIyOjzPGSkhLcu3cPjo6ONX694OBgAEBsbCyaNGlSYRtjY2MYGxvX+DmJiOqS6tJFc7y5+Tx+PBaPMSGesDE3EjsWaRKlAkg8AeSmAxYOgEcIIJVV2FQQBHyxVzU26OVgd7hYmzZkUq0gaiFkZ2cHOzu7att16tQJmZmZiIiIQLt27QAABw8ehFKpVBc3NREZGQkAcHJyeqK8REQNoW8rR7R0luNySjZWh8fi//r7iR2JNEX0LiDsXSD7kYlCcmegzxLAb2C55uFXb+P8zUyYGP63nQuVpRVjhFq0aIE+ffpg0qRJOHPmDI4fP46pU6dixIgRcHZ2BgAkJyfD19cXZ86cAQDcuHEDn3zyCSIiIpCQkIBdu3Zh9OjR6Nq1K1q3bi3m2yEiqpJUKsHs3j4AgPUnE5Ga9UDkRKQRoncBW0eXLYIAIDtVdTx6V5nDj/YGjenkCXu5SUMl1SpaUQgBqtlfvr6+6NmzJ/r164fOnTtj7dq16seLi4tx9epV9awwIyMj7N+/H8899xx8fX0xa9YsvPjii/jzzz/FegtERDXWvbkdOnjaoKhEiW8OxIodh8SmVKh6giBU8GDpsbC5qnal/rmchssp2TA3kuH1buwNqoxEEISKPlUqlZ2dDSsrK2RlZXENIiJqUGcT7uGlNSchk0qwf2Y37gulz+KPAusHVN9uzG7AqwtKFEr0+fooYjNy8dYzTTHrOZ/6z6hhavr9rTU9QkRE+qa9pw16+NhBoRTw5b5rYschMeWm16rdb+dvITYjF9ZmhpjU1bseg2k/FkJERBrs4VihPy+mIDqFC7zqLQuHGrcrKFbgq32qPcWm9mgKuYlhPQbTfiyEiIg0WEtnKwxorZrp+nDgK+khjxDV7DBIKmkgAeQugEcI1p1IQFp2AVysTfFKR4+GTKmVWAgREWm4Wc/5QCaV4OCVDJyJvyd2HBKDVKaaIg+gfDFUer/PYmQVKLHqkGpw/cxnm8PEsOL1heg/LISIiDScV2NzDG+v2u5n4Z4YcI6LnvIbCAz7BZA/thae3Fl13G8gVh2ORXZBCXwcLDG4jYs4ObWMqAsqEhFRzczo1Qw7LyQjMikTe6LS0L81F4bVS34DAd/+Fa4snZr1AOuOJwAA3u2r6kWk6rFHiIhIC9hbmuC10tk/S/+5gqISpciJSDRSGeDVBfAfqvpZur3G8n3XUViiRAcvG/Twsa/mSeghFkJERFpiUhdv2FkaI/FuPjaeShQ7DmmQa+k52Bah2iR8bl9fSCTsDaopFkJERFrC3NgAb/dqDgD49uB1ZD0oFjkRaYqFe2KgFIA+LR3R1r2R2HG0CgshIiItMizIFU3tLXA/vxirw2+IHYc0wOFrtxF+9TYMZRLM7esrdhytw0KIiEiLGMikmFf6ZffT8XgkZ3JDVn1WolDis7+iAag2VvXkNiy1xkKIiEjLPONrj47eqg1Zv/iHiyzqsy3nknAtXbWVxlvPNBM7jlZiIUREpGUkEgne69cCALDjQjIu3LwvciISQ05BMb7cq9qDbkbPZrAy41YaT4KFEBGRFmrtao2h7VwBAB/9GQ2lkoss6ptV4TdwN68I3nbmGMWtNJ4YCyEiIi01p7cPzI1kiEzKxM7IZLHjUANKupePH4/FAwD+r18LGMr4df6k+MkREWkpe7kJppaOC1n89xXkFZaInIgaypIw1aKaoU1t8YwvF098GiyEiIi02PjOnvCwNUNGTiGn0+uJkzfuYve/qZBKgP/r58fFE58SCyEiIi1mbCDD/5UOnF57NA5J9/JFTkT1qVihxIJdlwEAr3T0gJ+zXORE2o+FEBGRlnvWzwGdmzZGUYkSC/fEiB2H6tGGk4m4mp4DG3MjzHy2udhxdAILISIiLSeRSPDBAD/IpBL8fSkNx2PviB2J6kFGTgG+2qeaLj+ntw+szYxETqQbWAgREekAH0dLvFo6hfqDPy6hsEQhciKqa0v+voqcwhIEuFphWJCb2HF0BgshIiIdMfO55mhsYYy423n4/kic2HGoDkUk3sNv528BAD4a1ApSKQdI1xUWQkREOkJuYogPBqgGTn97MJYDp3WEQingwz9UA6SHB7kh0M1a3EA6hoUQEZEOGRjgjJAmtigsUWL+rssQBK44re02nkrE5ZRsyE0MMKePj9hxdA4LISIiHSKRSPDxoFYwlElw8EoG9kanix2JnkJq1gN8Xrqx7ju9fWBrYSxyIt3DQoiISMc0tbfAa129AQAf7brMFae12Pw/LiO3sARt3K0xKpj7idUHFkJERDpoao9mcG1kipSsAnxz4LrYcegJhF1Kw97odBhIJVj0gj8HSNcTFkJERDrI1EiGjwa2BAD8cCwel5KzRE5EtZFTUKxeQfq1rt7wdeQK0vWFhRARkY7q2cIB/Vs7QaEU8M72f1GsUIodiWroi3+uIi27AB62ZpjWs5nYcXQaCyEiIh320cCWaGRmiJjUbKzhpqxa4fzN+/jlVCIAYOEQf5gYykROpNtYCBER6bDGFsaY/7zqEtm3B2NxPT1H5ERUlcISBeb9FgVBAF5o64LQpo3FjqTzWAgREem4QYHOeMbXHkUKJd7Z/i8USq4tpKm+3n9dvanq+/39xI6jF1gIERHpOIlEgs+GtIKlsQEikzLx8/F4sSNRBS7cvI81h1WXLz8b3Ao25txUtSGwECIi0gNOVqaY10+1/cYXe68i8W6eyInoUQXFCszadhFKQbU6eF9/J7Ej6Q0WQkREemJkBzd08rZFQbESM7deRAlnkWmML/65irjbebCzNMbHg1qKHUevsBAiItITEokES4e2hqWxASIS72M1Z5FphDPx9/Bj6eXKxS/4w9qMl8QaEgshIiI94mZjho9Kexy+PnAdF5MyxQ2k5/KLSvDO9osQBGBoO1f0bOEgdiS9w0KIiEjPDGnjgv6tnVCiFPD2lkjkF3EvMrF8sjsaiXfz4WRlgg+f5ywxMbAQIiLSMxKJBJ8NbgVHuQni7uThs79ixI6kl/76NxX/O5MEiQT44qUAyE0MxY6kl1gIERHpIWszIywbFgAA2HT6Jg5eSa/dEygVQPxRIGq76qdSUQ8pdVfSvXzM/f1fAMDkbk24cKKIWAgREemp0KaNMbGzFwDgnW3/Ij27oGYnRu8ClrcC1g8Afpug+rm8leo4VatYocS0Xy8gp6AEbdyt8fazzcWOpNdYCBER6bHZvX3QwkmOu3lFeGvzheqn1EfvAraOBrJTyh7PTlUdZzFUra/2XcOFm5mwNDHANyPawFDGr2Ix8dMnItJjJoYyrBrVFhbGBjiTcA+f771aeWOlAgh7F0BFW3SUHguby8tkVTgeewerS1ePXvxCa7jZmImciFgIERHpOa/G5vh8aGsAwHeH47AvupLxQoknyvcElSEA2cmqdlROenYBZmyJhCCoFrfs35qrR2sCFkJERIS+/k4YH6oaLzRraySS7uWXb5RbwwHVNW2nRwpLFHhjYwRu5xTCx8ESHw7g6tGagoUQEREBAOb29UUbd2tkF5Rg8qYIFBQ/donLooaL/dW0nR5ZsOsyLtzMhNzEAGtHt4OpkUzsSFSKhRAREQEAjAykWPlyWzQyM8Sl5Gx8sPMSBOGR8UAeIYDcGYCkkmeQAHIXVTtS23z6pnq9oG9GtoGHrbnYkegRLISIiEjN2doUX49oA6kE2BZxC2uPxP33oFQG9FlSeufxYqj0fp/FqnYEAIhIvIf5uy4BAGY/54PuPvYiJ6LHsRAiIqIyuja3w4cDVNs9LA67UnbwtN9AYNgvgPyxgb5yZ9Vxv4ENmFSzpWcX4I2N51GsENDP3xFTujcROxJVQCKU6fekx2VnZ8PKygpZWVmQy+VixyEiahCCIOD9nZew6fRNmBnJsP2NEPg5P/JvoFKhmh2Wm64aE+QRwp6gR+QWlmDE2pO4lJyN5g4W2DElFObGBmLH0is1/f5mjxAREZUjkUiwYGBLhDa1RX6RAhPXn0VGziMrT0tlgFcXwH+o6ieLILVihRJTNp3HpeRs2Job4fvRQSyCNBgLISIiqpChTIpVL7eDd2NzpGQVYNIvEdypvhqCIOC936Nw5NptmBrK8OPY9hwcreFYCBERUaWszAzx49j2sDYzxMWkTLy+IQKFJVw5ujJf7b+ObRG3IJUAK15ug0A3a7EjUTVYCBERUZW8Gpvjp7HtYWYkw9HrdzDj18jq9yTTQ/87cxPfHLgOAPh0sD96tuB6StqAhRAREVWrrXsjrH01CEYyKf6+lIa5v0dBqeRcm4d2XUzB/+2IAgC89UxTvBzsLnIiqikWQkREVCOdmzXGNyPbQCaVYHvELXzyVzQ48VhVBM349QKUAjCivRtmPttc7EhUCyyEiIioxvq0csTSF1UbtP58PAFL/7mq18XQn48UQcOCXLFwiD8kkspW3iZNxEKIiIhq5cV2rljwvGrBxdXhNzB/12W9vEy2+98UzNgSCaUAvNTOFYtfaA2plEWQtmEhREREtTY21AufDm4FiQT45WQiZm27qFcDqP+ITMb0XyOhUAoY2s4VS15kEaStWAgREdETeaWjB5YPD4RMKsGOC8mYvOl8+R3rdYwgCFgdfoNFkA5hIURERE9sUKALvnulHYwMpNgXnY7x684i60Gx2LHqRYlCifd3XsKSsCsAgPGhXljyYmvIWARpNRZCRET0VHr5OWDduPYwN5LhxI27GLzyOGIzclT7kcUfBaK2q34qtbe3KK+wBJN+OYdNp29CJlFidWgePvSMhizxmFa/L+Kmq9XipqtERDVzKTkLr2+IQHLmAww2jsBis00weZD2XwO5M9BnidbtUJ90Lx+TN0XgUnI2Bhiew+cWm2GqA+9L13HTVSIialCtXKywa2oo3nKMxpdYBqP8tLINslOBraOB6F3iBHwCf0elot83R3EpORtDzc7jW9lXZYsgQCvfF/2HhRAREdUZWzMDzFT8BIkEKD90pvQCRNhcjb+cVFCswPs7ozB503nkFJSgnZslFpttggQVXUTRnvdF5bEQIiKiupN4ApKcFFQ+fFgAspOBxBMNGKp2rqfnYPDK49h46iYA4I1uTbClD2CQm1rFWZr/vqhiBmIHICIiHZKbXrftGlB+UQm+ORCLH4/FoVghwNbcCF8OD0S35nZA1KWaPYkGvi+qGgshIiKqOxY123F9d5wSvXwVMDGU1XOg6gmCgLBLafhkdzRSsgoAAD197bHoBX/Yy01UjWr4vmrcjjQGCyEiIqo7HiGqWVTZqUAF42mUANIEW0w7aQqH6HC83as5XmjrAgOZOCM1zt+8j6/2XcPR63cAAK6NTLHg+Zbo5fdYQVPN+wIkqsc9Quo9M9UtjhEiIqK6I5WpppIDQLmRQhJIIEF80PtwtDJDalYB5vz2L3ovP4IdF2412KrUSqWAg1fSMWzNSbyw6gSOXr8DIwMppvVshv0zu5UvgoBq3xcAoM9iVTvSKlqzjtBnn32Gv/76C5GRkTAyMkJmZma15wiCgPnz5+P7779HZmYmQkNDsXr1ajRr1qzGr8t1hIiInkD0LiDsXSA75b9jchdVseA3EAXFCmw8lYgVh2KRma9aidrK1BBD2rhgRAc3+DrW/b+3GTkF+OdyOjacTMC19FwAgKFMgsGBLnizR1N4NjZ/6vdFmqOm399aUwjNnz8f1tbWuHXrFn788ccaFUJLlizBokWLsH79enh5eeGDDz5AVFQUoqOjYWJiUqPXZSFERPSElArVLKrcdNXYGY+Qcj0m2QXFWH88Ab+eTUJy5gP18QBXK3TzsUdHbxu0dW/0RGOJBEFA0r0H2BudhrBLaYi4eR8Pv/EsjA3wcrA7xod6wdGqZt8HtXlfJD6dK4QeWrduHWbMmFFtISQIApydnTFr1izMnj0bAJCVlQUHBwesW7cOI0aMqNHrsRAiIqp/CqWAo9dvY8vZJOyLTkeJ8r+vJiMDKdq4WaOVixUc5SZwtFLdGlsYQ6FUoqBYiQfFChQUK5CS+QBX0nJwJTUHV9KycT+/7L5ngW7WGNDaCS8FucHK1LCh3yY1oJp+f+vsYOn4+HikpaWhV69e6mNWVlYIDg7GyZMnKy2ECgsLUVhYqL6fnZ1d71mJiPSdTCpBdx97dPexx+2cQuyPScepuLs4FXcX6dmFOB1/D6fj7z3R87b3bIS+rZzwXEsHOFmZ1kN60mY6WwilpamWQHdwKDvozcHBQf1YRRYtWoSPPvqoXrMREVHl7CyNMbKDO0Z2cIcgCEi4m49TcXeRcCcPqVkFSMsuQFpWAe7mFsJAJoWJoRSmhjKYGMpga2EEX0c5fB0t0cJJjqb2FhoxRZ80l6iF0Ny5c7FkyZIq28TExMDX17eBEgHz5s3DzJkz1fezs7Ph5ubWYK9PRET/kUgk8GpsDq+aDGQmegKiFkKzZs3C2LFjq2zj7e39RM/t6OgIAEhPT4eTk5P6eHp6OgIDAys9z9jYGMbGxk/0mkRERKRdRC2E7OzsYGdnVy/P7eXlBUdHRxw4cEBd+GRnZ+P06dOYPHlyvbwmERERaRetWVDx5s2biIyMxM2bN6FQKBAZGYnIyEjk5uaq2/j6+mLHjh0AVN2pM2bMwKeffopdu3YhKioKo0ePhrOzMwYPHizSuyAiIiJNojWDpT/88EOsX79efb9NmzYAgEOHDqF79+4AgKtXryIrK0vdZs6cOcjLy8Nrr72GzMxMdO7cGWFhYTVeQ4iIiIh0m9atI9TQuI4QERGR9qnp97fWXBojIiIiqmsshIiIiEhvsRAiIiIivcVCiIiIiPQWCyEiIiLSWyyEiIiISG+xECIiIiK9xUKIiIiI9BYLISIiItJbWrPFhlgeLrydnZ0tchIiIiKqqYff29VtoMFCqBo5OTkAADc3N5GTEBERUW3l5OTAysqq0se511g1lEolUlJSYGlpCYlEUmfPm52dDTc3NyQlJXEPswrw86kaP5+q8fOpGj+fyvGzqZo2fT6CICAnJwfOzs6QSisfCcQeoWpIpVK4urrW2/PL5XKN/2USEz+fqvHzqRo/n6rx86kcP5uqacvnU1VP0EMcLE1ERER6i4UQERER6S0WQiIxNjbG/PnzYWxsLHYUjcTPp2r8fKrGz6dq/Hwqx8+marr4+XCwNBEREekt9ggRERGR3mIhRERERHqLhRARERHpLRZCREREpLdYCIlk5cqV8PT0hImJCYKDg3HmzBmxI2mEI0eO4Pnnn4ezszMkEgl27twpdiSNsmjRIrRv3x6Wlpawt7fH4MGDcfXqVbFjaYTVq1ejdevW6oXeOnXqhL///lvsWBpr8eLFkEgkmDFjhthRNMKCBQsgkUjK3Hx9fcWOpVGSk5PxyiuvwNbWFqampvD398e5c+fEjvXUWAiJYMuWLZg5cybmz5+P8+fPIyAgAL1790ZGRobY0USXl5eHgIAArFy5UuwoGunw4cN48803cerUKezbtw/FxcV47rnnkJeXJ3Y00bm6umLx4sWIiIjAuXPn8Mwzz2DQoEG4fPmy2NE0ztmzZ/Hdd9+hdevWYkfRKC1btkRqaqr6duzYMbEjaYz79+8jNDQUhoaG+PvvvxEdHY1ly5ahUaNGYkd7apw+L4Lg4GC0b98eK1asAKDaz8zNzQ1vvfUW5s6dK3I6zSGRSLBjxw4MHjxY7Cga6/bt27C3t8fhw4fRtWtXseNoHBsbG3z++eeYMGGC2FE0Rm5uLtq2bYtVq1bh008/RWBgIJYvXy52LNEtWLAAO3fuRGRkpNhRNNLcuXNx/PhxHD16VOwodY49Qg2sqKgIERER6NWrl/qYVCpFr169cPLkSRGTkTbKysoCoPrCp/8oFAr8+uuvyMvLQ6dOncSOo1HefPNN9O/fv8y/QaRy/fp1ODs7w9vbG6NGjcLNmzfFjqQxdu3ahaCgILz00kuwt7dHmzZt8P3334sdq06wEGpgd+7cgUKhgIODQ5njDg4OSEtLEykVaSOlUokZM2YgNDQUrVq1EjuORoiKioKFhQWMjY3xxhtvYMeOHfDz8xM7lsb49ddfcf78eSxatEjsKBonODgY69atQ1hYGFavXo34+Hh06dIFOTk5YkfTCHFxcVi9ejWaNWuGf/75B5MnT8a0adOwfv16saM9Ne4+T6Sl3nzzTVy6dInjGB7h4+ODyMhIZGVlYfv27RgzZgwOHz7MYghAUlISpk+fjn379sHExETsOBqnb9++6v/dunVrBAcHw8PDA1u3buWlVaj+8AoKCsLChQsBAG3atMGlS5ewZs0ajBkzRuR0T4c9Qg2scePGkMlkSE9PL3M8PT0djo6OIqUibTN16lTs3r0bhw4dgqurq9hxNIaRkRGaNm2Kdu3aYdGiRQgICMDXX38tdiyNEBERgYyMDLRt2xYGBgYwMDDA4cOH8c0338DAwAAKhULsiBrF2toazZs3R2xsrNhRNIKTk1O5PyhatGihE5cPWQg1MCMjI7Rr1w4HDhxQH1MqlThw4ADHMlC1BEHA1KlTsWPHDhw8eBBeXl5iR9JoSqUShYWFYsfQCD179kRUVBQiIyPVt6CgIIwaNQqRkZGQyWRiR9Qoubm5uHHjBpycnMSOohFCQ0PLLdVx7do1eHh4iJSo7vDSmAhmzpyJMWPGICgoCB06dMDy5cuRl5eHcePGiR1NdLm5uWX+AouPj0dkZCRsbGzg7u4uYjLN8Oabb2Lz5s34448/YGlpqR5XZmVlBVNTU5HTiWvevHno27cv3N3dkZOTg82bNyM8PBz//POP2NE0gqWlZbmxZObm5rC1teUYMwCzZ8/G888/Dw8PD6SkpGD+/PmQyWQYOXKk2NE0wttvv42QkBAsXLgQw4YNw5kzZ7B27VqsXbtW7GhPTyBRfPvtt4K7u7tgZGQkdOjQQTh16pTYkTTCoUOHBADlbmPGjBE7mkao6LMBIPz8889iRxPd+PHjBQ8PD8HIyEiws7MTevbsKezdu1fsWBqtW7duwvTp08WOoRGGDx8uODk5CUZGRoKLi4swfPhwITY2VuxYGuXPP/8UWrVqJRgbGwu+vr7C2rVrxY5UJ7iOEBEREektjhEiIiIivcVCiIiIiPQWCyEiIiLSWyyEiIiISG+xECIiIiK9xUKIiIiI9BYLISIiItJbLISIiIhIb7EQIiIiIr3FQoiIiIj0FgshItIrt2/fhqOjIxYuXKg+duLECRgZGeHAgQMiJiMiMXCvMSLSO3v27MHgwYNx4sQJ+Pj4IDAwEIMGDcKXX34pdjQiamAshIhIL7355pvYv38/goKCEBUVhbNnz8LY2FjsWETUwFgIEZFeevDgAVq1aoWkpCRERETA399f7EhEJAKOESIivXTjxg2kpKRAqVQiISFB7DhEJBL2CBGR3ikqKkKHDh0QGBgIHx8fLF++HFFRUbC3txc7GhE1MBZCRKR33nnnHWzfvh0XL16EhYUFunXrBisrK+zevVvsaETUwHhpjIj0Snh4OJYvX44NGzZALpdDKpViw4YNOHr0KFavXi12PCJqYOwRIiIiIr3FHiEiIiLSWyyEiIiISG+xECIiIiK9xUKIiIiI9BYLISIiItJbLISIiIhIb7EQIiIiIr3FQoiIiIj0FgshIiIi0lsshIiIiEhvsRAiIiIivcVCiIiIiPTW/wNgc3s1eE6gggAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ], + "source": [ + "initial_conditions = np.random.choice(condition_pool, size=10, replace=False)\n", + "initial_observations = run_experiment(initial_conditions)\n", + "\n", + "# plot sampled conditions against ground-truth\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", + "plt.plot(initial_conditions, initial_observations, 'o', label='Sampled Conditions')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Sine Function')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fmIVAHdIUYM7" + }, + "source": [ + "Certain theorists and experimentalists may need to have knowledge about the experimental variables, such as the domain from which new experiment conditions are sampled. To provide this information, we can utilize a ``VariableCollection`` object. In the context of our synthetic experiment, we have a single *independent variable* (``IV``) denoted as $x$, and a single *dependent* variable (``DV``) denoted as $y$." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "id": "feMzX1JfUYM7" + }, + "outputs": [], + "source": [ + "from autora.variable import DV, IV, ValueType, VariableCollection\n", + "\n", + "# Specify independent variable\n", + "iv = IV(\n", + " name=\"x\", # name of the independent variable\n", + " value_range=(0, 2 * np.pi), # specify the domain\n", + " allowed_values=condition_pool, # alternatively, we can specify the pool of allowed conditions directly\n", + ")\n", + "\n", + "# specify dependent variable\n", + "dv = DV(\n", + " name=\"y\", # name of the dependent variable\n", + " type=ValueType.REAL, # specify the variable type (some theorists require this to optimize)\n", + ")\n", + "\n", + "# Variable collection with ivs and dvs\n", + "metadata = VariableCollection(\n", + " independent_variables=[iv],\n", + " dependent_variables=[dv],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G-s-foKEUYM8" + }, + "source": [ + "**Note**: *For expository reasons, we focus in this tutorial on simple synthetic experiments. In general, ``autora`` provides functionality for automating [more complex synthetic experiments](https://github.com/autoresearch/autora-synthetic/), as well as real-world experiments, such as [behavioral data collection via web-based experiments](https://autoresearch.github.io/autora/user-guide/experiment-runners/firebase-prolific/), experiments with electrical circuits via [Tinkerforge](https://en.wikipedia.org/wiki/Tinkerforge), and other automated experimentation platforms.*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6_0yTjh-UYM8" + }, + "source": [ + "## Theorists\n", + "\n", + "The AutoRA framework includes and interfaces with different methods for scientific model discovery. These methods are referred to as *theorists* and are implemented as [sklearn estimators](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html). For general information about theorists, see the respective [AutoRA Documentation](https://autoresearch.github.io/autora/theorist/).\n", + "\n", + "\"Theorist\n", + "\n", + "\n", + "Theorists **take as input a set of conditions and observations**. Conditions and observations can typically be passed as *two-dimensional numpy arrays* (with columns corresponding to variables and rows corresponding to different instances of those variables). Theorists then **identify and fit a model** which may be used to predict observations based on experiment conditions." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "w_7QGhwFUYM8" + }, + "source": [ + "### Types\n", + "\n", + "There are different types of theorists within the AutoRA framework, each with its own approach to scientific model discovery.\n", + "\n", + "Some theorists focus on *fitting the parameters of a pre-specified model* to the given data (see the scikit learn documentation for a [selection of basic regressors](https://scikit-learn.org/stable/supervised_learning.html)). The model architecture in such cases is typically fixed, while the parameters are adjusted to optimize the model's performance. Linear regression is an example of a parameter-fitting theorist.\n", + "\n", + "Other theorists are concerned with *identifying both the architecture of a model and its parameters*. The model architectures can take various forms, such as equations, causal models, or process models. Implemented as scikit-learn estimators, these theorists aim to discover a model architecture that accurately describes the data. They often operate within a user-defined search space, which specifies the allowable operations or components that can be included in the model. This approach provides more flexibility in exploring different model architectures." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b4BS1lGEUYM8" + }, + "source": [ + "### Usage\n", + "\n", + "In this tutorial, we delve into two types of theorists: (1) a linear regression theorist, which focuses on fitting a linear model, and (2) a Bayesian Machine Scientist (Guimerà et al., 2020, in *Science Advances*), which specializes in identifying and fitting a non-linear equation.\n", + "\n", + "Theorists are commonly instantiated as regressors within the ``sklearn`` library:" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "id": "-ICqZZikUYM8" + }, + "outputs": [], + "source": [ + "from sklearn import linear_model\n", + "from autora.theorist.bms import BMSRegressor\n", + "\n", + "theorist_lr = linear_model.LinearRegression()\n", + "theorist_bms = BMSRegressor(epochs=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3dKtzWCrUYM9" + }, + "source": [ + "Once instantiated, we can fit the theorist to link experimental conditions with observations. However, before doing so, we should convert both inputs into 2-dimensional numpy arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 144 + }, + "id": "5o0fJnXiUYM9", + "outputId": "01e843d0-5049-488f-8d8c-5f3bcc2f93ad" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Size of the initial conditions: (10, 1),\n", + "Size of the initial observations: (10, 1)\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:13<00:00, 7.41it/s]\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "BMSRegressor(epochs=100)" + ], + "text/html": [ + "
BMSRegressor(epochs=100)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ] + }, + "metadata": {}, + "execution_count": 103 + } + ], + "source": [ + "# convert data to 2-dimensional numpy array\n", + "initial_conditions = initial_conditions.reshape((len(initial_conditions), 1))\n", + "initial_observations = initial_observations.reshape((len(initial_observations), 1))\n", + "print(f\"Size of the initial conditions: {initial_conditions.shape},\\nSize of the initial observations: {initial_observations.shape}\\n\")\n", + "\n", + "# fit theorists\n", + "theorist_lr.fit(initial_conditions, initial_observations)\n", + "theorist_bms.fit(initial_conditions, initial_observations)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qwx7lc64UYM9" + }, + "source": [ + "For some theorists, we can inspect the resulting model architecture. For instance, in the BMS theorist, we can call obtain the model formula via ``theorist_bms.repr()``.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IOT7OzP2UYM9", + "outputId": "f35d8400-5d10-4526-c210-c22b30aa2d8d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model of BMS theorist: sin(X0)\n" + ] + } + ], + "source": [ + "print(\"Model of BMS theorist: \" + theorist_bms.repr())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "77tpR0taUYM-" + }, + "source": [ + "We may now obtain predictions from both theorists for the entire pool of experiment conditions." + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "id": "cSB2gLmPUYM-" + }, + "outputs": [], + "source": [ + "# convert condition pool into 2-dimensional numpy array before generating respective predictions\n", + "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", + "\n", + "# obtain predictions\n", + "predicted_observations_lr = theorist_lr.predict(condition_pool)\n", + "predicted_observations_bms = theorist_bms.predict(condition_pool)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eCe8VNt6UYM-" + }, + "source": [ + "In the next code segment, we plot the theorists' predictions against the ground truth. For the BMS theorist, we can obtain a latex expression of the model architecture using ``theorist_bms.latex()``." + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 489 + }, + "id": "TUpwLukrUYM-", + "outputId": "a09cca9f-a140-4b23-e70c-aa1c80807d3c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 106 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "# obtain latex expression of BMS theorist\n", + "bms_model = theorist_bms.latex()\n", + "\n", + "# plot model predictions against ground-truth\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", + "plt.plot(initial_conditions, initial_observations, 'o', label='Data Used for Model Identification')\n", + "plt.plot(condition_pool, predicted_observations_lr, label='Linear Regression')\n", + "plt.plot(condition_pool, predicted_observations_bms, label='Bayesian Machine Scientist: $' + bms_model + '$')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Model Predictions')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r5Ti6ULGUYM_" + }, + "source": [ + "**Note**: *There are various other types of theorists you can combine with AutoRA as long as they are implemented as ``sklearn`` estimators. This includes [autora modules](theorist/index.md), any [scikit learn estimators](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html), as well as third-party packages, such as [PySR](https://github.com/MilesCranmer/PySR) for symbolic regression.*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nLQqPVtJUYM_" + }, + "source": [ + "## Experimentalists\n", + "\n", + "The primary goal of an experimentalist is to design experiments that yield scientific merit. The AutoRA framework offers various strategies for identifying informative new data points (e.g., by searching for experiment conditions that existing scientific models fail to explain, or by looking for novel conditions altogether).\n", + "\n", + "\"Experimentalist\n", + "\n", + "Experimentalists are implemented as functions that return a set of experiment conditions (e.g., in the form of a 2-dimensional numpy array in which columns correspond to independent variables), which can be subjected to an experiment. To determine these conditions, experimentalists may use information about candidate models obtained from a theorist, experimental conditions that have already been probed, or respective dependent measures. For more detailed information about experimentalists, please refer to the corresponding [AutoRA Documentation](https://autoresearch.github.io/autora/experimentalist/).\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Types\n", + "\n", + "There are generally three types of experimentalist functions: **poolers**, **samplers**, and **pipelines**.\n", + "\n", + "**Poolers** generate a novel set of experimental conditions \"from scratch\", e.g., by sampling from a grid. They usually require metadata describing independent variables of the experiment (e.g., their range or the set of allowed values).\n", + "\n", + "**Samplers** operate on an existing pool of experimental conditions. They typically require experimental conditions to be represented as a 2-dimensional numpy array in which columns correspond to independent variables and rows to different conditions. They then select experiment conditions from this pool.\n", + "\n", + "**Pipelines** Pipelines connect multiple experimentalists into a unified workflow. This is beneficial when various steps are required to process experiment conditions. For example, apart from identifying novel experimental conditions, experimentalist functions may perform other operations on the set of conditions, such as rearranging the rows of a condition matrix or adding new experiment conditions as columns. Experiment pipelines may begin with a pooler that generates all possible experiment conditions, followed by a sampler that selects a subset of conditions from the pool, and then proceed to additional functions that arrange the selected conditions in a specific order necessary for conducting the experiment." + ], + "metadata": { + "id": "L7VIntSjO2ga" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fI5uCcT8UYM_" + }, + "source": [ + "### Usage: Poolers\n", + "\n", + "Experimentalist poolers are implemented as functions and can be called directly. For instance, the following **grid pooler** generates a grid based on the ``allowed_values`` of all independent variables in the ``metadata`` object that we defined above. We can simply add a list of allowed values to each independent variable. In this case, we only have one variable." + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": { + "id": "qJDNd9F3UYM_" + }, + "outputs": [], + "source": [ + "allowed_values = np.linspace(0, 2 * np.pi, 100)\n", + "metadata.independent_variables[0].allowed_values = allowed_values" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vRnf1nMoUYM_" + }, + "source": [ + "Now we can pass the grid pooler the list of independent variables from the ``metadata`` object." + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "id": "prol6MweUYM_" + }, + "outputs": [], + "source": [ + "from autora.experimentalist.pooler.grid import grid_pool\n", + "\n", + "new_conditions = grid_pool(ivs = metadata.independent_variables)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "87h_mB5xUYM_" + }, + "source": [ + "The resulting condition pool contains all experiment conditions from the grid:" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Y1FNUmOnUYM_", + "outputId": "f3479f02-8f74-4e2d-ebe8-4764109961d8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(0.6981317007977318,)\n", + "(0.7615982190520711,)\n", + "(0.8250647373064104,)\n", + "(0.8885312555607496,)\n", + "(0.9519977738150889,)\n", + "(1.0154642920694281,)\n", + "(1.0789308103237674,)\n", + "(1.1423973285781066,)\n", + "(1.2058638468324459,)\n", + "(1.269330365086785,)\n", + "(1.3327968833411243,)\n" + ] + } + ], + "source": [ + "# return first 10 conditions\n", + "for idx, condition in enumerate(new_conditions):\n", + " print(condition)\n", + " if idx > 9:\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MZnaSr1YUYNA" + }, + "source": [ + "Alternatively, we may use the **random pooler** to randomly draw experimental conditions from the domains of each independent variable. The random pooler requires as input a list of discrete values from which to sample from. In this case, we can pass it ``metadata.independent_variables[0].allowed_values`` for the independent variable. We can also specify the input argument ``n`` to obtain 10 random samples." + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tF2PVwB8UYNA", + "outputId": "2430af40-9cb1-46c9-ea3c-b9eb0a2a2078" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(0.8250647373064104,)\n", + "(0.7615982190520711,)\n", + "(4.1253236865320515,)\n", + "(4.188790204786391,)\n", + "(3.236792430971302,)\n", + "(3.8714576135146945,)\n", + "(3.3637254674799806,)\n", + "(6.092785752416569,)\n", + "(4.886921905584122,)\n", + "(4.950388423838462,)\n" + ] + } + ], + "source": [ + "from autora.experimentalist.pooler.random_pooler import random_pool\n", + "\n", + "# generate random pool of 10 conditions\n", + "num_samples = 10\n", + "new_conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", + " n=num_samples)\n", + "\n", + "# print conditons\n", + "for idx, condition in enumerate(new_conditions):\n", + " print(condition)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DdGnRYHKUYNA" + }, + "source": [ + "### Usage: Samplers\n", + "\n", + "An experiment sampler typically requires an existing pool of conditions as input along with additional arguments. For instance, the **[novelty sampler](https://autoresearch.github.io/autora/user-guide/experimentalists/samplers/novelty/)** requires, aside from a pool of conditions, a list of prior conditions. The user may also specify the number of samples ``num_samples`` to select from the pool.\n", + "\n", + "The novelty sampler will then select novel experiment conditions from the pool which are most dissimilar to some reference conditions, such as the ``initial_conditions`` obtained above:" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "o-XwmGmVUYNA", + "outputId": "de0f2980-a453-4a8a-feb3-c93f9a867472" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[0. ]\n", + " [0.06346652]]\n" + ] + } + ], + "source": [ + "from autora.experimentalist.sampler.novelty import novelty_sample\n", + "\n", + "new_conditions_novelty = novelty_sample(condition_pool = condition_pool,\n", + " reference_conditions = initial_conditions,\n", + " num_samples = 2)\n", + "\n", + "print(new_conditions_novelty)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HD-VCIVxUYNJ" + }, + "source": [ + "Another example for an experiment sampler is the **[falsification sampler](https://autoresearch.github.io/autora/falsification/docs/sampler/)**. The falsification sampler identifies experiment conditions under which the loss of a candidate model (returned by the theorist) is predicted to be the highest. This loss is approximated with a neural network, which is trained to predict the loss of the candidate model, given some initial experimental conditions, respective initial observations, and the metadata.\n", + "\n", + "The following code segment calls on the falsification sampler to return novel conditions based on the candidate model of the linear regression theorist introduced above. As with the novelty sampler, we seek to select 2 conditions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kPpATSzgUYNJ", + "outputId": "6007b817-7ddc-4a73-ff7e-61a088f5df79" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[0. ]\n", + " [0.06346652]]\n" + ] + } + ], + "source": [ + "from autora.experimentalist.sampler.falsification import falsification_sample\n", + "\n", + "new_conditions_falsification = falsification_sample(\n", + " condition_pool=condition_pool,\n", + " model=theorist_lr,\n", + " reference_conditions=initial_conditions,\n", + " reference_observations=initial_observations,\n", + " metadata=metadata,\n", + " num_samples=2\n", + " )\n", + "\n", + "print(new_conditions_falsification)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kDRWmikdUYNK" + }, + "source": [ + "We can plot the selected conditions for both samples relative to the selected samples. Since we don't have observations for those conditions, we plot them as vertical lines." + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 489 + }, + "id": "3skhpCOSUYNK", + "outputId": "ecdb0936-1a8f-4970-d7f0-a12d060d42c3" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 124 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "# plot model predictions against ground-truth\n", + "import matplotlib.pyplot as plt\n", + "\n", + "y_min = np.min(initial_observations)\n", + "y_max = np.max(initial_observations)\n", + "\n", + "# plot conditions obtained by novelty sampler\n", + "for idx, condition in enumerate(new_conditions_novelty):\n", + " if idx == 0:\n", + " plt.plot([condition[0], condition[0]], [y_min, y_max], '--r', label='novelty conditions')\n", + " else: # we want to omit the label for all other conditions\n", + " plt.plot([condition[0], condition[0]], [y_min, y_max], '--r')\n", + "\n", + "# plot conditions obtained by falsification sampler\n", + "for idx, condition in enumerate(new_conditions_falsification):\n", + " if idx == 0:\n", + " plt.plot([condition[0], condition[0]], [y_min, y_max], '--g', label='falsification conditions')\n", + " else: # we want to omit the label for all other conditions\n", + " plt.plot([condition[0], condition[0]], [y_min, y_max], '--g')\n", + "\n", + "plt.plot(condition_pool, ground_truth(condition_pool), '-', label='Ground Truth')\n", + "plt.plot(initial_conditions, initial_observations, 'o', label='Initial Data')\n", + "plt.plot(condition_pool, predicted_observations_lr, '-k', label='Prediction from Linear Regression')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Sampled Experimental Conditions')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cdDcYoJcUYNK" + }, + "source": [ + "\n", + "### Usage: Pipelines\n", + "\n", + "Experimentalists can be connected in a **[pipeline](https://autoresearch.github.io/autora/core/docs/pipeline/Experimentalist%20Pipeline%20Examples/)**, where each element passes its output to the next element, ensuring compatibility between the inputs and outputs. Pipelines offer a flexible and efficient way to orchestrate the workflow involving complex experimentalists (e.g., for processing of experimental conditions) and experiment runners (e.g., for preprocessing of collected observations). They allow for the integration of poolers, samplers, and other design manipulations into a cohesive stream of experimental conditions.\n", + "\n", + "Let's examine the following pipeline example:\n", + "\n", + "
    \n", + "
  1. Generate a grid of all possible experimental conditions.\n", + "
  2. Filter out conditions where the independent variable falls within the range -1 to 1.\n", + "
  3. Sample 10 conditions using the novelty sampler.\n", + "
  4. Select 5 conditions from the sampled set using the falsification sampler.\n", + "
\n", + "\n", + "Before creating the pipeline, let's define an additional function that removes experiment conditions falling within the range of -1 to 1, specifically $-1 \\leq x \\leq 1$. This function will be used in the second step of the pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "O3F9XdjMUYNK" + }, + "outputs": [], + "source": [ + "from typing import Iterable\n", + "\n", + "def condition_exclusion(conditions):\n", + " # first we need to make sure that conditions is a 2-dimensional numpy array\n", + " if isinstance(conditions, Iterable):\n", + " conditions = np.array(list(conditions))\n", + "\n", + " if conditions.ndim == 1:\n", + " conditions = conditions.reshape(-1, 1)\n", + "\n", + " # now we can sub-select conditions\n", + " conditions_to_keep = conditions[(-1 > conditions) | (conditions > 1)]\n", + " conditions_to_keep = conditions_to_keep.reshape(-1, 1)\n", + " return conditions_to_keep" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "daq8R8LsUYNK" + }, + "source": [ + "A pipeline can be defined as a list of functions, such as ``[grid_pool, value_exclusion, novelty_sample, falsification_sample]``. However, to create a pipeline object, we need to specify the required parameters for each element in the pipeline. We can achieve this by providing nested dictionaries containing the additional parameters, as shown in the code block below.\n", + "\n", + "**Note**: *Each element of the pipeline passes its output to the next element as the first argument of the element's function. Thus, we need to make sure that the output of one pipeline element is compatible with the required first input argument for the next element. In our case, the first argument for each pipeline element (except for poolers) is assumed to be a 2-dimensional numpy array specifying a set of experimental conditions.*\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YxwKnlEZUYNK" + }, + "outputs": [], + "source": [ + "from autora.experimentalist.pipeline import make_pipeline\n", + "\n", + "experimentalist_pipeline = make_pipeline([grid_pool,\n", + " condition_exclusion,\n", + " novelty_sample,\n", + " falsification_sample],\n", + " params={\"grid_pool\":\n", + " {\"ivs\": metadata.independent_variables},\n", + " \"novelty_sample\":\n", + " {\"reference_conditions\": initial_conditions,\n", + " \"num_samples\": 10},\n", + " \"falsification_sample\":\n", + " {\"model\": theorist_bms,\n", + " \"reference_conditions\": initial_conditions,\n", + " \"reference_observations\": initial_observations,\n", + " \"metadata\": metadata,\n", + " \"num_samples\": 5}})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KYWnkPvFUYNL" + }, + "source": [ + "In the declaration of the ``params`` parameter, we first specify the name of the pipeline object we seek to parameterize as a dictionary key, e.g., ``\"grid_pool\"``, and then nest within it, another dictionary with the names of the input arguments as keys (e.g., ``\"ivs\"``) along with their values (e.g., ``metadata.independent_variables``).\n", + "\n", + "Once specified, we can run the pipeline object to obtain novel experimental conditions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1FbRRSAEUYNL", + "outputId": "544781ac-9a20-4bd7-c2c7-69cb193d27ff" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[1.90399555]\n", + " [3.93492413]\n", + " [5.83891968]\n", + " [5.9023862 ]\n", + " [5.96585272]]\n" + ] + } + ], + "source": [ + "new_conditions = experimentalist_pipeline.run()\n", + "\n", + "print(new_conditions)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tXpSt5jOUYNL" + }, + "source": [ + "**Hint**: *A common error for running pipelines is that the output of one pipeline element is incompatible with the input of the next pipeline element (e.g., not providing a 2-dimensional numpy array to ``novelty_sample``). In such cases, it can be helpful to \"manually\" pass the inputs from one element to another element, to check if they are compatible.*\n", + "\n", + "**Note**: *Pipelines may be used for other purposes, such as linking an experiment runner with multiple pre-processing steps.*" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Next Notebook\n", + "\n", + "Follow this link for the next notebook tutorial:\n", + "[AutoRA Basic Tutorial II: Loop Constructs](www.addlink.com)
" + ], + "metadata": { + "id": "8Qmp6-J-Xa5j" + } + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/tutorials/Introduction.ipynb b/docs/tutorials/Introduction.ipynb deleted file mode 100644 index acf362d98..000000000 --- a/docs/tutorials/Introduction.ipynb +++ /dev/null @@ -1,3046 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction to AutoRA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", - "\n", - "This notebook provides a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", - "\n", - "**How to use this notebook**: *You can progress through the notebook section by section or directly navigate to specific sections. If you choose the latter, it is recommended to execute all cells in the notebook initially, allowing you to easily rerun the cells in each section later without issues.*\n", - "\n", - "## Overview\n", - "\n", - "1. Installation\n", - "2. Automated Empirical Research Components\n", - " - Experiment Runners\n", - " - Theorists\n", - " - Experimentalists\n", - "3. Automated Empirical Research With Basic Loop Constructs\n", - "4. Automated Empirical Research With AutoRA Workflow Logic\n", - " - Basic Workflows\n", - " - Advanced Workflows\n", - "5. Customizing Automated Empirical Research Components\n", - " - Custom Theorists\n", - " - Custom Experimentalists\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Installation\n", - "\n", - "The AutoRA ecosystem is a comprehensive collection of packages that together establish a framework for closed-loop empirical research. At the core of this framework is the ``autora`` package, which serves as the parent package and is essential for end users to install. It provides functionalities for automating workflows in empirical research and includes vetted modules with minimal dependencies.\n", - "\n", - "However, the flexibility of autora extends further with the inclusion of *optional* modules as additional dependencies. Users have the freedom to selectively install these modules based on their specific needs and preferences.\n", - "\n", - "\"AutoRA\n", - "\n", - "*Optional dependencies enable users to customize their autora environment without worrying about conflicts with other packages within the broader autora ecosystem. To install an optional module, simply use the command ``autora[dependency-name]``, where ``dependency-name`` corresponds to the name of the desired module (see example below).*\n", - "\n", - "To begin, we will install all the relevant optional dependencies. Our focus will be on two experimentalists: ``experimentalist-falsification`` and ``experimentalist-sampler-novelty``, along with a Bayesian Machine Scientist (BMS) implemented in the ``theorist-bms`` package. It's important to note that installing a module will automatically include the main autora package, as well as any required dependencies for workflow management and running synthetic experiments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m237.5/237.5 kB\u001b[0m \u001b[31m27.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m51.1/51.1 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h" - ] - } - ], - "source": [ - "!pip install -q \"autora[experimentalist-falsification]\"\n", - "!pip install -q \"autora[experimentalist-sampler-novelty]\"\n", - "!pip install -q \"autora[experimentalist-sampler-model-disagreement]\"\n", - "!pip install -q \"autora[theorist-bms]\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To make all simulations in this notebook replicable, we will set some seeds." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "import torch\n", - "\n", - "np.random.seed(42)\n", - "torch.manual_seed(42)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Automated Empirical Research Components\n", - "\n", - "The goal of this section is to set up all ``autora`` components to enable a closed-loop discovery workflow with synthetic data. This involves specifying (1) the experiment environment, (2) a theorist for model discovery, (3) an experimentalist for identifying novel experiment conditions.\n", - "\n", - "\"AutoRA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Experiments\n", - "\n", - "``autora`` provides support for experiment runners, which serve as interfaces for conducting both real-world and synthetic experiments. An experiment runner typically accepts experiment conditions as input (e.g., a 2-dimensional numpy array with columns representing different independent variables) and produces collected observations as output (e.g., a 2-dimensional numpy array with columns representing different dependent variables). These experiment runners can be combined with other ``autora`` components to facilitate closed-loop scientific discovery.\n", - "\n", - "\"AutoRA\n", - "\n", - "#### Types\n", - "\n", - "AutoRA offers two types of experiment runners: **real-world experiments** and **synthetic experiments**.\n", - "\n", - "For **real-world experiments**, experiment runners can include interfaces for various scenarios such as web-based experiments for behavioral data collection (e.g., using [Firebase and Prolific](https://autoresearch.github.io/autora/user-guide/experiment-runners/firebase-prolific/)) or experiments involving electrical circuits (e.g., using [Tinkerforge](https://en.wikipedia.org/wiki/Tinkerforge)). These runners often require external components such as databases to store collected observations or servers to host the experiments. You may refer to the respective tutorials for these interfaces on how to set up all required components.\n", - "\n", - "**Synthetic experiments** are conducted on synthetic experiment runners, which are functions that take experimental conditions as input and generate simulated observations as output. These experiments serve multiple purposes, including *testing autora components* before applying them to real-world experiments, *benchmarking methods for automated scientific discovery*, or *conducting computational metascientific experiments*.\n", - "\n", - "In this introductory tutorial, we primarily focus on simple synthetic experiments. For more complex synthetic experiments implementing various scientific models, you can utilize the[autora-synthetic](https://github.com/autoresearch/autora-synthetic/) module." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Usage\n", - "\n", - "To create a synthetic experiment runner, we begin with **defining a ground truth** from which to generate data. Here, we consider a simple sine function:\n", - "\n", - "$y = f(x) = \\sin(x)$\n", - "\n", - "In this case, $x$ corresponds to an *independent* variable (the variable we can manipulate in an experiment), $y$ corresponds to a *dependent* variable (the variable we can observe after conducting the experiment), and $f(x)$ is the *ground-truth function* (or \"mechanism\") that we seek to uncover via a combination of experimentation and model discovery.\n", - "\n", - "However, we assume that observations are obtained with a measurement error when running the experiment.\n", - "\n", - "$\\hat{y} = \\hat{f}(x) = f(x) + \\epsilon, \\quad \\epsilon \\sim \\mathcal{N}(0,0.01^{2})$\n", - "\n", - "where $\\epsilon$ is the measurement error sampled from a normal distribution with zero mean and a standard deviation of $0.01$.\n", - "\n", - "The following code block defines ground truth $f(x)$ and the experiment runner $\\hat{f}(x)$ as ``lambda`` functions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ground_truth = lambda x: np.sin(x)\n", - "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we generate a pool of all possible experimental conditions from the domain $[0, 2\\pi]$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "condition_pool = np.linspace(0, 2 * np.pi, 100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In order to run a simple synthetic experiment, we can first sample from the pool of possible experiment conditions (without replacement), and then pass these conditions to the synthetic experiment runner:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "initial_conditions = np.random.choice(condition_pool, size=10, replace=False)\n", - "initial_observations = run_experiment(initial_conditions)\n", - "\n", - "# plot sampled conditions against ground-truth\n", - "import matplotlib.pyplot as plt\n", - "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", - "plt.plot(initial_conditions, initial_observations, 'o', label='Sampled Conditions')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('Sine Function')\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Certain theorists and experimentalists may need to have knowledge about the experimental variables, such as the domain from which new experiment conditions are sampled. To provide this information, we can utilize a ``VariableCollection`` object. In the context of our synthetic experiment, we have a single *independent variable* (``IV``) denoted as $x$, and a single *dependent* variable (``DV``) denoted as $y$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.variable import DV, IV, ValueType, VariableCollection\n", - "\n", - "# Specify independent variable\n", - "iv = IV(\n", - " name=\"x\", # name of the independent variable\n", - " value_range=(0, 2 * np.pi), # specify the domain\n", - " allowed_values=condition_pool, # alternatively, we can specify the pool of allowed conditions directly\n", - ")\n", - "\n", - "# specify dependent variable\n", - "dv = DV(\n", - " name=\"y\", # name of the dependent variable\n", - " type=ValueType.REAL, # specify the variable type (some theorists require this to optimize)\n", - ")\n", - "\n", - "# Variable collection with ivs and dvs\n", - "metadata = VariableCollection(\n", - " independent_variables=[iv],\n", - " dependent_variables=[dv],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note**: *For expository reasons, we focus in this tutorial on simple synthetic experiments. In general, ``autora`` provides functionality for automating [more complex synthetic experiments](https://github.com/autoresearch/autora-synthetic/), as well as real-world experiments, such as [behavioral data collection via web-based experiments](https://autoresearch.github.io/autora/user-guide/experiment-runners/firebase-prolific/), experiments with electrical circuits via [Tinkerforge](https://en.wikipedia.org/wiki/Tinkerforge), and other automated experimentation platforms.*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Theorists\n", - "\n", - "The AutoRA framework includes and interfaces with different methods for scientific model discovery. These methods are referred to as *theorists* and are implemented as [sklearn estimators](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html). For general information about theorists, see the respective [AutoRA Documentation](https://autoresearch.github.io/autora/theorist/).\n", - "\n", - "\"Theorist\n", - "\n", - "\n", - "Theorists **take as input a set of conditions and observations**. Conditions and observations can typically be passed as *two-dimensional numpy arrays* (with columns corresponding to variables and rows corresponding to different instances of those variables). Theorists then **identify and fit a model** which may be used to predict observations based on experiment conditions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Types\n", - "\n", - "There are different types of theorists within the AutoRA framework, each with its own approach to scientific model discovery.\n", - "\n", - "Some theorists focus on *fitting the parameters of a pre-specified model* to the given data (see the scikit learn documentation for a [selection of basic regressors](https://scikit-learn.org/stable/supervised_learning.html)). The model architecture in such cases is typically fixed, while the parameters are adjusted to optimize the model's performance. Linear regression is an example of a parameter-fitting theorist.\n", - "\n", - "Other theorists are concerned with *identifying both the architecture of a model and its parameters*. The model architectures can take various forms, such as equations, causal models, or process models. Implemented as scikit-learn estimators, these theorists aim to discover a model architecture that accurately describes the data. They often operate within a user-defined search space, which specifies the allowable operations or components that can be included in the model. This approach provides more flexibility in exploring different model architectures." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Usage\n", - "\n", - "In this tutorial, we delve into two types of theorists: (1) a linear regression theorist, which focuses on fitting a linear model, and (2) a Bayesian Machine Scientist (Guimerà et al., 2020, in *Science Advances*), which specializes in identifying and fitting a non-linear equation.\n", - "\n", - "Theorists are commonly instantiated as regressors within the ``sklearn`` library:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn import linear_model\n", - "from autora.theorist.bms import BMSRegressor\n", - "\n", - "theorist_lr = linear_model.LinearRegression()\n", - "theorist_bms = BMSRegressor(epochs=100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once instantiated, we can fit the theorist to link experimental conditions with observations. However, before doing so, we should convert both inputs into 2-dimensional numpy arrays." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:07<00:00, 13.91it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "data": { - "text/html": [ - "
BMSRegressor(epochs=100)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "BMSRegressor(epochs=100)" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# convert data to 2-dimensional numpy array\n", - "initial_conditions = initial_conditions.reshape((len(initial_conditions), 1))\n", - "initial_observations = initial_observations.reshape((len(initial_observations), 1))\n", - "\n", - "# fit theorists\n", - "theorist_lr.fit(initial_conditions, initial_observations)\n", - "theorist_bms.fit(initial_conditions, initial_observations)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For some theorists, we can inspect the resulting model architecture. For instance, in the BMS theorist, we can call obtain the model formula via ``theorist_bms.model_.__repr__()``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model of BMS theorist: sin(X0)\n" - ] - } - ], - "source": [ - "print(\"Model of BMS theorist: \" + theorist_bms.model_.__repr__())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We may now obtain predictions from both theorists for the entire pool of experiment conditions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# convert condition pool into 2-dimensional numpy array before generating respective predictions\n", - "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", - "\n", - "# obtain predictions\n", - "predicted_observations_lr = theorist_lr.predict(condition_pool)\n", - "predicted_observations_bms = theorist_bms.predict(condition_pool)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next code segment, we plot the theorists' predictions against the ground truth. For the BMS theorist, we can obtain a latex expression of the model architecture using ``theorist_bms.model_.latex()``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# obtain latex expression of BMS theorist\n", - "bms_model = theorist_bms.model_.latex()\n", - "\n", - "# plot model predictions against ground-truth\n", - "import matplotlib.pyplot as plt\n", - "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", - "plt.plot(initial_conditions, initial_observations, 'o', label='Data Used for Model Identification')\n", - "plt.plot(condition_pool, predicted_observations_lr, label='Linear Regression')\n", - "plt.plot(condition_pool, predicted_observations_bms, label='Bayesian Machine Scientist: $' + bms_model + '$')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('Model Predictions')\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note**: *There are various other types of theorists you can combine with AutoRA as long as they are implemented as ``sklearn`` estimators. This includes [autora modules](theorist/index.md), any [scikit learn estimators](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html), as well as third-party packages, such as [PySR](https://github.com/MilesCranmer/PySR) for symbolic regression.*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Experimentalists\n", - "\n", - "The primary goal of an experimentalist is to design experiments that yield scientific merit. The AutoRA framework offers various strategies for identifying informative new data points (e.g., by searching for experiment conditions that existing scientific models fail to explain, or by looking for novel conditions altogether).\n", - "\n", - "\"Experimentalist\n", - "\n", - "Experimentalists are implemented as functions that return a set of experiment conditions (e.g., in the form of a 2-dimensional numpy array in which columns correspond to independent variables), which can be subjected to an experiment. To determine these conditions, experimentalists may use information about candidate models obtained from a theorist, experimental conditions that have already been probed, or respective dependent measures. For more detailed information about experimentalists, please refer to the corresponding [AutoRA Documentation](https://autoresearch.github.io/autora/experimentalist/).\n", - "\n", - "#### Types\n", - "\n", - "There are generally three types of experimentalist functions: **poolers**, **samplers**, and **pipelines**.\n", - "\n", - "**Poolers** generate a novel set of experimental conditions \"from scratch\", e.g., by sampling from a grid. They usually require metadata describing independent variables of the experiment (e.g., their range or the set of allowed values).\n", - "\n", - "**Samplers** operate on an existing pool of experimental conditions. They require typically require experimental conditions to be represented as a 2-dimensional numpy array in which columns correspond to independent variables and rows to different conditions). They then select experiment conditions from this pool.\n", - "\n", - "**Pipelines** Pipelines connect multiple experimentalists into a unified workflow. This is beneficial when various steps are required to process experiment conditions. For example, apart from identifying novel experimental conditions, experimentalist functions may perform other operations on the set of conditions, such as rearranging the rows of a condition matrix or adding new experiment conditions as columns. Experiment pipelines may begin with a pooler that generates all possible experiment conditions, followed by a sampler that selects a subset of conditions from the pool, and then proceed to additional functions that arrange the selected conditions in a specific order necessary for conducting the experiment." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Usage: Poolers\n", - "\n", - "Experimentalist poolers are implemented as functions and can be called directly. For instance, the following **grid pooler** generates a grid based on the ``allowed_values`` of all independent variables in the ``metadata`` object that we defined above. We can simply add a list of allowed values to each independent variable. In this case, we only have one variable." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "allowed_values = np.linspace(0, 2 * np.pi, 100)\n", - "metadata.independent_variables[0].allowed_values = allowed_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can pass the grid pooler the list of independent variables from the ``metadata`` object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.experimentalist.pooler.grid import grid_pool\n", - "\n", - "new_conditions = grid_pool(ivs = metadata.independent_variables)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The resulting condition pool contains all experiment conditions from the grid:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.0,)\n", - "(0.06346651825433926,)\n", - "(0.12693303650867852,)\n", - "(0.1903995547630178,)\n", - "(0.25386607301735703,)\n", - "(0.3173325912716963,)\n", - "(0.3807991095260356,)\n", - "(0.4442656277803748,)\n", - "(0.5077321460347141,)\n", - "(0.5711986642890533,)\n", - "(0.6346651825433925,)\n" - ] - } - ], - "source": [ - "# return first 10 conditions\n", - "for idx, condition in enumerate(new_conditions):\n", - " print(condition)\n", - " if idx > 9:\n", - " break" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, we may use the **random pooler** to randomly draw experimental conditions from the domains of each independent variable. The random pooler requires as input a list of discrete values from which to sample from. In this case, we can pass it ``metadata.independent_variables[0].allowed_values`` for the independent variable. We can also specify the input argument ``n`` to obtain 10 random samples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(5.521587088127515,)\n", - "(0.9519977738150889,)\n", - "(1.0154642920694281,)\n", - "(4.6330558325667655,)\n", - "(3.681058058751677,)\n", - "(5.775453161144872,)\n", - "(5.013854942092801,)\n", - "(4.442656277803748,)\n", - "(3.998390650023373,)\n", - "(0.9519977738150889,)\n" - ] - } - ], - "source": [ - "from autora.experimentalist.pooler.random_pooler import random_pool\n", - "\n", - "# generate random pool of 10 conditions\n", - "num_samples = 10\n", - "new_conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", - " n=num_samples)\n", - "\n", - "# print conditons\n", - "for idx, condition in enumerate(new_conditions):\n", - " print(condition)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Usage: Samplers\n", - "\n", - "An experiment sampler typically requires an existing pool of conditions as input along with additional arguments. For instance, the **[novelty sampler](https://autoresearch.github.io/autora/user-guide/experimentalists/samplers/novelty/)** requires, aside from a pool of conditions, a list of prior conditions. The user may also specify the number of samples ``num_samples`` to select from the pool.\n", - "\n", - "The novelty sampler will then select novel experiment conditions from the pool which are most dissimilar to some reference conditions, such as the ``initial_conditions`` obtained above:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[6.28318531]\n", - " [6.21971879]]\n" - ] - } - ], - "source": [ - "from autora.experimentalist.sampler.novelty import novelty_sample\n", - "\n", - "new_conditions_novelty = novelty_sample(condition_pool = condition_pool,\n", - " reference_conditions = initial_conditions,\n", - " num_samples = 2)\n", - "\n", - "print(new_conditions_novelty)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another example for an experiment sampler is the **[falsification sampler](https://autoresearch.github.io/autora/falsification/docs/sampler/)**. The falsification sampler identifies experiment conditions under which the loss of a candidate model (returned by the theorist) is predicted to be the highest. This loss is approximated with a neural network, which is trained to predict the loss of the candidate model, given some initial experimental conditions, respective initial observations, and the metadata.\n", - "\n", - "The following code segment calls on the falsification sampler to return novel conditions based on the candidate model of the linear regression theorist introduced above. As with the novelty sampler, we seek to select 10 conditions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0. ]\n", - " [0.06346652]]\n" - ] - } - ], - "source": [ - "from autora.experimentalist.sampler.falsification import falsification_sample\n", - "\n", - "new_conditions_falsification = falsification_sample(\n", - " condition_pool=condition_pool,\n", - " model=theorist_lr,\n", - " reference_conditions=initial_conditions,\n", - " reference_observations=initial_observations,\n", - " metadata=metadata,\n", - " num_samples=2\n", - " )\n", - "\n", - "print(new_conditions_falsification)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can plot the selected conditions for both samples relative to the selected samples. Since we don't have observations for those conditions, we plot them as vertical lines." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot model predictions against ground-truth\n", - "import matplotlib.pyplot as plt\n", - "\n", - "y_min = np.min(initial_observations)\n", - "y_max = np.max(initial_observations)\n", - "\n", - "# plot conditions obtained by novelty sampler\n", - "for idx, condition in enumerate(new_conditions_novelty):\n", - " if idx == 0:\n", - " plt.plot([condition[0], condition[0]], [y_min, y_max], '--r', label='novelty conditions')\n", - " else: # we want to omit the label for all other conditions\n", - " plt.plot([condition[0], condition[0]], [y_min, y_max], '--r')\n", - "\n", - "# plot conditions obtained by falsification sampler\n", - "for idx, condition in enumerate(new_conditions_falsification):\n", - " if idx == 0:\n", - " plt.plot([condition[0], condition[0]], [y_min, y_max], '--g', label='falsification conditions')\n", - " else: # we want to omit the label for all other conditions\n", - " plt.plot([condition[0], condition[0]], [y_min, y_max], '--g')\n", - "\n", - "plt.plot(condition_pool, ground_truth(condition_pool), '-', label='Ground Truth')\n", - "plt.plot(initial_conditions, initial_observations, 'o', label='Initial Data')\n", - "plt.plot(condition_pool, predicted_observations_lr, '-k', label='Prediction from Linear Regression')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('Sampled Experimental Conditions')\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "#### Usage: Pipelines\n", - "\n", - "Experimentalists can be connected in a **[pipeline](https://autoresearch.github.io/autora/core/docs/pipeline/Experimentalist%20Pipeline%20Examples/)**, where each element passes its output to the next element, ensuring compatibility between the inputs and outputs. Pipelines offer a flexible and efficient way to orchestrate the workflow involving complex experimentalists (e.g., for processing of experimental conditions) and experiment runners (e.g., for preprocessing of collected observations). They allow for the integration of poolers, samplers, and other design manipulations into a cohesive stream of experimental conditions.\n", - "\n", - "Let's examine the following pipeline example:\n", - "\n", - "
    \n", - "
  1. Generate a grid of all possible experimental conditions.\n", - "
  2. Filter out conditions where the independent variable falls within the range -1 to 1.\n", - "
  3. Sample 10 conditions using the novelty sampler.\n", - "
  4. Select 5 conditions from the sampled set using the falsification sampler.\n", - "
\n", - "\n", - "Before creating the pipeline, let's define an additional function that removes experiment conditions falling within the range of -1 to 1, specifically $-1 \\leq x \\leq 1$. This function will be used in the second step of the pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Iterable\n", - "\n", - "def condition_exclusion(conditions):\n", - " # first we need to make sure that conditions is a 2-dimensional numpy array\n", - " if isinstance(conditions, Iterable):\n", - " conditions = np.array(list(conditions))\n", - "\n", - " if conditions.ndim == 1:\n", - " conditions = conditions.reshape(-1, 1)\n", - "\n", - " # now we can sub-select conditions\n", - " conditions_to_keep = conditions[(-1 > conditions) | (conditions > 1)]\n", - " conditions_to_keep = conditions_to_keep.reshape(-1, 1)\n", - " return conditions_to_keep" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A pipeline can be defined as a list of functions, such as ``[grid_pool, value_exclusion, novelty_sample, falsification_sample]``. However, to create a pipeline object, we need to specify the required parameters for each element in the pipeline. We can achieve this by providing nested dictionaries containing the additional parameters, as shown in the code block below.\n", - "\n", - "**Note**: *Each element of the pipeline passes its output to the next element as the first argument of the element's function. Thus, we need to make sure that the output of one pipeline element is compatible with the required first input argument for the next element. In our case, the first argument for each pipeline element (except for poolers) is assumed to be a 2-dimensional numpy array specifying a set of experimental conditions.*\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.experimentalist.pipeline import make_pipeline\n", - "\n", - "experimentalist_pipeline = make_pipeline([grid_pool,\n", - " condition_exclusion,\n", - " novelty_sample,\n", - " falsification_sample],\n", - " params={\"grid_pool\":\n", - " {\"ivs\": metadata.independent_variables},\n", - " \"novelty_sample\":\n", - " {\"reference_conditions\": initial_conditions,\n", - " \"num_samples\": 10},\n", - " \"falsification_sample\":\n", - " {\"model\": theorist_bms,\n", - " \"reference_conditions\": initial_conditions,\n", - " \"reference_observations\": initial_observations,\n", - " \"metadata\": metadata,\n", - " \"num_samples\": 5}})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the declaration of the ``params`` parameter, we first specify the name of the pipeline object we seek to parameterize as a dictionary key, e.g., ``\"grid_pool\"``, and then nest within it, another dictionary with the names of the input arguments as keys (e.g., ``\"ivs\"``) along with their values (e.g., ``metadata.independent_variables``).\n", - "\n", - "Once specified, we can run the pipeline object to obtain novel experimental conditions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1.96746207]\n", - " [6.28318531]\n", - " [6.21971879]\n", - " [6.15625227]\n", - " [6.09278575]]\n" - ] - } - ], - "source": [ - "new_conditions = experimentalist_pipeline.run()\n", - "\n", - "print(new_conditions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Hint**: *A common error for running pipelines is that the output of one pipeline element is incompatible with the input of the next pipeline element (e.g., not providing a 2-dimensional numpy array to ``novelty_sample``). In such cases, it can be helpful to \"manually\" pass the inputs from one element to another element, to check if they are compatible.*\n", - "\n", - "**Note**: *Pipelines may be used for other purposes, such as linking an experiment runner with multiple pre-processing steps.*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Automated Empirical Research With Basic Loop Constructs\n", - "\n", - "After defining all the components required for the empirical research process, we can create an automated workflow using basic loop constructs in Python.\n", - "\n", - "The following code block demonstrates how to build such a workflow using the components introduced in the preceding sections, such as\n", - "\n", - "- ``metadata`` (object specifying variables of the experiment),
\n", - "- ``run_experiment`` (function for collecting data),
\n", - "- ``theorist_bms`` (scikit learn estimator for discoverying requations using the Bayesian Machine Scientist),
\n", - "- ``random_pool`` (function for generating a random pool of experimental conditions), and
\n", - "- ``falsification_sample`` (function for identifying novel experiment conditions using the falsification .sampler)
\n", - "\n", - "We begin with implementing the following workflow:\n", - "1. Generate 3 seed experimental conditions using ``random_pool``\n", - "2. Generate 3 seed observations using ``run_experiment``\n", - "3. Loop through the following steps 5 times\n", - " - Identify a model relating conditions to observations using ``theorist_bms``\n", - " - Identify 3 new experimental conditions using ``falsification_sample``\n", - " - Collect 3 new observations using ``run_experiment``\n", - " - Add new conditions and observations to the dataset\n", - "\n", - "We will iteratre through this workflow 5 times." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:08<00:00, 11.50it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 0: 0.0\n", - "Discovered Model: sin(X0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 13.36it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 1: 0.0\n", - "Discovered Model: sin(X0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 13.41it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 2: 0.5830078060480123\n", - "Discovered Model: _a0_\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 12.81it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 3: 0.0\n", - "Discovered Model: sin(X0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 13.67it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 4: 0.0\n", - "Discovered Model: sin(X0)\n" - ] - } - ], - "source": [ - "num_cycles = 5 # number of empirical research cycles\n", - "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", - "\n", - "# generate an initial set of experimental conditions\n", - "conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", - " n=measurements_per_cycle)\n", - "# convert iterator into 2-dimensional numpy array\n", - "conditions = np.array(list(conditions)).reshape(-1, 1)\n", - "\n", - "# collect initial set of observations\n", - "observations = run_experiment(conditions)\n", - "\n", - "for cycle in range(num_cycles):\n", - "\n", - " # use BMS theorist to fit the model to the data\n", - " theorist_bms.fit(conditions, observations)\n", - "\n", - " # obtain new conditions\n", - " new_conditions = falsification_sample(\n", - " condition_pool=condition_pool,\n", - " model=theorist_bms,\n", - " reference_conditions=conditions,\n", - " reference_observations=observations,\n", - " metadata=metadata,\n", - " num_samples=measurements_per_cycle,\n", - " )\n", - "\n", - " # obtain new observations\n", - " new_observations = run_experiment(new_conditions)\n", - "\n", - " # combine old and new conditions and observations\n", - " conditions = np.concatenate((conditions, new_conditions))\n", - " observations = np.concatenate((observations, new_observations))\n", - "\n", - " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", - " loss = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", - " print(\"Loss in cycle {}: {}\".format(cycle, loss))\n", - " print(\"Discovered Model: \" + theorist_bms.model_.__repr__())\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can easily replace components in the workflow above. For instance, we could replace ``falsification_sample`` with the ``experimentalist_pipeline`` defined above.\n", - "\n", - "In the following code block, we add a linear regression theorist, to fit a linear model to the data. In addition, we replace ``falsification_sample`` with ``model_disagreement_sampler`` to sample experimental conditions that differentiate most between the linear model and the model discovered by the BMS theorist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:07<00:00, 12.60it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 0: 0.7663165942701162\n", - "Discovered BMS Model: _a0_\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:08<00:00, 12.32it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 1: 0.0\n", - "Discovered BMS Model: sin(relu(X0))\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:06<00:00, 14.84it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 2: 0.5278979868526005\n", - "Discovered BMS Model: _a0_\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:06<00:00, 14.46it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 3: 0.5084168918123109\n", - "Discovered BMS Model: _a0_\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:06<00:00, 15.53it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 4: 0.0\n", - "Discovered BMS Model: sin(X0)\n" - ] - } - ], - "source": [ - "from autora.experimentalist.sampler.model_disagreement import model_disagreement_sampler\n", - "\n", - "num_cycles = 5 # number of empirical research cycles\n", - "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", - "\n", - "# generate an initial set of experimental conditions\n", - "conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", - " n=measurements_per_cycle)\n", - "# convert iterator into 2-dimensional numpy array\n", - "conditions = np.array(list(conditions)).reshape(-1, 1)\n", - "\n", - "# collect initial set of observations\n", - "observations = run_experiment(conditions)\n", - "\n", - "for cycle in range(num_cycles):\n", - "\n", - " # use BMS theorist to fit the model to the data\n", - " theorist_bms.fit(conditions, observations)\n", - " theorist_lr.fit(conditions, observations)\n", - "\n", - " # obtain new conditions\n", - " new_conditions = model_disagreement_sampler(\n", - " condition_pool,\n", - " models = [theorist_bms, theorist_lr],\n", - " num_samples = measurements_per_cycle\n", - " )\n", - "\n", - " # obtain new observations\n", - " new_observations = run_experiment(new_conditions)\n", - "\n", - " # combine old and new conditions and observations\n", - " conditions = np.concatenate((conditions, new_conditions))\n", - " observations = np.concatenate((observations, new_observations))\n", - "\n", - " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", - " loss = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", - " print(\"Loss in cycle {}: {}\".format(cycle, loss))\n", - " print(\"Discovered BMS Model: \" + theorist_bms.model_.__repr__())\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While the workflow logic with basic loop constructs is flexible, there are more convenient ways to specify a research cycle in ``autora``. The next section illustrates the use of these constructs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Automated Empirical Research With AutoRA Workflow Logic\n", - "\n", - "Workflows in ``autora`` implement the *autonomous empirical research paradigm*. This paradigm centers around the dynamic interplay between automated theorists and automated experimentalists. As outlined above, theorists rely–among other things–on existing data to construct computational models by linking experimental conditions to dependent measures. Experimentalist design follow-up experiments to refine and validate models generated by the theorist. Together, these agents enable a closed-loop scientific discovery process.\n", - "\n", - "The following sections introduce ways of specifying workflows directly in ``autora``. For more information on workflows, please refer to the [corresponding documentation](https://autoresearch.github.io/autora/user-guide/workflow/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Basic Workflows\n", - "\n", - "This section provides an introduction to handling workflows with the controller object. Here, we focus on workflows implementing the **default execution order**: (1) generate experiment conditions using the ``eperimentalist``, (2) collect observations using the ``experiment_runner``, and (3), generate a model that links experiment conditions to observations using the ``theorist``.\n", - "\n", - "At the end of this section, we will able to implement the following workflow:\n", - "\n", - "We begin with implementing the following workflow:\n", - "1. Generate seed experimental conditions\n", - "2. Iterate 5 times through the following steps\n", - " - Collect observations using ``run_experiment``\n", - " - Identify a model relating conditions to observations using ``theorist_bms``\n", - " - Identify 3 new experimental conditions using ``falsification_sample``\n", - "\n", - "#### Declaration\n", - "\n", - "We begin with defining a simple workflow. Workflows can be encapsulated in a ``Controller`` object. For instance, the following code block sets up a closed-loop cycle between (1) a grid pooler for sampling experimental conditions, (2) an experiment runner for obtaining respective observations, and (3) a BMS theorist for discoverying an equation relating experimental conditions to observations.\n", - "\n", - "As with pipelines, we can pass the ``Controller`` object static parameters for each component. In this case, we provide the grid experimentalist with information about the independent variables to sample.\n", - "\n", - "**Note**: *We haven't included the ``falsification_sample`` experimentalist into our workflow yet because it requires us to specify state-dependent input arguments (e.g., the model generated by the theorist), which we will cover at the end of this section.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.workflow import Controller\n", - "\n", - "controller = Controller(\n", - " variables=metadata,\n", - " experimentalist=grid_pool,\n", - " experiment_runner=run_experiment,\n", - " theorist=theorist_bms,\n", - " params={\n", - " \"experimentalist\":\n", - " {\"ivs\": metadata.independent_variables}\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the declaration of the ``params`` parameter, we first specify the type of the component we seek to parameterize as a dictionary key, e.g., ``\"experimentalist\"``. Then we nest within it, another dictionary with the input arguments to the respective component as keys (e.g., ``\"ivs\"`` is an input argument to the ``grid_pool`` experimentalist) along with their values (e.g., ``metadata.independent_variables``).\n", - "\n", - "#### Monitoring\n", - "\n", - "Before we execute the controller, lets also add a **monitor function** which is executed with every autonomous empirical research step. The following code block prints the last generated result of the workflow defined by the controller. All workflow results are stored in the ``state.history`` object. We can access the kind of the latest result using ``state.history[-1].kind``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# define monitor function\n", - "def monitor(state):\n", - " print(f\"MONITOR: Generated new {state.history[-1].kind}\")\n", - "\n", - "# add monitor function to controller\n", - "controller.monitor = monitor" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Execution\n", - "\n", - "The controller is defined as an iterator. We can execute a single step in the workflow by passing the ``controller`` object to the ``next()`` method. The following code block executes three steps of the default research cycle." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.workflow.base:getting step_name='experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x1494cea60>\n", - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x14abcaf70>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x14abcaf70>\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new CONDITION\n", - "MONITOR: Generated new OBSERVATION\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:11<00:00, 8.50it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(controller)\n", - "next(controller)\n", - "next(controller)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As indicated by the monitor, the **default execution order** is as follows: (1) generate experiment conditions, (2) collect observations, and (3), generate a model. After executing step (3), the controller would then continue with step (1):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.workflow.base:getting step_name='experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x1499671f0>\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new CONDITION\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(controller)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since ``controller`` is an iterator, we can use [itertools](https://docs.python.org/3/library/itertools.html) for efficient looping. The following example uses ``takewhile`` to define a loop that stops as soon as we obtained three models from the theorist.\n", - "\n", - "We begin with defining a lambda function which returns true whenever the controller has less then 5 models. As explained in the next subsection, we can obtain a list of generated models by accessing the controller's state via ``controller.state.models``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "continue_criterion = lambda controller: len(controller.state.models) < 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can run a for-loop using the ``controller`` as an iterator, and ``takewhile`` as iterator logic that continues to execute steps of the controller as long as ``continue_criterion`` returns ``True``. In this way, we can execute 3 research cycles." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x14ab130d0>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x1493d8f70>\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.47it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.workflow.base:getting step_name='experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x149e84280>\n", - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x1493d8f70>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x149e84280>\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 2\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 2\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:09<00:00, 11.05it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.workflow.base:getting step_name='experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x14ab130d0>\n", - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x149e84280>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x14ab130d0>\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 3\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 3\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 3\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.41it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.workflow.base:getting step_name='experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x1493d8dc0>\n", - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x149967040>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x1493d89d0>\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 4\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 4\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 4\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.87it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n" - ] - } - ], - "source": [ - "from itertools import takewhile\n", - "\n", - "for step in takewhile(continue_criterion, controller):\n", - " print(f\"Number of models: {len(step.state.models)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Result Inspection\n", - "\n", - "After each executed step, we can observe the result generated by the ``controller``. All results are stored in in ``controller.state.history``. Each result is composed of a value specifying its ``kind`` (``CONDITION``, ``OBSERVATION``, or ``MODEL``) and the respective ``data``.\n", - "\n", - "We can obtain the observations collected in the last step of the workflow as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ResultKind.MODEL\n", - "BMSRegressor(epochs=100)\n" - ] - } - ], - "source": [ - "result = controller.state.history[-1]\n", - "\n", - "print(result.kind)\n", - "print(result.data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also specify the kind of result we are looking for directly. For instance, we can obtain all models generated by the theorist using ``controller.state.models``. The following code block prints the last model discovered by the BMS theorist (note that ``model_.__repr__()`` is a function specific to the BMS theorist which returns its model as a string)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sin(X0)\n" - ] - } - ], - "source": [ - "print(controller.state.models[-1].model_.__repr__())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, we can access probed experimental conditions via ``controller.state.conditions`` and observations via ``controller.state.observations``, respectively. The following code block requests the latest experimental conditions identified by the experimentalist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0. ],\n", - " [0.06346652],\n", - " [0.12693304],\n", - " [0.19039955],\n", - " [0.25386607],\n", - " [0.31733259],\n", - " [0.38079911],\n", - " [0.44426563],\n", - " [0.50773215],\n", - " [0.57119866],\n", - " [0.63466518],\n", - " [0.6981317 ],\n", - " [0.76159822],\n", - " [0.82506474],\n", - " [0.88853126],\n", - " [0.95199777],\n", - " [1.01546429],\n", - " [1.07893081],\n", - " [1.14239733],\n", - " [1.20586385],\n", - " [1.26933037],\n", - " [1.33279688],\n", - " [1.3962634 ],\n", - " [1.45972992],\n", - " [1.52319644],\n", - " [1.58666296],\n", - " [1.65012947],\n", - " [1.71359599],\n", - " [1.77706251],\n", - " [1.84052903],\n", - " [1.90399555],\n", - " [1.96746207],\n", - " [2.03092858],\n", - " [2.0943951 ],\n", - " [2.15786162],\n", - " [2.22132814],\n", - " [2.28479466],\n", - " [2.34826118],\n", - " [2.41172769],\n", - " [2.47519421],\n", - " [2.53866073],\n", - " [2.60212725],\n", - " [2.66559377],\n", - " [2.72906028],\n", - " [2.7925268 ],\n", - " [2.85599332],\n", - " [2.91945984],\n", - " [2.98292636],\n", - " [3.04639288],\n", - " [3.10985939],\n", - " [3.17332591],\n", - " [3.23679243],\n", - " [3.30025895],\n", - " [3.36372547],\n", - " [3.42719199],\n", - " [3.4906585 ],\n", - " [3.55412502],\n", - " [3.61759154],\n", - " [3.68105806],\n", - " [3.74452458],\n", - " [3.8079911 ],\n", - " [3.87145761],\n", - " [3.93492413],\n", - " [3.99839065],\n", - " [4.06185717],\n", - " [4.12532369],\n", - " [4.1887902 ],\n", - " [4.25225672],\n", - " [4.31572324],\n", - " [4.37918976],\n", - " [4.44265628],\n", - " [4.5061228 ],\n", - " [4.56958931],\n", - " [4.63305583],\n", - " [4.69652235],\n", - " [4.75998887],\n", - " [4.82345539],\n", - " [4.88692191],\n", - " [4.95038842],\n", - " [5.01385494],\n", - " [5.07732146],\n", - " [5.14078798],\n", - " [5.2042545 ],\n", - " [5.26772102],\n", - " [5.33118753],\n", - " [5.39465405],\n", - " [5.45812057],\n", - " [5.52158709],\n", - " [5.58505361],\n", - " [5.64852012],\n", - " [5.71198664],\n", - " [5.77545316],\n", - " [5.83891968],\n", - " [5.9023862 ],\n", - " [5.96585272],\n", - " [6.02931923],\n", - " [6.09278575],\n", - " [6.15625227],\n", - " [6.21971879],\n", - " [6.28318531]])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "controller.state.conditions[-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Seeding\n", - "\n", - "The default execution order always begins with an experimentalist. This is problematic if we want to use an experimentalist that depends on prior steps (e.g., the falsification experimentalist requires a model generated by the theorist). We can circumvent this problem by seeding the controller with experiment conditons.\n", - "\n", - "The following code block seeds the controller with 3 experiment conditions. We first generate the ``seed_conditions``, and then pass them, encapsulated in a list, to the ``seed`` function of the ``controller`` object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x149e84e50>\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new OBSERVATION\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# generate initial pool of 3 experimental conditions\n", - "seed_conditions = np.linspace(0,2*np.pi,3)\n", - "\n", - "# define controller\n", - "controller = Controller(\n", - " monitor=monitor,\n", - " variables=metadata,\n", - " experimentalist=grid_pool,\n", - " experiment_runner=run_experiment,\n", - " theorist=theorist_bms,\n", - " params={\n", - " \"experimentalist\":\n", - " {\"ivs\": metadata.independent_variables}\n", - " }\n", - ")\n", - "\n", - "# seed controller\n", - "controller.seed(conditions=[seed_conditions])\n", - "\n", - "next(controller)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that, since we seeded the controller with initial experimental conditions, the next step is to execute the ``experiment_runner``. This is why the first step of reported by the monitor involves the generation of observations (based on the seed experimental conditions)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Accessing State-Dependent Properties\n", - "\n", - "Some automated empirical research components require input arguments that depend on the result of the last step in the workflow. For instance, the ``falsification_sample`` experimentalist depends on the previously collected experimental conditions, observations, and the fitted model. For such cases, it is possible to use \"state-dependent properties\" in the ``params`` dictionary. These are the following strings, which will be replaced during execution by their respective current values:\n", - "\n", - "- ``\"%observations.ivs[-1]%\"``: the last observed independent variables
\n", - "- ``\"%observations.dvs[-1]%\"``: the last observed dependent variables
\n", - "- ``\"%observations.ivs%\"``: all the observed independent variables (observations), concatenated into a single array
\n", - "- ``\"%observations.dvs%\"``: all the observed dependent variables (experimental conditions), concatenated into a single array
\n", - "- ``\"%models[-1]%\"``: the last fitted theorist
\n", - "- ``\"%models%\"``: all the fitted theorists
\n", - "\n", - "In the following example, we use the ``\"%observations.ivs%\"``, ``\"%observations.dvs%\"``, and ``\"%models%\"`` properties for the ``falsification_sample`` experimentalist which seeks to identify experimental conditions that are predicted to maximize the loss of the fitted model.\n", - "\n", - "The code block below implements the following workflow:\n", - "1. Generate 3 seed experimental conditions\n", - "2. Iterate 5 times through the following steps\n", - " - Collect observations using ``run_experiment``\n", - " - Identify a model relating conditions to observations using ``theorist_bms``\n", - " - Identify 3 new experimental conditions using ``falsification_sample``\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# generate initial pool of 3 experimental conditions\n", - "seed_conditions = np.linspace(0,2*np.pi,3)\n", - "\n", - "\n", - "# define controller\n", - "controller = Controller(\n", - " monitor=monitor,\n", - " variables=metadata,\n", - " experimentalist=falsification_sample,\n", - " experiment_runner=run_experiment,\n", - " theorist=theorist_bms,\n", - " params={\n", - " \"experimentalist\":\n", - " {\"condition_pool\": condition_pool,\n", - " \"model\": \"%models[-1]%\", # access last model generated by theorist\n", - " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", - " \"reference_observations\": \"%observations.dvs%\", # access all observations collected so far\n", - " \"metadata\": metadata,\n", - " \"num_samples\": 3}\n", - " }\n", - ")\n", - "\n", - "# seed controller\n", - "controller.seed(conditions=[seed_conditions])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using ``takewhile``, we can now specify a workflow logic that executes the automated research process 5 times. Accordingly, we stop execution of the ``controller`` as soon as it accumulated 5 models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x14a286160>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x149802940>\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 13.76it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.workflow.base:getting step_name='experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x14ac66790>\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x14ba651f0>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x14ba65a60>\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new CONDITION\n", - "Number of models: 1\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 13.54it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.workflow.base:getting step_name='experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x1493d8f70>\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x14c1aef70>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x14ab13820>\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new CONDITION\n", - "Number of models: 2\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:06<00:00, 14.66it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.workflow.base:getting step_name='experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x14ab13550>\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 3\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x149967d30>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x1496dd820>\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new CONDITION\n", - "Number of models: 3\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 3\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:06<00:00, 16.52it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.workflow.base:getting step_name='experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x149967d30>\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 4\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x14ac66550>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x14a875160>\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new CONDITION\n", - "Number of models: 4\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 4\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:06<00:00, 14.89it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.workflow.base:getting step_name='experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x149967ca0>\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x14a770e50>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x14a770e50>\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new CONDITION\n", - "Number of models: 5\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 14.08it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n" - ] - } - ], - "source": [ - "from itertools import takewhile\n", - "\n", - "continue_criterion = lambda controller: len(controller.state.models) < 6\n", - "\n", - "for step in takewhile(continue_criterion, controller):\n", - " print(f\"Number of models: {len(step.state.models)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Advanced Workflows\n", - "\n", - "In some cases, we may want to condition the sequence of steps taken in the empirical research process on the current state of the process. For instance, one might want to switch from a novelty sampling strategy to a falsification sampling strategy as soon as one has probed enough novel experiment conditions. This section provides a basic introduction to the``BaseController``, which enables the implementation of such arbitrary execution orders.\n", - "\n", - "In this section, we consider a scenario in which we switch experimentalists, depending on the amount of observations collected:\n", - "- If no observations are collected, we sample some seed experimental conditions\n", - "- If less than 7 observations are collected, we sample experimental conditions with ``novelty_sample``\n", - "- If 7 or more observations are collected, we sample experimental conditions with ``falsification_sample``\n", - "\n", - "#### Planner Declaration\n", - "\n", - "We begin with defining an ``experimentalist_planner`` function. Such planner function will be provided as input to the ``BaseController``, and will be used to determine the next step of the workflow, depending on the current state. The code block below implements a planner that selects the experimentalist to be executed depending on the amount of observations collected:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.workflow.planner import last_result_kind_planner\n", - "\n", - "def experimentalist_planner(state):\n", - " # We're going to reuse the \"last_result_kind_planner\" planner, and modify its output.\n", - " proposed_next_step = last_result_kind_planner(state)\n", - "\n", - " # Obtain a list of all observations collected so far\n", - " all_observations = [item for sublist in state.observations for item in sublist]\n", - " num_observations = len(all_observations)\n", - "\n", - " # Determine next experimentalist\n", - " if proposed_next_step == \"experimentalist\":\n", - " if num_observations < 1:\n", - " next_step = \"seed_experimentalist\"\n", - " elif num_observations > 0 and num_observations < 7:\n", - " next_step = \"novelty_experimentalist\"\n", - " else:\n", - " next_step = \"falsification_experimentalist\"\n", - " else:\n", - " next_step = proposed_next_step\n", - "\n", - " print(\"PLANNER: Next step: \" + next_step)\n", - " return next_step" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ``experimentalist_planner`` function accepts a ``controller``'s state as input and returns the next step to be executed. Here, we call the ``last_result_kind_planner`` to obtain the default next step. For instance, according to the autonomous empirical research paradigm, if the last step involved executing the ``\"theorist\"``, the next step would be executing the ``experimentalist``.\n", - "\n", - "If the next default step is the ``experimentalist``, the ``experimentalist_planner`` will select the type of experimentalist based on the total number of collected observations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Executor Collection Declaration\n", - "\n", - "In order for the ``BaseController`` to work with the ``experimentalist_planner``, we need to specify the experimentalists that it selects to be executed. In the next code block, we define all experimentalists by wrapping each of them into a ``Pipeline``. However, at this point, we don't need to provide the respective paramters for each experimentalist–we will provide these later, directly to the ``BaseController`` object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.experimentalist.pipeline import make_pipeline\n", - "\n", - "seed_pipeline = make_pipeline([np.linspace(0, 2*np.pi, 3)])\n", - "novelty_pipeline = make_pipeline([novelty_sample])\n", - "falsification_pipeline = make_pipeline([falsification_sample])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now wrap all elements of our research process–this includes all experimentalists as well as the theorist and experiment runner–into a collection of executors. The following code block defines this collection using ``ChainedFunctionMapping``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.workflow.executor import (ChainedFunctionMapping, from_experimentalist_pipeline,\n", - " from_experiment_runner_callable, from_theorist_estimator)\n", - "\n", - "executor_collection = ChainedFunctionMapping(\n", - " seed_experimentalist=\n", - " [from_experimentalist_pipeline, seed_pipeline],\n", - " novelty_experimentalist=\n", - " [from_experimentalist_pipeline, novelty_pipeline],\n", - " falsification_experimentalist=\n", - " [from_experimentalist_pipeline, falsification_pipeline],\n", - " experiment_runner=[from_experiment_runner_callable, run_experiment],\n", - " theorist=[from_theorist_estimator, theorist_bms],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the ``ChainedFunctionMapping``, we specify each element by its type, followed by its function. For instance, the ``seed_experimentalist`` is defined as an experimentalist pipeline. Thus, we specify it as ``from_experimentalist_pipeline``, and chain it with its respectie function ``seed_experimentalist`` defined above." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Base Controller Declaration\n", - "\n", - "So far, we have defined a ``experimentalist_planner`` function which determines the next step in our workflow. We have also defined a ``executor_collection`` defining each step of the workflow. Both will be provided to a special ``Controller`` called ``BaseController``. The ``BaseController`` does not require us to specify a ``theorist``, ``experimentalist``, or ``experiment_runner``. Instead, we can provide it with an ``executor_collection`` specifying all the elements of the workflow we require.\n", - "\n", - "The ``BaseController`` also requires us to specify an intiial ``state``. Here, we can instantiate a state as a ``History`` object which entails all variables of the experiment (as declared in ``metadata``) along with the parameters provided to each element in the ``executor_collection``. Let's begin with defining the parameters for all elements in the ``executor_collection``. Here, only two of the elements (``novelty_experimentalist`` and ``falsification_experimentalist``) require us to specify additional parameters.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "params = {\"novelty_experimentalist\":\n", - " {\"novelty_sample\":\n", - " {\"condition_pool\": condition_pool,\n", - " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", - " \"num_samples\": 3},\n", - " },\n", - " \"falsification_experimentalist\":\n", - " {\"falsification_sample\":\n", - " {\"condition_pool\": condition_pool,\n", - " \"model\": \"%models[-1]%\", # access last model generated by theorist\n", - " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", - " \"reference_observations\": \"%observations.dvs%\", # access all observations collected so far\n", - " \"metadata\": metadata,\n", - " \"num_samples\": 3}\n", - " }\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the ``metadata`` and ``params``, we can instantiate an initial ``state`` for the workflow." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.workflow.state import History\n", - "\n", - "state = History(variables=metadata, params=params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For convenience, let us also define a monitor function which can print the current total number of observations. We will provide this monitor to the ``BaseController``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def monitor(state):\n", - " all_observations = [item for sublist in state.observations for item in sublist]\n", - " num_observations = len(all_observations)\n", - " print(f\"MONITOR: Number of observations {num_observations}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now have all the required input arguments for the ``BaseController``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.workflow.base import BaseController\n", - "\n", - "# define controller\n", - "controller = BaseController(\n", - " state=state,\n", - " monitor=monitor,\n", - " planner=experimentalist_planner,\n", - " executor_collection=executor_collection,\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's execute the controller for 5 research cycles, measured in terms of the number of generated models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.workflow.base:getting step_name='seed_experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x14a875280>\n", - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x14c1ae5e0>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x14c1ae5e0>\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PLANNER: Next step: seed_experimentalist\n", - "MONITOR: Number of observations 0\n", - "MONITOR: Number of models: 0\n", - "PLANNER: Next step: experiment_runner\n", - "MONITOR: Number of observations 3\n", - "MONITOR: Number of models: 0\n", - "PLANNER: Next step: theorist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:06<00:00, 14.96it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.workflow.base:getting step_name='novelty_experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x14c1ae790>\n", - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x14a875940>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x14b6a05e0>\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 3\n", - "MONITOR: Number of models: 1\n", - "PLANNER: Next step: novelty_experimentalist\n", - "MONITOR: Number of observations 3\n", - "MONITOR: Number of models: 1\n", - "PLANNER: Next step: experiment_runner\n", - "MONITOR: Number of observations 6\n", - "MONITOR: Number of models: 1\n", - "PLANNER: Next step: theorist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:06<00:00, 14.48it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.workflow.base:getting step_name='novelty_experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x1497e4430>\n", - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x14995df70>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x14995df70>\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 6\n", - "MONITOR: Number of models: 2\n", - "PLANNER: Next step: novelty_experimentalist\n", - "MONITOR: Number of observations 6\n", - "MONITOR: Number of models: 2\n", - "PLANNER: Next step: experiment_runner\n", - "MONITOR: Number of observations 9\n", - "MONITOR: Number of models: 2\n", - "PLANNER: Next step: theorist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 13.28it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.workflow.base:getting step_name='falsification_experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x14937e700>\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 9\n", - "MONITOR: Number of models: 3\n", - "PLANNER: Next step: falsification_experimentalist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x1496dd040>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x14937e700>\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 9\n", - "MONITOR: Number of models: 3\n", - "PLANNER: Next step: experiment_runner\n", - "MONITOR: Number of observations 12\n", - "MONITOR: Number of models: 3\n", - "PLANNER: Next step: theorist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:06<00:00, 15.86it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.workflow.base:getting step_name='falsification_experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x14a286040>\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 12\n", - "MONITOR: Number of models: 4\n", - "PLANNER: Next step: falsification_experimentalist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.workflow.base:getting step_name='experiment_runner'\n", - "INFO:autora.workflow.base:running next_function=._executor_experiment_runner at 0x14937e700>\n", - "INFO:autora.workflow.base:getting step_name='theorist'\n", - "INFO:autora.workflow.base:running next_function=._executor_theorist at 0x14937e700>\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 12\n", - "MONITOR: Number of models: 4\n", - "PLANNER: Next step: experiment_runner\n", - "MONITOR: Number of observations 15\n", - "MONITOR: Number of models: 4\n", - "PLANNER: Next step: theorist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:06<00:00, 14.46it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 15\n" - ] - } - ], - "source": [ - "from itertools import takewhile\n", - "\n", - "continue_criterion = lambda controller: len(controller.state.models) < 5\n", - "\n", - "for step in takewhile(continue_criterion, controller):\n", - " print(f\"MONITOR: Number of models: {len(step.state.models)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can observe that the controller begins with sampling experiment condition using the ``seed_experimentalist``. It then proceeds to sample condition using the ``novelty_experimentalist`` until it has collected 7 or more observations, at which it switches to the ``falsification_experimentalist``." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Customizing Automated Empirical Research Components\n", - "\n", - "``autora`` is a flexible framework in which users can integrate their own theorists, experimentalists, and experiment_runners in a automated empirical research workflow. This section illustrates the integration of custom theorists and experimentalists. For more information on how to contribute your own modules to the ``autora`` ecosystem, please refer to the [Contributor Documentation](https://autoresearch.github.io/autora/contribute/modules/).\n", - "\n", - "To illustrate the use of custom theorists and experimentalists, we consider a simple workflow introduced above:\n", - "1. Generate 3 seed experimental conditions\n", - "2. Iterate through the following steps\n", - " - Collect observations using ``run_experiment``\n", - " - Identify a model relating conditions to observations using ``theorist_bms``\n", - " - Identify 3 new experimental conditions using ``falsification_sample``" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# generate initial pool of 3 experimental conditions\n", - "seed_conditions = np.linspace(0,2*np.pi,3)\n", - "\n", - "params = {\n", - " \"experimentalist\":\n", - " {\"condition_pool\": condition_pool,\n", - " \"model\": \"%models[-1]%\", # access last model generated by theorist\n", - " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", - " \"reference_observations\": \"%observations.dvs%\", # access all observations collected so far\n", - " \"metadata\": metadata,\n", - " \"num_samples\": 3}\n", - " }\n", - "\n", - "# define controller\n", - "controller = Controller(\n", - " monitor=monitor,\n", - " variables=metadata,\n", - " experimentalist=falsification_sample,\n", - " experiment_runner=run_experiment,\n", - " theorist=theorist_bms,\n", - " params=params,\n", - ")\n", - "\n", - "# seed controller\n", - "controller.seed(conditions=[seed_conditions])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Custom Theorists\n", - "\n", - "What if we wanted to replace the ``theorist_bms`` with a custom theorist?\n", - "\n", - "We can implement our theorist as a class that inherits from `sklearn.base.BaseEstimator`. The class must implement the following methods:\n", - "\n", - "- `fit(self, conditions, observations)`\n", - "- `predict(self, conditions)`\n", - "\n", - "The follwing code block implements such a theorist that fits a polynomial of a specified degree." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "Example Theorist\n", - "\"\"\"\n", - "\n", - "import numpy as np\n", - "from sklearn.base import BaseEstimator\n", - "\n", - "\n", - "class PolynomialRegressor(BaseEstimator):\n", - " \"\"\"\n", - " This theorist fits a polynomial function to the data.\n", - " \"\"\"\n", - "\n", - " def __init__(self, degree: int = 3):\n", - " self.degree = degree\n", - "\n", - " def fit(self, conditions, observations):\n", - "\n", - " # polyfit expects a 1D array\n", - " if conditions.ndim > 1:\n", - " conditions = conditions.flatten()\n", - "\n", - " if observations.ndim > 1:\n", - " observations = observations.flatten()\n", - "\n", - " # fit polynomial\n", - " self.coeff = np.polyfit(conditions, observations, 2)\n", - " self.polynomial = np.poly1d(self.coeff)\n", - " pass\n", - "\n", - " def predict(self, conditions):\n", - " return self.polynomial(conditions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now assign the theorist to a new controller." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "theorist_poly = PolynomialRegressor(degree = 3)\n", - "\n", - "# define controller\n", - "controller_with_polynomial_theorist = Controller(\n", - " monitor=monitor,\n", - " variables=metadata,\n", - " experimentalist=falsification_sample,\n", - " experiment_runner=run_experiment,\n", - " theorist=theorist_poly,\n", - " params=params,\n", - ")\n", - "\n", - "# seed controller\n", - "controller_with_polynomial_theorist.seed(conditions=[seed_conditions])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's run the controller with the new theorist for 3 research cycles, defined by the number of models generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.workflow.base:getting step_name='experimentalist'\n", - "INFO:autora.workflow.base:running next_function=._executor_experimentalist at 0x14a875550>\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 15\n" - ] - } - ], - "source": [ - "from itertools import takewhile\n", - "\n", - "continue_criterion = lambda controller: len(controller.state.models) < 5\n", - "\n", - "for step in takewhile(continue_criterion, controller_with_polynomial_theorist):\n", - " print(f\"MONITOR: Number of models: {len(step.state.models)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can plot the last model identified by our custom theorist against the ground truth." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "last_polynomial_model = controller_with_polynomial_theorist.state.models[-1]\n", - "\n", - "predicted_observations_polynomial = last_polynomial_model.predict(condition_pool)\n", - "\n", - "# plot model predictions against ground-truth\n", - "import matplotlib.pyplot as plt\n", - "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", - "plt.plot(condition_pool, predicted_observations_polynomial, label='Polynomial Fit')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('Model Predictions')\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Custom Experimentalists\n", - "\n", - "We can also implement custom experimentalists. Experimentalists are generally implemented as functions that can be integrated into an\n", - "[Experimentalist Pipeline](https://autoresearch.github.io/autora/core/docs/pipeline/Experimentalist%20Pipeline%20Examples/). For instance, an experimentalist sampler function expects a pool of experimental conditions–typically passed as a 2D numpy array named ``condition_pool``–and returns a modified set of experimental conditions.\n", - "\n", - "The following code block implements a basic experimentalist that considers two models, and identifies experimental conditions for which the two models differ most in their predictions. This is a special case of the [Model Disagreement Sampler](https://autoresearch.github.io/autora/user-guide/experimentalists/samplers/model-disagreement/)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def model_disagreement_sample(condition_pool, model_a, model_b, num_samples = 1):\n", - "\n", - " # get predictions from both models\n", - " prediction_a = model_a.predict(condition_pool)\n", - " prediction_b = model_b.predict(condition_pool)\n", - "\n", - " # compute mean squared distance between predictions\n", - " disagreement = np.mean((prediction_a - prediction_b) ** 2, axis=1)\n", - "\n", - " # sort the summed disagreements and select the top n\n", - " selected_conditions_idx = (-disagreement).argsort()[:num_samples]\n", - "\n", - " return condition_pool[selected_conditions_idx]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can illustrate our new experimentalist sampler by fitting two different theorists to an initial set of conditions and observations. Here, we consider the BMS theorist and our custom polynomial theorist from above. We then sample 3 experimental conditions using our new experimentalist ``model_disagreement_sample``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:10<00:00, 9.61it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - } - ], - "source": [ - "# fit two theorists\n", - "theorist_bms.fit(initial_conditions, initial_observations)\n", - "theorist_poly.fit(initial_conditions, initial_observations)\n", - "\n", - "# sample experimental conditions with our custom experimentalist sampler function\n", - "selected_conditions = model_disagreement_sample(condition_pool,\n", - " theorist_bms,\n", - " theorist_poly,\n", - " num_samples = 4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After fitting both theorists, we can compare their predictions across the entire pool of experimental conditions. We will add the sampled experimental conditions to the plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot model predictions against ground-truth\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# obtain predictions for both theorists\n", - "predicted_observations_bms = theorist_bms.predict(condition_pool)\n", - "predicted_observations_poly = theorist_poly.predict(condition_pool)\n", - "\n", - "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", - "plt.plot(condition_pool, predicted_observations_bms, label='Predictions of BMS Theorist')\n", - "plt.plot(condition_pool, predicted_observations_poly, label='Predictions of Polynomial Theorist')\n", - "\n", - "y_min = -2.5\n", - "y_max = 1\n", - "\n", - "# plot conditions obtained by novelty sampler\n", - "for idx, condition in enumerate(selected_conditions):\n", - " if idx == 0:\n", - " plt.plot([condition[0], condition[0]],\n", - " [y_min, y_max],\n", - " '--r', label='selected conditions')\n", - " else: # we want to omit the label for all other conditions\n", - " plt.plot()\n", - "\n", - "\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('Model Disagreement')\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can integrate our custom experimentalist and theorist into a closed-loop empirical research workflow, e.g., using basic loop constructs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.89it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss of BMS theorist in cycle 0: 1.2163627818326361\n", - "Loss of polynomial theorist in cycle 0: 20.625978925891836\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.87it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss of BMS theorist in cycle 1: 0.0\n", - "Loss of polynomial theorist in cycle 1: 0.5257924709018393\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:08<00:00, 12.09it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss of BMS theorist in cycle 2: 0.0\n", - "Loss of polynomial theorist in cycle 2: 0.8453161612839792\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:09<00:00, 10.20it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss of BMS theorist in cycle 3: 0.0\n", - "Loss of polynomial theorist in cycle 3: 0.2918752703795088\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.57it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss of BMS theorist in cycle 4: 0.5358783173609328\n", - "Loss of polynomial theorist in cycle 4: 0.2796160348658682\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "num_cycles = 5 # number of empirical research cycles\n", - "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", - "\n", - "# generate an initial set experimental conditions\n", - "conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", - " n=measurements_per_cycle)\n", - "# convert iterator into 2-dimensional numpy array\n", - "conditions = np.array(list(conditions)).reshape(-1, 1)\n", - "\n", - "# collect initial set of observations\n", - "observations = run_experiment(conditions)\n", - "\n", - "for cycle in range(num_cycles):\n", - "\n", - " # use BMS theorist and custom polynomial theorist to fit the model to the data\n", - " theorist_bms.fit(conditions, observations)\n", - " theorist_poly.fit(conditions, observations)\n", - "\n", - " # obtain new conditions from custrom experimentalist sampler\n", - " new_conditions = model_disagreement_sample(condition_pool,\n", - " theorist_bms,\n", - " theorist_poly,\n", - " num_samples = 3)\n", - "\n", - " # obtain new observations\n", - " new_observations = run_experiment(new_conditions)\n", - "\n", - " # combine old and new conditions and observations\n", - " conditions = np.concatenate((conditions, new_conditions))\n", - " observations = np.concatenate((observations, new_observations))\n", - "\n", - " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", - " loss_bms = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", - " loss_poly = np.mean(np.square(theorist_poly.predict(condition_pool) - ground_truth(condition_pool)))\n", - " print(\"Loss of BMS theorist in cycle {}: {}\".format(cycle, loss_bms))\n", - " print(\"Loss of polynomial theorist in cycle {}: {}\".format(cycle, loss_poly))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Help\n", - "We hope that this tutorial helped demonstrate the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments. We encourage you to explore other [tutorials](https://autoresearch.github.io/autora/tutorials/) and check out the [documentation](https://autoresearch.github.io/).\n", - "\n", - "If you encounter any issues, bugs, or questions, please reach out to us through the [AutoRA Forum](https://github.com/orgs/AutoResearch/discussions). Feel free to report any bugs by [creating an issue in the AutoRA repository](https://github.com/AutoResearch/autora/issues).\n", - "\n", - "You may also post questions directly into the [User Q&A Section](https://github.com/orgs/AutoResearch/discussions/categories/using-autora).\n" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From 5e2a1feb516ea9e02869b3295889c0bc807df3fe Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Wed, 5 Jul 2023 08:00:52 -0700 Subject: [PATCH 02/32] Completed tutorials --- .../Tutorial-I-Components.ipynb | 51 +- .../Tutorial-II-Loop-Constructs.ipynb | 430 ++++++ .../Tutorial-III-Workflow-Logic.ipynb | 1266 +++++++++++++++++ .../Tutorial-IV-Customization.ipynb | 710 +++++++++ 4 files changed, 2432 insertions(+), 25 deletions(-) create mode 100644 docs/tutorials/basic tutorial/Tutorial-II-Loop-Constructs.ipynb create mode 100644 docs/tutorials/basic tutorial/Tutorial-III-Workflow-Logic.ipynb create mode 100644 docs/tutorials/basic tutorial/Tutorial-IV-Customization.ipynb diff --git a/docs/tutorials/Basic Tutorial/Tutorial-I-Components.ipynb b/docs/tutorials/Basic Tutorial/Tutorial-I-Components.ipynb index b6c9a6452..64db4c442 100644 --- a/docs/tutorials/Basic Tutorial/Tutorial-I-Components.ipynb +++ b/docs/tutorials/Basic Tutorial/Tutorial-I-Components.ipynb @@ -25,7 +25,7 @@ "\n", "[AutoRA Basic Tutorial I: Components](www.addlink.com)
\n", "[AutoRA Basic Tutorial II: Loop Constructs](www.addlink.com)
\n", - "[AutoRA Basic Tutorial III: Workflow](www.addlink.com)
\n", + "[AutoRA Basic Tutorial III: Workflow Logic](www.addlink.com)
\n", "[AutoRA Basic Tutorial IV: Customization](www.addlink.com)
\n", "\n", "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": null, "metadata": { "id": "2S9mfSxVUYM3" }, @@ -91,7 +91,7 @@ "id": "tJNNbhskVMNq", "outputId": "a54371dd-79ab-4849-abdc-4862bd116e94" }, - "execution_count": 93, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -177,12 +177,12 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 507 + "height": 508 }, "id": "8P3iMrqN-pOU", "outputId": "92dede11-65c8-423d-b8a5-ac0b6191404c" }, - "execution_count": 125, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -285,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "metadata": { "id": "DM9HkPNqUYM5" }, @@ -306,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "metadata": { "id": "cjnQjoANUYM6" }, @@ -328,11 +328,11 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 489 + "height": 490 }, "id": "epUuwg8rUYM6", "outputId": "590bf1d8-7c66-468e-982a-55a097dba006" @@ -384,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": null, "metadata": { "id": "feMzX1JfUYM7" }, @@ -467,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": null, "metadata": { "id": "-ICqZZikUYM8" }, @@ -491,11 +491,11 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 144 + "height": 147 }, "id": "5o0fJnXiUYM9", "outputId": "01e843d0-5049-488f-8d8c-5f3bcc2f93ad" @@ -553,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -585,7 +585,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": null, "metadata": { "id": "cSB2gLmPUYM-" }, @@ -610,11 +610,11 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 489 + "height": 490 }, "id": "TUpwLukrUYM-", "outputId": "a09cca9f-a140-4b23-e70c-aa1c80807d3c" @@ -711,7 +711,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": null, "metadata": { "id": "qJDNd9F3UYM_" }, @@ -732,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "metadata": { "id": "prol6MweUYM_" }, @@ -754,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -800,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -854,7 +854,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -895,7 +895,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -946,11 +946,11 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 489 + "height": 490 }, "id": "3skhpCOSUYNK", "outputId": "ecdb0936-1a8f-4970-d7f0-a12d060d42c3" @@ -1154,6 +1154,7 @@ "cell_type": "markdown", "source": [ "# Next Notebook\n", + "After defining all the components required for the empirical research process, we can create an automated workflow using basic loop constructs. The next notebook illustrates the use of these loop constructs.\n", "\n", "Follow this link for the next notebook tutorial:\n", "[AutoRA Basic Tutorial II: Loop Constructs](www.addlink.com)
" diff --git a/docs/tutorials/basic tutorial/Tutorial-II-Loop-Constructs.ipynb b/docs/tutorials/basic tutorial/Tutorial-II-Loop-Constructs.ipynb new file mode 100644 index 000000000..96551199d --- /dev/null +++ b/docs/tutorials/basic tutorial/Tutorial-II-Loop-Constructs.ipynb @@ -0,0 +1,430 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "_q7iLq3GUYMz" + }, + "source": [ + "# Introduction\n", + "## Basic Tutorial II: Loop Constructs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + }, + "id": "5mfUKtGTUYM1" + }, + "source": [ + "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", + "\n", + "This notebook is the second of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", + "\n", + "[AutoRA Basic Tutorial I: Components](www.addlink.com)
\n", + "[AutoRA Basic Tutorial II: Loop Constructs](www.addlink.com)
\n", + "[AutoRA Basic Tutorial III: Workflow Logic](www.addlink.com)
\n", + "[AutoRA Basic Tutorial IV: Customization](www.addlink.com)
\n", + "\n", + "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", + "\n", + "**How to use this notebook** *You can progress through the notebook section by section or directly navigate to specific sections. If you choose the latter, it is recommended to execute all cells in the notebook initially, allowing you to easily rerun the cells in each section later without issues.*" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Tutorial Setup\n", + "This tutorial is self-contained so that you do not need to run the previous notebook to begin. However, the four notebooks are continuous so that what we define in a previous notebook should still exist within this notebook. As such, we will here re-run relevant code from past tutorials. We will not again walk you through these, but if you need a reminder what they are then go see the descriptions in previous notebooks." + ], + "metadata": { + "id": "7bD8W7cfhZ5n" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2S9mfSxVUYM3" + }, + "outputs": [], + "source": [ + "#### Installation ####\n", + "!pip install -q \"autora[experimentalist-falsification]\"\n", + "!pip install -q \"autora[experimentalist-sampler-model-disagreement]\"\n", + "!pip install -q \"autora[theorist-bms]\"\n", + "\n", + "#### Import modules ####\n", + "import numpy as np\n", + "import torch\n", + "from autora.variable import DV, IV, ValueType, VariableCollection\n", + "from autora.experimentalist.pooler.random_pooler import random_pool\n", + "from autora.experimentalist.sampler.falsification import falsification_sample\n", + "from autora.experimentalist.sampler.model_disagreement import model_disagreement_sample\n", + "from autora.theorist.bms import BMSRegressor\n", + "from sklearn import linear_model\n", + "\n", + "#### Set seeds ####\n", + "np.random.seed(42)\n", + "torch.manual_seed(42)\n", + "\n", + "#### Define ground truth and experiment runner ####\n", + "ground_truth = lambda x: np.sin(x)\n", + "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape)\n", + "\n", + "#### Define condition pool ####\n", + "condition_pool = np.linspace(0, 2 * np.pi, 100)\n", + "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", + "\n", + "#### Define metadata ####\n", + "iv = IV(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=condition_pool)\n", + "dv = DV(name=\"y\", type=ValueType.REAL)\n", + "metadata = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "#### Define theorists ####\n", + "theorist_lr = linear_model.LinearRegression()\n", + "theorist_bms = BMSRegressor(epochs=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0ZISUerxUYNL" + }, + "source": [ + "# Loop Constructs\n", + "After defining all the components required for the empirical research process, we can create an automated workflow using basic loop constructs in Python.\n", + "\n", + "The following code block demonstrates how to build such a workflow using the components introduced in the preceding notebook, such as\n", + "\n", + "- ``metadata`` (object specifying variables of the experiment),
\n", + "- ``run_experiment`` (function for collecting data),
\n", + "- ``theorist_bms`` (scikit learn estimator for discoverying equations using the Bayesian Machine Scientist),
\n", + "- ``random_pool`` (function for generating a random pool of experimental conditions), and
\n", + "- ``falsification_sample`` (function for identifying novel experiment conditions using the falsification .sampler)
\n", + "\n", + "We begin with implementing the following workflow:\n", + "1. Generate 3 seed experimental conditions using ``random_pool``\n", + "2. Generate 3 seed observations using ``run_experiment``\n", + "3. Loop through the following steps 5 times\n", + " - Identify a model relating conditions to observations using ``theorist_bms``\n", + " - Identify 3 new experimental conditions using ``falsification_sample``\n", + " - Collect 3 new observations using ``run_experiment``\n", + " - Add new conditions and observations to the dataset\n", + "\n", + "We will here begin using the naming convention ``cycle`` to refer to an entire AutoRA loop where the loop encounters all AutoRA components - experiment runner, theorist, experimentalist. Within the scientific method, a cycle would then be running a single iteration of the experiment. This requires the collection of data, the modelling of that data, and the conceptualization of the next iteration of this experiment. For example, if our research concerns how much information a person acquires from a photo (dependent variable) dependent on how bright the photo is (independent variable), we may first collect data with conditions of (let's say) 10%, 50%, and 90% brightness, then model our collected data to determine the relationship between brightness and photo perception, and finally determine which other brightness conditions may help us understand the true relationship. Probing other conditions - such as a brightness of 25% and of 75% would then be the next iteration of the experiment and thus, for us, the next cycle. The following code block will iterate through five of these cycles." + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Example 1: Falsification Sampler" + ], + "metadata": { + "id": "kO_HTQMPm7LQ" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PiDfcDVNUYNL", + "outputId": "278b7307-51f8-40c0-c1e3-025dffdf3c4f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:26<00:00, 3.84it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss in cycle 0: 0.0\n", + "Discovered Model: sin(X0)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:28<00:00, 3.56it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss in cycle 1: 0.0\n", + "Discovered Model: sin(X0)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:14<00:00, 7.10it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss in cycle 2: 0.5526484578348648\n", + "Discovered Model: _a0_\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:17<00:00, 5.60it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss in cycle 3: 0.0\n", + "Discovered Model: sin(X0)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:16<00:00, 6.25it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss in cycle 4: 0.0\n", + "Discovered Model: sin(X0)\n" + ] + } + ], + "source": [ + "num_cycles = 5 # number of empirical research cycles\n", + "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", + "\n", + "# generate an initial set of experimental conditions\n", + "conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", + " n=measurements_per_cycle)\n", + "\n", + "# convert iterator into 2-dimensional numpy array\n", + "conditions = np.array(list(conditions)).reshape(-1, 1)\n", + "\n", + "# collect initial set of observations\n", + "observations = run_experiment(conditions)\n", + "\n", + "for cycle in range(num_cycles):\n", + "\n", + " # use BMS theorist to fit the model to the data\n", + " theorist_bms.fit(conditions, observations)\n", + "\n", + " # obtain new conditions\n", + " new_conditions = falsification_sample(\n", + " condition_pool=condition_pool,\n", + " model=theorist_bms,\n", + " reference_conditions=conditions,\n", + " reference_observations=observations,\n", + " metadata=metadata,\n", + " num_samples=measurements_per_cycle,\n", + " )\n", + "\n", + " # obtain new observations\n", + " new_observations = run_experiment(new_conditions)\n", + "\n", + " # combine old and new conditions and observations\n", + " conditions = np.concatenate((conditions, new_conditions))\n", + " observations = np.concatenate((observations, new_observations))\n", + "\n", + " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", + " loss = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", + " print(\"Loss in cycle {}: {}\".format(cycle, loss))\n", + " print(\"Discovered Model: \" + theorist_bms.repr())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B0lVQWuCUYNL" + }, + "source": [ + "## Example 2: Model Disagreement Sampler\n", + "We can easily replace components in the workflow above. For instance, we could replace ``falsification_sample`` with the ``experimentalist_pipeline`` defined in Tutorial I.\n", + "\n", + "In the following code block, we add a linear regression theorist, to fit a linear model to the data. In addition, we replace ``falsification_sample`` with ``model_disagreement_sample`` to sample experimental conditions that differentiate most between the linear model and the model discovered by the BMS theorist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NXd-ON5uUYNL", + "outputId": "b5a7d8d7-f3e9-419c-9f52-618d713641bd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:40<00:00, 2.47it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss in cycle 0: 0.0\n", + "Discovered BMS Model: sin(X0)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:18<00:00, 5.53it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss in cycle 1: 0.0\n", + "Discovered BMS Model: sin(X0)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:19<00:00, 5.16it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss in cycle 2: 0.0\n", + "Discovered BMS Model: sin(X0)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:15<00:00, 6.37it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss in cycle 3: 0.0\n", + "Discovered BMS Model: sin(X0)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:24<00:00, 4.01it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss in cycle 4: 0.0\n", + "Discovered BMS Model: sin(X0)\n" + ] + } + ], + "source": [ + "num_cycles = 5 # number of empirical research cycles\n", + "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", + "\n", + "# generate an initial set of experimental conditions\n", + "conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", + " n=measurements_per_cycle)\n", + "# convert iterator into 2-dimensional numpy array\n", + "conditions = np.array(list(conditions)).reshape(-1, 1)\n", + "\n", + "# collect initial set of observations\n", + "observations = run_experiment(conditions)\n", + "\n", + "for cycle in range(num_cycles):\n", + "\n", + " # use BMS theorist to fit the model to the data\n", + " theorist_bms.fit(conditions, observations)\n", + " theorist_lr.fit(conditions, observations)\n", + "\n", + " # obtain new conditions\n", + " new_conditions = model_disagreement_sample(\n", + " condition_pool,\n", + " models = [theorist_bms, theorist_lr],\n", + " num_samples = measurements_per_cycle\n", + " )\n", + "\n", + " # obtain new observations\n", + " new_observations = run_experiment(new_conditions)\n", + "\n", + " # combine old and new conditions and observations\n", + " conditions = np.concatenate((conditions, new_conditions))\n", + " observations = np.concatenate((observations, new_observations))\n", + "\n", + " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", + " loss = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", + " print(\"Loss in cycle {}: {}\".format(cycle, loss))\n", + " print(\"Discovered BMS Model: \" + theorist_bms.model_.__repr__())\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Next Notebook\n", + "While the basic loop construct is flexible, there are more convenient ways to specify a research cycle in ``autora``. The next notebook illustrates the use of these constructs.\n", + "\n", + "Follow this link for the next notebook tutorial:\n", + "[AutoRA Basic Tutorial III: Workflow Logic](www.addlink.com)
" + ], + "metadata": { + "id": "NZgqanLagOv_" + } + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/tutorials/basic tutorial/Tutorial-III-Workflow-Logic.ipynb b/docs/tutorials/basic tutorial/Tutorial-III-Workflow-Logic.ipynb new file mode 100644 index 000000000..5f4697aff --- /dev/null +++ b/docs/tutorials/basic tutorial/Tutorial-III-Workflow-Logic.ipynb @@ -0,0 +1,1266 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "_q7iLq3GUYMz" + }, + "source": [ + "# Introduction\n", + "## Basic Tutorial III: Workflow Logic" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + }, + "id": "5mfUKtGTUYM1" + }, + "source": [ + "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", + "\n", + "This notebook is the third of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", + "\n", + "[AutoRA Basic Tutorial I: Components](www.addlink.com)
\n", + "[AutoRA Basic Tutorial II: Loop Constructs](www.addlink.com)
\n", + "[AutoRA Basic Tutorial III: Workflow Logic](www.addlink.com)
\n", + "[AutoRA Basic Tutorial IV: Customization](www.addlink.com)
\n", + "\n", + "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", + "\n", + "**How to use this notebook** *You can progress through the notebook section by section or directly navigate to specific sections. If you choose the latter, it is recommended to execute all cells in the notebook initially, allowing you to easily rerun the cells in each section later without issues.*" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Tutorial Setup\n", + "This tutorial is self-contained so that you do not need to run the previous notebook to begin. However, the four notebooks are continuous so that what we define in a previous notebook should still exist within this notebook. As such, we will here re-run relevant code from past tutorials. We will not again walk you through these, but if you need a reminder what they are then go see the descriptions in previous notebooks." + ], + "metadata": { + "id": "BuCna7-ytMBB" + } + }, + { + "cell_type": "code", + "source": [ + "#### Installation ####\n", + "!pip install -q \"autora[experimentalist-falsification]\"\n", + "!pip install -q \"autora[experimentalist-sampler-novelty]\"\n", + "!pip install -q \"autora[theorist-bms]\"\n", + "\n", + "#### Import modules ####\n", + "import numpy as np\n", + "import torch\n", + "from autora.variable import DV, IV, ValueType, VariableCollection\n", + "from autora.experimentalist.pooler.grid import grid_pool\n", + "from autora.experimentalist.sampler.falsification import falsification_sample\n", + "from autora.experimentalist.sampler.novelty import novelty_sample\n", + "from autora.theorist.bms import BMSRegressor\n", + "\n", + "#### Set seeds ####\n", + "np.random.seed(42)\n", + "torch.manual_seed(42)\n", + "\n", + "#### Define ground truth and experiment runner ####\n", + "ground_truth = lambda x: np.sin(x)\n", + "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape)\n", + "\n", + "#### Define condition pool ####\n", + "condition_pool = np.linspace(0, 2 * np.pi, 10)\n", + "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", + "\n", + "#### Define metadata ####\n", + "iv = IV(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 10))\n", + "dv = DV(name=\"y\", type=ValueType.REAL)\n", + "metadata = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "#### Define theorists ####\n", + "theorist_bms = BMSRegressor(epochs=100)" + ], + "metadata": { + "id": "RuCZVkP7tM6L" + }, + "execution_count": 28, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fjJRnOXhUYNM" + }, + "source": [ + "# Workflow Logic\n", + "\n", + "Workflows in ``autora`` implement the *autonomous empirical research paradigm*. This paradigm centers around the dynamic interplay between automated theorists and automated experimentalists. As outlined above, theorists rely–among other things–on existing data to construct computational models by linking experimental conditions to dependent measures. Experimentalists design follow-up experiments to refine and validate models generated by the theorist. Together, these agents enable a closed-loop scientific discovery process.\n", + "\n", + "The following sections introduce ways of specifying workflows directly in ``autora``. For more information on workflows, please refer to the [corresponding documentation](https://autoresearch.github.io/autora/user-guide/workflow/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lWV6x-oUUYNM" + }, + "source": [ + "## Basic Workflows\n", + "\n", + "This section provides an introduction to handling workflows with the controller object. Here, we focus on workflows implementing the **default execution order**: (1) generate experiment conditions using the ``eperimentalist``, (2) collect observations using the ``experiment_runner``, and (3), generate a model that links experiment conditions to observations using the ``theorist``.\n", + "\n", + "We begin with implementing the following workflow:\n", + "1. Generate seed experimental conditions\n", + "2. Iterate 5 times through the following steps\n", + " - Collect observations using ``run_experiment``\n", + " - Identify a model relating conditions to observations using ``theorist_bms``\n", + " - Identify 3 new experimental conditions using ``falsification_sample``\n", + "\n", + "### Declaration\n", + "\n", + "We begin with defining a simple workflow. Workflows can be encapsulated in a ``Controller`` object. For instance, the following code block sets up a closed-loop cycle between (1) a grid pooler for sampling experimental conditions, (2) an experiment runner for obtaining respective observations, and (3) a BMS theorist for discoverying an equation relating experimental conditions to observations.\n", + "\n", + "As with pipelines, we can pass the ``Controller`` object static parameters for each component. In this case, we provide the grid experimentalist with information about the independent variables to sample.\n", + "\n", + "**Note**: *We haven't included the ``falsification_sample`` experimentalist into our workflow yet because it requires us to specify state-dependent input arguments (e.g., the model generated by the theorist), which we will cover at the end of this section.*" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "crMFLnTgUYNM" + }, + "outputs": [], + "source": [ + "from autora.workflow import Controller\n", + "\n", + "controller = Controller(\n", + " variables=metadata,\n", + " experimentalist=grid_pool,\n", + " experiment_runner=run_experiment,\n", + " theorist=theorist_bms,\n", + " params={\n", + " \"experimentalist\":\n", + " {\"ivs\": metadata.independent_variables}\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p1ZHI56hUYNM" + }, + "source": [ + "In the declaration of the ``params`` parameter, we first specify the type of the component we seek to parameterize as a dictionary key, e.g., ``\"experimentalist\"``. Then we nest within it, another dictionary with the input arguments to the respective component as keys (e.g., ``\"ivs\"`` is an input argument to the ``grid_pool`` experimentalist) along with their values (e.g., ``metadata.independent_variables``).\n", + "\n", + "### Monitoring\n", + "\n", + "Before we execute the controller, lets also add a **monitor function** which is executed with every autonomous empirical research step. The following code block prints the last generated result of the workflow defined by the controller. All workflow results are stored in the ``state.history`` object. We can access the kind of the latest result using ``state.history[-1].kind``." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "AyKM11z6UYNN" + }, + "outputs": [], + "source": [ + "# define monitor function\n", + "def monitor(state):\n", + " print(f\"MONITOR: Generated new {state.history[-1].kind}\")\n", + "\n", + "# add monitor function to controller\n", + "controller.monitor = monitor" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QWhI8SvuUYNN" + }, + "source": [ + "### Execution\n", + "\n", + "The controller is defined as an iterator. We can execute a single step in the workflow by passing the ``controller`` object to the ``next()`` method. The following code block executes three steps of the default research cycle." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "wIwF6i70UYNN", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b0fbf84c-85ac-4f89-c9c8-705c4a5a4e22" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new CONDITION\n", + "MONITOR: Generated new OBSERVATION\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:21<00:00, 4.66it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new MODEL\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 31 + } + ], + "source": [ + "next(controller)\n", + "next(controller)\n", + "next(controller)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0OJ8WdFlUYNN" + }, + "source": [ + "As indicated by the monitor, the **default execution order** is as follows: (1) generate experiment conditions, (2) collect observations, and (3), generate a model. After executing step (3), the controller would then continue with step (1):" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "FZXvTUG2UYNN", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "848dcef6-cff4-4f1b-a781-2db960f58f06" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new CONDITION\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 32 + } + ], + "source": [ + "next(controller)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r7gneVACUYNN" + }, + "source": [ + "Since ``controller`` is an iterator, we can use [itertools](https://docs.python.org/3/library/itertools.html) for efficient looping. The following example uses ``takewhile`` to define a loop that stops as soon as we obtained three models from the theorist.\n", + "\n", + "We begin with defining a lambda function which returns true whenever the controller has less then 5 models. As explained in the next subsection, we can obtain a list of generated models by accessing the controller's state via ``controller.state.models``." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "id": "Hanf3vpEUYNO" + }, + "outputs": [], + "source": [ + "continue_criterion = lambda controller: len(controller.state.models) < 5" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "krPjzb8hUYNO" + }, + "source": [ + "Now we can run a for-loop using the ``controller`` as an iterator, and ``takewhile`` as iterator logic that continues to execute steps of the controller as long as ``continue_criterion`` returns ``True``. In this way, we can execute 5 research cycles." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "id": "8FFj4NFLUYNO", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "15ee6b27-ebbe-450b-c99b-8a1e1eef66a5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 1\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:20<00:00, 5.00it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 2\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 2\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 2\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:11<00:00, 9.04it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 3\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 3\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 3\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:11<00:00, 8.92it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 4\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 4\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 4\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:11<00:00, 8.92it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new MODEL\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ], + "source": [ + "from itertools import takewhile\n", + "\n", + "for step in takewhile(continue_criterion, controller):\n", + " print(f\"Number of models: {len(step.state.models)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ipgISi2QUYNO" + }, + "source": [ + "### Result Inspection\n", + "\n", + "After each executed step, we can observe the result generated by the ``controller``. All results are stored in in ``controller.state.history``. Each result is composed of a value specifying its ``kind`` (``CONDITION``, ``OBSERVATION``, or ``MODEL``) and the respective ``data``.\n", + "\n", + "We can obtain the observations collected in the last step of the workflow as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "id": "xaigjyMpUYNO", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6315b7ee-c334-4702-c6e1-c0f2d5480317" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "ResultKind.MODEL\n", + "BMSRegressor(epochs=100)\n" + ] + } + ], + "source": [ + "result = controller.state.history[-1]\n", + "\n", + "print(result.kind)\n", + "print(result.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p3mLxYfyUYNO" + }, + "source": [ + "We can also specify the kind of result we are looking for directly. For instance, we can obtain all models generated by the theorist using ``controller.state.models``. The following code block prints the last model discovered by the BMS theorist (note that ``repr()`` is a function specific to the BMS theorist which returns its model as a string)." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "id": "DEoLhXo6UYNO", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a8205c81-3052-4a01-db3c-bbc7d03fcdf5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "sin(X0)\n" + ] + } + ], + "source": [ + "print(controller.state.models[-1].repr())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XT3kZJJmUYNP" + }, + "source": [ + "Alternatively, we can access probed experimental conditions via ``controller.state.conditions`` and observations via ``controller.state.observations``, respectively. The following code block requests the latest experimental conditions identified by the experimentalist and the corresponding observations collected by the experiment runner" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "id": "oQu4pF_yUYNP", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "846cdb8d-f07a-4ca8-f878-6c06cbfec54a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Conditions:\n", + "[[0. ]\n", + " [0.6981317 ]\n", + " [1.3962634 ]\n", + " [2.0943951 ]\n", + " [2.7925268 ]\n", + " [3.4906585 ]\n", + " [4.1887902 ]\n", + " [4.88692191]\n", + " [5.58505361]\n", + " [6.28318531]]\n", + "Observations:\n", + "[[ 0. 0.07384666]\n", + " [ 0.6981317 0.65992444]\n", + " [ 1.3962634 0.97324292]\n", + " [ 2.0943951 0.83591503]\n", + " [ 2.7925268 0.19416794]\n", + " [ 3.4906585 -0.41400456]\n", + " [ 4.1887902 -0.91208928]\n", + " [ 4.88692191 -0.87909553]\n", + " [ 5.58505361 -0.60842578]\n", + " [ 6.28318531 -0.17630402]]\n" + ] + } + ], + "source": [ + "print(f\"Conditions:\\n{controller.state.conditions[-1]}\")\n", + "print(f\"Observations:\\n{controller.state.observations[-1]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aabSIf3fUYNP" + }, + "source": [ + "### Seeding\n", + "\n", + "The default execution order always begins with an experimentalist. This is problematic if we want to use an experimentalist that depends on prior steps (e.g., the falsification experimentalist requires a model generated by the theorist). We can circumvent this problem by seeding the controller with experiment conditons.\n", + "\n", + "The following code block seeds the controller with 3 experiment conditions. We first generate the ``seed_conditions``, and then pass them, encapsulated in a list, to the ``seed`` function of the ``controller`` object." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "id": "jyF6yXDCUYNP", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "97bb5399-ff3e-49ed-aec1-3783551dd994" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new OBSERVATION\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 38 + } + ], + "source": [ + "# generate initial pool of 3 experimental conditions\n", + "seed_conditions = np.linspace(0,2*np.pi,3)\n", + "\n", + "# define controller\n", + "controller = Controller(\n", + " monitor=monitor,\n", + " variables=metadata,\n", + " experimentalist=grid_pool,\n", + " experiment_runner=run_experiment,\n", + " theorist=theorist_bms,\n", + " params={\n", + " \"experimentalist\":\n", + " {\"ivs\": metadata.independent_variables}\n", + " }\n", + ")\n", + "\n", + "# seed controller\n", + "controller.seed(conditions=[seed_conditions])\n", + "\n", + "next(controller)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CTNPh9LAUYNP" + }, + "source": [ + "Note that, since we seeded the controller with initial experimental conditions, the next step is to execute the ``experiment_runner``. This is why the first step reported by the monitor involves the generation of observations (based on the seed experimental conditions).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "atOAyk5iUYNP" + }, + "source": [ + "### Accessing State-Dependent Properties\n", + "\n", + "Some automated empirical research components require input arguments that depend on the result of the last step in the workflow. For instance, the ``falsification_sample`` experimentalist depends on the previously collected experimental conditions, observations, and the fitted model. For such cases, it is possible to use \"state-dependent properties\" in the ``params`` dictionary. These are the following strings, which will be replaced during execution by their respective current values:\n", + "\n", + "- ``\"%observations.ivs[-1]%\"``: the last observed independent variables
\n", + "- ``\"%observations.dvs[-1]%\"``: the last observed dependent variables
\n", + "- ``\"%observations.ivs%\"``: all the observed independent variables (observations), concatenated into a single array
\n", + "- ``\"%observations.dvs%\"``: all the observed dependent variables (experimental conditions), concatenated into a single array
\n", + "- ``\"%models[-1]%\"``: the last fitted theorist
\n", + "- ``\"%models%\"``: all the fitted theorists
\n", + "\n", + "In the following example, we use the ``\"%observations.ivs%\"``, ``\"%observations.dvs%\"``, and ``\"%models%\"`` properties for the ``falsification_sample`` experimentalist which seeks to identify experimental conditions that are predicted to maximize the loss of the fitted model.\n", + "\n", + "The code block below implements the following workflow:\n", + "1. Generate 3 seed experimental conditions\n", + "2. Iterate 5 times through the following steps\n", + " - Collect observations using ``run_experiment``\n", + " - Identify a model relating conditions to observations using ``theorist_bms``\n", + " - Identify 3 new experimental conditions using ``falsification_sample``\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "id": "B8Bt0YeQUYNP" + }, + "outputs": [], + "source": [ + "# generate initial pool of 3 experimental conditions\n", + "seed_conditions = np.linspace(0,2*np.pi,3)\n", + "\n", + "# define controller\n", + "controller = Controller(\n", + " monitor=monitor,\n", + " variables=metadata,\n", + " experimentalist=falsification_sample,\n", + " experiment_runner=run_experiment,\n", + " theorist=theorist_bms,\n", + " params={\n", + " \"experimentalist\":\n", + " {\"condition_pool\": condition_pool,\n", + " \"model\": \"%models[-1]%\", # access last model generated by theorist\n", + " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", + " \"reference_observations\": \"%observations.dvs%\", # access all observations collected so far\n", + " \"metadata\": metadata,\n", + " \"num_samples\": 3}\n", + " }\n", + ")\n", + "\n", + "# seed controller\n", + "controller.seed(conditions=[seed_conditions])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GIlhC3UZUYNQ" + }, + "source": [ + "Using ``takewhile``, we can now specify a workflow logic that executes the automated research process 5 times. Accordingly, we stop execution of the ``controller`` as soon as it accumulated 5 models." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "id": "L_TPQmSJUYNQ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e329e8db-2839-4d42-b678-f051c388cd86" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 0\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:10<00:00, 9.51it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 1\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 1\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 1\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:11<00:00, 8.80it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 2\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 2\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 2\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:11<00:00, 8.70it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 3\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 3\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 3\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:11<00:00, 8.98it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 4\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 4\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 4\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:10<00:00, 9.47it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 5\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 5\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 5\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:09<00:00, 10.86it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new MODEL\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ], + "source": [ + "from itertools import takewhile\n", + "\n", + "continue_criterion = lambda controller: len(controller.state.models) < 6\n", + "\n", + "for step in takewhile(continue_criterion, controller):\n", + " print(f\"Number of models: {len(step.state.models)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "agcGRk5NUYNQ" + }, + "source": [ + "## Advanced Workflows\n", + "\n", + "In some cases, we may want to condition the sequence of steps taken in the empirical research process on the current state of the process. For instance, one might want to switch from a novelty sampling strategy to a falsification sampling strategy as soon as one has probed enough novel experiment conditions. This section provides a basic introduction to the``BaseController``, which enables the implementation of such arbitrary execution orders.\n", + "\n", + "In this section, we consider a scenario in which we switch experimentalists, depending on the amount of observations collected:\n", + "- If no observations are collected, we sample some seed experimental conditions\n", + "- If less than 7 observations are collected, we sample experimental conditions with ``novelty_sample``\n", + "- If 7 or more observations are collected, we sample experimental conditions with ``falsification_sample``\n", + "\n", + "#### Planner Declaration\n", + "\n", + "We begin with defining an ``experimentalist_planner`` function. Such planner function will be provided as input to the ``BaseController``, and will be used to determine the next step of the workflow, depending on the current state. The code block below implements a planner that selects the experimentalist to be executed depending on the amount of observations collected:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "id": "Rld_TIygUYNQ" + }, + "outputs": [], + "source": [ + "from autora.workflow.planner import last_result_kind_planner\n", + "\n", + "def experimentalist_planner(state):\n", + " # We're going to reuse the \"last_result_kind_planner\" planner, and modify its output.\n", + " proposed_next_step = last_result_kind_planner(state)\n", + "\n", + " # Obtain a list of all observations collected so far\n", + " all_observations = [item for sublist in state.observations for item in sublist]\n", + " num_observations = len(all_observations)\n", + "\n", + " # Determine next experimentalist\n", + " if proposed_next_step == \"experimentalist\":\n", + " if num_observations < 1:\n", + " next_step = \"seed_experimentalist\"\n", + " elif num_observations > 0 and num_observations < 7:\n", + " next_step = \"novelty_experimentalist\"\n", + " else:\n", + " next_step = \"falsification_experimentalist\"\n", + " else:\n", + " next_step = proposed_next_step\n", + "\n", + " print(\"PLANNER: Next step: \" + next_step)\n", + " return next_step" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Yufzx-_8UYNQ" + }, + "source": [ + "The ``experimentalist_planner`` function accepts a ``controller``'s state as input and returns the next step to be executed. Here, we call the ``last_result_kind_planner`` to obtain the default next step. For instance, according to the autonomous empirical research paradigm, if the last step involved executing the ``\"theorist\"``, the next step would be executing the ``experimentalist``.\n", + "\n", + "If the next default step is the ``experimentalist``, the ``experimentalist_planner`` will select the type of experimentalist based on the total number of collected observations." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qRvGhjFlUYNQ" + }, + "source": [ + "### Executor Collection Declaration\n", + "\n", + "In order for the ``BaseController`` to work with the ``experimentalist_planner``, we need to specify the experimentalists that it selects to be executed. In the next code block, we define all experimentalists by wrapping each of them into a ``Pipeline``. However, at this point, we don't need to provide the respective parameters for each experimentalist–we will provide these later, directly to the ``BaseController`` object.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "id": "eTzX1-nBUYNR" + }, + "outputs": [], + "source": [ + "from autora.experimentalist.pipeline import make_pipeline\n", + "\n", + "seed_pipeline = make_pipeline([np.linspace(0, 2*np.pi, 3)])\n", + "novelty_pipeline = make_pipeline([novelty_sample])\n", + "falsification_pipeline = make_pipeline([falsification_sample])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2H4eIWLmUYNR" + }, + "source": [ + "We can now wrap all elements of our research process–this includes all experimentalists as well as the theorist and experiment runner–into a collection of executors. The following code block defines this collection using ``ChainedFunctionMapping``." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "id": "FfjGXQvDUYNR" + }, + "outputs": [], + "source": [ + "from autora.workflow.executor import (ChainedFunctionMapping, from_experimentalist_pipeline,\n", + " from_experiment_runner_callable, from_theorist_estimator)\n", + "\n", + "executor_collection = ChainedFunctionMapping(\n", + " seed_experimentalist=\n", + " [from_experimentalist_pipeline, seed_pipeline],\n", + " novelty_experimentalist=\n", + " [from_experimentalist_pipeline, novelty_pipeline],\n", + " falsification_experimentalist=\n", + " [from_experimentalist_pipeline, falsification_pipeline],\n", + " experiment_runner=[from_experiment_runner_callable, run_experiment],\n", + " theorist=[from_theorist_estimator, theorist_bms],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qx9FNV_jUYNR" + }, + "source": [ + "In the ``ChainedFunctionMapping``, we specify each element by its type, followed by its function. For instance, the ``seed_experimentalist`` is defined as an experimentalist pipeline. Thus, we specify it as ``from_experimentalist_pipeline``, and chain it with its respective function ``seed_experimentalist`` defined above." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "h1xwRNm5UYNS" + }, + "source": [ + "### Base Controller Declaration\n", + "\n", + "So far, we have defined a ``experimentalist_planner`` function which determines the next step in our workflow. We have also defined a ``executor_collection`` defining each step of the workflow. Both will be provided to a special ``Controller`` called ``BaseController``. The ``BaseController`` does not require us to specify a ``theorist``, ``experimentalist``, or ``experiment_runner``. Instead, we can provide it with an ``executor_collection`` specifying all the elements of the workflow we require.\n", + "\n", + "The ``BaseController`` also requires us to specify an initial ``state``. Here, we can instantiate a state as a ``History`` object which entails all variables of the experiment (as declared in ``metadata``) along with the parameters provided to each element in the ``executor_collection``. Let's begin with defining the parameters for all elements in the ``executor_collection``. Here, only two of the elements (``novelty_experimentalist`` and ``falsification_experimentalist``) require us to specify additional parameters.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "id": "Zq0HDjTcUYNS" + }, + "outputs": [], + "source": [ + "params = {\"novelty_experimentalist\":\n", + " {\"novelty_sample\":\n", + " {\"condition_pool\": condition_pool,\n", + " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", + " \"num_samples\": 3},\n", + " },\n", + " \"falsification_experimentalist\":\n", + " {\"falsification_sample\":\n", + " {\"condition_pool\": condition_pool,\n", + " \"model\": \"%models[-1]%\", # access last model generated by theorist\n", + " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", + " \"reference_observations\": \"%observations.dvs%\", # access all observations collected so far\n", + " \"metadata\": metadata,\n", + " \"num_samples\": 3}\n", + " }\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AX60ukJeUYNS" + }, + "source": [ + "Using the ``metadata`` and ``params``, we can instantiate an initial ``state`` for the workflow." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "id": "dmG183YXUYNS" + }, + "outputs": [], + "source": [ + "from autora.workflow.state import History\n", + "\n", + "state = History(variables=metadata, params=params)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wtv0ORp5UYNS" + }, + "source": [ + "For convenience, let us also define a monitor function which can print the current total number of observations. We will provide this monitor to the ``BaseController``." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "id": "CWEAY4-9UYNS" + }, + "outputs": [], + "source": [ + "def monitor(state):\n", + " all_observations = [item for sublist in state.observations for item in sublist]\n", + " num_observations = len(all_observations)\n", + " print(f\"MONITOR: Number of observations {num_observations}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "W94jN2YDUYNS" + }, + "source": [ + "We now have all the required input arguments for the ``BaseController``." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "id": "sQ2aBbWAUYNT" + }, + "outputs": [], + "source": [ + "from autora.workflow.base import BaseController\n", + "\n", + "# define controller\n", + "controller = BaseController(\n", + " state=state,\n", + " monitor=monitor,\n", + " planner=experimentalist_planner,\n", + " executor_collection=executor_collection,\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XSPAMShJUYNT" + }, + "source": [ + "Finally, let's execute the controller for 5 research cycles, measured in terms of the number of generated models." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "id": "8tvuuhoGUYNT", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d523e8c1-7dca-4ab6-bd50-f697be43342a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "PLANNER: Next step: seed_experimentalist\n", + "MONITOR: Number of observations 0\n", + "MONITOR: Number of models: 0\n", + "PLANNER: Next step: experiment_runner\n", + "MONITOR: Number of observations 3\n", + "MONITOR: Number of models: 0\n", + "PLANNER: Next step: theorist\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:09<00:00, 10.08it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Number of observations 3\n", + "MONITOR: Number of models: 1\n", + "PLANNER: Next step: novelty_experimentalist\n", + "MONITOR: Number of observations 3\n", + "MONITOR: Number of models: 1\n", + "PLANNER: Next step: experiment_runner\n", + "MONITOR: Number of observations 6\n", + "MONITOR: Number of models: 1\n", + "PLANNER: Next step: theorist\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:10<00:00, 9.26it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Number of observations 6\n", + "MONITOR: Number of models: 2\n", + "PLANNER: Next step: novelty_experimentalist\n", + "MONITOR: Number of observations 6\n", + "MONITOR: Number of models: 2\n", + "PLANNER: Next step: experiment_runner\n", + "MONITOR: Number of observations 9\n", + "MONITOR: Number of models: 2\n", + "PLANNER: Next step: theorist\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:12<00:00, 7.91it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Number of observations 9\n", + "MONITOR: Number of models: 3\n", + "PLANNER: Next step: falsification_experimentalist\n", + "MONITOR: Number of observations 9\n", + "MONITOR: Number of models: 3\n", + "PLANNER: Next step: experiment_runner\n", + "MONITOR: Number of observations 12\n", + "MONITOR: Number of models: 3\n", + "PLANNER: Next step: theorist\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:10<00:00, 9.73it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Number of observations 12\n", + "MONITOR: Number of models: 4\n", + "PLANNER: Next step: falsification_experimentalist\n", + "MONITOR: Number of observations 12\n", + "MONITOR: Number of models: 4\n", + "PLANNER: Next step: experiment_runner\n", + "MONITOR: Number of observations 15\n", + "MONITOR: Number of models: 4\n", + "PLANNER: Next step: theorist\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:10<00:00, 9.46it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Number of observations 15\n" + ] + } + ], + "source": [ + "from itertools import takewhile\n", + "\n", + "continue_criterion = lambda controller: len(controller.state.models) < 5\n", + "\n", + "for step in takewhile(continue_criterion, controller):\n", + " print(f\"MONITOR: Number of models: {len(step.state.models)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VymXaj-gUYNT" + }, + "source": [ + "We can observe that the controller begins with sampling experiment condition using the ``seed_experimentalist``. It then proceeds to sample condition using the ``novelty_experimentalist`` until it has collected 7 or more observations, at which it switches to the ``falsification_experimentalist``." + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Next Notebook\n", + "This concludes the tutorial on ``autora`` functionality. However, ``autora`` is a flexible framework in which users can integrate their own theorists, experimentalists, and experiment_runners in an automated empirical research workflow. The next notebook illustrates how to add your own custom theorists and experimentalists to use with ``autora``.\n", + "\n", + "Follow this link for the next notebook tutorial:\n", + "[AutoRA Basic Tutorial IV: Customization](www.addlink.com)
" + ], + "metadata": { + "id": "oS5TJBr6s-kJ" + } + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/tutorials/basic tutorial/Tutorial-IV-Customization.ipynb b/docs/tutorials/basic tutorial/Tutorial-IV-Customization.ipynb new file mode 100644 index 000000000..726424d1c --- /dev/null +++ b/docs/tutorials/basic tutorial/Tutorial-IV-Customization.ipynb @@ -0,0 +1,710 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "_q7iLq3GUYMz" + }, + "source": [ + "# Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + }, + "id": "5mfUKtGTUYM1" + }, + "source": [ + "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", + "\n", + "This notebook is the fourth of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", + "\n", + "[AutoRA Basic Tutorial I: Components](www.addlink.com)
\n", + "[AutoRA Basic Tutorial II: Loop Constructs](www.addlink.com)
\n", + "[AutoRA Basic Tutorial III: Workflow Logic](www.addlink.com)
\n", + "[AutoRA Basic Tutorial IV: Customization](www.addlink.com)
\n", + "\n", + "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", + "\n", + "**How to use this notebook** *You can progress through the notebook section by section or directly navigate to specific sections. If you choose the latter, it is recommended to execute all cells in the notebook initially, allowing you to easily rerun the cells in each section later without issues.*" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Tutorial Setup\n", + "This tutorial is self-contained so that you do not need to run the previous notebook to begin. However, the four notebooks are continuous so that what we define in a previous notebook should still exist within this notebook. As such, we will here re-run relevant code from past tutorials. We will not again walk you through these, but if you need a reminder what they are then go see the descriptions in previous notebooks." + ], + "metadata": { + "id": "bfcWh4lramqo" + } + }, + { + "cell_type": "code", + "source": [ + "#### Installation ####\n", + "!pip install -q \"autora[experimentalist-falsification]\"\n", + "!pip install -q \"autora[theorist-bms]\"\n", + "\n", + "#### Import modules ####\n", + "import numpy as np\n", + "import torch\n", + "from autora.variable import DV, IV, ValueType, VariableCollection\n", + "from autora.workflow import Controller\n", + "from autora.experimentalist.sampler.falsification import falsification_sample\n", + "from autora.experimentalist.pooler.random_pooler import random_pool\n", + "from autora.theorist.bms import BMSRegressor\n", + "\n", + "#### Set seeds ####\n", + "np.random.seed(42)\n", + "torch.manual_seed(42)\n", + "\n", + "#### Define ground truth and experiment runner ####\n", + "ground_truth = lambda x: np.sin(x)\n", + "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape)\n", + "\n", + "#### Define condition pool ####\n", + "condition_pool = np.linspace(0, 2 * np.pi, 100)\n", + "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", + "\n", + "#### Define data ####\n", + "initial_conditions = np.random.choice(np.linspace(0, 2 * np.pi, 100), size=10, replace=False).reshape(-1,1)\n", + "initial_observations = run_experiment(initial_conditions).reshape(-1,1)\n", + "\n", + "#### Define metadata ####\n", + "iv = IV(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 100))\n", + "dv = DV(name=\"y\", type=ValueType.REAL)\n", + "metadata = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "#### Define theorists ####\n", + "theorist_bms = BMSRegressor(epochs=100)\n", + "\n", + "#### Define monitor ####\n", + "def monitor(state):\n", + " print(f\"MONITOR: Generated new {state.history[-1].kind}\")" + ], + "metadata": { + "id": "eT6HTGF7aoJT" + }, + "execution_count": 50, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MN8yBMeeUYNT" + }, + "source": [ + "# Customizing Automated Empirical Research Components\n", + "\n", + "``autora`` is a flexible framework in which users can integrate their own theorists, experimentalists, and experiment_runners in a automated empirical research workflow. This section illustrates the integration of custom theorists and experimentalists. For more information on how to contribute your own modules to the ``autora`` ecosystem, please refer to the [Contributor Documentation](https://autoresearch.github.io/autora/contribute/modules/).\n", + "\n", + "To illustrate the use of custom theorists and experimentalists, we consider a simple workflow introduced above:\n", + "1. Generate 3 seed experimental conditions\n", + "2. Iterate through the following steps\n", + " - Collect observations using ``run_experiment``\n", + " - Identify a model relating conditions to observations using ``theorist_bms``\n", + " - Identify 3 new experimental conditions using ``falsification_sample``" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "id": "LpLsdNJkUYNT" + }, + "outputs": [], + "source": [ + "# generate initial pool of 3 experimental conditions\n", + "seed_conditions = np.linspace(0,2*np.pi,3)\n", + "\n", + "params = {\n", + " \"experimentalist\":\n", + " {\"condition_pool\": condition_pool,\n", + " \"model\": \"%models[-1]%\", # access last model generated by theorist\n", + " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", + " \"reference_observations\": \"%observations.dvs%\", # access all observations collected so far\n", + " \"metadata\": metadata,\n", + " \"num_samples\": 3}\n", + " }\n", + "\n", + "# define controller\n", + "controller = Controller(\n", + " monitor=monitor,\n", + " variables=metadata,\n", + " experimentalist=falsification_sample,\n", + " experiment_runner=run_experiment,\n", + " theorist=theorist_bms,\n", + " params=params,\n", + ")\n", + "\n", + "# seed controller\n", + "controller.seed(conditions=[seed_conditions])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mhF2f9S4UYNT" + }, + "source": [ + "## Custom Theorists\n", + "\n", + "What if we wanted to replace the ``theorist_bms`` with a custom theorist?\n", + "\n", + "We can implement our theorist as a class that inherits from `sklearn.base.BaseEstimator`. The class must implement the following methods:\n", + "\n", + "- `fit(self, conditions, observations)`\n", + "- `predict(self, conditions)`\n", + "\n", + "The following code block implements such a theorist that fits a polynomial of a specified degree." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "id": "UXCQxvfsUYNU" + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Example Theorist\n", + "\"\"\"\n", + "\n", + "import numpy as np\n", + "from sklearn.base import BaseEstimator\n", + "\n", + "\n", + "class PolynomialRegressor(BaseEstimator):\n", + " \"\"\"\n", + " This theorist fits a polynomial function to the data.\n", + " \"\"\"\n", + "\n", + " def __init__(self, degree: int = 3):\n", + " self.degree = degree\n", + "\n", + " def fit(self, conditions, observations):\n", + "\n", + " # polyfit expects a 1D array\n", + " if conditions.ndim > 1:\n", + " conditions = conditions.flatten()\n", + "\n", + " if observations.ndim > 1:\n", + " observations = observations.flatten()\n", + "\n", + " # fit polynomial\n", + " self.coeff = np.polyfit(conditions, observations, 2)\n", + " self.polynomial = np.poly1d(self.coeff)\n", + " pass\n", + "\n", + " def predict(self, conditions):\n", + " return self.polynomial(conditions)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FxJvKQHdUYNU" + }, + "source": [ + "We can now assign the theorist to a new controller." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "id": "n2pA8wyAUYNU" + }, + "outputs": [], + "source": [ + "theorist_poly = PolynomialRegressor(degree = 3)\n", + "\n", + "# define controller\n", + "controller_with_polynomial_theorist = Controller(\n", + " monitor=monitor,\n", + " variables=metadata,\n", + " experimentalist=falsification_sample,\n", + " experiment_runner=run_experiment,\n", + " theorist=theorist_poly,\n", + " params=params,\n", + ")\n", + "\n", + "# seed controller\n", + "controller_with_polynomial_theorist.seed(conditions=[seed_conditions])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eoERktO5UYNU" + }, + "source": [ + "Let's run the controller with the new theorist for 3 research cycles, defined by the number of models generated." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "id": "tnyjVXcUUYNU", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7ffd90ad-801e-469d-862b-e466720eda24" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 0\n", + "MONITOR: Generated new MODEL\n", + "Number of models: 1\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new CONDITION\n", + "Number of models: 1\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 1\n", + "MONITOR: Generated new MODEL\n", + "Number of models: 2\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MONITOR: Generated new CONDITION\n", + "Number of models: 2\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 2\n", + "MONITOR: Generated new MODEL\n", + "Number of models: 3\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 3\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 3\n", + "MONITOR: Generated new MODEL\n" + ] + } + ], + "source": [ + "from itertools import takewhile\n", + "\n", + "continue_criterion = lambda controller: len(controller.state.models) < 4\n", + "\n", + "for step in takewhile(continue_criterion, controller_with_polynomial_theorist):\n", + " print(f\"Number of models: {len(step.state.models)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J7bMGWnxUYNU" + }, + "source": [ + "We can plot the last model identified by our custom theorist against the ground truth." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "id": "JpnsqFeKUYNV", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 489 + }, + "outputId": "4bd2658a-4870-493b-e2a7-254b035bae12" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 61 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "last_polynomial_model = controller_with_polynomial_theorist.state.models[-1]\n", + "\n", + "predicted_observations_polynomial = last_polynomial_model.predict(condition_pool)\n", + "\n", + "# plot model predictions against ground-truth\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", + "plt.plot(condition_pool, predicted_observations_polynomial, label='Polynomial Fit')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Model Predictions')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kwM8vJR_UYNV" + }, + "source": [ + "## Custom Experimentalists\n", + "\n", + "We can also implement custom experimentalists. Experimentalists are generally implemented as functions that can be integrated into an\n", + "[Experimentalist Pipeline](https://autoresearch.github.io/autora/core/docs/pipeline/Experimentalist%20Pipeline%20Examples/). For instance, an experimentalist sampler function expects a pool of experimental conditions–typically passed as a 2D numpy array named ``condition_pool``–and returns a modified set of experimental conditions.\n", + "\n", + "The following code block implements a basic experimentalist that considers two models, and identifies experimental conditions for which the two models differ most in their predictions. This is a special case of the [Model Disagreement Sampler](https://autoresearch.github.io/autora/user-guide/experimentalists/samplers/model-disagreement/)." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "id": "Bx4cSZ9nUYNV" + }, + "outputs": [], + "source": [ + "def basic_model_disagreement_sample(condition_pool, model_a, model_b, num_samples = 1):\n", + "\n", + " # get predictions from both models\n", + " prediction_a = model_a.predict(condition_pool)\n", + " prediction_b = model_b.predict(condition_pool)\n", + "\n", + " # compute mean squared distance between predictions\n", + " disagreement = np.mean((prediction_a - prediction_b) ** 2, axis=1)\n", + "\n", + " # sort the summed disagreements and select the top n\n", + " selected_conditions_idx = (-disagreement).argsort()[:num_samples]\n", + "\n", + " return condition_pool[selected_conditions_idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AXCRsOhUUYNV" + }, + "source": [ + "We can illustrate our new experimentalist sampler by fitting two different theorists to an initial set of conditions and observations. Here, we consider the BMS theorist and our custom polynomial theorist from above. We then sample 3 experimental conditions using our new experimentalist ``basic_model_disagreement_sample``." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "id": "P2jBBdKwUYNV", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3ba61e72-c768-4492-b644-942a5bd932d9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:07<00:00, 13.30it/s]\n" + ] + } + ], + "source": [ + "# fit two theorists\n", + "theorist_bms.fit(initial_conditions, initial_observations)\n", + "theorist_poly.fit(initial_conditions, initial_observations)\n", + "\n", + "# sample experimental conditions with our custom experimentalist sampler function\n", + "selected_conditions = basic_model_disagreement_sample(condition_pool,\n", + " theorist_bms,\n", + " theorist_poly,\n", + " num_samples = 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vP_bRY4GUYNV" + }, + "source": [ + "After fitting both theorists, we can compare their predictions across the entire pool of experimental conditions. We will add the sampled experimental conditions to the plot." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "id": "lbUKDXL-UYNV", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 489 + }, + "outputId": "dd97872b-0748-4048-d736-c3edd82579df" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 67 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "# plot model predictions against ground-truth\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# obtain predictions for both theorists\n", + "predicted_observations_bms = theorist_bms.predict(condition_pool)\n", + "predicted_observations_poly = theorist_poly.predict(condition_pool)\n", + "\n", + "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", + "plt.plot(condition_pool, predicted_observations_bms, label='Predictions of BMS Theorist')\n", + "plt.plot(condition_pool, predicted_observations_poly, label='Predictions of Polynomial Theorist')\n", + "\n", + "y_min = -2.5\n", + "y_max = 1\n", + "\n", + "# plot conditions obtained by novelty sampler\n", + "for idx, condition in enumerate(selected_conditions):\n", + " if idx == 0:\n", + " plt.plot([condition[0], condition[0]],\n", + " [y_min, y_max],\n", + " '--r', label='selected conditions')\n", + " else: # we want to omit the label for all other conditions\n", + " plt.plot()\n", + "\n", + "\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Model Disagreement')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Dk-Zd-DKUYNW" + }, + "source": [ + "Finally, we can integrate our custom experimentalist and theorist into a closed-loop empirical research workflow, e.g., using basic loop constructs." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "id": "4_8wXhOkUYNW", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1ea515a0-2aa3-4d34-aaba-aebe2e94b2b5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:08<00:00, 11.88it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss of BMS theorist in cycle 0: 0.0\n", + "Loss of polynomial theorist in cycle 0: 0.8717052095923039\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:08<00:00, 12.49it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss of BMS theorist in cycle 1: 0.0\n", + "Loss of polynomial theorist in cycle 1: 3.619766689361933\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:07<00:00, 12.97it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss of BMS theorist in cycle 2: 0.0\n", + "Loss of polynomial theorist in cycle 2: 0.5193832163876795\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:07<00:00, 14.26it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss of BMS theorist in cycle 3: 0.0\n", + "Loss of polynomial theorist in cycle 3: 0.36300053098571583\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 100/100 [00:07<00:00, 13.45it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loss of BMS theorist in cycle 4: 0.4967273581732591\n", + "Loss of polynomial theorist in cycle 4: 0.288261165753893\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ], + "source": [ + "num_cycles = 5 # number of empirical research cycles\n", + "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", + "\n", + "# generate an initial set experimental conditions\n", + "conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", + " n=measurements_per_cycle)\n", + "# convert iterator into 2-dimensional numpy array\n", + "conditions = np.array(list(conditions)).reshape(-1, 1)\n", + "\n", + "# collect initial set of observations\n", + "observations = run_experiment(conditions)\n", + "\n", + "for cycle in range(num_cycles):\n", + "\n", + " # use BMS theorist and custom polynomial theorist to fit the model to the data\n", + " theorist_bms.fit(conditions, observations)\n", + " theorist_poly.fit(conditions, observations)\n", + "\n", + " # obtain new conditions from custrom experimentalist sampler\n", + " new_conditions = basic_model_disagreement_sample(condition_pool,\n", + " theorist_bms,\n", + " theorist_poly,\n", + " num_samples = 3)\n", + "\n", + " # obtain new observations\n", + " new_observations = run_experiment(new_conditions)\n", + "\n", + " # combine old and new conditions and observations\n", + " conditions = np.concatenate((conditions, new_conditions))\n", + " observations = np.concatenate((observations, new_observations))\n", + "\n", + " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", + " loss_bms = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", + " loss_poly = np.mean(np.square(theorist_poly.predict(condition_pool) - ground_truth(condition_pool)))\n", + " print(\"Loss of BMS theorist in cycle {}: {}\".format(cycle, loss_bms))\n", + " print(\"Loss of polynomial theorist in cycle {}: {}\".format(cycle, loss_poly))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q-eKiaHRUYNW" + }, + "source": [ + "# Help\n", + "We hope that this tutorial helped demonstrate the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments. We encourage you to explore other [tutorials](https://autoresearch.github.io/autora/tutorials/) and check out the [documentation](https://autoresearch.github.io/).\n", + "\n", + "If you encounter any issues, bugs, or questions, please reach out to us through the [AutoRA Forum](https://github.com/orgs/AutoResearch/discussions). Feel free to report any bugs by [creating an issue in the AutoRA repository](https://github.com/AutoResearch/autora/issues).\n", + "\n", + "You may also post questions directly into the [User Q&A Section](https://github.com/orgs/AutoResearch/discussions/categories/using-autora).\n" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 8cffef90d70e1a51222c14928c6f53a9684adb0e Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Wed, 5 Jul 2023 08:11:57 -0700 Subject: [PATCH 03/32] Updated path and links --- .../Tutorial-I-Components.ipynb | 262 ++++++++++-------- .../Tutorial-II-Loop-Constructs.ipynb | 87 +++--- .../Tutorial-III-Workflow-Logic.ipynb | 189 +++++++------ .../Tutorial-IV-Customization.ipynb | 117 ++++---- 4 files changed, 370 insertions(+), 285 deletions(-) rename docs/tutorials/{Basic Tutorial => basic}/Tutorial-I-Components.ipynb (98%) rename docs/tutorials/{basic tutorial => basic}/Tutorial-II-Loop-Constructs.ipynb (94%) rename docs/tutorials/{basic tutorial => basic}/Tutorial-III-Workflow-Logic.ipynb (96%) rename docs/tutorials/{basic tutorial => basic}/Tutorial-IV-Customization.ipynb (98%) diff --git a/docs/tutorials/Basic Tutorial/Tutorial-I-Components.ipynb b/docs/tutorials/basic/Tutorial-I-Components.ipynb similarity index 98% rename from docs/tutorials/Basic Tutorial/Tutorial-I-Components.ipynb rename to docs/tutorials/basic/Tutorial-I-Components.ipynb index 64db4c442..b18b2b4b0 100644 --- a/docs/tutorials/Basic Tutorial/Tutorial-I-Components.ipynb +++ b/docs/tutorials/basic/Tutorial-I-Components.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "_q7iLq3GUYMz" @@ -11,22 +12,23 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { + "id": "5mfUKtGTUYM1", "pycharm": { "name": "#%% md\n" - }, - "id": "5mfUKtGTUYM1" + } }, "source": [ "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", "\n", "This notebook is the first of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", "\n", - "[AutoRA Basic Tutorial I: Components](www.addlink.com)
\n", - "[AutoRA Basic Tutorial II: Loop Constructs](www.addlink.com)
\n", - "[AutoRA Basic Tutorial III: Workflow Logic](www.addlink.com)
\n", - "[AutoRA Basic Tutorial IV: Customization](www.addlink.com)
