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"# MIT License\n",
"#\n",
"#@title Copyright (c) 2021 CCAI Community Authors { display-mode: \"form\" }\n",
"#\n",
"# Permission is hereby granted, free of charge, to any person obtaining a\n",
"# copy of this software and associated documentation files (the \"Software\"),\n",
"# to deal in the Software without restriction, including without limitation\n",
"# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
"# and/or sell copies of the Software, and to permit persons to whom the\n",
"# Software is furnished to do so, subject to the following conditions:\n",
"#\n",
"# The above copyright notice and this permission notice shall be included in\n",
"# all copies or substantial portions of the Software.\n",
"#\n",
"# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
"# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
"# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
"# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
"# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
"# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
"# DEALINGS IN THE SOFTWARE."
],
"execution_count": null,
"outputs": []
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"metadata": {
"id": "13i7KQ9t-CV8"
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"source": [
"# Open Catalyst Project Tutorial Notebook\n",
"Author(s):\n",
"* [Muhammed Shuaibi](https://mshuaibii.github.io/), CMU, mshuaibi@andrew.cmu.edu\n",
"* [Abhishek Das](https://abhishekdas.com/), FAIR, abhshkdz@fb.com \n",
"* [Adeesh Kolluru](https://adeeshkolluru.github.io/), CMU, akolluru@andrew.cmu.edu\n",
"* [Brandon Wood](https://wood-b.github.io/), NERSC, bwood@lbl.gov \n",
"* [Janice Lan](https://www.linkedin.com/in/janice-lan), FAIR, janlan@fb.com\n",
"* [Anuroop Sriram](https://www.linkedin.com/in/anuroopsriram), FAIR, anuroops@fb.com\n",
"* [Zachary Ulissi](https://ulissigroup.cheme.cmu.edu/), CMU, zulissi@andrew.cmu.edu\n",
"* [Larry Zitnick](http://larryzitnick.org/), FAIR, zitnick@fb.com\n",
"\n",
"FAIR - Facebook AI Research\n",
"\n",
"CMU - Carnegie Mellon University\n",
"\n",
"NERSC - National Energy Research Scientific Computing Center\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "E_qIKf8erkfC"
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"source": [
"## Table of Contents\n",
"\n",
"* [Background](#background)\n",
"* [Objective](#objective)\n",
"* [Climate Impact](#climate-impact)\n",
"* [Target Audience](#target-audience)\n",
"* [Background & Prerequisites](#background-and-prereqs)\n",
"* [Software Requirements](#software-requirements)\n",
"* [Dataset Overview & Visualization](#data-description)\n",
" * [Download](#download)\n",
" * [Visualization](#visual)\n",
" * [Data contents](#contents)\n",
"* [Tasks](#tasks)\n",
" * [S2EF](#s2ef)\n",
" * [IS2RE](#is2re)\n",
" * [IS2RS](#is2rs)\n",
"* [OCP Calculator](#calc)\n",
"* [Model development](#model-dev)\n",
"* [Running on command line](#cmd)\n",
"* [Limitations](#limit)\n",
"* [Next steps](#steps)\n",
"* [References](#references)"
]
},
{
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"metadata": {
"id": "JkjKcVJ47hSN"
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"source": [
"## Background \n",
"The discovery of efficient and economic catalysts (materials) are needed to enable the widespread use of renewable energy technologies. A common approach in discovering high performance catalysts is using molecular simulations. Specifically, each simulation models the interaction of a catalyst surface with molecules that are commonly seen in electrochemical reactions. By predicting these interactions accurately, the catalyst's impact on the overall rate of a chemical reaction may be estimated.\n",
"\n",
"An important quantity in screening catalysts is their adsorption energy for the molecules, referred to as `adsorbates', involved in the reaction of interest. The adsorption energy may be found by simulating the interaction of the adsorbate molecule on the surface of the catalyst to find their resting or relaxed energy, i.e., how tightly the adsorbate binds to the catalyst's surface (visualized below). The rate of the chemical reaction, a value of high practical importance, is then commonly approximated using simple functions of the adsorption energy. The goal of this tutorial specifically and the project overall is to encourage research and benchmark progress towards training ML models to approximate this relaxation.\n",
"\n",
"Specifically, during the course of a relaxation, given an initial set of atoms and their positions, the task is to iteratively estimate atomic forces and update atomic positions until a relaxed state is reached. The energy corresponding to the relaxed state is the structure's 'relaxed energy'.\n",
"\n",
"As part of the [Open Catalyst Project](https://github.com/Open-Catalyst-Project/ocp) (OCP), we identify three key tasks ML models need to perform well on in\n",
"order to effectively approximate DFT --\n",
"\n",
" 1) Given an **I**nitial **S**tructure, predict the **R**elaxed **E**nergy of the relaxed strucutre (**IS2RE**),\n",
"\n",
" 2) Given an **I**nitial **S**tructure, predict the **R**elaxed **S**tructure (**IS2RS**),\n",
"\n",
" 3) Given any **S**tructure, predict the structure **E**nergy and per-atom **F**orces (**S2EF**)."
]
},
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"cell_type": "markdown",
"metadata": {
"id": "FPeCifZbtiKJ"
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"source": [
""
]
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"source": [
"## Objective \n",
"This notebook serves as a tutorial for interacting with the Open Catalyst Project.\n",
"\n",
"By the end of this tutorial, users will have gained:\n",
"* Intuition to the dataset and it's properties\n",
"* Knowledge of the various OCP tasks: IS2RE, IS2RS, S2EF\n",
"* Steps to train, validate, and predict a model on the various tasks\n",
"* A walkthrough on creating your own model\n",
"* (Optional) Creating your own dataset for other molecular/catalyst applications \n",
"* (Optional) Using pretrained models directly with an [ASE](https://wiki.fysik.dtu.dk/ase/#:~:text=The%20Atomic%20Simulation%20Environment%20(ASE,under%20the%20GNU%20LGPL%20license.)-style calculator."
]
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"id": "99jkSa_KmrDH"
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"source": [
"\n",
"# Climate Impact\n",
"\n",
"Scalable and cost-effective solutions to renewable energy storage are essential to addressing the world’s rising energy needs while reducing climate change. As illustrated in the figure below, as we increase our reliance on renewable energy sources such as wind and solar, which produce intermittent power, storage is needed to transfer power from times of peak generation to peak demand. This may require the storage of power for hours, days, or months. One solution that offers the potential of scaling to nation-sized grids is the conversion of renewable energy to other fuels, such as hydrogen. To be widely adopted, this process requires cost-effective solutions to running chemical reactions.\n",
"\n",
"An open challenge is finding low-cost catalysts to drive these reactions at high rates. Through the use of quantum mechanical simulations (Density Functional Theory, DFT), new catalyst structures can be tested and evaluated. Unfortunately, the high computational cost of these simulations limits the number of structures that may be tested. The use of AI or machine learning may provide a method to efficiently approximate these calculations; reducing the time required from 24} hours to a second. This capability would transform the search for new catalysts from the present day practice of evaluating O(1,000) of handpicked candidates to the brute force search over millions or even billions of candidates.\n",
"\n",
"As part of OCP, we publicly released the world's largest quantum mechanical simulation dataset -- [OC20](https://github.com/Open-Catalyst-Project/ocp/blob/master/DATASET.md) -- in the Fall of 2020 along with a suite of baselines and evaluation metrics. The creation of the dataset required over 70 million hours of compute. This dataset enables the exploration of techniques that will generalize across different catalyst materials and adsorbates. If successful, models trained on the dataset could enable the computational testing of millions of catalyst materials for a wide variety of chemical reactions. However, techniques that achieve the accuracies required** for practical impact are still beyond reach and remain an open area for research, thus encouraging research in this important area to help in meeting the world's energy needs in the decades ahead.\n",
"\n",
"** The computational catalysis community often aims for an adsorption energy MAE of 0.1-0.2 eV for practical relevance."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jcpOlBcTsYVa"
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"source": [
""
]
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"source": [
"\n",
"# Target Audience\n",
"\n",
"This tutorial is designed for those interested in application of ML towards climate change. More specifically, those interested in material/catalyst discovery and Graph Nueral Networks (GNNs) will find lots of benefit here. Little to no domain chemistry knowledge is necessary as it will be covered in the tutorial. Experience with GNNs is a plus but not required. \n",
"\n",
"We have designed this notebook in a manner to get the ML communnity up to speed as far as background knowledge is concerned, and the catalysis community to better understand how to use the OCP's state-of-the-art models in their everyday workflows.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gQgijl46pYzn"
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"source": [
"\n",
"# Background & Prerequisites\n",
"\n",
"Basic experience training ML models. Familiarity with PyTorch. Familiarity with Pytorch-Geometric could be helpful for development, but not required.\n",
"No background in chemistry is assumed.\n",
"\n",
"For those looking to apply our pretrained models on their datasets, familiarity with the [Atomic Simulation Environment](https://wiki.fysik.dtu.dk/ase/#:~:text=The%20Atomic%20Simulation%20Environment%20(ASE,under%20the%20GNU%20LGPL%20license.) is useful."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7BpQklEEIFDD"
},
"source": [
"\n",
"## Background References\n",
"\n",
"To gain an even better understanding of the Open Catalyst Project and the problems it seeks to address, we strongly recommend the following resources:\n",
"\n",
"* To learn more about electrocatalysis, see our [white paper](https://arxiv.org/pdf/2010.09435.pdf).\n",
"* To learn about the OC20 dataset and the associated tasks, please see the [OC20 dataset paper](https://arxiv.org/pdf/2010.09990.pdf).\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rSRCNgYzUwaf"
},
"source": [
"\n",
"# Software Requirements\n",
"\n",
"All required dependencies can be found here - https://github.com/Open-Catalyst-Project/ocp#installation.\n",
"\n",
"For the following Colab Notebook, we manually install the dependencies below.\n",
"\n",
"For the purpose of the demo, we hihgly recommend you use a GPU. Google Colab provides access to 1 GPU (Runtime -> Change runtime type -> select GPU). The tutorial will function without a GPU, but will be slower for training times."
]
},
{
"cell_type": "code",
"metadata": {
"id": "58AKzWydvkVu"
},
"source": [
"# %%bash\n",
"pip install torch==1.7.1+cu110 -f https://download.pytorch.org/whl/torch_stable.html \n",
"pip install demjson==2.2.4 lmdb==1.1.1 ase==3.21 pymatgen==2020.12.31 pyyaml==5.4 tensorboard==2.4 wandb==0.11.2\n",
"pip install torch-scatter==2.0.6 torch-sparse==0.6.9 torch-cluster==1.5.9 torch-spline-conv==1.2.1 torch-geometric==1.6.3 -f https://data.pyg.org/whl/torch-1.7.1+cu110.html\n",
"git clone https://github.com/Open-Catalyst-Project/ocp.git"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "0NDOYuyAvmtO",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e3508b8f-8ade-4000-cdd8-7c5f75865a96"
},
"source": [
"%cd ocp\n",
"!pip install -e ."
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/ocp\n",
"Obtaining file:///content/ocp\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
"Installing collected packages: ocp-models\n",
" Running setup.py develop for ocp-models\n",
"Successfully installed ocp-models-0.0.3\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LS0Tllp95tSu",
"outputId": "c2821fbe-093a-4a8d-ad43-6f2e61a9499a"
},
"source": [
"import torch\n",
"torch.cuda.is_available()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jXoiLncsU3pe"
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"source": [
"\n",
"# Dataset Overview\n",
"\n",
"The Open Catalyst 2020 Dataset (OC20) will be used throughout this tutorial. More details can be found [here](https://github.com/Open-Catalyst-Project/ocp/blob/master/DATASET.md) and the corresponding [paper](https://arxiv.org/abs/2010.09990). Data is stored in PyTorch Geometric [Data](https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html) objects and stored in LMDB files. For each task we include several sized training splits. Validation/Test splits are broken into several subsplits: In Domain (ID), Out of Domain Adsorbate (OOD-Ads), Out of Domain Catalyast (OOD-Cat) and Out of Domain Adsorbate and Catalyst (OOD-Both). Split sizes are summarized below:\n",
"\n",
"Train\n",
"* S2EF - 200k, 2M, 20M, 134M(All)\n",
"* IS2RE/IS2RS - 10k, 100k, 460k(All)\n",
"\n",
"Val/Test\n",
"* S2EF - ~1M across all subsplits\n",
"* IS2RE/IS2RS - ~25k across all splits\n",
"\n",
"#### **Tutorial Use**\n",
"\n",
"For the sake of this tutorial we provide much smaller splits (100 train, 20 val for all tasks) to allow users to easily store, train, and predict across the various tasks. Please refer [here](https://github.com/Open-Catalyst-Project/ocp#download-data) for details on how to download the full datasets for general use.\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FIiwpALzBKaH"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PoF-BxSM5Jkc"
},
"source": [
"## Data Download [~1min] \n",
"FOR TUTORIAL USE ONLY"
]
},
{
"cell_type": "code",
"metadata": {
"id": "LEITxr5no8kh"
},
"source": [
"%%bash\n",
"mkdir data\n",
"cd data\n",
"wget -q http://dl.fbaipublicfiles.com/opencatalystproject/data/tutorial_data.tar.gz -O tutorial_data.tar.gz\n",
"tar -xzvf tutorial_data.tar.gz\n",
"rm tutorial_data.tar.gz"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "bSt6h_Q-oqjK"
},
"source": [
"## Data Visualization "
]
},
{
"cell_type": "code",
"metadata": {
"id": "HodnfJpE8D0u"
},
"source": [
"import matplotlib\n",
"matplotlib.use('Agg')\n",
"\n",
"import os\n",
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"params = {\n",
" 'axes.labelsize': 14,\n",
" 'font.size': 14,\n",
" 'font.family': ' DejaVu Sans',\n",
" 'legend.fontsize': 20,\n",
" 'xtick.labelsize': 20,\n",
" 'ytick.labelsize': 20,\n",
" 'axes.labelsize': 25,\n",
" 'axes.titlesize': 25,\n",
" 'text.usetex': False,\n",
" 'figure.figsize': [12, 12]\n",
"}\n",
"matplotlib.rcParams.update(params)\n",
"\n",
"\n",
"import ase.io\n",
"from ase.io.trajectory import Trajectory\n",
"from ase.io import extxyz\n",
"from ase.calculators.emt import EMT\n",
"from ase.build import fcc100, add_adsorbate, molecule\n",
"from ase.constraints import FixAtoms\n",
"from ase.optimize import LBFGS\n",
"from ase.visualize.plot import plot_atoms\n",
"from ase import Atoms\n",
"from IPython.display import Image"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "VRR5C88U8mH1"
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"source": [
"### Understanding the data\n",
"We use the Atomic Simulation Environment (ASE) library to interact with our data. This notebook will provide you with some intuition on how atomic data is generated, how the data is structured, how to visualize the data, and the specific properties that are passed on to our models."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hEDcCSGD86Hg"
},
"source": [
"### Generating sample data\n",
"\n",
"The OC20 dataset was generated using density functional theory (DFT), a quantum chemistry method for modeling atomistic environments. For more details, please see our dataset paper. In this notebook, we generate sample data in the same format as the OC20 dataset; however, we use a faster method that is less accurate called effective-medium theory (EMT) because our DFT calculations are too computationally expensive to run here. EMT is great for demonstration purposes but not accurate enough for our actual catalysis applications. Below is a structural relaxation of a catalyst system, a propane (C3H8) adsorbate on a copper (Cu) surface. Throughout this tutorial a surface may be referred to as a slab and the combination of an adsorbate and a surface as an adslab."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "y6Hx8JtXEbW-"
},
"source": [
"### Structural relaxations\n",
"\n",
"A structural relaxation or structure optimization is the process of iteratively updating atom positions to find the atom positions that minimize the energy of the structure. Standard optimization methods are used in structural relaxations — below we use the Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithm. The step number, time, energy, and force max are printed at each optimization step. Each step is considered one example because it provides all the information we need to train models for the S2EF task and the entire set of steps is referred to as a trajectory. Visualizing intermediate structures or viewing the entire trajectory can be illuminating to understand what is physically happening and to look for problems in the simulation, especially when we run ML-driven relaxations. Common problems one may look out for - atoms excessively overlapping/colliding with each other and atoms flying off into random directions."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GEpQz9In9GrX",
"outputId": "96cd7bc8-2877-4b35-e133-80a10ad81b61"
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"source": [
"###DATA GENERATION - FEEL FREE TO SKIP###\n",
"\n",
"# This cell sets up and runs a structural relaxation \n",
"# of a propane (C3H8) adsorbate on a copper (Cu) surface\n",
"\n",
"adslab = fcc100(\"Cu\", size=(3, 3, 3))\n",
"adsorbate = molecule(\"C3H8\")\n",
"add_adsorbate(adslab, adsorbate, 3, offset=(1, 1)) # adslab = adsorbate + slab\n",
"\n",
"# tag all slab atoms below surface as 0, surface as 1, adsorbate as 2\n",
"tags = np.zeros(len(adslab))\n",
"tags[18:27] = 1\n",
"tags[27:] = 2\n",
"\n",
"adslab.set_tags(tags)\n",
"\n",
"# Fixed atoms are prevented from moving during a structure relaxation. \n",
"# We fix all slab atoms beneath the surface. \n",
"cons= FixAtoms(indices=[atom.index for atom in adslab if (atom.tag == 0)])\n",
"adslab.set_constraint(cons)\n",
"adslab.center(vacuum=13.0, axis=2)\n",
"adslab.set_pbc(True)\n",
"adslab.set_calculator(EMT())\n",
"\n",
"os.makedirs('data', exist_ok=True)\n",
"\n",
"# Define structure optimizer - LBFGS. Run for 100 steps, \n",
"# or if the max force on all atoms (fmax) is below 0 ev/A.\n",
"# fmax is typically set to 0.01-0.05 eV/A, \n",
"# for this demo however we run for the full 100 steps.\n",
"\n",
"dyn = LBFGS(adslab, trajectory=\"data/toy_c3h8_relax.traj\")\n",
"dyn.run(fmax=0, steps=100)\n",
"\n",
"traj = ase.io.read(\"data/toy_c3h8_relax.traj\", \":\")\n",
"\n",
"# convert traj format to extxyz format (used by OC20 dataset)\n",
"columns = (['symbols','positions', 'move_mask', 'tags'])\n",
"with open('data/toy_c3h8_relax.extxyz','w') as f:\n",
" extxyz.write_xyz(f, traj, columns=columns)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" Step Time Energy fmax\n",
"*Force-consistent energies used in optimization.\n",
"LBFGS: 0 01:59:21 15.804700* 6.7764\n",
"LBFGS: 1 01:59:21 12.190607* 4.3232\n",
"LBFGS: 2 01:59:21 10.240169* 2.2655\n",
"LBFGS: 3 01:59:22 9.779223* 0.9372\n",
"LBFGS: 4 01:59:22 9.671525* 0.7702\n",
"LBFGS: 5 01:59:22 9.574461* 0.6635\n",
"LBFGS: 6 01:59:22 9.537502* 0.5718\n",
"LBFGS: 7 01:59:22 9.516673* 0.4466\n",
"LBFGS: 8 01:59:22 9.481330* 0.4611\n",
"LBFGS: 9 01:59:22 9.462255* 0.2931\n",
"LBFGS: 10 01:59:22 9.448937* 0.2490\n",
"LBFGS: 11 01:59:22 9.433813* 0.2371\n",
"LBFGS: 12 01:59:22 9.418884* 0.2602\n",
"LBFGS: 13 01:59:23 9.409649* 0.2532\n",
"LBFGS: 14 01:59:23 9.404838* 0.1624\n",
"LBFGS: 15 01:59:23 9.401753* 0.1823\n",
"LBFGS: 16 01:59:23 9.397314* 0.2592\n",
"LBFGS: 17 01:59:23 9.387947* 0.3450\n",
"LBFGS: 18 01:59:23 9.370825* 0.4070\n",
"LBFGS: 19 01:59:23 9.342222* 0.4333\n",
"LBFGS: 20 01:59:23 9.286822* 0.5002\n",
"LBFGS: 21 01:59:23 9.249910* 0.5241\n",
"LBFGS: 22 01:59:23 9.187179* 0.5120\n",
"LBFGS: 23 01:59:24 9.124811* 0.5718\n",
"LBFGS: 24 01:59:24 9.066185* 0.5409\n",
"LBFGS: 25 01:59:24 9.000116* 1.0798\n",
"LBFGS: 26 01:59:24 8.893632* 0.7528\n",
"LBFGS: 27 01:59:24 8.845939* 0.3321\n",
"LBFGS: 28 01:59:24 8.815173* 0.2512\n",
"LBFGS: 29 01:59:24 8.808721* 0.2143\n",
"LBFGS: 30 01:59:24 8.794643* 0.1546\n",
"LBFGS: 31 01:59:24 8.789162* 0.2014\n",
"LBFGS: 32 01:59:24 8.782320* 0.1755\n",
"LBFGS: 33 01:59:25 8.780394* 0.1037\n",
"LBFGS: 34 01:59:25 8.778410* 0.1076\n",
"LBFGS: 35 01:59:25 8.775079* 0.1797\n",
"LBFGS: 36 01:59:25 8.766987* 0.3334\n",
"LBFGS: 37 01:59:25 8.750249* 0.5307\n",
"LBFGS: 38 01:59:25 8.725928* 0.6851\n",
"LBFGS: 39 01:59:25 8.702312* 0.5823\n",
"LBFGS: 40 01:59:25 8.661515* 0.3996\n",
"LBFGS: 41 01:59:25 8.643432* 0.5585\n",
"LBFGS: 42 01:59:25 8.621201* 0.3673\n",
"LBFGS: 43 01:59:26 8.614414* 0.1394\n",
"LBFGS: 44 01:59:26 8.610785* 0.1372\n",
"LBFGS: 45 01:59:26 8.608134* 0.1464\n",
"LBFGS: 46 01:59:26 8.604928* 0.1196\n",
"LBFGS: 47 01:59:26 8.599151* 0.1354\n",
"LBFGS: 48 01:59:26 8.594063* 0.1479\n",
"LBFGS: 49 01:59:26 8.589493* 0.1538\n",
"LBFGS: 50 01:59:26 8.587274* 0.0885\n",
"LBFGS: 51 01:59:26 8.584633* 0.0938\n",
"LBFGS: 52 01:59:26 8.580239* 0.1409\n",
"LBFGS: 53 01:59:27 8.572938* 0.2543\n",
"LBFGS: 54 01:59:27 8.563343* 0.2919\n",
"LBFGS: 55 01:59:27 8.554117* 0.1966\n",
"LBFGS: 56 01:59:27 8.547597* 0.1291\n",
"LBFGS: 57 01:59:27 8.542086* 0.1280\n",
"LBFGS: 58 01:59:27 8.535432* 0.0982\n",
"LBFGS: 59 01:59:27 8.533622* 0.1277\n",
"LBFGS: 60 01:59:27 8.527487* 0.1167\n",
"LBFGS: 61 01:59:27 8.523863* 0.1218\n",
"LBFGS: 62 01:59:28 8.519229* 0.1305\n",
"LBFGS: 63 01:59:28 8.515424* 0.1019\n",
"LBFGS: 64 01:59:28 8.511240* 0.2122\n",
"LBFGS: 65 01:59:28 8.507967* 0.2666\n",
"LBFGS: 66 01:59:28 8.503903* 0.2377\n",
"LBFGS: 67 01:59:28 8.497575* 0.1623\n",
"LBFGS: 68 01:59:28 8.485434* 0.2022\n",
"LBFGS: 69 01:59:28 8.466738* 0.2159\n",
"LBFGS: 70 01:59:28 8.467607* 0.3348\n",
"LBFGS: 71 01:59:29 8.454037* 0.1063\n",
"LBFGS: 72 01:59:29 8.448980* 0.1197\n",
"LBFGS: 73 01:59:29 8.446550* 0.0992\n",
"LBFGS: 74 01:59:29 8.444705* 0.0562\n",
"LBFGS: 75 01:59:29 8.443403* 0.0388\n",
"LBFGS: 76 01:59:29 8.442646* 0.0548\n",
"LBFGS: 77 01:59:29 8.442114* 0.0614\n",
"LBFGS: 78 01:59:29 8.440960* 0.0588\n",
"LBFGS: 79 01:59:29 8.439820* 0.0482\n",
"LBFGS: 80 01:59:29 8.438600* 0.0513\n",
"LBFGS: 81 01:59:30 8.437429* 0.0541\n",
"LBFGS: 82 01:59:30 8.435695* 0.0672\n",
"LBFGS: 83 01:59:30 8.431957* 0.0857\n",
"LBFGS: 84 01:59:30 8.423485* 0.1332\n",
"LBFGS: 85 01:59:30 8.413846* 0.2078\n",
"LBFGS: 86 01:59:30 8.404849* 0.1787\n",
"LBFGS: 87 01:59:30 8.385339* 0.1690\n",
"LBFGS: 88 01:59:30 8.386849* 0.1876\n",
"LBFGS: 89 01:59:30 8.371078* 0.1181\n",
"LBFGS: 90 01:59:31 8.368801* 0.0942\n",
"LBFGS: 91 01:59:31 8.366226* 0.0670\n",
"LBFGS: 92 01:59:31 8.361680* 0.0550\n",
"LBFGS: 93 01:59:31 8.360631* 0.0473\n",
"LBFGS: 94 01:59:31 8.359692* 0.0242\n",
"LBFGS: 95 01:59:31 8.359361* 0.0155\n",
"LBFGS: 96 01:59:31 8.359163* 0.0143\n",
"LBFGS: 97 01:59:31 8.359102* 0.0156\n",
"LBFGS: 98 01:59:31 8.359048* 0.0155\n",
"LBFGS: 99 01:59:31 8.358986* 0.0142\n",
"LBFGS: 100 01:59:32 8.358921* 0.0132\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/ase/io/extxyz.py:302: UserWarning: Skipping unhashable information adsorbate_info\n",
" '{0}'.format(key))\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Kb77jRtz9fws"
},
"source": [
"### Reading a trajectory"
]
},
{
"cell_type": "code",
"metadata": {
"id": "mUbvcij59d6I"
},
"source": [
"identifier = \"toy_c3h8_relax.extxyz\"\n",
"\n",
"# the `index` argument corresponds to what frame of the trajectory to read in, specifiying \":\" reads in the full trajectory.\n",
"traj = ase.io.read(f\"data/{identifier}\", index=\":\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "b_e6zDVx9pTC"
},
"source": [
"### Viewing a trajectory\n",
"\n",
"Below we visualize the initial, middle, and final steps in the structural relaxation trajectory from above. Copper atoms in the surface are colored orange, the propane adsorbate on the surface has grey colored carbon atoms and white colored hydrogen atoms. The adsorbate’s structure changes during the simulation and you can see how it relaxes on the surface. In this case, the relaxation looks normal; however, there can be instances where the adsorbate flies away (desorbs) from the surface or the adsorbate can break apart (dissociation), which are hard to detect without visualization. Additionally, visualizations can be used as a quick sanity check to ensure the initial system is set up correctly and there are no major issues with the simulation.\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 680
},
"id": "CV5qe6IP9vZg",
"outputId": "256f97d6-daa7-40fa-ef50-7ba0ca005f9d"
},
"source": [
"fig, ax = plt.subplots(1, 3)\n",
"labels = ['initial', 'middle', 'final']\n",
"for i in range(3):\n",
" ax[i].axis('off')\n",
" ax[i].set_title(labels[i])\n",
"ase.visualize.plot.plot_atoms(traj[0], \n",
" ax[0], \n",
" radii=0.8, \n",
" rotation=(\"-75x, 45y, 10z\"))\n",
"ase.visualize.plot.plot_atoms(traj[50], \n",
" ax[1], \n",
" radii=0.8, \n",
" rotation=(\"-75x, 45y, 10z\"))\n",
"ase.visualize.plot.plot_atoms(traj[-1], \n",
" ax[2], \n",
" radii=0.8, \n",
" rotation=(\"-75x, 45y, 10z\"))"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 7
},
{
"output_type": "display_data",
"data": {