\n", + "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", + "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", + "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", + "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", "\n", "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", "\n", @@ -34,6 +36,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "BWl9iqpgUYM2" @@ -67,23 +70,18 @@ ] }, { + "attachments": {}, "cell_type": "markdown", - "source": [ - "To make all simulations in this notebook replicable, we will set some seeds." - ], "metadata": { "id": "B4DahNFBVNo3" - } + }, + "source": [ + "To make all simulations in this notebook replicable, we will set some seeds." + ] }, { "cell_type": "code", - "source": [ - "import numpy as np\n", - "import torch\n", - "\n", - "np.random.seed(42)\n", - "torch.manual_seed(42)" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -91,21 +89,28 @@ "id": "tJNNbhskVMNq", "outputId": "a54371dd-79ab-4849-abdc-4862bd116e94" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 93, "metadata": {}, - "execution_count": 93 + "output_type": "execute_result" } + ], + "source": [ + "import numpy as np\n", + "import torch\n", + "\n", + "np.random.seed(42)\n", + "torch.manual_seed(42)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "bSGRphhDUYM4" @@ -126,17 +131,56 @@ ] }, { + "attachments": {}, "cell_type": "markdown", + "metadata": { + "id": "F_lZhwcg8wW8" + }, "source": [ "## Toy Example of the Components\n", "Before jumping into each component in detail, we will present a toy example to provide you with an overview on how these components work together within a closed-loop. After some setup, you will see steps 1-3, which uses the three componens - namely, the EXPERIMENTALIST to propose new conditions, the EXPERIMENT RUNNER to retrieve new observations from those conditions, and the THEORIST to model the new data. We then finish this example by plotting our data and findings." - ], - "metadata": { - "id": "F_lZhwcg8wW8" - } + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 508 + }, + "id": "8P3iMrqN-pOU", + "outputId": "92dede11-65c8-423d-b8a5-ac0b6191404c" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:14<00:00, 6.96it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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YayLO/kKpvj3yX3Dtf2DSYpi2Q5Ig4bI++OADqqqqGDBgAPPnz2fPnj3s27ePL7/8kr179za6kOM///lP5s6dy5w5czhw4ABvvPEGP/zwA4888ghgaVUaMmQIr7zyCnv27GHFihU88cQTzY7zwQcf5NNPP+Wzzz5j//79PP300+zateucXnN9mhJrbGwsiqKwePFisrOzKSoqsmsMQjiaSyVCxcXFJCYm8v777zdp/5SUFK688kpGjRpFcnIy06ZN48477+TXX7Up4HQp3cdZWnmCompuD4qGG76AkTOg13UQP0K6w4RL69ixI1u3bmX06NHMnDmTxMREBgwYwLvvvssjjzzC888/3+Djx48fz9tvv83rr79Ojx49+Oijj/jss88YOXKkbZ9PP/2Uqqoq+vfvz7Rp03jhhReaHeeNN97Ik08+yaOPPkr//v1JTU3lH//4R7OfpzGNxdq2bVueffZZHnvsMSIiIpgyZYrdYxDCkRS1qeNMnYyiKCxcuJDx48fXu8+MGTP4+eef2blzp23bTTfdRF5eHkuWLGnScQoKCggODiY/P5+goKDzDdv1yLpioonKyspISUkhPj4eHx8frcMRQniAhs47Tf3+duti6bVr1zJ69Oga25KSkmQ9nObQ6S2tPs1QWmHieF4J6fll6BSFLhGBtA40tlCAQoi6VJrMlFWaUBQFnQI+Bj063fmv1C2Eu3HrRCgjI4OIiJojoCIiIigoKKC0tLTOESHl5eU1hssWFBS0eJzuQFVV1qec4psNR/llZwYVVTVHikQEGblpYHsmDYsjzF+m8ReiJZjMZrKLKsgvqaS8qmYxs05RCPQxEOrnTaBP0wvKhXB3bp0InYuXX36ZZ599VuswXEpWQRn/WriD3/ecni8k0GggMtiHKrPKkZxiMgvKeXvZAT766xAPXNyZey/qiF6uToWwC1VVySmqIKuwjCrz6WoHo8HSjW0yq1SZzeSXVpJfWkmgjxfRwT4YmzjNgBDuzK0TocjISDIza04OmJmZSVBQUJ2tQQAzZ85k+vTpttsFBQXExMS0aJyubOnuTB75dhv5pZV46RWu69+Omwa2p3e7YNsVZ2mFiWV7M/loxWF2HM/ntV/3sfZQDm/e2Ee6zIQ4TyazStqpEgrKLPP9GA16IoKMBBgNGPSn5wUqqzSRW1JJTnEFhWWV7C+von2oL8F+0kIrPJtbJ0JDhw7ll19+qbFt6dKlDc7GajQaMRrly7kpfthyjEe+3YZZhZ5tg3j9+kQSImsXpPl667mqdzRX9oriu83HeOqnXaw6eJLx76/mm7uHEBPmp0H0Qri+iiozR3KKbbVA0cE+hPl71+r2UhQFX28Dvt4Gwv29OZ5XSlF5FamnSogyqXJBIjyaSw2fLyoqIjk52TbBV0pKCsnJyRw9ehSwtOZMnDjRtv+9997L4cOHefTRR9m7dy8ffPABCxYs4KGHHtIifLfy1fpUpi+wJEHX92/HwvuG15kEnUlRFK4fEMN/HxhOh1b+HM8rZcIn6ziRV+qgqIVwH1UmMyknLUmQl15Hx1b+hAcYG639MXrpia/eFyA9v5SsgnNfr0wIV+dSidCmTZvo27cvffv2BSyLG/bt25ennnoKsMyGak2KAOLj4/n5559ZunQpiYmJzJ49m3//+98kJWkz7b+7+H13Jk/8aJmSYPKwOF69tjde+qb/KXVqE8j/3T2EuHA/juVakqFTxRUtFa4QbsdkVjmSU0J5VXUS1DoAP2PTG/itrUeRwZbhxhkFZeSVyGdQeCaXnUfIUTx+HqGzHMwqZPz7aygqr+Lmwe15cXzPcx59ciKvlBs+Wsux3FIu7NKazyYPlAJqFybzCDmGqqocPVVCfmklep1Cx9YB+JxH0XN6XinZReUoikKHVv74NyOhEkJr9phHyKVahIS2CsoquevzzRSVVzEoPoxnxvY4ryG40SG+/HvSAHy8dPy1P5t3/zhgx2iFcE85xRXkl1aiKApx4f7nlQQBRAb7EOzrZUuwqkyySKrwLJIIiSZ7cfEeUk4W0zbElw9u6Ye34fz/fBIig3hxfC8A3l52gNUHT573cwrhTs5cV7G0wkR6vqWeJyrIp8mtN5MnT653Fn5FUYgJ9cNo0FNpMnM8rxRHdxTMnTuXkJAQ2+1nnnmGPn36NPiYI0eOoCjKOS1gK8SZJBESTbJ8XxbzN6WhKPDmjX1oFWC/USbX9m/HhEExqCo89sN2Siqq7PbcwgWZTZCyEnZ8Z/nX3LKrnGdnZ/OPf/yD9u3bYzQaiYyMJCkpqdaK8lozV7fYqKpKkI8X4QH2G/au0ym0C/Xh+6/mcdXoiwgMDCQkJIQBAwbw1ltvUVJSYrdjNcUjjzzCsmXLbLfrSuRiYmJIT0+nZ8+eDo1NuB/pDBaNKiirZOYPOwBLcfSg+DC7H+PxK7uzYl82aadKeev3A/zrim52P4ZwAbsXwZIZUHDi9LagaBjzqmUh4BZw7bXXUlFRwbx58+jQoQOZmZksW7aMnJycFjneuTpZWE55lQmDTke7UF+7zwx9zx238cMPP3DnAw/z+Auv0T8hlt07d/DWW28RFxfX4LqO9hYQEEBAQECD++j1eiIjIx0UkXBn0iIkGjX7132k55cRG+7HP5O6tsgxAowGXrjGcmX375WH2Xk8v0WOI5zY7kWwYGLNJAigIN2yffciux8yLy+PlStX8uqrrzJq1ChiY2MZNGgQM2fOZNy404nXG2+8Qa9evfD39ycmJob77ruPoqIi2/3Wrp3FixfTtWtX/Pz8uO666ygpKWHevHnExcURGhrK1KlTMZlOt3DFxcXx/PPPM2HCBPz9/Wnbti3vv/9+rTgrTWayCi1L/5iKsrl5wk2EhIQQFhbG1VdfzZEjR2z7mkwmpk+fTkhICOHh4Tz66KONdnUtWLCAr776iq+//poHHn6U7ol98QmN5Oqrr+aPP/5g1KhRAJjNZp577jnatWuH0WikT58+NRawtnZX/fDDD4waNQo/Pz8SExNZu3ZtjePNnTuX9u3b4+fnxzXXXFMr6Tyza+yZZ55h3rx5/PTTTyiKgqIoLF++vM6usRUrVjBo0CCMRiNRUVE89thjVFWdbmEeOXIkU6dO5dFHHyUsLIzIyEieeeYZ2/2qqvLMM8/YWgejo6OZOnVqg++dcH2SCIkGHcgs5Mv1likJXr6mF37eLdeIeHFCBGMTozGr8PiPOx1epyA0ZDZZWoKo63devW3JY3bvJrO2PPz444811hg8m06n45133mHXrl3MmzePP/74g0cffbTGPiUlJbzzzjt88803LFmyhOXLl3PNNdfwyy+/8Msvv/DFF1/w0Ucf8d1339V43GuvvUZiYiJbt27lscce48EHH2Tp0qU19jlVXIFZVTHqVG4cP5bAwEBWrlzJ6tWrCQgIYMyYMVRUWIa/z549m7lz5/Lpp5+yatUqTp06xcKFCxt8H7766iu6du3K+PHjaRtsmXU/t6SC4vIqFEUhODgYgLfffpvZs2fz+uuvs337dpKSkhg3bhwHDtQc6PD444/zyCOPkJycTJcuXZgwYYItIVm/fj133HEHU6ZMITk5mVGjRvHCCy/UG9sjjzzCDTfcwJgxY0hPTyc9PZ1hw4bV2u/48eNcccUVDBw4kG3btjFnzhz+85//1HruefPm4e/vz/r165k1axbPPfec7f3+/vvvefPNN/noo484cOAAP/74I7169WrwvRNuQBUNys/PVwE1Pz9f61A0MenT9WrsjMXqnfM2OuR4mQWlarcn/6fGzlisLt52wiHHFPZRWlqq7t69Wy0tLW3+gw//papPBzX+c/gvu8f93XffqaGhoaqPj486bNgwdebMmeq2bdsafMy3336rhoeH225/9tlnKqAePHjQtu2ee+5R/fz81MLCQtu2pKQk9Z577rHdjo2NVceMGVPjuW+88Ub18ssvt90G1Dc/+VLdfixP/fSzeWrXrl1Vs9lsu7+8vFz19fVVf/31V1VVVTUqKkqdNWuW7f7Kykq1Xbt26tVXX13v6+nWrZs6btw42+2jOcXqtrRcdX9GQY1jRUdHqy+++GKNxw4cOFC97777VFVV1ZSUFBVQ//3vf9vu37Vrlwqoe/bsUVVVVSdMmKBeccUVtV5zcHCw7fbTTz+tJiYm2m5PmjSpVvzWY23dulVVVVX917/+Veu9ef/999WAgADVZDKpqqqqF110kXrBBRfUin/GjBmqqqrq7Nmz1S5duqgVFRX1vlfCuTR03mnq97e0CIl6rdifzfJ92XjpFYfV7LQJ9OHuCzsAMOvXvbVWsRduqiiz8X2as18zXHvttZw4cYJFixYxZswYli9fTr9+/Zg7d65tn99//51LLrmEtm3bEhgYyK233kpOTk6NImI/Pz86duxoux0REUFcXFyNWpeIiAiysk4vTgzUWvJn6NCh7NmzB6BGq2i4vze7d+3g4MGDBAYG2lqzwsLCKCsr49ChQ+Tn55Oens7gwYNtjzMYDAwYMKDB90A9q/U1MtgHvaJQWmkir8SyhllBQQEnTpxg+PDhNfYdPny4LV6r3r172/4fFRUFYHvde/bsqRFfXe/BudizZw9Dhw6tUTs1fPhwioqKOHbsWJ2xWeOzxnb99ddTWlpKhw4duOuuu1i4cGGNrjXhniQREnUym1Ve/sVycps4NI74Vv4OO/ZdIzrQKsBIak4JX69PddhxhYYCIuy7XzP5+Phw6aWX8uSTT7JmzRomT57M008/DVjqXq666ip69+7N999/z+bNm211PNbuKAAvL68az6koSp3bzOamJ/f5pZYkRKdAm0AjRUVF9O/f37bUkPVn//793Hzzzef02gG6dOnC3r17T78WvY7WQZaRoZmFZZib2U195uu2JibNed0tqaHfSUxMDPv27eODDz7A19eX++67jwsvvJDKykotQhUOIomQqNNvuzPZm1FIgNHAAxd3cuix/Y0GHrq0MwDv/HFQhtN7gthhltFh1DcSSoGgtpb9HKB79+4UFxcDsHnzZsxmM7Nnz2bIkCF06dKFEydONPIMTbdu3bpat7t164ZZVckssNQtBfl6YdDr6NevHwcOHKBNmzZ06tSpxk9wcDDBwcFERUWxfv162/NVVVWxefPmBmO4+eab2b9/Pz/99JNtW7i/EYNOR3mliaPp2QQFBREdHV1rWoHVq1fTvXv3Jr/ebt261YivrvfgbN7e3jWKzOt73rVr19Zo3Vq9ejWBgYG0a9euyfH5+voyduxY3nnnHZYvX87atWvZsWNHkx8vXI8kQqIWVVV5Z5ml+HHysDhC/Ow3X0lT3TgghthwP04VV/D1+qONP0C4Np3eMkQeqJ0MVd8e84plPzvKycnh4osv5ssvv2T79u2kpKTw7bffMmvWLK6++moAOnXqRGVlJe+++y6HDx/miy++4MMPP7RbDKtXr2bWrFns37+f999/n2+//ZYHH3yQvJIKyqssX/5BPpZWjFtuuYVWrVpx9dVXs3LlSlJSUli+fDlTp061df88+OCDvPLKK/z444/s3buX++67j7y8vAZjuOGGG7jxxhuZMGECL730Eps2buTYwd3sXLGIf0wYx+Ilv2NWVf75z3/y6quvMn/+fPbt28djjz1GcnIyDz74YJNf79SpU1myZAmvv/46Bw4c4L333qsx8qwucXFxbN++nX379nHy5Mk6W2juu+8+0tLSeOCBB9i7dy8//fQTTz/9NNOnT0ena9pX3dy5c/nPf/7Dzp07OXz4MF9++SW+vr7ExsY2+fUJ1yOJkKjl9z1Z7E4vwN9bzx0XxGsSg0Gv496LLPUWn6w8bPtCEG6s+zi44XMIiqq5PSjasr0F5hEKCAhg8ODBvPnmm1x44YX07NmTJ598krvuuov33nsPgMTERN544w1effVVevbsyVdffcXLL79stxgefvhh24LSL7zwAm+88QaXXXYZ2YWnu9101Wvw+fn58ddff9G+fXv+9re/0a1bN+644w7Kyspsayk9/PDD3HrrrUyaNImhQ4cSGBjINddc02AMiqLw9ddf88Ybb/DjD99z0UUX0XvAEGa98hI3JQ3l1gs7UZx/kqlTpzJ9+nQefvhhevXqxZIlS1i0aBGdO3du8usdMmQIn3zyCW+//TaJiYn89ttvPPHEEw0+5q677qJr164MGDCA1q1b1znZZdu2bfnll1/YsGEDiYmJ3Hvvvdxxxx2NPveZQkJC+OSTTxg+fDi9e/fm999/57///S/h4eFNfg7hemTR1UZ42qKrqqoy7r3V7Diezz9GdmTGmATNYimvMnHRrOVkFJTx0jW9uHlwe81iEY2z26KrZhOkrrEURgdEWLrD7NwS5Czi4uKYNm0a06ZNq7E9v6SC1FMl6HUKCZFBjluMuDQPclNqbVZVLA1zofEoviGOiUWIJpBFV4XdrT2Uw47j+fh66blTo9YgK6NBbxtB9uGKQ7IYpKfQ6SF+BPS6zvKvmyZB9VFVlewiS21QuL/RcUmQqkL+sTrvUhRABTXvWHVWJIT7kERI1PCfVZarwesHtCPcjuuJnasJg9oT7u/N0VMl/G9nhtbhCNHiisurKKkwoVMUu64n1qiKIjDXPzpKUUCnVlr2E8KNSCIkbA5nF7Fsr2U+jcnD4rQNppqvt56/D7EUKs5bc0TbYISwsyNHjtTqFssustQGhfp546V34Cna1LQh4g3NwC2EK5JESNjMrU40LkloQ4fWDS946Ei3DG6PQaewKTWXHcdkDTLhvsorTRSWWRKSVo5sDQLQezW+D5BbLl1jwr1IIuQJzCZIWQk7vrP8W8d6TfkllXy7yVIfcLvGtUFnaxPkw5W9LSOJ5kqrkHBjOcWW1qBAHy+MXg6ujfIOAF39yZAKVKgGssu9qJQZ34UbkUTI3e1eBG/1hHlXwfd3WP59q2etlby/23KM0koTCZGBDOvofENFJ1V31f132wlOFknTvHA/ZrNKboklEQr3d/zcXSgKBNc/8aACnDK0RgVOlVTUu58QrkYSIXe2exEsmAgFZ82CW5Bu2V6dDKmqyv9tsExa+PchsTXW6nEWfWNCSGwXTIXJzPyNaVqHI4Td5ZVWYjKreOt1BPoYtAnCNwRC42u3DOm8IDQen8AwAE4VV9Ran0wIVyWJkLsym2DJDCwN2mer3rbkMTCb2JSay8GsIny99FzdJ9qRUTaZoijcOjQOgPkb0zCb5SQs3IeqquRUt3SGBXhrezHiGwIRPSC8E4TEWv6N6AG+IZalPnQ6Kk1mCspk6RvhHiQRclepa2q3BNWgQsFxSF3D/1UvYTEuMZpAn6YVTGrhil6RBBgNHD1VwrqUHK3DEcJuyipNlFaaUBSFMA2WtKlFUcAYCH5hln+rEzOdohDqbzlHnCqW7jHhHiQRcldFmU3arTjnOIt3pANw06CYlozovPl5GxhX3WIl3WPCnZwqsYwUC/YxYKhnyPzkyZMZP368A6OqW1h1/VJhWSUVdlj6ZuTIkbWmEDiboij8+OOP532slnTkyBEURSE5ObnFj/XMM8/Qp08fuz9vRUUFnTp1Ys2aNXZ/7nNx0003MXv27BY/jiRC7iogokm7/ZWuo6LKTEJkIH1iQlo2Jju4cYAlWfvfzgzyS5o274kQDZk8eTKKoth+wsPDGTNmDNu3b3fI8c2qSl518XFoA0XSb7/9NnPnzm3xeOLi4lAUhW+++abWfT169MDHy8CShZb7ch30GUxPT+fyyy9v0WOYTCZeeeUVEhIS8PX1JSwsjMGDB/Pvf/+7SY+PiYkhPT2dnj172jWuupLARx55hGXLljXp8c1Jmj788EPi4+MZNmxYM6Os3/Lly+nXrx9Go5FOnTo162/4iSee4MUXXyQ/v2WnTZFEyF3FDrMsVllrJW8rBYLa8sHhNgDcNDDGKYukz9a7XTAJkYFUVJn5Mfm41uEINzFmzBjS09NJT09n2bJlGAwGrrrqKoccu6C6SNpLryPAWH+RdHBwMCEhIQ6JKSYmhs8++6zGtnXr1pGRkYG/vz/+3pY4c0scUzQdGRmJ0diyM90/++yzvPnmmzz//PPs3r2bP//8k7vvvpu8vLwmPV6v1xMZGYnB0PKF7gEBAXZfCFZVVd577z3uuOMOuz1nSkoKV155JaNGjSI5OZlp06Zx55138uuvvzbp8T179qRjx458+eWXdoupLpIIuSudHsa8Wn3j7ATHcvvE0KfZkV6MQacwrk9bh4Z3rhRF4caBllahBZuke0zYh9FoJDIyksjISPr06cNjjz1GWloa2dnZtn1mzJhBly5d8PPzo0OHDjz55JNUVlpaRI4cOYJOp2PTpk01nvett94iNjYWs9ky787OnTu5/PLLCQgIICIigltvvZVDaZau6VA/L77//nt69eqFr68v4eHhjB49muLiYqB219iSJUu44IILCAkJITw8nKuuuopDhw7Z7rd21fzwww+MGjUKPz8/EhMTWbt2baPvxy233MKKFStISzv9Gfv000+55ZZbMBgM+Hrp0SkKFVVmXn3tdXr16oW/vz8xMTHcd999FBXVXIZj9erVjBw5Ej8/P0JDQ0lKSiI3N9d2v9ls5tFHHyUsLIzIyEieeeaZGo8/s1Wkqa9r1apVjBgxAl9fX2JiYpg6dartvazLokWLuO+++7j++uuJj48nMTGRO+64g0ceeaRGnLNmzaJTp04YjUbat2/Piy++WCOuM7vG6vp9nzx50nb/yJEjmTp1ar2vPS4uDoBrrrkGRVFst89u5Vm+fDmDBg3C39+fkJAQhg8fTmpqKnPnzuXZZ59l27ZtthbP+lpkNm/ezKFDh7jyyivrfY/O1tBnAk63MM2ePZtu3boxZcoUrrvuOt58880mH2Ps2LF1tk7akyRC7qz7OLjhcwiKqrk9KBpu+JzP8xIBGJXQxtbv7wqu7tMWg05h14kCDmYVah2OqIeqqpRUlmjycz6tFEVFRXz55Zd06tSpxlV3YGAgc+fOZffu3bz99tt88sknthN6XFwco0ePrtWK8tlnnzF58mR0Oh15eXlcfPHF9O3bl02bNrFkyRIyMjL4x+23AlCWn8OECRO4/fbb2bNnD8uXL+dvf/tbva+luLiY6dOns2nTJpYtW4ZOp+Oaa66xJV1Wjz/+OI888gjJycl06dKFCRMmUFXV8IiviIgIkpKSmDdvHgAlJSXMnz+f22+/HQCdTiHY11I0XVal8s4777Br1y7mzZvHH3/8waOPPmp7ruTkZC655BK6d+/O2rVrWbVqFWPHjsVkOl1fNG/ePPz9/Vm/fj2zZs3iueeeY+nSpQ3G2NDrOnToEGPGjOHaa69l+/btzJ8/n1WrVjFlypR6ny8yMpI//vijRvJ7tpkzZ/LKK6/w5JNPsnv3br7++msiIuouQ6jr952ZmckNN9xQY7+GXvvGjRsBy99Renq67faZqqqqGD9+PBdddBHbt29n7dq13H333ZaLxhtv5OGHH6ZHjx62Fs8bb7yxznhXrlxJly5dCAwMrPf1n62hzwTA2rVrGT16dI3HJCUlNSkZtxo0aBAbNmxo2aVdVNGg/Px8FVDz8/O1DuXcmapU9fBfqrr9W8u/piq1ymRWh7z0uxo7Y7H6y/YTWkfYbLd/tkGNnbFYfW3JXq1DEdVKS0vV3bt3q6WlpaqqqmpxRbHac25PTX6KK4qbHPekSZNUvV6v+vv7q/7+/iqgRkVFqZs3b27wca+99prav39/2+358+eroaGhallZmaqqqrp582ZVURQ1JSVFVVVVff7559XLLrusxnMk7zmgAurSNVvUzZs3q4B65MiReuO8+uqr640nOztbBdQdO3aoqqqqKSkpKqD++9//tu2za9cuFVD37NlT7/PExsaqb775pvrjjz+qHTt2VM1mszpv3jy1b9++qqqqanBwsPrZZ5+phWWV6ra0XHXnsTzVZDLbHv/tt9+q4eHhttsTJkxQhw8fXu/xLrroIvWCCy6osW3gwIHqjBkzbLcBdeHChU1+XXfccYd6991313jOlStXqjqdzvb3ebZdu3ap3bp1U3U6ndqrVy/1nnvuUX/55Rfb/QUFBarRaFQ/+eSTOh9vjWvr1q2qqtb9+05LS1MBdd++fef02q2efvppNTExUVVVVc3JyVEBdfny5XXGdea+DXnwwQfViy++uNH9GnL2Z6Jz587qSy+9VGOfn3/+WQXUkpKSJj3ntm3bGvxcnH3eOVNTv7+lRcgT6PQQPwJ6XWf5V6dn3eEc0vPLCPIxcHG3NlpH2Gzj+1q68n5MPi4Tu4nzZq1hSE5OZsOGDSQlJXH55ZeTmppq22f+/PkMHz6cyMhIAgICeOKJJzh69Kjt/vHjx6PX61m4cCEAc+fOZdSoUbbujG3btvHnn38SEBBg+xnWvw8AOelpJCYmcskll9CrVy+uv/56PvnkkxrdR2c7cOAAEyZMoEOHDgQFBdmOc2ZMAL1797b9PyrK0jqclZXV6Hty5ZVXUlRUxF9//cWnn35qaw2y8vfW463XsfqvPxl18cW0bduWwMBAbr31VnJycigpKQFOtwg15MwYrXE2FmNDr2vbtm3MnTu3xnudlJSE2WwmJSWlzufr3r07O3fuZN26ddx+++1kZWUxduxY7rzzTgD27NlDeXl5o6/Fqq7fd0JCAkCNLsxzee1nCgsLY/LkySQlJTF27Fjefvtt0tPTm/x4q9LSUnx8fJr1mMY+E/bg6+sLYPt7agkaTV8qtPbDFkuh8ZW9ozEaHLymkR2M7haBv7eeY7mlbE7NZUBcmNYhibP4GnxZf/N6zY7dHP7+/nTq1Ml2+9///jfBwcF88sknvPDCC6xdu5ZbbrmFZ599lqSkJIKDg/nmm29qDO319vZm4sSJfPbZZ/ztb3/j66+/5u2337bdX1RUxNixY3n1VUvtXnmliZSTxSgoXJDYCb1ez9KlS1mzZg2//fYb7777Lo8//jjr168nPr72+n9jx44lNjaWTz75hOjoaMxmMz179qSioub8Pl5ep+cGsw6IOLv7rC4Gg4Fbb72Vp59+mvXr19sSvDOfqzA7nQduu4m/T76TV195mbCwMFatWsUdd9xBRUUFfn5+ti+yhpwZo/W5G4uxoddVVFTEPffcw9SpU2s9rn379vU+p06nY+DAgQwcOJBp06bx5Zdfcuutt/L444836XWc6ezf95msidvZr8P6Wpry+znTZ599xtSpU1myZAnz58/niSeeYOnSpQwZMqTJz9GqVSt27NjR5P2b8pmIjIwkM7PmVC6ZmZkEBQU1+f08deoUAK1bt25ybM0liZAHKqs08euuDAD+1s81iqTP5uutZ0zPKL7fcowfk49LIuSEFEXBz8tP6zDOiaIo6HQ6SktLAVizZg2xsbE8/vjjtn3ObC2yuvPOO+nZsycffPABVVVV/O1vf7Pd169fP77//nvi4uIwGAxkFJRhCiwj0MeL4CB/23GHDx/O8OHDeeqpp4iNjWXhwoVMnz69xnFycnLYt28fn3zyCSNGjAAsxcH2dvvtt/P6669z4403EhoaWuv+g3u2Yzabmfr48/SIDsag17FgwYIa+/Tu3Ztly5bx7LPP2j2++vTr14/du3fXSG7PRffu3QFLPVbnzp3x9fVl2bJltlaixmI48/d9rry8vGrUU9Wnb9++9O3bl5kzZzJ06FC+/vprhgwZgre3d5MfP2fOHFRVbdII4qZ8JoYOHcovv/xSY9vSpUsZOnRoo89vtXPnTtq1a0erVq2a/Jjmkq4xD7RifzZF5VVEB/vQv33tk5urGN/XMrni4u3pVJpkNWxx7srLy8nIyCAjI4M9e/bwwAMP2K7oATp37szRo0f55ptvOHToEO+8806tFhKAbt26MWTIEGbMmMGECRNqXPXef//9nDp1igkTJrBhwwZ27N7L6uXLePyh+zCZTKxfv56XXnqJTZs2cfToUX744Qeys7Pp1q1breOEhoYSHh7Oxx9/zMGDB/njjz9qJUv20K1bN06ePFmrCNyqe0IXqior+frTj9i+Zz9ffPEFH374YY19Zs6cycaNG7nvvvvYvn07e/fuZc6cOTVGT9nbjBkzWLNmDVOmTCE5OZkDBw7w008/NVgsbR3NtH79elJTU1m+fDn3338/Xbp0ISEhAR8fH2bMmMGjjz7K559/zqFDh1i3bh3/+c9/6ny+M3/fGzdu5NChQ/z666/cdtttTUpMrOLi4li2bBkZGRl1dpWmpKQwc+ZM1q5dS2pqKr/99hsHDhyw/d3ExcWRkpJCcnIyJ0+erLfoeNSoURQVFbFr164mxdWUz8S9997L4cOHefTRR9m7dy8ffPABCxYs4KGHHmry61+5ciWXXXZZk/c/F5IIeaCft1v6j6/oFYVO5/xzB9VnWMdWtAowkldSyeqDLXdSFe5vyZIlREVFERUVxeDBg9m4cSPffvstI0eOBGDcuHE89NBDTJkyhT59+rBmzRqefPLJOp/L2i10dk1NdHQ0q1evxmQykZSUxLhRw3jt2X/ROjwUnU5HUFAQf/31F1dccQVdunThiSeeYPbs2XVOJKjT6fjmm2/YvHkzPXv25KGHHuK1116z+/sCEB4eXm83RmJiIs++9CqfffA2wwf146uvvuLll18+vYOq0iU2it8Wfce2rVsYNGgQQ4cO5aeffmrR+XZ69+7NihUr2L9/PyNGjKBv37489dRTREfXv5ZiUlIS//3vfxk7dixdunRh0qRJJCQk8Ntvv9liffLJJ3n44Yd56qmn6NatGzfeeGO99Txn/r4vu+wyevXqxbRp0wgJCUGna/pX7+zZs1m6dCkxMTH07du31v1+fn7s3buXa6+9li5dunD33Xdz//33c8899wBw7bXXMmbMGEaNGkXr1q35v//7vzqPEx4ezjXXXMNXX33VpLia8pmIj4/n559/ZunSpSQmJjJ79mz+/e9/k5SU1KRjlJWV8eOPP3LXXXc1af9zpahSadqggoICgoODyc/PJygoSOtwzltZpYl+zy+lpMLEwvuG0deFW4QAnvhxB1+uO8qNA2J49brejT9AtJiysjJSUlKIj49vdtGlO3n++ef59ttv65+ZWlXJOXWK4tIyjEYjEa3CbWt5uaLyShP7MgtRUEiICsTLukRIaR7kHwPzGbNP67wguJ1lYVfhdLZv386ll17KoUOHCAgI0Doc5syZw8KFC/ntt9/q3aeh805Tv7+lRcjD/Lk3i5IKE21DfF1iSY3GXNHLUnT46+4M6R4TmioqKmLnzp289957PPDAA3XvVJqHmrmL8PKjtNdlEVGZBpm7LEmDizJ66fH10qOiUlBanfSU5kFuSs0kCCy3c1Nc+vW6s969e/Pqq6/WO7LO0by8vHj33Xdb/DiSCHkY6wKrV/WOcoklNRozOD6ccH9v8koqWXtIVqQX2pkyZQr9+/dn5MiRtbrFACjNddvkIMTPMvIpr7QSVNXSEtSQ/GOW/YTTmTx5Mr169eKll16qMfT/zB97rPt29OjRep8/ICCAo0ePcuedd9K1a1c7vKqGyagxD1JSUcUfeyz92Vf2jmpkb9eg1ykk9Yzk6/VH+WVHOhd2abkhlkI0ZO7cufUvKFmaC7lHgPpX/yP/GPgEu2Q3WbCvF+n5ZZSUV2EqK0R/drJ3NnMlVBSBsemzGAvHuvfee2vNgm3V3KkE6hIdHV1jOZK67ncUSYQ8yF/7symtNBET5kuvtsFah2M3V/aK4uv1R/l1VwbPj+95ukZBCGdQmmdLghrkwsmBt8HSPVZaaaK0rJwmVZeYHLNyvTg3YWFhhIW13LQkBoPhvKc3sBf5xvAgv+2yTGyV1D3SLbrFrAbHhxHm701uSSXrD5/SOhyPJ+MvztCUbqIzuXByEFS99lhhU1+C3qvxfYRohD3ON5IIeYhKk5nf91gSoct6RGocjX0Z9Dou625Z+PC33RkaR+O59HrLDOVnz2zs0SqKatcENcSFkwPrIqw5lV6oukZeh84LvLUflSRcn3XpjbNn6G4O6RrzEOsPn6KgrIpwf2/6x7r2kPm6XNo9gm82prF0dybPjuvhVi1ersJgMODn50d2djZeXl7NmivFbZUWQ1UTr1gVA5gNUFbWsjG1EFVV8VJNVFSZyPUOx6+igfWugtpAS64mLtyeqqqUlJSQlZVFSEiI7ULsXEgi5CGsLSWju0Wgd+FJFOszvFMr/Lz1pOeXsfN4Ab3auU8NlKtQFIWoqChSUlLqXH7CI1WVQVF20/b1bwVFR1o0nJaWX1pJYVkVRd56wrxVS32Uuer0DjqDZQ6h4hxARnmK8xcSEkJk5Pn1ckgi5AHMZvV0fVDPCI2jaRk+Xnou7NyaJbsyWLo7QxIhjXh7e9O5c2fpHrMym2Dew1CcDdTXMqSHpBehcw9HRtYi9mUU8PBXW/Dx0rPwvmF464ATW6EkB/zCIbov6FxvkWfhnLy8vM6rJchKEiEPsON4PhkFZfh76xnWseUWrtPaZT0iWLIrg992ZzL9spafe0LUTafTefTM0rVcNA0WTMSMWndR5nXzoOdVDg6qZfSONVKlGDh0qpytJ0q4qEtr6HSB1mEJ0SDpxPcA1iLpi7q2xsfLfa/GLk5og16nsDejkKM5JVqHI4RF93EUj/+MTPWsochBbeGGL6DneE3CagmKonBxgqXVeVn1eUcIZyeJkAdYVj2J4uhu7tktZhXi582gOMuXjYweE87kdwYxvPwd/un/Elz7H5i0GKbtgO7jtA7N7kZ3awPA77szZSoF4RIkEXJzJ/JK2Z1egKLAyK5ttA6nxV3a3Xo1WveK0EJo4bfdmZjR0ab3JdDrOogf4ba1MsM7tcLHS8eJ/DL2pBdqHY4QjZJEyM39sdeSEPRrH0qYv7fG0bS8ixMsyd7GI6coLHPdyemE+6ioMrNin2XkmLu3yoJl4MIFnSy1iNI9JlyBJEJuzpoIWRMEdxfXyp8OrfypMqusOnBS63CEYHNqLkXlVbQK8CaxXYjW4TjE6IRWDNHtpjx5AaSstIyeE8JJSSLkxkorTKw+aEkGLunmGYkQwKjqpM+aBAqhpeX7LX+HF3Zujc4N5/CqZfcirl95Od94v8AjhbNg3lXwVk/YvUjryISokyRCbmzNoZOUV5lpG+JL1wjXW8jxXFlbv/7cl43ZLMWaQlvWbrGLurbWOBIH2L0IFkxEX3TWrNIF6bBgoiRDwilJIuTGlp3RLeZJS04MjAsjwGjgZFE5O0/kax2O8GCZBWXszShEUWBEZzdPhMwmWDKDuieOrN625DHpJhNORxIhN6Wqqu1KdFSCm5+Az+Jt0NmKNaV7TGhpxX7LZ7B3uxD3H6yQugYKTjSwgwoFxy37CeFEJBFyF2aTpShxx3eQspJDmfkczyvF26BjSIdwraNzOFv3mCRCQkPWROiiLh5wMVLUxBFiTd1PCAeRJTbcwe5FlibpM67GIo0RJOkmUBJ/BX7envdrttZjbD+eT25xBaHufjUunE6VyWwbuegRiVBAE6cGaOp+QjiItAi5uurixLObpP3KM5nj9RaTQrZrFJi2IoJ86BoRiKrCmkOyyrVwvG3H8skvrSTY14tET1gEOHYYBEUD9dUjKpZlRWKHOTIqIRoliZAra6A40fqLvSjlDY8tTrygs6VOaOWBbI0jEZ5oxT5Lt+wFnVth0HvAqVanhzGvVt+omQyZ1eqz1JhX3HZGbeG6PODT6cYaKU7UKeBVdMJjixNH2BKhk7LmkXA4j6oPsuo+Dm74HIKiamzOIJzkoe+45dpqwvV5XvGIO5HixAYNjg/HW6/jeF4pKSeL6dA6QOuQhIfIKSpn+3HL1A0jPSkRAkuyk3Cl5QKsKJO520t5bkcIN5XE0Vfr2ISog7QIuTIpTmyQr7eeAXGhgKVVSAhHWXXwJKoK3aKCaBPko3U4jqfTWxaW7XUd7ftfhhkdf+3PlpZZ4ZQkEXJljRQnqlKcaJvEThIh4Ui22aQ9rTWoDkM6WFpmj+WWcvhksdbhCFGLJEKurKHiROsWDy9OtNYJrT10kkqTWeNohCcwm1X+OiCJkJWft8HWMvvXfhm4IJyPJEKurp7ixCLvCMt2Dy9O7B4VRJi/N8UVJrYezdM6HOEBdqcXcLKoAn9vPf1jQ7UOxylYE0JJhIQzkkTIHXQfB9N2ok76L4/rp3FTxRPsvH6lxydBADqdwvDq5TZWyTB64QDLq4fND+vUCm+DnGIBLqxOhNYdPkV5lWdO5yGcl3xK3YVOzwG/vnxVPIhkfU/6xbXSOiKnYe0e+0vqhIQDrDpo+Tu7ULrFbBIiA2kTaKS00sSmI7lahyNEDZIIuRHrdP4D48Lw8fLcuqCzWROh7cfyyC+p1Dga4c7KKk1sSc0DYHhHz1vjrz6KotgGLqyQ7jHhZCQRciNrDlkSIWtXkLCICvalU5sAzOrp90iIlrA5NZcKk5nIIB/iW/lrHY5TubCLtYtaPoPCuUgi5CaqTGbWHT4FwPCOkgidTbrHhCNYE+1hHcNRlPrW3PJMw6rPS3syCsgtrtA4GiFOk0TITWw7lk9ReRXBvl50jw7SOhynM+KMdcdkUjfRUqwL/A6TVtlaWgca6dwmAFWF9SmyELJwHpIIuYk1B09fiep1ciV6tsHx4XjpFY7llpKaU6J1OMINFZZVsv2YZVmNoVIfVKdh1e+LNWEUwhm4XCL0/vvvExcXh4+PD4MHD2bDhg317jt37lwURanx4+PjntPdW0eqyJVo3fyNBvq1t8zpYn2vhLCnDSmnMJlV4sL9aBviq3U4TsmaIK6VREg4EZdKhObPn8/06dN5+umn2bJlC4mJiSQlJZGVlVXvY4KCgkhPT7f9pKamOjBixyipqLJNFniBJEL1stYorD0sJ2Fhf9ZWjqFSo1evwfHhKAocyCoiq7BM63CEAFwsEXrjjTe46667uO222+jevTsffvghfn5+fPrpp/U+RlEUIiMjbT8REe63AOnGI5aRKtHBPsSF+2kdjtOyXo2uP5wjdULC7mz1QdItVq9Qf2+6RVpqGK2DO4TQmsskQhUVFWzevJnRo0fbtul0OkaPHs3atWvrfVxRURGxsbHExMRw9dVXs2vXLkeE61DW+qDhnVrJSJUGJMYEYzToOFlUwcGsIq3DEW7kVHEFe9ILAMsio6J+w2zdY9JFLZyDyyRCJ0+exGQy1WrRiYiIICMjo87HdO3alU8//ZSffvqJL7/8ErPZzLBhwzh27Fi9xykvL6egoKDGj7NbdVDmD2oKo0FvW/xxnXSPCTuy/j11jQikdaBR42icm9QJCWfjMonQuRg6dCgTJ06kT58+XHTRRfzwww+0bt2ajz76qN7HvPzyywQHB9t+YmJiHBhx8+UWV7C7+kp0WCe5Em3MkPjqk7AkQsKObPMHyWewUYPiw9DrFI7klHA8r1TrcIRwnUSoVatW6PV6MjMza2zPzMwkMjKySc/h5eVF3759OXjwYL37zJw5k/z8fNtPWlraecXd0tYezkFVoUtEAG0C3XNEnD0Nqb4aXXf4lNQJCbs5XR8krbKNCfTxomfbYEBahYRzcJlEyNvbm/79+7Ns2TLbNrPZzLJlyxg6dGiTnsNkMrFjxw6ioqLq3cdoNBIUFFTjx5mtlRNwsyS2C8HHS8ep4goOSJ2QsIOM/DIOZxejUyytHaJxw6R7TDgRl0mEAKZPn84nn3zCvHnz2LNnD//4xz8oLi7mtttuA2DixInMnDnTtv9zzz3Hb7/9xuHDh9myZQt///vfSU1N5c4779TqJdiddYbWIR3kBNwU3gYdA2It75WchIU9WLvFerUNJtjXS+NoXMPQDqcLpqVlVmjNoHUAzXHjjTeSnZ3NU089RUZGBn369GHJkiW2AuqjR4+i053O7XJzc7nrrrvIyMggNDSU/v37s2bNGrp3767VS7CrU8UV7M+0tGoMipfahKYa2jGcVQdPsu5wDpOGxWkdjnBxMn9Q8w2IC8VLr3Aiv4yjp0qIDZcFaoV2XCoRApgyZQpTpkyp877ly5fXuP3mm2/y5ptvOiAqbWyobg3qEhFAmL+3xtG4Dmvr2brDOZjNKjpZkkScI1VVz+ielouRpvLzNtA3JpQNR06x5lCOJEJCUy7VNSZqsk5INlhag5qlV9sQfL305JZUsj+rUOtwhAs7esoy8slLrzAwTrqnm2OI1AkJJyGJkAtbn1KdCEl9ULN4G3S2+YTkJCzOh/UzmNguBF9vvcbRuJYzF2CVOiGhJUmEXFR+SSV7MyzzB8lIleazzv4rEyuK87GhOhGSz2Dz9W0fUj3Te7nM9C40JYmQi9p45BSqCh1a+cv8QefAtu5YyinMZrkaFedGEqFzZzTo6R9raZm1tqwJoQVJhFyUddi8dIudm15tg/Hz1pNXUsneDKkTEs2Xnl/K0VMl6BRsX+iieax1VRskERIakkTIRdnqg6RQ+px46XUMiDs9ekyI5rJ+efeIDibQR+YPOheD408nQlInJLQiiZALKiyrZOfxfEBahM6HbVI3SYTEOZBusfPXt30oBp1CRkEZx3Jl3TGhDUmEXNCm1FzMKrQP8yMq2FfrcFyWdT6hDVInJM6BJELnz9dbT692lnXHpHtMaEUSIRe03jZ/kJyAz0fPtsH4eunJL5X5hETz5BSV29aqk/mDzs8gqRMSGpNEyAWdLpSW+qDz4aXX0S82BICNR3K1DUa4FOvfi8zqfv6sLWobj0giJLQhiZCLKamoYsex6vogaRE6b9ar+Y1yNSqaQbrF7GdAbBiKAodPFpNVWKZ1OMIDSSLkYrak5lFlVmkb4ktMmJ/W4bg8WyJ0REatiKbbcMTSKiuLHZ+/YD8vukYEArAxRVpmheNJIuRibN1iciVqF33bh2DQKaTny6gV0TQFZZXsPlE9q7vUB9nFYOkeExqSRMjF2AqlZdi8Xfh5G+jR1jJqZVOqnIRF4zZXj9qMDfcjMlhmdbeHgdWJkMwwLbQgiZALKas0kZyWB0iTvD0Nql6AdYM0y4smsNUHSWuQ3Vjfy70ZBeSXVmocjfA0kgi5kB3H86kwmWkdaCQuvLo+yGyClJWw4zvLv2aTtkG6oDPrhIRojBRK21+bIB/iwv1QVdgsLbPCwQxaByCazvpFPTAuFEVRYPciWDIDCk6c3ikoGsa8Ct3HaRSl67EutXEwq4hTxRUyHFrUq7TCxPZjeYAsb2Nvg+LDOJJTwoaUXC5OiNA6HOFBpEXIhWyqnrukf2yYJQlaMLFmEgRQkG7ZvnuRBhG6pjB/bzq1CQBgk7QKiQZsTcul0qQSGeRDTJjM6m5PpxdglSVvhGNJIuQizGbV9iU9sH2QpSWIuoZ7V29b8ph0kzWDdI+JpjizW0xRFI2jcS/WFrbtx/IprZBzl3AcSYRcxMHsIgrKqvDz1tOjclftlqAaVCg4DqlrHBafqxsUX10wLTNMiwbYuqelPsjuYsJ8iQgyUmVW2Zomn0PhOJIIuQjrCbhv+xD0JVlNe1BRZgtG5F6sLUK7judTUlGlcTTCGVWZzGw9mgdY6vSEfSmKYhsNK+uOCUeSRMhFWOuDBsSGQUATCwmbup+gXagf0cE+lqvR6i87Ic60N6OQkgoTgT4GurQJ1Doct2QdiSeJkHAkSYRchLVFaEBcKMQOs4wOo74aBQWC2lr2E002QOqERAOsfxf92oei00l9UEuwtrQlp+VRZTJrHI3wFJIIuYCM6uUfdAr0bR8KOr1liDxQOxmqvj3mFct+oskGyjT/ogGbUi2tstIt1nI6twkk0GigpMLE3oxCrcMRHkISIRdgXfqhe3QQAcbqqZ+6j4MbPoegqJo7B0Vbtss8Qs1mnd12S2oelXI1Ks6gqiqbz5y+QrQIvU6hb6wl0dycKgXTwjFkQkUXUKM+6Ezdx0HClZbRYUWZlpqg2GHSEnSOOrcJINjXi/zSSnadKKBPTIjWIQkncTyvlIyCMgw6Rf4uWlj/9qH8tT+bTam5TBoWp3U4wgNIIuQCatQHnU2nh/gRDo7IPel0CgPjQvl9TxabjpySLzxhY70Y6REdhK+3XGi0JOt5bou0CAkHka4xJ1dYVsme9AKgjhYhYXfWbg9plhdnsnZPD5CFVltcYkwIOsXSCpeeX6p1OMIDSCLk5LYezcOsWiYbiwz20Toct9e/uj5hU2ouqlrXzN3CE53unpZC6ZYWYDTQLSoIkAsS4RiSCDk527Ia0hrkEL3bBeOlV8guLOdYrlyNejSzCVJWUrLlG0Ky1qPDTH8ZMeYQtgsSmeldOIDUCDk565BdOQE7ho+Xnh7RwSSn5bHlaC4xYX5ahyS0sHuRZT2/ghP4Ad94Q5YSTpu0t2REpgP0jw3l87WpbDkqiZBoedIi5MQqa0zpLy1CjtJfhu96tt2LYMHEWuv5tVZzLNt3L9IoMM9h/QzuOlEgS96IFieJkBPbfaKA0koTwb5edGodoHU4HkOa5T2Y2WRpCaJ2fZht6tIlj1n2Ey2mbYgvkUE+mMwq29LytQ5HuDlJhJyYbdh8rEzp70jWRGhvRgFF5XI16lFS19RqCapJhYLjlv1Ei1EUxVYOsDlVZnoXLUsSISdm7R/vJyNVHCoiyIe2Ib6YVdiWlqd1OMKRijLtu584Z/3bSxe1cAxJhJyUqqq2E0B/SYQcTuqEPFRAhH33E+dsQNzpz6DZLFNZiJYjiZCTOpFfRmZBOXqdQmK7EK3D8TiSCHmo2GGW9fpqLWZspUBQW8t+okV1iwrC10tPQVkVh7KLtA5HuDFJhJyUdXr57lEypb8WrInQlqNyNepRdHoY8yoAaq1kqPr2mFdkPT8H8NLrSIwJBk5PIyJES5BEyElZWyL6tQ/RNhAPlRAZiJ+3nsKyKg7K1ahn6T4ObvgcU0Bkze1B0XDD5zKPkANJy6xwBJlQ0UltlUJpTRn0OvrEhLDmUA6bU3PpEhGodUjCkbqPY3FZH775bj4Dwit45G8XWrrDpCXIoSzrKx6SREi0KGkRckJllSZ2nbAstNqvvSRCWpH5hDzblrQC1pm7U9LlGogfIUmQBvpWt4innCwmp6hc22CE25JEyAltP5ZPlVmlTaCRdqG+WofjsfqdUSckPI+M2tReiJ83ndtYJpOVViHRUiQRckK2+YPah6IoMpGiVvrFWL4A5WrU85RUVLE3oxCAfrEh2gbj4Wx1QnJBIlqIJEJOyFYoLSdgTQX7edmuRrdUr/kmPMO2tHxMZpWoYB+igqVVVku2REi6qEULkUTIyaiqaiuUliZ57cmoFc8ks7o7D+tncPvxfMqrZI03YX+SCDmZo6dKOFlUgZdeoUd0sNbheDxbnZAkQh5lS+rp7mmhrfhW/oT5e1NRZWbn8QKtwxFuSBIhJ2O9Eu3ZNhgfLxmlorUB1YnQtmN5VFSZNY5GOIKqqmfU6YVoG4xAURRbQioXJKIlSCLkZLak5gFyJeos4lv5E+rnRXmVmd3pcjXqCVJOFpNbUom3QSetsk7Cuu7YJlmJXrQASYSczGZpkncqiqKcMZ+QnIQ9gbUwvnfbYLwNcop0BgNstXp5qKoseSPsSz7lTqS4vIq9GdUTKcqIMach8wl5FimUdj492wbjrddxsqico6dKtA5HuBlJhJzItrQ8zCpEy5Bdp9K//emRY3I16v6kUNr5+Hjp6dk2CJARnML+JBFyInIl6px6twvBoFPILCjneF6p1uGIFlRYVsm+TJlI0RnZuqglERJ2JomQE5H6IOfk662nR7RcjXqCbWn5qCq0C/WlTaCP1uGIM/SXqSxEC5FEyEmoqsrWtDxAWoSckcwn5BlkfTHnZb1A3J9ZSGFZpcbRCHciiZCTOHyymLySSowGHd2jgrQOR5xlQGwYIOsdubsz1/kTzqVNkA/tQn0xq5aWOyHsRRIhJ2G9Eu3dTobsOiNrvcie9EKKy6u0DUa0CLP59PI2kgg5J9vEinJBIuxIvnGdxFYplHZqUcG+tA3xxWRW2VbdhSncy6HsIgrKqvD10pMQFah1OKIOsvafaAmSCDkJKZR2fn2rl1uQq1H3ZP299m4XjJdeTo3OyHp+3Ho0F7NZprIQ9iGfdidQUFbJgawiQBIhZ3a6WT5P20BEi7AtbyOtsk4rISoQXy89BWVVHMou0joc4SYkEXICyUfzUFVoH+ZH60Cj1uGIevQ/Y4ZpmVjR/VgL4fvLxYjT8tLr6N3Osv6btMwKe5FEyAmc7hYL0TYQ0aBuUUEYDTrySio5fLJY63CEHeWXVHKwulW2r3wOndrpqSzytA1EuA1JhJyA9cpG5i5xbt6GM65GpVjTrWxNs/w+41v5Ex4grbLOzLbkjbQICTsxaB2ApzObVZKra076nkOTvKqqlFbJsg+O0ru9LxtTM9mQms6VieFahyPsZH1KOigV9I4Jp6RSFvV0ZgnRRlAqOJh9ioyCAoJ85WvMGfgafFEUReswzomiSrFDgwoKCggODiY/P5+gIPtPdLgvo5Ckt/7Cz1vP9qcvw9DM0SollSUM/nqw3eMSQgghmmr9zevx8/LTOowamvr9LV1jGrPWByW2C2l2EiSEEEKI8yNtiho7veJ8yDk93tfgy/qb19sxItGYpDf/Ii23lE8m9md4p1ZahyPO076MQq75YA0BRj3rZl6CTueazfueZP6mNJ5dtJuhHcP4z6SBWocjsHwXuSpJhDR2voXSiqI4XXOku+vfPoK0UyfYdbyMS7vJe+/qdp/IBtWbxHbhBBj9tQ5HNMGQuChQD7L9aClGvS96SV7FeZC+GA3lFldwONsyDLtvjIwYcxW24bsysaJbsA7DlvmDXEeXiEACjAaKK0zsyyjUOhzh4pqdCE2aNIm//vqrJWLxONYhux1a+xPq761xNKKpZJp/92Jtle0r01e4DL1OoU9MCCATK4rz1+xEKD8/n9GjR9O5c2deeukljh8/3hJxeQRZX8w1JUQG4uetp7CsioMyzb9LO1VcQcpJa6tsiLbBiGbpFysr0Qv7aHYi9OOPP3L8+HH+8Y9/MH/+fOLi4rj88sv57rvvqKysbIkYa3j//feJi4vDx8eHwYMHs2HDhgb3//bbb0lISMDHx4devXrxyy+/tHiMTWVb20gSIZdi0MvEiu5ia/WXaMfW/oT4SausK7HOxC+fQXG+zqlGqHXr1kyfPp1t27axfv16OnXqxK233kp0dDQPPfQQBw4csHecAMyfP5/p06fz9NNPs2XLFhITE0lKSiIrK6vO/desWcOECRO444472Lp1K+PHj2f8+PHs3LmzReJrjiqTmW3H8gCZUdoV9ZerUbdgbZWVz6DrsdZVHskpIaeoXONohCs7r2Lp9PR0li5dytKlS9Hr9VxxxRXs2LGD7t278+abb9orRps33niDu+66i9tuu43u3bvz4Ycf4ufnx6efflrn/m+//TZjxozhn//8J926deP555+nX79+vPfee3aPrbn2ZhRSUmEi0Gigc5sArcMRzWRtxdssV6MuzTZ9hbTKupxgPy/buVMGLojz0exEqLKyku+//56rrrqK2NhYvv32W6ZNm8aJEyeYN28ev//+OwsWLOC5556za6AVFRVs3ryZ0aNHnw5ep2P06NGsXbu2zsesXbu2xv4ASUlJ9e4PUF5eTkFBQY2flmBtku/TPkTmLXFB1uVQDmUXk1dSoXE04lxUmcxsS8sHTtebCNdiTWClZdZ1fbEulUe/28a6wzmaxdDsRCgqKoq77rqL2NhYNmzYwKZNm7j33ntrTF89atQoQkJC7BknJ0+exGQyERERUWN7REQEGRkZdT4mIyOjWfsDvPzyywQHB9t+YmJizj/4OkihtGsL8/cmvpVlzpmtaXnaBiPOyd6MQkorTQT6GOjUWlplXZG1S1NaZl3XrzszWLDpGAeytBt40uxE6M033+TEiRO8//779OnTp859QkJCSElJOd/YNDFz5kzy8/NtP2lpaS1ynJsHxzL1ks5cnNCmRZ5ftDzb1aichF2SrVU2RlplXZV1Rv7tx/KoNJm1DUY0m8msklx9IWktftdCs2eWvvXWW1sijka1atUKvV5PZmZmje2ZmZlERkbW+ZjIyMhm7Q9gNBoxGo3nH3AjBsWHMSg+rMWPI1pOv9gQvt9yTJrlXZQUSru+Dq0CCPb1Ir+0kj3pBfRuF6J1SKIZDmQVUlRehZ+3nq4RgZrF4TIzS3t7e9O/f3+WLVtm22Y2m1m2bBlDhw6t8zFDhw6tsT/A0qVL691fiOawtgglH83DJBMruhxrga10T7sunU6hrwyjd1nWKWS0XnTcZRIhgOnTp/PJJ58wb9489uzZwz/+8Q+Ki4u57bbbAJg4cSIzZ8607f/ggw+yZMkSZs+ezd69e3nmmWfYtGkTU6ZM0eolCDci0/y7rpNF5Rw9VYKiWAYsCNfVv70seeOqznfRcXtxqUVXb7zxRrKzs3nqqafIyMigT58+LFmyxFYQffToUXS607ndsGHD+Prrr3niiSf417/+RefOnfnxxx/p2bOnVi9BuBHrNP+rDp5ky9FcukcHNf4g4RSsrQed2wQQ5OOlcTTifPSTgmmX5SzTV7hUIgQwZcqUelt0li9fXmvb9ddfz/XXX9/CUQlP1a/96UTo70NitQ5HNJF0i7mPxJgQdAoczysls6CMiCAfrUMSTZBXcsai4xp/Dl2qa0wIZ2Nb70iuRl2K9fcl8we5vgCjga6RltZY+Ry6jq3VFyPxrfwJ03jRcUmEhDgPMs2/66k0mdl+PA+QFiF3YVt3TEZwugzr76qvE9ToSSIkxHkI9vOiU/U0/1ulWNMl7EkvoKzSTLCvFx2qJ8UUrk0mVnQ9zlIfBJIICXHerKNWNsvVqEuwdp/0leVt3Ib1y3Tn8QLKq0waRyMaYzKrJDtRnZ4kQkKcJ+vQT6lPcA2bnegELOwjNtyPcH9vKkxmdh5vmfUhhf3szyykuMKEv7eerpHaTaRoJYmQEOfJ+oW6/Vi+TPPvArbIjNJuR1EU28ijrdIy6/Ss3WKJMSHonaBVVhIhIc5Tx9YBBPkYKK00sTddJlZ0ZlkFZRzPK0WnWE7Cwn1YE1spmHZ+W52sVVYSISHOk2WafzkJuwLr78c6K7hwH9aRY5tTc1FVWfLGmTnLjNJWkggJYQf9JBFyCbaJFKVbzO30bheCQaeQWVDOifwyrcMR9ThzIsU+Mc7xOZRESAg7sF7ZyPBd52b9/ThLk7ywH19vvW2ZG/kcOi9nmkjRShIhIeygT0wIigLHckvJKpSrUWdUUWVmx/F8QAql3ZWtZVYSIaflTBMpWkkiJIQdBPp40TXCMgx0S2qetsGIOu06kU9FlZkwf2/iwv20Dke0gH5SMO30nGkiRStJhISwEymYdm7W+qC+MSEoivZDdoX9WQumd58ooLRCJlZ0Ns42kaKVJEJC2El/WYDVqZ0eqeI8J2BhX21DfIkIMlJlVtl+LE/rcMRZnG0iRStJhISwE+vV6Pbjli4Y4VzOXFpDuCdFUc4YwZmnbTCiFmsRu7NMpGgliZAQdhLfyp9QPy8qqszsOpGvdTjiDOn5paTnl6HXKSS2C9E6HNGCZGJF52W9GBngZK2ykggJYSdyNeq8rAXsCZGB+MtEim6t7xkjx2RiReeyybq8TVyYxpHUJImQEHYko1ackzOOVBEto2fbILz1OnKKKzh6qkTrcES1rMIyjp4qQVEs0404E0mEhLAja/3JVimYdiq2iRSdZEp/0XKMBj0928rEis7G2irbpU0gwb5e2gZzFkmEhLCjxHaWIsAT+WWk55dqHY4AyipNtpotaRHyDFIn5Hw2p54CoH+c830GJRESwo78jQYSImViRWey60Q+lSaVVgHetA+TiRQ9gTXh3SyfQadhbZ3r74QXI5IICWFnsgCrc7EmpH3bh8pEih7CWqu3L6OAovIqjaMRZZUmdh4vAJxzeRtJhISwM+sHXeoTNGI2QcpK2PEdpKxka+pJQLrFPElEkA9tQ3wxq7AtLU/rcDzezuP5VJjMtArwJtYJl7eRcaRC2Jn1C3fXiXzKKk34eOk1jsiD7F4ES2ZAwQnbpqcJx6S7lX7th2gYmHC0frGhHM8rZUtqLsM7tdI6HI9mG6zgpK2y0iIkhJ3FhPnSKsCbSpMqEys60u5FsGBijSQIoLWawxyvt+hTtFKjwIQW+leP4JQuau3Z6oOcsFsMJBESwu4URbFN6ibdYw5iNllagqg9gZ5OARQw/v4vy37CI5ye0ysPs1kmVtSKqqq28+AAJxwxBpIICdEiTi/AmqdtIJ4idU2tlqAz6QAKjlv2Ex6hW1QQPl468ksrOXyyWOtwPFZqTgk5xRV463X0iA7WOpw6SSIkRAuwDd89KtP8O0RRpn33Ey7PS6+jd/W6clukZVYz1tagnm2DnLZeUhIhIVpA73bBGHQK2YXlHMuViRVbXECEffcTbkEmVtTeJlu3mHOtL3YmSYSEaAE+Xnp6RFum+ZeTsAPEDoOgaKDuESkqCgS1tewnPEY/qdXT3JZU51/nTxIhIVqItWB6q6xE3/J0ehjzavWNmsmQat0y5hXLfsJj9KseOXYgq4j80kptg/FA+aWV7M8qBJx7nT9JhIRoIf1kYkXH6j4ObvgcgqJqbC42Rli2dx+nUWBCK+EBRuKqJ/DbKi2zDrf1aC6qCu3D/GgT6KN1OPWSREiIFmKtT9iTXkBphQzbdoju42DaTswT/8sMpnJTxRMcvmWtJEEe7Mxh9MKxrN1iA5x0/iArSYSEaCHRwT5EBBmpMqtsP5andTieQ6fngF9f5pcNYZu+F93aOvdJWLQs29p/0jLrcJurW+H6SSIkhGdSFKXGMHrhOJtSTwHQJyYEL72c5jyZ9TOYnJaHSSZWdJgqk5nk6lY4Z51R2krOEEK0IJlYURubj1gSz4FOOpOtcJyukYH4e+spKq/iQHXhrmh5ezMKKa4wEWg00CUiUOtwGiSJkBAtyDpybItMrOhQ1rlL+jvx3CXCMfQ6hT7Vo8dk4ILjWKcN6dM+BL3O+RZaPZMkQkK0oJ5tgzAadJwqrpBp/h0kq6CMo6dKUBToW/0FKDxb//bSMutozr7Q6pkkERKiBRkNehJjQgDYdOSUtsF4CGtrUNeIQIJ8vDSORjiDvjLDtMNtOmIdMeb8rbKSCAnRwqx1KhtS5CTsCJts9UHOfwIWjtEvxvIZTDlZzKniCo2jcX8Z+WUczytFp0BijHMutHomSYSEaGHWL2TrSCbRsjZXv88DpFBaVAv286JTmwBAhtE7grXlrWtkEIEu0CoriZAQLaxfbCiKAqk5JWQVlGkdjlsrqahi14kCwDVqE4TjWOuENkki1OJOd4u5xmdQEiEhWliQjxcJkZYFWOUk3LKS0/KoMqtEBvnQNsRX63CEE7G2EG6UWr0WZ503zVUuRiQREsIBBspJ2CGs8wcNiAtFUZx7yK5wrEHxli7q7cfyKKuUJW9aSmmFiV3H8wFJhIQQZxhgrRM6Ii1CLWmTi6xtJBzPsvCnkUqTSnJantbhuK2tablUmVUigoy0C3WNVllJhIRwAGuL0K4T+RSVV2kcjXsymVVbkeYAGTEmzqIoCgPjrRck0jLbUjamnB616SqtspIICeEAUcG+tA3xxazCVpnLpEXszyyksKwKP289CZHOPaW/0Mag6gR5g7TMthhr97+1K9IVSCIkhINYTwwb5STcIqzdYv3ah2KQhVZFHawF01tSc2UB1hZQaTLbWmUlERJC1GI9CUuzfMuwvq+uUqApHC8hMohAo4Gi8ir2pBdoHY7b2XWigJIKE8G+XnRp4zqtspIICeEg1okVtx7No9Jk1jga96KqKhtSLInQYBe6EhWOpdcp9LfN9C4XJPa2sfo9HRAbis7JF1o9kyRCQjhIp9YBBPt6UVppYvcJuRq1p2O5paTnl2HQKfRtLy1Con7WCxKZysL+NlS/pwNd7GJEEiEhHESnU2zDuuUkbF/Wq/ve7YLx9dZrHI1wZmfW6qmq1AnZi9ms2rqnXW2dP0mEhHCgAXI12iKsidCg+HCNIxHOrne7YLwNOk4WlXMkp0TrcNzGoewicksq8fHS0aut8y+0eiZJhIRwoIG2gmm5GrUna5O81AeJxhgNehLbWb6oN0qdkN1YP4N9Y0LxNrhWauFa0Qrh4npVX43mFFeQcrJY63DcQlZBGSkni1EUbIWwQjRkoG0+IUmE7MXaKutq9UEgiZAQDmU06OnTLgSQ7jF7sX6ZdY8KIsjHS+NohCsYGC9d1PZmbV0b5GL1QSCJkBAOZy3WXH9YTsL2cLo+yPVOwEIb/WNDURRIzSkhq6BM63Bc3rHcEk7kl6HXKfRtH6J1OM0miZAQDja4Q3UilHJK6oTsQOYPEs0V5ONFt8ggQGZ6twdry1rPtsH4Gw0aR9N8kggJ4WD9Y0Mx6BSO55VyLLdU63BcWl5JBXszCgHXG7IrtGUduCDdY+dvQ/VCq4NctEZPEiEhHMzP20Dv6lEr6w7naByNa7NezXds7U94gFHjaIQrsdYJyQzT52+ji84fZCWJkBAaGNzBMt/NOqkTOi8bUiyJpMwfJJrLWtS7J6OA/NJKjaNxXTlF5RzMKgIkERJCNIO1nmV9irQInQ+pDxLnqk2QDx1a+aOq0ip0Pqytsp3bBBDq761xNOdGEiEhNDAgLgy9TuFYbinHcmV223NRVF7Fzuo122TEmDgXQzpaW2blguScmE1kbf+dcbo13ND6CJhNWkd0TiQREkIDAUYDPaunoZdh9OdmS2ouJrNKu1BfokN8tQ5HuKAh1V3Uaw9JItRsuxfBWz2ZuP9+3vF+j7sOTYW3elq2uxhJhITQyBDpHjsvMn+QOF9DOpyuE8orqdA4GheyexEsmIhacKLm9oJ0WDDR5ZIhSYSE0Ij1anS91CecE6kPEuerTaAPHVtLnVCzmE2wZAagotS6s3petCWPuVQ3mSRCQmhkQFwouurZbdPzZT6h5iirNJGclgfIiDFxfmzdY1In1DSpa+DslqAaVCg4btnPRUgiJIRGAn286BEtdULnYuvRPCpMZtoEGokL99M6HOHChnaUqSyapSjTvvs5AUmEhNDQkA5SJ3Qu1h46CcCwjuEoSu0GeiGaanB1i+Ke9AJyi6VOqFEBEfbdzwlIIiSEhqwnYbkabZ7V1aN8hnVspXEkwtW1DjTSuU0AIPV6TRI7DIKi66wQslAgqK1lPxfhMonQqVOnuOWWWwgKCiIkJIQ77riDoqKiBh8zcuRIFEWp8XPvvfc6KGIhGjcwPgxFgZSTxbIKdhMVlVexrbo+yNqtIcT5GNJB5hNqMp0exrwKqJhrrRldnRyNecWyn4twmUTolltuYdeuXSxdupTFixfz119/cffddzf6uLvuuov09HTbz6xZsxwQrRBNE+zrRfcoyyrY6+RqtEk2HjlFlVklJsyXmDCpDxLnTxKhZuo+jmd8HiODs0ZsBkXDDZ9D93HaxHWODFoH0BR79uxhyZIlbNy4kQEDBgDw7rvvcsUVV/D6668THR1d72P9/PyIjIx0VKhCNNvg+HB2nShg3eEcxiXW/7csLKyT3w3rIN1iwj4GV9fq7c0o5FRxBWEuulSEo2QVlDEvrzdfKu+wbVIAAZU5lpqg2GEu1RJk5RItQmvXriUkJMSWBAGMHj0anU7H+vXrG3zsV199RatWrejZsyczZ86kpKTh5QzKy8spKCio8SNES7IWTMvVaNOssRZKd5JuMWEfrQKMdImorhOSz2GjrFMNJESFEJAwCnpdB/EjXDIJAhdJhDIyMmjTpk2NbQaDgbCwMDIyMup93M0338yXX37Jn3/+ycyZM/niiy/4+9//3uCxXn75ZYKDg20/MTExdnkNQtRncHw4igKHs4vJyJc6oYbklVSwq3p9MakPEvYk3WNNZ32PrO+Zq9M0EXrsscdqFTOf/bN3795zfv67776bpKQkevXqxS233MLnn3/OwoULOXToUL2PmTlzJvn5+baftLS0cz6+EE0R7OdF7+p1x1YfPKlxNM5t3eEcVNWy0nWbQB+twxFuZGgHGcHZVNbu6aFukghpWiP08MMPM3ny5Ab36dChA5GRkWRlZdXYXlVVxalTp5pV/zN48GAADh48SMeOHevcx2g0YjQam/ycQtjD8E6t2HYsn1UHT3Jt/3Zah+O01tiGzbvHCVg4j8HVX+r7MgvJKSonPEC+B+qSnl/KkZwSdAoM6uAey9tomgi1bt2a1q1bN7rf0KFDycvLY/PmzfTv3x+AP/74A7PZbEtumiI5ORmAqKioc4pXiJZyQadWfLD8EKsOnkRVVZkksB7WRGiozB8k7CzM35uEyED2ZhSyPuUUV/SS74m6WFuDerYNJsjHS+No7MMlaoS6devGmDFjuOuuu9iwYQOrV69mypQp3HTTTbYRY8ePHychIYENGzYAcOjQIZ5//nk2b97MkSNHWLRoERMnTuTCCy+kd+/eWr4cIWrpFxuK0aAju7CcA1kNz4/lqbIKyjiYVYSinC4wF8KerDUvq6SLul7u1i0GLpIIgWX0V0JCApdccglXXHEFF1xwAR9//LHt/srKSvbt22cbFebt7c3vv//OZZddRkJCAg8//DDXXnst//3vf7V6CULUy8dLz6DqVdRXHZCTcF2sI1V6RAcR4ifDm4X9XdDJ0tIon8H6WT+HQ9yoe9ol5hECCAsL4+uvv673/ri4OFT19DSXMTExrFixwhGhCWEXF3RqxcoDJ1l98CS3XxCvdThOZ81BWVZDtKwhHcMx6BSOniohNaeY2HB/rUNyKmmnSjiWW4pepzAwzn1aZV2mRUgIdze8+mp03eEcKk1mjaNxPmsOW67SZdi8aCkBRgP92ocCsFJahWpZsT8bgH7tQwgwukw7SqMkERLCSXSPCiLUz4viCpNtLS1hkXaqhLRTpRh0CoPc6EpUOJ8RnS0XJCsPZGscifOxvicXdm58kJMrkURICCeh0ykMs9YoSLFmDdbZpPvEhODvRleiwvmM6GL5kl9zKIcqaZm1qTKZbd3T1vfIXUgiJIQTkWLNuq0+aB02L91iomX1ahtMsK8XhWVVbDuWr3U4TiM5LY/C8ipC/LzoVT0BrLuQREgIJ2JNhLam5VFYVqlxNM7BZFZtTfIj3KxJXjgfvU5hePU6dnJBctpf1e/F8E6t0Ovca54zSYSEcCIxYX60D/PDZFbZkCJT/QPsOJ5PbkklgUYDfduHaB2OcFdmE6SshB3fcW3YEXSYpU7oDH9VF0pf5IYXI9LZLoSTGd6pFUc3HGXVwZNc0i1C63A0t2Kf5QQ8vFMrvPRy7SZawO5FsGQGFJwA4BJglTGM549NoqBsoNvMoHyu8koq2H4sD4ARXdxv+go5qwjhZKzdY7IAq8WK/ZZ1Bi/q6n5XosIJ7F4ECybakiCrSOUU7xve5PCK/9MoMOex+mAO5urFjqOCfbUOx+4kERLCyQzrGI6iwP7MIrIKyrQOR1N5JRUkV08lcJGbjVQRTsBssrQEoda6y/rlGLfpect+Hszda/QkERLCyYT6e9Mz2jIqw9OH0a86eBKzCl0iAogOcb8rUaGx1DW1WoLOpFMgpDLLsp+HUlXVNrnkhW7YLQaSCAnhlC6ontTNOpOrp7LWB0lrkGgRRZlN2i0n42gLB+K8DmUXczyvFG+DjsHx7jl9hSRCQjihkdVf/H/tz8Zkrt1s7wlUVbUlghd1aaNxNMItBTRtMMLWXJ8WDsR5WbvFBsWF4eut1zialiGJkBBOqF9sKIFGA7kllbbRGp5mb0YhWYXl+HrpGRAXqnU4wh3FDoOgaKDueXFU4IQazg85MQ4Ny5lYh81blx5xR5IICeGEvPQ6W/fY8n2e2T1mfd1DO4bj4+WeV6JCYzo9jHm1+sbZyZACKDxbeSurDuV65HIb5VUm1h22zGfmroXSIImQEE5rZPVw8eUeWidkGzYv9UGiJXUfBzd8DkFRNbcHRWO+bh7rjMMpKKuyjV70JJuP5FJaaaJVgJFuUYFah9NiZEJFIZyUtS5m+7E8corKCQ8wahyR4xSVV7HpSC5wOiEUosV0HwcJV1pGhxVlWmqHYoeh1+m5cMdW/rvtBH/szWJAXJjWkTrUCttq861QFPdaVuNM0iIkhJOKDPYhITIQVcU2fNVTrDl4kiqzSly4H7Hh/lqHIzyBTg/xI6DXdZZ/dZbu2IsTLIn4H3uztIxOE8v3Vg9WcPOLEUmEhHBiI7taWoX+3OdZJ+HTo8Xc+wQsnN9FXdqgKJbi/RN5pVqH4zBpp0rYl1mITnH/z6EkQkI4sYsTLInQ8n3ZHlOsWWPYvJtfiQrnF+bvTd+YEMCzLkisr3VAbBghft4aR9OyJBESwon1ax9CiJ8X+aWVbE7N1TochziUXcyx3FK89TqGdHDPCdyEa7FekPzpQd1jy/ZYXuvF3dx/Di9JhIRwYga9jlHV3WPLPOQk/Psey2y/gzuE4ect4zmE9kZVJ0KrD+ZQVun+646VVFSx9nAOAJckSCIkhNDYJdVXZNYEwd39vtvyOi/r3rRZf4Voad2jgogM8qG00sS66gTBna0+mENFlZmYMF86tQnQOpwWJ4mQEE7uwi6tMegUDmcXk3KyWOtwWlROUTmbj1q6AC/pJomQcA6KotguSJbudv8Lkj/2Wl7jJQkRbj1s3koSISGcXJCPF4M7WOYvWebmrUJ/7M1CVaFHdJCsNi+cyqXVLZS/78nEbFbBbIKUlbDjO8u/ZvfoMjObVVt90CgP6BYDmVBRCJdwSUIEqw/m8PueTO4c0UHrcFqMtftvtLQGCScztGM4AUYDmQXlpK7+hviNz0HBidM7BEVbluvoPk67IO1ga1oeWYXlBBoNDPWQwQrSIiSEC7AmBhuP5JJbXKFxNC2jrNLEX/stE0deKvVBwskYDXou6tqaJN0G4pbdWzMJAihIhwUTYfcibQK0k992ZwAwMqEN3gbPSBE841UK4eLahxq5odURrmQ121ctdptm+DOtOXSS0koTkUE+9IgO0jocIWq5rFsrnvb6vJ57Vcs/Sx5z2c+nqqr8tsvSKpvUw3MuRqRrTAhnt3sRLJnBrKIT4A2sBXa5RzP8mX7dWd0t1r2NRxRoCtdzid8hApRTDeyhQsFxy5pl8SMcFpe9HMgqIuVkMd56nW1We08gLUJCOLPdiyzN7Wc1w6tu0gxvVWUy25rkL+8Z1cjeQmgjoKKJQ+eLXHNQw2+7LJ/B4Z0s9VCeQhIhIZyV2QRLZmBrcj+D4gbN8Gdan3KK3JJKQv28GBzvWSt8CxcS0MTuoqbu52R+tXWLRWociWNJIiSEs0pdU7sgs4YzmuFd3P92pgNwWfdIDHo5LQknFTsMU0AU5trXJtUUCGoLscMcGZVdHMstYcfxfHQKjPawwQpyxhHCWTW1ed1Fm+GtTGbVdiV6eS/PuhIVLkanR3/FLBSFOpKh6rq2Ma+ATu/oyM7b/3ZYusUGxIXRKsCocTSOJYmQEM7KzZvhrTan5pJdWE6gj4FhHVtpHY4QDes+jj97zyaDs7pwg6Lhhs9ddgDD4u2W1uexvT2vRs9zqqGEcDWxwywn14J06qoTUgHFRZvhz2TtFru0W4THzFsiXFuP0X9n2MYIBip7mTOuLaERMZbPoQu2BAGknSph2zFLt9gYDxysIGcdIZyVTm8ZIg/Ymt2rmVVQVSgf/ZLLnnzB0i32yw5LInR5L887AQvXFBHkQ7/YcNaZu7OwaqhlqLwLfw5/rv4MDukQTutAz+oWA0mEhHBu3cdZmtuDaiYJ2bpW/KNyGr8zWKPA7GN9Sg6ZBeUE+Ri4sIt0iwnXcUV14m5NIlzZz9str+Gq3tEaR6IN6RoTwtl1HwcJV1pGhxVlQkAE8/aG8euKIyjbTnClC/fp/3ebpS7hil5RGA2ue0UtPM8VvaJ4bvFuNqfmknaqhJgwP61DOidHThaz43g+ep3iUbNJn0lahIRwBTq9pfm913UQP4KrEmMA+GNfFoVllRoHd27Kq0z8Uj1SZVwfz7wSFa4rIsiHYR0ti5L+lHxc42jOnfViZFjHcMI9bLSYlSRCQrigblGBdGztT0WV2bY2kKv5a/9J8ksraRNoZHC8Z6xyLdzL1X3aArBw63FUtd7JhZyWqqos3GpJ4qyvxRNJIiSEC1IUhXGJp0/CrmhR9ZXo2MRo9DpZW0y4njE9IzEadBzKLmbXiQKtw2m2bcfyOXyyGB8vHWN6eu4cXpIICeGi/tbPkgitPnSSE3mlGkfTPIVllSytXltsXKJ0iwnXFOTjxehulrqaH13wguSHLccAy5IanrS22NkkERLCRcWE+TE4PgxVdb1WoZ+3p1NWaaZja396twvWOhwhztnV1fVti7adwFT/2htOp6LKbKsPuqav53aLgSRCQri0a/u3A+D7Lcdcqkbh282WK9HrB8SgKNItJlzXyK5tCPHzIquwnL8OZGsdTpOt2J9NbkklrQKMXNDJs6eukERICBd2Ra8ofL30HM4uJjktT+twmuRgVhGbU3PR6xT+5uFXosL1eRt0thaV+RvSNI6m6b7bbIl1fJ9oj1/o2LNfvRAuLsBosBU5flfdyuLsvtt8DB1m7ok5TpvUxZCyEswmrcMS4pzdONAyncXvezI5WVSucTSNyyosY9meLACuG9BO42i0J4mQEC7uuurusZ+ST1BcXqVxNA2rMpk5tek7Vhmn8mjmI/D9HTDvKnirJ+xepHV4QpyThMggEmNCqDKrtgJkZ/bd5mNUmVX6tQ8hITJI63A0J4mQEC5uaIdw4sL9KCqvshU/Oqtdy77klarXiFRO1byjIB0WTJRkSLisGwdYWoXmb0xz6no9s1nlm+ouvAmD2mscjXOQREgIF6fTKdw82HJC+3J9qvOehM0m2q1/FqjrxFMd85LHpJtMuKSxiZZ6vUPZxWxKzdU6nHqtOZTD0VMlBPoYPHZtsbNJIiSEG7iufwzeBh07jxew/Vi+1uHUKX37H4SbTlL/3IkqFBy3rKkmhIsJ9PFibKJl3b/P16ZqHE39/m/DUcAyZN7XW9b3A0mEhHALYf7eXFm9GvZX653zJLx2266m7VjkmkuGCDFxaBwA/9uRTmZBmbbB1CEjv4xfd1kmMr1poHSLWUkiJISbuKW6e2zRthPkFldoHE1NpRUmFqeYm7ZzgGeugC1cX8+2wQyKC6PKrPLVOue7IPl87RGqzCqD4sPoHi1F0laSCAnhJvrHhtIjOoiySrPTtQr9lHyc5WWdyVLCUamvb0yBoLYQO8yhsQlhT5OGxQHw9YajlFc5T71baYWJr6u7xW4fHq9xNM5FEiEh3ISiKNx9YQcA5q5JpazSOU7CZrPKxysPY0bHth4zq9Ogs5Oh6ttjXgGd1C0I13VZjwiign04WVTB4m3pWodj88PWY+SVVBIT5sul3aXV9UySCAnhRq7oFVV9Ei5nUbJzDKVfuieTw9nFBPoYGHLVZLjhcwiKqrlTULRle/dxmsQohL146XX8fUgsAJ+sPIzZCdYfM5tVPl2VAsDkYfHo6x+x4JEkERLCjXjpdbZm74+d4CSsqiofrjgEwK1DYgn08bIkO9N2wqTFcO1/LP9O2yFJkHAbfx8cS4DRwN6MQpbtzdI6HP7Ym8Wh7GICjAZukJmka5FESAg3c9OgGAKNBg5mFWl+Et6QcoqtR/PwNui47cy6BJ0e4kdAr+ss/0p3mHAjwX5etlah9/48qOncXqqq8vayAwD83XoxImqQREgINxPo48WtQy0n4TeX7te0Vej95ZbWoOv6t6N1oFGzOIRwtDsuiMdo0LEtLY81h3I0i2P5vmx2HM/H10vPXSOkSLoukggJ4YbuGtGBAKOB3ekF/LY7Q5MYNqSc4q/92Rh0CvdUF3EL4SlaBxptS1i8s+yAJq1CZ7YG3To0lvAAuRipiyRCQrihUH9vbh8eB8CbSw84vFVIVVVmLdkLwA0DY4gN93fo8YVwBndf2AFvvY71KadYvj/b4cdfsT+b5LQ8fLx03DVCLkbqI4mQEG7qjhEdCPQxsC+zkP9ud+wIsj/2ZrEpNRejQcfUizs79NhCOIvoEF8mV1+QvPq/vZgceEFSZTLz8i+Wi5G/D46VrukGSCIkhJsK9vXi7uqrwFlL9lFa4Zh5hUxmldd+3QfA5GFxRAb7OOS4Qjij+0d2ItjXi70Zhfyw5ZjDjjt/Uxr7MgsJ9vViysWdHHZcVySJkBBu7M4RHWgb4svxvFLmVA9jb2lfrU9lb0YhgT4G7r2oo0OOKYSzCvbz4v5Rls/B7N/2U1JR1eLHLCir5I3f9gMwbXRnQvy8W/yYrkwSISHcmK+3nieu7AbAhysOkXaqpEWPl1VYxmtLLK1B/0zqSqi/nICFmDg0jrYhvmQUlPHm0v0tfrz3/jhITnEFHVr524bxi/pJIiSEmxvTM5JhHcOpqDLzzKJdLTp65aWf91BYXkXvdsHcMlhOwEIA+HjpeWF8TwD+syqFHcfyW+xY29Ly+PfKwwA8fmU3vPTyNd8YeYeEcHOKovDsuB546RWW7c3i203nUKdgNkHKStjxneVfc+16oz/3ZfFj8gkUBV4Y31Om8RfiDKMS2jAuMRqzCjO+306lyWz3Y5RVmnjk222YVRiXGM0l3WRNsaaQREgID9A5IpCHL+sKwLP/3cXRnDq6yOpLdnYvgrd6wryr4Ps7LP++1dOyvVpWQRmPLNgGwKShcfRuF9LSL0kIl/PU2O6E+HmxO72Ad6rn97Gnt5cd4EBWEa0CvHlmXA+7P7+7kkRICA9x14gODIoPo7jCxEMLkqmoOuOKtL5k57cnYcFEKDhr+H1BumX77kWYzSoPLUgmp7iChMhAHrs8wbEvTAgX0SrAyLPVCcq7fxzkTzsugfPn3izbun4vjO9FmNTnNZkkQkJ4CL1OYfb1iQQaDWxOzeXxhTss9UK7F9WT7JyANe8AddUUVW9b8hhvLd3D6oM5+Hrpee/mvvh4ybphQtTn6j5tubW6gHna/GS7DGA4lF3E1P/biqrChEHtGdMz8ryf05NIIiSEB4kJ8+OdCX3RKfDt5mPM+XM/LJlB3clOY1QoOM6GFT8D8Pz4nnRqE2jXeIVwR09c1Y3EmBDySyuZ/NkGsgvLz/m5ThVXcNfnmygsr2JgXKitxUk0nSRCQniYUQltbCfLv35fVLslqJnakMdDo7twXf929ghPCLdnNOj54JZ+RAf7cCi7mFv+vY6couYnQ5kFZdz40VoOZxcTHezDB7f0x9sgX+vN5TLv2IsvvsiwYcPw8/MjJCSkSY9RVZWnnnqKqKgofH19GT16NAcO2L9ATQhXc+vQOO65qANtyDvv5+rbvStTL5GZa4VojrYhvnx91xAigozszyzixo/XcSCzsMmPP5xdxA0freVAVhGRQT58fsdgWUbjHLlMIlRRUcH111/PP/7xjyY/ZtasWbzzzjt8+OGHrF+/Hn9/f5KSkigrK2vBSIVwDY+NSeCSgb3P+fFmFfK92jDppptRFBkqL0RzxbXytyVDB7OKGPveKr7ZcLTBNcnMZpXPVqdwxTsrSc0pISbMl2/vHUqnNgEOjNy9KGpLzq7WAubOncu0adPIy8trcD9VVYmOjubhhx/mkUceASA/P5+IiAjmzp3LTTfd1KTjFRQUEBwcTH5+PkFBQecbvhDOxWyidFY3jKWZ1Dftj+UEoaCcUUdkVi3zEyk3fA7dxzkiUiHcVnZhOdMXJLPywEkAOrTy544R8Qzr2IrYMD/MqkpmYTm/7szgm41H2Z9ZBMCwjuG8eWMfIoJkPb+6NPX72+DAmBwqJSWFjIwMRo8ebdsWHBzM4MGDWbt2bb2JUHl5OeXlp/tqCwoKWjxWITSz92d8lQqoIwmyXpR+YrqKsfo1RCunbPdVBUThfeUsSYKEsIPWgUbm3TaIT1Ye5v0/D3L4ZDGPL9wJgNGgo9Jk5sxGIj9vPTMvT+CWwbHoZOLS8+a2iVBGRgYAERE1Z9aMiIiw3VeXl19+mWeffbZFYxPCKViHzdczYiyPAGZW3smv5kF87jeJCRHHuKw9dOnUGe/YYaCTYfJC2ItOp3DPRR25ZUgs32w4yqJtJ9iXUUh59XxfBp1C9+ggrh8Qw7jEaIJ9vTSO2H1omgg99thjvPrqqw3us2fPHhISHDdB28yZM5k+fbrtdkFBATExMQ47vhAOYTY1OGxeBUICg3juzkd5TqenTaBR6oCEcIAAo4E7R3TgzhEdMJlVjuWW4Outp5W/UVp/WoimidDDDz/M5MmTG9ynQ4cO5/TckZGWCaUyMzOJioqybc/MzKRPnz71Ps5oNGI0SuW9cHOpaxocNq8AStEJInK3QPwIx8UlhLDR6xRiw/21DsPtaZoItW7dmtatW7fIc8fHxxMZGcmyZctsiU9BQQHr169v1sgzIdxSUaZ99xNCCBflMsPnjx49SnJyMkePHsVkMpGcnExycjJFRUW2fRISEli4cCFgGdEybdo0XnjhBRYtWsSOHTuYOHEi0dHRjB8/XqNXIYSTCGjiqtRN3U8IIVyUyxRLP/XUU8ybN892u2/fvgD8+eefjBw5EoB9+/aRn59v2+fRRx+luLiYu+++m7y8PC644AKWLFmCj48MNRQeLnYYBEVbFk+ts05IsdwfO8zRkQkhhEO53DxCjibzCAm3ZRs1BjWToeqCTJkjSAjhwpr6/e0yXWNCCDvrPs6S7ARF1dweFC1JkBDCY7hM15gQogV0HwcJV1pGkRVlWmqCZI4gIYQHkURICE+n08sQeSGEx5KuMSGEEEJ4LEmEhBBCCOGxJBESQgghhMeSREgIIYQQHksSISGEEEJ4LEmEhBBCCOGxJBESQgghhMeSREgIIYQQHksSISGEEEJ4LJlZuhHWNWkLCgo0jkQIIYQQTWX93m5sbXlJhBpRWFgIQExMjMaRCCGEEKK5CgsLCQ4Orvd+RW0sVfJwZrOZEydOEBgYiKIodnvegoICYmJiSEtLIygoyG7P6y7k/WmYvD8Nk/enYfL+1E/em4a50vujqiqFhYVER0ej09VfCSQtQo3Q6XS0a9euxZ4/KCjI6f+YtCTvT8Pk/WmYvD8Nk/enfvLeNMxV3p+GWoKspFhaCCGEEB5LEiEhhBBCeCxJhDRiNBp5+umnMRqNWofilOT9aZi8Pw2T96dh8v7UT96bhrnj+yPF0kIIIYTwWNIiJIQQQgiPJYmQEEIIITyWJEJCCCGE8FiSCAkhhBDCY0kipJH333+fuLg4fHx8GDx4MBs2bNA6JKfw8ssvM3DgQAIDA2nTpg3jx49n3759WofllF555RUURWHatGlah+I0jh8/zt///nfCw8Px9fWlV69ebNq0SeuwnILJZOLJJ58kPj4eX19fOnbsyPPPP9/oOkzu6q+//mLs2LFER0ejKAo//vhjjftVVeWpp54iKioKX19fRo8ezYEDB7QJVgMNvT+VlZXMmDGDXr164e/vT3R0NBMnTuTEiRPaBXweJBHSwPz585k+fTpPP/00W7ZsITExkaSkJLKysrQOTXMrVqzg/vvvZ926dSxdupTKykouu+wyiouLtQ7NqWzcuJGPPvqI3r17ax2K08jNzWX48OF4eXnxv//9j927dzN79mxCQ0O1Ds0pvPrqq8yZM4f33nuPPXv28OqrrzJr1izeffddrUPTRHFxMYmJibz//vt13j9r1izeeecdPvzwQ9avX4+/vz9JSUmUlZU5OFJtNPT+lJSUsGXLFp588km2bNnCDz/8wL59+xg3bpwGkdqBKhxu0KBB6v3332+7bTKZ1OjoaPXll1/WMCrnlJWVpQLqihUrtA7FaRQWFqqdO3dWly5dql500UXqgw8+qHVITmHGjBnqBRdcoHUYTuvKK69Ub7/99hrb/va3v6m33HKLRhE5D0BduHCh7bbZbFYjIyPV1157zbYtLy9PNRqN6v/93/9pEKG2zn5/6rJhwwYVUFNTUx0TlB1Ji5CDVVRUsHnzZkaPHm3bptPpGD16NGvXrtUwMueUn58PQFhYmMaROI/777+fK6+8ssbfkIBFixYxYMAArr/+etq0aUPfvn355JNPtA7LaQwbNoxly5axf/9+ALZt28aqVau4/PLLNY7M+aSkpJCRkVHjMxYcHMzgwYPlPF2P/Px8FEUhJCRE61CaTRZddbCTJ09iMpmIiIiosT0iIoK9e/dqFJVzMpvNTJs2jeHDh9OzZ0+tw3EK33zzDVu2bGHjxo1ah+J0Dh8+zJw5c5g+fTr/+te/2LhxI1OnTsXb25tJkyZpHZ7mHnvsMQoKCkhISECv12MymXjxxRe55ZZbtA7N6WRkZADUeZ623idOKysrY8aMGUyYMMElFmI9myRCwmndf//97Ny5k1WrVmkdilNIS0vjwQcfZOnSpfj4+GgdjtMxm80MGDCAl156CYC+ffuyc+dOPvzwQ0mEgAULFvDVV1/x9ddf06NHD5KTk5k2bRrR0dHy/ohzVllZyQ033ICqqsyZM0frcM6JdI05WKtWrdDr9WRmZtbYnpmZSWRkpEZROZ8pU6awePFi/vzzT9q1a6d1OE5h8+bNZGVl0a9fPwwGAwaDgRUrVvDOO+9gMBgwmUxah6ipqKgounfvXmNbt27dOHr0qEYROZd//vOfPPbYY9x000306tWLW2+9lYceeoiXX35Z69CcjvVcLOfphlmToNTUVJYuXeqSrUEgiZDDeXt7079/f5YtW2bbZjabWbZsGUOHDtUwMuegqipTpkxh4cKF/PHHH8THx2sdktO45JJL2LFjB8nJybafAQMGcMstt5CcnIxer9c6RE0NHz681lQL+/fvJzY2VqOInEtJSQk6Xc1Tvl6vx2w2axSR84qPjycyMrLGebqgoID169fLebqaNQk6cOAAv//+O+Hh4VqHdM6ka0wD06dPZ9KkSQwYMIBBgwbx1ltvUVxczG233aZ1aJq7//77+frrr/npp58IDAy09ccHBwfj6+urcXTaCgwMrFUr5e/vT3h4uNRQAQ899BDDhg3jpZde4oYbbmDDhg18/PHHfPzxx1qH5hTGjh3Liy++SPv27enRowdbt27ljTfe4Pbbb9c6NE0UFRVx8OBB2+2UlBSSk5MJCwujffv2TJs2jRdeeIHOnTsTHx/Pk08+SXR0NOPHj9cuaAdq6P2JioriuuuuY8uWLSxevBiTyWQ7V4eFheHt7a1V2OdG62Frnurdd99V27dvr3p7e6uDBg1S161bp3VITgGo8+ezzz7TOjSnJMPna/rvf/+r9uzZUzUajWpCQoL68ccfax2S0ygoKFAffPBBtX379qqPj4/aoUMH9fHHH1fLy8u1Dk0Tf/75Z53nmkmTJqmqahlC/+STT6oRERGq0WhUL7nkEnXfvn3aBu1ADb0/KSkp9Z6r//zzT61DbzZFVT10WlEhhBBCeDypERJCCCGEx5JESAghhBAeSxIhIYQQQngsSYSEEEII4bEkERJCCCGEx5JESAghhBAeSxIhIYQQQngsSYSEEEII4bEkERJCCCGEx5JESAghhBAeSxIhIYRHyc7OJjIykpdeesm2bc2aNXh7e9dYbVwI4RlkrTEhhMf55ZdfGD9+PGvWrKFr16706dOHq6++mjfeeEPr0IQQDiaJkBDCI91///38/vvvDBgwgB07drBx40aMRqPWYQkhHEwSISGERyotLaVnz56kpaWxefNmevXqpXVIQggNSI2QEMIjHTp0iBMnTmA2mzly5IjW4QghNCItQkIIj1NRUcGgQYPo06cPXbt25a233mLHjh20adNG69CEEA4miZAQwuP885//5LvvvmPbtm0EBARw0UUXERwczOLFi7UOTQjhYNI1JoTwKMuXL+ett97iiy++ICgoCJ1OxxdffMHKlSuZM2eO1uEJIRxMWoSEEEII4bGkRUgIIYQQHksSISGEEEJ4LEmEhBBCCOGxJBESQgghhMeSREgIIYQQHksSISGEEEJ4LEmEhBBCCOGxJBESQgghhMeSREgIIYQQHksSISGEEEJ4LEmEhBBCCOGxJBESQgghhMf6f7dC5m+4g7oRAAAAAElFTkSuQmCC", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#Setup: Import modules\n", "import numpy as np\n", @@ -173,57 +217,21 @@ "plt.ylabel('y')\n", "plt.title('Sine Function')\n", "plt.legend()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 508 - }, - "id": "8P3iMrqN-pOU", - "outputId": "92dede11-65c8-423d-b8a5-ac0b6191404c" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|██████████| 100/100 [00:14<00:00, 6.96it/s]\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 125 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } ] }, { + "attachments": {}, "cell_type": "markdown", + "metadata": { + "id": "zGaP3IQNBff2" + }, "source": [ "\n", "***WARNING:*** *Do not stop here! We have now shown you the three components and how they work together. At this point, it may be tempting to start working on your own project, but we urge you to continue through the tutorials. ``autora`` has a lot of embedded functionality that you are going to want to use, and this toy example has stripped those away. So, keep going and see how much ``autora`` has to offer!*" - ], - "metadata": { - "id": "zGaP3IQNBff2" - } + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "KbUon6UcUYM5" @@ -237,7 +245,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", + "metadata": { + "id": "SL3Si-rIFqlQ" + }, "source": [ "\n", "### Types\n", @@ -255,12 +267,10 @@ "**Synthetic experiments** are conducted on synthetic experiment runners, which are functions that take experimental conditions as input and generate simulated observations as output. These experiments serve multiple purposes, including *testing autora components* before applying them to real-world experiments, *benchmarking methods for automated scientific discovery*, or *conducting computational metascientific experiments*.\n", "\n", "In this introductory tutorial, we primarily focus on simple synthetic experiments. For more complex synthetic experiments implementing various scientific models, you can utilize the [autora-synthetic](https://github.com/autoresearch/autora-synthetic/) module." - ], - "metadata": { - "id": "SL3Si-rIFqlQ" - } + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "_UWBS-P-UYM5" @@ -296,6 +306,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "hBbzNQjXUYM5" @@ -318,6 +329,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "Ejj3Yd_FUYM6" @@ -339,24 +351,24 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 97, "metadata": {}, - "execution_count": 97 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -374,6 +386,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "fmIVAHdIUYM7" @@ -413,6 +426,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "G-s-foKEUYM8" @@ -422,6 +436,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "6_0yTjh-UYM8" @@ -438,6 +453,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "w_7QGhwFUYM8" @@ -453,6 +469,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "b4BS1lGEUYM8" @@ -481,6 +498,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "3dKtzWCrUYM9" @@ -502,8 +520,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Size of the initial conditions: (10, 1),\n", "Size of the initial observations: (10, 1)\n", @@ -511,24 +529,24 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:13<00:00, 7.41it/s]\n" ] }, { - "output_type": "execute_result", "data": { - "text/plain": [ - "BMSRegressor(epochs=100)" - ], "text/html": [ "
BMSRegressor(epochs=100)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "BMSRegressor(epochs=100)" ] }, + "execution_count": 103, "metadata": {}, - "execution_count": 103 + "output_type": "execute_result" } ], "source": [ @@ -543,6 +561,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "qwx7lc64UYM9" @@ -563,8 +582,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Model of BMS theorist: sin(X0)\n" ] @@ -575,6 +594,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "77tpR0taUYM-" @@ -600,6 +620,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "eCe8VNt6UYM-" @@ -621,24 +642,24 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 106, "metadata": {}, - "execution_count": 106 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", 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c+P+jLEeOHKFSpUqYmppSvXp17t27lyDrrFmzsLe3x9zcnD59+hAZmfTPAjMzMxwcHLQPPT09zM3Ntc87duzIoEGDGDp0KDlz5sTV1TXZrD4+PtSrVw8Aa2trVCoVPXv21LbVaDSMGjUKGxsbHBwc8PDwSOV3Q4jML0sVQidOnKBly5bkzp0blUrFjh07Ptr+3Q+j/z4CAgIyJnASFEXBUG2IChVb7m+hyz9d8A721mkmIZKjKArh0bE6eSiKkqKMr1694uDBg3z//ffkyJEj0Tb/HULy8PCgTZs23Lx5k969e7N9+3aGDBnC8OHDuXXrFt9++y29evXi2LFjqX7Pxo0bx/z587l06RL6+vr07t1b+9rmzZvx8PBgxowZXLp0CUdHR5YsWZLqPt63Zs0aDA0NOX36NEuXLk22vZOTE3///TcA9+7dw9/fn0WLFiXYXo4cOTh//jxz5sxhypQpHDp06LMyCpHZZKkLKoaFhVG2bFl69+7NN998k+L17t27h4WFhfZ5rly50iNeiqlUKgaVH0QF+wq4n3TnftB9OuzpwISvJtCyYMvkNyCEDkTExFFi4gGd9H1niiumhsn/uHr48CGKolC0aNEEy3PmzKk92vL9998ze/Zs7WudO3emV69e2uedOnWiZ8+efPfddwAMGzaMc+fOMW/ePO3Rk5SaPn06derUAWDMmDE0b96cyMhIjI2NWbhwIX369KFPnz4ATJs2jcOHD3/0qFByChcuzJw5c7TPfXx8PtpeT08PGxsbIP7nopWVVYLXy5Qpw6RJk7Tb/uWXXzhy5AiNGjX65IxCZDZZ6ohQ06ZNmTZtGm3atEnVerly5UpwSFmtzhy7XT13dba23EoVhypExEYw9tRYJp6eSERshK6jCfFFuXDhAteuXaNkyZJERUUleK1SpUoJnnt6elKjRo0Ey2rUqIGnp2eq+y1Tpoz23+9uAfDulgCenp5UrVo1Qftq1aqluo/3VaxY8bPW/6/380P8PrzLL8SXIksdEfpU5cqVIyoqilKlSuHh4fHBDzldsjO1Y1mjZfx+43eWXl/K9ofbufHiBvPrzqegVUFdxxNCy8RAjztTXHXWd0oUKlQIlUr1wVycAgUKxG/nf5fkf19SQ2hJefeH1PvDdUlNsjYwMND++92QnEajSVV/qfHffUlN1sS8nx/i9yE98wuhC5nj0Eg6cXR0ZOnSpfz999/8/fffODk5UbduXa5cuZLkOlFRUYSEhCR4pDc9tR7flfuOFY1XkNMkJ17BXnTc05HtD7aneG6EEOlNpVJhaqivk0dKTw23tbWlUaNG/PLLL4SFhX3SfhYvXpzTp08nWHb69GlKlCgBgJ2dHUCCs7Ten4ycmn7Onz+fYNm5c+dSvZ2PSUlWQ0NDIP7+ckJkR1/0EaGiRYsmmCtQvXp1vLy8WLBgQYKzQN43c+ZMJk+enFERE6jiWIUtLbcw9uRYzvqfZeKZiVwMuMj4r8ZjaiD3fBIiJZYsWUKNGjWoVKkSHh4elClTBrVazcWLF7l7926yw0cjR47Ezc2N8uXL07BhQ3bv3s22bdu0p92bmJjw1VdfMWvWLPLnz09gYCDjx49Pdc4hQ4bQs2dPKlWqRI0aNdiwYQO3b9/WHr1KCynJ6uzsjEqlYs+ePTRr1gwTExPMzMzSLIMQmd0XfUQoMVWqVOHhw4dJvu7u7k5wcLD28eTJkwxMBzlNcrK00VJ+KP8DapWa3Y9203FvR+69vpf8ykIIChYsyNWrV2nYsCHu7u6ULVuWSpUqsXjxYkaMGMHUqVM/uv7XX3/NokWLmDdvHiVLluT3339n1apV1K1bV9tm5cqVxMbGUrFiRYYOHcq0adNSnbNDhw5MmDCBUaNGUbFiRR4/fszAgQNTvZ3kJJc1T548TJ48mTFjxmBvb8+gQYPSPIMQmZlKyaJjLyqViu3bt/P111+nar1GjRphbm7Otm3bUtQ+JCQES0tLgoODE5x5lhEuBVxi9MnRBIYHYqRnxOgqo2lXuJ3cCFOku8jISLy9vcmfPz/Gxsa6jiOEEIn62M+qlP7+zlJHhEJDQ7l27Zp2jNvb25tr167h6+sLxB/N6d69u7b9woUL2blzJw8fPuTWrVsMHTqUo0eP8v333+sifqpVcqjElpZbqJmnJlFxUUw5O4XRJ0YTGh2q62hCCCHEFyFLFUKXLl2ifPnylC9fHoi/vkf58uWZOHEiED8h8F1RBPGXmB8+fDilS5emTp06XL9+ncOHD9OgQQOd5P8UNsY2/NrgV4ZVHIaeSo99PvvosKcDnq9SfyqvEEIIIRLKskNjGUWXQ2P/dS3wGqNOjMI/zB8DtQGjKo+iQ9EOMlQm0pwMjQkhsoJsNzSW3ZXLVY4tLbdQ16kuMZoYpp+fzvB/h/M2+q2uowkhhBBZkhRCWYylkSU/1/uZUZVHoa/W59DjQ7Tf3Z5bL2/pOpoQQgiR5UghlAWpVCq6lejGuqbryGOWB79QP7rt68b6O+vlAoxCCCFEKkghlIWVylmKzS0308i5EbGaWGZfnM2QY0MIjgrWdTQhhBAiS5BCKIuzMLRgfp35jK06FgO1AceeHKP97vZcf3Fd19GEEEKITE8KoS+ASqWiU7FOrG+2HidzJ/zD/Om5ryerbq1Co8gNEoUQQoikSCH0BSlhW4LNLTbTxKUJsUosP13+iR+O/kBQZJCuowkhdMTHxweVSvXRG8OGh4fTtm1bLCwsUKlUvHnzJsPypZW6desydOjQFLdfvXo1VlZWn9VnSt7btOLh4UG5cuU+WGZvb49KpWLHjh307Nkz1Xdb+BTv+vtSSCH0hTEzNGNO7TlMrDYRQ7UhJ56eoN3udlx+flnX0UR2pYkD75Nwc2v8V0363uW8Z8+eqFQqVCoVBgYG2Nvb06hRI1auXIlGk7ojpGnxy/IdFxcXFi5c+MHyxH7BZbQ1a9Zw8uRJzpw5g7+/P5aWlmm27XfFgp6eHn5+fgle8/f3R19fH5VKhY+PT5r1mVZ09Qs/sX5HjBjBkSNHtM89PT2ZPHkyv//+O/7+/jRt2pRFixaxevXqNMuR1GfzXX9fCimEvkAqlYr2RdqzsflGXCxcCAwPpM+BPiy/sVyGykTGurMLFpaCNS3g7z7xXxeWil+ejpo0aYK/vz8+Pj7s27ePevXqMWTIEFq0aEFsbGy69p0VeXl5Ubx4cUqVKoWDg8MnXaQ1Li7uo4Vmnjx5WLt2bYJla9asIU+ePKnuKzsyMzPD1tZW+9zLywuA1q1b4+DggJGREZaWlmlWuH/Mu/6+FFIIfcGK2hRlU4tNtCjQgjgljp+v/syAQwN4FfFK19FEdnBnF2zuDiHPEi4P8Y9fno7FkJGREQ4ODuTJk4cKFSowduxYdu7cyb59+xL8xfzTTz9RunRpcuTIgZOTE9999x2hofH38jt+/Di9evUiODhYe4TJw8MDgHXr1lGpUiXMzc1xcHCgc+fOBAYGpkn2rVu3Urp0aUxMTLC1taVhw4aEhYVpX1+xYgXFixfH2NiYYsWKsWTJkgTrX7hwgfLly2NsbEylSpW4evXqR/urW7cu8+fP58SJE6hUKurWrQtAUFAQ3bt3x9raGlNTU5o2bcqDBw+06707WrZr1y5KlCiBkZFRglsc/VePHj1YtWpVgmWrVq2iR48eH7T9999/qVKlCkZGRjg6OjJmzJgEBWxYWBjdu3fHzMwMR0dH5s+f/8E2oqKiGDFiBHny5CFHjhxUrVqV48ePf/S9SE5K3ttbt27RtGlTzMzMsLe3p1u3brx8+VL7et26dRk8eDCjRo3CxsYGBwcH7ecK4o8cArRp0waVSqV9/v7RGQ8PD1q2bAmAWq3WFq7/HRrTaDTMmTOHQoUKYWRkRL58+Zg+fbr29dGjR1OkSBFMTU0pUKAAEyZMICYmBoj//k6ePJnr169rP//v/u/894jVzZs3qV+/vvYz279/f+3/o/dzzZs3D0dHR2xtbfn++++1femaFEJfOFMDU2bUnMGU6lMw1jPmrP9Z2u1uxwX/C7qOJr5kmjjYPxpI7LpW/1u2f0y6D5O9r379+pQtW5Zt27Zpl6nVan7++Wdu377NmjVrOHr0KKNGjQKgevXqLFy4EAsLC/z9/fH392fEiBEAxMTEMHXqVK5fv86OHTvw8fGhZ8+en53R39+fTp060bt3bzw9PTl+/DjffPON9vpgGzZsYOLEiUyfPh1PT09mzJjBhAkTWLNmDRB/Y+oWLVpQokQJLl++jIeHhzZzUrZt20a/fv2oVq0a/v7+2venZ8+eXLp0iV27dnH27FkURaFZs2YJfnmFh4cze/ZsVqxYwe3bt8mVK1eS/bRq1YqgoCBOnToFwKlTpwgKCtL+Qn/Hz8+PZs2aUblyZa5fv85vv/3GH3/8wbRp07RtRo4cyb///svOnTs5ePAgx48f58qVKwm2M2jQIM6ePctff/3FjRs3aN++PU2aNElQzKVGSt7bN2/eUL9+fcqXL8+lS5fYv38/z58/x83NLUG7NWvWkCNHDs6fP8+cOXOYMmUKhw4dAuDixYtAfJHo7++vff6+ESNGaIvKd5/NxLi7uzNr1iwmTJjAnTt32LhxI/b29trXzc3NWb16NXfu3GHRokUsX76cBQsWANChQweGDx9OyZIltX106NDhgz7CwsJwdXXF2tqaixcvsmXLFg4fPsygQYMStDt27BheXl4cO3aMNWvWsHr16jQdxvssivio4OBgBVCCg4N1HeWzPXj9QGm9vbVSanUppcyaMsqSq0uU2LhYXccSmVBERIRy584dJSIi4tM28OiEokyySP7x6ETaBlcUpUePHkrr1q0Tfa1Dhw5K8eLFk1x3y5Ytiq2trfb5qlWrFEtLy2T7vHjxogIob9++TbKNs7OzsmDBgg+WT5o0SSlbtqyiKIpy+fJlBVB8fHwS3UbBggWVjRs3Jlg2depUpVq1aoqiKMrvv/+u2NraJvi+/fbbbwqgXL16NclsQ4YMUerUqaN9fv/+fQVQTp8+rV328uVLxcTERNm8ebOiKPHvDaBcu3Ytye0qiqJ4e3tr+x86dKjSq1cvRVEUpVevXsqPP/6oXL16VQEUb29vRVEUZezYsUrRokUVjUaj3cavv/6qmJmZKXFxccrbt28VQ0NDbQ5FUZRXr14pJiYmypAhQxRFUZTHjx8renp6ip+fX4IsDRo0UNzd3bX5k/veAsr27dsVRUnZezt16lSlcePGCbbx5MkTBVDu3bunKIqi1KlTR6lZs2aCNpUrV1ZGjx6daL/vvP85URRF2b59u/LfX+Hvf/ZDQkIUIyMjZfny5R/dx/fNnTtXqVixYpJ9JpZv2bJlirW1tRIaGqp9fe/evYparVYCAgK0uZydnZXY2P//fdO+fXulQ4cOKc6WlI/9rErp7299HdReQkcKWRdiY/ONzLwwkx0Pd7Dk+hIuP7/MzFozsTO103U88SUJfZ627dKIoigJ5r8cPnyYmTNncvfuXUJCQoiNjSUyMpLw8HBMTU2T3M67IwLXr18nKChIOzfG19eXEiVKfHK+smXL0qBBA0qXLo2rqyuNGzemXbt2WFtbExYWhpeXF3369KFfv37adWJjY7WTmz09PSlTpkyCm09Wq1Yt1Tk8PT3R19enatWq2mW2trYULVoUT09P7TJDQ0PKlCmT4u327t2b6tWrM2PGDLZs2cLZs2c/mLPl6elJtWrVEnyfatSoQWhoKE+fPiUoKIjo6OgE2WxsbChatKj2+c2bN4mLi6NIkSIJth0VFZVgnk1qpOS9vX79OseOHcPMzOyD9b28vLR5/vueOTo6ptnQ6vt5o6KiaNCgQZJtNm3axM8//4yXlxehoaHExsam+ubinp6elC1blhw5cmiX1ahRA41Gw71797RHoEqWLImenp62jaOjIzdv3kzlXqUPKYSyGVMDU6bWmEoVhypMPTeV8wHnabe7HTNrzaR67uq6jie+FGb2ybdJTbs04unpSf78+YH4s5latGjBwIEDmT59OjY2Npw6dYo+ffoQHR2dZCH0bijA1dWVDRs2YGdnh6+vL66urkRHRyfZt4WFBcHBH171/c2bN9pCRk9Pj0OHDnHmzBkOHjzI4sWLGTduHOfPn9fmWb58eYIi4N16umBiYpKqidWlS5emWLFidOrUSTs5Oz1OPQ8NDUVPT4/Lly9/8N4kVqSkZb8tW7Zk9uzZH7zm6Oio/beBgUGC11QqVarPaEyOiYnJR18/e/YsXbp0YfLkybi6umJpaclff/2V6HyrtJAR+/ypZI5QNtWyYEv+avEXha0L8zryNQMODeDnKz8Tq5EzakQacK4OFrmBpH5JqsAiT3y7DHL06FFu3rxJ27ZtgfijOhqNhvnz5/PVV19RpEgRnj1LOLHb0NCQuLiE85ju3r3Lq1evmDVrFrVq1aJYsWIp+mu+aNGiXL784WUsrly5kuDIhUqlokaNGkyePJmrV69iaGjI9u3bsbe3J3fu3Dx69IhChQoleLwr7ooXL86NGzeIjIzUbu/cuXMpf5P+p3jx4sTGxnL+/HntslevXnHv3r3POuIF8UeFjh8/Tu/evZPs+92cpHdOnz6Nubk5efPmpWDBghgYGCTIFhQUxP3797XPy5cvT1xcHIGBgR+8Vw4ODp+UOyXvbYUKFbh9+zYuLi4f9Pv+EZPkGBgYfPC5S63ChQtjYmKS4JT79505cwZnZ2fGjRtHpUqVKFy4MI8fP07QJrHP/38VL16c69evJ5jQf/r0adRqdYKjdJmZFELZWAHLAmxstpF2RdqhoLD85nL6HOjD87CMHa4QXyC1HjR591fxf4uh/z1vMiu+XTqIiooiICAAPz8/rly5wowZM2jdujUtWrSge/fuABQqVIiYmBgWL17Mo0ePWLduHUuXLk2wHRcXF0JDQzly5AgvX74kPDycfPnyYWhoqF1v165dTJ06NdlMP/74I3v37tVOdL516xbjxo3j7NmzDBkyBIDz588zY8YMLl26hK+vL9u2bePFixcUL14cgMmTJzNz5kx+/vln7t+/z82bN1m1ahU//fQTAJ07d0alUtGvXz/u3LnDP//8w7x581L9/hUuXJjWrVvTr18/Tp06xfXr1+natSt58uShdevWqd7e+/r168eLFy/o27dvoq9/9913PHnyhB9++IG7d++yc+dOJk2axLBhw1Cr1ZiZmdGnTx9GjhzJ0aNHuXXrFj179kSt/v9fZ0WKFKFLly50796dbdu24e3tzYULF5g5cyZ79+79pNwpeW+///57Xr9+TadOnbh48SJeXl4cOHCAXr16paqwcXFx4ciRIwQEBBAU9GkXxDU2Nmb06NGMGjWKtWvX4uXlxblz5/jjjz+A+O+xr68vf/31F15eXvz8889s3779gxze3t5cu3aNly9fEhUV9UE/Xbp0wdjYmB49enDr1i2OHTvGDz/8QLdu3RJMzM7UPnum0hfuS5os/TH/PPpHqbK+ilJqdSml1p+1lBNP0n4Sq8g6Pnuy9Du3dyrK/GIJJ0jPLx6/PJ306NFDIf7UNEVfX1+xs7NTGjZsqKxcuVKJi4tL0Pann35SHB0dFRMTE8XV1VVZu3atAihBQUHaNgMGDFBsbW0VQJk0aZKiKIqyceNGxcXFRTEyMlKqVaum7Nq1K9kJyYqiKAcOHFBq1KihWFtbK7a2tkrdunWVf//9V/v6nTt3FFdXV8XOzk4xMjJSihQpoixevDjBNjZs2KCUK1dOMTQ0VKytrZXatWsr27Zt075+9uxZpWzZsoqhoaFSrlw55e+//071ZGlFUZTXr18r3bp1UywtLbXvz/3797Wvp3Qi+fuTpRPz38nSiqIox48fVypXrqwYGhoqDg4OyujRo5WYmBjt62/fvlW6du2qmJqaKvb29sqcOXOUOnXqaCdLK4qiREdHKxMnTlRcXFwUAwMDxdHRUWnTpo1y48aNFOfnP5OWU/Le3r9/X2nTpo1iZWWlmJiYKMWKFVOGDh2qnfz935yKoiitW7dWevTooX2+a9cupVChQoq+vr7i7OysKErqJ0sriqLExcUp06ZNU5ydnRUDAwMlX758yowZM7Svjxw5UrG1tVXMzMyUDh06KAsWLEjwnkRGRipt27ZVrKysFEBZtWpVou/LjRs3lHr16inGxsaKjY2N0q9fvwQnDiR2AkNin7lPkRaTpVWK8t7xR/GBkJAQLC0tCQ4OTvUksqzmcchjRvw7gruv7wLQq1Qvfij/AwZqg2TWFF+ayMhIvL29yZ8/f4LJoZ9EEwePz8RPjDazjx8OS6cjQUKI7OVjP6tS+vtbhsaElrOFM+ubradj0Y4ArLq1it77e+Mfmvg1KoRIEbUe5K8FpdvFf5UiSAiRiUghJBIw0jNi3FfjmF9nPmYGZlx7cY12u9tx/MlxXUcTQggh0pwUQiJRjV0as7nlZkraliQkOoQfjv7A3ItziYnLHJdEF0IIIdKCFEIiSU7mTqxruo6uxbsCsPbOWnrs78HTt091nEwIIYRIG1IIiY8y0DNgdJXRLKq3CHNDc26+vInbbjcOPz6s62hCCCHEZ5NCSKRI/Xz12dpyK2XsyvA25i0/Hv+RGednEB2X9JV0hRBCiMxOCiGRYrnNcrO6yWp6leoFwJ93/6TrP13xDfHVcTIhhBDi00ghJFLFQG3AsIrD+LXBr1gZWeH52hO3PW7s99mv62hCCCFEqkkhJD5J7by12dJyCxVyVSAsJoyR/45k6tmpRMZGJr+yEEIIkUlIISQ+mUMOB/5w/YN+pfuhQsXm+5vp8k8XvIO9dR1NiCSpVCp27Nih6xjZjoeHB+XKldN1DCE+IIWQ+Cz6an0GVxjM0oZLsTG24X7QfTrs6cBur926jiayqZ49e/L1118n+bq/vz9NmzbNuECppFKptA8LCwsqV67Mzp07dR3rs40YMSLJO6ELoUtSCIk0UT1Pdba23Eplh8pExEYw9tRYJp6eSERshK6jCZGAg4MDRkZGOs2gKAqxsbFJvr5q1Sr8/f25dOkSNWrUoF27dty8eTNdM0VHp+8ZoGZmZtja2qZrH0J8CimERJqxM7VjeaPlDCw7EBUqtj/cTue9nfF646XraEJovT805uPjg0qlYtu2bdSrVw9TU1PKli3L2bNnE6xz6tQpatWqhYmJCU5OTgwePJiwsDDt6+vWraNSpUqYm5vj4OBA586dCQwM1L5+/PhxVCoV+/bto2LFihgZGXHq1KkkM1pZWeHg4ECRIkWYOnUqsbGxHDt2TPv6kydPcHNzw8rKChsbG1q3bo2Pj4/29djYWAYPHoyVlRW2traMHj2aHj16JDhSVrduXQYNGsTQoUPJmTMnrq6uANy6dYumTZtiZmaGvb093bp14+XLl9r1tm7dSunSpTExMcHW1paGDRtq34vjx49TpUoVcuTIgZWVFTVq1ODx48fAh0NjGo2GKVOmkDdvXoyMjChXrhz79///SRcp/d4I8bmkEBJpSk+tx3flvmN54+XkNMnJwzcP6binIzse7tB1NPGZFEUhPCZcJw9FUdJ138aNG8eIESO4du0aRYoUoVOnTtojNl5eXjRp0oS2bdty48YNNm3axKlTpxg0aJB2/ZiYGKZOncr169fZsWMHPj4+9OzZ84N+xowZw6xZs/D09KRMmTLJ5oqNjeWPP/4AwNDQUNuXq6sr5ubmnDx5ktOnT2NmZkaTJk20R3Vmz57Nhg0bWLVqFadPnyYkJCTReVFr1qzB0NCQ06dPs3TpUt68eUP9+vUpX748ly5dYv/+/Tx//hw3NzcgflixU6dO9O7dG09PT44fP84333yjPcL19ddfU6dOHW7cuMHZs2fp378/KpUq0X1btGgR8+fPZ968edy4cQNXV1datWrFgwcPUvy9ESItqJT0/gmTxYWEhGBpaUlwcDAWFha6jpOlvIx4iftJd875nwOgZYGWjP9qPKYGpjpOJpITGRmJt7c3+fPnx9jYGIDwmHCqbqyqkzznO59P8eemZ8+evHnzJskJ0SqViu3bt/P111/j4+ND/vz5WbFiBX369AHgzp07lCxZEk9PT4oVK0bfvn3R09Pj999/127j1KlT1KlTh7CwMO37875Lly5RuXJl3r59i5mZGcePH6devXrs2LGD1q1bfzS/SqXC2NgYPT09IiIi0Gg0uLi4cPnyZWxsbFi/fj3Tpk3D09NTW2RER0djZWXFjh07aNy4MQ4ODowYMYIRI0YAEBcXR4ECBShfvrz2falbty4hISFcuXJF2/e0adM4efIkBw4c0C57+vQpTk5O3Lt3j9DQUCpWrIiPjw/Ozs4Jcr9+/RpbW1uOHz9OnTp1PtgvDw8PduzYwbVr1wDIkycP33//PWPHjtW2qVKlCpUrV+bXX39N0fdGiMR+Vr2T0t/fckRIpJucJjn5vdHv/FD+B9QqNbsf7abj3o7cD7qv62hCJPD+0RlHR0cA7dDW9evXWb16NWZmZtqHq6srGo0Gb+/4MyQvX75My5YtyZcvH+bm5tpCwNc34cVGK1WqlKI8CxYs4Nq1a+zbt48SJUqwYsUKbGxstHkePnyIubm5No+NjQ2RkZF4eXkRHBzM8+fPqVKlinZ7enp6VKxY8YN+/rvs+vXrHDt2LMG+vis4vLy8KFu2LA0aNKB06dK0b9+e5cuXExQUBICNjQ09e/bE1dWVli1bsmjRIvz9/RPdv5CQEJ49e0aNGjUSLK9Rowaenp4Jln3seyNEWtDXdQDxZVOr1PQv058KuSow+sRovIO96by3M2OqjKFt4bZJHjYXmY+JvgnnO5/XWd/pycDAQPvvd59JjUYDQGhoKN9++y2DBw/+YL18+fIRFhaGq6srrq6ubNiwATs7O3x9fXF1df1gAnKOHDlSlMfBwYFChQpRqFAhVq1aRbNmzbhz5w65cuXSHpXZsGHDB+vZ2dmleJ8TyxMaGkrLli2ZPXv2B20dHR3R09Pj0KFDnDlzhoMHD7J48WLGjRvH+fPnyZ8/P6tWrWLw4MHs37+fTZs2MX78eA4dOsRXX32Vqlzv+9j3Roi0IIWQyBCVHCqxpdUWxp4ay2m/00w+O5kLAReY+NVEzAzNdB1PpIBKpcqWw5oVKlTgzp07FCpUKNHXb968yatXr5g1axZOTk5A/NBYWqlSpQoVK1Zk+vTpLFq0iAoVKrBp0yZy5cqV5OF+e3t7Ll68SO3atYH4obErV64kex2fChUq8Pfff+Pi4oK+fuK/HlQqFTVq1KBGjRpMnDgRZ2dntm/fzrBhwwAoX7485cuXx93dnWrVqrFx48YPCiELCwty587N6dOnEwyjnT59OsGRLCEyggyNiQxjY2zDkgZL+LHij+ip9NjnvY8Oezrg+coz+ZWFSIXg4GCuXbuW4PHkyZNP2tbo0aM5c+YMgwYN4tq1azx48ICdO3dqJ0vny5cPQ0NDFi9ezKNHj9i1axdTp05Ny91h6NCh/P777/j5+dGlSxdy5sxJ69atOXnyJN7e3hw/fpzBgwfz9OlTAH744QdmzpzJzp07uXfvHkOGDCEoKCjZI7Dff/89r1+/plOnTly8eBEvLy8OHDhAr169iIuL4/z588yYMYNLly7h6+vLtm3bePHiBcWLF8fb2xt3d3fOnj3L48ePOXjwIA8ePKB48eKJ9jVy5Ehmz57Npk2buHfvHmPGjOHatWsMGTIkTd87IZIjhZDIUGqVmt6lerO6yWoccjjg+9aXLv904a+7f6X7mUEi+zh+/Lj2yMS7x+TJkz9pW2XKlOHff//l/v371KpVi/LlyzNx4kRy584NxA9HrV69mi1btlCiRAlmzZrFvHnz0nJ3aNKkCfnz52f69OmYmppy4sQJ8uXLxzfffEPx4sXp06cPkZGR2iNEo0ePplOnTnTv3p1q1app5zUlNrH7fe+O0sTFxdG4cWNKly7N0KFDsbKyQq1WY2FhwYkTJ2jWrBlFihRh/PjxzJ8/n6ZNm2Jqasrdu3dp27YtRYoUoX///nz//fd8++23ifY1ePBghg0bxvDhwyldujT79+9n165dFC5cOE3fOyGSI2eNJUPOGks/byLfMOH0BI4/PQ5AI+dGeFT3wMJQ3mdd+9iZGCLr0Wg0FC9eHDc3tzQ/WiWELslZYyJLszK24uf6PzOy0kj0VfocenwIt91u3H55W9fRMidNHHifhJtb479q4nSdSGRSjx8/Zvny5dy/f5+bN28ycOBAvL296dy5s66jCZHpSCEkdEqlUtG9ZHfWNl1LHrM8+IX60XVfV9bfWS9DZe+7swsWloI1LeDvPvFfF5aKXy7Ef6jValavXk3lypWpUaMGN2/e5PDhw0nO1xEiO5OhsWTI0FjGCYkOYdLpSRz2PQxAfaf6TKkxBUsjSx0n07E7u2Bzd+C//1X/N/HVbS2UaJWmXcrQmBAiK5ChMfFFsTC04Ke6P+FexR0DtQFHnxzFbbcb119c13U03dHEwf7RfFgE8f/L9o+RYTIhhPhEUgiJTEWlUtG5eGfWNVuHk7kTz8Ke0XNfT1bfWo1GyYYXUXt8BkKefaSBAiF+8e2EEEKkmhRCIlMqaVuSzS0208SlCbFKLPMvz+eHoz/wJvKNrqNlrNDnadtOCCFEAlIIiUzLzNCMObXnMOGrCRiqDTnx9ATtdrfjyvMrya/8pTCzT9t2QgghEpBCSGRqKpUKt6JubGy+ERcLF56HP6f3gd6suLkiewyVOVcHi9xoJ0Z/QAUWeeLbCSGESDUphESWUNSmKJtabKJ5gebEKXEsurKI7w5/x6uIV7qOlr7UetDk3Q0w/1sM/e95k1nx7YQQQqRaliqETpw4QcuWLcmdOzcqlYodO3Yku87x48epUKECRkZGFCpUiNWrV6d7TpE+TA1MmVlzJlOqT8FYz5jTz07Tfnd7LgZc1HW0z/exiyWWaBV/iryFY8J1LHKny6nzQgiRnWSpu8+HhYVRtmxZevfuzTfffJNse29vb5o3b86AAQPYsGEDR44coW/fvjg6OuLq6poBiUVaU6lUtCnchtI5SzP83+E8Cn5E34N9GVB2AP1L90cvKx4ZubMr/hT5988Os8gdfyToXZFTohUUax5/dljo8/g5Qc7V5UiQEEJ8pix1RKhp06ZMmzaNNm3apKj90qVLyZ8/P/Pnz6d48eIMGjSIdu3asWDBgnROKtJbIetC/Nn8T1oXbI1G0bDk2hK+PfQtLyNe6jpa6ry7WOJ/T5EP8Y9f/v6Vo9V6kL8WlG4X/1WKoCytbt26DB06VNcx0lxK9iur7HtG5tT1e/K5/b969YpcuXLh4+OTZpk6duzI/Pnz02x7SclShVBqnT17loYNGyZY5urqytmzZ3WUSKQlUwNTptWcxoyaMzDRN+F8wHna7WrH2Wdp//2Ni43D38uXq/tOcHbTXk6t2c7xZX9xZPFajixey5k/d3PzyFke337I26AQNJoUTOSWiyWmm549e6JSqbQPW1tbmjRpwo0bN3QdTWvbtm0ZcgPUd+/FgAEDPnjt+++/R6VS0bNnz3TP8b6M2vcXL14wcOBA8uXLh5GREQ4ODri6unL69OkUrZ8eOZMqOFLTV3oUTZ+7r9OnT6d169a4uLhol2k0GooVK8a4ceMStN27dy+GhoZs27bto9scP34806dPJzg4+JNzpUSWGhpLrYCAAOztE55WbG9vT0hICBEREZiYmHywTlRUFFFRUdrnISEh6Z5TfJ6WBVtSMmdJRvw7ggdBD/j20Lf0K9OPgWUHoq9O/Uc8KOAVdw6f5NXZi6i9H5LjdSDWIS8x0sRiDHzshhPh/3u8NcrBS/t8xDgXxLR4MXJXKkOx6hXQ03/vKE5qLpaYv1aq9yO7a9KkCatWrQLifxaMHz+eFi1a4Ovrq+Nk8WxsbDKsLycnJ/766y8WLFig/bkXGRnJxo0byZcvX4bleCej9r1t27ZER0ezZs0aChQowPPnzzly5AivXqXsJIuM/B5lZF9p3X94eDh//PEHBw4cSLBcrVbj7u7OkCFDGDVqFJaWlly5coUOHTowe/bsZKe4lCpVioIFC7J+/Xq+//77T86XnC/6iNCnmDlzJpaWltqHk5OTriOJFChgWYCNzTbStnBbFBSW3VhG34N9eR6W/IUGY6KiObf5H3b2HMLRag0IqFsTm2nuFD6yjYKPbuDwJgAjTSxxKjUvc9jgl9MJX/v8+OQpgne+EnjnK45fTidem1oR/b/CyzwqjPy+nhQ5uYe8y+ah7t+dSxWrsqt9X44sXsvLp8/lYonp7N0RAAcHB8qVK8eYMWN48uQJL168AGD//v3UrFkTKysrbG1tadGiBV5eXtr1165di62tbYI/jAC+/vprunXrBsT/xTtz5kzy58+PiYkJZcuWZevWrdq2W7dupXTp0piYmGBra0vDhg0JCwsDPvyrPrk879YZPHgwo0aNwsbGBgcHBzw8PJJ9LypUqICTk1OCv8C3bdtGvnz5KF++fIK2Kcmh0WiYM2cOhQoVwsjIiHz58jF9+vQEr38s43/3PSX7ldx7/V9v3rzh5MmTzJ49m3r16uHs7EyVKlVwd3enVatWKdqP/+ZMLkNy+9GzZ0/+/fdfFi1apD1a+W4o6f2+Pva5+dg2kpOaz2NqPmv//PMPRkZGfPXVVx+81qVLF2xsbPjll1/w9fWlRYsW9OrVix9//DFFmVu2bMlff/2Voraf6os+IuTg4MDz5wl/iTx//hwLC4tEjwYBuLu7M2zYMO3zkJAQKYayCGN9Yzyqe1DFoQqTz07m8vPLtN/dnhm1ZlAzT80EbWOiormy+ygBO/ficOMsllFhvH9r1+eWuQguWALjMqWxKlQA+yIFyF3EGUNjo49m0Gg0hL55y+Nrdwm4coPwu/cw8PHCPsAbi6gwLG6ehpunef7rLO44OFImnwkWTpGoDT5y7+NMcrFERVFQIiJ00rfKxASVKqlrKSUvNDSU9evXU6hQIWxtbYH4ky+GDRtGmTJlCA0NZeLEibRp04Zr166hVqtp3749gwcPZteuXbRv3x6AwMBA9u7dy8GDB4H4P5zWr1/P0qVLKVy4MCdOnKBr167Y2dlRpEgROnXqxJw5c2jTpg1v377l5MmTJHWf6+TyvLNmzRqGDRvG+fPnOXv2LD179qRGjRo0atToo+9B7969WbVqFV26dAFg5cqV9OrVi+PHj6c6h7u7O8uXL2fBggXUrFkTf39/7t69+1kZk1vnY+91nTp1PtiemZkZZmZm7Nixg6+++gojow//7ya3H/+Vkgwf249FixZx//59SpUqxZQpUwCws7NL0Ie/v/9HPzdJbWP16tX06tUryc9Xctv9lO/JOydPnqRixYqJbkNfX5/Ro0czfvx4/vzzTypXrsyiRYuS7PO/qlSpwvTp04mKikr0e5gmlCwKULZv3/7RNqNGjVJKlSqVYFmnTp0UV1fXFPcTHBysAEpwcPCnxBQ64hPso7Tb1U4ptbqUUmp1KeWnSz8p0XHRyqtngcruMbOUU+W/Uu4ULaZ9nCtTUdnRdZByfMVm5fnjZ2meJyoiUrmw45Cyc6C7cqh6wwR9e5Ysojxr6qSEf2+raCZaKMqkdw9LRZlfXFHiYtM8T3IiIiKUO3fuKBEREdplcWFhCXJn5CMuLCxV+Xv06KHo6ekpOXLkUHLkyKEAiqOjo3L58uUk13nx4oUCKDdv3tQuGzhwoNK0aVPt8/nz5ysFChRQNBqNEhkZqZiamipnzpxJsJ0+ffoonTp1Ui5fvqwAio+PT6L91alTRxkyZEiq8tSpU0epWbNmgnaVK1dWRo8eneR2evToobRu3VoJDAxUjIyMFB8fH8XHx0cxNjZWXrx4obRu3Vrp0aNHinOEhIQoRkZGyvLly5Pcr+Qy/nffk1snufc6KVu3blWsra0VY2NjpXr16oq7u7ty/fr1FO3Hf3OmJMOn7Pt/lyf3uUlqG9u2bVOKFi2a5Dqp/Tym5rPWunVrpXfv3kn2/fbtW8XY2FgpVaqUEvaf/8u7d+9WihQpohQqVCjR78X169c/mjuxn1XvpPT3d5Y6IhQaGsrDhw+1z729vbl27Ro2Njbky5cPd3d3/Pz8WLt2LQADBgzgl19+YdSoUfTu3ZujR4+yefNm9u7dq6tdEBnE2cKZ9c3WM/fiXDbd28SB439gMn8LX11/S8G4WADeGpriX7YaDq1aUKFVfQyMDNMtj6GxEZVbN4TW8ZP3/e77cHXln+Q6sh3zt2958ygHbx7lwMQ2mpwl32LqGI1ahVws8TPUq1eP3377DYCgoCCWLFlC06ZNuXDhAs7Ozjx48ICJEydy/vx5Xr58qZ3g7uvrS6lSpQDo168flStXxs/Pjzx58rB69Wrt5OOHDx8SHh7+wV/H0dHRlC9fnrJly9KgQQNKly6Nq6srjRs3pl27dlhbWyeaNyV5AMqUKZNgPUdHRwIDA5N9P+zs7GjevDmrV69GURSaN29Ozpw5U53D09OTqKgoGjRokGRfn5LxY+sk914npW3btjRv3pyTJ09y7tw59u3bx5w5c1ixYgUlSpRIdj/el9IMn/r9eSe1n5t32rRp89Ezqj9luyndl4iICIyNk549OWjQIABevnyZ4OhmbGwsw4YN49ixY1haWlKxYkXatGmjPWoLaEdvwsPDk9z+58pShdClS5eoV6+e9vm7IawePXqwevVq/P39E0yEzJ8/P3v37uXHH39k0aJF5M2blxUrVsg1hLIJIz0jOhm1pejOC5S68wAIAuBJzpyo2vai1redqGKa+BBpestTxIU8s9zRaEZzf/ksjHesIfKxiohXhjw5YYu+jYbwNu0pVaQ5maUMUpmYUPTKZZ31nVo5cuSgUKFC2ucrVqzA0tKS5cuXM23aNFq2bImzszPLly8nd+7caDQaSpUqRXR0tHaddwXN2rVrady4Mbdv39b+IRUaGgrEnwGTJ0+eBH0bGRmhp6fHoUOHOHPmDAcPHmTx4sWMGzeO8+fPkz9//g/ypiQPgIGBQcL3RqVK2VmKxA+Pvful9OuvvybaJrkcSU0r+NyMH1snuff6Y4yNjWnUqBGNGjViwoQJ9O3bl0mTJrFnz55k9+N9Kc3wOd8fINWfm/Tcbkr3JWfOnAQFBSW6jQkTJrB3717OnTtHw4YN+eOPP7QTny9cuEDJkiW172fTpk05ePAgnTp10q7/+vVr4MMhxLSUpQqhunXrfnQ8M7GrRtetW5erV6+mYyqRGQV4P+X85LkUOH+YUooGDXC1SA52VY7E0ymI7iVfUddI9x9/tVpNsW/HQr/RvDy7i3vLN2F28Ta8jsXwj785tuM8liNGUbnNx+d/ZASVSoXK1FTXMT6ZSqVCrVYTERHBq1evuHfvHsuXL6dWrfgz8k6dOpXoen379mXhwoX4+fnRsGFD7ZzBEiVKYGRkhK+vb6JzVN71WaNGDWrUqMHEiRNxdnZm+/btCeYhAqnK8zmaNGlCdHQ0KpUq0T8IU5KjcOHCmJiYaC9QmxFS8l6nZls7duxI9X6kVQZDQ0Pi4j5+SYzkPjcp2canbPdTlS9fnvXr13+wfPny5cyfP5+jR49StmxZhg4dypw5c+jfvz8GBgY8e/YsQVGZJ08e/Pz8Emzj1q1b5M2bN9Gjl2lF978JhEhD4aHhHB4/B6dD2ykSF/8XrFeBshQcOxK3aqXxu7wAT8/1rL2zlquBV5lTew55zfPqODWg1iNnjTbkrNGGl0+fc2b2YvIc20OeV0/BfTC71lal/MyJOBUroOukWUZUVBQBAQFA/NDYL7/8QmhoKC1btsTa2hpbW1uWLVuGo6Mjvr6+jBkzJtHtdO7cmREjRrB8+XLtsDuAubk5I0aM4Mcff0Sj0VCzZk2Cg4M5ffo0FhYWFCtWjCNHjtC4cWNy5crF+fPnefHiBcWLF/+gj9Tk+Rx6enp4enpq//0pOYyNjRk9ejSjRo3C0NCQGjVq8OLFC27fvk2fPn3SPDMk/1736NHjg3VevXpF+/bt6d27N2XKlMHc3JxLly4xZ84cWrduner9+JQMiXFxceH8+fP4+PhgZmaGjY1NguGi8+fPJ/u5SWwbO3fuxN3dPcnJ3inZ7qdydXXF3d2doKAg7VDbP//8w6BBg/jrr7+0Z5MNGjSIuXPnsm7dOnr37p2ibZ88eZLGjRt/dsaPkUJIfDEubDtI+KzpFA6JH8P2tS+AzbAfadH6/y+qObrKaCo5VGLC6QncfHkTt91uTK0xlQbOKZsnkBFy5rWn1eJpvPIbzKnxMyh49iCFPc/zqu3XXGvSngYewzA1z6HrmJne/v37cXSMvz+bubk5xYoVY8uWLdStWxeAv/76i8GDB1OqVCmKFi3Kzz//rH3tfZaWlrRt25a9e/fy9ddfJ3ht6tSp2NnZMXPmTB49eoSVlRUVKlRg7NixWFhYcOLECRYuXEhISAjOzs7Mnz+fpk2bftCHWq1OcZ7PZWFhkeRrKc0xYcIE9PX1mThxIs+ePcPR0THRCzampY+914kxMzOjatWqLFiwAC8vL2JiYnBycqJfv37adVK7H6nNkJgRI0bQo0cPSpQoQUREBN7e3gkuQpiSz01i2wgODubevXtJ9puaz2NqlS5dmgoVKrB582a+/fZbLl++jJubm/YMtXcsLS0ZPHgws2bNokePHuTOnTvBESA/Pz+qVKmifR4ZGcmOHTvYv3//Z2f8GJXysbEmQUhICJaWlgQHB3/0B4jQnVd+gZwaOYkiV44DEGRiQfSAodTu1yHBX1rvexb6jJH/juTGy/grDXcu1pnhlYZjqJd+E6Y/leepy/h4TMXlafwPuQBLe2ymTadsoxrp1mdkZCTe3t7kz5//o5Mgs4sGDRpQsmRJfv75Z11HESJT2rt3LyNHjuTWrVtJ/tz9r9jYWIoXL87x48e1k6XPnDmjnSz922+/sX37du3lKhLzsZ9VKf39LRdUFFnav39s4UGz5toi6P5XjSlxcB91v+300f+Muc1ys7rpanqW7AnAxrsb6fpPV56EPMmA1KlTvGZFXA9uI2DoBIJMLHAIfo7eD/3YNXgiURGRuo73RQsKCmL79u0cP348Xa9sK0RW17x5c/r37//BHJ+P0dfXZ/78+dSrV49y5coxfPjwBGeMGRgYsHjx4vSIm4AcEUqGHBHKnMLfhnHoh7EUORf/l4K/tSNW4ydSoXndVG/rxNMTjD01luCoYHIY5MCjugdNXJqkceK08dr/BacGj6HwzTMA+OV0Is+cWRSvXiFN+5EjQvFcXFwICgpiwoQJjBgxQtdxhBD/kRZHhKQQSoYUQpnPvXPX8PtxOI5B8ffpul//a5rMn4SRyaf/wg4IC2DUiVFcDYw/w9CtiBujqozCSC+drmT6mY4v+wvTX+dhHhVGjFoP/+7f0WjUgBQfkk6OFEJCiKxAhsZEtqLRaDgwaykRvbvhGPSMN8bmvJk8j9ZLZn5WEQTgkMOBla4r6Vs6/jTazfc302VvF3yCfdIgedqr278jzjt34VWoPAaaOPKtXszuzgMICw7VdTQhhMhSpBASWUJURCS7u/9AvtWLMNLE8ih/aQrs2EG1Ds3TrA99tT5DKgxhacOl2BjbcC/oHh32dGDPo9RdeC2j2Lvkptmu9Tz6phdxqChy7SRnm7Xh0bWk75UkhBAiISmERKYX6OvPseZuFLl0lDhUeLXpSZPdf2Lvkjtd+quRpwZbWm6hskNlwmPDcT/pzqQzk4iI1c0NRz9GrVbTfMYoImYuItjYnDyvnhLUrROn1mzXdTQhhMgSpBASmdrtExe536Ytzs8eEG5gTPDEWbSYORo9/fS98UQu01wsb7ScgWUHokLFtgfb6Ly3M4/ePErXfj9V5TaNcP57K48dC2EaE4n1zHHsm7zws7crUwiFEJlZWvyMkkJIZFr//rGF6IF9sQ0L4rlFLixWrqNG51YZ1r+eWo/vyn3H8sbLyWmSk4dvHtJxb0d2PtyZYRlSw7FgPurv28r9Kg1Ro+Dy5+/s7DOc2JjYVG/r3T2G0vNGh0II8bne/Yz6733RUkPOGkuGnDWmGwdmLSHv6l9Qo/Aof2mqr/oNawfb5FdMJy8jXuJ+0p1z/ucAaFWwFeOqjsPUIPPde0uj0bBv/DwKbFsFwMPCFai37jfMrFL3+fX39+fNmzfkypULU1NTVCpVesQVQohUUxSF8PBwAgMDsbKy0l5F/n1y+nwakUIoY2k0GvaOnE6hvRsBuF+5Ic1WzMfASPdXfI7TxLHi5gqWXF+CRtGQ3zI/8+rMo4h1EV1HS9Tx3//EetEMDDWxPLVzpsy6lamaV6UoCgEBAbx58yb9QgohxGewsrLCwcEh0T/UpBBKI1IIZZzYmFj29htBkXMHAHjQpAMtfpqYZtfGSSsXAy4y5sQYAiMCMdIzwr2KO98U/iZTHjG5+s+/RLsPxyIqjABLe4qsW02eIi6p2kZcXBwxMTFpF0oTB8+uQvgrMLWF3OVBnb5zvoQQXx4DA4NEbx78jhRCaUQKoYwRFRHJgc4DKOx5Hg0qnnT7jibjBuk6VpJeR75m7KmxnPY7DUDT/E2ZVG0SOQwy381Qva564t+3D7ZhQbw0s8Vp5UpcyujoKNadXbB/NIQ8+/9lFrmhyWwokXHzv4QQXz65oKLIMiLDIzjUvjeFPc8To9bjxY8TMnURBGBjbMOSBksYWmEoeio99nnvo8OeDtx9nfmu4VOwfHHyrVtHoIUdOUNf8axHdx5cuJHxQe7sgs3dExZBACH+8cvv7Mr4TEKIbE8KIaFTkeERHG7fi4IPrxKl1ifcYw51v+2k61gpolap6VO6D6uarMLe1J7HIY/psrcLm+5uynSnnecrUZCif23A3zo31hHBvO7Xm9snLmZcAE1c/JEgEntf/rds/5j4dkIIkYGkEBI6Ex4azuF2vSjodZ0oPQOipszlK7dmuo6VauVzlWdry63UzVuXaE00085PY8S/I3gb/VbX0RJwKOBEmS0beWqXD4uoMMIHDeDOyUsZ0/njMx8eCUpAgRC/+HZCCJGBpBASOhEeGs5Rt14UfBRfBEVPmUvVdpnzju8pYWVsxc/1f2ZEpRHoq/Q5+PggbrvduP3ytq6jJZAzrz2Vt/2Jr0MBzKLDCRk0kPvnr6d/x6HP07adEEKkESmERIaLiojkqFtvCj66EV8ETZ1Hlbauuo712VQqFT1K9mBN0zXkzpGbp6FP6bqvK+vvrM9UQ2VWdjZU2byWp3b5sIwK5eWA/ul/fzIz+7RtJ4QQaUQKIZGhYmNiOdB1oPZIUMy0+VT5prGuY6WpMnZl2NxyMw3yNSBWE8vsi7MZemwowVHBuo6mZZ3Llgp/reOZTR6sI0J41qc3j28/TL8OnavHnx1GUpcYUIFFnvh2QgiRgaQQEhlGo9Gwt8+PFL59jhiVHhETZlK5TSNdx0oXlkaWLKi7gDFVxmCgNuDok6O47XbjxgsdnK2VBNs8uSi1cS0BVg7YhgXxuEdPnj3wTZ/O1Hrxp8gDHxZD/3veZJZcT0gIkeGkEBIZZs/giRS5cBgNKl4NGUu1js11HSldqVQquhTvwrpm68hrlpdnYc/osa8Ha26vQaNodB0PAHuX3BTbsIZAczvsQl/h2aMXQYGv0qezEq3AbS1Y/OdS+Ba545fLdYSEEDogF1RMhlxQMW3sHTtHe+8r355DcB0zQMeJMtbb6Ld4nPHg4OODANTOW5vpNaZjZWyl22D/43vHi6ddOmMdEYJPniLU2fknpmbpdB81TVz82WGhz+PnBDlXlyNBQog0J1eWTiNSCH2+QwtWkvf3uQB4telJi5mjdZxINxRFYcv9Lcy+MJtoTTT2pvbMrTOX8rnK6zoaAJ6nLhM+sC+mMZE8LFaZpltWom+gr+tYQgjxSeTK0iJTOL91Pw7L5gPwoG7rbFsEQfxQmVtRNzY034CzhTPPw5/Ta38vVtxckSmGyorXrIhmyhxi1HoUunuRvf1HoNHoPpcQQqQnKYREurl37hp6k93RVzQ8KFWN5r9M13WkTKGYTTE2tdhEs/zNiFPiWHRlEd8d/o5XEek0NycVKrdpxKvBY9GgosjZA/wzeqauIwkhRLqSQkiki4BHT3jx/XfkiInkce7CNFz9C3r6Mg/knRwGOZhVaxaTq0/GSM+I089O0353ey4GZOBtL5JQb0BnfDt/C0DB3es5umS9jhMJIUT6kUJIpLm3QSHc7NEX27AgnlvmovK6Fek38TYLU6lUfFP4G/5s/if5LfPzIuIFfQ/2Zen1pcTp+J5bTScO4X6d+LO4bH+ZzdUDJ3WaRwgh0osUQiJNxcbE8m+3AeR94UuIUQ6cly3DNk8uXcfK1ApbF+av5n/RqmArNIqGX6/9yreHv+VlxEud5mr+y3S8CpXHUBNL1Ojh+N7x0mkeIYRID1IIiTT1z9BJ2jvJG876ifxli+o6UpZgamDK9JrTmVZjGib6Jpz3P0+7Xe04539OZ5n0DfSps24pfrZ5sYx8y4O+3xLyOvNcHVsIIdKCFEIizRz9dR2Fj2wD4PV3IynftLaOE2U9rQu15q/mf1HIqhCvIl/R/2B/frn6i86GysytLSj6x++8MTYn92s/TnQbQGxMrE6yCCFEepBCSKSJm0fPY/PrHAAe1G9D/UHddZwo6ypgVYCNzTfStnBbFBR+v/E7fQ/2JTA8UCd5nIoVwGT2T0Sp9SnodY1/Bk/QSQ4hhEgPUgiJzxbg/ZSQEUMx0sTiVbAszRZN0XWkLM9E3wSP6h7MqjULU31TLj2/RLtd7Tjtd1onecq51iTohzEAFD62g2O/bdBJDiGESGtSCInPEhkewbVeA7AJf0OAlQM1Vy+RqxGnoeYFmrOpxSaKWhclKCqIAYcHsPDyQmI1GT88VW9gF+7X/xoAq1/m4HnmSoZnEEKItCaFkPgsB74dhXOAF6EGJuT7bQlWdja6jvTFcbF0YUPzDXQo2gGAP279Qe8DvQkIC8jwLM0XTeWRc0mM46J5MWQIQQG6vwikEEJ8DimExCc79NMfFLkYfzf52HFTKVi+ePwNNb1Pws2t8V91fD2cL4WRnhHjvxrPvDrzMDMw42rgVdrtbse/T/7N0Bz6Bvp8tXIJL81ssXv7ktO9v5PJ00KILE0KIfFJbp+4iN2KhQA8atGJah2bw51dsLAUrGkBf/eJ/7qwVPxykSZcXVzZ3GIzJWxLEBwVzKCjg5h3cR4xcTEZlsE2Ty5sflpAlJ4BBR/d4J9hHhnWtxBCpDUphESqBQW84tXwYfGTowuUpdmssfHFzubuEPIsYeMQ//jlUgylGScLJ9Y1XUfX4l0BWHNnDT3398Qv1C/DMpSsXZnXA0cAUPjQ35xauz3D+hZCiLQkhZBIlbjYOE73/QG7ty95aWZL9T8Wo6cG9o8GlETW+N+y/WNkmCwNGeoZMrrKaBbWW4i5oTk3Xt6g/e72HPE9kmEZ6g/qzv0azQAwmjdNrjwthMiSpBASqbJv7GwKPrxKtFof67nzsXG0g8dnPjwSlIACIX7x7USaapCvAVtbbqVMzjK8jX7L0GNDmXVhFtFx0RnSf5NfpuNrnx+z6HA8B/5AVERkhvQrhBBpRQohkWIXdx7GZVf8ncif9/qBUvWqxr8Q+jxlG0hpO5Equc1ys7rpanqW7AnABs8NdNvXjSchT9K9byMTY4ot+ZlQQ1PyPfdm/w/j071PIYRIS1IIiRR58cSfGI/x6KFwv1xtGo/s//8vmtmnbCMpbSdSzUBtwPBKw/m1wa9YGlly59Ud3Pa4ccDnQLr37VyyEFHD4wugIqf28u+KTenepxBCpBUphESy4mLjOP/tUKwjggmwcqDB0rkJGzhXB4vcgCqJLajAIk98O5GuauetzdaWWymfqzyhMaGM+HcE085NIyouKl37rdmjDQ/qfQ2A2aJZeF+/l679CSFEWpFCSCRrv8dPFHx0gyi1Pg7z5mFmZZGwgVoPmsz+35P/FkP/e95kVnw7ke4ccjiw0nUlfUv3BWDTvU102dsFn2CfdO23yQIPHjsWwjQmkoeDhsh8ISFEliCFkPioawdOke/v1QA87z6Q4jUrJt6wRCtwWwsWjgmXW+SOX16iVfoGFQnoq/UZUmEISxsuxdrImntB9+iwpwN7H+1Ntz4NjY0o/fti3hqakvfFYw786JFufQkhRFpRKYqS2DnP4n9CQkKwtLQkODgYCwuL5Ff4ggQFvuJWs9bkDH3Fg+JVafH3StTqZGpnTVz82WGhz+PnBDlXlyNBOhYYHsjoE6O59PwSAG0Lt2V0ldGY6JukS38nVm7Fbk78HerfTJ5HtQ7N06UfIYT4mJT+/pZCKBnZtRDSaDTsadebwnfO88LMljL7dsl9xLKwWE0sS68vZdmNZSgoFLIqxPw68ylgVSBd+tvZcwhFzh3kjbE5BXftJFc+x+RXEkKINJTS399Zbmjs119/xcXFBWNjY6pWrcqFCxeSbLt69WpUKlWCh7GxcQamzbqOLFpN4TvniVWpsZw5W4qgLE5frc+g8oP4vdHv2Brb8vDNQzru7cjOhzvTpb9Gi2fgb50bq8i3XPh2CHGxcjFNIUTmlKUKoU2bNjFs2DAmTZrElStXKFu2LK6urgQGBia5joWFBf7+/trH48ePMzBx1uR9/R62f/wMwOOWXSjbqIaOE4m0Ui13Nba22kpVx6pExEYw/vR4xp0aR3hMeJr2Y2qeg9zz5xOl1qeg900OTFmYptsXQoi0kqUKoZ9++ol+/frRq1cvSpQowdKlSzE1NWXlypVJrqNSqXBwcNA+7O3lWjYfEx0Zxf3BwzCJjcInTxGaTB+l60gijeU0ycnvDX/n+3Lfo1ap2eW1i057O/Eg6EGa9lOsejkCug4AIO+WVdw+cTFNty+EEGkhyxRC0dHRXL58mYYNG2qXqdVqGjZsyNmzZ5NcLzQ0FGdnZ5ycnGjdujW3b9/OiLhZ1v4xM8n3/BFhBsaUXPwT+gb6uo4k0oGeWo8BZQewovEKcpnk4lHwIzrt7cS2B9tIy2mDjccM5GGRChgocTwfPYbw0LQ98iSEEJ8ryxRCL1++JC4u7oMjOvb29gQEBCS6TtGiRVm5ciU7d+5k/fr1aDQaqlevztOnT5PsJyoqipCQkASP7OLK3uMU2L8ZgJCBw8lXoqCOE4n0VtmhMltabaFG7hpExUUx6cwk3E+5ExYTlibbV6vVfPXrPN4Ym+MY9IxDwyalyXaFECKtZJlC6FNUq1aN7t27U65cOerUqcO2bduws7Pj999/T3KdmTNnYmlpqX04OTllYGLdCX4ZRNik+FtoPChbi/rfddV1JJFBbIxtWNJwCUMqDEFPpcfeR3vpuKcj916nzdWh7Zwc0YwYC0CRE3s4v3V/mmxXCCHSQpYphHLmzImenh7Pnye8cefz589xcHBI0TYMDAwoX748Dx8+TLKNu7s7wcHB2seTJ+l/48rM4PjgseQMfcVLM1vq/Do7+RXEF0WtUtO3dF9WNVmFvak9PiE+dN7bmc33NqfJUFmNrl9zv0r8sHbsjMkEBbz67G0KIURayDKFkKGhIRUrVuTIkSPaZRqNhiNHjlCtWrUUbSMuLo6bN2/i6Jj0NU2MjIywsLBI8PjSnVqznSJXjqNBhcmEyVjmtNZ1JKEj5XOVZ2vLrdTJW4doTTRTz01l5ImRvI1++9nbbvDzdAIt7LAJf8PJQTIJXwiROWSZQghg2LBhLF++nDVr1uDp6cnAgQMJCwujV69eAHTv3h13d3dt+ylTpnDw4EEePXrElStX6Nq1K48fP6Zv37662oVM5+XT5+gtmAnAwzotqdS6gY4TCV2zMrZicf3FjKg0An2VPgd8DuC2243brz7vRAMzKwssp84gTqWm8K0zHP11XRolFkKIT5elCqEOHTowb948Jk6cSLly5bh27Rr79+/XTqD29fXF399f2z4oKIh+/fpRvHhxmjVrRkhICGfOnKFEiRK62oVMRaPRcOaHUVhFvsXf2pFG82Uiq4inUqnoUbIHq5uuJneO3DwNfUq3f7qxwXPDZw2VlXOtiXcTNwDMf1+Iv5dvWkUWQohPIrfYSMaXfIuNo7+sxfGXmcSq1KiWrKRUvaq6jiQyoeCoYCaensjRJ0cBaJCvAZOrT8bSyPKTthcdGcW/jVqT98VjvAqWpdnujcnfw04IIVLpi73Fhkgbzx74Yv77QgB8mneUIkgkydLIkoX1FjKmyhj01foc8T2C2243bry48UnbMzQ2It/cWcSo9SjodZ0jC5K+IKoQQqQ3KYSyIY1Gw5XBIzCLieBJLheaTB+t60gik1OpVHQp3oX1TdeT1ywvz8Ke0WNfD9bcXvNJQ2VFvyqHb+tuANis/pWn97zTOrIQQqSIFELZ0JGf/qCg902i1frknzcbAyNDXUcSWUTJnCXZ3HIzjZ0bE6vEMu/SPH44+gNvIt+keltNpgzH16EApjGRXB8yCo1Gk/aBhRAiGVIIZTN+932wWbMEgKdfd6NwlTI6TiSyGnNDc+bVmcf4quMxVBvy79N/abe7HVcDr6ZqO/oG+hScP5sotT4FfG5xaM7SdEoshBBJk0IoG9FoNFwdMgrTmEh8HQrgOnmYriOJLEqlUtGhWAc2NN+As4Uzz8Of02t/L1bcXIFGee/IjiYOvE/Cza3xXzVxCbZTqGIpnrXvDYDd+t95fCttb/wqhBDJkbPGkvElnTV2aP4K8i6fT7RaH9PVG+RokEgTYTFhTDk7hX+8/wGgRp4azKg5A5tHp2D/aAh59v+NLXJDk9lQopV2UVxsHAddv8HF7z7e+UrQZP8WOYtMCPHZ5KwxkYDffR+sV/8KwNOvu0oRJNJMDoMczKo1C49qHhjpGXHa7zTtt7Xg4q5+CYsggBB/2Nwd7uzSLtLT16Po/NlE6RmQ3/cOh+ctz+A9EEJkZ1IIZQMajYarQ0eTIyYSX/sCuE4erutI4gujUqloW6QtG5tvJL9FfgJj3tLXwY6lVhYkHAz73wHo/WMSDJMVKFeMZ9/0AMB23VL87vtkVHQhRDYnhVA2cPTnNRR8dINotT4F5s5E30Bf15HEF6qIdRH+Kj2YVm9D0ahU/GptxbcOuXip9/6PGgVC/ODxmQTrNp44BF/7+LPIrg4dI2eRCSEyhBRCX7jnPs+wXPkLAE9adZYhMZHuTCPeMP3la6a9eIWJRsN5E2Pa5XbknLFRwoahzxM81TfQJ/+cGfEXWnx0nWO/rM3A1EKI7EoKoS/c+R/HYhYdzlO7fDIkJjKGWfy9/1qHhvHnswAKRUfzSl+P/g65+NXK8v+Hyv7X7n1FqpbFt0VnACxWLCbA+2kGhRZCZFdSCH3B/v1jC4U9zxOnUpN7+jS5cKLIGM7V488OQ0XBmFg2PntO27ehKCoVS60t6euQi0DLPPHtEtF4ynCe5nTCLDqci0PHZmx2IUS2I4XQFyoo4BWGv8wD4FHDNpSsXVnHiUS2odaLP0UeABUmioLHy9fMCnyJqUbDJRNj2uey4LT/uURXNzQ2IveM6cSq1BS6d5F/V2zKuOxCiGxHCqEv1Ilh47GKCCHA0p6GM+WvapHBSrQCt7Vg4ahd1DwsnE3BCkVNHHgdG8aAwwNYeHkhsZrYD1YvWbsy3g3bAGDw608EBb7KsOhCiOxFLqiYjKx4QcXzW/ZjMeFHNKiImr+ECs3r6jqSyK40cfFnh4U+j58T5FydKCWWORfmsPn+ZgDK5yrPnNpzcMjhkGDV8NBwLjRoin1wIPcrN6D1ul90sQdCiCxKLqiYTYW+CSFq9jQAHlZ3lSJI6JZaD/LXgtLt4r+q9TDSM2JCtQnMrTOXHAY5uBp4lXa723Hi6YkEq5qamWI2diIARS4e4eL2Q7rYAyHEF04KoS/MUfeZ2IW+4lUOa+rN89B1HCGS1MSlCVtabKGEbQmCo4L5/sj3zLs4jxhNjLZNpdYNuF+lIQBhM6cR/jZMV3GFEF8oKYS+ILeOX6DA8f/dumDISCxsLHUbSIhkOFk4sa7pOroU7wLAmjtr6LmvJ89C///WHHXmTybIxBL7kEAOu8/UVVQhxBdKCqEvRExUNP4TJqCnaHhQoio1u7fRdSQhUsRQz5AxVcawsO5CzA3NufHyBu12t+OI7xEArOxsiB08AoACR7Zz+8RFXcYVQnxhpBD6Qhyatpi8L3wJNTChyvxpuo4jRKo1cG7AlpZbKJ2zNG+j3zL02FBmX5hNTFwMtXu140HxKugpGvzGTyQ25sMzzYQQ4lNIIfQFeHzrAY7b4m9HENTtWxzy59VxIiE+TR6zPKxpsoYeJeJvwLrecz3d9nXjydsnVJk/jTADY5wCfTg0Q84gE0KkDSmEsjiNRsPNkeMwjovGJ29RGo7op+tIQnwWAz0DRlQewS/1f8HSyJLbr27jttuN6+o7vOwU//m237Kap/e8dZxUCPElkEIoizv+2wYKet8kWq1P4dnTUKvlWyq+DHWc6rC15VbK2ZUjNCaUEf+O4FKdVzx2LIBJbBRXR47XdUQhxBdAfmtmYUEBrzBdvhgA32ZuFKpYSseJhEhbDjkcWNlkJX1K9QFg08PNbG2vxN9+4/4V/v1ji44TCiGyOimEsrATIz2wjHyLv5UDjaaM0HUcIdKFgdqAoRWH8lvD37A2suZ8jifsrqYHgP6vPxH8MijplTVx4H0Sbm6N/6qJy6DUQoisQgqhLOrK3uMUuXgYAAv38Ribmug4kRDpq2aemmxpuYWK9hXZWl2DvzXYhL/h2EiPxFe4swsWloI1LeDvPvFfF5aKXy6EEP8jhVAWFBURyZtpUwG4X7EelVo30HEiITKGfQ57VjReQa+K37KiSfxRocJnD3J4198JG97ZBZu7Q8izhMtD/OOXSzEkhPgfKYSyoEMeC3AMekawkRm15k3WdRwhMpS+Wp8fyv/AD/2Wc7KUIWoget4Ett/dFt9AEwf7RwOJ3U/6f8v2j5FhMiEEIIVQluN9/R55d28EIKzP99g42uk4kRC6US13NerM3ECokZr8gQrnF09g/KnxhD869uGRoAQUCPGDx2cyLKsQIvOSQigL0Wg03Bk9HiNNLN75SlBvUHddRxJCp4oWLsXrzoMA6HBCw6mrO+h0eToPDQySXzn0eTqnE0JkBVIIZSH//v4nBXxuEa3Wp6hcM0gIABoO789jx0IYx0DvQ3o8inxJp9z2bDfLkejgmJaZfUZFFEJkYvKbNIt48+I1xst+BsC3qRsFyxfXcSIhMgc9fT1cpk8lVqWm8v1oWj8tQKRazUQ7W9ztbAlTqf6zhgos8oBzdZ3kFUJkLlIIZRH/jp6KVUQIzy1z0WiqXDNIiPcVq14O73qtAWiy7QU/2NRFT1HYa5aDjrkduGf4bqjsf0VRk1mg1tNNWCFEpiKFUBZw88hZCp05AIDJSHe5ZpAQiWgwcywvzWyxDQvCfo8pK4v3J1ecgo+hAZ0dHdhsboZikRvc1kKJVrqOK4TIJKQQyuRiY2IJ8PBAjcKD0jWo2q6JriMJkSnlsDRDPXQUAAVP7MEkpgZbOx6ntk0potUqpua0YWSFpoQWqq/jpEKIzEQKoUzu0MxfyfvClzADY6rOnaLrOEJkajW6tuJh0croKRoeT/DAwtCaxS02MLzicPRV+hx4fBC3PW7cfnVb11GFEJlEqguhHj16cOLEifTIIv7D38uXXJtXAfCyUz/sXXLrOJEQmV/5OZOJ0DfE2f8hR35agVqlpmepnqxuuhrHHI48efuEbv90Y4PnBhTlo+eVCSGygVQXQsHBwTRs2JDChQszY8YM/Pz80iOXAC6OmoRpbBS+9gVoNOpbXccRIkvIWzQ/AW3ir7FltX4ZL5/GXy+orF1ZtrTcQj2nesRoYph1YRY/Hv+R4KhgXcYVQuhYqguhHTt24Ofnx8CBA9m0aRMuLi40bdqUrVu3EhMTkx4Zs6Vzm/+h8O1zxKEiz+RJ6OnLGS5CpFSj8T/gZ5sX8+hwTo/y0C63NLJkUb1FjKkyBn21Pkd8j9BhTwduvripu7BCCJ36pDlCdnZ2DBs2jOvXr3P+/HkKFSpEt27dyJ07Nz/++CMPHjxI65zZSnhoOFHzZgHgVbMZpepW0XEiIbIWAyNDbMeNB6DIleNc3n1U+5pKpaJL8S6sa7qOPGZ58Av1o/u+7qy5vUaGyoTIhj5rsrS/vz+HDh3i0KFD6Onp0axZM27evEmJEiVYsGBBWmXMdo5MnEeukBcEmVhSd9Y4XccRIksq36wO9ys3ACB4xnSiI6MSvF4qZym2tNxCI+dGxCqxzLs0j8FHB8tQmRDZTKoLoZiYGP7++29atGiBs7MzW7ZsYejQoTx79ow1a9Zw+PBhNm/ezJQpcobTp3h07S759m8BIOrbwVjmtNZxIiGyrlpzJhFilAPHoGccmrLog9fNDc2ZX2c+46qOw0BtwPGnx2m3ux3XAq9lfFghhE6kuhBydHSkX79+ODs7c+HCBS5dusSAAQOwsLDQtqlXrx5WVlZpmTNb0Gg0eLpPxFATyyPnktTp31HXkYTI0mwc7QjpMRCA3Ds38OTuow/aqFQqOhbryIZmG8hnno+AsAB67u/Jylsr0SiajI4shMhgKiWVg+Lr1q2jffv2GBsbp1emTCUkJARLS0uCg4MTFHvp4fjvf2K/YArRan3MNmyW+4kJkQbiYuM41LgNzs8e8LBoJVruXJdk27CYMCafncw+730A1MxTk+k1p2NjbJNRcYUQaSSlv79TfUSoW7du2aYIykghr4MxXBp/6N7XtZ0UQUKkET19PVymTSFOpabQvUucXr8jybY5DHIwu9ZsPKp5YKRnxCm/U7Tf1Z5LAZcyLrAQIkPJlaUziWNjpmEdEUyguR0Npo7UdRwhvijFqpfjUZ2WAMQtnEf427Ak26pUKtoWacvG5hvJb5mfwIhA+hzsw7Iby2SoTIgvkBRCmcCdk5coePIfAAyGjcTUzFTHiYT48tSbOZbXplbYhb7i8PjZybYvYl2Ev5r/RauCrdAoGhZfXcyAQwN4GfEyA9IKITKKFEI6Fhcbh+/EyegpGh4Ur0L1Ti11HUmIL5K5tQWxA4cC4HJoGw8v30p2HVMDU6bXnM60GtMw0TfhrP9Z2u9uz3n/8+mcVgiRUbJcIfTrr7/i4uKCsbExVatW5cKFCx9tv2XLFooVK4axsTGlS5fmn3/+yaCkKXN04Uqc/R8SoW9IxdmTdR1HiC9arT7t8SpQBgNNHPfdJ6HRpGyoq3Wh1vzZ/E8KWRXiZcRL+h3sx5JrS4jTxKVzYiFEestShdCmTZsYNmwYkyZN4sqVK5QtWxZXV1cCAwMTbX/mzBk6depEnz59uHr1Kl9//TVff/01t24l/5dgRnjlF4jlut8BCPi6G3mKuOg2kBBfOLVaTcmZ8Wdm5ve9w7+//5nidQtaFWRj8418U/gbFBR+u/4b/Q7140X4i3RMLIRIb6k+fV6XqlatSuXKlfnll1+A+OvuODk58cMPPzBmzJgP2nfo0IGwsDD27NmjXfbVV19Rrlw5li5dmqI+0/P0+Z3dBlHk4hGe2eSm1tF/MDQ2StPtCyESt3v4VArt3UiQiQUlD+1P9YVL9z7ay5SzUwiPDcfG2IaZNWdSPU/1dEorxJdNo9GgVqf9cZl0O31eV6Kjo7l8+TINGzbULlOr1TRs2JCzZ88mus7Zs2cTtAdwdXVNsj1AVFQUISEhCR7p4eqBkxS5eAQAq7HjpQgSIgM1mDycQAs7rCNCOO4+I9XrNy/QnE0tNlHUuiivI18z4PAAfr7yM7Ga2HRIK8SX6+TqbfzTvCPe1+/pLEOWKYRevnxJXFwc9vb2CZbb29sTEBCQ6DoBAQGpag8wc+ZMLC0ttQ8nJ6fPD5+I57PnAnC/Ql0qtqiXLn0IIRJnamaK4fDRABQ8uZfbJy6mehsuli6sb7ae9kXao6Cw/OZy+hzoQ0BY0j9fhBD/L/RNCCyeT0Hvm9xcnvSFTtNblimEMoq7uzvBwcHax5MnT9Kln1K/LeJ+udpUnz0xXbYvhPi4ah2a86B4VfRQeDJxMnGxqZ/4bKxvzMRqE5lbey45DHJwJfAK7Xe358TTE+mQWIgvy9Hxc8gZ9ppXOaypP22UznJkmUIoZ86c6Onp8fz58wTLnz9/joODQ6LrODg4pKo9gJGRERYWFgke6SFv0fy0/ut37Jwc02X7QojkVZozhQh9I5wDvDiy4I9P3k6T/E3Y3GIzxW2K8ybqDd8f+Z6fLv1EjCYmDdMK8eV4cOEGLkd2AKAZNAwzq/S9hdXHZJlCyNDQkIoVK3LkyBHtMo1Gw5EjR6hWrVqi61SrVi1Be4BDhw4l2V4Ikb3kLpyPgDbdALBa9zsvnz5PZo2k5bPIx/pm6+lcrDMAq26votf+XviH+qdJViG+FBqNhgfjJmGgxOFVsBw1e3yj0zxZphACGDZsGMuXL2fNmjV4enoycOBAwsLC6NWrFwDdu3fH3d1d237IkCHs37+f+fPnc/fuXTw8PLh06RKDBg3S1S4IITKZhuMG8cwmD+bR4Zwe/XnX8jLUM8S9qjsL6y7E3MCc6y+u0253O476Hk2jtEJkfceXrCf/k7tEqfUpPXtKupwxlhpZqhDq0KED8+bNY+LEiZQrV45r166xf/9+7YRoX19f/P3//6+v6tWrs3HjRpYtW0bZsmXZunUrO3bsoFSpUrraBSFEJmNobIT1uPEAFLl8jKv//PvZ22zg3IDNLTdTyrYUIdEhDDk2hNkXZhMTJ0NlInt78+I1JiviL4HztHlHnEsV1nGiLHYdIV1Iz+sICSEyj52dB1LkynH8bPNS5+heDIwMP3ubMXExLLyykLV31gJQyrYUc+vMJa953s/ethBZ0c4+wyhyeh8BlvZUO7YPY1OTdOvri7uOkBBCpKcaczx4a2hKnldPOTR9cZps00DPgJGVR7K4/mIsDC249eoWbrvdOPT4UJpsX4is5Nax8xQ8vR8A05Fj0rUISg0phIQQAsiZ1543XfsD4LBtHc8e+KbZtus61WVry62UsyvH25i3DDs+jOnnphMVF5VmfQiRmcXGxPJskgd6KDwoVY2q7ZroOpKWFEJCCPE/DYb15bFDQUxio7g0Om2v8eVo5sjKJivpXao3AH/d+4tu/3TDNyTtCi4hMqvDc5fhFOhDuIExVeZkrhuMSyEkhBD/o6evR76pHsShovCd85zdtDdNt2+gNuDHij/yW8PfsDayxvO1J2573NjnvS9N+xEiM3nu8wzbP1cAENi+Jw4F0ueODZ9KCiEhhHhPiVqV8KrVHIDo+bMJDw1P8z5q5qnJlpZbqGhfkbCYMEadGMXks5OJjI1M876E0LXzoz0wi4ngaU4nGrl/r+s4H5BCSAgh/qPuzLEEmViSK+QFRybMTZc+7HPYs6LxCvqX6Y8KFVvvb6XzP515FPwoXfoTQhcubj9E4esn0aAi16RJ6Bvo6zrSB6QQEkKI/7DMaU30gCEAOB/YgtdVz3TpR1+tzw/lf+D3Rr9ja2zLg6AHdNzTkd1eu9OlPyEyUlREJG9nzwDgYdWGlG1UQ8eJEieFkBBCJKJ2vw48yl8aA00cd8dMQKPRpFtf1XJXY2urrVR1qEpEbARjT41lwukJhMek/bCcEBnl0OQFOL4JINjIjNqzJ+k6TpKkEBJCiESo1WqKz5xCtFqfAo9vc3zpxnTtL6dJTn5v9DvflfsOtUrNjoc76Ly3Mw+DHqZrv0Kkh8e3H5J3958AhPb6DmsHWx0nSpoUQkIIkYQC5Yrh29QNAJPli3nz4nW69qen1mNg2YGsaLwCOxM7vIK96LS3E9sfbEduAiCyCo1Gw81R4zGKi8Enb1HqD+6h60gfJYWQEEJ8RKOpI3hukQuriBD+HT01Q/qs7FCZLS23UD13dSLjIpl4ZiJjT42VoTKRJZxc9TcFva4To9Kj0Azd31Q1OZk7nRBC6JixqQkmI90BKHjmADePnM2Qfm1NbPmt4W8MqTAEPZUeex7tocOeDtx7fS9D+hfiU4S8Dkb/158A8Gn0DYWrlNFxouRJISSEEMmo2r4JD0pVRw+FAI/JxMbEZki/apWavqX7stJ1JblMc+ET4kPnvZ3ZfG+zDJWJTOnYmOnYhL/hhXlOGk4fres4KSKFkBBCpECVuZMJMzAm74vHHJrxS4b2XcG+AltbbqV23tpEa6KZem4qo06MIjQ6NENzCPExt09cpODJ+Kux6w8bhal5Dh0nShkphIQQIgUc8uflZad+AOTasjpNb8qaEtbG1iyuv5jhFYejr9Jnv89+3Pa4cefVnQzNIURi4mLjeDrBAz1Fw4MSVaneqaWuI6WYFEJCCJFCjUZ9i69DAUxjo7g0akKG969WqelZqiermqzCMYcjT94+oes/XdnouVGGyoROHZrzO/mePyJc34jKc6foOk6qSCEkhBAppKevR75pU4hTqSnseYHT63fpJEe5XOXY0nIL9ZzqEaOJYeaFmQz/dzgh0SE6ySOyt4BHT8j553IAAt164Vgwn44TpY4UQkIIkQrFa1bkUZ34w/6ahXMIC9bNPB1LI0sW1VvE6Mqj0Vfrc+jxIdx2u3Hr5S2d5BHZ14WRE8kRE8mTXM6Z8qaqyZFCSAghUqn+rLG8ymFNztBXHHGfobMcKpWKriW6sq7pOvKY5cEv1I9u+7qx9vZaGSoTGeL0xl0Uvn2OOFTknjIlU95UNTlSCAkhRCqZWVnA4JEA5D+2E88zV3Sap1TOUmxuuZlGzo2I1cQy99JcBh8bTHBUsE5ziS9bWHAomp/mAOBVuwWl6lbRcaJPI4WQEEJ8gpo92vCwaGX0FQ2+7hMy7NpCSbEwtGB+nfmMrToWA7UBx58cp/3u9lwLvKbTXOLLdcR9BjlDX/EqhzX1Z4/XdZxPJoWQEEJ8oorzphGub0S+5484PPs3XcdBpVLRqVgnNjTbQD7zfPiH+dNzf09W3VqFRtHoOp74gnieuUL+Yzvjnwweibm1hW4DfQYphIQQ4hPlLpyPFx37AGD31x8Zfm2hpBS3Lc6mFpto6tKUOCWOny7/xKAjgwiKDNJ1NPEFiIuNw3fsBPQVDQ+LVqZmjza6jvRZpBASQojP0HD0QHzt468tdHlE5hkeMDM0Y3bt2UysNhEjPSNO+p2k3e52XH5+WdfRRBZ3aNYS8gXEXzOoQha7ZlBipBASQojPoG+gT77pU4hVqSl07yInV2/TdSQtlUpF+yLt2dBsAy4WLgSGB9LnQB+W31guQ2Xik/jd98Hurz8AeNGxD3mKuOg2UBqQQkgIIT5T8ZoV8a7/NQDqn+cS8jpzna1V1KYom1psomWBlsQpcfx89WcGHBrAq4hXuo4mshCNRsOV4eMwjY3isUNBGo35TteR0oQUQkIIkQYazhrLC/Oc2IS/4djIybqO8wFTA1Nm1JrB1BpTMdE34az/WdrtbscF/wu6jiayiJN/bKHQgyvEqPRwmTENPX09XUdKE1IICSFEGjA1z4HhSHcACp3ez9V9J3ScKHFfF/qaP5v/SSGrQryMeEm/Q/347dpvxGnidB1NZGJBga8wWPITAD6Nv6FY9XK6DZSGpBASQog08pVbM+6Xq40ahaApHkRFROo6UqIKWhVkY/ONfFP4GzSKhiXXl9D/UH9ehL/QdTSRSZ0Y4YF1RAjPLXPRcPoYXcdJU1IICSFEGqo5fyrBxuY4BvlzcNwcXcdJkom+CZOrT2ZmrZmY6JtwIeAC7Xa348yzM7qOJjKZy7uPUuTCYQByjJ2AqZmpjhOlLSmEhBAiDdnmyUXEt0MAcN63mfvnr+s40ce1KNCCTS02UcS6CK8jXzPg0AB+vvIzsRrdXilbZA7hoeG8nRY/5+1+xXpUbt1Qx4nSnhRCQgiRxup82wmvguUwUOJ4NHoccbGZe/5Nfsv8bGi2AbcibigoLL+5nD4H+hAQFqDraELHDo+ZgX1wIEEmFtReMFXXcdKFFEJCCJHG1Go15X6aQbi+Ec4BXhyc+auuIyXLWN+YCdUmMLf2XHIY5OBK4BXa727PiaeZc9K3SH+3jl+gwJHtAMT+MBLrXLY6TpQ+pBASQoh0kLdofl506guA/aaV+N7x0nGilGmSvwmbW2ymuE1x3kS94fsj3/PTpZ+I0cToOprIQNGRUQSMH4+eouFBiarU7t1O15HSjRRCQgiRThqNHsjj3IUxiY3i5rAxaDRZ42rO+Szysb7ZejoX6wzAqtur6LW/F/6h/jpOJjLKQY+fyPPyCW8NTan60wxdx0lXUggJIUQ60dPXo9DcmUSp9Sngc4sjC1bqOlKKGeoZ4l7VnQV1F2BuYM71F9dpt7sdx3yP6TqaSGcPL9/CaddGAEL6DMLeJbeOE6UvKYSEECIdFapYEr+vuwFgvfpX/L0yxx3qU6qhc0M2t9xMKdtShESHMPjYYOZcnENMnAyVfYniYuN4OHIshppYvPKXpv4PPXQdKd1JISSEEOms8aShPMnlQo6YSC4PyTpDZO/kNc/L2qZr6VYivqBbd2cd3fd15+nbpzpOJtLawemLcX72gAh9I8rMn4la/eWXCV/+HgohhI4ZGBmSb9ZMYlR6FHx4leNL1us6UqoZ6BkwqvIofq73MxaGFtx6dQu33W4cfnxY19FEGnl07S6Om+OHbwM79ydfiYI6TpQxpBASQogMUKx6OR437wCA2bJFPPd5puNEn6ZevnpsbbmVsnZleRvzlh+P/8iM8zOIjovWdTTxGeJi47g3fDRGcTF45ytOo1Hf6jpShpFCSAghMojrtFH42ebFPDqcC0Oz3hDZO45mjqxqsopepXoB8OfdP+n6T1d8Q7LW/Cfx/w7O+AUXv/tE6BtS8qfZX8yd5VNCCiEhhMgghsZGOEyfTqxKTaG7Fzn+2wZdR/pkBmoDhlUcxq8NfsXKyArP15647XFjv/d+XUcTqeR9/R4Om/43JNapH86lCus4UcaSQkgIITJQqbpV8GkaP0Rm/vtCAh490XGiz1M7b222tNxChVwVCIsJY+SJkUw5O4XI2EhdRxMpEBcbx93hozGOi8bbqRiNRg/UdaQMJ4WQEEJkMNcZo3lqlw+z6HAuDR6dZYfI3nHI4cAfrn/Qr3Q/VKjYcn8LXf7pgnewt66jiWQcnPELLk/vxQ+JLZiTrYbE3pFCSAghMpihsRFOs2cTo44/i+zIotW6jvTZ9NX6DK4wmKWNlmJjbMP9oPt02NOB3V67dR1NJOHh5ds4bvoDgMCOfbPdkNg7UggJIYQOFKteDt9WXQCwXrkYv/s+ug2URqrnrs7Wllup4lCFiNgIxp4ay8TTE4mIjdB1NPGemKhovIaNwiguhkfOJWk05jtdR9IZKYSEEEJHXCcPx9e+ADliIrk2ZFSWHyJ7x87UjmWNlvFdue9Qq9Rsf7idzns74/Uma9x4Njs4MH4u+Z4/IszAmLI/z82WQ2LvSCEkhBA6YmBkSIF5s+PvReZ9k4OzftN1pDSjp9ZjYNmBrGi8gpwmOXn45iEd93Rkx8Mduo6W7d06fgHnPfH3EnvTdzB5i+bXcSLdyjKF0OvXr+nSpQsWFhZYWVnRp08fQkNDP7pO3bp1UalUCR4DBgzIoMRCCJG8wpVL8cytNwAOG37n4eVbOk6Utio7VGZry61Uz12dyLhIJpyewLhT4wiPCdd1tGwpPDSc52Pd0Vc0PCxaOVvcSyw5WaYQ6tKlC7dv3+bQoUPs2bOHEydO0L9//2TX69evH/7+/trHnDlzMiCtEEKknOv4wTxyLhk/X+PHkURHRuk6UpqyNbHlt4a/Mbj8YNQqNbu8dtFxb0fuB93XdbRs5/DIqeR+/YxgY3O++mV2triXWHKyxDvg6enJ/v37WbFiBVWrVqVmzZosXryYv/76i2fPPn6ZelNTUxwcHLQPCwuLDEothBApo6evR7nF8wg1MMEp0If9Y2bqOlKaU6vU9CvTj5WuK8llmgvvYG867+3M1vtbURRF1/GyhYs7D1Pw2E4AYoaOwc7JUceJMocsUQidPXsWKysrKlWqpF3WsGFD1Go158+f/+i6GzZsIGfOnJQqVQp3d3fCwz9+ODYqKoqQkJAEDyGESG95irgQ+t1wAAoc2MLVAyd1nCh9VLSvyNaWW6mVpxZRcVFMPjuZ0SdGExr98akO4vMEBb4iaspE1Cjcr1CXWj2/0XWkTCNLFEIBAQHkypUrwTJ9fX1sbGwICAhIcr3OnTuzfv16jh07hru7O+vWraNr164f7WvmzJlYWlpqH05OTmmyD0IIkZx6A7vwoHQN9BQNIRPG8Tboy/xDzNrYml8a/MKwisPQU+mxz2cfHfZ0wPOVp66jfZE0Gg0nB47ANiyIQAs76v86W9eRMhWdFkJjxoz5YDLzfx9379795O33798fV1dXSpcuTZcuXVi7di3bt2/HyyvpUzjd3d0JDg7WPp48ydqXvxdCZC21fp3NqxzW5Ap5wdHBY3UdJ92oVWp6lerF6iarccjhgO9bX7r804W/7v4lQ2XJ0cSB90m4uTX+qybuo82PLl5D4dvniFOpsZo+E3NrmSLyPn1ddj58+HB69uz50TYFChTAwcGBwMDABMtjY2N5/fo1Dg4OKe6vatWqADx8+JCCBQsm2sbIyAgjI6MUb1MIIdKSdS5bDMd5oBk7lCIXj3B82V/U7d9R17HSTblc5djacivjT4/n+JPjTD8/nQsBF/Co7oGFofzC/sCdXbB/NIS8Nz/WIjc0mQ0lWn3Q/PGtB1gvXwSAT4vOtGhUI6OSZhkqJQuU3p6enpQoUYJLly5RsWJFAA4ePEiTJk14+vQpuXPnTtF2Tp8+Tc2aNbl+/TplypRJ0TohISFYWloSHBwsE62FEBlm16DxFD78N6EGJjhs2YpTsQK6jpSuFEVh3Z11LLiygFhNLHnM8jCvzjxK5Syl62iZx51dsLk78N9f26r4L25rExRDMVHRHGnSFmf/hzzOXZiGB7ahb6DT4x8ZKqW/v7PEHKHixYvTpEkT+vXrx4ULFzh9+jSDBg2iY8eO2iLIz8+PYsWKceHCBQC8vLyYOnUqly9fxsfHh127dtG9e3dq166d4iJICCF0pcm8CfjaF8AsJoLb3w0lJipa15HSlUqlonvJ7qxtspY8ZnnwC/Wj275urLuzTobKIH74a/9oPiyC+P9l+8ckGCbb7z4LZ/+HhBsYU+KXBdmqCEqNLFEIQfzZX8WKFaNBgwY0a9aMmjVrsmzZMu3rMTEx3Lt3T3tWmKGhIYcPH6Zx48YUK1aM4cOH07ZtW3bvlhsACiEyP0NjI4ouXkC4vhHOzx58kafUJ6a0XWk2t9xMw3wNidXEMufiHAYfG0xwVLCuo+nW4zMJh8M+oECIX3w74k+Vz//PXwAEf/sj+UokPh1EZJGhMV2SoTEhhC4d/XUdjotnEIeKqLm/ULFlfV1HyhCKovDn3T+Zd2keMZoYHHM4Mqf2HMrlKqfraLpxcyv83Sf5dm3/4IVVDR60aoN1RDD3K9Sl9cYv59YtqfFFDY0JIUR2Vf/7btwvVxs9FCI8xvPa/4WuI2UIlUpF5+KdWd9sPU7mTviH+dNrfy9W3VqFRvkybk6bKmb2KWoWZ5KT898OxToiGH8rBxoskVPlkyOFkBBCZHL1f5vLc4tc2IYFcabvYOJiP3669JekhG0JNrfYTBOXJsQqsfx0+ScGHRlEUGSQrqNlLOfq8WeHvZsY/QEVWORh/8pzFHx0gyi1Prl/WoCZlYxkJEcKISGEyOTMrS2wmzePaLU+Bb2usX/ifF1HylBmhmbMqT2HCV9NwFBtyEm/k7Tb3Y7Lzy/rOlrGUevFnyIPfFgMxT9/aNOLfNvXAfC8+/cUq14uw+JlZVIICSFEFlCydmUCun8PgPP2NVzec0zHiTKWSqXCragbG5tvxMXChcDwQPoc6MPyG8uzz1BZiVbxp8hb/OceYRa5edtwCf6Ld6CvaHhQ8isajUr+puQinkyWToZMlhZCZBYajYY9HfpT+OZpgkwsKbxre7a8cWZ4TDhTz01lz6M9AFTPXZ0ZNWdga2Kr42QZRBMXf3ZY6HMwsycuT1X2telOQa/rvDDPSZl/dmJlZ6PrlDonk6WFEOILo1arqbfsJwKsHLCOCOZCv8HExvxfe/cdHkWd+HH8vbtJNj20VAghdKSFroTigTQ5BAuggqCIAoLSBc8CKFU8BZVyICeoR/8dSq8iIh00GnoLNRBqKum7vz/ioShVkswm+3k9Tx7J7JTPzKPOh5nvzGYZHSvfebp6MrbRWN5t+C7uFne2xm6l47KO7Dq/y+ho+cNsgfDGUP0pCG/MqjcnUu7Yz6SbXSg28Z8qQfdIRUhEpADxKepL8IcfkW5xpeyJvaxykvcL/ZHJZOLxCo8zr+08yvqV5WLqRXqu7cm0qGlk3+G7twqT7QtXEr7sPwBcfLE/1R6ub3CigkdFSESkgKncMIKLPQcAUHbFPLZ8tdTYQAYqX7Q889rOo0P5DtjsNqb+PJVe63pxKfWS0dHy3OmDxzGPeQczdg7XbUaLwT2NjlQgqQiJiBRALQb24HCDFpixY50wguNRB42OZBhPV0/ei3yPsY3G4uHiwY7zO3hy6ZNsi91mdLQ8k3Ytlf29+uGTnsIZ/9K0mD7R6EgFloqQiEgB1Xr6+5wMLo9XZhon+vQl6Wqi0ZEM1a5cO+b/fT4VilbgStoVeq3rxcc/fkyWrfCNo1rTZxil42JIdvOk4rRP8fT2NDpSgaUiJCJSQFk93ImYNZWrHr4EX41lY4/XsNmc5FHyWyjrV5a5j87lqYpPYcfOzOiZ9Fzbk7iUOKOj5Zo146dSccc6bJjIeH0EYdUqGB2pQFMREhEpwILKhuIx5n0yTRYqHNjBqrd0i8TdxZ0RD43g/Sbv4+XqxZ64PXRc1pEfzv5gdLT7tvubDYTMmQJATPvniOz6mMGJCj4VIRGRAq7Wo02Jfe4VAML+O4et85YZnMgxtAlvw4K/L6BKsSpcTb9Kn/V9+GjPR2TaMo2O9pec2n+M7HeG5bw0sdpDPDpumNGRCgUVIRGRQqDl8N4crtsMC3asY97m0PYooyM5hDDfML589EuervQ0AP/e+296rO7BueRzBie7N8nxiRx+qTe+vw6Obj7rY8xmncJzg46iiEghYDabaT3zn5woVQnPrHQu9OvHxdMF62SfV6wWK28++CYfPvwhPq4+RF2MouPyjnx3+jujo90Vm83Gty+8SsnLZ4h396HKZ9Px8vM2OlahoSIkIlJIWD3cqT9nBhd8/CmRfJk93V7mWvI1o2M5jBZhLVjQbgHVilcjIT2BV799lYm7JpKZ7di3ypYPGEmFAzvJNFmwjnmfUpXCjY5UqKgIiYgUIsVLBhAydSrJrh6EnTvK+h79nf5Jst8L9QnlizZf0LVKVwC+2P8F3VZ140zSGYOT3dzaiTOosHYRAHEvDaR224eNDVQIqQiJiBQyFepVw/bOWLJNZir88gMrXx9rdCSH4mpxZVj9YUz+22R83HzYe3kvnZZ1Yv3J9UZHu8G2BSsI+fdkAI626kSLQS8anKhwUhESESmEGnRszZnn+gJQbvl/WPfhLIMTOZ5mpZuxuN1iavjXICkziYHfDWTsjrFkZGcYHY39m3fj9t6bWOw2jtRoRNuPRhgdqdBSERIRKaRa/+MVDjfrAEDwzA/ZMtd5v5PsVkK8Q5jdejYvVH0BgHkH59F1ZVdOJZ4yLFPskVNc7f8qnlnpxJSqTKs5n+gJsTykIysiUoj9/ePRHKnZGIvdhueYt/l53RajIzkcV7Mrg+oOYkrzKRSxFuHAlQN0Wt6J1TGr8z3L1fOX2f/8ixS7Fs+5IkE89NVnWD3c8z2HM1EREhEpxCwuFlrP+YTj4dVxz84gbUh/jv10wOhYDqlJqSYsareI2gG1SclMYej3Q3l327ukZaXly/aT4xPZ8czz1x+TLzdrJkWDiufLtp2ZipCISCHn5m6lydzPOB0Qhm96Cmdfeonzx08bHcshBXkFMavVLF6q/hImTCw6vIguK7sQkxCTp9tNT01j0zM9CTt3lGRXD4p8Oo2wquXzdJuSQ0VIRMQJ+BT1JeKrz7ng449/8mX2d+muFy7egovZhddqv8b0R6ZTzL0Yh68epvPyziw/vjxPtpeVmcWaLr0pGxNNmsUNl/cnUaVRnTzZlvyZipCIiJMIKB1Mmc9nccWzCMFXz/Hz0924cu6i0bEcVsOSDVnUbhH1guqRmpXKG5vfYMTWEaRmpebaNmw2Gyt6DKTC/h1kmiykvTOWWm2a5Nr65c5UhEREnEhYtQoEzvyMeHcfSl4+w+7O3Yi/eMXoWA4rwDOAmS1m0qdmH0yY+O+R//Lsimc5Fn/svtdts9lY1msYFXetJxsTVwa+xUOd2+ZCarkXKkIiIk6mfJ2qFJs+g0SrF6EXTrC9U3eSribe20ps2RCzGaIX5/zTlp03YR2AxWzhlYhXmNlyJiU8SnA0/ijPrHiGr49+/ZfXabPZWPbSUCpuzrndFvviAB5++elcSiz3wmS32+1Gh3BkiYmJ+Pn5kZCQgK+vr9FxRERyzb7vd3Gtby+8M1M5UaoSkQvn4FvM784L7l8Kq4dBYuxv03xDoPUEeOCxvAvsAC6lXuKNzW+w/dx2AB4r9xhvNngTT1fPu16HzWZjWc/BVNya83j+6R4DaPl6rzzJ68zu9vytK0IiIk6qapN6uH34CddcrJQ5c4htTzzL1fOXb7/Q/qWwsNuNJQgg8VzO9P2F+6WNJTxKMP2R6fSL6IfZZGbpsaU8veJpDl89fFfL22w2lr04iIpbV2PDxJmeg1SCDKYiJCLixGq2iMR10lSS3Twpff44e5565tZPk9myc64EcbMbCb9OWz28UN8mg5xbZb1q9mJWy1kEeAQQkxDDsyueZfHhxdzuJkt2VjbLXhhAxW1rsGEi9qVBtBjyUj4ml5tRERIRcXI1HmmI99QZJLj7UPLSafZ2fJbYIzf5iomTW/98JegGdkg8mzOfE6gbVJdFjy0ismQk6dnpjNo2imGbh5GckfynedOupbKic08q7liXU4J6DaHF4J4GpJY/UhESERGqNKpDiVmfc8WzCEHx5zn6zLOc+OUPt3uS4+5uZXc7XyFQzL0YU5tPZWCdgVhMFlbFrKLz8s4cuPzb27sTrySw4fHnqLBvO5kmC3Gv/YMWA3sYmFp+T0VIRESAnKfJSn355fWXLp5/rgtRa374bQbvwLtb0d3OV0iYTWZ6VOvB7NazCfIK4lTSKbqu7Mr8g/OJOxnLjvadKXtyH6kublwb9T7NXulqdGT5HRUhERG5LqxqeSot+A9nSoTil54MA/uw6bMFv37YMOfpMEy3WNoEviVz5nNCEQERLG63mIdLPUyGLYOZq0ezv/OjlLp4kkSrF66Tp/Ngp0eNjil/oCIkIiI3CCobyoPLFnOsXE2stixKfDCKlW//ExumnEfkgT+XoV9/bz0ezJb8jOtQ/Kx+fNzsY15Jb8e42dkExadzwddM8uR3qN78IaPjyU2oCImIyJ/4FPWl1X+/4PBDrTBjJ3zRZyzrMZD08JbQ6QvwDb5xAd+QnOmF/D1Cd2Kz2VgzdgqNP/oGnzQ4FuzC291MDDk9gq/2f3Xbp8rEGHqh4h3ohYoi4sxsNhurR35E2MJZmLFzMrg8NWZMIaRcyZynw5LjcsYEhTV06itBkPMN8qt7DaXizvUAHKkeyYPTRzPulwmsP5UzrVloM96NfBc/6128uFLuy92ev1WE7kBFSEQEfpizBPcP3sUrM40Edx/M74ym/hMtjY7lMGKPnOLn3q9S5uxhbJg48UR32oweitlsxm63M+/gPD7Y/QGZtkxCvEJ4v+n71PSvaXTsQk1FKJeoCImI5Ij5+RDHXulHyctnyMbEice78+iYnJO9M/thzhLcPhyDT3oK11yspA0fSWTXDn+ab9/lfQzdNJTTSadxMbnQv3Z/ulXthtnk3Mcvr6gI5RIVIRGR36QkJLO+12AqRn0PwLHw6tT59AOCy5U2OFn+u5Z8jXX936bilpUAnCkRSvjkjyhfp+otl0nOSGbktpGsObEGgCalmjAmcgxF3IvkR2Snou8aExGRXOfl5027udM49Xx/MswulIuJ5szjj/Ptp19gs9mMjpdvDu/4ma2tO1wvQYeb/J3Ga7+5bQkC8HbzZmKTibz94Nu4md34/sz3PLXsKX6M+zE/YstN6IrQHeiKkIjIzR3Z+QsxQ14n9MJJAI5WrseDn0zAP/TXJ8ps2YVuQPW15GtseGsC4WsWY7HbuGZ1J23IO0Q+9/g9r+vQlUMM2TSEE4knsJgs9KvVjx7VeuhWWS7RrbFcoiIkInJrGWnprHnrfcqsmI+L3UaC1Zuk516mWasSWNb948bvJvMNyXkPUQF9xH73NxvIHvMPfBMTAfAOSSOoXjyugUF/eb9SMlMYvX00y48vB6BhSEPGNhpLcY/iuZrdGakI5RIVIRGRO9u7cQcX/vEPgq/mFB/3YhkE1k7As0Tm7+b69aWLBex9Q+djzrDjrbFU3LMRAIt7NkG1E/AJTcNkgvvdL7vdztdHv2bsjrGkZafh7+HPhCYTqBdUL/d2wgmpCOUSFSERkbuTnprGhjEfE/71vyErpxz4lbmGf81EXD3+N37IlHNlaEC0w98mS7h0lU3vfUTo+m9wz84AoEi5FAJqJmJx++Op8/7368jVIwzZNITjCccxm8z0rtmbl6u/jMXBj5Oj0mBpERHJV1YPdx598SEqtI3DL/waAAknPDm2LJDzu/3ITLEAdkg8mzN2yEGlXUtl5YiPONi8BRXWLMI9O4NLAYGENb9EcL2Em5QgyI39qlC0AvPazqN9ufbY7DamRk2l17peXEq99Nd3Ru5IRUhERHJPchwuHjZCGsRTpsVFPEqkY7eZuHrUi6PLA4jd6UdGkiVnALWDuXDqHMsGv8dPDZsSvmAGvukpnPcL5MKQkUR+8gqe/hl3Xsl97penqyejG41mTKMxeLh4sOP8Dp5c+iTbYrfd13rl1lyMDiAiIoWId+D1P3oUzySs+WWuXXDj0j4frl2wknDci4TjnsRFfwZPZvLgcx1w9/QwMDDs+34XR6f/m/CozZS3ZQNwxbMIKU8/z9/6v4Cr1Q1iNt/dyn63//fjsXKPUa1ENQZ/N5ij8Ufpta4XL9V4iT41++Bi1qk7NxWYMUJjxoxhxYoVREVF4ebmRnx8/B2XsdvtjBgxgpkzZxIfH09kZCTTpk2jQoUKd71djRESEbkHtmyYVA0SzwE3nl6uXXLl0j4fUs65X5+WaPXifIO/UbbzEzzQtD4Wl/wZD3Nk114Ozl+C19bvrg/wBjgVGI5r52dp1KMjbu7W3xa4zX7lyJuxT2lZaYzfOZ7/O/J/ANQJrMOExhMI9MqdwlWYFbrB0iNGjKBIkSKcOXOGWbNm3VURmjBhAuPGjWPOnDmEh4fz9ttvEx0dzf79+3F3d7/j8qAiJCJyz/YvhYXdfv3l96eYnAHU52qPZ9faM5TYso6iqYnXP01w9+FCpQi8mzSmZodWFC8ZkGuRrl64zP61W7i8fSdeP24n5MrZ659lmiycqFKX0F4vUqtV47+8X3n5NNzK4ysZtW0U17KuUdRalLGNx9KoZKM82VZhUeiK0P/Mnj2bAQMG3LEI2e12QkJCGDx4MEOGDAEgISGBwMBAZs+ezdNPP31X21MREhH5C/YvhdXD/vAeoZLQevz1spCZnsGOBSu4suRrSh75Gc+s9BtWccm7OPEhYRBeHt9qD1CiUlmKBPlTNCQAT2/Pm242/uIVzh44xqUjJ0g5cYrMkyfwPnqAoCuxmH9XXrJMZk6GV8faoiV1u7SnaMBdvrfnLvYrr5xIOMHQ74dy8MpBAHpU60G/Wv1wNbvm6XYLKqcvQsePH6dcuXL89NNPREREXJ/etGlTIiIimDx58k2XS09PJz39t/8YExMTCQ0NVRESEblX9/Bm6fTUNH5ZvZnYdd/iHbXrhis2N5Pq4kaqmycmuw2X7CxcbNm4ZGfhas++5TJxvgEklKuCZ7161HnmMYoF++f5fuW29Ox0Ju6ayIJDCwCI8I9gYtOJBHkF5cv2C5K7LUKFdsTV+fPnAQgMvPE+amBg4PXPbmbcuHGMGjUqT7OJiDgFswXCb3Or6XesHu7Ue7wFPN4CgMtnL3BsRxSXftlH5uHDeJ6OwTv5Kt5pybjYbXhkZeCRdfOnuBKs3iQU8Se9RCAEl8QvojqVH2lMlTIh+b5fuc1qsfLWg29RL6geI7eOJOpiFE8te4rRkaN5OPRhQzIVdIYWoeHDhzNhwoTbznPgwAEqV66cT4ngjTfeYNCgQdd//98VIRERyT/FSwZQ/ImW8ETLG6bbbDYSL8dzNfYiyZeuYHZ1wc3diovVipuHFV//YlQpWviv3rcq04oHij/A0E1D2Xd5H69++yrdHujGgNoDcLXoVtm9MLQIDR48mOeff/6285QtW/YvrTsoKOcyYVxcHMHBwdenx8XF3XCr7I+sVitWq/WWn4uIiHHMZjNF/ItRxL+Y0VEMF+oTyhdtvuCjPR/x1YGv+GL/F/x04ScmNp1ISe+SRscrMAwtQv7+/vj7/8V7tHcQHh5OUFAQGzZsuF58EhMT2bFjB3369MmTbYqIiOQnN4sbw+oPo15QPd7a8hbRl6LpuKwj7zV8j+ZhzY2OVyAUmDdLnzp1iqioKE6dOkV2djZRUVFERUWRnJx8fZ7KlSuzZMkSAEwmEwMGDGD06NEsXbqU6OhounXrRkhICB06dDBoL0RERHJfs9LNWNxuMTX8a5CUkcSA7wYwbsc4MrLv4m3YTq7ADJZ+5513mDNnzvXfa9WqBcDGjRt5+OGHATh06BAJCQnX53n99ddJSUnh5ZdfJj4+nkaNGrF69eq7foeQiIhIQRHiHcLs1rP55MdP+Hzf58w9OJeoi1F80OQDQn011vVWCtzj8/lN7xESEZGC5vsz3/PmD28Snx6Pl6sXIxuOpHWZ1kbHylf69nkREREn1aRUExa1W0TtgNqkZKYwdNNQ3tv2HunZ6Xde2MmoCImIiBRCQV5BzGo1i57VewKw8PBCuqzoQkxCjMHJHIuKkIiISCHlYnahf+3+TH9kOsXci3Ho6iE6L+/M8uPLjY7mMFSERERECrnIkpEsareIekH1SM1K5Y3NbzBi6whSs1KNjmY4FSEREREnEOAZwMwWM+ldszcmTPz3yH95dsWzHIs/ZnQ0Q6kIiYiIOAmL2ULfiL7MbDmT4u7FORp/lGdWPMM3R78xOpphVIREREScTIPgBix+bDEPBj9IalYqb215izd/eJNrmdeMjpbvVIREREScUAmPEkx/ZDr9IvphNplZemwpT694msNXDxsdLV+pCImIiDgpi9lCr5q9+KzlZwR4BBCTEMOzK55l8eHFOMv7llWEREREnFy9oHosemwRkSUjSc9OZ9S2UQzbPIyUzBSjo+U5FSERERGhmHsxpjafyoDaA7CYLKyKWUXn5Z05eOWg0dHylIqQiIiIAGA2mXmx+ot83vpzAj0DOZl4ki4rurDg4IJCe6tMRUhERERuUCugFovbLaZpqaZk2DIYvWM0QzYNISkjyehouU5FSERERP6kiHsRPmn2CUPqDsHF5MLak2vptKwT+y7tMzparlIREhERkZsymUx0r9qdOW3mEOIVwpnkM3Rd1ZX/HPhPoblVpiIkIiIit1XDvwYL2y2kWWgzsmxZjN85ngEbB5CQnmB0tPumIiQiIiJ35Gf1Y9LfJjG8/nBczC58e/pbOi3rxC8XfzE62n1RERIREZG7YjKZ6FKlC189+hWlvEsRmxJL91XdmbNvToG9VaYiJCIiIvekavGqLGy3kJZhLcmyZ/HB7g949dtXiU+LNzraPVMREhERkXvm4+bDB00/4O0H38bN7MamM5t4atlT/HThJ6Oj3RMVIREREflLTCYTnSp1Ym7buZTxLUPctTheWP0Cn0V/hs1uMzreXVEREhERkftSqVgl5v99Pm3LtiXbns3kHyfzyoZXuJJ2xehod6QiJCIiIvfNy9WLcY3GMarhKNwt7mw5u4WOSzuy6/wuo6PdloqQiIiI5AqTycQTFZ5gbtu5lPUry4XUC/Rc25PpP08n25ZtdLybUhESERGRXFWhaAXmtZ1H+3LtsdltTImaQq/1vbiUesnoaH+iIiQiIiK5ztPVk9GNRjOm0Rg8XDzYcW4HTy59km2x24yOdgMVIREREckzj5V7jPlt51O+SHmupF2h17pefPrTp2TZsoyOBqgIiYiISB4rW6Qs89rO48kKT2LHzr9++Rc91/bkwrULRkdTERIREZG85+7izsiGIxnfeDyeLp7sidvDU0ufYsvZLYbmUhESERGRfNO2bFsW/H0BlYtV5mr6VXqv783svbMNy6MiJCIiIvmqjF8Zvnr0KzpX6ozFZKFaiWqGZTHZC+rXxeaTxMRE/Pz8SEhIwNfX1+g4IiIihUpMQgzhfuG5vt67PX/ripCIiIgYJi9K0L1QERIRERGnpSIkIiIiTktFSERERJyWipCIiIg4LRUhERERcVoqQiIiIuK0VIRERETEaakIiYiIiNNSERIRERGnpSIkIiIiTktFSERERJyWipCIiIg4LRUhERERcVouRgdwdHa7HYDExESDk4iIiMjd+t95+3/n8VtREbqDpKQkAEJDQw1OIiIiIvcqKSkJPz+/W35ust+pKjk5m81GbGwsPj4+mEymXFtvYmIioaGhnD59Gl9f31xbb2Gh43N7Oj63p+Nzezo+t6Zjc3sF6fjY7XaSkpIICQnBbL71SCBdEboDs9lMqVKl8mz9vr6+Dv8vk5F0fG5Px+f2dHxuT8fn1nRsbq+gHJ/bXQn6Hw2WFhEREaelIiQiIiJOS0XIIFarlREjRmC1Wo2O4pB0fG5Px+f2dHxuT8fn1nRsbq8wHh8NlhYRERGnpStCIiIi4rRUhERERMRpqQiJiIiI01IREhEREaelImSQKVOmUKZMGdzd3WnQoAE7d+40OpJD+P7772nXrh0hISGYTCa+/vproyM5lHHjxlGvXj18fHwICAigQ4cOHDp0yOhYDmHatGnUqFHj+oveHnroIVatWmV0LIc1fvx4TCYTAwYMMDqKQxg5ciQmk+mGn8qVKxsdy6GcPXuWrl27Urx4cTw8PKhevTq7d+82OtZ9UxEywIIFCxg0aBAjRozgxx9/pGbNmrRq1YoLFy4YHc1wKSkp1KxZkylTphgdxSFt2rSJvn37sn37dtatW0dmZiYtW7YkJSXF6GiGK1WqFOPHj2fPnj3s3r2bZs2a0b59e/bt22d0NIeza9cu/vWvf1GjRg2joziUqlWrcu7cues/P/zwg9GRHMbVq1eJjIzE1dWVVatWsX//fv75z39StGhRo6PdNz0+b4AGDRpQr149Pv30UyDn+8xCQ0N59dVXGT58uMHpHIfJZGLJkiV06NDB6CgO6+LFiwQEBLBp0yaaNGlidByHU6xYMSZOnMiLL75odBSHkZycTO3atZk6dSqjR48mIiKCSZMmGR3LcCNHjuTrr78mKirK6CgOafjw4WzZsoXNmzcbHSXX6YpQPsvIyGDPnj088sgj16eZzWYeeeQRtm3bZmAyKYgSEhKAnBO+/CY7O5v58+eTkpLCQw89ZHQch9K3b1/atm17w/+DJMeRI0cICQmhbNmydOnShVOnThkdyWEsXbqUunXr0rFjRwICAqhVqxYzZ840OlauUBHKZ5cuXSI7O5vAwMAbpgcGBnL+/HmDUklBZLPZGDBgAJGRkVSrVs3oOA4hOjoab29vrFYrvXv3ZsmSJTzwwANGx3IY8+fP58cff2TcuHFGR3E4DRo0YPbs2axevZpp06YRExND48aNSUpKMjqaQzh+/DjTpk2jQoUKrFmzhj59+vDaa68xZ84co6PdN337vEgB1bdvX/bu3atxDL9TqVIloqKiSEhIYPHixXTv3p1NmzapDAGnT5+mf//+rFu3Dnd3d6PjOJw2bdpc/3ONGjVo0KABYWFhLFy4ULdWyfmLV926dRk7diwAtWrVYu/evUyfPp3u3bsbnO7+6IpQPitRogQWi4W4uLgbpsfFxREUFGRQKilo+vXrx/Lly9m4cSOlSpUyOo7DcHNzo3z58tSpU4dx48ZRs2ZNJk+ebHQsh7Bnzx4uXLhA7dq1cXFxwcXFhU2bNvHxxx/j4uJCdna20REdSpEiRahYsSJHjx41OopDCA4O/tNfKKpUqVIobh+qCOUzNzc36tSpw4YNG65Ps9lsbNiwQWMZ5I7sdjv9+vVjyZIlfPvtt4SHhxsdyaHZbDbS09ONjuEQmjdvTnR0NFFRUdd/6tatS5cuXYiKisJisRgd0aEkJydz7NgxgoODjY7iECIjI//0qo7Dhw8TFhZmUKLco1tjBhg0aBDdu3enbt261K9fn0mTJpGSksILL7xgdDTDJScn3/A3sJiYGKKioihWrBilS5c2MJlj6Nu3L3PnzuWbb77Bx8fn+rgyPz8/PDw8DE5nrDfeeIM2bdpQunRpkpKSmDt3Lt999x1r1qwxOppD8PHx+dNYMi8vL4oXL64xZsCQIUNo164dYWFhxMbGMmLECCwWC88884zR0RzCwIEDadiwIWPHjqVTp07s3LmTGTNmMGPGDKOj3T+7GOKTTz6xly5d2u7m5mavX7++ffv27UZHcggbN260A3/66d69u9HRHMLNjg1g//zzz42OZrgePXrYw8LC7G5ubnZ/f3978+bN7WvXrjU6lkNr2rSpvX///kbHcAidO3e2BwcH293c3OwlS5a0d+7c2X706FGjYzmUZcuW2atVq2a3Wq32ypUr22fMmGF0pFyh9wiJiIiI09IYIREREXFaKkIiIiLitFSERERExGmpCImIiIjTUhESERERp6UiJCIiIk5LRUhEREScloqQiIiIOC0VIREREXFaKkIiIiLitFSERMSpXLx4kaCgIMaOHXt92tatW3Fzc2PDhg0GJhMRI+i7xkTE6axcuZIOHTqwdetWKlWqREREBO3bt+fDDz80OpqI5DMVIRFxSn379mX9+vXUrVuX6Ohodu3ahdVqNTqWiOQzFSERcUqpqalUq1aN06dPs2fPHqpXr250JBExgMYIiYhTOnbsGLGxsdhsNk6cOGF0HBExiK4IiYjTycjIoH79+kRERFCpUiUmTZpEdHQ0AQEBRkcTkXymIiQiTmfo0KEsXryYn3/+GW9vb5o2bYqfnx/Lly83OpqI5DPdGhMRp/Ldd98xadIkvvzyS3x9fTGbzXz55Zds3ryZadOmGR1PRPKZrgiJiIiI09IVIREREXFaKkIiIiLitFSERERExGmpCImIiIjTUhESERERp6UiJCIiIk5LRUhEREScloqQiIiIOC0VIREREXFaKkIiIiLitFSERERExGmpCImIiIjT+n825eBEIJSLuAAAAABJRU5ErkJggg==\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -658,6 +679,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "r5Ti6ULGUYM_" @@ -667,6 +689,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "nLQqPVtJUYM_" @@ -682,7 +705,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", + "metadata": { + "id": "L7VIntSjO2ga" + }, "source": [ "### Types\n", "\n", @@ -693,12 +720,10 @@ "**Samplers** operate on an existing pool of experimental conditions. They typically require experimental conditions to be represented as a 2-dimensional numpy array in which columns correspond to independent variables and rows to different conditions. They then select experiment conditions from this pool.\n", "\n", "**Pipelines** Pipelines connect multiple experimentalists into a unified workflow. This is beneficial when various steps are required to process experiment conditions. For example, apart from identifying novel experimental conditions, experimentalist functions may perform other operations on the set of conditions, such as rearranging the rows of a condition matrix or adding new experiment conditions as columns. Experiment pipelines may begin with a pooler that generates all possible experiment conditions, followed by a sampler that selects a subset of conditions from the pool, and then proceed to additional functions that arrange the selected conditions in a specific order necessary for conducting the experiment." - ], - "metadata": { - "id": "L7VIntSjO2ga" - } + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "fI5uCcT8UYM_" @@ -722,6 +747,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "vRnf1nMoUYM_" @@ -744,6 +770,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "87h_mB5xUYM_" @@ -764,8 +791,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "(0.6981317007977318,)\n", "(0.7615982190520711,)\n", @@ -790,6 +817,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "MZnaSr1YUYNA" @@ -810,8 +838,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "(0.8250647373064104,)\n", "(0.7615982190520711,)\n", @@ -840,6 +868,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "DdGnRYHKUYNA" @@ -864,8 +893,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "[[0. ]\n", " [0.06346652]]\n" @@ -883,6 +912,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "HD-VCIVxUYNJ" @@ -905,15 +935,15 @@ }, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "[[0. ]\n", " [0.06346652]]\n" @@ -936,6 +966,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "kDRWmikdUYNK" @@ -957,24 +988,24 @@ }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 124, "metadata": {}, - "execution_count": 124 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -1008,6 +1039,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "cdDcYoJcUYNK" @@ -1055,6 +1087,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "daq8R8LsUYNK" @@ -1093,6 +1126,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "KYWnkPvFUYNL" @@ -1115,15 +1149,15 @@ }, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "[[1.90399555]\n", " [3.93492413]\n", @@ -1140,6 +1174,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "tXpSt5jOUYNL" @@ -1151,17 +1186,18 @@ ] }, { + "attachments": {}, "cell_type": "markdown", + "metadata": { + "id": "8Qmp6-J-Xa5j" + }, "source": [ "# Next Notebook\n", "After defining all the components required for the empirical research process, we can create an automated workflow using basic loop constructs. The next notebook illustrates the use of these loop constructs.\n", "\n", "Follow this link for the next notebook tutorial:\n", - "[AutoRA Basic Tutorial II: Loop Constructs](www.addlink.com)
" - ], - "metadata": { - "id": "8Qmp6-J-Xa5j" - } + "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
" + ] } ], "metadata": { @@ -1179,4 +1215,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/docs/tutorials/basic tutorial/Tutorial-II-Loop-Constructs.ipynb b/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb similarity index 94% rename from docs/tutorials/basic tutorial/Tutorial-II-Loop-Constructs.ipynb rename to docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb index 96551199d..1549186aa 100644 --- a/docs/tutorials/basic tutorial/Tutorial-II-Loop-Constructs.ipynb +++ b/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "_q7iLq3GUYMz" @@ -11,22 +12,23 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { + "id": "5mfUKtGTUYM1", "pycharm": { "name": "#%% md\n" - }, - "id": "5mfUKtGTUYM1" + } }, "source": [ "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", "\n", "This notebook is the second of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", "\n", - "[AutoRA Basic Tutorial I: Components](www.addlink.com)
\n", - "[AutoRA Basic Tutorial II: Loop Constructs](www.addlink.com)
\n", - "[AutoRA Basic Tutorial III: Workflow Logic](www.addlink.com)
\n", - "[AutoRA Basic Tutorial IV: Customization](www.addlink.com)
\n", + "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", + "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", + "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", + "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", "\n", "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", "\n", @@ -34,14 +36,15 @@ ] }, { + "attachments": {}, "cell_type": "markdown", + "metadata": { + "id": "7bD8W7cfhZ5n" + }, "source": [ "## Tutorial Setup\n", "This tutorial is self-contained so that you do not need to run the previous notebook to begin. However, the four notebooks are continuous so that what we define in a previous notebook should still exist within this notebook. As such, we will here re-run relevant code from past tutorials. We will not again walk you through these, but if you need a reminder what they are then go see the descriptions in previous notebooks." - ], - "metadata": { - "id": "7bD8W7cfhZ5n" - } + ] }, { "cell_type": "code", @@ -89,6 +92,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "0ZISUerxUYNL" @@ -118,13 +122,14 @@ ] }, { + "attachments": {}, "cell_type": "markdown", - "source": [ - "## Example 1: Falsification Sampler" - ], "metadata": { "id": "kO_HTQMPm7LQ" - } + }, + "source": [ + "## Example 1: Falsification Sampler" + ] }, { "cell_type": "code", @@ -138,80 +143,80 @@ }, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:26<00:00, 3.84it/s]\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loss in cycle 0: 0.0\n", "Discovered Model: sin(X0)\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:28<00:00, 3.56it/s]\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loss in cycle 1: 0.0\n", "Discovered Model: sin(X0)\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:14<00:00, 7.10it/s]\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loss in cycle 2: 0.5526484578348648\n", "Discovered Model: _a0_\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:17<00:00, 5.60it/s]\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loss in cycle 3: 0.0\n", "Discovered Model: sin(X0)\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:16<00:00, 6.25it/s]\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loss in cycle 4: 0.0\n", "Discovered Model: sin(X0)\n" @@ -261,6 +266,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "B0lVQWuCUYNL" @@ -284,75 +290,75 @@ }, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:40<00:00, 2.47it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loss in cycle 0: 0.0\n", "Discovered BMS Model: sin(X0)\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:18<00:00, 5.53it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loss in cycle 1: 0.0\n", "Discovered BMS Model: sin(X0)\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:19<00:00, 5.16it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loss in cycle 2: 0.0\n", "Discovered BMS Model: sin(X0)\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:15<00:00, 6.37it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loss in cycle 3: 0.0\n", "Discovered BMS Model: sin(X0)\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:24<00:00, 4.01it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loss in cycle 4: 0.0\n", "Discovered BMS Model: sin(X0)\n" @@ -399,17 +405,18 @@ ] }, { + "attachments": {}, "cell_type": "markdown", + "metadata": { + "id": "NZgqanLagOv_" + }, "source": [ "# Next Notebook\n", "While the basic loop construct is flexible, there are more convenient ways to specify a research cycle in ``autora``. The next notebook illustrates the use of these constructs.\n", "\n", "Follow this link for the next notebook tutorial:\n", - "[AutoRA Basic Tutorial III: Workflow Logic](www.addlink.com)
" - ], - "metadata": { - "id": "NZgqanLagOv_" - } + "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
" + ] } ], "metadata": { @@ -427,4 +434,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/docs/tutorials/basic tutorial/Tutorial-III-Workflow-Logic.ipynb b/docs/tutorials/basic/Tutorial-III-Workflow-Logic.ipynb similarity index 96% rename from docs/tutorials/basic tutorial/Tutorial-III-Workflow-Logic.ipynb rename to docs/tutorials/basic/Tutorial-III-Workflow-Logic.ipynb index 5f4697aff..8fd517314 100644 --- a/docs/tutorials/basic tutorial/Tutorial-III-Workflow-Logic.ipynb +++ b/docs/tutorials/basic/Tutorial-III-Workflow-Logic.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "_q7iLq3GUYMz" @@ -11,22 +12,23 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { + "id": "5mfUKtGTUYM1", "pycharm": { "name": "#%% md\n" - }, - "id": "5mfUKtGTUYM1" + } }, "source": [ "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", "\n", "This notebook is the third of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", "\n", - "[AutoRA Basic Tutorial I: Components](www.addlink.com)
\n", - "[AutoRA Basic Tutorial II: Loop Constructs](www.addlink.com)
\n", - "[AutoRA Basic Tutorial III: Workflow Logic](www.addlink.com)
\n", - "[AutoRA Basic Tutorial IV: Customization](www.addlink.com)
\n", + "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", + "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", + "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", + "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", "\n", "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", "\n", @@ -34,17 +36,23 @@ ] }, { + "attachments": {}, "cell_type": "markdown", + "metadata": { + "id": "BuCna7-ytMBB" + }, "source": [ "## Tutorial Setup\n", "This tutorial is self-contained so that you do not need to run the previous notebook to begin. However, the four notebooks are continuous so that what we define in a previous notebook should still exist within this notebook. As such, we will here re-run relevant code from past tutorials. We will not again walk you through these, but if you need a reminder what they are then go see the descriptions in previous notebooks." - ], - "metadata": { - "id": "BuCna7-ytMBB" - } + ] }, { "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "RuCZVkP7tM6L" + }, + "outputs": [], "source": [ "#### Installation ####\n", "!pip install -q \"autora[experimentalist-falsification]\"\n", @@ -79,14 +87,10 @@ "\n", "#### Define theorists ####\n", "theorist_bms = BMSRegressor(epochs=100)" - ], - "metadata": { - "id": "RuCZVkP7tM6L" - }, - "execution_count": 28, - "outputs": [] + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "fjJRnOXhUYNM" @@ -100,6 +104,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "lWV6x-oUUYNM" @@ -148,6 +153,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "p1ZHI56hUYNM" @@ -177,6 +183,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "QWhI8SvuUYNN" @@ -191,44 +198,44 @@ "cell_type": "code", "execution_count": 31, "metadata": { - "id": "wIwF6i70UYNN", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "wIwF6i70UYNN", "outputId": "b0fbf84c-85ac-4f89-c9c8-705c4a5a4e22" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new CONDITION\n", "MONITOR: Generated new OBSERVATION\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:21<00:00, 4.66it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new MODEL\n" ] }, { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 31, "metadata": {}, - "execution_count": 31 + "output_type": "execute_result" } ], "source": [ @@ -238,6 +245,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "0OJ8WdFlUYNN" @@ -250,29 +258,29 @@ "cell_type": "code", "execution_count": 32, "metadata": { - "id": "FZXvTUG2UYNN", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "FZXvTUG2UYNN", "outputId": "848dcef6-cff4-4f1b-a781-2db960f58f06" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new CONDITION\n" ] }, { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 32, "metadata": {}, - "execution_count": 32 + "output_type": "execute_result" } ], "source": [ @@ -280,6 +288,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "r7gneVACUYNN" @@ -302,6 +311,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "krPjzb8hUYNO" @@ -314,31 +324,31 @@ "cell_type": "code", "execution_count": 34, "metadata": { - "id": "8FFj4NFLUYNO", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "8FFj4NFLUYNO", "outputId": "15ee6b27-ebbe-450b-c99b-8a1e1eef66a5" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new OBSERVATION\n", "Number of models: 1\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:20<00:00, 5.00it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new MODEL\n", "Number of models: 2\n", @@ -349,15 +359,15 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:11<00:00, 9.04it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new MODEL\n", "Number of models: 3\n", @@ -368,15 +378,15 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:11<00:00, 8.92it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new MODEL\n", "Number of models: 4\n", @@ -387,22 +397,22 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:11<00:00, 8.92it/s]" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new MODEL\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "\n" ] @@ -416,6 +426,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "ipgISi2QUYNO" @@ -432,16 +443,16 @@ "cell_type": "code", "execution_count": 35, "metadata": { - "id": "xaigjyMpUYNO", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "xaigjyMpUYNO", "outputId": "6315b7ee-c334-4702-c6e1-c0f2d5480317" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "ResultKind.MODEL\n", "BMSRegressor(epochs=100)\n" @@ -456,6 +467,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "p3mLxYfyUYNO" @@ -468,16 +480,16 @@ "cell_type": "code", "execution_count": 36, "metadata": { - "id": "DEoLhXo6UYNO", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "DEoLhXo6UYNO", "outputId": "a8205c81-3052-4a01-db3c-bbc7d03fcdf5" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "sin(X0)\n" ] @@ -488,6 +500,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "XT3kZJJmUYNP" @@ -500,16 +513,16 @@ "cell_type": "code", "execution_count": 37, "metadata": { - "id": "oQu4pF_yUYNP", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "oQu4pF_yUYNP", "outputId": "846cdb8d-f07a-4ca8-f878-6c06cbfec54a" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Conditions:\n", "[[0. ]\n", @@ -542,6 +555,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "aabSIf3fUYNP" @@ -558,29 +572,29 @@ "cell_type": "code", "execution_count": 38, "metadata": { - "id": "jyF6yXDCUYNP", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "jyF6yXDCUYNP", "outputId": "97bb5399-ff3e-49ed-aec1-3783551dd994" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new OBSERVATION\n" ] }, { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 38, "metadata": {}, - "execution_count": 38 + "output_type": "execute_result" } ], "source": [ @@ -607,6 +621,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "CTNPh9LAUYNP" @@ -617,6 +632,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "atOAyk5iUYNP" @@ -677,6 +693,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "GIlhC3UZUYNQ" @@ -689,32 +706,32 @@ "cell_type": "code", "execution_count": 40, "metadata": { - "id": "L_TPQmSJUYNQ", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "L_TPQmSJUYNQ", "outputId": "e329e8db-2839-4d42-b678-f051c388cd86" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new OBSERVATION\n", "Number of models: 0\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:10<00:00, 9.51it/s]\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new MODEL\n", "Number of models: 1\n", @@ -725,16 +742,16 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:11<00:00, 8.80it/s]\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new MODEL\n", "Number of models: 2\n", @@ -745,16 +762,16 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:11<00:00, 8.70it/s]\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new MODEL\n", "Number of models: 3\n", @@ -765,16 +782,16 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:11<00:00, 8.98it/s]\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new MODEL\n", "Number of models: 4\n", @@ -785,16 +802,16 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:10<00:00, 9.47it/s]\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new MODEL\n", "Number of models: 5\n", @@ -805,22 +822,22 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:09<00:00, 10.86it/s]" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new MODEL\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "\n" ] @@ -836,6 +853,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "agcGRk5NUYNQ" @@ -889,6 +907,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "Yufzx-_8UYNQ" @@ -900,6 +919,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "qRvGhjFlUYNQ" @@ -926,6 +946,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "2H4eIWLmUYNR" @@ -958,6 +979,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "qx9FNV_jUYNR" @@ -967,6 +989,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "h1xwRNm5UYNS" @@ -1006,6 +1029,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "AX60ukJeUYNS" @@ -1028,6 +1052,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "Wtv0ORp5UYNS" @@ -1051,6 +1076,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "W94jN2YDUYNS" @@ -1079,6 +1105,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "XSPAMShJUYNT" @@ -1091,16 +1118,16 @@ "cell_type": "code", "execution_count": 48, "metadata": { - "id": "8tvuuhoGUYNT", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "8tvuuhoGUYNT", "outputId": "d523e8c1-7dca-4ab6-bd50-f697be43342a" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "PLANNER: Next step: seed_experimentalist\n", "MONITOR: Number of observations 0\n", @@ -1112,15 +1139,15 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:09<00:00, 10.08it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Number of observations 3\n", "MONITOR: Number of models: 1\n", @@ -1134,15 +1161,15 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:10<00:00, 9.26it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Number of observations 6\n", "MONITOR: Number of models: 2\n", @@ -1156,16 +1183,16 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:12<00:00, 7.91it/s]\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Number of observations 9\n", "MONITOR: Number of models: 3\n", @@ -1179,16 +1206,16 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:10<00:00, 9.73it/s]\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Number of observations 12\n", "MONITOR: Number of models: 4\n", @@ -1202,15 +1229,15 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:10<00:00, 9.46it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Number of observations 15\n" ] @@ -1226,6 +1253,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "VymXaj-gUYNT" @@ -1235,17 +1263,18 @@ ] }, { + "attachments": {}, "cell_type": "markdown", + "metadata": { + "id": "oS5TJBr6s-kJ" + }, "source": [ "# Next Notebook\n", "This concludes the tutorial on ``autora`` functionality. However, ``autora`` is a flexible framework in which users can integrate their own theorists, experimentalists, and experiment_runners in an automated empirical research workflow. The next notebook illustrates how to add your own custom theorists and experimentalists to use with ``autora``.\n", "\n", "Follow this link for the next notebook tutorial:\n", - "[AutoRA Basic Tutorial IV: Customization](www.addlink.com)
" - ], - "metadata": { - "id": "oS5TJBr6s-kJ" - } + "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
" + ] } ], "metadata": { @@ -1263,4 +1292,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/docs/tutorials/basic tutorial/Tutorial-IV-Customization.ipynb b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb similarity index 98% rename from docs/tutorials/basic tutorial/Tutorial-IV-Customization.ipynb rename to docs/tutorials/basic/Tutorial-IV-Customization.ipynb index 726424d1c..1d85d7e92 100644 --- a/docs/tutorials/basic tutorial/Tutorial-IV-Customization.ipynb +++ b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "_q7iLq3GUYMz" @@ -10,22 +11,23 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { + "id": "5mfUKtGTUYM1", "pycharm": { "name": "#%% md\n" - }, - "id": "5mfUKtGTUYM1" + } }, "source": [ "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", "\n", "This notebook is the fourth of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", "\n", - "[AutoRA Basic Tutorial I: Components](www.addlink.com)
\n", - "[AutoRA Basic Tutorial II: Loop Constructs](www.addlink.com)
\n", - "[AutoRA Basic Tutorial III: Workflow Logic](www.addlink.com)
\n", - "[AutoRA Basic Tutorial IV: Customization](www.addlink.com)
\n", + "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", + "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", + "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", + "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", "\n", "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", "\n", @@ -33,17 +35,23 @@ ] }, { + "attachments": {}, "cell_type": "markdown", + "metadata": { + "id": "bfcWh4lramqo" + }, "source": [ "## Tutorial Setup\n", "This tutorial is self-contained so that you do not need to run the previous notebook to begin. However, the four notebooks are continuous so that what we define in a previous notebook should still exist within this notebook. As such, we will here re-run relevant code from past tutorials. We will not again walk you through these, but if you need a reminder what they are then go see the descriptions in previous notebooks." - ], - "metadata": { - "id": "bfcWh4lramqo" - } + ] }, { "cell_type": "code", + "execution_count": 50, + "metadata": { + "id": "eT6HTGF7aoJT" + }, + "outputs": [], "source": [ "#### Installation ####\n", "!pip install -q \"autora[experimentalist-falsification]\"\n", @@ -85,14 +93,10 @@ "#### Define monitor ####\n", "def monitor(state):\n", " print(f\"MONITOR: Generated new {state.history[-1].kind}\")" - ], - "metadata": { - "id": "eT6HTGF7aoJT" - }, - "execution_count": 50, - "outputs": [] + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "MN8yBMeeUYNT" @@ -146,6 +150,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "mhF2f9S4UYNT" @@ -206,6 +211,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "FxJvKQHdUYNU" @@ -239,6 +245,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "eoERktO5UYNU" @@ -251,23 +258,23 @@ "cell_type": "code", "execution_count": 54, "metadata": { - "id": "tnyjVXcUUYNU", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "tnyjVXcUUYNU", "outputId": "7ffd90ad-801e-469d-862b-e466720eda24" }, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new OBSERVATION\n", "Number of models: 0\n", @@ -276,15 +283,15 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new CONDITION\n", "Number of models: 1\n", @@ -295,15 +302,15 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "MONITOR: Generated new CONDITION\n", "Number of models: 2\n", @@ -329,6 +336,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "J7bMGWnxUYNU" @@ -341,33 +349,33 @@ "cell_type": "code", "execution_count": 61, "metadata": { - "id": "JpnsqFeKUYNV", "colab": { "base_uri": "https://localhost:8080/", "height": 489 }, + "id": "JpnsqFeKUYNV", "outputId": "4bd2658a-4870-493b-e2a7-254b035bae12" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 61, "metadata": {}, - "execution_count": 61 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -386,6 +394,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "kwM8vJR_UYNV" @@ -423,6 +432,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "AXCRsOhUUYNV" @@ -435,16 +445,16 @@ "cell_type": "code", "execution_count": 66, "metadata": { - "id": "P2jBBdKwUYNV", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "P2jBBdKwUYNV", "outputId": "3ba61e72-c768-4492-b644-942a5bd932d9" }, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:07<00:00, 13.30it/s]\n" ] @@ -463,6 +473,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "vP_bRY4GUYNV" @@ -475,33 +486,33 @@ "cell_type": "code", "execution_count": 67, "metadata": { - "id": "lbUKDXL-UYNV", "colab": { "base_uri": "https://localhost:8080/", "height": 489 }, + "id": "lbUKDXL-UYNV", "outputId": "dd97872b-0748-4048-d736-c3edd82579df" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 67, "metadata": {}, - "execution_count": 67 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -536,6 +547,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "Dk-Zd-DKUYNW" @@ -548,91 +560,91 @@ "cell_type": "code", "execution_count": 68, "metadata": { - "id": "4_8wXhOkUYNW", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "4_8wXhOkUYNW", "outputId": "1ea515a0-2aa3-4d34-aaba-aebe2e94b2b5" }, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:08<00:00, 11.88it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loss of BMS theorist in cycle 0: 0.0\n", "Loss of polynomial theorist in cycle 0: 0.8717052095923039\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:08<00:00, 12.49it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loss of BMS theorist in cycle 1: 0.0\n", "Loss of polynomial theorist in cycle 1: 3.619766689361933\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:07<00:00, 12.97it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loss of BMS theorist in cycle 2: 0.0\n", "Loss of polynomial theorist in cycle 2: 0.5193832163876795\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:07<00:00, 14.26it/s]\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loss of BMS theorist in cycle 3: 0.0\n", "Loss of polynomial theorist in cycle 3: 0.36300053098571583\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:07<00:00, 13.45it/s]" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loss of BMS theorist in cycle 4: 0.4967273581732591\n", "Loss of polynomial theorist in cycle 4: 0.288261165753893\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "\n" ] @@ -678,6 +690,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "id": "Q-eKiaHRUYNW" @@ -707,4 +720,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From 3a5ed5d282ac92311df3c8365158b978d1b23414 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Wed, 5 Jul 2023 08:13:36 -0700 Subject: [PATCH 04/32] Updated nav --- mkdocs.yml | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/mkdocs.yml b/mkdocs.yml index ea53225d8..776cdc2f8 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -157,7 +157,11 @@ nav: - Introduction: 'index.md' - Tutorials: - Home: 'tutorials/index.md' - - Introduction to AutoRA: 'tutorials/Introduction.ipynb' + - Basic Tutorial: + -I - Components: 'tutorials/basic/Tutorial-I-Components.ipynb' + -II - Loop Constructs: 'tutorials/basic/Tutorial-II-Loop-Constructs.ipynb' + -III - Workflow Logic: 'tutorials/basic/Tutorial-III-Workflow-Logic.ipynb' + -IV - Customization: 'tutorials/basic/Tutorial-IV-Customization.ipynb' - Theorists: 'tutorials/Theorist.ipynb' - Experimentalists: 'tutorials/Experimentalist.ipynb' - Closed-Loop Discovery: 'workflow-tutorial/docs/interactive/Basic Usage.ipynb' From c0fcedf808f8fb908cbf10f3a86e32c57af98057 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Wed, 5 Jul 2023 09:01:37 -0700 Subject: [PATCH 05/32] Resolved a comment --- docs/tutorials/basic/Tutorial-I-Components.ipynb | 6 ------ 1 file changed, 6 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-I-Components.ipynb b/docs/tutorials/basic/Tutorial-I-Components.ipynb index b18b2b4b0..fb029b6f2 100644 --- a/docs/tutorials/basic/Tutorial-I-Components.ipynb +++ b/docs/tutorials/basic/Tutorial-I-Components.ipynb @@ -258,12 +258,6 @@ "\n", "For **real-world experiments**, experiment runners can include interfaces for various scenarios such as web-based experiments for behavioral data collection (e.g., using [Firebase and Prolific](https://autoresearch.github.io/autora/user-guide/experiment-runners/firebase-prolific/)) or experiments involving electrical circuits (e.g., using [Tinkerforge](https://en.wikipedia.org/wiki/Tinkerforge)). These runners often require external components such as databases to store collected observations or servers to host the experiments. You may refer to the respective tutorials for these interfaces on how to set up all required components.\n", "\n", - "---\n", - "CHAD Suggestion: You mention to refer to the respective tutorials, but the tinkerforge link brings you to wikipedia. Is there any Autora page describing the tinkerforge interface that we could link instead? Even if it's just a description and not a tutorial. \n", - "\n", - "---\n", - "\n", - "\n", "**Synthetic experiments** are conducted on synthetic experiment runners, which are functions that take experimental conditions as input and generate simulated observations as output. These experiments serve multiple purposes, including *testing autora components* before applying them to real-world experiments, *benchmarking methods for automated scientific discovery*, or *conducting computational metascientific experiments*.\n", "\n", "In this introductory tutorial, we primarily focus on simple synthetic experiments. For more complex synthetic experiments implementing various scientific models, you can utilize the [autora-synthetic](https://github.com/autoresearch/autora-synthetic/) module." From a76c77b6ff011d84acef597697f858f27c3edc45 Mon Sep 17 00:00:00 2001 From: Sebastian Musslick Date: Thu, 6 Jul 2023 08:09:43 -0400 Subject: [PATCH 06/32] Update mkdocs.yml Co-authored-by: Younes Strittmatter --- mkdocs.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/mkdocs.yml b/mkdocs.yml index 776cdc2f8..1c02b3ec3 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -158,10 +158,10 @@ nav: - Tutorials: - Home: 'tutorials/index.md' - Basic Tutorial: - -I - Components: 'tutorials/basic/Tutorial-I-Components.ipynb' - -II - Loop Constructs: 'tutorials/basic/Tutorial-II-Loop-Constructs.ipynb' - -III - Workflow Logic: 'tutorials/basic/Tutorial-III-Workflow-Logic.ipynb' - -IV - Customization: 'tutorials/basic/Tutorial-IV-Customization.ipynb' + - I - Components: 'tutorials/basic/Tutorial-I-Components.ipynb' + - II - Loop Constructs: 'tutorials/basic/Tutorial-II-Loop-Constructs.ipynb' + - III - Workflow Logic: 'tutorials/basic/Tutorial-III-Workflow-Logic.ipynb' + - IV - Customization: 'tutorials/basic/Tutorial-IV-Customization.ipynb' - Theorists: 'tutorials/Theorist.ipynb' - Experimentalists: 'tutorials/Experimentalist.ipynb' - Closed-Loop Discovery: 'workflow-tutorial/docs/interactive/Basic Usage.ipynb' From c0d8adfc28a4faf2eaa92343abc81aa26f678438 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 11:43:44 -0400 Subject: [PATCH 07/32] chore: cleaned up notebooks, removing excess metadata --- .../basic/Tutorial-I-Components.ipynb | 2162 ++++++++-------- .../basic/Tutorial-II-Loop-Constructs.ipynb | 791 +++--- .../basic/Tutorial-III-Workflow-Logic.ipynb | 2271 ++++++++--------- .../basic/Tutorial-IV-Customization.ipynb | 1281 +++++----- 4 files changed, 3046 insertions(+), 3459 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-I-Components.ipynb b/docs/tutorials/basic/Tutorial-I-Components.ipynb index fb029b6f2..fe890dc81 100644 --- a/docs/tutorials/basic/Tutorial-I-Components.ipynb +++ b/docs/tutorials/basic/Tutorial-I-Components.ipynb @@ -1,1212 +1,1040 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "_q7iLq3GUYMz" - }, - "source": [ - "# Introduction to AutoRA\n", - "## Basic Tutorial I: Components" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "5mfUKtGTUYM1", - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", - "\n", - "This notebook is the first of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", - "\n", - "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", - "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", - "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", - "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", - "\n", - "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", - "\n", - "**How to use this notebook** *You can progress through the notebook section by section or directly navigate to specific sections. If you choose the latter, it is recommended to execute all cells in the notebook initially, allowing you to easily rerun the cells in each section later without issues.*" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "BWl9iqpgUYM2" - }, - "source": [ - "## Installation\n", - "\n", - "The AutoRA ecosystem is a comprehensive collection of packages that together establish a framework for closed-loop empirical research. At the core of this framework is the ``autora`` package, which serves as the parent package and is essential for end users to install. It provides functionalities for automating workflows in empirical research and includes vetted modules with minimal dependencies.\n", - "\n", - "However, the flexibility of autora extends further with the inclusion of *optional* modules as additional dependencies. Users have the freedom to selectively install these modules based on their specific needs and preferences.\n", - "\n", - "\"AutoRA\n", - "\n", - "*Optional dependencies enable users to customize their autora environment without worrying about conflicts with other packages within the broader autora ecosystem. To install an optional module, simply use the command ``pip install autora[dependency-name]``, where ``dependency-name`` corresponds to the name of the desired module (see example below).*\n", - "\n", - "To begin, we will install all the relevant optional dependencies. Our main focus will be on two experimentalists: ``experimentalist-falsification`` and ``experimentalist-sampler-novelty``, along with a Bayesian Machine Scientist (BMS) implemented in the ``theorist-bms`` package. It's important to note that installing a module will automatically include the main autora package, as well as any required dependencies for workflow management and running synthetic experiments.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "2S9mfSxVUYM3" - }, - "outputs": [], - "source": [ - "!pip install -q \"autora[experimentalist-falsification]\"\n", - "!pip install -q \"autora[experimentalist-sampler-novelty]\"\n", - "!pip install -q \"autora[experimentalist-sampler-model-disagreement]\"\n", - "!pip install -q \"autora[theorist-bms]\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "B4DahNFBVNo3" - }, - "source": [ - "To make all simulations in this notebook replicable, we will set some seeds." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "tJNNbhskVMNq", - "outputId": "a54371dd-79ab-4849-abdc-4862bd116e94" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "import torch\n", - "\n", - "np.random.seed(42)\n", - "torch.manual_seed(42)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "bSGRphhDUYM4" - }, - "source": [ - "# Automated Empirical Research Components\n", - "\n", - "The goal of this section is to set up all ``autora`` components to enable a closed-loop discovery workflow with synthetic data. This involves specifying (1) the experiment runner, (2) a theorist for model discovery, (3) an experimentalist for identifying novel experiment conditions.\n", - "\n", - "\n", - "* **Experiment Runner:** The experiment runner collects observations reflecting experimental conditions.\n", - "* **Theorist:** The theorist automates the construction of models from data. These can take many forms, for example linear regression and the bayesian machine scientist.\n", - "* **Experimentalist:** Each experimentalist identifies experimental conditions that yield scientific merit.\n", - "\n", - "\"AutoRA\n", - "\n", - "Each of these components automates a process of the scientific method that is generally conducted manually. The experiment runner parallels a *research assistant* that collects data from participants. The theorist takes the place of a *computational scientist* that applies modelling techniques to discover how to best describe the data. The experimentalist acts as a *research design expert* to determine the next iteration of experimentation. Each of these steps in the scientific method can be arduous and time consuming to conduct manually, and so ``autora`` allows for the automation of these steps and thus quickens the scientific method by leveraging data-driven techniques." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "F_lZhwcg8wW8" - }, - "source": [ - "## Toy Example of the Components\n", - "Before jumping into each component in detail, we will present a toy example to provide you with an overview on how these components work together within a closed-loop. After some setup, you will see steps 1-3, which uses the three componens - namely, the EXPERIMENTALIST to propose new conditions, the EXPERIMENT RUNNER to retrieve new observations from those conditions, and the THEORIST to model the new data. We then finish this example by plotting our data and findings." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 508 - }, - "id": "8P3iMrqN-pOU", - "outputId": "92dede11-65c8-423d-b8a5-ac0b6191404c" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:14<00:00, 6.96it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 125, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Setup: Import modules\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from autora.theorist.bms import BMSRegressor\n", - "from autora.experimentalist.sampler.random_sampler import random_sample #Note that this sampler is embedded within the autora-core module and so does not need to be explicitly installed\n", - "\n", - "#Step 0: Defining variables\n", - "ground_truth = lambda x: np.sin(x) #Define a ground truth model that we will attempt to recover - here a sine wave\n", - "initial_X = np.linspace(0, 4 * np.pi, 200) #Define initial data\n", - "\n", - "#Step 1: EXPERIMENTALIST: Sample using the experimentalist\n", - "new_conditions = random_sample(initial_X, n = 20)\n", - "new_conditions = np.array(new_conditions).reshape(-1,1) #Turn variable into a 2D array\n", - "\n", - "#Step 2: EXPERIMENT RUNNER: Define and then obtain observations using the experiment runner\n", - "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape) #Define the runner, which here is simply the ground truth with noise\n", - "new_observations = run_experiment(new_conditions) #Obtain observations from the runner for the conditions proposed by the experimentalist\n", - "new_observations = new_observations.reshape(-1,1) #Turn variable into a 2D array\n", - "\n", - "#Step 3: THEORIST: Initiate and fit a model using the theorist\n", - "theorist_bms = BMSRegressor(epochs=100) #Initiate the BMS theorist\n", - "theorist_bms.fit(new_conditions, new_observations) #Fit a model to the data\n", - "\n", - "#Wrap-Up: Plot data and model\n", - "sort_index = np.argsort(new_conditions, axis=0)[:,0] #We will first sort our data\n", - "new_conditions = new_conditions[sort_index,:]\n", - "new_observations = new_observations[sort_index,:]\n", - "\n", - "plt.plot(initial_X, ground_truth(initial_X), label='Ground Truth')\n", - "plt.plot(new_conditions, new_observations, 'o', label='Sampled Conditions')\n", - "plt.plot(new_conditions, theorist_bms.predict(new_conditions), label=f'Bayesian Machine Scientist ({theorist_bms.repr()})')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('Sine Function')\n", - "plt.legend()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "zGaP3IQNBff2" - }, - "source": [ - "\n", - "***WARNING:*** *Do not stop here! We have now shown you the three components and how they work together. At this point, it may be tempting to start working on your own project, but we urge you to continue through the tutorials. ``autora`` has a lot of embedded functionality that you are going to want to use, and this toy example has stripped those away. So, keep going and see how much ``autora`` has to offer!*" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "KbUon6UcUYM5" - }, - "source": [ - "## Experiment Runners\n", - "\n", - "``autora`` provides support for experiment runners, which serve as interfaces for conducting both real-world and synthetic experiments. An experiment runner typically accepts experiment conditions as input (e.g., a 2-dimensional numpy array with columns representing different independent variables) and produces collected observations as output (e.g., a 2-dimensional numpy array with columns representing different dependent variables). These experiment runners can be combined with other ``autora`` components to facilitate closed-loop scientific discovery.\n", - "\n", - "\"AutoRA\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "SL3Si-rIFqlQ" - }, - "source": [ - "\n", - "### Types\n", - "\n", - "AutoRA offers two types of experiment runners: **real-world experiments** and **synthetic experiments**.\n", - "\n", - "For **real-world experiments**, experiment runners can include interfaces for various scenarios such as web-based experiments for behavioral data collection (e.g., using [Firebase and Prolific](https://autoresearch.github.io/autora/user-guide/experiment-runners/firebase-prolific/)) or experiments involving electrical circuits (e.g., using [Tinkerforge](https://en.wikipedia.org/wiki/Tinkerforge)). These runners often require external components such as databases to store collected observations or servers to host the experiments. You may refer to the respective tutorials for these interfaces on how to set up all required components.\n", - "\n", - "**Synthetic experiments** are conducted on synthetic experiment runners, which are functions that take experimental conditions as input and generate simulated observations as output. These experiments serve multiple purposes, including *testing autora components* before applying them to real-world experiments, *benchmarking methods for automated scientific discovery*, or *conducting computational metascientific experiments*.\n", - "\n", - "In this introductory tutorial, we primarily focus on simple synthetic experiments. For more complex synthetic experiments implementing various scientific models, you can utilize the [autora-synthetic](https://github.com/autoresearch/autora-synthetic/) module." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "_UWBS-P-UYM5" - }, - "source": [ - "### Usage\n", - "\n", - "To create a synthetic experiment runner, we begin with **defining a ground truth** from which to generate data. Here, we consider a simple sine function:\n", - "\n", - "$y = f(x) = \\sin(x)$\n", - "\n", - "In this case, $x$ corresponds to an *independent* variable (the variable we can manipulate in an experiment), $y$ corresponds to a *dependent* variable (the variable we can observe after conducting the experiment), and $f(x)$ is the *ground-truth function* (or \"mechanism\") that we seek to uncover via a combination of experimentation and model discovery.\n", - "\n", - "However, we assume that observations are obtained with a measurement error when running the experiment.\n", - "\n", - "$\\hat{y} = \\hat{f}(x) = f(x) + \\epsilon, \\quad \\epsilon \\sim \\mathcal{N}(0,0.1)$\n", - "\n", - "where $\\epsilon$ is the measurement error sampled from a normal distribution with zero mean and a standard deviation of $0.1$.\n", - "\n", - "The following code block defines the ground truth $f(x)$ and the experiment runner $\\hat{f}(x)$ as ``lambda`` functions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "DM9HkPNqUYM5" - }, - "outputs": [], - "source": [ - "ground_truth = lambda x: np.sin(x)\n", - "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "hBbzNQjXUYM5" - }, - "source": [ - "Next, we generate a pool of all possible experimental conditions from the domain $[0, 2\\pi]$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "cjnQjoANUYM6" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "condition_pool = np.linspace(0, 2 * np.pi, 100)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "Ejj3Yd_FUYM6" - }, - "source": [ - "In order to run a simple synthetic experiment, we can first sample from the pool of possible experiment conditions (without replacement), and then pass these conditions to the synthetic experiment runner:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 490 - }, - "id": "epUuwg8rUYM6", - "outputId": "590bf1d8-7c66-468e-982a-55a097dba006" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "initial_conditions = np.random.choice(condition_pool, size=10, replace=False)\n", - "initial_observations = run_experiment(initial_conditions)\n", - "\n", - "# plot sampled conditions against ground-truth\n", - "import matplotlib.pyplot as plt\n", - "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", - "plt.plot(initial_conditions, initial_observations, 'o', label='Sampled Conditions')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('Sine Function')\n", - "plt.legend()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "fmIVAHdIUYM7" - }, - "source": [ - "Certain theorists and experimentalists may need to have knowledge about the experimental variables, such as the domain from which new experiment conditions are sampled. To provide this information, we can utilize a ``VariableCollection`` object. In the context of our synthetic experiment, we have a single *independent variable* (``IV``) denoted as $x$, and a single *dependent* variable (``DV``) denoted as $y$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "feMzX1JfUYM7" - }, - "outputs": [], - "source": [ - "from autora.variable import DV, IV, ValueType, VariableCollection\n", - "\n", - "# Specify independent variable\n", - "iv = IV(\n", - " name=\"x\", # name of the independent variable\n", - " value_range=(0, 2 * np.pi), # specify the domain\n", - " allowed_values=condition_pool, # alternatively, we can specify the pool of allowed conditions directly\n", - ")\n", - "\n", - "# specify dependent variable\n", - "dv = DV(\n", - " name=\"y\", # name of the dependent variable\n", - " type=ValueType.REAL, # specify the variable type (some theorists require this to optimize)\n", - ")\n", - "\n", - "# Variable collection with ivs and dvs\n", - "metadata = VariableCollection(\n", - " independent_variables=[iv],\n", - " dependent_variables=[dv],\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "G-s-foKEUYM8" - }, - "source": [ - "**Note**: *For expository reasons, we focus in this tutorial on simple synthetic experiments. In general, ``autora`` provides functionality for automating [more complex synthetic experiments](https://github.com/autoresearch/autora-synthetic/), as well as real-world experiments, such as [behavioral data collection via web-based experiments](https://autoresearch.github.io/autora/user-guide/experiment-runners/firebase-prolific/), experiments with electrical circuits via [Tinkerforge](https://en.wikipedia.org/wiki/Tinkerforge), and other automated experimentation platforms.*" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "6_0yTjh-UYM8" - }, - "source": [ - "## Theorists\n", - "\n", - "The AutoRA framework includes and interfaces with different methods for scientific model discovery. These methods are referred to as *theorists* and are implemented as [sklearn estimators](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html). For general information about theorists, see the respective [AutoRA Documentation](https://autoresearch.github.io/autora/theorist/).\n", - "\n", - "\"Theorist\n", - "\n", - "\n", - "Theorists **take as input a set of conditions and observations**. Conditions and observations can typically be passed as *two-dimensional numpy arrays* (with columns corresponding to variables and rows corresponding to different instances of those variables). Theorists then **identify and fit a model** which may be used to predict observations based on experiment conditions." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "w_7QGhwFUYM8" - }, - "source": [ - "### Types\n", - "\n", - "There are different types of theorists within the AutoRA framework, each with its own approach to scientific model discovery.\n", - "\n", - "Some theorists focus on *fitting the parameters of a pre-specified model* to the given data (see the scikit learn documentation for a [selection of basic regressors](https://scikit-learn.org/stable/supervised_learning.html)). The model architecture in such cases is typically fixed, while the parameters are adjusted to optimize the model's performance. Linear regression is an example of a parameter-fitting theorist.\n", - "\n", - "Other theorists are concerned with *identifying both the architecture of a model and its parameters*. The model architectures can take various forms, such as equations, causal models, or process models. Implemented as scikit-learn estimators, these theorists aim to discover a model architecture that accurately describes the data. They often operate within a user-defined search space, which specifies the allowable operations or components that can be included in the model. This approach provides more flexibility in exploring different model architectures." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "b4BS1lGEUYM8" - }, - "source": [ - "### Usage\n", - "\n", - "In this tutorial, we delve into two types of theorists: (1) a linear regression theorist, which focuses on fitting a linear model, and (2) a Bayesian Machine Scientist (Guimerà et al., 2020, in *Science Advances*), which specializes in identifying and fitting a non-linear equation.\n", - "\n", - "Theorists are commonly instantiated as regressors within the ``sklearn`` library:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "-ICqZZikUYM8" - }, - "outputs": [], - "source": [ - "from sklearn import linear_model\n", - "from autora.theorist.bms import BMSRegressor\n", - "\n", - "theorist_lr = linear_model.LinearRegression()\n", - "theorist_bms = BMSRegressor(epochs=100)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "3dKtzWCrUYM9" - }, - "source": [ - "Once instantiated, we can fit the theorist to link experimental conditions with observations. However, before doing so, we should convert both inputs into 2-dimensional numpy arrays." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 147 - }, - "id": "5o0fJnXiUYM9", - "outputId": "01e843d0-5049-488f-8d8c-5f3bcc2f93ad" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Size of the initial conditions: (10, 1),\n", - "Size of the initial observations: (10, 1)\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:13<00:00, 7.41it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
BMSRegressor(epochs=100)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "BMSRegressor(epochs=100)" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# convert data to 2-dimensional numpy array\n", - "initial_conditions = initial_conditions.reshape((len(initial_conditions), 1))\n", - "initial_observations = initial_observations.reshape((len(initial_observations), 1))\n", - "print(f\"Size of the initial conditions: {initial_conditions.shape},\\nSize of the initial observations: {initial_observations.shape}\\n\")\n", - "\n", - "# fit theorists\n", - "theorist_lr.fit(initial_conditions, initial_observations)\n", - "theorist_bms.fit(initial_conditions, initial_observations)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "qwx7lc64UYM9" - }, - "source": [ - "For some theorists, we can inspect the resulting model architecture. For instance, in the BMS theorist, we can call obtain the model formula via ``theorist_bms.repr()``.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IOT7OzP2UYM9", - "outputId": "f35d8400-5d10-4526-c210-c22b30aa2d8d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model of BMS theorist: sin(X0)\n" - ] - } - ], - "source": [ - "print(\"Model of BMS theorist: \" + theorist_bms.repr())" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "77tpR0taUYM-" - }, - "source": [ - "We may now obtain predictions from both theorists for the entire pool of experiment conditions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "cSB2gLmPUYM-" - }, - "outputs": [], - "source": [ - "# convert condition pool into 2-dimensional numpy array before generating respective predictions\n", - "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", - "\n", - "# obtain predictions\n", - "predicted_observations_lr = theorist_lr.predict(condition_pool)\n", - "predicted_observations_bms = theorist_bms.predict(condition_pool)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "eCe8VNt6UYM-" - }, - "source": [ - "In the next code segment, we plot the theorists' predictions against the ground truth. For the BMS theorist, we can obtain a latex expression of the model architecture using ``theorist_bms.latex()``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 490 - }, - "id": "TUpwLukrUYM-", - "outputId": "a09cca9f-a140-4b23-e70c-aa1c80807d3c" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# obtain latex expression of BMS theorist\n", - "bms_model = theorist_bms.latex()\n", - "\n", - "# plot model predictions against ground-truth\n", - "import matplotlib.pyplot as plt\n", - "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", - "plt.plot(initial_conditions, initial_observations, 'o', label='Data Used for Model Identification')\n", - "plt.plot(condition_pool, predicted_observations_lr, label='Linear Regression')\n", - "plt.plot(condition_pool, predicted_observations_bms, label='Bayesian Machine Scientist: $' + bms_model + '$')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('Model Predictions')\n", - "plt.legend()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "r5Ti6ULGUYM_" - }, - "source": [ - "**Note**: *There are various other types of theorists you can combine with AutoRA as long as they are implemented as ``sklearn`` estimators. This includes [autora modules](theorist/index.md), any [scikit learn estimators](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html), as well as third-party packages, such as [PySR](https://github.com/MilesCranmer/PySR) for symbolic regression.*" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "nLQqPVtJUYM_" - }, - "source": [ - "## Experimentalists\n", - "\n", - "The primary goal of an experimentalist is to design experiments that yield scientific merit. The AutoRA framework offers various strategies for identifying informative new data points (e.g., by searching for experiment conditions that existing scientific models fail to explain, or by looking for novel conditions altogether).\n", - "\n", - "\"Experimentalist\n", - "\n", - "Experimentalists are implemented as functions that return a set of experiment conditions (e.g., in the form of a 2-dimensional numpy array in which columns correspond to independent variables), which can be subjected to an experiment. To determine these conditions, experimentalists may use information about candidate models obtained from a theorist, experimental conditions that have already been probed, or respective dependent measures. For more detailed information about experimentalists, please refer to the corresponding [AutoRA Documentation](https://autoresearch.github.io/autora/experimentalist/).\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "L7VIntSjO2ga" - }, - "source": [ - "### Types\n", - "\n", - "There are generally three types of experimentalist functions: **poolers**, **samplers**, and **pipelines**.\n", - "\n", - "**Poolers** generate a novel set of experimental conditions \"from scratch\", e.g., by sampling from a grid. They usually require metadata describing independent variables of the experiment (e.g., their range or the set of allowed values).\n", - "\n", - "**Samplers** operate on an existing pool of experimental conditions. They typically require experimental conditions to be represented as a 2-dimensional numpy array in which columns correspond to independent variables and rows to different conditions. They then select experiment conditions from this pool.\n", - "\n", - "**Pipelines** Pipelines connect multiple experimentalists into a unified workflow. This is beneficial when various steps are required to process experiment conditions. For example, apart from identifying novel experimental conditions, experimentalist functions may perform other operations on the set of conditions, such as rearranging the rows of a condition matrix or adding new experiment conditions as columns. Experiment pipelines may begin with a pooler that generates all possible experiment conditions, followed by a sampler that selects a subset of conditions from the pool, and then proceed to additional functions that arrange the selected conditions in a specific order necessary for conducting the experiment." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "fI5uCcT8UYM_" - }, - "source": [ - "### Usage: Poolers\n", - "\n", - "Experimentalist poolers are implemented as functions and can be called directly. For instance, the following **grid pooler** generates a grid based on the ``allowed_values`` of all independent variables in the ``metadata`` object that we defined above. We can simply add a list of allowed values to each independent variable. In this case, we only have one variable." - ] - }, + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction to AutoRA\n", + "## Basic Tutorial I: Components" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", + "\n", + "This notebook is the first of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", + "\n", + "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", + "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", + "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", + "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", + "\n", + "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", + "\n", + "**How to use this notebook** *You can progress through the notebook section by section or directly navigate to specific sections. If you choose the latter, it is recommended to execute all cells in the notebook initially, allowing you to easily rerun the cells in each section later without issues.*" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installation\n", + "\n", + "The AutoRA ecosystem is a comprehensive collection of packages that together establish a framework for closed-loop empirical research. At the core of this framework is the ``autora`` package, which serves as the parent package and is essential for end users to install. It provides functionalities for automating workflows in empirical research and includes vetted modules with minimal dependencies.\n", + "\n", + "However, the flexibility of autora extends further with the inclusion of *optional* modules as additional dependencies. Users have the freedom to selectively install these modules based on their specific needs and preferences.\n", + "\n", + "\"AutoRA\n", + "\n", + "*Optional dependencies enable users to customize their autora environment without worrying about conflicts with other packages within the broader autora ecosystem. To install an optional module, simply use the command ``pip install autora[dependency-name]``, where ``dependency-name`` corresponds to the name of the desired module (see example below).*\n", + "\n", + "To begin, we will install all the relevant optional dependencies. Our main focus will be on two experimentalists: ``experimentalist-falsification`` and ``experimentalist-sampler-novelty``, along with a Bayesian Machine Scientist (BMS) implemented in the ``theorist-bms`` package. It's important to note that installing a module will automatically include the main autora package, as well as any required dependencies for workflow management and running synthetic experiments.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -q \"autora[experimentalist-falsification]\"\n", + "!pip install -q \"autora[experimentalist-sampler-novelty]\"\n", + "!pip install -q \"autora[experimentalist-sampler-model-disagreement]\"\n", + "!pip install -q \"autora[theorist-bms]\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To make all simulations in this notebook replicable, we will set some seeds." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "qJDNd9F3UYM_" - }, - "outputs": [], - "source": [ - "allowed_values = np.linspace(0, 2 * np.pi, 100)\n", - "metadata.independent_variables[0].allowed_values = allowed_values" + "data": { + "text/plain": [ + "" ] - }, + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import torch\n", + "\n", + "np.random.seed(42)\n", + "torch.manual_seed(42)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Automated Empirical Research Components\n", + "\n", + "The goal of this section is to set up all ``autora`` components to enable a closed-loop discovery workflow with synthetic data. This involves specifying (1) the experiment runner, (2) a theorist for model discovery, (3) an experimentalist for identifying novel experiment conditions.\n", + "\n", + "\n", + "* **Experiment Runner:** The experiment runner collects observations reflecting experimental conditions.\n", + "* **Theorist:** The theorist automates the construction of models from data. These can take many forms, for example linear regression and the bayesian machine scientist.\n", + "* **Experimentalist:** Each experimentalist identifies experimental conditions that yield scientific merit.\n", + "\n", + "\"AutoRA\n", + "\n", + "Each of these components automates a process of the scientific method that is generally conducted manually. The experiment runner parallels a *research assistant* that collects data from participants. The theorist takes the place of a *computational scientist* that applies modelling techniques to discover how to best describe the data. The experimentalist acts as a *research design expert* to determine the next iteration of experimentation. Each of these steps in the scientific method can be arduous and time consuming to conduct manually, and so ``autora`` allows for the automation of these steps and thus quickens the scientific method by leveraging data-driven techniques." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Toy Example of the Components\n", + "Before jumping into each component in detail, we will present a toy example to provide you with an overview on how these components work together within a closed-loop. After some setup, you will see steps 1-3, which uses the three componens - namely, the EXPERIMENTALIST to propose new conditions, the EXPERIMENT RUNNER to retrieve new observations from those conditions, and the THEORIST to model the new data. We then finish this example by plotting our data and findings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "vRnf1nMoUYM_" - }, - "source": [ - "Now we can pass the grid pooler the list of independent variables from the ``metadata`` object." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:14<00:00, 6.96it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "prol6MweUYM_" - }, - "outputs": [], - "source": [ - "from autora.experimentalist.pooler.grid import grid_pool\n", - "\n", - "new_conditions = grid_pool(ivs = metadata.independent_variables)" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "87h_mB5xUYM_" - }, - "source": [ - "The resulting condition pool contains all experiment conditions from the grid:" + "data": { + "image/png": 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YayLO/kKpvj3yX3Dtf2DSYpi2Q5Ig4bI++OADqqqqGDBgAPPnz2fPnj3s27ePL7/8kr179za6kOM///lP5s6dy5w5czhw4ABvvPEGP/zwA4888ghgaVUaMmQIr7zyCnv27GHFihU88cQTzY7zwQcf5NNPP+Wzzz5j//79PP300+zateucXnN9mhJrbGwsiqKwePFisrOzKSoqsmsMQjiaSyVCxcXFJCYm8v777zdp/5SUFK688kpGjRpFcnIy06ZN48477+TXX7Up4HQp3cdZWnmCompuD4qGG76AkTOg13UQP0K6w4RL69ixI1u3bmX06NHMnDmTxMREBgwYwLvvvssjjzzC888/3+Djx48fz9tvv83rr79Ojx49+Oijj/jss88YOXKkbZ9PP/2Uqqoq+vfvz7Rp03jhhReaHeeNN97Ik08+yaOPPkr//v1JTU3lH//4R7OfpzGNxdq2bVueffZZHnvsMSIiIpgyZYrdYxDCkRS1qeNMnYyiKCxcuJDx48fXu8+MGTP4+eef2blzp23bTTfdRF5eHkuWLGnScQoKCggODiY/P5+goKDzDdv1yLpioonKyspISUkhPj4eHx8frcMRQniAhs47Tf3+duti6bVr1zJ69Oga25KSkmQ9nObQ6S2tPs1QWmHieF4J6fll6BSFLhGBtA40tlCAQoi6VJrMlFWaUBQFnQI+Bj063fmv1C2Eu3HrRCgjI4OIiJojoCIiIigoKKC0tLTOESHl5eU1hssWFBS0eJzuQFVV1qec4psNR/llZwYVVTVHikQEGblpYHsmDYsjzF+m8ReiJZjMZrKLKsgvqaS8qmYxs05RCPQxEOrnTaBP0wvKhXB3bp0InYuXX36ZZ599VuswXEpWQRn/WriD3/ecni8k0GggMtiHKrPKkZxiMgvKeXvZAT766xAPXNyZey/qiF6uToWwC1VVySmqIKuwjCrz6WoHo8HSjW0yq1SZzeSXVpJfWkmgjxfRwT4YmzjNgBDuzK0TocjISDIza04OmJmZSVBQUJ2tQQAzZ85k+vTpttsFBQXExMS0aJyubOnuTB75dhv5pZV46RWu69+Omwa2p3e7YNsVZ2mFiWV7M/loxWF2HM/ntV/3sfZQDm/e2Ee6zIQ4TyazStqpEgrKLPP9GA16IoKMBBgNGPSn5wUqqzSRW1JJTnEFhWWV7C+von2oL8F+0kIrPJtbJ0JDhw7ll19+qbFt6dKlDc7GajQaMRrly7kpfthyjEe+3YZZhZ5tg3j9+kQSImsXpPl667mqdzRX9oriu83HeOqnXaw6eJLx76/mm7uHEBPmp0H0Qri+iiozR3KKbbVA0cE+hPl71+r2UhQFX28Dvt4Gwv29OZ5XSlF5FamnSogyqXJBIjyaSw2fLyoqIjk52TbBV0pKCsnJyRw9ehSwtOZMnDjRtv+9997L4cOHefTRR9m7dy8ffPABCxYs4KGHHtIifLfy1fpUpi+wJEHX92/HwvuG15kEnUlRFK4fEMN/HxhOh1b+HM8rZcIn6ziRV+qgqIVwH1UmMyknLUmQl15Hx1b+hAcYG639MXrpia/eFyA9v5SsgnNfr0wIV+dSidCmTZvo27cvffv2BSyLG/bt25ennnoKsMyGak2KAOLj4/n5559ZunQpiYmJzJ49m3//+98kJWkz7b+7+H13Jk/8aJmSYPKwOF69tjde+qb/KXVqE8j/3T2EuHA/juVakqFTxRUtFa4QbsdkVjmSU0J5VXUS1DoAP2PTG/itrUeRwZbhxhkFZeSVyGdQeCaXnUfIUTx+HqGzHMwqZPz7aygqr+Lmwe15cXzPcx59ciKvlBs+Wsux3FIu7NKazyYPlAJqFybzCDmGqqocPVVCfmklep1Cx9YB+JxH0XN6XinZReUoikKHVv74NyOhEkJr9phHyKVahIS2CsoquevzzRSVVzEoPoxnxvY4ryG40SG+/HvSAHy8dPy1P5t3/zhgx2iFcE85xRXkl1aiKApx4f7nlQQBRAb7EOzrZUuwqkyySKrwLJIIiSZ7cfEeUk4W0zbElw9u6Ye34fz/fBIig3hxfC8A3l52gNUHT573cwrhTs5cV7G0wkR6vqWeJyrIp8mtN5MnT653Fn5FUYgJ9cNo0FNpMnM8rxRHdxTMnTuXkJAQ2+1nnnmGPn36NPiYI0eOoCjKOS1gK8SZJBESTbJ8XxbzN6WhKPDmjX1oFWC/USbX9m/HhEExqCo89sN2Siqq7PbcwgWZTZCyEnZ8Z/nX3LKrnGdnZ/OPf/yD9u3bYzQaiYyMJCkpqdaK8lozV7fYqKpKkI8X4QH2G/au0ym0C/Xh+6/mcdXoiwgMDCQkJIQBAwbw1ltvUVJSYrdjNcUjjzzCsmXLbLfrSuRiYmJIT0+nZ8+eDo1NuB/pDBaNKiirZOYPOwBLcfSg+DC7H+PxK7uzYl82aadKeev3A/zrim52P4ZwAbsXwZIZUHDi9LagaBjzqmUh4BZw7bXXUlFRwbx58+jQoQOZmZksW7aMnJycFjneuTpZWE55lQmDTke7UF+7zwx9zx238cMPP3DnAw/z+Auv0T8hlt07d/DWW28RFxfX4LqO9hYQEEBAQECD++j1eiIjIx0UkXBn0iIkGjX7132k55cRG+7HP5O6tsgxAowGXrjGcmX375WH2Xk8v0WOI5zY7kWwYGLNJAigIN2yffciux8yLy+PlStX8uqrrzJq1ChiY2MZNGgQM2fOZNy404nXG2+8Qa9evfD39ycmJob77ruPoqIi2/3Wrp3FixfTtWtX/Pz8uO666ygpKWHevHnExcURGhrK1KlTMZlOt3DFxcXx/PPPM2HCBPz9/Wnbti3vv/9+rTgrTWayCi1L/5iKsrl5wk2EhIQQFhbG1VdfzZEjR2z7mkwmpk+fTkhICOHh4Tz66KONdnUtWLCAr776iq+//poHHn6U7ol98QmN5Oqrr+aPP/5g1KhRAJjNZp577jnatWuH0WikT58+NRawtnZX/fDDD4waNQo/Pz8SExNZu3ZtjePNnTuX9u3b4+fnxzXXXFMr6Tyza+yZZ55h3rx5/PTTTyiKgqIoLF++vM6usRUrVjBo0CCMRiNRUVE89thjVFWdbmEeOXIkU6dO5dFHHyUsLIzIyEieeeYZ2/2qqvLMM8/YWgejo6OZOnVqg++dcH2SCIkGHcgs5Mv1likJXr6mF37eLdeIeHFCBGMTozGr8PiPOx1epyA0ZDZZWoKo63devW3JY3bvJrO2PPz444811hg8m06n45133mHXrl3MmzePP/74g0cffbTGPiUlJbzzzjt88803LFmyhOXLl3PNNdfwyy+/8Msvv/DFF1/w0Ucf8d1339V43GuvvUZiYiJbt27lscce48EHH2Tp0qU19jlVXIFZVTHqVG4cP5bAwEBWrlzJ6tWrCQgIYMyYMVRUWIa/z549m7lz5/Lpp5+yatUqTp06xcKFCxt8H7766iu6du3K+PHjaRtsmXU/t6SC4vIqFEUhODgYgLfffpvZs2fz+uuvs337dpKSkhg3bhwHDtQc6PD444/zyCOPkJycTJcuXZgwYYItIVm/fj133HEHU6ZMITk5mVGjRvHCCy/UG9sjjzzCDTfcwJgxY0hPTyc9PZ1hw4bV2u/48eNcccUVDBw4kG3btjFnzhz+85//1HruefPm4e/vz/r165k1axbPPfec7f3+/vvvefPNN/noo484cOAAP/74I7169WrwvRNuQBUNys/PVwE1Pz9f61A0MenT9WrsjMXqnfM2OuR4mQWlarcn/6fGzlisLt52wiHHFPZRWlqq7t69Wy0tLW3+gw//papPBzX+c/gvu8f93XffqaGhoaqPj486bNgwdebMmeq2bdsafMy3336rhoeH225/9tlnKqAePHjQtu2ee+5R/fz81MLCQtu2pKQk9Z577rHdjo2NVceMGVPjuW+88Ub18ssvt90G1Dc/+VLdfixP/fSzeWrXrl1Vs9lsu7+8vFz19fVVf/31V1VVVTUqKkqdNWuW7f7Kykq1Xbt26tVXX13v6+nWrZs6btw42+2jOcXqtrRcdX9GQY1jRUdHqy+++GKNxw4cOFC97777VFVV1ZSUFBVQ//3vf9vu37Vrlwqoe/bsUVVVVSdMmKBeccUVtV5zcHCw7fbTTz+tJiYm2m5PmjSpVvzWY23dulVVVVX917/+Veu9ef/999WAgADVZDKpqqqqF110kXrBBRfUin/GjBmqqqrq7Nmz1S5duqgVFRX1vlfCuTR03mnq97e0CIl6rdifzfJ92XjpFYfV7LQJ9OHuCzsAMOvXvbVWsRduqiiz8X2as18zXHvttZw4cYJFixYxZswYli9fTr9+/Zg7d65tn99//51LLrmEtm3bEhgYyK233kpOTk6NImI/Pz86duxoux0REUFcXFyNWpeIiAiysk4vTgzUWvJn6NCh7NmzB6BGq2i4vze7d+3g4MGDBAYG2lqzwsLCKCsr49ChQ+Tn55Oens7gwYNtjzMYDAwYMKDB90A9q/U1MtgHvaJQWmkir8SyhllBQQEnTpxg+PDhNfYdPny4LV6r3r172/4fFRUFYHvde/bsqRFfXe/BudizZw9Dhw6tUTs1fPhwioqKOHbsWJ2xWeOzxnb99ddTWlpKhw4duOuuu1i4cGGNrjXhniQREnUym1Ve/sVycps4NI74Vv4OO/ZdIzrQKsBIak4JX69PddhxhYYCIuy7XzP5+Phw6aWX8uSTT7JmzRomT57M008/DVjqXq666ip69+7N999/z+bNm211PNbuKAAvL68az6koSp3bzOamJ/f5pZYkRKdAm0AjRUVF9O/f37bUkPVn//793Hzzzef02gG6dOnC3r17T78WvY7WQZaRoZmFZZib2U195uu2JibNed0tqaHfSUxMDPv27eODDz7A19eX++67jwsvvJDKykotQhUOIomQqNNvuzPZm1FIgNHAAxd3cuix/Y0GHrq0MwDv/HFQhtN7gthhltFh1DcSSoGgtpb9HKB79+4UFxcDsHnzZsxmM7Nnz2bIkCF06dKFEydONPIMTbdu3bpat7t164ZZVckssNQtBfl6YdDr6NevHwcOHKBNmzZ06tSpxk9wcDDBwcFERUWxfv162/NVVVWxefPmBmO4+eab2b9/Pz/99JNtW7i/EYNOR3mliaPp2QQFBREdHV1rWoHVq1fTvXv3Jr/ebt261YivrvfgbN7e3jWKzOt73rVr19Zo3Vq9ejWBgYG0a9euyfH5+voyduxY3nnnHZYvX87atWvZsWNHkx8vXI8kQqIWVVV5Z5ml+HHysDhC/Ow3X0lT3TgghthwP04VV/D1+qONP0C4Np3eMkQeqJ0MVd8e84plPzvKycnh4osv5ssvv2T79u2kpKTw7bffMmvWLK6++moAOnXqRGVlJe+++y6HDx/miy++4MMPP7RbDKtXr2bWrFns37+f999/n2+//ZYHH3yQvJIKyqssX/5BPpZWjFtuuYVWrVpx9dVXs3LlSlJSUli+fDlTp061df88+OCDvPLKK/z444/s3buX++67j7y8vAZjuOGGG7jxxhuZMGECL730Eps2buTYwd3sXLGIf0wYx+Ilv2NWVf75z3/y6quvMn/+fPbt28djjz1GcnIyDz74YJNf79SpU1myZAmvv/46Bw4c4L333qsx8qwucXFxbN++nX379nHy5Mk6W2juu+8+0tLSeOCBB9i7dy8//fQTTz/9NNOnT0ena9pX3dy5c/nPf/7Dzp07OXz4MF9++SW+vr7ExsY2+fUJ1yOJkKjl9z1Z7E4vwN9bzx0XxGsSg0Gv496LLPUWn6w8bPtCEG6s+zi44XMIiqq5PSjasr0F5hEKCAhg8ODBvPnmm1x44YX07NmTJ598krvuuov33nsPgMTERN544w1effVVevbsyVdffcXLL79stxgefvhh24LSL7zwAm+88QaXXXYZ2YWnu9101Wvw+fn58ddff9G+fXv+9re/0a1bN+644w7Kyspsayk9/PDD3HrrrUyaNImhQ4cSGBjINddc02AMiqLw9ddf88Ybb/DjD99z0UUX0XvAEGa98hI3JQ3l1gs7UZx/kqlTpzJ9+nQefvhhevXqxZIlS1i0aBGdO3du8usdMmQIn3zyCW+//TaJiYn89ttvPPHEEw0+5q677qJr164MGDCA1q1b1znZZdu2bfnll1/YsGEDiYmJ3Hvvvdxxxx2NPveZQkJC+OSTTxg+fDi9e/fm999/57///S/h4eFNfg7hemTR1UZ42qKrqqoy7r3V7Diezz9GdmTGmATNYimvMnHRrOVkFJTx0jW9uHlwe81iEY2z26KrZhOkrrEURgdEWLrD7NwS5Czi4uKYNm0a06ZNq7E9v6SC1FMl6HUKCZFBjluMuDQPclNqbVZVLA1zofEoviGOiUWIJpBFV4XdrT2Uw47j+fh66blTo9YgK6NBbxtB9uGKQ7IYpKfQ6SF+BPS6zvKvmyZB9VFVlewiS21QuL/RcUmQqkL+sTrvUhRABTXvWHVWJIT7kERI1PCfVZarwesHtCPcjuuJnasJg9oT7u/N0VMl/G9nhtbhCNHiisurKKkwoVMUu64n1qiKIjDXPzpKUUCnVlr2E8KNSCIkbA5nF7Fsr2U+jcnD4rQNppqvt56/D7EUKs5bc0TbYISwsyNHjtTqFssustQGhfp546V34Cna1LQh4g3NwC2EK5JESNjMrU40LkloQ4fWDS946Ei3DG6PQaewKTWXHcdkDTLhvsorTRSWWRKSVo5sDQLQezW+D5BbLl1jwr1IIuQJzCZIWQk7vrP8W8d6TfkllXy7yVIfcLvGtUFnaxPkw5W9LSOJ5kqrkHBjOcWW1qBAHy+MXg6ujfIOAF39yZAKVKgGssu9qJQZ34UbkUTI3e1eBG/1hHlXwfd3WP59q2etlby/23KM0koTCZGBDOvofENFJ1V31f132wlOFknTvHA/ZrNKboklEQr3d/zcXSgKBNc/8aACnDK0RgVOlVTUu58QrkYSIXe2exEsmAgFZ82CW5Bu2V6dDKmqyv9tsExa+PchsTXW6nEWfWNCSGwXTIXJzPyNaVqHI4Td5ZVWYjKreOt1BPoYtAnCNwRC42u3DOm8IDQen8AwAE4VV9Ran0wIVyWJkLsym2DJDCwN2mer3rbkMTCb2JSay8GsIny99FzdJ9qRUTaZoijcOjQOgPkb0zCb5SQs3IeqquRUt3SGBXhrezHiGwIRPSC8E4TEWv6N6AG+IZalPnQ6Kk1mCspk6RvhHiQRclepa2q3BNWgQsFxSF3D/1UvYTEuMZpAn6YVTGrhil6RBBgNHD1VwrqUHK3DEcJuyipNlFaaUBSFMA2WtKlFUcAYCH5hln+rEzOdohDqbzlHnCqW7jHhHiQRcldFmU3arTjnOIt3pANw06CYlozovPl5GxhX3WIl3WPCnZwqsYwUC/YxYKhnyPzkyZMZP368A6OqW1h1/VJhWSUVdlj6ZuTIkbWmEDiboij8+OOP532slnTkyBEURSE5ObnFj/XMM8/Qp08fuz9vRUUFnTp1Ys2aNXZ/7nNx0003MXv27BY/jiRC7iogokm7/ZWuo6LKTEJkIH1iQlo2Jju4cYAlWfvfzgzyS5o274kQDZk8eTKKoth+wsPDGTNmDNu3b3fI8c2qSl518XFoA0XSb7/9NnPnzm3xeOLi4lAUhW+++abWfT169MDHy8CShZb7ch30GUxPT+fyyy9v0WOYTCZeeeUVEhIS8PX1JSwsjMGDB/Pvf/+7SY+PiYkhPT2dnj172jWuupLARx55hGXLljXp8c1Jmj788EPi4+MZNmxYM6Os3/Lly+nXrx9Go5FOnTo162/4iSee4MUXXyQ/v2WnTZFEyF3FDrMsVllrJW8rBYLa8sHhNgDcNDDGKYukz9a7XTAJkYFUVJn5Mfm41uEINzFmzBjS09NJT09n2bJlGAwGrrrqKoccu6C6SNpLryPAWH+RdHBwMCEhIQ6JKSYmhs8++6zGtnXr1pGRkYG/vz/+3pY4c0scUzQdGRmJ0diyM90/++yzvPnmmzz//PPs3r2bP//8k7vvvpu8vLwmPV6v1xMZGYnB0PKF7gEBAXZfCFZVVd577z3uuOMOuz1nSkoKV155JaNGjSI5OZlp06Zx55138uuvvzbp8T179qRjx458+eWXdoupLpIIuSudHsa8Wn3j7ATHcvvE0KfZkV6MQacwrk9bh4Z3rhRF4caBllahBZuke0zYh9FoJDIyksjISPr06cNjjz1GWloa2dnZtn1mzJhBly5d8PPzo0OHDjz55JNUVlpaRI4cOYJOp2PTpk01nvett94iNjYWs9ky787OnTu5/PLLCQgIICIigltvvZVDaZau6VA/L77//nt69eqFr68v4eHhjB49muLiYqB219iSJUu44IILCAkJITw8nKuuuopDhw7Z7rd21fzwww+MGjUKPz8/EhMTWbt2baPvxy233MKKFStISzv9Gfv000+55ZZbMBgM+Hrp0SkKFVVmXn3tdXr16oW/vz8xMTHcd999FBXVXIZj9erVjBw5Ej8/P0JDQ0lKSiI3N9d2v9ls5tFHHyUsLIzIyEieeeaZGo8/s1Wkqa9r1apVjBgxAl9fX2JiYpg6dartvazLokWLuO+++7j++uuJj48nMTGRO+64g0ceeaRGnLNmzaJTp04YjUbat2/Piy++WCOuM7vG6vp9nzx50nb/yJEjmTp1ar2vPS4uDoBrrrkGRVFst89u5Vm+fDmDBg3C39+fkJAQhg8fTmpqKnPnzuXZZ59l27ZtthbP+lpkNm/ezKFDh7jyyivrfY/O1tBnAk63MM2ePZtu3boxZcoUrrvuOt58880mH2Ps2LF1tk7akyRC7qz7OLjhcwiKqrk9KBpu+JzP8xIBGJXQxtbv7wqu7tMWg05h14kCDmYVah2OqIeqqpRUlmjycz6tFEVFRXz55Zd06tSpxlV3YGAgc+fOZffu3bz99tt88sknthN6XFwco0ePrtWK8tlnnzF58mR0Oh15eXlcfPHF9O3bl02bNrFkyRIyMjL4x+23AlCWn8OECRO4/fbb2bNnD8uXL+dvf/tbva+luLiY6dOns2nTJpYtW4ZOp+Oaa66xJV1Wjz/+OI888gjJycl06dKFCRMmUFXV8IiviIgIkpKSmDdvHgAlJSXMnz+f22+/HQCdTiHY11I0XVal8s4777Br1y7mzZvHH3/8waOPPmp7ruTkZC655BK6d+/O2rVrWbVqFWPHjsVkOl1fNG/ePPz9/Vm/fj2zZs3iueeeY+nSpQ3G2NDrOnToEGPGjOHaa69l+/btzJ8/n1WrVjFlypR6ny8yMpI//vijRvJ7tpkzZ/LKK6/w5JNPsnv3br7++msiIuouQ6jr952ZmckNN9xQY7+GXvvGjRsBy99Renq67faZqqqqGD9+PBdddBHbt29n7dq13H333ZaLxhtv5OGHH6ZHjx62Fs8bb7yxznhXrlxJly5dCAwMrPf1n62hzwTA2rVrGT16dI3HJCUlNSkZtxo0aBAbNmxo2aVdVNGg/Px8FVDz8/O1DuXcmapU9fBfqrr9W8u/piq1ymRWh7z0uxo7Y7H6y/YTWkfYbLd/tkGNnbFYfW3JXq1DEdVKS0vV3bt3q6WlpaqqqmpxRbHac25PTX6KK4qbHPekSZNUvV6v+vv7q/7+/iqgRkVFqZs3b27wca+99prav39/2+358+eroaGhallZmaqqqrp582ZVURQ1JSVFVVVVff7559XLLrusxnMk7zmgAurSNVvUzZs3q4B65MiReuO8+uqr640nOztbBdQdO3aoqqqqKSkpKqD++9//tu2za9cuFVD37NlT7/PExsaqb775pvrjjz+qHTt2VM1mszpv3jy1b9++qqqqanBwsPrZZ5+phWWV6ra0XHXnsTzVZDLbHv/tt9+q4eHhttsTJkxQhw8fXu/xLrroIvWCCy6osW3gwIHqjBkzbLcBdeHChU1+XXfccYd6991313jOlStXqjqdzvb3ebZdu3ap3bp1U3U6ndqrVy/1nnvuUX/55Rfb/QUFBarRaFQ/+eSTOh9vjWvr1q2qqtb9+05LS1MBdd++fef02q2efvppNTExUVVVVc3JyVEBdfny5XXGdea+DXnwwQfViy++uNH9GnL2Z6Jz587qSy+9VGOfn3/+WQXUkpKSJj3ntm3bGvxcnH3eOVNTv7+lRcgT6PQQPwJ6XWf5V6dn3eEc0vPLCPIxcHG3NlpH2Gzj+1q68n5MPi4Tu4nzZq1hSE5OZsOGDSQlJXH55ZeTmppq22f+/PkMHz6cyMhIAgICeOKJJzh69Kjt/vHjx6PX61m4cCEAc+fOZdSoUbbujG3btvHnn38SEBBg+xnWvw8AOelpJCYmcskll9CrVy+uv/56PvnkkxrdR2c7cOAAEyZMoEOHDgQFBdmOc2ZMAL1797b9PyrK0jqclZXV6Hty5ZVXUlRUxF9//cWnn35qaw2y8vfW463XsfqvPxl18cW0bduWwMBAbr31VnJycigpKQFOtwg15MwYrXE2FmNDr2vbtm3MnTu3xnudlJSE2WwmJSWlzufr3r07O3fuZN26ddx+++1kZWUxduxY7rzzTgD27NlDeXl5o6/Fqq7fd0JCAkCNLsxzee1nCgsLY/LkySQlJTF27Fjefvtt0tPTm/x4q9LSUnx8fJr1mMY+E/bg6+sLYPt7agkaTV8qtPbDFkuh8ZW9ozEaHLymkR2M7haBv7eeY7mlbE7NZUBcmNYhibP4GnxZf/N6zY7dHP7+/nTq1Ml2+9///jfBwcF88sknvPDCC6xdu5ZbbrmFZ599lqSkJIKDg/nmm29qDO319vZm4sSJfPbZZ/ztb3/j66+/5u2337bdX1RUxNixY3n1VUvtXnmliZSTxSgoXJDYCb1ez9KlS1mzZg2//fYb7777Lo8//jjr168nPr72+n9jx44lNjaWTz75hOjoaMxmMz179qSioub8Pl5ep+cGsw6IOLv7rC4Gg4Fbb72Vp59+mvXr19sSvDOfqzA7nQduu4m/T76TV195mbCwMFatWsUdd9xBRUUFfn5+ti+yhpwZo/W5G4uxoddVVFTEPffcw9SpU2s9rn379vU+p06nY+DAgQwcOJBp06bx5Zdfcuutt/L444836XWc6ezf95msidvZr8P6Wpry+znTZ599xtSpU1myZAnz58/niSeeYOnSpQwZMqTJz9GqVSt27NjR5P2b8pmIjIwkM7PmVC6ZmZkEBQU1+f08deoUAK1bt25ybM0liZAHKqs08euuDAD+1s81iqTP5uutZ0zPKL7fcowfk49LIuSEFEXBz8tP6zDOiaIo6HQ6SktLAVizZg2xsbE8/vjjtn3ObC2yuvPOO+nZsycffPABVVVV/O1vf7Pd169fP77//nvi4uIwGAxkFJRhCiwj0MeL4CB/23GHDx/O8OHDeeqpp4iNjWXhwoVMnz69xnFycnLYt28fn3zyCSNGjAAsxcH2dvvtt/P6669z4403EhoaWuv+g3u2Yzabmfr48/SIDsag17FgwYIa+/Tu3Ztly5bx7LPP2j2++vTr14/du3fXSG7PRffu3QFLPVbnzp3x9fVl2bJltlaixmI48/d9rry8vGrUU9Wnb9++9O3bl5kzZzJ06FC+/vprhgwZgre3d5MfP2fOHFRVbdII4qZ8JoYOHcovv/xSY9vSpUsZOnRoo89vtXPnTtq1a0erVq2a/Jjmkq4xD7RifzZF5VVEB/vQv33tk5urGN/XMrni4u3pVJpkNWxx7srLy8nIyCAjI4M9e/bwwAMP2K7oATp37szRo0f55ptvOHToEO+8806tFhKAbt26MWTIEGbMmMGECRNqXPXef//9nDp1igkTJrBhwwZ27N7L6uXLePyh+zCZTKxfv56XXnqJTZs2cfToUX744Qeys7Pp1q1breOEhoYSHh7Oxx9/zMGDB/njjz9qJUv20K1bN06ePFmrCNyqe0IXqior+frTj9i+Zz9ffPEFH374YY19Zs6cycaNG7nvvvvYvn07e/fuZc6cOTVGT9nbjBkzWLNmDVOmTCE5OZkDBw7w008/NVgsbR3NtH79elJTU1m+fDn3338/Xbp0ISEhAR8fH2bMmMGjjz7K559/zqFDh1i3bh3/+c9/6ny+M3/fGzdu5NChQ/z666/cdtttTUpMrOLi4li2bBkZGRl1dpWmpKQwc+ZM1q5dS2pqKr/99hsHDhyw/d3ExcWRkpJCcnIyJ0+erLfoeNSoURQVFbFr164mxdWUz8S9997L4cOHefTRR9m7dy8ffPABCxYs4KGHHmry61+5ciWXXXZZk/c/F5IIeaCft1v6j6/oFYVO5/xzB9VnWMdWtAowkldSyeqDLXdSFe5vyZIlREVFERUVxeDBg9m4cSPffvstI0eOBGDcuHE89NBDTJkyhT59+rBmzRqefPLJOp/L2i10dk1NdHQ0q1evxmQykZSUxLhRw3jt2X/ROjwUnU5HUFAQf/31F1dccQVdunThiSeeYPbs2XVOJKjT6fjmm2/YvHkzPXv25KGHHuK1116z+/sCEB4eXm83RmJiIs++9CqfffA2wwf146uvvuLll18+vYOq0iU2it8Wfce2rVsYNGgQQ4cO5aeffmrR+XZ69+7NihUr2L9/PyNGjKBv37489dRTREfXv5ZiUlIS//3vfxk7dixdunRh0qRJJCQk8Ntvv9liffLJJ3n44Yd56qmn6NatGzfeeGO99Txn/r4vu+wyevXqxbRp0wgJCUGna/pX7+zZs1m6dCkxMTH07du31v1+fn7s3buXa6+9li5dunD33Xdz//33c8899wBw7bXXMmbMGEaNGkXr1q35v//7vzqPEx4ezjXXXMNXX33VpLia8pmIj4/n559/ZunSpSQmJjJ79mz+/e9/k5SU1KRjlJWV8eOPP3LXXXc1af9zpahSadqggoICgoODyc/PJygoSOtwzltZpYl+zy+lpMLEwvuG0deFW4QAnvhxB1+uO8qNA2J49brejT9AtJiysjJSUlKIj49vdtGlO3n++ef59ttv65+ZWlXJOXWK4tIyjEYjEa3CbWt5uaLyShP7MgtRUEiICsTLukRIaR7kHwPzGbNP67wguJ1lYVfhdLZv386ll17KoUOHCAgI0Doc5syZw8KFC/ntt9/q3aeh805Tv7+lRcjD/Lk3i5IKE21DfF1iSY3GXNHLUnT46+4M6R4TmioqKmLnzp289957PPDAA3XvVJqHmrmL8PKjtNdlEVGZBpm7LEmDizJ66fH10qOiUlBanfSU5kFuSs0kCCy3c1Nc+vW6s969e/Pqq6/WO7LO0by8vHj33Xdb/DiSCHkY6wKrV/WOcoklNRozOD6ccH9v8koqWXtIVqQX2pkyZQr9+/dn5MiRtbrFACjNddvkIMTPMvIpr7QSVNXSEtSQ/GOW/YTTmTx5Mr169eKll16qMfT/zB97rPt29OjRep8/ICCAo0ePcuedd9K1a1c7vKqGyagxD1JSUcUfeyz92Vf2jmpkb9eg1ykk9Yzk6/VH+WVHOhd2abkhlkI0ZO7cufUvKFmaC7lHgPpX/yP/GPgEu2Q3WbCvF+n5ZZSUV2EqK0R/drJ3NnMlVBSBsemzGAvHuvfee2vNgm3V3KkE6hIdHV1jOZK67ncUSYQ8yF/7symtNBET5kuvtsFah2M3V/aK4uv1R/l1VwbPj+95ukZBCGdQmmdLghrkwsmBt8HSPVZaaaK0rJwmVZeYHLNyvTg3YWFhhIW13LQkBoPhvKc3sBf5xvAgv+2yTGyV1D3SLbrFrAbHhxHm701uSSXrD5/SOhyPJ+MvztCUbqIzuXByEFS99lhhU1+C3qvxfYRohD3ON5IIeYhKk5nf91gSoct6RGocjX0Z9Dou625Z+PC33RkaR+O59HrLDOVnz2zs0SqKatcENcSFkwPrIqw5lV6oukZeh84LvLUflSRcn3XpjbNn6G4O6RrzEOsPn6KgrIpwf2/6x7r2kPm6XNo9gm82prF0dybPjuvhVi1ersJgMODn50d2djZeXl7NmivFbZUWQ1UTr1gVA5gNUFbWsjG1EFVV8VJNVFSZyPUOx6+igfWugtpAS64mLtyeqqqUlJSQlZVFSEiI7ULsXEgi5CGsLSWju0Wgd+FJFOszvFMr/Lz1pOeXsfN4Ab3auU8NlKtQFIWoqChSUlLqXH7CI1WVQVF20/b1bwVFR1o0nJaWX1pJYVkVRd56wrxVS32Uuer0DjqDZQ6h4hxARnmK8xcSEkJk5Pn1ckgi5AHMZvV0fVDPCI2jaRk+Xnou7NyaJbsyWLo7QxIhjXh7e9O5c2fpHrMym2Dew1CcDdTXMqSHpBehcw9HRtYi9mUU8PBXW/Dx0rPwvmF464ATW6EkB/zCIbov6FxvkWfhnLy8vM6rJchKEiEPsON4PhkFZfh76xnWseUWrtPaZT0iWLIrg992ZzL9spafe0LUTafTefTM0rVcNA0WTMSMWndR5nXzoOdVDg6qZfSONVKlGDh0qpytJ0q4qEtr6HSB1mEJ0SDpxPcA1iLpi7q2xsfLfa/GLk5og16nsDejkKM5JVqHI4RF93EUj/+MTPWsochBbeGGL6DneE3CagmKonBxgqXVeVn1eUcIZyeJkAdYVj2J4uhu7tktZhXi582gOMuXjYweE87kdwYxvPwd/un/Elz7H5i0GKbtgO7jtA7N7kZ3awPA77szZSoF4RIkEXJzJ/JK2Z1egKLAyK5ttA6nxV3a3Xo1WveK0EJo4bfdmZjR0ab3JdDrOogf4ba1MsM7tcLHS8eJ/DL2pBdqHY4QjZJEyM39sdeSEPRrH0qYv7fG0bS8ixMsyd7GI6coLHPdyemE+6ioMrNin2XkmLu3yoJl4MIFnSy1iNI9JlyBJEJuzpoIWRMEdxfXyp8OrfypMqusOnBS63CEYHNqLkXlVbQK8CaxXYjW4TjE6IRWDNHtpjx5AaSstIyeE8JJSSLkxkorTKw+aEkGLunmGYkQwKjqpM+aBAqhpeX7LX+HF3Zujc4N5/CqZfcirl95Od94v8AjhbNg3lXwVk/YvUjryISokyRCbmzNoZOUV5lpG+JL1wjXW8jxXFlbv/7cl43ZLMWaQlvWbrGLurbWOBIH2L0IFkxEX3TWrNIF6bBgoiRDwilJIuTGlp3RLeZJS04MjAsjwGjgZFE5O0/kax2O8GCZBWXszShEUWBEZzdPhMwmWDKDuieOrN625DHpJhNORxIhN6Wqqu1KdFSCm5+Az+Jt0NmKNaV7TGhpxX7LZ7B3uxD3H6yQugYKTjSwgwoFxy37CeFEJBFyF2aTpShxx3eQspJDmfkczyvF26BjSIdwraNzOFv3mCRCQkPWROiiLh5wMVLUxBFiTd1PCAeRJTbcwe5FlibpM67GIo0RJOkmUBJ/BX7envdrttZjbD+eT25xBaHufjUunE6VyWwbuegRiVBAE6cGaOp+QjiItAi5uurixLObpP3KM5nj9RaTQrZrFJi2IoJ86BoRiKrCmkOyyrVwvG3H8skvrSTY14tET1gEOHYYBEUD9dUjKpZlRWKHOTIqIRoliZAra6A40fqLvSjlDY8tTrygs6VOaOWBbI0jEZ5oxT5Lt+wFnVth0HvAqVanhzGvVt+omQyZ1eqz1JhX3HZGbeG6PODT6cYaKU7UKeBVdMJjixNH2BKhk7LmkXA4j6oPsuo+Dm74HIKiamzOIJzkoe+45dpqwvV5XvGIO5HixAYNjg/HW6/jeF4pKSeL6dA6QOuQhIfIKSpn+3HL1A0jPSkRAkuyk3Cl5QKsKJO520t5bkcIN5XE0Vfr2ISog7QIuTIpTmyQr7eeAXGhgKVVSAhHWXXwJKoK3aKCaBPko3U4jqfTWxaW7XUd7ftfhhkdf+3PlpZZ4ZQkEXJljRQnqlKcaJvEThIh4Ui22aQ9rTWoDkM6WFpmj+WWcvhksdbhCFGLJEKurKHiROsWDy9OtNYJrT10kkqTWeNohCcwm1X+OiCJkJWft8HWMvvXfhm4IJyPJEKurp7ixCLvCMt2Dy9O7B4VRJi/N8UVJrYezdM6HOEBdqcXcLKoAn9vPf1jQ7UOxylYE0JJhIQzkkTIHXQfB9N2ok76L4/rp3FTxRPsvH6lxydBADqdwvDq5TZWyTB64QDLq4fND+vUCm+DnGIBLqxOhNYdPkV5lWdO5yGcl3xK3YVOzwG/vnxVPIhkfU/6xbXSOiKnYe0e+0vqhIQDrDpo+Tu7ULrFbBIiA2kTaKS00sSmI7lahyNEDZIIuRHrdP4D48Lw8fLcuqCzWROh7cfyyC+p1Dga4c7KKk1sSc0DYHhHz1vjrz6KotgGLqyQ7jHhZCQRciNrDlkSIWtXkLCICvalU5sAzOrp90iIlrA5NZcKk5nIIB/iW/lrHY5TubCLtYtaPoPCuUgi5CaqTGbWHT4FwPCOkgidTbrHhCNYE+1hHcNRlPrW3PJMw6rPS3syCsgtrtA4GiFOk0TITWw7lk9ReRXBvl50jw7SOhynM+KMdcdkUjfRUqwL/A6TVtlaWgca6dwmAFWF9SmyELJwHpIIuYk1B09fiep1ciV6tsHx4XjpFY7llpKaU6J1OMINFZZVsv2YZVmNoVIfVKdh1e+LNWEUwhm4XCL0/vvvExcXh4+PD4MHD2bDhg317jt37lwURanx4+PjntPdW0eqyJVo3fyNBvq1t8zpYn2vhLCnDSmnMJlV4sL9aBviq3U4TsmaIK6VREg4EZdKhObPn8/06dN5+umn2bJlC4mJiSQlJZGVlVXvY4KCgkhPT7f9pKamOjBixyipqLJNFniBJEL1stYorD0sJ2Fhf9ZWjqFSo1evwfHhKAocyCoiq7BM63CEAFwsEXrjjTe46667uO222+jevTsffvghfn5+fPrpp/U+RlEUIiMjbT8REe63AOnGI5aRKtHBPsSF+2kdjtOyXo2uP5wjdULC7mz1QdItVq9Qf2+6RVpqGK2DO4TQmsskQhUVFWzevJnRo0fbtul0OkaPHs3atWvrfVxRURGxsbHExMRw9dVXs2vXLkeE61DW+qDhnVrJSJUGJMYEYzToOFlUwcGsIq3DEW7kVHEFe9ILAMsio6J+w2zdY9JFLZyDyyRCJ0+exGQy1WrRiYiIICMjo87HdO3alU8//ZSffvqJL7/8ErPZzLBhwzh27Fi9xykvL6egoKDGj7NbdVDmD2oKo0FvW/xxnXSPCTuy/j11jQikdaBR42icm9QJCWfjMonQuRg6dCgTJ06kT58+XHTRRfzwww+0bt2ajz76qN7HvPzyywQHB9t+YmJiHBhx8+UWV7C7+kp0WCe5Em3MkPjqk7AkQsKObPMHyWewUYPiw9DrFI7klHA8r1TrcIRwnUSoVatW6PV6MjMza2zPzMwkMjKySc/h5eVF3759OXjwYL37zJw5k/z8fNtPWlraecXd0tYezkFVoUtEAG0C3XNEnD0Nqb4aXXf4lNQJCbs5XR8krbKNCfTxomfbYEBahYRzcJlEyNvbm/79+7Ns2TLbNrPZzLJlyxg6dGiTnsNkMrFjxw6ioqLq3cdoNBIUFFTjx5mtlRNwsyS2C8HHS8ep4goOSJ2QsIOM/DIOZxejUyytHaJxw6R7TDgRl0mEAKZPn84nn3zCvHnz2LNnD//4xz8oLi7mtttuA2DixInMnDnTtv9zzz3Hb7/9xuHDh9myZQt///vfSU1N5c4779TqJdiddYbWIR3kBNwU3gYdA2It75WchIU9WLvFerUNJtjXS+NoXMPQDqcLpqVlVmjNoHUAzXHjjTeSnZ3NU089RUZGBn369GHJkiW2AuqjR4+i053O7XJzc7nrrrvIyMggNDSU/v37s2bNGrp3767VS7CrU8UV7M+0tGoMipfahKYa2jGcVQdPsu5wDpOGxWkdjnBxMn9Q8w2IC8VLr3Aiv4yjp0qIDZcFaoV2XCoRApgyZQpTpkyp877ly5fXuP3mm2/y5ptvOiAqbWyobg3qEhFAmL+3xtG4Dmvr2brDOZjNKjpZkkScI1VVz+ielouRpvLzNtA3JpQNR06x5lCOJEJCUy7VNSZqsk5INlhag5qlV9sQfL305JZUsj+rUOtwhAs7esoy8slLrzAwTrqnm2OI1AkJJyGJkAtbn1KdCEl9ULN4G3S2+YTkJCzOh/UzmNguBF9vvcbRuJYzF2CVOiGhJUmEXFR+SSV7MyzzB8lIleazzv4rEyuK87GhOhGSz2Dz9W0fUj3Te7nM9C40JYmQi9p45BSqCh1a+cv8QefAtu5YyinMZrkaFedGEqFzZzTo6R9raZm1tqwJoQVJhFyUddi8dIudm15tg/Hz1pNXUsneDKkTEs2Xnl/K0VMl6BRsX+iieax1VRskERIakkTIRdnqg6RQ+px46XUMiDs9ekyI5rJ+efeIDibQR+YPOheD408nQlInJLQiiZALKiyrZOfxfEBahM6HbVI3SYTEOZBusfPXt30oBp1CRkEZx3Jl3TGhDUmEXNCm1FzMKrQP8yMq2FfrcFyWdT6hDVInJM6BJELnz9dbT692lnXHpHtMaEUSIRe03jZ/kJyAz0fPtsH4eunJL5X5hETz5BSV29aqk/mDzs8gqRMSGpNEyAWdLpSW+qDz4aXX0S82BICNR3K1DUa4FOvfi8zqfv6sLWobj0giJLQhiZCLKamoYsex6vogaRE6b9ar+Y1yNSqaQbrF7GdAbBiKAodPFpNVWKZ1OMIDSSLkYrak5lFlVmkb4ktMmJ/W4bg8WyJ0REatiKbbcMTSKiuLHZ+/YD8vukYEArAxRVpmheNJIuRibN1iciVqF33bh2DQKaTny6gV0TQFZZXsPlE9q7vUB9nFYOkeExqSRMjF2AqlZdi8Xfh5G+jR1jJqZVOqnIRF4zZXj9qMDfcjMlhmdbeHgdWJkMwwLbQgiZALKas0kZyWB0iTvD0Nql6AdYM0y4smsNUHSWuQ3Vjfy70ZBeSXVmocjfA0kgi5kB3H86kwmWkdaCQuvLo+yGyClJWw4zvLv2aTtkG6oDPrhIRojBRK21+bIB/iwv1QVdgsLbPCwQxaByCazvpFPTAuFEVRYPciWDIDCk6c3ikoGsa8Ct3HaRSl67EutXEwq4hTxRUyHFrUq7TCxPZjeYAsb2Nvg+LDOJJTwoaUXC5OiNA6HOFBpEXIhWyqnrukf2yYJQlaMLFmEgRQkG7ZvnuRBhG6pjB/bzq1CQBgk7QKiQZsTcul0qQSGeRDTJjM6m5PpxdglSVvhGNJIuQizGbV9iU9sH2QpSWIuoZ7V29b8ph0kzWDdI+JpjizW0xRFI2jcS/WFrbtx/IprZBzl3AcSYRcxMHsIgrKqvDz1tOjclftlqAaVCg4DqlrHBafqxsUX10wLTNMiwbYuqelPsjuYsJ8iQgyUmVW2Zomn0PhOJIIuQjrCbhv+xD0JVlNe1BRZgtG5F6sLUK7judTUlGlcTTCGVWZzGw9mgdY6vSEfSmKYhsNK+uOCUeSRMhFWOuDBsSGQUATCwmbup+gXagf0cE+lqvR6i87Ic60N6OQkgoTgT4GurQJ1Doct2QdiSeJkHAkSYRchLVFaEBcKMQOs4wOo74aBQWC2lr2E002QOqERAOsfxf92oei00l9UEuwtrQlp+VRZTJrHI3wFJIIuYCM6uUfdAr0bR8KOr1liDxQOxmqvj3mFct+oskGyjT/ogGbUi2tstIt1nI6twkk0GigpMLE3oxCrcMRHkISIRdgXfqhe3QQAcbqqZ+6j4MbPoegqJo7B0Vbtss8Qs1mnd12S2oelXI1Ks6gqiqbz5y+QrQIvU6hb6wl0dycKgXTwjFkQkUXUKM+6Ezdx0HClZbRYUWZlpqg2GHSEnSOOrcJINjXi/zSSnadKKBPTIjWIQkncTyvlIyCMgw6Rf4uWlj/9qH8tT+bTam5TBoWp3U4wgNIIuQCatQHnU2nh/gRDo7IPel0CgPjQvl9TxabjpySLzxhY70Y6REdhK+3XGi0JOt5bou0CAkHka4xJ1dYVsme9AKgjhYhYXfWbg9plhdnsnZPD5CFVltcYkwIOsXSCpeeX6p1OMIDSCLk5LYezcOsWiYbiwz20Toct9e/uj5hU2ouqlrXzN3CE53unpZC6ZYWYDTQLSoIkAsS4RiSCDk527Ia0hrkEL3bBeOlV8guLOdYrlyNejSzCVJWUrLlG0Ky1qPDTH8ZMeYQtgsSmeldOIDUCDk565BdOQE7ho+Xnh7RwSSn5bHlaC4xYX5ahyS0sHuRZT2/ghP4Ad94Q5YSTpu0t2REpgP0jw3l87WpbDkqiZBoedIi5MQqa0zpLy1CjtJfhu96tt2LYMHEWuv5tVZzLNt3L9IoMM9h/QzuOlEgS96IFieJkBPbfaKA0koTwb5edGodoHU4HkOa5T2Y2WRpCaJ2fZht6tIlj1n2Ey2mbYgvkUE+mMwq29LytQ5HuDlJhJyYbdh8rEzp70jWRGhvRgFF5XI16lFS19RqCapJhYLjlv1Ei1EUxVYOsDlVZnoXLUsSISdm7R/vJyNVHCoiyIe2Ib6YVdiWlqd1OMKRijLtu584Z/3bSxe1cAxJhJyUqqq2E0B/SYQcTuqEPFRAhH33E+dsQNzpz6DZLFNZiJYjiZCTOpFfRmZBOXqdQmK7EK3D8TiSCHmo2GGW9fpqLWZspUBQW8t+okV1iwrC10tPQVkVh7KLtA5HuDFJhJyUdXr57lEypb8WrInQlqNyNepRdHoY8yoAaq1kqPr2mFdkPT8H8NLrSIwJBk5PIyJES5BEyElZWyL6tQ/RNhAPlRAZiJ+3nsKyKg7K1ahn6T4ObvgcU0Bkze1B0XDD5zKPkANJy6xwBJlQ0UltlUJpTRn0OvrEhLDmUA6bU3PpEhGodUjCkbqPY3FZH775bj4Dwit45G8XWrrDpCXIoSzrKx6SREi0KGkRckJllSZ2nbAstNqvvSRCWpH5hDzblrQC1pm7U9LlGogfIUmQBvpWt4innCwmp6hc22CE25JEyAltP5ZPlVmlTaCRdqG+WofjsfqdUSckPI+M2tReiJ83ndtYJpOVViHRUiQRckK2+YPah6IoMpGiVvrFWL4A5WrU85RUVLE3oxCAfrEh2gbj4Wx1QnJBIlqIJEJOyFYoLSdgTQX7edmuRrdUr/kmPMO2tHxMZpWoYB+igqVVVku2REi6qEULkUTIyaiqaiuUliZ57cmoFc8ks7o7D+tncPvxfMqrZI03YX+SCDmZo6dKOFlUgZdeoUd0sNbheDxbnZAkQh5lS+rp7mmhrfhW/oT5e1NRZWbn8QKtwxFuSBIhJ2O9Eu3ZNhgfLxmlorUB1YnQtmN5VFSZNY5GOIKqqmfU6YVoG4xAURRbQioXJKIlSCLkZLak5gFyJeos4lv5E+rnRXmVmd3pcjXqCVJOFpNbUom3QSetsk7Cuu7YJlmJXrQASYSczGZpkncqiqKcMZ+QnIQ9gbUwvnfbYLwNcop0BgNstXp5qKoseSPsSz7lTqS4vIq9GdUTKcqIMach8wl5FimUdj492wbjrddxsqico6dKtA5HuBlJhJzItrQ8zCpEy5Bdp9K//emRY3I16v6kUNr5+Hjp6dk2CJARnML+JBFyInIl6px6twvBoFPILCjneF6p1uGIFlRYVsm+TJlI0RnZuqglERJ2JomQE5H6IOfk662nR7RcjXqCbWn5qCq0C/WlTaCP1uGIM/SXqSxEC5FEyEmoqsrWtDxAWoSckcwn5BlkfTHnZb1A3J9ZSGFZpcbRCHciiZCTOHyymLySSowGHd2jgrQOR5xlQGwYIOsdubsz1/kTzqVNkA/tQn0xq5aWOyHsRRIhJ2G9Eu3dTobsOiNrvcie9EKKy6u0DUa0CLP59PI2kgg5J9vEinJBIuxIvnGdxFYplHZqUcG+tA3xxWRW2VbdhSncy6HsIgrKqvD10pMQFah1OKIOsvafaAmSCDkJKZR2fn2rl1uQq1H3ZP299m4XjJdeTo3OyHp+3Ho0F7NZprIQ9iGfdidQUFbJgawiQBIhZ3a6WT5P20BEi7AtbyOtsk4rISoQXy89BWVVHMou0joc4SYkEXICyUfzUFVoH+ZH60Cj1uGIevQ/Y4ZpmVjR/VgL4fvLxYjT8tLr6N3Osv6btMwKe5FEyAmc7hYL0TYQ0aBuUUEYDTrySio5fLJY63CEHeWXVHKwulW2r3wOndrpqSzytA1EuA1JhJyA9cpG5i5xbt6GM65GpVjTrWxNs/w+41v5Ex4grbLOzLbkjbQICTsxaB2ApzObVZKra076nkOTvKqqlFbJsg+O0ru9LxtTM9mQms6VieFahyPsZH1KOigV9I4Jp6RSFvV0ZgnRRlAqOJh9ioyCAoJ85WvMGfgafFEUReswzomiSrFDgwoKCggODiY/P5+gIPtPdLgvo5Ckt/7Cz1vP9qcvw9DM0SollSUM/nqw3eMSQgghmmr9zevx8/LTOowamvr9LV1jGrPWByW2C2l2EiSEEEKI8yNtiho7veJ8yDk93tfgy/qb19sxItGYpDf/Ii23lE8m9md4p1ZahyPO076MQq75YA0BRj3rZl6CTueazfueZP6mNJ5dtJuhHcP4z6SBWocjsHwXuSpJhDR2voXSiqI4XXOku+vfPoK0UyfYdbyMS7vJe+/qdp/IBtWbxHbhBBj9tQ5HNMGQuChQD7L9aClGvS96SV7FeZC+GA3lFldwONsyDLtvjIwYcxW24bsysaJbsA7DlvmDXEeXiEACjAaKK0zsyyjUOhzh4pqdCE2aNIm//vqrJWLxONYhux1a+xPq761xNKKpZJp/92Jtle0r01e4DL1OoU9MCCATK4rz1+xEKD8/n9GjR9O5c2deeukljh8/3hJxeQRZX8w1JUQG4uetp7CsioMyzb9LO1VcQcpJa6tsiLbBiGbpFysr0Qv7aHYi9OOPP3L8+HH+8Y9/MH/+fOLi4rj88sv57rvvqKysbIkYa3j//feJi4vDx8eHwYMHs2HDhgb3//bbb0lISMDHx4devXrxyy+/tHiMTWVb20gSIZdi0MvEiu5ia/WXaMfW/oT4SausK7HOxC+fQXG+zqlGqHXr1kyfPp1t27axfv16OnXqxK233kp0dDQPPfQQBw4csHecAMyfP5/p06fz9NNPs2XLFhITE0lKSiIrK6vO/desWcOECRO444472Lp1K+PHj2f8+PHs3LmzReJrjiqTmW3H8gCZUdoV9ZerUbdgbZWVz6DrsdZVHskpIaeoXONohCs7r2Lp9PR0li5dytKlS9Hr9VxxxRXs2LGD7t278+abb9orRps33niDu+66i9tuu43u3bvz4Ycf4ufnx6efflrn/m+//TZjxozhn//8J926deP555+nX79+vPfee3aPrbn2ZhRSUmEi0Gigc5sArcMRzWRtxdssV6MuzTZ9hbTKupxgPy/buVMGLojz0exEqLKyku+//56rrrqK2NhYvv32W6ZNm8aJEyeYN28ev//+OwsWLOC5556za6AVFRVs3ryZ0aNHnw5ep2P06NGsXbu2zsesXbu2xv4ASUlJ9e4PUF5eTkFBQY2flmBtku/TPkTmLXFB1uVQDmUXk1dSoXE04lxUmcxsS8sHTtebCNdiTWClZdZ1fbEulUe/28a6wzmaxdDsRCgqKoq77rqL2NhYNmzYwKZNm7j33ntrTF89atQoQkJC7BknJ0+exGQyERERUWN7REQEGRkZdT4mIyOjWfsDvPzyywQHB9t+YmJizj/4OkihtGsL8/cmvpVlzpmtaXnaBiPOyd6MQkorTQT6GOjUWlplXZG1S1NaZl3XrzszWLDpGAeytBt40uxE6M033+TEiRO8//779OnTp859QkJCSElJOd/YNDFz5kzy8/NtP2lpaS1ynJsHxzL1ks5cnNCmRZ5ftDzb1aichF2SrVU2RlplXZV1Rv7tx/KoNJm1DUY0m8msklx9IWktftdCs2eWvvXWW1sijka1atUKvV5PZmZmje2ZmZlERkbW+ZjIyMhm7Q9gNBoxGo3nH3AjBsWHMSg+rMWPI1pOv9gQvt9yTJrlXZQUSru+Dq0CCPb1Ir+0kj3pBfRuF6J1SKIZDmQVUlRehZ+3nq4RgZrF4TIzS3t7e9O/f3+WLVtm22Y2m1m2bBlDhw6t8zFDhw6tsT/A0qVL691fiOawtgglH83DJBMruhxrga10T7sunU6hrwyjd1nWKWS0XnTcZRIhgOnTp/PJJ58wb9489uzZwz/+8Q+Ki4u57bbbAJg4cSIzZ8607f/ggw+yZMkSZs+ezd69e3nmmWfYtGkTU6ZM0eolCDci0/y7rpNF5Rw9VYKiWAYsCNfVv70seeOqznfRcXtxqUVXb7zxRrKzs3nqqafIyMigT58+LFmyxFYQffToUXS607ndsGHD+Prrr3niiSf417/+RefOnfnxxx/p2bOnVi9BuBHrNP+rDp5ky9FcukcHNf4g4RSsrQed2wQQ5OOlcTTifPSTgmmX5SzTV7hUIgQwZcqUelt0li9fXmvb9ddfz/XXX9/CUQlP1a/96UTo70NitQ5HNJF0i7mPxJgQdAoczysls6CMiCAfrUMSTZBXcsai4xp/Dl2qa0wIZ2Nb70iuRl2K9fcl8we5vgCjga6RltZY+Ry6jq3VFyPxrfwJ03jRcUmEhDgPMs2/66k0mdl+PA+QFiF3YVt3TEZwugzr76qvE9ToSSIkxHkI9vOiU/U0/1ulWNMl7EkvoKzSTLCvFx2qJ8UUrk0mVnQ9zlIfBJIICXHerKNWNsvVqEuwdp/0leVt3Ib1y3Tn8QLKq0waRyMaYzKrJDtRnZ4kQkKcJ+vQT6lPcA2bnegELOwjNtyPcH9vKkxmdh5vmfUhhf3szyykuMKEv7eerpHaTaRoJYmQEOfJ+oW6/Vi+TPPvArbIjNJuR1EU28ijrdIy6/Ss3WKJMSHonaBVVhIhIc5Tx9YBBPkYKK00sTddJlZ0ZlkFZRzPK0WnWE7Cwn1YE1spmHZ+W52sVVYSISHOk2WafzkJuwLr78c6K7hwH9aRY5tTc1FVWfLGmTnLjNJWkggJYQf9JBFyCbaJFKVbzO30bheCQaeQWVDOifwyrcMR9ThzIsU+Mc7xOZRESAg7sF7ZyPBd52b9/ThLk7ywH19vvW2ZG/kcOi9nmkjRShIhIeygT0wIigLHckvJKpSrUWdUUWVmx/F8QAql3ZWtZVYSIaflTBMpWkkiJIQdBPp40TXCMgx0S2qetsGIOu06kU9FlZkwf2/iwv20Dke0gH5SMO30nGkiRStJhISwEymYdm7W+qC+MSEoivZDdoX9WQumd58ooLRCJlZ0Ns42kaKVJEJC2El/WYDVqZ0eqeI8J2BhX21DfIkIMlJlVtl+LE/rcMRZnG0iRStJhISwE+vV6Pbjli4Y4VzOXFpDuCdFUc4YwZmnbTCiFmsRu7NMpGgliZAQdhLfyp9QPy8qqszsOpGvdTjiDOn5paTnl6HXKSS2C9E6HNGCZGJF52W9GBngZK2ykggJYSdyNeq8rAXsCZGB+MtEim6t7xkjx2RiReeyybq8TVyYxpHUJImQEHYko1ackzOOVBEto2fbILz1OnKKKzh6qkTrcES1rMIyjp4qQVEs0404E0mEhLAja/3JVimYdiq2iRSdZEp/0XKMBj0928rEis7G2irbpU0gwb5e2gZzFkmEhLCjxHaWIsAT+WWk55dqHY4AyipNtpotaRHyDFIn5Hw2p54CoH+c830GJRESwo78jQYSImViRWey60Q+lSaVVgHetA+TiRQ9gTXh3SyfQadhbZ3r74QXI5IICWFnsgCrc7EmpH3bh8pEih7CWqu3L6OAovIqjaMRZZUmdh4vAJxzeRtJhISwM+sHXeoTNGI2QcpK2PEdpKxka+pJQLrFPElEkA9tQ3wxq7AtLU/rcDzezuP5VJjMtArwJtYJl7eRcaRC2Jn1C3fXiXzKKk34eOk1jsiD7F4ES2ZAwQnbpqcJx6S7lX7th2gYmHC0frGhHM8rZUtqLsM7tdI6HI9mG6zgpK2y0iIkhJ3FhPnSKsCbSpMqEys60u5FsGBijSQIoLWawxyvt+hTtFKjwIQW+leP4JQuau3Z6oOcsFsMJBESwu4URbFN6ibdYw5iNllagqg9gZ5OARQw/v4vy37CI5ye0ysPs1kmVtSKqqq28+AAJxwxBpIICdEiTi/AmqdtIJ4idU2tlqAz6QAKjlv2Ex6hW1QQPl468ksrOXyyWOtwPFZqTgk5xRV463X0iA7WOpw6SSIkRAuwDd89KtP8O0RRpn33Ey7PS6+jd/W6clukZVYz1tagnm2DnLZeUhIhIVpA73bBGHQK2YXlHMuViRVbXECEffcTbkEmVtTeJlu3mHOtL3YmSYSEaAE+Xnp6RFum+ZeTsAPEDoOgaKDuESkqCgS1tewnPEY/qdXT3JZU51/nTxIhIVqItWB6q6xE3/J0ehjzavWNmsmQat0y5hXLfsJj9KseOXYgq4j80kptg/FA+aWV7M8qBJx7nT9JhIRoIf1kYkXH6j4ObvgcgqJqbC42Rli2dx+nUWBCK+EBRuKqJ/DbKi2zDrf1aC6qCu3D/GgT6KN1OPWSREiIFmKtT9iTXkBphQzbdoju42DaTswT/8sMpnJTxRMcvmWtJEEe7Mxh9MKxrN1iA5x0/iArSYSEaCHRwT5EBBmpMqtsP5andTieQ6fngF9f5pcNYZu+F93aOvdJWLQs29p/0jLrcJurW+H6SSIkhGdSFKXGMHrhOJtSTwHQJyYEL72c5jyZ9TOYnJaHSSZWdJgqk5nk6lY4Z51R2krOEEK0IJlYURubj1gSz4FOOpOtcJyukYH4e+spKq/iQHXhrmh5ezMKKa4wEWg00CUiUOtwGiSJkBAtyDpybItMrOhQ1rlL+jvx3CXCMfQ6hT7Vo8dk4ILjWKcN6dM+BL3O+RZaPZMkQkK0oJ5tgzAadJwqrpBp/h0kq6CMo6dKUBToW/0FKDxb//bSMutozr7Q6pkkERKiBRkNehJjQgDYdOSUtsF4CGtrUNeIQIJ8vDSORjiDvjLDtMNtOmIdMeb8rbKSCAnRwqx1KhtS5CTsCJts9UHOfwIWjtEvxvIZTDlZzKniCo2jcX8Z+WUczytFp0BijHMutHomSYSEaGHWL2TrSCbRsjZXv88DpFBaVAv286JTmwBAhtE7grXlrWtkEIEu0CoriZAQLaxfbCiKAqk5JWQVlGkdjlsrqahi14kCwDVqE4TjWOuENkki1OJOd4u5xmdQEiEhWliQjxcJkZYFWOUk3LKS0/KoMqtEBvnQNsRX63CEE7G2EG6UWr0WZ503zVUuRiQREsIBBspJ2CGs8wcNiAtFUZx7yK5wrEHxli7q7cfyKKuUJW9aSmmFiV3H8wFJhIQQZxhgrRM6Ii1CLWmTi6xtJBzPsvCnkUqTSnJantbhuK2tablUmVUigoy0C3WNVllJhIRwAGuL0K4T+RSVV2kcjXsymVVbkeYAGTEmzqIoCgPjrRck0jLbUjamnB616SqtspIICeEAUcG+tA3xxazCVpnLpEXszyyksKwKP289CZHOPaW/0Mag6gR5g7TMthhr97+1K9IVSCIkhINYTwwb5STcIqzdYv3ah2KQhVZFHawF01tSc2UB1hZQaTLbWmUlERJC1GI9CUuzfMuwvq+uUqApHC8hMohAo4Gi8ir2pBdoHY7b2XWigJIKE8G+XnRp4zqtspIICeEg1okVtx7No9Jk1jga96KqKhtSLInQYBe6EhWOpdcp9LfN9C4XJPa2sfo9HRAbis7JF1o9kyRCQjhIp9YBBPt6UVppYvcJuRq1p2O5paTnl2HQKfRtLy1Con7WCxKZysL+NlS/pwNd7GJEEiEhHESnU2zDuuUkbF/Wq/ve7YLx9dZrHI1wZmfW6qmq1AnZi9ms2rqnXW2dP0mEhHCgAXI12iKsidCg+HCNIxHOrne7YLwNOk4WlXMkp0TrcNzGoewicksq8fHS0aut8y+0eiZJhIRwoIG2gmm5GrUna5O81AeJxhgNehLbWb6oN0qdkN1YP4N9Y0LxNrhWauFa0Qrh4npVX43mFFeQcrJY63DcQlZBGSkni1EUbIWwQjRkoG0+IUmE7MXaKutq9UEgiZAQDmU06OnTLgSQ7jF7sX6ZdY8KIsjHS+NohCsYGC9d1PZmbV0b5GL1QSCJkBAOZy3WXH9YTsL2cLo+yPVOwEIb/WNDURRIzSkhq6BM63Bc3rHcEk7kl6HXKfRtH6J1OM0miZAQDja4Q3UilHJK6oTsQOYPEs0V5ONFt8ggQGZ6twdry1rPtsH4Gw0aR9N8kggJ4WD9Y0Mx6BSO55VyLLdU63BcWl5JBXszCgHXG7IrtGUduCDdY+dvQ/VCq4NctEZPEiEhHMzP20Dv6lEr6w7naByNa7NezXds7U94gFHjaIQrsdYJyQzT52+ji84fZCWJkBAaGNzBMt/NOqkTOi8bUiyJpMwfJJrLWtS7J6OA/NJKjaNxXTlF5RzMKgIkERJCNIO1nmV9irQInQ+pDxLnqk2QDx1a+aOq0ip0Pqytsp3bBBDq761xNOdGEiEhNDAgLgy9TuFYbinHcmV223NRVF7Fzuo122TEmDgXQzpaW2blguScmE1kbf+dcbo13ND6CJhNWkd0TiQREkIDAUYDPaunoZdh9OdmS2ouJrNKu1BfokN8tQ5HuKAh1V3Uaw9JItRsuxfBWz2ZuP9+3vF+j7sOTYW3elq2uxhJhITQyBDpHjsvMn+QOF9DOpyuE8orqdA4GheyexEsmIhacKLm9oJ0WDDR5ZIhSYSE0Ij1anS91CecE6kPEuerTaAPHVtLnVCzmE2wZAagotS6s3petCWPuVQ3mSRCQmhkQFwouurZbdPzZT6h5iirNJGclgfIiDFxfmzdY1In1DSpa+DslqAaVCg4btnPRUgiJIRGAn286BEtdULnYuvRPCpMZtoEGokL99M6HOHChnaUqSyapSjTvvs5AUmEhNDQkA5SJ3Qu1h46CcCwjuEoSu0GeiGaanB1i+Ke9AJyi6VOqFEBEfbdzwlIIiSEhqwnYbkabZ7V1aN8hnVspXEkwtW1DjTSuU0AIPV6TRI7DIKi66wQslAgqK1lPxfhMonQqVOnuOWWWwgKCiIkJIQ77riDoqKiBh8zcuRIFEWp8XPvvfc6KGIhGjcwPgxFgZSTxbIKdhMVlVexrbo+yNqtIcT5GNJB5hNqMp0exrwKqJhrrRldnRyNecWyn4twmUTolltuYdeuXSxdupTFixfz119/cffddzf6uLvuuov09HTbz6xZsxwQrRBNE+zrRfcoyyrY6+RqtEk2HjlFlVklJsyXmDCpDxLnTxKhZuo+jmd8HiODs0ZsBkXDDZ9D93HaxHWODFoH0BR79uxhyZIlbNy4kQEDBgDw7rvvcsUVV/D6668THR1d72P9/PyIjIx0VKhCNNvg+HB2nShg3eEcxiXW/7csLKyT3w3rIN1iwj4GV9fq7c0o5FRxBWEuulSEo2QVlDEvrzdfKu+wbVIAAZU5lpqg2GEu1RJk5RItQmvXriUkJMSWBAGMHj0anU7H+vXrG3zsV199RatWrejZsyczZ86kpKTh5QzKy8spKCio8SNES7IWTMvVaNOssRZKd5JuMWEfrQKMdImorhOSz2GjrFMNJESFEJAwCnpdB/EjXDIJAhdJhDIyMmjTpk2NbQaDgbCwMDIyMup93M0338yXX37Jn3/+ycyZM/niiy/4+9//3uCxXn75ZYKDg20/MTExdnkNQtRncHw4igKHs4vJyJc6oYbklVSwq3p9MakPEvYk3WNNZ32PrO+Zq9M0EXrsscdqFTOf/bN3795zfv67776bpKQkevXqxS233MLnn3/OwoULOXToUL2PmTlzJvn5+baftLS0cz6+EE0R7OdF7+p1x1YfPKlxNM5t3eEcVNWy0nWbQB+twxFuZGgHGcHZVNbu6aFukghpWiP08MMPM3ny5Ab36dChA5GRkWRlZdXYXlVVxalTp5pV/zN48GAADh48SMeOHevcx2g0YjQam/ycQtjD8E6t2HYsn1UHT3Jt/3Zah+O01tiGzbvHCVg4j8HVX+r7MgvJKSonPEC+B+qSnl/KkZwSdAoM6uAey9tomgi1bt2a1q1bN7rf0KFDycvLY/PmzfTv3x+AP/74A7PZbEtumiI5ORmAqKioc4pXiJZyQadWfLD8EKsOnkRVVZkksB7WRGiozB8k7CzM35uEyED2ZhSyPuUUV/SS74m6WFuDerYNJsjHS+No7MMlaoS6devGmDFjuOuuu9iwYQOrV69mypQp3HTTTbYRY8ePHychIYENGzYAcOjQIZ5//nk2b97MkSNHWLRoERMnTuTCCy+kd+/eWr4cIWrpFxuK0aAju7CcA1kNz4/lqbIKyjiYVYSinC4wF8KerDUvq6SLul7u1i0GLpIIgWX0V0JCApdccglXXHEFF1xwAR9//LHt/srKSvbt22cbFebt7c3vv//OZZddRkJCAg8//DDXXnst//3vf7V6CULUy8dLz6DqVdRXHZCTcF2sI1V6RAcR4ifDm4X9XdDJ0tIon8H6WT+HQ9yoe9ol5hECCAsL4+uvv673/ri4OFT19DSXMTExrFixwhGhCWEXF3RqxcoDJ1l98CS3XxCvdThOZ81BWVZDtKwhHcMx6BSOniohNaeY2HB/rUNyKmmnSjiWW4pepzAwzn1aZV2mRUgIdze8+mp03eEcKk1mjaNxPmsOW67SZdi8aCkBRgP92ocCsFJahWpZsT8bgH7tQwgwukw7SqMkERLCSXSPCiLUz4viCpNtLS1hkXaqhLRTpRh0CoPc6EpUOJ8RnS0XJCsPZGscifOxvicXdm58kJMrkURICCeh0ykMs9YoSLFmDdbZpPvEhODvRleiwvmM6GL5kl9zKIcqaZm1qTKZbd3T1vfIXUgiJIQTkWLNuq0+aB02L91iomX1ahtMsK8XhWVVbDuWr3U4TiM5LY/C8ipC/LzoVT0BrLuQREgIJ2JNhLam5VFYVqlxNM7BZFZtTfIj3KxJXjgfvU5hePU6dnJBctpf1e/F8E6t0Ovca54zSYSEcCIxYX60D/PDZFbZkCJT/QPsOJ5PbkklgUYDfduHaB2OcFdmE6SshB3fcW3YEXSYpU7oDH9VF0pf5IYXI9LZLoSTGd6pFUc3HGXVwZNc0i1C63A0t2Kf5QQ8vFMrvPRy7SZawO5FsGQGFJwA4BJglTGM549NoqBsoNvMoHyu8koq2H4sD4ARXdxv+go5qwjhZKzdY7IAq8WK/ZZ1Bi/q6n5XosIJ7F4ECybakiCrSOUU7xve5PCK/9MoMOex+mAO5urFjqOCfbUOx+4kERLCyQzrGI6iwP7MIrIKyrQOR1N5JRUkV08lcJGbjVQRTsBssrQEoda6y/rlGLfpect+Hszda/QkERLCyYT6e9Mz2jIqw9OH0a86eBKzCl0iAogOcb8rUaGx1DW1WoLOpFMgpDLLsp+HUlXVNrnkhW7YLQaSCAnhlC6ontTNOpOrp7LWB0lrkGgRRZlN2i0n42gLB+K8DmUXczyvFG+DjsHx7jl9hSRCQjihkdVf/H/tz8Zkrt1s7wlUVbUlghd1aaNxNMItBTRtMMLWXJ8WDsR5WbvFBsWF4eut1zialiGJkBBOqF9sKIFGA7kllbbRGp5mb0YhWYXl+HrpGRAXqnU4wh3FDoOgaKDueXFU4IQazg85MQ4Ny5lYh81blx5xR5IICeGEvPQ6W/fY8n2e2T1mfd1DO4bj4+WeV6JCYzo9jHm1+sbZyZACKDxbeSurDuV65HIb5VUm1h22zGfmroXSIImQEE5rZPVw8eUeWidkGzYv9UGiJXUfBzd8DkFRNbcHRWO+bh7rjMMpKKuyjV70JJuP5FJaaaJVgJFuUYFah9NiZEJFIZyUtS5m+7E8corKCQ8wahyR4xSVV7HpSC5wOiEUosV0HwcJV1pGhxVlWmqHYoeh1+m5cMdW/rvtBH/szWJAXJjWkTrUCttq861QFPdaVuNM0iIkhJOKDPYhITIQVcU2fNVTrDl4kiqzSly4H7Hh/lqHIzyBTg/xI6DXdZZ/dZbu2IsTLIn4H3uztIxOE8v3Vg9WcPOLEUmEhHBiI7taWoX+3OdZJ+HTo8Xc+wQsnN9FXdqgKJbi/RN5pVqH4zBpp0rYl1mITnH/z6EkQkI4sYsTLInQ8n3ZHlOsWWPYvJtfiQrnF+bvTd+YEMCzLkisr3VAbBghft4aR9OyJBESwon1ax9CiJ8X+aWVbE7N1TochziUXcyx3FK89TqGdHDPCdyEa7FekPzpQd1jy/ZYXuvF3dx/Di9JhIRwYga9jlHV3WPLPOQk/Psey2y/gzuE4ect4zmE9kZVJ0KrD+ZQVun+646VVFSx9nAOAJckSCIkhNDYJdVXZNYEwd39vtvyOi/r3rRZf4Voad2jgogM8qG00sS66gTBna0+mENFlZmYMF86tQnQOpwWJ4mQEE7uwi6tMegUDmcXk3KyWOtwWlROUTmbj1q6AC/pJomQcA6KotguSJbudv8Lkj/2Wl7jJQkRbj1s3koSISGcXJCPF4M7WOYvWebmrUJ/7M1CVaFHdJCsNi+cyqXVLZS/78nEbFbBbIKUlbDjO8u/ZvfoMjObVVt90CgP6BYDmVBRCJdwSUIEqw/m8PueTO4c0UHrcFqMtftvtLQGCScztGM4AUYDmQXlpK7+hviNz0HBidM7BEVbluvoPk67IO1ga1oeWYXlBBoNDPWQwQrSIiSEC7AmBhuP5JJbXKFxNC2jrNLEX/stE0deKvVBwskYDXou6tqaJN0G4pbdWzMJAihIhwUTYfcibQK0k992ZwAwMqEN3gbPSBE841UK4eLahxq5odURrmQ121ctdptm+DOtOXSS0koTkUE+9IgO0jocIWq5rFsrnvb6vJ57Vcs/Sx5z2c+nqqr8tsvSKpvUw3MuRqRrTAhnt3sRLJnBrKIT4A2sBXa5RzP8mX7dWd0t1r2NRxRoCtdzid8hApRTDeyhQsFxy5pl8SMcFpe9HMgqIuVkMd56nW1We08gLUJCOLPdiyzN7Wc1w6tu0gxvVWUy25rkL+8Z1cjeQmgjoKKJQ+eLXHNQw2+7LJ/B4Z0s9VCeQhIhIZyV2QRLZmBrcj+D4gbN8Gdan3KK3JJKQv28GBzvWSt8CxcS0MTuoqbu52R+tXWLRWociWNJIiSEs0pdU7sgs4YzmuFd3P92pgNwWfdIDHo5LQknFTsMU0AU5trXJtUUCGoLscMcGZVdHMstYcfxfHQKjPawwQpyxhHCWTW1ed1Fm+GtTGbVdiV6eS/PuhIVLkanR3/FLBSFOpKh6rq2Ma+ATu/oyM7b/3ZYusUGxIXRKsCocTSOJYmQEM7KzZvhrTan5pJdWE6gj4FhHVtpHY4QDes+jj97zyaDs7pwg6Lhhs9ddgDD4u2W1uexvT2vRs9zqqGEcDWxwywn14J06qoTUgHFRZvhz2TtFru0W4THzFsiXFuP0X9n2MYIBip7mTOuLaERMZbPoQu2BAGknSph2zFLt9gYDxysIGcdIZyVTm8ZIg/Ymt2rmVVQVSgf/ZLLnnzB0i32yw5LInR5L887AQvXFBHkQ7/YcNaZu7OwaqhlqLwLfw5/rv4MDukQTutAz+oWA0mEhHBu3cdZmtuDaiYJ2bpW/KNyGr8zWKPA7GN9Sg6ZBeUE+Ri4sIt0iwnXcUV14m5NIlzZz9str+Gq3tEaR6IN6RoTwtl1HwcJV1pGhxVlQkAE8/aG8euKIyjbTnClC/fp/3ebpS7hil5RGA2ue0UtPM8VvaJ4bvFuNqfmknaqhJgwP61DOidHThaz43g+ep3iUbNJn0lahIRwBTq9pfm913UQP4KrEmMA+GNfFoVllRoHd27Kq0z8Uj1SZVwfz7wSFa4rIsiHYR0ti5L+lHxc42jOnfViZFjHcMI9bLSYlSRCQrigblGBdGztT0WV2bY2kKv5a/9J8ksraRNoZHC8Z6xyLdzL1X3aArBw63FUtd7JhZyWqqos3GpJ4qyvxRNJIiSEC1IUhXGJp0/CrmhR9ZXo2MRo9DpZW0y4njE9IzEadBzKLmbXiQKtw2m2bcfyOXyyGB8vHWN6eu4cXpIICeGi/tbPkgitPnSSE3mlGkfTPIVllSytXltsXKJ0iwnXFOTjxehulrqaH13wguSHLccAy5IanrS22NkkERLCRcWE+TE4PgxVdb1WoZ+3p1NWaaZja396twvWOhwhztnV1fVti7adwFT/2htOp6LKbKsPuqav53aLgSRCQri0a/u3A+D7Lcdcqkbh282WK9HrB8SgKNItJlzXyK5tCPHzIquwnL8OZGsdTpOt2J9NbkklrQKMXNDJs6eukERICBd2Ra8ofL30HM4uJjktT+twmuRgVhGbU3PR6xT+5uFXosL1eRt0thaV+RvSNI6m6b7bbIl1fJ9oj1/o2LNfvRAuLsBosBU5flfdyuLsvtt8DB1m7ok5TpvUxZCyEswmrcMS4pzdONAyncXvezI5WVSucTSNyyosY9meLACuG9BO42i0J4mQEC7uuurusZ+ST1BcXqVxNA2rMpk5tek7Vhmn8mjmI/D9HTDvKnirJ+xepHV4QpyThMggEmNCqDKrtgJkZ/bd5mNUmVX6tQ8hITJI63A0J4mQEC5uaIdw4sL9KCqvshU/Oqtdy77klarXiFRO1byjIB0WTJRkSLisGwdYWoXmb0xz6no9s1nlm+ouvAmD2mscjXOQREgIF6fTKdw82HJC+3J9qvOehM0m2q1/FqjrxFMd85LHpJtMuKSxiZZ6vUPZxWxKzdU6nHqtOZTD0VMlBPoYPHZtsbNJIiSEG7iufwzeBh07jxew/Vi+1uHUKX37H4SbTlL/3IkqFBy3rKkmhIsJ9PFibKJl3b/P16ZqHE39/m/DUcAyZN7XW9b3A0mEhHALYf7eXFm9GvZX653zJLx2266m7VjkmkuGCDFxaBwA/9uRTmZBmbbB1CEjv4xfd1kmMr1poHSLWUkiJISbuKW6e2zRthPkFldoHE1NpRUmFqeYm7ZzgGeugC1cX8+2wQyKC6PKrPLVOue7IPl87RGqzCqD4sPoHi1F0laSCAnhJvrHhtIjOoiySrPTtQr9lHyc5WWdyVLCUamvb0yBoLYQO8yhsQlhT5OGxQHw9YajlFc5T71baYWJr6u7xW4fHq9xNM5FEiEh3ISiKNx9YQcA5q5JpazSOU7CZrPKxysPY0bHth4zq9Ogs5Oh6ttjXgGd1C0I13VZjwiign04WVTB4m3pWodj88PWY+SVVBIT5sul3aXV9UySCAnhRq7oFVV9Ei5nUbJzDKVfuieTw9nFBPoYGHLVZLjhcwiKqrlTULRle/dxmsQohL146XX8fUgsAJ+sPIzZCdYfM5tVPl2VAsDkYfHo6x+x4JEkERLCjXjpdbZm74+d4CSsqiofrjgEwK1DYgn08bIkO9N2wqTFcO1/LP9O2yFJkHAbfx8cS4DRwN6MQpbtzdI6HP7Ym8Wh7GICjAZukJmka5FESAg3c9OgGAKNBg5mFWl+Et6QcoqtR/PwNui47cy6BJ0e4kdAr+ss/0p3mHAjwX5etlah9/48qOncXqqq8vayAwD83XoxImqQREgINxPo48WtQy0n4TeX7te0Vej95ZbWoOv6t6N1oFGzOIRwtDsuiMdo0LEtLY81h3I0i2P5vmx2HM/H10vPXSOkSLoukggJ4YbuGtGBAKOB3ekF/LY7Q5MYNqSc4q/92Rh0CvdUF3EL4SlaBxptS1i8s+yAJq1CZ7YG3To0lvAAuRipiyRCQrihUH9vbh8eB8CbSw84vFVIVVVmLdkLwA0DY4gN93fo8YVwBndf2AFvvY71KadYvj/b4cdfsT+b5LQ8fLx03DVCLkbqI4mQEG7qjhEdCPQxsC+zkP9ud+wIsj/2ZrEpNRejQcfUizs79NhCOIvoEF8mV1+QvPq/vZgceEFSZTLz8i+Wi5G/D46VrukGSCIkhJsK9vXi7uqrwFlL9lFa4Zh5hUxmldd+3QfA5GFxRAb7OOS4Qjij+0d2ItjXi70Zhfyw5ZjDjjt/Uxr7MgsJ9vViysWdHHZcVySJkBBu7M4RHWgb4svxvFLmVA9jb2lfrU9lb0YhgT4G7r2oo0OOKYSzCvbz4v5Rls/B7N/2U1JR1eLHLCir5I3f9gMwbXRnQvy8W/yYrkwSISHcmK+3nieu7AbAhysOkXaqpEWPl1VYxmtLLK1B/0zqSqi/nICFmDg0jrYhvmQUlPHm0v0tfrz3/jhITnEFHVr524bxi/pJIiSEmxvTM5JhHcOpqDLzzKJdLTp65aWf91BYXkXvdsHcMlhOwEIA+HjpeWF8TwD+syqFHcfyW+xY29Ly+PfKwwA8fmU3vPTyNd8YeYeEcHOKovDsuB546RWW7c3i203nUKdgNkHKStjxneVfc+16oz/3ZfFj8gkUBV4Y31Om8RfiDKMS2jAuMRqzCjO+306lyWz3Y5RVmnjk222YVRiXGM0l3WRNsaaQREgID9A5IpCHL+sKwLP/3cXRnDq6yOpLdnYvgrd6wryr4Ps7LP++1dOyvVpWQRmPLNgGwKShcfRuF9LSL0kIl/PU2O6E+HmxO72Ad6rn97Gnt5cd4EBWEa0CvHlmXA+7P7+7kkRICA9x14gODIoPo7jCxEMLkqmoOuOKtL5k57cnYcFEKDhr+H1BumX77kWYzSoPLUgmp7iChMhAHrs8wbEvTAgX0SrAyLPVCcq7fxzkTzsugfPn3izbun4vjO9FmNTnNZkkQkJ4CL1OYfb1iQQaDWxOzeXxhTss9UK7F9WT7JyANe8AddUUVW9b8hhvLd3D6oM5+Hrpee/mvvh4ybphQtTn6j5tubW6gHna/GS7DGA4lF3E1P/biqrChEHtGdMz8ryf05NIIiSEB4kJ8+OdCX3RKfDt5mPM+XM/LJlB3clOY1QoOM6GFT8D8Pz4nnRqE2jXeIVwR09c1Y3EmBDySyuZ/NkGsgvLz/m5ThVXcNfnmygsr2JgXKitxUk0nSRCQniYUQltbCfLv35fVLslqJnakMdDo7twXf929ghPCLdnNOj54JZ+RAf7cCi7mFv+vY6couYnQ5kFZdz40VoOZxcTHezDB7f0x9sgX+vN5TLv2IsvvsiwYcPw8/MjJCSkSY9RVZWnnnqKqKgofH19GT16NAcO2L9ATQhXc+vQOO65qANtyDvv5+rbvStTL5GZa4VojrYhvnx91xAigozszyzixo/XcSCzsMmPP5xdxA0freVAVhGRQT58fsdgWUbjHLlMIlRRUcH111/PP/7xjyY/ZtasWbzzzjt8+OGHrF+/Hn9/f5KSkigrK2vBSIVwDY+NSeCSgb3P+fFmFfK92jDppptRFBkqL0RzxbXytyVDB7OKGPveKr7ZcLTBNcnMZpXPVqdwxTsrSc0pISbMl2/vHUqnNgEOjNy9KGpLzq7WAubOncu0adPIy8trcD9VVYmOjubhhx/mkUceASA/P5+IiAjmzp3LTTfd1KTjFRQUEBwcTH5+PkFBQecbvhDOxWyidFY3jKWZ1Dftj+UEoaCcUUdkVi3zEyk3fA7dxzkiUiHcVnZhOdMXJLPywEkAOrTy544R8Qzr2IrYMD/MqkpmYTm/7szgm41H2Z9ZBMCwjuG8eWMfIoJkPb+6NPX72+DAmBwqJSWFjIwMRo8ebdsWHBzM4MGDWbt2bb2JUHl5OeXlp/tqCwoKWjxWITSz92d8lQqoIwmyXpR+YrqKsfo1RCunbPdVBUThfeUsSYKEsIPWgUbm3TaIT1Ye5v0/D3L4ZDGPL9wJgNGgo9Jk5sxGIj9vPTMvT+CWwbHoZOLS8+a2iVBGRgYAERE1Z9aMiIiw3VeXl19+mWeffbZFYxPCKViHzdczYiyPAGZW3smv5kF87jeJCRHHuKw9dOnUGe/YYaCTYfJC2ItOp3DPRR25ZUgs32w4yqJtJ9iXUUh59XxfBp1C9+ggrh8Qw7jEaIJ9vTSO2H1omgg99thjvPrqqw3us2fPHhISHDdB28yZM5k+fbrtdkFBATExMQ47vhAOYTY1OGxeBUICg3juzkd5TqenTaBR6oCEcIAAo4E7R3TgzhEdMJlVjuWW4Outp5W/UVp/WoimidDDDz/M5MmTG9ynQ4cO5/TckZGWCaUyMzOJioqybc/MzKRPnz71Ps5oNGI0SuW9cHOpaxocNq8AStEJInK3QPwIx8UlhLDR6xRiw/21DsPtaZoItW7dmtatW7fIc8fHxxMZGcmyZctsiU9BQQHr169v1sgzIdxSUaZ99xNCCBflMsPnjx49SnJyMkePHsVkMpGcnExycjJFRUW2fRISEli4cCFgGdEybdo0XnjhBRYtWsSOHTuYOHEi0dHRjB8/XqNXIYSTCGjiqtRN3U8IIVyUyxRLP/XUU8ybN892u2/fvgD8+eefjBw5EoB9+/aRn59v2+fRRx+luLiYu+++m7y8PC644AKWLFmCj48MNRQeLnYYBEVbFk+ts05IsdwfO8zRkQkhhEO53DxCjibzCAm3ZRs1BjWToeqCTJkjSAjhwpr6/e0yXWNCCDvrPs6S7ARF1dweFC1JkBDCY7hM15gQogV0HwcJV1pGkRVlWmqCZI4gIYQHkURICE+n08sQeSGEx5KuMSGEEEJ4LEmEhBBCCOGxJBESQgghhMeSREgIIYQQHksSISGEEEJ4LEmEhBBCCOGxJBESQgghhMeSREgIIYQQHksSISGEEEJ4LJlZuhHWNWkLCgo0jkQIIYQQTWX93m5sbXlJhBpRWFgIQExMjMaRCCGEEKK5CgsLCQ4Orvd+RW0sVfJwZrOZEydOEBgYiKIodnvegoICYmJiSEtLIygoyG7P6y7k/WmYvD8Nk/enYfL+1E/em4a50vujqiqFhYVER0ej09VfCSQtQo3Q6XS0a9euxZ4/KCjI6f+YtCTvT8Pk/WmYvD8Nk/enfvLeNMxV3p+GWoKspFhaCCGEEB5LEiEhhBBCeCxJhDRiNBp5+umnMRqNWofilOT9aZi8Pw2T96dh8v7UT96bhrnj+yPF0kIIIYTwWNIiJIQQQgiPJYmQEEIIITyWJEJCCCGE8FiSCAkhhBDCY0kipJH333+fuLg4fHx8GDx4MBs2bNA6JKfw8ssvM3DgQAIDA2nTpg3jx49n3759WofllF555RUURWHatGlah+I0jh8/zt///nfCw8Px9fWlV69ebNq0SeuwnILJZOLJJ58kPj4eX19fOnbsyPPPP9/oOkzu6q+//mLs2LFER0ejKAo//vhjjftVVeWpp54iKioKX19fRo8ezYEDB7QJVgMNvT+VlZXMmDGDXr164e/vT3R0NBMnTuTEiRPaBXweJBHSwPz585k+fTpPP/00W7ZsITExkaSkJLKysrQOTXMrVqzg/vvvZ926dSxdupTKykouu+wyiouLtQ7NqWzcuJGPPvqI3r17ax2K08jNzWX48OF4eXnxv//9j927dzN79mxCQ0O1Ds0pvPrqq8yZM4f33nuPPXv28OqrrzJr1izeffddrUPTRHFxMYmJibz//vt13j9r1izeeecdPvzwQ9avX4+/vz9JSUmUlZU5OFJtNPT+lJSUsGXLFp588km2bNnCDz/8wL59+xg3bpwGkdqBKhxu0KBB6v3332+7bTKZ1OjoaPXll1/WMCrnlJWVpQLqihUrtA7FaRQWFqqdO3dWly5dql500UXqgw8+qHVITmHGjBnqBRdcoHUYTuvKK69Ub7/99hrb/va3v6m33HKLRhE5D0BduHCh7bbZbFYjIyPV1157zbYtLy9PNRqN6v/93/9pEKG2zn5/6rJhwwYVUFNTUx0TlB1Ji5CDVVRUsHnzZkaPHm3bptPpGD16NGvXrtUwMueUn58PQFhYmMaROI/777+fK6+8ssbfkIBFixYxYMAArr/+etq0aUPfvn355JNPtA7LaQwbNoxly5axf/9+ALZt28aqVau4/PLLNY7M+aSkpJCRkVHjMxYcHMzgwYPlPF2P/Px8FEUhJCRE61CaTRZddbCTJ09iMpmIiIiosT0iIoK9e/dqFJVzMpvNTJs2jeHDh9OzZ0+tw3EK33zzDVu2bGHjxo1ah+J0Dh8+zJw5c5g+fTr/+te/2LhxI1OnTsXb25tJkyZpHZ7mHnvsMQoKCkhISECv12MymXjxxRe55ZZbtA7N6WRkZADUeZ623idOKysrY8aMGUyYMMElFmI9myRCwmndf//97Ny5k1WrVmkdilNIS0vjwQcfZOnSpfj4+GgdjtMxm80MGDCAl156CYC+ffuyc+dOPvzwQ0mEgAULFvDVV1/x9ddf06NHD5KTk5k2bRrR0dHy/ohzVllZyQ033ICqqsyZM0frcM6JdI05WKtWrdDr9WRmZtbYnpmZSWRkpEZROZ8pU6awePFi/vzzT9q1a6d1OE5h8+bNZGVl0a9fPwwGAwaDgRUrVvDOO+9gMBgwmUxah6ipqKgounfvXmNbt27dOHr0qEYROZd//vOfPPbYY9x000306tWLW2+9lYceeoiXX35Z69CcjvVcLOfphlmToNTUVJYuXeqSrUEgiZDDeXt7079/f5YtW2bbZjabWbZsGUOHDtUwMuegqipTpkxh4cKF/PHHH8THx2sdktO45JJL2LFjB8nJybafAQMGcMstt5CcnIxer9c6RE0NHz681lQL+/fvJzY2VqOInEtJSQk6Xc1Tvl6vx2w2axSR84qPjycyMrLGebqgoID169fLebqaNQk6cOAAv//+O+Hh4VqHdM6ka0wD06dPZ9KkSQwYMIBBgwbx1ltvUVxczG233aZ1aJq7//77+frrr/npp58IDAy09ccHBwfj6+urcXTaCgwMrFUr5e/vT3h4uNRQAQ899BDDhg3jpZde4oYbbmDDhg18/PHHfPzxx1qH5hTGjh3Liy++SPv27enRowdbt27ljTfe4Pbbb9c6NE0UFRVx8OBB2+2UlBSSk5MJCwujffv2TJs2jRdeeIHOnTsTHx/Pk08+SXR0NOPHj9cuaAdq6P2JioriuuuuY8uWLSxevBiTyWQ7V4eFheHt7a1V2OdG62Frnurdd99V27dvr3p7e6uDBg1S161bp3VITgGo8+ezzz7TOjSnJMPna/rvf/+r9uzZUzUajWpCQoL68ccfax2S0ygoKFAffPBBtX379qqPj4/aoUMH9fHHH1fLy8u1Dk0Tf/75Z53nmkmTJqmqahlC/+STT6oRERGq0WhUL7nkEnXfvn3aBu1ADb0/KSkp9Z6r//zzT61DbzZFVT10WlEhhBBCeDypERJCCCGEx5JESAghhBAeSxIhIYQQQngsSYSEEEII4bEkERJCCCGEx5JESAghhBAeSxIhIYQQQngsSYSEEEII4bEkERJCCCGEx5JESAghhBAeSxIhIYRHyc7OJjIykpdeesm2bc2aNXh7e9dYbVwI4RlkrTEhhMf55ZdfGD9+PGvWrKFr16706dOHq6++mjfeeEPr0IQQDiaJkBDCI91///38/vvvDBgwgB07drBx40aMRqPWYQkhHEwSISGERyotLaVnz56kpaWxefNmevXqpXVIQggNSI2QEMIjHTp0iBMnTmA2mzly5IjW4QghNCItQkIIj1NRUcGgQYPo06cPXbt25a233mLHjh20adNG69CEEA4miZAQwuP885//5LvvvmPbtm0EBARw0UUXERwczOLFi7UOTQjhYNI1JoTwKMuXL+ett97iiy++ICgoCJ1OxxdffMHKlSuZM2eO1uEJIRxMWoSEEEII4bGkRUgIIYQQHksSISGEEEJ4LEmEhBBCCOGxJBESQgghhMeSREgIIYQQHksSISGEEEJ4LEmEhBBCCOGxJBESQgghhMeSREgIIYQQHksSISGEEEJ4LEmEhBBCCOGxJBESQgghhMf6f7dC5m+4g7oRAAAAAElFTkSuQmCC", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Setup: Import modules\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autora.theorist.bms import BMSRegressor\n", + "from autora.experimentalist.sampler.random_sampler import random_sample #Note that this sampler is embedded within the autora-core module and so does not need to be explicitly installed\n", + "\n", + "#Step 0: Defining variables\n", + "ground_truth = lambda x: np.sin(x) #Define a ground truth model that we will attempt to recover - here a sine wave\n", + "initial_X = np.linspace(0, 4 * np.pi, 200) #Define initial data\n", + "\n", + "#Step 1: EXPERIMENTALIST: Sample using the experimentalist\n", + "new_conditions = random_sample(initial_X, n = 20)\n", + "new_conditions = np.array(new_conditions).reshape(-1,1) #Turn variable into a 2D array\n", + "\n", + "#Step 2: EXPERIMENT RUNNER: Define and then obtain observations using the experiment runner\n", + "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape) #Define the runner, which here is simply the ground truth with noise\n", + "new_observations = run_experiment(new_conditions) #Obtain observations from the runner for the conditions proposed by the experimentalist\n", + "new_observations = new_observations.reshape(-1,1) #Turn variable into a 2D array\n", + "\n", + "#Step 3: THEORIST: Initiate and fit a model using the theorist\n", + "theorist_bms = BMSRegressor(epochs=100) #Initiate the BMS theorist\n", + "theorist_bms.fit(new_conditions, new_observations) #Fit a model to the data\n", + "\n", + "#Wrap-Up: Plot data and model\n", + "sort_index = np.argsort(new_conditions, axis=0)[:,0] #We will first sort our data\n", + "new_conditions = new_conditions[sort_index,:]\n", + "new_observations = new_observations[sort_index,:]\n", + "\n", + "plt.plot(initial_X, ground_truth(initial_X), label='Ground Truth')\n", + "plt.plot(new_conditions, new_observations, 'o', label='Sampled Conditions')\n", + "plt.plot(new_conditions, theorist_bms.predict(new_conditions), label=f'Bayesian Machine Scientist ({theorist_bms.repr()})')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Sine Function')\n", + "plt.legend()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "***WARNING:*** *Do not stop here! We have now shown you the three components and how they work together. At this point, it may be tempting to start working on your own project, but we urge you to continue through the tutorials. ``autora`` has a lot of embedded functionality that you are going to want to use, and this toy example has stripped those away. So, keep going and see how much ``autora`` has to offer!*" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Experiment Runners\n", + "\n", + "``autora`` provides support for experiment runners, which serve as interfaces for conducting both real-world and synthetic experiments. An experiment runner typically accepts experiment conditions as input (e.g., a 2-dimensional numpy array with columns representing different independent variables) and produces collected observations as output (e.g., a 2-dimensional numpy array with columns representing different dependent variables). These experiment runners can be combined with other ``autora`` components to facilitate closed-loop scientific discovery.\n", + "\n", + "\"AutoRA\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Types\n", + "\n", + "AutoRA offers two types of experiment runners: **real-world experiments** and **synthetic experiments**.\n", + "\n", + "For **real-world experiments**, experiment runners can include interfaces for various scenarios such as web-based experiments for behavioral data collection (e.g., using [Firebase and Prolific](https://autoresearch.github.io/autora/user-guide/experiment-runners/firebase-prolific/)) or experiments involving electrical circuits (e.g., using [Tinkerforge](https://en.wikipedia.org/wiki/Tinkerforge)). These runners often require external components such as databases to store collected observations or servers to host the experiments. You may refer to the respective tutorials for these interfaces on how to set up all required components.\n", + "\n", + "**Synthetic experiments** are conducted on synthetic experiment runners, which are functions that take experimental conditions as input and generate simulated observations as output. These experiments serve multiple purposes, including *testing autora components* before applying them to real-world experiments, *benchmarking methods for automated scientific discovery*, or *conducting computational metascientific experiments*.\n", + "\n", + "In this introductory tutorial, we primarily focus on simple synthetic experiments. For more complex synthetic experiments implementing various scientific models, you can utilize the [autora-synthetic](https://github.com/autoresearch/autora-synthetic/) module." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Usage\n", + "\n", + "To create a synthetic experiment runner, we begin with **defining a ground truth** from which to generate data. Here, we consider a simple sine function:\n", + "\n", + "$y = f(x) = \\sin(x)$\n", + "\n", + "In this case, $x$ corresponds to an *independent* variable (the variable we can manipulate in an experiment), $y$ corresponds to a *dependent* variable (the variable we can observe after conducting the experiment), and $f(x)$ is the *ground-truth function* (or \"mechanism\") that we seek to uncover via a combination of experimentation and model discovery.\n", + "\n", + "However, we assume that observations are obtained with a measurement error when running the experiment.\n", + "\n", + "$\\hat{y} = \\hat{f}(x) = f(x) + \\epsilon, \\quad \\epsilon \\sim \\mathcal{N}(0,0.1)$\n", + "\n", + "where $\\epsilon$ is the measurement error sampled from a normal distribution with zero mean and a standard deviation of $0.1$.\n", + "\n", + "The following code block defines the ground truth $f(x)$ and the experiment runner $\\hat{f}(x)$ as ``lambda`` functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ground_truth = lambda x: np.sin(x)\n", + "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we generate a pool of all possible experimental conditions from the domain $[0, 2\\pi]$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "condition_pool = np.linspace(0, 2 * np.pi, 100)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to run a simple synthetic experiment, we can first sample from the pool of possible experiment conditions (without replacement), and then pass these conditions to the synthetic experiment runner:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Y1FNUmOnUYM_", - "outputId": "f3479f02-8f74-4e2d-ebe8-4764109961d8" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.6981317007977318,)\n", - "(0.7615982190520711,)\n", - "(0.8250647373064104,)\n", - "(0.8885312555607496,)\n", - "(0.9519977738150889,)\n", - "(1.0154642920694281,)\n", - "(1.0789308103237674,)\n", - "(1.1423973285781066,)\n", - "(1.2058638468324459,)\n", - "(1.269330365086785,)\n", - "(1.3327968833411243,)\n" - ] - } - ], - "source": [ - "# return first 10 conditions\n", - "for idx, condition in enumerate(new_conditions):\n", - " print(condition)\n", - " if idx > 9:\n", - " break" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "MZnaSr1YUYNA" - }, - "source": [ - "Alternatively, we may use the **random pooler** to randomly draw experimental conditions from the domains of each independent variable. The random pooler requires as input a list of discrete values from which to sample from. In this case, we can pass it ``metadata.independent_variables[0].allowed_values`` for the independent variable. We can also specify the input argument ``n`` to obtain 10 random samples." + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "initial_conditions = np.random.choice(condition_pool, size=10, replace=False)\n", + "initial_observations = run_experiment(initial_conditions)\n", + "\n", + "# plot sampled conditions against ground-truth\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", + "plt.plot(initial_conditions, initial_observations, 'o', label='Sampled Conditions')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Sine Function')\n", + "plt.legend()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Certain theorists and experimentalists may need to have knowledge about the experimental variables, such as the domain from which new experiment conditions are sampled. To provide this information, we can utilize a ``VariableCollection`` object. In the context of our synthetic experiment, we have a single *independent variable* (``IV``) denoted as $x$, and a single *dependent* variable (``DV``) denoted as $y$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.variable import DV, IV, ValueType, VariableCollection\n", + "\n", + "# Specify independent variable\n", + "iv = IV(\n", + " name=\"x\", # name of the independent variable\n", + " value_range=(0, 2 * np.pi), # specify the domain\n", + " allowed_values=condition_pool, # alternatively, we can specify the pool of allowed conditions directly\n", + ")\n", + "\n", + "# specify dependent variable\n", + "dv = DV(\n", + " name=\"y\", # name of the dependent variable\n", + " type=ValueType.REAL, # specify the variable type (some theorists require this to optimize)\n", + ")\n", + "\n", + "# Variable collection with ivs and dvs\n", + "metadata = VariableCollection(\n", + " independent_variables=[iv],\n", + " dependent_variables=[dv],\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**: *For expository reasons, we focus in this tutorial on simple synthetic experiments. In general, ``autora`` provides functionality for automating [more complex synthetic experiments](https://github.com/autoresearch/autora-synthetic/), as well as real-world experiments, such as [behavioral data collection via web-based experiments](https://autoresearch.github.io/autora/user-guide/experiment-runners/firebase-prolific/), experiments with electrical circuits via [Tinkerforge](https://en.wikipedia.org/wiki/Tinkerforge), and other automated experimentation platforms.*" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Theorists\n", + "\n", + "The AutoRA framework includes and interfaces with different methods for scientific model discovery. These methods are referred to as *theorists* and are implemented as [sklearn estimators](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html). For general information about theorists, see the respective [AutoRA Documentation](https://autoresearch.github.io/autora/theorist/).\n", + "\n", + "\"Theorist\n", + "\n", + "\n", + "Theorists **take as input a set of conditions and observations**. Conditions and observations can typically be passed as *two-dimensional numpy arrays* (with columns corresponding to variables and rows corresponding to different instances of those variables). Theorists then **identify and fit a model** which may be used to predict observations based on experiment conditions." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Types\n", + "\n", + "There are different types of theorists within the AutoRA framework, each with its own approach to scientific model discovery.\n", + "\n", + "Some theorists focus on *fitting the parameters of a pre-specified model* to the given data (see the scikit learn documentation for a [selection of basic regressors](https://scikit-learn.org/stable/supervised_learning.html)). The model architecture in such cases is typically fixed, while the parameters are adjusted to optimize the model's performance. Linear regression is an example of a parameter-fitting theorist.\n", + "\n", + "Other theorists are concerned with *identifying both the architecture of a model and its parameters*. The model architectures can take various forms, such as equations, causal models, or process models. Implemented as scikit-learn estimators, these theorists aim to discover a model architecture that accurately describes the data. They often operate within a user-defined search space, which specifies the allowable operations or components that can be included in the model. This approach provides more flexibility in exploring different model architectures." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Usage\n", + "\n", + "In this tutorial, we delve into two types of theorists: (1) a linear regression theorist, which focuses on fitting a linear model, and (2) a Bayesian Machine Scientist (Guimerà et al., 2020, in *Science Advances*), which specializes in identifying and fitting a non-linear equation.\n", + "\n", + "Theorists are commonly instantiated as regressors within the ``sklearn`` library:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import linear_model\n", + "from autora.theorist.bms import BMSRegressor\n", + "\n", + "theorist_lr = linear_model.LinearRegression()\n", + "theorist_bms = BMSRegressor(epochs=100)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once instantiated, we can fit the theorist to link experimental conditions with observations. However, before doing so, we should convert both inputs into 2-dimensional numpy arrays." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "tF2PVwB8UYNA", - "outputId": "2430af40-9cb1-46c9-ea3c-b9eb0a2a2078" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.8250647373064104,)\n", - "(0.7615982190520711,)\n", - "(4.1253236865320515,)\n", - "(4.188790204786391,)\n", - "(3.236792430971302,)\n", - "(3.8714576135146945,)\n", - "(3.3637254674799806,)\n", - "(6.092785752416569,)\n", - "(4.886921905584122,)\n", - "(4.950388423838462,)\n" - ] - } - ], - "source": [ - "from autora.experimentalist.pooler.random_pooler import random_pool\n", - "\n", - "# generate random pool of 10 conditions\n", - "num_samples = 10\n", - "new_conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", - " n=num_samples)\n", - "\n", - "# print conditons\n", - "for idx, condition in enumerate(new_conditions):\n", - " print(condition)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Size of the initial conditions: (10, 1),\n", + "Size of the initial observations: (10, 1)\n", + "\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "DdGnRYHKUYNA" - }, - "source": [ - "### Usage: Samplers\n", - "\n", - "An experiment sampler typically requires an existing pool of conditions as input along with additional arguments. For instance, the **[novelty sampler](https://autoresearch.github.io/autora/user-guide/experimentalists/samplers/novelty/)** requires, aside from a pool of conditions, a list of prior conditions. The user may also specify the number of samples ``num_samples`` to select from the pool.\n", - "\n", - "The novelty sampler will then select novel experiment conditions from the pool which are most dissimilar to some reference conditions, such as the ``initial_conditions`` obtained above:" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:13<00:00, 7.41it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "o-XwmGmVUYNA", - "outputId": "de0f2980-a453-4a8a-feb3-c93f9a867472" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0. ]\n", - " [0.06346652]]\n" - ] - } + "data": { + "text/html": [ + "
BMSRegressor(epochs=100)
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" ], - "source": [ - "from autora.experimentalist.sampler.novelty import novelty_sample\n", - "\n", - "new_conditions_novelty = novelty_sample(condition_pool = condition_pool,\n", - " reference_conditions = initial_conditions,\n", - " num_samples = 2)\n", - "\n", - "print(new_conditions_novelty)" + "text/plain": [ + "BMSRegressor(epochs=100)" ] - }, + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# convert data to 2-dimensional numpy array\n", + "initial_conditions = initial_conditions.reshape((len(initial_conditions), 1))\n", + "initial_observations = initial_observations.reshape((len(initial_observations), 1))\n", + "print(f\"Size of the initial conditions: {initial_conditions.shape},\\nSize of the initial observations: {initial_observations.shape}\\n\")\n", + "\n", + "# fit theorists\n", + "theorist_lr.fit(initial_conditions, initial_observations)\n", + "theorist_bms.fit(initial_conditions, initial_observations)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For some theorists, we can inspect the resulting model architecture. For instance, in the BMS theorist, we can call obtain the model formula via ``theorist_bms.repr()``.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "HD-VCIVxUYNJ" - }, - "source": [ - "Another example for an experiment sampler is the **[falsification sampler](https://autoresearch.github.io/autora/falsification/docs/sampler/)**. The falsification sampler identifies experiment conditions under which the loss of a candidate model (returned by the theorist) is predicted to be the highest. This loss is approximated with a neural network, which is trained to predict the loss of the candidate model, given some initial experimental conditions, respective initial observations, and the metadata.\n", - "\n", - "The following code segment calls on the falsification sampler to return novel conditions based on the candidate model of the linear regression theorist introduced above. As with the novelty sampler, we seek to select 2 conditions.\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Model of BMS theorist: sin(X0)\n" + ] + } + ], + "source": [ + "print(\"Model of BMS theorist: \" + theorist_bms.repr())" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We may now obtain predictions from both theorists for the entire pool of experiment conditions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# convert condition pool into 2-dimensional numpy array before generating respective predictions\n", + "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", + "\n", + "# obtain predictions\n", + "predicted_observations_lr = theorist_lr.predict(condition_pool)\n", + "predicted_observations_bms = theorist_bms.predict(condition_pool)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next code segment, we plot the theorists' predictions against the ground truth. For the BMS theorist, we can obtain a latex expression of the model architecture using ``theorist_bms.latex()``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kPpATSzgUYNJ", - "outputId": "6007b817-7ddc-4a73-ff7e-61a088f5df79" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0. ]\n", - " [0.06346652]]\n" - ] - } - ], - "source": [ - "from autora.experimentalist.sampler.falsification import falsification_sample\n", - "\n", - "new_conditions_falsification = falsification_sample(\n", - " condition_pool=condition_pool,\n", - " model=theorist_lr,\n", - " reference_conditions=initial_conditions,\n", - " reference_observations=initial_observations,\n", - " metadata=metadata,\n", - " num_samples=2\n", - " )\n", - "\n", - "print(new_conditions_falsification)" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "kDRWmikdUYNK" - }, - "source": [ - "We can plot the selected conditions for both samples relative to the selected samples. Since we don't have observations for those conditions, we plot them as vertical lines." + "data": { + "image/png": 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+ "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# obtain latex expression of BMS theorist\n", + "bms_model = theorist_bms.latex()\n", + "\n", + "# plot model predictions against ground-truth\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", + "plt.plot(initial_conditions, initial_observations, 'o', label='Data Used for Model Identification')\n", + "plt.plot(condition_pool, predicted_observations_lr, label='Linear Regression')\n", + "plt.plot(condition_pool, predicted_observations_bms, label='Bayesian Machine Scientist: $' + bms_model + '$')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Model Predictions')\n", + "plt.legend()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**: *There are various other types of theorists you can combine with AutoRA as long as they are implemented as ``sklearn`` estimators. This includes [autora modules](theorist/index.md), any [scikit learn estimators](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html), as well as third-party packages, such as [PySR](https://github.com/MilesCranmer/PySR) for symbolic regression.*" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Experimentalists\n", + "\n", + "The primary goal of an experimentalist is to design experiments that yield scientific merit. The AutoRA framework offers various strategies for identifying informative new data points (e.g., by searching for experiment conditions that existing scientific models fail to explain, or by looking for novel conditions altogether).\n", + "\n", + "\"Experimentalist\n", + "\n", + "Experimentalists are implemented as functions that return a set of experiment conditions (e.g., in the form of a 2-dimensional numpy array in which columns correspond to independent variables), which can be subjected to an experiment. To determine these conditions, experimentalists may use information about candidate models obtained from a theorist, experimental conditions that have already been probed, or respective dependent measures. For more detailed information about experimentalists, please refer to the corresponding [AutoRA Documentation](https://autoresearch.github.io/autora/experimentalist/).\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Types\n", + "\n", + "There are generally three types of experimentalist functions: **poolers**, **samplers**, and **pipelines**.\n", + "\n", + "**Poolers** generate a novel set of experimental conditions \"from scratch\", e.g., by sampling from a grid. They usually require metadata describing independent variables of the experiment (e.g., their range or the set of allowed values).\n", + "\n", + "**Samplers** operate on an existing pool of experimental conditions. They typically require experimental conditions to be represented as a 2-dimensional numpy array in which columns correspond to independent variables and rows to different conditions. They then select experiment conditions from this pool.\n", + "\n", + "**Pipelines** Pipelines connect multiple experimentalists into a unified workflow. This is beneficial when various steps are required to process experiment conditions. For example, apart from identifying novel experimental conditions, experimentalist functions may perform other operations on the set of conditions, such as rearranging the rows of a condition matrix or adding new experiment conditions as columns. Experiment pipelines may begin with a pooler that generates all possible experiment conditions, followed by a sampler that selects a subset of conditions from the pool, and then proceed to additional functions that arrange the selected conditions in a specific order necessary for conducting the experiment." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Usage: Poolers\n", + "\n", + "Experimentalist poolers are implemented as functions and can be called directly. For instance, the following **grid pooler** generates a grid based on the ``allowed_values`` of all independent variables in the ``metadata`` object that we defined above. We can simply add a list of allowed values to each independent variable. In this case, we only have one variable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "allowed_values = np.linspace(0, 2 * np.pi, 100)\n", + "metadata.independent_variables[0].allowed_values = allowed_values" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can pass the grid pooler the list of independent variables from the ``metadata`` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.experimentalist.pooler.grid import grid_pool\n", + "\n", + "new_conditions = grid_pool(ivs = metadata.independent_variables)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The resulting condition pool contains all experiment conditions from the grid:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 490 - }, - "id": "3skhpCOSUYNK", - "outputId": "ecdb0936-1a8f-4970-d7f0-a12d060d42c3" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Nm0aVKlXo378/r169SlJ33bp1al0027ZtIygoSPWBUr58eQoXLszcuXMJDQ1Nsv+HjzEzZVRcRkZGySY3WlpaSV63v/76K3FxcV/Ujru7O0ZGRvTq1Yvnz58n2e7v78+iRYuAhNcOwMKFC9XqzJ8/H4AmTZqkuf3GjRtz5swZzp07pyp7+fIlGzdu/Oy+RkZGAKlKAhs3bkxcXJza/xbAggULUCgUaU5c4uLiknRFWllZYWdnl+5TUUhZgzwjJOU4gwcPJjw8nFatWlG8eHGio6M5ffo0mzdvpkCBAnTv3h2A77//Hl1dXZo1a0bfvn0JDQ1l5cqVWFlZqc4apSd9fX0OHjyIm5sblSpV4sCBA+zbt49x48ZhaWmZ4n4zZ87k2LFjVKpUid69e+Pk5MSbN2+4dOkSXl5evHnzBoDevXuzZMkSunbtysWLF7G1tWX9+vUYGhqmKc4DBw6ovk1/qEqVKhQqVAg/Pz8mTpxIt27daNasGZCwPIeLiwsDBgxgy5YtavvlypWLatWq0b17d54/f87ChQtxdHSkd+/eQMLYqT/++INGjRpRsmRJunfvTt68eXny5AnHjh3D1NSUv//+O02PIT1kVFzly5fHy8uL+fPnY2dnR8GCBalUqRJNmzZl/fr1mJmZ4eTkhI+PD15eXuTOnfuL4i9cuDB//vknHTp0oESJEmozS58+fZqtW7eq5jBydnbGzc2NFStW8O7dO2rWrMm5c+dYu3YtLVu2pHbt2mlu393dnfXr19OwYUOGDh2KkZERK1aswMHBgatXr342dnNzc5YtW4aJiQlGRkZUqlQp2bFhzZo1o3bt2owfP54HDx7g7OzM4cOH2b17N8OGDVMbGJ0a79+/J1++fLRt2xZnZ2eMjY3x8vLi/PnzamfrpBxEA1eqSVKGOnDggOjRo4coXry4MDY2Frq6usLR0VEMHjxYPH/+XK3unj17RJkyZYS+vr4oUKCAmDVrlli9enWSy3sdHBxEkyZNkrQFJLnMNiAgQABizpw5qjI3NzdhZGQk/P39xffffy8MDQ2FtbW1mDx5stp8Q4nH/PDyeSGEeP78uRg4cKCwt7cXOjo6wsbGRtStW1esWLFCrV5gYKBo3ry5MDQ0FHny5BFDhw5VzfHzNZfP89+lzLGxscLV1VXky5dPvHv3Tm3/RYsWCUBs3rxZCPH/y7z/+usvMXbsWGFlZSUMDAxEkyZN1KYbSHT58mXRunVrkTt3bqGnpyccHBxE+/btxdGjR1V1Ei9T/3C6gY+3ffxcpubv82G8W7duTbe4Ep/TD19Lt27dEjVq1BAGBgYCUF1K//btW9G9e3eRJ08eYWxsLBo0aCBu3bolHBwc1C63T+08Qonu3LkjevfuLQoUKCB0dXWFiYmJqFq1qvj1119FZGSkql5MTIyYMmWKKFiwoNDR0RH29vZi7NixanWESPl/4eNL4IUQ4urVq6JmzZpCX19f5M2bV0ybNk2sWrXqs5fPCyHE7t27hZOTk9DW1la7lP7jy+eFEOL9+/di+PDhws7OTujo6IgiRYqIOXPmqE17IETyr4fEx5T4HEdFRYmffvpJODs7CxMTE2FkZCScnZ3F77//nsyzK+UECiGyweg2ScrmunXrxrZt25LtYsmpjh8/Tu3atdm6davaJf2SJElZiRwjJEmSJEnSN0smQpIkSZIkfbNkIiRJkiRJ0jdLjhGSJEmSJOmbJc8ISZIkSZL0zZKJkCRJkiRJ3yw5oeJnxMfH8/TpU0xMTNI07bskSZIkSZojhOD9+/fY2dmhVKZ83kcmQp/x9OnTJKtUS5IkSZKUPTx69Ih8+fKluF0mQp+RuIjfo0ePMDU11XA0kiRJkiSlRkhICPb29mqL8SZHJkKfkdgdZmpqKhMhSZIkScpmPjesRQ6WliRJkiTpmyUTIUmSJEmSvlkyEZIkSZIk6ZslxwhJkpRh4uLiiImJ0XQYkiTlQDo6OmhpaX31cWQiJElSuhNC8OzZM969e6fpUCRJysHMzc2xsbH5qnn+ZCIkSVK6S0yCrKysMDQ0lJORSpKUroQQhIeH8+LFCwBsbW2/+FgyEZIkKV3FxcWpkqDcuXNrOhxJknIoAwMDAF68eIGVldUXd5PJwdKSJKWrxDFBhoaGGo5EkqScLvF95mvGIspESJKkDCG7wyRJymjp8T4jEyFJkiRJkr5ZMhGSJEnKYmrVqsWwYcM0HUa6KVCgAAsXLlTdVygU7Nq165P7dOvWjZYtW2ZoXJIEMhGSJEnK8j5OJLK7oKAgGjVqBMCDBw9QKBT4+vqq1Vm0aBGenp6ZH5z0zZGJkIbExMRw8OBBTYchSZKU6WxsbNDT0/tkHTMzM8zNzTMnIOmbJhMhDZkwYQKNGjWiX79+REREaDocSZJI6JIaMmQI7u7u5MqVCxsbGzw8PNTqPHz4kBYtWmBsbIypqSnt27fn+fPnANy5cweFQsGtW7fU9lmwYAGFCxdW3b9+/TqNGjXC2NgYa2trunTpwqtXr1KMKTAwkOHDh6NQKFAoFISFhWFqasq2bdvU6u7atQsjIyPev3+f7LHi4+OZPXs2jo6O6OnpkT9/fqZPn67afu3aNerUqYOBgQG5c+emT58+hIaGqrYndlfNnTsXW1tbcufOzcCBA9Wu2Hnx4gXNmjXDwMCAggULsnHjxiRxfNg1VrBgQQDKli2LQqGgVq1aam0lioqKYsiQIVhZWaGvr0+1atU4f/68avvx48dRKBQcPXqUChUqYGhoSJUqVbh9+7aqzpUrV6hduzYmJiaYmppSvnx5Lly4kOxzJX07slUidOLECZo1a4adnV2q+pgT/zE+vj179ixzAk6BEAI9PT0UCgXLly/nu+++U/tnlaQcKyws5VtkZOrrfvzlIaV6X2Dt2rUYGRlx9uxZZs+ezdSpUzly5AiQkEi0aNGCN2/e8O+//3LkyBHu379Phw4dAChatCgVKlRI8uG/ceNGOnXqBMC7d++oU6cOZcuW5cKFCxw8eJDnz5/Tvn37ZOPZsWMH+fLlY+rUqQQFBREUFISRkREdO3ZkzZo1anXXrFlD27ZtMTExSfZYY8eOZebMmUycOJGbN2/y559/Ym1t/d9TGEaDBg2wsLDg/PnzbN26FS8vLwYNGqR2jGPHjuHv78+xY8dYu3Ytnp6eal1Y3bp149GjRxw7doxt27bx+++/qya9S865c+cA8PLyIigoiB07diRbz93dne3bt7N27VouXbqEo6MjDRo04M2bN2r1xo8fz7x587hw4QLa2tr06NFDta1z587ky5eP8+fPc/HiRcaMGYOOjk6KsUnfCJGN7N+/X4wfP17s2LFDAGLnzp2frH/s2DEBiNu3b4ugoCDVLS4uLtVtBgcHC0AEBwd/ZfRJHT58WFhaWgpAGBkZifXr16d7G5KU2SIiIsTNmzdFRERE0o2Q8q1xY/W6hoYp161ZU71unjzJ10ujmjVrimrVqqmVubq6itGjRwshEv5ntbS0xMOHD1Xbb9y4IQBx7tw5IYQQCxYsEIULF1Ztv337tgCEn5+fEEKIadOmie+//16tjUePHqneqxLjGDp0qGq7g4ODWLBggdo+Z8+eFVpaWuLp06dCCCGeP38utLW1xfHjx5N9bCEhIUJPT0+sXLky2e0rVqwQFhYWIjQ0VFW2b98+oVQqxbNnz4QQQri5uQkHBwcRGxurqtOuXTvRoUMHtcea+FwIIYSfn58A1OL/8P07ICBAAOLy5ctq8bi5uYkWLVoIIYQIDQ0VOjo6YuPGjart0dHRws7OTsyePVsI8f/3ey8vL7X4AdVr0cTERHh6eib7+KXs6VPvN6n9/M5WZ4QaNWrEzz//TKtWrdK0n5WVFTY2NqqbUpk1Hnb9+vVVp2rDwsLo0qULPXv2JDw8XNOhSdI3q0yZMmr3bW1tVWc0/Pz8sLe3x97eXrXdyckJc3Nz/Pz8AOjYsSMPHjzgzJkzQMLZoHLlylG8eHEgoXvm2LFjGBsbq26J2/z9/VMdZ8WKFSlZsiRr164FYMOGDTg4OFCjRo1k6/v5+REVFUXdunVT3O7s7IyRkZGqrGrVqsTHx6udsS5ZsqTaDL4fPz/a2tqUL19etb148eJfPdbH39+fmJgYqlatqirT0dGhYsWKquc90Yd/v8RlFxLjGzFiBL169aJevXrMnDkzTc+3lHNljYwgg7m4uGBra0v9+vXx9vbWdDhqbG1tOXLkCJMnT0ahULB69WpcXV25efOmpkOTpPQXGprybft29bovXqRc98AB9boPHiRf7wt83FWiUCiIj49P9f42NjbUqVOHP//8E4A///yTzp07q7aHhobSrFkzfH191W53795NMYlJSa9evVTdUmvWrKF79+4pTjCXuBzB1/ra5yejfRhf4nORGJ+Hhwc3btygSZMm/PPPPzg5ObFz506NxCllHTk6EbK1tWXZsmVs376d7du3Y29vT61atbh06VKK+0RFRRESEqJ2y2haWlp4eHhw9OhRbGxsuHnzJhUqVGDNmjUIITK8fUnKNEZGKd/09VNf9+MP9ZTqpbMSJUrw6NEjHj16pCq7efMm7969w8nJSVXWuXNnNm/ejI+PD/fv36djx46qbeXKlePGjRsUKFAAR0dHtZtRCjHr6uoSFxeXpPzHH38kMDCQxYsXc/PmTdzc3FKMvUiRIhgYGHD06NEUH9uVK1cI+2Bslbe3N0qlkmLFiqX8pHygePHixMbGcvHiRVXZ7du3effuXYr76OrqAiT7+BIVLlwYXV1dtS+yMTExnD9/Xu15T42iRYsyfPhwDh8+TOvWrZOMs5K+PTk6ESpWrBh9+/alfPnyVKlShdWrV1OlShUWLFiQ4j4zZszAzMxMdfvwFHhGq127Nr6+vtSvX5+IiAh69OiBm5ub2lUbkiRpTr169ShdujSdO3fm0qVLnDt3jq5du1KzZk0qVKigqte6dWvev39P//79qV27NnZ2dqptAwcO5M2bN/zwww+cP38ef39/Dh06RPfu3VNMBgoUKMCJEyd48uSJ2tVlFhYWtG7dmp9++onvv/+efPnypRi7vr4+o0ePxt3dnXXr1uHv78+ZM2dYtWoVkJC86evr4+bmxvXr1zl27BiDBw+mS5cuqgHVn1OsWDEaNmxI3759OXv2LBcvXqRXr16fPBtlZWWFgYGBatB4cHBwkjpGRkb079+fn376iYMHD3Lz5k169+5NeHg4PXv2TFVsERERDBo0iOPHjxMYGIi3tzfnz5+nRIkSqdpfyrlydCKUnIoVK3Lv3r0Ut48dO5bg4GDV7cNvfpnB2tqagwcP8vPPP6NUKlm/fj2urq5cvXo1U+OQJCkphULB7t27sbCwoEaNGtSrV49ChQqxefNmtXomJiY0a9aMK1euqHWLAdjZ2eHt7U1cXBzff/89pUuXZtiwYZibm6c4fnHq1Kk8ePCAwoULY2lpqbatZ8+eREdHq10dlZKJEycycuRIJk2aRIkSJejQoYNq/IyhoSGHDh3izZs3uLq60rZtW+rWrcuSJUvS8hSxZs0a7OzsqFmzJq1bt6ZPnz5YWVmlWF9bW5vFixezfPly7OzsaNGiRbL1Zs6cSZs2bejSpQvlypXj3r17HDp0CAsLi1TFpaWlxevXr+natStFixalffv2NGrUiClTpqTp8Uk5j0Jk074XhULBzp070zwFe/369TExMUnxEs2PhYSEYGZmRnBwMKampl8Q6Zc7ceIEnTp14smTJ+jr67No0SJ69+4tF7OUsrTIyEgCAgIoWLAg+h93d0npbv369QwfPpynT5+qupkk6Vvxqfeb1H5+Z6szQqGhoaqBhQABAQH4+vry8OFDIOFsTteuXVX1Fy5cyO7du7l37x7Xr19n2LBh/PPPPwwcOFAT4adZjRo1uHz5Mo0aNSIyMpK+ffvSqVOnTBm3JElS1hYeHo6/vz8zZ86kb9++MgmSpC+UrRKhCxcuULZsWcqWLQskXApZtmxZJk2aBCSsX5OYFAFER0czcuRISpcuTc2aNbly5QpeXl4pXj6aFVlaWrJ3715mz56NlpYWmzZtonz58ly+fFnToUmSpEGzZ8+mePHi2NjYMHbsWE2HI0nZVrbtGsssmuwa+5iPjw8dO3bk4cOH6OrqsmDBAvr37y+7yqQsRXaNSZKUWb65rrFvXeXKlbl8+TLNmzcnOjqagQMH0r59+2SvspAkSZIk6fNkIpTN5MqVi127drFgwQJ0dHTYtm0bZcuWVVt8UJIkSZKk1JGJUDakUCgYNmwY3t7eFChQgICAAKpWrcqiRYvkBIySJEmSlAYyEcrGXF1duXz5Mm3atCEmJoZhw4bRqlWrJKsxS5IkSZKUPJkIZXPm5uZs3bqVJUuWoKury+7duylbtqxqwUdJkiRJklImE6EcQKFQMHDgQHx8fChcuDAPHz6kevXqzJkzJ0sthihJkiRJWY1MhHKQcuXKcenSJTp06EBsbCzu7u40b95cbW0iSZJSJoSgT58+5MqVC4VCoZq89VMePHjwVXW9vb0pXbo0Ojo6tGzZkuPHj6NQKD65UGl66NatW5pn5s8uPn4OPT09MTc3/+x+CoWCXbt2ZWhsUtYjE6EcxtTUlL/++ovly5ejp6fHvn37cHFx4eTJk5oOTZKyvIMHD+Lp6cnevXsJCgqiVKlS6Xp8e3v7JMcdMWIELi4uBAQE4OnpSZUqVQgKCsLMzCxd2kwpUVu0aBGenp7p0kZW16FDB+7cuaO67+HhgYuLS5J6QUFBNGrUKBMjk7ICmQjlQAqFgj59+nDu3DmKFi3KkydPqF27Nr/88ovsKpOkT/D398fW1pYqVapgY2ODtrZ2uh5fS0sryXH9/f2pU6cO+fLlw9zcHF1dXWxsbDJ8olQzM7NUnSXJCQwMDD658GsiGxsb9PT0MiEiKSuRiVAOVqZMGS5evMiPP/5IXFwc48ePp2HDhqrVpiVJ+r9u3boxePBgHj58iEKhoECBAkDCWaJq1aphbm5O7ty5adq0Kf7+/ike5+3bt3Tu3BlLS0sMDAwoUqQIa9asAdTPziT+/vr1a3r06IFCocDT0zPZrjFvb29q1aqFoaEhFhYWNGjQgLdv36YqvoIFCwJQtmxZFAoFtWrVUj3eD7vGoqKiGDJkCFZWVujr61OtWjW1+ckS4zp69CgVKlTA0NCQKlWqcPv27U8+r48fP+aHH34gV65cGBkZUaFCBc6ePavavnTpUgoXLoyuri7FihVj/fr1avsrFAr++OMPWrVqhaGhIUWKFGHPnj1qdfbv30/RokUxMDCgdu3aPHjwQG37h11jnp6eTJkyhStXrqBQKFTPe2JbH3aNXbt2jTp16mBgYEDu3Lnp06cPoaGhqu2Jz+HcuXOxtbUld+7cDBw4kJiYGFWd33//nSJFiqCvr4+1tTVt27b95PMlZT6ZCOVwxsbGrFu3jlWrVmFgYMCRI0dwdnbm2LFjmg5N+gaFRYeleIuMjUx13YiYiFTVTYtFixYxdepU8uXLR1BQkCoJCAsLY8SIEVy4cIGjR4+iVCpp1apVimdXJ06cyM2bNzlw4AB+fn4sXbqUPHnyJKmX2E1mamrKwoULCQoKokOHDknq+fr6UrduXZycnPDx8eHUqVM0a9aMuLi4VMV37tw5ALy8vAgKCmLHjh3Jxu3u7s727dtZu3Ytly5dwtHRkQYNGiSZjmP8+PHMmzePCxcuoK2tTY8ePVJ8TkNDQ6lZsyZPnjxhz549XLlyBXd3d1VsO3fuZOjQoYwcOZLr16/Tt29funfvnuT9acqUKbRv356rV6/SuHFjOnfurIrr0aNHtG7dmmbNmuHr60uvXr0YM2ZMijF16NCBkSNHUrJkSYKCglJ83sPCwmjQoAEWFhacP3+erVu34uXlxaBBg9TqHTt2DH9/f44dO8batWvx9PRUJVYXLlxgyJAhTJ06ldu3b3Pw4EFq1KiRYmyShgjpk4KDgwUggoODNR3KV7t+/bpwcnISgFAqlcLDw0PExsZqOiwph4mIiBA3b94UERERSbbhQYq3xhsbq9U1nG6YYt2aa2qq1c0zO0+y9dJqwYIFwsHB4ZN1Xr58KQBx7do1IYQQAQEBAhCXL18WQgjRrFkz0b1792T3/biuEEKYmZmJNWvWqO4fO3ZMAOLt27dCCCF++OEHUbVq1VQ/hs/Fl8jNzU20aNFCCCFEaGio0NHRERs3blRtj46OFnZ2dmL27NlqcXl5eanq7Nu3TwDJ/q2FEGL58uXCxMREvH79OtntVapUEb1791Yra9eunWjc+P+vBUBMmDBBdT80NFQA4sCBA0IIIcaOHSucnJzUjjF69Gi153DNmjXCzMxMtX3y5MnC2dk5STyA2LlzpxBCiBUrVggLCwsRGhqq9niVSqV49uyZECLhOXRwcFB7H23Xrp3o0KGDEEKI7du3C1NTUxESEpLs45e+3qfeb1L7+S3PCH1DSpYsyblz5+jevTvx8fF4eHjw/fffExQUpOnQJCnLunv3Lj/88AOFChXC1NRU1WX28OHDZOv379+fTZs24eLigru7O6dPn/6q9hPPCKVXfMnx9/cnJiaGqlWrqsp0dHSoWLEifn5+anXLlCmj+t3W1hYgxe52X19fypYtS65cuZLd7ufnp9YmQNWqVT/ZppGREaampqo2/fz8qFSpklr9ypUrJ9teWvj5+eHs7IyRkZFabPHx8WrdgSVLlkRLS0t139bWVhVb/fr1cXBwoFChQnTp0oWNGzcSHh7+1bFJ6St9RwJKWZ6RkRGrV6+mdu3a9O/fn3/++QcXFxc2bNhA/fr1NR2elMOFjg1NcZuWUkvt/otRKY9lUyrUv8M9GPrgq+L6lGbNmuHg4MDKlSuxs7MjPj6eUqVKER0dnWz9Ro0aERgYyP79+zly5Ah169Zl4MCBzJ0794vaNzAwSNf4vpaOjo7q98QB3Sl1E34u9i9pM7HdrHLhx6diMzEx4dKlSxw/fpzDhw8zadIkPDw8OH/+/DczUD07kGeEvlFdunThwoULlC5dmhcvXtCgQQMmTJhAbGyspkOTcjAjXaMUb/ra+qmua6BjkKq6X+v169fcvn2bCRMmULduXUqUKKEapPwplpaWuLm5sWHDBhYuXMiKFSu+OIYyZcpw9OjRL45PV1cXQDWmKDmJg5W9vb1VZTExMZw/fx4nJ6evit3X1zfFZX9KlCih1iYkDAxPS5slSpRQjYNK9LmZ9XV1dT/5fCQe98qVK4SF/X+smbe3N0qlkmLFiqU6Pm1tberVq8fs2bO5evUqDx484J9//kn1/lLGk4nQN6x48eKcPXuWPn36IIRg+vTp1KlThydPnmg6NEnKEiwsLMidOzcrVqzg3r17/PPPP4wYMeKT+0yaNIndu3dz7949bty4wd69eylRosQXxzB27FjOnz/PgAEDuHr1Krdu3WLp0qW8evUqVfFZWVlhYGDAwYMHef78OcHBwUnaMDIyon///vz0008cPHiQmzdv0rt3b8LDw+nZs+cXx/7DDz9gY2NDy5Yt8fb25v79+2zfvh0fHx8AfvrpJzw9PVm6dCl3795l/vz57Nixg1GjRqW6jX79+nH37l1++uknbt++zZ9//vnZ+ZESF6v29fXl1atXREVFJanTuXNn9PX1cXNz4/r16xw7dozBgwfTpUsXrK2tUxXb3r17Wbx4Mb6+vgQGBrJu3Tri4+PTlEhJGU8mQt84AwMDli9fzl9//YWxsTEnT57ExcWFAwcOaDo0SdI4pVLJpk2buHjxIqVKlWL48OHMmTPnk/vo6uoyduxYypQpQ40aNdDS0mLTpk1fHEPRokU5fPgwV65coWLFilSuXJndu3ejra2dqvi0tbVZvHgxy5cvx87OjhYtWiTbzsyZM2nTpg1dunShXLly3Lt3j0OHDmFhYfHFsevq6nL48GGsrKxo3LgxpUuXZubMmaoxNS1btmTRokXMnTuXkiVLsnz5ctasWaO6xD818ufPz/bt29m1axfOzs4sW7aMX3755ZP7tGnThoYNG1K7dm0sLS3566+/ktQxNDTk0KFDvHnzBldXV9q2bUvdunVZsmRJqmMzNzdnx44d1KlThxIlSrBs2TL++usvSpYsmepjSBlPIYQQmg4iKwsJCcHMzIzg4GBMTU01HU6Gunv3Lu3bt1fNQOvu7s7PP/+cpA9ckj4lMjKSgIAAChYsiL6+/ud3kCRJ+kKfer9J7ee3PCMkqRQpUgQfHx8GDhwIwOzZs6lVq1aarj6RJEmSpOxEJkKSGn19fZYsWcLWrVsxNTXl9OnTuLi48Pfff2s6NEmSJElKdzIRkpLVtm1bLl++TIUKFXj79i3Nmzdn5MiRGXZJriRJkiRpgkyEpBQVKlQIb29vhg0bBsD8+fOpXr06AQEBmg1MkiRJktKJTISkT9LV1WXBggXs2rULc3Nzzp07R9myZVNcr0iSJEmSshOZCEmp0qJFC3x9ffnuu+8IDg6mTZs2DB48ONn5NyRJkiQpu5CJkJRqDg4OnDhxAnd3dwCWLFlClSpVuHfvnoYjkyRJkqQvIxMhKU10dHSYNWsW+/btI3fu3Fy6dIly5cqxZcsWTYcmSZIkSWkmEyHpizRu3BhfX1+qVavG+/fv6dChA/379yciIkLToUmSJElSqslESPpi+fLl49ixY4wbNw6FQsGyZcv47rvvuH37tqZDk6RvjoeHBy4uLpoOA4BatWqprjaVpKxOJkLSV9HW1mb69OkcPHgQS0tLrl69Svny5dmwYYOmQ5OkL/Ls2TOGDh2Ko6Mj+vr6WFtbU7VqVZYuXUp4eLimw/siHh4eKBSKT96+xPHjx1EoFLx79y59A5akTCQTISldfP/991y5coVatWoRFhZGly5d6NmzZ7b94JC+Tffv36ds2bIcPnyYX375hcuXL+Pj44O7uzt79+7Fy8srxX1jYmIyMdK0GTVqFEFBQapbvnz5mDp1qlrZh+TEqdK3RCZCUrqxtbXFy8uLyZMno1AoWL16NRUrVuTmzZuaDk2SUmXAgAFoa2tz4cIF2rdvT4kSJShUqBAtWrRg3759NGvWTFVXoVCwdOlSmjdvjpGREdOnTwdg6dKlFC5cGF1dXYoVK8b69etV+zx48ACFQqFa2Bjg3bt3KBQKjh8/Dvz/LMvRo0epUKEChoaGVKlSJUmX88yZM7G2tsbExISePXsSGRmZ4uMyNjbGxsZGddPS0sLExER1v2PHjgwaNIhhw4aRJ08eGjRo8NlYHzx4QO3atQGwsLBAoVDQrVs3Vd34+Hjc3d3JlSsXNjY2eHh4pPGvIUmZQyZCUrrS0tLCw8MDLy8vbGxsuHHjBhUqVMDT01PToUkaJIQgPDpWIzchRKpifP36NYcPH2bgwIEYGRklW+fjLiQPDw9atWrFtWvX6NGjBzt37mTo0KGMHDmS69ev07dvX7p3786xY8fS/JyNHz+eefPmceHCBbS1tenRo4dq25YtW/Dw8OCXX37hwoUL2Nra8vvvv6e5jQ+tXbsWXV1dvL29WbZs2Wfr29vbs337dgBu375NUFAQixYtUjuekZERZ8+eZfbs2UydOpUjR458VYySlBG0NR2AlDPVqVMHX19ffvzxR7y8vOjevTv//PMPv//+O8bGxpoOT8pkETFxOE06pJG2b05tgKHu59/q7t27hxCCYsWKqZXnyZNHdbZl4MCBzJo1S7WtU6dOdO/eXXX/hx9+oFu3bgwYMACAESNGcObMGebOnas6e5Ja06dPp2bNmgCMGTOGJk2aEBkZib6+PgsXLqRnz5707NkTgJ9//hkvL69PnhX6nCJFijB79mzV/QcPHnyyvpaWFrly5QLAysoKc3Nzte1lypRh8uTJqmMvWbKEo0ePUr9+/S+OUZIygjwjJGUYa2trDh06xM8//4xSqWT9+vW4urpy7do1TYcmSal27tw5fH19KVmyZJKZ1CtUqKB238/Pj6pVq6qVVa1aFT8/vzS3W6ZMGdXvtra2ALx48ULVTqVKldTqV65cOc1tfKh8+fJftf/HPowfEh5DYvySlJXIM0JShlIqlYwfP57q1avzww8/cOvWLSpWrMjixYvp1avXF1+tImUvBjpa3JzaQGNtp4ajoyMKhSLJWJxChQolHMfAIMk+KXWhpUSpTPju+WF3XUqDrHV0dFS/J/6fxMfHp6m9tPj4saQl1uR8GD8kPIaMjF+SvpQ8IyRliho1auDr60vDhg2JjIykT58+dO7cmZCQEE2HJmUChUKBoa62Rm6pTbZz585N/fr1WbJkCWFhYV/0OEuUKIG3t7dambe3N05OTgBYWloCqF2l9eFg5LS0c/bsWbWyM2fOpPk4n5KaWHV1dQGIi4tL17YlKTPJREjKNJaWluzbt49Zs2ahpaXFX3/9Rfny5bl8+bKmQ5MkAH7//XdiY2OpUKECmzdvxs/Pj9u3b7NhwwZu3bqFltanzy799NNPeHp6snTpUu7evcv8+fPZsWMHo0aNAhLOKn333XfMnDkTPz8//v33XyZMmJDmOIcOHcrq1atZs2YNd+7cYfLkydy4ceOLHnNKUhOrg4MDCoWCvXv38vLlS0JDQ9M1BknKDDIRkjKVUqnE3d2dEydOYG9vz7179/juu+/4/fffU311jyRllMKFC3P58mXq1avH2LFjcXZ2pkKFCvz666+MGjWKadOmfXL/li1bsmjRIubOnUvJkiVZvnw5a9asoVatWqo6q1evJjY2lvLlyzNs2DB+/vnnNMfZoUMHJk6ciLu7O+XLlycwMJD+/fun+Tif87lY8+bNy5QpUxgzZgzW1tYMGjQo3WOQpIymEPLT55NCQkIwMzMjODgYU1NTTYeTo7x+/Zru3bvz999/A9C2bVtWrlyZ5OoTKXuJjIwkICCAggULoq+vr+lwJEnKwT71fpPaz295RkjSmNy5c7N7927mz5+PtrY227Zto1y5cly4cEHToWVN8XEQcBKubUv4GS/HZUiSJH0tmQhJGqVQKBg+fDje3t4UKFCAgIAAqlSpwqJFi2RX2Ydu7oGFpWBtU9jeM+HnwlIJ5ZIkSdIXk4mQlCVUrFiRy5cv07p1a2JiYhg2bBitW7fm7du3mg5N827ugS1dIeSpenlIUEK5TIYkSZK+mEyEpCzD3Nycbdu28euvv6Krq8uuXbsoW7Zsul8WnK3Ex8HB0UByZ8f+Kzs4RnaTSZIkfSGZCElZikKhYNCgQZw+fZrChQsTGBhI9erVmTt37rc5GVvg6aRngtQICHmSUE+SJElKM5kIaUDY2xcopihQTFEQ9vZFimXfsvLly3Pp0iU6dOhAbGwsP/30E82bN+f169eaDi1zhT5P33qSJEmSGpkISVmWqakpf/31F8uWLUNPT499+/bh4uLCqVOnNB1a5jG2Tt96kiRJkhqZCElZmkKhoG/fvpw9e5aiRYvy+PFjatWqxYwZM76NrjKHKmBqB6S0TIQCTPMm1JMkSZLSTCZCUrbg7OzMxYsX6dy5M3FxcYwbN47GjRvn/NWslVrQcNZ/dz5Ohv6733BmQj1JkiQpzbJVInTixAmaNWuGnZ0dCoWCXbt2fXaf48ePU65cOfT09HB0dMTT0zPD45QyhrGxMevXr2fVqlUYGBhw6NAhXFxc+PfffzUd2tf71GSJTs2h/TowtVXfx9QuodypeebGKqlJzXtRt27daNmyZaqP+eDBAxQKxRctyCpJUtpkq0QoLCwMZ2dnfvvtt1TVDwgIoEmTJtSuXRtfX1+GDRtGr169OHToUAZHKmUUhUJBjx49OH/+PCVKlCAoKIg6deowderU7LsCdmomS3RqDsOug9teaLMq4eewazk/Ccrk2bTTmrBAwursjRo1AlJOYBYtWpTuX8Jq1aqFQqFAoVCgp6dH3rx5adasGTt27EjzsTw8PHBxcUnX+CQpu9DWdABp0ahRI9UbTmosW7aMggULMm/ePABKlCjBqVOnWLBgAQ0aNMioMD/LwDQXAW1Oqn5PqUxKWcmSJTl//jyDBg3C09OTyZMn8++//7Jx40ZsbGw0HV7qJU6W+PE8QYmTJX54xkepBQWrZ3qIGnNzT8IcSh9OH2Bql9BVmIUSwNS83szMzDKk7d69ezN16lRiY2N5/PgxO3fupGPHjnTr1o0VK1ZkSJuSlNNkqzNCaeXj40O9evXUyho0aICPj4+GIkqg1NKmQKlqFChVDaWWdopl0qcZGRmxZs0a1q1bh6GhIf/88w8uLi54eXmle1vx8YLnIZFcDHzLiTsv8br5nP3Xgth1+Qm7Lj/h+O0XXH38jkdvwgmLik3d8iByssSUZZHZtGvVqsWQIUNwd3cnV65c2NjY4OHhoVbnw66xggULAlC2bFkUCoVq1fmPzzQdPHiQatWqYW5uTu7cuWnatCn+/v5pjs/Q0BAbGxvy5cvHd999x6xZs1i+fDkrV65U+z8YPXo0RYsWxdDQkEKFCjFx4kRiYmIA8PT0ZMqUKVy5ckV1hinx7NX8+fMpXbo0RkZG2NvbM2DAAEJDQ9McpyRlZTn6E/fZs2dYW6tfVmxtbU1ISAgREREYGBgk2ScqKoqoqCjV/ZCQkAyPU/o6Xbp0wdXVlfbt23Pt2jW+//57xo8fz+TJk9HWTvtLPDg8hosP33D+wVv8gkJ49Cacx28jiIpN/VVqFoY6lLA1pbiNKSVsTXCyM6WEjSlK5QcDntMyWeK3dCboswmiIiFBLN4kUwaJr127lhEjRnD27Fl8fHzo1q0bVatWpX79+knqnjt3jooVK+Ll5UXJkiXR1dVN9phhYWGMGDGCMmXKEBoayqRJk2jVqhW+vr4olV/3/dTNzY2RI0eyY8cO1RdBExMTPD09sbOz49q1a/Tu3RsTExPc3d3p0KED169f5+DBg6rkKfEMllKpZPHixRQsWJD79+8zYMAA3N3d+f33378qRknKSnJ0IvQlZsyYwZQpUzK0jeiIUMb/XBuA6ROOoWtgnGyZlHrFixfn7NmzDB06lJUrV/Lzzz9z4sQJ/vzzT/LmzfvJfWPj4vG5/5pDN55xLuANd54n/41XS6nAxlQfMwMddLSV6Gkp0dFWIAS8C4/hbXg0r8OiiY6N5214DKf9X3Pa//8TQOYy0qVGkTzUKmZFjaKW5JKTJSYviyWIZcqUYfLkyQAUKVKEJUuWcPTo0WQTIUtLSwBy5879yS6zNm3aqN1fvXo1lpaW3Lx5k1KlSn1VvEqlkqJFi/LgwQNV2YQJE1S/FyhQgFGjRrFp0ybc3d0xMDDA2NgYbW3tJDEPGzZMbb+ff/6Zfv36yURIylFydCJkY2PD8+fqHyLPnz/H1NQ02bNBAGPHjmXEiBGq+yEhIdjb26drXDGR4czVvQCAR2Q4ugbGyZZJaWNgYMCKFSuoXbs2ffr04cSJE7i4uLB+/XoaNmyoVjc2Lp6zAW/YezWIQzee8SYsWm17oTxGVChggbO9OQVyG2FvYYituT46Wp/+ti6EICw6joCXYfgFheD3LAS/oBCuPwnhTVg0u3yfssv3KQoFdLV5RapS7m9tssQsliCWKVNG7b6tre1XT9tw9+5dJk2axNmzZ3n16pVqTqyHDx9+dSIECa9DheL/Zx83b97M4sWL8ff3JzQ0lNjYWExNTT97HC8vL2bMmMGtW7cICQkhNjaWyMhIwsPDMTQ0/Oo4JSkryNGJUOXKldm/f79a2ZEjR6hcuXKK++jp6aGnp5fRoUkZ6IcffqBChQq0b98eX19fGjVqxOjRo5k2bRphMYK/zj1inc8DgoIjVfvkMtKlQUkbaha1pEIBC/IYf9lrQKFQYKynTel8ZpTO9/8BsjFx8VwMfMvx2y/5985L/IJCWB+Ul756ubDhDcpk50tUJAwO/tYmS8xis2nr6Oio3VcoFF89mWezZs1wcHBg5cqV2NnZER8fT6lSpYiOjv78zp8RFxfH3bt3cXV1BRLGSnbu3JkpU6bQoEEDzMzM2LRpk+oikpQ8ePCApk2b0r9/f6ZPn06uXLk4deoUPXv2JDo6WiZCUo6RrRKh0NBQ7t27p7ofEBCAr68vuXLlIn/+/IwdO5YnT56wbt06APr168eSJUtwd3enR48e/PPPP2zZsoV9+/Zp6iFImaRIkSL4+PgwcuRIfv/9d2bNmsXGXYfQ/X4YcYZ5ADA31KFRKRualLbju0K50P7M2Z6voaOl5LtCufmuUG7GNCpOUHAEOy8/YalPb6ZEziJeoJYMCRQJ0yV+i5MlJs6mHRJE8uOEsm6CmDgm6FNTObx+/Zrbt2+zcuVKqldP6NpLz2Vj1q5dy9u3b1Xdb6dPn8bBwYHx48er6gQGBiaJ++OYL168SHx8PPPmzVONW9qyZUu6xSlJWUW2SoQuXLhA7dq1VfcTu7Dc3Nzw9PQkKCiIhw8fqrYXLFiQffv2MXz4cBYtWkS+fPn4448/NHrpvJR59PX1GTVlNv7aDhxeNoXHt31RBg7G5cdxjO33I82cbdHT1kySYWtmwIBajoiaY7lz3AFr78mYx75UbX+lzM3jSpNxLt4sZ1/amZzE2bS3dCVh9uwPk6GsPZu2lZUVBgYGHDx4kHz58qGvr5/k0nkLCwty587NihUrsLW15eHDh4wZM+aL2gsPD+fZs2dql88vWLCA/v37q94rixQpwsOHD9m0aROurq7s27ePnTt3qh2nQIECqi+W+fLlw8TEBEdHR2JiYvj1119p1qwZ3t7eLFu27MueGEnKwrLVe2ytWrUQQiS5JV7q6enpyfHjx5Psc/nyZaKiovD396dbt26ZHreU+V68j2TCrmvUn/8vtwxKYtdjMXkKliA+8j2X/hiLz58LUGSBy9IVCgXFanfGfNxt3rbfwa7CU+kaN4lK4QtpdSw3TX49hc8HA66/Gdl0Nm1tbW0WL17M8uXLsbOzo0WLFknqKJVKNm3axMWLFylVqhTDhw9nzpw5X9TeypUrsbW1pXDhwrRu3ZqbN2+yefNmtcHMzZs3Z/jw4QwaNAgXFxdOnz7NxIkT1Y7Tpk0bGjZsSO3atbG0tOSvv/7C2dmZ+fPnM2vWLEqVKsXGjRuZMWPGF8UpSVmZQqRqwpNvV0hICGZmZgQHB6dqcGFqhL19gfHihPENoUOeY2RhlWyZlHaRMXH8ftyflSfuExGTkOjULmaJe8PiFMqlx+jRo1m0aBEAFStWZNOmTaq5X7KKN2HRrDp1n7WnAwmNigWgSWlbxjUpQV7z5Af5ZyWRkZEEBARQsGBB9PX1v+5g8XEJV4eFPk8YE+RQJUueCZIkSTM+9X6T2s/vbHVGSJI+5bT/KxotOsnio3eJiInDxd6cTX2+Y033ipSwNUVPT4+FCxeyc+dOzM3NOXfuHGXLlk3STaBpuYx0+alBcU6616ZrZQeUCth3LYi6846zyOsukTGaP5OVaRJn0y7dNuGnTIIkSUpn8ozQZ2TEGaH4uFj8ziYM2C5RqQlKLe1ky6TUeRsWzS/7/dh68TEAViZ6TG5WksalbdQuIf5QYGAgHTp04OzZswAMHjyYOXPmZMkrBv2CQvDYc4OzAW8AKGRpxPz2LrjYm2s2sBSk6xkhSZKkT0iPM0IyEfqMjEiEpPRz8Pozxu+8xuv/5gH68bv8uDcsjqm+zmf2hJiYGMaNG8fcuXMBKFeuHFu2bKFw4cIZGvOXEEKw71oQU/++yYv3USgVMKCWI0PqFkFXO2ud2JWJkCRJmUV2jUnfrMiYOCbsuka/DRd5HRZNEStjtvWrzM8tS6cqCYKE+WHmzJnD3r17yZUrF5cuXaJs2bJZ8hJhhUJB0zJ2HB5egxYudsQLWHLsHi1/8+bWM7kMjCRJ0peSiZAGREeE4uFRCw+PWkRHhKZYJiXvzvP3NF9yig1nEqZK6FuzEPuGVKdCgVxfdLwmTZrg6+tL1apVef/+PR06dKB///5ERkZ+fudMZm6oy6KOZfm9czksDHW4GRRC81+92Xg2MHULvUqSJElqZCKkATGR4UxR/MsUxb/ERIanWCapE0Lw59mHNPv1FHeeh5LHWI91PSoytlGJr+4esre35/jx44wdOxaAZcuW8d1333Hnzp30CD3dNS5ty+HhNalXworouHjG77zOyK1XiIj+hgZSS5IkpQOZCEnZQnRsPGN3XGPczmtExcZTo6glB4ZWp0ZRy3RrQ1tbm19++YWDBw9iaWnJlStXKF++PBs3bky3NtKTpYkeK7tWYGyj4igVsOPSE1r97s2DV2GaDk2SJCnbkImQlOW9Co2i8x9n2HT+EUoFjG5YHM9urliaZMwVXg0aNMDX15datWoRGhrKjz/+SK9evQgPz3pn6hQKBX1rFmZjr+/IY6zLrWfvafbrKbxufmMr1kuSJH0hmQhJWdqNp8G0WOLN+QdvMdHTZlU3V/rXKowy+VVK042dnR1eXl5MnjwZhULBqlWrqFSpEn5+fhna7peqXDh3wjgpBwveR8XSe/0F1ngHaDosSZKkLE8mQlKWdfD6M9ou9eHJuwgK5jFi58Cq1C6WeTNua2lp4eHhgZeXFzY2Nly/fp0KFSqwdu3aTIshLaxN9fmrz3d0qpQfIWDK3zfx2HODuHg5iDor6tatGy1btlTdr1WrFsOGDfuqY6bHMVLD29ub0qVLo6Ojo/YYsqqPn2spczx48ACFQoGvr6+mQ/kkmQhJWdLGs4H033iRiJg4ahS1ZNeAqjhaGWskljp16uDr60u9evUIDw+nW7duuLm5ERqa9a7u09FSMr1lKcY2Kg6A5+kH9F1/kfDoWA1Hlj1069YNhUKBQqFAV1cXR0dHpk6dSmxsxj9/O3bsYNq0aamqe/z4cRQKBe/evfviY3yNESNG4OLiQkBAgGqtR01K6flItGjRoiwRZ0o+fN3p6OhQsGBB3N3ds+SVq2lhb29PUFAQpUqV0nQonyQTISlLEUKwyOsu43deRwj4oWJ+VrtVwMwwdXMDZRRra2sOHjzItGnTUCqVrFu3DldXV65du6bRuJKTOG7ot07l0NVW4uX3nA7Lz/DyfZSmQ8sWGjZsSFBQEHfv3mXkyJF4eHikuChqdHR0urWbK1cuTExMNH6M1PD396dOnTrky5cPc3PzJNuFEJmSPKaWmZlZsnFmtk+9XhJfd/fv32fBggUsX76cyZMnZ2g8cXFxxMfHZ9jxtbS0sLGxQVs7a6+UIBMhDdA3NudcFU/OVfFE39g8xbJvTVy8YNLuGyzwSrhkfUjdIvzSqhTaWlnjZaqlpcWECRP4559/sLOz49atW1SsWJE//vgjS87h06SMLX/1rkQuI12uPQmmwwofgoIjNB0WCAFR7yH8TcLPLPbc6enpYWNjg4ODA/3796devXrs2bMH+H8Xy/Tp07Gzs6NYsWIAPHr0iPbt22Nubk6uXLlo0aIFDx48UB0zLi6OESNGYG5uTu7cuXF3d0/ymvm4WysqKorRo0djb2+Pnp4ejo6OrFq1igcPHlC7dm0ALCwsUCgUdOvWLdljvH37lq5du2JhYYGhoSGNGjXi7t27qu2enp6Ym5tz6NAhSpQogbGxseoDOTmJXR2vX7+mR48eKBQKPD09VWdkDhw4QPny5dHT0+PUqVNERUUxZMgQrKys0NfXp1q1apw/f151vMT9Dh06RNmyZTEwMKBOnTq8ePGCAwcOUKJECUxNTenUqdNXXayQXDfkkCFDcHd3J1euXNjY2ODh4aG2z7t37+jVqxeWlpaYmppSp04drly5otru7+9PixYtsLa2xtjYGFdXV7y8vNSOUaBAAaZNm0bXrl0xNTWlT58+KcaY+Lqzt7enZcuW1KtXjyNHjqi2x8fHM2PGDAoWLIiBgQHOzs5s27ZN7Rh79uyhSJEi6OvrU7t2bdauXat2pizx771nzx6cnJzQ09Pj4cOHREVFMWrUKPLmzYuRkRGVKlXi+PHjquMGBgbSrFkzLCwsMDIyomTJkuzfvx9IeI117twZS0tLDAwMKFKkCGvWrAGS7xr7999/qVixInp6etja2jJmzBi1pDk1f5v0ljU+Yb4xWjq6uNZ3w7W+G1o6uimWfUuiY+MZ8tdl1p8JRKGAaS1KMqJ+0RTXCtOkmjVr4uvrS8OGDYmMjKR379507tyZ9+/fazq0JMo75GJ7/yrYmelz/2UY7Zb58PB15l/9JoQgLCyMsFdPCAs4T9ija4Q9vZXwM+B8QnlYWIbcvjZJNTAwUPsmf/ToUW7fvs2RI0fYu3cvMTExNGjQABMTE06ePIm3t7cqoUjcb968eXh6erJ69WpOnTrFmzdvPrvYb9euXfnrr79YvHgxfn5+LF++HGNjY+zt7dm+fTsAt2/fJigoiEWLFiV7jG7dunHhwgX27NmDj48PQggaN25MTEyMqk54eDhz585l/fr1nDhxgocPHzJq1Khkj5fY1WFqasrChQsJCgqiQ4cOqu1jxoxh5syZ+Pn5UaZMGdzd3dm+fTtr167l0qVLODo60qBBA968eaN2XA8PD5YsWcLp06dVSeXChQv5888/2bdvH4cPH+bXX3/95POVVmvXrsXIyIizZ88ye/Zspk6dqpZ4tGvXTpWQXbx4kXLlylG3bl1V7KGhoTRu3JijR49y+fJlGjZsSLNmzXj48KFaO3PnzsXZ2ZnLly8zceLEVMV2/fp1Tp8+ja7u/z8LZsyYwbp161i2bBk3btxg+PDh/Pjjj/z7778ABAQE0LZtW1q2bMmVK1fo27cv48ePT3Ls8PBwZs2axR9//MGNGzewsrJi0KBB+Pj4sGnTJq5evUq7du1o2LChKmkeOHAgUVFRnDhxgmvXrjFr1iyMjROGKkycOJGbN29y4MAB/Pz8WLp0KXny5En2cT158oTGjRvj6urKlStXWLp0KatWreLnn39O098m3Qnpk4KDgwUggoODNR1KjhUZEyt6ep4TDqP3iiLj9ou9V55qOqRUiYuLEzNnzhRaWloCEEWKFBGXL1/WdFjJevQmTNSc/Y9wGL1XVJx+RNx9HpJhbUVERIibN2+KiIgIVVloaKgANHILDQ1Ndexubm6iRYsWQggh4uPjxZEjR4Senp4YNWqUaru1tbWIiopS7bN+/XpRrFgxER8fryqLiooSBgYG4tChQ0IIIWxtbcXs2bNV22NiYkS+fPlUbQkhRM2aNcXQoUOFEELcvn1bAOLIkSPJxnns2DEBiLdv36qVf3iMO3fuCEB4e3urtr969UoYGBiILVu2CCGEWLNmjQDEvXv3VHV+++03YW1t/cnnyczMTKxZsyZJPLt27VKVhYaGCh0dHbFx40ZVWXR0tLCzs1M9F4n7eXl5qerMmDFDAMLf319V1rdvX9GgQYMU40np+Uj04d9ViITnqVq1amp1XF1dxejRo4UQQpw8eVKYmpqKyMhItTqFCxcWy5cvTzGOkiVLil9//VV138HBQbRs2TLF+h/Gp6WlJYyMjISenp4AhFKpFNu2bRNCCBEZGSkMDQ3F6dOn1fbr2bOn+OGHH4QQQowePVqUKlVKbfv48ePVnpfEv7evr6+qTmBgoNDS0hJPnjxR27du3bpi7NixQgghSpcuLTw8PJKNvVmzZqJ79+7JbgsICBCA6n1x3LhxSf5XfvvtN2FsbCzi4uKEEJ//23wsufebRKn9/M7aHXc5VHREKIsWJHyLGjp8M7oGxsmWfQuiYuPov+ES/9x6gZ62kj/cKlC9SPpNkpiRlEolo0ePplq1anTs2JG7d+/y3XffsWDBAvr165elzmblszBkS9/K/LjqLHeeh9J++RnW96xISTuzzAkgi3V/fcrevXsxNjYmJiaG+Ph4OnXqpHZqvnTp0mrf1K9cucK9e/eSjM2JjIzE39+f4OBggoKCqFSpkmqbtrY2FSpUSPFsla+vL1paWtSsWfOLH4efnx/a2tpq7ebOnZtixYqpTQNhaGiottCwra0tL168+KI2K1SooPrd39+fmJgYqlatqirT0dGhYsWKSaahKFOmjOp3a2trDA0NKVSokFrZuXPnviimlHzYJqg/7itXrhAaGkru3LnV6kRERODv7w8knBHy8PBg3759BAUFERsbS0RERJIzQh8+J59Su3Ztli5dSlhYGAsWLEBbW5s2bdoAcO/ePcLDw6lfv77aPtHR0ZQtWxZIODvo6uqqtr1ixYpJ2tHV1VV77NeuXSMuLo6iRYuq1YuKilI9/iFDhtC/f38OHz5MvXr1aNOmjeoY/fv3p02bNly6dInvv/+eli1bUqVKlWQfo5+fH5UrV1Z7b6xatSqhoaE8fvyY/PnzA5/+22QEmQhpQExkOO4xCf2rAyLD0TUwTrYsp4uMiaP/hoscu/0SfR0lq9xcqeqY/CnVrKxq1ar4+vrSvXt3/v77bwYMGMCxY8dYuXIlZmaZlGikgpWpPpv6VKbr6rNcfxJCp5Vn+av3dzjZpbwqc3ox1I4n9K735yvmKgR66TvY19DQME31Ez+QdHV1sbOzSzLQ08jISO1+aGhoijOQW1p+WVJvYGDwRft9CR0d9QsRFArFF3cnfvzcfEkMiVdOfRxTeg/q/VQboaGh2Nraqo2TSZQ46HrUqFEcOXKEuXPn4ujoiIGBAW3btk0yIDq1z4mRkRGOjo4ArF69GmdnZ1atWkXPnj1VV6ju27ePvHnzqu2np5e2iWUNDAzUEpHQ0FC0tLS4ePEiWlpaanUTu7969epFgwYNVN2UM2bMYN68eQwePJhGjRoRGBjI/v37OXLkCHXr1mXgwIHMnTs3TXF9KDP+/h+SY4QkjYiMiaPfB0nQ6myaBCXKnTs3u3fvZt68eWhra7N161bKlSvHhQsXNB2amlxGuvzZ+zvK5jcnOCKGLqvOcvd5xo9tUsTHYmRo8Pmbvi5GRkbpekvrmbnED6T8+fOn6mqXcuXKcffuXaysrHB0dFS7mZmZYWZmhq2tLWfPnlXtExsby8WLF1M8ZunSpYmPj1eN//hY4hmpuLiU15YrUaIEsbGxau2+fv2a27dv4+Tk9NnH9bUKFy6Mrq4u3t7/T4BjYmI4f/58prT/NcqVK8ezZ8/Q1tZO8jdNHP/i7e1Nt27daNWqFaVLl8bGxkZtgPzXUCqVjBs3jgkTJhAREaE2sPnjeOzt7QEoVqxYkvebDwemp6Rs2bLExcXx4sWLJMe2sbFR1bO3t6dfv37s2LGDkSNHsnLlStU2S0tL3Nzc2LBhAwsXLmTFihXJtlWiRAnVWLVE3t7emJiYkC9fvjQ9R+lJJkJSpouOjaf/hoscT0yCurlSJRsnQYkUCgUjRozg1KlTODg4cP/+fapUqcKiRYuy1FVlpvo6eHavSKm8prwOi6bzH2czfn0yrVROf5DaellI586dyZMnDy1atODkyZMEBARw/PhxhgwZwuPHjwEYOnQoM2fOZNeuXdy6dYsBAwakOOcNJFxt5ObmRo8ePdi1a5fqmFu2bAHAwcEBhULB3r17efnyZbJzWhUpUoQWLVrQu3dvTp06xZUrV/jxxx/JmzcvLVq0yJDn4kNGRkb079+fn376iYMHD3Lz5k169+5NeHg4PXv2zJA2r127hq+vr+r24VVeaVGvXj0qV65My5YtOXz4MA8ePOD06dOMHz9elWwUKVKEHTt2qNrp1KlTup61aNeuHVpaWvz222+YmJgwatQohg8fztq1a/H39+fSpUv8+uuvqgle+/bty61btxg9ejR37txhy5YtqrmTPvVloGjRonTu3JmuXbuyY8cOAgICOHfuHDNmzGDfvn0ADBs2jEOHDhEQEMClS5c4duwYJUqUAGDSpEns3r2be/fucePGDfbu3ava9rEBAwbw6NEjBg8ezK1bt9i9ezeTJ09mxIgRKJWaS0dkIiRlqrh4wYgtvqozQWu6VaRK4eyfBH2oUqVKXL58mVatWhETE8OwYcNo3bo1b9++1XRoKmYGOqzvUYniNia8eB9Fp5VnePQmA68m0zUG5WeSHKVOQr1sxtDQkBMnTpA/f35at25NiRIl6NmzJ5GRkZiaJnQ7jhw5ki5duuDm5kblypUxMTGhVatWnzzu0qVLadu2LQMGDKB48eL07t2bsLCEhDVv3rxMmTKFMWPGYG1tzaBBg5I9xpo1ayhfvjxNmzalcuXKCCHYv39/kq6HjDJz5kzatGlDly5dKFeuHPfu3ePQoUNYWFhkSHs1atSgbNmyqlv58uW/6DgKhYL9+/dTo0YNunfvTtGiRenYsSOBgYFYW1sDMH/+fCwsLKhSpQrNmjWjQYMGlCtXLt0ei7a2NoMGDWL27NmEhYUxbdo0Jk6cyIwZMyhRogQNGzZk3759FCxYEICCBQuybds2duzYQZkyZVi6dKnqqrHPdZ+tWbOGrl27MnLkSIoVK0bLli05f/68asxOXFwcAwcOVLVbtGhRfv/9dyDh7OTYsWMpU6YMNWrUQEtLi02bNiXbTt68edm/fz/nzp3D2dmZfv360bNnTyZMmJBeT9sXUYis9FU1CwoJCcHMzIzg4GDVm9rXCnv7AuPFCf9MoUOeY2RhlWxZTiOEYPyu6/x59iE6Wgr+cHOlZjquHp/VCCFYsmQJo0aNIjo6GgcHBzZv3qw2eFXTXr6PouMKH/xfhmGfy4CtfatgY6b/VceMjIwkICCAggULoq//wbEi3sHbT6x/ZlEQDMy/qm1Jkv5v+vTpLFu2jEePHmk6lAyT4vsNqf/8lmeEpEwz59Bt/jz7EIUCFnRwydFJECR8qxw8eDCnT5+mUKFCBAYGUq1aNebNm5ehA//SwtJEjz97f4dDbkMevYnAbfU5giNiPr/jlzAwT0h2Pj4zpNSRSZAkpYPff/+d8+fPc//+fdavX8+cOXNwc3PTdFhZnkyEpEyx/F9/fj+ecNnp9JalaVrGTsMRZZ7y5ctz6dIl2rVrR2xsLKNGjaJ58+a8fv1a06EBCYu1buxVCSsTPW4/f0/vdReIjEl5EO5XMTAH65KQ2xHMHRJ+WpeUSZAkpYO7d+/SokULnJycmDZtmmqJGOnTZNfYZ2RE11hcTDQn9yX0r1ZvMgAtHd1ky3KKbRcfM2prwqDF0Q2L079W4c/skTMJIVi+fDnDhg0jKiqKfPnysWnTJrV5VjTJLyiE9st8eB8VS6NSNizpVA4tZdrnQvrUqWpJkqT0lB5dYzIR+oyMSIS+Jd73XuG2+hyx8YI+NQoxrnHyVxN8S3x9fWnfvj13795FS0uLn3/+GXd3d41eNZHIx/81bqvPER0XT9fKDkxpXjLNl5/LREiSpMwixwhJWdqd5+/pt+EisfGCZs52jGlYXNMhZQkuLi5cvHiRTp06ERcXx9ixY2ncuHGGzpyaWpUL52Z+B2cUCljnE8hvx+598bHkdyxJkjJaerzPyERIA2Iiw/ltbnt+m9uemMjwFMuysxchkXRfc573kbG4FrBgTtsyKL+gmyWnMjExYcOGDfzxxx/o6+tz6NAhXFxcUpxALzM1LWPH5KYJE97NPXyHPVeepmn/xEuzv2a1cEmSpNRIfJ/5mikhZNfYZ8jL59MuLCqWDit8uP4khEJ5jNjevwoWRjlnzFN6u379Ou3atePWrVsolUo8PDwYN25ckunuM9v0fTdZeTIAXW0lm/p8R7n8qZ/7JSgoiHfv3mFlZYWhoWGWWndNkqTsTwhBeHg4L168wNzcHFtb2yR1Uvv5Ldcak9JVXLxg6KbLXH8SQi4jXdZ0d5VJ0GeUKlWKCxcuMHDgQNauXcukSZP4999/2bBhg9oU95ltTKMSBLwKw8vvBX3WXWDXwKrks0jdul2JcWeF7j5JknIuc3Pzr36flImQlK7mHr6Nl1/CSvIru1bAIfeXLcL4rTEyMsLT05PatWszYMAAjh49iouLCxs3bqRu3boaiUlLqWBRx7K0XeaDX1AIvdZeYFv/Khjrff5tQ6FQYGtri5WVFTExGTQvkSRJ3zQdHZ10OXMuEyEp3ez2fcLS/+YKmt22DOUdMmYa/ZzMzc2NihUr0r59e65fv079+vWZMGECkydP1khXmZGeNn+4VaDFEm9uPXvP0L8us6JrhVRfVq+lpaXxLj5JkqRPkYOlpXRx7XEw7tuuAtCvZmFauOTVcETZV4kSJTh79iy9evVCCMG0adOoW7cuT5+mbdByeslrbsDKruXR01Zy9NYLZh+8pZE4JEmSMoJMhKSv9uJ9JL3XXSAqNp46xa34qUExTYeU7RkaGrJy5Uo2btyIsbEx//77L87Ozhw6dEgj8ZTNb8Hcds4ALD9xn7/TeCWZJElSViUTIemrRMXG0W/9RZ6FRFLY0oiFHV2+aDZiKXmdOnXi4sWLODs78+rVKxo2bMjYsWOJjY3N9FiaOdvRt2YhANy3XeXWs5BMj0GSJCm9yURIA/SMTNnrOJm9jpPRMzJNsSw78Nhzk0sP32Gqr80fbq6Y6n/5XA5S8ooWLcqZM2cYMGAAADNnzqRWrVoaWVHavUFxqhfJQ0RMHH3WXSQ4XA6EliQpe5PzCH2GXGIjZVsvPOKnbVdRKMCze8WE1eTj4yDwNIQ+B2NrcKgCSjlYNr1s3bqVXr16ERISQq5cuVi7di1NmzbN1BjehkXTbMkpHr+NoFYxS1a5ucqzgJIkZTlyiQ0pQ914GsyEXdcBGF6vaEISdHMPLCwFa5vC9p4JPxeWSiiX0kW7du24dOkS5cuX582bNzRr1oxRo0YRHR2daTFYGOmyvEt59HWUHL/9kvlHbmda25IkSelNJkIaEBMZjueSXngu6aW2xMbHZVlVcHgM/TdcIio2ntrFLBlU2zEh2dnSFUI+GkQbEpRQLpOhdFO4cGG8vb0ZOnQoAPPmzaNGjRo8ePAg02IoaWfGrDZlAPjtmD9H/Z5nWtuSJEnpSSZCGhAdEUr316vo/noV0RGhKZZlRfHxgpFbfXn4Jpx8FgYs6OCCkng4OBpIrpf1v7KDYxK6zaR0oaenx8KFC9m5cyfm5uacPXuWsmXLsmvXrkyLoYVLXrpVKQDAiC1XePw2ayfwkiRJyZGJkJQmS//1x8vvBbraSpb9WB5zQ92EMUEfnwlSIyDkSUI9KV21bNkSX19fKlWqxLt372jVqhVDhw4lKioqU9of17gEzvbmBEfEMPDPy0THxmdKu5IkSelFJkJSqp25/5p5hxPGg0xrUZJSec0SNoSmslsktfWkNHFwcODkyZOMGjUKgMWLF1O1alX8/f0zvG1dbSVLfiiLmYEOVx69Y8YBvwxvU5IkKT3JREhKldehUQzddJl4AW3K5aODa/7/bzS2Tt1BUltPSjMdHR3mzJnD3r17yZUrFxcvXqRcuXJs3bo1w9u2z2XIvP8mW1zj/YCD14MyvE1JkqT0IhMh6bMSxgVd4XlIFIUtjZjWsqR6BYcqYGoHpHQJtQJM8ybUkzJUkyZN8PX1pWrVqoSEhNC+fXsGDBhAZGRkhrZbz8mavjUSJlv8aetVAl+HZWh7kiRJ6UUmQtJn/XHqPsdvv0RPW8mSTuUw1P1orV6lFjSc9d+dj5Oh/+43nCnnE8ok9vb2HD9+nLFjxwKwdOlSvvvuO+7cuZOh7Y5qUIwKDha8j4plkBwvJElSNiETIemTLj98y+yDCeOCJjVzooRtCpNSOTWH9uvA1Fa93NQuodypeQZHKn1IW1ubX375hYMHD5InTx6uXLlC+fLl+fPPPzOsTR0tJb92Kou5oQ7XngQz97CcX0iSpKxP+/NVpPSmZ2TKlnzDVb+nVKZpwRExDP7rMrHxgialbelUMf+nd3BqDsWbyJmls5AGDRpw5coVOnXqxL///kvnzp05duwYixYtwtDQMN3bszUzYFabMvRdf5EVJ+5TzTEPNYpapns7kiRJ6UUusfEZ3+oSG0IIBv11mX1Xg7DPZcC+IdXlOmLZWGxsLFOnTuXnn39GCEGpUqXYsmULJUqUyJD2Juy6xoYzD8ljrMfBYdXJY6yXIe1IkiSlJMcusfHbb79RoEAB9PX1qVSpEufOnUuxrqenJwqFQu2mr6+fidFmX9svPWHf1SC0lQp+/aGcTIKyOW1tbaZOncrhw4extrbm+vXrVKhQgbVr12ZIexOaOFHU2phXoVGM3HKF+Hj5fUuSpKwpWyVCmzdvZsSIEUyePJlLly7h7OxMgwYNePHiRYr7mJqaEhQUpLoFBgZmYsTJi42OZOuqEWxdNYLY6MgUyzQl8HUYk3f/t45Y/aK42JtrNB4p/dSrVw9fX1/q1q1LeHg43bp1o1u3boSFpe9VXvo6Wvz6Qzn0tJX8e+clq70D0vX4kiRJ6SVbJULz58+nd+/edO/eHScnJ5YtW4ahoSGrV69OcR+FQoGNjY3qZm2t+blsosJCaP94Ae0fLyAqLCTFMk2IiYtn6CZfwqLjqFggF/1qFtZYLFLGsLGx4dChQ0ydOhWlUsnatWtxdXXl+vXr6dpOMRsTJjR1AmDWwVvceBqcrseXJElKD9kmEYqOjubixYvUq1dPVaZUKqlXrx4+Pj4p7hcaGoqDgwP29va0aNGCGzduZEa42dav/9zD99E7TPS1md/BGS1lSnMDSdmZlpYWEydO5J9//sHOzg4/Pz9cXV1ZtWoV6Tls8MdK+anvZE1MnGD4Zl8iY+R6c5IkZS3ZJhF69eoVcXFxSc7oWFtb8+zZs2T3KVasGKtXr2b37t1s2LCB+Ph4qlSpwuPHj1NsJyoqipCQELXbt+LCgzcs+ecuANNblSafRfpfVSRlLTVr1sTX15cGDRoQGRlJr1696NKlC+/fv0+X4ysUCma2Lk0eYz3uPA9lziF5Sb0kSVlLtkmEvkTlypXp2rUrLi4u1KxZkx07dmBpacny5ctT3GfGjBmYmZmpbvb29pkYsea8j4xh2GZf4gW0LpuX5s52mg5JyiSWlpbs37+fGTNmoKWlxcaNG6lQoQJXrlxJl+PnNtZjdtvSAKw6FYD3vVfpclxJkqT0kG0SoTx58qClpcXz5+oLdz5//hwbG5tUHUNHR4eyZcty7969FOuMHTuW4OBg1e3Ro0dfFXd2MW3vTR6/jSCfhQFTWpT8/A5SjqJUKhkzZgz//vsv+fLl486dO1SqVIlly5alS1dZneLWdKqUMA/VqK1XCA6P+epjSpIkpYdskwjp6upSvnx5jh49qiqLj4/n6NGjVK5cOVXHiIuL49q1a9ja2qZYR09PD1NTU7VbTud18zlbLjxGoYB57ZwxkZfKf7OqVq2Kr68vTZs2JSoqiv79+9OxY0eCg79+oPOEJiUokNuQoOBIJu5O34HZkiRJXyrbJEIAI0aMYOXKlaxduxY/Pz/69+9PWFgY3bt3B6Br166q9ZUA1bwp9+/f59KlS/z4448EBgbSq1cvTT2ELOdNWDRjdlwDoFe1glQqlFvDEUmaljt3bvbs2cPcuXPR1tZmy5YtlCtXjosXL37VcQ11tVnQwQUtpYI9V56y2/dJOkUsSZL05bLVEhsdOnTg5cuXTJo0iWfPnuHi4sLBgwdVA6gfPnyIUvn/3O7t27f07t2bZ8+eYWFhQfny5Tl9+jROTk6aeggA6BoYsyZ3T9XvKZVlNCEEE3Zd41VoFEWsjBn5fbFMaVfK+hQKBSNHjqRq1ap07NiR+/fvU6VKFebOncugQYNQKL7sasKy+S0YVNuRRUfvMmn3Db4rlBtrUznJqSRJmiOX2PiMnLzExm7fJwzd5Iu2UsGugVUplddM0yFJWdDbt2/p0aMHu3btAqBVq1asWrUKCwuLLzpeTFw8rX8/zbUnwdQpbsUqtwpfnFhJkiSlJMcusSGlj2fBkUzclTBOY0jdIjIJklJkYWHBjh07WLRoETo6OuzcuZOyZcty9uzZLzqejpaSee2d0dVS8s+tF2y9mPJ0FpIkSRlNJkIaEBsdyb6NHuzb6KG2xMbHZRlFCMHo7VcJiYzFOZ8ZA2rJ2aOlT1MoFAwZMoTTp09TqFAhAgMDqVatGvPmzfuiq8qKWpswvH5RAKb9fZOn7yLSO2RJkqRUkYmQBkSFhdD03hSa3puitsTGx2UZZeuFx/x75yW62gnfzLW15MtASp0KFSpw6dIl2rVrR2xsLKNGjaJ58+a8fv06zcfqU6MQZfOb8z4qltHbr6brjNaSJEmpJT8BvzFBwRFM23sTgJH1i+JoZaLhiKTsxszMjM2bN/P777+jp6fH3r17cXFxwdvbO03H0VIqmNvOGT1tJSfvvuLPcw8zKGJJkqSUyUToGyKEYMz2a7yPiqVsfnN6VS+k6ZCkbEqhUNC/f3/OnDlDkSJFePz4MTVr1mTmzJnEx8f/v2J8HASchGvbEn7Gq681VtjSGPeGxQGYvs+PR2/CM/NhSJIkyUToW7L14v+7xOa0LSMXVJW+mouLCxcvXqRTp07ExcUxduxYmjRpwsuXL+HmHlhYCtY2he09E34uLJVQ/oHuVQpQsUAuwqPjGLNDdpFJkpS5ZCL0jfiwS2yE7BKT0pGJiQkbNmxg5cqV6Ovrc/DgQVxKFeff2Z0g5Kl65ZAg2NJVLRlSKhXMblsGfR0l3vdes/n8t7GsjSRJWYNMhL4BQgjG7rjG+8hYXOzN6S27xKR0plAo6NWrF+fOnaN48eI8ffGGOuvCmPZvFHHxH57h+e/3g2PUuskK5DFi1H8Tek7f50dQsLyKTJKkzCEToW/AjktPOH47oUtsbjvZJSZlnNKlS3Nh2yLcnHWIFzDpeBQNNoTzLPSDcUMICHkCgafV9u1etSAu9glXkY3bcU12kUmSlClkIqQBugbGLDFqxxKjdmpLbHxclh5evo9i6n9dYsPqFZFdYlKGM4oPwbOlAZ4t9DHUgaMBcbgsC+Po/Vj1iqHP1e5qKRXMaVsGXS0lx26/ZJdci0ySpEwgl9j4jOy+xMbAjZfYdy2IUnlN2TWgqpwzSMp4AScTBkYDN1/G0X5rBDdexqMAJtbQZVJNvYSzkm57oWD1JLv/duwecw7dxsxAhyMjamBlItcikyQp7eQSGxIHrz9j37UgtJQKZrUpI5MgKXM4VAFTO0CBk6UW53ob0ausDgKYeiKauuvCeSqsEuolo0+NQpS0MyU4IoZJu25kauiSJH175CejBsTFRHN810KO71pIXEx0imVfIzg8hom7E9YS61ezECXt5FpiUiZRakHDWf/dUWCoo2BlcwM2tjbAWBf+DYzDZclLDh3xSnZ3HS0ls9uWQVup4OCNZxy8HpR5sUuS9M2RiZAGRIa+o/aV4dS+MpzI0Hcpln2N6ftv8vJ9FIUsjRhcp8hXH0+S0sSpObRfB6a2qqJOpXW4OKIQzsUK8vJNMA0bNmTs2LHExsYm2b2knRl9ayZc3Thp9w2CI2IyLXRJkr4tMhHKgU7dfcWWC49RKGB2mzLo62hpOiTpW+TUHIZdTxgL1GYVuO2l6PQ7nPG9Sb9+/QCYOXMmtWrV4tGjpHMHDa5ThEJ5jHjxPoqZB/wyO3pJkr4RMhHKYcKjYxm78yoAXb9zoEKBXBqOSPqmKbUSBkSXbpvwU6mFvr4+S5cuZfPmzZiYmODt7Y2Liwv79u1T21VfR4sZrUsD8Ne5R/j4p31hV0mSpM+RiVAOs8jrLo/eRGBnps9P/63hJElZUfv27bl8+TLly5fnzZs3NG3alFGjRhET8/9usEqFctOpUn4Axu28RmRMXEqHkyRJ+iIyEcpBrj8J5o9TAQBMa1kKYz1tDUckSZ9WuHBhvL29GTJkCADz5s2jevXqBAYGquqMaVQca1M9Al6FsfjoXU2FKklSDiUToRwiNi6eMTuuEhcvaFLGlrolrDUdkiSlip6eHosWLWLHjh2Ym5tz9uxZXFxc2LVrFwCm+jpMa1EKgOUn7nPjabAGo5UkKaeRiVAO4Xn6AdefhGCqr83kZk6aDkeS0qxVq1ZcvnyZihUr8u7dO1q1asWwYcOIjo7m+5I2NC5tQ1y8YNyOax+tXyZJkvTlZCKkATr6hszWacxsncbo6BumWJZaj96EM+/wHQDGNS4hZ+KVsq0CBQpw8uRJRo4cCcCiRYuoWrUq9+/fx6NZSUz0tbnyOJh1Pg80G6gkSTmGXGLjM7L6EhtCCLqtOc+/d15SqWAuNvX5DoVCLqoqZX979+7Fzc2NN2/eYGpqyh9//EGUfUUm7LqOka4WR0bUxM7cQNNhSpKURcklNr4Re6485d87CSvL/9K6tEyCpByjadOm+Pr6UqVKFUJCQmjfvj2n183Cxc6QsOg4Ju+Ry29IkvT1ZCKkAXEx0Zw/spbzR9aqLbHxcdnnBIfHMO2/leUH1XaksGX6rVovSVmBvb09x48fZ8yYMQAsXbqUOyuHEf/uKUduPufg9WcajlCSpOxOJkIaEBn6joqnu1HxdDe1JTY+LvucmQdv8So0GkcrY9VyBJKU0+jo6DBjxgwOHDhAnjx58Lt+lRfrhhF281889tzgfeQnlt+Ij4OAk3BtW8LPeDkPkSRJ6uREM9nUhQdv+OvcQwCmtyyFnrZcRkPK2Ro2bIivry+dOnXixIkTRP09h8iHV5nhaMYv7Ssk3eHmHjg4GkKe/r/M1C5hQVin5pkXuCRJWZo8I5QNRcfGM27nNQA6VLCnUqHcGo5IkjJH3rx5OXr0KBMmTEChUBB65RBzB7Vj57Fz6hVv7oEtXdWTIICQoITym3syL2hJkrI0mQhlQytP3ufO81ByG+kytrFcRkP6tmhrazNt2jQOHz6MoVkuYl4+oF3Dmqzx9EyoEB+XcCaI5C6I/a/s4BjZTSZJEiAToWwn8PX/lxmY0LQE5oa6Go5IkjSjXr16nLt4CeOCLsRFR9Kje3e6d+9OmN/RpGeC1AgIeQKBpzMtVkmSsi6ZCGUjQggm7LpOVGw8VR1z09Ilr6ZDkiSNKlnYgeUbd2BWrTMolHh6euLavAc3XqTibE/o84wPUJKkLE8mQtnI3qtBnLz7Cl1tJT+3lHMGSRJAx0oFqNdpANYdf8bQPA9+95/gujKM1Zej+eR8scZyPT5JkuRVYxqho2/IZFFT9XtKZR8KiYxh6n9zBg2s5UjBPEaZFK0kZW1KpYLprUrT5NE7dH5cSIlLK7l4+l967onkn4A4ljbRx0Tvwy8NioSrxxyqaCxmSZKyDrnExmdklSU2Ju++zlqfQArlMeLAsOrycnlJ+sjMA7dY9q8/tia6NH6xlim/biROQNHcSra0NcDZRgv4LyFqv05eQi9JOZxcYiMHufr4HevOBAIwTc4ZJEnJGlq3CPksDAh6H43y+3EcXzuDvGba3HkdT6U/wlh+IRphYiuTIEmS1MhESAPi42K5cXo3N07vJj4uNsUygLh4wbid1xACWrrYUdUxj6bClqQszUBXi2ktSgGw2vsBuesNwPfOY5rUrkxUHPTbF0nHs6UJyVdLs4FKkpSlyERIAyJC3lDqSEtKHWlJRMibFMsA1vs84PqTEEz0tRnfxElTIUtStlC7uBUNS9oQF59whWWuPFbs8TrFnDlz0NbWZsvWrZQrV46LFy9qOlRJkrKINCdCbm5unDhxIiNikT7yPCSSuYfvADC6YXEsTfQ0HJEkZX2TmjlhqKvFxcC3bLnwCKVSyahRozh58iT58+fH39+fKlWq8Ouvv376qjJJkr4JaU6EgoODqVevHkWKFOGXX37hyZMnGRGXBEzbe5PQqFhc7M3pVDG/psORpGzBztyAEfWLAgkLE78Jiwbgu+++4/Lly7Ro0YLo6GiGDBlCmzZtePv2rSbDlSRJw9KcCO3atYsnT57Qv39/Nm/eTIECBWjUqBHbtm0jJuYTq0BLaXLy7kv2Xg1CqYCfW5ZCqZRzBklSanWrUoDiNia8C49hxn4/VXmuXLnYuXMnixYtQkdHh507d1KuXDnOnTv3iaNJkpSTfdEYIUtLS0aMGMGVK1c4e/Ysjo6OdOnSBTs7O4YPH87du3fTO85vSlRsPBN3XQfArUoBSuU103BEkpS9aGspmd4qYeD01ouPORfw/3F3CoWCIUOG4O3tTcGCBXnw4AFVq1Zl/vz5sqtMkr5BXzVYOigoiCNHjnDkyBG0tLRo3Lgx165dw8nJiQULFqRXjN+cVWeCePA6HGtTPdUpfkmS0qa8Qy5+qGgPwIRd14iJi1fb7urqyuXLl2nbti2xsbGMHDmSFi1a8ObNm+QOJ0lSDpXmRCgmJobt27fTtGlTHBwc2Lp1K8OGDePp06esXbsWLy8vtmzZwtSpUzMi3hxPO96WlWeCAJjY1AkTfR0NRyRJ2dfohsXJZaTLneehrD4VkGS7mZkZW7Zs4bfffkNXV5e///4bFxcXTp+WC7JK0rcizYmQra0tvXv3xsHBgXPnznHhwgX69eunNmtj7dq1MTc3T884cxQdfUNGRVdgVHQFtSU2RkZVoFzUaKLjBNWL5KFJaVsNRypJ2Zu5oS5jGxUHYKHXXZ68i0hSR6FQMGDAAM6cOYOjoyOPHj2iRo0azJ49m/j4+CT1JUnKWdK8xMb69etp164d+vr6GRVTlpKZS2zsuxrEwD8voaut5NCwGnI9MUlKB/Hxgg4rfDj/4C0NSlqzvEuFFOu+f/+evn378tdffwHQqFEj1q5di6WlZWaFK0lSOsmwJTa6dOnyzSRBmSk0Kpape28A0L9mYZkESVI6USoV/NyyNFpKBYduPOefW89TrGtiYsLGjRtZuXIl+vr6HDhwABcXFzl3miTlYHJmaQ2Ij4vlwfVTPLh+SrWcxvzDt3geEoWdsZJ+1R00HKEk5SzFbEzoWa0gAJP33CAyJi7FugqFgl69enHu3DmKFy/O06dPqV27NtOnT5ddZZKUA8lESAMiQt5QcHt1Cm6vTkTIG24+DWHt6YRFVS9HTyA+IljDEUpSzjO0bhFsTPV59CaC347d+2z90qVLc/78ebp27Up8fDwTJkygYcOGPH+e8hklSZKyH5kIaVi8EEzcfZ04AWHKU0RqXdJ0SJKUIxnpaTO5WcJ6fcv/vY//y9DP7mNsbMzatWvx9PTE0NCQI0eO4OLiwj///JPR4UqSlEmyXSL022+/UaBAAfT19alUqdJnZ4TdunUrxYsXR19fn9KlS7N///5MijR1dl17xcXAtxjoKHmru1LT4UhSjtawlA21ilkSHRfPpN3XUz2BopubG+fPn6dkyZI8e/aMevXq4eHhQVxcyl1skiRlD9kqEdq8eTMjRoxg8uTJXLp0CWdnZxo0aMCLFy+SrX/69Gl++OEHevbsyeXLl2nZsiUtW7bk+vXrmRx58pTChHnHHwMwqFpe4hSvNRyRJOVsCoWCKc1LoqutxPvea/ZeDUr1vk5OTpw7d46ePXsihGDKlCnUq1ePoKDUH0OSpKwnWyVC8+fPp3fv3nTv3h0nJyeWLVuGoaEhq1evTrb+okWLaNiwIT/99BMlSpRg2rRplCtXjiVLlmRy5Mkzj3HjXUQsxaxN6FzeStPhSNI3wSG3EQNrOQIJCxu/j0z9GomGhob88ccfbNy4EWNjY44fP46zszOHDx/OqHAlKcfT9NI22SYRio6O5uLFi9SrV09VplQqqVevHj4+Psnu4+Pjo1YfoEGDBinWB4iKiiIkJETtlhF044thEtcQgJ9blUJHK9v8KSQp2+tbsxAFchvy4n0UC46kfW3ETp06cfHiRZydnXn58iUNGzZk/PjxxMbGZkC0kpRzHbn5nG5rzhP4OkxjMWSbT99Xr14RFxeHtbW1Wrm1tTXPnj1Ldp9nz56lqT7AjBkzMDMzU93s7e2/PvhkWMT0AKBV6Ty4FsiVIW1IkpQ8fR0tprZIWJTV83QAN56m/UrNokWL4uPjQ9++fRFC8Msvv1CnTh0eP36c3uFKUo4UHh2Lx54b/HvnJZvPP9JYHNkmEcosY8eOJTg4WHV79Cj9/zjauvo0jttDPnER9/qFVWUDwksxILwU2rpywkpJymg1ilrSpLQt8QIm7rpOfHzaT88bGBiwbNkyNm3ahImJCSdPnsTFxSXLXZQhSVnRkn/u8eRdBHnNDRhUx1FjcWSbRChPnjxoaWklmcPj+fPn2NjYJLuPjY1NmuoD6OnpYWpqqnZLb3pGpqyedYpTsyZha5VHVfbbrGv8NusaekYZu5SHJEkJJjZ1wkhXi0sP37H14pd/6enQoQOXLl2iXLlyvH79miZNmuDu7k5MTOrHH0nSt+Tei/esPHkfgMnNnDDU1dZYLNkmEdLV1aV8+fIcPXpUVRYfH8/Ro0epXLlysvtUrlxZrT7AkSNHUqwvSdK3xcZMn+H1iwIw48At3oRFf/GxHB0dOX36NIMHDwZgzpw51KxZk4cPH6ZLrJKUUwghmLjrBjFxgrrFrajvZP35nTJQtkmEAEaMGMHKlStZu3Ytfn5+9O/fn7CwMLp37w5A165dGTt2rKr+0KFDOXjwIPPmzePWrVt4eHhw4cIFBg0apKmHAICIj+flQz9ePvRD/Ddlf3JlkiRlPLcqBShuY8K78BhmHbj1VcfS09Nj8eLF7NixAzMzM3x8fHBxcWH37t3pFK0kZX97rjzF5/5r9LSVeDQviUKh0Gg82SoR6tChA3PnzmXSpEm4uLjg6+vLwYMHVQOiHz58qDanR5UqVfjzzz9ZsWIFzs7ObNu2jV27dlGqVClNPQQAwoNfYbXGCas1ToQHv0qxTJKkjKejpeTnlgnvCZsvPOJi4JuvPmarVq24fPkyrq6uvH37lpYtWzJ8+HCio7/8jJMk5QQhkTFM2+sHwOA6jtjnMtRwRKAQmr6AP4sLCQnBzMyM4ODgdBsvFPb2BcaLE5K30CHPMbKwSrZMkqTM477tClsuPKa4jQl7B1dDOx2mtIiOjmbs2LHMnz8fAFdXVzZv3kzBggW/+tiSlB157LmB5+kHFLI04sDQ6uhpa2VYW6n9/M5WZ4QkSZIyyphGJTA31OHWs/d4nn6QLsfU1dVl3rx57NmzBwsLC86fP0/ZsmXZvn17uhxfkrKT60+CWefzAIBpLUplaBKUFjIRkiRJAnIZ6TKmYXEAFhy5w7PgyHQ7drNmzfD19aVKlSoEBwfTtm1bBg0aRGRk+rUhSVlZXLxg/M5rxAto5mxHVcc8mg5JRSZCkiRJ/2lfwZ5y+c0Ji45j2r6b6Xrs/Pnzc/z4cUaPHg0kLCBdpUoV7t27l67tSFJW9Ne5h1x5HIyJnjYTm5TQdDhqZCIkSZL0H6VSwc8tS6NUwL6rQZy48zJdj6+jo8PMmTM5cOAAefLk4fLly5QrV45NmzalazuSlJW8fB/F7IMJV2SO/L4oVqZZa9JgmQhJkiR9wMnOlG5VEgYzT9p9nciYuHRvo2HDhvj6+lKjRg3ev3/PDz/8QN++fYmIiEj3tiRJ02bs9yMkMpaSdqZ0qVxA0+EkIRMhDdDW1cftfWHc3hdWLaeRXJkkSZoxvH4RrE31ePA6nKXH/TOkjbx583L06FEmTJiAQqFgxYoVVKpUiVu3vm4uI0nKSnz8X7Pj8hMUCpjeqjRaSs3OGZQcefn8Z2TE5fOSJGV9+64GMfDPS+hqKTk0vAYF8xhlWFteXl78+OOPPH/+HCMjI5YuXUqXLl0yrD1JygzRsfE0XnySey9C6VwpP9Nblc7U9uXl85IkSV+hcWkbahS1JDounkm7r5OR3xnr1auHr68vderUISwsjK5du9KjRw/CwsIyrE1Jymh/nLrPvReh5DbSxb1BcU2HkyKZCGmAiI8n7O0Lwt6+UFti4+MySZI0R6FQMLV5SXS1lZy8+4q/rwZ9fqevYGNjw+HDh5kyZQpKpZI1a9ZQsWJFbty4kaHtSlJGePQmnMVH7wIwrnEJzAx1NBxRymQipAHhwa8wXmyN8WJrtSU2Pi6TJEmzCuQxYmAtRwCm7b1JSGTGriavpaXFpEmTOHr0KLa2tty8eRNXV1dWr16doWekJCk9CSGYvOcGkTHxVCqYi9bl8mo6pE+SiZAkSdIn9KtViIJ5jHj5Pop5h25nSpu1atXC19eX77//noiICHr27EnXrl0JDQ3NlPYl6WscuvGcf269QEdLwfRWpTS+qOrnyERIkiTpE/S0tZjWImFR1vVnArn6+F2mtGtlZcWBAwf45Zdf0NLSYsOGDZQvX56rV69mSvuS9CVCo2KZ8ndCd27fGoVxtDLRcESfJxMhSZKkz6hWJA/Nne2IFzB+53Xi4jOnm0qpVDJ27FiOHz9O3rx5uXPnDhUrVmT58uWyq0zKkhYeuUNQcCT5cxkyqI6jpsNJFZkISZIkpcKEpiUw0dfm2gcLR2aWatWq4evrS5MmTYiKiqJfv3788MMPhISEZGockvQpN54Gs+a/BYuntiiJvk7WWFT1c2QiJEmSlApWJvqM/m9R1nmH03dR1tTIkycPe/bsYc6cOWhra7N582bKlSvHpUuXMjUOSUpOfLxQnS1tUsaWWsWsNB1SqslESJIkKZU6VcxP2fzmhEbFMnVv5l/WrlQqGTVqFCdOnCB//vz4+/tTuXJllixZIrvKJI3689xDfB+9w1hPm0lNnTQdTprIREgDtHR0aRucl7bBedHS0U2xTJKkrEWpVPDLf8sE7L/2jGO3XmgkjsqVK3P58mVatGhBdHQ0gwcPpl27drx7904j8Ujfthchkcz6b1HVUd8XxTqLLar6OXKJjc+QS2xIkvSxX/b7seLEffJZGHBkeE0MdDUzFkIIweLFi/npp5+IiYmhYMGCbN68GVdXV43EI32bBv15ib1XgyiTz4ydA6pmmfXE5BIbkiRJGWRYvSLkNTfg8dsIFv03e64mKBQKhg4dire3NwULFiQgIICqVauyYMEC2VUmZYpjt1+w92oQSgWqs6XZjUyEJEmS0shQV5spzUsC8MfJ+9x6ptmrt1xdXbl06RJt2rQhJiaGESNG0LJlS968eaPRuKScLSI6jom7rgPQo2pBSuU103BEX0YmQhoQ9vYFiikKFFMUhL19kWKZJElZVz0naxqWtCE2XjBm+7VMm1soJebm5mzdupUlS5agq6vLnj17KFu2LD4+PhqNS8q5Fh29y+O3EdiZ6TO8flFNh/PFZCIkSZL0hTyal8RYTxvfR+/YeDZQ0+GgUCgYOHAgZ86cwdHRkYcPH1K9enXmzJlDvFzMWUpHt56F8MfJ+wBMbVEKIz1tDUf05WQiJEmS9IVszPRxb1gMgNkHb2f63EIpKVu2LBcvXqRjx47ExcXh7u5Os2bNePVKLugsfb34eMHYHdeIjRc0LGlDPSdrTYf0VWQiJEmS9BU6V3LAxT5hbiGPPZk/t1BKTE1N+fPPP1m+fDn6+vrs378fFxcXTp48qenQpGxuw9lALj9MmDNocvPsNWdQcmQiJEmS9BW0lApmtC6NtlLBwRvPOHLzuaZDUlEoFPTp04ezZ89SrFgxnjx5Qu3atfnll19kV5n0RYKCI5h98DYA7g2LYWtmoOGIvp5MhCRJkr5SCVtTetcoBMCk3dcJjYrVcETqypQpw4ULF+jSpQtxcXGMHz+ehg0b8uKFvDBDSj0hBBN33SA0KpZy+c35sZKDpkNKFzIRkiRJSgdD6xYhfy5DgoIjmXvotqbDScLY2Jh169axZs0aDA0NOXLkCM7Ozhw7dkzToUnZxMHrz/Dye46OloIZrcugzIZzBiVHJkIaoKWjS+N3ljR+Z6m2xMbHZZIkZR/6OlpMb1UKgLU+D7gY+FbDESWvW7dunD9/npIlS/Ls2TPq1avHlClTiIuL03RoUhYWHBHD5P/GwPWrWZhiNiYajij9yCU2PkMusSFJUlqM3HKF7ZceU8TKmH1DqqOrnTW/b4aHhzNkyBBWrVoFQO3atdm4cSO2trYajkzKisbtvMafZx9SKI8R+4dWR19HM8vKpIVcYkOSJEkDJjQpQR5jXe6+COX34/c0HU6KDA0N+eOPP9iwYQNGRkYcO3YMFxcXjhw5ounQpCzmXMAb/jz7EIBfWpfOFklQWshESJIkKR1ZGOkyuVnC8hu/HbvH3efvNRzRp3Xu3JmLFy9SpkwZXrx4QYMGDZgwYQKxsVlrwLekGZExcYzZfhWADhXs+a5Qbg1HlP5kIqQBYW9fYDRegdF49SU2Pi6TJCl7alrGlrrFrYiJE4zefpV4DS+/8TnFihXjzJkz9OvXDyEE06dPp3bt2jx+/FjToUkatvjoXe6/CsPKRI9xTUpoOpwMIRMhDQnXTbh9rkySpOxHoVAwrWUpjPW0ufTwHevPaH75jc8xMDBg6dKlbNq0CRMTE06dOoWLiwv79+/XdGiShlx/EszyEwnLaExrWQozAx0NR5QxZCIkSZKUAezMDRitWn7jFo/fhms4otTp0KEDly5doly5crx+/ZomTZrg7u5OTEyMpkOTMlFMXDzu264SFy9oUsaWBiVtNB1ShpGJkCRJUgbpXMkB1wIWhEXHMXbHNbLLRbqOjo6cPn2awYMHAzBnzhxq1qzJw4cPNRyZlFlWnrzPzaAQzA118PhvzFtOJRMhSZKkDKJUKpjVpgx62kpO3n3F1ovZZ8yNnp4eixcvZvv27ZiZmeHj44OLiwt79uzRdGhSBvN/GcpCr7sATGrqhKWJnoYjylgyEZIkScpAhSyNGVG/KADT9t7keUjWWKE+tVq3bs3ly5dxdXXl7du3tGjRghEjRhAdHa3p0KQMEB8vGLP9KtGx8dQsakmrsnk1HVKGk4mQJElSButZrSDO+cx4HxnL+J3Zp4ssUcGCBTl16hTDhw8HYMGCBVSrVo2AgAANRyalt7U+Dzj/4C1GugkzpSsUOWMZjU+RiZAGKLW0qfnWjJpvzVBqaadYJklSzqCtpWR2W2d0tBR4+b1gz5Wnmg4pzXR1dZk/fz67d+/GwsKC8+fPU7ZsWXbs2KHp0KR08uBVGLMO3gJgbOMS5LMw1HBEmUMusfEZcokNSfpfe3ceFlW9+HH8PcMuqyioKOJShokKIqi4lkZW16W6pUWpZZmmmUuL3l/X5VourWappWnmklreNNOictc0wGWMNDWX3PeFRQSBmd8fJDdMkRI4A/N5Pc886OHMmc+cx8f5zFm+Xykuk1b+ytvf78GvggvfD25bZq+9OHToEN27d2fTpk0ADBgwgDfffBM3t7L5fiTvlFj36T+SeOAcMXUrMbd3szI/qaqm2BARsTP92tWlfjUfLmRkM+LLn8vcKbIratasydq1a3nppZcAeP/994mJiWHvXvudUkQKN3vTbyQeOEcFVycmPFh+ZpYvChUhEZFS4uJk5o1/NsLZbOKbn0+UyVNkV7i4uDBhwgSWL19OpUqV8sceWrhwodHR5C86ePYiE+J3AzD8nlCC/R3jlNgVKkIGuHj+FAHDzAQMMxeYYuPqZSJS/oRV92XAnbcAMOLLHZwqY3eRXe3ee+/FYrHQunVr0tLS6N69O3379uXSpUtGR5MisFptvLToJy5l59KiTiXimoUYHanUqQgZ5IyHjTMethsuE5Hyp/8dtxBW3YeUS9n8qwzeRXa1GjVqsGrVKv7v//4Pk8nEhx9+SPPmzdm9e7fR0eQGZm/6jYTfT4m9/k/HOiV2hYqQiEgpc3Ey89ZD4bg6mVnxyyn+u/Wo0ZFumrOzM6+++irffvstgYGB/PTTT0RGRjJ37lyjo8l17D+dzvjf7xIb5oCnxK5QERIRMcBtVb0ZdNetAIz+agfHU8rHqaS77roLi8XCHXfcwcWLF3n88cfp3bs3GRllY641R5GTa2XIZ9vJzLbS6pbKPOaAp8SuUBESETFIn9Z1CA/2Iy0zh5f/W/ZPkV1RrVo1vv/+e0aPHo3ZbGbmzJlER0ezc+dOo6PJ7z5Yuw/L4Qt4uzs77CmxK1SEREQM4uxk5s2HGuPmbGbdntPMTSg/k5o6OTkxYsQIVq5cSdWqVdmxYwdNmzZl1qxZRkdzeD8fTcmfS2x05wYE+XkYnMhYZaYInTt3jri4OHx8fPDz86N3796kp6cX+px27dphMpkKPPr27VtKiUVEbuyWQC9e7hgKwGvLd7LvdOH/r5U17dq1Y/v27cTGxnLp0iWeeOIJevbsecP/v6VkZGbnMvSz7eRYbXRsUNUh5hK7kTJThOLi4tixYwfff/89y5YtY926dfTp0+eGz3v66ac5fvx4/uP1118vhbSFMzs50/RCBZpeqFBgio2rl4mIY+gVU4tWt1QmM9vKkIUWsnOtRkcqVoGBgXzzzTe89tprmM1mZs+eTVRUFMnJyUZHczjvfL+H3SfTqOzl6jBzid1ImZhi45dffuH2228nKSmJpk2bAhAfH8+9997LkSNHCAoKuubz2rVrR3h4OBMnTvzbr60pNkSkNBxPucTd76wjNTOHge1vzZ+xvrxZv349jzzyCEePHsXd3Z1Jkybx1FNP6QO5FPy4/yyPTP8Rmw2m92jKXbdXMTpSiSpXU2xs2rQJPz+//BIE0KFDB8xmMwkJCYU+d968eVSuXJmwsDCGDx9+wzsXsrKySE1NLfAQESlp1Xw9eO3+hgBMXr2XrYfOG5yoZLRu3RqLxcK9995LZmYmffr04dFHH9X/tSUs5VI2QxZasNng4aY1yn0J+ivKRBE6ceIEgYGBBZY5Ozvj7+/PiRMnrvu8Rx99lLlz57J69WqGDx/OnDlzeOyxxwp9rXHjxuHr65v/CA4OLpb3ICJyI50aB9E1PIhcq40hCy1czMoxOlKJqFy5Ml999RWvv/46Tk5OLFiwgMjISLZt22Z0tHLJZrPxf4uTOZaSSa1KFRjZqYHRkeyKoUVo2LBhf7qY+erHrl27/vb2+/Tpw913303Dhg2Ji4tj9uzZLF68mH379l33OcOHDyclJSX/cfjw4b/9+teTkXKGWi84U+sFZzJSzlx3mYg4ntFdwgjydee3sxm8urz83m5uNpt58cUXWb9+PcHBwezdu5fmzZszZcqUcjOMQImx5sKB9ZC8KO+nNbfQ1RdvO8qyn47jZDYxsXsEnm66DvWPDN0bQ4cOpVevXoWuU6dOHapWrcqpUwXn38rJyeHcuXNUrVq1yK/XrFkzAPbu3UvdunWvuY6bmxtubm5F3ubfYbNaOeidm//n6y0TEcfj6+HCmw83Ju6jBOYnHqb1rQHc27Ca0bFKTIsWLbBYLDzxxBMsXbqU/v37s3r1aqZPn46fn5/R8ezPzqUQ/zKk/mHCXp8g6DgBbu/8p9UPn8tgxJc7ABjc4VbCg/1KKWjZYegRoYCAAEJDQwt9uLq60qJFCy5cuMCWLVvyn7tq1SqsVmt+uSkKi8UC5A32JSJir2LqVqZf27wva8P++xNHL5SPUaevx9/fnyVLlvD222/j4uLCokWLaNKkCUlJSUZHsy87l8JnPQqWIIDU43nLdy4tsDgn18qghRbSs3KIqlWRfu1uKcWwZUeZuEaofv36dOzYkaeffprExER++OEHBgwYQPfu3fPvGDt69CihoaEkJiYCsG/fPsaMGcOWLVv47bffWLp0KT169KBNmzY0atTIyLcjInJDg++qR3iwH6mZOQxasI2ccnZL/dVMJhODBw9mw4YN1KpViwMHDtCyZUsmTpyoU2WQd/or/mXgWvvi92XxwwqcJnt/9V62HDyPt5sz73QLx8mBR48uTJkoQpB391doaCjt27fn3nvvpVWrVkybNi3/99nZ2ezevTv/rjBXV1dWrFhBbGwsoaGhDB06lAcffJCvvvrKqLcgIlJkLk5mJnWPwMvNmaTfzvPeqr1GRyoV0dHRbNu2jQceeIDs7GwGDx5M165dOXfunNHRjHVw45+PBBVgg9SjeeuRd6v8pJV5o0e/en8YNSo65oSqRVFmrpjy9/fn008/ve7va9WqVeBbQ3BwMGvXri2NaCIiJaJmpQq8dn8Yzy+w8N6qX2l5S2Wia/sbHavE+fn5sWjRIiZPnszQoUNZunQpERERLFiwgBYtWhgdzxjpJ4u83tn0LJ5fsA2rDR6KrEGXcI0eXZgyc0RIRMQRdQmvzoNNamC1waAF27iQcdnoSKXCZDIxYMAANm3aRN26dTl06BBt2rThjTfewOqIN5R4FW3cH6tnIEM/387J1CxuCfRidBfdKn8jKkIGMJnN3J7ixu0pbpjM5usuExEB+E+XBtSu7MmxlEyGfrYdq9Vxrplp0qQJW7dupVu3buTk5PDSSy/RqVMnzpxxsGFGQmLy7g7jetf5mMCnOh8dqsqa3adxczYz+dEmVHAtMyd+DFMmptgwkqbYEBF7sONYCvdP2cjlHCvD7wnlmbbXHgKkvLLZbEybNo3nn3+erKwsqlevzvz582ndurXR0UrPlbvGgIIXTeeVo/13TiU23pccq42x9zfk0WY1Sz2iPSlXU2yIiDi6BkG+jPp9RODXv91N0m+OdfGwyWTimWeeISEhgXr16nH06FHuuOMOxo4d6zinym7vDA/PBp+rhoDxCeJi1495fGMVcqw2/tGoGo9Ea1aEotIRoRvQESERsRc2m43BCy0ssRyjio8bXw9sTSWvkh0A1h6lp6fTr18/5s6dC0BsbCxz5sz501RM5ZY1N+/usPST4FUFa3ALnpq7jVW7TlHTvwLLBrbCx93F6JSG0xEhO5aRcoYGQ9xpMMS9wBQbVy8TEfkjk8nEa/c3pG6AJydTsxi00EKuA10vdIWXlxezZ89mxowZeHh48N133xEeHs6aNWuMjlY6zE5QuzU0/CfUbs2UdQdYtesUbs5mpsQ1UQn6i1SEDGCzWtnpm8VO36wCU2xcvUxE5Gqebs5MiYvE3cXM+l/P8L6DjC90NZPJxJNPPklSUhL169fn+PHjtG/fntGjR5ObW/jcW+XJ+l9P89b3ewAY0zWMsOq+Bicqe1SERETKmNuqevNq14YATFy5h9W7Tt3gGeVXgwYNSEpK4oknnsBqtTJq1ChiY2M5ceKE0dFK3NELlxg4fxs2G3SPCubhprou6O9QERIRKYP+GVmDuGY1sdlg4IJt/HbmotGRDOPp6cnMmTOZPXs2np6erFq1isaNG7NixQqjo5WYrJxcnp23lfMZ2YRV92FUZ40X9HepCImIlFEjOzUgMqQiaZk59JmzmYtZOUZHMtTjjz/O5s2badiwIadOnSI2NpZXXnmFnJzyt1/+89VOth++gK+HC1PjInF3cTI6UpmlIiQiUka5OpuZGteEQG839pxM56VFPzn8BKWhoaEkJCTQp08fbDYbr732Gu3bt+fo0aNGRys28xIOMi/hECYTTOwWTrC/5hG7GSpCIiJlWKCPO1Mfa4KLk4nlycf5cN1+oyMZzsPDgw8//JD58+fj7e3NunXrCA8PJz4+3uhoNy1h/1lGfrkDgBdib+OOUAcZMqAEqQgZwGQ2E5LmREiaU4EpNq5eJiJSFJEh/oy8Mthi/C7W7Hbci6f/qHv37mzZsoWIiAjOnDnDPffcw7Bhw8jOzjY62t9y5HwG/eZtJcdqo1PjIJ5t51iji5cUDah4AxpQUUTKApvNxvAvklmQdBgvN2e+eDaGelW8jY5lFzIzM3nhhReYPHkyADExMcyfP5+aNcvOFBQZl3N4YMpGdp1II6y6D58/E4OHq64LKowGVBQRcSAmk4n/dAmjWW1/0rNyeHJWEmfTs4yOZRfc3d15//33WbRoEb6+vmzcuJGIiAi++uoro6MVic1m44XPt7PrRBqVvdyY9nhTlaBipCIkIlJOuDqb+eCxSEIqVeDI+Uv0mbOFzGzHGVzwRh588EG2bt1KVFQU586do3PnzgwdOpTLly8bHa1Qb323h6+TT+DiZOKDx5oQ5OdhdKRyRUXIAJdSzxE12JOowZ5cSj133WUiIn9VRU9XZvSMwsfdmS0HzzP8i2SHv5Psj+rUqcOGDRsYNGgQAG+//TatWrXiwIEDxga7joVJh3h/dd7o4WPvb0jTWv4GJyp/VIQMYM3NYbNfBpv9MrDm5lx3mYjI33FLoBdT4iJxMptYvO2ow07DcT2urq688847LFmyBD8/P5KSkoiIiOCLL74wOloB6/ac5l+LfwZg4J238JBGji4RKkIiIuVQq1srM/r30Ybf+n4Pn28+bHAi+9OlSxcsFgvNmzcnJSWFBx98kOeee46sLOOvrdp5LJVn520l12rj/ojqDL6rntGRyi0VIRGRcuqx5iE807YOAMO+SGa1bqv/k5CQENatW8eLL74IwPvvv09MTAx79xp3FO1ESiZPzkoiPSuH5nX8mfBgI0wmk2F5yjsVIRGRcuzlu0N5IKI6uVYbz87diuXwBaMj2R0XFxdef/11li1bRqVKldi6dStNmjRh4cKFpZ4lJSObXh8nciI1k1sCvfjwsaa4OuujuiRp74qIlGNms4kJ/2xEm3oBXMrO5clZSRxw4AlaC3PfffdhsVho1aoVaWlpdO/enb59+3Lp0qVSef2Myzk8+UlS/m3yH/eKwreCS6m8tiNTERIRKedcnPLmJGtUw5dzFy/TY2YCp1IzjY5ll2rUqMHq1av517/+hclk4sMPP6R58+bs3r27RF/3co6VvnO3suXgeXzcnZnTO1pziJUSFSGDVL5kovIl0w2XiYgUB083Z2b2iiKkUgUOn7tE3EcJGnDxOpydnXnttdeIj48nICCAn376icjISObNm1cir5drtTH4Mwvr9pzGw8WJj5+Ipn41zWRQWjTFxg1oig0RKU8On8vgoQ82cSI1k/rVfJj/dDP8KrgaHctuHTt2jLi4ONasWQNA7969mTRpEhUqFM/RGpvNxr8W/8z8xEO4OJmY0TOKNvUCimXbjk5TbIiIyJ8E+1fg06ebUdnLjV+Op9JzZiKpmWVzEtLSEBQUxIoVKxg5ciQmk4kZM2YQHR3Nzp07b3rbNpuNMct+YX7iIcwmeLd7hEqQAVSEREQcTJ0ALz59uhn+nq5sP5LCEx8ncTHrLw7kas2FA+sheVHeT2v5ncrDycmJUaNGsWLFCqpWrcqOHTuIiopi1qxZf3ubNpuN0V/tZOYPeSNaj3ugIfc2rFZMieWvUBEywKXUc7Qb5Ee7QX4Fpti4epmISEmpV8WbOb2j86fiuDJuTZHsXAoTw+CTf8B/e+f9nBiWt7wcu/POO7FYLHTo0IGMjAyeeOIJevbsSXp6+l/ajs1mY9TSHcza+BsA4x9oSLeomiWQWIpCRcgA1twc1lZMYW3FlAJTbFy9TESkJDUI8mV272Z4uTmTcOAcj89IICXjBqfJdi6Fz3pA6rGCy1OP5y0v52WoSpUqxMfHM2bMGMxmM7NnzyYqKork5OQiPd9mszFy6Q4+2XQQkwlef7AR3aNVgoykIiQi4sDCg/2Y91QzfD1c2HboAo9M//H6d5NZcyH+ZeBa99j8vix+WLk+TQZ5p8peeeUVVq9eTVBQELt27SI6Oprp06cXOsGt1WrjlSU/M/v3EjThwUY8HKX5w4ymIiQi4uAaB/uxoE9zKnu5svN4Kg9/uIkTKdcYZ+jgxj8fCSrABqlH89ZzAG3atMFisdCxY0cyMzPp06cPcXFxpKam/mndrJxcBi7YxryEQ5hM8MY/G/OwJlG1CypCIiJC/Wo+fPZMC6r5urPv9EUe+nAjh85mFFwp/WTRNlbU9cqBgIAAli9fzoQJE3BycmL+/PlERkaybdu2/HXSs3LoPWszy346jouTiXe7R/DPyBoGppY/UhESEREg726yz55pkT/o4v1TfmDbofP/W8GrStE2VNT1ygmz2cxLL73E+vXrCQ4OZu/evbRo0YIpU6ZwOi2TR6b9yIa9Z6jg6sTMXlF0bhxkdGT5AxUhERHJF+xfgc+faUGDIB/OXrxM92k/Ev/z8bxfhsSATxBwvRHwTeBTPW89B9SiRQssFgudOnUiKyuL/v37c3vLu9m+/xj+nq4s6NOc1rdqnCB7oyJkkAqX8x43WiYiUtoCfdz57JkW3BkaSFaOlX7ztvLR+v3YTGboOOH3ta4uQ7//veN4MDuVZly74u/vz5dffslz/xqDycmZM8nrODV7ECOau9Gohp/R8eQaNMXGDWiKDRFxVDm5Vv6zbCezNx0E4LHmNRnxjwa47lmWd/fYHy+c9qmeV4Ju72xQWvtgs9mY++NBRn21k4wju0hd/iYZ547j4uLCG2+8wcCBAzGZNKdkaSjq57eK0A2oCImII7PZbMzYcIDXvv4Fmw0iQyoy+dEmVPV2ybs7LP1k3jVBITEOfSQI8maQH/XVDj5NOARA1/AghrWvSf++ffjiiy/ylnXtysyZM6lYsaKRUR2CilAxURESEYEVO08y+DMLaZk5VPZyZdIjEcTUrWx0LLtxIiWTgfO3kfjbOUwmeLljKM+0qYPJZMJmszF58mSGDh3K5cuXCQkJYcGCBTRv3tzo2OWaJl21Y5npF7hvcCD3DQ4kM/3CdZeJiNiLDrdXYdlzrahfzYcz6Zd57KMEpq7ZV+gAgo5ixc6T3PPuOhJ/O4eXmzMzejalb9u6+afATCYTAwYMYOPGjdStW5eDBw/SunVr3nzzTaxWq8HpRUXIALnZl/na7zRf+50mN/vydZeJiNiTkEqefNEvhgeb1MBqgwnxu+j1cRInU68x+KIDyMzOZdTSHTw1ezPnM7JpEOTD0gEtuTP02sMHREZGsnXrVh5++GFycnJ48cUX6dy5M2fPni3l5PJHKkIiIlJkHq5OvPlQI8be3xBXZzNr95wm9p11fGk56lBHh349mcYDUzbmT5zau1Vtvng2hjoBXoU+z8fHhwULFvDBBx/g5ubG8uXLCQ8PZ8OGDaWQWq5FRUhERP4Sk8nEo81q8vXAVjSq4UvKpWyeX2Dh2XlbC85TZs2FA+sheVHez3IwB1lmdi7vfPcLo977kFtOxhNbYQ8f92jCv/9xO27ORbtY3GQy8cwzz5CQkEC9evU4cuQI7dq1Y9y4cTpVZgBdLH0DJXGx9MXzp/CalHfoNH3gSTwrBl5zmYiIvcvOtTJ1zT4mrfyVHKuNSp6uvNwxlH9W2Ir522FX3WIflDcOURm9xT5h/1m+/mwaz1yaRpDp3P9+cRPvKy0tjWeffZa5c+cCEBsby5w5cwgM1GfAzdLF0iIiUuJcnMwMbH8rS/q3pF4VL85evMzKxR9h+rwHtqsnaE09Dp/1gJ1LjQn7N51Ky+TlRT8x86NJjLw0nqp/LEFwU+/L29ub2bNnM2PGDDw8PPjuu+8IDw9nzZo1xRNebkhFSEREblpYdV+WPdeaV+6px2iXOdhs15qI4/cTEPHDysRpsrTMbN76bjdtX1/D55sPMtJlNibTtT44b+59mUwmnnzySRITE6lfvz7Hjx+nffv2/Oc//yE31/73U1mnIiQiIsXC1dnMUzVPUNV0FvN1B0+2QerRvMEY7VRWTi4zNhyg7RtreG/VXi5l5/JIlSMEmc5dd5a14nhfYWFhJCUl0atXL6xWKyNHjiQ2NpYTJ0787W3KjTkbHcAReVYMxDbSdsNlIiJlTvrJ4l2vFJ1Jz2Lej4eY8+NBzvx+0XedAE9euvs27rZmwBdF2MhNvi9PT08+/vhj7rjjDvr168eqVato3Lgx8+bNo0OHDje1bbk2HRESEZHi43XtMXSuNnb9eZb/dJysHONP/ew4lsKLn28nZtwq3lmxhzPpWVTzdWf8Aw35blAbOoZVw+RdtWgbK+L7v5EePXqwZcsWwsLCOHXqFLGxsfz73/8mJyenWLYv/1NmitBrr71GTEwMFSpUwM/Pr0jPsdlsjBgxgmrVquHh4UGHDh349ddfSzaoiIgjC4nJu4vqOieRrMAxWyU+OlSV/p9upcW4VYxZtpOfjlzAai29o+J7T6Xz7opfiX1nLfdN2sDnW45wOddK42A/Jj0SwbqX7qB7dE2cncxFel9gypt4NiSm2DKGhoaSmJjI008/jc1m49VXX6V9+/YcPXq02F5DytDt8yNHjsTPz48jR44wY8YMLly4cMPnTJgwgXHjxvHJJ59Qu3Zt/v3vf5OcnMzOnTtxd3cv0utqrjERkb9o59K8u6iA/AuJgSsl4tQ905l1viGLthzhVNr/xh2q7OVKm1sDaHtbAG1uDaCip2uxRUq5lM3Wg+dJ+u0cq3adYteJtPzfuTiZiG1Qld6tatOkZiGTod7gffHw7BIbGmD+/Pn06dOH9PR0KleuzJw5c+jYsWOJvFZ5UW4nXZ01axaDBg26YRGy2WwEBQUxdOhQXnjhBQBSUlKoUqUKs2bNonv37kV6PRUhEZG/YedSiH/5qnGEqkPH8fllISfXyto9p/nv1iOs23OG9KyCp31qVPSgfjUf6lf1pn41H2pWqoC/pysVK7ji7nLtwQtTM7M5fC6Dw+cuceR8BvvPXGTrwfPsPpnGHz/tnM0mWt9amfsaBXHX7VXw9XAptvdVUvbs2UO3bt2wWCwAvPzyy4wZMwYXlyJmdzAOX4T2799P3bp12bZtG+Hh4fnL27ZtS3h4OO++++41n5eVlUVW1v++oaSmphIcHKwiJCLyV1lz8+6iSj+Zd+1MSAyYr11gLudY2XLwPGv2nGLt7tMFjthcSwVXJ3w9XMi12ricayU7x5r3M/f6H2m1K3sSGVKRZrX9uev2KvhV+JtHnP7C+ypumZmZDB06lClTpgAQExPDggULCA4OLpXXL0uKWoTK7V1jV243rFKl4IVrVapUKfRWxHHjxjF69OgSzSYi4hDMTlC7dZFWdXU206JuJVrUrcTwe+pz/uJlfjmRyq7jafxyPJVdJ9I4kZrJ+YuXybHayLicS8bla19oXcnTlRr+FQiu6EFN/wo0quFLZIg/Ad5upf6+ipu7uzuTJ0+mXbt2PPXUU2zcuJHw8HBmzZpFp06dDMlU1hlahIYNG8aECRMKXeeXX34hNDS0lBLB8OHDGTJkSP7frxwREhGR0lPR05WYupWJqVu5wHKbzUZaVg7nL14m5VI2TmYTbs5mXJzMuDqb8XF3wdOt3H7Hz/fQQw8RGRlJt27d2Lx5M507d2bIkCGMGzcOV9fiu7bKERj6r2Xo0KH06tWr0HXq1Knzt7ZdtWrerY4nT56kWrVq+ctPnjxZ4FTZ1dzc3HBzK6ZvDSIiUqxMJhM+7i74uOu6mDp16rBhwwaGDRvGxIkTefvtt9mwYQMLFy6kVq1aRscrMwwtQgEBAQQEBJTItmvXrk3VqlVZuXJlfvFJTU0lISGBfv36lchrioiIlCY3Nzfeeecd2rVrR69evUhMTCQiIoKZM2dy//33Gx2vTCgz4wgdOnQIi8XCoUOHyM3NxWKxYLFYSE9Pz18nNDSUxYsXA3nfGgYNGsSrr77K0qVLSU5OpkePHgQFBdG1a1eD3oWIiEjx69KlCxaLhebNm3PhwgUeeOABBg4cWODmH7m2MlOERowYQUREBCNHjiQ9PZ2IiAgiIiLYvHlz/jq7d+8mJSUl/+8vvfQSzz33HH369CEqKor09HTi4+OLPIaQiIhIWRESEsK6det48cUXAXjvvfdo2bIl+/btMziZfStzt8+XNo0jJCIiZc3y5cvp2bMnZ8+exdvbm48++oiHH37Y6Filqqif32XmiJCIiIgUzX333YfFYqFVq1akpaXRrVs3+vXrR2ZmptHR7I6KkIiISDlUo0YNVq9ezfDhwwH44IMPaN68Obt37zY4mX1RERIRESmnnJ2dGTt2LPHx8QQEBLB9+3YiIyOZN2+e0dHshoqQiIhIOXf33XdjsVho164dFy9e5LHHHuOpp54iIyPD6GiGUxESERFxAEFBQaxYsYIRI0ZgMpmYMWMG0dHR7Ny50+hohlIREhERcRBOTk6MHj2aFStWUKVKFXbs2EFUVBSffPKJ0dEMoyIkIiLiYO688062b99Ohw4dyMjIoFevXvTs2bPAIMWOQkVIRETEAVWpUoX4+HjGjBmD2Wxm9uzZREVFkZycbHS0UqUiJCIi4qCcnJx45ZVXWLVqFUFBQezatYvo6GimT5+Oo4y3rCIkIiLi4Nq2bYvFYqFjx45kZmbSp08f4uLiSEtLMzpaiVMREhEREQICAli+fDnjx4/HycmJ+fPnExkZicViMTpaiVIREhEREQDMZjMvv/wya9eupUaNGvz66680b96cqVOnlttTZSpCIiIiUkDLli2xWCz84x//ICsri2effZZu3bqRkpJidLRipyIkIiIif1KpUiWWLl3Km2++ibOzM59//jlNmjRh8+bNRkcrVipCIiIick0mk4mhQ4eyYcMGQkJC2L9/PzExMUyaNKncnCpTERIREZFCNWvWjG3bttG1a1eys7N5/vnneeCBBzh//rzR0W6aipCIiIjcUMWKFfniiy949913cXFxYcmSJURERJCQkGB0tJuiIiQiIiJFYjKZGDhwIJs2baJOnTocPHiQVq1a8dZbb5XZU2UqQiIiIvKXREZGsnXrVh566CFycnJ44YUX6Ny5M2fPnjU62l+mIiQiIiJ/ma+vLwsXLmTq1Km4ubmxbNkywsPD+eGHH4yO9peoCImIiMjfYjKZ6Nu3LwkJCdSrV48jR47Qtm1bxo8fj9VqNTpekagIiYiIyE1p3LgxmzdvJi4ujtzcXIYPH859993H6dOnjY52QypCIiIictO8vb2ZM2cOH330ER4eHsTHxxMeHs7atWuNjlYoFSEREREpFiaTid69e5OYmEj9+vU5duwYd955J2PGjCE3N9foeNekIiQiIiLFKiwsjKSkJHr16oXVamXEiBHcfffdnDhxwuhof6IiJCIiIsXO09OTjz/+mE8++YQKFSqwcuVKGjduzIoVK4yOVoCKkIiIiJSYHj16sHnzZsLCwjh16hSxsbGMGDGCnJwco6MBKkIiIiJSwurXr09iYiJPPfUUNpuNMWPG0L59e44dO2Z0NBUhERERKXkeHh5Mnz6defPm4eXlxbp162jcuDHffvutoblUhERERKTUPProo2zZsoXw8HDOnDlDx44defPNNw3LoyIkIiIipapevXps2rSJZ599FicnJ6Kjow3LYrKV1eliS0lqaiq+vr6kpKTg4+NjdBwREZFyZffu3dx2223Fvt2ifn7riJCIiIgYpiRK0F+hIiQiIiIOS0VIREREHJaKkIiIiDgsFSERERFxWCpCIiIi4rBUhERERMRhqQiJiIiIw1IREhEREYelIiQiIiIOS0VIREREHJaKkIiIiDgsFSERERFxWCpCIiIi4rCcjQ5g72w2GwCpqakGJxEREZGiuvK5feVz/HpUhG4gLS0NgODgYIOTiIiIyF+VlpaGr6/vdX9vst2oKjk4q9XKsWPH8Pb2xmQyFdt2U1NTCQ4O5vDhw/j4+BTbdssL7Z/Caf8UTvuncNo/16d9U7iytH9sNhtpaWkEBQVhNl//SiAdEboBs9lMjRo1Smz7Pj4+dv+PyUjaP4XT/imc9k/htH+uT/umcGVl/xR2JOgKXSwtIiIiDktFSERERByWipBB3NzcGDlyJG5ubkZHsUvaP4XT/imc9k/htH+uT/umcOVx/+hiaREREXFYOiIkIiIiDktFSERERByWipCIiIg4LBUhERERcVgqQgaZPHkytWrVwt3dnWbNmpGYmGh0JLuwbt06OnXqRFBQECaTiSVLlhgdya6MGzeOqKgovL29CQwMpGvXruzevdvoWHZh6tSpNGrUKH+gtxYtWvDNN98YHctujR8/HpPJxKBBg4yOYhdGjRqFyWQq8AgNDTU6ll05evQojz32GJUqVcLDw4OGDRuyefNmo2PdNBUhAyxcuJAhQ4YwcuRItm7dSuPGjbn77rs5deqU0dEMd/HiRRo3bszkyZONjmKX1q5dS//+/fnxxx/5/vvvyc7OJjY2losXLxodzXA1atRg/PjxbNmyhc2bN3PnnXfSpUsXduzYYXQ0u5OUlMSHH35Io0aNjI5iVxo0aMDx48fzHxs2bDA6kt04f/48LVu2xMXFhW+++YadO3fy1ltvUbFiRaOj3TTdPm+AZs2aERUVxfvvvw/kzWcWHBzMc889x7BhwwxOZz9MJhOLFy+ma9euRkexW6dPnyYwMJC1a9fSpk0bo+PYHX9/f9544w169+5tdBS7kZ6eTpMmTZgyZQqvvvoq4eHhTJw40ehYhhs1ahRLlizBYrEYHcUuDRs2jB9++IH169cbHaXY6YhQKbt8+TJbtmyhQ4cO+cvMZjMdOnRg06ZNBiaTsiglJQXI+8CX/8nNzWXBggVcvHiRFi1aGB3HrvTv35/77ruvwP9BkufXX38lKCiIOnXqEBcXx6FDh4yOZDeWLl1K06ZNeeihhwgMDCQiIoLp06cbHatYqAiVsjNnzpCbm0uVKlUKLK9SpQonTpwwKJWURVarlUGDBtGyZUvCwsKMjmMXkpOT8fLyws3Njb59+7J48WJuv/12o2PZjQULFrB161bGjRtndBS706xZM2bNmkV8fDxTp07lwIEDtG7dmrS0NKOj2YX9+/czdepUbr31Vr799lv69evHwIED+eSTT4yOdtM0+7xIGdW/f39+/vlnXcfwB7fddhsWi4WUlBQWLVpEz549Wbt2rcoQcPjwYZ5//nm+//573N3djY5jd+655578Pzdq1IhmzZoREhLCZ599plOr5H3xatq0KWPHjgUgIiKCn3/+mQ8++ICePXsanO7m6IhQKatcuTJOTk6cPHmywPKTJ09StWpVg1JJWTNgwACWLVvG6tWrqVGjhtFx7Iarqyu33HILkZGRjBs3jsaNG/Puu+8aHcsubNmyhVOnTtGkSROcnZ1xdnZm7dq1TJo0CWdnZ3Jzc42OaFf8/PyoV68ee/fuNTqKXahWrdqfvlDUr1+/XJw+VBEqZa6urkRGRrJy5cr8ZVarlZUrV+paBrkhm83GgAEDWLx4MatWraJ27dpGR7JrVquVrKwso2PYhfbt25OcnIzFYsl/NG3alLi4OCwWC05OTkZHtCvp6ens27ePatWqGR3FLrRs2fJPQ3Xs2bOHkJAQgxIVH50aM8CQIUPo2bMnTZs2JTo6mokTJ3Lx4kWeeOIJo6MZLj09vcA3sAMHDmCxWPD396dmzZoGJrMP/fv359NPP+XLL7/E29s7/7oyX19fPDw8DE5nrOHDh3PPPfdQs2ZN0tLS+PTTT1mzZg3ffvut0dHsgre395+uJfP09KRSpUq6xgx44YUX6NSpEyEhIRw7doyRI0fi5OTEI488YnQ0uzB48GBiYmIYO3YsDz/8MImJiUybNo1p06YZHe3m2cQQ7733nq1mzZo2V1dXW3R0tO3HH380OpJdWL16tQ3406Nnz55GR7ML19o3gO3jjz82OprhnnzySVtISIjN1dXVFhAQYGvfvr3tu+++MzqWXWvbtq3t+eefNzqGXejWrZutWrVqNldXV1v16tVt3bp1s+3du9foWHblq6++soWFhdnc3NxsoaGhtmnTphkdqVhoHCERERFxWLpGSERERByWipCIiIg4LBUhERERcVgqQiIiIuKwVIRERETEYakIiYiIiMNSERIRERGHpSIkIiIiDktFSERERByWipCIiIg4LBUhEXEop0+fpmrVqowdOzZ/2caNG3F1dWXlypUGJhMRI2iuMRFxOF9//TVdu3Zl48aN3HbbbYSHh9OlSxfefvtto6OJSClTERIRh9S/f39WrFhB06ZNSU5OJikpCTc3N6NjiUgpUxESEYd06dIlwsLCOHz4MFu2bKFhw4ZGRxIRA+gaIRFxSPv27ePYsWNYrVZ+++03o+OIiEF0REhEHM7ly5eJjo4mPDyc2267jYkTJ5KcnExgYKDR0USklKkIiYjDefHFF1m0aBHbt2/Hy8uLtm3b4uvry7Jly4yOJiKlTKfGRMShrFmzhokTJzJnzhx8fHwwm83MmTOH9evXM3XqVKPjiUgp0xEhERERcVg6IiQiIiIOS0VIREREHJaKkIiIiDgsFSERERFxWCpCIiIi4rBUhERERMRhqQiJiIiIw1IREhEREYelIiQiIiIOS0VIREREHJaKkIiIiDgsFSERERFxWP8PNdVqHCwIQykAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot model predictions against ground-truth\n", - "import matplotlib.pyplot as plt\n", - "\n", - "y_min = np.min(initial_observations)\n", - "y_max = np.max(initial_observations)\n", - "\n", - "# plot conditions obtained by novelty sampler\n", - "for idx, condition in enumerate(new_conditions_novelty):\n", - " if idx == 0:\n", - " plt.plot([condition[0], condition[0]], [y_min, y_max], '--r', label='novelty conditions')\n", - " else: # we want to omit the label for all other conditions\n", - " plt.plot([condition[0], condition[0]], [y_min, y_max], '--r')\n", - "\n", - "# plot conditions obtained by falsification sampler\n", - "for idx, condition in enumerate(new_conditions_falsification):\n", - " if idx == 0:\n", - " plt.plot([condition[0], condition[0]], [y_min, y_max], '--g', label='falsification conditions')\n", - " else: # we want to omit the label for all other conditions\n", - " plt.plot([condition[0], condition[0]], [y_min, y_max], '--g')\n", - "\n", - "plt.plot(condition_pool, ground_truth(condition_pool), '-', label='Ground Truth')\n", - "plt.plot(initial_conditions, initial_observations, 'o', label='Initial Data')\n", - "plt.plot(condition_pool, predicted_observations_lr, '-k', label='Prediction from Linear Regression')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('Sampled Experimental Conditions')\n", - "plt.legend()" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.6981317007977318,)\n", + "(0.7615982190520711,)\n", + "(0.8250647373064104,)\n", + "(0.8885312555607496,)\n", + "(0.9519977738150889,)\n", + "(1.0154642920694281,)\n", + "(1.0789308103237674,)\n", + "(1.1423973285781066,)\n", + "(1.2058638468324459,)\n", + "(1.269330365086785,)\n", + "(1.3327968833411243,)\n" + ] + } + ], + "source": [ + "# return first 10 conditions\n", + "for idx, condition in enumerate(new_conditions):\n", + " print(condition)\n", + " if idx > 9:\n", + " break" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we may use the **random pooler** to randomly draw experimental conditions from the domains of each independent variable. The random pooler requires as input a list of discrete values from which to sample from. In this case, we can pass it ``metadata.independent_variables[0].allowed_values`` for the independent variable. We can also specify the input argument ``n`` to obtain 10 random samples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "cdDcYoJcUYNK" - }, - "source": [ - "\n", - "### Usage: Pipelines\n", - "\n", - "Experimentalists can be connected in a **[pipeline](https://autoresearch.github.io/autora/core/docs/pipeline/Experimentalist%20Pipeline%20Examples/)**, where each element passes its output to the next element, ensuring compatibility between the inputs and outputs. Pipelines offer a flexible and efficient way to orchestrate the workflow involving complex experimentalists (e.g., for processing of experimental conditions) and experiment runners (e.g., for preprocessing of collected observations). They allow for the integration of poolers, samplers, and other design manipulations into a cohesive stream of experimental conditions.\n", - "\n", - "Let's examine the following pipeline example:\n", - "\n", - "
    \n", - "
  1. Generate a grid of all possible experimental conditions.\n", - "
  2. Filter out conditions where the independent variable falls within the range -1 to 1.\n", - "
  3. Sample 10 conditions using the novelty sampler.\n", - "
  4. Select 5 conditions from the sampled set using the falsification sampler.\n", - "
\n", - "\n", - "Before creating the pipeline, let's define an additional function that removes experiment conditions falling within the range of -1 to 1, specifically $-1 \\leq x \\leq 1$. This function will be used in the second step of the pipeline." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.8250647373064104,)\n", + "(0.7615982190520711,)\n", + "(4.1253236865320515,)\n", + "(4.188790204786391,)\n", + "(3.236792430971302,)\n", + "(3.8714576135146945,)\n", + "(3.3637254674799806,)\n", + "(6.092785752416569,)\n", + "(4.886921905584122,)\n", + "(4.950388423838462,)\n" + ] + } + ], + "source": [ + "from autora.experimentalist.pooler.random_pooler import random_pool\n", + "\n", + "# generate random pool of 10 conditions\n", + "num_samples = 10\n", + "new_conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", + " n=num_samples)\n", + "\n", + "# print conditons\n", + "for idx, condition in enumerate(new_conditions):\n", + " print(condition)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Usage: Samplers\n", + "\n", + "An experiment sampler typically requires an existing pool of conditions as input along with additional arguments. For instance, the **[novelty sampler](https://autoresearch.github.io/autora/user-guide/experimentalists/samplers/novelty/)** requires, aside from a pool of conditions, a list of prior conditions. The user may also specify the number of samples ``num_samples`` to select from the pool.\n", + "\n", + "The novelty sampler will then select novel experiment conditions from the pool which are most dissimilar to some reference conditions, such as the ``initial_conditions`` obtained above:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "O3F9XdjMUYNK" - }, - "outputs": [], - "source": [ - "from typing import Iterable\n", - "\n", - "def condition_exclusion(conditions):\n", - " # first we need to make sure that conditions is a 2-dimensional numpy array\n", - " if isinstance(conditions, Iterable):\n", - " conditions = np.array(list(conditions))\n", - "\n", - " if conditions.ndim == 1:\n", - " conditions = conditions.reshape(-1, 1)\n", - "\n", - " # now we can sub-select conditions\n", - " conditions_to_keep = conditions[(-1 > conditions) | (conditions > 1)]\n", - " conditions_to_keep = conditions_to_keep.reshape(-1, 1)\n", - " return conditions_to_keep" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0. ]\n", + " [0.06346652]]\n" + ] + } + ], + "source": [ + "from autora.experimentalist.sampler.novelty import novelty_sample\n", + "\n", + "new_conditions_novelty = novelty_sample(condition_pool = condition_pool,\n", + " reference_conditions = initial_conditions,\n", + " num_samples = 2)\n", + "\n", + "print(new_conditions_novelty)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another example for an experiment sampler is the **[falsification sampler](https://autoresearch.github.io/autora/falsification/docs/sampler/)**. The falsification sampler identifies experiment conditions under which the loss of a candidate model (returned by the theorist) is predicted to be the highest. This loss is approximated with a neural network, which is trained to predict the loss of the candidate model, given some initial experimental conditions, respective initial observations, and the metadata.\n", + "\n", + "The following code segment calls on the falsification sampler to return novel conditions based on the candidate model of the linear regression theorist introduced above. As with the novelty sampler, we seek to select 2 conditions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "daq8R8LsUYNK" - }, - "source": [ - "A pipeline can be defined as a list of functions, such as ``[grid_pool, value_exclusion, novelty_sample, falsification_sample]``. However, to create a pipeline object, we need to specify the required parameters for each element in the pipeline. We can achieve this by providing nested dictionaries containing the additional parameters, as shown in the code block below.\n", - "\n", - "**Note**: *Each element of the pipeline passes its output to the next element as the first argument of the element's function. Thus, we need to make sure that the output of one pipeline element is compatible with the required first input argument for the next element. In our case, the first argument for each pipeline element (except for poolers) is assumed to be a 2-dimensional numpy array specifying a set of experimental conditions.*\n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "YxwKnlEZUYNK" - }, - "outputs": [], - "source": [ - "from autora.experimentalist.pipeline import make_pipeline\n", - "\n", - "experimentalist_pipeline = make_pipeline([grid_pool,\n", - " condition_exclusion,\n", - " novelty_sample,\n", - " falsification_sample],\n", - " params={\"grid_pool\":\n", - " {\"ivs\": metadata.independent_variables},\n", - " \"novelty_sample\":\n", - " {\"reference_conditions\": initial_conditions,\n", - " \"num_samples\": 10},\n", - " \"falsification_sample\":\n", - " {\"model\": theorist_bms,\n", - " \"reference_conditions\": initial_conditions,\n", - " \"reference_observations\": initial_observations,\n", - " \"metadata\": metadata,\n", - " \"num_samples\": 5}})" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0. ]\n", + " [0.06346652]]\n" + ] + } + ], + "source": [ + "from autora.experimentalist.sampler.falsification import falsification_sample\n", + "\n", + "new_conditions_falsification = falsification_sample(\n", + " condition_pool=condition_pool,\n", + " model=theorist_lr,\n", + " reference_conditions=initial_conditions,\n", + " reference_observations=initial_observations,\n", + " metadata=metadata,\n", + " num_samples=2\n", + " )\n", + "\n", + "print(new_conditions_falsification)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the selected conditions for both samples relative to the selected samples. Since we don't have observations for those conditions, we plot them as vertical lines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "KYWnkPvFUYNL" - }, - "source": [ - "In the declaration of the ``params`` parameter, we first specify the name of the pipeline object we seek to parameterize as a dictionary key, e.g., ``\"grid_pool\"``, and then nest within it, another dictionary with the names of the input arguments as keys (e.g., ``\"ivs\"``) along with their values (e.g., ``metadata.independent_variables``).\n", - "\n", - "Once specified, we can run the pipeline object to obtain novel experimental conditions." + "data": { + "text/plain": [ + "" ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "1FbRRSAEUYNL", - "outputId": "544781ac-9a20-4bd7-c2c7-69cb193d27ff" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1.90399555]\n", - " [3.93492413]\n", - " [5.83891968]\n", - " [5.9023862 ]\n", - " [5.96585272]]\n" - ] - } - ], - "source": [ - "new_conditions = experimentalist_pipeline.run()\n", - "\n", - "print(new_conditions)" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot model predictions against ground-truth\n", + "import matplotlib.pyplot as plt\n", + "\n", + "y_min = np.min(initial_observations)\n", + "y_max = np.max(initial_observations)\n", + "\n", + "# plot conditions obtained by novelty sampler\n", + "for idx, condition in enumerate(new_conditions_novelty):\n", + " if idx == 0:\n", + " plt.plot([condition[0], condition[0]], [y_min, y_max], '--r', label='novelty conditions')\n", + " else: # we want to omit the label for all other conditions\n", + " plt.plot([condition[0], condition[0]], [y_min, y_max], '--r')\n", + "\n", + "# plot conditions obtained by falsification sampler\n", + "for idx, condition in enumerate(new_conditions_falsification):\n", + " if idx == 0:\n", + " plt.plot([condition[0], condition[0]], [y_min, y_max], '--g', label='falsification conditions')\n", + " else: # we want to omit the label for all other conditions\n", + " plt.plot([condition[0], condition[0]], [y_min, y_max], '--g')\n", + "\n", + "plt.plot(condition_pool, ground_truth(condition_pool), '-', label='Ground Truth')\n", + "plt.plot(initial_conditions, initial_observations, 'o', label='Initial Data')\n", + "plt.plot(condition_pool, predicted_observations_lr, '-k', label='Prediction from Linear Regression')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Sampled Experimental Conditions')\n", + "plt.legend()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Usage: Pipelines\n", + "\n", + "Experimentalists can be connected in a **[pipeline](https://autoresearch.github.io/autora/core/docs/pipeline/Experimentalist%20Pipeline%20Examples/)**, where each element passes its output to the next element, ensuring compatibility between the inputs and outputs. Pipelines offer a flexible and efficient way to orchestrate the workflow involving complex experimentalists (e.g., for processing of experimental conditions) and experiment runners (e.g., for preprocessing of collected observations). They allow for the integration of poolers, samplers, and other design manipulations into a cohesive stream of experimental conditions.\n", + "\n", + "Let's examine the following pipeline example:\n", + "\n", + "
    \n", + "
  1. Generate a grid of all possible experimental conditions.\n", + "
  2. Filter out conditions where the independent variable falls within the range -1 to 1.\n", + "
  3. Sample 10 conditions using the novelty sampler.\n", + "
  4. Select 5 conditions from the sampled set using the falsification sampler.\n", + "
\n", + "\n", + "Before creating the pipeline, let's define an additional function that removes experiment conditions falling within the range of -1 to 1, specifically $-1 \\leq x \\leq 1$. This function will be used in the second step of the pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Iterable\n", + "\n", + "def condition_exclusion(conditions):\n", + " # first we need to make sure that conditions is a 2-dimensional numpy array\n", + " if isinstance(conditions, Iterable):\n", + " conditions = np.array(list(conditions))\n", + "\n", + " if conditions.ndim == 1:\n", + " conditions = conditions.reshape(-1, 1)\n", + "\n", + " # now we can sub-select conditions\n", + " conditions_to_keep = conditions[(-1 > conditions) | (conditions > 1)]\n", + " conditions_to_keep = conditions_to_keep.reshape(-1, 1)\n", + " return conditions_to_keep" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A pipeline can be defined as a list of functions, such as ``[grid_pool, value_exclusion, novelty_sample, falsification_sample]``. However, to create a pipeline object, we need to specify the required parameters for each element in the pipeline. We can achieve this by providing nested dictionaries containing the additional parameters, as shown in the code block below.\n", + "\n", + "**Note**: *Each element of the pipeline passes its output to the next element as the first argument of the element's function. Thus, we need to make sure that the output of one pipeline element is compatible with the required first input argument for the next element. In our case, the first argument for each pipeline element (except for poolers) is assumed to be a 2-dimensional numpy array specifying a set of experimental conditions.*\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.experimentalist.pipeline import make_pipeline\n", + "\n", + "experimentalist_pipeline = make_pipeline([grid_pool,\n", + " condition_exclusion,\n", + " novelty_sample,\n", + " falsification_sample],\n", + " params={\"grid_pool\":\n", + " {\"ivs\": metadata.independent_variables},\n", + " \"novelty_sample\":\n", + " {\"reference_conditions\": initial_conditions,\n", + " \"num_samples\": 10},\n", + " \"falsification_sample\":\n", + " {\"model\": theorist_bms,\n", + " \"reference_conditions\": initial_conditions,\n", + " \"reference_observations\": initial_observations,\n", + " \"metadata\": metadata,\n", + " \"num_samples\": 5}})" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the declaration of the ``params`` parameter, we first specify the name of the pipeline object we seek to parameterize as a dictionary key, e.g., ``\"grid_pool\"``, and then nest within it, another dictionary with the names of the input arguments as keys (e.g., ``\"ivs\"``) along with their values (e.g., ``metadata.independent_variables``).\n", + "\n", + "Once specified, we can run the pipeline object to obtain novel experimental conditions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "tXpSt5jOUYNL" - }, - "source": [ - "**Hint**: *A common error for running pipelines is that the output of one pipeline element is incompatible with the input of the next pipeline element (e.g., not providing a 2-dimensional numpy array to ``novelty_sample``). In such cases, it can be helpful to \"manually\" pass the inputs from one element to another element, to check if they are compatible.*\n", - "\n", - "**Note**: *Pipelines may be used for other purposes, such as linking an experiment runner with multiple pre-processing steps.*" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "8Qmp6-J-Xa5j" - }, - "source": [ - "# Next Notebook\n", - "After defining all the components required for the empirical research process, we can create an automated workflow using basic loop constructs. The next notebook illustrates the use of these loop constructs.\n", - "\n", - "Follow this link for the next notebook tutorial:\n", - "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1.90399555]\n", + " [3.93492413]\n", + " [5.83891968]\n", + " [5.9023862 ]\n", + " [5.96585272]]\n" + ] } + ], + "source": [ + "new_conditions = experimentalist_pipeline.run()\n", + "\n", + "print(new_conditions)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Hint**: *A common error for running pipelines is that the output of one pipeline element is incompatible with the input of the next pipeline element (e.g., not providing a 2-dimensional numpy array to ``novelty_sample``). In such cases, it can be helpful to \"manually\" pass the inputs from one element to another element, to check if they are compatible.*\n", + "\n", + "**Note**: *Pipelines may be used for other purposes, such as linking an experiment runner with multiple pre-processing steps.*" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Next Notebook\n", + "After defining all the components required for the empirical research process, we can create an automated workflow using basic loop constructs. The next notebook illustrates the use of these loop constructs.\n", + "\n", + "Follow this link for the next notebook tutorial:\n", + "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb b/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb index 1549186aa..b35cc0bc8 100644 --- a/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb +++ b/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb @@ -1,437 +1,406 @@ { - "cells": [ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction\n", + "## Basic Tutorial II: Loop Constructs" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", + "\n", + "This notebook is the second of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", + "\n", + "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", + "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", + "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", + "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", + "\n", + "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", + "\n", + "**How to use this notebook** *You can progress through the notebook section by section or directly navigate to specific sections. If you choose the latter, it is recommended to execute all cells in the notebook initially, allowing you to easily rerun the cells in each section later without issues.*" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tutorial Setup\n", + "This tutorial is self-contained so that you do not need to run the previous notebook to begin. However, the four notebooks are continuous so that what we define in a previous notebook should still exist within this notebook. As such, we will here re-run relevant code from past tutorials. We will not again walk you through these, but if you need a reminder what they are then go see the descriptions in previous notebooks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#### Installation ####\n", + "!pip install -q \"autora[experimentalist-falsification]\"\n", + "!pip install -q \"autora[experimentalist-sampler-model-disagreement]\"\n", + "!pip install -q \"autora[theorist-bms]\"\n", + "\n", + "#### Import modules ####\n", + "import numpy as np\n", + "import torch\n", + "from autora.variable import DV, IV, ValueType, VariableCollection\n", + "from autora.experimentalist.pooler.random_pooler import random_pool\n", + "from autora.experimentalist.sampler.falsification import falsification_sample\n", + "from autora.experimentalist.sampler.model_disagreement import model_disagreement_sample\n", + "from autora.theorist.bms import BMSRegressor\n", + "from sklearn import linear_model\n", + "\n", + "#### Set seeds ####\n", + "np.random.seed(42)\n", + "torch.manual_seed(42)\n", + "\n", + "#### Define ground truth and experiment runner ####\n", + "ground_truth = lambda x: np.sin(x)\n", + "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape)\n", + "\n", + "#### Define condition pool ####\n", + "condition_pool = np.linspace(0, 2 * np.pi, 100)\n", + "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", + "\n", + "#### Define metadata ####\n", + "iv = IV(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=condition_pool)\n", + "dv = DV(name=\"y\", type=ValueType.REAL)\n", + "metadata = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "#### Define theorists ####\n", + "theorist_lr = linear_model.LinearRegression()\n", + "theorist_bms = BMSRegressor(epochs=100)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loop Constructs\n", + "After defining all the components required for the empirical research process, we can create an automated workflow using basic loop constructs in Python.\n", + "\n", + "The following code block demonstrates how to build such a workflow using the components introduced in the preceding notebook, such as\n", + "\n", + "- ``metadata`` (object specifying variables of the experiment),
\n", + "- ``run_experiment`` (function for collecting data),
\n", + "- ``theorist_bms`` (scikit learn estimator for discoverying equations using the Bayesian Machine Scientist),
\n", + "- ``random_pool`` (function for generating a random pool of experimental conditions), and
\n", + "- ``falsification_sample`` (function for identifying novel experiment conditions using the falsification .sampler)
\n", + "\n", + "We begin with implementing the following workflow:\n", + "1. Generate 3 seed experimental conditions using ``random_pool``\n", + "2. Generate 3 seed observations using ``run_experiment``\n", + "3. Loop through the following steps 5 times\n", + " - Identify a model relating conditions to observations using ``theorist_bms``\n", + " - Identify 3 new experimental conditions using ``falsification_sample``\n", + " - Collect 3 new observations using ``run_experiment``\n", + " - Add new conditions and observations to the dataset\n", + "\n", + "We will here begin using the naming convention ``cycle`` to refer to an entire AutoRA loop where the loop encounters all AutoRA components - experiment runner, theorist, experimentalist. Within the scientific method, a cycle would then be running a single iteration of the experiment. This requires the collection of data, the modelling of that data, and the conceptualization of the next iteration of this experiment. For example, if our research concerns how much information a person acquires from a photo (dependent variable) dependent on how bright the photo is (independent variable), we may first collect data with conditions of (let's say) 10%, 50%, and 90% brightness, then model our collected data to determine the relationship between brightness and photo perception, and finally determine which other brightness conditions may help us understand the true relationship. Probing other conditions - such as a brightness of 25% and of 75% would then be the next iteration of the experiment and thus, for us, the next cycle. The following code block will iterate through five of these cycles." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 1: Falsification Sampler" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "_q7iLq3GUYMz" - }, - "source": [ - "# Introduction\n", - "## Basic Tutorial II: Loop Constructs" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:26<00:00, 3.84it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "5mfUKtGTUYM1", - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", - "\n", - "This notebook is the second of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", - "\n", - "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", - "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", - "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", - "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", - "\n", - "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", - "\n", - "**How to use this notebook** *You can progress through the notebook section by section or directly navigate to specific sections. If you choose the latter, it is recommended to execute all cells in the notebook initially, allowing you to easily rerun the cells in each section later without issues.*" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss in cycle 0: 0.0\n", + "Discovered Model: sin(X0)\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "7bD8W7cfhZ5n" - }, - "source": [ - "## Tutorial Setup\n", - "This tutorial is self-contained so that you do not need to run the previous notebook to begin. However, the four notebooks are continuous so that what we define in a previous notebook should still exist within this notebook. As such, we will here re-run relevant code from past tutorials. We will not again walk you through these, but if you need a reminder what they are then go see the descriptions in previous notebooks." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:28<00:00, 3.56it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "2S9mfSxVUYM3" - }, - "outputs": [], - "source": [ - "#### Installation ####\n", - "!pip install -q \"autora[experimentalist-falsification]\"\n", - "!pip install -q \"autora[experimentalist-sampler-model-disagreement]\"\n", - "!pip install -q \"autora[theorist-bms]\"\n", - "\n", - "#### Import modules ####\n", - "import numpy as np\n", - "import torch\n", - "from autora.variable import DV, IV, ValueType, VariableCollection\n", - "from autora.experimentalist.pooler.random_pooler import random_pool\n", - "from autora.experimentalist.sampler.falsification import falsification_sample\n", - "from autora.experimentalist.sampler.model_disagreement import model_disagreement_sample\n", - "from autora.theorist.bms import BMSRegressor\n", - "from sklearn import linear_model\n", - "\n", - "#### Set seeds ####\n", - "np.random.seed(42)\n", - "torch.manual_seed(42)\n", - "\n", - "#### Define ground truth and experiment runner ####\n", - "ground_truth = lambda x: np.sin(x)\n", - "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape)\n", - "\n", - "#### Define condition pool ####\n", - "condition_pool = np.linspace(0, 2 * np.pi, 100)\n", - "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", - "\n", - "#### Define metadata ####\n", - "iv = IV(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=condition_pool)\n", - "dv = DV(name=\"y\", type=ValueType.REAL)\n", - "metadata = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", - "\n", - "#### Define theorists ####\n", - "theorist_lr = linear_model.LinearRegression()\n", - "theorist_bms = BMSRegressor(epochs=100)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss in cycle 1: 0.0\n", + "Discovered Model: sin(X0)\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "0ZISUerxUYNL" - }, - "source": [ - "# Loop Constructs\n", - "After defining all the components required for the empirical research process, we can create an automated workflow using basic loop constructs in Python.\n", - "\n", - "The following code block demonstrates how to build such a workflow using the components introduced in the preceding notebook, such as\n", - "\n", - "- ``metadata`` (object specifying variables of the experiment),
\n", - "- ``run_experiment`` (function for collecting data),
\n", - "- ``theorist_bms`` (scikit learn estimator for discoverying equations using the Bayesian Machine Scientist),
\n", - "- ``random_pool`` (function for generating a random pool of experimental conditions), and
\n", - "- ``falsification_sample`` (function for identifying novel experiment conditions using the falsification .sampler)
\n", - "\n", - "We begin with implementing the following workflow:\n", - "1. Generate 3 seed experimental conditions using ``random_pool``\n", - "2. Generate 3 seed observations using ``run_experiment``\n", - "3. Loop through the following steps 5 times\n", - " - Identify a model relating conditions to observations using ``theorist_bms``\n", - " - Identify 3 new experimental conditions using ``falsification_sample``\n", - " - Collect 3 new observations using ``run_experiment``\n", - " - Add new conditions and observations to the dataset\n", - "\n", - "We will here begin using the naming convention ``cycle`` to refer to an entire AutoRA loop where the loop encounters all AutoRA components - experiment runner, theorist, experimentalist. Within the scientific method, a cycle would then be running a single iteration of the experiment. This requires the collection of data, the modelling of that data, and the conceptualization of the next iteration of this experiment. For example, if our research concerns how much information a person acquires from a photo (dependent variable) dependent on how bright the photo is (independent variable), we may first collect data with conditions of (let's say) 10%, 50%, and 90% brightness, then model our collected data to determine the relationship between brightness and photo perception, and finally determine which other brightness conditions may help us understand the true relationship. Probing other conditions - such as a brightness of 25% and of 75% would then be the next iteration of the experiment and thus, for us, the next cycle. The following code block will iterate through five of these cycles." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:14<00:00, 7.10it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "kO_HTQMPm7LQ" - }, - "source": [ - "## Example 1: Falsification Sampler" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss in cycle 2: 0.5526484578348648\n", + "Discovered Model: _a0_\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "PiDfcDVNUYNL", - "outputId": "278b7307-51f8-40c0-c1e3-025dffdf3c4f" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:26<00:00, 3.84it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 0: 0.0\n", - "Discovered Model: sin(X0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:28<00:00, 3.56it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 1: 0.0\n", - "Discovered Model: sin(X0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:14<00:00, 7.10it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 2: 0.5526484578348648\n", - "Discovered Model: _a0_\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:17<00:00, 5.60it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 3: 0.0\n", - "Discovered Model: sin(X0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:16<00:00, 6.25it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 4: 0.0\n", - "Discovered Model: sin(X0)\n" - ] - } - ], - "source": [ - "num_cycles = 5 # number of empirical research cycles\n", - "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", - "\n", - "# generate an initial set of experimental conditions\n", - "conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", - " n=measurements_per_cycle)\n", - "\n", - "# convert iterator into 2-dimensional numpy array\n", - "conditions = np.array(list(conditions)).reshape(-1, 1)\n", - "\n", - "# collect initial set of observations\n", - "observations = run_experiment(conditions)\n", - "\n", - "for cycle in range(num_cycles):\n", - "\n", - " # use BMS theorist to fit the model to the data\n", - " theorist_bms.fit(conditions, observations)\n", - "\n", - " # obtain new conditions\n", - " new_conditions = falsification_sample(\n", - " condition_pool=condition_pool,\n", - " model=theorist_bms,\n", - " reference_conditions=conditions,\n", - " reference_observations=observations,\n", - " metadata=metadata,\n", - " num_samples=measurements_per_cycle,\n", - " )\n", - "\n", - " # obtain new observations\n", - " new_observations = run_experiment(new_conditions)\n", - "\n", - " # combine old and new conditions and observations\n", - " conditions = np.concatenate((conditions, new_conditions))\n", - " observations = np.concatenate((observations, new_observations))\n", - "\n", - " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", - " loss = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", - " print(\"Loss in cycle {}: {}\".format(cycle, loss))\n", - " print(\"Discovered Model: \" + theorist_bms.repr())\n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:17<00:00, 5.60it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "B0lVQWuCUYNL" - }, - "source": [ - "## Example 2: Model Disagreement Sampler\n", - "We can easily replace components in the workflow above. For instance, we could replace ``falsification_sample`` with the ``experimentalist_pipeline`` defined in Tutorial I.\n", - "\n", - "In the following code block, we add a linear regression theorist, to fit a linear model to the data. In addition, we replace ``falsification_sample`` with ``model_disagreement_sample`` to sample experimental conditions that differentiate most between the linear model and the model discovered by the BMS theorist." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss in cycle 3: 0.0\n", + "Discovered Model: sin(X0)\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "NXd-ON5uUYNL", - "outputId": "b5a7d8d7-f3e9-419c-9f52-618d713641bd" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:40<00:00, 2.47it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 0: 0.0\n", - "Discovered BMS Model: sin(X0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:18<00:00, 5.53it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 1: 0.0\n", - "Discovered BMS Model: sin(X0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:19<00:00, 5.16it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 2: 0.0\n", - "Discovered BMS Model: sin(X0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:15<00:00, 6.37it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 3: 0.0\n", - "Discovered BMS Model: sin(X0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:24<00:00, 4.01it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 4: 0.0\n", - "Discovered BMS Model: sin(X0)\n" - ] - } - ], - "source": [ - "num_cycles = 5 # number of empirical research cycles\n", - "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", - "\n", - "# generate an initial set of experimental conditions\n", - "conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", - " n=measurements_per_cycle)\n", - "# convert iterator into 2-dimensional numpy array\n", - "conditions = np.array(list(conditions)).reshape(-1, 1)\n", - "\n", - "# collect initial set of observations\n", - "observations = run_experiment(conditions)\n", - "\n", - "for cycle in range(num_cycles):\n", - "\n", - " # use BMS theorist to fit the model to the data\n", - " theorist_bms.fit(conditions, observations)\n", - " theorist_lr.fit(conditions, observations)\n", - "\n", - " # obtain new conditions\n", - " new_conditions = model_disagreement_sample(\n", - " condition_pool,\n", - " models = [theorist_bms, theorist_lr],\n", - " num_samples = measurements_per_cycle\n", - " )\n", - "\n", - " # obtain new observations\n", - " new_observations = run_experiment(new_conditions)\n", - "\n", - " # combine old and new conditions and observations\n", - " conditions = np.concatenate((conditions, new_conditions))\n", - " observations = np.concatenate((observations, new_observations))\n", - "\n", - " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", - " loss = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", - " print(\"Loss in cycle {}: {}\".format(cycle, loss))\n", - " print(\"Discovered BMS Model: \" + theorist_bms.model_.__repr__())\n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:16<00:00, 6.25it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "NZgqanLagOv_" - }, - "source": [ - "# Next Notebook\n", - "While the basic loop construct is flexible, there are more convenient ways to specify a research cycle in ``autora``. The next notebook illustrates the use of these constructs.\n", - "\n", - "Follow this link for the next notebook tutorial:\n", - "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss in cycle 4: 0.0\n", + "Discovered Model: sin(X0)\n" + ] } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true + ], + "source": [ + "num_cycles = 5 # number of empirical research cycles\n", + "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", + "\n", + "# generate an initial set of experimental conditions\n", + "conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", + " n=measurements_per_cycle)\n", + "\n", + "# convert iterator into 2-dimensional numpy array\n", + "conditions = np.array(list(conditions)).reshape(-1, 1)\n", + "\n", + "# collect initial set of observations\n", + "observations = run_experiment(conditions)\n", + "\n", + "for cycle in range(num_cycles):\n", + "\n", + " # use BMS theorist to fit the model to the data\n", + " theorist_bms.fit(conditions, observations)\n", + "\n", + " # obtain new conditions\n", + " new_conditions = falsification_sample(\n", + " condition_pool=condition_pool,\n", + " model=theorist_bms,\n", + " reference_conditions=conditions,\n", + " reference_observations=observations,\n", + " metadata=metadata,\n", + " num_samples=measurements_per_cycle,\n", + " )\n", + "\n", + " # obtain new observations\n", + " new_observations = run_experiment(new_conditions)\n", + "\n", + " # combine old and new conditions and observations\n", + " conditions = np.concatenate((conditions, new_conditions))\n", + " observations = np.concatenate((observations, new_observations))\n", + "\n", + " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", + " loss = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", + " print(\"Loss in cycle {}: {}\".format(cycle, loss))\n", + " print(\"Discovered Model: \" + theorist_bms.repr())\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 2: Model Disagreement Sampler\n", + "We can easily replace components in the workflow above. For instance, we could replace ``falsification_sample`` with the ``experimentalist_pipeline`` defined in Tutorial I.\n", + "\n", + "In the following code block, we add a linear regression theorist, to fit a linear model to the data. In addition, we replace ``falsification_sample`` with ``model_disagreement_sample`` to sample experimental conditions that differentiate most between the linear model and the model discovered by the BMS theorist." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:40<00:00, 2.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss in cycle 0: 0.0\n", + "Discovered BMS Model: sin(X0)\n" + ] }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:18<00:00, 5.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss in cycle 1: 0.0\n", + "Discovered BMS Model: sin(X0)\n" + ] }, - "language_info": { - "name": "python" + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:19<00:00, 5.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss in cycle 2: 0.0\n", + "Discovered BMS Model: sin(X0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:15<00:00, 6.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss in cycle 3: 0.0\n", + "Discovered BMS Model: sin(X0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:24<00:00, 4.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss in cycle 4: 0.0\n", + "Discovered BMS Model: sin(X0)\n" + ] } + ], + "source": [ + "num_cycles = 5 # number of empirical research cycles\n", + "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", + "\n", + "# generate an initial set of experimental conditions\n", + "conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", + " n=measurements_per_cycle)\n", + "# convert iterator into 2-dimensional numpy array\n", + "conditions = np.array(list(conditions)).reshape(-1, 1)\n", + "\n", + "# collect initial set of observations\n", + "observations = run_experiment(conditions)\n", + "\n", + "for cycle in range(num_cycles):\n", + "\n", + " # use BMS theorist to fit the model to the data\n", + " theorist_bms.fit(conditions, observations)\n", + " theorist_lr.fit(conditions, observations)\n", + "\n", + " # obtain new conditions\n", + " new_conditions = model_disagreement_sample(\n", + " condition_pool,\n", + " models = [theorist_bms, theorist_lr],\n", + " num_samples = measurements_per_cycle\n", + " )\n", + "\n", + " # obtain new observations\n", + " new_observations = run_experiment(new_conditions)\n", + "\n", + " # combine old and new conditions and observations\n", + " conditions = np.concatenate((conditions, new_conditions))\n", + " observations = np.concatenate((observations, new_observations))\n", + "\n", + " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", + " loss = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", + " print(\"Loss in cycle {}: {}\".format(cycle, loss))\n", + " print(\"Discovered BMS Model: \" + theorist_bms.model_.__repr__())\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Next Notebook\n", + "While the basic loop construct is flexible, there are more convenient ways to specify a research cycle in ``autora``. The next notebook illustrates the use of these constructs.\n", + "\n", + "Follow this link for the next notebook tutorial:\n", + "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/docs/tutorials/basic/Tutorial-III-Workflow-Logic.ipynb b/docs/tutorials/basic/Tutorial-III-Workflow-Logic.ipynb index 8fd517314..5196f9cd0 100644 --- a/docs/tutorials/basic/Tutorial-III-Workflow-Logic.ipynb +++ b/docs/tutorials/basic/Tutorial-III-Workflow-Logic.ipynb @@ -1,1295 +1,1156 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "_q7iLq3GUYMz" - }, - "source": [ - "# Introduction\n", - "## Basic Tutorial III: Workflow Logic" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "5mfUKtGTUYM1", - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", - "\n", - "This notebook is the third of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", - "\n", - "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", - "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", - "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", - "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", - "\n", - "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", - "\n", - "**How to use this notebook** *You can progress through the notebook section by section or directly navigate to specific sections. If you choose the latter, it is recommended to execute all cells in the notebook initially, allowing you to easily rerun the cells in each section later without issues.*" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "BuCna7-ytMBB" - }, - "source": [ - "## Tutorial Setup\n", - "This tutorial is self-contained so that you do not need to run the previous notebook to begin. However, the four notebooks are continuous so that what we define in a previous notebook should still exist within this notebook. As such, we will here re-run relevant code from past tutorials. We will not again walk you through these, but if you need a reminder what they are then go see the descriptions in previous notebooks." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "id": "RuCZVkP7tM6L" - }, - "outputs": [], - "source": [ - "#### Installation ####\n", - "!pip install -q \"autora[experimentalist-falsification]\"\n", - "!pip install -q \"autora[experimentalist-sampler-novelty]\"\n", - "!pip install -q \"autora[theorist-bms]\"\n", - "\n", - "#### Import modules ####\n", - "import numpy as np\n", - "import torch\n", - "from autora.variable import DV, IV, ValueType, VariableCollection\n", - "from autora.experimentalist.pooler.grid import grid_pool\n", - "from autora.experimentalist.sampler.falsification import falsification_sample\n", - "from autora.experimentalist.sampler.novelty import novelty_sample\n", - "from autora.theorist.bms import BMSRegressor\n", - "\n", - "#### Set seeds ####\n", - "np.random.seed(42)\n", - "torch.manual_seed(42)\n", - "\n", - "#### Define ground truth and experiment runner ####\n", - "ground_truth = lambda x: np.sin(x)\n", - "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape)\n", - "\n", - "#### Define condition pool ####\n", - "condition_pool = np.linspace(0, 2 * np.pi, 10)\n", - "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", - "\n", - "#### Define metadata ####\n", - "iv = IV(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 10))\n", - "dv = DV(name=\"y\", type=ValueType.REAL)\n", - "metadata = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", - "\n", - "#### Define theorists ####\n", - "theorist_bms = BMSRegressor(epochs=100)" - ] - }, + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction\n", + "## Basic Tutorial III: Workflow Logic" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", + "\n", + "This notebook is the third of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", + "\n", + "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", + "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", + "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", + "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", + "\n", + "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", + "\n", + "**How to use this notebook** *You can progress through the notebook section by section or directly navigate to specific sections. If you choose the latter, it is recommended to execute all cells in the notebook initially, allowing you to easily rerun the cells in each section later without issues.*" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tutorial Setup\n", + "This tutorial is self-contained so that you do not need to run the previous notebook to begin. However, the four notebooks are continuous so that what we define in a previous notebook should still exist within this notebook. As such, we will here re-run relevant code from past tutorials. We will not again walk you through these, but if you need a reminder what they are then go see the descriptions in previous notebooks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#### Installation ####\n", + "!pip install -q \"autora[experimentalist-falsification]\"\n", + "!pip install -q \"autora[experimentalist-sampler-novelty]\"\n", + "!pip install -q \"autora[theorist-bms]\"\n", + "\n", + "#### Import modules ####\n", + "import numpy as np\n", + "import torch\n", + "from autora.variable import DV, IV, ValueType, VariableCollection\n", + "from autora.experimentalist.pooler.grid import grid_pool\n", + "from autora.experimentalist.sampler.falsification import falsification_sample\n", + "from autora.experimentalist.sampler.novelty import novelty_sample\n", + "from autora.theorist.bms import BMSRegressor\n", + "\n", + "#### Set seeds ####\n", + "np.random.seed(42)\n", + "torch.manual_seed(42)\n", + "\n", + "#### Define ground truth and experiment runner ####\n", + "ground_truth = lambda x: np.sin(x)\n", + "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape)\n", + "\n", + "#### Define condition pool ####\n", + "condition_pool = np.linspace(0, 2 * np.pi, 10)\n", + "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", + "\n", + "#### Define metadata ####\n", + "iv = IV(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 10))\n", + "dv = DV(name=\"y\", type=ValueType.REAL)\n", + "metadata = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "#### Define theorists ####\n", + "theorist_bms = BMSRegressor(epochs=100)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Workflow Logic\n", + "\n", + "Workflows in ``autora`` implement the *autonomous empirical research paradigm*. This paradigm centers around the dynamic interplay between automated theorists and automated experimentalists. As outlined above, theorists rely–among other things–on existing data to construct computational models by linking experimental conditions to dependent measures. Experimentalists design follow-up experiments to refine and validate models generated by the theorist. Together, these agents enable a closed-loop scientific discovery process.\n", + "\n", + "The following sections introduce ways of specifying workflows directly in ``autora``. For more information on workflows, please refer to the [corresponding documentation](https://autoresearch.github.io/autora/user-guide/workflow/)." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Workflows\n", + "\n", + "This section provides an introduction to handling workflows with the controller object. Here, we focus on workflows implementing the **default execution order**: (1) generate experiment conditions using the ``eperimentalist``, (2) collect observations using the ``experiment_runner``, and (3), generate a model that links experiment conditions to observations using the ``theorist``.\n", + "\n", + "We begin with implementing the following workflow:\n", + "1. Generate seed experimental conditions\n", + "2. Iterate 5 times through the following steps\n", + " - Collect observations using ``run_experiment``\n", + " - Identify a model relating conditions to observations using ``theorist_bms``\n", + " - Identify 3 new experimental conditions using ``falsification_sample``\n", + "\n", + "### Declaration\n", + "\n", + "We begin with defining a simple workflow. Workflows can be encapsulated in a ``Controller`` object. For instance, the following code block sets up a closed-loop cycle between (1) a grid pooler for sampling experimental conditions, (2) an experiment runner for obtaining respective observations, and (3) a BMS theorist for discoverying an equation relating experimental conditions to observations.\n", + "\n", + "As with pipelines, we can pass the ``Controller`` object static parameters for each component. In this case, we provide the grid experimentalist with information about the independent variables to sample.\n", + "\n", + "**Note**: *We haven't included the ``falsification_sample`` experimentalist into our workflow yet because it requires us to specify state-dependent input arguments (e.g., the model generated by the theorist), which we will cover at the end of this section.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.workflow import Controller\n", + "\n", + "controller = Controller(\n", + " variables=metadata,\n", + " experimentalist=grid_pool,\n", + " experiment_runner=run_experiment,\n", + " theorist=theorist_bms,\n", + " params={\n", + " \"experimentalist\":\n", + " {\"ivs\": metadata.independent_variables}\n", + " }\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the declaration of the ``params`` parameter, we first specify the type of the component we seek to parameterize as a dictionary key, e.g., ``\"experimentalist\"``. Then we nest within it, another dictionary with the input arguments to the respective component as keys (e.g., ``\"ivs\"`` is an input argument to the ``grid_pool`` experimentalist) along with their values (e.g., ``metadata.independent_variables``).\n", + "\n", + "### Monitoring\n", + "\n", + "Before we execute the controller, lets also add a **monitor function** which is executed with every autonomous empirical research step. The following code block prints the last generated result of the workflow defined by the controller. All workflow results are stored in the ``state.history`` object. We can access the kind of the latest result using ``state.history[-1].kind``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define monitor function\n", + "def monitor(state):\n", + " print(f\"MONITOR: Generated new {state.history[-1].kind}\")\n", + "\n", + "# add monitor function to controller\n", + "controller.monitor = monitor" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Execution\n", + "\n", + "The controller is defined as an iterator. We can execute a single step in the workflow by passing the ``controller`` object to the ``next()`` method. The following code block executes three steps of the default research cycle." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "fjJRnOXhUYNM" - }, - "source": [ - "# Workflow Logic\n", - "\n", - "Workflows in ``autora`` implement the *autonomous empirical research paradigm*. This paradigm centers around the dynamic interplay between automated theorists and automated experimentalists. As outlined above, theorists rely–among other things–on existing data to construct computational models by linking experimental conditions to dependent measures. Experimentalists design follow-up experiments to refine and validate models generated by the theorist. Together, these agents enable a closed-loop scientific discovery process.\n", - "\n", - "The following sections introduce ways of specifying workflows directly in ``autora``. For more information on workflows, please refer to the [corresponding documentation](https://autoresearch.github.io/autora/user-guide/workflow/)." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new CONDITION\n", + "MONITOR: Generated new OBSERVATION\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "lWV6x-oUUYNM" - }, - "source": [ - "## Basic Workflows\n", - "\n", - "This section provides an introduction to handling workflows with the controller object. Here, we focus on workflows implementing the **default execution order**: (1) generate experiment conditions using the ``eperimentalist``, (2) collect observations using the ``experiment_runner``, and (3), generate a model that links experiment conditions to observations using the ``theorist``.\n", - "\n", - "We begin with implementing the following workflow:\n", - "1. Generate seed experimental conditions\n", - "2. Iterate 5 times through the following steps\n", - " - Collect observations using ``run_experiment``\n", - " - Identify a model relating conditions to observations using ``theorist_bms``\n", - " - Identify 3 new experimental conditions using ``falsification_sample``\n", - "\n", - "### Declaration\n", - "\n", - "We begin with defining a simple workflow. Workflows can be encapsulated in a ``Controller`` object. For instance, the following code block sets up a closed-loop cycle between (1) a grid pooler for sampling experimental conditions, (2) an experiment runner for obtaining respective observations, and (3) a BMS theorist for discoverying an equation relating experimental conditions to observations.\n", - "\n", - "As with pipelines, we can pass the ``Controller`` object static parameters for each component. In this case, we provide the grid experimentalist with information about the independent variables to sample.\n", - "\n", - "**Note**: *We haven't included the ``falsification_sample`` experimentalist into our workflow yet because it requires us to specify state-dependent input arguments (e.g., the model generated by the theorist), which we will cover at the end of this section.*" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:21<00:00, 4.66it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "id": "crMFLnTgUYNM" - }, - "outputs": [], - "source": [ - "from autora.workflow import Controller\n", - "\n", - "controller = Controller(\n", - " variables=metadata,\n", - " experimentalist=grid_pool,\n", - " experiment_runner=run_experiment,\n", - " theorist=theorist_bms,\n", - " params={\n", - " \"experimentalist\":\n", - " {\"ivs\": metadata.independent_variables}\n", - " }\n", - ")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new MODEL\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "p1ZHI56hUYNM" - }, - "source": [ - "In the declaration of the ``params`` parameter, we first specify the type of the component we seek to parameterize as a dictionary key, e.g., ``\"experimentalist\"``. Then we nest within it, another dictionary with the input arguments to the respective component as keys (e.g., ``\"ivs\"`` is an input argument to the ``grid_pool`` experimentalist) along with their values (e.g., ``metadata.independent_variables``).\n", - "\n", - "### Monitoring\n", - "\n", - "Before we execute the controller, lets also add a **monitor function** which is executed with every autonomous empirical research step. The following code block prints the last generated result of the workflow defined by the controller. All workflow results are stored in the ``state.history`` object. We can access the kind of the latest result using ``state.history[-1].kind``." + "data": { + "text/plain": [ + "" ] - }, + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "next(controller)\n", + "next(controller)\n", + "next(controller)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As indicated by the monitor, the **default execution order** is as follows: (1) generate experiment conditions, (2) collect observations, and (3), generate a model. After executing step (3), the controller would then continue with step (1):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "id": "AyKM11z6UYNN" - }, - "outputs": [], - "source": [ - "# define monitor function\n", - "def monitor(state):\n", - " print(f\"MONITOR: Generated new {state.history[-1].kind}\")\n", - "\n", - "# add monitor function to controller\n", - "controller.monitor = monitor" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new CONDITION\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "QWhI8SvuUYNN" - }, - "source": [ - "### Execution\n", - "\n", - "The controller is defined as an iterator. We can execute a single step in the workflow by passing the ``controller`` object to the ``next()`` method. The following code block executes three steps of the default research cycle." + "data": { + "text/plain": [ + "" ] - }, + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "next(controller)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since ``controller`` is an iterator, we can use [itertools](https://docs.python.org/3/library/itertools.html) for efficient looping. The following example uses ``takewhile`` to define a loop that stops as soon as we obtained three models from the theorist.\n", + "\n", + "We begin with defining a lambda function which returns true whenever the controller has less then 5 models. As explained in the next subsection, we can obtain a list of generated models by accessing the controller's state via ``controller.state.models``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "continue_criterion = lambda controller: len(controller.state.models) < 5" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can run a for-loop using the ``controller`` as an iterator, and ``takewhile`` as iterator logic that continues to execute steps of the controller as long as ``continue_criterion`` returns ``True``. In this way, we can execute 5 research cycles." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "wIwF6i70UYNN", - "outputId": "b0fbf84c-85ac-4f89-c9c8-705c4a5a4e22" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new CONDITION\n", - "MONITOR: Generated new OBSERVATION\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:21<00:00, 4.66it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(controller)\n", - "next(controller)\n", - "next(controller)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 1\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "0OJ8WdFlUYNN" - }, - "source": [ - "As indicated by the monitor, the **default execution order** is as follows: (1) generate experiment conditions, (2) collect observations, and (3), generate a model. After executing step (3), the controller would then continue with step (1):" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:20<00:00, 5.00it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "FZXvTUG2UYNN", - "outputId": "848dcef6-cff4-4f1b-a781-2db960f58f06" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new CONDITION\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(controller)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 2\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 2\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 2\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "r7gneVACUYNN" - }, - "source": [ - "Since ``controller`` is an iterator, we can use [itertools](https://docs.python.org/3/library/itertools.html) for efficient looping. The following example uses ``takewhile`` to define a loop that stops as soon as we obtained three models from the theorist.\n", - "\n", - "We begin with defining a lambda function which returns true whenever the controller has less then 5 models. As explained in the next subsection, we can obtain a list of generated models by accessing the controller's state via ``controller.state.models``." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:11<00:00, 9.04it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "id": "Hanf3vpEUYNO" - }, - "outputs": [], - "source": [ - "continue_criterion = lambda controller: len(controller.state.models) < 5" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 3\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 3\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 3\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "krPjzb8hUYNO" - }, - "source": [ - "Now we can run a for-loop using the ``controller`` as an iterator, and ``takewhile`` as iterator logic that continues to execute steps of the controller as long as ``continue_criterion`` returns ``True``. In this way, we can execute 5 research cycles." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:11<00:00, 8.92it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8FFj4NFLUYNO", - "outputId": "15ee6b27-ebbe-450b-c99b-8a1e1eef66a5" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:20<00:00, 5.00it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 2\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 2\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:11<00:00, 9.04it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 3\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 3\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 3\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:11<00:00, 8.92it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 4\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 4\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 4\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:11<00:00, 8.92it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from itertools import takewhile\n", - "\n", - "for step in takewhile(continue_criterion, controller):\n", - " print(f\"Number of models: {len(step.state.models)}\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 4\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 4\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 4\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "ipgISi2QUYNO" - }, - "source": [ - "### Result Inspection\n", - "\n", - "After each executed step, we can observe the result generated by the ``controller``. All results are stored in in ``controller.state.history``. Each result is composed of a value specifying its ``kind`` (``CONDITION``, ``OBSERVATION``, or ``MODEL``) and the respective ``data``.\n", - "\n", - "We can obtain the observations collected in the last step of the workflow as follows:" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:11<00:00, 8.92it/s]" + ] }, { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xaigjyMpUYNO", - "outputId": "6315b7ee-c334-4702-c6e1-c0f2d5480317" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ResultKind.MODEL\n", - "BMSRegressor(epochs=100)\n" - ] - } - ], - "source": [ - "result = controller.state.history[-1]\n", - "\n", - "print(result.kind)\n", - "print(result.data)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new MODEL\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "p3mLxYfyUYNO" - }, - "source": [ - "We can also specify the kind of result we are looking for directly. For instance, we can obtain all models generated by the theorist using ``controller.state.models``. The following code block prints the last model discovered by the BMS theorist (note that ``repr()`` is a function specific to the BMS theorist which returns its model as a string)." - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from itertools import takewhile\n", + "\n", + "for step in takewhile(continue_criterion, controller):\n", + " print(f\"Number of models: {len(step.state.models)}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Result Inspection\n", + "\n", + "After each executed step, we can observe the result generated by the ``controller``. All results are stored in in ``controller.state.history``. Each result is composed of a value specifying its ``kind`` (``CONDITION``, ``OBSERVATION``, or ``MODEL``) and the respective ``data``.\n", + "\n", + "We can obtain the observations collected in the last step of the workflow as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "DEoLhXo6UYNO", - "outputId": "a8205c81-3052-4a01-db3c-bbc7d03fcdf5" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sin(X0)\n" - ] - } - ], - "source": [ - "print(controller.state.models[-1].repr())" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "ResultKind.MODEL\n", + "BMSRegressor(epochs=100)\n" + ] + } + ], + "source": [ + "result = controller.state.history[-1]\n", + "\n", + "print(result.kind)\n", + "print(result.data)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also specify the kind of result we are looking for directly. For instance, we can obtain all models generated by the theorist using ``controller.state.models``. The following code block prints the last model discovered by the BMS theorist (note that ``repr()`` is a function specific to the BMS theorist which returns its model as a string)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "XT3kZJJmUYNP" - }, - "source": [ - "Alternatively, we can access probed experimental conditions via ``controller.state.conditions`` and observations via ``controller.state.observations``, respectively. The following code block requests the latest experimental conditions identified by the experimentalist and the corresponding observations collected by the experiment runner" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "sin(X0)\n" + ] + } + ], + "source": [ + "print(controller.state.models[-1].repr())" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can access probed experimental conditions via ``controller.state.conditions`` and observations via ``controller.state.observations``, respectively. The following code block requests the latest experimental conditions identified by the experimentalist and the corresponding observations collected by the experiment runner" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oQu4pF_yUYNP", - "outputId": "846cdb8d-f07a-4ca8-f878-6c06cbfec54a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Conditions:\n", - "[[0. ]\n", - " [0.6981317 ]\n", - " [1.3962634 ]\n", - " [2.0943951 ]\n", - " [2.7925268 ]\n", - " [3.4906585 ]\n", - " [4.1887902 ]\n", - " [4.88692191]\n", - " [5.58505361]\n", - " [6.28318531]]\n", - "Observations:\n", - "[[ 0. 0.07384666]\n", - " [ 0.6981317 0.65992444]\n", - " [ 1.3962634 0.97324292]\n", - " [ 2.0943951 0.83591503]\n", - " [ 2.7925268 0.19416794]\n", - " [ 3.4906585 -0.41400456]\n", - " [ 4.1887902 -0.91208928]\n", - " [ 4.88692191 -0.87909553]\n", - " [ 5.58505361 -0.60842578]\n", - " [ 6.28318531 -0.17630402]]\n" - ] - } - ], - "source": [ - "print(f\"Conditions:\\n{controller.state.conditions[-1]}\")\n", - "print(f\"Observations:\\n{controller.state.observations[-1]}\")" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Conditions:\n", + "[[0. ]\n", + " [0.6981317 ]\n", + " [1.3962634 ]\n", + " [2.0943951 ]\n", + " [2.7925268 ]\n", + " [3.4906585 ]\n", + " [4.1887902 ]\n", + " [4.88692191]\n", + " [5.58505361]\n", + " [6.28318531]]\n", + "Observations:\n", + "[[ 0. 0.07384666]\n", + " [ 0.6981317 0.65992444]\n", + " [ 1.3962634 0.97324292]\n", + " [ 2.0943951 0.83591503]\n", + " [ 2.7925268 0.19416794]\n", + " [ 3.4906585 -0.41400456]\n", + " [ 4.1887902 -0.91208928]\n", + " [ 4.88692191 -0.87909553]\n", + " [ 5.58505361 -0.60842578]\n", + " [ 6.28318531 -0.17630402]]\n" + ] + } + ], + "source": [ + "print(f\"Conditions:\\n{controller.state.conditions[-1]}\")\n", + "print(f\"Observations:\\n{controller.state.observations[-1]}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Seeding\n", + "\n", + "The default execution order always begins with an experimentalist. This is problematic if we want to use an experimentalist that depends on prior steps (e.g., the falsification experimentalist requires a model generated by the theorist). We can circumvent this problem by seeding the controller with experiment conditons.\n", + "\n", + "The following code block seeds the controller with 3 experiment conditions. We first generate the ``seed_conditions``, and then pass them, encapsulated in a list, to the ``seed`` function of the ``controller`` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "aabSIf3fUYNP" - }, - "source": [ - "### Seeding\n", - "\n", - "The default execution order always begins with an experimentalist. This is problematic if we want to use an experimentalist that depends on prior steps (e.g., the falsification experimentalist requires a model generated by the theorist). We can circumvent this problem by seeding the controller with experiment conditons.\n", - "\n", - "The following code block seeds the controller with 3 experiment conditions. We first generate the ``seed_conditions``, and then pass them, encapsulated in a list, to the ``seed`` function of the ``controller`` object." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new OBSERVATION\n" + ] }, { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "jyF6yXDCUYNP", - "outputId": "97bb5399-ff3e-49ed-aec1-3783551dd994" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new OBSERVATION\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# generate initial pool of 3 experimental conditions\n", - "seed_conditions = np.linspace(0,2*np.pi,3)\n", - "\n", - "# define controller\n", - "controller = Controller(\n", - " monitor=monitor,\n", - " variables=metadata,\n", - " experimentalist=grid_pool,\n", - " experiment_runner=run_experiment,\n", - " theorist=theorist_bms,\n", - " params={\n", - " \"experimentalist\":\n", - " {\"ivs\": metadata.independent_variables}\n", - " }\n", - ")\n", - "\n", - "# seed controller\n", - "controller.seed(conditions=[seed_conditions])\n", - "\n", - "next(controller)" + "data": { + "text/plain": [ + "" ] - }, + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# generate initial pool of 3 experimental conditions\n", + "seed_conditions = np.linspace(0,2*np.pi,3)\n", + "\n", + "# define controller\n", + "controller = Controller(\n", + " monitor=monitor,\n", + " variables=metadata,\n", + " experimentalist=grid_pool,\n", + " experiment_runner=run_experiment,\n", + " theorist=theorist_bms,\n", + " params={\n", + " \"experimentalist\":\n", + " {\"ivs\": metadata.independent_variables}\n", + " }\n", + ")\n", + "\n", + "# seed controller\n", + "controller.seed(conditions=[seed_conditions])\n", + "\n", + "next(controller)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that, since we seeded the controller with initial experimental conditions, the next step is to execute the ``experiment_runner``. This is why the first step reported by the monitor involves the generation of observations (based on the seed experimental conditions).\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Accessing State-Dependent Properties\n", + "\n", + "Some automated empirical research components require input arguments that depend on the result of the last step in the workflow. For instance, the ``falsification_sample`` experimentalist depends on the previously collected experimental conditions, observations, and the fitted model. For such cases, it is possible to use \"state-dependent properties\" in the ``params`` dictionary. These are the following strings, which will be replaced during execution by their respective current values:\n", + "\n", + "- ``\"%observations.ivs[-1]%\"``: the last observed independent variables
\n", + "- ``\"%observations.dvs[-1]%\"``: the last observed dependent variables
\n", + "- ``\"%observations.ivs%\"``: all the observed independent variables (observations), concatenated into a single array
\n", + "- ``\"%observations.dvs%\"``: all the observed dependent variables (experimental conditions), concatenated into a single array
\n", + "- ``\"%models[-1]%\"``: the last fitted theorist
\n", + "- ``\"%models%\"``: all the fitted theorists
\n", + "\n", + "In the following example, we use the ``\"%observations.ivs%\"``, ``\"%observations.dvs%\"``, and ``\"%models%\"`` properties for the ``falsification_sample`` experimentalist which seeks to identify experimental conditions that are predicted to maximize the loss of the fitted model.\n", + "\n", + "The code block below implements the following workflow:\n", + "1. Generate 3 seed experimental conditions\n", + "2. Iterate 5 times through the following steps\n", + " - Collect observations using ``run_experiment``\n", + " - Identify a model relating conditions to observations using ``theorist_bms``\n", + " - Identify 3 new experimental conditions using ``falsification_sample``\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# generate initial pool of 3 experimental conditions\n", + "seed_conditions = np.linspace(0,2*np.pi,3)\n", + "\n", + "# define controller\n", + "controller = Controller(\n", + " monitor=monitor,\n", + " variables=metadata,\n", + " experimentalist=falsification_sample,\n", + " experiment_runner=run_experiment,\n", + " theorist=theorist_bms,\n", + " params={\n", + " \"experimentalist\":\n", + " {\"condition_pool\": condition_pool,\n", + " \"model\": \"%models[-1]%\", # access last model generated by theorist\n", + " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", + " \"reference_observations\": \"%observations.dvs%\", # access all observations collected so far\n", + " \"metadata\": metadata,\n", + " \"num_samples\": 3}\n", + " }\n", + ")\n", + "\n", + "# seed controller\n", + "controller.seed(conditions=[seed_conditions])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using ``takewhile``, we can now specify a workflow logic that executes the automated research process 5 times. Accordingly, we stop execution of the ``controller`` as soon as it accumulated 5 models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "CTNPh9LAUYNP" - }, - "source": [ - "Note that, since we seeded the controller with initial experimental conditions, the next step is to execute the ``experiment_runner``. This is why the first step reported by the monitor involves the generation of observations (based on the seed experimental conditions).\n", - "\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 0\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "atOAyk5iUYNP" - }, - "source": [ - "### Accessing State-Dependent Properties\n", - "\n", - "Some automated empirical research components require input arguments that depend on the result of the last step in the workflow. For instance, the ``falsification_sample`` experimentalist depends on the previously collected experimental conditions, observations, and the fitted model. For such cases, it is possible to use \"state-dependent properties\" in the ``params`` dictionary. These are the following strings, which will be replaced during execution by their respective current values:\n", - "\n", - "- ``\"%observations.ivs[-1]%\"``: the last observed independent variables
\n", - "- ``\"%observations.dvs[-1]%\"``: the last observed dependent variables
\n", - "- ``\"%observations.ivs%\"``: all the observed independent variables (observations), concatenated into a single array
\n", - "- ``\"%observations.dvs%\"``: all the observed dependent variables (experimental conditions), concatenated into a single array
\n", - "- ``\"%models[-1]%\"``: the last fitted theorist
\n", - "- ``\"%models%\"``: all the fitted theorists
\n", - "\n", - "In the following example, we use the ``\"%observations.ivs%\"``, ``\"%observations.dvs%\"``, and ``\"%models%\"`` properties for the ``falsification_sample`` experimentalist which seeks to identify experimental conditions that are predicted to maximize the loss of the fitted model.\n", - "\n", - "The code block below implements the following workflow:\n", - "1. Generate 3 seed experimental conditions\n", - "2. Iterate 5 times through the following steps\n", - " - Collect observations using ``run_experiment``\n", - " - Identify a model relating conditions to observations using ``theorist_bms``\n", - " - Identify 3 new experimental conditions using ``falsification_sample``\n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:10<00:00, 9.51it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "id": "B8Bt0YeQUYNP" - }, - "outputs": [], - "source": [ - "# generate initial pool of 3 experimental conditions\n", - "seed_conditions = np.linspace(0,2*np.pi,3)\n", - "\n", - "# define controller\n", - "controller = Controller(\n", - " monitor=monitor,\n", - " variables=metadata,\n", - " experimentalist=falsification_sample,\n", - " experiment_runner=run_experiment,\n", - " theorist=theorist_bms,\n", - " params={\n", - " \"experimentalist\":\n", - " {\"condition_pool\": condition_pool,\n", - " \"model\": \"%models[-1]%\", # access last model generated by theorist\n", - " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", - " \"reference_observations\": \"%observations.dvs%\", # access all observations collected so far\n", - " \"metadata\": metadata,\n", - " \"num_samples\": 3}\n", - " }\n", - ")\n", - "\n", - "# seed controller\n", - "controller.seed(conditions=[seed_conditions])" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 1\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 1\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 1\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "GIlhC3UZUYNQ" - }, - "source": [ - "Using ``takewhile``, we can now specify a workflow logic that executes the automated research process 5 times. Accordingly, we stop execution of the ``controller`` as soon as it accumulated 5 models." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:11<00:00, 8.80it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "L_TPQmSJUYNQ", - "outputId": "e329e8db-2839-4d42-b678-f051c388cd86" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.51it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 1\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 1\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:11<00:00, 8.80it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 2\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 2\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:11<00:00, 8.70it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 3\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 3\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 3\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:11<00:00, 8.98it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 4\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 4\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 4\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.47it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 5\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 5\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:09<00:00, 10.86it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from itertools import takewhile\n", - "\n", - "continue_criterion = lambda controller: len(controller.state.models) < 6\n", - "\n", - "for step in takewhile(continue_criterion, controller):\n", - " print(f\"Number of models: {len(step.state.models)}\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 2\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 2\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 2\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "agcGRk5NUYNQ" - }, - "source": [ - "## Advanced Workflows\n", - "\n", - "In some cases, we may want to condition the sequence of steps taken in the empirical research process on the current state of the process. For instance, one might want to switch from a novelty sampling strategy to a falsification sampling strategy as soon as one has probed enough novel experiment conditions. This section provides a basic introduction to the``BaseController``, which enables the implementation of such arbitrary execution orders.\n", - "\n", - "In this section, we consider a scenario in which we switch experimentalists, depending on the amount of observations collected:\n", - "- If no observations are collected, we sample some seed experimental conditions\n", - "- If less than 7 observations are collected, we sample experimental conditions with ``novelty_sample``\n", - "- If 7 or more observations are collected, we sample experimental conditions with ``falsification_sample``\n", - "\n", - "#### Planner Declaration\n", - "\n", - "We begin with defining an ``experimentalist_planner`` function. Such planner function will be provided as input to the ``BaseController``, and will be used to determine the next step of the workflow, depending on the current state. The code block below implements a planner that selects the experimentalist to be executed depending on the amount of observations collected:" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:11<00:00, 8.70it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "id": "Rld_TIygUYNQ" - }, - "outputs": [], - "source": [ - "from autora.workflow.planner import last_result_kind_planner\n", - "\n", - "def experimentalist_planner(state):\n", - " # We're going to reuse the \"last_result_kind_planner\" planner, and modify its output.\n", - " proposed_next_step = last_result_kind_planner(state)\n", - "\n", - " # Obtain a list of all observations collected so far\n", - " all_observations = [item for sublist in state.observations for item in sublist]\n", - " num_observations = len(all_observations)\n", - "\n", - " # Determine next experimentalist\n", - " if proposed_next_step == \"experimentalist\":\n", - " if num_observations < 1:\n", - " next_step = \"seed_experimentalist\"\n", - " elif num_observations > 0 and num_observations < 7:\n", - " next_step = \"novelty_experimentalist\"\n", - " else:\n", - " next_step = \"falsification_experimentalist\"\n", - " else:\n", - " next_step = proposed_next_step\n", - "\n", - " print(\"PLANNER: Next step: \" + next_step)\n", - " return next_step" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 3\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 3\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 3\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "Yufzx-_8UYNQ" - }, - "source": [ - "The ``experimentalist_planner`` function accepts a ``controller``'s state as input and returns the next step to be executed. Here, we call the ``last_result_kind_planner`` to obtain the default next step. For instance, according to the autonomous empirical research paradigm, if the last step involved executing the ``\"theorist\"``, the next step would be executing the ``experimentalist``.\n", - "\n", - "If the next default step is the ``experimentalist``, the ``experimentalist_planner`` will select the type of experimentalist based on the total number of collected observations." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:11<00:00, 8.98it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "qRvGhjFlUYNQ" - }, - "source": [ - "### Executor Collection Declaration\n", - "\n", - "In order for the ``BaseController`` to work with the ``experimentalist_planner``, we need to specify the experimentalists that it selects to be executed. In the next code block, we define all experimentalists by wrapping each of them into a ``Pipeline``. However, at this point, we don't need to provide the respective parameters for each experimentalist–we will provide these later, directly to the ``BaseController`` object.\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 4\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 4\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 4\n" + ] }, { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "id": "eTzX1-nBUYNR" - }, - "outputs": [], - "source": [ - "from autora.experimentalist.pipeline import make_pipeline\n", - "\n", - "seed_pipeline = make_pipeline([np.linspace(0, 2*np.pi, 3)])\n", - "novelty_pipeline = make_pipeline([novelty_sample])\n", - "falsification_pipeline = make_pipeline([falsification_sample])" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:10<00:00, 9.47it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "2H4eIWLmUYNR" - }, - "source": [ - "We can now wrap all elements of our research process–this includes all experimentalists as well as the theorist and experiment runner–into a collection of executors. The following code block defines this collection using ``ChainedFunctionMapping``." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new MODEL\n", + "Number of models: 5\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 5\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 5\n" + ] }, { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "id": "FfjGXQvDUYNR" - }, - "outputs": [], - "source": [ - "from autora.workflow.executor import (ChainedFunctionMapping, from_experimentalist_pipeline,\n", - " from_experiment_runner_callable, from_theorist_estimator)\n", - "\n", - "executor_collection = ChainedFunctionMapping(\n", - " seed_experimentalist=\n", - " [from_experimentalist_pipeline, seed_pipeline],\n", - " novelty_experimentalist=\n", - " [from_experimentalist_pipeline, novelty_pipeline],\n", - " falsification_experimentalist=\n", - " [from_experimentalist_pipeline, falsification_pipeline],\n", - " experiment_runner=[from_experiment_runner_callable, run_experiment],\n", - " theorist=[from_theorist_estimator, theorist_bms],\n", - ")" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:09<00:00, 10.86it/s]" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "qx9FNV_jUYNR" - }, - "source": [ - "In the ``ChainedFunctionMapping``, we specify each element by its type, followed by its function. For instance, the ``seed_experimentalist`` is defined as an experimentalist pipeline. Thus, we specify it as ``from_experimentalist_pipeline``, and chain it with its respective function ``seed_experimentalist`` defined above." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new MODEL\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "h1xwRNm5UYNS" - }, - "source": [ - "### Base Controller Declaration\n", - "\n", - "So far, we have defined a ``experimentalist_planner`` function which determines the next step in our workflow. We have also defined a ``executor_collection`` defining each step of the workflow. Both will be provided to a special ``Controller`` called ``BaseController``. The ``BaseController`` does not require us to specify a ``theorist``, ``experimentalist``, or ``experiment_runner``. Instead, we can provide it with an ``executor_collection`` specifying all the elements of the workflow we require.\n", - "\n", - "The ``BaseController`` also requires us to specify an initial ``state``. Here, we can instantiate a state as a ``History`` object which entails all variables of the experiment (as declared in ``metadata``) along with the parameters provided to each element in the ``executor_collection``. Let's begin with defining the parameters for all elements in the ``executor_collection``. Here, only two of the elements (``novelty_experimentalist`` and ``falsification_experimentalist``) require us to specify additional parameters.\n" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from itertools import takewhile\n", + "\n", + "continue_criterion = lambda controller: len(controller.state.models) < 6\n", + "\n", + "for step in takewhile(continue_criterion, controller):\n", + " print(f\"Number of models: {len(step.state.models)}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Advanced Workflows\n", + "\n", + "In some cases, we may want to condition the sequence of steps taken in the empirical research process on the current state of the process. For instance, one might want to switch from a novelty sampling strategy to a falsification sampling strategy as soon as one has probed enough novel experiment conditions. This section provides a basic introduction to the``BaseController``, which enables the implementation of such arbitrary execution orders.\n", + "\n", + "In this section, we consider a scenario in which we switch experimentalists, depending on the amount of observations collected:\n", + "- If no observations are collected, we sample some seed experimental conditions\n", + "- If less than 7 observations are collected, we sample experimental conditions with ``novelty_sample``\n", + "- If 7 or more observations are collected, we sample experimental conditions with ``falsification_sample``\n", + "\n", + "#### Planner Declaration\n", + "\n", + "We begin with defining an ``experimentalist_planner`` function. Such planner function will be provided as input to the ``BaseController``, and will be used to determine the next step of the workflow, depending on the current state. The code block below implements a planner that selects the experimentalist to be executed depending on the amount of observations collected:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.workflow.planner import last_result_kind_planner\n", + "\n", + "def experimentalist_planner(state):\n", + " # We're going to reuse the \"last_result_kind_planner\" planner, and modify its output.\n", + " proposed_next_step = last_result_kind_planner(state)\n", + "\n", + " # Obtain a list of all observations collected so far\n", + " all_observations = [item for sublist in state.observations for item in sublist]\n", + " num_observations = len(all_observations)\n", + "\n", + " # Determine next experimentalist\n", + " if proposed_next_step == \"experimentalist\":\n", + " if num_observations < 1:\n", + " next_step = \"seed_experimentalist\"\n", + " elif num_observations > 0 and num_observations < 7:\n", + " next_step = \"novelty_experimentalist\"\n", + " else:\n", + " next_step = \"falsification_experimentalist\"\n", + " else:\n", + " next_step = proposed_next_step\n", + "\n", + " print(\"PLANNER: Next step: \" + next_step)\n", + " return next_step" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``experimentalist_planner`` function accepts a ``controller``'s state as input and returns the next step to be executed. Here, we call the ``last_result_kind_planner`` to obtain the default next step. For instance, according to the autonomous empirical research paradigm, if the last step involved executing the ``\"theorist\"``, the next step would be executing the ``experimentalist``.\n", + "\n", + "If the next default step is the ``experimentalist``, the ``experimentalist_planner`` will select the type of experimentalist based on the total number of collected observations." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Executor Collection Declaration\n", + "\n", + "In order for the ``BaseController`` to work with the ``experimentalist_planner``, we need to specify the experimentalists that it selects to be executed. In the next code block, we define all experimentalists by wrapping each of them into a ``Pipeline``. However, at this point, we don't need to provide the respective parameters for each experimentalist–we will provide these later, directly to the ``BaseController`` object.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.experimentalist.pipeline import make_pipeline\n", + "\n", + "seed_pipeline = make_pipeline([np.linspace(0, 2*np.pi, 3)])\n", + "novelty_pipeline = make_pipeline([novelty_sample])\n", + "falsification_pipeline = make_pipeline([falsification_sample])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now wrap all elements of our research process–this includes all experimentalists as well as the theorist and experiment runner–into a collection of executors. The following code block defines this collection using ``ChainedFunctionMapping``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.workflow.executor import (ChainedFunctionMapping, from_experimentalist_pipeline,\n", + " from_experiment_runner_callable, from_theorist_estimator)\n", + "\n", + "executor_collection = ChainedFunctionMapping(\n", + " seed_experimentalist=\n", + " [from_experimentalist_pipeline, seed_pipeline],\n", + " novelty_experimentalist=\n", + " [from_experimentalist_pipeline, novelty_pipeline],\n", + " falsification_experimentalist=\n", + " [from_experimentalist_pipeline, falsification_pipeline],\n", + " experiment_runner=[from_experiment_runner_callable, run_experiment],\n", + " theorist=[from_theorist_estimator, theorist_bms],\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the ``ChainedFunctionMapping``, we specify each element by its type, followed by its function. For instance, the ``seed_experimentalist`` is defined as an experimentalist pipeline. Thus, we specify it as ``from_experimentalist_pipeline``, and chain it with its respective function ``seed_experimentalist`` defined above." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Base Controller Declaration\n", + "\n", + "So far, we have defined a ``experimentalist_planner`` function which determines the next step in our workflow. We have also defined a ``executor_collection`` defining each step of the workflow. Both will be provided to a special ``Controller`` called ``BaseController``. The ``BaseController`` does not require us to specify a ``theorist``, ``experimentalist``, or ``experiment_runner``. Instead, we can provide it with an ``executor_collection`` specifying all the elements of the workflow we require.\n", + "\n", + "The ``BaseController`` also requires us to specify an initial ``state``. Here, we can instantiate a state as a ``History`` object which entails all variables of the experiment (as declared in ``metadata``) along with the parameters provided to each element in the ``executor_collection``. Let's begin with defining the parameters for all elements in the ``executor_collection``. Here, only two of the elements (``novelty_experimentalist`` and ``falsification_experimentalist``) require us to specify additional parameters.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "params = {\"novelty_experimentalist\":\n", + " {\"novelty_sample\":\n", + " {\"condition_pool\": condition_pool,\n", + " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", + " \"num_samples\": 3},\n", + " },\n", + " \"falsification_experimentalist\":\n", + " {\"falsification_sample\":\n", + " {\"condition_pool\": condition_pool,\n", + " \"model\": \"%models[-1]%\", # access last model generated by theorist\n", + " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", + " \"reference_observations\": \"%observations.dvs%\", # access all observations collected so far\n", + " \"metadata\": metadata,\n", + " \"num_samples\": 3}\n", + " }\n", + " }" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the ``metadata`` and ``params``, we can instantiate an initial ``state`` for the workflow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.workflow.state import History\n", + "\n", + "state = History(variables=metadata, params=params)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For convenience, let us also define a monitor function which can print the current total number of observations. We will provide this monitor to the ``BaseController``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def monitor(state):\n", + " all_observations = [item for sublist in state.observations for item in sublist]\n", + " num_observations = len(all_observations)\n", + " print(f\"MONITOR: Number of observations {num_observations}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have all the required input arguments for the ``BaseController``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.workflow.base import BaseController\n", + "\n", + "# define controller\n", + "controller = BaseController(\n", + " state=state,\n", + " monitor=monitor,\n", + " planner=experimentalist_planner,\n", + " executor_collection=executor_collection,\n", + ")\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's execute the controller for 5 research cycles, measured in terms of the number of generated models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "id": "Zq0HDjTcUYNS" - }, - "outputs": [], - "source": [ - "params = {\"novelty_experimentalist\":\n", - " {\"novelty_sample\":\n", - " {\"condition_pool\": condition_pool,\n", - " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", - " \"num_samples\": 3},\n", - " },\n", - " \"falsification_experimentalist\":\n", - " {\"falsification_sample\":\n", - " {\"condition_pool\": condition_pool,\n", - " \"model\": \"%models[-1]%\", # access last model generated by theorist\n", - " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", - " \"reference_observations\": \"%observations.dvs%\", # access all observations collected so far\n", - " \"metadata\": metadata,\n", - " \"num_samples\": 3}\n", - " }\n", - " }" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "PLANNER: Next step: seed_experimentalist\n", + "MONITOR: Number of observations 0\n", + "MONITOR: Number of models: 0\n", + "PLANNER: Next step: experiment_runner\n", + "MONITOR: Number of observations 3\n", + "MONITOR: Number of models: 0\n", + "PLANNER: Next step: theorist\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "AX60ukJeUYNS" - }, - "source": [ - "Using the ``metadata`` and ``params``, we can instantiate an initial ``state`` for the workflow." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:09<00:00, 10.08it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "id": "dmG183YXUYNS" - }, - "outputs": [], - "source": [ - "from autora.workflow.state import History\n", - "\n", - "state = History(variables=metadata, params=params)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Number of observations 3\n", + "MONITOR: Number of models: 1\n", + "PLANNER: Next step: novelty_experimentalist\n", + "MONITOR: Number of observations 3\n", + "MONITOR: Number of models: 1\n", + "PLANNER: Next step: experiment_runner\n", + "MONITOR: Number of observations 6\n", + "MONITOR: Number of models: 1\n", + "PLANNER: Next step: theorist\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "Wtv0ORp5UYNS" - }, - "source": [ - "For convenience, let us also define a monitor function which can print the current total number of observations. We will provide this monitor to the ``BaseController``." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:10<00:00, 9.26it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "id": "CWEAY4-9UYNS" - }, - "outputs": [], - "source": [ - "def monitor(state):\n", - " all_observations = [item for sublist in state.observations for item in sublist]\n", - " num_observations = len(all_observations)\n", - " print(f\"MONITOR: Number of observations {num_observations}\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Number of observations 6\n", + "MONITOR: Number of models: 2\n", + "PLANNER: Next step: novelty_experimentalist\n", + "MONITOR: Number of observations 6\n", + "MONITOR: Number of models: 2\n", + "PLANNER: Next step: experiment_runner\n", + "MONITOR: Number of observations 9\n", + "MONITOR: Number of models: 2\n", + "PLANNER: Next step: theorist\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "W94jN2YDUYNS" - }, - "source": [ - "We now have all the required input arguments for the ``BaseController``." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:12<00:00, 7.91it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "id": "sQ2aBbWAUYNT" - }, - "outputs": [], - "source": [ - "from autora.workflow.base import BaseController\n", - "\n", - "# define controller\n", - "controller = BaseController(\n", - " state=state,\n", - " monitor=monitor,\n", - " planner=experimentalist_planner,\n", - " executor_collection=executor_collection,\n", - ")\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Number of observations 9\n", + "MONITOR: Number of models: 3\n", + "PLANNER: Next step: falsification_experimentalist\n", + "MONITOR: Number of observations 9\n", + "MONITOR: Number of models: 3\n", + "PLANNER: Next step: experiment_runner\n", + "MONITOR: Number of observations 12\n", + "MONITOR: Number of models: 3\n", + "PLANNER: Next step: theorist\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "XSPAMShJUYNT" - }, - "source": [ - "Finally, let's execute the controller for 5 research cycles, measured in terms of the number of generated models." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:10<00:00, 9.73it/s]\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8tvuuhoGUYNT", - "outputId": "d523e8c1-7dca-4ab6-bd50-f697be43342a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PLANNER: Next step: seed_experimentalist\n", - "MONITOR: Number of observations 0\n", - "MONITOR: Number of models: 0\n", - "PLANNER: Next step: experiment_runner\n", - "MONITOR: Number of observations 3\n", - "MONITOR: Number of models: 0\n", - "PLANNER: Next step: theorist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:09<00:00, 10.08it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 3\n", - "MONITOR: Number of models: 1\n", - "PLANNER: Next step: novelty_experimentalist\n", - "MONITOR: Number of observations 3\n", - "MONITOR: Number of models: 1\n", - "PLANNER: Next step: experiment_runner\n", - "MONITOR: Number of observations 6\n", - "MONITOR: Number of models: 1\n", - "PLANNER: Next step: theorist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.26it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 6\n", - "MONITOR: Number of models: 2\n", - "PLANNER: Next step: novelty_experimentalist\n", - "MONITOR: Number of observations 6\n", - "MONITOR: Number of models: 2\n", - "PLANNER: Next step: experiment_runner\n", - "MONITOR: Number of observations 9\n", - "MONITOR: Number of models: 2\n", - "PLANNER: Next step: theorist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:12<00:00, 7.91it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 9\n", - "MONITOR: Number of models: 3\n", - "PLANNER: Next step: falsification_experimentalist\n", - "MONITOR: Number of observations 9\n", - "MONITOR: Number of models: 3\n", - "PLANNER: Next step: experiment_runner\n", - "MONITOR: Number of observations 12\n", - "MONITOR: Number of models: 3\n", - "PLANNER: Next step: theorist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.73it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 12\n", - "MONITOR: Number of models: 4\n", - "PLANNER: Next step: falsification_experimentalist\n", - "MONITOR: Number of observations 12\n", - "MONITOR: Number of models: 4\n", - "PLANNER: Next step: experiment_runner\n", - "MONITOR: Number of observations 15\n", - "MONITOR: Number of models: 4\n", - "PLANNER: Next step: theorist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.46it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 15\n" - ] - } - ], - "source": [ - "from itertools import takewhile\n", - "\n", - "continue_criterion = lambda controller: len(controller.state.models) < 5\n", - "\n", - "for step in takewhile(continue_criterion, controller):\n", - " print(f\"MONITOR: Number of models: {len(step.state.models)}\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Number of observations 12\n", + "MONITOR: Number of models: 4\n", + "PLANNER: Next step: falsification_experimentalist\n", + "MONITOR: Number of observations 12\n", + "MONITOR: Number of models: 4\n", + "PLANNER: Next step: experiment_runner\n", + "MONITOR: Number of observations 15\n", + "MONITOR: Number of models: 4\n", + "PLANNER: Next step: theorist\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "VymXaj-gUYNT" - }, - "source": [ - "We can observe that the controller begins with sampling experiment condition using the ``seed_experimentalist``. It then proceeds to sample condition using the ``novelty_experimentalist`` until it has collected 7 or more observations, at which it switches to the ``falsification_experimentalist``." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:10<00:00, 9.46it/s]\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "oS5TJBr6s-kJ" - }, - "source": [ - "# Next Notebook\n", - "This concludes the tutorial on ``autora`` functionality. However, ``autora`` is a flexible framework in which users can integrate their own theorists, experimentalists, and experiment_runners in an automated empirical research workflow. The next notebook illustrates how to add your own custom theorists and experimentalists to use with ``autora``.\n", - "\n", - "Follow this link for the next notebook tutorial:\n", - "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Number of observations 15\n" + ] } + ], + "source": [ + "from itertools import takewhile\n", + "\n", + "continue_criterion = lambda controller: len(controller.state.models) < 5\n", + "\n", + "for step in takewhile(continue_criterion, controller):\n", + " print(f\"MONITOR: Number of models: {len(step.state.models)}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can observe that the controller begins with sampling experiment condition using the ``seed_experimentalist``. It then proceeds to sample condition using the ``novelty_experimentalist`` until it has collected 7 or more observations, at which it switches to the ``falsification_experimentalist``." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Next Notebook\n", + "This concludes the tutorial on ``autora`` functionality. However, ``autora`` is a flexible framework in which users can integrate their own theorists, experimentalists, and experiment_runners in an automated empirical research workflow. The next notebook illustrates how to add your own custom theorists and experimentalists to use with ``autora``.\n", + "\n", + "Follow this link for the next notebook tutorial:\n", + "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/docs/tutorials/basic/Tutorial-IV-Customization.ipynb b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb index 1d85d7e92..eb873918a 100644 --- a/docs/tutorials/basic/Tutorial-IV-Customization.ipynb +++ b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb @@ -1,723 +1,652 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "_q7iLq3GUYMz" - }, - "source": [ - "# Introduction" - ] - }, + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", + "\n", + "This notebook is the fourth of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", + "\n", + "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", + "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", + "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", + "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", + "\n", + "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", + "\n", + "**How to use this notebook** *You can progress through the notebook section by section or directly navigate to specific sections. If you choose the latter, it is recommended to execute all cells in the notebook initially, allowing you to easily rerun the cells in each section later without issues.*" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tutorial Setup\n", + "This tutorial is self-contained so that you do not need to run the previous notebook to begin. However, the four notebooks are continuous so that what we define in a previous notebook should still exist within this notebook. As such, we will here re-run relevant code from past tutorials. We will not again walk you through these, but if you need a reminder what they are then go see the descriptions in previous notebooks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#### Installation ####\n", + "!pip install -q \"autora[experimentalist-falsification]\"\n", + "!pip install -q \"autora[theorist-bms]\"\n", + "\n", + "#### Import modules ####\n", + "import numpy as np\n", + "import torch\n", + "from autora.variable import DV, IV, ValueType, VariableCollection\n", + "from autora.workflow import Controller\n", + "from autora.experimentalist.sampler.falsification import falsification_sample\n", + "from autora.experimentalist.pooler.random_pooler import random_pool\n", + "from autora.theorist.bms import BMSRegressor\n", + "\n", + "#### Set seeds ####\n", + "np.random.seed(42)\n", + "torch.manual_seed(42)\n", + "\n", + "#### Define ground truth and experiment runner ####\n", + "ground_truth = lambda x: np.sin(x)\n", + "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape)\n", + "\n", + "#### Define condition pool ####\n", + "condition_pool = np.linspace(0, 2 * np.pi, 100)\n", + "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", + "\n", + "#### Define data ####\n", + "initial_conditions = np.random.choice(np.linspace(0, 2 * np.pi, 100), size=10, replace=False).reshape(-1,1)\n", + "initial_observations = run_experiment(initial_conditions).reshape(-1,1)\n", + "\n", + "#### Define metadata ####\n", + "iv = IV(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 100))\n", + "dv = DV(name=\"y\", type=ValueType.REAL)\n", + "metadata = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "#### Define theorists ####\n", + "theorist_bms = BMSRegressor(epochs=100)\n", + "\n", + "#### Define monitor ####\n", + "def monitor(state):\n", + " print(f\"MONITOR: Generated new {state.history[-1].kind}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Customizing Automated Empirical Research Components\n", + "\n", + "``autora`` is a flexible framework in which users can integrate their own theorists, experimentalists, and experiment_runners in a automated empirical research workflow. This section illustrates the integration of custom theorists and experimentalists. For more information on how to contribute your own modules to the ``autora`` ecosystem, please refer to the [Contributor Documentation](https://autoresearch.github.io/autora/contribute/modules/).\n", + "\n", + "To illustrate the use of custom theorists and experimentalists, we consider a simple workflow introduced above:\n", + "1. Generate 3 seed experimental conditions\n", + "2. Iterate through the following steps\n", + " - Collect observations using ``run_experiment``\n", + " - Identify a model relating conditions to observations using ``theorist_bms``\n", + " - Identify 3 new experimental conditions using ``falsification_sample``" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# generate initial pool of 3 experimental conditions\n", + "seed_conditions = np.linspace(0,2*np.pi,3)\n", + "\n", + "params = {\n", + " \"experimentalist\":\n", + " {\"condition_pool\": condition_pool,\n", + " \"model\": \"%models[-1]%\", # access last model generated by theorist\n", + " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", + " \"reference_observations\": \"%observations.dvs%\", # access all observations collected so far\n", + " \"metadata\": metadata,\n", + " \"num_samples\": 3}\n", + " }\n", + "\n", + "# define controller\n", + "controller = Controller(\n", + " monitor=monitor,\n", + " variables=metadata,\n", + " experimentalist=falsification_sample,\n", + " experiment_runner=run_experiment,\n", + " theorist=theorist_bms,\n", + " params=params,\n", + ")\n", + "\n", + "# seed controller\n", + "controller.seed(conditions=[seed_conditions])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom Theorists\n", + "\n", + "What if we wanted to replace the ``theorist_bms`` with a custom theorist?\n", + "\n", + "We can implement our theorist as a class that inherits from `sklearn.base.BaseEstimator`. The class must implement the following methods:\n", + "\n", + "- `fit(self, conditions, observations)`\n", + "- `predict(self, conditions)`\n", + "\n", + "The following code block implements such a theorist that fits a polynomial of a specified degree." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "Example Theorist\n", + "\"\"\"\n", + "\n", + "import numpy as np\n", + "from sklearn.base import BaseEstimator\n", + "\n", + "\n", + "class PolynomialRegressor(BaseEstimator):\n", + " \"\"\"\n", + " This theorist fits a polynomial function to the data.\n", + " \"\"\"\n", + "\n", + " def __init__(self, degree: int = 3):\n", + " self.degree = degree\n", + "\n", + " def fit(self, conditions, observations):\n", + "\n", + " # polyfit expects a 1D array\n", + " if conditions.ndim > 1:\n", + " conditions = conditions.flatten()\n", + "\n", + " if observations.ndim > 1:\n", + " observations = observations.flatten()\n", + "\n", + " # fit polynomial\n", + " self.coeff = np.polyfit(conditions, observations, 2)\n", + " self.polynomial = np.poly1d(self.coeff)\n", + " pass\n", + "\n", + " def predict(self, conditions):\n", + " return self.polynomial(conditions)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now assign the theorist to a new controller." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "theorist_poly = PolynomialRegressor(degree = 3)\n", + "\n", + "# define controller\n", + "controller_with_polynomial_theorist = Controller(\n", + " monitor=monitor,\n", + " variables=metadata,\n", + " experimentalist=falsification_sample,\n", + " experiment_runner=run_experiment,\n", + " theorist=theorist_poly,\n", + " params=params,\n", + ")\n", + "\n", + "# seed controller\n", + "controller_with_polynomial_theorist.seed(conditions=[seed_conditions])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's run the controller with the new theorist for 3 research cycles, defined by the number of models generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "5mfUKtGTUYM1", - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", - "\n", - "This notebook is the fourth of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", - "\n", - "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", - "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", - "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", - "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", - "\n", - "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", - "\n", - "**How to use this notebook** *You can progress through the notebook section by section or directly navigate to specific sections. If you choose the latter, it is recommended to execute all cells in the notebook initially, allowing you to easily rerun the cells in each section later without issues.*" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "bfcWh4lramqo" - }, - "source": [ - "## Tutorial Setup\n", - "This tutorial is self-contained so that you do not need to run the previous notebook to begin. However, the four notebooks are continuous so that what we define in a previous notebook should still exist within this notebook. As such, we will here re-run relevant code from past tutorials. We will not again walk you through these, but if you need a reminder what they are then go see the descriptions in previous notebooks." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 0\n", + "MONITOR: Generated new MODEL\n", + "Number of models: 1\n" + ] }, { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "id": "eT6HTGF7aoJT" - }, - "outputs": [], - "source": [ - "#### Installation ####\n", - "!pip install -q \"autora[experimentalist-falsification]\"\n", - "!pip install -q \"autora[theorist-bms]\"\n", - "\n", - "#### Import modules ####\n", - "import numpy as np\n", - "import torch\n", - "from autora.variable import DV, IV, ValueType, VariableCollection\n", - "from autora.workflow import Controller\n", - "from autora.experimentalist.sampler.falsification import falsification_sample\n", - "from autora.experimentalist.pooler.random_pooler import random_pool\n", - "from autora.theorist.bms import BMSRegressor\n", - "\n", - "#### Set seeds ####\n", - "np.random.seed(42)\n", - "torch.manual_seed(42)\n", - "\n", - "#### Define ground truth and experiment runner ####\n", - "ground_truth = lambda x: np.sin(x)\n", - "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape)\n", - "\n", - "#### Define condition pool ####\n", - "condition_pool = np.linspace(0, 2 * np.pi, 100)\n", - "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", - "\n", - "#### Define data ####\n", - "initial_conditions = np.random.choice(np.linspace(0, 2 * np.pi, 100), size=10, replace=False).reshape(-1,1)\n", - "initial_observations = run_experiment(initial_conditions).reshape(-1,1)\n", - "\n", - "#### Define metadata ####\n", - "iv = IV(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 100))\n", - "dv = DV(name=\"y\", type=ValueType.REAL)\n", - "metadata = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", - "\n", - "#### Define theorists ####\n", - "theorist_bms = BMSRegressor(epochs=100)\n", - "\n", - "#### Define monitor ####\n", - "def monitor(state):\n", - " print(f\"MONITOR: Generated new {state.history[-1].kind}\")" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "MN8yBMeeUYNT" - }, - "source": [ - "# Customizing Automated Empirical Research Components\n", - "\n", - "``autora`` is a flexible framework in which users can integrate their own theorists, experimentalists, and experiment_runners in a automated empirical research workflow. This section illustrates the integration of custom theorists and experimentalists. For more information on how to contribute your own modules to the ``autora`` ecosystem, please refer to the [Contributor Documentation](https://autoresearch.github.io/autora/contribute/modules/).\n", - "\n", - "To illustrate the use of custom theorists and experimentalists, we consider a simple workflow introduced above:\n", - "1. Generate 3 seed experimental conditions\n", - "2. Iterate through the following steps\n", - " - Collect observations using ``run_experiment``\n", - " - Identify a model relating conditions to observations using ``theorist_bms``\n", - " - Identify 3 new experimental conditions using ``falsification_sample``" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new CONDITION\n", + "Number of models: 1\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 1\n", + "MONITOR: Generated new MODEL\n", + "Number of models: 2\n" + ] }, { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "id": "LpLsdNJkUYNT" - }, - "outputs": [], - "source": [ - "# generate initial pool of 3 experimental conditions\n", - "seed_conditions = np.linspace(0,2*np.pi,3)\n", - "\n", - "params = {\n", - " \"experimentalist\":\n", - " {\"condition_pool\": condition_pool,\n", - " \"model\": \"%models[-1]%\", # access last model generated by theorist\n", - " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", - " \"reference_observations\": \"%observations.dvs%\", # access all observations collected so far\n", - " \"metadata\": metadata,\n", - " \"num_samples\": 3}\n", - " }\n", - "\n", - "# define controller\n", - "controller = Controller(\n", - " monitor=monitor,\n", - " variables=metadata,\n", - " experimentalist=falsification_sample,\n", - " experiment_runner=run_experiment,\n", - " theorist=theorist_bms,\n", - " params=params,\n", - ")\n", - "\n", - "# seed controller\n", - "controller.seed(conditions=[seed_conditions])" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "mhF2f9S4UYNT" - }, - "source": [ - "## Custom Theorists\n", - "\n", - "What if we wanted to replace the ``theorist_bms`` with a custom theorist?\n", - "\n", - "We can implement our theorist as a class that inherits from `sklearn.base.BaseEstimator`. The class must implement the following methods:\n", - "\n", - "- `fit(self, conditions, observations)`\n", - "- `predict(self, conditions)`\n", - "\n", - "The following code block implements such a theorist that fits a polynomial of a specified degree." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "MONITOR: Generated new CONDITION\n", + "Number of models: 2\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 2\n", + "MONITOR: Generated new MODEL\n", + "Number of models: 3\n", + "MONITOR: Generated new CONDITION\n", + "Number of models: 3\n", + "MONITOR: Generated new OBSERVATION\n", + "Number of models: 3\n", + "MONITOR: Generated new MODEL\n" + ] + } + ], + "source": [ + "from itertools import takewhile\n", + "\n", + "continue_criterion = lambda controller: len(controller.state.models) < 4\n", + "\n", + "for step in takewhile(continue_criterion, controller_with_polynomial_theorist):\n", + " print(f\"Number of models: {len(step.state.models)}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the last model identified by our custom theorist against the ground truth." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "id": "UXCQxvfsUYNU" - }, - "outputs": [], - "source": [ - "\"\"\"\n", - "Example Theorist\n", - "\"\"\"\n", - "\n", - "import numpy as np\n", - "from sklearn.base import BaseEstimator\n", - "\n", - "\n", - "class PolynomialRegressor(BaseEstimator):\n", - " \"\"\"\n", - " This theorist fits a polynomial function to the data.\n", - " \"\"\"\n", - "\n", - " def __init__(self, degree: int = 3):\n", - " self.degree = degree\n", - "\n", - " def fit(self, conditions, observations):\n", - "\n", - " # polyfit expects a 1D array\n", - " if conditions.ndim > 1:\n", - " conditions = conditions.flatten()\n", - "\n", - " if observations.ndim > 1:\n", - " observations = observations.flatten()\n", - "\n", - " # fit polynomial\n", - " self.coeff = np.polyfit(conditions, observations, 2)\n", - " self.polynomial = np.poly1d(self.coeff)\n", - " pass\n", - "\n", - " def predict(self, conditions):\n", - " return self.polynomial(conditions)" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "FxJvKQHdUYNU" - }, - "source": [ - "We can now assign the theorist to a new controller." + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "last_polynomial_model = controller_with_polynomial_theorist.state.models[-1]\n", + "\n", + "predicted_observations_polynomial = last_polynomial_model.predict(condition_pool)\n", + "\n", + "# plot model predictions against ground-truth\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", + "plt.plot(condition_pool, predicted_observations_polynomial, label='Polynomial Fit')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Model Predictions')\n", + "plt.legend()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom Experimentalists\n", + "\n", + "We can also implement custom experimentalists. Experimentalists are generally implemented as functions that can be integrated into an\n", + "[Experimentalist Pipeline](https://autoresearch.github.io/autora/core/docs/pipeline/Experimentalist%20Pipeline%20Examples/). For instance, an experimentalist sampler function expects a pool of experimental conditions–typically passed as a 2D numpy array named ``condition_pool``–and returns a modified set of experimental conditions.\n", + "\n", + "The following code block implements a basic experimentalist that considers two models, and identifies experimental conditions for which the two models differ most in their predictions. This is a special case of the [Model Disagreement Sampler](https://autoresearch.github.io/autora/user-guide/experimentalists/samplers/model-disagreement/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def basic_model_disagreement_sample(condition_pool, model_a, model_b, num_samples = 1):\n", + "\n", + " # get predictions from both models\n", + " prediction_a = model_a.predict(condition_pool)\n", + " prediction_b = model_b.predict(condition_pool)\n", + "\n", + " # compute mean squared distance between predictions\n", + " disagreement = np.mean((prediction_a - prediction_b) ** 2, axis=1)\n", + "\n", + " # sort the summed disagreements and select the top n\n", + " selected_conditions_idx = (-disagreement).argsort()[:num_samples]\n", + "\n", + " return condition_pool[selected_conditions_idx]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can illustrate our new experimentalist sampler by fitting two different theorists to an initial set of conditions and observations. Here, we consider the BMS theorist and our custom polynomial theorist from above. We then sample 3 experimental conditions using our new experimentalist ``basic_model_disagreement_sample``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "id": "n2pA8wyAUYNU" - }, - "outputs": [], - "source": [ - "theorist_poly = PolynomialRegressor(degree = 3)\n", - "\n", - "# define controller\n", - "controller_with_polynomial_theorist = Controller(\n", - " monitor=monitor,\n", - " variables=metadata,\n", - " experimentalist=falsification_sample,\n", - " experiment_runner=run_experiment,\n", - " theorist=theorist_poly,\n", - " params=params,\n", - ")\n", - "\n", - "# seed controller\n", - "controller_with_polynomial_theorist.seed(conditions=[seed_conditions])" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:07<00:00, 13.30it/s]\n" + ] + } + ], + "source": [ + "# fit two theorists\n", + "theorist_bms.fit(initial_conditions, initial_observations)\n", + "theorist_poly.fit(initial_conditions, initial_observations)\n", + "\n", + "# sample experimental conditions with our custom experimentalist sampler function\n", + "selected_conditions = basic_model_disagreement_sample(condition_pool,\n", + " theorist_bms,\n", + " theorist_poly,\n", + " num_samples = 3)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After fitting both theorists, we can compare their predictions across the entire pool of experimental conditions. We will add the sampled experimental conditions to the plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "eoERktO5UYNU" - }, - "source": [ - "Let's run the controller with the new theorist for 3 research cycles, defined by the number of models generated." + "data": { + "text/plain": [ + "" ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "tnyjVXcUUYNU", - "outputId": "7ffd90ad-801e-469d-862b-e466720eda24" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 0\n", - "MONITOR: Generated new MODEL\n", - "Number of models: 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new CONDITION\n", - "Number of models: 1\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 1\n", - "MONITOR: Generated new MODEL\n", - "Number of models: 2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new CONDITION\n", - "Number of models: 2\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 2\n", - "MONITOR: Generated new MODEL\n", - "Number of models: 3\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 3\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 3\n", - "MONITOR: Generated new MODEL\n" - ] - } - ], - "source": [ - "from itertools import takewhile\n", - "\n", - "continue_criterion = lambda controller: len(controller.state.models) < 4\n", - "\n", - "for step in takewhile(continue_criterion, controller_with_polynomial_theorist):\n", - " print(f\"Number of models: {len(step.state.models)}\")" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot model predictions against ground-truth\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# obtain predictions for both theorists\n", + "predicted_observations_bms = theorist_bms.predict(condition_pool)\n", + "predicted_observations_poly = theorist_poly.predict(condition_pool)\n", + "\n", + "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", + "plt.plot(condition_pool, predicted_observations_bms, label='Predictions of BMS Theorist')\n", + "plt.plot(condition_pool, predicted_observations_poly, label='Predictions of Polynomial Theorist')\n", + "\n", + "y_min = -2.5\n", + "y_max = 1\n", + "\n", + "# plot conditions obtained by novelty sampler\n", + "for idx, condition in enumerate(selected_conditions):\n", + " if idx == 0:\n", + " plt.plot([condition[0], condition[0]],\n", + " [y_min, y_max],\n", + " '--r', label='selected conditions')\n", + " else: # we want to omit the label for all other conditions\n", + " plt.plot()\n", + "\n", + "\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Model Disagreement')\n", + "plt.legend()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can integrate our custom experimentalist and theorist into a closed-loop empirical research workflow, e.g., using basic loop constructs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "J7bMGWnxUYNU" - }, - "source": [ - "We can plot the last model identified by our custom theorist against the ground truth." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:08<00:00, 11.88it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 61, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 489 - }, - "id": "JpnsqFeKUYNV", - "outputId": "4bd2658a-4870-493b-e2a7-254b035bae12" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "last_polynomial_model = controller_with_polynomial_theorist.state.models[-1]\n", - "\n", - "predicted_observations_polynomial = last_polynomial_model.predict(condition_pool)\n", - "\n", - "# plot model predictions against ground-truth\n", - "import matplotlib.pyplot as plt\n", - "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", - "plt.plot(condition_pool, predicted_observations_polynomial, label='Polynomial Fit')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('Model Predictions')\n", - "plt.legend()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss of BMS theorist in cycle 0: 0.0\n", + "Loss of polynomial theorist in cycle 0: 0.8717052095923039\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "kwM8vJR_UYNV" - }, - "source": [ - "## Custom Experimentalists\n", - "\n", - "We can also implement custom experimentalists. Experimentalists are generally implemented as functions that can be integrated into an\n", - "[Experimentalist Pipeline](https://autoresearch.github.io/autora/core/docs/pipeline/Experimentalist%20Pipeline%20Examples/). For instance, an experimentalist sampler function expects a pool of experimental conditions–typically passed as a 2D numpy array named ``condition_pool``–and returns a modified set of experimental conditions.\n", - "\n", - "The following code block implements a basic experimentalist that considers two models, and identifies experimental conditions for which the two models differ most in their predictions. This is a special case of the [Model Disagreement Sampler](https://autoresearch.github.io/autora/user-guide/experimentalists/samplers/model-disagreement/)." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:08<00:00, 12.49it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "id": "Bx4cSZ9nUYNV" - }, - "outputs": [], - "source": [ - "def basic_model_disagreement_sample(condition_pool, model_a, model_b, num_samples = 1):\n", - "\n", - " # get predictions from both models\n", - " prediction_a = model_a.predict(condition_pool)\n", - " prediction_b = model_b.predict(condition_pool)\n", - "\n", - " # compute mean squared distance between predictions\n", - " disagreement = np.mean((prediction_a - prediction_b) ** 2, axis=1)\n", - "\n", - " # sort the summed disagreements and select the top n\n", - " selected_conditions_idx = (-disagreement).argsort()[:num_samples]\n", - "\n", - " return condition_pool[selected_conditions_idx]" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss of BMS theorist in cycle 1: 0.0\n", + "Loss of polynomial theorist in cycle 1: 3.619766689361933\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "AXCRsOhUUYNV" - }, - "source": [ - "We can illustrate our new experimentalist sampler by fitting two different theorists to an initial set of conditions and observations. Here, we consider the BMS theorist and our custom polynomial theorist from above. We then sample 3 experimental conditions using our new experimentalist ``basic_model_disagreement_sample``." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:07<00:00, 12.97it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 66, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "P2jBBdKwUYNV", - "outputId": "3ba61e72-c768-4492-b644-942a5bd932d9" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 13.30it/s]\n" - ] - } - ], - "source": [ - "# fit two theorists\n", - "theorist_bms.fit(initial_conditions, initial_observations)\n", - "theorist_poly.fit(initial_conditions, initial_observations)\n", - "\n", - "# sample experimental conditions with our custom experimentalist sampler function\n", - "selected_conditions = basic_model_disagreement_sample(condition_pool,\n", - " theorist_bms,\n", - " theorist_poly,\n", - " num_samples = 3)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss of BMS theorist in cycle 2: 0.0\n", + "Loss of polynomial theorist in cycle 2: 0.5193832163876795\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "vP_bRY4GUYNV" - }, - "source": [ - "After fitting both theorists, we can compare their predictions across the entire pool of experimental conditions. We will add the sampled experimental conditions to the plot." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:07<00:00, 14.26it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 67, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 489 - }, - "id": "lbUKDXL-UYNV", - "outputId": "dd97872b-0748-4048-d736-c3edd82579df" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot model predictions against ground-truth\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# obtain predictions for both theorists\n", - "predicted_observations_bms = theorist_bms.predict(condition_pool)\n", - "predicted_observations_poly = theorist_poly.predict(condition_pool)\n", - "\n", - "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", - "plt.plot(condition_pool, predicted_observations_bms, label='Predictions of BMS Theorist')\n", - "plt.plot(condition_pool, predicted_observations_poly, label='Predictions of Polynomial Theorist')\n", - "\n", - "y_min = -2.5\n", - "y_max = 1\n", - "\n", - "# plot conditions obtained by novelty sampler\n", - "for idx, condition in enumerate(selected_conditions):\n", - " if idx == 0:\n", - " plt.plot([condition[0], condition[0]],\n", - " [y_min, y_max],\n", - " '--r', label='selected conditions')\n", - " else: # we want to omit the label for all other conditions\n", - " plt.plot()\n", - "\n", - "\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('Model Disagreement')\n", - "plt.legend()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss of BMS theorist in cycle 3: 0.0\n", + "Loss of polynomial theorist in cycle 3: 0.36300053098571583\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "Dk-Zd-DKUYNW" - }, - "source": [ - "Finally, we can integrate our custom experimentalist and theorist into a closed-loop empirical research workflow, e.g., using basic loop constructs." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:07<00:00, 13.45it/s]" + ] }, { - "cell_type": "code", - "execution_count": 68, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4_8wXhOkUYNW", - "outputId": "1ea515a0-2aa3-4d34-aaba-aebe2e94b2b5" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:08<00:00, 11.88it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss of BMS theorist in cycle 0: 0.0\n", - "Loss of polynomial theorist in cycle 0: 0.8717052095923039\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:08<00:00, 12.49it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss of BMS theorist in cycle 1: 0.0\n", - "Loss of polynomial theorist in cycle 1: 3.619766689361933\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 12.97it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss of BMS theorist in cycle 2: 0.0\n", - "Loss of polynomial theorist in cycle 2: 0.5193832163876795\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 14.26it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss of BMS theorist in cycle 3: 0.0\n", - "Loss of polynomial theorist in cycle 3: 0.36300053098571583\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 13.45it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss of BMS theorist in cycle 4: 0.4967273581732591\n", - "Loss of polynomial theorist in cycle 4: 0.288261165753893\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "num_cycles = 5 # number of empirical research cycles\n", - "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", - "\n", - "# generate an initial set experimental conditions\n", - "conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", - " n=measurements_per_cycle)\n", - "# convert iterator into 2-dimensional numpy array\n", - "conditions = np.array(list(conditions)).reshape(-1, 1)\n", - "\n", - "# collect initial set of observations\n", - "observations = run_experiment(conditions)\n", - "\n", - "for cycle in range(num_cycles):\n", - "\n", - " # use BMS theorist and custom polynomial theorist to fit the model to the data\n", - " theorist_bms.fit(conditions, observations)\n", - " theorist_poly.fit(conditions, observations)\n", - "\n", - " # obtain new conditions from custrom experimentalist sampler\n", - " new_conditions = basic_model_disagreement_sample(condition_pool,\n", - " theorist_bms,\n", - " theorist_poly,\n", - " num_samples = 3)\n", - "\n", - " # obtain new observations\n", - " new_observations = run_experiment(new_conditions)\n", - "\n", - " # combine old and new conditions and observations\n", - " conditions = np.concatenate((conditions, new_conditions))\n", - " observations = np.concatenate((observations, new_observations))\n", - "\n", - " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", - " loss_bms = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", - " loss_poly = np.mean(np.square(theorist_poly.predict(condition_pool) - ground_truth(condition_pool)))\n", - " print(\"Loss of BMS theorist in cycle {}: {}\".format(cycle, loss_bms))\n", - " print(\"Loss of polynomial theorist in cycle {}: {}\".format(cycle, loss_poly))" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss of BMS theorist in cycle 4: 0.4967273581732591\n", + "Loss of polynomial theorist in cycle 4: 0.288261165753893\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "Q-eKiaHRUYNW" - }, - "source": [ - "# Help\n", - "We hope that this tutorial helped demonstrate the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments. We encourage you to explore other [tutorials](https://autoresearch.github.io/autora/tutorials/) and check out the [documentation](https://autoresearch.github.io/).\n", - "\n", - "If you encounter any issues, bugs, or questions, please reach out to us through the [AutoRA Forum](https://github.com/orgs/AutoResearch/discussions). Feel free to report any bugs by [creating an issue in the AutoRA repository](https://github.com/AutoResearch/autora/issues).\n", - "\n", - "You may also post questions directly into the [User Q&A Section](https://github.com/orgs/AutoResearch/discussions/categories/using-autora).\n" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] } + ], + "source": [ + "num_cycles = 5 # number of empirical research cycles\n", + "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", + "\n", + "# generate an initial set experimental conditions\n", + "conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", + " n=measurements_per_cycle)\n", + "# convert iterator into 2-dimensional numpy array\n", + "conditions = np.array(list(conditions)).reshape(-1, 1)\n", + "\n", + "# collect initial set of observations\n", + "observations = run_experiment(conditions)\n", + "\n", + "for cycle in range(num_cycles):\n", + "\n", + " # use BMS theorist and custom polynomial theorist to fit the model to the data\n", + " theorist_bms.fit(conditions, observations)\n", + " theorist_poly.fit(conditions, observations)\n", + "\n", + " # obtain new conditions from custrom experimentalist sampler\n", + " new_conditions = basic_model_disagreement_sample(condition_pool,\n", + " theorist_bms,\n", + " theorist_poly,\n", + " num_samples = 3)\n", + "\n", + " # obtain new observations\n", + " new_observations = run_experiment(new_conditions)\n", + "\n", + " # combine old and new conditions and observations\n", + " conditions = np.concatenate((conditions, new_conditions))\n", + " observations = np.concatenate((observations, new_observations))\n", + "\n", + " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", + " loss_bms = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", + " loss_poly = np.mean(np.square(theorist_poly.predict(condition_pool) - ground_truth(condition_pool)))\n", + " print(\"Loss of BMS theorist in cycle {}: {}\".format(cycle, loss_bms))\n", + " print(\"Loss of polynomial theorist in cycle {}: {}\".format(cycle, loss_poly))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Help\n", + "We hope that this tutorial helped demonstrate the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments. We encourage you to explore other [tutorials](https://autoresearch.github.io/autora/tutorials/) and check out the [documentation](https://autoresearch.github.io/).\n", + "\n", + "If you encounter any issues, bugs, or questions, please reach out to us through the [AutoRA Forum](https://github.com/orgs/AutoResearch/discussions). Feel free to report any bugs by [creating an issue in the AutoRA repository](https://github.com/AutoResearch/autora/issues).\n", + "\n", + "You may also post questions directly into the [User Q&A Section](https://github.com/orgs/AutoResearch/discussions/categories/using-autora).\n" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } From 1b12e92b713c999f9d1564bf7414f4258982a6ee Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Tue, 18 Jul 2023 15:37:17 -0700 Subject: [PATCH 08/32] Updated tutorial 1 with autora subpackage updates --- .../basic/Tutorial-I-Components.ipynb | 140 +++++++++++------- 1 file changed, 89 insertions(+), 51 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-I-Components.ipynb b/docs/tutorials/basic/Tutorial-I-Components.ipynb index fe890dc81..f6a298613 100644 --- a/docs/tutorials/basic/Tutorial-I-Components.ipynb +++ b/docs/tutorials/basic/Tutorial-I-Components.ipynb @@ -16,11 +16,11 @@ "source": [ "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", "\n", - "This notebook is the first of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", + "This notebook is the third of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", "\n", "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", - "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", + "[AutoRA Basic Tutorial III: Functional Workflow](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Functional-Workflow/)