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Tcl0rYXt86ySx/RPbG9l+kXQ3eALQmrlZn1Qs8h0ASctJWjWKRUInyMUQy+YbOiR9StLDwL75JXMDD5Eeqz9qe07b+0PKK7V9Y01PFbYkpQBVphWn7b/b3tr2uflb55OKRd5QKrp8StJ/ci77LJI2jGKR0CkkzSNpE0nTS5pZ0q2k9BuAWUjX2dbEzLq2V7H9ev5zedWD1Wxu4JdVHrDH+fqC7d1t75O/dQfwZ9LkEMCJSgXUqwFI+phSAbX+96iD16CeYW3NHObN7wEz2N5P0qeBQ4AjbZ+Tc1Vesf1sqVgBJN0M7Gr75gI/exZghO0nJe1CGgx82/aFkg4E3gJ+2ZfcvhD6I795T5ufCGwNbAYcBDxLKnD6u+0v59zRWUgFT8WeCkj6Fqlw8sACP3taYKGc17cM8AvgHtt7SPo4qZjk3FxsEkItWjOHkpYDvkRKaTlHqVPGoqQbuqeADwF32x5bMNYlgYttVzYp1MbPFrAgKbVwrKQzSekEy5Ley74NXGX7jwMdWycZNDOskmaS9PFcOIGkH5Jy1VbOF7XXgKsAbF+Y7+jOydv3lh6sZtsB95T4wbZfsf1k/vwU2yvavjB/+xHSm887+W75YUm/AMjbC8adYmhXfsqxdf78o8CLpJxwSDMwVwFjbL9texnbXwaw/VyeOS2dwnIKcFyJH5x/J/fnz++2vY7tPfK3nyO9988KIOlcpe4HC+TtpXMBSgh9JmkRSdtKmjPP7N9NmkkEGA88CPwXIM82bmD7SSf/KTlYzR4jdRIYcPl38Gjrd2D7c7aXdOoB+26ObX4ASTtJukXSNnl7IUkzlYh7oDVuhjX/w71j+3VJPwKWI/0n/CDwE+AM26crtY4Z0wEnSaPkx5GLA3PZ/qekDYHfkn7vX8/bc5Lac9XxWCd0kTwwmsn2i0r9hHcCDrP9b0l/JlXF7ylpBKkdTSfcODZKnpH+IKk93njSbPV0theTND/wadLM2O0FwwwdIE88zOlUVLQoqTjxDttHSPoGqc3iwU4FjQuRzt/SN46NImk4qaj75fwE5WhgD1LqxI2SvkTqLnJpTamJxXT1gFVpJajNSInM50v6JulR/lY5kXk70mzClU34h8uJ2v+y/doUX9xhlKs3JW1J6mt3tO3rJZ1O6k6wP2lBhOG2X5/csUL3ysVCK5HysZcGbiYVHnxJqY3UPKTK9xcLhlkJScuTbp6LPBWZGj3O1yVIXRQesP1jSbsBGwDH5PN3hjhfmyv/+69POifvlnQTqb3awqRH1Z8Errd9d7koq5HT3lZxhUVXA6VHeuO7pOKuJYGtSE8+TwQutH1irjt5u1vHQx0/YM13dPORBqXP50f5m5Ia/s4A/By4xPYpeRbmzabe0Ul6APh461FfE+RBykqkf8e5SY+NLrO9haQFSSfezbZfKhhmaEN+yjGH7QclfYK0CMZxtn+fZwMAvgWMI83kNXLAo9SX8TXbPywdS1WU2gp9BPiP7XskXQEsA6wCPE2ajb3d9kPFggxtyU85liStojg7cCqp3/fuOSVnY+AE2zdLmrEbJ0z6QtKHgF/ZXrl0LFXJk3rrAtj+s6S9gCOAfW2fllMk3yLNkhdpB9aOjhuwKvUL3YlUXHGcpF2BHwBftf07SeuR8k1v9SBb41ep8nn9Jl8McrHInE6tedYhPXK6OM/u7AwsBJxm+5GScYaJ8sz/OqQ3wiGk7hJ/sL1jnqFZELjJ9isFwxxwkn4MvGj7iNKx1KVHscjjwPTAWaTi1Q0lfQDYFfiz7b8VDDP0kM/JLUk9Qa+SdCmwCOlGZDTpqeUtth8uGOaAU6rQP8H2qqVjqVOeSR5i+yVJB5EWJtnO9l2SjgMeAH7mThscUqjoStI0Sq2Rlsjb35H0SK5mfYdUMdjKLT3V9ly2fwdg+8qcoD2oBqvZxkxsNdVIuVjkifz51blYpLWU5QOk/7PTAEi6SdKVkqbL/6dWiGKR6uUCivmVlyCUtI6k23IKDsBSpLv0oU49gme0vSOA09LCVwy2wWp2NGm2qrF6FIu869Ry6NO2N8zffoVULNIq7josF4usm7eXHizFIgNNqch4LUkj83vj1Ur9TCH1652LlIoFsKntpWw/n99//zjYBqvZnaR0tUZzKqB+KX/+A6cC6rty/cmtwGy2rdS68mFNbGc5r6QF8k1qEbXOsOa/mGy/q9SYfh6nZt0bAceTljw8Ked6vQU81NTH+aF6uVhkOdtXKlU4/w14PM/urERqMH257XuLBtpFJA21PT7Pmn4KOIbUw/ReUmrGNvn3viBwV1Mf54fq9SgWecz2M5LOIc3mLUQa3H4DuDFmY/uux/m6CLAbaQGM30k6HlgN2MFpBai1gXsdha6hjzSxgHq47dvyE87DSe0sT5L0ufzSi9yH5eKrUNkMq6Thkj4qaY28/TXgJVKCPsB0wO0Atv/q1LLhpLx9Z56JicHqZEjaXNGsfwKn9kVX5s8ft700sEn+9jBgRdKa0q3ZnXM0cTGImUvE3EkkrShpix6fP0pK0IeUH34/MDbPni1hexuY8Hv/TwxWJ0/SKkqrXQVSE3XbN9h+Jm9vQ+oQ8QIwnFTEsxmApPUkXSZp27w9Y76ADlr5KccnJM2bt68CnsgTQ0PIkz4Atve2vVrrZt32P2OwOnmS5lIqCg1Aft+/z/ZteftUUp3Jyfklw4HNgRFKi7dcJekYSKl9SiuHVaqyGVZJJ5AaA//L9keUimlmJVUXRuuoCkh6gbSm8vOlY+k2Of1kDVJB13OSHiQNahclDc42JuVZDopHYTlf62pSCs68gEhVpZc599sNU0epF/Fttn9eOpZuI2lWUk7li7b/JekI0hLYW9i+XNKmpA4wtwyGiQ6lKvCHSAOGb9o+WqkLzt2kgpnG/w7qJml94CDbMWhtk9Iy72sB89s+S9KHSSuXnWT7K0rL7c5BeoIyur8/p8o71vmB7YHWNPGcpIKZHQAk7SXpgPw4MfTPUFKfxNCmPIP/mx6zDIsDa+Y3+tlI/08PhAlLe56r1Be0qZYE/khq4/Ia6W55e/IMq6Q1JB3eemIS+iXO136y/bLti2z/K28fQEpDuS6/ZGPSwgzT5XzNCyX9oFC4A2EY6Zq6MHBa/trqwHnAKElDJZ0gqdKlgAeZOF/7yWmZ93/aPitvXwvMTCqYh3S9+TapRRqSfizpt+2OB6scsL5Muht+FN6zWtQv8vcfIyV6D8vTx3dL+mMu6BguabGSybxdYlVSFWeYSrkC8rl8Z/g0aUWW1trViwAfZuLj8SZ6C3i+xyPDZ51Wnvl0/v4rpKKMOQEk/UzStZI+mLeXUOroESbtYODc0kE0yCukJwKQBq63ATOR0s1WB3ZRWhGtiYaQ8vOfyoWN2P6K7cWdFtOYlpRnviCApI0k/VfSl/P2vJJmKxV8l7gW+GLpIJrCqU1WK7f1HtL52rISsBHwvXaOWaStVR6YLgQsZvsKSSsAFwPX2N4uz+osDVxh+/EBDzA0jlI18lrAS7ZvkHQksDdpTfXbSGvS32b7ovz6WUlFgLOWirmT5N/HB0m/o5eUKo5XAkaR2hntTGogfn3BMEND5GvEGsC8tv+QcwvPA463/W2l/qAzktqnvZL3uQL4oe0rigXeIZTaAy5HWrjiDqVVqA4GdrR9gaQdSYOJS22/WTLW0Ay58G814ArS5M/dwDjby+XC+o1IKWf/za//ErCC7S/19WcUSWJ38kjrjcX27bYXAFqPM0YAG5IuiEg6WtKpkubJ25Un83YDSdvnGcEwCbk4Y3j+/EhJf8jfWpq0Ys+SeftoYJTtW/P/x8Nag9VsHPCbAQu8w+VHtFd6YjuUdUi/v7dJ6QRLAK0Crk/npyeb5O0ZBuPTE0kfyW/iYRIkDWvN/EnaWtKfJK2cn4B8F2jlE94ALAt8B8D2ubZP8XvbpV0GRP41E9oD3mL7jrz9Y1JR28X5JXMDuwNDlFpfXaHUk7NVMDOsSOAFSVpQqfd3mIT8RLz11G0JSb+R9NX87e1JPV1ny6l2GwMrwITC+qNbg9XsXtKsdt9/foVFVwcAf3INS7QptShaHfgdafnOF0gtOlaXNDtpVH+zG77OuKR3SC0mxpWOpRNImpG06tnrti+SdDApD3Vz23+TtAPpcf8VLvEooYPlGav5bZ9Rw7HnIS0k8KDT2tank/6dNrJ9Sy6YeRi4p8n/LpLOJi2JeFbpWDpF/n/3Ads/zU/WrgdOtr2vUuulOUlLacfKdj3kJ0SH2d63hmNPB6wHzGz7HEmfJi0AcajtwyWtTEq7uNX2G5M7VjfLNQufs71l6Vg6hVJXnfVI5+R9km4n3fgsTDpXNyUV2g/I8tNVzrB+gpzvVrV8p/gL2686LRgwijSSh1ThvB9p+UckbZGLRZarI5bCBl1SeL6jm0/SqLx9hKSbldp7zQhsQ/r/AHAsqU3O3wBykdXlTR4UTYXlSDd6lbP9tO2zbd+Yv7QTqcXY3XmmdXvSBVE5t+7nkpp4kRh05ytMeMqxSP78U0pN6zfP394SWFhpgY+7SEv47gsTWi/9IQarvRrBxGtepWy/ZftS2+fk7QtJ9San5JesChxHSqlqtQj8RutJVoMMZWKO9KChtLDEsvlaO3d+ynF8/vaqpMnC1lPttW0v6NTy6hnbpw7UYBWqHbA+S5r9rJ3td3oUd91he0Pb++VvP0oqFhkBIOliSdf0SCdYsYuLRRaz/W7pIOokaXpJu+f8FoA9gVuY+Gjwb8AepNysp2xvafs0ANtjXFF7F6WehzdO+ZVdawwwIO3RcsrFU7bfzJ9/3vYH8//lt5i4hnmrm8idkrbK24ure4tF9gYuKR1E3SRtIukHkkbkx4XPkCqCAe4DDgWuggn9Qb9qe1x+H6+s5WF+r1+pquN1GAOPDNgPs19z7qhi+ySnAupWbvCdpMmpt/MN5xOtAY6k2SUt2qUpQJcA+5QOom6Slsw3HB/JX7oUuIA0c/oKcDpwFIDtM23vbPvWvF1Z0Xd+rz+4rX2aPvmkicUi15Bag9xAWkJyWUmLklbzuar1DxLql2dXlgTesP2QpFb7sw1I3SR+QSrAO015JZcCMS5KSiWIHMQBlG8mlwVetv2IpJ+SKnfXdlptZT/gQVL6UbPfvDpEHnzMR1qp8EalfpVHAafbPib/m8xEesIxmrROeYlz9iZgd9s3DfTPHqzy/42FSDPlN0r6BOn9+zSn4rhPktoGXuLoHz5gcgrJ8qTJHpPyu9+yvVHO090cOMupALnUNfZA0hPRA/u8z2B8z9fE5eyWAvYF7rZ9vKR9gXWBH+V/yJmBVzvhwqiUBL99HTmHdclvZiKdMF8jFekckN/UjgWOsH1yzmV7k5Tz2BGPUPNM0b6227oDDNXTe1c4+j6woO0dJS1LWuL5PNsnShpBelPuiMd6kjYkvbc8UTqWvpI0xGkp7U1JaV5HAo+TbhL+ZXt7SXMDCwD/dQetdpZna86w/VjpWAa7HtfYrUhpID+yfbuk80kV5HuS0mVmcOoDXZzS4jJz2b6mdCx91eN8XQzYhZRnfK6kX5Em6ra1/aCkdUnvRR1T55PfY6a1fUGf96lqLCbpMFLy/COVHLAASQuS8nRuzP/IV5Nme5YFXiTlEN3iAmvTSxoJPGt7xED/7L7IOaUrk9pY3Cjpm6S84i1sXyXpO8DD3TTgbjKlwoppbf++dCz9lc+JjwDjnYrs9gV+BOxh+zeS1iPdCN1s+60C8V0K/NT2Xwb6Z/dFHvAvbvtPkj4E/IG0Lvg+kj5LmlU9q5MucoNVTmnbz/bXS8cyNSStDixn+9T8FOsO4GLbWystY7wE6fr7QoHYdiUtJrPLQP/svpA0F/Ah0oqMz0q6hnTzvqBScdR2pDZl/y4aaI2qHLDeDuzgvO5sE+QZwnmBp0gFPqcCw2xvLmkV0h3NH/PFUnXOxObZ3sdtz1TXz+hjHNOQUirezIPSD5EG8suRfj9n5Nnq+YG3HetXdyRJ3wamsX1I6ViqpNQ5Yojt0XnGbQvSkrOPkIpI7siPsWs9X3MsfwOOtP3XOn/OFGIQMML2GEkbk1JvTnRa7vQyUs7atqSiinlJvYc74ilHmEjSMsD5tpcuHUuV8pPD2fMAbH3gWzEAfaMAACAASURBVKTeuidK2geYB/i57ccH4Bq7B/Ah27vX9TP6GMfIfL7OR/p9PGz7J0q9dDcEDrB9c/4/8Ygb3Lnh/aosunqcVOzUGLlA5Mn88VXbW9luVbs+R1q9oVUpeVQuFlkbQNIKFReLvEaa6R0wSgVQH5e0Wd7ei9Rs+lP5Jc+Sqr2HOvVZW8328QC2n+jmwaqk5SRdVTqOGr3MABVdDSSnYpHR+fPDbH/I9sOkCuCrSe15ANbOxSKHwIQejFUXi3wO+GeFx5siSatI2kNpNcGFgZeAX+VvjyU19X4EwPbGtrfJ729jnZYv7trBqqTr8qPRJnoHeKh0EFVzKrx7Nn9+he31bbdWGLyNVJTZcr9Sx4mh+dq0sqotoD4T+GaFx5sipeLe7fLgs3WT+3R+YvkGcD/pfQvbP3YqML85b9/dzYNVSQdJ+kpb+3RAemYjaGKxyOO2X5B0Lqlx7gLA26QT4TrblxUMs1c5QXuY0wpG3wA2IyVlvwv8npQwf0ye5R3XSXlrdVHqPXiy7ZVLxxKqlwemCwPT275b0m6kpvQH2/61pJ1JOXZ/7JQcu5b8lGNO20/m3LT9gd869dA8jjRb+hVS15bZu/nGsR2SHiT1+n2wdCyhenkCaCnb1+V80/OBB/ITzzVJbfouLZGyNzn5vWZ+0pPakcAJpNqYvXLqzTbAsbavybUTL7jh3YAgtagkrTx5RF/3KbLSVRM59bK7pZV7Y3trYFbbr5JmdaYhNVNH0ieUVhbZJm/PqimsLKLUMuZzUxun0soy2+XHH+QYnmJij7/bSOv7jrX9Sr6jOyb/nUYPhsFq9iJwTukgQj3yrOLDzgud2P6V7fmBVo61SEsJTqO08s+/JR0DE548zDiln6HUg3SuqY1V0lqSDpA0m1Lvy1eAk/K3nwZOY+IszD62d8kzzeMHy2A1O5P0uwkNZPsl29flz++3vTypoAvS5MqSwFIAko6V9AdN7Ac8akpPTyR9QGlZ+KmSZ033lPTR/KXfAzeScsLHAJeTOilg+/f5ye01efu5wTBYza4H2uroUWUO63GklTEG0xtkv0iahdSM9wXbN0k6ilQ1uantqyVtQboQ3dh6RCdpAdIM7fx9OH7rjm6oU2ugr5BaA+0I3E5acvQW20cprTk9vpsfBYb2Sfo8MNrvXY429EJpOeRVSG2dLsizmheTcuu+ppTPPgq43j2WCpV0PfDV1kV2Cj9jZmBhp9Zdq5KWDr7Y9o+Ulj5cgJQP+7Sk6R3rvw8qSgVJX7R9UOlYukFODVmV1GN0NCkN5l1gUWBW0s3ojT1n4yV9jXSO79+H409LGhw/THqScT4w0vb6edC7G3Cm7b/H+VqdKgesj5F6JT5ayQEHmTxjM86pmOlYYA1gbVJ/w+OAW4Evky5q7rGfbFvSLqSFBb6l1GftbOBo20cqNdMeSio4GfBq6dB5JB0NPGn7qNKxdKP8RGRG2y/npxS7Az+x/RdJPyJdHDch3Yje2Dpne5yvHyG1jfq17XslPUwqeFo/PxZcHrjN9osl/n6hs+RH3sfYnuoZwMEoT+LMntP1FgOOAB61vX+eINqQlAL0Bqmo6f3n6zzAF0hFTmfnCboNSW2jbpW0CSk94YESf7/BYrKPodv0IO9NkA5t6JknZ3tCIrKkt0l3idOQ7hh3knQoqSHwCqQ+sheSmjc/k0/Mf9iep8fxbhmQv0SD5AHFgbY/UTqWmjwPDHjrmKZw6vX6cv78HN6bPnIl6YZzM9J74iuSbgbmIC1csjNp6cs3mPieuWjrIpmfUv19AP4ajSLpAWANF2iJNADeIl1jQz/kc6uVrvcgqXNIy13AgqRJoVuAi5Rabj0PLJGfbk5HOn//k/fZt+fEke1La/9LNEx+sny37ZP7vE8UXXWXPCBdjNShYClSy4vBkvMyYJRarBxk+2NTfHEIk5GLRf4IXERqKdW1lb2dTNLLpKdML5WOJXSvXED9deDDpFadTbwBKk7SL0k9ZU+a4ouzKLrqErngYrt8V/cEKe/0wRis1uZx4LzSQYTulYsbZ8sDqNeA+2KwWqtf0LDWimHgSFpN0qo5be5x4LkYrNbqClJNTZ9VmcN6OvBl22MqOWB4D0kfIK06s3wuAtnE9sWl4wrdSdKXgPttX146lqaSdA+wue17JK1GypmLVaNC2yR9EPik7UNLx9JUSqt1vmX70JwGMCrS6TpLlTOsW5AKe0I9hpKSwsntamKwGqbGqqS851CfYUw8Z2+IwWqYCvOSlg0P9el5jX08Bqudp8oB613AuAqPF97rTmBdmNBLNRra10jSZpLOLh1HjZ4kiq7qthp5dSJJSyst0BFqIullpUUVmmgMUXRVt8OAYwEkzaW0UlyoiaRTJW095VdOVNmA1fYaHjxN5Qec0xJ2L+fNWYFi65MPEtMB05YOoi62D7F9Yek4miw3Om/1N/4F8KGS8QwCs5BnyJrG9j9s7106jiZzWp64NYb5LKnwKtRnZLs7RNFVl5A0b4+7kaGktaVDfe4HLigdROheknaV1HpTjnO2Rrl7yk+iCDX0l6R1cq4wxPk6EC4idTvqs0qKrvKbxQWkAoPok1UDSWsDh9v+iKTpgbVsR6/G0C+SDgCusX1t6ViaStKzwIq2n8mr39zb4ylJCH2W3//XsH1k6ViaStLxpHP0uJwOMNx56ebQGapaOGAIqYIxBqv16ZkQ/ibRWDxMndWBWJWlXj3P2esLxxK62wJA1C3Uq+f5+kjZUEJvqkoJEBNXgAj1uIa0lCOShkuKfLgaSdpBUp9X4OhCDwPRYL1eiwIvQmpLJGlE4XgaS9IISU1exvYVouiqbvsBpwBIWkBSdFGpkaQ/Sdq4nX0qGbDmgqDVqjhW6F3+Hbd63M7Pe5eCDNWblgbneNve3/aVpeNoMtuv9sipPB1YomQ8DTeMtHx1I9m+xPbBpeNoMttv5EUDAL4A7F4ynkFgONDWU/nGXpCbRtKikrbMm5EQXr/bgeh1G/pN0v/1aLMU52y93gZ+UjqI0L0kbSpp2bwZ52v9zia3/eurqoquRgKn2f7sVB8s9ErSJ4E9bG+WHy2uYPu60nGF7iTpB8AfbN9cOpamkvQWMLPtNyWtCdwRKwGG/pC0KbCY7eNKx9JUks4E/mL7t5IWBbDd1oAq1KuqoqvpgPUrOlboXc+E8LFADFbD1FiTtJZzqE/PczbO1zA1FgaWndKLwlTpeb7GQLUDVZUS8C5wY0XHCr37M7ANgKQZo+iqXvlx7tGl46jRvaRCjlCfmcmPFSWtLml44XgaS9J8kh4rHUeNnqfNx6ehbV8AzgOQtJikBQrH02iSrs3t/vqskhnW3FuwrWqv0B7b7zAxp2Zp4OfAKuUiarxpaDMhvJvY/lLpGJouPwlpORdYB3ikTDSNV9XTwo5k+/elY2i6HgVXAF8CniHyous0LWmys8+i6KpLSFpO0qfy5jAiIbxu1xHL34Z+kjRUUs+lHeOcrder5HXgQ+gPSVtJanXyiPO1fqcAT7azQ1VFV3OTlsX7/FQfLPRK0g7ARrZ3kDQTKQH/ltJxhe4k6QTgp7bvKx1LE+XH/y/bnj5vrwnc/L5ZnBD6RNI2wIy2m9wbuihJFwM/t/1nSYsDb9p+onRcYaKqZlhHAGtVdKzQu2FMTAh/NQar9VJWOo4arQWMnOKrQn9NKOCAVHQVg9X6DILzdbH8J9SnZ9HVAzFYrVd/zteqBqxvAzdUdKzQuzOA3QAkzSYplumr13eB75QOoka3Aa+VDqLBxgKztTYkrdujJ2uo3nLAnaWDqNGTpNXpQn0+SU4Dk7SspPkKx9N0d0tapp0dqlrp6nHb21ZxrNA72+Ntj8ubHwKOKBnPIPCeGbKmsb2T7ftLx9FUTnrOqF4IzFAqnkGg0Y3ebZ9u+5el42gy2+Nst97z9wc2KRnPIND2NTaKrrqEpFUltU6gCekBoTZXAFeVDiJ0p7y2/X49vhTnbL2eBY4vHUToXpK+IGnBvBnna/1+CrzQzg5VFV0tDnzT9q5TfbDQK0n7AkvY3kfSrMA8tu8qHVfoTpJ+SzpnI0+rBpLmAW6xPXfeXgO4sccMTgh9Jml3YIzt35WOpakkXQscYPua3C1gtO3nSscVJqqqd93MQORU1qtnQvjLwMtlw2k2ScNIT3abOsD4MKkPXqjH+4uuri8YS+NJGgIM7ZE21TRLkBYPCPXpeY2NdKmaSZoWGOc2Zk2rSgkYSxRd1e1YYD9IbcQkrVQ4nqY7CtindBA1+jfweukgmirPXE9YKUfSJg2vYi/tw8CVpYOo0UPAo6WDaLg1gesBJK2U23WG+jwGtPU7rqro6h7be1ZxrNC7XMTRWhXio8CBJeMZBJpedLWt7WdKx9FkrfM1z/79pXA4Tdf08/Xnts8pHUeT5Wtsa7bvYNJNUKhPFF01laR1JK2fNyMhvH5/Av5VOojQnSSNktSaoR8KjG/n0Vdo28PASaWDCN1L0l6S5sqbcY2t32HAmHZ2qKroaiVgF9t7T/XBQq8kfRuYxvYhkmYHZrH9YOm4QnfKq7p8zvYrpWNpIknLAn+wvUyeYf2Q7RtLxxW6U17m937bF5SOpakk/RfYxvaduejqhVwvEjpEVUVXswFtNYANbRO5z6DtF4EXy4bTbJKmI82KNbW341rEE5Y6DQHGwYTUgBis1igXSQ5t8GpiSwKjSwcxCETR1QCRNAPwRomiq1eIN+Ra2f6e7e8BSFpI0oqlY2q4XwKfLx1Eja4GmnpxL872nbZXAJA0jaSPl46p4T4O/KF0EDW6G3i8dBBNZns523cDSFpT0hylY2q4l4Hp2tmhqqKrm2x/s4pjhT7ZGPhy6SAarulFHJvbHls6jkFiRiD6Z9ar6efr0bajcG/gHAasUDqIhouiq6aStKmktfNmJITX7yzgptJBhO4kaUFJX8qbjV42tEP8Fzi1dBChe0k6QNIseTOusfX7Bm3+jqsquloX+LjtA6b6YKFXko4GnrR9VH5UMdz2Y6XjCt0pr+qyToNzdIuStBZwlO01JU0DLGP79tJxhe4k6TDgatt/Kx1LU0l6EljN9pOSlgSetv1a6bjCRFXNsI4CFq/oWKF37wJvA9h+Pgar9cprwU9TOo4arUX6PxXqIeANANvjYrBaL0nT5iKOploKmLV0EA03nomFzffFYLU+SmZud7+qBqzPEo9Pa2X7a7aPA5C0pKQPlI6p4X4HfKJ0EHXIKy79pcdCFKFitq+1/TGYcPOzSemYGm4bUqFkU90KPFU6iCazvaDtZwEkrScpbhDqMz1p3NiWqoqu/mn7h1UcK/TJFsAOpYNouMbmHeYFXTYtHccgMjdwYukgGq7pRVc/sH1N6TgGkaOARUsH0WD9Ol+j6KpLSNpK0up5s9Fvzh3iZFIhRwhtk7SUpF3zZpyv9bsROLN0EKF7STo099+GOGfr9jbw9XZ3qqro6lPAyra/O9UHC72SdCpwre1TctHV0FgLPvRHzvW7zPbaU3xx6BdJmwL72P54vgguZPu+0nGF7iTpOOAs27FcdE0kvQ7MYXtsXunqCdtvlI4rTFTVSldzAgtUdKzQu3HkRu+2ny8cS+PlhPDXbY8rHUsNpiF6DNbNwFiAvPpSDFZrJGk4MKTBvYWXBkaWDqLh3iBWuhoQebnqWWy/1M5+VaUEPA7cUtGxQi9s72H7twCSPpDXKg/1uYhUSd9E7wIXlw6iyWz/xfZWAJJmlbRR6ZgablfgiNJB1Oh6+lGkEvrO9uy234QJfc/jBqE+cwF3tbtTVUVXl9k+vopjhT7ZHti8dBAN19gcJtuv2d6+dByDyKLAj0oH0XCNPV8BbB9i+7bScQwiJ5CeHId6RNFVk0n6oqQV82aj35w7xNHAg6WDCN1J0kqSdsybcb7W70rg96WDCN1J0hBJR/X4Upyz9RoNfKvdnaoqutoBmNv2kVN9sNArSecDv7H9R0lzAuNtv1g6rtB9JM0NnGE7HlPXRNL2wCdtb5fzK+ey/UjhsEKXkvRb4MexAEU9JE0LjLU9Td5eHHi0oTUMXauqoqu5SL0GQ33ezH+w/VzhWBpP0ihgdEPfsKYnrZwT6jMeeA0gVxo/UjSahmvlG9oeUzqWmiwFTDfFV4X+GgpMmACy/UDBWBpP0jBS0dUL7exXVUrAA0Dk19TI9na2LwGQtGpe6zjU5+9AUwvb3iCKrmpl+xzbuwNImkvSBqVjarj9gANKB1GjK+kxoArVsv2G7QmTbrnvedwg1GcJoO2FMCqZYbV9QRXHCX22M3AH0SqnTo3NYcrLD+5VOo5BZHlSvtblpQNpsMaerwC2v1E6hkHmV8Bi5FaSoXJRdNVkkvaR1HqM29hlQzvI94AnSwcRupOktSVtmzcbPZjqEBflPyG0TdJIST3bosU5W6+ngO+0u1NVRVd7k5YoP2GqDxZ6JelK4Pu2r8xFV2/ZHl06rtB9cjrJkbY/XTqWppL0ZWA523vllcVmsf1U6bhCd5J0MbC37YdLx9JE+Zp6p+058/aiwCO23y0bWeipqqKruYmp87q9xsSVrqLoqmaS5gWes93EmewZgIVLB9Fwb5Fat2D7deD1suE0m6RZgXdsv1Y6lposRXXX6/C/BEy4rtp+qGAsjZfzg2fJ6Wl9VlVKwJ35T6iJ7U+11pGW9NF8Bxjqcz0wX+kgajIa+EvpIJrM9sm2DwSQtJCk9UrH1HCHALuXDqJGlwCvlg6iqWw/a3v51rakL+TlQ0M9VgYubHenqla6Otv2+VUcK/TJXsDqpYNouMbmMNl+2PY3S8cxiKwC7FM6iIZr7PkKYHvfdmejwlT5dekAGm4Y/ajDiTuILiHpW5IWypuNfnPuEPsDL5UOInQnSZtI2iJvxvlav7OAv5YOInQnSXNIOjR/PgQg8ldrdT/ww3Z3qqro6hDgMdunT/XBQq8k3QLsbPsWSXMAr9seWzqu0H0krQLsb3u70rE0laSDgBlsHyRpRP78+dJxhe4k6TrSymltNVoPfZMLUf9se0lJAha0/WjpuMJ7VTXDOjcwY0XHCr17iYlFV8/HYLVekhaWNLR0HDUZCcxbOoiGe52JRVdjY7BarzxD1uRr0BKkwqBQDwPPQGp3FIPVekmaIS8R3paqBqw3AvdUdKzQC9vr274LQNLGkhYoHVPD3U4a2DXRc8BlpYNoMtvH2P4xgKSlJH20dEwNdwSwdekganQeaYW6UAPb99v+KICkaSR9oXRMDbcucFq7O1VVdPVr27GKy8D5Kmn1nFCfxuYd2r7Ldtv5Q6Hf1gbiAlivxp6vALb3tD2mdByDxAzAcaWDaLhY6arJJB2emxtDw9+cO8SuwJulgwjdKa9FvmnejPO1fr8E/lE6iNCdJC2Sa3EgzteBcAtwVLs7VVV0dTRwje0/TvXBQq8kPQx8zPbDkkYBY2zHgCq0TdIGwLa2dy0dS1NJOpK08MSRuehqWtsvl44rdCdJ9wIr2I4FemogaXXgZ7ZXz10C5rL9dOm4wntVNcM6F2kaPdTnGeBtANsvxGC1PkqWLB1HjUYCo0oH0XCvAq/AhKKrGKzWSNK8DS+6WgyINkv1eYe0vj22343Bar0kzSRpnnb3q2rA+k/ggYqOFXphe03bTwJI2qI/FXahz4bQ7CLCx4HIOa+R7UNt/wpA0oqSPlw6poY7HtiodBA1OoN4TF0b2zfZ3gJA0khJO5aOqeE2A45ud6eqiq5+Yfv6Ko4V+uQAYJHSQTRYo3OY8pvz8aXjGEQ2BLYsHUTDNf2c3Tka2Q+Y2YFDSwfRcFF01WSSTuzxyKvRb84dYDzwudJBhO4l6YuSPpY343yt31HAf0oHEbqTpOUlfSNvDiPO17pdA5zQ7k5VFV2dBpxlO5bGq4mkV4BFbL8saVZS0dW40nGF7iNpK2Bt2/9XOpamknQy8G/bv5I0AzAk2hKF/pA0PXCn7cVLx9JUkjYBvmp747xgzCy2XywdV3ivqmZY5wSmqehYoXePAuMAbL8cg9X6SBoqaenScdRoRmCm0kE03AtMLLp6PQar9cor0zV1oY9hpNUkQ33eBJ4AsD0+Bqv1kjSbpLZXW6xqwPpX4LGKjhV6YXvF1kVP0uckzV46pgabBfhX6SBqdD9wVekgmsz2N23/HkDSGpJWKx1Tw50KNPV3PJ709ws1sX2V7V0AJI2StH3pmBpuW+CQKb7qfaoquvqp7TuqOFbok0NIs9qhHo3OObR9je3TS8cxiGwGbFw6iIZrbN6h7Tds71s6jkFkflJhc6hPFF01maQzc0NjaPiAqgO8CnyxdBChe0naV9KaeTPO1/p9G/hv6SBCd8pPQVo5/XG+1u8vwMnt7lRV0dUFwBG2r5vqg4X/IUmkptFDbFvSzKSiqzipQtsk7Uoq4DuodCxNJek84Bzbv5c0HNJMWeGwQheSNBfwV9srlo6lqSRtC2xhextJw4AZbL9aOq7wXlXNsM5R4bHC/xoC3O18d2F7dAxW6yNpOknLlI6jRjMSK9PV7WlgNEx4pBuD1RpJWjJ3Y2iiaYHZSgfRcGOYWHT1TgxW6yVprpIrXV1AeoMONchVi8u2tiXt3uCK2E4wP3Bx6SBqdDupD16oie19Wm3+JK0naaXSMTXcOUBTO3uMBSLnvEa2/2x7fwBJ80vapnRMDbcL0HZedlVFV0fafqiKY4U++T5pPfhQj0bnMNm+wvYfSscxiHwG+EjpIBqusees7ZdsH1w6jkFkcWCv0kE0XBRdNZWkGSSd0eNLja2I7RBPA3uWDiJ0L0kHS2rlHDZ2MNVBvgI8XDqI0J0kbSBpj7wZ52v9zgXObHenqoqu/gF8yXZUadYgr2z1sO1Z8vZI4PVYWzr0h6SvA8NsH146lqaSdAVwuO3LJU0H2PbbpeMK3UfSUsCvba85xReHfpG0J7CS7T3ySlfT2X69dFzhvaqaYY0m9vUycOeEDXtMDFbrI2mEpGWn/MquNZJUyBHq8yipPRq234rBar0kfSAvYdpE0xIpYHV7hfeudBWD1RrlPOG2V2+rasB6JhBLmdXE9iu2J+TASdpPUiyFW5+lgd+WDqJG/85/Qk1s72z7BgBJn5C0fOmYGu5PQNtLPXaJl2j2+1Fxts+2fSiApMUlfaZ0TA33f8CO7e5UVdHVD20/U8WxQp8cTsqzCfUYCrxTOoi62L7E9qWl4xhEtgM+WDqIhmvsOWv7SdtHlI5jEFmefgymQlv6db5G0VUXkDSHpF/1+FIkhdfrPuCrpYMI3UvSjyUtkTfjfK3fTsDzpYMI3UnS5pK+kDfjfK3fycD57e5UyYBV0t2S5q/iWKFXI4ENe2yPoKGzCZ0gp2BcWzqOukg6XNLepeNouPWBmfPnO5KqYkNNbP+9qYsz5GVD/1Y6joZbGmgtFnM+sG3BWBrP9l222+7qUdUM6yykpUNDPd4B7mht5CKOqW/vEHolaeaGF12NIFJK6nYfafUcbI+LlenqJWnVBuf1Tws0taCsUzzPxKKrd6NIsl6SFs1LDrelqgHrScBrFR0rvI/tx21/EkDSUEnfLB1Tw60CHFc6iBpdCdxUOogms72d7XsAJG0tacnSMTXcX0lLDjfRk8BZpYNoMtun2D4eJnSc+FTpmBruQODT7e5UVdHVd23HgHVgTAt8t3QQDdfohRlsn287lmYdOF8AYsBaryYXXT1o+8TScQwiHyKtThfqE0VXTSVpEUnH581ICK/fTUDMYod+k3SSpPnyZpyz9dsciN6ZoV8kfUHS1nkzztf6HQVc1u5OVRVdPSepqY9jOsEsTFyLfCwwU8FYGs/2C7ZvLh1HXfJgKtq21GsDYHj+fFP68eYc+i4XXTVyhlXSJpLarqgObVkWWDR/fiqwW8FYGs/2f20/2e5+Vc2wzkjckdTpTXLRlZP4XddI0qiGF10Nn/JLwlS6A3gDJhRxRFFqjSStLUml46jJtKQ0pVCfJ/OfuMYOAEnLSJqj3f2qGrAeBURVXU1s3217BwBJI/Na8KE+6wKHlg6iRhfTo+tEqJ7tzVszCJJ2lrRQ6Zga7h9AUwesDwC/Lx1Ek9n+me3fAEhaXdLHS8fUcN8H1mt3p6qKrg5u6uOYDjQjsH/pIBqusQUcALbPsX1L6TgGkV2ABUoH0VSShpAmxho5i517Vp5ROo5BZE1g49JBNFwUXTVVbrPxk7zZ6MFUh7iKdAcYQr9IOkfSrK1NImWqTgY+VjqI0L0k7SPpk3kzrrH1+zbwz3Z3muq8GElDgbG2o7FxfUaRWm1g+wkgVhWrke1ngWdLx1EXSecBv7F9YelYGmwD8uIMttcqHEuj5UVUriodR10kbQds2koLC7VYHhgHYPuowrE0nu07+7NfFTOswyo6Tpi0MUTO4YCRNK+kZab8yq41HWlWKtTnRiKvf0BIGiZpndJx1KipK3h1koeAp0sHMVhI+qCk2dvdr4qB5rvADyo4TpgE2zfa3hcmVLB/tXRMDbcpzc4TPge4t3QQTWZ7E9uvwoTHjXOXjqnBRgJNflpwB3BB6SCazPYRrSdOkj4maf3SMTXckcDK7e401QPWvE7296b2OKHP5gB2Lx1EwzV9pavf2o4B68DZk5TWE+rR9PP1Ftt/KB3HILIOsHbpIBquX+fsVA9YlU3tccKkSfqwpFabpSGkvqyhPheS7gAbKc7X+km6TFLrUe54ooijTqOBT5QOoi5xvtZP0kE9ZlVNpPPUbV+g7cV5lPLV+0/SKOBe223nI4S+kbQlsIPtLUrHErqfpKuA79m+snQsTSVpPDBtNCAPU0vS3sAytr9cOpamknQOcL7ts0vHEiatihzWWHe3fi8C/aqqC+2TtLCkpUrHUaM4Z+t3DSm/P9RM0nBJHy0dR43ifK3fPTS4M0ynkbSGpJnb3a+KAetY4IgKjhMmwfbVtg8BkLRgvuMO9dmKZq8lfSrwSOkgmsz2OrndEpIOvkhlyAAAIABJREFUlDRL6ZgabC6gyY31/w1cUjqIJrP9ndYTJ0mflBQ5rPU6Hliy3Z2qKLoaE33LBtT8wOdKB9FwTS/iOM32Y6XjGET2JlWyh3o0/Xy93valpeMYRDYg9z0PtSmz0lWuuZrqBQjCpEnaRNK3enxpbLFgBoczgBNKB1GX3LcyCjlqIml6SX/t8aW3iKKrOj0BfKZ0EHWRNDQvPxtqIunHktbMm++QztlQnx2B+9rdqYqiq8WBy2wvNlUHCpMkaWdgbdtfLB1L6H6SbgW+aPuW0rE0kaSZgCdtz1g6ltD9JB0MDLd9UOlYmkrSZcAxMZPd2aoquorZg3o9A/y3dBCDhaSlJC1ROo4aRRFHvd4F/l46iMFC0kxRdBWm0m3AC6WDGCwkrSup7TSpKgasLwKRw1oj+//Zu+8wqar7f+Dv98zs1J1dFgSkNwugUoIKFqqJGoygqIgVY6yBROxfNVYsscQYJUYUSwJ20Z9gRVETBBXRICooqFhABOmwZbbM+/fHXMgqu+wyM3fP7p3zeh6fh9m59+57kDtz5t7zOR+9JOkOACDZnaRtHOCu0wCcaDqEi+6GbUPoGmde/8htj0neQjJsMpPHdUXq37RXvQHgNdMhvEzSZZIWAADJMSQPNJ3J46YA2OXuf9koulor6f5Mj2PVW1cAdj1Wd3m9iOMBST+azpFDLkDqKpnlDq+fr3MkzTGdI4f8GkBP0yE8zlinKx/JUKbHsWpHcjTJCc7DJIDNJvPkgEkAHjEdwi1OUZAt4nAJyZYkZ1T70RZ4eEDVCCwBcKrpEG4hGbSFze4iOZnkfs7DBGw3SbcdDeD7Xd0pGx9a/QDMzcJxrNq1B9AJACS9IsnLt6uNk7RCkpdvmX8G59+T5YoIgL7bHkhqLcl+ALpEUrGkJaZzuOhmABeaDuFxfQBEAUDSObbjlbskfSxpl1disJ2umoZvACw2HSJXkOxFsqvpHC6y56y7ErBFVw2G5G4eX+jdnq/uew/ABtMhcgXJX6dzZz4bA9bvANyTheNYtZA0XdIDAEDyFyTPMBzJ685Dah6TV90M++bsGkmrJY0Ftk+Zust0Jo/bD8BE0yFcNBPAf0yH8DJJf5S0FABInkWyt+lMHjcNwC4v+5eNoquVkqZlehyr3roDONx0CI/z9BUNSf+QtMV0jhwRAPB70yE8zuvn6xvbKtitBnE0gM6mQ3hcWudsNoquAiSjmR7Hqh3Js0me6zysBLDJZJ4cMBHA06ZDuIVk3Ha6cg/JriSfdB76AawxmScHvAfgbNMh3EIyQjLPdA4vI/k0yS7Ow2IApSbz5IBDkUbxeDamBAwFMKPOraxMdICzZpmkpySdbziPpzlFV+tM53DRCgCFpkN4WD6cZXEklUpqbziPp0naIukr0zlcdA+AM0yH8LjeAIIAIOlkSbPq2N7KgKRPJDX8FVZ4/HZMI7EMwOemQ+QKkgeS9HIVvT1n3bUVwJumQ+QKkm1JHmI6h4vs+eq+t5Bafs5qACSPS2dpRUrK9Bd3BtBX0nMZHciqF6catoOkx0xn8SqSjwF4UdKjprO4geR4AJMlVZjO4nVO+8HrJF1iOotXkTwWwFhJx5jO4gaShwNYKcm2524AJC9E6v1/qeksXkWyCkBIUuWu7JeNoquv7WC1Qe2H1PwPyz2evqIhaZIdrDaYKIDTTYfwOD9Sc/s9SdIsO1htUMcijbahVv049RM+GCq6CjlXESyXkLyU5CnOwwSAjSbz5ICLAbxsOoRbSDa3RVfuIdmH5CPbHgLwchOKxmAWPLywvlMkGTSdw8tIvkaypfNwI2ynK7ftozRu72ej3dsIACcCOD4Lx7Jq1hGpgSokPWg4i+dJWmE6g1ucgeo6ZGf+ulWzAgDdgNSarEgVdFgukbQZ3m5X/TCAJ+HhlUsagd5w3hMljTCcxdOcgWpajZBs0VXTsAjAF6ZD5AqSg0m2M53DJX4AyXS+3Vr1th6pIg6rATjLiA0wncNFnp7y0Ei8DLuUVYNwlkIdnc6+2RiwfgjgoSwcx6qFpAckvQQAJIeTPM50Jo+7HKne0l4kAH8wHcLLnCVbrgYAkq1I3mw6k8cNAXBuXRs1YVMAfGQ6hJdJGutcqQfJa0h2NJ3JwyJIc8yYjaKrpZJezfQ4Vr31BdDPdAiP8+xdA0lVku41nSOHNIOdLuU2z56vACDpRY+vM9vYHI/UeWu5I+3zNRtFV1GSBZkex6odyZtIbluyZStStxwt95wJYI7pEG5wetu3Mp3Dy5wpJdu+FAjASpN5csBTAK4yHcItJIts0ZW7SH5AMuw8/BFOzYjlii1I86JbNqYEnALgziwcx6pdR6QKOSDpb5LuMJzH0yStlFRsOodLCgHY9QXd1QxAewCQtEzSUMN5PE3SJqe4zaueATDIdAiP2w+pL5eQdJgk26jHJc5dvrRqcmzRVdMwH8By0yFyBckjPXwV0p6v7lsFj16hb4xI9iB5gOkcLrLnrPumwxa2NQiSMZJpTZPKxoB1DgBPdgRqLCTdI2kOAJAcTfIo05k87hoAe5oO4ZJiABeZDuFlkuZLuh0ASHYheZ3hSF73awAnmw7hojthW3O7StJJ23rbk7yj2pqsVva1APDXdHbMRtHVp5L+k+lxrHo7EEBP0yE8zrNXNCSVSvqn6Rw5pBWA4aZDeJxnz1cAkDRD0vemc+SQE5GqZLfcYbToKk6yMNPjWLUj+XeSv3QebnD+s9xzLICFpkO4wVkDz7YddBHJESRvdx6WA/jOZJ4ccD8Azy4d5iyNZouuXEIySPKTaj9agdR5a7ljBYCB6eyYjU5X5wNoCeDSLBzLqllHpHqSQ9JNhrN4nsevZnQA8CaAzoZzeFkRgNYAIOm/AOy6yS6StMl0Bpe9jNQ6swtMB/GoAJzOdAAg6SCDWTxPUgXS/BJvi66ahv/AXqVpMCSPJVlkOodL7Pnqvq8BzDMdIleQ7Euyr+kcLrLnrLuSAB43HSJXOMu0jUpr30w7NJLsDSBPkv321wBI/g7A15Jmm87iVSQXAzhB0qems2SbM33ncEm2L3kDILkfgN9IusV0Fq8ieQOAKknXm87iBpLHAviPpHWms+QCkg8AmODhpQ2NItkLwKOS9tvVfbNRdPWRHaw2qIMAdDUdwuM827vbWbPSDlYbTjukWoda7vH0FUhJz9nBaoMag9S/KcsdRouuikjaNmYuIvk4yQHOwzWwRVduGwzAk60QnQKDtqZzeBnJ06stZVUK4FuDcXLBbQDuNh3CLSTbk8wzncOrSLYk+V61H30Jj16waCQ+BXBEOjtmYw7rpQDGZeE4Vu3aAwgCgKQrJT1jOI+nSfrBmRjuRfsAeNF0CI8rAtAcACT9W9LZhvN4mnPXYLPpHC76N4BOpkN4WBCpYlQAgKQ+kkoM5vE0SeXpdqazRVdNw6sAfjAdIleQPJVkvukcLgnAnq9u+wzAu6ZD5AqSBzvz4rzKfsa6qwzAk6ZD5AqSu5Mcmda+WSi66gcgIemTOje2MkZyAoD5kmwVsktIrgLQz4vLW5HcDUB/SfYqawMgeRCAAZLS6uxi1Y3k3QC+lPQ301ncQPI4AK9K2mo6i9eRJIAnAIxRpoMjq0YkBwG4SdIur8WajaKrD+xgtUEdilQhh+Uez17RkLTWDlYbVCcAA+rcysqEZ89XAJA03Q5WG4wPqRVi7GDVPUaLrlrZoit3kXyF5D7Ow5UANprMkwP2BbDWdAg3kIySbG86h5eR/CPJbY1UtsAWXbntCgAPmQ7hFpJdSdqqdZeQ3IPkm85DH4BFJvPkgLkA0lqHNRtzWK8DcEoWjmPVrj2c/1eSLpD0muE8niZpjSSvXrEZAGCq6RAeVwQgHwAkvSjJdgF0kaTNHi+SeR+pf1OWO0IAWgGpLkyS+hjO42lO0dX6dPa1RVdNw3MA7Dp8DYTkOR5eRsaer+5bCOAD0yFyBcnDSHY3ncNF9px11yYAduWdBkKyM8nfpLVvFoquDgSwXtIXGR3IqheS1wCY6fQot1xAshRACy9etSHZBkB3SW/WubGVMZK/ArCnpHtNZ/Eqkv8C8Lqkf5nO4gaSxwOYIancdBavc1aH+Yek00xn8SqSIwCcLenoXd03kOkvlzQ/02NYu2QQgHdMh/A4z17RkLQKwCrTOXJINwC9TYfwOE8v1WbX3W5QYQDDTYfwOKNFV+2c/uSWS0i+T3LbwsbLAXh5kezGoA0AT17NIFlgi67cRfJakuc7D9fDFl257Rx4+JYuyR7OckuWC0j2I7lt5ZQkbNGV214EcHo6O2Z8hRXA7U6AR7NwLKtmbQAIAGzXHPd5vG/3LwGcBuBY00E8rBmcL5WSnjKcxfNyYMmnTwHkwcNXkQ0LI3XOwikGGmo2jrc5U1vSuiBki66ahmkAvP6m3CiQ9JH8o+kcLrLnq/veAfCR6RC5guQIkt1M53CDc2WVSF35s9yxBqnCZqsBkOxO8si09s1C0dUAACslfZfRgax6IfkXAJMlLTWdxYtIhgBskRQ0ncUNztSS9pLsPOgGQHIUgEJJD5vO4lUkZwJ4QNIM01myzRmwHi/padNZcgHJ1kh1YTrLdBavInk6gF9K2uVpAdkourI9sxvWEACPmw7hYZ6+Aul8sbRfLhvOXrBraLrNs+es03HJDlYbThx2SoDbjBZddSFZkOlxrNqR/LJaYdtnsNMD3FQKoLXpEG4h2bxaAZ/lApJ3kdzWTGU1bNGV204AMMt0CDeQ9JPc13QOLyM5jOS2ueblsNN53PYogHHp7JiNoqu/A5gE4KUsHMuqWWs430gk2a5iLnKuaHh5FYZjAAwE8FvTQTysGVJFMrBTAdwnqdh0BhfFkJoTHTcdxMMiSP09Q9K3SLNtqFU/pouuPL0GXiNxPzy6zFJjQzJKMq1vf02EZ2+fNiKzASwxHSJXkBzj4bsG9nx137cAPDf/ubEi+QuSh6W1bxaKrg4BsEzSmowOZNULySkArpO0wnQWL3Im3X8sqZXpLG4g2RlAke2U1jBIjgWQkPSE6SxeRXIOgKsk/cd0lmwjGQTwK0kv1rmxlTFntYkLJY03ncWrSF4AoJukXV6NJ+MrrJLm2sFqgxqK1Lpxljs8fUVD0td2sNqgegDobDqEx3n2nJVUbgerDaoIwEGmQ3ic0aKrvZ3+u5YLnHVBN1brdLIQgOd63DciqwB0NR3CLSRb205X7iI51emXDQArnP8s9wwF4MnVakgGSe5nOoeXkTyO5IPOwxLYoiu33Q3g8nR2zMYc1kcA9MrCcaya+QHkO8VAkHScpO8NZ/IspZSazuGiUwBcbDqExxXAeW+VNEnSNMN5PE1SQpInr7AC2B22oNltETh3LSUtlnSm4TyeJqnSKbzaZdnqdFWZheNYNROAv5oOkSucZZ/OM53DRfZ8dd9MAF+YDpErSP7OmXvuRfZ8dd/nsF8KGgzJQ0gOSmvfLBRdHQrgE0kbMzqQVS8knwFwtqQNprN4Ecm9AcyQtLfpLG5wigqCkmwVewNwCgyWe7ELU2NB8mMAp0haZDpLtpGMAegv6Q3TWXIByT4ATpNk70K5hOS1APySrtnVfbPR6ertTI9h7ZKhSH3rttzh6WXaJH1pOkOO6QkgYTqEx3m56KoYgB2sNpzdAPQxHcLjjBZd9SIZzfQ4Vs1IFpGsXrTxHuwHoJsWA+htOoRbSLYn2c50Di8j+SLJIc7DLwGsNBgnF/SCR9e9ddaFtjUiLiJ5Nsm7nIebYIuu3HY9gBvT2TEbna6eBHAcUh/0VvYFUG0ZK0nDDWbxPKe4rcJ0Dhedi9Tru8F0EA/bvmqKpNtMBskFkrw8x3MPpFpZ2pUC3BOGMxaS9D6A983G8bZMCiRt0VXjV4rUMhBWAyDZjuRZpnO4yJ6v7nsCqe45VgMgeQHJZqZzuMSer+77EMBrpkPkCpKHk0xrrdtsFF0NBPChx/s5NxokXwfwa0levgpoDMkBAP4mqb/pLG4guReASklfmc6SC0heDeAdSa+bzuJVJL8DcIjTB95TSBYA2EfSO6az5AKnev1Xkq42ncWrSP4VwHeS7tzVfbNRdDUn02NYu2QoUktdWe7wbAEHAEhaajpDjtkXwDLTITzOs+espM0A7GC14bRGqjud5R6jRVcHkgxlehyrZiS7kvys2o/egkffnBsDSXMBHGI6h1tIdiPZ1nQOLyP5LslfOA8XA/jBZJ4c0A6AJ5upkCy0RVfuInkZyYnOw7UAPLc8WmMi6Y9Ic5pjNuawPg+gRRaOY9UsgGrLWEk6TJnO47B2yuN/vxMAHG86hMdFACQBQNL1kt4yG8fbnO50Xj1newOYZDqEx4UAEAAkvSnJFqS6LN3z1RZdNX4bANxrOkSuILkHybGmc7jInq/umwJ7VbXBkLyKZLjuLZukAOz56ra3kbpzaTUAkseQ7JfWvlkqunov3d6wVv05b8ovSRpmOotXkTwSwIWSjjCdxQ1OJ69iSSvq3NjKGMk7AEy3RTPuIbkVQBtJW0xnyTaSzQF0kvRf01lyAcnfAOgl6WbTWbyK5MMA5kh6aFf3tUVXTUsegANMh/A4AvDsly9Jn5vOkGP2A2BXCHBXEh6d1y9pPYD1pnPkkDYAupoOkQPSumuQjaKrQSRtq1CXkPwFyQXOQ8GuF+cqSS9LOtp0DreQ7EFyd9M5vIzkFyS7OQ//C+BHk3m8TlKBpBLTOdxAcjeStmmAi0jeSvIy5+H3AD4xmcfrJP1W0r/S2Tcbna5eRaroypNvGI1AAM4yVpK2AhhlNo7VxF2J1JeetN4wrHrJg3MFQdL/Gc5iNW2HAPgdgBGmg3hYAP8rknzRcBZrJ7JRdGUnhbvrewD3mw6RK0j2Inmy6Rwusuer+/4GYKPpELmAKV6eb2jPV/fNAjDPdIhcQfIUkvumtW+Wiq7e9vCyIo0GyVYAHpZ0lOksXkVyDIBjJZ1oOosbSHYHsF7SGtNZcgHJKQDulmTXdnQByQCAhCRPTksjuRuAVpIWm86SC5yLFa0k3WU6i1eRfA7AvyQ9t6v72qKrRo4kkfpikQQQBmAXkXZfmekAbpH0Wd1bWZkg6YOzPChSRVdRw5G8zA/As23BJa1FajF7yyU/O1/bIdXtynJPEkBareUzmhJA0kfyl5kcw6rTMPyvyjgBW3TlKklPSPLsOqwk+5BsaTqHx60DUOT8eR5slbdrJCUkFZjO4RaSbdO9fWrV230AznH+vBzApwazeJ6k4yS9kM6+mc5hDQJI6xdb9ba9766k1ZLONJzHatomAhhgOoTHVT9nL5S01HAeq+n6FYBLTYfwuOrn6zOSHjacx6pFpgPWADy6/l0j8iUAewI1EJIDSJ5gOoeL7Dnrvonw8LSSxoRktFofeC+y56v7ngOwoM6trKwgeR7JPdPaN5NaKWf91f0lvZf2Qax6c9Z2vFWS7QXvEpLnAegr6VzTWdxAsgeAHyRtMJ0lF5CcDuBSSV+ZzuJFTlHS55JamM7iBpKtAcQlfWE6Sy4geT6AKkl2ZR6XkHwLwPWS3tzVfTMqupJUBcAOVl3kfCnwSaoAEAOwt+FIXlcFbxdxLDGdwetIhgCUVyu6yjMcyct8ADaZDuEWSasBrDadw8tI5gFIOuOZ9gBKDUfyujKk2U0y06KrEMnDMjmGVadjADzp/HkLgNkGs3iepAckXWQ6h1ucKQ/NTefwuDKkWvwCqfN1s8EsniZpjSTPttIk2ZlkT9M5PO4xAMc5f/7M+c9yiaQjJc1NZ99M57AWAXg0w2NYO1d9QvhySRMM57GatjsA7GM6hFc5S+TAWYYOks6XtMpsKqsJGwnAk9OTGpHqn7FTJT1jOI9VC1t01fh9DGCa6RC5guRhJL3cBtEP2znHbZebDpArSLYgeY3pHC6yn7Hum4bU56zVAEheQrJDWvtmWHQVBNBD0kdpH8SqN5J9AVwk6TTTWbyK5BUACr3aA94puvpO0lbTWXIByTcAnCjpR9NZvIjkHgBeldTNdBY3kNwdQJ6k70xnyQXO+/+3kuydY5eQXAjgt5L+u6v7Zlp0VQ7ADlZd5EwI90lKAIgD6Gw2keeVA/DsYM4WXbnL6UwXlbStcG9fZH4ny9o5zzZmkPSD6QxeRzKCVJHktqIrO+fcXVthqOgq3xZdue50AP9w/rwOwBsGs3iepL9IutF0DreQHELSs52BGoECANXnrL4AW3XsGklfSDrAdA63kNybZHfTOTxuBoBt45iPACwzmMXzJB0qKa1uYpl+828P4O8ZHsPaue1zDiV9Kulaw3mspm0SgI6mQ3jY9gIOAJB0piR7xcZK1xgAJ5kO4XHVi67ulzTLcB6rFrboqvF7D8DTpkPkCpIjSR5hOoeL7DnrrlIAV5kOkStIdiTpyfnmDnu+uu8+2KuqDYbk9SRbprVvhkVXEQCdJNl1yxoAySEATvJqF6bGgOStANZLutV0Fjc4RVfLJdnWoQ3AKTAYYP++3UHyAAD/kLS/6SxuINkGgOxc1oZB8jYAcyU9bzqLV5H8GsAQSV/v6r6ZFl2Vwi6y6yqSYaS+WJQiNT+ureFIXlcKW3RlpclZhzW/2jSAngDSvypg1SUJwLMrMNg1fN1HMg6gVFIlUtMc8w1H8roNACrS2THToqvmJIdlcgyrTuMBbCsC+h7AW+aieJ+k6yR5dl42yeEko6ZzeFgHAJ9Ue/wU7Lq3rpH0gaRfm87hFpK9Se5pOofHvQHgF86f3wGw3GAWz5PUV9LKdPbNdA7r3gBuzvAY1s5VnxC+QNJfDOexmrYHANjWrO75edHVqc5yOZaVjjMBHGU6hMdV/4y9R9I8w3msWmQ6YP3Jm7PlitkA7HyaBkLyFJKDTedwkT1n3bUOwHWmQ+QKkt1JXmQ6h4vs+eq+2wHYxgwNhORfSaY17SLToqsYgNaSvkr7IFa9kRwJYJCki01n8SqSkwF8KGmy6SxucNZ0/MKZr2W5yOkEuFBST9NZvIrkrwBcJulXprO4wSm6Kpe0znSWXEDyfgBPSpptOotXkdwIoLOkjbu6b6ZFV8UA7GDVRc6XAkkqAdAMQFrLQVj1thnAFtMh3GJX9HAXyQCAuKQNSL2/djEcyesqAKwxHcIttujKfSSbA9hcregqbDiS161GmvP6My26auPx26eNwRUAtl1R/QrAHINZPE/SpZIeM53DLSSPc9r9Wu7YF/8rjEwCmGYuivdJekvSKaZzuIVkf5L2S4+73gPQ1fnzG7DTA1wlaW9Jaa3Ek+kc1j5IDags91SfED5H0gOG81hN278AhEyH8LDq52uZpLMN57Gatt8DsBeF3FX9nL1D0iLDeaxaZKPoys6Fc9fzAF4xHSJXkDyX5ADTOVxkizjc9R3syikNhmQ/kuNN53CRPV/ddw08vJZvY0PyQWe96l3fN8Oiq3wAhemuqWXtGpJjAXSTdI3pLF5F8kkAz0p60nQWNzhrOn4pKWk6i9c57QdfkdTPdBavInkCgNGSTjCdxQ1O0VVxtUYUlotIPg3gdknzTWfxImegWgXApzQGn5kWXW2Fh7sCNQYkCwFUOgVuzQAUGo7kdevg7aIr2zPbRSRDSHW6WgcgCKCN4UheV4ZUEYcn2aIr95HcHcCPznrJ7ZDhuMjaKR+Ar9IZrG7bOW0kO5McmMkxrDrdhNTi0QCwBKlOHJZLJP1e0kumc7iBKZ4tUGkkDgLwrPPnUgCeLeBrDCTNlOTZKQEkB5PsYDqHx30MoIXz5xfh4S9ApkmqlNQt3f0zncN6MFKTwi33VJ8QPkvSE4bzWE1XAMA/TYfwuOrn63pJlxjOYzVtFwGwU0rctb0WR9JNkr40nMeqhe101fg9CuBN0yFyBclLSfY2ncMl9nx132cA7jAdIlc4VyDPMp3DRfacdd8EAMWmQ+QCklGnOUN6+2dYdBUHEJVkL6E3AJIXAAhLutV0Fq8iOQvAXyS9ajpLtjkT3jvbznQNg+QeAB6SNMh0Fq8ieQ6AA7y6fJhTdLXZqWGwXEZyNoDxkpaYzuJFTpOGLyUVpbN/pkVXW+DhApXGgORuAEqrFV3VelXcabt5JFLfyv8j6f2GSekpP8CjhYTOygB2sOoiklEAMUk/IrXebYs6drEyswWpc9aTbNGV+0h2BPCdUwjUBgANR/K6L9LdMaMBqzNAai5pXibHsXbqLgCvApgK4EMAO1wSJ1lQWFj4TGFh4aEnnHCCLxQK8ZlnnqkoLCxctnnz5qMkfd/QoZsqSaebzuAWkmEAo7zcyasROBzAbwGMBLARgCeXR2ssJD1uOoObSB4J4CM7cHXVVwAiSLX5fRrAerNxvEvSegAHpLt/pnNYhwE4LcNjWDtXvYhjpqQXfr5BQUHBCyNHjhy0Zs2ayAMPPBCaNGlS8Pvvv49deOGF++bn588hGWzw1FZjVADgb6ZDeFz183WlpBsM57GatisA7G06hMdVP2evleTZK/ZNnS26avzuA/BubU+SPCASifzioYceCgWD/xuX+nw+XHvttYGePXu2BHBMA+T0BJITncX1vcier+77AMA9pkPkCpJHkTzVdA4X2XPWRSQJ4EzbSKVhkGxJMu33x0wHrI8g1dbM2gXOepi/KSoq+ndBQcGaoqKiL4LB4BUkd5iILOnf24pkSF5Hclz15/Py8kadddZZEb/fX9PvwXnnnRcvKiqyV8Hr70gAaU0IbwLWwC6R4ypJX0t6E9jeNvRl05k8bh8AXl3VAwCOA2BrEVyilIe3PSa5kGRbk5k8Lg7gN+nunNGAVdIWZ06CVU8kmZ+f/0Dnzp2fuOuuuwb997//bTlz5sxuxxxzzNWxWOxTku1/tn0bkjHnYSFS3XO28/v90cLCwlr/P8bjcfh8vmj2X4lnfQOPLnEiqcq2UXYXyTjJVs7DMFLTMCz3bIC3i65WSyozncOrSPpIdq32o9aooU7EyppKAEvT3TnTTld9SB5qEWhJAAAgAElEQVSYyTFy0PGtW7ces2jRotjYsWPRrVs3HHrooXjqqacil112WcvCwsKfF8RMATDU+fNcAAurP1lWVjbv2WefrXWlhhkzZpRu3rx5dnZfgndJOl7Sp6ZzuIFkAckTTefwuOMA3O78eTWAZwxm8TxJD0j6i+kcbiE5ylkpxnJHFKlOV9s8Ao9esGgMJH0r6Yh09890HdYrAcQlXZH2QXJMUVHRgvvvv7/fCSecsMNzZWVlaNWqVdmWLVv2Rer27UmhUGgiSV9VVVWQZGUgEPi6vLz835WVlY9K+oBkMBqNfj9t2rQWxx577E+O995772Ho0KHFpaWlXSWtaZhXaDVWJPcC8KIkr87RNY7k7wAcIunMOje2rDqQ/AjA6ZI+Mp3Fi0gWIrWklb0T0gRktKwV7ITwXVZWVtZjyJAhNT4XDodxwAEHlL/11lu3+3y+I/faa69k7969Y+3bt0d+fj4qKyuxZs2a5suXL+8zb968cyORyHIAZ5SUlBxx6qmnzj7uuOOCp59+eiQYDOKZZ55JPPjgg1WlpaWj7WC1/kj+DcBtHr11bs9X9/0bgCev0DdGJE8CUCHJq1eyA7DnrJtKAXi5U1qjQrIzgD9Iujit/TO8wprvHMM2D6ineDy+dv78+S169OhR4/P77LNPcuvWreVnnnlmuFmzZj95bvr06ejYsSMOOOAAJJNJfPDBB3rmmWfKksnk7RUVFZPy8vLOjsfjJwLwl5aWvlJaWnqPpG8a4GV5BsllAI6SlPY8m8aKZABAkbOoveUykr8EcJ6k401n8SqStwDYIulm01ncQLIlgI2SKkxnyQUkVwDoLsmTzWNMI/kLAA9K6pvO/pkWXW21g9VdI+mx+++/v8Y3n08//RQrVqzw/eEPf9hhsAqkpgwkk6nVN3w+Hw444ABeeeWVkebNm18cDAb/VFFRccu6det6r1u3bt+SkpJL7GA1LcuQ+tbtOZIq7WDVXSSLflZ0FTGZJwesQWqusCdJ+tEOVt1DMkCyW7Uf7QZ7RdtNCQCfp7tzpkVXBzkj5pxEMo9kB6c/br0UFxfffv/995c89NBD2jb4BIBly5bh6KOPxhFHHIG8vLwa9917773Rpk2bn/yssLAQf/zjH2PRaPR3AGxBTYYkDZf0nekcbiDZiuSOk6etbBoL4Ernz8sB/D+DWTxP0l8lPWg6h1tInkbSzq90z+4A/lPt8SSkOl5ZLpD0qaQx6e6f6ZSA2wCsk3Rr2gdpgkiGI5HItQB+Hw6HAyUlJYFIJPLRxo0bL5X073rsv288Hp8ej8fbHXLIIfr222+DH3/8cfDXv/41Bg4cmFamb775BpMmTdpSUVHRVdLatA5ieZqzosffJaXdGs/aOZKXAGizszlazgDkcAAxAP+VtKih8llNC8lvAQy0d8vcQbITgDmSOprOYtXNdrraRSTz4vH460OHDr3gww8/LFi/fn108+bNwUmTJh0Qj8dfJlnnoriSPtmyZUv377//ftjTTz99yYIFCzRhwoS0B6sA0KlTJ/Tu3Tvg9/vPTfsgFkg+UlMDB4/IufPVgBcATK3pCWcN5hvD4fAPgwcPfvD444+f1KJFi3cKCws/dIoRrF1E8hySR5nO4SJ7zrprLYDfmw6RK0juSzLt+eaZDlivAXBvhsdoak7ae++9+8yYMSPSvXt3AEAwGMQpp5yCF198MRKNRh9xilt2yumwMR9A1V577VXZqlWrunbB1KlT8fHHH9f6/ODBgyN+v/8Cp92clZ6jANQ8J6PpW4BUJy/LJZI+k7QQAEgeT3J7F51YLHZTly5dJixbtizy1ltvFTz99NP5P/zwQ/RPf/pTr1gs9q6Hvyi5qS+ATqZDuGg/AKtMh/AqScWSXgC2z2fdYDqTx7UCMCDdnTMtuiqWVJLJMZqaoqKiCX/6059iNbVCHThwILp27RoE8Mv6Hi8Sify6d+/esbq3BBKJBHY2haN9+/bw+Xz5ADrU9/dbO/gYQLnpEG6QVCFpo+kcTZnTVnkYyT+RvJzkfj97vtXPiq6Czs+bVVVVXThr1qxY+/b/a2YXCARw6aWX+o866qiCQCBwToO9EO9YAW8XXa2XZK+wuoRkmOS2dakDSDUSsNxTDINFV8N+/obtdVVVVW179uxZ6/O9evXyAWhf6wY/I2n/du3a1Wvb/fbbDy1btqz1eZJo165dOVJXHaw0SBrm1UEdyY4kj617S6smJLvm5+cv69Kly/OXX3759ePHj7+xqKjovcLCwjeqFcaMw/9uMS5BaooAABw1ePDgip8XTW5z/vnnR+Lx+G/dfQXeI+kmSdNN53ALyfNIhk3n8LA98L/CyCQAz3ZNawwkvSfp/HT3z3RKwBgAB2V4jCbF7/evWrJkSa3Pf/zxx1UA6r3ofDKZjOfn59dr2/79+++wSsDPFRQUBADUe9UCK6d0h52vlRaSsVgsNvfGG2/s8uWXX+b/+c9/9t1zzz2B1atXR4455piDCwoKZjibbp9zKOkDSY87P89v3br1jrdlHC1btkQymazXnRYrp9wGIGQ6hIdVP1/LJV1Zx/aWQZkOWHOuC8eGDRvuuummm4qrqnZ82XPnzsWXX35ZCeA1ACDZxefzXRWLxd6IRCKrQqHQukgk8m00Gp1BcjzJ5iSrajpWuioqKpKwy3KkjeRzJIOmc7jEFnCk76QBAwbkX3DBBb7qU8Tz8vLw4IMPhoLB4AEk+wJ4HMDTNez/0ezZs5PVl7Kr7o033hCAhW4E9zKSl5AcZjqHi+w5665vAFxoOkSucJZCvTrd/TMdsI4HMC3DYzQ1jy9ZsmTRscceW7Z0aaoZUnl5OZ544gkMHz68pKSk5EwA7SKRyKxgMLj44IMPvuaEE04YOmHChN0vv/zy5uPHj+9wzDHHHN27d+9bA4HASr/fr9Wr6zcF67777sOyZct2us2qVasEwHNdmhpQnas8NGGvA7Bdl9LQvHnzk88+++wab4UEAgGMHTs2RPJoZ53Bz4DtFex3O5u9t3nz5u+nTJmywyT0NWvWYOLEiaWbNm263cWX4FX9kFpL06vaIjXvz3KBpI2SZgMAyd1IerEld2PSFhlMWayzmn1ncq3gCkjdNiB52OzZs2/o06fPudFo1FdSUpIXCoU+2bx58yU+n6+N3+//dNiwYaEhQ4YEgsEdL9a1b98e/fv3j27duhX33ntv4KuvvsI+++xT5++uqNj5hdOysjJs2LAhAsCu65i+BfDoFQ2nY469+p4GkqFYrPY79vF43E8yRLI9gITTUSwE56KAJJEceeGFF86dP39+9Jxzzgm3aNECr732mq677rqSkpKSv0n6T62/wKrNVwA8271N0ibTGbzMaS/f1mnFHUCGYyKrThuRQdFVpo0DfgPgi21XFHKNc+u4DYASST/6/f7fRSKRe8aNGxdp27ZtvY6xcuVKTJ48Gddeey1qWnmgunnz5mHvvfdGixYtanx+zpw5evHFF18uLS1Na11Ckl2QmoS+FsBCZfKPw2p0SO6FVJ/sGXVubP1EKBS64bTTTrt0ypQpNRbA9OzZc8uSJUtOQqohwHJJd5E8GEALSTO3bUeydSgUGh8Oh09LJpNRv9//4caNG2+T9EYDvRSrCSF5MYA77XuxO0gOBHCLpEOdwsnxktJeJ9RyV6ZTAsYC6JWNIE2RM0n7G0k/ktw/Ly/vngsuuKDeg1UAaNeuHXbbbTe8//77dW578MEH1zpYLS8vx6xZs0rKyspuq/cvd5Dco1mzZvMKCgo+PfDAA59u06bNf+Lx+Nck7Zqd3tIHwKmmQ7iN5O7RaPQv+fn5a4PBYKJZs2ZLfT7fOfVZH7k25eXl9z322GPJd999d4fnHn74YX377bebAbyC1JzDSgCQNK/6YNX52eqysrKrN27c2Hnz5s2tNmzYcKQdrFo1IekDcIcdrLqq+vm62Q5WGzfb6SoLSOaFQqGnTjjhhHB9GgD83KhRo/DCCy9g48b0V1OaMWNGory8/LWft4YlWUCyE8m2zhvgz7N3iEaj86+77rr+a9asibz33nuFK1euzH/66ac7FhQUTM+lQatzS/eFurdssjx/vpLsEo1GPx47duz4+fPnt1i7dm3wqaee2rNfv353xuPxl9IdtEr6vrS0dPSwYcNKzjvvvMSsWbPw/PPPY+TIkcXjx49fX1xc/CtnvczJAF7M7quyakNyIsm0FyJv5Dx/vjYCnwCwKwM0EJKHO+2r09s/wykBIQBVkirTPogHkDyuXbt2D19yySXxdJtMzZ49GwsWLMC4ceNQ2zJXf/nLXzB69Gh06PDTvgBz5sxJzpw588fy8vJ9AKwH0D8cDk+QNLSysrIoFAqVJ5NJX0VFhS8UCi0pKSm5H8A0SVvy8/PvO++888684447duju9NJLL+Gkk076avPmzXvkwrd8Zz7TakmeXF7IGawFJJWZzuKWZs2avX3FFVcMuPzyy38yv6aiogIDBw4smT9//sXJZPK+dI9Psn04HB4Xi8V+BaB8w4YNTySTyX/WNNeQ5P8BiEu6Kt3fZ+0cyZcB3CPpJdNZss3pWBiV5JmiK2eh/rHNmjX7Y0VFReu8vLzvN2zYcBdSn0dGG7aQ3APAi5L2NpnDy0j+DsAhks5Ma/8cGIe4LhqNvnP88ccP6NevX9rHkISXXnoJ77//PsaOHYsuXbrssM1tt92Gk08+Gds65ZSXl2PGjBmJ+fPnbywvLz8YAMLh8OPBYHCfIUOGRHr27Olr1aoVfL7UhdXi4mJ8/fXXmDt3bvHSpUtRVVV1UTAYvPPzzz+Pdeq0Y3fDZDKJtm3bFq9evfogSbX3hPUIkjEAz0k63HQWa9eR7BqPxz9Zs2ZNJBzecarp7Nmzcdxxx325cePGPVz43YUkTw+HwydWVVX1qKysjEui3+8vDgQCL5eVlT0G4CXbtSi7SN4J4ElJ75nOYu0cyVg8Hp/Tp0+fva+88spo9+7d8emnn2LixInFixcvXrRly5ZhDfll2imQHBMKhYb4/f52yWQymkgk2gO4VdLrAN7JhQs1DYnkCAA9Jf05rf0zvMJ6IoD3JX2V9kGaOJIBv99ffOONNwYjkUjGx3vllVcwe/bs5L777psYPHhwpFOnTth21XbOnDno3bs3AoEAFixYoNdee62koqLi1bKysnMAHJKXl/f4kUceGRo6dKh/2yC1NqtWrcLDDz9cvGbNmlhlZWWtBV99+/bduHDhwmN+PtXAanpI9gHQRtLLprO4geSRBx100BPz5s0rrOn54uJiNGvWrLKiomKHuwkZ/M5QXl7e9ZL+2KNHj2Tfvn1jHTp0QEFBAb744gts2LABkjB37twt69evLy0vLz9H0vPZ+v2Wdzkdrs6X9FfTWbIhPz//7uHDh5/9xBNPhKt/PlVVVWHkyJGls2fPvqu0tNT12/Mk9wyHw/dUVVUN3m+//ditW7dQy5YtkUgksGjRIsRisYr//ve/iUQi8WNZWdmlXu6k1tRkOmB9DcDtkmZlL1LTQrJnQUHBezfccEP92lXVobS0FFdeeWU5yavz8vIulLR727ZtNxcUFPgqKyv1ww8/aNOmTZG8vLxXy8rKbpM0h+TwUCj0zLhx4yIdO3as9++qqqrCxIkT9fLLL3PAgB2ngZWUlKBVq1ZlxcXFe0pakY3XZ5lD8lwA/SR5smc9yf6dOnV6bfny5TVOzVm2bBn69Omzsbi4uChLv69LKBR6vUuXLruPGTMm2qxZs51uv2zZMkydOrUkkUjMTCQSp5u+BWo1biSbA/hSUlb+vZpEMhwOh9cuWbIk1rlz5x2eX7JkCfbff/9NJSUlu7k5xTAQCIzz+Xy3H3nkkcFDDz3UHwrV3ERMEj7//HM88cQTxaWlpW8mEomTJW3Z2bGd/18nAGiF1HJrz0oqzfqLyGG26CpzzWOxWNb+DiKRCEj6k8nkPWVlZW3Ky8tP/vrrr09ZtGjR7xcvXnzu+vXrh1VVVcVLS0uPdgaru+fl5T1x7rnn7tJgFQD8fj8GDRrEK664AjV127rrrruqAoHAvFwZrJJsQfIZ0zlc5PXz9f1169YVz5kzp8YnJ02aVAHgX9n4RSQ7B4PB+cOHD+907rnn1jhYlYTqFwT23HNPXHXVVdEuXbocHQqFZmayaoGVQvJukvuZzuESL52v7QsKClTTYBUAevTogUAgEATQ0q0AwWDwxoKCgtsuu+yyyGGHHbbDYLX6+UoS3bt3x5VXXhnr1avXr0Kh0DyS8dqOHY1G/y8cDq8cNWrUX6644orrhgwZ8o9wOLzG7/ePcuv1NEUkjyc5Lt39Mx2wHgHgzQyP0dRVZLO1ajKZhCQf/rfUxuOSXpA0FcBNADZKSmzbPhwO3zdw4MBQ165d0/p9gwYNwqpVqzBs2DC8/fbbSCQSWLp0KcaNG1d+0003rd+0aVNak6ObqAiAg0yHcNFkAH80HcItkpIlJSXjjz322JI5c+Zs//BJJBK46667klOmTNlSUlJS67JvJNuT/F04HH4wFou9lZ+f/1Y4HH6Y5NkkO1bbzh8KhWYeccQRRYMHD/bXVmg5c+ZMzJ49+yc/C4VCOOuss6K77777oYFAwFYnZ+4AAFm5u9UIrUWqM5AXbN26dWtebc1vysrKkEgk/ADq1YyIpJ/kiGg0Oj0SiXzn8/kqSSaDweDmWCw2LxAIXEGyVbXtR0cikQsvuuiiaMuWNY+Jv/32W9x5550/+VkwGMTJJ58c6t27956hUOgp1nCyh0Khs1u1anX10qVLw9OnT4/dfPPNvjfffDP+9ttv5+fn508leUh9XlOO6ASgW7o7Z9rpynbNAb5Yv359JJlMoq55o/Xx448/IhgMri0rK6twTo4Bkt5xnv7JN26S7YPB4BFHHHHEju206ikQCODss8/G5MmTq4YPH15aXFwcCYVCWwE8XFpaequkHzJ7RU1KBYC5pkO4xSn48coVmxpVVVVN9/v9HD58+KSWLVtG27dvn1y0aFEQwMclJSWnSNqh9SLJfSORyJ3BYHBgjx49qrp06RJr3rw5AGDDhg2Dly9ffsLixYv90Wh0Xmlp6UWBQODwNm3adBk6dOhOO33U9p4QCARwxhlnRG+55Zb/I/m0pCVZevm5aCFS3XM8xyn48cS0EUk/FBUVLXn22Wf7nHjiiTs8//jjjyMSicwvKyurs7MXyZHBYPCB3XbbLXzIIYfEO3fujG3FxVu2bImvWLHioIULF/b56KOPrgmHw1MTicTNeXl5D5x55pnReLzWi6S1nq8kccIJJ4S++OKLgYlEYjSAJ6s958/Pz7/xySefjP589Z5+/frhzjvvjF5yySUTAQyr63XliJWo55eSmmQ6h/VMAK/W9CGQS8Lh8JoJEya0bNOmTcbHmj9/Pp577rlXS0pKjnQ6aRVLygMAkhcAeGTbEjo+n+/K/v37XzNmzJiaJ+LsgnXr1uGWW24prqysLJCUzPR4VuND8iAAMacC1tOcNYf7AygCsEzSshq2YSAQuMrn81151FFHhQcMGMDa5rQlEgnMnz9fM2fOLJPECy+8MFxXg5DPPvsM4XAYtd0GfeWVVyrffPPNx8rKysbu4suzcgDJZgBOk3SP6SzZQHJoQUHBCzNnzowOGjQIQOo2/Ouvv45Ro0aVbN269ZfVLs7UtH9eKBR6JBQKHXP66adH99hj54t9FBcXY/r06aWLFi2q+sUvfhE8+eSTd3phZ8OGDVi6dCn69+9f4/PLli3DlClTViQSiY7bVg8guV/btm3nrVy5ssar/CUlJYjH41XJZDKc68t/ZkOmc6jOR2rh3ZwesCaTyWnvvPPOuFGjRqV9pXObOXPmlJSWls52KkQJZ2oAAEj6W/VtI5HIL7t3757xYBUAWrRogVAohMrKyj0ALM3GMa1GZxCAFgA8P2B1vnTt7MOPoVBoSvPmzU8855xzIkVFO69rCYVCGDhwIPPy8iJvv/026tPNrnv37jt9/uCDDw68/vrro0n+3ktrbVpZ0xzAhQA8MWCV9CbJE4cPH/5wx44dQz179uSiRYu0atWqkq1bt55Wx2DVHwqFnuvYsePQs846K1rbF8vqYrEYTjnllMjnn3+OoUOH1rl9UVFRrYNVANhjjz2Qn5/fLJFIDMH/pkKGotForRd4wuHwtvV0A6j2WW6lxxZdZUFFRcU97777btXmzZszOs7y5cuxevXqaMeOHa+ORCI/RqPR6wCM2PY8yWD1OTRVVVX7ZOOq7jbt2rWrAuDVAoY6kexMcprpHC6y56sjEAhcWlRUdOIFF1wQq2uwWt3q1avRq1f9ulFXVVUhmaz9ZkVBQQGaN29ejlTL3IyRDJD8JcmTSR5c03w7ryH5L5I7LlrtDQF47HyV9EJxcfHuS5YsGTV9+vRxy5YtG7l169a2kl7b2X5+v/+SVq1aDT333HN/MlhNJBIoL6991sTq1asRjUZRn8/JZDJZY/HxNiTRr1+/qN/vr75O95IVK1YEVqyouS559uzZyM/PX+7lZi27guSZzp35tGQ6YO0H4MMMj9HkSVou6e+PPfZYSbpTLMrLy/Hkk0/isccewzfffBNftGhR/oABA8bH4/ELqn3wrEbqFicAIJlMhmtaID1d4XDYB8CTXZ7qKR9AX9MhXHQLgJzvukSyu8/nu+6ss86K7er5s3Llyu2NO+ryzDPP4J13ar1oBADo3LlzHrIwYPX7/aNisdgPPXv2nD5ixIj7OnTo8Go8Hl9O8uBMj93IHQgge2+CjYikpQA813VJUpWk1yX9S9KbdU1BI9nN7/dfc8YZZ0QDgQAkYcGCBbj77rtx9dVX46qrrsLkyZOxePHiHfb97rvvdugMWZulS5di8uTJO92mQ4cOvlAoNKjaaykOBAIPjxs3rrSy8qcXUDdv3owLLrigePPmzTfVK0A1JH0e/cLZBUD93kBrkNGAVY5MjuEVFRUVf1q+fPk3M2fOLN/Vv5LKykpMmzYNhx12GI477jgAqdsPL730UrSwsHAYgMHOpj+5Qubz+RKJRGLHA6YpkUgkAeTyrckSeLvoSl6dn0yyjXNVsc4P+HA4fNPhhx8e2m233Xb595SWliIajdZr2/oUYsbj8RCAQufuyW8CgcBNsVhsdn5+/nuxWOz1QCBwI8nhJGttdkByeEFBwbRZs2a1+PTTTwuef/75+DfffJP/z3/+s1M0Gv13PB7/rqioaFk0Gv2z093HS94FsNP1MZsyr52vJKN+v/+PRUVFS2Ox2MaioqLFPp/vHKfNe42CweAlAwcOzGvRogUA4NVXX8X8+fMxadIklJaWori4GNdddx1eeOEFzJs37yf7bt26FYWFNfYR2UF9ztfCwkJIak0yQnJsLBabWVFRcdyLL74YjkQiaNOmDQ477DCcdtpp2muvvYq/++67xyQ9Up/fT9Ln8/nObtas2ZckKwOBQFlhYeHTJPep1wtoGr4C8GW6O2c0h5XkHwA8LmltJsdpikju7vf7x0YikT0TicR3AP6ZSCQGz5079+1169Z1GD16dCQWq/ti5Y8//oipU6eib9++ePjhh3/yXCgUwoUXXhi54YYbzgXwFoDrAWxfiNjv9y/+/vvvB7dq1QrZsHLlSj9Sc5JzktOxzZOL6gMAycMAVHqpaxnJjoWFhVOi0ejALl26JH744Ye8Zs2ardi0adP5kt6oYfvd8vLyhh900EFpfVkPBAI7vW1YXe/evev8sCwrK6vy+/2DfT7fpa1bt/b16NEj1q5dO38oFEIikcDKlSuHLF68uPjHH39M5uXl/aWysvK26g0HSLKwsPDuadOmRQ4++H8XU0ni2GOPxcSJEwOzZs1qf80112DatGkTHnnkkXEkj5A0r8ZATYykM0xncAvJNgCOkfQP01mygWRBPB6f279//65XXHFFdK+99sInn3xSOHHixL9+/PHHvyU59Oe3zkkGA4HA6YceemgekOrQuGDBAixZsgTbvnD6fD6MHj0a/fr1Q9++fdGrVy/k5+dv23+n03Kqa9Wq1U7nsAKpaT5VVVUFeXl5azp16oQDDzwwv0OHDtg2rWjdunX49ttvMWfOnKr169ejsrJyfj3/bhiPxx/v2rXrUXfccUds6NCh2LBhQ/Chhx4adf311/+a5JGS3q7XC2nEJD1c91a1y3SVgGUAjnJuXeSMcDg8geQtJ554Ivr16xf+7LPPyv/5z38mAfy9uLj42mAw+Fe/33/KYYcdFhowYIB/28lT3dq1azFnzpzyefPm+Q4//PDA888/X2N71JkzZ+KMM874aP369WMBfCdp/bbn/H7/1QMGDLhq9OjRGRderV+/HjfffHOJ0wPdU9/qrRSStwDYIulm01mygeTu0Wh00eWXX978oosu8ufn5yOZTGLGjBk4/fTTS7Zs2TLy5ysikDxujz32eGj8+PEF6fzOxx9/HB06dMChhx6acf5Vq1bhnnvuSXbo0KHimGOOCe1snt2qVavw3HPPlXzzzTerEonECEmLAYDkHs2bN//oxx9/jNZ0dWjjxo1o06YNtmzZgkAggFdeeQWjRo3aWFpa2sbOq2vcSPYD8ICkX5jOkg3xeHzyyJEjx06dOjVU/W53tdasfy0tLf3JlCWSfYuKiv597bXXxgFgxowZOOigg3DjjTfW+DtOOeUUlJSUYMiQIQCAhQsX4v3338fZZ5+dcf6tW7di0qRJ8Pv9Vaeffrq/devWO91+xYoVmDp1avGmTZveLysrG7GzTlkkR3br1u3Rjz/+OLZu3Tp88MEHyMvLw6BBg/DWW29hzJgxq4qLi9vn+mdzNjqteGpSeF1Ijtx9991vevfdd8OdOnXa9uPgDTfcgEMOOeS85cuXf51IJM4hOfm111677OWXXz6moKAg0a5dO4RCIV9xcXHVihUr/GVlZSI5paKiYl1eXt4Vfr+/xvuMHyxYAFaV9WjTLPiftVvKw9Ggb1NewPfhliscyw4AACAASURBVLKqWRJmLliw4MqRI0eiPlWTOzN37twKki8DGEZyqaRvMzpgE+R0zLlA0lmms7jkJ6tONHXRaPT/zjjjjMJrrrlm+zc9n8+HY445Bo8++mj01FNPnUxyj+rTlvx+/wHdunVLe6H5jh07Yvny5fUasFZWVoJkjV9Ev/vuO0yePBkjRozw9e/f/ycf4DVp06YNzj///Oi7777b9dlnn32H5GBJCwEUFBUVVdZ2K7NZs2YgibKyMuTn5+PII4/E/vvvH5gzZ87xAJp8gSHJmQB+J2mN6Swu8CG1NnSTRzIaDodP+/Of/7zDv3W/349bb701cuCBB44jeY2zXvQ2vTt27Lh9h02bNqFv39rLDPbff3/MnDlz++MOHTpg+vTp9brdX1VVBUkIBHYcFpWUlODvf/87unfvjhEjRvjrs+Z6+/btcdlll8WeeuqpAQsXLnyb5CGStta0bVFR0UWXXHJJ7IwzzsBrr72GQw45BMXFxTj11FMxfvx4tGnTJv+LL74YCmB2Tfs3FSQvArBS0pN1blyDTOew7ikp7fkITVGzZs0m3nvvvdFqg1UAqWWhpk6dGgsGg9eQ9Ev6oKys7MSqqqr4hg0bhnzyySe//+CDDyZ89tln52zdunX/ysrKwvLy8gsBPPryyy8HP/vssx1+1/r16zH5H5Nw55hOwecm7FdQUYXgQ+f0aHnxrzsecfi+zW8MBfiOn9j4+uuvZzQI2bhxI95+++3AbrvtduSBBx74TCwW+7xZs2ZvkKzfbHXvKATQw3QIt0j6P0m1dnpqaiT9dsKECTUuJXfUUUchEom0BrBv9Z8Hg8EuRUVFab/v9erVC59++ilKSupe+3ratGn46KOPdvj51q1bMWXKFIwePRoDBgxAfWsrSOKggw7iKaecEs/Ly5tNp9f8ypUrg2vW1Dxee//999G6dWtUn540YsSI/EgksvN7n03HAUjN7fccSe9L8sr/pw5FRUVVtRUs7rPPPnDmsbb42VOF+fn52+dvx2IxLFu2w5LK233++ec/mWPevHlzxOPxne6zzaJFizB16tQan3vyySfRrVs3jBw5cpcaBPn9fowZMybco0ePvUKh0N9r266qqmqPf/zjH2jVqhW+/fZbzJw5E2+88QYWLlyIN954A5FIJAJg5wvPNg1dAaQ9hzHz1kw5hGR+cXFxj6OPPrrG5w844ABEo9EIgD23/UxSuaQPJU2TNEXSk5I+k5QkOTqU51u4R8tAcsigQ/Hoo48ikUggmUzi5ZdfxqCD++PwHjHs3SaKZDJ1kahLywiO6NUc1x/XJTLjol7hk/q3aPXvt94KfPPNN2m9pmQyialTp+Kkk07iypUrY++9917hmjVrwhdffPHAWCz2Psldr0xpujYC8MTcvlxQVlYWr21Rfp/Phw4dOlRix97kyUymQcXjcey777548826O1Ink8kar65Onz59+1y7dPTp04cHHnhgLBQK3S9pU15e3vSrr756h2LPyspKXH311Tj//PN/Mihev359srKyssYrPU3Qm6g2r99qtLZs3ry51tasxcXFKC8v92PHot9EeXn59iuuffv2xb333lvjF8a1a9fiiSeewP7777/9ZyRx6KGHYvbs2ajrvK/tKuxHH32EH374ASNHjqz3l8vqSGLMmDHhvLy840nW2PGqsrKyMh6P4+6770b1KYQdO3bEzJkz8dVXXwUAeGE6wBIAy9PdOaMBK8krSXq1j3NNfCR3+g0rEAgIdfy9kmyZH/a/sHth8KG/j92r4MGzugevOrI5Jt14MeL5MUTCIVxy/uk4vmclzh/6v8/bPxz+02+nhdEAzhnW1nf1yPa4f/J9qG0tuNpUVVXhX//6FwoLC3H//fdvPxmj0SiuvvrqwPHHH98sFApN2KWDNmGSPpF0qekcbiE5guQA0zmyJT8//4cPP6x5Vb1EIoHPP/88iJ+9OSYSiaVr/z975xkeVbX18f8+02fSK2mQhEASepeiFBWRjigqogiIVAui16tXvV4Vr71RBASUjoIURXoH6UVqKiWk9zp9zjnr/TAJUhKS6cB7f8/jB8nZZ++ZOfvstfda67+Kix3ySAwaNAiHDx9GZubfUTOiKKKiogI63d/rbefOnW8pMJCXl4dLly5hwIABjgwBQ4YMUVSrB8RXVVW9vHLlyoyBAwfq9+7di6ysLGzcuBF9+vSBVCrFtGl/T2GLxYKFCxcaLRbLGocGcIdARCOJ6J4szcoYi2WM3RPhSUSUK5PJUtavX1/r35cuXUoajeZALQU0UrKzs69ZudHR0YiIiMCjjz6K672SJ0+eRO/evdG1a1fcrKvcpUsXaLVaHDt2Y/6TxWLBmTNnsG/fPpw+fRqhoaE3GLs17Nq1C0OGDIFMVqdQR70olUoMGTJErVKpPqzt7zKZzDBlypRaDWJ/f38MGTIEsHoA72qIaA4R/WFve0djWKcDWADgXtmt10eVSqXK2LlzZ9wjjzxyyx/Pnz+P8vJyAUCd/gfGWBOljDs0qF1Q0JSHI+QKmdW27dLUB12a+sDCh0MgQCm70eblOIZR3a1B3uU6Hn+cLsXFEhFKKdC7uQpvDWiET+fMQt9+/dGzZ8963RYFBQVYsmSJUavVynJzcyW1TcZp06Yp1q1bNx7Au/V9Mf/jrqA/gHOwSgHd9RgMhm/fe++9/2zdulV18/M+b948keO400R0g8EqiuLxixcv6gHYlXQFWKVtRowYgUWLFmHSpElISUnB4cOHwfM8zGYzIiIi0KtXL7RufWsNjoMHD6Jr166Qyx0riqdQKNCjRw/pgQMHXjWZTFMYYx23b98+/vDhw1MtFkuQVCpVde3aVbp69WpJTV96vR5jx441mkymw0T0/14/+y4gBsAzABZ6eiDOoLy8/LXx48dvCg4OVvXu3RuMMRARNm3ahDfeeMOg1+v/WUuzvwoLC1UmkwkKhQKMMYwYMQK7d+9Gt27dEBAQAJ7nYTQa0bNnT/To0eOWG0ilUowaNQrff/89goODERsbi7/++gu//fYbWrdujVatWuHChQvYsGFDjWF4jZycHFRVVSEx0fFIsfbt22Pt2rUdGWPRRJRx/d8kEonpdkmX0dHRBKBhenr3MI4arDzuwaSras3DBzmG+7yUkl4WgdqaedGHAdKqygpx4sSJOHbsGIKD/z791Gq1eOGFF3SiKH5JRLX6PRhjUUoZd2zig+GBI7uF1hp3JZNyqG0fR0TQm0SczKjCx3/kYciQwRg1ehBKSkqwcP4cyMxVmP1sDD7dvBv79u1F7959qGXLliwwMPDars1oNOLq1as4dOiQPikpCTzPr+zTp88IjUZT684tMjISZrPZnzE2EsAFAEn3cj1kxlgPAE8R0SueHouLEAHUXRbmLoPn+ZlHjhwZMXDgwJYzZsxQdejQAbm5uZg5c6Ywa9Ysg8FgWMQY6wurmzGp+iRuX05Ojqy8vBx+fn529922bVuUlZVh9uzZaNOmDTZv3oyOHTuC53n8/vvvmDp1KsrLy3HfffddCwswm804c+YMJk+e7JTP365dO9nBgwcHVv8vLwjCkvLy8sWw6gn7HjlyZH1ERESHIUOGwGw208aNGzmpVLqtsrJylFMGcAfAGDsAoB8R1R9UfPfBANwzSg7VpVlHDBky5MewsDBVfHw8zp07x5WUlJj0ev1GuZQ9q5Rxz1sEqhIJGQBOADgvl8v3nzx58qHu3bszwBoX2rdvX/Tp0weFhYXgOA4hISG3PaSJiIjAU089hXnz5uH+Hj1w7vx57Ny5Ex07drx2zdGjRzFo0CCoVKprBurp06fRtGlTm+JW60ImkyE2NtaSnJzcnTGWCUAFQA7A5OXldXLfvn0t+vTpU6tNtnPnTi2AWysj3GUwxj4AcNzeU1aHZK3uNRhjkTIJmyTh2NTIAAXXJdZb1SJCI4sPUyPYWw6phMEiiJi3pxA7krUY/+JEdOjYEampqZg1axZvMBg263S6x2qTnmCMqVVyLumFXmERz/ZoZPNGQWsUMPirs1Cp1di2c88NrgtRFDFl0gSkH92Ejx8Lx84LZfjkjxyBF2EgIplKpTLzPM8ZjUa5QqFIN5lMC0RRXAIgPCAg4FhBQYG6tszIzZs3Y/yYURTrT9q0fD3KtLxSJefSDRbxF16g+URUYOvnuJNhjA0CMImIBnl6LP+jYTDGVFKp9AOFQjFVr9erpVIp5DIJH6wmvbdKSqII6EwCcspMapmElUo4dsIocN49evToNmzYMPt9fACWLl2MstIyXEhKhjUn4m8uXLiAtm3bYsyYMYiNjcXxY8dwYP9uVFTp8dlnn9ca22orPM/jn/98k6SMKiwC+cgkjCcAvEBSpYwrkUrYqUqDkAyr0XMFwPabT3budhhjRgD+RPS/ONa7gOrqTT2VUu4TiyB2VMo4oWWkxtIyUuOtUUgYA2DiCVeKDLrz2TqhsNKskku4TJnKK/Ldd99VOOKZ2LZ1MwrTjiGjjDB3/gKMGDHihr//8MMPWL16NbKzs/HUU0/h0J8HcOjwIQwYONgpMnYAsH37dtq1Y1u5hRe8OcbAcRAFkSQABC9vX1lSUhK7OYxo586dGDJkSJnBYGh0vQbz3Qizlj7fRkS1Z7fVgzNkre56GGMqpYz7VCFlEx5tG8ieui9EERuiqvVaqUSC1/qFYVgHAzYdWYlTO1dCyQR0Chdpf4qhr1rOrWCMTb45rkol477qFucbYo+xCgCCSOBFwuSpL98SZ8NxHL6dORsRYauRXWpC31YBCPWRS15eli6YLWIni8VCsMqjZOn1er76M8s5hgd5i1m2YMGCW059zGYzPnr/XbzQ3ZcNah/kDQA6o4CUPH2LTaeL39p1oextL6Vku84kfnAPuReLcI+4y/8/wBh7yEspeYsXxAceSVSha9NQtIjQINhHLsVNLn9BJFwtNoYm5+oHrjteqDt48KCsU6dODS6zejO5ubm4dPESPvnk01uMVcCa9ezj7Y2VK5aBRBH3x/vjtYeC8N3uEqcYq4DV1emtUbFPH4/wa9vYC4wxOQCIIiGzxBSckqfrl5Sj77PjfKnFIlCW1iiYGWO/3GPG3WbcQ1Jt9yrMaqmO1ii4D7yV0sBR3UM1D7b0Z4FWAYDaFlsNABjMAo5frmr6+eYcfsOG9Xjyyafs6j87OxsHD+zD98/FYvKyDAwbNuyWawRBQGxsLA4fPowvP/8EA9sGonWUFxpSAKihaDQa1rmpn/9Hw6Ogkv/9HqjQ85Lvd+ejc8f2+HDGfzFw4EBotVosXbpU+Oqrr4wGg+Gxu91YreYvAHZLZjpaOOATAO/dzW5ixlg3lZz7pXOMd+DbQ5qo/TX2H7roTQJmbs82bj1bqjdaxOeIaHN1Hw94KyVbf32lldpXbd8ewWgRMWJOGrbtPlCnDt2op0cg2nAcQzpYE/u/3pJl+uOv4m06kzD0+usYY+3Ucm5NfJg6bHD7QM3cvSV4/oUJmDxlKsLCwnDo0CH85923Iddn4aNh4ZBwtwaCVxl4/HG6RFywJ9ckEuYYLeK7ROS8OrH/w+lUh3ak3u0bDMZYsEbBLVLLJQ+O6xWm7tc6gKkVthmBKw8XYNmRSrz62nSbQwMqKysxZ+Y38FZJ8OOKdXjggQdqvW7YsKEQMv7E20OjIeEYckpNmLjsKt59/yOb+rsdH/z7Hfw0LhaN/Oo+eRJEwtGLlVhxqEB7IUdnMlrE0TXvpv9x58IYawWgIxEt8fRYHIEx1lij4FYEe8vbvzEgStMxxtvmbPsKPY/RC9Jx3wMP4cEHH7KpbWFhIebNmYnX+gajTZQXJizLRn5R6S3XnT17FsXFxRj19JP49qlGiA5S4t11WQht3RedO3e2qc+62LdvH1jOIbw5oPaN8rFLlVj7VxXOZlaBwGDhed5iNv1h5ul5Iqp0yiDuYhwNzPgHrHE2dyUyCTdRo+B2vTc0OurzkXEOGasAoFZI8NbgJsqvnokLCNBI1yhl3MeMMU4l51a8M7SJ3cYqYE3C8vdR3bbMnCgKuN62nPJwhEKjkDzEGHsEsNYqVsq4/6jk3KHp/aOafj+muWZAuyAsGBODrMO/oHOHNvDz9cZL455C98ACfDi0dmMVALxVUozsFsqtebmVqn0TrylqOZfCGLNPp+d/uIshAOI9PQhHYIw9ppRx6YPbB/Vb80orzWOdgm02VgHgmW6heKaLD2Z/9/UN2f71kZWVhTkzv8bQNhrEBMpRm35yDaLFiHbR3tfmUKC3DBVVetQl7WMrJpMJOoMRgV63f69IOIbuzX0xZ0xzr6+eiQsM8JKu0Sgkqxhj9gfx/g93EA9gaL1X3cFIOPa0QsqSRnVv1HX55BaaTrE+dklD+aqlmP98U5w8tBurViyDwVC/k4CIcOrUKcye+Q0m9QpC31YBCPSSgQQLzp+/tQJ5mzZt0KhRIwi8CZH+1kI80QFS5Ofl2jzeuijMy0ZMYN2byy5NffDZExHYMj0BW6fH47dXEqQPtwx4VCnjLlXH4/+/xtET1ioAvq4sF1adANUSQEeVnOsul7CWAJQACIDOaBFPmXg6AuAkgPSGjkUu5V71Vkr+O39cvDoqUOn0cZdqLZj0U6quoMK8I8hb9vCvr7TysnWi8gKhoMIMg0WEIIhYebQEbfo+h8+/+OqWa3U6HaLCG+HHcdEI8/u76tXvp4oxc3v2Xq1ReFgt55ZF+CuGfDUqThPi41iW8vUQEbacKaXPN2VqjRaxPxEddNrN3QhjbACAnkT0lqfH4goYY0sBbCCidW7qLwRW4X4vWMOPjAByYE2Astlqk0u51zUKyYdfjGyqbh3lHDW9HedL8eWWXLRp3wm9+zyIwMCbdcutlJSU4M8D+3Dq+FG81i8cj7YJwKH0Ciw8QTh15sItlebS0tLQuUNb/PpSPHxUfxuUI+elY+jT41CXfqwtXLp0CVvXLsWyCbbpietNAr7blm3afq60yGARe9ytVe0YY1IAR4moY70X34Uwxh4DMJyInnNTfyoArWHVLlbAmqBZBuDs7cqK1oVcyk1Vy7nPZ41urm4e5pwEd51JwMwdediXWoWu3R/AfbXIWJnNZiQnJ+PQ/j0wVBbj/aGRaBn5t1t/0f4CZHLNsHHzthukqioqKjBi+DA0oYsY38uqyHMovQJzD5kw9dXXHR47EeGLT2fgw0FBaGXj++vIxQr8e+0VvcEs/tvMi7caAHcJjLHvYF2D6heyrq39nZp0xRhrqpRxLxPRC35qKbWM1HBtorw0jQOVqJGC0pkEXC4y0NlMrTYpR8+ZLKLeItC3vEgLiKiorntLODbKRyX94ccXE9Th/o6VNL0dFXoe4xYki80bqdgnT8XVa60KIuFwegWOXqpESp4eF/MN8FFJoFFIwIuE7FITlEoVtm7bdoMbkud5jB39LIpT9uM/Q28M2DaaRfT7/LSRY2xbXCNV35nPNVNfHzvjTI5crMBbv1zWGS1iHyI63tB2jDEOQHMAHWUS1kEuZYGMMbkokt5oETNEwkkAJ11dfpEx9hysGcfPurKfexXGmBrAUz4qySiLQO1FkbyaBCkNPiopOA7MZCHKKzdxpdbkvcsWgXYbLeI8Ijpb373lUm66n1r60YIXEtS3c3/bQ4nWguUHC/H7X6Xw8vVHbEwMQqrrhJeXFiMnMwMFRcXo38Yfo3uEIMjbusiJIuHfv+WC/Jvh21nfo1WrVhBFEVu3bsXYMWNQXlaMNlFe6BLrg/hwNRLC1Pj5SBEuidEY/sSTDo97zS+rkKjIwvjedcvh3I6VhwqEH/bklhotYid7jVbGWBiAUFgNHAuACgBX3FHznDGmBFBBRK57id/D1CRAaRTci4yxHkazGBnqK9cH+8hILmHMIhCV6Xhkl5rUChlXyBiOaY3CTwA231Q+9RakEvaCt1I6c9H4BHVEgPN/nvR8PVYfK8G282WQSCSIaBQMuUyKysoqFJaUIT7CByM6+qFPoh9k0hsdyZcL9JixKR/MKxRvvf0OWrRogeTkZEyf/hoqysvRJUaNxHANEsLVaBGuxlPfp2LKK9MRWv1OsJeMjAz8vHQB1r0UD64Oz+XtyCs3YeKPqfoKPf+x0SL+154xVG/yImCNGyZYi27k1Pd7OgvG2B8A5hPRxnovrq39nWawMsZivBSSRSJRt6EdgyRPdA6RNfSBT87R4ecjhYa9yWVMKmG/6kziK0RUdtP9myhk3IWFL8RrmjVyvaxZYaUZz89PxtfPxCExovbg7TKdBRtPlWDdiSIEaKR4sKU/EsOtFa68lFbjMqfUhJeWpuGNAVH4aGMeetx/PwYMGoqioiIs/GE+QtU8vhgRgdrcoxMWJfM6E9GCF+Jl9rhPbeFAajneXXO5wsRT/O1UBKpfll01Cu51E0+DvJUSS4twDbWK0nj5qKRMwgCLQMgrN1nOZGr1FwsMSo5jFbxA8ywCzSOiPGePnTHWB0BrIprp7Hvfy1RrC78hEo1tHeVFQzsEeSWGqxEZoKjV/aczCUjP1+PYpSphzbFCM4C0KqPwKYDVdShsPOanli5fPCHR6cbq9RgtIn7al0urjhSy/m2DIZdyCPGRIDFMjVaRXlDK/174LLyIfSnl+PVYIdIKzOCkMmjUapjMJgR6yfBIghJ//FWC/m0DUGUUkJKrR1q+HonhGiTlmfD2O+/B29vb7rFWVlbis09m4JcpCdcMaHtYeahAWLAnN89gEVsRUUV91zPGYqQcG61WcA+ZeWrDGBQBGqlZKuFIEAlVRkGqMwlStZxLMVnEAyaeVgE4TC5YaKpLeS4mopHOvve9DGNMxTG8oJJz//BWSgOe6hqiadvYizUNUV07DLoeXiBcKTLgfLYOvx4rrMotM5uqD4bm1Fa0gTHWWS3n9i2ekKhqHOR87+X1GM0Cxi9MQXyYGn1bB8BPLUVsiArym4xUCy9iT3I51h4vQk6pCe2baCCVcsjXSaE1iQj0kkJBBviopOjS1AcpuXok5ehwqcCAiEAVNKFN8ezosXaPk4iwcP73eKiJAc90s9/wLaw0Y+wPyfpyPf8yL9CP9V1fnYg5WCXjHpVKWA+DWYxTKzizXMqJAGC2iBK9WZSqFVy6mac/jRZxC4BNrspLYoy9A2ArEZ20q72975HqL2IGEb1p1w1uvR8n5dhUiQSfju8VrhhxX4jkZvH8hlKh5zF3V05N8tNoItpU3QfTKLg/n+3RqMvYnmFuU0jYerYESw/kY/HExBsmEhFh85lSzN6ejR7NffF45+A6jVqtUcCB1HL0bxsInUnA9nOluFgsWMXKLRYcTqvAkI7BGN877IY+LhUYMHlxKpZObHHbxAxnMmt7tnn9yaLdepM4oLaFijH2mEbBfa6SS8JGdgtV9W8TwAV43X7RJSKk5Rvw67FC4/ZzpUwm4XZoTcKrRHTZZR/kHoMxNgHAn0TkFD2/6+fs8E7BshFdQmS2eix4gXAgtRwL9+Zq8yvMKTqT+DQRXbquj2CljEufNbqZr7PCAOrj6y1ZKNdZ8OETsbf8TRQJG04WY9G+XDQJUuLxziHolWANBS2oMEMmZagJt9l5vhQdY7xRExtvNIvYcb4U8/fmIySyKSZMmGDX+IgIixfOR9uASkx5yL7T1euZsSHDuDupbK3OJNTqWajeXA7wUkreFATqMqBdINcpxlueEK5GI1/5LZuSCj2PlDw9LmTrxHUnigwGs5CvN4mfE7D8HtVLdQmMsS4AmhLRKifes6tKzq1uFakJGPNAmKZDtJfNMaUpuTqsOFRgOJBaYbh+fa2+v0It51LfGtyk8SOtA9yS31JQYT0Umv18c8SF3io4sOtCGb7ZkoXoYCWGdw5Gz3g/SCW3Di0lVwdeoBvc9QUVZqw5VoA1x8vx3OjRaNWqlV1jPHb0KI7s+QNLxsfdcuJrK1eKDBgzP1lv4qnVzYVRamCMRcqlbArH2OSYYKWkb6sA78RwNZqHqaG56dCqysAjLd+A5FwdbTtbqs0qNVl4kWZWS1fmOzRYJ+OIweoFoICIHNZ8YIx5axTc5nA/RfuPnojVRAc7Z1d28koV/r32st5gFlfpzeJEjuG56GDl7KUTW2hqe2BdBRHhnz9fQvMwNcb3trrsiyrN+HRjJgorzXhvWDQcjfEp0Vrw+R+ZyCwx4t/DopEYoQEvEF5clIIhHYLwWKebS6q7DpNFxNNzLujyy80viES/1Px7dXb3Qo1C8vBbg5uouzb1scs1ojUK+PV4obB4f76JF+htXqTZ7nBB3u0wxrYC+I6ItjjhXo01Cu7XcD9FC2fMWUEkrDpcICzYm2cSBHqr+jclL6Xk98Htg/pNezTKPbstWA3LZ+cl4aW+Eeid+Hd8XG6ZCf/9/Sr0ZgFvD24Cez00JouIkXPT0K5rL9RWMa8+dmzfiuRTB7F4fNwtJ0n2oDMJeGLmeX2Zjh9ORNuu/1v177zSTy1rO7ZnI6+HWwXcUoXvdogi4fiVKiw/mK87n6WrNFjEp4lov8OD/n8AY+wFAD2IaJwT7iVTyrjPJRwm/mtItOqhlv71N6qH69bXP/RmcTwRVankki86RHtN+eqZOLU9yVX28tvJYqw7UYRF4xOuGaOlWgu+3JyJS4VGvDu0CRzZ8J68Uol//JKJFydOQkxMjE1tU1NTsWLpj5jzXKzd74ybWfZnvvDT/ryTerPY7fq1jzEmkUrYa1KOfdC/bQD3ZJcQZUwdEp11kZanx+qjhcYdF8oEXhDfFETMu1PWV0cMVh8A54mosUMDYMxPLef+fLClf9O3BzdR1pWVbi86k4BXl6XrLhcadgDU/ouRzZp0jLHfFWcvWSVGTFiUig2vtUZeuRmvLkvDwHZBGNuzUYN2XLxA0BoF+GnqPhgmIuw8X4avt2Th7SFNUKHnseVsCeY839yuzExHOJ+tw9TFqeUmnkKIyMIYe0gh49YO6xikmvxQhNze0/PruVpsxHu/EWAxVQAAIABJREFUXtbllJmSdNbT3GJH7lct+xRHRM7THbqDYIytBzCLiHY7eJ9EpYw78PwDjfxG399I4sw5e7XYiNdXXtQVV1kWGy3i+mBv2W9rXmmlccbzYgunr2rx3q+Xse7VVpBJOexPKcd/f7+KUd1DMbJbaK0nNDdTprPAWymt9drCCjMmLbmMZq06YNDghtUpN5vN2LTxN1xOOY25o5s6FApwM8cuVeLNny8VGy1iJBGZGGNMwmG8VMJ9M+aBRornejSSOrrJ359Sjo82ZOh5gZYaLOJ0R/VgGWOBAH4jIueout9hMMbGAGhPRK86eB+VWs79kRih6TrjiRiH1XCuR28S8NkfmaYDqeWX9GbxcYWU/bVuWmtlYD0eM2dDRJiyOA1DOgShf9tApOXp8frKi+jXJgAv9g6vNdThZnQmAQyoNawOAA6nV+C9dVno++gAPPDAA/VWvxIEAXv37Mb+PTvx2ZPRaNfEeR4iQSSMmZ+su1hgeEUka2gAY6y5Ws6tjg5Sxn34RKwm0sHY4StFBry75oour9x0Xm8Wn3ZG0RHG2BIAs23JcbmhvSdjWKsn0p/92gS0fHNgY4WrjCqTRcTLS9OMGcVG6dZ/tJE2pMyahRdx6qoWyTk6pOTpkZ6vR6VBgEUgyCUMvmopmodZEylaRGjQtrFXvYvYtGXp6BTjjZ+PFGLSQ+EY1D6owZ8hPV+P/6zLwIopLeq9NilHhzdWXoRKzuH1AY3RvVmtlVfrpMZFUFxlgZkXIZUwaBQSxIWqEOZ3qwuwLsb+kFyVnKsfD8CkknOrvhwZp3L2ZkEQCXN2ZJvXnyzONZjF7o7EtjLGXgYQT0QvOXGI9xSMsWZKGXf0zYGN/Qa0C3TJhK008Ji8OE2fW2YqfblvRMTwziEN7qekyoKUPD1ScnXILjXBxItgYFDIODQJUiAhzJpM0RCJuSmLU62eCQK+2ZqFL5+JQ4s6QnZq44mZ5/H1M3GoK46vTGfBp5tykFxIeHTAILRp0wa1VZzjeR5nzpzBjq1/oEWoBP8cENGg8dvKhEUpVWezdJMBrFLKuFmBXrLnP3+6qaZpLW5We6nQ85jxW4bh5JWqJL1ZfKghcbN1UZ3w9RcRNXLaAO8xGGNytZzb3qWpT5cZT8SqXOFZJCLM3J5t3nCyuKpnvK/mg8djG+xusfAiLhUakZKnQ0aREQazCJEICimHMH8FEsLUN+Ry3I79KeVY9mc+pvePwvSVF/GPgY3xYIuGnyR/vzMHajmHMT3rDrPJKDLiPxuyUCGo8NDDfdGmTRvcXH3LaDTi9OnT+HPvLgSreLw3JAKuSO4+cbkS//zl0lWdSYwB0E0hZdumPByhHtElhLPHc1kbvEBYfjCf/+lAvs5kTab+y5H7Mcb+BPA2ER2wq70nDVa1XDK3c6z3858+1VTlrC+4LvQmAS8sTMGo7qG3NRQLKszYcLIIv58qRrifAq2iNNcSoPzUUsgkDBaBUKK1IDVPj5RcPc5kalGms2BYp2AMaR+EwDpOPraeKcEXmzMxrV8UBndouLEKACm5evz39wwsnVS/wQpYE9CmLknDd882Q+vGt9/ZERFOXKnCptMlOJ+tQ6nWgrhQFRr5yiGVcBCJUGngkZ5vgJkXkRCuQa8EP/RrE3BLPMz17LpQho82ZKQzhsi5Y5qrEsKdVzHkZhbuzbWsOFSQZTCLnYnoVlXoBsAY6w8glIgWO3d09waMMR+ljEud1i8yeFinYJdm71UaeIxbkILhnYLwTPfb2yOVBh6bT5dg/clilGkt1zLymwQpoZRx1lRYs4grRYZryU/hfgoM7xyMfq0D6jxR2XWhDIv25qJcz2PW6Oaw1XAb/u05zBzdHPWddBxILccP+4qRWWJEXGwTREZFQ6FUwmQ0ID/nKq5kZCIuVI1RXQPQo/mNm0+tUUBqnh7JuTpklphgtIgAERQyDpEBthno+5LL8eGGK+dEwpFIf8Uzc8Y011wvyeUsRJHw+aZM0/Zzpel66ybTZskkAGCMBQD4mIgm13vx/1M0CsnSNo29Hv9yZJzalWFwRIRPN17FpUIjfhh3+yx4QSQcTKvAuuNF+OtqFSL8FUgIV6NpiApqhQQ15VmzSo1IzdXjYoEBEQEKDOsYhP5tAqGpw3jlBcKQr89CJOBfQ5qgZ4JtMsOztmfDVy3F6Ptv/74RRMLe5DJ8tS0fFToTwoIDEBjgD5GA0tISFJVWoGOMH0Z09ke3uL91Zy28iLNZOiTn6pCSq0d6gQFaIw9eIMilHPzUUsSHqZEQrkbLCOu8vd3hEBFh6DfntIWVljeVMu7LT56MVXez8XCqoexJKqMP1mdojRaxlyNGK2PsQwAriCjVrvYOhAT4AniTiN6xs31Pb6VkiyPVn2wlPV+Pl5emY+nERIT43rgr0psEfL8zB9vPlaJfmwAM7xQMW2I/UvP0WHe8CLuTyjCsYxDG1+KG+Pevl2uKC9g89jKdBaevatHHhh3jrgtl+GFPLpZOTKzVJWKyiNhwsgjrjhdBKuHwWKcgdIz2RuMgZZ0FA4qrLEjK0WHzmRKculKFR1oHYGS3UNSm5JBbZsLI7y/gu2eboV0T14ZhEBG+2pJl3nKm5IDOJPZ1RVby3Q5jbDqsGnh2JaqpFZIlvRP9nnz/sRjXpv5Wk1lixIsLU7BgfAIa16KVXGngMXdXDnaeL0O3ZtakxTZRmno9AKJo3aCtrV4wB7cPwvjeYbhZ7i2vzISR3ydh9vPN0SrS9s3W9nOl6NHMt84F9mbWnyjE/N15GNQ+CIIIqOQMcSEqtIjQ3JAwWZO8te5EEa4WGxEXqkJCuAYxwUqoqpUMjBYRmcUmpOTpkJqnRyNfOYZ1DMaAtrdf8Pt9dtoS5CPjF41PVDXkVMteiAgfbbhq3JdSdkxnEnv/b77eCmPsQQB+9uomM8b6B2ikv655pZX6dgcLzkIQCRMWpWJAu0A83vnWnAlBJPx6rBCrDhciyFuG4Z2C0TvRr84NYw28QDiTqcXa44U4frkK/VoH4MU+4bdswniB8MTM8xjVPRQj7guxefzns7RQyLgGx5kazALGzE9GvzaBiAtVgTEgxEeO2GDlDWF+1x+ChfrK0SrSCwlh1gQoP7UUEs6qiFNU+bd36PRVLaQShuGdgtG/bWCdJ8zzd+XQqiOFwidPxkpdZazWsDupjD5cn1FhtIitiSjbpZ3VgSMGaySAI0RkczFuxphKJeMuffB4TJituyBHWbQ3F+ezdfjm2WbX/u3klSp8/FsG2jXxwrRHo+DIqUKJ1oIvNmVa4yuHRV9zIe5PKcfMbdlYNjnxloXRlfxr9SWE+Snw8iM3/kwXsnX4aEMGIgMUeO7+Rg1a6G+msMKM9SeLsP5EMcb1CsMTnYOv7ayJCK+tuIgW4WpMeDDCaZ/ndlh4ESO/T9LllJomi0TL3NLpXQRj7AiAaUR0xI62D/uppb/9+kortSsNmZv5+XAB9iSXY+6Y5jec2hxILcfnf2SiV6IfxvUMQ30qE3VRUGHGvF05OJulxTtDo9Eh2rqxIiK8vvIi4sPUmOim5xcAZmzIgFzK4c1Bt6YGGM0iftqfhw0ni9AqyguPdw7GfU196txc1iCKhL+uarHuRBGOX6pEvzYBmNAnHN43vecuFhgw8ccUrJzSEqG+rs9x4wXC6PlJuqvFxjd4gea5vMO7DMbYGwAaEdEbdrT1Vcq4S1+MbBrYOdbHBaOrnStFBkz+KQ0/vphwgxs8o8iIGb9lQC5leOWRSNjrbSuqNGPJgXzsSynHm4Ma44H4v+2HJQfycOxSJWa7MWcjOUeH6SsvYvmkFrd4VnUmAbN3ZGPX+TI80iYAj9twCEZEOJWhxbrjRTh2uRJjHmiEp7uF3jDXRdGaVP1gS3+MqscL5SwW7Mm1/Hyk4LCnNpmOZDIwAHWK89fDMy0iNd7uNlYB4PkHwpBRbERSjg4AsOJQAf6z7gqm94/Cvx+LcchYBYBALxk+eTIW43qG4fWVF/H7qWKYeRFfbMrEO0Ob2G2smiwiSrW2l3R8Y0BjbDlTgsuF1vwGQSTM3ZmDf6y6iBd6h+GLkU3RtrHt0iYAEOIrx8QHI/DDuHjsOF+KqUvSkF9uBgD88VcJSrUWjOsVXs9dnIdMymHGE7EauZR9zxizuWPG2CTGmOMlTe5cKgGYbG1UXV548b+HRbvVWAWAJ+8LgUiEbeesUR68QPjv71fx7dYsfPB4DN4Y0NhuYxUAQn3leH94DKY9GoX3117BnB3ZEEXCljOlKK6y4AUHnt/CSjME0bZ3+qv9IvFnWjlOXrnRS34mU4vn5iUht9yEnyYk4qtn4tC9mW+9xioAcBxDxxhvfDwiFiuntgQvEkbNTcKh9L/DR3mB8NGGDLzySKRbjFUAkEoYPn4iViOTcF8yxqJtbc8Yi2GMbXf+yO4YTLDOWZtRyrgPH2rp7+VOYxUAYoJVGNktBLO2/30At+FEESb+mIJH2wRg9ujmdhurABDsI8cbAxvjg8dj8N3WbMzYkAELL+JqsRGrDhfi3WHRdhurFXoeepNt+vmJERoMaR+Er7bcWHfj2KVKjPo+CbxAWPtqK7wxoLFNHlvGqufsk7H4aUIi/kyrwKQfU3G12HjtmtVHCyGVMIzs6lhBA1sY2zNMFuQt78gx2KVcwRj7nTGWYG//bo9htWqhStI+HhET1zXOtUfYdbH0z3xkFhsR7q/AtnOlmPlcM5e8pDOLjXhlWTo6RHuhuMqCmaOb23wPQbTGy566UoVfjhbis6eaItBb1qCFqoaFe3NRorVg+qNReH/dFVQaBHzweAycmckpiITlB/Ox/kQxvnomDtOWp+OLkU0dejnZy7dbsywbThYvMJiFqba0Y4y9B0BBRO+6aGh3JYyxfo0DFWt+eamlt7vVJgDgz9Ry/LQ/H/PGNse/1lyGIBJmPBFbryvRVir0PN5YeRFNgpT4K6MK7z0W41Bm76Ofn8HKKS1sNqi3nS3F76eKMWdMcxARftiTi42nSvDGwKgbZLYc4fjlSvz396voFueL6f2jsPZ4EQ6lV+DbZ+Pcrijy0/48fsXBgj1VRt4mjS/GWCKA9URk9wJ4L8IYU8ulrPDnqS01rqzkWBc6k4DHvjmH5ZNbYMvZEmw8VYJvno2Ds0ugG8wC3l97BRaBEOwjQ4iP/JpspD189sdVNA1R4YkutoUTmCwihn1zDj+8EI/IAAUWH8jH+hNFeHtwEzjLTS+KhLXHi7BoXx7eGdoEieEajPr+AhaOT3D691of6fl6jF+YojPxFHVzYab6YIxdAPAUEZ23p29PGKxdA71kOzdOb61xdaJVXZTpLBj+7XkEesswf2x8nUlSziC3zIQXFqSgX5sATHs0qt7rBZFwKL0CJ69UXUsQUck5MDBUGXl4K6XQm4VrcWsdor1wf/PahZBrKKo0Y+ScC9fcnTNGxDpFv7E2/virGHN25KCRnxw/TUh0SR/1kVduwlOzL+jNVkktXUPbVdfulhDRry4c3l2Ht0q66+W+EX2Gdgz2yIQVRMLwb8+hSZASKrkEM56IcVh8uy4MZgGvLktHbpkJv09vXa90DWB13xVVWZCSa03CvFRogN4s4FRGFdo38UKEvxKJ4epriSX1jd3Cixj6zTnMer4Z1hwtQlq+Hl89EwdnyhEBVsPiX6svQyXjkF6gx3+GxzikVWkvRrOI/l+cMRosYqIt0jnVYWkvE9E/XTe6uw/G2NiOMd4z5zzf3P0/ZjVfbspEVqkRuWVmzB0b71QJtuvhBcL76y7jSHollk9ugbAGGug11btScvVIydOjuMqC5FwdfFQSxIWqretrtULBzaEztTF7RzZEEZBJGP5Mq8DM55q5xK6oUQDqEO0Nb5UE/xxkez6MM3j7l0u6fSnl7wkifWNLO8bY57BqgefY068jMaxhACYQ0Qe2tFPIuK+f69Ho1Rf7hLtXWPE6LhUYMOHHFCyb1MIlchM3c7HAgJeWpOGnCQkI86u9v1KtBb+fKsaGk8UI8pahZ4LftQnjq5aiqNKMiwUGdGvmiyoDb1UoyNNjf0o58srNGNoxCEM7BCHYp/aT4pFzLsBfLcW3zzVzmbFaw6/HCrH0z3ysfqnVDeUs3cnLS9O0xy9XTSOiRR4ZwB0IY+x9AAuIKNeGNo0UMu7K1n+0Uboz9vpmXluehgqDgLlj4hukqegIOqOAcQuT8fz9YRjQLrDO6/TVFefWnShCYaUFCdXqBHGhKngrpTiZUYkW4RoUVhuzqXk6FFRY8EjrADzeOfi2qgNzd2bjyKVKyKUcvn222W3VOBzBzIv458+XkJqnx+/TW0Mq8cx8/Wpzpun3U8WzjRbR5njNexXG2GAA4vWVpBqCj0p69v3HolvfH+/+kLsaNv1VjG+3ZWPZJNdXWLTwIl5Zlo52TbzqjTe/WGDAuuNF2H6uFIHeMiRUZ+WH+sqRU2qESi6BUsYhLd+AlFwdLhYYkBiuweOdg9Ezoe6DoZxSE0bPT0KIjxzzxsa7RHauhvR8PSb+mIqX+kZgeGfbk8ucwZlMLV5bnp6rN4tR7iwq4IjB2hrAKiKyqVaZr1p64oPhMR1dndFWFzXVnwZ3CMJwN1Z/WnIgDyeuVGHmc81ucLkRWUs9zt+di54JfhjeKRgJ4bZVw0jP12PdiSLsvlCGcb3CMaJL8A0JKmcztXjz50v4eWrL2xYecCb/XnsZARpZg06VXcH+lHLM+C3jRIWe7+yRAdyBMMZSAAwjohQb2gxt19hrybxx8Z6ZsLAmckxclIrFExPdssEErNVeXl2WjqWTEm/ZBJp5EYv35+PX44Vo38QbwzsHo3OMd4OqthVWmvH7qWL8drIYkQEKTO8fVWtW8vKD+Vh7rAhLJyU26ITHEYwWEVMWp+KhlgEY1d198XDXk1lsxHPzkqpMPPkTkW2BhPcojLEZAIxENMOGNnIJB+2ut9rLPHVYYDALeHZuEqY9GnVDUpQrKa6y4Lm5Sfjm2Wa1rp/JOTp8ty0bOWUmDO0YhCEdgq6VUL4dFl7E3pRyrDtehOxSE567v9ENycU1JOXo8MrSdKyc2qJB93WUk1eq8P7aK1gxpYVLjeO6ICI8MfNCVU6ZaZijhWhswZEnWgRgU51ZxhgzmMUWthpkzuSXIwXwVkrwWEfbdFAdZVT3RtAZBWw6XXLt3/LKTXh5aTo2nirG3DHN8a8hTWw2VgGgWSM1/jmoCRaMT8CuC6WYsjgN2aXW3BqjRcSM3zLw5qDGbjNWAeD1/o2x83wZTl+1S2LRYVpHaWAwiy2ZDQF5jLG3GWMTXTkuD1MCwGxLAwmHzm0be3nMtSiI1mSgiQ+Gu81YBYDmYWoM7xyMTzdm4vpNfVKODs/PT8alQgOWTmyBz55uivtsKDFcE2e3flpr9G8biFeWpmPR3lzwwt99FFdZsOJgAT55KtblxioAKGUcPno8FksP5N2Q1OFOGgcp4aWUMgANjkdljLVljP3mwmF5Gh1sT7pqFewtN3rKWAWAOTtz0CbKy23GKgAEecvwSr9IfFSdhFWDmRcxd1cOpq+8iEHtA7F+WmuM7x3eYKNSJuXQt1UA5o6Nx9ej4rDzvHV9zSr5e56YeREfbcjAPwZGucVYBYCOMd54sKU/vt6S5Zb+boYxhoda+qslHPrY2O4gY8xu2RW7n2oiukBED9vYrIlKxpGzY7EaCi8QVh0uxCv9otyeWCCVMLz0SCSWHSwAESE1T48XF6aic6w3fnghwaYMwrpoHKjE3LHx6JXohxcXpeB8tg6rjxaiaYjKpoofzsBXLcX0AVH4eksWPKB+AX+NDGqFRATQ1IZmQQDcX7fXTRBRD1s1WDUKSa/ECLXHYgEOplUABGvFKTcz5oFGyCg2IClHD8CqKPL6yosY2zMMnz4V65CrUyphGNIhCEsmJuJ8tg7jF6agpMqqAvL5H1cxtGOQWxMWIwIUGN87HDM2ZNisbOAsWkSoAaCjDU3UADxzJOwGiOgTIpppY7OOrSI1HrNW88vN2H621COetUfbBMBfI8X282XXxjJuQQquFBqwfFILDGof1KCSynXRrJH62vo6fmEKtlcrlyw5kI/GgUo80jrAKZ+joUx+KBwXsnXWd6QHSAxXSzQKSS8bm0XDqjBlF+5+sH01Sgnv5j6vsT+lHFGBCsQ5sdSgLbRv4gUJA347VYxpy9MxfUAUnn8gzKFJdDMSjmFkt1C8OzQab6y8iJ8PF+D5BzxTubB3gh8MZhHnsxuc9+RUEsLUAoAONjQ5AeCci4ZzVyISoiI8kGlcw9rjRRhxX0iDTzCdiUzKYXinYKw9XoR5u3Kw8VQxlkxIxCOtA5y24Q3xlePrUXHomeCHiT+l4s/UcqQXGDCuV93lIV3F452DYRYIh9M9swC2beylUcq4bjY0KQaw0VXjuRthQOPoYKX7pVmq2XCyCP3aBHjETc0Yw9NdQ7D2eCGyS02Y9FMq+rcNxGdPN3VaAlTN+vr9mHjM2p6N1UcLsOZYIV7tF+n2QzCVXIIpD0dg2Z82ObqdRkK4Gmae2tjYbA0Arb192m2wMsbiGGP/srGZTMoxj1U0WXu8CMNrqcDhLhhj6NvKH7O2Z+MfA2yrc2wrPZr74t2hTWCwiPBWuv/lAVj1H4d3ti74niDYWyYD0OBtLxGtIqJtLhySR2GMfcUYs+mhIyKFq5P06iKzxIi0PL3bvQPXM6h9EPYklWFvcjnmjY2/pUKeM2CMYVyvMDzWMQgzfruK/m0CXJ4YWRscx/DkfSEem6+NA5VMIWUNlhYhonQi+tiVY/IkjLFnGGM2eTFlUual8NB8tfAifj9V7NE1tlszXxRVWjBlcSpG398Io7qHusSQbBqqwtyx8VhyIB9hvnK3hitdT88EP+SUmXCxwOD2vhv5ymERRA1jrMEbJCKaRkTl9vbpyJMdBqC/jW2MFoE8Io1jNIs4n61FTw9mThIRTmZo8eR9IXiwpesX4fvjrVWAZvyWAdFDbr6B7QJxILUcOhsFmZ2BXMpxADx3PHjnMQo2fh8MTPDUs7P7Qhn6tg5wuSrA7bhUaIBcxjBrdDOXx4CP6tEIfVv5IzlX75EwGgB4qKW1/5oYeHdSbWh5LsHhzqM7bIjpBQCRwIseenZOZlQhIkCBmGDPeDABq69ZKeMwuH2gyw3nyAAFZo1ujrxyM64Uud9gBP4OLVrngU0mYwwyCccDcNsP7shKYAGQZ2ObgjKdRemJl3F6gR7RwSqPLn5//FWCSgPvUPUcW3mme6i12oaHTk18VFLEBKuQlqd3e99mXhRhQ2UnxtjnjLFnXDgkT5MH67xtMIxBp/XAZgMAknP1aB3pMe8mDGYB//3tKt4ZEl2nXJyzefmRSBRWmq9V9nI3ShmHvq38sfuCTXrgTkEkAhhrsEQOY6wnY2ylK8fkYSqq/2swvEDlVUbBIxM2KUePNh7Q8b2edSeK4K2SuK3CYmyIChMeDMeMDVc9Fvv9SOuAG6rWuRkGawJ+wy5mLJkxZneeiCNJV0eI6Ekb2xRxjGk9sXtPztUj0YPqBJUGHnN25uC9YdFOjVmtDwnH8O6w6GvVrjxBQpgaybnuN1iLqiwWALas/EEA3Fs2xI0QUXsiKqn/yuvaAKc94W4CgNQ8PeI9OGfn7spFqygN3FlCWi7l8N6waHy3LftaEpa7aR3lheRc98edG8wiYEOhDwAaAJ6LF3ExRPQOES2zsdmFC9k6jyQNpObp7VK5cRa5ZSYs2JOL9x+LsakSpKMM7xQMpYzDqsMFbuvzeqICFKgy8qjQuzc9iIhgEUQpAFsWiMawwcC9GbcfN8qk7FSKB07brhQZEOuETHx72XS6BF2a+tSquehqmgQp0SvRHxtPFbu9b8AanO2J3zwlTy8BcMqGJgcANFij9P8DWqNw4Hy2zu0Wa6WBR6WBR6SHYsNKtBZsPl2C1zyQ7ZwQrsFDLfyx5lih2/sGrBtMT8zXi4UGUW8WT9jQJAfAZleN5y7lZFq+XuYJL2Zqnh7xYZ4zWJcdzMfjnUPQJMi9Zw4cx/DPwY2x/GABjBa3aejf0H/zRmqkuPlQKLvUBLmEqyAiW9aHxbDRy3c9jiRdtWOMvW5rO61R2HX8cqXbj1iNZtFl1WLqQxQJ644X4XEPBqMP7xSM9SeKPOK2CPNToLjKJvlPhynTWaA3CRyASw1tQ0Q/EdEhFw7LozDGFjLGbLUAT57JdP/RfJVBgJ9a6hF1AADYeKoYD7b090i2MwA80SUEv58qvkFT0l1EBihQobduGNzJmUyt1iLQsYZeT0RniWiWK8fkSRhjExhj99vYLJsXiC+ocP/pfKWBR6CHJCt1RgE7z5fhsU7u1VevoXGgEi0i1Nh53jOhPHGhKlx2cxxtSp4eMin7y5Y2RDSViOw2Bhw5YW0MoLetjUTC8u3nykjv5rg4gQAPrX04lVEFhYxDmyjPxeMlhKsR7CP3iGSNXMpgsrjXUD6XpYNKzl0gT2Wv3JmMtqPNyeIqC7k7qcAikFtDZ65HEAnrTxS5tRLezUQHKxEbosLeZLsTau2G4xh81VJUGdz3jq7WppYCOOm2Tu98esGqW9lgiIikErZp+/lSt8exenLObjlbgi6xPm6LNa+NxzuHeCT5CQA0Cok1pMaNJGXrLFqjsNedfTpisBpgdcnYBBFlSyVs/7ZzpW41JBRSBpMHjusB4FSGFvc393W7TtvN3N/cF6cy7JZAsxtBdP+LbM2xQm2lQZhnSxvG2ALG2BBXjekO4AoAmxYyIjKLRHPXHC1yq1dELmWwCJ7ZayTn6OCllHo0Hg8ABrQNxO4k9yc/AYBM4t7v/2yWDqJI5QCuNrQNY2woY+wHFw7L0xTBxqQrANCZxK9WHS4wutubJpOwGyq2uZNdF8owsF2gR/quoWucD/IrzMgtc3+ODseYW72nokjYfr7MLBJ2NLQNY4xjjNmaqH9eAPPKAAAgAElEQVQDjiRd7SCiSfa01RqFLxYfyNe5090V5qdAlgeSvQAgOVfn8cUPqIkldX88fpVRgMqNpQJzy0w4k6nlAKyysWkgAM/4gN0AEcUTkc1+XjNP328+U0LulCbzVUlRruM9EsKSnKtHSw+qE9TQKlLjkVhSADDzBLnUfZvMnw8X6IwW8UsbPSIa3NuV6aYRkc2FEYjohEWgLHd703xUUpTo3B+KIIrWypGenrMSjiExwjNz1sSLblVAOna5EiaLmAegwSE8ACSwJjbbjac0nnZVGfjjP+7Pc9vT7anEHyJCSp4eiW4ss1gXCWEapObp3a7Jmp5vcGt1sdVHCy0cY4uJyNYffDsAm0qX/n+AiLIkHDbO3Znjth2fRilBoLfMI7XtrfPV8xvMyAAFKg3uz/4VREKFnoevyj17t1KtBQfTKyQiYbGNTS/COmf/x01ojcI7X27O0rnTqxgfpkaqB9bYrFITfNVSj8WbX09CmBqpHlDEySkzoZELiprUxcrDBVqdSfjcjpA7m7yeN+NI0lUPxthUe9oSEenN4rMrDxWY3PWAJ4SpkZKrc7sgt8EsQm8SEOLjmWD06/HTSCHlGMrdvACm5OqQ4Kbs0ZRcPdafKDIZLaLNFXCIaB4RnXbFuDwNY0zGGFtqb3udSZz8x+kSw+mrVc4c1m3xlBzaxQKDR9Q8bobjGJqFqpGe797v4GqxEYHeMmiU7klSnbUj2yjl2DIisin+gYiOEdFPrhqXp2GMvcEY62hPWyJaV2Xk987fneO2bNf4MDWSctzvwUvP16P5HTBfAet3kObm+QpY1z13rbGnr1bhTKaOJ8AmDWQishDRy4707cgJa1MAXe1tTES5Jp6mvrnqIl+uc70BVarjQQRccHNdexMvQinjPB6/WoNSxsHkxlAMIkJyrh4JbjhhtvAi3v31ss7M0xQiynV5h3cXMgAj7G1MRCVGizjm3TVXTFqje0IDBJFw4nKlW/q6Hq2Rh6/KM4oiN+OjksDdhRtS3KhZfTi9AnuSyqv0ZtFmxZn/B/SBtaKkXehM4ri1J4pMZzLdk7cQ7C3D0UuemK9WRZE7AV+1FO56P9ZQruNRZeQRGeB6CUCjWcR7v17RmyziGLJNM9kpOGKwVsGOpKsaGGNMKeO6CAR6dXmaS91eGUVGTFmcis6x3lh3wr1ZfAx3hqFaA8G9YzqVoYVCyiHC37XuCiLCd9uzzWU6yxEClttzD8bYWsZYbycP7U6BwXGN2UqdSZBMX5EOo4szUvenlOPY5UocSKuAzs0LAC+QW4XHb4fUA4kspzO1btlgVhp4fLA+Q2+0iM8Qkc1H94yxMYyxr10xtjuEXACOWIA8EbT/WHURl1xc/KOo0ox5u3JQXGXBlUL3KorwHkjqrQt3JysC1njS1pFebpEAnLUj26QzCVuJ6Ddb2zLG/BljFx3p35Gkq/VE9JYDfT/pq5aOWTE5