\n", "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", "\n", "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", @@ -50,7 +50,26 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 23.0.1 -> 23.2\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n", + "\n", + "[notice] A new release of pip is available: 23.0.1 -> 23.2\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n", + "\n", + "[notice] A new release of pip is available: 23.0.1 -> 23.2\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n", + "\n", + "[notice] A new release of pip is available: 23.0.1 -> 23.2\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n" + ] + } + ], "source": [ "!pip install -q \"autora[experimentalist-falsification]\"\n", "!pip install -q \"autora[experimentalist-sampler-novelty]\"\n", @@ -74,7 +93,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -127,13 +146,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:14<00:00, 6.96it/s]\n" + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:05<00:00, 19.85it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -142,7 +163,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -163,7 +184,7 @@ "initial_X = np.linspace(0, 4 * np.pi, 200) #Define initial data\n", "\n", "#Step 1: EXPERIMENTALIST: Sample using the experimentalist\n", - "new_conditions = random_sample(initial_X, n = 20)\n", + "new_conditions = random_sample(initial_X, num_samples = 20)\n", "new_conditions = np.array(new_conditions).reshape(-1,1) #Turn variable into a 2D array\n", "\n", "#Step 2: EXPERIMENT RUNNER: Define and then obtain observations using the experiment runner\n", @@ -294,7 +315,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -303,7 +324,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -331,7 +352,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Certain theorists and experimentalists may need to have knowledge about the experimental variables, such as the domain from which new experiment conditions are sampled. To provide this information, we can utilize a ``VariableCollection`` object. In the context of our synthetic experiment, we have a single *independent variable* (``IV``) denoted as $x$, and a single *dependent* variable (``DV``) denoted as $y$." + "Certain theorists and experimentalists may need to have knowledge about the experimental variables, such as the domain from which new experiment conditions are sampled. To provide this information, we can utilize a ``VariableCollection`` object. In the context of our synthetic experiment, we have a single *independent variable* (``iv``) denoted as $x$, and a single *dependent* variable (``dv``) denoted as $y$." ] }, { @@ -340,17 +361,17 @@ "metadata": {}, "outputs": [], "source": [ - "from autora.variable import DV, IV, ValueType, VariableCollection\n", + "from autora.variable import Variable, ValueType, VariableCollection\n", "\n", "# Specify independent variable\n", - "iv = IV(\n", + "iv = Variable(\n", " name=\"x\", # name of the independent variable\n", " value_range=(0, 2 * np.pi), # specify the domain\n", " allowed_values=condition_pool, # alternatively, we can specify the pool of allowed conditions directly\n", ")\n", "\n", "# specify dependent variable\n", - "dv = DV(\n", + "dv = Variable(\n", " name=\"y\", # name of the dependent variable\n", " type=ValueType.REAL, # specify the variable type (some theorists require this to optimize)\n", ")\n", @@ -437,6 +458,13 @@ "execution_count": null, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -450,16 +478,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:13<00:00, 7.41it/s]\n" + "100%|██████████| 100/100 [00:04<00:00, 20.14it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { "text/html": [ - "
BMSRegressor(epochs=100)
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + "
sin(X0)
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ - "BMSRegressor(epochs=100)" + "sin(X0)" ] }, "execution_count": null, @@ -541,7 +570,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -550,7 +579,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -669,17 +698,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "(0.6981317007977318,)\n", - "(0.7615982190520711,)\n", - "(0.8250647373064104,)\n", - "(0.8885312555607496,)\n", - "(0.9519977738150889,)\n", - "(1.0154642920694281,)\n", - "(1.0789308103237674,)\n", - "(1.1423973285781066,)\n", - "(1.2058638468324459,)\n", - "(1.269330365086785,)\n", - "(1.3327968833411243,)\n" + "(0.0,)\n", + "(0.06346651825433926,)\n", + "(0.12693303650867852,)\n", + "(0.1903995547630178,)\n", + "(0.25386607301735703,)\n", + "(0.3173325912716963,)\n", + "(0.3807991095260356,)\n", + "(0.4442656277803748,)\n", + "(0.5077321460347141,)\n", + "(0.5711986642890533,)\n", + "(0.6346651825433925,)\n" ] } ], @@ -708,16 +737,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "(0.8250647373064104,)\n", - "(0.7615982190520711,)\n", - "(4.1253236865320515,)\n", - "(4.188790204786391,)\n", - "(3.236792430971302,)\n", - "(3.8714576135146945,)\n", + "(3.490658503988659,)\n", "(3.3637254674799806,)\n", - "(6.092785752416569,)\n", - "(4.886921905584122,)\n", - "(4.950388423838462,)\n" + "(5.14078797860148,)\n", + "(3.42719198573432,)\n", + "(6.283185307179586,)\n", + "(5.711986642890533,)\n", + "(6.156252270670908,)\n", + "(5.96585271590789,)\n", + "(1.3327968833411243,)\n", + "(0.0,)\n" ] } ], @@ -726,8 +755,8 @@ "\n", "# generate random pool of 10 conditions\n", "num_samples = 10\n", - "new_conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", - " n=num_samples)\n", + "new_conditions = random_pool(metadata.independent_variables,\n", + " num_samples=num_samples)\n", "\n", "# print conditons\n", "for idx, condition in enumerate(new_conditions):\n", @@ -832,7 +861,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -841,7 +870,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -985,11 +1014,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[1.90399555]\n", - " [3.93492413]\n", - " [5.83891968]\n", - " [5.9023862 ]\n", - " [5.96585272]]\n" + "[[6.28318531]\n", + " [6.21971879]\n", + " [6.15625227]\n", + " [6.09278575]\n", + " [3.55412502]]\n" ] } ], @@ -1028,11 +1057,20 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", - "name": "python3" + "display_name": "autoraKernel", + "language": "python", + "name": "autorakernel" }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" } }, "nbformat": 4, From 1f85f96e0981029b6f23dd158859c2baff2a6091 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Tue, 18 Jul 2023 15:40:12 -0700 Subject: [PATCH 09/32] updated typo --- docs/tutorials/basic/Tutorial-I-Components.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/basic/Tutorial-I-Components.ipynb b/docs/tutorials/basic/Tutorial-I-Components.ipynb index f6a298613..e3e86c755 100644 --- a/docs/tutorials/basic/Tutorial-I-Components.ipynb +++ b/docs/tutorials/basic/Tutorial-I-Components.ipynb @@ -16,7 +16,7 @@ "source": [ "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", "\n", - "This notebook is the third of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", + "This notebook is the first of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", "\n", "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", From 6c4aa70be62e652c36166130f00ed04528f47e3d Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Tue, 18 Jul 2023 15:47:52 -0700 Subject: [PATCH 10/32] Updated tutorial 2 with autora updates --- .../basic/Tutorial-II-Loop-Constructs.ipynb | 137 +++++++++--------- 1 file changed, 69 insertions(+), 68 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb b/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb index b35cc0bc8..f881a148c 100644 --- a/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb +++ b/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb @@ -20,7 +20,7 @@ "\n", "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", - "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", + "[AutoRA Basic Tutorial III: Functional Workflow](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Functional-Workflow/)
\n", "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", "\n", "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", @@ -51,7 +51,7 @@ "#### Import modules ####\n", "import numpy as np\n", "import torch\n", - "from autora.variable import DV, IV, ValueType, VariableCollection\n", + "from autora.variable import Variable, ValueType, VariableCollection\n", "from autora.experimentalist.pooler.random_pooler import random_pool\n", "from autora.experimentalist.sampler.falsification import falsification_sample\n", "from autora.experimentalist.sampler.model_disagreement import model_disagreement_sample\n", @@ -71,8 +71,8 @@ "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", "\n", "#### Define metadata ####\n", - "iv = IV(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=condition_pool)\n", - "dv = DV(name=\"y\", type=ValueType.REAL)\n", + "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=condition_pool)\n", + "dv = Variable(name=\"y\", type=ValueType.REAL)\n", "metadata = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", "\n", "#### Define theorists ####\n", @@ -125,8 +125,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:26<00:00, 3.84it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:03<00:00, 27.81it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" ] }, { @@ -141,8 +144,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:28<00:00, 3.56it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + "100%|██████████| 100/100 [00:03<00:00, 27.69it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" ] }, { @@ -157,24 +162,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:14<00:00, 7.10it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + "100%|██████████| 100/100 [00:04<00:00, 24.21it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Loss in cycle 2: 0.5526484578348648\n", - "Discovered Model: _a0_\n" + "Loss in cycle 2: 0.0\n", + "Discovered Model: sin(X0)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:17<00:00, 5.60it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" + "100%|██████████| 100/100 [00:03<00:00, 26.50it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" ] }, { @@ -189,7 +198,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:16<00:00, 6.25it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 26.33it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] }, @@ -207,8 +217,8 @@ "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", "\n", "# generate an initial set of experimental conditions\n", - "conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", - " n=measurements_per_cycle)\n", + "conditions = random_pool(metadata.independent_variables,\n", + " num_samples=measurements_per_cycle)\n", "\n", "# convert iterator into 2-dimensional numpy array\n", "conditions = np.array(list(conditions)).reshape(-1, 1)\n", @@ -264,7 +274,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:40<00:00, 2.47it/s]\n" + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:04<00:00, 23.59it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" ] }, { @@ -279,7 +292,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:18<00:00, 5.53it/s]\n" + "100%|██████████| 100/100 [00:03<00:00, 26.18it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" ] }, { @@ -291,48 +306,25 @@ ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:19<00:00, 5.16it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 2: 0.0\n", - "Discovered BMS Model: sin(X0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:15<00:00, 6.37it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 3: 0.0\n", - "Discovered BMS Model: sin(X0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:24<00:00, 4.01it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 4: 0.0\n", - "Discovered BMS Model: sin(X0)\n" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[6], line 16\u001b[0m\n\u001b[0;32m 11\u001b[0m observations \u001b[39m=\u001b[39m run_experiment(conditions)\n\u001b[0;32m 13\u001b[0m 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thread\u001b[39;00m\n\u001b[0;32m 551\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 552\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[0;32m 553\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpub_thread\n\u001b[0;32m 554\u001b[0m \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpub_thread\u001b[39m.\u001b[39mthread \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 557\u001b[0m ):\n\u001b[0;32m 558\u001b[0m \u001b[39m# request flush on the background thread\u001b[39;00m\n\u001b[1;32m--> 559\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpub_thread\u001b[39m.\u001b[39;49mschedule(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_flush)\n\u001b[0;32m 560\u001b[0m \u001b[39m# wait for flush to actually get through, if we can.\u001b[39;00m\n\u001b[0;32m 561\u001b[0m evt \u001b[39m=\u001b[39m threading\u001b[39m.\u001b[39mEvent()\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\ipykernel\\iostream.py:251\u001b[0m, in \u001b[0;36mIOPubThread.schedule\u001b[1;34m(self, f)\u001b[0m\n\u001b[0;32m 249\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_events\u001b[39m.\u001b[39mappend(f)\n\u001b[0;32m 250\u001b[0m \u001b[39m# wake event thread (message content is ignored)\u001b[39;00m\n\u001b[1;32m--> 251\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_event_pipe\u001b[39m.\u001b[39;49msend(\u001b[39mb\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m 252\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 253\u001b[0m f()\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\zmq\\sugar\\socket.py:696\u001b[0m, in \u001b[0;36mSocket.send\u001b[1;34m(self, data, flags, copy, track, routing_id, group)\u001b[0m\n\u001b[0;32m 689\u001b[0m data \u001b[39m=\u001b[39m zmq\u001b[39m.\u001b[39mFrame(\n\u001b[0;32m 690\u001b[0m data,\n\u001b[0;32m 691\u001b[0m track\u001b[39m=\u001b[39mtrack,\n\u001b[0;32m 692\u001b[0m copy\u001b[39m=\u001b[39mcopy \u001b[39mor\u001b[39;00m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m 693\u001b[0m copy_threshold\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcopy_threshold,\n\u001b[0;32m 694\u001b[0m )\n\u001b[0;32m 695\u001b[0m data\u001b[39m.\u001b[39mgroup \u001b[39m=\u001b[39m group\n\u001b[1;32m--> 696\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49msend(data, flags\u001b[39m=\u001b[39;49mflags, copy\u001b[39m=\u001b[39;49mcopy, track\u001b[39m=\u001b[39;49mtrack)\n", + "File \u001b[1;32mzmq\\backend\\cython\\socket.pyx:742\u001b[0m, in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mzmq\\backend\\cython\\socket.pyx:789\u001b[0m, in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mzmq\\backend\\cython\\socket.pyx:250\u001b[0m, in \u001b[0;36mzmq.backend.cython.socket._send_copy\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\zmq\\backend\\cython\\checkrc.pxd:13\u001b[0m, in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -341,8 +333,8 @@ "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", "\n", "# generate an initial set of experimental conditions\n", - "conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", - " n=measurements_per_cycle)\n", + "conditions = random_pool(metadata.independent_variables,\n", + " num_samples=measurements_per_cycle)\n", "# convert iterator into 2-dimensional numpy array\n", "conditions = np.array(list(conditions)).reshape(-1, 1)\n", "\n", @@ -372,7 +364,7 @@ " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", " loss = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", " print(\"Loss in cycle {}: {}\".format(cycle, loss))\n", - " print(\"Discovered BMS Model: \" + theorist_bms.model_.__repr__())\n" + " print(\"Discovered BMS Model: \" + theorist_bms.repr())\n" ] }, { @@ -384,7 +376,7 @@ "While the basic loop construct is flexible, there are more convenient ways to specify a research cycle in ``autora``. The next notebook illustrates the use of these constructs.\n", "\n", "Follow this link for the next notebook tutorial:\n", - "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
" + "[AutoRA Basic Tutorial III: Functional Workflow](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
" ] } ], @@ -394,11 +386,20 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", - "name": "python3" + "display_name": "autoraKernel", + "language": "python", + "name": "autorakernel" }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" } }, "nbformat": 4, From 6af57ca58e48c75d469f385de184451f3e44f812 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Fri, 18 Aug 2023 09:42:59 -0700 Subject: [PATCH 11/32] Updated tutorial I with latest autora --- .../basic/Tutorial-I-Components.ipynb | 220 ++++-------------- 1 file changed, 44 insertions(+), 176 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-I-Components.ipynb b/docs/tutorials/basic/Tutorial-I-Components.ipynb index e3e86c755..bd8ec03d6 100644 --- a/docs/tutorials/basic/Tutorial-I-Components.ipynb +++ b/docs/tutorials/basic/Tutorial-I-Components.ipynb @@ -56,16 +56,16 @@ "output_type": "stream", "text": [ "\n", - "[notice] A new release of pip is available: 23.0.1 -> 23.2\n", + "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n", "\n", - "[notice] A new release of pip is available: 23.0.1 -> 23.2\n", + "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n", "\n", - "[notice] A new release of pip is available: 23.0.1 -> 23.2\n", + "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n", "\n", - "[notice] A new release of pip is available: 23.0.1 -> 23.2\n", + "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } @@ -93,7 +93,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -134,7 +134,9 @@ "metadata": {}, "source": [ "## Toy Example of the Components\n", - "Before jumping into each component in detail, we will present a toy example to provide you with an overview on how these components work together within a closed-loop. After some setup, you will see steps 1-3, which uses the three componens - namely, the EXPERIMENTALIST to propose new conditions, the EXPERIMENT RUNNER to retrieve new observations from those conditions, and the THEORIST to model the new data. We then finish this example by plotting our data and findings." + "Before jumping into each component in detail, we will present a toy example to provide you with an overview on how these components work together within a closed-loop. After some setup, you will see steps 1-3, which uses the three componens - namely, the EXPERIMENTALIST to propose new conditions, the EXPERIMENT RUNNER to retrieve new observations from those conditions, and the THEORIST to model the new data. We then finish this example by plotting our data and findings.\n", + "\n", + "*Do not stop with this toy example! At this point, it may be tempting to start working on your own project, but we urge you to continue through the tutorials. ``autora`` has a lot of embedded functionality that you are going to want to use, and this toy example has stripped those away. So, keep going and see how much ``autora`` has to offer!*" ] }, { @@ -146,15 +148,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:05<00:00, 19.85it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:03<00:00, 26.33it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -163,7 +171,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -210,15 +218,6 @@ "plt.legend()" ] }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "***WARNING:*** *Do not stop here! We have now shown you the three components and how they work together. At this point, it may be tempting to start working on your own project, but we urge you to continue through the tutorials. ``autora`` has a lot of embedded functionality that you are going to want to use, and this toy example has stripped those away. So, keep going and see how much ``autora`` has to offer!*" - ] - }, { "attachments": {}, "cell_type": "markdown", @@ -315,7 +314,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -324,7 +323,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -478,14 +477,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 20.14it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 26.00it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { "text/html": [ - "
sin(X0)
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + "
sin(X0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "sin(X0)" @@ -498,8 +497,8 @@ ], "source": [ "# convert data to 2-dimensional numpy array\n", - "initial_conditions = initial_conditions.reshape((len(initial_conditions), 1))\n", - "initial_observations = initial_observations.reshape((len(initial_observations), 1))\n", + "initial_conditions = initial_conditions.reshape(-1, 1)\n", + "initial_observations = initial_observations.reshape(-1, 1)\n", "print(f\"Size of the initial conditions: {initial_conditions.shape},\\nSize of the initial observations: {initial_observations.shape}\\n\")\n", "\n", "# fit theorists\n", @@ -512,7 +511,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For some theorists, we can inspect the resulting model architecture. For instance, in the BMS theorist, we can call obtain the model formula via ``theorist_bms.repr()``.\n" + "For some theorists, we can inspect the resulting model architecture. For instance, in the BMS theorist, we can obtain the model formula via ``theorist_bms.repr()``.\n" ] }, { @@ -547,7 +546,7 @@ "outputs": [], "source": [ "# convert condition pool into 2-dimensional numpy array before generating respective predictions\n", - "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", + "condition_pool = condition_pool.reshape(-1, 1)\n", "\n", "# obtain predictions\n", "predicted_observations_lr = theorist_lr.predict(condition_pool)\n", @@ -570,7 +569,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -579,7 +578,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -633,13 +632,11 @@ "source": [ "### Types\n", "\n", - "There are generally three types of experimentalist functions: **poolers**, **samplers**, and **pipelines**.\n", + "There are generally two types of experimentalist functions: **poolers** and **samplers**.\n", "\n", "**Poolers** generate a novel set of experimental conditions \"from scratch\", e.g., by sampling from a grid. They usually require metadata describing independent variables of the experiment (e.g., their range or the set of allowed values).\n", "\n", - "**Samplers** operate on an existing pool of experimental conditions. They typically require experimental conditions to be represented as a 2-dimensional numpy array in which columns correspond to independent variables and rows to different conditions. They then select experiment conditions from this pool.\n", - "\n", - "**Pipelines** Pipelines connect multiple experimentalists into a unified workflow. This is beneficial when various steps are required to process experiment conditions. For example, apart from identifying novel experimental conditions, experimentalist functions may perform other operations on the set of conditions, such as rearranging the rows of a condition matrix or adding new experiment conditions as columns. Experiment pipelines may begin with a pooler that generates all possible experiment conditions, followed by a sampler that selects a subset of conditions from the pool, and then proceed to additional functions that arrange the selected conditions in a specific order necessary for conducting the experiment." + "**Samplers** operate on an existing pool of experimental conditions. They typically require experimental conditions to be represented as a 2-dimensional numpy array in which columns correspond to independent variables and rows to different conditions. They then select experiment conditions from this pool." ] }, { @@ -737,16 +734,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "(3.490658503988659,)\n", - "(3.3637254674799806,)\n", - "(5.14078797860148,)\n", - "(3.42719198573432,)\n", + "(4.823455387329783,)\n", "(6.283185307179586,)\n", - "(5.711986642890533,)\n", - "(6.156252270670908,)\n", + "(3.0463928762082846,)\n", + "(4.886921905584122,)\n", + "(0.25386607301735703,)\n", + "(1.4597299198498028,)\n", "(5.96585271590789,)\n", - "(1.3327968833411243,)\n", - "(0.0,)\n" + "(0.8885312555607496,)\n", + "(4.3157232412950695,)\n", + "(5.331187533364497,)\n" ] } ], @@ -784,8 +781,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0. ]\n", - " [0.06346652]]\n" + "[[1.07893081]\n", + " [1.01546429]]\n" ] } ], @@ -825,8 +822,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0. ]\n", - " [0.06346652]]\n" + "[[1.65012947]\n", + " [1.58666296]]\n" ] } ], @@ -861,7 +858,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -870,7 +867,7 @@ }, { "data": { - "image/png": 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Nm0aVKlXo378/r169SlJ33bp1al0027ZtIygoSPWBUr58eQoXLszcuXMJDQ1Nsv+HjzEzZVRcRkZGySY3WlpaSV63v/76K3FxcV/Ujru7O0ZGRvTq1Yvnz58n2e7v78+iRYuAhNcOwMKFC9XqzJ8/H4AmTZqkuf3GjRtz5swZzp07pyp7+fIlGzdu/Oy+RkZGAKlKAhs3bkxcXJza/xbAggULUCgUaU5c4uLiknRFWllZYWdnl+5TUUhZgzwjJOU4gwcPJjw8nFatWlG8eHGio6M5ffo0mzdvpkCBAnTv3h2A77//Hl1dXZo1a0bfvn0JDQ1l5cqVWFlZqc4apSd9fX0OHjyIm5sblSpV4sCBA+zbt49x48ZhaWmZ4n4zZ87k2LFjVKpUid69e+Pk5MSbN2+4dOkSXl5evHnzBoDevXuzZMkSunbtysWLF7G1tWX9+vUYGhqmKc4DBw6ovk1/qEqVKhQqVAg/Pz8mTpxIt27daNasGZCwPIeLiwsDBgxgy5YtavvlypWLatWq0b17d54/f87ChQtxdHSkd+/eQMLYqT/++INGjRpRsmRJunfvTt68eXny5AnHjh3D1NSUv//+O02PIT1kVFzly5fHy8uL+fPnY2dnR8GCBalUqRJNmzZl/fr1mJmZ4eTkhI+PD15eXuTOnfuL4i9cuDB//vknHTp0oESJEmozS58+fZqtW7eq5jBydnbGzc2NFStW8O7dO2rWrMm5c+dYu3YtLVu2pHbt2mlu393dnfXr19OwYUOGDh2KkZERK1aswMHBgatXr342dnNzc5YtW4aJiQlGRkZUqlQp2bFhzZo1o3bt2owfP54HDx7g7OzM4cOH2b17N8OGDVMbGJ0a79+/J1++fLRt2xZnZ2eMjY3x8vLi/PnzamfrpBxEA1eqSVKGOnDggOjRo4coXry4MDY2Frq6usLR0VEMHjxYPH/+XK3unj17RJkyZYS+vr4oUKCAmDVrlli9enWSy3sdHBxEkyZNkrQFJLnMNiAgQABizpw5qjI3NzdhZGQk/P39xffffy8MDQ2FtbW1mDx5stp8Q4nH/PDyeSGEeP78uRg4cKCwt7cXOjo6wsbGRtStW1esWLFCrV5gYKBo3ry5MDQ0FHny5BFDhw5VzfHzNZfP89+lzLGxscLV1VXky5dPvHv3Tm3/RYsWCUBs3rxZCPH/y7z/+usvMXbsWGFlZSUMDAxEkyZN1KYbSHT58mXRunVrkTt3bqGnpyccHBxE+/btxdGjR1V1Ei9T/3C6gY+3ffxcpubv82G8W7duTbe4Ep/TD19Lt27dEjVq1BAGBgYCUF1K//btW9G9e3eRJ08eYWxsLBo0aCBu3bolHBwc1C63T+08Qonu3LkjevfuLQoUKCB0dXWFiYmJqFq1qvj1119FZGSkql5MTIyYMmWKKFiwoNDR0RH29vZi7NixanWESPl/4eNL4IUQ4urVq6JmzZpCX19f5M2bV0ybNk2sWrXqs5fPCyHE7t27hZOTk9DW1la7lP7jy+eFEOL9+/di+PDhws7OTujo6IgiRYqIOXPmqE17IETyr4fEx5T4HEdFRYmffvpJODs7CxMTE2FkZCScnZ3F77//nsyzK+UECiGyweg2ScrmunXrxrZt25LtYsmpjh8/Tu3atdm6davaJf2SJElZiRwjJEmSJEnSN0smQpIkSZIkfbNkIiRJkiRJ0jdLjhGSJEmSJOmbJc8ISZIkSZL0zZKJkCRJkiRJ3yw5oeJnxMfH8/TpU0xMTNI07bskSZIkSZojhOD9+/fY2dmhVKZ83kcmQp/x9OnTJKtUS5IkSZKUPTx69Ih8+fKluF0mQp+RuIjfo0ePMDU11XA0kiRJkiSlRkhICPb29mqL8SZHJkKfkdgdZmpqKhMhSZIkScpmPjesRQ6WliRJkiTpmyUTIUmSJEmSvlkyEZIkSZIk6ZslxwhJkpRh4uLiiImJ0XQYkiTlQDo6OmhpaX31cWQiJElSuhNC8OzZM969e6fpUCRJysHMzc2xsbH5qnn+ZCIkSVK6S0yCrKysMDQ0lJORSpKUroQQhIeH8+LFCwBsbW2/+FgyEZIkKV3FxcWpkqDcuXNrOhxJknIoAwMDAF68eIGVldUXd5PJwdKSJKWrxDFBhoaGGo5EkqScLvF95mvGIspESJKkDCG7wyRJymjp8T4jEyFJkiRJkr5ZMhGSJEnKYmrVqsWwYcM0HUa6KVCgAAsXLlTdVygU7Nq165P7dOvWjZYtW2ZoXJIEMhGSJEnK8j5OJLK7oKAgGjVqBMCDBw9QKBT4+vqq1Vm0aBGenp6ZH5z0zZGJkIbExMRw8OBBTYchSZKU6WxsbNDT0/tkHTMzM8zNzTMnIOmbJhMhDZkwYQKNGjWiX79+REREaDocSZJI6JIaMmQI7u7u5MqVCxsbGzw8PNTqPHz4kBYtWmBsbIypqSnt27fn+fPnANy5cweFQsGtW7fU9lmwYAGFCxdW3b9+/TqNGjXC2NgYa2trunTpwqtXr1KMKTAwkOHDh6NQKFAoFISFhWFqasq2bdvU6u7atQsjIyPev3+f7LHi4+OZPXs2jo6O6OnpkT9/fqZPn67afu3aNerUqYOBgQG5c+emT58+hIaGqrYndlfNnTsXW1tbcufOzcCBA9Wu2Hnx4gXNmjXDwMCAggULsnHjxiRxfNg1VrBgQQDKli2LQqGgVq1aam0lioqKYsiQIVhZWaGvr0+1atU4f/68avvx48dRKBQcPXqUChUqYGhoSJUqVbh9+7aqzpUrV6hduzYmJiaYmppSvnx5Lly4kOxzJX07slUidOLECZo1a4adnV2q+pgT/zE+vj179ixzAk6BEAI9PT0UCgXLly/nu+++U/tnlaQcKyws5VtkZOrrfvzlIaV6X2Dt2rUYGRlx9uxZZs+ezdSpUzly5AiQkEi0aNGCN2/e8O+//3LkyBHu379Phw4dAChatCgVKlRI8uG/ceNGOnXqBMC7d++oU6cOZcuW5cKFCxw8eJDnz5/Tvn37ZOPZsWMH+fLlY+rUqQQFBREUFISRkREdO3ZkzZo1anXXrFlD27ZtMTExSfZYY8eOZebMmUycOJGbN2/y559/Ym1t/d9TGEaDBg2wsLDg/PnzbN26FS8vLwYNGqR2jGPHjuHv78+xY8dYu3Ytnp6eal1Y3bp149GjRxw7doxt27bx+++/qya9S865c+cA8PLyIigoiB07diRbz93dne3bt7N27VouXbqEo6MjDRo04M2bN2r1xo8fz7x587hw4QLa2tr06NFDta1z587ky5eP8+fPc/HiRcaMGYOOjk6KsUnfCJGN7N+/X4wfP17s2LFDAGLnzp2frH/s2DEBiNu3b4ugoCDVLS4uLtVtBgcHC0AEBwd/ZfRJHT58WFhaWgpAGBkZifXr16d7G5KU2SIiIsTNmzdFRERE0o2Q8q1xY/W6hoYp161ZU71unjzJ10ujmjVrimrVqqmVubq6itGjRwshEv5ntbS0xMOHD1Xbb9y4IQBx7tw5IYQQCxYsEIULF1Ztv337tgCEn5+fEEKIadOmie+//16tjUePHqneqxLjGDp0qGq7g4ODWLBggdo+Z8+eFVpaWuLp06dCCCGeP38utLW1xfHjx5N9bCEhIUJPT0+sXLky2e0rVqwQFhYWIjQ0VFW2b98+oVQqxbNnz4QQQri5uQkHBwcRGxurqtOuXTvRoUMHtcea+FwIIYSfn58A1OL/8P07ICBAAOLy5ctq8bi5uYkWLVoIIYQIDQ0VOjo6YuPGjart0dHRws7OTsyePVsI8f/3ey8vL7X4AdVr0cTERHh6eib7+KXs6VPvN6n9/M5WZ4QaNWrEzz//TKtWrdK0n5WVFTY2NqqbUpk1Hnb9+vVVp2rDwsLo0qULPXv2JDw8XNOhSdI3q0yZMmr3bW1tVWc0/Pz8sLe3x97eXrXdyckJc3Nz/Pz8AOjYsSMPHjzgzJkzQMLZoHLlylG8eHEgoXvm2LFjGBsbq26J2/z9/VMdZ8WKFSlZsiRr164FYMOGDTg4OFCjRo1k6/v5+REVFUXdunVT3O7s7IyRkZGqrGrVqsTHx6udsS5ZsqTaDL4fPz/a2tqUL19etb148eJfPdbH39+fmJgYqlatqirT0dGhYsWKquc90Yd/v8RlFxLjGzFiBL169aJevXrMnDkzTc+3lHNljYwgg7m4uGBra0v9+vXx9vbWdDhqbG1tOXLkCJMnT0ahULB69WpcXV25efOmpkOTpPQXGprybft29bovXqRc98AB9boPHiRf7wt83FWiUCiIj49P9f42NjbUqVOHP//8E4A///yTzp07q7aHhobSrFkzfH191W53795NMYlJSa9evVTdUmvWrKF79+4pTjCXuBzB1/ra5yejfRhf4nORGJ+Hhwc3btygSZMm/PPPPzg5ObFz506NxCllHTk6EbK1tWXZsmVs376d7du3Y29vT61atbh06VKK+0RFRRESEqJ2y2haWlp4eHhw9OhRbGxsuHnzJhUqVGDNmjUIITK8fUnKNEZGKd/09VNf9+MP9ZTqpbMSJUrw6NEjHj16pCq7efMm7969w8nJSVXWuXNnNm/ejI+PD/fv36djx46qbeXKlePGjRsUKFAAR0dHtZtRCjHr6uoSFxeXpPzHH38kMDCQxYsXc/PmTdzc3FKMvUiRIhgYGHD06NEUH9uVK1cI+2Bslbe3N0qlkmLFiqX8pHygePHixMbGcvHiRVXZ7du3effuXYr76OrqAiT7+BIVLlwYXV1dtS+yMTExnD9/Xu15T42iRYsyfPhwDh8+TOvWrZOMs5K+PTk6ESpWrBh9+/alfPnyVKlShdWrV1OlShUWLFiQ4j4zZszAzMxMdfvwFHhGq127Nr6+vtSvX5+IiAh69OiBm5ub2lUbkiRpTr169ShdujSdO3fm0qVLnDt3jq5du1KzZk0qVKigqte6dWvev39P//79qV27NnZ2dqptAwcO5M2bN/zwww+cP38ef39/Dh06RPfu3VNMBgoUKMCJEyd48uSJ2tVlFhYWtG7dmp9++onvv/+efPnypRi7vr4+o0ePxt3dnXXr1uHv78+ZM2dYtWoVkJC86evr4+bmxvXr1zl27BiDBw+mS5cuqgHVn1OsWDEaNmxI3759OXv2LBcvXqRXr16fPBtlZWWFgYGBatB4cHBwkjpGRkb079+fn376iYMHD3Lz5k169+5NeHg4PXv2TFVsERERDBo0iOPHjxMYGIi3tzfnz5+nRIkSqdpfyrlydCKUnIoVK3Lv3r0Ut48dO5bg4GDV7cNvfpnB2tqagwcP8vPPP6NUKlm/fj2urq5cvXo1U+OQJCkphULB7t27sbCwoEaNGtSrV49ChQqxefNmtXomJiY0a9aMK1euqHWLAdjZ2eHt7U1cXBzff/89pUuXZtiwYZibm6c4fnHq1Kk8ePCAwoULY2lpqbatZ8+eREdHq10dlZKJEycycuRIJk2aRIkSJejQoYNq/IyhoSGHDh3izZs3uLq60rZtW+rWrcuSJUvS8hSxZs0a7OzsqFmzJq1bt6ZPnz5YWVmlWF9bW5vFixezfPly7OzsaNGiRbL1Zs6cSZs2bejSpQvlypXj3r17HDp0CAsLi1TFpaWlxevXr+natStFixalffv2NGrUiClTpqTp8Uk5j0Jk074XhULBzp070zwFe/369TExMUnxEs2PhYSEYGZmRnBwMKampl8Q6Zc7ceIEnTp14smTJ+jr67No0SJ69+4tF7OUsrTIyEgCAgIoWLAg+h93d0npbv369QwfPpynT5+qupkk6Vvxqfeb1H5+Z6szQqGhoaqBhQABAQH4+vry8OFDIOFsTteuXVX1Fy5cyO7du7l37x7Xr19n2LBh/PPPPwwcOFAT4adZjRo1uHz5Mo0aNSIyMpK+ffvSqVOnTBm3JElS1hYeHo6/vz8zZ86kb9++MgmSpC+UrRKhCxcuULZsWcqWLQskXApZtmxZJk2aBCSsX5OYFAFER0czcuRISpcuTc2aNbly5QpeXl4pXj6aFVlaWrJ3715mz56NlpYWmzZtonz58ly+fFnToUmSpEGzZ8+mePHi2NjYMHbsWE2HI0nZVrbtGsssmuwa+5iPjw8dO3bk4cOH6OrqsmDBAvr37y+7yqQsRXaNSZKUWb65rrFvXeXKlbl8+TLNmzcnOjqagQMH0r59+2SvspAkSZIk6fNkIpTN5MqVi127drFgwQJ0dHTYtm0bZcuWVVt8UJIkSZKk1JGJUDakUCgYNmwY3t7eFChQgICAAKpWrcqiRYvkBIySJEmSlAYyEcrGXF1duXz5Mm3atCEmJoZhw4bRqlWrJKsxS5IkSZKUPJkIZXPm5uZs3bqVJUuWoKury+7duylbtqxqwUdJkiRJklImE6EcQKFQMHDgQHx8fChcuDAPHz6kevXqzJkzJ0sthihJkiRJWY1MhHKQcuXKcenSJTp06EBsbCzu7u40b95cbW0iSZJSJoSgT58+5MqVC4VCoZq89VMePHjwVXW9vb0pXbo0Ojo6tGzZkuPHj6NQKD65UGl66NatW5pn5s8uPn4OPT09MTc3/+x+CoWCXbt2ZWhsUtYjE6EcxtTUlL/++ovly5ejp6fHvn37cHFx4eTJk5oOTZKyvIMHD+Lp6cnevXsJCgqiVKlS6Xp8e3v7JMcdMWIELi4uBAQE4OnpSZUqVQgKCsLMzCxd2kwpUVu0aBGenp7p0kZW16FDB+7cuaO67+HhgYuLS5J6QUFBNGrUKBMjk7ICmQjlQAqFgj59+nDu3DmKFi3KkydPqF27Nr/88ovsKpOkT/D398fW1pYqVapgY2ODtrZ2uh5fS0sryXH9/f2pU6cO+fLlw9zcHF1dXWxsbDJ8olQzM7NUnSXJCQwMDD658GsiGxsb9PT0MiEiKSuRiVAOVqZMGS5evMiPP/5IXFwc48ePp2HDhqrVpiVJ+r9u3boxePBgHj58iEKhoECBAkDCWaJq1aphbm5O7ty5adq0Kf7+/ike5+3bt3Tu3BlLS0sMDAwoUqQIa9asAdTPziT+/vr1a3r06IFCocDT0zPZrjFvb29q1aqFoaEhFhYWNGjQgLdv36YqvoIFCwJQtmxZFAoFtWrVUj3eD7vGoqKiGDJkCFZWVujr61OtWjW1+ckS4zp69CgVKlTA0NCQKlWqcPv27U8+r48fP+aHH34gV65cGBkZUaFCBc6ePavavnTpUgoXLoyuri7FihVj/fr1avsrFAr++OMPWrVqhaGhIUWKFGHPnj1qdfbv30/RokUxMDCgdu3aPHjwQG37h11jnp6eTJkyhStXrqBQKFTPe2JbH3aNXbt2jTp16mBgYEDu3Lnp06cPoaGhqu2Jz+HcuXOxtbUld+7cDBw4kJiYGFWd33//nSJFiqCvr4+1tTVt27b95PMlZT6ZCOVwxsbGrFu3jlWrVmFgYMCRI0dwdnbm2LFjmg5N+gaFRYeleIuMjUx13YiYiFTVTYtFixYxdepU8uXLR1BQkCoJCAsLY8SIEVy4cIGjR4+iVCpp1apVimdXJ06cyM2bNzlw4AB+fn4sXbqUPHnyJKmX2E1mamrKwoULCQoKokOHDknq+fr6UrduXZycnPDx8eHUqVM0a9aMuLi4VMV37tw5ALy8vAgKCmLHjh3Jxu3u7s727dtZu3Ytly5dwtHRkQYNGiSZjmP8+PHMmzePCxcuoK2tTY8ePVJ8TkNDQ6lZsyZPnjxhz549XLlyBXd3d1VsO3fuZOjQoYwcOZLr16/Tt29funfvnuT9acqUKbRv356rV6/SuHFjOnfurIrr0aNHtG7dmmbNmuHr60uvXr0YM2ZMijF16NCBkSNHUrJkSYKCglJ83sPCwmjQoAEWFhacP3+erVu34uXlxaBBg9TqHTt2DH9/f44dO8batWvx9PRUJVYXLlxgyJAhTJ06ldu3b3Pw4EFq1KiRYmyShgjpk4KDgwUggoODNR3KV7t+/bpwcnISgFAqlcLDw0PExsZqOiwph4mIiBA3b94UERERSbbhQYq3xhsbq9U1nG6YYt2aa2qq1c0zO0+y9dJqwYIFwsHB4ZN1Xr58KQBx7do1IYQQAQEBAhCXL18WQgjRrFkz0b1792T3/biuEEKYmZmJNWvWqO4fO3ZMAOLt27dCCCF++OEHUbVq1VQ/hs/Fl8jNzU20aNFCCCFEaGio0NHRERs3blRtj46OFnZ2dmL27NlqcXl5eanq7Nu3TwDJ/q2FEGL58uXCxMREvH79OtntVapUEb1791Yra9eunWjc+P+vBUBMmDBBdT80NFQA4sCBA0IIIcaOHSucnJzUjjF69Gi153DNmjXCzMxMtX3y5MnC2dk5STyA2LlzpxBCiBUrVggLCwsRGhqq9niVSqV49uyZECLhOXRwcFB7H23Xrp3o0KGDEEKI7du3C1NTUxESEpLs45e+3qfeb1L7+S3PCH1DSpYsyblz5+jevTvx8fF4eHjw/fffExQUpOnQJCnLunv3Lj/88AOFChXC1NRU1WX28OHDZOv379+fTZs24eLigru7O6dPn/6q9hPPCKVXfMnx9/cnJiaGqlWrqsp0dHSoWLEifn5+anXLlCmj+t3W1hYgxe52X19fypYtS65cuZLd7ufnp9YmQNWqVT/ZppGREaampqo2/fz8qFSpklr9ypUrJ9teWvj5+eHs7IyRkZFabPHx8WrdgSVLlkRLS0t139bWVhVb/fr1cXBwoFChQnTp0oWNGzcSHh7+1bFJ6St9RwJKWZ6RkRGrV6+mdu3a9O/fn3/++QcXFxc2bNhA/fr1NR2elMOFjg1NcZuWUkvt/otRKY9lUyrUv8M9GPrgq+L6lGbNmuHg4MDKlSuxs7MjPj6eUqVKER0dnWz9Ro0aERgYyP79+zly5Ah169Zl4MCBzJ0794vaNzAwSNf4vpaOjo7q98QB3Sl1E34u9i9pM7HdrHLhx6diMzEx4dKlSxw/fpzDhw8zadIkPDw8OH/+/DczUD07kGeEvlFdunThwoULlC5dmhcvXtCgQQMmTJhAbGyspkOTcjAjXaMUb/ra+qmua6BjkKq6X+v169fcvn2bCRMmULduXUqUKKEapPwplpaWuLm5sWHDBhYuXMiKFSu+OIYyZcpw9OjRL45PV1cXQDWmKDmJg5W9vb1VZTExMZw/fx4nJ6evit3X1zfFZX9KlCih1iYkDAxPS5slSpRQjYNK9LmZ9XV1dT/5fCQe98qVK4SF/X+smbe3N0qlkmLFiqU6Pm1tberVq8fs2bO5evUqDx484J9//kn1/lLGk4nQN6x48eKcPXuWPn36IIRg+vTp1KlThydPnmg6NEnKEiwsLMidOzcrVqzg3r17/PPPP4wYMeKT+0yaNIndu3dz7949bty4wd69eylRosQXxzB27FjOnz/PgAEDuHr1Krdu3WLp0qW8evUqVfFZWVlhYGDAwYMHef78OcHBwUnaMDIyon///vz0008cPHiQmzdv0rt3b8LDw+nZs+cXx/7DDz9gY2NDy5Yt8fb25v79+2zfvh0fHx8AfvrpJzw9PVm6dCl3795l/vz57Nixg1GjRqW6jX79+nH37l1++uknbt++zZ9//vnZ+ZESF6v29fXl1atXREVFJanTuXNn9PX1cXNz4/r16xw7dozBgwfTpUsXrK2tUxXb3r17Wbx4Mb6+vgQGBrJu3Tri4+PTlEhJGU8mQt84AwMDli9fzl9//YWxsTEnT57ExcWFAwcOaDo0SdI4pVLJpk2buHjxIqVKlWL48OHMmTPnk/vo6uoyduxYypQpQ40aNdDS0mLTpk1fHEPRokU5fPgwV65coWLFilSuXJndu3ejra2dqvi0tbVZvHgxy5cvx87OjhYtWiTbzsyZM2nTpg1dunShXLly3Lt3j0OHDmFhYfHFsevq6nL48GGsrKxo3LgxpUuXZubMmaoxNS1btmTRokXMnTuXkiVLsnz5ctasWaO6xD818ufPz/bt29m1axfOzs4sW7aMX3755ZP7tGnThoYNG1K7dm0sLS3566+/ktQxNDTk0KFDvHnzBldXV9q2bUvdunVZsmRJqmMzNzdnx44d1KlThxIlSrBs2TL++usvSpYsmepjSBlPIYQQmg4iKwsJCcHMzIzg4GBMTU01HU6Gunv3Lu3bt1fNQOvu7s7PP/+cpA9ckj4lMjKSgIAAChYsiL6+/ud3kCRJ+kKfer9J7ee3PCMkqRQpUgQfHx8GDhwIwOzZs6lVq1aarj6RJEmSpOxEJkKSGn19fZYsWcLWrVsxNTXl9OnTuLi48Pfff2s6NEmSJElKdzIRkpLVtm1bLl++TIUKFXj79i3Nmzdn5MiRGXZJriRJkiRpgkyEpBQVKlQIb29vhg0bBsD8+fOpXr06AQEBmg1MkiRJktKJTISkT9LV1WXBggXs2rULc3Nzzp07R9myZVNcr0iSJEmSshOZCEmp0qJFC3x9ffnuu+8IDg6mTZs2DB48ONn5NyRJkiQpu5CJkJRqDg4OnDhxAnd3dwCWLFlClSpVuHfvnoYjkyRJkqQvIxMhKU10dHSYNWsW+/btI3fu3Fy6dIly5cqxZcsWTYcmSZIkSWkmEyHpizRu3BhfX1+qVavG+/fv6dChA/379yciIkLToUmSJElSqslESPpi+fLl49ixY4wbNw6FQsGyZcv47rvvuH37tqZDk6RvjoeHBy4uLpoOA4BatWqprjaVpKxOJkLSV9HW1mb69OkcPHgQS0tLrl69Svny5dmwYYOmQ5OkL/Ls2TOGDh2Ko6Mj+vr6WFtbU7VqVZYuXUp4eLimw/siHh4eKBSKT96+xPHjx1EoFLx79y59A5akTCQTISldfP/991y5coVatWoRFhZGly5d6NmzZ7b94JC+Tffv36ds2bIcPnyYX375hcuXL+Pj44O7uzt79+7Fy8srxX1jYmIyMdK0GTVqFEFBQapbvnz5mDp1qlrZh+TEqdK3RCZCUrqxtbXFy8uLyZMno1AoWL16NRUrVuTmzZuaDk2SUmXAgAFoa2tz4cIF2rdvT4kSJShUqBAtWrRg3759NGvWTFVXoVCwdOlSmjdvjpGREdOnTwdg6dKlFC5cGF1dXYoVK8b69etV+zx48ACFQqFa2Bjg3bt3KBQKjh8/Dvz/LMvRo0epUKEChoaGVKlSJUmX88yZM7G2tsbExISePXsSGRmZ4uMyNjbGxsZGddPS0sLExER1v2PHjgwaNIhhw4aRJ08eGjRo8NlYHzx4QO3atQGwsLBAoVDQrVs3Vd34+Hjc3d3JlSsXNjY2eHh4pPGvIUmZQyZCUrrS0tLCw8MDLy8vbGxsuHHjBhUqVMDT01PToUkaJIQgPDpWIzchRKpifP36NYcPH2bgwIEYGRklW+fjLiQPDw9atWrFtWvX6NGjBzt37mTo0KGMHDmS69ev07dvX7p3786xY8fS/JyNHz+eefPmceHCBbS1tenRo4dq25YtW/Dw8OCXX37hwoUL2Nra8vvvv6e5jQ+tXbsWXV1dvL29WbZs2Wfr29vbs337dgBu375NUFAQixYtUjuekZERZ8+eZfbs2UydOpUjR458VYySlBG0NR2AlDPVqVMHX19ffvzxR7y8vOjevTv//PMPv//+O8bGxpoOT8pkETFxOE06pJG2b05tgKHu59/q7t27hxCCYsWKqZXnyZNHdbZl4MCBzJo1S7WtU6dOdO/eXXX/hx9+oFu3bgwYMACAESNGcObMGebOnas6e5Ja06dPp2bNmgCMGTOGJk2aEBkZib6+PgsXLqRnz5707NkTgJ9//hkvL69PnhX6nCJFijB79mzV/QcPHnyyvpaWFrly5QLAysoKc3Nzte1lypRh8uTJqmMvWbKEo0ePUr9+/S+OUZIygjwjJGUYa2trDh06xM8//4xSqWT9+vW4urpy7do1TYcmSal27tw5fH19KVmyZJKZ1CtUqKB238/Pj6pVq6qVVa1aFT8/vzS3W6ZMGdXvtra2ALx48ULVTqVKldTqV65cOc1tfKh8+fJftf/HPowfEh5DYvySlJXIM0JShlIqlYwfP57q1avzww8/cOvWLSpWrMjixYvp1avXF1+tImUvBjpa3JzaQGNtp4ajoyMKhSLJWJxChQolHMfAIMk+KXWhpUSpTPju+WF3XUqDrHV0dFS/J/6fxMfHp6m9tPj4saQl1uR8GD8kPIaMjF+SvpQ8IyRliho1auDr60vDhg2JjIykT58+dO7cmZCQEE2HJmUChUKBoa62Rm6pTbZz585N/fr1WbJkCWFhYV/0OEuUKIG3t7dambe3N05OTgBYWloCqF2l9eFg5LS0c/bsWbWyM2fOpPk4n5KaWHV1dQGIi4tL17YlKTPJREjKNJaWluzbt49Zs2ahpaXFX3/9Rfny5bl8+bKmQ5MkAH7//XdiY2OpUKECmzdvxs/Pj9u3b7NhwwZu3bqFltanzy799NNPeHp6snTpUu7evcv8+fPZsWMHo0aNAhLOKn333XfMnDkTPz8//v33XyZMmJDmOIcOHcrq1atZs2YNd+7cYfLkydy4ceOLHnNKUhOrg4MDCoWCvXv38vLlS0JDQ9M1BknKDDIRkjKVUqnE3d2dEydOYG9vz7179/juu+/4/fffU311jyRllMKFC3P58mXq1avH2LFjcXZ2pkKFCvz666+MGjWKadOmfXL/li1bsmjRIubOnUvJkiVZvnw5a9asoVatWqo6q1evJjY2lvLlyzNs2DB+/vnnNMfZoUMHJk6ciLu7O+XLlycwMJD+/fun+Tif87lY8+bNy5QpUxgzZgzW1tYMGjQo3WOQpIymEPLT55NCQkIwMzMjODgYU1NTTYeTo7x+/Zru3bvz999/A9C2bVtWrlyZ5OoTKXuJjIwkICCAggULoq+vr+lwJEnKwT71fpPaz295RkjSmNy5c7N7927mz5+PtrY227Zto1y5cly4cEHToWVN8XEQcBKubUv4GS/HZUiSJH0tmQhJGqVQKBg+fDje3t4UKFCAgIAAqlSpwqJFi2RX2Ydu7oGFpWBtU9jeM+HnwlIJ5ZIkSdIXk4mQlCVUrFiRy5cv07p1a2JiYhg2bBitW7fm7du3mg5N827ugS1dIeSpenlIUEK5TIYkSZK+mEyEpCzD3Nycbdu28euvv6Krq8uuXbsoW7Zsul8WnK3Ex8HB0UByZ8f+Kzs4RnaTSZIkfSGZCElZikKhYNCgQZw+fZrChQsTGBhI9erVmTt37rc5GVvg6aRngtQICHmSUE+SJElKM5kIaUDY2xcopihQTFEQ9vZFimXfsvLly3Pp0iU6dOhAbGwsP/30E82bN+f169eaDi1zhT5P33qSJEmSGpkISVmWqakpf/31F8uWLUNPT499+/bh4uLCqVOnNB1a5jG2Tt96kiRJkhqZCElZmkKhoG/fvpw9e5aiRYvy+PFjatWqxYwZM76NrjKHKmBqB6S0TIQCTPMm1JMkSZLSTCZCUrbg7OzMxYsX6dy5M3FxcYwbN47GjRvn/NWslVrQcNZ/dz5Ohv6733BmQj1JkiQpzbJVInTixAmaNWuGnZ0dCoWCXbt2fXaf48ePU65cOfT09HB0dMTT0zPD45QyhrGxMevXr2fVqlUYGBhw6NAhXFxc+PfffzUd2tf71GSJTs2h/TowtVXfx9QuodypeebGKqlJzXtRt27daNmyZaqP+eDBAxQKxRctyCpJUtpkq0QoLCwMZ2dnfvvtt1TVDwgIoEmTJtSuXRtfX1+GDRtGr169OHToUAZHKmUUhUJBjx49OH/+PCVKlCAoKIg6deowderU7LsCdmomS3RqDsOug9teaLMq4eewazk/Ccrk2bTTmrBAwursjRo1AlJOYBYtWpTuX8Jq1aqFQqFAoVCgp6dH3rx5adasGTt27EjzsTw8PHBxcUnX+CQpu9DWdABp0ahRI9UbTmosW7aMggULMm/ePABKlCjBqVOnWLBgAQ0aNMioMD/LwDQXAW1Oqn5PqUxKWcmSJTl//jyDBg3C09OTyZMn8++//7Jx40ZsbGw0HV7qJU6W+PE8QYmTJX54xkepBQWrZ3qIGnNzT8IcSh9OH2Bql9BVmIUSwNS83szMzDKk7d69ezN16lRiY2N5/PgxO3fupGPHjnTr1o0VK1ZkSJuSlNNkqzNCaeXj40O9evXUyho0aICPj4+GIkqg1NKmQKlqFChVDaWWdopl0qcZGRmxZs0a1q1bh6GhIf/88w8uLi54eXmle1vx8YLnIZFcDHzLiTsv8br5nP3Xgth1+Qm7Lj/h+O0XXH38jkdvwgmLik3d8iByssSUZZHZtGvVqsWQIUNwd3cnV65c2NjY4OHhoVbnw66xggULAlC2bFkUCoVq1fmPzzQdPHiQatWqYW5uTu7cuWnatCn+/v5pjs/Q0BAbGxvy5cvHd999x6xZs1i+fDkrV65U+z8YPXo0RYsWxdDQkEKFCjFx4kRiYmIA8PT0ZMqUKVy5ckV1hinx7NX8+fMpXbo0RkZG2NvbM2DAAEJDQ9McpyRlZTn6E/fZs2dYW6tfVmxtbU1ISAgREREYGBgk2ScqKoqoqCjV/ZCQkAyPU/o6Xbp0wdXVlfbt23Pt2jW+//57xo8fz+TJk9HWTvtLPDg8hosP33D+wVv8gkJ49Cacx28jiIpN/VVqFoY6lLA1pbiNKSVsTXCyM6WEjSlK5QcDntMyWeK3dCboswmiIiFBLN4kUwaJr127lhEjRnD27Fl8fHzo1q0bVatWpX79+knqnjt3jooVK+Ll5UXJkiXR1dVN9phhYWGMGDGCMmXKEBoayqRJk2jVqhW+vr4olV/3/dTNzY2RI0eyY8cO1RdBExMTPD09sbOz49q1a/Tu3RsTExPc3d3p0KED169f5+DBg6rkKfEMllKpZPHixRQsWJD79+8zYMAA3N3d+f33378qRknKSnJ0IvQlZsyYwZQpUzK0jeiIUMb/XBuA6ROOoWtgnGyZlHrFixfn7NmzDB06lJUrV/Lzzz9z4sQJ/vzzT/LmzfvJfWPj4vG5/5pDN55xLuANd54n/41XS6nAxlQfMwMddLSV6Gkp0dFWIAS8C4/hbXg0r8OiiY6N5214DKf9X3Pa//8TQOYy0qVGkTzUKmZFjaKW5JKTJSYviyWIZcqUYfLkyQAUKVKEJUuWcPTo0WQTIUtLSwBy5879yS6zNm3aqN1fvXo1lpaW3Lx5k1KlSn1VvEqlkqJFi/LgwQNV2YQJE1S/FyhQgFGjRrFp0ybc3d0xMDDA2NgYbW3tJDEPGzZMbb+ff/6Zfv36yURIylFydCJkY2PD8+fqHyLPnz/H1NQ02bNBAGPHjmXEiBGq+yEhIdjb26drXDGR4czVvQCAR2Q4ugbGyZZJaWNgYMCKFSuoXbs2ffr04cSJE7i4uLB+/XoaNmyoVjc2Lp6zAW/YezWIQzee8SYsWm17oTxGVChggbO9OQVyG2FvYYituT46Wp/+ti6EICw6joCXYfgFheD3LAS/oBCuPwnhTVg0u3yfssv3KQoFdLV5RapS7m9tssQsliCWKVNG7b6tre1XT9tw9+5dJk2axNmzZ3n16pVqTqyHDx9+dSIECa9DheL/Zx83b97M4sWL8ff3JzQ0lNjYWExNTT97HC8vL2bMmMGtW7cICQkhNjaWyMhIwsPDMTQ0/Oo4JSkryNGJUOXKldm/f79a2ZEjR6hcuXKK++jp6aGnp5fRoUkZ6IcffqBChQq0b98eX19fGjVqxOjRo5k2bRphMYK/zj1inc8DgoIjVfvkMtKlQUkbaha1pEIBC/IYf9lrQKFQYKynTel8ZpTO9/8BsjFx8VwMfMvx2y/5985L/IJCWB+Ul756ubDhDcpk50tUJAwO/tYmS8xis2nr6Oio3VcoFF89mWezZs1wcHBg5cqV2NnZER8fT6lSpYiOjv78zp8RFxfH3bt3cXV1BRLGSnbu3JkpU6bQoEEDzMzM2LRpk+oikpQ8ePCApk2b0r9/f6ZPn06uXLk4deoUPXv2JDo6WiZCUo6RrRKh0NBQ7t27p7ofEBCAr68vuXLlIn/+/IwdO5YnT56wbt06APr168eSJUtwd3enR48e/PPPP2zZsoV9+/Zp6iFImaRIkSL4+PgwcuRIfv/9d2bNmsXGXYfQ/X4YcYZ5ADA31KFRKRualLbju0K50P7M2Z6voaOl5LtCufmuUG7GNCpOUHAEOy8/YalPb6ZEziJeoJYMCRQJ0yV+i5MlJs6mHRJE8uOEsm6CmDgm6FNTObx+/Zrbt2+zcuVKqldP6NpLz2Vj1q5dy9u3b1Xdb6dPn8bBwYHx48er6gQGBiaJ++OYL168SHx8PPPmzVONW9qyZUu6xSlJWUW2SoQuXLhA7dq1VfcTu7Dc3Nzw9PQkKCiIhw8fqrYXLFiQffv2MXz4cBYtWkS+fPn4448/NHrpvJR59PX1GTVlNv7aDhxeNoXHt31RBg7G5cdxjO33I82cbdHT1kySYWtmwIBajoiaY7lz3AFr78mYx75UbX+lzM3jSpNxLt4sZ1/amZzE2bS3dCVh9uwPk6GsPZu2lZUVBgYGHDx4kHz58qGvr5/k0nkLCwty587NihUrsLW15eHDh4wZM+aL2gsPD+fZs2dql88vWLCA/v37q94rixQpwsOHD9m0aROurq7s27ePnTt3qh2nQIECqi+W+fLlw8TEBEdHR2JiYvj1119p1qwZ3t7eLFu27MueGEnKwrLVe2ytWrUQQiS5JV7q6enpyfHjx5Psc/nyZaKiovD396dbt26ZHreU+V68j2TCrmvUn/8vtwxKYtdjMXkKliA+8j2X/hiLz58LUGSBy9IVCgXFanfGfNxt3rbfwa7CU+kaN4lK4QtpdSw3TX49hc8HA66/Gdl0Nm1tbW0WL17M8uXLsbOzo0WLFknqKJVKNm3axMWLFylVqhTDhw9nzpw5X9TeypUrsbW1pXDhwrRu3ZqbN2+yefNmtcHMzZs3Z/jw4QwaNAgXFxdOnz7NxIkT1Y7Tpk0bGjZsSO3atbG0tOSvv/7C2dmZ+fPnM2vWLEqVKsXGjRuZMWPGF8UpSVmZQqRqwpNvV0hICGZmZgQHB6dqcGFqhL19gfHihPENoUOeY2RhlWyZlHaRMXH8ftyflSfuExGTkOjULmaJe8PiFMqlx+jRo1m0aBEAFStWZNOmTaq5X7KKN2HRrDp1n7WnAwmNigWgSWlbxjUpQV7z5Af5ZyWRkZEEBARQsGBB9PX1v+5g8XEJV4eFPk8YE+RQJUueCZIkSTM+9X6T2s/vbHVGSJI+5bT/KxotOsnio3eJiInDxd6cTX2+Y033ipSwNUVPT4+FCxeyc+dOzM3NOXfuHGXLlk3STaBpuYx0+alBcU6616ZrZQeUCth3LYi6846zyOsukTGaP5OVaRJn0y7dNuGnTIIkSUpn8ozQZ2TEGaH4uFj8ziYM2C5RqQlKLe1ky6TUeRsWzS/7/dh68TEAViZ6TG5WksalbdQuIf5QYGAgHTp04OzZswAMHjyYOXPmZMkrBv2CQvDYc4OzAW8AKGRpxPz2LrjYm2s2sBSk6xkhSZKkT0iPM0IyEfqMjEiEpPRz8Pozxu+8xuv/5gH68bv8uDcsjqm+zmf2hJiYGMaNG8fcuXMBKFeuHFu2bKFw4cIZGvOXEEKw71oQU/++yYv3USgVMKCWI0PqFkFXO2ud2JWJkCRJmUV2jUnfrMiYOCbsuka/DRd5HRZNEStjtvWrzM8tS6cqCYKE+WHmzJnD3r17yZUrF5cuXaJs2bJZ8hJhhUJB0zJ2HB5egxYudsQLWHLsHi1/8+bWM7kMjCRJ0peSiZAGREeE4uFRCw+PWkRHhKZYJiXvzvP3NF9yig1nEqZK6FuzEPuGVKdCgVxfdLwmTZrg6+tL1apVef/+PR06dKB///5ERkZ+fudMZm6oy6KOZfm9czksDHW4GRRC81+92Xg2MHULvUqSJElqZCKkATGR4UxR/MsUxb/ERIanWCapE0Lw59mHNPv1FHeeh5LHWI91PSoytlGJr+4esre35/jx44wdOxaAZcuW8d1333Hnzp30CD3dNS5ty+HhNalXworouHjG77zOyK1XiIj+hgZSS5IkpQOZCEnZQnRsPGN3XGPczmtExcZTo6glB4ZWp0ZRy3RrQ1tbm19++YWDBw9iaWnJlStXKF++PBs3bky3NtKTpYkeK7tWYGyj4igVsOPSE1r97s2DV2GaDk2SJCnbkImQlOW9Co2i8x9n2HT+EUoFjG5YHM9urliaZMwVXg0aNMDX15datWoRGhrKjz/+SK9evQgPz3pn6hQKBX1rFmZjr+/IY6zLrWfvafbrKbxufmMr1kuSJH0hmQhJWdqNp8G0WOLN+QdvMdHTZlU3V/rXKowy+VVK042dnR1eXl5MnjwZhULBqlWrqFSpEn5+fhna7peqXDh3wjgpBwveR8XSe/0F1ngHaDosSZKkLE8mQlKWdfD6M9ou9eHJuwgK5jFi58Cq1C6WeTNua2lp4eHhgZeXFzY2Nly/fp0KFSqwdu3aTIshLaxN9fmrz3d0qpQfIWDK3zfx2HODuHg5iDor6tatGy1btlTdr1WrFsOGDfuqY6bHMVLD29ub0qVLo6Ojo/YYsqqPn2spczx48ACFQoGvr6+mQ/kkmQhJWdLGs4H033iRiJg4ahS1ZNeAqjhaGWskljp16uDr60u9evUIDw+nW7duuLm5ERqa9a7u09FSMr1lKcY2Kg6A5+kH9F1/kfDoWA1Hlj1069YNhUKBQqFAV1cXR0dHpk6dSmxsxj9/O3bsYNq0aamqe/z4cRQKBe/evfviY3yNESNG4OLiQkBAgGqtR01K6flItGjRoiwRZ0o+fN3p6OhQsGBB3N3ds+SVq2lhb29PUFAQpUqV0nQonyQTISlLEUKwyOsu43deRwj4oWJ+VrtVwMwwdXMDZRRra2sOHjzItGnTUCqVrFu3DldXV65du6bRuJKTOG7ot07l0NVW4uX3nA7Lz/DyfZSmQ8sWGjZsSFBQEHfv3mXkyJF4eHikuChqdHR0urWbK1cuTExMNH6M1PD396dOnTrky5cPc3PzJNuFEJmSPKaWmZlZsnFmtk+9XhJfd/fv32fBggUsX76cyZMnZ2g8cXFxxMfHZ9jxtbS0sLGxQVs7a6+UIBMhDdA3NudcFU/OVfFE39g8xbJvTVy8YNLuGyzwSrhkfUjdIvzSqhTaWlnjZaqlpcWECRP4559/sLOz49atW1SsWJE//vgjS87h06SMLX/1rkQuI12uPQmmwwofgoIjNB0WCAFR7yH8TcLPLPbc6enpYWNjg4ODA/3796devXrs2bMH+H8Xy/Tp07Gzs6NYsWIAPHr0iPbt22Nubk6uXLlo0aIFDx48UB0zLi6OESNGYG5uTu7cuXF3d0/ymvm4WysqKorRo0djb2+Pnp4ejo6OrFq1igcPHlC7dm0ALCwsUCgUdOvWLdljvH37lq5du2JhYYGhoSGNGjXi7t27qu2enp6Ym5tz6NAhSpQogbGxseoDOTmJXR2vX7+mR48eKBQKPD09VWdkDhw4QPny5dHT0+PUqVNERUUxZMgQrKys0NfXp1q1apw/f151vMT9Dh06RNmyZTEwMKBOnTq8ePGCAwcOUKJECUxNTenUqdNXXayQXDfkkCFDcHd3J1euXNjY2ODh4aG2z7t37+jVqxeWlpaYmppSp04drly5otru7+9PixYtsLa2xtjYGFdXV7y8vNSOUaBAAaZNm0bXrl0xNTWlT58+KcaY+Lqzt7enZcuW1KtXjyNHjqi2x8fHM2PGDAoWLIiBgQHOzs5s27ZN7Rh79uyhSJEi6OvrU7t2bdauXat2pizx771nzx6cnJzQ09Pj4cOHREVFMWrUKPLmzYuRkRGVKlXi+PHjquMGBgbSrFkzLCwsMDIyomTJkuzfvx9IeI117twZS0tLDAwMKFKkCGvWrAGS7xr7999/qVixInp6etja2jJmzBi1pDk1f5v0ljU+Yb4xWjq6uNZ3w7W+G1o6uimWfUuiY+MZ8tdl1p8JRKGAaS1KMqJ+0RTXCtOkmjVr4uvrS8OGDYmMjKR379507tyZ9+/fazq0JMo75GJ7/yrYmelz/2UY7Zb58PB15l/9JoQgLCyMsFdPCAs4T9ija4Q9vZXwM+B8QnlYWIbcvjZJNTAwUPsmf/ToUW7fvs2RI0fYu3cvMTExNGjQABMTE06ePIm3t7cqoUjcb968eXh6erJ69WpOnTrFmzdvPrvYb9euXfnrr79YvHgxfn5+LF++HGNjY+zt7dm+fTsAt2/fJigoiEWLFiV7jG7dunHhwgX27NmDj48PQggaN25MTEyMqk54eDhz585l/fr1nDhxgocPHzJq1Khkj5fY1WFqasrChQsJCgqiQ4cOqu1jxoxh5syZ+Pn5UaZMGdzd3dm+fTtr167l0qVLODo60qBBA968eaN2XA8PD5YsWcLp06dVSeXChQv5888/2bdvH4cPH+bXX3/95POVVmvXrsXIyIizZ88ye/Zspk6dqpZ4tGvXTpWQXbx4kXLlylG3bl1V7KGhoTRu3JijR49y+fJlGjZsSLNmzXj48KFaO3PnzsXZ2ZnLly8zceLEVMV2/fp1Tp8+ja7u/z8LZsyYwbp161i2bBk3btxg+PDh/Pjjj/z7778ABAQE0LZtW1q2bMmVK1fo27cv48ePT3Ls8PBwZs2axR9//MGNGzewsrJi0KBB+Pj4sGnTJq5evUq7du1o2LChKmkeOHAgUVFRnDhxgmvXrjFr1iyMjROGKkycOJGbN29y4MAB/Pz8WLp0KXny5En2cT158oTGjRvj6urKlStXWLp0KatWreLnn39O098m3Qnpk4KDgwUggoODNR1KjhUZEyt6ep4TDqP3iiLj9ou9V55qOqRUiYuLEzNnzhRaWloCEEWKFBGXL1/WdFjJevQmTNSc/Y9wGL1XVJx+RNx9HpJhbUVERIibN2+KiIgIVVloaKgANHILDQ1Ndexubm6iRYsWQggh4uPjxZEjR4Senp4YNWqUaru1tbWIiopS7bN+/XpRrFgxER8fryqLiooSBgYG4tChQ0IIIWxtbcXs2bNV22NiYkS+fPlUbQkhRM2aNcXQoUOFEELcvn1bAOLIkSPJxnns2DEBiLdv36qVf3iMO3fuCEB4e3urtr969UoYGBiILVu2CCGEWLNmjQDEvXv3VHV+++03YW1t/cnnyczMTKxZsyZJPLt27VKVhYaGCh0dHbFx40ZVWXR0tLCzs1M9F4n7eXl5qerMmDFDAMLf319V1rdvX9GgQYMU40np+Uj04d9ViITnqVq1amp1XF1dxejRo4UQQpw8eVKYmpqKyMhItTqFCxcWy5cvTzGOkiVLil9//VV138HBQbRs2TLF+h/Gp6WlJYyMjISenp4AhFKpFNu2bRNCCBEZGSkMDQ3F6dOn1fbr2bOn+OGHH4QQQowePVqUKlVKbfv48ePVnpfEv7evr6+qTmBgoNDS0hJPnjxR27du3bpi7NixQgghSpcuLTw8PJKNvVmzZqJ79+7JbgsICBCA6n1x3LhxSf5XfvvtN2FsbCzi4uKEEJ//23wsufebRKn9/M7aHXc5VHREKIsWJHyLGjp8M7oGxsmWfQuiYuPov+ES/9x6gZ62kj/cKlC9SPpNkpiRlEolo0ePplq1anTs2JG7d+/y3XffsWDBAvr165elzmblszBkS9/K/LjqLHeeh9J++RnW96xISTuzzAkgi3V/fcrevXsxNjYmJiaG+Ph4OnXqpHZqvnTp0mrf1K9cucK9e/eSjM2JjIzE39+f4OBggoKCqFSpkmqbtrY2FSpUSPFsla+vL1paWtSsWfOLH4efnx/a2tpq7ebOnZtixYqpTQNhaGiottCwra0tL168+KI2K1SooPrd39+fmJgYqlatqirT0dGhYsWKSaahKFOmjOp3a2trDA0NKVSokFrZuXPnviimlHzYJqg/7itXrhAaGkru3LnV6kRERODv7w8knBHy8PBg3759BAUFERsbS0RERJIzQh8+J59Su3Ztli5dSlhYGAsWLEBbW5s2bdoAcO/ePcLDw6lfv77aPtHR0ZQtWxZIODvo6uqqtr1ixYpJ2tHV1VV77NeuXSMuLo6iRYuq1YuKilI9/iFDhtC/f38OHz5MvXr1aNOmjeoY/fv3p02bNly6dInvv/+eli1bUqVKlWQfo5+fH5UrV1Z7b6xatSqhoaE8fvyY/PnzA5/+22QEmQhpQExkOO4xCf2rAyLD0TUwTrYsp4uMiaP/hoscu/0SfR0lq9xcqeqY/CnVrKxq1ar4+vrSvXt3/v77bwYMGMCxY8dYuXIlZmaZlGikgpWpPpv6VKbr6rNcfxJCp5Vn+av3dzjZpbwqc3ox1I4n9K735yvmKgR66TvY19DQME31Ez+QdHV1sbOzSzLQ08jISO1+aGhoijOQW1p+WVJvYGDwRft9CR0d9QsRFArFF3cnfvzcfEkMiVdOfRxTeg/q/VQboaGh2Nraqo2TSZQ46HrUqFEcOXKEuXPn4ujoiIGBAW3btk0yIDq1z4mRkRGOjo4ArF69GmdnZ1atWkXPnj1VV6ju27ePvHnzqu2np5e2iWUNDAzUEpHQ0FC0tLS4ePEiWlpaanUTu7969epFgwYNVN2UM2bMYN68eQwePJhGjRoRGBjI/v37OXLkCHXr1mXgwIHMnTs3TXF9KDP+/h+SY4QkjYiMiaPfB0nQ6myaBCXKnTs3u3fvZt68eWhra7N161bKlSvHhQsXNB2amlxGuvzZ+zvK5jcnOCKGLqvOcvd5xo9tUsTHYmRo8Pmbvi5GRkbpekvrmbnED6T8+fOn6mqXcuXKcffuXaysrHB0dFS7mZmZYWZmhq2tLWfPnlXtExsby8WLF1M8ZunSpYmPj1eN//hY4hmpuLiU15YrUaIEsbGxau2+fv2a27dv4+Tk9NnH9bUKFy6Mrq4u3t7/T4BjYmI4f/58prT/NcqVK8ezZ8/Q1tZO8jdNHP/i7e1Nt27daNWqFaVLl8bGxkZtgPzXUCqVjBs3jgkTJhAREaE2sPnjeOzt7QEoVqxYkvebDwemp6Rs2bLExcXx4sWLJMe2sbFR1bO3t6dfv37s2LGDkSNHsnLlStU2S0tL3Nzc2LBhAwsXLmTFihXJtlWiRAnVWLVE3t7emJiYkC9fvjQ9R+lJJkJSpouOjaf/hoscT0yCurlSJRsnQYkUCgUjRozg1KlTODg4cP/+fapUqcKiRYuy1FVlpvo6eHavSKm8prwOi6bzH2czfn0yrVROf5DaellI586dyZMnDy1atODkyZMEBARw/PhxhgwZwuPHjwEYOnQoM2fOZNeuXdy6dYsBAwakOOcNJFxt5ObmRo8ePdi1a5fqmFu2bAHAwcEBhULB3r17efnyZbJzWhUpUoQWLVrQu3dvTp06xZUrV/jxxx/JmzcvLVq0yJDn4kNGRkb079+fn376iYMHD3Lz5k169+5NeHg4PXv2zJA2r127hq+vr+r24VVeaVGvXj0qV65My5YtOXz4MA8ePOD06dOMHz9elWwUKVKEHTt2qNrp1KlTup61aNeuHVpaWvz222+YmJgwatQohg8fztq1a/H39+fSpUv8+uuvqgle+/bty61btxg9ejR37txhy5YtqrmTPvVloGjRonTu3JmuXbuyY8cOAgICOHfuHDNmzGDfvn0ADBs2jEOHDhEQEMClS5c4duwYJUqUAGDSpEns3r2be/fucePGDfbu3ava9rEBAwbw6NEjBg8ezK1bt9i9ezeTJ09mxIgRKJWaS0dkIiRlqrh4wYgtvqozQWu6VaRK4eyfBH2oUqVKXL58mVatWhETE8OwYcNo3bo1b9++1XRoKmYGOqzvUYniNia8eB9Fp5VnePQmA68m0zUG5WeSHKVOQr1sxtDQkBMnTpA/f35at25NiRIl6NmzJ5GRkZiaJnQ7jhw5ki5duuDm5kblypUxMTGhVatWnzzu0qVLadu2LQMGDKB48eL07t2bsLCEhDVv3rxMmTKFMWPGYG1tzaBBg5I9xpo1ayhfvjxNmzalcuXKCCHYv39/kq6HjDJz5kzatGlDly5dKFeuHPfu3ePQoUNYWFhkSHs1atSgbNmyqlv58uW/6DgKhYL9+/dTo0YNunfvTtGiRenYsSOBgYFYW1sDMH/+fCwsLKhSpQrNmjWjQYMGlCtXLt0ei7a2NoMGDWL27NmEhYUxbdo0Jk6cyIwZMyhRogQNGzZk3759FCxYEICCBQuybds2duzYQZkyZVi6dKnqqrHPdZ+tWbOGrl27MnLkSIoVK0bLli05f/68asxOXFwcAwcOVLVbtGhRfv/9dyDh7OTYsWMpU6YMNWrUQEtLi02bNiXbTt68edm/fz/nzp3D2dmZfv360bNnTyZMmJBeT9sXUYis9FU1CwoJCcHMzIzg4GDVm9rXCnv7AuPFCf9MoUOeY2RhlWxZTiOEYPyu6/x59iE6Wgr+cHOlZjquHp/VCCFYsmQJo0aNIjo6GgcHBzZv3qw2eFXTXr6PouMKH/xfhmGfy4CtfatgY6b/VceMjIwkICCAggULoq//wbEi3sHbT6x/ZlEQDMy/qm1Jkv5v+vTpLFu2jEePHmk6lAyT4vsNqf/8lmeEpEwz59Bt/jz7EIUCFnRwydFJECR8qxw8eDCnT5+mUKFCBAYGUq1aNebNm5ehA//SwtJEjz97f4dDbkMevYnAbfU5giNiPr/jlzAwT0h2Pj4zpNSRSZAkpYPff/+d8+fPc//+fdavX8+cOXNwc3PTdFhZnkyEpEyx/F9/fj+ecNnp9JalaVrGTsMRZZ7y5ctz6dIl2rVrR2xsLKNGjaJ58+a8fv1a06EBCYu1buxVCSsTPW4/f0/vdReIjEl5EO5XMTAH65KQ2xHMHRJ+WpeUSZAkpYO7d+/SokULnJycmDZtmmqJGOnTZNfYZ2RE11hcTDQn9yX0r1ZvMgAtHd1ky3KKbRcfM2prwqDF0Q2L079W4c/skTMJIVi+fDnDhg0jKiqKfPnysWnTJrV5VjTJLyiE9st8eB8VS6NSNizpVA4tZdrnQvrUqWpJkqT0lB5dYzIR+oyMSIS+Jd73XuG2+hyx8YI+NQoxrnHyVxN8S3x9fWnfvj13795FS0uLn3/+GXd3d41eNZHIx/81bqvPER0XT9fKDkxpXjLNl5/LREiSpMwixwhJWdqd5+/pt+EisfGCZs52jGlYXNMhZQkuLi5cvHiRTp06ERcXx9ixY2ncuHGGzpyaWpUL52Z+B2cUCljnE8hvx+598bHkdyxJkjJaerzPyERIA2Iiw/ltbnt+m9uemMjwFMuysxchkXRfc573kbG4FrBgTtsyKL+gmyWnMjExYcOGDfzxxx/o6+tz6NAhXFxcUpxALzM1LWPH5KYJE97NPXyHPVeepmn/xEuzv2a1cEmSpNRIfJ/5mikhZNfYZ8jL59MuLCqWDit8uP4khEJ5jNjevwoWRjlnzFN6u379Ou3atePWrVsolUo8PDwYN25ckunuM9v0fTdZeTIAXW0lm/p8R7n8qZ/7JSgoiHfv3mFlZYWhoWGWWndNkqTsTwhBeHg4L168wNzcHFtb2yR1Uvv5Ldcak9JVXLxg6KbLXH8SQi4jXdZ0d5VJ0GeUKlWKCxcuMHDgQNauXcukSZP4999/2bBhg9oU95ltTKMSBLwKw8vvBX3WXWDXwKrks0jdul2JcWeF7j5JknIuc3Pzr36flImQlK7mHr6Nl1/CSvIru1bAIfeXLcL4rTEyMsLT05PatWszYMAAjh49iouLCxs3bqRu3boaiUlLqWBRx7K0XeaDX1AIvdZeYFv/Khjrff5tQ6FQYGtri5WVFTExGTQvkSRJ3zQdHZ10OXMuEyEp3ez2fcLS/+YKmt22DOUdMmYa/ZzMzc2NihUr0r59e65fv079+vWZMGECkydP1khXmZGeNn+4VaDFEm9uPXvP0L8us6JrhVRfVq+lpaXxLj5JkqRPkYOlpXRx7XEw7tuuAtCvZmFauOTVcETZV4kSJTh79iy9evVCCMG0adOoW7cuT5+mbdByeslrbsDKruXR01Zy9NYLZh+8pZE4JEmSMoJMhKSv9uJ9JL3XXSAqNp46xa34qUExTYeU7RkaGrJy5Uo2btyIsbEx//77L87Ozhw6dEgj8ZTNb8Hcds4ALD9xn7/TeCWZJElSViUTIemrRMXG0W/9RZ6FRFLY0oiFHV2+aDZiKXmdOnXi4sWLODs78+rVKxo2bMjYsWOJjY3N9FiaOdvRt2YhANy3XeXWs5BMj0GSJCm9yURIA/SMTNnrOJm9jpPRMzJNsSw78Nhzk0sP32Gqr80fbq6Y6n/5XA5S8ooWLcqZM2cYMGAAADNnzqRWrVoaWVHavUFxqhfJQ0RMHH3WXSQ4XA6EliQpe5PzCH2GXGIjZVsvPOKnbVdRKMCze8WE1eTj4yDwNIQ+B2NrcKgCSjlYNr1s3bqVXr16ERISQq5cuVi7di1NmzbN1BjehkXTbMkpHr+NoFYxS1a5ucqzgJIkZTlyiQ0pQ914GsyEXdcBGF6vaEISdHMPLCwFa5vC9p4JPxeWSiiX0kW7du24dOkS5cuX582bNzRr1oxRo0YRHR2daTFYGOmyvEt59HWUHL/9kvlHbmda25IkSelNJkIaEBMZjueSXngu6aW2xMbHZVlVcHgM/TdcIio2ntrFLBlU2zEh2dnSFUI+GkQbEpRQLpOhdFO4cGG8vb0ZOnQoAPPmzaNGjRo8ePAg02IoaWfGrDZlAPjtmD9H/Z5nWtuSJEnpSSZCGhAdEUr316vo/noV0RGhKZZlRfHxgpFbfXn4Jpx8FgYs6OCCkng4OBpIrpf1v7KDYxK6zaR0oaenx8KFC9m5cyfm5uacPXuWsmXLsmvXrkyLoYVLXrpVKQDAiC1XePw2ayfwkiRJyZGJkJQmS//1x8vvBbraSpb9WB5zQ92EMUEfnwlSIyDkSUI9KV21bNkSX19fKlWqxLt372jVqhVDhw4lKioqU9of17gEzvbmBEfEMPDPy0THxmdKu5IkSelFJkJSqp25/5p5hxPGg0xrUZJSec0SNoSmslsktfWkNHFwcODkyZOMGjUKgMWLF1O1alX8/f0zvG1dbSVLfiiLmYEOVx69Y8YBvwxvU5IkKT3JREhKldehUQzddJl4AW3K5aODa/7/bzS2Tt1BUltPSjMdHR3mzJnD3r17yZUrFxcvXqRcuXJs3bo1w9u2z2XIvP8mW1zj/YCD14MyvE1JkqT0IhMh6bMSxgVd4XlIFIUtjZjWsqR6BYcqYGoHpHQJtQJM8ybUkzJUkyZN8PX1pWrVqoSEhNC+fXsGDBhAZGRkhrZbz8mavjUSJlv8aetVAl+HZWh7kiRJ6UUmQtJn/XHqPsdvv0RPW8mSTuUw1P1orV6lFjSc9d+dj5Oh/+43nCnnE8ok9vb2HD9+nLFjxwKwdOlSvvvuO+7cuZOh7Y5qUIwKDha8j4plkBwvJElSNiETIemTLj98y+yDCeOCJjVzooRtCpNSOTWH9uvA1Fa93NQuodypeQZHKn1IW1ubX375hYMHD5InTx6uXLlC+fLl+fPPPzOsTR0tJb92Kou5oQ7XngQz97CcX0iSpKxP+/NVpPSmZ2TKlnzDVb+nVKZpwRExDP7rMrHxgialbelUMf+nd3BqDsWbyJmls5AGDRpw5coVOnXqxL///kvnzp05duwYixYtwtDQMN3bszUzYFabMvRdf5EVJ+5TzTEPNYpapns7kiRJ6UUusfEZ3+oSG0IIBv11mX1Xg7DPZcC+IdXlOmLZWGxsLFOnTuXnn39GCEGpUqXYsmULJUqUyJD2Juy6xoYzD8ljrMfBYdXJY6yXIe1IkiSlJMcusfHbb79RoEAB9PX1qVSpEufOnUuxrqenJwqFQu2mr6+fidFmX9svPWHf1SC0lQp+/aGcTIKyOW1tbaZOncrhw4extrbm+vXrVKhQgbVr12ZIexOaOFHU2phXoVGM3HKF+Hj5fUuSpKwpWyVCmzdvZsSIEUyePJlLly7h7OxMgwYNePHiRYr7mJqaEhQUpLoFBgZmYsTJi42OZOuqEWxdNYLY6MgUyzQl8HUYk3f/t45Y/aK42JtrNB4p/dSrVw9fX1/q1q1LeHg43bp1o1u3boSFpe9VXvo6Wvz6Qzn0tJX8e+clq70D0vX4kiRJ6SVbJULz58+nd+/edO/eHScnJ5YtW4ahoSGrV69OcR+FQoGNjY3qZm2t+blsosJCaP94Ae0fLyAqLCTFMk2IiYtn6CZfwqLjqFggF/1qFtZYLFLGsLGx4dChQ0ydOhWlUsnatWtxdXXl+vXr6dpOMRsTJjR1AmDWwVvceBqcrseXJElKD9kmEYqOjubixYvUq1dPVaZUKqlXrx4+Pj4p7hcaGoqDgwP29va0aNGCGzduZEa42dav/9zD99E7TPS1md/BGS1lSnMDSdmZlpYWEydO5J9//sHOzg4/Pz9cXV1ZtWoV6Tls8MdK+anvZE1MnGD4Zl8iY+R6c5IkZS3ZJhF69eoVcXFxSc7oWFtb8+zZs2T3KVasGKtXr2b37t1s2LCB+Ph4qlSpwuPHj1NsJyoqipCQELXbt+LCgzcs+ecuANNblSafRfpfVSRlLTVr1sTX15cGDRoQGRlJr1696NKlC+/fv0+X4ysUCma2Lk0eYz3uPA9lziF5Sb0kSVlLtkmEvkTlypXp2rUrLi4u1KxZkx07dmBpacny5ctT3GfGjBmYmZmpbvb29pkYsea8j4xh2GZf4gW0LpuX5s52mg5JyiSWlpbs37+fGTNmoKWlxcaNG6lQoQJXrlxJl+PnNtZjdtvSAKw6FYD3vVfpclxJkqT0kG0SoTx58qClpcXz5+oLdz5//hwbG5tUHUNHR4eyZcty7969FOuMHTuW4OBg1e3Ro0dfFXd2MW3vTR6/jSCfhQFTWpT8/A5SjqJUKhkzZgz//vsv+fLl486dO1SqVIlly5alS1dZneLWdKqUMA/VqK1XCA6P+epjSpIkpYdskwjp6upSvnx5jh49qiqLj4/n6NGjVK5cOVXHiIuL49q1a9ja2qZYR09PD1NTU7VbTud18zlbLjxGoYB57ZwxkZfKf7OqVq2Kr68vTZs2JSoqiv79+9OxY0eCg79+oPOEJiUokNuQoOBIJu5O34HZkiRJXyrbJEIAI0aMYOXKlaxduxY/Pz/69+9PWFgY3bt3B6Br166q9ZUA1bwp9+/f59KlS/z4448EBgbSq1cvTT2ELOdNWDRjdlwDoFe1glQqlFvDEUmaljt3bvbs2cPcuXPR1tZmy5YtlCtXjosXL37VcQ11tVnQwQUtpYI9V56y2/dJOkUsSZL05bLVEhsdOnTg5cuXTJo0iWfPnuHi4sLBgwdVA6gfPnyIUvn/3O7t27f07t2bZ8+eYWFhQfny5Tl9+jROTk6aeggA6BoYsyZ3T9XvKZVlNCEEE3Zd41VoFEWsjBn5fbFMaVfK+hQKBSNHjqRq1ap07NiR+/fvU6VKFebOncugQYNQKL7sasKy+S0YVNuRRUfvMmn3Db4rlBtrUznJqSRJmiOX2PiMnLzExm7fJwzd5Iu2UsGugVUplddM0yFJWdDbt2/p0aMHu3btAqBVq1asWrUKCwuLLzpeTFw8rX8/zbUnwdQpbsUqtwpfnFhJkiSlJMcusSGlj2fBkUzclTBOY0jdIjIJklJkYWHBjh07WLRoETo6OuzcuZOyZcty9uzZLzqejpaSee2d0dVS8s+tF2y9mPJ0FpIkSRlNJkIaEBsdyb6NHuzb6KG2xMbHZRlFCMHo7VcJiYzFOZ8ZA2rJ2aOlT1MoFAwZMoTTp09TqFAhAgMDqVatGvPmzfuiq8qKWpswvH5RAKb9fZOn7yLSO2RJkqRUkYmQBkSFhdD03hSa3puitsTGx2UZZeuFx/x75yW62gnfzLW15MtASp0KFSpw6dIl2rVrR2xsLKNGjaJ58+a8fv06zcfqU6MQZfOb8z4qltHbr6brjNaSJEmpJT8BvzFBwRFM23sTgJH1i+JoZaLhiKTsxszMjM2bN/P777+jp6fH3r17cXFxwdvbO03H0VIqmNvOGT1tJSfvvuLPcw8zKGJJkqSUyUToGyKEYMz2a7yPiqVsfnN6VS+k6ZCkbEqhUNC/f3/OnDlDkSJFePz4MTVr1mTmzJnEx8f/v2J8HASchGvbEn7Gq681VtjSGPeGxQGYvs+PR2/CM/NhSJIkyUToW7L14v+7xOa0LSMXVJW+mouLCxcvXqRTp07ExcUxduxYmjRpwsuXL+HmHlhYCtY2he09E34uLJVQ/oHuVQpQsUAuwqPjGLNDdpFJkpS5ZCL0jfiwS2yE7BKT0pGJiQkbNmxg5cqV6Ovrc/DgQVxKFeff2Z0g5Kl65ZAg2NJVLRlSKhXMblsGfR0l3vdes/n8t7GsjSRJWYNMhL4BQgjG7rjG+8hYXOzN6S27xKR0plAo6NWrF+fOnaN48eI8ffGGOuvCmPZvFHHxH57h+e/3g2PUuskK5DFi1H8Tek7f50dQsLyKTJKkzCEToW/AjktPOH47oUtsbjvZJSZlnNKlS3Nh2yLcnHWIFzDpeBQNNoTzLPSDcUMICHkCgafV9u1etSAu9glXkY3bcU12kUmSlClkIqQBugbGLDFqxxKjdmpLbHxclh5evo9i6n9dYsPqFZFdYlKGM4oPwbOlAZ4t9DHUgaMBcbgsC+Po/Vj1iqHP1e5qKRXMaVsGXS0lx26/ZJdci0ySpEwgl9j4jOy+xMbAjZfYdy2IUnlN2TWgqpwzSMp4AScTBkYDN1/G0X5rBDdexqMAJtbQZVJNvYSzkm57oWD1JLv/duwecw7dxsxAhyMjamBlItcikyQp7eQSGxIHrz9j37UgtJQKZrUpI5MgKXM4VAFTO0CBk6UW53ob0ausDgKYeiKauuvCeSqsEuolo0+NQpS0MyU4IoZJu25kauiSJH175CejBsTFRHN810KO71pIXEx0imVfIzg8hom7E9YS61ezECXt5FpiUiZRakHDWf/dUWCoo2BlcwM2tjbAWBf+DYzDZclLDh3xSnZ3HS0ls9uWQVup4OCNZxy8HpR5sUuS9M2RiZAGRIa+o/aV4dS+MpzI0Hcpln2N6ftv8vJ9FIUsjRhcp8hXH0+S0sSpObRfB6a2qqJOpXW4OKIQzsUK8vJNMA0bNmTs2LHExsYm2b2knRl9ayZc3Thp9w2CI2IyLXRJkr4tMhHKgU7dfcWWC49RKGB2mzLo62hpOiTpW+TUHIZdTxgL1GYVuO2l6PQ7nPG9Sb9+/QCYOXMmtWrV4tGjpHMHDa5ThEJ5jHjxPoqZB/wyO3pJkr4RMhHKYcKjYxm78yoAXb9zoEKBXBqOSPqmKbUSBkSXbpvwU6mFvr4+S5cuZfPmzZiYmODt7Y2Liwv79u1T21VfR4sZrUsD8Ne5R/j4p31hV0mSpM+RiVAOs8jrLo/eRGBnps9P/63hJElZUfv27bl8+TLly5fnzZs3NG3alFGjRhET8/9usEqFctOpUn4Axu28RmRMXEqHkyRJ+iIyEcpBrj8J5o9TAQBMa1kKYz1tDUckSZ9WuHBhvL29GTJkCADz5s2jevXqBAYGquqMaVQca1M9Al6FsfjoXU2FKklSDiUToRwiNi6eMTuuEhcvaFLGlrolrDUdkiSlip6eHosWLWLHjh2Ym5tz9uxZXFxc2LVrFwCm+jpMa1EKgOUn7nPjabAGo5UkKaeRiVAO4Xn6AdefhGCqr83kZk6aDkeS0qxVq1ZcvnyZihUr8u7dO1q1asWwYcOIjo7m+5I2NC5tQ1y8YNyOax+tXyZJkvTlZCKkATr6hszWacxsncbo6BumWJZaj96EM+/wHQDGNS4hZ+KVsq0CBQpw8uRJRo4cCcCiRYuoWrUq9+/fx6NZSUz0tbnyOJh1Pg80G6gkSTmGXGLjM7L6EhtCCLqtOc+/d15SqWAuNvX5DoVCLqoqZX979+7Fzc2NN2/eYGpqyh9//EGUfUUm7LqOka4WR0bUxM7cQNNhSpKURcklNr4Re6485d87CSvL/9K6tEyCpByjadOm+Pr6UqVKFUJCQmjfvj2n183Cxc6QsOg4Ju+Ry29IkvT1ZCKkAXEx0Zw/spbzR9aqLbHxcdnnBIfHMO2/leUH1XaksGX6rVovSVmBvb09x48fZ8yYMQAsXbqUOyuHEf/uKUduPufg9WcajlCSpOxOJkIaEBn6joqnu1HxdDe1JTY+LvucmQdv8So0GkcrY9VyBJKU0+jo6DBjxgwOHDhAnjx58Lt+lRfrhhF281889tzgfeQnlt+Ij4OAk3BtW8LPeDkPkSRJ6uREM9nUhQdv+OvcQwCmtyyFnrZcRkPK2Ro2bIivry+dOnXixIkTRP09h8iHV5nhaMYv7Ssk3eHmHjg4GkKe/r/M1C5hQVin5pkXuCRJWZo8I5QNRcfGM27nNQA6VLCnUqHcGo5IkjJH3rx5OXr0KBMmTEChUBB65RBzB7Vj57Fz6hVv7oEtXdWTIICQoITym3syL2hJkrI0mQhlQytP3ufO81ByG+kytrFcRkP6tmhrazNt2jQOHz6MoVkuYl4+oF3Dmqzx9EyoEB+XcCaI5C6I/a/s4BjZTSZJEiAToWwn8PX/lxmY0LQE5oa6Go5IkjSjXr16nLt4CeOCLsRFR9Kje3e6d+9OmN/RpGeC1AgIeQKBpzMtVkmSsi6ZCGUjQggm7LpOVGw8VR1z09Ilr6ZDkiSNKlnYgeUbd2BWrTMolHh6euLavAc3XqTibE/o84wPUJKkLE8mQtnI3qtBnLz7Cl1tJT+3lHMGSRJAx0oFqNdpANYdf8bQPA9+95/gujKM1Zej+eR8scZyPT5JkuRVYxqho2/IZFFT9XtKZR8KiYxh6n9zBg2s5UjBPEaZFK0kZW1KpYLprUrT5NE7dH5cSIlLK7l4+l967onkn4A4ljbRx0Tvwy8NioSrxxyqaCxmSZKyDrnExmdklSU2Ju++zlqfQArlMeLAsOrycnlJ+sjMA7dY9q8/tia6NH6xlim/biROQNHcSra0NcDZRgv4LyFqv05eQi9JOZxcYiMHufr4HevOBAIwTc4ZJEnJGlq3CPksDAh6H43y+3EcXzuDvGba3HkdT6U/wlh+IRphYiuTIEmS1MhESAPi42K5cXo3N07vJj4uNsUygLh4wbid1xACWrrYUdUxj6bClqQszUBXi2ktSgGw2vsBuesNwPfOY5rUrkxUHPTbF0nHs6UJyVdLs4FKkpSlyERIAyJC3lDqSEtKHWlJRMibFMsA1vs84PqTEEz0tRnfxElTIUtStlC7uBUNS9oQF59whWWuPFbs8TrFnDlz0NbWZsvWrZQrV46LFy9qOlRJkrKINCdCbm5unDhxIiNikT7yPCSSuYfvADC6YXEsTfQ0HJEkZX2TmjlhqKvFxcC3bLnwCKVSyahRozh58iT58+fH39+fKlWq8Ouvv376qjJJkr4JaU6EgoODqVevHkWKFOGXX37hyZMnGRGXBEzbe5PQqFhc7M3pVDG/psORpGzBztyAEfWLAgkLE78Jiwbgu+++4/Lly7Ro0YLo6GiGDBlCmzZtePv2rSbDlSRJw9KcCO3atYsnT57Qv39/Nm/eTIECBWjUqBHbtm0jJuYTq0BLaXLy7kv2Xg1CqYCfW5ZCqZRzBklSanWrUoDiNia8C49hxn4/VXmuXLnYuXMnixYtQkdHh507d1KuXDnOnTv3iaNJkpSTfdEYIUtLS0aMGMGVK1c4e/Ysjo6OdOnSBTs7O4YPH87du3fTO85vSlRsPBN3XQfArUoBSuU103BEkpS9aGspmd4qYeD01ouPORfw/3F3CoWCIUOG4O3tTcGCBXnw4AFVq1Zl/vz5sqtMkr5BXzVYOigoiCNHjnDkyBG0tLRo3Lgx165dw8nJiQULFqRXjN+cVWeCePA6HGtTPdUpfkmS0qa8Qy5+qGgPwIRd14iJi1fb7urqyuXLl2nbti2xsbGMHDmSFi1a8ObNm+QOJ0lSDpXmRCgmJobt27fTtGlTHBwc2Lp1K8OGDePp06esXbsWLy8vtmzZwtSpUzMi3hxPO96WlWeCAJjY1AkTfR0NRyRJ2dfohsXJZaTLneehrD4VkGS7mZkZW7Zs4bfffkNXV5e///4bFxcXTp+WC7JK0rcizYmQra0tvXv3xsHBgXPnznHhwgX69eunNmtj7dq1MTc3T884cxQdfUNGRVdgVHQFtSU2RkZVoFzUaKLjBNWL5KFJaVsNRypJ2Zu5oS5jGxUHYKHXXZ68i0hSR6FQMGDAAM6cOYOjoyOPHj2iRo0azJ49m/j4+CT1JUnKWdK8xMb69etp164d+vr6GRVTlpKZS2zsuxrEwD8voaut5NCwGnI9MUlKB/Hxgg4rfDj/4C0NSlqzvEuFFOu+f/+evn378tdffwHQqFEj1q5di6WlZWaFK0lSOsmwJTa6dOnyzSRBmSk0Kpape28A0L9mYZkESVI6USoV/NyyNFpKBYduPOefW89TrGtiYsLGjRtZuXIl+vr6HDhwABcXFzl3miTlYHJmaQ2Ij4vlwfVTPLh+SrWcxvzDt3geEoWdsZJ+1R00HKEk5SzFbEzoWa0gAJP33CAyJi7FugqFgl69enHu3DmKFy/O06dPqV27NtOnT5ddZZKUA8lESAMiQt5QcHt1Cm6vTkTIG24+DWHt6YRFVS9HTyA+IljDEUpSzjO0bhFsTPV59CaC347d+2z90qVLc/78ebp27Up8fDwTJkygYcOGPH+e8hklSZKyH5kIaVi8EEzcfZ04AWHKU0RqXdJ0SJKUIxnpaTO5WcJ6fcv/vY//y9DP7mNsbMzatWvx9PTE0NCQI0eO4OLiwj///JPR4UqSlEmyXSL022+/UaBAAfT19alUqdJnZ4TdunUrxYsXR19fn9KlS7N///5MijR1dl17xcXAtxjoKHmru1LT4UhSjtawlA21ilkSHRfPpN3XUz2BopubG+fPn6dkyZI8e/aMevXq4eHhQVxcyl1skiRlD9kqEdq8eTMjRoxg8uTJXLp0CWdnZxo0aMCLFy+SrX/69Gl++OEHevbsyeXLl2nZsiUtW7bk+vXrmRx58pTChHnHHwMwqFpe4hSvNRyRJOVsCoWCKc1LoqutxPvea/ZeDUr1vk5OTpw7d46ePXsihGDKlCnUq1ePoKDUH0OSpKwnWyVC8+fPp3fv3nTv3h0nJyeWLVuGoaEhq1evTrb+okWLaNiwIT/99BMlSpRg2rRplCtXjiVLlmRy5Mkzj3HjXUQsxaxN6FzeStPhSNI3wSG3EQNrOQIJCxu/j0z9GomGhob88ccfbNy4EWNjY44fP46zszOHDx/OqHAlKcfT9NI22SYRio6O5uLFi9SrV09VplQqqVevHj4+Psnu4+Pjo1YfoEGDBinWB4iKiiIkJETtlhF044thEtcQgJ9blUJHK9v8KSQp2+tbsxAFchvy4n0UC46kfW3ETp06cfHiRZydnXn58iUNGzZk/PjxxMbGZkC0kpRzHbn5nG5rzhP4OkxjMWSbT99Xr14RFxeHtbW1Wrm1tTXPnj1Ldp9nz56lqT7AjBkzMDMzU93s7e2/PvhkWMT0AKBV6Ty4FsiVIW1IkpQ8fR0tprZIWJTV83QAN56m/UrNokWL4uPjQ9++fRFC8Msvv1CnTh0eP36c3uFKUo4UHh2Lx54b/HvnJZvPP9JYHNkmEcosY8eOJTg4WHV79Cj9/zjauvo0jttDPnER9/qFVWUDwksxILwU2rpywkpJymg1ilrSpLQt8QIm7rpOfHzaT88bGBiwbNkyNm3ahImJCSdPnsTFxSXLXZQhSVnRkn/u8eRdBHnNDRhUx1FjcWSbRChPnjxoaWklmcPj+fPn2NjYJLuPjY1NmuoD6OnpYWpqqnZLb3pGpqyedYpTsyZha5VHVfbbrGv8NusaekYZu5SHJEkJJjZ1wkhXi0sP37H14pd/6enQoQOXLl2iXLlyvH79miZNmuDu7k5MTOrHH0nSt+Tei/esPHkfgMnNnDDU1dZYLNkmEdLV1aV8+fIcPXpUVRYfH8/Ro0epXLlysvtUrlxZrT7AkSNHUqwvSdK3xcZMn+H1iwIw48At3oRFf/GxHB0dOX36NIMHDwZgzpw51KxZk4cPH6ZLrJKUUwghmLjrBjFxgrrFrajvZP35nTJQtkmEAEaMGMHKlStZu3Ytfn5+9O/fn7CwMLp37w5A165dGTt2rKr+0KFDOXjwIPPmzePWrVt4eHhw4cIFBg0apKmHAICIj+flQz9ePvRD/Ddlf3JlkiRlPLcqBShuY8K78BhmHbj1VcfS09Nj8eLF7NixAzMzM3x8fHBxcWH37t3pFK0kZX97rjzF5/5r9LSVeDQviUKh0Gg82SoR6tChA3PnzmXSpEm4uLjg6+vLwYMHVQOiHz58qDanR5UqVfjzzz9ZsWIFzs7ObNu2jV27dlGqVClNPQQAwoNfYbXGCas1ToQHv0qxTJKkjKejpeTnlgnvCZsvPOJi4JuvPmarVq24fPkyrq6uvH37lpYtWzJ8+HCio7/8jJMk5QQhkTFM2+sHwOA6jtjnMtRwRKAQmr6AP4sLCQnBzMyM4ODgdBsvFPb2BcaLE5K30CHPMbKwSrZMkqTM477tClsuPKa4jQl7B1dDOx2mtIiOjmbs2LHMnz8fAFdXVzZv3kzBggW/+tiSlB157LmB5+kHFLI04sDQ6uhpa2VYW6n9/M5WZ4QkSZIyyphGJTA31OHWs/d4nn6QLsfU1dVl3rx57NmzBwsLC86fP0/ZsmXZvn17uhxfkrKT60+CWefzAIBpLUplaBKUFjIRkiRJAnIZ6TKmYXEAFhy5w7PgyHQ7drNmzfD19aVKlSoEBwfTtm1bBg0aRGRk+rUhSVlZXLxg/M5rxAto5mxHVcc8mg5JRSZCkiRJ/2lfwZ5y+c0Ji45j2r6b6Xrs/Pnzc/z4cUaPHg0kLCBdpUoV7t27l67tSFJW9Ne5h1x5HIyJnjYTm5TQdDhqZCIkSZL0H6VSwc8tS6NUwL6rQZy48zJdj6+jo8PMmTM5cOAAefLk4fLly5QrV45NmzalazuSlJW8fB/F7IMJV2SO/L4oVqZZa9JgmQhJkiR9wMnOlG5VEgYzT9p9nciYuHRvo2HDhvj6+lKjRg3ev3/PDz/8QN++fYmIiEj3tiRJ02bs9yMkMpaSdqZ0qVxA0+EkIRMhDdDW1cftfWHc3hdWLaeRXJkkSZoxvH4RrE31ePA6nKXH/TOkjbx583L06FEmTJiAQqFgxYoVVKpUiVu3vm4uI0nKSnz8X7Pj8hMUCpjeqjRaSs3OGZQcefn8Z2TE5fOSJGV9+64GMfDPS+hqKTk0vAYF8xhlWFteXl78+OOPPH/+HCMjI5YuXUqXLl0yrD1JygzRsfE0XnySey9C6VwpP9Nblc7U9uXl85IkSV+hcWkbahS1JDounkm7r5OR3xnr1auHr68vderUISwsjK5du9KjRw/CwsIyrE1Jymh/nLrPvReh5DbSxb1BcU2HkyKZCGmAiI8n7O0Lwt6+UFti4+MySZI0R6FQMLV5SXS1lZy8+4q/rwZ9fqevYGNjw+HDh5kyZQpKpZI1a9ZQsWJFbty4kaHtSlJGePQmnMVH7wIwrnEJzAx1NBxRymQipAHhwa8wXmyN8WJrtSU2Pi6TJEmzCuQxYmAtRwCm7b1JSGTGriavpaXFpEmTOHr0KLa2tty8eRNXV1dWr16doWekJCk9CSGYvOcGkTHxVCqYi9bl8mo6pE+SiZAkSdIn9KtViIJ5jHj5Pop5h25nSpu1atXC19eX77//noiICHr27EnXrl0JDQ3NlPYl6WscuvGcf269QEdLwfRWpTS+qOrnyERIkiTpE/S0tZjWImFR1vVnArn6+F2mtGtlZcWBAwf45Zdf0NLSYsOGDZQvX56rV69mSvuS9CVCo2KZ8ndCd27fGoVxtDLRcESfJxMhSZKkz6hWJA/Nne2IFzB+53Xi4jOnm0qpVDJ27FiOHz9O3rx5uXPnDhUrVmT58uWyq0zKkhYeuUNQcCT5cxkyqI6jpsNJFZkISZIkpcKEpiUw0dfm2gcLR2aWatWq4evrS5MmTYiKiqJfv3788MMPhISEZGockvQpN54Gs+a/BYuntiiJvk7WWFT1c2QiJEmSlApWJvqM/m9R1nmH03dR1tTIkycPe/bsYc6cOWhra7N582bKlSvHpUuXMjUOSUpOfLxQnS1tUsaWWsWsNB1SqslESJIkKZU6VcxP2fzmhEbFMnVv5l/WrlQqGTVqFCdOnCB//vz4+/tTuXJllixZIrvKJI3689xDfB+9w1hPm0lNnTQdTprIREgDtHR0aRucl7bBedHS0U2xTJKkrEWpVPDLf8sE7L/2jGO3XmgkjsqVK3P58mVatGhBdHQ0gwcPpl27drx7904j8Ujfthchkcz6b1HVUd8XxTqLLar6OXKJjc+QS2xIkvSxX/b7seLEffJZGHBkeE0MdDUzFkIIweLFi/npp5+IiYmhYMGCbN68GVdXV43EI32bBv15ib1XgyiTz4ydA6pmmfXE5BIbkiRJGWRYvSLkNTfg8dsIFv03e64mKBQKhg4dire3NwULFiQgIICqVauyYMEC2VUmZYpjt1+w92oQSgWqs6XZjUyEJEmS0shQV5spzUsC8MfJ+9x6ptmrt1xdXbl06RJt2rQhJiaGESNG0LJlS968eaPRuKScLSI6jom7rgPQo2pBSuU103BEX0YmQhoQ9vYFiikKFFMUhL19kWKZJElZVz0naxqWtCE2XjBm+7VMm1soJebm5mzdupUlS5agq6vLnj17KFu2LD4+PhqNS8q5Fh29y+O3EdiZ6TO8flFNh/PFZCIkSZL0hTyal8RYTxvfR+/YeDZQ0+GgUCgYOHAgZ86cwdHRkYcPH1K9enXmzJlDvFzMWUpHt56F8MfJ+wBMbVEKIz1tDUf05WQiJEmS9IVszPRxb1gMgNkHb2f63EIpKVu2LBcvXqRjx47ExcXh7u5Os2bNePVKLugsfb34eMHYHdeIjRc0LGlDPSdrTYf0VWQiJEmS9BU6V3LAxT5hbiGPPZk/t1BKTE1N+fPPP1m+fDn6+vrs378fFxcXTp48qenQpGxuw9lALj9MmDNocvPsNWdQcmQiJEmS9BW0lApmtC6NtlLBwRvPOHLzuaZDUlEoFPTp04ezZ89SrFgxnjx5Qu3atfnll19kV5n0RYKCI5h98DYA7g2LYWtmoOGIvp5MhCRJkr5SCVtTetcoBMCk3dcJjYrVcETqypQpw4ULF+jSpQtxcXGMHz+ehg0b8uKFvDBDSj0hBBN33SA0KpZy+c35sZKDpkNKFzIRkiRJSgdD6xYhfy5DgoIjmXvotqbDScLY2Jh169axZs0aDA0NOXLkCM7Ozhw7dkzToUnZxMHrz/Dye46OloIZrcugzIZzBiVHJkIaoKWjS+N3ljR+Z6m2xMbHZZIkZR/6OlpMb1UKgLU+D7gY+FbDESWvW7dunD9/npIlS/Ls2TPq1avHlClTiIuL03RoUhYWHBHD5P/GwPWrWZhiNiYajij9yCU2PkMusSFJUlqM3HKF7ZceU8TKmH1DqqOrnTW/b4aHhzNkyBBWrVoFQO3atdm4cSO2trYajkzKisbtvMafZx9SKI8R+4dWR19HM8vKpIVcYkOSJEkDJjQpQR5jXe6+COX34/c0HU6KDA0N+eOPP9iwYQNGRkYcO3YMFxcXjhw5ounQpCzmXMAb/jz7EIBfWpfOFklQWshESJIkKR1ZGOkyuVnC8hu/HbvH3efvNRzRp3Xu3JmLFy9SpkwZXrx4QYMGDZgwYQKxsVlrwLekGZExcYzZfhWADhXs+a5Qbg1HlP5kIqQBYW9fYDRegdF49SU2Pi6TJCl7alrGlrrFrYiJE4zefpV4DS+/8TnFihXjzJkz9OvXDyEE06dPp3bt2jx+/FjToUkatvjoXe6/CsPKRI9xTUpoOpwMIRMhDQnXTbh9rkySpOxHoVAwrWUpjPW0ufTwHevPaH75jc8xMDBg6dKlbNq0CRMTE06dOoWLiwv79+/XdGiShlx/EszyEwnLaExrWQozAx0NR5QxZCIkSZKUAezMDRitWn7jFo/fhms4otTp0KEDly5doly5crx+/ZomTZrg7u5OTEyMpkOTMlFMXDzu264SFy9oUsaWBiVtNB1ShpGJkCRJUgbpXMkB1wIWhEXHMXbHNbLLRbqOjo6cPn2awYMHAzBnzhxq1qzJw4cPNRyZlFlWnrzPzaAQzA118PhvzFtOJRMhSZKkDKJUKpjVpgx62kpO3n3F1ovZZ8yNnp4eixcvZvv27ZiZmeHj44OLiwt79uzRdGhSBvN/GcpCr7sATGrqhKWJnoYjylgyEZIkScpAhSyNGVG/KADT9t7keUjWWKE+tVq3bs3ly5dxdXXl7du3tGjRghEjRhAdHa3p0KQMEB8vGLP9KtGx8dQsakmrsnk1HVKGk4mQJElSButZrSDO+cx4HxnL+J3Zp4ssUcGCBTl16hTDhw8HYMGCBVSrVo2AgAANRyalt7U+Dzj/4C1GugkzpSsUOWMZjU+RiZAGKLW0qfnWjJpvzVBqaadYJklSzqCtpWR2W2d0tBR4+b1gz5Wnmg4pzXR1dZk/fz67d+/GwsKC8+fPU7ZsWXbs2KHp0KR08uBVGLMO3gJgbOMS5LMw1HBEmUMusfEZcokNSfpfe3ceFlW9+HH8PcMuqyioKOJShokKIqi4lkZW16W6pUWpZZmmmUuL3l/X5VourWappWnmklreNNOictc0wGWMNDWX3PeFRQSBmd8fJDdMkRI4A/N5Pc886OHMmc+cx8f5zFm+Xykuk1b+ytvf78GvggvfD25bZq+9OHToEN27d2fTpk0ADBgwgDfffBM3t7L5fiTvlFj36T+SeOAcMXUrMbd3szI/qaqm2BARsTP92tWlfjUfLmRkM+LLn8vcKbIratasydq1a3nppZcAeP/994mJiWHvXvudUkQKN3vTbyQeOEcFVycmPFh+ZpYvChUhEZFS4uJk5o1/NsLZbOKbn0+UyVNkV7i4uDBhwgSWL19OpUqV8sceWrhwodHR5C86ePYiE+J3AzD8nlCC/R3jlNgVKkIGuHj+FAHDzAQMMxeYYuPqZSJS/oRV92XAnbcAMOLLHZwqY3eRXe3ee+/FYrHQunVr0tLS6N69O3379uXSpUtGR5MisFptvLToJy5l59KiTiXimoUYHanUqQgZ5IyHjTMethsuE5Hyp/8dtxBW3YeUS9n8qwzeRXa1GjVqsGrVKv7v//4Pk8nEhx9+SPPmzdm9e7fR0eQGZm/6jYTfT4m9/k/HOiV2hYqQiEgpc3Ey89ZD4bg6mVnxyyn+u/Wo0ZFumrOzM6+++irffvstgYGB/PTTT0RGRjJ37lyjo8l17D+dzvjf7xIb5oCnxK5QERIRMcBtVb0ZdNetAIz+agfHU8rHqaS77roLi8XCHXfcwcWLF3n88cfp3bs3GRllY641R5GTa2XIZ9vJzLbS6pbKPOaAp8SuUBESETFIn9Z1CA/2Iy0zh5f/W/ZPkV1RrVo1vv/+e0aPHo3ZbGbmzJlER0ezc+dOo6PJ7z5Yuw/L4Qt4uzs77CmxK1SEREQM4uxk5s2HGuPmbGbdntPMTSg/k5o6OTkxYsQIVq5cSdWqVdmxYwdNmzZl1qxZRkdzeD8fTcmfS2x05wYE+XkYnMhYZaYInTt3jri4OHx8fPDz86N3796kp6cX+px27dphMpkKPPr27VtKiUVEbuyWQC9e7hgKwGvLd7LvdOH/r5U17dq1Y/v27cTGxnLp0iWeeOIJevbsecP/v6VkZGbnMvSz7eRYbXRsUNUh5hK7kTJThOLi4tixYwfff/89y5YtY926dfTp0+eGz3v66ac5fvx4/uP1118vhbSFMzs50/RCBZpeqFBgio2rl4mIY+gVU4tWt1QmM9vKkIUWsnOtRkcqVoGBgXzzzTe89tprmM1mZs+eTVRUFMnJyUZHczjvfL+H3SfTqOzl6jBzid1ImZhi45dffuH2228nKSmJpk2bAhAfH8+9997LkSNHCAoKuubz2rVrR3h4OBMnTvzbr60pNkSkNBxPucTd76wjNTOHge1vzZ+xvrxZv349jzzyCEePHsXd3Z1Jkybx1FNP6QO5FPy4/yyPTP8Rmw2m92jKXbdXMTpSiSpXU2xs2rQJPz+//BIE0KFDB8xmMwkJCYU+d968eVSuXJmwsDCGDx9+wzsXsrKySE1NLfAQESlp1Xw9eO3+hgBMXr2XrYfOG5yoZLRu3RqLxcK9995LZmYmffr04dFHH9X/tSUs5VI2QxZasNng4aY1yn0J+ivKRBE6ceIEgYGBBZY5Ozvj7+/PiRMnrvu8Rx99lLlz57J69WqGDx/OnDlzeOyxxwp9rXHjxuHr65v/CA4OLpb3ICJyI50aB9E1PIhcq40hCy1czMoxOlKJqFy5Ml999RWvv/46Tk5OLFiwgMjISLZt22Z0tHLJZrPxf4uTOZaSSa1KFRjZqYHRkeyKoUVo2LBhf7qY+erHrl27/vb2+/Tpw913303Dhg2Ji4tj9uzZLF68mH379l33OcOHDyclJSX/cfjw4b/9+teTkXKGWi84U+sFZzJSzlx3mYg4ntFdwgjydee3sxm8urz83m5uNpt58cUXWb9+PcHBwezdu5fmzZszZcqUcjOMQImx5sKB9ZC8KO+nNbfQ1RdvO8qyn47jZDYxsXsEnm66DvWPDN0bQ4cOpVevXoWuU6dOHapWrcqpUwXn38rJyeHcuXNUrVq1yK/XrFkzAPbu3UvdunWvuY6bmxtubm5F3ubfYbNaOeidm//n6y0TEcfj6+HCmw83Ju6jBOYnHqb1rQHc27Ca0bFKTIsWLbBYLDzxxBMsXbqU/v37s3r1aqZPn46fn5/R8ezPzqUQ/zKk/mHCXp8g6DgBbu/8p9UPn8tgxJc7ABjc4VbCg/1KKWjZYegRoYCAAEJDQwt9uLq60qJFCy5cuMCWLVvyn7tq1SqsVmt+uSkKi8UC5A32JSJir2LqVqZf27wva8P++xNHL5SPUaevx9/fnyVLlvD222/j4uLCokWLaNKkCUlJSUZHsy87l8JnPQqWIIDU43nLdy4tsDgn18qghRbSs3KIqlWRfu1uKcWwZUeZuEaofv36dOzYkaeffprExER++OEHBgwYQPfu3fPvGDt69CihoaEkJiYCsG/fPsaMGcOWLVv47bffWLp0KT169KBNmzY0atTIyLcjInJDg++qR3iwH6mZOQxasI2ccnZL/dVMJhODBw9mw4YN1KpViwMHDtCyZUsmTpyoU2WQd/or/mXgWvvi92XxwwqcJnt/9V62HDyPt5sz73QLx8mBR48uTJkoQpB391doaCjt27fn3nvvpVWrVkybNi3/99nZ2ezevTv/rjBXV1dWrFhBbGwsoaGhDB06lAcffJCvvvrKqLcgIlJkLk5mJnWPwMvNmaTfzvPeqr1GRyoV0dHRbNu2jQceeIDs7GwGDx5M165dOXfunNHRjHVw45+PBBVgg9SjeeuRd6v8pJV5o0e/en8YNSo65oSqRVFmrpjy9/fn008/ve7va9WqVeBbQ3BwMGvXri2NaCIiJaJmpQq8dn8Yzy+w8N6qX2l5S2Wia/sbHavE+fn5sWjRIiZPnszQoUNZunQpERERLFiwgBYtWhgdzxjpJ4u83tn0LJ5fsA2rDR6KrEGXcI0eXZgyc0RIRMQRdQmvzoNNamC1waAF27iQcdnoSKXCZDIxYMAANm3aRN26dTl06BBt2rThjTfewOqIN5R4FW3cH6tnIEM/387J1CxuCfRidBfdKn8jKkIGMJnN3J7ixu0pbpjM5usuExEB+E+XBtSu7MmxlEyGfrYdq9Vxrplp0qQJW7dupVu3buTk5PDSSy/RqVMnzpxxsGFGQmLy7g7jetf5mMCnOh8dqsqa3adxczYz+dEmVHAtMyd+DFMmptgwkqbYEBF7sONYCvdP2cjlHCvD7wnlmbbXHgKkvLLZbEybNo3nn3+erKwsqlevzvz582ndurXR0UrPlbvGgIIXTeeVo/13TiU23pccq42x9zfk0WY1Sz2iPSlXU2yIiDi6BkG+jPp9RODXv91N0m+OdfGwyWTimWeeISEhgXr16nH06FHuuOMOxo4d6zinym7vDA/PBp+rhoDxCeJi1495fGMVcqw2/tGoGo9Ea1aEotIRoRvQESERsRc2m43BCy0ssRyjio8bXw9sTSWvkh0A1h6lp6fTr18/5s6dC0BsbCxz5sz501RM5ZY1N+/usPST4FUFa3ALnpq7jVW7TlHTvwLLBrbCx93F6JSG0xEhO5aRcoYGQ9xpMMS9wBQbVy8TEfkjk8nEa/c3pG6AJydTsxi00EKuA10vdIWXlxezZ89mxowZeHh48N133xEeHs6aNWuMjlY6zE5QuzU0/CfUbs2UdQdYtesUbs5mpsQ1UQn6i1SEDGCzWtnpm8VO36wCU2xcvUxE5Gqebs5MiYvE3cXM+l/P8L6DjC90NZPJxJNPPklSUhL169fn+PHjtG/fntGjR5ObW/jcW+XJ+l9P89b3ewAY0zWMsOq+Bicqe1SERETKmNuqevNq14YATFy5h9W7Tt3gGeVXgwYNSEpK4oknnsBqtTJq1ChiY2M5ceKE0dFK3NELlxg4fxs2G3SPCubhprou6O9QERIRKYP+GVmDuGY1sdlg4IJt/HbmotGRDOPp6cnMmTOZPXs2np6erFq1isaNG7NixQqjo5WYrJxcnp23lfMZ2YRV92FUZ40X9HepCImIlFEjOzUgMqQiaZk59JmzmYtZOUZHMtTjjz/O5s2badiwIadOnSI2NpZXXnmFnJzyt1/+89VOth++gK+HC1PjInF3cTI6UpmlIiQiUka5OpuZGteEQG839pxM56VFPzn8BKWhoaEkJCTQp08fbDYbr732Gu3bt+fo0aNGRys28xIOMi/hECYTTOwWTrC/5hG7GSpCIiJlWKCPO1Mfa4KLk4nlycf5cN1+oyMZzsPDgw8//JD58+fj7e3NunXrCA8PJz4+3uhoNy1h/1lGfrkDgBdib+OOUAcZMqAEqQgZwGQ2E5LmREiaU4EpNq5eJiJSFJEh/oy8Mthi/C7W7Hbci6f/qHv37mzZsoWIiAjOnDnDPffcw7Bhw8jOzjY62t9y5HwG/eZtJcdqo1PjIJ5t51iji5cUDah4AxpQUUTKApvNxvAvklmQdBgvN2e+eDaGelW8jY5lFzIzM3nhhReYPHkyADExMcyfP5+aNcvOFBQZl3N4YMpGdp1II6y6D58/E4OHq64LKowGVBQRcSAmk4n/dAmjWW1/0rNyeHJWEmfTs4yOZRfc3d15//33WbRoEb6+vmzcuJGIiAi++uoro6MVic1m44XPt7PrRBqVvdyY9nhTlaBipCIkIlJOuDqb+eCxSEIqVeDI+Uv0mbOFzGzHGVzwRh588EG2bt1KVFQU586do3PnzgwdOpTLly8bHa1Qb323h6+TT+DiZOKDx5oQ5OdhdKRyRUXIAJdSzxE12JOowZ5cSj133WUiIn9VRU9XZvSMwsfdmS0HzzP8i2SHv5Psj+rUqcOGDRsYNGgQAG+//TatWrXiwIEDxga7joVJh3h/dd7o4WPvb0jTWv4GJyp/VIQMYM3NYbNfBpv9MrDm5lx3mYjI33FLoBdT4iJxMptYvO2ow07DcT2urq688847LFmyBD8/P5KSkoiIiOCLL74wOloB6/ac5l+LfwZg4J238JBGji4RKkIiIuVQq1srM/r30Ybf+n4Pn28+bHAi+9OlSxcsFgvNmzcnJSWFBx98kOeee46sLOOvrdp5LJVn520l12rj/ojqDL6rntGRyi0VIRGRcuqx5iE807YOAMO+SGa1bqv/k5CQENatW8eLL74IwPvvv09MTAx79xp3FO1ESiZPzkoiPSuH5nX8mfBgI0wmk2F5yjsVIRGRcuzlu0N5IKI6uVYbz87diuXwBaMj2R0XFxdef/11li1bRqVKldi6dStNmjRh4cKFpZ4lJSObXh8nciI1k1sCvfjwsaa4OuujuiRp74qIlGNms4kJ/2xEm3oBXMrO5clZSRxw4AlaC3PfffdhsVho1aoVaWlpdO/enb59+3Lp0qVSef2Myzk8+UlS/m3yH/eKwreCS6m8tiNTERIRKedcnPLmJGtUw5dzFy/TY2YCp1IzjY5ll2rUqMHq1av517/+hclk4sMPP6R58+bs3r27RF/3co6VvnO3suXgeXzcnZnTO1pziJUSFSGDVL5kovIl0w2XiYgUB083Z2b2iiKkUgUOn7tE3EcJGnDxOpydnXnttdeIj48nICCAn376icjISObNm1cir5drtTH4Mwvr9pzGw8WJj5+Ipn41zWRQWjTFxg1oig0RKU8On8vgoQ82cSI1k/rVfJj/dDP8KrgaHctuHTt2jLi4ONasWQNA7969mTRpEhUqFM/RGpvNxr8W/8z8xEO4OJmY0TOKNvUCimXbjk5TbIiIyJ8E+1fg06ebUdnLjV+Op9JzZiKpmWVzEtLSEBQUxIoVKxg5ciQmk4kZM2YQHR3Nzp07b3rbNpuNMct+YX7iIcwmeLd7hEqQAVSEREQcTJ0ALz59uhn+nq5sP5LCEx8ncTHrLw7kas2FA+sheVHeT2v5ncrDycmJUaNGsWLFCqpWrcqOHTuIiopi1qxZf3ubNpuN0V/tZOYPeSNaj3ugIfc2rFZMieWvUBEywKXUc7Qb5Ee7QX4Fpti4epmISEmpV8WbOb2j86fiuDJuTZHsXAoTw+CTf8B/e+f9nBiWt7wcu/POO7FYLHTo0IGMjAyeeOIJevbsSXp6+l/ajs1mY9TSHcza+BsA4x9oSLeomiWQWIpCRcgA1twc1lZMYW3FlAJTbFy9TESkJDUI8mV272Z4uTmTcOAcj89IICXjBqfJdi6Fz3pA6rGCy1OP5y0v52WoSpUqxMfHM2bMGMxmM7NnzyYqKork5OQiPd9mszFy6Q4+2XQQkwlef7AR3aNVgoykIiQi4sDCg/2Y91QzfD1c2HboAo9M//H6d5NZcyH+ZeBa99j8vix+WLk+TQZ5p8peeeUVVq9eTVBQELt27SI6Oprp06cXOsGt1WrjlSU/M/v3EjThwUY8HKX5w4ymIiQi4uAaB/uxoE9zKnu5svN4Kg9/uIkTKdcYZ+jgxj8fCSrABqlH89ZzAG3atMFisdCxY0cyMzPp06cPcXFxpKam/mndrJxcBi7YxryEQ5hM8MY/G/OwJlG1CypCIiJC/Wo+fPZMC6r5urPv9EUe+nAjh85mFFwp/WTRNlbU9cqBgIAAli9fzoQJE3BycmL+/PlERkaybdu2/HXSs3LoPWszy346jouTiXe7R/DPyBoGppY/UhESEREg726yz55pkT/o4v1TfmDbofP/W8GrStE2VNT1ygmz2cxLL73E+vXrCQ4OZu/evbRo0YIpU6ZwOi2TR6b9yIa9Z6jg6sTMXlF0bhxkdGT5AxUhERHJF+xfgc+faUGDIB/OXrxM92k/Ev/z8bxfhsSATxBwvRHwTeBTPW89B9SiRQssFgudOnUiKyuL/v37c3vLu9m+/xj+nq4s6NOc1rdqnCB7oyJkkAqX8x43WiYiUtoCfdz57JkW3BkaSFaOlX7ztvLR+v3YTGboOOH3ta4uQ7//veN4MDuVZly74u/vz5dffslz/xqDycmZM8nrODV7ECOau9Gohp/R8eQaNMXGDWiKDRFxVDm5Vv6zbCezNx0E4LHmNRnxjwa47lmWd/fYHy+c9qmeV4Ju72xQWvtgs9mY++NBRn21k4wju0hd/iYZ547j4uLCG2+8wcCBAzGZNKdkaSjq57eK0A2oCImII7PZbMzYcIDXvv4Fmw0iQyoy+dEmVPV2ybs7LP1k3jVBITEOfSQI8maQH/XVDj5NOARA1/AghrWvSf++ffjiiy/ylnXtysyZM6lYsaKRUR2CilAxURESEYEVO08y+DMLaZk5VPZyZdIjEcTUrWx0LLtxIiWTgfO3kfjbOUwmeLljKM+0qYPJZMJmszF58mSGDh3K5cuXCQkJYcGCBTRv3tzo2OWaJl21Y5npF7hvcCD3DQ4kM/3CdZeJiNiLDrdXYdlzrahfzYcz6Zd57KMEpq7ZV+gAgo5ixc6T3PPuOhJ/O4eXmzMzejalb9u6+afATCYTAwYMYOPGjdStW5eDBw/SunVr3nzzTaxWq8HpRUXIALnZl/na7zRf+50mN/vydZeJiNiTkEqefNEvhgeb1MBqgwnxu+j1cRInU68x+KIDyMzOZdTSHTw1ezPnM7JpEOTD0gEtuTP02sMHREZGsnXrVh5++GFycnJ48cUX6dy5M2fPni3l5PJHKkIiIlJkHq5OvPlQI8be3xBXZzNr95wm9p11fGk56lBHh349mcYDUzbmT5zau1Vtvng2hjoBXoU+z8fHhwULFvDBBx/g5ubG8uXLCQ8PZ8OGDaWQWq5FRUhERP4Sk8nEo81q8vXAVjSq4UvKpWyeX2Dh2XlbC85TZs2FA+sheVHez3IwB1lmdi7vfPcLo977kFtOxhNbYQ8f92jCv/9xO27ORbtY3GQy8cwzz5CQkEC9evU4cuQI7dq1Y9y4cTpVZgBdLH0DJXGx9MXzp/CalHfoNH3gSTwrBl5zmYiIvcvOtTJ1zT4mrfyVHKuNSp6uvNwxlH9W2Ir522FX3WIflDcOURm9xT5h/1m+/mwaz1yaRpDp3P9+cRPvKy0tjWeffZa5c+cCEBsby5w5cwgM1GfAzdLF0iIiUuJcnMwMbH8rS/q3pF4VL85evMzKxR9h+rwHtqsnaE09Dp/1gJ1LjQn7N51Ky+TlRT8x86NJjLw0nqp/LEFwU+/L29ub2bNnM2PGDDw8PPjuu+8IDw9nzZo1xRNebkhFSEREblpYdV+WPdeaV+6px2iXOdhs15qI4/cTEPHDysRpsrTMbN76bjdtX1/D55sPMtJlNibTtT44b+59mUwmnnzySRITE6lfvz7Hjx+nffv2/Oc//yE31/73U1mnIiQiIsXC1dnMUzVPUNV0FvN1B0+2QerRvMEY7VRWTi4zNhyg7RtreG/VXi5l5/JIlSMEmc5dd5a14nhfYWFhJCUl0atXL6xWKyNHjiQ2NpYTJ0787W3KjTkbHcAReVYMxDbSdsNlIiJlTvrJ4l2vFJ1Jz2Lej4eY8+NBzvx+0XedAE9euvs27rZmwBdF2MhNvi9PT08+/vhj7rjjDvr168eqVato3Lgx8+bNo0OHDje1bbk2HRESEZHi43XtMXSuNnb9eZb/dJysHONP/ew4lsKLn28nZtwq3lmxhzPpWVTzdWf8Aw35blAbOoZVw+RdtWgbK+L7v5EePXqwZcsWwsLCOHXqFLGxsfz73/8mJyenWLYv/1NmitBrr71GTEwMFSpUwM/Pr0jPsdlsjBgxgmrVquHh4UGHDh349ddfSzaoiIgjC4nJu4vqOieRrMAxWyU+OlSV/p9upcW4VYxZtpOfjlzAai29o+J7T6Xz7opfiX1nLfdN2sDnW45wOddK42A/Jj0SwbqX7qB7dE2cncxFel9gypt4NiSm2DKGhoaSmJjI008/jc1m49VXX6V9+/YcPXq02F5DytDt8yNHjsTPz48jR44wY8YMLly4cMPnTJgwgXHjxvHJJ59Qu3Zt/v3vf5OcnMzOnTtxd3cv0utqrjERkb9o59K8u6iA/AuJgSsl4tQ905l1viGLthzhVNr/xh2q7OVKm1sDaHtbAG1uDaCip2uxRUq5lM3Wg+dJ+u0cq3adYteJtPzfuTiZiG1Qld6tatOkZiGTod7gffHw7BIbGmD+/Pn06dOH9PR0KleuzJw5c+jYsWOJvFZ5UW4nXZ01axaDBg26YRGy2WwEBQUxdOhQXnjhBQBSUlKoUqUKs2bNonv37kV6PRUhEZG/YedSiH/5qnGEqkPH8fllISfXyto9p/nv1iOs23OG9KyCp31qVPSgfjUf6lf1pn41H2pWqoC/pysVK7ji7nLtwQtTM7M5fC6Dw+cuceR8BvvPXGTrwfPsPpnGHz/tnM0mWt9amfsaBXHX7VXw9XAptvdVUvbs2UO3bt2wWCwAvPzyy4wZMwYXlyJmdzAOX4T2799P3bp12bZtG+Hh4fnL27ZtS3h4OO++++41n5eVlUVW1v++oaSmphIcHKwiJCLyV1lz8+6iSj+Zd+1MSAyYr11gLudY2XLwPGv2nGLt7tMFjthcSwVXJ3w9XMi12ricayU7x5r3M/f6H2m1K3sSGVKRZrX9uev2KvhV+JtHnP7C+ypumZmZDB06lClTpgAQExPDggULCA4OLpXXL0uKWoTK7V1jV243rFKl4IVrVapUKfRWxHHjxjF69OgSzSYi4hDMTlC7dZFWdXU206JuJVrUrcTwe+pz/uJlfjmRyq7jafxyPJVdJ9I4kZrJ+YuXybHayLicS8bla19oXcnTlRr+FQiu6EFN/wo0quFLZIg/Ad5upf6+ipu7uzuTJ0+mXbt2PPXUU2zcuJHw8HBmzZpFp06dDMlU1hlahIYNG8aECRMKXeeXX34hNDS0lBLB8OHDGTJkSP7frxwREhGR0lPR05WYupWJqVu5wHKbzUZaVg7nL14m5VI2TmYTbs5mXJzMuDqb8XF3wdOt3H7Hz/fQQw8RGRlJt27d2Lx5M507d2bIkCGMGzcOV9fiu7bKERj6r2Xo0KH06tWr0HXq1Knzt7ZdtWrerY4nT56kWrVq+ctPnjxZ4FTZ1dzc3HBzK6ZvDSIiUqxMJhM+7i74uOu6mDp16rBhwwaGDRvGxIkTefvtt9mwYQMLFy6kVq1aRscrMwwtQgEBAQQEBJTItmvXrk3VqlVZuXJlfvFJTU0lISGBfv36lchrioiIlCY3Nzfeeecd2rVrR69evUhMTCQiIoKZM2dy//33Gx2vTCgz4wgdOnQIi8XCoUOHyM3NxWKxYLFYSE9Pz18nNDSUxYsXA3nfGgYNGsSrr77K0qVLSU5OpkePHgQFBdG1a1eD3oWIiEjx69KlCxaLhebNm3PhwgUeeOABBg4cWODmH7m2MlOERowYQUREBCNHjiQ9PZ2IiAgiIiLYvHlz/jq7d+8mJSUl/+8vvfQSzz33HH369CEqKor09HTi4+OLPIaQiIhIWRESEsK6det48cUXAXjvvfdo2bIl+/btMziZfStzt8+XNo0jJCIiZc3y5cvp2bMnZ8+exdvbm48++oiHH37Y6Filqqif32XmiJCIiIgUzX333YfFYqFVq1akpaXRrVs3+vXrR2ZmptHR7I6KkIiISDlUo0YNVq9ezfDhwwH44IMPaN68Obt37zY4mX1RERIRESmnnJ2dGTt2LPHx8QQEBLB9+3YiIyOZN2+e0dHshoqQiIhIOXf33XdjsVho164dFy9e5LHHHuOpp54iIyPD6GiGUxESERFxAEFBQaxYsYIRI0ZgMpmYMWMG0dHR7Ny50+hohlIREhERcRBOTk6MHj2aFStWUKVKFXbs2EFUVBSffPKJ0dEMoyIkIiLiYO688062b99Ohw4dyMjIoFevXvTs2bPAIMWOQkVIRETEAVWpUoX4+HjGjBmD2Wxm9uzZREVFkZycbHS0UqUiJCIi4qCcnJx45ZVXWLVqFUFBQezatYvo6GimT5+Oo4y3rCIkIiLi4Nq2bYvFYqFjx45kZmbSp08f4uLiSEtLMzpaiVMREhEREQICAli+fDnjx4/HycmJ+fPnExkZicViMTpaiVIREhEREQDMZjMvv/wya9eupUaNGvz66680b96cqVOnlttTZSpCIiIiUkDLli2xWCz84x//ICsri2effZZu3bqRkpJidLRipyIkIiIif1KpUiWWLl3Km2++ibOzM59//jlNmjRh8+bNRkcrVipCIiIick0mk4mhQ4eyYcMGQkJC2L9/PzExMUyaNKncnCpTERIREZFCNWvWjG3bttG1a1eys7N5/vnneeCBBzh//rzR0W6aipCIiIjcUMWKFfniiy949913cXFxYcmSJURERJCQkGB0tJuiIiQiIiJFYjKZGDhwIJs2baJOnTocPHiQVq1a8dZbb5XZU2UqQiIiIvKXREZGsnXrVh566CFycnJ44YUX6Ny5M2fPnjU62l+mIiQiIiJ/ma+vLwsXLmTq1Km4ubmxbNkywsPD+eGHH4yO9peoCImIiMjfYjKZ6Nu3LwkJCdSrV48jR47Qtm1bxo8fj9VqNTpekagIiYiIyE1p3LgxmzdvJi4ujtzcXIYPH859993H6dOnjY52QypCIiIictO8vb2ZM2cOH330ER4eHsTHxxMeHs7atWuNjlYoFSEREREpFiaTid69e5OYmEj9+vU5duwYd955J2PGjCE3N9foeNekIiQiIiLFKiwsjKSkJHr16oXVamXEiBHcfffdnDhxwuhof6IiJCIiIsXO09OTjz/+mE8++YQKFSqwcuVKGjduzIoVK4yOVoCKkIiIiJSYHj16sHnzZsLCwjh16hSxsbGMGDGCnJwco6MBKkIiIiJSwurXr09iYiJPPfUUNpuNMWPG0L59e44dO2Z0NBUhERERKXkeHh5Mnz6defPm4eXlxbp162jcuDHffvutoblUhERERKTUPProo2zZsoXw8HDOnDlDx44defPNNw3LoyIkIiIipapevXps2rSJZ599FicnJ6Kjow3LYrKV1eliS0lqaiq+vr6kpKTg4+NjdBwREZFyZffu3dx2223Fvt2ifn7riJCIiIgYpiRK0F+hIiQiIiIOS0VIREREHJaKkIiIiDgsFSERERFxWCpCIiIi4rBUhERERMRhqQiJiIiIw1IREhEREYelIiQiIiIOS0VIREREHJaKkIiIiDgsFSERERFxWCpCIiIi4rCcjQ5g72w2GwCpqakGJxEREZGiuvK5feVz/HpUhG4gLS0NgODgYIOTiIiIyF+VlpaGr6/vdX9vst2oKjk4q9XKsWPH8Pb2xmQyFdt2U1NTCQ4O5vDhw/j4+BTbdssL7Z/Caf8UTvuncNo/16d9U7iytH9sNhtpaWkEBQVhNl//SiAdEboBs9lMjRo1Smz7Pj4+dv+PyUjaP4XT/imc9k/htH+uT/umcGVl/xR2JOgKXSwtIiIiDktFSERERByWipBB3NzcGDlyJG5ubkZHsUvaP4XT/imc9k/htH+uT/umcOVx/+hiaREREXFYOiIkIiIiDktFSERERByWipCIiIg4LBUhERERcVgqQgaZPHkytWrVwt3dnWbNmpGYmGh0JLuwbt06OnXqRFBQECaTiSVLlhgdya6MGzeOqKgovL29CQwMpGvXruzevdvoWHZh6tSpNGrUKH+gtxYtWvDNN98YHctujR8/HpPJxKBBg4yOYhdGjRqFyWQq8AgNDTU6ll05evQojz32GJUqVcLDw4OGDRuyefNmo2PdNBUhAyxcuJAhQ4YwcuRItm7dSuPGjbn77rs5deqU0dEMd/HiRRo3bszkyZONjmKX1q5dS//+/fnxxx/5/vvvyc7OJjY2losXLxodzXA1atRg/PjxbNmyhc2bN3PnnXfSpUsXduzYYXQ0u5OUlMSHH35Io0aNjI5iVxo0aMDx48fzHxs2bDA6kt04f/48LVu2xMXFhW+++YadO3fy1ltvUbFiRaOj3TTdPm+AZs2aERUVxfvvvw/kzWcWHBzMc889x7BhwwxOZz9MJhOLFy+ma9euRkexW6dPnyYwMJC1a9fSpk0bo+PYHX9/f9544w169+5tdBS7kZ6eTpMmTZgyZQqvvvoq4eHhTJw40ehYhhs1ahRLlizBYrEYHcUuDRs2jB9++IH169cbHaXY6YhQKbt8+TJbtmyhQ4cO+cvMZjMdOnRg06ZNBiaTsiglJQXI+8CX/8nNzWXBggVcvHiRFi1aGB3HrvTv35/77ruvwP9BkufXX38lKCiIOnXqEBcXx6FDh4yOZDeWLl1K06ZNeeihhwgMDCQiIoLp06cbHatYqAiVsjNnzpCbm0uVKlUKLK9SpQonTpwwKJWURVarlUGDBtGyZUvCwsKMjmMXkpOT8fLyws3Njb59+7J48WJuv/12o2PZjQULFrB161bGjRtndBS706xZM2bNmkV8fDxTp07lwIEDtG7dmrS0NKOj2YX9+/czdepUbr31Vr799lv69evHwIED+eSTT4yOdtM0+7xIGdW/f39+/vlnXcfwB7fddhsWi4WUlBQWLVpEz549Wbt2rcoQcPjwYZ5//nm+//573N3djY5jd+655578Pzdq1IhmzZoREhLCZ599plOr5H3xatq0KWPHjgUgIiKCn3/+mQ8++ICePXsanO7m6IhQKatcuTJOTk6cPHmywPKTJ09StWpVg1JJWTNgwACWLVvG6tWrqVGjhtFx7Iarqyu33HILkZGRjBs3jsaNG/Puu+8aHcsubNmyhVOnTtGkSROcnZ1xdnZm7dq1TJo0CWdnZ3Jzc42OaFf8/PyoV68ee/fuNTqKXahWrdqfvlDUr1+/XJw+VBEqZa6urkRGRrJy5cr8ZVarlZUrV+paBrkhm83GgAEDWLx4MatWraJ27dpGR7JrVquVrKwso2PYhfbt25OcnIzFYsl/NG3alLi4OCwWC05OTkZHtCvp6ens27ePatWqGR3FLrRs2fJPQ3Xs2bOHkJAQgxIVH50aM8CQIUPo2bMnTZs2JTo6mokTJ3Lx4kWeeOIJo6MZLj09vcA3sAMHDmCxWPD396dmzZoGJrMP/fv359NPP+XLL7/E29s7/7oyX19fPDw8DE5nrOHDh3PPPfdQs2ZN0tLS+PTTT1mzZg3ffvut0dHsgre395+uJfP09KRSpUq6xgx44YUX6NSpEyEhIRw7doyRI0fi5OTEI488YnQ0uzB48GBiYmIYO3YsDz/8MImJiUybNo1p06YZHe3m2cQQ7733nq1mzZo2V1dXW3R0tO3HH380OpJdWL16tQ3406Nnz55GR7ML19o3gO3jjz82OprhnnzySVtISIjN1dXVFhAQYGvfvr3tu+++MzqWXWvbtq3t+eefNzqGXejWrZutWrVqNldXV1v16tVt3bp1s+3du9foWHblq6++soWFhdnc3NxsoaGhtmnTphkdqVhoHCERERFxWLpGSERERByWipCIiIg4LBUhERERcVgqQiIiIuKwVIRERETEYakIiYiIiMNSERIRERGHpSIkIiIiDktFSERERByWipCIiIg4LBUhEXEop0+fpmrVqowdOzZ/2caNG3F1dWXlypUGJhMRI2iuMRFxOF9//TVdu3Zl48aN3HbbbYSHh9OlSxfefvtto6OJSClTERIRh9S/f39WrFhB06ZNSU5OJikpCTc3N6NjiUgpUxESEYd06dIlwsLCOHz4MFu2bKFhw4ZGRxIRA+gaIRFxSPv27ePYsWNYrVZ+++03o+OIiEF0REhEHM7ly5eJjo4mPDyc2267jYkTJ5KcnExgYKDR0USklKkIiYjDefHFF1m0aBHbt2/Hy8uLtm3b4uvry7Jly4yOJiKlTKfGRMShrFmzhokTJzJnzhx8fHwwm83MmTOH9evXM3XqVKPjiUgp0xEhERERcVg6IiQiIiIOS0VIREREHJaKkIiIiDgsFSERERFxWCpCIiIi4rBUhERERMRhqQiJiIiIw1IREhEREYelIiQiIiIOS0VIREREHJaKkIiIiDgsFSERERFxWP8PNdVqHCwIQykAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -909,135 +906,6 @@ "plt.legend()" ] }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Usage: Pipelines\n", - "\n", - "Experimentalists can be connected in a **[pipeline](https://autoresearch.github.io/autora/core/docs/pipeline/Experimentalist%20Pipeline%20Examples/)**, where each element passes its output to the next element, ensuring compatibility between the inputs and outputs. Pipelines offer a flexible and efficient way to orchestrate the workflow involving complex experimentalists (e.g., for processing of experimental conditions) and experiment runners (e.g., for preprocessing of collected observations). They allow for the integration of poolers, samplers, and other design manipulations into a cohesive stream of experimental conditions.\n", - "\n", - "Let's examine the following pipeline example:\n", - "\n", - "
    \n", - "
  1. Generate a grid of all possible experimental conditions.\n", - "
  2. Filter out conditions where the independent variable falls within the range -1 to 1.\n", - "
  3. Sample 10 conditions using the novelty sampler.\n", - "
  4. Select 5 conditions from the sampled set using the falsification sampler.\n", - "
\n", - "\n", - "Before creating the pipeline, let's define an additional function that removes experiment conditions falling within the range of -1 to 1, specifically $-1 \\leq x \\leq 1$. This function will be used in the second step of the pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Iterable\n", - "\n", - "def condition_exclusion(conditions):\n", - " # first we need to make sure that conditions is a 2-dimensional numpy array\n", - " if isinstance(conditions, Iterable):\n", - " conditions = np.array(list(conditions))\n", - "\n", - " if conditions.ndim == 1:\n", - " conditions = conditions.reshape(-1, 1)\n", - "\n", - " # now we can sub-select conditions\n", - " conditions_to_keep = conditions[(-1 > conditions) | (conditions > 1)]\n", - " conditions_to_keep = conditions_to_keep.reshape(-1, 1)\n", - " return conditions_to_keep" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A pipeline can be defined as a list of functions, such as ``[grid_pool, value_exclusion, novelty_sample, falsification_sample]``. However, to create a pipeline object, we need to specify the required parameters for each element in the pipeline. We can achieve this by providing nested dictionaries containing the additional parameters, as shown in the code block below.\n", - "\n", - "**Note**: *Each element of the pipeline passes its output to the next element as the first argument of the element's function. Thus, we need to make sure that the output of one pipeline element is compatible with the required first input argument for the next element. In our case, the first argument for each pipeline element (except for poolers) is assumed to be a 2-dimensional numpy array specifying a set of experimental conditions.*\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.experimentalist.pipeline import make_pipeline\n", - "\n", - "experimentalist_pipeline = make_pipeline([grid_pool,\n", - " condition_exclusion,\n", - " novelty_sample,\n", - " falsification_sample],\n", - " params={\"grid_pool\":\n", - " {\"ivs\": metadata.independent_variables},\n", - " \"novelty_sample\":\n", - " {\"reference_conditions\": initial_conditions,\n", - " \"num_samples\": 10},\n", - " \"falsification_sample\":\n", - " {\"model\": theorist_bms,\n", - " \"reference_conditions\": initial_conditions,\n", - " \"reference_observations\": initial_observations,\n", - " \"metadata\": metadata,\n", - " \"num_samples\": 5}})" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the declaration of the ``params`` parameter, we first specify the name of the pipeline object we seek to parameterize as a dictionary key, e.g., ``\"grid_pool\"``, and then nest within it, another dictionary with the names of the input arguments as keys (e.g., ``\"ivs\"``) along with their values (e.g., ``metadata.independent_variables``).\n", - "\n", - "Once specified, we can run the pipeline object to obtain novel experimental conditions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[6.28318531]\n", - " [6.21971879]\n", - " [6.15625227]\n", - " [6.09278575]\n", - " [3.55412502]]\n" - ] - } - ], - "source": [ - "new_conditions = experimentalist_pipeline.run()\n", - "\n", - "print(new_conditions)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Hint**: *A common error for running pipelines is that the output of one pipeline element is incompatible with the input of the next pipeline element (e.g., not providing a 2-dimensional numpy array to ``novelty_sample``). In such cases, it can be helpful to \"manually\" pass the inputs from one element to another element, to check if they are compatible.*\n", - "\n", - "**Note**: *Pipelines may be used for other purposes, such as linking an experiment runner with multiple pre-processing steps.*" - ] - }, { "attachments": {}, "cell_type": "markdown", From 4d273b105b3a5c360682703567db8f86ef4e647e Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Fri, 18 Aug 2023 09:52:43 -0700 Subject: [PATCH 12/32] Updated Tutorial II with latest autora version --- .../basic/Tutorial-II-Loop-Constructs.ipynb | 101 +++++++++++++----- 1 file changed, 73 insertions(+), 28 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb b/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb index f881a148c..beefa85fc 100644 --- a/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb +++ b/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb @@ -41,7 +41,23 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n", + "\n", + "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n", + "\n", + "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n" + ] + } + ], "source": [ "#### Installation ####\n", "!pip install -q \"autora[experimentalist-falsification]\"\n", @@ -90,11 +106,11 @@ "\n", "The following code block demonstrates how to build such a workflow using the components introduced in the preceding notebook, such as\n", "\n", - "- ``metadata`` (object specifying variables of the experiment),
\n", - "- ``run_experiment`` (function for collecting data),
\n", - "- ``theorist_bms`` (scikit learn estimator for discoverying equations using the Bayesian Machine Scientist),
\n", - "- ``random_pool`` (function for generating a random pool of experimental conditions), and
\n", - "- ``falsification_sample`` (function for identifying novel experiment conditions using the falsification .sampler)
\n", + "- ``metadata`` (object specifying variables of the experiment)
\n", + "- ``run_experiment`` (function for collecting data)
\n", + "- ``theorist_bms`` (scikit learn estimator for discovering equations using the Bayesian Machine Scientist)
\n", + "- ``random_pool`` (function for generating a random pool of experimental conditions)
\n", + "- ``falsification_sample`` (function for identifying novel experiment conditions using the falsification sampler)
\n", "\n", "We begin with implementing the following workflow:\n", "1. Generate 3 seed experimental conditions using ``random_pool``\n", @@ -260,7 +276,7 @@ "metadata": {}, "source": [ "## Example 2: Model Disagreement Sampler\n", - "We can easily replace components in the workflow above. For instance, we could replace ``falsification_sample`` with the ``experimentalist_pipeline`` defined in Tutorial I.\n", + "We can easily replace components in the workflow above.\n", "\n", "In the following code block, we add a linear regression theorist, to fit a linear model to the data. In addition, we replace ``falsification_sample`` with ``model_disagreement_sample`` to sample experimental conditions that differentiate most between the linear model and the model discovered by the BMS theorist." ] @@ -275,7 +291,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 23.59it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 23.84it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", "INFO:autora.theorist.bms.regressor:BMS fitting started\n" ] @@ -292,7 +308,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:03<00:00, 26.18it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 27.23it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", "INFO:autora.theorist.bms.regressor:BMS fitting started\n" ] @@ -306,25 +322,53 @@ ] }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[6], line 16\u001b[0m\n\u001b[0;32m 11\u001b[0m observations \u001b[39m=\u001b[39m run_experiment(conditions)\n\u001b[0;32m 13\u001b[0m \u001b[39mfor\u001b[39;00m cycle \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(num_cycles):\n\u001b[0;32m 14\u001b[0m \n\u001b[0;32m 15\u001b[0m \u001b[39m# use BMS theorist to fit the model to the data\u001b[39;00m\n\u001b[1;32m---> 16\u001b[0m theorist_bms\u001b[39m.\u001b[39;49mfit(conditions, observations)\n\u001b[0;32m 17\u001b[0m theorist_lr\u001b[39m.\u001b[39mfit(conditions, observations)\n\u001b[0;32m 19\u001b[0m \u001b[39m# obtain new conditions\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\theorist\\bms\\regressor.py:133\u001b[0m, in \u001b[0;36mBMSRegressor.fit\u001b[1;34m(self, X, y, num_param, root, custom_ops, seed)\u001b[0m\n\u001b[0;32m 120\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madd_primitive(root)\n\u001b[0;32m 121\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpms \u001b[39m=\u001b[39m Parallel(\n\u001b[0;32m 122\u001b[0m Ts\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mts,\n\u001b[0;32m 123\u001b[0m variables\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvariables,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 131\u001b[0m seed\u001b[39m=\u001b[39mseed,\n\u001b[0;32m 132\u001b[0m )\n\u001b[1;32m--> 133\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel_, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mloss_, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcache_ \u001b[39m=\u001b[39m utils\u001b[39m.\u001b[39;49mrun(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpms, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mepochs)\n\u001b[0;32m 134\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodels_ \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpms\u001b[39m.\u001b[39mtrees\u001b[39m.\u001b[39mvalues())\n\u001b[0;32m 136\u001b[0m _logger\u001b[39m.\u001b[39minfo(\u001b[39m\"\u001b[39m\u001b[39mBMS fitting finished\u001b[39m\u001b[39m\"\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\theorist\\bms\\utils.py:34\u001b[0m, in \u001b[0;36mrun\u001b[1;34m(pms, num_steps, thinning)\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 21\u001b[0m \n\u001b[0;32m 22\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 31\u001b[0m \n\u001b[0;32m 32\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 33\u001b[0m desc_len, model, model_len \u001b[39m=\u001b[39m [], pms\u001b[39m.\u001b[39mt1, np\u001b[39m.\u001b[39minf\n\u001b[1;32m---> 34\u001b[0m \u001b[39mfor\u001b[39;00m n \u001b[39min\u001b[39;00m tqdm(\u001b[39mrange\u001b[39;49m(num_steps)):\n\u001b[0;32m 35\u001b[0m pms\u001b[39m.\u001b[39mmcmc_step()\n\u001b[0;32m 36\u001b[0m pms\u001b[39m.\u001b[39mtree_swap()\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\tqdm\\std.py:1093\u001b[0m, in \u001b[0;36mtqdm.__init__\u001b[1;34m(self, iterable, desc, total, leave, file, ncols, mininterval, maxinterval, miniters, ascii, disable, unit, unit_scale, dynamic_ncols, smoothing, bar_format, initial, position, postfix, unit_divisor, write_bytes, lock_args, nrows, colour, delay, gui, **kwargs)\u001b[0m\n\u001b[0;32m 1089\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpos \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_free_pos(\u001b[39mself\u001b[39m) \u001b[39mif\u001b[39;00m position \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m \u001b[39m-\u001b[39mposition\n\u001b[0;32m 1091\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m gui:\n\u001b[0;32m 1092\u001b[0m \u001b[39m# Initialize the screen printer\u001b[39;00m\n\u001b[1;32m-> 1093\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msp \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstatus_printer(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfp)\n\u001b[0;32m 1094\u001b[0m \u001b[39mif\u001b[39;00m delay \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m 1095\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrefresh(lock_args\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlock_args)\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\tqdm\\std.py:336\u001b[0m, in \u001b[0;36mtqdm.status_printer\u001b[1;34m(file)\u001b[0m\n\u001b[0;32m 334\u001b[0m fp_flush \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(fp, \u001b[39m'\u001b[39m\u001b[39mflush\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mlambda\u001b[39;00m: \u001b[39mNone\u001b[39;00m) \u001b[39m# pragma: no cover\u001b[39;00m\n\u001b[0;32m 335\u001b[0m \u001b[39mif\u001b[39;00m fp \u001b[39min\u001b[39;00m (sys\u001b[39m.\u001b[39mstderr, sys\u001b[39m.\u001b[39mstdout):\n\u001b[1;32m--> 336\u001b[0m \u001b[39mgetattr\u001b[39;49m(sys\u001b[39m.\u001b[39;49mstderr, \u001b[39m'\u001b[39;49m\u001b[39mflush\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39mlambda\u001b[39;49;00m: \u001b[39mNone\u001b[39;49;00m)()\n\u001b[0;32m 337\u001b[0m \u001b[39mgetattr\u001b[39m(sys\u001b[39m.\u001b[39mstdout, \u001b[39m'\u001b[39m\u001b[39mflush\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mlambda\u001b[39;00m: \u001b[39mNone\u001b[39;00m)()\n\u001b[0;32m 339\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfp_write\u001b[39m(s):\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\ipykernel\\iostream.py:559\u001b[0m, in \u001b[0;36mOutStream.flush\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 548\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"trigger actual zmq send\u001b[39;00m\n\u001b[0;32m 549\u001b[0m \n\u001b[0;32m 550\u001b[0m \u001b[39msend will happen in the background thread\u001b[39;00m\n\u001b[0;32m 551\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 552\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[0;32m 553\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpub_thread\n\u001b[0;32m 554\u001b[0m \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpub_thread\u001b[39m.\u001b[39mthread \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 557\u001b[0m ):\n\u001b[0;32m 558\u001b[0m \u001b[39m# request flush on the background thread\u001b[39;00m\n\u001b[1;32m--> 559\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpub_thread\u001b[39m.\u001b[39;49mschedule(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_flush)\n\u001b[0;32m 560\u001b[0m \u001b[39m# wait for flush to actually get through, if we can.\u001b[39;00m\n\u001b[0;32m 561\u001b[0m evt \u001b[39m=\u001b[39m threading\u001b[39m.\u001b[39mEvent()\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\ipykernel\\iostream.py:251\u001b[0m, in \u001b[0;36mIOPubThread.schedule\u001b[1;34m(self, f)\u001b[0m\n\u001b[0;32m 249\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_events\u001b[39m.\u001b[39mappend(f)\n\u001b[0;32m 250\u001b[0m \u001b[39m# wake event thread (message content is ignored)\u001b[39;00m\n\u001b[1;32m--> 251\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_event_pipe\u001b[39m.\u001b[39;49msend(\u001b[39mb\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m 252\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 253\u001b[0m f()\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\zmq\\sugar\\socket.py:696\u001b[0m, in \u001b[0;36mSocket.send\u001b[1;34m(self, data, flags, copy, track, routing_id, group)\u001b[0m\n\u001b[0;32m 689\u001b[0m data \u001b[39m=\u001b[39m zmq\u001b[39m.\u001b[39mFrame(\n\u001b[0;32m 690\u001b[0m data,\n\u001b[0;32m 691\u001b[0m track\u001b[39m=\u001b[39mtrack,\n\u001b[0;32m 692\u001b[0m copy\u001b[39m=\u001b[39mcopy \u001b[39mor\u001b[39;00m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m 693\u001b[0m copy_threshold\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcopy_threshold,\n\u001b[0;32m 694\u001b[0m )\n\u001b[0;32m 695\u001b[0m data\u001b[39m.\u001b[39mgroup \u001b[39m=\u001b[39m group\n\u001b[1;32m--> 696\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49msend(data, flags\u001b[39m=\u001b[39;49mflags, copy\u001b[39m=\u001b[39;49mcopy, track\u001b[39m=\u001b[39;49mtrack)\n", - "File \u001b[1;32mzmq\\backend\\cython\\socket.pyx:742\u001b[0m, in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mzmq\\backend\\cython\\socket.pyx:789\u001b[0m, in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mzmq\\backend\\cython\\socket.pyx:250\u001b[0m, in \u001b[0;36mzmq.backend.cython.socket._send_copy\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\zmq\\backend\\cython\\checkrc.pxd:13\u001b[0m, in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:04<00:00, 23.79it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss in cycle 2: 0.0\n", + "Discovered BMS Model: sin(X0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:03<00:00, 26.81it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss in cycle 3: 0.49765210053720216\n", + "Discovered BMS Model: -0.05\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:03<00:00, 26.17it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss in cycle 4: 0.0009571292126012284\n", + "Discovered BMS Model: (0.96 * sin(X0))\n" ] } ], @@ -335,6 +379,7 @@ "# generate an initial set of experimental conditions\n", "conditions = random_pool(metadata.independent_variables,\n", " num_samples=measurements_per_cycle)\n", + "\n", "# convert iterator into 2-dimensional numpy array\n", "conditions = np.array(list(conditions)).reshape(-1, 1)\n", "\n", From a7d4a02805b8ee456d3b4c649a8e4b618a5a3c3c Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Fri, 18 Aug 2023 14:50:02 -0700 Subject: [PATCH 13/32] Added Tutorial III with the new state functionality --- .../Tutorial-III-Functional-Workflow.ipynb | 974 ++++++++++++++++++ 1 file changed, 974 insertions(+) create mode 100644 docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb diff --git a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb new file mode 100644 index 000000000..521982107 --- /dev/null +++ b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb @@ -0,0 +1,974 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction\n", + "## Basic Tutorial III: Functional Workflow" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", + "\n", + "This notebook is the third of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", + "\n", + "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", + "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", + "[AutoRA Basic Tutorial III: Functional Workflow](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Functional-Workflow/)
\n", + "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", + "\n", + "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", + "\n", + "**How to use this notebook** *You can progress through the notebook section by section or directly navigate to specific sections. If you choose the latter, it is recommended to execute all cells in the notebook initially, allowing you to easily rerun the cells in each section later without issues.*" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tutorial Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#### Installation ####\n", + "!pip install -q \"autora[theorist-bms]\"\n", + "\n", + "#### Import modules ####\n", + "import numpy as np\n", + "import pandas as pd\n", + "import torch\n", + "\n", + "#### Set seeds ####\n", + "np.random.seed(42)\n", + "torch.manual_seed(42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## States\n", + "\n", + "Using the functions and objects in `autora.state`, we can build flexible pipelines and cycles which operate on state\n", + "objects. State objects are containers with specialized functionality that will allow you to build processing pipelines containing experimentatlists, experiment runners, and theorists.\n", + "\n", + "In tutorial I, we had experimentalists define new conditions, experiment runners collect new observations, and theorists model the data. To do this, we used the output of one as the input of the other, such as: \n", + "\n", + "`conditions = experimentalist(...)` $\\rightarrow$
\n", + "`observations = experiment_runner(conditions,...)` $\\rightarrow$
\n", + "`model = theorist(conditions, observations)`
\n", + "\n", + "This chaining is embedded within the `State` functionality. To use a state, we must independently wrap our experimentalist, experiment_runner, and theorist into the same state, so that they become functions that:\n", + "- operate on the `State`, and\n", + "- return a modified object of the **same type** `State`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Defining The State\n", + "\n", + "We use the `StandardState` object bundled with `autora`: `StandardState`. Let's begin by initiating the state while only providing *variable information* (`variables`), *seed condition data* (`conditions`), and a *dataframe* (`pd.DataFrame(columns=[\"x\",\"y\"])`) that will hold our conditions (`x`) and observations (`y`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.variable import Variable, ValueType, VariableCollection\n", + "from autora.experimentalist.random_ import random_pool\n", + "from autora.state.bundled import StandardState\n", + "\n", + "#### Define variable data ####\n", + "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 10))\n", + "dv = Variable(name=\"y\", type=ValueType.REAL)\n", + "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "#### Define seed condition data ####\n", + "conditions = random_pool(variables, num_samples=10)\n", + "\n", + "#### Initialize State ####\n", + "s = StandardState(\n", + " variables = variables,\n", + " conditions=conditions,\n", + " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"])\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Viewing the State\n", + "\n", + "Now, let's view the contents of the state we just initialized." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.6981317 , 1.3962634 , 2.0943951 , 2.7925268 ,\n", + " 3.4906585 , 4.1887902 , 4.88692191, 5.58505361, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 0.000000\n", + "1 0.000000\n", + "2 4.188790\n", + "3 2.792527\n", + "4 2.094395\n", + "5 6.283185\n", + "6 5.585054\n", + "7 2.094395\n", + "8 4.188790\n", + "9 0.000000, experiment_data=Empty DataFrame\n", + "Columns: [x, y]\n", + "Index: [], models=[])\n" + ] + } + ], + "source": [ + "print(s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Within the state, we can view all of the content we provided it more directly if we choose." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mThe variables we provided:\u001b[0m\n", + "VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.6981317 , 1.3962634 , 2.0943951 , 2.7925268 ,\n", + " 3.4906585 , 4.1887902 , 4.88692191, 5.58505361, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[])\n", + "\u001b[1mThe conditions we provided:\u001b[0m\n", + " x\n", + "0 0.000000\n", + "1 0.000000\n", + "2 4.188790\n", + "3 2.792527\n", + "4 2.094395\n", + "5 6.283185\n", + "6 5.585054\n", + "7 2.094395\n", + "8 4.188790\n", + "9 0.000000\n", + "\n", + "\u001b[1mThe dataframe we provided:\u001b[0m\n", + "Empty DataFrame\n", + "Columns: [x, y]\n", + "Index: []\n" + ] + } + ], + "source": [ + "print(\"\\033[1mThe variables we provided:\\033[0m\")\n", + "print(s.variables)\n", + "\n", + "print(\"\\033[1mThe conditions we provided:\\033[0m\")\n", + "print(s.conditions)\n", + "\n", + "print(\"\\n\\033[1mThe dataframe we provided:\\033[0m\")\n", + "print(s.experiment_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## AutoRA Components and the State\n", + "\n", + "Now that we have initialized the state, we need to start adding components of `AutoRA` to the state - namely, experiment runners, experimentalists, and theorists. \n", + "\n", + "These components are defined in the same way as past tutorials. All we need to do so that these can function within the state is to wrap them in specialized state functions. The wrappers are:\n", + "- `on_state()` for experiment runners and experimentalists\n", + "- `state_fn_from_estimator()` for theorists\n", + "\n", + "The first input for each wrapper should be your corresponding function (i.e., the experiment runner, experimentalist, and the theorist). The `on_state` wrapper takes a second input, `output`, to determine where in the state the component is acting on. For the experimentalist this will be `output=[\"conditions\"]`, and for the experiment runner this will be `output=[\"experiment_data\"]`.\n", + "\n", + "Once the components are wrapped, their functionality changes to act on the state, meaning that they now expect a state as the first input and will return a modified version of that state." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping Components to Work with State" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Experimentalist Defined and Wrapped with State\n", + "\n", + "We will use autora's `random_sample` sampler for our experimentalist. We import this and then wrap it so that it functions with the state." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.experimentalist.random_ import random_sample\n", + "from autora.state.delta import on_state\n", + "\n", + "experimentalist = on_state(random_sample, output=[\"conditions\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Experiment Runner Defined and Wrapped with State\n", + "We define the same experiment runner from the first two tutorials and then wrap it so that it functions with the state." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def experiment_runner(conditions: pd.DataFrame):\n", + " x = conditions[\"x\"]\n", + " y = np.sin(x) + np.random.normal(0, 0.5, size=x.shape)\n", + " observations = conditions.assign(y = y)\n", + " print(observations)\n", + " return observations\n", + "\n", + "experiment_runner = on_state(experiment_runner, output=[\"experiment_data\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Theorist Defined and Wrapped with State\n", + "\n", + "We will use autora's BMSRegressor theorist. We import this and then wrap it so that if functions with the state." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.theorist.bms import BMSRegressor\n", + "from autora.state.wrapper import state_fn_from_estimator\n", + "\n", + "theorist = state_fn_from_estimator(BMSRegressor(epochs=100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running Each Component Within the State" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the Experimentalist\n", + "\n", + "Let's run the experimentalist within the state and see how the state changes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mPrevious Conditions:\u001b[0m\n", + " x\n", + "0 0.000000\n", + "1 0.000000\n", + "2 4.188790\n", + "3 2.792527\n", + "4 2.094395\n", + "5 6.283185\n", + "6 5.585054\n", + "7 2.094395\n", + "8 4.188790\n", + "9 0.000000\n", + "\n", + "\u001b[1mUpdated Conditions:\u001b[0m\n", + " x\n", + "8 4.188790\n", + "1 0.000000\n", + "5 6.283185\n", + "0 0.000000\n", + "7 2.094395\n" + ] + } + ], + "source": [ + "print('\\033[1mPrevious Conditions:\\033[0m')\n", + "print(s.conditions)\n", + "\n", + "s = experimentalist(s, num_samples=5)\n", + "\n", + "print('\\n\\033[1mUpdated Conditions:\\033[0m')\n", + "print(s.conditions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the Experiment Runner\n", + "\n", + "Let's run the experiment runner and see how the state changes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mPrevious Data:\u001b[0m\n", + "Empty DataFrame\n", + "Columns: [x, y]\n", + "Index: []\n", + " x y\n", + "8 4.188790 -0.726505\n", + "1 0.000000 0.505258\n", + "5 6.283185 -0.290439\n", + "0 0.000000 -0.262585\n", + "7 2.094395 0.580335\n", + "\n", + "\u001b[1mUpdated Data:\u001b[0m\n", + " x y\n", + "0 4.188790 -0.726505\n", + "1 0.000000 0.505258\n", + "2 6.283185 -0.290439\n", + "3 0.000000 -0.262585\n", + "4 2.094395 0.580335\n" + ] + } + ], + "source": [ + "print(\"\\033[1mPrevious Data:\\033[0m\")\n", + "print(s.experiment_data)\n", + "\n", + "s = experiment_runner(s) #TODO: Why does it print the experiment data automatically?\n", + "\n", + "print(\"\\n\\033[1mUpdated Data:\\033[0m\")\n", + "print(s.experiment_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the Theorist\n", + "\n", + "Let's run the theorist and see how the state changes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mPrevious Model:\u001b[0m\n", + "None\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:03<00:00, 26.73it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mUpdated Model:\u001b[0m\n", + "-0.04\n" + ] + } + ], + "source": [ + "print(\"\\033[1mPrevious Model:\\033[0m\")\n", + "print(f\"{s.model}\\n\")\n", + "\n", + "s = theorist(s)\n", + "\n", + "print(\"\\n\\033[1mUpdated Model:\\033[0m\")\n", + "print(s.model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Component Chaining and Looping\n", + "\n", + "As such, we have our `AutoRA` components wrapped to work with the state. Remember, this means that they take the state as an input and returns the updated state as an output." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Component Chaining\n", + "\n", + "As the components all act on the state, they can be chained in a nested fashion." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " x y\n", + "1 0.000000 -0.462041\n", + "5 6.283185 -1.219553\n", + "8 4.188790 -0.564305\n", + "0 0.000000 -0.125522\n", + "7 2.094395 0.784092\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:03<00:00, 26.29it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.6981317 , 1.3962634 , 2.0943951 , 2.7925268 ,\n", + " 3.4906585 , 4.1887902 , 4.88692191, 5.58505361, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "1 0.000000\n", + "5 6.283185\n", + "8 4.188790\n", + "0 0.000000\n", + "7 2.094395, experiment_data= x y\n", + "0 4.188790 -0.726505\n", + "1 0.000000 0.505258\n", + "2 6.283185 -0.290439\n", + "3 0.000000 -0.262585\n", + "4 2.094395 0.580335\n", + "5 0.000000 -0.462041\n", + "6 6.283185 -1.219553\n", + "7 4.188790 -0.564305\n", + "8 0.000000 -0.125522\n", + "9 2.094395 0.784092, models=[-0.18, -0.18])\n" + ] + } + ], + "source": [ + "s = theorist(experiment_runner(experimentalist(s, num_samples=5)))\n", + "print(s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chain Looping with Number of Cycles\n", + "\n", + "Moreover, we can use these chained components within a loop to run multiple cycles." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 1:\u001b[0m\n", + " x y\n", + "1 0.000000 -0.146700\n", + "8 4.188790 -0.880945\n", + "7 2.094395 0.913588\n", + "5 6.283185 0.332327\n", + "4 2.094395 0.795916\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:03<00:00, 25.24it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 1 model: sin(x)\u001b[0m\n", + "\n", + "\u001b[1mRunning Cycle 2:\u001b[0m\n", + " x y\n", + "1 0.000000 -0.016597\n", + "4 2.094395 0.859277\n", + "7 2.094395 0.337170\n", + "5 6.283185 0.411272\n", + "8 4.188790 -1.476447\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:03<00:00, 25.75it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 2 model: 0.11\u001b[0m\n", + "\n", + "\u001b[1mRunning Cycle 3:\u001b[0m\n", + " x y\n", + "1 0.000000 -0.664093\n", + "7 2.094395 0.964456\n", + "5 6.283185 0.369233\n", + "8 4.188790 -0.780341\n", + "4 2.094395 0.808201\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:03<00:00, 25.62it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 3 model: sin(x)\u001b[0m\n", + "\n", + "\u001b[1mRunning Cycle 4:\u001b[0m\n", + " x y\n", + "8 4.188790 -1.016577\n", + "1 0.000000 0.230408\n", + "5 6.283185 0.042962\n", + "7 2.094395 0.111047\n", + "4 2.094395 1.226777\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:03<00:00, 25.24it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 4 model: sin(x)\u001b[0m\n", + "\n", + "\u001b[1mRunning Cycle 5:\u001b[0m\n", + " x y\n", + "1 0.000000 -0.021768\n", + "7 2.094395 0.728375\n", + "8 4.188790 -1.647559\n", + "5 6.283185 -0.397815\n", + "4 2.094395 1.331318\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:03<00:00, 25.29it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 5 model: sin(x)\u001b[0m\n" + ] + } + ], + "source": [ + "#### First, let's reinitialize the state object to get a clean state ####\n", + "s = StandardState(\n", + " variables = variables,\n", + " conditions = conditions,\n", + " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"])\n", + ")\n", + "\n", + "### Then we cycle through the pipeline we built five times ###\n", + "num_cycles = 5 # number of empirical research cycles\n", + "for cycle in range(num_cycles):\n", + " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", + " s = theorist(experiment_runner(experimentalist(s, num_samples=5)))\n", + " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If everything went well in terms of our theorist, we should have recovered our ground truth model `sin(x)`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sin(x)\n" + ] + } + ], + "source": [ + "print(s.model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chain Looping with Stopping Criterion\n", + "\n", + "Alternatively, we can run the chain until we reach a stopping criterion. For example, here we will loop until we get 30 datapoints." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 1, number of datapoints: 0\u001b[0m\n", + " x y\n", + "2 4.188790 -0.527142\n", + "9 0.000000 -0.088866\n", + "7 2.094395 0.660834\n", + "5 6.283185 0.589858\n", + "8 4.188790 -1.315129\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:03<00:00, 27.83it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 1 model: -0.14\u001b[0m\n", + "\n", + "\u001b[1mRunning Cycle 2, number of datapoints: 5\u001b[0m\n", + " x y\n", + "9 0.000000 0.180818\n", + "5 6.283185 -0.322560\n", + "2 4.188790 -0.685328\n", + "8 4.188790 -0.097007\n", + "7 2.094395 0.848112\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:04<00:00, 24.68it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 2 model: sin(x)\u001b[0m\n", + "\n", + "\u001b[1mRunning Cycle 3, number of datapoints: 10\u001b[0m\n", + " x y\n", + "7 2.094395 1.648347\n", + "5 6.283185 0.043524\n", + "8 4.188790 -1.015529\n", + "2 4.188790 -0.820145\n", + "9 0.000000 -0.993784\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:03<00:00, 27.33it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 3 model: -0.13\u001b[0m\n", + "\n", + "\u001b[1mRunning Cycle 4, number of datapoints: 15\u001b[0m\n", + " x y\n", + "5 6.283185 -0.709298\n", + "7 2.094395 0.961194\n", + "2 4.188790 -0.798148\n", + "8 4.188790 -0.561981\n", + "9 0.000000 0.352491\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:03<00:00, 25.85it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 4 model: -0.13\u001b[0m\n", + "\n", + "\u001b[1mRunning Cycle 5, number of datapoints: 20\u001b[0m\n", + " x y\n", + "2 4.188790 -0.685564\n", + "8 4.188790 -0.918654\n", + "9 0.000000 -0.477673\n", + "5 6.283185 -0.207382\n", + "7 2.094395 0.166655\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:03<00:00, 26.22it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 5 model: sin(x)\u001b[0m\n", + "\n", + "\u001b[1mRunning Cycle 6, number of datapoints: 25\u001b[0m\n", + " x y\n", + "7 2.094395 0.711381\n", + "2 4.188790 -0.529463\n", + "8 4.188790 -0.994340\n", + "5 6.283185 -0.183913\n", + "9 0.000000 0.636867\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:04<00:00, 24.70it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 6 model: sin(x)\u001b[0m\n", + "\n", + "\u001b[1mNumber of datapoints: 30\u001b[0m\n", + "Determined Model: sin(x)\n" + ] + } + ], + "source": [ + "#### First, let's reinitialize the state object to get a clean state ####\n", + "s = StandardState(\n", + " variables = variables,\n", + " conditions = conditions,\n", + " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"])\n", + ")\n", + "\n", + "### Then we cycle through the pipeline we built until we reach our stopping criteria ###\n", + "cycle = 0\n", + "while len(s.experiment_data) < 30:\n", + " print(f\"\\n\\033[1mRunning Cycle {cycle+1}, number of datapoints: {len(s.experiment_data)}\\033[0m\")\n", + " s = theorist(experiment_runner(experimentalist(s, num_samples=5)))\n", + " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", + " cycle += 1\n", + "\n", + "print(f\"\\n\\033[1mNumber of datapoints: {len(s.experiment_data)}\\033[0m\")\n", + "print(f\"\\033[1mDetermined Model: {s.model}\\033[0m\")\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Next Notebook\n", + "This concludes the tutorial on ``autora`` functionality. However, ``autora`` is a flexible framework in which users can integrate their own theorists, experimentalists, and experiment_runners in an automated empirical research workflow. The next notebook illustrates how to add your own custom theorists and experimentalists to use with ``autora``.\n", + "\n", + "Follow this link for the next notebook tutorial:\n", + "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "autoraKernel", + "language": "python", + "name": "autorakernel" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 6209792c55c78ec7a188bd8c2b82f3961db8f8c7 Mon Sep 17 00:00:00 2001 From: chadcwilliams Date: Fri, 18 Aug 2023 14:53:33 -0700 Subject: [PATCH 14/32] Removed old tutorial III --- .../basic/Tutorial-III-Workflow-Logic.ipynb | 1156 ----------------- 1 file changed, 1156 deletions(-) delete mode 100644 docs/tutorials/basic/Tutorial-III-Workflow-Logic.ipynb diff --git a/docs/tutorials/basic/Tutorial-III-Workflow-Logic.ipynb b/docs/tutorials/basic/Tutorial-III-Workflow-Logic.ipynb deleted file mode 100644 index 5196f9cd0..000000000 --- a/docs/tutorials/basic/Tutorial-III-Workflow-Logic.ipynb +++ /dev/null @@ -1,1156 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction\n", - "## Basic Tutorial III: Workflow Logic" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**[AutoRA](https://pypi.org/project/autora/)** (**Au**tomated **R**esearch **A**ssistant) is an open-source framework designed to automate various stages of empirical research, including model discovery, experimental design, and data collection.\n", - "\n", - "This notebook is the third of four notebooks within the basic tutorials of ``autora``. We suggest that you go through these notebooks in order as each builds upon the last. However, each notebook is self-contained and so there is no need to *run* the content of the last notebook for your current notebook. We will here provide a link to each notebook, but we will also provide a link at the end of each notebook to navigate you to the next notebook.\n", - "\n", - "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", - "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", - "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", - "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", - "\n", - "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", - "\n", - "**How to use this notebook** *You can progress through the notebook section by section or directly navigate to specific sections. If you choose the latter, it is recommended to execute all cells in the notebook initially, allowing you to easily rerun the cells in each section later without issues.*" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tutorial Setup\n", - "This tutorial is self-contained so that you do not need to run the previous notebook to begin. However, the four notebooks are continuous so that what we define in a previous notebook should still exist within this notebook. As such, we will here re-run relevant code from past tutorials. We will not again walk you through these, but if you need a reminder what they are then go see the descriptions in previous notebooks." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#### Installation ####\n", - "!pip install -q \"autora[experimentalist-falsification]\"\n", - "!pip install -q \"autora[experimentalist-sampler-novelty]\"\n", - "!pip install -q \"autora[theorist-bms]\"\n", - "\n", - "#### Import modules ####\n", - "import numpy as np\n", - "import torch\n", - "from autora.variable import DV, IV, ValueType, VariableCollection\n", - "from autora.experimentalist.pooler.grid import grid_pool\n", - "from autora.experimentalist.sampler.falsification import falsification_sample\n", - "from autora.experimentalist.sampler.novelty import novelty_sample\n", - "from autora.theorist.bms import BMSRegressor\n", - "\n", - "#### Set seeds ####\n", - "np.random.seed(42)\n", - "torch.manual_seed(42)\n", - "\n", - "#### Define ground truth and experiment runner ####\n", - "ground_truth = lambda x: np.sin(x)\n", - "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape)\n", - "\n", - "#### Define condition pool ####\n", - "condition_pool = np.linspace(0, 2 * np.pi, 10)\n", - "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", - "\n", - "#### Define metadata ####\n", - "iv = IV(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 10))\n", - "dv = DV(name=\"y\", type=ValueType.REAL)\n", - "metadata = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", - "\n", - "#### Define theorists ####\n", - "theorist_bms = BMSRegressor(epochs=100)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Workflow Logic\n", - "\n", - "Workflows in ``autora`` implement the *autonomous empirical research paradigm*. This paradigm centers around the dynamic interplay between automated theorists and automated experimentalists. As outlined above, theorists rely–among other things–on existing data to construct computational models by linking experimental conditions to dependent measures. Experimentalists design follow-up experiments to refine and validate models generated by the theorist. Together, these agents enable a closed-loop scientific discovery process.\n", - "\n", - "The following sections introduce ways of specifying workflows directly in ``autora``. For more information on workflows, please refer to the [corresponding documentation](https://autoresearch.github.io/autora/user-guide/workflow/)." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Basic Workflows\n", - "\n", - "This section provides an introduction to handling workflows with the controller object. Here, we focus on workflows implementing the **default execution order**: (1) generate experiment conditions using the ``eperimentalist``, (2) collect observations using the ``experiment_runner``, and (3), generate a model that links experiment conditions to observations using the ``theorist``.\n", - "\n", - "We begin with implementing the following workflow:\n", - "1. Generate seed experimental conditions\n", - "2. Iterate 5 times through the following steps\n", - " - Collect observations using ``run_experiment``\n", - " - Identify a model relating conditions to observations using ``theorist_bms``\n", - " - Identify 3 new experimental conditions using ``falsification_sample``\n", - "\n", - "### Declaration\n", - "\n", - "We begin with defining a simple workflow. Workflows can be encapsulated in a ``Controller`` object. For instance, the following code block sets up a closed-loop cycle between (1) a grid pooler for sampling experimental conditions, (2) an experiment runner for obtaining respective observations, and (3) a BMS theorist for discoverying an equation relating experimental conditions to observations.\n", - "\n", - "As with pipelines, we can pass the ``Controller`` object static parameters for each component. In this case, we provide the grid experimentalist with information about the independent variables to sample.\n", - "\n", - "**Note**: *We haven't included the ``falsification_sample`` experimentalist into our workflow yet because it requires us to specify state-dependent input arguments (e.g., the model generated by the theorist), which we will cover at the end of this section.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.workflow import Controller\n", - "\n", - "controller = Controller(\n", - " variables=metadata,\n", - " experimentalist=grid_pool,\n", - " experiment_runner=run_experiment,\n", - " theorist=theorist_bms,\n", - " params={\n", - " \"experimentalist\":\n", - " {\"ivs\": metadata.independent_variables}\n", - " }\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the declaration of the ``params`` parameter, we first specify the type of the component we seek to parameterize as a dictionary key, e.g., ``\"experimentalist\"``. Then we nest within it, another dictionary with the input arguments to the respective component as keys (e.g., ``\"ivs\"`` is an input argument to the ``grid_pool`` experimentalist) along with their values (e.g., ``metadata.independent_variables``).\n", - "\n", - "### Monitoring\n", - "\n", - "Before we execute the controller, lets also add a **monitor function** which is executed with every autonomous empirical research step. The following code block prints the last generated result of the workflow defined by the controller. All workflow results are stored in the ``state.history`` object. We can access the kind of the latest result using ``state.history[-1].kind``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# define monitor function\n", - "def monitor(state):\n", - " print(f\"MONITOR: Generated new {state.history[-1].kind}\")\n", - "\n", - "# add monitor function to controller\n", - "controller.monitor = monitor" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Execution\n", - "\n", - "The controller is defined as an iterator. We can execute a single step in the workflow by passing the ``controller`` object to the ``next()`` method. The following code block executes three steps of the default research cycle." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new CONDITION\n", - "MONITOR: Generated new OBSERVATION\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:21<00:00, 4.66it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(controller)\n", - "next(controller)\n", - "next(controller)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As indicated by the monitor, the **default execution order** is as follows: (1) generate experiment conditions, (2) collect observations, and (3), generate a model. After executing step (3), the controller would then continue with step (1):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new CONDITION\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(controller)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since ``controller`` is an iterator, we can use [itertools](https://docs.python.org/3/library/itertools.html) for efficient looping. The following example uses ``takewhile`` to define a loop that stops as soon as we obtained three models from the theorist.\n", - "\n", - "We begin with defining a lambda function which returns true whenever the controller has less then 5 models. As explained in the next subsection, we can obtain a list of generated models by accessing the controller's state via ``controller.state.models``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "continue_criterion = lambda controller: len(controller.state.models) < 5" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can run a for-loop using the ``controller`` as an iterator, and ``takewhile`` as iterator logic that continues to execute steps of the controller as long as ``continue_criterion`` returns ``True``. In this way, we can execute 5 research cycles." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:20<00:00, 5.00it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 2\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 2\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:11<00:00, 9.04it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 3\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 3\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 3\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:11<00:00, 8.92it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 4\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 4\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 4\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:11<00:00, 8.92it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from itertools import takewhile\n", - "\n", - "for step in takewhile(continue_criterion, controller):\n", - " print(f\"Number of models: {len(step.state.models)}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Result Inspection\n", - "\n", - "After each executed step, we can observe the result generated by the ``controller``. All results are stored in in ``controller.state.history``. Each result is composed of a value specifying its ``kind`` (``CONDITION``, ``OBSERVATION``, or ``MODEL``) and the respective ``data``.\n", - "\n", - "We can obtain the observations collected in the last step of the workflow as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ResultKind.MODEL\n", - "BMSRegressor(epochs=100)\n" - ] - } - ], - "source": [ - "result = controller.state.history[-1]\n", - "\n", - "print(result.kind)\n", - "print(result.data)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also specify the kind of result we are looking for directly. For instance, we can obtain all models generated by the theorist using ``controller.state.models``. The following code block prints the last model discovered by the BMS theorist (note that ``repr()`` is a function specific to the BMS theorist which returns its model as a string)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sin(X0)\n" - ] - } - ], - "source": [ - "print(controller.state.models[-1].repr())" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, we can access probed experimental conditions via ``controller.state.conditions`` and observations via ``controller.state.observations``, respectively. The following code block requests the latest experimental conditions identified by the experimentalist and the corresponding observations collected by the experiment runner" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Conditions:\n", - "[[0. ]\n", - " [0.6981317 ]\n", - " [1.3962634 ]\n", - " [2.0943951 ]\n", - " [2.7925268 ]\n", - " [3.4906585 ]\n", - " [4.1887902 ]\n", - " [4.88692191]\n", - " [5.58505361]\n", - " [6.28318531]]\n", - "Observations:\n", - "[[ 0. 0.07384666]\n", - " [ 0.6981317 0.65992444]\n", - " [ 1.3962634 0.97324292]\n", - " [ 2.0943951 0.83591503]\n", - " [ 2.7925268 0.19416794]\n", - " [ 3.4906585 -0.41400456]\n", - " [ 4.1887902 -0.91208928]\n", - " [ 4.88692191 -0.87909553]\n", - " [ 5.58505361 -0.60842578]\n", - " [ 6.28318531 -0.17630402]]\n" - ] - } - ], - "source": [ - "print(f\"Conditions:\\n{controller.state.conditions[-1]}\")\n", - "print(f\"Observations:\\n{controller.state.observations[-1]}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Seeding\n", - "\n", - "The default execution order always begins with an experimentalist. This is problematic if we want to use an experimentalist that depends on prior steps (e.g., the falsification experimentalist requires a model generated by the theorist). We can circumvent this problem by seeding the controller with experiment conditons.\n", - "\n", - "The following code block seeds the controller with 3 experiment conditions. We first generate the ``seed_conditions``, and then pass them, encapsulated in a list, to the ``seed`` function of the ``controller`` object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new OBSERVATION\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# generate initial pool of 3 experimental conditions\n", - "seed_conditions = np.linspace(0,2*np.pi,3)\n", - "\n", - "# define controller\n", - "controller = Controller(\n", - " monitor=monitor,\n", - " variables=metadata,\n", - " experimentalist=grid_pool,\n", - " experiment_runner=run_experiment,\n", - " theorist=theorist_bms,\n", - " params={\n", - " \"experimentalist\":\n", - " {\"ivs\": metadata.independent_variables}\n", - " }\n", - ")\n", - "\n", - "# seed controller\n", - "controller.seed(conditions=[seed_conditions])\n", - "\n", - "next(controller)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that, since we seeded the controller with initial experimental conditions, the next step is to execute the ``experiment_runner``. This is why the first step reported by the monitor involves the generation of observations (based on the seed experimental conditions).\n", - "\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Accessing State-Dependent Properties\n", - "\n", - "Some automated empirical research components require input arguments that depend on the result of the last step in the workflow. For instance, the ``falsification_sample`` experimentalist depends on the previously collected experimental conditions, observations, and the fitted model. For such cases, it is possible to use \"state-dependent properties\" in the ``params`` dictionary. These are the following strings, which will be replaced during execution by their respective current values:\n", - "\n", - "- ``\"%observations.ivs[-1]%\"``: the last observed independent variables
\n", - "- ``\"%observations.dvs[-1]%\"``: the last observed dependent variables
\n", - "- ``\"%observations.ivs%\"``: all the observed independent variables (observations), concatenated into a single array
\n", - "- ``\"%observations.dvs%\"``: all the observed dependent variables (experimental conditions), concatenated into a single array
\n", - "- ``\"%models[-1]%\"``: the last fitted theorist
\n", - "- ``\"%models%\"``: all the fitted theorists
\n", - "\n", - "In the following example, we use the ``\"%observations.ivs%\"``, ``\"%observations.dvs%\"``, and ``\"%models%\"`` properties for the ``falsification_sample`` experimentalist which seeks to identify experimental conditions that are predicted to maximize the loss of the fitted model.\n", - "\n", - "The code block below implements the following workflow:\n", - "1. Generate 3 seed experimental conditions\n", - "2. Iterate 5 times through the following steps\n", - " - Collect observations using ``run_experiment``\n", - " - Identify a model relating conditions to observations using ``theorist_bms``\n", - " - Identify 3 new experimental conditions using ``falsification_sample``\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# generate initial pool of 3 experimental conditions\n", - "seed_conditions = np.linspace(0,2*np.pi,3)\n", - "\n", - "# define controller\n", - "controller = Controller(\n", - " monitor=monitor,\n", - " variables=metadata,\n", - " experimentalist=falsification_sample,\n", - " experiment_runner=run_experiment,\n", - " theorist=theorist_bms,\n", - " params={\n", - " \"experimentalist\":\n", - " {\"condition_pool\": condition_pool,\n", - " \"model\": \"%models[-1]%\", # access last model generated by theorist\n", - " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", - " \"reference_observations\": \"%observations.dvs%\", # access all observations collected so far\n", - " \"metadata\": metadata,\n", - " \"num_samples\": 3}\n", - " }\n", - ")\n", - "\n", - "# seed controller\n", - "controller.seed(conditions=[seed_conditions])" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using ``takewhile``, we can now specify a workflow logic that executes the automated research process 5 times. Accordingly, we stop execution of the ``controller`` as soon as it accumulated 5 models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.51it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 1\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 1\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:11<00:00, 8.80it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 2\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 2\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:11<00:00, 8.70it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 3\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 3\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 3\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:11<00:00, 8.98it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 4\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 4\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 4\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.47it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n", - "Number of models: 5\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 5\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:09<00:00, 10.86it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new MODEL\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from itertools import takewhile\n", - "\n", - "continue_criterion = lambda controller: len(controller.state.models) < 6\n", - "\n", - "for step in takewhile(continue_criterion, controller):\n", - " print(f\"Number of models: {len(step.state.models)}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Advanced Workflows\n", - "\n", - "In some cases, we may want to condition the sequence of steps taken in the empirical research process on the current state of the process. For instance, one might want to switch from a novelty sampling strategy to a falsification sampling strategy as soon as one has probed enough novel experiment conditions. This section provides a basic introduction to the``BaseController``, which enables the implementation of such arbitrary execution orders.\n", - "\n", - "In this section, we consider a scenario in which we switch experimentalists, depending on the amount of observations collected:\n", - "- If no observations are collected, we sample some seed experimental conditions\n", - "- If less than 7 observations are collected, we sample experimental conditions with ``novelty_sample``\n", - "- If 7 or more observations are collected, we sample experimental conditions with ``falsification_sample``\n", - "\n", - "#### Planner Declaration\n", - "\n", - "We begin with defining an ``experimentalist_planner`` function. Such planner function will be provided as input to the ``BaseController``, and will be used to determine the next step of the workflow, depending on the current state. The code block below implements a planner that selects the experimentalist to be executed depending on the amount of observations collected:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.workflow.planner import last_result_kind_planner\n", - "\n", - "def experimentalist_planner(state):\n", - " # We're going to reuse the \"last_result_kind_planner\" planner, and modify its output.\n", - " proposed_next_step = last_result_kind_planner(state)\n", - "\n", - " # Obtain a list of all observations collected so far\n", - " all_observations = [item for sublist in state.observations for item in sublist]\n", - " num_observations = len(all_observations)\n", - "\n", - " # Determine next experimentalist\n", - " if proposed_next_step == \"experimentalist\":\n", - " if num_observations < 1:\n", - " next_step = \"seed_experimentalist\"\n", - " elif num_observations > 0 and num_observations < 7:\n", - " next_step = \"novelty_experimentalist\"\n", - " else:\n", - " next_step = \"falsification_experimentalist\"\n", - " else:\n", - " next_step = proposed_next_step\n", - "\n", - " print(\"PLANNER: Next step: \" + next_step)\n", - " return next_step" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ``experimentalist_planner`` function accepts a ``controller``'s state as input and returns the next step to be executed. Here, we call the ``last_result_kind_planner`` to obtain the default next step. For instance, according to the autonomous empirical research paradigm, if the last step involved executing the ``\"theorist\"``, the next step would be executing the ``experimentalist``.\n", - "\n", - "If the next default step is the ``experimentalist``, the ``experimentalist_planner`` will select the type of experimentalist based on the total number of collected observations." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Executor Collection Declaration\n", - "\n", - "In order for the ``BaseController`` to work with the ``experimentalist_planner``, we need to specify the experimentalists that it selects to be executed. In the next code block, we define all experimentalists by wrapping each of them into a ``Pipeline``. However, at this point, we don't need to provide the respective parameters for each experimentalist–we will provide these later, directly to the ``BaseController`` object.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.experimentalist.pipeline import make_pipeline\n", - "\n", - "seed_pipeline = make_pipeline([np.linspace(0, 2*np.pi, 3)])\n", - "novelty_pipeline = make_pipeline([novelty_sample])\n", - "falsification_pipeline = make_pipeline([falsification_sample])" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now wrap all elements of our research process–this includes all experimentalists as well as the theorist and experiment runner–into a collection of executors. The following code block defines this collection using ``ChainedFunctionMapping``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.workflow.executor import (ChainedFunctionMapping, from_experimentalist_pipeline,\n", - " from_experiment_runner_callable, from_theorist_estimator)\n", - "\n", - "executor_collection = ChainedFunctionMapping(\n", - " seed_experimentalist=\n", - " [from_experimentalist_pipeline, seed_pipeline],\n", - " novelty_experimentalist=\n", - " [from_experimentalist_pipeline, novelty_pipeline],\n", - " falsification_experimentalist=\n", - " [from_experimentalist_pipeline, falsification_pipeline],\n", - " experiment_runner=[from_experiment_runner_callable, run_experiment],\n", - " theorist=[from_theorist_estimator, theorist_bms],\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the ``ChainedFunctionMapping``, we specify each element by its type, followed by its function. For instance, the ``seed_experimentalist`` is defined as an experimentalist pipeline. Thus, we specify it as ``from_experimentalist_pipeline``, and chain it with its respective function ``seed_experimentalist`` defined above." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Base Controller Declaration\n", - "\n", - "So far, we have defined a ``experimentalist_planner`` function which determines the next step in our workflow. We have also defined a ``executor_collection`` defining each step of the workflow. Both will be provided to a special ``Controller`` called ``BaseController``. The ``BaseController`` does not require us to specify a ``theorist``, ``experimentalist``, or ``experiment_runner``. Instead, we can provide it with an ``executor_collection`` specifying all the elements of the workflow we require.\n", - "\n", - "The ``BaseController`` also requires us to specify an initial ``state``. Here, we can instantiate a state as a ``History`` object which entails all variables of the experiment (as declared in ``metadata``) along with the parameters provided to each element in the ``executor_collection``. Let's begin with defining the parameters for all elements in the ``executor_collection``. Here, only two of the elements (``novelty_experimentalist`` and ``falsification_experimentalist``) require us to specify additional parameters.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "params = {\"novelty_experimentalist\":\n", - " {\"novelty_sample\":\n", - " {\"condition_pool\": condition_pool,\n", - " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", - " \"num_samples\": 3},\n", - " },\n", - " \"falsification_experimentalist\":\n", - " {\"falsification_sample\":\n", - " {\"condition_pool\": condition_pool,\n", - " \"model\": \"%models[-1]%\", # access last model generated by theorist\n", - " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", - " \"reference_observations\": \"%observations.dvs%\", # access all observations collected so far\n", - " \"metadata\": metadata,\n", - " \"num_samples\": 3}\n", - " }\n", - " }" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the ``metadata`` and ``params``, we can instantiate an initial ``state`` for the workflow." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.workflow.state import History\n", - "\n", - "state = History(variables=metadata, params=params)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For convenience, let us also define a monitor function which can print the current total number of observations. We will provide this monitor to the ``BaseController``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def monitor(state):\n", - " all_observations = [item for sublist in state.observations for item in sublist]\n", - " num_observations = len(all_observations)\n", - " print(f\"MONITOR: Number of observations {num_observations}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now have all the required input arguments for the ``BaseController``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.workflow.base import BaseController\n", - "\n", - "# define controller\n", - "controller = BaseController(\n", - " state=state,\n", - " monitor=monitor,\n", - " planner=experimentalist_planner,\n", - " executor_collection=executor_collection,\n", - ")\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's execute the controller for 5 research cycles, measured in terms of the number of generated models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PLANNER: Next step: seed_experimentalist\n", - "MONITOR: Number of observations 0\n", - "MONITOR: Number of models: 0\n", - "PLANNER: Next step: experiment_runner\n", - "MONITOR: Number of observations 3\n", - "MONITOR: Number of models: 0\n", - "PLANNER: Next step: theorist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:09<00:00, 10.08it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 3\n", - "MONITOR: Number of models: 1\n", - "PLANNER: Next step: novelty_experimentalist\n", - "MONITOR: Number of observations 3\n", - "MONITOR: Number of models: 1\n", - "PLANNER: Next step: experiment_runner\n", - "MONITOR: Number of observations 6\n", - "MONITOR: Number of models: 1\n", - "PLANNER: Next step: theorist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.26it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 6\n", - "MONITOR: Number of models: 2\n", - "PLANNER: Next step: novelty_experimentalist\n", - "MONITOR: Number of observations 6\n", - "MONITOR: Number of models: 2\n", - "PLANNER: Next step: experiment_runner\n", - "MONITOR: Number of observations 9\n", - "MONITOR: Number of models: 2\n", - "PLANNER: Next step: theorist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:12<00:00, 7.91it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 9\n", - "MONITOR: Number of models: 3\n", - "PLANNER: Next step: falsification_experimentalist\n", - "MONITOR: Number of observations 9\n", - "MONITOR: Number of models: 3\n", - "PLANNER: Next step: experiment_runner\n", - "MONITOR: Number of observations 12\n", - "MONITOR: Number of models: 3\n", - "PLANNER: Next step: theorist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.73it/s]\n", - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 12\n", - "MONITOR: Number of models: 4\n", - "PLANNER: Next step: falsification_experimentalist\n", - "MONITOR: Number of observations 12\n", - "MONITOR: Number of models: 4\n", - "PLANNER: Next step: experiment_runner\n", - "MONITOR: Number of observations 15\n", - "MONITOR: Number of models: 4\n", - "PLANNER: Next step: theorist\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:10<00:00, 9.46it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Number of observations 15\n" - ] - } - ], - "source": [ - "from itertools import takewhile\n", - "\n", - "continue_criterion = lambda controller: len(controller.state.models) < 5\n", - "\n", - "for step in takewhile(continue_criterion, controller):\n", - " print(f\"MONITOR: Number of models: {len(step.state.models)}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can observe that the controller begins with sampling experiment condition using the ``seed_experimentalist``. It then proceeds to sample condition using the ``novelty_experimentalist`` until it has collected 7 or more observations, at which it switches to the ``falsification_experimentalist``." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Next Notebook\n", - "This concludes the tutorial on ``autora`` functionality. However, ``autora`` is a flexible framework in which users can integrate their own theorists, experimentalists, and experiment_runners in an automated empirical research workflow. The next notebook illustrates how to add your own custom theorists and experimentalists to use with ``autora``.\n", - "\n", - "Follow this link for the next notebook tutorial:\n", - "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From a7a835578b21a93d3e3f30ec55e515ce2fc90a65 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Mon, 21 Aug 2023 15:48:25 -0700 Subject: [PATCH 15/32] Simplified the experimentalist use --- .../Tutorial-III-Functional-Workflow.ipynb | 657 ++++++++++-------- 1 file changed, 351 insertions(+), 306 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb index 521982107..b41cdefa1 100644 --- a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb +++ b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb @@ -33,7 +33,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Tutorial Setup" + "## Tutorial Setup\n", + "\n", + "We will here import some standard python packages, set seeds for replicability, and define a plotting function." ] }, { @@ -49,16 +51,6 @@ "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -69,10 +61,23 @@ "import numpy as np\n", "import pandas as pd\n", "import torch\n", + "import matplotlib.pyplot as plt\n", "\n", "#### Set seeds ####\n", "np.random.seed(42)\n", - "torch.manual_seed(42)" + "torch.manual_seed(42)\n", + "\n", + "#### Define plot function ####\n", + "def plot_from_state(state):\n", + " experiment_data = state.experiment_data.sort_values(by=[\"x\"])\n", + " plt.plot(experiment_data[\"x\"], experiment_data[\"y\"], 'o', label = None)\n", + " plt.plot(experiment_data[\"x\"], state.model.predict(experiment_data[\"x\"].values.reshape(-1,1)), alpha=.8, label='Model')\n", + " ground_x = np.linspace(state.variables.independent_variables[0].value_range[0],state.variables.independent_variables[0].value_range[1],100)\n", + " plt.plot(ground_x, np.sin(ground_x), alpha=.8, label='Ground Truth (sin(x))')\n", + " plt.xlabel('x')\n", + " plt.ylabel('y')\n", + " plt.legend()\n", + " plt.show()" ] }, { @@ -82,7 +87,7 @@ "## States\n", "\n", "Using the functions and objects in `autora.state`, we can build flexible pipelines and cycles which operate on state\n", - "objects. State objects are containers with specialized functionality that will allow you to build processing pipelines containing experimentatlists, experiment runners, and theorists.\n", + "objects. State objects are containers with specialized functionality that will hold ou variables, data, and models. This state can be acted upon by experimentalists, experiment runners, and theorists. \n", "\n", "In tutorial I, we had experimentalists define new conditions, experiment runners collect new observations, and theorists model the data. To do this, we used the output of one as the input of the other, such as: \n", "\n", @@ -90,7 +95,7 @@ "`observations = experiment_runner(conditions,...)` $\\rightarrow$
\n", "`model = theorist(conditions, observations)`
\n", "\n", - "This chaining is embedded within the `State` functionality. To use a state, we must independently wrap our experimentalist, experiment_runner, and theorist into the same state, so that they become functions that:\n", + "This chaining is embedded within the `State` functionality. To act on a state, we must wrap each of our experimentalist(s), experiment_runner(s), and theorist(s) so that they:\n", "- operate on the `State`, and\n", "- return a modified object of the **same type** `State`." ] @@ -101,7 +106,7 @@ "source": [ "### Defining The State\n", "\n", - "We use the `StandardState` object bundled with `autora`: `StandardState`. Let's begin by initiating the state while only providing *variable information* (`variables`), *seed condition data* (`conditions`), and a *dataframe* (`pd.DataFrame(columns=[\"x\",\"y\"])`) that will hold our conditions (`x`) and observations (`y`)." + "We use the `StandardState` object bundled with `autora`: `StandardState`. Let's begin by populating the state with *variable information* (`variables`), *seed condition data* (`conditions`), and a *dataframe* (`pd.DataFrame(columns=[\"x\",\"y\"])`) that will hold our conditions (`x`) and observations (`y`)." ] }, { @@ -115,7 +120,7 @@ "from autora.state.bundled import StandardState\n", "\n", "#### Define variable data ####\n", - "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 10))\n", + "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 100))\n", "dv = Variable(name=\"y\", type=ValueType.REAL)\n", "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", "\n", @@ -148,18 +153,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.6981317 , 1.3962634 , 2.0943951 , 2.7925268 ,\n", - " 3.4906585 , 4.1887902 , 4.88692191, 5.58505361, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 0.000000\n", - "1 0.000000\n", - "2 4.188790\n", - "3 2.792527\n", - "4 2.094395\n", - "5 6.283185\n", - "6 5.585054\n", - "7 2.094395\n", - "8 4.188790\n", - "9 0.000000, experiment_data=Empty DataFrame\n", + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.06346652, 0.12693304, 0.19039955, 0.25386607,\n", + " 0.31733259, 0.38079911, 0.44426563, 0.50773215, 0.57119866,\n", + " 0.63466518, 0.6981317 , 0.76159822, 0.82506474, 0.88853126,\n", + " 0.95199777, 1.01546429, 1.07893081, 1.14239733, 1.20586385,\n", + " 1.26933037, 1.33279688, 1.3962634 , 1.45972992, 1.52319644,\n", + " 1.58666296, 1.65012947, 1.71359599, 1.77706251, 1.84052903,\n", + " 1.90399555, 1.96746207, 2.03092858, 2.0943951 , 2.15786162,\n", + " 2.22132814, 2.28479466, 2.34826118, 2.41172769, 2.47519421,\n", + " 2.53866073, 2.60212725, 2.66559377, 2.72906028, 2.7925268 ,\n", + " 2.85599332, 2.91945984, 2.98292636, 3.04639288, 3.10985939,\n", + " 3.17332591, 3.23679243, 3.30025895, 3.36372547, 3.42719199,\n", + " 3.4906585 , 3.55412502, 3.61759154, 3.68105806, 3.74452458,\n", + " 3.8079911 , 3.87145761, 3.93492413, 3.99839065, 4.06185717,\n", + " 4.12532369, 4.1887902 , 4.25225672, 4.31572324, 4.37918976,\n", + " 4.44265628, 4.5061228 , 4.56958931, 4.63305583, 4.69652235,\n", + " 4.75998887, 4.82345539, 4.88692191, 4.95038842, 5.01385494,\n", + " 5.07732146, 5.14078798, 5.2042545 , 5.26772102, 5.33118753,\n", + " 5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,\n", + " 5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,\n", + " 6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 0.634665\n", + "1 6.092786\n", + "2 0.126933\n", + "3 1.586663\n", + "4 0.571199\n", + "5 5.965853\n", + "6 5.013855\n", + "7 1.967462\n", + "8 2.157862\n", + "9 0.634665, experiment_data=Empty DataFrame\n", "Columns: [x, y]\n", "Index: [], models=[])\n" ] @@ -173,7 +196,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Within the state, we can view all of the content we provided it more directly if we choose." + "We can view all of the content we provided the state more directly if we choose." ] }, { @@ -186,20 +209,39 @@ "output_type": "stream", "text": [ "\u001b[1mThe variables we provided:\u001b[0m\n", - "VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.6981317 , 1.3962634 , 2.0943951 , 2.7925268 ,\n", - " 3.4906585 , 4.1887902 , 4.88692191, 5.58505361, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[])\n", + "VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.06346652, 0.12693304, 0.19039955, 0.25386607,\n", + " 0.31733259, 0.38079911, 0.44426563, 0.50773215, 0.57119866,\n", + " 0.63466518, 0.6981317 , 0.76159822, 0.82506474, 0.88853126,\n", + " 0.95199777, 1.01546429, 1.07893081, 1.14239733, 1.20586385,\n", + " 1.26933037, 1.33279688, 1.3962634 , 1.45972992, 1.52319644,\n", + " 1.58666296, 1.65012947, 1.71359599, 1.77706251, 1.84052903,\n", + " 1.90399555, 1.96746207, 2.03092858, 2.0943951 , 2.15786162,\n", + " 2.22132814, 2.28479466, 2.34826118, 2.41172769, 2.47519421,\n", + " 2.53866073, 2.60212725, 2.66559377, 2.72906028, 2.7925268 ,\n", + " 2.85599332, 2.91945984, 2.98292636, 3.04639288, 3.10985939,\n", + " 3.17332591, 3.23679243, 3.30025895, 3.36372547, 3.42719199,\n", + " 3.4906585 , 3.55412502, 3.61759154, 3.68105806, 3.74452458,\n", + " 3.8079911 , 3.87145761, 3.93492413, 3.99839065, 4.06185717,\n", + " 4.12532369, 4.1887902 , 4.25225672, 4.31572324, 4.37918976,\n", + " 4.44265628, 4.5061228 , 4.56958931, 4.63305583, 4.69652235,\n", + " 4.75998887, 4.82345539, 4.88692191, 4.95038842, 5.01385494,\n", + " 5.07732146, 5.14078798, 5.2042545 , 5.26772102, 5.33118753,\n", + " 5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,\n", + " 5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,\n", + " 6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[])\n", + "\n", "\u001b[1mThe conditions we provided:\u001b[0m\n", " x\n", - "0 0.000000\n", - "1 0.000000\n", - "2 4.188790\n", - "3 2.792527\n", - "4 2.094395\n", - "5 6.283185\n", - "6 5.585054\n", - "7 2.094395\n", - "8 4.188790\n", - "9 0.000000\n", + "0 0.634665\n", + "1 6.092786\n", + "2 0.126933\n", + "3 1.586663\n", + "4 0.571199\n", + "5 5.965853\n", + "6 5.013855\n", + "7 1.967462\n", + "8 2.157862\n", + "9 0.634665\n", "\n", "\u001b[1mThe dataframe we provided:\u001b[0m\n", "Empty DataFrame\n", @@ -212,7 +254,7 @@ "print(\"\\033[1mThe variables we provided:\\033[0m\")\n", "print(s.variables)\n", "\n", - "print(\"\\033[1mThe conditions we provided:\\033[0m\")\n", + "print(\"\\n\\033[1mThe conditions we provided:\\033[0m\")\n", "print(s.conditions)\n", "\n", "print(\"\\n\\033[1mThe dataframe we provided:\\033[0m\")\n", @@ -225,7 +267,7 @@ "source": [ "## AutoRA Components and the State\n", "\n", - "Now that we have initialized the state, we need to start adding components of `AutoRA` to the state - namely, experiment runners, experimentalists, and theorists. \n", + "Now that we have initialized the state, we need to start preparing components of `AutoRA` to work with the state - namely, experiment runners, experimentalists, and theorists. \n", "\n", "These components are defined in the same way as past tutorials. All we need to do so that these can function within the state is to wrap them in specialized state functions. The wrappers are:\n", "- `on_state()` for experiment runners and experimentalists\n", @@ -249,7 +291,7 @@ "source": [ "### Experimentalist Defined and Wrapped with State\n", "\n", - "We will use autora's `random_sample` sampler for our experimentalist. We import this and then wrap it so that it functions with the state." + "We will use autora's `random_pool` pooler for our experimentalist. We import this and then wrap it so that it functions with the state." ] }, { @@ -258,10 +300,10 @@ "metadata": {}, "outputs": [], "source": [ - "from autora.experimentalist.random_ import random_sample\n", + "from autora.experimentalist.random_ import random_pool\n", "from autora.state.delta import on_state\n", "\n", - "experimentalist = on_state(random_sample, output=[\"conditions\"])" + "experimentalist = on_state(random_pool, output=[\"conditions\"])" ] }, { @@ -278,14 +320,15 @@ "metadata": {}, "outputs": [], "source": [ - "def experiment_runner(conditions: pd.DataFrame):\n", + "from autora.state.delta import on_state\n", + "\n", + "def run_experiment(conditions: pd.DataFrame, added_noise: float = 0.5):\n", " x = conditions[\"x\"]\n", - " y = np.sin(x) + np.random.normal(0, 0.5, size=x.shape)\n", + " y = np.sin(x) + np.random.normal(0, added_noise, size=x.shape)\n", " observations = conditions.assign(y = y)\n", - " print(observations)\n", " return observations\n", "\n", - "experiment_runner = on_state(experiment_runner, output=[\"experiment_data\"])" + "experiment_runner = on_state(run_experiment, output=[\"experiment_data\"])" ] }, { @@ -313,7 +356,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Running Each Component Within the State" + "## Running Each Component With the State" ] }, { @@ -322,7 +365,7 @@ "source": [ "### Run the Experimentalist\n", "\n", - "Let's run the experimentalist within the state and see how the state changes." + "Let's run the experimentalist with the state and see how the state changes." ] }, { @@ -336,24 +379,29 @@ "text": [ "\u001b[1mPrevious Conditions:\u001b[0m\n", " x\n", - "0 0.000000\n", - "1 0.000000\n", - "2 4.188790\n", - "3 2.792527\n", - "4 2.094395\n", - "5 6.283185\n", - "6 5.585054\n", - "7 2.094395\n", - "8 4.188790\n", - "9 0.000000\n", + "0 0.634665\n", + "1 6.092786\n", + "2 0.126933\n", + "3 1.586663\n", + "4 0.571199\n", + "5 5.965853\n", + "6 5.013855\n", + "7 1.967462\n", + "8 2.157862\n", + "9 0.634665\n", "\n", "\u001b[1mUpdated Conditions:\u001b[0m\n", " x\n", - "8 4.188790\n", - "1 0.000000\n", - "5 6.283185\n", - "0 0.000000\n", - "7 2.094395\n" + "0 5.140788\n", + "1 3.554125\n", + "2 5.077321\n", + "3 3.998391\n", + "4 1.205864\n", + "5 0.380799\n", + "6 4.696522\n", + "7 0.444266\n", + "8 2.602127\n", + "9 5.204254\n" ] } ], @@ -361,7 +409,7 @@ "print('\\033[1mPrevious Conditions:\\033[0m')\n", "print(s.conditions)\n", "\n", - "s = experimentalist(s, num_samples=5)\n", + "s = experimentalist(s, num_samples=10)\n", "\n", "print('\\n\\033[1mUpdated Conditions:\\033[0m')\n", "print(s.conditions)" @@ -389,20 +437,19 @@ "Empty DataFrame\n", "Columns: [x, y]\n", "Index: []\n", - " x y\n", - "8 4.188790 -0.726505\n", - "1 0.000000 0.505258\n", - "5 6.283185 -0.290439\n", - "0 0.000000 -0.262585\n", - "7 2.094395 0.580335\n", "\n", "\u001b[1mUpdated Data:\u001b[0m\n", " x y\n", - "0 4.188790 -0.726505\n", - "1 0.000000 0.505258\n", - "2 6.283185 -0.290439\n", - "3 0.000000 -0.262585\n", - "4 2.094395 0.580335\n" + "0 5.140788 -0.661275\n", + "1 3.554125 -0.470063\n", + "2 5.077321 -0.610304\n", + "3 3.998391 0.005765\n", + "4 1.205864 0.817071\n", + "5 0.380799 0.254594\n", + "6 4.696522 -0.210268\n", + "7 0.444266 0.813512\n", + "8 2.602127 0.278940\n", + "9 5.204254 -0.610173\n" ] } ], @@ -410,7 +457,7 @@ "print(\"\\033[1mPrevious Data:\\033[0m\")\n", "print(s.experiment_data)\n", "\n", - "s = experiment_runner(s) #TODO: Why does it print the experiment data automatically?\n", + "s = experiment_runner(s)\n", "\n", "print(\"\\n\\033[1mUpdated Data:\\033[0m\")\n", "print(s.experiment_data)" @@ -450,7 +497,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:03<00:00, 26.73it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 26.37it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -462,6 +509,16 @@ "\u001b[1mUpdated Model:\u001b[0m\n", "-0.04\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -471,7 +528,9 @@ "s = theorist(s)\n", "\n", "print(\"\\n\\033[1mUpdated Model:\\033[0m\")\n", - "print(s.model)" + "print(s.model)\n", + "\n", + "plot_from_state(s)" ] }, { @@ -504,23 +563,11 @@ "INFO:autora.theorist.bms.regressor:BMS fitting started\n" ] }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " x y\n", - "1 0.000000 -0.462041\n", - "5 6.283185 -1.219553\n", - "8 4.188790 -0.564305\n", - "0 0.000000 -0.125522\n", - "7 2.094395 0.784092\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:03<00:00, 26.29it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 22.78it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -528,29 +575,94 @@ "name": "stdout", "output_type": "stream", "text": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.6981317 , 1.3962634 , 2.0943951 , 2.7925268 ,\n", - " 3.4906585 , 4.1887902 , 4.88692191, 5.58505361, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "1 0.000000\n", - "5 6.283185\n", - "8 4.188790\n", - "0 0.000000\n", - "7 2.094395, experiment_data= x y\n", - "0 4.188790 -0.726505\n", - "1 0.000000 0.505258\n", - "2 6.283185 -0.290439\n", - "3 0.000000 -0.262585\n", - "4 2.094395 0.580335\n", - "5 0.000000 -0.462041\n", - "6 6.283185 -1.219553\n", - "7 4.188790 -0.564305\n", - "8 0.000000 -0.125522\n", - "9 2.094395 0.784092, models=[-0.18, -0.18])\n" + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.06346652, 0.12693304, 0.19039955, 0.25386607,\n", + " 0.31733259, 0.38079911, 0.44426563, 0.50773215, 0.57119866,\n", + " 0.63466518, 0.6981317 , 0.76159822, 0.82506474, 0.88853126,\n", + " 0.95199777, 1.01546429, 1.07893081, 1.14239733, 1.20586385,\n", + " 1.26933037, 1.33279688, 1.3962634 , 1.45972992, 1.52319644,\n", + " 1.58666296, 1.65012947, 1.71359599, 1.77706251, 1.84052903,\n", + " 1.90399555, 1.96746207, 2.03092858, 2.0943951 , 2.15786162,\n", + " 2.22132814, 2.28479466, 2.34826118, 2.41172769, 2.47519421,\n", + " 2.53866073, 2.60212725, 2.66559377, 2.72906028, 2.7925268 ,\n", + " 2.85599332, 2.91945984, 2.98292636, 3.04639288, 3.10985939,\n", + " 3.17332591, 3.23679243, 3.30025895, 3.36372547, 3.42719199,\n", + " 3.4906585 , 3.55412502, 3.61759154, 3.68105806, 3.74452458,\n", + " 3.8079911 , 3.87145761, 3.93492413, 3.99839065, 4.06185717,\n", + " 4.12532369, 4.1887902 , 4.25225672, 4.31572324, 4.37918976,\n", + " 4.44265628, 4.5061228 , 4.56958931, 4.63305583, 4.69652235,\n", + " 4.75998887, 4.82345539, 4.88692191, 4.95038842, 5.01385494,\n", + " 5.07732146, 5.14078798, 5.2042545 , 5.26772102, 5.33118753,\n", + " 5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,\n", + " 5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,\n", + " 6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 1.967462\n", + "1 1.142397\n", + "2 5.648520\n", + "3 3.807991\n", + "4 4.696522\n", + "5 6.092786\n", + "6 2.792527\n", + "7 2.221328\n", + "8 4.506123\n", + "9 5.140788\n", + "10 1.078931\n", + "11 5.267721\n", + "12 5.458121\n", + "13 2.475194\n", + "14 0.571199\n", + "15 1.650129\n", + "16 4.442656\n", + "17 6.156252\n", + "18 0.698132\n", + "19 4.886922, experiment_data= x y\n", + "0 5.140788 -0.661275\n", + "1 3.554125 -0.470063\n", + "2 5.077321 -0.610304\n", + "3 3.998391 0.005765\n", + "4 1.205864 0.817071\n", + "5 0.380799 0.254594\n", + "6 4.696522 -0.210268\n", + "7 0.444266 0.813512\n", + "8 2.602127 0.278940\n", + "9 5.204254 -0.610173\n", + "10 1.967462 0.690645\n", + "11 1.142397 0.676767\n", + "12 5.648520 -0.471927\n", + "13 3.807991 -1.574799\n", + "14 4.696522 -1.862333\n", + "15 6.092786 -0.470395\n", + "16 2.792527 -0.164395\n", + "17 2.221328 0.952886\n", + "18 4.506123 -1.432814\n", + "19 5.140788 -1.615784\n", + "20 1.078931 1.614278\n", + "21 5.267721 -0.962614\n", + "22 5.458121 -0.700828\n", + "23 2.475194 -0.094215\n", + "24 0.571199 0.268449\n", + "25 1.650129 1.052316\n", + "26 4.442656 -1.539339\n", + "27 6.156252 0.061257\n", + "28 0.698132 0.342468\n", + "29 4.886922 -1.130655, models=[sin(x), sin(x)])\n" ] + }, + { + "data": { + "image/png": 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jY1m0aBHLli3jzjvvpFu3bnz77bdFuu6/P7fQ0FDS0tLIysrCx8cn33nbtm0jNTUVs9lMXFzcBctdJSYmAlClSpUL9kdGRhZYz+VSZ2kRESkXISEhdO/enffee6/Ec8M0adLkgj42a9euzetrkvsl+s9RWv/sjFyc62zYsCHfvt9++63Yr1OYotSaGyZsNttlX69Vq1bs2bPngg7ugYGB3HXXXXz00Ud89dVXfPfdd3mhpLhy51n6ZydoOB9mBg4cyDPPPMPAgQPp168f6enp+Y7ZsWMH3t7eNG3a9IL9rVq1KlE9RaEWIRERKTfvv/8+nTp1om3btkyYMIHmzZtjNpvZtGkTe/bsueTtoyeffJI777yTVq1a0a1bN+bPn8/333+fN+zez8+PK6+8kldffZW6deuSkJDAs88+W+w6hw8fzsCBA2nbti2dOnXi888/Z+fOnZfVWfrfilJr7dq1MZlMLFiwgF69euHn51esYen/1KVLF1JSUti5cyfNmjUD4M033yQiIoJWrVphNpv55ptvCA8Pv6A/UlFVqVKF1q1bs2bNmnyTTw4ZMoSaNWvy7LPPkpmZSatWrRg9ejTTpk3LO2b16tVcddVV+VoGDx48yLFjx+jWrVuJ6ikKtQiJiEi5iYyM5Pfff6dbt26MGzeOFi1a0LZtW959911Gjx7Niy++WOj5ffr04e233+b111+nadOmfPDBB3zyySdce+21ecd8/PHH5OTk0KZNG0aMGMFLL71U7DrvuusunnvuOcaMGUObNm04dOgQQ4cOLfbrXMqlaq1evToTJ05k7NixVK1alWHDhpX4WiEhIdx66618/vnnefsCAgKYMmUKbdu2pV27dhw8eJCFCxfm3bYriQceeCDfNT799FMWLlzIZ599hpeXFxUqVGD27Nl89NFHLFq0KO+4L7/8kgcffDDfa33xxRdcf/31RerEXVJaa+wStNaYiDgbrTUmJfXHH3/QvXt39u/fX+KWpUtJT0+nUaNGfPXVV0RHRxfpnEWLFjFq1Cj++OMPvLzO36zKysqiQYMGzJkzh06dOl30PK01JiIiIkXWvHlzJk+eTGxsbJldw8/Pj08//ZRTp04V+ZzU1FQ++eSTvBAE54f2P/300wWGoNKiFqFLUIuQiDgbtQiJnKcWIREREZHLoCAkIuKi1KAvnq40fgcUhEREXEzuhHZlOduuiCvI/R349+SSxaF5hEREXIzFYqFSpUp5yyn4+/tfdEkDEXflcDhIS0sjISGBSpUqYbFYSvxaCkIiIi4oPDwcoNC1pUTcXaVKlfJ+F0pKQUhExAWZTCYiIiIICwsr1vpUIu7C29v7slqCcikIiYi4MIvFUipfBiKeSp2lRURExGMpCImIiIjHUhASERERj6UgJCIiIh5LQUhEREQ8loKQiIiIeCwFIREREfFYCkIiIiLisRSERERExGMpCImIiIjH0hIbIqXIZnewMTaRhHMZhAVYaV83GItZq4KLiDgrBSGRUrJ4RxwT5+8iLikjb19EkJXxvaPo2SzCwMpERKQgujUmUgoW74hj6Oyt+UIQQHxSBkNnb2XxjjiDKhMRkcIoCIlcJpvdwcT5u3Bc5LncfRPn78Jmv9gRIiJiJAUhkcu0MTbxgpagf3IAcUkZbIxNLL+iRESkSBSERC5TwrmCQ1BJjhMRkfKjICRymcICrKV6nIiIlB+XCkKrVq2id+/eVKtWDZPJxLx58y55zooVK2jdujW+vr7Ur1+fmTNnlnmd4lna1w0mIshKQYPkTZwfPda+bnB5liUiIkXgUkEoNTWVFi1aMG3atCIdHxsby4033kiXLl2IiYlhxIgRPPDAAyxZsqSMKxVPYjGbGN87CuCCMJT7eHzvKM0nJCLihEwOh8Mlh7KYTCbmzp1Lnz59Cjzmqaee4qeffmLHjh15++6++27Onj3L4sWLL3pOZmYmmZmZeY+Tk5OpWbMmSUlJBAYGllr94vzSc9KJS40jMT2RxIzz25mMM2TaMsmx52Bz2Mi2ZwPg7+1P3Klstu05il/6ObJyKrE/uwnBfjWYcFNrzSMkIlLOkpOTCQoKuuT3t1tPqLh+/Xq6deuWb1+PHj0YMWJEgedMmjSJiRMnlnFl4mwybZnsTdzLgaQDHEw6yMHkg8SnxuO46KB4wG6DnMz/3zLO/9eWReVAB/aKDryB5qaVWKwV+XZfFdYlNqJBaBSNgxvTsHJDgnyDyvX9/ZNmvxYR+ZtbB6H4+HiqVq2ab1/VqlVJTk4mPT0dPz+/C84ZN24cI0eOzHuc2yIk7sXhcHAo+RB/nPqDP07+we7E3eTYcy44LsgniBAvf4IdEJyVSXDGOfxSTuGVkYQFsPz/cWmYSDP5kuptJaVCEKcyEjmek0pi5jnOZZ5j75lY9h5ZxQKfiuBbkYiAmjQLbUa78HZEhUThbfYul/et2a9FRPJz6yBUEr6+vvj6+hpdhpSRI+eOsObYGtYcW8Op9FP5nguxBtPIL5w6Di/qZGVSJyWRoOMHIfPcRV7JBwKrQUh9CG34//9tAP4hYDKBwwGn/iR9/3JOHFzJ4XOH2WvLZk/mKY6mnCAu+ThxJ3ey9MBC/H2DaFO1De3C29E6rDXelrIJRbmzX/+7jSt39uvp/XULT0Q8j1sHofDwcE6cOJFv34kTJwgMDLxoa5C4p3NZ51h5dCWrj67mYPLB8zsdDnztdqJ8KtPC4UPztBSqxf+FybbrwhcwW6By3fNBJ6T+35tvxYIvajJBlUb4VWlEnSsfoc7ZI1x9cDXEriYlYSd7snP4PSOJTeZTJHn7sDrpMKsPLSPAL4Sra1xD19pdqV6xeql9Bpea/drE+dmvu0eF6zaZiHgUtw5C0dHRLFy4MN++pUuXEh0dbVBFUp6OnjvKothFrDr8C1lZKZCTgSUni5Z2C1elZ9LGbsGHk/lP8vaHkMi/W3pCG0Cl2uDlc3nFVKoJLe+BlvdQMeUkbQ+toW3sagbH/c6fOVlszExhfeoZEr2O8dOZA/y091uaVG3N9XWup0NEByxmy6WvUYjizH4dHRlyWdcSEXElLhWEUlJS2LdvX97j2NhYYmJiCA4OplatWowbN45jx47x6aefAjBkyBDee+89xowZw/33388vv/zC119/zU8//WTUW5Ay5rDb2bF3Lgv2zSMmOfZ8R2Z7DnUcFrrafLjS7sX5sQNe4Ff577CT28oTWB3MZTyrRMUq0PRWaHor5oxkGh/+jcYHV9H/yAZiMlJZln2O39PPsPvcMXYf/pWqATW5pWl/rq7VtcS3zTT7tYjIxblUENq8eTNdunTJe5zbqXnAgAHMnDmTuLg4Dh8+nPd83bp1+emnn3jiiSd4++23qVGjBv/973/p0aNHudcuZW/vnwv4Yss77M5IAM7f7mlr96aXrQJNAmpjyg08ueHH3wkmOLQGQsProeH1mLMzaH10E60Prub0odX8kp3IElsyJzJ28OHKp/nWGkzv2tfTtfUQfH2LN5WDZr8WEbk4l51HqLwUdR4CMc6B2F/4atObxKQeBcALM10rR9Gr1nWER7Q9f6vLp4LBVRaTLQfitpFx4FeWH/yZ+TmnOGOyAxDs5U/flkPp3KwfZlPRWq9sdgedJ/9CfFLGRfsJmYDwICtrnrpOfYRExC0U9ftbQegSFISc15nE/Xy+8hlWn90DgBkTXUKacVvHZwgNbWxwdaXIbic7YScrd3zG3GMrOWU/P+FnvUqR3NvpeaLCWhTpZXJHjQH5wlBu7NGoMRFxJwpCpURByPnkZGewZO0rfH3wJzIcNkxAp0qNuOPKsYRHtDK6vDKVlXqKhb+MY+6pzWTgAIsP7Wp3ZWD7UYT6hV7yfM0jJCKeQkGolCgIOZdde+bxv81vcDT7/Nw+kT7BDL5yHJGR3Q2urHwl/fUz36x/heW2s9gxYa1QhbtaD6Nn5E2XvF2mmaVFxBMoCJUSBSHnkJ6dxue/PMXS46sBCDB707fBf+jSYRRmi0v1+S89GckcWfUK/z2yjD3mHLD4Ur9aOx5q/yS1A2sbXZ2IiKEUhEqJgpDxdiX8wfQVY0hIjQegW/AV9L1uChUDdCsHwH5gJb+sfpHZ9tOkAxb/EPo0H8xtje7Ay+yhIVFEPJ6CUClxxyDkKrdGMm2ZfLH9Yxbt+Ayy0wh1mBnSdABXdBhe4Dmu8t5KXfpZEldNYebRpWwwZ4OXL/Uj2vNY9DOEVwg3ujoRkXKnIFRK3C0IuUpn2SPJR3hrw8sci9sKtiy6UoH+107Cv+7VBZ7jKu+tTB1YwbrVL/GR/TRpgLViVQa2G821tbtiMnlAIBQR+X8KQqXEnYJQQYtuOtPwaYfDwa9HfuWTre+SdfYwle0OhvhUp2WvtyG4XoHnucJ7KzdpiZxaOYn3jv/KbnMOePnSoW4PHu7wFBW8XWw+JRGREirq93cZryUgzuJSi27C+UU3bXbjcnF6TjrvxbzHBxumkHXmIM1tJiZXakvL22YVGoJc4b2VK/9gQntO4fmrJ9HXVAlLTiYb/prPuAX9OXRmv9HViYg4FQUhD1GcRTeNcPTcUcatGsuavXMxp8Rzt82XcbVvJujm9y65FIazvzdDmEyYG3Snz53f82LY1VRxmDhxNpZnF9zLqt1fG12diIjTUBDyEM686OaWE1t4ZvU44uK2EJx2lvHZFbi11RDMXZ8r0qrvzvzeDOcfTGSvt5kUPYEWJn+yctKY9tskPl7yGNk5Hvh5iIj8i4KQh3DGRTcdDgdz/5rLa7+9QsbpP2mSkcGr9so0vu4FaDMQiti51xnfm1MxmQhocjNj//MDtwdFAQ6WHF/NC9/0JunccaOrExExlIKQh2hfN5iIICsFRQsT50dYta9bPiuyZ9oyeef3d/hyx0wcZw/RPdPBs17VCer9LtTvWqzXcrb35qzMFatw562fM+aKh/E3efFnxkme/eFujpzcaXRpIiKGURDyEBazifG9owAuCAy5j8f3jiqXOXeSMpOYsG4C62KXYDl7hAezvHkgqClet30AVaOK/XrO9N6cnslEm7ZDeem6dwgz+5KQnczziwbzx+FVRlcmImIIBSEP0rNZBNP7tyY8KP8tovAga7kNLz+ecpxn1zzDgbhNBCTF8Wy2H91qXAO3TIOAkk/85wzvzZVUr9WRl3t9QmNLRdJsGUxaMZpl6kQtIh5I8whdgjvNI5TLqNmX9ybuZcrGV0lJ3E/VjBTGZfsTccXdcOUjYC6dTO6xM0uXUHbSET6YP5DV2afB7MUtzQbQt/UwTb4oIi5PEyqWEncMQkbYELeBd7e8RfbZQ9TPymJMTkWCOo+CqJuNLs3jOVJP8/2PA/g64yiYLFzboA8PRT+NxWwxujQRkRLThIriNJYdWsZbG14lO3E/bTKzed4cTlCvNxWCislmd7B+/2l+iDnG+v2nS22CSFOFEG6/dQ4PVWiA2WFjxV9zeXPlOLJsWaXy+iIizkwtQpegFqHL8+P+H/l824eQfJzuOV4M8o/EcsOrULm20aW5lHJZRy0rjU0LhvB20naygSbVoxnT5XX8vf1L5/VFRMqRWoTEUA6Hg6/2fMXnW6dB0jFuzfFmcJUOWG6bUWohqKxaSJxN7jpq/549Oz4pg6Gzt7J4R1zpXMjHn3Y3f8TTodH4AbuPrWfC0qEkZSaVzuuLiDghtQhdglqEis/usPPpjpks2vkZpJ/hnhwrtzS4Fa4aCRbvUrmGp6w0b7M76Dz5lwKXEDFxfmTcmqeuK71O4bYcDv48llfilpNkghqhTXiu+/tUslYqndcXESkHahESQ9gddj7Y+i6L/vgfpJ/hfpsft7QbDteMKdUQVC4tJE7AkHXULF7U6TGFCbVvprLDxNFTu5i4+EFOp58uvWuIiDgJBSEpNXaHnQ82vc6KnXMwZ6XyqCOIHl0nQ8u+RV4u41I8baV5w9ZRM5updt0EJjS4mxCHmeNn/mLiosGcSjtZutcRETGYgpCUCrvDzgdrX2TF7q8w2zJ5zCucq3t/BHWvLtXreNpK84auo2YyEd75SSY0fYAwh5kTSQeZuHAQCaknSv9aIiIGURCSy2Z32PnglzGs+GseZruNx/wb0PHWz6BKw1K/lqetNG/4OmomE2EdHmF8y8cJd5hJOHeUF38ayKlUtQyJiHtQEJLLYrfl8MGih1lxeBlmHDwW2oGOt30KFauUyfU8baV5Z1lHLbT1QMa3G0tVh4WE1DheXNCfM6kJZXpNEZHyoCAkJeaw2/l44QOsOLEJM/BY7ZvoeON08PYrs2sa3kJiAGdZRy34ijt5vuN4wrAQn3aCF+f3I0lhSERcnIbPX4KGz1+cw+Fgzs+P8+Px1ZiAYU0G0PnKJ8rl2rmjxoB8naZzw5G7LrLqLOuoJfy1hPFrniGRHGr5hvD8zXMIqFi13OsQESmMhs9LmZq34ll+PL4agAcb3FluIQicp4WkvFnMJqIjQ7ilZXWiI0MMW0w2rEEPnr9mMpVMXhzOPM0rP/QlTX2GRMRFqUXoEtQidKFF6yYzc+8XANxXuyc3XveqIXU4SwuJpzpyaBUTV4zinD2bJn7hPHPrd3j7VjC6LBERQC1CUkZ+3TKdmXu/BOA/4R25scskw2pxlhYST1Wz9tU8ffVkrCYLu9PjmbrgXmw52UaXJSJSLApCUmSbd37Fh398BDjoFXwF/7n+3VKbKFFcU7261zGm3Vi8MbE5+QAfLnoQh91udFkiIkWmICRFsvfAUqZumoIdO9cGRHLfjR9jsliMLkucQNOmdzD8iocwAytOxTB7+Uh0x11EXIWCkFzSkeObmLL6abIdNlr7RfDQzZ9h8iqddcPEPbRrO5Qh9f8DwIKjK5i/9mWDKxIRKRoFISnUqdN/8sqyx0mxZ9PQpzIjbv4Mi4+/0WWJE7rmqme5t3oXAD7/6ztW//6RwRWJiFyagpAUKOVcHJMWPUCiLZ3qlgqMuWkWvv6hRpclTuymbm9wY3BzwMGMbdPZ8eePRpckIlIoBSG5qOysNF5fMICj2ckEm315uueHBATVMroscXZmM/1v/C/RFWqR47Dz+voXOHz0N6OrEhEpkMsFoWnTplGnTh2sVisdOnRg48aNBR47c+ZMTCZTvs1qdY81qMqSw25n+k/3szsjAT+TF+O6vEFoWFOjyxIXYfby4dHen9HEN5R0ew6TfnmC06f/MrosEZGLcqkg9NVXXzFy5EjGjx/P1q1badGiBT169CAhoeD1jgIDA4mLi8vbDh06VI4Vu6Zvlo9m7dk9WDAxqt1oatXqbHRJ4mK8/YIYfdNMqntVJNGWzqRFgzX7tIg4JZcKQm+++SYPPvgggwYNIioqihkzZuDv78/HH39c4Dkmk4nw8PC8rWpVrYlUmF83vsN3R38B4MGGd3JF07sNrkhcVcXAGozrMYPKZl+OZCfz1k+DNOGiiDgdlwlCWVlZbNmyhW7duuXtM5vNdOvWjfXr1xd4XkpKCrVr16ZmzZrccsst7Ny5s9DrZGZmkpycnG/zFNv3/shHO2cCcGvVK+nSaZyxBYnLqxLWjDFXvYwvZv5IPconSx7RHEMi4lRcJgidOnUKm812QYtO1apViY+Pv+g5jRo14uOPP+aHH35g9uzZ2O12OnbsyNGjRwu8zqRJkwgKCsrbatasWarvw1kdjdvKm7+9iA07nSrW5a4e04wuSdxEvXrdeOyKhzABSxM2sWj9ZKNLEhHJ4zJBqCSio6O57777aNmyJddccw3ff/89VapU4YMPPijwnHHjxpGUlJS3HTlypBwrNsa5lBNMXv44afZsGvsEM/SmmZo1WkpVu7ZD6FfjOgA+2/s1W3Z/Z3BFIiLnuUwQCg0NxWKxcOLEiXz7T5w4QXh4eJFew9vbm1atWrFv374Cj/H19SUwMDDf5s6yczJ446dBJGSnEGa2MuqG/+LtF2R0WeKGbrruNboG1MeOnXc2vsrBuK1GlyQi4jpByMfHhzZt2rB8+fK8fXa7neXLlxMdHV2k17DZbGzfvp2IiIiyKtOlOOx2/rfwYXanHcdqsjDmmskEBtczuixxUyaLhftv+pgrfILJsGczZflwklILHvEpIlIeXCYIAYwcOZKPPvqIWbNmsXv3boYOHUpqaiqDBg0C4L777mPcuL87+L7wwgv8/PPPHDhwgK1bt9K/f38OHTrEAw88YNRbcCoLV0/k19PbMANPtHyMmnWuMbokcXNe1kCe6PkREWZfTmef4/WFg8nOyTK6LBHxYC4VhO666y5ef/11nn/+eVq2bElMTAyLFy/O60B9+PBh4uLi8o4/c+YMDz74IE2aNKFXr14kJyezbt06oqKijHoLTuP3P2Yz+8D55Q/617mJli0HGluQeIwKIZGM6fwS/pj5M+UI/102XCPJRMQwJof+BipUcnIyQUFBJCUluU1/oWPHNvD00kfIcNjoGnwFD/aehcnsUplY3EDMxneZvPN/2IEBTQfSq/0Io0sSETdS1O9vfft5mNSUeF77ZSQZDhtNrGEM6vWBQpAYomX7x+gffn7W8s92fcq2fQsNrkhEPJG+AT2I3ZbDOwsfIC4nlRCLlSdu+BBvb3+jyxIP1qv7W1xboRZ2h523100k7tReo0sSEQ+jIORBvvj5cWJSj+JtMjP6qlcIqlTH6JLEw5m8vHngxo9p6BVIqi2T138eSnqm58zmLiLGUxDyEGs3vceP8esAGNr0furVva5UXtdmd7B+/2l+iDnG+v2nsdnV5UyKx7tCKKO6v0tlkxdHMxOZvughHHa70WWJiIfwMroAKXsHD65kxs7zC9PeHB5Np3bDSuV1F++IY+L8XcQlZeTtiwiyMr53FD2baa4mKbpK4S0Y2e4pJm58hQ1n9jBv9XhuveZFo8sSEQ+gFiE3l5KVwhs7PiQLaFmhBn2vf7dUXnfxjjiGzt6aLwQBxCdlMHT2VhbviCvgTJGLa9j0Du6vdwsAXx1YwNadXxhckYh4AgUhN2Z32Hnn93dIIIew8FY81uu/mC2X3whoszuYOH8XF7sJlrtv4vxduk0mxdb16vF0r9wUBw7e3fQGcXG/G12SiLg5BSE39vXer9l2chs+Zh9GRT9HxYpFW5PtUjbGJl7QEvRPDiAuKYONsYmlcj3xICYTA3p9SCPfENIcOby+/HHS088YXZWIuDEFITe1MW4jc/fNBeDhFg9TJ6hOqb12wrmCQ1BJjhP5J2+fCozs+RGVzb4czT7Hh4seVudpESkzCkJu6FjKMabFTAOgV91edK7euVRfPyzAWqrHifsq6ajCSsH1eOLKp7FgYl3Snyxc+0oZVyoinkqjxtxMek46b2x+gwxbBk2Cm9CvSb8SvY7N7mBjbCIJ5zIIC7DSvm4wFrMJgPZ1g4kIshKflHHRfkImIDzo/DniuS53VGGjRrdw3/FNfHJwAbP3fU+9au1pEnl9WZYsIh5Ia41dgiutNeZwOHjn93dYd3wdla2VmXzVZIJ8gwoNNRdTlC+w3FFjQL4wlPuq0/u31hB6D5b78/Hvv1yK+/PhsNl4d+5/WHsulkoWP17t8w2VA2uUer0i4n6K+v2tIHQJrhSEFsUuYubOmVhMFsZHj6dRcKNi/6u8OF9gmkdILsZmd9B58i8FdqjPbTFc89R1hQbyXBkpJ3nu+z4ctqXSqEJ1nr99Hl4W71KuWkTcjYJQKXGVILQ3cS8T10/E5rAxIGoAver1Kva/ykvyBVbc1iZxf+v3n6bvR79d8rgvHryS6MiQIr1m/KHVjPtlOGnY6VXjOgZ0f/NyyxQRN6fV5z1IUmYSb219C5vDRsdqHbmh7g0lmuunJMPiLWYT0ZEh3NKyOtGRIQpBUiajCsNrX8WjjfsDsPDor6zXZIsiUkoUhFyczW7j7a1vcybjDNUrVueh5g9hMplKFGo0LF5KQ1mNKmx75RPcXLkZ4GDG5rc4fmpPCaoTEclPQcjFff3n1+w8vROrxcrINiPx8/IDShZqNCxeSkPuqMKC2gZNnO9LVuxRhSYTd/ecThOvIDLsWby57DEys9Mvt1wR8XAKQi5s64mtzNs3D4CHmz9MjYC/R9OUJNSU2ReYeBSL2cT43lEAF/ws5T4e3zuqRLdRLdYAhnebShAWjqSf5L/LRqBujiJyORSEXNTJtJN5kyb2qNODjtU75nu+JKGmLL/AxLP0bBbB9P6tCQ/KH8jDg6yXPbVC5YhWjGg+BDOwKn4jv8R8dJnViogn06ixS3DGUWPZ9mwmrJvAvrP7iAyKZGLHiXhfZDhxSef60bB4KS1lOarwhwUPMefkRrzNXrzY4yPqhrcqldcVEfeg4fOlxBmD0Cc7PmHxwcVU9K7IpKsmEeYfVuCxJQ01GhYvzs6Rlc5r3/RmS9YpqvpW5tXbf8TfN8DoskTESSgIlRJnC0Lrj69n6tapAIxpN4Y2Vdtc8hyFGnFXKSf3Mvan/px0ZNMhtAVP3DQTk0k/2yKieYTcUlxKHDO2zQDg5sibixSCQHP9iPuqWKURI1o/gRew4dQ2lmx+x+iSRMTFKAi5iGxbNm9tfStvMdW7G91tdEkiTqF+83voH3E1AJ/t/JR9R9cbXJGIuBIFIRcxa9csDiUfItAnkMdaPYbFbDG6JBGn0bPb63SwhpPjsDF1xVOkpJ8xuiQRcREKQi5g3bF1LD20FBMmhrUaRohf0dZnEvEUJi8fhvScTpjJh5PZyby/5BHNLyQiRaIg5OTiUuL44I8PAOhTvw8tqrQwuCIR5+RfuS5PtB+LF7DlzG4W/va60SWJiAtQEHJiWbasfP2C7mh4h9EliTi1elG3cV+N7gB8vvcL/jq82uCKRMTZKQg5sVk7z/cLCvIJYnjr4eoXJFIE1183iQ5+1bE57Ly9chwpaaeMLklEnJiCkJNad2wdyw4vy+sXVNla2eiSRFyCyeLFkBtmEGb25WROCtMXq7+QiBRMQcgJ/btfUPMqzQ2uSMS1+AfV5Ino5/DCxOakP1m49hWjSxLxKDa7g/X7T/NDzDHW7z+Nze68/xjxMroAye/f8wWpX5BIydRreBP3Hl3PJ4d+Ys5f39GoRkfq1+lidFkibs/V1qtUi5CT+Wz3ZxxKPkSAT4DmCxK5TD2ueYEOFWqRg523Vz9DamqC0SWJuLXcxb7/GYIA4pMyGDp7K4t3xBlUWcEUhJzIb3G/seTgEgCGtdR8QSKXy2Sx8HDPGYSZrSTkpPHB4qE47HajyxJxSza7g4nzd3Gxm2C5+ybO3+V0t8kUhJzEidQT+dYRaxnW0tiCRNxEhcBqjOg0AS9MbEjez5I1Lxldkohb2hibeEFL0D85gLikDDbGJpZfUUWgIOQEsu3ZvL31bdJz0mlYuSF3NbrL6JJE3Epk/Z7cU7c3AJ/tn0vsgeUGV2QMV+rAKq4n4VzBIagkx5UXdZZ2AnN2z2F/0n4qeldkeOvheJn1v0WktPW6egI7T/3BlnMHmbrmOSaFNcO/YlWjyyo3rtaBVVxPWIC1VI8rLy7XIjRt2jTq1KmD1WqlQ4cObNy4sdDjv/nmGxo3bozVauWKK65g4cKF5VRp0WyO38zC2PM1DW0xlFC/UIMrEnFPJrOZR3p+QIiXP/G2ND5a9DAOm83ossqFK3ZgFdfTvm4wEUFWTAU8b+J8+G5fN7g8y7oklwpCX331FSNHjmT8+PFs3bqVFi1a0KNHDxISLj4SZN26dfTt25fBgwfz+++/06dPH/r06cOOHTvKufKLO5V+iunbpgPQq24v2oa3NbgiEfdWsWJVhnd6ATNm1qUc5JfVLxhdUplz1Q6s4nosZhPje0cBXBCGch+P7x2FxVxQVDKGSwWhN998kwcffJBBgwYRFRXFjBkz8Pf35+OPP77o8W+//TY9e/bkySefpEmTJrz44ou0bt2a9957r5wrv1COPYe3t75NSnYKkUGR3NPkHqNLEvEIjep14+7IWwD4JPZHjuxbYnBFZctVO7CKa+rZLILp/VsTHpT/9ld4kJXp/VtfcBs2IyeDXad3lWeJF3CZzihZWVls2bKFcePG5e0zm81069aN9evXX/Sc9evXM3LkyHz7evTowbx58wq8TmZmJpmZmXmPk5OTL6/wAny992v+PPMnfl5+DG89HG+zd5lcR0Qu1Lvzs+w8uY1tyQd4a+0EXqnaHGuAe/aTcdUOrOK6ejaLoHtUOBtjE0k4l0FYwPnbYRdrCfpkxyesPLqSfk360TuytwHVulCL0KlTp7DZbFStmr9zY9WqVYmPj7/oOfHx8cU6HmDSpEkEBQXlbTVr1rz84v/F4XBgNpkxYeLh5g9TtYLndNgUcQZms4VHe7xPZa8KHLOn88miIeCm8wu5agdWcW0Ws4noyBBuaVmd6MiQi4agVUdXseLoCkyYiKwUaUCV57lMECov48aNIykpKW87cuRIqV/DZDJxd+O7ee2a14iuFl3qry8ilxZUMZzHOk/EbDKzIvUQq1Y8Z3RJZcJVO7CKezuWcoz/bf8fALc3vJ2okCjDanGZIBQaGorFYuHEiRP59p84cYLw8PCLnhMeHl6s4wF8fX0JDAzMt5WVmgGl39okIkXXtG43bq9/KwD/O7SQY3t/Mrii0ueqHVjFfWXZsnh7y9tk2DJoFtKM2xrcZmg9LhOEfHx8aNOmDcuX/z0Rmt1uZ/ny5URHX7xVJTo6Ot/xAEuXLi3weBHxPLd1eoamQfXJwMHbv71IVtJRo0sqdcXtwCpSlj7d9SmHzh0iyCeIYa2GYTYZG0VcprM0wMiRIxkwYABt27alffv2TJ06ldTUVAYNGgTAfffdR/Xq1Zk0aRIAw4cP55prruGNN97gxhtv5Msvv2Tz5s18+OGHRr4NEbdgszuK1BnS2ZlNZh67fhpj5t3OoewUZi0awoN3zAWLew1gKE4HVpGysu74OpYeWooJE8NaDaOytbLRJblWELrrrrs4efIkzz//PPHx8bRs2ZLFixfndYg+fPgwZvPfybJjx47MmTOHZ599lqeffpoGDRowb948mjVrZtRbEHEL7jZLceWKVRnW+QUmrXiSZelHafrLs3TsPtnoskpdbgdWESPEp8bzwbYPAOhTvw/NqzQ3uKLzTA6HQ7NoFSI5OZmgoCCSkpLKtL+QiKvInaX4339x5LYruPKtli/XvszcP7/BionJV44nvEkfo0sScQvZtmyeW/ccsUmxNA5uzPNXPo/FbCnTaxb1+9tl+giJiPHcfZbiO6LH0rhyQzJwMHXDJLLPxBpdkohbmL17NrFJsQR4B/B4q8fLPAQVh4KQiBSZu89SbDFbeLz7ewT4BBDryGT2okchJ/PSJ4pIgTbEbWDxwcUAPNrqUUL8nOv2rIKQiBSZJ8xSHFIhjEc6TQSzhcWZx9mwfNylTxKRi0pIS2DGthkA3Bx5M63CWhlc0YWKHYQGDBjAqlWryqIWEXFynjJLces613FzwzsAmHH8V05s/8bgikRcT3Z2GlM3vkZaThoNKzfkrkZ3GV3SRRU7CCUlJdGtWzcaNGjAK6+8wrFjx8qiLhFxQuU1S7HN7mD9/tP8EHOM9ftPG9Ln6K4Oo2kY3Jg0HLy9+TWyT/1Z7jWIuLI5P49g/6EVVHSYGN56OF5m5xyoXuwgNG/ePI4dO8bQoUP56quvqFOnDjfccAPffvst2dnZZVGjiDiJ8pilePGOODpP/oW+H/3G8C9j6PvRb3Se/AuLd8SV+DVLwsvsxfBu71DRN5D9ZDFnyWOQlVauNYi4qk0xH7MwYSPYsxlaswehfqFGl1SgEvURqlKlCiNHjmTbtm1s2LCB+vXrc++991KtWjWeeOIJ/vrrr9KuU0ScRFnOUpw7NP/fHbLjkzIYOntruYeh0AphDO08EcxeLMw6wcalT4FmHBEpVMKJHUyPeR+AG6u2p23LQQZXVLjLaqeKi4tj6dKlLF26FIvFQq9evdi+fTtRUVFMmTKFJ554orTqFBEnUhazFF9qaL6J80Pzu0eFl+tsyG1rdaF34zuZv+sLpiesoc622YS1vLfcri/iSrKz05i67HFSHTk08A3lnu7vGF3SJRW7RSg7O5vvvvuOm266idq1a/PNN98wYsQIjh8/zqxZs1i2bBlff/01L7zwQlnUKyJOIneW4ltaVic6MuSyw4kzD82/u91IGoRGkYaDqb+/Q/aJHeVeg/zNGfqQycXN+Xk4+7MSqWjyYni3t/Hydv6BE8VuEYqIiMBut9O3b182btxIy5YtLzimS5cuVKpUqRTKExFP4cxD873MXozoOpWnfriT/Rln+OznEdx/x/dg1Wzz5c3dlndxJxt//y8LEzYBMLTVMKqENTW4oqIpdovQW2+9xfHjx5k2bdpFQxBApUqViI3VjKwiUnTOPjQ/1L8Kw656CSzeLMk5xfqfR6u/UDlztj5k8rcT8duY/v/zBfUOj6Zti4HGFlQMxQ5C9957L1ar8zd1iYhrKa+h+ZejVY1O3NKkH2BixulNxG3+0LBaPI27L+/iyrLTk3hr2WOkOXJo6BvK3d3eMrqkYtHM0iLiFMpjaH5puKvNYzQJa0EGDt7a/hFZx7YYWo+ncOY+ZB7NbmfWwgeJzU4mwOzD8B7vu0S/oH9SEBIRp1GWQ/NLi8Vs4bEurxPoF8ohUw4zl4+C9DNGl+X2nLkPmSdbs/J5lib/iQkTw658mtCQhkaXVGzOOc2jiHisshiaX9pC/EN57OpXeGXZYyy3naXx4uFcfctMMOvflmXF2fuQeaKjO7/ho4M/AXBr5C20bNTH2IJKSL+1IuJ0SntoflloXq09t18xEExmPjq7ncPrpxpdkltzhT5kniTjxA7e2jiFDBw0q9yYO6563uiSSkxBSESkhG5v+TAtItqThYM3935O2qG1RpfktlylD5kncKSe5r9LhnGUbCr7VubxHtMxm1w3Trhu5SIiBjObzAzrMoXgCuHEmWx8sOIpHOcSjC7LbblCHzK3Z8tm2cJHWG07i9niw+NdXiPIr7LRVV0Wk8OhiTAKk5ycTFBQEElJSQQGavI0EbnQnyd3MnHxYHJyMhhQsQG9bvsCLOqCWVZsdodT9yFzWw4H+5Y/y/gjP5FjstCv9TBubu6864gV9ftbLUIiIpepYZWm3Nv6cTCZmZ3yF3tXTzK6JLfmCn3I3FHytjm8eXgROUCHWl3ofcVAo0sqFQpCIiKloEdUXzrW6oINeOvAXJL+WmJ0SSKlxn50M+9uncppk52IypEMueoFTCb3CKAKQiIipcBkMvHQ1S9RPbA2Z0x23lo7HlvSEaPLErl8yXF8u3w0f5iy8bFWZmSXN/H39je6qlKjICQiUkr8vPwY1e1drN4V2O3I4ItFj0BOltFliZRcVhq/L3yM7+xnwcvKgx2fpVZQbaOrKlUKQiIipah6UC0e6fg8mC3MTz/C+uVPG12SSMnY7cQve5Z30/aD2YvuTfpyde2uRldV6hSERERKWYd6Pbi5wW0AzDi+nKPbvzK4IpHiy9j8X96IX0mqCRpUv5IBrR4xuqQyoSAkIlIG7r5yLE1DmpCBgze2vE7ayT1GlyRSZI79K/hg+0ccNtkIqlyXJzpNxNvibXRZZUJBSESkDFjMFoZ3f59g30ocd2Tz3pJHsWelGl2WyKWd2sfClc+xzpyNxS+EJ656mRC/EKOrKjMKQiIiZSTIrzKjr3sTb7M3W7JP893iYaA5bMWZpZ9h55KRzDadA58K3NtuJE1CmhhdVZlSEBIRKUOR4a15oOVQwMS3p39n88b3jC5J5OJs2ZxaMpapmUewW3zo3PBWetbrZXRVZU5BSESkjF3b4n56VL8KgPd2fcLxw+sMrkjkQplr3uS1xM0km03UrtaOh1o/5jaTJhZGQUhEpBzcd93rNKlQjXTsvL7iSdJSTxpdkkgex465fLDvWw6abARUjuTJjhPwtfgaXVa5UBASESkHXl4+PNHjQ4ItfhyzpfLuT/djt2UbXZYIHI9h/obXWGvOxlIhjJGdJ1LFv4rRVZUbBSERkXISFFSD0Z1fxNtkZmvqEb5cNESdp8VYyXHELB3DHHMa+AYyoN0ookKijK6qXCkIiYiUozp1unJD9Xux2R3MPbGZVateMrok8VRZacQtHs07tpM4vKxc1+Qurq9zfYleymZ3sH7/aX6IOcb6/aex2V0n4HsZXYCIiKdYvCOOifN3EZfUkFbBTbEFbeftPd+QmB5Kn55DjS5PXJjN7mBjbCIJ5zIIC7DSvm4wFnMhHZ3tdlJ+eYHJKbtJtVhoWPMq7m/+UIk6R//9c52Rty8iyMr43lH0bBZRkrdTrhSERETKweIdcQydvZXcfyf/ntiXNj6nyfY7zlex07GurE7Pa242tEZxTSUJIjlbZzL12DLizA5CQpsw6spnSjRz9L9/rnPFJ2UwdPZWpvdv7fRhSLfGRETKmM3uYOL8Xf/6sjCz9cRDWLKDSDU7mLtjAumn9htUobiq3CDyzxAEfweRxTviLjzpwEo+jZnBdnMO1qAajOn8IpWslYp97Yv/XJ+Xu2/i/F1Of5tMQUhEpIxtjE284IsKwOGwsiv+ARx2X+K8spk6/37saYkGVCiuqERB5PR+Fq98jiWWLEx+lRnW8XnqBNUp0fUL+rn+Zw1xSRlsjHXun2mXCUKJiYn069ePwMBAKlWqxODBg0lJSSn0nGuvvRaTyZRvGzJkSDlVLCJyXsK5gr8sMnOqcOhEf2x4scV+lq/nD4aczHKsTlxVsYNI+ln+WPQEs0gGb3/6tnmcduHtSnz9wn6uS3KcUVwmCPXr14+dO3eydOlSFixYwKpVq3jooYcued6DDz5IXFxc3jZlypRyqFZE5G9hAdZCn0/OaMCpUzeCycLctFhWLnoM7PZyqk5cVbGCiC2HI0vG8Fb2+eUzrm78H26u3+eyrn+pn+viHmcUlwhCu3fvZvHixfz3v/+lQ4cOdO7cmXfffZcvv/yS48ePF3quv78/4eHheVtgYGChx2dmZpKcnJxvExG5HO3rBhMRZKWg8TgmwGzuwq1N+gImPjy1kV2rXi7HCsUVFSeIJK15nSmJm0kzmWhUoxMPtbr85TOK8nMdEXR+BJszc4kgtH79eipVqkTbtm3z9nXr1g2z2cyGDRsKPffzzz8nNDSUZs2aMW7cONLS0go9ftKkSQQFBeVtNWvWLJX3ICKey2I2Mb73+Unq/v2lkft4fO8o+l45ig41riIHeOPA98T9Pqs8yxQXU9Qg0iLtVybv/4YEk53w0ChGd55YohFi/1bUn+tCh/E7AZcIQvHx8YSFheXb5+XlRXBwMPHx8QWed8899zB79mx+/fVXxo0bx2effUb//v0Lvda4ceNISkrK244cOVIq70FEPFvPZhFM79+a8KD8/4oPD7LmDTE2m8wMu+516gc3IsXk4NXf3yFp/3KDKhZnV5QgMqWTnfc3TWG/yUZAQHXGXvsagT6F3xkpjqL8XDs7Q+cRGjt2LJMnTy70mN27d5f49f/Zh+iKK64gIiKCrl27sn//fiIjIy96jq+vL76+nrHQnIiUr57NIugeFV7oxHc+Fh+evH4Gz83vS3xqPFNWPc3zFT/Gt2pTAysXZ5UbRP49j1B4kJVXuoVyYNfjbDJl4W0N4skubxBRsfSDSVF+rp2ZyeEwbqGbkydPcvr06UKPqVevHrNnz2bUqFGcOXMmb39OTg5Wq5VvvvmGW2+9tUjXS01NpWLFiixevJgePXoU6Zzk5GSCgoJISkq6ZP8iEZHScizpMM8v6E9KVjJtzAGMvu0bzAHhRpclTuqCmaVr+LF07j18kn4QvKwMv2YSHWt1MbrMclXU729DW4SqVKlClSqXXuE2Ojqas2fPsmXLFtq0aQPAL7/8gt1up0OHDkW+XkxMDAAREc7fVCcinq16UC2e7DqVl5Y+wpacc3w8fyCDb/sGkzXA6NLECVnMJqIjQ84/cDhYt2AoM9MPgtmLvq0e8bgQVBwu0UeoSZMm9OzZkwcffJCNGzeydu1ahg0bxt133021atUAOHbsGI0bN2bjxo0A7N+/nxdffJEtW7Zw8OBBfvzxR+677z6uvvpqmjdvbuTbEREpksbhrXms4wRMZi+WZsbz408Pgi3H6LLEye1c+xrTTv2GAxM9GtzKLU3vNbokp+YSQQjOj/5q3LgxXbt2pVevXnTu3JkPP/ww7/ns7Gz27t2bNyrMx8eHZcuWcf3119O4cWNGjRrF7bffzvz58416CyIixdYhsicDWj4CJjNzkvew4ucnwLgeDR6pKCurO8vq6wd3fcdrf31BDtChWjQDo8dd9jB5d2doHyFXoD5CIuIMZq99kfl/fo8ZByMj76Dd1c8YXZJHKMqCps6y+nrC8U089/NQzjpyaBJUn2du+aJUhsm7qqJ+fysIXYKCkIg4A4fDwYxlw1lxdBXemHi65eNEtRpkdFluraCV1XPbV6b3bw1wyWPKIwwlnT3I+B/7EWdLpZY1hAm3fk8Fa1CZX9eZFfX722VujYmIeDKTycRDXd+iTXBTsnEwOeZdYv9aZHRZbqsoC5pO+HEnE340fvX1lKQjvLzgPuJsqYRa/Bh3w/88PgQVh4KQiIiLsJgtjOj1P5pUqEEGdl5Z+zxxxzYZXZZbKsqCpvHJmcQnG7v6enrycSbP78+h7GSCzL482/19givVKbPruSMFIRERF+LjbWVM78+o4xtMsiObF5c9RkLc70aX5XZKc8X0slp9PTvlBG/82I8/s5OoaPblmW7vEhHRqkyu5c4UhEREXIy/X2We7vUJ1bwCOG3P4MWfh3BaYahUleaK6WWx+npOSgJvzbuH7dlnsJp9GHvdG9Su3r7Ur+MJFIRERFxQUKXaPHvTLKp6B5Bgz+TFn4dwRmGo1FxqQdOiKKvV122pJ5n2wz1syT6Nt9mbMddOpkHNzqV6DU+iICQi4qJCKtfj+RtnUcU7gDh7Ji8tGULS8a1Gl+UWClvQtCjKavV1W+pJps3ry7qsU1jM3oy86kWa1tas0ZdDQUhExIWFVq7HszfOJNgnkKOOTF7+eSjnjm42uiy3UNDK6kVR0tXXC5uY8XwIuoe1/x+Cnug0kdb1eha7NslP8whdguYREhFXcPzMPiYsvJ+krGRq4cOzXacSVKuj0WW5BZvdwVtL/+S9X/dd8tj7omtzQ7OIEq2+XtjEjNfX9WbavL6syTqJxezNiI7jad/gpmK/F0+ieYRERDxItcr1ef6Gj6nkE8Rhsnhh+QjOHFxldFluwWI20al+aJGOvaFZBNGRISUKQUNnb71gyH58UgZPzV7JlK/uUAgqIwpCIiJuokZwfSbcOItg38ocJYuJv47k9IFfjC7LLVyq8/TldIwubPLGiiRxVfjbrM85jdnszfCOzysElTIFIRERNxJRqQ4TbvqMKtYQ4shhwsoxnPxrsdFlubzCOk9fbsfogiZvDDCdpWPEOxz2S8HuMNOr/kg6NOhd7NeXwikIiYi4maqBNRh/02eE+VUhgRzGr3mGY7vnGV2Wyyuo83RJO0bnutiEiwGmRNpFvEOcNRWTw0JC/F0EVri6RK8vhfMyugARESl9VQKqMaH357y04F6Op51g/G8vMDY7jfrN7zG6NJfWs1kE3aPC2RibSMK5DMICrCXqGP1P/55wMcB8ilYR0zjlk47FYeF43D2cyGxaJhMzilqERETcVkiFMCbe8iWRFWtyDjsvbnmNbVs/Mrosl2cxm4iODOGWltVL1DH63/7Z/yjccogrqr3HWZ90vO1exB2/l4TMpmUyMaOcpyAkIuLGAq2Vee6WL7kiMJIMHEzZ9j7rfptqdFnyD7n9j6J8NlOn+oekeGdgtXlx/PgATmQ1Bkp/Ykb5m4KQiIib8/OpwFO3zKFjcBQ5OHhn90zmL34cR2aq0aUJgMNBtXNfUrn2XNK97FizK/DnsUeIy25w2f2P5NI0oeIlaEJFEXEXdruNWYuGsDhhEwDdvCozqPMLeNW9yuDKPFhOFquWPMGMhLXYgCaBdbk26nWSMq2l0v/IkxX1+1tB6BIUhETEnTgcDhZtfo9Pd3+Kw5bNFXYvnqhxPRU6j4IKIUaX51Hs507wzU8P8X36IcBEp2rRDO06FW8vH6NLcwuaWVpERC5gMpno1e4xnuz6DtYKYWw323ju2GLiv74Hdv0AdrvRJXqE9ONbeeP7/5wPQSYLNze+i2HXv6cQZAC1CF2CWoRExF0dTDrI5LXjSTy9l4rZmTyW40fLsFZw9WgIrmt0eW4r/o8veG3LGxwlB28vKw91eIqrG95qdFluR7fGSomCkIi4szMZZ3ht0xT2x2/BlHqKO3N86OPwx9zyHmh1H6iFovTYbWxfMYGpBxeQYnJQ2S+E0V3fpn6VZkZX5pYUhEqJgpCIuLtsWzYzd85kWexiSDlB64wMhuX4UyGoJlw1Cqq3NrpEl2dPP8N3Pz3M9+f+xA7UD27EqO7TCPYv2mKuUnwKQqVEQUhEPMWvh3/lfzv+R3Z6ImEpiTyR6UU9hwUa3QBXDgVrkNEluqQzcdt4b9nj7MhJApOZLnWuZ/BVL+Jt8Ta6NLemIFRKFIRExJMcSDrAW5vfIiE1Hq+009yVks5NNm/M1koQPQwadAeThnMX1R/bPuW9398hyZGD1eLL4LYjuTrqLqPL8ggKQqVEQUhEPE1KVgof/vEhG+I3QHY6TdPO8WhKDiGYoUZb6DwSgqobXaZTy85M5atfRrEg/jccQC1rCCO6v0f10CZGl+YxFIRKiYKQiHgih8PBiiMrmLlzJhk56VTMTGVw0jk65pjA4gNtB8EVd4JFa3f/2779S3l/3QscyzkHQLfQlgzo8T4+Pv4GV+ZZFIRKiYKQiHiyuJQ43v39XfYn7YecLNplZjHobPL51qGQSLhqNFSNMrpMp5Cdmcp3v47lh7g12HFQyezDgy0eoW3LgUaX5pEUhEqJgpCIeLocew7f//U98/bNw+aw4ZeVwT3nztEtIwezyQxRfaD9g+BTwehSDbNrzzw+3vwmR7KTAegU1JBB3d4iIFC3EI2iIFRKFIRERM47nHyYD/74gH1n94HdRsOsLB5ITKS2wwIVqkCn4eBh65adSdzP7JVPs+bsXgACzT48cMUDdGj9kMGViYJQKVEQEhH5m91h5+eDP/PFni/IsGVgzk7nmtRU7krNojLm80Go43CoWMXoUstUTnYGi9e+zDcHF5LhsGECuoY05+5rJ6kVyEkoCJUSBSERkQudSj/FZ7s+47e438DhwJqeRO9zydyU443Vu8L5W2VRfcDsXkta2m05rN3yPt/s+YoTtlQA6vsGc3+HcURGdje4OvknBaFSoiAkIlKwvYl7+XTXp+dvl+VkUjk1kd5pWXS1+2ANa3p+3bKQSKPLvGwOu51N2z7mq52zOJp9fjRYoNmHvg3+w7UdRmL+1+g5m93BxthEEs5lEBZgpX3dYCxmzb9UnhSESomCkIhI4RwOB+uPr2fOnjmcTDsJGWcJSD3NjTle9HD44d+8L7QZAF6+RpdabDmpJ9m47RPmxy7kQNZZACqYvOhdows9O47Dzz/4gnMW74hj4vxdxCVl5O2LCLIyvncUPZtFlFfpHk9BqJQoCImIFE22LZtVR1cxb/88Es4dh9QE/DNTuM7mQzf/GkRcPe78hIzOLjuDlP3LWb7rCxYn7SYRGwBWk4UbwjvSu/MzVKgYftFTF++IY+jsrfz7izW3LWh6/9YKQ+VEQaiUKAiJiBSPzW5j3fF1zNs3j6On90DKCbDnEGX3onvElbS7+nm8K4YZXWZ+djv2Y5vZvfMrVsX9xnrSyPz/OBPkXZHrq11F9zZDCQqqVeBL2OwOOk/+JV9L0D+ZgPAgK2ueuk63ycpBUb+/NSWoiIiUKovZwlU1rqJT9U78nvA7y2IXE3NwObvSE9l1Yg0Vv+1F20oNaVfnelo0uQNvX4PmH3I4cJz8k0O7vmHt4V9Yk3OWRJP9/9+EN7UDatGr8V10anhrkRZI3RibWGAIAnAAcUkZbIxNJDoypJTehFwulwlCL7/8Mj/99BMxMTH4+Phw9uzZS57jcDgYP348H330EWfPnqVTp05Mnz6dBg0alH3BIiIezmwy06ZqG9pUbcOpFg/z6845/LLnaxKzU1hxZhcrzuzCGvMuLQJq06pqWxpXbU14WHNMAeFlOtrs3Ol9bN8xh21HV/NH5um/w4/Zgr9fFaJrXE3nRrfRJCQKUzEWmE04V3AIKslxUj5cJghlZWVxxx13EB0dzf/+978inTNlyhTeeecdZs2aRd26dXnuuefo0aMHu3btwmq1lnHFIiKSK9QvlDvaPs7trR5h918/smnfT2w4vZNEewYbkg+wIfkA/PU1gQ4TjfGhoV8YNQJqElEpkiohjbBUrg1BNcBaCQoLJ7ZsSDsNqSch9SQpycc5dHYfseeOcDAtntissxyzpf7dh8dkwtsaRMuw1lzV+D+0Dm9fpNafiwkLKNr3SlGPk/Lhcn2EZs6cyYgRIy7ZIuRwOKhWrRqjRo1i9OjRACQlJVG1alVmzpzJ3XffXaTrqY+QiEjZcNjtHIhdzqZ989mTtJ99mafJzsmEf3U19gLCHGZCHGb8Lb5UsFaiol8oVmsl7Dnp2DJTsGUmk5OVQkpOKmdwcNpkJ9HkIOOCbsvn1bKG0jy8HS0a9aFx1Vb4WHwu+/3k9hGKT7r4VdVHqHx5fB+h2NhY4uPj6datW96+oKAgOnTowPr16wsMQpmZmWRmZuY9Tk5OLvNaRUQ8kclsJjKye95EhNm2bA6c3c/euA3sO7mD+HNHiUtPICsng+O2LI7bssGRA+mpkH7s4i9qBjCBxQvMXmD2JswnkDoVqlEnsDa1KzcgMqItlSvXK/X3YzGbGN87iqGzt2Iif5zLjT3je0cpBDkZtw1C8fHxAFStWjXf/qpVq+Y9dzGTJk1i4sSJZVqbiIhcyNviTaOQxjQKaZy3z+6wk5iRSFxKHGfTTpJ67ihpycdISYknPeMMFm8/vHwCsfgG4GWthJ9/KMEBNQjxCyHYGkxla2WsXuV3K6pnswim9299wTxC4ZpHyGkZGoTGjh3L5MmTCz1m9+7dNG7cuNBjStO4ceMYOXJk3uPk5GRq1qxZbtcXEZG/mU1mQv1CCfULNbqUIuvZLILuUeGaWdpFGBqERo0axcCBAws9pl69kjVfhoefn+zqxIkTRET8ncBPnDhBy5YtCzzP19cXX1/Xm/1URESch8Vs0hB5F2FoEKpSpQpVqpTNCsV169YlPDyc5cuX5wWf5ORkNmzYwNChQ8vkmiIiIuJaXGZZ4MOHDxMTE8Phw4ex2WzExMQQExNDSkpK3jGNGzdm7ty5AJhMJkaMGMFLL73Ejz/+yPbt27nvvvuoVq0affr0MehdiIiIiDNxmc7Szz//PLNmzcp73KpVKwB+/fVXrr32WgD27t1LUlJS3jFjxowhNTWVhx56iLNnz9K5c2cWL16sOYREREQEcMF5hMqb5hESERFxPR4/j5CIiMg/2ewOjeSSCygIiYiI21u8I+6CuX0iNLeP4EKdpUVEREpi8Y44hs7eesHK8PFJGQydvZXFO+IMqkycgYKQiIi4LZvdwcT5uy669lfuvonzd2Gzq7usp1IQEhERt7UxNvGClqB/cgBxSRlsjE0sv6LEqSgIiYiI20o4V3AIKslx4n4UhERExG2FBRRt3riiHifuR0FIRETcVvu6wUQEWSlokLyJ86PH2tcNLs+yxIkoCImIiNuymE2M7x0FcEEYyn08vneU5hPyYApCIiLi1no2i2B6/9aEB+W//RUeZGV6/9aaR8jDaUJFERFxez2bRdA9KlwzS8sFFIRERMQjWMwmoiNDjC5DnIxujYmIiIjHUhASERERj6UgJCIiIh5LQUhEREQ8loKQiIiIeCwFIREREfFYCkIiIiLisRSERERExGMpCImIiIjHUhASERERj6UgJCIiIh5LQUhEREQ8loKQiIiIeCwFIREREfFYCkIiIiLisRSERERExGMpCImIiIjH8jK6ABER8Vw2u4ONsYkknMsgLMBK+7rBWMwmo8sSD6IgJCIihli8I46J83cRl5SRty8iyMr43lH0bBZhYGXiSXRrTEREyt3iHXEMnb01XwgCiE/KYOjsrSzeEWdQZeJpFIRERKRc2ewOJs7fheMiz+Xumzh/Fzb7xY4QKV0KQiIiUq42xiZe0BL0Tw4gLimDjbGJ5VeUeCwFIRERKVcJ5woOQSU5TuRyKAiJiEi5CguwlupxIpdDQUhERMpV+7rBRARZKWiQvInzo8fa1w0uz7LEQ7lMEHr55Zfp2LEj/v7+VKpUqUjnDBw4EJPJlG/r2bNn2RYqIiKFsphNjO8dBXBBGMp9PL53lOYTknLhMkEoKyuLO+64g6FDhxbrvJ49exIXF5e3ffHFF2VUoYiIFFXPZhFM79+a8KD8t7/Cg6xM799a8whJuXGZCRUnTpwIwMyZM4t1nq+vL+Hh4WVQkYiIXI6ezSLoHhWumaXFUC4ThEpqxYoVhIWFUblyZa677jpeeuklQkJCCjw+MzOTzMzMvMfJycnlUaaIiNsraDmN6MiC/04WKWtuHYR69uzJbbfdRt26ddm/fz9PP/00N9xwA+vXr8disVz0nEmTJuW1PomISOnQchrirAztIzR27NgLOjP/e9uzZ0+JX//uu+/m5ptv5oorrqBPnz4sWLCATZs2sWLFigLPGTduHElJSXnbkSNHSnx9ERHRchri3AxtERo1ahQDBw4s9Jh69eqV2vXq1atHaGgo+/bto2vXrhc9xtfXF19f31K7pohIWXCVVdsvtZyGifPLaXSPCnfK+sX9GRqEqlSpQpUqVcrtekePHuX06dNERKgZVkRclyvdZirOchrqKyRGcJnh84cPHyYmJobDhw9js9mIiYkhJiaGlJSUvGMaN27M3LlzAUhJSeHJJ5/kt99+4+DBgyxfvpxbbrmF+vXr06NHD6PehojIZXG120xaTkOcnct0ln7++eeZNWtW3uNWrVoB8Ouvv3LttdcCsHfvXpKSkgCwWCz88ccfzJo1i7Nnz1KtWjWuv/56XnzxRd36EhGX5Iq3mbSchjg7lwlCM2fOvOQcQg7H3389+Pn5sWTJkjKuSkSk/Ljibabc5TTikzIuGuBMnJ9E0ZWW03CV/llSNC4ThEREPJ0r3mbKXU5j6OytmCBfGHLF5TRcqX+WFI3L9BESEfF0rnqbyV2W03C1/llSNGoREhFxEa58m8nVl9Nwxf5ZUjRqERIRcRGuvmp77nIat7SsTnRkiNPWeTHF6Z8lrkVBSETEhbjLbSZX44r9s6RodGtMRMTFuPptJlfkqv2z5NIUhEREXJBWbS9frtw/SwqnW2MiIiKX4Or9s6RgCkIiIiJFoP5Z7km3xkRERIpI/bPcj4KQiIhIMah/lnvRrTERERHxWApCIiIi4rEUhERERMRjKQiJiIiIx1IQEhEREY+lICQiIiIeS0FIREREPJaCkIiIiHgsBSERERHxWApCIiIi4rEUhERERMRjKQiJiIiIx9KiqyIi4lJsdodWf5dSoyAkIiIuY/GOOCbO30VcUkbevoggK+N7R9GzWYSBlYmr0q0xERFxCYt3xDF09tZ8IQggPimDobO3snhHnEGViStTEBIREadnszuYOH8Xjos8l7tv4vxd2OwXO0KkYApCIiLi9DbGJl7QEvRPDiAuKYONsYnlV5S4BQUhERFxegnnCg5BJTlOJJeCkIiIOL2wAGupHieSS0FIREScXvu6wUQEWSlokLyJ86PH2tcNLs+yxA0oCImIiNOzmE2M7x0FcEEYyn08vneU5hOSYlMQEhERl9CzWQTT+7cmPCj/7a/wICvT+7fWPEJSIppQUUREXEbPZhF0jwrXzNJSahSERETEpVjMJqIjQ4wuQ9yEbo2JiIiIx1IQEhEREY+lICQiIiIeyyWC0MGDBxk8eDB169bFz8+PyMhIxo8fT1ZWVqHnZWRk8OijjxISEkLFihW5/fbbOXHiRDlVLSIiIs7OJYLQnj17sNvtfPDBB+zcuZO33nqLGTNm8PTTTxd63hNPPMH8+fP55ptvWLlyJcePH+e2224rp6pFRETE2ZkcDodLLtX72muvMX36dA4cOHDR55OSkqhSpQpz5szhP//5D3A+UDVp0oT169dz5ZVXXvS8zMxMMjMz8x4nJydTs2ZNkpKSCAwMLP03IiIiIqUuOTmZoKCgS35/u0SL0MUkJSURHFzwVOpbtmwhOzubbt265e1r3LgxtWrVYv369QWeN2nSJIKCgvK2mjVrlmrdIiIi4jxcMgjt27ePd999l4cffrjAY+Lj4/Hx8aFSpUr59letWpX4+PgCzxs3bhxJSUl525EjR0qrbBEREXEyhgahsWPHYjKZCt327NmT75xjx47Rs2dP7rjjDh588MFSr8nX15fAwMB8m4iIiLgnQ2eWHjVqFAMHDiz0mHr16uX9+fjx43Tp0oWOHTvy4YcfFnpeeHg4WVlZnD17Nl+r0IkTJwgPDy9yjbldqJKTk4t8joiIiBgr93v7kl2hHS7i6NGjjgYNGjjuvvtuR05OziWPP3v2rMPb29vx7bff5u3bs2ePA3CsX7++yNc9cuSIA9CmTZs2bdq0ueB25MiRQr/nXWLU2LFjx7j22mupXbs2s2bNwmKx5D2X27pz7Ngxunbtyqeffkr79u0BGDp0KAsXLmTmzJkEBgby2GOPAbBu3boiX9tut3P8+HECAgIwmUpvUb/c0WhHjhzR7beL0OdTOH0+hdPnUzh9PgXTZ1M4V/p8HA4H586do1q1apjNBfcEcolFV5cuXcq+ffvYt28fNWrUyPdcbo7Lzs5m7969pKWl5T331ltvYTabuf3228nMzKRHjx68//77xbq22Wy+4JqlSf2QCqfPp3D6fAqnz6dw+nwKps+mcK7y+QQFBV3yGJdoEXJHRZ3fwFPp8ymcPp/C6fMpnD6fgumzKZw7fj4uOXxeREREpDQoCBnE19eX8ePH4+vra3QpTkmfT+H0+RROn0/h9PkUTJ9N4dzx89GtMREREfFYahESERERj6UgJCIiIh5LQUhEREQ8loKQiIiIeCwFIYNMmzaNOnXqYLVa6dChAxs3bjS6JKewatUqevfuTbVq1TCZTMybN8/okpzKpEmTaNeuHQEBAYSFhdGnTx/27t1rdFlOYfr06TRv3jxvorfo6GgWLVpkdFlO69VXX8VkMjFixAijS3EKEyZMuGDR78aNGxtdllM5duwY/fv3JyQkBD8/P6644go2b95sdFmXTUHIAF999RUjR45k/PjxbN26lRYtWtCjRw8SEhKMLs1wqamptGjRgmnTphldilNauXIljz76KL/99htLly4lOzub66+/ntTUVKNLM1yNGjV49dVX2bJlC5s3b+a6667jlltuYefOnUaX5nQ2bdrEBx98QPPmzY0uxak0bdqUuLi4vG3NmjVGl+Q0zpw5Q6dOnfD29mbRokXs2rWLN954g8qVKxtd2mXT8HkDdOjQgXbt2vHee+8B59czq1mzJo899hhjx441uDrnYTKZmDt3Ln369DG6FKd18uRJwsLCWLlyJVdffbXR5Tid4OBgXnvtNQYPHmx0KU4jJSWF1q1b8/777/PSSy/RsmVLpk6danRZhpswYQLz5s0jJibG6FKc0tixY1m7di2rV682upRSpxahcpaVlcWWLVvo1q1b3j6z2Uy3bt1Yv369gZWJK0pKSgLOf+HL32w2G19++SWpqalER0cbXY5TefTRR7nxxhvz/R0k5/31119Uq1aNevXq0a9fPw4fPmx0SU7jxx9/pG3bttxxxx2EhYXRqlUrPvroI6PLKhUKQuXs1KlT2Gw2qlatmm9/1apViY+PN6gqcUV2u50RI0bQqVMnmjVrZnQ5TmH79u1UrFgRX19fhgwZwty5c4mKijK6LKfx5ZdfsnXrViZNmmR0KU6nQ4cOzJw5k8WLFzN9+nRiY2O56qqrOHfunNGlOYUDBw4wffp0GjRowJIlSxg6dCiPP/44s2bNMrq0y+YSq8+LyIUeffRRduzYoX4M/9CoUSNiYmJISkri22+/ZcCAAaxcuVJhCDhy5AjDhw9n6dKlWK1Wo8txOjfccEPen5s3b06HDh2oXbs2X3/9tW6tcv4fXm3btuWVV14BoFWrVuzYsYMZM2YwYMAAg6u7PGoRKmehoaFYLBZOnDiRb/+JEycIDw83qCpxNcOGDWPBggX8+uuv1KhRw+hynIaPjw/169enTZs2TJo0iRYtWvD2228bXZZT2LJlCwkJCbRu3RovLy+8vLxYuXIl77zzDl5eXthsNqNLdCqVKlWiYcOG7Nu3z+hSnEJERMQF/6Bo0qSJW9w+VBAqZz4+PrRp04bly5fn7bPb7Sxfvlx9GeSSHA4Hw4YNY+7cufzyyy/UrVvX6JKcmt1uJzMz0+gynELXrl3Zvn07MTExeVvbtm3p168fMTExWCwWo0t0KikpKezfv5+IiAijS3EKnTp1umCqjj///JPatWsbVFHp0a0xA4wcOZIBAwbQtm1b2rdvz9SpU0lNTWXQoEFGl2a4lJSUfP8Ci42NJSYmhuDgYGrVqmVgZc7h0UcfZc6cOfzwww8EBATk9SsLCgrCz8/P4OqMNW7cOG644QZq1arFuXPnmDNnDitWrGDJkiVGl+YUAgICLuhLVqFCBUJCQtTHDBg9ejS9e/emdu3aHD9+nPHjx2OxWOjbt6/RpTmFJ554go4dO/LKK69w5513snHjRj788EM+/PBDo0u7fA4xxLvvvuuoVauWw8fHx9G+fXvHb7/9ZnRJTuHXX391ABdsAwYMMLo0p3CxzwZwfPLJJ0aXZrj777/fUbt2bYePj4+jSpUqjq5duzp+/vlno8tyatdcc41j+PDhRpfhFO666y5HRESEw8fHx1G9enXHXXfd5di3b5/RZTmV+fPnO5o1a+bw9fV1NG7c2PHhhx8aXVKp0DxCIiIi4rHUR0hEREQ8loKQiIiIeCwFIREREfFYCkIiIiLisRSERERExGMpCImIiIjHUhASERERj6UgJCIiIh5LQUhEREQ8loKQiIiIeCwFIREREfFYCkIi4lFOnjxJeHg4r7zySt6+devW4ePjw/Llyw2sTESMoEVXRcTjLFy4kD59+rBu3ToaNWpEy5YtueWWW3jzzTeNLk1EypmCkIh4pEcffZRly5bRtm1btm/fzqZNm/D19TW6LBEpZwpCIuKR0tPTadasGUeOHGHLli1cccUVRpckIgZQHyER8Uj79+/n+PHj2O12Dh48aHQ5ImIQtQiJiMfJysqiffv2tGzZkkaNGjF16lS2b99OWFiY0aWJSDlTEBIRj/Pkk0/y7bffsm3bNipWrMg111xDUFAQCxYsMLo0ESlnujUmIh5lxYoVTJ06lc8++4zAwEDMZjOfffYZq1evZvr06UaXJyLlTC1CIiIi4rHUIiQiIiIeS0FIREREPJaCkIiIiHgsBSERERHxWApCIiIi4rEUhERERMRjKQiJiIiIx1IQEhEREY+lICQiIiIeS0FIREREPJaCkIiIiHis/wNEnSYMiKMy4AAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "s = theorist(experiment_runner(experimentalist(s, num_samples=5)))\n", - "print(s)" + "s = theorist(experiment_runner(experimentalist(s, num_samples=10)))\n", + "print(s)\n", + "\n", + "plot_from_state(s)" ] }, { @@ -579,45 +691,38 @@ "output_type": "stream", "text": [ "\n", - "\u001b[1mRunning Cycle 1:\u001b[0m\n", - " x y\n", - "1 0.000000 -0.146700\n", - "8 4.188790 -0.880945\n", - "7 2.094395 0.913588\n", - "5 6.283185 0.332327\n", - "4 2.094395 0.795916\n" + "\u001b[1mRunning Cycle 1:\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:03<00:00, 25.24it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + "100%|██████████| 100/100 [00:03<00:00, 25.98it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1mCycle 1 model: sin(x)\u001b[0m\n", - "\n", - "\u001b[1mRunning Cycle 2:\u001b[0m\n", - " x y\n", - "1 0.000000 -0.016597\n", - "4 2.094395 0.859277\n", - "7 2.094395 0.337170\n", - "5 6.283185 0.411272\n", - "8 4.188790 -1.476447\n" + "\u001b[1mCycle 1 model: sin(x)\u001b[0m\n" ] }, + { + "data": { + "image/png": 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", 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"INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1mCycle 3 model: sin(x)\u001b[0m\n", - "\n", - "\u001b[1mRunning Cycle 4:\u001b[0m\n", - " x y\n", - "8 4.188790 -1.016577\n", - "1 0.000000 0.230408\n", - "5 6.283185 0.042962\n", - "7 2.094395 0.111047\n", - "4 2.094395 1.226777\n" + "\u001b[1mCycle 2 model: sin(x)\u001b[0m\n" ] }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:03<00:00, 25.24it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", "INFO:autora.theorist.bms.regressor:BMS fitting started\n" ] }, @@ -673,22 +770,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1mCycle 4 model: sin(x)\u001b[0m\n", "\n", - "\u001b[1mRunning Cycle 5:\u001b[0m\n", - " x y\n", - "1 0.000000 -0.021768\n", - "7 2.094395 0.728375\n", - "8 4.188790 -1.647559\n", - "5 6.283185 -0.397815\n", - "4 2.094395 1.331318\n" + "\u001b[1mRunning Cycle 3:\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:03<00:00, 25.29it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 22.97it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -696,64 +786,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1mCycle 5 model: sin(x)\u001b[0m\n" + "\u001b[1mCycle 3 model: sin(x)\u001b[0m\n" ] - } - ], - "source": [ - "#### First, let's reinitialize the state object to get a clean state ####\n", - "s = StandardState(\n", - " variables = variables,\n", - " conditions = conditions,\n", - " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"])\n", - ")\n", - "\n", - "### Then we cycle through the pipeline we built five times ###\n", - "num_cycles = 5 # number of empirical research cycles\n", - "for cycle in range(num_cycles):\n", - " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", - " s = theorist(experiment_runner(experimentalist(s, num_samples=5)))\n", - " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If everything went well in terms of our theorist, we should have recovered our ground truth model `sin(x)`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "sin(x)\n" - ] - } - ], - "source": [ - "print(s.model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Chain Looping with Stopping Criterion\n", - "\n", - "Alternatively, we can run the chain until we reach a stopping criterion. For example, here we will loop until we get 30 datapoints." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ + "data": { + "image/png": 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0.180818\n", - "5 6.283185 -0.322560\n", - "2 4.188790 -0.685328\n", - "8 4.188790 -0.097007\n", - "7 2.094395 0.848112\n" + "\u001b[1mCycle 4 model: sin(x)\u001b[0m\n" ] }, + { + "data": { + "image/png": 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"INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1mCycle 3 model: -0.13\u001b[0m\n", - "\n", - "\u001b[1mRunning Cycle 4, number of datapoints: 15\u001b[0m\n", - " x y\n", - "5 6.283185 -0.709298\n", - "7 2.094395 0.961194\n", - "2 4.188790 -0.798148\n", - "8 4.188790 -0.561981\n", - "9 0.000000 0.352491\n" + "\u001b[1mCycle 5 model: sin(x)\u001b[0m\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:03<00:00, 25.85it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#### First, let's reinitialize the state object to get a clean state ####\n", + "s = StandardState(\n", + " variables = variables,\n", + " conditions = conditions,\n", + " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"])\n", + ")\n", + "\n", + "### Then we cycle through the pipeline we built five times ###\n", + "num_cycles = 5 # number of empirical research cycles\n", + "for cycle in range(num_cycles):\n", + " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", + " s = theorist(experiment_runner(experimentalist(s, num_samples=10)))\n", + " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", + " plot_from_state(s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If everything went well in terms of our theorist, we should have recovered our ground truth model `sin(x)`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1mCycle 4 model: -0.13\u001b[0m\n", - "\n", - "\u001b[1mRunning Cycle 5, number of datapoints: 20\u001b[0m\n", - " x y\n", - "2 4.188790 -0.685564\n", - "8 4.188790 -0.918654\n", - "9 0.000000 -0.477673\n", - "5 6.283185 -0.207382\n", - "7 2.094395 0.166655\n" + "sin(x)\n" ] - }, + } + ], + "source": [ + "print(s.model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chain Looping with Stopping Criterion\n", + "\n", + "Alternatively, we can run the chain until we reach a stopping criterion. For example, here we will loop until we get 30 datapoints." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:03<00:00, 26.22it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", "INFO:autora.theorist.bms.regressor:BMS fitting started\n" ] }, @@ -884,33 +946,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1mCycle 5 model: sin(x)\u001b[0m\n", "\n", - "\u001b[1mRunning Cycle 6, number of datapoints: 25\u001b[0m\n", - " x y\n", - "7 2.094395 0.711381\n", - "2 4.188790 -0.529463\n", - "8 4.188790 -0.994340\n", - "5 6.283185 -0.183913\n", - "9 0.000000 0.636867\n" + "\u001b[1mRunning Cycle 1, number of datapoints: 0\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 24.70it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1mCycle 6 model: sin(x)\u001b[0m\n", - "\n", - "\u001b[1mNumber of datapoints: 30\u001b[0m\n", - "Determined Model: sin(x)\n" + " 89%|████████▉ | 89/100 [00:03<00:00, 26.24it/s]" ] } ], @@ -924,14 +968,15 @@ "\n", "### Then we cycle through the pipeline we built until we reach our stopping criteria ###\n", "cycle = 0\n", - "while len(s.experiment_data) < 30:\n", + "while len(s.experiment_data) < 50:\n", " print(f\"\\n\\033[1mRunning Cycle {cycle+1}, number of datapoints: {len(s.experiment_data)}\\033[0m\")\n", - " s = theorist(experiment_runner(experimentalist(s, num_samples=5)))\n", + " s = theorist(experiment_runner(experimentalist(s, num_samples=10)))\n", " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", " cycle += 1\n", + " plot_from_state(s)\n", "\n", "print(f\"\\n\\033[1mNumber of datapoints: {len(s.experiment_data)}\\033[0m\")\n", - "print(f\"\\033[1mDetermined Model: {s.model}\\033[0m\")\n" + "print(f\"\\033[1mDetermined Model: {s.model}\\033[0m\")" ] }, { From af7cf88b14e25049fb40bd868a600d4a2e33f71c Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Mon, 21 Aug 2023 17:42:52 -0700 Subject: [PATCH 16/32] Re-designed Tutorial IV with new state functionality --- .../basic/Tutorial-IV-Customization.ipynb | 615 ++++++------------ 1 file changed, 196 insertions(+), 419 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-IV-Customization.ipynb b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb index eb873918a..fbc4ce42d 100644 --- a/docs/tutorials/basic/Tutorial-IV-Customization.ipynb +++ b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb @@ -19,7 +19,7 @@ "\n", "[AutoRA Basic Tutorial I: Components](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-I-Components/)
\n", "[AutoRA Basic Tutorial II: Loop Constructs](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-II-Loop-Constructs/)
\n", - "[AutoRA Basic Tutorial III: Workflow Logic](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Workflow-Logic/)
\n", + "[AutoRA Basic Tutorial III: Functional Workflow](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-III-Functional-Workflow/)
\n", "[AutoRA Basic Tutorial IV: Customization](https://autoresearch.github.io/autora/tutorials/basic/Tutorial-IV-Customization/)
\n", "\n", "These notebooks provide a comprehensive introduction to the capabilities of ``autora``. **It demonstrates the fundamental components of ``autora``, and how they can be combined to facilitate automated (closed-loop) empirical research through synthetic experiments.**\n", @@ -32,8 +32,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Tutorial Setup\n", - "This tutorial is self-contained so that you do not need to run the previous notebook to begin. However, the four notebooks are continuous so that what we define in a previous notebook should still exist within this notebook. As such, we will here re-run relevant code from past tutorials. We will not again walk you through these, but if you need a reminder what they are then go see the descriptions in previous notebooks." + "## Tutorial Setup" ] }, { @@ -43,62 +42,85 @@ "outputs": [], "source": [ "#### Installation ####\n", - "!pip install -q \"autora[experimentalist-falsification]\"\n", "!pip install -q \"autora[theorist-bms]\"\n", "\n", "#### Import modules ####\n", "import numpy as np\n", + "import pandas as pd\n", "import torch\n", "from autora.variable import DV, IV, ValueType, VariableCollection\n", - "from autora.workflow import Controller\n", - "from autora.experimentalist.sampler.falsification import falsification_sample\n", - "from autora.experimentalist.pooler.random_pooler import random_pool\n", + "from autora.state.bundled import StandardState\n", + "from autora.state.delta import on_state\n", + "from autora.state.wrapper import state_fn_from_estimator\n", + "from autora.experimentalist.random_ import random_pool\n", "from autora.theorist.bms import BMSRegressor\n", "\n", "#### Set seeds ####\n", "np.random.seed(42)\n", - "torch.manual_seed(42)\n", - "\n", - "#### Define ground truth and experiment runner ####\n", - "ground_truth = lambda x: np.sin(x)\n", - "run_experiment = lambda x: ground_truth(x) + np.random.normal(0, 0.1, size=x.shape)\n", + "torch.manual_seed(42)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Customizing Automated Empirical Research Components\n", "\n", - "#### Define condition pool ####\n", - "condition_pool = np.linspace(0, 2 * np.pi, 100)\n", - "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", + "``autora`` is a flexible framework in which users can integrate their own theorists, experimentalists, and experiment_runners in aa automated empirical research workflow. This section illustrates the integration of custom theorists and experimentalists. For more information on how to contribute your own modules to the ``autora`` ecosystem, please refer to the [Contributor Documentation](https://autoresearch.github.io/autora/contribute/modules/).\n", "\n", - "#### Define data ####\n", - "initial_conditions = np.random.choice(np.linspace(0, 2 * np.pi, 100), size=10, replace=False).reshape(-1,1)\n", - "initial_observations = run_experiment(initial_conditions).reshape(-1,1)\n", + "To illustrate the use of custom theorists and experimentalists, we consider a simple workflow:\n", + "1. Generate 10 seed experimental conditions using `random_pool`\n", + "2. Iterate through the following steps\n", + " - Collect observations using the ``experiment_runner``\n", + " - Identify a model relating conditions to observations using a ``theorist``\n", + " - Identify 3 new experimental conditions using an ``experimentalist``\n", "\n", + "Once this workflow is setup, we will replace each component with a custom function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "#### Define metadata ####\n", - "iv = IV(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 100))\n", + "iv = IV(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 10))\n", "dv = DV(name=\"y\", type=ValueType.REAL)\n", - "metadata = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", "\n", - "#### Define theorists ####\n", - "theorist_bms = BMSRegressor(epochs=100)\n", + "#### Define condition pool ####\n", + "conditions = random_pool(variables, num_samples=10)\n", "\n", - "#### Define monitor ####\n", - "def monitor(state):\n", - " print(f\"MONITOR: Generated new {state.history[-1].kind}\")" + "#### Define state ####\n", + "s = StandardState(\n", + " variables = variables,\n", + " conditions = conditions,\n", + " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"])\n", + ")\n", + "\n", + "#### Define experiment runner and wrap with state functionality ####\n", + "def run_experiment(conditions: pd.DataFrame):\n", + " x = conditions[\"x\"]\n", + " y = np.sin(x) + np.random.normal(0, 0.5, size=x.shape)\n", + " observations = conditions.assign(y = y)\n", + " return observations\n", + "\n", + "experiment_runner = on_state(run_experiment, output=[\"experiment_data\"])\n", + "\n", + "#### Define theorist and wrap with state functionality ####\n", + "theorist = state_fn_from_estimator(BMSRegressor(epochs=100))\n", + "\n", + "#### Define experimentalist and wrap with state functionality ####\n", + "experimentalist = on_state(random_pool, output=[\"conditions\"])" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# Customizing Automated Empirical Research Components\n", - "\n", - "``autora`` is a flexible framework in which users can integrate their own theorists, experimentalists, and experiment_runners in a automated empirical research workflow. This section illustrates the integration of custom theorists and experimentalists. For more information on how to contribute your own modules to the ``autora`` ecosystem, please refer to the [Contributor Documentation](https://autoresearch.github.io/autora/contribute/modules/).\n", - "\n", - "To illustrate the use of custom theorists and experimentalists, we consider a simple workflow introduced above:\n", - "1. Generate 3 seed experimental conditions\n", - "2. Iterate through the following steps\n", - " - Collect observations using ``run_experiment``\n", - " - Identify a model relating conditions to observations using ``theorist_bms``\n", - " - Identify 3 new experimental conditions using ``falsification_sample``" + "We should quickly test to make sure everything works as expected." ] }, { @@ -107,31 +129,14 @@ "metadata": {}, "outputs": [], "source": [ - "# generate initial pool of 3 experimental conditions\n", - "seed_conditions = np.linspace(0,2*np.pi,3)\n", - "\n", - "params = {\n", - " \"experimentalist\":\n", - " {\"condition_pool\": condition_pool,\n", - " \"model\": \"%models[-1]%\", # access last model generated by theorist\n", - " \"reference_conditions\": \"%observations.ivs%\", # access all conditions probed so far\n", - " \"reference_observations\": \"%observations.dvs%\", # access all observations collected so far\n", - " \"metadata\": metadata,\n", - " \"num_samples\": 3}\n", - " }\n", - "\n", - "# define controller\n", - "controller = Controller(\n", - " monitor=monitor,\n", - " variables=metadata,\n", - " experimentalist=falsification_sample,\n", - " experiment_runner=run_experiment,\n", - " theorist=theorist_bms,\n", - " params=params,\n", - ")\n", + "print('\\033[1mPrevious State:\\033[0m')\n", + "print(s)\n", "\n", - "# seed controller\n", - "controller.seed(conditions=[seed_conditions])" + "for cycle in range(2):\n", + " s = theorist(experiment_runner(experimentalist(s)))\n", + "\n", + "print('\\n\\033[1mUpdated State:\\033[0m')\n", + "print(s)" ] }, { @@ -141,7 +146,7 @@ "source": [ "## Custom Theorists\n", "\n", - "What if we wanted to replace the ``theorist_bms`` with a custom theorist?\n", + "What if we wanted to replace the ``theorist`` with a custom theorist?\n", "\n", "We can implement our theorist as a class that inherits from `sklearn.base.BaseEstimator`. The class must implement the following methods:\n", "\n", @@ -157,14 +162,9 @@ "metadata": {}, "outputs": [], "source": [ - "\"\"\"\n", - "Example Theorist\n", - "\"\"\"\n", - "\n", "import numpy as np\n", "from sklearn.base import BaseEstimator\n", "\n", - "\n", "class PolynomialRegressor(BaseEstimator):\n", " \"\"\"\n", " This theorist fits a polynomial function to the data.\n", @@ -183,12 +183,14 @@ " observations = observations.flatten()\n", "\n", " # fit polynomial\n", - " self.coeff = np.polyfit(conditions, observations, 2)\n", + " self.coeff = np.polyfit(conditions, observations, self.degree)\n", " self.polynomial = np.poly1d(self.coeff)\n", " pass\n", "\n", " def predict(self, conditions):\n", - " return self.polynomial(conditions)" + " return self.polynomial(conditions)\n", + " \n", + "custom_theorist = state_fn_from_estimator(PolynomialRegressor())" ] }, { @@ -196,7 +198,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can now assign the theorist to a new controller." + "Let's run the controller with the new theorist for 3 research cycles, defined by the number of models generated." ] }, { @@ -205,152 +207,17 @@ "metadata": {}, "outputs": [], "source": [ - "theorist_poly = PolynomialRegressor(degree = 3)\n", - "\n", - "# define controller\n", - "controller_with_polynomial_theorist = Controller(\n", - " monitor=monitor,\n", - " variables=metadata,\n", - " experimentalist=falsification_sample,\n", - " experiment_runner=run_experiment,\n", - " theorist=theorist_poly,\n", - " params=params,\n", - ")\n", + "#### First, let's reinitialize the state object to get a clean state ####\n", + "s = StandardState(variables = variables, conditions = conditions, experiment_data = pd.DataFrame(columns=[\"x\",\"y\"]))\n", "\n", - "# seed controller\n", - "controller_with_polynomial_theorist.seed(conditions=[seed_conditions])" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's run the controller with the new theorist for 3 research cycles, defined by the number of models generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 0\n", - "MONITOR: Generated new MODEL\n", - "Number of models: 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new CONDITION\n", - "Number of models: 1\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 1\n", - "MONITOR: Generated new MODEL\n", - "Number of models: 2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MONITOR: Generated new CONDITION\n", - "Number of models: 2\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 2\n", - "MONITOR: Generated new MODEL\n", - "Number of models: 3\n", - "MONITOR: Generated new CONDITION\n", - "Number of models: 3\n", - "MONITOR: Generated new OBSERVATION\n", - "Number of models: 3\n", - "MONITOR: Generated new MODEL\n" - ] - } - ], - "source": [ - "from itertools import takewhile\n", + "print('\\033[1mPrevious State:\\033[0m')\n", + "print(s)\n", "\n", - "continue_criterion = lambda controller: len(controller.state.models) < 4\n", + "for cycle in range(5):\n", + " s = custom_theorist(experiment_runner(experimentalist(s)))\n", "\n", - "for step in takewhile(continue_criterion, controller_with_polynomial_theorist):\n", - " print(f\"Number of models: {len(step.state.models)}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can plot the last model identified by our custom theorist against the ground truth." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "last_polynomial_model = controller_with_polynomial_theorist.state.models[-1]\n", - "\n", - "predicted_observations_polynomial = last_polynomial_model.predict(condition_pool)\n", - "\n", - "# plot model predictions against ground-truth\n", - "import matplotlib.pyplot as plt\n", - "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", - "plt.plot(condition_pool, predicted_observations_polynomial, label='Polynomial Fit')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('Model Predictions')\n", - "plt.legend()" + "print('\\n\\033[1mUpdated State:\\033[0m')\n", + "print(s)\n" ] }, { @@ -372,27 +239,25 @@ "metadata": {}, "outputs": [], "source": [ - "def basic_model_disagreement_sample(condition_pool, model_a, model_b, num_samples = 1):\n", + "def uniform_experimentalist(variables: VariableCollection, conditions: pd.DataFrame, num_samples = 1):\n", "\n", - " # get predictions from both models\n", - " prediction_a = model_a.predict(condition_pool)\n", - " prediction_b = model_b.predict(condition_pool)\n", - "\n", - " # compute mean squared distance between predictions\n", - " disagreement = np.mean((prediction_a - prediction_b) ** 2, axis=1)\n", - "\n", - " # sort the summed disagreements and select the top n\n", - " selected_conditions_idx = (-disagreement).argsort()[:num_samples]\n", + " \"\"\"\n", + " An experimentalist that selects the least represented datapoints\n", + " \"\"\"\n", "\n", - " return condition_pool[selected_conditions_idx]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can illustrate our new experimentalist sampler by fitting two different theorists to an initial set of conditions and observations. Here, we consider the BMS theorist and our custom polynomial theorist from above. We then sample 3 experimental conditions using our new experimentalist ``basic_model_disagreement_sample``." + " #Retrieve the possible values\n", + " allowed_values = variables.independent_variables[0].allowed_values\n", + " \n", + " #Determine the representation of each value\n", + " conditions_count = np.array([conditions[\"x\"].isin([value]).sum(axis=0) for value in allowed_values])\n", + " \n", + " #Sort to determine the least represented values\n", + " conditions_sort = conditions_count.argsort()\n", + " values_count = allowed_values[conditions_sort]\n", + " \n", + " return pd.DataFrame({\"x\": values_count[:num_samples]})\n", + "\n", + "custom_experimentalist = on_state(uniform_experimentalist, output=[\"conditions\"])" ] }, { @@ -401,223 +266,126 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:07<00:00, 13.30it/s]\n" + "\u001b[1mPrevious State:\u001b[0m\n", + "StandardState(variables=VariableCollection(independent_variables=[IV(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.6981317 , 1.3962634 , 2.0943951 , 2.7925268 ,\n", + " 3.4906585 , 4.1887902 , 4.88692191, 5.58505361, 6.28318531]), units='', type=, variable_label='Independent Variable', rescale=1, is_covariate=False)], dependent_variables=[DV(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='Dependent Variable', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 4.886922\n", + "1 4.886922\n", + "2 0.698132\n", + "3 2.792527\n", + "4 0.698132\n", + "5 4.886922\n", + "6 0.000000\n", + "7 4.886922\n", + "8 0.698132\n", + "9 1.396263, experiment_data=Empty DataFrame\n", + "Columns: [x, y]\n", + "Index: [], models=[])\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Length of values (5) does not match length of index (10)", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[89], line 8\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[39mprint\u001b[39m(s)\n\u001b[0;32m 7\u001b[0m \u001b[39mfor\u001b[39;00m cycle \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m5\u001b[39m):\n\u001b[1;32m----> 8\u001b[0m s \u001b[39m=\u001b[39m theorist(experiment_runner(custom_experimentalist(s, num_samples \u001b[39m=\u001b[39;49m \u001b[39m5\u001b[39;49m)))\n\u001b[0;32m 10\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\033\u001b[39;00m\u001b[39m[1mUpdated State:\u001b[39m\u001b[39m\\033\u001b[39;00m\u001b[39m[0m\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m 11\u001b[0m \u001b[39mprint\u001b[39m(s)\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state\\delta.py:1033\u001b[0m, in \u001b[0;36mdelta_to_state.._f\u001b[1;34m(state_, **kwargs)\u001b[0m\n\u001b[0;32m 1031\u001b[0m \u001b[39m@wraps\u001b[39m(f)\n\u001b[0;32m 1032\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_f\u001b[39m(state_: S, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m S:\n\u001b[1;32m-> 1033\u001b[0m delta \u001b[39m=\u001b[39m f(state_, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1034\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39misinstance\u001b[39m(delta, Mapping), (\n\u001b[0;32m 1035\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mOutput of \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m must be a `Delta`, `UserDict`, \u001b[39m\u001b[39m\"\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mor `dict`.\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m f\n\u001b[0;32m 1036\u001b[0m )\n\u001b[0;32m 1037\u001b[0m new_state \u001b[39m=\u001b[39m state_ \u001b[39m+\u001b[39m delta\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state\\delta.py:768\u001b[0m, in \u001b[0;36minputs_from_state.._f\u001b[1;34m(state_, **kwargs)\u001b[0m\n\u001b[0;32m 761\u001b[0m \u001b[39mif\u001b[39;00m (\u001b[39m\"\u001b[39m\u001b[39mconditions\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m arguments \u001b[39mand\u001b[39;00m \u001b[39misinstance\u001b[39m(arguments[\u001b[39m\"\u001b[39m\u001b[39mconditions\u001b[39m\u001b[39m\"\u001b[39m], pd\u001b[39m.\u001b[39mDataFrame) \u001b[39mand\u001b[39;00m\n\u001b[0;32m 762\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mvariables\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m [i\u001b[39m.\u001b[39mname \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m fields(state_)]):\n\u001b[0;32m 763\u001b[0m arguments[\u001b[39m\"\u001b[39m\u001b[39mconditions\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m (\n\u001b[0;32m 764\u001b[0m align_dataframe_to_ivs(arguments[\u001b[39m\"\u001b[39m\u001b[39mconditions\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 765\u001b[0m \u001b[39mgetattr\u001b[39m(state_, \u001b[39m\"\u001b[39m\u001b[39mvariables\u001b[39m\u001b[39m\"\u001b[39m)\u001b[39m.\u001b[39mindependent_variables)\n\u001b[0;32m 766\u001b[0m )\n\u001b[1;32m--> 768\u001b[0m result \u001b[39m=\u001b[39m f(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39marguments)\n\u001b[0;32m 769\u001b[0m \u001b[39mreturn\u001b[39;00m result\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state\\delta.py:851\u001b[0m, in \u001b[0;36moutputs_to_delta..decorator..inner\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 849\u001b[0m \u001b[39m@wraps\u001b[39m(f)\n\u001b[0;32m 850\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minner\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m--> 851\u001b[0m result \u001b[39m=\u001b[39m f(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 852\u001b[0m delta \u001b[39m=\u001b[39m Delta(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m{output[\u001b[39m0\u001b[39m]: result})\n\u001b[0;32m 853\u001b[0m \u001b[39mreturn\u001b[39;00m delta\n", + "Cell \u001b[1;32mIn[88], line 17\u001b[0m, in \u001b[0;36muniform_experimentalist\u001b[1;34m(variables, conditions, num_samples)\u001b[0m\n\u001b[0;32m 14\u001b[0m conditions_sort \u001b[39m=\u001b[39m conditions_count\u001b[39m.\u001b[39margsort()\n\u001b[0;32m 15\u001b[0m values_count \u001b[39m=\u001b[39m allowed_values[conditions_sort]\n\u001b[1;32m---> 17\u001b[0m \u001b[39mreturn\u001b[39;00m conditions\u001b[39m.\u001b[39;49massign(x\u001b[39m=\u001b[39;49mvalues_count[:num_samples])\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\pandas\\core\\frame.py:4844\u001b[0m, in \u001b[0;36mDataFrame.assign\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m 4841\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcopy(deep\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m)\n\u001b[0;32m 4843\u001b[0m \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems():\n\u001b[1;32m-> 4844\u001b[0m data[k] \u001b[39m=\u001b[39m com\u001b[39m.\u001b[39mapply_if_callable(v, data)\n\u001b[0;32m 4845\u001b[0m \u001b[39mreturn\u001b[39;00m data\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\pandas\\core\\frame.py:3950\u001b[0m, in \u001b[0;36mDataFrame.__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m 3947\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_setitem_array([key], value)\n\u001b[0;32m 3948\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 3949\u001b[0m \u001b[39m# set column\u001b[39;00m\n\u001b[1;32m-> 3950\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_set_item(key, value)\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\pandas\\core\\frame.py:4143\u001b[0m, in \u001b[0;36mDataFrame._set_item\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m 4133\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_set_item\u001b[39m(\u001b[39mself\u001b[39m, key, value) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 4134\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 4135\u001b[0m \u001b[39m Add series to DataFrame in specified column.\u001b[39;00m\n\u001b[0;32m 4136\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 4141\u001b[0m \u001b[39m ensure homogeneity.\u001b[39;00m\n\u001b[0;32m 4142\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 4143\u001b[0m value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sanitize_column(value)\n\u001b[0;32m 4145\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[0;32m 4146\u001b[0m key \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\n\u001b[0;32m 4147\u001b[0m \u001b[39mand\u001b[39;00m value\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m 4148\u001b[0m \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m is_extension_array_dtype(value)\n\u001b[0;32m 4149\u001b[0m ):\n\u001b[0;32m 4150\u001b[0m \u001b[39m# broadcast across multiple columns if necessary\u001b[39;00m\n\u001b[0;32m 4151\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mis_unique \u001b[39mor\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns, MultiIndex):\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\pandas\\core\\frame.py:4870\u001b[0m, in \u001b[0;36mDataFrame._sanitize_column\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m 4867\u001b[0m \u001b[39mreturn\u001b[39;00m _reindex_for_setitem(Series(value), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mindex)\n\u001b[0;32m 4869\u001b[0m \u001b[39mif\u001b[39;00m is_list_like(value):\n\u001b[1;32m-> 4870\u001b[0m com\u001b[39m.\u001b[39;49mrequire_length_match(value, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindex)\n\u001b[0;32m 4871\u001b[0m \u001b[39mreturn\u001b[39;00m sanitize_array(value, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mindex, copy\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, allow_2d\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\pandas\\core\\common.py:576\u001b[0m, in \u001b[0;36mrequire_length_match\u001b[1;34m(data, index)\u001b[0m\n\u001b[0;32m 572\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 573\u001b[0m \u001b[39mCheck the length of data matches the length of the index.\u001b[39;00m\n\u001b[0;32m 574\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 575\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(data) \u001b[39m!=\u001b[39m \u001b[39mlen\u001b[39m(index):\n\u001b[1;32m--> 576\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 577\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mLength of values \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 578\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m(\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(data)\u001b[39m}\u001b[39;00m\u001b[39m) \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 579\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mdoes not match length of index \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 580\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m(\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(index)\u001b[39m}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 581\u001b[0m )\n", + "\u001b[1;31mValueError\u001b[0m: Length of values (5) does not match length of index (10)" ] } ], "source": [ - "# fit two theorists\n", - "theorist_bms.fit(initial_conditions, initial_observations)\n", - "theorist_poly.fit(initial_conditions, initial_observations)\n", - "\n", - "# sample experimental conditions with our custom experimentalist sampler function\n", - "selected_conditions = basic_model_disagreement_sample(condition_pool,\n", - " theorist_bms,\n", - " theorist_poly,\n", - " num_samples = 3)" + "#### First, let's reinitialize the state object to get a clean state ####\n", + "s = StandardState(variables = variables, conditions = conditions, experiment_data = pd.DataFrame(columns=[\"x\",\"y\"]))\n", + "\n", + "print('\\033[1mPrevious State:\\033[0m')\n", + "print(s)\n", + "\n", + "for cycle in range(5):\n", + " s = theorist(experiment_runner(custom_experimentalist(s, num_samples = 5)))\n", + "\n", + "print('\\n\\033[1mUpdated State:\\033[0m')\n", + "print(s)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "After fitting both theorists, we can compare their predictions across the entire pool of experimental conditions. We will add the sampled experimental conditions to the plot." + "## Custom Experiment Runner" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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EEELkEqqq8vDhQwoWLIhO9/R2HymEnuPWrVtyd4gQQgiRS12/fp3ChQs/9XUphJ7j0QSV169fx87OTuM0QgghhMiI6OhoPD090000/SRSCD3Ho8thdnZ2UggJIYQQuczzurVIZ2khhBBC5FtSCAkhhBAi35JCSAghhBD5lhRCQgghhMi3pBASQgghRL4lhZAQQggh8i0phIQQQgiRb0khJIQQQoh8SwohIYQQQuRbUggJIYQQIt/KVYXQvn37aN26NQULFkRRFDZs2PDcffbs2UP16tUxNzenVKlSLFu2LMtzCiGEECJ3yFWFUGxsLFWqVGH+/PkZ2j4kJIQ33niDRo0aERgYyLBhw+jbty/bt2/P4qRCCCGEyA1y1aSrLVu2pGXLlhnefsGCBRQvXpyvvvoKgPLly3PgwAFmz55N8+bNsyqmyMNUg4GkpASSkxJJTU4iOTkRDCrW9k5YWFqj6HLV3xZCCJHv5apC6EX5+fnRpEmTdOuaN2/OsGHDnrpPYmIiiYmJxufR0dFZFU/kULEPI7kedJSokGMQcRHTuHCsE+/gkBJBAfUB5ooB8yfsl6Sa8FCxJlZnQ7SpC7E2xVCdSmLpUYYCRSpSqEQFKZSEECKHydOFUFhYGG5ubunWubm5ER0dTXx8PJaWlo/t8+WXXzJx4sTsiihygLBrF7l2dDO60H24xQRRyHCbcor65I2V9E8NatoKnaJipqRQgCgKGKIg8SYkBsI94GLatg+wI9SqIokeNXEo+xolqryGmblFlr0vIYTI0WJjwcYm7XFMDFhbaxIjTxdCL2PcuHGMGDHC+Dw6OhpPT08NE4nMlpqSwrlDfxJ7ejMe9/woariB+783UOAujtyyLE28fWmwL4yZkyc2Lp44uBXF0sYeU1MzTM0s0JuYoBoMxMREERMZQVz0feKjIoi7G0LK3UuYR13BPv4ahVJu4KhE4xh3CC4fgstzidlsyWk7X9Ryb1C2Xjts7Z20+kiEECLfytOFkLu7O+Hh4enWhYeHY2dn98TWIABzc3PMzZ904UPkZqrBwKVTB7l3aAWl7mynEpHG11JVhYum5XjgURfrknUoWK4WLu6euGTw2IpOh42dIzZ2jk/dJikxgeDTh3hwfh/mt49SNPYUTko03g93w9HdJB35iFOW1Umq2IFKTbpibmH1am9YCCFEhuTpQqh27dps2bIl3bodO3ZQu3ZtjRKJ7PYw6j5nN39HwUu/UNpwk9L/rI/EhguODTEt14wStd6gnKNzluYwM7egbI3XocbrABhSUwk+sZf7x9dTKGwXRQw3qZxwFAKO8iBgEifc3sTj9Q8oWrZqluYSQoj8LlcVQjExMVy6dMn4PCQkhMDAQJycnChSpAjjxo3j5s2b/PTTTwB88MEHzJs3jzFjxtC7d2/+/vtvVq9ezebNm7V6CyKb3LwSxPVts6kYvhFfJR6ABNWUs3b10FfpSIXX3qaWhv1zdHp9usLo6vnj3Dr4MyWur8eNe/iG/wq//spZs0qk1h5GpQbvSEdrIUTeotNBgwb/f6wRRVXVp/QKzXn27NlDo0aNHlvfo0cPli1bRs+ePQkNDWXPnj3p9hk+fDjnzp2jcOHCfPbZZ/Ts2TPD54yOjsbe3p6oqCjs7Owy4V2IrBRy7igPtkyi6sP96P7p8HxVV5iwcj2p0LxPju+Hk5qSwpl9v2MIWE6l2MOYKAYALulLEl3zQ6o27YZOr9c4pRBC5HwZ/f7OVYWQFqQQyh2uXzpN+MYJVI/aZSyATlnUBN8BVHytba4sHsJvXCbkz5lUDluHlZI2pMNVXWEivIdTvWVvaSESQohnkEIok0ghlLOF37jM1d8/pfqDbcbWk+M2DXB643OKla+hcbrM8eDubYL/mEmFG79hRywAwSZlUZtOppxPM43TCSFEziSFUCaRQihnSkyI4/iqKVS58oOxtSTQ0hfbluMpWbmOxumyxsOo+5xZO40qV5cZ3/Nx6/q4vv0lhUtV1DidEEK8oNhYKFYs7XFoaKaPIySFUCaRQijnObX7dxz3fYaneguAINMKKM0nU65GY42TZY+IsGtcWf0x3vc2oVdUklQ9x4r0pnqXSXLbvRAi98jiARWlEMokUgjlHHdvhXJj5UCqxR0EIAIHQquPxfvN9/Nlf5mQc0d5uHEslRMCALimK0RM06+oUDvj8/EJIYRmckghlP++PUSuoxoMHN0wH/NFdagWd5BkVc9ht06YDz9BjbcG5MsiCKB4hZpUGrODY7XmEIEDRQw3qbD9PY7M7ULU/btaxxNCiFxBWoSeQ1qEtHX3Vig3V7xP1fjDAFw0KY3JO99TvEJNjZPlLFEPIji/cgQ+9/4A4A5OhL8+h0r122icTAghnkJahIR4tmObF2O2qA5V4w+TpOo5XGwQxT86JEXQE9g7OuMz5CeCWq7mmq4Qrtyn0t/dOfz9+yTEx2odTwghcixpEXoOaRHKfvGxDzn94wBq3f8TgIv6Upi0WyAFUAbFxz7k1JLB+NzbAECIrhi0W0RxLx9NcwkhRDrSIiTE464GHSP8qzrUuv8nBlXBr3Bvikkr0AuxtLbFZ8hyAl9byH3sKG4IpdDqVvivmYVqMGgdTwgh0uh0UKNG2iJTbORc0iKUfY6s/4ZKgV9gqSQRgQNhjb+h4mvSx+VVRIRd5+byPlSJ9wfgqH0LKvZfjKW1rcbJhBAia0mLkMg1khIT8P+2O7VOfoalksRp8+rwwQEpgjKBs7snlUdv43CJD0lVFWpGbePW169x88pZraMJIUSOIIWQ0FRE2HUuz3odn3t/pF0KK/oBXmN24uzuqXW0PEPR6fDt/gVBzVZwD3tKpoZg+1MTAnf9pnU0IYTQnBRCQjMXT+wjdUEDyiefJRorTjdYRO1e03PlBKm5QcW6rUntt5fzJuWxI47K+z7g8Mrx0m9ICKGNuLi0KTaKFUt7rBEphIQmAjYtwnPDO7hxj6u6wkR23kqV1ztoHSvPcy1UnBKj9+BfoC06RcX30hyOftuVpMQEraMJIfIbVYWrV9MWDbsrSyEkspVqMHB42cfUCBiNhZJMoKUvjh/uo0iZqlpHyzfMzC2oNWgph8uOIVVVqPVgMxe/akrUvXCtowkhRLaTQkhkm5TkJI7O645v6HwADrt3ofKoLdg5FNA4Wf6j6HT4dvqEMw0WEaNa4pV0iuh5Dblx6YzW0YQQIltJISSyRezDSM5+1Ypa9/8kVVXwLz8O3w++k/5AGqvyegfudviD27jgqd7CamVLLgbu1zqWEEJkGymERJaLCLvG7TmvUyXhKPGqGafqzsOn41itY4l/FPfywfSD3VzSl8SJaAquf5fT+/7QOpYQQmQLKYRElrp9NZiEhc0olXqZ+9hxrfUqqjXrqnUs8R/O7p64fbiTM+ZVsVYSKLurF8c2L9Y6lhBCZDkphESWuRociG5pSwqrt7mluBLXdStla7yudSzxFLb2TpQevpXjNg0wU1KpdmQUh3+dqnUsIURepShQoULaoiiaxZBCSGSJSycPYvtr639uj/fEpO9fFC5VUetY4jnMLayoMmwd/s7vpN1eHzwdv+WfaB1LCJEXWVnB2bNpi5WVZjGkEBKZLsh/O27r2uFENJf0JbH9YDuuhYprHUtkkN7EhFoDf8TPsy8AtUPm4ffjKBl4UQiRJ0khJDLV2UNbKLqlG7ZKPOdMK+I6ZAdOroW0jiVekKLTUbvPV/gVHwRA7es/cHjxUCmGhBB5jhRCItOcPbiZ4tt7YqUkcsqiBsWHbZMxgnK52j2mcrjMqLTHt37Cf8EHUgwJITJHXBx4eaUtMsWGyO3OHtxM8b96/VME1aTM0I1YWttqHUtkAt/On+FfIa2fkO+dVRz5rq8UQ0KIV6eqcO5c2iJTbIjc7OzBzZT4K60l6KRFTcoM/QMLS2utY4lM5NNhDEcrT8KgKvhErMV/4QAphoQQeYIUQuKVnD20hRJ/9cRSSeKkZS3KShGUZ9V8ZygBlT4HwDf8Nw7/IH2GhBC5nxRC4qUFB/xNse29/l8EfbhBiqA8rta7I/Av/zEAtW//xOGlozVOJIQQr0YKIfFSLp8+jMemrlgrCZwxrypFUD7i0/Gj/3egvr4Yv2UyXYoQIveSQki8sKvBgTiubY8dsQSZVqDEkI1SBOUzvp0/43DJoQDUDv2ew79O0TiREEK8HCmExAu5FXIey1/fNg6WWGjQJqxs7LWOJTTg220SfkXeT3scPIOjf3yncSIhRK6iKFC0aNoiU2yI3CDi1lXUn97ClfuE6jwp8MFmGScon/PtOY3Drh0AqHb8EwJ3/qpxIiFErmFlBaGhaYtMsSFyuujIe0T92IZCajg3FTes+27C0cVD61hCY4pOR633F3DUvjkmioHy+4dw9uBmrWMJIUSGmWgdQOR8CfGxXP+uDV6pIUTgAN3W41KwmNaxXolBNRCbHGtcYpJjiE2OJTElkURDYtp/UxNJNiSTakjFoBpIVdP+qygKCgp6RY+iKJjoTDDTm2GmM8Ncb46Z3gxLE0usTa3TLTamNigaNv9mFZ1eT7XBKzkxuw3V4g5R9K8+XLReQ+mqr2kdTQghnksKIfFMqSkpnJvXkepJp4lRLYl85xdKlfDSOtYTGVQDEfERhMWGcSfuDhHxEcblXvw9HiQ+ICoxisjESKKTojGo2TsGjl7RY29uj52ZHQ7mDjhaOFLAsgDOls4UsEj7r5uVGx42HjhZOKFTck+DrYmpGeWH/M7Z2S3xSjqJ04Yu3LTbTqES5bWOJoTIqeLjoX79tMf79oGlpSYxFFXVcFzrXCA6Ohp7e3uioqKws7PTOk62Ug0Gjszvic+9P0hSTbjQdCkV672laaaoxCiuRV/j+sPrxuVmzE1ux94mPC6cFEPKCx3PRGeCjamNsdXGwsTC2Kpjrkv7r07RGVt/9IoeFRWDakBVVVLVVFLVVJJSk9IWQxKJKYnEpcQRlxxHbEpai9PL5HKzcqOgTUE8bT0pbFM47b+2hSlqVxRbs5w5fcnDqPvc+aYxJVOvcF0piPWAnTLprhDiyWJjwcYm7XFMDFhn7t3HGf3+lkLoOfJzIeS39CNqX12AQVU44TMb71a9su3cEfERXHhwgUsPLhESHUJIVNpyP+H+M/fTK3pcrVxxsXLBxdIFZ0vntBYXywI4mTthb26Pg7lDWsuMuR3mevNseT+JqYlEJUYZW6SiEqO4n3Cfe/H30rVchcWFEREf8dzWKmdLZ4rbF6e4XXFKOJSglEMpyjiWwdHCMVvez7NE3LpK8qLGeHCXYJOyFBm+S+adE0I8TgqhlzN//nxmzpxJWFgYVapU4dtvv6VWrVpP3HbZsmX06pX+y9vc3JyEhIQMny+/FkJHN8ynZmDaCML+5cfh0zFrBs0zqAZCo0M5d+8cQfeCCH4QzMUHF59Z8LhaulLYNq2F5FErSUGbgnhYe+Bs6YyJLndf8U02JHM37i5hsWHcir3F9YfXufHwBjce3uD6w+vcjb/71H2dLZ0p7VCack7lqFCgAuULlMfT1jPbL7NdDQ7E/tc3cCCGQKvaVBy+ERNTs2zNIITI4XJIIZSrvjFWrVrFiBEjWLBgAT4+PsyZM4fmzZsTHByMq6vrE/exs7MjODjY+DwvdlbNbGcObKTqic9AAT+PrtTOxCIoPDacUxGnOHX3FKcjThN0L4i4lLjHtlNQKGpXlFIOpSjhUILi9sUpYV+CYnbFsDLV7jbL7GCqM6WgTUEK2hSkOtUfez0mKYbQ6FBjK9mlyEtcfHCRGzE3jC1Lfrf9jNvbmNpQvkB5KjlXorJLZaq4VMHZ0jlL30PRslU533IpFls6UzXOD/8Ffak1aBmKLvf0exJC5A+5qkXIx8eHmjVrMm/ePAAMBgOenp4MGTKEsWMf/7JetmwZw4YNIzIy8qXPmd9ahEKDAnBa1Ro74jhm24hqw9ai0+tf6liphlQuRV4iIDyAE3dOcPLuScJiwx7bzkJvQVmnsmktGE7lKeNYhhIOJbA00abjXG4VlxzHpchLXHhwgfP3z3Pu3jmC7weTZEh6bNuC1gWp4loFb1dvvN28KeFQIktajY5vX0HVQ0PQKSp+JT6kdvcvMv0cQohcSlqEXkxSUhLHjh1j3LhxxnU6nY4mTZrg5+f31P1iYmIoWrQoBoOB6tWrM3XqVLy8nn7XU2JiIomJicbn0dHRmfMGcoGIW1exWPUedsQRZOqF18CfX6gIMqgGgu8H43/bn6PhRzlx5wQPkx6m20an6CjjWIbKzpWp5FIJrwJeFLcvnusvZ+UEVqZWVHapTGWXysZ1yYZkrkRe4ey9s5y6e4pTEae49OASt2JvcSvkFltDtgJgb25PNddq+Lj7UMujFqUdSmdK62n15t04fP86vsHTqX3lG45vLUH1ltnX10wIIZ4n13z7REREkJqaipubW7r1bm5unD9//on7lC1bliVLllC5cmWioqKYNWsWderU4ezZsxQuXPiJ+3z55ZdMnDgx0/PndLEPI4n88W1KcZfrSkE83l+XofnDbjy8waFbhzh8+zBHw44SmRiZ7nVrU2uqulSlult1qrlWw6uAV56/tJWTmOpMKetUlrJOZXmn9DsAxCbHcibiDMfvHOdY+DFO3T1FVGIUe67vYc/1PQA4WThRy70Wvh6+1C1UF3dr95fO4NvpY/znX8bn7u9UODyaYJeilK3x+qu/OSFE7uectZfpMyLXXBq7desWhQoV4tChQ9SuXdu4fsyYMezduxd/f//nHiM5OZny5cvTqVMnvvjiyU30T2oR8vT0zNOXxgypqZz86k2qxR3iPnbEd//rqeO/JKQkcCz8GAduHuDAzQOERoeme93KxIoa7jWo5V6LGu41KOtYVlp7crhkQzLn753naPhRjtw+wvE7x4lPiU+3TSmHUtQtWJe6heri7eaNmf7FOj6npqRw+qs3qBp/mHvYk9hzBwWLlc3MtyGEEOnkuUtjzs7O6PV6wsPD060PDw/H3T1jf62amppSrVo1Ll269NRtzM3NMTfPnluqcwr/xR9SO+4Qiaopd95YQrn/FEER8RHsv7Gf3dd343fLj4TU/991p1f0VHGpQu2CtfH18MXL2QtTnWl2vwXxCkx1plRyqUQll0r0rtib5NRkTkWcwv+2P4duHeJ0xGkuRV7iUuQllp9bjpWJFXUL1aVB4Qa8Vvg1nCycnnsOvYkJpQeu4vKcRpRMvULoT+2I/nCvzFUnhNBcrmkRgrTO0rVq1eLbb78F0jpLFylShMGDBz+xs/R/paam4uXlRatWrfj6668zdM683ln6yPpvqXXyUwACqk+nxlsfAHA9+jo7ru3g72t/c+ruKVT+/2viauXKa4Veo16hevh4+OTYwf1E5ohKjMLvth8Hbx7kwM0DRMRHGF9TUKjmWo3GRRrTpGgTCtoUfOaxwm9cRlncBFfuc8rCmwojt8lt9UKILJEnxxFatWoVPXr0YOHChdSqVYs5c+awevVqzp8/j5ubG927d6dQoUJ8+eWXAEyaNAlfX19KlSpFZGQkM2fOZMOGDRw7dowKFSpk6Jx5uRA657eVUtu6YKak4le4N27vDuGvq3+x8+pOgh8Ep9vWq4AXDT0b0tCzIWUdy8owBPmUQTVw7t459lzfw94bezl/P33/vAoFKtC0aFOaFW1GEbsiTzzGpZMHKbjubayURA67dsB34A/ZkFwIkePEx0PLlmmPt27N9Ck28tylMYCOHTty9+5dPv/8c8LCwqhatSrbtm0zdqC+du0aun+NU/LgwQP69etHWFgYjo6OeHt7c+jQoQwXQXnZzStnKbi9L3dMYYljFU66XOPCH22Mr+sVPTXda9K0aFMaejbE1erJ4zSJ/EWn6KjoXJGKzhUZXG0wt2Nu8/f1v9l5dSfH7xzn3L1znLt3jrnH5+JVwIuWxVvSvFjzdJ2tS1Wpy4mwmVTz+xDfO6s58nt5ar07QsN3JYTQhMEAe/f+/7FGclWLkBbyYovQtTtX2PjzW/hZJXHK4v/9oUx0JtT2qE3Tok1p5NkIBwsH7UKKXOde/D12X9/NX6F/cSTsCKlqqvG16q7VeaPEGzQv1hx7c3vg/1O4JKt6LjRfiVedVlpFF0JoIYeMIySF0HPklUIoKTWJvTf2svHSRvZf30PqP1e2dOio6VGTlsVa0qRoE+OXlBCv4l78PXZe3cnW0K0cDz9u7GNmqjOloWdD3izxJvU86nL6m/fwfvg3D7AlvsdOChYvp3FyIUS2kUIod8jthdD5++dZd3Edm69sJjrp/4NDlk9MwrdYJ7q/NjjLp1sQ+VtYbBjbQrax8cpGLj64aFzvZOFEyyLNqbX3d15PuEyorggFhu7F1v75d6EJIfIAKYRyh9xYCEUnRbPlyhbWXVxH0P0g43pHnQ3v3L9J65hYHlSaTI23BmiYUuRHwfeD+fPyn2wO2Zzu7rMKCal0eBhJQbUyPiM2v/S0LkKIXEQKodwhNxVCZyPOsvrCaraGbDUOiGeiM+F1z9fx1Zel5c5PsFGSOOzeBd8PvtM4rcjPUgwpHLh5gHUX17Hvxj5jfyJrg4GaFGNwm68p6yQDLgqRp0khlDvk9EIoISWBrSFbWRW8irP3zhrXl3IoxTul3+HNEm9iiIol+fsGuBPBKYuaeI3aht4kV90wKPKwiPgI/rz8Jz+fWEy44f+Xb6u4VKFj2Y40L9b8hUeyFkLkArGx4PrPHcl37kghlFPl1ELodsxtfgv+jbUX1xKVGAWkdURtWrQpHct2pJprNRRFITkpkQuzGuOVdJrrSkHsPtyPvaP0CRI5j0E18NPCDpwynOBvK0tS/xmrysnCifZl2tOhbAcZxkEIkWFSCGWSnFQIqarK8TvH+TnoZ3Zd24VBTRt3oZBNITqU7UDbUm0fm+7g8Hf98b2zihjVknudt1G0bFUNkguRMY8Kd7fUsyyx9WCbhxt3E9L6EpkoJjQt2pRuFbpRyaWSxkmFEDmdFEKZJCcUQimGFHZc3cHys8vTXf7y8fChc7nONCjcAL3u8c6lARsXUOP4RwCcqDOfas26ZltmIV7W/Ts3SfquAe7c5ZhlTe60H8Fvwas4fue4cZvqrtXpXqE7DT0bPvF3XwghpBDKJFoWQrHJsay9sJaVQSu5HXsbAHO9OW+WeJMu5btQ2rH0U/e9dPIghda1xVJJwq9wb2r3nZ1dsYV4ZZdOHqDwurZYKMn4efajdp9ZBN0LYmXQSraEbCHFkAKAp60n3Sp04+1Sb2NhYqFxaiHEC0lIgHbt0h6vXQsWmfv/sBRCmUSLQuh+wn1+DvqZX8//ysOkh0BaP4n3yr5Hx3Idnzvbd2REGHHzX6OgeoeTFjWpKJ2jRS509I/vqHliHACBry2kauP3ALgTd4dfz//K6uDVxrGxnCyc6Fq+Kx3LdcTOLOf05RNCPIPcNZY7ZGchdDvmNsvOLmPdxXUkpCYAUMyuGD28evBmiTcz9BdvakoK52Y2pVLicW4o7tgOOYC9k0uW5hYiq/jP64VPxDqisSK66w4Kl6pofC0uOY4Nlzbw07mfuBlzEwBrU2s6lOlAtwrdcLGS33shcjQphHKH7CiEbjy8weLTi/nj8h/GJv8KBSrQp2IfGhdp/EJ9IPx+GEbtm0uJU80J7/Anxb18siSzENkhKTGBK7MaUS75HCG6YriN2IeVTfppYFIMKWwP3c6PZ340jlxtpjPj3TLv0qtir3QTvgohchAphHKHrCyErkZf5YdTP7DpyibjgHK13GvRp1IfanvURvnn9uGMCtzxC1UPpo0WHVBjJjXe7J+peYXQwt1boSiLGuBMJAG2jfEe/juKTvfYdqqqsv/mfhadWsTJuyeBtCEl2pZqS59KfShkUyi7owshnkUKodwhqwqhqf5TWRW8yngLfN2CdXm/yvtUc632Use7cekMdiubYkcch13a4ztocaZlFUJr5w5vo/TWzpgqqRwuMxrfzp8+dVtVVfEP82fhyYUEhAcAabfev136bfpX7i8tRELkFDmkEHr8zyqRLRzMHTCoBuoXrs/PrX5mQdMFL10Excc+JOmXLtgRx3nTClTvOy+T0wqhrQq+LThWbiQA3sFfc/7IjqduqygKvh6+LG2xlKXNl+Lj4UOKmsKaC2tota4VX/p/yd24u9kVXQiRw0mL0HNkVYtQVGIUN2Ju4FXA65WOoxoMBMztSM2ov7iHPan99uJaqHgmpRQi51ANBo7Pfgfvh7u5gxP6Afsp4FY4Q/sGhAUwP3C+sYXIXG9Op3Kd6FOxDw4WDlmYWgjxVDmkRUgKoefICQMqPov/6pn4nJtMiqojuNlKvOq+oXUkIbJM7MNIImbXo6jhOmfNqlB29E5MTDM2D5mqqhwJO8K8E/MIvBsIgI2pDb0q9qJr+a5YmVplYXIhRHaTS2P5wMUT+6h2dhoAAaU/lCJI5HnWtg7Q4SfiVHO8kk5ydOmoDO+rKAo+Hj781PIn5jeeT1nHssQkx/DtiW9pta4Vv53/jWRDctaFF0LkSNIi9Bw5tUUo6l44cd/WxYO7nLCqS9VRm554J40QedGxzYvxPprWZyiw7vdUbdr5hY9hUA1sDdnKvBPzuBFzA0gbt2tY9WG8XuT1F75rUwiRs0iLUB5mSE0ldHE3PLjLDcWdEv1+kiJI5Cveb/TlsEt7AEocHMnNK0EvfAydouONEm+wse1GPvb5GCcLJ0KjQxm2Zxg9t/Xk1N1TmR1bCPFvCQnQvn3akpCgWQxpEXqOnNgi5Lf8Y2qHzCdRNeVGu42UrFxH60hCZLukxARCZjagbMp5LulLUnjUfiwsX76zZUxSDEvOLGHFuRXGkd1bFmvJcO/heNh4ZFZsIcQjOaSztDQj5DJnDv5JrSvfAXCy8qdSBIl8y8zcAvseP/MAW0qlXubk4oGvdDwbMxs+rP4hf779J21KtkFBYWvoVlpvaM28E/OIS47LpORCiJxECqFcJOLWVTx2DEKvqBx1aEnNtz/UOpIQmnL3LMX1BnMwqAo+9zYQ8OfCVz+mtTuT601m1Zur8HbzJjE1kYWnFtJ6fWv+vPyncRBUIUTeIIVQLpGSnET4si4UIIoQXTEq9vtB+gUJAVRu9C7+nr0AqBDwGVfPH8+U45YvUJ6lzZfydcOvKWRTiDvxd/j4wMf02NqDoHsv3idJCJEzyTdpLnF02Wi8kk4Tq1qgf285lta2WkcSIseo1XMmZ8yrYqUkoq7uTlxMVKYcV1EUmhZtyh9t/2Bo9aFYmlgSeDeQ9za/x+TDk4lKzJzzCCG0I4VQLnBy9xpq31wGwPlaUyhSpqqmeYTIafQmJrj3WsldHClmuM7ZH/qiGjLvEpa53py+lfqyse1GWhZriUE1sCp4FW+uf5PfL/wul8uEyMWkEMrhwq5fosje4QD4O7+D9xt9NU4kRM7k7O7J3ebfk6oq1Iz6i4AN32b6Odyt3ZnRYAZLmi+hlEMpIhMjmeg3ke5buxN8PzjTzyeEyHpSCOVgyUmJRC7viiMPuaQvSdW+87WOJESOVqF2S46USLt7rNLJLwg5658l56npXpPVrVczusZorEysOHn3JB03dWTW0Vlyd5kQGWVllXbbfExM2mONSCGUgx37cSjlUoKIxgrLLj9jbiFzIQnxPD5dv+CURU0slGT0a3sRE/0gS85jqjOlu1d3/mj7B02LNiVVTWX5ueW8teEt/r72d5acU4g8RVHSxg6ytk57rBEphHKowB2/4Bv+KwCXas+gUInyGicSInfQ6fV49lnBHZwoYrjJ+cWZ21/ov9yt3fm64dfMbzyfQjaFCI8LZ+juoYzYM4K7cXez7LxCiMwhhVAOdPtqMMUPpk0medi1A9Wbd9M4kRC5i6OLB/dbLiRF1VEjeidH183J8nPWL1yfDW020KdiH/SKnh1Xd9BmQxvWXFgjnamFeJLEROjZM21JTNQshkyx8RzZPcVG2rQB9SmbEswFkzIUG70fM3OLLD+vEHnR4Z8+w/fKN2nT0by7iZKVfLPlvMH3g5lwaAJn7p0BoLprdSbVnURRu6LZcn4hcgWZYkM8yfElwyibEkw01th0XSlFkBCvoFaXCZy09MFcScZsXdb1F/qvsk5lWdlqJWNqjsHSxJLjd47TbmM7lp9dTqohNVsyCCEyRgqhHOTEXyuN/YIu15lBwWJlNU4kRO6m0+sp2ucnwimAp3qL8z/0ydL+Qv+m1+npVqEb69usx9fDl8TURGYFzKL71u5cjrycLRmEEM8nhVAOcSs0mJKHxgBw2O09qjXrqnEiIfIGB2d3HrT6p7/Qw13Z0l/o3wrZFGJR00VMrDMRG1MbTkWcov2f7Vl8ejEphpRszSKEeJwUQjlAUmICD1d2w45YLpiUoXrvuVpHEiJPKVerKQGlhgBQ5fRULp8+nK3nVxSFd0q/w/o266lfuD7JhmTmHp9Lj609CIkKydYsQoj0pBDKAaRfkBBZr1bn8Zr0F/o3d2t35r0+j8l1J2NramtsHfrp7E9yZ5kQGsl1hdD8+fMpVqwYFhYW+Pj4cOTIkWduv2bNGsqVK4eFhQWVKlViy5Yt2ZQ0Y/49XpD0CxIi6zzWXyiLxxd6GkVRaFOqDevarKNOwTokpiYyM2Amvbf35mbMzWzPI0R+l6sKoVWrVjFixAjGjx/P8ePHqVKlCs2bN+fOnTtP3P7QoUN06tSJPn36cOLECdq2bUvbtm05c+ZMNid/svTjBXWUfkFCZLF0/YWid3J0vXaXod2t3VnQZAGf+X6GpYklx8KP0W5jOzZe3oiMaiLyBSsruHMnbdFwio1cNY6Qj48PNWvWZN68eQAYDAY8PT0ZMmQIY8eOfWz7jh07Ehsby6ZNm4zrfH19qVq1KgsWLMjQObNqHKHkpESuzKhP2ZTzMl6QENns0fhCCaopt9pvpkRFH03zXH94nU8OfMKJOycAaFq0KZ/7fo6DhYOmuYTIarEPIwk5uY+K9d7K9GPnuXGEkpKSOHbsGE2aNDGu0+l0NGnSBD8/vyfu4+fnl257gObNmz91e4DExESio6PTLVnh2JLhlE05TzRW2HRZIUWQENmoVpcJnPxnPjKTdb2IfRipaR5PW0+WNl/Kh9U+xEQxYcfVHbyz8R0O3TqkaS4hspJqMHBucT8q7uzG4ZXjNcuRawqhiIgIUlNTcXNzS7fezc2NsLCwJ+4TFhb2QtsDfPnll9jb2xsXT0/PVw//H6rBgGpijkFVuFR7BgWLl8v0cwghnk6n11PkX/ORBf2gTX+hf9Pr9PSr3I+Vb6ykuH1x7sbf5f0d7/NVwFckpyZrmk2IrBDw+xxq/vIHhs0J2Ht6a5Yj1xRC2WXcuHFERUUZl+vXr2f6ORSdjtp9Z3Ojyx6ZR0wIjTi6eHCv5YJ/+gvtIGDDt1pHAsCrgBer3lxFx7IdAVh2dhldtnSR2+xFnhIaFEDFE1MhIBldQBLlazTWLEuuKYScnZ3R6/WEh4enWx8eHo67u/sT93F3d3+h7QHMzc2xs7NLt2SVImWqZtmxhRDPV96nOUdLDACg0skvCDl3VONEaSxNLPnU91O+afQNDuYOBN0PouOmjqy7uE46UotcLy4mCmVNTyyVJK2jALmoEDIzM8Pb25tdu3YZ1xkMBnbt2kXt2rWfuE/t2rXTbQ+wY8eOp24vhMh/fLp+wSmLGlgoyeh+76l5f6F/a1SkEWvfWouPhw/xKfGMPzSej/Z9RExSjNbRhHhpZxe/T1HDde7ioHUUIBcVQgAjRozghx9+YPny5QQFBTFgwABiY2Pp1asXAN27d2fcuHHG7YcOHcq2bdv46quvOH/+PBMmTCAgIIDBgwdr9RaEEDmMTq+nUK/l3MGJooYbnFvcT/P+Qv/mauXKoqaLGO49HBPFhK2hW+mwqQNn753VOpoQL+zohnnUjNxKqqpwt/EcreMAuawQ6tixI7NmzeLzzz+natWqBAYGsm3bNmOH6GvXrnH79m3j9nXq1OGXX35h0aJFVKlShd9//50NGzZQsWJFrd6CECIHKuBWmIjm35GqKtSM+oujf8zTOlI6OkVH74q9WdZyGQWtC3L94XW6bunKynMr5VKZyDWuBh3D68QkAI4Ue58Kvs01TpQmV40jpIWsGkdICJHzHF72Mb6h84lXzQh/byvFytfQOtJjohKjGH9oPLuupV32b+TZiMn1JmNnJv8+iZwrLiaKO1/XpZjhOqfNq1Fh9E70iYlgY5O2QUwMWFtn6jnz3DhCQgiR1Wp1+4JTFt5YKkkoa3oSFxOldaTH2JvbM7vhbD72+RhTnSm7r++mw59yqUzkbGcXv08xw3UicMCj1wr0JiZaRzKSQkgIIf6R1l/op3/6C13n7A/9tY70RIqi0KlcJ1a0WkEhm0LcjLlJty3dWHV+lVwqEznOkfXfGvsFhTf9Dmf3f8bns7SEkJC0xdJSs3xSCAkhxL+k7y+0jSPrv9E60lM9GnOokWcjkg3JTPafzNj9Y4lLjtM6mhBA2nhBlQL/3y/Iq+4b/39Rp4NixdIWnXbliBRCQgjxHxVqt+Ro8YEAVArMOeMLPYm9uT1zG81lpPdI9IqeLSFb6LKlC6FRoVpHE/lcXEwU/DNe0Gnz6tTqNkXrSE8khZAQQjxBWn+hmlgqSTlufKH/UhSFnhV7sqT5EpwtnbkUeYn3Nr/Hrqu7nr+zEFnk7A/9KWa4zl0cKdj7Cf2CkpJg9Oi0JUm7wRWlEBJCiCfQ6fV4/jMfWVHDDYJ+6JOjxhd6kupu1Vn95mqqu1YnNjmWYXuGMefYHFIMKVpHE/nMkfXfUDNqG6mqwp1m31HArfDjGyUnw6xZaUuydvPpSSEkhBBP4ejiwf2WC/+Zj2wnR9fN0TrSc7lYubC4+WK6VUibx/DHMz/ywc4PiEyI1DaYyDeunPGn8qN+QSUG4lWnlcaJnk0KISGEeIZyPs0IKJk2Gn2V01O5fOqQxomez1RnypiaY5hZfyaWJpb43/bnvc3vEXw/WOtoIo+LiX6A6dqeWCjJnLSoiU/XL7SO9FxSCAkhxHPU6jKBk5Y+mCvJmK/vRXTkPa0jZUiL4i1Y2WolhW0Kp91iv7Ub20K3aR1L5FGqwUDwD73xVG8RTgGK9FmBTq/XOtZzSSEkhBDPodPrKdrnJ8JwobAaxqUfeub4/kKPlHEsw29v/kadgnWIT4ln9N7RzD42m1RDqtbRRB5z5PdZeD/8m2RVz4NWC3F08dA6UoZIISSEEBng4OxO9FuLSVL1VI/dh/9vU7WOlGH25vZ81/g7elVMm6B6yZklDPl7CA+THmqcTOQVFwP3U+3sdACOlRlKuVpNNU6UcVIICSFEBpWp3pDj5UYB4B38NecDcs/t6XqdnhHeI5hZfyYWegv239xPly1duBp9VetoIpeLun8X6z96Y6akcMKqDj6dPtM60guRQkgIIV6AT8exHLdpgKmSisOm/kRGhGkd6YW0KN6CZS2X4WblRkhUCJ03d8bvlp/WsUQuZUhNJeSHrhRU73BLcaNEvxUoGR0l2tISzpxJW2SKDSGEyB0UnY7S/ZZxXSmIOxFcW9wVQ2ru6m/jVcCL3978jcoulYlOimbAzgH8HPSzzFMmXpj/ys+pGn+YRNWUuLZLsXd0zvjOOh14eaUtMsWGEELkHrb2TiS3W0aCakrlhKP4Lx+ndaQX5mzpzJLmS3ir5FukqqlMOzKNKf5TZPBFkWFnD26m1pX5AJys/AmlqtTVONHLkUJICCFeQomKPpyqOh4An6uLOL13ncaJXpy53pzJdScz3Hs4CgqrglcxaNcgopOitY4mcriIW1dx2zEQvaJy1L4FNd8e+uIHSUqCCRPSFg2n2FBUaQt9pujoaOzt7YmKisLOzk7rOEKIHObIN12pdf9PHmBLYu/duBcprXWkl7Lr2i7G7R9HfEo8JexLMK/xPDxtPbWOJXKg5KRELs5qTIWk04ToiuE+8gCW1rYvfqDYWLCxSXscEwPW1pmaM6Pf39IiJIQQr6Byv4Vc0pfEkYdE/9SZxIQ4rSO9lMZFGrOsxTJcLV25EnWFLpu7cOLOCa1jiRzo2I9DqZB0mhjVEpNOP71cEZSDSCEkhBCvwMLSGquuvxCFNWVSLhC4eJDWkV5ahQIV+OWNXyjvVJ4HiQ/ou72vjEQt0jm2ZSm+4b8CcLHOdDxLV9E40auTQkgIIV5RweLlCK0/BwCfiHUEbFygbaBX4GbtxrIWy2jk2YgkQxKj947mx9M/yh1lgqvnj1POfywAfh5dqda8h8aJMocUQkIIkQmqvN6Bw4X7AOB17DMunz6scaKXZ2VqxeyGs+lavisAc47PYaLfRJINyRonE1qJiX4Aq7thrSRw1qwKNXvP1jpSppFCSAghMknNnjM4ZVEDSyUJi3Xdibp/V+tIL02v0/NRrY8YW2ssCgprL65lyK4hxCbHah1NZDPVYODCoh4UNdzgDk649f4ZE1MzrWNlGimEhBAik+hNTCja7xduKW4UUsMJXdQp1w22+F9dyndhbqO5WJpYcvDWQXpt68XduNxb4IkX5//LJKrH7CVJ1XP/jR9wds9bdxNKISSEEJnIvoAbcW2XkqCaUiXhKP7Lxmgd6ZU1KtKIJc2X4GThRND9ILpu6cqVqCtaxxLZ4MyBjdS8OAeAExXGUK5mk8w7uIUFHDmStlhYZN5xX5AUQkIIkclKVanL6WoTAah9fTGBO3/VONGrq+hckZUtV1LEtgi3Ym/RfWt3ub0+j7t9NZhCO/8/aGKt9plc1Ov1ULNm2qLXZ+6xX4AUQkIIkQVqth2Ev/M7AJTcP5xrFwK1DZQJPO08WdFqBZWdKxOVGEW/v/qx6+ourWOJLJAQF0PsT51w5CEX9aWo9P6PGZ9MNZfJm+9KCCFygGr9vifItAK2Sjzqb114GHVf60ivzMnCicXNF9OwcEMSUxMZsXcEay6s0TqWyESqwcDphb0plXqZB9hh2+M3LKxsMv9ESUkwc2baouEUG1IICSFEFjEzt8Clzyru4ERRww0uLeyS6ztPA1iaWDK70WzalW6HQTUwyW8SC04ukLGG8gj/VdOoGbWdFFXHzabfZd20McnJMGZM2pKs3dAMUggJIUQWcnYvQuRbS0lSTagWdwj/ZR9pHSlTmOhMGF97PO9Xfh+A+YHzmeo/lVRD7i/08rMzB/+kxvmZAASUHUHFuq01TpT1pBASQogsVqZ6QwKrTgCg9vUfOPHXSm0DZRJFURhcbTDjao1DQeG34N8Ys28MSanaXeYQL+9WyHkK7RiAiWIgwK4pPu99onWkbCGFkBBCZINabw/hsEt7AMocHEloUIDGiTJP5/KdmdFgBiY6E/66+heDdw0mLjl3Tj6bX8U+jCRxRce0ztEmpan4wbI82zn6v/LHuxRCiBzAu998zppVxlpJwHR1ZyIjwrSOlGlaFGvB902+x9LEEr/bfvT7qx9RiVFaxxIZYEhN5cKCLhQ3hBKBA/a91mRN5+gcSgohIYTIJqZm5hTst5pbiiuF1HBuLmpPclKi1rEyja+HL4ubLcbe3J5TEafoua0nd+LuaB1LPIf/so+oFnuAJNWEiDeX4FqouNaRspUUQkIIkY0cXTxIbP8LsaoFXkmnOL6wv9aRMlVll8osa74MV0tXLkVeovvW7lyPvq51LPEUx7cto/b1HwAIrDqBcjUaa5wo+0khJIQQ2ax4hZpcfG0OBlXB594G/FdN1zpSpirlWIqfWv2Ep60nN2Nu0n1bdy4+uKh1LPEfFwP3U95vNACHXTtQ6+0h2RvAwgJ2705bZIoNIYTIX6o26cSRkmlfPN7npnFm/x8aJ8pchWwK8VPLnyjtWJqI+Ah6be/F2XtntY4l/nHnZgj2G7pjqSRxyqImNfrNz/4Qej00bJi2yBQbQgiR//h0nchR+2aYKAaK7BqQJ6bh+DdnS2eWNl9KJedKRCVG0Xd7X46HH9c6Vr4XFxNF9JJ2uHKfUJ0nxT5YhYmpmdaxNJNrCqH79+/TpUsX7OzscHBwoE+fPsTExDxzn4YNG6IoSrrlgw8+yKbEQgjxbIpOR6UPlnHepDx2xKL7tSMP7t7WOlamsje354dmP1DDrQYxyTG8v+N9Dt06pHWsfMuQmsr577sYp88w6/Y7dg4FtAmTnAzz56ctMrL083Xp0oWzZ8+yY8cONm3axL59++jf//mdDPv168ft27eNy4wZM7IhrRBCZIyFpTUu/X7nluJGYTWMsEXvkBAfq3WsTGVtas13Tb6jXqF6JKQmMHjXYHZf2611rHzJ/8fhVI/dT5JqQnjLHylYvJx2YZKSYPDgtEXmGnu2oKAgtm3bxuLFi/Hx8aFevXp8++23/Pbbb9y6deuZ+1pZWeHu7m5c7Ozssim1EEJkTAG3wiR3/I1orCiffI4z33dHNRi0jpWpLE0s+abRNzQt2pRkQzIj9ozgr9C/tI6VrxxdN5fat5YDcLL6F5TzaaZxopwhVxRCfn5+ODg4UKNGDeO6Jk2aoNPp8Pf3f+a+P//8M87OzlSsWJFx48YRF/fs0U4TExOJjo5OtwghRFYrWq461xovIFnVUyN6J4eXjtY6UqYz1Zsyo/4MWhVvRYqawuh9o9l0ZZPWsfKF03vXUe3kBAD8CvemZpuB2gbKQXJFIRQWFoarq2u6dSYmJjg5OREW9vSRWTt37szKlSvZvXs348aNY8WKFXTt2vWZ5/ryyy+xt7c3Lp6enpnyHoQQ4nkqvtaGE5U/B6D29cUc3aDBnTxZzERnwtR6U2lbqi0G1cDH+z9m/cX1WsfK0y6fPkzxvwca5xDz7f2V1pFyFE0LobFjxz7Wmfm/y/nz51/6+P3796d58+ZUqlSJLl268NNPP7F+/XouX7781H3GjRtHVFSUcbl+XQYCE0Jkn1rthuFXsDsAVU98xul9ea9I0Ov0TKwzkQ5lOqCi8vmhz1l1fpXWsfKk8BuXsV3bCRslnrNmlak8aGW+mUMso0y0PPnIkSPp2bPnM7cpUaIE7u7u3LmTfpj2lJQU7t+/j7u7e4bP5+PjA8ClS5coWbLkE7cxNzfH3Nw8w8cUQojM5tNnDgFzblLj4S5K7PqAS/aulKpSV+tYmUqn6PjU91PM9GasDFrJZP/JGDDQqVwnraPlGQ+j7hO75B1KcJ+rOk8Kf7AOM3PtBi7MqTQthFxcXHBxcXnudrVr1yYyMpJjx47h7e0NwN9//43BYDAWNxkRGBgIgIeHx0vlFUKI7KDT66k0aCVnv26BV9JJHNZ35pb9XxQsVlbraJlKURTG1ByDqc6UpWeXMtV/KgbVQJfyXbSOluslJSYQ+t07VPpnIlXT7muxd3r+921+lCvax8qXL0+LFi3o168fR44c4eDBgwwePJj33nuPggULAnDz5k3KlSvHkSNHALh8+TJffPEFx44dIzQ0lI0bN9K9e3fq169P5cqVtXw7QgjxXOYWVngOXE+IrhjORJL80ztE3QvXOlamUxSF4d7D6V2xNwDTjkxjxbkVGqfK3QypqZye14lKiSeIU8150GZFziyizc1h06a0RcMrMbmiEIK0u7/KlStH48aNadWqFfXq1WPRokXG15OTkwkODjbeFWZmZsbOnTtp1qwZ5cqVY+TIkbRr144///xTq7cghBAvxM6hANZ9NhCGM0UNN7i1oC3xsQ+1jpXpFEVhWPVh9K3UF4AZR2fw09mfNE6VO6kGA0cWvI/3w79JVvVcfn0BpavV1zrWk5mYwBtvpC0m2l2gUlRVVTU7ey4QHR2Nvb09UVFRMgaREEITV4OO4biqNXbEctLShwrD/8TULO/1ZVRVlXmB81h0Ku2P3FE1RtHDq4fGqXIXv+UfUzsk7W7DAO8Z1Gj9vsaJtJPR7+9c0yIkhBD5VdHy3txquYx41Ywq8f6cnNcZQ2qq1rEynaIoDK46mA+qpE2FNCtgFivPrdQ4Ve5xZO0cYxF0uMzonF8EJSfDsmVpi0yxIYQQ4lnK+TTjQsPvjAMuHv2+b54bfRrSiqGBVQbSr1I/AKYfnc4vQb9onCrnO75tGd6nJgDgV7A7vp0/1TRPhiQlQa9eaYtMsSGEEOJ5qjRqz8la0zGoCj4R6/BfMkrrSFlCURSGVBtCn4p9APjyyJcyztAznNy9hop+I9ArKkccWuHbd67WkXIVKYSEECIXqfFGP456fQyA740fOfzzRI0TZQ1FURhafSi9vHoBMNl/MmsurNE4Vc5z9tAWyu4ZgJmSyjGbhngPXiEDJr4g+bSEECKX8ekwBr9iAwDwvfg1/quma5woazy6tb57hbSRtif5TZLpOP7lwvE9FNveCwslmUBLXyoNWYVew7uvcisphIQQIhfy7T7VOBWHT9BUjqydrXGirKEoCqNqjDIOsjj+0Hg2X9mscSrtXTnjj9vGzlgrCZwxr0q5D2XU6JclhZAQQuRCik6Hb9+5HHZLm5KixqmJHN0wT+NUWUNRFD6q+RHty7RHReWTA5+w4+oOrWNpJuSsP46/v4s9sZw3KU/xwX9gYWmtdaxcSwohIYTIpRSdDp/3v8PfuR06RcX7xKcE/LlQ61hZQlEUPvX9lDYl25CqpjJm7xj2XN+jdaxsd+WMPw5r3sWRaC7qS+ExaBPWtg5ax8rVpBASQohcTNHpqDVwMf4F2qBTVKoFfETApkXP3zEX0ik6JtaZSMtiLUlRUxixZwSHbh3SOla2uXLGH8ff26UVQSalcR28HXtHZ61jvTxzc1i9Om3RcIoNGVn6OWRkaSFEbmBITSXg267UityCQVUIqPoFtd4eonWsLJFsSGbM3jHsvLYTC70FC5supLpbda1jZanLpw/jtPZdHHnIBZMyuA3amruLoGwgI0sLIUQ+otPrqTFkpbFlqNbJT/FfM0vrWFnCVGfKjPozqFeoHgmpCQzaNYiz985qHSvLXDp5QIqgLCSFkBBC5BE6vZ5ag5Zx2LUDAD5nv+DwL5M1TpU1TPWmzG44mxpuNYhJjuGDHR9w6cElrWNlunOHt+G2Lo8WQSkpsGZN2pKSolkMKYSEECIPUXQ6fD5YiJ9H2q31vhdm4rf84zw5HYeFiQXzGs+jknMlIhMj6bejH9eir2kdK9Oc2v07xbd2w1aJ56xZJTyG5PI+Qf+VmAgdOqQtiYmaxZBCSAgh8hhFp8O331z8ivQHoHbIfPwXDsiTE7Vam1rzfZPvKeNYhoj4CPr+1Zew2DCtY72yY1uWUm5PfyyVJE5a+lBy2DZs7Z20jpUnvXAh1KNHD/bt25cVWYQQQmQSRaejdu+ZHC49EgDf8N84PrcDSYkJGifLfPbm9ixsupBidsW4HXub/jv68yDhgdaxXtqRtXOo6j88bdoM20aUH7YRCysbrWPlWS9cCEVFRdGkSRNKly7N1KlTuXnzZlbkEkIIkQl8u3xOQPVpxlnrz89+g9iHkVrHynTOls4saroINys3QqJC+GDnB8QkxWgd64WoBgN+P46i1unx6BUVf6e3qDr0dxkxOou9cCG0YcMGbt68yYABA1i1ahXFihWjZcuW/P777yQnJ2dFRiGEEK+gxlsDCGq4iDjVnMoJAdyc25R74Te0jpXpPGw8WNRsEY7mjpy7d44hfw8hISV3tIAlJSYQMLcTta//AIBfoZ7UGrxc5g7LBi/VR8jFxYURI0Zw8uRJ/P39KVWqFN26daNgwYIMHz6cixcvZnZOIYQQr6Byo3e53vo3HmBLmZQLJC5oRGhQgNaxMl0J+xIsaLoAa1NrAsIDGL13NMmGnP1H+sOo+wR/3YKaUdtIUXX4e31O7X5zZRb5bPJKn/Lt27fZsWMHO3bsQK/X06pVK06fPk2FChWYPTtvTgAohBC5Vdkar/Ow8yZuKO4UVO/g/NubnNr9u9axMl2FAhX49vVvMdebs+fGHsYfHI9BzZl3zYVdv8TduY2olHiCONWcsw0X4dN+pNax8pUXHlk6OTmZjRs3snTpUv766y8qV65M37596dy5s3HkxvXr19O7d28ePMi9ndUekZGlhRB5TWREGLcWvUuFpNOkqgpHy43Bt9PHWsfKdHuv72Xo7qGkqql0r9CdUTVGoSiK1rGMzh3ehvu2fjgRTQQORL79M6Wq1NM6VvZJToaff0573KULmJpm6uEz+v39woWQs7MzBoOBTp060a9fP6pWrfrYNpGRkVSrVo2QkJAXDp7TSCEkhMiLkhITCPy+F7UitwDgX6At1fovzHMdczde3sgnBz4BYLj3cHpX7K1xojT+q2dS/eyXmCqpXNaXwKr7b3gULat1rDwlywqhFStW0L59eyws8tb/LE8jhZAQIq9SDQb8V46n1uVv0SkqwSblcOj5C26FS2odLVMtP7ucWQFp041MqjOJt0u/rVmWpMQETizsh8/9jQAcs21EhQ9WYGltq1mmvCrLCqH8RgohIUReF7jrN0rsH4EdsTzAjpuN51HxtTZax8pUXx/7mqVnlqJTdMxpOIdGRRple4aw65eI/Kkb5ZLPYVAV/EsOwbfrxPzbKTolBbZvT3vcvDlk8h1yUghlEimEhBD5wc0rQST83JmSqVdIVRWOlBiIT9cv0On1WkfLFKqq8vmhz9lwaQNmOjN+aPZDts5YH7jjF4odHI0DMURjRUj9uVR5vUO2nT9Hio0Fm38GioyJAWvrTD28zD4vhBAiwwqVKE+hkfs54tAKvaJSO2Q+Z2Y2I+LWVa2jZQpFURhfezwNCzckyZDE4L8HcznycpafNykxgcPf9afqwQE4EMNFk9I87P63FEE5iBRCQgghALCwsqHmhz9zpOJ4ElRTKicEoF9UlxN/rdQ6WqYw0Zkwo8EMqrhU4WHSQz7Y+UGWzkt2/dJprs6sh++dVQAcdnuPoqMPUKhE+Sw7p3hxUggJIYQwUnQ6ar07gvD3tnNZXwJHHlLt0CCOzO2SJ6bmsDSxZN7r8yhmV4yw2DAG7BxAdFJ0pp7DkJrK4V++wHnF65ROuUgkNgTWW4DvgLx3V15eIIWQEEKIxxQt743nGD/8PLpiUBVqPdjEg699OLP/D62jvTIHCwcWNF2As6UzlyIvMfTvoSSmJmbKsW9cOkPwtNfwvTALSyWJM+ZVSeizl6pNOmXK8UXmk0JICCHEE5mZW1D7/fkENfuZMJwprIZRcVd3js7uwIO7t7WO90oK2RRiQZMF2JjaEBAewMf7P36l0adTkpM4/MtkCqxoRPnks8SqFvhX+BSvj3bj7lkqE5OLzCaFkBBCiGfyqvsG1sOP4u/yLgZVoWbUdphfk6N/fIdqyJlTV2REWaeyzGk0BxOdCX9d/YuvAr56qeME+W/n6rRa+F6YaWwFiuq1D58Oo/PvrfG5iNw+/xxy+7wQQvxfcMDfmG0ZTnFDKADnTCti0nIKZao31DTXq9h8ZTNj948FYGytsXQp3yVD+0XcukrobyOpEb0DgCisOe81gprvDM8zww5kqeRkWLQo7XH//rlnio38RgohIYRILzkpkYBfJ1H1yiIslSQAAmwbU7DdlxQsljuniVh8ejFzj89FQeHrhl/TpGiTp24b+zCS02tnUCnkR6yVBAyqwtECrSnTaQaOLh7ZmFo8ixRCmUQKISGEeLLwG5e59vsneD/Yhk5RSVRNOeHRgVJtx+Hs7ql1vBeiqipT/KewKngV5npzFjdbTFXXqum2SYiLIXD9V5S5uBgn0u40CzYpi+6NWZSuVl+D1OJZpBDKJFIICSHEs106eZCELR9TMTEQgATVlJOubSjaeizuRUprG+4FpBhSGL57OHtu7MHe3J6VLVdSzL4Y8bEPObXpO4oHLcCV+wDcUDwIqz6c6q36ymWwl5WaCvv3pz1+7TXI5M9RCqFMIoWQEEI8n2owcGrP71gemkmZlAsAJKt6Tjg2x6XZCIpXqKlxwoyJS46jz/Y+nLl3hkKWHoyILEGtW3/iQAwAYbhwvfIQqrUegImpmcZpc7kcMsWGFELPIYWQEEJknGowcPbgn3Dga2MLEcB5k/JEe3WhUrOeOXqmdUNqKocPreWTi18SoU+hakIii8PCuYcb18v1pmqbDzG3sNI6Zt4ghVDuIIWQEEK8nPMBu4jbPZtKMYcwVVIBiMaKoALNsK7WjnI+LXJMq0rIWX/CDqyk+O0tuBPBZVMTunm481Cvw8eiHN+/8zOmOSRrniGF0IuZMmUKmzdvJjAwEDMzMyIjI5+7j6qqjB8/nh9++IHIyEjq1q3L999/T+nSGb9mLYWQEEK8moiwa1zcvpAioWsopIYb10diw0X7eph4taZM7TextnXItkyJCXFcPLqDh+f+wj18P8UN/59c9qFqSZBjI27UaMIXV+aRoqbQt1JfhlYfmm358gUphF7M+PHjcXBw4MaNG/z4448ZKoSmT5/Ol19+yfLlyylevDifffYZp0+f5ty5c1hYZGy+FymEhBAicxhSUzl7cBPxx3+jdOR+HHlofC1F1RFiUoJ7BapjWqwOnlUa4uJRNNMGJIwIu8bNIH/irp3A6vYRysSfNN76D5Ck6jlr7YtaqT0VGnbAwjLtS/mPS3/w6cFPAZhQewLtyrTLlDwCKYRe1rJlyxg2bNhzCyFVVSlYsCAjR45k1KhRAERFReHm5sayZct47733MnQ+KYSEECLzpSQnEXx0Jw8D11Pk7h4Kqnce2yZGteS2SSGirIqS7FACvUMhTKwcMLV2wsLOCQtrOwwGFUNKIinJSaSmJJH48D6J92+QGnULfcxtLOJuUzDhEs5EPnb8uzgSau+DUup1Stdpi30BtydmnXdiHgtPLcREMeH7pt/j6+Gb2R9H/pRDCiGTTD1rDhISEkJYWBhNmvx/UCx7e3t8fHzw8/N7aiGUmJhIYuL/J9+Ljs7cWYmFEEKAiakZXnVaQZ1WAIRdu8iNU7tJDfXD+UEgxVJCsFHiKZ16CR5egoe74PrLn8+gKlzXF+KudVlS3KvgVrUFxcrXxCUDLU6Dqg7i+sPrbAnZwog9I1jZaiUl7Eu8fBiRo+TZQigsLAwAN7f0Fb6bm5vxtSf58ssvmThxYpZmE0IIkZ57kdL/jDnUH0jrw3Mj9Dz3r50jMfwCuvuXMUuIwDzlIRapMVgbHmKlxpOq6EjBhBRMSFVMiNdZEWPmQqKlG6k2HujtC2FXtBJFytWgqI09RV8im6IoTKo7iVsxtwi8G8ignYP45Y1fcLRwzNTPIN8xNYUZM/7/WCOaFkJjx45l+vTpz9wmKCiIcuXKZVMiGDduHCNGjDA+j46OxtMzd42QKoQQuZ25hRVFy1WnaLnqWkcBwFxvztzX59J5c2duxNxg2O5h/NDsB8z0cifZSzMzg9GjtU6hbSE0cuRIevbs+cxtSpR4ueZHd3d3AMLDw/Hw+P/cL+Hh4VStWvWp+5mbm2Nubv5S5xRCCJF3OVk4Mb/xfLpu6crxO8eZcGgCU+pNQVEUraOJV6BpIeTi4oKLi0uWHLt48eK4u7uza9cuY+ETHR2Nv78/AwYMyJJzCiGEyNtKOpTkq4ZfMXDnQP688ifF7YvTr3I/rWPlTqmpcPx42uPq1TN9io2Mypz7ErPBtWvXCAwM5Nq1a6SmphIYGEhgYCAxMTHGbcqVK8f69euBtGu6w4YNY/LkyWzcuJHTp0/TvXt3ChYsSNu2bTV6F0IIIXK7OgXr8LHPxwB8c+Ibdl7dqXGiXCohAWrVSlsSEjSLkWs6S3/++ecsX77c+LxatWoA7N69m4YNGwIQHBxMVFSUcZsxY8YQGxtL//79iYyMpF69emzbti3DYwgJIYQQT9KhbAcuR17ml/O/8PGBjylkU4jyBcprHUu8hFw3jlB2k3GEhBBCPEmKIYVBuwZx6NYh3Kzc+O3N33C2dNY6Vu6RQ8YRyjWXxoQQQoicxERnwswGMylmV4zwuHCG/j2UxNTE5+8ochQphIQQQoiXZGdmx7zG87Azs+NUxCnGHxqPXGjJXaQQEkIIIV5BUbuizG44GxPFhM1XNrP07FKtI4kXIIWQEEII8YpqedRibK2xAMw5Nod9N/ZpnEhklBRCQgghRCboWK4jHcp0QEVlzL4xXIm8onWknM3UFMaPT1s0nGJD7hp7DrlrTAghREYlpybTb0c/joUfo4htEX554xfsze21jpUvyV1jQgghRDYz1ZvydcOvKWhdkGsPrzFq7yhSDClaxxLPIIWQEEIIkYmcLJz45vVvsDSx5PDtw3wV8JXWkXImgwHOnk1bDAbNYkghJIQQQmSysk5lmVpvKgArg1ay8fJGjRPlQPHxULFi2hIfr1kMKYSEEEKILNCkaBPer/w+ABMPTeRsxFmNE4knkUJICCGEyCIDqw6kQeEGJBmSGLp7KBHxEVpHEv8hhZAQQgiRRXSKji9f+9I4DcfIPSNJTk3WOpb4FymEhBBCiCxka2bLN69/g42pDcfvHGf60elaRxL/IoWQEEIIkcWK2xdnev3pKCisCl7F+ovrtY4k/iGFkBBCCJEN6heuz8CqAwGYfHiydJ7OIaQQEkIIIbJJ/8r9aejZkCRDEsP2DONe/D2tI2nH1BRGjUpbZIqNnEum2BBCCJGZHiY9pPPmzoRGh1LTvSaLmi7CRGeidaw8R6bYEEIIIXIgWzNb5jSag5WJFUfDjjL72GytI+VrUggJIYQQ2aykQ0km15sMwE/nfmJryFaNE2nAYIDQ0LRFptgQQggh8pemRZvSp2IfAMYfGs+lB5c0TpTN4uOhePG0RabYEEIIIfKfIdWG4OvhS3xKPMP3DCcmKUbrSPmOFEJCCCGERvQ6PdPrT8fd2p3Q6FA+Pfgpcg9T9pJCSAghhNCQk4UTXzf4GlOdKbuu7WLp2aVaR8pXpBASQgghNFbJpRJja40FYO7xufjf9tc4Uf4hhZAQQgiRA7Qv0542JdtgUA2M2TeGsNgwrSPlC1IICSGEEDmAoih86vsp5ZzKcT/hPqP3jibZIDPVZzUphIQQQogcwsLEgq8bfI2tqS2BdwOZc2yO1pGyjokJDByYtphoN7K2FEJCCCFEDuJp58kX9b4A0gZb3HF1h8aJsoi5Ocyfn7aYm2sWQwohIYQQIodpXKQxvbx6AfDZwc+4Gn1V40R5lxRCQgghRA40pPoQqrtWJzY5lhF7RhCfot3oy1lCVeHu3bRFw7GTpBASQgghciBTnSkzG8zEycKJCw8uMOXwFK0jZa64OHB1TVvi4jSLIYWQEEIIkUO5Wrkyo/4MdIqOPy7/wYZLG7SOlOdIISSEEELkYD4ePgyoMgCAKYencPHBRY0T5S1SCAkhhBA5XP/K/alTsA4JqQmM3DuSuGTtLiXlNVIICSGEEDmcTtHx5Wtf4mrpSkhUCJMOT5LJWTOJFEJCCCFELuBk4cSMBjPQK3o2X9nM2otrtY6UJ0ghJIQQQuQS3m7eDKk2BIAv/b8k+H6wxolyv1xTCE2ZMoU6depgZWWFg4NDhvbp2bMniqKkW1q0aJG1QYUQQogs1KtiL14r9BpJhiRG7R2Ve/sLmZhAjx5pi0yx8XxJSUm0b9+eAQMGvNB+LVq04Pbt28bl119/zaKEQgghRNbTKTqm1JuCq5UrodGhfHH4i9zZX8jcHJYtS1tkio3nmzhxIsOHD6dSpUovtJ+5uTnu7u7GxdHRMYsSCiGEENnD0cLROL7Qpiub+OPyH1pHyrVyTSH0svbs2YOrqytly5ZlwIAB3Lt375nbJyYmEh0dnW4RQgghchpvN28GVR0EwFT/qVyOvKxxohekqhAbm7bIFBtZo0WLFvz000/s2rWL6dOns3fvXlq2bElqaupT9/nyyy+xt7c3Lp6entmYWAghhMi4vpX6UtujNvEp8YzaOyp3zUcWFwc2NmlLfp1iY+zYsY91Zv7vcv78+Zc+/nvvvcdbb71FpUqVaNu2LZs2beLo0aPs2bPnqfuMGzeOqKgo43L9+vWXPr8QQgiRlXSKjqmvTaWARQEuRV5i+pHpWkfKdbTrpg2MHDmSnj17PnObEiVKZNr5SpQogbOzM5cuXaJx48ZP3Mbc3BxzDTttCSGEEC/C2dKZafWn0f+v/qy9uBZfD19aFJc7pDNK00LIxcUFFxeXbDvfjRs3uHfvHh4eHtl2TiGEECKr+Xr40rdSX344/QMT/SZS0bkihW0Lax0rV8g1fYSuXbtGYGAg165dIzU1lcDAQAIDA4mJiTFuU65cOdavXw9ATEwMo0eP5vDhw4SGhrJr1y7atGlDqVKlaN68uVZvQwghhMgSA6sOpKpLVWKSY/ho30ckG5K1jpQr5JpC6PPPP6datWqMHz+emJgYqlWrRrVq1QgICDBuExwcTFRUFAB6vZ5Tp07x1ltvUaZMGfr06YO3tzf79++XS19CCCHyHBOdCdPrT8fWzJZTEaeYd2Ke1pFyBUXNlaMwZZ/o6Gjs7e2JiorCzs5O6zhCCCHEM+28upPhe4YDsLDJQuoUqqNxoqeIjU27YwwgJgasrTP18Bn9/s41LUJCCCGEeL4mRZvQoUwHAMYdGEdEfITGiZ5Cr4d3301b9HrNYkghJIQQQuQxo2uOppRDKe4n3OfTg59iUA1aR3qchQWsWZO2WFhoFkMKISGEECKPsTCxYGb9mZjrzTl48yA/B/2sdaQcSwohIYQQIg8q5ViK0TVGAzD72GyC7gVpnChnkkJICCGEyKM6lO1AI89GJBuS+Wj/R8QlazeVxWNiY0FR0pbYWM1iSCEkhBBC5FGKojCxzkRcLV0JiQphZsBMrSPlOFIICSGEEHmYo4UjU1+bioLC7xd+Z+fVnVpHylGkEBJCCCHyOB8PH3pX7A3A+EPjCYsN0zhRziGFkBBCCJEPDKo2iIoFKhKdFM0nBz7JmbfUa0AKISGEECIfMNWZMq3+NCxNLDkSdoTlZ5drHSlH0HT2+bwkNTWV5GSZ4E4IkTVMTU3Razj6rsgbitoVZWytsYw/NJ5vTnyDj4cPFQpU0DqWpqQQekWqqhIWFkZkZKTWUYQQeZyDgwPu7u4oiqJ1FJGLvV3qbfbf2M/Oazv5aN9HrG69GksTy+wPotdDq1b/f6wRmXT1OZ43advt27eJjIzE1dUVKysr+QdKCJHpVFUlLi6OO3fu4ODggIeHh9aRRC4XmRBJu43tuBN/hw5lOvBZ7c+0jpTpMjrpqrQIvYLU1FRjEVSgQAGt4wgh8jBLy7S/2O/cuYOrq6tcJhOvxMHCgcn1JtN/R39WX1hNvUL1aFSkkdaxNCGdpV/Boz5BVlZWGicRQuQHj/6tkf6IIjPULlibHhV6ADDBb0LOnaU+i0khlAnkcpgQIjvIvzUis31Y/UNKO5bmfsJ9xh8aT7b2lomNBWvrtEWm2BBCCCFEdjPTmzHttWmY6kzZd2Mfay6syd4AcXFpi4akEBK5yoQJE6hatarWMQBo2LAhw4YN0zqGEEK8kjKOZRhafSgAswJmERoVqm2gbCaFUD4VFhbG0KFDKVWqFBYWFri5uVG3bl2+//574jSuzl/WhAkTUBTlmcvL2LNnD4qiyBAJQog8q1uFbvi4+xCfEs/HBz4m2ZB/+qFJIZQPXblyhWrVqvHXX38xdepUTpw4gZ+fH2PGjGHTpk3s3Pn0CflycifNUaNGcfv2beNSuHBhJk2alG7dvyUlJWmUVAghchadomNyvcnYmtlyOuI0P5z6QetI2UYKoXxo4MCBmJiYEBAQQIcOHShfvjwlSpSgTZs2bN68mdatWxu3VRSF77//nrfeegtra2umTJkCwPfff0/JkiUxMzOjbNmyrFixwrhPaGgoiqIQGBhoXBcZGYmiKOzZswf4fyvLrl27qFGjBlZWVtSpU4fg4OB0WadNm4abmxu2trb06dOHhISEp74vGxsb3N3djYter8fW1tb4/L333mPw4MEMGzYMZ2dnmjdv/tysoaGhNGqUdkupo6MjiqLQs2dP47YGg4ExY8bg5OSEu7s7EyZMeMGfhhBC5Azu1u586vMpAItOLeLU3VMaJ8oeUghlIlVViUtK0WTJaE//e/fu8ddffzFo0CCsra2fuM1/LyFNmDCBt99+m9OnT9O7d2/Wr1/P0KFDGTlyJGfOnOH999+nV69e7N69+4U/s08++YSvvvqKgIAATExM6N27t/G11atXM2HCBKZOnUpAQAAeHh589913L3yOf1u+fDlmZmYcPHiQBQsWPHd7T09P1q5dC0BwcDC3b99m7ty56Y5nbW2Nv78/M2bMYNKkSezYseOVMgohhFZalWhFy+ItSVVT+eTAJ8SnxGsdKcvJgIqZKD45lQqfb9fk3OcmNcfK7Pk/zkuXLqGqKmXLlk233tnZ2djaMmjQIKZPn258rXPnzvTq1cv4vFOnTvTs2ZOBAwcCMGLECA4fPsysWbOMrScZNWXKFBo0aADA2LFjeeONN0hISMDCwoI5c+bQp08f+vTpA8DkyZPZuXPnM1uFnqd06dLMmDHD+Dw0NPSZ2+v1epycnABwdXXFwcEh3euVK1dm/PjxxmPPmzePXbt20bRp05fOKIQQWvrE5xOOhR0jNDqU2cdm87HPx1lzIp0O/vn3H5127TLSIiQAOHLkCIGBgXh5eZGYmJjutRo1aqR7HhQURN26ddOtq1u3LkFBQS983sqVKxsfP5o24M6dO8bz+Pj4pNu+du3aL3yOf/P29n6l/f/r3/kh7T08yi+EELmRvbk9X9T9AoBfz//KoVuHsuZElpawZ0/aYqnBXGf/kBahTGRpqufcpOaanTsjSpUqhaIoj/XFKVGiRNpxnvDL+LRLaE+j+6ey//fluqd1sjY1NTU+fnRJzmAwvND5XsR/38uLZH2Sf+eHtPeQlfmFECI71ClUh45lO7IqeBWfHfyMdW+tw97cXutYWUJahDKRoihYmZlosmT01vACBQrQtGlT5s2bR+xLjuRZvnx5Dh48mG7dwYMHqVChAgAuLi4A6e7S+ndn5Bc5j7+/f7p1hw8ffuHjPEtGspqZmQFpc8sJIUR+McJ7BEXtinIn7g5fHvlS6zhZRgqhfOi7774jJSWFGjVqsGrVKoKCgggODmblypWcP3/+uZM5jh49mmXLlvH9999z8eJFvv76a9atW8eoUaOAtFYlX19fpk2bRlBQEHv37uXTTz994ZxDhw5lyZIlLF26lAsXLjB+/HjOnj37Uu/5aTKStWjRoiiKwqZNm7h79y4xMTGZmkEIIXIiK1Mrptabik7RsfnKZraHZnIf2NhYcHFJW2SKDZGdSpYsyYkTJ2jSpAnjxo2jSpUq1KhRg2+//ZZRo0bxxRdfPHP/tm3bMnfuXGbNmoWXlxcLFy5k6dKlNGzY0LjNkiVLSElJwdvbm2HDhjF58uQXztmxY0c+++wzxowZg7e3N1evXmXAgAEvfJzneV7WQoUKMXHiRMaOHYubmxuDBw/O9AxCCJETVXapTN9KfQGYfHhy5k/MGhGRtmhIUbN1hrXcJzo6Gnt7e6KiorCzs0v3WkJCAiEhIRQvXhwLCwuNEgoh8gv5N0doITk1mc5bOnP+/nkaejbkm0bfZM4EwLGxYGOT9jgmJm3y1Uz0rO/vf5MWISGEEEI8lanelCn1pmCiM2HP9T1svLxR60iZSgohIYQQQjxTGccyDKo6CIBpR6YRFhumcaLMI4WQEEIIIZ6rp1dPKrtUJiY5hs8OfpbhGQ1yOimEhBBCCPFcJjoTptSdgoXegsO3D7MqeJXWkTKFFEJCCCGEyJBi9sUY5j0MgK+Pfc316OsvfzCdDmrUSFtkig0hhBBC5AadynWipntN4lPi+fTgpxjUlxxN39ISjh5NWzScYkMKISGEEEJkmE7RManOJCxNLDl+5zi/BP2idaRXIoWQEEIIIV5IYdvCjPQeCcDc43O5Gn1V40QvL1cUQqGhofTp04fixYtjaWlJyZIlGT9+PElJSc/cLyEhgUGDBlGgQAFsbGxo164d4eHh2ZRaCCGEyLval22Pj4cPCakJfHbwM1INLzgfY1wcFCuWtsTFZUXEDMkVhdD58+cxGAwsXLiQs2fPMnv2bBYsWMDHH3/8zP2GDx/On3/+yZo1a9i7dy+3bt3inXfeyabUAqBnz560bdvW+Lxhw4YMGzbslY6ZGcfIDhMmTMDNzQ1FUdiwYYPWcV6Zlu9jwoQJVK1aVZNzCyGe7NElMisTK07cOcHPQT+/2AFUFa5eTVu0vBVfzaVmzJihFi9e/KmvR0ZGqqampuqaNWuM64KCglRA9fPzy/B5oqKiVECNiop67LX4+Hj13Llzanx8/IuF11iPHj1UQAVUU1NTtWTJkurEiRPV5OTkLDlXmzZtjM/v3bunRkdHZ2jf3bt3q4D64MGDdOtf5BhaOXfunAqo69evV2/fvq0mJCQ8tk1ISIjx5/Dvn8UXX3yhGgwG43bjx49XAbV58+aPHWPGjBkqoDZo0MC4LjY2Vh07dqxaokQJ1dzcXHV2dlbr16+vbtiw4YlZGzRokC7Hf5dHx370frTw8OFDNSIiIkPbjh8/Xq1SpUrWBtJIbv03R+Rtq4NXqxWXVVS9V3irVyKvZHzHmBhVTSuB0h5nsmd9f/+bSfaWXZknKioKJyenp75+7NgxkpOTadKkiXFduXLlKFKkCH5+fvj6+j5xv8TERBITE43Po6OjMy90DtKiRQuWLl1KYmIiW7ZsYdCgQZiamjJu3LjHtk1KSsLMzCxTzvusn1l2HiOrXb58GYA2bdo8d06enTt34uXlRWJiIgcOHKBv3754eHjQp08f4zYeHh7s3r2bGzduULhwYeP6JUuWUKRIkXTH++CDD/D39+fbb7+lQoUK3Lt3j0OHDnHv3r0nnn/dunXGy8zXr1+nVq1axkxApv3sX4aqqqSmpmJjY4PNozmJhBA5yrul32VH6A78bvvx2cHPWN5iOXqdXutYGZYrLo3916VLl/j22295//33n7pNWFgYZmZmODg4pFvv5uZGWNjThwb/8ssvsbe3Ny6enp4ZD6aqkBSrzfKCzYrm5ua4u7tTtGhRBgwYQJMmTdi4MW3+mEeXs6ZMmULBggUpW7YskPYl2aFDBxwcHHBycqJNmzaEhoYaj5mamsqIESNwcHCgQIECjBkz5rGRR/97WSsxMZGPPvoIT09PzM3NKVWqFD/++COhoaE0atQIAEdHRxRFoWfPnk88xoMHD+jevTuOjo5YWVnRsmVLLl68aHx92bJlODg4sH37dsqXL4+NjQ0tWrTg9u3bxm327NlDrVq1sLa2xsHBgbp163L16tM7/50+fZrXX38dS0tLChQoQP/+/YmJiQHSLuO0bt0aAJ1O99xCqECBAsafRZcuXahbty7Hjx9Pt42rqyvNmjVj+fLlxnWHDh0iIiKCN954I922Gzdu5OOPP6ZVq1YUK1YMb29vhgwZQu/evZ94ficnJ9zd3XF3d8fFxSVdJnd393SFZ0REBG+//TZWVlaULl3a+DvzyJkzZ2jZsiU2Nja4ubnRrVs3Iv41s3RiYiIffvghrq6uWFhYUK9ePY4ePWp8fc+ePSiKwtatW/H29sbc3JwDBw48dmnsaT+vZcuWMXHiRE6ePImiKCiKwrJly575+QshXo2iKEysMxFrU2tO3j354pfINKZpITR27FjjP1ZPW86fP59un5s3b9KiRQvat29Pv379Mj3TuHHjiIqKMi7Xr7/AYFHJcTC1oDZL8qt1NLO0tEzX+XzXrl0EBwezY8cONm3aRHJyMs2bN8fW1pb9+/dz8OBBY0HxaL+vvvqKZcuWsWTJEg4cOMD9+/dZv379M8/bvXt3fv31V7755huCgoJYuHAhNjY2eHp6snbtWgCCg4O5ffs2c+fOfeIxevbsSUBAABs3bsTPzw9VVWnVqhXJycnGbeLi4pg1axYrVqxg3759XLt2jVGjRgGQkpJC27ZtadCgAadOncLPz4/+/fs/tYCJjY2lefPmODo6cvToUdasWcPOnTsZPHgwAKNGjWLp0qUA3L59O13B9TwBAQEcO3YMHx+fx17r3bt3ui/1JUuW0KVLl8dabNzd3dmyZQsPHz7M8HkzauLEiXTo0IFTp07RqlUrunTpwv379wGIjIzk9ddfp1q1agQEBLBt2zbCw8Pp0KGDcf8xY8awdu1ali9fzvHjxylVqhTNmzc3HuORsWPHMm3aNIKCgqhcuXK615718+rYsSMjR47Ey8vL+Nl37Ngx0z8HIUR6HjYejKyRdhfZtye+5Vr0NY0TZZyml8ZGjhxp/Cv/aUqUKGF8fOvWLRo1akSdOnVYtGjRM/dzd3cnKSmJyMjIdK1C4eHhuLu7P3U/c3NzzM3NM5Q/L1BVlV27drF9+3aGDBliXG9tbc3ixYuNX7IrV67EYDCwePFiY4GwdOlSHBwc2LNnD82aNWPOnDmMGzfO2CF9wYIFbN++/annvnDhAqtXr2bHjh3GS5j//nk/aolwdXV9rGXvkYsXL7Jx40YOHjxInTp1APj555/x9PRkw4YNtG/fHoDk5GQWLFhAyZIlARg8eDCTJk0C0i5/RkVF8eabbxpfL1++/FNz//LLLyQkJPDTTz9hbW0NwLx582jdujXTp0/Hzc3NmPdZv2uP1KlTB51OR1JSEsnJyfTv35/u3bs/tt2bb77JBx98wL59+/D29mb16tUcOHCAJUuWpNtu0aJFdOnShQIFClClShXq1avHu+++S926dZ+b5Xl69uxJp06dAJg6dSrffPMNR44coUWLFsybN49q1aoxdepU4/ZLlizB09OTCxcuUKhQIb7//nuWLVtGy5YtAfjhhx/YsWMHP/74I6NHjzbuN2nSJJo2bfrEDM/7ednY2GBiYpKhz14IkXneLf0u20O343/bn88Pfc6S5kvQKTn/wpOmhZCLi4uxKf55bt68SaNGjfD29mbp0qXonjMct7e3N6ampuzatYt27doBaS0L165do3bt2q+c/YlMreDjW1lz7Iyc+wVs2rQJGxsbkpOTMRgMdO7cmQkTJhhfr1SpUrqWhpMnT3Lp0iVsbW3THSchIYHLly8TFRXF7du307VkmJiYUKNGjadOzBcYGIher6dBgwYvlP3fgoKCMDExSXfeAgUKULZsWYKCgozrrKysjF+akNbn5s6dO0BawdWzZ0+aN29O06ZNadKkCR06dMDDw+Op56xSpYqxCAKoW7cuBoOB4OBg3NzcXug9rFq1ivLly5OcnMyZM2cYMmQIjo6OTJs2Ld12pqamdO3alaVLl3LlyhXKlCnzWGsJQP369bly5QqHDx/m0KFD7Nq1i7lz5zJx4kQ+++yzF8r2X/8+n7W1NXZ2dsbP8eTJk+zevfuJfXkuX75MQkICycnJ6QoyU1NTatWqle5nBVCjRo2nZnjRn5cQIns8ukT29h9vcyz8GL+d/43O5Ts/aweoUOH/jzWS80s10oqghg0bUqRIEWbNmsXdu3cJCwtL19fn5s2blCtXjiNHjgBgb29Pnz59GDFiBLt37+bYsWP06tWL2rVrP7Wj9CtTFDCz1mZ5wV+iRo0aERgYyMWLF4mPj2f58uXpvtj//RggJiYGb29vAgMD0y0XLlygc+dn/KI/g2U2Dqluamqa7rmiKOkKtKVLl+Ln50edOnVYtWoVZcqU4fDhw9mSzdPTk1KlSlG+fHnat2/PsGHD+Oqrr0hISHhs2969e7NmzRrmz5//1D4/kPZ+X3vtNT766CP++usvJk2axBdffPHcsbee50mfo8GQNrx+TEwMrVu3fux35OLFi9SvX/+FzvPf37//0vLnJYR4ukI2hRjhPQKAOcfncOPhjadvbGUFZ8+mLVYv9sd8ZsoVhdCOHTu4dOkSu3btonDhwnh4eBiXR5KTkwkODibuX4MyzZ49mzfffJN27dpRv3593N3dWbdunRZvIcextramVKlSFClSBBOT5zcMVq9enYsXL+Lq6kqpUqXSLY86lnt4eODv72/cJyUlhWPHjj31mJUqVcJgMLB3794nvv6oRSo19emDdJUvX56UlJR057137x7BwcFUePSXRgZVq1aNcePGcejQISpWrMgvvzx52Pjy5ctz8uRJYmNjjesOHjyITqczdix/FXq9npSUlCcWLV5eXnh5eXHmzJkXKkArVKhASkrKE4urzFK9enXOnj1LsWLFHvsdsba2pmTJkpiZmXHw4EHjPsnJyRw9evSFf1bw9J+XmZnZM39nhBBZq0PZDsa5yCYcmvDUqwI5Ra4ohHr27Imqqk9cHilWrBiqqtKwYUPjOgsLC+bPn8/9+/eJjY1l3bp10m/gJXXp0gVnZ2fatGnD/v37CQkJYc+ePXz44YfcuJFW8Q8dOpRp06axYcMGzp8/z8CBA4mMjHzqMYsVK0aPHj3o3bs3GzZsMB5z9erVABQtWhRFUdi0aRN379413pX1b6VLl6ZNmzb069ePAwcOcPLkSbp27UqhQoVo06ZNht5bSEgI48aNw8/Pj6tXr/LXX39x8eLFp/YT6tKlCxYWFvTo0YMzZ86we/duhgwZQrdu3V74shikFW5hYWHcuHGDrVu3MnfuXBo1aoSdnd0Tt//777+5ffv2U/tNNWzYkIULF3Ls2DFCQ0PZsmULH3/88TOPmRkGDRrE/fv36dSpE0ePHuXy5cts376dXr16kZqairW1NQMGDGD06NFs27aNc+fO0a9fP+Li4tINFfA8z/t5FStWjJCQEAIDA4mIiEg3HIYQIuvpFB0Ta0/E0sQS/zB/fr/4u9aRnilXFEJCe1ZWVuzbt48iRYrwzjvvUL58efr06UNCQoLxy3XkyJF069aNHj16ULt2bWxtbXn77befedzvv/+ed999l4EDB1KuXDn69etnbGkpVKgQEydOZOzYsbi5uRnvyvqvpUuX4u3tzZtvvknt2rVRVZUtW7Y8dhnnWe/t/PnztGvXjjJlytC/f38GDRr01OEZrKys2L59O/fv36dmzZq8++67NG7cmHnz5mXofP/VpEkTPDw8KFasGP3796dVq1asWrXqqds/umX8aZo3b87y5ctp1qwZ5cuXZ8iQITRv3txYYGaVggULcvDgQVJTU2nWrBmVKlVi2LBhODg4GPv0TZs2jXbt2tGtWzeqV6/OpUuX2L59O46Ojhk+z/N+Xu3ataNFixY0atQIFxcXfv311yx5v0KIp/O08+TDah8C8FXAV4TFPmHYmrg48PJKWzScYkNRc3qblcaio6Oxt7cnKirqsb+mExISCAkJoXjx4lhYWGiUUAiRX8i/OSI3STWk0mNbD07ePUn9wvWZ9/q89MOSxMbCo5srYmLgOX0DX9Szvr//TVqEhBBCCJHp9Do9k+pMwlRnyr4b+9gcslnrSE8khZAQQgghskQJhxIMqDIAgOlHpnMv/slT/WhJCiEhhBBCZJmeFXtSzqkckYmRfHnkS63jPEYKISGEEEJkGVOdKZPqTEKv6Nkeup1d13ZpHSkdKYSEEEIIkaXKFyhPr4q9AJh8eDLRSdEaJ/o/KYSEEEIIkeU+qPIBxeyKEREfwdcBX6fNiFC0aNoiU2wIIYQQIi8z15szoc4EANZeXMuRqDMQGpq2yBQbQgghhMjrvN286Vi2IwAT/CaQkJJ10/5klBRCIkv17NmTtm3bGp83bNiQYcOGvdIxM+MY2WHChAm4ubmhKAobNmzIknMUK1aMOXPmZMmxM9uLfg4TJkygatWqr3ROLX9X9uzZg6Ioz5xmRoj8aFj1YbhauXL94XW+O/md1nGkEMqPevbsiaIoKIqCmZkZpUqVYtKkSaSkpGT5udetW8cXX3yRoW2f9kXyIsfQSlBQEBMnTmThwoXcvn2bli1bPrZNaGio8eegKAoFChSgWbNmnDhxQoPEWe9pn8PLmDBhQrrP7kmL1urUqcPt27ext7d/7rZSNIn8xMbMhk99PsU8yUDz9p8SX60ixMdrlkcKoXyqRYsW3L59m4sXLzJy5EgmTJjAzJkzn7jtk2ZBf1lOTk7Y2tpqfoysdvnyZQDatGmDu7s75ubmT912586d3L59m+3btxMTE0PLli3z5Bfi8z6HFzFq1Chu375tXAoXLsykSZPSrdNScnIyZmZmuLu754iiTIicplGRRjTzbIJXSDyWgWdJTtFucmQphPIpc3Nz3N3dKVq0KAMGDKBJkyZs3LgR+P/lrClTplCwYEHKli0LwPXr1+nQoQMODg44OTnRpk0bQkNDjcdMTU1lxIgRODg4UKBAAcaMGcN/p7L776WKxMREPvroIzw9PTE3N6dUqVL8+OOPhIaG0qhRIwAcHR1RFIWePXs+8RgPHjyge/fuODo6YmVlRcuWLbl48aLx9WXLluHg4MD27dspX748NjY2xkLwkT179lCrVi3jhKZ169bl6tWrT/38Tp8+zeuvv46lpSUFChSgf//+xMTEAGmtFa1btwZAp9M994uwQIECuLu7U6NGDWbNmkV4eDj+/v4ArF27Fi8vL8zNzSlWrBhfffXVU4/Tu3dv3nzzzXTrkpOTcXV15ccffzR+dh9++CFjxozByckJd3d3JkyYkG6fa9eu0aZNG2xsbLCzs6NDhw6Eh4cbX390yWrJkiUUKVIEGxsbBg4cSGpqKjNmzMDd3R1XV1emTJmS7rj/vTT20UcfUaZMGaysrChRogSfffYZycnJz/ysHrGxscHd3d246PV6bG1t0617xGAwPPP9RkZG0rdvX1xcXLCzs+P111/n5MmT6bb5/vvvKVmyJGZmZpQtW5YVK1Y89t6+//573nrrLaytrZkyZcpjrTxXr16ldevWODo6Ym1tjZeXF1u2bHnm77oQedmIGiOMj38594tmOaQQykSqqhKXHKfJ8qpz51paWqZr+dm1axfBwcHs2LGDTZs2kZycTPPmzbG1tWX//v0cPHjQWFA82u+rr75i2bJlLFmyhAMHDnD//n3Wr1//zPN2796dX3/9lW+++YagoCAWLlyIjY0Nnp6erF27FoDg4GBu377N3Llzn3iMnj17EhAQwMaNG/Hz80NVVVq1apXuSzUuLo5Zs2axYsUK9u3bx7Vr1xg1ahQAKSkptG3blgYNGnDq1Cn8/Pzo37//UwuY2NhYmjdvjqOjI0ePHmXNmjXs3LmTwYMHA2mtFUuXLgV44dYJS0tLIK0V7tixY3To0IH33nuP06dPM2HCBD777DOWLVv2xH379u3Ltm3b0p1v06ZNxMXF0bFjR+O65cuXY21tjb+/PzNmzGDSpEns2LEDSCsa2rRpw/3799m7dy87duzgypUr6faHtBavrVu3sm3bNn799Vd+/PFH3njjDW7cuMHevXuZPn06n376qbGgexJbW1uWLVvGuXPnmDt3Lj/88AOzZ8/O8GeVUc96vwDt27fnzp07bN26lWPHjlG9enUaN27M/fv3AVi/fj1Dhw5l5MiRnDlzhvfff59evXqxe/fudOeZMGECb7/9NqdPn6Z3796P5Rg0aBCJiYns27eP06dPM3369Bf+XRciL3G2dDY+ru5WXbsgqnimqKgoFVCjoqIeey0+Pl49d+6cGh8fr6qqqsYmxaoVl1XUZIlNis3we+rRo4fapk0bVVVV1WAwqDt27FDNzc3VUaNGGV93c3NTExMTjfusWLFCLVu2rGowGIzrEhMTVUtLS3X79u2qqqqqh4eHOmPGDOPrycnJauHChY3nUlVVbdCggTp06FBVVVU1ODhYBdQdO3Y8Mefu3btVQH3w4EG69f8+xoULF1RAPXjwoPH1iIgI1dLSUl29erWqqqq6dOlSFVAvXbpk3Gb+/Pmqm5ubqqqqeu/ePRVQ9+zZ87yPTlVVVf1fe/ceFlWd/wH8PYDDxeEul0GHiyIwBCKKGlKAC4W2a2DXTVMyZdeVvGQXZfUXlq7WVq5Wxqrtglv5iE+KlamAJmSAohikOXJzUFJQMgOGxAvz+f3hepYRGIZEzuB8Xs9znodzznfOec+XYebDOd8zZ+PGjeTo6EgajUZY9tVXX5GZmRnV19cTEVFWVhZ19+elVqsJAH333XdERHT58mWaMmUKyWQyqq+vp6lTp9JDDz2k85hXXnmFAgMDhXkvLy/6xz/+IcwHBgbSW2+9JcxPnjyZnnvuOWE+KiqKHnjgAZ1tjhkzhhYvXkxERDk5OWRubk5nz54V1v/www8EgIqLi4mIKDU1lWxsbKipqUloExcXR97e3tTW1iYs8/f3p9WrVwvzACgrK6vL/nj77bdp9OjRwnxqaiqFhIR02b692/vB0Od78OBBsrOzo9bWVp02w4YNow0bNhAR0fjx4ykpKUln/ZNPPkmPPPKIMA+AFi5cqNPm9tdvcHAwLV++vNP8Xb3Wb3f7ew5j/ZpGQwTcnNq9n/YWfZ/f7fERIRO1a9cuyGQyWFlZYdKkSXj66ad1ThkEBwdDKpUK82VlZaiqqoKtrS1kMhlkMhmcnJzQ2tqK6upqNDY2oq6uDuPGjRMeY2FhgbCwsC4zlJaWwtzcHFFRUb/5eahUKlhYWOjs19nZGf7+/lCpVMIyGxsbDBs2TJiXy+W4ePEigJtjjp577jnExcVh8uTJWLdund6jOCqVCiEhIRg4cKCwLCIiAlqtFuXl5T1+DuPHj4dMJoOjoyPKysqQmZkJNzc3qFQqRERE6LSNiIhAZWUl2traOt3W7NmzhaNRFy5cwJ49ezocnRgxYoTOfPu+UKlUUCgUUCgUwvrAwEA4ODjo9Ke3t7fOOC03NzcEBgbCzMxMZ9mt7XYmMzMTERERcHd3h0wmw7Jly3D27Nku2/9W+p5vWVkZNBoNnJ2dhde1TCaDWq0Wxnl19Xto3x8A9L7WAWD+/PlYuXIlIiIikJqaiu+///5OnxpjrBdYiB3gXmJtYY3DU7s+FXC3990TEyZMQFpaGqRSKTw8PGBhoftSaP8hDwAajQajR4/Gp59+2mFbLi4uPQ+M/50G6gsDBgzQmZdIJDqnE9PT0zF//nzs3bsXmZmZWLZsGXJzc3H//fff9WyZmZkIDAyEs7MzHBwc7mhbM2bMwJIlS1BUVITCwkL4+PjgwQcf1GnTWV9otdoe7aezbfRku0VFRZg2bRpef/11xMXFwd7eHlu3btU7Buq30pdLo9FALpcjLy+vw+N6+ru4/W/mdrNnz0ZcXBy++uor5OTkYPXq1Xj33Xcxb968Hu2HMda7uBDqRRKJBDYDxPt2zJ4YOHAgfH19DW4/atQoZGZmwtXVFXZ2dp22kcvlOHz4MCIjIwHcHHtza8xFZ4KDg6HVapGfn4/Y2NgO628dkerq6AcAKJVK3LhxA4cPH8b48eMBAJcuXUJ5eTkCAwMNfn4AEBoaitDQUKSkpCA8PBxbtmzptBBSKpXIyMhAS0uL8OFXUFAAMzMzYWB5TygUCp2jVe33U1BQoLOsoKAAfn5+MDc373Rbzs7OSEhIQHp6OoqKijBz5sweZVEqlaitrUVtba1wVOjkyZP45Zdfetyf+hQWFsLLywtLly4VlukbnH63jBo1CvX19bCwsIC3t3enbW79HhITE4VlBQUFv6k/FAoF5syZgzlz5iAlJQWbNm3CvHnzDHqtM3ZPGjSo+zZ3GZ8aYwaZNm0aBg0ahPj4eBw8eBBqtRp5eXmYP38+fvzxRwDAggUL8Oabb2Lnzp04deoU5s6dq/cycG9vbyQmJuL555/Hzp07hW1u27YNAODl5QWJRIJdu3ahoaFBuCqrveHDhyM+Ph5JSUn49ttvUVZWhmeffRaDBw9GfHy8Qc9NrVYjJSUFRUVFOHPmDHJyclBZWQmlUtllX1hZWSExMREnTpzAgQMHMG/ePEyfPh1ubm4G7dMQL730Evbv348VK1agoqICmzdvxgcffCAM8u7K7NmzsXnzZqhUKp0Pb0PExsYiODgY06ZNw7Fjx1BcXIwZM2YgKiqq21M/PTF8+HCcPXsWW7duRXV1Nd57771uB9bfDbGxsQgPD0dCQgJycnJQU1ODwsJCLF26FEePHgUAvPLKK8jIyEBaWhoqKyuxZs0a7Nixo9vfw+0WLlyI7OxsqNVqHDt2DAcOHBBeY4a81hm75wwcCDQ03Jy6OaJ6N3EhxAxiY2ODb775Bp6ennjsscegVCoxa9YstLa2CkeIXnrpJUyfPh2JiYkIDw+Hra0tpkyZone7aWlpeOKJJzB37lwEBAQgKSkJLS0tAIDBgwfj9ddfx5IlS+Dm5iZclXW79PR0jB49Gn/4wx8QHh4OIsLu3bs7nBLR99xOnTqFxx9/HH5+fvjTn/6E5ORk/PnPf+6yfXZ2Nn7++WeMGTMGTzzxBGJiYvDBBx8YtD9DjRo1Ctu2bcPWrVsRFBSE1157DW+88Ua3l1bHxsZCLpcjLi4OHh4ePdqnRCLB559/DkdHR0RGRiI2NhZDhw5FZmbmHTyTjh599FG8+OKLeOGFFzBy5EgUFhbi//7v/3p1H4aQSCTYvXs3IiMjMXPmTPj5+eGPf/wjzpw5IxS1CQkJWLduHd555x3cd9992LBhA9LT0xEdHd2jfbW1tSE5ORlKpRITJ06En58fPvzw5rfqGvpaZ4z1PgnRHV53fY9ramqCvb09GhsbO5wSam1thVqtho+PD6ysrERKyJgujUaDwYMHIz09HY899pjYcVgv4vccxgyn7/O7PT4ixNg9QqvV4uLFi1ixYgUcHBzw6KOPih2JMca6duUKEB19cxLxFhs8WJqxe8TZs2fh4+ODIUOGICMjo8OVgIwxZlS0WiA//38/i4TfKRm7R3h7e9/xN4wzxpip4VNjjDHGGDNZXAgxxhhjzGRxIcQYY4wxk8WFEGOMMcZMFg+WZowxxpg4bMS/LRUXQowxxhjrewMHAv+9k4CY+NQY65GamhpIJBKUlpaKHaUDY84mkUiwc+dOAIbnjI6OxsKFC+96NsYYM2VcCDFRGXPxcrcoFArU1dUhKCgIAJCXlweJRNLhBrU7duzAihUrREjIGGOmg0+NMdbHzM3N4e7u3m07JyenPkjDGGMiaW0FHn/85s/btwMi3T+PjwiZoM8++wzBwcGwtraGs7MzYmNjhTu+A8BHH30EpVIJKysrBAQECHfI7sqJEycwadIkyGQyuLm5Yfr06fjpp5+E9VqtFn//+9/h6+sLS0tLeHp64m9/+xsAwMfHBwAQGhoKiUSic0fv7nIUFxcjNDQUVlZWCAsLw3fffdftc7969SoWL14MhUIBS0tL+Pr64l//+pewPj8/H2PHjoWlpSXkcjmWLFmCGzduCOujo6Mxf/58vPrqq3BycoK7uzuWL1+us4/KykpERkbCysoKgYGByM3N1Vnf/ihYTU0NJkyYAABwdHSERCIR7i5/+6mxy5cvY8aMGXB0dISNjQ0mTZqEyspKYX1GRgYcHByQnZ0NpVIJmUyGiRMnoq6uTmiTl5eHsWPHYuDAgXBwcEBERATOnDnTbb8xxliva2sDdu++ObW1iZeDmF6NjY0EgBobGzusu3LlCp08eZKuXLmiu0Kj6XrqSdtffzWsbQ+cP3+eLCwsaM2aNaRWq+n777+n9evXU3NzMxERffLJJySXy2n79u10+vRp2r59Ozk5OVFGRgYREanVagJA3333HRERXb58mVxcXCglJYVUKhUdO3aMHnroIZowYYKwz1dffZUcHR0pIyODqqqq6ODBg7Rp0yYiIiouLiYAtG/fPqqrq6NLly4ZlKO5uZlcXFxo6tSpdOLECfryyy9p6NChOtk689RTT5FCoaAdO3ZQdXU17du3j7Zu3UpERD/++CPZ2NjQ3LlzSaVSUVZWFg0aNIhSU1OFx0dFRZGdnR0tX76cKioqaPPmzSSRSCgnJ4eIiNra2igoKIhiYmKotLSU8vPzKTQ0lABQVlZWhz68ceMGbd++nQBQeXk51dXV0S+//CLsa8GCBcK+H330UVIqlfTNN99QaWkpxcXFka+vL127do2IiNLT02nAgAEUGxtLR44coZKSElIqlTR16lQiIrp+/TrZ29vTyy+/TFVVVXTy5EnKyMigM2fO9Og1xMTT5XsOY/2RRkME3Jx6+FlmCH2f3+1xIdSN31QI3frFdjY98ohuWxubrttGRem2HTSo83Y9UFJSQgCopqam0/XDhg2jLVu26CxbsWIFhYeHE1HHQmjFihX08MMP67Svra0VPtibmprI0tJSKHxud/v2DM2xYcMGcnZ21un7tLQ0vYVQeXk5AaDc3NxO1//1r38lf39/0mq1wrL169eTTCajtrY2IrpZnDzwwAM6jxszZgwtXryYiIiys7PJwsKCzp07J6zfs2dPl4UQEdGBAwcIAF2+fFlnu+0LoYqKCgJABQUFwvqffvqJrK2tadu2bUR0sxACQFVVVTr53dzciIjo0qVLBIDy8vI6ff7M+HEhxO4pRlII9YtTYzU1NZg1axZ8fHxgbW2NYcOGITU1FdeuXdP7uOjoaEgkEp1pzpw5fZTaOIWEhCAmJgbBwcF48sknsWnTJly+fBkA0NLSgurqasyaNQsymUyYVq5cierq6k63V1ZWhgMHDui0DwgIAABUV1dDpVLh6tWriImJMTijITlUKhVGjBgBq3bnlMPDw/Vut7S0FObm5oiKiup0vUqlQnh4OCQSibAsIiICGo0GP/74o7BsxIgROo+Ty+W4ePGisA2FQgEPDw+DcxlCpVLBwsIC48aNE5Y5OzvD398fKpVKWGZjY4Nhw4Z1ms3JyQnPPfcc4uLiMHnyZKxbt07ntBljjJmifjFY+tSpU9BqtdiwYQN8fX1x4sQJJCUloaWlBe+8847exyYlJeGNN94Q5m364subNJqu15mb687/90OqU2a31ak1Nb850v92b47c3FwUFhYiJycH77//PpYuXYrDhw8LfbNp0yadD9xbj+uMRqPB5MmT8dZbb3VYJ5fLcfr06R5n1Py3/3qSwxDW1ta/+bHtDRgwQGdeIpFAq9X2yrbvVGfZqN0d6dPT0zF//nzs3bsXmZmZWLZsGXJzc3H//ff3dVTGGDMK/aIQmjhxIiZOnCjMDx06FOXl5UhLS+u2ELKxsTHoCp1eNXCg+G31kEgkiIiIQEREBF577TV4eXkhKysLixYtgoeHB06fPo1p06YZtK1Ro0Zh+/bt8Pb2hoVFx5fT8OHDYW1tjf3792P27Nkd1kulUgBAW7uBcm5ubt3mUCqV+Pjjj9Ha2iocFTp06JDerMHBwdBqtcjPz0dsbGyn29y+fTuISDgqVFBQAFtbWwwZMkTvtttvo7a2FnV1dZDL5Qbl6qwPOtvujRs3cPjwYYwfPx4AcOnSJZSXlyMwMNCgbLeEhoYiNDQUKSkpCA8Px5YtW7gQYoyZrH5xaqwzjY2NBl1e/Omnn2LQoEEICgpCSkoKfv31V73tr169iqamJp3pXnL48GGsWrUKR48exdmzZ7Fjxw40NDRAqVQCAF5//XWsXr0a7733HioqKnD8+HGkp6djzZo1nW4vOTkZP//8M5555hkcOXIE1dXVyM7OxsyZM9HW1gYrKyssXrwYr776Kv7zn/+guroahw4dEq7UcnV1hbW1Nfbu3YsLFy6gsbHRoBxTp06FRCJBUlISTp48id27d3dbFHt7eyMxMRHPP/88du7cCbVajby8PGzbtg0AMHfuXNTW1mLevHk4deoUPv/8c6SmpmLRokUwu/3oXBdiY2Ph5+eHxMRElJWV4eDBg1i6dKnex3h5eUEikWDXrl1oaGgQjoi1N3z4cMTHxyMpKQnffvstysrK8Oyzz2Lw4MGIj483KJtarUZKSgqKiopw5swZ5OTkoLKyUvjdM8aYSer10Ul9oLKykuzs7Gjjxo16223YsIH27t1L33//PX3yySc0ePBgmjJlit7HpKamEoAOU48GSxuxkydPUlxcHLm4uJClpSX5+fnR+++/r9Pm008/pZEjR5JUKiVHR0eKjIykHTt2EFHng5srKipoypQp5ODgQNbW1hQQEEALFy4UBh23tbXRypUrycvLiwYMGECenp60atUq4fGbNm0ihUJBZmZmFNVugLi+HERERUVFFBISQlKplEaOHClcfaXvqrErV67Qiy++SHK5nKRSKfn6+tK///1vYX1eXh6NGTOGpFIpubu70+LFi+n69evC+tuv5CIiio+Pp8TERGG+vLycHnjgAZJKpeTn50d79+7VO1iaiOiNN94gd3d3kkgkwrZu39fPP/9M06dPJ3t7e7K2tqa4uDiqqKgQ1qenp5O9vb1OtqysLLr1Z15fX08JCQnCc/fy8qLXXntNGAjOjF9/fM9hTCyGDpaWELUbQNDHlixZ0unYkvZUKpUw+BYAzp07h6ioKERHR+Ojjz7q0f6+/vprxMTEoKqqSmdAaXtXr17F1atXhfmmpiYoFAo0NjbCzs5Op21rayvUajV8fHx0Bu0yxtjdwO85jBmuqakJ9vb2nX5+tyfqGKGXXnpJ+PK4rgwdOlT4+fz585gwYQLGjx+PjRs39nh/twbe6iuELC0tYWlp2eNtM8YYY6z/EbUQcnFxgYuLi0Ftz507hwkTJmD06NFIT083eMxGe7fuZ3VrECtjjDHGTFu/GCx97tw5REdHw9PTE++88w4aGhpQX1+P+vp6nTYBAQEoLi4GcPM7bFasWIGSkhLU1NTgiy++wIwZMxAZGdnhe2AYY4wxZpr6xeXzubm5qKqqQlVVVYfLmG8Ncbp+/TrKy8uFq8KkUin27duHtWvXoqWlBQqFAo8//jiWLVvW5/kZY4wxZpxEHSzdH+gbbMUDFxljfYnfcxgznKGDpfvFqTFjx7UkY6wv8HsNY72PC6E7cOt2Bt19SSNjjPWGW+81t99KhTH22/WLMULGytzcHA4ODsJNLW1sbHRu2MkYY72BiPDrr7/i4sWLcHBwuKN77jHGdHEhdIdu3cfsor6bpzLGWC9wcHDo+3snMnaP40LoDkkkEsjlcri6uuL69etix2GM3aMGDBjAR4IYuwu4EOol5ubm/CbFGGOM9TM8WJoxxhhjJosLIcYYY4yZLC6EGGOMMWayeIxQN259gVlTU5PISRhjjDFmqFuf2919ESkXQt1obm4GACgUCpGTMMYYY6ynmpubYW9v3+V6vtdYN7RaLc6fPw9bW9te/bLEpqYmKBQK1NbW6r0Hiqni/tGP+0c/7h/9uH+6xn2jX3/qHyJCc3MzPDw8YGbW9UggPiLUDTMzsw53vO9NdnZ2Rv9iEhP3j37cP/px/+jH/dM17hv9+kv/6DsSdAsPlmaMMcaYyeJCiDHGGGMmiwshkVhaWiI1NRWWlpZiRzFK3D/6cf/ox/2jH/dP17hv9LsX+4cHSzPGGGPMZPERIcYYY4yZLC6EGGOMMWayuBBijDHGmMniQogxxhhjJosLIZGsX78e3t7esLKywrhx41BcXCx2JKPwzTffYPLkyfDw8IBEIsHOnTvFjmRUVq9ejTFjxsDW1haurq5ISEhAeXm52LGMQlpaGkaMGCF80Vt4eDj27Nkjdiyj9eabb0IikWDhwoViRzEKy5cvh0Qi0ZkCAgLEjmVUzp07h2effRbOzs6wtrZGcHAwjh49KnasO8aFkAgyMzOxaNEipKam4tixYwgJCUFcXBwuXrwodjTRtbS0ICQkBOvXrxc7ilHKz89HcnIyDh06hNzcXFy/fh0PP/wwWlpaxI4muiFDhuDNN99ESUkJjh49it/97neIj4/HDz/8IHY0o3PkyBFs2LABI0aMEDuKUbnvvvtQV1cnTN9++63YkYzG5cuXERERgQEDBmDPnj04efIk3n33XTg6Oood7Y7x5fMiGDduHMaMGYMPPvgAwM37mSkUCsybNw9LliwROZ3xkEgkyMrKQkJCgthRjFZDQwNcXV2Rn5+PyMhIseMYHScnJ7z99tuYNWuW2FGMhkajwahRo/Dhhx9i5cqVGDlyJNauXSt2LNEtX74cO3fuRGlpqdhRjNKSJUtQUFCAgwcPih2l1/ERoT527do1lJSUIDY2VlhmZmaG2NhYFBUViZiM9UeNjY0Abn7gs/9pa2vD1q1b0dLSgvDwcLHjGJXk5GT8/ve/13kPYjdVVlbCw8MDQ4cOxbRp03D27FmxIxmNL774AmFhYXjyySfh6uqK0NBQbNq0SexYvYILoT72008/oa2tDW5ubjrL3dzcUF9fL1Iq1h9ptVosXLgQERERCAoKEjuOUTh+/DhkMhksLS0xZ84cZGVlITAwUOxYRmPr1q04duwYVq9eLXYUozNu3DhkZGRg7969SEtLg1qtxoMPPojm5maxoxmF06dPIy0tDcOHD0d2djb+8pe/YP78+di8ebPY0e4Y332esX4qOTkZJ06c4HEM7fj7+6O0tBSNjY347LPPkJiYiPz8fC6GANTW1mLBggXIzc2FlZWV2HGMzqRJk4SfR4wYgXHjxsHLywvbtm3jU6u4+Y9XWFgYVq1aBQAIDQ3FiRMn8M9//hOJiYkip7szfESojw0aNAjm5ua4cOGCzvILFy7A3d1dpFSsv3nhhRewa9cuHDhwAEOGDBE7jtGQSqXw9fXF6NGjsXr1aoSEhGDdunVixzIKJSUluHjxIkaNGgULCwtYWFggPz8f7733HiwsLNDW1iZ2RKPi4OAAPz8/VFVViR3FKMjl8g7/UCiVynvi9CEXQn1MKpVi9OjR2L9/v7BMq9Vi//79PJaBdYuI8MILLyArKwtff/01fHx8xI5k1LRaLa5evSp2DKMQExOD48ePo7S0VJjCwsIwbdo0lJaWwtzcXOyIRkWj0aC6uhpyuVzsKEYhIiKiw1d1VFRUwMvLS6REvYdPjYlg0aJFSExMRFhYGMaOHYu1a9eipaUFM2fOFDua6DQajc5/YGq1GqWlpXBycoKnp6eIyYxDcnIytmzZgs8//xy2trbCuDJ7e3tYW1uLnE5cKSkpmDRpEjw9PdHc3IwtW7YgLy8P2dnZYkczCra2th3Gkg0cOBDOzs48xgzAyy+/jMmTJ8PLywvnz59HamoqzM3N8cwzz4gdzSi8+OKLGD9+PFatWoWnnnoKxcXF2LhxIzZu3Ch2tDtHTBTvv/8+eXp6klQqpbFjx9KhQ4fEjmQUDhw4QAA6TImJiWJHMwqd9Q0ASk9PFzua6J5//nny8vIiqVRKLi4uFBMTQzk5OWLHMmpRUVG0YMECsWMYhaeffprkcjlJpVIaPHgwPf3001RVVSV2LKPy5ZdfUlBQEFlaWlJAQABt3LhR7Ei9gr9HiDHGGGMmi8cIMcYYY8xkcSHEGGOMMZPFhRBjjDHGTBYXQowxxhgzWVwIMcYYY8xkcSHEGGOMMZPFhRBjjDHGTBYXQowxxhgzWVwIMcYYY8xkcSHEGGOMMZPFhRBjzKQ0NDTA3d0dq1atEpYVFhZCKpVi//79IiZjjImB7zXGGDM5u3fvRkJCAgoLC+Hv74+RI0ciPj4ea9asETsaY6yPcSHEGDNJycnJ2LdvH8LCwnD8+HEcOXIElpaWYsdijPUxLoQYYybpypUrCAoKQm1tLUpKShAcHCx2JMaYCHiMEGPMJFVXV+P8+fPQarWoqakROw5jTCR8RIgxZnKuXbuGsWPHYuTIkfD398fatWtx/PhxuLq6ih2NMdbHuBBijJmcV155BZ999hnKysogk8kQFRUFe3t77Nq1S+xojLE+xqfGGGMmJS8vD2vXrsXHH38MOzs7mJmZ4eOPP8bBgweRlpYmdjzGWB/jI0KMMcYYM1l8RIgxxhhjJosLIcYYY4yZLC6EGGOMMWayuBBijDHGmMniQogxxhhjJosLIcYYY4yZLC6EGGOMMWayuBBijDHGmMniQogxxhhjJosLIcYYY4yZLC6EGGOMMWayuBBijDHGmMn6f1QxuWkandOwAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# plot model predictions against ground-truth\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# obtain predictions for both theorists\n", - "predicted_observations_bms = theorist_bms.predict(condition_pool)\n", - "predicted_observations_poly = theorist_poly.predict(condition_pool)\n", - "\n", - "plt.plot(condition_pool, ground_truth(condition_pool), label='Ground Truth')\n", - "plt.plot(condition_pool, predicted_observations_bms, label='Predictions of BMS Theorist')\n", - "plt.plot(condition_pool, predicted_observations_poly, label='Predictions of Polynomial Theorist')\n", - "\n", - "y_min = -2.5\n", - "y_max = 1\n", - "\n", - "# plot conditions obtained by novelty sampler\n", - "for idx, condition in enumerate(selected_conditions):\n", - " if idx == 0:\n", - " plt.plot([condition[0], condition[0]],\n", - " [y_min, y_max],\n", - " '--r', label='selected conditions')\n", - " else: # we want to omit the label for all other conditions\n", - " plt.plot()\n", - "\n", - "\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('Model Disagreement')\n", - "plt.legend()" + "def sine_experiment_runner(conditions: pd.DataFrame, added_noise: float = 0.5):\n", + " x = conditions[\"x\"]\n", + " y = np.sin(x) + np.random.normal(0, added_noise, size=x.shape)\n", + " observations = conditions.assign(y = y)\n", + " return observations\n", + "\n", + "custom_experiment_runner = on_state(sine_experiment_runner, output=[\"experiment_data\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#### First, let's reinitialize the state object to get a clean state ####\n", + "s = StandardState(variables = variables, conditions = conditions, experiment_data = pd.DataFrame(columns=[\"x\",\"y\"]))\n", + "\n", + "print('\\033[1mPrevious State:\\033[0m')\n", + "print(s)\n", + "\n", + "for cycle in range(5):\n", + " s = theorist(custom_experiment_runner(experimentalist(s, num_samples = 5)))\n", + "\n", + "print('\\n\\033[1mUpdated State:\\033[0m')\n", + "print(s)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, we can integrate our custom experimentalist and theorist into a closed-loop empirical research workflow, e.g., using basic loop constructs." + "## Altogether Now" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:08<00:00, 11.88it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss of BMS theorist in cycle 0: 0.0\n", - "Loss of polynomial theorist in cycle 0: 0.8717052095923039\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:08<00:00, 12.49it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss of BMS theorist in cycle 1: 0.0\n", - "Loss of polynomial theorist in cycle 1: 3.619766689361933\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 12.97it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss of BMS theorist in cycle 2: 0.0\n", - "Loss of polynomial theorist in cycle 2: 0.5193832163876795\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 14.26it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss of BMS theorist in cycle 3: 0.0\n", - "Loss of polynomial theorist in cycle 3: 0.36300053098571583\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 13.45it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss of BMS theorist in cycle 4: 0.4967273581732591\n", - "Loss of polynomial theorist in cycle 4: 0.288261165753893\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ - "num_cycles = 5 # number of empirical research cycles\n", - "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", - "\n", - "# generate an initial set experimental conditions\n", - "conditions = random_pool(metadata.independent_variables[0].allowed_values,\n", - " n=measurements_per_cycle)\n", - "# convert iterator into 2-dimensional numpy array\n", - "conditions = np.array(list(conditions)).reshape(-1, 1)\n", - "\n", - "# collect initial set of observations\n", - "observations = run_experiment(conditions)\n", - "\n", - "for cycle in range(num_cycles):\n", - "\n", - " # use BMS theorist and custom polynomial theorist to fit the model to the data\n", - " theorist_bms.fit(conditions, observations)\n", - " theorist_poly.fit(conditions, observations)\n", - "\n", - " # obtain new conditions from custrom experimentalist sampler\n", - " new_conditions = basic_model_disagreement_sample(condition_pool,\n", - " theorist_bms,\n", - " theorist_poly,\n", - " num_samples = 3)\n", - "\n", - " # obtain new observations\n", - " new_observations = run_experiment(new_conditions)\n", - "\n", - " # combine old and new conditions and observations\n", - " conditions = np.concatenate((conditions, new_conditions))\n", - " observations = np.concatenate((observations, new_observations))\n", - "\n", - " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", - " loss_bms = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", - " loss_poly = np.mean(np.square(theorist_poly.predict(condition_pool) - ground_truth(condition_pool)))\n", - " print(\"Loss of BMS theorist in cycle {}: {}\".format(cycle, loss_bms))\n", - " print(\"Loss of polynomial theorist in cycle {}: {}\".format(cycle, loss_poly))" + "#### First, let's reinitialize the state object to get a clean state ####\n", + "s = StandardState(variables = variables, conditions = conditions, experiment_data = pd.DataFrame(columns=[\"x\",\"y\"]))\n", + "\n", + "print('\\033[1mPrevious State:\\033[0m')\n", + "print(s)\n", + "\n", + "for cycle in range(5):\n", + " s = custom_theorist(custom_experiment_runner(custom_experimentalist(s, num_samples = 5)))\n", + "\n", + "print('\\n\\033[1mUpdated State:\\033[0m')\n", + "print(s)" ] }, { @@ -640,11 +408,20 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", - "name": "python3" + "display_name": "autoraKernel", + "language": "python", + "name": "autorakernel" }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" } }, "nbformat": 4, From 4c23eebcd17f7091f52e42f625e9d7ff6cdba12f Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Mon, 21 Aug 2023 20:30:05 -0700 Subject: [PATCH 17/32] Updated plotting --- .../Tutorial-III-Functional-Workflow.ipynb | 483 ++++++++++-------- 1 file changed, 282 insertions(+), 201 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb index b41cdefa1..dcdb2e87a 100644 --- a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb +++ b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb @@ -68,12 +68,15 @@ "torch.manual_seed(42)\n", "\n", "#### Define plot function ####\n", - "def plot_from_state(state):\n", + "def plot_from_state(state): \n", + " model_label = f\"Model: {state.model.repr()}\" if state.model.repr() else \"Model\"\n", " experiment_data = state.experiment_data.sort_values(by=[\"x\"])\n", - " plt.plot(experiment_data[\"x\"], experiment_data[\"y\"], 'o', label = None)\n", - " plt.plot(experiment_data[\"x\"], state.model.predict(experiment_data[\"x\"].values.reshape(-1,1)), alpha=.8, label='Model')\n", " ground_x = np.linspace(state.variables.independent_variables[0].value_range[0],state.variables.independent_variables[0].value_range[1],100)\n", - " plt.plot(ground_x, np.sin(ground_x), alpha=.8, label='Ground Truth (sin(x))')\n", + "\n", + " f = plt.figure(figsize=(4,3))\n", + " plt.plot(experiment_data[\"x\"], experiment_data[\"y\"], 'o', label = None)\n", + " plt.plot(ground_x, state.model.predict(ground_x.reshape(-1, 1)), alpha=.8, label=model_label)\n", + " plt.plot(ground_x, np.sin(ground_x), alpha=.8, label='Ground Truth: sin(x)')\n", " plt.xlabel('x')\n", " plt.ylabel('y')\n", " plt.legend()\n", @@ -173,16 +176,16 @@ " 5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,\n", " 5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,\n", " 6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 0.634665\n", - "1 6.092786\n", - "2 0.126933\n", - "3 1.586663\n", - "4 0.571199\n", - "5 5.965853\n", - "6 5.013855\n", - "7 1.967462\n", - "8 2.157862\n", - "9 0.634665, experiment_data=Empty DataFrame\n", + "0 4.633056\n", + "1 2.792527\n", + "2 2.284795\n", + "3 4.252257\n", + "4 3.173326\n", + "5 4.633056\n", + "6 2.855993\n", + "7 1.205864\n", + "8 3.998391\n", + "9 4.125324, experiment_data=Empty DataFrame\n", "Columns: [x, y]\n", "Index: [], models=[])\n" ] @@ -232,16 +235,16 @@ "\n", "\u001b[1mThe conditions we provided:\u001b[0m\n", " x\n", - "0 0.634665\n", - "1 6.092786\n", - "2 0.126933\n", - "3 1.586663\n", - "4 0.571199\n", - "5 5.965853\n", - "6 5.013855\n", - "7 1.967462\n", - "8 2.157862\n", - "9 0.634665\n", + "0 4.633056\n", + "1 2.792527\n", + "2 2.284795\n", + "3 4.252257\n", + "4 3.173326\n", + "5 4.633056\n", + "6 2.855993\n", + "7 1.205864\n", + "8 3.998391\n", + "9 4.125324\n", "\n", "\u001b[1mThe dataframe we provided:\u001b[0m\n", "Empty DataFrame\n", @@ -356,7 +359,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Running Each Component With the State" + "## Running Each Component with the State" ] }, { @@ -379,29 +382,29 @@ "text": [ "\u001b[1mPrevious Conditions:\u001b[0m\n", " x\n", - "0 0.634665\n", - "1 6.092786\n", - "2 0.126933\n", - "3 1.586663\n", - "4 0.571199\n", - "5 5.965853\n", - "6 5.013855\n", - "7 1.967462\n", - "8 2.157862\n", - "9 0.634665\n", + "0 4.633056\n", + "1 2.792527\n", + "2 2.284795\n", + "3 4.252257\n", + "4 3.173326\n", + "5 4.633056\n", + "6 2.855993\n", + "7 1.205864\n", + "8 3.998391\n", + "9 4.125324\n", "\n", "\u001b[1mUpdated Conditions:\u001b[0m\n", " x\n", - "0 5.140788\n", - "1 3.554125\n", - "2 5.077321\n", - "3 3.998391\n", - "4 1.205864\n", - "5 0.380799\n", - "6 4.696522\n", - "7 0.444266\n", - "8 2.602127\n", - "9 5.204254\n" + "0 3.554125\n", + "1 0.698132\n", + "2 1.586663\n", + "3 2.729060\n", + "4 1.078931\n", + "5 5.331188\n", + "6 1.713596\n", + "7 0.825065\n", + "8 5.204254\n", + "9 4.950388\n" ] } ], @@ -440,16 +443,16 @@ "\n", "\u001b[1mUpdated Data:\u001b[0m\n", " x y\n", - "0 5.140788 -0.661275\n", - "1 3.554125 -0.470063\n", - "2 5.077321 -0.610304\n", - "3 3.998391 0.005765\n", - "4 1.205864 0.817071\n", - "5 0.380799 0.254594\n", - "6 4.696522 -0.210268\n", - "7 0.444266 0.813512\n", - "8 2.602127 0.278940\n", - "9 5.204254 -0.610173\n" + "0 3.554125 -0.152573\n", + "1 0.698132 0.573655\n", + "2 1.586663 1.323718\n", + "3 2.729060 1.162445\n", + "4 1.078931 0.764377\n", + "5 5.331188 -0.931644\n", + "6 1.713596 1.779428\n", + "7 0.825065 1.118309\n", + "8 5.204254 -1.116191\n", + "9 4.950388 -0.700532\n" ] } ], @@ -497,7 +500,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:03<00:00, 26.37it/s]\n", + "100%|██████████| 100/100 [00:05<00:00, 19.93it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -507,14 +510,14 @@ "text": [ "\n", "\u001b[1mUpdated Model:\u001b[0m\n", - "-0.04\n" + "sin(x)\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -548,7 +551,7 @@ "source": [ "### Component Chaining\n", "\n", - "As the components all act on the state, they can be chained in a nested fashion." + "As the components all act on the state, they can be chained." ] }, { @@ -560,14 +563,83 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:04<00:00, 24.05it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.06346652, 0.12693304, 0.19039955, 0.25386607,\n", + " 0.31733259, 0.38079911, 0.44426563, 0.50773215, 0.57119866,\n", + " 0.63466518, 0.6981317 , 0.76159822, 0.82506474, 0.88853126,\n", + " 0.95199777, 1.01546429, 1.07893081, 1.14239733, 1.20586385,\n", + " 1.26933037, 1.33279688, 1.3962634 , 1.45972992, 1.52319644,\n", + " 1.58666296, 1.65012947, 1.71359599, 1.77706251, 1.84052903,\n", + " 1.90399555, 1.96746207, 2.03092858, 2.0943951 , 2.15786162,\n", + " 2.22132814, 2.28479466, 2.34826118, 2.41172769, 2.47519421,\n", + " 2.53866073, 2.60212725, 2.66559377, 2.72906028, 2.7925268 ,\n", + " 2.85599332, 2.91945984, 2.98292636, 3.04639288, 3.10985939,\n", + " 3.17332591, 3.23679243, 3.30025895, 3.36372547, 3.42719199,\n", + " 3.4906585 , 3.55412502, 3.61759154, 3.68105806, 3.74452458,\n", + " 3.8079911 , 3.87145761, 3.93492413, 3.99839065, 4.06185717,\n", + " 4.12532369, 4.1887902 , 4.25225672, 4.31572324, 4.37918976,\n", + " 4.44265628, 4.5061228 , 4.56958931, 4.63305583, 4.69652235,\n", + " 4.75998887, 4.82345539, 4.88692191, 4.95038842, 5.01385494,\n", + " 5.07732146, 5.14078798, 5.2042545 , 5.26772102, 5.33118753,\n", + " 5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,\n", + " 5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,\n", + " 6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 4.569589\n", + "1 4.759989\n", + "2 2.475194\n", + "3 2.792527\n", + "4 5.838920\n", + "5 3.236792\n", + "6 4.569589\n", + "7 4.379190\n", + "8 5.140788\n", + "9 0.253866, experiment_data= x y\n", + "0 3.554125 -0.152573\n", + "1 0.698132 0.573655\n", + "2 1.586663 1.323718\n", + "3 2.729060 1.162445\n", + "4 1.078931 0.764377\n", + "5 5.331188 -0.931644\n", + "6 1.713596 1.779428\n", + "7 0.825065 1.118309\n", + "8 5.204254 -1.116191\n", + "9 4.950388 -0.700532\n", + "10 3.554125 -0.632639\n", + "11 0.698132 0.409923\n", + "12 1.586663 1.120855\n", + "13 2.729060 -0.555710\n", + "14 1.078931 0.018994\n", + "15 5.331188 -1.095720\n", + "16 1.713596 0.483406\n", + "17 0.825065 0.891715\n", + "18 5.204254 -1.335465\n", + "19 4.950388 -1.677963, models=[sin(x), sin(x)])\n" ] }, + { + "data": { + "image/png": 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0.268449\n", - "25 1.650129 1.052316\n", - "26 4.442656 -1.539339\n", - "27 6.156252 0.061257\n", - "28 0.698132 0.342468\n", - "29 4.886922 -1.130655, models=[sin(x), sin(x)])\n" + " 6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 2.221328\n", + "1 0.571199\n", + "2 6.092786\n", + "3 4.886922\n", + "4 3.807991\n", + "5 0.507732\n", + "6 3.871458\n", + "7 0.444266\n", + "8 3.681058\n", + "9 0.000000, experiment_data= x y\n", + "0 3.554125 -0.152573\n", + "1 0.698132 0.573655\n", + "2 1.586663 1.323718\n", + "3 2.729060 1.162445\n", + "4 1.078931 0.764377\n", + "5 5.331188 -0.931644\n", + "6 1.713596 1.779428\n", + "7 0.825065 1.118309\n", + "8 5.204254 -1.116191\n", + "9 4.950388 -0.700532\n", + "10 3.554125 -0.632639\n", + "11 0.698132 0.409923\n", + "12 1.586663 1.120855\n", + "13 2.729060 -0.555710\n", + "14 1.078931 0.018994\n", + "15 5.331188 -1.095720\n", + "16 1.713596 0.483406\n", + "17 0.825065 0.891715\n", + "18 5.204254 -1.335465\n", + "19 4.950388 -1.677963\n", + "20 2.221328 1.528586\n", + "21 0.571199 0.427753\n", + "22 6.092786 -0.155487\n", + "23 4.886922 -1.697182\n", + "24 3.807991 -0.890350\n", + "25 0.507732 0.541658\n", + "26 3.871458 -1.242266\n", + "27 0.444266 0.617644\n", + "28 3.681058 -0.813997\n", + "29 0.000000 -0.145847, models=[sin(x), sin(x), sin(x)])\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -659,9 +721,18 @@ } ], "source": [ - "s = theorist(experiment_runner(experimentalist(s, num_samples=10)))\n", + "#==========OPTION 1==========#\n", + "s = theorist(s)\n", + "s = experiment_runner(s)\n", + "s = experimentalist(s, num_samples=10)\n", + "\n", "print(s)\n", + "plot_from_state(s)\n", "\n", + "#==========OPTION 2==========#\n", + "s = theorist(experiment_runner(experimentalist(s, num_samples=10)))\n", + "\n", + "print(s)\n", "plot_from_state(s)" ] }, @@ -698,7 +769,86 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:03<00:00, 25.98it/s]\n", + " 12%|█▏ | 12/100 [00:00<00:04, 21.17it/s]" + ] + } + ], + "source": [ + "#### First, let's reinitialize the state object to get a clean state ####\n", + "s = StandardState(\n", + " variables = variables,\n", + " conditions = conditions,\n", + " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"])\n", + ")\n", + "\n", + "### Then we cycle through the pipeline we built five times ###\n", + "num_cycles = 5 # number of empirical research cycles\n", + "for cycle in range(num_cycles):\n", + " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", + " s = theorist(experiment_runner(experimentalist(s, num_samples=10)))\n", + " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", + " print(s.models)\n", + " plot_from_state(s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If everything went well in terms of our theorist, we should have recovered our ground truth model `sin(x)`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sin(x)\n" + ] + } + ], + "source": [ + "print(s.model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chain Looping with Stopping Criterion\n", + "\n", + "Alternatively, we can run the chain until we reach a stopping criterion. For example, here we will loop until we get 30 datapoints." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 1, number of datapoints: 0\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:04<00:00, 23.78it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -711,7 +861,7 @@ }, { "data": { - "image/png": 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/5tdeyoX5wfkZEhISLrt/eno6Pj55z94NHDiQm2++mTp16tCjRw9uu+02unXrlu/jXvhzq1u3Lunp6Xh7e19ycPjMmTOpXbs2ZrOZXbt2XbTP+bNd/xww7+vrW6SD6FWERERKAZPJlO9LVEapWbMmJpPporE45y83XXjZ57zLXUK7HLPZOfT1wrFLlxtk7enpmfv1+V/Kdru9QMcriH9+loJkvZQL84PzM1wpf/ny5dmxY0eebc2aNePw4cP89NNP/Prrr9xzzz107dqVr776Kl/H/efPrXz58qSlpZGVlYWXl1ee123bto3U1FTMZjOxsbG5Jeq8xETn/GehoaEXba9Ro8Zl81wvDZYWEZFiUa5cOW6++WamTJlyzXPD1KtX76IxNmvWrMkda3L+l+iFd2ldOBi5IMfZsGFDnm3r168v8PtcSX6yni8TNpvtuo/XtGlT9u7de9EA98DAQPr06cOMGTNYsGABX3/9dW4pKajz8yxdOAganGVm4MCBPP/88wwcOJB+/fqRnp6eZ5+dO3fi6elJ/fr1L9retGnTa8qTHzojJCIixeaDDz6gffv2tGjRgvHjx9OoUSPMZjObNm1i7969V7189Mwzz3DPPffQtGlTunbtyg8//MA333yTe9u9r68vbdq04dVXXyUqKoqEhAReeOGFAud84oknGDhwIC1atKB9+/Z89tln7Nq167oGS/9TfrJWrVoVk8nEokWLuOWWW/D19S3QbekX6ty5MykpKezatYsGDRoA8NZbbxEREUHTpk0xm818+eWXhIeHXzQeKb9CQ0Np1qwZq1evzjP55NChQ4mMjOSFF14gMzOTpk2b8vTTT/P+++/n7rNq1SpuuOGGPGcGjxw5wokTJ+jates15ckPnRESEZFiU6NGDf744w+6du3K2LFjady4MS1atOC9997j6aef5r///e8VX9+7d2/eeecd3njjDerXr8+HH37IJ598QqdOnXL3mTlzJjk5OTRv3pyRI0fy0ksvFThnnz59+Pe//83o0aNp3rw5R48eZdiwYQV+n6u5WtZKlSoxYcIExowZQ4UKFRgxYsQ1H6tcuXLceeedfPbZZ7nbAgICeO2112jRogUtW7bkyJEjLF68OPey3bV48MEH8xxjzpw5LF68mLlz5+Lh4UGZMmX49NNPmTFjBj/99FPufvPnz+ehhx7K816ff/453bp1y9cg7mtlchR0Egg3k5SURFBQEFarlcDAQKPjiIiQkZHB4cOHiYqKumjwq8iVbN++nZtvvplDhw5d85mlq0lPT6dOnTosWLCAtm3b5us1P/30E0899RTbt2/Hw8N5sSorK4tatWoxb9482rdvf8nXXenPQn5/f+uMkIiIiJto1KgRkyZN4vDhw0V2DF9fX+bMmcPp06fz/ZrU1FQ++eST3BIEzlv7n3vuucuWoMKiM0JXoTNCIlLS6IyQiJPOCImIiIhcBxUhEREXpRP64u4K48+AipCIiIs5P6FdUc62K+IKzv8Z+OfkkgWheYRERFyMxWIhODg4dzkFPz+/Sy5pIFJaORwO0tLSSEhIIDg4GIvFcs3vpSIkIuKCwsPDAa64tpRIaRccHJz7Z+FaqQiJiLggk8lEREQEYWFhBVqfSqS08PT0vK4zQeepCImIuDCLxVIovwxE3JUGS4uIiIjbUhESERERt6UiJCIiIm5LRUhERETcloqQiIiIuC0VIREREXFbKkIiIiLitlSERERExG2pCImIiIjbUhESERERt+VSRej333+nV69eVKxYEZPJxMKFC6/6mhUrVtCsWTO8vb2pWbMms2bNKvKcIiIi4hpcqgilpqbSuHFj3n///Xztf/jwYW699VY6d+5MTEwMI0eO5MEHH+Tnn38u4qQiIiLiClxq0dWePXvSs2fPfO8/bdo0oqKiePPNNwGoV68eq1ev5u2336Z79+5FFVNKOYfDQY49hxxHDtn2bBwOB2U8y+Bhdqk/TiIigosVoYJat24dXbt2zbOte/fujBw58rKvyczMJDMzM/f7pKSkooonJVhqdioHzh7gWPIxTqWd4lT6KU6lneJ0+mkybBmXfI2fhx/+Xv4EegVS3rc8VQKqUCWwClUCqhDqF4rZ5FInYEVE3EKpLkJxcXFUqFAhz7YKFSqQlJREeno6vr6+F71m4sSJTJgwobgiymXY7A42Hk4kITmDsAAfWkWFYDGbiux41kwrMQkx7E3cy/6z+zmRcgIHjgK9R1pOGmk5aSSkJXDw3EHWx67Pfc7H4kN0uWgahzamcWhjwsuEYzIV3ecREZH8KdVF6FqMHTuWUaNG5X6flJREZGSkgYncz5KdsUz4YTex1v+deYkI8mFcr2h6NIgotOOcST/DpvhNbIjdwN4ze7Fjz/N8Bb8K1AiuQQW/CoT6hhLmF0Z53/L4efrhafbE0+yJxWwBICU7heSsZFKyUkjKSiI2NZZjScc4lnyMEyknyLBlsDVhK1sTtgIQ6htKswrN6Fi5I9WDqqsUiYgYpFQXofDwcOLj4/Nsi4+PJzAw8JJngwC8vb3x9vYujnhyCUt2xjLs060XnYuJs2Yw7NOtTO3f7LrKUI49h83xm/n5yM/sPrM7z3NRQVE0LN+Q2mVrU7tsbYK8g/L9voFegQR6BV7yOZvdxrHkY2w/tZ3tp7az9+xeTqWf4ucjP/PzkZ+p7F+ZjpEd6VCpAyE+Idf82UREpOBKdRFq27YtixcvzrNt6dKltG3b1qBEciU2u4MJP+y+5AUpB2ACJvywm5ujwwt8mcyaaWXZsWUsPbqUxIzE3O21y9amVXgrWke0Jswv7LryX47FbCEqKIqooCjuqHkHGTkZ7DqzizUn1rAxbiN/pfzFZ3s+4/M9n9M6ojV31LyDqKCoIskiIiJ5uVQRSklJ4eDBg7nfHz58mJiYGEJCQqhSpQpjx47lxIkTzJkzB4ChQ4cyZcoURo8ezeDBg/ntt9/44osv+PHHH436CHIFGw8n5rkc9k8OINaawcbDibStUS5f75mYkcg3B75h+bHl5DhyAAjyCqJLlS50qdqF8r7lCyN6gfh4+NC8QnOaV2hOWnYa62LXsfL4Svad3ce62HWsi11H49DG3FHzDqJDonXZTESkCLlUEdq8eTOdO3fO/f78WJ4BAwYwa9YsYmNjOXbsWO7zUVFR/Pjjjzz55JO88847VK5cmY8++ki3zpdQCcmXL0EF3c+aaeW7g9/xy9FfyLZnA1AruBbdq3WnTUQbPC2e15W1sPh5+jlLWZUuHE06yncHv2PdyXVsO7WNbae2UbtsbfrX60+dkDpGRxURKZVMDoejYLfGuJmkpCSCgoKwWq0EBl56DIgUjnWHztB3xvqr7vf5Q20ue0Yoy5bFd4e+Y9GhRbm3udcpW4d7695LdLnoQs1bVOJT41n05yKWH1+eW+LaRrTlvnr3FdnlOxGR0ia/v79VhK5CRaj42OwOOkz6jThrxiXHCZmA8CAfVj970yXHCG2N38onOz8hIT0BgOpB1elTpw+NQxu75OWlsxlnWbBvASuOr8CBA0+zJ7dE3cKdte7E1+PSg/1FRMRJRaiQqAgVr/N3jQF5ytD5GnOpu8ZOp59m1s5ZbIrfBECITwgPRD9Am4g2LlmA/umI9Qhzd89l55mdAJTzKcfDjR6mSVgTY4OJiJRgKkKFREWo+OV3HiG7w84vR35h3t55ZNoysZgs3Fr9Vv5V61+l7oyJw+FgS/wWZu+anXvGq1PlTtwffT/+Xv4GpxMRKXlUhAqJipAxrjazdGJGItO2TWPbqW0A1Aupx5AGQ4gMLN2TX2bkZLBg96f8dGQJDnsOwRZfHqzdh5Z1ehsdTUSkRFERKiQqQiXPhtgNTN8+nZTsFDzNnvSP7k/3qt1d7zJYThZkJjkfGRf+MxkyrM5/Zlr/t+3887Ys9plymOqRTqzJORv2zVE9GXDDf0rM3XAiIkbL7+9vl7p9Xtxbpi2TmTtnsuL4CsA5E/SIJiOoHFDZ0FzYsv9RXi4oNrnbz399QeHJyd90AZdSx+TNa5byfGFO5Yec0yw98jP7s6082fbfRPgX3jIkIiKlnc4IXYXOCJUM8anxvLnlTY4mHcWEiTtq3sH/1f4/PM2FeAbEbrt0YfnnmZl/Fp7stGs/pskM3gHgEwjeQX//MwC8A//xdZDzn94Bzq89fcFkArudmO8fYkriVpI9PPApX4dHGj9Ku0rtCu/nIiLignRprJCoCBkvJiGG9/54j5TsFIK8gnii2RPUL1//8i+w2yEr5YLCcuFZmX+erblgW1bKtYc0mcDL/x+FJTBvecktNxeUHM8yYDZf+3EBMpI4880Q3ks/zB4fbwisTLdq3RhQfwAeZp30FRH3pCJUSFSEjONwOFi47wsW7PsChz2Hmn7hjIrsQTm74zLjaZKcX2elwPX8Z+3l/48SE5i3xOT5+u+S4+V//YXmepw5hO27R/nKfo5vA/xxlClPw/INGdlspO4qExG3pCJUSFSEjJGdncn7iwexLtG5QnwXmxeDbD54UoAB0Z5+l7jUdL68BF+i5Py9n9lSNB+qqB1aDr+OZ7Mpm/dCQ8nw9CGiTASjW46mon9Fo9OJiBQrFaFCoiJU/JKzknn9x8HsO3cAD2CwI5AuXmF/X166sLwEXabk/L3dHe+g2jAdYj7jqIeF1ypW4bQtnTKeZXiy2ZM0DG1odDoRkWKjIlRIVISKV3xqPK8uHc7Jswfxw8TTjYZTv/mDRsdyHXY7LBkDxzdg9Q/ljcga7E86ghkzDzd6mM5VOl/9PURESoH8/v42cFCDSF6Hzh3i30sf5eTZg5RzmPlP9EMqQQVlNsNNL0BgJYJSTvGiNZMbKnbAjp1p26fx/aHvjU4oIlKiqAhJibD91HYmLH8K69k/qeqw8FKtfkS2ftToWK7JJxC6vwSevnie/IPhmRZur3E7AJ/t+YzP9nyGTgSLiDipCInhNsdtZtKa8WRaj9HIbmFCldsJaf+k0bFcW0h16DQGANP2BfTzrEC/ev0A+P7Q90zbPg2b3WZkQhGREkFFSAy19sRa3towkZxzR2ltszA6vBO+nZ839lb00qJ6J2ja3/n1yte4PSiaoY2GYsbMiuMrmLx1Mtn2bCMTiogYTr9txDArjq/gvS1vYTt3lA42M0+UbY5nt/+CRZMAFpoWQyCyNeRkwi/P0zm0KaNajMLT7MnGuI1M3qIyJCLuTUVIDLH06FKm/jEF+7lj3JRtZniZOlhumeRcOkIKz/nB00GVITkOlk2gZVgznmn5DJ5mTzbHb+btLW+rDImI21IRkmL327Hf+Gj7DEg6Qc8seNg7EvOtbzrn/5HC5xMI3V5yTjB5YitsmEbj0Ma5ZWhL/BaVIRFxWypCUqxW/bWK6ds+hKST3JJhY4BHKKZb34CACkZHK91ConIHT7P9Czjw66XLkE1lSETci4qQFJsNsRv4IOYDHClx3JyexQOmIEw9Jjl/SUvRq97xgsHTk+D0gYvK0Ht/vIfdYTc2p4hIMVIRkmKxJX4L72x9B3tKPJ1S0xls98PUdQKENzA6mntpMQSqtAFbFvzyAqSfyy1DHmYPNsRtYPr26ZpnSETchoqQFLldp3fx1pa3sKWepl1qMo/YfDB3fBaqtjM6mvu5xOBp7DYahzbm8aaPY8bM8uPLNemiiLgNFSEpUkesR3ht02vkpCXSMvksw3N8MbceCnV6Gh3NfXkH5B08vX4qAK0jWvNwo4cB+OHPH1h4cKGBIUVEioeKkBSZhLQEJm6cSEb6GeolneaJHF88Gt4DjfsaHU1CoqDzWOfXO76E/b8A0LlKZ+6Pvh+A+fvms/ToUqMSiogUCxUhKRLWTCuvbHiFcylxVLUmMDrbF89a3aDNo2AyGR1PAKJuhGbO0sPvr8Op/QDcVv027qx5JwAf7/iYTXGbjEooIlLkVISk0KXnpDNp4yRirUcJTYplTJY3fpGtoeOzWjqjpGk+GKq0vWDw9FkA+tTpQ9cqXXHg4N2t73Lg7AGDg4qIFA39VpJCZbPbmLxlMocS9xGQdJLnMrwICasPXSeAxdPoePJPZjPc9Lxz8HRKPPw6Hmw5mEwmBjcYTNOwpmTZs3ht02vEpcYZnVZEpNCpCEmhcTgczNo1i5j4LXgnneDZDA8qBleDHq+Cl5/R8eRyvAOg+8vOwdMnY2CDc/C0xWzhiWZPEBUURVJWEq9ufJXkrGRjs4qIFDIVISk0S44s4ZcjP2NKOsFjGWZq+YXDLW+Ab7DR0eRqylZznhkC2PFV7uBpXw9fnm35LOV9yxObGsvrm17X7NMiUqqoCEmh2Bq/lTm7ZkPSSe5Ld9DSMwRueR0Cwo2OJvlVrQM0H+D8+vfX4dQ+AMr6lGVMqzH4efix7+w+pm6bqjmGRKTUUBGS63Y06ahz1ujkWDqnZ9HL5A/dX4GQ6kZHk4JqNhCqtv/f4Om0RAAiAyJ5usXTWEwW1pxcozmGRKTUUBGS63Iu4xyvbXqNjKS/qJ+WypDzS2dENDI6mlwLsxk6PwfBkZCSkDt4GqB++foMajAIgAX7FrA5brOBQUVECoeKkFyzbHs2b255k9NnDhCRepZROX543vgMVGtvdDS5Ht7+0O3vwdOx22D9B7lP3Vz1ZrpV7YYDB+/98R7Hko4ZGFRE5PqpCMk1m7VzFvtjN+OXeopns/3wb/Uw1L3V6FhSGMpWda5JBrDza9j3U+5TA+oPoH65+mTYMnh90+skZSUZFFJE5PqpCMk1+fXor/x68DtMyXE8ke1LRMM+0KSf0bGkMFVrD80HOr9e9RYk7AXAw+zBk82fpIJfBRLSE3hr81vk2HOMyykich1UhKTA9iXu45M/PoCkk/SxedOkRg9oM1xLZ5RGzQZccvB0gFcAz7R8Bh+LD3sS9/DZns8MDioicm1UhKRAzqSf4c31L5NjPUprm4Xe4e2g01gtnVFanZ95OrgKpJ7KM3g6MiCSEU1HALD48GLWnFhjYFARkWuj316Sb9m2bN7aMBHrqT1UscGw4MaYur2kpTNKO68yzpmnvco4B0+vm5L7VMvwlrkLtH64/UMNnhYRl6MiJPk2d/tHHDy+Cn9bDk/71sD3lte1dIa7CK4Cnf+eeXrXt3kGT99T5x4alW9Epi2TNza/QWp2qkEhRUQKTkVI8mX1seX8vGsO5GQywiOcCre+A75ljY4lxalae2jhnEfowsHTZpOZx5s9TqhvKPFp8Uz5Ywp2h93AoCIi+aciJFd13HqU6avHQXY6dxJI01unQGCE0bHECE0fcC7FcYnB06NajMLT7MnWhK2aeVpEXIaKkFxRenYab//yKJmZSTRweHFPt8lQrobRscQouTNPnx88PQ7+XoS1elB1hjQYAsCX+75k1+ldRiYVEckXFSG5LIfDwfSfh3Mi5QRlMfN4h/9grtTM6FhitDyDp7fDuvdzn+pcpTOdIjthx867f7yLNdNqYFARkatTEZLL+mXtq6w99QdmYGTDoQTV7mF0JCkpgqs4Z542mZyDp/cuzn1qcIPBVPavzLnMc7z3x3saLyQiJZqKkFzSkRMbmXPgSwDuq9KNui0eNjiRlDhV20HzvwdPr34L4ncD4G3xZmTzkXhbvNlxegffHPjGwJAiIlfmckXo/fffp1q1avj4+NC6dWs2btx42X1nzZqFyWTK8/Dx8SnGtK4pPSuVySufJcdhp5lvRW7rNNHoSFJSNb3/78HT2bD037mDpyMDInmw4YMAfLX/K3ae3mlkShGRy3KpIrRgwQJGjRrFuHHj2Lp1K40bN6Z79+4kJCRc9jWBgYHExsbmPo4ePVqMiV3TJ8ufJTbzLCEmD4Z1fQeTxWJ0JCmpzGbn/EJlq0LqaWcZ+nvw9I2Vb6RTZCccOHh3q8YLiUjJ5FJF6K233uKhhx5i0KBBREdHM23aNPz8/Jg5c+ZlX2MymQgPD899VKhQoRgTu57f933Dyti1mIHH6g8hsHwtoyNJSeflB91eBi9/iNsJa9/LfWpwg8FEBkRizbLyQcwHGi8kIiWOyxShrKwstmzZQteuXXO3mc1munbtyrp16y77upSUFKpWrUpkZCR33HEHu3Zd+ZbezMxMkpKS8jzcRWzyCT7e9CY47NwVUJvoFkONjiSuIjgSuvzbOXh693ew90fAOV7oiWZP4Gn2JOZUDD8d/ukqbyQiUrxcpgidPn0am8120RmdChUqEBcXd8nX1KlTh5kzZ/Ldd9/x6aefYrfbadeuHX/99ddljzNx4kSCgoJyH5GRkYX6OUqqbFs2k5c/RUZ2KtH48K9uk7WavBRMlTbQwjmPEKvfhnjnXzoiAyJ5IPoBAObtmcdh62GjEoqIXMRlitC1aNu2LQ888ABNmjShY8eOfPPNN4SGhvLhhx9e9jVjx47FarXmPo4fP16MiY0z/4+pHDmzjwCHiceaj8QcWNHoSOKKmvaHqBv/Hjz9Yu7g6Zur3kzLCi3JceTw7tZ3ycjJMDioiIiTyxSh8uXLY7FYiI+Pz7M9Pj6e8PDwfL2Hp6cnTZs25eDBg5fdx9vbm8DAwDyP0m57fAyL9swDHAwNaU5Iwz5GRxJXZTJBp7FQtppz8PQvzsHTJpOJRxo/QohPCCdTTzJr1yyjk4qIAC5UhLy8vGjevDnLli3L3Wa321m2bBlt27bN13vYbDZ27NhBRITWyTovKSuJD9a8CDkZ3Iw/LbpO1CUxuT5eftDtJefg6fidsOYdwLke2YimIzBhYvnx5aw7efmxfSIixcVlihDAqFGjmDFjBrNnz2bPnj0MGzaM1NRUBg1yTur2wAMPMHbs2Nz9//Of//DLL7/w559/snXrVvr378/Ro0d58MEHjfoIJYrD4eDDDa9x1nqcSg4z97d5FvxDjY4lpUFwJHR50Vmq9/zgfAD1y9Wnd83eAMzYMYMz6WcMDCki4mJFqE+fPrzxxhu8+OKLNGnShJiYGJYsWZI7gPrYsWPExsbm7n/27Fkeeugh6tWrxy233EJSUhJr164lOjraqI9Qovx2ZCmbj/yKBw4eC2uPd93bjI4kpUmV1hcMnp7svLUe+L/a/0fN4JqkZqfqlnoRMZzJ4XA4jA5RkiUlJREUFITVai1V44VOppxkzE+DyUyJo78pmF53fwVlyhsdS0obh8M5aPrw7+BXDv41HcqUJzYllmdXPUumLZMHoh/g1uq3Gp1UREqZ/P7+dqkzQlI4cuw5TFk/kcyUeBraPbi13ViVICkaFw6eTjsDS8dBThYR/hG5t9R/vvdzjiUdMzaniLgtFSE39O3+rzgUuxF/BwyL6IS5VjejI0lp5uUH3V8G7wDn4Om17wLQpUoXmoU1I9uezXt/vEf230tziIgUJxUhN3Po3CG+2f4R5GQyxFKOcp2e011iUvSCKsNN//7f4Ond32MymRjaeCiBXoEcSz7Ggn0LjE4pIm5IRciNZNoymbJ+IvbUM7Sze9Kuw3PgF2J0LHEXVVpDy4ecX695B+J2EuQdxCONHgFg0Z+L2HXmykvgiIgUNhUhNzJv11xOntpBWYeJIZVvhho3GR1J3E2T+6B6J7DnOFeqTz1Ni/AW3BR5Ew4cTIuZRnpOutEpRcSNqAi5iR2ndrBkz+eQk8lQSyj+Nz5jdCRxRyYTdHwWQqo7l99Y+iLkZPFA/QcI8w0jIT2BubvnGp1SRNyIipAbSM1O5YNNr0NaIjfbvGhyw3PgW9boWOKuzs887R3gXJh1zTv4WnwY1mQYAMuOLSMmIcbYjCLiNlSE3MDsHTNJPL2PcIeJ/tV6QvWORkcSdxdU6e+Zp82wdxHs+Z7octHcEnULANO2TSMlK8XgkCLiDlSESrnNcZtZeeBbTLZMHrVUwKfDKKMjiThFtoJW5wdPvwtxO+hbty8Vy1TkbOZZPtn1ibH5RMQtqAiVYilZKczY/DakneU2mzd1Oj4PPkFGxxL5n8Z9oUZn5+DpX/6NV7qVR5s8ihkzq0+sZkPsBqMTikgppyJUin2y4yPOJR6gksPEPVG3QrUORkcSyevCwdPpZ2Hpi9QKqModNe8AnAuzWjOtBocUkdJMRaiU2hi7kdUHvsdsy2KYZ0W82o80OpLIpXn6/m/m6YTdsGYyd9X6F1UDqpKclczMnTONTigipZiKUCmUlJXER1vehvREbrd5U6vjC+BTehaMlVIosCJ0Hf/34Okf8dy7mKFNhmLGzPrY9aw7uc7ohCJSSqkIlUKfbJuBNfEQlR0W/q/GHVCljdGRRK6ucgto9bDz67XvUj0thTtr3QnAzJ0zdYlMRIqEilApsyF2A2sPLcJsy2K4Z0U82z1udCSR/Gt8r3PGc7sNlr7InRE3UCWgCklZSXyyU3eRiUjhUxEqRZKzkvl482RIP8sdNm+qd3oRvP2NjiWSfyYTdBwN5WpA+lk8fx3PsAYPYsbMuth1rI9db3RCESllVIRKkdnbP8J69iCVHGbuqvUviGxpdCSRgvP0hW5/D54+tZfqOxbS+++7yD7e8bEukYlIoVIRKiW2xG9h1cHvMNuyGeYdiWfbx4yOJHLtAiOg6wTn4Ol9i/lXlpnIgEiSspKYvWu20elEpBRRESoFUrNTmbHpTUg/x602b2p1etG5npOUaja7g3WHzvBdzAnWHTqDze4wOlLhqtwcWj8CgOeGaTwa0RkzZtacXMPmuM0GhxOR0sLD6ABy/ebs+JizZw4Q4TBzT517oFJzoyNJEVuyM5YJP+wm1pqRuy0iyIdxvaLp0SDCwGSFrFEfOL0fDi6j+voZ3NaoB9//tZyPdnxEvXL1KONZxuiEIuLidEbIxW07tY0V+77BZM9mqHcVvFoPMzqSFLElO2MZ9unWPCUIIM6awbBPt7JkZ6xByYqAyQQ3joZyNSH9LHcf2kKEXwXOZp5l7u65RqcTkVJARciFpeekM2PD65Bxju42L+p2Hq9LYqWcze5gwg+7udRFsPPbJvywu3RdJvP0gW4vgU8gXqf380iODwDLjy9nx6kdBocTEVenIuTCFuyaw6kzewl1mLm3bl+o2MToSFLENh5OvOhM0IUcQKw1g42HE4svVHEIjIAu48Fkpt7hDXT3qQTA9O3TSc9JNzabiLg0FSEXtS9xH0t2zwN7Dg/5VMFXl8TcQkLy5UvQteznUio3hzbO/877Hv6D8iYvEtITmL93vsHBRMSVqQi5oGxbNh+ufwVHhpWOdi8a3/Rf5+UDKfXCAvL37zm/+7mchndDrZvxtdt5+HQC2HL4+cjP7EvcZ3QyEXFRKkIu6Ns98zhxahdBDhMP1LkPwhsaHUmKSauoECKCfDBd5nkTzrvHWkWFFGes4mMywQ1PQ7maNM7IoGNaGg6HnQ+3f0i2LdvodCLiglSEXMzRpKMs3DET7DkM9qmCfxtdEnMnFrOJcb2iAS4qQ+e/H9crGov5clWpFLhg8PQDSakEpZ3lRMoJvj34rdHJRMQFqQi5ELvDzodr/4stw0oruxetb3oFPLyNjiXFrEeDCKb2b0Z4UN7LX+FBPkzt36x0zSN0OYER0HU8/iYLA5NSIf0s3x38juNJx41OJiIuRhMqupCf9n/NofgY/DAxqG4/TOH1jY4kBunRIIKbo8PZeDiRhOQMwgKcl8NK9Zmgf6rUHNo8Stt177E66SxbLN5M2z6N/7b/L2aT/o4nIvmjIuQiEtISWLD1fbDn0N+nKiG6S8ztWcwm2tYoZ3QMYzX8P0yn9zHkwM/sTo7joMWLJYeXcEv1W4xOJiIuQn9tcgEOh4MZq8eTmXGOaIcnN930Knh4GR1LxHh/D54uV74O/bM8IOkE8/fOIyEtwehkIuIiVIRcwKo/f2J77AY8MfFw3b6YKtQzOpJIyfH34OmbvMpTLyuHzHPH+Gj7RzgcpWh2bREpMipCJZw1w8qcja+D3cZdPpWJaP2Y0ZFESp6AcMw3/4eHbX54Ziaz7fgKVp9YbXQqEXEBKkIl3Jw1/yE54yxVHR706vI6WDyNjiRSMlVsSsW2j3OXzRtSTjH7jykkZSUZnUpESjgVoRIs5tjvrD6+AjPwSO178Qira3QkkZKtwV3cVv02Ih1mkhMPMvePqUYnEpESTkWohErPTuOjtf8Bh40ePpWo0Xak0ZFESj6TCc8bn+GRgHqY7DZ+3/8t22M3G51KREowFaES6ou1L3Mq/TShWOjT5XWwaKYDkXzx8KZWjzfpbg6CnAxm/P48mTmlcBFaESkUKkIl0KHYzSw5vASAB2vejU9YtMGJRFxMQAXuvWkS5RxmEtLi+WrFC0YnEpESSkWohMmxZfPhyuexO2y094mgSftnjI4k4pJ8I9swpF4/ABYdX8bhw8sNTiQiJZGKUAmzeN0kjqbH44+ZAZ0ngdlidCQRl9W8zSjaBtbAjoPpaydg1wr1IvIPKkIlSFzCbr78ewXt+6v3Jii8kcGJRFycycTAm97Az+TBn1nn+GnVBKMTiUgJoyJUQjjsdj5a/gxZDhsNfELp2OF5oyOJlArBZaPoX/seAL44sphTCTsMTiQiJYmKUAmxavfn7Eg7gSdmHur0KiaLLomJFJbObZ6inm84GQ47Hy1/FofdbnQkESkhVIRKgKSsJOacWA7BVbir1p2ERzQ3OpJIqWI2W3joxpfxwExM2knWbXrP6EgiUkKoCJUAc3fPJTk7mciQ2vRqN9boOCKlUqWKzbmzSlcAZu35lBTrcYMTiUhJoCJksB2ndvD7X79jwsTDjR7Gw6yJE0WKyh03TKCSZyBWRzaf/qapKUTEBYvQ+++/T7Vq1fDx8aF169Zs3Ljxivt/+eWX1K1bFx8fHxo2bMjixYuLKenVZdmymLFjBgDdqnWjdtnaBicSKd08vXx5pM1zgInl5/aye9cCoyOJiMFcqggtWLCAUaNGMW7cOLZu3Urjxo3p3r07CQkJl9x/7dq19O3blyFDhvDHH3/Qu3dvevfuzc6dO4s5+aV9feBr4tPiCfEJ4d469xodR8Qt1KnZg66hzQCYseUdstOtBicSESOZHA6Hw+gQ+dW6dWtatmzJlClTALDb7URGRvLYY48xZsyYi/bv06cPqampLFq0KHdbmzZtaNKkCdOmTbvkMTIzM8nMzMz9PikpicjISKxWK4GBgYX2WY4mHWXsqrHYHDaebvE0LcNbFtp7i8iVpaadZtRXt3HOlsFd4W25p6dWqRcxwq4zu1h2dBkD6g8gyDuoUN87KSmJoKCgq/7+LvAZoQEDBvD7779fV7hrkZWVxZYtW+jatWvuNrPZTNeuXVm3bt0lX7Nu3bo8+wN07979svsDTJw4kaCgoNxHZGRk4XyAf/hk5yfYHDZahbdSCRIpZmX8yjOo8TAAvotbz1+HVxgbSMQNZduymbF9BmtOrmHhwYWG5ShwEbJarXTt2pVatWrxyiuvcOLEiaLIdZHTp09js9moUKFCnu0VKlQgLi7ukq+Ji4sr0P4AY8eOxWq15j6OHy+aO0sebPggTcOaMrD+wCJ5fxG5staNHqB5YE1ycDBjzQTsOZlXf5GIFJpvDnxDbGosZb3Lcnftuw3LUeAitHDhQk6cOMGwYcNYsGAB1apVo2fPnnz11VdkZ7v+Oj7e3t4EBgbmeRSFygGVGdNqDOV8yxXJ+4vIlZlMJgbf9Bo+Zg/2Zp/lt1UvGR1JxG0cTzrOd4e+A2BQg0H4efoZluWaBkuHhoYyatQotm3bxoYNG6hZsyb3338/FStW5Mknn+TAgQOFnZPy5ctjsViIj4/Psz0+Pp7w8PBLviY8PLxA+4uIeylftjp9ajn/JvrZkR85G6/lN0SKmt1hZ/qO6dgcNlpUaEGr8FaG5rmuu8ZiY2NZunQpS5cuxWKxcMstt7Bjxw6io6N5++23CysjAF5eXjRv3pxly5blbrPb7Sxbtoy2bdte8jVt27bNsz/A0qVLL7u/iLifHm2epoZvBdKwM2v5GNDyGyJF6tejv7L/7H58LD4MbjAYk8lkaJ4CF6Hs7Gy+/vprbrvtNqpWrcqXX37JyJEjOXnyJLNnz+bXX3/liy++4D//+U+hhx01ahQzZsxg9uzZ7Nmzh2HDhpGamsqgQYMAeOCBBxg79n8zMz/xxBMsWbKEN998k7179zJ+/Hg2b97MiBEjCj2biLgms9nCwze8hNlkZn36CTZvmmJ0JJFS60z6GebtnQdA37p9S8TwkAJPYxwREYHdbqdv375s3LiRJk2aXLRP586dCQ4OLoR4efXp04dTp07x4osvEhcXR5MmTViyZEnugOhjx45hNv+v27Vr14558+bxwgsv8Nxzz1GrVi0WLlxIgwYNCj2biLiuapVaclvlm/j++K/M3DOX+nXvwjeoktGxRFyGze5g4+FEEpIzCAvwoVVUCBbzxWd6Zu+aTXpOOjWDa9KtWjcDkl6swPMIzZ07l7vvvhsfH5+iylSi5HceAhFxbZnZ6Ty9oDsJ2UncElSXAf+ab3QkEZewZGcsE37YTaw1I3dbRJAP43pF06NBRO62TXGbeGPzG1hMFibeMJGqgVWLNFeRzSN0//33u00JEhH34e3py4OtnwVM/GTdy8GdWn5D5GqW7Ixl2Kdb85QggDhrBsM+3cqSnbEApGWnMXPnTABurX5rkZeggnCpJTZERIpS41q3ckNYMxzA9K3vkJN+zuhIIiWWze5gwg+7udRlpfPbJvywG5vdwfx980nMSKSCXwVD5wy6FBUhEZEL3N/pVQIsvhy1pfHj8rFXf4GIm9p4OPGiM0EXcgCx1gy+2bmJX478AsBDDR/Cy+JVTAnzR0VIROQCQWVCeaDxQwB8Gb+euMPLDU4kUjIlJF++BP2PjW/+nIUDBx0rd6RhaMMiz1VQKkIiIv9wQ6NBNAyqQTYOPlozAUe2lt8Q+aewgKuPF/YI2kqyLZ5Ar0Duj76/GFIVnIqQiMg/mEwmHuw0CS+zJzuyz7FydeHPiybi6lpFhRAR5MPlpkM0e5yjTPktlPHyYED9AQR4BRRrvvxSERIRuYTwkJrcXev/AJh75CescdsNTiRSsljMJsb1iga4qAyZsONV/jcqBnvSOKwx7Su2L/6A+aQiJCJyGbe2foqqfuGkYGfWCi2/IfJPPRpEMLV/M8KD8l4mK1/hIFGVzhLq78+DDR80fBmNKynwzNIiIu7CYvFg6A0v8fwvD7M2/SQ3bnyXpm1GGh1LpETp0SCCm6PDc2eW9vXJYN6Rr0jP8aRPnT6E+YUZHfGKdEZIROQKqldswS2RXQCYsfcz0s8dMziRSMljMZtoW6McdzSpxM6U70jPSaNGUA16RvU0OtpVqQiJiFzF3TdMIMwriDOObBYsexoKtjKRiNvYGLuRDXEbMGPm4UYPYzaV/JpR8hOKiFwjm93BukNn+C7mBOsOncFmv7YC4+Plx0NtxgImliTtZ9+OTws3qEgpkJqdmruMxu01b6daUDVjA+WTxgiJSKmU34Ug86tRjR503Pc1K+M3Mf2P93m1xi14lilXmJFFXNq8TW9zNvMsEWUiuKvWXUbHyTedERKRUie/C0EW1P2dXiXQw4+/7Bks/O3ZwogqUirs3ruQX/d9Bda/eLj+4BK3jMaVqAiJSKlSkIUgCyrArxyDmg4HYOHpzRw/8NO1BxUpJbLSzzF942sA3ORfneiwRgYnKhgVIREpVfK7EOTGw4nX9P5t699H87J1yQE+XD8Re3b6tQUVKSW+/HUUsbY0ylp86d/1LaPjFJiKkIiUKvlbCDL/+/2TyWRiSOfX8DF7cSAniZ9X/Pua3kekNPhz9zcsOr0VgCHNHqdMmVCDExWcipCIlCr5WQiyIPtdSrmgKvSL7g/A/L+WkfDXxmt+LxFXlZN2hmmbXscOtC3XiJYN+hod6ZqoCIlIqXK1hSBNOO8eaxUVcl3H6dpiBPX8I8nAwfTfn8Nhy7mu9xNxKQ4HPywdxVF7Ov4evgzq8qbRia6ZipCIlCpXXgjSaVyvaCzm61v7yGwy80inSXiaLOzIPM2KtROv6/1EXMmJXV/ydeJ2wMSAZo8T5IKXxM5TERKRUudyC0GGB/kwtX+za5pH6FIiQqO5p8YdAMw5tJDftq6/7skbRUo6e0oCH25+i2wcNCnfgBui7zU60nUxORyaK/5KkpKSCAoKwmq1EhgYaHQcESkAm92RuxBkWIDzctj1ngm66Bi2HEZ9ejNHMs9QNi2YFXHPAObrmrxRpMRyOPjp2/uZZd2Jj6cfb9zxNaEBJfO/8fz+/tYZIREptS5cCLJtjXKFXoIAlu45xcaDd4ADEn3P0sz/Z+D6J28UKYnit33K59ZdgIl+TR4tsSWoIFSERESu0fnJG2Oza+FrdY5LspRbTQXzieuevFGkpLGfO86Hf0whEwfRoY3oWv8+oyMVChUhEZFrdOHkjZsS++Cf7UOm2UaXsPd5yLKIQFKua/JGkRLDbmfZ0qfZRSZenmV4pOOrLrGyfH6Ujk8hImKACydltOHFgYT7SMeHnb52qvuvZbrXW/S1LOPM2bMGphS5fqe3fMynKfvBZObepo8SXgouiZ2nIiQico3+OSnjqazaHDnXnROO8swP9sJuzqCv5Tc6bH4cdnwFOVkGJRW5do4zh/hwxwwycFA7rBE9o11z4sTLURESEblGl5q8MftsS1KzIzhgKscLgdEkeoQRZEqFte/BFw/A/l/Abjcss0iB2LJZ8etotpuy8PQKYNgNL5eaS2Lnla5PIyJSjC49eaMHWae6AiZi/c6yq8vjmG54CvzKQXIsLH8Zvh4CR9eBZi+REu70xg+YnfYnmCzc03QYFQMqGR2p0KkIiYhch0tN3mjPqoBvZmuqlvNjW+r3JNfsDPfOg9aPgHcAJP4JS8bA949B3E4D04tcniN+N9P2fEo6DmpVaMpt9Vx74sTL0YSKV6EJFUUkP/45eWPTKgE8v2Ysf6X8RfuK7Xm82ePOHTOSIGYe7PwabH+PGaraHlo9BCFRxn0AkQtlZ/Drl//HjMy/8PQJ5rVe86joX9HoVAWiCRVFRIrRPydv9PH04tEmj2LGzJqTa9gQu8G5o08gtBnqPENU7zYwmeHoGvhqECyfCMlxxn4QESBh7dvMzTwBZg/6NhvhciWoIFSERESKSI3gGtxR07kW2YwdM7BmWv/3pH8o3PgM3D0Lom50jhfavwQW9Ie1UyD9nCGZRex/bWLawa/JwEHdiJb0rP0voyMVKRUhEZEidFetu6gaUJXkrGRmbJ/BRaMRylaFbv+FOz+ESs3Alg07voTP+8KW2ZCVZkxwcU+ZKfyy/N/sMufg7VuOYe3Hlbq7xP6pdH86ERGDeVo8ebTJo3iYPNgUv4lVJ1ZdesewunDrW3DLG1C+FmSnweaZMP8+2PmNsyCJFLHYVZOYlxMPFk/ua/EE4WXCjY5U5FSERESKWLWgavxf7f8D4JOdn3Am/cyldzSZILIl3DkdurwIQZUh/Sysecc5B9GBXzUHkRQZ2+FVvH90MZk4qF+pHd1q3GZ0pGKhIiQiUgxur3E7NYNrkpaTxrRt0y6+RHYhsxlqdoG7Z0OHJ8EvBJJOwm//hW8egmMbNAeRFK70s3z/+zgOmG34lqnAo+3+XeoviZ3nHp9SRMRgFrOF4U2G42n2ZPvp7Sw9ujQfL/KA+r2dd5i1fBC8ysCZg/DTaPjhCYjfVeS5xQ04HBxePoEv7Ylg8WZw69GU9y1vdKpioyIkIlJMKvpX5L669wHw6Z5PiU2Jzd8LPX2h2f3Q93No1AcsXhC7DRY+Cj8/D2ePFF1oKfWy9/3ElLhV2DDROqobN1S5yehIxUpFSESkGPWI6kHD8g3JtGXy3h/vkWPPyf+LfYKg7aNw72dQ5xbnHERHVsOXg2DFJEhJKLrgUjqlnGL+uon8ZbIRFFCJIa2exmQyXf11pYiKkIhIMTKbzAxrPAx/T38OWQ/xzYFvCv4m/mHQ6Vn4v5lQrQM47LBvMczvB+s+gAzr1d9DxOFg97Ln+JFk8PDh4fYvEuQdZHSqYqciJCJSzMr5lmNIwyEAfHvgW/Yl7ru2NwqJgu4vQ+8PIKKxc8mO7QuccxBtnQvZ6YWYWkqb1B0LmHJmCw5MdK7VmxYRrYyOZAgVIRERA7Sr2I4bKt2AHTvvx7xPes51lJYK9aHXO9DzNShXE7JSYdNHzjmIdi0EWwEuv4lbcJw7zkdb3uGMyU542ZoMaPGE0ZEMoyIkImKQwQ0GU963PPFp8czZNef63sxkgiqt4V8z4KZ/Q0AEpCXC6rfhywFwcJnmIBInu51VS0ezlnTMnmV47MaX8fXwNTqVYVSEREQM4ufpx/AmwzFh4rfjv/1vYdbrYTZDra7QZy60fwJ8y4L1L1j2H/j2ETi+SXMQubn4zdP5OGUvmMzc0/ghaobUNjqSoVSEREQMFF0umttr3A7A9O3TOZ1+unDe2OIJDf7lnIOoxWDw9IPT+2Hx07DoSUjYUzjHEZdiO72fKTs/di6oGtaEOxo8YHQkw7lMEUpMTKRfv34EBgYSHBzMkCFDSElJueJrOnXqhMlkyvMYOnRoMSUWEcmfe+rcQ83gmqRkpzDljynYHYV4CcvLD5oPgL7zoOHdzoJ08g/4dij88m84d6zwjiUlW04W3ywdxX5TNr7eQYzo+KrbzB59JS7zE+jXrx+7du1i6dKlLFq0iN9//52HH374qq976KGHiI2NzX289tprxZBWRCT/PMwePNb0MXwsPuxJ3HNtt9RfjW9ZaDcC+nwKtXs4xxQd/h2+GAC/vw4ppwr/mFKi7F37Bt9k/AVmCw+2GUNomTCjI5UILlGE9uzZw5IlS/joo49o3bo1HTp04L333mP+/PmcPHnyiq/18/MjPDw89xEYGHjF/TMzM0lKSsrzEBEpauFlwnNvqf96/9fXfkv91QSEQ+exzjmIqrZ3zkG0Z5HzDrP10yBD/88rjZKPb+Sdg19hB26IvIkO1XsaHanEcIkitG7dOoKDg2nRokXutq5du2I2m9mw4cqDCz/77DPKly9PgwYNGDt2LGlpaVfcf+LEiQQFBeU+IiMjC+UziIhczY2Vb8y9pf7dP94lNTu16A4WUh16vAJ3TIHwhs45iLZ97ixEf3wG2RlFd2wpVo6sNKaufJZEk52IMuEMufE/RkcqUVyiCMXFxREWlvcUnoeHByEhIcTFxV32dffddx+ffvopy5cvZ+zYscydO5f+/ftf8Vhjx47FarXmPo4fP14on0FEJD8GNxhMmF8Yp9NP8+G2D6+8Sn1hCG8It78HPV51lqPMZNg4HRb0g93faw6iUmDJsmfZkn0WD7MnT3R63a1vlb8UQ4vQmDFjLhrM/M/H3r17r/n9H374Ybp3707Dhg3p168fc+bM4dtvv+XQoUOXfY23tzeBgYF5HiIixcXP048nmj6Bh8mDDXEb+Pnoz0V/UJMJqraFuz6Gzs87L5+lnoZVbzrnIDq0XLfcu6g/9/3Ap3GrALi//kCiwhoanKjk8TDy4E899RQDBw684j7Vq1cnPDychIS8iwnm5OSQmJhIeHh4vo/XunVrAA4ePEiNGjUKnFdEpDjULFuT++rdx5zdc5i7ey61gmtRI7gY/p9lNkPtblC9E+z5Hv6Y65yD6NfxEFoXWj0MlZsXfQ4pFOmpp3hn/SvkAC3L1qV780eNjlQiGVqEQkNDCQ0Nvep+bdu25dy5c2zZsoXmzZ1/CH/77TfsdntuucmPmJgYACIiIq4pr4hIcbkl6hZ2n9nN5vjNTN4ymVdvfJUynmWK5+AeXtDw/5wr3G9fANu/gFN74cdRULmFsxCF1imeLHJNHLYcZvz0MHH2dMpZ/Bja7T23W1U+v1xijFC9evXo0aMHDz30EBs3bmTNmjWMGDGCe++9l4oVKwJw4sQJ6taty8aNGwE4dOgQ//3vf9myZQtHjhzh+++/54EHHuDGG2+kUaNGRn4cEZGrMplMDGs8jDDfMBLSE4pnvNA/eflBi0Fw72fQ4C4we8Bfm+Gbh51nic5pDGWJZLfzy0/DWZN8GDNmnmjzPP5+Vz/p4K5cogiB8+6vunXr0qVLF2655RY6dOjA9OnTc5/Pzs5m3759uXeFeXl58euvv9KtWzfq1q3LU089xV133cUPP/xg1EcQESkQfy9/nmhWzOOFLsUvBNo/7pyDqFY355iiQ8vhiwfg9zec44mkZHA4OLhiPHNObQBM9IvuT53atxqdqkQzOYr9rxiuJSkpiaCgIKxWqwZOi4ghFv+5mNm7Z+Nh8mBCuwnULFvT2EBnDsHGGXBsnfN7D29o8H/QpC94Bxibzc0lr5vCmD0zOW2y07ryjTzZ9R23vSSW39/fLnNGSETEXfWM6knr8NbkOHJ4c8ubWDOtxgYqVwN6vgq3vwsVGkBOJsR8Bp/3hZjPnd9LsbP/8RlTds/itMlOeEhthnZ61W1LUEGoCImIlHAmk4mhjYdSsUxFEjMSeWfrO9jsNqNjQURj54SM3V+BstWccxBtmAbz+8HeH6EkZHQXuxaycMt7xJhz8PQPZ1Tn1/Hz9DM6lUtQERIRcQF+nn6MajEKH4sPu87sYv6++UZHcjKZoFp7+L9PoNMY8A+D1FOw8jX4ciD8uVJzEBW1/b+wbe3rfGHJAL9yPNhmDFUDqxqdymWoCImIuIjIgEiGNh4KwPeHvmd97HqDE13AbIY6PaHPZ9B2BPgEOle2X/oiLBwGJ7YanbB0OrKauBUv8Y4lDYdvWW6qew+dIjsZncqlqAiJiLiQthXb0qt6LwCmxkzlr+S/DE70Dx5e0OhuuPdzaPYAePpCwh5Y9CQsfgZOHzA6Yenx1xbSfx3H6x6ppPoGUqtyOwY3GGx0KpejIiQi4mL61u1L/XL1ybBl8MbmN0jJSjE60sW8/aHlELh3HtS/E8wWOL4Rvn4Qfp0A1hNGJ3RtcTtx/PIcU01J/OXjS3C5Ooxq8RSeFk+jk7kcFSERERdjMVt4otkTlPctT2xqLJO3Ti4Zg6cvxS8EOoyEe+ZCzS7ObYd+gy/uh9VvQ1qiofFc0umDsGQMC+1WNvh44REYyagWTxHiE2J0MpekIiQi4oKCvIN4psUzeFu82XF6B5/u+dToSFcWVAm6vOhc2DWytfOOsl0Lnbfcb5wBmSXwrFZJdO44LH6arVmJLPAxQWBFBjd6kDohWvLkWqkIiYi4qGpB1RjeZDgAiw8vZtmxZQYnyofyNeGW16DXZAiLhpwM+ONTmN/XuaZZTpbRCUuu5Hj4cRTHM07zji84Aitzc7XudKnSxehkLk1FSETEhbWOaM3dte8GYOaOmew5s8fgRPlUsSn0/gC6/ReCq0BGEqx7Hxb0g72LwW43OmHJkpYIP47CmhLHJF8HGUEVqVe+PgPqDzA6mctTERIRcXF31bqLthFtc2eeTkhLMDpS/phMEHUj3D0LOj4LZUIhJQFWToKvBsGR1ZqDCJwlcfHTZFmP85qvg1OB4UQEVObpFk/jadbg6OulIiQi4uJMJhPDmgwjKiiK5KxkJm6YWDLvJLscswXq3uJc5b7NMOd6ZWePwM/Pw3cj4GSM0QmNk5UGS8ZgP3OQqT4ODgaG4u8TzLMtn8Xfy9/odKWCipCISCngbfFmdMvRlPMpx8nUk7yx+Q2ybdlGxyoYD29ofC/0/Rya9nd+H78TfngCfnrWudirO8nJgl9egPhdfOVtYm1QOTw8/XiqxVNE+EcYna7UUBESESklQnxCGNtqLL4evuxJ3MMH2z7A7nDBsTbeAdDqIeekjNG3g8kMx9bD10Pgt5cgKdbohEXPlgPLJsCJLSz3hK+DgsHDm4caPUR0uWij05UqKkIiIqVIZGAkTzV/CovJwtqTa5m/t4SsSXYtypSDG56Ce+ZAjZuc44UOLIUF/WHNO6V3DiK73TlO6shqtnrA9LJB4OlD75q9tXxGEVAREhEpZRqGNuSRRo8A8N2h71h6dKnBia5TcCR0HQf/mg6VW4I9B3Z+A/Pvg80zISvV6ISFx+GANZPhwC/stzh4OyQYu6cvN1a+kXvr3Gt0ulJJRUhEpBTqGNkx97b6j3d8zLqT6wxOVAhC68Ctb8Btb0FoXchOhy2znZMybv+ydMxBtHEG7P6OEyY7k8qXI8vThyahTXik0SOYTCaj05VKKkIiIqXUXbXuomuVrjhwMOWPKcQkxBgdqXBUag53ToObJ0BQZciwwropzmU79v/sunMQ/fEZxHxGInYmhlUgxcOLGkE1eLL5k3iYPYxOV2qpCImIlFImk4khDYfQrmI7chw5vLXlLfYl7jM6VuEwmaB6J+f4oRufhjLlITkOlr8CXw+GI2tcaw6iXd/CxukkY+eVChGc8rAQUSaCZ1s9i4+Hj9HpSjUVIRGRUsxsMvNok0dpEtqETFsmkzZN4mjSUaNjFR6zBer1gj6fQetHnHecJR6Gn5+D70dA3A6jE17d/l9g9WRScfBSWAWOe5go612W51o/R5B3kNHpSj0VIRGRUs7T7MmoFqOoU7YOqdmpvLz+ZWJTStkt6J4+0OQ+uHee858WL4jb6ZyQcclzJXcOosOrYMVE0nEwMbQ8RzwtBHoF8kKbFwjzCzM6nVtQERIRcQPeFm+ebfUsVQOrYs2yMmH9hNJXhgB8Ap1nhu6d5zxTZDLD0TXOOYiWv+K8fFZS/LUFlk0g02FjUrkQDnh64u/pzwttXqByQGWj07kNFSERETdRxrMMz7d+nsiASM5mnGXCulJahgD8Q51jh+6ZDdU7OscL7f/ZOQfR2vcg/ayx+eJ2ws/PkW3L4vWQYPZ4e+Pr6cvzrZ+namBVY7O5GZPD4UqjyYpfUlISQUFBWK1WAgMDjY4jInLdrJlW/rv+vxxPPk5Z77KMazuu9C/ZkLAXNn4IJ7Y6v/f0g8Z9oOE94OVXvFlOH4RFI8nITOKNYH92+Pnj4+HLc62fo05IneLNUorl9/e3itBVqAiJSGnklmXI4YATW2DDh3B6v3ObbzA0ewDq3Q6WYljJ/dxx+P4x0tITmRTozV7/YHw8/Hi21bNaOqOQqQgVEhUhESmtrJlWXlr/EseSjxHsHcxzrZ9zj8sydjscXgGbPgbrX85tARHQcgjU6ALmIho1khwP348gJSWeV/zNHAoIxc/LnzGtxuhMUBFQESokKkIiUppZM628vOFljiYdxc/Dj9EtR1OvXD2jYxUPWw7s+xG2zPrfumXlakCrhyGytXOuosKSlgjfP4bVeoyX/eBoUAUCvIN5rs1zVA+qXnjHkVwqQoVERUhESrvU7FRe3/Q6exL34Gn2ZGSzkbQIb2F0rOKTnQ47v4aYzyErxbktorGzEIU3uP73z0iCRSNJOHOAib42TgZFEOxXnhfavEBkQOT1v79ckopQIVEREhF3kGXLYvLWyWyJ34IZM0MbD6VjZEejYxWvDCvEzHMu6Gr7e92yah2g5YMQEnVt75mVBouf5lDCdiZ5Z2ENrky5MuH8u82/S/+YLIOpCBUSFSERcRc2u40Pt3/Iyr9WAnBvnXvpXbO3+y32mZIAmz+B/UvAYXfORVS7OzQfBAEV8v8+OVmw5Fm2nNzAO15ZZAZXpmpIbca0GkOIT0jR5RdARajQqAiJiDuxO+x8tuczFv25CIAOlTowtNFQPIvjjqqS5uwR2PSRc/ZncM5WXb83NO0PPldZ+sKWA0tfZOnx5cz0zMQeFEnjiq15svmT+Hr4FnVyQUWo0KgIiYg7Wnp0KTN3zMSOnVrBtXim5TPuu+5V/G7nHEQnY5zfe5WBxn2h4f+B5yVKjd1OzvKX+OzwIhZbsiGoMp1r3MqDDR/UKvLFSEWokKgIiYi72nFqB5O3TiYlO4VyPuUY3XI01YKqGR3LGA4HHN8IG6fDmYPObb5lofkAqNsLLB65+1lXTuTtQ9+wx2yDwIrc03Aw/6r1L/e7xGgwFaFCoiIkIu4sNiWW1za9xsnUk3hbvBncYDCdIjsZHcs4djv8+ZtzDqKkk85tgZWg5WCofhMHVr/KWwe/ItFkxye4KsPbPE+riFbGZnZTKkKFREVIRNxdanYq72x9h22ntgHQsXJHBjcYjI+Hj8HJDGTLhj0/wNY5kH4WBw6WlfHnk+yT5ACVytXlqZvepJJ/JaOTui0VoUJSXEXIZnew8XAiCckZhAX40CoqBItZp1FFpGSwO+x8d/A7vtj3BXbsVPKvxJPNniQy0M3nwclKI3nbZ8zYOZMNpAPQOrwlw25+V4OiDaYiVEiKowgt2RnLhB92E2vNyN0WEeTDuF7R9GigeSZEpOTYfWY37/7xLmczzuJl9qJ/dH9urnozZlMRLUtRwm07tY2pMVM5m34ajwwrfSJuoFeHf2s8UAmgIlRIiroILdkZy7BPt/LPfwnn/whN7d9MZUhEShRrppX3Y97PvVQWXS6aRxo9QniZcIOTFZ8sWxbz9szjpyM/AVDJvxIjmo7QchkliIpQISnKImSzO+gw6bc8Z4IuZALCg3xY/exNukwmIiWK3WHn5yM/8/nez8m0ZeJl9uLeuvfSM6pnqT87FJMQw8ydM4lPiwege7Xu9KvXD2+Lt8HJ5EL5/f2tCQ0MtPFw4mVLEIADiLVmsPFwIm1rlCu+YCIiV2E2mekZ1ZNmYc2Yvn06O8/sZM7uOaw7uY5BDQZRI7iG0REL3Zn0M8zePZsNsRsAKOtTlqGNhtIkrImxweS6qAgZKCH58iXoWvYTESluFcpU4IU2L/Dbsd+Ys3sOB84d4LnVz3FDpRvoW7cv5Xxd/y9x2bZslhxZwlf7vyLDloEZZwm8u87dGhBdCqgIGSgsIH+3nuZ3PxERI5hMJrpU7UKTsCbM3zef3//6nVUnVrEhdgO9avSiV41eLlkYcuw5rDy+kq8PfM2ZjDMA1C5bmyENhrjvxJKlkMYIXUVxjBGKs2ZcNFgaNEZIRFzToXOHmLN7DnsT9wLg7+lPj6ge9KjWgwCvAIPTXZ3dYWf1idV8tf+r3HFAIT4h3F37bjpFdir1Y6BKCw2WLiTFddcYkKcM6a4xEXFlDoeDjXEbmbdnHnFpcQB4W7zpUqULt1a/lfK+5Q1OeLGUrBSWH1/OL0d/ISEtAYBAr0B61+xNt6rd3HPhWRemIlRINI+QiMi1szvsrI9dz3cHv+NI0hEAzJhpEtaEjpEdaR7W3NCC4XA4OJJ0hF+O/MLqE6vJsmcBzrNYt1W/jR5RPVzysp6UwiL08ssv8+OPPxITE4OXlxfnzp276mscDgfjxo1jxowZnDt3jvbt2zN16lRq1aqV7+NqZmkRkevncDjYfno7Cw8uZPeZ3bnb/T39aVexHW0rtqV22drFsjq7w+HgsPUwG+I2sCF2A7GpsbnPVQ2oSvdq3Wlfqb17LyFSCpS6IjRu3DiCg4P566+/+Pjjj/NVhCZNmsTEiROZPXs2UVFR/Pvf/2bHjh3s3r0bH5/8/QeutcZERArXiZQTrDy+klUnVpGYkZi73cfiQ/3y9WkU2ojG5RtToUyFQhmP43A4iE+LZ//Z/ew/u59tCdtISE/Ifd7D7EHLCi3pEdWDOmXraFboUqLUFaHzZs2axciRI69ahBwOBxUrVuSpp57i6aefBsBqtVKhQgVmzZrFvffem6/jqQiJiBQNu8POjtM7WPXXKrad2kZSVlKe530sPlQOqEyVgCpUCaxCqG8oAV4BBHoF4u/lTxnPMtgcNnLsOdjsNrLt2SRnJZOQlsDp9NMkpCUQlxbHwbMHsWZZ87y3t8WbJqFNaB3RmqZhTfHz9CvOjy7FwO0nVDx8+DBxcXF07do1d1tQUBCtW7dm3bp1ly1CmZmZZGZm5n6flJR0yf1EROT6mE1mGoc2pnFoY+wOO0eSjrD91Ha2n9rO/rP7ybBlcPDcQQ6eO3jdx/Iwe1A9qDq1gmtRr1w9GoU20kzQApTiIhQX57xLoUKFCnm2V6hQIfe5S5k4cSITJkwo0mwiIpKX2WSmelB1qgdVp3fN3tjsNmJTYzmWfIzjScc5nnycxIxEkrOSSc5OJj0n/aL3MGGijGcZQv1CCfMNI9QvlFC/UKICo6geVF13fcklGVqExowZw6RJk664z549e6hbt24xJYKxY8cyatSo3O+TkpKIjIwstuOLiAhYzBYqB1SmckBlqHjx89n2bNJz0rGYLHiYPfAweWAxW4o/qLg8Q4vQU089xcCBA6+4T/Xq17aSb3i4cxXk+Ph4IiL+dwt6fHw8TZo0uezrvL298fbW6VIRkZLM0+yJp5fO8Mj1M7QIhYaGEhoaWiTvHRUVRXh4OMuWLcstPklJSWzYsIFhw4YVyTFFRERKAk3Jkn8uM0bo2LFjJCYmcuzYMWw2GzExMQDUrFkTf39/AOrWrcvEiRO58847MZlMjBw5kpdeeolatWrl3j5fsWJFevfubdwHERERKUKapLdgXKYIvfjii8yePTv3+6ZNmwKwfPlyOnXqBMC+ffuwWv93i+To0aNJTU3l4Ycf5ty5c3To0IElS5bkew4hERERV3J+2aZ/zosTZ81g2KdbtWzTJbjcPELFTfMIiYiIKzi/kPeFZ4Iu5G4Leef397eW0BURESkFNh5OvGwJAufC3rHWDDYeTrzsPu5IRUhERKQUSEi+fAm6lv3chYqQiIhIKRAWkL/xr/ndz12oCImIiJQCraJCiAjy4XKjf0w47x5rFRVSnLFKPBUhERGRUsBiNjGuVzTARWXo/PfjekW7xUDpglAREhERKSV6NIhgav9mhAflvfwVHuSjW+cvw2XmERIREZGr69EggpujwzWzdD6pCImIiJQyFrOJtjXKGR3DJejSmIiIiLgtFSERERFxWypCIiIi4rZUhERERMRtqQiJiIiI21IREhEREbelIiQiIiJuS0VIRERE3JaKkIiIiLgtFSERERFxWypCIiIi4rZUhERERMRtqQiJiIiI21IREhEREbelIiQiIiJuS0VIRERE3JaKkIiIiLgtFSERERFxWypCIiIi4rZUhERERMRtqQiJiIiI21IREhEREbelIiQiIiJuS0VIRERE3JaKkIiIiLgtFSERERFxWypCIiIi4rZUhERERMRtqQiJiIiI21IREhEREbelIiQiIiJuS0VIRERE3JaKkIiIiLgtFSERERFxWypCIiIi4rZUhERERMRteRgdQERExNXZ7A42Hk4kITmDsAAfWkWFYDGbjI4l+aAiJCIich2W7Ixlwg+7ibVm5G6LCPJhXK9oejSIMDCZ5IfLXBp7+eWXadeuHX5+fgQHB+frNQMHDsRkMuV59OjRo2iDioiI21iyM5Zhn27NU4IA4qwZDPt0K0t2xhqUTPLLZYpQVlYWd999N8OGDSvQ63r06EFsbGzu4/PPPy+ihCIi4k5sdgcTftiN4xLPnd824Yfd2OyX2kNKCpe5NDZhwgQAZs2aVaDXeXt7Ex4eXgSJRETEnW08nHjRmaALOYBYawYbDyfStka54gsmBeIyZ4Su1YoVKwgLC6NOnToMGzaMM2fOXHH/zMxMkpKS8jxERET+KSH58iXoWvYTY5TqItSjRw/mzJnDsmXLmDRpEitXrqRnz57YbLbLvmbixIkEBQXlPiIjI4sxsYiIuIqwAJ9C3U+MYWgRGjNmzEWDmf/52Lt37zW//7333svtt99Ow4YN6d27N4sWLWLTpk2sWLHisq8ZO3YsVqs193H8+PFrPr6IiJReraJCiAjy4XI3yZtw3j3WKiqkOGNJARk6Ruipp55i4MCBV9ynevXqhXa86tWrU758eQ4ePEiXLl0uuY+3tzfe3t6FdkwRESmdLGYT43pFM+zTrZggz6Dp8+VoXK9ozSdUwhlahEJDQwkNDS224/3111+cOXOGiAjN6yAiItevR4MIpvZvdtE8QuGaR8hluMxdY8eOHSMxMZFjx45hs9mIiYkBoGbNmvj7+wNQt25dJk6cyJ133klKSgoTJkzgrrvuIjw8nEOHDjF69Ghq1qxJ9+7dDfwkIiJSmvRoEMHN0eGaWdpFuUwRevHFF5k9e3bu902bNgVg+fLldOrUCYB9+/ZhtVoBsFgsbN++ndmzZ3Pu3DkqVqxIt27d+O9//6tLXyIiUqgsZpNukXdRJofDoZmeriApKYmgoCCsViuBgYFGxxEREZF8yO/v71J9+7yIiIjIlagIiYiIiNtSERIRERG3pSIkIiIibktFSERERNyWipCIiIi4LRUhERERcVsqQiIiIuK2XGZmaaOcn28yKSnJ4CQiIiKSX+d/b19t3mgVoatITk4GIDIy0uAkIiIiUlDJyckEBQVd9nktsXEVdrudkydPEhAQgMlUeAvoJSUlERkZyfHjx7V0xyXo53Nl+vlcmX4+V6afz+XpZ3NlrvTzcTgcJCcnU7FiRczmy48E0hmhqzCbzVSuXLnI3j8wMLDE/8dkJP18rkw/nyvTz+fK9PO5PP1srsxVfj5XOhN0ngZLi4iIiNtSERIRERG3pSJkEG9vb8aNG4e3t7fRUUok/XyuTD+fK9PP58r087k8/WyurDT+fDRYWkRERNyWzgiJiIiI21IREhEREbelIiQiIiJuS0VIRERE3JaKkEHef/99qlWrho+PD61bt2bjxo1GRyoRfv/9d3r16kXFihUxmUwsXLjQ6EglysSJE2nZsiUBAQGEhYXRu3dv9u3bZ3SsEmHq1Kk0atQod6K3tm3b8tNPPxkdq8R69dVXMZlMjBw50ugoJcL48eMxmUx5HnXr1jU6Voly4sQJ+vfvT7ly5fD19aVhw4Zs3rzZ6FjXTUXIAAsWLGDUqFGMGzeOrVu30rhxY7p3705CQoLR0QyXmppK48aNef/9942OUiKtXLmS4cOHs379epYuXUp2djbdunUjNTXV6GiGq1y5Mq+++ipbtmxh8+bN3HTTTdxxxx3s2rXL6GglzqZNm/jwww9p1KiR0VFKlPr16xMbG5v7WL16tdGRSoyzZ8/Svn17PD09+emnn9i9ezdvvvkmZcuWNTraddPt8wZo3bo1LVu2ZMqUKYBzPbPIyEgee+wxxowZY3C6ksNkMvHtt9/Su3dvo6OUWKdOnSIsLIyVK1dy4403Gh2nxAkJCeH1119nyJAhRkcpMVJSUmjWrBkffPABL730Ek2aNGHy5MlGxzLc+PHjWbhwITExMUZHKZHGjBnDmjVrWLVqldFRCp3OCBWzrKwstmzZQteuXXO3mc1munbtyrp16wxMJq7IarUCzl/48j82m4358+eTmppK27ZtjY5TogwfPpxbb701z/+DxOnAgQNUrFiR6tWr069fP44dO2Z0pBLj+++/p0WLFtx9992EhYXRtGlTZsyYYXSsQqEiVMxOnz6NzWajQoUKebZXqFCBuLg4g1KJK7Lb7YwcOZL27dvToEEDo+OUCDt27MDf3x9vb2+GDh3Kt99+S3R0tNGxSoz58+ezdetWJk6caHSUEqd169bMmjWLJUuWMHXqVA4fPswNN9xAcnKy0dFKhD///JOpU6dSq1Ytfv75Z4YNG8bjjz/O7NmzjY523bT6vIiLGj58ODt37tQ4hgvUqVOHmJgYrFYrX331FQMGDGDlypUqQ8Dx48d54oknWLp0KT4+PkbHKXF69uyZ+3WjRo1o3bo1VatW5YsvvtClVZx/8WrRogWvvPIKAE2bNmXnzp1MmzaNAQMGGJzu+uiMUDErX748FouF+Pj4PNvj4+MJDw83KJW4mhEjRrBo0SKWL19O5cqVjY5TYnh5eVGzZk2aN2/OxIkTady4Me+8847RsUqELVu2kJCQQLNmzfDw8MDDw4OVK1fy7rvv4uHhgc1mMzpiiRIcHEzt2rU5ePCg0VFKhIiIiIv+QlGvXr1ScflQRaiYeXl50bx5c5YtW5a7zW63s2zZMo1lkKtyOByMGDGCb7/9lt9++42oqCijI5VodrudzMxMo2OUCF26dGHHjh3ExMTkPlq0aEG/fv2IiYnBYrEYHbFESUlJ4dChQ0RERBgdpURo3779RVN17N+/n6pVqxqUqPDo0pgBRo0axYABA2jRogWtWrVi8uTJpKamMmjQIKOjGS4lJSXP38AOHz5MTEwMISEhVKlSxcBkJcPw4cOZN28e3333HQEBAbnjyoKCgvD19TU4nbHGjh1Lz549qVKlCsnJycybN48VK1bw888/Gx2tRAgICLhoLFmZMmUoV66cxpgBTz/9NL169aJq1aqcPHmScePGYbFY6Nu3r9HRSoQnn3ySdu3a8corr3DPPfewceNGpk+fzvTp042Odv0cYoj33nvPUaVKFYeXl5ejVatWjvXr1xsdqURYvny5A7joMWDAAKOjlQiX+tkAjk8++cToaIYbPHiwo2rVqg4vLy9HaGioo0uXLo5ffvnF6FglWseOHR1PPPGE0TFKhD59+jgiIiIcXl5ejkqVKjn69OnjOHjwoNGxSpQffvjB0aBBA4e3t7ejbt26junTpxsdqVBoHiERERFxWxojJCIiIm5LRUhERETcloqQiIiIuC0VIREREXFbKkIiIiLitlSERERExG2pCImIiIjbUhESERERt6UiJCIiIm5LRUhERETcloqQiIiIuC0VIRFxK6dOnSI8PJxXXnkld9vatWvx8vJi2bJlBiYTESNo0VURcTuLFy+md+/erF27ljp16tCkSRPuuOMO3nrrLaOjiUgxUxESEbc0fPhwfv31V1q0aMGOHTvYtGkT3t7eRscSkWKmIiQibik9PZ0GDRpw/PhxtmzZQsOGDY2OJCIG0BghEXFLhw4d4uTJk9jtdo4cOWJ0HBExiM4IiYjbycrKolWrVjRp0oQ6deowefJkduzYQVhYmNHRRKSYqQiJiNt55pln+Oqrr9i2bRv+/v507NiRoKAgFi1aZHQ0ESlmujQmIm5lxYoVTJ48mblz5xIYGIjZbGbu3LmsWrWKqVOnGh1PRIqZzgiJiIiI29IZIREREXFbKkIiIiLitlSERERExG2pCImIiIjbUhESERERt6UiJCIiIm5LRUhERETcloqQiIiIuC0VIREREXFbKkIiIiLitlSERERExG39P86te4osFSqIAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -731,14 +881,14 @@ "output_type": "stream", "text": [ "\n", - "\u001b[1mRunning Cycle 2:\u001b[0m\n" + "\u001b[1mRunning Cycle 2, number of datapoints: 10\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:03<00:00, 27.34it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 23.25it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -751,7 +901,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -771,14 +921,14 @@ "output_type": "stream", "text": [ "\n", - "\u001b[1mRunning Cycle 3:\u001b[0m\n" + "\u001b[1mRunning Cycle 3, number of datapoints: 20\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 22.97it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 21.79it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -791,7 +941,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -811,14 +961,14 @@ "output_type": "stream", "text": [ "\n", - "\u001b[1mRunning Cycle 4:\u001b[0m\n" + "\u001b[1mRunning Cycle 4, number of datapoints: 30\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 24.54it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 21.88it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -831,7 +981,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -851,14 +1001,14 @@ "output_type": "stream", "text": [ "\n", - "\u001b[1mRunning Cycle 5:\u001b[0m\n" + "\u001b[1mRunning Cycle 5, number of datapoints: 40\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 23.52it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 22.23it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -871,90 +1021,21 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "#### First, let's reinitialize the state object to get a clean state ####\n", - "s = StandardState(\n", - " variables = variables,\n", - " conditions = conditions,\n", - " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"])\n", - ")\n", - "\n", - "### Then we cycle through the pipeline we built five times ###\n", - "num_cycles = 5 # number of empirical research cycles\n", - "for cycle in range(num_cycles):\n", - " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", - " s = theorist(experiment_runner(experimentalist(s, num_samples=10)))\n", - " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", - " plot_from_state(s)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If everything went well in terms of our theorist, we should have recovered our ground truth model `sin(x)`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sin(x)\n" - ] - } - ], - "source": [ - "print(s.model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Chain Looping with Stopping Criterion\n", - "\n", - "Alternatively, we can run the chain until we reach a stopping criterion. For example, here we will loop until we get 30 datapoints." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", - "\u001b[1mRunning Cycle 1, number of datapoints: 0\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 89%|████████▉ | 89/100 [00:03<00:00, 26.24it/s]" + "\u001b[1mNumber of datapoints: 50\u001b[0m\n", + "\u001b[1mDetermined Model: sin(x)\u001b[0m\n" ] } ], From ad364c9ee6d8699bd7fa1561f44e0f471633d8c2 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Mon, 21 Aug 2023 20:31:20 -0700 Subject: [PATCH 18/32] Removed debugging print --- .../Tutorial-III-Functional-Workflow.ipynb | 474 ++++++++++++------ 1 file changed, 328 insertions(+), 146 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb index dcdb2e87a..fb1ab68c6 100644 --- a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb +++ b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb @@ -176,16 +176,16 @@ " 5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,\n", " 5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,\n", " 6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 4.633056\n", - "1 2.792527\n", - "2 2.284795\n", - "3 4.252257\n", - "4 3.173326\n", - "5 4.633056\n", + "0 1.078931\n", + "1 5.775453\n", + "2 4.696522\n", + "3 0.761598\n", + "4 3.744525\n", + "5 5.140788\n", "6 2.855993\n", - "7 1.205864\n", - "8 3.998391\n", - "9 4.125324, experiment_data=Empty DataFrame\n", + "7 1.015464\n", + "8 1.142397\n", + "9 1.523196, experiment_data=Empty DataFrame\n", "Columns: [x, y]\n", "Index: [], models=[])\n" ] @@ -235,16 +235,16 @@ "\n", "\u001b[1mThe conditions we provided:\u001b[0m\n", " x\n", - "0 4.633056\n", - "1 2.792527\n", - "2 2.284795\n", - "3 4.252257\n", - "4 3.173326\n", - "5 4.633056\n", + "0 1.078931\n", + "1 5.775453\n", + "2 4.696522\n", + "3 0.761598\n", + "4 3.744525\n", + "5 5.140788\n", "6 2.855993\n", - "7 1.205864\n", - "8 3.998391\n", - "9 4.125324\n", + "7 1.015464\n", + "8 1.142397\n", + "9 1.523196\n", "\n", "\u001b[1mThe dataframe we provided:\u001b[0m\n", "Empty DataFrame\n", @@ -382,29 +382,29 @@ "text": [ "\u001b[1mPrevious Conditions:\u001b[0m\n", " x\n", - "0 4.633056\n", - "1 2.792527\n", - "2 2.284795\n", - "3 4.252257\n", - "4 3.173326\n", - "5 4.633056\n", + "0 1.078931\n", + "1 5.775453\n", + "2 4.696522\n", + "3 0.761598\n", + "4 3.744525\n", + "5 5.140788\n", "6 2.855993\n", - "7 1.205864\n", - "8 3.998391\n", - "9 4.125324\n", + "7 1.015464\n", + "8 1.142397\n", + "9 1.523196\n", "\n", "\u001b[1mUpdated Conditions:\u001b[0m\n", " x\n", - "0 3.554125\n", - "1 0.698132\n", - "2 1.586663\n", - "3 2.729060\n", - "4 1.078931\n", - "5 5.331188\n", - "6 1.713596\n", - "7 0.825065\n", - "8 5.204254\n", - "9 4.950388\n" + "0 2.411728\n", + "1 5.902386\n", + "2 3.236792\n", + "3 3.744525\n", + "4 0.063467\n", + "5 1.523196\n", + "6 4.315723\n", + "7 4.379190\n", + "8 3.046393\n", + "9 5.204254\n" ] } ], @@ -443,16 +443,16 @@ "\n", "\u001b[1mUpdated Data:\u001b[0m\n", " x y\n", - "0 3.554125 -0.152573\n", - "1 0.698132 0.573655\n", - "2 1.586663 1.323718\n", - "3 2.729060 1.162445\n", - "4 1.078931 0.764377\n", - "5 5.331188 -0.931644\n", - "6 1.713596 1.779428\n", - "7 0.825065 1.118309\n", - "8 5.204254 -1.116191\n", - "9 4.950388 -0.700532\n" + "0 2.411728 0.915126\n", + "1 5.902386 -0.440795\n", + "2 3.236792 0.228788\n", + "3 3.744525 0.194455\n", + "4 0.063467 -0.053653\n", + "5 1.523196 0.881799\n", + "6 4.315723 -0.132748\n", + "7 4.379190 -0.561283\n", + "8 3.046393 -0.139681\n", + "9 5.204254 -0.610173\n" ] } ], @@ -500,7 +500,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:05<00:00, 19.93it/s]\n", + "100%|██████████| 100/100 [00:05<00:00, 19.41it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -510,14 +510,14 @@ "text": [ "\n", "\u001b[1mUpdated Model:\u001b[0m\n", - "sin(x)\n" + "0.03\n" ] }, { "data": { - "image/png": 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", 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" + "
" ] }, "metadata": {}, @@ -564,7 +564,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 24.05it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 25.80it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -592,43 +592,43 @@ " 5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,\n", " 5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,\n", " 6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 4.569589\n", - "1 4.759989\n", - "2 2.475194\n", - "3 2.792527\n", - "4 5.838920\n", - "5 3.236792\n", - "6 4.569589\n", - "7 4.379190\n", - "8 5.140788\n", - "9 0.253866, experiment_data= x y\n", - "0 3.554125 -0.152573\n", - "1 0.698132 0.573655\n", - "2 1.586663 1.323718\n", - "3 2.729060 1.162445\n", - "4 1.078931 0.764377\n", - "5 5.331188 -0.931644\n", - "6 1.713596 1.779428\n", - "7 0.825065 1.118309\n", - "8 5.204254 -1.116191\n", - "9 4.950388 -0.700532\n", - "10 3.554125 -0.632639\n", - "11 0.698132 0.409923\n", - "12 1.586663 1.120855\n", - "13 2.729060 -0.555710\n", - "14 1.078931 0.018994\n", - "15 5.331188 -1.095720\n", - "16 1.713596 0.483406\n", - "17 0.825065 0.891715\n", - "18 5.204254 -1.335465\n", - "19 4.950388 -1.677963, models=[sin(x), sin(x)])\n" + "0 5.521587\n", + "1 5.902386\n", + "2 5.711987\n", + "3 6.092786\n", + "4 3.871458\n", + "5 0.698132\n", + "6 2.919460\n", + "7 4.061857\n", + "8 4.315723\n", + "9 3.934924, experiment_data= x y\n", + "0 2.411728 0.915126\n", + "1 5.902386 -0.440795\n", + "2 3.236792 0.228788\n", + "3 3.744525 0.194455\n", + "4 0.063467 -0.053653\n", + "5 1.523196 0.881799\n", + "6 4.315723 -0.132748\n", + "7 4.379190 -0.561283\n", + "8 3.046393 -0.139681\n", + "9 5.204254 -0.610173\n", + "10 2.411728 0.435060\n", + "11 5.902386 -0.604527\n", + "12 3.236792 0.025925\n", + "13 3.744525 -1.523700\n", + "14 0.063467 -0.799035\n", + "15 1.523196 0.717724\n", + "16 4.315723 -1.428770\n", + "17 4.379190 -0.787877\n", + "18 3.046393 -0.358956\n", + "19 5.204254 -1.587605, models=[0.03, 0.03])\n" ] }, { "data": { - "image/png": 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", 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fLvxEYmYijtaOjAoapXQ4Fm/ryRQmrjpaIjkApGbkMXHVUbaeTCmxXaVS8UKHF7BWW3P86nF+T/69JsMVosZZTIKwsbGha9eu7Nixw7BNp9OxY8eOEk8JZdFqtZw4cQJvb+UL1Zjq6u2rfHfmOwBGBI7AxdZF4Ygsm1anZ87mWO5tTMKwbc7m2Huam7wdvRncajAAK0+tJKcwx7yBCqEgi0kQABERESxdupSoqCji4uKYOHEiOTk5jB1bvLjas88+S2RkpGH/N998k19++YXz589z9OhRRo4cSWJiIs8//7xSH6FS9Ho9y08tJ1+bT6BbID19eyodksU7eOHGPU8Od9MDKRl5HLxw456fDWgxAJ8GPmQUZLDm9BozRimEsiyqD2Lo0KFcvXqVmTNnkpqaSqdOndi6dauh4zopKQm1+s+cd/PmTcaPH09qaioNGzaka9eu7Nu3j6CgIKU+QqUcSTvCkbQjWKmsGNdhHGqVReX1Wik9q/TkUN5+1hprxnUYx1v732Jb4jZ6+vYkwDWgukMUQnEqvV5v7Clb/L/MzExcXFzIyMhQpD8iX5vP9F3TuZp7lYEBAxkeOLzGY6iLohOuM2zp/nL3+3b8/YQFNDL6s4XHFrL3yl5auLTgnQfekcQtLEZF72vyG13LbTi7gau5V2lk18jQ9i2qLrS5G94udpQ2BklF8Wim0OZupZ5jVOAoHKwcOJ9xnl8SfzFLnEIoSRJELZaSncKm88VVzUa3G42dVd1ZR0ppGrWKWQOKmxr/miTuvJ81IKjM+tmudq4MbTsUKJ4bkZGfYYZIhVCOJIhaSq/Xs+LUCop0RQS7BxPqFap0SHVO3/beLB7ZBS+XkonXy8WOxSO73DMPwphH/B7B39mfnMIc6bAWdY5FdVLXJ4fTDhNzNQYrtRVj242VCVlm0re9N48EeXHwwg3Ss/LwcCpuVirryeFuGrWGce3HMXPfTHZe2snDfg/TsmFLM0ctRM2QJ4haqFBbyMpTKwF4vMXjeDta3rwNS6JRqwgLaMTATk0IC2hU4eRwRxu3NjzY5EH06Fl2chk6vc5MkQpRsyRB1EKbz28mPTcdNzs3BrUcpHQ4ogJGBo7ETmNHQkYCuy7tUjocIaqFJIha5lruNX48+yNQfNORxfgsg6udK0+1eQqANafXcLvwtsIRCVF1kiBqmVWxqyjQFRDoFkh3n+5KhyNMEN4s3DDDWqrPibpAEkQtEns9luiUaNSoGdNujHRMWxhrtTWj240G4H8X/kdydrLCEQlRNZIgagmdXkfUqSgAevv1pplLM2UDEpXSyaMTXTy6oNVrDf+eQlgqSRC1xM5LO7mYeREHKwepEmfhng16FiuVFTFXY4hJj6nUOSpSxEgIc5N5ELXA7cLbrD29FoC/t/67LOVt4bwdvenXvB+bz29mZexK2jduj5W64n9qphQxEsKc5AmiFvjx3I9kFGTg08CH8GbhSocjqsHgVoNxtnHmSvYVtiVuq/BxphYxEsKcJEEoLDUnlZ/O/wTAs+2eNembpqi9HKwdGNqmeJ2m7898T1ZBVrnHVLaIkamk+UpUlNyNFLY6bjVF+iI6Nu5IJ/dOSocjqlFvv978cvEXErMS+e7MdzzX/rky9zeliFFpS5CXR5qvhCnkCUJBsddjOZB6ADVqnm33rAxrrWPUKrVh2Ou2i9u4kn2lzP2rUsSoIqT5SphKEoRC7h7W+rD/w/g6+SockTCHdo3bEeIZgg4dq2JXlbmvh1PFlnOv6H53q6nmK1G3SIJQyG+XfzMMa32q9VNKhyPMaGTgSDQqDUfTj3Li6olS96uOIkalqUoNblF/SYJQQF5RnqF2wJOtnpRhrXWct6O3YXTa17Ffl7raa3UUMSqNuZuvRN0kCUIBm89v5mb+TTzsPejXrJ/S4YgaMKTVEBytHUnMSmTnpZ2l7lcdRYyMMWfzlai7ZBRTDbuRd4PNCZsBGBY4DGuNtcIRiZrgaOPI4FaDWRm7krWn19Ldp3upK/VWtYiRMXear1Iz8oz2Q6goTkKVab4SdZc8QdSwtfFrydfm07pha8K8w5QOR9SgR5s9ipeDFxkFGYYvCaWpahEjY+czV/OVqLskQdSgixkX2X1pNwCjgkbJsNZ6xlptzfDA4QBsTtjM9dzrNXp9czVfibpLmphqiF6vZ1XcKvTo6e7TndYNWysdklBAqFcobd3acvrGadbFr2Nip4k1en1zNF+JukueIGrI8avHOXHtBFZqK4a1HaZ0OEIhKpWKUYGjANh9eTcXMy7WeAzV3Xwl6i5JEDVAq9PydezXAPRr1g8PBw+FIxJKatmwJT18eqBHz9exX6PXy+Q0UTtJgqgBuy7t4nL2ZRytHRnUcpDS4Yha4Jm2z2CltuLk9ZPEXI255+eyoJ6oDaQPwsxyi3JZG19c62FIqyE42jgqHJGoDTwcPOjfvD+bEjaxKnYVHRt3RKPWALKgnqg95AnCzDYnbCajIAMvBy8eafaI0uGIWmRQy0E4WjtyOfsyuy7tAmRBPVG7WNwTxKJFi/jwww9JTU0lODiYhQsXEhoaWur+3333HW+88QYXL16kVatWvP/++/Tv39+8Qer1UJTHjbybbEnYBHodw1sNwVpbBNoi815bWIwGqBnSYgBRp79h7elv6da4M+9tOoYNBUb3VwHvbTrGI61cpGNZlGRlB2YYNm9RCWLt2rVERESwZMkSunXrxvz58wkPDyc+Ph4Pj3s7fvft28ewYcOYO3cujz/+OKtXr2bQoEEcPXqU9u3bmy/QojxY1pd1mlzyNQW01mkI/el17p2iJOq7R9Cz1TqbNJWOH74OZ0FeIdiUcUAe5H7eAEdbi/rTFeb23FawNj4zvyosqonp448/Zvz48YwdO5agoCCWLFmCg4MDy5YtM7r/p59+St++fXn55ZcJDAzkrbfeokuXLnz22WdmjzVJpWWXpvib4EitHSpJDsIIa1QM1xZPXNtqXUimuvzO6CLpsBb/T2904ZTqYzFfQwoKCjhy5AiRkZGGbWq1mj59+hAdHW30mOjoaCIiIkpsCw8PZ8OGDaVeJz8/n/z8fMP7zMxM04O1suObDuHor/1BN8/7aNP5X6afQ9Qb3fR6Wh94m2Nppxl/0xfdtZ5l7r+yXyjdWsiaSfVdobaQmQfe5gGfMB5VazDHqm4WkyCuXbuGVqvF09OzxHZPT09Onz5t9JjU1FSj+6emppZ6nblz5zJnzpwqxXr21jlirp/ESm3D0MBRRCfdllmrolQqYGS70Zy5NRMbl7NkZXRBV9jY6H5eLnaEtPIB+R2q97Ymbed8ViI3EzPp08I8/aoWkyBqSmRkZImnjszMTHx9Tav21tK1JTNCZ/C/uFP8/TMZrijK18atDd18upGZt5fz2fsoSHuiROOBLKgn7pZdkM2P534E4Jk2z2CrsTXLdSymD6Jx48ZoNBrS0tJKbE9LS8PLy8voMV5eXibtD2Bra4uzs3OJl6lUKhVp6V4s/V9DGa4oKmx42+G4OdjToulV3BuXfMqVBfXE3dafXU9OYQ7+Tv78renfzHYdi0kQNjY2dO3alR07dhi26XQ6duzYQViY8WWzw8LCSuwPsG3btlL3ry5S/1dUhlcDLx5t9ijO9tY8dN9pvnk+lE+f6cS34+9n76u9JTkIAFJzUvnl4i8AjAwaiVplvtu4xSQIgIiICJYuXUpUVBRxcXFMnDiRnJwcxo4dC8Czzz5bohN7ypQpbN26lXnz5nH69Glmz57N4cOHmTx5slnjlPq/orIGtxqMg5UDSVlJFNqekgX1xD2+Pf0tRfoiOrl3oqN7R7Ney6ISxNChQ/noo4+YOXMmnTp1IiYmhq1btxo6opOSkkhJ+bPppnv37qxevZovvviC4OBgvv/+ezZs2GDeORBI/V9ReU42TgxuNRiANfFryNfml3OEqE/ib8SzP2U/atSMCBxh9uup9LKUZJkyMzNxcXEhIyOjwv0R0QnXGbZ0f7n7fTv+fsICGlU1RFHHFGoLmbZrGldzr/JMm2d4stWTSodUKVqdXupOVCO9Xs/MfTM5c/MMPX17MjG48rVEKnpfk1FMZiD1f0V5yrp5WmusGdZ2GAuOLWDDuQ309uuNi62LwhGbRhYcrH4HUg9w5uYZbDW2DG0ztEauaVFNTJZC6v+Ksmw9mcID7//KsKX7mbImhmFL9/PA+7+WGNkW5hNGgEsAedo81sWvUzBa08mCg9WvUFfIN3HfADAgYABudjXz5VIShJlI/V9hTEVvnmqVmlFBxZXnfk36lUtZl2o81sqQEXzm8cvFX0i/nU5D24YMaDGgxq4rTUxmJPV/xd3Ku3mqKL55PhLkhUatIrBRIKFeoRxMPciq2FVEdos0cmTtYsoIPul/q5jsgmx+OPsDAE+3eRo7K7tyjqg+8gRhZlL/V9xRmeHPw9sOR6PSEHM1hj+u/lEDUVaNjOCrfuvPrie7MBs/Jz96+vas0WtLghCihlTm5unt6E14s3AAVsWuQqfXmSW26uLhVLFvtxXdr76ryUlxxkiCEKKGVPbmObjVYBpYNyAxK9FQea62ujOCr7TnZBXFo5lkBF/FrI5bbZgUF+weXOPXlwQhRA2p7M3z7slza+PXkluUa95Aq0BG8FWf2OuxHEg9gBo1I4NGKhKDJAghakhVbp7hzcLxcvDiVv4tNp3bZN5Aq0hG8FWdTq9jVewqAHr79cbXybQVpauLzKQuR2VmUgtRlspOIjuYcpB5R+ZhrbZmfq/5NLa/t2ZEbSIzqSvvt8u/sShmEXYaOxb0XlDtEyVlJrUQtVRlhz/f53UfgW6BxN2I49vT3/KvWl6p8M4IPmGavKI8Vp9eDRT3Pyk5i16amIRQQGWGP6tUKp4NehYVKvZe2cvZm2drIFJR0zaf38zNvJt42HvQv7l5KsVVlCQIISxIC9cWPNT0IQCiTkUhLcR1y/Xc64Y+puGBw7HWmKPSdMVJghDCwgxtOxQ7jR1nb51lX/I+pcMR1ejb099SoCugrVtb7ve+X+lwJEEIYWnc7NwY1HIQAN/EfSM1I+qIMzfPsOfKHoDipkSV8h36JieI0aNH89tvv5kjFiFEBT3W4jEa2zfmet51NidsVjocUUU6vY6oU1EA9GzakwDXAIUjKmZygsjIyKBPnz60atWKd999lytXrpgjLiFEGWw0NoxoW1xRbFPCJq7lXlM4IlEVe6/s5dytc9hp7Him7TNKh2NgcoLYsGEDV65cYeLEiaxdu5ZmzZrRr18/vv/+ewoLC80RoxDCiDCfMNq6tSVfm2+oFSAsT25RrmFY66CWg2ho11DhiP5UqT4Id3d3IiIiOH78OAcOHKBly5aMGjUKHx8fpk2bxtmzMvxOiLJodXqiE66zMeYK0QnXK1UfQaVSMabdGFSo2Je8j7jrcWaIVJjbxnMbDcNaH2/xuNLhlFClTuqUlBS2bdvGtm3b0Gg09O/fnxMnThAUFMQnn3xSXTEKUadUpKJcRTV3aU5vv94ALD+1vNav9ipKSs1JZfP54j6kUUGjFB/W+lcmJ4jCwkLWr1/P448/jr+/P9999x1Tp04lOTmZqKgotm/fzrp163jzzTfNEa8QFs0c5TiHthmKg5UDiZmJ/Jr0a3WFKmrAytiVFOmK6NC4A/d53ad0OPcwOUF4e3szfvx4/P39OXjwIIcPH2bChAkl1vPo1asXrq6u1RmnEBbPXOU4XWxd+HvrvwOw5vQasguyqxaoqBEx6TEcSTuCRqUpbiqsBcNa/8rkBPHJJ5+QnJzMokWL6NSpk9F9XF1duXDhQlVjE6JOqUxFuYoKbxZOU8emZBVmsSZ+TRWiFDWhUFfIilMrAOjbrC9NnZoqG1ApTE4Qo0aNws5OqkEJYSpzluO0UlsxrsM4ALYnbud8xnmTzyFqzk/nfyIlJwUXmz+f/mojmUktRA0xdznOoEZB9PDpgR49y09Kh3VtdS33Gt+fWU9OfhEt7fpyPCm3UqPYaoIkCCFqSE2U4xwROAI7jR1nbp7ht8uy4kFtNHv3f/jjyjXOXnLhPz/ZVGkUm7lJghCihtREOc5G9o0MTRar4lZJh3Uts2T/Dn69uI9CrZ6C6w9x51++KqPYzEkShBA1qCbKcfZr3q+4w7ogi29Pf1vl84nqkVdYwJKYrwAozAxGX/hnRcCqjGIzJ6koJ0QNq2xFuYqyUlvxfIfnmR09m+1J23nI9yFaN2xdLecWlffpwdUUcAO9tgGFN7vd8/O7R7HVlkp88gQhhAIqU1HOFIGNAunZtCcAX574Eq1OW63nF6ZJzk5md/J/ASi48SDobUrdtzKj2MzFYhLEjRs3GDFiBM7Ozri6ujJu3Diys8tuX+3ZsycqlarEa8KECTUUsRDKGh44HEdrRxIzE/npwk9Kh1Nv6fV6vjrxFWq1Dm2uP9qclmXuX9lRbOZgMQlixIgRnDp1im3btrFlyxZ+++03XnjhhXKPGz9+PCkpKYbXBx98UAPRCqE8F1sXhgcOB+C7M9+Rfjtd4Yjqpz1X9nDy+klc7O1xLXgUVSnj2KpjFFt1s4gEERcXx9atW/nyyy/p1q0bDzzwAAsXLmTNmjUkJyeXeayDgwNeXl6G191LgghR1/Xy7UWgWyD52ny+OvGV1LCuYVkFWXwd+zUAf281hDmPdQfMN4qtullEgoiOjsbV1ZWQkBDDtj59+qBWqzlw4ECZx37zzTc0btyY9u3bExkZye3bt8vcPz8/n8zMzBIvISyVWqVmfMfxWKmtiLkaQ3RytNIh1StRp6LILMikqWNTHg94vEZGsVUnixjFlJqaioeHR4ltVlZWuLm5kZqaWupxw4cPx9/fHx8fH/744w9effVV4uPj+eGHH0o9Zu7cucyZM6faYhdCaU0cm/Bkyyf57sx3rDi1gg7uHXCycaqx62t1erON2KrNYtJj2HNlDypUTAiegLW6eClvc49iq06KJogZM2bw/vvvl7lPXFzli6Dc3UfRoUMHvL29efjhh0lISCAgwHjN18jISCIiIgzvMzMz8fX1rXQMQtQGAwMGEp0czeXsy0SdimJy58k1ct2tJ1OYszm2xCKF3i52zBoQVOu+LVen3KJclp5YChTPS2nVsFWJn98ZxVbbKZogpk+fzpgxY8rcp0WLFnh5eZGeXrKDraioiBs3buDl5VXh63XrVjz2+Ny5c6UmCFtbW2xtbSt8TiEsgbXGmgnBE5j5+0z2XNlDd5/udPHsYtZr3ql98ddejzuzhmtjk0p1WXt6Lddyr+Fh78HQNkOVDqfSFE0Q7u7uuLu7l7tfWFgYt27d4siRI3Tt2hWAX3/9FZ1OZ7jpV0RMTAxQXNNCiPqmVcNW9G/Rny3nt/DFiS+Y5zaPBtYNzHKt8mpfqCieNfxIkFetbFqpitjrsWy9uBWA5zs+j53VvcNWLaXZzSL6IAIDA+nbty/jx49nyZIlFBYWMnnyZJ555hl8fHwAuHLlCg8//DArV64kNDSUhIQEVq9eTf/+/WnUqBF//PEH06ZN429/+xsdO3ZU+BMJoYyn2zzN4dTDpN5O5evYr5kQbJ55QabUvlCiqcVcN+i8ojwWH1+MHj29fXsT7B58zz6W1OxmEaOYoHg0Utu2bXn44Yfp378/DzzwAF988YXh54WFhcTHxxtGKdnY2LB9+3YeffRR2rZty/Tp0xkyZAibN29W6iMIoThbjS3/CP4HADsv7SQmPcYs1zFn7Yuqqs6a4H+1+vRq0m+n08iuEaOCRhm9dnWXnDUnlV4GRpcpMzMTFxcXMjIyZA6FqDOiTkXx04WfaGjbkA8f+rDaRzVFJ1xn2NL95e737fj7a/QJorR+kTvPDlXpFzl17RRv7n8TgNe6vUZH95ItFVqdngfe/7XUJysVxcNd977a2+zNTRW9r1nME4QQovoMazuMJo5NuJl/k2Unl1X7+Wui9oWpzFUTHOB24W3+c/w/APTx63NPcgDzlpw1F0kQQtRDNhobJnWahBo1+5L3se/Kvmo9f03UvjCVOW/QX538imu51/B08GRk0Eij+9TmZrfSSIIQop4KcA1gcOvBAHx58kuu5V6r1vPXtlnD5rpB/37ld/Ze2YsaNZM6TcLeyt7ofuYuOWsOFjGKSQhhHk+2fJKY9BjO3TrHZ8c+Y2bYTNSq6vveWJtmDZvjBn0t9xpfnvgSgCdbPUkbtzal7nun2S01I89oM9edPghZrE8IUStYqa34V+d/YaexI+5GHD+e/bHar2Hu2hcVVd39IlqdloXHFnK76DYtXVsyuNXgMvevjc1u5ZEEIUQ959XAi3EdxgHw/Znvib8Rr3BE5lHdN+jvznzH6RunsdPY8a/O/8JKXX6DTG1rdiuPDHMthwxzFfXFZ8c+Y8+VPTSya8R7f3sPZ5u6+fteHRPVTlw9wTsH3kGPnimdp9C9SXeTYlB6JnVF72uSIMohCULUF7lFuUTuiSQlJ4Vg92BmhM6o1v6I2qQqN+hbebd45bdXyCjI4GG/h3mhY/mFy2obmQchhDCJvZU9EV0jsFHbcPzqcTac26B0SGZT2X6RIl0R84/OJ6MgA18nX0a3G23mSJUlCUIIYeDn7MfzHZ4HYF38Ov64+ofCEdUu38R9Q9yNOOw0dkR0jcBWU7dXfpYEIYQo4SHfh+jl2ws9ej49+impOaUX5apP9l7Zy08XfgJgcufJ+Dj6KByR+UmCEELcY1z7cbR0bUl2YTYfHvqQ3KLcSp9Lq9MTnXCdjTFXiE64XqmlLJR2IeMCnx//HCieO3Kf130KR1QzZKKcmSk9WkGIyrDWWDM9ZDr/3vNvLmdf5rNjnzE9ZLrJndaWtLR1aW7k3eCDQx9QoCsg2D2Yp9s8rXRINUZGMZWjKqOY6sIfh6jfztw8w5zoORTpingi4AlGBI6o8LHmXDm1puRr85m9bzbnM87TxLEJb/V4y2xFlmqSjGJSmKWt+y6EMa0btuYfHYvrR2xK2MTPF3+u0HHmXDm1puj0OhYdW8T5jPM42Tjx6n2v1onkYApJEGZQF/44hLjjb03/xtOti5tVVpxcweHUw+UeY4lLW99Nr9ez4tQKDqQewEptxUshL+HZwFPpsGqcJAgzsPQ/DiH+anCrwfTy7YUOHZ8e/bTc5TgscWnru/1w9gd+vvgzKlRMCp5EW7e2SoekCEkQZmDpfxxC/JVKpeL5Ds/Tyb0TBboC5h6cy/mM86Xub4lLW9+xLXEb686sA2BM+zEmL6NRl0iCMANL/uMQojRWaisiQiIIdAsktyiXd/e/y6WsS0b3rY0V5SpiZ9JOvjrxFQBDWg2hb7O+CkekLEkQZmCpfxxClMdWY8uroa/S0rUlWYVZvBX9ltEkYYlLW+9M2snnf3yOHj19m/XlqdZPKR2S4iRBmIEl/nEIUVH2VvZEhkbi7+xPRkEGc/bN4fyte5ubLGlp6x1JO0okhzHtxqBSyd+nzIMoh8yDEMK47IJs5h6cy7lb5wxJw1hFtdo8WVSv17Ph3AbWxK8BqDfJQZb7riZVXe67Nv9xCFFVtwtv88GhD4i7EYe12prJnSdzv/f9SodVITq9jqhTUWy9uBWAgQEDGdZ2WJ1PDiAJotpIPQghypavzWf+kfkcTT8KwPC2w3ki4IlafaO9XXibRTGLOJxWPKdjdNBo+rfor3BUNUdmUgshaoStxpaX73uZfs36AbD69GoWxSwir6h2DuNOyU7h9d9f53DaYazUVkzpPKVeJQdTyGJ9QogqU6vUjGk/Bi9HL6JORrHnyh4uZFxgatep+Dr5Kh2ewf6U/Sw5voTcolzc7NyY3nU6LRu2VDqsWkuamMohTUxCmCb2eiwLji7gZv5NbDW2jAwcSR//PoqWL71deJvlp5bz2+XfAGjTsA0RXSNwtXNVLCYlSR9ENZEEIYTpMvIzWHhsISeunQAg0C2Qf3T8B96ONTt6T6/XcyTtCCtOreBq7lXUqBnYciBDWg/BWm1do7HUJpIgqokkCCEqR6fX8fPFn1lzeg152jys1db0a96PgQEDcbRxNPv1U7JTiIqN4lj6MQA8HDyY3Gmy0aG49U2d66R+55136N69Ow4ODri6ulboGL1ez8yZM/H29sbe3p4+ffpw9uxZ8wYqhACK+yX6Ne/Hhw99SLB7MIW6QjYlbGLKzilsPLeR24W3zXLd5OxkFscsZvru6RxLP4aVyoqBAQP54G8fSHIwkcU8QcyaNQtXV1cuX77MV199xa1bt8o95v3332fu3LlERUXRvHlz3njjDU6cOEFsbCx2dhVbB0meIISoOr1ez9H0o6yOW83l7MsA2GnseLDpg4T7h+PrXLWO7CJdETHpMey+vJtDqYfQ///C+p3cOzGm3Zgab9qq7epsE9OKFSuYOnVquQlCr9fj4+PD9OnTeemllwDIyMjA09OTFStW8Mwzz1ToepIghKg+Or2OPZf3sClhkyFRAPg08CHEK4RO7p1o7tIcB2uHcs91K+8Wp66f4tT1UxxKPURmQabhZ109uzK45WAZoVSKit7X6uww1wsXLpCamkqfPn0M21xcXOjWrRvR0dEVThBCiOqjVql5yPch/tb0b5y6foqtF7Zy7OoxknOS2ZSwiU0JmwDwbuCNp4MnLrYuONs4o0dPoa6QvKI80m6nkZKdQkZBRolzu9i40KNJD3r79q7yE4koVmcTRGpqKgCeniWrQHl6ehp+Zkx+fj75+fmG95mZmaXuK4SoHJVKRfvG7WnfuD23C28Tkx7DobRDnLl5hmu510jJSSElp+yyvCpU+Dv7065ROzq6d6RD4w5o1Joa+gT1g6IJYsaMGbz//vtl7hMXF0fbtjVXzWnu3LnMmTOnxq4nRH3nYO1A9ybdDYV5MvIzuJh5kRu5N8goyCCrIAu1So212hprtTUeDh54NfDCu4F3hZqiROUpmiCmT5/OmDFjytynRYsWlTq3l5cXAGlpaXh7/9lBlZaWRqdOnUo9LjIykoiICMP7zMxMfH3lcVWImuJi60Kwe7DSYQgUThDu7u64u7ub5dzNmzfHy8uLHTt2GBJCZmYmBw4cYOLEiaUeZ2tri62trVliEkIIS2Ix8yCSkpKIiYkhKSkJrVZLTEwMMTExZGdnG/Zp27YtP/74I1Dcxjl16lTefvttNm3axIkTJ3j22Wfx8fFh0KBBCn0KIYSwHBbTST1z5kyioqIM7zt37gzAzp076dmzJwDx8fFkZPw5suGVV14hJyeHF154gVu3bvHAAw+wdevWCs+BMIVWq6WwsLDazytEbWFjY4NabTHfKUU1sLh5EDWtvPHCer2e1NTUCk3cE8KSqdVqmjdvjo2NjdKhiCqq9/Mgasqd5ODh4YGDg0OtLpIiRGXpdDqSk5NJSUnBz89Pfs/rCUkQVaDVag3JoVGjRkqHI4RZubu7k5ycTFFREdbW9Xcl1PpEGhSr4E6fg4ODjMUWdd+dpiWtVqtwJKKmSIKoBvK4LeoD+T2vfyRBCCGEMEoShDCLXbt2oVKpTBrd1axZM+bPn2+2mIQQppEEUQ+NGTMGlUrFhAkT7vnZpEmTUKlU5S6BUlvk5eUxadIkGjVqhKOjI0OGDCEtLa3MYypSSOqJJ57Az88POzs7vL29GTVqFMnJyeb8KELUOpIg6ilfX1/WrFlDbm6uYVteXh6rV6/Gz89PwchMM23aNDZv3sx3333H7t27SU5OZvDgwWUe88EHH7BgwQKWLFnCgQMHaNCgAeHh4eTl5Rn26dWrF+vWrSM+Pp7169eTkJDA3//+d3N/HCFqFUkQ1Umvh8JcZV4mznfs0qULvr6+/PDDD4ZtP/zwA35+foZZ6nfk5+fz4osv4uHhgZ2dHQ888ACHDh0qsc9PP/1E69atsbe3p1evXly8ePGea+7du5cHH3wQe3t7fH19efHFF8nJyTEp7rtlZGTw1Vdf8fHHH9O7d2+6du3K8uXL2bdvH/v37zd6jF6vZ/78+bz++usMHDiQjh07snLlSpKTk9mwYYNhv2nTpnH//ffj7+9P9+7dmTFjBvv375fZ8qJekXkQ1akoD5b1Vebaz20Fa3vTDnnuOZYvX86IESMAWLZsGWPHjmXXrl0l9nvllVdYv349UVFR+Pv788EHHxAeHs65c+dwc3Pj0qVLDB48mEmTJvHCCy9w+PBhpk+fXuIcCQkJ9O3bl7fffptly5Zx9epVJk+ezOTJk1m+fLnR+MaMGcPFixfvieeOI0eOUFhYWKIoVNu2bfHz8yM6Opr777//nmMqU0jqxo0bfPPNN3Tv3l3G/4t6RZ4g6rGRI0eyd+9eEhMTSUxM5Pfff2fkyJEl9snJyWHx4sV8+OGH9OvXj6CgIJYuXYq9vT1fffUVAIsXLyYgIIB58+bRpk0bRowYcU8fxty5cxkxYgRTp06lVatWdO/enQULFrBy5coSTTt38/b2LrO5KzU1FRsbG1xdXUtsL6solCmFpF599VUaNGhAo0aNSEpKYuPGjaXGIkRdJE8Q1cnKrvibvFLXNpG7uzuPPfYYK1asQK/X89hjj9G4ceMS+yQkJFBYWEiPHj0M26ytrQkNDSUuLg4oLurUrVu3EseFhYWVeH/8+HH++OMPvvnmG8M2vV6PTqfjwoULBAYG3hPf3LlzTf5M1enll19m3LhxJCYmMmfOHJ599lm2bNki8wFEvSEJojqpVCY38yjtueeeY/LkyQAsWrTIbNfJzs7mH//4By+++OI9P6tsp7iXlxcFBQXcunWrxFNEWlqaoWCUsWPu7FNeIanGjRvTuHFjWrduTWBgIL6+vuzfv/+e5CdEXSVNTPVc3759KSgooLCwkPDw8Ht+HhAQgI2NDb///rthW2FhIYcOHSIoKAiAwMBADh48WOK4v3YSd+nShdjYWFq2bHnPq7Krg3bt2hVra2t27Nhh2BYfH09SUlKpN/G7C0ndcaeQVFk3fp1OB1CiXrkQdZ0kiHpOo9EQFxdHbGwsGs29Bd8bNGjAxIkTefnll9m6dSuxsbGMHz+e27dvM27cOAAmTJjA2bNnefnll4mPj2f16tWsWLGixHleffVV9u3bx+TJk4mJieHs2bNs3LjR8PRiTGRkJM8++2ypP3dxcWHcuHFERESwc+dOjhw5wtixYwkLCyvRQW1qIakDBw7w2WefERMTQ2JiIr/++ivDhg0jICBAnh5EvSJNTKLM9eAB3nvvPXQ6HaNGjSIrK4uQkBB+/vlnGjZsCBQ3Ea1fv55p06axcOFCQkNDeffdd3nuuecM5+jYsSO7d+/mtdde48EHH0Sv1xMQEMDQoUNLvW5KSgpJSUllxvbJJ5+gVqsZMmQI+fn5hIeH85///KfEPqYWknJwcOCHH35g1qxZ5OTk4O3tTd++fXn99delHK2oV6RgUDnKKqyRl5fHhQsXaN68uVmq1AlRm8jve91R0YJB0sQkhBDCKEkQQgghjJIEIYQQwihJEEIIIYySBCGEEMIoSRBCCCGMknkQQgiz0Or0HLxwg/SsPDyc7Aht7oZGLetYWRJJEEKIarf1ZApzNseSkvHnSr3eLnbMGhBE3/beZRwpahNpYhJCVKutJ1OYuOpoieQAkJqRx8RVR9l6MkWhyISpJEEIizJ79ux7Vl1VSs+ePZk6dapZr9GsWTPmz59v8nFvvPEGL7zwQoX3X7JkCQMGDDD5On+l1emZszkWY8sz3Nk2Z3MsWp0s4GAJJEHUU6mpqUyZMoWWLVtiZ2eHp6cnPXr0YPHixdy+fVvp8Cpl9uzZqFSqMl+VsWvXLlQqFbdu3aregCvg0KFDJt3oofjf9tNPP+W1116r8DHPPfccR48eZc+ePaaGWMLBCzfueXK4mx5Iycjj4IUbVbqOqBmSIOqh8+fP07lzZ3755Rfeffddjh07RnR0NK+88gpbtmxh+/btpR5bm2syv/TSS6SkpBheTZs25c033yyx7W4FBQUKRVpx7u7uODg4mHTMl19+Sffu3fH396/wMTY2NgwfPpwFCxaYGmIJ6VmlJ4fK7CeUJQmiGun1evKK8hR5mbLm4j//+U+srKw4fPgwTz/9NIGBgbRo0YKBAwfy3//+t0RTg0qlYvHixTzxxBM0aNCAd955B/izzKiNjQ1t2rTh66+/Nhxz8eJFVCoVMTExhm23bt1CpVIZ6kvf+Va+Y8cOQkJCcHBwoHv37sTHx5eI9b333sPT0xMnJyfGjRtXanlSAEdHR7y8vAwvjUaDk5OT4f0zzzzD5MmTmTp1Ko0bNyY8PLzcWC9evEivXr0AaNiwISqVqkQ5VZ1OxyuvvIKbmxteXl7Mnj27wv8OUPw7M3v2bPz8/LC1tcXHx6dEUaW/NjGpVCq+/PJLnnzySRwcHGjVqhWbNm0qcc41a9aU+De8evUqXl5evPvuu4Zt+/btw8bGpkRdjAEDBrBp0yZyc3NN+gx383Cq2CJ+Fd1PKMtiRjG98847/Pe//yUmJgYbG5sKPe6PGTOGqKioEtvCw8PZutU8ZUHztfmM3jraLOcuT1TfKOwqUHb0+vXrhieHBg0aGN3nr00xs2fP5r333mP+/PlYWVnx448/MmXKFObPn0+fPn3YsmULY8eOpWnTpoabaUW99tprzJs3D3d3dyZMmMBzzz1nKE60bt06Zs+ezaJFi3jggQf4+uuvWbBgAS1atDDpGneLiopi4sSJJQoglcXX15f169czZMgQ4uPjcXZ2xt7+z6qBUVFRREREcODAAaKjoxkzZgw9evTgkUceAYp/By9evGhIjH+1fv16PvnkE9asWUO7du1ITU3l+PHjZcY0Z84cPvjgAz788EMWLlzIiBEjSExMxM3NjRs3bhAbG0tISIhhf3d3d5YtW8agQYN49NFHadOmDaNGjWLy5Mk8/PDDhv1CQkIoKiriwIED9OzZs0L/f/4qtLkb3i52pGbkGe2HUAFeLsVDXkXtZzEJoqCggKeeeoqwsDC++uqrCh/Xt29fli9fbnhf39fzP3fuHHq9njZt2pTY3rhxY8O380mTJvH+++8bfjZ8+HDGjh1reD9s2DDGjBnDP//5TwAiIiLYv38/H330kckJ4p133uGhhx4CYMaMGTz22GPk5eVhZ2fH/PnzGTdunKEw0dtvv8327dvLfIooT6tWrfjggw8M7y9evFjm/hqNBje34puZh4dHidKmUFznYtasWYZzf/bZZ+zYscOQILy9vQ3V6IxJSkrCy8uLPn36YG1tjZ+fH6GhoWXGNGbMGIYNGwbAu+++y4IFCzh48CB9+/YlKSkJvV6Pj49PiWP69+/P+PHjGTFiBCEhIdg7OPDqG3PIziuiga0GlUqFg4MDLi4uJCYmlnn9smjUKmYNCGLiqqOooESSuPO1Y9aAIJkPYSEsJkHMmTMH4J5KZeWxtbUttT5xdbPV2BLVN6r8Hc107ao4ePAgOp2OESNG3FNW8+5vowBxcXH3dJz26NGDTz/91OTrduzY0fDfd2pEp6en4+fnR1xcHBMmTCixf1hYGDt37jT5Ond07dq10scac3f8UPwZ0tPTDe/nzp1b5vFPPfUU8+fPp0WLFvTt25f+/fszYMAArKxK/9O8+5oNGjTA2dnZcM07zUPG6jV89NFHBLVrx7rvvmPNf3eSlqOFnGysNWp8XO1wsbfB3t6+yoMU+rb3ZvHILvfMg/CSeRAWx2ISRGXt2rULDw8PGjZsSO/evXn77bdp1KhRqfvn5+eXuEFmZmZW+FoqlapCzTxKatmyJSqV6p62/jvNNnc3n9xRWlNUadTq4q6tu/tFSuvctra2Nvz3naatsr5xV9VfP4spsRpzd/xQ/BlMid/X15f4+Hi2b9/Otm3b+Oc//8mHH37I7t277zl3Ra7ZuHFjAG7evIm7u3uJ/WJOnSYlOQW9TseVy0m0CmwHQKFWR+L12/g3ghs3btxzXGX0be/NI0FeMpPawtXpTuq+ffuycuVKduzYwfvvv8/u3bvp168fWq221GPmzp2Li4uL4eXr61uDEZtfo0aNeOSRR/jss8/Iycmp1DkCAwPvacP//fffCQoKAjDcYO4eNXR3J7Ap1zlw4ECJbfv37zf5PGWpSKw2NjYAZf7eVIW9vT0DBgxgwYIF7Nq1i+joaE6cOFGpcwUEBODs7ExsbGyJ7fn5+YwdPZrwAU8y6aV/M+eVKVy/drXEPvtjYsnLy6Nz586V/ix306hVhAU0YmCnJoQFNJLkYIEUTRAzZswod9z66dOnK33+Z555hieeeIIOHTowaNAgtmzZwqFDh0rtMASIjIwkIyPD8Lp06VKlr19b/ec//6GoqIiQkBDWrl1LXFwc8fHxrFq1itOnT6PRaMo8/uWXX2bFihUsXryYs2fP8vHHH/PDDz/w0ksvAcU3vPvvv5/33nuPuLg4du/ezeuvv25ynFOmTGHZsmUsX76cM2fOMGvWLE6dOlWpz1yaisTq7++PSqViy5YtXL16lezs7AqfPzIykmeffbbUn69YsYKvvvqKkydPcv78eVatWoW9vb1JQ1Tvplar6dOnD3v37i2x/ZUZ/yYrK4NX33yPsf+cin/zAGa9NLnEPgej99G8eQsCAgIqdW1R9yiaIKZPn05cXFyZr6qMWPmrFi1a0LhxY86dO1fqPra2tjg7O5d41TUBAQEcO3aMPn36EBkZSXBwMCEhISxcuJCXXnqJt956q8zjBw0axKeffspHH31Eu3bt+Pzzz1m+fHmJkS/Lli2jqKiIrl27MnXqVN5++22T4xw6dChvvPEGr7zyCl27diUxMZGJEyeafJ7ylBdrkyZNmDNnDjNmzMDT05PJkyeXcqZ7paSkkJSUVOrPXV1dWbp0KT169KBjx45s376dzZs3l9kMWp7nn3+eNWvWGJqddu3axX8+W8A785fg6OSMWq3mnU8/59jBaNat/HPAx/82rmfUmOcqfV1LotXpiU64zsaYK0QnXJeZ3aVQ6U0ZQF8LrFixgqlTp1ZqVuvly5fx8/Njw4YNPPHEExU6pqzi3lLEXdRGer2ebt26MW3aNMNop+y8Is5fK/3J51x8HOOfGcgfJ+No4mk8OdWV33dZSLDs+9rdLKYPIikpiZiYGJKSktBqtcTExBATE1Picb9t27b8+OOPAGRnZ/Pyyy+zf/9+Ll68yI4dOxg4cCAtW7YkPDxcqY8hhNmpVCq++OILioqKDNsa2Gqw1pT+534tPY33FizBx6Nuz0+QhQRNYzGjmGbOnFli0tudjrSdO3camjbi4+PJyMgAisev//HHH0RFRXHr1i18fHx49NFHeeutt+r9XAhR93Xq1KnEooYqlQofVzsSrxsfwnr/gz3xb+RQ6fWqLEF5CwmqKF5I8JEgL+lQ/38WkyBWrFhR7hyIu1vL7O3t+fnnn80clRCWw8XeBv9GkHwrj0Ltn0Nx754HUZeZspBgWEDl+4DqEotJEEKIqnOxt8HZzpqcfC1FOh1WarVhJnVdJwsJmk4SRDWwsH5+Uc+pVCoc7Uz/07f033NZSNB0FtNJXRvdmdFqqfUThDDFneXRy5snU1vdWUiwtGclFcWjmWQhwT/JE0QVaDQaXF1dDevgODjU7U4+UX/pdDquXr2Kg4NDmetE1WaykKDpLPNfuha5sxDg3Qu0CVEXqdVq/Pz8LPpLkCwkaBqLmyhX0yo6oUSr1dbqamtCVJWNjY1hcUNLp9Xp6/VCghW9r8kTRDXRaDQW2zYrRH1zZyFBUba68XVACCFEtZMEIYQQwihJEEIIIYySPohy3OnDN6WynBBC1GZ37mfljVGSBFGOrKwsgDpXWU4IIbKysnBxcSn15zLMtRw6nY7k5GScnJxMGv+dmZmJr68vly5dspiiQxJzzbC0mC0tXpCYy6PX68nKysLHx6fMocvyBFEOtVpN06ZNK328JValk5hrhqXFbGnxgsRclrKeHO6QTmohhBBGSYIQQghhlCQIM7G1tWXWrFkWVb1OYq4ZlhazpcULEnN1kU5qIYQQRskThBBCCKMkQQghhDBKEoQQQgijJEEIIYQwShKEGSxatIhmzZphZ2dHt27dOHjwoNIhlem3335jwIAB+Pj4oFKp2LBhg9IhlWnu3Lncd999ODk54eHhwaBBg4iPj1c6rDItXryYjh07GiZBhYWF8b///U/psEzy3nvvoVKpmDp1qtKhlGr27NmoVKoSr7Zt2yodVrmuXLnCyJEjadSoEfb29nTo0IHDhw8rHZYkiOq2du1aIiIimDVrFkePHiU4OJjw8PBaXZI0JyeH4OBgFi1apHQoFbJ7924mTZrE/v372bZtG4WFhTz66KPk5OQoHVqpmjZtynvvvceRI0c4fPgwvXv3ZuDAgZw6dUrp0Crk0KFDfP7553Ts2FHpUMrVrl07UlJSDK+9e/cqHVKZbt68SY8ePbC2tuZ///sfsbGxzJs3j4YNGyodGuhFtQoNDdVPmjTJ8F6r1ep9fHz0c+fOVTCqigP0P/74o9JhmCQ9PV0P6Hfv3q10KCZp2LCh/ssvv1Q6jHJlZWXpW7Vqpd+2bZv+oYce0k+ZMkXpkEo1a9YsfXBwsNJhmOTVV1/VP/DAA0qHYZQ8QVSjgoICjhw5Qp8+fQzb1Go1ffr0ITo6WsHI6raMjAwA3NzcFI6kYrRaLWvWrCEnJ4ewsDClwynXpEmTeOyxx0r8XtdmZ8+excfHhxYtWjBixAiSkpKUDqlMmzZtIiQkhKeeegoPDw86d+7M0qVLlQ4LkCamanXt2jW0Wi2enp4ltnt6epKamqpQVHWbTqdj6tSp9OjRg/bt2ysdTplOnDiBo6Mjtra2TJgwgR9//JGgoCClwyrTmjVrOHr0KHPnzlU6lArp1q0bK1asYOvWrSxevJgLFy7w4IMPGpbtr43Onz/P4sWLadWqFT///DMTJ07kxRdfJCoqSunQZDVXYdkmTZrEyZMna307M0CbNm2IiYkhIyOD77//ntGjR7N79+5amyQuXbrElClT2LZtG3Z2dkqHUyH9+vUz/HfHjh3p1q0b/v7+rFu3jnHjxikYWel0Oh0hISG8++67AHTu3JmTJ0+yZMkSRo8erWhs8gRRjRo3boxGoyEtLa3E9rS0NLy8vBSKqu6aPHkyW7ZsYefOnVVakr2m2NjY0LJlS7p27crcuXMJDg7m008/VTqsUh05coT09HS6dOmClZUVVlZW7N69mwULFmBlZYVWq1U6xHK5urrSunVrzp07p3QopfL29r7nS0JgYGCtaBqTBFGNbGxs6Nq1Kzt27DBs0+l07NixwyLami2FXq9n8uTJ/Pjjj/z66680b95c6ZAqRafTkZ+fr3QYpXr44Yc5ceIEMTExhldISAgjRowgJiYGjUajdIjlys7OJiEhAW9vb6VDKVWPHj3uGaZ95swZ/P39FYroT9LEVM0iIiIYPXo0ISEhhIaGMn/+fHJychg7dqzSoZUqOzu7xDesCxcuEBMTg5ubG35+fgpGZtykSZNYvXo1GzduxMnJydC/4+Ligr29vcLRGRcZGUm/fv3w8/MjKyuL1atXs2vXLn7++WelQyuVk5PTPf06DRo0oFGjRrW2v+ell15iwIAB+Pv7k5yczKxZs9BoNAwbNkzp0Eo1bdo0unfvzrvvvsvTTz/NwYMH+eKLL/jiiy+UDk2GuZrDwoUL9X5+fnobGxt9aGiofv/+/UqHVKadO3fqgXteo0ePVjo0o4zFCuiXL1+udGileu655/T+/v56Gxsbvbu7u/7hhx/W//LLL0qHZbLaPsx16NChem9vb72NjY2+SZMm+qFDh+rPnTundFjl2rx5s759+/Z6W1tbfdu2bfVffPGF0iHp9Xq9Xpb7FkIIYZT0QQghhDBKEoQQQgijJEEIIYQwShKEEEIIoyRBCCGEMEoShBBCCKMkQQghhDBKEoQQQgijJEEIIYQwShKEEEIIoyRBCKGgq1ev4uXlZagFALBv3z5sbGxKrAoshBJkLSYhFPbTTz8xaNAg9u3bR5s2bejUqRMDBw7k448/Vjo0Uc9JghCiFpg0aRLbt28nJCSEEydOcOjQIWxtbZUOS9RzkiC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" + "
" ] }, "metadata": {}, @@ -639,7 +639,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 21.03it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 24.20it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -667,53 +667,53 @@ " 5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,\n", " 5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,\n", " 6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 2.221328\n", - "1 0.571199\n", - "2 6.092786\n", - "3 4.886922\n", - "4 3.807991\n", - "5 0.507732\n", - "6 3.871458\n", - "7 0.444266\n", - "8 3.681058\n", - "9 0.000000, experiment_data= x y\n", - "0 3.554125 -0.152573\n", - "1 0.698132 0.573655\n", - "2 1.586663 1.323718\n", - "3 2.729060 1.162445\n", - "4 1.078931 0.764377\n", - "5 5.331188 -0.931644\n", - "6 1.713596 1.779428\n", - "7 0.825065 1.118309\n", - "8 5.204254 -1.116191\n", - "9 4.950388 -0.700532\n", - "10 3.554125 -0.632639\n", - "11 0.698132 0.409923\n", - "12 1.586663 1.120855\n", - "13 2.729060 -0.555710\n", - "14 1.078931 0.018994\n", - "15 5.331188 -1.095720\n", - "16 1.713596 0.483406\n", - "17 0.825065 0.891715\n", - "18 5.204254 -1.335465\n", - "19 4.950388 -1.677963\n", - "20 2.221328 1.528586\n", - "21 0.571199 0.427753\n", - "22 6.092786 -0.155487\n", - "23 4.886922 -1.697182\n", - "24 3.807991 -0.890350\n", - "25 0.507732 0.541658\n", - "26 3.871458 -1.242266\n", - "27 0.444266 0.617644\n", - "28 3.681058 -0.813997\n", - "29 0.000000 -0.145847, models=[sin(x), sin(x), sin(x)])\n" + "0 3.998391\n", + "1 3.871458\n", + "2 1.967462\n", + "3 2.982926\n", + "4 3.300259\n", + "5 3.934924\n", + "6 1.967462\n", + "7 4.950388\n", + "8 3.807991\n", + "9 5.521587, experiment_data= x y\n", + "0 2.411728 0.915126\n", + "1 5.902386 -0.440795\n", + "2 3.236792 0.228788\n", + "3 3.744525 0.194455\n", + "4 0.063467 -0.053653\n", + "5 1.523196 0.881799\n", + "6 4.315723 -0.132748\n", + "7 4.379190 -0.561283\n", + "8 3.046393 -0.139681\n", + "9 5.204254 -0.610173\n", + "10 2.411728 0.435060\n", + "11 5.902386 -0.604527\n", + "12 3.236792 0.025925\n", + "13 3.744525 -1.523700\n", + "14 0.063467 -0.799035\n", + "15 1.523196 0.717724\n", + "16 4.315723 -1.428770\n", + "17 4.379190 -0.787877\n", + "18 3.046393 -0.358956\n", + "19 5.204254 -1.587605\n", + "20 3.998391 -0.022925\n", + "21 3.871458 -0.779657\n", + "22 1.967462 0.956118\n", + "23 2.982926 -0.554373\n", + "24 3.300259 -0.430193\n", + "25 3.934924 -0.657233\n", + "26 1.967462 0.346858\n", + "27 4.950388 -0.783963\n", + "28 3.807991 -0.918478\n", + "29 5.521587 -0.835926, models=[-0.31, -0.31, -0.31])\n" ] }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, @@ -769,8 +769,191 @@ "name": "stderr", "output_type": "stream", "text": [ - " 12%|█▏ | 12/100 [00:00<00:04, 21.17it/s]" + "100%|██████████| 100/100 [00:04<00:00, 23.38it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 1 model: sin(x)\u001b[0m\n", + "[sin(x)]\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 2:\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:04<00:00, 24.21it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 2 model: sin(x)\u001b[0m\n", + "[sin(x), sin(x)]\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 3:\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:04<00:00, 23.25it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 3 model: sin(x)\u001b[0m\n", + "[sin(x), sin(x), sin(x)]\n" ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 4:\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:04<00:00, 22.62it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 4 model: sin(x)\u001b[0m\n", + "[sin(x), sin(x), sin(x), sin(x)]\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 5:\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:04<00:00, 24.55it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mCycle 5 model: 0.17\u001b[0m\n", + "[0.17, 0.17, 0.17, 0.17, 0.17]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -787,7 +970,6 @@ " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", " s = theorist(experiment_runner(experimentalist(s, num_samples=10)))\n", " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", - " print(s.models)\n", " plot_from_state(s)" ] }, @@ -807,7 +989,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "sin(x)\n" + "0.17\n" ] } ], @@ -848,7 +1030,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 23.78it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 26.12it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -861,9 +1043,9 @@ }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, @@ -888,7 +1070,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 23.25it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 22.26it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -901,9 +1083,9 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" + "
" ] }, "metadata": {}, @@ -928,7 +1110,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 21.79it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 24.07it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -936,14 +1118,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1mCycle 3 model: sin(x)\u001b[0m\n" + "\u001b[1mCycle 3 model: 0.25\u001b[0m\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] }, "metadata": {}, @@ -968,7 +1150,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 21.88it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 24.79it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -981,9 +1163,9 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" + "
" ] }, "metadata": {}, @@ -1008,7 +1190,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 22.23it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 21.43it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1021,9 +1203,9 @@ }, { "data": { - "image/png": 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+ "image/png": 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/Tq9evcjIyMDV1ZU5c+YwdOjQ/E1S3n33XbZs2VJi67409evX57333sv//fz58yUer9Pp8PO7PYYTEBCAr69vgedbtGjBpEmT8s/98ccfs3Xr1vxkHxQUlL82flHu3DTd2dk5f+P0kuRtmg4wbdo05s2bx6FDh+jevbtRm6a3a9dOtU3TJdkLu+Cic2Fp96WqXbs8Dh06hMFgoH///mRmZhZ47s5WItzeYPzuQcPQ0FDmzp1r8nXv3AA8KOh2F1p8fDw1atTg9OnTDB8+vMDxnTp1Yvv27SZfJ0/btm3L/Nqi3Bk/3P4b8jYwB9k0/W6S7IVd0Gg0RnWlqKlevXpoNJpCfeN5XSN3dlHkKa67pzha7e1huDvHEYob2L1zA/C87qOSWsLldfffYkqsRSlpA3NjONqm6TJAK4SFVK5cmW7duvHxxx+TlpZWpnM0bty4UJ/33r17adLkdqVTXrK4s/rlzgFQU65z8ODBAo/dvZF5eRkTq16vByA3N9es186Tt2n6vHnz2LFjB/v37+fEiRNlOtedm6bf6c5N09955x1efPHFAncgYJlN06VlL4QFffLJJ4SGhtKuXTsmT55MixYt0Gq1HD58mD/++KPUro6xY8fyzDPP0Lp1a8LCwli3bh0//vhjfsmmm5sbHTt2ZMaMGdSuXZv4+Hjeeustk+McNWoUgwYNol27doSGhvLNN9/w+++/l2uA9m7GxFqzZk00Gg3r16+nZ8+euLm54enpadT5J0yYwOXLl/nqq6+KfH7JkiXk5ubSoUMH3N3dzbppep8+ffIfv3PTdE9PT37++WeGDBnC+vXr84+xxKbp0rIXwoLq1q3Lr7/+SlhYGBMmTKBly5a0a9eOjz76iDFjxvDOO++U+Po+ffowd+5cPvjgA5o2bcpnn33G4sWLC1RwfPnll+Tk5NC2bVsiIyN59913TY6zb9++TJw4kXHjxtG2bVsuXLjAiBEjTD5PaUqLtVq1akyZMoXXX3+dqlWrMnLkSKPPLZumFyQbjotSWdtmI7LhuLBWFbVpujk2HJduHFEi2WxECOPlbZpuSr+/pTZNl2QvipW32cjdt355m42Ud9arEPbIWjdNlz57USTZbEQI+yLJXhRJNhsRwr5IshdFsoXNRhyotkA4OHN81iXZiyJZ82YjebMYbXXtdyFMlfdZL25mrzFkgFYUKW+zkbikjCL77TXcXppYjc1GdDodvr6++bMQ3d3dy7w5hhDWTFEU0tPTiY+Px9fXF51OV+ZzSbIXRcrbbGTE18fQQIGEbw2bjQQGBgIUmnYuhD3y9fXN/8yXlUyqEiWy9jr73Nxcq97BSYjycnZ2LrFFL5OqRCFlmQlbUZuNmItOpyvXra0QjkKSvYMoTwvdUTcbEcKeSDWOA8ibCXt33XzeTNgNJ2OLeaXxcg0K+6NvsCbqMvujb8hkKyGsjLTs7VxpM2E13J4J261JYJm7Zqy9X18IIS17u1fRM2EtcdcghCg/SfZ2riJnwsr6OULYDkn2dq4iZ8LK+jlC2A7ps7dzFTkT1lrWz7G2zVWEsEaS7O1cRc6E9fdwMetxZSGDw0IYR7pxHED3ZkEsGNCGQJ+CXTWBPq7l24DE2O+HCmpky+CwEMaTlr2DqIiZsNdTM816nCksUVIqhD2RZO9AzD0TVs1lkE0ZHJbZv0LYWDfOrl27CA8PJzg4GI1Gw+rVq9UOyaHlDf4W127WcLv/vCKWQbaWwWEhbIVNJfu0tDRatmzJ/Pnz1Q5F8O/gLxTuls/7fWKvxhyKSTD7MgrWvLmKENbIprpxevToQY8ePdQOwyEYW86YN/h7d0VMoI8rj7UM4p2fThd43NfNmcGhtRj5YP1y9aVb8+YqQlgjm0r2wjKKK2d8q3sIbYPSSUy6SOqtBHINWeTmZuOrdWLBoz5cTtaTThWCAxqQdMtAxPJjhRJx4q1sZm85y+J955nxRPMyVwJZ++YqQlgbu072mZmZZGb+WwmSnJysYjS2Ia+c0UWTQhP3X6nkdhad83Uy9al8digbvZO21ATq/KcWzS0nulb1JiOjOvEZDbiY2RTDHR+3xPRsRnx9rFylnyXdVUidvRAF2XWynz59OlOmTFE7DJsRG3eC7za9T2jwWVJcUlGAlLuOMeRAkJs7XjpXnLRadBodBsVAem4mqblZJBoyyTTkkqnLBvdb4H4Vb47SxqDDPaMyKemNOJ3alQzFC4Xyl0da++YqQlgLm92WUKPRsGrVKvr06VPsMUW17ENCQmRbwjtk3LrJ/qgv2BLzC3/euk5mjiH/OfccPfqMKtzKDCYxqyaxWXVJNfjwv2Gdiy1nzMnOYOX+3SzZsZVKLudxcokl3SWJLG1u/jE6RYNXeiBXUzpw5lb7Es8nhCiZbEsIuLi44OJScVP1bdmN63+y/uAH7Lh2jHQl559HNfhkepOR2oiY9Hu4nhNS5GtLKmd0cnalRrU2nE7PgvSu/5w1hzquxwl0P47B4wJpTpkkesTi4rGaztkb+O1ED9pVH4+zi4e5/0whxD9sKtmnpqZy7ty5/N9jYmKIiorCz8+PGjVqqBiZ7bh69TfWHHyfnTdOkvPPsGZVnQdh1e/Dy/8ZBi+/XOo5SitnzKuUyetHV3AiOqMt0RltIcFAXdcogr32k+ZxmVTnDLbcWMOxFRt4vHYvHuowRpK+EBXAprpxduzYwQMPPFDo8YEDB7JkyZJSX2/s7Y49Sk2JZeWuSWyKP5yf5Ju4VaV30xdo0aQvWp0TuQaFLjO3lVrOuGf8g6X2iW84Gcvwr4+VeIynNpH2/ptwrXyGG4bbXwz+Ojeeqv8k97WPRKuzqbaIEKowNq/ZVLIvL0dM9obcHDbueZfvY9aT9k93TXP3YJ5u/TINGzxa6Pi8ahwoupzRlOqZDSdjef3HEySmZxd67s7zPdTQhx0H57Ayeg03DbfHWOrq/RjcYRz163U36lpFkaWPhSOQZF8ER0v2f8VsZdG+d/kr6yYANZ196N9qBC2bPVvi64qqs/fzcObxVtUIaxJoUtLMNSh8vO0ci/fGkHjr36Rf1DLEt24ls2zjZHbe2EEWuWi1Gh70a87zD83C3TPAlD9dlj4WDkOSfRFsOdmb0krNzkzju21jWB93AAMKHhon+tV7goc6jTO6ayTveptPxbE66goJaVn5z5UlaZYW/53J2Ud3lZaVV5DkGYezTkMVJzeGtXqZNi1fMOpaeXcnd3+wy3J3IoS1k2RfBFtN9qa0Us+f38n8PRO5mH17AlmoT31eeOA9fCvVLtN1y5s0jfmSKu469V2P4F9lLdmuOei0Gu6v1JTBj3yMq1ulEq/XZea2YlfENGXcQQhbIMm+CLaY7I1NuIrBwC+7p/DNX2vJQcFbq+ellsO5p9WQMl3XHEnTmC+p0q7jokmja9UVpPr8hQIEOXnwSpd3qFP7wSKP3x99g36LDpT69/1vWEep7Rd2wdi8ZlOrXjqa0jbogNszUFNSrjL7xydZ+tcaclBo51mTD3p/V+ZED8avF38g+kaRzxe3i1TsXbtIlXadTMWDLXFD6VdnNH5aV2Jz0pi44zU27PovisFQ6HhZ+liIokmyt2LGJFzdrSjG/dCHgykxOKFhcN0nGPPkKnx8a5Xr2sYmw4jlhbf/K+lLKi/uKetOkWtQjL6OwetB3nt8Jfd41iIHhcXRPzJ/zXNkZiQVOE6WPhaiaJLsrVhpibC55xYCgr/imiGdKjo3/nvve3S/92002vL/bzU2GSbeyi6032tpX1Lw7y5Sxl7nnZ9Os/eiltee/JHna4ejRcPuxD+Y+MNjXIv/Pf84NTdUEcKaSbK3YsUnQgOd/ZaiqbKFHI2BBvqqzHj8R+rW7Wa2a5eWNO+W11IHiEu6ZdRr4pJuGX2dm2lZjPj6GBtPXeXR+9/hrU4T8dbquZCdxJu/DObPc78Axm2oIksfC0ckyd6KFZUIncjk3oCPSPU5DUDArbpM7rsOTy/zlhLemTRLc+d+r0CBMs2SJKRlGX2dO8cocg0KTRs9wfReX1HT2YckQxb/3f0m+w7f3sGse7MgXrq3Npq78rlGAy/dW1vKLoVDkmRvxe5upXpoE+kUPItEj1g0gP5GR54KW4BeXzGLveWtF+/r5mzU8XndTn6exsWTd1zedfw8Sr7O3V8q/v6NmPLEKtp41iAbA3NPLmLd9jfZcDKWhbtiuHsHRIMCC3fFFBpjKItcg8L+6Btm325RiIoii49YubxE+OH6Lfh7ziPJOQMnRYs+pTeDHhtR4a3U7s2C8HJ1pv/nB0s9Nq/bKdDbuH74O4/r3iyIW9kGXv02qtTX3TmW4ebux9gnfmTZhhH8HH+Yr8//hNfx08BAivt4l3cNfZmdK2yRtOxtQCv/qzQI/oRstywqO7nyYqt3+ea1KRZLLB3rVDZp0DOv+6kkRQ2SGvslcfdYhlbnxMBeixhQqxcGg0K821m6BnyMjsLdSXffHZiquJLSuLtKSoWwNpLsrdzFi3uYtPElruXeIsjZk5mPLqZnx3CLDjCaOuiZd7ymmOM1FD1IWt5KmvAHptLN/0k0aEj0iCM0cB7OFF0VVJY6e2PnPUiXjrBGkuyt2F8x25iyLZJEQxY1nL2Z8uhXBFRtpkosed1JgXe12AN9XItcNqG4433cnIkMq0+3JoGFrmGOSprmzSLIvtodHRoS3a7TMWgOLpq0QseVpc7e2IlmZb1rEKIiyXIJVio6ejNTd08gTcmhnosfE8K/MXvFTVmYumywKate5ilPn3hWjoGO07dSxbALj6prydEY8Mn04lBsJJmKR7nWxlkTdZlRK6JKPW7us63o3aqaSecWoqxkW0IbdvbcBqbueYtbSg4NXPyZ0GcF7u7+aocF3G59m7KmzOZTcczZ8mehro+8Pu7i7grKsol43pdEQloWCXSkbpwe38AfSHJJoX3QHA7/k/DLWmdvjbNzZc1+YSxJ9lYmOnpzfqJv7BrA+N4rcHO3zdmepfVxayi+MsbUL5WiFoyLzmhD7VgtfkHfkeSSQqdq83gq9PMyD2znjSmUtpOXpWbnSlWQMIX02VuR8+d3Mm33hPxE/3qfb2020YPl+rhL+lKJyWzFzbincUZLukcqx85Ekp1ZuA/fGNY0O1eqgoSpJNlbiUuX9vPuzjGk/tN1M773ihLXbbcFllqBsrQvlb8yWpMY+xTOipaTt+L4cG0/crLLdk1TB6orglQFibKQbhwrcPXqb0zdFkmKIZu6ej8mPLbcplv0eSzVx23Ml0V0RhsGVavOxisfcSz1Ih+vHcArfVaUaVPzso4pmIspd0yyZr/II8leZTcTonl343BuGjIJcfbmjce+KXK/VVsciKuIPu6i3gdjvyzq1XuM5sF63j/yAfuTz+G6fhD/F/5VmVYJNXVMwZxkzX5RFpLsVZSaEsvUnwcTn5tOgM6dN3suLrK80lYH4vL6uEd8fQwNFEj4ZenjLu59mNirsdFfKjptf17JTmPO8QVsTziJ18aR9O/xSRn/QnVYY1WQsH7SZ6+SzIwkZq57nkvZyVTSujDxkYVU8qtb6DhbH4gzVx93Se9DxPJfeazl7fMYM3Daoc1LDGvYD4C1cftYt/0t0/4olcma/ZZjTwveyaQqFeTmZDFr1dMcTb2Ah8aJKQ/NJSQktPBxdrR5dnm6oYx9Hyb2asI7Pxl/B7Rm2+ssv7ABgIgmg7m3wyjT/zCV5H35QdF3TJYaLLZntnJHLZOqrJRiMPD5T0M5mnoBZ7SM7TSxyEQP9jUQV54+bmPfh0oeevaMf9DoL5XH7p9G8oabrL96kE9PLcHXM5AWTfuWKUZLy7tjujsZBVphMrJFRc3bgJInA1o7SfYWtnLrGLYlnECLhlGtImjcsHexx8pA3G2mvA+mfKlotFr6PzKfm2v6sTfpLLMOv8cUj0Bq1bqvPOFajNpVQfaqPJMBrZn02VvQzgMf8v3f2wAYUv9p7mk9tMTjZSDutop8H7Q6J0b0WkJTt0AylFym7xxbYE9ba5f35da7VTU61a1sU8nHWtnrgneS7C3kxO/f89npZQD0DgqlW5c3Sn2NDMTdVtHvg7OLB2Me/Yoazt4kGrKYsWk46anxZY5X2DZ7vaOWZG8Bly7tZdbhmeSi0Nm7Hs92m2vU66xper6aLPE+uHsGML77QippXfg7O4UP1w8s8yxbYdvs9Y5akn0FS0o8z8ztY/LXu3n50aUmzdq0hun51sAS74O/fyPG3zcTV42OE7di+fznF1EMhnKfV9gWe72jltLLCpSVmcKUlX04l3mDQCcP3u39HV7eZVvn3Npn0FoqPktc59jxr3j/2GwMKPSv2ZPHHpxm1vML62dLpa3G5jVJ9hXEkJvD3NV9OZAcjafGiXcf/oyg4LYVek212Eo9sik27JrC4uhVaIDXWv2n1MF0YX9s5XMtyb4Ilkz2322KZOXlHTih4c1Ok2jSqE+FXk8txdUjW2MLyFRfrBvMpuu/4qLRMeX+WdSudb/aIQkLs/Y7ajA+r0mffQXYd3g+Ky/vAODFRv3tNtHb+1K7g3p8Rgv3YDKVXN7b+To3E6LVDklYmD2VtkqyN7Nz0Zv45OQXAIQHduSBTmNUjqji2Gs9ch6dk57IXl9SzcmLBEMGH2x4qcwbnwihNpOT/cCBA9m1a1dFxGLzblz/kw/2TCQbA208a/Dcwx+rHVKFstd65Dt5eAYyLmwenhonzmXe4LOfh1ZIhY49LbglrJPJyT4pKYmwsDDq16/PtGnTuHz5ckXEZXOyMlOY9c+69NWdvfhPry/LtDGGLbHXeuS7BQa15tX249GiYXfiH6zZPsGs599wMpYuM7fRb9EBRq2Iot+iA3SZuc3qVzQVtsXkZL969WouX77MiBEj+Pbbb6lVqxY9evTghx9+IDs7uyJitHqKwcBnP79IdFYCnhonxnWbj7u7v9phVTh7rUcuSrMmTzO43pMArLi4kaPHl5rlvLa+hLWjssU7sTL12VepUoXRo0dz/PhxDh48SL169Xj++ecJDg7m1Vdf5ezZs+aO06qt2/EWexLPoEXD6A4TqFq1hdohWYSjzfB9uOtbdKvcEgWY9+s8/v77QLnOZ+8D3PbKVu/EyjVAGxsby+bNm9m8eTM6nY6ePXty4sQJmjRpwuzZs80Vo1X79bevWX7hZwAG1XuCpo2fVDkiy3K0Gb4DeyygsWsAGUou729/jdSUsv8Dt/cBbntky3diJncqZ2dns3btWhYvXsymTZto0aIFkZGRPPfcc/k1nqtWrWLIkCG8+uqrZg/Ymly5coR5x+agAGGVW/Bw6Jtqh6QKR1pq19nZnVd7fs4ba/oSl5PG3J+HMuGptWUan3GEAW57YutLH5v8CQ0KCsJgMNCvXz8OHTpEq1atCh3zwAMP4Ovra4bwrFd6ajzvb40kXcmhoas/g3p8WqaNq+2FmhtwW5qPTw3G3juDiTte5bf0K/xv03/o32OByedxlAFue2HrmwmZnJ1mz57NlStXmD9/fpGJHsDX15eYmJjyxma1DLk5fPTLi1zJScVP68prPT7H2dld7bCEBdWqdR8jmt5eQmFt3H72HPrI5HM40gC3PbD1OzGTk/3zzz+Pq6tjtzR+2PIax1Iv4oyWMV3ewce3ltohCRV0vieC3kG3t5T89PfF/BWzzaTXO9oAt62z9Tsxx+13KKODxxay8spOAF5qMpC6dbupHJFQ07Pd5tLKozrZGJi1+02Ski6a9HpHG+C2ZbZ+J2ZzyX7+/PnUqlULV1dXOnTowKFDhyx27UuX9vPJb58B0DOgPfd2GGWxa1ckW6wZVlvee7buxFU6N32fICcPrufeYs4vw0ze9KR7syD2jH+Q/w3ryNxnW/G/YR3ZM/5BSfRWxtbvxGxq1ctvv/2WF154gU8//ZQOHTowZ84cvv/+e86cOUNAQECpry/PqpepKbG8seopruam0cwtkDeeWovOSV/WP8Vq2MoyrtakqPeseaXzePt/QY7WQI8q7Rj06OcqRigqkrX9m7HLJY47dOjAPffcw8cf315zxmAwEBISwn/+8x9ef/31Ul9f1mRvyM1h5so+RKX9TRWdG9N6f4e3T0iZ/w5rYc/LE1eUkt6zZh7bcAnegk6r4eXGL3Bfx9FqhCgswJqWPra7JY6zsrI4evQoYWFh+Y9ptVrCwsLYv39/hV77282RRKX9jV6j5bV7p9tFopfZm6Yr7T07mfYgPsmNAVh0+muiozdbND5hOeZe+tiQm8PyDRHcuP6nmSIszGaS/fXr18nNzaVq1aoFHq9atSpxcXFFviYzM5Pk5OQCP6Y6ErWE1bF7ABjedIjdbGAhszdNZ8x7tu1qPxo5VyMbAx/smUhS4nmLxSds1w9bXmNN7F4m/zyowpbRtplkXxbTp0/Hx8cn/yckxPQWeeN63WnlUZ1Hq3Yg9J6RFRClOmy9ZlgNxrwXCk60qD+NICcPEgwZzP7lJZMHbIVjOfTr5/kVfk83fBpnF48KuY7NJHt/f390Oh1Xr14t8PjVq1cJDAws8jUTJkwgKSkp/+fSpUsmX9fDM5DxT67muYdNnzRjzWy9ZlgNxr4Xwf7VGfPALFw1Ok5nxPPVhpcrODJhqy5d2s/847dnX/cMuId7O1TcEjM2k+z1ej1t27Zl69at+Y8ZDAa2bt1Kp06dinyNi4sL3t7eBX7KQqtzsovKmzvZes2wGkx5z6pX78jIVhEAbLx+jO37P7BYnMI2pKXGMWvHGDKUXJq4VaV/BTcobSbZA4wePZpFixaxdOlSTp8+zYgRI0hLS2Pw4MFqh2ZzbL1mWA2mvmf3tBrCU9UeAODzP5ZzLnqThSIV1s6Qm8O8n18kNicNf50bkT0W4eRcsXfRNpXs+/btywcffMDbb79Nq1atiIqKYsOGDYUGbYVxZPam6Ux9z5586H3aedYkBwOz9rxN4k37XTNKGO+7za/mV/iNuXc6Pj41KvyaNlVnX17lmVRlz6ypZlgtpr4Hphyfnn6dt1Y+zuWcFBq6+jPxqbWycJ4D23/0U+b89ikA/2k6lC7t/1Ou89nlpKrykmQvimKJGZGxV47yxqb/I13JIaxyC4Y99pVZzitsy4ULu5m4PZJMJZfwwI4M6PFpuc9pd5OqhKgIltp5KCi4La+0iUQDbLnxG5v3TDPLeS1B1k4yj+SkS3ywczyZSi7N3YPp122eRa9v+vY6QtiJrBwDb6w6abGdh1q3GMCz107wv4sbWXz2e6pXaUrjhr3Lfd6KZG3rwNiqnOwM5vwyjPjcdAJ07kT2/MLiFX7SshcOacPJWDpO30JCWlaxx1TELOLeD0yns3c9clH48MBUrsX/brZzm5st77dqbZZtjOD3W3G4anSMe2AWnl6W/6KUZC8cTl4SS0jLNup4c84i1mi1DO/1BbX0viQbsnh/08tk3LpptvObi6ydZD5b981kw7WjAIxs+TIhIUXPC6pokuyFQykpiRXH3LOIXVx9GPvwAny0ei5kJ7Hg5xdRDIYSX2PpfnNZO8k8/jizli/PrADgmeoPcU/roarFIn32wqGUlsTupOF2/XxFzCL2r9KY0R3f4p19kziQHM3KrWN4qtuHRR6rRr+5rJ1Uftfif2fWgXfJQaGjd12eeOh9VeORlr1wKKYmp4qcRdyo4WMMbdgPgO//3sbBYwsLHaNWv7msnVQ+t9ITeH/TyyQbsqil92VEz8/RaNVNt5LsTSAlaLbP2ORU2UNvkVnED3YeR8+A9gDM/+0zYs7vyH+utGohqLh+c1k7qewMuTl8/NNgLmQn4aPVM/bhBbi6VVI7LOnGMZaUoNmHvCQWl5RRbL+9n4cz+yc8hN7JMm2hAY98zOWVj3M8/TLv7Xydqd5fc+iKB2+sOlHiIPKd/ead6lY2a0x56wCN+PoYGijwXsnaSSVbsXkUR1Iv4IyW1zpPwr9KY7VDAqRlbxQpQbMfpS1mpgGmPd7cYokeQOekZ1SvL6jm5EmCIYP/rhnCqG92q1ItdCdZO8l0uw7OZk3sXgD+r+kgGtbvpXJE/5LlEkqRa1DoMnNbsYN6eYN4e8Y/KK0cG2KNd2pxcVG8uWEY17My8UnzZ/fVUShG3Hz/b1hHs7fs7yRrJxnn1B+rmbp/Cjko9AnqQr/uH1vkusbmNenGKYUpJWgV+Q9OmFf3ZkF0axJo8SRWUuIMDGzFY3UiWXx6Jonu1+js9xV7E4YUe66KrBa6U95+q6J4sVeO5lfedPCuQ99uc9QOqRBJ9qWQEjT7ZekkZtTdhPf9aK7vBf/dpPj8Sbuc1RxJ7lPsOaXfXH2pKbHM3PoKqUoO9Vz8GPnoUrQ660ut0mdfCilBE+Zg7LhPgJcrv6b0wiupEQDZlQ/QyG1fofNZqlpIlCw7M4331r/A5axUvBUXwpp9iM7ZU+2wiiTJvhRSgibKy5SlB/I+b/sSXsA3LRAF8Ki6nhD9qfzX5FULSaJXlyE3h6nfPcfxlKsYsuHU+b4MXn6ZLjO3WWXRhiT7Usj2faK8TBn3+ffzpuVA/HB8Mr3I1hgIDlyOv+6KKtVComizvx/GsfS/0CiQevUxLmc1BKy3Sk8+MUaQEjRRHqaO++R93ir7+PJr3Mt4ZrtyS5dD42qLmPdUiHzerMDPO6ewK/n24maa6104c6tj/nPWulCc9Y0iWCm1qjeE7SvLuM+dn7dLsdXZdHYcKWRx9OwYujdfhbOLR4nnknLJirPvyCcsiV6Foih4JDZjf0rhWnprrNKTZG8CKUETZVHarN3iSijzPm+d6t5Ph5BZTNr+Kqcz4pm3bgCvPv59sRUf1jiHwF6cPPU98098jkFR8Empye6bz5V4vDVV6Uk3jhAVzBzjPjVrdmVsxzdwQsuhlBg++H4Aq49dKrRGk8z2/pe517L6K2Yr7x+aQQ4GWrjWZM/1YZSWQq2pSk9m0AphIeZocS/9eRar/l6KQVHwSmrA3oQh+efo1iRQZnv/w9x3N5cvH2LSlghSDNk0cavK+D4reWD2wVLv1izxXhub1yTZC2FB5elLz2u1t/VeTVblAwB4JDbnwM3+AESG1Wf2lrOlnqeil1dQW977dHdiy3uXTS2quBb/O2//MpQEQwZ19X689fh3uLv7518Hil4ozlLFG8bmNenGEcKC8vrhe7eqRqe6lY1O9HfW6h9J7oP7zRYApPme4B6flQAs3nveqHNZUz+yuZl7O8WbCdFM3fgSCYYMqjl58fqjS3F39wdsr0pPBmiFsAF31+ofSHyOUG0WKT5/kOF3mDYGPUdTwo06lzX1I5ubOdeySko8zzs/DSI2J40AnTtv9vgCb5+QAsfYUpWeJHshbEBRrfG9CS/QRbOIZO8Ysv330hotMTl9SLqVbVLVjz0x11pWKcmXeXf9C1zOScFP68rERxZS2b9BkcfaSpWedOMIYQOKbo1r2XNjGD4pNQDI9d/Ncw12A6VX/djrrmvmWMsqOekS76x9jovZyVTSuvB2t/kEVG1mrhBVIy17IWxA8bX6WvZcf4mums9I8vqbYxnreCtUz+cnOxfozgi8oxLFnuvwyzqnIU9S4nneXf8CF7OT8dHqmRj2EUHBbSs0ZkuRahwhbERJ1R8achjWYjlRmWfQAEPqP4NX0P8V6kc2d6WKNSprlUzizRje+ekF/s5OoZLWhYlhH1GtWvuKD7icpBpHCCtnaldKSdUfnwxoz/i+3/CIfxsU4Iuz35FwaW6Bqh9zV6pYq7JUycRfPcmk9QP4O/t2H/2kbp/YRKI3hbTshVBBebpSSqrVVwwGlm+MYG3cfgCeqvYAT4XNQqPVsj/6Bv0WHSg1Nnupwzd2TsOlS/uZtu1VEgwZVNG58dbDCwgMbGX5gMtItiUUwkoV15WSt6RBaV0pJVV/aLRanntkPq5bx/Ld31v54fJ2EtcPYmivLx1u1zVjqmTORW9ixp43STFkU93Zize6f1Fs1Y2tk24cISzIEl0pGq2WJ7vN4sX6z6ABttz4jdmrnsbPNduo19tzHf6djkQtYcru10kxZFNX78fkx1aUK9Fbe4WTtOyFsCBLbmDfrcsbeLv7M+/4pxxKiSHh2FDq+wzlXJKPw9bh59mw678sjV6FAYVWHtWJfPQr3NzL/nfbQoWTtOyFsCBLd6V0aPMSb3Z6G0+NE+cyb1Cz6hyq6/9w2F3XcrIzWLx+KIujf8SAwoN+zRn3xI/lTvS2sNKoJHshLEiNDeybNOrDuw9/RpCTBymaLGrU/IbO/jsLHFPe9VysvQsDbs+Knb6yDxuu3d5h6tmQbrwUvhSdk77M57SlCifpxhHCgso76aesgoLb8m6fH/jwp8H8fisOKm/itVo3qV7nLQJ9PMu1nostdGH8FbON2bvfIj43HVeNjpEtX+ae1kPLfV5LdsuVl7TshbAgNTew9/QK4o0nV9Mz4Hb9+J6Uw/z+53Aa+yWXK9FbcxeGYjCwec803t4xhvjcdAJ07rzz4FyzJHqwfLdceUiyF8LC1Fwa18nZlYG9FvJKsxdx0eg4cSuWcWv7EnXiG5PPZe1dGGmpccxb9Qyfn/2ObAy09azJ9MdXUqNGF7NdQ41uubKSbhwhVFARS+OasjFK6D0jqRHYlnk7x3MxO5npR96nx4Xt9AubhYurj1HXs+YujBO/f88nR2aRYMhAi4bnavXk0fveQaM1b/tWrW65spBkL4RKzLk0bln6zUNCOjHt6V9Ytuk/bLx+jF+uHeHYdz0Z3n4sTRr1KfWa1tiFkZ5+nRVbx7Hx+u21cQKdPIjo9BYN6vWokOvldcuN+PoYGopei8daKpykG0cIG1eefnNnFw+GhH/JhHZj8dO6cjU3jSn7J7No7QukJF8u8br+ni5GxWeJLgzFYODgsYWM/r5XfqLv5t+KmU+tr7BEn8dWdqyStXGEsGG5BsVsm4ynp8bz9bbX2HrjBACeGieeqdubsE7jC5UnbjgZy+S1vxOXnFns+Sy16falS3tZtm8ax9NvfzkFOnnwYptImjd9usKuWZTy7C9cHrLheBEk2Qt7UxGLm536YzVLjszmQnYSANWcPHmq8XN0bP0SWp1TsWv73MkSSyZfv/4HK/dOZUfCSQwoOKHhseCuPH7/VPQuXhVyTWskC6EJ4QAqot+8SaM+TK/Xky37Z/L9X2u5nJPK3BMLWf3Ht4Q3fIZ3NtcpMdFDwc1SzC0u9lfWHPqQXQknyfknko7edekXOtGmVqu0NJtJ9lOnTuWnn34iKioKvV5PYmKi2iEJobqKKv3TOel5pOtEurYdwU97prL+yi4uZCcx77eFVPfVEaJtzKnkbiQbqhT5+g+eaklofX+TrlkSQ24Ov536lk1/fMevqRcx/JPkm7hV5dm2r9Cwfi+zXcte2Uyyz8rK4umnn6ZTp0588cUXaocjhFWo6NI/d3d/nn54Nt2TL7Px4CzWX9xFgi4TKv1G7Uq/4XPLj6TUlpxN60i68m/J5vW04vvyjaUYDMRc2M7+P35g/7UoruXeyn+ulUd1nmg1nIYNHi33dRyFzST7KVOmALBkyRJ1AxHCiliq9M/LuxpPdfuQwD8uMnvlNNy8o0jWp5HolgBu26lfZQfemV7k3KrB9Vv1qaRvaPI1DLk5xF87yZ8XtvN77GFOJsdw/Y4E76Fx4r4qrQlrNczudpGyBJtJ9kKIouWV/t1dZ18R/eadGoQwVvs0cZfDCdKfpbbXLgzuF0lzyiTJJRlcTuJe6XcWHFzHyqPuVNX7UtU9AG8XX9z1XrjqPVAMBnIM2WTlZJCYcZ3EjJtczUjg76xEMpTcAtdz0eho7VWLjrW60aZpP6MnfInC7DrZZ2Zmkpn57+1kcnKyitEIUXEqYkZuUe68k4jNqs+VG/XhBlR1iqGWx2FcXC+h90klhSzictKIy0mD9JLr9e/kjJaaLr40qdSIpiFdaFyvlyR4M1E12b/++uvMnDmzxGNOnz5No0aNynT+6dOn53f/CGHvzDkjtyRF3UlczamNlsZM6nb7TuJmQjRX4o9zNeEc8cmXSM1KJj0nnVs5GWg0Wpw0Wpx1enxdfKnkHkBlr2pUC2jJpVu1uJ5uIMDLleYWqlN3FKrW2V+7do0bN26UeEydOnXQ6/+d0LFkyRIiIyONqsYpqmUfEhIidfZCmIE5JxHZwjLJ1som6uyrVKlClSpFl26Zg4uLCy4uxk3pFkKYxlx3EuXdgF0Yx2bWxrl48SJRUVFcvHiR3NxcoqKiiIqKIjU1Ve3QhBBlZO3LJNsTmxmgffvtt1m6dGn+761btwZg+/bt3H///SpFJYQoD2teJtne2EzLfsmSJSiKUuhHEr0Qtssal0m2VzaT7IUQ9seWdnqydZLshRCqyVvuobgaHg23q3KsYacnWyfJXgihGjU3YHc0kuyFEKqylZ2ebJ3NVOMIIeyXpZZ7cGSS7IUQVsFSyz04KunGEUIIByDJXgghHIAkeyGEcACS7IUQwgFIshdCCAcgyV4IIRyAJHshhHAAkuyFEMIBSLIXQggHIMleCCEcgCR7IYRwAJLshRDCAUiyF0IIByDJXgghHIAkeyGEcACS7IUQwgFIshdCCAcgyV4IIRyAJHshhHAAsgetEMJouQZFNgW3UZLshRBG2XAylinrThGblJH/WJCPK5PCm9C9WZCKkQljSDeOEKJUG07GMuLrYwUSPUBcUgYjvj7GhpOxKkUmjCXJXghRolyDwpR1p1CKeC7vsSnrTpFrKOoIYS0k2QshSnQoJqFQi/5OChCblMGhmATLBSVMJsleCFGi+JTiE31ZjhPqkGQvhChRgJerWY8T6pBkL4QoUfvafgT5uFJcgaWG21U57Wv7WTIsYSJJ9kKIEum0GiaFNwEolPDzfp8U3kTq7a2cJHshRKm6NwtiwYA2BPoU7KoJ9HFlwYA2UmdvA2RSlRDCKN2bBdGtSaDMoLVRkuyFEEbTaTV0qltZ7TBEGUg3jhBCOABJ9kII4QAk2QshhANwqD57Rbm9dkdycrLKkQghhHnk5bO8/FYch0r2KSkpAISEhKgciRBCmFdKSgo+Pj7FPq9RSvs6sCMGg4ErV67g5eWFRmN8uVhycjIhISFcunQJb2/vCozQfCTmimdr8YLEbCmWjFlRFFJSUggODkarLb5n3qFa9lqtlurVq5f59d7e3jbzYcsjMVc8W4sXJGZLsVTMJbXo88gArRBCOABJ9kII4QAk2RvBxcWFSZMm4eLionYoRpOYK56txQsSs6VYY8wONUArhBCOSlr2QgjhACTZCyGEA5BkL4QQDkCSvRBCOABJ9qWYP38+tWrVwtXVlQ4dOnDo0CG1QyrRrl27CA8PJzg4GI1Gw+rVq9UOqUTTp0/nnnvuwcvLi4CAAPr06cOZM2fUDqtECxYsoEWLFvkTZjp16sQvv/yidlgmmTFjBhqNhsjISLVDKdbkyZPRaDQFfho1aqR2WKW6fPkyAwYMoHLlyri5udG8eXOOHDmidliS7Evy7bffMnr0aCZNmsSxY8do2bIljzzyCPHx8WqHVqy0tDRatmzJ/Pnz1Q7FKDt37iQiIoIDBw6wefNmsrOzefjhh0lLS1M7tGJVr16dGTNmcPToUY4cOcKDDz5I7969+f3339UOzSiHDx/ms88+o0WLFmqHUqqmTZsSGxub/7Nnzx61QyrRzZs3CQ0NxdnZmV9++YVTp04xa9YsKlWqpHZooIhitW/fXomIiMj/PTc3VwkODlamT5+uYlTGA5RVq1apHYZJ4uPjFUDZuXOn2qGYpFKlSsrnn3+udhilSklJUerXr69s3rxZue+++5RRo0apHVKxJk2apLRs2VLtMEwyfvx4pUuXLmqHUSRp2RcjKyuLo0ePEhYWlv+YVqslLCyM/fv3qxiZfUtKSgLAz89P5UiMk5uby4oVK0hLS6NTp05qh1OqiIgIevXqVeBzbc3Onj1LcHAwderUoX///ly8eFHtkEq0du1a2rVrx9NPP01AQACtW7dm0aJFaocFSDdOsa5fv05ubi5Vq1Yt8HjVqlWJi4tTKSr7ZjAYiIyMJDQ0lGbNmqkdTolOnDiBp6cnLi4uDB8+nFWrVtGkSRO1wyrRihUrOHbsGNOnT1c7FKN06NCBJUuWsGHDBhYsWEBMTAxdu3bNX6rcGv31118sWLCA+vXrs3HjRkaMGMErr7zC0qVL1Q7NsVa9FNYtIiKCkydPWn2/LEDDhg2JiooiKSmJH374gYEDB7Jz506rTfiXLl1i1KhRbN68GVdXV7XDMUqPHj3y/7tFixZ06NCBmjVr8t133zF06FAVIyuewWCgXbt2TJs2DYDWrVtz8uRJPv30UwYOHKhqbNKyL4a/vz86nY6rV68WePzq1asEBgaqFJX9GjlyJOvXr2f79u3lWobaUvR6PfXq1aNt27ZMnz6dli1bMnfuXLXDKtbRo0eJj4+nTZs2ODk54eTkxM6dO5k3bx5OTk7k5uaqHWKpfH19adCgAefOnVM7lGIFBQUV+sJv3LixVXQ/SbIvhl6vp23btmzdujX/MYPBwNatW22ib9ZWKIrCyJEjWbVqFdu2baN27dpqh1QmBoOBzMxMtcMo1kMPPcSJEyeIiorK/2nXrh39+/cnKioKnU6ndoilSk1NJTo6mqCgILVDKVZoaGih0uE///yTmjVrqhTRv6QbpwSjR49m4MCBtGvXjvbt2zNnzhzS0tIYPHiw2qEVKzU1tUDLJyYmhqioKPz8/KhRo4aKkRUtIiKC5cuXs2bNGry8vPLHQ3x8fHBzc1M5uqJNmDCBHj16UKNGDVJSUli+fDk7duxg48aNaodWLC8vr0LjIB4eHlSuXNlqx0fGjBlDeHg4NWvW5MqVK0yaNAmdTke/fv3UDq1Yr776Kp07d2batGk888wzHDp0iIULF7Jw4UK1Q5PSy9J89NFHSo0aNRS9Xq+0b99eOXDggNohlWj79u0KUOhn4MCBaodWpKJiBZTFixerHVqxhgwZotSsWVPR6/VKlSpVlIceekjZtGmT2mGZzNpLL/v27asEBQUper1eqVatmtK3b1/l3LlzaodVqnXr1inNmjVTXFxclEaNGikLFy5UOyRFURRFljgWQggHIH32QgjhACTZCyGEA5BkL4QQDkCSvRBCOABJ9kII4QAk2QshhAOQZC+EEA5Akr0QQjgASfZCCOEAJNkLIYQDkGQvhJlcu3aNwMDA/LXMAfbt24dery+weqoQapC1cYQwo59//pk+ffqwb98+GjZsSKtWrejduzcffvih2qEJByfJXggzi4iIYMuWLbRr144TJ05w+PBhXFxc1A5LODhJ9kKY2a1bt2jWrBmXLl3i6NGjNG/eXO2QhJA+eyHMLTo6mitXrmAwGDh//rza4QgBSMteCLPKysqiffv2tGrVioYNGzJnzhxOnDhBQECA2qEJByfJXggzGjt2LD/88APHjx/H09OT++67Dx8fH9avX692aMLBSTeOEGayY8cO5syZw7Jly/D29kar1bJs2TJ2797NggUL1A5PODhp2QshhAOQlr0QQjgASfZCCOEAJNkLIYQDkGQvhBAOQJK9EEI4AEn2QgjhACTZCyGEA5BkL4QQDkCSvRBCOABJ9kII4QAk2QshhAOQZC+EEA7g/wGPEPzCMFNVBAAAAABJRU5ErkJggg==", "text/plain": [ - "
" + "