UdYpxgeTf0pFjguSos5kajF1SSr8VBJEBSixP6XCrTFhChmD0SJ6rDb4zRjNIhQy903u5QfzUVRldrmo8YI9edh0uiRfZxKfdEDKyh+ei+t2KUSkI6L29rZnjIUqZGzD5yObSiMDFHhtRbrLDMndF8rw0YYM+Gtk6BTtjS1nbSrO5TByKeexbOebsSY/ue+R1JkEbD9X6vITVoNZwEtL0kwmXvyRiHbaeRs17u3KdC8S0X572jLGmEbBrRzUPijw9f5ReGVZmsvewbllJkz8MRUGs4g+iX5Y6+ZDIZmEg9kDesW1YeZFyNyYrAhY19jIANfHr/5ytJA2nS4p1ZvFF+28hRSAryNj8MjibF38uB/+OyJW46uW4aW+ERjaMRjjFiRjzdFCpyQFGS0iZm7Lxtu/XMLbg5tg9ph4bDpdgo4xXlh+MN8Jn6JhqOQS+KikyC1zr3B+bRRXWQAGt7lPCivNOJOpxTtDozF9xUWcvOL8+EdBJMzekY2NfxWjWpDYkU42AMhy0tDuGRhjTK3gFj/eOUTZOdYH7w6NRtNQFcb8kIyzTnQ3Gi0iZm3PxjdbszD7+WaIClBAreCw5mihWw3IAC8ZCis9P18B6xwK0LjP3bn1bAlig5V4f+0Vl53KaY0CXl2WjqwSk9RgFh2RpToPYI+zxnUvwYCnfVTS+6f1i5T3axOINwc2wbTl6fj1mHPW1xp2XSjD+IUpGNktFNP6R+Jslhbbzpa69VDIXyO1rm13AEVVFgS4sXjCxQIDCist2J1Uhk2nXbexX3O0EPN35cAiiKuIyN7qCCYADsnQOZJ09Shj7AV72moU3E+Pdw5W1shQMMbwVNcQzB+XgO3nSjF1SRpOXqmy61SSFwi7LpTh+XlJKKg0Y8WUFrg/3g8hPnJM7RuBzGITNp0ucWtwuKcUCm4mJVeH+DC1W+JpiQifbryKJ7oE45HWAfjoiRj8e+1lfLcty2nl664WGzHpx1Qk5eiwbFIiEsI1YXIp+5cDY55JROlOGdwdBmPMjzG20K62wJO+KukDE/uEywFrMtAbAxpjysMReHv1JXy3LQsGs2OnrWcytRg9Lwn5FWYsnZSI+DAN3h7cBAfTKhDgJcPiAw7J99lEvIdKk96MmRdxtdiIODcllPACYfXRIkx+OAIvPxKJl5akYcuZEqd6h5JydBi/MAXNG6nx8iMRTC3nVjPG7LLIiWg/Ea1x2uDuMBhjHzLGEu1oFyqXsvkfj4jV1JzO90r0w/xx8dh6thQvLU1DtoPezFKtBf9afQk/7MnF5083xYj7QjC4fRBCfRWICVbi6y3u2/fXzNc7wYuZkuve8rSL9ubiyftC8P2YBPywOwezd2Q7VW/eYBbw5eZM/HKkEF+PimNyCTeZMdbSnnsRUSURvePIeBw5YW0OoJ2tjRhj7aQc61Wz+F1PdLAS88bFo1/rAHy5ORNPz0nCL0cKkV1quu3DyAuEiwUGLNiTi2HfnMOvxwoxtW8kPh4RC//rdjsD2wXCVy3FQy398dGGDLcVEogNVuF8tvsF+2/GmkzhHnmtnRfKkF1qwgu9wgEAnWJ9sGJySxRXWTB6XhIOpVXYvdPXGv+PveuOjqra3t+502fSk0lIJaGE0GtQ0aeiCCogSNGHiL2BhWfXh12eFXtBsCsgSu92EQWk1/Tey6RPv23//pjAo6RNjY/1+9ZyuRa555x779xzzj57f/vbEr76oxp3f5aDCUMi8P5NqQgzqPDctSkGBWOPM8YG+/JZzhHoAUx0txFjjNOpuTefmpJsOFPnb+yAcCyfOxANFhHXvn0c7/5QjrL6rstQCaKMn443YO7nOXhqVSHuuSz+tDkbHarGbZfEQafisGavKWDZ8mmx+oAnZ7aFgho7EiO10AZIX/HLP6oQG6rGqJRgjB8cgbdu7INlO6vx2MoCrz1YvChj8c8VeHhFPm67JBYPX52Ia0cZudQe+kRvDpnnOMYBiHC3kVrJHrl6aKR6QPzpa33PKC2W3NYPF/YNxa1Ls/DktwXYX9jilqGXVWHFwvXFuP79DMSGafDl3f0xKDEIgMvx9MSkniiqteNYuQU7sgNTpS06RAUiwNTS/V7WnCpbwDTXt2c1Ir/GjhsuiEGyUYvP7+qPykYnbl6SheM+WL8OFptx4+JM2JwSPr0zDcN6BuOBCQkavZpb5ekh01swT08ljLE5AOKJ6BV32hk0ii9nj4m54fZL4zp8YCLC4RIL1h2ow+ESM+y8jH6xeiRFaqFRMRABNl5GUa0d+TV2RAWrMLp3CKaNMqJ3B5qfv2Y04rs9NVBwDEFaJV66rpdfEywEUcbtn2Sj2S5h3fxB3VYbnYgw64NMPHRVIkb3DvHrWLXNPOZ8lIk3Z/dtU8x5e1YjPvu9CjanjGvTjbhqSAQigjoOoxARcqvtWLffhF8yGjG6dwjmXR6P+DMyI1ftqaUlv1X+ZraLl7t734yx7QAeJqJzrjwkYywGwAoicuu9MMauSAjXrF31wMCgjjzzFQ1OrDtgwpZD9YiP0KB/nB5pcQb0jdEhSKuAkmNwiDKqmnjkVNqQXWXF4RILUow6TE834uK0sDYTJ6wOCVPfPoZZF0Rj86F6fHF3f4T4WR/0YLEZT3xbgG2PDu3W5KvlO6tRUu/Ev6/p6fex8mvsuO/LXHx5d3/EnKLnyIsyPt9RhdV7Tbh8YDimpxvdkvyqMwvYeLAO6/abMCDegMcmJiEy+L9zvbqJx3XvH7fzIsW7K2vFGHsQQBgRPetOu/8VMMaWA3iNiI640UajUTLTV/cMCO4Z1T691+qU8P3RBqzZWwteJAxJMiAtzoC0WD2iglUnk/2abCJyqmzIrrTheLkFNl7GtaOMmDw88jRn0Kn493cFMAar8P2xBnx+Z3/Ehfs3e10QZUx75zjuuyIBE4a4bd/7DA5BxuQ3juK7+we2+258hSariNmLM/DSdb0xNCnotL/9ktGIN7eVYlBCEKalG5GeEtxlu0OSCX/lt2DNvlrkVdvx2KQk/KNf2Mm/ExFuXZptya6y3UNEy925Z8ZYLwDriGioO+1O6yOQbnTGWKhayarWzR+sO3XR6goaLAKyq2yoaHCCF2UwxqBRcUiO0qJfrB5BXdQMFCXCpDeOQJIJceEa9I3R48lrevplY+JFGY9/W4C8KhtC9UrMn+B/Y7E9HC214F/L8vDC9BRcdMoH6Gs0WgXcsjQb00ZF4eZ/tK/IQkTIKLdizT4Tfs9uQqheibRYPVJj9QjWKqBUMPAioabZlbCVU2VDiE6BScOjcM2IKES18/04eBlXvX7EYRfkNCLqcplHAGCMHQJwOxEddOuhz2EE65Q/3jsufty1o4xdmiBOQcbxciuyq2zIqbQiv8YOGy9DkgkaJQdjiAppcXqkxRowKMFw1oGjLby0oQR7CpphDFYDDHj7xr5dnu/uot4i4I5PssEx4F9XJp62WAcSRIRr3jyGRycm4eI0/96D2S7irs9y8M/zYzBlZNuFaOrNAjYcrMP6AyYYg9UY1jOo9XfUIzpEDbXSZeC02CWXgVNlQ0a5FUfLLBg3KBzTRrVv6D75bYH19+ympyWZ3nLnvhljCwAYiOj/PbStYIzdMDjR8NHHt6d1qQIYESGv2o7MSuvJdbbJJkIQXZn3IToF+vZw/c5pcXoMiDd0ulceKDLjmTWFUCkYlAoOi29JhTHEP0lBokR4fGU+SuudMIao8OEt/fwyTlew+VAdvvqzGt/eN9CvtDunIOO+r3IxJDEI949PaPMam1PCj8cbsGavCQ5BxoWpoSfX3aRIzUkDVpIJJXUOZFe65uwfOU0I1ysxLd2IcYMi2ozubM9qxMINJcfMdnGIO/fNGOsHYBMRpbr/1K19BNhgnZXeK3jJezeldls5PZtTwrR3juPJyT2R3isYj39bAK2Kw4JrkhHmw+SG2hYeL6wrhkrBkFlhxZQRUSipd+LVf/b22Rju4ImVBQjSKrAztxmPT07Cpf3DfT5GbTOPe7/MRb1FwKoHBiGyE6/pCcgyoazBiexKG/JqbLA6JYiSy8CJDFadXCy7emp9Y2upc8PBunedgvyYO/fPGHsYwEoi8liu7VwCY0yv5FjTj48PVek13adL+u/vCkAELJyZgre2leNYuRWLZvX2+SZYVu/AQ8vzwZgrfCpIhLdv7OvTMbqKfYUteG5tEUJ1CnxwSz+/eWysTglzP89BVJAKb8zu0+lGK0qEA8VmZFa4DJzsSisarSJ4iaDkGAwazmXgtBqz5/cJ7fRwcaTUggeX5VXaeDmRiLrM02KMjQOgIqJtXW1zriNEp/z1kauTxnanp3FfYQueWlWIlfcNxOZD9Vh/wIS3ZvdFUgceX09gc0p4Zk0RTC08eElGi13C+zelIiU6cFUVT8UNH2bAzssYPygC91we5xej1SHIeGRFPsrqHVj7r8GdHh6ICBkVNhwsNiO79VBS3cxDreRARBAkl+PuxB47KiW4U0k7USJMXHTE1myXLnSn0A5jLBrArUT0alfbnAmPLTTG2EwAanfcwholu2BUSnBQ51f6Dx/+XIHz+4Tgkv4ur8UbN/TBR79UYvbiDDx8dRIuG+CdIUdE2HqkAe//WI5p6UbcenEsfs5owJc7qtDikHC01IIhSYF9BTlVNhwsMWPV/YMwY3Q0Hv0mH0dKLbh7bDy0at/w437NbMQbW0sx64IYNNtEvL6lFC9f16tLk5bjGHpGadEzSosJ7lO2zsK0UUbNpkP1twNwy2Aloje8HvxvCsZYPIB/E5E71emGxoWr7XqNotvKtO0vbMHxciuWzx0ABcfh4asT8cUf1bjpoyw8MCEBVw6J8HpjkGXC6n0mfLq9EndfFo+L08Jw44cZkOGqohPoqldEhC92VOG2S2JRbxZxz2c5eHN23y55o91BvUXAoyvyERumwdFSMxqtYqfUHKWC4bzeITjvjEgREXn8OwxJNCBEpwy28fxoAH91tZ0Xclj/E2CMvQ3gLXciRYJEwwcndl8ZcKtTwksbS/DstBSEG1SYc1EPhOiUuOuzHNx2SSxmpBt9Qos7UGTGfzYUY2RKMF6+rhee/K4QYXoJK3bXYMGUZO8fxE0cLjHD5pTxyR398PCKAjRYBTx4ZSJ8edCvaebx7JoiRAWrUNvCsD2rCZcP7NheYYxhUIIrmnUCokStUWqXhJ+70WWlgmFaulHzze7aWwHM72o7IqoF4LGxCniXdDUAgFv+d42KuzgtztBtpLDDJWb8nt2Eh65KPPlvaiWHByYk4KXreruSApbnY29Bi9sJQZJM+DOnCfd/lYdvdtfgnTl9cefYOCgVDBMGRyAxUouhSUFYuKHYZ1nyXYEgynhmTSEevDLRFXaP0+PrewagzixgzkeZ8LbUZr1FwFOrCvHRLxV4+breuPHCHrjj0jiU1Dnw83G3KGk+Q7JRCwAGxpjHVWLOQYQCuMzNNiMHJ3bRTe4HOAQZ/9lYgicm9URwK2+VMYZbL47FWzf2xfJdNXjkmwLkepjRT0Q4WmrBvV/m4qfjDfj49jRMSzciKliF+VcmQqNkWLihOOCarFuP1KPRJuKa4VG467I4zBgdjds/ycaafSafSBIREX481oA5izMxJjUUr1zfCxOHR+H1LaUeZ1p7c2hgjGFUSrAKwGiPOzk3MR5Al70bjLEeALSxYYGrKX8mFv9SgZHJwRjT979ym1NGRmHpbf3w03GXApA3Cj21zTxe21yK59YW4eGrk7BgSjJUSg6PT0pCYa0du/KaA14dzyHI+M+GEjwwIQFRwWp8eHMqJBmY81GmT6QciQgbD9bh5iVZGN07BC9MT8EzU1PwxtZSNFjcTzRTKhj0GgV0aoXHVMjhPYMVamXgi+x4k3R1D+Cqv97VNmolZ9/88BBtaDeVUXtoeR7G9g/H5BFt87Qcgozvj9Rj9T4TeFHGpGFRGJRoQL8e+jbrapvtLkL6kVILNh2qR0SQEtNGGTFhcARUZ4h9VzY6ccvSLIxMDkaPUDXmX5l4Vn/+wAc/laOg1o43bjg73PdbZiPe+aEcUcEqTE834rIB4TgzE7wtEBGOt/JP/8xpxpSRUbhzbNxpfJeMcise/SYf6/41uEt9+hp3fZrdfLTMeiMRbe5qG8bYMQDXEpFX1Tj+jmCM9QHwChHN6GqbYK1yxbxx8bOmpRv9eGftY/OhOvyS0Yi32gnLC6KMlX/VYtXeWsSEqDEt3YgLU0M7TcpqsorYntWINftMsAsy/nl+NK4dZTxt8SYi3PFJNmQCLu4Xiltb1S78jTqzgNkfZuCdOamnZRsXmex4cX0x9GoF5o2Lx5kZ4F1FQY0dS3+rRGm9A89MTUb/1n6cgow5H2Xi4auScF6fwPPs1x8w4f2fKtaY7WKXv0/G2AsAWohokR9vrdvAGFsH4CEiKuri9VcOjDes/PTONK/E2T1Fk1XEzPeOY/UDg9DWHi/JhLX7TFi+qwYRBhdPcuyAcBg68UKKEuFgsRlr9plwqNiMK4dG4o5LY8+a58t31WBXbhMqm3gsmzug0359hbe2laHeImDhzF6n/fufOU14dXMpRqYE47rzot2esycSoFbsqoHZIeLpqcmnRXve/aEcVqeEJwOQlHkmmm0iJr1x1ClIpO8qjYcxNgLAe0R0oafjemw5umOoAgBjTAFAG9JNNborGpzIrLDhpeva55BqVRymjjJiysgoHC2z4qfjDfjw5ybk19gRHaJCmF4JlYKDIMlosIhosAro20OP/nF6vHxdr5OLf1uIC9dgaFIQUmN1WLazBomRWvjbENhyuA6bDtVj2dwBbXpAxg4Ix8VpYdiZ24y1+0x46/syDIw3nCRnx4S6kikk2ZVMkVvtyhbNaD0hTxtlPOm5PRMDEwxI7aHHr5mNuGpopF+fsy0MTQoyZJRb0wF02WCFy5sR2DqgAUKrEd5lYwAAFByifcnrdhdr9plw56XtG4oqJYc5F/XArAtisCuvGWv3m/D6llJEGFToF6dHcpTWdVgiwC5IKKx1IKfKBrNDxKiUENx3RQLSe7WdQcsYw4zR0Vh/wIRlu2owJCkYI1P8S713CjKe+LYA09Ojz5LGSTHqsPS2NKzZV4sFqwoRpldieroRl6SFnfQ+twerU3K9n30mlDc4MS3diBdnpJxWQUuj4jB7TA+s3lfbLQZrWqwBANLdbKYD0P16gX4CEV3rZpPwyKAAlkU7AxsP1eGStLA29wMAUHAMM8+LxrR048lM9EVbyxATqkb/OD16ReugV7tC1E5BRlmDE1mVVhTWOpAUqcHUkUY8c21yu4bopGGR+GJHFdLi9Hh+XRFeua6331V5/sxpwk/HG7B83oCz/nZRvzAMTQrC+gN1J+fsxGGRGJRgQO9o3VlOLcA1V3OrbDhcasHGg3UIN7icYFcOiTxLRWX2hTH45/sZuPeKeL8rp5yJUL0SQVqF0GgV+wLI6WIzVet/HiOQT6niGKRWwzXgWLvfhKuHRnZJ05AxhqFJQSflIkTJlUlndogQJIJKwSFUr0BSpNYtl/rUkVF4YV0xLu4Xhs93VIEx4NpR/jFatxyuw2ubS/HoxKR2M+oB1yJycVoYLk4Lg6mFR2aFS3Jo/QET6i0C+NZsUYNGgT4xOoxKCcaNF8agd7Su08VgWroRX/1Z3S0GqzFErdSouHg3my0BEBjxwP8NaJTdJOuUWWFFs03skvGkVPz3G5ZkQmmdA9lVNpQ3ONFsE8Exl0E2bmA47r0iHgnhmi5tZJcNCMcbW0sRrFVgwapCvPbP3n7jn/OijEdX5qPRKuD2S9tmsigVDNefH4MZo6OxJ78Fa/ab8Oa2spMGei+jFlq1AgyuaFFJ63uoaeYxKMGAmaOj25UPA4Dxg8Pxwc/lqGpyIjbMv1JEZyIqWAVRInflELbDu8p25xrUym7SYZNkwrr9Jrx0hpexLSg4hgtTQ3FhaihEiVBksiO70oZCkx1VTTJk2TVfEyM0uGJQOPr20HfJWxqqV2J4zyAcKDYjPlyD17e49j9/Ga37Clvw1OoivD27T7tJkcE6JeZc1AM3jInBnvwW/JLZiHX761De4EDPKC1C9UooOQZBIphaeNS0COgdrcOAeD1emtmxEywySIUL+oRg6+F6/POCGL88Y0eIDFJJjVYxGl03WKsAfOnNmN4kXd0OoJGI1naxCS8TFN6Q873BztxmPDctxaO2SgXrUNu1q8iutCHFqMPTU5NR0ejE/K/zXNWaLvNd8pNTkPHJ9kr8eKwBd10Wh6931uCKQRFdCssbQ9S4JER9MiHNW1yYGoo3tpaioMbuk/fnDtQKBo4xtwZ1V1P4fwmtkiJ3E9FDbjRzBJq/eQJ/5jTjikERbnOsFBxDSrTOJ5nCpfUOAAxLbktDkcmOx1cW4MlrevpcZqrJKuLpNYXQKDnYnBKyKm2nJUmcCQXHMCY1FGNSQ10Ger1LlqakzgGLmQeRa8NP7+U6XKYYde0aqadCp1bgyiGR2HyoHneODQwF4gRUCgZJJre8L0S0xV/383cAY+wLAI8SkamLTZyCJAcuQeIUFJkcUHKsQwOrLSgVDH176H2S2Ogyfh2YPyERlw8Ix8Mr8vHcumL8e3JPn+2vJ/Dz8Qa8sbUMqbE67MpvwbDkjqMvp85ZwCW/WFBrh6VVEUetZAjTK7s8V09gWroRr20u7RaDVa1kANDlky0RlQL4wJsxvfGwDoIbddeJSNaoOGu9RTR05PHzB2xOCdXNPPoE2Gg6FXnVNqzaa8JXd/cHxzEkRroqU7yxtRRzPsrEU1OTzxIAdhcZ5VYs3FCMnlFafHZXf0QGqZBRbsXH2ytx3xVt67X5EwqOYVSvEBwrswTcYBVlgkzU9bJL5z4iAZzvTgNBonJTN9Xozq6y4pp2uOaBgCgRXlxfjPkTEhATqkZMqBqvzeqNZ9YU4ffsJvxrQkKnofiuYHtWIxZtKcP4wRG494p4/JrZiIXri/Hl3f27dMhUcAwpRh1SjL6ZX+f1DsHKv2p80pc7EGUCxxC4AvT/GxgPwJ1SlqaaZr5bTpjZlVa3jVVf44s/qpAQocHk4ZFgjOHtG/vi5U0lmPNRJhZMScawnt5HR5ptIt7cVoaMCiveurEvjCEq3Lg4E5ekhbVZJKc9aNWcW9e3h8GJQahu5mG2iz5Zj9yB4HJm8IEc05tjRzlcLt4uQ6viMrIrA1/yMLfajl5GrVsnF1+CiPDyxhLcOy4e0adUkQnVK/HCjF6494p4LPiuEI+syMfuPPdKlsoyYW9BCx5fWYBHv3GVPXz5ul4nNVAfnZiEbUfqA1bW8kykdVNd9karKPMi1brThjFWxRjrPivJv7AByHCrAS/vPlZmCfiEJSJkV9pO8Bq7BSv/qkFkkAqThv+XzjI4MQjL5g6ARslh9uJMbD5U57HiR161DQtWFeKDnyqwcGYvPDAhAQqOYdzAcKQYtfjqz2pfPYpbSIvVI6cy8HXZm20ilArmFh+VMfYBYx+YtIEAACAASURBVOxuf93T3wBHATjduP5wscmh84WShLtwzdfAyr+dipI6B1bvNeHJa3qejOBq1Ryen56C+65IwFOrCrFoSykqG915nf+FQ5Cx+VAdblyciTC9EsvuGYC0OD0ig1R46MpEvLSx2CcKHu5CwTH0idEhpxv22GabyAHoshQQY+wyxtgP3ozpTdKV25qVdl7+PavSNuqifmEBJYYX1NrRJ8BaiqfiSKkVVqd02uZ3Ki7tH47zeofgp+ONWPxLBRZtLcU/+oW1WZlClgnljc5W0W4b/sxtgkrBYcbotgnp4QYVrj8/Bt/tqe0Wfbq0OD02H64P+LhHyywWUXa7YpUO527S1WEAd7rZbP/xcmvAQ4zNNgmCRIgJ7R5FLVEifPtXLd5sQ0jfoFHgsUlJGFccjq//rMb7P1Vg4rBIjB8cgd7RHYfz6s0C9ha2YO1+E2qaeVw7yoinpySfFq5kjGHuuHjc/WkObrqoR8AVNiKDVdCoOFQ18X4vqXkqcqpsUHDskJvN1AC6h7MSABDRlW5eX2/QKJpL653GVmm/gKGg1u4zKpknWLW3FtNGGRHdRjGRS/qHYVjPIHy+owq3Ls3CoMQgXDsyCsOTgzvkxkoyobDWju+PNmDL4XoMiNfjxRm9zvLUjhsUjq93VmNfobl7EhbjDMiusmFUr8CNbeclNFhELYBsN5qpAHjlNQyoD1mQaPfeghbLnWPjAvqr2pwSgrqxUs/afbWYlm7skLurUytwzYgoTB4eicwKGw4UmbEjuwlLfq1EbQsPBcegYAy8JMMYrD5ZSeapKckYnGjosO/JwyNx3XsZuH+8GPBswvhwDWqaAxo1AADkVNmUAPa72WwRALsfbud/FcfrzIKmySr6tApcZ7DxEgwaRbdw3QHgz9wmV9nmDg65I5KDMSI5GOUNTqzbb8Iza4pQ08yjV7QWfWP00LVmO4sSoaLRiZwqG5yCjMGJQbhxTA9cmBrarnGbFKlFamz3KWzEh2tQ3RxYgzWjwsq32KXf3Wy2Ba5I3/+jFQqO7TlSapkUaIPVxsvdtsfanBJ+PNqAZXPPztQ/gVC9Ev+6MhH3XBaPn4434OudNXhqdRFiQlRIizMgJlQFtZKDJBPMrYo4edV2RAarcElaGD65Iw0J7RTuYIxhWroRa7pJYSM+XIOy+sCy33Kr7dCpucIWu+gOZ6wQQJcLTbUFb5KuHgSQRUTfu9Hs+9xqG1fe4Gz3x/cHZCIouknso9EqYHd+Cx6dmNSl6xljGJhgOI3fIogysiqtiAxSIzpE1aYcRkcIN6hwYWpot2QTapQcnAEslAC4xKUdgkwAit1pR0QL/XNH3Q/G2Ci4NGa7zIkjIj5Iq9iw6VDd9DkX9QjYDJLk7puvALB2nwnTuyg5lxChwf3jE3D/+ARYnRLyqm0oqHWgzszDxsuIDVNjaFIQHroqEbFh6i4b4dPTjfh6Z/cobKiVLOBz9mCR2QHggDttiGi9n27nb4FWHdbZRNTleK/ZIS359q+aS6aMjApo+XNZJr9LSLWHH481YHhy8Gl0u/agVXOYPCIKk0dEnVQoyKmyobZFQHmDAwkRWiRGKjF2QDhSe+i6zAudMDgCH/5cgZpmHjFduA9fQqNicIqBna/Hyy0kybTbnTZElAcgz5txvXGbDAHQ7E4DIrLr1IpP1+yrnTt/QmLAflW1kgMvdg+f/3CJBUMSg7wiRKuUHIYkebf+XJIWhs3dYLC6DguBXcg2HqoTlRxb7aRuIBX9fREDYLi7jaxO+Y1vdtdcPXtMjCFQG5JGyYEXu+enE0QZR8useOX69vWa24NBo8CwnsEY1tN7W+GCPiF4enUh7LwEnTqwniuZENA5W1RrR0UTTwD+CNig/xuYAPdDqNuqmnhHVoU1OJBJUK49tlsECrCvyIxLPaAj+FKhQKdWID0lBAeLzQE/ZMpyYOcrEWH1XpPVxstfBWzQVnjjxygA4HZmgEOQ31t/oE422wNnQEaHqFHZ5BnZ2ltkV9nOEgH3qJ9KG1q8eGdpcXp0R8Kb1SlD52NJkY4gSoTv9tTyNl5+0922jDGRMdZ9Svn+RRPcTLpqxV6nKFf8nh04edowgxItdhEOPvAbYKHJgdgwtdc1wGubeZTWeR6mUyk59IrWIbc68AwVm1MK6Jz9bm+tg4g+JCK3uEOMsW8YY//01339DbAHcE85gYgkQaK3v/ozsB9OdIgKVU2Bp34BQI4PEr4EUcbhEu9qUPSL03dL8pM1wPP1UIkFLXaxES4d5C6DMTadMbbam7E9fkoiWkhEWz1oVwBgxaKtZQGbUGmxemRXdk+WfE6lDf18kD35zg9lyK/x/JX1CFVDbBUnDiTya2zo5QNNzK7i9+wmyIQ8IjrqTjvmitUqcO4mXe0kokc9aEdWpzz3lU0ltkAdMtVKDj2jtMirCfyc9VW28/bsJny31y2RirPQHeuWJLu0LH0lk9UZzHYRW480gBfpQw+aK3GOzlcAIKKxROS2p0WU6b3d+S2WXXluBUC9QlqcHlkVgXeItNhdFSeTorzj7DbbJSxYVeBVH91lZ+TX2NErQPMVAJbtrLHZefl1cl9KRAnAKy9EtzDF7Lw8f0d2U8vO3MBMqLhwNeyCjPpu0JQsqXcgxQcEeJdOoeduf8Zceo2l9YH1NGdXBU7uxOKQ8NqWUpvFIT3sYRfPeTAJz3kQ0a+8RCsXbQvcIbNfNy3+pfW+MdYkmaDwMmksJVrXWrwgcCipcyAyWIUgbWBoCIu2ldk5hhVE5Eny1Cp4FjU4p0FEZocgz3p+bVHADplpsQYcLQ+8wVpW70SimxUn24Lk5f4KAL2idSjxIqriKXwVxe0KduY241Cx2SwTPvegeQaA77wZ32ODlTH2LGPsEk/aEpHFLsiznltbZKsKQKje5pSh4pi8t7DF72OdCacgQ+8DDtpjE5PQ10vxfa068AlQWRU29AvQZFq0tdTBi/K3RPSLu23Jhef9cV9/BzDGxjLGnvK0vZ2X5/+e1dTyw9H6gBj0phZB3JHTFHDvmVPwDYXl8oHhuO68aK/60KkCP18zK6wBO2Duym3G71lNZjsv/8uT9kT0HRFl+vq+/g5gjCkYYz962p6IfuElWvn8umK7FAAqv0SEvGobLI7ATlmHIEPvg/kablB6xFs/FToVF/DkJ6tDQk0zH5CIiNku4oV1RTa7IM8iIrf5E0R0nIi6hxIAYBhc1XM8AhH95hTkp+7+LMdW68cwtdUp4f6vc60Wp/TbN7trAl532ldLRd8eehi89HowBFa0sMkq4nCpGSM7KVvnC/yW2Yjtrs1vviftmQvnKn8VAOIAtK/70gmIyOIQ5HEvbyo1/57lXz7r5zuqxMOl5sojpRa+NsCSaL6aH9EhasR7qYTCWOBFRrccrscFff0vzWNq4fH8uiKbw7X5ebQutxp13ZOa7n8oAVzqTQd2Xr73YLH5yH82FDv8abRmVVjx1KpCm0rB9mw7EpgDra+hVvqg8hQDAh2f+zmjEUOTDH4viiTLhP9sKHHwEn1DRL950gdjjGOMeXW68KZxFoCu1jhuE05RfqvZJi68ZUmWrcjk+2hjvUXA3Z/lWItMjrWCRFeV1jvFQFd80ig5j6vhnIpjZRZYnd6dXh2CDI0ycOv7xoMmWSaIuX5+53sLWvD8umKLQ5Cv8nTzAxAEV2LSuQoTAK+8UUR03CHIlz+ztqhl06E6n7MnJJnw3o/l/Fd/VFc6BBqj4NiytftNAZX38NV8rWhweq2N6JqvgWNtldU7kFlhlTPKrU5/MmOarCLu/jzHZhfkV4joVy+62gpXJv25Cnd1aU8DETlsvDx+e1bT0QWrCu3+SGLcW9CCeV/m2my8PMvqlB9fvqvGGkhWlUbJfDJf7bzkddKVQ5ADWuiDiLBsZzVfUGN3NNv8t0wSEV7fWurcW9iSaeflB7zo6jYAH3tzL94kXf2biLyWIXEI8svNdvG+W5dm25btrJZ8cRIkIvx4rIFmvnvcXt7gfNfOyzcTkSDJ9M4n26sCmj2ZEKFBiQ94aK9s8rysHOB6JyV1Lp25QECSCSv/qrXbefneR78psO7wU5b571lNeGxlgdUhyBOJyC0dxzNwziZcAQAR/egLnVki2u8U5Ave3FZW+sg3BbYGi2944UUmO25ekmVdt990yC7Io4iows7Lb6/aWyv4czE+EwkRGp/wRn841oCtR7yr8FZc5wioXvWqvSYewJLvjzYUvrypxClKvjc8app53Ppxlq3BIr7Pi15/j+fsnCUiJxFd4YN+zDZevnRvYcvWme8ft3prlJ2AnZfw6uYS52MrCxrtvDyRiDYC2NFiF007sgOX7JUQoUVpvcPrsqjVzTxe3lTiVR8ldQ4kBnC+Hi+3os4iNFoc0uLbPs7yS6RalAgLN5Q4fjjakGfj5cvd0QRuAwq4qXpxJrpRnvu/ECX63CHIgz/fUXXoliVZ1kPFZo9rWedV2/DQ8nzby5tKSm28fImdl/59whUkSPT63sKWRn8ZT23BV9mTLjF1z72jJrMAmRCwcpcr/6qRnKKcSURLHYI89pk1RQ0vbypx2rz0Ep+AzSnhpY0ljmfXFtU7BHksEe3wsksngBd8cW/nOogo087LaQeKzEtmvnfcvv6AyWMJqmabiE+2V0qtUZbHbLw8hohMJ8aRZXz56ubSwCmKxOmR5YNkL18kcWRXBi6ZotjkwIaDJtEp0is2Xj7/l4zGvTcvybIW1frm1RMRth6up1kfZNjrLcJzdl563Acu+s8B5Pvi/s5lEJHd6pBmmFqEG/+1LK/p1c0lzmoPJahEifBrZiNmvJth++FowwaHIPcmou2t45CNl29duKHYHqhDZphBiWCtEuUN3uXCSDLgbVQ9qzJw+RqSTHhtc6mVF+XnHCI9XG8RF/7z/Qz790frfRb5KqixY85HmdbtWY1/ta7L3hpO+wB4VeyDefpwjLG3ACwnInfLX3bUJ8cx3K5Vcc+E6pVhs8fEGC4bEM4igjo2siwOCbvymrF8V425pM4hSjK9JUj0WluSIIyxfwRrFT+sfmCQLlTvf8riT8cbsPFgHd67KdWrfvKqbUiM0J5Wd9wd7Mhuwtr9Jrx9Y1+v7qMrKKlz4KYlWTanIA9plTEDYyxcr+Y+0qm5SQuuSdZf0DfEo9Kbskz4K78FCzcW2xy8vNHGy3N9MJHOeTDGpgDoQ0Rv+Ljf0UFaxWuSROdNHhHFTRkRpU4xajuseiNKhMwKK1bvrbVvz27iVAq2yeqUHyOiojb6N+hUXN7z01NiL07zf61yXpQx7uXD+PGJYdB6Ed6rbeZBgMdVb0SJMO6Vw9jy8BCvueudQZIJtyzNshbVOv4tSPK7gIvTreBwj5Jji275R6z6+vOjlZ5q05bVO/DallJbRrm12sbLM4jokE8f4BwEYywUwDIimuzjfiN1Km6hRHTT8J7B9M/zow3Dk4M7/NaJCFVNPLYeqRdX7anlJUK+xSE92Z6spV6jWHJh39A5C2f2CojW0kPL83DlkEiMHxzhcR8OXkZZg8OrIgLPrC7E6N4hmDQ8yuM+uoqv/6yWvvij6qDVKZ9PRDLgqmaoV3OrhiYFRT9ydZLeUw691Slh5e4a8audNU5Rkh+UZHzyd1HP8cZg/Q3AC54ScDvpmwG4NEireNTBS5dqVBxiQtQI0ip0Co6ByDWJLE7JaTILktUpKfRqxUGzQ1oEYBMRdRin1KkVX6T20N30/s2pTO1njtjb35dh/YE6bH1kiNdi5N7gxfVF6Bmpw03/6OHXcQRRxu2fZFsLax0LBEl+58y/M8YmGjTcu3qNIvqGC2L0E4dFciFdqALWYhex5XC9vGJ3jc3mlGqtTvl+T3SA20PrN6f2RPfwfwGMsXsBDCSieX7qP0Wj4u5TMNwoiHJYdKhGCDcotUoFU7gyhwgOgcRGq+CsMwtqnVpRaRekxZKMT4morpO+LzFouF8+uSNN4e9s2KwKKx74Og//vqYnxg4I9+tYHeGv/GYs/qUCX97tcZ5cl/HlH1XSV39WH7I65fNObH4nwBhLDtIoFosyXXr10Eg28zyjpiu/gSTTCSeCpTXC9LZTpBfdLQ7QERhjGgDCmfd8LoAxFgPgGBF5JzXRfv9BDJgVpFU8aHVKfSIMSj4qRKXSKDk1x1x7rCiT3GwTHbVmXgGCQ8GxDTZefpOIjnTSt0Gr4krvHRcfMdNLpYzOYHNKuP79DAxKMOBlL7P8vYFTkHHNW8fw2R1pXidbdobCWjtuXZplc4o06MxDPmNMo1VxzxDRAwMTDLjhgpigC/qGdik6W1Bjx3d7a53fH20gFcd+tTiluURU6qv7PpHUTEQeu9+9cTEeBuAdSatjSCBIjDEoOYaoYBUGJBgoWKNgYAy8KKOw1s5svCxbnZIKrqSZYLieqV2DlTFm1CrZ5Q5BxlOrCvHijF5+I0p/vbMaGw/WYURyEH441oBrR3WtPnlb2F/YgkEJQR55WM12ET8fb8S8cf4NV0gyYcHqQmd5g3OXKNN7bV1DRFsYY1utTnnMp9urHvngp4qrjcFKR98eelWEQaU7KWXAXP01WgR7Qa1dMJlFrUbFbbE6pUUAdvvhxJcAYHfr/89FVMC/FCCJiEQwBsYxBGsVGBCnR1SIGioFgyARapt5drTMQo1WkWQirYJjoZJMHZKqGWNMp+ZuC9crpflf5ynenZOKZB/oGreF7Eob7vsqF2P7h2HtfpNXBmuRyQ61gvN481q2swaS7PK0+jMD+Puj9fT5jupmhyDPbMvwI6JiAFcxxhI3H66bu/VI/Vy1kqn7xujE2DB1kErBcYRWNQMiWJ2yUFxnt5XWOXVqFZdvcUivAFhFRP4QqNwBYD6Av/zQd3dDhJdJV51AJhf/V2YMTKPikBSpRS+jDmolByKC2SmxjHIrmu2i7OBlFWMIARDEGGOdrL+XAKT/4o8qaFUcJo/wj8fR5pTwwNd5CNEpsK/QjHqLgMhOorHtocUuosjkwNCkII/a/5rZCFkm+FuzorLRiXlf5NoFie5pKyLV6nBZwBhbeLDYcl1utf1xXpR7J0dp7SlGnUGn5k6z+ZyCLFc389aCWruCF8kpy/QhL9FiB1GFH27/QbhKhD/iaQcee1j9AeZyc83Ra7gXgrTKyNljYgyXDwxnnX2EDl7GvqIWfLO7xpJRbgVj7GOHID9HRKcJrzLGmF7N/XDNiKhL7x0Xr3p2bRGabRJemJ6CyGDfcTsFUcZHv1bi9+wmOAUZU0dG4besJnx9T3+PwuAAMOXNo1hyWxp6hLkfYvxmdw1+zWhEVROPuy+L88sCIogynllbhL/yW2Q7Lw/vrNIUY0wNYGqwVjGPF+VRHMfUfWJ0iAlRc2olY4JEVG8R5PwaO9l5mXRq7pjZLi0lYAUR+VyhmjGWDOB3Iurp677PZTDGhgdpFYskicZMGh7Jpow0anp1QgmQZUJBrR1r95uc2440kJJjOyxO6REiOnbmtRxjs2PD1Uu+vmeA4bfMRiz+pQIvTO+FkSm+lUrbmduMheuLkWLUwhiiwt5CM5bc2s/jCjpvf18GY4gas8fEuN22uonHrA8zMDDegGCtAs9PT4E/IkFbDtdh0ZYy8JL8uijRY51dzxgbqVdzDzCG8U6BjAkRGinZqFXo1BwjAlkcEhXU2qXaFl6pVytKeYnWOQX5/bY2Vl+AMbYfwFwi2ueP/s9FMMbCdCruBZnotiFJQTTr/JigESkdUwIAoN4s4MfjDfKKXTV2Gy/V2Jzy0wR8c6bhyhiL1ShZ7ns3pQaF6pWY/3UerhkRhZv/0cOn9e6rmpxYsKoQoTol8qptGNUrBMlRWtxycaxH/R0rs+CdH8rxyR1pHrWf/WEGUnvocajEgnfn9PW68lZbKK1zYN4XuTA7xFKnSL2IqMOEEMZYjErB7tKquNl2Xu4dZlBKfWJ0ilCdkik4MIcgU3mDUyw2ORRKBWtmjO2wOqX3AGz3BwWAMfYEgHAietzjPv4uBitjLNGg4ZYbg9UjHrk60TAyJdgj4668wYlPtlc6tmc1mR2CfAMR/XzibxxjN8SGq5euvHegQa3kIEqET7ZXYsPBOsyfkIAJgyM8NihPILvShhfXF6NHqBoLpvREcZ0Dz6wuRJBWiTvHxuHygZ55bSYtOorP7kpDdIh7BqvFIeH694/j1et7I0SnxPxleRjTNxT3jov3GUWh2OTAi+uLERWswvCeQfLS3yozbbw8rK0JxRgLVyvZo4yxeb2jtdz158UED+sZhOgQVbvvvsEi4Hi5FWv2mSyHSsycgrGv7IL8EhGV+eQBXPcVBuB6Ilriqz7PZTDG1Bole1bBsQfnjYvXThwWyXQeFMiwOSVsOlRHH/1S6ZCIXuVFeukEpYcxFqtRcTlLbk0NTotzaSTuymvGyxtLcEn/MNw7Lh6ejHkqzHYRb/9QjoPFZjwzNRn9YvWY81EmBsQbIBPwn5m9POr3zW1liA9X4/rz3TdYn1lTiEiDCnPHxePZNUWoaebx9NRkpPioxLGdl/DhzxX4I6cZT07uice/LbA7BHkEEWWfeW0rVeaGIK1igUrBel5/XrTmwtRQRYpR167n18HLyKux4afjjfymg3WyQsH2WBzS876mjzHGbgbwAxFV+7LfcxWMsau0Km7ZuEHh+tsujtXGhbvv/Zdlwr4iM17fUmpttAp7rE55DhFVtvbP9Brup5np0RfPHRevAlyqEM+vK4JTIDw9Ndnr6AgRYf2BOiz5tRI3XBCDORfFYNHWMtQ088iqtGL53IEIM7gfOD5cYsaHP1dg6e3uG6y7cpvx2pZSrH5gELYdqceHP1dg/pW+sSeAE6pHjXj7hzLcPTYOGw/VWfOq7S/wovxaW9czxoYbNIpnRUmecPnAcJo4LEqXFtu+jrssE8oanNhb0EIrdtdYm21ik0OQX5UJSzqjV7oDxtj5ADRE5HHkwBsO6+cAXm1rkXMXSo5dr1SwT2/+R6zmpot6KH0RAtud14zn1xXbeFH+xsbL9wCI0Ki4/I9uSQ3uH3+6QHBWhRUvri9GTKjLI+KJsVxa58CqvbX4+Xgj7h+fgKuG/vdjXbS1FBUNTuRW27Bs7gCEG9z35uZV25AcpYXKTU/Ls2uKoFNzeGKyy3F4YoM+VGzGv6/piVG9PBcJFyXCyr9qsGxnDe4cG4drR7o8t3d9lmPNrbL9xynKL596PWNsolbFfTW2f5h+zkU9tL082ICrm3h8t7dWWLO3lhck+pdM+PTvQgj/O4MxNgdAMJFHNdvP7Ku3Xs19PzDeEPfMtcl6o5uHqLZQ3cTjuXVF1twqW5mNl68kopIgreKH6enRY+e1bn4n0GwT8fb3ZThaZsFNF8Vi/OBwtw1Xi0PCtiP1+HpnNS5KDcO9V8TD0HqAO1BkxrNrCqFVKzBvXDwu84AaUNPMQ6lgbocod+U245XNJVh570DoNYqzNugbxsR4RRE4WGzGfzYUY2hSEOZPSESoXolVe2rlxb9UZNhckZGTh0zGWLJew33TI1Q9eO7l8YYxXeTCnQoHL+On4w348JcKm1OQ19l4+V4iCpzu0f8oGGMJAN4iopk+6EutV3OfaFXc9Bemp+i9WfNPQBBlfLajSlixq8bJSzRHlmk9x9js+AjNkm/mDTCcuk/JMmHtfhM+/q0Sk0dEYdooI9w1lmWZ8FdBC5btrIadl/H01GSc2D9sTgk3Ls5EaqweKgXDizPcP2RanRJMLYLbBrXZLuL6DzLw4ilRn6wKKxZuKEZcuAaPT+qJKC+it3VmAa9tLkF5gxNPT01G/3gDKhqcmL040+4Q5OFElHPi2lb+6oscw313jI3TTB4WyQV3IT/kVBARDpdYsPS3SmtOla3SxsszO+MsBxLeGKyHAdzqbcanSsHN02u4Re/flKpL9XFJQItDwqPf5Ntyqmx/8KJ86KqhkQ8umJLc5kzhRRkbD9Zh7T4TJAKmjozC8J7B6B3dtpFIRChvcOJ4uRVbj9Qjv8aOycOjcN150Wd9oDanhGvfPoZL+4fD7BDxn5m9fHLy6gy78prxyqYSfHPvwJOb8cm/tZ4Ko0NUmJ4ejbEDwrocdqw3C9hwsA7rD5iQHKXFE5N7nrYAVTY6MeuDDLtTpL5EVMEY0+vV3Oc6tWLSC9NT9L4I5+bX2PHUqkKrycwfsjrlGURU401/jDEFAK0/6AZ/BzDGFgAwENG/vexnoFbF7Zg3Lj5s5mgj58vvmIiwYneN9PFvVY0OQb4tWKtYueWRIfr2vsv9hS34dk8tjpRacNWQSFzULxT9YvVoL4mvySoiu8qK37Oa8HNGI0b3CsH150djSBu8tWfWFCFUp8CvmY0eHzLdxYnN74VpKWcdJKuanHh1UykKau24dpQR14yI6vJGKIgyfstqwpp9JlQ3OfHIxCT8o99/FRdkmXDHp9nWrArbw3JrhEGpYHeoFNzbt17cQzN7jPdOBKtDwlvflzl+zmi0tvJlvfa2MsYMAOznaNJVP7gSiL2Sl2GM6fRqbuvgxKDRL13XS3/mPuAtMiusmP91nt3qlO7TqrjnX5/VJ6G99b2qyYlv/6rFtiP1GJQYhKuGRGBAvAGxYeo290OnICO/xo4DxWZsOGBCkFaJGelGXDU08qxD2/asRnz1ZzUsDgn3XO7ZIdMTPL26EMFaBR6bdDqTjBdlfPZ7FVbvNeHitDBMTzdiQLy+S/s+ESGzwoY1+0zYkd2E6aONuP2S2NP252//qpU//q1yr9khXgAAjLHBejW3cWhSUPRTU5L13lIciQhbDtfTG9vKHLJMrzlFet5bx1BrkiR5k3jpjcG6GMDrRFTo6eBKBbs1WKt8/9M70jyWYOgMgijjsZUF9sOlFuXSW/up+nZiFJ84YWw8VIcjJRbUtvAIN6igVLCTZdeICE02EVoVh/5xBkwYEo7LB0Z0aPC9/X0ZQISfMhoxnKmLzAAAIABJREFUdaQRd46Nc+s5duY247zeIV32ruTX2DH38xy8cn3vdvl+okT4M9e1keXX2DGiZzD6xenRO1p7ktNEYCCZ0GQXkVdtR1alFdmVNlw2IBzT0o3o1877fHljiWPbkfo3eIkW6dXcrxf0De2/4JqeWl8qJYgSYcmvFcLqvaZauyCP8SajkTE2GC5O1iCf3eDfCIyxGwCoiOhLL/roo1Vxe5+YnBR25ZBIv524Nh6sk9/cViZMHhGpePiqpE5dBNVNPNYfMGF3fguKau3QaTjoVIrW/D3XpLXxMhyCjN7RWlzYNwxTRxk7NPiOlVnw/LpipPbQoazeiaW393PLi5tTZUOIToHYsK6taw5BxrwvcjAw3oCHr05q97q8ahvW7jPh54xG9IvVo3+cHv1i9Qg3KMEYg9y6SNl5GcUmB7KrbDhYbEbvaB2mpRtxcb+wNteQA0VmPPpNfqmNl5M1SvZiqF754Ds39tX7ioZwAnvyW/DEdwV2hyDPkWVa401fjLEcAFN8EeX7u4ExlgTgaSK604s+VHo1ty29V8iY/8zspfNX8l5pnQN3fJrt5Bho26NDtZ0ZZSe87j8db0B2lQ28KCNMr3KVIiZXSWJRIjRYBcSGqjGsZxCuHRXdocEnSoTp7xzDrAtisPS3SnxwcyrOjKR2hHqLgMpGJwYndj3p6qs/q7B2Xx1WzBvQLr2uySpi06E6rNtvgk7NYXBiENLi9EgI14Bjrsw3BkCQgaomB3Kr7DhaZoWNlzBtlBGThke2eVgWRBlXLTpqtzikUQDCtCru+8cmJgVdNTTCp9WKa1t4PLwi31be4Nxg5+U5nfFmOwJj7D9wHTA9LhjSbRzWVs2wHZ/f1V/X0w8E5VPBizLmfZGL8/uE4I5L2zcUTz3ZbM9qRFKk9uSm0CNMDZWCg0wEs11CbrUN2VU2ZFfaEBOqxvR0I8YPimg3i7+0zoHbPs5CQqQGZruEa0ZE4aaLenTZ03rZS4ew6aGuaTLmVdsw94tcXNwvFM9cm9Lp9Q5Bxpq9tdiV34yyeicarCJCtAqoWsu4OgWC1SkhOkSFFKMOVwxyGegdLYAFNXbc9nFWk4JjpeMHR/R7bGKSpqNEHG+wfFe19PFvVbUOQR5JRFWe9MEYGwbgCyIa5uPbOyfAGNPq1FzWvePik2aMjvZ7wZFlO6ux9Ug9vrp7QIffWZ1ZwMaDrg1BrWToF6tHWqweKdE66FsNTIcgo6TOgaxKK3KqbDA7JFwzPApTR0W1a1ASEWa+dxzNNhHDk4NhtktYNKtPlzVRX1xfjGFJQV1KcLTzEh5ano/Segc2PjSk07A7EWF3Xgs2HapDYa0d1c081EoOOjUHjjEIkowWu4QQnQKJEVqMTAnGdedFoyPdaSLC1LePWxoswqroENX1S29P03uacd0ZcqpsmPdFjt3qlK8nok2e9sMYywdwFRHl+fD2zhno1IpF/eP0c9+7KVXv71rzBTV23PN5Dj68JbVDLVNelPFbZhNW76tFicmBfnGu+Zoaq0eoTgkFxyBKhNoWvnW+2lFksuOCvqGYnm7E8J5B7e6ZS36twIYDdRjdOxh7CsxYNKsPBiZ0zWjdlduM7/bWdlmnfMWuGiz9rRJLb01FalznY1Q0OrHsz2pkVdpQ2eSEU5ARrFNCxTFIRLA4JDAGxIVpkBqrxz/Pj+5UE3bxLxXiN7trNnOMXfHK9b0M5/cJ7dK9uws7L+GBr/Ns+TX2da1Gq0dGI2PsFQDNRPRypxe3A/8r57cBxphGr+ZWPT4pSetvYxUA1EoOL13XCzd/lIVL0sLa/BCOllrw1vdlaLaJmJZuxAPjB3dI3r6sNXnqBLdmzT4T3v+pHP88PwY3XdTjrE22upmHSsHh3TmpcAoy5n+dh8pGHg9MSDgrXN8WJJnAdcFM+CWjEYu2lmLW+TFYu78WTVax3ecoq3dg7X4Tth6ux8AEA8b2D0danAF9YnRnZY1aHRJyqm3IqrRh9V4TPvy5AlNGGjFlZFSbPL3eMToEaRVBI5KD+z82MUnlL2MVAGaP6aFw8HLU8l01PzPGhnlIFK8CsMjX93auQKviXhyWFBQ9Pd0YkOp4s8fE4ECRGV/9WY3bLjk787fRKuC9H8vxR04zxg0Mx5uz+3S4wF+Y+t/FvNjkwLr9Jty8JAsjk4Px4JWJiD5D4N/mlGFxyHh+ei+c1zsEi7aW4p7Pc/D89BR0hXsty9ShUsIJlNY58NzaIiRGalDbwuOPnCZc2r/tcKbV6eLdrt1ngiQTJgyJxDUjopAWpz/LCyNKhCKTHTlVNuwtNGPGu8dxcVoYZqQb2/Q8McYwKiXY8Fd+801LbktT+MtYBYB+sXq8d1Oqbt4XuSsZY+cR0XEPu3oJgMmX93augDE22qDh5i30o2f1VPSO0eG+K+Lx4vpifHZn/7P2P0kmrNpTiy//rEafGB1mj4nBRalte/tP4MRhz+KQsPVIPV7dXAoFA+ZPSMR5fc7m4VY0OpHeKxjPTeuFP3Ka8MiKfDwwIQFXDuk8+UkigqILziMHL+ODn8uxp6AFY/uHYdnuWrwwvW2nEBFhT6ttcKTUgnEDIzB7TAz6nfCwnrI+EBFqWwRkV9pwrNyCh5bnIyFCg2mjjBg7ILzN93RZ/zDlyt01U1+ckQJ/GasAoFMr8M6NffV3fZYztbTO8SRc884TbAHglbydN5SAdQDuJ6Jyd9vq1IpXh/cMuu/N2X30geBynsDmQ3X4bk/taRPKIchY8msFfjzWgH9dmYjLB4R3aaNpCxUNTizaWop6i4CnpiTjBCfX6pQw+8NMPDE56eSHZXFIePv7MhwoNmNBF5Kf8mvs6EguqNEqYNGWMuTX2PD01GQMSgzCOz+Uoc4snEVCF0QZX/xRjTX7TJg8PBLXekCCz61yeaJ/z27CPZfHYcqIqNMWhV8yGvHhzxVYMW+A33RuTwURYe4XudbMCuvrTkF+3u8D/o+BMXY/gBZPKAGMsXS9mvt91f2DdL6Uf+sMtc08bl6ShXdv6nuaMfprRiPe2FaKK4dE4taLYxHkYSUoOy9hxa4arNprwr3j4jFpeOTJb/iVTSUQZcJTU5IBuL6vDQfq8NGvlZh1QTRmjzn7UHoqapp5aFVcu15NSSZ8+1ctvvyjCrdfGocZ6UYcK7diwXeFWD5vwFntTvDRB8QbMHN0NEYkt+9paguNVgGbDtVj9d5anNc7BA+MT8CpCRkNFgGzP8zEohu67pXyFusPmOR3fyjPsfHyEG/ExM9FMMYGAHiCiG7yoK1Wr+ayn5jcM2n84IiAbbBEhAeX52NIYtBph8zSOgcWbigGxxgen5TksdoFEWFnbjMWbS07+Q2fiHjsyW/By5tKsHzugJP/dkKxJy5c3Wnyk9UhodkudrgPHi6xYOGGYgyIN+DhqxKhUXK48aNM3H9FAi7pf3oVvuomHi9tLEadWcD158e4nRgqSoQd2U34bk8trE4JT0/9rz1x4l08+V0h4sLUeGBCYpf79QZVTU7c8EGm3S7I6USUEZBBz4A3BmsxgLHuauwxxqLVSlaybv5gbSA3P8D1I9/3ZR6uHhaJicMiUdnoxIPL89E3RodHrk7ySA6jrTG2HK7H+z9V4I5LYzFjdDTe2FoKhyBjQevmdypOZAX3jzNgeroRo1KC3TKYq5qcWL+/DhsP1eHqoZG4c2zcSe+oQ5Bx00eZuH98wskki9wq1ySODlHj8clJbstknYmCGjteXF+MEJ0CC6YkIyZUjQaLgBsXZ+K1WX0wKECbH9CqX/lBht0uyKPd9dowxlQAdGdq954rYIwtAlBDRK+72zZYp9w9f3zCeZNHRAXudNmK1XtrsTu/BW/c0AeiRHh5UwmOlVnw9NRkt/hmHSGv2jUnYsM0eH56CjIrrHhhXfFpm98JVDU58dLGEtSbBcw8LxoTBke4JQ/n4GX8eLwBq/bUIkjrmjMJp/D33/6+DC128SSVx+KQ8M4PZdhXaMaCKT3xf+ydd5gUVdbG37c6zfQEchhyZgAVFTCAYlxFcc05B9TVFXX3M6y7hjWHVTGtWcy6oAgmzAooOQgSZsgMYWBy7Nxd5/uje1xWCdPdU3WHmvo9j89jjV11X2Ru17n3nvOeEWlWePtCMTz39VbMXhu3tDq8fyuICP4+eQO6tfXgz38wr2+GiOBPr6/xFRT7Hg1F9PuTvT9hRVdr0aKrQwE8KyKHJHuvRl5zcK+cJ5+7rH+WmRtCQHyhdvELq/DhjfuhldeJr5dX4onpm3HlUV1wziEdUt4M2pn6YAzPfL0VC9bHvxe6tfXgvOdW4o4/9vzdzms4quP1WdsxdVE5xh4Y35zplkS9jIhgyaZ6fJTYJb11bI//CU6XFtXjzg82YNL4eGGziODjJeV48btinH9YR1w8as8L28aMP31ZJZ77eivOHNEBV4zOg9NBfLO8EhNnbccb1wwyZUOogamLyvRnv05tkZkokoxKGt0k07W1ul1ESpO5z+3U7jx+vzZ/v+eM3qb0Gf4tP66uxhuzduCeM3rhhrfW4JJRnWFE+7htlSHc+PYajDmgHSbPL8WkG4ag7W6O2fyhGL5eXokPF5bFe5kPaYv8LvHc2Q65bogIflhVjWMGt0YoIr/mz85fX4vlW+px8tD4RNxVesX3K6vwwYJSvHDFQMxbV4N/frQJ40/ohpOHNo1HHBBfDb710w5MXVSGpy7uj6mLyuB0EDePMWfltzNTFpbKC98Wz60LRkclcx/J0QAeFJEjDZKmFJLjAFSIyNQk7xuY5dF+/uLWoZlGtzHeFcGwjtMm/IKXrxqI577ZBl2Pe6Om0vFtT0SiOu7/uAhltfGd0WMGt8Gpu8k/FREs3FCHjxaVYcnGOhw7pA0OSBRT9GyfAYdGLN9Sj065brTLcWFzRTB+1LfFh+9WVmL/7tk4a0QHHNo393cvcF8w7ijy7vWDoWnETW+vxaAuXtw8pnujUocay4L1tbh/2iZcPjoPvdpn/Lo7ZebLD4gvAM57bmUwHJUeIpLU8T7JEgBDrejDSjIfwDgRSaorEElmebR1D5/bt88hfdO3r0qFe6ZsRH4XL7xuB16dUYwJF/dHv05N/7r/8pcKPP3V1vjpxBYfnrpk9/mnWytDmLqoDJ8vrUB+Fy8O75eLgXlZGNA5E16PAzuqwyivj2C/blmoqIugcLsfBcU+fLuiCiRw1ogOOOmAdrvMYb9j0noM75OLM4a1x4Qvt2DJpnrcd1Zv9G3CP3NpbRj3T9sEt1PDvWf0wvn/XoVHzu9r6oYQEP/uG/fqat/Kbb7xIvJ6MveS/DeAAhF5LtXxTS26IunIdGk7XrhiYPv8Lsa2Cd0dMV1w+oTlEADXHNNlty+lpqCsNoyrX1uNtllOTLxm0F4/35Dz8vGScqzd4UdtIIZITBCN6YjEAI+TcDoIr9uBLq3dOKRvLi44vCO8nt3vDEdjgjOeWo4rR+fh5R+K8cj5fVNuP7c3vl5eiae+3IJQRMf7NwxJe/c2FUIRHSf9a1kwsQJsdDEGyWMA3CMiRxunbt8j0+3499mHdLj6hj90M/c4ZCcmfLEZizfWoUsbDx46t69hrUp1XfDAx5sws7Aa027eH43xMNxRHcI7s0vwy5Z6lNdFEIrqiMYE4ajAocXz5z0uDW2ynBjSNQsXjeyEXh32/CJ77LPNyHQTP62pxfFD2mDc0XmG2OBtqwxh/FtrkJPhwB8Pbo+zDzG27/vuuGfKxsB3K6seiMT0pHLjSJYDGJRsoGtlSI7skOv66uOb9882sm5gT/yyuR53TFoPTSOev3wAurczrk5lRkEV7pu6CX/+Q1ecNWLvv7/BiI6pC8vw45oabK0MIhDWEYnpiEQFOgCPU4PbSWRnONC3YyZOHtoORw9qvcf5t2hDLZ6YvhkH9szB+tIAnriwX6O+O5IlGhPcN3UjNpUH4fVoePGK1LpypcuctTW4+8ONa+pDsfxkCrBIvghgmYi8kOrYZhddHdE+1+VRFawCcQsJj0vDyUPbGRqsAkCHXDeev3wArnylEIXFPuTvoZpw7Q4/PlpUhu9WVGFw1yz8Yb+2yM/LQv/OmfC4iFOfXI5pf9kf26vDKCyO77B+sawSMwqq4w4FuzmadDqIUQNa4blvt2LCRf136TnZVJywf1vouuDx6VvgVPRl6XFpOGN4B23KwrIbAYxP4ta1AJ42SNY+CUl6nLzkjGEdlAWrQPwlAsYNwY0sINE04u+n9kJZ7Vq8N7cE1x7bdbefrfH/164mw+XAQT3jO6wD87zokOPGvVM34oT922JI1yys3hF3E1m51YfrXl+DsQe126N5+qkHt8ef31yNcw7pmLT9XTJ0bevBc5cNwBUvFyC7if05k+H8wzpmziqsvpnko0na5twBoM4oXfsiXrc27txDOnpVBasA0NrrgC+s49Vx+YYGqwBw9KA2qPJF8eGCUpx6UPvdNtaJxgQ/ro5bOK4vDWBEn1wcMaBV/FSkXQZmFVZj8aY63HhiN2wsDaJwe9y+8eFPivD18kqcNaLDbhsKDeudg9pADKu2+fD8FQOb9CRkZ5wO4u4zeuO2/6xDMKwuC+awvrlwO9kVIYwAsCCJWychzSLJdFICZgA4XUSqk7jnljOHd3jwtlN6mL/1lmDSvFJ8s6ISL105sEl7G++JLxMddV6/ZtDvvFpLa8N47LPNWL3dj9OHtcepB7fHbzsH6bqgqCKI3r/ZmWlokzdlQdw8/brju/6u+CkS1XHxiwW4ZFQnnHKQsQF6A89+vRXFVSE8dK45DRJ+S3FVCOc/t9IXjkmuFfPbUoHknQAKReTDJO7plZ3hWPnt3w5UtsLcXBHE1a8W4pVx+ehh8MuvgfK6CC55YRUmXNzvd4vMhvSX9+eW4MiBrXDmiI4Ysgt/yJKaMLIzHL97eW2uiDsUTF9agdH5rX9X/AQAr/xQjGWb6/Hspf1NmT8rtvpw2/vr8M51g3ebtmQ05z23sq6oPHiGiHynREAzI5HDeqWIXJvMfbmZztVPXNhvgJEbE3tC1wXXvbEGR+W3xoUjk29LnAoiglveX4+Bnb245tjfL/Dmr6vFI58VoUPO7pvk1AWiCET0350K7uzM4XTwf4qpGygs9mP8W2vw3vWDf/fuNoJgRMdlL63CNcd0TbnVe7q89eMO/Y0ft7/lC8WuMHPcdBKWDkR8w7LR5GQ4jhrSLUtZsLqtMoSJM4tx9+m9TAtWAeDEA9qiaxsPXp/1X4tQEcFnP5fjshcLkN/Fi49u2g9XHd1ll7/wmsbfBasNPz+0by4eu6Afnr98IKYtKsdNb6/Fjur/NpJ448cd6NrGjbEHtjPmD7cLrjmmCzaWBfHtyirTxtyZLm08DblGjTPVQ9xqLVHEYVV6AUj2223YwDyvsuptEcFDHxfhitF5pgWrANA+x4UbT+yG+6dtQjT23wX92h1+XPVqIX7ZUo93rhuMu8/ojf26Ze0yqOzUyr3LnZYe7TJw04nd8dHN+8PpIC56YRXmrP1vp9LV2+ONAe45o5dpi739umXh5APb4fHpKffdSJuR/XMzNOLwZO4h2bFJXdKbF60Rn7ONhqTbH471HrAX/04j+WBBfAPt/MPMSy8hib+d0gNTF5Vh9Xb/rz/3BWN4+JMiPPxpEW4/pQdeviofJx6w6wY/OZnOXaawZXkcOPuQjnj3+sE477BOuOnttXjlh2JEovF9kEhUx/3TNuEvY7qZEqwCQIZLw12n9cIT0zejsj4VB8f0GdY7R3NoHJ3MPSRbkUzrizydgPVzAElVe+kiw/KbuP1qMrw7pwRnjuiAHiZ4v+4MSdwytgemLCiDLxhDNCa4f1oRJs0rxdOX9Me4o7vs9iijsfTtlIlXx+VjWO8cXPFyAZYW1WFLRRBTFpbhjj/2NHWn0+PScNfpvTDhiy0IhFNujJEW+V28OoBhSdxyIoC3DZLTHFgKIKmudE6Nw4b2yFazVQNgyaZ61AaihhRF7o0xB7RFToYTMwrii65vV1Ri/FtrcfaIDphwUT90apXeyynL48Dtp/TEXaf3wmOfbcYrPxRD13U8+mkRxp9g3suvgauP7oL1pUHMWVOz9w8bwOAuWa7sDMdRSd62FYCyDRCDKQfwU5L3DO6Q4w40dUFiY4lEdbz103bcNrZHk7gBJEOHXDcuH52Ht36M198VV4Vw2csFEADvXDc4bZ9Skhh7YDu8+adBKCz24/o316AuEMV/5pWic2s3Thpq3oYQgHhr26Ht8Py320wdt4F+nTLhD+vdkwxAnwdwdjrjppzDKiIXJXtPJCqtGtv/uqnxBWP4dkUl3r1+sJLxO+a6MaJvLj5fWo6lm+vhD+t45ar8Jq12djqIy47MQ36XLPxt0gYc3Ct7lykGZjCkWxaGdMvC18urcNowc1IRdmZoj+zsJRvrDgPwXiNvcQBQE12bQCqVmR6X1q1Djsll4zsxZWEpzjqko6mnIQ2QxDmHdsSHC0oRiQme/3Ybnv2NH2xTMKJPLiZenY+b31mLrZVB1ASiGHNA2yYdozF4XBouP7IzJs8vxcgBxpmQ746BXbyIxOSgJG+z7JwVkcUAFid5W4f2OS5lKVAzCqvRq31mk1bHJ8MpB7bDqzOKsXxzPe78cAMuNsABqGOuG/+6oC+e/morrn9jDar9ETxxoTmpO7/l0iM64+xnVuCGPTQHMgqPS0PnVu7AtqrQAWh8Hmva89XUl1FM4HSZ0HVjV3zxSwWG98lRErw1cNaIDnj9xx3whWJ47Py+TW7N08ChfXPx4Dl9MGdtLfbvbq7txc6cNaIDpiwshYr2v51buelxaXvvS/tflgFIuXrRimhEpqr5WlobxqINdUqCtwaOym+N9SUBPPv11t81L2hK2ma78NxlA1BQ7Eev9rtvDmI0xw5ug9Xb/dhSkVYzmpTo3MqNYERPNmXlelg0YE0Rj6sx7ZoMYsqCMpw1ooOq4ZGV4cCRA1vhtknrcemReYadzGgacfOYbji4Vw4iMezSTtIMWnmdOCq/NT79uVzJ+Hmt3QLg920Id89rABamM2bKERPJX0gmVQ6nEbFIzPzgBQBmFFSbvm3/WyrqI/A4NTxyXt9d5tE0JcN65+Dvp/bEc99sQyiiZtF9SJ9c+EM6Cor9e/9wE+N2aiDQ6KW+iGwQka+M1KQSko+TPCmZe0QQVDVff1pdgyMGtjKs4rYx1Aai0AV49Py+u8whb0pyM514/vKBKCj2Y8VWn6Fj7Q6PS8PYg9ph2mLzX4AuByECLZl3ioi8lGpf8+YOyRNJPpnkbWFV87XaF8XaEj9G56srAxARlNSEccJ+bQwPnMl40DqkWxZem7l97zcYxJkjOmDqojIlm0Iel0YAjY7WReQbEVmXzpgpRU2JRPf9ASQVCbkcrKvymV/DISJYvd2PIbvooW0WlfURTPhiCx44p09SLdrS4YT926Jvxwy8MqPYlPF+i6YRh/fPxc+bzHeeiekCAcJ7/2Qckl6SakouzaEXgKTyUUNRvbTKF1XyBiwo9mGwwvkqIvjX55tx2rD2TdZRa2+0z3HhLyd1xwPTNilbZI7q3wpLi+pNH1eP/5YJGvlOYRzjPL/U0wbA7n3Vdk1llS+qZIe1cLsPA/O8htrO7Y1vV1Sh0hfF9ceb062NJP5+ak989nM5Vm1Ts8gc1MWLQFhHWa35xVfRmAiARg9Msp2qoisCmJzs6tbp4NKdq/jMYltVGJluTZllCxBvuXjyge1M70xxy8k9MH1pBQqL1Uyo/C5ZKFTwd14XiCGmIxmbgrMBPGOUnmbAXABJlYFHYrLwly315kcviFfL78m32GhmFFRjY1kQ1xjog7orjh/SBr07ZOCNH9Xs2gzI82J9aeB/3BHMoC4Qg8vBYBLvlEwA643UpJitAOYkec/KkpqwV8Vip7DYj/w8dfO1xh/Fk19uwZ2n9TK1W1u7bBf+MqY77p+2CTHd/LU9SQzs4lVyilkbiALJ+SC/BeD4dMZM6W9WRHQROS/Z++qCsZmrtvlMXwqs3eGHSquP4qoQ5q2vxVVHJZPu0TS0zXbhkiM64725SXXQbTLy87woVDCZVhX7gr5QbH4St1i2gAMAROQJEUnm/wcALC4o9ptedBXTBZvKgoa0c2ws78wuwfXHdzW9VSlJjD+hG6YsLFPisJHlcaBTrgsbywKmjrtmhx+Zbq3Rnelg/fn6k4gk1chERAIZbm3L+lJz/+4AYG1JAAPy1M3Xz34ux6F9czHE5A0hADh+vzbwuh3/Y09nJvl5XhRuN3dDSkSwsSyYAeCXJG5zAkjriN3Ub2MRLFy8qc702eQLxZBrQKu0xvLx4nKcPLSdaakAv2Xsge0wd22NEs+2nu0zsK0qZHqOzfItvhCSq7KdDWCiQXL2Vdb7QzGttLbRmRVNQjCiw6ERGYoMCgqLfaioj2CUgmp5IO4jvH/3bHyzQo2PcY/2GdhamZRjYdoUbvdLOCrJ2DiFEC+6stkJEZm7fEu96Vt99UF171hdF0xdVI6zFRV8kUwUGKvpENyzfQa2mTxfi6vCIOATkZIkbnsKwIp0xk01hzWb5JIUbp25pSJo+uo9GhNluTWRqI5Pfi7HmcPVVU/mZjpx9CA11YROB0HC1CPGYETHtspQFuLeo41CRNaIyCwDZSmF5Gskj0zmHhHRXQ5+8OmSclMTz1XOVwCYsrAMZwxvr8ROq4GzFb4AM1waQlFzj5V/3lTnC0b0uY39vIiEReQtIzWphOS5JB9I9j5fSH/jg/llPrM3CKIxUdaOe8GGWmRlOJTsrjZw3JA2WF3sN32hB8SLJUMmlxqs2uaDy8lGv18BQES+EJGt6Yyb6haGC0CfZG8SkbAAL3wwv8zUv1W3U/u1M4XZrN7uR4ccl+nNCn7L8UPaYt66WjWDC0z1qft+ZRUy3Np8EWn0OQnJXJLqPJSMpyeSqOhswB/WJ0yeXxq/RgucAAAgAElEQVQ2c8HhcWoIK5qvADBvXS2OG6L2V+HQvrnYVhlClfkZVBABNBPna40/isWb6lwAGu3SQdJJsruBslTTFkAquxzfVfoitcu3mHtE7HYSqhwK5q6txXFD2ijxQm3A49JwxMBWmL9ewTtWALP/6NMWl9fXBmLvJHMPyTySnnTGTTVgjQKYnMqN4ag8P31ZhdQFzNu0aZftQonJx5oNFBT7MUhh8UgD+V28WL3dD93kxPBQRAdJU3fM3p2zo64+GHs0ydsuB3CvAXKaCz8ASNouQkSWxgTrZxZWGyBp13hc8d+XGr/5jiIV9RGEIjq6tlHbQEnTiIGK8r8DYd3UdIxPfy7XXQ5+KiLJbCl3ROMNy/dF1gNINuccIqIHI/oT78zeYeovTrtsF0pq1LxjC7f7MaiLuhqVBvK7ZCkpbvaHY6bO122VIazYWg8A/0ny1ikAhqczdqpFV3Uick2K927WiHcfn77FtLyAgXlqgjUgPpnym8FkauV1orXXiS0mH1lsLAugZ/u0FlVJUbDNh+KqcAjA9CRvtXoRx4MisjKVe+uDsZsf+6zIb9YikyQGdFYTrK0u9mNAnlfpbk0D+V28Shw2NpQFTDNDj8YE788tCfhC+hNJ3mr1+fqNiKSUU68LXlm4oa5+rolFQKp+V2O6YO0OPwYqbPnegKoC441lQVObF3ywoDRCcqKIJBvD7Vudrhrwh/WbZxVW15rVt7p1lhNZHge2VpmfX7KxLIC+HdVVT+5M306Z2GByBWlhsR8DTbI7icYED3xc5AvH9LtFJNmJ8RWAd43Qta8jIt+HYzLp8S/MW2QO6pKFAgW7FRvKAkrdCXamX6dMbDR5vtb4o6jxR9G9rTmLzHfn7IgGI/pyJL+bWAXgZgMk7fOISF0gol9479RN/vqgOTF9fp4XK7ea74BXUhNGdoZDaVF1A307ZWJDWcD0AuO4pZg5AfuG0gCmLi4PByNJLzAB4D4AShoHdCGZTEXn/yAi9YGIfuE/p270m1GBHNMFDo2xJQoM7INhXWm3np3xuh0ImuzRF0+JMGcyvTtnR3RHTWhlTMdLyd4rIqtEJK22cc0Zkh+RPDDV+wNh/aaZBdV1362sNOXbOBTVZfaaGtOPRAItfL7GF5heU9rDbiwN4PVZO0K+kH5Bsp7eIlIvIh8apU01JK8m+bdU7xeR78JR/YMHpm0KmnGymJ3pwOaKkOlpPMFI85mvGS4NECBm4pTVdUl4Vhv/jo3GBHd+sMEXjem3iEhSnt4AICKfi0hald+p7rB6AKTVTkJEvg9G9Puvnbjab6Tdkq4LHvy4KFhRF1n7n7mlpi8BYwoSoneHxl87yphCJKpjVmE1RvTJNXysNdv9mDhze9gX0s8XkaS/Mki2bQFFVylvQ4hIXTCij7lvWpFv3jpjT0ZmFVbj0yVl9Wt2+ANm27XoIs1mvpLx7w8z+XpFJUb0yTF8nGBExz8+2OCLxPRbRWRTsveTdJPsaYC05kI7xLtdpYw/rF+/YEPtqn9N3xwyctevvC6C8W+u9bsc/OXTn8tNXWHFdEEzma4A4rnnZjYQWLSxDh1zXWiTZXxTpImztkdLasO/pLIhBAAke5BMqzgg1YDVD+CDdAYGgFBEf6TKF51wxcsF/mIDjuvDUR13TdkYmFFQtTIck8NKasMBs1uoeZxUWvG8M+GowO00b3rPKKxGOCZ6tcGr7s0VQfz5zTX+cFQuF5GNKT5mPKx9xPg5gLR8kkTk51BEH3P7pA313680xiP0q18q5K4pG+vCMRxP8rUpC8tMreTwODVlbVF/Sziqw21isWJ9MIZvV1RKeX0kbGSAE4nquO39df4dNeFvYjpeTPEx/QB82ZS6mhkrASxK5wEi4veH9eO++qVyzX3TNgWNcPrYUhHE5S8V+GsC0Yf9Yf3a9+eWBMysFfG4NITVdI/+HTFdEI2Z+459d05JpCYQDRmd+jFtcZn+3pySSl9IPyfZ05Cd+BJA/3R0pFp0VSIit6YzcAOBcOzOKl/0Hxc9vyrwyZLyNP5f/C+FxT5c8O9Vvnnramb4w/poEamJRGXC67PMzQzPa+0xvdBpd2ypDCKvtXkFUO/OKanzhWIP3fzOWr9Rdh/rSgIY90phIBDRb9JF0llEWb2I424RKWqC58wORfTR903bVHbf1E3BpvqirA1EcdeHGwIPf7q5NBTRR4nIgmBEf2bq4jLdzGPGvNYeJV6Ku2JLZQhd2pg3X6cvqxCng998saxy61NfbTUkaA2Gddz87jr/im2+n/xh/dw0vvCtPl8/lfS+zxqeU+0P6yNnFVbPvvD5lb61TWQeoOuCKQtL9UtfLAjUBKK3hCL6AwDmByN68QwTHUU65rhRVhduFptC2ypD6NTKbVrBZmlNGD8X1UV9odi7414t9BllgTdpXknsqS+3VgUj+hEisi2NR+2bRVe/JRTVnwpE9EOf+nLLmuvfWONLp9CgNhDFv7/ZGrn29TV1xVWha30hfayI+AEgqsszizbU1cwycULFrWnMLx75LaGIjqJy89pd/ri6GpvKgj4A9wUj+pjb3l9X9+9vtkaa6otF1wWT55fq414tDNQFY1dGovqraT7yQwAfNYU2qyMiPwcjer8fCqomn/3MCv+MgqqUj8GiMcF3K6tw9jMr/D+tqXknGNH7icjyxDjrRPDmY59vNq3ySFW1864ws5iixh/FKz8UB3wh/Z5AWB/x6ZLyVddOXO3bXt10wfuKrT6c/++VvoJtvs/8If0UEUnnDbsVwO1Npc3KiEi9L6T/YWtl6MZxr62uf/n7bdF0HD/WlwRw7eurff/+ZltBIKIPC0f1FxLjiC+kX/3wJ0UBsxaZGW4N3dp6TC8m3hVmOwI9/fXWgEPjK8GIjNtRE37u/OdW+puykL3GH8Udk9YHXvyuuDgY0YeLSDKtk3fFLUjBWnFnmMoCl+QgAE+LyAnpDL6L57rcTt5G8tZ+HTO1C0d2yhk9sBVczj3H1SKCgmI/Js0rDfxQUKW5HPzUF9JvFJHtuxjjyJwMx1cf3rhfZiuv8ZWFc9fWYOLM7XhlXL7hY+2JVdt8eOiTIrxz3WDDx6oNRHH2Myv8tYHYWBGZAcRNg7M82lutva7D7z2rd9Z+aXQl2VwexD+nbvQVlQfX+0L6uSKyuqm0WxWSMwFcISIbmvi5f8jyOJ52O9nj/MM6ZvzxoPaOttl7z6eqqIvg4yXlsUnzS0OxmGyoD8XGN/yu/Ob5WZkube29Z/XOG53fuiml7xJdFxz38FJM+8v+MOP7YU+cPmE5nrm0P3q0M96y5s4PNgRmr6152x+KXQvEjfndDt7u0PiPG07o5jnt4PZaql7K/lAMr84oDk9ZVB4MRfRrAExusqM0i0LyNgABEXm2iZ/bLcujPRuNyZjj92sr5x7aMXNA58y97gqGozpmFlTjnTkldUXlwWhMl4cjMZkgIr+LTL0ex0uj+re65IFz+piyO3LPlI04qGc2TlfYTRIAnvlqK1p5HbjsyDzDx5pVWI17pmzcHojo/Rsa5JA8NsOlvX9Ufuucv4zpntk6K7XvLxHBDwXVePiTokBUlzcCYf2Whk0/1aQasB4EYKKIHNT0kuIJ9QDOyM5w3B4M6/t1a+sJHNAj2z0wz5vhdWvQGM8LLaoIRpdtrvet3RHIIFEdicozUV1eFZHSPT3f63E8P6Rr1mVPXdzfa7Sh/RdLK/DwZ0X4/P8OQI5C641XZxSjyhfFrWN7GDqOiOCuDzcGZq+teccfiv2PVy9JErjQ49Ke6dHO47poZOecYwa3hnsvCxIgnh80b10t3p2zo37FVh9FcF8kJk+kYF+1S0h2AhARkcqmeF5zg+R6ACeIyHqDnj88y6P9NRSRM1tnOcODu3p5QPfsrDZZLroc8S44lfURWba5vr6g2MeaQMzldnCyP6xPEJGf9/Ls0dkexxcTr8n3Gh28ldWGcdELq/T/O6mHduIB6mrwNpYF8Oc31uCz/zvA8Ir9xMtvRyC+u/0/x0Ekh2R5HG86NAw655CO7jOGd3C2z2lcgcfG0gAmLygNfbGsEg6NX/pCsWv29t3cWEh6AXRMpWBrX4DkIwCqReQRg57f2engNS6NN5DI6d/ZGxraIzu7e1uPw+3UoIvAF4qhcLs/uHyLL7StMpSV4dZ+TjRk+WRPu+MkszJc2tq/ntS906kHtzf0FFfXBVe+Uhhu5XU4nr5kgDK7ABHBhc+vwm1je+CgXsYWLdb4ozj7mRWBumBszG/biZPM9rq1J6K6XHZUfuvY+Yd18g7u2jhf6bpAFJ8trdDfn1virw/GSvxh/XIRSdkN6reQ7AegKJ2TlVQD1l4ALhWR+1IdOImxsgEcCGBYlkcb7tCYA8CpC3yBUKwwJlgIYPGudlP38EyX1619NaJP7mEPntMn06igde7aGtwxeUO9pmHJtcd0OfK8wzopKWiMxgQnP74Ml4zqJJcckWeohtdmFEfemVOyORDWDxSRXboykHQC+GN2huO2aEwOGtA5MzS0R3bWoK5ZjvbZLridRDQmqPZHUbjdry8tqq8vKPa7CBTVh2KPApiUgmnxHiE5AcAWEXmyKZ/bXCD5KIB/pWsr0ohxnADyAQzLcGmHup3sBCBDBIFwVN8Risp8AIsBrE5mseF08JpWmc4Jr47L9xqV11lRF8HVrxX6S2rDH/fr5D3lzWsHGV8uvxse+bRIX7a5Xn/3usFOIwPWVdt8+POba/yBsH6CiMze3edIDvW6tb9EdTm3S2tP5IAeWe4hXbMzerb3wOPUoAsQCMewvjSI5VvqfSu2+vQqX0QEeD4cledFZEtT6iY5EsDjIjKyKZ/bXCB5DoB6EfnC4HEIoAuAYQ4NI7xux0ASWQAiMV1qfSF9EeLzdWmSra4HZLi0+X8/tWerE/Zva8gvsK4LHvlsc+ib5RUFMcHAD8fvl9mxlZoOdUuL6vB/762T964bzE4G1on4QzFcM3G1b1tV6EV/KHbL7j5Hsr1Dw1Vup/ZXr1vzDu6ahaE9srP6d/Yyy+OAUwNCUUFxVQgrt/nCyzbXB4rKghkup/alLxR7HMDspj4FIVkMYEQ6ebApBaxWgGSm1619vl+3rEMfPKePtyl3P0UEX/xSKY99trk+GNFPBODs1Mo9fdrN+2Wr6KAzs6Aa90/btC4S0zv947Re2UZ8gYgIJs7cHnl7dklZIt+lUQsIknkAhjs0jMj2OEYL0FEEHhJhAtWBsD47HJP5ABal4QDQGB3PAFgvIk8bNYZNerid2g1et/bY05f0z8xv4nbHm8qCGP/WGn9tIPp4KCoPZLi00lfH5bdW0UTAH4rh5Md/CQFSMGpA64H3ntnbkEX18i31uOnttQF/WD9PRD5tzD0kswAcBGBYToZjNIkBIsgkoRPwR2Ky1B/WZyMe5CxPM091TzpGA3hQRI404vk26UNy/wyXNvO647q2OvfQDlpTvvsC4Rge+LgoOHdtzSp/WD820609cf5hHS+79tiuSo4xb//Pet9Pa6q/b5vlOu6lKwcasqiuC0Qx/u21vs3lwan+sH5pYwJKkhqAQYhvIIz0ODlCgGwROEkEABTVBmIzEZ+vi0XEsAIfkiUAhorIjpSf0VIDViC+05rp1p53O3jhPWf09o4c0CrtZ1bUR/DQx0X+JUV15YGw/kcR+YUkvW5tzd9P7dnv+P3MPWaMxgSXvrSqfkNp8FoAyzNc2oxzDumQc/UxXVyNOYpvDLWBKB77bHNg9tqa4kBYPzKZ3e7mQiLNpb4JEsttDEQjz3E7OfGCwzt5rjoqz7W3/Pa9EdMF780pib06szgU0/GXSFR/GQA8Lu2fh/bNve1fF/QzPWJ9fdb26LuzS76vD8XO8Lq16X06Zg6/76zeWU31EtR1wdTFZfqzX28LBiP62Ubv4hkByQ4A9hORH1Rrsdk9JPt53dqnAzp7e/zzzN7ezq3T3wFdsqkOd3240R+IxL7wh/RLRcQfT13RFk65cf+UczdTZWNpAJe/XOALRaWby8HLMlzaQ/88s7d3VBPEEw0UbPPh7x9s8FX5ou8EI/r1qXiNq4bkqQC+FpFgys9IMSXgMAC3i8gZqQ7cnGhIVh41oFXuFaPzMlLZVfGFYvhiWYW88N22YEzHC8GI/o+d/2JIHp7tcXz3wY1DMs0w+W3gzR+3R9/6acdiX0gfKSI6yc6J4qeRD57TOyvdnarZa2pw79SN/khM3guE9b/sLg3ARi0klwM4VkTS8mJtDpDskuXR3m6T5Tr0xhO6ZY3s3wrJ7kDqumDhhjo8981WX3F1aFWi4cSvBWkkMzPd2pq7Tu/V7djBafm3J8XG0gCueKXQF4zo+4nIpkTx020OjXfecEI3z5nD26e1U1VcFcI9H230rS8JbErYSq1qQvk2TQTJhxFPm3lDtZZ0Iel0O/l3B/m3K4/K85x6cHstlYLGzeVBvD17R+ibFVX+YES/XEQ+2fm/e92OZw/pm3vVo+f3NW2RGY0JLn1xla+oInhLNCYvAgDJYzJd2vtHDmyVe+vYHpnpnN6Gozpe+aE4MnlBWTAc0f8kwPstuWAx1YD1GAB3i8gxTS9JDSSz3Q7ermm8oVf7DMcFh3fKGdY7B3sqMAhFdKzd4cenSytCX/5SKS4HZ9QHY3eJyC4NnzPdjqcP6ZMz7rEL+pniffHbl1/DzxPFTxe4nXzx4F45vODwTtnDe+c0urgjEtUxs7Aa784pqdtUFvQFIvqF+/pOB8luAPwWLrqqADBARCpUa2kKErl3Z2dnOO52aOxz3qEdPccMbu3o0S4Djt38Huu6YGtVCD+trtHfm1sSCIT1Hf5Q7AEB3trVjgXJkdkex7dmLTITpyG+ovLgrdGYvPAbLYO9bm1ymyxnz4tGds468YC2TKYlZWGxD/+ZVxr4YVUVRPBAOCaP7arCe1+BZC6AthYuunoJwBIRSamrUHMksQt6XzQmJx89qI2cPqx95qAuWchw7/6UpNoXxZKiOkyaV1JfWOwXkK+EIvqDu/qeJunNdGlr7jqjV1ezFplvzNoefXv2joW+kD5q50CSZHamW3sKwEWnD2uvnTWio7tb28afkFTUR/DJkvLYpHmloUhM/8kX0i9L5yi9OUByCIDCdAqlUw1Y8wGMEZGnUh24uULSBeDUnAzHTaGofrDbqXFgnjfava3HnenWHJGoSE0gGlm1zR/bURP2Zrq1LaGI/l4kJi/sLZk4sWuz4srReT0uOaKzoecWVb4ILn+50F9eF745GpNXdqMnm8CFXo92u9ft6HjUoNbuIV2z3Pl5XnRvl/HrrlU4qmNDaRCF231YsdUXnFlQLQBW1cUrRqcZladmJiRfB/CjiExUrcUISD4J4M7mYk/SlJA8yOvRbobgxEhM2vbqkOEf0NnrzvI4nATgD8ei60oC4Q2lgUxNY61GzPCF9CcBzNvbboXX7Xiyd8eMa56/fGBWhsu4gmcRwUOfFAW/W1W10B/Sj9qVrkSQfmy2x3FbRJfRo/q30of2yPbmd/Gif6dMZLo1kISuC7ZXh1G43Y9V23zR2WtqAiU14VAkJhMa46KyL0ByLIA/i8jJqrUYAcmLAWxqyirt5gLJjk6N4zLd2uX+cKxXhxx3cHBXr9Ymy+VyO8lgRNeLq8LhwmKf5g/rWqZbW14biD0L4MO9HSeTHOl1a9+8dOVAb//Oxu4LzVlTgzsmr68LRWV/2U1TFpJ9M1zaDSIybkBnr35I39zsQV28Wn6XLLTLdoIkRAS1gRhWb/ejcLtfFm+s8/1cVOd0OTjFF9KfFJElhv5BTIKkH0CHZIr3fveMFry7vFcSL4ieAIYB6AogA0AEQB2AXwD8kmw+BsluGS5t8bXHdml3weGdDLHhqKyP4E+vr/aV1EaeC4Zjf2uEJgI4BMDonAzH0TFdhvnDekeNEAAQAbI8jq0gFtQHY7MAfCsiBUZoVwXJtwB8JyJvqtZikzok2yBeFJQPIBMAAQQArEO8qCAplwSSmtetTR6Y5z1pwkX9vXvaDUoVXRc8+cWW8PRlFev8Yf0wEalrhK5uAE7yurVRDo2H+0KxPiJwaBr0mA5Hhkurdju5zBeKzYjpmA3g+6aygGsOJPLhrhaRP6rWYpM6CQvL/QAMBdAKgBtAEEAJ4oVAG5LN19TIc7IzHG8+f/mATKOC1nnravC3SRv8wYh+nIjM29vnSWYCOMnl4GGZbu2oYEQfEo5KlkNDLKbD4dQY8nq01eGo/BSM6HMBfGZkAZQKSIYAtDI9h9UmPUj2zHBpc04f1r7D9cd3bbLiJwBYs92PW95f568NxJ5O5NGm9BecCGI9AHTE/Ukt/YtCcjiAUhHZrFqLTfOCpNPr1t7t1tYz9pHz+jZZ8RMQr/x96NOi4Px1tav9Yf0YEalKRycAF4DQvliUkQwJd5FeIjJXtRab5odGnpfp1ibed1Zv7xEDm67hiIhg6uJyeearrfXBiD5GROak+iySDsQD9Mi+nJ7TWEieBuDTdL6bUk0JGAPgYhG5ONWBWzokO2Z5tLdbZTpHPXBOn6zBXdMrfopEdUyctT3y3tySUCQqN8R0e6fQ5r8kPPB6iUhYtZZ9EZJaIsf9rvEndPWcMayDlq4/6py1Nbj3o43+cEwmBcL6+HSOymysRSKH9WsRmaJay74KydGZLm3yEYnip9w0rSt3VIfxz482+tbs8G/xh/Wz7IJF80k1YD0TwCVWcQlQRaL46Xy3S3vpuMFtXBcc3jEj2SOMYETHtysqMXHm9vqaQHShL6Rfko4xb0uFZB8AlVY7hmmAZAyA20rHwipoKH7q3i6j5+VHds4+cmDrpBwKRARLi+rxzpwS/+KNdb5gRL9ARL4zULIlIdkW8eNFw7yZVUJyEoCPRGSSai37MonipwlOjRdeekTnjD8e1F5L1vZqR3UYUxaVRj6cXxaNiTwSjspDLWFHtClJnNgeKHvparjX56QYsB4IYLiIvJrO4DZxSHZwOXi9Q+ON3dt6XKcNa58zpGsW+nbK3GXb0vK6CAqLfZi/vjb82c8VusPBBYmWeV9Y/ejeKEhOBfC2iHykWosRkHwWwI3270f6JI7ez83JcNxGov+Zwzt4DuqV48jP82JXdj2BcAxrdgSwYmu9fLigzFfjj9YEI/q/dMHExuSr2vwekhcBGCsiF6rWYgQkrwTwc7oveJs4jLeOvi0Sk1OPym8dOyq/tTe/Sxa6tnH/rm1pTBdsLg+ioNiPL5dX1C8tqtcc5FuBiP6k7dOdGiQ9AOpEJC0jXjuHtRnxa8tSj+N8EIcGw3rXjrkuf5bH4XZoZCSmB0tqI65wVJcMl7YiENa/jcRk4s7+kTapQfITAK/+1tvPxmZPkBzqcfIKj0s72h/W83MyHNG2WU7d6WCWLqir9Ue18vpIhtft2BjTZa4/rL8F4Ad74ZAeJC8FcLyIXKpai82+A8n2GnFZdoZjbCQqQ3UgK6+1O+h2MJtEIBDWY9urw16Xg5VOBxfVBmLTEPc+tdN10oCkF0C5iKRVBWcHrM0UktcB2AxgO4BrALQG8AyAYgBF9guvaSF5CIDN+7rXnY0aEqdOpwF4H3GXggkAzgNQg7j3oJ073ISQ7A6gvb0DaZMqJN8GcDeAzgDeAvAYgGUA1lg1NUwVic24P6TbVS/VlIALARwpItelM7jN7iH5GoC5IvJqwhuW9kvPJhUSq9v1IpKnWotVIXkSgJtEZEyif7dHRAKqddnsm5D8AMArIvK1ai1WhWQ9gM4iUk8yA0DY6u4a+zqp+illIm55ZGMc2wCUA4CIROxg1VhIDkp0z7EiTgDZqkVYnHoA6wFARHQ7WDUWkp1I9lKtw0BaIe4hbGMcqwBEAUBEgnawahwkXSQPTvc5qQasKwF8le7gNrtHRO4WkWlA3J6D5BGqNVmc5xFvEGFFIgCeVS3CyojIjyLyZyBuWUfyKtWaLM7ZAG5TLcJApgLYpFqElRGRQxpM7EleS7Kdak0Wpi2AL9N9SEoBq4jMs+02TOVEAEerFmFxnAAsafkkIgER+btqHS2IrgDGqxZhcRxI7I5ZERF5QURWq9bRgrgV8aDKxhiaZL4a1xzbJi1I3r7TrqoDFg2mmhG3AFiuWoTNvgnJI0jenri056vxfAzgBdUibPZNSGokP97pR/acNZZKAGlb0KVadPVnAD1FxMpHMkohOQVxO40PEy3cYJu+26QCyS4AZopIf9VarArJCwCcJiLnJ0yyHba5uE2qkPwewB0iMl+1FiuSKGQOiIgzce0EELPdd5o3qe6wZiB+hGpjHBsRX5VARGJ2sGosJA8kadXCJBfsIkmjqcZ/i67EDlaNhWR3kj1V6zAQq34XNRcIYGHDhYhE7WDVOEhmkky7RiTVgHURgO/THdxm94jILSLyPRC3zEn4hNoYxxsArLoDWQf7+NRQROQLEfkHAJDsSfJyxZKszqUArlUtwkDeRtxz28YARCQsIoc3XJO8hWSWSk0WpwfiHtVpkWrR1UwR+SzdwW0azSkA7IDVWCxbxCEilSLysGodLYg+AC5XLcLiWHa+AoCIPCsiW1TraEHcgbhdp40x2EVXVobkQzv5ltkJ4cZzNeJpGDY2SZM4BbkxcWnPV+N5G/FTERubpCGZQ3LyTj+y56yxbAZwZboPSbXo6s7EvfenK8Bm1ySS7h8Uke8S17RzbGxSgeRgAO+KyEGqtViVRCvloSLyp8S1PV9tUobkMgDni0iBai1WhGQHAAUi0j5xTSCef65UmM0eSXWH1QO7C4fRFCJeyAHAnkhGQ/IwklY9EnIhvoNgYxylSBRdAfZ8NRqSfUn2UK3DQDJh7/gZSQzAgoaLRKGkPWcNgmSuyqKrWQB+THdwm90jIteLyGIAIHkWyQNVa7I4/wHQWbUIgygD8IpqEVZGRKaIyL8AgGQ+yYtUa7I41wE4T7UIA3kJQIVqEVYlkdd/cnGE8wMAACAASURBVMM1yXsS1lY2xjAITVD4m2rR1Tci8kO6g9s0mjMBDFEtwuI4YdEiDhEpFhG7Nat5DAJwjmoRFsey8xUAROQJEbEDVvO4W7UAi9Mk89UuumqmkHyO5MCdfmTZL+dmwtmIH+va2CQNyfNIXt1wCSCsUk8L4FkAH6gWYbNvQjKP5NuJfycAHXYKhpGsAHB9ug9JKWAlOYHkzekObrNHRiJhHi0iF4nIJMV6LI2IzBORkGodRkByFMmfVOuwOH0S/0BEPhKRcxXrsTQisl5EtqrWYRQki0nmqdZhYbIBHA78mr/qsnNYjUNEakRkabrPSXWH1Yn4isTGOJYBqFUtoqVA8uhEuz4rYs9X4ymGbYtmGiQHk+yqWoeBuGDv+BlJCDsVXdkYC8l2O9l0pkyqAesXAOwexwYiIleIyFoAIHkZyUGqNVmcaQCs2umkCMDrqkVYGRF5U0ReBgCSB5O0c1iN5a8ATlItwkAeB1CvWoRVEZHNInIhAJB0k7xPtSaLMxxA2s1rUi26mi4idsBqHuchcdxoYxhOWHRHQ0Q2iYgdsJrHUABjVYuwOJadrwAgIo+KiF+1jhaCB/EFkI1xqCu6Iqk1GO3aGAPJd0h2S1zGAERU6mkBHAfAki8IxrELLA2E5DU7WVkJgKBKPS2ABwB8rlqEUdjvWGMhOYBkg9WfBsCnUk8LYA6AW9J9SKovsYkALkt3cJs9MhLxlR9E5I8i8rViPZZGROaLiFV3bMYC+FS1CIvTD0A3ABCRNxo6XtkYg4isExEru3r4AWSoFmFhWgE4CPi1IKiTYj2WRkSqmqJrW6oBq91313jmw171mUaiF7xVdzTs+Wo8GwBsUi2ipUDyIItX0dtz1ljqYRddmUbCRizt1uCpBqxTAKRtUWCze0TkAhHZAQAk/0yyt2pNViURqE5XrcNACgG8o1qElRGRFxus50geSfI01Zoszj8AjFItwkDuge29bRgiUiAi1wMAyVYk71KtyeIcBeD2dB+SatHVNBFZnu7gNo3mIgBdVIuwMA4AulV9+ERktYhMVq2jBTECwNGqRVgcqxddPSQithWdOeQCuFa1CIvTJPM11aIrl13EYSwkPyPZJnEZhN05x0h0AIepFmEUJB12n2xjIXkLyTMSl1FYtICvGXErgBmqRRhBokjSo1qHlSE5nOTTDZcAalTqaQF8iSZof5vqS+xDxH0dp6UrwGa3jERiQSEixyrWYmkSOxkLVeswkAsQ96y8aG8ftEmZAQDqAEBEnlGsxfI0eFRbFA/iAZQdtBpHWwCDgbgnK4AhauVYGxEpB1Ce7nPsoqvmywzEu3HYGEzixMDKJuQO2PlwRlMAYItqES0FkoeT7KBah0HY71fjqQSwSLWIlgLJXiSHpvucVAPWNxH/grYxCBE5U0TqAYDk7STtHFbjyAbwnmoRBrIEgJ3DaiAiMkFEpgMAyTEWXwA1B+5HvEGDFYkAuFO1CCsjIotE5A7g1wr2v6nWZHHGALgu3YekWnT1gYisS3dwm0ZzGYA2e/2UTapYvYBjuYhY1mS9GTISwCGqRVgcy85ZEQmLyJOqdbQgOsBOlzIapUVXmSQd6Q5us3tIzibpSlzWwy66MpJqAMeoFmEUiV7Zdj6cgZB8gOQJicswbA9lo7kaFj3STXS5ylKtw8qQPJbkI4lLQTxFwMY4JgF4ZK+f2gtMxcmH5EwA94jIjHQF2OwakjoAp21tYpMuJMcDGCgiN6jWYlVITgYwpcGL1cYmVUh2BrDM7r5kHCTPBXCOiJyjWotN47GLrpohCSP7L+xg1RxIZpEco1qHgdjz1XiWAtimWkRLgeQxO9n+WQ17vhpPCYDFqkW0FEgOJLlfus9JNWB9DvFWhDYGIHHGNlyTfNDCX87NgU4AXlAtwkB+hG1BZygJo/efAIDk2SRtKzpjeRxAX9UiDKIWwD9Vi7AyIjJTRB4BAJJ9Sf6fak0W50wAF6f7kFSLrv4jIvZugnlcASBTtQgLY+kdDRFZLCI/qNbRghgNIO3dBJs9YlmrNhGpE5GXVetoQXQFYLdSNpYmecemWnSVYxddGQdJL8mfdvpRFSz65dxM2AzgZNUijIJkBskM1TqsDMlnSTb0tvcjXihpYxxnA1itWoQRJDrT5arWYWVInk6yofNSDECFSj0tgJcApN1QJdVOVz8hbrW0NF0BNrvEBeCAhgsRsbtwGIiIhACsUa3DQG5FvGuO7e1oHAMR9/OFiNiejgZjcVvF/oin8OSrFmJhOgLoDgAiMhvAbLVyrI2IlDXFc+yiq+ZJDMCnqkW0FEi2IXmiah0GYs9X45mHeCGHjQmQHEsyR7UOg7Dnq/FsBvCzahEtBZIHkByU9nNStLW6GPEqdnsb3QRIPgvgVhEJqtZiRUgeDOA1ETlItRYjIHkY4rV881VraQmQvBzAGhGZo1qLVSG5AcAfRGS9ai1NDcn2AE4UkXdVa2kJkDwAwGgReU61FqtC8lEAVQ2FbqmSatHVO3awairjAFC1CAtj6R0NEZlnB6umciyAfqpFWBzLzlkRKbeDVVPpBeCEvX3IJi2UFl21s4uujINkJ5Lf7vSjHbDol3MzYQXiRRyWhGQ2Sa9qHVaG5DskG3rb18AuujKaY2FR31uSLtvG0FhIXkbylsRlGECT5Fja7JbHALyW7kNSzWFdgkTCso0hZAAY0HAhIr1FxG7NahAiEhCRTap1GMi9AK5XLcLiDES8sA0iMl5EPlKsx9KIyHoRiajWYRDDAXyhWoTF6ZT4ByLypYhcpViPpRGRUhFJu/2tXXTVPAkA+Ey1iJZCYkf7D6p1GIg9X43nB9jWOKaRaM5gVas2e74azxoAy1SLaCmQPJRk/7Sfk2LR1WWI9822j70MJpF68ZKIjFOtxaokuhLdJSLHqNZiBCSPAFArIr+o1tISIDkewGwRWaJai1UhWQmgvxVrKUjmAThMRKaq1tISIHk4gP3tZg3GQfJlAItF5KV0npNq0dWbdrBqGk4Al6gWYXEsvaMhIj/ZwaqpnACgm2oRFseyc1ZEttvBqqn0B3CEahEWR2nRVWeSqaYT2OwFkv1JNviwagA2qtTTApgN4FLVIoyCZGu76MpYSH5Osk/ishx20ZXRHAigVrUII0h0pmurWoeVIXkTyesSlwEApSr1tAD+BmByug9JNehcA8Cqps3NAS+AnsCvBUF2xxMDERG/iBSr1mEgTwC4ULUIizMAic6BInKFiHyvWI+lEZGNIqKr1mEQxwB4T7UIi9MZQBsAEJEPROSWvXzeJg1EpExE0l5g2kVXzZMaANNVi2gpkOxB8jjVOgzEnq/GMx0W3fFrjiRsiax6ymfPV+NZDmClahEtBZJH7XQClfpzUiy6ugLA2yISTVeAzZ4hmQvgERGxbYkMguSZAC4RkTNUazECkkcB2C4ia1RraQmQ/AeAj0SkQLUWq0JSB+ASEcsFdiR7ABgkIl+p1tISSDjE5InIW6q1WBWSkxEv1J+UznNSLbp63Q5WTcML4CzVIiyOA4Blf59FZKYdrJrKSQDaqxZhVUgS8c5/lkwJEJHNdrBqKoMR9761MQ6lRVc9El8aNgZAcjjJ/yQuBYDl+mU3M6bDwsb6JDvYRVfGQnIuyQ6Jy2IAPpV6WgC9JJXjwX0Aklkk7QWPgZC8h2SD+04tgBKVeloAf0ITNMNwJntDIm+oSETsgNU4sgB0AQARKQEwUq0cayMiPlg7wHgR8SKOKaqFWJh+iO/6QUTOVazF0iQC1SLVOgzkVACnAThftRAL0xmJdqwi8rpiLZZHRJqk9W0qO6x2QrjxlAL4UrWIlgLJgYk8T6vihD1njeYDxO1xbAyGpCtRR2FV7Hes8SwAUKhaREuB5Ekku6f9nGRPVRKdly4WkTfTHdxm75DsDOB2EfmLai1WheRVAEaJyJWqtRhBwgFhrYhsVq2lJUDyUQDPi4iVdwGVkShE3SYilrRWJNkXQFcRmaVaS0sgUXTrFJG0fUJtdg3JrwE8kW5udtI7rCISs4NVU2kN4GTVIiyOpXc0ROQ7O1g1lbEAclWLsDBWn6/r7WDVVA4AMES1CIujpuiKpINkr3QHttk9JI8j+VriMgJgrUo9LYB3ANyqWoRRkOxCMlO1DitDcjXJjMTlJtjpAUZSA8CyzVRIttqpgM/GAEg+RbLBxrACdqcrozkXwI/pPiSVHNZWAH5Od2CbPZKDhC1OYrV9imI9libR6apatQ4DeQd24Z7R9EHCZklEThGRdYr1WBYR0UVkh2odBnIhgPtVi7A4nQBkAoCIPCsi/1asx9KISIWIhNJ9jl101TzZAuAb1SJaCiSHkhylWoeB2HPWeN6Chb18mxMks0leqlqHgdjz1XhmwbaLNA2SZybqcdJ7TgpFVxkAThGRD9Md3GbvkOwH4GoRuV21FqtC8v8QL3L4q2otRkDyBADLEhZpNgZD8gUAdzeVlYvN/0KyJ4AfRaSHai1GQHIggFwRWahaS0uA5GUAqkTkE9VarArJBQDGi8j8dJ6TStFV0A5WTaU9ACtbLjUHLL2jISJf28GqqfwRQMZeP2WTKlafr6vtYNVUDgLQV7UIi6Os6MpFsk+6A9vsHpJnkXwmcRkEYLfVNJbnANyrWoRRkOy1U0GQTRND0kNy5+PF1QDSztey2S1FsHArTZLt7KIrYyH5JsnjE5c7YBddGc3xAJal+5BUcli7A/gu3YFt9kgOErY4IrJURKycr6WcRNFVvWodBvIRbNsWI3Ei3jkHACAix4mI/QI0iIS1YoVqHQZyLQBLpic1IzoCcAGAiDwiIu8q1mNpRKRKRCLpPscuumqerAXwg2oRLQWSh5E8VLUOA7HnrLHoAF7b66dsmgSS7UlepFqHgdjz1Xi+BGB7U5sEyUtItk37OSkUXeUAOFJEpqc7uM3eIXkQgNNE5J+qtVgVkvcDiIjIfaq1GAHJMQDmWdy6q9lA8j3ECyV9qrVYEZJDAbwtIgeo1mIEJIcA0ERkuWotLQGS4wEUiojtzGMQJNcgXqyfVnpjKkVXdXawaiqdARymWoTFsfSOhoh8aQerpnIqUju9smkcVp+vK+1g1VSGAeimWoTFcUJR0VVmwmrJxiBIXkXyocRlHeyiK6O5H8CTqkUYBckBJD2qdVgVkp1I7hxgLIPtyWoky2Bh5xSSne2iK2Mh+clOaWCbAdgWdMZyIOIdANMilV2AQQAmpzuwzR7JAeAFABH5SURuVKzH0ohIQESs3ErzS9g7CEbiAtCm4UJERln890kpiaKrWtU6DOSvAK5ULcLidEAi/hGRu0XkM8V6LI2I1IqI+TusaKKtXZs98guaoO+uTeMgeQzJg1XrMBBLH6E2A/wA3lAtoqVAshvJ81XrMBB7vhrPR4jbWdmYAMk/kcxK+zkpFF21BTBUROwqdhMgeSSAI0TkYdVarArJZwGsFZFn9vrhfRCSJwGYKSJ+1VqsDkkC+FRETlGtxaqQ/9/emcf7NpZt/Hsd5xzDOWbHPE8h81yGMhSRpEJSEkqoFN5CSRmSDJV4DUUSkqIolMxDRObMM5nn2cFxvX88z3Y275n23uvZz2+v3/39fHystffaa11n79+z1rPu577uWx8C9re9dm0tJZC0LPCa7btra+kGJO1Nuj9eWVtLW5H0FPD+gZb7G97XH7D9LFFyaTCZF1iutoiW0+qIhu3zamvoIqYCNqwtouW0fbzeXFtDl7EqcHttES2nWqer6cN0VRZJe+S3PoBnCdNVab4FHFdbRCkkLSOpzy+nwZSRTW1X591hwNWTOj4YMJcCG9UWUQpJ80marbaONiPpn5KWzLv3AG1uRNEJzEeaywyI/uSwrkqLH+4dwmhgJIDtv9v+fmU9rcb22Ca6cHQwlwIz1hbRYkaSxiy237C9ZmU9rSabrtpsatsH+HRtES1nFsAAtvewfVllPa3G9ivua/7pBOhvp6so2VKWa4gozaAh6eOSlq6toyAxZsvyLPDb2iK6BUmLSvpMbR0FifFant/SQMQvmDLyqvGAV/n6Y7qaHVjYdkyoBgFJGwGL2/5ZbS1tRdLJwN9tt3LSkT9D59uOh2BhcifAE21HhKwQkjYFtrf9idpaSiBpeeBZ29E6dBCQdDBwmu0bamtpK5LeBEbZfmMg5+mP6epJYEBOr6BPLAAsUVtEy2m7iSM60w0e09DiovYdQtvH6421NXQZqwFhTC1LNdPVzGG6KoukH+X+xgBPAFHepCxfAH5fW0QpJK2Syy0FBZC0mqQL8+444KqaerqAPwNb1hZRipzyMEttHW1G0p2S5s67twLRuros0wJvD/Qk/ckpWBfYGvjUQC8eTJRRjO/CcWZlLa2nC5bKryG94Q446T2YICOBqeGdsn+b1JXTbmy/DQxoabHD+RFwBi1+ie4AZiJPoGzvUllL67E9tonzhOmqM7kYuK62iG5B0haSFq+towSShgHOD/mgDI8Ap9YW0S3kMm2b1tZRkHjGludo4OXaIroBScMl7dnIufphupoLmDMSlAeH3IJwRtvH1tbSViT9FTjW9l9qa2maPGH9qO2/1dbSDeT746G2t66tpa1I+iKwru0v1tZSgtwm+lHb0Tp0EJB0LHCY7ah3XoDckvUp29MN9Fz9MV09Bjw20AsHU8xCRA3N0rTWxJEjqzFZHTxGk0wcQTmG09LxCmD7+toauozVgQFPpoKJ0tjztT+mqznCdFUWScfmKALAw6ROHEE5NqalLlFJU0mKCVRBJG0g6ay8+zphuirNCcAOtUWUQtJSkmauraPNSHomR/4gpd+9WFNPy3mJlDM8YPqTw/pxYO/JHhUMhGl7NmyfbPtXNcW0HdtvN9GFo0MZDfyjtoiWM5IURcD2w7a/UFlPq3GizTnZPwNWqS2i5YwmR/1sb2f7vsp6Wkser3UirERC+GDwV+CW2iK6BUlfkrRgbR2FiPFannuA02uL6BZyGbGP1dZRkOHEmC3NIUCb23F3DJKml7RHI+fqh+lqXmB627c3ISCYNJK+ArzW1i5MnYCkK4C9bF9eW0vTSBoJfND2JbW1dAO52sR3bG9fW0tbkfRNYCHbu9bWUgJJKwH35xJpQWEk/R7YzfYjtbW0EUnzANfannuyB0+G/piu/jvQiwZ9YhGi53FpREtNHLkV3iW1dXQRMwLL1RbRckSLI5C2o6Th4LI6MKK2iBYzFQ1Fs/tjuppX0iJNXDyYMJL+KGmzvHsvcH9NPW3H9hq2/1lbRwkkTROmq7JI+qyknhWQl4Cra+ppO7Z/anv32jpKIWkFSTPU1tFWJI2Q9FqvL10JvFpLT9ux/ZDtBZo4V39yWLcEojNEWUYyvgvHcbYjPy7oL3MAf6gtouW8E52xfYftr9UUEwx5jgOWqC2ixQwnRekBsP05209W1BNMIf2ZsEZCeHlOB+6sLaJbkPT1XPC9jcR4Lc9/SP3tg0FA0rqS1q+toyAxZssyjtT+NhgEJM2e884Hfq5+mK7mB0bYvrcJAcGkkfRt4B7bZ9bW0lYk/QfYynbrKjNImhZY1va/amvpBiStDGwbUdZySPoB6dm1b20tJcifoTttv1RbSzcg6Xxgc9sv1NbSRiQtDfze9vsHeq7+mK4eGuhFg0kjSb3qgi4KxEAqyzhaGtGw/RoQk9WCvGe8zkws55bGtLgkke1/19bQZiQJUn3Q/KXV6JUiEDTOMFJDlUZO1CckLSJpoSYuHkyUiyStm7dvBR6sKabt2F6urWXaJI0O01Vxdpb0v3n7WcJ0VRTbP7R9YG0dpZD0gV5dmILmGQP0zlm9AHijkpbWY/tm2ys1ca7+5LB+Cdi6iYsHE+Wd3ru2f247esEH/WURkokjKEfv8Xqd7e9V1hMMbU4CBlyzMpgo72qmYvvTtqNKwBCgv6arVtas7CBOAB6oLaJbkLRXi3t3x3gtz7+Ac2qL6BYkbSJp7do6ChJjtiwvAz+pLaJbkLSApEYqS/XHdLUg8FY0EBgcJP0IuNT232traSuS/gus3sbPtKTRwKK2b6ytpRuQtB6wge1v19bSViT9DHjQ9k9raylBNl39x3YjeX/BpJF0LbCa7bdra2kjktYEDra9xkDP1R/T1QMDvWgwaSQNB8blpPBFgZsqS2o7r9Ne09XLQExWCyJpKgDb44BZgcjxL8tbNGTi6ETCdFWWbLqayvZbeXtlkpEvKMfLTZykP6arJSU10rUgmCjXAivk7euA1kX+Ognbi9p+vLaOEkiaJUxXxdkT2D9vP0Yav0EhbO9h++jaOkohaR1JU9fW0WIWB27L28OAs9zXpeZgirF9he0NmjhXf3JYdwE2aeLiwUTpbeI42PaVlfUEQ5dliXyt0vQer5fbjt93MBBOB6I1azl6j9dxtj9ZWU8whYTpqjM5ghSpCQYBSQdJmqa2jkK8yxEbFOFi4B+1RXQLkj7b8lWDeMaW5Sng8NoiuoW8Kv+VRs7VT9PVa7afaEJAMGkkHQX8zvYVtbW0FUmvAHPkfM9WIWlGYC7bd9TW0g1I2gxYzvYPamtpK5JOJBlRf11bSwkkrQTcZDteNAuTTann2/5gbS1tRdLHgZ1sbzzQc4XpqgPJ7TTHZtfiYkAUkS7Ly7Q0opHbDUantIJIGgm8nScYY4B5KktqO2Npt+nqutoa2kw2SY7IVRhGAEtWltR23gZebOJE/TFdrSBp3iYuHkyU24AF8/Y/gVYagjoF23PkFqatQ9KcklapraPlHAjslrcfJBklg0LY3tH272rrKIWkjXoqTwRFWBm4LG+PA86qqKX12D7X9lZNnKs/Oay7Ax9u4uLBRHkn79D2D2xHWaugv6wOfLe2iJbT28Txd9vHVNYTDG3Oon/P5mDK6D1eX7S9bV05wZQSpqvO5EfAc7VFdAOShkk6oraOgsR4Lc9fgEtri+gWJO0gabnaOgoSY7YsDwK/qC2iW5C0sqRtGzlXP0xXCwEv2H62CQHBpJF0KvCT6FRUBkkjgFdtj6itpQS55ezMtu+rraUbyDfm2aO0VTkknQWcYLuVS7mSVrR9fW0d3YCkuYGTbK9fW0tbkfR5YEPbnx/oufpjurp/oBcNJo2k6YGXe3W6iiLS5ZgKeLK2iFLYfo6I1hclmyTH2X4DmJ1kvArK8TLtNl3FZLUgOUgxwvarwDTAIpUltZ23aMj42x/T1QckzdXExYOJ8jAwU96+AHi6opZWY/t12611dUuaP5fJCcpxBLBt3r4TuKGelPZje2vbf6+towSShkv6RG0dLWdd4M95+1Xg7IpaWo/t02zv0sS5+pPD+j0gHoBl6Z0UvrfteyvrCYYu6wFfry2i5fQer2fZPrWynmDoMi0Qn5+y9B6vj9vetbKeYArpz4T1nT92UIw9afGSVychaZSkw2rrKEiM1/KcBlxdW0S3IOlbkt5XW0chYryW53bg2NoiugVJH5LUSFmr/pqunrb9UhMCgkkj6W/ALhFlLYOkMcBttluZdyhpFmA62/+traUbkPRN4E3bR9XW0lYkXQp833brKjPk+qtL2L61tpZuQNISwCG2N6mtpa1I+hrpM/21gZ4rTFcdiKRZgWez6WoR0lt3UAYBj9YWUYpczSMqehQkmyTfsD2WZLp6pbKktvM8LV2Bsj0OiMlqQSRNDYzMQbfpgGiEVJbXacj42x/T1To5KhUUQJJ4t8nqL0RrzWLYftJ2a2s6Slpc0gq1dbScXwM9RpmbgFsqamk9tje1/a/aOkogaVpJEe0ryyeB4/P2c8BfK2ppPbZ/ZXufJs7VnxzWA4C25g91AsMA5+gqtnez/URlTcHQZWPgi7VFtJzeJo7f2w7XcdBfZgOOri2i5fQer/c3NZkKytNf09VbTQsJ3sHAgHM9gilD0hhJB9XWUZAwcZTnl0A09hgkJH1f0gK1dRQixmt5riOtigSDgKSNJX2qkXP1w3S1IPCE7deaEBBMGknXAB+33dri9jWRtBhwru3FamspQc6HHh5R+sFB0g+B+2z/praWtiLpJmAb2zfV1tI0kkYC89u+p7aWbkDSasButresraWtSNqHlDM84Eh2f0xXDwz0osHEkTQMmK3XBHWhmnq6AAOP1BZRCtvP1NbQdnIlhld6ma7i5aAsTwFja4soQe6WFpPVgkiajvQS/yIwijRmg3K8TDI3D5j+mK42ljTT5I8M+skMwN299n9P6sYRFMD2PbY/XFtHKSQtI2nZ2jpazunA2nn7alKdx6AQtte3fUdtHSWQNKOkjWvraDlfAA7N208A51XU0nps/9T24U2cq88RVuAQYHNSaZGged6Vw9RE7bKgq/kMKYp8c20hLaa3iSNSAYKBMD9wMHBObSEt5h0fTq53G2XEhgjR6arzeBX4Vm0R3YKkBXPeYVuJ8Vqew4FWRvw6EUmHSmrrMm6M1/JcRrS/HTQkfVbSRo2cqx+mqwWAx3KuTVAYSfcAS9tuZaHs2khaFTjS9qq1tZQgm66cGwgEhZF0BHCJ7TNra2krkh4C1rL9YG0tTZOL2s9h+6HaWroBSR8FPmt7u9pa2oqkw4FHbA+4BXqfI6y2H4zJajkkDZc0V68vLUC8cZfkTeDh2iJKYfuZmKyWRdIc2d0NMAaYuqaeLuBhoJXPINtjY7JaFkkzSJox744GwpNTlueo2Olqc0mjmrh4MEHmJRk3ejiBmLAWw/YNtj9dW0cpJK0qaanaOlrOOUBPt7SLCZd3UWyvYfux2jpKIGn2ppZPg4myM7B33n4Q+EdFLa3H9v62T2jiXP3JYf058UZSkvearna0/XZFPcHQ5vPAR2qLaDm9TVfH2b62sp5g6PI+xk+mgjL0Hq/X2Y7OYkOEMF11Hk8B36ktoluQtLSkNj8gYryW54ekSE0wCEg6TtLo2joKEeO1POcAZ9QW0S1I2kHSuo2cqx+mq/mAR23HoCpMTsC/y3Zb2xBWR9IGwO62P1pbSwly8EVpjgAAIABJREFUUfu3cpHsoDCSfgucZDuWGQsh6UVgPtsv1NbSNJKmAWay/XhtLd2ApC2BD9retbaWtiLpBOBK28cP9Fz9MV09HJPVckiaWtI8eXc4MFtNPV3Aa0BrTQ62n43JalkkzSdpRN6djf7Vtw6mnHtJZsnWYfv1mKyWRdIsvZofjc7/BeV4koqmq2163ZyD5lmK8UWjxwHHVdTSemxfZnuH2jpKIWltSYvX1tFyLgYWzNvnEukBRbG9gu1Wdv/LLz8b1NbRcr4N7JS37ySN36AQtvdsqsxff3JYjwFGTvaooL/0Tgh/3XY0EQgGwg7AB2qLaDm9x+wvbN9WWU8wdFke+HptES2n93i9wvbJlfUEU0iYrjqPB4B9aovoFiStLmn32joKEuO1PLuTlr2CwihxuiTV1lKI4eS2oUExTiNa3w4aknaT1EjQpD+mq7lJna769oNBn5E0B3Cx7aijWYicdP9p21vU1lICSTMDY9u6hNppSDoH2N/21ZM9OOgzkqYC3rTdn2BLxyNpWmA628/U1tINSPoqycD33dpa2oqkM4FTbA+4MkN/TFePxmS1HJKmkzRv3h0BzDip44MB8xItzjm0/VxMVssiaWFJPUarWYC2Rv86gWHALbVFlML2azFZLUtuztDzXB0FTFNTTxfwX+D5Jk7UpwmrpGGSdmziwsFEWQ3oyal5BfhVRS2tx/a5tv+nto5SSNpQ0kK1dbScaxjfTOWPQCu7MHUCtt+0vdzkjxyaSFpc0nq1dbSc/YDP5e0bgcsqamk9tr9h+8ImztXXCOtw4MgmLhxMlN4J4c/Z3reynmBoszOwbG0RLWc448fsYbYfqCsnGMKsDmxbW0TL6f2MvdD2WZX1BFNIXyesYeAoz3+AA2uL6BYkfUTSLrV1FCTGbHl2IK2GBIWRNEpSm13dMV7L80ugkYhfMHkk/UBSI6sifTJdZWfmGNvhiB0EJL0PONn2KrW1tJWc4rKS7a/U1lKCXCD7Ndtja2vpBiRdDWwXpa3KIGlW4G7bs9TWUoJsuhrZxi5enYikvQBsH1RbS1uRdBFwgO2LBnquPkVYnYjJakEkzZDb30KqdzttTT1dwLO023T1fExWyyJpqV5llmYCwpRajreBm2qLKEU2XcVktSCS5n2P6So605XlfqCRbot9NV1NK6m1XYE6hPWAX+TtZ4AT60lpP7b/YLu1KRiSPtWr6kRQhltIS7mQxmu4vAuR8/rXqa2jFJKWlfSh2jpazqHARnn7n8BVFbW0Htvb2/53E+fqaw7rDMCPmrhwMFF6J4Q/avvQynqCoc03gUVqi2grObI6jPFj9sexChUMgA8Dn6ktouX0fsaea/uCynqCKSRMV53H1aQ3wGAQkLSZpO1q6yhIjNnybBG1qQcHSXNIOr62joLEeC3P4aTIajAISPqppMUaOVcfTVfDgBlsN1IENpg0klYDftzmJbDaSPoOMKvtb9fWUgJJM5BMV2/W1tINSLoXWMv2o7W1tBFJiwD/sL1wbS0lkDQNMCyafQwOkg4BHrQd5ToLIekGYHvb1w/0XH01Xb0dk9WySJrlPaarETX1dAFPAA/VFlEK2y/GZLUcuZnKMr2+NJpkDArK8Cap2Hsrsf16TFbLkjvTTZ93pyU605XmduDlJk7UV9PVzJK+1MSFg4myKbB/3v4v47teBQWwfWKb364lfV7S7LV1tJhRvNu0cSQN3ZyD/4/th2x/qraOUkhaTdIatXW0nKOBnt/xBUAjhqBgwtj+nO27mjhXX3NYZwf2auLCwUTpnRB+v+1jKusJhjbfBuasLaLFvCvn0Pb+tmPCGvSXDYANa4toOb2fsX+2HVUChghhuuo8LiDa3w4akraVtFVtHQV5p21oUIRXGd+XPCiMpEUk/W9tHQWJZ2x59qXFaSWdhqQTJM3TxLn6WjD3DmDlJi4cTJjch/wBAEkbArvY3qSmppazBNDmQt2rAK/XFtFWbL8BnNOzL+l5YB7b0aq1DDMBq9cWUZAoG1kY21f2bOeKE5faPqmipLazDjB1Eyfqj+kqbsQFyWVbekxXI4iE8NI8nP9rJbZfsR0Rm0JIGilp2V5fmpaIkJXkVVocHbM9NjrTlUXSEpJG592pCZNkaW4EXmviRH01Xc0laZsmLhxMlM8Bu+Xtu4HTKmppPbaPst1aY5ukr/ZqQxg0zxzAub32f0xysgcFsH277dbWTZa0jqRVa+toOb8Bls7bZ5E61QWFsL2Z7ceaOFdfc1jnB77WxIWDifJOzqHtO9o8mQoGhe8CMWEtx1TAWz07tveNiHYwADYB1qotouW8M2Zza+6bKusJppAwXXUeZwJt7uTSUUj6hqRNa+soSIzZsjwJtDbi12lIWl7S4bV1FCTGa3m+SVq9DAYBSWc2tcrXV9PV1aRex0EhbN/bs53d65vYDhdyOZai3Uu4CwORE1eIXOT9IgBJI4BXbI+sq6rVzAosV1tEQfYAos1vQWxf0bMt6SzgBNtnVZTUdtYhvYgNmP6YruLhVxBJ80maN+8OJxLCS3MP8EhtEaXInXPiAVgISdNJ6plAxXgtz4u023T1pu23Jn9k0F8kLSdp2rwbZf/KcxXwRhMn6qvpaiFJEe0ry5eB7fP2zcAZFbW0HtuH2j67to5SSNo99ycPyrAIcEreHsf4LnVBAWxfa3v32jpKIWljScvX1tFyTgcWyNunAndW1NJ6bG/UVDOVvuawLg5s28SFg4nSOyH8Jtt/qqwnGNr8AIgl6nL0Hq9v2D6wsp5gaPMZYMXaIlpO7zF7iu3IZx0ihOmq8zgR+B2ApGGS+vo3CvqApH0kfaS2joLEmC3LfcBOAEo0kqsVTBhJa0s6oLaOgsR4Lc+XgEcBJE0lKWqdF0TSRZL66peaIH2dDJ0HfLyJCwcTxvbdtu/LuzsRbVpL835gttoiCjI9qdh6UADbL/bqRT4GeLymni5gdmDJ2iIKsi3w29oi2ozty7NZEuBiYO2aerqAdWgot7+vpitHjcGySFpY0tx5N962y3Mb0EhR407E9rgwXZVD0oy9Ol3FeC3P06Tc/laSjc1h3CuIpNUk9aRJxZgtSI5eX9TUZ7qvpqulJG3exIWDifJNoOd3fBXwl4paWo/t/WxfUltHCfIS9b6x5FWU5YCj8vbLRC/4oti+xPYPa+sohaQtJC1VW0fLORuYJW8fBzxQT0q7yUHO9Zo6X19TApYhJYUH5eidEH6t7fMr6wmGLsOAfSPCWpTe4/Ul20dU1hMMbbYClqgtouX0HrO/sf3fynqCKSRMV53HEaT+xkga0VSycjBhJB0q6YO1dRTiXW1DgyLcCOwG75gkp66sp9Xksk/fra2jIMOIZ2xpNgNeAJA0dRiby5HrVF/Q1Pn6msN6KrB1UxcP/j+27+z1xvdtYL+aerqApYFG2sZ1GrbfAGICVRDbz9m+Ie8uCvynpp4uYC5S97ZWYntT0pJ1UIhsuurpbngtaeU4KMNIYJWmTtbnN4tYXiyLpCUlzZV3I6JdnutJ/eBbSYzXskia7T2mq4hol+VR4JbaIkoSY7Yskj7Uq/xcPGPLMg74R1MnU1/GhqRVgHls/7kpAcG7kXQScKHt30hag/Q3umJyPxcE7yW3H9wtitmXQ9IngC/b3kTSbMAnbJ9QW1cwNJH0JeAy2/fW1tJWJI0FZrA9VtL2wFm2n66tK5g8fY2wrgR8rISQACSNAmYFxkgaZvvKmKwGA2A6oLVtLGuTozTzA9NKms720zFZDQbINoxvGxo0SK6aMhcwHJgZwPbxMVkdOvQn2fiNxlV0KZLWkrSfpPnyw+9hUvHxrwN/kTSNpFUk7SCptXlbNZH0K0nL1dZRiGHA67VFtAVJ80raXVJP85Rfk14IpgeeljRTrsu6h6S16iltL5K2kvSt2joK8jaRVtIIeYK6g6Tv5C9tSkonuQS4XdI2kkZL+pykT+eAUdAgkuaQ9NemztdX09Uxtr/e1MW7AUlzSlozby8r6TpJh+ZvLwKIlLY0DpjN9qq2FyA5GfcndT5Zm5y4LOkASb+RtFDej0E2MJYGpq0togS2n7I99+SPDHqQNErSqpJmzftnS+oxUs1Iin71dMnZ1vZCtlcDZgLeB1xEirpunn/+I/kcm+X96aIu7oCYB5i3tohS2F7P9mW1dQwVcmvVJXpq10raVdI9ktbMucDLMn68ng2MyXVBZyG1QL+NZJb8ImmlZISkCyUdmM83sleTgaDvTEuDprYomdQQeQn/7Tw53Rz4re1/k0pUvShpA+BBYBfgVgDbJ/Y+R+9uELbfyFHX+2wf1uuw04DVgZfyg+9uSS8AS5EiPR8CrrP9aKF/atu4Cni2tohgcOk1XucgjclHbR8D/A/wCWBn4BngINK4xfatwDd6zjGR8fqG7W+MvxI3Aicx3th3MLC1pE1tX57vC48At0WHoynifmK8dh35WTfM9jhJXyU17NgZWB44HTiWNPn8G/B34B6A3mPxPePVwJt5zJ5ge798neGkMT9nPnRN4K+SfmZ772ywnAG4wfYrJf/NLeF1oLGyVn01XX0IGGX73KYEDEUkLUoaKGeS3vgvBm62/SlJHyENpjNs3z/A63wYeMn2dZM4ZipgXtsPZl1HAQ/Y3lHSx0h9fE+zff1AtARDD0mzkAxBB9fWUpP8e1gZuMf2fZL+AGxI6ks/mjQJvdj2RQO8zjzAWrZPm8xxcwAv235F0nHAGsAKwCjShPaSXEIw6DIkfR34UzcXs5c0AlgRmNr2ZdkY9WNgV9unStoNeAn4te0BpU9I+gpwqu2XJ3HMdCST1uOSvgB8DfiR7bMk7UNywh9p+8WBaAkmT18nrHsCM9v+zmQPHuLkN7rpbb8oaXVgJ+DM/CH9DWl58IvAK8BiwN0DHTxNI2kZUrTo8jzwTyctV25t+978tni37deqCg2KkF9g/m57kdpaBgNJo0nLf6OAA4HXbH8nP2S2Aw60fYGkxYDHbb9UUe7/Q9KMwOdJ9+UjJW0D7A0clKuGLEx6gX2qqtCgGJJuALbvhgBDjmZOnV/cdgI2IJnOpiZFSf9se7/8gjeMNGY7quRXTvVZA9gHGAHcAZxve1tJM5E8KffG6kkz9NV0NY6WmjgkLSPpy9k4MQ1pCe+c/O1XgSvI9f9sf9H2J22/YPst27eXmKzmfLp+F363fYvtA3vlRO1AWvJ8LEdmjycNMCTNkw0ljRX5HQpIOkNSWyd0Ir1QtQ5Jsyv1XV81759IGrPzk8brg+Txa/u3ttexfUHev7vEZDXnv03f35/P95OjbB+Zv/Q7UnpRT6WQ7YC7euXEfz2bRaaawOlaiaSdJO1YW0dB3gDenOxRQ4yca7qBpC3z/qeBF4Ht8yFPA6cAb+Xc+xV7lultP2H7sRKTVUkzDySn3PafbO+Rgz4vAasBR+dvL0+qQXpYvtYGkrbX+DrrrUfSYnlFqxH6aro6xPa+TV18sJE0XNICeXs5SX+W1JPj8knSm9Jo268D7yeZnbB9s+1f2r5vkCX/jBTFbQTbLzp1+XjV9jjbqzC+a8wI0sP+wwBK7sqzJK2d92dsqVlkadK/vXXkidmykz+yM1FibknT5u1fSzojf3tJUt/12fP+7qQVkQfyZ/uwCuaVdYEzJnvUFGL7zfzSeW/e/x7JLPLPfMiMwBbA25Lml3SppN3hHXNXGz/X8zD+b946bK9me8g2RlBy3c+et78i6WJJSwAGvknyWkDKNR1j+wgA23/I/706wROX4yGS92PAOPGw7X/l/UtsLwjskQ8ZQfKY9MxBTpF0oqTp8/2tjR0XRwGLN3WyVpuuJC0PrE/KCbqXlLD/JKmebM8bXc+Ha//eP2u7E7ofFe/C4VSdANsPALv2+ta5wPOMN4v8FlhD0kq2H5C0BXCrkxFlKHMh6U0/qIxS4f3NgCdsn016YfscsJHtayWdz3gzxaXApT0/a/uZCpLfy2CMV5Me/tg+oOfrkp4iVRXpcTRvCRwlaXfbRyuV2TLJkDmUU4DuoKWrfEMRSZ8CFrJ9WA5u/I2Ub7ofyQT1I+DhvCT+Tg33DjIsDeYz9q9A7xJPh5LmIq8AswH3S7rC9oY5sLYMcK3tJ0rqK8yLpGdsI/Q1h3Ujkgu2MdfXQMl5a0vY/nfO8ToeuMX2N3Lu2srAUbbvkjS17bFVBfcBSesBj9m+rbYWeMcs8jRpkP+WlM/8UUnvJ709n237L5LUablG3YikeYEtbB9eW0sPOW9tMeAp209LOh5YlWRUXBj4HqnzzJ9yOswbQ+WzJGlBYBnbf6ksBXjHLDLS9vNKRpUtgR1s3yLpGFLaxMHk4FBNrUFC0t7Ace6QYvZ5VW0u0srjXTmv+lvAd2yfL+lI4CnSBHUqxpdoHBLk3Nlflkjp64eWqUhR58clfRDYF7jA9iGSvky6bx7r5D/pymdsXyesPwZesH1QOUkTvbZsO0dNPw2ca/sqSVcC05CW84cDHwRuGuJvJUOKnJPzKeBJ23+Q9EPSw3FX239XMn897jCLDCqSVibd4FaqcO2e8Toj8GXglRzp25OUS71zfuB9CHgcuKsbb8A1yJOQrYBFbO+f76l/A060vadStYNRpKoKYRYZRCQ9AKzjAVaY6ee1e8bslsAHSEvZC5FSUE5wMjAuQSrrdMsQj9QPKZSM3+sDv7d9t6SbSLn665JynlekC/4mfTVdvQkU/4VImkvShpKmzknR15NuqJByuMz4Zdw1ba9k+3XbL9s+vy2T1Zw3Ok1tHZMjJ8QfZbsnufoAUm7djXn/W6R6sXNLGibph5I2qSL2PUi6qCfnqqUUT3dQMgd+QNm8Jul/SZ2fZid17pmPNCkFONj2orbPh7S0b/vONkxWlTrTdXweWs61O7VXGtRNpCj3yXl/beB8UvkeJH1eySwy8+CrfTeS9pK0dW0dBXmVwqarfA9eKkfxkPQpSfcBPWa2eUm5nSNIKTizO1cGsn2H7WvaMDHKeaND4t5v+2rbB9i+O39pbaDH7DU7cBw53UDSCkoG6qUryX0HSStJOqmx89V8TvQs0edf7I7Apbb/KOkEYEFSiZfHSF2ebu2gvJdBQcld9wfbp9fWMlByVAdS54s9SQaZb+UI236kmnonKpUCecX2oDhlJT0BLGf78cke3OXk5XyTlv6+D4zIUZfNSX/TH9k+Q6nrzLOkXNQhPxGdUnJk6tO2t6itpQl6Rdy2JtWt3YvU5OByUn7ijqRJzbS2XxgkTUcBt3t8JYVgIuR77ginphZbARuRqsS8SAomnGd7V0lzkgx89wyl5fyBkpfg37Tdnxb1Hcd7VqG3B66w/XtJPyKZ3fa2fZukMcDTg3FvVqol/0PbH2rifIP2h5K0kKQtJY3Jb3h3MD4C9zbJEHUHgO3tbK9r+9EcDbim2yarmaloSV9pj+dV29+33dMP/AZSRPbOvP9N4AXlfu2SPiFpTZVzPP+VQVg1GGpImiGvcvSUjTqU9KBbmvErLZfDOw7flWyfkfdvs91xNRMHgeIGjsGk5+9n+xTbX3AqZi/Sisk/8veXBR5RaoCApOUlfVy5tW0BbgDunuxRXUaOFn5AqVwUktYldWr7bj7kLVKDm9fyPXhx27sC5LF6ZzdNVjNtHa832v667d/nbx1HMpj35EVfDDyuVDVpdF5BeV8hWU+T2lU3Ql9zWDcn5SJePpHvi5Q0/KRS0fLvk/JJD5O0FylSupftO5UMCg934SCZYiRtCNxZI5+pJkpmEdt+Tamn8/r5v7eAE4HLbB+l3F6zotSORtLiwEcnFY1Sqhv6Vv5dHwIsbPvTeaJ6ECnyfXKOwrzUpS+OU4RSQ4IF3EGm1MEgR6pmsP2cpE+QWt3+Mq+W7QtMBxxq+6kYs5NG0kHAARMbZ3mVYwbbzypVavk8sK/tG5SqaNxtexclM/KotqTHlSB/brez/cvaWgYbSbPafkYpX/1w4BnbO+c5x2bAyU6toztqvPZ1wnoMcKNTz+2eG/Q6pKX8OyXdSCrPsED+/8bAVbZvb1x50HVImpZk7hpt+9gcTTiSlBf5s/yW+DbRWQQASesDe9peP+/PQCot86pTNYd9SEv5mzp1gPoCKQXnwi6MjgYFkLQOyQh7JCkq/zhwte2NcvrPYiSzSJSqAiQ9Dyxo+/m8vy6p8sTPlToTXk16GdhVqVTZ7KS2ws/WUx20hexD2JDUav5ySb8jNUDY3PZ/lMxf97hSFYu+TliPJ/1jrrX9yZwX81HgiPyGN4Ojn25jKNWlfDlu5hMmR/TnBaZyqg27G6kv/M62z1Uqov4cqVf0BH+H+SXrA26BieC95IfdKaSJwkqkSNcRwF+c8oVHk5YIY5WjASSNIuUMPl9bS6eiZEpb0PZNklYilSH8l+0dc3RnKVJLzgk2aZF0MCkI8ufBUz14SHqWlP99sO1fKpWNehP4Nil/fOpY5WgGScOAOWw/VltLp5JT8ZYE7iUZAs8jjdEFSW1ndyEFLCdYazU/gza3vVMTevqaw/o94N+kKAzAy8AcpA5JPQJbkcDcIZwGrFVbRKeSc2Ifdmp6gO3DnTqLnJcPeZXkpnxLqZXnTXnJDaXuIjORBmMro4m2LwL+CDyQv/QMabz2dB55i5Z2+arE54Gf1BbRyTi1n70pb19ne3ngq/nbr5IehHMDSDpNqdvemLw/L+kFdfSgCx88diXl6PYEft4iRVHncKoV2upmP4PMzMBQb3xTFKduezfbfiU/bzckpT29zfj543IAkrZT6ra3Ud6fk1TVqbFWtAOqEpAjgGsAjzgV7j8a2JqUN3d1zmd6mJTH2vVLtH1F0sXA/nniEQyA/Ka4IilH83c5+now6eVrIXdA4ejS5N/BB0l55n+UtAHwJ+Aw2/so1W2dHvi37Zdqah2KSNqZtHzbSDSh21FqBLMe6cUd4AnSy+VHbV9ZTdggolRxYxXSOH2FlFLxuO1l8vN3LdKK538ryhySKJW0utX2mNpa2oBS2bs1SU1hrpZ0Falc3i9tf3XSPz2F12g6VU3SLKQcudezc3RFYDVSTuuPSZ0bTpG6s1NDX5C0MXB9LFn0D0krkAor/5FUV/Ax4EHbq0man1Qc+ybbd1SUWRWlblKjcwL+NsBXgP2civofREonONz2yzFmJ02eXMzcLZOppsmR1E2BR3NKz1GkBiQfySlnnwf+a/uSmjprktOg5rT9mKQlgUOAO2zvodQmdU3gJNs3xnidND2eCNun1NYyVMk+knlzjvX6wNmkINtBSiWtpielDDSSKjpodVjz8usWwDjbx0vakVQT7gdOLuT3kVIMHo1BFkwpOQ9zsfxAWxw4htQv/X8kbUdKGD/C9j2Spol84ClH0qakl819SXUa7yDlv34pR3fmIFWxaH10OmiG7HJflNQV71lJvyYtKa4EvI9U6/VM22cpNU0ZG8+DKUOpo+DHSCasayWdR8oz/KTt/ypV/rjN9stVhQZDhp4XJFLFiXskfQn4OrC77YslHUuas/0wr+C55POgWuOAXFJiMVI09iGl4rZfBja2fY2kb5OiYqd3azqBUsvTZ22Pra2lNj3RAkkrAp8kTZyulfTvfMgHSE0JVida8xYh5yTNlV8O1geOJk0uvpMnt3MBZ9t+tKrQSmRD0TDbz9XWUpte43VmUhHzF7KJ6PvAF4Ev274omzIeJb34xMS0QbIJcFmS72QYqW7yaNtL5XSLLYCLbF9TUWY18gRrdtuP1NbSCfQas1uR0lD2IBmsLgGOs723pPeTcshvdgWjcjWDlO1xTm3eHsr7e5OSy6/Ns/oRwAa235b0Pkn/zHmHPUXNO75laQP8hXTD6SokzSlpA6XCxmOUWvOenb89G6l4eU+UYBXbK+fk8Bfdota8nYZTgfEb8vYFthcjlcWCVIB7FVLUlWyWOVXStEqNQmaro3pQ2Ynxv4+uQak17+pKtbXJqWBP5VU1SKbcp/L2/rYX6cnLt31Rfg7EZLVhslHmqnxvHGt7VeD9+dsiRV9XBpD0TUnnSloj78+m9huo5yc3P+kmJE0laUmlElVI2kKpNe92+ZAFSB3tRpBMaWPy/Azbt9r+V43JKlRuzTql5FyTVUnliy6S9BXg58A38lv7+qQH5tW1fpElkHQDsL3t62trKYGkkU5tA5cFdiDV/zxL0skkp/BnSc72lUnJ8bGUNUTIf9PlSP3p5ya18rzC9sZKTUWWIY3X1uRnS/ouaels79paSpCX898GRpI6KMn293Ju6bdIuc9n5aXpp0nmoM5/wAQ9q3mrkSJn90n6G8mguTjJ7LYNcIPtmyvKbJScQnZOfvFuHT2Bv/yM3RrYANiNVCbtOtJq2G6S5gZmINVX7ej0riExYZ0QOcI6wvZLOfL6KWBb4B7gN8BdwIE9xw/FG6ekTYAr3YKi0JIWIkXgLiCVbLmdlA6yXJ7cfAT4m+0oM9JCcrRm5mzuWosUiTzXqWPZN4CFgF/kh2VHdVeZUvLneKqeKPRQJqc3rE7qOX6dpJ+RDHkrk3KZv0fKFT+nosygIHlV5BlgFMkbMI3tzyhVE9kF+KPtc4bweJ0RWMctqOmbJ6erkcqfnaVUAeZU4Ke2D5D0OVLK3B+aMkDVYMhOWCdGfjB+Bpjf9qGSVgPOYXwOxoKkAurd2Du5KHnQzGr76fz2+l1SiaRfKLVoXB74n5y8vTDJsR9/gy4nj9EPA6fZflDSXcDzpJJ5w0iGnJsjwt482bT4llNVl8OAeWx/VtKawH6kkjS/y1GYFxxF67ue/FnYhFTO8q/5c7MpKS/5YqWGEI8Bjw3FQFEnk1c5Rtt+XtJnga1Iz9lbgYtIXeO+odTVcDrgiTb9DVo3YZ0Q2Swyg+278vLVvsBPcjrBl4GpSA/LjupQI2kB0qB/o7aWCZEnpR8iJe7fK+kW0tLCgiQDzsdIEeKuLRsV9J1sFlkiR/YWIJUle9z2Jjnv6sPAX20ZMgTSAAAGuElEQVT/p6bO95IjUuM61XSVI0obAC/ZPk/SD0mVWjayfYmSA/hh2xdUFRoMKZQM1IuTJkfPSjqJdO9fjFQW7wek7mRnT/wsg09epR1j++HaWiaGpPVI98Kj8ovA5cDRtndXans8KymVriPvOU3T9qRq4B2zyF15++Scs/Kr/O1XSUvVI7PJ5/psGiAbRmatoxqAC0kJ0NXIv5MllJhb0p/z8iCkJcM1GN95Zi1S20XbftT28TFZDfpKNotcl7cftL0K8In87bEks8giAJJ+Jumc/PJE/ozWuq/twfiuTVXoNU5nyfuHSLpGyRE9MykvvKdQ+qHA9M51TW3/OiarQV9xMlDf3pO6Znsbkvv+eVK+c88zFkkbSbpS0hfy/mxKtaBr8H6gejqAUtfFBfL2ZpIukfSx/O0tgUXzS8HNpAn27gC2L7b9x26ZrEKXRFinlPygWwaYLy91fAD4G3CK7Z0lrUJqDXiF7acmda6G9NwPrOeJ9NUudM33kSYHV9i+StKlwDykBhBvARuRmhkMmqYgmBhK7TpXBi4DngP+Q4o69BgJNiP1qr9tELQcQuryMmjtWSVNR2oJ67xitCspv3RH22fmXLbnSPmmkX4TVEXS9KQUnxecyuMdSDLsbWL7QklbAE+Snj9FDUA5FekXTtUTBg2l1qVrkFJuxgB3kuYYX1EqGzU3qXtZR634dgIxYZ0MeRI7Kpu7NiXVij0mT2h/Qir9cEA2kwxvcpDl613YdO5evmksDdxAirKfRzJAfSwvM2xKGkDXNv1vCoLSSJrB9os5anEgKZ1gD0mfJH22j7d9RYHxuiLwetOT45y3tjhpjD6gVLN6K2BtUrmoo4FLbP8mHzuuTXlrQbvJS/O2PVapu94awPqklbvjSd0xj8pRxreb+mznFJ6VbP+9ifP1Oq9Ik845c1rTBqQ24L/M/47vkPJLDwNeItVujpfJKSAmrANA0tqkgvW/yF96ktSGbOM8GBanollE2b0paRHgS6SyJGcodZdZBtgceABYh1Q2KuqXBq0lG/0+Atxo+1+S/kQaBx+3fYdSK8G7qGQWyQ865d09SHn331Nquflj4MA8KV2etMx6bzzograSc9k/Dkxt+6ScRvBTYN888VuGVM6ymoG61zN2Y1Le7o9J84D7SN6ObbJJbR7SM/bVGjrbQkxYG0TJcTuf7dvzcsORpKWNb+XozhKkzl1TtJwuaTHg/imJAkmanbRsfx2pBuKVpDe8hZV6Tm9JKiPUlV1NguC95GjkYsD9wBvAuaSc8aWA+YCdSdGdKcrrzObOsVOSU6ZUW3r5fPz1kr5HqpH4cdv/lHQAqUf8yf34pwVB68gvdHOSIpKPKHXD/AqpVvmlkvYhTRZPsP3mFJxvFKmqzUNTeP2lSB6Nc5XMn78jdfrbXak71JzAyYORLtitxIR1kMjR2E8Cv8tL7WeTqhN8gZRjthDwgHvVs5P0NLBk7wEgaZTtV3Lu3p6kKMtPJe0NrAvsYfvGnAvzgKMMTRD0mRwV2YFUh/R/JX2NtAy/v+2/5fH3tO3Xe/3MMaS2wEf3+toI0gN2rKS9SBHdrYEVgGNJD9ejc/rC67HKEQT9Q9JOpGYlO5FePM8hTSj3UWoRPMz2M72OXx/Yy/Z6vb4mYLr8jP0Y8Dng57b/Leli4PH8tdGkCep9scoxeMSEtRL5AbUiqeXo9CQH4BO2V8lL+GuTunl9k1Qa6k5JF5A6fo3JP/MFUgT32hr/hiDoFpRKQq0APOTU3OAkUkrNMk51hXci1aa8GzgvT2q/QVoi/JztPyuV0HsW+JOHYKH1IBgq5BfF95OqYFyuVLP0WNIL56F5MroasHH++vGkyiPXkErmbZNThBYmrUw+XuPfEbybmLB2EJKms/1qzlHbg5S4/TxwmO0r85LjU/FGFwT1yWaRN0grJT8lGRlHkNJ+fp6jOmMjby0I6pMN1COdmmRsT1oxGUbycXyNNJZnjVWOziUmrEEQBEEQBEFH0xWNA4IgCIIgCIKhS0xYgyAIgiAIgo4mJqxBEARBEARBRxMT1iAIgiAIgqCjiQlrEARBEARB0NHEhDUIgiAIgiDoaGLCGgRBEARBEHQ0MWENgiAIgiAIOpqYsAZBEARBEAQdTUxYgyAIgiAIgo4mJqxBEARBEARBRxMT1iAIgiAIgqCjiQlrEARBEARB0NHEhDUIgiAIgiDoaGLCGgRBEARBEHQ0MWENgiAIgiAIOpqYsAZBEARBEAQdTUxYgyAIgiAIgo4mJqxBEARBEARBRxMT1iAIgiAIgqCj+T9JLLHvpgNAcwAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SSR1vQZ1_Ojq"
},
"source": [
"### Data contents \n",
"\n",
"Here we take a closer look at what information is contained within these trajectories."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9x8w3o17_May",
"outputId": "a6ed3414-774f-4e9c-f211-73379999f6a0"
},
"source": [
"i_structure = traj[0]\n",
"i_structure"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Atoms(symbols='Cu27C3H8', pbc=True, cell=[7.65796644025031, 7.65796644025031, 33.266996999999996], energies=..., forces=..., tags=..., constraint=FixAtoms(indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]), calculator=SinglePointCalculator(...))"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4CgeShkN_bdJ"
},
"source": [
"#### Atomic numbers"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cMGTQRIz_f2c",
"outputId": "20442973-b999-4723-ec66-ac169203dfbe"
},
"source": [
"numbers = i_structure.get_atomic_numbers()\n",
"print(numbers)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29\n",
" 29 29 29 6 6 6 1 1 1 1 1 1 1 1]\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ol4Zi2Gh_qU_"
},
"source": [
"#### Atomic symbols"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cwbxks-i_uVq",
"outputId": "4960d233-b6c8-42bb-979d-879b6a20cfd4"
},
"source": [
"symbols = np.array(i_structure.get_chemical_symbols())\n",
"print(symbols)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu'\n",
" 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'C' 'C'\n",
" 'C' 'H' 'H' 'H' 'H' 'H' 'H' 'H' 'H']\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "x57XplOw_yNw"
},
"source": [
"#### Unit cell\n",
"\n",
"The unit cell is the volume containing our system of interest. Express as a 3x3 array representing the directional vectors that make up the volume. Illustrated as the dashed box in the above visuals."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VWMMzn_i_0vM",
"outputId": "9fd0343a-9599-4fcb-911d-87ac48974bc0"
},
"source": [
"cell = np.array(i_structure.cell)\n",
"print(cell)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[ 7.65796644 0. 0. ]\n",
" [ 0. 7.65796644 0. ]\n",
" [ 0. 0. 33.266997 ]]\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XHRbOyaA_97r"
},
"source": [
"#### Periodic boundary conditions (PBC)\n",
"\n",
"x,y,z boolean representing whether a unit cell repeats in the corresponding directions. The OC20 dataset sets this to [True, True, True], with a large enough vacuum layer above the surface such that a unit cell does not see itself in the z direction. Although the original structure shown above is what get's passed into our models, the presence of PBC allows it to effectively repeat infinitely in the x and y directions. Below we visualize the same structure with a periodicity of 2 in all directions, what the model may effectively see."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "htvwgCuFAOSB",
"outputId": "578202d3-f9c5-4857-c2c1-86ee6aaf5aa0"
},
"source": [
"pbc = i_structure.pbc\n",
"print(pbc)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[ True True True]\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 400
},
"id": "Flzo7aO-RgyA",
"outputId": "36835a5f-cc91-48d1-ee8b-8fc5112c0cb6"
},
"source": [
"fig, ax = plt.subplots(1, 3)\n",
"labels = ['initial', 'middle', 'final']\n",
"for i in range(3):\n",
" ax[i].axis('off')\n",
" ax[i].set_title(labels[i])\n",
"\n",
"ase.visualize.plot.plot_atoms(traj[0].repeat((2,2,1)), \n",
" ax[0], \n",
" radii=0.8, \n",
" rotation=(\"-75x, 45y, 10z\"))\n",
"ase.visualize.plot.plot_atoms(traj[50].repeat((2,2,1)), \n",
" ax[1], \n",
" radii=0.8, \n",
" rotation=(\"-75x, 45y, 10z\"))\n",
"ase.visualize.plot.plot_atoms(traj[-1].repeat((2,2,1)), \n",
" ax[2], \n",
" radii=0.8, \n",
" rotation=(\"-75x, 45y, 10z\"))"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 13
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TWGXcH7AARpy"
},
"source": [
"#### Tags\n",
"\n",
"The OC20 dataset consists of systems with several different types of atoms. To help with identifying the index of certain atoms, we tag each atom according to where it is found in the system. There are three categories of atoms: \n",
"- *sub-surface slab atoms*: these are atoms in the bottom layers of the catalyst, furthest away from the adsorbate\n",
"- *surface slab atoms*: these are atoms in the top layers of the catalyst, close to where the adsorbate will be placed \n",
"- *adsorbate atoms*: atoms that make up the adsorbate molecule on top of the catalyst.\n",
"\n",
"Tag:\n",
"\n",
"0 - Sub-surface slab atoms\n",
"\n",
"1 - Surface slab atoms\n",
"\n",
"2 - Adsorbate atoms\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "SGZzFhsrB5A2",
"outputId": "3b2e4e3e-b82f-4e1a-ed88-e53e3040240b"
},
"source": [
"tags = i_structure.get_tags()\n",
"print(tags)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2\n",
" 2]\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0zVhbDL2B8cd"
},
"source": [
"#### Fixed atoms constraint\n",
"\n",
"In reality, surfaces contain many, many more atoms beneath what we've illustrated as the surface. At an infinite depth, these subsurface atoms would look just like the bulk structure. We approximate a true surface by fixing the subsurface atoms into their “bulk” locations. This ensures that they cannot move at the “bottom” of the surface. If they could, this would throw off our calculations. Consistent with the above, we fix all atoms with tags=0, and denote them as \"fixed\". All other atoms are considered \"free\"."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FBMUmGrrCD_h",
"outputId": "4d0aad44-f6bd-491b-d734-5edf5be04031"
},
"source": [
"cons = i_structure.constraints[0]\n",
"print(cons, '\\n')\n",
"\n",
"# indices of fixed atoms\n",
"indices = cons.index\n",
"print(indices, '\\n')\n",
"\n",
"# fixed atoms correspond to tags = 0\n",
"print(tags[indices])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"FixAtoms(indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]) \n",
"\n",
"[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17] \n",
"\n",
"[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_DHAYeBUCHbN"
},
"source": [
"#### Adsorption energy\n",
"\n",
"The energy of the system is one of the properties of interest in the OC20 dataset. It's important to note that absolute energies provide little value to researchers and must be referenced properly to be useful. The OC20 dataset references all it's energies to the bare slab + gas references to arrive at adsorption energies. Adsorption energies are important in studying catalysts and their corresponding reaction rates. In addition to the structure relaxations of the OC20 dataset, bare slab and gas (N2, H2, H2O, CO) relaxations were carried out with DFT in order to calculate adsorption energies."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5XxYqdM7CMdd",
"outputId": "c2f5ea9c-1614-42ef-fbc0-75fddd7c976f"
},
"source": [
"final_structure = traj[-1]\n",
"relaxed_energy = final_structure.get_potential_energy()\n",
"print(f'Relaxed absolute energy = {relaxed_energy} eV')\n",
"\n",
"# Corresponding raw slab used in original adslab (adsorbate+slab) system. \n",
"raw_slab = fcc100(\"Cu\", size=(3, 3, 3))\n",
"raw_slab.set_calculator(EMT())\n",
"raw_slab_energy = raw_slab.get_potential_energy()\n",
"print(f'Raw slab energy = {raw_slab_energy} eV')\n",
"\n",
"\n",
"adsorbate = Atoms(\"C3H8\").get_chemical_symbols()\n",
"# For clarity, we define arbitrary gas reference energies here.\n",
"# A more detailed discussion of these calculations can be found in the corresponding paper's SI. \n",
"gas_reference_energies = {'H': .3, 'O': .45, 'C': .35, 'N': .50}\n",
"\n",
"adsorbate_reference_energy = 0\n",
"for ads in adsorbate:\n",
" adsorbate_reference_energy += gas_reference_energies[ads]\n",
"\n",
"print(f'Adsorbate reference energy = {adsorbate_reference_energy} eV\\n')\n",
"\n",
"adsorption_energy = relaxed_energy - raw_slab_energy - adsorbate_reference_energy\n",
"print(f'Adsorption energy: {adsorption_energy} eV')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Relaxed absolute energy = 8.358921451420816 eV\n",
"Raw slab energy = 8.127167122751231 eV\n",
"Adsorbate reference energy = 3.4499999999999993 eV\n",
"\n",
"Adsorption energy: -3.218245671330415 eV\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EchgyYxXCUit"
},
"source": [
"#### Plot energy profile of toy trajectory\n",
"\n",
"Plotting the energy profile of our trajectory is a good way to ensure nothing strange has occured. We expect to see a decreasing monotonic function. If the energy is consistently increasing or there's multiple large spikes this could be a sign of some issues in the optimization. This is particularly useful for when analyzing ML-driven relaxations and whether they make general physical sense."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 482
},
"id": "WffoTL5pCSrg",
"outputId": "86e7a0fb-7a34-42ee-db58-edd30323eb54"
},
"source": [
"energies = [image.get_potential_energy() - raw_slab_energy - adsorbate_reference_energy for image in traj]\n",
"\n",
"plt.figure(figsize=(7, 7))\n",
"plt.plot(range(len(energies)), energies, lw=3)\n",
"plt.xlabel(\"Step\", fontsize=24)\n",
"plt.ylabel(\"Energy, eV\", fontsize=24)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0, 0.5, 'Energy, eV')"
]
},
"metadata": {},
"execution_count": 17
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "erpOSowgCeuS"
},
"source": [
"#### Force\n",
"\n",
"Forces are another important property of the OC20 dataset. Unlike datasets like QM9 which contain only ground state properties, the OC20 dataset contains per-atom forces necessary to carry out atomistic simulations. Physically, forces are the negative gradient of energy w.r.t atomic positions: $F = -\\frac{dE}{dx}$. Although not mandatory (depending on the application), maintaining this energy-force consistency is important for models that seek to make predictions on both properties.\n",
"\n",
"The \"apply_constraint\" argument controls whether to apply system constraints to the forces. In the OC20 dataset, this controls whether to return forces for fixed atoms (apply_constraint=False) or return 0s (apply_constraint=True)."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NtgLDiT2Cmff",
"outputId": "61a720bd-4117-4403-eb07-4d49fd5ddc22"
},
"source": [
"# Returning forces for all atoms - regardless of whether \"fixed\" or \"free\"\n",
"i_structure.get_forces(apply_constraint=False)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[-1.07900000e-05, -3.80000000e-06, 1.13560540e-01],\n",
" [-0.00000000e+00, -4.29200000e-05, 1.13302410e-01],\n",
" [ 1.07900000e-05, -3.80000000e-06, 1.13560540e-01],\n",
" [-1.84600000e-05, 0.00000000e+00, 1.13543430e-01],\n",
" [ 0.00000000e+00, -0.00000000e+00, 1.13047800e-01],\n",
" [ 1.84600000e-05, 0.00000000e+00, 1.13543430e-01],\n",
" [-1.07900000e-05, 3.80000000e-06, 1.13560540e-01],\n",
" [-0.00000000e+00, 4.29200000e-05, 1.13302410e-01],\n",
" [ 1.07900000e-05, 3.80000000e-06, 1.13560540e-01],\n",
" [-1.10430500e-02, -2.53094000e-03, -4.84573700e-02],\n",
" [ 1.10430500e-02, -2.53094000e-03, -4.84573700e-02],\n",
" [ 0.00000000e+00, -2.20890000e-04, -2.07827000e-03],\n",
" [-1.10430500e-02, 2.53094000e-03, -4.84573700e-02],\n",
" [ 1.10430500e-02, 2.53094000e-03, -4.84573700e-02],\n",
" [-0.00000000e+00, 2.20890000e-04, -2.07827000e-03],\n",
" [-3.49808000e-03, -0.00000000e+00, -7.85544000e-03],\n",
" [ 3.49808000e-03, -0.00000000e+00, -7.85544000e-03],\n",
" [-0.00000000e+00, -0.00000000e+00, -5.97640000e-04],\n",
" [-3.18144370e-01, -2.36420450e-01, -3.97089230e-01],\n",
" [ 0.00000000e+00, -2.18895316e+00, -2.74768262e+00],\n",
" [ 3.18144370e-01, -2.36420450e-01, -3.97089230e-01],\n",
" [-5.65980520e-01, 0.00000000e+00, -6.16046990e-01],\n",
" [ 0.00000000e+00, 0.00000000e+00, -4.47152822e+00],\n",
" [ 5.65980520e-01, -0.00000000e+00, -6.16046990e-01],\n",
" [-3.18144370e-01, 2.36420450e-01, -3.97089230e-01],\n",
" [ 0.00000000e+00, 2.18895316e+00, -2.74768262e+00],\n",
" [ 3.18144370e-01, 2.36420450e-01, -3.97089230e-01],\n",
" [-0.00000000e+00, 0.00000000e+00, -3.96835355e+00],\n",
" [-0.00000000e+00, -3.64190926e+00, 5.71458646e+00],\n",
" [-0.00000000e+00, 3.64190926e+00, 5.71458646e+00],\n",
" [-2.18178516e+00, -0.00000000e+00, 1.67589182e+00],\n",
" [ 2.18178516e+00, 0.00000000e+00, 1.67589182e+00],\n",
" [-0.00000000e+00, 2.46333681e+00, 1.78299828e+00],\n",
" [-0.00000000e+00, -2.46333681e+00, 1.78299828e+00],\n",
" [ 6.18714050e+00, 2.26336330e-01, -5.99485570e-01],\n",
" [-6.18714050e+00, 2.26336330e-01, -5.99485570e-01],\n",
" [-6.18714050e+00, -2.26336330e-01, -5.99485570e-01],\n",
" [ 6.18714050e+00, -2.26336330e-01, -5.99485570e-01]])"
]
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QVgvU-OgCqzx",
"outputId": "1a4bed0b-3554-4b42-b41e-7ca84741d66e"
},
"source": [
"# Applying the fixed atoms constraint to the forces\n",
"i_structure.get_forces(apply_constraint=True)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [ 0. , 0. , 0. ],\n",
" [-0.31814437, -0.23642045, -0.39708923],\n",
" [ 0. , -2.18895316, -2.74768262],\n",
" [ 0.31814437, -0.23642045, -0.39708923],\n",
" [-0.56598052, 0. , -0.61604699],\n",
" [ 0. , 0. , -4.47152822],\n",
" [ 0.56598052, -0. , -0.61604699],\n",
" [-0.31814437, 0.23642045, -0.39708923],\n",
" [ 0. , 2.18895316, -2.74768262],\n",
" [ 0.31814437, 0.23642045, -0.39708923],\n",
" [-0. , 0. , -3.96835355],\n",
" [-0. , -3.64190926, 5.71458646],\n",
" [-0. , 3.64190926, 5.71458646],\n",
" [-2.18178516, -0. , 1.67589182],\n",
" [ 2.18178516, 0. , 1.67589182],\n",
" [-0. , 2.46333681, 1.78299828],\n",
" [-0. , -2.46333681, 1.78299828],\n",
" [ 6.1871405 , 0.22633633, -0.59948557],\n",
" [-6.1871405 , 0.22633633, -0.59948557],\n",
" [-6.1871405 , -0.22633633, -0.59948557],\n",
" [ 6.1871405 , -0.22633633, -0.59948557]])"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uzDp10XsoHdo"
},
"source": [
"### Interacting with the OC20 datasets\n",
"\n",
"The OC20 datasets are stored in LMDBs. Here we show how to interact with the datasets directly in order to better understand the data. We use two seperate classes to read in the approriate datasets:\n",
"\n",
"*S2EF* - We use the [TrajectoryLmdbDataset](https://github.com/Open-Catalyst-Project/ocp/blob/master/ocpmodels/datasets/trajectory_lmdb.py) object to read in a **directory** of LMDB files containing the dataset.\n",
"\n",
"*IS2RE/IS2RS* - We use the [SinglePointLmdbDataset](https://github.com/Open-Catalyst-Project/ocp/blob/master/ocpmodels/datasets/single_point_lmdb.py) class to read in a **single LMDB file** containing the dataset.\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "7F7BjxNQoGLn",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "36fcd255-facc-43dd-efda-c238cac9c5d9"
},
"source": [
"from ocpmodels.datasets import TrajectoryLmdbDataset, SinglePointLmdbDataset\n",
"\n",
"# TrajectoryLmdbDataset is our custom Dataset method to read the lmdbs as Data objects. Note that we need to give the path to the folder containing lmdbs for S2EF\n",
"dataset = TrajectoryLmdbDataset({\"src\": \"data/s2ef/train_100/\"})\n",
"\n",
"print(\"Size of the dataset created:\", len(dataset))\n",
"print(dataset[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Size of the dataset created: 100\n",
"Data(atomic_numbers=[86], cell=[1, 3, 3], cell_offsets=[2964, 3], edge_index=[2, 2964], fid=[1], fixed=[86], force=[86, 3], id=\"0_0\", natoms=86, pos=[86, 3], sid=[1], tags=[86], total_frames=74, y=6.282500615000004)\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "pD5B_TymoJ8S",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "72b21c2a-9472-4b08-afe9-c1bd28a5b399"
},
"source": [
"data = dataset[0]\n",
"data"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Data(atomic_numbers=[86], cell=[1, 3, 3], cell_offsets=[2964, 3], edge_index=[2, 2964], fid=[1], fixed=[86], force=[86, 3], id=\"0_0\", natoms=86, pos=[86, 3], sid=[1], tags=[86], total_frames=74, y=6.282500615000004)"
]
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "rL4u0glIoL8h",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a29c8dfc-617f-48fa-9195-e851b23033e1"
},
"source": [
"energies = torch.tensor([data.y for data in dataset])\n",
"energies"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"tensor([ 6.2825e+00, 4.1290e+00, 3.1451e+00, 3.0260e+00, 1.7921e+00,\n",
" 1.6451e+00, 1.2257e+00, 1.2161e+00, 1.0712e+00, 7.4727e-01,\n",
" 5.9575e-01, 5.7016e-01, 4.2819e-01, 3.1616e-01, 2.5283e-01,\n",
" 2.2425e-01, 2.2346e-01, 2.0530e-01, 1.6090e-01, 1.1807e-01,\n",
" 1.1691e-01, 9.1254e-02, 7.4997e-02, 6.3274e-02, 5.2782e-02,\n",
" 4.8892e-02, 3.9609e-02, 3.1746e-02, 2.7179e-02, 2.7007e-02,\n",
" 2.3709e-02, 1.8005e-02, 1.7676e-02, 1.4129e-02, 1.3162e-02,\n",
" 1.1374e-02, 7.4124e-03, 7.7525e-03, 6.1224e-03, 5.2787e-03,\n",
" 2.8587e-03, 1.1835e-04, -1.1200e-03, -1.3011e-03, -2.6812e-03,\n",
" -5.9202e-03, -6.1644e-03, -6.9261e-03, -9.1364e-03, -9.2114e-03,\n",
" -1.0665e-02, -1.3760e-02, -1.3588e-02, -1.4895e-02, -1.6190e-02,\n",
" -1.8660e-02, -1.4980e-02, -1.8880e-02, -2.0218e-02, -2.0559e-02,\n",
" -2.1013e-02, -2.2129e-02, -2.2748e-02, -2.3322e-02, -2.3382e-02,\n",
" -2.3865e-02, -2.3973e-02, -2.4196e-02, -2.4755e-02, -2.4951e-02,\n",
" -2.5078e-02, -2.5148e-02, -2.5257e-02, -2.5550e-02, 5.9721e+00,\n",
" 9.5081e+00, 2.6373e+00, 4.0946e+00, 1.4385e+00, 1.2700e+00,\n",
" 1.0081e+00, 5.3797e-01, 5.1462e-01, 2.8812e-01, 1.2429e-01,\n",
" -1.1352e-02, -2.2293e-01, -3.9102e-01, -4.3574e-01, -5.3142e-01,\n",
" -5.4777e-01, -6.3948e-01, -7.3816e-01, -8.2163e-01, -8.2526e-01,\n",
" -8.8313e-01, -8.8615e-01, -9.3446e-01, -9.5100e-01, -9.5168e-01])"
]
},
"metadata": {},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "mkOm2roAoNY2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 737
},
"outputId": "aed9b4de-99de-49ab-a21c-3a372166747a"
},
"source": [
"plt.hist(energies, bins = 50)\n",
"plt.yscale(\"log\")\n",
"plt.xlabel(\"Energies\")\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RtECvWIPCu0b"
},
"source": [
"### Additional Resources\n",
"\n",
"More helpful resources, tutorials, and documentation can be found at ASE's webpage: https://wiki.fysik.dtu.dk/ase/index.html. We point to specific pages that may be of interest:\n",
"\n",
"* Interacting with Atoms Object: https://wiki.fysik.dtu.dk/ase/ase/atoms.html\n",
"* Visualization: https://wiki.fysik.dtu.dk/ase/ase/visualize/visualize.html\n",
"* Structure optimization: https://wiki.fysik.dtu.dk/ase/ase/optimize.html\n",
"* More ASE Tutorials: https://wiki.fysik.dtu.dk/ase/tutorials/tutorials.html"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qa9Iuu2GU52Z"
},
"source": [
"\n",
"# Tasks\n",
"\n",
"In this section, we cover the different types of tasks the OC20 dataset presents and how to train and predict their corresponding models.\n",
"\n",
"1. Structure to Energy and Forces (S2EF)\n",
"2. Initial Structure to Relaxed Energy (IS2RE)\n",
"3. Initial Structure to Relaxed Structure (IS2RS)\n",
"\n",
"Tasks can be interrelated. The figure below illustrates several approaches to solving the IS2RE task:\n",
"\n",
"(a) the traditional approach uses DFT along with an optimizer,\n",
"such as BFGS or conjugate gradient, to iteratively update\n",
"the atom positions until the relaxed structure and energy are found.\n",
"\n",
"(b) using ML models trained to predict the energy and forces of a\n",
"structure, S2EF can be used as a direct replacement for DFT. \n",
"\n",
"(c) the relaxed structure could potentially be directly regressed from\n",
"the initial structure and S2EF used to find the energy.\n",
"\n",
"(d) directly compute the relaxed energy from the initial state.\n",
"\n",
"\n",
"**NOTE** The following sections are intended to demonstrate the inner workings of our codebase and what goes into running the various tasks. We do not recommend training to completion within a notebook setting. Please see the [running on command line](#cmd) section for the preferred way to train/evaluate models."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "W7aZpLzmuNra"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yWXsiZ5freTG"
},
"source": [
"## Structure to Energy and Forces (S2EF) \n",
"\n",
"The S2EF task takes an atomic system as input and predicts the energy of the entire system and forces on each atom. This is our most general task, ultimately serving as a surrogate to DFT. A model that can perform well on this task can accelerate other applications like molecular dynamics and transitions tate calculations.\n",
"\n",
"### Steps for training an S2EF model\n",
"1) Define or load a configuration (config), which includes the following\n",
"* task\n",
"* model\n",
"* optimizer\n",
"* dataset\n",
"* trainer\n",
"\n",
"2) Create a ForcesTrainer object\n",
"\n",
"3) Train the model\n",
"\n",
"4) Validate the model\n",
"\n",
"**For storage and compute reasons we use a very small subset of the OC20 S2EF dataset for this tutorial. Results will be considerably worse than presented in our paper.**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2snWOAxnPPyd"
},
"source": [
"### Imports"
]
},
{
"cell_type": "code",
"metadata": {
"id": "l-1rNyuk_1Mo"
},
"source": [
"from ocpmodels.trainers import ForcesTrainer\n",
"from ocpmodels.datasets import TrajectoryLmdbDataset\n",
"from ocpmodels import models\n",
"from ocpmodels.common import logger\n",
"from ocpmodels.common.utils import setup_logging\n",
"setup_logging()\n",
"\n",
"import numpy as np\n",
"import copy\n",
"import os"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "OmkUDMQgP5he"
},
"source": [
"### Dataset"
]
},
{
"cell_type": "code",
"metadata": {
"id": "1SHl_1eQP4mW"
},
"source": [
"train_src = \"data/s2ef/train_100\"\n",
"val_src = \"data/s2ef/val_20\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZUpFFV2OWyYJ"
},
"source": [
"### Normalize data\n",
"\n",
"If you wish to normalize the targets we must compute the mean and standard deviation for our energy values. Because forces are physically related by the negative gradient of energy, we use the same multiplicative energy factor for forces."