" ] }, "metadata": {}, From 7237dd2294b35d2c7918db89cff6f8bbee3bcdc0 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Mon, 21 Aug 2023 23:37:11 -0700 Subject: [PATCH 19/32] Added conditional cycle --- .../Tutorial-III-Functional-Workflow.ipynb | 290 +++++++++++++++++- 1 file changed, 274 insertions(+), 16 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb index fb1ab68c6..92b62f555 100644 --- a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb +++ b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb @@ -540,18 +540,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Component Chaining and Looping\n", + "## Component Chaining\n", "\n", - "As such, we have our `AutoRA` components wrapped to work with the state. Remember, this means that they take the state as an input and returns the updated state as an output." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Component Chaining\n", - "\n", - "As the components all act on the state, they can be chained." + "As such, we have our `AutoRA` components wrapped to work with the state. Remember, this means that they take the state as an input and returns the updated state as an output. As the components all act on the state, they can be chained." ] }, { @@ -740,11 +731,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Chain Looping with Number of Cycles\n", + "## The Cycle\n", "\n", "Moreover, we can use these chained components within a loop to run multiple cycles." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cycle using Number of Cycles" + ] + }, { "cell_type": "code", "execution_count": null, @@ -967,8 +965,11 @@ "### Then we cycle through the pipeline we built five times ###\n", "num_cycles = 5 # number of empirical research cycles\n", "for cycle in range(num_cycles):\n", - " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", + " #Run pipeline\n", " s = theorist(experiment_runner(experimentalist(s, num_samples=10)))\n", + " \n", + " #Report metrics\n", + " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", " plot_from_state(s)" ] @@ -1001,7 +1002,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Chain Looping with Stopping Criterion\n", + "### Cycle using Stopping Criteria\n", "\n", "Alternatively, we can run the chain until we reach a stopping criterion. For example, here we will loop until we get 30 datapoints." ] @@ -1232,16 +1233,273 @@ "### Then we cycle through the pipeline we built until we reach our stopping criteria ###\n", "cycle = 0\n", "while len(s.experiment_data) < 50:\n", - " print(f\"\\n\\033[1mRunning Cycle {cycle+1}, number of datapoints: {len(s.experiment_data)}\\033[0m\")\n", + " #Run pipeline\n", " s = theorist(experiment_runner(experimentalist(s, num_samples=10)))\n", + " \n", + " #Report metrics\n", + " print(f\"\\n\\033[1mRunning Cycle {cycle+1}, number of datapoints: {len(s.experiment_data)}\\033[0m\")\n", " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", - " cycle += 1\n", " plot_from_state(s)\n", + " \n", + " #Increase count\n", + " cycle += 1\n", + "\n", "\n", "print(f\"\\n\\033[1mNumber of datapoints: {len(s.experiment_data)}\\033[0m\")\n", "print(f\"\\033[1mDetermined Model: {s.model}\\033[0m\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Conditional Cycle\n", + "\n", + "Because `AutoRA` components (theorist, experiment runner, experimentalist) act on the state, building a pipeline can have a lot of flexibility. Above, we demonstrated using a single set of components in different loops, but the components can also change respective to your criteria. In other words, you can use `if-else` statements to control which component is acting on the state.\n", + "\n", + "For example, we can choose a different experimentalist depending on the number of datapoints we have collected." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#### We will first define a new experimentalist\n", + "def uniform_sample(variables: VariableCollection, conditions: pd.DataFrame, num_samples = 1):\n", + "\n", + " \"\"\"\n", + " An experimentalist that selects the least represented datapoints\n", + " \"\"\"\n", + "\n", + " #Retrieve the possible values\n", + " allowed_values = variables.independent_variables[0].allowed_values\n", + " \n", + " #Determine the representation of each value\n", + " conditions_count = np.array([conditions[\"x\"].isin([value]).sum(axis=0) for value in allowed_values])\n", + " \n", + " #Sort to determine the least represented values\n", + " conditions_sort = conditions_count.argsort()\n", + " \n", + " conditions_count = conditions_count[conditions_sort]\n", + " values_count = allowed_values[conditions_sort]\n", + " \n", + " #Sample from values with the smallest frequency\n", + " x = values_count[conditions_count<=conditions_count[num_samples-1]]\n", + " x = np.random.choice(x,num_samples)\n", + " \n", + " return pd.DataFrame({\"x\": x})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using random pooler experimentalist...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:07<00:00, 14.11it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 1:\u001b[0m\n", + "\u001b[1mCycle 1 model: 0.1\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using random pooler experimentalist...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:07<00:00, 12.88it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 2:\u001b[0m\n", + "\u001b[1mCycle 2 model: sin(x)\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using uniform sampler experimentalist...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:07<00:00, 12.65it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 3:\u001b[0m\n", + "\u001b[1mCycle 3 model: sin(x)\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using uniform sampler experimentalist...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:08<00:00, 12.22it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 4:\u001b[0m\n", + "\u001b[1mCycle 4 model: sin(x)\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from autora.experimentalist.random_ import random_pool\n", + "\n", + "#### First, let's reinitialize the state object to get a clean state ####\n", + "s = StandardState(\n", + " variables = variables,\n", + " conditions = conditions,\n", + " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"])\n", + ")\n", + "\n", + "#### Initiate both experimentalists ####\n", + "uniform_experimentalist = on_state(uniform_sample, output=[\"conditions\"])\n", + "random_experimentalist = on_state(random_pool, output=['conditions'])\n", + "\n", + "### Then we cycle through the pipeline we built until we reach our stopping criteria ###\n", + "cycle = 0\n", + "while len(s.experiment_data) < 20:\n", + " \n", + " #Run pipeline\n", + " if len(s.experiment_data) < 10: #Conditional experimentalist: random for first half of cyles\n", + " print('\\033[1mUsing random pooler experimentalist...\\033[0m')\n", + " s = random_experimentalist(s, num_samples=5)\n", + " else: #Uniform for last half of cycles\n", + " print('\\033[1mUsing uniform sampler experimentalist...\\033[0m')\n", + " s = uniform_experimentalist(s, num_samples=5)\n", + " \n", + " s = experiment_runner(s)\n", + " s = theorist(s)\n", + " \n", + " #Report metrics\n", + " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", + " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", + " plot_from_state(s)\n", + " \n", + " #Increase count\n", + " cycle += 1" + ] + }, { "attachments": {}, "cell_type": "markdown", From 713a35b09e385ece14ad256461fa68b9f3afe6d3 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Tue, 22 Aug 2023 00:05:37 -0700 Subject: [PATCH 20/32] Added second conditional cycle example --- .../Tutorial-III-Functional-Workflow.ipynb | 873 ++++++++++-------- 1 file changed, 483 insertions(+), 390 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb index 92b62f555..8ab2c3f00 100644 --- a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb +++ b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb @@ -123,7 +123,7 @@ "from autora.state.bundled import StandardState\n", "\n", "#### Define variable data ####\n", - "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 100))\n", + "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 30))\n", "dv = Variable(name=\"y\", type=ValueType.REAL)\n", "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", "\n", @@ -156,36 +156,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.06346652, 0.12693304, 0.19039955, 0.25386607,\n", - " 0.31733259, 0.38079911, 0.44426563, 0.50773215, 0.57119866,\n", - " 0.63466518, 0.6981317 , 0.76159822, 0.82506474, 0.88853126,\n", - " 0.95199777, 1.01546429, 1.07893081, 1.14239733, 1.20586385,\n", - " 1.26933037, 1.33279688, 1.3962634 , 1.45972992, 1.52319644,\n", - " 1.58666296, 1.65012947, 1.71359599, 1.77706251, 1.84052903,\n", - " 1.90399555, 1.96746207, 2.03092858, 2.0943951 , 2.15786162,\n", - " 2.22132814, 2.28479466, 2.34826118, 2.41172769, 2.47519421,\n", - " 2.53866073, 2.60212725, 2.66559377, 2.72906028, 2.7925268 ,\n", - " 2.85599332, 2.91945984, 2.98292636, 3.04639288, 3.10985939,\n", - " 3.17332591, 3.23679243, 3.30025895, 3.36372547, 3.42719199,\n", - " 3.4906585 , 3.55412502, 3.61759154, 3.68105806, 3.74452458,\n", - " 3.8079911 , 3.87145761, 3.93492413, 3.99839065, 4.06185717,\n", - " 4.12532369, 4.1887902 , 4.25225672, 4.31572324, 4.37918976,\n", - " 4.44265628, 4.5061228 , 4.56958931, 4.63305583, 4.69652235,\n", - " 4.75998887, 4.82345539, 4.88692191, 4.95038842, 5.01385494,\n", - " 5.07732146, 5.14078798, 5.2042545 , 5.26772102, 5.33118753,\n", - " 5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,\n", - " 5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,\n", - " 6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 1.078931\n", - "1 5.775453\n", - "2 4.696522\n", - "3 0.761598\n", - "4 3.744525\n", - "5 5.140788\n", - "6 2.855993\n", - "7 1.015464\n", - "8 1.142397\n", - "9 1.523196, experiment_data=Empty DataFrame\n", + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", + " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", + " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", + " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", + " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", + " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 4.333231\n", + "1 5.633201\n", + "2 3.899908\n", + "3 5.849862\n", + "4 3.249923\n", + "5 0.216662\n", + "6 4.549893\n", + "7 2.383277\n", + "8 4.549893\n", + "9 4.116570, experiment_data=Empty DataFrame\n", "Columns: [x, y]\n", "Index: [], models=[])\n" ] @@ -212,39 +198,25 @@ "output_type": "stream", "text": [ "\u001b[1mThe variables we provided:\u001b[0m\n", - "VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.06346652, 0.12693304, 0.19039955, 0.25386607,\n", - " 0.31733259, 0.38079911, 0.44426563, 0.50773215, 0.57119866,\n", - " 0.63466518, 0.6981317 , 0.76159822, 0.82506474, 0.88853126,\n", - " 0.95199777, 1.01546429, 1.07893081, 1.14239733, 1.20586385,\n", - " 1.26933037, 1.33279688, 1.3962634 , 1.45972992, 1.52319644,\n", - " 1.58666296, 1.65012947, 1.71359599, 1.77706251, 1.84052903,\n", - " 1.90399555, 1.96746207, 2.03092858, 2.0943951 , 2.15786162,\n", - " 2.22132814, 2.28479466, 2.34826118, 2.41172769, 2.47519421,\n", - " 2.53866073, 2.60212725, 2.66559377, 2.72906028, 2.7925268 ,\n", - " 2.85599332, 2.91945984, 2.98292636, 3.04639288, 3.10985939,\n", - " 3.17332591, 3.23679243, 3.30025895, 3.36372547, 3.42719199,\n", - " 3.4906585 , 3.55412502, 3.61759154, 3.68105806, 3.74452458,\n", - " 3.8079911 , 3.87145761, 3.93492413, 3.99839065, 4.06185717,\n", - " 4.12532369, 4.1887902 , 4.25225672, 4.31572324, 4.37918976,\n", - " 4.44265628, 4.5061228 , 4.56958931, 4.63305583, 4.69652235,\n", - " 4.75998887, 4.82345539, 4.88692191, 4.95038842, 5.01385494,\n", - " 5.07732146, 5.14078798, 5.2042545 , 5.26772102, 5.33118753,\n", - " 5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,\n", - " 5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,\n", - " 6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[])\n", + "VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", + " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", + " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", + " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", + " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", + " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[])\n", "\n", "\u001b[1mThe conditions we provided:\u001b[0m\n", " x\n", - "0 1.078931\n", - "1 5.775453\n", - "2 4.696522\n", - "3 0.761598\n", - "4 3.744525\n", - "5 5.140788\n", - "6 2.855993\n", - "7 1.015464\n", - "8 1.142397\n", - "9 1.523196\n", + "0 4.333231\n", + "1 5.633201\n", + "2 3.899908\n", + "3 5.849862\n", + "4 3.249923\n", + "5 0.216662\n", + "6 4.549893\n", + "7 2.383277\n", + "8 4.549893\n", + "9 4.116570\n", "\n", "\u001b[1mThe dataframe we provided:\u001b[0m\n", "Empty DataFrame\n", @@ -382,29 +354,29 @@ "text": [ "\u001b[1mPrevious Conditions:\u001b[0m\n", " x\n", - "0 1.078931\n", - "1 5.775453\n", - "2 4.696522\n", - "3 0.761598\n", - "4 3.744525\n", - "5 5.140788\n", - "6 2.855993\n", - "7 1.015464\n", - "8 1.142397\n", - "9 1.523196\n", + "0 4.333231\n", + "1 5.633201\n", + "2 3.899908\n", + "3 5.849862\n", + "4 3.249923\n", + "5 0.216662\n", + "6 4.549893\n", + "7 2.383277\n", + "8 4.549893\n", + "9 4.116570\n", "\n", "\u001b[1mUpdated Conditions:\u001b[0m\n", " x\n", - "0 2.411728\n", - "1 5.902386\n", - "2 3.236792\n", - "3 3.744525\n", - "4 0.063467\n", - "5 1.523196\n", - "6 4.315723\n", - "7 4.379190\n", - "8 3.046393\n", - "9 5.204254\n" + "0 4.983216\n", + "1 5.633201\n", + "2 5.633201\n", + "3 4.333231\n", + "4 1.949954\n", + "5 3.033262\n", + "6 3.466585\n", + "7 0.866646\n", + "8 0.866646\n", + "9 6.283185\n" ] } ], @@ -443,16 +415,16 @@ "\n", "\u001b[1mUpdated Data:\u001b[0m\n", " x y\n", - "0 2.411728 0.915126\n", - "1 5.902386 -0.440795\n", - "2 3.236792 0.228788\n", - "3 3.744525 0.194455\n", - "4 0.063467 -0.053653\n", - "5 1.523196 0.881799\n", - "6 4.315723 -0.132748\n", - "7 4.379190 -0.561283\n", - "8 3.046393 -0.139681\n", - "9 5.204254 -0.610173\n" + "0 4.983216 -0.715193\n", + "1 5.633201 -0.674306\n", + "2 5.633201 -0.281330\n", + "3 4.333231 -0.167462\n", + "4 1.949954 0.811900\n", + "5 3.033262 -0.008949\n", + "6 3.466585 0.470305\n", + "7 0.866646 1.145879\n", + "8 0.866646 0.527425\n", + "9 6.283185 0.271280\n" ] } ], @@ -500,7 +472,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:05<00:00, 19.41it/s]\n", + " 1%| | 1/100 [00:00<00:10, 9.26it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:06<00:00, 14.36it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -510,12 +489,12 @@ "text": [ "\n", "\u001b[1mUpdated Model:\u001b[0m\n", - "0.03\n" + "0.14\n" ] }, { "data": { - "image/png": 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", + "image/png": 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VFfv27WPo0KG0a9eOwMBABg0axNq1a0t1NahUKubPn88DDzyAg4MDb731FvDPNqM2NjaEhISwZMkS0zlnz55FpVKZ9lEAuHbtGiqVyrS/9I1v5Zs2bSIyMhJ7e3t69OhhWnr7hrfffhtPT0+cnJwYN25chduTAjg6OpZarluj0eDk5GR6/sgjjzBp0iQmT55MixYt6NevX5VZz549a7pXqlmzZqhUqlLbqRoMBl588UXc3Nzw8vKq8WqwRqORGTNm4Ofnh62tLT4+PjzzzDOmn9/axaRSqfj666958MEHsbe3p02bNqxatarUey5btqzU/8NLly7h5eXFrFmzTK/t3LkTGxubUntpxMbGsmrVKq5fv16j36EmLOpGOVG/Eq4kMPfgXAwYiPaLZlhIxUs7K0VvMBKXlElGTgEeTlq6BbihUatQq9T8X+f/43rxdfak7eGDfR/wes/XaenYssL3KtQXMmr9qAZM/4/FMYvRVmPb0StXrphaDg4ODuUec2vrbsaMGbz99tvMmTMHKysrVq5cybPPPsucOXOIjo5mzZo1jBkzhlatWpW68bQ6XnnlFT744APc3d156qmnGDt2rGlzouXLlzNjxgzmzZtHr169WLJkCXPnziUwMLBG17jZ4sWLmTBhQqkNkCrj6+vLihUrGDJkCCdOnCiz7trixYuZOnUqe/bsYdeuXYwePZqePXty7733AjB69GjOnj1rKoy3WrFiBR999BHLli2jffv2pKWlcejQoUozzZw5k3fffZf33nuPTz75hBEjRpCcnIybmxuZmZkkJCQQGRlpOt7d3Z0FCxYwePBg7rvvPkJCQnjssceYNGkS99xzj+m4yMhIiouL2bNnT73tnCcFQgCQlpfGB/s+oNhQTHev7ozrOM7supXWH0ll5uoEUrP++Vbq7aJlemwYMR28UavUTOoyicxdmZy6doq397zNm73eVHzW1e04ffo0RqORkJDS04pbtGhh+nY+ceJE3nnnHdPPHn300VL7JwwfPpzRo0fz9NNPAyUrEOzevZv333+/xgXirbfeonfv3kDJTm4DBw6koKAArVbLnDlzGDdunGljojfffJONGzdW2oqoSps2bXj33XdNz8+ePVvp8RqNBjc3NwA8PDxwdXUt9fNOnToxffp003t/+umnbNq0yVQgvL29MRgMVOTcuXN4eXkRHR2NtbU1fn5+dOvWrdJMo0ePZvjw4QDMmjWLuXPnEhcXR0xMDOfOncNoNOLj41PqnAEDBjB+/HhGjBhBZGQkDg4OZfb8tre3x8XFheTk5EqvfzukQAjydfm8u/ddcnW5BLkEManLJNQq8+p9XH8klQlLD3Brx0xaVgETlh5g/siuxHTwxkZjwwt3vMCrO14lPT+d9/a+x/So6eW+p63GlsUxi+s/fAXXvh1xcXEYDAZGjBhRaoMroNS3UYBjx46VGTjt2bMnH3/8cY2v26lTJ9N/e3t7A5CRkYGfnx/Hjh3jqaeeKnV8VFQUf/75Z42vc0NEREStzy3Pzfmh5HfIyMgwPb/1Q/hWDz/8MHPmzCEwMJCYmBgGDBhAbGysaR/sqq7p4OCAs7Oz6Zo3uofKu7v//fffp0OHDvz444/s37+/3FWm7ezs6nWSgnl9CogGZzCW9NlfzL1IM20znr/jeWw0tdtzuL7oDUZmrk4oUxwA02szVyegN5Q8c7F1YVq3aaY7rhcdXVTu+6pUKrRWWkUe1W2dBQcHo1KpyvT1BwYGEhwcXO6y9RV1RVVErS75GLh5XKSiwW1r63+mOd/4HSr7xn27bv1dapK1PDfnh5LfoSb5fX19OXHiBJ999hl2dnY8/fTT/Otf/6o0Q2XXbNGiBQBXr14tc15iYiIpKSkYDIYKW06ZmZm4u7tXO39NSYFo4n46+RMHMw5irbbmhcgXcNO6KR2pjLikzFLdSrcyAqlZBcQlZZpe83b05pkuz6BCxcZzG9mdsrsBkta95s2bc++99/Lpp5+Sl5dXq/do165dmT78HTt2EBYWBmD6gLl51tDNg8A1uc6ePaVnkO3eXbd/7tXJamNT8gVHr9fX6bVvsLOzIzY2lrlz57JlyxZ27drF4cOHa/VeQUFBODs7k5BQelJFUVERI0eOZNiwYbzxxhs88cQTpVo6UFJACgoK6NKlS61/l6pIgWjCDl06xM+nfgbgyU5PEuQapHCi8mXkVK8P+9bjwj3CGRoyFIAVp1agM5jvlM/KfPbZZxQXFxMZGckPP/zAsWPHOHHiBEuXLuX48eNoNJWvh/XCCy+waNEi5s+fz6lTp/jwww/5+eefef7554GSD7w777yTt99+m2PHjrF161b++9//1jjns88+y4IFC1i4cCEnT55k+vTpHD16tFa/c0Wqk9Xf3x+VSsWaNWu4dOkSubm51X7/adOm8fjjj1f480WLFvHNN99w5MgRzpw5w9KlS7Gzs6vRFNWbqdVqoqOj2b59e6nXX3nlFbKyspg7dy4vvfQSbdu2LbVnOMC2bdsIDAwkKKj+/t1KgWiirly/wicHP8GIkWi/aP7V6l9KR6qQh1P1Vl8t77jBwYOJ8Iyg2FBMTlEOekP9fKusT0FBQRw8eJDo6GimTZtG586diYyM5JNPPuH555/njTfeqPT8wYMH8/HHH/P+++/Tvn17vvjiCxYuXFhq5suCBQsoLi4mIiKCyZMn8+abb9Y457Bhw3j11Vd58cUXiYiIIDk5mQkTJtT4fapSVdaWLVsyc+ZMXn75ZTw9PZk0aVK13/vWLQNu5erqyldffUXPnj3p1KkTGzduZPXq1TRvXvt9UJ544gmWLVtm6nbasmULc+bMYcmSJTg7O6NWq1myZAnbtm1j/vz5pvP+97//MX78+Fpftzpkue8qNMblvvUGPa/vfp3jmcfxd/bnrZ5vmc0SGuXRG4z0emczaVkF5Y5DqAAvFy3bX7objbps336+Lp9Z22dxn8t9BLYOpGWzlmY3Q0s0XUajke7duzNlyhTTbKeqHD16lLvvvpuTJ0/i4lL+LD1Z7lvUys+nf+Z45nHsrOyYGjHVrIsDgEatYnpsSX/5rR/rN55Pjw0rtzgA2FvbMyJsBAD5xfnk6qrf5SBEfVOpVHz55ZcUFxdX+5zU1FS+/fbbCotDXZFprk3Mqaun+PlkybjDEx2fwMvBS+FE1RPTwZv5I7uWuQ/C66b7ICoT4BJAfnrJdMDL1y9jq7E1u9laoukKDw+v0aKG0dHR9RfmJlIgmpCC4gLmxc/DgIEePj3o1bKX0pFqJKaDN/eGeZV7J3V12FnbodVo0Rl1XMq/hI+jj3Q1CVEJKRBNyJKEJaTmpeKmdWNch3FKx6kVjVpFVFDtBgRVqHCzc+OS7hIF+gKuFV6jmbZZHScUovGQMYgm4u9Lf7PxXMmqn093fhpHG0eFEylDo9LQwu7/3ZxUeJUivfmvECpEbdTF/CMpEE3A9eLrfPn3lwD0a92Pju4dFU7U8G7czZqfn4+jtSMO1g4YjUYy8jPq5B+SEObmxhIct97JXRPSxdQELDu+jEvXL+Fu587w0OpNo2tsNBoNrq6uprtRHbQO5OnyyC/KJ8OQYdEL+glxM6PRSH5+PhkZGbi6ulZ5I2VlpEA0csczj7P+7Hqg5G5pO6uya/c0FV5eJTO2bhSJguICcnQ5pJNOM9tmaNS1/4ckhLlxdXU1/Z2vLSkQjZhOr+OLQ18A0Me3D53cO1VxRuOmUqnw9vbGw8MDnU6H0Wjki0NfcOLqCYJdg3k6/GmZ1SQaBWtr69tqOdwgBaIRW31mNSl5KbjYuPBYu8eUjmM2NBqN6R/P8I7DeX7r88RdiaPrpa709avZ/ghCNGYySN1IpeWlmRbiG9V+VJOdtVQVTwdPHg55GIClx5aSVZilcCIhzIcUiEbIaDSy8MhCdAYdHVt0pIdPD6UjmbWBAQPxd/YnV5fL98e/VzqOEGZDCkQjtCdtD/GX4rFSWzGug/ltHWpuNGqN6cbBLee3cPLqSWUDCWEmpEA0MgXFBSw+WrKN5qCgQXg7Vr5GkSgR4hZCn1Z9AFhwZAEGY/3tkiaEpZAC0cisSlxFZkEmHnYeDA4erHQci/Jou0ext7InKSuJjckblY4jhOKkQDQiGfkZrEpcBcDIsJGyWmkNudi6MCxkGAA/nPiBnKIchRMJoSwpEI3I0oSl6Aw6OjTvQDevbkrHsUj3+t+Ln5Mfubpcfjr5k9JxhFCUFIhG4sjlI+xJ24MaNaPbj5aB6VrSqDWMaj8KgD/O/sGFnAsKJxJCOVIgGgGD0cC3R78F4N7W9+Lr7KtwIsvWoUUHIjwjMGBg6bGlSscRQjFSIBqBree3kpyTjIO1Aw+3fVjpOI3CY+0ew0plxcGMg8RnxCsdRwhFSIGwcAXFBfxw4gcAHgx+ECcbp0qP1xuM7Eq8wq/xF9mVeAW9QZa6Lo+3ozf9WvcDSjZa0hv0CicSouHJWkwWbs2ZNVwtvIqHnQcxrWMqPXb9kdQyezp7V3NP56ZoSNshbL2wlQu5F9h6YSt3+92tdCQhGpS0ICzY1YKrrE5cDcDwdsOx1lS8Mcj6I6lMWHqgVHEASMsqYMLSA6w/klqvWS2Rg7UDD7V5CIDlJ5dTqC9UOJEQDcviCsS8efNo3bo1Wq2W7t27ExcXV+GxixYtQqVSlXpotdoGTFu/fjr5EwX6Atq4tiHKO6rC4/QGIzNXJ1BeZ9KN12auTpDupnLc538fHnYeXC24yroz65SOI0SDsqgC8cMPPzB16lSmT5/OgQMH6Ny5M/369TNtAFMeZ2dnUlNTTY/k5OQGTFx/UnNT2XxuM1ByU1xl01rjkjLLtBxuZgRSswqIS8qs65gWz1pjzbDQkpvnfjn9i6z2KpoUiyoQH374IePHj2fMmDGEhYXx+eefY29vz4IFCyo8R6VS4eXlZXp4eno2YOL6s+zEMgwY6OrRlVC30EqPzcipuDjU5rimpodPDwJcAijQF5iWUDcXMulA1CeLKRBFRUXs37+f6Oho02tqtZro6Gh27dpV4Xm5ubn4+/vj6+vLoEGDOHr0aKXXKSwsJDs7u9TD3CReS2R36m5UqKq1x7SHU/W61ap7XFOjVqkZEToCgI3JG7mUf0nhRCXWH0ml1zubGf7Vbp5dFs/wr3bT653NMp4k6ozFFIjLly+j1+vLtAA8PT1JS0sr95yQkBAWLFjAr7/+ytKlSzEYDPTo0YMLFyq+O3b27Nm4uLiYHr6+5nfT2f+O/w+AXi174efsV+Xx3QLc8HbRUlEnlIqS2UzdAtzqLmQj09G9Ix1bdKTYWGwWS3DIpAPRECymQNRGVFQUjz/+OOHh4fTu3Zuff/4Zd3d3vvjiiwrPmTZtGllZWabH+fPnGzBx1Q5fOszhy4exUlsxNGRotc7RqFVMjw0DKFMkbjyfHhuGRi3Lc1TmxkJ+f134S9ElOGTSgWgoFlMgWrRogUajIT09vdTr6enpeHl5Ves9rK2t6dKlC6dPn67wGFtbW5ydnUs9zIXRaDTdFBftF42HvUe1z43p4M38kV3xcindjeTlomX+yK5yH0Q1tGnWhjs878CAgeUnlyuWQyYdiIZiMTfK2djYEBERwaZNmxg8eDAABoOBTZs2MWnSpGq9h16v5/DhwwwYMKAek9afgxkHOXXtFDZqGx4MfrDG58d08ObeMC/ikjLJyCnAw6mkW0laDtU3LGQY+9L3sSd1D2eunSHQNbDBM8ikA9FQLKYFATB16lS++uorFi9ezLFjx5gwYQJ5eXmMGTMGgMcff5xp06aZjn/99df5448/OHPmDAcOHGDkyJEkJyfzxBNPKPUr1JrBaDC1HmICYnDVutbqfTRqFVFBzRkU3pKooOZSHGrI19mXXi17AZj+fzQ0mXQgGorFtCAAhg0bxqVLl3jttddIS0sjPDyc9evXmwauz507h1r9T827evUq48ePJy0tjWbNmhEREcHOnTsJCwtT6leotb1pezmbfRatRssDQQ8oHadJ+3fbf7Pj4g7iL8VzIvMEIW4hDXr9G5MO0rIKyh2HUFHSdSiTDsTtUhmNRhnJqkR2djYuLi5kZWUpNh5hMBp4YesLXMi9wJA2Q6o9OC3qz/xD89lyfgsdW3Tkv3f+t8Gvf2MWE1CqSNxoD8q4kqhMdT/XLKqLqananbKbC7kXcLB2YGDgQKXjCGBImyFoVBoOXz7M8czjDX59mXQgGoJFdTE1RQajgZ9Olcy7vz/wfhysHRROJAA87D3o69uXjec28uOJH3k16tUGzyCTDkR9kwJh5nan7OZi7kUcrR2rXM5bNKwH2zzIlvNbOHLlCEevHKV98/YNnuHGpAMh6oN0MZmxm1sPAwMHYm9tr3AicbMWdi1Me0SsOLlC4TRC1D0pEGZMWg/mb1DwIKxUVhy9cpRjV44pHUeIOiUFwkxJ68EytLBrQR/fPgCsOCWtCNG4SIEwU7tTS1oPDtYO0nowc4OCB5lmNJ3IPKF0HCHqjBQIM2QwGlh5aiUA/QP6S+vBzHnYe9C7VW9AWhGicZECYYb2p+/nXM45tBot/Vv3VzqOqIbBwYNRo+bQpUOcunpK6ThC1AkpEGbGaDSavoX2D+iPo42jwolEdXg6ePKvVv8CYOXplQqnEaJuSIEwM/GX4knKSsJWY8uAQMtcdbapGhQ8CBUq9qfv52zWWaXjCHHbpECYEaPRaNrz+F7/e3G2MZ+9KETVfBx9iPKJAuCX078oG0Y0eg2xH7ncSW1GEjITOHn1JNZqa2IDY5WOI2phcPBgdqbsZHfqblJyU/Bx9FE6kmiE1h9JZebqhFIbR3m7aJkeG1an63BJC8KM3Ji51Ne3b633exDK8nf2J8IzAiNGaUWIetGQ+5FLgTATp6+e5vDlw2hUGtnvwcI9FPwQANsubCMjP0PhNKIxaej9yKVAmIkbM196teyFu727wmnE7QhuFkzHFh0xYGDNmTVKxxGNSEPvRy4Fwgyczz7PvvR9qFAxKHiQ0nFEHbixZ/jmc5vJKsxSOI1oLBp6P3IpEGbgl8RfAOjm3Y2Wji2VDSPqRFjzMIJdg9EZdKw9s1bpOKKRaOj9yKVAKCwjP4OdF3cCMDhosLJhRJ1RqVQMDh4MwB/Jf5Cvy1c2kGgUbuxHXtGWUCpKZjPV1X7kUiAUtubMGgwY6NSiE4GugUrHEXUowjOCVo6tuF58nd/P/q50HNEIaNQqpseGAZQpEjeeT48Nq7NdBaVAKCirMIvN5zYDmL5tisZDrVKbxpTWJa2jSF+kcCLRGDTkfuRyo5yCfkv6DZ1BR7BrMGHNw5SOI+pBD58eLD+xnEvXL/Hn+T/p17qf0pFEI9BQ+5FLC0Ih+bp8U7fD4ODBqFSy0XxjZKW24v6g+wFYk7gGvUGvcCLRWNzYj3xQeEuigprXeXEAKRCK2XhuI/nF+bR0bEmEZ4TScUQ96uvbFycbJzKuZ7A7dbfScYSoNikQCtDpdaw7sw6A2MBY1Cr539CY2WpsTft6/Jr4K0Zj3S+qJkR9kE8mBWy7uI2rhVdx07rRq1UvpeOIBtCvdT+0Gi3J2ckcunRI6ThCVIsUiAZmMBpYnbgagAEBA7BWWyucSDQERxtH7va7GyhpRQhhCWQWU30wGqG4/Fvd96XvIyX3Ag5WDkT79ALd9QYOJ5Qy0Pcefk/6jYTLRzh96QjBrkFKRxKNhZUW6mGiixSI+lBcAAtiyrxsxMiv1nmg0nOf3ha7bwc3fDahmBZAT6t8/lLrWLVqDFOL7ZWOJBqLsevB2q7O31a6mBrQMZWe0yo91qjor7dROo5QwAN6WwDi1DpSkSmvwrxJC6I+WGlLKvotVu//EC7F09u3Ly7txygQTCjNF+i6/0MOXIpnjW9fxsvfA1EXrOpmcb4yb1sv79rUqVRlmnvnc85z4PLfqFQaYts8VC/NQWEZYts8yIHLf7M1ZSdD243AxdZF6UhClEu6mBrImsSSjWO6eXfDy8FL4TRCSe3c2tHGtQ06g47fkn5TOo4QFapxgRg1ahR//fVXfWRptK5cv8L2i9sBGBQkGwI1dSqVyrSt7B/Jf3C9WGayCfNU4wKRlZVFdHQ0bdq0YdasWVy8eLE+cjUqvyX9RrGxmHZu7QiSqY0CiPSKxNvBmzxdnmlFXyHMTY0LxC+//MLFixeZMGECP/zwA61bt6Z///789NNP6HS6+sho0fJ0eWxI3gAg24kKE7VKzf2BJYv4rUtaR7GhWOFEQpRVqzEId3d3pk6dyqFDh9izZw/BwcE89thj+Pj4MGXKFE6dOlXXOS3WxuSNFOgLaOXYinD3cKXjCDPSu1VvXGxcuHz9MrtSdikdR4gybmuQOjU1lQ0bNrBhwwY0Gg0DBgzg8OHDhIWF8dFHH9VVRoul0/8zCPlA0AOypLcoxVpjTf+AkkX8VieulkX8hNmpcYHQ6XSsWLGC+++/H39/f3788UcmT55MSkoKixcvZuPGjSxfvpzXX3+9PvIyb948WrdujVarpXv37sTFxVV6/I8//khoaCharZaOHTuybt26eslVnu0Xt5sW5evRskeDXVeYN73ByK7EK/wafxEXQwS2GluSc2QRP2F+anwfhLe3NwaDgeHDhxMXF0d4eHiZY/r27Yurq2sdxCvthx9+YOrUqXz++ed0796dOXPm0K9fP06cOIGHh0eZ43fu3Mnw4cOZPXs2999/P99//z2DBw/mwIEDdOjQoc7z3cxgNLD6jCzKJ0pbfySVmasTSM36Z62u5j5+eHgnsObMGsI9wpULJ8QtVMYatmuXLFnCww8/jFZbP3fuVaZ79+7ccccdfPrppwAYDAZ8fX35z3/+w8svv1zm+GHDhpGXl8eaNWtMr915552Eh4fz+eefV+ua2dnZuLi4kJWVhbOzc7Wz7k/fz7t738XOyo7P7vkMe2tZd6epW38klQlLD3DrPzi1VTbaVt/i39yOefe9T6BLoCL5hOUxGo216rqu7udajbuYHnvsMUWKQ1FREfv37yc6Otr0mlqtJjo6ml27yh/g27VrV6njAfr161fh8QCFhYVkZ2eXetTGjSW97/W/V4qDQG8wMnN1QpniAGAodkaf14aUawWsOr26wbMJy6TT63hp20v8evpXivRF9XINi7mT+vLly+j1ejw9PUu97unpSVpaWrnnpKWl1eh4gNmzZ+Pi4mJ6+Pr61jjrmawzHMs8hpXKyjQIKZq2uKTMUt1KtyrK6opOb2DT2e1k5Gc0YDJhqbZe2EpydjJ/JP9Rb7tSWkyBaCjTpk0jKyvL9Dh//nyN3yPAOYBp3abxSOgjuGnd6iGlsDQZORUXBwBjkTv6674U6fWm7WiFqIjBaGDNmZKu8wEBA7BS18+yehZTIFq0aIFGoyE9Pb3U6+np6Xh5lb+2kZeXV42OB7C1tcXZ2bnUo6ZUKhXhHuHEBsXW+FzROHk4Vd0tq8vqirVGxebzm8kpymmAVMJS7U/fT2peKo7W/+xUWB8spkDY2NgQERHBpk2bTK8ZDAY2bdpEVFRUuedERUWVOh5gw4YNFR4vRH3pFuCGt4uWioYTVYCHbRvatQiiUF9ouvteiPKsSlwFlIxx2lnV38rQFlMgAKZOncpXX33F4sWLOXbsGBMmTCAvL48xY0rW1H/88ceZNm2a6fhnn32W9evX88EHH3D8+HFmzJjBvn37mDRpklK/gmiiNGoV02PDAMoUiRvPZ8S2Z1BwySJ+65PW19vAo7BsJzJPcPLqSazV1sS0LrtzZV2yqAIxbNgw3n//fV577TXCw8OJj49n/fr1poHoc+fOkZqaajq+R48efP/993z55Zd07tyZn376iV9++aXe74EQojwxHbyZP7IrXi6lu5u8XLTMH9mVmA7e3Ol9J+527mQVZfHXBVk1WZT1a+KvAPyr1b9w1brW67VqfB9EU1Pb+yCEqIjeYCQuKZOMnAI8nLR0C3BDo/6nXbHuzDoWJyzG28GbD/t8WG8zVITluZh7kalbpqJCxYd9PsTH0adW71Nv90EIIW6PRq0iKqg5g8JbEhXUvFRxAOjr1xdHa0dS81LZm7ZXoZTCHN24vyrSM7LWxaEmpEAIYWbsrOy4z/8+oGQwUhr5AiCzIJNtF7cBNNgMSSkQQpihmIAYrNXWnL52mmOZx5SOI8zAb0m/UWwoJtQtlBC3kAa5phQIIcyQi60LvVv1Bv7pVhBNV74u3zT1OTaw4e6vkgIhhJm6P/B+VKg4kHGA89k1v6NfNB6bzm3ievF1Wjq2pKtn1wa7rhQIIcyUt6M33by7AZiWjrckN+97sSvxCnqDjKXUhs6gY11SyfIrsYGxDTqrrX4W8BBC1IlBQYPYk7qH7Re3MzRkKC3sWigdqVrK2/fC20XL9NgwYjp4K5jM8my/sJ3MgkyaaZvRq2WvBr22tCCEMGNBrkGENQ9Db7ScRfxu7Htx6+q1aVkFTFh6gPVHUis4U9zq5o3HBgYMxFrTsBuPSYEQwswNChoElPRD5xblKpymcpXte3HjtZmrE6S7qZr2p+/nYu5F7K3sucfvnga/vhQIIcxcZ/fO+Dv5U6AvMPtF/Kra98IIpGYVEJeU2XChLNjNi/IpsfGYFAghzJxKpTLdGLUuaZ1ZL+JX1b4XNT2uKTueedy0KN+AgAGKZJACIYQF6OHTAw87D7KLsvnz/J9Kx6lQdfa9qMlxTdmvp0sW5evdqne9L8pXESkQQlgAjVrDwKCBAKxJXIPeoFc4Ufmqs++Ft0vJAoWiYueyz3Eg4wAqVNwfeL9iOaRACGEh+vr2xcnGiYzrGexO3a10nHJVZ9+L6bFhZRYoFKXdGHvo7t0db0flpgVLgRDCQthqbOnfuj9Q0v1grov4VWffC1GxjPwMdlzcAcCg4EGKZpEb5YSwIP1a92NV4iqSc5KJvxRPF48uSkcqV0wHb+4N86p03wtRvjVn1mDAQKcWnQh0CVQ0ixQIISyIo40j0f7RrDmzhl9O/2K2BQL+2feiKahqE6jqyirMYvO5zQAMDh5cxylrTgqEEBZmYOBA1p9dz/HM4xy7cox2zdspHalJq8tlRX5L+g2dQUewazBhzcPqOmqNyRiEEBbGTetGn1Z9APjl9C+KZmnq6nJZkXxdPuvPrgdK7p5XqZTvjpMCIYQFig2KRY2a+EvxnM06q3ScJqmulxX5/ezvXC++TivHVkR6RdZZztshBUIIC+Tl4EWUTxQgrQil1OWyIoX6QtOS3oOCBzXokt6VMY8UQogauzEFcnfqblJyUxRO0/TU5bIim89tJrsoGw87D3r69LzdaHVGCoQQFsrf2Z8IzwiMGE3LMoiGU1fLiugMOtONcYOCB6FRa247W12RAiGEBXso+CEA/rrwFxn5GQqnaVrqalmRbRe2lWwIZNvMtA+5uZACIYQFC24WTMcWHTFgMH0LFQ2jLpYV0Rv0rDy9EoD7g+5v8A2BqiIFQggL91CbklbEn+f/JLNA9lloSLe7rMiOlB1k5GfgZONEtF90fUatFblRTggL186tHaFuoRzPPM7qxNWMaj9K6UhNSm2XFTEYDaw8VdJ6GBgwEK2V+S2BLi0IISycSqXiweAHAdiYvJGswiyFEzU9N5YVGRTekqig5tVaZmN3ym5S8lJwtHakX+t+DZCy5qRACNEIdHbvTLBrMEWGItacWaN0HFEFg9HAz6d/BiAmIEaR7USrQwqEEI2ASqXi323/DZTckSutCPO2L20f53POo9VoTUu4myMpEEI0EuHu4QS6BFKoL2TtmbVKxxEVMBgN/HTqJwD6B/TH0cZR4UQVkwIhRCOhUqkY0mYIUNKKyCnKUTiRKM++tH0kZyej1WgV3U60OqRACNGIRHhG4O/sT4G+QMYizNDNrYcBAQPMuvUAUiCEaFRUKhUPt30YgPVJ68kuylY4kbjZza2HgYEDlY5TJSkQQjQykZ6RtHZuTYG+gNWJq5WOI/4fS2s9gBQIIRodlUrF0JChgMxoMid70/ZaVOsBpEAI0Sh19ehKsGswhfpCWenVDBiMBpafWA6UbBlrCa0HkAIhRKN081jEH8l/yBpNCttxcQcXci/gaO1oMa0HsKACkZmZyYgRI3B2dsbV1ZVx48aRm5tb6Tl9+vRBpVKVejz11FMNlFgIZXV270zbZm3RGXSy65yCig3F/HSyZOzh/sD7cbB2UDhR9VlMgRgxYgRHjx5lw4YNrFmzhr/++osnn3yyyvPGjx9Pamqq6fHuu+82QFohlKdSqXgk9BEANiVvkv0iFPLXhb9Iy0/D2caZmIAYpePUiEUUiGPHjrF+/Xq+/vprunfvTq9evfjkk09YtmwZKSmVb7Vob2+Pl5eX6eHs7NxAqYVQXvvm7enYoiPFxmJ+PPmj0nGaHJ1ex4pTKwAYHDwYOys7hRPVjEUUiF27duHq6kpkZKTptejoaNRqNXv27Kn03O+++44WLVrQoUMHpk2bRn5+fqXHFxYWkp2dXeohhCUbHjocKNm57HzOeYXTNC1/JP/B5euXcdO6ca//vUrHqTGLKBBpaWl4eHiUes3Kygo3NzfS0tIqPO/RRx9l6dKl/Pnnn0ybNo0lS5YwcuTISq81e/ZsXFxcTA9fX986+R2EUEqQaxDdvbpjxGiaSSPqX74u37Rb3L/b/hsbjY3CiWpO0QLx8ssvlxlEvvVx/PjxWr//k08+Sb9+/ejYsSMjRozg22+/ZeXKlSQmJlZ4zrRp08jKyjI9zp+Xb1zC8j0c8jAqVMSlxXHq6iml4zQJa86sIacoh5aOLenTqo/ScWpF0R3lnnvuOUaPHl3pMYGBgXh5eZGRUXqArbi4mMzMTLy8vKp9ve7duwNw+vRpgoKCyj3G1tYWW1vbar+nEJbA18mX3q16s+XCFr479h3To6ajUlW9qY2onazCLNOKusNChqFRaxROVDuKFgh3d3fc3d2rPC4qKopr166xf/9+IiIiANi8eTMGg8H0oV8d8fHxAHh7V75PrBCN0cMhD7MjZQfHMo+xP30/kV6RVZ8kamXFqRUU6AsIdg2mm1c3pePUmkWMQbRr146YmBjGjx9PXFwcO3bsYNKkSTzyyCP4+PgAcPHiRUJDQ4mLiwMgMTGRN954g/3793P27FlWrVrF448/zr/+9S86deqk5K8jhCJa2LWgf0DJ5jT/O/4/9Aa9wokap9TcVDYmbwTg0dBHLbqlZhEFAkpmI4WGhnLPPfcwYMAAevXqxZdffmn6uU6n48SJE6ZZSjY2NmzcuJH77ruP0NBQnnvuOYYMGcLq1bJ4mWi6BgcPxtHakQu5F9hyYYvScRql749/j96op6tHV9q3aK90nNuiMhqNRqVDmLPs7GxcXFzIysqSeyhEo7D2zFq+TfiWZrbNmNN3DlorrdKRGo2EKwnM3DUTNWre6/0erZxaKR2pXNX9XLOYFoQQom7c538fHnYeXC28yqrEVUrHaTQMRgNLE5YCcI//PWZbHGpCCoQQTYy1xpqRYSX3A61KXMXl65cVTtQ47EzZSWJWIlqN1rRQoqWTAiFEE9TNqxvt3NqhM+j4/tj3SsexeAXFBXx37DugZJzHxdZF4UR1QwqEEE2QSqXi8faPo0LFjpQdnLx6UulIFm1V4ioyCzLxsPOwqOW8qyIFQogmKtAlkN6+vQFYfHQxBqNB4USWKSM/wzSWMzJspEUuqVERKRBCNGGPhDyCVqPl9LXTbDm/Rek4FmlpwlJ0eh3u1sGkprVkV+IV9IbGMTlU0TuphRDKaqZtxsMhD7MkYQnfH/+ebl7dLGY7THNw+NJhNiRtJ+VaIXHnQlijOwSAt4uW6bFhxHSw7FUbpAUhRBMX0zqGVo6tyCnK4YcTPygdx2Lo9Dpm7fyM5Cv55F9tj1HX3PSztKwCJiw9wPojqQomvH1SIIRo4qzUVoztOBaADckbOJN1RuFElmFV4moSMpIx6u3RXbuz1M9udDDNXJ1g0d1NUiCEELRv3p6ePj0xYuTrv7+26AFrvcHIrsQr/Bp/sd7GA9Lz0vn2yHJ0egNFmb3AUHYFaCOQmlVAXFJmnV+/ocgYhBACKJmBcyDjAIlZifx+9nfTwn6WZP2RVGauTiA1q8D0Wl2PBxiNRhYcWUBBcRH6Al/0eW0rPT4jp6DSn5szaUEIIQBw07oxot0IAJYdX2Zxd1ivP5LKhKUHShUHqPvxgN2pu4m/FI/Wypqiy32Ayldr9XCy3LWupEAIIUzu8buHts3aUqAv4JvD32Apa3nqDUZmrk6gvLR1OR6QXZTNwiMLAXg0bAheDl4VlgcVJa2XbgFut3VNJUmBEEKYqFVqnuz0JFYqKw5kHGBX6i6lI1VLXFJmmZbDzepqPGDx0cVkFWXRyrEVD7V9kOmxYUDZNsSN59Njw9CoZT8IIUQj4evky6DgQQAsOLKArMIshRNVrbr9/LczHnAg/QDbL25HjZoJnSdgrbYmpoM380d2xculdDeSl4uW+SO7Wvx9EDJILYQo48E2D7IvbR/JOcl8ffhrpkZMNeud0arbz1/b8YA8XR5fHf4KgAGBAwhuFmz6WUwHb+4N8yIuKZOMnAI8nEq6lSy55XCDtCCEEGVYq615OvxpNCoNcWlxbL+4XelIleoW4Ia3i7bexgMWHFlAZkEmXvZeDA0ZWubnGrWKqKDmDApvSVRQ80ZRHEAKhBCiAq1dWjOkzRAAFh5dyJXrVxROVDGNWlVv4wE7L+40dS09Hf40tpqy9zw0VlIghBAVGhQ8iECXQPJ0eXwW/5lZ30BXH+MBV65f4esjXwMl3W4hbiF1ktVSyBiEEKJCVmor/tPlP7y87WWOXDnCqsRVDA4erHSsCtXleIDBaGD+ofnk6fIIcgnioTYP1UNi8yYtCCFEpXwcfRjTfgwAPxz/wew3F6qr8YBfT//K4cuHsVHbMKnLJKzUTe/7tBQIIUSV+vj2oYdPDwwY+OTAJ+Tp8pSOVK+OXjnK8hPLARjXcRw+jj4KJ1KGFAghRJVUKhXjO47Hw86DjOsZfHrwU7Mej7gd1wquMffAXAwY6NOqD318+ygdSTFSIIQQ1WJvbc+UyClYq605kHGAFadWKB2pzhUbivn44MdcK7yGr5OvaRn0pkoKhBCi2gJdAhnfcTwAP538if3p+xVOVLcWH11MwpUEtBotUyKmNKkpreWRAiGEqJHevr3p17ofAJ8c/ITzOecVTlQ3fj/7O38k/4EKFf/p8h9aOrZUOpLipEAIIWrssbDHaOfWjuvF13k77m2uFlxVOtJtOXL5CIuOLAJgeOhwIr0ilQ1kJqRACCFqzFptzXORz+Ht4M3l65d5Z+87XC++rnSsWjmbdZb3972PAQN3tbyLB4IeUDqS2ZACIYSoFScbJ17u9jJONk4kZSXx8YGP0Rl0SseqkbS8NGbtmcX14uu0c2vH/3X6P7NelLChSYEQQtSal4MXL97xItZqaw5mHOSTA59QbChWOla1XC24ylt73iKrKAt/Z/+S30NjrXQssyIFQghxW9o2a8vzkc9jpbZiT9oe5sXPM/t7JK5cv8Lru14nIz8DT3tP/r9u/x/21vZKxzI7UiCEELct3COcqRFT0ag07EzZyacHPzXb7qbL1y8zc9dMUvJSaGHXgle6v4Kr1lXpWGZJCoQQok5EeEbwbNdn0ag07EjZwXt73zO7gevU3FRm7JxBen46HvYezIiagaeDp9KxzJYUCCFEnenu3Z0X73gRW40thy4d4o1db5jNlqVHrxzlvzv+y6Xrl/B28GZG1Azc7d2VjmXWpEAIIepUuEc4r975Kk7WTiRmJTJt2zROXz2taKbN5zbz1u63yNXlEuwazMweM2lu11zRTJZAZTQajUqHMGfZ2dm4uLiQlZWFs7Oz0nGEsBgXcy/y3t73SM1LxUptxZj2Y7jH754GnUZ6vfg6i44sYsuFLQD08OnBhM4TsNHYNFgGc1TdzzUpEFWQAiFE7eXr8vks/jP2pu8F4A7POxjbcSxu2trtDV0Tp6+eZu7BuaTnp6NCxb/b/pshbYbIfQ5IgagzUiCEuD1Go5HVZ1az7Pgy9EY99lb2jGw3kr5+fVGr6r6XO7som+UnlrMpeRMGDLSwa8HE8ImENQ+r82tZqup+rlnMGMRbb71Fjx49sLe3x9XVtVrnGI1GXnvtNby9vbGzsyM6OppTp07Vb1AhRCkqlYoHgh7g7bveJsgliPzifL48/CUvbH2BPal76uyeievF11l7Zi1T/pzChuQNGDDQ06cn7/7rXSkOtWQxLYjp06fj6urKhQsX+Oabb7h27VqV57zzzjvMnj2bxYsXExAQwKuvvsrhw4dJSEhAq9VWeT5IC0KIumQwGvgt6TdWnFph2pXOz8mPu/3uplfLXjjZONX4PdPy0th0bhMbkzeSX5wPgL+zP6Pbj5bCUIFG28W0aNEiJk+eXGWBMBqN+Pj48Nxzz/H8888DkJWVhaenJ4sWLeKRRx6p1vWkQAhR9/J0eaw9s5a1Z9ZSoC8AwEplRUf3joQ1DyPULRRfJ1/srOxKnWc0GsksyOR8znlOZJ5gb/reUsuNezt4ExsUS1/f+um+aiyq+7nWaHfhTkpKIi0tjejoaNNrLi4udO/enV27dlVYIAoLCyksLDQ9z87OrvesQjQ1DtYODA0ZyoCAAWxP2c6W81tIykriYMZBDmYcNB2n1Whppm0GQKG+kHxdvqmg3KBGTfsW7YlpHUNXz65SGOpQoy0QaWlpAHh6lr5L0tPT0/Sz8syePZuZM2fWazYhRAlHG0diWscQ0zqG89nn+fvy3xzPPM7xzONkF2VToC8gNS+11DkalQZvB2/8nf0J9winq0dXHG0cFfoNGjdFC8TLL7/MO++8U+kxx44dIzQ0tIESwbRp05g6darpeXZ2Nr6+vg12fSGaKl9nX3ydfRkYOBAoGXS+VnCNq4VXUavU2Kht0Fppcbdzl1VXG4iiBeK5555j9OjRlR4TGBhYq/f28vICID09HW9vb9Pr6enphIeHV3iera0ttrZNex9aIcyBnZUddo52eDt6V32wqBeKFgh3d3fc3etnLZSAgAC8vLzYtGmTqSBkZ2ezZ88eJkyYUC/XFEKIxsRiRnPOnTtHfHw8586dQ6/XEx8fT3x8PLm5uaZjQkNDWblyJVAy93ry5Mm8+eabrFq1isOHD/P444/j4+PD4MGDFfothBDCcljMIPVrr73G4sWLTc+7dOkCwJ9//kmfPn0AOHHiBFlZ/6wc+eKLL5KXl8eTTz7JtWvX6NWrF+vXr6/2PRBCCNGUWdx9EA1N7oMQQjQ2jW6pDSGEEA1LCoQQQohySYEQQghRLosZpFbKjSEaWXJDCNFY3Pg8q2oIWgpEFXJycgDkbmohRKOTk5ODi4tLhT+XWUxVMBgMpKSk4OTkVKOdqG4s0XH+/HmLmf0kmRuGpWW2tLwgmatiNBrJycnBx8cHtbrikQZpQVRBrVbTqlWrWp/v7OxsMX9Bb5DMDcPSMltaXpDMlams5XCDDFILIYQolxQIIYQQ5ZICUU9sbW2ZPn26Ra0MK5kbhqVltrS8IJnrigxSCyGEKJe0IIQQQpRLCoQQQohySYEQQghRLikQQgghyiUFoh7MmzeP1q1bo9Vq6d69O3FxcUpHqtRff/1FbGwsPj4+qFQqfvnlF6UjVWr27NnccccdODk54eHhweDBgzlx4oTSsSo1f/58OnXqZLoJKioqit9++03pWDXy9ttvm3ZqNFczZsxApVKVeoSGhiodq0oXL15k5MiRNG/eHDs7Ozp27Mi+ffuUjiUFoq798MMPTJ06lenTp3PgwAE6d+5Mv379yMjIUDpahfLy8ujcuTPz5s1TOkq1bN26lYkTJ7J79242bNiATqfjvvvuIy8vT+loFWrVqhVvv/02+/fvZ9++fdx9990MGjSIo0ePKh2tWvbu3csXX3xBp06dlI5Spfbt25Oammp6bN++XelIlbp69So9e/bE2tqa3377jYSEBD744AOaNWumdDQwijrVrVs348SJE03P9Xq90cfHxzh79mwFU1UfYFy5cqXSMWokIyPDCBi3bt2qdJQaadasmfHrr79WOkaVcnJyjG3atDFu2LDB2Lt3b+Ozzz6rdKQKTZ8+3di5c2elY9TISy+9ZOzVq5fSMcolLYg6VFRUxP79+4mOjja9plariY6OZteuXQoma9xu7EPu5uamcJLq0ev1LFu2jLy8PKKiopSOU6WJEycycODAUn+vzdmpU6fw8fEhMDCQESNGcO7cOaUjVWrVqlVERkby8MMP4+HhQZcuXfjqq6+UjgVIF1Odunz5Mnq9Hk9Pz1Kve3p6kpaWplCqxs1gMDB58mR69uxJhw4dlI5TqcOHD+Po6IitrS1PPfUUK1euJCwsTOlYlVq2bBkHDhxg9uzZSkeplu7du7No0SLWr1/P/PnzSUpK4q677jIt22+Ozpw5w/z582nTpg2///47EyZM4JlnnmHx4sVKR5PVXIVlmzhxIkeOHDH7fmaAkJAQ4uPjycrK4qeffmLUqFFs3brVbIvE+fPnefbZZ9mwYQNarVbpONXSv39/03936tSJ7t274+/vz/Llyxk3bpyCySpmMBiIjIxk1qxZAHTp0oUjR47w+eefM2rUKEWzSQuiDrVo0QKNRkN6enqp19PT0/Hy8lIoVeM1adIk1qxZw59//nlbS7I3FBsbG4KDg4mIiGD27Nl07tyZjz/+WOlYFdq/fz8ZGRl07doVKysrrKys2Lp1K3PnzsXKygq9Xq90xCq5urrStm1bTp8+rXSUCnl7e5f5ktCuXTuz6BqTAlGHbGxsiIiIYNOmTabXDAYDmzZtsoi+ZkthNBqZNGkSK1euZPPmzQQEBCgdqVYMBgOFhYVKx6jQPffcw+HDh4mPjzc9IiMjGTFiBPHx8Wg0GqUjVik3N5fExES8vb2VjlKhnj17lpmmffLkSfz9/RVK9A/pYqpjU6dOZdSoUURGRtKtWzfmzJlDXl4eY8aMUTpahXJzc0t9w0pKSiI+Ph43Nzf8/PwUTFa+iRMn8v333/Prr7/i5ORkGt9xcXHBzs5O4XTlmzZtGv3798fPz4+cnBy+//57tmzZwu+//650tAo5OTmVGddxcHCgefPmZjve8/zzzxMbG4u/vz8pKSlMnz4djUbD8OHDlY5WoSlTptCjRw9mzZrF0KFDiYuL48svv+TLL79UOppMc60Pn3zyidHPz89oY2Nj7Natm3H37t1KR6rUn3/+aQTKPEaNGqV0tHKVlxUwLly4UOloFRo7dqzR39/faGNjY3R3dzfec889xj/++EPpWDVm7tNchw0bZvT29jba2NgYW7ZsaRw2bJjx9OnTSseq0urVq40dOnQw2traGkNDQ41ffvml0pGMRqPRKMt9CyGEKJeMQQghhCiXFAghhBDlkgIhhBCiXFIghBBClEsKhBBCiHJJgRBCCFEuKRBCCCHKJQVCCCFEuaRACCGEKJcUCCGEEOWSAiGEgi5duoSXl5dpLwCAnTt3YmNjU2pVYCGUIGsxCaGwdevWMXjwYHbu3ElISAjh4eEMGjSIDz/8UOlooomTAiGEGZg4cSIbN24kMjKSw4cPs3fvXmxtbZWOJZo4KRBCmIHr16/ToUMHzp8/z/79++nYsaPSkYSQMQghzEFiYiIpKSkYDAbOnj2rdBwhAGlBCKG4oqIiunXrRnh4OCEhIcyZM4fDhw/j4eGhdDTRxEmBEEJhL7zwAj/99BOHDh3C0dGR3r174+Liwpo1a5SOJpo46WISQkFbtmxhzpw5LFmyBGdnZ9RqNUuWLGHbtm3Mnz9f6XiiiZMWhBBCiHJJC0IIIUS5pEAIIYQolxQIIYQQ5ZICIYQQolxSIIQQQpRLCoQQQohySYEQQghRLikQQgghyiUFQgghRLmkQAghhCiXFAghhBDlkgIhhBCiXP8/HaZFvdApnugAAAAASUVORK5CYII=", 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" ] @@ -555,7 +534,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:03<00:00, 25.80it/s]\n", + "100%|██████████| 100/100 [00:06<00:00, 15.87it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -563,61 +542,47 @@ "name": "stdout", "output_type": "stream", "text": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.06346652, 0.12693304, 0.19039955, 0.25386607,\n", - " 0.31733259, 0.38079911, 0.44426563, 0.50773215, 0.57119866,\n", - " 0.63466518, 0.6981317 , 0.76159822, 0.82506474, 0.88853126,\n", - " 0.95199777, 1.01546429, 1.07893081, 1.14239733, 1.20586385,\n", - " 1.26933037, 1.33279688, 1.3962634 , 1.45972992, 1.52319644,\n", - " 1.58666296, 1.65012947, 1.71359599, 1.77706251, 1.84052903,\n", - " 1.90399555, 1.96746207, 2.03092858, 2.0943951 , 2.15786162,\n", - " 2.22132814, 2.28479466, 2.34826118, 2.41172769, 2.47519421,\n", - " 2.53866073, 2.60212725, 2.66559377, 2.72906028, 2.7925268 ,\n", - " 2.85599332, 2.91945984, 2.98292636, 3.04639288, 3.10985939,\n", - " 3.17332591, 3.23679243, 3.30025895, 3.36372547, 3.42719199,\n", - " 3.4906585 , 3.55412502, 3.61759154, 3.68105806, 3.74452458,\n", - " 3.8079911 , 3.87145761, 3.93492413, 3.99839065, 4.06185717,\n", - " 4.12532369, 4.1887902 , 4.25225672, 4.31572324, 4.37918976,\n", - " 4.44265628, 4.5061228 , 4.56958931, 4.63305583, 4.69652235,\n", - " 4.75998887, 4.82345539, 4.88692191, 4.95038842, 5.01385494,\n", - " 5.07732146, 5.14078798, 5.2042545 , 5.26772102, 5.33118753,\n", - " 5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,\n", - " 5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,\n", - " 6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 5.521587\n", - "1 5.902386\n", - "2 5.711987\n", - "3 6.092786\n", - "4 3.871458\n", - "5 0.698132\n", - "6 2.919460\n", - "7 4.061857\n", - "8 4.315723\n", - "9 3.934924, experiment_data= x y\n", - "0 2.411728 0.915126\n", - "1 5.902386 -0.440795\n", - "2 3.236792 0.228788\n", - "3 3.744525 0.194455\n", - "4 0.063467 -0.053653\n", - "5 1.523196 0.881799\n", - "6 4.315723 -0.132748\n", - "7 4.379190 -0.561283\n", - "8 3.046393 -0.139681\n", - "9 5.204254 -0.610173\n", - "10 2.411728 0.435060\n", - "11 5.902386 -0.604527\n", - "12 3.236792 0.025925\n", - "13 3.744525 -1.523700\n", - "14 0.063467 -0.799035\n", - "15 1.523196 0.717724\n", - "16 4.315723 -1.428770\n", - "17 4.379190 -0.787877\n", - "18 3.046393 -0.358956\n", - "19 5.204254 -1.587605, models=[0.03, 0.03])\n" + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", + " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", + " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", + " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", + " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", + " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 1.516631\n", + "1 3.033262\n", + "2 4.333231\n", + "3 3.033262\n", + "4 1.949954\n", + "5 1.949954\n", + "6 5.633201\n", + "7 5.199877\n", + "8 0.866646\n", + "9 5.633201, experiment_data= x y\n", + "0 4.983216 -0.715193\n", + "1 5.633201 -0.674306\n", + "2 5.633201 -0.281330\n", + "3 4.333231 -0.167462\n", + "4 1.949954 0.811900\n", + "5 3.033262 -0.008949\n", + "6 3.466585 0.470305\n", + "7 0.866646 1.145879\n", + "8 0.866646 0.527425\n", + "9 6.283185 0.271280\n", + "10 4.983216 -1.195259\n", + "11 5.633201 -0.838039\n", + "12 5.633201 -0.484193\n", + "13 4.333231 -1.885617\n", + "14 1.949954 0.066518\n", + "15 3.033262 -0.173025\n", + "16 3.466585 -0.825717\n", + "17 0.866646 0.919286\n", + "18 0.866646 0.308150\n", + "19 6.283185 -0.706152, models=[0.14, 0.14])\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -630,7 +595,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 24.20it/s]\n", + "100%|██████████| 100/100 [00:05<00:00, 17.23it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -638,71 +603,57 @@ "name": "stdout", "output_type": "stream", "text": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.06346652, 0.12693304, 0.19039955, 0.25386607,\n", - " 0.31733259, 0.38079911, 0.44426563, 0.50773215, 0.57119866,\n", - " 0.63466518, 0.6981317 , 0.76159822, 0.82506474, 0.88853126,\n", - " 0.95199777, 1.01546429, 1.07893081, 1.14239733, 1.20586385,\n", - " 1.26933037, 1.33279688, 1.3962634 , 1.45972992, 1.52319644,\n", - " 1.58666296, 1.65012947, 1.71359599, 1.77706251, 1.84052903,\n", - " 1.90399555, 1.96746207, 2.03092858, 2.0943951 , 2.15786162,\n", - " 2.22132814, 2.28479466, 2.34826118, 2.41172769, 2.47519421,\n", - " 2.53866073, 2.60212725, 2.66559377, 2.72906028, 2.7925268 ,\n", - " 2.85599332, 2.91945984, 2.98292636, 3.04639288, 3.10985939,\n", - " 3.17332591, 3.23679243, 3.30025895, 3.36372547, 3.42719199,\n", - " 3.4906585 , 3.55412502, 3.61759154, 3.68105806, 3.74452458,\n", - " 3.8079911 , 3.87145761, 3.93492413, 3.99839065, 4.06185717,\n", - " 4.12532369, 4.1887902 , 4.25225672, 4.31572324, 4.37918976,\n", - " 4.44265628, 4.5061228 , 4.56958931, 4.63305583, 4.69652235,\n", - " 4.75998887, 4.82345539, 4.88692191, 4.95038842, 5.01385494,\n", - " 5.07732146, 5.14078798, 5.2042545 , 5.26772102, 5.33118753,\n", - " 5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,\n", - " 5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,\n", - " 6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 3.998391\n", - "1 3.871458\n", - "2 1.967462\n", - "3 2.982926\n", - "4 3.300259\n", - "5 3.934924\n", - "6 1.967462\n", - "7 4.950388\n", - "8 3.807991\n", - "9 5.521587, experiment_data= x y\n", - "0 2.411728 0.915126\n", - "1 5.902386 -0.440795\n", - "2 3.236792 0.228788\n", - "3 3.744525 0.194455\n", - "4 0.063467 -0.053653\n", - "5 1.523196 0.881799\n", - "6 4.315723 -0.132748\n", - "7 4.379190 -0.561283\n", - "8 3.046393 -0.139681\n", - "9 5.204254 -0.610173\n", - "10 2.411728 0.435060\n", - "11 5.902386 -0.604527\n", - "12 3.236792 0.025925\n", - "13 3.744525 -1.523700\n", - "14 0.063467 -0.799035\n", - "15 1.523196 0.717724\n", - "16 4.315723 -1.428770\n", - "17 4.379190 -0.787877\n", - "18 3.046393 -0.358956\n", - "19 5.204254 -1.587605\n", - "20 3.998391 -0.022925\n", - "21 3.871458 -0.779657\n", - "22 1.967462 0.956118\n", - "23 2.982926 -0.554373\n", - "24 3.300259 -0.430193\n", - "25 3.934924 -0.657233\n", - "26 1.967462 0.346858\n", - "27 4.950388 -0.783963\n", - "28 3.807991 -0.918478\n", - "29 5.521587 -0.835926, models=[-0.31, -0.31, -0.31])\n" + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", + " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", + " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", + " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", + " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", + " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 3.033262\n", + "1 0.649985\n", + "2 3.899908\n", + "3 2.599939\n", + "4 2.816600\n", + "5 6.283185\n", + "6 1.083308\n", + "7 4.333231\n", + "8 0.649985\n", + "9 6.066524, experiment_data= x y\n", + "0 4.983216 -0.715193\n", + "1 5.633201 -0.674306\n", + "2 5.633201 -0.281330\n", + "3 4.333231 -0.167462\n", + "4 1.949954 0.811900\n", + "5 3.033262 -0.008949\n", + "6 3.466585 0.470305\n", + "7 0.866646 1.145879\n", + "8 0.866646 0.527425\n", + "9 6.283185 0.271280\n", + "10 4.983216 -1.195259\n", + "11 5.633201 -0.838039\n", + "12 5.633201 -0.484193\n", + "13 4.333231 -1.885617\n", + "14 1.949954 0.066518\n", + "15 3.033262 -0.173025\n", + "16 3.466585 -0.825717\n", + "17 0.866646 0.919286\n", + "18 0.866646 0.308150\n", + "19 6.283185 -0.706152\n", + "20 3.033262 0.840943\n", + "21 0.649985 0.492286\n", + "22 3.899908 -0.653935\n", + "23 2.599939 -0.196820\n", + "24 2.816600 0.047110\n", + "25 6.283185 0.055461\n", + "26 1.083308 0.308015\n", + "27 4.333231 -0.741128\n", + "28 0.649985 0.304855\n", + "29 6.066524 -0.360817, models=[sin(x), sin(x), sin(x)])\n" ] }, { "data": { - "image/png": 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9PTTMHBxSbjnyXoG92HFpBwnXElhydAkzus+Q5UxEvWYzNYimTZtiZ2fH5cuXyxy/fPkyfn5+1bqHg4MDd9xxB6dPn67wHCcnJ9zd3cu8rMXGsxs5n3UeVwdXngh5wtLh2LzYY2lMXnm4THIASM/MZ/LKw8QeSytzXKVS8XSXp3FQO/D7ld/ZnbrbnOEKYXY2kyAcHR3p1q0bW7Zs0R/T6XRs2bKlTC2hMlqtlqNHj+Lvb/mNaox15dYVvjn5DQCjgkfh4eRh4Yhsm1anMHt9AuUbk9Afm70+oVxzk7+rP4+0fwSAFcdXkFuUa9pAhbAgm0kQANOnT2fx4sXExMSQmJjI5MmTyc3NZdy4ksXVnnzySWbMmKE//4033uCXX37hzJkzHD58mNGjR3P+/HmeeuopS/0INaIoCsuOL6NAW0CwVzC9AntZOiSbt//s9XI1h9spQFpmPvvPXi/32eA2gwloFEBmYSarT6w2YZRCWJZN9UEMGzaMK1eu8Prrr5Oenk5YWBixsbH6juuUlBTU6j9z3o0bN5g4cSLp6ek0btyYbt26sWfPHkJCQiz1I9TIocuHOHT5EPYqeyZ0mYBaZVN53SplZFecHKo6z8HOgQldJvDm3jfZfH4zvQJ70dazbV2HKITFqRRFMVTLFv8vKysLDw8PMjMzLdIfUaAt4IVtL3Al7wpD2g5hZPBIs8dQH8UlX2PE4r1VnvfVxLuJbNvE4Gfzj8xn16VdtPFow9v3vC2JW9iM6j7X5Dfayv1w6geu5F2hiaaJvu27oavOsNSqdG/thb+HhorGIKkoGc3UvbVXhfd4IvgJXOxdOJN5hl/O/2J0DEJYO5tqYmpo0nLSWHemZFezMZ3GoLGvP+tI1ZQxw1IrY6dWMXNwCJNXHkYFZTqrS5PGzMEhle6f7anxZFjQMJYdW8aapDVE+kfK4AFRr0gNwkopisLy48sp1hUT6h1Kd7/ulg7J4owdllqVAZ39WTj6Tvw8yiZePw8NC0ffWa2E069FP1q6tyS3KFc6rEW9IzUIK3Xw8kHir8Rjr7ZnXKdxDX5CVlXDUlWUDEvtF+JX6bf+vxrQ2Z9+IX7sP3udjOx8fNxKmpWqew87tR0TOk/g9T2vs/XCVvq26Eu7xu2qXb4Q1kxqEFaoSFvEiuMrAPhbm7/h72p78zbqWm2GpVbFTq0ism0ThoQ1I7JtE6MSDEBHr47c2+xeFBSWHluKTtEZHYMQ1kgShBVaf2Y9GXkZeGm8GNpuqKXDsQq1GZZqDqODR6Ox05Ccmcy2C9ssEoMQdU0ShJW5mneVtafWAiUPHVmMr4SPW/U66Kt7Xl3z1HjyWMfHAFh9YjW3im5ZJA4h6pIkCCuzMmElhbpCgr2C6RHQw9LhWI26GJZqav1b9dfPsJbd50R9IAnCiiRcSyAuLQ41asZ2GtvgO6ZvVzosFSiXJKo7LNXUHNQOjOk0BoCfz/5Mak6qxWIRoi5IgrASOkVHzPEYAPq06EMrj1aWDcgK1cWwVFML8wnjTp870Spa/b+nELZKhrlaia0XtnIu6xwu9i6yS1wlajss1RyeDHmSP678QfyVeOIz4gnzCTP6HlqdYtU/o2gYJEFYgVtFt/j6xNcA/L3D32U2bhVKh6VaK39Xfwa2Hsj6M+tZkbCCzk07Y6+u/n+1upotLkRtSROTFVh7ei2ZhZkENAqgf6v+lg5H1IFH2j+Cu6M7l3Iusfn85mpfV9ezxYWoDUkQFpaem87GMxsBeLLTk0Z90xTWy8XBhWEdhwHw7clvyS7MrvKamm5iZKy6WOxQNAzyNLKwVYmrKFaK6dq0K2HeYZYOR9ShPi368Mu5XziffZ5vTn7D+M7jKz3fmNniNW1ik+YrYQypQVhQwrUE9qXvQ42aJzs9KcNa6xm1Sq0f9rr53GYu5Vyq9HxTzxaX5ithLEkQFnL7sNa+LfsS6BZo4YiEKXRq2olw33B06FiZsLLSc005W9xczVeifpEEYSE7Lu7QD2t9rMNjlg5HmNDo4NHYqew4nHGYo1eOVnieKWeLm3KxQ1F/SYKwgPzifP3eAQ+3f1iGtdZz/q7++tFpXyR8UeFqr6acLW7tix0K6yQJwgLWn1nPjYIb+Dj7MLDVQEuHI8zg0faP4urgyvns82y9sLXC80w1W9zaFzsU1klGMZnZ9fzrrE9eD8CI4BE42DlYOCJhDq6OrjzS/hFWJKzg6xNf0yOgR4Ur9Zpitnhp81V6Zr7BfggVJUnIkosdCusjNQgz+zrpawq0BXRo3IFI/0hLhyPM6IFWD+Dn4kdmYab+S0JFaruJkaH7Wftih8L6SIIwo3OZ59h+YTsAT4Q8IcNaGxgHtQMjg0cCsD55Pdfyrpm1fFtY7FBYF2liMhNFUViZuBIFhR4BPejQuIOlQxIW0N2vO0FeQZy4foI1SWuYHDbZrOXbwmKHwnpIDcJMfr/yO0evHsVebc+IoBGWDkdYiEql4ongJwDYfnE75zLPmT2Gum6+EvWXJAgz0Oq0fJHwBQADWw3Ex8XHwhEJS2rXuB09A3qioPBFwhcoikxOE9ZJEoQZbLuwjYs5F3F1cGVou6GWDkdYgeFBw7FX23Ps2jHir8SX+1wW1BPWQPogTCyvOI+vk0r2eni0/aO4OrpaOCJhDXxcfBjUehDrktexMmElXZt2xU5tB8iCesJ6SA3CxNYnryezMBM/Fz/6tepn6XCEFRnabiiuDq5czLnItgvbAFlQT1gXm0sQCxYsoFWrVmg0GiIiIti/f3+l53/zzTcEBQWh0Wjo0qULGzduNFOkJZPiNpzZAMDI4JE4qGVSnPhTI4dGPNr+UaBkfkxO4S1ZUE9YFZtKEF9//TXTp09n5syZHD58mNDQUPr3709GRobB8/fs2cOIESOYMGECR44cYejQoQwdOpRjx46ZJd41SWv0k+K6+3U3S5nCtvRr1Q9fF18yCzP5ZP9qq1tQT/pCGjaVYkNDKCIiIrjrrrv45JNPANDpdAQGBvLPf/6TV199tdz5w4YNIzc3lw0bNuiP3X333YSFhbFo0aJqlZmVlYWHhweZmZm4u7tXO9aUrBRe3vEyCgpv9HiDjl4dq32taFj2pu3lo0MfkZMHCfGPoGgr76f6eHgYQ8KamTwu6Quxfoqi1GjCbXWfazZTgygsLOTQoUNERUXpj6nVaqKiooiLizN4TVxcXJnzAfr371/h+QAFBQVkZWWVedXEl4lfoqAQ4R8hyUFUKsIvgg6NO6BSF+PQeF+V55tjQT3pC7F+Rdoi/rXrX/x05ieKdEUmKcNmEsTVq1fRarX4+vqWOe7r60t6errBa9LT0406HyA6OhoPDw/9KzDQ+I18Tt04RfyVeOxV9gzrMEKq6KJSKpWK0cGjaeRoj7PHCdQOVw2fR833gzCGbC5kG2LPxXIm8wzrz6yvcAn52pJhrn8xY8YMpk+frn+flZVldJJo59mOV7u/ys+Jx/n7J1JFF1Xr6NWRiIAIsvJ3cSZnD4WXHyrzgDbngnrm2Btb1E5OYQ5rT68FYHjH4TjZOZmkHJupQTRt2hQ7OzsuX75c5vjly5fx8/MzeI2fn59R5wM4OTnh7u5e5mUslUrF5Qw/Fv/cWKrootpGBo3Ey8WZNs2v4N20bC3XnAvqyeZC1u+7U9+RW5RLS7eW3Nf8PpOVYzMJwtHRkW7durFlyxb9MZ1Ox5YtW4iMNLxsdmRkZJnzATZv3lzh+XVFquiiJvwa+fFAqwdwd3bg/rtO8OVT3fl4eBhfTbybXa/0MVutUzYXsm7puen8cu4XAEaHjEatMt1j3GYSBMD06dNZvHgxMTExJCYmMnnyZHJzcxk3bhwATz75JDNmzNCf/9xzzxEbG8ucOXM4ceIEs2bN4uDBg0ydOtWkccr+v6KmHmn/CC72LqRkp1DkdNwiC+qZcm9sUXtfnfiKYqWYMO8wunp3NWlZNpUghg0bxocffsjrr79OWFgY8fHxxMbG6juiU1JSSEv7s+mmR48erFq1is8++4zQ0FC+/fZbfvjhBzp37mzSOKWKLmrKzdGNR9o/AsDqpNUUaAvMHoNsLmS9kq4nsTdtL2rUjAoeZfLybGoehCXUZB5EXPI1RizeW+V5X028Wzr5RDlF2iKe3/Y8V/KuMLzjcB5u/7BF4qjtPAitTpF9J+qQoii8vud1Tt44Sa/AXkwOrfleItV9rskoJhOQ/X9FVSp7eDrYOTAiaATzjszjh9M/0KdFHzycPMweY202F5JJdnVvX/o+Tt44iZOdE8M6DjNLmZIgTKC0ij555WFUYLHhisI6VefhGRkQyU9nfiI5M5k1SWuY2HWiRWIt3VzIGKWT7P765ah0BJ9sb2q8Il0RXyZ+CcDgtoPx0pjny6VN9UHYEtn/VxhS3RnKapWaJ0JKdp77LeU3LmRfMHusNSEj+Ezjl3O/kHErg8ZOjRncZrDZypUahAnJ/r/idlU9PFWUPDz7hfhhp1YR3CSY7n7d2Z++n5UJK5kRMcPAldZFJtnVvZzCHL4/9T0Aj3d8HI29+YYXSw3CxGT/X1GqJsOfRwaNxE5lR/yVeP648ocZoqwdGcFX97479R05RTm0cGtBr8BeZi1bEoQQZlKTh6e/qz/9W/UHYGXCSpOtuVNXZJJd3TLnpDhDJEEIYSY1fXg+0v4RGjk04nz2ef3Oc9ZKJtnVrVWJq/ST4kK9Q81eviQIIcykpg/P2yfPfZ30NXnFeaYNtBZkkl3dSbiWwL70fahRMzpktEVikAQhhJnU5uHZv1V//Fz8uFlwk3Wn15k20FqSEXy1p1N0rExYCUCfFn0IdDN+24G6IDOpq1DTHeWEqEhNJ5HtT9vPnENzcFA7MLf3XJo6NzVHuDUmM6lrbsfFHSyIX4DGTsO8PvPqfKKkzKQWwkrVdPjzXX53EewVTOL1RL468RX/vOOfZoq4ZmoyyU5AfnE+q06sAkr6nywxi76UNDEJYQE1Gf6sUql4MuRJVKjYdWkXp26cMkOkwtzWn1nPjfwb+Dj7MKj1IIvGIglCCBvSxrMN9ze/H4CY4zFIC3H9ci3vmr6PaWTwSBzsHCwajyQIIWzMsKBhaOw0nLp5ij2peywdjqhDX534ikJdIUFeQdztf7elw5EEIYSt8dJ4MbTdUAC+TPzSIntGiLp38sZJdl7aCVDSlKiyfIe+0QlizJgx7NixwxSxCCGq6cE2D9LUuSnX8q+xPnm9pcMRtaRTdMQcjwGgV/NetPVsa+GIShidIDIzM4mKiqJ9+/a88847XLp0yRRxCSEq4WjnyKigkh3F1iWv42reVQtHJGpj16VdnL55Go2dhuFBwy0djp7Rw1x/+OEHrly5whdffEFMTAwzZ84kKiqKCRMmMGTIEBwcLNupYhUUBYplMTJhWpHeYWzybMeJGyf58lgMz4U9Y+mQRA3kFeezKmElKDqGtn6QxnYaKDJytry9BkzQJFXriXKHDx9m2bJlLFmyBFdXV0aPHs0zzzxD+/bt6ypGi6rRRLmiPFg6wLSBCZumALkFxRTrFOzVKho52Ve4BEdlzqq0zHDIQQFmFTUiWJGpTbZmtV0+a+0K8FHU/LfIFYea/CaMjwUH52qfXt3nWq06qdPS0ti8eTObN2/Gzs6OQYMGcfToUUJCQvjoo49qc2sh6q3M/CJOpGdz5mouKddvceZqLifSs8nMLzL6Xq0VO/poHQFYZp+PzuBuE8JapaNjvV3JIIMntJqaJQcTMroGUVRUxLp161i2bBm//PILXbt25amnnmLkyJH6TLR27VrGjx/PjRs3TBK0OdWoBiFNTKICvySk8+xX8eUe46WPhXkjwnggxM+oe2YWZjFtx8vcKr7FxE7jiArsXSexCtN7/9BHHLpyhC5NOvNa+Es1H7lkZBOTyZba8Pf3R6fTMWLECPbv309YWFi5c3r37o2np6ext64/VCqjqnuiYdDqFGZuPEM+jgY/VwEzN56hb5dWRq1Z5OHgzN87Ps6KhBWsPvU9dze/D1dH1zqKWphKfEY8h67+jp3agbFdJqBydLF0SOUY3cT00UcfkZqayoIFCwwmBwBPT0/Onj1b29iEqFdqsqNcdfVv1Z/mrs3JLspmddLqWkQpzKFIV8Ty48sBGNBqAM3dmls2oAoYnSCeeOIJNBrZDUoIY9V0O06tTiEu+Ro/xl8iLvkaWl35VmF7tT0TukwA4Nfzv3Im80ztAxYms/HMRtJy0/Bw9ODvHf5u6XAqJEMehDCTmuwoZ8zS4CFNQugZ0JPdqbtZdmwZs3vMNvsWlaJqV/Ou8u3J78gtKKab+wB+T8mje2tnq1wKXX57hDATY3eUiz2WxuSVh8s1S6Vn5jN55WFij6WVu8eo4FFo7DScvHGSHRdlxQNrNGv7//jj0lVOXfDgfxsdGbF4L/e895vBf09LkwQhhJkYs6OcVqcwe32CwUGrpcdmr08o19zUxLmJvsliZeJKcgpz6ix+UXuL9m7ht3N7KNIqFF67n9J/+cqSviVJghDCjKq7HWdtOrQHth5Y0mFdmM1XJ76q0/hFzeUXFbIo/nMAirJCUYr+3BGwsqRvSdIHIYSZVWdHuZp2aENJh/VTXZ5iVtwsfk35lfsD76dD4w51Fr+omY/3r6KQ6yjaRhTdiCj3+e1J31p24pMahBAWUNWOcjXp0L5dcJNgejXvBcCSo0vQ6rS1ilfUTmpOKttTfwKg8Pq9oBieCwPV/3JgDjaTIK5fv86oUaNwd3fH09OTCRMmkJNTeftqr169UKlUZV6TJk0yU8RC1JyxHdqGjAweiauDK+ezzrPx7EaTxCmqpigKnx/9HLVahzavJdrcdpWeX90vB+ZgMwli1KhRHD9+nM2bN7NhwwZ27NjB008/XeV1EydOJC0tTf96//33zRCtELVjTId2RTycPBgZPBKAb05+Q8atDBNEKqqy89JOjl07hoezM56FD6CqIO1XJ+mbm00kiMTERGJjY1myZAkRERHcc889zJ8/n9WrV5OamlrptS4uLvj5+elf1V5PSQgLq26HdmV6B/Ym2CuYAm0Bnx/9XPawNrPswmy+SPgCgL+3f5TZD/YAap70zc0mEkRcXByenp6Eh4frj0VFRaFWq9m3b1+l13755Zc0bdqUzp07M2PGDG7dulXp+QUFBWRlZZV5CVEXqjMj+q8GdPZn1yt9+Gri3Xw8PIyvJt7Nrlf6VCs5AKhVaiZ2nYi92p74K/HEpcbV9scQRog5HkNWYRbNXZvzt7Z/q5Okb042MYopPT0dHx+fMsfs7e3x8vIiPT29wutGjhxJy5YtCQgI4I8//uCVV14hKSmJ77//vsJroqOjmT17dp3FLgQYNyP6r0o7tGuqmWszHm73MN+c/Iblx5fTxbsLbo5uNb6fsbQ6pdIRW/VVfEY8Oy/tRIWKSaGTcFCXbKZWnVFs1sKiCeLVV1/lvffeq/ScxMTEGt//9j6KLl264O/vT9++fUlOTqZtW8N7vs6YMYPp06fr32dlZREYGFjjGIQonRH91/pC6eQoc3xzHNJ2CHGpcVzMuUjM8Rim3jHVpOWVqk1itGV5xXksProYKJmX0r5x2Q3Uapv0zcWiTUwvvPACiYmJlb7atGmDn58fGRllO9iKi4u5fv06fn7VXzs/IqJk7PHp06crPMfJyQl3d/cyLyFqqqYzouuag50Dk0InoUbNzks7OXz5sEnLg5otFVJffH3ia67mXcXH2YdhHYdZOpwas2gNwtvbG29v7yrPi4yM5ObNmxw6dIhu3boB8Ntvv6HT6fQP/eqIj48HSva0EMIcjJkRbepvlO0bt2dQm0FsOLOBz45+xhyvOTRyaGSSsqpKjCpKEmO/ED+rbFqpjYRrCcSeiwXgqa5PobEvP2zVVprdbKIPIjg4mAEDBjBx4kQWLVpEUVERU6dOZfjw4QQEBABw6dIl+vbty4oVK+jevTvJycmsWrWKQYMG0aRJE/744w+ef/557rvvPrp27Wrhn0g0FLWZEW0Kj3d8nIPpB0m/lc4XCV8wKdQ084KsKTEaYqoHdH5xPgt/X4iCQp/APoR6h5Y7x5aa3WxiFBOUjEYKCgqib9++DBo0iHvuuYfPPvtM/3lRURFJSUn6UUqOjo78+uuvPPDAAwQFBfHCCy/w6KOPsn79ekv9CKIBqu2M6LrmZOfEP0L/AcDWC1uJz4g3STnWlhhvF3ssjXve+40Ri/fy3Or4Ol1NddWJVWTcyqCJpglPhDxhsGxbanaziRoEgJeXF6tWrarw81atWpUZ4x0YGMj27dvNEZoQFSqdEZ2emW+wuUVFyRBHc06OCmkSwqDWg9h4diOLfl/EB/d/UOejmqwtMZYy5YCB41ePs+ncJgAmhU7CxaHsFqK22OxmMzUIIWxRXcyINoURQSNo5tqMGwU3WHpsaZ3fvy6WCqlrphwwcKvoFv/7/X8ARLWIoqt3+WZsU245ayqSIIQwMWucHOVo58iUsCmoUbMndQ97Lu3Rf1aTCX1/ZY2J0ZQP6M+Pfc7VvKv4uvgyOmS0wXOsudmtIjbTxCSELbPGyVFtPdvySIdH+Pbktyw5toQOXh04mFxUZx2opYnxr/fzs1CHrKke0Lsv7WbXpV2oUTMlbArO9s4Gz7PWZrfKSIIQwkyscXLUw+0eJj4jntM3T/PKb+/z2857Uf7SsFCb9nlrSoymeEBfzbvKkqNLAHi4/cN09OpY4bnW2B9VFWliEqIBs1fb8887/onGTsPB1GPYex4sd05t2+er2vvCXOq6X0Sr0zL/yHxuFd+inWc7Hmn/SKXnW2OzW1UkQQjRwPk18qNn08cp0upw8NyP2qn8CsnW2IFqrLp+QH9z8htOXD+Bxk7DP+/4J/bqqhtkrLE/qjLSxCSEwNchjOKcjti7JuHks4m8S8NBV74t3Zo6UGuirvpFjl45yg+nfwDgH13/gV+j6i/5Y03NblWRBCGEwMdNQ+G1XqidLqN2uImT9y8UXB7MXxsZrKkDtaZq+4C+mX+T+Ufmo6DQt0VfejTrYXQM1tgfZYgkCCFESfu8uzuXMwaiCfgGO+cUHDwOUZR5F2CdHai1UdMHdLGumLmH55JZmEmgWyBjOo0xQXTWQ/oghBD69nmlqCmF13oB4NB4H2pNitV2oFrCl4lfkng9EY2dhundpuNk52TpkExKEoQQAvizfd7b7g6Kc0IABSefWHwa51tlB6q57bq0i41nNwIw9Y6pBLgGWDgi05MmJiEEUDKD2sPZkZf7d+RKTnN23lzEjaIUOoTs4f6gQbW6rzk6ZE1ZztnMs3z6+6dAydyRu/zuqpP7WjtJECZmK+u+i4bN0BLUfo170azDD1zKucgnRz7hhfAXUKuMa3Qw19LWpiznev513j/wPoW6QkK9Q3m84+O1DddmqJTbl0AV5WRlZeHh4UFmZqbRu8vZ0rrvouGqaIVTFaB2SieoyyZcNSoeavsQo4JH1cl9gTprtjJlOQXaAmbtmcWZzDM0c23Gmz3fNNkmS+ZU3eea9EGYiK2t+y4apqpWONUV+JGRci8osC55nX4569reF+pmq1VTlqNTdCw4soAzmWdwc3TjlbteqRfJwRiSIEzAWvYhFqIq1Vnh9EpGG7p5lfRBLD+2nIPp5ZfjqMl962JmtqnKURSF5ceXsy99H/Zqe14MfxHfRr5A3ax2ayukD8IErH27RSFKVXdmdAeXvri75rH1wlY+Pvwx/77735UuTGeupa1NVc73p75n07lNqFAxJXQKQV5BQMNrNpYahAnY4rrvomGq7sxoX3dnnuryFGHeYRTqConeH82ZzDO1vm9tZ2abopzN5zez5uQaAMZ2HqufKd0Qm40lQZiALa77LhomY1Y4tVfbMz18OsFeweQV5/HO3ne4kH2h1vc1V/zVsTVlK58f/RyAR9s/yoBWA4CG22wsCcIErHG7RSEMMXaFUyc7J17p/grtPNuRXZTNm3FvGkwS5lraui7L2ZqylU//+BQFhQGtBvBYh8f0n9nidqF1QRKECdjiuu+i4TJ2CWpne2dmdJ9BS/eWZBZmMnvPbM7cLN/cZK6lreuinC0pW8okh7GdxqJS/fn/s6E2G8s8iCrIPAjRUBg7qTOnMIfo/dGcvnlanzQMdVxb80xqRVH44fQPrE5aDWAwOQDEJV9jxOK9Vcbw1cS7bWLgSXWfa5IgqlCbBAEyk1pYt9r+ft4qusX7B94n8XoiDmoHpt4xlbv97zZhxHVHp+iIOR5D7LlYAIa0HcKIoBHlkgOU/D3d895vVW4XuuuVPjbx/1sSRB2pbYIQwlrVVQ23QFvA3ENzOZxxGICRQSN5qO1DBh+01uJW0S0WxC/g4OWSOR1jQsYwqE3l602VjmICyiSJup4Zbg6SIOqIJAhRH9X18hQ6RceK4yv4+dzPANzb7F6e6vIUGnvrG6mXlpPGBwc/4FLOJezV9kwJnVLtTX/qS7OxJIg6IglC1DelzSUVjcqpTXNJ7LlYYo7FoENHc9fmTOs2jUC3wDqIum7sTdvLot8XkVech5fGixe6vUC7xu2Mukd9aDau7nNNZlIL0cCYcqb/gFYDaOHWgnmH53Ex5yKv7XqN0cGjiWoZZfRKsHXpVtEtlh1fxo6LOwDo2Lgj07tNx1PjafS9bGW70Logw1yFaGBMPWQzpEkI7933Hl2adqFAW8Dnxz7njbg3SMsx/0xjRVE4mH6Ql3e8zI6LO1Cj5uF2D/OfyP/UKDk0NFKDEKKBMcdMfw8nD/4V8S82ndvE6hOrSbyeyEs7XmJg64EMaTsEV0fXGt+7utJy0ohJiOFIxhEAfFx8mBo2tdI1pERZNlODePvtt+nRowcuLi54enpW6xpFUXj99dfx9/fH2dmZqKgoTp06ZdpAhbBy5prpr1apGdh6IB/c/wGh3qEU6YpYl7yO57Y+x4+nf+RW0a1a3b8iqTmpLIxfyAvbX+BIxhHsVfYMaTuE9+97X5KDkWymk3rmzJl4enpy8eJFPv/8c27evFnlNe+99x7R0dHExMTQunVr/vOf/3D06FESEhLQaKr37Ug6qUV9ZO4hm4qicDjjMKsSV3Ex5yIAGjsN9za/l/4t+xPoXruO7GJdMfEZ8Wy/uJ0D6QdQ/v+nCvMOY2ynsfi72s4II3Oot6OYli9fzrRp06pMEIqiEBAQwAsvvMCLL74IQGZmJr6+vixfvpzhw4dXqzxJEKK+ssSQTZ2iY+fFnaxLXqdPFAABjQII9wsnzDuM1h6tcXFwqfJeN/NvcvzacY5fO86B9ANkFWbpP+vm241H2j1i9AilhqLBj2I6e/Ys6enpREVF6Y95eHgQERFBXFxctROEEPXVgM7+9AvxM+uQTbVKzf2B93Nf8/s4fu04sWdjOXLlCKm5qaxLXse65HUA+Dfyx9fFFw8nD9wd3VFQKNIVkV+cz+Vbl0nLSSOzMLPMvT0cPejZrCd9AvvUukYiStTbBJGeng6Ar69vmeO+vr76zwwpKCigoKBA/z4rK6vCc4WwdZYasqlSqejctDOdm3bmVtEt4jPiOXD5ACdvnORq3lXSctNIy6181JMKFS3dW9KpSSe6enelS9Mu2KntzPQTNAwWTRCvvvoq7733XqXnJCYmEhQUZKaIIDo6mtmzZ5utPCEaOhcHF3o066GfzZxZkMm5rHNcz7tOZmEm2YXZqFVqHNQOOKgd8HHxwa+RH/6N/KvVFCVqzqIJ4oUXXmDs2LGVntOmTZsa3dvPzw+Ay5cv4+//Z3vq5cuXCQsLq/C6GTNmMH36dP37rKwsAgOluiqEuXg4eRDqHWrpMAQWThDe3t54e3ub5N6tW7fGz8+PLVu26BNCVlYW+/btY/LkyRVe5+TkhJOTk0liEkIIW2Iz8yBSUlKIj48nJSUFrVZLfHw88fHx5OTk6M8JCgpi7dq1QEkb57Rp03jrrbdYt24dR48e5cknnyQgIIChQ4da6KcQQgjbYTOd1K+//joxMTH693fccQcAW7dupVevXgAkJSWRmfnnyIaXX36Z3Nxcnn76aW7evMk999xDbGxstedAGEOr1VJUVFTn9xXCWjg6OqJW28x3SlEHbG4ehLlVNV5YURTS09OrNXFPCFumVqtp3bo1jo6Olg5F1FKDnwdhLqXJwcfHBxcXF6veJEWImtLpdKSmppKWlkaLFi3k97yBkARRC1qtVp8cmjRpGMv/iobL29ub1NRUiouLcXBwsHQ4wgykQbEWSvscXFxkLLao/0qblrRarYUjEeYiCaIOSHVbNATye97wSIIQQghhkCQIYRLbtm1DpVIZNbqrVatWzJ0712QxCSGMIwmiARo7diwqlYpJkyaV+2zKlCmoVKoql0CxZtevX2fUqFG4u7vj6enJhAkTykyoNOQf//gHbdu2xdnZGW9vb4YMGcKJEyfKnPPss8/SrVs3nJycKl2uRYj6QhJEAxUYGMjq1avJy8vTH8vPz2fVqlW0aNHCgpHV3qhRozh+/DibN29mw4YN7Nixg6effrrSa7p168ayZctITExk06ZNKIrCAw88UK5Ddvz48QwbNsyU4QthNSRB1CVFgaI8y7yMnO945513EhgYyPfff68/9v3339OiRQv9LPVSBQUFPPvss/j4+KDRaLjnnns4cOBAmXM2btxIhw4dcHZ2pnfv3pw7d65cmbt27eLee+/F2dmZwMBAnn32WXJzc42KuyqJiYnExsayZMkSIiIiuOeee5g/fz6rV68mNTW1wuuefvpp7rvvPlq1asWdd97JW2+9xYULF8r8HPPmzWPKlCk1XkBSCFsj8yDqUnE+LB1gmbLHx4KDs3GXjB/PsmXLGDVqFABLly5l3LhxbNu2rcx5L7/8Mt999x0xMTG0bNmS999/n/79+3P69Gm8vLy4cOECjzzyCFOmTOHpp5/m4MGDvPDCC2XukZyczIABA3jrrbdYunQpV65cYerUqUydOpVly5YZjG/s2LGcO3euXDyViYuLw9PTk/DwcP2xqKgo1Go1+/bt4+GHH67yHrm5uSxbtozWrVvLSr6iQZMaRAM2evRodu3axfnz5zl//jy7d+9m9OjRZc7Jzc1l4cKFfPDBBwwcOJCQkBAWL16Ms7Mzn3/+OQALFy6kbdu2zJkzh44dOzJq1KhyfRjR0dGMGjWKadOm0b59e3r06MG8efNYsWIF+fn5GOLv7290c1d6ejo+Pj5ljtnb2+Pl5VXpRlEA//vf/3B1dcXV1ZWff/6ZzZs3y7ISokGTGkRdsteUfJO3VNlG8vb25sEHH2T58uUoisKDDz5I06ZNy5yTnJxMUVERPXv21B9zcHCge/fuJCYmAiXNOhEREWWui4yMLPP+999/548//uDLL7/UH1MUBZ1Ox9mzZwkODi4XX3R0dKXxT5o0iZUrV+rfV9URXZVRo0bRr18/0tLS+PDDD3n88cfZvXu3SRZ3FMIWSIKoSyqV0c08ljZ+/HimTp0KwIIFC0xWTk5ODv/4xz949tlny31W007xN954gxdffLHMMT8/PzIyMsocKy4u5vr16/pNpCri4eGBh4cH7du35+6776Zx48asXbuWESNG1Cg+IWydJIgGbsCAARQWFqJSqejfv3+5z9u2bYujoyO7d++mZcuWQMkSIwcOHGDatGkABAcHs27dujLX7d27t8z7O++8k4SEBNq1a1dnsfv4+JRrToqMjOTmzZscOnSIbt26AfDbb7+h0+nK1XIqoygKiqKU2Z9ciIZG+iAaODs7OxITE0lISMDOrvyG740aNWLy5Mm89NJLxMbGkpCQwMSJE7l16xYTJkwASpp6Tp06xUsvvURSUhKrVq1i+fLlZe7zyiuvsGfPHqZOnUp8fDynTp3ixx9/1NdeDJkxYwZPPvmkUT9PcHAwAwYMYOLEiezfv5/du3czdepUhg8fTkBAAACXLl0iKCiI/fv3A3DmzBmio6M5dOgQKSkp7Nmzh8ceewxnZ2cGDRqkv/fp06eJj48nPT2dvLw8/aZVhYWFRsUohK2QGoSodD14gHfffRedTscTTzxBdnY24eHhbNq0icaNGwMlTUTfffcdzz//PPPnz6d79+688847jB8/Xn+Prl27sn37dl577TXuvfdeFEWhbdu2lc4pSEtLIyUlxeif58svv2Tq1Kn07dsXtVrNo48+yrx58/SfFxUVkZSUxK1btwDQaDTs3LmTuXPncuPGDXx9fbnvvvvYs2dPmRrKU089xfbt2/XvS4cDnz17llatWhkdpxDWTjYMqkJlG2vk5+dz9uxZWrduLR2Zot6T3/f6o7obBkkTkxBCCIMkQQghhDBIEoQQQgiDJEEIIYQwSBKEEEIIgyRBCCGEMEjmQQghTEKrU9h/9joZ2fn4uGno3toLO7Xsa21LJEEIIepc7LE0Zq9PIC3zz5V6/T00zBwcwoDO/haMTBhDmpiEEHUq9lgak1ceLpMcANIz85m88jCxx9IsFJkwliQIYVNmzZplNftB9+rVS79goam0atWKuXPnGn3df/7znyq3Wb3dokWLGDx4sNHl/JVWpzB7fQKGlmcoPTZ7fQJanSzgYAskQTRQ6enpPPfcc7Rr1w6NRoOvry89e/Zk4cKF+jWKbM2sWbNQqVSVvmpi27ZtqFQqbt68WbcBV8OBAweMetBDyb/txx9/zGuvvVbta8aPH8/hw4fZuXOnsSGWsf/s9XI1h9spQFpmPvvPXq9VOcI8JEE0QGfOnOGOO+7gl19+4Z133uHIkSPExcXx8ssvs2HDBn799dcKry0qKjJjpMZ58cUXSUtL07+aN2/OG2+8UebY7WxhFVZvb29cXFyMumbJkiX06NFDvzx7dTg6OjJy5MgyixrWREZ2xcmhJucJy5IEUYcURSG/ON8iL2PWXHzmmWewt7fn4MGDPP744wQHB9OmTRuGDBnCTz/9VKapQaVSsXDhQh566CEaNWrE22+/Dfy5zaijoyMdO3bkiy++0F9z7tw5VCoV8fHx+mM3b95EpVLp95cu/Va+ZcsWwsPDcXFxoUePHiQlJZWJ9d1338XX1xc3NzcmTJhQ4fakAK6urvj5+elfdnZ2uLm56d8PHz6cqVOnMm3aNJo2bUr//v2rjPXcuXP07t0bgMaNG6NSqcpsp6rT6Xj55Zfx8vLCz8+PWbNmVfvfAUp+Z2bNmkWLFi1wcnIiICCgzKZKf21iUqlULFmyhIcffhgXFxfat29fbi+O1atXl/k3vHLlCn5+frzzzjv6Y3v27MHR0ZEtW7bojw0ePJh169aRl5dn1M9wOx+36i3iV93zhGXZzCimt99+m59++on4+HgcHR2rVd0fO3YsMTExZY7179+f2FjTbAtaoC1gTOwYk9y7KjEDYtBUY9vRa9eu6WsOjRo1MnjOX5tiZs2axbvvvsvcuXOxt7dn7dq1PPfcc8ydO5eoqCg2bNjAuHHjaN68uf5hWl2vvfYac+bMwdvbm0mTJjF+/Hh2794NwJo1a5g1axYLFizgnnvu4YsvvmDevHm0adPGqDJuFxMTw+TJk/VlVCUwMJDvvvuORx99lKSkJNzd3XF2/nPXwJiYGKZPn86+ffuIi4tj7Nix9OzZk379+gElv4Pnzp3TJ8a/+u677/joo49YvXo1nTp1Ij09nd9//73SmGbPns3777/PBx98wPz58xk1ahTnz5/Hy8uL69evk5CQQHh4uP58b29vli5dytChQ3nggQfo2LEjTzzxhH5J9FLh4eEUFxezb98+evXqVa2/n7/q3toLfw8N6Zn5BvshVICfR8mQV2H9bCZBFBYW8thjjxEZGcnnn39e7esGDBjAsmXL9O+dnJxMEZ7NOH36NIqi0LFjxzLHmzZtqv92PmXKFN577z39ZyNHjmTcuHH69yNGjGDs2LE888wzAEyfPp29e/fy4YcfGp0g3n77be6//34AXn31VR588EHy8/PRaDTMnTuXCRMm6Dcmeuutt/j1118rrUVUpX379rz//vv69+fOnav0fDs7O7y8Sh5mPj4+eHp6lvm8a9euzJw5U3/vTz75hC1btugThL+/PzqdrsL7p6Sk4OfnR1RUFA4ODrRo0YLu3btXGtPYsWP126C+8847zJs3j/379zNgwABSUlJQFEW/OVKpQYMGMXHiREaNGkV4eDjOLi688p/Z5OQX08jJDpVKhYuLCx4eHpw/f77S8itjp1Yxc3AIk1ceRgVlkkTp146Zg0NkPoSNsJkEMXv2bIByO5VVxcnJqcq9iOuKk50TMQNiqj7RRGXXxv79+9HpdIwaNarcNpu3fxsFSExMLNdx2rNnTz7++GOjy+3atav+z/7+JePjMzIyaNGiBYmJiUyaNKnM+ZGRkWzdutXockqVbkNaV26PH0p+htv3xI6Ojq70+scee4y5c+fSpk0bBgwYwKBBgxg8eDD29hX/17y9zEaNGuHu7q4vs7R5yNB+DR9++CEhnTqx5ptvWP3TVi7naiE3Bwc7NQGeGjycHXF2dq71IIUBnf1ZOPrOcvMg/GQehM2xmQRRU9u2bcPHx4fGjRvTp08f3nrrLZo0aVLh+QUFBWUekFlZWdUuS6VSVauZx5LatWuHSqUq19Zf2mxze/NJqYqaoiqiVpd0bd3eL1JR57aDg4P+z6VNW5V9466tv/4sxsRqyO3xQ8nPYEz8gYGBJCUl8euvv7J582aeeeYZPvjgA7Zv317u3tUps2nTpgDcuHEDb2/vMufFHz9BWmoaik7HpYsptA/uBECRVsf5a7do2QSuX79e7rqaGNDZn34hfjKT2sbV607qAQMGsGLFCrZs2cJ7773H9u3bGThwIFqttsJroqOj8fDw0L8CAwPNGLHpNWnShH79+vHJJ5+Qm5tbo3sEBweXa8PfvXs3ISEhAPoHzO2jhm7vBDamnH379pU5tnfvXqPvU5nqxOro6AhQ6e9NbTg7OzN48GDmzZvHtm3biIuL4+jRozW6V9u2bXF3dychIaHM8YKCAsaNGUP/wQ8z5cV/Mfvl57h29UqZc/bGJ5Cfn6/fSrW27NQqIts2YUhYMyLbNpHkYIMsmiBeffXVKsetnzhxosb3Hz58OA899BBdunRh6NChbNiwgQMHDlTYYQgwY8YMMjMz9a8LFy7UuHxr9b///Y/i4mLCw8P5+uuvSUxMJCkpiZUrV3LixAns7Owqvf6ll15i+fLlLFy4kFOnTvHf//6X77//nhdffBEoeeDdfffdvPvuuyQmJrJ9+3b+/e9/Gx3nc889x9KlS1m2bBknT55k5syZHD9+vEY/c0WqE2vLli1RqVRs2LCBK1eukJOTU+37z5gxgyeffLLCz5cvX87nn3/OsWPHOHPmDCtXrsTZ2dmoIaq3U6vVREVFsWvXrjLHX371X2RnZ/LKG+8y7plptGzdlpkvTi1zzv64PbRu3Ya2bdvWqGxR/1g0QbzwwgskJiZW+qrNiJW/atOmDU2bNuX06dMVnuPk5IS7u3uZV33Ttm1bjhw5QlRUFDNmzCA0NJTw8HDmz5/Piy++yJtvvlnp9UOHDuXjjz/mww8/pFOnTnz66acsW7aszMiXpUuXUlxcTLdu3Zg2bRpvvfWW0XEOGzaM//znP7z88st069aN8+fPM3nyZKPvU5WqYm3WrBmzZ8/m1VdfxdfXl6lTp1Zwp/LS0tJISUmp8HNPT08WL15Mz5496dq1K7/++ivr16+vtBm0Kk899RSrV6/WNztt27aN/30yj7fnLsLVzR21Ws3bH3/Kkf1xrFnx54CPn3/8jifGjq9xubZEq1OIS77Gj/GXiEu+JjO7K6BSjBlAbwWWL1/OtGnTajSr9eLFi7Ro0YIffviBhx56qFrXVLa5t2ziLqyRoihERETw/PPP60c75eQXc+ZqxTWf00mJTBw+hD+OJdLM13Byqi+/77KQYOXPtdvZTB9ESkoK8fHxpKSkoNVqiY+PJz4+vkx1PygoiLVr1wKQk5PDSy+9xN69ezl37hxbtmxhyJAhtGvXjv79+1vqxxDC5FQqFZ999hnFxcX6Y42c7HCwq/i/+9WMy7w7bxEBPvV7foIsJGgcmxnF9Prrr5eZ9FbakbZ161Z900ZSUhKZmZlAyfj1P/74g5iYGG7evElAQAAPPPAAb775ZoOfCyHqv7CwsDKLGqpUKgI8NZy/ZngI69339qJlE5car1dlC6paSFBFyUKC/UL8pEP9/9lMgli+fHmVcyBuby1zdnZm06ZNJo5KCNvh4exIyyaQejOfIu2fQ3FvnwdRnxmzkGBk25r3AdUnNpMghBC15+HsiLvGgdwCLcU6HfZqtX4mdX0nCwkaTxJEHbCxfn7RwKlUKlw1xv/Xt/Xfc1lI0Hg200ltjUpntNrq/glCGKN0efSq5slYq9KFBCuqK6koGc0kCwn+SWoQtWBnZ4enp6d+HRwXl/rdyScaLp1Ox5UrV3Bxcal0nShrJgsJGs82/6WtSOlCgLcv0CZEfaRWq2nRooVNfwmShQSNY3MT5cytuhNKtFqtVe+2JkRtOTo66hc3tHVandKgFxKs7nNNahB1xM7OzmbbZoVoaEoXEhSVqx9fB4QQQtQ5SRBCCCEMkgQhhBDCIOmDqEJpH74xO8sJIYQ1K32eVTVGSRJEFbKzswHq3c5yQgiRnZ2Nh4dHhZ/LMNcq6HQ6UlNTcXNzM2r8d1ZWFoGBgVy4cMFmNh2SmM3D1mK2tXhBYq6KoihkZ2cTEBBQ6dBlqUFUQa1W07x58xpfb4u70knM5mFrMdtavCAxV6aymkMp6aQWQghhkCQIIYQQBkmCMBEnJydmzpxpU7vXSczmYWsx21q8IDHXFemkFkIIYZDUIIQQQhgkCUIIIYRBkiCEEEIYJAlCCCGEQZIgTGDBggW0atUKjUZDREQE+/fvt3RIldqxYweDBw8mICAAlUrFDz/8YOmQKhUdHc1dd92Fm5sbPj4+DB06lKSkJEuHVamFCxfStWtX/SSoyMhIfv75Z0uHZZR3330XlUrFtGnTLB1KhWbNmoVKpSrzCgoKsnRYVbp06RKjR4+mSZMmODs706VLFw4ePGjpsCRB1LWvv/6a6dOnM3PmTA4fPkxoaCj9+/e36i1Jc3NzCQ0NZcGCBZYOpVq2b9/OlClT2Lt3L5s3b6aoqIgHHniA3NxcS4dWoebNm/Puu+9y6NAhDh48SJ8+fRgyZAjHjx+3dGjVcuDAAT799FO6du1q6VCq1KlTJ9LS0vSvXbt2WTqkSt24cYOePXvi4ODAzz//TEJCAnPmzKFx48aWDg0UUae6d++uTJkyRf9eq9UqAQEBSnR0tAWjqj5AWbt2raXDMEpGRoYCKNu3b7d0KEZp3LixsmTJEkuHUaXs7Gylffv2yubNm5X7779fee655ywdUoVmzpyphIaGWjoMo7zyyivKPffcY+kwDJIaRB0qLCzk0KFDREVF6Y+p1WqioqKIi4uzYGT1W2ZmJgBeXl4WjqR6tFotq1evJjc3l8jISEuHU6UpU6bw4IMPlvm9tmanTp0iICCANm3aMGrUKFJSUiwdUqXWrVtHeHg4jz32GD4+Ptxxxx0sXrzY0mEB0sRUp65evYpWq8XX17fMcV9fX9LT0y0UVf2m0+mYNm0aPXv2pHPnzpYOp1JHjx7F1dUVJycnJk2axNq1awkJCbF0WJVavXo1hw8fJjo62tKhVEtERATLly8nNjaWhQsXcvbsWe699179sv3W6MyZMyxcuJD27duzadMmJk+ezLPPPktMTIylQ5PVXIVtmzJlCseOHbP6dmaAjh07Eh8fT2ZmJt9++y1jxoxh+/btVpskLly4wHPPPcfmzZvRaDSWDqdaBg4cqP9z165diYiIoGXLlqxZs4YJEyZYMLKK6XQ6wsPDeeeddwC44447OHbsGIsWLWLMmDEWjU1qEHWoadOm2NnZcfny5TLHL1++jJ+fn4Wiqr+mTp3Khg0b2Lp1a62WZDcXR0dH2rVrR7du3YiOjiY0NJSPP/7Y0mFV6NChQ2RkZHDnnXdib2+Pvb0927dvZ968edjb26PVai0dYpU8PT3p0KEDp0+ftnQoFfL39y/3JSE4ONgqmsYkQdQhR0dHunXrxpYtW/THdDodW7ZssYm2ZluhKApTp05l7dq1/Pbbb7Ru3drSIdWITqejoKDA0mFUqG/fvhw9epT4+Hj9Kzw8nFGjRhEfH4+dnZ2lQ6xSTk4OycnJ+Pv7WzqUCvXs2bPcMO2TJ0/SsmVLC0X0J2liqmPTp09nzJgxhIeH0717d+bOnUtubi7jxo2zdGgVysnJKfMN6+zZs8THx+Pl5UWLFi0sGJlhU6ZMYdWqVfz444+4ubnp+3c8PDxwdna2cHSGzZgxg4EDB9KiRQuys7NZtWoV27ZtY9OmTZYOrUJubm7l+nUaNWpEkyZNrLa/58UXX2Tw4MG0bNmS1NRUZs6ciZ2dHSNGjLB0aBV6/vnn6dGjB++88w6PP/44+/fv57PPPuOzzz6zdGgyzNUU5s+fr7Ro0UJxdHRUunfvruzdu9fSIVVq69atClDuNWbMGEuHZpChWAFl2bJllg6tQuPHj1datmypODo6Kt7e3krfvn2VX375xdJhGc3ah7kOGzZM8ff3VxwdHZVmzZopw4YNU06fPm3psKq0fv16pXPnzoqTk5MSFBSkfPbZZ5YOSVEURZHlvoUQQhgkfRBCCCEMkgQhhBDCIEkQQgghDJIEIYQQwiBJEEIIIQySBCGEEMIgSRBCCCEMkgQhhBDCIEkQQgghDJIEIYQQwiBJEEJY0JUrV/Dz89PvBQCwZ88eHB0dy6wKLIQlyFpMQljYxo0bGTp0KHv27KFjx46EhYUxZMgQ/vvf/1o6NNHASYIQwgpMmTKFX3/9lfDwcI4ePcqBAwdwcnKydFiigZMEIYQVyMvLo3Pnzly4cIFDhw7RpUsXS4ckhPRBCGENkpOTSU1NRafTce7cOUuHIwQgNQghLK6wsJDu3bsTFhZGx44dmTt3LkePHsXHx8fSoYkGThKEEBb20ksv8e233/L777/j6urK/fffj4eHBxs2bLB0aKKBkyYmISxo27ZtzJ07ly+++AJ3d3fUajVffPEFO3fuZOHChZYOTzRwUoMQQghhkNQghBBCGCQJQgghhEGSIIQQQhgkCUIIIYRBkiCEEEIYJAlCCCGEQZIghBBCGCQJQgghhEGSIIQQQhgkCUIIIYRBkiCEEEIYJAlCCCGEQf8HqM6pHJ7vYuoAAAAASUVORK5CYII=", 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", 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" ] @@ -755,19 +706,11 @@ "INFO:autora.theorist.bms.regressor:BMS fitting started\n" ] }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 1:\u001b[0m\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 23.38it/s]\n", + "100%|██████████| 100/100 [00:06<00:00, 15.95it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -775,13 +718,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1mCycle 1 model: sin(x)\u001b[0m\n", - "[sin(x)]\n" + "\n", + "\u001b[1mRunning Cycle 1:\u001b[0m\n", + "\u001b[1mCycle 1 model: -0.32\u001b[0m\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -793,22 +737,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 2:\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:04<00:00, 24.21it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:06<00:00, 16.31it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -816,13 +746,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1mCycle 2 model: sin(x)\u001b[0m\n", - "[sin(x), sin(x)]\n" + "\n", + "\u001b[1mRunning Cycle 2:\u001b[0m\n", + "\u001b[1mCycle 2 model: sin(x)\u001b[0m\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -834,22 +765,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 3:\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:04<00:00, 23.25it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:06<00:00, 16.41it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -857,13 +774,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1mCycle 3 model: sin(x)\u001b[0m\n", - "[sin(x), sin(x), sin(x)]\n" + "\n", + "\u001b[1mRunning Cycle 3:\u001b[0m\n", + "\u001b[1mCycle 3 model: sin(x)\u001b[0m\n" ] }, { "data": { - "image/png": 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", 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wGKuh3/lcxt7XLGqqDVEwK7WKIN+ST5tcGWVoMxgSNcQk114Vsgp7I5YdvXPnjv7JoUqVKgbLPFoVM2PGDD744APmzZuHtbU1GzduZMKECcybN4/g4GC2bt3KsGHDqF27drGnnXnrrbf4+OOPcXNzY9SoUQwfPly/ONE333zDjBkzWLhwIZ06dWLNmjUsWLCABg0aFOsaD1u1ahWjR48ucqGvXD4+PmzYsIFnn31Wv9Swg8PfqwauWrWKSZMmceTIEaKjoxk6dCgdO3akR48eQM4a2JcuXWLPnj0Gz79hwwbmzp3L+vXradasGYmJifz666+FxhQeHs6HH37InDlz+PTTTxk0aBCXL1/G1dWVpKQkYmNjCQwM1Jd3c3Nj+fLl9O/fn549e9K4cWNefPFFxo0bR/fu3fXlAgMDyc7O5siRI3Tp0sWo/5/ikgQhhBm7ePEiiqLQuHHjPPtr1qxJenpO9+WxY8cye/Zs/XsvvPACw4YN078eOHAgQ4cOZcyYMUDOHGaHDx/mo48+KnaCeP/99+ncuTMA06ZNo2/fvqSnp2Nvb8+8efN4+eWX9QsTvffee+zYsUMfZ0k0atSIDz/8UP/60qVLhZa3srLC1TWnU4ahpYZbtGhBWFiY/tyfffYZO3fu1CcILy8v/doShly5cgVPT0+Cg4OxsbGhTp06tG3bttCYhg4dqp+KfNasWSxYsICjR48SEhLClStXUBQl3zobffr0YeTIkQwaNIjAwECqVKmSb81vR0dHnJ2duXz5cqHXfxySIESlZWdlx6qQVSa79uM4evQoOp2OQYMGkZGRkee9h7+NApw7dy5fw2nHjh2ZP39+sa/bokUL/b9zp82/efMmderU4dy5c4waNSpP+aCgIHbv3l3s6+Rq06ZNiY815OH4IeczPLwc6aM34Uc999xzzJs3jwYNGhASEkKfPn0IDQ3F2rrgW+nD16xSpQpOTk76a6alpQEYnB/so48+wt/fn2+//ZYTJ05gZ5f/d8bBwaFMOylIghBlzlxnmlWpVEZV85hSw4YNUalUnD9/Ps/+3Gqbh6tPchVUFVUQtTqnr4ry0LydBTVu29jY6P+dW7VV2Dfux/XoZylOrIY8HD/kfIbixO/j48P58+fZsWMH27dvZ8yYMcyZM4e9e/fmO7cx16xZsyYAd+/exc3NLU+5uLg4rl+/jk6n49KlSzRv3jzfuZOSkvIdV5qkF5MoU1FnEug0excDlxxmwvoYBi45TKfZu2QSQSPVqFGDHj168Nlnn5GamlqiczRt2jRfHf7Bgwfx88vpIp17g3m411BMTEyJrnPkyJE8+w4fPlzs8xTGmFhtbW0B0Gq1pXrtXA4ODoSGhrJgwQL27NlDdHQ0p0+fLtG5fH19cXJyIjY2Ns/+zMxMBg8ezIABA5g5cyYjRozI86QDOQkkPT2dVq1alfizFEWeIESZyZ1p9tGpAnNnmpVuuMb5/PPP6dixI4GBgcyYMYMWLVqgVqs5duwYv//+e5HVMFOmTOHf//43rVq1Ijg4mC1btvD999/ru8c6ODjQvn17PvjgA+rXr8/Nmzf573//W+w4J0yYwNChQwkMDKRjx458+eWXnD179rEaqR9lTKx169ZFpVKxdetW+vTpg4ODA1WrVjXq/I8uOvaolStXotVqadeuHY6OjqxduxYHB4didVF9mFqtJjg4mAMHDuRZPvmtt95Co9GwYMECqlatyk8//cTw4cPZunWrvsz+/ftp0KABvr6+Jbq2UfGV2ZlFpSYzzZYeX19fTp06RXBwMNOnT6dly5YEBgby6aef8sYbbzBz5sxCj+/fvz/z58/no48+olmzZnzxxResWLEiT8+X5cuXk52dTZs2bZg4cSLvvfdeseMcMGAAb7/9Nm+++SZt2rTh8uXLjB49utjnKUpRsdaqVYvw8HCmTZuGh4cH48aNM/rcjy469igXFxeWLFlCx44d9QuQbdmyhRo1St6DcMSIEaxfv15f7bRnzx7mzZvHmjVrcHJyQq1Ws2bNGvbv309kZKT+uK+++oqRI0eW+LrGkAWDimAJCwaZo+i4OwxcUnT1wlcj25dL91xZMEiYK0VRaNeuHa+//rq+t1NRzp49S7du3fjjjz8KXFtbFgwSZktmmhXCOCqVisWLF5OdnW30MQkJCaxevbrA5FBapA1ClAmZaVYI4wUEBBRrUsPg4OCyC+Yh8gQhyoTMNCuE5bOoBLFv3z5CQ0Px9vZGpVKxadOmIo/Zs2cPrVu3xs7OjoYNG7Jy5coyj1P8PdNsQQ1cCjLTrBDmzqISRGpqKi1btmThwoVGlY+Pj6dv37507dqVmJgYJk6cyIgRI9i2bVsZRyrMlfTJEJVFafyuW1QbRO/evendu7fR5RctWkT9+vX5+OOPgZyBPAcOHGDu3Ln06tWrrMIU5HRznfZ94YOHpn1/mh5+nuXyFJE7mvXBgwcGRx8LUdHkTsFR0AhvY1hUgiiu6OjofI05vXr1KnRVqYyMjDxz2yQnJ5dVeBVWVkYqUceiaGj9Mw41E1FZp6C1TiNbnYlOBcr/Kp6sdVZMWfM+no4ueFf1wsu5PvU8WlOvbmesrG1LNSYrKytcXFz0o1EdHR1LvCCNEOZMURQePHjAzZs3cXFxwcrKqsTnqtAJIjExEQ8Pjzz7PDw8SE5OJi0tzeA3yYiICMLDw8srxArj6tWDnLrwIzE3T3I+7QZpWh3ZNXQUtsRLplrLpax7XEvRcCLlMiQehvNfYa+y4gkHDwI8AmnXbCA13ZqWSoyenp4A+aYsEKIicnFx0f/Ol1SFThAlMX36dCZNmqR/nZycjI+PjwkjMl/37sazP2YZ+6/t53KWJs97jlijSndEyaxJelYN0rJdSdVWJ1uxRauzQaXOxkGdQs8m9gTUyiBBc5m/Uq8Tl3aTB0o2vz24zm/xP7A6/gd8bV3pXKcrT7YZjaNjzRLHq1Kp8PLywt3d3axXWhPicdnY2DzWk0OuCp0gPD09uXHjRp59N27cyLeIyMPs7OwMTqsr/nbp0l62nork0L3zaHOri1DTvEotAjwDadnwn8Sl+DB4+bEiz9UpsB0dG/5909dps7ly9SBnL+3kWMJhfk+/SVxmEnEXN7AubhMdXf0IDZyIl3fJp4G2srIqlT8eISq6Cp0ggoKC+Omnn/Ls2759O0FBQSaKyLL9Gb+T9Uc/4dcH1/T7GtnVpEud7rRvOZSq1f6eeM9dp+DiaMO9BwV/U6/uaEP7Bnmn2VBbWVOvXmfq1etMX0Bz7xIHf13Bjiu7uJZ9n513TrN72wiCnBvyzD8m4eMjP0shyopFzcWUkpLCxYsXAWjVqhWffPIJXbt2xdXVlTp16uSbiTE+Ph5/f3/Gjh3L8OHD2bVrF+PHj+fHH380uheTzMUEiQmnWH/ofaKTc/7v1aho7+TLPwNexde3R4HHRZ1JYNTakwW+v6gYs7kqOh3n/viBradX5LRXkDPYrluN5gx4aibOLvWM/jxCVHbG3tcsKkHs2bPH4BKJQ4YMYeXKlQbXk92zZw+vv/46sbGx1K5dm7fffpuhQ4cafc3KnCCyMlLZtPe/bL62lyx0qIBOLk14rsN/8PBoUeTxkJMkZvwQS2Ly33MueTnbExbqV+KpvuMv7eH74/M5ej8eAHuVFc/W6UWfJ9/B2kam7hCiKBUyQZhCZU0Qp89+y5IT87ihzVmkprmjNy+2n07duk8W+1xltaLc7+d/YPXxecRlJgFQz9aFUR1nUL9el8c+txAVmSSIUlLZEkRGuoZ1OyYRdesEAK5qe4b4D6Ndq5Go1OY38F6nzWbv0XmsPb+eFCUbNSr6eXXi/7rPkacJIQogCaKUVKYE8Wf8LhYceJuE7Jynhh41AxjU/RMcHM1/Qj3NvUss3zmZw8lxADS0c2V893lGV4UJUZlIgigllSFBKDodOw59wMoL35GNjupqO0a1mUCA/wumDq3Yok8sYsnppaQq2dgoarq4PUczv1dKrVpLiIpAEkQpqegJIj3tLkuiRnHg3nkA2lSty5iQRXm6rFqaTdH72HRiOkk2OdOkOCX78mfWKN55uqWsgS0EsqKcMMLt278T9v0zHLh3HjUqBtXtw5RnN1p0cog6k8Drm++z7683qZbcCIBkpzh8q7zHlHXbiTqTYOIIhbAckiAqqQsXo3jrx5e4lHmPamob3g4K4+lus8yyIfphWp1CdNwdNsdcIzruDlqdkue98C2xKIAOaw7eeRludcdaUaOx19Cs1nw+/fGHPMcIIQpWoUdSC8OOnVrG/JiFZKHDx8aJN3t8jruHv6nDKlLUmQTCt8SSoDE8puJofFKe9wB+S+mBd2Yd6nh+yQPrTBycIvnxYDWeftLy2leEKG/m/XVRlLrtB2bxScxnZKGjddU6zPzXJotJDqPXnsyXABI16Yxee5KoMwncvJ9u8NjrmY2J/es1nDKrkKnW8uUfH3L4xBflEbYQFk0SRCWh6HR8+8vrLL3wDToUurk2541nvrOILqwPVx09Kndf+JZYXB0LXkMiWefGseuTcEmriQ6F+b8tYtehD4sVQ0FVW0JUVFLFVAkoOh1rokbx442jADzr3Znnesw1+/aGXIaqjh6mAAmadH5PLHxxpwylCvsTJzLGcz3H087xxfl1pGZoCO36fqHHFVW1JURFZRl3CFFiOm02y34crk8Ow3yf4d+95pdrcnjcb98FVR096urdtCLLKFjT6IkPedqzAwBrL/3Id9snFVjemKotISoqeYKowHTabBZvHcrupDOogJGNB9K9w9RyjaE0vn27VzNuyoy6ro5GlfNwdiSo9ec47nyT9Vd+4du/dqH88jrP9Zybp1xRVVsqcqq2ymtdbSHKmzxBVFCKTseyH4ezO+kMalSM8RtmkuRQGt++29Z3xcvZnoJuwSpyks6LQfWMKte2fk67yzPdP2RQ3T4AfHdtN9/8MjFPeWOrto7GJxn1OYSwNJIgKiBFp2PFjyPYcec31KgY22w4T7WbUK4xGNuwbEx1k5VaRVioH0C+m3/u67BQP2yt1UaVe/jb/tPdZjG43j8B2HBtDxt3vql/z9iqLWPLCWFpJEFUMIpOx5fbxrDt9klUwKimL9Kp7WvlHkdpf/sO8fcicnBrPJ3zVjd5OtsT+dDCQ8aWe1ho1/cYXK8vAOuv/ELUvnDA+KotY8sJYWmkDaKC2bR7GlsSDwM5bQ6d2xfcAFuWyuLbd4i/Fz38PItcW8LYcg8L7fo+6b884Ltru1kRtxE7myo81W4yXs72JGrSDT4JqchJPLlVVkJUNJIgKpAdByJYf+UXAF6sH1rubQ4PK6tv31ZqFUG+NUqt3MP+L/hj0v7XHXjx719S1b46YaF9GL0252ns4SRRUJWVEBWJVDFVEIdPfMHSC18D8IzXk/yzy0yTxmNsw7I5fftWqdW8GLKIrq7+OYPpYhZSz+ZYsausiiKD7oSlkOm+i2AJ032fO7+Z9w+Fk4WO4BotGPHPlWYxCC63FxMY/vZdkhtsWS1fmuca2Zl8vPE5TqRcporKmvDu8/Gu1aFUriuD7oQ5kPUgSom5J4irV6MJ2/kaqUo2/6haj0n/+g61lfnUHJbmDbE8b64Z6Rre+/5Z/si4javanvf6rqZGzSce65y5CfPRP7jHSZhClESFXQ9i4cKF1KtXD3t7e9q1a8fRo0cLLLty5UpUKlWezd6+4vQ4uZsUxwe7J5GqZPOEXU3GP73GrJID5DQYH5jaja9Gtmf+8wF8NbI9B6Z2K1FyKM8RzXb2zkwNXUMt66ok6dKZHTWStAclH+9Qmt1+hSgvFpUgvv76ayZNmkRYWBgnT56kZcuW9OrVi5s3bxZ4jJOTEwkJCfrt8uXL5Rhx2clI1zDn55Hc1qbhZV2FKX1XYGtXzdRhGZTbYNwvoBZBvjWKXTVjqptr1WpeTOu5CGe1LZezNMzfOgRtdmaJziWD7oQlsqgE8cknnzBy5EiGDRuGn58fixYtwtHRkeXLlxd4jEqlwtPTU795eHiUY8RlQ6fNZuHWocRlJlFNbcO0HgtxcvYxdVhlxpQ3V3cPf97s9B62KjWnUq+y6udRJTqPDLoTlshiEkRmZiYnTpwgODhYv0+tVhMcHEx0dHSBx6WkpFC3bl18fHzo168fZ8+eLfQ6GRkZJCcn59nMzfrtEzhyPx5rVExu/188PQNMHVKZMvXNtaFvT15rOQYVsO32SbbtN9xDrLDeSTLoTlgi86qwLsTt27fRarX5ngA8PDz4/fffDR7TuHFjli9fTosWLdBoNHz00Ud06NCBs2fPUrt2bYPHREREEB4eXurxl5Z9R+ayOeEgAK/6DaVp434mjqjsmcPNtW2rETyfdIGvrmxj5cXv8XZtQvNmz+nfL6oBPbfbrwy6E5bEYp4gSiIoKIiXXnqJgIAAOnfuzPfff4+bmxtffFHwamLTp09Ho9Hot6tXr5ZjxIW7cDGKxbFrgJyxDuU9v5KpmMuYin5dI3jSpQk6FOYem83168cB4xrQjZ1PSgbdCXNiMQmiZs2aWFlZcePGjTz7b9y4gaenp1HnsLGxoVWrVly8eLHAMnZ2djg5OeXZzEFS0kU+PhhGFjoCq9bl3z3mFn1QBWEuN1eVWs2rfZbxhF1NUpVs5uycyP37N4xuQC/JPFFCmJLFJAhbW1vatGnDzp079ft0Oh07d+4kKCjIqHNotVpOnz6Nl5dl/SFmZaTycdQr3NVlUNumGuP6rjC77qxlzVxurjZ2VXijz1Jc1fZcz07ho83DSdSkFFj+0Qb00ur2K0R5sKi7zKRJkxgyZAiBgYG0bduWefPmkZqayrBhwwB46aWXqFWrFhEREQC8++67tG/fnoYNG3Lv3j3mzJnD5cuXGTFihCk/RrEoOh1Lf36FixlJVFVZ82aPhRaxjnRZKMkkfGXB2aUeb3SaSdi+qZzN+Isg1y85lDSk0GMebkAvyTxRQpiCRSWIAQMGcOvWLd555x0SExMJCAggKipK33B95coV1A9NMXH37l1GjhxJYmIi1atXp02bNhw6dAg/Pz9TfYQ8jJk2YvvBWey5exY1Kib+YyoeHi1MFK15MJebq69vD165HcunZ5aT4nyO5hm7OJ3arcDy0jtJWCKZaqMIZTXVhjHTRpy/8CPvHvgv2SgMqtuHp7vNKrXri9KxcutINl4/gpVORcJfL5OQ7Zvn/dzeSQemdpMGaGE2KuxUGxWBMb1e7t2NZ+6hd8lGob2TL6Fd3jNRtKIwg3t9yhN2NclW6ajvuQZ71X39e9I7SVg6SRDlzJhpI9794VfmRb2qb5Qe1XuJWczOKvKztrHnraeX4WZjT6ptBm3dFwM6QHonCctnUW0QFYEx00Y0sFnE2Qc3cLSyZnK3uZW2UdpSuFSvz5tPzmDGgf+S5nSHyT67CWz1lkka0IUoTfK1tJwVNR1E86o7SHaKQwFGN38Fb+/A8glMPJbGjfoyxLc/arWK6Pu7cdUdluQgLJ4kiHJWWG8WD+t4bGruAqCTUyvat3m1vMISpaBXp7cJcmqIFoV5h2ai0VwxdUhCPBZJEOWsoGkjbFVpNPRcTbZKh2u2E6P7RZokPlFyKrWaV3svxvt/a0h8GjUKnTbb1GEJUWKSIMrZw9NGPKxdzeXct0nDVmfFM20+xM7WwQTRicfl4OjKpC5zsFNZcfrBdTbummrqkIQoMUkQJhDi78UrT9Unt4q6VbWf0FS9igpoU/0l+rXvaNL4xOPx8QliRNPBAHz31y7Onttg4oiEKBlJECYQdSaBxfvi0SngZR2HqsYBAKpqmrA4pnGpL58pyt9T7V6nS/Vm6FBYcHQ2mnuXTB2SEMUmCaKcPTwOwlaVRgPPtWSrdDhnOHEoKedbp6xNXDEMD/mc2jbVuKfL5NOo0dIeISyOJIhy9vA4iNx2BzudNadvDEfBWtYmrkDs7J15Pbc9Ii2BTbunmTokIYpFEkQ5yx0H0bLqNjRVcxYjun+zD/e0ngbLCctWu3Z7fXvEt1d3Evv7JtMGJEQxSIIoZ+7V7HGzvoxVzb0AVNM05ve0DgbLiYrhqXav09mlaU57xJEIGR8hLIYkiHLWqrY9fl5ryFbpcMqoRnTSi3neL6/lM0X5GtZrIbWsq3JXl0HktrFGtUdodQrRcXfYHHON6Lg70i4lyp3MxVTOvt05iVT7B9hmWvH7jWHoHvoRyOyfFZeDoysTnorgrV0TOJV6lZ/3v0vfLu8WWN6Y6eCFKGvyBFGOTv22lq03jmClVtGv3jBsqjbI877M/lmx1a37JC81/BcA6+K38Gf8ToPljJkOXojyIAsGFaG0FgxKSrrIm1sGcl+XRYhbG4b9c5lRK8qJikXR6Zj7/bMcuR+Ph1UVPvi/zTg61tS/r9UpdJq9q8AZf2UBIlEaZMEgM6LTZrPwl3Hc12VR18aZwT0WAH8vn9kvoBZBvjXkD74SUKnVvNLrc2paOXBDm8qybWNRdDr9+8ZMBy/doEV5kQRRDjbvns6ZtETsVVZM7PYxNnZVTB2SMKGq1bwYH/Rf1Kg4cO88+4/N179nbPdm6QYtyoMkiDJ2/sKPfHN1BwDDmrwg6zsIIGf9iH/7BAOw7NxaEq6fAIzv3izdoEV5sLgEsXDhQurVq4e9vT3t2rXj6NGjhZb/9ttvadKkCfb29jRv3pyffvqpnCKF1JREPo1+Dx0KHZ0b0bnt6+V2bWH++nWNwM/Bg3RFy7zdk8nKSC1wOvhc0g1alCeLShBff/01kyZNIiwsjJMnT9KyZUt69erFzZs3DZY/dOgQAwcO5OWXX+bUqVP079+f/v37c+bMmTKPVdHpWLxtLLe0abhbOTIiJFLWlRZ5qK2sGRf8KdXUNlzKvMe6Ha/nmQ7+0SQh3aBFeSv2HWvIkCHs27evLGIp0ieffMLIkSMZNmwYfn5+LFq0CEdHR5YvX26w/Pz58wkJCWHKlCk0bdqUmTNn0rp1az777LMyj3XX4TkcTo7DChUTOs7I01NFiFw1aj7B6IBxAPx08yinfltLiL8XkYNb4+mctxpJukGLR0Xte5fbt38vs/MXO0FoNBqCg4Np1KgRs2bN4tq1a2URVz6ZmZmcOHGC4OBg/T61Wk1wcDDR0dEGj4mOjs5THqBXr14FlgfIyMggOTk5z1Zcf/11mJV/fA3A83V709C3Z7HPISqPNi2H0Nstp21q4an53E2KI8TfiwNTu/HVyPbMfz6Ar0a258DUbpIchN7xmJWsiPueqVsHcz+5bO7DxU4QmzZt4tq1a4wePZqvv/6aevXq0bt3b7777juysrLKIkYAbt++jVarxcPDI89+Dw8PEhMTDR6TmJhYrPIAERERODs76zcfH59ix1rNqQ4NbWpTT+1GDZ8JMkWCKNKgHvOpa+PMfV0WC38Zh06bLd2gRYHu3P6DyJicmpAu7m2o5lSrTK5TokpxNzc3Jk2axK+//sqRI0do2LAhL774It7e3rz++utcuHChtOMsN9OnT0ej0ei3q1evFuv4qDMJhCw8x4rTI9n0+0gGLTtOp9m7ZPSrKJSNXRUmdP17avAf9rxl6pCEmdJps/l0+zhSlGwa2Fbn+R5zy+xaj9VqmpCQwPbt29m+fTtWVlb06dOH06dP4+fnx9y5pRt0zZo1sbKy4saNG3n237hxA09PT4PHeHp6Fqs8gJ2dHU5OTnk2Y+WdIkFNhpIz3kGmSBDGqFWrLcMaDwTg6yu/8MfFn00ckTBHm3ZP41z6TexVVozv+hE2No5ldq1iJ4isrCw2bNjAP//5T+rWrcu3337LxIkTuX79OqtWrWLHjh188803vPtuwRORlYStrS1t2rRh586/56/R6XTs3LmToKAgg8cEBQXlKQ+wffv2Ass/jodXintU7j5ZKU4UpUu7SXRwaogOhU8PzeRBiuEeeqJyOv/HVr69mnNPe7npYLy825Tp9Yo9m6uXlxc6nY6BAwdy9OhRAgIC8pXp2rUrLi4upRBeXpMmTWLIkCEEBgbStm1b5s2bR2pqKsOGDQPgpZdeolatWkRERAAwYcIEOnfuzMcff0zfvn1Zv349x48fZ/HixaUeW3GmSAjyrVHq1xcVg0qtZmSvz7n4fX9uah+wZNsYxj/zjXSRFqTcT2DB4ffRodDJpTFP/mNCmV+z2Ali7ty5PPfcc9jbFzyS08XFhfj4+McKzJABAwZw69Yt3nnnHRITEwkICCAqKkrfEH3lyhXUD/0hdejQgXXr1vHf//6X//znPzRq1IhNmzbh7+9f6rHJFAmitDhWdWd8h3cI2z+dQ8kXaX74I7p1eNPUYQkTUnQ6vtg2htvaNDytqzCiV/mMq5LZXItg7KyH0XF3GLjkcJHn+2pke3mCEEbZvGsa6y5HYatSM6v7Qnx8Sr9qVFiGX/a/x7KL32GNipldPqJB/e6PdT6ZzbWcyRQJorSFdn6P5o7eZCo65u+dSmbGfVOHJEzg8uX9rL74PQAv1A997ORQHJIgSolMkSBKm9rKmtd6fY6z2parWcms3jbO1CGJcpaedpf5+6aThY5WVXzo89SMcr2+JIhSJFMkiNLm7FKPsW0mAbD9zq9En1hk4ohEeVqxbRzXslOorrZjTK/Py72zgqxJXcpC/L3o4ecpK8WJUtPS/3n6/bWfzQkH+eL0UhrU7oCHRwtThyXK2P6jC9hz9yxqVIxvNx0n5+LP6vC45AmiDMgUCaK0/bv7xzxhV5M0JZv5OyaSlfXA1CGJMpRw/QRLY1cB8K9aXfBr0t8kcUiCEMICWNvYM6HHp1RVWROXmcRX2yeaOiRRRrIyUpm7axLpipam9u48232OyWKRBCGEhajp1pRRAWMA+PHGUY7HrDRtQKJMrNo2hstZGpzUtozv9TlqK9O1BEiCEMKC/CNgOH3c2wIQGfMZt26eNXFEojQdOv452+/8CsDYNhNxdW1o0ngkQQhhYV7oOY+Gdq6kKNnM2zFe2iMqiITrJ/jizDIA+nt1IsD/BRNHJAlCCItjY+PIhO4LqKKy5mLGHdb9MtHUIYnHlJlxP0+7w7+DPzF1SIAkCCEskruHP2MeWqr0yMnSn4BSlJ/lUaO5nKXB+X/tDlbWtqYOCZAEIYTFCgwYytOeOfMzff7bFyRcP2HiiERJ7D38CbuTzqACXgucYvJ2h4dJghDCgg0InktTe3fSFS1zd00iI11j6pBEMVy6tJcl59YC8H+1u9G82XMmjigvSRBCWDBrG3vG/2++pstZGpb+PApFpzN1WMIIqSmJfLLvP2ShI6BKbf7V7UNTh5SPJAghLJyra0MmtJ2KGhX77p1j+8FZJT6XVqcQHXeHzTHXiI67IysglhFFp+Pzn1/lhjYVNysHxoV8YdLxDgUxv4iEEMXWrOmzDEw4wZeXf2LVxQ3U82rDEw17F+scUWcSCN8Sm2dlRC9ne8JC/WSiyVK2afc0jqdcxho1k558j2pOtUwdkkHyBCFEBRHa5T3aVatPNgpzD4ajuXfJ6GOjziQweu3JfMvmJmrSGb32JFFnEko52sor5vSXfH3lFwCGNx5Qrus7FJckCCEqCJVazeg+y6hlXZUkXTpzf36F7Kyil7jV6hTCt8RiqDIpd1/4llipbioFiYkxLDgxFwUIrtGC7h2mmjqkQkmCEKICcXB0ZXK3T7BXWXEu/Saro8YUeczR+KR8Tw4PU4AETTpH45NKMdLKJ+1BEh/vGE+qkk0ju5oM7RVp6pCKZDEJIikpiUGDBuHk5ISLiwsvv/wyKSkphR7TpUsXVCpVnm3UqFHlFLEQplGrVlteC3gNgG23T7LrUOG9Y27eL/opozjlRH46bTaRP73MlaxknNW2vN7zc2zsqpg6rCJZTIIYNGgQZ8+eZfv27WzdupV9+/bxyiuvFHncyJEjSUhI0G8ffmh+XcmEKG2BAUP5d+2cuu1l59dz/sKPBZZ1r2Zf4HslKSfy27BzCkfux2ONmskdwqhR8wlTh2QUi0gQ586dIyoqiqVLl9KuXTs6derEp59+yvr167l+/Xqhxzo6OuLp6anfnJycyilqIUzrmW6z/9dorePjQ+EFzvzatr4rXs72+dZSz6UipzdT2/quZRZrRXbk5GK+u7YbgBFNXqBxo74mjsh4FpEgoqOjcXFxITAwUL8vODgYtVrNkSNHCj32yy+/pGbNmvj7+zN9+nQePJCZL0XloLayZnSfZdS1cUajy2TOL2NIe5C/HcFKrSIs1A8gX5LIfR0W6icrI5ZA/KU9LPztCwD6uP+DrkFvmDii4rGIBJGYmIi7u3uefdbW1ri6upKYmFjgcS+88AJr165l9+7dTJ8+nTVr1jB48OBCr5WRkUFycnKeTQhL5eDoypu9FulHWn/64zB02ux85UL8vYgc3BpP57zVSJ7O9kQObi3jIErgzu0/+HDvNDIULc0dvRnY4zOLG4Ro0oFy06ZNY/bs2YWWOXfuXInP/3AbRfPmzfHy8qJ79+7ExcXh6+tr8JiIiAjCw8NLfE0hiqLVKRyNT+Lm/XTcq+VU3ZTlt/Oabk15o2M47+5/ixMpl1m7bQwv9ck/+2uIvxc9/DzLNbaK4tGfaQtPFXO2vUqSLp1a1tVo6fsBnT8+YHGDEFWKopgsjd26dYs7d+4UWqZBgwasXbuWyZMnc/fuXf3+7Oxs7O3t+fbbb3nmmWeMul5qaipVq1YlKiqKXr16GSyTkZFBRkaG/nVycjI+Pj5oNBppvxCPzZSjlQ8dW8j8M0sAGOb7L0KeeqdMr1dZPPozVZFNt1rz0TjewcXalr6NZjPph7R840xy064pntCSk5NxdnYu8r5m0icINzc33NzciiwXFBTEvXv3OHHiBG3atAFg165d6HQ62rVrZ/T1YmJiAPDyKviHYWdnh52dndHnFMJYuaOVH71R5I5WLusbRYd/jOWG5hLrr25nVdxG3Jzr0qblkDK7XmWQ/2eqo1PNJdyyvYU6W0X3OuP5cK9S4CBEFTmDEHv4eZrlk5pFtEE0bdqUkJAQRo4cydGjRzl48CDjxo3j+eefx9vbG4Br167RpEkTjh49CkBcXBwzZ87kxIkTXLp0iR9++IGXXnqJp556ihYtWpjy44hKyFxGK/fvNpuurv7oUJh/agEXLkaV6fUqMkM/0/Yu69FUuwyAcqsrcw57WPQgRItIEJDTG6lJkyZ0796dPn360KlTJxYv/rseNSsri/Pnz+t7Kdna2rJjxw569uxJkyZNmDx5Ms8++yxbtmwx1UcQlZi5jFZWqdWM6LOUFo7eZChaZh98m+vXj5fpNUuLuc00++jPNKDqzzyo/hsADkmt+DWlJ0mpWUady1wHIVrMbK6urq6sW7euwPfr1avHw80pPj4+7N27tzxCE6JI5jRa2drGnkmha5m58f+Iy0xi1o7XePefa8xqJbNHmeNMsw//rPwc96G47QPAKbk+BzQDinUucx2EaDFPEEJYMnMbrezg6MrUvsvxtK7CLW0as34azv3ka+Vy7eIy15lmc39WDe2PY+f+MwoKLg88OXjn5TzlXKvYWuwgREkQQpQDcxyt7OxSj7d6fkF1tR1Xs5KJ2PIiDx7cLrfrG8Nc2m4MaVvflZbVL+Ls+T1alYJLWnUO3hiD8r+Kmdyf6Xv9/PWvH2YJgxAlQQhRDsx1tLK7hz9vdZtHNbUNcZlJfLj5BbNa19pc2m4MuX7tMC5ua8hS6XDOqMaRG+PQYgvk/Zn2aVG8QYjm1NZiMW0QQli63NHKj9ale5q4Lt3HJ4j/PDWbd/e+ybn0m8zZPJA3+3+NrV01k8TzMHNqu3nYtWtHmblrAumqbBrYVufXpAlkKH8ngEd/psYOQjS3thaTDpSzBMYOKBHCWOU9ktpY5y/8yKyD75CuaGnu4MWU/uuxs3c2aUzRcXcYuORwkeW+GtmeIN8a5RARJFw/Qfj2MdzVZVDXxpl3+q3Hocrjj0AvaJxMWQyoM/a+JgmiCJIgRGXy+/kfiIgON5skodUpdJq9i0RNusF2CBU539YPTO1WLkn26tWDzNz1OhpdJj42TrwT+iVOzj6Pfd7cz1lQdVppf05j72vSBiGE0GvS+Gn+0yEce5UVp9MSiNj4HA9SbposHnNqu4m/tIfwXRPR6DKpa+PM2/9cXSrJAcy3rUUShBAij8ZP/JP/dHwXR5U159JvEr7532juXTJZPOYw0+zv539g5p43uK/LwtfWlXf6rcfZpV6pnd9c21qkkVoIkU/jRn0Js6nKrL1TuJR5j7Atg/hPzy9w9/A3STymnGn22KllzI9ZSBY6GtvXZFroOhyruhd9YDGY2ziZXPIEIYQwqF69zoT3iKSmlQMJ2an8N2o4cXHbTRaPlVpFkG8N+gXUIsi3Rrkkhx0HIvgk5jOy0NGmal3eemZDqScHMM9xMiAJQghRCC/vNrzbZ5V+Vbrw/dM4HrPSJLEYOz6gNMYR6LTZrP7pFZZc+BodCt1cmzP5mW8LbLB/3GuaU1tLnmtLL6bCSS8mIeDBg9vM3zqUmNS/UAEDfHrQv9tsVOry+Y5p7PiA0hhH8ODBbRZsHcap1KsA/F+trvxf8McFftbSHLtQXuMgpJtrKZEEIUQObXYmK34ayfY7vwLQrlp9RvdZhoNj2VZ7GDs+oDTGEVy9Gs3cPVO4lp2CDWrGNB9Bh8Axjx1bcZTHOBlJEKVEEoQQee08+AHL//iGbHTUsq7KhKciqFv3yTK5lrHjA/ZO6UrnObsfaxzBviPzWXpuNRmKFle1PW90momvb4/Hjq28xmgUh4yDEEKUie4dpxH25PtUV9txLTuFt3ZNIGrfuyg6Xalfy9jxAWuiL5V4HMGDB7eJ3DSIhbEryPjfAMEP+q0vNDkUJzZzXQzIGJIghBDF9kTD3szu/y2tq/iQhY4Vcd8z+7t+3Ln9R6lex9h+/5eTHpTofLG/b+LN70LZc/csKnLaG/7z3BajxjiY69iF0iQJQghRIs7OdXjz/zYztEF/rFFzKvUqk7YOZMeBCHTa7FK5hrH9/uu6OhbrfKkpiSzfMpx3o2dwS5uGu5Uj7wTN4Lmec1FbGTc8zFzHLpQmSRBCiBJTqdX07jyDD4IX0tCuBumKliUXvibs65BSGTNh7PiAF9rVpahqfrUKWvs4s//oAl7f8DTbbuc0Lndzbc6H/7cVvyb9yyQ2c10MyBiSIIQQj83HJ4iZA7bxUv2nsVNZ8UfGbf6zbwoLNw7k9q1zJT6vseMDYq7eo6ihB43sDzP9q+58dnY5Gl0m3tZVebvdW7zab02JemKZ69iF0iS9mIogvZiEKJ47t/9g/f4w9t3LSQzWqHjK1Z/+7afg4dGiROcsanzA5phrTFgfY+BIHY0dDuPusheNvQZbazWOVtY8U7sb/3xyBjZ2VUoUT3FiM0cVrpvr+++/z48//khMTAy2trbcu3evyGMURSEsLIwlS5Zw7949OnbsSGRkJI0aNTL6upIghCiZi3G/8NWxTziTlgiAGhWB1erRo+nz+Dd51ui6/lyFjQ94dN0IO1UqzaruxsH5BPdt0vTXf6p6S17uObNUJ9orKjZzVOESRFhYGC4uLvz1118sW7bMqAQxe/ZsIiIiWLVqFfXr1+ftt9/m9OnTxMbGYm9vXMORJAghHs/5P7ay4VQkvz64pt/nbuVIe7cA2j/xDA3qd3/sEdlanUKXD36kWvYBalY9SYrjDbJVOd1urRQVVVN8uJ39LFvfHGbWN+7yUuESRK6VK1cyceLEIhOEoih4e3szefJk3njjDQA0Gg0eHh6sXLmS559/3qjrSYIQonRcvRrN9pgl7LvzG2nK372cqqvtaFrVhybuLWno3R4vr1Y4OtYs8nwazRUuXT1E/I1TnLtzll/vX+NB9t/ndcy2RZXyBGc0fUjRuZbb1OCWwNj7WoWd7js+Pp7ExESCg4P1+5ydnWnXrh3R0dEFJoiMjAwyMjL0r5OTk8s8ViEqAx+fIIb7BPFC2l1OnlnHkSu7iLl/ibu6DA4lX+RQ8kW4uAEAF7Ut1a2rUNXKHkfrnKf9LEVLljaLpKwUkrRpeZIMAGpws3FAdd+D+Ltt+S09AFDj5WzPHDNuDzBnFTZBJCbm1Ht6eHjk2e/h4aF/z5CIiAjCw8PLNDYhKjN7h+p0+MdYOvxjLJkZ97nw5y+c++sgv985x5WMO2h0mdzTZXIvM7PIc3lZV6G+oxe+NZrS0rcPtWu1Q4fKotoDzJlJE8S0adOYPXt2oWXOnTtHkyZNyikimD59OpMmTdK/Tk5OxsendJYVFELkZWtXjWZNn6VZ02f1+1JTEkm8cZrk1BukpCeRmn4PtUqNlZUNNlb2VK/qTY3q9XGt7ou9Q/V857QCgnxrlOOnqLhMmiAmT57M0KFDCy3ToEGDEp3b09MTgBs3buDl9fej5Y0bNwgICCjwODs7O+zs7Ep0TSHE46tS1RPfqp6mDkNg4gTh5uaGm5tbmZy7fv36eHp6snPnTn1CSE5O5siRI4wePbpMrimEEBWJxYykvnLlCjExMVy5cgWtVktMTAwxMTGkpKToyzRp0oSNGzcCoFKpmDhxIu+99x4//PADp0+f5qWXXsLb25v+/fub6FMIIYTlsJhG6nfeeYdVq1bpX7dq1QqA3bt306VLFwDOnz+PRqPRl3nzzTdJTU3llVde4d69e3Tq1ImoqCijx0AIISo3SxsAV9osbhxEeZNxEEJUTpY4hYaxZMEgIYQoodylRB9dEChRk87otSeJOpNgosjKlyQIIYR4iFanEL4lNt8604B+X/iWWLRFTR9bAUiCEEJUKFqdQnTcHTbHXCM67k6xb+SVYSlRY1lMI7UQQhSlNNoNKsNSosaSJwghRIVQWu0GlWEpUWNJghBCWLzSbDeoDEuJGksShBDC4pVmu0FlWErUWJIghBAWr7TbDUL8vYgc3BpP57zVSJ7O9pVqXQlppBZCWLyyaDcI8feih59npR5JLQlCCGHxctsNEjXpBtshVOR8+y9uu4GVWlWppw6XKiYhhMWTdoOyIQlCCFEhSLtB6ZMqJiFEhSHtBqVLEoQQokKp7O0GpUmqmIQQQhgkCUIIIYRBkiCEEEIYJAlCCCGEQZIghBBCGCQJQgghhEEWkyDef/99OnTogKOjIy4uLkYdM3ToUFQqVZ4tJCSkbAMVQogKwmLGQWRmZvLcc88RFBTEsmXLjD4uJCSEFStW6F/b2dmVRXhCCFHhWEyCCA8PB2DlypXFOs7Ozg5PT88yiEgIISo2i6liKqk9e/bg7u5O48aNGT16NHfu3Cm0fEZGBsnJyXk2IYSojCp0gggJCWH16tXs3LmT2bNns3fvXnr37o1Wqy3wmIiICJydnfWbj49POUYshBDmw6QJYtq0afkakR/dfv/99xKf//nnn+fpp5+mefPm9O/fn61bt3Ls2DH27NlT4DHTp09Ho9Hot6tXr5b4+kIIYclM2gYxefJkhg4dWmiZBg0alNr1GjRoQM2aNbl48SLdu3c3WMbOzk4asoUQAhMnCDc3N9zc3Mrten/99Rd37tzBy0vmhRdCiKJYTBvElStXiImJ4cqVK2i1WmJiYoiJiSElJUVfpkmTJmzcuBGAlJQUpkyZwuHDh7l06RI7d+6kX79+NGzYkF69epnqYwghhMWwmG6u77zzDqtWrdK/btWqFQC7d++mS5cuAJw/fx6NRgOAlZUVv/32G6tWreLevXt4e3vTs2dPZs6cKVVIQghhBJWiKIbW+Bb/k5ycjLOzMxqNBicnJ1OHI4QQj83Y+5rFVDEJIYQoXxZTxSSEqJi0OkXWkDZTkiCEECYTdSaB8C2xJGjS9fu8nO0JC/UjxF96G5qaVDEJIUwi6kwCo9eezJMcABI16Yxee5KoMwkmikzkkgQhhCh3Wp1C+JZYDPWQyd0XviUWrU760JiSJAghRLk7Gp+U78nhYQqQoEnnaHxS+QUl8pEEIYQodzfvF5wcSlJOlA1JEEKIcudezb5Uy4myIQlCCFHu2tZ3xcvZnoI6s6rI6c3Utr5reYYlHiEJQghR7qzUKsJC/QDyJYnc12GhfjIewsQkQQghTCLE34vIwa3xdM5bjeTpbE/k4NYyDsIMyEA5IYTJhPh70cPPU0ZSmylJEEIIk7JSqwjyrWHqMIQBUsUkhBDCIEkQQgghDJIEIYQQwiBpgyhC7npKycnJJo5ECCFKR+79rKj14iRBFOH+/fsA+Pj4mDgSIYQoXffv38fZ2bnA92XJ0SLodDquX79OtWrVUKmM73qXnJyMj48PV69etZilSiXm8mFpMVtavCAxF0VRFO7fv4+3tzdqdcEtDfIEUQS1Wk3t2rVLfLyTk5PF/ILmkpjLh6XFbGnxgsRcmMKeHHJJI7UQQgiDJEEIIYQwSBJEGbGzsyMsLAw7OztTh2I0ibl8WFrMlhYvSMylRRqphRBCGCRPEEIIIQySBCGEEMIgSRBCCCEMkgQhhBDCIEkQZWDhwoXUq1cPe3t72rVrx9GjR00dUqH27dtHaGgo3t7eqFQqNm3aZOqQChUREcE//vEPqlWrhru7O/379+f8+fOmDqtQkZGRtGjRQj8IKigoiJ9//tnUYRXLBx98gEqlYuLEiaYOpUAzZsxApVLl2Zo0aWLqsIp07do1Bg8eTI0aNXBwcKB58+YcP37c1GFJgihtX3/9NZMmTSIsLIyTJ0/SsmVLevXqxc2bN00dWoFSU1Np2bIlCxcuNHUoRtm7dy9jx47l8OHDbN++naysLHr27ElqaqqpQytQ7dq1+eCDDzhx4gTHjx+nW7du9OvXj7Nnz5o6NKMcO3aML774ghYtWpg6lCI1a9aMhIQE/XbgwAFTh1Sou3fv0rFjR2xsbPj555+JjY3l448/pnr16qYODRRRqtq2bauMHTtW/1qr1Sre3t5KRESECaMyHqBs3LjR1GEUy82bNxVA2bt3r6lDKZbq1asrS5cuNXUYRbp//77SqFEjZfv27Urnzp2VCRMmmDqkAoWFhSktW7Y0dRjFMnXqVKVTp06mDsMgeYIoRZmZmZw4cYLg4GD9PrVaTXBwMNHR0SaMrGLTaDQAuLq6mjgS42i1WtavX09qaipBQUGmDqdIY8eOpW/fvnl+r83ZhQsX8Pb2pkGDBgwaNIgrV66YOqRC/fDDDwQGBvLcc8/h7u5Oq1atWLJkianDAqSKqVTdvn0brVaLh4dHnv0eHh4kJiaaKKqKTafTMXHiRDp27Ii/v7+pwynU6dOnqVq1KnZ2dowaNYqNGzfi5+dn6rAKtX79ek6ePElERISpQzFKu3btWLlyJVFRUURGRhIfH8+TTz6pn7bfHP35559ERkbSqFEjtm3bxujRoxk/fjyrVq0ydWgym6uwbGPHjuXMmTNmX88M0LhxY2JiYtBoNHz33XcMGTKEvXv3mm2SuHr1KhMmTGD79u3Y29ubOhyj9O7dW//vFi1a0K5dO+rWrcs333zDyy+/bMLICqbT6QgMDGTWrFkAtGrVijNnzrBo0SKGDBli0tjkCaIU1axZEysrK27cuJFn/40bN/D09DRRVBXXuHHj2Lp1K7t3736sKdnLi62tLQ0bNqRNmzZERETQsmVL5s+fb+qwCnTixAlu3rxJ69atsba2xtramr1797JgwQKsra3RarWmDrFILi4uPPHEE1y8eNHUoRTIy8sr35eEpk2bmkXVmCSIUmRra0ubNm3YuXOnfp9Op2Pnzp0WUddsKRRFYdy4cWzcuJFdu3ZRv359U4dUIjqdjoyMDFOHUaDu3btz+vRpYmJi9FtgYCCDBg0iJiYGKysrU4dYpJSUFOLi4vDy8jJ1KAXq2LFjvm7af/zxB3Xr1jVRRH+TKqZSNmnSJIYMGUJgYCBt27Zl3rx5pKamMmzYMFOHVqCUlJQ837Di4+OJiYnB1dWVOnXqmDAyw8aOHcu6devYvHkz1apV07fvODs74+DgYOLoDJs+fTq9e/emTp063L9/n3Xr1rFnzx62bdtm6tAKVK1atXztOlWqVKFGjRpm297zxhtvEBoaSt26dbl+/TphYWFYWVkxcOBAU4dWoNdff50OHTowa9Ys/v3vf3P06FEWL17M4sWLTR2adHMtC59++qlSp04dxdbWVmnbtq1y+PBhU4dUqN27dytAvm3IkCGmDs0gQ7ECyooVK0wdWoGGDx+u1K1bV7G1tVXc3NyU7t27K7/88oupwyo2c+/mOmDAAMXLy0uxtbVVatWqpQwYMEC5ePGiqcMq0pYtWxR/f3/Fzs5OadKkibJ48WJTh6QoiqLIdN9CCCEMkjYIIYQQBkmCEEIIYZAkCCGEEAZJghBCCGGQJAghhBAGSYIQQghhkCQIIYQQBkmCEEIIYZAkCCGEEAZJghBCCGGQJAghTOjWrVt4enrq1wIAOHToELa2tnlmBRbCFGQuJiFM7KeffqJ///4cOnSIxo0bExAQQL9+/fjkk09MHZqo5CRBCGEGxo4dy44dOwgMDOT06dMcO3YMOzs7U4clKjlJEEKYgbS0NPz9/bl69SonTpygefPmpg5JCGmDEMIcxMXFcf36dXQ6HZcuXTJ1OEIA8gQhhMllZmbStm1bAgICaNy4MfPmzeP06dO4u7ubOjRRyUmCEMLEpkyZwnfffcevv/5K1apV6dy5M87OzmzdutXUoYlKTqqYhDChPXv2MG/ePNasWYOTkxNqtZo1a9awf/9+IiMjTR2eqOTkCUIIIYRB8gQhhBDCIEkQQgghDJIEIYQQwiBJEEIIIQySBCGEEMIgSRBCCCEMkgQhhBDCIEkQQgghDJIEIYQQwiBJEEIIIQySBCGEEMIgSRBCCCEM+n/zU5/oNe7ZMQAAAABJRU5ErkJggg==", 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" ] @@ -875,22 +793,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 4:\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:04<00:00, 22.62it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:06<00:00, 15.88it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -898,13 +802,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1mCycle 4 model: sin(x)\u001b[0m\n", - "[sin(x), sin(x), sin(x), sin(x)]\n" + "\n", + "\u001b[1mRunning Cycle 1:\u001b[0m\n", + "\u001b[1mCycle 1 model: sin(x)\u001b[0m\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -916,7 +821,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:06<00:00, 16.55it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { @@ -924,14 +831,26 @@ "output_type": "stream", "text": [ "\n", - "\u001b[1mRunning Cycle 5:\u001b[0m\n" + "\u001b[1mRunning Cycle 2:\u001b[0m\n", + "\u001b[1mCycle 2 model: sin(x)\u001b[0m\n" ] }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 24.55it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:05<00:00, 17.34it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -939,13 +858,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1mCycle 5 model: 0.17\u001b[0m\n", - "[0.17, 0.17, 0.17, 0.17, 0.17]\n" + "\n", + "\u001b[1mRunning Cycle 3:\u001b[0m\n", + "\u001b[1mCycle 3 model: 0.01\u001b[0m\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -962,8 +882,10 @@ " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"])\n", ")\n", "\n", - "### Then we cycle through the pipeline we built five times ###\n", - "num_cycles = 5 # number of empirical research cycles\n", + "#==========OPTION 1==========#\n", + "\n", + "### Then we cycle through the pipeline we built three times ###\n", + "num_cycles = 3 # number of empirical research cycles\n", "for cycle in range(num_cycles):\n", " #Run pipeline\n", " s = theorist(experiment_runner(experimentalist(s, num_samples=10)))\n", @@ -971,6 +893,21 @@ " #Report metrics\n", " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", + " plot_from_state(s)\n", + " \n", + "#==========OPTION 2==========#\n", + "\n", + "### Then we cycle through the pipeline we built three more times ###\n", + "num_cycles = 3 # number of empirical research cycles\n", + "for cycle in range(num_cycles):\n", + " #Run pipeline\n", + " s = experimentalist(s, num_samples=10)\n", + " s = experiment_runner(s)\n", + " s = theorist(s)\n", + " \n", + " #Report metrics\n", + " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", + " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", " plot_from_state(s)" ] }, @@ -990,7 +927,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.17\n" + "sin(x)\n" ] } ], @@ -1016,22 +953,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 1, number of datapoints: 0\u001b[0m\n" + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + " 0%| | 0/100 [00:00" ] @@ -1056,22 +988,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 2, number of datapoints: 10\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:04<00:00, 22.26it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:06<00:00, 16.66it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1079,12 +997,14 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "\u001b[1mRunning Cycle 2, number of datapoints: 20\u001b[0m\n", "\u001b[1mCycle 2 model: sin(x)\u001b[0m\n" ] }, { "data": { - "image/png": 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", + "image/png": 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" ] @@ -1096,22 +1016,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 3, number of datapoints: 20\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:04<00:00, 24.07it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:06<00:00, 15.42it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1119,12 +1025,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1mCycle 3 model: 0.25\u001b[0m\n" + "\n", + "\u001b[1mRunning Cycle 3, number of datapoints: 30\u001b[0m\n", + "\u001b[1mCycle 3 model: sin(x)\u001b[0m\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1136,22 +1044,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 4, number of datapoints: 30\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:04<00:00, 24.79it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:07<00:00, 13.97it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1159,12 +1053,14 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "\u001b[1mRunning Cycle 4, number of datapoints: 40\u001b[0m\n", "\u001b[1mCycle 4 model: sin(x)\u001b[0m\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1176,22 +1072,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 5, number of datapoints: 40\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:04<00:00, 21.43it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:07<00:00, 13.82it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1199,12 +1081,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1mCycle 5 model: sin(x)\u001b[0m\n" + "\n", + "\u001b[1mRunning Cycle 5, number of datapoints: 50\u001b[0m\n", + "\u001b[1mCycle 5 model: (0.53 ** x)\u001b[0m\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1218,7 +1102,7 @@ "text": [ "\n", "\u001b[1mNumber of datapoints: 50\u001b[0m\n", - "\u001b[1mDetermined Model: sin(x)\u001b[0m\n" + "\u001b[1mDetermined Model: (0.53 ** x)\u001b[0m\n" ] } ], @@ -1230,9 +1114,9 @@ " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"])\n", ")\n", "\n", - "### Then we cycle through the pipeline we built until we reach our stopping criteria ###\n", + "### Then we cycle through the pipeline we built until we reach our stopping criterion ###\n", "cycle = 0\n", - "while len(s.experiment_data) < 50:\n", + "while len(s.experiment_data) < 50: #Run until we have at least 50 datapoints\n", " #Run pipeline\n", " s = theorist(experiment_runner(experimentalist(s, num_samples=10)))\n", " \n", @@ -1244,7 +1128,6 @@ " #Increase count\n", " cycle += 1\n", "\n", - "\n", "print(f\"\\n\\033[1mNumber of datapoints: {len(s.experiment_data)}\\033[0m\")\n", "print(f\"\\033[1mDetermined Model: {s.model}\\033[0m\")" ] @@ -1253,10 +1136,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### The Conditional Cycle\n", - "\n", - "Because `AutoRA` components (theorist, experiment runner, experimentalist) act on the state, building a pipeline can have a lot of flexibility. Above, we demonstrated using a single set of components in different loops, but the components can also change respective to your criteria. In other words, you can use `if-else` statements to control which component is acting on the state.\n", + "### The Conditional Cycle \n", "\n", + "Because `AutoRA` components (theorist, experiment runner, experimentalist) act on the state, building a pipeline can have a lot of flexibility. Above, we demonstrated using a single set of components in different loops, but the components can also change respective to your criteria. In other words, you can use `if-else` statements to control which component is acting on the state." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "For example, we can choose a different experimentalist depending on the number of datapoints we have collected." ] }, @@ -1308,14 +1196,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Using random pooler experimentalist...\n" + "\u001b[1mUsing random pooler experimentalist...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:07<00:00, 14.11it/s]\n", + "100%|██████████| 100/100 [00:05<00:00, 17.78it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1325,12 +1213,12 @@ "text": [ "\n", "\u001b[1mRunning Cycle 1:\u001b[0m\n", - "\u001b[1mCycle 1 model: 0.1\u001b[0m\n" + "\u001b[1mCycle 1 model: -0.34\u001b[0m\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1349,14 +1237,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Using random pooler experimentalist...\n" + "\u001b[1mUsing random pooler experimentalist...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:07<00:00, 12.88it/s]\n", + "100%|██████████| 100/100 [00:06<00:00, 16.23it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1366,12 +1254,12 @@ "text": [ "\n", "\u001b[1mRunning Cycle 2:\u001b[0m\n", - "\u001b[1mCycle 2 model: sin(x)\u001b[0m\n" + "\u001b[1mCycle 2 model: -0.16\u001b[0m\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1390,14 +1278,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Using uniform sampler experimentalist...\n" + "\u001b[1mUsing uniform sampler experimentalist...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:07<00:00, 12.65it/s]\n", + "100%|██████████| 100/100 [00:05<00:00, 17.31it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1412,7 +1300,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1431,14 +1319,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Using uniform sampler experimentalist...\n" + "\u001b[1mUsing uniform sampler experimentalist...\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:08<00:00, 12.22it/s]\n", + "100%|██████████| 100/100 [00:06<00:00, 16.27it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1453,7 +1341,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1484,7 +1372,7 @@ " if len(s.experiment_data) < 10: #Conditional experimentalist: random for first half of cyles\n", " print('\\033[1mUsing random pooler experimentalist...\\033[0m')\n", " s = random_experimentalist(s, num_samples=5)\n", - " else: #Uniform for last half of cycles\n", + " else: #Conditional experimentalist: uniform for last half of cycles\n", " print('\\033[1mUsing uniform sampler experimentalist...\\033[0m')\n", " s = uniform_experimentalist(s, num_samples=5)\n", " \n", @@ -1500,6 +1388,211 @@ " cycle += 1" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition, we can dynamically change parameters across cycles." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:05<00:00, 18.92it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 1:\u001b[0m\n", + "\u001b[1mCycle 1 model: -0.38\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:06<00:00, 15.39it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 2:\u001b[0m\n", + "\u001b[1mCycle 2 model: sin(x)\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:06<00:00, 16.67it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 3:\u001b[0m\n", + "\u001b[1mCycle 3 model: sin(x)\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:07<00:00, 14.26it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 4:\u001b[0m\n", + "\u001b[1mCycle 4 model: (sin(x) / 0.97)\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:10<00:00, 9.10it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mRunning Cycle 5:\u001b[0m\n", + "\u001b[1mCycle 5 model: cos((x * -0.52))\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from autora.experimentalist.random_ import random_pool\n", + "\n", + "#### First, let's reinitialize the state object to get a clean state ####\n", + "s = StandardState(\n", + " variables = variables,\n", + " conditions = conditions,\n", + " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"])\n", + ")\n", + "\n", + "#### Initiate both experimentalists ####\n", + "random_experimentalist = on_state(random_pool, output=['conditions'])\n", + "\n", + "### Then we cycle through the pipeline we built until we reach our stopping criteria ###\n", + "for cycle, num_samples in enumerate([5, 10, 20, 50, 100]):\n", + " \n", + " #Run pipeline\n", + " s = random_experimentalist(s, num_samples=num_samples)\n", + " s = experiment_runner(s)\n", + " s = theorist(s)\n", + " \n", + " #Report metrics\n", + " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", + " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", + " plot_from_state(s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", + " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", + " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", + " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", + " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", + " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.variables" + ] + }, { "attachments": {}, "cell_type": "markdown", From ebc5c1120818a1d3854a0d4f3e50f8c431506295 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Thu, 31 Aug 2023 09:36:25 -0700 Subject: [PATCH 21/32] Completed tutorial 3, adding as many seeds as possible --- .../Tutorial-III-Functional-Workflow.ipynb | 630 ++++++------------ 1 file changed, 218 insertions(+), 412 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb index 8ab2c3f00..95eca5b77 100644 --- a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb +++ b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb @@ -68,14 +68,14 @@ "torch.manual_seed(42)\n", "\n", "#### Define plot function ####\n", - "def plot_from_state(state): \n", - " model_label = f\"Model: {state.model.repr()}\" if state.model.repr() else \"Model\"\n", - " experiment_data = state.experiment_data.sort_values(by=[\"x\"])\n", - " ground_x = np.linspace(state.variables.independent_variables[0].value_range[0],state.variables.independent_variables[0].value_range[1],100)\n", + "def plot_from_state(s): \n", + " model_label = f\"Model: {s.model.repr()}\" if s.model.repr() else \"Model\"\n", + " experiment_data = s.experiment_data.sort_values(by=[\"x\"])\n", + " ground_x = np.linspace(s.variables.independent_variables[0].value_range[0],s.variables.independent_variables[0].value_range[1],100)\n", "\n", " f = plt.figure(figsize=(4,3))\n", " plt.plot(experiment_data[\"x\"], experiment_data[\"y\"], 'o', label = None)\n", - " plt.plot(ground_x, state.model.predict(ground_x.reshape(-1, 1)), alpha=.8, label=model_label)\n", + " plt.plot(ground_x, s.model.predict(ground_x.reshape(-1, 1)), alpha=.8, label=model_label)\n", " plt.plot(ground_x, np.sin(ground_x), alpha=.8, label='Ground Truth: sin(x)')\n", " plt.xlabel('x')\n", " plt.ylabel('y')\n", @@ -90,7 +90,7 @@ "## States\n", "\n", "Using the functions and objects in `autora.state`, we can build flexible pipelines and cycles which operate on state\n", - "objects. State objects are containers with specialized functionality that will hold ou variables, data, and models. This state can be acted upon by experimentalists, experiment runners, and theorists. \n", + "objects. State objects are containers with specialized functionality that will hold our variables, data, and models. This state can be acted upon by experimentalists, experiment runners, and theorists. \n", "\n", "In tutorial I, we had experimentalists define new conditions, experiment runners collect new observations, and theorists model the data. To do this, we used the output of one as the input of the other, such as: \n", "\n", @@ -109,7 +109,9 @@ "source": [ "### Defining The State\n", "\n", - "We use the `StandardState` object bundled with `autora`: `StandardState`. Let's begin by populating the state with *variable information* (`variables`), *seed condition data* (`conditions`), and a *dataframe* (`pd.DataFrame(columns=[\"x\",\"y\"])`) that will hold our conditions (`x`) and observations (`y`)." + "We use the `StandardState` object bundled with `autora`: `StandardState`. Let's begin by populating the state with *variable information* (`variables`), *seed condition data* (`conditions`), and a *dataframe* (`pd.DataFrame(columns=[\"x\",\"y\"])`) that will hold our conditions (`x`) and observations (`y`).\n", + "\n", + "*Note: Some `AutoRA` components have a `random_state` parameter that sets the seed for random number generators. Using this parameter ensures reproducibility of your code, but is optional.*" ] }, { @@ -128,7 +130,7 @@ "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", "\n", "#### Define seed condition data ####\n", - "conditions = random_pool(variables, num_samples=10)\n", + "conditions = random_pool(variables, num_samples=10, random_state=0)\n", "\n", "#### Initialize State ####\n", "s = StandardState(\n", @@ -162,16 +164,16 @@ " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 4.333231\n", - "1 5.633201\n", - "2 3.899908\n", - "3 5.849862\n", - "4 3.249923\n", + "0 5.416539\n", + "1 4.116570\n", + "2 3.249923\n", + "3 1.733292\n", + "4 1.949954\n", "5 0.216662\n", - "6 4.549893\n", - "7 2.383277\n", - "8 4.549893\n", - "9 4.116570, experiment_data=Empty DataFrame\n", + "6 0.433323\n", + "7 0.000000\n", + "8 1.083308\n", + "9 5.199877, experiment_data=Empty DataFrame\n", "Columns: [x, y]\n", "Index: [], models=[])\n" ] @@ -207,16 +209,16 @@ "\n", "\u001b[1mThe conditions we provided:\u001b[0m\n", " x\n", - "0 4.333231\n", - "1 5.633201\n", - "2 3.899908\n", - "3 5.849862\n", - "4 3.249923\n", + "0 5.416539\n", + "1 4.116570\n", + "2 3.249923\n", + "3 1.733292\n", + "4 1.949954\n", "5 0.216662\n", - "6 4.549893\n", - "7 2.383277\n", - "8 4.549893\n", - "9 4.116570\n", + "6 0.433323\n", + "7 0.000000\n", + "8 1.083308\n", + "9 5.199877\n", "\n", "\u001b[1mThe dataframe we provided:\u001b[0m\n", "Empty DataFrame\n", @@ -242,13 +244,13 @@ "source": [ "## AutoRA Components and the State\n", "\n", - "Now that we have initialized the state, we need to start preparing components of `AutoRA` to work with the state - namely, experiment runners, experimentalists, and theorists. \n", + "Now that we have initialized the state, we need to start preparing components of `AutoRA` to work with the state - namely, experimentalists, experiment runners, and theorists. \n", "\n", "These components are defined in the same way as past tutorials. All we need to do so that these can function within the state is to wrap them in specialized state functions. The wrappers are:\n", "- `on_state()` for experiment runners and experimentalists\n", - "- `state_fn_from_estimator()` for theorists\n", + "- `state_fn_from_estimator()` for theorists (specifically, scikit-learn estimators)\n", "\n", - "The first input for each wrapper should be your corresponding function (i.e., the experiment runner, experimentalist, and the theorist). The `on_state` wrapper takes a second input, `output`, to determine where in the state the component is acting on. For the experimentalist this will be `output=[\"conditions\"]`, and for the experiment runner this will be `output=[\"experiment_data\"]`.\n", + "The first argument for each wrapper should be your corresponding function (i.e., the experiment runner, the experimentalist, and the theorist). The `on_state` wrapper takes a second argument, `output`, to determine where in the state the component is acting on. For the experimentalist this will be `output=[\"conditions\"]`, and for the experiment runner this will be `output=[\"experiment_data\"]`.\n", "\n", "Once the components are wrapped, their functionality changes to act on the state, meaning that they now expect a state as the first input and will return a modified version of that state." ] @@ -286,7 +288,9 @@ "metadata": {}, "source": [ "### Experiment Runner Defined and Wrapped with State\n", - "We define the same experiment runner from the first two tutorials and then wrap it so that it functions with the state." + "We define a sine experiment runner and then wrap it so that it functions with the state.\n", + "\n", + "To create our experiment runner, we will use an `AutoRA` function called `equation_experiment()`. This function takes in an equation wrapped as a `sympy` object using `sp.simplify()` and then allows us to solve for any input (`x`) given. Further, we constrain the values that this function can output by passing it the `variable` information." ] }, { @@ -295,15 +299,21 @@ "metadata": {}, "outputs": [], "source": [ + "\n", + "import sympy as sp\n", + "from autora.experiment_runner.synthetic.abstract.equation import equation_experiment\n", "from autora.state.delta import on_state\n", "\n", - "def run_experiment(conditions: pd.DataFrame, added_noise: float = 0.5):\n", - " x = conditions[\"x\"]\n", - " y = np.sin(x) + np.random.normal(0, added_noise, size=x.shape)\n", - " observations = conditions.assign(y = y)\n", - " return observations\n", + "#### Define variable data ####\n", + "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 30))\n", + "dv = Variable(name=\"y\", type=ValueType.REAL)\n", + "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "#### Equation Experiment Method ####\n", + "sin_experiment = equation_experiment(sp.simplify('sin(x)'), variables.independent_variables, variables.dependent_variables[0])\n", + "sin_runner = sin_experiment.experiment_runner\n", "\n", - "experiment_runner = on_state(run_experiment, output=[\"experiment_data\"])" + "experiment_runner = on_state(sin_runner, output=[\"experiment_data\"])" ] }, { @@ -312,7 +322,7 @@ "source": [ "### Theorist Defined and Wrapped with State\n", "\n", - "We will use autora's BMSRegressor theorist. We import this and then wrap it so that if functions with the state." + "We will use autora's `BMSRegressor` theorist. We import this and then wrap it so that if functions with the state." ] }, { @@ -354,29 +364,29 @@ "text": [ "\u001b[1mPrevious Conditions:\u001b[0m\n", " x\n", - "0 4.333231\n", - "1 5.633201\n", - "2 3.899908\n", - "3 5.849862\n", - "4 3.249923\n", + "0 5.416539\n", + "1 4.116570\n", + "2 3.249923\n", + "3 1.733292\n", + "4 1.949954\n", "5 0.216662\n", - "6 4.549893\n", - "7 2.383277\n", - "8 4.549893\n", - "9 4.116570\n", + "6 0.433323\n", + "7 0.000000\n", + "8 1.083308\n", + "9 5.199877\n", "\n", "\u001b[1mUpdated Conditions:\u001b[0m\n", " x\n", - "0 4.983216\n", - "1 5.633201\n", - "2 5.633201\n", - "3 4.333231\n", - "4 1.949954\n", - "5 3.033262\n", - "6 3.466585\n", - "7 0.866646\n", - "8 0.866646\n", - "9 6.283185\n" + "0 0.433323\n", + "1 4.983216\n", + "2 4.116570\n", + "3 2.816600\n", + "4 2.599939\n", + "5 5.416539\n", + "6 0.433323\n", + "7 4.333231\n", + "8 1.299969\n", + "9 0.433323\n" ] } ], @@ -384,7 +394,7 @@ "print('\\033[1mPrevious Conditions:\\033[0m')\n", "print(s.conditions)\n", "\n", - "s = experimentalist(s, num_samples=10)\n", + "s = experimentalist(s, num_samples=10, random_state=42)\n", "\n", "print('\\n\\033[1mUpdated Conditions:\\033[0m')\n", "print(s.conditions)" @@ -415,16 +425,16 @@ "\n", "\u001b[1mUpdated Data:\u001b[0m\n", " x y\n", - "0 4.983216 -0.715193\n", - "1 5.633201 -0.674306\n", - "2 5.633201 -0.281330\n", - "3 4.333231 -0.167462\n", - "4 1.949954 0.811900\n", - "5 3.033262 -0.008949\n", - "6 3.466585 0.470305\n", - "7 0.866646 1.145879\n", - "8 0.866646 0.527425\n", - "9 6.283185 0.271280\n" + "0 0.433323 0.724606\n", + "1 4.983216 -2.003534\n", + "2 4.116570 -0.077238\n", + "3 2.816600 1.259866\n", + "4 2.599939 -1.435481\n", + "5 5.416539 -2.064342\n", + "6 0.433323 0.547730\n", + "7 4.333231 -1.245219\n", + "8 1.299969 0.946749\n", + "9 0.433323 -0.433155\n" ] } ], @@ -432,7 +442,7 @@ "print(\"\\033[1mPrevious Data:\\033[0m\")\n", "print(s.experiment_data)\n", "\n", - "s = experiment_runner(s)\n", + "s = experiment_runner(s, added_noise=1.0, random_state=42)\n", "\n", "print(\"\\n\\033[1mUpdated Data:\\033[0m\")\n", "print(s.experiment_data)" @@ -472,14 +482,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " 1%| | 1/100 [00:00<00:10, 9.26it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:06<00:00, 14.36it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 26.91it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -489,12 +492,12 @@ "text": [ "\n", "\u001b[1mUpdated Model:\u001b[0m\n", - "0.14\n" + "-0.38\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -521,7 +524,7 @@ "source": [ "## Component Chaining\n", "\n", - "As such, we have our `AutoRA` components wrapped to work with the state. Remember, this means that they take the state as an input and returns the updated state as an output. As the components all act on the state, they can be chained." + "As such, we have our `AutoRA` components wrapped to work with the state. Remember, this means that they take the state as an input and returns the updated state as an output. As the components all act on the state, they can easily be chained." ] }, { @@ -534,68 +537,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:06<00:00, 15.87it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", - " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", - " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", - " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", - " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", - " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 1.516631\n", - "1 3.033262\n", - "2 4.333231\n", - "3 3.033262\n", - "4 1.949954\n", - "5 1.949954\n", - "6 5.633201\n", - "7 5.199877\n", - "8 0.866646\n", - "9 5.633201, experiment_data= x y\n", - "0 4.983216 -0.715193\n", - "1 5.633201 -0.674306\n", - "2 5.633201 -0.281330\n", - "3 4.333231 -0.167462\n", - "4 1.949954 0.811900\n", - "5 3.033262 -0.008949\n", - "6 3.466585 0.470305\n", - "7 0.866646 1.145879\n", - "8 0.866646 0.527425\n", - "9 6.283185 0.271280\n", - "10 4.983216 -1.195259\n", - "11 5.633201 -0.838039\n", - "12 5.633201 -0.484193\n", - "13 4.333231 -1.885617\n", - "14 1.949954 0.066518\n", - "15 3.033262 -0.173025\n", - "16 3.466585 -0.825717\n", - "17 0.866646 0.919286\n", - "18 0.866646 0.308150\n", - "19 6.283185 -0.706152, models=[0.14, 0.14])\n" - ] - }, - { - "data": { - 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ng7gP0KLljlvu4G7vu+t0XGNrOotyD3Z7kJ4uPSnSFLE8djklmhJF4pA+JWEqZpMgOnTogIWFBZmZmZW2Z2Zm4uFRt8XorKys6NOnD2fPVl+L2MbGBkdHx0qv5kCn07H6+GquFF3Bw96Dyb0m19jvcLPG1nQWoFapmdFnBo7WjpzPO8+GxA2KxCF9SsJUzCZBWFtb069fP3bu3KnfptVq2blzJ2FhYXU6h0ajISEhAU9P87sZ7kjZwcGMg1iqyjtNG1LbYWigJ/tevJsvptzO+48G88WU29n34t2SHOrB2daZ6cHTAfj1/K+KLOonfUrCVMwmQQDMmTOHNWvWEB0dzalTp4iIiKCgoICJEycC8MQTTzB//nz9/q+99hq//vorf//9N3FxcYwbN47z58/z5JNPKvUVGiQ1L5Xok9EAjOkxhs7tOjf4XI2t6Swg2C2Y4X7DAVh1fBVXi66a9PrSpyRMxawSxOjRo1m6dCmvvvoqwcHBxMfHs23bNn3HdUpKCunp/xu9cfXqVaZMmUKPHj0YPnw4ubm57N+/n4CAAKW+Qr2VakpZcXQFpdpSglyD9DcmoazHuj+Gr6MveSV5fBT/kUnXa5I+JWEqKp2Uz6pRbm4uTk5O5OTkKNIfsTFxI1v+3oKDtQNL/28p7WzbmTwGYdiFvAvM3zufEm0JTwQ8wYjOI0x6fVlbSzRUXe9rslhfM3bi8gl9dbOpvadKcmhmOjp05ImeT/BJwid8cfoLenfojbej6YZFDw305N4ADw4lZ5OVV4SbQ3mzkjw5CGMxqyam1qSgtICV8SvRoeMen3u4zeM2o5xXlmYwrnCfcPq69aVUW8qK+BWUakpNen3pUxJNSZ4gmqm1J9aSXZSNh70HTwQ8YZRzSpOE8alUKqb2nsrzvz/P+dzz/OfP/8jS4KLFkCeIZigmLYZ9F/ehRs30PtOxtWz8cEVZmqHptLNtx1O9ngJgS9IWEq8kKhyREMYhCaKZuVp0lU8TPgXgga4PcGv7Wxt9Tlmaoen19+zPYO/B6NARFR9FYVmh0iGJFs4UzcXSxNSMVMyWzivNo5NjJ/3aP41Vn6UZwroYLjgkajc+YDwnLp8gqzCLjYkbmdJ7itIhiRbKVM3F8gTRjOxO3U1cVhxWaitm9JmBldrKKOeVpRlMw97KnoigCKB85nt8VryyAYkWyZTNxZIgmonLhZeJTiyfLf2I/yN4OxhvuKQszWA6gR0CGdppKFA+yzq/JF/hiERLYurmYkkQzYBOp2PVsVUUlhVya/tba6wO1xCyNINpPdbjMTzbeHK16CrrE9crHY5oQUy9kq8kiGZgZ8pOEi4nYK225umgp1GrjPvPIkszmJaNhQ0RQRGoULHnwh5iM2OVDkm0EKZuLpYEobBL1y/pl41+tPujeLZtmvkIsty3afk7++uX3lhzfI00NQmjMHVzsYxiUpBOp+Pj4x9TpCmih3MPhvkNa9LrydIMpjXafzRxmXGkFaSx7uQ6ZvSZoXRIwsxVNBdn5BQZ7IdQUf5Ln7Gai+UJQkG7Unbpm5am9p5q9KYlQ2RpBtOxtrBmWtA01KjZe3GvNDWJRjN1c7EkCIVcLrys78BsyqYloSx/Z3+Gdy5fon1NwhoKSgsUjkiYO1M2F0sTkwIqJsQVaYrwb+/f5E1LQlmj/UcTmxlLekE66xPX6+dKCNFQpmoulicIBey5sIdjl45hpbYqb4IwQdOSUE5FU5MKFbtTd8sEOmEUpmgulieIpqDTQZnhYWbZRVdZf2Id6LQ80mUUXjbtoVTW7Wnpujv4MsT7bralbGf1sVW8OygSOyMswigEAJa2oDJ+gpCKcrVoUEW50kJYO7TKZh063rG8Tqy6jC46C14vbYNFtdPXREtThI65VvlkqbTcp7FmssZO6ZBESzFpG1jV/eeprvc1adswoRh1GbHqMiyBaWV2khwaQAfkF5dxrbCU/OIyg0P9mitbVDxVVv6f+FeLEhJVZQpHJETNpImpKVjalmf0G+SW5LF27zwozWNU13/i0/WfCgVnvn5NzGDxj6dIzy3Wb/N0tOGlET24L8BDwcjqrhdwz4m17Lywm9X2Hrw98A2sLayVDkuYuyZqrpQE0RRUqiqPe+sS1pBXVoCPYydG+T8MRlqptbXYdiKdiC8qFin73w31fK6OqV8kEjXO1mxmg48LnMjRKydIL8ziq+StUoFONFvSxGQCsZmx/JH2B2rUTAuaZrRlvFuLllbwyN7Knid7PQnA1qStJF1LUjgiIQyTBNHErpde55OETwAY0XkEXdp1UTgi82PqFSxNoZ97PwZ6DUSLllXHVlGqLVU6JCGqMLsEsXLlSjp16oStrS2hoaEcOnSoxv2/+uorunfvjq2tLb169eKnn34yUaTlPj/1OdlF2bjbu/Ow/8MmvXZL0dIKHlWUinTT3oe2zJaU3BS2JG1ROiwhqjCrBPHll18yZ84cFixYQFxcHEFBQQwZMoSsrCyD++/fv58xY8YwefJkjh49yqhRoxg1ahQnTpwwSbyJVxLZkbIDgKlBU7GxsDHJdVuallTwaNuJdAYt2cWYNQeY93UScQnBnM7I49Njm0nNS1U6PCEqMat5EKGhodx22218+OGHAGi1Wry9vXnmmWeYN29elf1Hjx5NQUEBW7du1W+7/fbbCQ4OZtWqVXW6ZoPmQQAlmhLm7plLxvUMwn3CpT5xI2i0OgYt2VXrCpb7Xry7WS8+WFEqsvJ30GHjthUL+3OEduzJpyPekZn1osm1uHkQJSUlxMbGEh4ert+mVqsJDw8nJibG4DExMTGV9gcYMmRItfsb01d/fkXG9QycbZ1llEojtYSCR9V3tKsouXIX6KyISz/Fz39vM3C0EFXpdDrWnVjH2atnm+waZpMgLl++jEajwd3dvdJ2d3d3MjIyDB6TkZFRr/0BiouLyc3NrfSqr+ScZLYmlT+1TA6cjL2Vfb3PISoz94JHNXW06zRtKckeSKlGy5rjG8i6brjJVIgb7b24l5/P/cxrB14jrySvSa4h8yBuEhkZyaJFixp1jo5tO/KPrv/gSuEVQjxCjBSZMOeCR7V1oJfl9cSizV8Ull5l9fHVvBz6MqomWFtHtAw5xTlEn4wG4MFuD+Jg7dAk1zGbBNGhQwcsLCzIzMystD0zMxMPD8OzaD08POq1P8D8+fOZM2eO/n1ubi7e3t71itXKwoox3cdgRt07ZqNiBUtzU3sHupqSy3dj67OFhMsJ7Lmwh8Heg00RmjBDa0+sJb80H19HX+7vfH+TXcdsmpisra3p168fO3fu1G/TarXs3LmTsLAwg8eEhYVV2h9g+/bt1e4PYGNjg6OjY6VXQ8lvgKJCRanI6n4iVIBHGw8m9hoDwIbEDVwrumaq8IQZOZR+iAPpB/QTby3VTfd7vtkkCIA5c+awZs0aoqOjOXXqFBERERQUFDBx4kQAnnjiCebPn6/ff+bMmWzbto13332X06dPs3DhQo4cOcKMGVIbWJhWXTvaR3a5Hz8nP/JL81l7Yq1JYxTNX0Fpgf7nYmSXkXR26tyk1zOrBDF69GiWLl3Kq6++SnBwMPHx8Wzbtk3fEZ2SkkJ6erp+/wEDBrBp0yZWr15NUFAQX3/9Nd9//z2BgYFKfQXRitWlo91CbcG03tOwUFlwMOMgB9MPKhStaI42JG7gavFVPNt48q9b/9Xk1zOreRBKaOg8CCGqo9Hqau1o33x6M9+d/Q4nayfeG/weba3bKhStaC4SLiXwxsE3AFgYtpAeLj0afK4WNw9CiJaiLqUiH+r2ELe0vYWckhw2JG5QIMrGq1hS5L/xF4lJumI2iyk2R0VlRaw+vhqAIZ2GNCo51IfZjGISoqWoyxOElYUVU3tPZcH+Bey+sJswrzCC3YKVCbgBtp1IZ9GWxEpzPzydbFkwMqDZz1lpjr488yVZhVl0sOvAmO5jTHZdeYIQwoRuXItp5uZ4xqw5wKAlu9h2Ir3Kvv7O/gzzGwbAmoQ1FJaZR+3yiiVFbp4YmJFTRMTGOIPfVVTvTPYZfk7+GYApvaZgZ2m6UrWSIIQwkYbcOEf7j8bNzo3LhZfZdGqTqUJtsJZWu0NppZpSVh1bhQ4dd3a80+RPkZIghDCBht44bS1tear3UwD8ev5XEq8kNm2gjdQSa3co6as/vyKtII12Nu14IuAJk19fEoQQJtCYG2cv117c43MPAKuOraJYU1xln+aipdXuUFLStSR9nZAnez2pyEg2SRBCmEBjb5zjeozD2daZzOuZbD692ZihGVVLqt2hpFJtedOSFi0DvAZwm8dtisQhCUIIE2jsjdPeyp6pvacC8HPyz5zJPmO02IypLkuKeDqVj9wS1fvur+9IyUvBwdqBCT0nKBaHJAghTMAYN85gt2AGdxyMDh1Rx6Io0ZQ0SayN0RJqdzSEMed8JOck8/3Z74HycgFONk5GirL+JEEIYQLGunE+HvA47W3bk16QzpdnvjR+oEZg7rU76qs+Q5drU6otJepYFBqdhlDPUMK8ql9Y1BRkqY1ayFIbwpiMMYEsLjOOJYeXoELFogGL8Hf2b6pwG6UuEwLNneEysv9L+vVNiF/9+RVf//k1DlYOvDv43SZ7eqjrfU0SRC0kQQhjM8aN86P4j9hzYQ+ebTx5+//extrCuomiFdWpqJVe3ei0+tZK/zvnb/69799odBpm9pnJgFsGGDni/5G1mIRopuqyFlNtxvccr29q+uL0F00QpaiNMed8lGpK+Sj+o2bTtFRBEoQQZqiNVZtKo5qa+wS6lsiYcz6+/utrUvNScbR2ZHLg5GZTbEwShBBmqo9bH+7yvqt8VFN8lNms1dRSGGvOx19X/+KHsz8A5WstKTlq6WaSIIQwY08EPIGrnStZhVlsTNyodDitijGGLhdrilkZvxItWgZ6DaS/Z/8mibWhJEEIYcbsreyJCI4AYEfKDuKz4pUNqBUxxtDlz099TnpBOu1t2zMpcFLTBNoI9U4Q48eP5/fff2+KWIQQDdDTpSfD/YYDEHUsirySPIUjaj0aM+fj+KXj/HLuFwAigiKaZdXAehcMysnJITw8HF9fXyZOnMj48eO55ZZbmiI2IUQdjek+hmOXjnEx/yJrjq9hdr/Zzaajs6UbGujJvQEe9Rq6nF+ST9SxKKC8QlyQa5Cpwq2Xej9BfP/991y8eJGIiAi+/PJLOnXqxLBhw/j6668pLS1tihiFELWwtrBmRp8ZWKgsOJhxkL0X9yodUqtSn6HLOp2OT098SnZRNp5tPHms+2MmjLR+GtQH4erqypw5czh27BgHDx6ka9euPP7443h5eTF79mz++usvY8cphKhFZ6fOPHzrwwB8mvApWdezFI5IGLL34l72p+1HjZoZwTOwtWy+K9s2qpM6PT2d7du3s337diwsLBg+fDgJCQkEBASwbNkyY8UohKijf3T5B7e2v5UiTREfHv0QjVajdEjiBlnXs/g04VMAHvZ/mK7tuyocUc3qnSBKS0v55ptvuP/++/H19eWrr75i1qxZpKWlER0dzY4dO/jPf/7Da6+91hTxCiFqYKG24Jk+z2BrYcuZq2f49uy3SofUKtRlNVeNVsOHRz+kSFOEf3t/RnUdZfpA66nendSenp5otVrGjBnDoUOHCA4OrrLPXXfdRbt27YwQnhCivtzs3Xiy15N8GP8h3/75Lb079G62C/q1BHVdgPHbs99y5uoZbC1smdFnBmpV859lUO8Ily1bRlpaGitXrjSYHADatWtHcnJyY2OrJDs7m7Fjx+Lo6Ei7du2YPHky+fn5NR4zePBgVCpVpde0adOMGpcQzdEdHe/gjlvuQIuWD49+yPXS60qH1CJVrOZ685pMGTlFRGyM0y/5nXglkW//LH+am9J7Cm72biaPtSHqnSAef/xxbG1N36kyduxYTp48yfbt29m6dSu///47Tz31VK3HTZkyhfT0dP3r7bffNkG0QihvUuAk3OzcyCrMYtXxVcjCzcal0epYtCWxylLfgH7boi2J5BTlseLoCrRoGdxxMINuGWTKMBul+T/jAKdOnWLbtm188sknhIaGMmjQIFasWMHmzZtJS0ur8Vh7e3s8PDz0L1myW7QW9lb2zOw7s3zoa/pBdqTsUDqkFqVuq7kWsmjfcv2Q1gmBE0wWnzGYRYKIiYmhXbt2hISE6LeFh4ejVqs5ePBgjcd+/vnndOjQgcDAQObPn8/16zU/ahcXF5Obm1vpJYS56tq+K2O6jwEg+mQ053PPKxxRy1GXVVotHY9xMvsolipLnu37LHaWdiaIzHjMIkFkZGTg5la5zc7S0hJnZ2cyMjKqPe6xxx5j48aN/Pbbb8yfP58NGzYwbty4Gq8VGRmJk5OT/uXt7W2U7yCEUkZ0HkFft76UaktZHrtcVn01ktpWaVXbZGDt/AdWFioe7/k4nZ06mygy41E0QcybN69KJ/LNr9OnTzf4/E899RRDhgyhV69ejB07lvXr1/Pdd9+RlJRU7THz588nJydH/0pNTW3w9YVoDtQqNRHBETjbOpNWkMbq46ulP8IIalzNVV2Ejes2rCzgbt9BDPEdYurwjKLew1yN6bnnnmPChAk17tO5c2c8PDzIyqo8K7SsrIzs7Gw8PDzqfL3Q0FAAzp49S5cuXQzuY2Njg42NTZ3PKYQ5cLR2ZFbfWSyKWcT+tP34t/dnqN9QpcMyaxWruUZsjEMFN3RWa7HpsB2VZR493HyZFjTVbNfFUjRBuLq64urqWut+YWFhXLt2jdjYWPr16wfArl270Gq1+pt+XcTHxwPlczmEaG38nf0Z12Mc0YnRbEjcgJ+Tn8yPaKSK1VxvnAdh1e4Itg4peLd3InLwPOyt7BWOsuFUOjN51hw2bBiZmZmsWrWK0tJSJk6cSEhICJs2bQLg4sWL3HPPPaxfv57+/fuTlJTEpk2bGD58OC4uLhw/fpzZs2fTsWNH9uzZU+fr1rW4txDmQKfT8X7c+8Skx+Bs68xbd7zVrCqYmSuNVseh5GyOZMTxS8bHtLG2ICI4gsHeg5UOzaC63tfMopMaykcjde/enXvuuYfhw4czaNAgVq9erf+8tLSUM2fO6EcpWVtbs2PHDu677z66d+/Oc889x0MPPcSWLVuU+gpCKE6lUjE1aCq3tL2F7KJs3ot9j1KtrMLcWBZqFZ09yojP30wbGwvu7XRvs00O9WE2TxBKkScI0RKl5afx0r6XKCwr5D7f+5jca7LSIZm1wrJCFvyxgPN55+narisLwxZiZWGldFjVanFPEEII4/Fq68UzfZ5BhYpfz//KzvM7lQ7JbGl15cuZJF5OoqTYlv9zmYhapWj3rtFIghCilern3o9H/B8B4NMTn3LyykmFIzJPC3d/wsb43/g7q4hjx/+PqdFnGLRkl34dJnMmCUKIVmxU11GEeYah0Wl478h7pOeb/03NlN7fv4Wvz3xLqUZL8eW70RaXD7u/ebE+cyUJQohWTK1S83Tw03Rt15X80nyWHF5CfknNqyQ3d3WpzWAMJy8nsvbkxwCU5vRFU9Bd/9mNi/U11fVN8T1bRkOZEKLBrC2smRsyl5f2vUR6QTpLjyzl5dCXm3Una3XqWpuhsdLy01iw7y1KNWVorneh9GpYlX3KF+sr4lByNmFdXIx2bTDd95QnCCEE7Wzb8WL/F7G1sOVU9ik+jP8QrU6rdFj1UtfaDI2VU5xD5KFI8kry0Ra7U3zpXmq6ldZlUb/6MNX3BEkQQoj/z9fRl7m3zcVSZcmB9ANEn4w2mzWb6lqbobHNMNdLr7P44GKyrmfhaudGUeb9oKv5Sau2Rf3qw1Tfs4IkCCGEXmCHQKYHTwdg27ltfH/2e2UDqqO61WYob+5pqBJNCW8ffptzuedwtHbkrcGv4ungbHixPkBFebNPfz/nBl/zZqb4njeSBCGEqGTALQN4IuAJADaf2cyPf/+ocES1q2szTkObe8q0ZSyPW86p7FPYWdrxUuhLdHTwYsHIAIAqSaLi/YKRAViojbdQX1N/z5tJghBCVDGi8wj+deu/AFifuL7ZT6SrazNOQ5p7NFoNK46uIDYzFiu1FS/c9gJ+Tn7A/xbr83CqfF4PJ1uixvU1aocxNO33NERGMQkhDPpXt39RXFbMlr+3sCZhDRZqi2a7vlBFbYaMnCKD7fMqym/a9W3u0Wg1fBj/IQfSD2CpsmROvzkEuARU2mdooCf3BnhwKDmbrLwi3BzKr2PMJ4cKTfU9qyNPEEKYsaYcC69SqRjbYyz3+d6HDh1Rx6LYcb551rWuqM0Axmvu0Wg1fHTsI/an7cdSZcnsfrPp69632uuHdXHhgeBbCOvi0iTJoeI6pmzWksX6aiGL9YnmylRj4XU6HetOrmPbuW0ATAycyNBOzbPYkLH+Tko1pbwf9z6HMw9jobJgdr/Z3OZxW7X7Vyz33dRPEBUa+z3rel+TBFELSRCiOaoYC3/zf96KW5Kx2791Oh0bT21k699bAXjk1kd4sNuDzbJSWmNv1oVlhSw9vJQTV05gpbZiVt9ZhHiEVLu/qRL1zRrzPSVBGIkkCNHcaLQ6Bi3ZVe1wx4p26H0v3m3U32J1Oh1f/fkV3/z1DQDhPuFM7jUZtarltFRfK7rG24ffJiknCVsLW1647QV6duhZ7f6mTtTGIst9C9FCmXosfAWVSsUj/o8wKXASKlTsSNnB0iNLKSwrNOp1lJKam8rLf7xMUk4Sba3a8srtr9SYHEw9aU0JkiCEMDOmHgt/syGdhjC732ys1FbEZsbyyh+vkFmQ2STXMpX4rHhe2f8Klwsv49nGkzcGvkHX9l1rPEapRG1KkiCEMDOmHgtvSKhnKK/c/grtbNqRmpfKS/teIuFSQpNdr6lodVq+/vNr3jr0FoVlhfRw7sHrA1/Hs23tzUJKJ2pTkAQhhJmpGAtvyiUeDPF39mfxoMV0cepCfmk+bx58ky9Pf4lGq2nS6xpLXkkeSw4t4as/v0KHjnCfcF4OfRkHa4c6Hd8cEnVTkwQhhJkx9Vj4mrjYubBwwELu8r4LHTq+PfstC2MWknU9q8mv3RhxmXE8v+d54i/FY6W24umgp5nSe0q9ljhvLom6KUmCEMIMmXqJh5pYW1gzLWgaM/vMxM7Sjj+v/sncPXP5OfnnZrdk+PXS66w+vpolh5dwrfgat7S9hTcGvsGd3nfW+1zNKVE3FRnmWgsZ5iqaM1NP0KpNZkEmHx37iNPZpwHo2q4rT/Z6Ur92kVK0Oi37Lu5jY+JGckpyUKFiuN9wHu3+KNYW1o06t1LzIBpD5kEYiSQIIepHq9OyM2UnGxM3UqQpQoWKQbcM4tHuj9LBroPJ40m8ksgXp7/gz6t/AuDZxpMpvafQ06X6Iaz11dwSdW0kQRiJJAghGuZK4RU+P/U5f6T9AYCV2orB3oO5v/P9eLTxaNJr63Q6Tmef5pu/viHhcvnoKlsLWx7s9iDDOw/HSm1+5VSNqcUliDfffJMff/yR+Ph4rK2tuXbtWq3H6HQ6FixYwJo1a7h27RoDBw4kKiqKbt261fm6kiCEaJyka0lsSNzAqexTAKhQ0d+jP4O9B9PbtTeWauMtKl1YVsgfF//g1/O/cj73PACWKksGew/mwW4P4mJn3NrQ5qrFJYgFCxbQrl07Lly4wKefflqnBLFkyRIiIyOJjo7Gz8+PV155hYSEBBITE7G1rdvQM0kQQjSeTqcjMTuRLUlbOJp1VL/d0dqR2z1vJ8g1iACXAOyt7Ot97qtFVzl26RiHMg5x/NJxSrWlQPkTy50d72RU11G42rsa7bu0BC0uQVRYt24ds2bNqjVB6HQ6vLy8eO6553j++ecByMnJwd3dnXXr1vHoo4/W6XqSIIQwrtTcVHal7mLfxX3kluTqt6tR4+vki4+DD94O3rjYutDWui1trdqiQ0eZtoxiTTFXCq9wqfASaflp/HXtLy4XXq50fs82ntzrey93dryTttZtTf31zEJd72sttmBQcnIyGRkZhIeH67c5OTkRGhpKTExMtQmiuLiY4uJi/fvc3FyD+wkhGsbb0ZvxPccztsdYjl86TlxWHMcvHSfzeibJOckk5yTX63wqVPg6+hLiEUJ/j/74OPg0y1VmzVGLTRAZGRkAuLu7V9ru7u6u/8yQyMhIFi1a1KSxCSHAUm1JX/e++iI8WdezOJdzjtS8VC7kX+Bq0VXyS/MpKC1AhQpLtSVWaitc7FxwtXPFzd6NLu260KVdF+ws7RT+Ni2Togli3rx5LFmypMZ9Tp06Rffu3U0UEcyfP585c+bo3+fm5uLt7W2y6wvRWrnZu+Fm70Z/z/5KhyL+P0UTxHPPPceECRNq3Kdz584NOreHR/kwuszMTDw9/zdZJTMzk+Dg4GqPs7GxwcbGpkHXFEKIlkTRBOHq6oqra9OMLvDz88PDw4OdO3fqE0Jubi4HDx4kIiKiSa4phBAtidmsxZSSkkJ8fDwpKSloNBri4+OJj48nPz9fv0/37t357rvvgPLiJrNmzeKNN97ghx9+ICEhgSeeeAIvLy9GjRql0LcQQgjzYTad1K+++irR0dH693369AHgt99+Y/DgwQCcOXOGnJwc/T4vvPACBQUFPPXUU1y7do1Bgwaxbdu2Os+BEEKI1szs5kGYmsyDEEK0NFKTWgghRKOYTROTEELUhbmtrNqcSYIQQrQY5liboTmTJiYhRIuw7UQ6ERvjKiUHgIycIiI2xrHtRLpCkZkvSRBCCLOn0epYtCURQyNuKrYt2pKIRitjcupDEoQQwuwdSs6u8uRwIx2QnlPEoeRs0wXVAkiCEEKYvay86pNDQ/YT5SRBCCHMnptD3Sa/1nU/UU4ShBDC7PX3c8bTyZbqBrOqKB/N1N/P2ZRhmT1JEEIIs2ehVrFgZABAlSRR8X7ByACZD1FPkiCEEC3C0EBPosb1xcOpcjOSh5MtUeP6yjyIBpCJckai0WgoLS1VOgwhmoy1tTVqdfP+nXJooCf3BnjITGojkQTRSDqdjoyMDK5du6Z0KEI0KbVajZ+fH9bW1kqHUiMLtYqwLi5Kh9EiSIJopIrk4Obmhr29vRRLFy2SVqslLS2N9PR0fHx85Oe8lZAE0QgajUafHFxc5DcW0bK5urqSlpZGWVkZVlZWSocjTKB5Nyg2cxV9Dvb29gpHIkTTq2ha0mg0CkciTEUShBHI47ZoDeTnvPWRBCGEEMIgSRCiSezevRuVSlWv0V2dOnVi+fLlTRaTEKJ+JEG0QhMmTEClUjFt2rQqn02fPh2VSsWECRNMH1gDFBUVMX36dFxcXGjbti0PPfQQmZmZNR7z7bffct999+Hi4oJKpSI+Pr7afXU6HcOGDUOlUvH9998bN3ghmjlJEK2Ut7c3mzdvprCwUL+tqKiITZs24ePjo2Bk9TN79my2bNnCV199xZ49e0hLS+PBBx+s8ZiCggIGDRrEkiVLaj3/8uXLpe1dtFqSIIxJp4PSQmVeuvoVQunbty/e3t58++23+m3ffvstPj4+9OnTp9K+xcXFPPvss7i5uWFra8ugQYM4fPhwpX1++uknbr31Vuzs7Ljrrrs4d+5clWvu27ePO+64Azs7O7y9vXn22WcpKCioV9w3ysnJ4dNPP+W9997j7rvvpl+/fnz22Wfs37+fAwcOVHvc448/zquvvkp4eHiN54+Pj+fdd99l7dq1DY5RCHMm8yCMqawI1g5V5tqTtoGVXf0OmTSJzz77jLFjxwKwdu1aJk6cyO7duyvt98ILL/DNN98QHR2Nr68vb7/9NkOGDOHs2bM4OzuTmprKgw8+yPTp03nqqac4cuQIzz33XKVzJCUlMXToUN544w3Wrl3LpUuXmDFjBjNmzOCzzz4zGN+ECRM4d+5clXgqxMbGUlpaWulG3717d3x8fIiJieH222+v19/Hja5fv85jjz3GypUr8fDwaPB5hDBnZvME8eabbzJgwADs7e1p165dnY6paGu/8TV0qEI38GZo3Lhx7Nu3j/Pnz3P+/Hn++OMPxo0bV2mfgoICoqKieOeddxg2bBgBAQGsWbMGOzs7Pv30UwCioqLo0qUL7777Lv7+/owdO7ZKH0ZkZCRjx45l1qxZdOvWjQEDBvDBBx+wfv16iooMF3Hx9PSssbkrIyMDa2vrKj8P7u7uZGRk1P8v5AazZ89mwIABPPDAA406jxDmzGyeIEpKSnj44YcJCwvT35jqYujQoZV+Q7WxsWmK8MpZ2pb/Jq8Ey/oXQnF1dWXEiBGsW7cOnU7HiBEj6NChQ6V9kpKSKC0tZeDAgfptVlZW9O/fn1OnTgFw6tQpQkNDKx0XFhZW6f2xY8c4fvw4n3/+uX6bTqdDq9WSnJxMjx49qsQXGRlZ7+9kDD/88AO7du3i6NGjilxfiObCbBLEokWLAFi3bl29jrOxsTFdE4FKVe9mHqVNmjSJGTNmALBy5comu05+fj5Tp07l2WefrfJZQzvFPTw8KCkp4dq1a5WeIjIzMxv1b75r1y6SkpKqPJk89NBD3HHHHdU2eQnR0phNE1ND7d69Gzc3N/z9/YmIiODKlStKh9SsDB06lJKSEkpLSxkyZEiVz7t06YK1tTV//PGHfltpaSmHDx8mIKC8QEuPHj04dOhQpeNu7iTu27cviYmJdO3atcqroauD9uvXDysrK3bu3KnfdubMGVJSUqo8wdTHvHnzOH78OPHx8foXwLJly6rtLxGiJTKbJ4iGGDp0KA8++CB+fn4kJSXx0ksvMWzYMGJiYrCwsDB4THFxMcXFxfr3ubm5pgpXERYWFvqmIkN/J23atCEiIoK5c+fi7OyMj48Pb7/9NtevX2fy5MkATJs2jXfffZe5c+fy5JNPEhsbW+VJ78UXX+T2229nxowZPPnkk7Rp04bExES2b9/Ohx9+aDC2+fPnc/HiRdavX2/wcycnJyZPnsycOXNwdnbG0dGRZ555hrCwsEod1N27dycyMpJ//vOfAGRnZ5OSkkJaWhpQnlSg/InkxtfNfHx88PPzq+mvU4gWRdEniHnz5lXpRL75dfr06Qaf/9FHH+Uf//gHvXr1YtSoUWzdupXDhw/X2EQQGRmJk5OT/uXt7d3g65sLR0dHHB0dq/38rbfe4qGHHuLxxx+nb9++nD17ll9++YX27dsD5TfOb775hu+//56goCBWrVrF4sWLK52jd+/e7Nmzhz///JM77riDPn368Oqrr+Ll5VXtddPT00lJSakx9mXLlnH//ffz0EMP8X//9394eHhUGroL5QkgJydH//6HH36gT58+jBgxAij/OenTpw+rVq2q8VpCtDYqna6eA+iN6NKlS7U2+XTu3LlSE8S6deuYNWtWgwv0uLq68sYbbzB16lSDnxt6gvD29iYnJ6fKTbSoqIjk5GT8/Pywta1/J7EQ5kR+3luO3NxcnJycDN7XbqRoE5Orqyuurq4mu96FCxe4cuUKnp7V16a1sbFp2pFOQghhJsymkzolJYX4+HhSUlLQaDT6zsP8/Hz9Pt27d+e7774DykfNzJ07lwMHDnDu3Dl27tzJAw88QNeuXQ12xgohhKjMbDqpX331VaKjo/XvK5aD+O233xg8eDBQua3ZwsKC48ePEx0dzbVr1/Dy8uK+++7j9ddflycEIYSoA7NJEOvWrat1DsSN3Sl2dnb88ssvTRyVEEK0XGbTxCSEEMK0JEEIIYQwSBKEEEIIgyRBCCGEMEgShBBCCIMkQQizsnDhQoKDg5UOA4DBgwcza9asJr1Gp06dWL58eb2Pe+WVV3jqqafqvP+qVasYOXJkva8jWjZJEK1URkYGM2fOpGvXrtja2uLu7s7AgQOJiori+vXrSofXIAsXLqx1ba+G2L17NyqVqsHLuzTG4cOH63Wjh/J/2/fff5+XX365zsdMmjSJuLg49u7dW98QRQsmCaIV+vvvv+nTpw+//vorixcv5ujRo8TExPDCCy+wdetWduzYUe2xpaWlJoy0fp5//nnS09P1r44dO/Laa69V2najkpIShSKtO1dXV+zt7et1zCeffMKAAQPw9fWt8zHW1tY89thjfPDBB/UNUbRgkiCMSKfTUVRWpMirPmsuPv3001haWnLkyBEeeeQRevToQefOnXnggQf48ccfKzU1qFQqoqKi+Mc//kGbNm148803gf+VGbW2tsbf358NGzbojzl37hwqlUpfRwHg2rVrqFQq/Uq6Fb+V79y5k5CQEOzt7RkwYIB+6e0Kb731Fu7u7jg4ODB58uRqy5MCtG3bttJy3RYWFjg4OOjfP/roo8yYMYNZs2bRoUMHhgwZUmus586d46677gKgffv2qFSqSuVUtVotL7zwAs7Oznh4eLBw4cI6/ztA+c/MwoUL8fHxwcbGBi8vr0pFlW5uYlKpVHzyySf885//xN7enm7duvHDDz9UOufmzZsr/RteunQJDw+PSivs7t+/H2tr60q1NEaOHMkPP/xAYWFhvb6DaLnMZia1OSjWFDN+23hFrh09NBrbOpQdvXLliv7JoU2bNgb3ubkpZuHChbz11lssX74cS0tLvvvuO2bOnMny5csJDw9n69atTJw4kY4dO+pvpnX18ssv8+677+Lq6sq0adOYNGmSvjjRf/7zHxYuXMjKlSsZNGgQGzZs4IMPPqBz5871usaNoqOjiYiIqFQAqSbe3t588803PPTQQ5w5cwZHR0fs7P5XNTA6Opo5c+Zw8OBBYmJimDBhAgMHDuTee+8Fyuuinzt3rtol5r/55huWLVvG5s2b6dmzJxkZGRw7dqzGmBYtWsTbb7/NO++8w4oVKxg7diznz5/H2dmZ7OxsEhMTCQkJ0e/v6urK2rVrGTVqFPfddx/+/v48/vjjzJgxg3vuuUe/X0hICGVlZRw8eFC/fI1o3SRBtDJnz55Fp9Ph7+9faXuHDh30v51Pnz6dJUuW6D977LHHmDhxov79mDFjmDBhAk8//TQAc+bM4cCBAyxdurTeCeLNN9/kzjvvBMrrg4wYMYKioiJsbW1Zvnw5kydP1hcmeuONN9ixY0eNTxG16datG2+//bb+/blz52rc38LCAmdnZwDc3NyqlCHt3bs3CxYs0J/7ww8/ZOfOnfoE4enpiVarrfb8KSkpeHh4EB4ejpWVFT4+PvTv37/GmCZMmMCYMWMAWLx4MR988AGHDh1i6NChpKSkoNPpqtTZGD58OFOmTGHs2LGEhITQpk2bKjW/7e3tcXJy4vz58zVeX7QekiCMyMbChuih0bXv2ETXboxDhw6h1WoZO3ZspXoYQKXfRgFOnTpVpeN04MCBvP/++/W+bu/evfV/rliGPSsrCx8fH06dOsW0adMq7R8WFsZvv/1W7+tU6NevX4OPNeTG+KH8O2RlZenf33wTvtnDDz/M8uXL6dy5M0OHDmX48OGMHDkSS8vq/2veeM02bdrg6Oiov2ZF85Cheg1Lly4lMDCQr776itjYWIOLVtrZ2ZntIAVhfJIgjEilUtWpmUdJXbt2RaVSVWnrr2i2ubH5pEJ1TVHVUavLu7Zu7BeprnPbyspK/+eKpq2afuNurJu/S31iNeTG+KH8O9Qnfm9vb86cOcOOHTvYvn07Tz/9NO+88w579uypcu66XLNDhw4AXL16tUqtlaSkJNLS0tBqtZw7d45evXpVOXd2drZJa7SI5k06qVsZFxcX7r33Xj788EMKCgoadI4ePXpUacP/448/CAgIANDfYG4cNXRjJ3B9rnPw4MFK2w4cOFDv89SkLrFWVDTUaDRGvXYFOzs7Ro4cyQcffMDu3buJiYkhISGhQefq0qULjo6OJCYmVtpeUlLCuHHjGD16NK+//jpPPvlkpScdKE8gRUVF+qX0hZAniFboo48+YuDAgYSEhLBw4UJ69+6NWq3m8OHDnD59utZmmLlz5/LII4/Qp08fwsPD2bJlC99++61+eKydnR233347b731Fn5+fmRlZfHvf/+73nHOnDmTCRMmEBISwsCBA/n88885efJkozqpb1aXWH19fVGpVGzdupXhw4djZ2dH27Zt63T++fPnc/HiRdavX2/w83Xr1qHRaAgNDcXe3p6NGzdiZ2dXryGqN1Kr1YSHh7Nv3z5GjRql3/7yyy+Tk5PDBx98QNu2bfnpp5+YNGkSW7du1e+zd+9eOnfuTJcuXRp0bdHyyBNEK9SlSxeOHj1KeHg48+fPJygoiJCQEFasWMHzzz/P66+/XuPxo0aN4v3332fp0qX07NmTjz/+mM8++6zSyJe1a9dSVlZGv379mDVrFm+88Ua94xw9ejSvvPIKL7zwAv369eP8+fNERETU+zy1qS3WW265hUWLFjFv3jzc3d2ZMWNGnc+dnp5OSkpKtZ+3a9eONWvWMHDgQHr37s2OHTvYsmULLi4uDf4+Tz75JJs3b9Y3O+3evZvly5ezYcMGHB0dUavVbNiwgb179xIVFaU/7osvvmDKlCkNvu7NNFodMUlX+G/8RWKSrqDR1n0otmgeVLr6DKBvhWoq7i1F3EVzpNPpCA0NZfbs2frRTrU5efIkd999N3/++SdOTk4G96nPz/u2E+ks2pJIes7/Rpx5OtmyYGQAQwOrrwkvTKOm+9qN5AlCiBZGpVKxevVqysrK6nxMeno669evrzY51Me2E+lEbIyrlBwAMnKKiNgYx7YT6dUcKZob6YMQogUKDg6u16KG4eHhRrmuRqtj0ZZEDDVL6AAVsGhLIvcGeGChbtjaWMJ05AlCCGE0h5Kzqzw53EgHpOcUcSg523RBiQaTBCGEMJqsvLrNcq/rfkJZkiCMQPr5RWtQl59zN4e6Ddao635CWZIgGqFiRqssTSBag4rl0S0sLKrdp7+fM55OtlTXu6CifDRTfz9n4wcojE46qRvBwsKCdu3a6Wek2tvbN7gojRDNmVar5dKlS9jb29e4TpSFWsWCkQFEbIxDBZU6qyv+ZywYGSAd1GbCLBLEuXPneP3119m1axcZGRl4eXkxbtw4Xn75Zf0yCIYUFRXx3HPPsXnzZoqLixkyZAgfffQR7u7uRovNw8MDoMqyBUK0NGq1Gh8fn1p/CRoa6EnUuL5V5kF4yDwIs2MWCeL06dNotVo+/vhjunbtyokTJ5gyZQoFBQUsXbq02uNmz57Njz/+yFdffYWTkxMzZszgwQcfrHMtgLpQqVR4enri5ubWrKutCdFY1tbW+sUNazM00JN7Azw4lJxNVl4Rbg7lzUry5GBezHYm9TvvvENUVBR///23wc9zcnJwdXVl06ZN/Otf/wLKE02PHj2IiYnh9ttvr9N16jrjUAghzEWLn0mdk5OjL+RiSGxsLKWlpZUmAHXv3h0fHx9iYmKqPa64uJjc3NxKLyGEaI3MMkGcPXuWFStWMHXq1Gr3ycjIwNraukoFMHd3dzIyMqo9LjIyEicnJ/3L29vbWGELIYRZUTRBzJs3D5VKVePr9OnTlY65ePEiQ4cO5eGHHzbqypMV5s+fT05Ojv6Vmppq9GsIIYQ5ULST+rnnnmPChAk17nPj2v9paWncddddDBgwgNWrV9d4nIeHByUlJVy7dq3SU0RmZqZ+5JEhNjY2lUoxVnTRSFOTEKKlqLif1doFrTMTFy5c0HXr1k336KOP6srKymrd/9q1azorKyvd119/rd92+vRpHaCLiYmp83VTU1N1lA/nlpe85CWvFvVKTU2t8f5nFqOYLl68yODBg/H19SU6OrrSTM6Kp4GLFy9yzz33sH79evr37w9AREQEP/30E+vWrcPR0ZFnnnkGgP3799f52lqtlrS0NBwcHOo1CS43Nxdvb29SU1PNZvSTxGwa5hazucULEnNtdDodeXl5eHl51Th02SzmQWzfvp2zZ89y9uxZOnbsWOmzivxWWlrKmTNnKi17sWzZMtRqNQ899FCliXL1oVarq1yzPhwdHc3mB7SCxGwa5hazucULEnNN6lL7wyyeIMyROc6fkJhNw9xiNrd4QWI2FrMc5iqEEKLpSYJoIjY2NixYsKDSiKjmTmI2DXOL2dziBYnZWKSJSQghhEHyBCGEEMIgSRBCCCEMkgQhhBDCIEkQQgghDJIE0QRWrlxJp06dsLW1JTQ0lEOHDikdUo1+//13Ro4ciZeXFyqViu+//17pkGoUGRnJbbfdhoODA25ubowaNYozZ84oHVaNoqKi6N27t34SVFhYGD///LPSYdXLW2+9hUqlYtasWUqHUq2FCxdWWfCze/fuSodVq4sXLzJu3DhcXFyws7OjV69eHDlyROmwJEEY25dffsmcOXNYsGABcXFxBAUFMWTIkGZdkrSgoICgoCBWrlypdCh1smfPHqZPn86BAwfYvn07paWl3HfffRQUFCgdWrU6duzIW2+9RWxsLEeOHOHuu+/mgQce4OTJk0qHVieHDx/m448/pnfv3kqHUquePXuSnp6uf+3bt0/pkGp09epVBg4ciJWVFT///DOJiYm8++67tG/fXunQMJvF+sxF//79ddOnT9e/12g0Oi8vL11kZKSCUdUdoPvuu++UDqNesrKydIBuz549SodSL+3bt9d98sknSodRq7y8PF23bt1027dv19155526mTNnKh1StRYsWKALCgpSOox6efHFF3WDBg1SOgyD5AnCiEpKSoiNja1UxU6tVhMeHl5jFTvRODk5OQA1VhhsTjQaDZs3b6agoICwsDClw6nV9OnTGTFiRKWf6+bsr7/+wsvLi86dOzN27FhSUlKUDqlGP/zwAyEhITz88MO4ubnRp08f1qxZo3RYgDQxGdXly5fRaDS4u7tX2l5bFTvRcFqtllmzZjFw4EACAwOVDqdGCQkJtG3bFhsbG6ZNm8Z3331HQECA0mHVaPPmzcTFxREZGal0KHUSGhrKunXr2LZtG1FRUSQnJ3PHHXeQl5endGjV+vvvv4mKiqJbt2788ssvRERE8OyzzxIdHa10aOaxmqsQ1Zk+fTonTpxo9u3MAP7+/sTHx5OTk8PXX3/N+PHj2bNnT7NNEqmpqcycOZPt27dja2urdDh1MmzYMP2fe/fuTWhoKL6+vvznP/9h8uTJCkZWPa1WS0hICIsXLwagT58+nDhxglWrVjF+/HhFY5MnCCPq0KEDFhYWZGZmVtpeWxU70TAzZsxg69at/Pbbb41akt1UrK2t6dq1K/369SMyMpKgoCDef/99pcOqVmxsLFlZWfTt2xdLS0ssLS3Zs2cPH3zwAZaWlmg0GqVDrFW7du249dZbOXv2rNKhVMvT07PKLwk9evRoFk1jkiCMyNramn79+rFz5079Nq1Wy86dO82irdlc6HQ6ZsyYwXfffceuXbvw8/NTOqQG0Wq1FBcXKx1Gte655x4SEhKIj4/Xv0JCQhg7dizx8fGVCnc1V/n5+SQlJeHp6al0KNUaOHBglWHaf/75J76+vgpF9D/SxGRkc+bMYfz48YSEhNC/f3+WL19OQUEBEydOVDq0auXn51f6DSs5OZn4+HicnZ3x8fFRMDLDpk+fzqZNm/jvf/+Lg4ODvn/HyckJOzs7haMzbP78+QwbNgwfHx/y8vLYtGkTu3fv5pdfflE6tGo5ODhU6ddp06YNLi4uzba/5/nnn2fkyJH4+vqSlpbGggULsLCwYMyYMUqHVq3Zs2czYMAAFi9ezCOPPMKhQ4dYvXo1q1evVjo0GebaFFasWKHz8fHRWVtb6/r37687cOCA0iHV6LfffjNYr3b8+PFKh2aQoVgB3WeffaZ0aNWaNGmSztfXV2dtba1zdXXV3XPPPbpff/1V6bDqrbkPcx09erTO09NTZ21trbvlllt0o0eP1p09e1bpsGq1ZcsWXWBgoM7GxkbXvXt33erVq5UOSafTmUlNaiGEEKYnfRBCCCEMkgQhhBDCIEkQQgghDJIEIYQQwiBJEEIIIQySBCGEEMIgSRBCCCEMkgQhhBDCIEkQQgghDJIEIYQQwiBJEEIo6NKlS3h4eOhrAQDs378fa2vrSqsCC6EEWYtJCIX99NNPjBo1iv379+Pv709wcDAPPPAA7733ntKhiVZOEoQQzcD06dPZsWMHISEhJCQkcPjwYWxsbJQOS7RykiCEaAYKCwsJDAwkNTWV2NhYevXqpXRIQkgfhBDNQVJSEmlpaWi1Ws6dO6d0OEIA8gQhhOJKSkro378/wcHB+Pv7s3z5chISEnBzc1M6NNHKSYIQQmFz587l66+/5tixY7Rt25Y777wTJycntm7dqnRoopWTJiYhFLR7926WL1/Ohg0bcHR0RK1Ws2HDBvbu3UtUVJTS4YlWTp4ghBBCGCRPEEIIIQySBCGEEMIgSRBCCCEMkgQhhBDCIEkQQgghDJIEIYQQwiBJEEIIIQySBCGEEMIgSRBCCCEMkgQhhBDCIEkQQgghDJIEIYQQwqD/B6RoitiOFM1OAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:05<00:00, 17.23it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 24.85it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -609,51 +551,41 @@ " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 3.033262\n", - "1 0.649985\n", - "2 3.899908\n", - "3 2.599939\n", - "4 2.816600\n", - "5 6.283185\n", - "6 1.083308\n", + "0 0.433323\n", + "1 4.983216\n", + "2 4.116570\n", + "3 2.816600\n", + "4 2.599939\n", + "5 5.416539\n", + "6 0.433323\n", "7 4.333231\n", - "8 0.649985\n", - "9 6.066524, experiment_data= x y\n", - "0 4.983216 -0.715193\n", - "1 5.633201 -0.674306\n", - "2 5.633201 -0.281330\n", - "3 4.333231 -0.167462\n", - "4 1.949954 0.811900\n", - "5 3.033262 -0.008949\n", - "6 3.466585 0.470305\n", - "7 0.866646 1.145879\n", - "8 0.866646 0.527425\n", - "9 6.283185 0.271280\n", - "10 4.983216 -1.195259\n", - "11 5.633201 -0.838039\n", - "12 5.633201 -0.484193\n", - "13 4.333231 -1.885617\n", - "14 1.949954 0.066518\n", - "15 3.033262 -0.173025\n", - "16 3.466585 -0.825717\n", - "17 0.866646 0.919286\n", - "18 0.866646 0.308150\n", - "19 6.283185 -0.706152\n", - "20 3.033262 0.840943\n", - "21 0.649985 0.492286\n", - "22 3.899908 -0.653935\n", - "23 2.599939 -0.196820\n", - "24 2.816600 0.047110\n", - "25 6.283185 0.055461\n", - "26 1.083308 0.308015\n", - "27 4.333231 -0.741128\n", - "28 0.649985 0.304855\n", - "29 6.066524 -0.360817, models=[sin(x), sin(x), sin(x)])\n" + "8 1.299969\n", + "9 0.433323, experiment_data= x y\n", + "0 0.433323 0.724606\n", + "1 4.983216 -2.003534\n", + "2 4.116570 -0.077238\n", + "3 2.816600 1.259866\n", + "4 2.599939 -1.435481\n", + "5 5.416539 -2.064342\n", + "6 0.433323 0.547730\n", + "7 4.333231 -1.245219\n", + "8 1.299969 0.946749\n", + "9 0.433323 -0.433155\n", + "10 0.433323 0.724606\n", + "11 4.983216 -2.003534\n", + "12 4.116570 -0.077238\n", + "13 2.816600 1.259866\n", + "14 2.599939 -1.435481\n", + "15 5.416539 -2.064342\n", + "16 0.433323 0.547730\n", + "17 4.333231 -1.245219\n", + "18 1.299969 0.946749\n", + "19 0.433323 -0.433155, models=[-0.38, -0.38])\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -663,16 +595,9 @@ } ], "source": [ - "#==========OPTION 1==========#\n", "s = theorist(s)\n", - "s = experiment_runner(s)\n", - "s = experimentalist(s, num_samples=10)\n", - "\n", - "print(s)\n", - "plot_from_state(s)\n", - "\n", - "#==========OPTION 2==========#\n", - "s = theorist(experiment_runner(experimentalist(s, num_samples=10)))\n", + "s = experiment_runner(s, added_noise=1.0, random_state=42)\n", + "s = experimentalist(s, num_samples=10, random_state=42)\n", "\n", "print(s)\n", "plot_from_state(s)" @@ -699,102 +624,12 @@ "execution_count": null, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:06<00:00, 15.95it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 1:\u001b[0m\n", - "\u001b[1mCycle 1 model: -0.32\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:06<00:00, 16.31it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 2:\u001b[0m\n", - "\u001b[1mCycle 2 model: sin(x)\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:06<00:00, 16.41it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 3:\u001b[0m\n", - "\u001b[1mCycle 3 model: sin(x)\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:06<00:00, 15.88it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 24.96it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -804,12 +639,12 @@ "text": [ "\n", "\u001b[1mRunning Cycle 1:\u001b[0m\n", - "\u001b[1mCycle 1 model: sin(x)\u001b[0m\n" + "\u001b[1mCycle 1 model: -0.38\u001b[0m\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -822,7 +657,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:06<00:00, 16.55it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 27.19it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -832,12 +667,12 @@ "text": [ "\n", "\u001b[1mRunning Cycle 2:\u001b[0m\n", - "\u001b[1mCycle 2 model: sin(x)\u001b[0m\n" + "\u001b[1mCycle 2 model: -0.27\u001b[0m\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -850,7 +685,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:05<00:00, 17.34it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 27.27it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -860,12 +695,12 @@ "text": [ "\n", "\u001b[1mRunning Cycle 3:\u001b[0m\n", - "\u001b[1mCycle 3 model: 0.01\u001b[0m\n" + "\u001b[1mCycle 3 model: sin(x)\u001b[0m\n" ] }, { "data": { - "image/png": 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", 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nvX4B8NDQUAAyMzOJiIjgyJEjjBkzptT2nTt3Ztu2bVU+T4n27dtXe9+yXF8/FL+H65cslEXTS5OwF7WCRqMxqSlFTY0bN0aj0dzUNl7SNHJ9E0WJ8pp7yqPVFt+Gu/4+Qnk3dq9fALyk+aiiK+GauvG9VKXWslS0gLkpHG3RdLlBK4SV1K1bl759+/Luu++Sm5tbrWM0a9bspjbvn376ybgAeUlYXN/75foboFU5z+7du0s9d+NC5jVlSq0lC6Lr9XqznruEIy2aLlf2QljRf/7zH6Kjo4mKimLatGm0atUKrVbL3r17+eOPPypt6pg0aRIPPvggbdu2pU+fPqxZs4avv/7a2GXT3d2dTp06MWvWLBo0aEBmZiYvv/xylescP348I0aMICoqiujoaD755BMOHTpUoxu0NzKl1sjISDQaDWvXrmXAgAG4u7vj5eVl0vFl0fQb6rPYkYUQN2nUqBG//vorffr0YcqUKbRu3ZqoqCgWLFjA888/z6uvvlrh/oMHD+add95h7ty5tGjRgvfff58lS5aU6sHx4YcfUlRURPv27ZkwYQKvvfZalescMmQIr7zyCi+88ALt27cnLS2NsWPHVvk4lams1ltuuYXp06czefJkgoODGTdunMnHlkXTS5MFx4XdscaC44qikJuvp8hgwEmrxdNVV+1RoMJxWGrRdHMsOC7NOELcIOtaAWcv51Go/+dmn7NOS5ifG77uLipWJmxdyaLpVWn3t9ai6RL2Qlwn61oBaX/f3P2tUG8g7e+rRNZFAl9UyFYXTZc2eyH+R1EUzl6ueNDQ2ctVmx5BCFshYS/E/+Tm60s13ZSlUG8gN98y3QCFsCQJe2G3zH2FXWTigBxTtxPCXMzxuy5hL+xOyShGcw8td9Ka9udg6nZCmEvJ73p5I3tNITdohd3R6XT4+fkZRyF6eHiY3C1SURSuFujRGwzotFo8XP7pUqlTFHSGogqv3J20WnRKIXl5RTV/I0JUQlEUrl69SmZmJn5+fuh0umofS8Je2KWQkBCAm4adV+RagZ6sa4UUGf75Suyk1eDr7oy7S/EfUX6Bnou5xQtrXP/FueSjxN/ThdTc6v/BCVEdfn5+xt/56pKwF3ZJo9EQGhpKUFCQSZNn/Xgsk2lrD5f7+rTY5nS7Nci47cJtJzh/5Z8ZKAO9XInr2Yjm/9tGCGtxdnau0RV9CRlBK2o9vUGh6xtbSc8qu1ulBgjxdWPHi73QaTXGffakXCQzJ48gbzc6NPA3viaELZERtEJVthSWe1Iulhv0UNxck56Vx56Ui3RuVDwvik6rMf5biNrArsL+hx9+YM6cOezbt4/09HRWrVpVanY5YRs2HExn+prDpQI21NeN+NjmxLQMtXo9mTmmra5k6nZC2CO76kOWm5tL69atWbhwodXOqTco7DzxN98mn2Hnib/RGxym1ataNhxMZ+zH+2+6ks7IymPsx/vZcDC9nD0tJ8jbtMnSTN1OCHtkV1f2/fv3p3///lY7n61doVqSOZpd9AaF6WsOU9bHoUJx2/j0NYfp2zzEqk06HRr4E+rrRkZWXpm1lbTZd2jgb7WahLA2uwp7ayq5Qr0xHEquUBOHtas1gV/Zh5piMJCdfZLMC3+QnXuO7KvnuVaQg96gx6AUodXocHP25PwVLX4F2WhcArhYGEae4l3qPGW1jVuDTqshPrY5Yz/ej4ayu1TGxzaXG7CiVqvVYZ+fn19qAefs7GyT9rPVK1RLuPFDTUMREa5HCFKO8+nG03y3N5fLmqvkKZXPB6M3KDiHGAgAAgA3gxOuBV4oBXXJzq9P2rVWZOmDVWkbj2kZSuKwdjd9qIXU0m9qQtyoVod9QkIC06dPr/J+1em9YY9KPtS8tOdp6vUTHu4nuOp2kQKtnnwgH8gq1ODmrEUD+Ovc8NN54O3sgYfODZ3WCZ1Gi14xkFeUx8W8q6Reu0S+rpBCrZ48bRF5bpfB7TJwgki24Fnkyoljzfjd+W6a3RqLs7OH1d5vTMtQ+jYPsZleQkJYU60O+ylTpjBx4kTjz9nZ2YSHh1e6nyP03rh69QJfbptPY4+tZPtnUQAU/O81Z0WLR74PhvxgsvMjGHVXDL3aROPs6lnhMUv6s2dk5eGmySbE5S/quqbi7nIWg9t5cpyvcdW5gN25B9i95wDue+fQ0a8pdza9j2a3DkKrs/yvo3SpFI6qVoe9q6srrq6uVd6vNvfe+PP4BjYf/Iidl49yVV9EgVvxPDDeBR4o1yI5d/V2UvJaYbjuVyPPtUWlQQ+l28bzFB9S8tuQkt8GKG768tRe5oWumVwt3Mf+y8fIMhSQdOkQSbsOEbB3Ln3CutIrKg5fv/oWeOdCODa7CvsrV65w/Phx488pKSkkJyfj7+9PRESE2c5T23pvGPRF7P3tQ9b+8QXH8i8Ynw/ReXHhfH1OXInm96LIcvevyodaxW3jvYxt4wZ9EX/8uZYdx1ay8+IRLuivseLUJr46tYUufk0ZdMd46tXrVI13K4Qoi11Nl5CUlETPnj1ven748OEsXbq00v2rMl1CyY1LKLv3hj30xjHoi9jxy0K+PvYF6UW5ADihJdqvKb1bDqNRgxi6zUmq9EPt+mkETFWVrpyF+bnsTF7ExhNrOJ5/0XjuDt4NuDdqPA3q96jSuYVwJKbmml2FfU1VdW4ce+1nrxgM7En+gM8PfcSZoisAeGmcuCu0C/06PItfnQbGbW3tQ+34ie9Z/Wsiu3NSjM918WnMg52nEBrW3mp1CGEvJOzLUJ2J0GxpjhdT/Hl8A8v3zuVoXnFzjZfGibvr9SSmyxTcPcpudrLFD7VTp3ayau/b/Jx1DAXQoqFvQFse7PE6Xt62+0ErhLVJ2JdB7VkvLfnBkZV1kk+3TSbpUvE0vi4aLbGh3bi72yt4eASoWltNpKZu5/M9c9mfewoAb60zQxrdS+/OL1il944Qtk7Cvgxqhn1Vrp6rErwGfRHbds3l0z+/4opSvHpSd79mDOk2nboBt1ruDVnZoSMrWbLvHU4VFg+Ma+zqzxPR04mM7KZyZdZnqx/MQh0S9mVQK+zLm3qhrHbxqnwoZGQk837Sixy+dg6ASGdfHu80mVsbW2/+IGvSFxWw6ecEVvy1hmtKETo03B3ahQd6zjapa2htYItNbkJdEvZlUCPsq7JwxqbDGSZ9KBj0RWzc8RqfpawhX9HjqtExpP5AYrq+jM7JxZJvxyZcvHicpVsnGW/i1nP2Ji56Gg0b9Fa5MsuqykWDcBym5ppdTXFsj0ydemHXib8rnI8HiufjOX/+KDO/jGXpX9+Qr+hp4R7CnP5LGNhjhkMEPYC/f2Mm/msVz7d5Gl+tC6cLc3g56Xm+2jQRfVFB5QewQ5XN1wTFvx8yBbcoj4S9hZk6pcLOvy5U+qEQoF/L82se4cC1dFw0Wh5r/C9efmAtwcGtzFStfbmj7WPMHbySTj6N0KPw5emtTP9yABfOH1G7NLOrynxNQpRFwt7CTB99Wv4NNmfy6BaQiCZwM7lKEQ1d6vDGXYu4q9vLDt8jxcc3nAn3fsnTLR7DTaPjaN4FXlj3KLv3L1K7NLNyhPmahGVJ2FtYydQL5UW5huIbbOVNzhXolEaHW2aT5Z0GwJ0+Ucx4cB1hYVGWKdgOabRaunZ4mtn9l9DY1Z9cpYi3fvsPy9Y9QVFh7Qi/2jxfk7AOCXsLK5kcrCLxsc3p1LDuTR8KzTx+JOKWReS4XMXFoMMzexBx939g1WmB7UlwcCumPbCW2JDiOXXWZ+5hxld3c/Hi8Ur2tH2mXjTYy3xNwvok7K0gpmUoT9zZgBu7Qms18MSdDYhpGVrqQ0FLEZ3rLMc5eB2FWj0+BV6kno5jeP846U9dCWdnD4b1f4/n2ozDXePE0bwLTF7zCEf/XKd2aTVy/e/Hjb8B1lhtS9Zitn/S9dIKqtJlbt2vf/D1jjjOu2QC4JsTwV8FcbxyT1vpVldF6Wf38dbWZzlZmI0TWh6/7RF6dn5e7bJqRI1+9tK337ZJP/sy2Ho/+0t//8HsjWNIK8xCo2jo4X83rW+fICMka+Da1Yskrn/M2Ce/f2AUj8b8x667qVpzBK307bd9EvZlUCPsd574m4cX76p0u3fvzmX1iblkGQrw1brwXJd4mjYZaIUKaz+DvohVW1/ki9NbAGjjWY/xdy81ac4gR1aVCxW5GFGPDKqyEaZ0hWvltYmPjyaQZSgg0tmX1/svkaA3I63Oifv7vsnE1k/hotGSnHuaqV/fx/nMQ2qXZtOkb3/tImFvYRV3hTPQye9TCNyCHgPtvCKYft8qAoNaWK0+R9Kx3RNM6/YGfloXThVm88p3o0hN3a52WTZL+vbXLhL2FlZelzkNRXQLeJ+rdX5Ho9EQE9SeSfd9Xe6c88I8GjXqy+sDPyLc2YdLhnzikyby28EVapdlk6Rvf+0iYW9hZXWZcyaPbsHvGAdK9Qvoy6iBHzj8aFhrCQi4jemDv6CFewh5ip439r7BD7vfUbssmyN9+2sXCXsrKFmEO8TXDQ9NFp3C3uSyx3l0aLj3lqE8dd+baLTyv8KaPL1CmHL/13T1a4oehYWHl7Bm28tql2VT1O7bL8xLeuNY0cVLqUz/9lHO6LNx1+iY1OklWjW/3+p1iH8Y9EV8sjGOted2AxAb0omh/f4jH77XkX72tk26XpZBzbDPPHeQ179/koyiXHy1Lvy7+xzq1+9u1RpE+VZv/TefpK0HoEedFjwZu0ya1a4jq2PZLgn7MqgV9qdP7+K1LeO5ZMgnSOfBS30XEhLa1mrnF6bZtnMui/74BAMKHX0a8vTdyx1mBSxhv6SfvY1ISU1i2panuWTIp56zN9Pv/shsQS/zlZhXz87PM6H1WJzQsjv7L+Z88yD5eVlqlyWEWciVvQUd/XMds36K56pSRCMXf6bELsfb5xazHFvaUS3n90OfM3fvbPIVPc3cgnhh0Kcy2lbYLLmyV9mhIyuZ+dNUripFNHML4uV7vzBr0I/9eP9NoxszsvIY+/F+NhxMN8t5HFWrFkN4KXoG7honjuRl8vo3D3IlR/6bCvsmYW8ByQc+IWHX6+Qpem73CGPKvV+a7cpQ1iK1jqZNBjK1+xt4aZw4nn+RGd8+RFbWSbXLEqLaJOzN7Jfkpcz55U0KMdDeK5IXB3+Jq5uv2Y4v85VYT8MGvZnW+x18tS6kFWYxY81Qsi6nql2WENUiYW9Gu/cv4q1f36EIAx29G/Ds4M/M3ptD5iuxrvDwaKb1/Q91tK6cLsxh+pphtWLlK+F4JOzNZOe+95j3WyJ6FKJ9mzB+8OcWWT5Q5iuxvrCwKKbd9R51dW6cKbrCjHXD+fvCsSodQ3pOCbXJqBEz+HnvQhYc/AADCt38buOpez622ICckvlKMrLyymy3L5ljXOYrMa+Q0LZM6/cBr258gvSiXKavH8nUAf8lIOC2SveVnlPCFsiVfQ3t2LPAGPQ96jS3aNCDzFeipqDglkyNWUyQzoNz+lymrx/FhfNHKtxHek4JWyFhXwM/7pnPwkMfYkChp39Lnoz9yCpD7K+fWO16Ib5uskychQUGtSC+/38J0nmQqb/K9O8eKzfwq9pzSpp6hCXJoKpq+nHPfP5zaAkGFHr5387ou5dUGvTmnl9E5itRz98XjjF9/UjO6XMJ0nkQ3/+/BAQ2K7WNqUtSfja6E1nXCqSpR1SLzI1TBnOF/Y49C4xX9L3r3s7jAysPemm3rX0qC/xvk88wfkVypccZFV2fJT+lyqLeolpkBK2FXB/0vfxND3ppt6196gbcSvyAJQTrPP9p0rnwh/F1U3tEfZN8VtVBctJ85BjsLuwXLlxI/fr1cXNzo2PHjuzZs8dq5/5578JSQW9q042MeK29bgr89aOM3TJNWenJ39OZi7kF5R7f0oPkNhxMp+sbW3l48S7Gr0jm4cW76PrGVrkAqYXsKuw///xzJk6cSHx8PPv376d169b069ePzMxMi5/7+u6VPf1bmhT0ICNeHUHdgFuZOuC6m7brR/L3hWMm9Zy6t41p8yVZYpCcfON0LHYV9m+99RajR49m5MiRNG/enPfeew8PDw8+/PBDi5535773rute2YIn7l5qcq8bGfFqWyzVZBEQcJuxl845fS4zvisO/Mp6TvVpHmLS8c09SE6+cdqeLT+/YdEJ9+xmUFVBQQH79u1jypQpxue0Wi19+vRh586dZe6Tn59Pfn6+8efs7Owqnzf5wCfM//19DCh092tW5RWMZMSr7bD0TfKAwGZMjVnMjA2jySjK5dXvRjJ14DJiWjamb/OQMntO6Q2KKoPkqvKNs3OjumY9t7jZyk3P8cXpLWxO2cCMf621yKI5dnNlf+HCBfR6PcHBwaWeDw4OJiMjo8x9EhIS8PX1NT7Cw8OrfN4G4dHc4uxNN7/bGHPP8ir3ozel3TZURrxanLWaLAKDWvBKv0UE6txJL8plxrrhXLp4Ap1WQ+dGdRnU5hY6N6pr7CKr1iA5+cZpO1ZtnsQXp7cA0Dm0s8VWR7ObsK+OKVOmkJWVZXycOnWqysfw9avPtEGfV3tkrIx4VZ+1myyCglsytd9iAq4L/MuXUsrdXo1BcgFermbdTlTPqi0vsOLUJgAeiYzhnl4zLXYuu2nGCQgIQKfTce7cuVLPnzt3jpCQsts9XV1dcXWt+S+rl3fN/thK/phvbEIIkX72VqFGk0Vx4L/P9I1PcLboCjPWPkp87Mf4+tUvc/uYlqHlNvVYhKmfa9JkbzHfbp3MipPfA/BQxF0M6jXLouezm7B3cXGhffv2bNmyhcGDBwNgMBjYsmUL48aNU7c4E1j9j1kYqdVkERzciql3vcf0jWM4U3SF6WseJT52ebmBX9LUYw0XcvMr36gK24mqWb3133yatgGAh8L7cm/v2RY/p10140ycOJHFixezbNkyjhw5wtixY8nNzWXkyJFql2aS8tpthWWpeZM8JKQN8Xcl4q9140xRDtPXPGoTC6BIxwH1rN76bz5JWw/AkPA+3NtnjlXOa1dhP2TIEObOncvUqVNp06YNycnJbNiw4aabtkJcT+2b5CGhbW0u8NtH1qGyaw2tpng7YT7fbp1sDPoH6/Xmvj5zrXbuKof98OHD+eGHHyxRi0nGjRtHWloa+fn57N69m44dO6pWi7APtnCTvKzAr+imraXtS7tEZfejDUrxdsI8vtnyorHp5oF6vbi/75tWPX+Vwz4rK4s+ffrQpEkTZs6cyZkzZyxRlxBmZQvTQt8Y+DPWPsqliycsft6ySNdL6/pmy4t8dnIjUHxF/6++b1m9hmrNenn+/HmWL1/OsmXLOHz4MH369OGxxx5j0KBBODs7W6JOszDnFMfCPtnCtNAZGcnM+H4Mf+vzCHXyZOrAZfj7N7ZqDVWZflkGVdXM15uf5/NTm4HiNnpzN91YdNbLwMBAJk6cyG+//cbu3btp3Lgxjz76KGFhYTz77LP8+eef1S5cCEuyhZvkISFtiO+3yNgPf/q64aVmy7QGte9jOALFYODL7581Bv1D4X2t2kZ/oxrdoE1PT2fTpk1s2rQJnU7HgAEDOHDgAM2bN+ftt982V41C1DrBwa2I71e8xGFGUfESh5nnDlrt/LZwH6M2UwwGPv9+PF+d2QbA0MgBVut1U54qh31hYSErV67k7rvvJjIyki+//JIJEyZw9uxZli1bxubNm/niiy+YMWOGJeoVotYICm5JfP///jM98sbRZGQkW+38tnAfozZSDAaWbxjDqvQfAfi/BvdYdGSsqarcZh8QEIDBYODhhx9m9OjRtGnT5qZtLl++TNu2bUlJUa+3QVmkzV7Yor8vHOPV70aSXpRLHa0rr/RZwC23dLDa+W3hPkZtYdAXsWT9aL6/8CsAIxvdR8ydUy16TostS7h8+XIeeOAB3Nzsb7CFhL2wVZcvpfDquv/jdGEOPloXXuo+h/r1u6tdlqgCg76I99cMJ+nSITTA6KYP07vLixY/r6xBWwa1w16uoERFsrNOMXPt/5FScAkvjRNTus2kcaO71C5LmKCw8Crvrn6UXdkn0KJhbPMR3NlxvFXOLWFfBjXDXhYcF6bIvZLBrNXDOJZ/ATeNjhc6/psWze5XuyxRgYL8HN7+9hH2557CCQ3PtB5Lx3ZPWO38suC4DZHl3+yPWotwe3qF8O/BX3C7eyh5ip6EXa/zS/JSq5xbVN3VK5nM/Pp+9ueewkWjZdIdk6wa9FUhV/YWpjcodH1ja7lT7JasRLTjxV7SpGMjbOFbWGF+Lu+sHsreK6lo0TCm2aN07zTRKucWpsm6nMrra4eTVpiFu8aJFzu/QrOmg6xeh1zZ2whZcNy+2Mq3MGdXT5699wu6+zXDgMJ/jnzE6q3/tsq5ReUyzx0kfs1Q0gqz8NW6MK3Hm6oEfVVI2FuYzEFiP2xtEW6dkwtj7llObEgnAD5JW8/y9U9i0BdZ5fyibCmpSbyyYRTpRbkE6tyZ3jfRLnpOSdhbmMwbbj9s8VuYVufEsP7vMTRyAABrz+1mwTcPUZifa7UabJ2p91fMcR/mwKEvmZb0HJcNBUQ6+zLj7uWEhrWv6VuwCrtZqcpelcxBkpGVV+YVY0mbvcxBoj5b/hZ2T6+Z+O0O5L3DH/Fz9nEufX0vk2I/wtOr7CU5HYWp91fMcR/mh91v8/7hjyhCoYV7CM/f/REeXkHmezMWJlf2FiZzkNgPW/8WdmfHZ5ncYTJuGh1H8jKZ+s0DVp1Px9aYen+lpvdhSiY0W3h4GUUodPFpzJR7V9pV0IOEvVXIHCT2wR5mgmzVYgjTe7xFHa0rpwtzeGnDKI7+uU61etRi6v2VgiJDje7DFObnsvDbR4wTmg0KjebpwStwdvWs6VuwOmnGsRJZcNz2lXwLG/vxfjRQKiBs6VtY/frded1rObM3PkFqwWVe3fEKYy+nEH3HOFXrsiZT768s35lq8n2YG+ftz7qcytz1j3Ms/wJaNDx+6xB6R0820zuwPrmytyJbmEtdVMxevoXVDbiVafd+TXuvSAoxMP/gB3y6Ic5heuqYet8k7eLVah0vNXU7U1Y/xLH8C3hqnJjSYbJdBz3Ilb3ZyLw3tYe9fAtz9/Dn+ftW8tn3T7M6Yyffpv/EyZX38vSAxbX+xq2p900i/T2qfLwdexbw/uElFCgGQp08ebH3fLvpcVMRCXszsIURl8K8Sr6F2Tqtzomh/ROpv/ddEg9+yK+5p/j3qvuZeOcsIiO7qV2exZjay+3RzvX5YEeKSb3higrz+OT7Z1ifuQeA1h638MyAD/Dyrh1/w9KMU0O2MuJSOLboO8Yxo8dcAnTuZBTl8vK28fywu/auFmdqLzcXJ61J212++CevfXWPMejvDe3G5H99W2uCHiTsa8TWRlwKx9awQS9mDf6KNp71KFAMLDy8jMRvhpJ37ZLapVmEqfdXKtsuRNnKi+uGciQvEzeNjufajOOhmAVodbWr4UMmQquBnSf+5uHFuyrd7rPRneyiSUDUDgZ9EV9vfYGvTm9FAW5x8uKZbq/bxZD+6jD1ftmN27W9xZWV2yaxOmMnAPVd/Hi219uEhLa19luoEVNzrXZ9dFmZLY+4FI5Lq3PiX33fotmRlSzYM5szRVd4eduzPNRgIAO6xde6K1ZT769cv93p07uY+tWLpBVmAdA3oA3D71pol/3nTSXNODVg6yMuhWNr0ex+Zg/+inZeERRiYHnKGmZ8McChR90a9EWsTXqFyZueIq0wC2+tM8+3fYbHY5fW6qAHCfsasYcRl8Kx+fiG88L93zC6yRDjNAuTvhvOd9unOUyf/BJnzuwh/vMYlqesoRADrTzCmBO7gjvajFK7NKuQsK8BmfdG2AONVkufrlOY3X8JzdyCyFP0LP3rG175vB9paT+qXZ7FFebn8uX3z/Li92OMyz2ObjKEfz+wljr+jdQuz2rkBq0ZSD97YS8M+iK27JzNJ8e/5ppShBYN/QLb86/ur9aqboYlkg98woe//odz+uIpodt41mN0zzkEBDZTuTLzkQXHy2DJZQllBK2wJxcvHmfp1knszkkBwFvrzIMNB9Gr0/M4Odv/PaaTJ3ewfOdMfr96FoA6WleGtxxJp7ZPoNHWrgYNCfsyqLEGrRC27MChL1n663xOF+YAEOLkyZDbHqFTuyftstdO5rmDrNyZwA+XDmNAwQkNMcEd+FePBNw9aue9Mwn7MkjYC3GzosI8Nu98g5Up68g2FAAQ4ezD4KYP0qntE+icXFSusHKZ5w7yze43SPr7IPr/DWns6NOQR6KnEhLSRt3iLEzCvgwS9kKU79rVi6z76TXWnNlOnqIHIFjnyYD6/bgz6ik8PAJUrvBmR/9cx7rfPmBvTiqG/4X87R5hPNAujqZNBqpcnXVI2JdBwl6Iyl3JSWfjrrl8d/YHcgyFALhpdHSrezs9mw+lYYPeqrZ7X8lJZ8evi9l2aiupBZeNz7fyCONfbZ+i6a13q1abGmpd2L/++uusW7eO5ORkXFxcuHz5cpWPIWEvhOnyrl0iae8Cvk/7njNFV4zPhzl50TWkI1G3DiYiPNoqwX/16gX2H/yM3Se38mtOGoUYAHBCS9c6zbi7/VOEh0dbvA5LqUkHj1oX9vHx8fj5+XH69Gn++9//StgLYSWKwcChP1ay5cjn/JLzFwWKwfhagM6dtn630iz0Dpo26ENAwG1mOWdBfg4pJ3/gcFoSv53/jT/zzlN03ZSD4c4+9ArvSbe2T+Dtc4tZzqmWmnbdrnVhX2Lp0qVMmDBBwl4IFVy7epE9vy9l96ntHMg9VSr4obiLY4RbABE+4YR4R1LXJxx/30g8PQNxd/PDxdUHDApF+nwKC3PJuZJOzpUMLmafIiMrhfScM6TlnuVUYbaxDb5EmJMXnYLa0bHp/URGdKsVXShLpki/MYRLrulNWR1NJkID8vPzyc/PN/6cnZ2tYjVC2D93D3+6d5pI904Tyc/L4uDRbzh4+if+uPwnqQWXuWTI59LVM/x29QxkVD4jbEV8tS409byF24Pb06rxQIKDW9eKgC9R2RTpGoqnSO/bPMQsY3ZqddgnJCQwffp0tcsQolZydfOlfevhtG89HCi+6j95Zienzv3OqcvHOX/tAhfyL3NRf5VrBj1FGG46hofGCR+dK75O7oS4BRLqXY8w/yY0iriTuv631qpwv5Gpi6aXtRh6daga9pMnT+aNN96ocJsjR45w223VawecMmUKEydONP6cnZ1NeHh4tY4lhKiYu4c/TZsMLLfLY2HhVQrys9FqXdDpnNHpXO2iD7+lWHuKdFXD/rnnnmPEiBEVbtOwYcNqH9/V1RVXV9dq7y+EMB9nZw+cnU1bANwRWHuKdFXDPjAwkMDAQDVLEEIIVZi6aLq5pki3mwaxkydPkpyczMmTJ9Hr9SQnJ5OcnMyVK1cq31kIIWyMtadIt5uulyNGjGDZsmU3Pb9t2zZ69Ohh0jGk66UQwtZIP3sLkLAXonpkCm/LssYI2lrd9VIIUXOyOI/lmbpoek3YTZu9EML6SkZ43tgfPCMrj7Ef72fDwXSVKhNVJWEvhChTZSM8oXiEp97gMC3Bdk3CXghRpqqM8BS2T8JeCFEma4/wFJYlYS+EKJO1R3gKy5KwF0KUqWSEZ3kdADUU98ox1whPYVkS9kKIMll7hKewLAl74VD0BoWdJ/7m2+Qz7Dzxt/QkqURMy1ASh7UjxLd0U02Ir5tJC2sI2yGDqoTDkMFB1RPTMpS+zUNkBK2dk+kShEMwx/JvQtgiU3NNmnFErSeDg4SQsBcOQAYHCSFhLxyADA4SQsJeOAAZHCSEhL1wADI4SAgJe+EAZHCQEBL2wkHI4CDh6GRQlXAYMjhIODIJe+FQrLH8mxC2SJpxhBDCAUjYCyGEA5CwF0IIByBhL4QQDkDCXgghHICEvRBCOAAJeyGEcAAS9kII4QAk7IUQwgFI2AshhAOQsBdCCAcgYS+EEA5Awl4IIRyAzHophLBJeoMi01GbkV2EfWpqKq+++ipbt24lIyODsLAwhg0bxksvvYSLi4va5QkhzGzDwXSmrzlMetY/i8CH+roRH9tcFpqpJrsI+z/++AODwcD7779P48aNOXjwIKNHjyY3N5e5c+eqXZ4Qwow2HExn7Mf7UW54PiMrj7Ef75eVxapJoyjKjf9N7cKcOXNITEzkr7/+Mnmf7OxsfH19ycrKwsfHx4LVCSGqQ29Q6PrG1lJX9NfTULyU5I4Xe1W5Sae2NguZmmt2cWVflqysLPz9/SvcJj8/n/z8fOPP2dnZli5LCFEDe1Iulhv0AAqQnpXHnpSLVVpxTJqF7LQ3zvHjx1mwYAFPPvlkhdslJCTg6+trfISHh1upQiFEdWTmlB/01dkO/mkWuvFDpKRZaMPB9CrVaK9UDfvJkyej0WgqfPzxxx+l9jlz5gwxMTE88MADjB49usLjT5kyhaysLOPj1KlTlnw7QogaCvJ2M+t2eoPC9DWHb2r/B4zPTV9zGL3BLluzq0TVZpznnnuOESNGVLhNw4YNjf8+e/YsPXv2pEuXLixatKjS47u6uuLq6lrTMoUQVtKhgT+hvm5kZOWVGdAlbfYdGlTchFvCUs1C9kjVsA8MDCQwMNCkbc+cOUPPnj1p3749S5YsQau1yxYoIUQFdFoN8bHNGfvxfjRQKvBLbqXGxzY3+caqJZqF7JVdJOaZM2fo0aMHERERzJ07l/Pnz5ORkUFGRobapQkhzCymZSiJw9oR4lu6qSbE163K3S7N3Sxkz+yiN86mTZs4fvw4x48fp169eqVes9Oeo0KICsS0DKVv85Aad5U0d7OQPbPbfvbVIf3shXA8Jb1xoOxmIXsfpGVqrtlFM44QQlSXOZuF7JldNOMIIURNmKtZyJ5J2AshHIJOq6n13SsrIs04QgjhACTshRDCAUjYCyGEA5CwF0IIByBhL4QQDkDCXgghHICEvRBCOAAJeyGEcAAyqEoIO1Nb11IVliVhL4QdkbVURXVJM44QdkLWUhU1IWEvhB2QtVRFTUnYC2EHqrKWqhBlkbAXwg7IWqqipiTshbADspaqqCkJeyHsQMlaquV1sNRQ3CvHEdZSFdUjYS+EHdBpNcTHNge4KfBLfo6PbS797UW5JOyFsBOylqqoCRlUJYQdkbVURXVJ2AthZxx9LVVRPdKMI4QQDkDCXgghHICEvRBCOACHarNXlOJ5Q7Kzs1WuRAghzKMkz0ryrTwOFfY5OTkAhIeHq1yJEEKYV05ODr6+vuW+rlEq+zioRQwGA2fPnsXb2xuNxvSuatnZ2YSHh3Pq1Cl8fHwsWKH5SM2WZ2/1gtRsLdasWVEUcnJyCAsLQ6stv2Xeoa7stVot9erVq/b+Pj4+dvPLVkJqtjx7qxekZmuxVs0VXdGXkBu0QgjhACTshRDCAUjYm8DV1ZX4+HhcXV3VLsVkUrPl2Vu9IDVbiy3W7FA3aIUQwlHJlb0QQjgACXshhHAAEvZCCOEAJOyFEMIBSNhXYuHChdSvXx83Nzc6duzInj171C6pQj/88AOxsbGEhYWh0Wj45ptv1C6pQgkJCdxxxx14e3sTFBTE4MGDOXr0qNplVSgxMZFWrVoZB8x07tyZ7777Tu2yqmTWrFloNBomTJigdinlmjZtGhqNptTjtttuU7usSp05c4Zhw4ZRt25d3N3duf322/nll1/ULkvCviKff/45EydOJD4+nv3799O6dWv69etHZmam2qWVKzc3l9atW7Nw4UK1SzHJ9u3biYuLY9euXWzatInCwkLuuusucnNz1S6tXPXq1WPWrFns27ePX375hV69ejFo0CAOHTqkdmkm2bt3L++//z6tWrVSu5RKtWjRgvT0dONjx44dapdUoUuXLhEdHY2zszPfffcdhw8f5s0336ROnTpqlwaKKFeHDh2UuLg44896vV4JCwtTEhISVKzKdICyatUqtcuokszMTAVQtm/frnYpVVKnTh3lgw8+ULuMSuXk5ChNmjRRNm3apHTv3l0ZP3682iWVKz4+XmndurXaZVTJiy++qHTt2lXtMsokV/blKCgoYN++ffTp08f4nFarpU+fPuzcuVPFymq3rKwsAPz9/VWuxDR6vZ4VK1aQm5tL586d1S6nUnFxcQwcOLDU77Ut+/PPPwkLC6Nhw4YMHTqUkydPql1ShVavXk1UVBQPPPAAQUFBtG3blsWLF6tdFiDNOOW6cOECer2e4ODgUs8HBweTkZGhUlW1m8FgYMKECURHR9OyZUu1y6nQgQMH8PLywtXVlTFjxrBq1SqaN2+udlkVWrFiBfv37ychIUHtUkzSsWNHli5dyoYNG0hMTCQlJYVu3boZpyq3RX/99ReJiYk0adKEjRs3MnbsWJ555hmWLVumdmmONeulsG1xcXEcPHjQ5ttlAZo2bUpycjJZWVl89dVXDB8+nO3bt9ts4J86dYrx48ezadMm3Nzc1C7HJP379zf+u1WrVnTs2JHIyEi++OILHnvsMRUrK5/BYCAqKoqZM2cC0LZtWw4ePMh7773H8OHDVa1NruzLERAQgE6n49y5c6WeP3fuHCEhISpVVXuNGzeOtWvXsm3bthpNQ20tLi4uNG7cmPbt25OQkEDr1q1555131C6rXPv27SMzM5N27drh5OSEk5MT27dvZ/78+Tg5OaHX69UusVJ+fn7ceuutHD9+XO1SyhUaGnrTB36zZs1sovlJwr4cLi4utG/fni1bthifMxgMbNmyxS7aZu2FoiiMGzeOVatWsXXrVho0aKB2SdViMBjIz89Xu4xy9e7dmwMHDpCcnGx8REVFMXToUJKTk9HpdGqXWKkrV65w4sQJQkND1S6lXNHR0Td1HT527BiRkZEqVfQPacapwMSJExk+fDhRUVF06NCBefPmkZuby8iRI9UurVxXrlwpdeWTkpJCcnIy/v7+REREqFhZ2eLi4vj000/59ttv8fb2Nt4P8fX1xd3dXeXqyjZlyhT69+9PREQEOTk5fPrppyQlJbFx40a1SyuXt7f3TfdBPD09qVu3rs3eH3n++eeJjY0lMjKSs2fPEh8fj06n4+GHH1a7tHI9++yzdOnShZkzZ/Lggw+yZ88eFi1axKJFi9QuTbpeVmbBggVKRESE4uLionTo0EHZtWuX2iVVaNu2bQpw02P48OFql1amsmoFlCVLlqhdWrlGjRqlREZGKi4uLkpgYKDSu3dv5fvvv1e7rCqz9a6XQ4YMUUJDQxUXFxfllltuUYYMGaIcP35c7bIqtWbNGqVly5aKq6urcttttymLFi1SuyRFURRFpjgWQggHIG32QgjhACTshRDCAUjYCyGEA5CwF0IIByBhL4QQDkDCXgghHICEvRBCOAAJeyGEcAAS9kII4QAk7IUQwgFI2AthJufPnyckJMQ4lznAzz//jIuLS6nZU4VQg8yNI4QZrV+/nsGDB/Pzzz/TtGlT2rRpw6BBg3jrrbfULk04OAl7IcwsLi6OzZs3ExUVxYEDB9i7dy+urq5qlyUcnIS9EGZ27do1WrZsyalTp9i3bx+333672iUJIW32QpjbiRMnOHv2LAaDgdTUVLXLEQKQK3shzKqgoIAOHTrQpk0bmjZtyrx58zhw4ABBQUFqlyYcnIS9EGY0adIkvvrqK3777Te8vLzo3r07vr6+rF27Vu3ShIOTZhwhzCQpKYl58+axfPlyfHx80Gq1LF++nB9//JHExES1yxMOTq7shRDCAciVvRBCOAAJeyGEcAAS9kII4QAk7IUQwgFI2AshhAOQsBdCCAcgYS+EEA5Awl4IIRyAhL0QQjgACXshhHAAEvZCCOEAJOyFEMIB/D/wSVe2OC+ilQAAAABJRU5ErkJggg==", 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" ] @@ -876,33 +711,24 @@ ], "source": [ "#### First, let's reinitialize the state object to get a clean state ####\n", + "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 30))\n", + "dv = Variable(name=\"y\", type=ValueType.REAL)\n", + "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "conditions = random_pool(variables, num_samples=10, random_state=42)\n", + "\n", "s = StandardState(\n", " variables = variables,\n", " conditions = conditions,\n", " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"])\n", ")\n", "\n", - "#==========OPTION 1==========#\n", - "\n", - "### Then we cycle through the pipeline we built three times ###\n", - "num_cycles = 3 # number of empirical research cycles\n", - "for cycle in range(num_cycles):\n", - " #Run pipeline\n", - " s = theorist(experiment_runner(experimentalist(s, num_samples=10)))\n", - " \n", - " #Report metrics\n", - " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", - " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", - " plot_from_state(s)\n", - " \n", - "#==========OPTION 2==========#\n", - "\n", "### Then we cycle through the pipeline we built three more times ###\n", "num_cycles = 3 # number of empirical research cycles\n", "for cycle in range(num_cycles):\n", " #Run pipeline\n", - " s = experimentalist(s, num_samples=10)\n", - " s = experiment_runner(s)\n", + " s = experimentalist(s, num_samples=10, random_state=42+cycle)\n", + " s = experiment_runner(s, added_noise=1.0, random_state=42+cycle)\n", " s = theorist(s)\n", " \n", " #Report metrics\n", @@ -911,37 +737,13 @@ " plot_from_state(s)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If everything went well in terms of our theorist, we should have recovered our ground truth model `sin(x)`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sin(x)\n" - ] - } - ], - "source": [ - "print(s.model)" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cycle using Stopping Criteria\n", "\n", - "Alternatively, we can run the chain until we reach a stopping criterion. For example, here we will loop until we get 30 datapoints." + "Alternatively, we can run the chain until we reach a stopping criterion. For example, here we will loop until we get 50 datapoints." ] }, { @@ -954,14 +756,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - " 0%| | 0/100 [00:00" ] @@ -989,7 +784,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:06<00:00, 16.66it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 23.27it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -999,12 +794,12 @@ "text": [ "\n", "\u001b[1mRunning Cycle 2, number of datapoints: 20\u001b[0m\n", - "\u001b[1mCycle 2 model: sin(x)\u001b[0m\n" + "\u001b[1mCycle 2 model: -0.27\u001b[0m\n" ] }, { "data": { - "image/png": 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7u39WDVy/fj1Tpkzh5MmThIaGMmLECDp37kyvXr2AnDWwr1y5oi+MT9q2bRuff/45W7ZsoXnz5sTHx3P27NkCM82ZM4ePP/6YRYsW8cUXXzBkyBCuXr2Kq6sriYmJREVF4evrq9+/Ro0arFmzhgEDBtC7d2+aNGnCa6+9xsSJE+nZs6d+P19fX7Kzszl58iTdu3c36u/HVFIgRC7BkXGM23Qmz/b45AzGbTrDiqHtKlSRKO+io6PR6XQ0adIk1/bq1avrv51PmDCBhQsX6p979dVXGTlypP7nwYMHM2LECN544w0ApkyZwokTJ/jkk09MLhAffvgh3bp1A+Ddd9+lf//+pKenY2try+LFixk9erR+YaJ58+axb9++Aq8iCtOoUSM+/vhj/c9XrlwpcH8LCwtcXV0BcHNzw8XFJdfzrVq1YtasWfrX/vLLL9m/f7++QHh4eOjXljDk2rVruLu74+/vj5WVFXXq1KFDhw4FZhoxYoR+KvKPPvqIpUuXEhYWRkBAANeuXUOn0+VZZ6Nfv36MHTuWIUOG4Ovri4ODQ541v+3t7XF2dubq1asFnr84pEBUMhqtjrDYRBIepOPmaEuH+q5YqFX6597dXvDyj+9uP0cvH3f9MebMxsKG9QHrFTt3cYSFhaHVahkyZAgZGRm5nnv82yjAhQsX8nScdu7cmSVLlph83latWun/7OGR80UhISGBOnXqcOHCBcaNG5drfz8/Pw4ePGjyeR5p3759kY815PH8kPMeHl+O9MkP4Se99NJLLF68mAYNGhAQEEC/fv0IDAzE0jL/j9LHz+ng4ICTk5P+nI+ahwytUfLJJ5/QokULvv/+e8LDw7Gxyfs7Y2dnV6qDFKRAVCLBkXHM2R1FXNI/3+g8nG2ZFehDQAsPTsTc5X5awZ2a99OyOBFzl86Nqpd23FKnUqmMauZRUsOGDVGpVHna+h812zzefPJIfk1R+VGrc7oiH+8Xya9z28rKSv/nR01bBX3jLq4n34spWQ15PD/kvAdT8nt5eXHx4kX27dvH3r17eeONN1i0aBGHDh3K89rGnLN69Zz/R/fu3aNGjRq59ouJieHmzZtotVquXLlCy5Yt87x2YmJinuNKknRSVxLBkXGM33QmV3EAiE9KZ/ymMwRHxhH61x2jXsvY/UTxVatWjV69evHll1+SmppapNdo1qxZnjb8Y8eO4ePjA6D/gHl81NDjncCmnOfkyZO5tp04ccLk1ymIMVmtra0B0Gg0JXruR+zs7AgMDGTp0qWEhIQQGhrKuXMFX3nnx9vbGycnJ6KionJtz8zMZOjQoQwaNIgPPviAMWPG5LrSgZwCkp6eTtu2bYv8XgpjVgXi8OHDBAYG4unpiUqlYufOnYUeExISQrt27bCxsaFhw4Z5FmevDDRaHXN2R2Fo3Myjbfk9b5j5Ny+Zk//9739kZ2fj6+vL1q1buXDhAhcvXmTTpk38+eefWFhYFHj8O++8w7p161i+fDmXL1/ms88+Y/v27bz99ttAzgfe008/zYIFC7hw4QKHDh3i/fffNznnpEmTWLNmDWvXruXSpUvMmjWL8+fPF+k958eYrHXr1kWlUrFnzx5u375NSkqK0a8fFBTEsGHD8n1+3bp1fP3110RGRvLXX3+xadMm7OzsTBqi+ji1Wo2/vz9Hjx7Ntf29994jKSmJpUuXMn36dBo3bsyoUaNy7XPkyBEaNGiAt7d3kc5tVL5Se+VSkJqaSuvWrVm2bJlR+8fGxtK/f3+effZZIiIimDx5MmPGjOHXX38t5aTlS1hsYp4rh8fpgLikdFzsrI16PT/voi8yL0zn7e3N77//jr+/P0FBQbRu3RpfX1+++OIL3n77bT744IMCjx8wYABLlizhk08+oXnz5nz11VesXbs218iXNWvWkJ2dTfv27Zk8eTLz5s0zOeegQYOYMWMG06ZNo3379ly9epXx48eb/DqFKSxrrVq1mDNnDu+++y41a9Zk4sSJRr92XFwc165dy/d5FxcXVq1aRefOnWnVqhX79u1j9+7dVKtW9P8TY8aMYcuWLfpmp5CQEBYvXszGjRtxcnJCrVazceNGjhw5wvLly/XHffvtt4wdO7bI5zWGSmfKgOxyRKVSsWPHDgYMGJDvPtOnT+enn34iMjJSv+2VV17h/v37BAcHG3UeYxf3Ls9+jLjBpC0Rhe73+cutmbMnqsB+iKr2Vpx+v1eRO6k12ZlkZaViobbGysa0tvLiKGgBdyGUpNPp6NixI2+99ZZ+tFNhzp8/T48ePbh06VK+a2sX9Dtv7Odahe6kDg0NzbP+bJ8+fQqcjyUjIyPXqJDk5OTSildm3ByN+0B0d7ZjwYstDQ5zfWT+iy2NKg5371zifMwvXE6IIC41nluZSdzVPETzWEOWrcoCJ7UNNa2dqe9cj/rVW9DUuw+urg2NyitERaBSqVi5cqVJ/RhxcXFs2LAh3+JQUip0gYiPj6dmzZq5ttWsWZPk5GQePnxocATI/PnzmTNnTllFLBMd6rvi4WxLfFK6wX4GFeDu/M+Q1xVD2zF7VxTxyYZHO+UnPu53Dp9bT+it09zMLrzdN12nIV2TRsLDNM49jIP4UIhcRUMbV3xr+tK5xTDcarYowjsWwry0adPGpEkNn/ziW1oqdIEoiqCgIKZMmaL/OTk5GS8vLwUTFZ+FWsWsQB/GbzqDCnIViUfXArMCffRXBgEtPOjl457v/RKP02qyOXNuE3subOZC+j+jLNSoqG9dFR/XxnhVbYy7a2OquTbExtoRKyt7NJoMkpNvkPTgBjfv/slfd6OISb5KbOY9ojMSib72G99d20u7KnUI8BlKi2YDUanNqstMCLNXoQuEu7s7t27dyrXt1q1beW6/f5yNjY3BG1LMXUALD5YPbZfnPgj3fK4MLNSqAjujdVotoWdWsOPPLVzLymmGU6Oipb0nXev3oZ3PyzhUcS8wk0MVdzxoT1Oeo8f/bbuXGEN41Hec+Psw5x7GcTrlKqfDPqTh2eW82nYCzZsNLNpfgBDCZBW6QPj5+fHzzz/n2rZ37178/PwUSqQsU64MChJ7JYS1ofO4mJ5zP4StyoLeNTsS8NRkqlVvXKyMVV298e8ShD9B3LgRxq9nVhBy9yzRGYnMPfEBrf74mhFdZlOrVsHTG+THTMdkCGGykvhdN6sCkZKSQnR0tP7n2NhYIiIicHV1pU6dOgQFBXHjxg02bNgAwLhx4/jyyy+ZNm0ao0aN4sCBA3z33Xf89NNPSr0FxRV2ZVCQ9If32LxvKnvv/I4WHTYqCwI9n6Gv3zSqOJb8/Ey1anVgVK0ODLx/hW1H5rLvzu/8kXaTab+NY6BXDwK7zcXKyriplR/dzZqWlpbv1aMQFcmjKTjyu8PbGGY1zDUkJMTg5GLDhw9n3bp1BmdiDAkJ4a233iIqKoratWszY8aMXNP/FqYiDHMtCZeif+F/ofOIy865m7ezcyOGPPNhsa8YTBEfH8Haw+8Tkfo3AHWsnHiz23y8vDobdXxcXBz379/Hzc0Ne3v7Ii9II0R5ptPpSEtLIyEhARcXF/2cWY8z9nPNrAqEEip7gdBqstlxYDo//H0ALTpc1ba84TuVls1fUiSPTqvl2OllrLuwgQfaLKxVakY2foVnn3670E5snU5HfHy8IiuNCVHWXFxccHd3N/hFSApECanMBSItJYEvfxlLeErOdMJdXJowqteSQjufy0LS/Sss+3UCZ9NuANDVpSmv91uFtY1jocdqNJpyvdKaEMVlZWVV4BQsUiBKSGUtEDdvnmbR/snczE7BCjVjmg2h+9NTlY6Vi1aTza6Q99h67Te06GhoU41p/b7G2aWe0tGEKNeM/VyTgeUij4uXf2LGb+O4mZ1CNQtb5jyzsNwVBwC1hSUDei7kfb8ZVFFZEp1xl//uGsy1a0cLP1gIUSgpECKX0xHrmHdsBim6bBraVGP+c1vw9u6ldKwCNW/6IvN6f4WHpQN3NA+ZeWAyFy/tUTqWEGZPCoTQO3zycz79fQmZOi3tHLyY8cIPZtNc4+HZnnkDfqCZrRsPddl8eHwWf5zfqnQsIcyaFAgBQMiJT/lf1Hq06OhetTlvv7gNW7uqSscySRVHD4Je+J7W9rXI0GlYGLaQUxFrlI4lhNmSAiE4GPoJKy5sRAf0rt6Wcc9txMLSuLUhyhsbW2feGfAdHR3rk42Wxb9/we9/bFI6lhBmSQpEJXckbClf/bkJHdCnejtG9f/a7CfFs7JxYNKArfg5NSQbHZ+Gf0Zk1PdKxxLC7Jj3J4Eolt//2MTy82v1Vw4j+682++LwiIWlNROf20T7KnXJQsuisAVcvFx5p1gRoigqxqeBMNnFS3v47MxnaNDRxaUJI/utqjDF4RFLK1veem4zrew9Sddp+PjYLG7cCFM6lhBmo2J9Igij/P33CRYen02mTksbh9qM/9d61BZmNW+j0axsHHj7+W9paFONFF02C/ZP5v69WKVjCWEWpEBUMklJ11h4YAqpumwa2VTnrcCNWFpV7DWabWydmdZ/Le6WDiRo0ljw8ygepiUqHUuIck8KRCWSlZHKpz+PIkGThpuFPe/0X2N2Q1mLytm5DkG9luGktiY28x5f/jQSrSZb6VhClGtSICoJnVbLip9HcTH9DvYqS6b3+Axn5zpKxypT7u5tmNZ5LlaoOZ1yle/2vqV0JCHKNSkQlcSeQzM5ev8iFqiY8tR0atd+WulIimjUMIDXfYYDsCPuCMdPLVM4kRDllxSISiAy6ns2X8kZ4jnM+wXF1nIoCRqtjtCYu/wYcYPQmLtotKZPRvxMx0k8556z7Oz/Ir/mr9gDJR1TiAqhYg5dEXq3E86z+NTHaNHRzaUZfbq8r3SkIguOjGPO7ijiktL12zycbZkV6ENAC9OWPB3c+wuub3uB31Ov8/mR95lffVuBy6ZqtLpir+UthLmR9SAKYc7rQWRlpDLrh0BiMhOpb12Vuf/eZdSCOuVRcGQc4zed4clf1kcf0cuHtjO5SKQ8iCNox0ASNGm0r1KXt1/cZnC4b0kWJiHKA1kPQrBp75vEZCZSRWXJ1F5fmm1x0Gh1zNkdlac4APptc3ZHmdzcVMXRg7e6zsMKNeEpV9l9KO/V1aPC9HhxAIhPSmf8pjMER8aZdE4hzIkUiArq5JmVBN8OB2BC2zep4dZc4URFFxabmOcD+nE6IC4pnbBY0+9taFC/ByMbvwzAlqu/8ufFXfrnSqswCWEupEBUQAm3Ilnxx0oAnnP3o13rYQonKp6EB/kXh6Ls96QeftPo6tIULTqWnvyIlAc5VwWlWZiEMAdSICqY7Kx0luyfRJoum8Y21Rnk/7nSkYrNzdG4O72N3e9JKrWa0X3+h7ulA3c16az89Q10Wm2pFyYhyjspEBXM9oPvEp1xF3uVJW/6L6kQ02h0qO+Kh7Mt+Y0ZUpHTadyhvmuRz2Fn78qkLnOxRMXJB7HsP76w1AuTEOWdFIgK5OLln9hx4xAAY5uPNOt+h8dZqFXMCvQByFMkHv08K9Cn2MNOG9TvyeD6/wJg3eXvqWUTXeqFSYjyTApEBZGWdocvQz9Ei46uLk3p9NQEpSOVqIAWHiwf2g5359zf1t2dbYs0xDU//brOopW9J1loWXEoiBn9vIHSLUxClFdyH0QhzOU+iOU7hxBy7zxuFvYs/Pcu7O2rKx2pVJTFDWt371xi2p5XSdFl8+9az+LoOU3ugxAVirGfa3IndQUQfnY9IffOowImdHy3whYHyGlu8vOuVqrnqFa9MaObj2RJ5Cq23wjhA+++HJ3eS+6kFpWONDGZuQfJN1gZkTPhXP+aHWna5DmFE1UMnZ6aQCenhmjRsez4XDRZKfh5V+P5NrXw864mxUFUClIgzNzafZO5r82klmUVBvX8ROk4Fcro3l9QVW3DzewUvj8wTek4QpQ5sysQy5Yto169etja2tKxY0fCwvJfY3jdunWoVKpcD1vbijMk8eSZlRxLuowaFW90mmm2U2mUV1UcPRjTejwAe+JPcDk6WOFEQpQtsyoQW7duZcqUKcyaNYszZ87QunVr+vTpQ0JCQr7HODk5ERcXp39cvXq1DBOXnpQHcXx9bjUAz3t0oaF3b4UTVUy+bUbQxaUJWnQsD/2QrIxUpSMJUWbMqkB89tlnjB07lpEjR+Lj48OKFSuwt7dnzZo1+R6jUqlwd3fXP2rWrFmGiUvPxv1vk/R/TUsDn12gdJwKbUTPz3BWW3Mj+wE/HJyudBwhyozZFIjMzEzCw8Px9/fXb1Or1fj7+xMaGprvcSkpKdStWxcvLy+ef/55zp8/X+B5MjIySE5OzvUob85GbtGPWvrP00FY2TgoHalCc3SqxZhWrwOwK+4YV64cUjiREGXDbArEnTt30Gg0ea4AatasSXx8vMFjmjRpwpo1a/jxxx/ZtGkTWq2WTp068ffff+d7nvnz5+Ps7Kx/eHl5lej7KK6HaYmsOrMEgIAavjRp1F/hRJVDh7Zj6OhYHy06Vh6bg1aTrXQkIUqd2RSIovDz82PYsGG0adOGbt26sX37dmrUqMFXX32V7zFBQUEkJSXpH9evXy/DxIX7PuRdbmseUsPCjkE9PlY6TqUysscn2KssiclM5Jcjc5WOI0SpM5sCUb16dSwsLLh161au7bdu3cLd3d2o17CysqJt27ZER0fnu4+NjQ1OTk65HuVF7JUQfrl1CoAxbSdiZ2/6HEAlsaZzZVXV1ZshDQcC8N2Vn7idUHBzpRDmzmwKhLW1Ne3bt2f//v36bVqtlv379+Pn52fUa2g0Gs6dO4eHh/lNj6DVZLPq2Fy06Ojk1JA2LYeY/BrBkXF0WXiAwatOMGlLBINXnaDLwgOyKpoJevi9QzNbN9J1Gr4OeRedVqt0JCFKjdkUCIApU6awatUq1q9fz4ULFxg/fjypqamMHDkSgGHDhhEUFKTff+7cufz222/89ddfnDlzhqFDh3L16lXGjBmj1Fsosl+PziMmMxF7lSXDnl1o8vGydGbJUFtYMvaZeVii4vfU64RFrFY6khClxqzmYho0aBC3b99m5syZxMfH06ZNG4KDg/Ud19euXUOt/qfm3bt3j7FjxxIfH0/VqlVp3749x48fx8fHR6m3UCR371xiS+xuAF5t+CJVXb1NOr6wpTNV5Cyd2cvHXaaQMEKtWh14zrMr228eZt25NbRq+u8iNfcJUd7JbK6FKA+zuS7e9m9Ck6NpZFOduYOCUVuYVtdDY+4yeNWJQvf7duzTpT4RXkWRmfGAqVv7kKBJo3/NDgzrt1LpSEIYzdjPNbNqYqqMzkZuITQ5GjUqxnSaYXJxgNJf07kysrZxZHTbnDU3frl1Su6NEBWSFIhyLCsjlbW/fwFAgJsv9ep1K9LryNKZpaNNyyE87eSNFh1fH/9A7o0QFY4UiHJs95FZxGWnUlVtw0vPzi/y65TFms6V1bBu87FVWXAp4w5HTi1VOo4QJUoKRDmVcCuS7dcPADDMZ1ixFgEqqzWdK6Nq1RszsE4fADZd3EJqiuG7+oUwR1IgyqmNR2aQhZbmdu74tR9f7NcrqzWdK6O+Xd+nlmUVkrWZbD34X6XjCFFizGqYa2VxNnILYQ9iUaNiZOdZqNQlU8cDWnjQy8ddls4sYVZW9oxsN4l5YR+y987v9LhyqMj9RUKUJ3IFUc5kZaWx7vcvAQhwewovL+PuEjfWozWdZenMktWy+Uv4/d8SpetCP5Q7rEWFIAWinPnlyDxuZqfgrLbmpW7zlI4j/o8xc1gN7TYPa5WaC+kJhIYvVyClECVLmpjKkXuJMWy79isArzYZhH0VN4UTCciZpmTO7qhc05R4ONsyK9AnV99N9epNeaF2D7Ze38fGqI20a/EqtnZVlYgsRImQK4hy5NtD75Ou09DQxpVnnpqkdByB6XNYBXadg5uFPYnadHYemlmWUYUocVIgyonomN84dP8CACM6TCvSHdNlraJPHV7YHFaQM4fV4+/bysaBYS1HA7A77hi3bv1R+kGFKCXl/1OoEtBqsll7MmeG1mdcmtGoYYDCiQpnbLOLOQuLTcxz5fA4HRCXlE5YbGKuOax8W4+k5cUfOPcwjm+OzGLKv3eUQVohSp5cQZQDR08vIzrjLrYqC141g47pyjJ1eFHnsFKp1Qzv/D5qVJx8EMv5C9tKI54QpU4KhMIepiWy+c/NALzg5W/yVN5lrSjNLuaqOHNYeXl1plf1tgCsD18q8zQJsyQFQmG7j8zhnjYDNwt7+ncp/52apjS7mLvizmH1UrcPqKKy5GpWEgdCF5VaTiFKixQIBd25fYFdN48AMLTFCKxsHBROVLjKNHV4ceewcnSqxb/r/wuALTHbZZ4mYXakQCho85GZZKGlma0bHdqYxzKolW3q8OLOYdWr07vUsqzCA20WO47MLsWkQpQ8GcWkkIuXf+JY0mVUwHC//5bYfEul7VGzS3xSusF+CBU5H54Vaerw4sxhZWlly2utx7Eg/BN+iT+Jf9zvuHu0LYPUoqLTaHWlPq+aFAgFaDXZbDj1KQDdqjanfr3uygYywaNml/GbzqCCXEWiIk8d/mgOq6Jo0+JVWl/4lrNpN/jm2FymyrBXUUxlNczc5K+tw4cP5/DhwyUWoDI6Hr6c6IxEbFUWvPLMXKXjmEymDjeNSq3mtU45w17DHsRy/s/tSkcSZqwsh5mbfAWRlJSEv78/devWZeTIkQwfPpxatWqVWKCKLjPjAZv//AaAAbV7lPthrfmRqcNN4+XlR8/qrdl7J4L1p5ewoNFzZnG3vChfChtmriJnmHkvH/cS+b9o8hXEzp07uXHjBuPHj2fr1q3Uq1ePvn378sMPP5CVlVXsQBXdT0fmcleTTjULW/p3Lf/DWgsiU4eb5qVn5mL3f8NeD59aonQcYYbKeph5kXpGa9SowZQpUzh79iwnT56kYcOGvPbaa3h6evLWW29x+fLlEglX0dy/F8vOv3OWEX216RCsbRwVTiTKkrNzHV6s0xuALRe/I/3hPYUTCXNT1sPMizV0Ji4ujr1797J3714sLCzo168f586dw8fHh88//7xEAlYkWw/P1M/W2tl3gtJxhAL6dn4PNwt77mkz2H3E/PqfhLLKepi5yQUiKyuLbdu28a9//Yu6devy/fffM3nyZG7evMn69evZt28f3333HXPnyi//465dO0pIYiQAw56aajbDWkXJsrJxYLDPUAB23zxMYmK0wolyq+gz9Jq74t7dbyqTe8k8PDzQarUMHjyYsLAw2rRpk2efZ599FhcXlxKIV3FsDP0ILTo6OjWgSaP+SscRCvJrN45fLm3nUsYdth6eyfgBm5WOBFSOGXrNXVkPMzf5a+znn3/OzZs3WbZsmcHiAODi4kJsbGxxs1UYEee+4Y+0m1ii4tXO5t0xLYpPpVbzWoepABy6F8WVK4cUTlR5ZuitCMpymLlKp9PJNWQBkpOTcXZ2JikpCScnJ5OP12RnMm1LT/7OekD/mh0Y1m9lKaQU5mjJtn9zPDmalnYevPfyT4o1O2q0OrosPJDv6JhHd8cfnd5DRqqVI8W5k9rYzzVpCC9lB098yt9ZD6iisuTFrnOUjiPKkcFdZmOJmnMP4/j93CbFclSmGXqFacyuQCxbtox69epha2tLx44dCQsLK3D/77//nqZNm2Jra0vLli35+eefyyhpzloP38XkTKvw7/r/ooqjtOOKfzqCQ+Oq0rFKzrxMm/5YhSY7U5E8lWmG3ooiODKOLgsPMHjVCSZtiWDwqhN0WXigxJsCzapAbN26lSlTpjBr1izOnDlD69at6dOnDwkJCQb3P378OIMHD2b06NH8/vvvDBgwgAEDBhAZGVkmeXcdmU2SNhN3Swf8O00rk3OK8u3J/9hf/d4DXQZcy0xmf+jHimSqbDP0mruy7C8yqwLx2WefMXbsWEaOHImPjw8rVqzA3t6eNWvWGNx/yZIlBAQE8M4779CsWTM++OAD2rVrx5dfflnqWe/eucSeuKMADGkxEisr+1I/pyjfDP3HTtW6wL12ZGZr2Xx5J2lpd0rl3AUNXy3roZOi6Mp6RUezKRCZmZmEh4fj7++v36ZWq/H39yc0NNTgMaGhobn2B+jTp0+++5ekrUdmkanLWevhqdajSv18onwr6D92eFIgDtk23MvO5MfDJd9PVVhzRHEXRhJl5/H+IhXZPOO2mGb2R/TPl4upNpRw584dNBoNNWvWzLW9Zs2axMcbXqkrPj7epP0BMjIySE5OzvUw1ZUrhzh8/wIAr3V4R26KEwV2BGuw5sHd7uh0OnbdPMqdO3/mbC+Bm9aMbY6QGXrNw+P9QG0cf+W+Qzz2br/goL6f737FIdNJPmH+/PnMmVO8b3G1PHwZWj+QWw/+xtu7VwklE+assP+wkWnd6JpxnIeWqWw5MpuGjT4v9k1rps78KTP0ln+P+oFsVKlYVw3lIWB1v0VOU6WB/YrLbL7aVq9eHQsLC27dupVr+61bt3B3dzd4jLu7u0n7AwQFBZGUlKR/XL9+3eSsVjYO/Kv7B4wOXGvysaJiKvw/rJrrdwNRASGJF1jw/fpid0IWZfiqzNBbvj3qL2rnsoOHFtnYaawIv/+i/vmS7i8ymwJhbW1N+/bt2b9/v36bVqtl//79+Pn5GTzGz88v1/4Ae/fuzXd/ABsbG5ycnHI9hCguYzqCM2w78EzVpmRpdHhV2wVoc+1jaiekDF+teCzUKoJ6VCHDOQqAjMROZJHz5aNcTLWhpClTprBq1SrWr1/PhQsXGD9+PKmpqYwcORKAYcOGERQUpN9/0qRJBAcH8+mnn/Lnn38ye/ZsTp8+zcSJE5V6C6KSMrYj2Kf+ZNQ6SLJ5QAv7vFNwmNIJKcNXK6Zbt5ahtoKq2VU4m/JPE3Zp9BeZVR/EoEGDuH37NjNnziQ+Pp42bdoQHBys74i+du0a6sc6hDt16sTmzZt5//33+e9//0ujRo3YuXMnLVq0UOotiErsUUfwk30L7o/1LfwYocU+qREPnC/hWC0Ei7TOaLDO81rGfOt/dNUSn5RusB/i0RQaMnzVfFy/HsqBu+ewUKuY1mM646y7lGp/kczFVIjizsUkxJMKmkMnNOYuo1b/Ros6H5OhzsY28SnCkgbmeY1vxz6Nn3e1Qs/1aBQTGJ75U0YomZf53/2LiNS/6eBYn6n/3lHk15G5mIQopwrqCO5Q3xUXJze41x4ArcuZXEMYTe2ElOGrFcfZyC1EpP6dMyt0p/fL5Jxm1cQkREX3qK9iwqZAOjqdJcUqnbZVv+fo3bFF7oSU4avmT6vJZlPE/wDo7fYUHp7ty+S8UiCEKGcCWniwbGgH1v8SAFY7SXH6ixpJV7F0aFLkxXseXbUI83Tw5Kdcy0qmisqSgc+U3azQ0sQkRDkU0MKDjW/NpqW9J5aWKp5r9itHp/eQJqFK6GFaIt9d3gbAi/X6lems0FIghCinLC0t+E+3GVip1URmXiU6+ielIwkF/Hh4Fve1mdS0cKB353fL9NxSIIQox+rU6cKz1XKGZW84/RlaTbbCiURZunP7AnvijgEwtGXZzwotBUKIcu7lrnOxVVkQnZHI8fDlSscRZejbo7PJQrlZoaVACFFOPZrN9dBVa/ycOwPwzYVvyEhPUjiZKAvRMb9x9P5FVMCwp99VZFZoGcUkRDkUHBmX645ra9Uz+NY5xm3rh+w5MpeBvT5VOKEoTTqtlvUnc1YYfMalGQ3q91Akh1xBCFHOGFrDIVNnR9rdzmRma9l27QCJidEKJhSlLTR8OZcy7mCjsmBQ17Ib1vokKRBClCMFreEQkdILp4wqpGo0bDk8s8yzibKRmfGATRc2AvB8re5Uq95YsSxSIIQoRwpew0HNjbv/QqfTcfDueWJi9pZpNlE29hyZw11NOtUsbAl8ZraiWaRACFGOFDZLa2xGG1xSPdAB68MWotNqC9xfmJfExGh2/n0QgKHNXsPaxlHRPFIghChHjFmbIfLuS9io1FxMv0OoDHutUL49NIMMnYbGNtXxaz9e6ThSIIQoT4xZec6mSgNe8OoJwMaojTLstYK4HB3M4fsXABjecZoiw1qfpHwCIYSesSvPPffMLGpY2JGoTWfX4dllmFCUBq0mm3VhOcNau1f1oaF3b4UT5ZACIUQ5Y8waDtY2jgxpPhyAH28c4nbCeSWiihJy5NRSojMSsVVZMLjbh0rH0ZMb5YQoh4xZw+Hptq/T7NJ2LqQnsOnITN4auE3BxKKoHqYlsvniFgAG1umDS9X6Cif6h1xBCFFOFbTyHIBKrWZkpxmoUXEiOYbIqO8VSiqKY/uh97ivzcTd0oG+nd9TOk4uUiCEKKcezcX0Y8QNQmPuotHmvX2ubt2u9KreFoC1Z5aiyc4s65iiGG7cCOPn+BMADG/1H6xsHBROlJs0MQlRDj05FxPkrEVtaEW5l7t/yLFtgfyd9YDfjn1E326zyzitKAqdVsu6o7PJRke7KnVo13qY0pHykCsIIcoZQ3MxAcQnpTN+0xmCI+Nyba/i6MErDV8E4LvYPSTdv1JWUUUxnD67lj/SbmKJmuFd5yodxyApEEKUIwXNxfRo25zdUXmam3r6TaO+dVXSdNl8czCo1HOK4snMeMCGc18DEOjRGXf3NsoGyocUCCHKkYLnYsopEnFJ6YTFJubarrawZHTH6QAcun+Bi5f2lGZMUUw7Q94nQZNGNQtbBnQrn1cPIAVCiHKlsLmYCtqvUcMAeri2BODrsEXSYV1Oxd0M58ebRwAY3mIUtnZVFU6UPykQQpQjxszFVNB+g7t/RBWVJVezkvjt2EclGU2UAJ1Wy5ojM8hGSxuH2nRoM0bpSAWSAiFEOWLMXEwezjk3zRni5Oyl77DeErtbFhYqZ078vpI/0m5ihZqRXT8oF/MtFaR8pxOikjF2LqYnb5p7XE+/aTS0qUa6TsOGg++WTlBhsodpiWyIXAfA87W64e7RVtlARpACIUQ5Y8xcTAVRW1gyttNM1KgITY4m4tw3pRlXGOm7g9NJ1KbjZmHP8+W4Y/pxcqOcEOWQMXMxFaRevW4EuD3FzwlhfP37Mj5t/Jzii89UZjExewlOOA3AmHZvms2/hdlcQSQmJjJkyBCcnJxwcXFh9OjRpKSkFHhM9+7dUalUuR7jxo0ro8RCKOvlZxfgqrYlQZPGNrk3QjGa7ExWnvgQLTo6OzemdYtXlI5kNLO5ghgyZAhxcXHs3buXrKwsRo4cyeuvv87mzZsLPG7s2LHMnfvP5Zy9vX1pRxWi2EyZaiM/dvaujGo1lk8ivmB33DE6XT1C3bpdSyuyyMcvRz/gSuZ9HFSWDO/xsdJxTGIWVxAXLlwgODiY1atX07FjR7p06cIXX3zBli1buHnzZoHH2tvb4+7urn84OTmVUWohisbUqTYK8lTb0XR0rI8GHV8dnYVWk13ScUUBEm5F8v2VnwEY0vglnF3qKRvIRGZRIEJDQ3FxccHX11e/zd/fH7VazcmTJws89ptvvqF69eq0aNGCoKAg0tLSCtw/IyOD5OTkXA8hykpRp9ooyIgei7BXWRKTmUjw0Q9KJKconE6rZWXINNJ1GprZuvFsx6lKRzKZWRSI+Ph43Nzccm2ztLTE1dWV+Pj4fI979dVX2bRpEwcPHiQoKIiNGzcydOjQAs81f/58nJ2d9Q8vL68SeQ9CGKOoU20UxNW1IUMaDgRga+weEm5FFjemMMKhsM8593/3PPyn23zUFmbToq+naIF4991383QiP/n4888/i/z6r7/+On369KFly5YMGTKEDRs2sGPHDmJiYvI9JigoiKSkJP3j+vXrRT6/EKYqzlQbBenh9w7NbN1I12lYefAddFptUeIJI92/F8uGP78F4OW6AXh4tlc4UdEoWtKmTp3KiBEjCtynQYMGuLu7k5CQkGt7dnY2iYmJuLu7G32+jh07AhAdHY23t7fBfWxsbLCxsTH6NYUoScWdaiM/agtLxnVfyDu/jubcwzj2H1+IfxcZ2VQadFota/ZPIVWXTQPrqvR/ZrbSkYpM0QJRo0YNatSoUeh+fn5+3L9/n/DwcNq3z6nEBw4cQKvV6j/0jREREQGAh4dxo0CEKGuPptqIT0o32A+hIueGufym2iiIu0dbXqn3LzbE7mJj9A+0bvwcNdyaFzuzyO3Y6WWcfBCLBSr+03k2FpbWSkcqMrPog2jWrBkBAQGMHTuWsLAwjh07xsSJE3nllVfw9PQE4MaNGzRt2pSwsDAAYmJi+OCDDwgPD+fKlSvs2rWLYcOG8cwzz9CqVSsl344Q+SqJqTYK0rfrTBrbVCddp+EraWoqcYmJ0ayNWg/AwNo9qFevm/45Y5aQLW/MokBAzmikpk2b0rNnT/r160eXLl1YuXKl/vmsrCwuXryoH6VkbW3Nvn376N27N02bNmXq1KkMHDiQ3bt3K/UWhDBKcafaKIjawpLx3RdghZpzaTf57diHxY0r/o9Oq2XVvrdI+b+mpee7//N3GxwZR5eFBxi86gSTtkQweNUJuiw8YNKQZSWodDpd+S9jCkpOTsbZ2ZmkpCS5h0KUKY1WV+SpNgrz86FZrP/rR6xVaj7uvcpsO1HLk4Ohn7Diz01YomaB/zK8vPyAf+5refKD9tG/ZHGLflEY+7lmNlcQQlQ2FmoVft7VeL5NLfy8q5VYcQAI6DKDlnYeZOq0fBnyjiwuVEzx8RGsu/ho1FJvfXEojftaypIUCCEqIbWFJeP8P8deZUl0RiI7Za6mItNkZ/Llgbf1N8QFdpunf6407mspS1IghKikqldvyshmrwHww98HuHj5J4UTmaftB6ZzOeMO9ipLJvT8PNcNcaV1X0tZkQIhRCXW9an/R2fnxmjR8UXoPFJT8p+ZQOR18fJPbL8RAsCY5iPyDBsurftayooUCCEqMZVazZiA/+FmYc9tzUNW/TpRhr4aKeVBHEtD56FFRxeXJnR+amKefYq7hKzSpEAIUcnZ21fnzU4zsfi/FegOnFikdKRyT6fV8r/gcdzRPMTd0oHRfZYZ3K+072spbVIghBA0ahjAoLp9AFh7cStXrhxSOFH59vPh2YSnXMUSNZO7foi9ffV89y3N+1pKm9wHUQi5D0JUFlpNNou2vcCZ1Ou4Wdiz4MXtOFQxfq6zyuJydDCzjwSRjY5RDQfSp+sMo44rzftaTCX3QQghTKK2sGRC35W4WdiToEnjf7/8R/ojnnD/XiyfHZtNNjqedvKmd+f3jD62NO9rKS1SIIQQelUcPZjceQ6WqDmdcpWdB99VOlK5kZ2VzuLg/5CoTaeWpSPj+q5Cpa7YH6EV+90JIUzm7d2LUY1fBmDrtd84HbFO2UDlxMZfJ3AhPQE7lSXv9FyMnX35HHlUkqRACCHy6Nn5XXpXb4sO+CLiC65fD1U6kqL2H1tA8O1wACa2mVhp5q6SAiGEMGh4wHJ87GqSrtPw8cGpPEi+oXQkRZw7/z1fX9oKwEu1e+DbZoSygcqQFAghhEGWVra81Xe1vtP64z3Dycx4oHSsMvX33yf47NRCNOjo7NyYgT0/UTpSmZICIYTIl5OzF9Oe/RQHlSWXMu7wxe5haDXZSscqE/fvxbLwwBTSdNk0sa3O+P5rK3yn9JMq17sVQpjMy8uPt59+H0vUhD2IZX0lGP6amhLPRz+NIEGThpuFPVP7rcHKxkHpWGVOCoQQolA+TQcwocVoAIJvh/PDvqkKJyo9mRkPWLR7GFezknBWW/Ne7//h7FxH6ViKkAIhhDBKp6cmMKLBAAB+uHGQXQf+q2ygUpCVkcrnP77KhfQE7FWW/LfbItzd2ygdSzFSIIQQRuvbbTav1OkNwDdXfyb48FyFE5WcrIxUPts1mDOp17FCzTS/GdSr103pWIqSAiGEMMkLPT/mRc9nAFgbs509IcbNRVSeZWWk8tmPr3Am5RpWqJneIYhmTZ5XOpbipEAIIUz2cq/FPOfeCYCNsbv5/re3zLbj+mFaIot2vqy/cpjeIYiWzV9SOla5IAVCCGEylVrNq32+5BWvXkBOn8SGX8aZ3RDYpPtX+GDHvzmbdgMblYUUhydIgRBCFIlKreYF/0UMb5DTFPNzQhifbh9I+sN7CiczTnx8BDN3DyEmMxFHtRUzunwoxeEJUiCEEMXSr9sc/l/z0foZYGdtf4G7dy4pHatAEee+4b/BY4jPTsXNwp4Peq2gUcMApWOVO1IghBDF1qXD/2Nmlw9wUltzJfM+7/40lLORW5SOlYdOq2XH/mksOL2IVF02DW2qMfdfGyvN5HumkhXlCiEryglhvIRbkXyydwJXs5JQAQM8uvKS/6dYWForHY3ExGhW7J3E2bScSQf9q7ViRJ/llfIOaVlRTghR5txqtmDeS3voVa01OmBH3BHe39pH0TWudVotx0//j3d2vcLZtBtYoWZso0GMfW5DpSwOppAriELIFYQQRXP81DJWn19Lqi4bNSoCPTrxYrd52NpVLbMMcTfDWXd0FhGpfwPQwLoqE7rNp3btp8ssQ3lk7OeaFIhCSIEQouju34tl7YG3OZEcA4Cz2pqBDQLp8fRUrKzsS+28SfevsDt0Ab/EnyQbHZaoeN7zGV7ssRBLK9tSO6+5qHBNTB9++CGdOnXC3t4eFxcXo47R6XTMnDkTDw8P7Ozs8Pf35/Lly6UbVAih51K1Pm8N3Mbbbd/EzcKeJG0ma6K3MeVbf34KmUlaSkKJnu/O7Qus2zOGiTtfZHf8CbLR0dbBi0/6rOblPkukOJjIbK4gZs2ahYuLC3///Tdff/019+/fL/SYhQsXMn/+fNavX0/9+vWZMWMG586dIyoqCltb435R5ApCiJKRlZXGgROfsu2v3SRpMwGwVVnQqaoPHRsE0LzxgCL1CaSl3eH0uY0cjv2NyIdxPPpA87Z25aWWo2jbamgJvouKocI2Ma1bt47JkycXWiB0Oh2enp5MnTqVt99+G4CkpCRq1qzJunXreOWVV4w6nxQIIUpW+sN7HAlfTvCVX/g7658V6mxVFjR3qE0DF28auLWhZvWmODt5YW9fA5VKTbYmnYz0JG4lRHIr8RKxd84Tde9P/sq4h5Z/PsZa2nnwfMtRtGg2sNIt8GMsYz/XLMswU5mKjY0lPj4ef39//TZnZ2c6duxIaGhovgUiIyODjIwM/c/JycmlnlWIysTWriq9uvwX/07vEnVpJ6GXfuT0vT+5p80gPOUq4SlX4e8D+v3VqNCho6Bvsp6WVeji3pGurUbiVrNF6b+JSqLCFoj4+HgAatasmWt7zZo19c8ZMn/+fObMmVOq2YQQOVN1NG/6Is2bvsgoTTZ/XTnAxetHib13idjUGyRmPyRNl53r6gDARW2Nu7UznvbuNHNvh493X6pXb6rQu6jYFC0Q7777LgsXLixwnwsXLtC0adn94wcFBTFlyhT9z8nJyXh5eZXZ+YWojNQWljT07k1D7965tmdlpPIgNQ61ygIrKzusrRzl3oUypGiBmDp1KiNGjChwnwYNGhTptd3d3QG4desWHh4e+u23bt2iTZs2+R5nY2ODjY1Nkc4phChZVjYOuNo0VDpGpaVogahRowY1atQoldeuX78+7u7u7N+/X18QkpOTOXnyJOPHjy+VcwohREViNl38165dIyIigmvXrqHRaIiIiCAiIoKUlBT9Pk2bNmXHjh0AqFQqJk+ezLx589i1axfnzp1j2LBheHp6MmDAAIXehRBCmA+z6aSeOXMm69ev1//ctm1bAA4ePEj37t0BuHjxIklJSfp9pk2bRmpqKq+//jr379+nS5cuBAcHG30PhBBCVGZmdx9EWZP7IIQQFU2lvw9CCGEeNFodYbGJJDxIx83Rlg71XbFQq5SOJZACIYRQUHBkHHN2RxGXlK7f5uFsy6xAHwJaeBRwpCgLZtNJLYSoWIIj4xi/6Uyu4gAQn5TO+E1nCI6MUyiZeEQKhBCizGm0OubsjjI4fcajbXN2R6HRShepkqRACCHKXFhsYp4rh8fpgLikdMJiE8sulMhDCoQQoswlPMi/OBRlP1E6pEAIIcqcm6Nx9yIZu58oHVIghBBlrkN9VzycbclvMKuKnNFMHeq7lmUs8QQpEEKIMmehVjEr0AcgT5F49POsQB+5H0JhUiCEEIoIaOHB8qHtcHfO3Yzk7mzL8qHt5D6IckBulBNCKCaghQe9fNzlTupySgqEEEJRFmoVft7VlI4hDJAmJiGEEAZJgRBCCGGQFAghhBAGSR9EIR4tl5GcnKxwEiGEKBmPPs8KWw5ICkQhHjx4AICXl5fCSYQQomQ9ePAAZ2fnfJ+XFeUKodVquXnzJo6OjqhUxg+9S05OxsvLi+vXr5vNSnSSuWyYW2ZzywuSuTA6nY4HDx7g6emJWp1/T4NcQRRCrVZTu3btIh/v5ORkNr+gj0jmsmFumc0tL0jmghR05fCIdFILIYQwSAqEEEIIg6RAlBIbGxtmzZqFjY2N0lGMJpnLhrllNre8IJlLinRSCyGEMEiuIIQQQhgkBUIIIYRBUiCEEEIYJAVCCCGEQVIgSsGyZcuoV68etra2dOzYkbCwMKUjFejw4cMEBgbi6emJSqVi586dSkcq0Pz583nqqadwdHTEzc2NAQMGcPHiRaVjFWj58uW0atVKfxOUn58fv/zyi9KxTLJgwQJUKhWTJ09WOkq+Zs+ejUqlyvVo2rSp0rEKdePGDYYOHUq1atWws7OjZcuWnD59WulYUiBK2tatW5kyZQqzZs3izJkztG7dmj59+pCQkKB0tHylpqbSunVrli1bpnQUoxw6dIgJEyZw4sQJ9u7dS1ZWFr179yY1NVXpaPmqXbs2CxYsIDw8nNOnT9OjRw+ef/55zp8/r3Q0o5w6dYqvvvqKVq1aKR2lUM2bNycuLk7/OHr0qNKRCnTv3j06d+6MlZUVv/zyC1FRUXz66adUrVpV6WigEyWqQ4cOugkTJuh/1mg0Ok9PT938+fMVTGU8QLdjxw6lY5gkISFBB+gOHTqkdBSTVK1aVbd69WqlYxTqwYMHukaNGun27t2r69atm27SpElKR8rXrFmzdK1bt1Y6hkmmT5+u69Kli9IxDJIriBKUmZlJeHg4/v7++m1qtRp/f39CQ0MVTFaxJSUlAeDq6qpwEuNoNBq2bNlCamoqfn5+Sscp1IQJE+jfv3+u3+vy7PLly3h6etKgQQOGDBnCtWvXlI5UoF27duHr68tLL72Em5sbbdu2ZdWqVUrHAqSJqUTduXMHjUZDzZo1c22vWbMm8fHxCqWq2LRaLZMnT6Zz5860aNFC6TgFOnfuHFWqVMHGxoZx48axY8cOfHx8lI5VoC1btnDmzBnmz5+vdBSjdOzYkXXr1hEcHMzy5cuJjY2la9eu+mn7y6O//vqL5cuX06hRI3799VfGjx/Pm2++yfr165WOJrO5CvM2YcIEIiMjy307M0CTJk2IiIggKSmJH374geHDh3Po0KFyWySuX7/OpEmT2Lt3L7a2tkrHMUrfvn31f27VqhUdO3akbt26fPfdd4wePVrBZPnTarX4+vry0UcfAdC2bVsiIyNZsWIFw4cPVzSbXEGUoOrVq2NhYcGtW7dybb916xbu7u4Kpaq4Jk6cyJ49ezh48GCxpmQvK9bW1jRs2JD27dszf/58WrduzZIlS5SOla/w8HASEhJo164dlpaWWFpacujQIZYuXYqlpSUajUbpiIVycXGhcePGREdHKx0lXx4eHnm+JDRr1qxcNI1JgShB1tbWtG/fnv379+u3abVa9u/fbxZtzeZCp9MxceJEduzYwYEDB6hfv77SkYpEq9WSkZGhdIx89ezZk3PnzhEREaF/+Pr6MmTIECIiIrCwsFA6YqFSUlKIiYnBw8ND6Sj56ty5c55h2pcuXaJu3boKJfqHNDGVsClTpjB8+HB8fX3p0KEDixcvJjU1lZEjRyodLV8pKSm5vmHFxsYSERGBq6srderUUTCZYRMmTGDz5s38+OOPODo66vt3nJ2dsbOzUzidYUFBQfTt25c6derw4MEDNm/eTEhICL/++qvS0fLl6OiYp1/HwcGBatWqldv+nrfffpvAwEDq1q3LzZs3mTVrFhYWFgwePFjpaPl666236NSpEx999BEvv/wyYWFhrFy5kpUrVyodTYa5loYvvvhCV6dOHZ21tbWuQ4cOuhMnTigdqUAHDx7UAXkew4cPVzqaQYayArq1a9cqHS1fo0aN0tWtW1dnbW2tq1Gjhq5nz5663377TelYJivvw1wHDRqk8/Dw0FlbW+tq1aqlGzRokC46OlrpWIXavXu3rkWLFjobGxtd06ZNdStXrlQ6kk6n0+lkum8hhBAGSR+EEEIIg6RACCGEMEgKhBBCCIOkQAghhDBICoQQQgiDpEAIIYQwSAqEEEIIg6RACCGEMEgKhBBCCIOkQAghhDBICoQQCrp9+zbu7u76tQAAjh8/jrW1da5ZgYVQgszFJITCfv75ZwYMGMDx48dp0qQJbdq04fnnn+ezzz5TOpqo5KRACFEOTJgwgX379uHr68u5c+c4deoUNjY2SscSlZwUCCHKgYcPH9KiRQuuX79OeHg4LVu2VDqSENIHIUR5EBMTw82bN9FqtVy5ckXpOEIAcgUhhOIyMzPp0KEDbdq0oUmTJixevJhz587h5uamdDRRyUmBEEJh77zzDj/88ANnz56lSpUqdOvWDWdnZ/bs2aN0NFHJSROTEAoKCQlh8eLFbNy4EScnJ9RqNRs3buTIkSMsX75c6XiikpMrCCGEEAbJFYQQQgiDpEAIIYQwSAqEEEIIg6RACCGEMEgKhBBCCIOkQAghhDBICoQQQgiDpEAIIYQwSAqEEEIIg6RACCGEMEgKhBBCCIOkQAghhDDo/wN2O3u0SdiBTAAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -1017,7 +812,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:06<00:00, 15.42it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 22.68it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1032,7 +827,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1045,7 +840,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:07<00:00, 13.97it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 22.91it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1055,12 +850,12 @@ "text": [ "\n", "\u001b[1mRunning Cycle 4, number of datapoints: 40\u001b[0m\n", - "\u001b[1mCycle 4 model: sin(x)\u001b[0m\n" + "\u001b[1mCycle 4 model: -0.17\u001b[0m\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1073,7 +868,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:07<00:00, 13.82it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 27.47it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1083,12 +878,12 @@ "text": [ "\n", "\u001b[1mRunning Cycle 5, number of datapoints: 50\u001b[0m\n", - "\u001b[1mCycle 5 model: (0.53 ** x)\u001b[0m\n" + "\u001b[1mCycle 5 model: -0.11\u001b[0m\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1102,23 +897,32 @@ "text": [ "\n", "\u001b[1mNumber of datapoints: 50\u001b[0m\n", - "\u001b[1mDetermined Model: (0.53 ** x)\u001b[0m\n" + "\u001b[1mDetermined Model: -0.11\u001b[0m\n" ] } ], "source": [ "#### First, let's reinitialize the state object to get a clean state ####\n", + "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 30))\n", + "dv = Variable(name=\"y\", type=ValueType.REAL)\n", + "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "conditions = random_pool(variables, num_samples=10, random_state=42)\n", + "\n", "s = StandardState(\n", " variables = variables,\n", " conditions = conditions,\n", " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"])\n", ")\n", "\n", + "\n", "### Then we cycle through the pipeline we built until we reach our stopping criterion ###\n", "cycle = 0\n", "while len(s.experiment_data) < 50: #Run until we have at least 50 datapoints\n", " #Run pipeline\n", - " s = theorist(experiment_runner(experimentalist(s, num_samples=10)))\n", + " s = experimentalist(s, num_samples=10, random_state=42+cycle)\n", + " s = experiment_runner(s, added_noise=1.0, random_state=42+cycle)\n", + " s = theorist(s)\n", " \n", " #Report metrics\n", " print(f\"\\n\\033[1mRunning Cycle {cycle+1}, number of datapoints: {len(s.experiment_data)}\\033[0m\")\n", @@ -1154,13 +958,18 @@ "metadata": {}, "outputs": [], "source": [ + "from typing import Optional\n", + "\n", "#### We will first define a new experimentalist\n", - "def uniform_sample(variables: VariableCollection, conditions: pd.DataFrame, num_samples = 1):\n", + "def uniform_sample(variables: VariableCollection, conditions: pd.DataFrame, num_samples = 1, random_state: Optional[int] = None):\n", "\n", " \"\"\"\n", " An experimentalist that selects the least represented datapoints\n", " \"\"\"\n", "\n", + " #Set random state\n", + " rng = np.random.default_rng(random_state)\n", + " \n", " #Retrieve the possible values\n", " allowed_values = variables.independent_variables[0].allowed_values\n", " \n", @@ -1175,7 +984,7 @@ " \n", " #Sample from values with the smallest frequency\n", " x = values_count[conditions_count<=conditions_count[num_samples-1]]\n", - " x = np.random.choice(x,num_samples)\n", + " x = rng.choice(x,num_samples)\n", " \n", " return pd.DataFrame({\"x\": x})" ] @@ -1196,6 +1005,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "#==================================================#\n", "\u001b[1mUsing random pooler experimentalist...\u001b[0m\n" ] }, @@ -1203,7 +1014,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:05<00:00, 17.78it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 27.02it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1213,12 +1024,12 @@ "text": [ "\n", "\u001b[1mRunning Cycle 1:\u001b[0m\n", - "\u001b[1mCycle 1 model: -0.34\u001b[0m\n" + "\u001b[1mCycle 1 model: -0.38\u001b[0m\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1237,6 +1048,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "#==================================================#\n", "\u001b[1mUsing random pooler experimentalist...\u001b[0m\n" ] }, @@ -1244,7 +1057,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:06<00:00, 16.23it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 27.46it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1254,12 +1067,12 @@ "text": [ "\n", "\u001b[1mRunning Cycle 2:\u001b[0m\n", - "\u001b[1mCycle 2 model: -0.16\u001b[0m\n" + "\u001b[1mCycle 2 model: -0.27\u001b[0m\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1278,6 +1091,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "#==================================================#\n", "\u001b[1mUsing uniform sampler experimentalist...\u001b[0m\n" ] }, @@ -1285,7 +1100,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:05<00:00, 17.31it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 24.25it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1300,7 +1115,7 @@ }, { "data": { - "image/png": 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", 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" ] @@ -1319,6 +1134,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "#==================================================#\n", "\u001b[1mUsing uniform sampler experimentalist...\u001b[0m\n" ] }, @@ -1326,7 +1143,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:06<00:00, 16.27it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 23.04it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1341,7 +1158,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1354,6 +1171,12 @@ "from autora.experimentalist.random_ import random_pool\n", "\n", "#### First, let's reinitialize the state object to get a clean state ####\n", + "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 30))\n", + "dv = Variable(name=\"y\", type=ValueType.REAL)\n", + "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "conditions = random_pool(variables, num_samples=10, random_state=42)\n", + "\n", "s = StandardState(\n", " variables = variables,\n", " conditions = conditions,\n", @@ -1366,17 +1189,19 @@ "\n", "### Then we cycle through the pipeline we built until we reach our stopping criteria ###\n", "cycle = 0\n", - "while len(s.experiment_data) < 20:\n", + "while len(s.experiment_data) < 40:\n", " \n", " #Run pipeline\n", - " if len(s.experiment_data) < 10: #Conditional experimentalist: random for first half of cyles\n", + " if len(s.experiment_data) < 20: #Conditional experimentalist: random for first half of cyles\n", + " print('\\n#==================================================#')\n", " print('\\033[1mUsing random pooler experimentalist...\\033[0m')\n", - " s = random_experimentalist(s, num_samples=5)\n", + " s = random_experimentalist(s, num_samples=10, random_state=42+cycle)\n", " else: #Conditional experimentalist: uniform for last half of cycles\n", + " print('\\n#==================================================#')\n", " print('\\033[1mUsing uniform sampler experimentalist...\\033[0m')\n", - " s = uniform_experimentalist(s, num_samples=5)\n", + " s = uniform_experimentalist(s, num_samples=10, random_state=42+cycle)\n", " \n", - " s = experiment_runner(s)\n", + " s = experiment_runner(s, added_noise=1.0, random_state=42+cycle)\n", " s = theorist(s)\n", " \n", " #Report metrics\n", @@ -1405,7 +1230,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:05<00:00, 18.92it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 26.11it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1415,12 +1240,12 @@ "text": [ "\n", "\u001b[1mRunning Cycle 1:\u001b[0m\n", - "\u001b[1mCycle 1 model: -0.38\u001b[0m\n" + "\u001b[1mCycle 1 model: -0.31\u001b[0m\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1433,7 +1258,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:06<00:00, 15.39it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 25.66it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1443,12 +1268,12 @@ "text": [ "\n", "\u001b[1mRunning Cycle 2:\u001b[0m\n", - "\u001b[1mCycle 2 model: sin(x)\u001b[0m\n" + "\u001b[1mCycle 2 model: -0.21\u001b[0m\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1461,7 +1286,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:06<00:00, 16.67it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 26.29it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1476,7 +1301,7 @@ }, { "data": { - "image/png": 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Dxu+eNIyIiGDWrFlmv++dB4AHBgYCkJKSQq1atTh58iTDhw8vcH3btm3Zvn272e+Tr1WrVqW+tzB3xg+3v4c7jyyUQ9MLkmQvKgSNRmNSKUVN9evXR6PR3FMbzy+NFLZexNztu7Xa29Nwd84jFDWxe+cB4Pnlo+JGwmV19/diTqyFKe4Ac1M42qHpMkErRDmpUqUKXbp04YsvviAzM7NUz2jcuPE9Ne+9e/caDyDPTxZ3dr/cOQFqzvscPHiwwGt3H2ReVqbEmn8gul6vt+h753OkQ9NlZC9EOfrPf/5DREQE4eHhTJw4kWbNmqHVajl8+DB//vlniaWON954g759+9KiRQsiIyNZv349q1evNrZsuru78+CDDzJt2jTq1q1LSkoK77zzjtlxjho1isGDBxMeHk5ERATffvstJ06cKNME7d1MibV27dpoNBo2bNhAjx49cHd3x9PT06Tnjx8/nosXL/LNN98U+vXFixej1+tp06YNHh4eFj00vU+fPsbX7zw03dPTk40bNzJ06FA2bNhgvKY8Dk2Xkb0Q5SgkJITffvuNyMhIxo8fT/PmzQkPD+fzzz/n9ddf54MPPij2/j59+jBr1iw+/vhjmjRpwpdffsmiRYsKdHB8/fXX5OXl0apVK0aPHs3kyZPNjrNfv368++67jB07llatWnHu3DlGjBhh9nNKUlKsNWrUYNKkSbz55ptUr16d6Ohok58th6YXJAeOC7sjB44LW2WtQ9Md6sBxIYSwdbZ8aLrU7IUQwoJs9dB0GdkLIYQDkGQvhBAOQJK9sFsO1FsgHJwl/q5Lshd2J38Vo73u/S6EufL/rhe1stcUMkEr7I5Op8PX19e4CtHDw6PUh2MIYcsUReHmzZukpKTg6+uLTqcr9bMk2Qu7FBAQAHDPsnMhKiJfX1/j3/nSkmQv7JJGoyEwMBB/f3+bPsFJiLJydnYu04g+nyR7Ydd0Op1F/iEIUdFJsheq0RsUDiVcIyU9C38vN1rX9UOnldq7ENYgyV6oYtPxJCatjyMp9X8nHwX6uDGhVyjdmgaqGJkQFZO0Xopyt+l4EiOWHS2Q6AGSU7MYsewom44nFXGnEKK0JNkL4HZJZX/8VX6Mvcj++KvoDdZZsKQ3KExaH0dhT89/bdL6OKu9vxCOSso4olxLKocSrt0zor+TAiSlZnEo4RptQ0q/r7gQoiAZ2Tu48i6ppKQXnehLc50QwjSS7B2YNUsqRZWF/L1MO2zE1OuEEKaRMo4Ds1ZJpbiyUJfQAAJ93EhOzSr0h4wGCPC53YYphLAcGdk7MGuUVEoqC22JS2ZCr1DgdmK/U/7nE3qFSr+9EBYmyd6BWbqkYmpZqEtoAHMHtiTAp+BzA3zcmDuwpfTZC2EFUsZxYK3r+lm0pGJOWahb00C6hAbIClohyokkewem02qY0CuUEcuOooECCb80JRVzy0I6rUbaK4UoJ1LGcXDdmgZarKQinTZC2C4Z2VuIPW/qZamSiqXLQkIIy5FkbwEVYVMvS5RULF0WEkJYjpRxykg29SrIkmUhIYTlyMi+DEpqNdTwv1ZDRxrNSqeNELZHkn0ZyKZeRZNOGyFsi5RxykA29RJC2AtJ9mUgrYZCCHshyb4M8lsNi6pEa7jdlSOthkIItUmyL4P8VkOQTb2EELZNkn0ZSauhEMIe2FU3zq5du/joo4+IiYkhKSmJNWvW0KdPH7XDklZDIYTNs6tkn5mZSfPmzRk6dChPPvmk2uEUIK2GQghbZlfJvnv37nTv3l3tMIQQwu7YVbI3V3Z2NtnZ2cbP09LSVIzG/igGAymXj3Mh6SgXrpwgKf0fruekkpqbSYYhG71iQI+CFg0eWmc8tK74unhR3cOf6l41qeXfnLq1OuDuUT7dSPa8GZ0Q1lahk/3UqVOZNGmS2mHYlatXTvPbn6s4cSmGuIzz3DDkmHRfqiEHyISca5BxDlIOQ/waNPuhhrMXTX0b0qJ2J0IbPo6Lq5fF464Im9EJYU0aRVEK29rF5mk0mhInaAsb2QcHB5Oamoq3t3c5RGkfUm+cZddvCzmYdIC/sq8U+JoTWmo4exFcKYCaXrWo7BmAr2cgXu7V0Olc0Olc0OtzuJV9g8xb17mekcil9PMkZSSRcOsSV/UFVw+7aLS08qpHu5DHaN6kL87OHmWOP38zurv/IueP6aUrSlRkaWlp+Pj4lJjXKvTI3tXVFVdXV7XDsEmKwcDxk6vYcnI5R9LPor8jVTZ0rUrzas0IDe5Ag7pdcHatVOr3uXE9gVMJW/j9n938dv0vrhmy2J92hv2/zcTr9zl0rt6GR8NfoUrVhqV6vmxGJ4RpKnSyF/fS5+Vw8LcF/Hj6e87m3DC+Xt+1Cu1rPkzrps/i51ffYu/nW7kubSq/RJuWL6EYDCSc287uuJXsuxLLDUMOa5P2sG79Xh70DuHJ1q8SHBxh1vNlMzohTGNXyT4jI4MzZ84YP09ISCA2NhY/Pz9q1aqlYmS2TzEYOPjbAlbEfUNSXiYArhodHas0p3OzIdSu3d7qMWi0WurV7Uy9up0ZmJfD0WNL+fnU95y4lcy+tDPs2xrFg94h/Kv16wQHtzXpmbIZnRCmsaua/Y4dO3jkkUfueX3QoEEsXry4xPtNrW1VNHF/rmVZzGzic64B4KV1pltQe7q2eQ0v7xoqRwdnz+5kTcxsDqTFA6BFQ6cq99O3w/v4+NYp9t798Vfpv+BAie/x3xcflJG9qJBMzWt2lezLytGS/fVr8Szb+RZ7bpwCwE2j47GgDjzW7t1ya4c0x/nze/j+0CccSk8AwF3jxNN1etCt3TvonFwKvUdvUGg3/dcSz73dM66T1OxFhSTJvhCOkuwN+jy27pvG8vi13FLy0ACdqzQzaaRsC+L+XMuSmJnGOYV6LpX5d8RE6tR5uNDr87txoPBzb6UbR1RkkuwL4QjJPuXScb7c/jrHbyUDEOLixwsPjickpIvKkZnHoM/j1/0f8e2ZVdxU8tCioXdgO57qPL3Qdk3psxeOSpJ9ISpyslcMBnYe+oxFfy4nS9HjotHSv24vurV7F63OrubhC7h+LZ7F28ca6/n1XCoT3XE6NWq0vudaWUErHJEk+0JU1GR/8+YVFm4ayd7U0wDc51aNER1nEBDYQuXILOdAzJcsOLaADCUPF42WwQ360qntWDRa2aVbODZJ9oWoiMn+7NmdfLrrLS7pM9GioW9wJL0fmWrXo/miXL1ymrnbRnPsZiIA7X3vY1i3ubi5V1Y5MiHUI8m+EBUt2e86OIsFJ5eQoxiopnPn5bbv0KhBT7XDsiqDPo91O95m5flfMKBQ09mL1zvNJDColdqhCaEKSfaFqCjJXp+Xw9JNI/n58hEAwirV5OXuC/D0cpyJyLg/1zL74FSuG7Lx0DgxKnwMYU2fVTssIcqdJPtCVIRkn5mRzMyfhvLH/5cyngp6mH9FflIhyzYluXE9gU9+fpHT2VfQomFA3cfo2WGSRer4Mtkr7IUk+0LYe7K/dOkPpv8SxcW8dFw1OqKbj6B1i2Fqh6Wq3OxMFv78EjuunwAgskozhvZYWOQiLFNIG6ewJ6bmNWllsBPx8Vt4Z9MwLualU1nrysSHP3L4RA/g7FqJ4Y8v5fm6j6MBtl79g49WP8Wtm9dK9bz8BVp3b66WnJrFiGVH2XQ8yQJRC1H+JNnbgdjjy3l/95ukGXKo4+LLhz2XUq9uJ7XDshkarZaeHd9nTFg0zmj5LfMCk9Y8ReqNs2Y9p6TtkuH2dsl6g8P8MiwqEEn2Nm7XwVnMOPwRWYqe+z2CmPjE6lLv/V7RtW4xjAntp+CtdSEh5zoT1g/gcsoJk+83Z7tkIeyNJHsbtnn3B8yJW4QehXa+jRj35A82uYGZLWlQvxvvd5lLVZ07SXmZvPfzC1y4sN+ke2W7ZFGRSbK3Uet+fYuvz6wCoHu1cKIe/9YiR/g5gsCgVrzfYwk1nb24Zshi0q+vkHB2R4n3+Xu5mfR8U68TwpZIsrcxisHAd7+M5ttzGwF4IrA9g3rMd8jWyrKoUrUhEx9fQYiLH+mGXD7Y8Trx8VuKvad1XT8CfdwoqsFSw+2unNZ15bcrYX8k2dsQxWBg5S+jWHVxBwD9a3XlmW6fy/4vpeTlXYN3eq+goWtVMpU83t/9Jqf++qnI63VaDRN6hQLck/DzP5/QK1T67YVdkixiI/IT/Zqk3QA8X/dx+nSernJU9s/D05+3+nxHYzd/shQ9U/a+V2zC79Y0kLkDW1Ldu2CpJsDHTfbFF3ZNkr2NuDPRD6rXm54d31c5oorD3cOP8U98TxP3ALIUPVP3TuBM/C8l3FWwvbKsaw/1BoX98Vf5MfYi++OvSvumKHeygtYGrN76OisvbAVgcL0+dH94oroBVVBZt64zbW0/Tmal4KFx4p32U+851CV/UdXd/yjKcuqVrMgV1iQraO3E+u3vGBP9wDqPSaK3Ijf3yozrvYL73KpxU8ljyu7xXLiw1/h1ayyqkhW5wlZIslfRlj1TWHZ2AwD9giPp9chklSOq+Nw9/Hiz90rqu/qRoeQx+ddXSUqMASy/qEpW5ApbIsm+HN1Zt12+eQZf/fUdAH0C2/Fk5McqR+c43D38GN/rW2o7+3DDkMPkrdFcufKnxRdVyYpcYUukebuc3Fm3DfXYiXP1TaCBh7zu55lHZ6sdnsPx9Ark7ceWMGH9AJLyMpny8zAeD5tr0r2mLqqSFbnClpg9sh80aBC7du2yRiw2yRJdFHfWbeu7HcG1+iYUFHwyqvPl733ZHHfJCpGLkvj41uGd7l/hp3XjYl4Gv/wxito+uRZbVCUrcoUtMTvZp6amEhkZSYMGDZgyZQoXL160Rlw2YdPxJNpN/5X+Cw4wakUs/RccoN30X82aVLuzblvD5RQ+1degR8H3lh97U0ai4CR1WxVVrXofb3eeiZfWmfica7QN+g9OZFtkUZWsyBW2xOxkv3btWi5evMiIESNYuXIlderUoXv37vzwww/k5uZaI0ZVWKqLIr9uW9XpAjUClpKr1eOd7cnBS1HocZG6rQ2oWfNB3mz3IW4aHQmGFJ5r+g0B3gUrnEUtqiruNz9ZkStsSZn77I8ePcqiRYtYuHAhnp6eDBw4kJEjR9KgQQNLxWgxpvaj6g0K7ab/WuTkmobb//j3jOtU4j/UH2Mv8vZ327m/xkwynbLxzHXjj8RRZBgqF7hu1jNh9A6rYfb3JCzn9+MrmH54OnoUHqv+IPUbfVjssYSm9s9Ln72wJlPzWpkmaJOSktiyZQtbtmxBp9PRo0cPjh07RmhoKDNmzODVV18ty+NVY04XRduQKsU+q4qbnrCA/5DmlI273olTSS/dk+hB6ra2oHnTZxieeYk5cYvYcOkAz3vMoncRK5mLWnyV/5vfnb8FdGsaSJfQADnTVqjK7DJObm4uq1at4rHHHqN27dp8//33jB49msTERJYsWcLWrVv57rvveP99+13ub6kuCoM+j4PHR5PuloGzouWf5Oe4qg8qcI3UbW1LhzajeLZ2NwC+SVjH/ph591xTmv55nVZD25Aq9A6rQduQKpLoRbkze2QfGBiIwWCgf//+HDp0iLCwsHuueeSRR/D19bVAeOqwVBfF0k0jOZT+N+46LdcSHycxp1GBr0vd1jY93nEKV39KYfOVo8z5Yz5+XjVp1PAx49ct+ZufEOXF7JH9Z599RmJiInPmzCk00QP4+vqSkJBQ1thUY4kuik273mdjyiEAXm42jDH/GkGAj+ykaA80Wi2De8ynlWdtcjHw0f5JxlW2IP3zwj6ZPbJ/7rnnrBGHTcnvohix7CgaCu5/aMpo/EjsYpbErwFu70kf8UA0gNRt7YhW58Qrjy3m/dVPEZ9zjalbX2Fy7xV4+wRL/7wwmd6g2My/edn1shil6aL4O2EbE3eOJVvR08nvfl7qtUQOH7FjqTfO8s66Z0nR36SRW1XeffJHtM4etJv+K8mpWYXW7c3p1hIVV3l1YZma1yTZl8Ccn8xXr5zm7Z+e47ohm2YeQYx7cjVOzjK6s3f//HOAd7dGc1PJI8KnIS/3WcHmuEuMWHYUKPw3PynPOTZrbJVdFNni2EJM7aK4dfMa0ze9yHVDNjWdvXi152JJ9BVEzZoPMuaBcejQsDf1NN9vHWM80UrmYcTdbHW3U9kIzQIM+jxm/zSYc7mp+GhdePPReXh4+qsdlrCg+5s8zbDUBL48tZxVF3cQeGg23Vq/IvMw4h622q0lyd4Clm4aydGM8zij5Y2I96nm30TtkIQVdHpoLEmpCaxL3s+8E4vxrxxCowY9pb1SFGCr3VpSximjbXunGVssR94/jAb1u6kckbCm/o9+zgOedcjDwMf7JpFy6bjaIZWZnI9rWbbarSUj+zI4cXIVX51eCUDfmp15KHykyhEJa9PqnIh+bBETVj/B2ZwbzNgykvefXI2HR1W1QysV2bfH8vLX6ZTUrVXeq+ZlZF9KSYkxfHpwKnoUInwa8mTnj9QOSZQTN/fKjO06n8paVy7kpvH5hiEY9Hlqh2U2OR/XOmx1t1NJ9qWQkZ7E9G2vkKHk0cC1KiN6LpJeegdTpWpD3mg3GWe0HM28wLLN9vVbna12jFQUttitJWUcM+nzcpj501CS8jKponPj9e4LcHatpHZYQgUhIV0Yef0vZh2bz0+XDlFz3ww6PTRW7bBMYqsdIxWJre12KsneTEt+Hs6xW0m4anSM7TAd38p11Q5JqOih8JFcvPYXP1zczlen/kuAX0NC7+ujdlglstWOkYomf52OLbC72sOcOXOoU6cObm5utGnThkOHDpXbe2/ZM4XNV26vmny5+Ujq1Hm43N5b2K5/RX5CW+/65KHw6cEP7aJDx1Y7RoT12FWyX7lyJWPGjGHChAkcPXqU5s2b07VrV1JSUqz+3sfjvufrv74H4JngLjzQ4gWrv6ewDxqtlhE9vyLExY90Qy7Tt4zg5s0raodVLDkf1/HYVbL/9NNPefHFFxkyZAihoaHMmzcPDw8Pvv76a6u+b3LSb3x2aDoGFNr5NqJPp+lWfT9hf1zdfHit6zwqa135Jzedz38aatMdOrbaMeLIMtKt2/1kN8k+JyeHmJgYIiMjja9ptVoiIyPZv39/ofdkZ2eTlpZW4MNcNzNSmLH1ZTKUPOq7+jG8x9fSeSMKVaBDJ+M8//3lZbVDKpYtdoxUNKYuWEtKjGH06sf58dc3UQwGq8RiNxO0V65cQa/XU7169QKvV69enT///LPQe6ZOncqkSZPK9L4nzmwgKS8TP60br3eTzhtRvJCQLoy8dppZxxewLnk/wQc/o0Mb2z2L2dY6RioSUxesZWYkM2PbKNINuRxJPkIPfRbOWg+Lx2M3yb40xo8fz5gxY4yfp6WlERwcbNYzHggbyjidK96eAVT2C7F0iMJGleXQiYceiOLCtVOsTtzFl3FLqe7XkEYNetpEbIWxpY6RisLUA+nzW7kT8zJut3L3WIizs+UTPdhRsq9atSo6nY5Lly4VeP3SpUsEBAQUeo+rqyuurq5lfu+w+weU+RnCflhiC4GnIz/lnzVPcyg9gU/2TWKKbz2qVmtsE7EJ6yppwZqG2wvWuoQGsGxzFH/cTDS2cvv41rFaXHZTfHZxcaFVq1Zs27bN+JrBYGDbtm20bdtWxchERWKpLQS0OidG9viK2s4+pBpymLF5OLduXrOJ2IR1mbpg7dufJ7Ix5TAAUc3+bfVWbrtJ9gBjxoxhwYIFLFmyhJMnTzJixAgyMzMZMmSI2qGJCsDSWwi4e/gxtus8fLQunMtN5T8bXyh1h45sb2A/TFmI1sDtIBuSfwSgX3AkbVq+ZO2w7CvZ9+vXj48//pj33nuPsLAwYmNj2bRp0z2TtkKUhjlbCJiqarXGvPbQBJzQcig9ge+2lG6y1hqxidIrrsumpIVo/k5n8am+DgWFCJ8GPNFphrXDBeyoZp8vOjqa6OhotcMQFZC1thBo1KAn/752hjlxi1iTtJugg7Po0GaUTcQmzFfSvElxWxx7aFKpH7iITJ2BULcqDO9Zfq3cdjWyF8KarLmFQIc2o+gT2A6AL+OWcOqvn2wmNmE6U+ZNilqwpiWP8IC5ZDhlU9XJjbHdF+Li6lVusUuyF+L/WXsLgX5dZhpPufrEzFOuZHsD9Zkzb1LYgrWIqgtIdU/F08mJdzrNKPdWbkn2Qvw/a28hoNU5EdXz6/916GwZafIeOvayvUFFPuLQ3HmTbk0D2TOuE/998UHGtt5FVuULuDlrGd0yirp1OpZP0HeQZC/EHay9hYC7hx/jui0wnnI1a8Ng9Hk5NhFbWW06nkS76b/Sf8EBRq2Ipf+CA7Sb/muFaQktzbyJTqvB/dZmdqf+glaroX+trrRuMcxaIRZLoyhKxfnRW4K0tDR8fHxITU3F29tb7XCEDbP0KtW7xcdvYeLuceQoBrpXC2fwYwttJrbSKGrFaH5UtvDDqKz2x1+l/4IDJV733xcfNK5IvnBhL+9uG8UtJY+HfRszove3Fp+QNTWvycheiELkbyHQO6wGbUOqWDyZhoR0IbrZcAB+vnyETbvet5nYzOUoawDMnTdJvXGW6dtf55aSR2M3f1587CtVN1GUZC+EStq0fIlna3cDYEn8Go7+/o3KEZWOo6wBMGfeJCc7nRkbX+Cy/hZVcOf+ulM5cv6Wqj/wJNkLoaLHO07hEb+mGFCY9dsszp7dqXZIZnOkNQCmzJsY9Hl8sf55Tt26giEbjsY/y2trk1Wfw7C7RVVCVCQarZZhPRZyZdUTHLuVxPSd45js+Q1VqjZUOzSTOdoagJK2hV7+SzT7Uv8mL08hM/lJkvPqGe+9e9fL8iQjeyFU5uTsxquPLaKmsxfXDFlM2zSMmxnWP2rTUhxxDUBR8ya/7J7M+uQD5OoVuNyBv7LCC9yn5hyGJHshbEAlzwDefHQevloXzuem8emGQeTl2kfZw17WAFjbkdjFLDqzCoNBweN6KLEZ3Qu9Tq05DEn2QtiIav5NGNdhGm4aHcduJfHlhiFWO6LO0mx9DYC1/XVmE7NiZ2NAoblbI/ZfL/kMjPKew5CavRA2pF7dToxOf5UZMZ+w68ZJ/H55hf7dvijVs8q7H99RjzhMTvqN6XvfJUcxEFapJh3CZrHw2NES7yvvOQxJ9kLYmBbNBvJSZjLz/lzG2qQ9VN71Pt06vGfWM9Q60crRjji8cT2BD7dEkW7Ipa5LZUY/9g0ubpWL3PUSbpe2AlSYw5AyjhA26JG2r9O3ZmcAFsevZn/MPJPvlROtysfNm1eY8tNgUvQ3qa6rxPiei3D38LPZOQxJ9kLYqCc7f0SXKs1RgC/++JJjJ74v8R5HWc2qttzsTD5eN5Bzuan4aF1469E5Bc6PtcU5DCnjCGGjNFotQ3t+RdravhxM+5uPD0/jPTdfQkK6FHmPOatZHancYkn6vBxmrRvAiVvJuGl0vNlhGgEBYfdcZ2tzGJLshSgHpZ0s1eqcePmxpdxc8y+O3Upi6p63mODiSXBw20Kvd6TVrGow6POYv2EwhzPO4oyWN1q/Sb26nYq83pbmMCTZC2FlZZ0sdXatxGuPf8sHa/5FfM41Pvx1NJO6LaB69Wb3XOtoq1nLk2IwsHTTSHZcj0OLhlEtomka+rTaYZlMavZCWJGlJkvdPfwY32spwc7eXDdkM3nzcK5eOX3PdY64mrW8rPxlFBtTDgEwIvR5HggbqnJE5pFkL4SVWHqy1Mu7Bm/3WESAUyVS9DeZ/PNQblxPKHCNrXaC2LvVW19nTdJuAIaEPEmHNq+qHJH5JNkLYSXW2Pq3sl8I73SdTxWdG4l5GXzw0/Ok3jhb4Bpb7ASxZ+u3v8PKC1sBGFinp9lrHmyF1OyFsBJrTZZW82/Ce4/OY9Lm4fyTm84HG57n3V7L8PGpZbzG1jpB7NX67e+w7OwGAPrW7EyvRz5UOaLSk5G9EGVQ3AHb1pwsDQgI470uc4xn2U5eP/CeEb6tnWhlb9Zvf9uY6J8KepgnO3+kckRlIyN7IUqppC6b/MlSay2bDwxqxXtd5jBpy0jO56Yxaf1zvNtzMZX9Qkr3DQmjdb++xbfnNgLwrxqP8PSjn6kcUdnJyF6IUjCly6Y8JkuDgsKZ2GUuflo3LualM/Gn57ly5c9SP8/RKQYD320eVeESPUiyF8Js5nTZmDNZWlxJqDiBQa2Y1G0h1XTuJOdlMnHjEJISY0r53TkuxWDgm5+Hsyrx9tGQ/Wt1rTCJHkCjKIrDbJKRlpaGj48PqampeHt7qx2OsFP746/Sf8GBEq/774sPGldPlrSC1hK7VF65fJLJm4aRlJeJt9aF8R2mFbu6U/xPXm4WC356gR3XTwC32yvtpevG1LwmI3shzFSaLpviJksttfCqarXGTOr1LXVdKpNmyGHSzjc4cXKVSfc6sqxb1/l4zdPsuH4CLRpGNH7ObhK9OSTZC2EmS3bZlFQSUjBv4ZWPbx3ee+J7Grv5k6XomXJgMrsOzjLpXkeUlnqBD9b8i98yL+CMltdajKLjg6+pHZZVSLIXwkyW3JKgpIVXYP7CKw+Pqrz95Boe9A4hD4U5cYtYvfV1uznisLz8888B3v6xH2eyr+KpceLddh8QHjZY7bCsRpK9EGayZJdNcpppJSFTr8vn7FqJUX1W0ivgQQBWXtjKnB+fJTc706znVFR/nFjJu1ujSdHfxF/nwaTIL2jUoKfaYVmVJHshSsFSWxJcy8i26HV30uqcGNh9HkPrP4UWDbtv/MnEVb24du2M2c+qKBSDgY07JzD10DRuKnnc51aNyb1XULPmg2qHZnWyqEqIUrLElgR+lVwset2d8juAsryG0rduTdafncOZ7Gu8tX4Aox98m/saPW72M+1ZdlYq839+iT03TgHQ3vc+/t3jK5xdK6kcWfmQZC9EGZT1cIoAH3eLXpfv3lZOf0J9/03d6ou5Yshk0r4J9L94gF4dJ6PRVvxf8BMTjzDz19c4l5uKFg3P1Xuc7u0nOMT3ns9xvlMhbFD+ZG9xzN1/vqhWzpM3gvj19Ms0ca6DAYVvz23ko1VPkJp6vlSx2wPFYGDHgU8Y/8u/jefFvtt2Aj0enuRQiR4k2QuhqvzJ3uI6e8zZUqGkVs4sxYuN56N4oUFfnNASk3GON9b+i9/+WFbK78B2ZaQn8fnaZ5h7cilZip6m7gFM67Wc0Pv6qB2aKiTZC6Gy/Mneu0f4gaXYf96UPfST03LwCvw3Hz7yGTWdvUg15DAt5mMWrHuemxkppf02bMqR2MW8tro3e1NPo0XDM8FdePvpDfj51Vc7NNVIzV4IG2DOZG9xWy+Ys7q3bdjDTAvcxPKtr7Ix5RBbr/5BzKpeDG32Iq1bDLPo92dNd/55eGsvcezMNPam3j6ysYaTJyPavkOD+t1UjlJ9kuyFsBGmTPaWtIeOuat7nV0rMajnfML/XM2Cw5+QlJfJJ7Ff0OrMep6LeI/AoFal/4bKQf6fx6XUDB7wWU1e5VjytAquOi29g9rSt9PHDtNtUxIp4whhJ0zZQ6e0q3ub3PckM/pu4onA9ujQEJNxjtc3D2PJTy+RkW7a3jyWVtIuoLf/PI5QNW8jDwZ/wC2/o+RqDHhlu3P5fD+qBL8nif4OsuulEHZAb1BoN/3XIuvx+Qeh7BnXiS1xyYxYdhSgwERt/g+AkuYB/vnnAMv2fchvmRcAcNPo6BEYQc+HxuPpVT7n15b0G0xenp4Bn03C2XULaa4ZALgYdHA9nJi0XoCT8c+jop/QZWpek2QvhB0wd1tlS2yZHHt8Octjv+RcbipwO+l3qtaKbuHRVK/erHTfiAnyf4O5OzFpAB05vN3uFL9f2cjf2dcB0CkaPNIaEnvjCTINvgXuuXOb6YrK1LxmNzX7Dz/8kJ9++onY2FhcXFy4ceOG2iEJUW7M3VbZEqt7w5o+S7PGfTnyx2J+OLGUc7mpbEw5xM8bn6elZ20eDulJyyYDLFoqKap1NMDpb+r7bCfbM4G1iXqcdRp0ioZKGXU4cf1xrusL/wFm7mHu5sRpb4e5202yz8nJ4emnn6Zt27Z89dVXaocjRLkqzbbKZV3dC7f312ndYhjhzQbzR9xKfj75X2Iz/yEm4xwxv/8Hjz/m09q3ES2CO9Cs0RN4ePqX6f3yW0c15FHD5QzBlQ6jrfQ36c63SPv/a1z1TrSsFM7y+A6kG4pfbFaaw9xLYonfmtRgd2WcxYsXM3r06FKN7KWMI+xVfs2+pMPLy6NGnZh4hO2/f82elKNcM/wv4WnRUM/VjwbedalX7X5qVQ/D378JHh5Vi32eQZ/Htetn+Ccphl1/HuLIxT+46XqdHK3eeI0G8M6qzI20cOIy2/Nx3weYsfmUVf48ihu1F1digpLnQ6yhwpVxSiM7O5vs7P/tFpiWllbM1ULYrvyVtiOWHUVD4ROvZT283FRBQeEMCAqnvz6Pk3+t40j8JmKvxZGYl8GZ7KucuXwVLh+BuNvXe2md8da64q5zwV3rigEFvaIn25BHat4tUg3Z6P//OzIYFLLdb++7r1M0eGX5kpkZyunMh0g3/O+3lAAfd6v8eRQ3au8SGlDs6mQNtw+a6RIaYJMlnQqd7KdOncqkSZPUDkMIi8hfaXt3MgpQqYSg1TnR5L4naXLfkwwCUi4d5/S57cRf/oMzqQkk5aaSbsg1fpBX9LN0aAh09iTYvTp/nHPnXFpDzmU1IQ/XAtflj9jzR9uW/PMoatSe39o6OrJBiauT8w+ascVJYVXLOG+++SbTp08v9pqTJ09y3333GT83p4xT2Mg+ODhYyjjCrtnT5ODNm1e4fPkkmbeucjPrBrdy0tBpdGi0Tly8oSdH8aN6lXq0a3I/ri63d/bMT7pgWuuoJf48TGlt9XF35sat3BKfNeuZMHqH1TDr/cvCLso4r732GoMHDy72mnr16pX6+a6urri6upZ8oRB2xBITr+XFw6MqtWu3L/DavaWSywT67DOOxs39DcYSfx6m7ClkSqIH60wKW4Kqyb5atWpUq1ZNzRCEEOWopFJJ/qjdEq2j5jC1RdPX3ZnUW7nFTgqbsx11ebKbmv358+e5du0a58+fR6/XExsbC0D9+vXx9PRUNzghRIlK2n757gnO8vwNxtTR+JCIuszcelr1SfLSsJtk/95777FkyRLj5y1atABg+/btdOzYUaWohBCmMqVUcucEZ3nOTeTvKVRSK2d0p/o0CvC0mUlyc9hdn31ZSJ+9EOr5MfYio1bElnjdrGfCcHXSlvvCJXMmhm1pktzUvCa7XgohyoWppZKzV26WuLunNeRPDAfcdYhMQCGHyOSXmHqH1aBtSBWbLd3cSUb2QohyYcoq4OreroCG5LSSd/e0VoK1pVG7KWRkL4SwKfmrgIF79tvP/7x/61pFJnooWNe3FnsctZtCkr0QotyUVCqpU9W0HTSttZtlRWY33ThCiIqhuB76/fFXTXqGrS5csmWS7IUQ5a6oHnpTWyBtdeGSLZMyjhDCZphS17flhUu2TJK9EMKmmNMCKUwnZRwhhM0p771xHIEkeyGETbKn3T3tgZRxhBDCAUiyF0IIByDJXgghHIAkeyGEcAAyQSuEsBh720TMkUiyF0JYxL1ny1p/D3phOinjCCHKLP/gj/Leg16YTpK9EKJMSjpbFm6fLas3KAXu2R9/lR9jL7I//mqBrwnrkDKOEKJMzD1bVso96pCRvRCiTEzdWz4lPUvKPSqSZC+EKBNT95avWsnV7HKPsBxJ9kKIMsnfg76oBksNt8s0aDC53CMsT5K9EKJMTN2D/kpGtknPkyMHrUOSvRCizEzZg97Uco8cOWgd0o0jhLCIkvaglyMH1SXJXghhMcXtQZ9f7hmx7CgaKJDw5chB65MyjhCi3MiRg+qRkb0QolzJkYPqkGQvhCh3cuRg+ZMyjhBCOABJ9kII4QAk2QshhANwqJq9otxu9kpLS1M5EiGEsIz8fJaf34riUMk+PT0dgODgYJUjEUIIy0pPT8fHx6fIr2uUkn4cVCAGg4HExES8vLzQaExv80pLSyM4OJgLFy7g7e1txQgtR2K2PnuLFyTm8lKeMSuKQnp6OkFBQWi1RVfmHWpkr9VqqVmzZqnv9/b2tpu/bPkkZuuzt3hBYi4v5RVzcSP6fDJBK4QQDkCSvRBCOABJ9iZwdXVlwoQJuLq6qh2KySRm67O3eEFiLi+2GLNDTdAKIYSjkpG9EEI4AEn2QgjhACTZCyGEA5BkL4QQDkCSfQnmzJlDnTp1cHNzo02bNhw6dEjtkIq1a9cuevXqRVBQEBqNhrVr16odUrGmTp3KAw88gJeXF/7+/vTp04dTp06pHVax5s6dS7NmzYwLZtq2bcvPP/+sdlhmmTZtGhqNhtGjR6sdSpEmTpyIRqMp8HHfffepHVaJLl68yMCBA6lSpQru7u7cf//9HDlyRO2wJNkXZ+XKlYwZM4YJEyZw9OhRmjdvTteuXUlJSVE7tCJlZmbSvHlz5syZo3YoJtm5cydRUVEcOHCALVu2kJuby6OPPkpmZqbaoRWpZs2aTJs2jZiYGI4cOUKnTp3o3bs3J06cUDs0kxw+fJgvv/ySZs2aqR1KiZo0aUJSUpLxY8+ePWqHVKzr168TERGBs7MzP//8M3FxcXzyySdUrlxZ7dBAEUVq3bq1EhUVZfxcr9crQUFBytSpU1WMynSAsmbNGrXDMEtKSooCKDt37lQ7FLNUrlxZWbhwodphlCg9PV1p0KCBsmXLFuXhhx9WRo0apXZIRZowYYLSvHlztcMwy7hx45R27dqpHUahZGRfhJycHGJiYoiMjDS+ptVqiYyMZP/+/SpGVrGlpqYC4Ofnp3IkptHr9axYsYLMzEzatm2rdjglioqKomfPngX+Xtuyv/76i6CgIOrVq8eAAQM4f/682iEVa926dYSHh/P000/j7+9PixYtWLBggdphAVLGKdKVK1fQ6/VUr169wOvVq1cnOTlZpagqNoPBwOjRo4mIiKBp06Zqh1OsY8eO4enpiaurK8OHD2fNmjWEhoaqHVaxVqxYwdGjR5k6daraoZikTZs2LF68mE2bNjF37lwSEhJo3769catyW/T3338zd+5cGjRowObNmxkxYgSvvPIKS5YsUTs0x9r1Uti2qKgojh8/bvN1WYBGjRoRGxtLamoqP/zwA4MGDWLnzp02m/AvXLjAqFGj2LJlC25ubmqHY5Lu3bsb/7tZs2a0adOG2rVr89133/HCCy+oGFnRDAYD4eHhTJkyBYAWLVpw/Phx5s2bx6BBg1SNTUb2RahatSo6nY5Lly4VeP3SpUsEBASoFFXFFR0dzYYNG9i+fXuZtqEuLy4uLtSvX59WrVoxdepUmjdvzqxZs9QOq0gxMTGkpKTQsmVLnJyccHJyYufOncyePRsnJyf0er3aIZbI19eXhg0bcubMGbVDKVJgYOA9P/AbN25sE+UnSfZFcHFxoVWrVmzbts34msFgYNu2bXZRm7UXiqIQHR3NmjVr+PXXX6lbt67aIZWKwWAgOztb7TCK1LlzZ44dO0ZsbKzxIzw8nAEDBhAbG4tOp1M7xBJlZGQQHx9PYGCg2qEUKSIi4p7W4dOnT1O7dm2VIvofKeMUY8yYMQwaNIjw8HBat27NzJkzyczMZMiQIWqHVqSMjIwCI5+EhARiY2Px8/OjVq1aKkZWuKioKJYvX86PP/6Il5eXcT7Ex8cHd3d3laMr3Pjx4+nevTu1atUiPT2d5cuXs2PHDjZv3qx2aEXy8vK6Zx6kUqVKVKlSxWbnR15//XV69epF7dq1SUxMZMKECeh0Ovr37692aEV69dVXeeihh5gyZQp9+/bl0KFDzJ8/n/nz56sdmrReluTzzz9XatWqpbi4uCitW7dWDhw4oHZIxdq+fbsC3PMxaNAgtUMrVGGxAsqiRYvUDq1IQ4cOVWrXrq24uLgo1apVUzp37qz88ssvaodlNltvvezXr58SGBiouLi4KDVq1FD69eunnDlzRu2wSrR+/XqladOmiqurq3Lfffcp8+fPVzskRVEURbY4FkIIByA1eyGEcACS7IUQwgFIshdCCAcgyV4IIRyAJHshhHAAkuyFEMIBSLIXQggHIMleCCEcgCR7IYRwAJLshRDCAUiyF8JCLl++TEBAgHEvc4B9+/bh4uJSYPdUIdQge+MIYUEbN26kT58+7Nu3j0aNGhEWFkbv3r359NNP1Q5NODhJ9kJYWFRUFFu3biU8PJxjx45x+PBhXF1d1Q5LODhJ9kJY2K1bt2jatCkXLlwgJiaG+++/X+2QhJCavRCWFh8fT2JiIgaDgbNnz6odjhCAjOyFsKicnBxat25NWFgYjRo1YubMmRw7dgx/f3+1QxMOTpK9EBb0xhtv8MMPP/D777/j6enJww8/jI+PDxs2bFA7NOHgpIwjhIXs2LGDmTNnsnTpUry9vdFqtSxdupTdu3czd+5ctcMTDk5G9kII4QBkZC+EEA5Akr0QQjgASfZCCOEAJNkLIYQDkGQvhBAOQJK9EEI4AEn2QgjhACTZCyGEA5BkL4QQDkCSvRBCOABJ9kII4QAk2QshhAP4P1Rlo9/SItZCAAAAAElFTkSuQmCC", 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", 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" ] @@ -1489,7 +1314,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:07<00:00, 14.26it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 22.58it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1499,12 +1324,12 @@ "text": [ "\n", "\u001b[1mRunning Cycle 4:\u001b[0m\n", - "\u001b[1mCycle 4 model: (sin(x) / 0.97)\u001b[0m\n" + "\u001b[1mCycle 4 model: ((-2.68 + x) / -2.68)\u001b[0m\n" ] }, { "data": { - "image/png": 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1+zvpdDomTpxIVFQUEREReo+Ljo7G19e36CssLMyCUQohhO2wy2Q/btw4jh8/Xu5Kd9OnTyclJaXo6/LlyxaKUAghbIvdlXHGjx/Pxo0b2blzJ7Vq1SrzWFdX16IJEUII4cjsJtkrisKLL77I2rVr2bFjB3Xr1rV2SEIIYTfsJtmPGzeOr7/+mnXr1uHt7V207Kivr2+J5UKFEEIUZzc1+6VLl5KSksIDDzxAaGho0deaNWusHZoQVUrhzlK24IEHHmDixIlmfY2KbsL++uuv89xzzxl8vKU2FtfHbpK9oiilftnTps3CsSQkJDBhwgQaNGiAm5sb1atXJyoqiqVLl5p13XJzmjVrlt4NtAu/KmLHjh2oVCqSk5NNG7ABDhw4YFTShoLf7aJFi5gxY4bBz7HYxuJ62E2yF8Ke/P3337Ru3ZpffvmFOXPm8Oeff7J3715effVVNm7cyLZt2/Q+986FtmzNK6+8Qnx8fNFXrVq1ePPNN4s9did9+7fakqCgIDw8PIx6zqeffkqnTp0IDw83+DkuLi4MGTKExYsXGxuiSUiyF3ZFURSy87Ot8mXMyiIvvPACTk5OHDx4kMcff5ymTZtSr149Hn74YTZt2lTsdl6lUrF06VIeeughPD09i/ZlLdyK0MXFhcaNGxfb4ejChQuoVCqOHDlS9FhycjIqlapoP9nC1vL27dtp164dHh4edOrUqcSaUnPnzqV69ep4e3vzzDPP6N3CEMDLy6vEBtre3t5F3z/xxBOMHz+eiRMnEhgYSK9evcqN9cKFC3Tt2hUo2EBcpVIVu2PX6XS8+uqr+Pv7ExISwqxZswz+PUDB38ysWbOoXbs2rq6u1KhRo9jKlHeXcVQqFZ9++imPPPIIHh4eNGzYkPXr1xc75+rVq4v9Dm/cuEFISEixlUj37NmDi4tLse0XLbGxuD5200ErBECONocRW0ZY5bVX9l6JmwFbE966dauoRa9v3aa7yx2zZs1i7ty5LFy4ECcnJ9auXcuECRNYuHAhPXr0YOPGjTz99NPUqlWrKDEaasaMGbz33nsEBQUxZswYRo0aVbRRyrfffsusWbNYsmQJnTt3ZtWqVSxevLjUnZYMtXLlSsaOHVtsM5ayhIWF8f333/Poo49y+vRpfHx8ig26KNz6b//+/ezdu5eRI0cSFRVVtEFIeZumf//993zwwQesXr2ae+65h4SEBI4ePVpmTLNnz2b+/Pm8++67fPjhhwwdOpSLFy/i7+9PUlISsbGxtGvXruj4oKAgPv/8cwYMGMCDDz5I48aNGT58OOPHj6d79+5Fx1liY3F9JNkLYWLnzp1DURQaN25c7PHAwMCiVvO4ceOYN29e0c+GDBnC008/XfT9k08+yciRI3nhhReAgj1f9+3bx4IFC4xO9u+88w73338/ANOmTaNfv35kZ2fj5ubGwoULeeaZZ4o2SXn77bfZtm1bma378jRs2JD58+cXfX/hwoUyj9doNEXLngQHB5dY871FixbMnDmz6NwfffQR27dvL0r2oaGhZa7GeenSJUJCQujRowfOzs7Url2b9u3blxnTyJEjefLJJwGYM2cOixcvJiYmht69e3Pp0iUURSmxT0Dfvn0ZPXo0Q4cOpV27dnh6epbY49cSG4vrI8le2BVXjSsre6+02mtXRkxMDDqdjqFDhxZbswko1kqEgg3G7+40jIqKYtGiRUa/bosWLYr+PzQ0FIDExERq167NyZMnGTNmTLHjIyMj+e2334x+nUL69litqDvjh5KblZe3afqgQYNYuHAh9erVo3fv3vTt25f+/fuX2MtW32t6enri4+NT9JqFJZjSVmJdsGABERER/Pe//+XQoUOlTuo098bi+kiyF3ZFpVIZVEqxpgYNGqBSqUrUxgtLI6XNCzF0me5ChZt73NmPoK9j98613AvLR+Zcl/7uazEm1tKUt1l5ecLCwjh9+jTbtm1j69atvPDCC7z77rv8/vvvete5L+s1AwMDAbh9+3aJDcLPnz/PtWvX0Ol0XLhwgebNm5c4t7k3FtdHOmiFMLGAgAB69uzJRx99REZGRoXO0bRp0xI17z/++KNo8+vCZHHn6Jc7O0CNeZ39+/cXe+zuPV4ry5BYCzdE12q1Jn3tQu7u7vTv35/FixezY8cO9u7dy7Fjxyp0rvr16+Pj41Nig/Tc3FyGDRvG4MGDeeutt3j22WeL3YGAZTYW10da9kKYwf/93/8RFRVFu3btmDVrFi1atECtVnPgwAFOnTpVbqljypQpPP7447Ru3ZoePXqwYcMGfvjhh6Ihm+7u7nTs2JG5c+dSt25dEhMTee2114yOc8KECYwcOZJ27doRFRXFf/7zH06cOFGpDtq7GRJreHg4KpWKjRs30rdvX9zd3fHy8jLo/NOnT+fq1at8+eWXpf58xYoVaLVaOnTogIeHB1999RXu7u5GDZu8k1qtpkePHuzevZsBAwYUPT5jxgxSUlJYvHgxXl5ebN68mVGjRrFx48aiYyyxsbjeuC3+ikI4gPr16/Pnn3/So0cPpk+fTsuWLWnXrh0ffvghr7zyCm+99VaZzx8wYACLFi1iwYIF3HPPPXzyySd88cUXxUZwfP755+Tn59O2bVsmTpzI22+/bXScgwcP5vXXX+fVV1+lbdu2XLx4kbFjxxp9nvKUF2vNmjWZPXs206ZNo3r16kWbjBuivE3T/fz8WL58OVFRUbRo0YJt27axYcMGAgICKnw9zz77LKtXry4q7ezYsYOFCxeyatUqfHx8UKvVrFq1il27drF06dKi51liY3F97GZbQlOQbQnti2xLKGyVoih06NCBSZMmFY3aKY8hG4vr41AbjgshhK1QqVQsW7aM/Px8g59jqY3F9ZGavRBCVECrVq2MWjCuR48e5gvGANKyF0IIByDJXgghHIAke2HzHGgMgRClMsW/AUn2wmYVzmK017XfhTCVwn8D+mb8GkI6aIXN0mg0+Pn5Fc1C9PDwqPDmGELYI0VRyMzMJDExET8/PzQaTYXPJcle2LSQkBCAEtPOhXAkfn5+Rf8WKkqSfRWn1SnExCWRmJZNsLcb7ev6o1HbT+tYpVIRGhpKcHCwTe/gJIS5ODs7V6pFX0iSfRW25Xg8szfEEp/yz9rkob5uzOzfjN4RoVaMzHgajcYkf/BCOCrpoK2ithyPZ+xXh4sleoCElGzGfnWYLcfj9TzTcrQ6hb3nb7HuyFX2nr+FViejboQwF2nZV0FancLsDbGUljoVQAXM3hBLz2YhVivpVKW7DiHsgbTsq6CYuKQSLfo7KUB8SjYxcUmWC+oO9nDXIURVI8m+CkpMM2z/0DuPs1RJpby7Dii465CSjhCmJWWcKijY27DlgAuPs2RJxZi7jsj6FV9vXAhRnLTsq6D2df0J9XVDXzVeRUEyb1/X3+IllYrcdQghKk+SfRWkUauY2b9gr9K7E37h94U/N1dJRV9ZyNi7DiGEaUgZp4rqHRHK0mFtSpRnQu4oz+w9f8ssJZWyykI9m4UQ6utGQkp2qR8yqv/F2L6uv8GvJ4QonyT7Kqx3RCg9m4XonUFrjpJKYVno7kReWBZaOqwNM/s3Y+xXh1FBsePuvOuwp1m+QtgDKeNUcRq1isj6ATzcqiaR9QOKJdFAT1eDzmHocYaOtOnZLISlw9oQ4lu8VBPi68bSYW1knL0QZiAte0dmaOPZwOOMGWlT3l2HEMK0JNk7sJvpOSY9ztiyUOFdhxDC/KSM48BMPTJGRtoIYbukZW8i9riUcOF4fGNGxpR1nRU5nyHs8b0VwtZIsjcBe13Uq3A8vqEjY8q7TmPPZwh7fW+FsDVSxqkke1/Uq3A8fnkjYwy9TkPPZwh7f2+FsCUqxRTbltuJ1NRUfH19SUlJwcfHp9Ln0+oUOs/7Ve8IlMKyxe6p3Wy+7FBWqaQi11nZ0ktVem+FMCdD85qUcSqhKi3qVdbImIpcZ2VH2lSl91YIWyBlnEpwlEW9rHGdjvLeCmEpkuwrwVGGGlrjOh3lvRXCUiTZV4IxSwnbM2tcp6O8t0JYiiT7SjB0KWF770C0xnU6ynsrhKVIsq8kUw41tGXWuE5HeW+FsAS7Gnq5c+dO3n33XQ4dOkR8fDxr165lwIABBj/f1EMv7+QoszytcZ2O8t4KURFVcuhlRkYGLVu2ZNSoUQwcONDa4RRjyFBDu0xaeVmQFAdJf0PKZTQZN4nMvAU5aaDLhwP5oNaAixe4eIJHIPjUKPgKaAC+YaCu3A2kLJgmROXZVbLv06cPffr0sXYYFWI30/7zc+Han3DtcMF/b56Bytz8uXpDUBOo1Q5qdwS/cFDZ+AecEFWQXSV7Y+Xk5JCT88/yvKmpqVaJw5Ddm6ya8HU6uBID53+FC39AbjoACgqJKFx28+Cypx/xrm7cVqtIQUe6Lh8tClp0qFHhoXLCQ6XBT1FRXaulek42tVNvUDcnFfcrB+DKAdi3tKDF36A7NOwFfmEmvQy7vHMSwkKqdLKPjo5m9uzZVo2hvN2bVPyze5M5ElOZCTDjFpzeBCc3QHoiALfQ8aeHOye8A4hV5ZGMAprCP5Psf1Y3KxaqQgp5oOQVfKsBPNTgHoxKm0tNlSsRufm0Tr1Bs9SruBxeBYdXQUgERDwKde8vKAVVgt3cOQlhJXbVQXsnlUpVbgdtaS37sLAws3TQ6rP3/C2eXL6v3OO+Gd3R5HVpfQlwTo8AumZvh9M/gTaPFHTsdHNiv3c1zqrywcm9KJk7qZ2o6VWTMO8wannVoppbNfxc/fB28Uaj0qBRa9DqtGTlZ5GRl8HtnNtcz7hOfEY8cSlx3Mq+9U9Aig6XvBza5qvonHKLljo1zqjAOwQiHoNmD4GTYVsg3n2dpd05FX4eWf3OSQgzqpIdtMZydXXF1dX45GFK1pr2X1oCDCCFQek/4L/hMMkBblx2V7PVx4eDLmq0Lp7/q6U706haI1oGtaRZQDMa+jXEWeNc4TiSs5M5ffs0R28c5c/EP0lSJbHXBfa6Vcc7N5vuGRk8mHaNgL0fwV+rofVwaNIPDHxNa985CWEvqnSytwXWmPZ/dwJ0I4eBmt08otmFM7kcc4d5zhq0gdXAxQOABn4N6FKrC+1D2uPvZrpZqX5ufnQI7UCH0A4oikJcShy7ru5iz7U9JKuT+dHVk/U5qXTMymZg5nXCdn8Af30LncZDeCcAcvN1rNp7gYtJmYT7ezA8sg4uTgUjfMy1YJrU/0VVY1fJPj09nXPnzhV9HxcXx5EjR/D396d27dpWjEy/wmn/ZSUkU0/7vzMBdlTH8rxmA/6qFE64ww8+bvyt8SQbFxqpvHioTk+61+5OuE+4yV5fH5VKRT2/etTzq8ewpsM4nHiYn+J+4sStE+xx9WGPWwodMzN5LO0SYVumQ+2OfJTdm/djMtHd0XR/Z/NJRnepy/S+zYy+czIkiVur/i8fMMKc7CrZHzx4kK5duxZ9P3nyZABGjBjBihUrrBRV2TRqFQ+1DOWTnXF6j3moZahJ/1EnpmUTQApjnDbQQX2SOBeFL32dOe7iSYbijqJzIz+1JU/eO5TBEY1M9rrG0Kg13BtyL/eG3MuFlAusPbeWffH72OfmQ0xmEt3SM7jv+G9Epm3nIVVP1imdUP434VunUPR+PtC4ukGvF+ztZlASt9bIKelgFuZmtx20FWHOGbT6lLcJBxT8ozblJhyxu9aS8st8dJpMfvKFHR6e3Fa80Sku5Ke0Ji+lNSguZukUroxLqZf475n/EpMQg5KfS1rCVR5KzSYyXcU5pTYL8x/lihJUdLxaBSdm96bbezvK3ff29X7NGPd12Z24PZuFWGXDFOlgFpVhaF6TtXHMrLyaMvxTU6607FTYNpvGsR9wyieL6BBnNroHkqT4kpfWnKzLT5GX3AGV4mKTK0bW9qnNy+1eZmbkTHTaGlzEn5W+fiwJVuHtcomFzkt4UH2AwvGfOgW+3n+x3AXTXu/XlLc26e/EhYJO3H1/3zK4/m8q5XUwF8am1TlMm0yYiSR7M7PYaJzEU/DDcySe38Yc5yzWhlYjjgCyc2qSfW0Qube6gs7DLlaMbBbQjKaa58m91ZUUrR/7nYJYEOzC7z45jHFayzSn1XiSBcDFpMxyF0yr5ulqUBLfe/6W3mPuZMqRU8Z0MAtRGXZVs7dHZh+NoyhwYi3KviX8rmTyhbuObO8a+Lr6MKx2H9b9EURCbm7R4SF2UgeuE+BFfloE2sy6aP13ctXzHN97p3HWLY1BScf4QHWNd/KHEu7fFChYIbNns5BSOzjXHblq4Ksa1no25cgp2ZFLWIokezMrHI1TXk25QiWV/FzYtYDMM1v41CmLPzxcwasWTQKbMbblWEI8Q3i1s32O8BgeWYd3Np9Ep/Uk90YftBnnuB34K/udXLkafJsBKbdYkLGUVsHhQD1A/4JphibnyHqBfH/4qnl+V3rIjlzCUqSMY2Zm24Qj4yZsmMCFs5uZ5pzBH97VUPuE8UTTIcyMnEmIZ0jR60fWD+DhVjWJrB9gF4kewMVJzegudYu+12Y2IPvqEDKz6nOBIL7xc2Z7dS3aHW/C/mUF6/voYeiuVx3rB1h8wxTZkUtYiiR7CzD5Jhw3z8Ha59l58wivu2Rz3a8GQQGNmNVpFo80fAS1qmr8Wqf3bcbz99WlMLcqWi9yrj9ETnIUeV61+DPQnxnOGcQf+RK2zYS80ksdhR+4+oo0Cv8kcUtvmCI7cglLkaGXFmSSSTOXY9BufYNVuiR+clGBb01ahdzLi61fxMvFyzyBW1lpM2jPpZxi8eHF3E65iEfadSbkudEqsDn0jgaPkq3gLcfjGfPVYb2v8fFdidzSE5xknL2oKEPzmiR7e3JqMxk757FQk8Ffri7gU5NHGw/isUaPVZnWvDGSs5N579B7nEk8ijr1KkNznennWRdVvwXgW7PouPLmOpQ2fr6sJRrMRWbQioqQZF8Ku072R77h+v7/Y55zBlfdvHD1C2d86xdpH9re2pFZVZ42j0+Pf8qOC1sh5TI9clWMcg5F0/ddCCqYHWzsyqPRm2NZviuu2BINahVFSzQIYUtkUlVVoSiw/xPOxyzhNed0rnr6US2oGbOiZjt8ogdw1jgzpsUYnmr+DCq/cLa5qng3/ypZG16E+KOAccMbozfH8snO4oke/lmiIXpzrKkvQQiLkGRvy3Q62P0+R46u5E3nDFK9AqkTei/vdH6Her71rB2dzVCpVPSr14/J976Ks399/nR1YTY3Sdn8Mlw5aPCwRX93F5bv0r+GEcDyXXHk5usf+SOErZJkb6t0Otj5LjtPfcd85yyyvarTvPb9zOo0iwB321nPxpa0D23PzE6z8QlqSpyrGzNVSdzYMoX26pMGDW88dT2tRIv+bjoFVu29YOLIhTA/Sfa2SKeD3+fy89kfWeKUjdY7hM4N+jG1/VTcndytHZ1Na1itIW9GvU1g9RbEu3nyhjqFa9teY2HHDKDs4Y2Xb2ca9BoXkww7TghbIsne1uh0sCOa9efW87lTNviE0qfJYMa1GoezuuI7RjmSUK9Q3ox6m1o17iXJzYvZmhSCz73H6l7aMsfPh/t7GHR+Q48TwpbIaBxbotOh/D6f/55fy/eaXPAJ5ZF7nmJw48GoVDIEz1hpuWlE75/D+ct/4JmdxgytD3V6zCFG16TU4Y25+TqavP5TmaUctQpOvdXH7MMwhTCUjMaxN4qCsvsD1tyR6J9s+RxPNHlCEn0Febt481rH12lU+z4y3Lx40ymVc9tnEOl6odTlI+5eoqE0o7vUlUQv7JL81doCRUHZu4Q1Z75lrSYHvEN4qvU4BjQYYO3I7J6Hswf/7jiDpuHdyHb1ZI4mhdM/T4GEY6Uef/cSDYXUKnj+PhlnL+yXJHsT0eoU9p6/xbojV9l7/pZxm038+RVrYlcVJfoRbV6kX71+5gvWRlTqPTOCu5M70zv+m3vCe5Dt4kG0+jbnfpoMN86Uenzr2tUI8nIp9liQlwuta1ercAyWulYh9JGavQlUal2T4z/ww775rNFkg2cwI++dRJ+6fUwWm62yxlow2fnZzN33DicvbMcjL4vX1CHUf3gZ+IUVi8vUWwTKujfCnKRmbyGFyeHudVcKN6jecjxe/5PPbWPD3v8leo8AhrV90WESfYXfs0pwc3Jjasd/0yT8ATKdXJmjTeDyppcg/QZgni0CrXWtQtxNkn0lVCo5XDnI1h0z+copG9yrMbj1C/Sv39+c4doEa++56u7kzrTI12kQ1pl0J2fezrlA/MaXIDvV5FsEWvtahbiTJPtKMDY5FNZtt+/aydZNk/hMkwGuPgxoOZqBjR61UNSls1RN2Rb2XHV3cmd6p1mE1+xAskbD25mnubllCjdSUg16vqFr7Vj7WqWfQNxJtiUsR1nLzhqzwFZh3VaXco3nvZewPjCDrDw3Wtd6mCeaPGnOSyiXJWvKtrLnqpeLFzM6v83MHa8QH3+IOUkHeD53ESq6o5TTBrpzrR1T/X2YmvQTiLsZnexHjBjBM888w3333WeOeGzKluPxzFp/goTUnKLHQnxcmfXQPfSOCDV4ga0LNzNZuO0MXmQy0fNTNgZmkIUzV9Lac2Z7Ix4ITbDaP0B9HZKFNWVT785kS3uu+rr68tp90by+fQJXb8SyMn0X473y+Si9j0F70JaXUK11rZb+nQr7YHQZJyUlhR49etCwYUPmzJnD1atXzRGX1RXubHRnogdISM1hzP861gzZPzTEx5VvYi7hRD4vuH/Bz0FJZKMhIaMVGTd6AWqr1W2tUVO2tT1XA90DmXH/PLyr1eW8SktK8B7+pd5V7haBhnS8WuNapZ9A6GN0sv/xxx+5evUqY8eOZc2aNdSpU4c+ffrw3XffkZeXZ44YLU6rU5j2Q+mTbgoV/ry8/UOfbF+b66mZjHb9ht3Bl8lUqbme3YSUGw8BGovUqPWxRk3ZFvdcreVdi2n3z8fNO5TTzjpqh2+lt/e5YsfcuYaOoQkVyv/7MPW1WrufQBRnS/0mFeqgDQoKYvLkyRw9epT9+/fToEEDhg8fTo0aNZg0aRJnz541dZwWte/8LZIzy/7gSs7MY9/5W+VuUF0n0JPHnX/mVPAJUtUqbuaGc+v6IFCKL2pm7hp1aaxVU7b0pt6GaFCtAZPvi0bjXo2DLlo6hX7Lj4MCWPREK74Z3ZHdU7sVxWVMQrX0tdpKn4goqA50nvcrTy7fx4TVR3hy+T46z/vVasNtK9VBGx8fz9atW9m6dSsajYa+ffty7NgxmjVrxvz585k0aZKp4rSovX/fNPi4qIaB9I4IpWezkFI76o799g1bg3dy0wlu51fnesIQ0JWs0VqiRl3R1zRHbGW9Z+ZUVodqy+BWjImayZKdM9iYm4H/8Rk8PPBr8C6+f4CxCdWS12pLfSKOzBb7TYxO9nl5eaxfv54vvviCX375hRYtWjBx4kSGDBlSNHtr7dq1jBo1ym6Tfcmb7vKP06hVRNYvnhR0Vw+xNW4hl10hVevH1YThKFrvEmcIsWCN+k6FNeWElGyDOiRNrbT3zJwMGaFyX9gD3G47ga8PvMeX+bfw3/wCkY+sApd/ljUO9HQ16PXuPM5S12rt36kov99ERUGZr2ezEIuWK40u44SGhjJ69GjCw8OJiYnh4MGDjBkzptg03a5du+Ln52fKOC3K0H+UZR6XcoVVWyexX5WDs5sPV64Phbzix1urRl3IFuvn5mLMTNaHGg+iV7OhoHZiSebfBQun6e7YitD4toDFONLv1FbZar+J0cn+gw8+4Nq1ayxZsoRWrVqVeoyfnx9xcWXv5WnLOtYLwM+j7I1Cqnk407GenmSfk8aWTWPYrKSCkxuTHnibjx572KZq1IVssX5uasaOUFGpVIxsM5624T3IA969sYf43e8WPedmek7JE5XC0ONMzRF+p7bMVvtNjC7jDB8+3Bxx2BSNWsXcgc0Z89VhvcdED2xeeutIp+XgTxNYmXMF1E482WY8UbUfALBKjdoQ1qqfW4oxLa3CuzW1Ss1LnWfxZuZ1zl//k+hza3jbvwE+EY/aRV28qv9ObZmt/n3IDFo9ekeE8vGwNsxaH0tCquGzEP/+/W0W3/4TnUpNt0YDebjZ0KKfWbpGbQxTx1ZWR6ilVbSl5ebkxtTuC3lt01NcT7nIgpi5vO5fj/Z1W9lFXdyW/96qssJ+k7IaGJacS1JIkn0ZjG0d3fprNfPj1pGjUmhRoxPPdHjVIXeZsrWp+pVpafm6+jK1+4e8vnkEp7Nvs3TrS7z4yLfM7N+MsV8dRgXFEr7UxYVGreKhlqF8slN/KfuhlqEW//uQhdDKUdg6Km0buztlXTnAvIMLuK3SUataAyZ1fRcnteN9ltrikr6Vnclay7c2kx94F42TG3/o0vjvptH0buwndXFRKq1OYf3Rsv/O1x+Nt/gEK8fLRmagS41n8faJXFTl4+sewLSe/4eHs0f5T6xibHXIWeEIlcq0xJuHtuPZDlP5ZO87fJ99hdAtE+j9r0+kLi5KKK+PCEr2EVmCtOwrKy+bVZue4bAuA2cnd6Z0X0SQZ7C1o7IKWx1yBqYZodKt0SM81GQwoOLjmzGc3vOewXd+wnFUmdE44g6KwvYtL7E5+xqoNbwQ+ToNgyKsHZXV2OofeSFTjFB5sv3LxCfHceDaHhac/g/vBDYhuPG/zBi1+dlSZ3pVIKNxqqATe97js5sxgIrHmw2nU4O+1g7Jqmz1j/xOlR2holapGd/9fWaue4ILqReYv+dN3qxWH4/gpiaM0nJsrTO9KrDVWcxSxqmg+DObeP/Mf9ACUTWjGNhugrVDsjpbW77YXNyc3Hi19zKqufpymVw+/OUFdFnJ1g7LaLbYmV4V2OosZkn2FZB+4zTz9swmHYWGPnUY2/0DhxxiebfCP3J9YwwUqs6QxADPYKZ0W4izxoXDebf5atPo4ksq2DhZ9968bHEWs5RxjKTNTmXhz2OIV3IJcPHllT7LcdaUvbSCqJrqh7TmhXunsGh/NJvSzlLr1+l06zHP2mEZpCKzioVxbG0Ws7TsjaHTsXLTsxzLu42r2plXeyzGzyPI2lHZjMLWoj6FQy+rUmuxU9NBPNbgEQA+u/wzsUdXWjkiw9h6Z3pVYUujtewu2S9ZsoQ6derg5uZGhw4diImJsdhrb93xGj+nngFUvNh+KnWqt7TYa9sDWx56aU6PRb1GZGBL8oH3Dy8m8eqBEsfY0o5FYB+d6cK07CrZr1mzhsmTJzNz5kwOHz5My5Yt6dWrF4mJiWZ/7eN//YfPL24G4In6A7i36WNmf01746itRZVKxdjeH1PfvTppaJm3fSKZ6QlFP99yPJ6ouduL7VgUNXe7VTtAHaUzXfzDrpL9+++/z+jRo3n66adp1qwZH3/8MR4eHnz++edmfd2E+EN8cPgDdEDngOYM6PKGWV/PXjlya9HV2Z2Xey+jmsadK9oMPtz0DDptnkEb11uDrY4YcWTpdzQQzMFukn1ubi6HDh2iR48eRY+p1Wp69OjB3r17S31OTk4Oqampxb6MlZlxg/nbJpCu5NPALZAxvZfJyBs9HL21GOAXzpT7onFWqTmceZX//PySQRvXW6ukY4sjRqoaQ8t38dcOMfG7/qz7dRqKmUZ12c1onJs3b6LVaqlevXqxx6tXr86pU6dKfU50dDSzZ8+u1OueOLOe+PwM/NVuvNJ7Gc4u7pU6nyHsdUajKdagsXf16zzACxHPsujYMtbH76G2RkMyffQeX7hxfVTDQAtG+Q9bGzFSlRg6YS0j4wbzt08kTcnjYMJB+mqzcVabfm0tu0n2FTF9+nQmT55c9H1qaiphYWFGnePe1s8w1dkDH48gqlWrp/c4UyVoe5/RWNhavPsaQuzoGqByv89O7V7g8s1Y1lzZhTpwJ2F5Nbmc3ULv8YUb11sittLIuvemZ+iG41ptPgs3P8O1/DQC1K4FQ7nNtIii3ST7wMBANBoN169fL/b49evXCQkJKfU5rq6uuLoatjl0WVpFPFnmz02VoG1xR/qKsPfWoil+n4N6LuTQF305RTzB1f9L8pUQ0rT6Fsgz/H2x98aAPajsh6kxq79+tW0Sf6VfwhU1r3Z5G99qdU11GSXYTc3excWFtm3bsn379qLHdDod27dvJzIy0mpxmWrKeVWb0WhL44uNYarfp1rjRL8Oi/HLcyVPnUfT0OWoVaWPQjK0VS3LG5jfluPxdJ73a7GRU53n/WrUe2voEOQvty1g87VdAIxrNoI69XpWNvwy2U2yB5g8eTLLly9n5cqVnDx5krFjx5KRkcHTTz9tlXhMmaAddYy6LTH1B27nexpzI3kU7jo12c5ptA7+FCje+VbmxvVmjE2UZKoPU0OGFoe4HWfzla8BGBzcgQ4dzL+2ll0l+8GDB7NgwQLeeOMNWrVqxZEjR9iyZUuJTltLMWWCdtQx6rbE1B+4GrWKCQMeIS+xNxogz+MKzfy/L3aM3o3rzRybKM7YD9OyRtmUN7TY3ek6IdW/BUVHlHsNHun1oYmuomx2U7MvNH78eMaPH2/tMADTJmhHHqNuK8zxgVtQR5/ITz8lEOdzGI3vYWrl1kCr6W5UnV0aA+ZlzIdpSlZumf0mZS1xrFJl0STkM3SaPBo4ezOm3+eonFzMc1F3sauWva0J9DKs89eQ4xx9jLotMNcHbu+IUN6f/Dn9vBrh6qSiVs1f+PwpH6M6VKUxYF6GfkhujU0ot9Sjf8KajoiQz9E6pxKkduLVbu/j4l364BJzkGRfGYaWRw04TmY0Wp85P3A1Gg2jH/2cjq7+KOTzwY6XSUy7ZhOxCcMbbj8euWZQqae0CWsNAr9D7X4FLycNM9pNpFqtDpUP3AiS7CvhZkZO+QcZcZzMaLQuc3/gqt18GdfrY8JVrqTkpjL/5+fJzMu0idhMxdYWfDOYgWEmZeSWeYo7+016R4Sye2o3vhndkfEP/I2f/1+4OauZGN6Huq2eMkHQxrG7mr0tMcettb2PUbd35p4U5h7UmKlRs5mx+99cTrvMom0TebXXUjRqjdVjqyx7ngNgaIPMEHeWhDRqFe6upziUuAqNSuFJ7ya07/a2yV7LGJLsK8Fce03KjEbrMuYDtyITcAIa9mbKzVPMOrWSIwkHWLVvLiM7zTB5bJZk7xMCTdnXcee5LifHsfD3aei0udzv5M/D/T4GAz7YzUGSfSXIWjBVlyEfuJVpydbvOIHxSWd5P/EPfjr7AyF+9end7AmTxWZJxswYtdV/C4Y03Kr7uAIqrqca1rhLyUlh3tZxZOWm0RQ3Rvf5BJW7n9muoTxSs68kqbM7pkpPwFGp6PDgewzxqAM6LSsPfcDhq3vMF7AZVYU5AIb0icx66B5mPWRYv0muNpf5W1/kRvo1grQqompO4mCKv1X7MFSKothJD0rlpaam4uvrS0pKCj4+PiY9t72uVCmMp9UpdJ73q94EV9jC2z21W9HfgL6/DyUtkU/WDuI3bQpurr7M7vsFdfz0L7hni9YducqE1UfKPW7RE614uFVN8wdUCYbcrZV3jE7RsfD36ew5/zNOeToC4+9jY06fUs9lCobmNSnjmIit3VoL8zF2s+7yksOzPT/i5k/PcCwnhXnbXuLtfisJcLefv6WqNAfAkD6R8o75+vAS/vh7K7p8LffcaMLKnN5Fz7VmH4aUcYQwkjGzWQ0p9ziFNmdS5GvUUjQkpV1h7vYJBg/JtAVVbQ6AIYv46Tvml3Pr2HBiFfn5+XRICuabjKHcWfSx5jpGkuyFMJKhLdRAT1eD11vxbPoQ0xoPx09RcenGCd7f+W/ydfmlntfQseyWGvNuL3MAzO3gtf18sW8+urwcOiS7szplNLk4lzjOWn0YUsYRwkiGDrlFhVHlnqDIl5iacpHZ13dw7MouPolZwAsdphbbBtPQEUCWHvNu63MAzO1s0hkW7ZyOLi+DTvlurL/9DMl4l/kcS69jJC17IYxkaEv2ZrphE3WK/tGr1dTrGc1Ez8aodVp2nvmB1cdXFB1n6AggfcfFm3nd+ztnjC56ohXfjO7I7qndqnyiT8hIYN5vL5OblUQrnTPdI94gTim/I9rSfRiS7IWoAEOG3Fao49LZjdb9lvCcUwhoc/nx6HK2nN9o8BK8ufk6vccVHmvOerG9blpTUcnZybzz62TSUi9TV9Ewse0k7onsa5N9GFLGEaKCyhuVUeEZ1p4BdO27hKT1T/NtXior9r/L1dvOxKeUXsOHf0pCq/ZeKLN0BMVLR6LiMvMymbNzKok3T1JdUTO93mO4t3wSVLY52VJa9sKhmLrTsqyWbKU6LgPqM7D7AnrqXFFyUthwai5qt8vlxhN3K8OguBNSsgw6TpQuT5vHgj2zuRh/EF8F/h3cBd/7XoX/9a/Y4mRLadkLh2GNhboq03Gpqt2eUZEzSN37FntzkgipvpqE+KfQ5erfmc3QtmJZqzeKsml1WhbFzOfExd9w0+mY5n0PIQ9Gl1jzxtbWMZJkLxyCNRfq6h0RSrcm1Vm19wIXkzIJ9/dgeGQdXJzKv7FWN3uIF9MTyDi2jFxNEtqQ1STGD0PJK16CKSwJtQ6rxqp9l8o9r7+B67eL4nSKjmVHlnDg3AactXlMcQ6jXr/F4OJR6vG2NNlSkr2o8qy9UFdpdxSf7o4z+I7C+d7RvJJ+g9fPf4dOk4A2ZA234oei5PsCxUtCvu6GbXEX4mP7s1ltjaIorDq+kh0n16DOz2aCKpCIfh+Ch31MFpOavajyrLlQV6UXTANQqXC/fyozQx+gnlpDqPMV/ELWoNKkA8XrwIWdwmWxp9mstmTNqdVsPr4CcjMYq/hwb++FUC3c2mEZTJK9qPKstVm3ocMlDeok1jjh3WsOswLaUc9JQ33PK3Rss4XlI5sWG8te2CmsovROYRWOMZvV1H448z1rjy6H7BSe1npwX/e5EBJR7vNsaecuSfaiyrPWQl0mv6Nwdqda3/d4zbMJwYqCLuMEW65+QHpearHDekeE8tx9dVHdlc9VKnjuvrpVfpKTqW04v4E1R5ZCVhLD8t3o3eV1CO9U7vO2HI+n87xfeXL5PiasPsKTy/fRed6vZpvUVh5J9qLKs9ZCXWa5o3DzIajfIt5wCcc/P58r12J46483SMlJKTpky/F4lu2M4+5GpE6BZTvjrJZs7NGG8xv46vBHkHGTx7Wu9O/wMjTuU+7zTFK+MzFJ9qLKM+dCXWXdppvtjsIriJB/LeYNpxpUy8vj8tX9vL1nFik5KWWWjgpZY8VFe7Th/Aa++nMJpF/nUa0rA1s+By0Glfs8k5bvTEiSvXAI5pjkUt5tulnvKPzCCP3XYt5QB+OXl8Olq/uZ/ccbbD/zt93vGmUL1p9fz1d//h+kJfCY1pXHmw1D1W6UQc+11Z27ZOilcBimnORi6Lh9s06b969HjX6LmLXxRd7MvcnVawf4LDkalaYHita2Vly0F4qi8N8z/+X74yuKEv2gxoMhcjwlOkH0sNaAgPJIy144FFMs1GXMbboxdxQVGrkR1JjQvguZrQoiKDebvLTjeIT+F5VTcplPC/Q0z6QqU44+sfRIFkVR+DL2S74/tgJS43lS68qgho9C1ESDEz3Y7s5d0rIXwkjGbktoyB1FpZZyqN6M4L7vM2vTJGbl3SDN+SLXQr8l6/oAdLnBpT/HDCMvTbkchaWXtsjX5bP82HJ2nF0PaQk8ne9K74YDoMsroDauTVzhBfDMTFr2QhipIrfpZd1RmGT9+er3ENj3fSZn+VE/L5eaTlfwCP0OtduVUg+/c619U7SgTTn6xNIjWbLzs1lwcAE7zq5DnRbP2Hw3ejd6FO571ehED7a7c5ckeyGMZMrb9PJGzxi1/nxIBKmdohmc6EGT3DxqquPxDFmLxvOU3thMMRbclKNPLD2SJTU3lbf2vcWfF37FOe06L+d58ECTx6DLyxVK9IVk1UshqgBT3qaXVxIC49afb966A0/8PJ4XEpeyNSANtft1rgZtIcspjbyUdqhQFcVmqsXhjC1rWepc5bmSdoV5MfNIvBmLV0YSr+Z70LjFMOgwxqgavT62tuqltOyFMJIpb9MNXVfe0OM0ahXPPNSd1/Keo+utanRN1xKmuoFXtV24Bm4F8otiN1UL2pSjTyw1kuWvG3/x+h+vkZh4jOCM28zO96RxuzEmS/SFbGnnLkn2QlSAqW7TDV1X3pj153tHhPL6sAd5z+0lmiaHMiBZoZbqJn6+x7g/agftG7iadCy4Kcta5h7JoigKm//eTPS+d8i8dZYmmem8ne9FrahXoM1wkyZ6WyNlHCEqyBS36YauK1+R9edv48u0vNHMSPuKUflxrAm4ya3UY/x7179p5TXEoHMY0oI2ZVnLnCNZcrQ5LPtrGbsv74CUq3TJ1fI81XB+cCbU6Wz0+eyNtOyFqITK3qYbuq68MevPbzkez5ivDnM9LYcM3JmZ/zSXs1rx/HUV1ZMTiU84yaZri3HyPQRlLqxgWAvalGUtc41kuZZ+jdf/eJ3dF7ahvn2REdkwzikU538tcohED5LshbAqU68/r9UpTPvhWLHH8nDi/fxBbMntyfOJKlom3cYj6xregX/gGrwJ1JklzmPsUg6mHH1iynMpisKOyzuYvms6FxOP4Zt8hddzXOnr2xjVgKVQvZnB57J3UsYRwooKW7Jjvjqs9xhjWrL7zt8iOTOvlJ+oWKPtylUlkJdufUd753y+dL/FWY981DWvk3uzO9qsOv870vjXBdOOPjHFudJz0/n8+Of8cXU3pCcSkZnOuHwP/OvcB11n6N1KsKqSZC9EFbLn75tl/ny3rjmX84L4NG8d852SeF9zm8OaPJKqbyA/LYLcpE6E+PhWeKaqKfdcrcy5DiYcZPmx5SRn3kCdeo3Hc+BhnSfqNk9B26crNYbeXkmyF8KKCicR6VPa/rhanaK3xXvtdvlDNC8qIXwWOJW5AZt47+JuvtZks84jn8xqZ/FrfJuJ7Z6jYw372eDkzvfDwy2H2IyN7In/A7JTqJmexNhcZxq6BkC31yCsvbXDtRpJ9kJYkbGTiMpbM6aGn7tBrxsQEAC93sH5xFpG7FtKu+xMlivXiXfK44PD79P2aluGNx1OqJdtJ/1/3o9MnHz+wtlvP+7OOdRxSefRvHwe17rhHNICus8EryBrh2tVkuyFsCJjJhEZMuO1U/1Aluw4X+75OtUPLBhTHjEQQltyz/Y3mX87jh/ybrHeI51DuhiOJh7lwToP8mjDR/Fy8arA1VVOWXcwULiGziHUHn/jVnMfauckvFWZNM9OY2CCjk6+vjh3eR5aPumQZZu7SbIXwooMnRwU6OnKK98d1TvjtbDc8/uUrvh5OOvppC3g5+FMxztr4QH1YeByXA6v5IkjX9M5PY+vcq/xp6cXm//ezK+XfqVv3b70q9fPYkm/vDuYfK2ON7ZsxjV0F2rXBFzIp5aSyiMpObTNgMtKCM+lDeOblkNkc/X/kWQvhBW1Da+GWkWJ/WLvVPBzxaByz6GLt5k7sHmZo3vmDmxeMgE6uUD70VD3fmrtnM+0m2c5kpvO1x45XPTw5odzP7A5bjPdanejd53eVPesbuSVGq7sO5gDTHxIxV/Jv5LudRJndASRTt/0DLqkgUZx4cv8bqzTRaHN05hkDZ2qwm6S/TvvvMOmTZs4cuQILi4uJCcnWzskISrt0MXbZSZ6KPgg2G/gFnaJadk83KomHw9rw6z1J0hI/Wcp4xAfV2Y9dE/Zo2yCGsEjy+DURlrFLKNFRioHs5L4zjOLi+6ebI7bzE9xP9Gmehvur3U/bYLb4KxxNig2Q+hb9VLllIyT9wmcvE7x+YlsQn2cCSKd7hmZdEvT4qNTEaNrwrL8f5FItWLvhzmUV2KyRXaT7HNzcxk0aBCRkZF89tln1g5HCJMwPBkZtqRvYVmoUuPU1Wpo9hDUewD14S9pH7uOdmk5/JV+k5+8fTjiouFQwiEOXT+Eh5MH7UPb0zq4NS0CW+DhXLmx6/90WOtQOSfh5BGHxvM8apcbADihJTAvh0G3crknMQsPnYqzSi2i8/twQqmr9/0wJUtvrGIqdpPsZ8+eDcCKFSusG4gQJmRoMoqsF8j3h68atWZMpce8u/lAp/HQYjDqP7+k1anNtErN4hpafvPIZbeHO0naPHZc3sGOyztQo6aeXz0a+jWknl89anvXJtgjuNwPAJ2iIyk7iStpV/j54lFcg2NQu11Fpc7+37UpeJBDkxyFvhnJNMmGOtU8iVGHsTj3PvbqmqLctRhAZdbQyc3XsWrvBS4mZRLu78HwyDq4OBWc31TLQluD3ST7isjJySEn55/b2NTUVCtGI0RJhi781bF+gHk3Ly+LV1DBZh5tRsKJH6gRu46hmWk8mZnDSZWWg35BHHFx4hq5nEs+x7nkc8We7u3sjY+rD+5O7rg7uaNTdGgVLTnaHFJyUkjJSUGraAHIyMlH45GBGh0eipY6OSq6ZqXQKicXT50KUHFUVx/njs+heDVn73/+LPU9q+j7Eb05luW74oqV1t7ZfJLRXeryau+mZS4LXdqcCFtSpZN9dHR00R2BELaocLkEQ5J44Zoxd5cQQixVQvAMKOjEbT0M4nahPrOFe64d5p7btxkBJKLjjIcX5z19OeesIV7JI03JJ01JIy0vTf95dVo0Oi2hGnfCnF1IzbhN66wkauYqOP3vXUhWvFmna8kWbXt0vrXY3aEbEWoVS4epTPZ+RG+O5ZOdcSXDU+CTnXFcS86y2MYq5qBSFMW8W7aXYdq0acybN6/MY06ePEmTJk2Kvl+xYgUTJ040qIO2tJZ9WFgYKSkp+Pj4VDhuIUzNmDqwTXUOpifChd1waS9c+xO0xYd8ZqJwQw0ZLu5kOrmRpXFCA6h1OrIys/DNTidQ0RLq4ozz/xJ7SnYeF29lkqD4c0jXiN26CGKVcArXbby7VGKK9yM3X0eT138qs7P87g9jfRY90YqHW9U06vUrIzU1FV9f33LzmlVb9i+//DIjR44s85h69epV+Pyurq64uhq/DrgQlmZMh6op15+pNK/ggolZEQMhLwtunIbE2IKv5Mt4pF4jXJsL2dlAwQdZSnYe15Kz8dDqAEgGEjROhNRtQu0GzfENbMTN1BrM3Jpo0IefKd6PVXsvlDsqytBWsTk6hU3Bqsk+KCiIoCDHnsIsRCGbSuIV4ewONVoVfBXS6SArCXJSITeDfacuMe/ns+SjIQ8nkhRvkvEiD2eIhaVt2tC7USjdgN1tLHcHczGp5DLPpfFw0ZCVqzX5xiqWYDc1+0uXLpGUlMSlS5fQarUcOXIEgAYNGuDlZfmp3EIIA6jV4BkInoFodQqTvkgkXmlY6qF3d3Ba8sMv3N+wIaN9I0L4/vBVy3eSm4DdLBjxxhtv0Lp1a2bOnEl6ejqtW7emdevWHDx40NqhCSEMYOy+t1qdwt7zt1h35Cp7z98yaPPzihoeWYfycrRaBXMGtjDZxiqWZjct+xUrVsgYeyHsmLGLvlly4pKLk5rRXeqWOhqn0OgudXFxUpt0kxZLsptkL4Swb4Z2XF64mcnCbWcsPnFpet+CLQrvHmevVhUk+sKfg332r1h16KWlGTpESQhhelqdQud5v5Y5gay6jyugIiG19LuAwk7Q3VO7ma0lXdYMWltkaF6z3SsQQlQphRPI4J8OzUKF3z/ZvrbeRA8l6/rm4OKk5pku9Xjz4Qie6VLPphO9MarGVQgh7ELhLGB9HZx1Aj0NOo+5VrOsyqRmL4SwqLI6OPeev2XQOWx14pItk2QvhDAZQ5cu0NfBaejCcLY6ccmWSbIXQpiEKYZLGrMwnDCO1OyFEJVWuM773ZOm4v83XHLL8XiDz1VeXd+WJy7ZMmnZCyEqRd9WgoUUjF/n3V4nLtkySfZCiEopbxkEqNg67/Y4ccmWSRlHCFEpCSlZJj1OmIckeyFEpSRl5Jr0OGEekuyFEJXi72XYBkGGHifMQ5K9EKJSQnwMm+Bk6HHCPCTZCyEqpXAiVFlCZSKU1UmyF0JUyp0LnOkjE6GsT5K9EEI4AEn2QohKKZxUpU/h3rJ3bitoyS0HRQGZVCWEqBRj9paNrB9g8S0HRQFp2QshKsXYvWVLW0MnoQJr6AjjSLIXQlSKoWvLB3q66l1Dp/Cxu8s9wnQk2QshKqVw6KW+sTYqCso0qDC43CNMT5K9EKJSDNlbdmb/ZtxMzzHofLLloHlIshdCVJoha9AbWu6RLQfNQ0bjCCFMorw16GXLQeuSZC+EMJmy1qCXLQetS8o4QgiLkS0HrUda9kIIi5ItB61Dkr0QwuJky0HLkzKOEEI4AEn2QgjhACTZCyGEA3Comr2iFAz2Sk1NtXIkQghhGoX5rDC/6eNQyT4tLQ2AsLAwK0cihBCmlZaWhq+vr96fq5TyPg6qEJ1Ox7Vr1/D29kalMnyYV2pqKmFhYVy+fBkfHx8zRmg6ErP52Vu8IDFbiiVjVhSFtLQ0atSogVqtvzLvUC17tVpNrVq1Kvx8Hx8fu/ljKyQxm5+9xQsSs6VYKuayWvSFpINWCCEcgCR7IYRwAJLsDeDq6srMmTNxdXW1digGk5jNz97iBYnZUmwxZofqoBVCCEclLXshhHAAkuyFEMIBSLIXQggHIMleCCEcgCT7cixZsoQ6derg5uZGhw4diImJsXZIZdq5cyf9+/enRo0aqFQqfvzxR2uHVKbo6GjuvfdevL29CQ4OZsCAAZw+fdraYZVp6dKltGjRomjCTGRkJD/99JO1wzLK3LlzUalUTJw40dqh6DVr1ixUKlWxryZNmlg7rHJdvXqVYcOGERAQgLu7O82bN+fgwYPWDkuSfVnWrFnD5MmTmTlzJocPH6Zly5b06tWLxMREa4emV0ZGBi1btmTJkiXWDsUgv//+O+PGjWPfvn1s3bqVvLw8HnzwQTIyMqwdml61atVi7ty5HDp0iIMHD9KtWzcefvhhTpw4Ye3QDHLgwAE++eQTWrRoYe1QynXPPfcQHx9f9LV7925rh1Sm27dvExUVhbOzMz/99BOxsbG89957VKtWzdqhgSL0at++vTJu3Lii77VarVKjRg0lOjrailEZDlDWrl1r7TCMkpiYqADK77//bu1QjFKtWjXl008/tXYY5UpLS1MaNmyobN26Vbn//vuVCRMmWDskvWbOnKm0bNnS2mEYZerUqUrnzp2tHUappGWvR25uLocOHaJHjx5Fj6nVanr06MHevXutGFnVlpKSAoC/v7+VIzGMVqtl9erVZGRkEBkZae1wyjVu3Dj69etX7O/alp09e5YaNWpQr149hg4dyqVLl6wdUpnWr19Pu3btGDRoEMHBwbRu3Zrly5dbOyxAyjh63bx5E61WS/Xq1Ys9Xr16dRISEqwUVdWm0+mYOHEiUVFRREREWDucMh07dgwvLy9cXV0ZM2YMa9eupVmzZtYOq0yrV6/m8OHDREdHWzsUg3To0IEVK1awZcsWli5dSlxcHF26dClaqtwW/f333yxdupSGDRvy888/M3bsWF566SVWrlxp7dAca9VLYdvGjRvH8ePHbb4uC9C4cWOOHDlCSkoK3333HSNGjOD333+32YR/+fJlJkyYwNatW3Fzc7N2OAbp06dP0f+3aNGCDh06EB4ezrfffsszzzxjxcj00+l0tGvXjjlz5gDQunVrjh8/zscff8yIESOsGpu07PUIDAxEo9Fw/fr1Yo9fv36dkJAQK0VVdY0fP56NGzfy22+/VWoZaktxcXGhQYMGtG3blujoaFq2bMmiRYusHZZehw4dIjExkTZt2uDk5ISTkxO///47ixcvxsnJCa1Wa+0Qy+Xn50ejRo04d+6ctUPRKzQ0tMQHftOmTW2i/CTJXg8XFxfatm3L9u3bix7T6XRs377dLmqz9kJRFMaPH8/atWv59ddfqVu3rrVDqhCdTkdOTo61w9Cre/fuHDt2jCNHjhR9tWvXjqFDh3LkyBE0Go21QyxXeno658+fJzQ01Nqh6BUVFVVi6PCZM2cIDw+3UkT/kDJOGSZPnsyIESNo164d7du3Z+HChWRkZPD0009bOzS90tPTi7V84uLiOHLkCP7+/tSuXduKkZVu3LhxfP3116xbtw5vb++i/hBfX1/c3d2tHF3ppk+fTp8+fahduzZpaWl8/fXX7Nixg59//tnaoenl7e1doh/E09OTgIAAm+0feeWVV+jfvz/h4eFcu3aNmTNnotFoePLJJ60dml6TJk2iU6dOzJkzh8cff5yYmBiWLVvGsmXLrB2aDL0sz4cffqjUrl1bcXFxUdq3b6/s27fP2iGV6bffflOAEl8jRoywdmilKi1WQPniiy+sHZpeo0aNUsLDwxUXFxclKChI6d69u/LLL79YOyyj2frQy8GDByuhoaGKi4uLUrNmTWXw4MHKuXPnrB1WuTZs2KBEREQorq6uSpMmTZRly5ZZOyRFURRFljgWQggHIDV7IYRwAJLshRDCAUiyF0IIByDJXgghHIAkeyGEcACS7IUQwgFIshdCCAcgyV4IIRyAJHshhHAAkuyFEMIBSLIXwkRu3LhBSEhI0VrmAHv27MHFxaXY6qlCWIOsjSOECW3evJkBAwawZ88eGjduTKtWrXj44Yd5//33rR2acHCS7IUwsXHjxrFt2zbatWvHsWPHOHDgAK6urtYOSzg4SfZCmFhWVhYRERFcvnyZQ4cO0bx5c2uHJITU7IUwtfPnz3Pt2jV0Oh0XLlywdjhCANKyF8KkcnNzad++Pa1ataJx48YsXLiQY8eOERwcbO3QhIOTZC+ECU2ZMoXvvvuOo0eP4uXlxf3334+vry8bN260dmjCwUkZRwgT2bFjBwsXLmTVqlX4+PigVqtZtWoVu3btYunSpdYOTzg4adkLIYQDkJa9EEI4AEn2QgjhACTZCyGEA5BkL4QQDkCSvRBCOABJ9kII4QAk2QshhAOQZC+EEA5Akr0QQjgASfZCCOEAJNkLIYQDkGQvhBAO4P8BmlVUauh8b3cAAAAASUVORK5CYII=", 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", 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" ] @@ -1517,7 +1342,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:10<00:00, 9.10it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 24.45it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -1527,12 +1352,12 @@ "text": [ "\n", "\u001b[1mRunning Cycle 5:\u001b[0m\n", - "\u001b[1mCycle 5 model: cos((x * -0.52))\u001b[0m\n" + "\u001b[1mCycle 5 model: sin(x)\u001b[0m\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1545,6 +1370,12 @@ "from autora.experimentalist.random_ import random_pool\n", "\n", "#### First, let's reinitialize the state object to get a clean state ####\n", + "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 30))\n", + "dv = Variable(name=\"y\", type=ValueType.REAL)\n", + "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "conditions = random_pool(variables, num_samples=10, random_state=42)\n", + "\n", "s = StandardState(\n", " variables = variables,\n", " conditions = conditions,\n", @@ -1558,8 +1389,8 @@ "for cycle, num_samples in enumerate([5, 10, 20, 50, 100]):\n", " \n", " #Run pipeline\n", - " s = random_experimentalist(s, num_samples=num_samples)\n", - " s = experiment_runner(s)\n", + " s = random_experimentalist(s, num_samples=num_samples, random_state=42+cycle)\n", + " s = experiment_runner(s, added_noise=1.0, random_state=42+cycle)\n", " s = theorist(s)\n", " \n", " #Report metrics\n", @@ -1568,31 +1399,6 @@ " plot_from_state(s)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", - " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", - " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", - " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", - " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", - " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.variables" - ] - }, { "attachments": {}, "cell_type": "markdown", From 313c712389b8ab96ef0e7d10f90f31d1481adb5d Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Thu, 31 Aug 2023 10:19:07 -0700 Subject: [PATCH 22/32] Replaced import --- docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb index 95eca5b77..ae4fa84e6 100644 --- a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb +++ b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb @@ -58,6 +58,7 @@ "!pip install -q \"autora[theorist-bms]\"\n", "\n", "#### Import modules ####\n", + "from typing import Optional\n", "import numpy as np\n", "import pandas as pd\n", "import torch\n", @@ -958,8 +959,6 @@ "metadata": {}, "outputs": [], "source": [ - "from typing import Optional\n", - "\n", "#### We will first define a new experimentalist\n", "def uniform_sample(variables: VariableCollection, conditions: pd.DataFrame, num_samples = 1, random_state: Optional[int] = None):\n", "\n", From 2aec6657ee971f38928c73edf81d695755498c78 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Thu, 31 Aug 2023 10:21:51 -0700 Subject: [PATCH 23/32] added type --- docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb index ae4fa84e6..95fff8a30 100644 --- a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb +++ b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb @@ -960,7 +960,7 @@ "outputs": [], "source": [ "#### We will first define a new experimentalist\n", - "def uniform_sample(variables: VariableCollection, conditions: pd.DataFrame, num_samples = 1, random_state: Optional[int] = None):\n", + "def uniform_sample(variables: VariableCollection, conditions: pd.DataFrame, num_samples: int = 1, random_state: Optional[int] = None):\n", "\n", " \"\"\"\n", " An experimentalist that selects the least represented datapoints\n", From c2f2a7f7ae03c26a7edad5ceb50e8a971326ada8 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Thu, 31 Aug 2023 10:52:07 -0700 Subject: [PATCH 24/32] Improved plotting function --- .../Tutorial-III-Functional-Workflow.ipynb | 831 +----------------- 1 file changed, 38 insertions(+), 793 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb index 95fff8a30..405fd86ad 100644 --- a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb +++ b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb @@ -44,12 +44,11 @@ "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", - "[notice] To update, run: python.exe -m pip install --upgrade pip\n" + "ename": "SyntaxError", + "evalue": "invalid syntax (1057721313.py, line 16)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m Cell \u001b[1;32mIn[1], line 16\u001b[1;36m\u001b[0m\n\u001b[1;33m def plot_from_state(s,'sin(x)'):\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" ] } ], @@ -64,20 +63,34 @@ "import torch\n", "import matplotlib.pyplot as plt\n", "\n", + "from autora.state.bundled import StandardState\n", + "\n", "#### Set seeds ####\n", "np.random.seed(42)\n", "torch.manual_seed(42)\n", "\n", "#### Define plot function ####\n", - "def plot_from_state(s): \n", + "def plot_from_state(s: StandardState, expr: str): \n", + " \n", + " \"\"\"\n", + " Plots the data, the ground truth model, and the current predicted model\n", + " \"\"\"\n", + " \n", + " #Determine labels and variables\n", " model_label = f\"Model: {s.model.repr()}\" if s.model.repr() else \"Model\"\n", " experiment_data = s.experiment_data.sort_values(by=[\"x\"])\n", " ground_x = np.linspace(s.variables.independent_variables[0].value_range[0],s.variables.independent_variables[0].value_range[1],100)\n", + " \n", + " #Determine predicted ground truth\n", + " equation = sp.simplify(expr)\n", + " ground_predicted_y = [equation.evalf(subs={'x':x}) for x in ground_x]\n", + " model_predicted_y = s.model.predict(ground_x.reshape(-1, 1))\n", "\n", + " #Plot the data and models\n", " f = plt.figure(figsize=(4,3))\n", " plt.plot(experiment_data[\"x\"], experiment_data[\"y\"], 'o', label = None)\n", - " plt.plot(ground_x, s.model.predict(ground_x.reshape(-1, 1)), alpha=.8, label=model_label)\n", - " plt.plot(ground_x, np.sin(ground_x), alpha=.8, label='Ground Truth: sin(x)')\n", + " plt.plot(ground_x, model_predicted_y, alpha=.8, label=model_label)\n", + " plt.plot(ground_x, ground_predicted_y, alpha=.8, label=f'Ground Truth: {expr}')\n", " plt.xlabel('x')\n", " plt.ylabel('y')\n", " plt.legend()\n", @@ -154,32 +167,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", - " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", - " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", - " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", - " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", - " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 5.416539\n", - "1 4.116570\n", - "2 3.249923\n", - "3 1.733292\n", - "4 1.949954\n", - "5 0.216662\n", - "6 0.433323\n", - "7 0.000000\n", - "8 1.083308\n", - "9 5.199877, experiment_data=Empty DataFrame\n", - "Columns: [x, y]\n", - "Index: [], models=[])\n" - ] - } - ], + "outputs": [], "source": [ "print(s)" ] @@ -195,39 +183,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1mThe variables we provided:\u001b[0m\n", - "VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", - " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", - " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", - " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", - " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", - " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[])\n", - "\n", - "\u001b[1mThe conditions we provided:\u001b[0m\n", - " x\n", - "0 5.416539\n", - "1 4.116570\n", - "2 3.249923\n", - "3 1.733292\n", - "4 1.949954\n", - "5 0.216662\n", - "6 0.433323\n", - "7 0.000000\n", - "8 1.083308\n", - "9 5.199877\n", - "\n", - "\u001b[1mThe dataframe we provided:\u001b[0m\n", - "Empty DataFrame\n", - "Columns: [x, y]\n", - "Index: []\n" - ] - } - ], + "outputs": [], "source": [ "print(\"\\033[1mThe variables we provided:\\033[0m\")\n", "print(s.variables)\n", @@ -358,39 +314,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1mPrevious Conditions:\u001b[0m\n", - " x\n", - "0 5.416539\n", - "1 4.116570\n", - "2 3.249923\n", - "3 1.733292\n", - "4 1.949954\n", - "5 0.216662\n", - "6 0.433323\n", - "7 0.000000\n", - "8 1.083308\n", - "9 5.199877\n", - "\n", - "\u001b[1mUpdated Conditions:\u001b[0m\n", - " x\n", - "0 0.433323\n", - "1 4.983216\n", - "2 4.116570\n", - "3 2.816600\n", - "4 2.599939\n", - "5 5.416539\n", - "6 0.433323\n", - "7 4.333231\n", - "8 1.299969\n", - "9 0.433323\n" - ] - } - ], + "outputs": [], "source": [ "print('\\033[1mPrevious Conditions:\\033[0m')\n", "print(s.conditions)\n", @@ -414,31 +338,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1mPrevious Data:\u001b[0m\n", - "Empty DataFrame\n", - "Columns: [x, y]\n", - "Index: []\n", - "\n", - "\u001b[1mUpdated Data:\u001b[0m\n", - " x y\n", - "0 0.433323 0.724606\n", - "1 4.983216 -2.003534\n", - "2 4.116570 -0.077238\n", - "3 2.816600 1.259866\n", - "4 2.599939 -1.435481\n", - "5 5.416539 -2.064342\n", - "6 0.433323 0.547730\n", - "7 4.333231 -1.245219\n", - "8 1.299969 0.946749\n", - "9 0.433323 -0.433155\n" - ] - } - ], + "outputs": [], "source": [ "print(\"\\033[1mPrevious Data:\\033[0m\")\n", "print(s.experiment_data)\n", @@ -462,51 +362,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1mPrevious Model:\u001b[0m\n", - "None\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:03<00:00, 26.91it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mUpdated Model:\u001b[0m\n", - "-0.38\n" - ] - }, - { - "data": { - "image/png": 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ScjGFZYeWMbrTaLVjCVFrrOYKws7OjvDwcDZt2mTeZjKZ2LRpE5GRkVV6D6PRyMGDB/H1LX+iu/pGp9UQ2bopJqd4zhQewlZny+TwybjZu6kdrVHyc/ZjYpeJaNCwMX0jm9M3qx1JiFpjNQUCICYmhsWLF7N8+XKSk5MZO3Ys+fn5jBgxAoCnn36aadOmmfd/4403+PXXXzl27Bh79+5l6NChnDx5kmeeeUatj3BTErMT+frI1wCMChlFcJNglRM1bmFeYeYV6D4/9Ll0WosGy2qamAAGDRrEuXPneP3118nKyiI0NJT169ebO67T09PRav+qeRcvXmT06NFkZWXRpEkTwsPD2bFjBx06dFDrI1Rb9pVs5u+bj4JC78DeMnV3PfFwm4dJM6SRcDaBOQlzmHX3LFztXNWOJUSN0ihye2iFcnNzcXNzw2Aw4Opatz8AxcZiXt/xOscMxwh2D2ZG5AxsddLvUF/kF+fz6rZXybqSRWfPzkztOlXmwBJWoaq/a/JtrseWHl7KMcMxXGxdeDH8RSkO9YyTrROTIyZjp7Vj/7n9sq61aHCkQNRTv5/+nU3pm9Cg4YUuL9DMoZnakYQFAa4BPNOxtE/r6yNfc/DcQZUTCVFzpEDUQ6cun+Kzg58B8Ohtj9LZs7PKiURF7vW/l/v970dBYd6+eVy4ekHtSELUCCkQ9UxBSQFzE+ZSaCykY7OOPNrmUbUjiSoYETKCQNdAcotymb9vPkaTUe1IQtwyKRD1zJJDSzidd5om9k14IewF6fS0EnY6O17s8iJ6nZ7knGS+OSIzvwrrJ78+9cjWU1vZenorWrRM6DJBboazMr7OvjzX6TkAvkv9jgPnDqicSIhbIwWinjiTd4bPD30OwGNtH6NDU+u5V0P8pXvz7kQFRKGgMH/ffC4WXFQ7khA3TQpEPVBkLOLjhI8pNBYS0jSEgcED1Y4kbsGw24cR6FLaH7EgcQEmxaR2JCFuihSIeuCLpC84efkkbnZujA8bL/0OVs5OZ8eELhOw19lz8PxB1qSuUTuSEDdFfolUtitzFxtObgBgXNg4muibqJxI1IQWLi0YcXvpHGFfpXxFSk6KyomEqD4pECo6f/U8/z5QujbFQ60fkvsdGpj7/O+jh18PTJiYt28eeUV5akcSolqkQKjEaDLyyb5PyC/Op7Vbax5v+7jakUQN02g0PNPxGbwdvTl/9TyLDy5Gpj4T1kQKhEq+S/2O5Jxk9Do9E7pMkMV/GihHW0cmdpmITqNjZ+ZONp+S9SOE9ZACoYKUnBS+PfItAM90fAYfp6qtiCesU2v31gxqOwiAZYeXcSbvjMqJhKgaKRB1LL84n/n75mPCxN3N7+buFnerHUnUgf6t+9OxWUcKjYV8vPdjio2ynrWo/6RA1CFFUfjs4Gecu3oOL0cvRoaMVDuSqCNajZbnQ5/Hxc6Fk7knWZ2yWu1IQlRKCkQd+v307+zI2FE6lUbYBBxtHdWOJOqQh96DMZ3GALDu2DqZikPUe1Ig6khWfhZLDi0B4PG2j9OmSRuVEwk1RPhE0DuwNwCfJn5KblGuyomEKJ8UiDpQYiph/r75FBgLaO/RngHBA9SOJFT0VIenaO7cnIuFF1m4f6EMfRX1lhSIOvDtkW9JvZSKk62TTKUhsNfZMyFsAjZaGxLOJrApfZPakYSwSH6palnyhWS+T/0eKB3SKkuHCoCWbi15st2TACw/vFyGvop6yeoKxIIFC2jZsiV6vZ5u3boRHx9f4f5ff/017dq1Q6/X07FjR3766ac6Slo6pPWTxE8wYeLeFvfS3a97nZ1b1H99g/rSsVlHikxFzNs7j2KTDH0V9YtVFYj//ve/xMTEMH36dPbu3Uvnzp3p06cP2dnZFvffsWMHgwcPZtSoUezbt4+BAwcycOBADh06VCd5Pz/4OeevnsfL0YsRISPq5JzCepiHvtq6cCL3BF+lfKV2JCHK0ChW1EPWrVs37rjjDj755BMATCYT/v7+vPDCC0ydOvWG/QcNGkR+fj7r1q0zb7vzzjsJDQ1l4cKFVTpnbm4ubm5uGAwGXF1dq5x12+ltfJL4CVq0zOwxk9ua3FblY0XjsjtrN7P3zEaDhn/d+S9CmoWoHUlYid9P/06oVyiudlX/bYKq/65ZzRVEUVERCQkJREVFmbdptVqioqKIi4uzeExcXFyZ/QH69OlT7v4AhYWF5ObmlnlUV/aVbPPqcI/e9qgUB1GhO3zuMK9CtyBxgcz6Kqrk0PlDfJr4KVO2Tqm14dJWUyDOnz+P0WjE29u7zHZvb2+ysrIsHpOVlVWt/QFmzZqFm5ub+eHv71/trDZaG4Ldg7mtyW08HPxwtY8Xjc9THZ7C18mXnIIcFh1YVO+HvhpNCnFpF1iTeIa4tAsYTfU7b0OTV5THgsQFKCh08e5S7SuIqrKplXe1YtOmTSMmJsb8PDc3t9pFwkPvwavdXuVK8RV0Wl1NRxQNkN5Gz4SwCfxr+7/YlbWLLae20DOgp9qxLFp/KJOZa5PINBSYt/m66ZnevwPRIb4qJmscFEVh0YFF5BTk4Ovky9Mdnq61c1nNFUSzZs3Q6XScPXu2zPazZ8/i42N5NlQfH59q7Q9gb2+Pq6trmcfN0Gq0ONs539SxonFq5d7KvC7IssPLyMov/0pXLesPZTJ25d4yxQEgy1DA2JV7WX8oU6VkjceWU1vYlbULnUbHhLAJ6G30tXYuqykQdnZ2hIeHs2nTXzcVmUwmNm3aRGRkpMVjIiMjy+wPsGHDhnL3F0JtD7V+iPYe7SkwFjB/33xKTCVqRzIzmhRmrk3CUmPStW0z1yZJc1MtysrPYtnhZQAMajuIVu6tavV8VlMgAGJiYli8eDHLly8nOTmZsWPHkp+fz4gRpUNIn376aaZNm2bef+LEiaxfv545c+bw559/MmPGDPbs2cP48ePV+ghCVEir0TI+bDxOtk6kXko1rxtSH8Qfz7nhyuF6CpBpKCD+eE7dhWpE/j5lT//W/Wv9nFZVIAYNGsTs2bN5/fXXCQ0NJTExkfXr15s7otPT08nM/OsSt3v37qxatYpFixbRuXNnvvnmG77//ntCQmQYoai/mjk0Y3TH0QB8n/o9yReSVU5UKvty+cXhZvYT1fPNkW9IvZSKs61znU3ZY1X3QajhZu+DEOJWxSbGsuX0Fpo5NOP9e97HydZJ1TxxaRcYvHhnpfv9Z/SdRLZuWgeJGo+kC0m8EfcGCgqTukwi0u/Wmskb3H0QQjQ2w0OG4+3ozfmr51l8YLHqQ1+7Bnng66ZHU87rGkpHM3UN8qjLWA3e9UNa7/O/75aLQ3VIgRCinnKwcWBC2AR0Gh1xmXFsPb1V1Tw6rYbp/TsA3FAkrj2f3r8DOm15JURU17VVKM9fPY+Pow/Dbx9ep+eXAiFEPRbcJJjHbnsMgKWHlpKZp+4w0ugQX2KHdsHHrezQSh83PbFDu8h9EDVsy6ktxGXGlQ5p7TIBBxuHOj2/3CgnRD03IHgAB84fIOlCEvP2zeONHm9gq7VVLU90iC+9O/gQfzyH7MsFeLmUNivJlUPNysjLYOnhpUDpkNbW7q3rPINcQQhRz2k1WsaHjsfZ1pljhmP1YtZXnVZDZOumDAhtTmTrplIcalixsZh5++ZRaCwkpGlInQxptUQKhBBWoKlDU8Z0HgPAD2k/cODcAZUTidr0nz//w3HDcZxtnXk+9HnVVqGUAiGElbjD5w56B/YGYEHiAgyFBpUTidqQmJ3Ij8d/BGBs57E0dVBvyLAUCCGsyNMdnqaFcwsuFV5iQeICTIpJ7UiiBl0suMiCxAUARLeMJsInQtU8UiCEsCJ2OjsmhU/CTmvH/nP7WXdsXeUHCatgUkwsSFxAblEuga6BDG0/VO1IUiCEsDb+Lv4Mu30YAKv/XM3Ri0dVTiRqwprUNRw8fxB7nT0Tu0zEVqfeSLVrpEAIYYV6BfTiTt87MSpG5u2dR35xvtqRxC1IvpBsHp02KmQUzZ2bq5yolBQIIayQRqPh2U7P4uXgRfbVbBbuX6j6VBzi5lwuusy8ffMwYeLu5ndzr/+9akcykwIhhJVysnViUvgkbDQ2xGfF88uJX9SOJKrpWr/DtdXhRnUcpXakMqRACGHFWru3Zkj7IQCsSFpB2qU0lROJ6vgh7Qf2Ze/DVmvLpC6T6nwqjcpIgRDCyvUN6ssd3ndQopQwN2EueUV5akcSVZB0IYn//vlfAEaEjKClW0t1A1kgczHVBkWBkppZNMVoUthz4iLZeQV4OeuJaNlEpjUQZWiAsbcP56ThGNlXsliw92Ne7jJJtbtvReUMhQbmJczFpJRwt18P7veJhOKrN/+GNnrQ1PzvQrUXDBo2bBijRo3innvuqfEw9dFNLRhUfBWWRN/yuQ0FxWRcKqDY+NfNULY6LX7uetz06g+BE/XLMY2R123zKUbhyRI9A0z2akcSFhhReNvmCoe1JTRXtLxT7Ez5q2xU0cj1YFv15qlaWzDIYDAQFRVFmzZteOeddzhz5kx130JUgaGgmJMXrpQpDgDFRhMnL1zBUFCsUjJRX7VSdAwvKZ2Ge7VNAYc1JSonEpb8V1fIYW0JejTElDjeenGoRTe15Oi5c+dYsWIFy5cvJykpiaioKEaNGsWAAQOwtW1Yf9ne1BXELTYxGU0KveZsITO30OLrGsDH1Z5Nk++T5iZRhqIoLDi4iG0Z23Gzc2NW95k01csKb/XF7rN7mb1vLgATO4+ju2+3mnnjajYxVfV37ZbXpN67dy9Lly7ls88+w9nZmaFDh/L888/Tpk2bW3nbekONNall7V9xKwqNhby2/TVO5p6kjXsbpkdOrxd35TZ2mXmZvPrHq1wpuUK/oH7mu+HVUCdrUmdmZrJhwwY2bNiATqejX79+HDx4kA4dOvDRRx/dylvfICcnhyFDhuDq6oq7uzujRo0iL6/i0Rr33XcfGo2mzGPMmDE1mqs2ZF+u2tVHVfcTjYu9zp6Y8BicbJ04eukoy5OWqx2p0btacpXZe2ZzpeQKbZu0NQ9Nru+qXSCKi4v59ttv+cc//kFgYCBff/01kyZNIiMjg+XLl7Nx40a++uor3njjjRoNOmTIEA4fPsyGDRtYt24dv//+O88++2ylx40ePZrMzEzz4/3336/RXLXBy0Vf+U7V2E80Pj5OPrwQ9gIaNGw4uYHf0n9TO1KjpSgKsYmxnM47TRN9E2LCY7DRWscA0mqn9PX1xWQyMXjwYOLj4wkNDb1hn549e+Lu7l4D8UolJyezfv16du/eTURE6fS38+fPp1+/fsyePRs/P79yj3V0dMTHx6fGstSFrkEe+LrpyTIUYKn9T0PpGsBdg6RtWZQvzCuM/7vt//j6yNd8fvBzmjs3p61HW7VjNTrfp37Prqxd2GhtiAmPwV3vrnakKqv2FcRHH31ERkYGCxYssFgcANzd3Tl+/PitZjOLi4vD3d3dXBwAoqKi0Gq17Nq1q8Jjv/zyS5o1a0ZISAjTpk3jypUrFe5fWFhIbm5umUdd02k1TO/fAeCG8Q3Xnk/v30E6qEWlHmnzCN18ulGilDBnzxwuXL2gdqRGZXfWbv6bUnoz3MiQkdzW5DaVE1VPtQvEU089hV5ft00bWVlZeHl5ldlmY2ODh4cHWVlZ5R735JNPsnLlSjZv3sy0adNYsWIFQ4dWPMf6rFmzcHNzMz/8/f1r5DNUV3SIL7FDu+DjVvbftY+bntihXYgO8VUll7AuWo2WsaFjCXAJwFBkYM6eORQZi9SO1Sik56bzyb5PUFDoHdibXgG91I5Ubao2hE2dOpX33nuvwn2Sk5Nv+v2v76Po2LEjvr6+9OrVi7S0NFq3bm3xmGnTphETE2N+npubq2qR6N3Bh/jjOWRfLsDLpbRZSa4cRHU42Djw8h0v8+q2V0kzpPFp4qdM6DJB7rSuRYZCAx/s/oACYwEhTUMYfvtwtSPdFFULxOTJkxk+fHiF+7Rq1QofHx+ys7PLbC8pKSEnJ6da/QvdupWOOU5NTS23QNjb22NvX3/uQNVpNTKUVdwyL0cvYiJieHvn28RlxuF3xI/H2z6udqwGqdhYzIcJH5J9NRtvR29eDH/Rajql/07V1J6ennh6ela6X2RkJJcuXSIhIYHw8HAAfvvtN0wmk/lHvyoSExOB0o52IRqbDk078EzHZ1h4YCHfHv0WXydf7m5xt9qxGhRFUYjdH8ufOX/iYOPAlDum4GznrHasm2YV15jt27cnOjqa0aNHEx8fz/bt2xk/fjxPPPGEeQTTmTNnaNeuHfHx8QCkpaXx5ptvkpCQwIkTJ/jhhx94+umnueeee+jUqZOaH0cI1fQM6MlDrR8CYOGBhSRdSFI5UcPyVcpXbM/Yjk6jY3L4ZFq4tFA70i2xigIBpaOR2rVrR69evejXrx933XUXixYtMr9eXFxMSkqKeZSSnZ0dGzdu5IEHHqBdu3ZMnjyZRx99lLVr16r1EYSoFwa3G1w6sslUwuw9szl1+ZTakRqEzemb+V/q/wAY3XE0HT07qpzo1t3yVBsNnRpTbQhR24qMRby5802OXDxCU31T3rrrLTxkzqabtvfsXj7Y/QEmTDwc/DBPtHtC7UgVqpOpNoQQ1slOZ8eUO6bg6+TLhYILvLvrXVlo6CYduXiEjxI+woSJe1rc06A6/6VACNFIudi5MK3rNNzs3Dh5+STv736fQqPlGYSFZacvn+a9+PcoMhUR6hnKc52ea1DDhxvOJxFCVJu3kzf/vPOfONk6kXIxhQ/3fEixSdYaqYqz+Wd5a9db5BXnEewebNXDWcsjBUKIRi7QNZBX7ngFO60diecS+WTfJxhNRrVj1WsXrl7grZ1vcbHgIi2cW/BK11fQ2zS8yTOlQAghaOvRlskRk7HR2LAzcycLEhdgUkyVH9gIGQoNvLXzLfONcP+681+42jXMASxSIIQQAIR6hfJi+IvoNDq2Z2wndn+sFIm/uVRwiZlxM8nIz6Cpvimv3fkaTfRN1I5Va6RACCHMInwimNhlIlq0/H76dz5N/FSam/6/nIIcZsbN5EzeGTz0Hrwe+TqejpXPBGHNpEAIIcro5tuNF8JeQIuWbWe28fG+jxt9x/X5q+d5I+4N85XD9Mjp+DhZ1zozN6NhdbkLIWpE9+bdsdXZMnfvXHZl7qLIWERMeAx2Oju1o9W505dP8/aut8kpyMHTwZPXI1/Hy9Gr8gMbALmCEEJYdIfPHbwc8TJ2Wjv2Ze/jzZ1vcrnostqx6lTapTRm7JhBTkEOzZ2bM6P7jEZTHEAKhBCiAqFeobza7VWcbJ04cvEIr29/newr2ZUf2AAknE3gjbg3uFx8mdZurZnRfQbNHJqpHatOSYEQQlSofdP2zOw+k6b6pmTkZ/Da9tc4evGo2rFqjaIo/HTsJ2bvnk2BsYCOzTryWuRrDXYoa0WkQAghKuXv4s+bPd4k0CWQS4WXmBE3g62ntqodq8YVm4r5/NDnLE9ajgkTvQJ6MbXrVBxsHNSOpgqZzbUSMpurEH+5WnKVBfsWsPvsbgD6BfVjSPshDWKKiQtXLzB371yOXDyCBg1D2g/hH63+gUbT8Jb4rervmhSISkiBEKIsk2LimyPf8O3RbwFo496GCV0mWHXn7aHzh5i3dx6GIgNOtk6MCx1HuHe42rFqjRSIGiIFQgjL4jPjWXhgIfnF+TjZOvFcp+fo5lv1JYDrg2JjMV8d+Yq1aWtRUAh0DSQmPKbB3+MgBaKGSIEQonzZV7L5eO/HpF5KBaCHXw9GhIzAxc5F5WSVO5l7kgWJCziZexKAXgG9GHb7MOx19ionq31SIGqIFAghKlZsKuabI9/wQ+oPmDDhZufG8NuHE+kXWS/b7wtKCvj26LesS1uHCRMudi6M6TSGCJ8ItaPVGSkQNUQKhBBVk3YpjU8TP+V03mkA2nu0Z0TICAJdA1VOVkpRFHZm7mRV8iqyr5bey9HNpxsjQ0birndXN1wdkwJRQ6RACFF1xcZi1qStYU3qGopMRWjRck+Le3i4zcOqtusnXUjiy+QvzU1hzRyaMeL2EY3qquF6UiBqiBQIIarv/NXzrExaSVxmHEBpofC/h3+0+gf+Lv51ksGkmNh7di9rj63lz5w/AdDr9Pyj9T/4R6t/NNp7G6ABFoi3336bH3/8kcTEROzs7Lh06VKlxyiKwvTp01m8eDGXLl2iR48exMbG0qZNmyqfVwqEEDfv6MWjfHPkGxLPJZq3tfdozwMtHyDCO6JWJv/LKchh2+ltbD61mcz8TABsNDb0DOjJ/7X5v0bXnGRJgysQ06dPx93dndOnT/P5559XqUC89957zJo1i+XLlxMUFMRrr73GwYMHSUpKQq+v2vKAUiCEuHUpOSn8eOxHdmftxkTpIkR6nZ4u3l24w/sObm92O272bjf13oqikJmfyb7sfezL3sfh84fN53CwcaB3YG/6BvXFQ+9RY5/H2jW4AnHNsmXLmDRpUqUFQlEU/Pz8mDx5Mi+99BIABoMBb29vli1bxhNPPFGl80mBEKLmXLh6gY0nN7LtzDbOXT1X5jU/Jz+CmwTj5+RHc+fmNNE3wdnWGUdbRwBKTCUUGYvIKczhwtULZOVncdxwnGOGY1wqvFTmvdo2acu9/vcS6RtpPl78paq/a9Z/f3w5jh8/TlZWFlFRUeZtbm5udOvWjbi4uHILRGFhIYWFhebnubm5tZ5ViMaiqUNTBrUbxONtHyftUho7M3dy4NwB0i+nk5GfQUZ+xk29r43GhvZN2xPmFUa4d3iDv9GtrjTYApGVlQWAt7d3me3e3t7m1yyZNWsWM2fOrNVsQjR2Go2G4CbBBDcJBiCvKI8/c/7k1OVTnMk7Q0ZeBrlFueQV53G15CpQWgRstDY00Tehqb4pzRybEeQaRJBbEC3dWjaKG9zqmqoFYurUqbz33nsV7pOcnEy7du3qKBFMmzaNmJgY8/Pc3Fz8/etm1IUQjZWznTMRPhEWh52aFBMaNPXypruGTtUCMXnyZIYPH17hPq1atbqp9/bxKb3EPHv2LL6+vubtZ8+eJTQ0tNzj7O3tsbeXv0SEqC+0GlmVQC2qFghPT088PT1r5b2DgoLw8fFh06ZN5oKQm5vLrl27GDt2bK2cUwghGhKrKc3p6ekkJiaSnp6O0WgkMTGRxMRE8vLyzPu0a9eO7777Diht45w0aRJvvfUWP/zwAwcPHuTpp5/Gz8+PgQMHqvQphBDCelhNJ/Xrr7/O8uXLzc/DwsIA2Lx5M/fddx8AKSkpGAwG8z5TpkwhPz+fZ599lkuXLnHXXXexfv36Kt8DIYQQjZnV3QdR1+Q+CCFEQ1PV3zWraWISQghRt6RACCGEsEgKhBBCCIusppNaCGFdjCaF+OM5ZF8uwMtFT9cgD3RaudnNmkiBEELUuPWHMpm5NolMQ4F5m6+bnun9OxAd4lvBkaI+kSYmIUSNWn8ok7Er95YpDgBZhgLGrtzL+kOZKiUT1SUFQghRY4wmhZlrk7A0dv7atplrkzCaZHS9NZACIYSoMfHHc264crieAmQaCog/nlN3ocRNkwIhhKgx2ZfLLw43s59QlxQIIUSN8XKp2jQ2Vd1PqEsKhBCixnQN8sDXTU95g1k1lI5m6hok60NbAykQQogao9NqmN6/A8ANReLa8+n9O8j9EFZCCoQQokZFh/gSO7QLPm5lm5F83PTEDu0i90FYEblRTghR46JDfOndwUfupLZyUiCEELVCp9UQ2bqp2jHELZACIUQVyLxCojGSAiFEJWReIdFYSYGoIUajkeLiYrVjiBq27Ug2b65NQgs0d9H99YKpmDfX7EdnKubu27xUy1eX7Ozs0GplXEtjIgXiFimKQlZWFpcuXVI7iqhhigI2VwqY0dNyAdAAuisXOHYsH00jaG3SarUEBQVhZ2endhRRR6RA3KJrxcHLywtHR0c0jeGXopHILyyh2PFKpft5N3HEyb5h/69kMpnIyMggMzOTgIAA+Z43ElbzrX777bf58ccfSUxMxM7Orkp/sQ8fPpzly5eX2danTx/Wr19fI5mMRqO5ODRtKqM1GpoCUxEam5JK99PZ2qHXN/y/qj09PcnIyKCkpARbW1u144g6YDUFoqioiMcee4zIyEg+//zzKh8XHR3N0qVLzc/t7e1rLNO1PgdHR8cae09Rf9hUsb29qvtZu2tNS0ajUQpEI2E1BWLmzJkALFu2rFrH2dvb4+PjUwuJ/iKX2w2Tk70OW52WYqOp3H1sdVqc7HXlvt6QyPe88Wnwf/ps2bIFLy8v2rZty9ixY7lw4YLakYSV0Gg0+LlXPOuon7tefjhFg9WgC0R0dDRffPEFmzZt4r333mPr1q307dsXo9FY7jGFhYXk5uaWeYjq27JlCxqNplqju1q2bMncuXNrLdPNcHOwI7CpI7a6sv+r2Oq0BDZ1xM2h4fc9iMZL1QIxdepUNBpNhY8///zzpt//iSee4KGHHqJjx44MHDiQdevWsXv3brZs2VLuMbNmzcLNzc388Pf3v+nz11fDhw9Ho9EwZsyYG14bN24cGo2G4cOH132wGpKTk8OQIUNwdXXF3d2dUaNGkZeXV+Exzz33HK1bt8bBwQFPT08GDBhg/u65OdjRzseFiyeSeWHow9wT0pLuHQJ5fGB/9u/fXxcfSQhVqFogJk+eTHJycoWPVq1a1dj5WrVqRbNmzUhNTS13n2nTpmEwGMyPU6dO1dj56xN/f39Wr17N1atXzdsKCgpYtWoVAQEBKia7dUOGDOHw4cNs2LCBdevW8fvvv/Pss89WeEx4eDhLly4lOTmZX375BUVReOCBB8xXm/n5+Twy4B+0CmrJrl27+OOPP3BxcaFPnz5yg6RouBQrs3TpUsXNze2mjj116pSi0WiUNWvWVPkYg8GgAIrBYLjhtatXrypJSUnK1atXSzeYTIpSdEWdh8lU5c80bNgwZcCAAUpISIiycuVK8/Yvv/xS6dSpkzJgwABl2LBh5u0FBQXKCy+8oHh6eir29vZKjx49lPj4+DLv+eOPPypt2rRR9Hq9ct999ylLly5VAOXixYvmfbZt26bcddddil6vV1q0aKG88MILSl5envn1wMBA5aOPPqry57AkKSlJAZTdu3ebt/3888+KRqNRzpw5U+X32b9/vwIoqampiqIoyu7duxVASU9PN+9z4MABBVCOHj16S5mtxQ3fd2G1Kvpdu57VjGJKT08nJyeH9PR0jEYjiYmJAAQHB+Ps7AxAu3btmDVrFg8//DB5eXnMnDmTRx99FB8fH9LS0pgyZQrBwcH06dOndkKWFMCS6Np578qMXA+2DtU7ZORIli5dypAhQwBYsmQJI0aMuKEJbsqUKXz77bcsX76cwMBA3n//ffr06UNqaioeHh6cOnWKRx55hHHjxvHss8+yZ88eJk+eXOY90tLSiI6O5q233mLJkiWcO3eO8ePHM378+DLDkK83fPhwTpw4UWGT4N/FxcXh7u5ORESEeVtUVBRarZZdu3bx8MMPV/oe+fn5LF26lKCgIHMTY9u2bWnatCmff/45r776Kkajkc8//5z27dvTsmXLKucTwppYTSf166+/TlhYGNOnTycvL4+wsDDCwsLYs2ePeZ+UlBQMBgMAOp2OAwcO8NBDD3HbbbcxatQowsPD2bZtW43eC2HNhg4dyh9//MHJkyc5efIk27dvZ+jQoWX2yc/PJzY2lg8++IC+ffvSoUMHFi9ejIODg/l+lNjYWFq3bs2cOXNo27YtQ4YMuaEPY9asWQwZMoRJkybRpk0bunfvzrx58/jiiy8oKLC8gL2vr2+1m7uysrLw8io7NYaNjQ0eHh5kZWVVeOynn36Ks7Mzzs7O/Pzzz2zYsME89t/FxYUtW7awcuVKHBwccHZ2Zv369fz888/Y2FjN31lCVIvVfLOXLVtW6T0QiqKY/9nBwYFffvmlllP9jY2+9C95NdhUfxF4T09PHnzwQZYtW4aiKDz44IM0a9aszD5paWkUFxfTo0cP8zZbW1u6du1KcnIyAMnJyXTr1q3McZGRkWWe79+/nwMHDvDll1+atymKgslk4vjx47Rv3/6GfLNmzaow/5gxY1i5cqX5eWUd0ZUZMmQIvXv3JjMzk9mzZ/P444+zfft29Ho9V69eZdSoUfTo0YP//Oc/GI1GZs+ezYMPPsju3btxcKje1ZsQ1sBqCoRV0Giq3cyjtpEjRzJ+/HgAFixYUGvnycvL47nnnmPChAk3vHazneJvvPEGL730UpltPj4+ZGdnl9lWUlJCTk5OpTdMXhu51qZNG+68806aNGnCd999x+DBg1m1ahUnTpwgLi7OPKPpqlWraNKkCWvWrOGJJ564qc8gRH0mBaKRi46OpqioCI1GY7FvpnXr1tjZ2bF9+3YCAwOB0ilGdu/ezaRJkwBo3749P/zwQ5njdu7cWeZ5ly5dSEpKIjg4uMaye3l53dCcFBkZyaVLl0hISCA8PByA3377DZPJdMNVTkUURUFRFAoLCwG4cuUKWq22zE1x156bTOXfaS2ENbOaPghRO3Q6HcnJySQlJaHT3ThlhJOTE2PHjuXll19m/fr1JCUlMXr0aK5cucKoUaOA0qaeo0eP8vLLL5OSksKqVatuaA585ZVX2LFjB+PHjycxMZGjR4+yZs0a89WLJdOmTePpp5+u1udp37490dHRjB49mvj4eLZv38748eN54okn8PPzA+DMmTO0a9eO+Ph4AI4dO8asWbNISEggPT2dHTt28Nhjj+Hg4EC/fv0A6N27NxcvXmTcuHEkJydz+PBhRowYgY2NDT179qxWRiGshRQIgaurK66uruW+/u677/Loo4/y1FNP0aVLF1JTU/nll19o0qQJUNpE9O233/L999/TuXNnFi5cyDvvvFPmPTp16sTWrVs5cuQId999N2FhYbz++uvmH21LMjMzSU9Pr/bn+fLLL2nXrh29evWiX79+3HXXXSxatMj8enFxMSkpKVy5UjqVt16vZ9u2bfTr14/g4GAGDRqEi4sLO3bsMF+htGvXjrVr13LgwAEiIyO5++67ycjIYP369fj6yqpyomHSKNf37Iob5Obm4ubmhsFguOFHtKCggOPHjxMUFIReX/1OYiGsiXzfG46KfteuJ1cQQgghLJICIYQQwiIpEEIIISySAiGEEMIiKRBCCCEskgIhhBDCIikQQgghLJICIYQQwiIpEEIIISySAiGsyowZMwgNDVU7BgD33XefecLC2tKyZUvmzp1b7eNee+21SpdZvd7ChQvp379/tc8jGjYpEI1UVlYWEydOJDg4GL1ej7e3Nz169CA2NtY8R5G1mTFjBhqNpsLHzdiyZQsajYZLly7VbOAq2L17d7V+6KH0v+3HH3/MP//5zyofM3LkSPbu3cu2bduqG1E0YFIgGqFjx44RFhbGr7/+yjvvvMO+ffuIi4tjypQprFu3jo0bN5Z7bHFxcR0mrZ6XXnqJzMxM86NFixa88cYbZbZdr6ioSKWkVefp6Ymjo2O1jvnss8/o3r27eXr2qrCzs+PJJ59k3rx51Y0oGjApEDVIURQKSgpUeVRnzsXnn38eGxsb9uzZw+OPP0779u1p1aoVAwYM4McffyzT1KDRaIiNjeWhhx7CycmJt99+G/hrmVE7Ozvatm3LihUrzMecOHECjUZjXjcc4NKlS2g0GvP60tf+Kt+0aRMRERE4OjrSvXt3UlJSymR999138fb2xsXFhVGjRpW7PCmAs7MzPj4+5odOp8PFxcX8/IknnmD8+PFMmjSJZs2a0adPn0qznjhxwjydd5MmTdBoNGWWUzWZTEyZMgUPDw98fHyYMWNGlf87QOl3ZsaMGQQEBGBvb4+fn1+ZRZX+3sSk0Wj47LPPePjhh3F0dKRNmzY3rMWxevXqMv8Nz507h4+PT5kZdnfs2IGdnR2bNm0yb+vfvz8//PADV69erdZnEA2XLBhUgwqNhQxbP0yVcy+PXo6+CsuOXrhwwXzl4OTkZHGfvzfFzJgxg3fffZe5c+diY2PDd999x8SJE5k7dy5RUVGsW7eOESNG0KJFi2qvjfDPf/6TOXPm4OnpyZgxYxg5ciTbt28H4KuvvmLGjBksWLCAu+66ixUrVjBv3jxatWpVrXNcb/ny5YwdO9Z8jsr4+/vz7bff8uijj5KSkoKrq2uZ5UWXL19OTEwMu3btIi4ujuHDh9OjRw969+4NwPDhwzlx4oS5MP7dt99+y0cffcTq1au5/fbbycrKYv/+/RVmmjlzJu+//z4ffPAB8+fPZ8iQIZw8eRIPDw9ycnJISkoiIiLCvL+npydLlixh4MCBPPDAA7Rt25annnqK8ePH06tXL/N+ERERlJSUsGvXLu67774q/fsRDZsUiEYmNTUVRVFo27Ztme3NmjUz/3U+btw43nvvPfNrTz75JCNGjDA/Hzx4MMOHD+f5558HICYmhp07dzJ79uxqF4i3336be++9F4CpU6fy4IMPUlBQgF6vZ+7cuYwaNcq8MNFbb73Fxo0bK7yKqEybNm14//33zc9PnDhR4f46nQ4PDw+gdAU7d3f3Mq936tSJ6dOnm9/7k08+YdOmTeYC4evrW+GKc+np6fj4+BAVFYWtrS0BAQF07dq1wkzDhw9n8ODBALzzzjvMmzeP+Ph4oqOjSU9PR1GUG9bZ6NevH6NHj2bIkCFERETg5OR0w5rfjo6OuLm5cfLkyQrPLxoPKRA1yF5nz/Lo5aqd+1bEx8djMpkYMmSIeZnNa67/axQgOTn5ho7THj168PHHH1f7vJ06dTL/87WFd7KzswkICCA5OZkxY8aU2T8yMpLNmzdX+zzXXFuGtKZcnx9KP8P1a2L//Uf47x577DHmzp1Lq1atiI6Opl+/fvTv3x8bm/L/17z+nE5OTri6uprPea15yNJ6DbNnzyYkJISvv/6ahIQE7O1v/M44ODhY7SCFa4wmhfjjOWRfLsDLRU/XIA902psboNDYWUUfxIkTJxg1ahRBQUE4ODjQunVrpk+fXmknY0FBAePGjaNp06Y4Ozvz6KOPcvbs2VrLqdFo0NvoVXlUdYROcHAwGo3mhrb+Vq1aERwcXKb55JrymqLKo9WWfq2u7xcpr3Pb1tbW/M/XPkNtrvH8989SnayWXJ8fqPYa1f7+/qSkpPDpp5/i4ODA888/zz333FNhhorO2axZMwAuXrx4w3FpaWlkZGRgMpnKvXLKycnB09Ozyvnrm/WHMrnrvd8YvHgnE1cnMnjxTu567zfWH8qs/GBxA6soEH/++Scmk4l///vfHD58mI8++oiFCxfy6quvVnjciy++yNq1a/n666/ZunUrGRkZPPLII3WUun5q2rQpvXv35pNPPiE/P/+m3qN9+/Y3tOFv376dDh06AJh/YK4fNXR9J3B1zrNr164y23bu3Fnt96lIVbLa2dkBYDQaa/Tc1zg4ONC/f3/mzZvHli1biIuL4+DBgzf1Xq1bt8bV1ZWkpKQy24uKihg6dCiDBg3izTff5JlnnilzpQOlBaSgoICwsLCb/ixqWn8ok7Er95JpKNsEmWUoYOzKvVIkboJVNDFFR0cTHR1tft6qVStSUlKIjY1l9uzZFo8xGAx8/vnnrFq1ivvvvx+ApUuX0r59e3bu3Mmdd95ZJ9nro08//ZQePXoQERHBjBkz6NSpE1qtlt27d/Pnn39W2gzz8ssv8/jjjxMWFkZUVBRr167lf//7n3l4rIODA3feeSfvvvsuQUFBZGdn869//avaOSdOnMjw4cOJiIigR48efPnllxw+fPiWOqn/ripZAwMD0Wg0rFu3jn79+uHg4ICzs3OV3n/atGmcOXOGL774wuLry5Ytw2g00q1bNxwdHVm5ciUODg7VGqJ6Pa1WS1RUFH/88QcDBw40b//nP/+JwWBg3rx5ODs789NPPzFy5EjWrVtn3mfbtm20atWK1q1b39S51WQ0Kcxcm4SlsXwKoAFmrk2idwcfaW6qBqu4grDEYDCYOw8tSUhIoLi4mKioKPO2du3aERAQQFxcXLnHFRYWkpubW+bR0LRu3Zp9+/YRFRXFtGnT6Ny5MxEREcyfP5+XXnqJN998s8LjBw4cyMcff8zs2bO5/fbb+fe//83SpUvLjHxZsmQJJSUlhIeHM2nSJN56661q5xw0aBCvvfYaU6ZMITw8nJMnTzJ27Nhqv09lKsvavHlzZs6cydSpU/H29mb8+PFVfu/MzEzS09PLfd3d3Z3FixfTo0cPOnXqxMaNG1m7di1Nmza96c/zzDPPsHr1anOz05YtW5g7dy4rVqzA1dUVrVbLihUr2LZtG7Gxsebj/vOf/zB69OibPq+a4o/n3HDlcD0FyDQUEH88p1rvazQpxKVdYE3iGeLSLmA0VX04eUOgUaozgL6eSE1NJTw8nNmzZ5f7hV61ahUjRoy4ocO1a9eu9OzZs8wonevNmDGDmTNn3rDd0uLesoi7qI8URaFbt268+OKL5tFOlTl8+DD3338/R44cwc3NzeI+9fn7vibxDBNXJ1a638dPhDIgtHmV3nP9oUxmrk0qU3h83fRM79+B6BDfm41aL+Tm5uLm5mbxd+16ql5BTJ06tdKpEf78888yx5w5c4bo6Ggee+yxWvlrZ9q0aRgMBvPj1KlTNX4OIWqTRqNh0aJFlJSUVPmYzMxMvvjii3KLQ33n5VK1glXV/aQ/o5SqfRCTJ08uc1eqJde3N2dkZNCzZ0+6d+/OokWLKjzOx8eHoqIiLl26VGbs+tmzZ/Hx8Sn3OHt7e4vD/4SwJqGhodWa1PD6plhr1DXIA183PVmGAov9EBrAx610yGtlpD/jL6oWCE9PzyoPqTtz5gw9e/YkPDycpUuXmocnlic8PBxbW1s2bdrEo48+CkBKSgrp6elERkbecnYhRP2h02qY3r8DY1fuRQNlftyv/YRP79+hSj/o1enPiGx9831F1sAqOqnPnDnDfffdR0BAALNnz+bcuXNkZWWRlZVVZp927doRHx8PgJubG6NGjSImJobNmzeTkJDAiBEjiIyMbNQjmIRoqKJDfIkd2gUft7LNSD5uemKHdqlyv0H25ardqV/V/ayZVQxz3bBhA6mpqaSmptKiRYsyr13rYy8uLiYlJaXMXaAfffQRWq2WRx99lMLCQvr06cOnn35a4/mssJ9fiGqzhu95dIgvvTv43NKd1DXdn2HNrHIUU12qqLffaDRy5MgRvLy8bmlYohDWwGAwkJGRQXBw8A13czckRpPCXe/9Vml/xh+v3G+1fRBVHcVkFVcQ9ZVOp8Pd3d18R6qjo+NNL0ojRH1mMpk4d+4cjo6OFc4T1RDUZH+GtWvY/6XrwLURUX+ftkCIhkar1RIQENAo/gi61p/x9/sgfBrIfRBVJU1MlajqpZjRaKzXq60Jcavs7OwqHT3Y0DTUmWGliamO6XQ6dDqd2jGEEDVIp9U0+KGsFWlcfw4IIYSoMikQQgghLJICIYQQwiLpg6jEtT78hjjttxCicbr2e1bZGCUpEJW4fPkyULo0pBBCNCSXL1+ucAZfGeZaCZPJREZGBi4uLtUa/52bm4u/vz+nTp2qcBhZfSKZ64a1Zba2vCCZK6MoCpcvX8bPz6/CoctyBVEJrVZ7w/xP1eHq6mo1X9BrJHPdsLbM1pYXJHNFqrL2h3RSCyGEsEgKhBBCCIukQNQSe3t7pk+fblWr00nmumFtma0tL0jmmiKd1EIIISySKwghhBAWSYEQQghhkRQIIYQQFkmBEEIIYZEUiFqwYMECWrZsiV6vp1u3bsTHx6sdqUK///47/fv3x8/PD41Gw/fff692pArNmjWLO+64AxcXF7y8vBg4cCApKSlqx6pQbGwsnTp1Mt8EFRkZyc8//6x2rGp599130Wg0TJo0Se0o5ZoxYwYajabMo127dmrHqtSZM2cYOnQoTZs2xcHBgY4dO7Jnzx61Y0mBqGn//e9/iYmJYfr06ezdu5fOnTvTp0+fer0kaX5+Pp07d2bBggVqR6mSrVu3Mm7cOHbu3MmGDRsoLi7mgQceID8/X+1o5WrRogXvvvsuCQkJ7Nmzh/vvv58BAwZw+PBhtaNVye7du/n3v/9Np06d1I5Sqdtvv53MzEzz448//lA7UoUuXrxIjx49sLW15eeffyYpKYk5c+bQpEkTtaOBImpU165dlXHjxpmfG41Gxc/PT5k1a5aKqaoOUL777ju1Y1RLdna2Aihbt25VO0q1NGnSRPnss8/UjlGpy5cvK23atFE2bNig3HvvvcrEiRPVjlSu6dOnK507d1Y7RrW88soryl133aV2DIvkCqIGFRUVkZCQQFRUlHmbVqslKiqKuLg4FZM1bAaDAQAPDw+Vk1SN0Whk9erV5OfnExkZqXacSo0bN44HH3ywzPe6Pjt69Ch+fn60atWKIUOGkJ6ernakCv3www9ERETw2GOP4eXlRVhYGIsXL1Y7FiBNTDXq/PnzGI1GvL29y2z39vYmKytLpVQNm8lkYtKkSfTo0YOQkBC141To4MGDODs7Y29vz5gxY/juu+/o0KGD2rEqtHr1avbu3cusWbPUjlIl3bp1Y9myZaxfv57Y2FiOHz/O3XffbZ62vz46duwYsbGxtGnThl9++YWxY8cyYcIEli9frnY0mc1VWLdx48Zx6NChet/ODNC2bVsSExMxGAx88803DBs2jK1bt9bbInHq1CkmTpzIhg0b0Ov1asepkr59+5r/uVOnTnTr1o3AwEC++uorRo0apWKy8plMJiIiInjnnXcACAsL49ChQyxcuJBhw4apmk2uIGpQs2bN0Ol0nD17tsz2s2fP4uPjo1Kqhmv8+PGsW7eOzZs339KU7HXFzs6O4OBgwsPDmTVrFp07d+bjjz9WO1a5EhISyM7OpkuXLtjY2GBjY8PWrVuZN28eNjY2GI1GtSNWyt3dndtuu43U1FS1o5TL19f3hj8S2rdvXy+axqRA1CA7OzvCw8PZtGmTeZvJZGLTpk1W0dZsLRRFYfz48Xz33Xf89ttvBAUFqR3ppphMJgoLC9WOUa5evXpx8OBBEhMTzY+IiAiGDBlCYmIiOp1O7YiVysvLIy0tDV9fX7WjlKtHjx43DNM+cuQIgYGBKiX6izQx1bCYmBiGDRtGREQEXbt2Ze7cueTn5zNixAi1o5UrLy+vzF9Yx48fJzExEQ8PDwICAlRMZtm4ceNYtWoVa9aswcXFxdy/4+bmhoODg8rpLJs2bRp9+/YlICCAy5cvs2rVKrZs2cIvv/yidrRyubi43NCv4+TkRNOmTettf89LL71E//79CQwMJCMjg+nTp6PT6Rg8eLDa0cr14osv0r17d9555x0ef/xx4uPjWbRoEYsWLVI7mgxzrQ3z589XAgICFDs7O6Vr167Kzp071Y5Uoc2bNyvADY9hw4apHc0iS1kBZenSpWpHK9fIkSOVwMBAxc7OTvH09FR69eql/Prrr2rHqrb6Psx10KBBiq+vr2JnZ6c0b95cGTRokJKamqp2rEqtXbtWCQkJUezt7ZV27dopixYtUjuSoiiKItN9CyGEsEj6IIQQQlgkBUIIIYRFUiCEEEJYJAVCCCGERVIghBBCWCQFQgghhEVSIIQQQlgkBUIIIYRFUiCEEEJYJAVCCCGERVIghFDRuXPn8PHxMa8FALBjxw7s7OzKzAoshBpkLiYhVPbTTz8xcOBAduzYQdu2bQkNDWXAgAF8+OGHakcTjZwUCCHqgXHjxrFx40YiIiI4ePAgu3fvxt7eXu1YopGTAiFEPXD16lVCQkI4deoUCQkJdOzYUe1IQkgfhBD1QVpaGhkZGZhMJk6cOKF2HCEAuYIQQnVFRUV07dqV0NBQ2rZty9y5czl48CBeXl5qRxONnBQIIVT28ssv880337B//36cnZ259957cXNzY926dWpHE42cNDEJoaItW7Ywd+5cVqxYgaurK1qtlhUrVrBt2zZiY2PVjicaObmCEEIIYZFcQQghhLBICoQQQgiLpEAIIYSwSAqEEEIIi6RACCGEsEgKhBBCCIukQAghhLBICoQQQgiLpEAIIYSwSAqEEEIIi6RACCGEsEgKhBBCCIv+H5Uef0sRBHl8AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "print(\"\\033[1mPrevious Model:\\033[0m\")\n", "print(f\"{s.model}\\n\")\n", @@ -516,7 +372,7 @@ "print(\"\\n\\033[1mUpdated Model:\\033[0m\")\n", "print(s.model)\n", "\n", - "plot_from_state(s)" + "plot_from_state(s,'sin(x)')" ] }, { @@ -532,76 +388,14 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 24.85it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", - " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", - " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", - " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", - " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", - " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 0.433323\n", - "1 4.983216\n", - "2 4.116570\n", - "3 2.816600\n", - "4 2.599939\n", - "5 5.416539\n", - "6 0.433323\n", - "7 4.333231\n", - "8 1.299969\n", - "9 0.433323, experiment_data= x y\n", - "0 0.433323 0.724606\n", - "1 4.983216 -2.003534\n", - "2 4.116570 -0.077238\n", - "3 2.816600 1.259866\n", - "4 2.599939 -1.435481\n", - "5 5.416539 -2.064342\n", - "6 0.433323 0.547730\n", - "7 4.333231 -1.245219\n", - "8 1.299969 0.946749\n", - "9 0.433323 -0.433155\n", - "10 0.433323 0.724606\n", - "11 4.983216 -2.003534\n", - "12 4.116570 -0.077238\n", - "13 2.816600 1.259866\n", - "14 2.599939 -1.435481\n", - "15 5.416539 -2.064342\n", - "16 0.433323 0.547730\n", - "17 4.333231 -1.245219\n", - "18 1.299969 0.946749\n", - "19 0.433323 -0.433155, models=[-0.38, -0.38])\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "s = theorist(s)\n", "s = experiment_runner(s, added_noise=1.0, random_state=42)\n", "s = experimentalist(s, num_samples=10, random_state=42)\n", "\n", "print(s)\n", - "plot_from_state(s)" + "plot_from_state(s,'sin(x)')" ] }, { @@ -624,92 +418,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 24.96it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 1:\u001b[0m\n", - "\u001b[1mCycle 1 model: -0.38\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:03<00:00, 27.19it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 2:\u001b[0m\n", - "\u001b[1mCycle 2 model: -0.27\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", 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nvX4B8NDQUAAyMzOJiIjgyJEjjBkzptT2nTt3Ztu2bVU+T4n27dtXe9+yXF8/FL+H65cslEXTS5OwF7WCRqMxqSlFTY0bN0aj0dzUNl7SNHJ9E0WJ8pp7yqPVFt+Gu/4+Qnk3dq9fALyk+aiiK+GauvG9VKXWslS0gLkpHG3RdLlBK4SV1K1bl759+/Luu++Sm5tbrWM0a9bspjbvn376ybgAeUlYXN/75foboFU5z+7du0s9d+NC5jVlSq0lC6Lr9XqznruEIy2aLlf2QljRf/7zH6Kjo4mKimLatGm0atUKrVbL3r17+eOPPypt6pg0aRIPPvggbdu2pU+fPqxZs4avv/7a2GXT3d2dTp06MWvWLBo0aEBmZiYvv/xylescP348I0aMICoqiujoaD755BMOHTpUoxu0NzKl1sjISDQaDWvXrmXAgAG4u7vj5eVl0vFl0fQb6rPYkYUQN2nUqBG//vorffr0YcqUKbRu3ZqoqCgWLFjA888/z6uvvlrh/oMHD+add95h7ty5tGjRgvfff58lS5aU6sHx4YcfUlRURPv27ZkwYQKvvfZalescMmQIr7zyCi+88ALt27cnLS2NsWPHVvk4lams1ltuuYXp06czefJkgoODGTdunMnHlkXTS5MFx4XdscaC44qikJuvp8hgwEmrxdNVV+1RoMJxWGrRdHMsOC7NOELcIOtaAWcv51Go/+dmn7NOS5ifG77uLipWJmxdyaLpVWn3t9ai6RL2Qlwn61oBaX/f3P2tUG8g7e+rRNZFAl9UyFYXTZc2eyH+R1EUzl6ueNDQ2ctVmx5BCFshYS/E/+Tm60s13ZSlUG8gN98y3QCFsCQJe2G3zH2FXWTigBxTtxPCXMzxuy5hL+xOyShGcw8td9Ka9udg6nZCmEvJ73p5I3tNITdohd3R6XT4+fkZRyF6eHiY3C1SURSuFujRGwzotFo8XP7pUqlTFHSGogqv3J20WnRKIXl5RTV/I0JUQlEUrl69SmZmJn5+fuh0umofS8Je2KWQkBCAm4adV+RagZ6sa4UUGf75Suyk1eDr7oy7S/EfUX6Bnou5xQtrXP/FueSjxN/ThdTc6v/BCVEdfn5+xt/56pKwF3ZJo9EQGhpKUFCQSZNn/Xgsk2lrD5f7+rTY5nS7Nci47cJtJzh/5Z8ZKAO9XInr2Yjm/9tGCGtxdnau0RV9CRlBK2o9vUGh6xtbSc8qu1ulBgjxdWPHi73QaTXGffakXCQzJ48gbzc6NPA3viaELZERtEJVthSWe1Iulhv0UNxck56Vx56Ui3RuVDwvik6rMf5biNrArsL+hx9+YM6cOezbt4/09HRWrVpVanY5YRs2HExn+prDpQI21NeN+NjmxLQMtXo9mTmmra5k6nZC2CO76kOWm5tL69atWbhwodXOqTco7DzxN98mn2Hnib/RGxym1ataNhxMZ+zH+2+6ks7IymPsx/vZcDC9nD0tJ8jbtMnSTN1OCHtkV1f2/fv3p3///lY7n61doVqSOZpd9AaF6WsOU9bHoUJx2/j0NYfp2zzEqk06HRr4E+rrRkZWXpm1lbTZd2jgb7WahLA2uwp7ayq5Qr0xHEquUBOHtas1gV/Zh5piMJCdfZLMC3+QnXuO7KvnuVaQg96gx6AUodXocHP25PwVLX4F2WhcArhYGEae4l3qPGW1jVuDTqshPrY5Yz/ej4ayu1TGxzaXG7CiVqvVYZ+fn19qAefs7GyT9rPVK1RLuPFDTUMREa5HCFKO8+nG03y3N5fLmqvkKZXPB6M3KDiHGAgAAgA3gxOuBV4oBXXJzq9P2rVWZOmDVWkbj2kZSuKwdjd9qIXU0m9qQtyoVod9QkIC06dPr/J+1em9YY9KPtS8tOdp6vUTHu4nuOp2kQKtnnwgH8gq1ODmrEUD+Ovc8NN54O3sgYfODZ3WCZ1Gi14xkFeUx8W8q6Reu0S+rpBCrZ48bRF5bpfB7TJwgki24Fnkyoljzfjd+W6a3RqLs7OH1d5vTMtQ+jYPsZleQkJYU60O+ylTpjBx4kTjz9nZ2YSHh1e6nyP03rh69QJfbptPY4+tZPtnUQAU/O81Z0WLR74PhvxgsvMjGHVXDL3aROPs6lnhMUv6s2dk5eGmySbE5S/quqbi7nIWg9t5cpyvcdW5gN25B9i95wDue+fQ0a8pdza9j2a3DkKrs/yvo3SpFI6qVoe9q6srrq6uVd6vNvfe+PP4BjYf/Iidl49yVV9EgVvxPDDeBR4o1yI5d/V2UvJaYbjuVyPPtUWlQQ+l28bzFB9S8tuQkt8GKG768tRe5oWumVwt3Mf+y8fIMhSQdOkQSbsOEbB3Ln3CutIrKg5fv/oWeOdCODa7CvsrV65w/Phx488pKSkkJyfj7+9PRESE2c5T23pvGPRF7P3tQ9b+8QXH8i8Ynw/ReXHhfH1OXInm96LIcvevyodaxW3jvYxt4wZ9EX/8uZYdx1ay8+IRLuivseLUJr46tYUufk0ZdMd46tXrVI13K4Qoi11Nl5CUlETPnj1ven748OEsXbq00v2rMl1CyY1LKLv3hj30xjHoi9jxy0K+PvYF6UW5ADihJdqvKb1bDqNRgxi6zUmq9EPt+mkETFWVrpyF+bnsTF7ExhNrOJ5/0XjuDt4NuDdqPA3q96jSuYVwJKbmml2FfU1VdW4ce+1nrxgM7En+gM8PfcSZoisAeGmcuCu0C/06PItfnQbGbW3tQ+34ie9Z/Wsiu3NSjM918WnMg52nEBrW3mp1CGEvJOzLUJ2J0GxpjhdT/Hl8A8v3zuVoXnFzjZfGibvr9SSmyxTcPcpudrLFD7VTp3ayau/b/Jx1DAXQoqFvQFse7PE6Xt62+0ErhLVJ2JdB7VkvLfnBkZV1kk+3TSbpUvE0vi4aLbGh3bi72yt4eASoWltNpKZu5/M9c9mfewoAb60zQxrdS+/OL1il944Qtk7Cvgxqhn1Vrp6rErwGfRHbds3l0z+/4opSvHpSd79mDOk2nboBt1ruDVnZoSMrWbLvHU4VFg+Ma+zqzxPR04mM7KZyZdZnqx/MQh0S9mVQK+zLm3qhrHbxqnwoZGQk837Sixy+dg6ASGdfHu80mVsbW2/+IGvSFxWw6ecEVvy1hmtKETo03B3ahQd6zjapa2htYItNbkJdEvZlUCPsq7JwxqbDGSZ9KBj0RWzc8RqfpawhX9HjqtExpP5AYrq+jM7JxZJvxyZcvHicpVsnGW/i1nP2Ji56Gg0b9Fa5MsuqykWDcBym5ppdTXFsj0ydemHXib8rnI8HiufjOX/+KDO/jGXpX9+Qr+hp4R7CnP5LGNhjhkMEPYC/f2Mm/msVz7d5Gl+tC6cLc3g56Xm+2jQRfVFB5QewQ5XN1wTFvx8yBbcoj4S9hZk6pcLOvy5U+qEQoF/L82se4cC1dFw0Wh5r/C9efmAtwcGtzFStfbmj7WPMHbySTj6N0KPw5emtTP9yABfOH1G7NLOrynxNQpRFwt7CTB99Wv4NNmfy6BaQiCZwM7lKEQ1d6vDGXYu4q9vLDt8jxcc3nAn3fsnTLR7DTaPjaN4FXlj3KLv3L1K7NLNyhPmahGVJ2FtYydQL5UW5huIbbOVNzhXolEaHW2aT5Z0GwJ0+Ucx4cB1hYVGWKdgOabRaunZ4mtn9l9DY1Z9cpYi3fvsPy9Y9QVFh7Qi/2jxfk7AOCXsLK5kcrCLxsc3p1LDuTR8KzTx+JOKWReS4XMXFoMMzexBx939g1WmB7UlwcCumPbCW2JDiOXXWZ+5hxld3c/Hi8Ur2tH2mXjTYy3xNwvok7K0gpmUoT9zZgBu7Qms18MSdDYhpGVrqQ0FLEZ3rLMc5eB2FWj0+BV6kno5jeP846U9dCWdnD4b1f4/n2ozDXePE0bwLTF7zCEf/XKd2aTVy/e/Hjb8B1lhtS9Zitn/S9dIKqtJlbt2vf/D1jjjOu2QC4JsTwV8FcbxyT1vpVldF6Wf38dbWZzlZmI0TWh6/7RF6dn5e7bJqRI1+9tK337ZJP/sy2Ho/+0t//8HsjWNIK8xCo2jo4X83rW+fICMka+Da1Yskrn/M2Ce/f2AUj8b8x667qVpzBK307bd9EvZlUCPsd574m4cX76p0u3fvzmX1iblkGQrw1brwXJd4mjYZaIUKaz+DvohVW1/ki9NbAGjjWY/xdy81ac4gR1aVCxW5GFGPDKqyEaZ0hWvltYmPjyaQZSgg0tmX1/svkaA3I63Oifv7vsnE1k/hotGSnHuaqV/fx/nMQ2qXZtOkb3/tImFvYRV3hTPQye9TCNyCHgPtvCKYft8qAoNaWK0+R9Kx3RNM6/YGfloXThVm88p3o0hN3a52WTZL+vbXLhL2FlZelzkNRXQLeJ+rdX5Ho9EQE9SeSfd9Xe6c88I8GjXqy+sDPyLc2YdLhnzikyby28EVapdlk6Rvf+0iYW9hZXWZcyaPbsHvGAdK9Qvoy6iBHzj8aFhrCQi4jemDv6CFewh5ip439r7BD7vfUbssmyN9+2sXCXsrKFmEO8TXDQ9NFp3C3uSyx3l0aLj3lqE8dd+baLTyv8KaPL1CmHL/13T1a4oehYWHl7Bm28tql2VT1O7bL8xLeuNY0cVLqUz/9lHO6LNx1+iY1OklWjW/3+p1iH8Y9EV8sjGOted2AxAb0omh/f4jH77XkX72tk26XpZBzbDPPHeQ179/koyiXHy1Lvy7+xzq1+9u1RpE+VZv/TefpK0HoEedFjwZu0ya1a4jq2PZLgn7MqgV9qdP7+K1LeO5ZMgnSOfBS30XEhLa1mrnF6bZtnMui/74BAMKHX0a8vTdyx1mBSxhv6SfvY1ISU1i2panuWTIp56zN9Pv/shsQS/zlZhXz87PM6H1WJzQsjv7L+Z88yD5eVlqlyWEWciVvQUd/XMds36K56pSRCMXf6bELsfb5xazHFvaUS3n90OfM3fvbPIVPc3cgnhh0Kcy2lbYLLmyV9mhIyuZ+dNUripFNHML4uV7vzBr0I/9eP9NoxszsvIY+/F+NhxMN8t5HFWrFkN4KXoG7honjuRl8vo3D3IlR/6bCvsmYW8ByQc+IWHX6+Qpem73CGPKvV+a7cpQ1iK1jqZNBjK1+xt4aZw4nn+RGd8+RFbWSbXLEqLaJOzN7Jfkpcz55U0KMdDeK5IXB3+Jq5uv2Y4v85VYT8MGvZnW+x18tS6kFWYxY81Qsi6nql2WENUiYW9Gu/cv4q1f36EIAx29G/Ds4M/M3ptD5iuxrvDwaKb1/Q91tK6cLsxh+pphtWLlK+F4JOzNZOe+95j3WyJ6FKJ9mzB+8OcWWT5Q5iuxvrCwKKbd9R51dW6cKbrCjHXD+fvCsSodQ3pOCbXJqBEz+HnvQhYc/AADCt38buOpez622ICckvlKMrLyymy3L5ljXOYrMa+Q0LZM6/cBr258gvSiXKavH8nUAf8lIOC2SveVnlPCFsiVfQ3t2LPAGPQ96jS3aNCDzFeipqDglkyNWUyQzoNz+lymrx/FhfNHKtxHek4JWyFhXwM/7pnPwkMfYkChp39Lnoz9yCpD7K+fWO16Ib5uskychQUGtSC+/38J0nmQqb/K9O8eKzfwq9pzSpp6hCXJoKpq+nHPfP5zaAkGFHr5387ou5dUGvTmnl9E5itRz98XjjF9/UjO6XMJ0nkQ3/+/BAQ2K7WNqUtSfja6E1nXCqSpR1SLzI1TBnOF/Y49C4xX9L3r3s7jAysPemm3rX0qC/xvk88wfkVypccZFV2fJT+lyqLeolpkBK2FXB/0vfxND3ppt6196gbcSvyAJQTrPP9p0rnwh/F1U3tEfZN8VtVBctJ85BjsLuwXLlxI/fr1cXNzo2PHjuzZs8dq5/5578JSQW9q042MeK29bgr89aOM3TJNWenJ39OZi7kF5R7f0oPkNhxMp+sbW3l48S7Gr0jm4cW76PrGVrkAqYXsKuw///xzJk6cSHx8PPv376d169b069ePzMxMi5/7+u6VPf1bmhT0ICNeHUHdgFuZOuC6m7brR/L3hWMm9Zy6t41p8yVZYpCcfON0LHYV9m+99RajR49m5MiRNG/enPfeew8PDw8+/PBDi5535773rute2YIn7l5qcq8bGfFqWyzVZBEQcJuxl845fS4zvisO/Mp6TvVpHmLS8c09SE6+cdqeLT+/YdEJ9+xmUFVBQQH79u1jypQpxue0Wi19+vRh586dZe6Tn59Pfn6+8efs7Owqnzf5wCfM//19DCh092tW5RWMZMSr7bD0TfKAwGZMjVnMjA2jySjK5dXvRjJ14DJiWjamb/OQMntO6Q2KKoPkqvKNs3OjumY9t7jZyk3P8cXpLWxO2cCMf621yKI5dnNlf+HCBfR6PcHBwaWeDw4OJiMjo8x9EhIS8PX1NT7Cw8OrfN4G4dHc4uxNN7/bGHPP8ir3ozel3TZURrxanLWaLAKDWvBKv0UE6txJL8plxrrhXLp4Ap1WQ+dGdRnU5hY6N6pr7CKr1iA5+cZpO1ZtnsQXp7cA0Dm0s8VWR7ObsK+OKVOmkJWVZXycOnWqysfw9avPtEGfV3tkrIx4VZ+1myyCglsytd9iAq4L/MuXUsrdXo1BcgFermbdTlTPqi0vsOLUJgAeiYzhnl4zLXYuu2nGCQgIQKfTce7cuVLPnzt3jpCQsts9XV1dcXWt+S+rl3fN/thK/phvbEIIkX72VqFGk0Vx4L/P9I1PcLboCjPWPkp87Mf4+tUvc/uYlqHlNvVYhKmfa9JkbzHfbp3MipPfA/BQxF0M6jXLouezm7B3cXGhffv2bNmyhcGDBwNgMBjYsmUL48aNU7c4E1j9j1kYqdVkERzciql3vcf0jWM4U3SF6WseJT52ebmBX9LUYw0XcvMr36gK24mqWb3133yatgGAh8L7cm/v2RY/p10140ycOJHFixezbNkyjhw5wtixY8nNzWXkyJFql2aS8tpthWWpeZM8JKQN8Xcl4q9140xRDtPXPGoTC6BIxwH1rN76bz5JWw/AkPA+3NtnjlXOa1dhP2TIEObOncvUqVNp06YNycnJbNiw4aabtkJcT+2b5CGhbW0u8NtH1qGyaw2tpng7YT7fbp1sDPoH6/Xmvj5zrXbuKof98OHD+eGHHyxRi0nGjRtHWloa+fn57N69m44dO6pWi7APtnCTvKzAr+imraXtS7tEZfejDUrxdsI8vtnyorHp5oF6vbi/75tWPX+Vwz4rK4s+ffrQpEkTZs6cyZkzZyxRlxBmZQvTQt8Y+DPWPsqliycsft6ySNdL6/pmy4t8dnIjUHxF/6++b1m9hmrNenn+/HmWL1/OsmXLOHz4MH369OGxxx5j0KBBODs7W6JOszDnFMfCPtnCtNAZGcnM+H4Mf+vzCHXyZOrAZfj7N7ZqDVWZflkGVdXM15uf5/NTm4HiNnpzN91YdNbLwMBAJk6cyG+//cbu3btp3Lgxjz76KGFhYTz77LP8+eef1S5cCEuyhZvkISFtiO+3yNgPf/q64aVmy7QGte9jOALFYODL7581Bv1D4X2t2kZ/oxrdoE1PT2fTpk1s2rQJnU7HgAEDOHDgAM2bN+ftt982V41C1DrBwa2I71e8xGFGUfESh5nnDlrt/LZwH6M2UwwGPv9+PF+d2QbA0MgBVut1U54qh31hYSErV67k7rvvJjIyki+//JIJEyZw9uxZli1bxubNm/niiy+YMWOGJeoVotYICm5JfP///jM98sbRZGQkW+38tnAfozZSDAaWbxjDqvQfAfi/BvdYdGSsqarcZh8QEIDBYODhhx9m9OjRtGnT5qZtLl++TNu2bUlJUa+3QVmkzV7Yor8vHOPV70aSXpRLHa0rr/RZwC23dLDa+W3hPkZtYdAXsWT9aL6/8CsAIxvdR8ydUy16TostS7h8+XIeeOAB3Nzsb7CFhL2wVZcvpfDquv/jdGEOPloXXuo+h/r1u6tdlqgCg76I99cMJ+nSITTA6KYP07vLixY/r6xBWwa1w16uoERFsrNOMXPt/5FScAkvjRNTus2kcaO71C5LmKCw8Crvrn6UXdkn0KJhbPMR3NlxvFXOLWFfBjXDXhYcF6bIvZLBrNXDOJZ/ATeNjhc6/psWze5XuyxRgYL8HN7+9hH2557CCQ3PtB5Lx3ZPWO38suC4DZHl3+yPWotwe3qF8O/BX3C7eyh5ip6EXa/zS/JSq5xbVN3VK5nM/Pp+9ueewkWjZdIdk6wa9FUhV/YWpjcodH1ja7lT7JasRLTjxV7SpGMjbOFbWGF+Lu+sHsreK6lo0TCm2aN07zTRKucWpsm6nMrra4eTVpiFu8aJFzu/QrOmg6xeh1zZ2whZcNy+2Mq3MGdXT5699wu6+zXDgMJ/jnzE6q3/tsq5ReUyzx0kfs1Q0gqz8NW6MK3Hm6oEfVVI2FuYzEFiP2xtEW6dkwtj7llObEgnAD5JW8/y9U9i0BdZ5fyibCmpSbyyYRTpRbkE6tyZ3jfRLnpOSdhbmMwbbj9s8VuYVufEsP7vMTRyAABrz+1mwTcPUZifa7UabJ2p91fMcR/mwKEvmZb0HJcNBUQ6+zLj7uWEhrWv6VuwCrtZqcpelcxBkpGVV+YVY0mbvcxBoj5b/hZ2T6+Z+O0O5L3DH/Fz9nEufX0vk2I/wtOr7CU5HYWp91fMcR/mh91v8/7hjyhCoYV7CM/f/REeXkHmezMWJlf2FiZzkNgPW/8WdmfHZ5ncYTJuGh1H8jKZ+s0DVp1Px9aYen+lpvdhSiY0W3h4GUUodPFpzJR7V9pV0IOEvVXIHCT2wR5mgmzVYgjTe7xFHa0rpwtzeGnDKI7+uU61etRi6v2VgiJDje7DFObnsvDbR4wTmg0KjebpwStwdvWs6VuwOmnGsRJZcNz2lXwLG/vxfjRQKiBs6VtY/frded1rObM3PkFqwWVe3fEKYy+nEH3HOFXrsiZT768s35lq8n2YG+ftz7qcytz1j3Ms/wJaNDx+6xB6R0820zuwPrmytyJbmEtdVMxevoXVDbiVafd+TXuvSAoxMP/gB3y6Ic5heuqYet8k7eLVah0vNXU7U1Y/xLH8C3hqnJjSYbJdBz3Ilb3ZyLw3tYe9fAtz9/Dn+ftW8tn3T7M6Yyffpv/EyZX38vSAxbX+xq2p900i/T2qfLwdexbw/uElFCgGQp08ebH3fLvpcVMRCXszsIURl8K8Sr6F2Tqtzomh/ROpv/ddEg9+yK+5p/j3qvuZeOcsIiO7qV2exZjay+3RzvX5YEeKSb3higrz+OT7Z1ifuQeA1h638MyAD/Dyrh1/w9KMU0O2MuJSOLboO8Yxo8dcAnTuZBTl8vK28fywu/auFmdqLzcXJ61J212++CevfXWPMejvDe3G5H99W2uCHiTsa8TWRlwKx9awQS9mDf6KNp71KFAMLDy8jMRvhpJ37ZLapVmEqfdXKtsuRNnKi+uGciQvEzeNjufajOOhmAVodbWr4UMmQquBnSf+5uHFuyrd7rPRneyiSUDUDgZ9EV9vfYGvTm9FAW5x8uKZbq/bxZD+6jD1ftmN27W9xZWV2yaxOmMnAPVd/Hi219uEhLa19luoEVNzrXZ9dFmZLY+4FI5Lq3PiX33fotmRlSzYM5szRVd4eduzPNRgIAO6xde6K1ZT769cv93p07uY+tWLpBVmAdA3oA3D71pol/3nTSXNODVg6yMuhWNr0ex+Zg/+inZeERRiYHnKGmZ8McChR90a9EWsTXqFyZueIq0wC2+tM8+3fYbHY5fW6qAHCfsasYcRl8Kx+fiG88L93zC6yRDjNAuTvhvOd9unOUyf/BJnzuwh/vMYlqesoRADrTzCmBO7gjvajFK7NKuQsK8BmfdG2AONVkufrlOY3X8JzdyCyFP0LP3rG175vB9paT+qXZ7FFebn8uX3z/Li92OMyz2ObjKEfz+wljr+jdQuz2rkBq0ZSD97YS8M+iK27JzNJ8e/5ppShBYN/QLb86/ur9aqboYlkg98woe//odz+uIpodt41mN0zzkEBDZTuTLzkQXHy2DJZQllBK2wJxcvHmfp1knszkkBwFvrzIMNB9Gr0/M4Odv/PaaTJ3ewfOdMfr96FoA6WleGtxxJp7ZPoNHWrgYNCfsyqLEGrRC27MChL1n663xOF+YAEOLkyZDbHqFTuyftstdO5rmDrNyZwA+XDmNAwQkNMcEd+FePBNw9aue9Mwn7MkjYC3GzosI8Nu98g5Up68g2FAAQ4ezD4KYP0qntE+icXFSusHKZ5w7yze43SPr7IPr/DWns6NOQR6KnEhLSRt3iLEzCvgwS9kKU79rVi6z76TXWnNlOnqIHIFjnyYD6/bgz6ik8PAJUrvBmR/9cx7rfPmBvTiqG/4X87R5hPNAujqZNBqpcnXVI2JdBwl6Iyl3JSWfjrrl8d/YHcgyFALhpdHSrezs9mw+lYYPeqrZ7X8lJZ8evi9l2aiupBZeNz7fyCONfbZ+i6a13q1abGmpd2L/++uusW7eO5ORkXFxcuHz5cpWPIWEvhOnyrl0iae8Cvk/7njNFV4zPhzl50TWkI1G3DiYiPNoqwX/16gX2H/yM3Se38mtOGoUYAHBCS9c6zbi7/VOEh0dbvA5LqUkHj1oX9vHx8fj5+XH69Gn++9//StgLYSWKwcChP1ay5cjn/JLzFwWKwfhagM6dtn630iz0Dpo26ENAwG1mOWdBfg4pJ3/gcFoSv53/jT/zzlN03ZSD4c4+9ArvSbe2T+Dtc4tZzqmWmnbdrnVhX2Lp0qVMmDBBwl4IFVy7epE9vy9l96ntHMg9VSr4obiLY4RbABE+4YR4R1LXJxx/30g8PQNxd/PDxdUHDApF+nwKC3PJuZJOzpUMLmafIiMrhfScM6TlnuVUYbaxDb5EmJMXnYLa0bHp/URGdKsVXShLpki/MYRLrulNWR1NJkID8vPzyc/PN/6cnZ2tYjVC2D93D3+6d5pI904Tyc/L4uDRbzh4+if+uPwnqQWXuWTI59LVM/x29QxkVD4jbEV8tS409byF24Pb06rxQIKDW9eKgC9R2RTpGoqnSO/bPMQsY3ZqddgnJCQwffp0tcsQolZydfOlfevhtG89HCi+6j95Zienzv3OqcvHOX/tAhfyL3NRf5VrBj1FGG46hofGCR+dK75O7oS4BRLqXY8w/yY0iriTuv631qpwv5Gpi6aXtRh6daga9pMnT+aNN96ocJsjR45w223VawecMmUKEydONP6cnZ1NeHh4tY4lhKiYu4c/TZsMLLfLY2HhVQrys9FqXdDpnNHpXO2iD7+lWHuKdFXD/rnnnmPEiBEVbtOwYcNqH9/V1RVXV9dq7y+EMB9nZw+cnU1bANwRWHuKdFXDPjAwkMDAQDVLEEIIVZi6aLq5pki3mwaxkydPkpyczMmTJ9Hr9SQnJ5OcnMyVK1cq31kIIWyMtadIt5uulyNGjGDZsmU3Pb9t2zZ69Ohh0jGk66UQwtZIP3sLkLAXonpkCm/LssYI2lrd9VIIUXOyOI/lmbpoek3YTZu9EML6SkZ43tgfPCMrj7Ef72fDwXSVKhNVJWEvhChTZSM8oXiEp97gMC3Bdk3CXghRpqqM8BS2T8JeCFEma4/wFJYlYS+EKJO1R3gKy5KwF0KUqWSEZ3kdADUU98ox1whPYVkS9kKIMll7hKewLAl74VD0BoWdJ/7m2+Qz7Dzxt/QkqURMy1ASh7UjxLd0U02Ir5tJC2sI2yGDqoTDkMFB1RPTMpS+zUNkBK2dk+kShEMwx/JvQtgiU3NNmnFErSeDg4SQsBcOQAYHCSFhLxyADA4SQsJeOAAZHCSEhL1wADI4SAgJe+EAZHCQEBL2wkHI4CDh6GRQlXAYMjhIODIJe+FQrLH8mxC2SJpxhBDCAUjYCyGEA5CwF0IIByBhL4QQDkDCXgghHICEvRBCOAAJeyGEcAAS9kII4QAk7IUQwgFI2AshhAOQsBdCCAcgYS+EEA5Awl4IIRyAzHophLBJeoMi01GbkV2EfWpqKq+++ipbt24lIyODsLAwhg0bxksvvYSLi4va5QkhzGzDwXSmrzlMetY/i8CH+roRH9tcFpqpJrsI+z/++AODwcD7779P48aNOXjwIKNHjyY3N5e5c+eqXZ4Qwow2HExn7Mf7UW54PiMrj7Ef75eVxapJoyjKjf9N7cKcOXNITEzkr7/+Mnmf7OxsfH19ycrKwsfHx4LVCSGqQ29Q6PrG1lJX9NfTULyU5I4Xe1W5Sae2NguZmmt2cWVflqysLPz9/SvcJj8/n/z8fOPP2dnZli5LCFEDe1Iulhv0AAqQnpXHnpSLVVpxTJqF7LQ3zvHjx1mwYAFPPvlkhdslJCTg6+trfISHh1upQiFEdWTmlB/01dkO/mkWuvFDpKRZaMPB9CrVaK9UDfvJkyej0WgqfPzxxx+l9jlz5gwxMTE88MADjB49usLjT5kyhaysLOPj1KlTlnw7QogaCvJ2M+t2eoPC9DWHb2r/B4zPTV9zGL3BLluzq0TVZpznnnuOESNGVLhNw4YNjf8+e/YsPXv2pEuXLixatKjS47u6uuLq6lrTMoUQVtKhgT+hvm5kZOWVGdAlbfYdGlTchFvCUs1C9kjVsA8MDCQwMNCkbc+cOUPPnj1p3749S5YsQau1yxYoIUQFdFoN8bHNGfvxfjRQKvBLbqXGxzY3+caqJZqF7JVdJOaZM2fo0aMHERERzJ07l/Pnz5ORkUFGRobapQkhzCymZSiJw9oR4lu6qSbE163K3S7N3Sxkz+yiN86mTZs4fvw4x48fp169eqVes9Oeo0KICsS0DKVv85Aad5U0d7OQPbPbfvbVIf3shXA8Jb1xoOxmIXsfpGVqrtlFM44QQlSXOZuF7JldNOMIIURNmKtZyJ5J2AshHIJOq6n13SsrIs04QgjhACTshRDCAUjYCyGEA5CwF0IIByBhL4QQDkDCXgghHICEvRBCOAAJeyGEcAAyqEoIO1Nb11IVliVhL4QdkbVURXVJM44QdkLWUhU1IWEvhB2QtVRFTUnYC2EHqrKWqhBlkbAXwg7IWqqipiTshbADspaqqCkJeyHsQMlaquV1sNRQ3CvHEdZSFdUjYS+EHdBpNcTHNge4KfBLfo6PbS797UW5JOyFsBOylqqoCRlUJYQdkbVURXVJ2AthZxx9LVVRPdKMI4QQDkDCXgghHICEvRBCOACHarNXlOJ5Q7Kzs1WuRAghzKMkz0ryrTwOFfY5OTkAhIeHq1yJEEKYV05ODr6+vuW+rlEq+zioRQwGA2fPnsXb2xuNxvSuatnZ2YSHh3Pq1Cl8fHwsWKH5SM2WZ2/1gtRsLdasWVEUcnJyCAsLQ6stv2Xeoa7stVot9erVq/b+Pj4+dvPLVkJqtjx7qxekZmuxVs0VXdGXkBu0QgjhACTshRDCAUjYm8DV1ZX4+HhcXV3VLsVkUrPl2Vu9IDVbiy3W7FA3aIUQwlHJlb0QQjgACXshhHAAEvZCCOEAJOyFEMIBSNhXYuHChdSvXx83Nzc6duzInj171C6pQj/88AOxsbGEhYWh0Wj45ptv1C6pQgkJCdxxxx14e3sTFBTE4MGDOXr0qNplVSgxMZFWrVoZB8x07tyZ7777Tu2yqmTWrFloNBomTJigdinlmjZtGhqNptTjtttuU7usSp05c4Zhw4ZRt25d3N3duf322/nll1/ULkvCviKff/45EydOJD4+nv3799O6dWv69etHZmam2qWVKzc3l9atW7Nw4UK1SzHJ9u3biYuLY9euXWzatInCwkLuuusucnNz1S6tXPXq1WPWrFns27ePX375hV69ejFo0CAOHTqkdmkm2bt3L++//z6tWrVSu5RKtWjRgvT0dONjx44dapdUoUuXLhEdHY2zszPfffcdhw8f5s0336ROnTpqlwaKKFeHDh2UuLg44896vV4JCwtTEhISVKzKdICyatUqtcuokszMTAVQtm/frnYpVVKnTh3lgw8+ULuMSuXk5ChNmjRRNm3apHTv3l0ZP3682iWVKz4+XmndurXaZVTJiy++qHTt2lXtMsokV/blKCgoYN++ffTp08f4nFarpU+fPuzcuVPFymq3rKwsAPz9/VWuxDR6vZ4VK1aQm5tL586d1S6nUnFxcQwcOLDU77Ut+/PPPwkLC6Nhw4YMHTqUkydPql1ShVavXk1UVBQPPPAAQUFBtG3blsWLF6tdFiDNOOW6cOECer2e4ODgUs8HBweTkZGhUlW1m8FgYMKECURHR9OyZUu1y6nQgQMH8PLywtXVlTFjxrBq1SqaN2+udlkVWrFiBfv37ychIUHtUkzSsWNHli5dyoYNG0hMTCQlJYVu3boZpyq3RX/99ReJiYk0adKEjRs3MnbsWJ555hmWLVumdmmONeulsG1xcXEcPHjQ5ttlAZo2bUpycjJZWVl89dVXDB8+nO3bt9ts4J86dYrx48ezadMm3Nzc1C7HJP379zf+u1WrVnTs2JHIyEi++OILHnvsMRUrK5/BYCAqKoqZM2cC0LZtWw4ePMh7773H8OHDVa1NruzLERAQgE6n49y5c6WeP3fuHCEhISpVVXuNGzeOtWvXsm3bthpNQ20tLi4uNG7cmPbt25OQkEDr1q1555131C6rXPv27SMzM5N27drh5OSEk5MT27dvZ/78+Tg5OaHX69UusVJ+fn7ceuutHD9+XO1SyhUaGnrTB36zZs1sovlJwr4cLi4utG/fni1bthifMxgMbNmyxS7aZu2FoiiMGzeOVatWsXXrVho0aKB2SdViMBjIz89Xu4xy9e7dmwMHDpCcnGx8REVFMXToUJKTk9HpdGqXWKkrV65w4sQJQkND1S6lXNHR0Td1HT527BiRkZEqVfQPacapwMSJExk+fDhRUVF06NCBefPmkZuby8iRI9UurVxXrlwpdeWTkpJCcnIy/v7+REREqFhZ2eLi4vj000/59ttv8fb2Nt4P8fX1xd3dXeXqyjZlyhT69+9PREQEOTk5fPrppyQlJbFx40a1SyuXt7f3TfdBPD09qVu3rs3eH3n++eeJjY0lMjKSs2fPEh8fj06n4+GHH1a7tHI9++yzdOnShZkzZ/Lggw+yZ88eFi1axKJFi9QuTbpeVmbBggVKRESE4uLionTo0EHZtWuX2iVVaNu2bQpw02P48OFql1amsmoFlCVLlqhdWrlGjRqlREZGKi4uLkpgYKDSu3dv5fvvv1e7rCqz9a6XQ4YMUUJDQxUXFxfllltuUYYMGaIcP35c7bIqtWbNGqVly5aKq6urcttttymLFi1SuyRFURRFpjgWQggHIG32QgjhACTshRDCAUjYCyGEA5CwF0IIByBhL4QQDkDCXgghHICEvRBCOAAJeyGEcAAS9kII4QAk7IUQwgFI2AthJufPnyckJMQ4lznAzz//jIuLS6nZU4VQg8yNI4QZrV+/nsGDB/Pzzz/TtGlT2rRpw6BBg3jrrbfULk04OAl7IcwsLi6OzZs3ExUVxYEDB9i7dy+urq5qlyUcnIS9EGZ27do1WrZsyalTp9i3bx+333672iUJIW32QpjbiRMnOHv2LAaDgdTUVLXLEQKQK3shzKqgoIAOHTrQpk0bmjZtyrx58zhw4ABBQUFqlyYcnIS9EGY0adIkvvrqK3777Te8vLzo3r07vr6+rF27Vu3ShIOTZhwhzCQpKYl58+axfPlyfHx80Gq1LF++nB9//JHExES1yxMOTq7shRDCAciVvRBCOAAJeyGEcAAS9kII4QAk7IUQwgFI2AshhAOQsBdCCAcgYS+EEA5Awl4IIRyAhL0QQjgACXshhHAAEvZCCOEAJOyFEMIB/D/wSVe2OC+ilQAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#### First, let's reinitialize the state object to get a clean state ####\n", "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 30))\n", @@ -735,7 +444,7 @@ " #Report metrics\n", " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", - " plot_from_state(s)" + " plot_from_state(s,'sin(x)')" ] }, { @@ -751,157 +460,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 23.33it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 1, number of datapoints: 10\u001b[0m\n", - "\u001b[1mCycle 1 model: -0.38\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 23.27it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 2, number of datapoints: 20\u001b[0m\n", - "\u001b[1mCycle 2 model: -0.27\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 22.68it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 3, number of datapoints: 30\u001b[0m\n", - "\u001b[1mCycle 3 model: sin(x)\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mNumber of datapoints: 50\u001b[0m\n", - "\u001b[1mDetermined Model: -0.11\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "#### First, let's reinitialize the state object to get a clean state ####\n", "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 30))\n", @@ -928,7 +487,7 @@ " #Report metrics\n", " print(f\"\\n\\033[1mRunning Cycle {cycle+1}, number of datapoints: {len(s.experiment_data)}\\033[0m\")\n", " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", - " plot_from_state(s)\n", + " plot_from_state(s,'sin(x)')\n", " \n", " #Increase count\n", " cycle += 1\n", @@ -992,180 +551,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "#==================================================#\n", - "\u001b[1mUsing random pooler experimentalist...\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:03<00:00, 27.02it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 1:\u001b[0m\n", - "\u001b[1mCycle 1 model: -0.38\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from autora.experimentalist.random_ import random_pool\n", "\n", @@ -1206,7 +592,7 @@ " #Report metrics\n", " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", - " plot_from_state(s)\n", + " plot_from_state(s,'sin(x)')\n", " \n", " #Increase count\n", " cycle += 1" @@ -1223,148 +609,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:03<00:00, 26.11it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 1:\u001b[0m\n", - "\u001b[1mCycle 1 model: -0.31\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 24.45it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mRunning Cycle 5:\u001b[0m\n", - "\u001b[1mCycle 5 model: sin(x)\u001b[0m\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from autora.experimentalist.random_ import random_pool\n", "\n", @@ -1395,7 +640,7 @@ " #Report metrics\n", " print(f\"\\n\\033[1mRunning Cycle {cycle+1}:\\033[0m\")\n", " print(f\"\\033[1mCycle {cycle+1} model: {s.model}\\033[0m\")\n", - " plot_from_state(s)" + " plot_from_state(s,'sin(x)')" ] }, { From cb0cc66dd94bf03ef2547817290948b16c6e5bb3 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Thu, 31 Aug 2023 11:11:57 -0700 Subject: [PATCH 25/32] Finished Tutorial 4 draft --- .../basic/Tutorial-IV-Customization.ipynb | 867 +++++++++++++++--- 1 file changed, 740 insertions(+), 127 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-IV-Customization.ipynb b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb index fbc4ce42d..65a0194ee 100644 --- a/docs/tutorials/basic/Tutorial-IV-Customization.ipynb +++ b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb @@ -32,32 +32,76 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Tutorial Setup" + "## Tutorial Setup\n", + "\n", + "We will here import some standard python packages, set seeds for replicability, and define a plotting function." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n" + ] + } + ], "source": [ "#### Installation ####\n", "!pip install -q \"autora[theorist-bms]\"\n", "\n", "#### Import modules ####\n", + "from typing import Optional\n", "import numpy as np\n", "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import sympy as sp\n", "import torch\n", - "from autora.variable import DV, IV, ValueType, VariableCollection\n", + "\n", + "from autora.variable import Variable, ValueType, VariableCollection\n", "from autora.state.bundled import StandardState\n", "from autora.state.delta import on_state\n", "from autora.state.wrapper import state_fn_from_estimator\n", "from autora.experimentalist.random_ import random_pool\n", "from autora.theorist.bms import BMSRegressor\n", + "from autora.experiment_runner.synthetic.abstract.equation import equation_experiment\n", "\n", "#### Set seeds ####\n", "np.random.seed(42)\n", - "torch.manual_seed(42)" + "torch.manual_seed(42)\n", + "\n", + "#### Define plot function ####\n", + "def plot_from_state(s: StandardState, expr: str): \n", + " \n", + " \"\"\"\n", + " Plots the data, the ground truth model, and the current predicted model\n", + " \"\"\"\n", + " \n", + " #Determine labels and variables\n", + " model_label = f\"Model: {s.model.repr()}\" if s.model.repr() else \"Model\"\n", + " experiment_data = s.experiment_data.sort_values(by=[\"x\"])\n", + " ground_x = np.linspace(s.variables.independent_variables[0].value_range[0],s.variables.independent_variables[0].value_range[1],100)\n", + " \n", + " #Determine predicted ground truth\n", + " equation = sp.simplify(expr)\n", + " ground_predicted_y = [equation.evalf(subs={'x':x}) for x in ground_x]\n", + " model_predicted_y = s.model.predict(ground_x.reshape(-1, 1))\n", + "\n", + " #Plot the data and models\n", + " f = plt.figure(figsize=(4,3))\n", + " plt.plot(experiment_data[\"x\"], experiment_data[\"y\"], 'o', label = None)\n", + " plt.plot(ground_x, model_predicted_y, alpha=.8, label=model_label)\n", + " plt.plot(ground_x, ground_predicted_y, alpha=.8, label=f'Ground Truth: {expr}')\n", + " plt.xlabel('x')\n", + " plt.ylabel('y')\n", + " plt.legend()\n", + " plt.show()" ] }, { @@ -67,14 +111,14 @@ "source": [ "# Customizing Automated Empirical Research Components\n", "\n", - "``autora`` is a flexible framework in which users can integrate their own theorists, experimentalists, and experiment_runners in aa automated empirical research workflow. This section illustrates the integration of custom theorists and experimentalists. For more information on how to contribute your own modules to the ``autora`` ecosystem, please refer to the [Contributor Documentation](https://autoresearch.github.io/autora/contribute/modules/).\n", + "``AutoRA`` is a flexible framework in which users can integrate their own experimentalists, experiment_runners, and theorists in an automated empirical research workflow. This section illustrates the integration of custom `AutoRA`. For more information on how to contribute your own modules to the ``AutoRA`` ecosystem, please refer to the [Contributor Documentation](https://autoresearch.github.io/autora/contribute/modules/).\n", "\n", - "To illustrate the use of custom theorists and experimentalists, we consider a simple workflow:\n", + "To illustrate the use of custom experimentalists, experiment runners, and theorists, we consider a simple workflow:\n", "1. Generate 10 seed experimental conditions using `random_pool`\n", "2. Iterate through the following steps\n", + " - Identify 3 new experimental conditions using an ``experimentalist``\n", " - Collect observations using the ``experiment_runner``\n", " - Identify a model relating conditions to observations using a ``theorist``\n", - " - Identify 3 new experimental conditions using an ``experimentalist``\n", "\n", "Once this workflow is setup, we will replace each component with a custom function." ] @@ -86,12 +130,12 @@ "outputs": [], "source": [ "#### Define metadata ####\n", - "iv = IV(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 10))\n", - "dv = DV(name=\"y\", type=ValueType.REAL)\n", + "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 30))\n", + "dv = Variable(name=\"y\", type=ValueType.REAL)\n", "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", "\n", "#### Define condition pool ####\n", - "conditions = random_pool(variables, num_samples=10)\n", + "conditions = random_pool(variables, num_samples=10, random_state=0)\n", "\n", "#### Define state ####\n", "s = StandardState(\n", @@ -100,20 +144,17 @@ " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"])\n", ")\n", "\n", + "#### Define experimentalist and wrap with state functionality ####\n", + "experimentalist = on_state(random_pool, output=[\"conditions\"])\n", + "\n", "#### Define experiment runner and wrap with state functionality ####\n", - "def run_experiment(conditions: pd.DataFrame):\n", - " x = conditions[\"x\"]\n", - " y = np.sin(x) + np.random.normal(0, 0.5, size=x.shape)\n", - " observations = conditions.assign(y = y)\n", - " return observations\n", + "sin_experiment = equation_experiment(sp.simplify('sin(x)'), variables.independent_variables, variables.dependent_variables[0])\n", + "sin_runner = sin_experiment.experiment_runner\n", "\n", - "experiment_runner = on_state(run_experiment, output=[\"experiment_data\"])\n", + "experiment_runner = on_state(sin_runner, output=[\"experiment_data\"])\n", "\n", "#### Define theorist and wrap with state functionality ####\n", - "theorist = state_fn_from_estimator(BMSRegressor(epochs=100))\n", - "\n", - "#### Define experimentalist and wrap with state functionality ####\n", - "experimentalist = on_state(random_pool, output=[\"conditions\"])" + "theorist = state_fn_from_estimator(BMSRegressor(epochs=100))" ] }, { @@ -127,13 +168,131 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mPrevious State:\u001b[0m\n", + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", + " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", + " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", + " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", + " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", + " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 5.416539\n", + "1 4.116570\n", + "2 3.249923\n", + "3 1.733292\n", + "4 1.949954\n", + "5 0.216662\n", + "6 0.433323\n", + "7 0.000000\n", + "8 1.083308\n", + "9 5.199877, experiment_data=Empty DataFrame\n", + "Columns: [x, y]\n", + "Index: [], models=[])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:04<00:00, 24.37it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "data": { + "image/png": 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Ekzm7ODs7o9FouHbtWp7l165dw93dvVj7MDc3p2XLlly8eLHAdSwtLbG3t8/zqmgFPQdhrUrB3OkQAFbJjbmj8anIWFWeb90udK7RDID/HZsjkwuJSs9kCoSFhQWtW7dm586dhmU6nY6dO3cSGFi8h8O0Wi2nTp3Cw8O450co6DmI1k7fkaHOwTbHkmO3Xi728xKi7PTt9Al2anP+yU5l6wF57kRUbiZTIADGjBnDkiVLWLlyJWfPnmXEiBGkp6czaNAgAF577TVCQ/+9PzxlyhR+//13/v77b44fP07//v25fPkyQ4cOVeojFEt+z0F4Wpwn1f5vAJJvBuHs4Ii/j5MyAauwanYe9Hu8NwA/XN7KraRohRMJUX5MqkD07t2b2bNnM3HiRFq0aMGJEyfYtm2boeH6ypUrxMf/O8rprVu3GDZsGA0bNqR79+6kpKRw6NAhGjUq/BkDpd3/HERukdBRt8aP6NHjeLcGZ++0JyykEZoH+8CKCtHBfzS+lk5k6LWs3fuh0nGEKDcqvV5f8Aw0gpSUFBwcHEhOTq7w9oh780E4a7egctmBBhUZt0bzzrPd8p0PQlSci9G/88G+3Pmrp7T7WB5UFCaluOc1k7qCqGqCm3iwa/QTeDx2CAszNc+4tGPL+EFSHIyAb90udKzeGIAVEbPlCWtRKUmBMHJbD04hmUxczKwZ0n2G3FYyIq92/BhrlRl/Z91iz5HPlI4jRJmTAmHEbt64wMZ/dgMwoNEALK0cFE4k7ufgWJtetbsDsO6vH7hz54bCiYQoW1IgjNi3+z4kU6+lgZULga1HKB1H5KNL2wl4mNmSrMti474wpeMIUaakQBipCxe3cjD5Airg9SdD5YlpI2VubsOAZm8A8Et8ONeu/alwIiHKjpx1jJBep+ObiDkAdKjemDo+TyucSBSmVdMBNLXxJAcd3+6XqwhReUiBMELhkQv5K/MGVioNvZ+arHQcUQSVWs3rbT5AjYojqTEyTpOoNKRAGJmszFS+PbsagOce64iTk6/CiURxeHm1NYzTtCpynnR7FZWCFAgj88uBqdzQ3sVJbcWz7ScqHUeUQK+nJmOl0vB31i0OHFugdBwhHpkUCCOSfPsSG2NzByPs26CvdGs1MQ6Otenp1RmAdefXkpWZqnAiIR6NFAgj8sP+yWTotdSxqE47v5FKxxGl0KPdRzhrrLmpzeCX/VOUjiPEI5ECYSTi4o6x48YJAPq3fgu1xkzZQKJULCzt6NOgLwAb/9kl05MKkyYFwkh8e3AqOvS0rlaLxg1eVDqOeARtW79JXYvc0V5/2C+90ITpkgJhBKLObeRY2mXUqHg1UOY7NnVqjRn9Wo8CYMeNE8TFHVM4kRClIwVCYXqdjm+Pzwegs3NzatZ8UuFEoiw0bvAirap5o0PP2kMfKx1HiFKRAqGww38s5mJmElYqDb3aT1I6jihDrz4ZihoVEakxnL+wRek4QpSYFAgFZWffYV3UKgCe9XwKB8faygYSZcrLK5COTk0AWH1sLnqdTuFEQpSMFAgF7QyfTUJOOg5qC55t95HScUQ56NU+DEuVhguZNzh6cpnScYQoESkQCrlz5wYbYnJvO7xc5zmsbZwUTiTKg5OTL93d2wCw9vQKtDlZCicSovikQChky/6ppOiy8DCz5eknxykdR5Sj59p9hJ3anLicNJl5TpgUKRAKuH0rhl/iDwDQt/HrmJlbKZxIlCebaq68+P8zz62/+BMZd28pnEiI4pECoYANB6aQodfia1kD/xZDlY4jKsAzT47HVWPDLV0mWw9OVzqOEMUiBaKCJcT/wc7/H1KjX6u3ZKa4KsLc0pZXGvQB4Od/dpOaclXhREIUTc5OFey7Qx+jRU8L25o0atBT6TiiArVt/Sa1zB24q89h4wEZyE8YPykQFejvmF0cSrmICujr/57ScUQFU2vMeLV57vzVv107yo3rZxVOJEThpEBUoLURswFo59iA2rU7KJxGKKF54740snYjGx3rD05TOo4QhZICUUFOR63nzztxmKHilTYfKh1HKESlVvOq37sA7LsVRWxsuMKJhCiYFIgKoNfpWPPHQgCCXFrh6tZE4URCSfV8gwmw80GHnnWHZyodR4gCSYGoABEnlhKdlTsg34sypIYAegfmDuR3LO0yFy5uVTqOEPmSAlHOtDlZrDvzDQA9PNvLgHwCgMce86eDU2MA1hz9XAbyE0ZJCkQ52xsxl7icNOzU5jzbTtoexL9ebvsh5qg5m5HIyah1SscR4iFSIMpRVmYq6//6EYAXanXDxsa5xPvQ6vSER9/k5xNXCY++iVanL+uYQiHOzg3o6vYEAGtOfI1Om6NwIiHyMrkCsWDBAmrXro2VlRUBAQFEREQUuv769etp0KABVlZWNG3alF9//bWCksJvhz4hSZdBDY0VXQL/W+Ltt52Op90nu+i75DDvrDtB3yWHaffJLradji+HtEIJPdtNxFplxuXsZMKPL1I6jhB5mFSB+O677xgzZgxhYWEcP36c5s2b07VrVxITE/Nd/9ChQ/Tt25chQ4bwxx9/0LNnT3r27Mnp06fLPeudtEQ2Xt4GQK96L2NuaVui7bedjmfE6uPEJ2fkWZ6QnMGI1celSFQSdvaP8VzNjgB8d3YtOdkZhW8gRAUyqQLx2WefMWzYMAYNGkSjRo1YtGgRNjY2LFuW/0Qs8+bNIzg4mPfee4+GDRsydepUWrVqxZdfflnuWTcfnEaaPofHzOx46om3S7StVqdn8uYo8ruZdG/Z5M1RcrupkujW5n0c1BZc06az58jnSscRJmTHgRkk375Ubvs3mQKRlZVFZGQkQUFBhmVqtZqgoCDCw/N/2Cg8PDzP+gBdu3YtcH2AzMxMUlJS8rxKKvn2JX6JPwhAnyavozGzKNH2ETFJD1053E8PxCdnEBGTVOJswvhY2zjxok8PAH6I3khmRrLCiYQpOH9hC0v++o7RG1/mTlr+d1EelckUiBs3bqDVanFzc8uz3M3NjYSEhHy3SUhIKNH6ADNmzMDBwcHw8vLyKnHWrKx0mtrWxNeyBk80H1zi7RNTi3ebobjrCePXOWCcYTjwbYdmKB1HGDm9TsfayPkABDo1xKaaa7kcx2QKREUJDQ0lOTnZ8IqNjS3xPlxcG/Ner5+Z+OKPpRrO29WueBMIFXc9YfzMLW3pVb83AD9f2UFaqrQxiYKdPLOWsxmJmKPmpTbl9/CtyRQIZ2dnNBoN165dy7P82rVruLu757uNu7t7idYHsLS0xN7ePs+rtCytHEq1nb+PEx4OVqgK+LkK8HCwwt9H5rGuTNr5jcTL3J50fQ6bZCA/UQCdNoc1JxcDEOzuTw3nx8vtWCZTICwsLGjdujU7d+40LNPpdOzcuZPAwMB8twkMDMyzPsD27dsLXN9YaNQqwkIaATxUJO69DwtphEZdUAkRpkitMaNPk9xbklsTwklKuqhwImGMwo8v4nJ2MtYqM55vW75D95hMgQAYM2YMS5YsYeXKlZw9e5YRI0aQnp7OoEGDAHjttdcIDQ01rP/OO++wbds25syZw7lz55g0aRLHjh1j1KhRSn2EYgtu4sHC/q1wd8h7G8ndwYqF/VsR3MRDoWSiPLVu9hqPWzqTpdfx44GpSscRRiYnO4Pvzq4F4PmanbCzf6xcj2dWrnsvY7179+b69etMnDiRhIQEWrRowbZt2wwN0VeuXEF93z3/Nm3asGbNGj788EPef/996tWrx8aNG2nSxDRGUw1u4sEzjdyJiEkiMTUDV7vc20py5VB5qdRqXm39FpMOhbHz5p/0iIvEw7O10rGEkdh1eDbXtOk4qC0IbhNa9AaPSKXX66UzfSFSUlJwcHAgOTn5kdojhCiJmd+H8Ed6LG3sfXnnpR+UjiOMQMbdW7zzfVdu67IYVPdFgp+aWOp9Ffe8ZlK3mISoKvr4j0MFHEq5yN8xO4tcX1R+Ww9O57YuC1eNDZ0Dx1XIMaVACGGEatfuQFvH+gCsjZijcBqhtNSUq/z8z24Aejfoi7m5TYUcVwqEEEbqlTYfYIaKP+/EcTpqvdJxhII2HpjCXX0OtcwdaNN6RIUdVwqEEEbKza0ZnZ1bArDmj4UyqVAVdeP6WX67dhSAV5u/gVpTcX2LpEAIYcReaj8RK5WG6KwkjvyxROk4QgHrD04jGx0NrVxp3rhvhR5bCoQQRszBsTY9PNsDsC7qG7Q5WQonEhUpNjacfbeiAOj3xJhSDd3zKKRACGHknm33IXZqc+Jz0tl9WBqsq5K1h2eiQ0+AnQ/1fIMr/PhSIIQwcjY2zrxUO3c48PXRP5Fx95bCiURFOH9hC5Fpl1Gjok+bDxTJIAVCCBMQ1GY8rhobbuuy2HpwutJxRDnT63R8GzkXgE41muDp6adIDikQQpgAc3Mb+jR8FYCN/+wiOfmKwolEeTp2cjnnM25goSrf4byLIgVCCBMR2Go4tS0cydBr+Wn/FKXjiHKizcli7enlAPRwb1uuw3kXpcQF4vXXX2ffvn3lkUUIUQi1xox+LXIfktp+PZJr1/5UOJEoD7sPz+FqThp2anOea6fc1QOUokAkJycTFBREvXr1mD59OlevXi2PXEKIfDRr3JtmNp7koGfdAbmKqGwy7t5iffRPALxYu3u5TSVaXCUuEBs3buTq1auMGDGC7777jtq1a9OtWzd++OEHsrOzyyOjEOI+/QL+axjI72L070rHEWXolwPTDAPyPfPkeKXjlK4NwsXFhTFjxnDy5EmOHDmCr68vAwYMwNPTk3fffZe//vqrrHMKIf5f7dodaO/YEIDVEbNlCI5K4vatGDZd3QNAn4avYm5pq2wgHrGROj4+nu3bt7N9+3Y0Gg3du3fn1KlTNGrUiM8//7ysMgohHtC7fRjmqDmbkcjxU6uUjiPKwPr9YWTotdS1cKJN6zeVjgOUokBkZ2ezYcMGnn32WWrVqsX69esZPXo0cXFxrFy5kh07dvD9998zZYrcHxWivDg7N6Cb+5MAfPvnEhmCw8TFxoaz6+YpAPq3frvCh9QoSImHBfTw8ECn09G3b18iIiJo0aLFQ+t06tQJR0fHMognhChIz/YT2b0hhKs5aewMn0WX9h8qHUmU0prDM9Chx69aLRo16Kl0HIMSl6nPP/+cuLg4FixYkG9xAHB0dCQmJuZRswkhCmFbzZ2XfUIAWP/3Ju7cuaFwIlEaZ85u4HjaFdSo6NdW2W6tDypxgRgwYABWVlblkUUIUUJBgePxNKtGii6Ln/dNUjqOKCGdNoeVkfMBCHJuodiQGgUxjhtdQohSMTO3ol/ToQD8En+IG9fPKpxIlMTeiLlczk7GRmXGy09NVjrOQ6RACGHiWjd7jUbWbmSjY83+iUrHEcV0904S313InUr2xVpdcXDwVjjRw6RACGHiVGo1rz0Zigo4mPwX5//6RelIohg275/MLV0mrhobgtu8r3ScfEmBEKIS8KndkQ7VGwPwzdE56LQ5CicShblx4xyb4/cD0L/JQKN4KC4/UiCEqCT6dpiGlUrDxcwkDhxboHQcUYg1+z4iS587z7R/i6FKxymQFAghKgnH6j684BUEwJpza2TmOSN17vwmDib/hQp47ckJRvNQXH6MN5kQosR6tJuIq8aGW7pMNu4tvwZrrU5PePRNfj5xlfDom2h1+nI7VmWi0+aw4ljuMESdnJpSx+dphRMVrsRPUgtRFWl1eiJikkhMzcDVzgp/Hyc0apXSsR5ibmlL/yYD+ezkV2yJP0ina3/i5tasTI+x7XQ8kzdHEZ+cYVjm4WBFWEgjgpt4lOmxKpu9EXOJybqFtcqMPh2mKh2nSFIghCiCqZ0Q/VsMpemFnzh1N55v9n3Ee71+LrN9bzsdz4jVx3nweiEhOYMRq4+zsH8ro/w7MQZ30hJZe+F7AF6u3Q0Hx9rKBioGucUkRCHunRDvLw7w7wlx2+l4hZIVTKVW83rbiahRcSztMidOrymT/Wp1eiZvjnqoOACGZZM3R8ntpgKs3/shybosPMxs6do2VOk4xSIFQogCmPIJ0csrkGDXJwBY+cdXZGffeeR9RsQkPVQo76cH4pMziIhJeuRjVTZXrhxgW+JRAAa1fAtzcxuFExWPyRSIpKQk+vXrh729PY6OjgwZMoS0tLRCt+nYsSMqlSrPa/jw4RWUWJg6Uz8h9uowDQe1BXE5aWzdP+2R95eYWvDfRWnWqyr0Oh3LDk5Bh54AOx+aN+mjdKRiM5kC0a9fP86cOcP27dvZsmUL+/bt44033ihyu2HDhhEfH294zZo1qwLSisrA1E+INtVcebV+bwB+uPIbN26ce6T9udoVb5DO4q5XVRw8toCzGYlYqNS81mG60nFKxCQKxNmzZ9m2bRtLly4lICCAdu3a8cUXX7Bu3Tri4uIK3dbGxgZ3d3fDy97evoJSC1NXGU6ITz3xDg2sXMjUa/lmz6Pd9/b3ccLDwYqC+m6pyG289/dxeqTjVCZ37txg9dnVALxYszPOLg0VTlQyJlEgwsPDcXR0xM/v36Fwg4KCUKvVHDlypNBtv/32W5ydnWnSpAmhoaHcuVP4vdjMzExSUlLyvETVVBlOiGqNGUPahqFGxZHUGE6c+rbU+9KoVYSFNAJ46O/k3vuwkEZG2f1XKd/vmsAtXSbuZrY8236S0nFKzCQKREJCAq6urnmWmZmZ4eTkREJCQoHbvfrqq6xevZrdu3cTGhrKqlWr6N+/f6HHmjFjBg4ODoaXl5dXmXwGYXoqywnR27sd3dxyG6yX/fEV2Znppd5XcBMPFvZvhbtD3qsmdwcr6eL6gL9jdvLb9UgAhrYabbTjLRVG0ecgJkyYwCeffFLoOmfPln58+/vbKJo2bYqHhwedO3cmOjqaunXr5rtNaGgoY8aMMbxPSUmRIlGF3TshPvgchLsRPweRn14dZ3JofTeuadP5cU8ovbvOL/W+gpt48Ewjd5N4cFApOm0OSw5NQ4eetg6P07RxL6UjlYqiBWLs2LEMHDiw0HXq1KmDu7s7iYmJeZbn5OSQlJSEu7t7sY8XEBAAwMWLFwssEJaWllhaWhZ7n6LyqwwnRGsbJwY1HcJnJ7/i57j9tIkNx8srsNT706hVBNatUYYJK5ffD07n76xb2KjMeK1T4V+CjZmiBcLFxQUXF5ci1wsMDOT27dtERkbSunVrAHbt2oVOpzOc9IvjxIkTAHh4mMa3PmE8KsMJ0b/FUFpH/0Jk2mUW7/+Iyb23odbIYApl7eaNC6z9O/fp9b51e+JY3UfhRKVnEm0QDRs2JDg4mGHDhhEREcHBgwcZNWoUffr0wdPTE4CrV6/SoEEDIiIiAIiOjmbq1KlERkZy6dIlNm3axGuvvcZTTz1Fs2ZlOzaNEKZApVYzuNMnWKk0XMi8wa7wT5WOVOnodTqW7h5Hhl7L45bOBLWZoHSkR2ISBQJyeyM1aNCAzp070717d9q1a8fixYsNP8/Ozub8+fOGXkoWFhbs2LGDLl260KBBA8aOHctLL73E5s2blfoIQijO2bkBfXxCAFh9cQM3b1xQOFHlEh65kONpVzBDzRvtp5r8FZpKr9cb3zgBRiQlJQUHBweSk5PlGQpRKei0OXz0XVcuZt6kla0X41/+2ajnJDAVqSlXGfPTC6TosuhV82lefuYzpSMVqLjnNfmtEKKKUWvMGN5+GmaoOZ4ey/6jXygdqVJYuXMsKbosvMzteb7jow9tYgykQAhRBXl5BfKyV2cAVp5dxa2kaIUTmbajf/yP/bfPoUbFf55832QG4yuKFAghqqiQDlPxsahOmj6HpTvHoNfplI5kklKSY1ny59cAhHi0oZ5vsMKJyo4UCCGqKDNzK0a0m4LZ/88bsTfic6UjmaRlO94lWZdFTXM7enWqXIOBSoEQogqrVas9vby7ALD83BoSr51WOJFpOXR0AeEpF1GjYmTbSSY5nEZhpEAIUcU91/Fj6ls5k6HX8tWuMei0OUpHMgk3rp9l6ZnlALzwWAfq+HRWOFHZkwIhRBWn1pjxZqfZWKk0nM1IZMveiUpHMno6bQ5f7niHdH0OvpZOvNhpptKRyoUUCCEE7u4teO3xVwBYd3kr0dHbFU5k3Dbt+YCzGYlYqTS81WkOZubGOyfIo5ACIYQA4Okn3yPAvg5a9Mw9GMadOzeUjmSULkb/zvdXfgdgUINXcfdoqXCi8iMFQggB5I7V9J+uX+GisSZRe4clW4dL19cHpKXGM/dgGFr0PGlflw7+7yodqVxJgRBCGNhWc+etwA9Ro+JQykV2hVeubpuPQq/T8dW24VzX3sVVY8N/ui6s9EOUVO5PJ4Qosfr1etD7XtfXC99Le8T/27J3IpFplzFHzbvtp2FTzbXojUycFAghxEOe6/gxravVIhsdnx2cSEpyrNKRFBV1biNrLv0CwMB6vajj87TCiSqGFAghxEPUGjNGdvsadzNbbmjv8sW2/1TZ5yOuJ57h8yPTDdOHdm7zX6UjVRgpEEKIfNlWc2dsh0+wUKn5804ca34fpXSkCpeZkczs7SNJ0WVR28KR/3RfUunbHe5XdT6pEKLEvL3bMaLxEAA2Jxxm16Gq02it1+lY9MsQLmXdxl5twXtdFmJp5aB0rAolBUIIUag2T4zk5cc6AbD0/FrOnPtR4UQV4/vtozmUchENKsY8+QHOLg2VjlThpEAIIYr0ctAc2tj7okXPZ4enc/VqhNKRytXOgzP5MW4fAMMa9KNh/ecVTqQMKRBCiCKp1GpGPLscX8sapOlz+Hjn25V2PusTp75l6YXvAHjpsY50ChyncCLlSIEQQhSLhaUd43ssx9OsGje1GXy8dQipKVeVjlWmzp3fxGeRn6FDz1OODekVZLzzSlcEKRBCiGJzcPDm/a5f46S24mpOKjO3vFZpxmyKjt7OjPDJZOq1NLd5jDee/V+V6rGUn6r96YUQJebi2pj3n/6caiozLmbeZPrGPtxJS1Q61iO5cuUA0/eHkqHX0tDKlbHPr6k080o/CikQQogS8/IK5IMOs6imMuOvzBt8vKmvyRaJ6OjtTNn9Lmn6HHwta/Df59dVue6sBZECIYQolTo+T/Nhx9mGK4lpP/ch+fYlpWOVSNS5jUzZP4FUXTZ1LZwIDVmNtY2T0rGMhhQIIUSp+dTuyEcd52CnNic6K4mPNvUjIf4PpWMVS+TJlUwPn2K4rfTRCz9Qzc5D6VhGRQqEEOKR1K7dganPLMJVY8M1bTof/f4f/rq4TelYBdLrdGzZ8xGfHv+cbHS0svXi/Rc3yJVDPqRACCEemYdna6aGfIuPRXVSdFlM2v8+Ow99onSsh2Rn3+Hrza+xKmYzeqBzjaaMfWE9FpZ2SkczSlIghBBlwrG6D2EvbMCvWi1y0LH4/FoW/dyf7Mx0paMBEB8XycTve7A76TRqVLxe53mGPbuy0s4nXRakQAghyoy1jRNjX9xAH+8uqFGxO+k0E9YHKzrpkF6nY9+ReUz4/Q3+zrpFNZUZ//UbR/cOk6v8cw5FUen1er3SIYxZSkoKDg4OJCcnY29vr3QcIUzGn2e+48tjc0jWZaFGxfMe7Xip00zMLW0rLEPitdMs2xvKH+m5Ex41tHLlrWe+pIbz4xWWwRgV97wmBaIIUiCEKL3UlKss3zGag8l/AeCisebVhv0JbD2iXL+9Z9y9xdaD09kQu5NsdJih4iWvzvTsNBO1xqzcjmsqinteM5nrq48//pg2bdpgY2ODo6NjsbbR6/VMnDgRDw8PrK2tCQoK4q+//irfoEIIAzv7x3j7xfWMaf4mTmorrmvvMu/0Ej5cF0TEH0vLfJa6jLu32LTrfd76vivrYreTjY7G1u7M6vI1LwbNluJQQiZzBREWFoajoyP//PMP//vf/7h9+3aR23zyySfMmDGDlStX4uPjw0cffcSpU6eIiorCyqp4DVNyBSFE2cjMSOaXA1P5+Z/dZOi1ALhqbOjq3ZnAJgMe6bZPzKU97D71DQdu/km6PrfouGlseaVBH9r6jZS2hgdU2ltMK1asYPTo0UUWCL1ej6enJ2PHjmXcuNzhepOTk3Fzc2PFihX06dOnWMcr7l+kVqslOzu72J9DCFNjYWGBugxOtLdvxbDtyGdsTwgnTf/vFcTjls60dG2Br8cT1PF+qsCH1nTaHG7ePM+lq4f5858DnLp9gficf3tKuWlsefHxF2nv9xYaM4tHzlsZFfe8Vmmvt2JiYkhISCAoKMiwzMHBgYCAAMLDwwssEJmZmWRmZhrep6SkFHocvV5PQkJCsa5ohDBlarUaHx8fLCwe7aTrWN2HPsFf0PPuLfZHLmR/7C7OZ9zgQuYNLsTugNgdEDEDO7U5DhorHMxs0aMnW6flri6Ta9npZKPLs08z1Pjb16FTg140afCS3EoqI5X2bzEhIQEANze3PMvd3NwMP8vPjBkzmDx5comOc/v2bVxdXbGxsUGlUpUusBBGTKfTERcXR3x8PN7e3mXye25lXZ1n2r3PM7xPUtJFjp5Zy/nrf3IxLY5r2nRSddmk6rL5Jzv1oW3NUOFuXo1GDr40q9mOxvVCsKnm+siZRF6KFogJEybwySeFP2159uxZGjRoUEGJIDQ0lDFjxhjep6Sk4OXlle+6Wq3WUBxq1KhRURGFUISLiwtxcXHk5ORgbm5epvt2cvKla/uP6Pr/79NS40m6FU1yajwpd66hUqkxN7PC0twWV6fHcXFpLLePKoCiBWLs2LEMHDiw0HXq1KlTqn27u7sDcO3aNTw8/r2Xee3aNVq0aFHgdpaWllhaWhbrGPfaHGxsZNx4Ufndu7Wk1WrLvEA8qJqdhwycZwQULRAuLi64uLiUy759fHxwd3dn586dhoKQkpLCkSNHGDFiRJkeS24riaqgpL/nWp2eiJgkElMzcLWzwt/HCY1a/q+YEpNpg7hy5QpJSUlcuXIFrVbLiRMnAPD19aVatWoANGjQgBkzZvDCCy+gUqkYPXo006ZNo169eoZurp6envTs2VO5DyJEFbDtdDyTN0cRn5xhWObhYEVYSCOCm8iVgakwmc7BEydOpGXLloSFhZGWlkbLli1p2bIlx44dM6xz/vx5kpOTDe/Hjx/PW2+9xRtvvMETTzxBWloa27ZtK/YzEKL09uzZg0qlKlHvrtq1azN37twyy1Da/X300Ue88cYbxV5/0aJFhISElPg4ldW20/GMWH08T3EASEjOYMTq42w7Ha9QMlFSJlMgVqxYgV6vf+jVsWNHwzp6vT5Pm4ZKpWLKlCkkJCSQkZHBjh07ePzxqj0GC8DAgQNRqVQMHz78oZ+NHDkSlUpVZNuQKTh69GiJTvSQ2ytt3rx5fPDBB8XeZvDgwRw/fpz9+/eXNGKlo9Xpmbw5ivwerrq3bPLmKLQ6k3r8qsoymQIhypaXlxfr1q3j7t27hmUZGRmsWbMGb29vBZOVHRcXlxJ3IFi6dClt2rShVq1axd7GwsKCV199lfnz55c0YqUTEZP00JXD/fRAfHIGETFJFRdKlJoUiLKk10P2XWVeJXwgvlWrVnh5efHjjz8alv344494e3vTsmXLPOtmZmby9ttv4+rqipWVFe3atePo0aN51vn11195/PHHsba2plOnTly6dOmhYx44cID27dtjbW2Nl5cXb7/9NunppZ8rQK/XM2nSJLy9vbG0tMTT05O3337b8PMHbzGpVCqWLl3KCy+8gI2NDfXq1WPTpk159rlu3bo8t4uuX7+Ou7s706dPNyw7dOgQFhYW7Ny507AsJCSETZs25Sm4VVFiasHFoTTrCWWZTCO1ScjJgGXByhx78DYwty7ZJoMHs3z5cvr16wfAsmXLGDRoEHv27Mmz3vjx49mwYQMrV66kVq1azJo1i65du3Lx4kWcnJyIjY3lxRdfZOTIkbzxxhscO3aMsWPH5tlHdHQ0wcHBTJs2jWXLlnH9+nVGjRrFqFGjWL58eb75Bg4cyKVLlx7Kc8+GDRv4/PPPWbduHY0bNyYhIYGTJ08W+pknT57MrFmz+PTTT/niiy/o168fly9fxsnJiaSkJKKiovDz8zOs7+LiwrJly+jZsyddunShfv36DBgwgFGjRtG5c2fDen5+fuTk5HDkyJE8tz2rGle74rXvFXc9oSy5gqjC+vfvz4EDB7h8+TKXL1/m4MGD9O/fP8866enpLFy4kE8//ZRu3brRqFEjlixZgrW1Nf/73/8AWLhwIXXr1mXOnDnUr1+ffv36PdSGMWPGDPr168fo0aOpV68ebdq0Yf78+XzzzTdkZOT/bdLDw6PQ211XrlzB3d2doKAgvL298ff3Z9iwYYV+5oEDB9K3b198fX2ZPn06aWlpREREGPZ3bwyv+3Xv3p1hw4bRr18/hg8fjq2tLTNmzMizjo2NDQ4ODly+fLnQ41d2/j5OeDhYUVBnVhW5vZn8fWT+Z1MgVxBlycwq95u8UscuIRcXF3r06GHoANCjRw+cnZ3zrBMdHU12djZt27Y1LDM3N8ff35+zZ88CuU+7BwQE5NkuMDAwz/uTJ0/y559/8u233xqW6fV6dDodMTExNGzY8KF8D56EH9SrVy/mzp1LnTp1CA4Opnv37oSEhGBmVvCvdbNmzQx/trW1xd7ensTERADD7aH8ernNnj2bJk2asH79eiIjI/N9mNLa2po7d+4Umrmy06hVhIU0YsTq46ggT2P1vaIRFtJInocwEVIgypJKVeLbPEobPHgwo0aNAmDBggXldpy0tDT+85//5GkjuKe0jeJeXl6cP3+eHTt2sH37dt58800+/fRT9u7dW+CTvg8uV6lU6HS5A7/dK463bt166AHO6Oho4uLi0Ol0XLp0iaZNmz6076SkpHJ78NOUBDfxYGH/Vg89B+Euz0GYHCkQVVxwcDBZWVmoVCq6du360M/r1q2LhYUFBw8eNPTsyc7O5ujRo4wePRqAhg0bPtTYe/jw4TzvW7VqRVRUFL6+vmWa39rampCQEEJCQhg5ciQNGjTg1KlTtGrVqsT7qlu3Lvb29kRFReXpDp2VlUX//v3p3bs39evXZ+jQoZw6dQpX138Hh4uOjiYjI+OhBv6qKriJB880cpcnqU2cFIgqTqPRGG4VaTSah35ua2vLiBEjeO+993BycsLb25tZs2Zx584dhgwZAsDw4cOZM2cO7733HkOHDiUyMpIVK1bk2c9///tfnnzySUaNGsXQoUOxtbUlKiqK7du38+WXX+abLTQ0lKtXr/LNN9/k+/MVK1ag1WoJCAjAxsaG1atXY21tXaIuqvdTq9UEBQVx4MCBPE/bf/DBByQnJzN//nyqVavGr7/+yuDBg9myZYthnf3791OnTh3q1q1bqmNXRhq1isC6MoilKZNGaoG9vX2hk4bMnDmTl156iQEDBtCqVSsuXrzIb7/9RvXq1YHcW0QbNmxg48aNNG/enEWLFuXpFgq59/737t3LhQsXaN++PS1btmTixIkPNQjfLz4+nitXrhT4c0dHR5YsWULbtm1p1qwZO3bsYPPmzY80su7QoUNZt26d4bbTnj17mDt3LqtWrcLe3h61Ws2qVavYv38/CxcuNGy3du3aIhvIhTA1JjejXEUrbOaljIwMYmJi8PHxkeE7Kgm9Xk9AQADvvvsuffv2LdY2Z86c4emnn+bChQs4ODiUc0LlyO975VHcGeXkCkKI+6hUKhYvXkxOTk7RK/+/+Ph4vvnmm0pdHETVJG0QQjygRYsWhc4Z8qD7p7UVojKRKwghhBD5kgIhhBAiX1IghBBC5EsKhBBCiHxJgRBCCJEvKRBCCCHyJQVCCCFEvqRACJMyadKkEj2jUJ46duxoGLCwvDw4K15xffTRRyWaj3vRokV5ZtITAqRAVFkJCQm88847+Pr6YmVlhZubG23btmXhwoUmO6fBpEmTUKlUhb5KY8+ePahUKm7fvl22gYvh6NGjJTrRQ+6/7bx58/jggw+Kvc3gwYM5fvw4+/fvL2lEUYlJgaiC/v77b1q2bMnvv//O9OnT+eOPPwgPD2f8+PFs2bKFHTt2FLhtdnZ2BSYtmXHjxhEfH2941axZkylTpuRZdr+srCyFkhafi4sLNjY2Jdpm6dKltGnTpkSj2lpYWPDqq68yf/78kkYUlZgUiDKk1+vJyMlQ5FWSMRfffPNNzMzMOHbsGK+88goNGzakTp06PP/88/zyyy95bjWoVCoWLlzIc889h62tLR9//DHw7zSjFhYW1K9fn1WrVhm2uXTpEiqVihMnThiW3b59G5VKZZhf+t638p07d+Ln54eNjQ1t2rTh/PnzebLOnDkTNzc37OzsGDJkSIHTkwJUq1YNd3d3w0uj0WBnZ2d436dPH0aNGsXo0aNxdnama9euRWa9dOkSnTp1AqB69eqoVKo806nqdDrGjx+Pk5MT7u7uTJo0qdj/DpD7OzNp0iS8vb2xtLTE09Mzz6RKD95iUqlULF26lBdeeAEbGxvq1av30Fwc69aty/NveP36ddzd3fOMsHvo0CEsLCzYuXOnYVlISAibNm0yzKwnhIzFVIYytZm8vu11RY69MnglVsWYdvTmzZuGKwdbW9t813nwVsykSZOYOXMmc+fOxczMjJ9++ol33nmHuXPnEhQUxJYtWxg0aBA1a9Y0nEyL64MPPmDOnDm4uLgwfPhwBg8ezMGDBwH4/vvvmTRpEgsWLKBdu3asWrWK+fPnU6dOnRId434rV65kxIgRhmMUxcvLiw0bNvDSSy9x/vx57O3tsbb+d9bAlStXMmbMGI4cOUJ4eDgDBw6kbdu2PPPMM0DuHNiXLl0yFMYHbdiwgc8//5x169bRuHFjEhISOHnyZKGZJk+ezKxZs/j000/54osv6NevH5cvX8bJyYmkpCSioqLw8/MzrO/i4sKyZcvo2bMnXbp0oX79+gwYMIBRo0bRuXNnw3p+fn7k5ORw5MgROnbsWKy/n8pOq9NX6UmPpEBUMRcvXkSv11O/fv08y52dnQ3fzkeOHMknn3xi+Nmrr77KoEGDDO/79u3LwIEDefPNNwEYM2YMhw8fZvbs2SUuEB9//DEdOnQAYMKECfTo0YOMjAysrKyYO3cuQ4YMMUxMNG3aNHbs2FHoVURR6tWrx6xZswzvL126VOj6Go0GJycnAFxdXXF0dMzz82bNmhEWFmbY95dffsnOnTsNBcLDw8Mwt0R+rly5gru7O0FBQZibm+Pt7Y2/v3+hmQYOHGgYinz69OnMnz+fiIgIgoODuXLlCnq9/qF5Nrp3786wYcPo168ffn5+2NraPjTnt42NDQ4ODly+fLnQ41cV207HPzRtqkcVmzZVCkQZstRYsjJ4pWLHfhQRERHodDr69etHZmZmnp/d/20U4OzZsw81nLZt25Z58+aV+LjNmjUz/NnDI/c/XWJiIt7e3pw9e5bhw4fnWT8wMJDdu3eX+Dj3tG7dutTb5uf+/JD7GRITEw3vHzwJP6hXr17MnTuXOnXqEBwcTPfu3QkJCcHMrOD/mvcf09bWFnt7e8Mx790eym++htmzZ9OkSRPWr19PZGQklpYP/85YW1ubbCeFsrTtdDwjVh/nwRu3CckZjFh9nIX9W1WJIiFtEGVIpVJhZWalyKu4PXR8fX1RqVQP3euvU6cOvr6+eW6f3FPQraiCqNW5v1b3t4sU1Lhtbm5u+PO9z1DYN+5H9eBnKUnW/NyfH3I/Q0nye3l5cf78eb766iusra158803eeqppwrNUNgxnZ2dAbh169ZD20VHRxMXF4dOpyvwyikpKQkXF5di56+MtDo9kzdHPVQcAMOyyZuj0Ooq/1xrUiCqmBo1avDMM8/w5Zdfkp6eXqp9NGzY8KF7+AcPHqRRo0YAhhPM/b2G7m8ELslxjhw5kmfZ4cOHS7yfwhQnq4WFBQBarbZMj32PtbU1ISEhzJ8/nz179hAeHs6pU6dKta+6detib29PVFRUnuVZWVn079+f3r17M3XqVIYOHZrnSgdyC0hGRgYtW7Ys9WepDCJikvLcVnqQHohPziAiJqniQilEbjFVQV999RVt27bFz8+PSZMm0axZM9RqNUePHuXcuXNF3oZ57733eOWVV2jZsiVBQUFs3ryZH3/80dA91tramieffJKZM2fi4+NDYmIiH374YYlzvvPOOwwcOBA/Pz/atm3Lt99+y5kzZx6pkfpBxclaq1YtVCoVW7ZsoXv37lhbW1OtWrVi7T80NJSrV6/yzTff5PvzFStWoNVqCQgIwMbGhtWrV2NtbV2iLqr3U6vVBAUFceDAAXr27GlY/sEHH5CcnMz8+fOpVq0av/76K4MHD2bLli2Gdfbv30+dOnWoW7duqY5dWSSmFq+Nq7jrmTK5gqiC6tatyx9//EFQUBChoaE0b94cPz8/vvjiC8aNG8fUqVML3b5nz57MmzeP2bNn07hxY77++muWL1+ep+fLsmXLyMnJoXXr1owePZpp06aVOGfv3r356KOPGD9+PK1bt+by5cuMGDGixPspSlFZH3vsMSZPnsyECRNwc3Nj1KhRxd53fHw8V65cKfDnjo6OLFmyhLZt29KsWTN27NjB5s2bqVGjRqk/z9ChQ1m3bp3httOePXuYO3cuq1atwt7eHrVazapVq9i/fz8LFy40bLd27VqGDRtW6uMaC61OT3j0TX4+cZXw6JslvhXkale8+baLu54pU+lL0oG+Cipscm+ZxF0YI71eT0BAAO+++66ht1NRzpw5w9NPP82FCxcKnFvbFH7fy6LnkVanp90nu0hIzsi3HUIFuDtYceC/T5tsl9fCzmv3M5kriI8//pg2bdpgY2PzUFfDggwcOPChoRaCg4PLN6gQClOpVCxevJicnJxibxMfH88333xTYHEwBfd6Hj3YfnCv59G20/EFbJmXRq0iLCS3Pe3B0/+992EhjUy2OJSEyRSIrKwsevXqVeJbDMHBwXmGWli7dm05JRTCeLRo0YIBAwYUe/2goCC6du1ajonKV1n3PApu4sHC/q1wd8h7peTuYFVluriCCTVST548Gcht1CsJS0tL3N3dyyGREMJYlKTnUWDd4rXvBDfx4JlG7vIkdWW2Z88eXF1dqV69Ok8//TTTpk17pAZAIYTxKa+eRxq1qtgFpTKq1AUiODiYF198ER8fH6Kjo3n//ffp1q0b4eHhaDSafLfJzMzM8yRxSkpKkceRdn5RFRjz77n0PCofirZBTJgwocjx+8+dO1fq/ffp04fnnnuOpk2b0rNnT7Zs2cLRo0cLHDgNcodGcHBwMLy8vLwKXPfeE60yNIGoCu4Nj17Qlysl+fs44eFg9VCj8j0qcnsz+fs4VWQsk6foFcTYsWPzDJ2cn7J8KKpOnTo4Oztz8eLFPKNY3i80NJQxY8YY3qekpBRYJDQaDY6OjoYnUm1sbEo9KY0Qxkyn03H9+nVsbGwKHSdKKfd6Ho1YfRwV5Gmsrmo9j8qSov/SLi4uFTruyz///MPNmzcNg8Llx9LSMt9BzApyrwH8wWELhKhs1Go13t7eRvsl6F7Powefg3CvYiOwliXj+ypQgCtXrpCUlMSVK1fQarWG8XJ8fX0Nwx40aNCAGTNm8MILL5CWlsbkyZN56aWXcHd3Jzo6mvHjx+Pr61um3flUKhUeHh64uroa9WxrQjwqCwsLw+CGxkp6HpUtkykQEydOZOXKf4fSvjeg2O7duw1DPJw/f57k5GQg9/bPn3/+ycqVK7l9+zaenp506dKFqVOnlugKobg0Go1R3psVoqqp6j2PypIMtVGE4j6SLoQQpqLSDbUhhBCiYkmBEEIIkS+TaYNQyr07cMV5YE4IIUzBvfNZUS0MUiCKkJqaClDoA3NCCGGKUlNTCx3BVxqpi6DT6YiLi8POzq5E/b/vPWAXGxtrMo3bkrlimFpmU8sLkrkoer2e1NRUPD09C+26LFcQRVCr1dSsWbPU29vb25vML+g9krlimFpmU8sLkrkwxZn7QxqphRBC5EsKhBBCiHxJgSgnlpaWhIWFlctT2+VFMlcMU8tsanlBMpcVaaQWQgiRL7mCEEIIkS8pEEIIIfIlBUIIIUS+pEAIIYTIlxSIcrBgwQJq166NlZUVAQEBREREKB2pUPv27SMkJARPT09UKhUbN25UOlKhZsyYwRNPPIGdnR2urq707NmT8+fPKx2rUAsXLqRZs2aGh6ACAwPZunWr0rFKZObMmahUKkaPHq10lAJNmjTpoXntGzRooHSsIl29epX+/ftTo0YNrK2tadq0KceOHVM6lhSIsvbdd98xZswYwsLCOH78OM2bN6dr165GPSVpeno6zZs3Z8GCBUpHKZa9e/cycuRIDh8+zPbt28nOzqZLly6kp6crHa1ANWvWZObMmURGRnLs2DGefvppnn/+ec6cOaN0tGI5evQoX3/9Nc2aNVM6SpEaN25MfHy84XXgwAGlIxXq1q1btG3bFnNzc7Zu3UpUVBRz5syhevXqSkcDvShT/v7++pEjRxrea7Vavaenp37GjBkKpio+QP/TTz8pHaNEEhMT9YB+7969SkcpkerVq+uXLl2qdIwipaam6uvVq6ffvn27vkOHDvp33nlH6UgFCgsL0zdv3lzpGCXy3//+V9+uXTulY+RLriDKUFZWFpGRkQQFBRmWqdVqgoKCCA8PVzBZ5XZvmlknJyeFkxSPVqtl3bp1pKenExgYqHScIo0cOZIePXrk+b02Zn/99Reenp7UqVOHfv36ceXKFaUjFWrTpk34+fnRq1cvXF1dadmyJUuWLFE6FiC3mMrUjRs30Gq1uLm55Vnu5uZGQkKCQqkqN51Ox+jRo2nbti1NmjRROk6hTp06RbVq1bC0tGT48OH89NNPNGrUSOlYhVq3bh3Hjx9nxowZSkcploCAAFasWMG2bdtYuHAhMTExtG/f3jBsvzH6+++/WbhwIfXq1eO3335jxIgRvP3226xcuVLpaDKaqzBtI0eO5PTp00Z/nxmgfv36nDhxguTkZH744Qdef/119u7da7RFIjY2lnfeeYft27djZWWldJxi6datm+HPzZo1IyAggFq1avH9998zZMgQBZMVTKfT4efnx/Tp0wFo2bIlp0+fZtGiRbz++uuKZpMriDLk7OyMRqPh2rVreZZfu3YNd3d3hVJVXqNGjWLLli3s3r37kYZkrygWFhb4+vrSunVrZsyYQfPmzZk3b57SsQoUGRlJYmIirVq1wszMDDMzM/bu3cv8+fMxMzNDq9UqHbFIjo6OPP7441y8eFHpKAXy8PB46EtCw4YNjeLWmBSIMmRhYUHr1q3ZuXOnYZlOp2Pnzp0mca/ZVOj1ekaNGsVPP/3Erl278PHxUTpSqeh0OjIzM5WOUaDOnTtz6tQpTpw4YXj5+fnRr18/Tpw4gUajUTpikdLS0oiOjsbDw0PpKAVq27btQ920L1y4QK1atRRK9C+5xVTGxowZw+uvv46fnx/+/v7MnTuX9PR0Bg0apHS0AqWlpeX5hhUTE8OJEydwcnLC29tbwWT5GzlyJGvWrOHnn3/Gzs7O0L7j4OCAtbW1wunyFxoaSrdu3fD29iY1NZU1a9awZ88efvvtN6WjFcjOzu6hdh1bW1tq1KhhtO0948aNIyQkhFq1ahEXF0dYWBgajYa+ffsqHa1A7777Lm3atGH69Om88sorREREsHjxYhYvXqx0NOnmWh6++OILvbe3t97CwkLv7++vP3z4sNKRCrV792498NDr9ddfVzpavvLLCuiXL1+udLQCDR48WF+rVi29hYWF3sXFRd+5c2f977//rnSsEjP2bq69e/fWe3h46C0sLPSPPfaYvnfv3vqLFy8qHatImzdv1jdp0kRvaWmpb9CggX7x4sVKR9Lr9Xq9DPcthBAiX9IGIYQQIl9SIIQQQuRLCoQQQoh8SYEQQgiRLykQQggh8iUFQgghRL6kQAghhMiXFAghhBD5kgIhhBAiX1IghBBC5EsKhBAKun79Ou7u7oa5AAAOHTqEhYVFnlGBhVCCjMUkhMJ+/fVXevbsyaFDh6hfvz4tWrTg+eef57PPPlM6mqjipEAIYQRGjhzJjh078PPz49SpUxw9ehRLS0ulY4kqTgqEEEbg7t27NGnShNjYWCIjI2natKnSkYSQNgghjEF0dDRxcXHodDouXbqkdBwhALmCEEJxWVlZ+Pv706JFC+rXr8/cuXM5deoUrq6uSkcTVZwUCCEU9t577/HDDz9w8uRJqlWrRocOHXBwcGDLli1KRxNVnNxiEkJBe/bsYe7cuaxatQp7e3vUajWrVq1i//79LFy4UOl4ooqTKwghhBD5kisIIYQQ+ZICIYQQIl9SIIQQQuRLCoQQQoh8SYEQQgiRLykQQggh8iUFQgghRL6kQAghhMiXFAghhBD5kgIhhBAiX1IghBBC5EsKhBBCiHz9H16DI+e3P3f9AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:03<00:00, 26.21it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mUpdated State:\u001b[0m\n", + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", + " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", + " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", + " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", + " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", + " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 3.249923\n", + "1 4.116570\n", + "2 2.599939\n", + "3 0.216662\n", + "4 3.683247\n", + "5 0.000000\n", + "6 1.733292\n", + "7 5.416539\n", + "8 2.816600\n", + "9 3.683247, experiment_data= x y\n", + "0 0.433323 0.572248\n", + "1 4.983216 -1.483542\n", + "2 4.116570 -0.452463\n", + "3 2.816600 0.789584\n", + "4 2.599939 -0.459964\n", + "5 5.416539 -1.413252\n", + "6 0.433323 0.483809\n", + "7 4.333231 -1.087098\n", + "8 1.299969 0.955149\n", + "9 0.433323 -0.006633\n", + "10 3.249923 0.013996\n", + "11 4.116570 -0.488600\n", + "12 2.599939 0.222789\n", + "13 0.216662 -0.239366\n", + "14 3.683247 -1.511473\n", + "15 0.000000 0.485811\n", + "16 1.733292 0.995155\n", + "17 5.416539 -0.659296\n", + "18 2.816600 -0.072496\n", + "19 3.683247 0.097695, models=[sin(x), sin(x)])\n" + ] + } + ], "source": [ "print('\\033[1mPrevious State:\\033[0m')\n", "print(s)\n", "\n", "for cycle in range(2):\n", - " s = theorist(experiment_runner(experimentalist(s)))\n", + " s = experimentalist(s, num_samples=10, random_state=42+cycle)\n", + " s = experiment_runner(s, added_noise=0.5, random_state=42+cycle)\n", + " s = theorist(s)\n", + " \n", + " plot_from_state(s, 'sin(x)')\n", "\n", "print('\\n\\033[1mUpdated State:\\033[0m')\n", "print(s)" @@ -144,16 +303,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Custom Theorists\n", - "\n", - "What if we wanted to replace the ``theorist`` with a custom theorist?\n", - "\n", - "We can implement our theorist as a class that inherits from `sklearn.base.BaseEstimator`. The class must implement the following methods:\n", + "## Custom Experimentalists\n", "\n", - "- `fit(self, conditions, observations)`\n", - "- `predict(self, conditions)`\n", + "We can also implement custom experimentalists. Experimentalists are generally implemented as functions that can be integrated into an\n", + "[Experimentalist Pipeline](https://autoresearch.github.io/autora/core/docs/pipeline/Experimentalist%20Pipeline%20Examples/). For instance, an experimentalist sampler function expects a pool of experimental conditions–typically passed as a 2D numpy array named ``condition_pool``–and returns a modified set of experimental conditions.\n", "\n", - "The following code block implements such a theorist that fits a polynomial of a specified degree." + "The following code block implements a basic experimentalist that considers two models, and identifies experimental conditions for which the two models differ most in their predictions. This is a special case of the [Model Disagreement Sampler](https://autoresearch.github.io/autora/user-guide/experimentalists/samplers/model-disagreement/)." ] }, { @@ -162,75 +317,270 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "from sklearn.base import BaseEstimator\n", + "def uniform_sample(variables: VariableCollection, conditions: pd.DataFrame, num_samples: int = 1, random_state: Optional [int] = None):\n", "\n", - "class PolynomialRegressor(BaseEstimator):\n", " \"\"\"\n", - " This theorist fits a polynomial function to the data.\n", + " An experimentalist that selects the least represented datapoints\n", " \"\"\"\n", + " #Set rng seed\n", + " rng = np.random.default_rng(random_state)\n", "\n", - " def __init__(self, degree: int = 3):\n", - " self.degree = degree\n", - "\n", - " def fit(self, conditions, observations):\n", - "\n", - " # polyfit expects a 1D array\n", - " if conditions.ndim > 1:\n", - " conditions = conditions.flatten()\n", - "\n", - " if observations.ndim > 1:\n", - " observations = observations.flatten()\n", - "\n", - " # fit polynomial\n", - " self.coeff = np.polyfit(conditions, observations, self.degree)\n", - " self.polynomial = np.poly1d(self.coeff)\n", - " pass\n", - "\n", - " def predict(self, conditions):\n", - " return self.polynomial(conditions)\n", + " #Retrieve the possible values\n", + " allowed_values = variables.independent_variables[0].allowed_values\n", " \n", - "custom_theorist = state_fn_from_estimator(PolynomialRegressor())" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's run the controller with the new theorist for 3 research cycles, defined by the number of models generated." + " #Determine the representation of each value\n", + " conditions_count = np.array([conditions[\"x\"].isin([value]).sum(axis=0) for value in allowed_values])\n", + " \n", + " #Sort to determine the least represented values\n", + " conditions_sort = conditions_count.argsort()\n", + " \n", + " conditions_count = conditions_count[conditions_sort]\n", + " values_count = allowed_values[conditions_sort]\n", + " \n", + " #Sample from values with the smallest frequency\n", + " x = values_count[conditions_count<=conditions_count[num_samples-1]]\n", + " x = rng.choice(x,num_samples)\n", + " \n", + " return pd.DataFrame({\"x\": x})\n", + "\n", + "custom_experimentalist = on_state(uniform_sample, output=[\"conditions\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mPrevious State:\u001b[0m\n", + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", + " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", + " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", + " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", + " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", + " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 5.416539\n", + "1 4.116570\n", + "2 3.249923\n", + "3 1.733292\n", + "4 1.949954\n", + "5 0.216662\n", + "6 0.433323\n", + "7 0.000000\n", + "8 1.083308\n", + "9 5.199877, experiment_data=Empty DataFrame\n", + "Columns: [x, y]\n", + "Index: [], models=[])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:04<00:00, 23.04it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:03<00:00, 27.31it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:04<00:00, 23.47it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:04<00:00, 24.90it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:04<00:00, 24.25it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mUpdated State:\u001b[0m\n", + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", + " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", + " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", + " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", + " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", + " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 4.983216\n", + "1 5.849862\n", + "2 3.466585\n", + "3 4.116570\n", + "4 1.733292\n", + "5 3.249923\n", + "6 3.249923\n", + "7 5.199877\n", + "8 3.683247\n", + "9 4.333231, experiment_data= x y\n", + "0 5.849862 -0.267531\n", + "1 1.516631 0.478541\n", + "2 2.383277 1.062925\n", + "3 3.683247 -0.045271\n", + "4 3.683247 -1.491071\n", + "5 2.599939 -0.135536\n", + "6 5.849862 -0.355969\n", + "7 2.383277 0.529578\n", + "8 4.766554 -1.006934\n", + "9 5.849862 -0.846411\n", + "10 2.816600 0.441416\n", + "11 1.949954 1.268066\n", + "12 3.466585 -0.612066\n", + "13 5.633201 -1.059511\n", + "14 3.033262 -0.887800\n", + "15 0.000000 0.485811\n", + "16 4.333231 -0.920648\n", + "17 0.649985 0.708040\n", + "18 6.066524 -0.606768\n", + "19 2.166616 1.440938\n", + "20 1.299969 1.686531\n", + "21 3.683247 -0.464401\n", + "22 5.849862 -0.256513\n", + "23 4.983216 -0.395319\n", + "24 1.299969 1.375672\n", + "25 2.383277 0.977178\n", + "26 3.899908 -0.877285\n", + "27 4.549893 -1.496263\n", + "28 5.416539 -0.574078\n", + "29 4.766554 -1.254513\n", + "30 1.949954 0.700044\n", + "31 0.216662 -0.096459\n", + "32 0.866646 0.829807\n", + "33 0.649985 1.027126\n", + "34 0.649985 0.530925\n", + "35 6.283185 0.131242\n", + "36 2.166616 1.093523\n", + "37 1.516631 1.343437\n", + "38 0.649985 0.159225\n", + "39 3.033262 0.342529\n", + "40 4.983216 -1.215008\n", + "41 5.849862 0.189056\n", + "42 3.466585 -0.454861\n", + "43 4.116570 -0.462597\n", + "44 1.733292 0.402174\n", + "45 3.249923 -0.822852\n", + "46 3.249923 -0.119755\n", + "47 5.199877 -1.107408\n", + "48 3.683247 -0.471516\n", + "49 4.333231 -0.666329, models=[-0.04, -0.04, -0.04, -0.04, -0.04])\n" + ] + } + ], "source": [ "#### First, let's reinitialize the state object to get a clean state ####\n", + "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 30))\n", + "dv = Variable(name=\"y\", type=ValueType.REAL)\n", + "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "conditions = random_pool(variables, num_samples=10, random_state=0)\n", + "\n", "s = StandardState(variables = variables, conditions = conditions, experiment_data = pd.DataFrame(columns=[\"x\",\"y\"]))\n", "\n", "print('\\033[1mPrevious State:\\033[0m')\n", "print(s)\n", "\n", "for cycle in range(5):\n", - " s = custom_theorist(experiment_runner(experimentalist(s)))\n", + " s = custom_experimentalist(s, num_samples = 10, random_state=42+cycle)\n", + " s = experiment_runner(s, added_noise=0.5, random_state=42+cycle)\n", + " s = theorist(s)\n", + " \n", + " plot_from_state(s,'sin(x)')\n", "\n", "print('\\n\\033[1mUpdated State:\\033[0m')\n", - "print(s)\n" + "print(s)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Custom Experimentalists\n", - "\n", - "We can also implement custom experimentalists. Experimentalists are generally implemented as functions that can be integrated into an\n", - "[Experimentalist Pipeline](https://autoresearch.github.io/autora/core/docs/pipeline/Experimentalist%20Pipeline%20Examples/). For instance, an experimentalist sampler function expects a pool of experimental conditions–typically passed as a 2D numpy array named ``condition_pool``–and returns a modified set of experimental conditions.\n", - "\n", - "The following code block implements a basic experimentalist that considers two models, and identifies experimental conditions for which the two models differ most in their predictions. This is a special case of the [Model Disagreement Sampler](https://autoresearch.github.io/autora/user-guide/experimentalists/samplers/model-disagreement/)." + "## Custom Experiment Runner" ] }, { @@ -239,25 +589,23 @@ "metadata": {}, "outputs": [], "source": [ - "def uniform_experimentalist(variables: VariableCollection, conditions: pd.DataFrame, num_samples = 1):\n", - "\n", - " \"\"\"\n", - " An experimentalist that selects the least represented datapoints\n", - " \"\"\"\n", - "\n", - " #Retrieve the possible values\n", - " allowed_values = variables.independent_variables[0].allowed_values\n", + "def run_experiment(conditions: pd.DataFrame, added_noise: int = 0.01, random_state: Optional[int] = None):\n", " \n", - " #Determine the representation of each value\n", - " conditions_count = np.array([conditions[\"x\"].isin([value]).sum(axis=0) for value in allowed_values])\n", + " #Set rng seed\n", + " rng = np.random.default_rng(random_state)\n", " \n", - " #Sort to determine the least represented values\n", - " conditions_sort = conditions_count.argsort()\n", - " values_count = allowed_values[conditions_sort]\n", + " #Extract conditions\n", + " x = conditions[\"x\"]\n", + " \n", + " #Compute data\n", + " y = (x + x**2) + rng.normal(0, added_noise, size=x.shape)\n", " \n", - " return pd.DataFrame({\"x\": values_count[:num_samples]})\n", + " #Assign to dataframe\n", + " observations = conditions.assign(y = y)\n", + " \n", + " return observations\n", "\n", - "custom_experimentalist = on_state(uniform_experimentalist, output=[\"conditions\"])" + "custom_experiment_runner = on_state(run_experiment, output=[\"experiment_data\"])" ] }, { @@ -265,57 +613,226 @@ "execution_count": null, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1mPrevious State:\u001b[0m\n", - "StandardState(variables=VariableCollection(independent_variables=[IV(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.6981317 , 1.3962634 , 2.0943951 , 2.7925268 ,\n", - " 3.4906585 , 4.1887902 , 4.88692191, 5.58505361, 6.28318531]), units='', type=, variable_label='Independent Variable', rescale=1, is_covariate=False)], dependent_variables=[DV(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='Dependent Variable', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 4.886922\n", - "1 4.886922\n", - "2 0.698132\n", - "3 2.792527\n", - "4 0.698132\n", - "5 4.886922\n", - "6 0.000000\n", - "7 4.886922\n", - "8 0.698132\n", - "9 1.396263, experiment_data=Empty DataFrame\n", + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", + " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", + " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", + " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", + " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", + " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 5.416539\n", + "1 4.116570\n", + "2 3.249923\n", + "3 1.733292\n", + "4 1.949954\n", + "5 0.216662\n", + "6 0.433323\n", + "7 0.000000\n", + "8 1.083308\n", + "9 5.199877, experiment_data=Empty DataFrame\n", "Columns: [x, y]\n", "Index: [], models=[])\n" ] }, { - "ename": "ValueError", - "evalue": "Length of values (5) does not match length of index (10)", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[89], line 8\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[39mprint\u001b[39m(s)\n\u001b[0;32m 7\u001b[0m \u001b[39mfor\u001b[39;00m cycle \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m5\u001b[39m):\n\u001b[1;32m----> 8\u001b[0m s \u001b[39m=\u001b[39m theorist(experiment_runner(custom_experimentalist(s, num_samples \u001b[39m=\u001b[39;49m \u001b[39m5\u001b[39;49m)))\n\u001b[0;32m 10\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\033\u001b[39;00m\u001b[39m[1mUpdated State:\u001b[39m\u001b[39m\\033\u001b[39;00m\u001b[39m[0m\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m 11\u001b[0m \u001b[39mprint\u001b[39m(s)\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state\\delta.py:1033\u001b[0m, in \u001b[0;36mdelta_to_state.._f\u001b[1;34m(state_, **kwargs)\u001b[0m\n\u001b[0;32m 1031\u001b[0m \u001b[39m@wraps\u001b[39m(f)\n\u001b[0;32m 1032\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_f\u001b[39m(state_: S, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m S:\n\u001b[1;32m-> 1033\u001b[0m delta \u001b[39m=\u001b[39m f(state_, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1034\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39misinstance\u001b[39m(delta, Mapping), (\n\u001b[0;32m 1035\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mOutput of \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m must be a `Delta`, `UserDict`, \u001b[39m\u001b[39m\"\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mor `dict`.\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m f\n\u001b[0;32m 1036\u001b[0m )\n\u001b[0;32m 1037\u001b[0m new_state \u001b[39m=\u001b[39m state_ \u001b[39m+\u001b[39m delta\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state\\delta.py:768\u001b[0m, in \u001b[0;36minputs_from_state.._f\u001b[1;34m(state_, **kwargs)\u001b[0m\n\u001b[0;32m 761\u001b[0m \u001b[39mif\u001b[39;00m (\u001b[39m\"\u001b[39m\u001b[39mconditions\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m arguments \u001b[39mand\u001b[39;00m \u001b[39misinstance\u001b[39m(arguments[\u001b[39m\"\u001b[39m\u001b[39mconditions\u001b[39m\u001b[39m\"\u001b[39m], pd\u001b[39m.\u001b[39mDataFrame) \u001b[39mand\u001b[39;00m\n\u001b[0;32m 762\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mvariables\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m [i\u001b[39m.\u001b[39mname \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m fields(state_)]):\n\u001b[0;32m 763\u001b[0m arguments[\u001b[39m\"\u001b[39m\u001b[39mconditions\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m (\n\u001b[0;32m 764\u001b[0m align_dataframe_to_ivs(arguments[\u001b[39m\"\u001b[39m\u001b[39mconditions\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 765\u001b[0m \u001b[39mgetattr\u001b[39m(state_, \u001b[39m\"\u001b[39m\u001b[39mvariables\u001b[39m\u001b[39m\"\u001b[39m)\u001b[39m.\u001b[39mindependent_variables)\n\u001b[0;32m 766\u001b[0m )\n\u001b[1;32m--> 768\u001b[0m result \u001b[39m=\u001b[39m f(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39marguments)\n\u001b[0;32m 769\u001b[0m \u001b[39mreturn\u001b[39;00m result\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state\\delta.py:851\u001b[0m, in \u001b[0;36moutputs_to_delta..decorator..inner\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 849\u001b[0m \u001b[39m@wraps\u001b[39m(f)\n\u001b[0;32m 850\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minner\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m--> 851\u001b[0m result \u001b[39m=\u001b[39m f(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 852\u001b[0m delta \u001b[39m=\u001b[39m Delta(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m{output[\u001b[39m0\u001b[39m]: result})\n\u001b[0;32m 853\u001b[0m \u001b[39mreturn\u001b[39;00m delta\n", - "Cell \u001b[1;32mIn[88], line 17\u001b[0m, in \u001b[0;36muniform_experimentalist\u001b[1;34m(variables, conditions, num_samples)\u001b[0m\n\u001b[0;32m 14\u001b[0m conditions_sort \u001b[39m=\u001b[39m conditions_count\u001b[39m.\u001b[39margsort()\n\u001b[0;32m 15\u001b[0m values_count \u001b[39m=\u001b[39m allowed_values[conditions_sort]\n\u001b[1;32m---> 17\u001b[0m \u001b[39mreturn\u001b[39;00m conditions\u001b[39m.\u001b[39;49massign(x\u001b[39m=\u001b[39;49mvalues_count[:num_samples])\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\pandas\\core\\frame.py:4844\u001b[0m, in \u001b[0;36mDataFrame.assign\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m 4841\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcopy(deep\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m)\n\u001b[0;32m 4843\u001b[0m \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems():\n\u001b[1;32m-> 4844\u001b[0m data[k] \u001b[39m=\u001b[39m com\u001b[39m.\u001b[39mapply_if_callable(v, data)\n\u001b[0;32m 4845\u001b[0m \u001b[39mreturn\u001b[39;00m data\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\pandas\\core\\frame.py:3950\u001b[0m, in \u001b[0;36mDataFrame.__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m 3947\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_setitem_array([key], value)\n\u001b[0;32m 3948\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 3949\u001b[0m \u001b[39m# set column\u001b[39;00m\n\u001b[1;32m-> 3950\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_set_item(key, value)\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\pandas\\core\\frame.py:4143\u001b[0m, in \u001b[0;36mDataFrame._set_item\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m 4133\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_set_item\u001b[39m(\u001b[39mself\u001b[39m, key, value) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 4134\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 4135\u001b[0m \u001b[39m Add series to DataFrame in specified column.\u001b[39;00m\n\u001b[0;32m 4136\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 4141\u001b[0m \u001b[39m ensure homogeneity.\u001b[39;00m\n\u001b[0;32m 4142\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 4143\u001b[0m value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sanitize_column(value)\n\u001b[0;32m 4145\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[0;32m 4146\u001b[0m key \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\n\u001b[0;32m 4147\u001b[0m \u001b[39mand\u001b[39;00m value\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m 4148\u001b[0m \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m is_extension_array_dtype(value)\n\u001b[0;32m 4149\u001b[0m ):\n\u001b[0;32m 4150\u001b[0m \u001b[39m# broadcast across multiple columns if necessary\u001b[39;00m\n\u001b[0;32m 4151\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mis_unique \u001b[39mor\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns, MultiIndex):\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\pandas\\core\\frame.py:4870\u001b[0m, in \u001b[0;36mDataFrame._sanitize_column\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m 4867\u001b[0m \u001b[39mreturn\u001b[39;00m _reindex_for_setitem(Series(value), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mindex)\n\u001b[0;32m 4869\u001b[0m \u001b[39mif\u001b[39;00m is_list_like(value):\n\u001b[1;32m-> 4870\u001b[0m com\u001b[39m.\u001b[39;49mrequire_length_match(value, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindex)\n\u001b[0;32m 4871\u001b[0m \u001b[39mreturn\u001b[39;00m sanitize_array(value, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mindex, copy\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, allow_2d\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n", - "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\pandas\\core\\common.py:576\u001b[0m, in \u001b[0;36mrequire_length_match\u001b[1;34m(data, index)\u001b[0m\n\u001b[0;32m 572\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 573\u001b[0m \u001b[39mCheck the length of data matches the length of the index.\u001b[39;00m\n\u001b[0;32m 574\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 575\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(data) \u001b[39m!=\u001b[39m \u001b[39mlen\u001b[39m(index):\n\u001b[1;32m--> 576\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 577\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mLength of values \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 578\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m(\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(data)\u001b[39m}\u001b[39;00m\u001b[39m) \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 579\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mdoes not match length of index \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 580\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m(\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(index)\u001b[39m}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 581\u001b[0m )\n", - "\u001b[1;31mValueError\u001b[0m: Length of values (5) does not match length of index (10)" + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:05<00:00, 18.87it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:05<00:00, 19.46it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:05<00:00, 19.88it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:06<00:00, 16.33it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:05<00:00, 17.36it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mUpdated State:\u001b[0m\n", + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", + " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", + " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", + " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", + " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", + " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 3.249923\n", + "1 5.849862\n", + "2 1.299969\n", + "3 0.433323\n", + "4 3.466585\n", + "5 1.733292\n", + "6 1.516631\n", + "7 3.899908\n", + "8 0.866646\n", + "9 6.066524, experiment_data= x y\n", + "0 0.433323 0.773451\n", + "1 4.983216 29.295665\n", + "2 4.116570 21.437941\n", + "3 2.816600 11.220120\n", + "4 2.599939 8.384103\n", + "5 5.416539 34.104345\n", + "6 0.433323 0.685012\n", + "7 4.333231 22.952003\n", + "8 1.299969 2.981489\n", + "9 0.433323 0.194570\n", + "10 3.249923 13.934041\n", + "11 4.116570 21.401805\n", + "12 2.599939 9.066856\n", + "13 0.216662 -0.190733\n", + "14 3.683247 16.253633\n", + "15 0.000000 0.485811\n", + "16 1.733292 4.745924\n", + "17 5.416539 34.858300\n", + "18 2.816600 10.358040\n", + "19 3.683247 17.862801\n", + "20 4.333231 23.833106\n", + "21 0.649985 1.123617\n", + "22 5.199877 32.401980\n", + "23 1.516631 4.385032\n", + "24 4.116570 21.474838\n", + "25 2.599939 9.649098\n", + "26 0.433323 0.431507\n", + "27 6.283185 45.252166\n", + "28 3.466585 15.671881\n", + "29 0.866646 1.361742\n", + "30 5.849862 39.841817\n", + "31 3.683247 16.938122\n", + "32 4.549893 25.319062\n", + "33 3.249923 14.233877\n", + "34 3.249923 13.737676\n", + "35 4.766554 27.617837\n", + "36 4.549893 25.517251\n", + "37 5.199877 32.583507\n", + "38 3.466585 15.037847\n", + "39 3.249923 14.046336\n", + "40 3.249923 13.560468\n", + "41 5.849862 40.679695\n", + "42 1.299969 2.854330\n", + "43 0.433323 0.986184\n", + "44 3.466585 14.899144\n", + "45 1.733292 4.022862\n", + "46 1.516631 3.805164\n", + "47 3.899908 18.885295\n", + "48 0.866646 1.661760\n", + "49 6.066524 43.131882, models=[((x / 1.0) * (1.0 + x)), ((x / 1.0) * (1.0 + x)), ((x / 1.0) * (1.0 + x)), ((x / 1.0) * (1.0 + x)), ((x / 1.0) * (1.0 + x))])\n" ] } ], "source": [ "#### First, let's reinitialize the state object to get a clean state ####\n", + "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 30))\n", + "dv = Variable(name=\"y\", type=ValueType.REAL)\n", + "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "conditions = random_pool(variables, num_samples=10, random_state=0)\n", + "\n", "s = StandardState(variables = variables, conditions = conditions, experiment_data = pd.DataFrame(columns=[\"x\",\"y\"]))\n", "\n", "print('\\033[1mPrevious State:\\033[0m')\n", "print(s)\n", "\n", "for cycle in range(5):\n", - " s = theorist(experiment_runner(custom_experimentalist(s, num_samples = 5)))\n", + " s = experimentalist(s, num_samples = 10, random_state=42+cycle)\n", + " s = custom_experiment_runner(s, added_noise=0.5, random_state=42+cycle)\n", + " s = theorist(s)\n", + " \n", + " plot_from_state(s, 'x + x**2')\n", "\n", "print('\\n\\033[1mUpdated State:\\033[0m')\n", "print(s)" @@ -325,7 +842,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Custom Experiment Runner" + "## Custom Theorists\n", + "\n", + "What if we wanted to replace the ``theorist`` with a custom theorist?\n", + "\n", + "We can implement our theorist as a class that inherits from `sklearn.base.BaseEstimator`. The class must implement the following methods:\n", + "\n", + "- `fit(self, conditions, observations)`\n", + "- `predict(self, conditions)`\n", + "\n", + "The following code block implements such a theorist that fits a polynomial of a specified degree." ] }, { @@ -334,29 +860,99 @@ "metadata": {}, "outputs": [], "source": [ - "def sine_experiment_runner(conditions: pd.DataFrame, added_noise: float = 0.5):\n", - " x = conditions[\"x\"]\n", - " y = np.sin(x) + np.random.normal(0, added_noise, size=x.shape)\n", - " observations = conditions.assign(y = y)\n", - " return observations\n", + "import numpy as np\n", + "from sklearn.base import BaseEstimator\n", + "\n", + "class PolynomialRegressor(BaseEstimator):\n", + "\n", + " def __init__(self, degree: int = 3):\n", + " self.degree = degree\n", + "\n", + " def fit(self, conditions, observations):\n", + " c = np.array(conditions)\n", + " o = np.array(observations)\n", + "\n", + " # polyfit expects a 1D array\n", + " if c.ndim > 1:\n", + " c = c.flatten()\n", "\n", - "custom_experiment_runner = on_state(sine_experiment_runner, output=[\"experiment_data\"])" + " if o.ndim > 1:\n", + " o = o.flatten()\n", + "\n", + " # fit polynomial\n", + " self.coeff = np.polyfit(c, o, self.degree)\n", + " self.polynomial = np.poly1d(self.coeff)\n", + " pass\n", + "\n", + " def predict(self, conditions):\n", + " c = np.array(conditions)\n", + " return self.polynomial(c)\n", + " \n", + "custom_theorist = state_fn_from_estimator(PolynomialRegressor())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mPrevious State:\u001b[0m\n", + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", + " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", + " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", + " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", + " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", + " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 5.416539\n", + "1 4.116570\n", + "2 3.249923\n", + "3 1.733292\n", + "4 1.949954\n", + "5 0.216662\n", + "6 0.433323\n", + "7 0.000000\n", + "8 1.083308\n", + "9 5.199877, experiment_data=Empty DataFrame\n", + "Columns: [x, y]\n", + "Index: [], models=[])\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute 'repr'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[88], line 18\u001b[0m\n\u001b[0;32m 15\u001b[0m s \u001b[39m=\u001b[39m experiment_runner(s, added_noise\u001b[39m=\u001b[39m\u001b[39m0.5\u001b[39m, random_state\u001b[39m=\u001b[39m\u001b[39m42\u001b[39m\u001b[39m+\u001b[39mcycle)\n\u001b[0;32m 16\u001b[0m s \u001b[39m=\u001b[39m custom_theorist(s)\n\u001b[1;32m---> 18\u001b[0m plot_from_state(s, \u001b[39m'\u001b[39;49m\u001b[39msin(x)\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m 20\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\033\u001b[39;00m\u001b[39m[1mUpdated State:\u001b[39m\u001b[39m\\033\u001b[39;00m\u001b[39m[0m\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m 21\u001b[0m \u001b[39mprint\u001b[39m(s)\n", + "Cell \u001b[1;32mIn[80], line 32\u001b[0m, in \u001b[0;36mplot_from_state\u001b[1;34m(s, expr)\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 28\u001b[0m \u001b[39mPlots the data, the ground truth model, and the current predicted model\u001b[39;00m\n\u001b[0;32m 29\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 31\u001b[0m \u001b[39m#Determine labels and variables\u001b[39;00m\n\u001b[1;32m---> 32\u001b[0m model_label \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mModel: \u001b[39m\u001b[39m{\u001b[39;00ms\u001b[39m.\u001b[39mmodel\u001b[39m.\u001b[39mrepr()\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m s\u001b[39m.\u001b[39;49mmodel\u001b[39m.\u001b[39;49mrepr() \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mModel\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 33\u001b[0m experiment_data \u001b[39m=\u001b[39m s\u001b[39m.\u001b[39mexperiment_data\u001b[39m.\u001b[39msort_values(by\u001b[39m=\u001b[39m[\u001b[39m\"\u001b[39m\u001b[39mx\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[0;32m 34\u001b[0m ground_x \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mlinspace(s\u001b[39m.\u001b[39mvariables\u001b[39m.\u001b[39mindependent_variables[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mvalue_range[\u001b[39m0\u001b[39m],s\u001b[39m.\u001b[39mvariables\u001b[39m.\u001b[39mindependent_variables[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mvalue_range[\u001b[39m1\u001b[39m],\u001b[39m100\u001b[39m)\n", + "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'repr'" + ] + } + ], "source": [ "#### First, let's reinitialize the state object to get a clean state ####\n", + "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 30))\n", + "dv = Variable(name=\"y\", type=ValueType.REAL)\n", + "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "conditions = random_pool(variables, num_samples=10, random_state=0)\n", + "\n", "s = StandardState(variables = variables, conditions = conditions, experiment_data = pd.DataFrame(columns=[\"x\",\"y\"]))\n", "\n", "print('\\033[1mPrevious State:\\033[0m')\n", "print(s)\n", "\n", "for cycle in range(5):\n", - " s = theorist(custom_experiment_runner(experimentalist(s, num_samples = 5)))\n", + " s = experimentalist(s, num_samples=10, random_state=42+cycle)\n", + " s = experiment_runner(s, added_noise=0.5, random_state=42+cycle)\n", + " s = custom_theorist(s)\n", + " \n", + " plot_from_state(s, 'sin(x)')\n", "\n", "print('\\n\\033[1mUpdated State:\\033[0m')\n", "print(s)" @@ -376,18 +972,35 @@ "outputs": [], "source": [ "#### First, let's reinitialize the state object to get a clean state ####\n", + "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 30))\n", + "dv = Variable(name=\"y\", type=ValueType.REAL)\n", + "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "\n", + "conditions = random_pool(variables, num_samples=10, random_state=0)\n", + "\n", "s = StandardState(variables = variables, conditions = conditions, experiment_data = pd.DataFrame(columns=[\"x\",\"y\"]))\n", "\n", "print('\\033[1mPrevious State:\\033[0m')\n", "print(s)\n", "\n", "for cycle in range(5):\n", - " s = custom_theorist(custom_experiment_runner(custom_experimentalist(s, num_samples = 5)))\n", + " s = custom_experimentalist(s, num_samples=10, random_state=42+cycle)\n", + " s = custom_experiment_runner(s, added_noise=0.5, random_state=42+cycle)\n", + " s = custom_theorist(s)\n", + " \n", + " plot_from_state(s, 'x + x**2')\n", "\n", "print('\\n\\033[1mUpdated State:\\033[0m')\n", "print(s)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's run the controller with the new theorist for 3 research cycles, defined by the number of models generated." + ] + }, { "attachments": {}, "cell_type": "markdown", From 2f3c10dd3fa5647eed3177f071bc1d415e2ca1dd Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Fri, 1 Sep 2023 12:45:02 -0700 Subject: [PATCH 26/32] Updated tutorials 1 and 2 with current Autora state (But problem) The problem is the autora changes are underway, so some of it is half done and so we need to update again once it is all done. --- .../basic/Tutorial-I-Components.ipynb | 190 ++++++------------ .../basic/Tutorial-II-Loop-Constructs.ipynb | 150 +++++--------- 2 files changed, 114 insertions(+), 226 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-I-Components.ipynb b/docs/tutorials/basic/Tutorial-I-Components.ipynb index bd8ec03d6..ab2d332e7 100644 --- a/docs/tutorials/basic/Tutorial-I-Components.ipynb +++ b/docs/tutorials/basic/Tutorial-I-Components.ipynb @@ -50,26 +50,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", - "[notice] To update, run: python.exe -m pip install --upgrade pip\n", - "\n", - "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", - "[notice] To update, run: python.exe -m pip install --upgrade pip\n", - "\n", - "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", - "[notice] To update, run: python.exe -m pip install --upgrade pip\n", - "\n", - "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", - "[notice] To update, run: python.exe -m pip install --upgrade pip\n" - ] - } - ], + "outputs": [], "source": [ "!pip install -q \"autora[experimentalist-falsification]\"\n", "!pip install -q \"autora[experimentalist-sampler-novelty]\"\n", @@ -89,18 +70,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import torch\n", @@ -148,21 +118,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:03<00:00, 26.33it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting started\n", + "100%|██████████| 100/100 [00:03<00:00, 26.52it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -171,7 +135,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -185,7 +149,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from autora.theorist.bms import BMSRegressor\n", - "from autora.experimentalist.sampler.random_sampler import random_sample #Note that this sampler is embedded within the autora-core module and so does not need to be explicitly installed\n", + "from autora.experimentalist.random import random_sample #Note that this sampler is embedded within the autora-core module and so does not need to be explicitly installed\n", "\n", "#Step 0: Defining variables\n", "ground_truth = lambda x: np.sin(x) #Define a ground truth model that we will attempt to recover - here a sine wave\n", @@ -211,7 +175,7 @@ "\n", "plt.plot(initial_X, ground_truth(initial_X), label='Ground Truth')\n", "plt.plot(new_conditions, new_observations, 'o', label='Sampled Conditions')\n", - "plt.plot(new_conditions, theorist_bms.predict(new_conditions), label=f'Bayesian Machine Scientist ({theorist_bms.repr()})')\n", + "plt.plot(initial_X, theorist_bms.predict(initial_X.reshape(-1,1)), label=f'Bayesian Machine Scientist ({theorist_bms.repr()})')\n", "plt.xlabel('x')\n", "plt.ylabel('y')\n", "plt.title('Sine Function')\n", @@ -314,7 +278,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -323,7 +287,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -376,7 +340,7 @@ ")\n", "\n", "# Variable collection with ivs and dvs\n", - "metadata = VariableCollection(\n", + "variables = VariableCollection(\n", " independent_variables=[iv],\n", " dependent_variables=[dv],\n", ")" @@ -477,17 +441,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:03<00:00, 26.00it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 24.69it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { "text/html": [ - "
sin(X0)
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" + "
0.03
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" ], "text/plain": [ - "sin(X0)" + "0.03" ] }, "execution_count": null, @@ -523,7 +487,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Model of BMS theorist: sin(X0)\n" + "Model of BMS theorist: 0.03\n" ] } ], @@ -569,7 +533,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -578,7 +542,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -656,7 +620,7 @@ "outputs": [], "source": [ "allowed_values = np.linspace(0, 2 * np.pi, 100)\n", - "metadata.independent_variables[0].allowed_values = allowed_values" + "variables.independent_variables[0].allowed_values = allowed_values" ] }, { @@ -664,7 +628,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we can pass the grid pooler the list of independent variables from the ``metadata`` object." + "Now we can pass the grid pooler the list of independent variables from the ``variables`` object." ] }, { @@ -673,9 +637,9 @@ "metadata": {}, "outputs": [], "source": [ - "from autora.experimentalist.pooler.grid import grid_pool\n", + "from autora.experimentalist.grid import grid_pool\n", "\n", - "new_conditions = grid_pool(ivs = metadata.independent_variables)" + "new_conditions = grid_pool(variables=variables)" ] }, { @@ -695,23 +659,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "(0.0,)\n", - "(0.06346651825433926,)\n", - "(0.12693303650867852,)\n", - "(0.1903995547630178,)\n", - "(0.25386607301735703,)\n", - "(0.3173325912716963,)\n", - "(0.3807991095260356,)\n", - "(0.4442656277803748,)\n", - "(0.5077321460347141,)\n", - "(0.5711986642890533,)\n", - "(0.6346651825433925,)\n" + "[0.]\n", + "[0.06346652]\n", + "[0.12693304]\n", + "[0.19039955]\n", + "[0.25386607]\n", + "[0.31733259]\n", + "[0.38079911]\n", + "[0.44426563]\n", + "[0.50773215]\n", + "[0.57119866]\n", + "[0.63466518]\n" ] } ], "source": [ "# return first 10 conditions\n", - "for idx, condition in enumerate(new_conditions):\n", + "for idx, condition in enumerate(new_conditions.values):\n", " print(condition)\n", " if idx > 9:\n", " break" @@ -722,7 +686,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Alternatively, we may use the **random pooler** to randomly draw experimental conditions from the domains of each independent variable. The random pooler requires as input a list of discrete values from which to sample from. In this case, we can pass it ``metadata.independent_variables[0].allowed_values`` for the independent variable. We can also specify the input argument ``n`` to obtain 10 random samples." + "Alternatively, we may use the **random pooler** to randomly draw experimental conditions from the domains of each independent variable. The random pooler requires as input a list of discrete values from which to sample from. In this case, we can pass it ``variables.independent_variables[0].allowed_values`` for the independent variable. We can also specify the input argument ``n`` to obtain 10 random samples." ] }, { @@ -734,29 +698,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "(4.823455387329783,)\n", - "(6.283185307179586,)\n", - "(3.0463928762082846,)\n", - "(4.886921905584122,)\n", - "(0.25386607301735703,)\n", - "(1.4597299198498028,)\n", - "(5.96585271590789,)\n", - "(0.8885312555607496,)\n", - "(4.3157232412950695,)\n", - "(5.331187533364497,)\n" + "[2.91945984]\n", + "[0.]\n", + "[2.15786162]\n", + "[1.07893081]\n", + "[2.53866073]\n", + "[3.99839065]\n", + "[1.96746207]\n", + "[5.2042545]\n", + "[1.96746207]\n", + "[0.50773215]\n" ] } ], "source": [ - "from autora.experimentalist.pooler.random_pooler import random_pool\n", + "from autora.experimentalist.random import random_pool\n", "\n", "# generate random pool of 10 conditions\n", "num_samples = 10\n", - "new_conditions = random_pool(metadata.independent_variables,\n", + "new_conditions = random_pool(variables=variables,\n", " num_samples=num_samples)\n", "\n", "# print conditons\n", - "for idx, condition in enumerate(new_conditions):\n", + "for idx, condition in enumerate(new_conditions.values):\n", " print(condition)" ] }, @@ -778,16 +742,19 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1.07893081]\n", - " [1.01546429]]\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'autora.experimentalist.novelty'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[39], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mautora\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mexperimentalist\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mnovelty\u001b[39;00m \u001b[39mimport\u001b[39;00m novelty_sample\n\u001b[0;32m 3\u001b[0m new_conditions_novelty \u001b[39m=\u001b[39m novelty_sample(condition_pool \u001b[39m=\u001b[39m condition_pool,\n\u001b[0;32m 4\u001b[0m reference_conditions \u001b[39m=\u001b[39m initial_conditions,\n\u001b[0;32m 5\u001b[0m num_samples \u001b[39m=\u001b[39m \u001b[39m2\u001b[39m)\n\u001b[0;32m 7\u001b[0m \u001b[39mprint\u001b[39m(new_conditions_novelty)\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'autora.experimentalist.novelty'" ] } ], "source": [ - "from autora.experimentalist.sampler.novelty import novelty_sample\n", + "from autora.experimentalist.novelty import novelty_sample\n", "\n", "new_conditions_novelty = novelty_sample(condition_pool = condition_pool,\n", " reference_conditions = initial_conditions,\n", @@ -801,7 +768,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Another example for an experiment sampler is the **[falsification sampler](https://autoresearch.github.io/autora/falsification/docs/sampler/)**. The falsification sampler identifies experiment conditions under which the loss of a candidate model (returned by the theorist) is predicted to be the highest. This loss is approximated with a neural network, which is trained to predict the loss of the candidate model, given some initial experimental conditions, respective initial observations, and the metadata.\n", + "Another example for an experiment sampler is the **[falsification sampler](https://autoresearch.github.io/autora/falsification/docs/sampler/)**. The falsification sampler identifies experiment conditions under which the loss of a candidate model (returned by the theorist) is predicted to be the highest. This loss is approximated with a neural network, which is trained to predict the loss of the candidate model, given some initial experimental conditions, respective initial observations, and the variables.\n", "\n", "The following code segment calls on the falsification sampler to return novel conditions based on the candidate model of the linear regression theorist introduced above. As with the novelty sampler, we seek to select 2 conditions.\n" ] @@ -810,23 +777,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1.65012947]\n", - " [1.58666296]]\n" - ] - } - ], + "outputs": [], "source": [ "from autora.experimentalist.sampler.falsification import falsification_sample\n", "\n", @@ -835,7 +786,7 @@ " model=theorist_lr,\n", " reference_conditions=initial_conditions,\n", " reference_observations=initial_observations,\n", - " metadata=metadata,\n", + " metadata=variables,\n", " num_samples=2\n", " )\n", "\n", @@ -854,28 +805,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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nc+bMGVatWsW0adOoXbs2gwYN4vnz58nqbtiwQaM7aNu2bYSGhqo/dKpWrUqJEiWYO3cukZGRyY5/9zlmp6yKy9TUNMUESFdXN9n7dvHixSQmJn7Udby8vDA1NaVfv348ffo02X5/f38WLVoEqN47AAsXLtSoM3/+fAC++OKLDF+/ZcuWnDt3jgsXLqjLnj17xsaNGz94rKmpKUC6EsWWLVuSmJio8X8LYMGCBSgUigwnN4mJicm6PW1sbHBwcMj0aTiknEG2LEn50rBhw4iOjqZdu3aUKVOGuLg4zpw5w+bNm3F2dqZ3794ANGvWDAMDA1q1asWAAQOIjIxk9erV2NjYqFufMpORkREHDhzA09OTmjVrsn//fvbu3cu3335L4cKFUz1u5syZHDt2jJo1a9K/f3/c3Nx4+fIlV65c4ciRI7x8+RKA/v37s2TJEnr27Mnly5ext7fnt99+w8TEJENx7t+/X/2t/F21a9emePHi+Pn5MWnSJHr16kWrVq0A1VIplSpVYvDgwWzZskXjuAIFClC3bl169+7N06dPWbhwIa6urvTv3x9QjeX69ddfadGiBeXKlaN3794UKVKER48ecezYMSwsLPjf//6XoeeQGbIqrqpVq3LkyBHmz5+Pg4MDLi4u1KxZky+//JLffvsNS0tL3NzcOHv2LEeOHKFgwYIfFX+JEiX4448/6NKlC2XLltWYwfvMmTNs3bpVPceTu7s7np6erFq1itevX1O/fn0uXLjA+vXradu2LQ0bNszw9b28vPjtt99o3rw5I0aMwNTUlFWrVuHk5MT169c/GLuVlRUrVqzA3NwcU1NTatasmeJYtVatWtGwYUO+++47AgMDcXd359ChQ/z111+MHDlSYzB3erx584aiRYvSsWNH3N3dMTMz48iRI1y8eFGj1U/KQ7RwB54kad3+/ftFnz59RJkyZYSZmZkwMDAQrq6uYtiwYeLp06cadXfv3i0qVqwojIyMhLOzs5g1a5ZYu3ZtslubnZycxBdffJHsWkCyW4wDAgIEIObMmaMu8/T0FKampsLf3180a9ZMmJiYCFtbWzF58mSN+ZiSzvnu1AFCCPH06VMxZMgQ4ejoKPT19YWdnZ1o3LixWLVqlUa9oKAg0bp1a2FiYiIKFSokRowYoZ4D6VOmDuDf27gTEhJE9erVRdGiRcXr1681jl+0aJEAxObNm4UQ/93i/ueff4qJEycKGxsbYWxsLL744guNqRaSXL16VbRv314ULFhQGBoaCicnJ9G5c2dx9OhRdZ2kW/TfnWrh/X3vv5bp+f28G+/WrVszLa6k1/Td99Lt27dFvXr1hLGxsQDU0wi8evVK9O7dWxQqVEiYmZkJDw8Pcfv2beHk5KQx1UB651lKcvfuXdG/f3/h7OwsDAwMhLm5uahTp45YvHixiImJUdeLj48XP/74o3BxcRH6+vrC0dFRTJw4UaOOEKn/X3j/9n8hhLh+/bqoX7++MDIyEkWKFBHTpk0Ta9as+eDUAUII8ddffwk3Nzehp6enMY3A+1MHCCHEmzdvxKhRo4SDg4PQ19cXJUuWFHPmzNGY8kGIlN8PSc8p6TWOjY0V48aNE+7u7sLc3FyYmpoKd3d3sWzZshReXSkvUAiRC0bbSVI+0KtXL7Zt25Zid05edfz4cRo2bMjWrVs1pjOQJEnKSeSYJUmSJEmSpDTIZEmSJEmSJCkNMlmSJEmSJElKgxyzJEmSJEmSlAbZsiRJkiRJkpQGmSxJkiRJkiSlQU5KmQmUSiWPHz/G3Nw8Q1PwS5IkSZKkPUII3rx5g4ODAzo6qbcfyWQpEzx+/DjZ6uSSJEmSJOUOISEhFC1aNNX9MlnKBEkLM4aEhGBhYaHlaCRJkiRJSo+IiAgcHR01FlhOiUyWMkFS15uFhYVMliRJkiQpl/nQEBo5wFuSJEmSJCkNMlmSJEmSJElKg0yWJEmSJEmS0iDHLEmSpFWJiYnEx8drOwxJkvIgfX19dHV1P/k8MlmSJEkrhBA8efKE169fazsUSZLyMCsrK+zs7D5pHkSZLEmSpBVJiZKNjQ0mJiZyQldJkjKVEILo6GjCwsIAsLe3/+hzyWRJkqRsl5iYqE6UChYsqO1wJEnKo4yNjQEICwvDxsbmo7vk5ABvSZKyXdIYJRMTEy1HIklSXpf0d+ZTxkbKZEmSJK2RXW+SJGW1zPg7I7vhJCk3UCZC0BmIfApmtuBUG3Q+/Q4PSZIk6cNky5Ik5XS+u2FheVj/JWzvq/p3YXlVuZQvNWjQgJEjR2o7jEzj7OzMwoUL1Y8VCgW7du1K85hevXrRtm3bLI1LkpLIZEmScjLf3bClJ0Q81iyPCFWVy4RJInmykduFhobSokULAAIDA1EoFPj4+GjUWbRoEevWrcv+4KR8SSZLkpRTKRPhwHhApLDz37IDE1T1JCkPsbOzw9DQMM06lpaWWFlZZU9AUr4nkyVJyqmCziRvUdIgIOKRqp6ULRo0aMDw4cPx8vKiQIEC2NnZMWXKFI06wcHBtGnTBjMzMywsLOjcuTNPnz4F4O7duygUCm7fvq1xzIIFCyhRooT68c2bN2nRogVmZmbY2trSo0cPnj9/nmpMQUFBjBo1CoVCgUKhICoqCgsLC7Zt26ZRd9euXZiamvLmzZsUz6VUKpk9ezaurq4YGhpSrFgxfvrpJ/X+Gzdu0KhRI4yNjSlYsCDffPMNkZGR6v1JXWNz587F3t6eggULMmTIEI27kMLCwmjVqhXGxsa4uLiwcePGZHG82w3n4uICQOXKlVEoFDRo0EDjWkliY2MZPnw4NjY2GBkZUbduXS5evKjef/z4cRQKBUePHqVatWqYmJhQu3Zt7ty5o65z7do1GjZsiLm5ORYWFlStWpVLly6l+FpJ+YtMlqTcKyoKFArVFhWVepkWRcVFofhRgeJHBVFxUamWpSjyafoukt56uUVUVOpbTEz66759++G6H2H9+vWYmppy/vx5Zs+ezdSpUzl8+DCgSjbatGnDy5cvOXHiBIcPH+bBgwd06dIFgFKlSlGtWrVkCcLGjRv56quvAHj9+jWNGjWicuXKXLp0iQMHDvD06VM6d+6cYjw7duygaNGiTJ06ldDQUEJDQzE1NaVr1654e3tr1PX29qZjx46Ym5uneK6JEycyc+ZMJk2ahK+vL3/88Qe2trb/vnxReHh4YG1tzcWLF9m6dStHjhxh6NChGuc4duwY/v7+HDt2jPXr17Nu3TqN7rJevXoREhLCsWPH2LZtG8uWLVNPGpiSCxcuAHDkyBFCQ0PZsWNHivW8vLzYvn0769ev58qVK7i6uuLh4cHLly816n333XfMmzePS5cuoaenR58+fdT7unfvTtGiRbl48SKXL19mwoQJ6OvrpxqblI+IXOTEiRPiyy+/FPb29gIQO3fu/OAxx44dE5UrVxYGBgaiRIkSwtvbO1mdJUuWCCcnJ2FoaChq1Kghzp8/n6G4wsPDBSDCw8MzdJz0iaKjhahWTbVFR6depkXRcdGi2qpqotqqaiI6LjrVshQ9OCnEZIsPbw9OZtOzyTxv374Vvr6+4u3bt8l3Qupby5aadU1MUq9bv75m3UKFktfJoPr164u6detqlFWvXl2MHz9eCCHEoUOHhK6urggODlbvv3XrlgDEhQsXhBBCLFiwQJQoUUK9/86dOwIQfn5+Qgghpk2bJpo1a6ZxjZCQEAGIO3fuqOMYMWKEer+Tk5NYsGCBxjHnz58Xurq64vHjx0IIIZ4+fSr09PTE8ePHU3xuERERwtDQUKxevTrF/atWrRLW1tYiMjJSXbZ3716ho6Mjnjx5IoQQwtPTUzg5OYmEhAR1nU6dOokuXbpoPNek10IIIfz8/ASgEf+7f98DAgIEIK5evaoRj6enp2jTpo0QQojIyEihr68vNm7cqN4fFxcnHBwcxOzZs4UQqs8CQBw5ckQjfkD9PjQ3Nxfr1q1L8flLuVdaf2/S+/mdq1qWoqKicHd3Z+nSpemqHxAQwBdffEHDhg3x8fFh5MiR9OvXj4MHD6rrbN68mdGjRzN58mSuXLmCu7s7Hh4eaX7TkXIIY2O4eFG1/TtLa4plWmSsb8zF/he52P8ixvrGqZalyKk2WDgAqc0RogCLIqp6UrapWLGixmN7e3v13ws/Pz8cHR1xdHRU73dzc8PKygo/Pz8AunbtSmBgIOfOnQNUrUpVqlShTJkygKor6NixY5iZmam3pH3+/v7pjrNGjRqUK1eO9evXA/D777/j5OREvXr1Uqzv5+dHbGwsjRs3TnW/u7s7pqam6rI6deqgVCo1urLKlSunMUvy+6+Pnp4eVatWVe8vU6bMJ4898vf3Jz4+njp16qjL9PX1qVGjhvp1T/Lu7y9p+Yuk+EaPHk2/fv1o0qQJM2fOzNDrLeVtuSpZatGiBdOnT6ddu3bpqr9ixQpcXFyYN28eZcuWZejQoXTs2JEFCxao68yfP5/+/fvTu3dv3NzcWLFiBSYmJqxduzarnoYkpY+OLjSf9e+D9xOmfx83n5n35luKjEx9275ds25YWOp19+/XrBsYmLzOR3i/W0ahUKBUKtN9vJ2dHY0aNeKPP/4A4I8//qB79+7q/ZGRkbRq1QofHx+N7d69e6kmOqnp16+fugvM29ub3r17pzpBn3Emfbn41Ncnq70bX9JrkRTflClTuHXrFl988QV///03bm5u7Ny5UytxSjlLnp6U8uzZszRp0kSjzMPDQz0/SVxcHJcvX2bixInq/To6OjRp0oSzZ8+met7Y2FhiY2PVjyMiIjI3cClXCY+Oxzc0Ar/QCG4/icD/WRRRsQnExCcSE6/kbXwiBno62FsaYWdhhL2lEfZWxlQsYkkVJ2uM9NNIdtxaQ+cNqrvi3h3sbeGgSpTcWmf9E8xu77RcaK3uRypbtiwhISGEhISoW5d8fX15/fo1bm5u6nrdu3fHy8uLbt268eDBA7p27areV6VKFbZv346zszN6eun7E21gYEBiYvK7Ir/++mu8vLz45Zdf8PX1xdPTM9VzlCxZEmNjY44ePUq/fv1SfG7r1q0jKipK3bp0+vRpdHR0KF26dLriLFOmDAkJCVy+fJnq1asDcOfOHV6/fp3mcwNSfH5JSpQogYGBAadPn8bJyQlQLW1x8eLFDM9HVapUKUqVKsWoUaPo1q0b3t7e6f6CLuVdeTpZevLkiXpwYhJbW1siIiJ4+/Ytr169IjExMcU679+t8q4ZM2bw448/ZknMUgZER0PSB5CvL5iYpFyWyYQQ3HwUwb6boRy8+YQHz9M3UPjZm1iuE65RZqCnQ5ViVtQqXoh6pQpRydEq+Td/t9ZQ5gs5g3cu0KRJEypUqED37t1ZuHAhCQkJDB48mPr161OtWjV1vfbt2zNo0CAGDRpEw4YNcXBwUO8bMmQIq1evplu3buq77u7fv8+mTZv49ddfU1wI1NnZmZMnT9K1a1cMDQ0pVKgQANbW1rRv355x48bRrFkzihYtmmrsRkZGjB8/Hi8vLwwMDKhTpw7Pnj3j1q1b9O3bl+7duzN58mQ8PT2ZMmUKz549Y9iwYfTo0SPZ39DUlC5dmubNmzNgwACWL1+Onp4eI0eOTLNVy8bGBmNjYw4cOEDRokUxMjLC0tJSo46pqSmDBg1i3LhxFChQgGLFijF79myio6Pp27dvumJ7+/Yt48aNo2PHjri4uPDw4UMuXrxIhw4d0nW8lLfl6WQpq0ycOJHRo0erH0dERGiMUZCyiRAQFPTfz6mVZZIHzyLZfDGEfTdDCXmpeadVUWtjytpbUNbOnFJ25lga62Osr4sgjtre1dHBkO0djvIqGkJeRrDg9O8YJpaDhIKce/CScw9esuDIXUramNGtRjE6VCmKpck73Rk6uuDyeaY+HynzKRQK/vrrL4YNG0a9evXQ0dGhefPmLF68WKOeubk5rVq1YsuWLcm6/B0cHDh9+jTjx4+nWbNmxMbG4uTkRPPmzdHRSXnkxNSpUxkwYAAlSpQgNjYW8c57v2/fvvzxxx8ad32lZtKkSejp6fHDDz/w+PFj7O3tGThwIKBajPTgwYOMGDGC6tWrY2JiQocOHZg/f36GXiNvb2/69etH/fr1sbW1Zfr06UyaNCnV+np6evzyyy9MnTqVH374gc8//5zjx48nqzdz5kyUSiU9evTgzZs3VKtWjYMHD2JtbZ2uuHR1dXnx4gU9e/bk6dOnFCpUiPbt28svxhIACiEy+RMlmygUCnbu3JnmdPf16tWjSpUqGjPbent7M3LkSMLDw4mLi8PExIRt27ZpnMfT05PXr1/z119/pSuWiIgILC0tCQ8Px8LC4iOfkZRhUVFgZqb6OTJS1c2SUtknuvEwnOUn7rP/5hN1/mWsr0vDMoVpUd6eeiULayY274YYF4XZDFU8kRMjMTUw/a9MwI1vwvAJjuaM/3OO3X7G23hVV4Ohng5fVLSnTx0XyhexTPHcuVlMTAwBAQG4uLhgZGSk7XDytN9++41Ro0bx+PFjdZeWJOUnaf29Se/nd55uWapVqxb79u3TKDt8+DC1atUCVH3hVatW5ejRo+pkSalUcvTo0WRzh0j5z7kHL1h67D7/3PtvMsAmZW3oWLUo9UvZYGzwid1gCnApZEJ5h8J8/ZkTETHx/HX1ERvPB3P7yRt2XHnEjiuPaFPJgbHNSuNYIPO7FKW8Kzo6mtDQUGbOnMmAAQNkoiRJnyBXJUuRkZHcv39f/TggIAAfHx91H/XEiRN59OgRGzZsAGDgwIEsWbIELy8v+vTpw99//82WLVvYu3ev+hyjR4/G09OTatWqUaNGDRYuXEhUVBS9e/fO9ucn5QxPI2KYuseXvddDAdDVUdDa3YGB9UtQ2i7lyfwyg4WRPj1qOfP1Z05cexjOmlMB/O/aY/7yecz+G0/oUcuJoQ1dsTaVH3rSh82ePZuffvqJevXqadzEIklSxuWqZOnSpUs0bNhQ/Thp3JCnpyfr1q0jNDSU4OBg9X4XFxf27t3LqFGjWLRoEUWLFuXXX3/Fw8NDXadLly48e/aMH374gSdPnlCpUiUOHDiQ7gGLUt6RkKhkw9kg5h++S2RsAjoK6FajGAPrl8jWVh2FQkElRysWd6vMgHrFmbHfj9P3X7DmVABbLoUwuVU5OlQpkuot4JIEqtvg31+KRZKkj5NrxyzlJHLMkpZk4pilm4/CGb/9Orceq6aBqORoxU/tylPO4dPGC6U5ZumdsrQIITh57zkz99/GL1QVX1M3W35uV4HC5mkvNppTyTFLkiRlFzlmScrfFIr/pglIamVJqSwNQgg2nA3ip71+xCUqsTDSY3yLMnSrXgwdnU9vuVEoFLgVdlP/nFrZh85Rv1Rh6roWYuVJfxYcvsth36dcDnrFT23L06KC/SfHKUmSJKVOtixlAtmylDtFxMQzcfsN9t5QjU1q5mbLz+0rUMgsZ7fW+D6OYPQWH24/Ua0c36lqUaa1LZ/25JY5jGxZkiQpu2RGy1KuWu5EkjLLzUfhtFp8ir03QtHTUTDpSzdW9qia4xMlADcHC/4aWofBDUqgo4Ctlx/SddU5wiJitB2aJElSniSTJSnf2XP9Me2XnyHoRTRFrIzZOrAWfeu65KoB04Z6ung1L8OGPjWxNNbHJ+Q1rZec5vrD19oOTZIkKc+RyZKUe0VHQ7lyqi06OvWyd6w5FcDQP64Sl6CkURkb9g6vS+Vi6Zvh96NCjI+m3LJylFtWjuj46FTLPlbdkoXYNaQOrjZmPImIodOKs+y+9vjDB0qSJEnpJpMlKfcSQrX+m6+v5nIn75cBSqXgp72+TNvjC0DPWk6s7lkNK5OsnbNICIHvM198n/mql6BIqexTuBQyZcfg2jQsXZjYBCXD/7zK8uP+n3xeKTkhBN988w0FChRAoVDg4+PzwWMCAwM/qe7p06epUKEC+vr6tG3bluPHj6NQKNJcfDYz9OrVK80VEnKz91/DdevWYWVl9cHjFAoFu3btytLYpJxJ3g0n5XmxCYmM3Xqd//3b4jK+eRkG1i+eq7rdPsTCSJ9fPasz68BtVp18wKwDt3kbn8ioJiXz1PPUtgMHDrBu3TqOHz9O8eLF1QvWZhZHR0dCQ0M1zjt69GgqVarE/v37MTMzw8TEhNDQ0GSLyX6swMBAXFxcuHr1KpUqVVKXL1q0KFOS+dygS5cutGzZUv14ypQp7Nq1K1mCGxoamu615qS8RSZLUp4WE59I/w2X+Ofec/R0FMzuWJH2VVJfeT0309VR8G3LsliZ6DP7wB1+OXqPt3EJfNuyrEyYMom/vz/29vbUrl07S86vq6uLnZ1dsmsOHDiQokX/e9++XycrZFYylhsYGxtjbGz8wXrZ8bpLOZPshpPyrJiERAb8dpl/7j3HxEAX797V82yi9K7BDVyZ3Eo1j9PqfwKY9NdNlMr80UKQlXr16sWwYcMIDg5GoVDg7OwMqFqb6tati5WVFQULFuTLL7/E3z/1btBXr17RvXt3ChcujLGxMSVLlsTb2xvQ7IZL+vnFixf06dMHhUKhbtV6vxvu9OnTNGjQABMTE6ytrfHw8ODVq1fpis/FxQWAypUro1AoaNCggfr5vtsNFxsby/Dhw7GxscHIyIi6dety8eJF9f6kuI4ePUq1atUwMTGhdu3a3LlzJ83X9eHDh3Tr1o0CBQpgampKtWrVOH/+vHr/8uXLKVGiBAYGBpQuXZrffvtN43iFQsGvv/5Ku3btMDExoWTJkuzevVujzr59+yhVqhTGxsY0bNiQwMBAjf3vdsOtW7eOH3/8kWvXrqFQKNSve9K13u2Gu3HjBo0aNcLY2JiCBQvyzTffEBkZqd6f9BrOnTsXe3t7ChYsyJAhQ4iPj1fXWbZsGSVLlsTIyAhbW1s6duyY5uslaYdMlqQ8KU5HjyHbfDlx9xlG+jp496rO5yULazusbNO7jguzOlRAoYDfzwUzfvv1XJMwRcVFpbrFJMSku+7b+LcfrJsRixYtYurUqRQtWpTQ0FB1ohAVFcXo0aO5dOkSR48eRUdHh3bt2qFUKlM8z6RJk/D19WX//v34+fmxfPnyFLvzkrrkLCwsWLhwIaGhoXTp0iVZPR8fHxo3boybmxtnz57l1KlTtGrVisTExHTFd+HCBQCOHDlCaGgoO3bsSDFuLy8vtm/fzvr167ly5Qqurq54eHjw8uVLjXrfffcd8+bN49KlS+jp6dGnT59UX9PIyEjq16/Po0eP2L17N9euXcPLy0sd286dOxkxYgRjxozh5s2bDBgwgN69e3Ps2DGN8/z444907tyZ69ev07JlS7p3766OKyQkhPbt29OqVSt8fHzo168fEyZMSDWmLl26MGbMGMqVK0doaGiqr3tUVBQeHh5YW1tz8eJFtm7dypEjR5Itwn7s2DH8/f05duwY69evZ926derk69KlSwwfPpypU6dy584dDhw4QL169VKNTdIiIX2y8PBwAYjw8HBth5K/REYKoRrGrfr537I4HV3xTdtvhdP4PaLUd/vE6XvPtBdibKRgCoIpiMjYyFTLssquqw9F8Yl7hdP4PWLK7ptCqVRm6fXS6+3bt8LX11e8ffs22b6k1yalreXGlhp1TX4ySbVufe/6GnULzS6UrE5GLViwQDg5OaVZ59mzZwIQN27cEEIIERAQIABx9epVIYQQrVq1Er17907x2PfrCiGEpaWl8Pb2Vj8+duyYAMSrV6+EEEJ069ZN1KlTJ93P4UPxJfH09BRt2rQRQggRGRkp9PX1xcaNG9X74+LihIODg5g9e7ZGXEeOHFHX2bt3rwBS/D0LIcTKlSuFubm5ePHiRYr7a9euLfr3769R1qlTJ9Gy5X/vA0B8//336seRkZECEPv37xdCCDFx4kTh5uamcY7x48drvIbe3t7C0tJSvX/y5MnC3d09WTyA2LlzpxBCiFWrVglra2sRGfnf/+G9e/cKHR0d8eTJEyGE6jV0cnISCQkJGvF36dJFCCHE9u3bhYWFhYiIiEjx+UuZI62/N+n9/JYtS1LupVCAk5Nq+3dMToIQjOzyAwdL18ZAV8HqntWo7Zq5g3AzFqICJ0snnCydNJY7eb8sq7SpVIR5ndwB8D4dyNJj97P0evnRvXv36NatG8WLF8fCwkLdPffuot7vGjRoEJs2baJSpUp4eXlx5syZT7p+UstSZsWXEn9/f+Lj46lTp466TF9fnxo1auDn56dRt2LFiuqf7e1VS/GEhYWlGnvlypUpUKBAivv9/Pw0rglQp06dNK9pamqKhYWF+pp+fn7UrFlTo36tWrVSvF5G+Pn54e7ujuk760/WqVMHpVKp0fVYrlw5dHX/m13f3t5eHVvTpk1xcnKiePHi9OjRg40bNxKdwpQnkvbJAd5S7mViAu+MPRBCMOlQAHuLVUVfV8GKHlWpV0q7XW8m+iYEjgz8YFlWalu5CC+j4pi6x5e5h+5ibWpA95pO2Xb9jIqcGJnqPl0dzSVdwsam/CEMoKPQ/C4YOCLwk+JKTatWrXBycmL16tU4ODigVCopX748cXFxKdZv0aIFQUFB7Nu3j8OHD9O4cWOGDBnC3LlzP+r6HxqYnNH4PpW+vr7656QvA6l1SaZnUHVGr5l03dSumd3Sis3c3JwrV65w/PhxDh06xA8//MCUKVO4ePFiuqYykLKPbFmS8oyVJx/w54VgFApY3K0yjcrYajukHKNPXReGNnQF4PtdN9l7PVTLEaXO1MA01c1IzyjddY31jT9Y91O9ePGCO3fu8P3339O4cWPKli2rHlidlsKFC+Pp6cnvv//OwoULWbVq1UfHULFiRY4ePfrR8RkYqOYaSxrjlJKkAdanT59Wl8XHx3Px4kXckhau/sjYfXx8ko17SlK2bFmNa4JqMHtGrlm2bFn1uKwk586dS/MYAwODNF+PpPNeu3aNqKj/xr6dPn0aHR0dSpcune749PT0aNKkCbNnz+b69esEBgby999/p/t4KXvIZEnKE/Zcf8zM/bcBmPSFG83L22s5opxnTLNSfFWzGELAyM1XOXP/ubZDyvWsra0pWLAgq1at4v79+/z999+MHj06zWN++OEH/vrrL+7fv8+tW7fYs2cPZcuW/egYJk6cyMWLFxk8eDDXr1/n9u3bLF++nOfPn6crPhsbG4yNjTlw4ABPnz4lPDw82TVMTU0ZNGgQ48aN48CBA/j6+tK/f3+io6Pp27fvR8ferVs37OzsaNu2LadPn+bBgwds376ds2fPAjBu3DjWrVvH8uXLuXfvHvPnz2fHjh2MHTs23dcYOHAg9+7dY9y4cdy5c4c//vhDPcA6Nc7OzgQEBODj48Pz58+JjY1NVqd79+4YGRnh6enJzZs3OXbsGMOGDaNHjx7Y2qbvi9qePXv45Zdf8PHxISgoiA0bNqBUKjOUbEnZQyZLUu719i1Ur86lJu0ZvfkaAL1qFKXPqM5Qvbpqv5a9jX9L9dXVqb66uvrurJTKsoNCoWBam/K0rGBHfKJg0MYrBD7P2B1hkiYdHR02bdrE5cuXKV++PKNGjWLOnDlpHmNgYMDEiROpWLEi9erVQ1dXl02bNn10DKVKleLQoUNcu3aNGjVqUKtWLf766y/09PTSFZ+enh6//PILK1euxMHBgTZt2qR4nZkzZ9KhQwd69OhBlSpVuH//PgcPHvykSRoNDAw4dOgQNjY2tGzZkgoVKjBz5kz1GJ+2bduyaNEi5s6dS7ly5Vi5ciXe3t7q6Q3So1ixYmzfvp1du3bh7u7OihUr+Pnnn9M8pkOHDjRv3pyGDRtSuHBh/vzzz2R1TExMOHjwIC9fvqR69ep07NiRxo0bs2TJknTHZmVlxY4dO2jUqBFly5ZlxYoV/Pnnn5QrVy7d55Cyh0KIfDJFaxaKiIjA0tKS8PBwLCwstB1O/hEVRYBjKdp/PYdXJpY0dbNlRbsy6FqYq/ZHRoLpp3e1fFKIcVGYzTBThTMxElMD0xTLslNMfCJdVp3jWshrXG3M2Dm4NuZG+h8+MDNjiIkhICAAFxcXjIyMPnyAJEnSR0rr7016P79ly5KUa0XEJNCn42RemVji7mDOoq6V0NWRM1V/iJG+Lqt6VMXWwpD7YZGM2ORDYi6Zg0mSJEkbZLIk5UpKpWD0X34EFChCkfAwfu1SARMDeXNnetlaGLGqRzUM9XT4+3YYcw6mPcuyJElSfiaTJSlXWnb8PkfuvsAgIY7lu36msJmBtkPKddwdrZjdUTU/zYoT/uy8+lDLEUmSJOVMMlmScp2Td58x7/BdAKYdXk7FJ3KixY/VplIRBjcoAcD47Te49Tj5nVCSJEn5nUyWpFwl5GU0wzddRQjoVtmeLtcPazukXG9ss9I0LF2YuAQlQ/+4SmRsgrZDkiRJylFksiTlGjHxiQzaeJnX0fFULGrJ5OauUKiQantXSmVaVMikEIVMCn2wTFt0dBTM71wJe0sjAp5H8e2OG8ibZCVJkv4jR8RKucb0vb7cfBSBtYk+y7+uipGlMTx7plnJ1DR5mRaZGpjybNyzD5Zpm7WpAUu+qkznlefYfe0xtUoUpFuNYtoOS5IkKUeQLUtSrnDo1hN+P6da+HNR18oUscqcNaWk/1R1KsA4D9XMwVN238IvNELLEUmSJOUMMlmScrynETGM334dgG/qFdf64rh52TefF6dB6cLEJigZ8scVouT4JUmSJJksSTmbUikYs+Uar6LjKedgwdhm76yZ9PYtNGig2pKWNkmpTIvexr+lwboGNFjXQGO5k/fLcoqk8Ut2FkY8eBbF5N23tB2SlEmmTJlCpUqVtB0GAA0aNGDkyJHaDkOS0k0mS1KO9uupB5y6/xwjfR0Wda2Mgd47b1mlEk6cUG1KZeplWqQUSk4EneBE0AmUQplqWU5SwNSAX7pU5DMdX+KubuHCsb9AmfYK7PnJkydPGDFiBK6urhgZGWFra0udOnVYvnw50dHR2g7vo0yZMgWFQpHm9jGOHz+OQqHg9evXmRuwJGUzOcBbyrFuPgpXzyw9uVU5XG3MtBxRPuG7mxoHxrPJ4LHq8QlIvOyAbstZ4NZau7Fp2YMHD6hTpw5WVlb8/PPPVKhQAUNDQ27cuMGqVasoUqQIrVun/BrFx8ejr5+9a/Cl19ixYxk4cKD6cfXq1fnmm2/o379/ivXj4uIwMJATwUr5h2xZknKkmPhEhm+6SnyiwKOcLV2rO2o7pPzBdzds6QkRjzWKFZGPEVt6qvbnY4MHD0ZPT49Lly7RuXNnypYtS/HixWnTpg179+6lVatW6roKhYLly5fTunVrTE1N+emnnwBYvnw5JUqUwMDAgNKlS/Pbb7+pjwkMDEShUODj46Mue/36NQqFguPHjwP/tdYcPXqUatWqYWJiQu3atblzR3PJmpkzZ2Jra4u5uTl9+/YlJiYm1edlZmaGnZ2detPV1cXc3Fz9uGvXrgwdOpSRI0dSqFAhPDw8PhhrYGAgDRs2BMDa2hqFQkGvXr3UdZVKJV5eXhQoUAA7OzumTJmSwd+GJGWfXJcsLV26FGdnZ4yMjKhZsyYXLlxItW6DBg1SbE7+4osv1HV69eqVbH/z5s2z46lIaZh36A4PnkVha2HIzPYVP7obQMoAZSIcGA8kn2NJ/YfiwIQs65ITQhAdl5DtW3rnlHrx4gWHDh1iyJAhmJqapljn/ffplClTaNeuHTdu3KBPnz7s3LmTESNGMGbMGG7evMmAAQPo3bs3x44dy/Dr9d133zFv3jwuXbqEnp4effr0Ue/bsmULU6ZM4eeff+bSpUvY29uzbNmyDF/jXevXr8fAwIDTp0+zYsWKD9Z3dHRk+/btANy5c4fQ0FAWLVqkcT5TU1POnz/P7NmzmTp1KocPy0lmpZwpV3XDbd68mdGjR7NixQpq1qzJwoUL8fDw4M6dO9jY2CSrv2PHDuLi4tSPX7x4gbu7O506ddKo17x5c7y9vdWPDQ0Ns+5JSB90OegVv54KAGBG+wpYm8rm/mwRdCZZi9K7FAiIeKSq5/J5pl/+bXwibj8czPTzfojvVI90LcJ8//59hBCULl1ao7xQoULqVpshQ4Ywa9Ys9b6vvvqK3r17qx9369aNXr16MXjwYABGjx7NuXPnmDt3rroVJr1++ukn6tevD8CECRP44osviImJwcjIiIULF9K3b1/69u0LwPTp0zly5EiarUsfUrJkSWbPnq1+HBgYmGZ9XV1dChQoAICNjQ1WVlYa+ytWrMjkyZPV516yZAlHjx6ladOmHx2jJGWVXNWyNH/+fPr370/v3r1xc3NjxYoVmJiYsHbt2hTrJzXvJm2HDx/GxMQkWbJkaGioUc/a2jo7no6Ugpj4RMZtu4YQ0KFKURqVsdV2SPlH5NN0VVO+eZLFgeQuFy5cwMfHh3LlyhEbG6uxr1q1ahqP/fz8qFOnjkZZnTp18PPzy/B1K1asqP7Z3t4egLCwMPV1atasqVG/Vq1aGb7Gu6pWrfpJx7/v3fhB9RyS4peknCbXtCzFxcVx+fJlJk6cqC7T0dGhSZMmnD17Nl3nWLNmDV27dk3WhH78+HFsbGywtramUaNGTJ8+nYIFC6Z6ntjYWI0/ihERcvK+zLLgyF0ePIvCxtyQH750+/ABJibpK9MiE/3k8aRUpnVm6UtMj4RAs4ofrpdRxvq6+E71yPwTp+O66eHq6opCoUg2Nqh48eKq8xgnnyg1te661OjoqL6/vts1GB8fn2LddweLJ3X/KbPwDtD3n0tGYk3J+4PdFQpFlsYvSZ8i17QsPX/+nMTERGxtNf+g29ra8uTJh7/pXrhwgZs3b9KvXz+N8ubNm7NhwwaOHj3KrFmzOHHiBC1atCAxMfVxGTNmzMDS0lK9OTrKwceZ4WrwK1affADAz+0qYGnygTuHTE0hKkq1Jf0hT6lMi0wNTIn6Noqob6MwNTBNtSxHcKoNFg5AyuPDBAoei4KMvWBGaHjmzw+lUCgwMdDL9i294+EKFixI06ZNWbJkCVFRUR/1HMuWLcvp06c1yk6fPo2bm+qLQeHCqglXQ0ND1fvfHUCdkeucP39eo+zcuXMZPk9a0hNr0h1zaf09laTcINe0LH2qNWvWUKFCBWrUqKFR3rVrV/XPFSpUoGLFipQoUYLjx4/TuHHjFM81ceJERo8erX4cEREhE6ZPFBOfiNe26ygFtK3kQBM32f2W7XR0ofks1d1wKNAc6K1KKH6zHEhEmJJJu26yume1fDfwftmyZdSpU4dq1aoxZcoUKlasiI6ODhcvXuT27dsf7KoaN24cnTt3pnLlyjRp0oT//e9/7NixgyNHjgCq1qnPPvuMmTNn4uLiQlhYGN9//32G4xwxYgS9evWiWrVq1KlTh40bN3Lr1i11K1hmSE+sTk5OKBQK9uzZQ8uWLTE2NsbMTE4BIuU+uaZlqVChQujq6vL0qea4iqdPn2JnZ5fmsVFRUWzatEk92DEtxYsXp1ChQty/fz/VOoaGhlhYWGhs0qdZ8vd97oVFUsjMkMmtymk7nPzLrTV03gAW9prlFg4oOm+gXfdB6OsqOOIXxr4b+W/sUokSJbh69SpNmjRh4sSJuLu7U61aNRYvXszYsWOZNm1amse3bduWRYsWMXfuXMqVK8fKlSvx9vamQYMG6jpr164lISGBqlWrMnLkSKZPn57hOLt06cKkSZPw8vKiatWqBAUFMWjQoAyf50M+FGuRIkX48ccfmTBhAra2tgwdOjTTY5Ck7KAQ6b1vNgeoWbMmNWrUYPHixYCqf75YsWIMHTqUCRMmpHrcunXrGDhwII8ePUpzLBLAw4cPKVasGLt27Up1crn3RUREYGlpSXh4uEycPsK9p29o+cs/xCcKVnxdhebl7T98EEBMDHTooPp5+3YwMkq5TItiEmLosEUVz/bO2zHSM0qxLMdRJqrueot8qhrL5FRb1fIEzD98l1+O3qOQmSFHR9f/cHdpCmJiYggICMDFxQUjLf+OJEnK29L6e5Pez+9c1Q03evRoPD09qVatGjVq1GDhwoVERUWpb83t2bMnRYoUYcaMGRrHrVmzhrZt2yZLlCIjI/nxxx/p0KEDdnZ2+Pv74+XlhaurKx4e2T/QND8SQvDdrpvEJwqalLXBo1zarYQaEhNh377/fk6tTIsSlYnsu7dP/XNqZTmOjm6q0wMMaViCvdcf4/8sip/3+TGrYxaM9pYkScpBclWy1KVLF549e8YPP/zAkydPqFSpEgcOHFAP+g4ODlbfoZHkzp07nDp1ikOHDiU7n66uLtevX2f9+vW8fv0aBwcHmjVrxrRp0+RcS9lk2+WHXAh4ibG+LlNal8t3Y2ByI0M9XWZ2qEinFWfZfCmENpUdqF2ikLbDkiRJyjK5KlkCGDp0aKr93knLAbyrdOnSqc7Qa2xszMGD2T8JnqTyKiqOn/ep5pcZ2aQkRa1z4O30UoqqOxfg68+K8fu5YL7feZP9Iz/HUC99t+BLkiTlNrlmgLeU98zY78er6HjK2JnTp66LtsORMsireRkKmRny4HkUa/6dcV2SJCkvksmSpBUXAl6y5dJDAH5qVx59XflWzG0sjPT57osyACw+ep9HrzN/7iVJkqScQH5CSdkuPlHJ97tuANCthiNVnQpoOSLpY7WtVIQazgV4G5/IT3t9tR2OJElSlpDJkpTt1p8J5O7TSAqaGjC+eRlthyN9AoVCwY9tyqGro2DfjSf8c++ZtkOSJEnKdDJZkrLV88hYFh25B4BX89JYmRh8/MlMTUEI1fbucifvl2mRqYEpYrJATBYay528X5ablbW3oGctJwAm/3WL2IQcOh2CJEnSR5LJkpSt5hy4w5vYBCoUsaRTVblETF4xqmkpOdhbkqQ8SyZLUra58TCcLZdDAJjS2g0dHTmnUl5hYaTPty3/G+z9WA72VlMoFOzatSvNOr169aJt27bpPmdgYCAKheKjFtmVJCnjZLIkZQshBFP+dwvx70K5mTKoOyYGOnVSbTExqZdpUUxCDJ22dqLT1k7EJMSkWpYXtKtchOrO1ryNT2T2gdvZc1FlIgT8Aze2qf7N4hnRM5rUAISGhtKiRQsg9SRn0aJFrFu3LnOC/FeDBg1QKBQoFAoMDQ0pUqQIrVq1YseOHRk+15QpU6hUqVKmxidJuYlMlqRs8ZfPYy4HvcLEQJcJLcpmzkkTE2HbNtX27nIn75dpUaIykW2+29jmu01juZP3y/IChULBD1+WQ6GAXT6P8Ql5nbUX9N0NC8vD+i9he1/VvwvLq8pzEDs7uw+uCGBpaYmVlVWmX7t///6Ehobi7+/P9u3bcXNzo2vXrnzzzTeZfi1JystksiRluajYBGbsV83UPaShK3aWcuHUvKpCUUvaVy4KwLQ9vqnOnv/JfHfDlp4Q8VizPCJUVZ5NCVODBg0YPnw4Xl5eFChQADs7O6ZMmaJR591uOBcX1eSrlStXRqFQ0KBBAyB5i9WBAweoW7cuVlZWFCxYkC+//BJ/f/8Mx2diYoKdnR1Fixbls88+Y9asWaxcuZLVq1dz5MgRdb3x48dTqlQpTExMKF68OJMmTSI+Ph5QLUT+448/cu3aNXVLVVIr2Pz586lQoQKmpqY4OjoyePBgIiMjMxynJOV0MlmSstzy4/48jYilWAET+sqZuvM8r+alMdbX5XLQK/ZcD838CygT4cB4IKVE7N+yAxOyvEsuyfr16zE1NeX8+fPMnj2bqVOncvjw4RTrXrhwAYAjR44QGhqaapdYVFQUo0eP5tKlSxw9ehQdHR3atWuHUqn85Hg9PT2xtrbWuLa5uTnr1q3D19eXRYsWsXr1ahYsWACo1uQcM2YM5cqVIzQ0lNDQULp06QKAjo4Ov/zyC7du3WL9+vX8/fffeHl5fXKMkpTT5Lq14aTc5fHrt6z+5wEA37Ysi5G+XD8sr7O1MGJg/RIsOHKXmftv09TNNnN/70FnkrcoaRAQ8UhVz+XzzLtuKipWrMjkyZMBKFmyJEuWLOHo0aM0bdo0Wd3ChQsDULBgQezs7FI9Z4cOHTQer127lsKFC+Pr60v58uU/KV4dHR1KlSpFYGCguuz7779X/+zs7MzYsWPZtGkTXl5eGBsbY2Zmhp6eXrKYR44cqXHc9OnTGThwIMuWLfukGCUpp5EtS1KWmnfoLrEJSmq4FMCjnK22w5GyyTf1imNvacSj128zfyqByKeZW+8TVaxYUeOxvb09YWFhn3TOe/fu0a1bN4oXL46FhQXOzs4ABAcHf9J5kwghUCj+uxt18+bN1KlTBzs7O8zMzPj+++/Tda0jR47QuHFjihQpgrm5OT169ODFixdER0dnSpySlFPIZEnKMrceh7Pjqmr9t+9altX44yzlbcYGung1Lw3AsmP3CXuTiXf9maUz6U5vvU+kr6+v8VihUHxyd1mrVq14+fIlq1ev5vz585w/fx6AuLi4TzovQGJiIvfu3VOPnzp79izdu3enZcuW7Nmzh6tXr/Ldd9998FqBgYF8+eWXVKxYke3bt3P58mWWLl2aaXFKUk4ikyUpSwghmLHvNkJAK3cH3B2ttB2SlM3auBfBvaglUXGJzD90N/NO7FQbLByA1JJvBVgUUdXLYQwMVDPWJ6Zxp+aLFy+4c+cO33//PY0bN6Zs2bK8evUq02JYv349r169Unf1nTlzBicnJ7777juqVatGyZIlCQoKShb3+zFfvnwZpVLJvHnz+OyzzyhVqhSPH6fVPSpJuZccsyRliRN3n3Hq/nMMdHXw8iidNRcxMYGkO29MTFIv0yITfRMiJ0aqf06tLC/S0VEw6Us3Oq44y5ZLIfSt60JJW/NMOLEuNJ+luusNBZoDvf9NoJrPVNXLYWxsbDA2NubAgQMULVoUIyMjLC0tNepYW1tTsGBBVq1ahb29PcHBwUyYMOGjrhcdHc2TJ09ISEjg4cOH7Ny5kwULFjBo0CAaNmwIqMZZBQcHs2nTJqpXr87evXvZuXOnxnmcnZ0JCAjAx8eHokWLYm5ujqurK/Hx8SxevJhWrVpx+vRpVqxY8XEvjCTlcLJlScp0iUpVqxKAZ20nHAtkUUKgUKjWfzM1Vf2cWpkWKRQKTA1MMTUwVXdDplSWV1VzVo1VUwqYffBO5p3YrTV03gAW9prlFg6qcrfWmXetTKSnp8cvv/zCypUrcXBwoE2bNsnq6OjosGnTJi5fvkz58uUZNWoUc+bM+ajrrV69Gnt7e0qUKEH79u3x9fVl8+bNGgOwW7duzahRoxg6dCiVKlXizJkzTJo0SeM8HTp0oHnz5jRs2JDChQvz559/4u7uzvz585k1axbly5dn48aNzJgx46PilKScTiGybCKU/CMiIgJLS0vCw8OxsLDItPPu2LGDhIQE2rdvj55e7mkE3HwxmPHbb2BprM/JcQ2xNNH/8EFSnnU/LJJmC06gFLBtYC2qORcgJiaGgIAAXFxcMDL6hHm3lImqu94in6rGKDnVzpEtSpIkaU9af2/S+/ktW5ZyqMTERMaNG0eXLl0oWbIkixYtyhWTvUXHJTDv3/Epwxq5Zm2iFBsLvXqpttjY1Mu0KDYhll67etFrVy9iE2JTLcvLXG3M6FJdtWjyzP23M3eiSh1d1fQAFTqq/pWJkiRJWUAmSzlUXFwcPXv2pFChQgQGBjJy5EgcHR2ZOHFijh5E6X06kLA3sTgWMKZHLaesvVhCAqxfr9oSElIv06IEZQLrr61n/bX1JCgTUi3L60Y0LoWRvg6Xgl5x1O/TbquXJEnKbjJZyqGMjY2ZPHkywcHBrFixgpIlS/L69WtmzpyJs7MzvXv35ubNm9oOU8Pr6DhWnFAtyTC2WWkM9eS3fEnFztKI3nVUt6rPOnCbRKXs/ZckKfeQyVIOZ2xszIABA7h9+za7du2iTp06xMfHs27dOipUqECLFi04evRo1q3BlQErTz7gTUwCZezMaVXRQdvhSDnMwPolsDTW515YJId8n2g7HEmSpHSTyVIuoaOjQ5s2bTh16hRnz56lY8eO6OjocODAAZo0aUKVKlXYuHGjevHL7BYWEYP3adVMzWOblUZHJ2/f5SVlnKWxPkMbugKw/nRgjkjwJUmS0kMmS7nQZ599xtatW7l79y5DhgzBxMQEHx8fvv76a4oXL868efOIiIjI1pgW/32fmHglVYpZ0bisTbZeW8o9etRywsHSiGeRsUTG5o/xWpIk5X4yWcrFSpQowZIlSwgODmb69OnY2try8OFDxo4di6OjI+PGjSMkJCTL4wh+Ec2fF1TrSHk1L5Pn5w6SPp6Rvi4jm5YCIDImQY5dkiQpV5DJUh5QsGBBvvvuOwIDA/n1118pW7YsERERzJ07l+LFi9OjRw98fHyy7PoLjtwlQSn4vGQhPiteMMuuI+UN7SsXoYi1CYlCdVOAJElSTieTpTzEyMiIvn37cvPmTfbu3UvDhg1JSEjg999/p3LlyjRt2pSDBw9m6liRO0/esMvnEQBeHmUy7bzpYmICYWGq7d3lTt4v0yITfRPCxoYRNjZMY7mT98vyEz1dHTz/nVbiZXQcCYmftuisJElSVpPJUh6ko6NDy5Yt+fvvv7l06RJdu3ZFV1eXI0eO0Lx5c9zd3Vm/fn2mrAw+99AdhICWFeyoUNTywwdkJoUCChdWbe8ud/J+mRYpFAoKmxamsGlhjeVO3i/LbxqWtkFfV4FSKXgeKVuXUtKrVy/atm2rftygQQNGjhz5SefMjHOkx+nTp6lQoQL6+voazyGnev+1lrJHYGAgCoUiS3s+MotMlvK4qlWr8ueff+Lv78+oUaMwMzPjxo0b9OrVCxcXF2bNmsXr168/6tw3HoZz2PcpOgoY3TSLFsuV8iQdHQUWRqrZ3Z9Hxuaa1qVevXqhUChQKBQYGBjg6urK1KlTSciGCVB37NjBtGnT0lX3+PHjKBSKZP+3M3KOTzF69GgqVapEQEAA69aty/LrfUhqr0eSRYsW5Yg4U/Pu+05fXx8XFxe8vLyIiYnRdmifxNHRkdDQUMqXL6/tUD5IJkv5hJOTE/PnzyckJIRZs2bh4ODA48ePmTBhAo6OjowaNYrAwMAMnXPBEdWyJm0rFcHVxiwLov6A2FgYMkS1vbvcyftlWhSbEMuQvUMYsneIxnIn75flR8YGuhjq6aIUgmdvcs/r0Lx5c0JDQ7l37x5jxoxhypQpqS50mxmtt0kKFCiAubm51s+RHv7+/jRq1IiiRYtiZWWVbL8QIlsSzPSytLRMMc7sltb7Jel99+DBAxYsWMDKlSuZPHlylsaTmJiIUpl1X2R0dXWxs7PLFWufymQpn7GyssLLy0v9ja98+fJERkaycOFCXF1d6dq1K5cuXfrgeXxCXvP37TB0dRQMa1wyGyJPQUICLFum2t5d7uT9Mi1KUCaw7NIyll1aprHcyftl+VUhcwMAXkTFEZ9LWpcMDQ2xs7PDycmJQYMG0aRJE3bv3g38153z008/4eDgQOnSqhbXkJAQOnfujJWVFQUKFKBNmzYaX04SExMZPXo0VlZWFCxYEC8vr2RjC9/vQouNjWX8+PE4OjpiaGiIq6sra9asITAwkIYNGwJgbW2NQqGgV69eKZ7j1atX9OzZE2tra0xMTGjRogX37t1T71+3bh1WVlYcPHiQsmXLYmZmpv7QTklSt8qLFy/o06cPCoWCdevWqVt29u/fT9WqVTE0NOTUqVPExsYyfPhwbGxsMDIyom7duly8eFF9vqTjDh48SOXKlTE2NqZRo0aEhYWxf/9+ypYti4WFBV999RXR0dEZ/l0mSanLc/jw4Xh5eVGgQAHs7OyYMmWKxjGvX7+mX79+FC5cGAsLCxo1asS1a9fU+/39/WnTpg22traYmZlRvXp1jhw5onEOZ2dnpk2bRs+ePbGwsOCbb75JNcak952joyNt27alSZMmHD58WL1fqVQyY8YMXFxcMDY2xt3dnW3btmmcY/fu3ZQsWRIjIyMaNmzI+vXrNVrckn7fu3fvxs3NDUNDQ4KDg4mNjWXs2LEUKVIEU1NTatasyfHjx9XnDQoKolWrVlhbW2Nqakq5cuXYt28foHqPde/encKFC2NsbEzJkiXx9vYGUu6GO3HiBDVq1MDQ0BB7e3smTJigkVin53eTFXJdsrR06VKcnZ0xMjKiZs2aXLhwIdW669atUzddJm3vrzgshOCHH37A3t4eY2NjmjRpovHHIq8yMDDA09OT69evc+DAAZo2bUpiYiKbN2+mevXqNGzYkD179qT6rWLBYVWrUrvKRXApZJqdoUt5iKmBHiYGeiiF4GlEDFFRUdm+feoND8bGxhotAkePHuXOnTscPnyYPXv2EB8fj4eHB+bm5vzzzz+cPn1anXQkHTdv3jzWrVvH2rVrOXXqFC9fvmTnzp1pXrdnz578+eef/PLLL/j5+bFy5UrMzMxwdHRk+/btANy5c4fQ0FAWLVqU4jl69erFpUuX2L17N2fPnkUIQcuWLTUmt42Ojmbu3Ln89ttvnDx5kuDgYMaOHZvi+ZK6VSwsLFi4cCGhoaF06dJFvX/ChAnMnDkTPz8/KlasiJeXF9u3b2f9+vVcuXIFV1dXPDw8ePnypcZ5p0yZwpIlSzhz5ow68Vy4cCF//PEHe/fu5dChQyxevDjN1yuj1q9fj6mpKefPn2f27NlMnTpVIznp1KmTOmm7fPkyVapUoXHjxurYIyMjadmyJUePHuXq1as0b96cVq1aERwcrHGduXPn4u7uztWrV5k0aVK6Yrt58yZnzpzBwMBAXTZjxgw2bNjAihUruHXrFqNGjeLrr7/mxIkTAAQEBNCxY0fatm3LtWvXGDBgAN99912yc0dHRzNr1ix+/fVXbt26hY2NDUOHDuXs2bNs2rSJ69ev06lTJ5o3b67+rBwyZAixsbGcPHmSGzduMGvWLMzMVL0NkyZNwtfXl/379+Pn58fy5cspVKhQis/r0aNHtGzZkurVq3Pt2jWWL1/OmjVrmD59eoZ+N1lC5CKbNm0SBgYGYu3ateLWrVuif//+wsrKSjx9+jTF+t7e3sLCwkKEhoaqtydPnmjUmTlzprC0tBS7du0S165dE61btxYuLi7i7du36Y4rPDxcACI8PPyTnp+2+fj4iB49egg9PT0BCECUKVNGrF69WuP1uBT4UjiN3yOKT9wrAp9Hai/gyEghQLVFRqZepkWRsZGCKQimICJjI1Mty2/evn0rfH19xdu3b8Wbt3HiWsgrce7uI/X7Lju3yAy8Tzw9PUWbNm2EEEIolUpx+PBhYWhoKMaOHaveb2trK2JjY9XH/Pbbb6J06dJCqVSqy2JjY4WxsbE4ePCgEEIIe3t7MXv2bPX++Ph4UbRoUfW1hBCifv36YsSIEUIIIe7cuSMAcfjw4RTjPHbsmADEq1evNMrfPcfdu3cFIE6fPq3e//z5c2FsbCy2bNkihFD9DQXE/fv31XWWLl0qbG1t03ydLC0thbe3d7J4du3apS6LjIwU+vr6YuPGjeqyuLg44eDgoH4tko47cuSIus6MGTMEIPz9/dVlAwYMEB4eHqnGk9rrkeTd36sQqtepbt26GnWqV68uxo8fL4QQ4p9//hEWFhYiJiZGo06JEiXEypUrU42jXLlyYvHixerHTk5Oom3btqnWfzc+XV1dYWpqKgwNDQUgdHR0xLZt24QQQsTExAgTExNx5swZjeP69u0runXrJoQQYvz48aJ8+fIa+7/77juN1yXp9+3j46OuExQUJHR1dcWjR480jm3cuLGYOHGiEEKIChUqiClTpqQYe6tWrUTv3r1T3BcQECAAcfXqVSGEEN9++22y/ytLly4VZmZmIjExUQjx4d9NSt79e/O+9H5+56qWpfnz59O/f3969+6Nm5sbK1aswMTEhLVr16Z6jEKhwM7OTr3Z2tqq9wkhWLhwId9//z1t2rShYsWKbNiwgcePH7Nr165seEY5i7u7Oxs2bCAgIIBx48ZhYWHB7du36d+/P05OTkyfPp0XL16w8N+xSh2rFMWpoGxVkj6NmZE+poZ6uWb5kz179mBmZoaRkREtWrSgS5cuGt0AFSpU0PjGf+3aNe7fv4+5uTlmZmaYmZlRoEABYmJi8Pf3Jzw8nNDQUGrWrKk+Rk9Pj2rVqqUag4+PD7q6utSvX/+jn4efnx96enoa1y1YsCClS5fGz89PXWZiYkKJEiXUj+3t7QkLC/uoa777nPz9/YmPj6dOnTrqMn19fWrUqKFxfYCKFSuqf7a1tcXExITixYtrlH1sTKl595qg+byvXbtGZGQkBQsWVP9OzczMCAgIwN9ftZh4ZGQkY8eOpWzZslhZWWFmZoafn1+ylqW0fs/vatiwIT4+Ppw/fx5PT0969+5Nhw4dALh//z7R0dE0bdpUI54NGzao47lz5w7Vq1fXOGeNGjWSXcfAwEDjud+4cYPExERKlSqlce4TJ06ozz18+HCmT59OnTp1mDx5MtevX1cfP2jQIDZt2kSlSpXw8vLizJkzqT5HPz8/atWqpXGXcJ06dYiMjOThw4fqsrR+N1kl54+q+ldcXByXL19m4sSJ6jIdHR2aNGnC2bNnUz0uMjISJycnlEolVapU4eeff6ZcuXKAqlnyyZMnNGnSRF3f0tKSmjVrcvbsWbp27ZriOWNjY4l9Z/Bwdi8tktWKFi3K7Nmz+f777/n1119ZuHAhISEhTJo0iek//YyBWyOsa7RjaKOG2g5VyiNszY2IjInn3N1HlLIxx0Av+77HmWRwPq6GDRuyfPlyDAwMcHBwSDY41dRU8wtEZGQkVatWZePGjcnOVbhw4YwHjKrrL7vo6+trPFYoFB+d2L7/2nxMDEl3hL0fU2YPRE7rGpGRkdjb22uM20mSNFB87NixHD58mLlz5+Lq6oqxsTEdO3ZMNog7va+Jqakprq6qtRXXrl2Lu7s7a9asoW/fvkRGRgKwd+9eihQponGcoaFhus6fxNjYWCNZiYyMRFdXl8uXL6Orq6tRN6mrrV+/fnh4eKi7RGfMmMG8efMYNmwYLVq0ICgoiH379nH48GEaN27MkCFDmDt3bobield2/P7fl2talp4/f05iYqJGyxCovlE8eZLyCualS5dm7dq1/PXXX/z+++8olUpq166tzlCTjsvIOUHVN2xpaaneHB0dP+Wp5VgWFhaMHj0af39/Nm7cSOXKlYmNecubK3sJXtmfUd/05Ny5c9oOU8oDzIz0MDPSx9jYhGihh6mpabZtGZ3rKulDq1ixYum6i6dKlSrcu3cPGxsbXF1dNbakvyH29vacP39efUxCQgKXL19O9ZwVKlRAqVSqx6O8L6llKzExMdVzlC1bloSEBI3rvnjxgjt37uDm5vbB5/WpSpQogYGBAadPn1aXxcfHc/HixWy5/qeoUqUKT548QU9PL9nvNGk8zunTp+nVqxft2rWjQoUK2NnZZfiO49To6Ojw7bff8v333/P27VuNwdjvx5P0+VS6dOlkN++8O5g+NZUrVyYxMZGwsLBk57azs1PXc3R0ZODAgezYsYMxY8awevVq9b7ChQvj6enJ77//zsKFC1m1alWK1ypbtqx67FyS06dPY25uTtGiRTP0GmW2XJMsfYxatWrRs2dPKlWqRP369dmxYweFCxdm5cqVn3TeiRMnEh4ert6yY/01bdLX1+err75i2dZD2HT9CZMS1UAItm/fTq1atahbty5//fVXlmf2Ut5ma676BvwyKo74hLzzXurevTuFChWiTZs2/PPPPwQEBHD8+HGGDx+u/uI2YsQIZs6cya5du7h9+zaDBw9Oc/4zZ2dnPD096dOnD7t27VKfc8uWLYBqqhCFQsGePXt49uyZuuXhXSVLlqRNmzb079+fU6dOce3aNb7++muKFClCmzZtsuS1eJepqSmDBg1i3LhxHDhwAF9fX/r37090dDR9+/bNkmveuHEDHx8f9fbu3WsZ0aRJE2rVqkXbtm05dOgQgYGBnDlzhu+++06dkJQsWZIdO3aor/PVV19l6t/ITp06oaury9KlSzE3N2fs2LGMGjWK9evX4+/vz5UrV1i8eDHr168HYMCAAdy+fZvx48dz9+5dtmzZop5bKq0vDKVKlaJ79+707NmTHTt2EBAQwIULF5gxYwZ79+4FYOTIkRw8eJCAgACuXLnCsWPHKFu2LAA//PADf/31F/fv3+fWrVvs2bNHve99gwcPJiQkhGHDhnH79m3++usvJk+ezOjRo9HR0W66kmuSpUKFCqGrq8vTp081yp8+faqR3aZFX1+fypUrc//+fQD1cRk9p6GhIRYWFhpbfrD42H2MndwZOWctN2/epE+fPupvhm3btqVMmTKsWLGCt2/fZk9AxsYQEKDakrolUirTImN9YwJGBBAwIgBjfeNUyyQwNdTD1EA1dulZZO6Zd+lDTExMOHnyJMWKFaN9+/aULVuWvn37EhMTo/7bMWbMGHr06IGnpye1atXC3Nycdu3apXne5cuX07FjRwYPHkyZMmXo378/UVFRABQpUoQff/yRCRMmYGtry9ChQ1M8h7e3N1WrVuXLL7+kVq1aCCHYt29fsm6OrDJz5kw6dOhAjx49qFKlCvfv3+fgwYNYW1tnyfXq1atH5cqV1VvVqlU/6jwKhYJ9+/ZRr149evfuTalSpejatStBQUHqnor58+djbW1N7dq1adWqFR4eHlSpUiXTnouenh5Dhw5l9uzZREVFMW3aNCZNmsSMGTMoW7YszZs3Z+/evbi4uADg4uLCtm3b2LFjBxUrVmT58uXqu+E+1FXn7e1Nz549GTNmDKVLl6Zt27ZcvHiRYsWKAaoWzCFDhqivW6pUKZYtWwaoWjknTpxIxYoVqVevHrq6umzatCnF6xQpUoR9+/Zx4cIF3N3dGThwIH379uX777/PrJft46U5/DuHqVGjhhg6dKj6cWJioihSpIiYMWNGuo5PSEgQpUuXFqNGjRJCqO5msbOzE3PnzlXXCQ8PF4aGhuLPP/9Md1x55W64tCTdAVdi4l4R8jJKXf748WPx7bffCisrK/XdRYUKFRKTJ08WYWFhWoxYyslSuzsl6c646w9fi7iERC1FJ0n5w/Tp00XRokW1HUaWy3d3w40ePZrVq1ezfv16/Pz8GDRoEFFRUfTu3RtQzTvy7gDwqVOncujQIR48eMCVK1f4+uuvCQoKol+/foDq28HIkSOZPn06u3fv5saNG/Ts2RMHBwe5TtB7Fv+tmk+jQ5WiFLX+b0Csvb09P/30EyEhISxatAhnZ2eeP3/Ojz/+SLFixRg4cCB3797VVthSLqPRupSLZvWWpNxg2bJlXLx4kQcPHvDbb78xZ84cPD09tR1WrpCrkqUuXbowd+5cfvjhBypVqoSPjw8HDhxQN3sGBwdrzCz76tUr+vfvT9myZWnZsiURERGcOXNGY/Cgl5cXw4YN45tvvqF69epERkZy4MCBZJNX5inKRAj4B25sU/2rTH0QKMD1h685fucZujoKBjcskWIdMzMzhg8fzr1799i0aRPVqlUjJiaGlStXUqZMGdq2bcupU6cy9/bwuDgYN061Jd1hklKZFsUlxjHu0DjGHRpHXGJcqmWSikKhwMbinbFLuWRWb0nKDe7du0ebNm1wc3Nj2rRp6uV6pA9TiEz99MqfIiIisLS0JDw8POePX/LdDQfGQ8Tj/8osHKD5LHBrneIh/Tdc4rDvU9pXLsL8LpXSdRkhBP/88w9z587lf//7n7q8Zs2ajB07lnbt2iW7DTXDoqLg31tXiYwEU9OUy7QoKi4KsxmqeCInRmJqYJpiWX4TExNDQEAALi4uKc6q7/8siui4BAqZGeJgJcd1SZL08dL6e5Pez+9c1bIkfSLf3bClp2aiBBARqir33Z3sEL/QCA77PkWhgMENXdN9KYVCQb169di9ezd+fn70798fQ0NDzp8/T6dOnShZsiSLFy9WD0iVpCQKhQLbd1qXEmTrkiRJWiaTpfxCmahqUSKlhsR/yw5MSNYlt+Rv1Z2DX1Swx9XG7KMuXaZMGVatWkVQUBCTJk2iQIECBAQEMHz4cBwdHfn+++/TnNdKyrtSa9g2M9TDWF8XpRA8j5RdlZIkfbzM6ECTyVJ+EXQmeYuSBgERj1T1/nU/7A37bqrGgA1tlP5WpdTY2toydepUQkJCWLp0Ka6urrx69YqffvoJJycn+vXrh6+v7ydfR8r5km5NT22l+HfHLr2IiiVRzuElSdJHSvo78ylTYuSa5U6kTxT59MN13qu35O/7CAHN3GwpY5d5Y7FMTEwYPHgwAwYMYPfu3cydO5czZ86wZs0a1qxZwxdffMHYsWOpX79+hmdXlnIHXV1drKys1Os5mZiYJPtdGyDQF4nExSXy5CUUNMvYsg2SJOVvQgiio6MJCwvDysrqk8bJymQpvzCz/XCdd+oFvYhi9zVVS9SwRiWzJCRdXV3atWtHu3btOHPmDPPmzWPnzp3s3buXvXv3UrVqVcaOHUvHjh3TtayElLskTfya1gKY0XEJvIyK54UC7CyNZPIsSVKGWVlZpXvy6tTIT6D8wqm26q63iFBSHrekUO13qg3AypMPUAqoX6owFYpaZnl4tWvXpnbt2ty/f58FCxbg7e3N5cuX6datGxMmTGDkyJH07dsXc3PzLI9Fyh4KhQJ7e3tsbGyIj49PsU5CohJP7ws8CY9hcANXOlTV7vpQkiTlLvr6+p9+5zVy6oBMkWumDki6Gw7QTJj+/bbeeQO4tSYsIoa6s44Rl6hk8zefUbN4weyOlOfPn7Ns2TKWLFnCs2fPALC0tGTAgAEMHz5ctbK2Ugl+fqoDypYFHZ2Uy7RIKZT4PVPFU7ZwWXQUOimWSanbeD6I73bexM7CiBNeDTDU+/Q/fJIkSZD+z2+ZLGWCXJMsQSrzLBWB5jPV8yzN2OfHypMPqOpkzbaBtbTa9fH27Vt+//135s2bx507d4D/FvYdM2YMFSpU0FpsUvaITUik3uxjPI2IZUb7CnSrUUzbIUmSlEfIZCkb5apkCVTTAwSdUQ3mNrNVdb3pqL6th0fHU3vmUaLiElnbqxqNyqRzrFMWUyqV7N27lzlz5vDPP/+oy5s1a8bYsWNp0qSJHM+Sh/36zwOm7/WjWAET/h5THz1d2RonSdKnk5NSSqnT0QWXz6FCR9W/Ov91a6w/G0hUXCJl7MxpWNpGi0Fq0tHRoVWrVpw8eZLz58/TuXNndHR0OHToEM2aNaNypUr8/vvvxEdFwZQpqi2HLHcy5fgUphyforHcyftlUtq+qlmMAqYGBL+MZt9NOSeXJEnZS7YsZYJc17KUiui4BOrM/JtX0fEs6lqJNpWKaDukNAXcusWC8uVZAyTN1lPEwYGRjx/TH7CUy53kKb8cvcf8w3cpa2/BvuF1ZUuiJEmfTLYsSRm26UIIr6LjcSpowhcV7LUdzge5ODvzCxAC/DxlCnZ2djx6/JhxgCMw9ttvCQkJ0W6QUqbpWcsJEwNd/EIjOHnvubbDkSQpH5HJkgRAXIKS1f88AGBAvRK5akxIAWDi2LEEBgaydvly3IA3wLxffqF48eJ8/fXXXL16VctRSp/KysRAPbh7+fH7Wo5GkqT8JPd8IkpZatfVR4SGx2BjbkiHqjm7+y01hoaG9O7RgxvAPqBR/fokJCSwceNGqlSpQuPGjdm/f3+mrBMkaUe/z13Q11Vw7sFLrga/0nY4kiTlEzJZklAqBStP+gOqD6PcPo+NDtACOLp3r3piS11dXf7++29atmxJhQoV8Pb2JjY2VtuhShlkb2msHku34oS/lqORJCm/kMmSxNHbYfg/i8LcSC/PzWFTpUoV/vjjD/z9/Rk1ahRmZmbcunWLPn364OzszIwZM3j1SrZQ5CYD6xcH4JDvU+6HRWo5GkmS8gOZLEms/Pcb+tefOWFu9PGrMudkTk5OzJ8/n5CQEGbPno2DgwNPnjzh22+/xdHRkREjRhAQEKDtMKV0cLUxp5mbLULAqpOydUmSpKwnk6V87lLgSy4FvcJAV4fetZ21HU7GGBnBhQuqzcgo9bJ3WFlZMW7cOAICAtiwYQMVK1YkKiqKX375BVdXV7p06cLFixczL0Q9Iy70u8CFfhcw0jNKtUzKmIENSgCw8+ojQsPfajkaSZLyOjnPUibIzfMs9Vt/iSN+T+la3ZGZHSpqO5xsJ4TgyJEjzJkzh8OHD6vL69Wrx9ixY/niiy/Q0fL6clLKuqw8y/mAl/Sr68L3X7ppOxxJknIhOc+S9EH3w95wxO8pCgX0r1dc2+FohUKhoGnTphw6dIhr167Rs2dP9PT0OHnyJK1bt8bNzY3Vq1cTExOj7VCl9yS1Lv15IZjwt/FajkaSpLxMJkv52KqTqnmVmrnZUqKwmZaj+QhxcTBnjmpLWtokpbJ0qlixIuvXrycgIAAvLy8sLS25c+cO33zzDU5OTkybNo3nzzM2GWJcYhxzTs9hzuk5GsudvF8mZVyDUoUpbWtOVFwif5wP1nY4kiTlYbIbLhPkxm64pxEx1J31N/GJgh2Da1OlmLW2Q8q4qCgw+zfJS1raJKWyjxQREcGaNWtYuHAhwcGqD2NjY2N69+7NqFGjcHV1/XCIcrmTLLXt8kPGbr2Gjbkh/4xvmOunvZAkKXvJbjgpTWtPBxCfKKjhXCB3JkrZwMLCglGjRuHv78+ff/5JlSpVePv2LcuWLaNUqVJ06NCBs2fPajvMfK21uwN2FkaEvYnlL5/H2g5HkqQ8SiZL+VBETDx/nFO1lAyonz/HKmWEnp4eXbt25dKlSxw7doyWLVsihGDHjh3Url2bOnXqsHPnThITE7Udar5joKdD7zrOAKw++QClUjaUS5KU+WSylA9tvhDCm9gEStqY0bC0jbbDyTUUCgUNGjRg79693Lx5kz59+mBgYMCZM2do3749ZcqUYfny5URHR2s71HylW81imBnqcS8skhN3n2k7HEmS8iCZLOUz8YlK1p5WTb7Y//Pi6OgotBxR7lSuXDnWrFlDYGAg3377LdbW1ty/f5/BgwdTrFgxJk+eTFhYmLbDzBcsjPTpVsMRQL1sjyRJUmaSyVI+s/d6KKHhMRQyM6RNZQdth5Pr2dvb89NPPxEcHMwvv/yCi4sLL168YOrUqRQrVoxhg4dBxm6gkz5C7zou6OmoFti9/vC1tsORJCmPkclSPiKEUE8X0Ku2k7xzKBOZmZkxbNgw7t69y5YtW6hevTqxsbF4r/GGJcAfcPrUaeTNp1nDwcqY1u6q5H/lv+9xSZKkzCKTpXzkjP8LfEMjMNbXpXtNJ22H8+mMjODYMdX27nIn75dlIz09PTp16sT58+fVE1sqFAq4Cx6NPfjss8/Ys3MPR7of4ZjnMbncSSZKmlh1/41Qgl/IcWOSJGUemSzlI6v/UX3j7lytKNamBlqOJhPo6kKDBqpNVzf1Mi1QKBR8/vnn/PXXX/j5+TFgwAAMDQ25cOECXbt2pX+z/tz43w3eRst1zTJLWXsLPi9ZCKVAPS5PkiQpM+S6ZGnp0qU4OztjZGREzZo1uXDhQqp1V69ezeeff461tTXW1tY0adIkWf1evXqhUCg0tubNm2f108h2d5684fidZ+gooE9dF22Hk6+ULl2aFStWEBwczOTJkylYsCABAQEMHz6cYsWK8d133xEaGqrtMPOE/p+rWpe2XAohPFougSJJUubIVcnS5s2bGT16NJMnT+bKlSu4u7vj4eGR6l1Hx48fp1u3bhw7doyzZ8/i6OhIs2bNePTokUa95s2bExoaqt7+/PPP7Hg62erXf1uVPMrZ4VQwj8wYHR8PS5eqtvj41Mu0KD4xnqUXlrL0wlKsC1ozZcoU/AP86eLVhcJFC/Pq1St+/vlnnJ2d6du3L7du3dJ2yLna5yULUcbOnOi4RP68KJdAkSQpc+Sq5U5q1qxJ9erVWbJkCQBKpRJHR0eGDRvGhAkTPnh8YmIi1tbWLFmyhJ49ewKqlqXXr1+za9euj44rpy93EhYRQ53cvrRJSrJ4uZPMkOZyJ0r4s/yfLF64mDNnzqiPadmyJWPHjqVBgwaq8U5Shmy9FMK4bdexszDipFdDDPRy1XdCSZKyUZ5b7iQuLo7Lly/TpEkTdZmOjg5NmjRJ95IT0dHRxMfHU6BAAY3y48ePY2NjQ+nSpRk0aBAvXrxI8zyxsbFERERobDnZ+rOBxCcKqjlZ551EKS/QgVZtWnH69Gn1xJYKhYJ9+/bRqFEjqlatyh9//EF8Dmghy01aV3KgsLkhTyJi2HtDLoEiSdKnyzXJ0vPnz0lMTMTW1laj3NbWlidPnqTrHOPHj8fBwUEj4WrevDkbNmzg6NGjzJo1ixMnTtCiRYs0l66YMWMGlpaW6s3R0fHjnlQ2iI5LYOO/K7L3+1yOVcqpatWqxfbt27l79y6DBw/G2NiYq1ev0r17d1xdXZk/f36OT8pzCkM9XTxrqe72XH0yQE7XIEnSJ8s1ydKnmjlzJps2bWLnzp0YvXNLedeuXWndujUVKlSgbdu27Nmzh4sXL3L8+PFUzzVx4kTCw8PVW0hISDY8g4+z/cojXkfHU6yACU3d7LQdjvQBrq6uLF26lODgYKZOnYqNjQ3BwcGMGTMGR0dHvLy8ePjwobbDzPG613TCSF8H39AIzj5Iu6VYkiTpQ3JNslSoUCF0dXV5+vSpRvnTp0+xs0s7CZg7dy4zZ87k0KFDVKxYMc26xYsXp1ChQty/fz/VOoaGhlhYWGhsOZFSKVh7SnULdZ86zujKpU1yjUKFCjFp0iSCgoJYvXo1ZcqUISIigjlz5uDi4kLPnj25du2atsPMsaxNDehUVdXi++s/choBSZI+Ta5JlgwMDKhatSpHjx5VlymVSo4ePUqtWrVSPW727NlMmzaNAwcOUK1atQ9e5+HDh7x48QJ7e/tMiVub/r4dRsDzKMyN9OhULed2FUqpMzIyol+/fty6dYvdu3dTv359EhIS+O2336hUqRLNmjXj0KFDsqspBX3ruqBQqP4f3A97o+1wJEnKxXJNsgQwevRoVq9ezfr16/Hz82PQoEFERUXRu3dvAHr27MnEiRPV9WfNmsWkSZNYu3Ytzs7OPHnyhCdPnhAZGQlAZGQk48aN49y5cwQGBnL06FHatGmDq6srHh4eWnmOmenXU6rpAr6qWQxTQz0tRyN9Ch0dHVq1asXx48e5cOECXbp0QUdHh8OHD+Ph4YG7uzsbNmwgLi5O26HmGM6FTGlaVjXGcc0p2bokSdLHy1XJUpcuXZg7dy4//PADlSpVwsfHhwMHDqgHfQcHB2tM7rd8+XLi4uLo2LEj9vb26m3u3LkA6Orqcv36dVq3bk2pUqXo27cvVatW5Z9//sHQ0FArzzGz3HwUzrkHL9HVUeBZy1nb4WQNQ0PYs0e1Jf2+UirTIkM9Q/Z028Oebnsw1DNMtSwjqlevzqZNm/D392fEiBGYmppy48YNPD09cXFxYfbs2bx+/TqTn0nu1O/fSSp3XHnEyyiZSEqS9HFy1TxLOVVOnGdp9GYfdlx9RGt3B37pVlnb4UhZ6NWrV6xcuZJffvlF/WXBzMyM/v37M2LECJyc8sA6gB9JCEHrJae58SicMU1LMaxxSW2HJElSDpLn5lmS0u9JeAy7r6nml5HTBeR91tbWTJgwgYCAANauXUu5cuWIjIxkwYIFlChRgq+++oorV65oO0ytUCgU6v8DG84FEZuQ+pQgkiRJqZHJUh604WwgCUpBDecCVCxqpe1wsk58PKxbp9reXe7k/TItik+MZ53POtb5rCM+MT7VssxgaGhI7969uXHjBvv376dx48YkJiby559/UrVqVRo1asS+fftQKpWZds3coGUFe+wsjHj2JpY91+QafJIkZZzshssEOakbLjougVoz/ib8bTwre1TFo1wenlspty938k5ZVrl69Spz585l8+bN6olW3dzcGDNmDN27d8/1Y/PSa9nx+8w+cAc3ewv2Dq8rl5GRJAmQ3XD51vYrjwh/q5qEsklZ2w8fIOVplStXZuPGjTx48IAxY8Zgbm6Or68vffv2xcnJiZ9//pmXL19qO8ws91WNYhjr6+IbGsG5B3n/+UqSlLlkspSHKJUC79OqW6R7y0kopXcUK1aMuXPnEhISwty5cylatChPnz7lu+++Uy9G/eDBA22HmWWsTAzoULUIAGtO5d3nKUlS1pDJUh5y4u4zHjyLwtxQTkIppczS0pIxY8bw4MEDfv/9d9zd3YmOjmbJkiWULFmSzp07c+HCBW2HmSV611EN9D7672StkiRJ6SWTpTxk7b+tSl2qO2ImJ6GU0qCvr0/37t25evUqR44cwcPDA6VSydatW6lZsyb16tVj9+7deWoweInCZjQqY4MQqFtgJUmS0kMmS3nEnSdv+Ofec3QU4FnbWdvhSLmEQqGgcePGHDhwgOvXr9OrVy/09fX5559/aNOmDWXLlmXVqlW8fftW26Fmir51Va1LWy89JDxa+3dLSpKUO8hkKY9IWjDXo5wdjgVMtByNlBtVqFABb29vAgMDmTBhApaWlty9e5cBAwbg5OTE1KlTef78ubbD/CS1SxSkjJ05b+MT2XQxWNvhSJKUS8hkKQ94ERnLTp9HwH/fnPMFQ0PYskW1vbvcyftlWmSoZ8iWjlvY0nGLxnIn75flJA4ODsyYMYOQkBAWLlyIk5MTz549Y/LkyTg6OjJ48GDu3bun7TA/ikKhoM+//0fWnwkkITHvdDNKkpR15DxLmUDb8yz9cvQe8w/fpWJRS/4aUkfOISNlqoSEBLZv386cOXO4fPkyoEo62rZty9ixY6ldu7aWI8yYmPhE6sz8mxdRcSz9qgpfVLTXdkiSJGmJnGcpn4hNSOS3c0GAqlVJJkpSZtPT06NLly5cvHiR48eP8+WXXyKEYOfOndSpU4datWqxfft29aSXOZ2Rvi7dP1Otl7dWDvSWJCkdZLKUy+25FsqzN7HYWhjSonw++4ackABbt6q2hITUy7QoQZnA1ltb2XprKwnKhFTLcgOFQkH9+vX53//+h6+vL/369cPAwIBz587RsWNHSpcuzdKlS4mKyvm35X/9WTH0dRVcDnqFT8hrbYcjSVIOJ7vhMoG2uuGEEHy5+BS3HkcwzqM0Qxq6Ztu1cwS53InWPXnyhKVLl7Js2TL1TOAFChRg8ODBDB06FFvbnDuL/OgtPuy48og2lRxY1LWytsORJEkLZDdcPnAx8BW3HkdgqKfDVzWKaTscKR+ys7Nj2rRpBAcHs2TJEooXL87Lly+ZPn06Tk5OfPPNN9y+fVvbYaaoz7+TVO69HsqT8BgtRyNJUk4mk6VcLGm6gPZVimBtaqDlaKT8zNTUlCFDhnD37l22bdtGzZo1iY2NZfXq1ZQtW5bWrVtz8uRJclJDdvkiltRwKUCCUvDbuUBthyNJUg4mk6VcKuRlNId8nwD/LeMgSdqmq6tLhw4dOHv2LKdOnaJt27YoFAr+97//Ub9+fWrWrMnmzZtJyAHjyeC/1qU/zgfzNi53DFCXJCn7yWQpl9pwNhClgM9LFqKUrbm2w5EkDQqFgjp16rBz507u3LnDwIEDMTIy4uLFi3Tt2pWSJUuyaNEiIiMjtRpnUzdbilob8yo6nl3/zlUmSZL0Ppks5UJRsQlsuhgCQO86ztoNRpI+oGTJkixfvpzg4GCmTJlCoUKFCAwMZOTIkTg6OjJx4kQeP36sldh0dRT0+nd5oLWnAnJUN6EkSTmHTJZyoe1XHvImJgGXQqY0KGWj7XAkKV0KFy7M5MmTCQ4OZsWKFZQsWZLXr18zc+ZMnJ2d6d27Nzdv3sz2uDpXd8TUQJd7YZGcuv8clIkQ8A/c2Kb6Vym75yQpv5NL0+cySqXA+3QgAL1qO6Ojk48noTQwAG/v/35OrUyLDHQN8G7jrf45tbL8xNjYmAEDBtC/f3/+97//MXfuXE6dOsW6detYt24dzZs3Z+zYsTRq1ChbJlm1MNKnUzVH1p0J5Nqh3/g8djVEvNPSZeEAzWeBW+ssj0WSpJxJzrOUCbJznqVjt8Pove4i5kZ6nJvYGFNDme9Kud+5c+eYN28eO3bsQKlUrddWqVIlxo4dS+fOndHX18/S6wc8j2LWgtks01uIQgGaKdq/jzpvkAmTJOUxcp6lPCppeYau1R1loiTlGZ999hlbt27l7t27DB06FBMTE3x8fPj6668pXrw48+bNIzw8PMuu71LAiJ+NfgfeT5QA/v0+eWCC7JKTpHxKJku5yL3Q18T7n6SN7hn6Oz6Wf7gTEmDvXtX27nIn75dpUYIygb1397L37l6N5U7eL5NUSpQoweLFiwkJCWH69OnY2try8OFDxo4di6OjI2PHjiUkJCTzLxx0hgKJz0m9V1tAxCMIOpP515YkKceT3XCZIFu64Xx383rnaKzin/1Xlt/HUsjlTvK8mJgY/vjjD+bOnYufnx+gWti3a9eujBkzhkqVKmXOhW5sg+19P1yvwxqo0DFzrilJktbJbri8xHc3YktPLOKeaZZHhMKWnuC7WztxSVIWMzIyok+fPty8eZM9e/bQsGFDEhIS+P3336lcuTJNmzbl4MGDn37Lv1k617BLbz1JkvIUmSzldMpEODAeECl0EcixFFL+oKOjwxdffMHff//NpUuX6NatG7q6uhw5coTmzZtTsWJF1q9fT1xc3MddwKk2WDggUhixpKIAiyKqepIk5TsyWcrpgs5AxONU/4TLsRRSflO1alX++OMP/P39GTlyJGZmZty8eZNevXrh4uLCrFmzeP36dcZOqqMLzWehQP0V5B3//u9rPlNVT5KkfEcmSzld5NPMrSdJeYSTkxMLFiwgJCSEWbNm4eDgwOPHj5kwYQKOjo6MHDmSwMDA9J/QrTV03oDSzF6z3MJBThsgSflchpMlT09PTp48mRWxSCmRYykkKU1WVlZ4eXkREBDAunXrqFChApGRkSxatIgSJUrQtWtXLl26lL6TubVGd/QtFhVdwPC4oaxwXgQjb8hESZLyuQwnS+Hh4TRp0oSSJUvy888/8+hR9i4+uXTpUpydnTEyMqJmzZpcuHAhzfpbt26lTJkyGBkZUaFCBfbt26exXwjBDz/8gL29PcbGxjRp0oR79+5l5VPIkBu65XgsCqBMdfyqHEshSQAGBgZ4enpy7do1Dh48SNOmTVEqlWzevJnq1avToEED9uzZo570MlU6utRt2o7dytrMv2/Li2g5vYMk5XcZTpZ27drFo0ePGDRoEJs3b8bZ2ZkWLVqwbds24uPjsyJGtc2bNzN69GgmT57MlStXcHd3x8PDg7CwsBTrnzlzhm7dutG3b1+uXr1K27Ztadu2rcb6U7Nnz+aXX35hxYoVnD9/HlNTUzw8PIiJicnS55Je3meD+TG+J6pVH94fuZTPx1IYGMCSJart3eVO3i/TIgNdA5a0WMKSFks0ljt5v0zKPAqFgmbNmnHo0CH1xJZ6enqcOHGCVq1aUa5cOX799dc0/49XKWaFe1FL4hKU/HE+OBujlyQpJ/rkeZauXLmCt7c3v/76K2ZmZnz99dcMHjyYkiVLZlaMajVr1qR69eosWbIEAKVSiaOjI8OGDWPChAnJ6nfp0oWoqCj27NmjLvvss8+oVKkSK1asQAiBg4MDY8aMYezYsYCq5czW1pZ169bRtWvXdMWVVfMshUXEUGfW38QnCo5/EYHzxanvrVlVRJUoyS4CSUrTw4cP+eWXX1i5ciUREREA2NjYMGzYMAYNGkTBggWTHbPr6iNGbvbBxtyQU+MbYaAnh3hKUl6TLfMshYaGcvjwYQ4fPoyuri4tW7bkxo0buLm5sWDBgk85dTJxcXFcvnyZJk2aqMt0dHRo0qQJZ8+eTfGYs2fPatQH8PDwUNcPCAjgyZMnGnUsLS2pWbNmqufMTr+fDyY+UVDVyRrnz7vByJvguUc1MZ7nHjmWQpLSqWjRosyePZuQkBDmzp2Lo6MjYWFhTJo0iWLFijFs2DD8/f01jmlZwR4bc0PC3sSy/2aoliKXJGnhkbusPOFPeHTW9l6lJcPJUnx8PNu3b+fLL7/EycmJrVu3MnLkSB4/fsz69es5cuQIW7ZsYerUqZka6PPnz0lMTMTWVnMgs62tLU+ePEnxmCdPnqRZP+nfjJwTIDY2loiICI0ts8UmJPLH+SAAetdxVhXq6ILL56oZhF0+z59db+9KTITjx1VbYmLqZVqUqEzkeOBxjgceJ/HfubBSKpOyh4WFBWPGjMHf35+NGzdSuXJloqOjWbJkCaVKlaJTp06cP38eAAM9Hb7+zAmAtacDtRi1JOVfr6PjWHHCnxn7b+P3JPM/a9Mrw8mSvb09/fv3x8nJiQsXLnDp0iUGDhyo0XzVsGFDrKysMjPOHGXGjBlYWlqqN0dHx0y/hq5CwQ+tytHMzRaPcnaZfv48ISYGGjZUbUnjT1Iq06KYhBgarm9Iw/UNiUmISbVMyl76+vp89dVXXL58maNHj9KiRQuUSiXbtm3js88+o27duuzatYsu1YpgoKvDtZDXXAl+pe2wJSnf2XQxhJh4JWXtLajpUkBrcWQ4WVqwYAGPHz9m6dKlqa7LZGVlRUBAwKfGpqFQoULo6ury9KnmfEJPnz7Fzi7lZMLOzi7N+kn/ZuScABMnTiQ8PFy9ZcXCnnq6OrR2d2BVz2ro68qxEpKUFRQKBY0aNWLfvn3cuHGD3r17o6+vz+nTp2nXrh2fV6+EU9hplPGxeMvWJUnKVgmJSjacCQRUPSwKRerTM2e1DH8K9+jRAyMjo6yIJU0GBgZUrVqVo0ePqsuUSiVHjx6lVq1aKR5Tq1YtjfoAhw8fVtd3cXHBzs5Oo05ERATnz59P9ZwAhoaGWFhYaGySJOVu5cuXZ+3atQQFBTFhwgSsrKy4d+8eR379iUfLe7Nx2Vxu+mf+FyNJklJ2yPcpj8NjKGhqQGt3B63GkquaLEaPHs3q1atZv349fn5+DBo0iKioKHr37g1Az549mThxorr+iBEjOHDgAPPmzeP27dtMmTKFS5cuMXToUED1rXLkyJFMnz6d3bt3c+PGDXr27ImDgwNt27bVxlOUJEnL7O3tmTFjBiEhISxatAhnZ2eUbyN4deoPKruVZODAgdy5c0fbYUpSnrf2lKqH6quaxTDS1+4Y3VyVLHXp0oW5c+fyww8/UKlSJXx8fDhw4IB6gHZwcDChof/dtVK7dm3++OMPVq1ahbu7O9u2bWPXrl2UL19eXcfLy4thw4bxzTffUL16dSIjIzlw4IBWWs8kSco5zMzMGD58OPfu3WPinBUY2JckIS6WlStXUrZsWdq2bcs///zDJ86+IklSCm48DOdS0Cv0dRXqGy206ZPnWZKybp4l6QOiosDMTPVzZCSYmqZcpkVRcVGYzVDFEzkxElMD0xTLpJwtUSmoN/tv/G9cokjIEa78c0S9r2bNmowdO5Z27dqhq5vP71CVpEwyerMPO64+om0lBxZ2rZxl18mWeZYkSZLyA10dBb1qu2DkWJ5C7Sbh6+vLN998g6GhIefPn6dTp06UKlWKJUuWEBUVpe1wJSlXC3sTw/+uqyZg7l3HRcvRqMhkScq99PVh9mzVpq+fepkW6evqM7vJbGY3mY2+rn6qZVLO17m6IyYGutx5+oZX+oVZuXIlQUFB/PDDDxQsWJAHDx4wbNgwHB0d+f7779Ocq02SpNRtPKeakLlKMSvcHa20HQ4gu+EyheyGk6T8YdKum/x2LogmZW351bOaujw6Opr169czf/587t+/D6ju4O3RowejR4/Gzc1NWyFLUq4Sm5BInZl/8zwyjsXdKtMqi++Ck91wkiRJmazXv7PpH739lKAX/3W3mZiYMGjQIG7fvs2OHTuoVasWcXFxrFmzhnLlyvHll19y/PhxORhckj5gz7VQnkfGYWdhRPPyOWdCZpksSblXYiJcvKja3l3u5P0yLUpUJnLx0UUuPrqosdzJ+2VS7lCisBkNShdGCFj372R579LV1aVdu3acOXOG06dP0759exQKBXv37qVhw4ZUr16dTZs2kZCQkP3BS1IOJ4Rg3an7fKbjy9QSfugHn4Yc8jdSdsNlAtkNpyXybjhJC07cfYbn2guYGepxdmIjzI3SHnd27949Fi5ciLe3N2/fvgWgWLFijBw5kn79+mFubp4dYUtSjnfv+B+YHvsOB8XL/wotHKD5rCxbNF52w0mSJGWBeiULUaKwKZGxCWy99PCD9UuWLMnSpUsJDg5m6tSpFC5cmODgYEaPHo2joyMTJkzg0aNH2RC5JOVgvrtxPT4IO15qlkeEwpae4LtbO3H9SyZLkiRJGaBQKNS3M68/G0iiMn2N84UKFWLSpEkEBQWxatUqSpcuTXh4OLNmzcLFxQVPT0+uX7+elaFLUs6kTCRhnxdCgE6y5d/+/f91YIJWu+RksiRJkpRB7asUwcJIj6AX0fx9OyxDxxobG9O/f398fX3ZvXs39erVIz4+ng0bNuDu7o6HhweHDx+Wg8Gl/CPoDHqRoSkkSkkERDyCoDPZGZUGmSxJkiRlkImBHt1qFAPA+3TAR51DR0eHVq1aceLECc6fP0/nzp3R0dHh0KFDNGvWjEqVKvHbb78RFxeXmaFLUo4T8+px+ipGPs3aQNIgkyVJkqSP0LO2M7o6Cs74v8AvNOKTzlWjRg02b97M/fv3GT58OKamply/fp2ePXtSvHhx5syZQ3h4eCZFLkk5y4nQdKYiZrZZG0gaZLIkSZL0EYpYGdO8nGoemHWnAzPlnC4uLixatIjg4GBmzJiBnZ0djx49wsvLC0dHR8aMGUNwcHCmXEuScgKlUjDbtwCPRQEEqfXDKcCiCDjVztbY3iWTJSn30teHyZNV27vLnbxfpkX6uvpMrj+ZyfUnayx38n6ZlDv1qesMwE6fR7yIjM208xYoUIAJEyYQGBiIt7c35cqV482bN8yfP5/ixYvTvXt3rl69mmnXkyRtOXH3Gf4vYpit6P1vyfsJ07+Pm88EHe0tVC3nWcoEcp4lScqfhBC0XXqaaw/DGdO0FMMal8yy6xw4cIB58+Zx9OhRdXmjRo0YO3YszZs3R6FIdXSsJOVYPdac5597z+lX14Xvi9+HA+Mh4p0xTBZFVImSludZkslSJpDJkiTlX3/5PGLEJh8KmxtyenwjDPSytsH+ypUrzJs3j82bN5P47yz15cqVY8yYMXz11VcYGhpm6fUlKbPcffqGZgtOoqOAE+Ma4ljARDU9QNAZ1WBuM1tV11sWtijJSSmlvE+phFu3VJtSmXqZFimFkltht7gVdgulUKZaJuVeLcrbY2thyLM3sey9kc67ej5BlSpV2LhxIw8ePGD06NGYmZlx69Yt+vTpg7OzMzNmzODVq1dZHockfaqkO0mbudmpEiVQJUYun0OFjqp/tdj19i6ZLEm519u3UL68avt3GYkUy7Tobfxbyi8vT/nl5Xkb/zbVMin3MtDToWctZwDWnArItvmRihUrxrx58wgJCeH/7d15fExn+8fxz8xkjyyWrGS1RcQe0thbam3RR6vaKEXpglLU9mupqqV4VKuonVqqWqUoai1q30IQS5AFiSCyR7aZ3x9DHkEiSHJmkuv9ep2XuOfMyXemKpf73HNfU6dOpWLFisTExDBmzBjc3NwYPHgwV68+37YGQhS1uJQM/jih37m+bzMvhdM8nRRLQgjxgt5p5I65iZoz1xM5FlG8szr29vZ8/vnnXLlyJWdjy5SUFH744QeqVKlCt27dOHLkSLFmEuJpVh2OID1LS62Kdvh7lFU6zlNJsSSEEC+onLUZ/6lfEYDF/yozm2NmZsZ7773HyZMn2b59O23btkWr1fLbb78REBBAixYt2LhxI1oDuD0tSreMLC0/H4wAoG9TL6P4cIIUS0IIUQge9Iv7+2wMUXGpiuVQqVS0bt2arVu3curUKXr27ImpqSl79+6lU6dO+Pr6smDBAu7du6dYRlG6/RVyg9ikdBxtzOlQy0XpOAUixZIQQhSCak42NKtaAa0Olh4IVzoOALVr12bZsmVcvXqVkSNHYmdnx4ULF+jfvz8eHh5MmDCB27dvKx1TlCI6nY5F92dfezX2LPJPjxYW40gphBBGoE9T/ezSr0ejSLqXqf8Y9NV9EPK7/leFuqZXrFiRKVOmEBUVxYwZM3B3dyc2NpaxY8fi7u7OgAEDCAsLUySbKF2Oht/lzPVEzE3UOf0VjYEUS0IIUUhaVHWgsoM1yelZHN68DGb6wbLXYG1f/a8z/eDcBsXy2djY8Nlnn3H58mV++eUX6tevT1paGnPmzKFatWp07dqVgwcPKpZPlHwP1vT9p34lylmbKZym4KRYEsbL1BSGD9cfD7c7eXRMQaYaU4YHDmd44PBc7U4eHRMlg1qtok9TL9qqj9Dq9DB0iY/su5QYDWt6KlowAZiYmNC9e3eOHTvG7t276dChAzqdjj/++IPGjRvTpEkT/vjjj5xNL4UoDFFxqWw7FwNAnyaeyoZ5RrKDdyGQHbyFEA+k3csgYYoPjro7qJ/4IR8V2LrCkBCD2XAP4OzZs8yYMYMVK1aQkZEBQOXKlRk6dCjvv/8+VlZWCicUxm7CpnMs+vcqzas58HOfRkrHAWQHbyGEUIRl9GGcyatQAtBB4nV9SwcDUrNmTRYtWkRERARjxoyhbNmyXL58mQEDBuDu7s7YsWOJjY1VOqYwUon3Mvn1aBSg3y7A2EixJIyXVgvh4frj4XYnj44pSKvTEh4fTnh8eK52J4+OiRIk+WbhnlfMnJ2dmThxIlFRUcyaNQsvLy/u3LnDhAkTcHd3p3///pw/f17pmMLIrDkaRXJ6FlUdy9C8agWl4zwzKZaE8UpLAy8v/fFwu5NHxxSUlpmG1/deeH3vlavdyaNjogQp41S45ynE2tqagQMHcunSJX777TcaNWpEeno6CxYsoEaNGnTq1Im9e/cWW3sXYbyysrUs2R8OwAfNjGMTykdJsSSEEIXJozHYuqIjrx8IKrCtqD/PCGg0Gt58800OHTrEvn376Ny5MyqVio0bN9KiRQsCAgJYs2YNWVlZSkcVBmrr2Riux6dR3tqMznUrKh3nuUixJIQQhUmtgXbfogIev8l6v4BqN8WgFncXhEqlomnTpqxfv57Q0FA+/PBDLCwsOHr0KG+//TZVq1blhx9+IDk5WemowoDodDoW7NNvF9DjJQ8sTI3rz/0DUiwJIURh8+0E3X4mw9I597itK3T7Wf+4EatevTo//fQTERERjBs3jvLlyxMeHs7gwYNxc3NjzJgxREdHKx1TGIATkXc5FRWPmYmaHi95KB3nuRlNsRQXF0dQUBC2trbY29vTt2/ffP8FExcXx6BBg6hevTqWlpa4u7vz6aefkpCQkOs8lUr12LF69eqifjlCiJLOtxNmw84yxOIbPs0YyJYGC/TbBRh5ofQwR0dHvvrqKyIjI5k7dy5VqlQhPj6eyZMn4+npSZ8+fTh79qzSMYWCFt6fVXqjbkUcbMwVTvP8jKZYCgoK4uzZs2zfvp1Nmzaxd+9e+vfvn+f5N27c4MaNG0yfPp0zZ86wdOlStm7dSt++fR87d8mSJURHR+ccXbp0KcJXIoQoLdQmJjRo2YkN2sZMCq1AtvH8lftMrKys+Oijjzh//jzr1q2jSZMmZGRksGTJEvz8/OjQoQO7du2SxeClTFRcKn+f1W9C2beZ8W0X8DCj+D83NDSUrVu3snDhQgICAmjatCmzZs1i9erV3Lhx44nP8fPzY+3atbz++utUrlyZV155hYkTJ7Jx48bHFiLa29vj7Oycc1hYWBTHyxJClAJv1q+EvZUpUXFpbLv/g6Ok0mg0dOnShX///ZcDBw7wn//8B5VKxZYtW2jVqhUNGjRg1apVZGZmKh1VFIMl+8PR6qB5NQeqOdkoHeeFGEWxdPDgQezt7fH3988Za926NWq1msOHDxf4Og926DQxMck1PmDAACpUqECjRo1YvHjxU//1k56eTmJiYq5DKMDEBD75RH88+G/6pDEFmahN+MT/Ez7x/wQTtUmeY6LksjTT0CNAv1Zjwb4rCqcpPoGBgaxdu5ZLly4xYMAALC0tOXnyJEFBQVSuXJkZM2bI350lmH4TykjAODehfIzOCEycOFFXrVq1x8YdHBx0c+bMKdA1bt26pXN3d9eNGTMm1/jXX3+t+/fff3UnTpzQTZkyRWdubq77/vvv873WuHHjdMBjR0JCQsFflBCi1LiZmKarOmazzmPkJt2x8Dil4yji9u3bugkTJugcHR1z/s60tbXVDR8+XBcZGal0PFHIfvonTOcxcpPu1Rn/6LRardJx8pSQkFCgn9+KziyNGjXqiQusHz4KY6fYxMREOnbsiK+vL1999VWux7788kuaNGlCvXr1GDlyJCNGjGDatGn5Xm/06NEkJCTkHFFRUS+cUQhRcjnaWNC5risAi/4tPbNLDytfvjxffPEFERERLFiwAB8fHxITE5k+fTre3t689957nDp1SumYohBkZD28CaW3UW5C+ShFi6Vhw4YRGhqa7+Ht7Y2zs/NjPYmysrKIi4vD2dk5j6vrJSUl0a5dO2xsbFi3bh2mT+lEHxAQwLVr10hPT8/zHHNzc2xtbXMdQgE6Hdy6pT8e3Dp90piCdDodt1JucSvlVs7t3SeNiZLvwQLXrWdiiIpLVTiNciwsLPjggw84e/YsmzZtomXLlmRlZbFixQrq1q1LmzZt2LZtm/y/YcT+CrlBTOI9HGzMc/6RYOwULZYcHBzw8fHJ9zAzMyMwMJD4+HiOHz+e89xdu3ah1WoJCAjI8/qJiYm0adMGMzMzNmzYUKCF28HBwZQtWxZzc+P9iGOpkZoKjo76IzU17zEFpWam4jjdEcfpjqRmpuY5Jko+H2dbmlWtgFYHi/69qnQcxanVajp27Mju3bs5evQo3bt3R6PRsH37dtq2bUudOnX4+eefycjIUDqqeAY6nY75e/V/vt9v7Im5iXFuQvkoo1jgXaNGDdq1a0e/fv04cuQI+/fvZ+DAgXTv3h1XV33Vev36dXx8fDhy5Ajwv0IpJSWFRYsWkZiYSExMDDExMWRnZwOwceNGFi5cyJkzZwgLC2Pu3LlMmjSJQYMGKfZahRAlV79m3gCsORZFQqp8IuwBf39/fvnlF8LCwhgyZAjW1taEhITQq1cvvLy8mDp1KvHx8UrHFAWwP+wOodGJWJpqCApwVzpOoTGKYglg5cqV+Pj40KpVKzp06EDTpk2ZP39+zuOZmZlcuHCB1PuzCSdOnODw4cOEhIRQpUoVXFxcco4Ha4xMTU2ZPXs2gYGB1K1bl3nz5jFjxgzGjRunyGsUQpRszapWwMfZhtSMbFYeiVA6jsHx9PTku+++IyoqiilTpuDi4sKNGzcYOXIkbm5uDB06lIgIed8M2fz7n/h8u6Eb9lZmCqcpPCqd3Bh+YYmJidjZ2eVsTSCKSUoKlCmj/zo5GaytnzymoJSMFMpM1udJHp2MtZn1E8dE6fH78WsM/+0UDjbm/Dvy5RJzm6IoZGRk8Msvv+RsLgz6vZy6devGsGHDaNCggcIJxcMuxCTRduZe1Cr4Z/jLuJe3UjrSUxX057fRzCwJIURJ0KmOK862FtxKSufPk0/eVFfomZmZ0atXL06fPp2zsWV2dja//PIL/v7+vPzyy/z1119otY+3LBbF78E+Yu39XIyiUHoWUiwJIUQxMjNR07uJJ6C/ZaHVyuT+06hUKtq1a8eOHTs4efIkPXr0wMTEhH/++YfXXnsNPz8/Fi1axL1795SOWmrdTLzHn8HXAfjAyFubPIkUS0IIUczeCXCnjLkJYbHJ/HMx9ulPEDnq1q3L8uXLuXLlCsOHD8fGxobQ0FA++OADPD09mThxInfu3FE6Zqmz9EA4mdk6GnqWpZ57WaXjFDoploTxMjGBXr30x8PtTh4dU5CJ2oRedXrRq06vXO1OHh0TpYuthSnv3v+k0Lw9pXOTyhfl5ubGtGnTiIqKYvr06VSqVImbN2/yxRdf4O7uzqBBg7hyRd7b4pCcnsWKQ/qF9w8+8VnSyALvQiALvIUQzyo6IY1m3+4mS6vjzwFNqONmr3Qko5aZmcmvv/7Kf//7X4KDgwH9Xk5du3Zl2LBh+e7JJ17Mwn1X+OavULwdrNnxWQvUauPZsVsWeAshhAFzsbOk0/3djefvlRmQF2VqakqPHj04ceIEO3bsoF27dmi1Wn777TdeeuklmjdvzoYNG2QxeCHLyNKycJ9+E8oPm3sbVaH0LKRYEsZLp9NvFZCSkrvdyaNjCtLpdKRkpJCSkZKr3cmjY6J06t9cf8tiy5loIu/Ibu6FQaVS0apVK7Zs2UJISAi9e/fG1NSUffv20blzZ2rUqMG8efNIS0tTOmqJsOGUvrWJo405XepVVDpOkZFiSRiv1FT9nkplyuRud/LomIJSM1MpM7kMZSaXydXu5NExUTr5ONvSopoDWh0sLKUNdouSn58fixcvJjw8nFGjRmFnZ8fFixf56KOP8PDwYPz48dy6dUvpmEZLq9Uxf+9lAPo09SrRe4ZJsSSEEAr6sPn/WqDcSc67gbd4fq6urkyePJmoqCi+++47PDw8uHXrFl999RXu7u58/PHHXLx4UemYRuefi7FcvJlMGXOTnA8slFRSLAkhhIICK5enTiU77mVqWXYgXOk4JZqNjQ1DhgwhLCyM1atX06BBA+7du8dPP/2Ej48Pb7zxBvv375fb4wX00z/62dCgAHdsLUwVTlO0pFgSQggFqVQqPmpRGYBlByNITkuHq/sg5Hf9r9pshROWPCYmJrz99tscPXqU3bt389prr6HT6Vi/fj1NmzalcePGrF27Nqfpunjc8Yi7HAmPw1SjoneTkrcJ5aOkWBJCCIW1qemMdwVrXkrfDzNrwbLXYG1f/a8z/eDcBqUjlkgqlYqWLVuyceNGzp07R79+/TA3N+fQoUO8+eabVKtWjdmzZ5OSkqJ0VIPzYK1Sl7oVcbazUDhN0ZNiSQghFKZRq/i62hXmms7EKv1m7gcTo2FNTymYiliNGjWYP38+ERERfPHFF5QrV44rV64wcOBA3N3d+eKLL4iJiVE6pkG4fCuZbef0f04/bFEyN6F8lBRLQgihNG02TS5NA9WT/lK+v35m6yi5JVcMnJycmDBhApGRkcyePZvKlSsTFxfHxIkT8fDwoF+/foSGhiodU1E//XMZnQ5a13CiiqON0nGKhRRLwnhpNPDmm/pDo8l7TEEatYY3fd/kTd830ag1eY6JUi7iAKqkG/n8hayDxOsQcaAYQ5Vu1tbWfPLJJ1y4cIG1a9fy0ksvkZGRwcKFC/H19eX1119nz549pW4x+I34NNad1DfM/eTlygqnKT7S7qQQSLsTIcQLCfldv0bpabouglpvFn0e8UQHDhxg+vTprF+/PqdIatCgAZ9//jldu3bFxAD6URa18RvPsmR/OC95l2N1/0Cl47wwaXcihBDGooxT4Z4nikTjxo35448/uHDhAh999BEWFhYcP36c7t27U7lyZb777juSkpKUjllk7iSns/pIFACftKyicJriJcWSEEIozaMx2LoCefXVUoFtRf15QnFVq1Zl7ty5REZGMn78eBwcHIiMjGTo0KG4ubkxcuRIrl+/rnTMQrf0QDhpmdnUqmhHs6oVlI5TrKRYEsYrJQVUKv3x4KO9TxpTUEpGCqrxKlTjVaRkpOQ5Jko5tQbafXv/N48WTPd/326K/jxhMBwcHBg7diwRERHMmzePatWqkZCQwNSpU/Hy8qJXr16cPn1a6ZiFIuleZs6mqZ+0rIxKVTIb5uZFiiUhhDAEvp2g289g65JrOMPaWT/u20mhYOJpLC0t6d+/P6Ghofz55580a9aMzMxMfv75Z+rUqUPbtm3Zvn27US8GX3U4ksR7WXg7WNO2prPScYqdFEtCCGEofDvBkDPQaxMrKo2le8YXDHBYKoWSkVCr1XTq1Im9e/dy6NAh3nrrLdRqNdu2baNNmzbUrVuX5cuXk5GRoXTUZ3IvM5uF/14F4KMWlVGrS9esEkixJIQQhkWtAa9mBHb+kMM6X7afv0NodKLSqcQzCggIYM2aNVy6dIlBgwZhZWXF6dOn6dmzJ97e3kybNo2EhASlYxbI78evcSspHVc7C7rUrah0HEVIsSSEEAaoskMZOtTS35KbvTtM4TTieXl7e/PDDz8QFRXFxIkTcXJy4vr164wYMQI3NzeGDRtGVFSU0jHzlJmtZd791ib9mntjZlI6y4bS+aqFEMIIDLj/8ey/QqK5citZ4TTiRZQrV44xY8YQERHBokWL8PX1JSkpiRkzZuDt7U2PHj04efKk0jEf82fwDaLi0qhQxozuDd2VjqMYKZaEEMJA+bra0srHEZ0O5v5zWek4ohCYm5vTp08fQkJC2Lx5My+//DJZWVmsXLmS+vXr07p1a7Zu3WoQi8GztTrm3J/V/KCZN5ZmpffTmFIsCeOl0UCHDvrj4XYnj44pSKPW0KFqBzpU7ZCr3cmjY0LkZcAr+tmldSevc+1uqsJpRGFRq9W0b9+eXbt2cezYMd555x00Gg07d+6kffv21K5dm6VLl5Kenq5Yxk2nb3Dldgr2Vqb0eMlDsRyGQNqdFAJpdyKEKErvzD/EwSt36Bnowded/ZSOI4pIREQE33//PQsWLCA5WX/b1cXFhU8//ZQPP/yQsmXLFlsWrVZHu+/3cvFmMsNercagVlWL7XsXJ2l3IoQQJcSg+7NLq49GcTPxnsJpRFHx8PBgxowZREVF8e233+Lq6kp0dDSjR4/Gzc2NIUOGEB4eXixZ/j4bw8WbydhYmNCriWexfE9DJsWSEEIYuMDK5WngUZaMLC0/7ZG1SyWdvb09I0aM4OrVqyxbtoxatWqRkpLC999/T+XKlenevTvHjh0rsu+v0+mYtUu/Vql3Y09sLUyL7HsZCymWhPFKSQFra/3xcLuTR8cUlJKRgvUka6wnWedqd/LomBD5UalUDGmtvw2y6nAksTK7VCqYmZnRs2dPTp06xd9//82rr76KVqvl119/pWHDhrRo0YJNmzah1WoL9fvuDI3lXHQi1mYa+jT1KtRrGyujKZbi4uIICgrC1tYWe3t7+vbtm3NPNy8tW7ZEpVLlOj766KNc50RGRtKxY0esrKxwdHTk888/JysrqyhfiihMqan642ljCkrNTCU1M/WpY0Lkp2mVCtR3tyc9S8tPe64oHUcUI5VKRZs2bdi2bRvBwcH07NkTExMT9u7dy+uvv07NmjVZuHAh9+69eBGt0+mYdf8TcO8FemJvZfbC1ywJjKZYCgoK4uzZs2zfvp1Nmzaxd+9e+vfv/9Tn9evXj+jo6Jxj6tSpOY9lZ2fTsWNHMjIyOHDgAMuWLWPp0qWMHTu2KF+KEEI8M5VKxeDW1QBYeTiC2CSZXSqN6tSpw7Jly7h69SojRozA1taW8+fP069fPzw8PPjmm2+4c+fOc19/76XbnIqKx8JUzQfNZFbpAaMolkJDQ9m6dSsLFy4kICCApk2bMmvWLFavXs2NGzfyfa6VlRXOzs45x8Or3bdt28a5c+dYsWIFdevWpX379kyYMIHZs2cbXe8eIUTJ17xqBeq66WeX5svsUqlWqVIlvv32W6KiopgxYwZubm7Exsby5Zdf4ubmxsCBA7l8+dnWt+l0Or7bfhGAoAAPKpQxL4roRskoiqWDBw9ib2+Pv79/zljr1q1Rq9UcPnw43+euXLmSChUq4Ofnx+jRo0l96PbMwYMHqVWrFk5OTjljbdu2JTExkbNnz+Z5zfT0dBITE3MdQghR1PSzS/q1SysOR3ArSbk9eIRhsLW15bPPPuPy5cusXLmSevXqkZaWxuzZs6latSpvvvkmhw4dKtC1/rl4i+D7s0oftahcxMmNi1EUSzExMTg6OuYaMzExoVy5csTExOT5vHfffZcVK1awe/duRo8ezfLly+nRo0eu6z5cKAE5v8/vupMnT8bOzi7ncHNze56XJYQQz6xlNQfqVLLjXqaWBftkdknomZqa8u6773L8+HF27dpF+/bt0el0rF27lsDAQJo2bcr69evJzs5+4vMfnlXqGeiJg43MKj1M0WJp1KhRjy3AfvQ4f/78c1+/f//+tG3bllq1ahEUFMTPP//MunXrnnlq8lGjR48mISEh5zDkJohCiJLl4dml5QcjuJ0ss0vif1QqFS+//DKbN2/mzJkz9O7dG1NTU/bv388bb7xBjRo1mDt3bq67LAC7zsdy+loClqYa+jf3Vii94VK0WBo2bBihoaH5Ht7e3jg7OxMbG5vruVlZWcTFxeHs7Fzg7xcQEABAWJh+pb+zszM3b97Mdc6D3+d3XXNzc2xtbXMdQgFqNbRooT/U6rzHFKRWqWnh0YIWHi1Qq9R5jgnxLF6u7kjtSnakZWYzT/ZdEnmoWbMmixcvJiIigjFjxlC2bFkuXbrEJ598goeHB+PGjSM2NlY/q7RDP6vUq7GnrFV6AqNodxIaGoqvry/Hjh2jQYMGgH5xdrt27bh27Rqurq4Fus7+/ftp2rQpp06donbt2mzZsoXXXnuN6OjonNt88+fP5/PPPyc2NhZz84L9gZF2J0KI4rb7Qiy9lxzF3ETNvhEv42hroXQkYeCSk5NZvHgx3333Xc5O4Obm5rTq9BbBdk2xd/Fg38hXKGdderYLKFHtTmrUqEG7du3o168fR44cYf/+/QwcOJDu3bvnFErXr1/Hx8eHI0eOAHD58mUmTJjA8ePHCQ8PZ8OGDfTs2ZPmzZtTu3ZtANq0aYOvry/vvfdezqZfX3zxBQMGDChwoSSEEEpoWc2BBh5lSc/SMvv+vjhC5KdMmTJ8+umnXLp0iTVr1tCwYUPS09PZ/NsKbiz8GNWO6Zw7eQQjmEMpdkZRLIH+U20+Pj60atWKDh060LRpU+bPn5/zeGZmJhcuXMi5D2tmZsaOHTto06YNPj4+DBs2jK5du7Jx48ac52g0GjZt2oRGoyEwMJAePXrQs2dPvv7662J/fUII8SxUKhXD2uj3XVp1JJJrd2WTU1EwJiYmvPXWWxw+fJipS/7AskoAoOPswZ00a9aMwMBAfv/99zwXg5dGRnEbztDJbTiFpKSAp6f+6/Dw/7U4eXRMQSkZKXh+76mPMzgcazPrJ44J8bzeXXCIA5fv0L2hG1O61lY6jjAiWq2ODj/s43xMEm9XVRN/ZD3Lli0jPV3/oQFvb28+++wzevfujbXCf5cWlRJ1G06IPN2+rT+eNqag26m3uZ16+6ljQjyPB7NLvx2/Rvht6TUoCm7DqRucj0nCxsKEMe+0Zt68eURGRjJ27FjKly/PlStXGDRoEG5ubnzxxRf5bqlT0kmxJIQQRqyBRzleru5AtlbH9zsvKR1HGInMbC0z7u+r9FGLythZmQLg6OjI+PHjiYyMZM6cOVSpUoW7d+8yceJEPDw8+OCDDzh37pyS0RUhxZIQQhi5oa9WB2B98HUu3UxSOI0wBr8ejSIyLpUKZczp3cTzscetrKz4+OOPOX/+PH/88QeBgYFkZGSwaNEiatasSceOHdm9e3epWQwuxZIQQhi5WpXsaFfTGZ2OnNkCIfKSlpHND/dnIQe9UgUrM5M8z9VoNLzxxhscOHAgZ2NLlUrF5s2beeWVV/D39+eXX34hMzOzuOIrQoolIYQoAT57tRoqFWw5E8OpqHil4wgDtuxgOLFJ6VQqa8k7jdwL/LzGjRvzxx9/cPHiRT7++GMsLS05ceIE7777LlWqVOG7774jKalkzmxKsSSEECVAdWcb3qhXEYApW86Xmtsj4tkkpGUy9x/9ru+fta6GmcmzlwFVqlRhzpw5REZG8vXXX+Pg4EBkZCRDhw7Fzc2NkSNHcv369cKOrigploTxUqvB319/PNzu5NExBalVavxd/fF39c/V7uTRMSEKw9BXq2GmUXPwyh32XpJPW4rHLdh7hYS0TKo5laHL/eL6eVWoUIEvv/ySiIgI5s2bR/Xq1UlISGDq1Kl4enrSs2dPTp8+XUjJlSX7LBUC2WdJCGEovtl0joX/XqWGiy1/DWqKWq1SOpIwELeS0mkxbTepGdnMe68BbWsWvLdqQWi1Wv766y+mT5/O3r17c8ZfffVVhg8fzquvvopKZVh/HmWfJSGEKIUGvFwFG3MTQqMT2XDqhtJxhAGZtesSqRnZ1HGzp42vU6FfX61W8/rrr7Nnzx4OHz5Mt27dUKvVbN++nbZt21K3bl2WL19ORkZGoX/voibFkhBClCBlrc34qGVlAKZvu0B6lrSsEHD5VjKrDkcCMKqdT5HP8DRq1Ihff/2VsLAwBg8ejLW1NadPn6Znz554e3szbdo0EhISijRDYZJiSRiv1FR9axNPT/3XeY0pKDUzFc+ZnnjO9CQ1MzXPMSEKU58mXjjamHPtbhorD0UqHUcYgG+3nCdLq6OVjyOBlcsX2/f18vJi5syZREVFMXnyZFxcXLh+/TojRozAzc2NYcOGERlp+H9GpVgSxkung4gI/fFg6d2TxhSk0+mISIggIiEi59NJTxoTojBZmmn47FV9G5Qfd4eRdK9k74Ej8nfkahzbzt1ErYJR7X0UyVC2bFlGjRrF1atXWbJkCTVr1iQpKYkZM2bg7e1NUFAQJ06cUCRbQUixJIQQJdBbDSrh7WBNXEoGP+25rHQcoRCdTsekzaEAdG/kTlUnG0XzmJub8/777xMSEsKWLVto1aoV2dnZrFq1igYNGvDKK6+wefNmtFqtojkfJcWSEEKUQCYaNaPa6WcRFu67yvX4NIUTCSX8FRJNcFQ8VmYahrSuqnScHCqVinbt2rFjxw5OnDhBUFAQGo2G3bt307FjR2rVqsWSJUtIT09XOiogxZIQQpRYr/o68ZJ3OdKztHy75bzScUQxS8/K5tut+v/uHzavjKONhcKJnqxevXqsWLGCK1euMGzYMGxsbDh37hx9+vTB09OTyZMnExcXp2hGKZaEEKKEUqlUfPmaLyoVbDh1gxORd5WOJIrR8oMRRMWl4WhjTr/mXkrHeSp3d3emT59OZGQkU6dOpWLFisTExDBmzBjc3NzYtWuXYtmkWBJCiBKspqsdbzWoBMDXG8/JhwpKibspGczaFQbod3bPr1muobG3t+fzzz/nypUrLF++nDp16mBiYoK/v79imYzn3RPiUSoV+Pr+7+u8xhSkUqnwdfDN+TqvMSGK0vA21dl0Wr92ZcOpG3Su+2JtLoThm7H9Iglpmfg42/CWv5vScZ6LmZkZPXr0ICgoiMuXLyvaIUPanRQCaXcihDB0P+66xPRtF3G1s2DnsJZYmmmUjiSKSGh0Ih1/2IdWB6v6BdC4cgWlIxksaXcihBAixwfNvKlob8mNhHss3HdF6TiiiOh0OsZvPItWBx1qOUuhVEikWBJCiFLAwlTDiHbVAZi75zLRCbKVQEm05UwMh67EYW6iZkyHGkrHKTGkWBLGKzUVatbUHw+3O3l0TEGpmanUnFOTmnNq5mp38uiYEMWhUx1XGniUJTUjm4l/hSodRxSye5n/++/6YYvKVCprpXCikkOKJWG8dDo4d05/PNzu5NExBel0Os7dOse5W+dytTt5dEyI4qBSqfi6c03UKth0OpoDYbeVjiQK0bw9V7gen4arnQUft6isdJwSRYolIYQoRWq62vHeSx4AjN1wlowsw2orIZ7P9fg05u7RbxUwukMNWcBfyKRYEkKIUmZom+qUtzYjLDaZpQeuKh1HFIKJf53jXqaWRl7leK22i9JxShwploQQopSxszRl5P3u8zN3XCIm4Z7CicSL2H0+ls0hMWjUKsZ3qin7txUBKZaEEKIUerN+Jeq72+sXe2+Wxd7GKi0jmy//PANA36Ze1HCRvf6KghRLQghRCqnVKr7u7IdaBRtP3ZDF3kZq1q5LXLurX9Q9uFVVpeOUWFIsCeOlUoGHh/54uN3Jo2MKUqlUeNh54GHnkavdyaNjQijBr6IdPe4v9v5i/RnuZWYrnEg8i4s3k5i/V7/B6PjOflibSwezoiLtTgqBtDsRQhirhLRMXp2xh9ikdAa+XIXhbasrHUkUgFar4+35BzkafpdXfZ1Y0FO5JrPGrMS1O4mLiyMoKAhbW1vs7e3p27cvycnJeZ4fHh6OSqV64vHbb7/lnPekx1evXl0cL0kIIRRnZ2nK151rAvDTnsuERicqnEgUxO/Hr3E0/C5WZhq+6lRT6TglntEUS0FBQZw9e5bt27ezadMm9u7dS//+/fM8383Njejo6FzH+PHjKVOmDO3bt8917pIlS3Kd16VLlyJ+NUIIYTja+bnQtqYTWVodo9aeJlsrNxwM2e3kdCZt0S/K/6x1NSraWyqcqOQzihucoaGhbN26laNHj+Lvr59qnDVrFh06dGD69Om4uro+9hyNRoOzs3OusXXr1tGtWzfKlCmTa9ze3v6xc4URSEuD5s31X+/dC5aWTx5TUFpmGs2X6vPsfX8vlqaWTxwTQmlfd/bjQNgdTl1LYNmBcPo09VI6ksjDuD/PEp+aiY+zDe838VQ6TqlgFDNLBw8exN7ePqdQAmjdujVqtZrDhw8X6BrHjx8nODiYvn37PvbYgAEDqFChAo0aNWLx4sXSgsJYaLVw7Jj+0GrzHlOQVqfl2I1jHLtxDK1Om+eYEEpzsrVg9P3Gq9O3XeDaXelbaIg2h0TzV0g0GrWK6W/VwVRjFD/GjZ5RvMsxMTE4OjrmGjMxMaFcuXLExMQU6BqLFi2iRo0aNG7cONf4119/zZo1a9i+fTtdu3blk08+YdasWfleKz09ncTExFyHEEIYu+4N3WjkWY7UjGz+b90Z+YejgYlLyeDL9fo9lT5pWRm/inYKJyo9FC2WRo0aleci7AfH+fPnX/j7pKWlsWrVqifOKn355Zc0adKEevXqMXLkSEaMGMG0adPyvd7kyZOxs7PLOdzc3F44oxBCKE2tVjG5ay3MNGr2XLzFb8evKR1JPGTchrPcScmgupMNA1+ponScUkXRYmnYsGGEhobme3h7e+Ps7ExsbGyu52ZlZREXF1egtUa///47qamp9OzZ86nnBgQEcO3aNdLT0/M8Z/To0SQkJOQcUVFRT3+xQghhBCo7lGFom2oAfL3xHFFxcjvOEGw9E8PGUzfQqFVMe6s25ibSKLc4KbrA28HBAQcHh6eeFxgYSHx8PMePH6dBgwYA7Nq1C61WS0BAwFOfv2jRIjp16lSg7xUcHEzZsmUxNzfP8xxzc/N8HxdCCGPWr5k3O87d5FjEXYb/dopf+r2EWi0bqCrlbkoGX9y//fZhc29qV7JXNlApZBRrlmrUqEG7du3o168fR44cYf/+/QwcOJDu3bvnfBLu+vXr+Pj4cOTIkVzPDQsLY+/evXzwwQePXXfjxo0sXLiQM2fOEBYWxty5c5k0aRKDBg0qltclhBCGSKNW8d9udbAy03D4ahyL919VOlKppdPpGLfhLLeT06nqWIbBraWliRKMolgCWLlyJT4+PrRq1YoOHTrQtGlT5s+fn/N4ZmYmFy5cIDU195Tx4sWLqVSpEm3atHnsmqampsyePZvAwEDq1q3LvHnzmDFjBuPGjSvy1yMKSYUK+uNpYwqqYFWBClYVnjomhCHxKG/NFx19AZj69wUu3kxSOFHptO7kdTbk3H6rI7ffFCLtTgqBtDsRQpREOp2O3kuP8s+FW9R0tWXdJ00wMzGaf2MbvYg7KXT4fh8pGdkMb1ONga/IrFJhK3HtToQQQhQvlUrF1K61sbcy5eyNRH7YeUnpSKVGZraWwauDScnIppFXOT5uKZ9+U5IUS0IIIfLkaGvBN138AJj9Txj/XrqtcKLS4YedlwiOisfWwoTv3q6LRhbYK0qKJWG80tKgZUv9kZaW95iC0jLTaLm0JS2XtiQtMy3PMSEM2Wu1XXmnkRs6HQz59SSxifeUjlSiHb5yhx93hwEw6T+1pPebATCK3nBCPJFWC3v2/O/rvMYUpNVp2ROxJ+frvMaEMHTjXq/Jych4zsckMXh1MCs+CCgdsx3abIg4AMk3oYwTeDQGddEtso5PzeCzX4PR6eCtBpV4rfbjvU9F8ZOZJSGEEE9lYarhx3frY2Wm4eCVO6Vj/dK5DTDTD5a9Bmv76n+d6acfLwJarY7Bq4O5kXAPz/JWfNWpZpF8H/HspFgSQghRIFUcyzDxDf36pR92XeJAWAlev3RuA6zpCYk3co8nRuvHi6BgmrnzEnsu3sLCVM2coAZYm8vNH0MhxZIQQogCe6NeJd72169f+nR1cMlcv6TNhq0jgSftrHN/bOso/XmFZGfozZzZusn/qYWvq2xDY0ikWBJCCPFMvupUk+pONtxOTqf/8uPcyyy8osEgRBx4fEYpFx0kXtefVwjCb6cw5NdgAHoGevBGvUqFcl1ReKRYEkII8UwszTTMe68BdpamBEfFM+aPEErU/sbJNwv3vHykZWTz0YrjJN3Lor67fc6u6cKwSLEkjJuVlf542piCrEytsDK1euqYEMbEs4I1c4Lqo1Gr+OPkdRbsu6J0pMJTxqlwz8uDTqdj5NrTnI9JokIZM+YENZAd0g2UtDspBNLuRAhRWi07EM64DWdRqWBxr4a87OOodKQXp83Wf+otMZonr1tSga0rDAl5oW0E/rvtArN2hWGiVrHigwBe8i7/3NcSz0fanQghhChyPQM9cjas/PSXk4TFloCGu2oNtPv2/m8e3Uvq/u/bTXmhQmnN0Shm7bq/8eQbtaRQMnBSLAkhhHhuKpWK8Z38aORVjqT0LHovPVoyPiHn2wm6/Qy2LrnHbV31476dnvvS+y7dYsy6EAAGvVKFbg3dXiSpKAZyG64QyG04hdy7B1276r9euxYsLJ48pqB7WffoukafZ223tViYWDxxTAhjdyc5nf/MPUDEnVR8nG34tX8gdlamSsd6cYW8g/f5mETemnuQpPQsOtd1ZebbdVGpSsFO6AaqoD+/ZccrYbyys2Hz5v99ndeYgrK12Wy+tDnn67zGhDB25cuYs7xPAF1/OsD5mCT6LjvK8r4BWJoVXWuQYqHWgFezQrlUdEIavZccJSk9i0Ze5Zj6Zm0plIyE3IYTQghRKNzLW/Fzn0bYWJhwLOIuA1adIDNb+h8CxCbe490Fh4lOuIe3gzXz32uAuYmRF5KliBRLQgghCk0NF1sWv98QcxM1u87HMvL302i1pXu1x62kdN5ZcIiI20m8ZnOZ35pcx/7m4ULdAVwULbkNJ4QQolA19CzH3B716ffzcf44eR1TjZpJ/6mFRl36bjndSU4naOEhqtzZzSqL5Thl3oGt9x+0ddV/6u4FFouL4iEzS0IIIQrdKz5OzOhWB7UKfj0WxdA1waXullx8agY9Fh3B69YufjKbiSN3cp9QhE15ReGSYkkIIUSR6Fy3IrPeqY+JWsWfwTcYsPIE6Vml49ZTbJJ+jdKF6Hi+NlsOPL5jU1E15RWFT4olIYQQRaZjbRfmvadv47Ht3E36/3yctIySXRhcuZXMf+Yc4Fx0Iq2twnDizhMKpQcKtymvKBpSLAnjZW0NOp3+sLbOe0xB1mbW6Mbp0I3TYW1mneeYECVZqxpOLO7VEEtTDXsu3qLn4sPEpWQoHatInIi8S9e5B7h2Nw2P8lZMbO1QsCcWQlNeUXSkWBJCCFHkmlatwM99G2FjbsLR8Lt0+vFfzsckKh2rUG0/d5N3FxzibmomtSvZsfbjxji4eBTsyS/YlFcULSmWhBBCFIuGnuVY+0lj3MtZce1uGl3nHGDb2RilY70wnU7H/L2X+XD5Me5lanm5ugO/9HuJCmXM9Tt+27rypBVLeiqwrag/TxgsKZaEEEIUm2pONvw5oAmNK5cnJSOb/suP8+OuSxhr56341Az6/XyMSZvPo9XB2/5uLOjpj7X5/Z15iqEpryh6UiwJIYQoVmWtzVjWpxG9AvW3qKZvu0jvpUe5aWQNeE9G3qXjD/+yIzQWMxM1E9/wY0rXWphoHvnRWoRNeUXxkEa6hUAa6QohxPNZdTiSrzaeJSNLi52lKRO6+NGpjqvSsfKVrdWx+N+rTP37PJnZOjzLW/Hju/Xxq2iX/xMLuSmveHEF/fktxVIhkGJJCCGe36WbSXy2Jpgz1/ULvjvWduGbzn6UtTZTONnjTkXF83/rQ/6XtZYLU7rWwsbCVOFk4nlIsVSMpFgSQogXk5mt5cddYfy4O4xsrY5y1mZ8+koV3g3wwMxE+RUjCWmZTPv7PCsPR6LTga2FCaM71KB7QzdUqtLXxqWkkGKpGEmxJIQQheP0tXiGrTnFpdhkADzKW/F52+p0rOWiSFGSlpHNmmNRzNoVxu3kdAD+U68iozvUwMHGvNjziMIlxVIxkmJJCCEKT2a2ll+PRjFzx6WcAqVOJTs+blmZVjWcMH10AXURSEjLZMWhCBb/e5U79zfQrOxgzYQufjSuXKHIv78oHgX9+a383GYBTZw4kcaNG2NlZYW9vX2BnqPT6Rg7diwuLi5YWlrSunVrLl26lOucuLg4goKCsLW1xd7enr59+5KcnFwEr0AIIURBmGrU9HjJgz2ft+Sz1tWwNtNw6loCH604QeMpu5j293mi4lIL/fvqdDpORcUzYdM5mkzZxbS/L3AnJYNKZS2Z0Lkmmwc3k0KplDKamaVx48Zhb2/PtWvXWLRoEfHx8U99zrfffsvkyZNZtmwZXl5efPnll4SEhHDu3DksLCwAaN++PdHR0cybN4/MzEx69+5Nw4YNWbVqVYGzycySEEIUnVtJ6SzZf5U1x67lzDSpVPCSV3maVatAoHd5alW0e/wj+wWQla3l1LV4NofEsPVMDNfj03Ieq+5kw8ctK/NabZfnurYwfCX2NtzSpUsZMmTIU4slnU6Hq6srw4YNY/jw4QAkJCTg5OTE0qVL6d69O6Ghofj6+nL06FH8/f0B2Lp1Kx06dODatWu4uhbs46tSLAkhRNHLyNKyI/QmvxyJZN+l27keK2NuQkPPslRzssHZzgIXOwuc7Swpa2VKepaWtIxs7mVmk5qZzdVbKYRGJxIak8jFm8lkZGlzrmNlpuEVH0feqFeRl6s7olbL4u2SrKA/v02KMVOxunr1KjExMbRu3TpnzM7OjoCAAA4ePEj37t05ePAg9vb2OYUSQOvWrVGr1Rw+fJg33njjiddOT08nPT095/eJiSWrv5EQQhgiMxM1HWq50KGWC5F3Utl5/iYHL9/h0JU7JN7LYveFW+y+cOuZr2tjbkKrGo60r+VCi2oOWJjK3kcitxJbLMXE6PsNOTnlbk7o5OSU81hMTAyOjo65HjcxMaFcuXI55zzJ5MmTGT9+fCEnFkIIUVDu5a3o3cSL3k28yNbqCI1O5MjVOK7dTSMmMY3ohHvEJNwjIS0TC1MNFiZqLMw0WJhoqFjWkhoutvi62FDDxRa3slYygyTypWixNGrUKL799tt8zwkNDcXHx6eYEhXM6NGjGTp0aM7vExMTcXNzUzCREEKUXhq1Cr+Kdk/fQVuI56RosTRs2DDef//9fM/x9vZ+rms7OzsDcPPmTVxc/teP5+bNm9StWzfnnNjY2FzPy8rKIi4uLuf5T2Jubo65ueyvIYQQQpQGihZLDg4OODg4FMm1vby8cHZ2ZufOnTnFUWJiIocPH+bjjz8GIDAwkPj4eI4fP06DBg0A2LVrF1qtloCAgCLJJYQQQgjjYjSfhYyMjCQ4OJjIyEiys7MJDg4mODg4155IPj4+rFu3DgCVSsWQIUP45ptv2LBhAyEhIfTs2RNXV1e6dOkCQI0aNWjXrh39+vXjyJEj7N+/n4EDB9K9e/cCfxJOCCGEECWb0SzwHjt2LMuWLcv5fb169QDYvXs3LVu2BODChQskJCTknDNixAhSUlLo378/8fHxNG3alK1bt+bssQSwcuVKBg4cSKtWrVCr1XTt2pUffviheF6UEEIIIQye0e2zZIhknyUhhBDC+JS4didCCCGEEEqQYkkIIYQQIh9SLAkhhBBC5EOKJSGEEEKIfEixJIQQQgiRDymWhBBCCCHyIcWSEEIIIUQ+pFgSQgghhMiHFEtCCCGEEPkwmnYnhuzBJuiJiYkKJxFCCCFEQT34uf20ZiZSLBWCpKQkANzc3BROIoQQQohnlZSUhJ2dXZ6PS2+4QqDVarlx4wY2NjaoVKpCu25iYiJubm5ERUVJz7knkPcnf/L+5E3em/zJ+5M/eX/yZ0zvj06nIykpCVdXV9TqvFcmycxSIVCr1VSqVKnIrm9ra2vwf+CUJO9P/uT9yZu8N/mT9yd/8v7kz1jen/xmlB6QBd5CCCGEEPmQYkkIIYQQIh9SLBkwc3Nzxo0bh7m5udJRDJK8P/mT9ydv8t7kT96f/Mn7k7+S+P7IAm8hhBBCiHzIzJIQQgghRD6kWBJCCCGEyIcUS0IIIYQQ+ZBiSQghhBAiH1IsGbDZs2fj6emJhYUFAQEBHDlyROlIBmHv3r28/vrruLq6olKpWL9+vdKRDMbkyZNp2LAhNjY2ODo60qVLFy5cuKB0LIMxd+5cateunbNZXmBgIFu2bFE6lsGaMmUKKpWKIUOGKB3FIHz11VeoVKpch4+Pj9KxDMb169fp0aMH5cuXx9LSklq1anHs2DGlYxUKKZYM1K+//srQoUMZN24cJ06coE6dOrRt25bY2FiloykuJSWFOnXqMHv2bKWjGJw9e/YwYMAADh06xPbt28nMzKRNmzakpKQoHc0gVKpUiSlTpnD8+HGOHTvGK6+8QufOnTl79qzS0QzO0aNHmTdvHrVr11Y6ikGpWbMm0dHROce///6rdCSDcPfuXZo0aYKpqSlbtmzh3Llz/Pe//6Vs2bJKRysUsnWAgQoICKBhw4b8+OOPgL7/nJubG4MGDWLUqFEKpzMcKpWKdevW0aVLF6WjGKRbt27h6OjInj17aN68udJxDFK5cuWYNm0affv2VTqKwUhOTqZ+/frMmTOHb775hrp16zJz5kylYynuq6++Yv369QQHBysdxeCMGjWK/fv3s2/fPqWjFAmZWTJAGRkZHD9+nNatW+eMqdVqWrduzcGDBxVMJoxNQkICoC8IRG7Z2dmsXr2alJQUAgMDlY5jUAYMGEDHjh1z/R0k9C5duoSrqyve3t4EBQURGRmpdCSDsGHDBvz9/XnrrbdwdHSkXr16LFiwQOlYhUaKJQN0+/ZtsrOzcXJyyjXu5ORETEyMQqmEsdFqtQwZMoQmTZrg5+endByDERISQpkyZTA3N+ejjz5i3bp1+Pr6Kh3LYKxevZoTJ04wefJkpaMYnICAAJYuXcrWrVuZO3cuV69epVmzZiQlJSkdTXFXrlxh7ty5VK1alb///puPP/6YTz/9lGXLlikdrVCYKB1ACFE0BgwYwJkzZ2RNxSOqV69OcHAwCQkJ/P777/Tq1Ys9e/ZIwQRERUUxePBgtm/fjoWFhdJxDE779u1zvq5duzYBAQF4eHiwZs2aUn8bV6vV4u/vz6RJkwCoV68eZ86c4aeffqJXr14Kp3txMrNkgCpUqIBGo+HmzZu5xm/evImzs7NCqYQxGThwIJs2bWL37t1UqlRJ6TgGxczMjCpVqtCgQQMmT55MnTp1+P7775WOZRCOHz9ObGws9evXx8TEBBMTE/bs2cMPP/yAiYkJ2dnZSkc0KPb29lSrVo2wsDCloyjOxcXlsX9w1KhRo8TcppRiyQCZmZnRoEEDdu7cmTOm1WrZuXOnrK0Q+dLpdAwcOJB169axa9cuvLy8lI5k8LRaLenp6UrHMAitWrUiJCSE4ODgnMPf35+goCCCg4PRaDRKRzQoycnJXL58GRcXF6WjKK5JkyaPbVNy8eJFPDw8FEpUuOQ2nIEaOnQovXr1wt/fn0aNGjFz5kxSUlLo3bu30tEUl5ycnOtfclevXiU4OJhy5crh7u6uYDLlDRgwgFWrVvHnn39iY2OTs8bNzs4OS0tLhdMpb/To0bRv3x53d3eSkpJYtWoV//zzD3///bfS0QyCjY3NY+vbrK2tKV++vKx7A4YPH87rr7+Oh4cHN27cYNy4cWg0Gt555x2loynus88+o3HjxkyaNIlu3bpx5MgR5s+fz/z585WOVjh0wmDNmjVL5+7urjMzM9M1atRId+jQIaUjGYTdu3frgMeOXr16KR1NcU96XwDdkiVLlI5mEPr06aPz8PDQmZmZ6RwcHHStWrXSbdu2TelYBq1Fixa6wYMHKx3DILz99ts6FxcXnZmZma5ixYq6t99+WxcWFqZ0LIOxceNGnZ+fn87c3Fzn4+Ojmz9/vtKRCo3ssySEEEIIkQ9ZsySEEEIIkQ8ploQQQggh8iHFkhBCCCFEPqRYEkIIIYTIhxRLQgghhBD5kGJJCCGEECIfUiwJIYQQQuRDiiUhhBBCiHxIsSSEEEIIkQ8ploQQQggh8iHFkhBCPOLWrVs4OzszadKknLEDBw5gZmbGzp07FUwmhFCC9IYTQogn2Lx5M126dOHAgQNUr16dunXr0rlzZ2bMmKF0NCFEMZNiSQgh8jBgwAB27NiBv78/ISEhHD16FHNzc6VjCSGKmRRLQgiRh7S0NPz8/IiKiuL48ePUqlVL6UhCCAXImiUhhMjD5cuXuXHjBlqtlvDwcKXjCCEUIjNLQgjxBBkZGTRq1Ii6detSvXp1Zs6cSUhICI6OjkpHE0IUMymWhBDiCT7//HN+//13Tp06RZkyZWjRogV2dnZs2rRJ6WhCiGImt+GEEOIR//zzDzNnzmT58uXY2tqiVqtZvnw5+/btY+7cuUrHE0IUM5lZEkIIIYTIh8wsCSGEEELkQ4olIYQQQoh8SLEkhBBCCJEPKZaEEEIIIfIhxZIQQgghRD6kWBJCCCGEyIcUS0IIIYQQ+ZBiSQghhBAiH1IsCSGEEELkQ4olIYQQQoh8SLEkhBBCCJEPKZaEEEIIIfLx//6uLIHCz9U1AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot model predictions against ground-truth\n", "import matplotlib.pyplot as plt\n", diff --git a/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb b/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb index beefa85fc..4de2d769f 100644 --- a/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb +++ b/docs/tutorials/basic/Tutorial-II-Loop-Constructs.ipynb @@ -68,7 +68,7 @@ "import numpy as np\n", "import torch\n", "from autora.variable import Variable, ValueType, VariableCollection\n", - "from autora.experimentalist.pooler.random_pooler import random_pool\n", + "from autora.experimentalist.random import random_pool\n", "from autora.experimentalist.sampler.falsification import falsification_sample\n", "from autora.experimentalist.sampler.model_disagreement import model_disagreement_sample\n", "from autora.theorist.bms import BMSRegressor\n", @@ -84,12 +84,11 @@ "\n", "#### Define condition pool ####\n", "condition_pool = np.linspace(0, 2 * np.pi, 100)\n", - "condition_pool = condition_pool.reshape((len(condition_pool), 1))\n", "\n", - "#### Define metadata ####\n", + "#### Define variables ####\n", "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=condition_pool)\n", "dv = Variable(name=\"y\", type=ValueType.REAL)\n", - "metadata = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", + "variables = VariableCollection(independent_variables=[iv],dependent_variables=[dv])\n", "\n", "#### Define theorists ####\n", "theorist_lr = linear_model.LinearRegression()\n", @@ -106,7 +105,7 @@ "\n", "The following code block demonstrates how to build such a workflow using the components introduced in the preceding notebook, such as\n", "\n", - "- ``metadata`` (object specifying variables of the experiment)
\n", + "- ``variables`` (object specifying variables of the experiment)
\n", "- ``run_experiment`` (function for collecting data)
\n", "- ``theorist_bms`` (scikit learn estimator for discovering equations using the Bayesian Machine Scientist)
\n", "- ``random_pool`` (function for generating a random pool of experimental conditions)