]
},
{
"cell_type": "code",
"metadata": {
"id": "HAJ3x4SnXE1o"
},
"source": [
"train_dataset = TrajectoryLmdbDataset({\"src\": train_src})\n",
"\n",
"energies = []\n",
"for data in train_dataset:\n",
" energies.append(data.y)\n",
"\n",
"mean = np.mean(energies)\n",
"stdev = np.std(energies)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ruspSf6CQIk4"
},
"source": [
"### Define the Config"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6R6IkYLCQPpH"
},
"source": [
"For this example, we will explicitly define the config; however, a set of default configs can be found [here](https://github.com/Open-Catalyst-Project/ocp/tree/master/configs). Default config yaml files can easily be loaded with the following [utility](https://github.com/Open-Catalyst-Project/ocp/blob/aa8e44d50229fce887b3a94a5661c4f85cd73eed/ocpmodels/common/utils.py#L361-L400). Loading a yaml config is preferrable when launching jobs from the command line. We have included our best models' config files here for reference. \n",
"\n",
"**Note** - we only train for a single epoch with a reduced batch size (GPU memory constraints) for demonstration purposes, modify accordingly for full convergence."
]
},
{
"cell_type": "code",
"metadata": {
"id": "j6Z_XbkiPGR9"
},
"source": [
"# Task\n",
"task = {\n",
" 'dataset': 'trajectory_lmdb', # dataset used for the S2EF task\n",
" 'description': 'Regressing to energies and forces for DFT trajectories from OCP',\n",
" 'type': 'regression',\n",
" 'metric': 'mae',\n",
" 'labels': ['potential energy'],\n",
" 'grad_input': 'atomic forces',\n",
" 'train_on_free_atoms': True,\n",
" 'eval_on_free_atoms': True\n",
"}\n",
"# Model\n",
"model = {\n",
" 'name': 'gemnet_t',\n",
" \"num_spherical\": 7,\n",
" \"num_radial\": 128,\n",
" \"num_blocks\": 3,\n",
" \"emb_size_atom\": 512,\n",
" \"emb_size_edge\": 512,\n",
" \"emb_size_trip\": 64,\n",
" \"emb_size_rbf\": 16,\n",
" \"emb_size_cbf\": 16,\n",
" \"emb_size_bil_trip\": 64,\n",
" \"num_before_skip\": 1,\n",
" \"num_after_skip\": 2,\n",
" \"num_concat\": 1,\n",
" \"num_atom\": 3,\n",
" \"cutoff\": 6.0,\n",
" \"max_neighbors\": 50,\n",
" \"rbf\": {\"name\": \"gaussian\"},\n",
" \"envelope\": {\n",
" \"name\": \"polynomial\",\n",
" \"exponent\": 5,\n",
" },\n",
" \"cbf\": {\"name\": \"spherical_harmonics\"},\n",
" \"extensive\": True,\n",
" \"otf_graph\": False,\n",
" \"output_init\": \"HeOrthogonal\",\n",
" \"activation\": \"silu\",\n",
" \"scale_file\": \"configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\",\n",
" \"regress_forces\": True,\n",
" \"direct_forces\": True,\n",
"}\n",
"# Optimizer\n",
"optimizer = {\n",
" 'batch_size': 1, # originally 32\n",
" 'eval_batch_size': 1, # originally 32\n",
" 'num_workers': 2,\n",
" 'lr_initial': 5.e-4,\n",
" 'optimizer': 'AdamW',\n",
" 'optimizer_params': {\"amsgrad\": True},\n",
" 'scheduler': \"ReduceLROnPlateau\",\n",
" 'mode': \"min\",\n",
" 'factor': 0.8,\n",
" 'patience': 3,\n",
" 'max_epochs': 1, # used for demonstration purposes\n",
" 'force_coefficient': 100,\n",
" 'ema_decay': 0.999,\n",
" 'clip_grad_norm': 10,\n",
" 'loss_energy': 'mae',\n",
" 'loss_force': 'l2mae',\n",
"}\n",
"# Dataset\n",
"dataset = [\n",
" {'src': train_src,\n",
" 'normalize_labels': True,\n",
" \"target_mean\": mean,\n",
" \"target_std\": stdev,\n",
" \"grad_target_mean\": 0.0,\n",
" \"grad_target_std\": stdev\n",
" }, # train set \n",
" {'src': val_src}, # val set (optional)\n",
"]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "8AsZpLjIQg-W"
},
"source": [
"### Create the trainer"
]
},
{
"cell_type": "code",
"metadata": {
"id": "0it4gs6gPGGz",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e7a98c1d-6d4f-425b-878f-4a3a7b42b2ed"
},
"source": [
"trainer = ForcesTrainer(\n",
" task=task,\n",
" model=copy.deepcopy(model), # copied for later use, not necessary in practice.\n",
" dataset=dataset,\n",
" optimizer=optimizer,\n",
" identifier=\"S2EF-example\",\n",
" run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n",
" is_debug=False, # if True, do not save checkpoint, logs, or results\n",
" is_vis=False,\n",
" print_every=5,\n",
" seed=0, # random seed to use\n",
" logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n",
" local_rank=0,\n",
" amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage),\n",
")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"amp: true\n",
"cmd:\n",
" checkpoint_dir: ./checkpoints/2021-11-22-17-14-40-S2EF-example\n",
" commit: bc04a90\n",
" identifier: S2EF-example\n",
" logs_dir: ./logs/tensorboard/2021-11-22-17-14-40-S2EF-example\n",
" print_every: 5\n",
" results_dir: ./results/2021-11-22-17-14-40-S2EF-example\n",
" seed: 0\n",
" timestamp_id: 2021-11-22-17-14-40-S2EF-example\n",
"dataset:\n",
" grad_target_mean: 0.0\n",
" grad_target_std: !!python/object/apply:numpy.core.multiarray.scalar\n",
" - &id001 !!python/object/apply:numpy.dtype\n",
" args:\n",
" - f8\n",
" - false\n",
" - true\n",
" state: !!python/tuple\n",
" - 3\n",
" - <\n",
" - null\n",
" - null\n",
" - null\n",
" - -1\n",
" - -1\n",
" - 0\n",
" - !!binary |\n",
" dPVlWhRA+D8=\n",
" normalize_labels: true\n",
" src: data/s2ef/train_100\n",
" target_mean: !!python/object/apply:numpy.core.multiarray.scalar\n",
" - *id001\n",
" - !!binary |\n",
" zSXlDMrm3D8=\n",
" target_std: !!python/object/apply:numpy.core.multiarray.scalar\n",
" - *id001\n",
" - !!binary |\n",
" dPVlWhRA+D8=\n",
"gpus: 1\n",
"logger: tensorboard\n",
"model: gemnet_t\n",
"model_attributes:\n",
" activation: silu\n",
" cbf:\n",
" name: spherical_harmonics\n",
" cutoff: 6.0\n",
" direct_forces: true\n",
" emb_size_atom: 512\n",
" emb_size_bil_trip: 64\n",
" emb_size_cbf: 16\n",
" emb_size_edge: 512\n",
" emb_size_rbf: 16\n",
" emb_size_trip: 64\n",
" envelope:\n",
" exponent: 5\n",
" name: polynomial\n",
" extensive: true\n",
" max_neighbors: 50\n",
" num_after_skip: 2\n",
" num_atom: 3\n",
" num_before_skip: 1\n",
" num_blocks: 3\n",
" num_concat: 1\n",
" num_radial: 128\n",
" num_spherical: 7\n",
" otf_graph: false\n",
" output_init: HeOrthogonal\n",
" rbf:\n",
" name: gaussian\n",
" regress_forces: true\n",
" scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\n",
"optim:\n",
" batch_size: 1\n",
" clip_grad_norm: 10\n",
" ema_decay: 0.999\n",
" eval_batch_size: 1\n",
" factor: 0.8\n",
" force_coefficient: 100\n",
" loss_energy: mae\n",
" loss_force: l2mae\n",
" lr_initial: 0.0005\n",
" max_epochs: 1\n",
" mode: min\n",
" num_workers: 2\n",
" optimizer: AdamW\n",
" optimizer_params:\n",
" amsgrad: true\n",
" patience: 3\n",
" scheduler: ReduceLROnPlateau\n",
"slurm: {}\n",
"task:\n",
" dataset: trajectory_lmdb\n",
" description: Regressing to energies and forces for DFT trajectories from OCP\n",
" eval_on_free_atoms: true\n",
" grad_input: atomic forces\n",
" labels:\n",
" - potential energy\n",
" metric: mae\n",
" train_on_free_atoms: true\n",
" type: regression\n",
"val_dataset:\n",
" src: data/s2ef/val_20\n",
"\n",
"2021-11-22 17:15:16 (INFO): Loading dataset: trajectory_lmdb\n",
"2021-11-22 17:15:16 (INFO): Loading model: gemnet_t\n",
"2021-11-22 17:15:20 (INFO): Loaded GemNetT with 31671825 parameters.\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"2021-11-22 17:15:20 (WARNING): Model gradient logging to tensorboard not yet supported.\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "4yGWsRq3PF8R"
},
"source": [
"trainer.model"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "vA8nDKt4QqkO"
},
"source": [
"### Train the model"
]
},
{
"cell_type": "code",
"metadata": {
"id": "WFmssq5oPFd_",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a80e93f3-637a-4394-9ec8-4c38bac27461"
},
"source": [
"trainer.train()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2021-11-22 17:15:33 (INFO): forcesx_mae: 2.37e+00, forcesy_mae: 3.27e+00, forcesz_mae: 3.07e+00, forces_mae: 2.90e+00, forces_cos: -4.09e-02, forces_magnitude: 5.73e+00, energy_mae: 4.82e+01, energy_force_within_threshold: 0.00e+00, loss: 8.53e+02, lr: 5.00e-04, epoch: 5.00e-02, step: 5.00e+00\n",
"2021-11-22 17:15:39 (INFO): forcesx_mae: 2.42e+00, forcesy_mae: 3.28e+00, forcesz_mae: 3.03e+00, forces_mae: 2.91e+00, forces_cos: -1.82e-02, forces_magnitude: 5.77e+00, energy_mae: 4.96e+01, energy_force_within_threshold: 0.00e+00, loss: 7.71e+02, lr: 5.00e-04, epoch: 1.00e-01, step: 1.00e+01\n",
"2021-11-22 17:15:46 (INFO): forcesx_mae: 1.78e+01, forcesy_mae: 8.20e+01, forcesz_mae: 2.61e+01, forces_mae: 4.20e+01, forces_cos: -1.39e-02, forces_magnitude: 9.52e+01, energy_mae: 2.10e+03, energy_force_within_threshold: 0.00e+00, loss: 1.45e+04, lr: 5.00e-04, epoch: 1.50e-01, step: 1.50e+01\n",
"2021-11-22 17:15:53 (INFO): forcesx_mae: 1.17e+01, forcesy_mae: 4.24e+01, forcesz_mae: 1.78e+01, forces_mae: 2.40e+01, forces_cos: -2.96e-02, forces_magnitude: 5.36e+01, energy_mae: 1.12e+03, energy_force_within_threshold: 0.00e+00, loss: 3.92e+03, lr: 5.00e-04, epoch: 2.00e-01, step: 2.00e+01\n",
"2021-11-22 17:15:59 (INFO): forcesx_mae: 1.40e+01, forcesy_mae: 3.46e+01, forcesz_mae: 1.56e+01, forces_mae: 2.14e+01, forces_cos: 9.12e-03, forces_magnitude: 4.50e+01, energy_mae: 4.24e+02, energy_force_within_threshold: 0.00e+00, loss: 4.87e+03, lr: 5.00e-04, epoch: 2.50e-01, step: 2.50e+01\n",
"2021-11-22 17:16:06 (INFO): forcesx_mae: 9.72e+01, forcesy_mae: 2.24e+02, forcesz_mae: 1.05e+02, forces_mae: 1.42e+02, forces_cos: -4.17e-02, forces_magnitude: 3.00e+02, energy_mae: 4.30e+03, energy_force_within_threshold: 0.00e+00, loss: 3.78e+04, lr: 5.00e-04, epoch: 3.00e-01, step: 3.00e+01\n",
"2021-11-22 17:16:12 (INFO): forcesx_mae: 1.33e+00, forcesy_mae: 1.43e+00, forcesz_mae: 1.35e+00, forces_mae: 1.37e+00, forces_cos: 6.92e-03, forces_magnitude: 2.72e+00, energy_mae: 2.62e+01, energy_force_within_threshold: 0.00e+00, loss: 2.00e+02, lr: 5.00e-04, epoch: 3.50e-01, step: 3.50e+01\n",
"2021-11-22 17:16:19 (INFO): forcesx_mae: 1.05e+02, forcesy_mae: 2.08e+02, forcesz_mae: 1.16e+02, forces_mae: 1.43e+02, forces_cos: -2.02e-02, forces_magnitude: 2.95e+02, energy_mae: 3.29e+03, energy_force_within_threshold: 0.00e+00, loss: 3.36e+04, lr: 5.00e-04, epoch: 4.00e-01, step: 4.00e+01\n",
"2021-11-22 17:16:25 (INFO): forcesx_mae: 2.25e+02, forcesy_mae: 5.61e+02, forcesz_mae: 2.86e+02, forces_mae: 3.57e+02, forces_cos: 7.29e-02, forces_magnitude: 7.71e+02, energy_mae: 7.83e+03, energy_force_within_threshold: 0.00e+00, loss: 7.47e+04, lr: 5.00e-04, epoch: 4.50e-01, step: 4.50e+01\n",
"2021-11-22 17:16:32 (INFO): forcesx_mae: 6.88e-01, forcesy_mae: 7.65e-01, forcesz_mae: 6.54e-01, forces_mae: 7.03e-01, forces_cos: -7.49e-02, forces_magnitude: 1.25e+00, energy_mae: 1.88e+01, energy_force_within_threshold: 0.00e+00, loss: 1.05e+02, lr: 5.00e-04, epoch: 5.00e-01, step: 5.00e+01\n",
"2021-11-22 17:16:38 (INFO): forcesx_mae: 5.71e-01, forcesy_mae: 6.43e-01, forcesz_mae: 6.73e-01, forces_mae: 6.29e-01, forces_cos: 1.62e-01, forces_magnitude: 9.06e-01, energy_mae: 2.06e+01, energy_force_within_threshold: 0.00e+00, loss: 9.64e+01, lr: 5.00e-04, epoch: 5.50e-01, step: 5.50e+01\n",
"2021-11-22 17:16:45 (INFO): forcesx_mae: 4.86e-01, forcesy_mae: 4.93e-01, forcesz_mae: 5.01e-01, forces_mae: 4.93e-01, forces_cos: -2.05e-02, forces_magnitude: 9.57e-01, energy_mae: 1.11e+01, energy_force_within_threshold: 0.00e+00, loss: 7.26e+01, lr: 5.00e-04, epoch: 6.00e-01, step: 6.00e+01\n",
"2021-11-22 17:16:51 (INFO): forcesx_mae: 9.37e-01, forcesy_mae: 2.66e+00, forcesz_mae: 1.30e+00, forces_mae: 1.63e+00, forces_cos: 2.07e-01, forces_magnitude: 2.77e+00, energy_mae: 8.03e+00, energy_force_within_threshold: 0.00e+00, loss: 2.04e+02, lr: 5.00e-04, epoch: 6.50e-01, step: 6.50e+01\n",
"2021-11-22 17:16:58 (INFO): forcesx_mae: 4.89e-01, forcesy_mae: 4.57e-01, forcesz_mae: 4.84e-01, forces_mae: 4.77e-01, forces_cos: 1.22e-01, forces_magnitude: 6.81e-01, energy_mae: 6.36e+00, energy_force_within_threshold: 0.00e+00, loss: 6.72e+01, lr: 5.00e-04, epoch: 7.00e-01, step: 7.00e+01\n",
"2021-11-22 17:17:04 (INFO): forcesx_mae: 1.61e+00, forcesy_mae: 1.96e+00, forcesz_mae: 1.58e+00, forces_mae: 1.72e+00, forces_cos: 5.39e-02, forces_magnitude: 3.33e+00, energy_mae: 1.70e+01, energy_force_within_threshold: 0.00e+00, loss: 1.97e+02, lr: 5.00e-04, epoch: 7.50e-01, step: 7.50e+01\n",
"2021-11-22 17:17:11 (INFO): forcesx_mae: 9.00e-01, forcesy_mae: 1.00e+00, forcesz_mae: 1.10e+00, forces_mae: 1.00e+00, forces_cos: 2.08e-02, forces_magnitude: 1.65e+00, energy_mae: 1.93e+01, energy_force_within_threshold: 0.00e+00, loss: 1.34e+02, lr: 5.00e-04, epoch: 8.00e-01, step: 8.00e+01\n",
"2021-11-22 17:17:17 (INFO): forcesx_mae: 6.05e-01, forcesy_mae: 1.65e+00, forcesz_mae: 8.28e-01, forces_mae: 1.03e+00, forces_cos: 5.95e-02, forces_magnitude: 1.87e+00, energy_mae: 1.63e+01, energy_force_within_threshold: 0.00e+00, loss: 1.30e+02, lr: 5.00e-04, epoch: 8.50e-01, step: 8.50e+01\n",
"2021-11-22 17:17:24 (INFO): forcesx_mae: 5.26e-01, forcesy_mae: 7.32e-01, forcesz_mae: 5.05e-01, forces_mae: 5.88e-01, forces_cos: 5.29e-04, forces_magnitude: 1.07e+00, energy_mae: 4.13e+00, energy_force_within_threshold: 0.00e+00, loss: 7.10e+01, lr: 5.00e-04, epoch: 9.00e-01, step: 9.00e+01\n",
"2021-11-22 17:17:30 (INFO): forcesx_mae: 4.01e-01, forcesy_mae: 4.67e-01, forcesz_mae: 3.45e-01, forces_mae: 4.04e-01, forces_cos: 6.19e-02, forces_magnitude: 7.39e-01, energy_mae: 3.07e+00, energy_force_within_threshold: 0.00e+00, loss: 5.64e+01, lr: 5.00e-04, epoch: 9.50e-01, step: 9.50e+01\n",
"2021-11-22 17:17:37 (INFO): forcesx_mae: 4.27e-01, forcesy_mae: 7.22e-01, forcesz_mae: 4.27e-01, forces_mae: 5.25e-01, forces_cos: 4.71e-02, forces_magnitude: 9.01e-01, energy_mae: 8.72e+00, energy_force_within_threshold: 0.00e+00, loss: 6.92e+01, lr: 5.00e-04, epoch: 1.00e+00, step: 1.00e+02\n",
"2021-11-22 17:17:39 (INFO): Evaluating on val.\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"device 0: 100%|██████████| 20/20 [00:02<00:00, 7.13it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2021-11-22 17:17:42 (INFO): forcesx_mae: 1.4760, forcesy_mae: 1.1875, forcesz_mae: 1.6235, forces_mae: 1.4290, forces_cos: -0.2961, forces_magnitude: 2.5544, energy_mae: 7.8576, energy_force_within_threshold: 0.0000, loss: 193.1406, epoch: 1.0000\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZHkrkULBQ1Xy"
},
"source": [
"### Validate the model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "paYx3_FBQ8OE"
},
"source": [
"#### Load the best checkpoint\n",
"\n",
"The `checkpoints` directory contains two checkpoint files:\n",
"\n",
"\n",
"\n",
"* `best_checkpoint.pt` - Model parameters corresponding to the best val performance during training. Used for predictions.\n",
"* `checkpoint.pt` - Model parameters and optimizer settings for the latest checkpoint. Used to continue training.\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "UW4ihgBdQ0Yt",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "8226c4d2-041d-46d3-c0d9-02ce85f8fc93"
},
"source": [
"# The `best_checpoint.pt` file contains the checkpoint with the best val performance\n",
"checkpoint_path = os.path.join(trainer.config[\"cmd\"][\"checkpoint_dir\"], \"best_checkpoint.pt\")\n",
"checkpoint_path"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'./checkpoints/2021-11-22-17-14-40-S2EF-example/best_checkpoint.pt'"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6jppgncMTivj",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a15e13a5-4c1d-4fd4-c2c3-ef9fa210a9dd"
},
"source": [
"# Append the dataset with the test set. We use the same val set for demonstration.\n",
"\n",
"# Dataset\n",
"dataset.append(\n",
" {'src': val_src}, # test set (optional)\n",
")\n",
"dataset"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'grad_target_mean': 0.0,\n",
" 'grad_target_std': 1.5156444102461508,\n",
" 'normalize_labels': True,\n",
" 'src': 'data/s2ef/train_100',\n",
" 'target_mean': 0.45158625849998374,\n",
" 'target_std': 1.5156444102461508},\n",
" {'src': 'data/s2ef/val_20'},\n",
" {'src': 'data/s2ef/val_20'}]"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "MaVROfxzRLaj",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0f143c63-1e1d-44c4-c641-34bac1706c2c"
},
"source": [
"pretrained_trainer = ForcesTrainer(\n",
" task=task,\n",
" model=model,\n",
" dataset=dataset,\n",
" optimizer=optimizer,\n",
" identifier=\"S2EF-val-example\",\n",
" run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n",
" is_debug=False, # if True, do not save checkpoint, logs, or results\n",
" is_vis=False,\n",
" print_every=10,\n",
" seed=0, # random seed to use\n",
" logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n",
" local_rank=0,\n",
" amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage)\n",
")\n",
"\n",
"pretrained_trainer.load_checkpoint(checkpoint_path=checkpoint_path)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"amp: true\n",
"cmd:\n",
" checkpoint_dir: ./checkpoints/2021-11-22-17-16-48-S2EF-val-example\n",
" commit: bc04a90\n",
" identifier: S2EF-val-example\n",
" logs_dir: ./logs/tensorboard/2021-11-22-17-16-48-S2EF-val-example\n",
" print_every: 10\n",
" results_dir: ./results/2021-11-22-17-16-48-S2EF-val-example\n",
" seed: 0\n",
" timestamp_id: 2021-11-22-17-16-48-S2EF-val-example\n",
"dataset:\n",
" grad_target_mean: 0.0\n",
" grad_target_std: !!python/object/apply:numpy.core.multiarray.scalar\n",
" - &id001 !!python/object/apply:numpy.dtype\n",
" args:\n",
" - f8\n",
" - false\n",
" - true\n",
" state: !!python/tuple\n",
" - 3\n",
" - <\n",
" - null\n",
" - null\n",
" - null\n",
" - -1\n",
" - -1\n",
" - 0\n",
" - !!binary |\n",
" dPVlWhRA+D8=\n",
" normalize_labels: true\n",
" src: data/s2ef/train_100\n",
" target_mean: !!python/object/apply:numpy.core.multiarray.scalar\n",
" - *id001\n",
" - !!binary |\n",
" zSXlDMrm3D8=\n",
" target_std: !!python/object/apply:numpy.core.multiarray.scalar\n",
" - *id001\n",
" - !!binary |\n",
" dPVlWhRA+D8=\n",
"gpus: 1\n",
"logger: tensorboard\n",
"model: gemnet_t\n",
"model_attributes:\n",
" activation: silu\n",
" cbf:\n",
" name: spherical_harmonics\n",
" cutoff: 6.0\n",
" direct_forces: true\n",
" emb_size_atom: 512\n",
" emb_size_bil_trip: 64\n",
" emb_size_cbf: 16\n",
" emb_size_edge: 512\n",
" emb_size_rbf: 16\n",
" emb_size_trip: 64\n",
" envelope:\n",
" exponent: 5\n",
" name: polynomial\n",
" extensive: true\n",
" max_neighbors: 50\n",
" num_after_skip: 2\n",
" num_atom: 3\n",
" num_before_skip: 1\n",
" num_blocks: 3\n",
" num_concat: 1\n",
" num_radial: 128\n",
" num_spherical: 7\n",
" otf_graph: false\n",
" output_init: HeOrthogonal\n",
" rbf:\n",
" name: gaussian\n",
" regress_forces: true\n",
" scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\n",
"optim:\n",
" batch_size: 1\n",
" clip_grad_norm: 10\n",
" ema_decay: 0.999\n",
" eval_batch_size: 1\n",
" factor: 0.8\n",
" force_coefficient: 100\n",
" loss_energy: mae\n",
" loss_force: l2mae\n",
" lr_initial: 0.0005\n",
" max_epochs: 1\n",
" mode: min\n",
" num_workers: 2\n",
" optimizer: AdamW\n",
" optimizer_params:\n",
" amsgrad: true\n",
" patience: 3\n",
" scheduler: ReduceLROnPlateau\n",
"slurm: {}\n",
"task:\n",
" dataset: trajectory_lmdb\n",
" description: Regressing to energies and forces for DFT trajectories from OCP\n",
" eval_on_free_atoms: true\n",
" grad_input: atomic forces\n",
" labels:\n",
" - potential energy\n",
" metric: mae\n",
" train_on_free_atoms: true\n",
" type: regression\n",
"test_dataset:\n",
" src: data/s2ef/val_20\n",
"val_dataset:\n",
" src: data/s2ef/val_20\n",
"\n",
"2021-11-22 17:17:43 (INFO): Loading dataset: trajectory_lmdb\n",
"2021-11-22 17:17:43 (INFO): Loading model: gemnet_t\n",
"2021-11-22 17:17:46 (INFO): Loaded GemNetT with 31671825 parameters.\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"2021-11-22 17:17:46 (WARNING): Model gradient logging to tensorboard not yet supported.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2021-11-22 17:17:46 (INFO): Loading checkpoint from: ./checkpoints/2021-11-22-17-14-40-S2EF-example/best_checkpoint.pt\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kWetMgsmRBZS"
},
"source": [
"#### Run on the test set"
]
},
{
"cell_type": "code",
"metadata": {
"id": "jbiPZNeJQ0WK",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "dd346bcd-f30a-4333-a1ca-e18c057cb238"
},
"source": [
"# make predictions on the existing test_loader\n",
"predictions = pretrained_trainer.predict(pretrained_trainer.test_loader, results_file=\"s2ef_results\", disable_tqdm=False)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2021-11-22 17:17:46 (INFO): Predicting on test.\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"device 0: 100%|██████████| 20/20 [00:02<00:00, 7.47it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2021-11-22 17:17:49 (INFO): Writing results to ./results/2021-11-22-17-16-48-S2EF-val-example/s2ef_s2ef_results.npz\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zaZGqeyqNCXz"
},
"source": [
"energies = predictions[\"energy\"]\n",
"forces = predictions[\"forces\"]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "o8L28axZ4NVj"
},
"source": [
"## Initial Structure to Relaxed Energy (IS2RE) \n",
"The IS2RE task predicts the relaxed energy (energy of the relaxed state) given the initial state of a system. One approach to this is by training a regression model mapping the initial structure to the relaxed energy. We call this the *direct* approach to the IS2RE task. \n",
"\n",
"An alternative is to perform a structure relaxation using an S2EF model to obtain the relaxed state and compute the energy of that state (see the IS2RS task below for details about relaxation).\n",
"\n",
"### Steps for training an IS2RE model\n",
"1) Define or load a configuration (config), which includes the following\n",
"* task\n",
"* model\n",
"* optimizer\n",
"* dataset\n",
"* trainer\n",
"\n",
"2) Create an EnergyTrainer object\n",
"\n",
"3) Train the model\n",
"\n",
"4) Validate the model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kEPPcr0YYHpH"
},
"source": [
"### Imports"
]
},
{
"cell_type": "code",
"metadata": {
"id": "d-0GsaGDW16G"
},
"source": [
"from ocpmodels.trainers import EnergyTrainer\n",
"from ocpmodels.datasets import SinglePointLmdbDataset\n",
"from ocpmodels import models\n",
"from ocpmodels.common import logger\n",
"from ocpmodels.common.utils import setup_logging\n",
"setup_logging()\n",
"\n",
"import numpy as np\n",
"import copy\n",
"import os"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "w20BJZ_GYWat"
},
"source": [
"### Dataset"
]
},
{
"cell_type": "code",
"metadata": {
"id": "BlL5gGPQW1te"
},
"source": [
"train_src = \"data/is2re/train_100/data.lmdb\"\n",
"val_src = \"data/is2re/val_20/data.lmdb\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "yT5qHT2wamPh"
},
"source": [
"### Normalize data\n",
"\n",
"If you wish to normalize the targets we must compute the mean and standard deviation for our energy values."
]
},
{
"cell_type": "code",
"metadata": {
"id": "vaY-ZUMaamPh"
},
"source": [
"train_dataset = SinglePointLmdbDataset({\"src\": train_src})\n",
"\n",
"energies = []\n",
"for data in train_dataset:\n",
" energies.append(data.y_relaxed)\n",
"\n",
"mean = np.mean(energies)\n",
"stdev = np.std(energies)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "K4SSW0UGYeYM"
},
"source": [
"### Define the Config\n",
"\n",
"For this example, we will explicitly define the config; however, a set of default configs can be found [here](https://github.com/Open-Catalyst-Project/ocp/tree/master/configs). Default config yaml files can easily be loaded with the following [utility](https://github.com/Open-Catalyst-Project/ocp/blob/aa8e44d50229fce887b3a94a5661c4f85cd73eed/ocpmodels/common/utils.py#L361-L400). Loading a yaml config is preferrable when launching jobs from the command line. We have included our best models' config files here for reference. \n",
"\n",
"**Note** - we only train for a single epoch with a reduced batch size (GPU memory constraints) for demonstration purposes, modify accordingly for full convergence."
]
},
{
"cell_type": "code",
"metadata": {
"id": "TiHmkTm6W1do"
},
"source": [
"# Task\n",
"task = {\n",
" \"dataset\": \"single_point_lmdb\",\n",
" \"description\": \"Relaxed state energy prediction from initial structure.\",\n",
" \"type\": \"regression\",\n",
" \"metric\": \"mae\",\n",
" \"labels\": [\"relaxed energy\"],\n",
"}\n",
"# Model\n",
"model = {\n",
" 'name': 'gemnet_t',\n",
" \"num_spherical\": 7,\n",
" \"num_radial\": 64,\n",
" \"num_blocks\": 5,\n",
" \"emb_size_atom\": 256,\n",
" \"emb_size_edge\": 512,\n",
" \"emb_size_trip\": 64,\n",
" \"emb_size_rbf\": 16,\n",
" \"emb_size_cbf\": 16,\n",
" \"emb_size_bil_trip\": 64,\n",
" \"num_before_skip\": 1,\n",
" \"num_after_skip\": 2,\n",
" \"num_concat\": 1,\n",
" \"num_atom\": 3,\n",
" \"cutoff\": 6.0,\n",
" \"max_neighbors\": 50,\n",
" \"rbf\": {\"name\": \"gaussian\"},\n",
" \"envelope\": {\n",
" \"name\": \"polynomial\",\n",
" \"exponent\": 5,\n",
" },\n",
" \"cbf\": {\"name\": \"spherical_harmonics\"},\n",
" \"extensive\": True,\n",
" \"otf_graph\": False,\n",
" \"output_init\": \"HeOrthogonal\",\n",
" \"activation\": \"silu\",\n",
" \"scale_file\": \"configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\",\n",
" \"regress_forces\": False,\n",
" \"direct_forces\": False,\n",
"}\n",
"# Optimizer\n",
"optimizer = {\n",
" 'batch_size': 1, # originally 32\n",
" 'eval_batch_size': 1, # originally 32\n",
" 'num_workers': 2,\n",
" 'lr_initial': 1.e-4,\n",
" 'optimizer': 'AdamW',\n",
" 'optimizer_params': {\"amsgrad\": True},\n",
" 'scheduler': \"ReduceLROnPlateau\",\n",
" 'mode': \"min\",\n",
" 'factor': 0.8,\n",
" 'patience': 3,\n",
" 'max_epochs': 1, # used for demonstration purposes\n",
" 'ema_decay': 0.999,\n",
" 'clip_grad_norm': 10,\n",
" 'loss_energy': 'mae',\n",
"}\n",
"# Dataset\n",
"dataset = [\n",
" {'src': train_src,\n",
" 'normalize_labels': True,\n",
" 'target_mean': mean,\n",
" 'target_std': stdev,\n",
" }, # train set \n",
" {'src': val_src}, # val set (optional)\n",
"]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "oG5w1sk-v1LI"
},
"source": [
"###Create EnergyTrainer"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ExmkV2K1W07H",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4e875ed0-258b-43eb-e191-d00274400128"
},
"source": [
"energy_trainer = EnergyTrainer(\n",
" task=task,\n",
" model=copy.deepcopy(model), # copied for later use, not necessary in practice.\n",
" dataset=dataset,\n",
" optimizer=optimizer,\n",
" identifier=\"IS2RE-example\",\n",
" run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n",
" is_debug=False, # if True, do not save checkpoint, logs, or results\n",
" is_vis=False,\n",
" print_every=5,\n",
" seed=0, # random seed to use\n",
" logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n",
" local_rank=0,\n",
" amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage) \n",
")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"amp: true\n",
"cmd:\n",
" checkpoint_dir: ./checkpoints/2021-11-22-17-21-04-IS2RE-example\n",
" commit: bc04a90\n",
" identifier: IS2RE-example\n",
" logs_dir: ./logs/tensorboard/2021-11-22-17-21-04-IS2RE-example\n",
" print_every: 5\n",
" results_dir: ./results/2021-11-22-17-21-04-IS2RE-example\n",
" seed: 0\n",
" timestamp_id: 2021-11-22-17-21-04-IS2RE-example\n",
"dataset:\n",
" normalize_labels: true\n",
" src: data/is2re/train_100/data.lmdb\n",
" target_mean: !!python/object/apply:numpy.core.multiarray.scalar\n",
" - &id001 !!python/object/apply:numpy.dtype\n",
" args:\n",
" - f8\n",
" - false\n",
" - true\n",
" state: !!python/tuple\n",
" - 3\n",
" - <\n",
" - null\n",
" - null\n",
" - null\n",
" - -1\n",
" - -1\n",
" - 0\n",
" - !!binary |\n",
" MjyJzgpQ978=\n",
" target_std: !!python/object/apply:numpy.core.multiarray.scalar\n",
" - *id001\n",
" - !!binary |\n",
" PnyyzMtk/T8=\n",
"gpus: 1\n",
"logger: tensorboard\n",
"model: gemnet_t\n",
"model_attributes:\n",
" activation: silu\n",
" cbf:\n",
" name: spherical_harmonics\n",
" cutoff: 6.0\n",
" direct_forces: false\n",
" emb_size_atom: 256\n",
" emb_size_bil_trip: 64\n",
" emb_size_cbf: 16\n",
" emb_size_edge: 512\n",
" emb_size_rbf: 16\n",
" emb_size_trip: 64\n",
" envelope:\n",
" exponent: 5\n",
" name: polynomial\n",
" extensive: true\n",
" max_neighbors: 50\n",
" num_after_skip: 2\n",
" num_atom: 3\n",
" num_before_skip: 1\n",
" num_blocks: 5\n",
" num_concat: 1\n",
" num_radial: 64\n",
" num_spherical: 7\n",
" otf_graph: false\n",
" output_init: HeOrthogonal\n",
" rbf:\n",
" name: gaussian\n",
" regress_forces: false\n",
" scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\n",
"optim:\n",
" batch_size: 1\n",
" clip_grad_norm: 10\n",
" ema_decay: 0.999\n",
" eval_batch_size: 1\n",
" factor: 0.8\n",
" loss_energy: mae\n",
" lr_initial: 0.0001\n",
" max_epochs: 1\n",
" mode: min\n",
" num_workers: 2\n",
" optimizer: AdamW\n",
" optimizer_params:\n",
" amsgrad: true\n",
" patience: 3\n",
" scheduler: ReduceLROnPlateau\n",
"slurm: {}\n",
"task:\n",
" dataset: single_point_lmdb\n",
" description: Relaxed state energy prediction from initial structure.\n",
" labels:\n",
" - relaxed energy\n",
" metric: mae\n",
" type: regression\n",
"val_dataset:\n",
" src: data/is2re/val_20/data.lmdb\n",
"\n",
"2021-11-22 17:20:24 (INFO): Loading dataset: single_point_lmdb\n",
"2021-11-22 17:20:24 (INFO): Loading model: gemnet_t\n",
"2021-11-22 17:20:26 (INFO): Loaded GemNetT with 22774037 parameters.\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"2021-11-22 17:20:26 (WARNING): Model gradient logging to tensorboard not yet supported.\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "tnJer5rGwjwi"
},
"source": [
"energy_trainer.model"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "pto2SpJPwlz1"
},
"source": [
"### Train the Model"
]
},
{
"cell_type": "code",
"metadata": {
"id": "iHMRkFplwsky",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "df58e36a-6bb9-411a-ce4a-b9258fc06a55"
},
"source": [
"energy_trainer.train()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"energy_mae: 6.21e+01, energy_mse: 3.86e+03, energy_within_threshold: 0.00e+00, loss: 6.76e+01, lr: 1.00e-04, epoch: 5.00e-02, step: 5.00e+00\n",
"energy_mae: 1.86e+02, energy_mse: 3.46e+04, energy_within_threshold: 0.00e+00, loss: 2.03e+02, lr: 1.00e-04, epoch: 1.00e-01, step: 1.00e+01\n",
"energy_mae: 2.88e+03, energy_mse: 8.31e+06, energy_within_threshold: 0.00e+00, loss: 3.14e+03, lr: 1.00e-04, epoch: 1.50e-01, step: 1.50e+01\n",
"energy_mae: 5.92e+02, energy_mse: 3.51e+05, energy_within_threshold: 0.00e+00, loss: 3.22e+02, lr: 1.00e-04, epoch: 2.00e-01, step: 2.00e+01\n",
"energy_mae: 4.49e+03, energy_mse: 2.02e+07, energy_within_threshold: 0.00e+00, loss: 2.45e+03, lr: 1.00e-04, epoch: 2.50e-01, step: 2.50e+01\n",
"energy_mae: 4.48e+01, energy_mse: 2.01e+03, energy_within_threshold: 0.00e+00, loss: 2.44e+01, lr: 1.00e-04, epoch: 3.00e-01, step: 3.00e+01\n",
"energy_mae: 1.29e+02, energy_mse: 1.68e+04, energy_within_threshold: 0.00e+00, loss: 7.05e+01, lr: 1.00e-04, epoch: 3.50e-01, step: 3.50e+01\n",
"energy_mae: 2.21e+02, energy_mse: 4.90e+04, energy_within_threshold: 0.00e+00, loss: 1.21e+02, lr: 1.00e-04, epoch: 4.00e-01, step: 4.00e+01\n",
"energy_mae: 2.20e+02, energy_mse: 4.84e+04, energy_within_threshold: 0.00e+00, loss: 1.20e+02, lr: 1.00e-04, epoch: 4.50e-01, step: 4.50e+01\n",
"energy_mae: 1.82e+01, energy_mse: 3.32e+02, energy_within_threshold: 0.00e+00, loss: 9.91e+00, lr: 1.00e-04, epoch: 5.00e-01, step: 5.00e+01\n",
"energy_mae: 2.80e+03, energy_mse: 7.84e+06, energy_within_threshold: 0.00e+00, loss: 1.52e+03, lr: 1.00e-04, epoch: 5.50e-01, step: 5.50e+01\n",
"energy_mae: 5.37e+01, energy_mse: 2.88e+03, energy_within_threshold: 0.00e+00, loss: 2.92e+01, lr: 1.00e-04, epoch: 6.00e-01, step: 6.00e+01\n",
"energy_mae: 4.53e+00, energy_mse: 2.05e+01, energy_within_threshold: 0.00e+00, loss: 2.46e+00, lr: 1.00e-04, epoch: 6.50e-01, step: 6.50e+01\n",
"energy_mae: 2.54e+03, energy_mse: 6.47e+06, energy_within_threshold: 0.00e+00, loss: 1.38e+03, lr: 1.00e-04, epoch: 7.00e-01, step: 7.00e+01\n",
"energy_mae: 5.55e+02, energy_mse: 3.08e+05, energy_within_threshold: 0.00e+00, loss: 3.02e+02, lr: 1.00e-04, epoch: 7.50e-01, step: 7.50e+01\n",
"energy_mae: 1.72e+02, energy_mse: 2.95e+04, energy_within_threshold: 0.00e+00, loss: 9.35e+01, lr: 1.00e-04, epoch: 8.00e-01, step: 8.00e+01\n",
"energy_mae: 1.04e+02, energy_mse: 1.08e+04, energy_within_threshold: 0.00e+00, loss: 5.67e+01, lr: 1.00e-04, epoch: 8.50e-01, step: 8.50e+01\n",
"energy_mae: 1.68e+02, energy_mse: 2.81e+04, energy_within_threshold: 0.00e+00, loss: 9.13e+01, lr: 1.00e-04, epoch: 9.00e-01, step: 9.00e+01\n",
"energy_mae: 4.73e+02, energy_mse: 2.24e+05, energy_within_threshold: 0.00e+00, loss: 2.58e+02, lr: 1.00e-04, epoch: 9.50e-01, step: 9.50e+01\n",
"energy_mae: 2.12e+01, energy_mse: 4.49e+02, energy_within_threshold: 0.00e+00, loss: 1.15e+01, lr: 1.00e-04, epoch: 1.00e+00, step: 1.00e+02\n",
"2021-11-22 17:23:24 (INFO): Evaluating on val.\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"device 0: 100%|██████████| 20/20 [00:10<00:00, 1.86it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2021-11-22 17:23:35 (INFO): energy_mae: 1028.9198, energy_mse: 3489562.4455, energy_within_threshold: 0.0000, loss: 560.1051, epoch: 1.0000\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MkAd2MBmw8wO"
},
"source": [
"### Validate the Model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gaauxWdNw_-4"
},
"source": [
"#### Load the best checkpoint"
]
},
{
"cell_type": "code",
"metadata": {
"id": "xkj0Bslqws_N",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "2680bf59-c13e-4113-b3bd-15aa62c9007e"
},
"source": [
"# The `best_checpoint.pt` file contains the checkpoint with the best val performance\n",
"checkpoint_path = os.path.join(energy_trainer.config[\"cmd\"][\"checkpoint_dir\"], \"best_checkpoint.pt\")\n",
"checkpoint_path"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'./checkpoints/2021-11-22-17-21-04-IS2RE-example/best_checkpoint.pt'"
]
},
"metadata": {},
"execution_count": 29
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BqmCqaFlbMZC",
"outputId": "fd9f2409-1b51-4b6a-90ca-0a00a40d2dfe"
},
"source": [
"# Append the dataset with the test set. We use the same val set for demonstration.\n",
"\n",
"# Dataset\n",
"dataset.append(\n",
" {'src': val_src}, # test set (optional)\n",
")\n",
"dataset"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'normalize_labels': True,\n",
" 'src': 'data/is2re/train_100/data.lmdb',\n",
" 'target_mean': -1.4570415561499996,\n",
" 'target_std': 1.8371084209427546},\n",
" {'src': 'data/is2re/val_20/data.lmdb'},\n",
" {'src': 'data/is2re/val_20/data.lmdb'}]"
]
},
"metadata": {},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "IkcqadZIxXP-",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "5a07d5c7-cbdf-4901-80db-1fcf19c1c42b"
},
"source": [
"pretrained_energy_trainer = EnergyTrainer(\n",
" task=task,\n",
" model=model,\n",
" dataset=dataset,\n",
" optimizer=optimizer,\n",
" identifier=\"IS2RE-val-example\",\n",
" run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n",
" is_debug=False, # if True, do not save checkpoint, logs, or results\n",
" is_vis=False,\n",
" print_every=10,\n",
" seed=0, # random seed to use\n",
" logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n",
" local_rank=0,\n",
" amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage)\n",
")\n",
"\n",
"pretrained_energy_trainer.load_checkpoint(checkpoint_path=checkpoint_path)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"amp: true\n",
"cmd:\n",
" checkpoint_dir: ./checkpoints/2021-11-22-17-23-12-IS2RE-val-example\n",
" commit: bc04a90\n",
" identifier: IS2RE-val-example\n",
" logs_dir: ./logs/tensorboard/2021-11-22-17-23-12-IS2RE-val-example\n",
" print_every: 10\n",
" results_dir: ./results/2021-11-22-17-23-12-IS2RE-val-example\n",
" seed: 0\n",
" timestamp_id: 2021-11-22-17-23-12-IS2RE-val-example\n",
"dataset:\n",
" normalize_labels: true\n",
" src: data/is2re/train_100/data.lmdb\n",
" target_mean: !!python/object/apply:numpy.core.multiarray.scalar\n",
" - &id001 !!python/object/apply:numpy.dtype\n",
" args:\n",
" - f8\n",
" - false\n",
" - true\n",
" state: !!python/tuple\n",
" - 3\n",
" - <\n",
" - null\n",
" - null\n",
" - null\n",
" - -1\n",
" - -1\n",
" - 0\n",
" - !!binary |\n",
" MjyJzgpQ978=\n",
" target_std: !!python/object/apply:numpy.core.multiarray.scalar\n",
" - *id001\n",
" - !!binary |\n",
" PnyyzMtk/T8=\n",
"gpus: 1\n",
"logger: tensorboard\n",
"model: gemnet_t\n",
"model_attributes:\n",
" activation: silu\n",
" cbf:\n",
" name: spherical_harmonics\n",
" cutoff: 6.0\n",
" direct_forces: false\n",
" emb_size_atom: 256\n",
" emb_size_bil_trip: 64\n",
" emb_size_cbf: 16\n",
" emb_size_edge: 512\n",
" emb_size_rbf: 16\n",
" emb_size_trip: 64\n",
" envelope:\n",
" exponent: 5\n",
" name: polynomial\n",
" extensive: true\n",
" max_neighbors: 50\n",
" num_after_skip: 2\n",
" num_atom: 3\n",
" num_before_skip: 1\n",
" num_blocks: 5\n",
" num_concat: 1\n",
" num_radial: 64\n",
" num_spherical: 7\n",
" otf_graph: false\n",
" output_init: HeOrthogonal\n",
" rbf:\n",
" name: gaussian\n",
" regress_forces: false\n",
" scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\n",
"optim:\n",
" batch_size: 1\n",
" clip_grad_norm: 10\n",
" ema_decay: 0.999\n",
" eval_batch_size: 1\n",
" factor: 0.8\n",
" loss_energy: mae\n",
" lr_initial: 0.0001\n",
" max_epochs: 1\n",
" mode: min\n",
" num_workers: 2\n",
" optimizer: AdamW\n",
" optimizer_params:\n",
" amsgrad: true\n",
" patience: 3\n",
" scheduler: ReduceLROnPlateau\n",
"slurm: {}\n",
"task:\n",
" dataset: single_point_lmdb\n",
" description: Relaxed state energy prediction from initial structure.\n",
" labels:\n",
" - relaxed energy\n",
" metric: mae\n",
" type: regression\n",
"test_dataset:\n",
" src: data/is2re/val_20/data.lmdb\n",
"val_dataset:\n",
" src: data/is2re/val_20/data.lmdb\n",
"\n",
"2021-11-22 17:23:36 (INFO): Loading dataset: single_point_lmdb\n",
"2021-11-22 17:23:36 (INFO): Loading model: gemnet_t\n",
"2021-11-22 17:23:38 (INFO): Loaded GemNetT with 22774037 parameters.\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"2021-11-22 17:23:38 (WARNING): Model gradient logging to tensorboard not yet supported.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2021-11-22 17:23:38 (INFO): Loading checkpoint from: ./checkpoints/2021-11-22-17-21-04-IS2RE-example/best_checkpoint.pt\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TcUvAI81xoSt"
},
"source": [
"#### Test the model"
]
},
{
"cell_type": "code",
"metadata": {
"id": "VtCEFtXxxr3u",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "eadd2568-ac65-4d3a-b234-cafe99cee575"
},
"source": [
"# make predictions on the existing test_loader\n",
"predictions = pretrained_energy_trainer.predict(pretrained_trainer.test_loader, results_file=\"is2re_results\", disable_tqdm=False)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2021-11-22 17:23:38 (INFO): Predicting on test.\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"device 0: 100%|██████████| 20/20 [00:03<00:00, 5.80it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2021-11-22 17:23:42 (INFO): Writing results to ./results/2021-11-22-17-23-12-IS2RE-val-example/is2re_is2re_results.npz\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1UcfxFi4x4aD"
},
"source": [
"energies = predictions[\"energy\"]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "gM9Wqk0GIxyU"
},
"source": [
"## Initial Structure to Relaxed Structure (IS2RS) \n",
"\n",
"We approach the IS2RS task by using a pre-trained S2EF model to iteratively run a structure optimization to arrive at a relaxed structure. While the majority of approaches for this task do this iteratively, we note it's possible to train a model to directly predict relaxed structures.\n",
"\n",
"## Steps for making IS2RS predictions\n",
"1) Define or load a configuration (config), which includes the following\n",
"* task with relaxation dataset information\n",
"* model\n",
"* optimizer\n",
"* dataset\n",
"* trainer\n",
"\n",
"2) Create a ForcesTrainer object\n",
"\n",
"3) Train a S2EF model or load an existing S2EF checkpoint\n",
"\n",
"4) Run relaxations\n",
"\n",
"**Note** For this task we'll be using a publicly released pre-trained checkpoint of our best model to perform relaxations."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tNSI3hUAJAWc"
},
"source": [
"#### Imports"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Z-WZXuRiI6Vo"
},
"source": [
"from ocpmodels.trainers import ForcesTrainer\n",
"from ocpmodels.datasets import TrajectoryLmdbDataset\n",
"from ocpmodels import models\n",
"from ocpmodels.common import logger\n",
"from ocpmodels.common.utils import setup_logging\n",
"setup_logging()\n",
"\n",
"import numpy as np"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "XFLZTpRvldZE"
},
"source": [
"### Dataset\n",
"\n",
"The IS2RS task requires an additional realxation dataset to be defined - `relax_dataset`. This dataset is read in similar to the IS2RE dataset - requiring an LMDB file. The same datasets are used for the IS2RE and IS2RS tasks."
]
},
{
"cell_type": "code",
"metadata": {
"id": "irrPcbs4ldZF"
},
"source": [
"train_src = \"data/s2ef/train_100\"\n",
"val_src = \"data/s2ef/val_20\"\n",
"relax_dataset = \"data/is2re/val_20/data.lmdb\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "7gJ01gabd6BR"
},
"source": [
"### Download pretrained checkpoint"
]
},
{
"cell_type": "code",
"metadata": {
"id": "MiOeqFN-d-7K"
},
"source": [
"!wget -q https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_08/s2ef/gemnet_t_direct_h512_all.pt\n",
"checkpoint_path = \"/content/ocp/gemnet_t_direct_h512_all.pt\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "fp1Ab8TGltP6"
},
"source": [
"### Define the Config"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JLOydGsmltP7"
},
"source": [
"Running an iterative S2EF model for the IS2RS task can be run from any S2EF config given the following additions to the `task` portion of the config:\n",
"\n",
"* relax_dataset - IS2RE LMDB dataset\n",
"* *write_pos* - Whether to save out relaxed positions\n",
"* *relaxation_steps* - Number of optimization steps to run\n",
"* *relax_opt* - Dictionary of optimizer settings. Currently only LBFGS supported\n",
" * *maxstep* - Maximum distance an optimization is allowed to make\n",
" * *memory* - Memory history to use for LBFGS\n",
" * *damping* - Calculated step is multiplied by this factor before updating positions\n",
" * *alpha* - Initial guess for the Hessian\n",
" * *traj_dir* - If specified, directory to save out the full ML relaxation as an ASE trajectory. Useful for debugging or visualizing results.\n",
"* *num_relaxation_batches* - If specified, relaxations will only be run for a subset of the relaxation dataset. Useful for debugging or wanting to visualize a few systems.\n",
"\n",
"A sample relaxation config can be found [here](https://github.com/Open-Catalyst-Project/ocp/blob/1044e311182c1120c6e6d137ce6db3f445148973/configs/s2ef/2M/dimenet_plus_plus/dpp_relax.yml#L24-L33).\n",
" "
]
},
{
"cell_type": "code",
"metadata": {
"id": "XU9DisuyltP8"
},
"source": [
"# Task\n",
"task = {\n",
" 'dataset': 'trajectory_lmdb', # dataset used for the S2EF task\n",
" 'description': 'Regressing to energies and forces for DFT trajectories from OCP',\n",
" 'type': 'regression',\n",
" 'metric': 'mae',\n",
" 'labels': ['potential energy'],\n",
" 'grad_input': 'atomic forces',\n",
" 'train_on_free_atoms': True,\n",
" 'eval_on_free_atoms': True,\n",
" 'relax_dataset': {\"src\": relax_dataset},\n",
" 'write_pos': True,\n",
" 'relaxation_steps': 200,\n",
" 'num_relaxation_batches': 1,\n",
" 'relax_opt': {\n",
" 'maxstep': 0.04,\n",
" 'memory': 50,\n",
" 'damping': 1.0,\n",
" 'alpha': 70.0,\n",
" 'traj_dir': \"ml-relaxations/is2rs-test\", \n",
" }\n",
"}\n",
"# Model\n",
"model = {\n",
" 'name': 'gemnet_t',\n",
" \"num_spherical\": 7,\n",
" \"num_radial\": 128,\n",
" \"num_blocks\": 3,\n",
" \"emb_size_atom\": 512,\n",
" \"emb_size_edge\": 512,\n",
" \"emb_size_trip\": 64,\n",
" \"emb_size_rbf\": 16,\n",
" \"emb_size_cbf\": 16,\n",
" \"emb_size_bil_trip\": 64,\n",
" \"num_before_skip\": 1,\n",
" \"num_after_skip\": 2,\n",
" \"num_concat\": 1,\n",
" \"num_atom\": 3,\n",
" \"cutoff\": 6.0,\n",
" \"max_neighbors\": 50,\n",
" \"rbf\": {\"name\": \"gaussian\"},\n",
" \"envelope\": {\n",
" \"name\": \"polynomial\",\n",
" \"exponent\": 5,\n",
" },\n",
" \"cbf\": {\"name\": \"spherical_harmonics\"},\n",
" \"extensive\": True,\n",
" \"otf_graph\": False,\n",
" \"output_init\": \"HeOrthogonal\",\n",
" \"activation\": \"silu\",\n",
" \"scale_file\": \"configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\",\n",
" \"regress_forces\": True,\n",
" \"direct_forces\": True,\n",
"}\n",
"# Optimizer\n",
"optimizer = {\n",
" 'batch_size': 1, # originally 32\n",
" 'eval_batch_size': 1, # originally 32\n",
" 'num_workers': 2,\n",
" 'lr_initial': 5.e-4,\n",
" 'optimizer': 'AdamW',\n",
" 'optimizer_params': {\"amsgrad\": True},\n",
" 'scheduler': \"ReduceLROnPlateau\",\n",
" 'mode': \"min\",\n",
" 'factor': 0.8,\n",
" 'ema_decay': 0.999,\n",
" 'clip_grad_norm': 10,\n",
" 'patience': 3,\n",
" 'max_epochs': 1, # used for demonstration purposes\n",
" 'force_coefficient': 100,\n",
"}\n",
"# Dataset\n",
"dataset = [\n",
" {'src': train_src, 'normalize_labels': False}, # train set \n",
" {'src': val_src}, # val set (optional)\n",
"]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "IsOqQIjnogkQ"
},
"source": [
"### Create the trainer"
]
},
{
"cell_type": "code",
"metadata": {
"id": "5KZvPu4hogkR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "fdbbfa5c-0d7c-449f-8be5-ef2e5d17860d"
},
"source": [
"trainer = ForcesTrainer(\n",
" task=task,\n",
" model=model,\n",
" dataset=dataset,\n",
" optimizer=optimizer,\n",
" identifier=\"is2rs-example\",\n",
" run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n",
" is_debug=False, # if True, do not save checkpoint, logs, or results\n",
" is_vis=False,\n",
" print_every=5,\n",
" seed=0, # random seed to use\n",
" logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n",
" local_rank=0,\n",
" amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage)\n",
")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"amp: true\n",
"cmd:\n",
" checkpoint_dir: ./checkpoints/2021-11-22-17-42-24-is2rs-example\n",
" commit: bc04a90\n",
" identifier: is2rs-example\n",
" logs_dir: ./logs/tensorboard/2021-11-22-17-42-24-is2rs-example\n",
" print_every: 5\n",
" results_dir: ./results/2021-11-22-17-42-24-is2rs-example\n",
" seed: 0\n",
" timestamp_id: 2021-11-22-17-42-24-is2rs-example\n",
"dataset:\n",
" normalize_labels: false\n",
" src: data/s2ef/train_100\n",
"gpus: 1\n",
"logger: tensorboard\n",
"model: gemnet_t\n",
"model_attributes:\n",
" activation: silu\n",
" cbf:\n",
" name: spherical_harmonics\n",
" cutoff: 6.0\n",
" direct_forces: true\n",
" emb_size_atom: 512\n",
" emb_size_bil_trip: 64\n",
" emb_size_cbf: 16\n",
" emb_size_edge: 512\n",
" emb_size_rbf: 16\n",
" emb_size_trip: 64\n",
" envelope:\n",
" exponent: 5\n",
" name: polynomial\n",
" extensive: true\n",
" max_neighbors: 50\n",
" num_after_skip: 2\n",
" num_atom: 3\n",
" num_before_skip: 1\n",
" num_blocks: 3\n",
" num_concat: 1\n",
" num_radial: 128\n",
" num_spherical: 7\n",
" otf_graph: false\n",
" output_init: HeOrthogonal\n",
" rbf:\n",
" name: gaussian\n",
" regress_forces: true\n",
" scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\n",
"optim:\n",
" batch_size: 1\n",
" clip_grad_norm: 10\n",
" ema_decay: 0.999\n",
" eval_batch_size: 1\n",
" factor: 0.8\n",
" force_coefficient: 100\n",
" lr_initial: 0.0005\n",
" max_epochs: 1\n",
" mode: min\n",
" num_workers: 2\n",
" optimizer: AdamW\n",
" optimizer_params:\n",
" amsgrad: true\n",
" patience: 3\n",
" scheduler: ReduceLROnPlateau\n",
"slurm: {}\n",
"task:\n",
" dataset: trajectory_lmdb\n",
" description: Regressing to energies and forces for DFT trajectories from OCP\n",
" eval_on_free_atoms: true\n",
" grad_input: atomic forces\n",
" labels:\n",
" - potential energy\n",
" metric: mae\n",
" num_relaxation_batches: 1\n",
" relax_dataset:\n",
" src: data/is2re/val_20/data.lmdb\n",
" relax_opt:\n",
" alpha: 70.0\n",
" damping: 1.0\n",
" maxstep: 0.04\n",
" memory: 50\n",
" traj_dir: ml-relaxations/is2rs-test\n",
" relaxation_steps: 200\n",
" train_on_free_atoms: true\n",
" type: regression\n",
" write_pos: true\n",
"val_dataset:\n",
" src: data/s2ef/val_20\n",
"\n",
"2021-11-22 17:42:56 (INFO): Loading dataset: trajectory_lmdb\n",
"2021-11-22 17:42:56 (INFO): Loading model: gemnet_t\n",
"2021-11-22 17:43:00 (INFO): Loaded GemNetT with 31671825 parameters.\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"2021-11-22 17:43:00 (WARNING): Model gradient logging to tensorboard not yet supported.\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wtMn792WpC4X"
},
"source": [
"### Load the best checkpoint\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "jFXQJBYxpC4Y",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f35be368-a350-465d-fb32-5a5795317bac"
},
"source": [
"trainer.load_checkpoint(checkpoint_path=checkpoint_path)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2021-11-22 17:43:00 (INFO): Loading checkpoint from: /content/ocp/gemnet_t_direct_h512_all.pt\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2rtga4JPot6i"
},
"source": [
"### Run relaxations\n",
"\n",
"We run a full relaxation for a single batch of our relaxation dataset (`num_relaxation_batches=1`)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "aQG-HEpuot6k",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f91a9a2a-4ea8-4b60-c6a1-a1255e482119"
},
"source": [
"trainer.run_relaxations()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2021-11-22 17:43:19 (INFO): Running ML-relaxations\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\r 0%| | 0/20 [00:00"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CN9RC25hxLlp"
},
"source": [
"Qualitatively, the ML relaxation is behaving as expected - decreasing energies over the course of the relaxation."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 198
},
"id": "6kxJBkV1wZUw",
"outputId": "f1f39a5f-feac-42bc-c208-c6c14aff88ef"
},
"source": [
"fig, ax = plt.subplots(1, 3)\n",
"labels = ['ml-initial', 'ml-middle', 'ml-final']\n",
"for i in range(3):\n",
" ax[i].axis('off')\n",
" ax[i].set_title(labels[i])\n",
"\n",
"ase.visualize.plot.plot_atoms(\n",
" ml_trajectory[0], \n",
" ax[0], \n",
" radii=0.8,\n",
" # rotation=(\"-75x, 45y, 10z\")) # uncomment to visualize at different angles\n",
")\n",
"ase.visualize.plot.plot_atoms(\n",
" ml_trajectory[100], \n",
" ax[1], \n",
" radii=0.8, \n",
" # rotation=(\"-75x, 45y, 10z\") # uncomment to visualize at different angles\n",
")\n",
"ase.visualize.plot.plot_atoms(\n",
" ml_trajectory[-1], \n",
" ax[2], \n",
" radii=0.8,\n",
" # rotation=(\"-75x, 45y, 10z\"), # uncomment to visualize at different angles\n",
")\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 99
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8LE2lrJwyblQ"
},
"source": [
"Qualitatively, the generated structures seem reasonable with no obvious issues we had previously mentioned to look out for."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MymFuumcRd8r"
},
"source": [
"# Model development \n",
"\n",
"In this section, we will walk through how to develop a simple Graph Neural Network model on the S2EF-200k dataset.\n",
"\n",
"Let's begin by setting up some imports and boilerplate config parameters."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mk71_j2i96X4"
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vK49MKgd9ufL"
},
"source": [
"import torch\n",
"\n",
"from typing import Optional\n",
"\n",
"from ocpmodels.trainers import ForcesTrainer\n",
"from ocpmodels import models\n",
"from ocpmodels.common import logger\n",
"from ocpmodels.common.utils import setup_logging, get_pbc_distances\n",
"from ocpmodels.common.registry import registry\n",
"\n",
"from ocpmodels.models.gemnet.layers.radial_basis import PolynomialEnvelope\n",
"\n",
"from torch_geometric.nn.models.schnet import GaussianSmearing\n",
"from torch_scatter import scatter"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Xj9QvWby-AI6"
},
"source": [
"setup_logging()\n",
"\n",
"# Dataset paths\n",
"train_src = \"data/s2ef/train_200k\"\n",
"val_src = \"data/s2ef/val\"\n",
"\n",
"# Configs\n",
"task = {\n",
" 'dataset': 'trajectory_lmdb', # dataset used for the S2EF task\n",
" 'description': 'Regressing to energies and forces for DFT trajectories from OCP',\n",
" 'type': 'regression',\n",
" 'metric': 'mae',\n",
" 'labels': ['potential energy'],\n",
" 'grad_input': 'atomic forces',\n",
" 'train_on_free_atoms': True,\n",
" 'eval_on_free_atoms': True\n",
"}\n",
"\n",
"# Optimizer\n",
"optimizer = {\n",
" 'batch_size': 16, # if hitting GPU memory issues, lower this\n",
" 'eval_batch_size': 8,\n",
" 'num_workers': 8,\n",
" 'lr_initial': 0.0001,\n",
" 'scheduler': \"ReduceLROnPlateau\",\n",
" 'mode': \"min\",\n",
" 'factor': 0.8,\n",
" 'patience': 3,\n",
" 'max_epochs': 80,\n",
" 'max_epochs': 5,\n",
" 'force_coefficient': 100,\n",
"}\n",
"\n",
"# Dataset\n",
"dataset = [\n",
" {'src': train_src, 'normalize_labels': True, 'target_mean': -0.7554450631141663, 'target_std': 2.887317180633545, 'grad_target_mean': 0.0, 'grad_target_std': 2.887317180633545}, # train set\n",
" {'src': val_src},\n",
"]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "bzp-Cyrm-JOE"
},
"source": [
"## Atom and Edge Embeddings\n",
"\n",
"Each atom is represented as a node with its features computed using a simple `torch.nn.Embedding` layer on the atomic number.\n",
"\n",
"All pairs of atoms with a defined cutoff radius (=6A) are assumed to have edges between them, with their features computed as the concatenation of 1) a Gaussian expansion of the distance between the atoms, and the 2) source and 3) target\n",
"node features.\n",
"\n",
"We will use the `GaussianSmearing` layer (reproduced below) from the PyTorch Geometric library for computing distance features:\n",
"\n",
"```\n",
"class GaussianSmearing(torch.nn.Module):\n",
" def __init__(self, start=0.0, stop=5.0, num_gaussians=50):\n",
" super(GaussianSmearing, self).__init__()\n",
" offset = torch.linspace(start, stop, num_gaussians)\n",
" self.coeff = -0.5 / (offset[1] - offset[0]).item()**2\n",
" self.register_buffer('offset', offset)\n",
"\n",
" def forward(self, dist):\n",
" dist = dist.view(-1, 1) - self.offset.view(1, -1)\n",
" return torch.exp(self.coeff * torch.pow(dist, 2))\n",
"```"
]
},
{
"cell_type": "code",
"metadata": {
"id": "dfMCS-pL-2X5"
},
"source": [
"class AtomEmbedding(torch.nn.Module):\n",
" def __init__(self, emb_size):\n",
" super().__init__()\n",
" self.embeddings = torch.nn.Embedding(83, emb_size) # We go up to Bi (83).\n",
"\n",
" def forward(self, Z):\n",
" h = self.embeddings(Z - 1) # -1 because Z.min()=1 (==Hydrogen)\n",
" return h\n",
"\n",
"class EdgeEmbedding(torch.nn.Module):\n",
" def __init__(self, atom_emb_size, edge_emb_size, out_size):\n",
" super().__init__()\n",
" in_features = 2 * atom_emb_size + edge_emb_size\n",
" self.dense = torch.nn.Sequential(\n",
" torch.nn.Linear(in_features, out_size, bias=False),\n",
" torch.nn.SiLU()\n",
" )\n",
"\n",
" def forward(self, h, m_rbf, idx_s, idx_t,\n",
" ):\n",
" h_s = h[idx_s] # indexing source node, shape=(num_edges, emb_size)\n",
" h_t = h[idx_t] # indexing target node, shape=(num_edges, emb_size)\n",
"\n",
" m_st = torch.cat([h_s, h_t, m_rbf], dim=-1) # (num_edges, 2 * atom_emb_size + edge_emb_size)\n",
" m_st = self.dense(m_st) # (num_edges, out_size)\n",
" return m_st\n",
"\n",
"class RadialBasis(torch.nn.Module):\n",
" def __init__(self, num_radial: int, cutoff: float, env_exponent: int = 5):\n",
" super().__init__()\n",
" self.inv_cutoff = 1 / cutoff\n",
" self.envelope = PolynomialEnvelope(env_exponent)\n",
" self.rbf = GaussianSmearing(start=0, stop=1, num_gaussians=num_radial)\n",
"\n",
" def forward(self, d):\n",
" d_scaled = d * self.inv_cutoff\n",
" env = self.envelope(d_scaled)\n",
" return env[:, None] * self.rbf(d_scaled) # (num_edges, num_radial)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "nhvCP4wzAE_K"
},
"source": [
"## Message passing \n",
"\n",
"We start by implementing a very simple message-passing scheme to predict system energy and forces.\n",
"\n",
"Given the node and edge features, we sum up edge features for all edges $e_{ij}$ connecting node $i$ to its neighbors $j$, and pass the resultant vector through a fully-connected layer to project it down to a scalar. This gives us a scalar energy contribution for each node $i$ in the structure. We then sum up all node energy contributions to predict the overall system energy.\n",
"\n",
"Similarly, to predict forces, we pass edge features through a fully-connected layer to project it down to a scalar representing the force magnitude per edge $e_{ij}$. We can then sum up these force magnitudes based on the original edge directions to predict the resultant force vector per node $i$."
]
},
{
"cell_type": "code",
"metadata": {
"id": "QMjBCLcSAQSp"
},
"source": [
"@registry.register_model(\"simple\")\n",
"class SimpleAtomEdgeModel(torch.nn.Module):\n",
" def __init__(self, num_atoms, bond_feat_dim, num_targets, emb_size=64, num_radial=64, cutoff=6.0, env_exponent=5):\n",
" super().__init__()\n",
"\n",
" self.radial_basis = RadialBasis(\n",
" num_radial=num_radial,\n",
" cutoff=cutoff,\n",
" env_exponent=env_exponent,\n",
" )\n",
"\n",
" self.atom_emb = AtomEmbedding(emb_size)\n",
" self.edge_emb = EdgeEmbedding(emb_size, num_radial, emb_size)\n",
"\n",
" self.out_energy = torch.nn.Linear(emb_size, 1)\n",
" self.out_forces = torch.nn.Linear(emb_size, 1)\n",
"\n",
" def forward(self, data):\n",
" batch = data.batch\n",
" atomic_numbers = data.atomic_numbers.long()\n",
" edge_index = data.edge_index\n",
" cell_offsets = data.cell_offsets\n",
" neighbors = data.neighbors\n",
"\n",
" # computing edges and distances taking periodic boundary conditions into account\n",
" out = get_pbc_distances(\n",
" data.pos,\n",
" edge_index,\n",
" data.cell,\n",
" cell_offsets,\n",
" neighbors,\n",
" return_offsets=True,\n",
" return_distance_vec=True,\n",
" )\n",
"\n",
" edge_index = out[\"edge_index\"]\n",
" D_st = out[\"distances\"]\n",
" V_st = -out[\"distance_vec\"] / D_st[:, None]\n",
"\n",
" idx_s, idx_t = edge_index\n",
"\n",
" # embed atoms\n",
" h_atom = self.atom_emb(atomic_numbers)\n",
"\n",
" # gaussian expansion of distances D_st\n",
" m_rbf = self.radial_basis(D_st)\n",
" # embed edges\n",
" m = self.edge_emb(h_atom, m_rbf, idx_s, idx_t)\n",
"\n",
" # read out energy\n",
" # \n",
" # x_E_i = \\sum_j m_ji -- summing up edge features m_ji for all neighbors j\n",
" # of node i to predict node i's energy contribution.\n",
" x_E = scatter(m, idx_t, dim=0, dim_size=h_atom.shape[0], reduce=\"sum\")\n",
" x_E = self.out_energy(x_E)\n",
"\n",
" # E = \\sum_i x_E_i\n",
" num_systems = torch.max(batch)+1\n",
" E = scatter(x_E, batch, dim=0, dim_size=num_systems, reduce=\"add\")\n",
" # (num_systems, 1)\n",
"\n",
" # read out forces\n",
" # \n",
" # x_F is the force magnitude per edge, we multiply that by the direction of each edge ji,\n",
" # and sum up all the vectors to predict the resultant force on node i\n",
" x_F = self.out_forces(m)\n",
" F_st_vec = x_F[:, :, None] * V_st[:, None, :]\n",
" F = scatter(F_st_vec, idx_t, dim=0, dim_size=atomic_numbers.size(0), reduce=\"add\")\n",
" # (num_atoms, num_targets, 3)\n",
" F = F.squeeze(1)\n",
"\n",
" return E, F\n",
"\n",
" @property\n",
" def num_params(self):\n",
" return sum(p.numel() for p in self.parameters())"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "-Vl3WEqVAith"
},
"source": [
"## Training the model"
]
},
{
"cell_type": "code",
"metadata": {
"id": "u7E7pLiqAmnL"
},
"source": [
"model_params = {\n",
" 'name': 'simple',\n",
" 'emb_size': 256,\n",
" 'num_radial': 128,\n",
" 'cutoff': 6.0,\n",
" 'env_exponent': 5,\n",
"}\n",
"\n",
"trainer = ForcesTrainer(\n",
" task=task,\n",
" model=model_params,\n",
" dataset=dataset,\n",
" optimizer=optimizer,\n",
" identifier=\"S2EF-simple\",\n",
" run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n",
" is_debug=False, # if True, do not save checkpoint, logs, or results\n",
" is_vis=False,\n",
" print_every=20,\n",
" seed=0, # random seed to use\n",
" logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n",
" local_rank=0,\n",
")\n",
"\n",
"trainer.train()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "thF9lWK9Ay90"
},
"source": [
"If you've wired everything up correctly, this model should be relatively small (~185k params) and achieve a force MAE of 0.0815, force cosine of 0.0321, energy MAE of 2.2772 in 2 epochs.\n",
"\n",
"We encourage the reader to try playing with the embedding size, cutoff radius, number of gaussian basis functions, and polynomial envelope exponent to see how it affects performance."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PSqVJXsxArvu"
},
"source": [
"## Incorporating triplets and training GemNet-T\n",
"\n",
"Recall how this model computes edge embeddings based only on a Gaussian expansion of edge distances.\n",
"\n",
"To better capture 3D geometry, we should also embed angles formed by triplets or quadruplets of atoms. A model that incorporates this idea and works quite well is GemNet (Klicpera et al., NeurIPS 2021); see the following figure.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Twh6yIC5GTrW"
},
"source": [
"You can train a GemNet-T (T = triplets) on S2EF-200k using the following config.\n",
"\n",
"Note that this is a significantly bulkier model (~3.4M params) than the one we developed above and will take longer to train."
]
},
{
"cell_type": "code",
"metadata": {
"id": "LVbM_S0sGlOr"
},
"source": [
"model_params = {\n",
" 'name': 'gemnet_t',\n",
" 'num_spherical': 7,\n",
" 'num_radial': 128,\n",
" 'num_blocks': 1,\n",
" 'emb_size_atom': 256,\n",
" 'emb_size_edge': 256,\n",
" 'emb_size_trip': 64,\n",
" 'emb_size_rbf': 16,\n",
" 'emb_size_cbf': 16,\n",
" 'emb_size_bil_trip': 64,\n",
" 'num_before_skip': 1,\n",
" 'num_after_skip': 1,\n",
" 'num_concat': 1,\n",
" 'num_atom': 3,\n",
" 'cutoff': 6.0,\n",
" 'max_neighbors': 50,\n",
" 'rbf': {'name': 'gaussian'},\n",
" 'envelope': {'name': 'polynomial', 'exponent': 5},\n",
" 'cbf': {'name': 'spherical_harmonics'},\n",
" 'extensive': True,\n",
" 'otf_graph': False,\n",
" 'output_init': 'HeOrthogonal',\n",
" 'activation': 'silu',\n",
" 'scale_file': 'configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json',\n",
" 'regress_forces': True,\n",
" 'direct_forces': True,\n",
"}\n",
"\n",
"trainer = ForcesTrainer(\n",
" task=task,\n",
" model=model_params,\n",
" dataset=dataset,\n",
" optimizer=optimizer,\n",
" identifier=\"S2EF-gemnet-t\",\n",
" run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n",
" is_debug=False, # if True, do not save checkpoint, logs, or results\n",
" is_vis=False,\n",
" print_every=20,\n",
" seed=0, # random seed to use\n",
" logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n",
" local_rank=0,\n",
")\n",
"\n",
"trainer.train()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "F-Pw3GCVHAwA"
},
"source": [
"This model should achieve a force MAE of 0.0668, a force cosine of 0.1180, and an energy MAE of 0.8106 in 2 epochs, significantly better than our simple model.\n",
"\n",
"Again, we encourage the reader to try playing with no. of blocks, choice of basis functions, the various embedding sizes to develop intuition for the interplay between these hyperparameters."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Rzx0lArZJ6r0"
},
"source": [
"# (Optional) OCP Calculator \n",
"\n",
"For those interested in using our pretrained models for other applications, we provide an [ASE](https://wiki.fysik.dtu.dk/ase/#:~:text=The%20Atomic%20Simulation%20Environment%20(ASE,under%20the%20GNU%20LGPL%20license.)-compatible Calculator to interface with ASE's functionality."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QGaXyeS_8yHp"
},
"source": [
"## Download pretrained checkpoint\n",
"\n",
"We have released checkpoints of all the models on the leaderboard [here](https://github.com/Open-Catalyst-Project/ocp/blob/master/MODELS.md). These trained models can be used as an ASE calculator for various calculations.\n",
"\n",
"For this tutorial we download our current best model checkpoint: GemNet-T"
]
},
{
"cell_type": "code",
"metadata": {
"id": "MBCRi69284Ve"
},
"source": [
"!wget -q https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_08/s2ef/gemnet_t_direct_h512_all.pt\n",
"checkpoint_path = \"/content/ocp/gemnet_t_direct_h512_all.pt\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "TNQ1dNVG93kH"
},
"source": [
"## Using the OCP Calculator\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "o_MHpzbhPKN_",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "fa4336cf-ba85-43b6-e608-551ffcf3763a"
},
"source": [
"from ocpmodels.common.relaxation.ase_utils import OCPCalculator\n",
"import ase.io\n",
"from ase.optimize import BFGS\n",
"from ase.build import fcc100, add_adsorbate, molecule\n",
"import os\n",
"from ase.constraints import FixAtoms\n",
"\n",
"# Construct a sample structure\n",
"adslab = fcc100(\"Cu\", size=(3, 3, 3))\n",
"adsorbate = molecule(\"C3H8\")\n",
"add_adsorbate(adslab, adsorbate, 3, offset=(1, 1))\n",
"tags = np.zeros(len(adslab))\n",
"tags[18:27] = 1\n",
"tags[27:] = 2\n",
"adslab.set_tags(tags)\n",
"cons= FixAtoms(indices=[atom.index for atom in adslab if (atom.tag == 0)])\n",
"adslab.set_constraint(cons)\n",
"adslab.center(vacuum=13.0, axis=2)\n",
"adslab.set_pbc(True)\n",
"\n",
"config_yml_path = \"configs/s2ef/all/gemnet/gemnet-dT.yml\"\n",
"\n",
"# Define the calculator\n",
"calc = OCPCalculator(config_yml=config_yml_path, checkpoint=checkpoint_path)\n",
"\n",
"# Set up the calculator\n",
"adslab.calc = calc\n",
"\n",
"os.makedirs(\"data/sample_ml_relax\", exist_ok=True)\n",
"opt = BFGS(adslab, trajectory=\"data/sample_ml_relax/toy_c3h8_relax.traj\")\n",
"\n",
"opt.run(fmax=0.05, steps=100)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"amp: false\n",
"cmd:\n",
" checkpoint_dir: /content/ocp/checkpoints/2021-11-22-18-03-44\n",
" commit: bc04a90\n",
" identifier: ''\n",
" logs_dir: /content/ocp/logs/tensorboard/2021-11-22-18-03-44\n",
" print_every: 100\n",
" results_dir: /content/ocp/results/2021-11-22-18-03-44\n",
" seed: null\n",
" timestamp_id: 2021-11-22-18-03-44\n",
"dataset: null\n",
"gpus: 0\n",
"logger: tensorboard\n",
"model: gemnet_t\n",
"model_attributes:\n",
" activation: silu\n",
" cbf:\n",
" name: spherical_harmonics\n",
" cutoff: 6.0\n",
" direct_forces: true\n",
" emb_size_atom: 512\n",
" emb_size_bil_trip: 64\n",
" emb_size_cbf: 16\n",
" emb_size_edge: 512\n",
" emb_size_rbf: 16\n",
" emb_size_trip: 64\n",
" envelope:\n",
" exponent: 5\n",
" name: polynomial\n",
" extensive: true\n",
" max_neighbors: 50\n",
" num_after_skip: 2\n",
" num_atom: 3\n",
" num_before_skip: 1\n",
" num_blocks: 3\n",
" num_concat: 1\n",
" num_radial: 128\n",
" num_spherical: 7\n",
" otf_graph: true\n",
" output_init: HeOrthogonal\n",
" rbf:\n",
" name: gaussian\n",
" regress_forces: true\n",
" scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\n",
"optim:\n",
" batch_size: 32\n",
" clip_grad_norm: 10\n",
" ema_decay: 0.999\n",
" energy_coefficient: 1\n",
" eval_batch_size: 32\n",
" eval_every: 5000\n",
" factor: 0.8\n",
" force_coefficient: 100\n",
" loss_energy: mae\n",
" loss_force: l2mae\n",
" lr_initial: 0.0005\n",
" max_epochs: 80\n",
" mode: min\n",
" num_workers: 2\n",
" optimizer: AdamW\n",
" optimizer_params:\n",
" amsgrad: true\n",
" patience: 3\n",
" scheduler: ReduceLROnPlateau\n",
"slurm: {}\n",
"task:\n",
" dataset: trajectory_lmdb\n",
" description: Regressing to energies and forces for DFT trajectories from OCP\n",
" eval_on_free_atoms: true\n",
" grad_input: atomic forces\n",
" labels:\n",
" - potential energy\n",
" metric: mae\n",
" train_on_free_atoms: true\n",
" type: regression\n",
"\n",
"2021-11-22 18:03:35 (INFO): Loading dataset: trajectory_lmdb\n",
"2021-11-22 18:03:35 (INFO): Loading model: gemnet_t\n",
"2021-11-22 18:03:38 (INFO): Loaded GemNetT with 31671825 parameters.\n",
"2021-11-22 18:03:38 (INFO): Loading checkpoint from: /content/ocp/gemnet_t_direct_h512_all.pt\n",
" Step Time Energy fmax\n",
"BFGS: 0 18:03:41 -4.099784 1.5675\n",
"BFGS: 1 18:03:43 -4.244461 1.1370\n",
"BFGS: 2 18:03:44 -4.403120 0.7635\n",
"BFGS: 3 18:03:46 -4.503653 0.8364\n",
"BFGS: 4 18:03:48 -4.558208 0.7339\n",
"BFGS: 5 18:03:49 -4.592069 0.4095\n",
"BFGS: 6 18:03:51 -4.619362 0.7312\n",
"BFGS: 7 18:03:53 -4.671468 0.9712\n",
"BFGS: 8 18:03:54 -4.796430 0.9211\n",
"BFGS: 9 18:03:56 -4.957961 0.9762\n",
"BFGS: 10 18:03:57 -5.109433 1.0384\n",
"BFGS: 11 18:03:59 -5.295604 1.2247\n",
"BFGS: 12 18:04:00 -5.498977 1.1271\n",
"BFGS: 13 18:04:02 -5.618095 1.0669\n",
"BFGS: 14 18:04:04 -5.737120 0.9509\n",
"BFGS: 15 18:04:05 -5.901926 0.9260\n",
"BFGS: 16 18:04:07 -6.076125 1.2738\n",
"BFGS: 17 18:04:08 -6.198373 1.2029\n",
"BFGS: 18 18:04:10 -6.250323 0.6851\n",
"BFGS: 19 18:04:11 -6.254094 0.2008\n",
"BFGS: 20 18:04:13 -6.293966 0.1779\n",
"BFGS: 21 18:04:14 -6.326333 0.2294\n",
"BFGS: 22 18:04:16 -6.324431 0.1700\n",
"BFGS: 23 18:04:17 -6.321288 0.1016\n",
"BFGS: 24 18:04:19 -6.328468 0.0847\n",
"BFGS: 25 18:04:20 -6.331809 0.0587\n",
"BFGS: 26 18:04:22 -6.332153 0.0444\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 106
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TUH5BaaXo-ca"
},
"source": [
"\n",
"# (Optional) Creating your own LMDBs for use in the OCP repository \n",
"\n",
"In order to interface with our repository, the data mustbe structured and organized in a specific format. Below we walk you through on how to create such datasets with your own non-OC20 data that may help with your research.\n",
"\n",
"For this tutorial we use the toy C3H8 trajectory we previously generated [here](#data-description)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "o7cG3WhLnuqg"
},
"source": [
"\n",
"\n",
"#### Initial Structure to Relaxed Energy (IS2RE) LMDBs\n",
"IS2RE/IS2RS LMDBs utilize the SinglePointLmdb dataset. This dataset expects the data to be contained in a **single** LMDB file. In addition to the attributes defined by AtomsToGraph, the following attributes must be added for the IS2RE/IS2RS tasks:\n",
"\n",
"- pos_relaxed: Relaxed adslab positions\n",
"- sid: Unique system identifier, arbitrary\n",
"- y_init: Initial adslab energy, formerly Data.y\n",
"- y_relaxed: Relaxed adslab energy\n",
"- tags (optional): 0 - subsurface, 1 - surface, 2 - adsorbate\n",
"\n",
"\n",
"As a demo, we will use the above generated data to create an IS2R* LMDB file.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "nweCG0y5nxlw"
},
"source": [
"from ocpmodels.preprocessing import AtomsToGraphs\n",
"\n",
"\"\"\"\n",
"args description:\n",
"\n",
"max neigh (int): maximum number of neighors to be considered while constructing a graph\n",
"radius (int): Neighbors are considered only within this radius cutoff in Angstrom\n",
"r_energy (bool): Stored energy value in the Data object; False for test data\n",
"r_forces (bool): Stores forces value in the Data object; False for test data\n",
"r_distances (bool): pre-calculates distances taking into account PBC and max neigh/radius\n",
" If you set it to False, make sure to add \"otf_graph = True\" under models in config for runs\n",
"r_fixed (bools): True if you want to fix the subsurface atoms\n",
"\"\"\"\n",
"\n",
"a2g = AtomsToGraphs(\n",
" max_neigh=50,\n",
" radius=6,\n",
" r_energy=True, \n",
" r_forces=True,\n",
" r_distances=False, \n",
" r_fixed=True,\n",
")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "K16pPnQdnzro"
},
"source": [
"import lmdb\n",
"\n",
"\"\"\"\n",
"For most cases one just needs to change the name of the lmdb as they require.\n",
"Make sure to give the entire path in the config (with .lmdb) for IS2RE tasks\n",
"\"\"\"\n",
"\n",
"db = lmdb.open(\n",
" \"data/toy_C3H8.lmdb\",\n",
" map_size=1099511627776 * 2,\n",
" subdir=False,\n",
" meminit=False,\n",
" map_async=True,\n",
")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "t_8oaE5qn1Za"
},
"source": [
"\"\"\"\n",
"This method converts extracts all features from trajectory file and convert to Data Object\n",
"\"\"\"\n",
"\n",
"def read_trajectory_extract_features(a2g, traj_path):\n",
" # Read the traj file\n",
" traj = ase.io.read(traj_path, \":\")\n",
"\n",
" # Get tags if you had defined those in the atoms object, if not skip this line\n",
" tags = traj[0].get_tags()\n",
"\n",
" # Collect only initial and final image as this is IS2RS task\n",
" images = [traj[0], traj[-1]]\n",
"\n",
" # Converts a list of atoms object to a list of Data object using a2g defined above\n",
" data_objects = a2g.convert_all(images, disable_tqdm=True)\n",
"\n",
" # Add tags to the data objects if you have them (we would suggest to do so), if not skip this\n",
" data_objects[0].tags = torch.LongTensor(tags)\n",
" data_objects[1].tags = torch.LongTensor(tags)\n",
"\n",
" return data_objects"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qSfOagphn7yy"
},
"source": [
"import torch\n",
"import pickle\n",
"system_paths = [\"data/toy_c3h8_relax.traj\"] # specify list of trajectory files you wish to write to LMDBs\n",
"idx = 0\n",
"\n",
"for system in system_paths:\n",
" # Extract Data object\n",
" data_objects = read_trajectory_extract_features(a2g, system)\n",
" initial_struc = data_objects[0]\n",
" relaxed_struc = data_objects[1]\n",
" \n",
" initial_struc.y_init = initial_struc.y # subtract off reference energy, if applicable\n",
" del initial_struc.y\n",
" initial_struc.y_relaxed = relaxed_struc.y # subtract off reference energy, if applicable\n",
" initial_struc.pos_relaxed = relaxed_struc.pos\n",
" \n",
" # Filter data if necessary\n",
" # OCP filters adsorption energies > |10| eV\n",
" \n",
" initial_struc.sid = idx # arbitrary unique identifier \n",
" \n",
" # no neighbor edge case check\n",
" if initial_struc.edge_index.shape[1] == 0:\n",
" print(\"no neighbors\", traj_path)\n",
" continue\n",
" \n",
" # Write to LMDB\n",
" txn = db.begin(write=True)\n",
" txn.put(f\"{idx}\".encode(\"ascii\"), pickle.dumps(initial_struc, protocol=-1))\n",
" txn.commit()\n",
" db.sync()\n",
" idx += 1\n",
"\n",
"db.close()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "p8ftTehrn9pG",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "74c95b8a-e260-4b6f-92c4-3544f28deda5"
},
"source": [
"from ocpmodels.datasets import SinglePointLmdbDataset\n",
"\n",
"# SinglePointLmdbDataset is out custom Dataset method to read the lmdbs as Data objects. Note that we need to give the entire path (including lmdb) for IS2RE\n",
"dataset = SinglePointLmdbDataset({\"src\": \"data/toy_C3H8.lmdb\"})\n",
"\n",
"print(\"Size of the dataset created:\", len(dataset))\n",
"print(dataset[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Size of the dataset created: 1\n",
"Data(atomic_numbers=[38], cell=[1, 3, 3], cell_offsets=[1733, 3], edge_index=[2, 1733], fixed=[38], force=[38, 3], natoms=38, pos=[38, 3], pos_relaxed=[38, 3], sid=0, tags=[38], y_init=15.80469962027714, y_relaxed=8.358921451420816)\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UWYBEis2n_ye"
},
"source": [
"#### Structure to Energy and Forces (S2EF) LMDBs\n",
"\n",
"S2EF LMDBs utilize the TrajectoryLmdb dataset. This dataset expects a directory of LMDB files. In addition to the attributes defined by AtomsToGraph, the following attributes must be added for the S2EF task:\n",
"\n",
"- tags (optional): 0 - subsurface, 1 - surface, 2 - adsorbate\n",
"- fid: Frame index along the trajcetory\n",
"- sid- sid: Unique system identifier, arbitrary\n",
"\n",
"Additionally, a \"length\" key must be added to each LMDB file.\n",
"\n",
"As a demo, we will use the above generated data to create an S2EF LMDB dataset"
]
},
{
"cell_type": "code",
"metadata": {
"id": "k74bbQJuoBwy"
},
"source": [
"os.makedirs(\"data/s2ef\", exist_ok=True)\n",
"db = lmdb.open(\n",
" \"data/s2ef/toy_C3H8.lmdb\",\n",
" map_size=1099511627776 * 2,\n",
" subdir=False,\n",
" meminit=False,\n",
" map_async=True,\n",
")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-6VuR1lBoDfY",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0c3e104b-d22f-4376-85f3-0cd505c8914d"
},
"source": [
"from tqdm import tqdm\n",
"tags = traj[0].get_tags()\n",
"data_objects = a2g.convert_all(traj, disable_tqdm=True)\n",
"\n",
"\n",
"for fid, data in tqdm(enumerate(data_objects), total=len(data_objects)):\n",
" #assign sid\n",
" data.sid = torch.LongTensor([0])\n",
" \n",
" #assign fid\n",
" data.fid = torch.LongTensor([fid])\n",
" \n",
" #assign tags, if available\n",
" data.tags = torch.LongTensor(tags)\n",
" \n",
" # Filter data if necessary\n",
" # OCP filters adsorption energies > |10| eV and forces > |50| eV/A\n",
"\n",
" # no neighbor edge case check\n",
" if data.edge_index.shape[1] == 0:\n",
" print(\"no neighbors\", traj_path)\n",
" continue\n",
"\n",
" txn = db.begin(write=True)\n",
" txn.put(f\"{fid}\".encode(\"ascii\"), pickle.dumps(data, protocol=-1))\n",
" txn.commit()\n",
" \n",
"txn = db.begin(write=True)\n",
"txn.put(f\"length\".encode(\"ascii\"), pickle.dumps(len(data_objects), protocol=-1))\n",
"txn.commit()\n",
"\n",
"\n",
"db.sync()\n",
"db.close()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 101/101 [00:00<00:00, 129.56it/s]\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rJ2ZXuBMH8xt"
},
"source": [
"# Running on command line [Preferred way to train models] "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aj8HsmxjISED"
},
"source": [
"The previous sections of this notebook are intended to demonstrate the inner workings of our codebase. For regular training, we suggest that you train and evaluate on command line.\n",
"\n",
"1. Clone our repo at https://github.com/Open-Catalyst-Project/ocp and set up the environment according to the readme.\n",
"2. Download relevant data ([see above for info](https://colab.research.google.com/drive/1oGZcrakB4Pbj8Xq74lSvcRDUHw9L-Dh5#scrollTo=jXoiLncsU3pe)).\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lAdwlMNOKwYj"
},
"source": [
"3. In the config file, modify the path of the data [train](https://github.com/Open-Catalyst-Project/ocp/blob/master/configs/is2re/10k/base.yml#L4) [val](https://github.com/Open-Catalyst-Project/ocp/blob/master/configs/is2re/10k/base.yml#L8), [normalization parameters](https://github.com/Open-Catalyst-Project/ocp/blob/master/configs/is2re/10k/base.yml#L5-L7) as well as any other [model](https://github.com/Open-Catalyst-Project/ocp/blob/master/configs/is2re/10k/dimenet_plus_plus/dpp.yml#L4-L16) or [training](https://github.com/Open-Catalyst-Project/ocp/blob/master/configs/is2re/10k/dimenet_plus_plus/dpp.yml#L23-L35) args. \n",
"\n",
"For a simple example, we'll train DimeNet++ on IS2RE demo data: \\\n",
"a. Modify the train data path in `/contents/ocp/configs/is2re/10k/base.yml` in \n",
"Line 4 to `/contents/ocp/data/is2re/train_10k/data.lmdb` and val data path in Line 8 to `/contents/ocp/data/is2re/val_2k/data.lmdb`. \\\n",
"b. Calculate the mean and std for train data and modify Lines 6-7 respectively \\\n",
"c. We can change the model parameters in `/contents/ocp/configs/is2re/10k/dimenet_plus_plus/dpp.yml` and we suggest you to change the lr_milestones and warmup_steps as the data here is smaller (these need to be tuned for every dataset).\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HjWsAaojKzpH"
},
"source": [
"4. Train: `python main.py --mode train --config-yml configs/is2re/10k/dimenet_plus_plus/dpp.yml --identifier dpp_is2re_sample`\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "mCgs4eGSO-HM"
},
"source": [
"# Optional block to try command line training \n",
"# Note that config args can be added in the command line. For example, --optim.batch_size=1"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "q1xRtYWTO8Xb"
},
"source": [
"5. Add a data path as a test set to `configs/is2re/10k/base.yml`\n",
"6. Run predictions with the trained model: \n",
"`python main.py --mode predict --config-yml configs/is2re/10k/dimenet_plus_plus/dpp.yml --checkpoint checkpoints/[datetime]-dpp_is2re_sample/checkpoint.pt`\n",
"7. View energy predictions at `results/[datetime]/is2re_predictions.npz`\n",
"\n",
"For more information on how to train and evaluate, see [this readme](https://github.com/Open-Catalyst-Project/ocp/blob/master/TRAIN.md). For checkpoints of publicly available trained models, see [MODELS.md](https://github.com/Open-Catalyst-Project/ocp/blob/master/MODELS.md)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oHIjM6eMwlXY"
},
"source": [
"# Limitations \n",
"The OpenCatalyst project is motivated by the problems we face due to climate change, many of which require innovative solutions to reduce energy usage and replace traditional chemical feedstocks with renewable alternatives. For example, one of the most energy intensive chemical processes is the development of new electrochemical catalysts for ammonia fertilizer production that helped to feed the world’s growing population during the 20th century. This is also an illustrative example of possible unintended consequences as advancements in chemistry and materials may be used for numerous purposes. As ammonia fertilization increased in use, its overuse in today’s farming has led to ocean “dead zones” and its production is very carbon intensive. Knowledge and techniques used to create ammonia were also transferred to the creation of explosives during wartime. We hope to steer the use of ML for atomic simulations to societally-beneficial uses by training and testing our approaches on datasets, such as OC20, that were specifically designed to address chemical reactions useful for addressing climate change."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CLLCQpv14Gsx"
},
"source": [
"# Next Steps \n",
"\n",
"While progress has been well underway - https://opencatalystproject.org/leaderboard.html, a considerable gap still exists between state-of-the-art models and our target goals. We offer some some general thoughts as to next steps for the readers to ponder on or explore:\n",
"\n",
"* GNN depth has consistenly improved model performance. What limitations to depth are there? How far can we push deeper models for OC20? \n",
"* Our best performing models have little to no physical biases encoded. Can we incorporate such biases to improve our models? Experiments with physically inspired embeddings have had no advantage vs. random initializations, are there better ways to incorporate this information into the models?\n",
"* Uncertainty estimation will play an important role in later stages of the project when it comes to large scale screening. How can we get reliable uncertainty estimates from large scale GNNs?\n",
"* Are we limited to message-passing GNNs? Can we leverage alternative architectures for similiar or better performance?\n",
"* Trajectories are nothing more than sequential data points. How can we use sequential modeling techniques to model the full trajectory?\n",
"\n",
"OC20 is a large and diverse dataset with many splits. For those with limited resources but unsure where to start, we provide some general recommendations:\n",
"\n",
"* The IS2RE-direct task is a great place to start. With the largest training set containing ~460k data points, this task is easily accesible for those with even just a single GPU.\n",
"* Those interested in the more general S2EF task don't need to train on the All set to get meaningful performance.\n",
" * Results on the 2M dataset are often sufficient to highlight model improvements.\n",
" * For a fixed compute budget (e.g. fixed number of steps), training on the All set often leads to better performance.\n",
"* The S2EF 200k dataset is fairly noisy, trying to find meaningful trends using this dataset can be difficult.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PkKqewK_-ZLD"
},
"source": [
"\n",
"# References\n",
"\n",
"* Open Catalyst codebase: https://github.com/Open-Catalyst-Project/ocp/\n",
"* Open Catalyst webpage: https://opencatalystproject.org/\n",
"* [Electrocatalysis white paper](https://arxiv.org/pdf/2010.09435.pdf): C. Lawrence Zitnick, Lowik Chanussot, Abhishek Das, Siddharth Goyal, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Thibaut Lavril, Aini Palizhati, Morgane Riviere, Muhammed Shuaibi, Anuroop Sriram, Kevin Tran, Brandon Wood, Junwoong Yoon, Devi Parikh, Zachary Ulissi: “An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage”, 2020; arXiv:2010.09435.\n",
"* [OC20 dataset paper](https://arxiv.org/pdf/2010.09990.pdf): L. Chanussot, A. Das, S. Goyal, T. Lavril, M. Shuaibi, M. Riviere, K. Tran, J. Heras-Domingo, C. Ho, W. Hu, A. Palizhati, A. Sriram, B. Wood, J. Yoon, D. Parikh, C. L. Zitnick, and Z. Ulissi. The Open Catalyst 2020 (oc20) dataset and community challenges. ACS Catalysis, 2021.\n",
"* [Gemnet model:](https://arxiv.org/abs/2106.08903) Johannes Klicpera, Florian Becker, and Stephan Günnemann. Gemnet: Universal directional graph neural networks for molecules, 2021.\n",
"\n",
"\n"
]
}
]
}