\n", @@ -142,7 +141,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:03<00:00, 27.81it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 23.40it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n", "INFO:autora.theorist.bms.regressor:BMS fitting started\n" @@ -152,15 +151,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loss in cycle 0: 0.0\n", - "Discovered Model: sin(X0)\n" + "[[0. ]\n", + " [0.06346652]\n", + " [0.12693304]]\n", + "Loss in cycle 0: 0.4950015317483731\n", + "Discovered Model: -0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:03<00:00, 27.69it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 24.71it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n", "INFO:autora.theorist.bms.regressor:BMS fitting started\n" @@ -170,7 +172,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loss in cycle 1: 0.0\n", + "[[6.28318531]\n", + " [6.21971879]\n", + " [6.15625227]]\n", + "Loss in cycle 1: 0.99\n", "Discovered Model: sin(X0)\n" ] }, @@ -178,7 +183,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 24.21it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 23.75it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n", "INFO:autora.theorist.bms.regressor:BMS fitting started\n" @@ -188,7 +193,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loss in cycle 2: 0.0\n", + "[[5.07732146]\n", + " [5.01385494]\n", + " [5.14078798]]\n", + "Loss in cycle 2: 0.99\n", "Discovered Model: sin(X0)\n" ] }, @@ -196,7 +204,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:03<00:00, 26.50it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 24.73it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n", "INFO:autora.theorist.bms.regressor:BMS fitting started\n" @@ -206,7 +214,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loss in cycle 3: 0.0\n", + "[[0. ]\n", + " [0.06346652]\n", + " [0.12693304]]\n", + "Loss in cycle 3: 0.99\n", "Discovered Model: sin(X0)\n" ] }, @@ -214,7 +225,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:03<00:00, 26.33it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 25.01it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", "WARNING:autora.utils.deprecation:Use `falsification_score_sample_from_predictions` instead. `falsification_score_sampler_from_predictions` is deprecated.\n" ] @@ -223,7 +234,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loss in cycle 4: 0.0\n", + "[[0.38079911]\n", + " [0.44426563]\n", + " [0.31733259]]\n", + "Loss in cycle 4: 0.99\n", "Discovered Model: sin(X0)\n" ] } @@ -233,11 +247,11 @@ "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", "\n", "# generate an initial set of experimental conditions\n", - "conditions = random_pool(metadata.independent_variables,\n", - " num_samples=measurements_per_cycle)\n", + "conditions = random_pool(variables=variables,\n", + " num_samples=measurements_per_cycle)\n", "\n", "# convert iterator into 2-dimensional numpy array\n", - "conditions = np.array(list(conditions)).reshape(-1, 1)\n", + "conditions = np.array(list(conditions.values)).reshape(-1, 1)\n", "\n", "# collect initial set of observations\n", "observations = run_experiment(conditions)\n", @@ -253,11 +267,12 @@ " model=theorist_bms,\n", " reference_conditions=conditions,\n", " reference_observations=observations,\n", - " metadata=metadata,\n", + " metadata=variables,\n", " num_samples=measurements_per_cycle,\n", " )\n", "\n", " # obtain new observations\n", + " print(new_conditions)\n", " new_observations = run_experiment(new_conditions)\n", "\n", " # combine old and new conditions and observations\n", @@ -265,7 +280,7 @@ " observations = np.concatenate((observations, new_observations))\n", "\n", " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", - " loss = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", + " loss = np.mean(np.square(theorist_bms.predict(condition_pool.reshape(-1,1)) - ground_truth(condition_pool)))\n", " print(\"Loss in cycle {}: {}\".format(cycle, loss))\n", " print(\"Discovered Model: \" + theorist_bms.repr())\n" ] @@ -291,84 +306,26 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 23.84it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 0: 0.0\n", - "Discovered BMS Model: sin(X0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:03<00:00, 27.23it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 1: 0.0\n", - "Discovered BMS Model: sin(X0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:04<00:00, 23.79it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 2: 0.0\n", - "Discovered BMS Model: sin(X0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:03<00:00, 26.81it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n", - "INFO:autora.theorist.bms.regressor:BMS fitting started\n" + "100%|██████████| 100/100 [00:03<00:00, 25.54it/s]\n", + "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Loss in cycle 3: 0.49765210053720216\n", - "Discovered BMS Model: -0.05\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:03<00:00, 26.17it/s]\n", - "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" + "[0. 0.06346652 0.12693304]\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loss in cycle 4: 0.0009571292126012284\n", - "Discovered BMS Model: (0.96 * sin(X0))\n" + "ename": "ValueError", + "evalue": "all the input arrays must have same number of dimensions, but the array at index 0 has 2 dimension(s) and the array at index 1 has 1 dimension(s)", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[27], line 33\u001b[0m\n\u001b[0;32m 31\u001b[0m \u001b[39m# combine old and new conditions and observations\u001b[39;00m\n\u001b[0;32m 32\u001b[0m conditions \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mconcatenate((conditions, new_conditions\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m,\u001b[39m1\u001b[39m)))\n\u001b[1;32m---> 33\u001b[0m observations \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mconcatenate((observations, new_observations))\n\u001b[0;32m 35\u001b[0m \u001b[39m# evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\u001b[39;00m\n\u001b[0;32m 36\u001b[0m loss \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mmean(np\u001b[39m.\u001b[39msquare(theorist_bms\u001b[39m.\u001b[39mpredict(condition_pool\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m,\u001b[39m1\u001b[39m)) \u001b[39m-\u001b[39m ground_truth(condition_pool)))\n", + "\u001b[1;31mValueError\u001b[0m: all the input arrays must have same number of dimensions, but the array at index 0 has 2 dimension(s) and the array at index 1 has 1 dimension(s)" ] } ], @@ -377,11 +334,11 @@ "measurements_per_cycle = 3 # number of data points to collect for each cycle\n", "\n", "# generate an initial set of experimental conditions\n", - "conditions = random_pool(metadata.independent_variables,\n", - " num_samples=measurements_per_cycle)\n", + "conditions = random_pool(variables=variables,\n", + " num_samples=measurements_per_cycle)\n", "\n", "# convert iterator into 2-dimensional numpy array\n", - "conditions = np.array(list(conditions)).reshape(-1, 1)\n", + "conditions = np.array(list(conditions.values)).reshape(-1, 1)\n", "\n", "# collect initial set of observations\n", "observations = run_experiment(conditions)\n", @@ -400,14 +357,15 @@ " )\n", "\n", " # obtain new observations\n", + " print(new_conditions)\n", " new_observations = run_experiment(new_conditions)\n", "\n", " # combine old and new conditions and observations\n", - " conditions = np.concatenate((conditions, new_conditions))\n", - " observations = np.concatenate((observations, new_observations))\n", + " conditions = np.concatenate((conditions, new_conditions.reshape(-1,1)))\n", + " observations = np.concatenate((observations, new_observations.reshape(-1,1)))\n", "\n", " # evaluate model of the theorist based on its ability to predict each observation from the ground truth, evaluated across the entire space of experimental conditions\n", - " loss = np.mean(np.square(theorist_bms.predict(condition_pool) - ground_truth(condition_pool)))\n", + " loss = np.mean(np.square(theorist_bms.predict(condition_pool.reshape(-1,1)) - ground_truth(condition_pool)))\n", " print(\"Loss in cycle {}: {}\".format(cycle, loss))\n", " print(\"Discovered BMS Model: \" + theorist_bms.repr())\n" ] From a75f292348ca1fe7db362ddf05ba35db8322ac69 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Fri, 1 Sep 2023 12:50:01 -0700 Subject: [PATCH 27/32] Updated to work with latest autora --- .../basic/Tutorial-III-Functional-Workflow.ipynb | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb index 405fd86ad..b392084bb 100644 --- a/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb +++ b/docs/tutorials/basic/Tutorial-III-Functional-Workflow.ipynb @@ -44,11 +44,12 @@ "metadata": {}, "outputs": [ { - "ename": "SyntaxError", - "evalue": "invalid syntax (1057721313.py, line 16)", - "output_type": "error", - "traceback": [ - "\u001b[1;36m Cell \u001b[1;32mIn[1], line 16\u001b[1;36m\u001b[0m\n\u001b[1;33m def plot_from_state(s,'sin(x)'):\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 23.2 -> 23.2.1\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n" ] } ], @@ -367,7 +368,7 @@ "print(\"\\033[1mPrevious Model:\\033[0m\")\n", "print(f\"{s.model}\\n\")\n", "\n", - "s = theorist(s)\n", + "s = theorist(s, seed=42)\n", "\n", "print(\"\\n\\033[1mUpdated Model:\\033[0m\")\n", "print(s.model)\n", From 1d8eb45fc1c7bbca0ef65a8d97a1dc7253cda77f Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Fri, 1 Sep 2023 13:03:19 -0700 Subject: [PATCH 28/32] Updated tutorial 4 to work with latest autora version --- .../basic/Tutorial-IV-Customization.ipynb | 192 +++++++++++++----- 1 file changed, 145 insertions(+), 47 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-IV-Customization.ipynb b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb index 65a0194ee..bc9bf35df 100644 --- a/docs/tutorials/basic/Tutorial-IV-Customization.ipynb +++ b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb @@ -65,10 +65,8 @@ "import torch\n", "\n", "from autora.variable import Variable, ValueType, VariableCollection\n", - "from autora.state.bundled import StandardState\n", - "from autora.state.delta import on_state\n", - "from autora.state.wrapper import state_fn_from_estimator\n", - "from autora.experimentalist.random_ import random_pool\n", + "from autora.state import StandardState, on_state, estimator_on_state\n", + "from autora.experimentalist.random import random_pool\n", "from autora.theorist.bms import BMSRegressor\n", "from autora.experiment_runner.synthetic.abstract.equation import equation_experiment\n", "\n", @@ -84,7 +82,7 @@ " \"\"\"\n", " \n", " #Determine labels and variables\n", - " model_label = f\"Model: {s.model.repr()}\" if s.model.repr() else \"Model\"\n", + " model_label = f\"Model: {s.model.repr()}\" if hasattr(s.model,'.repr') else \"Model\"\n", " experiment_data = s.experiment_data.sort_values(by=[\"x\"])\n", " ground_x = np.linspace(s.variables.independent_variables[0].value_range[0],s.variables.independent_variables[0].value_range[1],100)\n", " \n", @@ -154,7 +152,7 @@ "experiment_runner = on_state(sin_runner, output=[\"experiment_data\"])\n", "\n", "#### Define theorist and wrap with state functionality ####\n", - "theorist = state_fn_from_estimator(BMSRegressor(epochs=100))" + "theorist = estimator_on_state(BMSRegressor(epochs=100))" ] }, { @@ -205,13 +203,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 24.37it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 24.52it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -224,13 +222,13 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:03<00:00, 26.21it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 23.17it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -387,13 +385,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 23.04it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 26.22it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -406,13 +404,13 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:03<00:00, 27.31it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 25.54it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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fZ/jPP//g4+OTb4bdAwcOYGtry86dO/X7IiIi2Lx5s35lvbJgUY3Ue/fuZc6cORw7doyEhAQ2btxIjx49ijwnKiqKsWPHcvr0afz9/Xn77bcLrL8rKhdrdd7vohxtNu8eG1HocXbWatRlNEp7ZZeV2Bux7OjNmzf1JYcqVaoYPObBqphp06Yxa9YsFixYgLW1NRs3bmTUqFEsWLCA8PBwtm7dyqBBg6hevbr+y9RYb731FvPmzcPT05Nhw4YxePBg/eJE3377LdOmTWPRokW0bduW1atXs3DhQmrVqmXSPe63cuVKhg8fnm8BpKL4+/uzYcMGevbsyblz53BxccHB4X+rBq5cuZKxY8dy6NAhoqOjGThwIG3atOHJJ58E8tbAvnjxoj4xPmjDhg3Mnz+fdevW0ahRIxITEzlx4kSRMU2fPp3Zs2czZ84cPv74Y/r27culS5dwd3cnOTmZ2NhYQkJC9Md7enryxRdf0KNHDzp16kT9+vV58cUXGTlyJB07dtQfFxISQm5uLocOHaJ9+/ZGvT+msqgSREZGBs2aNTN6qcX4+HieeuopnnjiCWJiYhg9ejQvvfQSv/zySxlHKsxZFTsrbKyK/tNXqSiz5GCKCxcuoNPpqF+/fr79Hh4eODk54eTkxMSJE/M998ILLzBo0CBq1apFQEAAc+fOZeDAgbz66qvUq1ePsWPH8t///pe5c+eaHM+MGTNo164dQUFBTJo0iQMHDuhLCQsWLGDIkCEMGTKE+vXr8/777+tXsSupunXrMnv2bOrXr1/gPTDEysoKd/e8WX29vLzw8fHJN2tr06ZNmTp1KnXr1qV///6EhITk+1Xu6+tb5JKqly9fxsfHh/DwcAICAmjVqlWx60oMHDiQPn36UKdOHWbOnEl6erp+Rb/Lly+j0+kKrLPRrVs3hg4dSt++fRk2bBhVqlQpsOa3o6Mjrq6uXLp0qdj3paQsqgTRtWtXunbtavTxS5YsITAwkHnz5gF5Sy3u37+f+fPnG1yuUZSd9LQEbqdcIic3k5zcu1iprHFy8sa5ig8Ojh6o1OX3W0WlUuHnZs/FGxreaWn4x0aAuwMuZdjV1c7K7qHOP3z4MFqtlr59+5KVlZXvuft/jQKcOXOmQMNpmzZt+Oijj0y+b9OmTfX/9vXNmyAxKSmJgIAAzpw5w7Bhw/IdHxYWxu7du02+zz0tW7Ys8bmG3B8/5L2G+5cjffBL+EG9evViwYIF1KpViy5dutCtWzciIiKwti78q/T+e1apUgUXFxf9Pe9VDxlao2Tu3Lk0btyY9evXc+zYMezsCv7NODg4lGknBYtKEKaKjo4uMLVv586di+zqlpWVle8/XGpqalmFVyHptFoSE3/n7KXdnE86QVzaZf7JzeCOrvD5+h1U1gTYuRFQ5RHqeDWlab3uuLvXKdM4XR1sqekB125bmfU4iDp16qBSqQrU9d+rtrm/+uSewqqiCqP+Nznf3y5SWOO2jY2N/t/3qrbureZWFh58LabEasj98UPeazAlfn9/f86dO8eOHTvYvn07r776KnPmzGHPnj0Frm3MPT08PAC4desWnp6e+Y6Li4vj2rVraLVaLl68SJMmTQpcOzk5ucB5palCJ4jExES8vb3z7fP29iY1NZW7d+8a/M8VGRnJ9OnTyyvECuPSpX0cPLueQ0nHuZqbbvCYKiprbFVW2Kis0KAlXZtDlk7DXV0u5zJvcC7zBttvnoAzqwmwcSHUO4R2zYbg6dWoTGJ2dbDFxd7GrEdSV6tWjSeffJJPPvmE1157zeQvf8grOf/2228MGDBAv++3337TV//c+4JJSEigefPmAPkagU25z6FDh/KtS33wYPErAZrCmFhtbfOSu0ZTNr3QHBwciIiIICIighEjRtCgQQNOnjxJixYtTL5W7dq1cXFxITY2lnr16un3Z2dn069fP3r37k39+vV56aWXOHnyJF5eXvpj4uLiyMzM1L8PZaFCJ4iSmDx5MmPHjtU/Tk1Nxd/fX8GIzFdOzh0Ox3zBtj9/4HzWDf1+a9TUsa9GPbe61PNpiY9HEJ4eDbB3qFrgGtlZaVxP+oNLCce5dDOW08nn+Cs7mcs5qVz+exfr/95FYwcfujR4jpZN+qO2Kt0/WZVKhZO9ef83+PTTT2nTpg0hISFMmzaNpk2bolarOXLkCGfPni22Gmb8+PE899xzNG/enPDwcLZs2cL333+v7x7r4ODAY489xqxZswgMDCQpKYm3337b5DhHjRrFwIEDCQkJoU2bNnz11VecPn36oRqpH2RMrDVq1EClUrF161a6deuGg4MDTk5ORl1/8uTJXL16lVWrVhl8fsWKFWg0GkJDQ3F0dGTNmjU4ODiY1EX1fmq1mvDwcPbv35+vw81bb71FSkoKCxcuxMnJiZ9++onBgwezdetW/TH79u2jVq1a1K5du0T3NoZ5/894SD4+Ply/fj3fvuvXrxfo2XA/Ozs7g3V94n9yczKJOjSfDXGbSNbmNVBao6K5Uw0eq9GRFkG9cXTyKuYqeWztnPH3b4O/fxva/rsvJeUyJ85uYG/8r5y6m8Cpu4mc+n0h1U99Sfe6/0fbkBGlnijMWe3atfn999+ZOXMmkydP5u+//8bOzo6goCDeeOMNXn311SLP79GjBx999BFz585l1KhRBAYG8uWXX+br+fLFF18wZMgQWrZsSf369Zk9ezadOnUyKc7evXsTFxfHhAkTyMzMpGfPngwfPrzUO4UUF+sjjzzC9OnTmTRpEoMGDaJ///6sWLHCqGsnJCRw+fLlQp93c3Nj1qxZjB07Fo1GQ5MmTdiyZQvVqpV8CvqXXnqJoUOHMnv2bNRqNVFRUSxYsIDdu3fr14tevXo1zZo1Y/HixQwfPhyAr7/+utgG8oel0pnTcFETqFSqYru5Tpw4kZ9++omTJ0/q973wwgskJyezbds2o+5j7OLelYFOqyX62GLWnVnLdU0GAG5qW8J92/Dko6/jVjWw1O/5T9JpdhxfzC+JB7n7bztGDRtX+rcYSeOgXkZfp6gF3IVQkk6nIzQ0lDFjxtCnTx+jzjl9+jQdOnTg/Pnzha6tXdTfvLHfaxb1Myw9PZ0LFy7oH8fHxxMTE4O7uzsBAQEFiofDhg3jk08+YcKECQwePJhdu3bx7bff8uOPPyr1EixWwrVjfL73bU7ezRvd6qq25dma3Qh/bDw2dqbXixvL06sRfbp8Qvf0JH45OIctf+/mUk4K7x2aQcjpNQx+Yg7VPOoVfyEhzJRKpWLp0qX5fsgWJyEhgVWrVhWaHEqLRZUgoqKiDA7sGTBgACtWrDA4yCUqKooxY8YQGxtL9erVmTJlikkD5Sp7CUKTm83mqLf47spOctFig5oe1dvzdNspBtsUylpa6lXW75nC9hu/o0WHg8qa/vWe44nH3iiyq6yUIERlUxolCItKEEqozAki6fopPtk1mnOZeQ3QwVWqM+g/7+PjE6xsYMCVK9F8tm8Kf/7bON7M8RFGdF6Eq1tNg8dLghCVTWkkCIsaSS3KT/SxJUz4eSDnMm9gr7JiRNAAJv3fZrNIDgD+/mG823sb/Wo+hQ1qTty5ysRNvYk9+4PSoQlRYUiCEPloNbl89fNwFvyxhLu6XOrbezCn60r+EzqmXEc7G0NtZU3EEzOIDF9EdRtnbmmzeC96Oht3TkBXyOAnKTCLyqI0/tbN63+8UFR6WgIzv3uGzYnRADzj05qpvX7Cy7uxwpEVzd8/jBk9N9POrSFadKy7/CufbOpDTlaG/ph7o1ktde0EIUx172+9sBHexpA2iGJUljaIpOunmPXrMK7mpmOnsmJY4yG0Dim6f7052rE/ki/+/BYNOurZefBGt+X6domEhARu376Nl5cXjo6OZjViWojSotPpuHPnDklJSbi5uennzLqfNFKXksqQIOIvRjFrzwRua7NxV9szqf0catR4XOmwSuzk6fXMP/IBGbpcvKwcebvzEry9m6LT6UhMTFRkpTEhypubmxs+Pj4GfwhJgiglFT1BnD77PbMPziBTpyHAxoVJXZZXiHEF164dJXLH6yRp7lBVbcebT8wjICBvrLZGozHrldaEeFg2NjZYWVkV+rwkiFJSkRNEzKm1zD0ylxy0NHbwYdzTq4yeIsMS3EqOY+ZPg7ick0oVlTWT2r5HvTrGTxcvREUl3VxFkY7GrGDOv8mhpVMNJj27oUIlB4Cq7rWZ2v0b6tl5kKHLZcb+tzl/4WelwxLCYkiCqISOnVjJh79/RC5aQp0DGdPj6zKdLkNJTs6+vPXseho5+JCp0zBz/xT+vGDcPFxCVHaSICoIjVZHdNxNNsVcJTruJhqt4ZrDk6fX8+Hxj9Cgo7VLHUb1+AYbG8dyjrZ82TtUZUL3r2lo78Xdf0sScXHblQ5LCLMnbRDFsIQ2iG2nEpi+JZaElEz9Pl9Xe6ZGBNGl8f+6uJ09t5mZ0dPJ0mlo5RzI6B7fYGVtHiunlYfMu7eY9UNvzmQm4ay2YVrHj6le/TGlwxKi3EkbRCWx7VQCw9ccz5ccABJTMhm+5jjbTuXNvnrp0j5mRb9Llk5DcJXqvB6xplIlB8grSUzsvo7atu6kaXOYsXM0N/45o3RYQpgtSRAWTKPVMX1LLIaKgPf2Td8SS+L1U0RGvcFdXS4N7b0Y+8xXFbbNoTgOju5Menolj1g7kazNZMa2oaSmXFE6LCHMkiQIC3Y4PrlAyeF+OiAtLYH3f36FW9osqts488bTK7GzL9s55M2di6s/b3ZdTjUre67lpjPnx0H5puUQQuSRBGHBktIKTw4ANmTS3OdTEjXpuKvtebPL5zg5Fxx2Xxl5eDTgzQ4LqKKy5nzWDRZtHYBWk6t0WEKYFUkQFszLuah1DbSEeX1Gil0aDipr3uwwr0KMkC5N1as/xrjQyVijIjr1Al//+rrSIQlhViRBWLBWge74utpjaMq5sKpfc7tKAmqVinGPjsffv025x2cJGjXsyStB/QHYnHiAqIPzFI5ICPMhCcKCWalVTI0IAsiXJJpU2UmGW976tl29niK4yfMKRGc5/hM6hp5+7QBYduYrzv0pa5YLAZIgLF6Xxr4s7tcCH9e86iY/23PYeO5EpVLRxqUpr/SIVDhCy/B/4fNo5RxILlo+PDCdmzfOKx2SEIqTBFEBdGnsy/6JHVj5Yi2a1PwWKxto6eLHG72+VDo0i6G2subVbp8TYOPCbW02834ZJj2bRKUnCaKCUOk0RJ+ewG1VJt42jozuuqzSDYR7WA6O7ox/8lOc1TbEZSfz5S/DlA5JCEVJgqggNuwcz+8ZV7BBzdjHZ+Di6q90SBbhwTmsqnk24rWW41ABO2+eZHf0XKVDFEIx1koHIB7eiVPr2HB1NwAvB71IrcAOCkdkGQqfw6odz/kf5ZsrO/j87Fpq+LSQ91RUSlKCsHA3b5zn42Pz0AHh1Zryn9AxSodkEYqbw8reaxQtnALIQcv8fW+TkZ6oUKRCKMfiEsSiRYuoWbMm9vb2hIaGcvjw4UKPXbFiBSqVKt9mb1/U4DLLkpuTyYJfXyVNm0MNG1cGdl6sdEgWwZg5rN778TzDO3+Gl5UjSZo7fLZtODqttjzDFEJxFpUgvvnmG8aOHcvUqVM5fvw4zZo1o3PnziQlJRV6jouLCwkJCfrt0qVLZR6nsWszPKxvdozlfNYNHFTWjA3/yGwn4Cuv98NYxsxhlZCSyel/bBndZjrWqDiUFs+vv80ovyCFMAMW1Qbx4YcfMnToUAYNGgTAkiVL+PHHH/niiy+YNGmSwXNUKhU+Pj7lFqOxazM8rD9Of8PmxAMADGsyFB+f4FK7dmkqr/fDFMXNYXX/cWHBT/LClQhWxW9m1YWN1PN/nMCa7cs2QCHMhMWUILKzszl27Bjh4eH6fWq1mvDwcKKjows9Lz09nRo1auDv70/37t05ffp0mcVo7NoMDyvl9kU+OZo3JUR4taY81vKVUrluaSuv98NURc9hVfC4bv+ZRkunGuSi5aN9b5F591ZZhieE2bCYBHHjxg00Gg3e3t759nt7e5OYaLgBsX79+nzxxRds2rSJNWvWoNVqad26NX///Xeh98nKyiI1NTXfZgxj12Z42OoVrSaXT38dSYo2m+o2zgzovOihrldWyuv9KImi5rCCvGlLfF3taRXonvdYrWZ4509xV9uTkJvBykIm9TO3qjQhHpbFJIiSCAsLo3///gQHB9OuXTu+//57PD09+eyzzwo9JzIyEldXV/3m72/ceAJj67UPxyeb+jLy2bb/PWIy/sYGNaPbzcbWzvmhrldWyuv9KInC5rC6//HUiCCs1P971tnlEUa2Go8K2JV8kkPHl+Y7b9upBNp+sIs+yw4yal0MfZYdpO0HuxQrJQlRGiwmQXh4eGBlZcX169fz7b9+/brRbQw2NjY0b96cCxcuFHrM5MmTSUlJ0W9Xrhi32pgp9doldfnyftb+tQWA/nX+i79/WImvVdbK4/14GA/OYXWPj6s9i/u1MNg+0qhhT7r7tgVg6R9LuXHjLGC+VWlCPCyLSRC2tra0bNmSnTt36vdptVp27txJWJhxX5QajYaTJ0/i61t446idnR0uLi75NmOYWq9tqpysDD7e+yY5aGlRxZ8n27xZouuUl7J+P0rDvTmsvh76GB89H8zXQx9j/8QORTae9+o4l9q27qTrcvl0x2hycnPMtipNiIdlMQkCYOzYsSxbtoyVK1dy5swZhg8fTkZGhr5XU//+/Zk8ebL++HfffZdff/2Vv/76i+PHj9OvXz8uXbrESy+9VOqxmVqvbap1O8dyOScVF7Utwzp9jEpt3h9dWb8fpcVKrSKsdjW6Bz9CWO1q+aqVDLG2see1J+Zgp7Li9N1EVv30ltlWpQnxsMz7W+YBvXv3Zu7cubzzzjsEBwcTExPDtm3b9A3Xly9fJiHhf8X5W7duMXToUBo2bEi3bt1ITU3lwIEDBAUFlXpsJanXNtbpMxvYev0QAMOCR+DqVrPkgZaTsnw/lObr15IX6/QEYPs/2/GzPVfsOUpVpQnxMFQ6nU7KvkVITU3F1dWVlJQUo6qbSrvf/507N5jwXQT/aO4SXq0pQ59ZZfI1lGSO4yBKg06rZdZ3z3A87Qq2d+049PckcrEr9Pivhz5GWO1q5RihEIUz9ntNEkQxTE0QkNfd8XB8MklpmXg551WjlPSX8uIf+hJ16zReVo7M/r+tODgqWyVTEqX5fpiTW8lxvLH5eW5kZ+F0ux6/JQ8qcIyKvIbv/RM7VIjXLCoGY7/XLGoktaW4V6/9sI7GrCDq1mlUwKuhEy0yOUDpvR/mpqp7bV5qMoR5MYtJdz1HYMYfxGc11T9v6VVpQlhUG0RlkpZ6laUnPgXgKe9QGtbvrnBEwpCwlsN43K0uNtZqqnt/jw3/q0orqsusEJZAShBm6ssdY0jRZvOItTPPd/xQ6XBEEQY/+RGnNz5Lijqb11r+RI2671aoqjRReUkJwgwd+f1zfks5jxoVr7aeYraztIo8zi6P8HKzVwE4kH6MBlVijOoyK4S5kwRhZtJSr7Lsj7ypQJ72CaNO7U4KRySMERI8kP+4NUQHfHYwkpysDKVDEuKhSYIwMyt2jtVXLT3XYY7S4QgTDAj/EDe1LVdz01m/e4LS4Qjx0CRBmJFjJ1ay//Y51KgYHvaWVC1ZGCdnX15qNgyALQkHiIvbrnBEQjwcSRBm4k56Estj8pYMfconlLp1uigckSiJR4MH08a1Llp0LI5+j5ycO0qHJESJSYIwE2t2jSNZm4mPdRV6PfGB0uGIhzCw44e4qG25kpPKD7vfUjocIUpMEoQZOBW7np03TwLwyqPjsbN3VTgi8TBcXP0ZFDQAgB+uRnHlSuErHgphziRBKCwrM4XPjs4HoJNHc4Ia9FA2IFEqwloO/3eZUh1L9r2NVpOrdEhCmEwShMK+2z2JJM0d3NX29OkovZYqCpVazZAn5uCgsuZC1k1+3veu0iEJYTJJEAr6K34nWxMPAjA0+FUcHT0UjkiUpmoe9ej377Tg31z8kaTrpxSOSAjTSIJQSG5OJkt+exctOtq41qVFs/5KhyTKQIew8TS09yJLp2F51ER0Wq3SIQlhNEkQCvlx3zQu5aTgpLJmgAyIq7DUVta83G4mNqg5cecq+49+onRIQhhNEoQCEhN+Z/2lXwHo3+AFi1ghTpScn18IPf07ArDyzGpSU64oHJEQxpEEUc50Wi3L97xJDlqaOPjyn1ajlQ5JlIOn203H38aFNG0Oq3aNVzocIYwiCaKc7T28gJN3E7BBzUvtI1Gp5SOoDGxsHHnlsTdRAftun+Xk6fVKhyREseTbqRylpFxm1dm1APSq0Qkfn2BlAxLlqm6dLnT2bAnA0mPzycpMUTgiIYomCaIcrd41gXRdLjVsXHnq8WlKhyMU0LvDB7ir7UnS3GFD1JtKhyNEkSRBlJM/Tn/DvttnUQEvh72FtY290iEJBTg6ejC42ctA3oyvly7tUzgiIQonCaIcZGelsfzYQgA6e7aURYAquUeDBxPqHIgWHUt/mybTcAizJQmiHGzYPZnrmgzc1fb07iAztQoY0P4D7FVWXMi6ya+/zVQ6HCEMkgRRxq5c+Y0tCb8BMLjpUJlOQwB503D0qdUdgK//2kRy8gWFIxKiIItLEIsWLaJmzZrY29sTGhrK4cOHizx+/fr1NGjQAHt7e5o0acJPP/1UTpGCVpPL0n1T0aDjUaeaPNp8SLndW5i/Tm3epI5dNTJ1GlbI2AhhhiwqQXzzzTeMHTuWqVOncvz4cZo1a0bnzp1JSkoyePyBAwfo06cPQ4YM4ffff6dHjx706NGDU6fKZ9K0XdFzOJ91A3uVFYOemF0u9xSWQ21lzcttpqFGxaG0eI7GrFA6JCHyUel0Op3SQRgrNDSURx99lE8+yZvPRqvV4u/vz2uvvcakSZMKHN+7d28yMjLYunWrft9jjz1GcHAwS5YsMeqeqampuLq6kpKSgouLi9Gx3kqOY+zm3tzR5TKwVg+6tptm9Lmicvnq5+FsTozGw8qBuf/3Iw6O7kqHJCyETqst0WBbY7/XLKYEkZ2dzbFjxwgPD9fvU6vVhIeHEx1teMWu6OjofMcDdO7cudDjAbKyskhNTc23lcSq3RO5o8ultq07ndu+XaJriMqhZ/uZeFk5ckNzl/VRBX/olAaNVkd03E02xVwlOu4mGq3F/C4UhcjNyeS9b7ux5+CHZTZLsMkJYsCAAezdu7csYinSjRs30Gg0eHt759vv7e1NYmKiwXMSExNNOh4gMjISV1dX/ebv729yrOcv/MyB1AuoUTG0zTuoraxNvoaoPOwdqjKk+QgAfr5+hPiLUaV6/W2nEmj7wS76LDvIqHUx9Fl2kLYf7GLbqYRSvY8oX1v3TuX03URWn/uajIzrZXIPkxNESkoK4eHh1K1bl5kzZ3L16tWyiEsxkydPJiUlRb9duWL6zJt1a3Xm9cYv8Zx/OIE125d+kKLCCW7Sl9YuddCiY9lv75ba2IhtpxIYvuY4CSmZ+fYnpmQyfM1xSRIW6vr1P/ju8nYgb0ZoJ2ffMrmPyQnihx9+4OrVqwwfPpxvvvmGmjVr0rVrV7777jtycnLKIkYAPDw8sLKy4vr1/Jny+vXr+Pj4GDzHx8fHpOMB7OzscHFxybeZSqVW0+bRkTwbLus8COMN6DAHR5U1cdnJ/LL//Ye+nkarY/qWWAxVJt3bN31LrFQ3WRidVsvyqEnkoKWxgw+PPzqqzO5VojYIT09Pxo4dy4kTJzh06BB16tThxRdfxM/PjzFjxvDnn3+WdpzY2trSsmVLdu7cqd+n1WrZuXMnYWFhBs8JCwvLdzzA9u3bCz1eCCW5VQ2k779LlK6L38KNG2cf6nqH45MLlBzupwMSUjI5HJ/8UPcR5evAsU/54861vBmh/zOjTGeEfqgrJyQksH37drZv346VlRXdunXj5MmTBAUFMX/+/NKKUW/s2LEsW7aMlStXcubMGYYPH05GRgaDBg0CoH///kyePFl//KhRo9i2bRvz5s3j7NmzTJs2jaNHjzJy5MhSj02I0tAhbDz17DzI1Gn4cvfEh7pWUlrhyaEkxwnlpaclsDJ2FQDPVn8CX7+WZXo/kxNETk4OGzZs4Omnn6ZGjRqsX7+e0aNHc+3aNVauXMmOHTv49ttveffdd0s92N69ezN37lzeeecdgoODiYmJYdu2bfqG6MuXL5OQ8L861datW7N27VqWLl1Ks2bN+O677/jhhx9o3LhxqccmKrby6gWktrLm5cenY4WKo+mXOPL75yW+lpezcRNCGnucUN5XuyaQos3mEWsnnvlP6X/HPsjkcRAeHh5otVr69OnD0KFDCQ4OLnDM7du3ad68OfHx8aUVp2JKOg5CVBzbTiUwfUtsvuoaX1d7pkYE0aVx2TQOrtv2GhsT9uGutmder60lmqJFo9XR9oNdJKZkGmyHUAE+rvbsn9gBK7XqoWMWZevMuU1MOzAVgGmtp9OwfvcSX6vMxkHMnz+fa9eusWjRIoPJAcDNza1CJAchlOoF9N8nZuJtVYVkbSbf7CpZVZOVWsXUiCAgLxnc797jqRFBkhwsQE5WBssO53V66eDe5KGSgylMThAvvvgi9vZSJBUVn5K9gGztnBnacjQAv/xzjD8vbCvRdbo09mVxvxb4uOb/P+vjas/ifi3KrAQkStemvVO4mpuOq9qWvh3Kb9oeGcElRCFM6QUUVrtaqd+/SaNe/Of89+y9fYalB2cSWaN9iRaa6tLYlyeDfDgcn0xSWiZezva0CnSXkoOFuHbtKBv/jgJgQFD/MhvzYIjFTLUhRHkzh15AL3aYjbPahss5qfy4b1qJr2OlVhFWuxrdgx8hrHY1SQ4WQqvJZemeN8lFS3CV6rRu+Wq53l8ShBCFMIdeQC6u/vRv8AIA6y/9SmLC72V2L2F+dh+ax5nMJOxUVgxpN6tMxzwYIglCiEK0CnTH19W+QAPvPSryejO1Cizb2Vcff3QUTRx8yUHL8j1vltnEbMK83L4Vz1fn1wPwXM1ueHmXf/d8SRBCFMJcegGp1Gpeah+JrUrNybsJ7Dlc+oNQhflZsWs8GbpcatlWpWvbKYrEIAlCiCKYSy8gH59gegV0AWD12a9JuX2xXO4rlHE0ZgXR/84I/XKbqVhZ2yoSh0UtGKQEGSgnIK/Lq9K9gDS52Uxe9ySXclJo7VKHUT2/K9f7i4dj7N/QnfQkxm14hmRtJs/4tKZv109LPRZjv9ekm6sQRrjXC0jRGKxtGdbmHd6KeoMDqRdoe2IlLZsNUDQmYRxTRuOv3T2BZG0m3lZV6NXhg/IONR+pYhLCgtQK7MhTPqEALI9ZzJ07NxSOSBTHlNH4Z89tZvuNGABeeXQstnbO5RlqAZIghLAwvZ74QD8Nx9od45QORxTBlNH42VlpLDmcV2J4wr0xjRr2LLc4CyMJQggLY2fvyiuPjgVg+80TxJ79QdmARKFMGY2/ftdEEnIzqKq248WOc8svyCJIghDCAjVq2JOO1ZoA8NmROWRlpigckTDE2FH2f13cydbEaACGNHuFKk6Fr3pZniRBCGGh+nWYh7vansTcDL7ZNV7pcIQBxoyytyaLw1cWokVHa5c6PBo8uBwiM44kCCEslKOTF0OD8+bm+en6Yc79+aPCEYkH3RuNX5R23t+RqEvHWW3DwPAPyyky40iCEMKCtWjWn8fdGqADPjsYSU5WhtIhiftYqVU806zwwZQBtrGkOJ8GYFDQAFxdA8orNKNIghDCwg0Mn4+b2paruel8K1VNZkWj1bH5hOFFpazIxt9rPTlaHY86BZb7TK3GkAQhhIVzcvblpWbDANiaGM35Cz8rHJG4p6heTKHu60izuYuNRk3L+lPLfaZWY5hfREIIkz0aPJjH3RqgRcfi6BlkZ6UpHZKg8F5MAbaxZLieASD35uOkaj3LMyyjSYIQooIYGD6fqmo7ruWm87UMoDMLhnox2ZBJda9v0aHD7a4HMemdynRNkYchCUKICsLJ2ZdXmr8GwM9Jh2UAnRkwtKZIq2pfkW6TiZ3WihNJA/B1dSzzNUVKShKEEBVI86b9aF+1ETpg8eHZMleTwh5cU6S2/XHSXf4EIPNGB1K1nuWypkhJSYIQooIZ0OkjvKwcSdLcYeWvo5QOp9K7t6ZIgEsuXp6b0AFuGb7csH6qXNcUKQlJEEJUMI6OHrwaOhEVEHXrNEd+/1zpkCq9Lo196dtoA7n2OXja2DOk80fsn9jBrJMDWFCCSE5Opm/fvri4uODm5saQIUNIT08v8pz27dujUqnybcOGDSuniIVQTsP63YnwCQPgsz+WcPtWvMIRVW5HY1awM/kP1GoVYx6bQLtGDc22Wul+FpMg+vbty+nTp9m+fTtbt25l7969vPzyy8WeN3ToUBISEvTb7NmzyyFaIZT3XIe51LBxJU2bw+Ltr6HV5CodUqV0+1Y8S04sAuBp71AaB/VSOCLjWUSCOHPmDNu2bWP58uWEhobStm1bPv74Y9atW8e1a9eKPNfR0REfHx/9JsuGisrCxq4Kr7WbiQ1qYjL+Ztv+95QOqdLRabUs2f46adocati48nxH85prqTgWkSCio6Nxc3MjJCREvy88PBy1Ws2hQ4eKPPerr77Cw8ODxo0bM3nyZO7cuVPk8VlZWaSmpubbhLBU/v5t6Fe7BwBf/bWFS5f2KRtQJfPT3mn8nnEFG9R5ydquitIhmcQiEkRiYiJeXl759llbW+Pu7k5iYmKh573wwgusWbOG3bt3M3nyZFavXk2/fv2KvFdkZCSurq76zd/fv1RegxBK6dz2bVo4BZCLloX73pS1I8rJX/G7WBu/BYAX6zyLv38bhSMynaIJYtKkSQUakR/czp49W+Lrv/zyy3Tu3JkmTZrQt29fVq1axcaNG4mLiyv0nMmTJ5OSkqLfrly5UuL7C2EOVGo1wzp9gpvalr9z0ljxy0ilQ6rw7t5JZuH+KeSi41GnmnRq85bSIZWItZI3HzduHAMHDizymFq1auHj40NSUlK+/bm5uSQnJ+PjY/zKS6GheYu9X7hwgdq1axs8xs7ODjs7O6OvKYQlcHUN4LVHx/P+oRnsSj5Jo8Mf07bVa0qHVSHptFo+/+VVEnIzcFfbM6zLYrOciM8YiiYIT09PPD2Ln6QqLCyM27dvc+zYMVq2bAnArl270Gq1+i99Y8TExADg62vefY+FMJZGq+NwfDJJaZl4OdvTKtC90O6TjYN68d+/f2PD1SiWxa6gdvXW+Pq1LOeIK77dB+ey7/ZZ1Kh4LXQSTs6mfd+Y8pmWNZVOp9MpcmcTde3alevXr7NkyRJycnIYNGgQISEhrF27FoCrV6/SsWNHVq1aRatWrYiLi2Pt2rV069aNatWq8ccffzBmzBiqV6/Onj17jL5vamoqrq6upKSkSA8oYVa2nUpg+pbYfNNJ+7raMzUiqNABWFpNLu+tf4rYu9epYePK+722YmvnXF4hV3gXL+7h7d1jyEHL8wGdeLajad3qS/KZloSx32sWU+756quvaNCgAR07dqRbt260bduWpUuX6p/Pycnh3Llz+l5Ktra27Nixg06dOtGgQQPGjRtHz5492bJli1IvQYhSs+1UAsPXHC+w1kBiSibD1xxn2ynDi9Sorax5rdMiXNS2XMpJ4fOfX0Gn1ZZHyBXenTs3mL/vTXLQ0ryKP93bzzTp/JJ+pmXJYkoQSpEShDA3Gq2Oth/sKnQhGhXg42rP/okdCq2aOBW7nhmHZqJFx9C6vQlvO7kMI674dFot87/vyaG0eDysHJjV4zucXR4x+vzS+ExNUeFKEEKIPEWtUgagAxJSMjkcn1zoMY2DevF8jc4AfPnneuLitpd2mJXKD7sncSgtHmtUjG491aTkAKXzmZYFSRBCWJjCVikz9bhn2s/kUaea5KJl7v4ppNy+WArRVT4xJ7/im8u/AjC4Xm/q1uli8jVK6zMtbZIghLAwxq4+VtxxKrWaV7suxc/aiWRtJnN/eomcrIzSCLHSSEyMYeGx+eiA8GpN6dhmUomuU1qfaWmTBCGEhTG0Stn9VOT1fDFmlTJHJy/Gd1yAo8qa81k3+HybNFobKyM9kdnbXyNDl0tdOw8Gdl5c4muV5mdamiRBCGFhHlyl7H73HpuySpmfXwijWo5BjYrdyaf4ed/00gu2gtLkZrPgx8FczU3DXW3P2C5LHmqepdL+TEuLJAghLNC9Vcp8XPNXOfi42pdolbLgJn15oeZTAKz6axNHY1aUVqgV0sqfh/HHnWvYqayY0G4W7u51Hvqapf2Zlgbp5loM6eYqzFlpjrrVabUs2zqAnTdPYqeyYlq72dQK7FjKEVu+n/ZMZeVfmwAYFzySVs1fKtXrl8dIamO/1yRBFEMShKhMcnMymf19T07cuYqb2pb3u36Jp1cjpcMyGweOfsrCk0vRAS/U6EL3DrOUDqlEZByEEMJk1jb2jI5YSYCNC7e12cz45WXp/vqv02c2sOjkcnRAZ48WPGPiSGlLJAlCCJGPo6MHk7osx8PKgYTcDCJ/HMSdOzeUDktR8RejmHMokly0hDoHMrDbUoudodUUFf8VCiFMVs2jHm+Hf4KL2pb47FvM2dyX7Kw0pcNSxJUr0czYM567ulwa2nvxWsQa1FaKToRdbiRBCCEM8vVryeT/RGKvsiL27nXmbepT6QbSJSb8zoxdo0nT5lDb1p0JEWssbtnQhyEJQghRqFqBHZn42BTsVFbEZPzN3E29K02SSEyM4b3tr3JLm4W/jQuTI1bj6ORV/IkViCQIIUSRghr0YOJjb2GrUhOT8TfzNj1f4ZPEtWtHmfbLK9zQ3MXXugpTnl5l8gR8D9JodUTH3WRTzFWi426i0Zp/B1Lp5loM6eYqRJ7TZzYw69AMsnVaghy8Gf/MVzg6eigdVqm7cuU33ts1hhRtNtVtnJny1CrcqgY+1DXLayEgY0k3VyFEqWrUsCeTH3tH3ybx3sZepKRcVjqsUnX23Gam7RxFijabGjauvBPxVakkB3NbCMhYkiCEKGeWWNVwT1CDHkxtNxtntQ1/Zd9i6uY+JCb8rnRYpeLQ8aW8f2Aa6bpc6thV453u63B1DXioa2q0OqZvicXQJ3xv3/QtsWb7N1A5+moJYSbMraqhJGoFduRdu8XM2PEaCbkZvPXLUMY99jZBDXooHVqJ6LRaftw7lTXxW9ABLZ1qMCpiFXb2rg99bVMWAgqrXe2h71fapAQhRDmx5KqGB/n5hfD+02uobetOui6XGdHT2Xngg3zHWEJJKSszhU829WH1v8nhSY9gxj27vlSSA5jvQkDGkhKEEOWguKoGFXlVDU8G+ZT7lM4lVdW9NtP+bxOfbh1EdOoFlp77mnPXYxjc5VOi4jLNvqSUdP0Uc7eP4FJOCmpU9AuMoNt/ppXqCGlzXQjIWFKCEKIcmOuaww/L1s6Z13us43n/J1GjYs/tM4xb9zTvffu12ZaUdFotew99xPifB3ApJwUXtS1vh03hqfbvlvr0Gea6EJCxJEEIUQ4svaqhKGora54Nn8PbYVNwU9vyd04ano+sIKzqV6jJ1R9nDo2yaalXWbjxORbFfkmmTkN9ew8iu62kUYP/lsn9zHUhIGNJghCiHFh6VYMxGjX4L883X4rrHTe06MhwO0nYIzMJsDutP0apkpJOqyXq4DzGbOzBgdQLqFHR2z+cqb1+wsOzYZne2xwXAjKWtEEIUQ7uVTUkpmQabIdQkfeFYa5VDcZK1XqyN3E8zZ1/Ru1+gDTbO7j5raZm+iOcSH6OFI03UL4lpbi47aw+PIczmUkAVLdxZthjb1G3Tpdyi6FLY1+eDPIp84WASpskCCHKwb2qhuFrjqOCfEnCEqoajJVXAlLze9pTVL3TgiYeX3PbMYnbTlepXeUjHNJqc/LWM+VSUrpy5Te+PTSXw2nxANiprPi/gM50e/wdrG3Kv6RmpVaZZVfWolhMFdOMGTNo3bo1jo6OuLm5GXWOTqfjnXfewdfXFwcHB8LDw/nzzz/LNlAhCmHJVQ3Gur9R9pbGl73Xx5KR8Bwu2VXIVWlJc/mTOjXmc+r0OC5e3GPStY3pNqvV5HI0ZgXvr+vKGztGcDgtHjUq2rk1ZF63VTzTYaYiycFSWcxcTFOnTsXNzY2///6bzz//nNu3bxd7zgcffEBkZCQrV64kMDCQKVOmcPLkSWJjY7G3N+6PROZiEqWtPNYcVtK98R7wv5KSilwaOe7Drep+Mh3v6l9vDRtXHn+kLcF1nqb6I6GF9iIqaoBhp4aenLvwI4f+3MKhm6dI1uYdo0ZFK5da9AodT/Xqj5XdC7ZAFXZN6hUrVjB69OhiE4ROp8PPz49x48bxxhtvAJCSkoK3tzcrVqzg+eefN+p+kiCEMF1hX+jvPNWAAJuD/Bq7lqOp8eSi1T/vrranoXMANdxqEeDRGI+qtXF28uHAZR2jvo7BSpWNnfoOVa0TcbW5jpNtAlZ2CWicMshV/+86TiprOng/ypMtRuDl3bhcX7elMPZ7rcK2QcTHx5OYmEh4eLh+n6urK6GhoURHRxeaILKyssjKytI/Tk1NLfNYhahoim6U7Umjhj1JT0vgt5jlHL0WzZk7iSRrM/kt5Ty/pZyHS9v018rM0dIwMP/vWB1wb307lUZFNStbWrrWJTSwM80a9ir3RX0qaqmwwiaIxMREALy9vfPt9/b21j9nSGRkJNOnTy/T2ISoDIprlHVy9qXz41PoDGRnpXHmzx/5K/EYl1PiuXz3OimaTFI1OTxYyeGgscYm1wF1rhN3M/25ntmACS/2p01db8M3KmMVYX6twiiaICZNmsQHH3xQ5DFnzpyhQYMG5RQRTJ48mbFjx+ofp6am4u/vX273F6IysrVzplnj52nWOH/JfuPxS0z5bh9anZpcbMnR2aAz8LV1IyO3wL7ycK+95cF6+nujxi2984GiCWLcuHEMHDiwyGNq1apVomv7+PgAcP36dXx9//cBXb9+neDg4ELPs7Ozw87OrkT3FEKULh9XJ9K1VYs9TokBhhVxfq0HKZogPD098fT0LJNrBwYG4uPjw86dO/UJITU1lUOHDjF8+PAyuacQonSZ8wBDS5/K2xgWMw7i8uXLxMTEcPnyZTQaDTExMcTExJCenq4/pkGDBmzcuBEAlUrF6NGjef/999m8eTMnT56kf//++Pn50aNHD4VehRDCFOY8l1FFnl/rHotppH7nnXdYuXKl/nHz5s0B2L17N+3btwfg3LlzpKSk6I+ZMGECGRkZvPzyy9y+fZu2bduybds2o8dACCGUd2+A4YMNwT4KNwRXhvm1LG4cRHmTcRBCmAdz60qq0epo+8GuYqu/9k/sYHZtEJV+HIQQomIxt7mMKsP8WhbTBiGEEOamos+vJSUIIYR4CJY6lbcxJEEIIcRDMrfqr9IiVUxCCCEMkgQhhBDCIEkQQgghDJIEIYQQwiBJEEIIIQySBCGEEMIgSRBCCCEMkgQhhBDCIEkQQgghDJIEIYQQwiBJEEIIIQySBCGEEMIgSRBCCCEMkgQhhBDCIEkQQgghDJIEIYQQwiBJEEIIIQySBCGEEMIgSRBCCCEMkjWphagENFodh+OTSUrLxMvZnlaB7lipVUqHJcycJAghKrhtpxKYviWWhJRM/T5fV3umRgTRpbGvgpEJc2cxVUwzZsygdevWODo64ubmZtQ5AwcORKVS5du6dOlStoEKYUa2nUpg+Jrj+ZIDQGJKJsPXHGfbqQSFIhOWwGISRHZ2Nr169WL48OEmndelSxcSEhL029dff11GEQphXjRaHdO3xKIz8Ny9fdO3xKLRGjpCCAuqYpo+fToAK1asMOk8Ozs7fHx8yiAiIczb4fjkAiWH++mAhJRMDscnE1a7WvkFJiyGxZQgSioqKgovLy/q16/P8OHDuXnzZpHHZ2VlkZqamm8TwhIlpRWeHEpynKh8KnSC6NKlC6tWrWLnzp188MEH7Nmzh65du6LRaAo9JzIyEldXV/3m7+9fjhELUXq8nO1L9ThR+SiaICZNmlSgEfnB7ezZsyW+/vPPP88zzzxDkyZN6NGjB1u3buXIkSNERUUVes7kyZNJSUnRb1euXCnx/YVQUqtAd3xd7SmsM6uKvN5MrQLdyzMsYUEUbYMYN24cAwcOLPKYWrVqldr9atWqhYeHBxcuXKBjx44Gj7Gzs8POzq7U7imEUqzUKqZGBDF8zXFUkK+x+l7SmBoRJOMhRKEUTRCenp54enqW2/3+/vtvbt68ia+v9P0WlUOXxr4s7teiwDgIHxkHIYxgMb2YLl++THJyMpcvX0aj0RATEwNAnTp1cHJyAqBBgwZERkby7LPPkp6ezvTp0+nZsyc+Pj7ExcUxYcIE6tSpQ+fOnRV8JUKUry6NfXkyyEdGUguTWUyCeOedd1i5cqX+cfPmzQHYvXs37du3B+DcuXOkpKQAYGVlxR9//MHKlSu5ffs2fn5+dOrUiffee0+qkESlY6VWSVdWYTKVTqeTUTJFSE1NxdXVlZSUFFxcXJQORwghHpqx32sVupurEEKIkpMEIYQQwiCLaYNQyr0aOBlRLYSoKO59nxXXwiAJohhpaWkAMqJaCFHhpKWl4erqWujz0khdDK1Wy7Vr13B2dkalMr5bYGpqKv7+/ly5csViGrcl5vJhaTFbWrwgMRdHp9ORlpaGn58fanXhLQ1SgiiGWq2mevXqJT7fxcXFYv5A75GYy4elxWxp8YLEXJSiSg73SCO1EEIIgyRBCCGEMEgSRBmxs7Nj6tSpFjVqW2IuH5YWs6XFCxJzaZFGaiGEEAZJCUIIIYRBkiCEEEIYJAlCCCGEQZIghBBCGCQJogwsWrSImjVrYm9vT2hoKIcPH1Y6pCLt3buXiIgI/Pz8UKlU/PDDD0qHVKTIyEgeffRRnJ2d8fLyokePHpw7d07psIq0ePFimjZtqh8EFRYWxs8//6x0WCaZNWsWKpWK0aNHKx1KoaZNm1ZgXfsGDRooHVaxrl69Sr9+/ahWrRoODg40adKEo0ePKh2WJIjS9s033zB27FimTp3K8ePHadasGZ07dyYpKUnp0AqVkZFBs2bNWLRokdKhGGXPnj2MGDGCgwcPsn37dnJycujUqRMZGRlKh1ao6tWrM2vWLI4dO8bRo0fp0KED3bt35/Tp00qHZpQjR47w2Wef0bRpU6VDKVajRo1ISEjQb/v371c6pCLdunWLNm3aYGNjw88//0xsbCzz5s2jatWqSocGOlGqWrVqpRsxYoT+sUaj0fn5+ekiIyMVjMp4gG7jxo1Kh2GSpKQkHaDbs2eP0qGYpGrVqrrly5crHUax0tLSdHXr1tVt375d165dO92oUaOUDqlQU6dO1TVr1kzpMEwyceJEXdu2bZUOwyApQZSi7Oxsjh07Rnh4uH6fWq0mPDyc6OhoBSOr2O4tM+vu7q5wJMbRaDSsW7eOjIwMwsLClA6nWCNGjOCpp57K93dtzv7880/8/PyoVasWffv25fLly0qHVKTNmzcTEhJCr1698PLyonnz5ixbtkzpsACpYipVN27cQKPR4O3tnW+/t7c3iYmJCkVVsWm1WkaPHk2bNm1o3Lix0uEU6eTJkzg5OWFnZ8ewYcPYuHEjQUFBSodVpHXr1nH8+HEiIyOVDsUooaGhrFixgm3btrF48WLi4+N5/PHH9dP2m6O//vqLxYsXU7duXX755ReGDx/O66+/zsqVK5UOTWZzFZZtxIgRnDp1yuzrmQHq169PTEwMKSkpfPfddwwYMIA9e/aYbZK4cuUKo0aNYvv27djb2ysdjlG6du2q/3fTpk0JDQ2lRo0afPvttwwZMkTByAqn1WoJCQlh5syZADRv3pxTp06xZMkSBgwYoGhsUoIoRR4eHlhZWXH9+vV8+69fv46Pj49CUVVcI0eOZOvWrezevfuhpmQvL7a2ttSpU4eWLVsSGRlJs2bN+Oijj5QOq1DHjh0jKSmJFi1aYG1tjbW1NXv27GHhwoVYW1uj0WiUDrFYbm5u1KtXjwsXLigdSqF8fX0L/Eho2LChWVSNSYIoRba2trRs2ZKdO3fq92m1Wnbu3GkRdc2WQqfTMXLkSDZu3MiuXbsIDAxUOqQS0Wq1ZGVlKR1GoTp27MjJkyeJiYnRbyEhIfTt25eYmBisrKyUDrFY6enpxMXF4evrq3QohWrTpk2Bbtrnz5+nRo0aCkX0P1LFVMrGjh3LgAEDCAkJoVWrVixYsICMjAwGDRqkdGiFSk9Pz/cLKz4+npiYGNzd3QkICFAwMsNGjBjB2rVr2bRpE87Ozvr2HVdXVxwcHBSOzrDJkyfTtWtXAgICSEtLY+3atURFRfHLL78oHVqhnJ2dC7TrVKlShWrVqplte88bb7xBREQENWrU4Nq1a0ydOhUrKyv69OmjdGiFGjNmDK1bt2bmzJk899xzHD58mKVLl7J06VKlQ5NurmXh448/1gUEBOhsbW11rVq10h08eFDpkIq0e/duHVBgGzBggNKhGWQoVkD35ZdfKh1aoQYPHqyrUaOGztbWVufp6anr2LGj7tdff1U6LJOZezfX3r1763x9fXW2tra6Rx55RNe7d2/dhQsXlA6rWFu2bNE1btxYZ2dnp2vQoIFu6dKlSoek0+l0OpnuWwghhEHSBiGEEMIgSRBCCCEMkgQhhBDCIEkQQgghDJIEIYQQwiBJEEIIIQySBCGEEMIgSRBCCCEMkgQhhBDCIEkQQgghDJIEIYSC/vnnH3x8fPRrAQAcOHAAW1vbfLMCC6EEmYtJCIX99NNP9OjRgwMHDlC/fn2Cg4Pp3r07H374odKhiUpOEoQQZmDEiBHs2LGDkJAQTp48yZEjR7Czs1M6LFHJSYIQwgzcvXuXxo0bc+XKFY4dO0aTJk2UDkkIaYMQwhzExcVx7do1tFotFy9eVDocIQApQQihuOzsbFq1akVwcDD169dnwYIFnDx5Ei8vL6VDE5WcJAghFDZ+/Hi+++47Tpw4gZOTE+3atcPV1ZWtW7cqHZqo5KSKSQgFRUVFsWDBAlavXo2LiwtqtZrVq1ezb98+Fi9erHR4opKTEoQQQgiDpAQhhBDCIEkQQgghDJIEIYQQwiBJEEIIIQySBCGEEMIgSRBCCCEMkgQhhBDCIEkQQgghDJIEIYQQwiBJEEIIIQySBCGEEMIgSRBCCCEM+n9fj9sZUlA/lwAAAABJRU5ErkJggg==", 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", 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" ] @@ -425,13 +423,13 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 23.47it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 25.56it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -444,13 +442,13 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 24.90it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 23.35it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -463,13 +461,13 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 24.25it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 23.68it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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", 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DHx8fzp8/X+b9K0MShLB7xk6tNXbAu4irxpXlfZebJUZTuWoqV1L+wIED6HQ6hg4dWmz/EqDY0yjAyZMnbxs4jY6O5v333zf5vm3atNH/d1HZ/OTkZOrVq8fJkyeLjSkAREVFsX37dpPvU6RDhw4VPteQW+OHws+QnJys/7nkl3BJDz74IPPmzaNRo0b07duX/v37M3DgQJycSv8qvfWeNWrUwNvbW3/Pou4hQ5tYvfvuu7Rq1YpvvvmGw4cPG9yGwN3d3aKTFGQMQtg9Y6fWmjpArVKpcHNys8nL2Bk6jRs3RqVS3dbX36hRIxo3blys+6RIaV1RpVH/r7rtreMi+fn5Bo+9dcC76DOU9cRdWSU/iymxGlJywF6lUpkUf2hoKKdOneKjjz7C3d2dZ599ljvvvLPMGMq6p7+/PwA3bty47bwzZ85w5coVdDpdqS2nlJSUCg2MG0sShHAI5hrwdjS1atWiV69efPjhh2RlZVXoGs2bN7+tD3/v3r20aFGYdIu+YBIS/p0qfOsgsCn32b9/f7H39u3bZ/J1ymJMrEUznbRarVnvXcTd3Z2BAwcyf/58duzYQVxcHEePHq3QtcLCwvD29ubEiRPF3s/Ly+PRRx9l8ODBvPHGGzz11FPFWjpQmEBycnJo165dhT9LeaSLSTgMcwx4O6KPPvqI6OhoIiIimD59Om3atEGtVnPw4EH+/PPPcrthXnrpJR566CHatWtHTEwM69at47vvvmPLli1A4Rde586dmTVrFg0bNiQ5OZnXX3/d5DjHjRvH8OHDiYiIIDo6mlWrVnH8+PFKDVKXZEys9evXR6VSsX79evr374+7uzuenp5GXb/kpmMlLVu2DK1WS2RkJB4eHqxcuRJ3d3eTpqjeSq1WExMTw549e4ptn/zaa6+RlpbG/Pnz8fT05KeffmLEiBGsX79ef8zu3btp1KgRYWFhFbq3UfFZ7MpCWEB5U2uLaHUKcWeu82P8ZeLOXEerc9xtT8LCwvjtt9+IiYlh8uTJtG3bloiICD744ANefPFF3njjjTLPHzRoEO+//z7vvvsuLVu25OOPP+azzz4rNvNl6dKlFBQU0KFDB8aPH8+bb75pcpyDBw9mypQpvPzyy3To0IHz588zevRok69TnvJirVOnDjNmzGDSpEkEBgYyduxYo69dctOxknx9ffnkk0+Ijo7Wb0C2bt06atWq+CSMp556itWrV+u7nXbs2MG8efNYsWIF3t7eqNVqVqxYwe7du1m4cKH+vC+//LLUNSPmIhsGlUM2DHI8hvagCA/2YGr32rRo2tjggKAQtqIoCpGRkbzwwgv62U7lOX78OD169OCvv/7Cx8fH4DHm2DBIWhCiSimtJMfVzFxSsvLIyMkr9r6iKGTmFJCanUdmToFJC9iEMAeVSsXixYspKCgw+pyEhAQ+//zzUpODucgYhKgyyirJUSQ5PQ9/HwWVSkXazTyupOaQr/13FouzRk2Irxs+7vZd0kFULeHh4SYVNYyJibFcMLeQFoSoMowpyVGg05GVqyXtZh7nr2cXSw4A+Vod569nk3Yzz/BFhKhGpAVRRcg+CcaX2sjX6khML/vYK6k5eLs5y/7VolqTBFEFGBqUDfZxY9rAFlV2fYAhZZXaKJzEpICiUKBTbms5lJSvLWxpeLrJPxHhmMwxnuZQXUy7du1i4MCBhISEoFKp+OGHH8o9Z8eOHbRv3x5XV1caN27MsmXLLB6nNVX3fRJuVdYeFKk5OvK1CmpdPk4a41oFBRZcISyEpRWV4KhMuXeHejzKysqibdu2jBgxgvvvv7/c48+ePcuAAQMYNWoUq1atYuvWrTz11FMEBwfTp08fK0RsWdV9n4SSytqDIqdAYds/mQz1SSU73QUlX4Fyuo+0+U7k5EiSEI5FURSys7NJTk7G19cXjUZT4Ws5VILo168f/fr1M/r4RYsW0bBhQ+bMmQMUlgLYs2cPc+fOrRIJwpR9EipTTdWRFJXkKNnlFuTjRkz7OwjyV3Ej5TrX029SVi+Tk1qFc7ZbeTlECLvl6+tLUFBQpa7hUAnCVHFxcbdNB+vTp4/F96W1Fkvtk2AuOm0B1679ybUbp7mWdp607GQKtHnk6/JRocLLzZcabjXx865L3aD2eHvXQ6WufK9neSU5AgIC2LzlJF8fulTqNR6KqMsz4Q0rHYsQtuDs7FyplkORKp0gEhMTCQwMLPZeYGAg6enp3Lx502AlzNzc3GLlk9PT0y0eZ0XZ2z4JOm0Bf5/ZyG///MLp1NP8k3OVLMX4xT9eamcauQXQwr8VrRr0pFGDHqg1FfsrWuYeFCo1q48kkZBRejG31UeSGNurZbXomhOiNFU6QVREbGwsM2bMsHUYRrHUPgmmUHQ6Tv61lp1/fs3htL/J0BUve+yEmtpO7vg7e+Pr6oOz2hkntRM6RUtmfhaZ+VlczcsgWZtFhi6f37Mv8/uFy3DhF3z3uBBVqw1dmj1Ak0Z9zNK6gPK75qD6dc0JYUiVThBBQUEkJSUVey8pKem2bQhvNXnyZCZMmKD/OT09ndDQUIvGWVFlDcpWZp8EY9zMTmHL/vfYemk7CQX/lqH2VDnR1rshLQLaE1Y3itA6nXFyLr8Fk5ebwaUrBzl1YRfHkn/jRNZlUnV5/Hz1ED9fPUT9fbPoFzaQru1H4+xq2n4HJdl715wQ9qJKJ4ioqCh++umnYu9t3ryZqKioUs9xdXU1uHOTvSprUNYS6yAyMxL4Oe5tNl7ZTeb/uo/cVBqi/VoQfcd9NGv8HzROppepcHH1olHDHjRq2IN+QH5+Nn+c+Ia9ZzZwKP0M5/PTWPTnSr7462vuqdebPl1ewcXVq0Kfwd665oSwVw5VzTUzM5PTp08D0K5dO9577z3uvvtu/Pz8qFev3m213M+ePUurVq0YM2YMI0aMYNu2bTz//PNs2LDB6FlMjlLN1dIrqfPzs9m0dxbfnvtJP64Q7FSDgY0G0qXd07h7WK4bKzMjgW0HP2DjpW1c1xYmwZpqV/4vbBA9ol4yeZxCq1PoOntbuV1ze17pIWMQokoy9nvNoRLEjh07DG6yPmzYMJYtW8bw4cM5d+4cO3bsKHbOCy+8wIkTJ6hbty5Tpkxh+PDhRt/TURKEJcUfXcWy+I/0XUmhzt7c3+xhOrd7usKDyBWhLchj18H5fPP3Gn2iaOhSk6c6T6ZxWG+TrlW0wBAMd81V5V3qhKiSCcIWqnOCyEi/zPKtE9md+icAPmoXBjd5gLsjJ1o1MZSUn5vFprjZrDn3E9lKASogxj+coT3fM6klIyVKRHUlCcJMqlqCMLYr6lD8Mhb//hFpujzUqOgb0JEH756Jh4e/DaI2LC31HCu2T9InsACNB2OjXqNpkwFGX0OKHIrqSBKEmVSlBGHME3N+bhYrNz/PxquHAajj5MWoqFe5o7HxK9it7diJb/jo0Byua3NQo+K+kDv5v5g5Nm3lCGHPJEGYSVVJEEV97iX/sG/tcw/3T2Lu1vGcy0sFoH9AJx6JmVvpaaXWkJWZyGebx+tbE63dg3mu38f4+NSzcWRC2B9JEGZSFRJE0ayd0haHqYBOfoeoEbCOLKUAL7Uzz4Y/R/u2j1s3UDPYe/BDFh//jBxFi5/ajQnR02nSuK+twxLCrsie1EKv7JXDOjr5fE2OzxoytPk0dq3F7AGrHDI5AER3HMubPT8kxMmTFF0OM3a/yq8HF9g6LCEckiSIaqC0FcEqCujmv4ibfoVdT61dwpj+wFpq+d9h3QDNLDQ0irce+IEOnvXJR8f7xz7huy0vosj+DkKYRBJENWBoRbCL6ibdguaR5nUBAPeUcGI6f+wQ4w3G8PDw58X7v2VAYCcAvrq4hUVrH0NbIHtNC2EsSRDVQMmd1mqoU+kU/B6p7tfQoIKrPbnAcCLD7GcKqzmoNU483n8xTzV5CDUqdtw4zrwfBpOfm1X+yUIISRBVhVanEHfmOj/GXybuzHW0un/nHhQV9QPwUl8nPOR90l0zcNZpyEy4j6OZvSxW1M/cyvqcpenV9VVeCB+DE2oOZJxl1vcPcDM7xQrRCuHYZBZTORxhFpOxK4LX7t/PN4efI01zE1edhsSEoRS4t3eYlcOVXfl89Pg3vHtwFjmKlqZu/ky+92uL1pASwl7JNFczsfcEYcz6hr6tgkm9cZYZ6x/jSkEmXrgQ03A69UMjHWblsLGfszynz2zird2vkq0USJIQ1ZZMc60GtDqFGetOGKxIWvTejHUnuHHjAm9seJwrBZn4a9x5s99iHu7en6iwWg6RHIz9nMZ0NzUO683r3WLxUDlxKucasT8+JN1NQpRCEoQDK29nNAXIyEhg+o9DuZSfgZ/ajSm9FxIUFG61GM3BmM9ZtAOcMcLCehVLEu+sfUQGroUwQBKEAytvxzM3VQbhwR9yWZuOj9qFKb0+dLjkAJbZAa4oSbipNBy/mcjctY9QkC87yAlxK0kQDqysHc+cyKVj0ALSXbLwVDkzpcdcQkIirBid+VhqB7iwsF680nkKzqg5nHmej9Y9jk5bUJEQhaiSJEE4sJLrG4qoKKBL4ALS3FJxRs3kO98iNDTaJjGaQ2mfs4iKwtlMnRqaPtjcotkgJnaYgAYVe9P+YsXGZysVqxBViSQIB3br+oZbvzyj/T8l1SMZFSrubzCapo2N217VXpX2OW/9uTLrONq1eZRnW44A4KfkA2zYMbWCkQpRtUiCcHB9WwWz8NH2BPkUdq9E+nxNutdZVCoVg+o8zNA+o2wcoXmU/JxFgnzczLI9aNdOzzG0fn8APj+7ll8PfVSp6wlRFcg6iHLY+zqIIlqdwnfbFvD1+U8AGNqgH4N6zrJxVOZnyR3gFJ2OZT+NZOPVwzihZmr0f2l6x3/Mcm0h7Imsg6hmzpzdwg8XPkUBOtVowX/uesvWIVmERq0iKqwW94bXMfs6DpVazbB+H9PRswEF6Hg37r8kJx0z2/WFcDSSIKqAH/fHMXXTK2TkF+Ce6c3C+Ifp9s4ONh5LsHVodq9kbSdFpWHsfz6jgYsv6bo8Zm8eTXZmsq3DFMImpIupHPbexbTht9N8uWco6S7ZeOa78dvlCdxUvE0uQVEdlVXbqWNQBq9teIwbulzCa9TllQd+kD2uRZVRZbuYFixYQIMGDXBzcyMyMpIDBw6UeuyyZctQqVTFXm5ups2Vr4iKVBytiIICLd/vHkO6SzYuOg2nE4dzUyn8wza1BEV1U1TbqeQK7cS0HEavPMLBRC9evnMmLio18VmX+HLTczaKVAjbcagE8dVXXzFhwgSmTZvGkSNHaNu2LX369CE5ufQuAG9vbxISEvSv8+fPWzTGjccS6Dp7G0M+2ce41fEM+WQfXWdvs0h3z9L1L5HsegUVKtKT7yG5oEGx35tagsJSrJUwTYnHmNpO9ev3YNT/pr+uTYxj78EPrRajEPbAodrM7733HiNHjuSJJ54AYNGiRWzYsIGlS5cyadIkg+eoVCqCgoKsEl9pFUeLnkrN2d1z9Pg3bL6+DQCX6xH8fjOy1GNNKUFhbpUt0W0JptR2iu44lnPXTrA28VcWHV9KSO1WNGzQ3WqxCmFLDtOCyMvL4/Dhw8TExOjfU6vVxMTEEBcXV+p5mZmZ1K9fn9DQUO69916OHz9ukfjMWXG0PMlJx3j/0NuAgm9mHQ6m31fm8aaWoDCX8rpxbDWIbmptpyG95xNeoy55io73dr1KZobhuO2tpSREZTlMgrh27RparZbAwMBi7wcGBpKYmGjwnKZNm7J06VJ+/PFHVq5ciU6no0uXLly6dKnU++Tm5pKenl7sZQxzVxwtTV5uBu9teY4MXT6N3fz4J3csqlL+GCtTgqKyrJkwTWVqbSe1xonn+y8hUFODZG02H/w88raaTdbsWhTCWhwmQVREVFQUjz/+OOHh4dx1111899131K5dm48//rjUc2JjY/Hx8dG/QkNDjbqXJSqOGvLZxmc5m3cDL7UzE3t/xOv3dAAsU4KiMqyVMCuiIrWdangGMeGWQetvt76k/529tpSEqCyHSRD+/v5oNBqSkpKKvZ+UlGT0GIOzszPt2rXj9OnTpR4zefJk0tLS9K+LFy8adW1LVRy91Y59c9iWchQVMC7iZfxrN7d4CYqKqkjCtFYXTUVrOzVocBcjmz8GwLeXtxN/dJVdt5SEqCyHGaR2cXGhQ4cObN26lUGDBgGg0+nYunUrY8eONeoaWq2Wo0eP0r9//1KPcXV1xdXV1eT4ip5KE9NyDH5ZqCj80q5od8/587v59M9VADxYtyetWz6o/13fVsH0ahFksRIUFWFqwrT2YHZRYi15z6By7nln5Av8lfw7m6/F8+GReQwpCDO6pRQVVsvcH0MIi3KYBAEwYcIEhg0bRkREBJ06dWLevHlkZWXpZzU9/vjj1KlTh9jYWAD++9//0rlzZxo3bkxqairvvPMO58+f56mnnjJ7bEVPpaNXHkEFxZJEZbt7bmanMHfXJPIUHW096nBfj9kG729PX0CmJExrzv66VUUT67DeCziz5j/8k3eD9fGT0DAOLS5lnmPLmWRCVJTDdDEBDB48mHfffZepU6cSHh5OfHw8Gzdu1A9cX7hwgYSEf/t7b9y4wciRI2nevDn9+/cnPT2dX3/9lRYtWlgkPkt09yg6HZ9sfIaEgiz81G6M7bvIIVb0GtuNA9i0i6YitZ2cXWswPuZ93FVOXNLdIKrW5+WeY6uZZEJUhpTaKEdFSm2Ys+Lotl/f5uNTX6BGxfToNxyuumh5XUdxZ64z5JN95V7ny5Gd7aqFBLD/yGLe+/0jcvJ1FCT25Xj2XbcdU9RS2vNKD/3fAUtWpBXCGMZ+r9n/o6gDMld3z8WLe/nsr9UADK7X2+GSA5TfjWOt2V+WENn+afpdOcD6pIM4195ErUtNuK4N0f/eUNeiPS4cFKI0DtXFVJ3k5qQxb+e/4w73dHfc8t1ldeNYY/aXJQ3t9T5hbjXBRaFN8HJU/Ls+omTXokyHFY5GWhB26vNNz3EpPwNftQtj+ixwiHGHirD07C9Lc3atwfi75zBp09Nk18jklU6bCW40+baWUnnTYVUUjrX0ahEk3U3CbkgLwg7tP7KYLdf/QAWMjXgJH98Gtg7JYiy937Q1BId04MnmjwOwM20vTd0P39ZSsueFg0KURhKEnbl27U8W/7EYgIFBUcXWO1RV9rrYzxR3Ro6jm28zdCh8uD+WrMzi5V8ceaxFVF9Vs9/CQem0BXy4+XkylQLCXPx4KGaOrUOyGntc7GeqEX0+5NSae0jWZvPppud4btBXqNSFz2COPtYiqidpQdiRH3e8ysmcZNxUGp7vMQdnZw9bh2RVltxv2ho8PPx5rssU1KjYm/Y3uw9+oP9dReo/CWFrkiDsxJkzm/nmwmYAhjcdQlBwOxtHJCrijsb9eDC0JwCfnvycpKQ/gKox1iKqH0kQdiDn5g0++HU6WhQivRrSPXKCrUMSlTDo7lk0dwsgR9GyYNsEtAV5QNUYaxHVi6ykLkdFVlKb6pO1j7Pl+h/4qd145/7v8fSSLwpHl5x0jJd+HkaOouXh0F7cF/OO/neykrp6sqc/d1lJ7SAO/76cLdcLuyGejZgoyaGKCAhsxZPNH2fBic/45uIW2pzZTFhYL8D+CisKy3PUFfTSxWRDaWkX+Dh+AQD9AzpVakqrbHdpf7p1fI4o78ZoUfjg1+nk5qTZOiRhA468gl5aEDai6HQs3vQcabo86jp78UjM3Apfy1GfTqo6lVrNU70/4NR395FQkMWqzS8wYuBSW4clrMjRV9BLC8JGdux/j0OZ53FCxXNd38TZtUaFruPITyfVgadXMKMjXgDgl2tH+P3YahtHJKzJkivordFrIC0IG0hOOsayU18C8FD9PjRocHuZaGM4+tNJddGm5WD6/PMLv1w7wsLDc3m3/l0y1lRNWGoFvbV6DaQFYWU6bQEfbZtAjqKlqZs/A+96s8LXkvo+jmNor7mEOHlyQ5fLp5ues3U4wkossYLemr0GkiCs7KfdM/SrpZ+9+91KVWmV+j6Ow9XNh7FdpqFGxa/pp/n14AJbhySswNwr6MvrNQDz7sAoCcKKLl6MY/XZDQA83uRBgoLCK3U9qe/jWMLCenF/ne4AfHr8M1JSTts2IGFx5l5Bb+1eA5MTxLBhw9i1a5dZbl6dFOTnsGDXq+Sjo12NUHpEvVzpa0p9H8dz392xNHKpSaZSwMdbxqPodLYOSViYOVfQW7vXwOQEkZaWRkxMDE2aNGHmzJlcvnzZLIFUdd9tn8TZvBt4qpx4JmaevspnZUh9H8fj5OzGs3fOxBk18VmX2Bb3tq1DElbQt1Uwe17pwZcjO/P+w+F8ObIze17pYfKAsrV7DUz+lvrhhx+4fPkyo0eP5quvvqJBgwb069ePNWvWkJ+fb5agqpozZzbz/eWdADzZ8glq+oWZ7dpS38fxhIZG8XDDAQB8/vc3JCcds3FEwhrMUa3Y2r0Gla7FdOTIET777DOWLFmCp6cnjz76KM8++yxNmjQxS4C2VtlaTHm5GUz6uj+XCzLo4t2YcQ+ssUCU9lXnRZRPpy3gv1/352ROMs3dApj60E9VdltZYV5Fs5iAYoPVRf/ajXkwNPZ7rVL9HAkJCWzevJnNmzej0Wjo378/R48epUWLFsydW/GVwWVZsGABDRo0wM3NjcjISA4cOFDm8d988w3NmjXDzc2N1q1b89NPP1kkrtJ8tfVFLhcU7i09otf7FruPo++lUN2oNU6M7vEubioNJ3OS+Xn3f20dknAQVu01UEyUl5enrFmzRhkwYIDi7OysdOjQQVm4cKGSlpamP+a7775TfH19Tb10uVavXq24uLgoS5cuVY4fP66MHDlS8fX1VZKSkgwev3fvXkWj0Shvv/22cuLECeX1119XnJ2dlaNHjxp9z7S0NAUo9vmMdeLPH5TBS9sqDy1tqxyOX27y+aLq27x7pvLQ0rbK0KXtlEuX9ts6HOFACrQ65dfT15Qffruk/Hr6mlKg1Rl9rrHfayZ3Mfn7+6PT6RgyZAgjR44kPDz8tmNSU1Np164dZ8+eNU8W+5/IyEg6duzIhx9+CIBOpyM0NJTnnnuOSZMm3Xb84MGDycrKYv369fr3OnfuTHh4OIsWLTLqnhXtYrqZncLLa/5Dsjabu/1aMerelUafK6oPRacj9puB/J59mTAXP94YvBGNk4vZ7yNdkOJWFutimjt3LleuXGHBggUGkwOAr6+v2ZNDXl4ehw8fJiYmRv+eWq0mJiaGuLg4g+fExcUVOx6gT58+pR4PkJubS3p6erFXRaza8gLJ2mz8Ne483mteha4hqj6VWs0zPedSQ+XEmbwU1u54zez32Hgsga6ztzHkk32MWx3PkE/20XX2NqnT5eB02gLe+/Z+Dv++3GL3MDlBPPbYY7i5WX/h1bVr19BqtQQGBhZ7PzAwkMTERIPnJCYmmnQ8QGxsLD4+PvpXaGioybH+dfpnNl//HYDRERPw8PA3+Rqi+qjlfwfDmz8GwJqLWzh3bqfZri3FHKuuDbumsT/9H+b/Np+MdMssN5CV1CVMnjyZtLQ0/evixYsmX6Nxw148EXY/9wRF0apFxfd4ENVHt47P0cmrIQUofLRnKvm5WZW+prXLMgjruXgxjtXnfgZg2B2D8fKuY5H7OEyC8Pf3R6PRkJSUVOz9pKQkgoKCDJ4TFBRk0vEArq6ueHt7F3uZSq1xou+dUxnab2GZx8kmP6KISq3mqZi5eKmdOZ+fxprtr1T6mlLMsWoqyM/ho12vUvC/qgx3d37RYvdymATh4uJChw4d2Lp1q/49nU7H1q1biYqKMnhOVFRUseMBNm/eXOrx1iT9wqIkH98GjGw9EoC1CXv586+fKvUAIcUcq6YfdrzKP/+ryvB0zFyzVGUojUOtzJkwYQLDhg0jIiKCTp06MW/ePLKysnjiiScAePzxx6lTpw6xsbEAjBs3jrvuuos5c+YwYMAAVq9ezaFDh1i8eLEtP4a+X7jkP/eifmFZAW1/rDULKLL903Q7v40dKSd5Y+tUDl14mVylcDMpU+v9SzHHquefs9v47tJ2AEa0HI6fX2OL3s+hEsTgwYO5evUqU6dOJTExkfDwcDZu3KgfiL5w4QLqW7Jply5d+OKLL3j99dd59dVXadKkCT/88AOtWrWy1UeQTX4ckLW3dK0fOhl1wnAyNLl0rLWSPdeeAUx/gCgqy5CYlmPw75uKwsVVUszRMeTnZrFg7zS0KER6N6JLh2ctfs9Kl9qo6ipbaqOkuDPXGfLJvnKP+3JkZ6LCalX6fqJySmvtmVLWwBRanULX2dvwzd+Mc2BhafjcxEGcutlZf98gHzf2vNLDqAcIc5RlEPZh5c+jWJe4Dx+1C+8O+hZvH9NnWBaxSqkNYTrpF3YctpgFVDSwfDK7Gz6ZhV8AvrU34KFK09/XlIFlKeZYNZw89SPrEwsfLJ9u+2ylkoMpHKqLqSqQfmHHYcosIHO19m59MDh47XHC3d4h2ymPCP/l7Lr6vMHjytO3VTC9WgTJSmoHdTM7hY/2z0YButdsSUT4cKvdW1oQViab/DgOW7T2bn0wyFG8uHb1HgBSPa/QymO7weOMIcUcHdfKLeP1VRmGxVimCGppJEFYmWzy4zhs0dor+QBxOicC7/TCmSrutbfgo74qDxDVyG9/rGTL9T8AeLbji3h4Blj1/pIgbED6hR2DLVp7hh4g9l9/FM98N3LVWtoGLGPqgGbyAFENZKRfZtFv8wHoH9CJls0fsHoMMgZhI9IvbP+KvqxHrzyCCsOzgCzR2it6gCiaWpuPG5eSH6JW3RVke93ALetLwHKrZ4VlmLKWRtHpWLL5eVJ1edRx8mJIzBwrR1tIprmWw9zTXIXjsfY6iCIlv1ASz73N6gu/4KbS8E6/5QQE2m49jzDNxmMJTF97nMT0XP17Qd6uTL+npcG/Q7v2v8+CE5+hQcUbd75NWFgvs8Zj7PeaJIhySIIQYB/7Kei0BUz/ui+ncq7JNqUOZOOxBEb9by2KIYtKdCtfu3qSFzc8xk2lgMGhMdwf867ZY5J1EEKYkT3MAlJrnBjT4z39NqXrdr5u9RiEabQ6hUnfHS3zmEnfHdWvpdFpC1iwdTw3lQKauPpzb/eZ1gizVJIghHAggYFtGN50CABfn/+Fs+d22DYgUaZ9Z66Tmp1f5jGp2fnsO3MdgA27pnPiZhJuKg1j737HIrsLmkIShBAOpnvkBP3eER/snkJeboatQxKliPvnmtHHnTu3k9XnfgLg8SYPEhTczpKhGUUShBAORqVWM7LXfHzVLlwuyGDlpnG2DkmUyriuSEWbyfzdr1OAjgjP+vSIetnCcRlHEoQQDsjbJ5TRHcYD8Mu1Ixz5/XPbBiQMMrYEizpjHpcLMvBVu/BM7w8tuseDKewjCiGEycJbPUL/gI4ALIz/kNQbZ20ckSipc6Na+Ho4l3lMhO8eDmQfA2B0h/FWK8RnDEkQQjiwR2LmUd/Zh3RdHgs3P4dOW2DrkMQtNGoVgyPqlvp7X00ibrV+AaBf7QjCWz1irdCMIglCCAfm7FqD5+6ahTNq4rMu8fPu/9o6JJuzp73etTqFtb8b3kZYRQGtAz4jQymgvrMPQ3u9b+XoyierbIRwcKGhUTzW+D6Wnv6WL86uo3n97jRq2MPWYdmErVa9l6askvGRvl+T5paGRqeiZ5PXcHatYeXoyictCCGqgN7Rr9HRswEFKMzfM4Wb2cZtKFSVFO2eV/ILuWir1o3HDD/JW1JppeAbuP7BzZqFC+g0KZHkuLa0ZlhGkwQhRBWgUqsZ1XchtTRuJBRk8ekvlt+v2J7YYvc/YxgqBe+hSiMocA0KCr7ZtTmUfo/dbhAmCUIIB3Zrf/vRZBfGRL6KGhW7U/9k5773DB5njn55e+rnB9N2/7Om20vG64gIWEK2Jg8PrQtHrj5JsI+H3e7vIWMQQjgow/3tNRjSpAt7M/by6Z+raFQniuNpDUyqJFqx+9qunx/sd6/3kiXjO3j/SKrHVVSoSEp6gCydL+/a8QZh0oIQwgGV1d8+71BvGqoDyFW0zNryIuNW7S6WHAAS03MZVYF+eXvs5wf73uu9aH+PNjVPo611AAC3lLbkuEXa/QZhDpMgUlJSGDp0KN7e3vj6+vLkk0+SmZlZ5jndu3dHpVIVe40aNcpKEQthGeX1tys4cfjKcHzVrlzKTyey9pJSr3VrJdHK3hds088P9r/Xe7cGKuoFf4WTk4pWbnUZ93/vseeVHnadHMCBEsTQoUM5fvw4mzdvZv369ezatYunn3663PNGjhxJQkKC/vX2229bIVohLMeY/va/03xo7TsCFEitkUAH77UGj721kqg57muLfn6w773eFZ2Oj35+hmRtNkHONXj1/qV0aVLbbruVbuUQCeLkyZNs3LiRJUuWEBkZSdeuXfnggw9YvXo1V65cKfNcDw8PgoKC9C/Z9Ec4OmP70f9I64BHauGuc7pacdR3NbwvgbEVR+21n7+Ive71vn7nFA5lnscJNeOjZ+DpZd+thls5xCB1XFwcvr6+RERE6N+LiYlBrVazf/9+7rvvvlLPXbVqFStXriQoKIiBAwcyZcoUPDw8Sj0+NzeX3Nx/+2vT09PN8yGEMBNj+9FruGjYfONhurnOJdX9OkGBX5NyqQ4ZupLdLMY9ydpzP38Re9vr/fjJb/nifyW8hzf5P7NvHWppDtGCSExMJCAgoNh7Tk5O+Pn5kZiYWOp5jzzyCCtXrmT79u1MnjyZFStW8Oijj5Z5r9jYWHx8fPSv0FD7KZwlBBjf335/+7qF4xHJI/EocOGmJp/wwE9QUbxek7EVR+29n7+IOXf/q8x03mvX/mTegdnoUOjm24yYLpMqHIet2DRBTJo06bZB5JKvP//8s8LXf/rpp+nTpw+tW7dm6NChfP7553z//fecOXOm1HMmT55MWlqa/nXx4sUK318ISzC2v71LY398PZzJ0vlyOelhNIqKNLcbdPH7tzR4TQ9nOjcyLkHYcz+/JWw8lkDX2dsY8sk+xq2OZ8gn++g6e5tRM7Xyc7N475dnSdflUd/Zh5H9PrabEt6msGnEEydO5OTJk2W+GjVqRFBQEMnJycXOLSgoICUlhaCgIKPvFxkZCcDp06dLPcbV1RVvb+9iLyHsjTH97Rq1iln3twbgYl4LlGt3AZDh8xdtPQsriMbe39qkL3R77ec3t8pM51V0Opb8/DRn8lLwVDnxYq8FuLr5WDpki7DpGETt2rWpXbt2ucdFRUWRmprK4cOH6dChAwDbtm1Dp9Ppv/SNER8fD0BwcNX4Syyqt76tgunRLJAVcec4n5JNfT8PHotqgIuTutgxix5tz/S1J4hP70tX10uke59GU3sn73S7u0Jf6PbWz29u5U3nVVE4nbdXiyCDn/nn3TPYceM4alQ8H/EiAYGtTL6/vfy/VSmKYts18kbq168fSUlJLFq0iPz8fJ544gkiIiL44osvALh8+TI9e/bk888/p1OnTpw5c4YvvviC/v37U6tWLf744w9eeOEF6taty86dO42+b3p6Oj4+PqSlpUlrQtgVU1Y0F33pJKZlcvj4M/xdkIi/xp2Z93yJj28DK0du3+LOXGfIJ/vKPe7LkZ1vG7/5/dhqZh0sHHd4rOFA/tP9DZPuba1V6sZ+rzlMp9iqVato1qwZPXv2pH///nTt2pXFixfrf5+fn8+pU6fIzs4GwMXFhS1bttC7d2+aNWvGxIkTeeCBB1i3bp2tPoIQZmNqF0jRwO197eszadBnBDvV4Jr2Ju/89BT5uVnWDN3uVXQ675Urh5h36F10KHSv2YIBd84w6b72uErdYVoQtiItCGFvtDqFrrO3lbpoTUXhmMCeV3qU2jWRcOUwr216hiylgGifJjw36CuzD6LaU1eJKSrSgkhPu8iUtQ+TWJDFHa7+TH3gR5P2dzDHn6kpqlwLQghRyBwrmoNDOvBCx1fQoGJv2t+s2TLRrDFWZgaQrZk6nTc/N4t3NjxBYkEWARoPXuy/xOTNf+x1lbokCCEcjLlWNLdu+SBP3jEYgDWXt7M97t1Kxwb22VViClOm8+q0BSxYP4y/cq9RQ+XEKz3eq9CYjr2uUpcEIYSDMeeK5p7Rk7g3OBqAxX+uIv7oqkrFZs8F/Uxh7HTeLzaNJS79NE6omBg5mbp1O1fofva6St0hSm0IIf5V1AWSmJZj8Iu4qL/a2BXNQ3p/QMqPj7A79U/mHn6PqR4BFS4JYUpXibEruG2lvOm867a/xrrEwrGKZ1o8TsvmD1T4Xub+MzUXaUEI4WDMvaJZpVbzzH+W0to9mBxFS+yeV7l0qfxBWkPstaukokor27Fz33usPLcBgKH1+3Nn5AuVvo89rlKXBCGEAzL3imZnZw8m3LOCMBc/MnT5vLl1HMlJx0yOy167SszpYPxSFp1cAcDAoM7c02OmWa5rj6vUZZprOWSaq7Bn5p5KmpF+melrH+ZSfgaBmhrM+M/n1PQLMymerrO3ldtVYq7pmtYWf3QV7xx6lwIU7vJtzqh7VqDWmLen3hrTg439XpMEUQ5JEKK6SUk5zbT1j5OszSbEyZOpA5ablCSKZjEBxZJE0Veco9ZsOnr8G2YfiCUfHZ29w3j+3i/ROLnYOqwKkXUQQogK8fNrzJQ+i/HXuHOlIJP/bhjGjZTSKyCXZI9dJZV17MQ3vHNwFvnoiPCsz3P3rHLY5GAKaUGUQ1oQorpKSvqD//7yDNe0Nwlx8uT1fkup5X+H0ec76krqkn77YyVzDr9HPjrCa9TlxXu/MnkhnL2RLiYzkQQhbMUevmBvTRIBGg9e67WAoOB2Vo3Blg78toT34xdQgEKEZ33G3/OFwycHkARhNpIghC1Yq6qnMZKTjvHWpmdILMjCR+3Cq3e9Q4MGd1k1BlvY+utslpxajQ6FKO/GjL1nJU7OFZ99ZQ8Jv4gkCDORBCGsrWiQt+Q/TFsO8qalnuOt9cM4n5+Gh8qJCR1foXXLB60ag7UoOh1rtkxkzeXtAHSv2ZJnBi6v1Gwle0r4IIPUQjgkW5eqKG0PZh/fBkwf9A3N3QLIVgqIPTCTbb++bZEYbCk/P5tFax/TJ4f7Q+6s9FRWR65NJaU2hLCysroabFmqorynXA/PAF67/3sWbniCvWl/8fGpL0hIO8uQ3h+YfS2ALaSlnmPOz09xKucaalSMaPIgvbq+WqlrVnZ3Oltz/D9VIRxIeV/CtipVUVq3VtFTblG3lrNrDZ4btJrg/3XBrE2M4+w3A3mu38f4+NQza0zWdObMZt7dM4UUXQ4eKifGdXiB8NZDK31dR69NJV1MQliJMV0NtihVYWq3lkqt5sHecxnXaiRuKg1HbyYw6YeH+Ov0z7dd11B3lT1RdDo27JjK1F0vk6LLIcTJk5m9PzZLcgDHr00lLQghrMDYroadL91t9aqeFX3K7dJxDKFB7Xlvx0tcKchk2u5Xuf+fTdx3dyxbTt2wq0FZQ9LTLrJw0xiOZF4AoKNnA57ttxgPzwCz3cPRa1NJC0IIKzD2S/jw+RtWr+pZmafc0NAo3nrgB7p4N0aHwprL23lxZW/++/VXdjsoq+h0/HpwARN/eIAjmRdwRs2Ixg8w8YHvzJocwPTd6eyNJAghrMCUL2Frl6qo7FOuh4c/4x5Yw/OtnqKGyomz+akE1FlKtN9nuKqy9MfZw4ZB1679yZxv7+f9Y5+Qrssj1NmbN++eS59uU8y+JzfYbxlvY0kXkxBWYOqXcHmb1ZiTuTarie44lhynTizf+RKp7ilk+JyinedsslO6cjSzBwpONhuUvZmdwrrdM1h7ZTf56NCg4r463Rl091s4O3tY9N5FCb9kl1uQnXW5GSIJQggrqMiXcNFmNZZW9JQ7euURVBiuwGrsU26mqg67El+kdY0deNTaTrYmD2pvo4vvPlJS7uZkdjSgttqgbM7NG2zdP5d153/hhi4XgOZuAYyInkq9el2tEgNYN+Gbk8N0Mb311lt06dIFDw8PfH19jTpHURSmTp1KcHAw7u7uxMTE8Pfff1s2UCEMsPeuBnN1axW2gNQczerB7xdfxjOtOU6KmgznbJwDNxBd9w06eK2jlpvW5BhNmRV1I+UM3215kbFf9+bzs2u5ocslQOPBhLbPMm3wRqsmhyKl7U5nzxym1Ma0adPw9fXl0qVLfPrpp6SmppZ7zuzZs4mNjWX58uU0bNiQKVOmcPToUU6cOIGbm3FNfim1IczJ3koulFTZekGGNgzyUl+nbc3vyPL6B61KQaVSUdvVlSi/VkTfcR93NO5f7kI7Y/6/5dy8wR9/fsvO0+s4knkB3f8iCNB4cG+jgdzV8fkqUWjPHKpsLaZly5Yxfvz4chOEoiiEhIQwceJEXnzxRQDS0tIIDAxk2bJlPPzww0bdTxKEMDd7KtpmCaVtGOSlvkEr75+pEfA3Gao8/fs11a609G5Ay8AONAntRlBQ22LjAqUt4nNTZRHqepJBrdNI1/7F8awr5KPT//4OV396N76HLu1HVYu9G0xh7PdalR2DOHv2LImJicTExOjf8/HxITIykri4uFITRG5uLrm5ufqf09PTLR6rqF6sNbZgK6UNynp6BTPsP+/Sq1ktjv65hl//XseBtL+5octlT+op9qSeglNfoEFFkJMnfi5eeDp5cOxSLl1ra1GhA3U+Os1N8p1yyNHkowCbrqtwcy7sLQ/QeNDRvxV3t36C0NAoq33mqpr0q2yCSExMBCAwMLDY+4GBgfrfGRIbG8uMGTMsGpsQVV15g7LhrR4hvNUjjMzN4q9/NnLswk6Op5zgfO4NchQtlwsyuFyQgU6nkOumK/U+blon3PJ86dqgHf06PEjdOpEWma5aFnvvNqwMmyaISZMmMXv27DKPOXnyJM2aNbNSRDB58mQmTJig/zk9PZ3Q0FCr3V+IqsKYlpKzaw1aNn+Als0fAAoXsV1P+YvLifGkZSVx9NIl9p45D6hQFA06nRM3tTXJyA8gpSCYNG3hA+AD3cMJDa1j6Y90G2NrWDkqmyaIiRMnMnz48DKPadSoUYWuHRQUBEBSUhLBwf/+ASUlJREeHl7qea6urri6ulbonkKIylGp1fj7N8Pfv/Ch0Nn/Ou8c3FfuebYoVeHolVqNYdMEUbt2bWrXrm2Razds2JCgoCC2bt2qTwjp6ens37+f0aNHW+SeQgjzMtcivpLMMWbg6JVajeEwYxAXLlwgJSWFCxcuoNVqiY+PB6Bx48Z4enoC0KxZM2JjY7nvvvtQqVSMHz+eN998kyZNmuinuYaEhDBo0CDbfRAhhNHMuYiviLnGDBy9UqsxHCZBTJ06leXLl+t/bteucOP07du30717dwBOnTpFWlqa/piXX36ZrKwsnn76aVJTU+natSsbN240eg2EEML2zFmqwpxjBo5eqdUYDrcOwtpkHYQQ9sFci/hK6xYq6q7a80oPo65raFFgZa5nTbIntRCiSqlsqQpTxgyMjceey6eYgyQIIUS1YIkxA2uXZrc2hxmDEEKIyrDUmIGjVmo1hiQIIUSVUtpYhaWmzELVLZ8iCUIIUWWUN4XV3FNmqzoZgxBCOITy9oMomsJa1l7YVX3MwNykBSGEsHvltQxMKXtRlccMzE0ShBDCrhmzuM3H3cWkshdVdczA3KSLSQhht8prGUBhyyAxveqXvbAFSRBCCLtl7OK2lMzcUo+5lSOXvbAFSRBCCLtl7BO/Xw0Xgn3cblvRXERF4ZhFRaawVmeSIIQQdsvYJ/4gH/cqX/bCFiRBCCHsVtHiNmNaBjKF1fxkFpMQwm6Zuh+ETGE1Lyn3XQ4p9y2E7Zlrkx9RyNjvNWlBCCHsnrQMbEMShBDCIcjiNuuTQWohhBAGSYIQQghhkCQIIYQQBkmCEEIIYZAMUgtRDZS2y5o9sOfYqjuHSRBvvfUWGzZsID4+HhcXF1JTU8s9Z/jw4SxfvrzYe3369GHjxo0WilII+2PPawjsOTbhQF1MeXl5PPjgg4wePdqk8/r27UtCQoL+9eWXX1ooQiHsjzG7rNmKPccmCjlMC2LGjBkALFu2zKTzXF1dCQoKskBEQtg3U3ZZs3aXjj3HJv7lMC2IitqxYwcBAQE0bdqU0aNHc/369TKPz83NJT09vdhLCEdk7F4KB86mWC+o/7Hn2MS/qnSC6Nu3L59//jlbt25l9uzZ7Ny5k379+qHVaks9JzY2Fh8fH/0rNDTUihELYT7G7qVgi13W7Dk28S+bJohJkyahUqnKfP35558Vvv7DDz/MPffcQ+vWrRk0aBDr16/n4MGD7Nixo9RzJk+eTFpamv518eLFCt9fCFsydi8FW+yyZs+xiX/ZdAxi4sSJDB8+vMxjGjVqZLb7NWrUCH9/f06fPk3Pnj0NHuPq6oqrq6vZ7imErRTtpZCYlmOwr19F4V4JtthlzZ5jE/+yaYKoXbs2tWvXttr9Ll26xPXr1wkOlulzouozdS8FiU2U5DBjEBcuXCA+Pp4LFy6g1WqJj48nPj6ezMxM/THNmjXj+++/ByAzM5OXXnqJffv2ce7cObZu3cq9995L48aN6dOnj60+hhBWZc+7rNlzbKKQw0xznTp1arFFb+3atQNg+/btdO/eHYBTp06RlpYGgEaj4Y8//mD58uWkpqYSEhJC7969eeONN6QLSVQr9ryXgj3HJmRHuXLJjnJCiKrG2O81h+liEkIIYV2SIIQQQhgkCUIIIYRBDjNIbStFQzRSckMIUVUUfZ+VNwQtCaIcGRkZAFJyQwhR5WRkZODj41Pq72UWUzl0Oh1XrlzBy8sLlcr4qXfp6emEhoZy8eJFh5n9JDFbh6PF7GjxgsRcHkVRyMjIICQkBLW69JEGaUGUQ61WU7du3Qqf7+3t7TB/QYtIzNbhaDE7WrwgMZelrJZDERmkFkIIYZAkCCGEEAZJgrAQV1dXpk2b5lBlPSRm63C0mB0tXpCYzUUGqYUQQhgkLQghhBAGSYIQQghhkCQIIYQQBkmCEEIIYZAkCAtYsGABDRo0wM3NjcjISA4cOGDrkMq0a9cuBg4cSEhICCqVih9++MHWIZUpNjaWjh074uXlRUBAAIMGDeLUqVO2DqtMCxcupE2bNvpFUFFRUfz888+2Dssks2bNQqVSMX78eFuHUqrp06ejUqmKvZo1a2brsMp1+fJlHn30UWrVqoW7uzutW7fm0KFDtg5LEoS5ffXVV0yYMIFp06Zx5MgR2rZtS58+fUhOTrZ1aKXKysqibdu2LFiwwNahGGXnzp2MGTOGffv2sXnzZvLz8+nduzdZWVm2Dq1UdevWZdasWRw+fJhDhw7Ro0cP7r33Xo4fP27r0Ixy8OBBPv74Y9q0aWPrUMrVsmVLEhIS9K89e/bYOqQy3bhxg+joaJydnfn55585ceIEc+bMoWbNmrYODRRhVp06dVLGjBmj/1mr1SohISFKbGysDaMyHqB8//33tg7DJMnJyQqg7Ny509ahmKRmzZrKkiVLbB1GuTIyMpQmTZoomzdvVu666y5l3Lhxtg6pVNOmTVPatm1r6zBM8sorryhdu3a1dRgGSQvCjPLy8jh8+DAxMTH699RqNTExMcTFxdkwsqqtaB9yPz8/G0diHK1Wy+rVq8nKyiIqKsrW4ZRrzJgxDBgwoNjfa3v2999/ExISQqNGjRg6dCgXLlywdUhlWrt2LRERETz44IMEBATQrl07PvnkE1uHBUgXk1ldu3YNrVZLYGBgsfcDAwNJTEy0UVRVm06nY/z48URHR9OqVStbh1Omo0eP4unpiaurK6NGjeL777+nRYsWtg6rTKtXr+bIkSPExsbaOhSjREZGsmzZMjZu3MjChQs5e/Ys3bp105ftt0f//PMPCxcupEmTJvzyyy+MHj2a559/nuXLl9s6NKnmKhzbmDFjOHbsmN33MwM0bdqU+Ph40tLSWLNmDcOGDWPnzp12myQuXrzIuHHj2Lx5M25ubrYOxyj9+vXT/3ebNm2IjIykfv36fP311zz55JM2jKx0Op2OiIgIZs6cCUC7du04duwYixYtYtiwYTaNTVoQZuTv749GoyEpKanY+0lJSQQFBdkoqqpr7NixrF+/nu3bt1eqJLu1uLi40LhxYzp06EBsbCxt27bl/ffft3VYpTp8+DDJycm0b98eJycnnJyc2LlzJ/Pnz8fJyQmtVmvrEMvl6+vLHXfcwenTp20dSqmCg4Nve0ho3ry5XXSNSYIwIxcXFzp06MDWrVv17+l0OrZu3eoQfc2OQlEUxo4dy/fff8+2bdto2LChrUOqEJ1OR25urq3DKFXPnj05evQo8fHx+ldERARDhw4lPj4ejUZj6xDLlZmZyZkzZwgODrZ1KKWKjo6+bZr2X3/9Rf369W0U0b+ki8nMJkyYwLBhw4iIiKBTp07MmzePrKwsnnjiCVuHVqrMzMxiT1hnz54lPj4ePz8/6tWrZ8PIDBszZgxffPEFP/74I15eXvrxHR8fH9zd3W0cnWGTJ0+mX79+1KtXj4yMDL744gt27NjBL7/8YuvQSuXl5XXbuE6NGjWoVauW3Y73vPjiiwwcOJD69etz5coVpk2bhkajYciQIbYOrVQvvPACXbp0YebMmTz00EMcOHCAxYsXs3jxYluHJtNcLeGDDz5Q6tWrp7i4uCidOnVS9u3bZ+uQyrR9+3YFuO01bNgwW4dmkKFYAeWzzz6zdWilGjFihFK/fn3FxcVFqV27ttKzZ09l06ZNtg7LZPY+zXXw4MFKcHCw4uLiotSpU0cZPHiwcvr0aVuHVa5169YprVq1UlxdXZVmzZopixcvtnVIiqIoipT7FkIIYZCMQQghhDBIEoQQQgiDJEEIIYQwSBKEEEIIgyRBCCGEMEgShBBCCIMkQQghhDBIEoQQQgiDJEEIIYQwSBKEEEIIgyRBCGFDV69eJSgoSL8XAMCvv/6Ki4tLsarAQtiC1GISwsZ++uknBg0axK+//krTpk0JDw/n3nvv5b333rN1aKKakwQhhB0YM2YMW7ZsISIigqNHj3Lw4EFcXV1tHZao5iRBCGEHbt68SatWrbh48SKHDx+mdevWtg5JCBmDEMIenDlzhitXrqDT6Th37pytwxECkBaEEDaXl5dHp06dCA8Pp2nTpsybN4+jR48SEBBg69BENScJQggbe+mll1izZg2///47np6e3HXXXfj4+LB+/XpbhyaqOeliEsKGduzYwbx581ixYgXe3t6o1WpWrFjB7t27Wbhwoa3DE9WctCCEEEIYJC0IIYQQBkmCEEIIYZAkCCGEEAZJghBCCGGQJAghhBAGSYIQQghhkCQIIYQQBkmCEEIIYZAkCCGEEAZJghBCCGGQJAghhBAGSYIQQghh0P8DMg+BpsNont0AAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -548,7 +546,7 @@ "46 3.249923 -0.119755\n", "47 5.199877 -1.107408\n", "48 3.683247 -0.471516\n", - "49 4.333231 -0.666329, models=[-0.04, -0.04, -0.04, -0.04, -0.04])\n" + "49 4.333231 -0.666329, models=[sin(x), sin(x), sin(x), sin(x), sin(x)])\n" ] } ], @@ -649,13 +647,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:05<00:00, 18.87it/s]\n", + "100%|██████████| 100/100 [00:05<00:00, 19.70it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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", + "image/png": 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jmTRpkuO9586dS1xcHD169AAgLCwMTdPK/IypU6eyfv16nnvuOQ4ePMigQYN45JGKG//s6+vLF198QefOnfHz82P27Nls2LDBMavgmjVrmDJlCg8++CBJSUmMHDmSJ554ghEjRrB+/fpS1115L4ETJ07w7rvv8tZbb1XYd3EFSik+OvARp7NO42/2Z3TLkXhu+tfv0xD0nOa0d4LT7RDNAw88wIEDB4iPj3c82rRpw4ABAxz/29PTk7i4OMdrEhISOHPmzFX3C72cl5eXY270suZIdzc7d+4kPj6ee+65x/Gr+yVt2rQp8fzIkSMl7tEK0LFjR8c9Wm/E5SOfwsKK57i+dIy3PPd6vVHR0dG39PorXTlyKywsrMQx6unTp/PZZ5+V+R5ms5kvv/ySb775hoKCAmbNmlXm9mfOnMHX19fxeP755/npp59KLLtyPv4rxcTEMG7cOP75z3/y4osv0qlTJ8e6xMREVq5cyR/+8Afuuece4uLisNlsjvvnlrbucufOnaNXr148+eSTPPvss2VmcXc/nv2Rjf/biBEjo1qPovqhFZD4U/EwyAengl+I3hFLpdsevJ+fH82bNy+xrGrVqlSvXt2xfNiwYYwdO5agoCD8/f154YUXiImJ4d57762QTF4mLz7t9WmFvHd5Prs8GjZsiMFguOpYd/369QHw8bl6T6Jq1ao3lMVoLP53X112q4DSTs56ev4+1vfSKJfr7fHeiiu/y41kvZbL80Pxd7iZ/Fu3bgUgIyODjIyMMv/Mw8PDS5wzWLZsGd98802JG5pf+q2jNJqmsWXLFkwmEydOnCixbvjw4QAcPnwYKP4HaNy4cdddd0lSUhJdu3alQ4cOvP/++2XmcHcnM0+y6OAiAPo36U/zi8mw74vilfePh9DmZbxaf7qPoinLrFmzePjhh+nXrx/3338/oaGhFXrCx2Aw4O3hrcujvEMAq1evTo8ePZg7dy65ubk39T2bNm3Kli1bSizbsmULzZo1A6BmzZoAJUatXF5IN/I5O3bsKLHsdg9xLU9Ws9kMFB8CrAgnT55kzJgxfPDBB7Rv355BgwaV+Y+Eh4cHDRs2dDyCg4Px8fEpsex6Bf/mm29y9OhRNm3axJo1a1i0aNFV23Tp0qXUk8alrTt37hxdunRxDEu+9A9oZZRlzeLtPW9j02xEh0TziG+D4hkiAVo+DXf3LPsNnIBTTVVwaYTGJd7e3sybN4958+bpE8hJvffee3Ts2JE2bdowefJkIiMjMRqN7Nq1i6NHj173MMb48eN56qmnaNWqFd27d2fVqlUsW7bMMbzSx8eHe++9lxkzZlCvXj3S0tJ49dVXbzjnqFGjGDx4MG3atKFjx458+eWXHDp0yPHbxu1Qnqx16tTBYDCwevVqHnroIXx8fPD19S3X+0+cOJFz586VepjGbrczcOBAevbsyZAhQ+jVqxctWrRg5syZjB8//pa/37Xs27eP1157jaVLl9KxY0fefvttRo0aRefOnW/pz/ZSudepU4e33nqL9PR0x7qyznu5I01pzNk7h/P55wmtEsqIhk9iXDUG7Fao0xHausZhq8r7z7MLa9CgAfv27aN79+5MnDiRqKgo2rRpw7vvvus4LluWxx57jHfeeYe33nqLe+65h4ULF7Jo0aISY7E//vhjioqKiI6OZvTo0UydOvWGc/bv35+///3vTJgwgejoaE6fPu04RHA7XS/rXXfdxZQpU3j55ZcJCQm5oRuFJycnc+bMmVLXv/7665w+fZqFCxcCxcfw33//fV599VX2799/c1+oDAUFBQwcOJDBgwc7hsM+99xzdO3alWeeeeaWfktZv349J06cIC4ujlq1ahEWFuZ4VDb/SfgPB84fwMvkxYtRf6XKD/+A/ItQvSF0exVc5DcbuSer3E9SiJvmjj9Du1J28dbu4pFDL0SNoNOh7+H0luIRM39Y6BQnVeWerEIIcYPO5Zxj7r65QPHFTJ3+90txuZvMxcMhnaDcb4QUvBBCAPlF+czcPZMCewFNg5oyAH/4pXg6YLq8DCHN9A14E6TghRCVnlKK+fHzOZdzjiDvIMaEdcVjy5zilW2GQsMH9A14k6TghRCV3ooTK9iRsgMPowdjG/2RgI1vgNKg0YPQ+s96x7tpUvBCiEptX9o+vkr4CoBhjfrTaMt8sOYUX8R0/3i4yWmqnYEUPCWvghRClJ+r/+wk5yQzZ+8cFIoetbrS7dBayE4G/3B48HXwMOsd8ZZU6oK/dJm6q94gQwi9XfrZuXLKB1eQX5TPm7vfJK8oj7sDGzEoPRlSD4KXH/SaAT6Beke8ZU51JeudZjKZCAwMdEwuVaVKlZu+a5AQlYlSiry8PNLS0ggMDLxqJkpnpymNufvmci7nHNW8qzHWGIznr9+A0QQ9/gHV6ugd8bao1AUPv1+CffkMgkKI8gkMDHTJaQy+Of4Nu1N342H04MWgdlTb9dskg/ePh7tal/1iF1LpC95gMBAWFkZwcLBT385OCGfj6enpcnvuADuTd7L02FIAng3rSqPdv80O2foZaNxbx2S3X6Uv+EtMJpNL/mUVQpTfmawzjtvu9Q5pR5f4ZaDZoWF3aDNM53S3X6U+ySqEqDyyrFm8uetNCuwFNA9oyMCEbWDNhbBI6PySSw+HLI0UvBDC7RVpRbyz5x3S8tMI9q7O6NQkPHLTITDCLYZDlkYKXgjh9j47/BkHLxzE2+TNhHwjfhd+LR4G2fsN8Hbf23rKMXghhNuya4oFu1bw7enVeJqMvOhbk4ikXeDhBT2nF1/Q5Mak4IUQbmnNwWReW7OWHN//ABpRlmr4FfyIJdCHgEdfd8nZIW+UHKIRQridNQeT+euSDWRXWQFo3JXnz8u5h7HZNV5J7caanAZ6R7wjpOCFEG7Frikmr96HOWQ1BmMBAVYfXs88gQEDy+2d+K92L1NWHcauufY8OuUhBS+EcCvbf03notdKjJ4ZeNs9+GfGObzR+ElrwSf2nigg2VLAzsQMvaNWOCl4IYRbWZm4BJPPaTwUjL9wkWDNykGtLrOLnkBdVnlp2QU6prwz5CSrEMJt/HD6Bw5nbcKIxtCLBdxTlMsZFczrRQOxXVF3wX7ucZPwskjBCyHcwoH0A3x88GOqmk38Ic9Ot4IsLih/JtsGk4uPYzsDEBrgTbt6QfqFvUPkEI0QwuUl5SQxa+8s7JqdjlY7L5ryyMObKUWDOE+AY7tLkxFM6tsMk9H9pia4khS8EMKlZVuz+dfOf5Fry+Vuq43h59MJrFoVrcc/KfSvW2Lb0ABv5g9sTa/mYfqEvcPkEI0QwmXZ7DZm7p5JSl4KwdZCxp2/gKfBBF1foUODrvzcUbEzMYO07AKC/YoPy1SGPfdLpOCFEC5JKcXCXxZyJOMIPtZ8JpzPIAATdHgBGnQFwGQ0ENOgus5J9SMFL4RwSctPLOencz9htOYy5kImEcoErQZC8356R3MacgxeCOFyfj73M18lfAW2fIZetBClmaDxQ9D2//SO5lSk4IUQLuXIhSPM3z8figrpa8mkR5ER6nSE+8e55U07boUUvBDCZSTnJPPW7rcosuXT3pLB04VAaHN44DUwyi03ryQFL4RwCZZCC9N2TiOn0ELDrPPEFoAxqH7xvO6e7n9V6s2QghdCOD2r3cqbu94kLTeF4Kw0JuQpvPzC4KG33PqOTLdKCl4I4dQ0pTF331yOXzyGb1YKE3M1AnyCisu9ag294zk1XQt+/vz5REZG4u/vj7+/PzExMXz//feO9QUFBcTGxlK9enV8fX3p168fqampOiYWQtxpnx/+nB3JO/DITmFcrka4p1/xvVQDI/SO5vR0LfhatWoxY8YM9uzZw+7du+nWrRuPPvoohw4dAmDMmDGsWrWKr7/+mk2bNpGUlMTjjz+uZ2QhxB3031//y3e/fgc5KcTmFtHUWAV6TYeajfWO5hIMSimnuq1JUFAQb775Jk888QQ1a9Zk8eLFPPHEEwAcPXqUpk2bsm3bNu69995yvV9WVhYBAQFYLBb8/eVYnRCuYlvSNmbvnQ05aQzIzucR5QMPToW6HfWOprvy9prTHIO32+0sWbKE3NxcYmJi2LNnDzabje7duzu2adKkCbVr12bbtm06JhVCVCS7pliyfxv/3DKLQks6D+bk0lczQ+eXpNxvkO5TFRw4cICYmBgKCgrw9fVl+fLlNGvWjPj4eMxmM4GBgSW2DwkJISUlpdT3KywspLCw0PE8KyuroqILIW6zNQeTmfTdZrJ8FxNousB9BVncm2lif7vnaNm4l97xXI7ue/CNGzcmPj6eHTt2MHz4cAYNGsThw4dv+v2mT59OQECA4xERISdihHAFaw4m89d/byar6tf4my4Sbc2i/wUDX1q78odNYaw5mKx3RJeje8GbzWYaNmxIdHQ006dPJyoqinfeeYfQ0FCsViuZmZkltk9NTSU0NLTU95s4cSIWi8XxOHv2bAV/AyHErbJrismr92IO+RZfj3SaFWUy8IKB7+wdWWzvBsCUVYexa051ytDp6V7wV9I0jcLCQqKjo/H09CQuLs6xLiEhgTNnzhATE1Pq6728vBzDLi89hBDObevJVC56L8PXfI6G2kUGnzew1daGj+wPAQYUkGwpYGdiht5RXYqux+AnTpxI7969qV27NtnZ2SxevJiNGzeydu1aAgICGDZsGGPHjiUoKAh/f39eeOEFYmJiyj2CRgjh/DSlsfj4Qny9E6lLBoPPGzhsa8G79sf4/SZ7xdKyC3TJ6Kp0Lfi0tDT+/Oc/k5ycTEBAAJGRkaxdu5YePXoAMGvWLIxGI/369aOwsJCePXvy3nvv6RlZCHEbKaX4+ODHnMveRS3DBZ5JN5BU2JiZRU+irnGAIdhP5py5EU43Dv52k3HwQjivr499zdLDX2LIPMvjqXaKsusxuWgQVjxLbGeg+H6qP7/UrVLdcq805e013YdJCiEqp7Wn1rL08GKw/I+hNjPNghvT4+Ij2K5R7gCT+jaTcr9BTneSVQjh/rYmbWXR/vfBcpYnbCYerNaMWn+aw6yBMYQGlDwMExrgzfyBrenVPEyntK5L9uCFEHfU/vT9zNszG2U5w4M2I0/4NYY+xdP+9mruT49moexMzCAtu4BgP2/a1QuSPfebJAUvhLhjjl08xswdMyi6eIoYGwyp2hDDw7PAp5pjG5PRQEyD6jqmdB9yiEYIcUeczT7Lv7ZNpTDjJJE2jVjvehgfngVVpcwrihS8EKLCpeam8vrWyeRcSKCRzc6L5tp49p0NfiF6R3NrUvBCiAqVUZDB1K2TuZh2iAhbES971sK77zvgH653NLcnBS+EqDDZ1mxe3zqFtJR9BNusvGIKw/fh2XI3pjtECl4IUSHybHlM3/ZP/pe0k2o2K383hVCt7xwIqqd3tEpDCl4IcdsV2gt5Y8c0Tp79CT9rAa8YaxLc510Iqq93tEpFCl4IcVvZNBtv73yDI6fi8LEV8DdjMBF93oUaDfWOVulIwQshbhu7ZufdXW8Tf/J7zLYCXjbUpH6fOVDzbr2jVUpS8EKI20JTGvP3zmHH8ZV42PIYR3WaPDQHajbWO1qlJQUvhLhlSik+il/AT0e/xmTLYwxBRPV5F4Kb6B2tUpOCF0LcEqUUn/zyPj8c+gKjLY8RBNGmzzwIbqp3tEpP5qIRQtw0pRRfHlzEmgOfgC2fv1CNDlLuTkMKXghx0/5z+HNW7f8AbPk8awiiy0PvyWEZJyIFL4S4KUsPf8GyvfOhKJ8hhmp07zNfTqg6GSl4IcQNW354MV/vmQtFBQw0BNLr4fehRiO9Y4krSMELIW7It0eWsGTPO1BUyNPGavR9+EOo3kDvWOIapOCFEOW26si/+XL3bCgqpL8xiEf7fiRzyzgxKXghxDXZNVXi1nnpBWv4cs87YLfyhKk6j/f9GKrV0TumKIMUvBDiKmsOJjNl1WGSLQUABPn/TGiNNVQxafQ3B/PkI4sgoJbOKcX1SMELIUpYczCZ4V/sRf32vEbAJoKD1uGh7LTI8KVGl7ek3F3EDV/JOmjQIDZv3lwRWYQQOrNriimrDjvKPSRgAyFB6/DATqssP1ZdeIG//XAeu6bKfB/hHG644C0WC927d6dRo0ZMmzaNc+fOVUQuIYQOdiZmOA7LhAeup0bQekzYicwKZMWFUZwngGRLATsTM3ROKsrjhgt+xYoVnDt3juHDh/PVV19Rt25devfuzdKlS7HZbBWRUQhxh6RlFwCKiMDvCaq2ARMazbOCWHFhJBZ8r9hOOLubmmysZs2ajB07lv3797Njxw4aNmzIM888Q3h4OGPGjOH48eO3O6cQ4g6o6etFvWqrCKi2GSMa92TWZPmFkeRQpcR2wX7eOiUUN+KWZpNMTk5m/fr1rF+/HpPJxEMPPcSBAwdo1qwZs2bNul0ZhRC3mV1TbDt5gZXx59h28gJ2TaGU4kTSXPyqbcOIoklmOMsuvkA+v5e5AQgL8KZdvSD9wotyu+FRNDabjW+//ZZFixaxbt06IiMjGT16NE8//TT+/v4ALF++nKFDhzJmzJjbHlgIcWuuHAIJEBpgpk/jVRy0/ITZZKBOegQrMp/DellFGH7776S+zTAZDQjnd8MFHxYWhqZp/OlPf2Lnzp20bNnyqm26du1KYGDgbYgnhLidrhwCWUyjpuf77Eo/ipeHkefDOlHUbiJbVydc8Y+AN5P6NqNX87A7HVvcpBsu+FmzZvHkk0/i7V36MbjAwEASExNvKZgQ4va6cghksSJaBX+EvWoiBqB2RnO6Dp6DyWSixz13lbiStV29INlzdzE3XPDPPPNMReQQQlSwy4dAAhiw0iZkAYVVkjAC1c9H8032EzxxKpOYBtUxGQ3ENKiuX2Bxy+RKViEqicuHNnoY8okOfY9873Q8gCppnYjLffiq7YRrk4IXopK4NLTRy5hNq9C55HlZ8FQGDGk9+Dmv21XbCden6023p0+fTtu2bfHz8yM4OJjHHnuMhISEEtsUFBQQGxtL9erV8fX1pV+/fqSmpuqUWAjX1a5eEPUDsmkdPps8LwteykhByqPs/q3cZQik+9G14Ddt2kRsbCzbt29n/fr12Gw2HnzwQXJzcx3bjBkzhlWrVvH111+zadMmkpKSePzxx3VMLYRrupiRQOPQOeR65uKtmchI+iOHCu4FZAikuzIopZxm1qD09HSCg4PZtGkT999/PxaLhZo1a7J48WKeeOIJAI4ePUrTpk3Ztm0b995773XfMysri4CAACwWi2OcvhCVzbn/bef1uNFc0ArwU2bS0gfzy8Xf53IPkyGQLqW8veZUx+AtFgsAQUHFvyLu2bMHm81G9+7dHds0adKE2rVrl1rwhYWFFBYWOp5nZWVVcGohnNuvJ9Yw7ee/k61s3OXhx996f0C1oMYyBLIScJqC1zSN0aNH07FjR5o3bw5ASkoKZrP5qoumQkJCSElJueb7TJ8+nSlTplR0XCFcwsFfPufNPbMoQKOBV3VeevhTAvyL53KXIZDuT9dj8JeLjY3l4MGDLFmy5JbeZ+LEiVgsFsfj7NmztymhEK5l+7a3mL7nbQrQaF61Fq/+Yamj3EXl4BR78CNGjGD16tVs3ryZWrV+/wsYGhqK1WolMzOzxF58amoqoaGh13wvLy8vvLy8KjqyEM5LKdbHvcxHZ9eigPbVmvBCn0/w9JThj5WNrnvwSilGjBjB8uXL+fHHH6lXr+Td2aOjo/H09CQuLs6xLCEhgTNnzhATE3On4wrh9FSRlf+sGsqHv5V799AYRj/ypZR7JaXrHnxsbCyLFy9m5cqV+Pn5OY6rBwQE4OPjQ0BAAMOGDWPs2LEEBQXh7+/PCy+8QExMTLlG0AhRmdjzLXy0ahBxuacAA080eIQn7puMwSAnTysrXYdJlvYXb9GiRQwePBgovtDpxRdf5N///jeFhYX07NmT9957r9RDNFeSYZKiMiiwnOWd1YPZa72A0WBkWItn6R49XO9YooKUt9ecahx8RZCCF+7OkryPN9bFckLLw9Poyaj2E2nbRC4GdGcuOQ5eCHFjUo59z4ytr5GsbPh6+DCh69s0riXnp0QxKXghXNSxXQt44+D7ZKMR7FWNib0WEh50t96xhBORghfC1WgaOze8ypwz32NDUd+vNi899BGBVWrqnUw4GSl4IVyIKszlu++G83nmLyigdc2WjOz5Hj6eVfSOJpyQFLwQLsKencKnq4aytjAJMNCjXk+G3P86JqNJ72jCSUnBC+EC8pP3M2fdCPZq2WD0YGCLYTzc6nkZ4y7KJAUvhJM7f2Qlb2yfymlseHp480KH12jf4CG9YwkXIAUvhLNSipNb3+aNhC/INCgCvKoxvvs7NAqO1DuZcBFS8EI4I1sBW9eO5b30bdgMitr+dZnQcyE1fUP0TiZciBS8EE5GZafxzX+f4+v8U4CBVmHtGPnAbKrISBlxg6TghXAihUn7WLB+FFu1LDCaePjuJxlw7wSMBqe5dYNwIVLwQjiJ8wf+w1u73yQRGx4e3gxrO55uTfrpHUu4MCl4IfRmLyJh81RmJq7EYlD4eVfjxa5v0zS0ld7JhIuTghdCT/mZxH0Xy8dZhygyQJ3AhozrMZdg3/JNhy1EWaTghdBJUdpRPlsby9qiC2Aw0j6iM8M7T8PHw0fvaMJNSMELoQPLoRXM2jmNI1jBZKZ/5DD+EPWcXJkqbispeCHuJHsRJzZNZeapb8kwaHh7+fNCp6m0qX2/3smEG5KCF+JOyb1A3Hd/5eOcBIoMcFdAPcZ1f5dw/1p6JxNuSgpeiDvAdm4vH8WNYYPdAgYjbSM689f7X5eLl0SFkoIXoiIpRdreRcza/x6/Goowmrx4Kuo5Ho0cIhcviQonBS9ERbHmsm/dBOambSXHoPDzqc7IzjOIDGurdzJRSUjBC1EBtPPH+XrtCyyzpoDBQMMazRnzwCxqyG31xB0kBS/EbZZ54Gvm7HqTQwYrGD3peffjPNNuHJ4mT72jiUpGCl6I28WWz6EfX2POuR/INCi8zf482+EVOtXrqXcyUUlJwQtxG2gXTvLN2pEsKzyHZjBQq1pDxnabxV3+EXpHE5WYFLwQt0IpLh5cytzdb3GQQjB60KX+Qwzt8ApeJi+904lKTgpeiJtlzSP+h4nMS9lMlkHhbfZj2L0TuV/ulyqchBS8EDfBlnqIr9aPZZUtFQwG6lS7m1HdZsohGeFUpOCFuBGaRvKeD5hz4AN+NRT9NkrmDzzTbryMkhFORwpeiN/YNcXOxAzSsgsI9vOmXb0gTMbfZ3dUOefZuG4siywHKDQofL2r8XzHSbSt3UW/0EKUQQpeCGDNwWSmrDpMsqXAsSwswJtJfZvRq3kY2Sfj+ODnSezQcsBg5J7gVsR2eYPqVWromFqIsknBi0pvzcFkhn+xF3XF8hRLAaO+2M7MVptYnbuZiwYNk4c3T7YYxqNRw2QuGeH0pOBFpWbXFFNWHb6q3AHqGc/QvvrnfGDJwdvTSJhfBC90fZMG1Zvc8ZxC3AwpeFGp7UzMKHFYBsCARi/v7yisuYVDHooiZaJtcE/G9ZyCt4e3TkmFuHFS8KJSS8suWe6hpNMl6BOOBlxAA4rsvpxNf5IW7ftLuQuXo+tBxM2bN9O3b1/Cw8MxGAysWLGixHqlFK+99hphYWH4+PjQvXt3jh8/rk9Y4ZaC/S6VtqKz1wZa1JrN4YALFGGkILcxh/83luz8xpdtJ4Tr0LXgc3NziYqKYt68eddc/8YbbzBnzhwWLFjAjh07qFq1Kj179qSgoOCa2wtxo9rVC6Kpfx5PVnuHrPC1pHra0TQfktKe4FjaYAxaFcICiodMCuFqdD1E07t3b3r37n3NdUopZs+ezauvvsqjjz4KwGeffUZISAgrVqzgj3/8452MKtyRUiQd/DfNgmeRYC9AYaAory5Hz/8JZffn0gj4SX2blRgPL4SrcNpj8ImJiaSkpNC9e3fHsoCAANq3b8+2bdtKLfjCwkIKCwsdz7Oysio8q3A9RdmpfPvDWL7JPESRCXw9fDmf0Ztjaa3ht2oPvWwcvBCuyGkLPiUlBYCQkJASy0NCQhzrrmX69OlMmTKlQrMJF6YUv+7/nAXx8zitCgEDrYNb8n9d3iDQp0aZV7IK4WqctuBv1sSJExk7dqzjeVZWFhERMgGUgMLMs3z9w4v8N/sYGuDnWZXBbcbQsXE/DIbiIo9pUF3fkELcRk5b8KGhoQCkpqYSFvb7r8ipqam0bNmy1Nd5eXnh5SXzcIvLaBq/7JrHB0c+I03ZAAMdQtoyuOsMAnzk5KlwX05b8PXq1SM0NJS4uDhHoWdlZbFjxw6GDx+ubzjhMiypB/lsw0v8nH8OTVNUNfnSs9FInop5Sg6/CLena8Hn5ORw4sQJx/PExETi4+MJCgqidu3ajB49mqlTp9KoUSPq1avH3//+d8LDw3nsscf0Cy1cgmYrYMPP/2Txqe+xaHasdsDSlN0ZT7LxqDdzNv8oJ1CF2zMopa41DccdsXHjRrp27XrV8kGDBvHJJ5+glGLSpEm8//77ZGZm0qlTJ9577z3uvvvucn9GVlYWAQEBWCwW/P39b2d84aROn1jLh9unccxmwa4pDPm+/Jr2FBesDR3bXNp3nz+wtZS8cDnl7TVdC/5OkIKvPPKyk/l6w8usubAfDfA2elJwoR070noCpqu2N1A8FPLnl7rJ4RrhUsrba057DF6I8lJ2Oz/teocvE/5NpmYDoH1gEyIbvMz/LT5X+uuAZEsBOxMzZPSMcEtS8MKlJZ7ezKKt/yCh4DwAYZ5+DGkzhqgmj7My/hxQesFfcuWEY0K4Cyl44ZIs2Ul8venvxKXvRUPhZTDxh4gePHzfJDzNPgDlniBMJhIT7koKXrgUm93Kuu1vs/TEN+T9djimg18DBnaZRvUajUts265eEGEB3qRYCq55Q49Lx+BlIjHhrqTghUtQSrEnYTlf7JlDsjUTgLoefgyKHkmzZk9e8zUmo4FJfZsx/Iu9GKBEyctEYqIykIIXTu/XlH18ueWfHMz6FYAAgwf96z1M144TMXqUfdVyr+ZhzB/Y+qobastEYqIykIIXTis9+xxf/fwPfkrdBUrDEwN9gqJ4tMs/qRJQ/vmFejUPo0ezUJlITFQ6UvDC6WQXZrFix1usPbUGm90KQEfvMP4YM5Hguvff1HuajAYZCikqHSl44TQKigr4fv+HfHtkCXm2HACamXwZGPkcDSIHglHXG5AJ4XKk4IXubHYbcUf/w7JfPsJSkAFAHcw83fBRotqPwWCuonNCIVyTFLzQTZFWxOaTq1kWv5D0nBRAEaxMPBnWiU73vYrRt6beEYVwaVLw4o6za3a2nF7P0r3vkZr9P1Aa1ZSRfkFRdOn0Cp41GukdUQi3IAUv7hi7Zufn03Esi19AiuU0KDsBysCjvo3o0WEC5lpt9Y4ohFuRghcVzqbZ2HzqB1b88iFpllOg2fFTBvp616Jn+xfxrt8FDDJkUYjbTQpeVJhCeyE/Jq5j1YFFXMg6A1oRfsrAI+YwerR5AZ+7e8nIGCEqkBS8uO1yrDms+/W/fHfoS7JzkkAropoy0tccygOt/oJ3k75gkr96QlQ0+SkTt016XjrfHV/Oj8eXU5CbDloRwcrII56hdGn1HJ5N+4LJU++YQlQaUvDilp24eILVx5ay4/QPaHkXQdmpo0w8Yr6LmNbPYWr8kBS7EDqQghc3xa7Z2Zmyk++PLSMhZTcUWEBptNA86OtTl8jWz2Fo9KAcihFCR/LTJ26IpdDChrMbWHdsORcyE6EwGw8UHTVPHvJvRt3WQ6FeFzl5KoQTkIIX16WU4kTmCdadWsu2U+ux5aaDLQ9/ZaC7ZqZHcFuCWg+Bu1rLcEchnIgUfCVm11SZU+jmF+Wz5dwW1id+z6n0A5CfCXYr9ZWJB7UqdKrXE8+op0GuPBXCKUnBV1JrDiZfdROMsABvXnu4KXdH5BN3Jo4tp3+kIDcVCix4KsW9mgc9TcE0bPIHDC36gW+wjt9ACHE9UvCV0JqDyQz/Ym/J+5Qa8ziv4hm74SMa1DhPVS0HbHmEKSPd7WY6+zfAr/kT0KgnyOyOQrgEKfhKxq4ppqw6/Fu5F2GqcgoP36N4VzmJvyEHf/Lwytbo5OHFA5ofTSPuw9C8nxxfF8IFScFXMtt/PU9q4UnM1RPwrHocX1Mm/uThQyF32Qy0zjVQO9ePRvc+Sf37n5bDMEK4MCn4SkApxa+WX9matJUVR3+kWuhp/Ay5+FJAgKYRlWegVa6Jc9ZGrNXaslNrwtvh0dSXchfCpUnBu6lLpb49eTvbk7eTlvU/KMzCkGehPoXck2cgKs9AlYLq/Ki14W/2VqQT6Hh9sJ+3fuGFELeFFLwbsWt2jmYcZWfKTnal7OJCbgoUZkNhFl5FVqI1D2I0M4ZUD36y3sOHWisOqzoofr8oyQCEBhQPmRRCuDYpeBeXa8tlf9p+9qTtIT4tnpyCi7+VejbeRYW00jy4V/OkJYF4R9wLDbuzLrcB7/77EECJkTSXTqFO6tusxHh4IYRrkoJ3MZrSOJN1hvj0eOLT4knIOIpmywdrDlhz8LNZidY8aat5EEkA5vDWUL8L1LsffAIBeBCYb/K6ahx8aIA3k/o2o1fzMF2+mxDi9pKCr2DWIo3Pt53idEYedYKq8ExMXcweNzZPy4X8Cxw8f5AD5w/wS/ovWAovgjUPrLlgzeEuu0a05kkbzYNG+GC8KxrqdYa6naDKtQ+19GoeRo9moWVeySqEcG0GpZS6/mauKysri4CAACwWC/7+/nf0s6d/d5gPfkpEu+xP2GiAZ++rx8SHmpX6uoyCDI5eOMqhC4c4fOEwSTlJUFQAtuJS97bl00wzEaV50FrzJNizKkS0gzqdoPa94H1nv6cQ4s4qb6/JHnwFmf7dYRZuTrxquaZwLJ/4UDM0pXEu5xzHMo6RcDGBIxlHSMtNA3th8V66LQ+jLZ/6GrTQPGiheXC38sMzIOK3Uu8IoZHgYb7TX1EI4eSk4CuAtUjjg5+uLndQGEy5GM1pLDqwDVugN4lZJ8mz5vy2h54PRfkYbQXU1qCZZuIezYOmqipVzX4Q0RLuioaI9hBw153+WkIIF+MSBT9v3jzefPNNUlJSiIqK4t1336Vdu3Z6xyrV59tOoSkNg0cWRnM6Rq/zxf81p2My5eCFDS+DjV3HFFWMNrzsNhpoJu5WJppqJu5WVaniWRXCW0B4SwhrCTUbg9Gk91cTQrgQpy/4r776irFjx7JgwQLat2/P7Nmz6dmzJwkJCQQH63+lpc1uIyUvheScZM7lnONczjn+e+YAfnXO4GkoxGwowowNM8X/9cROzSIDtazQNN+TdlV8qK28MfmFQ3BTCG1R/AiqL4UuhLglTn+StX379rRt25a5c+cCoGkaERERvPDCC7z88svXff2tnmTVlEZmYSbn889zPu886bnJpGWdJTX7HKl5KZzPv4Cm2UArArsNNBvWwkLsdjseCkKKDIRZIcxmIMwGoTawaNX4VYVRs14LHuvetXjvvJTRLkIIcSW3OMlqtVrZs2cPEydOdCwzGo10796dbdu2XfM1hYWFFBYWOp5nZWXd8OdaspN569unuaBZuahsaEoBGmgaJS8NKuaNgXBl5C5l5C5lwj/fjCHDSlFRAKmqBmdUMLtVMP9TwSSqUPIongbg7ZZRUKfWDecTQojycOqCP3/+PHa7nZCQkBLLQ0JCOHr06DVfM336dKZMmXJLn1vFsyrHrBcdz41ANWUkWJmogYFgZSLE049gr0DCq4Ti7xuKoWpN8AsB31D2ZnjRf8lpbNf54w0L9LmlnEIIURanLvibMXHiRMaOHet4npWVRURExA29h6fZl3HR4wj0DiDIK4hA70BMnlXAXBU8fcCzapk3lY6qpajx/YUSV4leKUzmexFCVDCnLvgaNWpgMplITU0tsTw1NZXQ0NBrvsbLywsvL69b+2CjkbaRA2/65SajgUl9mzH8i72AzPcihNDHjV0zf4eZzWaio6OJi4tzLNM0jbi4OGJiYnRMdn29mocxf2BrQgNKTrsbGuDN/IGtZb4XIUSFc+o9eICxY8cyaNAg2rRpQ7t27Zg9eza5ubkMGTJE72jXJfO9CCH05PQF379/f9LT03nttddISUmhZcuWrFmz5qoTr87KZDQQ06C63jGEEJWQ04+Dv1V6TjYmhBAVoby95tTH4IUQQtw8KXghhHBTUvBCCOGmnP4k6626dIrhZqYsEEIIZ3Spz653CtXtCz47Oxvghq9mFUIIZ5ednU1AQECp691+FI2maSQlJeHn54fBUP7x55emODh79qzLjL6RzHeGq2V2tbwgma9HKUV2djbh4eEYy5g2xe334I1GI7Vq3fyMjf7+/i7zF+wSyXxnuFpmV8sLkrksZe25XyInWYUQwk1JwQshhJuSgi+Fl5cXkyZNuvWZKe8gyXxnuFpmV8sLkvl2cfuTrEIIUVnJHrwQQrgpKXghhHBTUvBCCOGmpOCFEMJNScFfw7x586hbty7e3t60b9+enTt36h2pTJs3b6Zv376Eh4djMBhYsWKF3pHKNH36dNq2bYufnx/BwcE89thjJCQk6B2rTPPnzycyMtJxEUtMTAzff/+93rFuyIwZMzAYDIwePVrvKKWaPHkyBoOhxKNJkyZ6x7quc+fOMXDgQKpXr46Pjw8tWrRg9+7deseSgr/SV199xdixY5k0aRJ79+4lKiqKnj17kpaWpne0UuXm5hIVFcW8efP0jlIumzZtIjY2lu3bt7N+/XpsNhsPPvggubm5ekcrVa1atZgxYwZ79uxh9+7ddOvWjUcffZRDhw7pHa1cdu3axcKFC4mMjNQ7ynXdc889JCcnOx4///yz3pHKdPHiRTp27Iinpyfff/89hw8fZubMmVSrVk3vaKBECe3atVOxsbGO53a7XYWHh6vp06frmKr8ALV8+XK9Y9yQtLQ0BahNmzbpHeWGVKtWTX344Yd6x7iu7Oxs1ahRI7V+/XrVuXNnNWrUKL0jlWrSpEkqKipK7xg35KWXXlKdOnXSO8Y1yR78ZaxWK3v27KF79+6OZUajke7du7Nt2zYdk7k3i8UCQFBQkM5Jysdut7NkyRJyc3OJiYnRO851xcbG0qdPnxJ/r53Z8ePHCQ8Pp379+gwYMIAzZ87oHalM3377LW3atOHJJ58kODiYVq1a8cEHH+gdC5BDNCWcP38eu91+1Q29Q0JCSElJ0SmVe9M0jdGjR9OxY0eaN2+ud5wyHThwAF9fX7y8vHj++edZvnw5zZo10ztWmZYsWcLevXuZPn263lHKpX379nzyySesWbOG+fPnk5iYyH333eeY9tsZ/frrr8yfP59GjRqxdu1ahg8fzsiRI/n000/1jub+s0kK5xYbG8vBgwed/jgrQOPGjYmPj8disbB06VIGDRrEpk2bnLbkz549y6hRo1i/fj3e3t56xymX3r17O/53ZGQk7du3p06dOvznP/9h2LBhOiYrnaZptGnThmnTpgHQqlUrDh48yIIFCxg0aJCu2WQP/jI1atTAZDKRmppaYnlqaiqhoaE6pXJfI0aMYPXq1WzYsOGWpnS+U8xmMw0bNiQ6Oprp06cTFRXFO++8o3esUu3Zs4e0tDRat26Nh4cHHh4ebNq0iTlz5uDh4YHdbtc74nUFBgZy9913c+LECb2jlCosLOyqf+SbNm3qFIeWpOAvYzabiY6OJi4uzrFM0zTi4uJc4lirq1BKMWLECJYvX86PP/5IvXr19I50UzRNo7CwUO8YpXrggQc4cOAA8fHxjkebNm0YMGAA8fHxmEwmvSNeV05ODidPniQsLEzvKKXq2LHjVcN8jx07Rp06dXRK9Ds5RHOFsWPHMmjQINq0aUO7du2YPXs2ubm5DBkyRO9opcrJySmxh5OYmEh8fDxBQUHUrl1bx2TXFhsby+LFi1m5ciV+fn6O8xsBAQH4+PjonO7aJk6cSO/evalduzbZ2dksXryYjRs3snbtWr2jlcrPz++q8xpVq1alevXqTnu+Y9y4cfTt25c6deqQlJTEpEmTMJlM/OlPf9I7WqnGjBlDhw4dmDZtGk899RQ7d+7k/fff5/3339c7mgyTvJZ3331X1a5dW5nNZtWuXTu1fft2vSOVacOGDQq46jFo0CC9o13TtbICatGiRXpHK9XQoUNVnTp1lNlsVjVr1lQPPPCAWrdund6xbpizD5Ps37+/CgsLU2azWd11112qf//+6sSJE3rHuq5Vq1ap5s2bKy8vL9WkSRP1/vvv6x1JKaWUTBcshBBuSo7BCyGEm5KCF0IINyUFL4QQbkoKXggh3JQUvBBCuCkpeCGEcFNS8EII4aak4IUQwk1JwQshhJuSghdCCDclBS/ELUhPTyc0NNQxFzjA1q1bMZvNJWYlFUIPMheNELfou+++47HHHmPr1q00btyYli1b8uijj/L222/rHU1UclLwQtwGsbGx/PDDD7Rp04YDBw6wa9cuvLy89I4lKjkpeCFug/z8fJo3b87Zs2fZs2cPLVq00DuSEHIMXojb4eTJkyQlJaFpGqdOndI7jhCA7MELccusVivt2rWjZcuWNG7cmNmzZ3PgwAGCg4P1jiYqOSl4IW7R+PHjWbp0Kfv378fX15fOnTsTEBDA6tWr9Y4mKjk5RCPELdi4cSOzZ8/m888/x9/fH6PRyOeff85PP/3E/Pnz9Y4nKjnZgxdCCDcle/BCCOGmpOCFEMJNScELIYSbkoIXQgg3JQUvhBBuSgpeCCHclBS8EEK4KSl4IYRwU1LwQgjhpqTghRDCTUnBCyGEm5KCF0IIN/X/FIjI9WefBgcAAAAASUVORK5CYII=", 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" ] @@ -668,13 +666,13 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:05<00:00, 19.46it/s]\n", + "100%|██████████| 100/100 [00:05<00:00, 19.91it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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yduxYGjRogJOTEwEBAXTo0IEFCxZU2bHEp02bhkajKfF1OzZs2IBGoyE1NbVsC75D06ZNK/UIl2U5MXlZSUhI4IknnqBRo0Zotdoif1m+8MILLFy4kJMnT1Z8geUpIwm2/vXUcfQw8K5bYLXVZuXt3W+TnJOMv7M/Y1uPrZCbqjeSgK+CTp48SatWrfj111+ZOXMmf/zxB9u2bWPSpEmsWLGCtWvXFruvxWKpwEpvzQsvvEBCQoLjFRISwmuvvVZg2fXMZrNKld4+i8XCnDlzCvx/SE5OLnEcnrKYmLw0pk2bVmgmr5Lk5eXh5+fHyy+/TIsWLYrcxtfXlx49erBgwYIyqrISsNlg4+xrwwBHPVpok8VHFrMvZR8mnYnno5/HzeimQqES8AUoikJufq4qr1sZ823kyJHo9Xp27drFgAEDaNKkCfXq1aNv377873//KzABhkajYcGCBTz44IO4uroyY8YM4No0fUajkYiICL788kvHPqdOnUKj0RQY7jY1NRWNRuO44rx6Vbxu3Tqio6NxcXHh7rvv5siRIwVqnT17NgEBAbi7uzNs2LBip/cDcHNzIzAw0PHS6XS4u7s73j/22GOMHj2acePGOYLjZrWeOnWKLl26AFCrVi00Gk2BELPZbEyaNAlvb28CAwOZNm1aqf8/XP0+GI1GNm/e7Fj2xhtv4O/vT1JSUqHtr/4V0rVrVw4cOMCyZcvo06cPISEhxX7GjROTF+WLL77Azc2twOQoI0eOpHHjxuX2F12dOnV4++23eeqpp/D09Cx2u2o3kfiBpXB+t32Gps6TCw0D/Pv53/n5xM8ADG8xnDqedVQo0k7GorlOnjWPwasGq/LZC3suxKkU0/ZdunTJceXu6upa5DY3NmVMmzaN2bNnM2/ePPR6PcuWLWPs2LHMmzePbt26sWLFCoYOHUpISIgjDEvrpZdeYs6cOfj5+TF8+HCefvppx+Qi3333HdOmTWP+/Pl07NiRL7/8knfeeYd69erd0mdcb+HChYwYMaLABCYlCQ0N5YcffqB///4cOXIEDw8Px7R4V483YcIEduzYwbZt2xgyZAgdOnSge/fuwM0nDr/ajv/kk0+yd+9eTp48ySuvvMKSJUsICAgotL1er+f555+na9eudOzYkbCwMLZv315iQN44MXlRnnrqKVasWMHAgQPZunUrq1ev5r///S/btm3DxaViuuQVp127dpw7d45Tp05Rp04dVWu5Y1dOw46//tq6azh4FewRE58WX+Cm6t3Bd1d0hQXIFXwVc/z4cRRFISIiosByX19f3NzccHNz48UXXyyw7oknnmDo0KHUq1ePsLAw3nrrLYYMGcLIkSNp1KgREyZM4KGHHuKtt9665XpmzJhBp06diIyMZPLkyWzdutVxlT5v3jyGDRvGsGHDiIiI4PXXX3fMInW7GjZsyBtvvEFERESh70FRdDod3t72Gez9/f0JDAwsEKZRUVFMnTqVhg0b8tRTTxEdHc26desc60szcfjrr79OrVq1ePbZZxk0aBCDBw/mwQcfLHJbq9XK22+/zXPPPccDDzzA/fffT8+ePVm1qvi5gIuamLwoH374IQkJCTz33HMMGzaMadOm0aZNm5vuV96u1n769GmVK7lDNitsmGWfPDskGiL7FVidlpfGW7vewmwz09KvpSo3VW8kV/DXMelMLOy5ULXPvhOxsbHYbDYGDhxIXl5egXXR0dEF3h86dIhnn322wLIOHTrw9ttv3/LnRkVFOb4OCgoC7G3KYWFhHDp0iOHDhxfYPiYmhvXr19/y51xV1oF1ff1gP4fk5GTH+9JMHG40Gvn666+JiooiPDycuXPnFrutzWbDYrGwbt06Zs6cSefOnfnXv/7FsmXLit2nuInJb1SrVi0++eQTevTowd13383kyZNL3H7z5s306tXL8d5sNqMoCt9//71j2YcffsjAgQNv+tklqTYTif/xJSQfAqMbdJpcYPJsi83C3Li5pOSkEOQaxHOtn1PlpuqNJOCvo9FoStVMoqYGDRqg0WgKtXVfbfa4vvnhquKacoqj/atN8fr7AsXdnDUYrj21d7VpqDzHdb/xXG6l1qJcXz/Yz+F26t+6dStgnzT78uXLxX7PDQYDL7zwQoFlAQEBhX4RXq+4icmLsmnTJnQ6HQkJCWRlZeHu7l7sttHR0QXuXbzzzjucP3+ef//73wVqu1PVYiLx5MMQ99fFX8dx4FbwXBYeWMihy4dw1jszse1EXA239jNXXtT/FSNuiY+PD927d+e9994jKyvrto7RpEmTQm3Yv//+u6P55OoP4vW9Vq4Pglv5nB07dhRYduNk3neqNLVenRS8vPqQnzhxgvHjx/Pxxx/Tvn17Bg8eXKpfEtOmTaNz58433a64iclvtHXrVv7973+zfPly3NzcSpwvF+wXA9dPnu7t7Y27u3uBZSX9giit/fv3YzAYaNq06R0fSxWWXFj/Oig2qN8FGnQrsHrt6bWsOb0GDRrGtBpDbbfaKhVamFzBV0Hvv/8+HTp0IDo6mmnTphEVFYVWq2Xnzp0cPnz4ps0YEydOZMCAAbRq1Ypu3bqxfPlyli5d6uhe6ezszF133cXs2bOpW7cuycnJvPzyy7dc59ixYxkyZAjR0dF06NCBr7/+mgMHDtzRTdYblabW8PBwNBoNK1as4IEHHsDZ2Rk3t9J1W5syZQrnz5/niy++KHK91Wpl0KBB9OjRg6FDh9KzZ0+aN2/OnDlzmDhx4h2fH9gnJl+4sOSmw4yMDJ588kmee+45evXqRUhICG3btqVPnz48/PDDZVJHUa7+Ms3MzOTixYvs2bMHo9FY4F7L5s2bueeee4r867JK2PEBpJ4FFx/oOKFA08zBSwf5dL99+N9HIx6lTYD69zwKuOXZXquY6jjptqIoyoULF5TRo0crdevWVQwGg+Lm5qa0a9dOefPNN5WsrCzHdoCybNmyQvu///77Sr169RSDwaA0atRI+eKLLwqsP3jwoBITE6M4OzsrLVu2VH799VcFUNavX68oiqKsX7++0KTcf/zxhwIo8fHxjmUzZsxQfH19FTc3N2Xw4MHKpEmTlBYtWpTqHG+cxLtTp07K2LFjC213s1oVRVFee+01JTAwUNFoNI6Jros63o0Tlt9s4vDp06crQUFBSkpKimPZDz/8oBiNRmXPnj2lOs+buXFi8qIMHTpUad68uZKbm+tYNmfOHMXb21s5d+5cqT5n6tSpBc69NChiIvEbJySPiIhQvvnmm2KPUal/Ds/ssE+c/cG99q+vk5SVpAxbPUwZsHyAMi9u3m1Nnn27SjvpdqUJ+FmzZilAgR+4nJwcZeTIkYq3t7fi6uqqPPTQQ0piYuItHbe6BryoWV544QXl2WefVbuMW7Zy5UqlSZMmisViKXabSvtzmH1FUb7oZw/3zXMLrrJkKxM3TFQGLB+gTN40WcnNzy3yEOWltAFfKdrgd+7cyYcfflioR8P48eNZvnw5S5YsYePGjVy4cIGHHnpIpSqFUE9VnZg8KyuLzz77DL2+irUGKwpsnmOfyKNWOLS/dhPcptiY/8d8TmecxtPoyQvRL9xxL7jyonrAZ2ZmMnDgQD7++GNq1arlWJ6WlsYnn3zCf/7zH7p27UqbNm347LPP2Lp1a5nfqBOisrs6MblWq/qP7C15+OGHad++YkdQLBNHfvlr+j09dHkZDNd613135Dt2Ju1Er9XzfPTz+Dj7qFhoyVT/1zJq1Ch69+5Nt24F70zHxcVhsVgKLG/cuDFhYWFs27atossUQtQUaeeuDSTWdhj4NXKs2nJ+C8uO259Z+GfUP4nwvvnDdmpS9e+mxYsXs3v3bnbu3FloXWJiIkajES8vrwLLAwICSExMLPaYeXl5BR70SU9PL7N6hRDVnDUffpthH0gsqAVEXXsa9diVY45hCB6s/yD3htyrVpWlptoV/NmzZxk7dixff/11qZ7SK61Zs2bh6enpeIWG3nz2FOUWBvoSQpStSvXzt3shJB8Ekzt0fdkxkFhKTgpv7nwTi81Cm4A2PN74cZULLR3VAj4uLo7k5GRat26NXq9Hr9ezceNG3nnnHfR6PQEBAZjN5kJjeCclJREYGFjscadMmUJaWprjdfbs2WK3vfoUY5V/hFqIKuzqsM+qj3Wf8Cf88ZX963smgJs/ADn5Obyx8w3SzGmEu4czptWYSjEMQWmo1kRz3333sW/fvgLLhg4dSuPGjXnxxRcJDQ3FYDCwbt06xzjYR44c4cyZM8TExBR7XJPJhMlUujvaOp0OLy8vx9gjLi4utz2phBDi1tlsNi5evIiLi4u6PW3yMuC3v55WbdQT6ne116fYeO+P9zidbu8xM7HtRJz1VeeBLdW+o+7u7jRr1qzAMldXV3x8fBzLhw0bxoQJE/D29sbDw4MxY8YQExPDXXfdVWZ1XP1r4PoBpoQQFUer1RIWFqbexdXVLpGZSeBRGzqMdaz65vA37ErahUFr4Pno5/FzqVrj6VTqzqlz585Fq9XSv39/8vLy6NGjB++//36ZfoZGoyEoKAh/f/9KPduRENWV0WhUt/vnkV/gxHrQ6uC+V8BoHz//tzO/FZi4o7L3mCmKRqlUdzjKXnp6Op6enqSlpeHh4aF2OUKIyiT1LCz9B1hyoN2z0Mo+NPKBlAPM2DEDq2Ll4UYP80ijR1QutKDS5lrVuFMghBBlLd8M66bbwz24FbSw94y5kHmB/8T9B6tipUNwBx5uWH6DtZU3CXghRM0U+xGkHAMnD+jyEmi1pJvTmR07m0xLJg29GjK8xfAq3fFCAl4IUfOc2Q77lti/7jQZ3PywWC28tfMtkrKT8Hf2Z2LbiRh1RnXrvEMS8EKImiXrEqyfaf+6WX+o0wFFUViwdwFHrhzBRe/CpHaT8DQVPxF6VSEBL4SoOWw2+O3/IDcNfBo4Ron87sh3/H7hd3QaHRPaTCDU/eZPwFcFEvBCiJrjjy/gwh9gcIZu00BvZP2Z9Sw9vhSAfzT/B839mqtbYxmSgBdC1AwX9lw3cfYE8Apl38V9fLzvYwD+3uDvdAnrol595UACXghR/eVcsTfNXB2KoNH9nE0/y5y4OVgVK3cH382AiAFqV1nmJOCFENWbzQbrZ0FWCniFQoexXM69zKzYWeTk59DEuwkjW4ysMgOI3Yrqd0ZCCHG9vd/A2R2gM0K36eRoNcyOnc2l3EsEuwbzQvQLGHQGtassFxLwQohqy3phL5lbPiA1x8Lh+k+T5xnG3Li5jtEhJ7ebjJvRTe0yy02lHmxMCCFu19rdR3BZMQ63/Aw22Frwn9Vu+Oz/F/6BJ/Bzc+PFdi8S4BqgdpnlSq7ghRDVzqp95zm/9GXc8q9wXvHl/fy+GLx2kmPYx+lLOdzlNYj6XvXVLrPcScALIaoVq00h7qf5tNIew4yB2fmPY3E7gcErFgDzpc58uUGH1VatB9IFJOCFENXMgV0b6G3+BYAF+X0455SLyfc3ACxp0eRnNCMhLZfY+MtqllkhJOCFENVH1iUCd76BFoW11jas14dg9P8FUMjPbIzlyrXZ4JIzctWrs4JIwAshqgebFdZNx8WazhklgA81HTEFLEejyceaE4Y5pStwbehff3cn9WqtIBLwQojqIfZjSNiLi6s7M21/Rxv4CxpdDjazP3nJvQCdY1OtBtqE11Kv1goiAS+EqPriN9sfaAL+aPgPLgdsQaNPx5bvSW5iH1AKjutuUyDu9BU1Kq1QEvBCiKot9SxsmAWApXl/3k/fidaYgmJ1IS+xL9hcitxN2uCFEKIys+TAmlfAnIUtoBnvGiwk5R1DUQzkJfVByS9+0g5pgxdCiMpKUWDTW3A5HsW5Fp+GRrAjKRYPJxPuWX9HMfsXuZsGCPJ0ol1d74qtVwUS8EKIqunAUji+FjRavm/SiTUJv6NBw+hWo3mtZy/g+j4zFHg/tU8kOm3VnUy7tCTghRBVT8KfsG0+AKsiOvF90nYAhjQbwt3Bd9OzWRALBrUm0LNgM0ygpxMLBrWmZ7OgCi9ZDTLYmBCiaslKgbVTwWbl99qRfJ5xGICHGz1Mzzo9HZv1bBZE98hAYuMvk5yRi7+7vVmmJly5XyUBL4SoOqwWWDMVsi+zx9OP+VxBQaFHnR483PDhQpvrtBpi6vuoUGjlIE00QoiqY+u7kLSfI0Yjc1x1WFHoENyBIU2HoNHUnCvz0pKAF0JUDYdWwMGfOKW1MbuWB2YNtPRryYiWI6rldHtlQb4rQojKL+kA/D6PBKzM9PYiW6+nsXdjJkRPwKCtntPtlQUJeCFE5ZZ1CX59hRRrHq97OpFmciHcI5xJbSdh0pnUrq5Sk4AXQlRe+WZY8wpp2Rd53UUhxbUWQa5BvNT+JVwNrmpXV+lJwAshKidFgS1zyUzazwyTmQQPf3xd/Hn5rpfxNBU/BIG4RtWAX7BgAVFRUXh4eODh4UFMTAy//PKLY31ubi6jRo3Cx8cHNzc3+vfvT1JSkooVCyEqzIGl5Bz5H7MN2Zz28MPT2ZeX27+Mr7Ov2pVVGaoGfEhICLNnzyYuLo5du3bRtWtX+vbty4EDBwAYP348y5cvZ8mSJWzcuJELFy7w0EMPqVmyEKIinIvDvPVd3tRnc8zNGzfXAF666yWC3GrGE6hlRaMoSqWaedbb25s333yThx9+GD8/PxYtWsTDD9sfYDh8+DBNmjRh27Zt3HXXXTc5kl16ejqenp6kpaXh4eFRnqULIcqA9cpZUhc/w3vai+x1ccHdtz6vxrxKfa/6apdWaZQ21yrNk6xWq5UlS5aQlZVFTEwMcXFxWCwWunXr5timcePGhIWFlRjweXl55OXlOd6np6eXe+1CiLKxZs9xND+PYq1HArudDZzP9MQ1qQvHQl2o76V2dVWP6jdZ9+3bh5ubGyaTieHDh7Ns2TIiIyNJTEzEaDTi5eVVYPuAgAASExOLPd6sWbPw9PR0vEJDQ8v5DIQQZWHVvvOc/X4KGzwS+NNZS4LNj5ykv3ExxY8RX+1m1f4EtUusclQP+IiICPbs2cOOHTsYMWIEgwcP5uDBg7d9vClTppCWluZ4nT17tgyrFUKUB6tN4dCPb3DW5wh7nSFB8SU7uTe23DCutiFPX34Qq61StShXeqo30RiNRho0aABAmzZt2LlzJ2+//TaPPvooZrOZ1NTUAlfxSUlJBAYGFns8k8mEySQPPwhRlRzduJgs1/XEuSgkKT6kJ/8Na049x3oFSEjLJTb+co0ePOxWqX4FfyObzUZeXh5t2rTBYDCwbt06x7ojR45w5swZYmJiVKxQCFGWlPN/sOHIf4h1VbiMB5cu9sGa3bDIbWvCPKplSdUr+ClTptCrVy/CwsLIyMhg0aJFbNiwgdWrV+Pp6cmwYcOYMGEC3t7eeHh4MGbMGGJiYkrdg0YIUbkpqWdZtGYsvxnMZOY7k3CxL9asxsVuXxPmUS1LqgZ8cnIyTz31FAkJCXh6ehIVFcXq1avp3r07AHPnzkWr1dK/f3/y8vLo0aMH77//vpolCyHKiJKTxjf/e4aflXS0Rmdy0h7Cltm0yG012GdjqgnzqJalStcPvqxJP3ghKh8l38x3y55gaeZx0BoYeteLYOnAiK9229dft+3VUd5r0lR7N1PaXKt0bfBCiGpOUfh+1Uh7uGu0DG41kp4RD8s8quVA9V40QoiaZcm6SXx/cRegYVCTJ3kgaqhjncyjWrYk4IUQFWbJlv/j+7NrABhYrw992o8vtE1Nn0e1LEkTjRCiQizZ+TbfH1sKwMCge3mw02sqV1T9yRW8EKJcKYrCkr0f8cPBLwCFgbWiePD+eWqXVSNIwAshyo2iKHy7/zOW/fkJ2KwMcqlHn94fgVYaDyqCBLwQolwoisKig1/w896PwWrmKUMgvfv8FwzysFJFkYAXQpQ5RVH44sBCVu79L1hyGKKpRa+/fQQu8qBSRZKAF0KUKUVR+Hz/Z6w68AWYMxlmc+f+B+aBV5japdU4EvBCiDJjU2x8su8T1h7+Dk1OKs9aXeja9XUIaqF2aTWSBLwQokxYbVYW7F3A5hP/Q5t1keH5znS6awLU66R2aTWWBLwQ4o7l2/J574/32HZ6LdqMRMbku3B38yeh+cNql1ajScALIe6IxWph7u65xJ37HX36BcblO9O2fm9o90+1S6vxJOCFELctNz+Xt3a9xb7EOAzp53nB7ETL2ndDp0nS170SuOX/A4MHD2bTpk3lUYsQogrJtmQzc8dM9iX9gVPaeabkmWjp2wy6vwY6g9rlCW4j4NPS0ujWrRsNGzZk5syZnD9/vjzqEkJUYml5aUzfNp0jlw7ikn6Bl/IMNPWoA73+DUYXtcsTf7nlgP/xxx85f/48I0aM4Ntvv6VOnTr06tWL77//HovFUh41CiEqkUs5l5i+bTqn0k7ikZHE1BwdjVwCofcccK6ldnniOrfVSObn58eECRPYu3cvO3bsoEGDBjz55JMEBwczfvx4jh07VtZ1CiEqgYTMBKZuncr5jHN4Z6QwPVOhjtELHngL3APVLk/c4I7ugiQkJLBmzRrWrFmDTqfjgQceYN++fURGRjJ37tyyqlEIoQKrTWHbiUv8tOc8205c4mRqPFO3TuVi9kWCstN4LSOfYL2rvVnGu67a5Yoi3HIvGovFws8//8xnn33Gr7/+SlRUFOPGjeOJJ55wzA24bNkynn76acaPLzyYvxCi8lu1P4Hpyw+SkJYLgNZ0Affa/yO4lpbmGgsvpWbjqTVCjxkQUPRE2UJ9txzwQUFB2Gw2Hn/8cWJjY2nZsmWhbbp06YKXl1cZlCeEqGir9icw4qvdjomvdc6nMPmvxGKzYkiwMl7JxNPZCbq+DCHRqtYqSnbLAT937lweeeQRnJyKH/LTy8uL+Pj4OypMCFHxrDaF6csPXgt318OY/NYCCvVzdbx6JYFUrQ7/+19GV7+LmqWKUrjlNvgnn3yyxHAXQlRdsfGXHc0yeo8/MPmtARTqZbnw2uVzGBUNC/J6EWu8S91CRanIo2ZCCIfkjFxAwVDrd4zeWwCon+HN62kn0KFhkfU+frbd/dd2orKToQqEqIGsNoXY+MskZ+Ti7+5Eu7re6LQafNz0GH3Xonc7DED91Nq8nr0LDRp+sN7LYqu9WcbfXf6Krwok4IWoYVbtT2DazwdJTL92FR7o4cS//laP/dmLcfY8isWqocGl+ryW9zsaNKyw3sVC6/1o0BDoaf+FICo/CXghapBV+xMY/tXuQssTMy8zaf1C6gZnElbLA82hEP7Psg4NCmusbfjY2hsNGgCm9olEp9VUdOniNkgbvBA1hNWmMHnpvkLLNfpUnIK/R2tK5vwleKtxf77wjMWo07De1pL3rP1Q0BLo6cSCQa3p2SxIherF7ZAreCFqiO0nL5GaXXC8KK0pEVPACjTaHJR8D8KTmhO0YT4eJg0ed/clte5I5mWaC7TTi6pDAl6IGmLbiUsF3utcTmLyWw2afGx5/kQkR/Kq7nuyc3R4NLofbdeXiNFJRFRl0kQjRI2hOL7Su+/F5L8SNPlYc+oQkRTFq7rvMZDPWY82cN9UkHCv8lQN+FmzZtG2bVvc3d3x9/enX79+HDlypMA2ubm5jBo1Ch8fH9zc3Ojfvz9JSUkqVSxE1RVTzxewYfDejNFnE6CQn9GMJsmNeFW/GAP57LA1wdL5FQn3akLVgN+4cSOjRo1i+/btrFmzBovFwv33309WVpZjm/Hjx7N8+XKWLFnCxo0buXDhAg899JCKVQtRNbWq44ZH8K8YPPYAYL5yN00vB/OKYREG8om1NeZDw5O0byjD/lYXGkVRlJtvVjEuXryIv78/Gzdu5N577yUtLQ0/Pz8WLVrEww/bZ2c/fPgwTZo0Ydu2bdx1180fl05PT8fT05O0tDTHaJdC1DSpuam8sfMN/kg8zOmUPPJSutEiG14yfO24cv93/mO8N6id9JKpAkqba5Xq77C0tDQAvL3tD1HExcVhsVjo1q2bY5vGjRsTFhZWbMDn5eWRl5fneJ+enl7OVQtRuZ1NP8vsnbNJyUmhtoc3D4cPYeevcTxr+AI9VrbZIvnC+Sne69tCwr2aqTQBb7PZGDduHB06dKBZs2YAJCYmYjQaCw09HBAQQGJiYpHHmTVrFtOnTy/vcoWoEvYk72Fu3FxyrbkEuQYxud1kApOO8LT/T2TlOpHg3Y6o9pPZVN9fukBWQ5Um4EeNGsX+/fvZsmXLHR1nypQpTJgwwfE+PT2d0NDQOy1PiCpn1alVLNy/EBs2mng34fno53E/Ewu/vY5GseHWtAcNu7xEQ61O7VJFOakUAT969GhWrFjBpk2bCAkJcSwPDAzEbDaTmppa4Co+KSmJwMCibwSZTCZMJlN5lyxEpWW1WVl4cCGrT60GoHNoZ55p/gyGo2tg0xugKNCoJ3R6EbTSU7o6U/X/rqIojB49mmXLlvHbb79Rt27BeR3btGmDwWBg3bp1jmVHjhzhzJkzxMTEVHS5QlR6meZMZsXOYvWp1WjQ8ETjJxgeNRzDweWw8d/2cG/SR8K9hlD1Cn7UqFEsWrSIn376CXd3d0e7uqenJ87Oznh6ejJs2DAmTJiAt7c3Hh4ejBkzhpiYmFL1oBGiJknITODfO/9NQlYCTjonRrcaTdvAtrDnG9jxgX2j5o9AzCjQSHt7TaBqN0lNMf/IPvvsM4YMGQLYH3R6/vnn+eabb8jLy6NHjx68//77xTbR3Ei6SYrqoLjx26/ak7yHt3e/TXZ+Nr7OvkyMnkgdj3DY+V/44yv7Rq0GQdtnJNyrgdLmWqXqB18eJOBFVbdqfwLTlx90TKUHEOTpxNQ+kfRoGsjK+JV8dfArbNhoVKsRL0S/gKfBHba+DQd+tO/Q/p/Q8gl1TkCUuSrZD14IUdCq/QmM+Go3N16FJablMuKrWPp1PcwFs318986hnXmm2TMY0MCGmXBsjf1qveN4iOxb8cUL1UnAC1FJWW0K05cfLBTuAOgyMfn/j99Op9AkyJOnIp+kV91eaPLzYO00OLMNtDro/C9o2K2oI4gaQAJeiEoqNv5ygWaZq7SmC5j8f0Gjy8ZsMfFgyCgeqNcR8jJg1RRI3Ad6E3R/DcKkM0JNJgEvRCWVnHFjuCvo3ff/NRKkDZvZh7zk3rhSF7JSYOVEuHwSjG7QcxYERalRtqhEJOCFqKT83Z2ue5eP0XcDerdD9ndZDTGn3AeKgRDNJfhpBmQkgos39HoTfBuoU7SoVCTghaik2tX1JsjTicSsZEx+v6A1JQMazFdiyE9rDWjo4J5E6z0fQm4aeIbAA2+BhwwYJuwk4IWopHRaDXdHZrHy/LdotLkoNifykntgyw0DoK3mMP82LUOTqwO/xtBrNjjXUrlqUZlIwAtRCdkUG8uO/cja5P+i0dqwmf3JS+6Fkm/v89xDG8sI/c9kZGmwRd6Httt0MLqoXLWobCTghahkMs2ZzN8zn81nY7FYbeRnRmJO6YT9x1VhkG4tA3QbAPjF3JrUhi8QI+EuiiABL0QlciL1BHPj5nIx5yI2m468lPuwZkYCYCCfMfqldNbuBWCxtSuLrF2pn5WvZsmiEpOAF6ISUBSF1adX8+WBL8lX8vF38eex8KcZH3cBADey+Zd+Ec208djQ8l5+P9ba2gA39rYR4hoJeCFUlm3J5qM/P2JbwjYA2gW2Y0SLEZh0zrzheRlN2nleNXxBsCaFHEzMsjzBHqUBGiDQ0z7wmBBFkYAXQkXxafHMjZtLUnYSOo2OJ5o8Qe+6vR0jrb51jwZWf4C7JpuLihev5T/JaSWQq+NBTu0TKVPtiWJJwAuhAkeTzMEvybfl4+vsy9jWY2lUq9G1jY6upsPhN0nzha1pYUzJfoJU3AH7lfvUPpEySbYokQS8EOWkuDHcM82ZfPjnh8QmxgLQJqANI1uMxM3oZt/RZrOP477nawA8m9zH/Z3/hdfZ7GLHgxeiKBLwQpSD4sZw/8d9JuIyviElJwW9Rs/AJgPto0BenYTDnA3rZ8CpvyafbzUIooeh02qJqe+swpmIqkwCXogyVvQY7jZSNJt4a3cs4T7ONPQJYVzrcdTzqndtk/QEWP0v+4BhOgPcOwka3V/B1YvqRAJeiDJU1BjuGl0GRr816JzOA5CUVJclff8Pt+sfTrqwB9a8ah9TxrkW9JgBAU0rtHZR/UjAC1GGbhzDXed6DKPPejTaPBTFgDmlM9lZjdl3NoeY+i6gKHBgGWx7D2xW8G0EPWaCm5+KZyGqCwl4IcqQYwx3jRmjzybH8L62vADyLt6Pku91bbt8M2yZC0dW2vdpcJ+9WcYgDy6JsiEBL0QZ8nd3ss+45LcGjT4d0GBJjcaS2hbQObarrc+An1+Di4dBo4X2wyFqgH0OVSHKiAS8EGXEYrMQb/4V95AfsVitKPnu5F3sji2vtmMbDdDZ/Rxt4j6C3FQwucN9UyG0rWp1i+pLAl6IMnA2/Szv7nmX0+mnCfYycfJ0GOZLnVAUo2MbDQoPaTfzitvv9jHcfRrA/a/LBB2i3EjAC3EHbIqNFSdX8O2Rb8m35eNucGd8m2dIbRJeoB+8C7m87PojvT1O4OlkgEY94J7n7ZNjC1FOJOCFuE0JmQm8v/d9jl45CkBr/9b8M+qfeDl5QRB0jwwkNv4yWQmHaX1kHrXyk9HoXODu56BJH2lvF+VOAl6IW2RTbKyKX8U3h7/BbDPjpHNiSNMhdA7tfO2JVECngRjzVjj8DljN4BYA3V8D/8YqVi9qEgl4If5S3Ngx10vMSmTB3gUcvnwYgGY+zRjeYjh+Ljf0Wzdnw5b/wLE19vdhMdBlCjh5VsSpCAFIwAsBFD92zNURG22KjZXxK/n28LeOq/ZBkYO4L+w+tBptwYOlHIO10yDtnL0LZLtnIepR0N6wnRDlTAJe1HhFjx0DiWm5jPhqN68/EsTBnB84nnocgOa+zXk26ln8XfwL7nD1qdTt74PVAm7+cN+rENi8Yk5EiBtIwIsaraixY65SsGLwiuON3XE0DnTF2eDMk5FP0jW0a4G2dgByUmHjG3D6d/v78A7QeTI4eZT3KQhRLAl4UaPdOHbMVVpTAkbf39AaLmOxQpBTJK90HI2Ps0/hg5yLgw0zISsFdEZo/09o1l96yQjVScCLGs0xdsxVmjyM3tvQu+8HFBSrM+bL99K5/SOFwz3fbJ+Y489v7e+9wuxPpfo2qJDahbgZVe/6bNq0iT59+hAcHIxGo+HHH38ssF5RFF599VWCgoJwdnamW7duHDt2TJ1iRbXk63r1QSMFnctxnEO+Ru++D1DIz2xMzvlBWLMa4ed2wwBgl0/CjyOuhXvkg/DQxxLuolJRNeCzsrJo0aIF8+fPL3L9G2+8wTvvvMMHH3zAjh07cHV1pUePHuTmFv6TWojbogGNPh1TwHJM/r+g0WVhs3iRm9gPc0p3sDk5tgPs0+ntXQxLn4VLx8HZyz687z3PyyiQotJRtYmmV69e9OrVq8h1iqIwb948Xn75Zfr27QvAF198QUBAAD/++COPPfZYRZYqqiGL1cLq0z/jXHspaPIBLZbUNlhSo7nxRyMlM88+49KGWZCw174wLAY6TQIX7wqvXYjSqLRt8PHx8SQmJtKtWzfHMk9PT9q3b8+2bduKDfi8vDzy8vIc79PT08u9VlH53fgQk7PbOT4/+BnHU8+CJh9rboh9cDBLUWGt0Pjyetj9FVhywOAMMaOg8d/kRqqo1CptwCcmJgIQEBBQYHlAQIBjXVFmzZrF9OnTy7U2UbVc/xCTRpeBwXsLzh4nCfZyIsTTl6TTnUlJrotC4bD25wovuiyn0bG//s0FtbB3f/QIrtiTEOI2VLtH66ZMmUJaWprjdfbsWbVLEiq6+hBTQlomes+dOId8hd71OBarwon4hvwtYDL/d//DgKZAvGuw8YB2B+8Z36Wb51k0OiPcPQb+Nk/CXVQZlfYKPjAwEICkpCSCgq6Nl52UlETLli2L3c9kMmEyyRCswt4sM235AbQuJzB4b0GrtzfXWXODMV/qBBZfZq+MZ8uLXVkwqLXjKj+YFEbrl9HKcIZgLyc867SGTi+CV6jKZyTEram0AV+3bl0CAwNZt26dI9DT09PZsWMHI0aMULc4USWsOPgnV5y/xVTL/lecYnXFfLkD1qxGXO0Wk5CWS2z8ZXo2C6J7Y1/if/sU78OLMJKPq6sfmvbPQuTfZRwZUSWpGvCZmZkcP37c8T4+Pp49e/bg7e1NWFgY48aN4/XXX6dhw4bUrVuXV155heDgYPr166de0aLSS8tL47sj3/HD4VXonLJA0WFJb4UltQ1cN8PSVckZuZB8CN2mt2hw6TiYgJC74J4XZLYlUaWpGvC7du2iS5cujvcTJkwAYPDgwXz++edMmjSJrKwsnn32WVJTU+nYsSOrVq3CyUn6G4vCLFYLK+NXsuz4MnLyc9BpIT+rAZYrHVDyix4TxplcWpz6HGLX2AcLM7nb29ob3i89ZESVp1EUpahxlqqN9PR0PD09SUtLw8NDBn6qjmyKja0XtrL48GIu5lwEoK5nXVq4P8iUxVeK2UvhHu0+ntGtpI2fDTeT3h7qMSPBuVbFFS/EbShtrlXaNnghSmPfxX18ffhr4tPiAfB28ubxxo/TsXZHlu9NAAoHfG0uMly/nBbaEwCkm+rj1vtfENKmIksXotxJwIsq6WTaSb459A1/pvwJgJPOiX4N+vFAvQcw6ey9qPzdCzblOZHHY7r19NVtRYcVC3q+s3bm712eJzhE2tpF9SMBL6qUhMwEvjv6HVsvbAVAr9HTPbw7f2/4dzxNBafDa1fXmyBPJ5LSsrlXu5chul/x1ti7Su60Nea/+Q+AZ21mNgis8PMQoiJIwIsqISUnhe+Pfs/GsxuxYUODhg61OzCg0QACXAOK3Een1fDmvQYSfvmICK29q2Si4s3H+b3Zpdgnvl7QJ7LQvKtCVBcS8KJSu5J7hR+P/8jaM2vJt+UD0Nq/NY9GPEodzzrF75iZDLEf0/HYr6T5WYhPdeIr8738ZO2ABX2B+VaFqK4k4EWllJqbys8nfubX079isVkAiPSJ5LGIx4jwjih+R3MW7FkEf34HVjMAnlF9aB79DP2Ttdzz12Bj7ep6y5W7qPYk4EWlciX3Cj+f+Jk1p9c4gr1RrUY8GvEoTX2aFp4L9SqrBQ4th7jPITfNviyohX3UR78IdECMe4WcghCVhgS8qBRSclL4+cTP/HbmN0ewN/BqwCONHqGFX4vig91mg5O/wc5PIf28fZlniH1e1Dr3yMNKokaTgBeqSshM4OcTP7Pp3CbyFXsbe6NajXi40cNE+UYVH+yKAme22+dEvfTXcBfOtSB6KET0Bp380xZCfgqEKuLT4vnp+E/sSNiBDRtgb2Pv37B/yU0xigLnd8OuTyDpgH2Z0RVaPA7N+oPRpYLOQIjKTwJelJkbZ0268UamoijsT9nPTyd+Yl/KPsfy1v6t6degX8k3TxUFLuy2t7En2B9uQm+Cpg9By8fBybP4fYWooSTgRZm4ftakq652Rbwv0pdtF7ax4uQKTqefBkCLlpjgGB6s/2DJ3R0VBc7Hwe6F14JdZ4AmD0KrQTIfqhAlkIAXd+zqrEk3jlqXmJHKmOUf0fLYabSGLABMOhNdQrvQu15v/F38iz+ozQZntsEfX0LyIfsyncE+D2rLgeDmVz4nI0Q1IgEv7ojVpjB9+cEC4a4xXMLg8Sd6t8Ogyedoipb24aH0qtuT7uHdcTO6lXDAfDixzt6X/cop+zK9CZr0gajHJNiFuAUS8OKOxMZf/qtZxobO5SR6jz/ROZ13rLeZfclMb8mTnQdxT8MSxnwxZ8Hh/8G+JfanUMF+8zSyHzR/WJpihLgNEvDijsRfScLgFYve/QAaXeZfSzVYs+thSW+BLTcY0HA5y1r0AdIT4MAyOLzCHvJg7+4YNcDezm4q4WpfCFEiCXhxy2yKjT8v/sm6M+vYcHo7Bq8MABSrM/mZTclPb4ZiLfjYaIGhexUFEvbC/h/g1BZQ7N0kqRUOUY9Cg+6gLzy1nhDi1kjAi1JLyUlh49mNbDi7geQcezOKi1GLyRZCxqVI8rPqc+M/KQ0Q6GnvMok5G46vhQNL4XL8tY1CoqH5IxDSTia3FqIMScDXYDfrtw72eU53Ju5k47mN/HnxT8dDSS56F+4NuZduYd04cEbPiK92o4GCN1v/+u+/u7ii2zoPjv4Klmz7Qr0TNOxufzjJu255n6oQNZIEfA1VUr/1Hk0DOXrlKJvPb2brha1kWbIc20T6RNIltAt3Bd2FUWdvRgltBgsGtS5wPCfy6Od2iDG1j1J776lrH+wZYr9xGtHTPsG1EKLcyKTbNVBx/da1+ivo3I7SpnEiij7NsdzHyYdOIZ3oFNqJQNfie8JYrVYOxG3GeGI1wVdicdfl26/itTr7wF+RD0JwaxkATIg7JJNuiyLd2G9do09H53oMvesxtMaLABy6qKVFiC/tg9pzb+17aerbFK2mhLbxK6fh2K/ojq8jKiPBvkwHeIXaH0xqeL90cxRCBRLwNUxs/GUSs5LRe5ywh7op6bq1Wqw5YeRlNmJYp8fo1Ci4+ANlJMHJ9XB8HaQcvbbc6Ar1u0CjXhDQVK7WhVCRBHwNoCgK5zLPsStxFz8c2oBzyJHr1mqw5gZjzWpk7wVjcwYgNbuIlruMJIjfBCc3QNL+a8u1OghtDw26QXgHMDgV3lcIUeEk4Kspq83K0StHiUuKY1fSLhKy7E0nWZZ8roV6A6zZ9VGsroX293d3svdXv3LK3lf91Ba4ePjaBhoNBEZBg/ugbidw9qqQ8xJClJ4EfDWSlpfGnxf/5I/kP9h7cS+ZlkzHOr1GTzPfZkQHtOWVxdkkpeoK3WQFMJDPve4JtE88AbHbr82SBNdCvV5nqHsvuPqW+zkJIW6fBHwVZrFZOHblGPsu7mPvxb2cTDuJcl1suxncaOXfijYBbWjh1wIXg30yDOvfEgr0Ww/gMi21x2mrPUqU9iQRbjq0+w32g+gMULsN1Olob36Rm6VCVBkS8OWsNA8TlZZNsXEm/Qz7L+3nQMoBDl46SK41t8A24e7htPRvSZuANjTwaoBOqyt0nJ71THzXLZu4bb9R33yYAM0VAAw6LcFeTnjW8oewGAiPgdrRMkuSEFWUBHwRyiqUV+1PYNrPB0hMz3MsC/QwMe3BpvRsFnTT/fNt+ZxOP82hy4c4eOkgRy4fKdDsAuBudKe5b3Na+LUgyi8Kb6cbrrAVBTISIXEfJP5pHwMm9QxtgWh/yMrLx6K4YfaJxK9JR7Rhd4FPAxkyQIhqQAL+BiU94VmaUL7+OMO/2l1oeWJ6HsO/2s0Hg1oXOl6GOYNjV45x7Moxjl45yrHUY+RZ8wps46RzoolPE5r6NKW5b3PCPMIK9lE3Z9u7LSYfguSD9nlLsy8VLtCnAZrgVrjVbg1BLeUqXYhqSJ5kvU5xT3hevXZfUEQoF8VqU2jz+hpSsy3FbuPpauPr4fU4nXGKE6knOJ56nKTspELbuRpciagVQROfJkR6R1LHsw567V+/l3PT4NJxuHTSHuopRyD1jP2q/XpaHfg0hKAWEBQFgc1lDlMhqrBq9STr/PnzefPNN0lMTKRFixa8++67tGvXrkw/o6iZia5SsIf89OUH6R4Z6GiuKa4pZ/uJS9eFuw2NPhOt4RIaYwpaYwpa00Us+jRe3OiKm1PB/wXBrsE0rNWQRrUaEeEdQW232mhz0+3BnXgYDq2CK/H27otZKUWfjKsf+DexvwKagm+E9E0Xogaq9AH/7bffMmHCBD744APat2/PvHnz6NGjB0eOHMHfv4Q5PW/RtZmJiqYACWm5xMZfJqa+TxFNOfkEeOfx1D2e7EuKx+h7GK3hChrjZTSaoq/kbflutA1oTn2PMOoZalFf64xbzhV7m/mx3yF9ib2bYl5G8YV7BINPffsVum8j+8vV5/a/EUKIaqPSN9G0b9+etm3b8t577wFgs9kIDQ1lzJgxTJ48+ab7l/ZPmZ/2nGfs4j0lHMmGRpfF5L/VJteWynubdqPRZ6AxpKHVp6HRZ3B1sFxPZz2ZOWa02NBhQwe4WJzxMJvwtegItGgIt+TTyU9DEw8z5KaXfBIaDbgFgFeY/VWrLtSqYx9m11j4ISUhRPVWLZpozGYzcXFxTJkyxbFMq9XSrVs3tm3bVuQ+eXl55OVduzGZnn6T8PzL9TMOGbUZdPT5nBydhVxdPnm6fHJ1+YDC6mN6MnPzCfe2oQE0KGhR0KDgpCj45SsEXFHwtYBfvgZ/C/jkg47CvXB8zU6Qa7K/MbjYJ5R2CwT3QHAPsg+t61kb3IOliUUIccsqdcCnpKRgtVoJCAgosDwgIIDDhw8Xuc+sWbOYPn36LX9Wu7reBHk6kZiWi1aBFLdrT3DqATdAp9Hgb9YRkmfFKx+8rOCdr8E73x7ibjbQ/BX7ADmYyFCcOYULGYoL6bhwRXEjHVcuK+482TKazi0jwM0fjG4yMJcQokxV6oC/HVOmTGHChAmO9+np6YSGht50P51Ww9Q+kYz4ajdWxRmn1OborUb0ViNGqwmd1cQ/Y5qQr2j56PezpKPjOHrMih4zBvIwkI2JXMVEDkaycEKh5L7kz9RpDz7SXi6EKB+VOuB9fX3R6XQkJRXsPpiUlERgYNETT5hMJkwm0219Xs9mQY6ZiWKvDHQsv9oPvnWzILaduMTazdtveixvVyOXs8zFrg+6Ok+pEEKUk0od8EajkTZt2rBu3Tr69esH2G+yrlu3jtGjR5fLZ/ZsFkT3yMBin2S9vimnqLvTVyeZfqV3JKMW2R90Kmqe0ql9Im97yAIhhCiNSv88+oQJE/j4449ZuHAhhw4dYsSIEWRlZTF06NBy+0ydVkNMfR/6tqxNTH2fAkF8tSkHKHTb9PrwfiDK/tdAoGfBm6OBnk6lfmBKCCHuRKW+ggd49NFHuXjxIq+++iqJiYm0bNmSVatWFbrxWpGub8q5vu984A1DGtzsrwEhhChPlb4f/J0qz0m3y3KkSCGEKK1q0Q++srvalCOEEJVRpW+DF0IIcXsk4IUQopqSgBdCiGqq2rfBX72HXNoxaYQQorK7mmc36yNT7QM+I8M+1G5phisQQoiqJCMjA0/P4ifvqfbdJG02GxcuXMDd3R3NLQzmdXUMm7Nnz5Z598ryIjVXjKpWc1WrF6Tmm1EUhYyMDIKDg9GWMH9ytb+C12q1hISE3Pb+Hh4eVeYf2FVSc8WoajVXtXpBai5JSVfuV8lNViGEqKYk4IUQopqSgC+GyWRi6tSptz30sBqk5opR1WquavWC1FxWqv1NViGEqKnkCl4IIaopCXghhKimJOCFEKKakoAXQohqSgK+CPPnz6dOnTo4OTnRvn17YmNj1S6pRJs2baJPnz4EBwej0Wj48ccf1S6pRLNmzaJt27a4u7vj7+9Pv379OHLkiNpllWjBggVERUU5HmKJiYnhl19+UbusWzJ79mw0Gg3jxo1Tu5RiTZs2DY1GU+DVuHFjtcu6qfPnzzNo0CB8fHxwdnamefPm7Nq1S+2yJOBv9O233zJhwgSmTp3K7t27adGiBT169CA5OVnt0oqVlZVFixYtmD9/vtqllMrGjRsZNWoU27dvZ82aNVgsFu6//36ysrLULq1YISEhzJ49m7i4OHbt2kXXrl3p27cvBw4cULu0Utm5cycffvghUVFRapdyU02bNiUhIcHx2rJli9ollejKlSt06NABg8HAL7/8wsGDB5kzZw61atVSuzRQRAHt2rVTRo0a5XhvtVqV4OBgZdasWSpWVXqAsmzZMrXLuCXJyckKoGzcuFHtUm5JrVq1lP/+979ql3FTGRkZSsOGDZU1a9YonTp1UsaOHat2ScWaOnWq0qJFC7XLuCUvvvii0rFjR7XLKJJcwV/HbDYTFxdHt27dHMu0Wi3dunVj27ZtKlZWvaWlpQHg7e2tciWlY7VaWbx4MVlZWcTExKhdzk2NGjWK3r17F/h3XZkdO3aM4OBg6tWrx8CBAzlz5ozaJZXo559/Jjo6mkceeQR/f39atWrFxx9/rHZZgDTRFJCSkoLVaiUgIKDA8oCAABITE1Wqqnqz2WyMGzeODh060KxZM7XLKdG+fftwc3PDZDIxfPhwli1bRmRkpNpllWjx4sXs3r2bWbNmqV1KqbRv357PP/+cVatWsWDBAuLj47nnnnscw35XRidPnmTBggU0bNiQ1atXM2LECJ577jkWLlyodmnVfzRJUbmNGjWK/fv3V/p2VoCIiAj27NlDWloa33//PYMHD2bjxo2VNuTPnj3L2LFjWbNmDU5OTmqXUyq9evVyfB0VFUX79u0JDw/nu+++Y9iwYSpWVjybzUZ0dDQzZ84EoFWrVuzfv58PPviAwYMHq1qbXMFfx9fXF51OR1JSUoHlSUlJBAYGqlRV9TV69GhWrFjB+vXr72hI54piNBpp0KABbdq0YdasWbRo0YK3335b7bKKFRcXR3JyMq1bt0av16PX69m4cSPvvPMOer0eq9Wqdok35eXlRaNGjTh+/LjapRQrKCio0C/5Jk2aVIqmJQn46xiNRtq0acO6descy2w2G+vWrasSba1VhaIojB49mmXLlvHbb79Rt25dtUu6LTabjby8PLXLKNZ9993Hvn372LNnj+MVHR3NwIED2bNnDzqdTu0SbyozM5MTJ04QFBSkdinF6tChQ6FuvkePHiU8PFyliq6RJpobTJgwgcGDBxMdHU27du2YN28eWVlZDB06VO3SipWZmVngCic+Pp49e/bg7e1NWFiYipUVbdSoUSxatIiffvoJd3d3x/0NT09PnJ2dVa6uaFOmTKFXr16EhYWRkZHBokWL2LBhA6tXr1a7tGK5u7sXuq/h6uqKj49Ppb3f8cILL9CnTx/Cw8O5cOECU6dORafT8fjjj6tdWrHGjx/P3XffzcyZMxkwYACxsbF89NFHfPTRR2qXJt0ki/Luu+8qYWFhitFoVNq1a6ds375d7ZJKtH79egUo9Bo8eLDapRWpqFoB5bPPPlO7tGI9/fTTSnh4uGI0GhU/Pz/lvvvuU3799Ve1y7pllb2b5KOPPqoEBQUpRqNRqV27tvLoo48qx48fV7usm1q+fLnSrFkzxWQyKY0bN1Y++ugjtUtSFEVRZLhgIYSopqQNXgghqikJeCGEqKYk4IUQopqSgBdCiGpKAl4IIaopCXghhKimJOCFEKKakoAXQohqSgJeCCGqKQl4IYSopiTghbgDFy9eJDAw0DEWOMDWrVsxGo0FRiUVQg0yFo0Qd2jlypX069ePrVu3EhERQcuWLenbty//+c9/1C5N1HAS8EKUgVGjRrF27Vqio6PZt28fO3fuxGQyqV2WqOEk4IUoAzk5OTRr1oyzZ88SFxdH8+bN1S5JCGmDF6IsnDhxggsXLmCz2Th16pTa5QgByBW8EHfMbDbTrl07WrZsSUREBPPmzWPfvn34+/urXZqo4STghbhDEydO5Pvvv2fv3r24ubnRqVMnPD09WbFihdqliRpOmmiEuAMbNmxg3rx5fPnll3h4eKDVavnyyy/ZvHkzCxYsULs8UcPJFbwQQlRTcgUvhBDVlAS8EEJUUxLwQghRTUnACyFENSUBL4QQ1ZQEvBBCVFMS8EIIUU1JwAshRDUlAS+EENWUBLwQQlRTEvBCCFFNScALIUQ19f+h/QmEDI4CwAAAAABJRU5ErkJggg==", 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", 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" ] @@ -687,13 +685,13 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:05<00:00, 19.88it/s]\n", + "100%|██████████| 100/100 [00:05<00:00, 19.39it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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", 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a+y71geKw8+YP48hynCfE4Ev84AXoDfVjrSTNhmhuuukmdu/eTXJysvMRGxvLiBEjnP/t6enJ6tWrna85cOAAx48fL7Ne6IW8vLycc6NXNke6u9myZQvJyclcffXVzn+6l4iNLT0vxr59+0qt0QrQu3dv5xqt1XFh51N4ePFq8CVjvFVZ67W6YmJqdm7tizu3wsPDS41RJyQk8OGHlU/5ajQaWbJkCV9++SVWq5XZs2dXevzx48fx9/d3Ph566CHWrVtXatvF8/FfLC4ujscff5z//Oc/PPbYY1x33XXOfampqSxfvpy//e1vXH311axevZqioiLn+rkV7bvQyZMnGTRoEHfddRcPPvhgpbW4u2W/PMnO/BN4omfKDS/ibwrXuqQq0+yvIZPJRMeOHUtt8/PzIzg42Ll97NixTJkyhaCgIAICAnjkkUeIi4vj2muvrZWavAxeLBq0qFbeuyqfXRVt2rRBp9OVGetu1aoVAD4+ZdeU9fOr3t11en3x3/vqBUsFVHRx1tPT0/nfJV0ulzrjvRIXf5fq1FqeC+uH4u9wOfVv2LABgLNnz3L27NlK/8wjIiJKXTP46quv+PLLL0staF7yr46KKIrC+vXrMRgMHD58uNS+8ePHA5CSkgIU/wX0+OOPX3JfibS0NPr27UuvXr1YsGBBpXW4u+TdS/j8f8Unmf/XYQQtW9yobUHVpHkXTWVmz57NrbfeyvDhw7nhhhsICwur1Qs+Op0Obw9vTR5VbQEMDg5mwIABvPnmm+Tn51/W9+zQoQPr168vtW39+vVER0cD0KRJE4BSXSsXBlJ1Pmfz5s2lttV0i2tVajUajUDxEGBtOHLkCJMnT+add96hZ8+ejB49utK/JDw8PGjTpo3zERISgo+PT6ltlwr4l19+mf3795OUlMTKlStZuHBhmWNuvPHGCi8aV7Tv5MmT3Hjjjc625JK/QBuizMzfmXvBJGI3XvuY1iVVm0sNJJV0aJTw9vZm3rx5zJun/cT5ruStt96id+/exMbGMmPGDDp16oRer2fr1q3s37//ksMYU6dO5e6776Zr167079+fFStW8NVXXznbK318fLj22muZNWsWLVu2JCsri6effrradU6cOJExY8YQGxtL7969WbJkCXv37nX+a6MmVKXWqKgodDod3377Lbfccgs+Pj74+/tX6f2nTZvGyZMnKxymcTgcjBw5koEDB3L//fczaNAgrrnmGl599VWmTp16xd+vPDt37uTZZ5/liy++oHfv3rz22mtMnDiRPn36XNGfbUm4R0VF8corr3Dq1Cnnvsque7kjm9XCq6seIU+108Yr2OUmEauqhvvXcz3WunVrdu7cSf/+/Zk2bRqdO3cmNjaWuXPnOsdlKzNs2DBef/11XnnlFa6++mrefvttFi5cWKoX+/3338dutxMTE8OkSZN4/vnnq13nPffcwzPPPMMTTzxBTEwMx44dcw4R1KRL1dq0aVNmzpzJU089RWhoaLUWCk9PT+f48eMV7n/hhRc4duwYb7/9NlA8hr9gwQKefvppdu3adXlfqBJWq5WRI0cyZswYZzvsuHHj6Nu3L6NGjbqif6WsWrWKw4cPs3r1apo1a0Z4eLjz0ZCoisK7PzzkvJlpys2JLjeJWFXJmqyynqQQl80df4dWrn2OhUe+Qo+Op+Oe4er2rjc9iqzJKoQQ1bTvwHI+PLIMgBEtb3XJcK8OCXghhKB4ZabZm17AgUqvgDYMuWGm1iVdMQl4IUSDV2TL57WfHsKiFBLlaeaft7yDzg06iOr/NxBCiCtQfFF1HIdtZ/HXefDYgLl4+zS69AvrAQl4IUSD9tP6F1hzbu+fM0Q+Tmhop0u/qJ6QgKf0XZBCiKqr7787+w4sZ9Hh4psn740aTOeOf9e4oprVoAO+5Db1+rpAhhBaK/nduXjKh/rg9Kl9vHbBRdWhN1b/Xg9X51J3stY1g8FAYGCgc3IpX1/fy141SIiGRFVVzp8/T1ZWFoGBgWVmonR1NquFV356mJw/L6o+NOQ9t7ioerEGHfDw1y3YF84gKISomsDAwHo3jYGqKLz9/f+RWngOk96TqTe/hZe3WeuyakWDD3idTkd4eDghISEuvZydEK7G09Oz3p25A3yz5l+stxzCgI4pPf9Nk5CrtS6p1jT4gC9hMBjq5Q+rEKJiDkVlS+pZsnKthJi88cz7jo+PrQTgvtZ/I7r9MG0LrGUS8EIIt7RyTzozV6SQbileQSzCeIiIpgvBAwY26czA66o/Q2p9IwEvhHA7K/ekM37xDkqaOP3154gK/Yh8FMz5JprGznDLi6oXc/9vKIRoUByKyswVKc5wN1BI19D55HsU4uPwZFfGOP7zfSoOpX738FeFBLwQwq1sST3rHJYB6NX4PSzeFjxUPekZ95KjNCHdYmVL6lkNq6wbEvBCCLeSlftXuMcGLMNiOgZA0an+HC+MLvc4dyUBL4RwK439ihewv8pnM0XBWwDwy+7I7vx+5R7nziTghRDuRQdhHn8QEPINKiqB58PYeO4f5R7n7iTghRBuJeP0CVqGL6JQ7yCg0I+NmQ9RXtSdzrPVfXF1TAJeCOE27EVWth96knwPGz4OT3ZnjKOI8teKDTG5xxqylZE+eCGEW1AVhYU/jOOIPQtPijtmLI7QMsfpgDCzNz1aBtV9kXVMzuCFEPWOQ1HZeOQMy5NPsvHIGRyKyvdrZ/Dzmd/RAcOaP8CJwugyw+wlz6cPjcagd/9BeDmDF0LUKxdPQQAQF7wee9B36PU6RrS4laF9J9IksuxxYWZvpg+NZlDHcC1Kr3MS8EKIeuPiKQgAmhtTsJm+w25XuNYUza19ngNgUMdwBkSHlZpsrEfLoAZx5l5CAl4IUS9cPAUBQJAhjfCwpRToFMxWM9+dGcOT6CiZF9ag1xHXOliLcl2CjMELIeqFi6cg8Nbl0iH8HQoMdvyLvNmROZ6TOUqDmIKgqiTghRD1woVTC+iw0yM0kVzPArwUA4cyHiBfCSxzXEMnAS+EqBf+6ltXuK7xO2T7nMWAjjOZd3LK3ryc44SMwQsh6oUeLYMIN3vTnEXOCcQcp/rxh7Ur0LD626tKzuCFEPWCQa9jfOdkCoJ2AuB7rhO/5w0AGl5/e1VJwAsh6oWDh3/gp8wlGD30hFij2JT9d+e+MLM3iSO7NZj+9qqSIRohhMvLSN/JS+ufpQiFWFNzpoz6gu0n8hpsf3tVScALIVxajuUEs36eQK5SREtjIybeugij0Yu41u4/n/uV0nSIJjExkU6dOhEQEEBAQABxcXH88MMPzv1Wq5X4+HiCg4Px9/dn+PDhZGZmalixEKIu2awWXvruftLt+TQx+PDULe/j4ysXUatK04Bv1qwZs2bNYvv27Wzbto1+/fpx++23s3fvXgAmT57MihUr+Pzzz0lKSiItLY077rhDy5KFEHVEcdh5Y8V9HLKdxl/nwVP9ZhPYqKXWZdUrOlVVXWpp8aCgIF5++WXuvPNOmjRpwtKlS7nzzjsB2L9/Px06dGDjxo1ce+21VXq/nJwczGYzFouFgICA2ixdCFEDHIrK5iOnSdo6kR0Fe/HSG3i61wzat7tN69JcRlVzzWXG4B0OB59//jn5+fnExcWxfft2ioqK6N+/v/OY9u3b07x580oD3mazYbP9tVJLTk5OrdcuhKgZJTNFttC/S37gHgCMuQM5WtSd9hrXVh9p3ia5e/du/P398fLy4qGHHmLZsmVER0eTkZGB0WgkMDCw1PGhoaFkZGRU+H4JCQmYzWbnIzIyspa/gRCiJpTMFBmufOEMd++zMWw43Yfxi3ewck+6xhXWP5oHfLt27UhOTmbz5s2MHz+e0aNHk5KSctnvN23aNCwWi/Nx4sSJGqxWCFEbSmaKbO+7Dkfj3wAIyGnDFstdztkjZ65IwaG41Iiyy9N8iMZoNNKmTRsAYmJi2Lp1K6+//jr33HMPhYWFZGdnlzqLz8zMJCwsrML38/LywstL2qeEqE+2pJ7Fy7oZn/AfsAOB+eGsOzPGuV8F0i1WtqSebdDT/1aX5mfwF1MUBZvNRkxMDJ6enqxevdq578CBAxw/fpy4uDgNKxRC1LRj/9tM47DPsOsUzNZANmQ9hFrO+afMFFk9mp7BT5s2jcGDB9O8eXNyc3NZunQpa9as4ccff8RsNjN27FimTJlCUFAQAQEBPPLII8TFxVW5g0YI4frOnD7IL3/MpFDvwFToy9aMeOyU/69wmSmyejQN+KysLO677z7S09Mxm8106tSJH3/8kQEDiicQmj17Nnq9nuHDh2Oz2Rg4cCBvvfWWliULIWpQXm46CSv/Dws2TIoXu9PHY1VNZY6TmSIvj8v1wdc06YMXwjXZrBZeWDacA9bTBOqNDG77XyZ/cx6g1LJ8JTPMyGRif6l3ffBCiIbDXmRlzjcjOWA9ja/Og3/d+CpRUdfjHVDcB3/h0nxhZm+mD42WcL8MEvBCiDqlOOy8/e397Mg/gSd6nox7lqio6wEY1DGcAdFhbEk9KzNF1gAJeCFEnVEVhY9Wjmdt9j706JjU9dEyUxAY9DpphawhLtcmKYRwX1+tnsr3WVsBGB89htguY7QtyM1JwAsh6sTKtc/x2f+K72sZ02oYN/ScqHFF7k8CXghR69Zuns3CI18BcGfTvgzuM0PbghoICXghRK3asvNdElM+BOCWkO7c2f9VjStqOCTghRC15ve9n/J68lsoqNzYKJpRgxLR6SV26or8SQshasX+A9/wytaXsKPQ09SScbd+gN4gjXt1SQJeCFHj/khdzayNz2FTHXTxa8Yjty/B4GHUuqwGRwJeCFGjTpzYyItrn6JAtdPBO4Qpty3B09NX67IaJAl4IUSNSU/bzn9+mUiuUkQbryCevP0TvLzNWpfVYEnACyFqRFbmHp5bFY9FKSTK08y0oUvw8ZXZH7UkAS+EuGKnT+/nPz+O46xipamHiX8P/Qh/k0wOpjUJeCHEFTl79jD/+WEsWY7zhHn48cyQDzCbm2tdlkACXghxBSzZR3n+uzFk2PMJMfjyzOD3aBTUWuuyxJ+kKVUIUS6HolY6ba/Fcpz/fHsfJ+15BBu8eWbgAho3bq9hxeJiEvBCiDJW7im78Eb4BQtv5Oac5IUVozhRlEMjvRfPDkgkJLSjhhWL8kjACyFKWbknnfGLd3DxWp4ZFivjF+/gjTubsW7/oxwrshCoN/LsgHmEhXfVpFZROQl4IYSTQ1GZuSKlTLhD8TqpJv05Pt/8H2y+Vsx6I8/c9AYREbF1XaaoIrnIKoRw2pJ6ttSwzIX89efoFDGXbI98fFUPnun3Os2aXVvHFYrqkIAXQjhl5VYc7p0j5pLreR4vxcDNrZ4hMjKujqsT1SUBL4RwCjF5l9l2cbinpd9Hi8jrNKhOVJeMwQshnGKiGqHXgfLnILxJf4ZOEW+S61ngDPf0onbERDXStlBRJdU+gx89ejRr166tjVqEEBrbfuzcBeF+imsi5v4Z7h6cTL+Pk4XtUNTi44Trq3bAWywW+vfvT9u2bXnxxRc5efJkbdQlhNBAyRi82ZDJNRHzyPO04q14cDJ9NGmF7cocJ1xbtQP+66+/5uTJk4wfP55PP/2UFi1aMHjwYL744guKiopqo0YhRB0JMXnTyJBOdMRbxeHu8ODEyftJK2xb5jjh+i7rImuTJk2YMmUKu3btYvPmzbRp04ZRo0YRERHB5MmTOXToUE3XKYSoQYV2hffW/cGzy/fw3ro/KLQrALQ2nSK62XzyPWz4ODw5ljaWdPtfc8voKL6jtUdLmQa4Priii6zp6emsWrWKVatWYTAYuOWWW9i9ezfR0dG89NJLTJ48uabqFELUkITvU3hnXapzrB3ghe/3Ma57If/LewmbsQhfm5Ejaf/HKftfs0KWzEIzfWh0qTlphOuq9hl8UVERX375JbfeeitRUVF8/vnnTJo0ibS0NBYtWsTPP//MZ599xnPPPVcb9QohrkDC9ym8vbZ0uAOEeR5mR8ZzpBWep5nRn793ex0Pv6tKH2P2JnFkNwZ1lHne64tqn8GHh4ejKAr33nsvW7ZsoUuXLmWO6du3L4GBgTVQnhCiphTaFRasSy2zvbkxhdDwJVj1Dvyt3vzr9g8JadKaoT0qn01SuL5qB/zs2bO566678Pau+CJLYGAgqallf5CEENpZtOEo6kVn7q28d9Io9AtsegemIh9+T5vA8n06HmwCBr2OuNbB2hQrakS1h2hGjRpVabgLIVzT1qNnSz1v672ZwLDPKdI7CLD5k5w2kVwluMxxov6SO1mFaCB8jQbnf1/tm4QxdCV2VMxWM1szJmBVTWWOE/WbzEUjRAMxvGszALr4/4BH6EocqAQWBLMpfbIz3C88TtR/mgZ8QkIC3bt3x2QyERISwrBhwzhw4ECpY6xWK/Hx8QQHB+Pv78/w4cPJzMzUqGIh6q9ebRsT12gZSpMkVFQC88NZnzGRIv4acvU1GujVtrGGVYqapGnAJyUlER8fz6ZNm1i1ahVFRUXcfPPN5OfnO4+ZPHkyK1as4PPPPycpKYm0tDTuuOMODasWov5RFYXlv0ylqPFWAMy5UazLiseBsdRxr93dWTpl3IhOVS++rq6dU6dOERISQlJSEjfccAMWi4UmTZqwdOlS7rzzTgD2799Phw4d2LhxI9dee+nFBnJycjCbzVgsFgICAmr7KwjhchSHnY9WPsz3WVsAiPPrydKDd5CVZ3ceE2oyMvP2jtLjXk9UNddc6iKrxWIBICio+Dbo7du3U1RURP/+/Z3HtG/fnubNm1cY8DabDZvN5nyek5NTy1UL4brsRVbmfzuGddn7ARjTahiD+8zgEUV63BsClwl4RVGYNGkSvXv3pmPH4tXZMzIyMBqNZW6aCg0NJSMjo9z3SUhIYObMmbVdrhAuz1pwjjkr7mNn/gn06BgfPYYbek4EpMe9oXCZLpr4+Hj27NnDJ598ckXvM23aNCwWi/Nx4sSJGqpQiPojLzed55fdxc78Exh1ep6IecwZ7qLhcIkz+AkTJvDtt9+ydu1amjX7q0UrLCyMwsJCsrOzS53FZ2ZmEhYWVu57eXl54eXlVdslC+GyTmXtJeHHf3LSnoe/zoMnes+kXdshWpclNKDpGbyqqkyYMIFly5bxyy+/0LJly1L7Y2Ji8PT0ZPXq1c5tBw4c4Pjx48TFyYK/Qlzs+PHfeOaHBzhpzyNI782Mm+ZKuDdgmp7Bx8fHs3TpUpYvX47JZHKOq5vNZnx8fDCbzYwdO5YpU6YQFBREQEAAjzzyCHFxcVXqoBGiIdm7/yte2fQi51U7zTxN/GvQewQ3vurSLxRuS9M2SZ2u/Kv2CxcuZMyYMUDxjU6PPfYYH3/8MTabjYEDB/LWW29VOERzMWmTFO7AcYmulw1b5zFvz3vYUWjv3YSpt36Iv0laHt1VVXPNpfrga4MEvKjvVu5JZ+aKFNItf62DGm72ZvrQaAZGh/Jt0jMsPvodAD1NLXlk6GI8vfy0KlfUgXrZBy+EKG3lnnTGL97BxWdhGRYrDy/ewj87f8YO614AbgnpzqhBiegN8mstislPghAuyqGozFyRUibcAYy6fHqGvsWG3LP4eOoZ2XIot974nzqvUbg2CXghXNSW1LOlhmVKBBoyuDpsAdnG8xhUHX9rOoZbb5xU9wUKlycBL4SLysotG+7NjPtpGraEXEMRRsXAuczhePe8S4PqRH0gAS+EiwoxlV45rYPPb/iEfk+BTsHP7sUf6WPItLcsc5wQJSTghXBRPVoGEW72JsNiJTbgKwqDt2AHzDYTOzMeJl9pRLi5uGVSiPK4zFw0QojSDHodzwy5iusaz8cWvAUVCMwPZ2Pa4+QpjVCB6UOjZRZIUSE5gxfCRZ3Py+K35P/DYkoDwC/7Gtaeuxc5LxNVJT8pQmjIoahsPHKG5ckn2XjkDA6luCkyIyOZp5cNZ681HYOqg1M3sfHcCC78ldUBM1ekOF8jxMXkDF4IjVR0h+rE7sdZ9b955DiK8LIbyMy8h2O2a8q8XgXSLVa2pJ6Vud1FuSTghdBARXeoNlOX8vGRrXh66GiqN7P25H2cc1Q+p0x57ZRCgAzRCFHnyrtD1UAh1zd+68+LqSqB+aHcce2iS4Y7lG2nFKKEBLwQdeziO1QD9KfoFfEyFtNxAHzPdeLntEfw8DARbvamoh4ZHUibpKiUBLwQdezCIZUor910aPYGFq9cPFQ9SuZANmX/A9BzOt/G9KHRAGVCvuS5tEmKykjAC1HHSoZUupq+IyhiKQWGIvzsXmSkPcCe831LHTeoYziJI7sRZi49DBNm9iZxZDcGdZQ530XF5CKrEHWsW6Qv/cMWkOXzBwCBBY3YnvVP8pVAoPjsPOyCoZdBHcMZEB1W6YIfQpRHAl6IWlLeKky5lqPMWflPsk2ZYAeTpT3rzo5E/fNXsaKhF4NeJ62Qotok4IWoBeX1uMcE7Saw8Zfk6YrwM3hwW/hIEs92QuWvY8L+XKlJhl5ETZCAF6KGle1xV+hh/gqbeTunilSaGU38e8AcmjbtwYhBla+1KsSVkIAXogZd3OPurculR8g7ZPtmARB4PoS9Zx8hLLw7IEMvonZJwAtRgy7scY8wHiAqdCnZHjZ06PA+25W1ljsBvUwvIOqEBLwQNaikx72r6TsI/o18nYqPw4PTWXewy9qtzHFC1CYJeCFqUJB3ETc0mUu2/0kAzFYzuzL/jxylSanjZHoBURck4IWoIf/73ya+3vYEFlM2qOBviea3s/9wtkBC2R53IWqTBLwQNSBp02u8t38JNtVBkIeR9BMD2VjQq9SEYjK9gKhrEvBCXIGC82d578eHWZe9H4BrfMKZMPAtNp30LtMHLz3uoq5JwAtxmY4cWcXr62eQ6chHj467Im9iWN9Z6A0eDGqETC8gNCcBL0Q1KQ473yY9y6fHfsCOSmODD49e+2/aXXVrqeOkx11oTQJeiD+VN3fMxWfcZ04fJPHniewuSAegp6kl4wa+hb9Jhl2E65GAF4KK10e9cMx8y853WfD72+QqRXjpDIxuexf94p5Ap5dZt4VrkoAXDV5F66NmWKyMX7yDuXe34sTx51lzbi8ALYyBTOz7ChERsXVfrBDVIAEvGrTy1kctoQJtvLezdPN0HN52dMBt4b2566aX8fT0reNKhag+CXjRoF28PmoJT6z0CF5CXsAh8oAwfJkUN43o9sPqvEYhLpcEvGjQypsTprkxhciQz8n1LAAgMD+cgV1eJrp957ouT4grounVobVr1zJ06FAiIiLQ6XR8/fXXpfarqsqzzz5LeHg4Pj4+9O/fn0OHDmlTrHBLF84Jo8dOXKOPCGr6EbmeBRgVA2rWANZmTSSicTMNqxTi8mga8Pn5+XTu3Jl58+aVu/+ll17ijTfeYP78+WzevBk/Pz8GDhyI1Soz8YnqcSgqG4+cYXnySTYeOYNDKR51j4lqhF4HTY0HiGv6IvmBe1FQCSxozMETk9idfxN6XfFxQtQ3mg7RDB48mMGDB5e7T1VV5syZw9NPP83tt98OwIcffkhoaChff/01f//73+uyVFGPVdYCaTJCz8DFFATuJRcVT8WAcrYXa3MHU3L+o6iw/dg5uWlJ1DsuOwafmppKRkYG/fv3d24zm8307NmTjRs3SsCLKqmsBfLFzxYTHfk1+YE5AAQWBLH71CjOOcretCTzt4v6yGUDPiMjA4DQ0NBS20NDQ537ymOz2bDZbM7nOTk5tVOgcHkVtUB6YqVH0MfkmQ+SrqoYFQOOi87aLybzt4v6yGUD/nIlJCQwc+ZMrcsQLqC8Fsg23tto0mQFuR7FJwGB+U04lv8Ax/PKH2OX+dtFfeay91iHhYUBkJmZWWp7Zmamc195pk2bhsVicT5OnDhRq3UK13XhsIqfPpvrG7+Fb/gX5HvY8FI8UE/1Z23mY9zUuSvw13ztJWT+dlHfuewZfMuWLQkLC2P16tV06dIFKB5u2bx5M+PHj6/wdV5eXnh5edVRlcKVNfbzAhQ6+/+MV/BaLHo7AOa8Zuw8M4I8pfis/ab2ofRoGSTztwu3o2nA5+XlcfjwYefz1NRUkpOTCQoKonnz5kyaNInnn3+etm3b0rJlS5555hkiIiIYNmyYdkWLeiPfksL14S9j8T6HFfCze3H61BB+t/YofaAOBnUMl/nbhdvRNOC3bdtG3759nc+nTJkCwOjRo/nggw944oknyM/PZ9y4cWRnZ3PdddexcuVKvL3lgpeoWJEtnxXrpvPZsdWc97ajR4evpT1bz95DEWV/dk7nFY/Hy/ztwt3oVFUtb54lt5GTk4PZbMZisRAQEKB1OaKW7drzCQt3ziXdno+iqHjnmTh86m7S7a0rfM2SsT3p3bZxHVYpxJWpaq657Bi8ENVx+vR+Pkz6F5tz/gDArDfSJ/wO/r3mai7ZSyCjMMJNScCLeq1kOGbZ/36hUFXQo2NQSCx39XmBVYeLgORLvkfJEI0Q7kYCXtRLqqKwbddCPtz9HlmO8wB08A7h/l7PEBV1PQAhpjNVei+5iUm4Kwl4Ue8cO7aORRtfYG9B8R3NQXpvRkWPIi5mfKnl83q0DCLc7E2GxVrugh5yE5NwdxLwot6wZB/ls7XP8suZ3SioeKLn1vDe3H7DTHx8y4a0Qa9j+tBoxi/egQ5KhbzcxCQaAgl44fJsVgvf//Y8X//vF6yqA4BrA1oz4rqZhIR2rPS1gzqGkziym9zEJBokCXjhshSHnaQtc/js4BecVYrDubUxiPtiJ9G+3W1Vfh+5iUk0VBLwwuWoisLO3Yv5ePe7HC8qng20scGHv7e/l94xD6M3VP/HVm5iEg2RBLxwKQcOfcfH215nnzULAD+dB3+LGsigXv/C08tP4+qEqF8k4IVLOHo0iU+3vsqOvOMAeKJncFhPbr/uWfxNMk4uxOWQgBeaOnlyC19seokNOcWTzunR0Sfoau7q/SzBja/SuDoh6jcJeKGJ9LTtfLn5ZdZnH0D5s4GxV0Ab7o6bRnhEjMbVCeEeJOBFnSov2Lv7t+DO7pNp0aKPxtUJ4V4k4EWNcShqha2IJ05sZNnW2Wy0HHIGeze/SO7qPolWLW/Ssmwh3JYEvLikyoK7xMo96WVuJgo3ezP52jP878zHbM5NdW6P8Y9ieLcJtG49oM6+gxANkQS8qFRFwX3hXaAr96QzfvGOC6YCUGjns4kQ3yQWH7Rg9NDjodfRw9SSYTGP0Kplvzr/HkI0RBLwokJlg7tYhsXK+MU7SBzZjQHRYcxckYIK6LFzjf9q/AM3k+t5HgvFc74E5ofzzLDpRDWPq/svIUQDJgEvyuVQVGdwX0ylOLhnrkjB5O1Jdk4WPc0/oDPv5byhkFzAoOrwz2vOvuxb2WWPZGzRVUTV7VcQosGTgBfl2pJ6ttSwzMVUwJ5/gJXr3qVDVAoFOgUAo2LAmNOe3ZYh5Cp/zfCYlVvxewkhaocEvChXxYFcPL4eal5Pts8Z9jr02HUKfnYviixd+T13IIWqT5lXyaIaQtQ9CXhRrosD2UeXwzUBqzAE/E6+h43sP7d38Arjj4webD4Ti1LOj5MsqiGEdiTgG7DK2h9LVkPysm4lMiCJPL+TWHXFI/Ieqh6/vGacc9zKcw8+xKqUDDbJohpCuBwJ+AaqsvbHG1oYWJ/8Lj3CfiC1MBvLn/v97V7YczqxJ2cABWoAiSO7YdDrZFENIVyUTlXV8hol3EZOTg5msxmLxUJAQIDW5biE8tofddhp672dsIBN2M2ncOiL9+oU8M8P5djZXhyydgP0ZfrgS1TlhighxJWraq7JGXwDc3H7Y4jHUdoEJKH4H+G8oZBsQOfQ0cbLTL9mfbm+64P4mZpVKbhlUQ0hXIsEfAOzJfUsebkn6GFei9FvHzleeeT9uc9T1eOb35STub0YPuKf9GrbxPk6CW4h6h8J+AbifF4WW/cs5ocDP9IqKg0rYKX4QmiANZDc3C7syb8em1q8atKp/EItyxVC1AAJeDeWn5fB9r2fsun4L+zKO4EdBUVRUQFToS/2vKs4mNeHc46yF0Glb12I+k8Cvp651IVMS/ZRtu75mK1p69mTfxL7BZdSm3mauDakO4t2XsWGc5HlTkMgfetCuA8JeBdxuVPyhgUYefy6AgyF69ietZPDttOlgrs41GO5tsOdREb2BsAUUdxFI33rQrg3aZN0AdWdktdbl0sbn2008kuh0CeDAkMRRg+9M5TbeAXRPbQ73dsPp2nTHpf9mUII11TVXJOAr2WFdoWPNh7l2NnzRAX5MiquBUYPvXN/RVPylpw/J47sRr+rgrh39usY2YmHzzFyvPJQL3iFAR1BhcHc03UAsdF3ExTUpkq1Sd+6EPWTBPyfLifgayr4Er5P4Z11qSgX/AnrdfDg9S2Zdks0DkXluv/+UmbWRh12Ir0OEO69B3//ExT6WbAUFZU6xtduxLMgnNPnO3KkIJZC1YePH7xW2hmFaADkRqfLVFNDFwnfp/D22tQy2xUV5/Yb24WSbrHipcunuddeGvscxMP7JOe9LBTpFAqAAsBD0eOpGPCzNaLgfEuOnY8hy96izHvLlLxCiAtJwF+gKisYVSXkC+0KC9aVDXcoPjsPN6ayNflb1HPZ9G76B3nG86hA/gXHeah6/K1mCq3NiW1zE/O2BaFe4n+XtDYKIS5ULwJ+3rx5vPzyy2RkZNC5c2fmzp1Ljx7lXzy8XFVdwWhAdJhzuKaioZxFG46iquCJlQivQzQ2HsXX6ySq8QznjfnYdQp2YFuBDrux+BO9HR542xphs0aSYe3ACVsH5/S7j3TuyZeHdpFhsUproxCiylw+4D/99FOmTJnC/Pnz6dmzJ3PmzGHgwIEcOHCAkJCQGvucqqxglG6xsiX1LHGtg0sN5Rh1BYR6phIVkE73qPMcPpNKXOQZznvYUIEicM7ICH8uZ1foTxNjJFl5zdh/7ipOOyIAfanPLAnua1sHM31otLQ2CiGqxeUvsvbs2ZPu3bvz5ptvAqAoCpGRkTzyyCM89dRTl3x9VS9GLE8+ycRPkivYqxCgP0OQZxqjYg0U2E6y5dhhVI8cCj3PU2AofQFUr9Oh/PnHalQM+BT5odqCyS9sRqatDemFrVHxoHfrYEbFRTF+8Q6g/OC+cFhIWhuFEOAmF1kLCwvZvn0706ZNc27T6/X079+fjRs3lvsam82GzWZzPs/JyanSZ104fm3Sn6Vrk0WohvMUGWzYPIpw/Bm/K7P0FDpUVP/Sfy96KgZ8inzQF5lRHE04U9CMjMLWnHOEcvGZeYlOkeZqzaU+qGM4A6LDpLVRCFElLh3wp0+fxuFwEBoaWmp7aGgo+/fvL/c1CQkJzJw5s9qfVbKCUYbFSqHqRbZvZplj/FRPmnqYybJ4YrcHYi0KxlIUQVZhFLlKIyoK8opc16Z4tsbqBLdMySuEqCqXDvjLMW3aNKZMmeJ8npOTQ2Rk5CVfZ9DrnOPchaofPme7YXWYybcHY7GHkG0PZe7IOGx2pZKhnL/4Gg2cL3RUuL+RryfXtvorqCW4hRA1rXqnnHWscePGGAwGMjNLn01nZmYSFhZW7mu8vLwICAgo9aiqkuGSMLM3my13sytvIIetsXj4XcXckXEM6hhe5VbEf97QutL9CXdcI0MrQoha5dJn8EajkZiYGFavXs2wYcOA4ousq1evZsKECbXymZcaLrlwKKeylsUJ/drQLsyfGd+kkJEjF0WFEHXPpQMeYMqUKYwePZrY2Fh69OjBnDlzyM/P5/7776+1z6xsuOTCoZxLtSzKRVEhhJZcPuDvueceTp06xbPPPktGRgZdunRh5cqVZS681qXqdL7I2LoQQisu3wd/pWpzNkmZjVEIoQW36IN3dXJ2LoRwZS7dRSOEEOLyScALIYSbkoAXQgg35fZj8CXXkKs6J40QQri6kjy7VI+M2wd8bm4uQJWmKxBCiPokNzcXs9lc4X63b5NUFIW0tDRMJhM6XdVbGEvmsDlx4oQmi3VfDqm5btS3mutbvSA1X4qqquTm5hIREYFeX/FIu9ufwev1epo1a3bZr6/ufDauQGquG/Wt5vpWL0jNlanszL2EXGQVQgg3JQEvhBBuSgK+Al5eXkyfPh0vLy+tS6kyqblu1Lea61u9IDXXFLe/yCqEEA2VnMELIYSbkoAXQgg3JQEvhBBuSgJeCCHclAR8OebNm0eLFi3w9vamZ8+ebNmyReuSKrV27VqGDh1KREQEOp2Or7/+WuuSKpWQkED37t0xmUyEhIQwbNgwDhw4oHVZlUpMTKRTp07Om1ji4uL44YcftC6rWmbNmoVOp2PSpElal1KhGTNmoNPpSj3at2+vdVmXdPLkSUaOHElwcDA+Pj5cc801bNu2TeuyJOAv9umnnzJlyhSmT5/Ojh076Ny5MwMHDiQrK0vr0iqUn59P586dmTdvntalVElSUhLx8fFs2rSJVatWUVRUxM0330x+fr7WpVWoWbNmzJo1i+3bt7Nt2zb69evH7bffzt69e7UurUq2bt3K22+/TadOnbQu5ZKuvvpq0tPTnY/ffvtN65Iqde7cOXr37o2npyc//PADKSkpvPrqqzRq1Ejr0kAVpfTo0UONj493Pnc4HGpERISakJCgYVVVB6jLli3TuoxqycrKUgE1KSlJ61KqpVGjRuq7776rdRmXlJubq7Zt21ZdtWqV2qdPH3XixIlal1Sh6dOnq507d9a6jGp58skn1euuu07rMsolZ/AXKCwsZPv27fTv39+5Ta/X079/fzZu3KhhZe7NYrEAEBQUpHElVeNwOPjkk0/Iz88nLi5O63IuKT4+niFDhpT6uXZlhw4dIiIiglatWjFixAiOHz+udUmV+uabb4iNjeWuu+4iJCSErl278s4772hdFiBDNKWcPn0ah8NBaGhoqe2hoaFkZGRoVJV7UxSFSZMm0bt3bzp27Kh1OZXavXs3/v7+eHl58dBDD7Fs2TKio6O1LqtSn3zyCTt27CAhIUHrUqqkZ8+efPDBB6xcuZLExERSU1O5/vrrndN+u6I//viDxMRE2rZty48//sj48eN59NFHWbRokdaluf9sksK1xcfHs2fPHpcfZwVo164dycnJWCwWvvjiC0aPHk1SUpLLhvyJEyeYOHEiq1atwtvbW+tyqmTw4MHO/+7UqRM9e/YkKiqKzz77jLFjx2pYWcUURSE2NpYXX3wRgK5du7Jnzx7mz5/P6NGjNa1NzuAv0LhxYwwGA5mZmaW2Z2ZmEhYWplFV7mvChAl8++23/Prrr1c0pXNdMRqNtGnThpiYGBISEujcuTOvv/661mVVaPv27WRlZdGtWzc8PDzw8PAgKSmJN954Aw8PDxwOh9YlXlJgYCBXXXUVhw8f1rqUCoWHh5f5S75Dhw4uMbQkAX8Bo9FITEwMq1evdm5TFIXVq1fXi7HW+kJVVSZMmMCyZcv45ZdfaNmypdYlXRZFUbDZbFqXUaGbbrqJ3bt3k5yc7HzExsYyYsQIkpOTMRgMWpd4SXl5eRw5coTw8HCtS6lQ7969y7T5Hjx4kKioKI0q+osM0VxkypQpjB49mtjYWHr06MGcOXPIz8/n/vvv17q0CuXl5ZU6w0lNTSU5OZmgoCCaN2+uYWXli4+PZ+nSpSxfvhyTyeS8vmE2m/Hx8dG4uvJNmzaNwYMH07x5c3Jzc1m6dClr1qzhxx9/1Lq0CplMpjLXNfz8/AgODnbZ6x2PP/44Q4cOJSoqirS0NKZPn47BYODee+/VurQKTZ48mV69evHiiy9y9913s2XLFhYsWMCCBQu0Lk3aJMszd+5ctXnz5qrRaFR79Oihbtq0SeuSKvXrr7+qQJnH6NGjtS6tXOXVCqgLFy7UurQKPfDAA2pUVJRqNBrVJk2aqDfddJP6008/aV1Wtbl6m+Q999yjhoeHq0ajUW3atKl6zz33qIcPH9a6rEtasWKF2rFjR9XLy0tt3769umDBAq1LUlVVVWW6YCGEcFMyBi+EEG5KAl4IIdyUBLwQQrgpCXghhHBTEvBCCOGmJOCFEMJNScALIYSbkoAXQgg3JQEvhBBuSgJeCCHclAS8EFfg1KlThIWFOecCB9iwYQNGo7HUrKRCaEHmohHiCn3//fcMGzaMDRs20K5dO7p06cLtt9/Oa6+9pnVpooGTgBeiBsTHx/Pzzz8TGxvL7t272bp1K15eXlqXJRo4CXghakBBQQEdO3bkxIkTbN++nWuuuUbrkoSQMXghasKRI0dIS0tDURSOHj2qdTlCAHIGL8QVKywspEePHnTp0oV27doxZ84cdu/eTUhIiNaliQZOAl6IKzR16lS++OILdu3ahb+/P3369MFsNvPtt99qXZpo4GSIRogrsGbNGubMmcNHH31EQEAAer2ejz76iHXr1pGYmKh1eaKBkzN4IYRwU3IGL4QQbkoCXggh3JQEvBBCuCkJeCGEcFMS8EII4aYk4IUQwk1JwAshhJuSgBdCCDclAS+EEG5KAl4IIdyUBLwQQrgpCXghhHBT/w+uatdhMqEAiwAAAABJRU5ErkJggg==", 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" ] @@ -706,13 +704,13 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:06<00:00, 16.33it/s]\n", + "100%|██████████| 100/100 [00:05<00:00, 19.16it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -725,13 +723,13 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:05<00:00, 17.36it/s]\n", + "100%|██████████| 100/100 [00:06<00:00, 16.36it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -810,7 +808,7 @@ "46 1.516631 3.805164\n", "47 3.899908 18.885295\n", "48 0.866646 1.661760\n", - "49 6.066524 43.131882, models=[((x / 1.0) * (1.0 + x)), ((x / 1.0) * (1.0 + x)), ((x / 1.0) * (1.0 + x)), ((x / 1.0) * (1.0 + x)), ((x / 1.0) * (1.0 + x))])\n" + "49 6.066524 43.131882, models=[(x * (x + (1.0 ** 2))), (x * (x + (1.0 ** 2))), (x * (x + (1.0 ** 2))), (x * (x + (1.0 ** 2))), (x * (x + (1.0 ** 2)))])\n" ] } ], @@ -888,7 +886,7 @@ " c = np.array(conditions)\n", " return self.polynomial(c)\n", " \n", - "custom_theorist = state_fn_from_estimator(PolynomialRegressor())" + "custom_theorist = estimator_on_state(PolynomialRegressor())" ] }, { @@ -918,19 +916,80 @@ "8 1.083308\n", "9 5.199877, experiment_data=Empty DataFrame\n", "Columns: [x, y]\n", - "Index: [], models=[])\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'NoneType' object has no attribute 'repr'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[88], line 18\u001b[0m\n\u001b[0;32m 15\u001b[0m s \u001b[39m=\u001b[39m experiment_runner(s, added_noise\u001b[39m=\u001b[39m\u001b[39m0.5\u001b[39m, random_state\u001b[39m=\u001b[39m\u001b[39m42\u001b[39m\u001b[39m+\u001b[39mcycle)\n\u001b[0;32m 16\u001b[0m s \u001b[39m=\u001b[39m custom_theorist(s)\n\u001b[1;32m---> 18\u001b[0m plot_from_state(s, \u001b[39m'\u001b[39;49m\u001b[39msin(x)\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m 20\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\033\u001b[39;00m\u001b[39m[1mUpdated State:\u001b[39m\u001b[39m\\033\u001b[39;00m\u001b[39m[0m\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m 21\u001b[0m \u001b[39mprint\u001b[39m(s)\n", - "Cell \u001b[1;32mIn[80], line 32\u001b[0m, in \u001b[0;36mplot_from_state\u001b[1;34m(s, expr)\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 28\u001b[0m \u001b[39mPlots the data, the ground truth model, and the current predicted model\u001b[39;00m\n\u001b[0;32m 29\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 31\u001b[0m \u001b[39m#Determine labels and variables\u001b[39;00m\n\u001b[1;32m---> 32\u001b[0m model_label \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mModel: \u001b[39m\u001b[39m{\u001b[39;00ms\u001b[39m.\u001b[39mmodel\u001b[39m.\u001b[39mrepr()\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m s\u001b[39m.\u001b[39;49mmodel\u001b[39m.\u001b[39;49mrepr() \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mModel\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 33\u001b[0m experiment_data \u001b[39m=\u001b[39m s\u001b[39m.\u001b[39mexperiment_data\u001b[39m.\u001b[39msort_values(by\u001b[39m=\u001b[39m[\u001b[39m\"\u001b[39m\u001b[39mx\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[0;32m 34\u001b[0m ground_x \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mlinspace(s\u001b[39m.\u001b[39mvariables\u001b[39m.\u001b[39mindependent_variables[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mvalue_range[\u001b[39m0\u001b[39m],s\u001b[39m.\u001b[39mvariables\u001b[39m.\u001b[39mindependent_variables[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mvalue_range[\u001b[39m1\u001b[39m],\u001b[39m100\u001b[39m)\n", - "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'repr'" + "Index: [], models=[])\n", + "None\n", + "None\n", + "None\n", + "None\n", + "None\n", + "\n", + "\u001b[1mUpdated State:\u001b[0m\n", + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", + " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", + " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", + " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", + " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", + " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 3.249923\n", + "1 5.849862\n", + "2 1.299969\n", + "3 0.433323\n", + "4 3.466585\n", + "5 1.733292\n", + "6 1.516631\n", + "7 3.899908\n", + "8 0.866646\n", + "9 6.066524, experiment_data= x y\n", + "0 0.433323 0.572248\n", + "1 4.983216 -1.483542\n", + "2 4.116570 -0.452463\n", + "3 2.816600 0.789584\n", + "4 2.599939 -0.459964\n", + "5 5.416539 -1.413252\n", + "6 0.433323 0.483809\n", + "7 4.333231 -1.087098\n", + "8 1.299969 0.955149\n", + "9 0.433323 -0.006633\n", + "10 3.249923 0.013996\n", + "11 4.116570 -0.488600\n", + "12 2.599939 0.222789\n", + "13 0.216662 -0.239366\n", + "14 3.683247 -1.511473\n", + "15 0.000000 0.485811\n", + "16 1.733292 0.995155\n", + "17 5.416539 -0.659296\n", + "18 2.816600 -0.072496\n", + "19 3.683247 0.097695\n", + "20 4.333231 -0.205995\n", + "21 0.649985 0.656327\n", + "22 5.199877 -0.720136\n", + "23 1.516631 1.566765\n", + "24 4.116570 -0.415567\n", + "25 2.599939 0.805032\n", + "26 0.433323 0.230304\n", + "27 6.283185 -0.509437\n", + "28 3.466585 -0.131217\n", + "29 0.866646 0.506182\n", + "30 5.849862 -0.648822\n", + "31 3.683247 -0.826983\n", + "32 4.549893 -0.919182\n", + "33 3.249923 0.313832\n", + "34 3.249923 -0.182368\n", + "35 4.766554 -0.867292\n", + "36 4.549893 -0.720993\n", + "37 5.199877 -0.538608\n", + "38 3.466585 -0.765251\n", + "39 3.249923 0.126291\n", + "40 3.249923 -0.359577\n", + "41 5.849862 0.189056\n", + "42 1.299969 0.827990\n", + "43 0.433323 0.784981\n", + "44 3.466585 -0.903954\n", + "45 1.733292 0.272093\n", + "46 1.516631 0.986897\n", + "47 3.899908 -0.911596\n", + "48 0.866646 0.806200\n", + "49 6.066524 0.047677, models=[None, None, None, None, None])\n" ] } ], @@ -952,6 +1011,7 @@ " s = experiment_runner(s, added_noise=0.5, random_state=42+cycle)\n", " s = custom_theorist(s)\n", " \n", + " print(s.model)\n", " plot_from_state(s, 'sin(x)')\n", "\n", "print('\\n\\033[1mUpdated State:\\033[0m')\n", @@ -969,7 +1029,45 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1mPrevious State:\u001b[0m\n", + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", + " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", + " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", + " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", + " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", + " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 5.416539\n", + "1 4.116570\n", + "2 3.249923\n", + "3 1.733292\n", + "4 1.949954\n", + "5 0.216662\n", + "6 0.433323\n", + "7 0.000000\n", + "8 1.083308\n", + "9 5.199877, experiment_data=Empty DataFrame\n", + "Columns: [x, y]\n", + "Index: [], models=[])\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute 'predict'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[21], line 18\u001b[0m\n\u001b[0;32m 15\u001b[0m s \u001b[39m=\u001b[39m custom_experiment_runner(s, added_noise\u001b[39m=\u001b[39m\u001b[39m0.5\u001b[39m, random_state\u001b[39m=\u001b[39m\u001b[39m42\u001b[39m\u001b[39m+\u001b[39mcycle)\n\u001b[0;32m 16\u001b[0m s \u001b[39m=\u001b[39m custom_theorist(s)\n\u001b[1;32m---> 18\u001b[0m plot_from_state(s, \u001b[39m'\u001b[39;49m\u001b[39mx + x**2\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m 20\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\033\u001b[39;00m\u001b[39m[1mUpdated State:\u001b[39m\u001b[39m\\033\u001b[39;00m\u001b[39m[0m\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m 21\u001b[0m \u001b[39mprint\u001b[39m(s)\n", + "Cell \u001b[1;32mIn[2], line 37\u001b[0m, in \u001b[0;36mplot_from_state\u001b[1;34m(s, expr)\u001b[0m\n\u001b[0;32m 35\u001b[0m equation \u001b[39m=\u001b[39m sp\u001b[39m.\u001b[39msimplify(expr)\n\u001b[0;32m 36\u001b[0m ground_predicted_y \u001b[39m=\u001b[39m [equation\u001b[39m.\u001b[39mevalf(subs\u001b[39m=\u001b[39m{\u001b[39m'\u001b[39m\u001b[39mx\u001b[39m\u001b[39m'\u001b[39m:x}) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m ground_x]\n\u001b[1;32m---> 37\u001b[0m model_predicted_y \u001b[39m=\u001b[39m s\u001b[39m.\u001b[39;49mmodel\u001b[39m.\u001b[39;49mpredict(ground_x\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m))\n\u001b[0;32m 39\u001b[0m \u001b[39m#Plot the data and models\u001b[39;00m\n\u001b[0;32m 40\u001b[0m f \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mfigure(figsize\u001b[39m=\u001b[39m(\u001b[39m4\u001b[39m,\u001b[39m3\u001b[39m))\n", + "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'predict'" + ] + } + ], "source": [ "#### First, let's reinitialize the state object to get a clean state ####\n", "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 30))\n", From 62c1a552dd170aa30eba578dd17a4daacdc8bd56 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Fri, 1 Sep 2023 13:08:53 -0700 Subject: [PATCH 29/32] Fixed custom theorist not working --- docs/tutorials/basic/Tutorial-IV-Customization.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/basic/Tutorial-IV-Customization.ipynb b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb index bc9bf35df..c438cb663 100644 --- a/docs/tutorials/basic/Tutorial-IV-Customization.ipynb +++ b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb @@ -880,7 +880,7 @@ " # fit polynomial\n", " self.coeff = np.polyfit(c, o, self.degree)\n", " self.polynomial = np.poly1d(self.coeff)\n", - " pass\n", + " return self\n", "\n", " def predict(self, conditions):\n", " c = np.array(conditions)\n", From db0fbbab471cfedb74634a867a4490c97d71cecf Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Fri, 1 Sep 2023 13:31:06 -0700 Subject: [PATCH 30/32] Added markdown, finishing the tutorial draft. --- .../basic/Tutorial-IV-Customization.ipynb | 275 ++++++++++++++++-- 1 file changed, 249 insertions(+), 26 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-IV-Customization.ipynb b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb index c438cb663..b924cfe80 100644 --- a/docs/tutorials/basic/Tutorial-IV-Customization.ipynb +++ b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb @@ -303,10 +303,14 @@ "source": [ "## Custom Experimentalists\n", "\n", - "We can also implement custom experimentalists. Experimentalists are generally implemented as functions that can be integrated into an\n", - "[Experimentalist Pipeline](https://autoresearch.github.io/autora/core/docs/pipeline/Experimentalist%20Pipeline%20Examples/). For instance, an experimentalist sampler function expects a pool of experimental conditions–typically passed as a 2D numpy array named ``condition_pool``–and returns a modified set of experimental conditions.\n", + "Experimentalists must be implemented as functions. For instance, an experimentalist sampler function expects a pool of experimental conditions and returns a modified set of experimental conditions. \n", "\n", - "The following code block implements a basic experimentalist that considers two models, and identifies experimental conditions for which the two models differ most in their predictions. This is a special case of the [Model Disagreement Sampler](https://autoresearch.github.io/autora/user-guide/experimentalists/samplers/model-disagreement/)." + "**Requirements for working with the state:**\n", + "- The function has a `variables` argument that accepts the `VariableCollection` type\n", + "- The function has a `conditions` argument that accepts a `pandas.DataFrame`\n", + "- The function returns a `pandas.DataFrame`\n", + "\n", + "The custom `uniform_sampler` below will select conditions that are the least represented in the data. " ] }, { @@ -560,9 +564,11 @@ "\n", "s = StandardState(variables = variables, conditions = conditions, experiment_data = pd.DataFrame(columns=[\"x\",\"y\"]))\n", "\n", + "#Report previous state\n", "print('\\033[1mPrevious State:\\033[0m')\n", "print(s)\n", "\n", + "#Cycle\n", "for cycle in range(5):\n", " s = custom_experimentalist(s, num_samples = 10, random_state=42+cycle)\n", " s = experiment_runner(s, added_noise=0.5, random_state=42+cycle)\n", @@ -570,6 +576,7 @@ " \n", " plot_from_state(s,'sin(x)')\n", "\n", + "#Report updated state\n", "print('\\n\\033[1mUpdated State:\\033[0m')\n", "print(s)" ] @@ -578,7 +585,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Custom Experiment Runner" + "## Custom Experiment Runner\n", + "\n", + "Experiment runners must be implemented as functions. \n", + "\n", + "**Requirements for working with the state:**\n", + "- The function has a `conditions` argument that accepts a `pandas.DataFrame`\n", + "- The function returns a `pandas.DataFrame`\n", + "\n", + "The custom `quadratic_experiment` below will apply a quadratic transform (`x + x**2`) to the conditions." ] }, { @@ -587,7 +602,7 @@ "metadata": {}, "outputs": [], "source": [ - "def run_experiment(conditions: pd.DataFrame, added_noise: int = 0.01, random_state: Optional[int] = None):\n", + "def quadratic_experiment(conditions: pd.DataFrame, added_noise: int = 0.01, random_state: Optional[int] = None):\n", " \n", " #Set rng seed\n", " rng = np.random.default_rng(random_state)\n", @@ -603,7 +618,7 @@ " \n", " return observations\n", "\n", - "custom_experiment_runner = on_state(run_experiment, output=[\"experiment_data\"])" + "custom_experiment_runner = on_state(quadratic_experiment, output=[\"experiment_data\"])" ] }, { @@ -822,9 +837,11 @@ "\n", "s = StandardState(variables = variables, conditions = conditions, experiment_data = pd.DataFrame(columns=[\"x\",\"y\"]))\n", "\n", + "#Report previous state\n", "print('\\033[1mPrevious State:\\033[0m')\n", "print(s)\n", "\n", + "#Cycle\n", "for cycle in range(5):\n", " s = experimentalist(s, num_samples = 10, random_state=42+cycle)\n", " s = custom_experiment_runner(s, added_noise=0.5, random_state=42+cycle)\n", @@ -832,6 +849,7 @@ " \n", " plot_from_state(s, 'x + x**2')\n", "\n", + "#Report updated state\n", "print('\\n\\033[1mUpdated State:\\033[0m')\n", "print(s)" ] @@ -842,13 +860,20 @@ "source": [ "## Custom Theorists\n", "\n", - "What if we wanted to replace the ``theorist`` with a custom theorist?\n", - "\n", - "We can implement our theorist as a class that inherits from `sklearn.base.BaseEstimator`. The class must implement the following methods:\n", + "Theorists must be implemented as classes that inherit from `sklearn.base.BaseEstimator`. The class must implement the following methods:\n", "\n", "- `fit(self, conditions, observations)`\n", "- `predict(self, conditions)`\n", "\n", + "\n", + "\n", + "\n", + "Experiment runners must be implemented as functions. \n", + "\n", + "**Requirements for working with the state:**\n", + "- The function has a `conditions` argument that accepts a `pandas.DataFrame`\n", + "- The function returns a `pandas.DataFrame`\n", + "\n", "The following code block implements such a theorist that fits a polynomial of a specified degree." ] }, @@ -866,7 +891,7 @@ " def __init__(self, degree: int = 3):\n", " self.degree = degree\n", "\n", - " def fit(self, conditions, observations):\n", + " def fit(self, conditions: pd.DataFrame, observations: pd.DataFrame):\n", " c = np.array(conditions)\n", " o = np.array(observations)\n", "\n", @@ -882,7 +907,7 @@ " self.polynomial = np.poly1d(self.coeff)\n", " return self\n", "\n", - " def predict(self, conditions):\n", + " def predict(self, conditions: pd.DataFrame):\n", " c = np.array(conditions)\n", " return self.polynomial(c)\n", " \n", @@ -917,11 +942,91 @@ "9 5.199877, experiment_data=Empty DataFrame\n", "Columns: [x, y]\n", "Index: [], models=[])\n", - "None\n", - "None\n", - "None\n", - "None\n", - "None\n", + "PolynomialRegressor()\n" + ] + }, + { + "data": { + "image/png": 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Gu98D/85VmLgGad4LBn+mDPLLvQw/Pwd/fXtt2pBy8qnnw4iQEQB8c/QbkrOSyzhCiNpHCkQNVN7Vx9zr6fjkz08wYaJ7o+7c4nOLcufw87Nw8bjSU+nueeAVUqV5axw3f7j3EwjupzxyipoPv70Ohoot+9kroBetPVpjMBn49K9P5VGTqHOsrkDMnz+fJk2aYG9vT+fOnYmOLnllsMWLF6PRaIq87O1r/tKP5V2l7IzhN85lncPNzo1RrUb90+bwwj/dWOsrjdEezaoxeQ1iYwc9noeuE5X2l5OblYGBFWiX0Gg0PN7mccujps2Jm6swsBA1j1UViJUrVzJ58mSmT5/OwYMHadu2LX369CE1teSeJi4uLiQnJ1teiYmJ1Zj4xlxdpaykhyJm4KleLqw7tRaAx1o/hpNWDxtfvvZY6a53lam46zKNBsIGw93vK1OHXzgKq5+AiyfLPvYfXo5ePNjyQQC+jvuai7kXqyqtEDWOVRWI9957j7Fjx/Loo48SGhrKJ598gqOjI19++WWJx2g0Gnx8fCwvb2/vakxcVUxsTv4aEyY6+3bmFq+OsG220iBr6wD93qx5U2aoqWE7GLQA3Pwg+wKs+R+c2Vvuw/s06UNw/WDyjHl8/tfnMoBO1BlWUyAKCgo4cOAAkZGRlm1arZbIyEiioqJKPC4rK4uAgAD8/PwYOHAgR44cKfU6+fn5ZGRkFHlVt6vdXEti6/InMSnHcbSpx+hWo2H/FxC/FbQ20PsN8G5VjWmthGsjGPh/0KgDGHJgwzQ4vqFch2o1Wp5o+wQ2WhtiLsQQlVTy35sQtYnVFIiLFy9iNBqvuwPw9vYmJSWl2GOCg4P58ssv+emnn1i2bBkmk4kuXbrw999/l3idOXPm4Orqann5+flV6s9RHqV1c9XYpGNbfw8Go4lO7oNwO71bGSUNcNsUaCyL3pTI3gX6vaX0dDIZYetsiPmmXD2cGjk14t5m9wKw+MhisgqyqjqtEKqzmgJxIyIiInjkkUdo164dPXr0YNWqVXh6evLpp5+WeMy0adNIT0+3vM6ePVuNiRUld3M1o2+wHTSFGPMa0zLbHna8o3yrwyMQ3LfaMlotnS3c/iK0fUD5eu8nyqscRWJg0EAaOTUivSCd5UeXV3FQIdRnNQXCw8MDnU7H+fPni2w/f/48Pj4+5TqHra0t7du35+TJkhsp7ezscHFxKfKqbiV1c9U5nkTnkAhmHfUudqT98Q+UT8JBd0L46GpOacW0Wrh1HNz6lPL1nytgx7tlTs9hq7NlbOuxAGw5s4W4S3FVnVQIVVlNgdDr9XTs2JEtW7ZYtplMJrZs2UJERES5zmE0Gjl06BC+vtdPdFeTFNvNVVOAvsEOAEzp7Zml34ArWdCgmdKdsyZP1V1TtR2mPJbTaCBurdLQbzKWekhIgxB6+vcEYOGhhRSaCqsjqRCqsJoCATB58mQ+//xzlixZQlxcHOPGjSM7O5tHH30UgEceeYRp06ZZ9n/ttdfYuHEjp06d4uDBg4wYMYLExEQee+wxtX6EcrnazRWwFAnb+lFodNmYDW6MzEyhm2sqGjtn6D0LbGv+2I4aK+RuuPMVZX6qE5vgt1lgLP1N/6GWD+Gid+HvrL/5JeGXagoqRPWzqgIxbNgw3nnnHV599VXatWtHTEwM69evtzRcnzlzhuTka1MiXL58mbFjxxISEkL//v3JyMhg9+7dhIbe+HTQ1aVvmC8LRnTAx9UerT4VWxdllbNbcwN4yisWVwe98sbm0lDlpLVAs57Qa5bSCyx+K2yZWWqRcNI7Wabh+P749zI2QtRash5EGdRek9pgNDJ+4/MkZp6ivUsr5iTtR2PIURqlbxlT7XlqtcQo2PSKMkV6YHfoOQN0NsXuajabmRk1k7i0ODr7dGZy+OTqzSrETZD1IGqJned+J914Fh9nJ6bmJCvFwScMOo5SO1rtExChjCPR6SFhB2x9vcQ2CY1Gw5iwMWjRsjdlLzGpMdWbVYhqIAWiBssqyOLro18DcL+NJ+4XToDe6dozc1H5/Dsr7TpXHzdtm1ti7yY/Fz/6N+0PwKLDizAYDdWZVIgqJwWiBltxbAWZBZk0tnGmb8J+ZeNtz4Fz+br1ihvkfytEzvin4Xqj0gW2hCexQ5oPob5dfVJyUlh3al315hSiikmBqKES0hOU2UPNZsakXcLGDLToC0F3qB2tbgjsriw+pNHC0XXK2tfFFAlHW0ceCnkIgNUnV0uDtahVpEDUQGazmUWHF2HGTBezHaGZacqay10mqB2tbgm6UxknAfDXymtTmvxH90bdaeneknxjPl/HfV2NAYWoWlIgaqDdSbs5dvkYdsZChqf8Mz35bVOUabxF9WrZHyL+Kcz7FsLhVdftotFoeLTVo2jRsjtpN0culT4hpBDWQgpEDZNbmMuyuGVgNnNvVg4eZg0E9687S4bWRG3uh44jlX/v/hDif7tulyauTYgMUGYaXnJ4CcYyRmQLYQ2kQNQwP538ibS8NLwK8rk7KxvqeUDEeLVjiY6PQqtBSjvE1n/W3viPocFDcbJ1IjEzkd/OXl9EhLA2UiBqkNScVKUnTGEBj1xOwxYNdHka7JzUjiY0GugyEQJvUwbSbXgZLp4osouz3pn7W9wPwMqjK2VKcGH1pEDUIMtil2EwGWidl0O4UQP+EcobkqgZtFplDIpvW2XRoV+nQmbRtUgiAyJp7NSYTEMm3x//XqWgQlQOKRA1ROylWPam7EWbl8kjGdlobOyh60SZpbWmsdFDn9ngHgg5afDr85Cfee3bWhtGtRoFwIbTGzibWf3riQhRWaRA1AAms4nFRxaDyUTP7Cz8zTrlmbdLzZ6WvM6yc1JWpqvnAZdPw8aXobDA8u3Wnq3p6N0REyaWxRbfNVYIayAFogbYenYriRmJOOalMzTPDG7+0Pp+tWOJ0jh5Qd83wdYRkmKUlf3+NZDu4ZCHsdEoa1jLPE3CWkmBUFluYS4rj66EwgLuy8zCBS10+V+Js4iKGsSjGfR6TRltfXxDkYF0vk6+9G7SG4CvYr+Sbq/CKkmBUNlPJ38ivSAd39xMehfaQEAX8OukdixRXn63QLdnlH/vWwgnr614OKT5EJxtnTmXdY5NZzapFFCIGycFQkUXci4o3VrzsxmemYOt1vbaOsnCeoTeA22GKv/eNhfOKyOpnfRO3B+sPCr87th3ZBuy1UooxA2RAqGib45+g8FooFVuFuFmG6Xdwc1P7VjiRnQeBwFdwVgAG16CrFQAIv2Vbq9ZhixWn1itckghKkYKhEqOXz7OrqRdaPLTeSSnEI29K7QfoXYscaO0WmX2V/emkHsZ1k8DQy46rY4Rocr/r7+e/pXz2edVDipE+UmBUIHZbGZp7FIwm+mRk0sTsw7aPyIjpq2d3hH6zgEHN7h0UpmSw2SinWc7Wnu0ptBUyPKjy9VOKUS5SYFQwZ7kPRy/fBy7vAyG5ZrA2RdCB6odS1QGZx/o/bqyIl3C7/DHV2g0Gh4OfRgNGvYk7+FY2jG1UwpRLlIgqpnBaFA+RZqMDMjKwR0t3PKYMkJX1A4+raH7s8q/9y+ChB0EuARwh5+y2NNXsV9hLmGFOiFqEikQ1WxD4gZSc1Kpn5/DgALAo7myMI2oXVr2h7Ahyr+3zoa0BIYGD8VeZ8/JKyeJSo5SN58Q5SAFohplFWSx6sQqMBUyLCMTezRwy1ilgVPUPrc+BY06KBP7bXiJ+ugYEDQAgG/ilB5sQtRk8s5UjX448QPZhmz8DYX0KNSAd5gMiqvNdDYQOUNpY8o4B7+9zt1N+lPfvj6pualsSNygdkIhSiUFopqkZKew8fRGMBYy4spltGggfLTM1lrb2bsqjdY2dnB2L/YxXzOsxTAAVp1YJWtGiBpNCkQ1WXF0BYXmQtqatLQt1ChrCjTqoHYsUR08msFtU5V//7GMHgYIcA4g25DNDyd+UDebEKWQAlENjl8+TlRyFBpjIcMvKiNs5e6hjmkeaZmhV7v9LUb4KetXbzy9kZTslNKOFEI1UiCqmNlstqwJ0ENTjwAT0KgjNGynai6hglvHWVaja7N/GW3dW1FoLmTF0RVqJxOiWFIgqlh0SjTHLh9Dj5ahqX8rGzs8om4ooQ6tTmm0rucBlxMZnpGBBg1RyVGcuHyizMOFqG5SIKpQoamQb45+A8Dduvo0MBrBJ0z5FFlORpOZqPhL/BRzjqj4SxhNMsDKqjm6Q+RM0OoISNxHD1sPAJbFLZPBc6LGsboCMX/+fJo0aYK9vT2dO3cmOjq61P2/++47WrZsib29Pa1bt+aXX36ppqSw+cxmkrOTcbWpxz3JJ5WN7R8ud9vD+sPJdHvzNx78fA8TV8Tw4Od76Pbmb6w/nFyFqUWV8wmDiPEADD1zGL2xkKNpR9mXsk/lYEIUZVUFYuXKlUyePJnp06dz8OBB2rZtS58+fUhNTS12/927d/Pggw8yZswY/vjjDwYNGsSgQYM4fPhwlWfNMeTw/fHvAbjP1gsHQ74yatqvc7mOX384mXHLDpKcnldke0p6HuOWHZQiYe1aDYagO2lgMnNX+hUwGVl+dDmFpkK1kwlhYVUF4r333mPs2LE8+uijhIaG8sknn+Do6MiXX35Z7P4ffPABffv2ZcqUKYSEhDBr1iw6dOjAxx9/XOVZf4r/icyCTBo5enPn2UPKxvYjynX3YDSZmbk2luIeOFzdNnNtrDxusmYaDdw2Bdz8uSfXgEvWBZKzktl8ZrPayYQVWXdqHRdzL1bZ+a2mQBQUFHDgwAEiIyMt27RaLZGRkURFFT+vTVRUVJH9Afr06VPi/gD5+flkZGQUeVXUxdyL/HzqZwAesvfHJj9LWQioyW3lOj46Ie26O4d/MwPJ6XlEJ6RVOJuoQfSO0Os1HG0cuC+7AHIu8sPxH8gx5KidTFiBIxePsDR2Kc9ue7bKViu0mgJx8eJFjEYj3t7eRbZ7e3uTklJ8P/KUlJQK7Q8wZ84cXF1dLS8/v4qv8KbVaOnk04nQ+iF0PLVX2dhmWLnnXErNLLk43Mh+ogZzD4TbptDTZItv9hUyspJZE79G7VSihjOZTSyNWwpAD78e1LOtVyXXsZoCUV2mTZtGenq65XX27NkKn8Pd3p2nOzzNi54RaLJTwaE+NO9T7uO9nO0rdT9RwzWPxCbkHoYb7SAzmXUnVnMp95LaqUQNtuvcLhLSE3CwcWBI8yFVdh2rKRAeHh7odDrOny+6ZOP58+fx8fEp9hgfH58K7Q9gZ2eHi4tLkdcNMZuxPaQ0UtNqUIXWe+gU6I6vqz0ltVZoAF9XezoFut9YNlHzdHmacLeWtCzUYEg/w7cyeE6UwGA0sOKY8vcxMGggrnauVXYtqykQer2ejh07smXLFss2k8nEli1biIiIKPaYiIiIIvsDbNq0qcT9K1VyDFw8Djo9hA6q0KE6rYbpA0IBrisSV7+ePiAUnVam6qg1bPRoer/GCI0bGHLZfnwVp9NPq51K1EC/nv6Vi7kXcbd3566md1XptaymQABMnjyZzz//nCVLlhAXF8e4cePIzs7m0UcfBeCRRx5h2rRplv0nTpzI+vXreffddzl69CgzZsxg//79TJgwoerD/vWt8r/B/ZQ1iiuob5gvC0Z0wMe16GMkH1d7FozoQN8w30oIKWoU18Y0v/1lIky2mHMu8fW+99ROJGqYzIJMfjz5IwDDgoeh11XtSpQ2VXr2SjZs2DAuXLjAq6++SkpKCu3atWP9+vWWhugzZ86g/VdDcJcuXVi+fDkvv/wyL774Is2bN+fHH38kLCysaoNePg2Ju5WujP9M0HYj+ob50ivUh+iENFIz8/ByVh4ryZ1DLdb0dh48M5B9Cd/z17ldxCRuo13A7WqnEjXEqhOryDZkE+AcwG2Ny9cr8mZozDK+v1QZGRm4urqSnp5e/vYIQx4c/xWunIWuT1dtQFH7FBaw5LtB/JKXhL+dO28O24BWZ6t2KqGylOwUnt32LIXmQl7q/BJtPNvc8LnK+75mVY+YrIatPbS6V4qDuDE2egZHvkc9jQ1n8tPYvn262olEDbD86HIKzYW082x3U8WhIqRACFEDOXu25N4W9wGwMnE9eYklD+4Utd+xtGPsTd6LFi3DQ4ZX23WlQAhRQ/Xt/Cxe9Xy5jImft78EOTJyvi4ym80si/tnTRm/Hvi7+FfbtaVACFFD2epsefDW50FnxxrjZa5smQEmk9qxRDXbk7yH45ePY6ezY2jw0Gq9thQIIWqwCL8eNGvUmTyNhu/O74G/VqodSVQjg9HA8qPLARgQNAB3++odHCsFQogaTKPRMKLdOKjnxW+6As7u+wTOx6odS1STDYkbSM1Jxc3Ojbub3l3t15cCIUQNF9IghM6BvTDZObNMmw1bXoP8LLVjiSqWWZDJqhOrAGVQnIONQ7VnkAIhhBV4KGQ4Ni6NiLHVEJN1Bna8CzKEqVb796C42/1uVyWDFAghrIBPPR96B/YD54YssynAFL8Fjv2qdixRRZKzktlwegMAI0JHoNWo81YtBUIIKzGk+RCcHD0461SfrVoD7PoArpxRO5aoAsvilmE0G2nv1b7aBsUVRwqEEFbCSe/E4OaDwcGdlfX05BTmwuaZUFigdjRRiY5cPML+8/vRomVEyAhVs0iBEMKK9G7SG18nX9KdPPjRXguXTkL0Z2rHEpXEZDaxJHYJAL2a9KKxc2NV80iBEMKK2GptlU+VWht+dnElFRMc+g7O7FU7mqgE289uJzEjEUcbxypdKa68pEAIYWU6enektUdrCm3t+drnn2kXts2WqTisXG5hrmWluMHNB1fpSnHlVeECMXLkSH7//feqyCKEKAeNRsPDoQ+jRcserYE4Nx/IvQJb35CpOKzY6hOruZJ/BW9Hb/o26at2HOAGCkR6ejqRkZE0b96c2bNnc+7cuarIJYQoRYBLAHf63wkaDUsaeGKy0cPf++HQt2pHEzfgfPZ5fk74GYCHQx/Gtoas/1HhAvHjjz9y7tw5xo0bx8qVK2nSpAn9+vXj+++/x2AwVEVGIUQxhgYPxcHGgYS8C2xvGalsjP4cLhxXN5iosK/jvqbQVEhrj9aEe4erHcfihtogPD09mTx5Mn/++Sd79+6lWbNmPPzwwzRs2JBnnnmGEydOVHZOIcR/uNq5ct8/a0Z8kx1PTkAEmAphy0ww5KqcTpTXkUtH2JuirPXwSOgjaDQ1Z0nhm2qkTk5OZtOmTWzatAmdTkf//v05dOgQoaGhvP/++5WVUQhRgj5N+uBbz5f0gnRWNQyCep6Q/jfs/ljtaKIcjCYjSw4r3Vp7BvSs1rUeyqPCBcJgMPDDDz9w9913ExAQwHfffcekSZNISkpiyZIlbN68mW+//ZbXXnutKvIKIf7FVmvLyFYjAfj17DaSI54AjQaOroNT21TNJsq2+cxmEjMTcbJ1YljwMLXjXMemogf4+vpiMpl48MEHiY6Opl27dtftc8cdd+Dm5lYJ8YQQZWnv1Z52nu2IuRDDkksHeKHdcPhjGfz+DniFgpOX2hFFMTILMvn2mNKpYGjwUJz1zionul6F7yDef/99kpKSmD9/frHFAcDNzY2EhISbzSaEKKdRrUZho7Hhj9Q/OODXFjxbQn4m/Pa6dH2toVYeW0mWIYsA5wAi/SPVjlOsCheIhx9+GHt7+6rIIoS4Qb5OvvRv2h+AxXFLMdzxAtg6QPKfEPO1yunEf51OP82WxC0AjAobhU6rUzlR8WQktRC1xODmg6lvX5/UnFTWXvwDuk5SvrH/S1mFrgYxmU18cfgLTJiI8I0gtEGo2pFKJAVCiFrCwcaBh0MeBpRRuRcad4CgO8Fsgt9mQUGOygkFwI6/d3D88nHsdfY8HPqw2nFKJQVCiHIwmsxExV/ip5hzRMVfwmiqmau5dWnYhRD3EApMBXwVtxS6TwYnb8hIUtaPqCTW8vuoabIN2SyLWwbAkBZDaODQQOVEpatwLyYh6pr1h5OZuTaW5PQ8yzZfV3umDwilb5ivismup9FoGBM2hqm/TyU6JZqDfnfQ4c6XYO0kOL4e/DpBs543dQ1r+n3UNCuPrSSjIINGTo3oF9hP7ThlkjsIIUqx/nAy45YdLPJmCJCSnse4ZQdZfzhZpWQl83Px466mdwGw6PAiCrxCoP0/C8/seA8yU2743Nb4+6gpTqWfYtPpTQCMDhuNrbZmzLdUGikQQpTAaDIzc20sxT08ubpt5trYGvl4ZUiLIbjbu5Oam8qPJ3+EjqPAuxUUZP3T9dVY4XNa8+9DbSaziYV/LcSEiS4NuxDmEaZ2pHKxmgKRlpbG8OHDcXFxwc3NjTFjxpCVlVXqMbfffjsajabI68knn6ymxMLaRSekXfdJ+d/MQHJ6HtEJNW8dBgcbB0a1GgXAT/E/kZyTCne+DLaOkHJIGUhXQdb8+1DbxsSNxKfH42jjyMjQkWrHKTerKRDDhw/nyJEjbNq0iXXr1vH777/z+OOPl3nc2LFjSU5OtrzeeuutakgraoPUzJLfDG9kv+rWyacT7TzbUWgq5LNDn2F29oVuzyjfPLAYUg5X6HzW/vtQS1peGiuOKgsBPdDyAdzs3dQNVAFWUSDi4uJYv349CxcupHPnznTr1o2PPvqIFStWkJSUVOqxjo6O+Pj4WF4uLi7VlFpYOy/n8g0ILe9+1U2j0TCm9Rj0Wj2xl2LZdnYbtOgNzSL/6fr6OuSXfhf+b9b++1DLV0e+IrcwlyDXIHoF9FI7ToVYRYGIiorCzc2N8PBr86RHRkai1WrZu7f0tXi//vprPDw8CAsLY9q0aeTklN4XPD8/n4yMjCIvUTd1CnTH19WekiZf1qD03ukU6F6dsSrEy9GL+4PvB2BZ3DLS89OVuwhnX8hMhl3zyn2u2vD7qG4Hzh8gKjkKLVrGthmLVmMVb7kWVpE2JSUFL6+iE47Z2Njg7u5OSkrJPTIeeughli1bxtatW5k2bRpLly5lxIgRpV5rzpw5uLq6Wl5+fn6V8jMI66PTapg+QBnl+t83xatfTx8Qik5bc+bvL85dgXcR4BJAliGLpbFLwc5JaY/QaOHEJji+sVznqS2/j+qSW5jLwkMLAbir6V0EugaqnKjiVC0QL7zwwnWNyP99HT169IbP//jjj9OnTx9at27N8OHD+eqrr1i9ejXx8fElHjNt2jTS09Mtr7Nnz97w9YX16xvmy4IRHfBxLfrYxMfVngUjOlhFv3+dVscTbZ5Ai5Yd53YQkxoDPmHQ8Z/G0p3vKwPpyqE2/D6qyzdHvyEtL63IXZy1UXWg3LPPPsuoUaNK3adp06b4+PiQmppaZHthYSFpaWn4+PiU+3qdO3cG4OTJkwQFBRW7j52dHXZ2duU+p6j9+ob50ivUh+iENFIz8/ByVh6jWNMn5SC3IPoG9uWXhF/47K/PeKfHOzi2f1hZxzrlEGyZBfd8BLqy3xJqw++jqh1LO8bG08qd2eOtH8dOZ53vKaoWCE9PTzw9PcvcLyIigitXrnDgwAE6duwIwG+//YbJZLK86ZdHTEwMoKxpIURF6LQaIoJq9rQIZRkWPIz95/eTmpPK8qPLeaz1Y8qjpu/HQGosHFgEncaW61y14fdRVQxGA5/+9SlmzNzudzutPVurHemGWUUbREhICH379mXs2LFER0eza9cuJkyYwAMPPEDDhg0BOHfuHC1btiQ6OhqA+Ph4Zs2axYEDBzh9+jRr1qzhkUce4bbbbqNNmzZq/jhCqMLexp4n2jwBwKbETRy5dAScfeC2Z5UdYr6GpBj1AtYS3x7/lnNZ53Czc7NMnmitrKJAgNIbqWXLlvTs2ZP+/fvTrVs3PvvsM8v3DQYDx44ds/RS0uv1bN68md69e9OyZUueffZZhgwZwtq1a9X6EYRQXZhHmGVxmk///JTcwlxlxtfg/mA2K11f86Tn3o06fvk46+LXATC29Vic9E4qJ7o5GrPZLOPiS5GRkYGrqyvp6ekyhkLUCjmGHJ7b/hyX8i7RO6A3Y1qPUaYCXzUW0v+GwO7Qa5aytrUoN4PRwPM7nudc1jm6N+rOhPYT1I5UovK+r1nNHYQQonI42joyru04QJkC4q8Lf4HeEXq+ClobSNgBcXKnXVErj620PFq6Os2JtZMCIUQd1NqzNX2a9AFgwZ8LyDZkg2cwdPpn+prdH0GarCtfXkcuHWHdqdrzaOkqKRBC1FHDQ4bjW8+XtLw0vjz8pbKx9f3KmhHGAtjyGhQWqBvSCmQbspn/x3zMmLnT707CfcLLPshKSIEQoo6y09nxVLun0KJl57md7Ph7B2i1cPs0cKgPaadgz/+pHbPG+/Lwl1zKu4S3ozePtHpE7TiVSgqEEHVYi/otGNxiMAALDy0kNScVHN3hjheVHY6shtM7VUxYs+0+t5ud53aiRcv4duNxsHFQO1KlkgIhRB03uNlggusHk2fM46M/PsJoMiqPmdo+oOywbS5kXVA3ZA2Ukp3Cp399CsC9ze8l2D1Y5USVTwqEEHWcTqvjf+3/h6ONI8cvH+e7498p37jlMfBsCfmZ8NssMJnUDVqDGEwGPjz4IXnGPFq6t2RI8yFqR6oSUiCEEHg6ejK2tTLNxo8nf1Qm9NPZQs9XlFXokv+Eg0vUDVmDLI9bTnx6PE62Tvyv/f/QaXVqR6oSUiCEEAB0adSFXgG9MGPm4z8+5mLuRXBtDN0nKzsc/Eqm4gD2p+znl4RfABjXdhweDh4qJ6o6UiCEEBYjQ0cS6BpIpiGTDw5+QKGpEJr3guB+11ahy72idkzVJGcl83HMxwD0D+xfq7q0FkcKhBDCwlZnyzMdnrG0RyyNXap8o8vT4OYH2Rdg+5vKvE11TG5hLu/sf4fcwlxaurdkeMhwtSNVOSkQQogivOt5M77deADWn17P1jNb/5mKYwbo9JC4Gw59r27IamY2m/nsr8/4O+tv3OzcmNRhEjZaVVdLqBZSIIQQ1wn3Cee+FvcBsPDwQo5fPg4ezSDiKWWHvZ9AapyKCavXT/E/sTtpNzqNjskdJ1Pfvr7akaqFFAghRLGGNB9CJ59OFJoKeW//e1zKvQShg6BpDzAVwuaZShfYWi46OZpvjn4DwKhWo2rleIeSSIEQQhRLq9HyVLun8HP243L+Zd7c9ya5xjy4bQo4+0JmMmx/q1a3RySkJ1gapfs06UPvJr1VTlS9pEAIIUrkYOPA1Fum4qp3JTEjkfcPvE+hrQNEzvhnavDflek4aqGLuRd5a99b5BvzaevZlpGhI9WOVO2kQAghSuXl6MXznZ7HTmfHnxf+ZOGhhZg9g+FWZU0J9vwfXDimbshKllGQwey9s0nLS6OxU2MmdZhUawfDlUYKhBCiTEFuQUzsMBEtWrae3ao8kw8bAk26gdEAm6bXmvaIvMI83ox+k3NZ53C3d2da52k42jqqHUsVUiCEEOXS0bsjj7V+DFB69aw++SP0eL5WtUcYjAbeO/AeJ6+cxMnWiZc6v1SrR0qXRQqEEKLcegb0ZETICABWHFvBr8m7irZHWPH4CIPRwLsH3uXPC3+i1+qZestUGjs3VjuWqqRACCEqZEDQAMsYicVHFvNzVjxEKAPr2LsAUg6rmO7GXL1z+CP1D/RaPS90eqFOdWctiRQIIUSF3df8PgYGDQTgq9ivWK0Hgu4AkxE2z7Cq+ZryCvN4Z/87HEw9iF6r5/lOz9PKo5XasWoEKRBCiArTaDQ82PJBhrYYCsCK4yv52jsAk2tjZb6m3163ivUjMgsyeX3P68RciLE8VgrzCFM7Vo0hBUIIcUM0Gg1DWgyxtEmsSVzPR34tMNjo4e99cHCxugHLcDH3ItN3T+fElRM42Trx8q0v09qztdqxahQpEEKImzIgaADj2o5Dp9Gx+3Isr/n6kY4JDiyBM3vUjlesuEtxTNsxzdKVdUaXGdLmUAwpEEKIm3a73+282PlF6tnW47gpm2nuThzTFCqPmjKS1Y5nYTab2Xh6I7P2zCKjIIMAlwBmdZ2Fn7Of2tFqJCkQQohKEeYRxqyus/Ct58sle2dmOphYZ7iIeePLUJivdjyyCrL46I+P+OLwFxjNRro07MKsrrPq9DiHskiBEEJUmkZOjZjdbTZdGnXF6NKQpXoDb6T/SerW11UdRHfk4hGm/D6FXUm70KLloZYP8XT7p7HT2amWyRpozGYrH/pYxTIyMnB1dSU9PR0XFxe14whhFcxmMxsTN7I05hMMl09jDwwLupe+3V9Gq6m+z6Xp+eksP7qcbWe3AeDj6MOE9hNoXr95tWWoicr7viYFogxSIIS4cclZyXy6bSpxFw4BGhr7duTBdk/S0bsjGo2myq5rMBrYlLiJ745/R05hDgCR/pGMCB2Bg41DlV3XWpT3fc1qHjG98cYbdOnSBUdHR9zc3Mp1jNls5tVXX8XX1xcHBwciIyM5ceJE1QYVQlj4Ovnyav/FPNYgHCcz/H0+hrej5zB993T2pezDZK7csRJ5hXmsO7WO//32P5bELiGnMIdA10Be6/IaY9uMleJQQVazqGpBQQH3338/ERERfPHFF+U65q233uLDDz9kyZIlBAYG8sorr9CnTx9iY2Oxt7ev4sRCCACtVkevvh/S5ccn+CnjOL9kJHNMo+PY5WN4OXjRM6Ant/reik89nxs6v9ls5vjl42z/ezu7k3aTW5gLQAP7BgxuPpg7/e+s1sdatYnVPWJavHgxkyZN4sqVK6XuZzabadiwIc8++yzPPfccAOnp6Xh7e7N48WIeeOCBcl1PHjEJUUnSz8HqJ7iUn84G3+ZssYMsQ7bl237OfrT1bEuQaxBN3Zri5ehV7Bt7jiGHlOwUEjMSOXzpMEcuHuFy/mXL930cfRjYbCDdG3fHVmtbLT+atSnv+5rV3EFUVEJCAikpKURGRlq2ubq60rlzZ6KiokosEPn5+eTnX+uSl5GRUeVZhagTXBtBz1dp8OvzPJQcz30RT7HLzYtdSbuIvRTL2cyznM08a9ldgwZnvTPOemdAaVfINeaSWXD9uhP2Ontu9b2V2/xuI8Q9RO4YKkmtLRApKSkAeHt7F9nu7e1t+V5x5syZw8yZM6s0mxB1ll8n6PQ47P0E/Z5PuKP/O9xx68tkFWTxR+ofHLt8jIT0BBIzEjGYDGQUZJBRcP2HNFe9Kw2dGhLiHkKYRxgt6rfAVid3C5VN1QLxwgsv8Oabb5a6T1xcHC1btqymRDBt2jQmT55s+TojIwM/PxllKUSlafsApJ2CExth83S49xOcXBvTvXF3ujfuDoDRZCTLkEV6fjqZBZloNBpstbbodXo8HTzr7Apv1U3VAvHss88yatSoUvdp2rTpDZ3bx0dp8Dp//jy+vr6W7efPn6ddu3YlHmdnZ4ednQyeEeJmGU1mohPSSM3Mw8vZnk6B7ui0GtBo4LYpkH4WUuNgw4sw8P/AzslyrE6rw9XOFVc7VxV/AqFqgfD09MTT07NKzh0YGIiPjw9btmyxFISMjAz27t3LuHHjquSaQgjF+sPJzFwbS3J6nmWbr6s90weE0jfMF2z00PsNWP04XE6ELTOh71zQ6lRMLf7Latogzpw5Q1paGmfOnMFoNBITEwNAs2bNcHJSPnm0bNmSOXPmcO+996LRaJg0aRKvv/46zZs3t3RzbdiwIYMGDar0fEajEYPBUOnnFaKm0Ov1aLVlN/6uP5zMuGUH+W/3yJT0PMYtO8iCER2UIlGvAfSZDWv+B2ejYfdH0G1SlWQXN8ZqCsSrr77KkiVLLF+3b98egK1bt3L77bcDcOzYMdLT0y37TJ06lezsbB5//HGuXLlCt27dWL9+faWOgTCbzaSkpJTZ7VYIa6fVagkMDESv15e4j9FkZuba2OuKA4AZ0AAz18bSK9RHedzkGQx3vASbXoUjq8HNH8IGV9WPICrI6sZBVLey+gsnJydz5coVvLy8cHR0rNLpA4RQi8lkIikpCVtbW/z9/Uv8O4+Kv8SDn5e9BsQ3Y28lIqjBtQ1/LIPoz0Gjhb5zwP/WyoouilHnx0FUB6PRaCkODRo0KPsAIayYp6cnSUlJFBYWYmtbfJfS1My8YreXuV+74ZD+Nxz7FTbPhHs+Ao9mNxtZ3CQZTXITrrY5ODpKlztR+119tGQ0Gkvcx8u5fI9vr9tPo4Huz0KjDmDIgfXPQ9aFG84qKocUiEogj5VEXVCev/NOge74utpT0p4alN5MnQLdr/+mzhZ6vQb1m0D2Rfh1KuRn3UxkcZOkQAghKo1Oq2H6gFCA64rE1a+nDwhVGqiLY+esdHd1dFcG0218GQoLqiyvKJ0UCFEltm3bhkajqVDvriZNmjBv3rwqyySqR98wXxaM6ICPa9HHSD6u9te6uJbGxRf6vQW2jpD0B2yfC6bKnRZclI8UiDpq1KhRaDQannzyyeu+N378eDQaTZmj3IUoSd8wX3Y+fyffjL2VDx5oxzdjb2Xn83eWXRyu8mgOvWcpA+dOboE9/6fqkqV1lRSIOszPz48VK1aQm5tr2ZaXl8fy5cvx9/dXMZmoDXRaDRFBDRjYrhERQQ1KfqxUksbh0OMF5d+HvoOYrys/pCiVFIjKZDaDIVed1w18uurQoQN+fn6sWrXKsm3VqlX4+/tbBiKCMgX6008/jZeXF/b29nTr1o19+/YVOdcvv/xCixYtcHBw4I477uD06dPXXW/nzp10794dBwcH/Pz8ePrpp8nOzr5uPyEsWvSGiAnKv6M/h7i16uapY2QcRGUqzIMv+6pz7dHrwbbiyymOHj2aRYsWMXz4cAC+/PJLHn30UbZt22bZZ+rUqfzwww8sWbKEgIAA3nrrLfr06cPJkydxd3fn7NmzDB48mPHjx/P444+zf/9+nn322SLXiY+Pp2/fvrz++ut8+eWXXLhwgQkTJjBhwgQWLVp0Uz+6qOXa3A95V5TBdDveBb0TBN2hdqo6Qe4g6rgRI0awc+dOEhMTSUxMZNeuXYwYMcLy/ezsbBYsWMDbb79Nv379CA0N5fPPP8fBwcGy9OuCBQsICgri3XffJTg4mOHDh1/XfjFnzhyGDx/OpEmTaN68OV26dOHDDz/kq6++Ii+vfIOrRB12y2MQcrdyp/zbLEiMUjtRnSB3EJXJxl75JK/WtW+Ap6cnd911F4sXL8ZsNnPXXXfh4eFh+X58fDwGg4GuXbtattna2tKpUyfi4uIAZc2Ozp07FzlvREREka///PNP/vrrL77++tpzZLPZjMlkIiEhgZCQkBvKL+oIjQa6PQsFORD/mzJ3U7+50Kij2slqNSkQlUmjuaHHPGobPXo0EyYoz3nnz59fJdfIysriiSee4Omnn77ue9IgLspFq1Um9jMWwOmdsOEl6P82+LRWO1mtJY+YBH379qWgoACDwUCfPn2KfC8oKAi9Xs+uXbss2wwGA/v27SM0VBkQFRISQnR0dJHj9uwpOmFbhw4diI2NpVmzZte9SpsdVIgidDbQczo0vkXpnPHLVEg5rHaqWksKhECn0xEXF0dsbCw6XdEFW+rVq8e4ceOYMmUK69evJzY2lrFjx5KTk8OYMWMAePLJJzlx4gRTpkzh2LFjLF++nMWLFxc5z/PPP8/u3buZMGECMTExnDhxgp9++sly5yJEudnooffr1+Zt+mUKnD+idqpaSQqEAMDFxaXEaX/nzp3LkCFDePjhh+nQoQMnT55kw4YN1K9fH1AeEf3www/8+OOPtG3blk8++YTZs2cXOUebNm3Yvn07x48fp3v37rRv355XX32Vhg0bVvnPJmohW3voMwcatr9WJOROotLJehBlKG3e9Ly8PBISEggMDKzURYiEqIlq5N+7IRfWvwBJMUr7X99/ioYoVXnXg5A7CCGE9bJ1gL5vKqOur7ZJnI0u+zhRLlIghBDW7erjpoAuSg+nDS/CqW1qp6oVpEAIIayfjV5ZS6JpDzAaYPMMmZajEkiBEELUDjpb6DkDQgYoI65/fwcOLpVZYG+CFAghRO2h1SpLl7b/Z7qYfQuV+ZtMJS+TKkomBUIIUbtoNNBpLHR9Wvl33FqlXaIgR+1kVkcKhBCidgobAr1mgU4PZ/bAmv9B5vkKncJoMhMVf4mfYs4RFX8Jo6luPa6SuZiEELVXYHcY8AFsmAaXTsLqJ5RR2D5hZR66/nAyM9fGkpx+bbZhX1d7pg8ILf/KeFZO7iCEELWbdyjc+xk0aAa5l2HdJIhbV+oh6w8nM27ZwSLFASAlPY9xyw6y/nByFQauOaRACKsyY8YM2rVrp3YMAG6//XYmTZpUpddo0qQJ8+bNq/Bxr7zyCo8//ni59//kk08YMGBAha9jNZy9YeDHEHib0g3297dh25tQmH/drkaTmZlrYynuYdLVbTPXxtaJx01SIOqolJQUJk6cSLNmzbC3t8fb25uuXbuyYMECcnKsszFvxowZaDSaUl83Ytu2bWg0Gq5cuVK5gcth3759FXqjB+X/2w8++ICXXnqp3MeMHj2agwcPsmPHjopGtB62DhA5U2nA1mjh2C/w41Nw5WyR3aIT0q67c/g3M5Ccnkd0QloVB1afFIg66NSpU7Rv356NGzcye/Zs/vjjD6Kiopg6dSrr1q1j8+bNJR5rMBiqMWnFPPfccyQnJ1tejRs35rXXXiuy7d8KCgpUSlp+np6eODo6VuiYhQsX0qVLFwICAsp9jF6v56GHHuLDDz+saETrotUqXWD7vwMObkq7xKqxcGy9ZbxEamb5Vjgs737WTApEJTKbzeQV5qnyqsici0899RQ2Njbs37+foUOHEhISQtOmTRk4cCA///xzkUcNGo2GBQsWcM8991CvXj3eeOMN4Noyo3q9nuDgYJYuXWo55vTp02g0GmJiYizbrly5gkajsax1ffVT+ZYtWwgPD8fR0ZEuXbpw7NixIlnnzp2Lt7c3zs7OjBkzptTlSZ2cnPDx8bG8dDodzs7Olq8feOABJkyYwKRJk/Dw8KBPnz5lZj19+jR33KGsf1y/fn00Gk2R5VRNJhNTp07F3d0dHx8fZsyYUe7/H0D5m5kxYwb+/v7Y2dnRsGHDIosq/fcRk0ajYeHChdx77704OjrSvHlz1qxZU+ScK1asKPL/4YULF/Dx8Skyw+7u3bvR6/Vs2bLFsm3AgAGsWbOG3NzcCv0MNU25eh417giDF4JvW2UOp21zYMtrkJeBl3P5JiIs737WTHoxVaJ8Yz4j149U5dpL+i7BvhzLjl66dMly51CvXr1i9/nvo5gZM2Ywd+5c5s2bh42NDatXr2bixInMmzePyMhI1q1bx6OPPkrjxo0tb6bl9dJLL/Huu+/i6enJk08+yejRoy2LE3377bfMmDGD+fPn061bN5YuXcqHH35I06ZNK3SNf1uyZAnjxo0rsgBSafz8/Pjhhx8YMmQIx44dw8XFBQeHa6sGLlmyhMmTJ7N3716ioqIYNWoUXbt2pVevXgCMGjWK06dPWwrjf/3www+8//77rFixglatWpGSksKff/5ZaqaZM2fy1ltv8fbbb/PRRx8xfPhwEhMTcXd3Jy0tjdjYWMLDwy37e3p68uWXXzJo0CB69+5NcHAwDz/8MBMmTKBnz56W/cLDwyksLGTv3r3cfvvt5fr91DQV6nnk5Al3z4OYr2H/l8pSpsl/0qnbZHxd7UlJzyu2HUID+Lja0ynQvSp/lBrBau4g3njjDbp06YKjoyNubm7lOmbUqFHXPYPu27dv1Qat4U6ePInZbCY4OLjIdg8PD5ycnHBycuL5558v8r2HHnqIRx99lKZNm+Lv788777zDqFGjeOqpp2jRogWTJ09m8ODBvPPOOxXO88Ybb9CjRw9CQ0N54YUX2L17t+UuYd68eYwZM4YxY8YQHBzM66+/blnF7kY1b96ct956i+Dg4Ot+B8XR6XS4uytvBF5eXvj4+ODq6mr5fps2bZg+fTrNmzfnkUceITw8vMincl9f31KXVD1z5gw+Pj5ERkbi7+9Pp06dGDt2bKmZRo0axYMPPkizZs2YPXs2WVlZlhX9zpw5g9lsvm6djf79+zN27FiGDx/Ok08+Sb169ZgzZ06RfRwdHXF1dSUxMbHM30tNdEM9j7Ra6PAwDJwPbn6QcwndxpdYGrgJV7L4b6vV1a+nDwhFp72xNi1rYjV3EAUFBdx///1ERETwxRdflPu4vn37smjRIsvXdnZ2VRFPObfOjiV9l1TZ+cu69s2Ijo7GZDIxfPhw8vOL9uz496dRgLi4uOsaTrt27coHH3xQ4eu2adPG8m9fX+UTXmpqKv7+/sTFxfHkk08W2T8iIoKtW7dW+DpXdexYuYvc/zs/KD9Damqq5ev/vgn/1/3338+8efNo2rQpffv2pX///gwYMAAbm5L/0/z3NevVq4eLi4vlmlcfDxW3XsM777xDWFgY3333HQcOHCj2vwUHBwer7KRQVs8jDUrPo16hPsW/sXuHwpAvYP8i+GslzdKj2OJ3gPcu38byrHaY//ks7VPHxkFYTYGYOXMmwHVLWZbFzs4OHx+fKkh0PY1GU67HPGpq1qwZGo3mumf9Vx/b/PvxyVUlPYoqiVar/Mf073aRkhq3bW1tLf+++mjLZDJV6HoV8d+fpSJZi/Pv/KD8DBXJ7+fnx7Fjx9i8eTObNm3iqaee4u2332b79u3Xnbs81/Tw8ADg8uXLeHp6FtkvPj6epKQkTCYTp0+fpnXr1tedOy0t7brjrEFFeh5FBDUoficbO7j1SWVG2B3v0uDiCV733MykxseJDXgIvX8nOgW614k7h6us5hHTjdq2bRteXl4EBwczbtw4Ll26VOr++fn5ZGRkFHnVJg0aNKBXr158/PHHZGdn39A5QkJCrnuGv2vXLsvjn6tvMP/uNfTvRuCKXGfv3r1Ftu3Zs6fC5ylNebLq9XoAjMaqmfDNwcGBAQMG8OGHH7Jt2zaioqI4dOjQDZ0rKCgIFxcXYmNji2wvKChgxIgRDBs2jFmzZvHYY48VudMBpYDk5eXRvr31rchWqT2PvEKUgXVdJ6LRO+GZf4Yex+cScexNdGnxN5nUuljNHcSN6Nu3L4MHDyYwMJD4+HhefPFF+vXrR1RUFDqdrthj5syZY7lbqa3+7//+j65duxIeHs6MGTNo06YNWq2Wffv2cfTo0TIfw0yZMoWhQ4fSvn17IiMjWbt2LatWrbJ0j3VwcODWW29l7ty5BAYGkpqayssvv1zhnBMnTmTUqFGEh4fTtWtXvv76a44cOXJTjdT/VZ6sAQEBaDQa1q1bR//+/XFwcMDJyalc5582bRrnzp3jq6++Kvb7ixcvxmg00rlzZxwdHVm2bBkODg4V6qL6b1qtlsjISHbu3MmgQYMs21966SXS09P58MMPcXJy4pdffmH06NGsW3dtRPGOHTto2rQpQUFBN3RtNVV6zyOtFsIGQ9AdcPAriP0Jzu5VXgFdla6y3jfXHmYNVL2DeOGFF8oc2HT06NEbPv8DDzzAPffcQ+vWrRk0aBDr1q1j3759JfYoAeU/6PT0dMvr7NmzJe5rrYKCgvjjjz+IjIxk2rRptG3blvDwcD766COee+45Zs2aVerxgwYN4oMPPuCdd96hVatWfPrppyxatKhIz5cvv/ySwsJCOnbsyKRJk3j99dcrnHPYsGG88sorTJ06lY4dO5KYmMi4ceMqfJ6ylJW1UaNGzJw5kxdeeAFvb28mTJhQ7nMnJydz5syZEr/v5ubG559/TteuXWnTpg2bN29m7dq1NGhQwmOQcnjsscdYsWKF5bHTtm3bmDdvHkuXLsXFxQWtVsvSpUvZsWMHCxYssBz3zTfflNlAXlN1CnTH19X+ukblqzQovZkq3PPIoT50nQhDv4KgO5XZYRN3wY/jlMn/4n8DY+HNxr8xmSmwZ4Gy5kUV0Zgr0oG+kl24cKHMRz5Nmza13OKD8olr0qRJNzyq1dPTk9dff50nnniiXPuXtrh3jVzEXdR5ZrOZzp0788wzz/Dggw+W65gjR45w5513cvz48SK9tP6tpv+9X+3FBBRprL5aNBaM6HDzjctXzkDMcji+Acz/tDXV84BmvaBZJDQIUopIVTGZIDkGjqyG0zuVDHbOMPx7ZenVcirtfe3fVH3E5OnpWa0NYn///TeXLl2y9JYRojbSaDR89tlnFWrHSE5O5quvviqxOFiDvmG+LBjR4bpxEJXa88jNH25/AcLHQNwaZa2J7Ivw5zfKq34T8I8A/87gHaascnezjAZIjYWEHXBqq3K9qxp1hNb3KVOaVwFV7yAq4syZM6SlpbFmzRrefvtty5wxzZo1szwPbtmyJXPmzOHee+8lKyuLmTNnMmTIEHx8fIiPj2fq1KlkZmZy6NChcnd3lTsIIRTW8vduNJmJTkgjNTMPL2f7qu15ZDRA4m44uRnORClfX2VjD54twCsUGjQH10bg0gjsS/7EjiEXslLhcgKknYLUOEj+Cwr/1biud1LaRsKGgHvgDcW2ijuIinj11VdZsuTaGIOrPS22bt1qefZ97Ngx0tPTAWWA019//cWSJUu4cuUKDRs2pHfv3syaNatKx0IIIdSl02pK7spa6RezVbrFNu0B+Zlw5p+G7L+jIfeK8uae/Nd/jtGDvh7YOYFGB6ZCZUnUvHQwlDAGxd4VGt+itIM0vgVsquaO4b+s5g5CLXIHIYRC/t4rwGSCK4lw4ajyeOjyachIKvp4qCS2DuAWAO5NlTaNhu2hfqDSs6qS1Lo7iJpMaqyoC+TvvAK0WuXxj3sgBPe7tt2QB3lXID8LCjKVGWQ1WtDaKHcJjg2UAlGVDd0VIAXiJlwd0ZqTk1PsCGQhapOr06OXNIZIlIOtPdj6gLPaQcpHCsRN0Ol0uLm5WUakOjo63vCiNELUZCaTiQsXLuDo6FjqPFGidpH/p2/S1Xme/jttgRC1jVarxd/fXz4E1SFSIG6SRqPB19cXLy+vGr3amhA3S6/XWyY3FHWDFIhKotPp5NmsEKJWkY8DQgghiiUFQgghRLGkQAghhCiWtEGU4ergoNq2cJAQou66+n5W1uBHKRBlyMzMBJSlIYUQojbJzMwsdQZfmYupDCaTiaSkJJydnSvU/zsjIwM/Pz/Onj1b6lwnNYlkrh7Wltna8oJkLovZbCYzM5OGDRuW2nVZ7iDKoNVqady48Q0f7+LiYjV/oFdJ5uphbZmtLS9I5tKUZ+0PaaQWQghRLCkQQgghiiUFoorY2dkxffp0q1qcSDJXD2vLbG15QTJXFmmkFkIIUSy5gxBCCFEsKRBCCCGKJQVCCCFEsaRACCGEKJYUiCowf/58mjRpgr29PZ07dyY6OlrtSKX6/fffGTBgAA0bNkSj0fDjjz+qHalUc+bM4ZZbbsHZ2RkvLy8GDRrEsWPH1I5VqgULFtCmTRvLIKiIiAh+/fVXtWNVyNy5c9FoNEyaNEntKCWaMWMGGo2myKtly5ZqxyrTuXPnGDFiBA0aNMDBwYHWrVuzf/9+tWNJgahsK1euZPLkyUyfPp2DBw/Stm1b+vTpU6OXJM3OzqZt27bMnz9f7Sjlsn37dsaPH8+ePXvYtGkTBoOB3r17k52drXa0EjVu3Ji5c+dy4MAB9u/fz5133snAgQM5cuSI2tHKZd++fXz66ae0adNG7ShlatWqFcnJyZbXzp071Y5UqsuXL9O1a1dsbW359ddfiY2N5d1336V+/fpqRwOzqFSdOnUyjx8/3vK10Wg0N2zY0DxnzhwVU5UfYF69erXaMSokNTXVDJi3b9+udpQKqV+/vnnhwoVqxyhTZmamuXnz5uZNmzaZe/ToYZ44caLakUo0ffp0c9u2bdWOUSHPP/+8uVu3bmrHKJbcQVSigoICDhw4QGRkpGWbVqslMjKSqKgoFZPVbunp6QC4u7urnKR8jEYjK1asIDs7m4iICLXjlGn8+PHcddddRf6ua7ITJ07QsGFDmjZtyvDhwzlz5ozakUq1Zs0awsPDuf/++/Hy8qJ9+/Z8/vnnascC5BFTpbp48SJGoxFvb+8i2729vUlJSVEpVe1mMpmYNGkSXbt2JSwsTO04pTp06BBOTk7Y2dnx5JNPsnr1akJDQ9WOVaoVK1Zw8OBB5syZo3aUcuncuTOLFy9m/fr1LFiwgISEBLp3726Ztr8mOnXqFAsWLKB58+Zs2LCBcePG8fTTT7NkyRK1o8lsrsK6jR8/nsOHD9f458wAwcHBxMTEkJ6ezvfff8/IkSPZvn17jS0SZ8+eZeLEiWzatAl7e3u145RLv379LP9u06YNnTt3JiAggG+//ZYxY8aomKxkJpOJ8PBwZs+eDUD79u05fPgwn3zyCSNHjlQ1m9xBVCIPDw90Oh3nz58vsv38+fP4+PiolKr2mjBhAuvWrWPr1q03NSV7ddHr9TRr1oyOHTsyZ84c2rZtywcffKB2rBIdOHCA1NRUOnTogI2NDTY2Nmzfvp0PP/wQGxsbjEaj2hHL5ObmRosWLTh58qTaUUrk6+t73YeEkJCQGvFoTApEJdLr9XTs2JEtW7ZYtplMJrZs2WIVz5qthdlsZsKECaxevZrffvuNwMBAtSPdEJPJRH5+vtoxStSzZ08OHTpETEyM5RUeHs7w4cOJiYlBp9OpHbFMWVlZxMfH4+vrq3aUEnXt2vW6btrHjx8nICBApUTXyCOmSjZ58mRGjhxJeHg4nTp1Yt68eWRnZ/Poo4+qHa1EWVlZRT5hJSQkEBMTg7u7O/7+/iomK9748eNZvnw5P/30E87Ozpb2HVdXVxwcHFROV7xp06bRr18//P39yczMZPny5Wzbto0NGzaoHa1Ezs7O17Xr1KtXjwYNGtTY9p7nnnuOAQMGEBAQQFJSEtOnT0en0/Hggw+qHa1EzzzzDF26dGH27NkMHTqU6OhoPvvsMz777DO1o0k316rw0Ucfmf39/c16vd7cqVMn8549e9SOVKqtW7eageteI0eOVDtasYrLCpgXLVqkdrQSjR492hwQEGDW6/VmT09Pc8+ePc0bN25UO1aF1fRursOGDTP7+vqa9Xq9uVGjRuZhw4aZT548qXasMq1du9YcFhZmtrOzM7ds2dL82WefqR3JbDabzTLdtxBCiGJJG4QQQohiSYEQQghRLCkQQgghiiUFQgghRLGkQAghhCiWFAghhBDFkgIhhBCiWFIghBBCFEsKhBBCiGJJgRBCCFEsKRBCqOjChQv4+PhY1gIA2L17N3q9vsiswEKoQeZiEkJlv/zyC4MGDWL37t0EBwfTrl07Bg4cyHvvvad2NFHHSYEQogYYP348mzdvJjw8nEOHDrFv3z7s7OzUjiXqOCkQQtQAubm5hIWFcfbsWQ4cOEDr1q3VjiSEtEEIURPEx8eTlJSEyWTi9OnTascRApA7CCFUV1BQQKdOnWjXrh3BwcHMmzePQ4cO4eXlpXY0UcdJgRBCZVOmTOH777/nzz//xMnJiR49euDq6sq6devUjibqOHnEJISKtm3bxrx581i6dCkuLi5otVqWLl3Kjh07WLBggdrxRB0ndxBCCCGKJXcQQgghiiUFQgghRLGkQAghhCiWFAghhBDFkgIhhBCiWFIghBBCFEsKhBBCiGJJgRBCCFEsKRBCCCGKJQVCCCFEsaRACCGEKJYUCCGEEMX6f2auW4WDahb4AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PolynomialRegressor()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PolynomialRegressor()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PolynomialRegressor()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PolynomialRegressor()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "\u001b[1mUpdated State:\u001b[0m\n", "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", @@ -989,7 +1094,7 @@ "46 1.516631 0.986897\n", "47 3.899908 -0.911596\n", "48 0.866646 0.806200\n", - "49 6.066524 0.047677, models=[None, None, None, None, None])\n" + "49 6.066524 0.047677, models=[PolynomialRegressor(), PolynomialRegressor(), PolynomialRegressor(), PolynomialRegressor(), PolynomialRegressor()])\n" ] } ], @@ -1003,9 +1108,11 @@ "\n", "s = StandardState(variables = variables, conditions = conditions, experiment_data = pd.DataFrame(columns=[\"x\",\"y\"]))\n", "\n", + "#Report previous state\n", "print('\\033[1mPrevious State:\\033[0m')\n", "print(s)\n", "\n", + "#Cycle\n", "for cycle in range(5):\n", " s = experimentalist(s, num_samples=10, random_state=42+cycle)\n", " s = experiment_runner(s, added_noise=0.5, random_state=42+cycle)\n", @@ -1014,6 +1121,7 @@ " print(s.model)\n", " plot_from_state(s, 'sin(x)')\n", "\n", + "#Report updated state\n", "print('\\n\\033[1mUpdated State:\\033[0m')\n", "print(s)" ] @@ -1056,15 +1164,127 @@ ] }, { - "ename": "AttributeError", - "evalue": "'NoneType' object has no attribute 'predict'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[21], line 18\u001b[0m\n\u001b[0;32m 15\u001b[0m s \u001b[39m=\u001b[39m custom_experiment_runner(s, added_noise\u001b[39m=\u001b[39m\u001b[39m0.5\u001b[39m, random_state\u001b[39m=\u001b[39m\u001b[39m42\u001b[39m\u001b[39m+\u001b[39mcycle)\n\u001b[0;32m 16\u001b[0m s \u001b[39m=\u001b[39m custom_theorist(s)\n\u001b[1;32m---> 18\u001b[0m plot_from_state(s, \u001b[39m'\u001b[39;49m\u001b[39mx + x**2\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m 20\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\033\u001b[39;00m\u001b[39m[1mUpdated State:\u001b[39m\u001b[39m\\033\u001b[39;00m\u001b[39m[0m\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m 21\u001b[0m \u001b[39mprint\u001b[39m(s)\n", - "Cell \u001b[1;32mIn[2], line 37\u001b[0m, in \u001b[0;36mplot_from_state\u001b[1;34m(s, expr)\u001b[0m\n\u001b[0;32m 35\u001b[0m equation \u001b[39m=\u001b[39m sp\u001b[39m.\u001b[39msimplify(expr)\n\u001b[0;32m 36\u001b[0m ground_predicted_y \u001b[39m=\u001b[39m [equation\u001b[39m.\u001b[39mevalf(subs\u001b[39m=\u001b[39m{\u001b[39m'\u001b[39m\u001b[39mx\u001b[39m\u001b[39m'\u001b[39m:x}) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m ground_x]\n\u001b[1;32m---> 37\u001b[0m model_predicted_y \u001b[39m=\u001b[39m s\u001b[39m.\u001b[39;49mmodel\u001b[39m.\u001b[39;49mpredict(ground_x\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m))\n\u001b[0;32m 39\u001b[0m \u001b[39m#Plot the data and models\u001b[39;00m\n\u001b[0;32m 40\u001b[0m f \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mfigure(figsize\u001b[39m=\u001b[39m(\u001b[39m4\u001b[39m,\u001b[39m3\u001b[39m))\n", - "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'predict'" + "data": { + "image/png": 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L9VHHxsby8ccfV3sOo9HIZ599xtdff43ZbGbRokXVHn/mzBm8vLzsj8cff5ytW7eW23b5fPyXi4yMZNasWbzwwgs89dRTDBw40L4vOTmZ7777jjvvvJOuXbuyceNGrFarff3cqvZd6uzZs4waNYp77rmHRx55pNpanFljT4WOQR+g05UQbHFlS9YjlFbSLg7yccypkjVrwXt7e9OtW7dy2xo1akSTJk3s26dMmUJMTAx+fn74+PjwxBNPEBkZSb9+/eqkJjeDGx+N+qhOzl2T966Jdu3aodPpKvR1X+z2uLT74aKqunKqoteX/d6/dKmAqi7Ourq62v/7YtfQlVq81+Pyz3I1tVbm0vqh7DNcS/07duwA4Pz585w/f77af/OQkJBy1wy++eYbvv7663ILml+pW0RRFLZv347BYCApKancvqlTpwKQmJgIlP0CmjVr1hX3XZSamsqQIUPo378/7733XrV1ODNFVdh9Yj46Yy5epTpOZzzIeSr+fwk2udOntWN2Y2k+iqY6ixYt4k9/+hPjx4/nlltuISgoqE4v+Oh0Otxd3DV51HQIYJMmTRg+fDhvvfUWhYWF1/Q5O3fuXKEPe/v27XTpUjYDnr+/P0C5USuXBtLVvM/u3bvLbavtIa41qdVoLOsftdlstfreF504cYKZM2eyfPly+vbtS1RUVLW/JFxcXGjXrp39ERAQgIeHR7ltVwr4V155haNHjxIXF8e6detYsWJFhWMGDx5c5UXjqvadPXuWwYMH24clX/wF2hB9ued19mfE42nQ4Z45mGO2zuX26/73mDu2Cwb9jV9vtSYcaqqCyy9kubu7s3TpUpYuXapNQQ7q7bffZsCAAURERDBv3jzCwsLQ6/Xs3buXo0ePXrEb4+mnn+bee++lZ8+eDBs2jDVr1vDNN9/Yh1d6eHjQr18/Fi5cSOvWrcnMzOTvf//7Vdc5ffp0Jk2aREREBAMGDOCzzz7j8OHD13WR9XI1qbVly5bodDrWrl3LbbfdhoeHB15eXjU6/5w5czh79myV3TQ2m42JEycycuRIJk+ezKhRo+jevTuvvfYaTz/99HV/vsr88ssvPP/883z11VcMGDCA119/nenTpzNo0KDr+re9GO4tW7bk1VdfJSsry76vuutezmj3yfV8c/TfgMrUoIEU9XmGxLVHSMv94/pRkMmduWO7MKpbsHaFXonq5HJzc1VAzc3NrbCvuLhYTUxMVIuLizWo7Pqkpqaq06ZNU1u3bq26urqqXl5eap8+fdRXXnlFLSwstB8HqKtWrarw+rfffltt06aN6urqqnbo0EH9+OOPy+1PTExUIyMjVQ8PD7VHjx7qjz/+qALqpk2bVFVV1U2bNqmAeuHCBftrfvnlFxVQk5OT7dteeukltWnTpqqXl5caFRWlzp49Ww0PD6/RZ2zZsqW6aNEi+/NBgwap06dPr3DclWpVVVX9xz/+oQYFBak6nU6Nioqq8nzjxo2z71dVVY2KilIHDRpUZY3z589Xg4OD1ezsbPu2r7/+WjUajWpCQkKNPueKFSuqfY9LFRcXq126dFEfffTRcttvv/12tX///mppaWmNzlNVHUClj+rqqa/foaokn/tNfeiTSPXeD8LVD1fepqolZZ+t1KaoO5Ky1W9/+V3dkZStltoUzWqsLtcuJWuyynqSQlwzZ/sO5Zpzefa7e8kqyqC73pM5d/wHg6m51mVVIGuyCiHEVbAqVhb99CRZRRkEqQZmDHrZIcP9akjACyEE8NGOBRzJ+hV3dDzd/TG8Wg288oscnAS8EKLBW3/kCzacWI0OlSeDbqF5hHOM/ZeAF0I0aAfT9vLhvtdBsfGAZ2t6D38ZrnHmUkcjAS+EaLDS81NZvHk2SqmFm/U+3H7bu+DimNMOXAsJeMrfBSmEqLn6/N0ptBby8k/TKDBfoB2uPDZ8CTrvAK3LqlUNOuAv3qZeXxfIEEJrF787l0/54Ohsio03Ns3mbM5J/FQ9syJm4xrSQ+uyap1D3cl6oxkMBnx9fe2TS3l6el7zqkFCNCSqqlJUVERmZia+vr4VZqJ0dJ/sXcSB1J0Y0fF0mztp3P0erUuqEw064OGPW7AvnUFQCFEzvr6+9W4ag59++5Yfjn4BqsI03x60ucV5V6xq8AGv0+kIDg4mICDAoZezE8LRuLq61ruW+8H0/by/52VQrNxvDKbv6CWgr1+f4Wo0+IC/yGAw1LsfViFEzZ3N/53XNz+NYi3iZl0j7hi9DNyrvs3fGTToi6xCiIYhrySPf/40naLic3RQXXhsyKvo/FppXVadk4AXQjg1q83K65tmk5FzggBVz6xe03Ft2TBWhZOAF0I4LVVVeXdXLEfS9uCBjtmtbscUXnFNVWclAS+EcFrfHP6YrUmr0asKMb69CB30d6eZhqAmJOCFEE5p2+mf+c8vy0ApZYpbc8JuWwyGhjWuRAJeCOF0jmQfZtn2eVBq5k86H4aNeQfcvLUu64aTgBdCOJW0gjRe3TSLUksefVU3JgxfDPV84Y5rJQEvhHAaeSV5LPx5JgUFabRTDUT3fx59s15al6UZCXghhFMosZXwyuZnSD93DH9Vz+wuD+PWaYzWZWlKAl4IUe8pqsJbO1/kt9TdNFLhmWbDMfX9i9ZlaU4CXghR7312YDm7T/6Ai2pjlimM5re+0KCGQ1ZFAl4IUa+tS1rN2oMrwGblcWMoXcYsARej1mU5BAl4IUS9tSd1Fx/ufhlKzdyv9+XmMW+Du0nrshyGBLwQol46dv4YS7Y8i1pSwHDVgztGvAm+oVqX5VAk4IUQ9U5qQSqvbJqFtfgcvRRXJg9agC64u9ZlORwJeCFEvZJjziH25xjy81JopxqY3nsGhrZDtC7LIUnACyHqjeLSYmLjZpN57hhBqp7Z7R/APfxBrctyWBLwQoh6wapYeW3bXE6lxWNSYU7wUEwDYmQ4ZDUk4IUQDk9RFZbteZWDp3/GXVX4q284QcNfAr1EWHXkX0cI4dBUVeWTX//F9t++xaCUEuPRhra3LQEXN61Lc3iaBvyyZcsICwvDx8cHHx8fIiMj+eGHH+z7zWYz0dHRNGnSBC8vL8aPH09GRoaGFQshbrQ1x1fx/cEPwWZhqksQ4WPedvrFsmuLpgHfvHlzFi5cSHx8PPv27WPo0KGMGzeOw4cPAzBz5kzWrFnDl19+SVxcHKmpqdx1111aliyEuIE2n/6Zz/a9DtYiJmLi5tuWgneg1mXVGzpVVVWti7iUn58fr7zyCnfffTf+/v6sXLmSu+++G4CjR4/SuXNndu7cSb9+/Wp0vry8PEwmE7m5ufj4yG99IeqLfel7eW3T0yjmHG5XPZkwaimE9NS6LIdQ01xzmD54m83G559/TmFhIZGRkcTHx2O1Whk2bJj9mE6dOtGiRQt27typYaVCiLp29PxRFm95FsWcwyDFyIODFki4XwPNFyg8ePAgkZGRmM1mvLy8WLVqFV26dCEhIQGj0Yivr2+54wMDA0lPT6/yfBaLBYvFYn+el5dXV6ULIerAqdxT/HPT01gLM+mluPBov2fQtR2sdVn1kuYt+I4dO5KQkMDu3buZOnUqUVFRJCYmXvP5YmNjMZlM9kdoqMxNIUR9kV6YTuzmWRTlpdBJcWFG90dx6SrX3a6V5gFvNBpp164dvXv3JjY2lvDwcN544w2CgoIoKSkhJyen3PEZGRkEBQVVeb45c+aQm5trf6SkpNTxJxBC1IYL5gu8tPlpcs6foKVqYHb7e3G76RGty6rXNA/4yymKgsVioXfv3ri6urJx40b7vmPHjnHmzBkiIyOrfL2bm5t92OXFhxDCsRWUFPBS3Gwysw4ToOr4W7ORNBo4S+5SvU6a9sHPmTOH0aNH06JFC/Lz81m5ciWbN29m/fr1mEwmpkyZQkxMDH5+fvj4+PDEE08QGRlZ4xE0QgjHV1xaTOzWZ0lJ209jBZ5rGonvrfPkLtVaoGnAZ2Zm8uc//5m0tDRMJhNhYWGsX7+e4cOHA7Bo0SL0ej3jx4/HYrEwcuRI3n77bS1LFkLUIqvNyms7/kHS79vxUhSe9e1BwKhXZEWmWuJw4+Brm4yDF8IxlSqlLNoVy76kNbjbrDzn2YF245bLXao1UNNc03yYpBCiYbApKnuSz5OZb6apl5H48x+xL2ktrjYrs40tafcnmYKgtknACyHq3LpDacxfk0harhlQ8WzyEy1MW/ExlDLTGELXscugUROty3Q6EvBCiDq17lAaUz/dT1lfsIqHXxzNfLbhppYwPMODgqHzwSdY4yqdk1ymFkLUGZuiMn9NIhcv9Ln77qKZaTMeWLjtgpEv8v+PZzblY1Oc+lKgZiTghRB1Zk/y+f91y4DRtI+Qxj/iiZmRua58l/swSWoz0nLN7Ek+r3GlzkkCXghRZzLzy8Ld1TuBZn7f40UxQ/MMbLgQRaLaqsJxonZJwAsh6kyAtzsu3gdp1nQN3hRxS76enecnkqC2q3CcqH1ykVUIUWfMLgdp4b8aL7WQAQV6Es7fx261s32/DggyudOntZ92RToxCXghRJ3Y9vtW3tv1An6GQnrm6jh2bjzblHD7/ouzzMwd2wWDXuacqQvSRSOEqHU7z+5k6fb5KEXnGKm6MapLDEe8B5Q7JsjkzrKJvRjVTYZI1hVpwQshatWetD28uf15lKJsBtuMTOk7G333u9k24o87WQO8y7plpOVetyTghRC1Zm/6XhZv/Tu2wiwGKq48dtMs9N3L1lQ26HVEtpW7VW8k6aIRQtSKfen7WLz1WWyFmQxQXInuNQN9+L1al9WgScALIa7bvvR9LNr6LKUFmfRXXInu+QT6nhO0LqvBk4AXQlyXP8I9g36KK9N6RGPo9WetyxJIH7wQ4jpcGu6RiitPhE/F0HuS1mWJ/5GAF0Jckz1pe3hj23OXhPtfMERM1roscQkJeCHEVdudtps3tj6HrbCsz32ahLtDkoAXQlyVHWd3lI1zL8xmoOLKX3pMw9A7SuuyRCUk4IUQNbYlZQvLdvwDpSibWxRXpvaaIaNlHJgEvBCiRjad+Zl3d76IWnSeITYjj0bEoO9xv9ZliWpIwAshrmh98no+2L0Qii8w3Gbk4T5Pow+7R+uyxBVIwAshqrUmaTWf7n0dzDncZjPy58hn0XUdp3VZogYk4IUQlVJVlW9++4r/xL8JljzusLlz/4Dn0HUeo3VpooYk4IUQFaiqysrET1h9YDlY8rnf5sGdg1+EdrdqXZq4ChLwQohyFFXhg1+Xs+HQx1BSyENKI/407J/QaqDWpYmrdNVz0URFRbFly5a6qEUIoTGbYuPt+DfYcPAjdCWFPKJ686cRiyTc66mrDvjc3FyGDRtG+/btWbBgAWfPnq2LuoQQN5jVZmXRnoVsPfIFemsRT9CYYaOXQmgfrUsT1+iqA/7bb7/l7NmzTJ06lS+++IJWrVoxevRovvrqK6xWa13UKISoY8WlxSzcPpe9v63GtdTCU3p/BvzpHQgO07o0cR2uabpgf39/YmJiOHDgALt376Zdu3Y89NBDhISEMHPmTI4fP17bdQoh6kh+ST4vbvkbh05twN1WwhyXECLGvgf+HbUuTVyn65oPPi0tjQ0bNrBhwwYMBgO33XYbBw8epEuXLixatKi2ahRC1JFzxeeYt3kWSSlb8Sot5Tlja7qO+xf4tda6NFELrnoUjdVqZfXq1axYsYIff/yRsLAwZsyYwYMPPoiPjw8Aq1at4uGHH2bmzJm1XrAQonakFaTxUtxfyco6jJ+i8rdGnQkd+xZ4+mldmqglVx3wwcHBKIrCAw88wJ49e+jRo0eFY4YMGYKvr28tlCeEqAsnc04Su+UZ8s4dJ1jV8WzjXviPfh3cfbQuTdSiqw74RYsWcc899+Du7l7lMb6+viQnJ19XYUKIunEw6yCvbn0Wc24KrVU9cwIHYRqxAFyr/k6L+umq++AfeuihasP9asTGxnLTTTfh7e1NQEAAd9xxB8eOHSt3jNlsJjo6miZNmuDl5cX48ePJyMiolfcXoqHZcXY7CzfPwpx7hm6Kgedb3I5p1MsS7k5K00W34+LiiI6OZteuXWzYsAGr1cqIESMoLCy0HzNz5kzWrFnDl19+SVxcHKmpqdx1110aVi1E/fTDif+yZOtz9sWxn+kyGc+hz4FBbmh3VjpVVVWti7goKyuLgIAA4uLiuOWWW8jNzcXf35+VK1dy9913A3D06FE6d+7Mzp076dev3xXPmZeXh8lkIjc3134RWIiG5I95Zf4FljxG2oxMkrnc67Wa5ppD/erOzc0FwM+v7Cp+fHw8VquVYcOG2Y/p1KkTLVq0qDLgLRYLFovF/jwvL6+OqxbCcVkVK+/uf5OtR78CaxH3K57ccfNcdB1Hal2auAE07aK5lKIozJgxgwEDBtCtWzcA0tPTMRqNFUbkBAYGkp6eXul5YmNjMZlM9kdoaGhdly6EQyqyFvHP7fPsUw9MVX25c+QSCfcGxGECPjo6mkOHDvH5559f13nmzJlDbm6u/ZGSklJLFQpRf1y8gengyfW4l5bwV0MQg8e+B80jtC5N3EAO0UUzbdo01q5dy5YtW2jevLl9e1BQECUlJeTk5JRrxWdkZBAUFFTpudzc3HBzc6vrkoVwWGfyzhC7ZQ7ns4/iqyj81aMdbcYsAZ9grUsTN5imLXhVVZk2bRqrVq3i559/pnXr8rdH9+7dG1dXVzZu3GjfduzYMc6cOUNkZOSNLlcIh/dr1q8899M0zmcl0kxRecHvJtrc+b6EewOlaQs+OjqalStX8t133+Ht7W3vVzeZTHh4eGAymZgyZQoxMTH4+fnh4+PDE088QWRkZI1G0AjRkGw68zPLd8ViK8yis+LCrNBReA19HlyMWpcmNKLpMEmdTlfp9hUrVjBp0iSg7Eanp556in//+99YLBZGjhzJ22+/XWUXzeVkmKRwBjZFZU/yeTLzzQR4u9OntR8Gfdn3R1EVvkhcybcH3gNLHgMUV6Z2nYxrn8dB7zCX2UQtqmmuOdQ4+LogAS/qu3WH0pi/JpG0XLN9W7DJnbljuzC0cxOWxS9mx2/fgrWI8TZ37omcg67rOO0KFnWuXo6DF0KUt+5QGlM/3c/lrbD0XDNTV27j1j4/k58Xj4uthEdVXwaNfFlWYBJ2EvBCOCibojJ/TWKFcAfA9Rz+QV+Qkfk7/i56njKG0nX0YmjS9gZXKRyZBLwQDmpP8vly3TIXGTxOERjwFU3152hiVZnp1pWud8k87qIiuQIjhIPKzL883FVcfeJpFvQpTfXZtCqB7hndOdXtJQl3USlpwQvhoAK8L53Ct5RGTX8i2HsXnpiJKNRReG4oS2zD+LevDB4QlZOAF8JB9WntR7DJnbT88zQOXEWg+1HcsDIi14UdOfeyXQkj2FQ2ZFKIykjAC+GgDHodg7uqbExZgb/LWRqpCqPOebOyIIoktWxKj9vDg+3j4YW4nPTBC+GgtqZsY0f6iwS5pBBQqjAiozlv5j9hD3eA1QfSsClOfSuLuA7SghfCwSiqwr8TP+Wr/csxqTm0t+gIyurBP63jsV72lU3LNbMn+TyRbZtoVK1wZBLwQjiQgpIC3tzzMgnJP2IoKaZfgZ6z50azVBkIVN4VU3G0jRBlJOCFcBBn8s7wyrbnyMw6jFGx8VBpY5Zm38kBtV21rys/2kaIP0gfvBAOYMfZ7fz9x8fJzPiVAJvCC406c+t9n5Hp062KdntZe15G0YjqSMALoaFSpZSPfn2fN+LmYMlPpbtiYEHoGFrd+T4G32bMHdsFqNg5c/H53LFdZBSNqJIEvBAayTHn8OKWOXz/v2l+x9ncmRMxC+9h/wBXDwBGdQtm2cReBJnKd8MEmdxZNrEXo7rJQh6iatIHL4QGjpw7wuLt88g5n4S7qhCtD6TPyH9CSI8Kx47qFszwLkFVzgcvRFUk4IW4gVRVZe2JNayMfwOl6BzNVQNP+fYiZMRC8PKv8nUGvU6GQoqrJgEvxA1SUFLAsvhF7DvxA1iLuFlx5f86PIB7/yfA4Kp1ecIJScALcQOcyDnB4u3zycxOxEUpJUo1MfyW59C1H6Z1acKJScALUYdUVWV98g98su8NSgszCVB1zPTsQJuRr0DjllqXJ5ycBLwQdaTQWsi78YvZfeJ7KCnkJsWVqa1vp9HNT4Or3Jwk6p4EvBB14LcLv/HmjhfIzD6Ci1LKRNWbUf2fQdd5jNaliQZEAl6IWqSoCmuSvuPz/UtRirIJUPXM8OxA2xH/BL/WWpcnGhgJeCFqyQXzBZbufZWDp38GazH9FVceaT0Oz5ufst+4JMSNJAEvRC2Iz4hn2a5Y8i8k46YqTFZ9GXzL32WUjNCUBLwQ16HEVsKnhz5kfeJKMOfQUjUw3SeMZsMXgKmZ1uWJBk4CXohrdCr3FEt2LeBsRgLYSrhNcePBrpNwvekRMMhXS2hPfgqFuEqKqrA2aQ1fJLxNaUEmvir8xTWE8CEvQPPeWpcnhJ0EvBBXIbMok7f3vsaRlK1gLSJCceWxZkPxGfw3cDdpXZ4Q5UjAC1EDqqqyOWUTH+57A3NeCu6qyiTVxODIWeg6/wl0MrOjcDwS8EJcQY45h/d+eZP45B/Bkk8HxcC0xj0JvHU+mJprXZ4QVZKAF6IKqqqyM3UnH+x7nfycU7gopdynePKnHo+h7zkR9AatSxSiWhLwQlQi15LL+wnL2H3yBzDn0lI1MM2zIy1ufQH8O2hdnhA1IgEvxCXsrfb4xeRfOIlBKeVOmzt3dpmAS5/HwMWodYlC1Jima7Ju2bKFsWPHEhISgk6n49tvvy23X1VVnn/+eYKDg/Hw8GDYsGEcP35cm2KF07tgvsBru2N5Y8sc8s/9RkubygK3ttwz5j1c+j8h4S7qHU0DvrCwkPDwcJYuXVrp/pdffpklS5bwzjvvsHv3bho1asTIkSMxm803uFLhzFRVZdOZTTy1/hH2HvsGgzmXu21uvNRhIq3u+RSCw7QuUYhromkXzejRoxk9enSl+1RVZfHixfz9739n3LhxAHz88ccEBgby7bffcv/999/IUoWTyijMYPkvb3HwTBxY8mijGnjcvS0tBz9X6QLYQtQnDtsHn5ycTHp6OsOG/TFZk8lkom/fvuzcubPKgLdYLFgsFvvzvLy8Oq9V1D+lSinfn/wvX/76PiX5qbgqNu6zeXBb5wcw9HlUFuQQTsFhAz49PR2AwMDActsDAwPt+yoTGxvL/Pnz67Q2Ub8lXUjivV/e5HRaPJQU0E1x4ZFGnQga8ncI7Kp1eULUGocN+Gs1Z84cYmJi7M/z8vIIDQ3VsCLhCGyKypak31mb/CUnzv2AW8k5vBSVPyte3BI+GV3Ph+QiqnA6DhvwQUFBAGRkZBAcHGzfnpGRQY8ePap8nZubG25ubnVdnnAQNkVlT/J5MvPNBHi706e1HwZ9+WkDfjiYyvMbvsHm9gNNXdJxp4TOZj33+XSn810vyEpLwmk5bMC3bt2aoKAgNm7caA/0vLw8du/ezdSpU7UtTjiEdYfSmL8mkbTcP0ZVBZvcmTu2C6O6lTUKVsb/wotbl9Ck0VEaU0CTUhiV48bWwtGMSevD26nujPLT6hMIUbc0DfiCggKSkpLsz5OTk0lISMDPz48WLVowY8YMXnzxRdq3b0/r1q157rnnCAkJ4Y477tCuaOEQ1h1KY+qn+1Ev256ea2bqp/t548Eu5Bq28/b+D2jlcR53bAzO02HI7crLpWM5hwkdMH9NIsO7BFVo9QvhDDQN+H379jFkyBD784t951FRUXz44YfMnj2bwsJCHn30UXJychg4cCDr1q3D3V1GODRkNkVl/prECuEOoKLi0ug4L+5cTnOPTPyVfDqadfS70JjPLePYq3a65FhIyzWzJ/k8kW2b3LD6hbhRdKqqVvY9cRp5eXmYTCZyc3Px8fHRuhxRC3aeOMcDy3dV2K4zZuHeZDN+7sfxI59gnZ5bsyGx8BY+tw3BQuUXUd+4vwfjesjyeqL+qGmuOWwfvBBVycy/7E5mfRHGxrswecfTVJeLh1rKoHwdA7zCmJE/hN/VgGrPF+AtfxEK5yQBL+qdpo0ujpIqxcV0AC/fnTTRZ9MIM92LdfTNMfF1yW30Gvt/2FYdRJdrrrQ7RwcEmcpG3gjhjDSdi0aIa6GiYvA8TqPmnxDs9wOh+rO0t1p4OMsF16xBzDbHEKeEo3fRM3dsF6AszC918fncsV3kAqtwWtKCF/XKsfPHWH7kXfwD99GEPHwVG8Nz9SgFHXildAxn8bcfm11gYVyPZiyb2KvCcMqgy4ZTCuGMJOBFvZBakMq/j/6bPWfiKM1Pp5lazM35Otrl+/ORdQx71Y5c3k6/2Lc+qlsww7sEXfGGKCGcjQS8cGjnzef5+rev+Tl5HUpBBvqSAkbYjHTKasS3xUN4S+lH6WU/xpX1rRv0OhkKKRocCXjhkApKCvjuxHf8cGIt1oJ0KM6ht2LgAZsPoZ3HsbHXbXz7n5MVXid960L8QQJeOJQiaxHfJ3/P2hNrKM5PhaJzdLTpeNDmSafmA6DfVPBrza3AMqOv9K0LUQ0JeOEQzKVmfjz9I6uTviM//ywUZtPSpnJvqTu9G3dCFxkNzXuXe430rQtRPQl4oSlzqZkNpzew+sRq8v4X7MGlpdxrc6efRzP0A/8P2g0HfeUjeqVvXYiqScALTRSXFrPh9AbWnFhDXkE6FGYRYC3hbpsbA139MURMgi7jZI52Ia6DBLy4oQqthaw/tZ7/nvwvBUVZUJhNQImZO21u3GLwx6XHPRB2P7h5aV2qEPWeBLy4IXItuXyf/D3rT62nuPgCFGUTbCnmTpsbA3SNcekyDnpOBE+ZNkCI2iIBL+pUemE6a0+uZXPKZqyWfCg6R6ilmLtsbvTDhL7jGOj1Z/CqfkIwIcTVk4AXdSLpQhKrT65mb9peFGsRFGXTzmLmjlI3euONvsMo6PkQmGSaXiHqigS8qDWKqrA3fS/fJ3/P0fNHwVoMRefoYbZwu82NLnijaze8rMXuKwuhC1HXJODFdSu0FrIpZRPrk9eTWZwJJYW4FF2gv6WEsTY3WuANHUaW9bGbmmtdrhANhgS8uGYp+SlsOL2BuJQ4zKVmKMnHqziX4eZSRtiM+Ol9oNNo6DEBfOTOUiFuNAl4cUU2RbXfLdrEywWDRzI/ndnAoXOHQFXBnEtzcyGjzTZuVlxxc/GC7n8qG+7o5X/lNxBC1AkJeFGtdYfSmL8mkfSCLFy8D+PinYjRWEyIyYgvhfQuLmakRaWbakDn5gtd74Ru48HDV+vShWjwJOBFlf57MIXpq77B4J2IR+MzgIorpQRYLQw6U8C9RjfauLmVDXHsfi90GgNGT63LFkL8jwS8qCAlL4WNZzaxeOd3GAMKAfDAQkeLjTFFuXQuBhd0JLk0peV9T2JoNxQM8qMkhKORb6UAyu403ZG6g7jf40jOTabQUopVKcBPKeWWIgvDC/NpYtMBOvYpHfnO1p8DJW35t0tvIiXchXBI8s1swCw2C/vS97Ht7DYOZB3AptoAMCg2ulkM3H3uAuEWCwZ0WDCyztaT1Up/flf/uHCamW+u6vRCCI1JwDcwVpuVA1kH2JG6g/iMeMy2/wW0Cm1cvRlUbCEy6zQGs42TlhIyVD/+a+vHBqU3hXhUON/FdU+FEI5HAr4BsNqs/Jr9K7vSdrEvfR9FpUX2fQFGXwbqGjEgM5nmBcf/t1WH0jaSt/Nb8mN+KxQqzsVe2bqnQgjHIgHvpIpLi0nITGBP+h72Z+z/o6UONHbzJdKjGf1zs2l36ld0qlq2w80bOt4Gncei9w3ljpZprP90PzpAveTcsu6pEPWDBLwTyS7OZn/GfuIz4jl07hClSql9X2P3xvT17UxkcREdTu1DX3T0jxcGdYPO46DNIHBxs28e1S2YZRN7ybqnQtRTEvD1WKlSyvELx0nISuCXjF84nX+63P4gzyD6+PfgplJol/IL+hPf/LHTzRs6jIJOt4FfmyrfQ9Y9FaL+koCvR1RVJb0wnYPZB+2P4tJi+349eto1bkdEQG8icCckJR7d3i/KZnUE0OmgWQR0HA2tbq7xcniy7qkQ9ZMEvIPLLs4m8Vwih7MPc/jcYbKKs8rt9zZ6E9Y0jJ4BPehhMOF9eifs/hQKLznO1LxsNsf2I8E78AZ/AiGEViTgHYiqqpwtOMuxC8c4dv4YR84dKZt+9xIuOhfaN25PuH84Yf5htFZd0J/cDDv+BRcu6aJx84Y2g8u6YQK7lrXehRANigS8hgpKCjiRe4KkC0kczznO8QvHKbAWlDtGj57WptZ0bdqVrk260smvE+5F5+HkZvg1FrKP/3GwwQgt+kH7ERDat8ZdMEII5yQBf4Pkl+RzKvcUyXnJJOcmcyLnBBlFGRWOM+qNtPVtSye/TnRu0pkOjTvg4eIBOWcgeQtsfat8qOv00DwC2g4t61d387qBn0oI4cjqRcAvXbqUV155hfT0dMLDw3nzzTfp06eP1mVVqri0mNSCVH7P/53fC37nTN4ZTuef5oL5QqXHB3oG0s63HR0ad6CdbztamVrhoncpm2c96xjs/xRObYMLp/54kU4PIT3LumBa3yJT8wohKuXwAf/FF18QExPDO++8Q9++fVm8eDEjR47k2LFjBAQEaFKTxWYhsyiTjMIMMooySC9MJ60wjdSCVM6Zz1X5ukDPQFqbWtPKpxVtfNvQ1tQWL+MlLe7SEvg9Hk5vh9M7yl8o1btAs97Q+mZoNRA8GtfhJxRCOAOdqqrqlQ/TTt++fbnpppt46623AFAUhdDQUJ544gmeeeaZK74+Ly8Pk8lEbm4uPj4+NXpPq81KUk4SF8wXOGc+x3nzec6Zz5FdlE12cTa5JbnVvt5kNNHcuznNvZsT6h1KC+8WtPBpUdbVcrmCLEjZBWd2wdn4P4Y0Arh6QPObyrpeWkaWXTgVQjR4Nc01h27Bl5SUEB8fz5w5c+zb9Ho9w4YNY+fOnZW+xmKxYLFY7M/z8vKu+n2LSouYt3Netcd4unji7+lPUKMgghsFE+QZRIhXCM28mpVvlV+utAQyDkHKHvh9D5w7UX5/o6bQIhJaDihrscuFUiHENXLogM/OzsZmsxEYWH7sdmBgIEePHq30NbGxscyfP/+63tfb6E0zr2b4GH3wc/fDz92PJh5NaOrRlCYeTQjwCKg+xC+lqnAhuazr5ew+SE2A0kum2NXpIKBL2aiXFpHQtL0MaRRC1AqHDvhrMWfOHGJiYuzP8/LyCA0Nvapz6HV6Xh/8+rUVoKqQdxZSfyl7nN0PxZddYPVoXNb10qJv2Z2lcpFUCFEHHDrgmzZtisFgICOj/HDCjIwMgoKCKn2Nm5sbbm5ule6rE4oCOach/SCkJUDar+UvjgK4uENwGIT0gtA+ZXO/SCtdCFHHHDrgjUYjvXv3ZuPGjdxxxx1A2UXWjRs3Mm3aNG2Kspoh6yhkJkL6IUj/FSz55Y/Ru5TdPRrSs+wR0EX60oUQN5xDBzxATEwMUVFRRERE0KdPHxYvXkxhYSGTJ0+u+zdXFMhNKRuPnplY9jiXBIqt/HEubhDQGYJ7QHB4WaC7ykpHQghtOXzA33fffWRlZfH888+Tnp5Ojx49WLduXYULr7WqIBM2LYDs36CksOJ+zyZlLfTAbhDUHZp2AFl4WgjhYBx+HPz1upZx8JRaYMXospa6i1tZgPt3gsAuZa1zr0DpQxdCaMYpxsFrxsUNbp1bNs1u41agN2hdkRBCXDUJ+Kq0GaR1BUIIcV30WhcghBCibkjACyGEk5KAF0IIJyUBL4QQTkoCXgghnJQEvBBCOCkJeCGEcFJOPw7+4o2617LwhxBCOKKLeXaliQicPuDz88tmerzaOeGFEMLR5efnYzKZqtzv9HPRKIpCamoq3t7e6K5i/piLC4WkpKTUfA4bjUnNN0Z9q7m+1QtS85Woqkp+fj4hISHo9VX3tDt9C16v19O8efNrfr2Pj0+9+QG7SGq+MepbzfWtXpCaq1Ndy/0iucgqhBBOSgJeCCGclAR8Fdzc3Jg7d+6NXd/1OknNN0Z9q7m+1QtSc21x+ousQgjRUEkLXgghnJQEvBBCOCkJeCGEcFIS8EII4aQk4CuxdOlSWrVqhbu7O3379mXPnj1al1StLVu2MHbsWEJCQtDpdHz77bdal1St2NhYbrrpJry9vQkICOCOO+7g2LFjWpdVrWXLlhEWFma/iSUyMpIffvhB67KuysKFC9HpdMyYMUPrUqo0b948dDpduUenTp20LuuKzp49y8SJE2nSpAkeHh50796dffv2aV2WBPzlvvjiC2JiYpg7dy779+8nPDyckSNHkpmZqXVpVSosLCQ8PJylS5dqXUqNxMXFER0dza5du9iwYQNWq5URI0ZQWFiodWlVat68OQsXLiQ+Pp59+/YxdOhQxo0bx+HDh7UurUb27t3Lu+++S1hYmNalXFHXrl1JS0uzP7Zt26Z1SdW6cOECAwYMwNXVlR9++IHExERee+01GjdurHVpoIpy+vTpo0ZHR9uf22w2NSQkRI2NjdWwqpoD1FWrVmldxlXJzMxUATUuLk7rUq5K48aN1X/9619al3FF+fn5avv27dUNGzaogwYNUqdPn651SVWaO3euGh4ernUZV+Wvf/2rOnDgQK3LqJS04C9RUlJCfHw8w4YNs2/T6/UMGzaMnTt3aliZc8vNzQXAz89P40pqxmaz8fnnn1NYWEhkZKTW5VxRdHQ0Y8aMKfdz7ciOHz9OSEgIbdq0YcKECZw5c0brkqq1evVqIiIiuOeeewgICKBnz54sX75c67IA6aIpJzs7G5vNRmBgYLntgYGBpKena1SVc1MUhRkzZjBgwAC6deumdTnVOnjwIF5eXri5ufH444+zatUqunTponVZ1fr888/Zv38/sbGxWpdSI3379uXDDz9k3bp1LFu2jOTkZG6++Wb7tN+O6OTJkyxbtoz27duzfv16pk6dypNPPslHH32kdWnOP5ukcGzR0dEcOnTI4ftZATp27EhCQgK5ubl89dVXREVFERcX57Ahn5KSwvTp09mwYQPu7u5al1Mjo0ePtv93WFgYffv2pWXLlvznP/9hypQpGlZWNUVRiIiIYMGCBQD07NmTQ4cO8c477xAVFaVpbdKCv0TTpk0xGAxkZGSU256RkUFQUJBGVTmvadOmsXbtWjZt2nRdUzrfKEajkXbt2tG7d29iY2MJDw/njTfe0LqsKsXHx5OZmUmvXr1wcXHBxcWFuLg4lixZgouLCzabTesSr8jX15cOHTqQlJSkdSlVCg4OrvBLvnPnzg7RtSQBfwmj0Ujv3r3ZuHGjfZuiKGzcuLFe9LXWF6qqMm3aNFatWsXPP/9M69attS7pmiiKgsVi0bqMKt16660cPHiQhIQE+yMiIoIJEyaQkJCAwWDQusQrKigo4MSJEwQHB2tdSpUGDBhQYZjvb7/9RsuWLTWq6A/SRXOZmJgYoqKiiIiIoE+fPixevJjCwkImT56sdWlVKigoKNfCSU5OJiEhAT8/P1q0aKFhZZWLjo5m5cqVfPfdd3h7e9uvb5hMJjw8PDSurnJz5sxh9OjRtGjRgvz8fFauXMnmzZtZv3691qVVydvbu8J1jUaNGtGkSROHvd4xa9Ysxo4dS8uWLUlNTWXu3LkYDAYeeOABrUur0syZM+nfvz8LFizg3nvvZc+ePbz33nu89957WpcmwyQr8+abb6otWrRQjUaj2qdPH3XXrl1al1StTZs2qUCFR1RUlNalVaqyWgF1xYoVWpdWpYcfflht2bKlajQaVX9/f/XWW29Vf/zxR63LumqOPkzyvvvuU4ODg1Wj0ag2a9ZMve+++9SkpCSty7qiNWvWqN26dVPd3NzUTp06qe+9957WJamqqqoyXbAQQjgp6YMXQggnJQEvhBBOSgJeCCGclAS8EEI4KQl4IYRwUhLwQgjhpCTghRDCSUnACyGEk5KAF0IIJyUBL4QQTkoCXojrkJWVRVBQkH0ucIAdO3ZgNBrLzUoqhBZkLhohrtP333/PHXfcwY4dO+jYsSM9evRg3LhxvP7661qXJho4CXghakF0dDQ//fQTERERHDx4kL179+Lm5qZ1WaKBk4AXohYUFxfTrVs3UlJSiI+Pp3v37lqXJIT0wQtRG06cOEFqaiqKonDq1CmtyxECkBa8ENetpKSEPn360KNHDzp27MjixYs5ePAgAQEBWpcmGjgJeCGu09NPP81XX33FgQMH8PLyYtCgQZhMJtauXat1aaKBky4aIa7D5s2bWbx4MZ988gk+Pj7o9Xo++eQTtm7dyrJly7QuTzRw0oIXQggnJS14IYRwUhLwQgjhpCTghRDCSUnACyGEk5KAF0IIJyUBL4QQTkoCXgghnJQEvBBCOCkJeCGEcFIS8EII4aQk4IUQwklJwAshhJP6f2qZbY+yxYJIAAAAAElFTkSuQmCC", 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", 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", 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++5kwYQLr1q2rct+VawkcP36ct99+m9dee63OvhdHFxvhxzf702nifgTPgNLf0iJzWrKq+LYKxzkiu/3YSU5OJisri65du+Li4oKLiwubN2/mrbfewsXFhdDQUIxGY4UbY5mZmYSFhVV5Xjc3N2tv9Op6pDubXbt2sW/fPtq1a2f91b1MXFyczdeHDx+2WaMVoHfv3tZFQ65F+Rvj4eGlD3KUjfHWZK3Xa9WtW7cbev+VrryxHx4ebjNGPXv2bJYuXVrtOXQ6HZ988gkrVqyguLiYuXPnVnv8mTNn8Pb2tr7GjRvHL7/8YrPtyn78V4qPj+eZZ57h//2//8c///lP+vTpY92XmprKN998w7333ku7du3YsGEDJpPJun5uVfvKO3fuHIMGDeKBBx7giSeeqLYWZxbi645Wqyc6ZDkaLNxU6MOP+r9VepwjstsV/B133MGBAwdsto0ZM4Y2bdrw3HPPERUVhaurKxs2bGDEiBEAHD16lDNnztxwSFTFTevGkkFL6uTcNfnsmmjZsiWKolQY6y4b9ig//FCmqqGcqmg0pT/3yy8VUNXNWVdXV+t/lw0NXe2K90Zc+b1cS62VKV8/lH4P11P/tm3bAMjJySEnJ6faP/OIiAibewZfffUVK1assFnQ/GrDIhaLha1bt6LVajl+/LjNvvHjxwOQkpIClP4AeuaZZ666r0xaWhq33347vXr14r333qu2DmcX5O1CbOiHqNpigkxadmY9jhFdhePCJOBt+fj40L59e5ttXl5eBAYGWrePHTuWKVOmEBAQgK+vL0899RTx8fH07NmzTmpSFKVGwyT2FBgYyIABA3jnnXd46qmnrjm8oXQN1q1bt5KQcHk4auvWrcTGxgIQHBwMQHp6unV8tnwgXcvn7Ny503rzDyqu9XqjalKrTlf6D9JsNtfqZ5c5ceIEkydP5v333+ezzz4jISGB9evXW3/4XMnFxcVmbduQkBA8PDyuab3bV199lSNHjrB582YGDhzIokWLbIbhoPT+QFWzc6rad+7cOW6//XbrrLWqvofG4ui5t9G4Z+FmgUuZ95KpVryvYo8mYjXl0E+yzp07F41Gw4gRIzAYDAwcOJAFCxbYuyy7W7BgAb179yYuLo4ZM2bQsWNHNBoNu3fv5siRI1cdxnj22Wd58MEH6dKlC/3792fVqlV89dVX1umVHh4e9OzZkzlz5tCsWTOysrL497//fc11Tpw4kdGjRxMXF0fv3r355JNPOHTo0A3dZL1STWqNiYlBURRWr17NXXfdhYeHB97e3jU6/7Rp0zh37lyVwzRms5lRo0YxcOBAxowZw6BBg+jQoYN1jdu68Ouvv/LCCy/w5Zdf0rt3b9544w0mTpxI3759b+jP9ty5c9x2223ExMTw2muvkZ2dbd1X3bCos/r5yJesO/U9rlqF0MxOrDbZDnXas4lYjalOTq/Xq4Cq1+sr7CsqKlJTUlLUoqIiO1R2Y9LS0tQJEyaozZo1U11dXVVvb2+1e/fu6quvvqoWFBRYjwPUlStXVnj/ggUL1ObNm6uurq7qzTffrC5dutRmf0pKihofH696eHionTt3VteuXasC6saNG1VVVdWNGzeqgHrx4kXre3799VcVUFNTU63bXn75ZTUoKEj19vZWExIS1KlTp6qdOnWq0fcYExOjzp071/p137591YkTJ1Y47mq1qqqqvvjii2pYWJiqKIqakJBQ5fmGDRtm3a+qqpqQkKD27du3yhpnzpyphoeHq+fPn7duW7FiharT6dR9+/bV6PtctGhRtZ9RXlFRkRobG6s++eSTNtvvuecetVevXmpJSUmNzlNVHZQuPlThVV09DfXfUHVOZqeoI5f2UB/8sJP6+ecj1B9+O6v2nLVejXlutfXVc9Z69YcDaXapr7pcK0/WZJX1JIW4bs74byjPkMv/fX0/WYVZdNH4MPX+lWi8gjBbVIdpIlbTNVkdeohGCCHqk0W18Ob6iWQVZhGClgn9XkXjFQQ4VhOxmpKAF0I0emVX52sPvcnB87/ioSg82/7veEfVzYSO+iIBL4Ro1NYcTGfmqhQKTTuJDP0WLWZic1uT4n430Vd/u0Nr3HOghBCN2pqD6Yz/eC/nC0/TNOSr0nDP8+WzCyMZ/8mvrDmYfvWTODAJeCFEo2S2qMxclYKqKaZl2BJcFAMxBhc2n3+c4j8fZpq5KgWzpeHOQ5GAx/YpSCFEzTXkfzu7UnNI1xfSIuRjXF1y8Dcr/JH5AOmEAKXzQ9P1xexKdcxOkTXRqAO+7DH1hrpAhhD2VvZv58qWDw1BVl4xEQE/4OlxAhcVfLN68au5U6XHNVSN+iarVqvF39/f2lzK09PzulcNEqIxUVWVwsJCsrKy8Pf3r9CJsiHIKdqOv982FFTa5cTwcfGQSo8L8Wm48/sbdcDD5Uewy3cQFELUjL+/f4NsY5Cac5yfTszFVbHQIdeHFbljUK8Y0FCAMAfuM1MTjT7gFUUhPDyckJAQh17OTghH4+rq2iCv3PUGPa9teBqjqYDOijvrLzxBEbZX6Q2iz0wNNPqAL6PVahvkX1YhRNWubC/QJcaHNzZM5nx+GuGqhqm3zyI+vh0zV6WQrr881h7m5870obEMah9ux+pvnAS8EMIplT3AdDm4VVpGrMLfay/eGoVn2jyKV8v+DAIGxIY5TJ+Z2iQBL4RwOmUPMJWfxOnvsxOd205MJWYe8OpGZM+J1n0Nsc9MTTTqaZJCCOdjfYCp3Dad+ylCg77HBTPd9H68euZBzDT8K/SrkYAXQjiV0geYLo+nKy4XuSn0E9ww0rbQle9ynuBUrtqgH2CqKQl4IYRTsXkwSTHQLGwp7po8mpoUjmSNIpOgisc5KQl4IYRTufxgkoXIkM/wdk3H1wxq5p3sV1tXcpzzkoAXQjiV7s0CCPdzJyhgLf6eR3BRoXlWB9aYbgNK57g78kLZtUkCXgjhVLQahfvj0wj224IGCz0uhPFp8UOA4jQPMNWUTJMUQjiVQ9m/kXxuIV4uFrrrvfkk73FMf0adszzAVFMS8EIIp5Gen84bPz2D2VjAXzSe/GPUEm7NC3S6B5hqSgJeCOEU8o35/HfDRPILs2ipahnX92VcQm4mPsTeldmPjMELIRo8k8XEGz9PIz3nGEGqhqkdxqNr0c/eZdmdBLwQokFTVZUPdr3OoT+24Q48FzkQv7ix9i7LIUjACyEatG8Of8rG379Co5qZ6NuO6H4zQRbuASTghRAN2Pazv/Dp3rfBbCTBNZyug98CF529y3IYEvBCiAbpWM7vzN/yHzAVMRhvBt21ADyd/+GlayEBL4RocDILMnll4xRMxZfoanHlb3e8DgHN7F2Ww5GAF0I0KPnGfP770xRyc//gJlXL092noonuYe+yHJIEvBCiwTCZTbz+y784dz6FAFXDc63+ikeHB+xdlsOSgBdCNAiqqvLu7ldJ+WML7sDzYX8hoPdke5fl0ORJViGEQ7lyoeyy9gKfHVzML79/jcZiZop3LDED5oBGrlGrIwEvhHAYFRfKLm3te3/vbHadeQ/MRp50jaDTkHfA1fn7ud8oCXghhEOobKFsgGzjYb4/vJQmLgYe1Ppz+xCZDllTdv39JikpiY4dO+Lr64uvry/x8fH88MMP1v3FxcUkJiYSGBiIt7c3I0aMIDMz044VCyHqQmULZQNodFlEhnyKF0W0y1e474650OQme5TYINk14CMjI5kzZw7Jycns2bOHfv36MWzYMA4dOgTA5MmTWbVqFV988QWbN28mLS2N++67z54lCyHqwJULZQMoLrlEhn2EryaXFgaFU5kj2G2MsVOFDZNdh2iGDh1q8/XLL79MUlISO3bsIDIykg8++IBly5bRr19pV7hFixbRtm1bduzYQc+ePe1RshCiDlRYAFtTRETYR/hrswkzKZB1B5stXbmvESyUXZsc5ha02Wxm+fLlFBQUEB8fT3JyMiaTif79+1uPadOmDdHR0Wzfvr3K8xgMBnJzc21eQgjHZrMAtmIiNPQzAlz/wM8MkVldWVlyR8XjxFXZPeAPHDiAt7c3bm5ujBs3jpUrVxIbG0tGRgY6nQ5/f3+b40NDQ8nIyKjyfLNnz8bPz8/6ioqKquPvQAhxo8oWylawEBiykiD33/FUVbpmtWSxcQQKSqNZKLs22T3gW7duzb59+9i5cyfjx48nISGBlJSU6z7ftGnT0Ov11tfZs2drsVohRF3QahReuLstvoFrCPXcjxsW/pIdzsLiv6H+GVONZaHs2mT3aZI6nY6WLVsC0K1bN3bv3s2bb77JQw89hNFo5NKlSzZX8ZmZmYSFhVV5Pjc3N9zc3Oq6bCFELcvTbKBF8G4sRgt3ZDfh3YKxGNAR3sgWyq5Ndg/4K1ksFgwGA926dcPV1ZUNGzYwYsQIAI4ePcqZM2eIj4+3c5VCiNq05sRqVvyahKtq5HG3EALueZNIs3+jXCi7Ntk14KdNm8bgwYOJjo4mLy+PZcuWsWnTJn788Uf8/PwYO3YsU6ZMISAgAF9fX5566ini4+NlBo0QTmT7uW0s3vkKmAp5AB8G3LUAgm+mm70LcwJ2DfisrCz+9re/kZ6ejp+fHx07duTHH39kwIABAMydOxeNRsOIESMwGAwMHDiQBQsW2LNkIUQtOpD9G+/88h9UQy4DLO6MuHMuBN9s77KchqKq6pUPjzmV3Nxc/Pz80Ov1+Pr62rscIRqtK5uIBTa5yMsbnqI4P50eFlcm/WUWmlYD7F1mg1DTXHO4MXghhPO5somY4nKJmKhFBLlk0wlXnop7RsK9DkjACyHq1JVNxBRtHuHhH+FLJkFFCkMiH8S100N2rdFZ2X0evBDCeVVoIqYpIjTsEwJd0ggsgaaZ3UhM6YrZ4tQjxXYjAS+EqDM2TcQUI8FhywnSncbPrNI+qy0fmu4jPdfArtQc+xbqpCTghRB1JiO3LNxLCAz9ghC3Y3hbVHpmNWOhYaT1KVXrcaJWScALIepMTr4BMOMf/DWhHil4qBb6ZkfwTvFoTOVuAZYeJ2qbBLwQos74e7rgG/wd4V77cMfMgPNBLCh8nGJs24kEeOnsVKFzk4AXQtQJVVX57dKnRHjvxo0SBp735938J8nDs8KxYX4edqjQ+UnACyFqnaqqfHzgQ46krcRdKWHgBW8+yPs7OVR8KEfaANcdCXghRK378sinrP7tf1BSzGOWJnya9wRZNKlwnIK0Aa5L8qCTEKJWrfp9JV/unQ+mIkbjz+AR76HL9LF5khWQNsD1QAJeCFFr1pz8no93vw7GAh5WfRh81zsQfDODgmFAbJhNLxppA1z3JOCFEDekrInY5rM/sevsm7iW5HOv6sW9d86DsPbW47QahfgWgfYrtBGSgBdCXLeyJmLZJb8SFfIFPhTSo1BLTOdpECkd3e1NbrIKIa5LWROxLNMBIkO+xIdC4go07Mt+hL/+qLDmYLq9S2z0JOCFENesrImYxvMEUSGf4UsBXQo1HD//INstpcMyM1elSBMxO5OAF0Jcs12pOWQZU4gMWY6vkk+nQoUz2SP42dIZABVI1xdLEzE7k4AXQlyz3el7iAxdjp+SR4cihczse9loqTjmnpUnTcTsSQJeCHFN9mXt45fTr+Cn5NKuSOFC1jDWWrpXemyIj3s9VyfKk1k0QohKXbmGavdmARw8v5/XNj2LxniB9gYN+qy7WWPpWeG9ChAmLQjsTgJeCFHBlWuoAoQEphMZvgxXUw5xFldub/k4D6RFogDlb6WWPbokLQjsT4ZohBA2yqY/lg93jftpPHwWU1KQTQejhkm3PEPcXX8naVRXwvxsh2HC/NxJGtVVWhA4ALmCF0JYVVhDFdB4nCIy9FP8FD1tihWK8+5G0/5BAAa1D5cWBA7smq/gExIS+Pnnn+uiFiGEndmsoQpoPVKJCl2Gv6KnbbFCYeYQPs7vYTP9sawFwbDOTYlvESjh7kCuOeD1ej39+/enVatWzJo1i3PnztVFXUIIOyg/rVHrcZLI0GX4KbnEFinkZQ5ltaVPheOE47rmgP/66685d+4c48eP57PPPuOmm25i8ODBfPnll5hMprqoUQhRT8qmNWo9jxMVugw/JY/2RQqXsu7he0uvCscJx3ZdN1mDg4OZMmUK+/fvZ+fOnbRs2ZJHH32UiIgIJk+ezLFjx2q7TiFEPejeLICQ4FSiQj/FV8mnY5HC+ax7WWOJB0pnyMgKTA3HDc2iSU9PZ926daxbtw6tVstdd93FgQMHiI2NZe7cubVVoxCinmz5YxNNg0t7y3QuVDiXdb/1ISaZ/tjwXHPAm0wmVqxYwd13301MTAxffPEFkyZNIi0tjSVLlrB+/Xo+//xzXnzxxbqoVwhRR9alrmHBlunozHn0V93QFz5q035Apj82PNc8TTI8PByLxcIjjzzCrl276Ny5c4Vjbr/9dvz9/WuhPCFEffju2Dcs3fUqGPMZbPEgYcBsLM368rBMf2zQrjng586dywMPPIC7e9U3Wfz9/UlNTb2hwoQQdU9VVVYe/ZzPkt8CYwH3qJ78tf/rKDHxaEFWYGrgrjngH3300bqoQwhRz1RVZdnBRXy7/30wFfEAPoy4cy5KZJy9SxO1RJ5kFaIRsqgWFv26gLWHPoaSYh7Fn7sHv2Ozhqpo+CTghWhkSiwlJO1+nS1HV6CYjTyuCaL/kIUQ1NLepYlaZtdmY7Nnz+aWW27Bx8eHkJAQhg8fztGjR22OKS4uJjExkcDAQLy9vRkxYgSZmZl2qliIhs1kNvHGtplsOfolWrORp1zC6T/sQwl3J2XXgN+8eTOJiYns2LGDdevWYTKZuPPOOykoKLAeM3nyZFatWsUXX3zB5s2bSUtL47777rNj1UI0TIWmQmb9/DzJJ37A1VzCM2430Xv4IvCPtndpoo4oqqo6zKq42dnZhISEsHnzZv7yl7+g1+sJDg5m2bJl3H///QAcOXKEtm3bsn37dnr2rLjQwJVyc3Px8/NDr9fj6+tb19+CEA5Jb9Aze/NUUtP24K5aeM6zNbFDF4CnPJHaENU01xyqH7xerwcgIKD0L11ycjImk4n+/ftbj2nTpg3R0dFs377dLjUK0dCcLzrP9PUTSD23C1+Lhel+XYgd/r6EeyPgMDdZLRYLkyZNonfv3rRvX3onPyMjA51OV+GhqdDQUDIyMio9j8FgwGAwWL/Ozc2ts5qFcHRn884y66cp5OQcJ0hV+HfoXwgf+F9wcbN3aaIeOEzAJyYmcvDgQbZs2XJD55k9ezYzZ86spaqEaFjKr6NqVP7gh2MvUpB3jkhVy/9F301gvxdAo7V3maKeOETAT5gwgdWrV/Pzzz8TGRlp3R4WFobRaOTSpUs2V/GZmZmEhYVVeq5p06YxZcoU69e5ublERUXVWe1COIry66hqPU4QHbocX00+bdDyn3YJeMdPAI1DjcqKOmbX/9uqqjJhwgRWrlzJTz/9RLNmzWz2d+vWDVdXVzZs2GDddvToUc6cOUN8fHyl53Rzc8PX19fmJYSzK7+Oqpv3AWLCPsFHyaNVERScvpMtfg9IuDdCdr2CT0xMZNmyZXzzzTf4+PhYx9X9/Pzw8PDAz8+PsWPHMmXKFAICAvD19eWpp54iPj6+RjNohGgMLq+jquLlt42IgLV4YKBToYbT2Q/wi6ULyatSGBAbJs3CGhm7BnxSUhIAt912m832RYsWMXr0aKC0uZlGo2HEiBEYDAYGDhzIggUL6rlSIRxX6TqqhfgH/kiY7zZ0mOiV58q2C3/jN7UVAOn6Ynal5kjzsEbGrgFfkyn47u7uzJ8/n/nz59dDRUI4pvI3T69s3XtOryc49AuCPX/DFTO3XfLk20tPkKra9m2XdVQbH4e4ySqEqFr5m6dlwv3cmT40lvhWnmw8/RKhnvvRYeG2C034KPdJsmlS4TyyjmrjIwEvhAMru3l65e+6Gfpi/rH8J/q0/xJD0Sm8VZX4rEjeK3yMfDxtjlUoXY1J1lFtfCTghXBQl2+eVqS4nSMm7GP0uZeIcnFhpF9fEk71x3TFP2lZR7Vxk3lTQjio0punFcfN3bwO0yz8Q3w0F2lqhCdCHiL+4bd4a1R3wvxsh2FkHdXGTa7ghXBQGblXhruKj98WwgPW4Y6RNsUazmcN52TvUXRSFAa1D2dAbFiVN2NF4yMBL4SDysk3lPvKTFDwKoK99+BKCd3zdew6/yi/qa3oXe44rUaRqZDCSgJeCAcV4KUr/Q9NEU1Dl9PE/RhaLNx60Zev9U/whxpse5wQV5CAF8JBhfl5oLjkEBO2FF/XDNxU6HE+gqX5Y8nFy+Y4ISojAS+Eg/LwOk2rqIW4k0sTMzTP6sC7xQ/ZzJTx93SV6Y+iShLwQjgYVVX58dhKFu96DQ8lj0iDgibzDhaV3MHliY+l5PapqI4EvBAOxGQxsWT3XNYd/QKLyUi7AheOZT3MbrV9pcdfLDRJjxlRJQl4IeyofI8ZLw8Tv6TO5kjGbhTVwlBLEO9l/ZU/1MrXPigjPWZEVSTghbCT8j1mNK5ZNA9bipfrBXy0GiYHdMbcZjp/nEi56nmkx4yoigS8EHZQvseMp9chIoNX4K4UEmCCsHNdye7+IgNaNyXc7yQZ+uLK2xUgPWZE9aRVgRD17HKPGQvBTdYSHfIp7kohzQ0a1PThLDeMYObqo0BpDxmoeDNVesyImpCAF6Ke7UrNIT3vEjFhSwj134SOErrmeXAgfTybSnqicnmBjkHtw0ka1VV6zIjrIkM0QtSzlOzfubnpO7i7nMdFhS454XyZOxY93jbHld08lR4z4npJwAtRj7acXMO63/8f7i6lDy+FZsWxuPhezGgrHFv+5qn0mBHXQwJeiHpgspj4eOdrrPl9BVhKaG50ITtzOJ+XxFU4Vm6eitoiAS9EHbtQeJ43f5rM0eyDgMp9ujBC4mbw2DcXUMBmhozcPBW1SQJeiDp0IG0nb22eRm5xDp4oJIb0Jm7Aq6DzJMmn4lqrYX+utSo3T0VtkIAXog5YVAtf//ounx9chGo2EqO6MKXTPwjrOgaU0itzuXkq6poEvBC1TF+s552fpvBb5l5A5XaXAB7rPw9deMcKx8rNU1GXJOCFuEbl+8dcedV9JCOZNzc9R07ReXQojA3oym0D54G7r32LFo2SBLwQ16B8/5gy4X7u/OfuNhiMK/n80FIsZiNNcWFy+yeIinvSOiQjRH2TgBeihsr3jykvM+8872x4Ek+fc2g1cKtLAGP7v4FHeGd7lCmElQS8EDVwuX+MLX/3w0SFfIGiLYQShb9HxNPvztfBzbvS8whRnyTghaiBXak5NsMyYKZlwDe4+e1BwUKwSYMx+0487nhOwl04DAl4IWqg/KIabi5ZtA5dCrrzALTM92XH+TGkqeHcn2+wV4lCVCABL0QNlPaFUYn02UKTwLWgmPBQIfh8R1blP2hdCFsW3xCORAJeiD9VN/2xbbiGW5q+j0GXCqhEGHT8kfUAO0s6WN8fLv1jhIORgBeCqqc/Th8aS7jnQZK2vYjZIxeNWSXmUhQbLo4mHy+bc9zTKVyeQhUORQJeNHpVTn/M1bNgzWR0AUfQKuBv1FCYOYhvDLdScY0l+HZ/OlMHtZWQFw5DAl40alVNfwzU/c5NoZ9hcinAVKJwq0cMS88+wAW16rYCZaswSesB4Sgk4EWjVnH6o4mOAV+i+h3AhAVvswbt+T6Yuk/ggnryqucrP9tGCHuz65qsP//8M0OHDiUiIgJFUfj6669t9quqygsvvEB4eDgeHh7079+fY8eO2adY4ZTKB3Kg7gS3RL4CfvtRsNC00I/UPxL5pfCuGrcbkFk0wpHYNeALCgro1KkT8+fPr3T/K6+8wltvvcXChQvZuXMnXl5eDBw4kOJiuUoStSPExx0FE12afErTpv/D4JqHh0XBLzueNZnPkW5pCkB88yDC/dwrGXkvpSCzaITjsesQzeDBgxk8eHCl+1RVZd68efz73/9m2LBhACxdupTQ0FC+/vprHn744fosVTipEN1R4qNfIV+bB0BooR8Hs0eRbokCLi+f17NFINOHxjL+472yCpNoMOx6BV+d1NRUMjIy6N+/v3Wbn58fPXr0YPv27VW+z2AwkJuba/MS4komYxGfrpvMv9dPwOhWgLtFg3d2L9ZnTrUJd7gc3IPah5M0qithfrbDMGF+7iSN6iqrMAmH47A3WTMyMgAIDQ212R4aGmrdV5nZs2czc+bMOq1NNGxHjv/AuztmkWYqvWrv7RNDq+ipvLzRgEr1y+fJKkyiIXHYgL9e06ZNY8qUKdavc3NziYqKsmNFwhGYLSpbDh9jy/7p7Cs+jEYBf40rj7d7jFu6jQNFYXD3qp9kLU9WYRINhcMGfFhYGACZmZmEh1++gsrMzKRz585Vvs/NzQ03N7e6Lk84iOraC5T54bdzLFs7F4PnBoq1JQCEFUcytNfL3BLX1XqcBLdwNg4b8M2aNSMsLIwNGzZYAz03N5edO3cyfvx4+xYnHMKag+nM+DaFjNxywyq+7sy45/KwyjfbNrJy13T0PhcB8CpxIyd7MOuKe7JuRTouHukydi6cll0DPj8/n+PHj1u/Tk1NZd++fQQEBBAdHc2kSZN46aWXaNWqFc2aNeM///kPERERDB8+3H5FC4ew5mA64z7eW2F7Rm4x4z7ey4KH22LKTuKjk2sxeljQoOChb82eiw9jUktvkirAzFUpDIgNkzF04ZTsGvB79uzh9ttvt35dNnaekJDA4sWLmTp1KgUFBTz55JNcunSJPn36sGbNGtzd5WGSxsxsUXn+qwNV7u/gvpXlW6dT5G7CiAVvgy8nsx8kw9TS5jgVaS8gnJuiquqVbTicSm5uLn5+fuj1enx9ZWV7Z7D1+HlG/m9nhe3hmrO0D/qMTK/ShTh8tG7oM3tyMO9OQFvl+d58uDPDOjetq3KFqHU1zTWHHYMXjVt1N0+3n7hgc6w7RfTy/4Jc/8NkKioqCp75MQSHTWJrDXrDSHsB4awk4IXDudrNU/XP50gVLPTw3IAu4GfOu5oAcDf6cub8vWQY2jK+XQThfmlk6IsrdIssfX/pXHdpLyCclcM+ySoap7Kbp+XDHS7fPF1zMB1/D1fauB5gYNhsCkM3cMnVhNbiivHCbew69xwZhrYABHjqmD40FqjYvV3aC4jGQAJeOIyr3TwFmPvVjxSmTsUz8hPSPPJQ0aDNa8OBs89xJHcQ5cfag7zdpL2AaNRkiEY4jB0nL3Cp0FTpPh/yuNVvBRf9j/KzSaEEcC0O5dj5EeSaoit9T5ifByDtBUTjJQEvHMaVN08B3DDwF6/vMDdJ5g9XMwBNNP5kXRrMb+c7UNnSeVCxda88pSoaIwl4Ua+qby1w+VaoFjO93TbhHbiZP9yMpRstbuRd7E2frk/wSPfAStdRhdLIl7F1ISTgRT1aczCdmatSbJbICy/XsTG+eRDzNx4jXreD0CbrOeFZyCXAggsluR04enEoqsWT3i3C6N0qiKRRXas9nxCNnTzoJOrFmoPplV5xl11jJ43swi2uv/LqD7M44pmLBTCjRS1owe85wykpKR1u8fd0JfnfA6xX5zVpNiaEs5EHnYTDMFtUZq5KqXQ4RUWlm/YAW9f/l0/88yjytVBSokEtiuR4znAMxgib4+fc18EmwGVsXYiqScCLOrcrNcdmGKWUSjfNQVo3+YFjPjnsV0CnutA6sDXNg0fz/mYdBqPBenSYrxsz7mknQy9CXAMJeFHnssq1C1CwcIv2N1r7/cjvvhc5qIAFBbMxiAGtE5nQ9z4URWF0vAy9CHGjJOBFnQvyckODhZ7aX2npv5YjPnr2/xnsJaYgzuUMIK+wPf8eFI+ilIa4DL0IceMk4EXdKjGiO/0Njwb+jxSffPYpYEGDyRRE2sX+5BW0x/pAtVygC1GrJOBF3TDkceG3T1l9eBk/mi6R72vGjAajKZi0iwMoKIjlyk4Z5/MNlZ9LCHFdJODFVV3TVMS8DP7Y+yGrTn7HFgooAcwaLYXGMP64OIDiwpupqgWStO0VonZJwItqXe3hpDJqxkEO7/0fq9K3slfzZz8ZrRuxwR25u/PfSVx8CUNh5Vfo0rZXiLohAd+IXe3KvKqHkzL0xYz/eC8L/9qBOzwOs3PfIr7LO8YJxQwaUFy9iIvoydDOT9I6oDUAM4aWnksBm/NJ214h6o48ydpIlS6qcYiM3MrnmpstKn3++1Ml89ehCbn0d91GmP9ujjcxkqNYAAVXD3/63jSQu9snEO5dcb56TX8bEEJUr6a5JgHfCJUtqlGVhaO64ueh45H3d5TbqtJBSSXefTMG32Mc8LRQAuh0OgJ8whnY5kH6txqGn5tftZ8trQWEuHHSqkBUqiaLajz/1QGmD20HgC8F9NUk08xnGye99ezQlV4PFKOj2BjB3a3/xuRbh+Oqda3R58v8diHqjwR8I7PjRNWLapTRFxpQ/tjDOPdlqD5H2edl5vSf89cLVC8KCtpyPjceiyGcW++Kr3G4CyHqlwR8I7P95Pkq94WSw20ue2jqtZtt5wo5El66wIYBHUUlAWTndqcovxOYvQDQKNAtpkm91C2EuHYS8I2O7Xi3F0X00hygnfsucr3P8ZunymEFNFoterM3RYUtuZDbE3NRNFfOX7eokHz6ogy5COGgJOAbmfgWgby78QhxmqPEuSajeP/OAc8Sfvrzb0IhbhSXBNE++C72J4eimr2rPV/5RmJCCMciAd9YmEvgXDLtTq9hSsAqDnkY2fznDVMjrhRYvMktiCUvvyu+mmgeHRjHml07r3paefpUCMclAd/AXNM0wxIjpO0l//g6dp/ZyDZzLgc1JZgCVQpLNOTjSVHRTeTkd8Vc0AJUHQBzRnWiZ/NAwv3cydAXV7nuqTx9KoRjk4B3EDUJ7ho9KGQshLM70Z/8ieQ/trDTkscBTQlmABcXcGtCyyY34+veh2+2+5B9ybXKc00fGitPnwrRgMmDTg6gJsFd3ZqmQVzi/b4GIkr2sScrmV0YOKopwQKgcQE3H6L8m9Or2WDim/a2PmVaaz9UhBD1Sp5k/ZOjB3xVwQ2l4Z00qisDYsNs2ga4UEIb5QzdNEeJdE9B73Ge3z1B7/HnLBetDnTexDRpRXyzO+ke0ZOm3k2vu0Z5+lQIxyJPsjYA1S1GDaXDIjNXpeDj5oI29yxDNCdo7XoYN49UzribOOKu8uufOVuMDi83fzqFd+aW6Nu5Jbw7IZ4htVKnPH0qRMMkAW9HlS9GDaASwiU6ak7SpvAYOT/+wZCgC5xwh/UupT8OzGgoxINisw95RS0pKryZWXF381C3m+v3mxBCOCwJ+ErU15BE2qUioHQh6iglm7bKaVq5nMDP7SQX3fNJdYNfXFW2aTWYXKEIHUW4YzCEoy9sjbkoBoshjLIHkKL95SpbCHGZBPwV6uWmYnEuZB1G++taJrvvxd39LFk6I6fd4GeXsgEbhWJcKcSNAF04GZdacPFSU0qKI8FiO/dcpiwKISojAV/O1Ra4SBrV1Sbka3SlX6yH88dRs4+Sk3WAk+cPkVqUyXHFTIrFRG64BQALSmnPF3SYTAHkFrfAWBSNpTiSvnGx/KNrkExZFEJckwYR8PPnz+fVV18lIyODTp068fbbb9O9e/da/YzqbniqlAbpzFUpDIgNQ6tRKlzpu1JCF59cpvZwo5P3Bc5lH+TMpROcMVzklGLmtGImV/nz7FpAqyNP8UJvdMVoCEVf3ByzIRxzcQRYPG0+X6PAoPbhJI3qWuG3izCZsiiEqILDB/xnn33GlClTWLhwIT169GDevHkMHDiQo0ePEhJSO7NEoLobnqVUIENfyN6U3zHlnGblmi3002bi45mJzvU8Ztc8sl1V5h9VKXDXoJRdTf8Z5rh4oHHxIMo3mmbB7WkRGMvJc17MWXXhz4Oq1jnSHygN+QGxYTJlUQhRIw4f8G+88QZPPPEEY8aMAWDhwoV89913fPjhhzz//PO19jnlm2a5YaSHJoUmLll4ac/j5nIJV5dL4FLA2i0azppLuBCtkn5FrlrQYMSVErOOm5oEE+17E1FBbbmpyc3c5HcTUT5R6LQ66/HbzReAHVxNRJPLV/QyZVEIUVMOHfBGo5Hk5GSmTZtm3abRaOjfvz/bt2+v9D0GgwGD4fI6o7m5uTX6rPJNs3TaXPTRy7lUyXGuLiqFihYTLphwQS3xosQUSIEphAJjOBZTAKqpCS/1u4NeLYOq/czuzQII93Ov9jeHcLl5KoS4Tg4d8OfPn8dsNhMaGmqzPTQ0lCNHjlT6ntmzZzNz5sxr/qyysM3QF5NnbkIh7pjRoJg9UEu8MZX4odGG079DFxb/cglLiS+qyZeq/giz8w2Vbi9Pq1Gs/V6qepJVbp4KIa6X5uqHNCzTpk1Dr9dbX2fPnq3R+8rCFkBBy/Ezz3Iy9d+cOPMsqWnj+SPrr0y745/0jxmEuegmVFMA1f18rGkb3bKbp+F+tseH+7lXmLUjhBDXwqGv4IOCgtBqtWRmZtpsz8zMJCwsrNL3uLm54ebmdl2fZztT5fL28jNVzBa11tvoys1TIURdcOiA1+l0dOvWjQ0bNjB8+HAALBYLGzZsYMKECXXymVcL2/LDKrU5J11ungohaptDBzzAlClTSEhIIC4uju7duzNv3jwKCgqss2rqwtXCVuakCyEaAocP+Iceeojs7GxeeOEFMjIy6Ny5M2vWrKlw47W+ybCKEMLRST94IYRoYGqaa043i0YIIUQpCXghhHBSEvBCCOGkHP4m640qu8VQ05YFQgjh6Mry7Gq3UJ0+4PPy8gCIioqycyVCCFG78vLy8PPzq3K/08+isVgspKWl4ePjg6LUfApjbm4uUVFRnD17tsHMvpGa60dDq7mh1QtS89WoqkpeXh4RERFoNFWPtDv9FbxGoyEyMvK63+/r69tg/oKVkZrrR0OruaHVC1Jzdaq7ci8jN1mFEMJJScALIYSTkoCvgpubG9OnT7/uzpT2IDXXj4ZWc0OrF6Tm2uL0N1mFEKKxkit4IYRwUhLwQgjhpCTghRDCSUnACyGEk5KAr8T8+fO56aabcHd3p0ePHuzatcveJVXr559/ZujQoURERKAoCl9//bW9S6rW7NmzueWWW/Dx8SEkJIThw4dz9OhRe5dVraSkJDp27Gh9iCU+Pp4ffvjB3mVdkzlz5qAoCpMmTbJ3KVWaMWMGiqLYvNq0aWPvsq7q3LlzjBo1isDAQDw8POjQoQN79uyxd1kS8Ff67LPPmDJlCtOnT2fv3r106tSJgQMHkpWVZe/SqlRQUECnTp2YP3++vUupkc2bN5OYmMiOHTtYt24dJpOJO++8k4KCAnuXVqXIyEjmzJlDcnIye/bsoV+/fgwbNoxDhw7Zu7Qa2b17N++++y4dO3a0dylX1a5dO9LT062vLVu22Lukal28eJHevXvj6urKDz/8QEpKCq+//jpNmjSxd2mgChvdu3dXExMTrV+bzWY1IiJCnT17th2rqjlAXblypb3LuCZZWVkqoG7evNnepVyTJk2aqP/73//sXcZV5eXlqa1atVLXrVun9u3bV504caK9S6rS9OnT1U6dOtm7jGvy3HPPqX369LF3GZWSK/hyjEYjycnJ9O/f37pNo9HQv39/tm/fbsfKnJterwcgICDAzpXUjNlsZvny5RQUFBAfH2/vcq4qMTGRIUOG2Py9dmTHjh0jIiKC5s2bM3LkSM6cOWPvkqr17bffEhcXxwMPPEBISAhdunTh/ffft3dZgAzR2Dh//jxms7nCgt6hoaFkZGTYqSrnZrFYmDRpEr1796Z9+/b2LqdaBw4cwNvbGzc3N8aNG8fKlSuJjY21d1nVWr58OXv37mX27Nn2LqVGevToweLFi1mzZg1JSUmkpqZy6623Wtt+O6KTJ0+SlJREq1at+PHHHxk/fjxPP/00S5YssXdpzt9NUji2xMREDh486PDjrACtW7dm37596PV6vvzySxISEti8ebPDhvzZs2eZOHEi69atw93d3d7l1MjgwYOt/92xY0d69OhBTEwMn3/+OWPHjrVjZVWzWCzExcUxa9YsALp06cLBgwdZuHAhCQkJdq1NruDLCQoKQqvVkpmZabM9MzOTsLAwO1XlvCZMmMDq1avZuHHjDbV0ri86nY6WLVvSrVs3Zs+eTadOnXjzzTftXVaVkpOTycrKomvXrri4uODi4sLmzZt56623cHFxwWw227vEq/L39+fmm2/m+PHj9i6lSuHh4RV+yLdt29YhhpYk4MvR6XR069aNDRs2WLdZLBY2bNjQIMZaGwpVVZkwYQIrV67kp59+olmzZvYu6bpYLBYMBoO9y6jSHXfcwYEDB9i3b5/1FRcXx8iRI9m3bx9ardbeJV5Vfn4+J06cIDw83N6lVKl3794Vpvn+/vvvxMTE2Kmiy2SI5gpTpkwhISGBuLg4unfvzrx58ygoKGDMmDH2Lq1K+fn5Nlc4qamp7Nu3j4CAAKKjo+1YWeUSExNZtmwZ33zzDT4+Ptb7G35+fnh4eNi5uspNmzaNwYMHEx0dTV5eHsuWLWPTpk38+OOP9i6tSj4+PhXua3h5eREYGOiw9zueeeYZhg4dSkxMDGlpaUyfPh2tVssjjzxi79KqNHnyZHr16sWsWbN48MEH2bVrF++99x7vvfeevUuTaZKVefvtt9Xo6GhVp9Op3bt3V3fs2GHvkqq1ceNGFajwSkhIsHdplaqsVkBdtGiRvUur0mOPPabGxMSoOp1ODQ4OVu+44w517dq19i7rmjn6NMmHHnpIDQ8PV3U6ndq0aVP1oYceUo8fP27vsq5q1apVavv27VU3Nze1TZs26nvvvWfvklRVVVVpFyyEEE5KxuCFEMJJScALIYSTkoAXQggnJQEvhBBOSgJeCCGclAS8EEI4KQl4IYRwUhLwQgjhpCTghRDCSUnACyGEk5KAF+IGZGdnExYWZu0FDrBt2zZ0Op1NV1Ih7EF60Qhxg77//nuGDx/Otm3baN26NZ07d2bYsGG88cYb9i5NNHIS8ELUgsTERNavX09cXBwHDhxg9+7duLm52bss0chJwAtRC4qKimjfvj1nz54lOTmZDh062LskIWQMXojacOLECdLS0rBYLJw6dcre5QgByBW8EDfMaDTSvXt3OnfuTOvWrZk3bx4HDhwgJCTE3qWJRk4CXogb9Oyzz/Lll1+yf/9+vL296du3L35+fqxevdrepYlGToZohLgBmzZtYt68eXz00Uf4+vqi0Wj46KOP+OWXX0hKSrJ3eaKRkyt4IYRwUnIFL4QQTkoCXgghnJQEvBBCOCkJeCGEcFIS8EII4aQk4IUQwklJwAshhJOSgBdCCCclAS+EEE5KAl4IIZyUBLwQQjgpCXghhHBS/x9qD0VP7L6E6AAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mUpdated State:\u001b[0m\n", + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", + " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", + " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", + " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", + " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", + " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", + "0 4.983216\n", + "1 5.849862\n", + "2 3.466585\n", + "3 4.116570\n", + "4 1.733292\n", + "5 3.249923\n", + "6 3.249923\n", + "7 5.199877\n", + "8 3.683247\n", + "9 4.333231, experiment_data= x y\n", + "0 5.849862 40.223108\n", + "1 1.516631 3.296808\n", + "2 2.383277 8.438513\n", + "3 3.683247 17.719834\n", + "4 3.683247 16.274034\n", + "5 2.599939 8.708530\n", + "6 5.849862 40.134670\n", + "7 2.383277 7.905166\n", + "8 4.766554 27.478194\n", + "9 5.849862 39.644228\n", + "10 2.816600 10.871952\n", + "11 1.949954 6.091364\n", + "12 3.466585 15.191032\n", + "13 5.633201 36.911813\n", + "14 3.033262 11.238020\n", + "15 0.000000 0.485811\n", + "16 4.333231 23.118453\n", + "17 0.649985 1.175330\n", + "18 6.066524 42.477437\n", + "19 2.166616 7.474088\n", + "20 1.299969 3.712871\n", + "21 3.683247 17.300704\n", + "22 5.849862 40.234126\n", + "23 4.983216 30.383888\n", + "24 1.299969 3.402012\n", + "25 2.383277 8.352766\n", + "26 3.899908 18.919606\n", + "27 4.549893 24.741981\n", + "28 5.416539 34.943519\n", + "29 4.766554 27.230615\n", + "30 1.949954 5.523342\n", + "31 0.216662 -0.047826\n", + "32 0.866646 1.685367\n", + "33 0.649985 1.494416\n", + "34 0.649985 0.998215\n", + "35 6.283185 45.892845\n", + "36 2.166616 7.126673\n", + "37 1.516631 4.161704\n", + "38 0.649985 0.626515\n", + "39 3.033262 12.468350\n", + "40 4.983216 29.564199\n", + "41 5.849862 40.679695\n", + "42 3.466585 15.348237\n", + "43 4.116570 21.427807\n", + "44 1.733292 4.152943\n", + "45 3.249923 13.097192\n", + "46 3.249923 13.800289\n", + "47 5.199877 32.014707\n", + "48 3.683247 17.293589\n", + "49 4.333231 23.372772, models=[PolynomialRegressor(), PolynomialRegressor(), PolynomialRegressor(), PolynomialRegressor(), PolynomialRegressor()])\n" ] } ], @@ -1078,9 +1298,11 @@ "\n", "s = StandardState(variables = variables, conditions = conditions, experiment_data = pd.DataFrame(columns=[\"x\",\"y\"]))\n", "\n", + "#Report previous state\n", "print('\\033[1mPrevious State:\\033[0m')\n", "print(s)\n", "\n", + "#Cycle\n", "for cycle in range(5):\n", " s = custom_experimentalist(s, num_samples=10, random_state=42+cycle)\n", " s = custom_experiment_runner(s, added_noise=0.5, random_state=42+cycle)\n", @@ -1088,6 +1310,7 @@ " \n", " plot_from_state(s, 'x + x**2')\n", "\n", + "#Report updated state\n", "print('\\n\\033[1mUpdated State:\\033[0m')\n", "print(s)" ] From b3e3796592e3d5700c5d850196126600c22e625f Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Tue, 5 Sep 2023 09:40:00 -0700 Subject: [PATCH 31/32] Added decorators --- .../basic/Tutorial-IV-Customization.ipynb | 437 +++++------------- 1 file changed, 105 insertions(+), 332 deletions(-) diff --git a/docs/tutorials/basic/Tutorial-IV-Customization.ipynb b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb index b924cfe80..b4f0c739d 100644 --- a/docs/tutorials/basic/Tutorial-IV-Customization.ipynb +++ b/docs/tutorials/basic/Tutorial-IV-Customization.ipynb @@ -203,7 +203,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:04<00:00, 24.52it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 20.11it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -222,7 +222,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 23.17it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 24.51it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -310,7 +310,9 @@ "- The function has a `conditions` argument that accepts a `pandas.DataFrame`\n", "- The function returns a `pandas.DataFrame`\n", "\n", - "The custom `uniform_sampler` below will select conditions that are the least represented in the data. " + "The custom `uniform_sampler` below will select conditions that are the least represented in the data. \n", + "\n", + "*Note that when building custom experimentalists, we can either wrap the function with `on_state(output=['conditions'])` as we did in tutorial III, or else we can use the `@on_state(output=['conditions'])` decorator.*" ] }, { @@ -319,6 +321,10 @@ "metadata": {}, "outputs": [], "source": [ + "#==================================================================#\n", + "# Option 1 - Wrapping our Component #\n", + "#==================================================================#\n", + "\n", "def uniform_sample(variables: VariableCollection, conditions: pd.DataFrame, num_samples: int = 1, random_state: Optional [int] = None):\n", "\n", " \"\"\"\n", @@ -345,7 +351,38 @@ " \n", " return pd.DataFrame({\"x\": x})\n", "\n", - "custom_experimentalist = on_state(uniform_sample, output=[\"conditions\"])" + "custom_experimentalist = on_state(uniform_sample, output=[\"conditions\"])\n", + "\n", + "#==================================================================#\n", + "# Option 2 - Using a Decorator #\n", + "#==================================================================#\n", + "\n", + "@on_state(output=[\"conditions\"])\n", + "def custom_experimentalist(variables: VariableCollection, conditions: pd.DataFrame, num_samples: int = 1, random_state: Optional [int] = None):\n", + "\n", + " \"\"\"\n", + " An experimentalist that selects the least represented datapoints\n", + " \"\"\"\n", + " #Set rng seed\n", + " rng = np.random.default_rng(random_state)\n", + "\n", + " #Retrieve the possible values\n", + " allowed_values = variables.independent_variables[0].allowed_values\n", + " \n", + " #Determine the representation of each value\n", + " conditions_count = np.array([conditions[\"x\"].isin([value]).sum(axis=0) for value in allowed_values])\n", + " \n", + " #Sort to determine the least represented values\n", + " conditions_sort = conditions_count.argsort()\n", + " \n", + " conditions_count = conditions_count[conditions_sort]\n", + " values_count = allowed_values[conditions_sort]\n", + " \n", + " #Sample from values with the smallest frequency\n", + " x = values_count[conditions_count<=conditions_count[num_samples-1]]\n", + " x = rng.choice(x,num_samples)\n", + " \n", + " return pd.DataFrame({\"x\": x})" ] }, { @@ -389,7 +426,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:03<00:00, 26.22it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 26.85it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -408,7 +445,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:03<00:00, 25.54it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 26.97it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -427,7 +464,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:03<00:00, 25.56it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 28.09it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -446,7 +483,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 23.35it/s]\n", + "100%|██████████| 100/100 [00:03<00:00, 25.75it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -465,7 +502,7 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:04<00:00, 23.68it/s]\n", + "100%|██████████| 100/100 [00:04<00:00, 23.64it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -593,7 +630,9 @@ "- The function has a `conditions` argument that accepts a `pandas.DataFrame`\n", "- The function returns a `pandas.DataFrame`\n", "\n", - "The custom `quadratic_experiment` below will apply a quadratic transform (`x + x**2`) to the conditions." + "The custom `quadratic_experiment` below will apply a quadratic transform (`x + x**2`) to the conditions.\n", + "\n", + "*Note that when building custom experiment runners, we can either wrap the function with `on_state(output=['experiment_data'])` as we did in tutorial III, or else we can use the `@on_state(output=['experiment_data'])` decorator.*" ] }, { @@ -602,6 +641,10 @@ "metadata": {}, "outputs": [], "source": [ + "#==================================================================#\n", + "# Option 1 - Wrapping our Component #\n", + "#==================================================================#\n", + "\n", "def quadratic_experiment(conditions: pd.DataFrame, added_noise: int = 0.01, random_state: Optional[int] = None):\n", " \n", " #Set rng seed\n", @@ -618,7 +661,28 @@ " \n", " return observations\n", "\n", - "custom_experiment_runner = on_state(quadratic_experiment, output=[\"experiment_data\"])" + "custom_experiment_runner = on_state(quadratic_experiment, output=[\"experiment_data\"])\n", + "\n", + "#==================================================================#\n", + "# Option 2 - Using a Decorator #\n", + "#==================================================================#\n", + "\n", + "@on_state(output=[\"experiment_data\"])\n", + "def quadratic_experiment(conditions: pd.DataFrame, added_noise: int = 0.01, random_state: Optional[int] = None):\n", + " \n", + " #Set rng seed\n", + " rng = np.random.default_rng(random_state)\n", + " \n", + " #Extract conditions\n", + " x = conditions[\"x\"]\n", + " \n", + " #Compute data\n", + " y = (x + x**2) + rng.normal(0, added_noise, size=x.shape)\n", + " \n", + " #Assign to dataframe\n", + " observations = conditions.assign(y = y)\n", + " \n", + " return observations" ] }, { @@ -662,7 +726,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [00:05<00:00, 19.70it/s]\n", + "100%|██████████| 100/100 [00:05<00:00, 18.61it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, @@ -681,13 +745,13 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:05<00:00, 19.91it/s]\n", + "100%|██████████| 100/100 [00:05<00:00, 19.14it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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pzmbLli0kJyfTvn1725/u58XFxdm937t3r90arQAJCQm2NVpr4sKRT2FhYQC2Pt7qrPVaU7GxsVd0/sUuHrkVFhZm10edlJTEhx9+WOU1TCYTn3zyCV999RXFxcW89tprVR5/7NgxvL29ba8xY8bwyy+/2G27eD7+i8XHx/P444/z/PPP89hjj3HddX+N4khNTWXZsmX87W9/o3379qxatYrS0lLb+rmV7bvQyZMnGTBgAHfeeSf/+Mc/qqzF2S09sITfDv+Aq1rGJPcYvG987tzyng2Abi14Hx8fOnToYLfNy8uLwMBA2/bRo0czadIkAgIC8PX15ZFHHiE+Pp6ePXvWSU1uRjc+GPBBnVy7Op9dHS1btkRRlHJ93c2bNwfAw6P8mrJeXl41qsVgOPd7X7tgqYDKbs66uv61gPD5US6XavFeiYu/lprUWpEL64dzX8Pl1L9hwwYAsrOzyc7OrvJ7Hh4ebnfPYMmSJXz11Vd2C5qf/6ujMqqqsn79eoxGIwcPHrTbN3bsWABSUlKAc7+AHn/88UvuOy8tLY0+ffpw7bXXsnDhwirrcHbJWcl8kbwASgv5u+ZHzICXwM1b77KqTfdRNFV57bXXuPXWWxk2bBjXX389oaGhdXrDR1EU3F3cdXlVdwhgYGAg/fr144033qCgoOCyvs62bduyfv16u23r16+nXbt2ADRt2hTAbtTKhYFUk8/ZvHmz3bbaHuJanVpNpnNPF1qt1lr97PMOHTrExIkTeeedd+jRowcjR46s8peEi4sLLVu2tL2Cg4Px8PCw23apgH/ppZfYt28fa9euZcWKFSxatKjcMTfccEOlN40r23fy5EluuOEG27Dk879AG6PMgkzmbXjeNonYDdc/CwHN9S6rRhxqfM/5ERrnubu7M3/+fObPn69PQQ7qzTffJCEhgbi4OKZPn06nTp0wGAxs3bqVffv2XbIbY/Lkydx111107dqVvn37snz5cpYsWWIbXunh4UHPnj2ZNWsWMTExZGVl8cwzz9S4zvHjxzNq1Cji4uJISEjgk08+Yc+ePba/NmpDdWqNjo5GURS+/fZbbrnlFjw8PPD2rl4rbOrUqZw8ebLSbhqr1cqIESPo378/Dz74IAMGDKBjx4688sorTJ48+Yq/vor89ttvPPfcc3z55ZckJCTw6quvMn78eHr37n1F39vz4R4dHc3LL7/MqVOnbPuquu/ljCxWC69sfJ78nFRaakZGtbnXoSYRq67G++u5AWvRogW//fYbffv2ZerUqXTu3Jm4uDjmzZtn65etytChQ3n99dd5+eWXad++PW+//TaLFi2yG4v93nvvUVZWRmxsLBMmTOCFF16ocZ133303zz77LFOmTCE2NpajR4/aughq06Vqveqqq5gxYwZPPvkkISEhNVooPD09nWPHjlW6/8UXX+To0aO8/fbbwLk+/IULF/LMM8+wc+fOy/uCqlBcXMyIESMYNWqUbTjsww8/TJ8+fbj//vuv6K+UlStXcvDgQVatWkVERARhYWG2V2OiaRrvJi/gaNoWfFWNSQFxuMY/ondZl0XWZJX1JIW4bM74M7QidQWLNr6IwZLHMy5X0X7YR+BZdZdZfZM1WYUQoob2ntnLh1tfBUsew1Uv2vf7t8OFe01IwAshBOdWZnpt/XSsBVlcq7oyqOcUCO1w6RMdmAS8EKLRK7WW8urGFzBnHyRaM/J/zW9HaVd3D6vVFwl4IUSjpmka7+58i4MnN+Ctqjzm1wn3Xo81mIeZqiIBL4Ro1H488iNrLpghMqT/bIdbmelyScBj/xSkEKL6GvrPzt4ze/lg6ytgyeNe1ZPO/V8C76Z6l1VrGnXAn39MvaEukCGE3s7/7Fw85UNDcLroNK+un2a7qTq45xMQ2lHvsmqVQz3JWt+MRiP+/v62yaU8PT0ve9UgIRoTTdMoLCwkKysLf3//cjNROjqL1cLLG2aQm32IaM3ImBZ3oLQfondZta5RBzz89Qj2hTMICiGqx9/fv8FNY6BpGm/vmEfqyc34qCqTm8Ti1mvSpU9sgBp9wCuKQlhYGMHBwQ69nJ0QjsbV1bXBtdwBvjm4jPUHlmK0ljDJNYKm/WeDseF1MVVHow/484xGY4P8xyqEqJxV1diSmk1WXjHBPu64eh3hvzvmQUkBD6jetBvwSoN+UvVSJOCFEE5pxe50ZixPId18bgUxxTWbiIj3CXE5TX/Njf69p0HT1pe4SsMmAS+EcDordqcz9uMd2AZxGooJDPkSf04RVgg9Iu5AadVPzxLrRaMeJimEcD5WVWPG8pS/wh0rfsHfEOx6lCZWjdanWjNmb1esasMew18dEvBCCKeyJTXb1i0D4BGwlhCP3XhqVvqcbsr8kntIyy1hS2q2jlXWDwl4IYRTycr7K9xdfHYR5vcL7pRwS7YHbxU+SBHu5Y5zVhLwQginEuR9bgF7g/sJQgO/xZsibso18lXeA2QSUO44ZyYBL4RwLhooLjkEB39BgGKmU5HCnrND2KPFlDvO2UnACyGcyoncbAJDP6epMYuIEgXXU9fyo9q93HGnCyw6VFe/JOCFEE6jTC1jfeZCgl2P4m/V6HCqJe9bB1V4bLCPc6whWxUJeCGEU9A0jUW/LyTzzK94oXLT6abMKxmOdlHMKUCYnzvdY5z3CdbzJOCFEE7hu8P/46c9i1HKihmjBbGg8CGKsW+ln58rdtrgdhgNzj9zrAS8EKLB2565nY+2vQol+QxXvblp6FxmjLiJUD/7gA/1c2fBiG4M6BCmU6X1S6YqEEI0aEfMR5j763S0wmxuspq4tfc0CO3AgFDo1y7UbrKx7jEBjaLlfp4EvBCiwTpTdIZ///IUxbkn6Ki68FDn/7ObY8ZoUIhvEahjhfqSLhohRINUVFbE7PXTyD69jwjNwMToQbjEPaR3WQ5FAl4I0eComsrcLS9xJG0zvqrGEwGxeN3wNMiSm3Yk4IUQDYqmabz/+zvsOPQdrtYyJrvHEDzgJXAx6V2aw5GAF0I0KN8d/h8/7P4IyooZZwjk6lteB3c/vctySBLwQogGY0v6Fj7a+tdwyJ79XwP/SL3LclgS8EKIBuHA2QPMXT8NrSibvlYTg3vPgNCOepfl0CTghRAOL6Mgg9nrnqI0L52uqgsPxY5HaXmT3mU5PAl4IYRDyy3JZda6p8jLPkSMZmR8y7swdrlP77IaBF0DfsGCBXTq1AlfX198fX2Jj4/n+++/t+0vLi4mMTGRwMBAvL29GTZsGJmZmTpWLISoTxarhdm/TiM9aydNNYUnQ/vg0esxGQ5ZTboGfEREBLNmzWL79u1s27aNG2+8kSFDhrBnzx4AJk6cyPLly/niiy9Yu3YtaWlp3H777XqWLISoJ+fGus/mjxO/4m1VedK3I/79ngeDUe/SGgxF0zSHWtckICCAl156iTvuuIOmTZuyePFi7rjjDgD27dtH27Zt2bhxIz179qzW9XJzc/Hz88NsNuPr61uXpQshaoFV1dh8+AxLDy7icOaXeKglPGOKps3f3gNP55/itzqqm2sOMxeN1Wrliy++oKCggPj4eLZv305paSl9+/a1HdOmTRuioqKqDHiLxYLF8tdKLbm5uXVeuxCidqzYnc6M5SmcYR1RAd/hRTE353iR0edZ2ki415juN1l37dqFt7c3bm5ujBkzhqVLl9KuXTsyMjIwmUz4+/vbHR8SEkJGRkal10tKSsLPz8/2ioyUMbJCNAQrdqcz9uMdZFl3cFXACjwp5mazK5/kjOLBJRms2J2ud4kNju4B37p1a5KTk9m8eTNjx45l5MiRpKSkXPb1pk6ditlstr2OHz9ei9UKIeqCVdWYsTwFxeMo4U2X4kMhCfkG1pwdzn7tXCNtxvIUrKpD9Sg7PN27aEwmEy1btgQgNjaWrVu38vrrr3P33XdTUlJCTk6OXSs+MzOT0NDQSq/n5uaGm5tbXZcthKhFW1KzySw6SljYZzQhjy6FCgfO3M5WrQ0AGpBuLmZLanajnv63pnRvwV9MVVUsFguxsbG4urqyatUq2779+/dz7Ngx4uPjdaxQCFHb9p85SkjoYgIN2bS0KBSc7scqNa7ccVl5xTpU13Dp2oKfOnUqAwcOJCoqiry8PBYvXsyaNWv44Ycf8PPzY/To0UyaNImAgAB8fX155JFHiI+Pr/YIGiGE4ztTdIafj/2bIGMWV5UqBGR1511rnwqPDfZxr3C7qJiuAZ+VlcUDDzxAeno6fn5+dOrUiR9++IF+/c6tyPLaa69hMBgYNmwYFouF/v378+abb+pZshCiFuWX5JP0y1NY8v8g2ApXZ7VjbtkQ/loe+xyFc+updo+RkTQ14XDj4GubjIMXwjFZrBZeXPcU+4+twd+qMsbYhbv3DqUUFy4MpfNR35gWy76U6uaaw/XBCyGcX5laxpxNM9l/fB2eqspTPu3peu8bzB3RnVA/+26YUD93CffLpPsoGiFE46JqKm/vmMuOQytwtZbxhFsM0YPmgsmLAR286NculC2p2WTlFRPsc65bxmiQuWcuhwS8EKLeaJrGR7//h3X7vsBgtTDBJYw2g9+0m4LAaFBkKGQtkS4aIUS9WbL/c77b/T6UFjFWCSRu0Hzwqfy5FnFlJOCFEPVixeHv+HzHG1BSwCh8uX7gPAhorndZTk0CXghR59YdW8Oizf8GSx53qJ4M7PcahLTTuyynJwEvhKhTW9I3s2DDv6DYzC2qG3fcMBMiYvUuq1GQgBdC1JnfT/3O6788i1qUzQ1WE/cnTENpcYPeZTUaEvBCiDqxL3sfL699grKCLHqorjzcYwqGNrfoXVajIgEvhKh1h3MOM2vNZCx56XRRXXikSyLGjnfoXVajIwEvhKhVx/OOM3P1YxSZj9NWdWFSu4dw7TZS77IaJQl4IUStSc9P5/mfJ5GXc4SWmpEnWt2DW8+xoMiTqHqQgBdC1Iqswiz+tXoi5uyDRGsGpjYbisd1kyTcdSQBL4S4YqeLTvP86sfIPr2fqzSFpyNvwfuGp8EgEaMn+e4LIa5IdnE2z69+nKxTewjVFJ4N74vfTdMl3B2A/B8QQlw2s8XMC6sfJyPrd4JVeDakN036vQgGo96lCSTghRCXyWwx8/yaxzmZmUygCs82TSCo/7/B6Kp3aeJPEvBCiBrLK8njxTVTOJ6+nSYqPBfYk+CBL4OLSe/SxAUk4IUQNZJfks/zayZzNH0r/qrGc03iCL3lVXBx07s0cREJeCFEteWX5PPC2ikcTduKn6rxbJNYwm99HVzdL32yqHeyopMQokJWVbNbOq/dVSaSfnmC1JOb8VVVnvXvSsSgueDqoXepohIS8EKIclbsTmfG8hTSzcXnNhiKiYj4jBD3PwgAnvPrSuStb4DJU9c6RdUk4IUQdlbsTmfsxzvQzm8wFNM0dDH+xoOYLCr/8OhI5GAJ94ZA+uCFEDZWVWPG8pQLwr2IpqGfEOJ2EB9V5fqsSKakjcDqIt0yDUGNA37kyJGsW7euLmoRQuhsS2r2Bd0yhQSHfUKI2yF8VJVemVG8XvR3juSe65sXjq/GAW82m+nbty+tWrVi5syZnDx5si7qEkLoICvvz3A3FhAS9jHBpsP4qirXZTbj9eK/U4yb/XHCodU44L/++mtOnjzJ2LFj+eyzz2jWrBkDBw7kyy+/pLS0tC5qFELUk2AfdxRjPiFhH9HUlIqfqnJtZgxzix/CgsnuOOH4LqsPvmnTpkyaNImdO3eyefNmWrZsyf333094eDgTJ07kjz/+qO06hRC1yKpqbDx0hmXJJ9l46AxW9Vyve4tQjcjIjwl2PUoTq0aPzJZ24a4AYX7udI8J0LF6UV1XNIomPT2dlStXsnLlSoxGI7fccgu7du2iXbt2zJ49m4kTJ9ZWnUKIWrJidzrTv0khI/evbpZQX3cmDGjKrydnEex6HC+LRtfMq5lneYDSP2Pi/Kzu0wa3w2iQOd4bAkXTNO3Sh/2ltLSUb775hkWLFvHjjz/SqVMn/v73v3Pffffh6+sLwNKlS3nooYc4e/ZsnRRdE7m5ufj5+WE2m231CdFYrdidzpiPd5TbrrhmExb6EeHuWUQoBh5wi+fRo7dxIrfMdkyYnzvTBrdjQIew+ixZVKC6uVbjFnxYWBiqqnLvvfeyZcsWunTpUu6YPn364O/vX9NLCyHqkFXVeHLJrnLbFdMpwkM/ItCYRZNihedaDSag73TWYrB7krV7TIC03BuYGgf8a6+9xp133om7e+U3Wfz9/UlNTb2iwoQQtWvT4TPkFNoPhDC4ZRAe+jEBhtOElSpEZsay9+ZHSDAYMQLxLQL1KVbUihrfZL3//vurDHchhGPaeOiM3XuD+wkiQj8gwHCaqBKFkIyevFs6jI2H9e9aFbVDpioQotH463ab0eMwESGf4qfk0sKi4JrVhw/L+nHuVmqNbssJByZTFQjRSMQ3DwLAxWsfkSGL8VNyaVOsYM0YyOdlN3N+nMz540TDp2vAJyUlcc011+Dj40NwcDBDhw5l//79dscUFxeTmJhIYGAg3t7eDBs2jMzMTJ0qFqLh6tkikCaBu4gK/gxfJZ/OhQo5mUP5Rr3edoy/pys9pd/daega8GvXriUxMZFNmzaxcuVKSktLufnmmykoKLAdM3HiRJYvX84XX3zB2rVrSUtL4/bbb9exaiEaHk3TWHbgc5oFL8ObIroXGDl06l5+VHvYHTfr9o4yUsaJ1HgcfF06deoUwcHBrF27luuvvx6z2UzTpk1ZvHgxd9xxBwD79u2jbdu2bNy4kZ49e17ymjIOXjR2qqby0e/v8t2uRVBaxECLO7+eHs5P+c1tx4T6ujH9tvYyxr2BqLNx8HXJbDYDEBBw7jHo7du3U1paSt++fW3HtGnThqioqEoD3mKxYLFYbO9zc3PruGohHFeZWsZb217ll/1fQZmFUUoTBg6dx/3B7WWMeyPgMAGvqioTJkwgISGBDh06AJCRkYHJZCr30FRISAgZGRkVXicpKYkZM2bUdblCOLzismLmbHye31J/xGAtZawxhOtvXQABzWWMeyPhMKNoEhMT2b17N59++ukVXWfq1KmYzWbb6/jx47VUoRANR35JPi+smcxvh1dgspYxxa0Z1w99HwKaX/Jc4TwcogU/btw4vv32W9atW0dERIRte2hoKCUlJeTk5Ni14jMzMwkNDa3wWm5ubri5udV1yUI4rFOFp0haM5mTWb/jrWpM8WlP61vngUcTvUsT9UzXFrymaYwbN46lS5fy888/ExMTY7c/NjYWV1dXVq1aZdu2f/9+jh07Rnx8fH2XK4TDO5Z7jGdXjuVk5k4CVJge1JPWQxZKuDdSurbgExMTWbx4McuWLcPHx8fWr+7n54eHhwd+fn6MHj2aSZMmERAQgK+vL4888gjx8fHVGkEjRGOy5/QeXl4zmcK8NCI0I09FDiTwxmlgdNW7NKETXYdJKkrFd+0XLVrEqFGjgHMPOj322GP897//xWKx0L9/f958881Ku2guJsMkhTOwqlqVo142nPiV+b8+R1lRNm1UFya3eQDv+HFgcJjbbKIWVTfXHGocfF2QgBcN3Yrd6cxYnvLXYtj8NTd7//ahfPvHUj7e+iqU5NNDdeWR2Mdw7XKPjhWLutYgx8ELIeyt2J3O2I93lJv+K8NczNiPt3Fvnz0cyvoayoq5RfPk/htexNCijx6lCgckAS+Eg7KqGjOWp1Q4t6OmlBIY/DU7j+7Ex1VjhOLPrQPnQWjHeq9TOC4JeCEc1JbUbLtumfMUYwHBIZ8S5HYIN03l72oEfYe9Df5ROlQpHJkEvBAOKiuvgnB3PU146CcEuGTgpWpcdyqckgGvSLiLCsktdiEcVLCP/cppBo8jRIW/S6BLOkFlGrGZbZhbOJYmgSE6VSgcnbTghXBQ3WMCCPNzJ8NcjMknmauCvsGbQpqVKHhnXceC0oGE+HnSPSZA71KFg5IWvBAOymhQePbWNvgErCQ6aAneFNK50EBh+lAWlw5CxcC0we1kFkhRKWnBC+GgCksL+e7wbK7yW4srVnqb3Vh7dgS/a630Lk00ENKCF8IBZRRk8PSPYzmauQYPzUq/M/4syR5nF+4KMGN5ClbVqZ9VFFdAAl4IB5Nyeg9Pr/g7JzJ34mtV6Z0Zzdu5j3KSpnbHaUC6uZgtqdn6FCocnnTRCOFAfjz8P97fPBtrsZnoMgXf9DjmlvwNK8ZKz6loOKUQIAEvhEMoU8t4f8d8Vu79L5QVk6CauKn5GAYdvvQQyIuHUwpxngS8EDozW8y89utz7D2xHkUt417Fn9v6v4waHkvY1p/JMBdXOF2BAoT6ucswSVEp6YMXQkeHzYeZ+v1o9h5bh7tq5XGPFgy5fTFKRBxGg8K0we2Ac2F+ofPvZZikqIoEvBA6WXdsNc99/3fOnD1ImKYwM6QPcbd/BL7htmMGdAhjwYhuhPrZd8OE+rmzYEQ3BnQIq++yRQMiXTRC1LMytYyPk9/m+5SPobSIrqorj3T6P7ziRkMFi+AM6BBGv3ahVS74IURFJOCFqCMVrcKUV2Jmzq/PsffkBlDLGIYvd9yUhKFZQpXXMhoU4lsE1lPlwllIwAtRBypahSk44AytIj+nrPgk7sA4jxZcM/B18IvQr1Dh1CTghahl5Vdh0vDw2YGP3//Iyy0k2uDCk5H9uOqmGeDqoWOlwtlJwAtRi8qtwqSUEBT0LUHev2GilA5FBrSCWwl9aAYYZYyDqFsS8ELUogtXYVJczxAR8il+ridwQaNXjicrcx5gn9aM246clT51Ueck4IWoReenDXDz3sNVQV/jreTha4Vup6P4oPABzHjbHSdEXZKAF6IWNfEyEBi0nCCfLbhRSguLguupXswrHYB2wWMnMr2AqA8S8ELUkhN5J1i2/ynCfXdi0FTic93YnH0fv2mtbcfI9AKiPknAC1EL1h75if9sSsJSdIamBoUO6eEsLhjJafxsx8j0AqK+ScALcQWKyor4z5ZX+OXQciiz0FFzYVy7B9jU43Zcv90PF4yDD/VzZ9rgdjK9gKg3EvBCXKZDZw/y+rqnyDx7EIOmcqchgKE3zsIQ2Z0BQL/2V8n0AkJXEvBC1JCqqXy77ws++20+ZZZcgjQDjwZ2p3W/WeD5V9+6TC8g9CYBL8SfKpo75uIW95miMyzY8C92nVgPahk9NDce7vJPvLvcDwZ5cEk4Fgl4Iah47piwi/rMt5zcwMINz5OXn44bCiNNEdzY9yWUkLZ6lS1ElSTgRaNXfu6YczLMxYz9eAev39eOk4WfsuaPZVBWTDPNyPjoWwnv9QSYPHWpWYjqkIAXjVq5uWMuoAEubieZt+F1AtxOoaBxm+LLnb2m49ryxvouVYgak4AXjdqFc8fYK6NJk7UE+a/DAwv+VhfGN+1Bu74zwUtunIqGQQJeNGoVzQmjmLKIbPolvqYTGFDpVGikf6t/0G7Q2ApXXBLCUel623/dunUMHjyY8PBwFEXh66+/ttuvaRrPPfccYWFheHh40LdvX/744w99ihVOyX5OGBVfv19oEb4Af9MxvFWVXmdC+DlzIi5t75ZwFw2OrgFfUFBA586dmT9/foX7Z8+ezdy5c3nrrbfYvHkzXl5e9O/fn+JimYlP1I7Y6CYYFFBcTxMVvpDIgO/xVIpoU2wgJO1GFuSOJ0MJIja6id6lClFjunbRDBw4kIEDB1a4T9M05syZwzPPPMOQIUMA+PDDDwkJCeHrr7/mnnvuqc9ShZPaeuQ03r6/ENLkZzyUItw1iDsbyP9yh3NUCz93kAbbj8r87aLhcdgnM1JTU8nIyKBv3762bX5+fvTo0YONGzfqWJlwFkfMqSza/iiRAd/joRRxdbGBiLTevG2e9Fe4/0nmbxcNkcPeZM3IyAAgJCTEbntISIhtX0UsFgsWi8X2Pjc3t24KFA3KhU+pBngZOVHwDcv3fEhpcR7emkq3s0F8l3tfuWA/T+ZvFw2Rwwb85UpKSmLGjBl6lyEcyIVPqRrdThDV9Ct8TJmYjBCveJCTfR3v5F5PGcZy58r87aIhc9gumtDQUAAyMzPttmdmZtr2VWTq1KmYzWbb6/jx43Vap3Bs559STc8zExL4DS3CF+Ljmo6XVSUuI4TY9m+QcNtjWDFy8RgZmb9dNHQOG/AxMTGEhoayatUq27bc3Fw2b95MfHx8pee5ubnh6+tr9xKNk1XVmL58D25eKbSImEOI70bcKaFzgSteJwezMO8Rpv5spl+7UBaM6Eaon303TKifOwtGdJP520WDpWsXTX5+PgcPHrS9T01NJTk5mYCAAKKiopgwYQIvvPACrVq1IiYmhmeffZbw8HCGDh2qX9Giwfhh335c3RcS4/kHrpQRVAYx2S1ZWnAX2Zz7xZ9uLmZLajYDOoTRr12ozN8unIquAb9t2zb69Oljez9p0iQARo4cyfvvv8+UKVMoKCjg4YcfJicnh+uuu44VK1bg7i43vETlSq2lLE/5hMU73qWJpxkj0C3Xi9+z/8Z/tA7ljj8/QkbmbxfORtE0raJ5lpxGbm4ufn5+mM1m6a5pBHZmbGfRppmk56SiWq2EF2i4n+nOMsstFONW4TmfjO5BQqugeq5UiMtX3VxzulE0onE6XXSaDzfPZvOxNWAtwU9TGOx6NS+l9+eYdok+dOmFEU5KAl40aKXWUpbv+Yiluz+gxGLGAAww+HPnNRNYaYnjWMrOS17jdL7lkscI0RBJwIsGSdM0tp3cwIdbZpNlPgZotNVceDBmCNHXTgA3H4IPnanWteQhJuGsJOBFg3M0J5UPNiWxJ3MHqGUEaAbu9+9A/PXTUIJa2o7rHhNAmJ87GebiChf0kIeYhLOTgBcNhtli5vPt8/j50P9Qy4pwReFWl6YM6TkFj5Z9y03nazQoTBvcjrEf70ABu5CXh5hEYyABLxyexWrhuz2f8PWeDykuzgGgJ54Mb38/wd1Gg4up0nMHdAhjwYhu5RbUDr1oQW0hnJEEvHBYqqay9vD3fL5jPtn56YBGC83IAxH9aJMwudpL58lDTKKxkoAXDkfTNH5L28x/t77KsZxDoFkJ0gzc06QTCb2ewRDUqsbXlIeYRGMkAS8cyv7TKfx380vsPb0L1DK8NIW/eUQzIGEqrlE99S5PiAZFAl44hCM5h/lsyyvsyNgK1hJcURjo0pQhsY/g3eZWMDjsvHhCOCwJeKGrk7nH+XLbHDac+BWsFgxAb4Mvd3YaTWDHe6u8gSqEqJoEvNBFel4aX217nfUn1qKWnRvdcq3ixV1t7iOs24Ng8tS5QiEaPgl4Ua/S807+GezrbMF+DR7c0fJvNLtmDLjLhHBC1BYJeFFrLlz39OKhiMfNx1i6/XU2nlxvC/ZuuHNny7/R/Jox4O6nZ+lCOCUJeFErLlz39LwwP3fG3OjOGfN/2ZyxBawlAMTiwbCWQ2gRNwY8/HWqWAjnJwEvrtj5dU//mgpAw909FV+PH/ks+TieLhouBoXuihdDW/2N5rEPS1eMEPVAAl5cEauqMWN5yp/hruLrtYtw/9UYTVkYUVGAVvnujO0xiii5eSpEvZKAF1dkS2o26bm5hPhtJMB3Ey4uOShouGhwdYEXmTnX82VpArc1vY4oCXch6pUEvLhsmfnprNg1h7bRq3FVigDwVKF5fiAHzX35rKwzGuceUDq/7qkQov5IwIsa0TSN3Rlb+T75HXac2om11IKrohJUphCUG8muvP4sVptz8Tp4sqiGEPVPAl5US2FpIev2f8mP+z7nZH4aaCoAXQ0e5Oe0YdXZG8mk/GResqiGEPqRgG/Eqhq3ft7hM/tZufNd1qdtwFJaAIA7CtebghnQ9h6u6nAPKw6Y+a8sqiGEw5GAb6QqG7c+bXA7rm/tx/r9S1h14CtSc4+BZgXgKs3IzYGdub7r3/GMjLetoDSgg6csqiGEA1I0TatouUqnkZubi5+fH2azGV9fGXsNFY1bB1Dxdk8lyucXfAKPg3YuqF2AHkZ/+jW/hTadR6H4BFd63er8RSCEuHLVzTVpwTcy9uPWwehylmbev+DhsxvFJQ8FjeIShZYuJm4M7ECvDg/gG3NDtabrlUU1hHAsEvCNzJbUbNLzsonw3UwT72RUt9MYOdcF46ZBVKEPublduPvOSXRv31LnaoUQV0ICvpEoLClg66Fv+Sb5K9pF/4ELZcC5fwBRxa6Q34qUguvZqkYDCreWeuharxDiyknAO7GCkny2H/wfmw5/x87sfZRZLaiqhgsqoaUGPAoiOZYXz3dlHbFitDtXxq0L0fBJwDsZc9FZth5YwtYjq9htPkSZ1WLbF4ELPZu0ZtWBlqzJ6UIx5VdLknHrQjgPCfgGTtM0jmcfYPuBpWw/uZGDBSfR1DLb/ghc6el/NT1bDCKy9W3g5o3P7nR+kHHrQjg9CfgGqKi0kN1HfiY59Ud+O72LM5YcLozqloo71wS045oWA7mq1aByMzgO6BAm49aFaAQk4OtYSZnKRxuPcDS7kOgAT+6Pb4bJ5dJDDi9kVa0czvyN3Yd/YGfGNg7kn8Cqltr2u6LQ0cWf2JBudGs5mIDo68DoWuU1B3QIo1+7UBm3LoQTkwedKlBbD+wkfZfCO7+kol7wHTYo8I9eMUy9pV2l56maytGsXaSkrmRPxnZS8o5QVFZkd0wILnTxjqLbVdfRrvUQTAHNbU+WCiGcmzzodJmqeoS/Jl0XSd+l8Pa61HLbVQ3b9vMhbykr5tCJjew/8Sv7Tv3O/vwT5QLdS1No7x5Eh6COdI7pT2jMDeDiVvMvUAjRaEjAX6DiR/ghw1zM2I93sGBEt2qFfEmZyju/lA/3c1R8XNNY+9tPBFHM4dxUjlhOoapWu6PcUWjr2oT2gW1pF5FATIsBGDxlZIsQovoaRMDPnz+fl156iYyMDDp37sy8efPo3r17rX7GxY/wX0jj3AiTGctT6Ncu1NZdU1lXzkcbj/zZLVNKkOkowW6H8HI7icF0imJTHppybpTLijQDLsZz12qCkavdg2kT2JY2EQlER9+A0Use+xdCXD6HD/jPPvuMSZMm8dZbb9GjRw/mzJlD//792b9/P8HBlU98VVNbUrPtumUupgHp5mK2pGYT3yLQrivHoJTQ1OUYzfzS6BlTyJGzR+gemUWRSzEK5+ZNt1xwLVcN/Es8uMrjKgZc3ZOrI3sRGBaL4ipdLkKI2uPwN1l79OjBNddcwxtvvAGAqqpERkbyyCOP8OSTT17y/OrejFiWfJLxnyZXslfFx5BNgGsaI65xpbjkJJtTD4BLLhbXQizGUi4cpmg0KFj/vLPqril4l3iilDSh2BLOaUsLjpW0oghP7u8ZxfNDO1b3WyGEEICT3GQtKSlh+/btTJ061bbNYDDQt29fNm7cWOE5FosFi+Wv9nJubm61PuvCR/N9DNl0bfoBmrGQUqOFYpdS1D8D/MdMAyVWDc3b/veim6bgWeqBsdQX1KZkF4ZxqiSGjLJIrJV8m7tGNqlWbUIIcTkcOuBPnz6N1WolJCTEbntISAj79u2r8JykpCRmzJhR48/qHhNAmJ87GeZiSjQ3cjwz7fYrgKfqQoSLH6fMLljL/CguDSSvNJTTJdGcUZuiXTSfy6WE+cuEXkKIuuPQAX85pk6dyqRJk2zvc3NziYyMvOR5RoPCtMHtGPvxDko0Lzyyu2Gx+lJQFkhuWTBny0KZOyIeS5laRVfOX/w9XMkpKq10f5jM9yKEqGM1e6SyngUFBWE0GsnMtG9NZ2ZmEhoaWuE5bm5u+Pr62r2q6/wj/KF+7mw230Vy/gD+KL4Gg1dr5o6IZ0CHsGrPsvhgQgwKf83vct75bTLfixCirjl0C95kMhEbG8uqVasYOnQocO4m66pVqxg3blydfOalHuG/sCunorvT52djHHdjS1qHest8L0II3Th0wANMmjSJkSNHEhcXR/fu3ZkzZw4FBQU8+OCDdfaZVS09d2FXzqVmY5T5XoQQenL4gL/77rs5deoUzz33HBkZGXTp0oUVK1aUu/Fan2oyG6OsUyqE0IvDj4O/Upcz2Vh11dakZEIIURNOMQ7e0UnrXAjhyBx6FI0QQojLJwEvhBBOSgJeCCGclNP3wZ+/h1zdOWmEEMLRnc+zS42RcfqAz8vLA6jWdAVCCNGQ5OXl4efnV+l+px8mqaoqaWlp+Pj4oNRgzdLzc9gcP3681odX1hWpuX40tJobWr0gNV+Kpmnk5eURHh6OwVB5T7vTt+ANBgMRERGXfX5N57NxBFJz/WhoNTe0ekFqrkpVLffz5CarEEI4KQl4IYRwUhLwlXBzc2PatGm4uTWcdVKl5vrR0GpuaPWC1FxbnP4mqxBCNFbSghdCCCclAS+EEE5KAl4IIZyUBLwQQjgpCfgKzJ8/n2bNmuHu7k6PHj3YsmWL3iVVad26dQwePJjw8HAUReHrr7/Wu6QqJSUlcc011+Dj40NwcDBDhw5l//79epdVpQULFtCpUyfbQyzx8fF8//33epdVI7NmzUJRFCZMmKB3KZWaPn06iqLYvdq0aaN3WZd08uRJRowYQWBgIB4eHnTs2JFt27bpXZYE/MU+++wzJk2axLRp09ixYwedO3emf//+ZGVl6V1apQoKCujcuTPz58/Xu5RqWbt2LYmJiWzatImVK1dSWlrKzTffTEFBgd6lVSoiIoJZs2axfft2tm3bxo033siQIUPYs2eP3qVVy9atW3n77bfp1KmT3qVcUvv27UlPT7e9fv31V71LqtLZs2dJSEjA1dWV77//npSUFF555RWaNGmid2mgCTvdu3fXEhMTbe+tVqsWHh6uJSUl6VhV9QHa0qVL9S6jRrKysjRAW7t2rd6l1EiTJk20d999V+8yLikvL09r1aqVtnLlSq13797a+PHj9S6pUtOmTdM6d+6sdxk18sQTT2jXXXed3mVUSFrwFygpKWH79u307dvXts1gMNC3b182btyoY2XOzWw2AxAQEKBzJdVjtVr59NNPKSgoID4+Xu9yLikxMZFBgwbZ/bt2ZH/88Qfh4eE0b96c4cOHc+zYMb1LqtI333xDXFwcd955J8HBwXTt2pV33nlH77IA6aKxc/r0aaxWKyEhIXbbQ0JCyMjI0Kkq56aqKhMmTCAhIYEOHTroXU6Vdu3ahbe3N25ubowZM4alS5fSrl07vcuq0qeffsqOHTtISkrSu5Rq6dGjB++//z4rVqxgwYIFpKam0qtXL9u0347o8OHDLFiwgFatWvHDDz8wduxYHn30UT744AO9S3P+2SSFY0tMTGT37t0O388K0Lp1a5KTkzGbzXz55ZeMHDmStWvXOmzIHz9+nPHjx7Ny5Urc3d31LqdaBg4caPvvTp060aNHD6Kjo/n8888ZPXq0jpVVTlVV4uLimDlzJgBdu3Zl9+7dvPXWW4wcOVLX2qQFf4GgoCCMRiOZmZl22zMzMwkNDdWpKuc1btw4vv32W1avXn1FUzrXF5PJRMuWLYmNjSUpKYnOnTvz+uuv611WpbZv305WVhbdunXDxcUFFxcX1q5dy9y5c3FxccFqtepd4iX5+/tz9dVXc/DgQb1LqVRYWFi5X/Jt27Z1iK4lCfgLmEwmYmNjWbVqlW2bqqqsWrWqQfS1NhSapjFu3DiWLl3Kzz//TExMjN4lXRZVVbFYLHqXUambbrqJXbt2kZycbHvFxcUxfPhwkpOTMRqNepd4Sfn5+Rw6dIiwsDC9S6lUQkJCuWG+Bw4cIDo6WqeK/iJdNBeZNGkSI0eOJC4uju7duzNnzhwKCgp48MEH9S6tUvn5+XYtnNTUVJKTkwkICCAqKkrHyiqWmJjI4sWLWbZsGT4+Prb7G35+fnh4eOhcXcWmTp3KwIEDiYqKIi8vj8WLF7NmzRp++OEHvUurlI+PT7n7Gl5eXgQGBjrs/Y7HH3+cwYMHEx0dTVpaGtOmTcNoNHLvvffqXVqlJk6cyLXXXsvMmTO566672LJlCwsXLmThwoV6lybDJCsyb948LSoqSjOZTFr37t21TZs26V1SlVavXq0B5V4jR47Uu7QKVVQroC1atEjv0ir10EMPadHR0ZrJZNKaNm2q3XTTTdqPP/6od1k15ujDJO+++24tLCxMM5lM2lVXXaXdfffd2sGDB/Uu65KWL1+udejQQXNzc9PatGmjLVy4UO+SNE3TNJkuWAghnJT0wQshhJOSgBdCCCclAS+EEE5KAl4IIZyUBLwQQjgpCXghhHBSEvBCCOGkJOCFEMJJScALIYSTkoAXQggnJQEvxBU4deoUoaGhtrnAATZs2IDJZLKblVQIPchcNEJcoe+++46hQ4eyYcMGWrduTZcuXRgyZAivvvqq3qWJRk4CXohakJiYyE8//URcXBy7du1i69atuLm56V2WaOQk4IWoBUVFRXTo0IHjx4+zfft2OnbsqHdJQkgfvBC14dChQ6SlpaGqKkeOHNG7HCEAacELccVKSkro3r07Xbp0oXXr1syZM4ddu3YRHBysd2mikZOAF+IKTZ48mS+//JKdO3fi7e1N79698fPz49tvv9W7NNHISReNEFdgzZo1zJkzh48++ghfX18MBgMfffQRv/zyCwsWLNC7PNHISQteCCGclLTghRDCSUnACyGEk5KAF0IIJyUBL4QQTkoCXgghnJQEvBBCOCkJeCGEcFIS8EII4aQk4IUQwklJwAshhJOSgBdCCCclAS+EEE7q/wGoCzqBq7oE2gAAAABJRU5ErkJggg==", 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", 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" ] @@ -700,13 +764,13 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:05<00:00, 19.39it/s]\n", + "100%|██████████| 100/100 [00:05<00:00, 18.50it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -719,13 +783,13 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:05<00:00, 19.16it/s]\n", + "100%|██████████| 100/100 [00:05<00:00, 19.57it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -738,13 +802,13 @@ "output_type": "stream", "text": [ "INFO:autora.theorist.bms.regressor:BMS fitting started\n", - "100%|██████████| 100/100 [00:06<00:00, 16.36it/s]\n", + "100%|██████████| 100/100 [00:05<00:00, 16.83it/s]\n", "INFO:autora.theorist.bms.regressor:BMS fitting finished\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -823,7 +887,7 @@ "46 1.516631 3.805164\n", "47 3.899908 18.885295\n", "48 0.866646 1.661760\n", - "49 6.066524 43.131882, models=[(x * (x + (1.0 ** 2))), (x * (x + (1.0 ** 2))), (x * (x + (1.0 ** 2))), (x * (x + (1.0 ** 2))), (x * (x + (1.0 ** 2)))])\n" + "49 6.066524 43.131882, models=[((x * x) + x), ((x * x) + x), ((x * x) + x), ((x * x) + x), ((x * x) + x)])\n" ] } ], @@ -865,16 +929,12 @@ "- `fit(self, conditions, observations)`\n", "- `predict(self, conditions)`\n", "\n", - "\n", - "\n", - "\n", - "Experiment runners must be implemented as functions. \n", - "\n", "**Requirements for working with the state:**\n", - "- The function has a `conditions` argument that accepts a `pandas.DataFrame`\n", - "- The function returns a `pandas.DataFrame`\n", + "- The fit module function has a `conditions` argument that accepts a `pandas.DataFrame`\n", + "- The fit module function has an `observations` argument that accepts a `pandas.DataFrame`\n", + "- the fit function returns `self` (i.e., the model itself)\n", "\n", - "The following code block implements such a theorist that fits a polynomial of a specified degree." + "The custom `PolynomialRegressor` below fits a polynomial of a specified degree." ] }, { @@ -941,160 +1001,21 @@ "8 1.083308\n", "9 5.199877, experiment_data=Empty DataFrame\n", "Columns: [x, y]\n", - "Index: [], models=[])\n", - "PolynomialRegressor()\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PolynomialRegressor()\n" + "Index: [], models=[])\n" ] }, { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PolynomialRegressor()\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PolynomialRegressor()\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mUpdated State:\u001b[0m\n", - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", - " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", - " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", - " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", - " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", - " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 3.249923\n", - "1 5.849862\n", - "2 1.299969\n", - "3 0.433323\n", - "4 3.466585\n", - "5 1.733292\n", - "6 1.516631\n", - "7 3.899908\n", - "8 0.866646\n", - "9 6.066524, experiment_data= x y\n", - "0 0.433323 0.572248\n", - "1 4.983216 -1.483542\n", - "2 4.116570 -0.452463\n", - "3 2.816600 0.789584\n", - "4 2.599939 -0.459964\n", - "5 5.416539 -1.413252\n", - "6 0.433323 0.483809\n", - "7 4.333231 -1.087098\n", - "8 1.299969 0.955149\n", - "9 0.433323 -0.006633\n", - "10 3.249923 0.013996\n", - "11 4.116570 -0.488600\n", - "12 2.599939 0.222789\n", - "13 0.216662 -0.239366\n", - "14 3.683247 -1.511473\n", - "15 0.000000 0.485811\n", - "16 1.733292 0.995155\n", - "17 5.416539 -0.659296\n", - "18 2.816600 -0.072496\n", - "19 3.683247 0.097695\n", - "20 4.333231 -0.205995\n", - "21 0.649985 0.656327\n", - "22 5.199877 -0.720136\n", - "23 1.516631 1.566765\n", - "24 4.116570 -0.415567\n", - "25 2.599939 0.805032\n", - "26 0.433323 0.230304\n", - "27 6.283185 -0.509437\n", - "28 3.466585 -0.131217\n", - "29 0.866646 0.506182\n", - "30 5.849862 -0.648822\n", - "31 3.683247 -0.826983\n", - "32 4.549893 -0.919182\n", - "33 3.249923 0.313832\n", - "34 3.249923 -0.182368\n", - "35 4.766554 -0.867292\n", - "36 4.549893 -0.720993\n", - "37 5.199877 -0.538608\n", - "38 3.466585 -0.765251\n", - "39 3.249923 0.126291\n", - "40 3.249923 -0.359577\n", - "41 5.849862 0.189056\n", - "42 1.299969 0.827990\n", - "43 0.433323 0.784981\n", - "44 3.466585 -0.903954\n", - "45 1.733292 0.272093\n", - "46 1.516631 0.986897\n", - "47 3.899908 -0.911596\n", - "48 0.866646 0.806200\n", - "49 6.066524 0.047677, models=[PolynomialRegressor(), PolynomialRegressor(), PolynomialRegressor(), PolynomialRegressor(), PolynomialRegressor()])\n" + "ename": "TypeError", + "evalue": "BaseEstimator.set_params() missing 1 required positional argument: 'self'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[19], line 18\u001b[0m\n\u001b[0;32m 16\u001b[0m s \u001b[39m=\u001b[39m experimentalist(s, num_samples\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m, random_state\u001b[39m=\u001b[39m\u001b[39m42\u001b[39m\u001b[39m+\u001b[39mcycle)\n\u001b[0;32m 17\u001b[0m s \u001b[39m=\u001b[39m experiment_runner(s, added_noise\u001b[39m=\u001b[39m\u001b[39m0.5\u001b[39m, random_state\u001b[39m=\u001b[39m\u001b[39m42\u001b[39m\u001b[39m+\u001b[39mcycle)\n\u001b[1;32m---> 18\u001b[0m s \u001b[39m=\u001b[39m custom_theorist(s)\n\u001b[0;32m 20\u001b[0m \u001b[39mprint\u001b[39m(s\u001b[39m.\u001b[39mmodel)\n\u001b[0;32m 21\u001b[0m plot_from_state(s, \u001b[39m'\u001b[39m\u001b[39msin(x)\u001b[39m\u001b[39m'\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state.py:1014\u001b[0m, in \u001b[0;36mdelta_to_state.._f\u001b[1;34m(state_, **kwargs)\u001b[0m\n\u001b[0;32m 1012\u001b[0m \u001b[39m@wraps\u001b[39m(f)\n\u001b[0;32m 1013\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_f\u001b[39m(state_: S, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m S:\n\u001b[1;32m-> 1014\u001b[0m delta \u001b[39m=\u001b[39m f(state_, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1015\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39misinstance\u001b[39m(delta, Mapping), (\n\u001b[0;32m 1016\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mOutput of \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m must be a `Delta`, `UserDict`, \u001b[39m\u001b[39m\"\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mor `dict`.\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m f\n\u001b[0;32m 1017\u001b[0m )\n\u001b[0;32m 1018\u001b[0m new_state \u001b[39m=\u001b[39m state_ \u001b[39m+\u001b[39m delta\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state.py:750\u001b[0m, in \u001b[0;36minputs_from_state.._f\u001b[1;34m(state_, **kwargs)\u001b[0m\n\u001b[0;32m 748\u001b[0m arguments_from_state[\u001b[39m\"\u001b[39m\u001b[39mstate\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m state_\n\u001b[0;32m 749\u001b[0m arguments \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(arguments_from_state, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m--> 750\u001b[0m result \u001b[39m=\u001b[39m f(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39marguments)\n\u001b[0;32m 751\u001b[0m \u001b[39mreturn\u001b[39;00m result\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state.py:1316\u001b[0m, in \u001b[0;36mestimator_on_state..theorist\u001b[1;34m(experiment_data, variables, **kwargs)\u001b[0m\n\u001b[0;32m 1314\u001b[0m dvs \u001b[39m=\u001b[39m [v\u001b[39m.\u001b[39mname \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m variables\u001b[39m.\u001b[39mdependent_variables]\n\u001b[0;32m 1315\u001b[0m X, y \u001b[39m=\u001b[39m experiment_data[ivs], experiment_data[dvs]\n\u001b[1;32m-> 1316\u001b[0m new_model \u001b[39m=\u001b[39m estimator\u001b[39m.\u001b[39mset_params(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\u001b[39m.\u001b[39mfit(X, y)\n\u001b[0;32m 1317\u001b[0m \u001b[39mreturn\u001b[39;00m Delta(model\u001b[39m=\u001b[39mnew_model)\n", + "\u001b[1;31mTypeError\u001b[0m: BaseEstimator.set_params() missing 1 required positional argument: 'self'" ] } ], @@ -1130,164 +1051,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Altogether Now" + "## Altogether Now\n", + "\n", + "We have now created custom experimentalists, experiment runners, and theorists. Let's add them all to the same workflow to see our first fully customized `AutoRA` workflow." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1mPrevious State:\u001b[0m\n", - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", - " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", - " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", - " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", - " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", - " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 5.416539\n", - "1 4.116570\n", - "2 3.249923\n", - "3 1.733292\n", - "4 1.949954\n", - "5 0.216662\n", - "6 0.433323\n", - "7 0.000000\n", - "8 1.083308\n", - "9 5.199877, experiment_data=Empty DataFrame\n", - "Columns: [x, y]\n", - "Index: [], models=[])\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mUpdated State:\u001b[0m\n", - "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(0, 6.283185307179586), allowed_values=array([0. , 0.21666156, 0.43332312, 0.64998469, 0.86664625,\n", - " 1.08330781, 1.29996937, 1.51663094, 1.7332925 , 1.94995406,\n", - " 2.16661562, 2.38327719, 2.59993875, 2.81660031, 3.03326187,\n", - " 3.24992343, 3.466585 , 3.68324656, 3.89990812, 4.11656968,\n", - " 4.33323125, 4.54989281, 4.76655437, 4.98321593, 5.1998775 ,\n", - " 5.41653906, 5.63320062, 5.84986218, 6.06652374, 6.28318531]), units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), conditions= x\n", - "0 4.983216\n", - "1 5.849862\n", - "2 3.466585\n", - "3 4.116570\n", - "4 1.733292\n", - "5 3.249923\n", - "6 3.249923\n", - "7 5.199877\n", - "8 3.683247\n", - "9 4.333231, experiment_data= x y\n", - "0 5.849862 40.223108\n", - "1 1.516631 3.296808\n", - "2 2.383277 8.438513\n", - "3 3.683247 17.719834\n", - "4 3.683247 16.274034\n", - "5 2.599939 8.708530\n", - "6 5.849862 40.134670\n", - "7 2.383277 7.905166\n", - "8 4.766554 27.478194\n", - "9 5.849862 39.644228\n", - "10 2.816600 10.871952\n", - "11 1.949954 6.091364\n", - "12 3.466585 15.191032\n", - "13 5.633201 36.911813\n", - "14 3.033262 11.238020\n", - "15 0.000000 0.485811\n", - "16 4.333231 23.118453\n", - "17 0.649985 1.175330\n", - "18 6.066524 42.477437\n", - "19 2.166616 7.474088\n", - "20 1.299969 3.712871\n", - "21 3.683247 17.300704\n", - "22 5.849862 40.234126\n", - "23 4.983216 30.383888\n", - "24 1.299969 3.402012\n", - "25 2.383277 8.352766\n", - "26 3.899908 18.919606\n", - "27 4.549893 24.741981\n", - "28 5.416539 34.943519\n", - "29 4.766554 27.230615\n", - "30 1.949954 5.523342\n", - "31 0.216662 -0.047826\n", - "32 0.866646 1.685367\n", - "33 0.649985 1.494416\n", - "34 0.649985 0.998215\n", - "35 6.283185 45.892845\n", - "36 2.166616 7.126673\n", - "37 1.516631 4.161704\n", - "38 0.649985 0.626515\n", - "39 3.033262 12.468350\n", - "40 4.983216 29.564199\n", - "41 5.849862 40.679695\n", - "42 3.466585 15.348237\n", - "43 4.116570 21.427807\n", - "44 1.733292 4.152943\n", - "45 3.249923 13.097192\n", - "46 3.249923 13.800289\n", - "47 5.199877 32.014707\n", - "48 3.683247 17.293589\n", - "49 4.333231 23.372772, models=[PolynomialRegressor(), PolynomialRegressor(), PolynomialRegressor(), PolynomialRegressor(), PolynomialRegressor()])\n" - ] - } - ], + "outputs": [], "source": [ "#### First, let's reinitialize the state object to get a clean state ####\n", "iv = Variable(name=\"x\", value_range=(0, 2 * np.pi), allowed_values=np.linspace(0, 2 * np.pi, 30))\n", From f13708775bfdb812a52685458886c87fe557d155 Mon Sep 17 00:00:00 2001 From: Chad C Williams Date: Tue, 5 Sep 2023 09:44:02 -0700 Subject: [PATCH 32/32] Updated nav --- mkdocs.yml | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/mkdocs.yml b/mkdocs.yml index 15fb3bbd4..0370695b5 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -158,16 +158,17 @@ nav: - Introduction: 'index.md' - Tutorials: - Home: 'tutorials/index.md' - - Basic Tutorial: + - Basic: - I - Components: 'tutorials/basic/Tutorial-I-Components.ipynb' - II - Loop Constructs: 'tutorials/basic/Tutorial-II-Loop-Constructs.ipynb' - - III - Workflow Logic: 'tutorials/basic/Tutorial-III-Workflow-Logic.ipynb' + - III - Functional Workflow: 'tutorials/basic/Tutorial-III-Functional-Workflow.ipynb' - IV - Customization: 'tutorials/basic/Tutorial-IV-Customization.ipynb' - - Theorists: 'tutorials/Theorist.ipynb' - - Experimentalists: 'tutorials/Experimentalist.ipynb' - - Functional Workflow: 'core/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb' - - Closed-Loop Discovery: 'workflow-tutorial/docs/interactive/Basic Usage.ipynb' - - Online Closed-Loop Discovery: 'user-cookiecutter/docs/index.md' + - Advanced: + - Equation Discovery: 'tutorials/Theorist.ipynb' + - Experimentalists: 'tutorials/Experimentalist.ipynb' + #- Functional Workflow: 'core/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb' + - Closed-Loop Discovery: 'workflow-tutorial/docs/interactive/Basic Usage.ipynb' + - Online Closed-Loop Discovery: 'user-cookiecutter/docs/index.md' - User Guide: - Installation: 'installation.md' - Theorists: