{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluating convergence\n", "\n", "Here we evaluate the convergence of a trajectory using the clustering ensemble similarity method and the dimensionality reduction ensemble similarity methods.\n", "\n", "**Last updated:** December 2022 with MDAnalysis 2.4.0-dev0\n", "\n", "**Last updated:** December 2022\n", "\n", "**Minimum version of MDAnalysis:** 1.0.0\n", "\n", "**Packages required:**\n", " \n", "* MDAnalysis (Michaud-Agrawal *et al.*, 2011, Gowers *et al.*, 2016)\n", "* MDAnalysisTests\n", "* [scikit-learn](https://scikit-learn.org/stable/)\n", " \n", "**Optional packages for visualisation:**\n", "\n", "* [matplotlib](https://matplotlib.org)\n", "\n", "\n", "
\n", " \n", "**Note**\n", "\n", "The metrics and methods in the `encore` module are from (Tiberti *et al.*, 2015). Please cite them when using the ``MDAnalysis.analysis.encore`` module in published work.\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:59:55.164483Z", "iopub.status.busy": "2021-05-19T05:59:55.163573Z", "iopub.status.idle": "2021-05-19T05:59:56.540827Z", "shell.execute_reply": "2021-05-19T05:59:56.541182Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "import MDAnalysis as mda\n", "from MDAnalysis.tests.datafiles import PSF, DCD\n", "from MDAnalysis.analysis import encore\n", "from MDAnalysis.analysis.encore.clustering import ClusteringMethod as clm\n", "from MDAnalysis.analysis.encore.dimensionality_reduction import DimensionalityReductionMethod as drm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The test files we will be working with here feature adenylate kinase (AdK), a phosophotransferase enzyme. (Beckstein *et al.*, 2009)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:59:56.546291Z", "iopub.status.busy": "2021-05-19T05:59:56.545647Z", "iopub.status.idle": "2021-05-19T05:59:56.785608Z", "shell.execute_reply": "2021-05-19T05:59:56.786089Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/pbarletta/mambaforge/envs/guide/lib/python3.9/site-packages/MDAnalysis/coordinates/DCD.py:165: DeprecationWarning: DCDReader currently makes independent timesteps by copying self.ts while other readers update self.ts inplace. This behavior will be changed in 3.0 to be the same as other readers. Read more at https://github.com/MDAnalysis/mdanalysis/issues/3889 to learn if this change in behavior might affect you.\n", " warnings.warn(\"DCDReader currently makes independent timesteps\"\n" ] } ], "source": [ "u = mda.Universe(PSF, DCD)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaluating convergence with similarity measures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The convergence of the trajectory is evaluated by the similarity of the conformation ensembles in windows of the trajectory. The trajectory is divided into windows that increase by `window_size` frames. For example, if your trajectory had 13 frames and you specified a `window_size=3`, your windows would be:\n", "\n", " - Window 1: ---\n", " - Window 2: ------\n", " - Window 3: ---------\n", " - Window 4: -------------\n", " \n", "Where `-` represents 1 frame.\n", "\n", "These are compared using either the similarity of their clusters (`ces_convergence`) or their reduced dimension coordinates (`dres_convergence`). The rate at which the similarity values drop to 0 is indicative of how much the trajectory keeps on resampling the same regions of the conformational space, and therefore is the rate of convergence. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using default arguments with clustering ensemble similarity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See [clustering_ensemble_similarity.ipynb](clustering_ensemble_similarity.ipynb#Calculating-clustering-similarity-with-default-settings) for an introduction to comparing trajectories via clustering. See the [API documentation for ces_convergence](https://docs.mdanalysis.org/stable/documentation_pages/analysis/encore/similarity.html#MDAnalysis.analysis.encore.similarity.ces_convergence) for more information." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:59:56.791388Z", "iopub.status.busy": "2021-05-19T05:59:56.790617Z", "iopub.status.idle": "2021-05-19T05:59:58.964292Z", "shell.execute_reply": "2021-05-19T05:59:58.964732Z" } }, "outputs": [], "source": [ "ces_conv = encore.ces_convergence(u, # universe\n", " 10, # window size\n", " select='name CA') # default" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output is an array of similarity values, with the shape (`number_of_windows`, `number_of_clustering_methods`)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.4819\n", " 0.4028\n", " 0.3170\n", " 0.2522\n", " 0.1983\n", " 0.1464\n", " 0.0991\n", " 0.0567\n", " 0.0000\n" ] } ], "source": [ "for row in ces_conv:\n", " for sim in row:\n", " print(\"{:>7.4f}\".format(sim))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This can be easily plotted as a line." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:59:58.991618Z", "iopub.status.busy": "2021-05-19T05:59:58.990939Z", "iopub.status.idle": "2021-05-19T05:59:59.088137Z", "shell.execute_reply": "2021-05-19T05:59:59.088508Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Jensen-Shannon divergence')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ces_fig, ces_ax = plt.subplots()\n", "plt.plot(ces_conv)\n", "ces_ax.set_xlabel('Window')\n", "ces_ax.set_ylabel('Jensen-Shannon divergence')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Comparing different clustering methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may want to try different clustering methods, or use different parameters within the methods. `encore.ces_convergence` allows you to pass a list of `clustering_methods` to be applied, much like [normal clustering ensemble similarity methods](clustering_ensemble_similarity.ipynb#Calculating-clustering-similarity-with-multiple-methods).\n", "\n", "
\n", " \n", "**Note**\n", "\n", "To use the other ENCORE methods available, you need to install [scikit-learn](https://scikit-learn.org/stable/).\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The KMeans clustering algorithm separates samples into $n$ groups of equal variance, with centroids that minimise the inertia. You must choose how many clusters to partition. [(See the scikit-learn user guide for more information.)](https://scikit-learn.org/stable/modules/clustering.html#k-means)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:59:59.094046Z", "iopub.status.busy": "2021-05-19T05:59:59.093346Z", "iopub.status.idle": "2021-05-19T05:59:59.095367Z", "shell.execute_reply": "2021-05-19T05:59:59.094973Z" } }, "outputs": [], "source": [ "km1 = clm.KMeans(12, # no. clusters\n", " init = 'k-means++', # default\n", " algorithm=\"auto\") # default\n", "\n", "km2 = clm.KMeans(6, # no. clusters\n", " init = 'k-means++', # default\n", " algorithm=\"auto\") # default\n", "\n", "km3 = clm.KMeans(3, # no. clusters\n", " init = 'k-means++', # default\n", " algorithm=\"auto\") # default" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we pass a list of clustering methods to `encore.ces_convergence`, the similarity values get saved in `ces_conv2` in order." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:59:59.099109Z", "iopub.status.busy": "2021-05-19T05:59:59.098472Z", "iopub.status.idle": "2021-05-19T06:00:01.094005Z", "shell.execute_reply": "2021-05-19T06:00:01.094590Z" } }, "outputs": [ { "data": { "text/plain": [ "(9, 3)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ces_conv2 = encore.ces_convergence(u, # universe\n", " 10, # window size\n", " select='name CA',\n", " clustering_method=[km1, km2, km3]\n", " )\n", "ces_conv2.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the number of clusters partitioned by KMeans has an effect on the resulting rate of convergence." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T06:00:01.112454Z", "iopub.status.busy": "2021-05-19T06:00:01.108370Z", "iopub.status.idle": "2021-05-19T06:00:01.241556Z", "shell.execute_reply": "2021-05-19T06:00:01.242120Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels = ['12 clusters', '6 clusters', '3 clusters']\n", "\n", "ces_fig2, ces_ax2 = plt.subplots()\n", "for data, label in zip(ces_conv2.T, labels):\n", " plt.plot(data, label=label)\n", "ces_ax2.set_xlabel('Window')\n", "ces_ax2.set_ylabel('Jensen-Shannon divergence')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using default arguments with dimension reduction ensemble similarity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See [dimension_reduction_ensemble_similarity.ipynb](dimension_reduction_ensemble_similarity.ipynb#Calculating-dimension-reduction-similarity-with-default-settings) for an introduction on comparing trajectories via dimensionality reduction. We now use the `dres_convergence` function ([API docs](https://docs.mdanalysis.org/stable/documentation_pages/analysis/encore/similarity.html#MDAnalysis.analysis.encore.similarity.dres_convergence))." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T06:00:01.247319Z", "iopub.status.busy": "2021-05-19T06:00:01.246438Z", "iopub.status.idle": "2021-05-19T06:00:03.689365Z", "shell.execute_reply": "2021-05-19T06:00:03.689783Z" } }, "outputs": [], "source": [ "dres_conv = encore.dres_convergence(u, # universe\n", " 10, # window size\n", " select='name CA') # default" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Much like `ces_convergence`, the output is an array of similarity values." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T06:00:03.696587Z", "iopub.status.busy": "2021-05-19T06:00:03.695214Z", "iopub.status.idle": "2021-05-19T06:00:03.699634Z", "shell.execute_reply": "2021-05-19T06:00:03.699083Z" }, "tags": [ "nbval-ignore-output" ] }, "outputs": [ { "data": { "text/plain": [ "array([[0.52983036],\n", " [0.41177493],\n", " [0.31770319],\n", " [0.24269804],\n", " [0.18980852],\n", " [0.13913721],\n", " [0.06342056],\n", " [0.03125632],\n", " [0. ]])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dres_conv" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T06:00:03.710016Z", "iopub.status.busy": "2021-05-19T06:00:03.709181Z", "iopub.status.idle": "2021-05-19T06:00:03.815587Z", "shell.execute_reply": "2021-05-19T06:00:03.816008Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Jensen-Shannon divergence')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dres_fig, dres_ax = plt.subplots()\n", "plt.plot(dres_conv)\n", "dres_ax.set_xlabel('Window')\n", "dres_ax.set_ylabel('Jensen-Shannon divergence')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Comparing different dimensionality reduction methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, you may want to compare the performance of different methods.\n", "\n", "Principal component analysis uses singular value decomposition to project data onto a lower dimensional space. [(See the scikit-learn user guide for more information.)](https://scikit-learn.org/stable/modules/decomposition.html#pca)\n", "\n", "The method provided by MDAnalysis.encore accepts any of the keyword arguments of [sklearn.decomposition.PCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) *except* `n_components`. Instead, use `dimension` to specify how many components to keep." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T06:00:03.820777Z", "iopub.status.busy": "2021-05-19T06:00:03.820060Z", "iopub.status.idle": "2021-05-19T06:00:03.821891Z", "shell.execute_reply": "2021-05-19T06:00:03.822264Z" } }, "outputs": [], "source": [ "pc1 = drm.PrincipalComponentAnalysis(dimension=1,\n", " svd_solver='auto')\n", "pc2 = drm.PrincipalComponentAnalysis(dimension=2,\n", " svd_solver='auto')\n", "pc3 = drm.PrincipalComponentAnalysis(dimension=3,\n", " svd_solver='auto')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T06:00:03.826293Z", "iopub.status.busy": "2021-05-19T06:00:03.825752Z", "iopub.status.idle": "2021-05-19T06:00:05.200317Z", "shell.execute_reply": "2021-05-19T06:00:05.200683Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "(9, 3)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dres_conv2 = encore.dres_convergence(u, # universe\n", " 10, # window size\n", " select='name CA',\n", " dimensionality_reduction_method=[pc1, pc2, pc3]\n", " )\n", "dres_conv2.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, the size of the subspace you choose to include in your similarity comparison, affects the apparent rate of convergence over the trajectory." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T06:00:05.216927Z", "iopub.status.busy": "2021-05-19T06:00:05.216371Z", "iopub.status.idle": "2021-05-19T06:00:05.325075Z", "shell.execute_reply": "2021-05-19T06:00:05.325585Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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V+LLxl4ypM4YNgRv4LfA3Hic+Zv7Z+Sy5uIQ3yr1Bryq9KOtQVumoQgihiPfff5+LFy9y+PDhF/6MTqcz+L5jUtyI3KFSQRF3/VXB99/7Wg1EBf1d6Pynlyfipr6n5+Fp/fVfVkX1xY5L5cyFj/XzuzwNwdnKmRG1RjCo+iB23t3J2qtrCYwKZH3getYHrqdJySb08epDw+INZaNAIcTLM7PW96Ao1XYOjRo1iq1bt3Lw4EFKlXqxHe81Gg03b96kXr16OW4vJ6S4EYalNgGncvqryuv/3tekwZPb+l6e//b2RN6BpCj9JoP/3WgQ9D06/xQ7LpX/Lnoqg4VdnvynmJuY07l8Z94o9wanH59m7dW1+N/35/DDwxx+eJhyDuXo7dWb18u+jqVp/p0cKIRQiEr1UkNDeU2n0zFq1Cg2b96Mv78/np6eL/zZNWvWEBUVxZtvvmnAhFLcCKWYmOkLE9fKme+nJUPEjb97eP5T+EQHQ/xj/XXHP/NnHEr//V3/KXxcKhlsubpKpaJesXrUK1aP+7H3+eX6L2y+uZnbMbf54tgXLDi7gLcrvk3Pyj1xtXY1SAYhhFDKyJEj+fXXX/nzzz+xs7MjNDQUAAcHB6ys/v17NzExkdDQUNLT03n48CGbNm1i3rx5jBgxgpYtWxo0oywFF/lDShyE3/jPfJ6/C5//7sXzX09bru7qpT8h3QDL1eNS49h8czO/Xv81Yym5qcqUdp7t6OPVh6pOVXO9TSFE/pZfl4I/a/h91apV9O/fH8i6iZ+TkxPe3t4MHDiQrl27PvO7ZZ+blyTFTQGTGPl3785/V25d1S9Xf5pMy9X/HtbKxeXq6dp0/O/7s/bqWs6Gnc24X8e1Dr29etPKvRUmRrwsXgiRd/JrcWNIUty8JCluCoHnLVd/mv8uV6/TR3/cxCu68uQKP1/9mV1Bu0j/+zT2krYleafyO3Sr0A0787yZKySEME5S3GQlxc1LkuKmEHvecvX/8h4AvtPA8tV/j4QlhrE+cD0bAjcQlRIFgLWpNV3Kd+HdKu9S2r70K7chhMh/pLjJSoqblyTFjcjin+Xq4dchcCecW6u/b18SOi3IvLT9FSSnJ7P9znZ+vvYzt6JvAaBCRXP35vSp0od6xerJUnIhChEpbrKS4uYlSXEjnuvuIdg6CqLu6p/XfAfazci1fXZ0Oh3HQ46z9upaDj08lHG/UtFK9PbqTQfPDliYWORKW0II4yXFTVZS3LwkKW7EC0lNhAPT4diPgA5sXOG1OeD1Rq42czfmLr9c+4Wtt7eS9PfQmKOlIz0q9aB7pe44WznnantCCOMhxU1WUty8JCluRI7cPwV/joSIQP1zry7Q8Vv98RO5KCYlho03N/LrtV95nPgYADO1GR09O9LbqzeVHSs/5xuEEPmNFDdZSXHzkqS4ETmWngIB38DheaDT6I+H6PANVH8718/AStOmsS94H2uvruVi+L9ncdUrVo/eVXrTvFRzWUouRAEhxU1WUty8JCluxEsLuaDvxQm9pH9esT28Pg/sDXOK74XwC/xy9Rf23NuDRqcBwN3OnXervEuX8l2wMTP+bdqFEM8mxU1WUty8JCluxCvRpMGR+fqeHE0qWNhD26+gTl+DnWQemhDKuuvr+OPGH8SmxgJga2ZLtwrdeKfyO5Sye7ED64QQxkWKm6xyq7hRGzLki1i4cGHGf4S3tzeHDh3K9v0pKSlMmTIFDw8PLCwsKFeuHCtXrsyjtKLQMzGDZpNg2CEoWRdSYmHbaPips345uQEUsynGOO9x+L3lxycNPqGMfRni0+L56epPvLb5NcYdGMeZx2coZP9OEUKIZ1K0uFm/fj1jx45lypQpnDt3jqZNm9KhQweCg4Of+Znu3buzb98+VqxYQWBgIOvWraNyZZlsKfKYa2UYtAfaTgdTK7gbAAsbwokloNUapElrM2u6V+rOn13+ZGHrhTQq0QitTsve4L3039Wfntt7su32NtI0aQZpXwghABYtWkSNGjWwt7fH3t6ehg0bsnPnzozXW7RogUqlQqVSYWFhQcmSJenUqRObNm3Ks4yKDkv5+PhQp04dFi1alHGvSpUqdOnShZkzZ2Z5/65du+jZsyd37tzB0fHl9hyRYSmR657chq2j4d5h/XP3BtD5B3CuYPCmb0Xd4pfrv7Dt9jZSNCkAuFi50LNyT96u+DZFLYsaPIMQ4uXk12Gpbdu2YWJiQvny5QFYs2YN3377LefOnaNq1aq0aNGCihUrMm3aNNLS0nj48CGbN29m3rx59O/fn6VLlz7zu/P9sFRqaipnzpyhbdu2me63bduWo0ePPvUzW7dupW7dunzzzTeULFmSihUrMnHiRJKSkp76ftAPY8XGxma6hMhVTuWg3zZ4bS6Y28L947CosX51lSbdoE2XL1qezxp+ht9bfoyuPRoXKxfCk8L5/tz3+P7hy+dHP+dm1E2DZhBCFC6dOnWiY8eOVKxYkYoVKzJ9+nRsbW05fvx4xnusra0pVqwY7u7uNGjQgK+//polS5awbNky9u7da/CMihU3ERERaDQa3NzcMt13c3MjNDT0qZ+5c+cOhw8f5vLly2zevJn58+fzxx9/MHLkyGe2M3PmTBwcHDIud3f3XP3vEAIAtRrqDYL3jkO51qBJgb2fw/LW8PiKwZsvalmUITWGsPvN3cxsOhMvJy9SNClsvLmRblu7MXTPUA4+OIhWZ5ghMyHEq9PpdCSmJSpyvewgjkaj4bfffiMhIYGGDRtm+95+/fpRtGjRPBmeMjV4C8/x/2fp6HS6Z56vo9VqUalU/PLLLzg4OAAwd+5c3nrrLX788UesrKyyfGby5MmMHz8+43lsbKwUOMJwirhD741wYR3s+ghCzsOS5tB0gv4yNTdo82YmZrxe9nVe83yN8+HnWXt1LfuC93Es5BjHQo5Rxr4Mg6sPpnP5zgbNIYTIuaT0JHx+9VGk7RO9TmBtZv3C77906RINGzYkOTkZW1tbNm/ejJeXV7afUavVVKxYkaCgoFdM+3yK9dw4OztjYmKSpZcmLCwsS2/OP4oXL07JkiUzChvQz9HR6XQ8ePDgqZ+xsLDImPT0zyWEQalUUKsXjDwJlV8HbRoEzIKlLeDh2TyKoKK2a23mtpjLjm476OfVD1szW4Jig5h6ZCrfnf1OVlcJIV5apUqVOH/+PMePH2fEiBH069ePq1evPvdz2XVg5CbFem7Mzc3x9vbGz8+Prl27Ztz38/Ojc+en/6uycePGbNiwgfj4eGxtbQG4ceMGarWaUqVkrw9hZOyKQY+f4cpm2DEJwq7oh6kajYIWk8Esa0+jIZS0LcnEehMZUWsEq6+sZvGFxSy7tIxUTSoT6k6Qk8iFMBJWplac6HVCsbZzwtzcPGNCcd26dTl16hQLFixgyZIlz/yMRqPh5s2b1KtX75WyvghFh6XGjx9Pnz59qFu3Lg0bNmTp0qUEBwczfPhwQD+k9PDhQ3766ScAevXqxZdffsmAAQP44osviIiIYNKkSQwcOPCpQ1JCKE6lgmrdwLM57PoQLm2AIwvg+nZ44wfwyH6MOjfZmNkwstZIiloUZebJmay5uoYUTQqTfSajVim+5ZUQhZ5KpcrR0JAx0el0pKSkZPueNWvWEBUVxZtvvmnwPIoWNz169ODJkydMmzaNkJAQqlWrxo4dO/Dw8AAgJCQk0543tra2+Pn5MWrUKOrWrYuTkxPdu3fnq6++Uuo/QYgXY+MEby6Hqt3gr3Hw5Bas6gD1h0LrT8HCNs+i9KrSC3MTc6Ydm8Zvgb+Rqk3l0wafyplVQogX8vHHH9OhQwfc3d2Ji4vjt99+w9/fn127dmW8JzExkdDQUNLT03n48CGbNm1i3rx5jBgxgpYtWxo8oxy/IEReS4qGPVPh3Fr98yKlodN3UM7wf+D/a9vtbUw9MhWtTstrZV/jq8ZfYapWfI2BEIVGft3nZtCgQezbt4+QkBAcHByoUaMGH374Ib6+voB+E7+AgABAP3zl5OSEt7c3AwcOzDQN5WnkbKmXJMWNMBq398PWMRDzd+9k7T76c6qsiuRZhF1Bu5h8cDLpunR8PXz5uunXmJmY5Vn7QhRm+bW4MaR8v4mfEIVeuVbw3jH90BToe3IWNoDAndl/Lhe1L9OeOS3mYKY2w++eH+P9x5OqSc2z9oUQwhCkuBFCSRa20PFbGLATHMtBXAis6wkbB0PCkzyJ0Kp0K75r9R0WJhb4P/Bn9P7RJKU/e9dvIYQwdlLcCGEMPBrBiCPQaDSo1PpVVT/Wh8ubIA9GjpuUbMKPrX/EytSKI4+OMHLfSBLTEg3erhBCGIIUN0IYCzMraPslDN4LLlUgMQL+GADre0Pc048kyU0+xX1Y3GYxNmY2nAo9xTC/YcSlxhm8XSGEyG1S3AhhbEp6w7AAaP4hqE3h+l/6Xpzzvxq8F6eOWx2W+S7DztyO8+HnGbJnCDEpMQZtU4jCrpCt68lWbv1aSHEjhDEytYCWH8NQfyheE5JjYMsI+OUtiL5v0Karu1RnRdsVFLEowpUnVxi0exCRyZEGbVOIwsjMTL8yMTFRhoD/kZqqX9BgYvJq+27JUnAhjJ0mHY5+B/6z9KeNm9uC7zTwHqA/jdxAbkXdYvCewTxJfkI5h3Isa7sMF2sXg7UnRGEUEhJCdHQ0rq6uWFtbF+rjULRaLY8ePcLMzIzSpUtn+bWQfW6yIcWNyLfCb8DW9+H+32fPeDSBN74Dp3IGazIoJohBewYRlhiGh70Hy9sup5hNMYO1J0Rho9PpCA0NJTo6WukoRkGtVuPp6Ym5uXmW16S4yYYUNyJf02rg5DLY9wWkJYKpFbT+BHyGg4GOT7gfd58he4bwMP4hJW1LsrztckrZyUG1QuQmjUZDWlqa0jEUZ25ujvoZPdJS3GRDihtRIETehW2j4e5B/fNS9fQHcbpWNkhzIfEhDN4zmOC4YNys3VjedjllHMoYpC0hhHga2aFYiILO0RP6boVOC8DcDh6cgiVN4eBs0OT+v/6K2xZnVftVeDp48jjxMQN2D+B29O1cb0cIIXKDFDdC5FcqFXj3h5EnoEJb0KTC/i9hWUsIuZjrzblau7Kq3SoqFq1IRFIEA3YN4Hrk9VxvRwghXpUUN0Lkdw4lodfv0HUpWBWF0Ev6Amffl5CekqtNOVk5sbLdSrycvIhKiWLg7oFcjricq20IIcSrkuJGiIJApYKaPWDkSfDqDNp0ODQbljSDB6dztSkHCweWt11OTZeaxKXGMXjPYM6FncvVNoQQ4lVIcSNEQWLrCt1/0l82LhB+HVb4wu4pkJp7G4XZmduxxHcJdd3qkpCWwDC/YZwKPZVr3y+EEK9CihshCiKvzvpenBo9QaeFYz/AokYQdDjXmrAxs2Fhm4U0LN6QpPQkRuwdwZGHR3Lt+4UQ4mVJcSNEQWXtCN2WQK8NYF8Sou7C6tdg50e5dkaVlakV37f+nualmpOiSWHU/lEcCD6QK98thBAvS4obIQq6im3hvWP6lVUAJxbBicW59vUWJhbMazEPXw9f0rRpjPcfz+6g3bn2/UIIkVNS3AhRGFg66PfE6fCt/vmeT+BR7k0CNjMx45tm39DRsyPpunQ+OPgB225vy7XvF0KInJDiRojCpP4QqPw6aNNgwwBIjs21rzZVmzKjyQy6lu+KVqdlyuEpbLq5Kde+XwghXpQUN0IUJioVdP4BHNz1c3C2j8+1+TcAJmoTPm/0OT0q9UCHjs+Ofsa66+ty7fuFEOJFSHGTi9I0WtI0WqVjCJE9q6Lw5gpQmcClDXD+l1z9erVKzRSfKfTx6gPAjBMzWHNlTa62IYQQ2ZHiJpckp2kYvvYMH268iFZbqM4iFflRaR9oNUX/eMckCA/M1a9XqVRMqjuJIdWHADD79GyWXFiSq20IIcSzvFRxc+vWLXbv3k1SUhIAhexg8ae6cD8a/xvhbDr7kOk7rsmviTB+jcdB2RaQlqiff5OWlKtfr1KpGF1nNO/Xeh+AH87/wHdnv5M/G0IIg8tRcfPkyRPatGlDxYoV6dixIyEhIQAMHjyYCRMmGCRgfuFT1olv36oBwIrDd1noLycmCyOnVuvPo7JxgbAr+l2MDWBYzWFM8Nb//bDs0jJmn54tBY4QwqByVNyMGzcOU1NTgoODsba2zrjfo0cPdu3alevh8ptudUrxyeteAHy7O5B1J4MVTiTEc9i5Qde/h4tOr4Crfxqkmf7V+jO5/mQAfrr6E9NPTEerk/lpQgjDyFFxs2fPHr7++mtKlSqV6X6FChW4d+9ergbLrwY18WRky3IATNl8iZ2XQhROJMRzlG8NjcfqH/85CqIM82e5V5VefN7wc1SoWB+4ni+OfYFGqzFIW0KIwi1HxU1CQkKmHpt/REREYGFhkWuh8ruJbSvxTv3SaHUw5rfzHLkVoXQkIbLXaiqUqgcpMbBxEGjSDNLMmxXfZHqT6ahVajbd3MSUI1NI16YbpC0hROGVo+KmWbNm/PTTTxnPVSoVWq2Wb7/9lpYtW+Z6uPxKpVLxVZdqdKhWjFSNlqE/nebig2ilYwnxbCZm+uXhlg7w4BTs/8pgTXUq14mvm32NqcqU7Xe288HBD0gzUDElhCicVLoczOy7evUqLVq0wNvbm/379/PGG29w5coVIiMjOXLkCOXKlTNk1lwRGxuLg4MDMTEx2NvbG7StlHQNA1ef4sitJzjamPP7sIaUd7U1aJtCvJKrf8LvffWPe2/SD1kZyP7g/UwMmEiaNo0WpVowu8VsLEykB1gI8XQ5+fmdo54bLy8vLl68SP369fH19SUhIYFu3bpx7ty5fFHY5DULUxOW9KlLjVIORCak0nfFCR5F5+5yWyFylVdnqDtI/3jzMIh7bLCmWpVuxXetvsPCxAL/B/6M3j+apHT58yGEeHU56rkpCPKy5+YfkQmpvLX4KHfCEyjvasuGYQ0pamOeJ20LkWNpSbC8DTy+DJ7Noc8W/bJxAzkRcoJR+0eRlJ5EvWL1+KHVD1ibZZ3bJ4Qo3AzWc7Nq1So2bNiQ5f6GDRtYs0a2V38WRxtz1g7yobiDJbfC4um/+hQJKTKJUhgpMyt4ayWYWcPdADgyz6DN+RT3YYnvEmzMbDgVeophfsOIS40zaJtCiIItR8XNrFmzcHZ2znLf1dWVGTNm5FqogqhkESvWDqpPUWszLtyPZvjPZ0hJl2Wwwki5VIKO3+of758OwccN2lxt19os812Gnbkd58PPM2TPEGJSYgzaphCi4MpRcXPv3j08PT2z3Pfw8CA4WDase57yrnasGlAfa3MTDt2MYPzvF9DIOVTCWNV6F6p3B50GNg6GxEiDNlfdpTor2q6giEURrjy5wqDdg4hMNmybQoiCKUfFjaurKxcvXsxy/8KFCzg5OeVaqIKslnsRlvapi5mJiu0XQ/j0z8uyFb0wTioVvD4XHMtCzH3YOgoM/Hu1ilMVVrVbhZOlE4FRgQzYNYDwxHCDtimEKHhyVNz07NmT0aNHc+DAATQaDRqNhv379zNmzBh69uxpqIwFTpMKzszvURuVCn45Ecw8vxtKRxLi6Szs9PNv1GZw/S84tdzgTZYvWp7V7Vfjau3KnZg79N/Vn9CEUIO3K4QoOHJU3Hz11Vf4+PjQunVrrKyssLKyom3btrRq1eql59wsXLgQT09PLC0t8fb25tChQ898r7+/PyqVKst1/fr1l2pbSa/VKM5XXaoB8N3+W6w6clfhREI8Q4na0PZL/ePdH0NI1t7b3FbGoQyr26+mpG1JguOC6b+rPw/iHhi8XSFEwZCj4sbc3Jz169dz/fp1fvnlFzZt2sTt27dZuXIl5uY5X9q8fv16xo4dy5QpUzh37hxNmzalQ4cOz52/ExgYSEhISMZVoUKFHLdtDN718WCCb0UAvth2lS3nHiqcSIhn8BkOFTuAJhX+GAAp8QZv0t3OndXtV1ParjQP4x/Sf1d/gmKCDN6uECL/U3SfGx8fH+rUqcOiRYsy7lWpUoUuXbowc+bMLO/39/enZcuWREVFUaRIkZdqU4l9brKj0+mY9tdVVh0JwlStYlnfurSs7Kp0LCGySoyERY0h7hHU7AVdFz3/M7kgPDGcwXsGcyfmDs5Wzixvu5xyRWTTUCEKG4Ptc6PRaFixYgW9evWiTZs2tGrVKtOVE6mpqZw5c4a2bdtmut+2bVuOHj2a7Wdr165N8eLFad26NQcOHMj2vSkpKcTGxma6jIlKpeKT17zoWrsk6VodI345w+kgWSEijJC1I7y5HFRquPArXPgtT5p1sXZhZbuVVCxakYikCAbsGsD1yPw3FC2EyDs5Km7GjBnDmDFj0Gg0VKtWjZo1a2a6ciIiIgKNRoObm1um+25uboSGPn3yYPHixVm6dCkbN25k06ZNVKpUidatW3Pw4MFntjNz5kwcHBwyLnd39xzlzAtqtYpv3qpBq8quJKdpGbj6FNdDjasIEwKAMo2h+Uf6x3+Nh4hbedKsk5UTK9utxMvJi6iUKAbuHsjliMt50rYQIv/J0bCUs7MzP/30Ex07dnzlhh89ekTJkiU5evQoDRs2zLg/ffp01q5d+8KThDt16oRKpWLr1q1PfT0lJYWUlJSM57Gxsbi7uxvNsNR/JaVq6LPiBKfvReFqZ8HGEY1wd5Rt6IWR0Wrgp84QdAiKVYfB+8A0bw68jEuNY8TeEVwIv4CNmQ2L2iyitmvtPGlbCKEsgw1LmZubU758+VcK9w9nZ2dMTEyy9NKEhYVl6c3JToMGDbh58+YzX7ewsMDe3j7TZayszE1Y0a8elYvZERaXQu8VJwiPS3n+B4XIS2oT6LYMrJ0g9BLs+STPmrYzt2OJ7xLqutUlIS2BYX7DOBlyMs/aF0LkDzkqbiZMmMCCBQtyZdM5c3NzvL298fPzy3Tfz8+PRo0avfD3nDt3juLFi79yHmPhYG3GTwPr4+5oxb0nifRdeZLY5DSlYwmRmX1x6LJY//jkEri+Pc+atjGzYWGbhTQq0Yik9CTe2/cehx8ezrP2hRDGL0fDUl27duXAgQM4OjpStWpVzMzMMr2+adOmHDW+fv16+vTpw+LFi2nYsCFLly5l2bJlXLlyBQ8PDyZPnszDhw/56aefAJg/fz5lypShatWqpKam8vPPPzNr1iw2btxIt27dXqhNY1st9SxBEQm8tfgYEfEp1Pd05KeB9bE0M1E6lhCZ7Z4Cx34AyyIw4gg4lMqzplM0KUzwn0DAgwDM1GbMaT6HlqVb5ln7Qoi8ZbBhqSJFitC1a1eaN2+Os7Nzpom6Dg4OOQ7ao0cP5s+fz7Rp06hVqxYHDx5kx44deHh4ABASEpJpz5vU1FQmTpxIjRo1aNq0KYcPH2b79u0vXNjkJ2WcbVgzsB52FqacvBvJ+7+eI12jVTqWEJm1/gxK1IHkaPhjEGjy7rR7CxML5rWYh6+HL2naNMb7j2d30O48a18IYbwU3edGCfml5+YfJ+48oe/Kk6Ska3mzTim+fasGarVK6VhC/CvyLixpBimx0GwStJqap82na9OZcngKO+7uQK1S81Xjr+hUrlOeZhBCGJ7Bem4A0tPT2bt3L0uWLCEuLg7Qr3yKjzf8jqWFkU9ZJ37sVQcTtYqNZx8wc+c1OWhTGBdHT+g0X//44Gy445+nzZuqTZnRZAZdy3dFq9My5fAUtt5++upJIUThkKPi5t69e1SvXp3OnTszcuRIwsP1p/V+8803TJw40SABBbTxcuPrN2sAsOzQXRYH3FE4kRD/p9qbUKcfoINNQyE+b0/yNlGb8Hmjz+lRqQc6dHxy5BP87vk9/4NCiAIpx5v41a1bl6ioKKysrDLud+3alX379uV6OPGvt7xLMfW1KgB8ves6v53M/vwtIfJc+1ngUhniH8OW4aDN2zliapWaj30+pkv5Lmh1Wj44+AGHHjz7IF4hRMGVo+Lm8OHDTJ06NcshmR4eHjx8KIc+GtrgpmUZ0UJ/ps7Hmy+x63KIwomE+A9za3hrFZhawq29cOz7PI+gVqn5vOHntC/TnnRtOuP8x3Eq9FSe5xBCKCtHxY1Wq0Wj0WS5/+DBA+zs7HItlHi2D9pVomc9d7Q6GL3uPEdvRygdSYh/uXlBh6/1j/dNgwen8zyCidqEGU1n0LxUc1I0Kby/730uhl/M8xxCCOXkqLjx9fVl/vz5Gc9VKhXx8fF89tlnuXIkg3g+lUrF9K7VaV+1GKkaLUPWnObSgxilYwnxrzr9oGpX0KbDHwMgKTrPI5ipzZjTYg4+xXxITE9kxN4RBEYG5nkOIYQycrQU/NGjR7Rs2RITExNu3rxJ3bp1uXnzJs7Ozhw8eBBXV1dDZs0V+W0p+LMkp2kYuPoUR28/wdHGnA3DG1LOxVbpWELoJcfA4qYQfQ+8usDbq0GV91sYJKYlMtRvKBfCL+Bo6cjq9qvxdPDM8xxCiFeXk5/fOd7nJikpiXXr1nH27Fm0Wi116tTh3XffzTTB2JgVlOIGID4lnXeWHufSwxhKFrHijxENKe6QP/5/EIXAgzOwsq2+B+f1+VB3gCIxYlNjGbR7ENcjr+Nm7caaDmsoaVtSkSxCiJdn0OImvytIxQ3Ak/gU3l58jDsRCVRwteX3YQ0pamP+/A8KkReOfAd+n+gnGQ/ZD25VFYkRmRxJ/139uRtzF3c7d9a0X4OLtYsiWYQQL8dgxc3WrU/fGEulUmFpaUn58uXx9DTuLt+CVtwAPIxO4s2FRwmNTaaWexF+GeyDjYWp0rGE0C8H/7U73PID50ow9ACY2ygS5XHCY/rt6sfD+IeUcyjHqvarKGpZVJEsQoicM1hxo1arUalUWXbI/eeeSqWiSZMmbNmyhaJFjfMvjYJY3ADcfBzH20uOEZ2YRtMKzqzoVw9z0xxvQC1E7kuIgEWNIT4UaveBzj8oFuVB3AP67epHWGIYVRyrsKLdCuzMZaWnEPmBwY5f8PPzo169evj5+RETE0NMTAx+fn7Ur1+fv/76i4MHD/LkyRPZrVgBFdzsWNW/HtbmJhy6GcH438+j0RaqEUdhrGyc4c1lgArOrYVLfygWpZRdKZa1XYajpSPXIq8xct9IEtMSFcsjhDCMHPXcVKtWjaVLl9KoUaNM948cOcLQoUO5cuUKe/fuZeDAgZlO8zYmBbXn5h8Hb4QzaM0p0jQ6+jTwYFrnqqgUWKUiRBb7p8PBb8DcDoYfBMeyikW5HnmdgbsHEpcaR4PiDfih9Q9YmFgolkcI8XwG67m5ffv2U7/Q3t6eO3f05x1VqFCBiAjZWE4pzSq6MLd7LVQqWHv8HvP33lQ6khB6zT+E0o0gNQ7+GAjpqYpFqexYmUVtFmFlasXxkONMDJhImjZNsTxCiNyVo+LG29ubSZMmZRyYCRAeHs4HH3xAvXr1ALh58yalSpXK3ZQiRzrVLMG0ztUAWLDvJquP3FU4kRCAial+eMqqKDw6B/u+UDROTZeafN/qe8zV5vjf92fK4SlotFl3YBdC5D85Km6WL1/O3bt3KVWqFOXLl6dChQqUKlWKoKAgli9fDkB8fDyffPKJQcKKF9engQfjfSsC8Pm2q/x5Xs7+EkbAoRR0Xqh/fOwHuLFb0Tg+xX2Y13IepipTdt7dyZfHv8yyYEIIkf/keJ8brVbLnj17uHHjBjqdjsqVK+Pr64tanT9W5hT0OTf/pdPp+GLbVVYfDcJUrWJZv7q0rGT8u0iLQmDnR3BiEVg5wogjYF9C0Ti7gnbx4cEP0eq09K7Smw/qfSBz1YQwMgZZCp6eno6lpSXnz5+nWrVquRJUCYWpuAHQanWM+/08f55/hKWZml8G++Dt4ah0LFHYpafA8jYQehE8mkC/raA2UTTSlltb+OSIvtd5WI1hvF/7fUXzCCEyM8iEYlNTUzw8PJ56KrgwXmq1itlv16RFJReS07QMWHWKwNA4pWOJws7UQn/elLkt3DsMB79VOhFdyndhcv3JACy5uIRVl1cpnEgI8bJyNJY0depUJk+eTGRkpKHyCAMwM1Gz6F1vvD2KEpucTp8VJ7gfKXt7CIU5lYPX5+kfB3wNQYeVzQP0qtKLMXXGADD3zFzWX1+vcCIhxMvI0Zyb2rVrc+vWLdLS0vDw8MDGJvM26mfPns31gLmtsA1L/VdMYhrdlxwj8HEcZZys2TC8ES52sreHUNiW9+D8L2BXAoYfBhsnpROx4OwCll/SL5KY0WQGncp1UjiRECInP79zdABRly5dXiWXUJiDtRk/DarPm4uOEvQkkf6rTrJuaAPsLc2UjiYKs47fwv2T8OQm/PkevPMbKDyZd3Tt0SSmJfLr9V+ZemQqlqaW+Hr4KppJCPHi5FTwQuhuRAJvLz5KRHwqPp6OrBlYH0szZSdzikIu9BIsaw2aFGg3Exq+p3QitDotnx39jC23tmCqNuW7lt/RtFRTpWMJUWgZbIdigOjoaJYvX55p7s3Zs2d5+FD2UckvPJ1tWD2gPnYWppy4G8modedI12iVjiUKs2LVod10/WO/T+Gh8kPcapWazxt+Trsy7UjXpjPOfxynQk8pHUsI8QJyVNxcvHiRihUr8vXXXzN79myio6MB2Lx5M5MnTzZEPmEg1Uo6sKxfXcxN1fhdfczkTZdk8zKhrHqDoUon0Kbpj2dIjlU6ESZqE2Y2mUmzUs1I0aTw/r73uRR+SelYQojnyFFxM378ePr378/NmzextLTMuN+hQwcOHjyY6+GEYTUo68QP79RGrYINZx4wa+d1pSOJwkylgje+B4fSEHUX/hoHRlBwm5mYMaf5HOoXq09ieiLD9w4nMDJQ6VhCiGzkqLg5deoUw4YNy3K/ZMmShIaG5lookXfaVi3GrDdrALDk4B0WB9xWOJEo1KyKwlsrQGUCl/+Acz8rnQgAS1NLvm/1PTVcahCbGstQv6EExQQpHUsI8Qw5Km4sLS2Jjc3aVRwYGIiLi0uuhRJ5q3tddz7uWBmAWTuv8/up+wonEoWae31oNVX/eMckCDeOXhJrM2sWtl5IZcfKRCZHMnjPYB7Gy1xDIYxRjoqbzp07M23aNNLS0gBQqVQEBwfz0Ucf8eabbxokoMgbQ5uVY3jzcgB8tOkiu69IT5xQUOOxULYlpCfBhv6QlqR0IgAcLBxY3GYxng6ePE58zJA9QwhPDFc6lhDi/+SouJk9ezbh4eG4urqSlJRE8+bNKV++PHZ2dkyfPt1QGUUe+bB9JXrUdUerg1HrznHs9hOlI4nCSq2GbkvBxhXCrsLuj5VOlMHJyollvssoaVuS+3H3GbJnCFHJUUrHEkL8x0vtc7N//37Onj2LVqulTp06tGnTxhDZDEL2ucleukbLyF/PsvvKY2wtTPltaAOqlXRQOpYorG7vh7XdAB28vQaqdlE6UYYHcQ/ot7MfYUlhVHGswop2K7Azt1M6lhAFlkFOBQcICgqiTJkyr5pPUVLcPF9ymoYBq05x7M4TnGzM2TC8IWVdbJWOJQqrvV/A4blg4QDDD0LRMkonynAn+g79d/UnKiWK2q61WdxmMdZm1krHEqJAMtgmfmXLlqVJkyYsWbJEDs8swCzNTFja15tqJe15kpBKnxUnCY1JVjqWKKxafgyl6kNKDPwxCDRpSifKULZIWZb4LsHOzI5zYecYe2AsKZoUpWMJUejlqLg5ffo0DRs25KuvvqJEiRJ07tyZDRs2kJIif5gLGjtLM1YPqI+nsw0Po5Pou/IE0YmpSscShZGJmX55uKUDPDwN+79UOlEmVZyqsLDNQqxMrTgWcoxJAZNI0xpPASZEYZSj4qZOnTp8++23BAcHs3PnTlxdXRk2bBiurq4MHDjQUBmFQpxtLVg7qD5u9hbceBzPgNWnSExNVzqWKIyKlIY3ftA/PrIAbu1VNs//qeVai+9bfY+52pwD9w8w5fAUNFqN0rGEKLRyfLYU6JeAt2zZkmXLlrF3717Kli3LmjVrcjubMAKlilqzdpAPDlZmnAuOZsTPZ0lJl7+0hQK83tAf0QCwaRjEGdd2BT7FfZjXch6mKlN23t3Jl8e/lCNNhFDISxU39+/f55tvvqFWrVrUq1cPGxsbfvjhh9zOJoxERTc7Vg2oh5WZCQE3wnln6XHC4mQOjlBA2+ngVg0SI2DTUDCy3pFmpZoxs9lM1Co1G29u5NvT30qBI4QCclTcLF26lObNm+Pp6cmaNWvo3r07t2/f5vDhw4wYMcJQGYURqFO6KCv61cXe0pSzwdF0/uEIlx7EKB1LFDZmlvDWKjCzhrsBcHie0omyaF+mPZ83/ByAtVfXsvDCQmUDCVEI5ai4+fLLL6lfvz6nT5/mypUrfPzxx6+8NHzhwoV4enpiaWmJt7c3hw4deqHPHTlyBFNTU2rVqvVK7YsX16i8M1tGNqasiw0hMcm8tfgoWy88UjqWKGxcKkLH2frHB2ZA8HFl8zxF1wpd+aj+RwAsvrCY1ZdXKxtIiEImR8VNcHAw3377ba4VFOvXr2fs2LFMmTKFc+fO0bRpUzp06EBwcHC2n4uJiaFv3760bt06V3KIF1fWxZYtIxvTspILKelaRq87xze7rqPVSte7yEO1ekGNHqDT6JeHJxrf1hTvVnmX0bVHAzDnzBx+D/xd4URCFB7P3cTv4sWLVKtWDbVazcWLF7P9sho1auSocR8fH+rUqcOiRYsy7lWpUoUuXbowc+bMZ36uZ8+eVKhQARMTE7Zs2cL58+dfuE3ZxC93aLQ6vtl9nSUBdwBoU8WVeT1qYWdppnAyUWikxMGS5hB5Gyq/Dj1+BpVK6VRZLDi7gOWXlqNCxfQm0+lUrpPSkYTIl3J1E79atWoRERGR8bh27drUqlUr4/rnee3atXMUMjU1lTNnztC2bdtM99u2bcvRo0ef+blVq1Zx+/ZtPvvssxdqJyUlhdjY2EyXeHUmahWTO1RhXo+amJuq2XstjG4LjxIUkaB0NFFYWNjBWyvBxByu/wUnlymd6KlG1x5Nr8q90KFj6pGp7L1nXMvYhSiInlvc3L17FxcXl4zHd+7c4e7duxnXP8/v3LmTo4YjIiLQaDS4ublluu/m5kZo6NOXeN68eZOPPvqIX375BVNT0xdqZ+bMmTg4OGRc7u7uOcopste1dik2DGuIm70FN8Pi6fzjEQ7fjFA6ligsStQC37839dszBUKy711Wgkql4sP6H9K5XGe0Oi2TDk7i8MPDSscSokB7bnHj4eGB6u+uXg8Pj2yvl6H6v25knU6X5R6ARqOhV69efPHFF1SsWPGFv3/y5MnExMRkXPfv33+pnOLZaroXYdv7TajlXoSYpDT6rTrJqiN3ZQmsyBs+w6BSR9Ckwh8DICVe6URZqFVqvmj0BW092pKuTWfsgbGcDj2tdCwhCqznzrnZunXrC3/ZG2+88cLvTU1Nxdramg0bNtC1a9eM+2PGjOH8+fMEBARken90dDRFixbFxMQk455Wq0Wn02FiYsKePXto1arVc9uVOTeGk5ym4ePNl9h09iEAPeq6M61LVSxMTZ7zSSFeUWIkLG4CsQ+h5jvQdbHSiZ4qTZPGWP+xHHxwEBszG5a3XU4152pKxxIiX8jVU8HV6sydOyqVKtO/yP/by6LR5GxDLR8fH7y9vVm48N99ILy8vOjcuXOWCcVarZarV69murdw4UL279/PH3/8gaenJzY2Ns9tU4obw9LpdKw4fJcZO66h1YG3R1EW9/bGxc5C6WiioLt3FFa/BjotdFoA3v2VTvRUyenJjNw3kpOhJ7E3t2dV+1VULPrivdFCFFa5OqFYq9VmXHv27KFWrVrs3LmT6OhoYmJi2LFjB3Xq1GHXrl05Djp+/HiWL1/OypUruXbtGuPGjSM4OJjhw4cD+iGlvn376oOq1VSrVi3T5erqiqWlJdWqVXuhwkYYnkqlYnDTsqzsXw87S1PO3IvijR8Oc/mhbPgnDMyjEbScon+8fQLc8Vc0zrNYmlryXavvqOFSg9jUWIbuGUpQTJDSsYQoUHK0z83YsWNZsGAB7dq1w97eHjs7O9q1a8fcuXMZPXp0jhvv0aMH8+fPZ9q0adSqVYuDBw+yY8eOjPk7ISEhz93zRhinFpVcs2z4t002/BOG1nQCVH8btOmwvi+EByqd6KlszGxY2HohlR0r8yT5CUP8hvAoXv58CJFbnjss9V9WVlacPHmS6tWrZ7p/8eJFfHx8SEpKyvWAuU2GpfJWTFIao9edI+BGOADvtyzPeN+KqNXGtx+JKCDSkuGnznD/OBTxgCH7wcZZ6VRP9STpCQN2D+BuzF3c7dxZ034NLtYuSscSwijl6rDUf9WrV4+xY8cSEhKScS80NJQJEyZQv379l0srCjQHKzNW9q/HsGZlAfjhwC2Grj1DfEq6wslEgWVmCT1/gaJlIPoe/NZLX/AYIScrJ5b6LqWkbUnux91nqN9QopKjlI4lRL6Xo+Jm5cqVhIWF4eHhQfny5SlfvjylS5cmJCSEFStWGCqjyOdM1Comd6zC3O7/bPj3mG4Lj3DviWz4JwzExhl6bQBLB7h/Av4cCUa6NUExm2Isa7sMVytXbkXfYvje4cSlxikdS4h8LUfDUqBfDePn58f169fR6XR4eXnRpk2bp+5NY4xkWEpZ5+9HM/Sn04TFpVDE2owfe9WhcXnjHDIQBcCdAPi5m34OTvOPoOVkpRM9053oO/Tf1Z+olCjquNZhUZtFWJtZKx1LCKORq0vBCxopbpT3ODaZoT+d5sKDGEzUKj593Yu+DT3yTYEs8pmzP8HWUfrH3ZZBje7K5snGtSfXGLR7EHFpcTQs3pAfWv+AuYm50rGEMAoGm3MjRG5ws7dk/bCGdK1dEo1Wx2dbrzB50yVS07VKRxMFUZ2+0His/vGfI/X74RipKk5VWNhmIVamVhwLOcbEgImkadOUjiVEviPFjVCEpZkJc7vX5OOOlVGr4LdT93l3+XEi4lOUjiYKotafQZVO+iMafnsXntxWOtEz1XKtxfetvsdcbc6B+weYengqGm3ONkgVorCT4kYoRqVSMbRZOVb0r4edhSmngqJ44/vDXHkkG/6JXKZWQ9elUKIOJEXCr90hyXhXJfkU92Fui7mYqkzZcXcHXx7/Us5qEyIHpLgRimtZyZXNIxtT1tmGRzHJvLnoKNsvhjz/g0LkhLk1vPMbOLjDk1uwvg+kpyqd6pmauzdnZtOZqFVqNt7cyOzTs6XAEeIF5XhCsVar5datW4SFhaHVZp4j0axZs1wNZwgyodh4xSSlMWrdOQ7+veHfqFblGddGNvwTuezxFVjRDlLjoHZveOMHMOLJ7JtvbubTo58CMKLmCN6r9Z7CiYRQhsFWSx0/fpxevXpx7969LP+CUKlUOT44UwlS3Bg3jVbHrJ3XWHboLgC+Xm7M61ELWwtThZOJAuWmn35oSqeFNp9Dk3FKJ8rWL9d+YdbJWQBM8J5A/2r9lQ0khAIMtlpq+PDh1K1bl8uXLxMZGUlUVFTGFRkZ+UqhhQD9hn9TXvNizts1MTdR43f1MW8uPErwk0Slo4mCpIIvdPhG/3jv53Bli5JpnuvdKu8yurb+/L45Z+bwx40/FE4khHHLUc+NjY0NFy5coHz58obMZFDSc5N/nA2OYtjaM4T/veHfwnfr0KicbPgnctHOD+HEYjC1hP47oJS30omyNf/MfFZcXoGpypRV7VdRy7WW0pGEyDMG67nx8fHh1q1brxROiBdVp3RRtr3fhBqlHIhOTKPPipOsPRYkkypF7mk3Ayq0g/RkWNcTooOVTpStMXXG0K5MO9J16UwMmEhksvSYC/E0Oeq52bx5M1OnTmXSpElUr14dMzOzTK/XqFEj1wPmNum5yX+S0zR8tPEiW84/AuCd+qX54o2qmJvKYj+RC1LiYGUHeHwJXL1g4G6wNN6/GxLSEuj5V0+CYoNoULwBi9ssxkRtonQsIQzOYBOK1eqsP0xUKhU6nU4mFAuD0ul0LDl4h693XUeng/plHFnYuw7OthZKRxMFQcwDWNYa4kOhfBt4Zz2YGO8k9ltRt+i1oxdJ6UkMqzGM92u/r3QkIQzOYMXNvXv3sn3dw8PjRb9KMVLc5G8Hrocxet054lLSKVnEiqV9valawkHpWKIgeHQOVnWEtESoNxg6zjbqJeLb72zno0MfAfBj6x9pVsr4t+IQ4lXIwZnZkOIm/7sVFsfgNacJepKIlZkJc7rXpGP14krHEgXBtb9gfW9AB+1nQYMRSifK1lfHv2J94Hrsze35vdPvlLQtqXQkIQzGoAdn3r59m1GjRtGmTRt8fX0ZPXo0t28b7zktouAp72rHnyOb0LSCM0lpGt775Sxz/W6g1RaqOl0YQpXXwXea/vGuyRC4U9k8z/FBvQ+o7lyd2NRYJvhPIFVjvDsuC5GXclTc7N69Gy8vL06ePEmNGjWoVq0aJ06coGrVqvj5+RkqoxBZOFibsap/PQY38QTgu303GfHLGRJS0hVOJvK9RqPAuz+ggz8GQchFpRM9k7mJObObz8bBwoErT67w9cmvlY4khFHI0bBU7dq1adeuHbNmzcp0/6OPPmLPnj2cPXs21wPmNhmWKng2nL7PlM2XSdVoqVzMjmV96+LuaK10LJGfadLgl7fgjj/YlYAh+8HeeIc+Dz88zHt730OHjhlNZtCpXCelIwmR6ww2LHXt2jUGDRqU5f7AgQO5evVqzlIKkUveruvOuqENcLGz4HpoHG/8cJhjt58oHUvkZyZm8PYacK4EcY9gXQ9ITVA61TM1KdmE4TWHAzDt2DRuRN1QOJEQyspRcePi4sL58+ez3D9//jyurq65lUmIHPP2KMrW9xtTvaQDUYlp9FlxgrXHs1/dJ0S2rIrAu7+DtTOEXICNQ0BrvNtdDKsxjEYlGpGsSWa8/3jiU+OVjiSEYnJU3AwZMoShQ4fy9ddfc+jQIQ4fPsysWbMYNmwYQ4cONVRGIV5IcQcrNgxvyBs1S5Cu1fHJlstM2XyJ1HTt8z8sxNMULQM9fwUTCwjcDn6fKp3omUzUJsxqOotiNsW4F3uPT49+Krt5i0IrR3NudDod8+fPZ86cOTx6pN8ttkSJEkyaNInRo0ejMuI9If4hc24KPp1Ox+KAO3yz++8N/zwdWfRuHZxkwz/xsi79ARv/HpJ/fR7UHahsnmxcDL9Iv139SNemM6nuJPpW7at0JCFyRZ7scxMXFweAnZ3dy3xcMVLcFB77rj1mzG/nif97w7/l/epSpbj8fy5eUsC3cOArUJlA7z+gXCulEz3TuuvrmHFiBiYqE1a2W0kdtzpKRxLilRl0n5t/2NnZ5bvCRhQurau4sfm9RpRxsuZhdBJvLjrKrsshSscS+VWziVCjJ+g08Hs/CLuudKJn6lmpJx08O6DRaZgYMJGIpAilIwmRp3JU3Dx+/Jg+ffpQokQJTE1NMTExyXQJYWwquNmxZWRjmpR3JjFVw/CfzzJPNvwTL0Olgje+g9KNICUWfn0b4sOVTvVUKpWKzxt+TlmHsoQnhfPhwQ9J18oeUKLwyNGwVIcOHQgODub999+nePHiWebYdO7cOdcD5jYZliqc0jVaZuy4zsojdwFoX7UYc7rXxMbCeA9HFEYqMRKWt4bIO1CqHvTbBmZWSqd6qjsxd3jnr3dITE9kcPXBjKkzRulIQrw0g825sbOz49ChQ9SqVetVMypGipvC7fdT95my5RJpGp1s+CdeXsRNWN4GkqOhajd4cwWoX3qU36B2Be1iUsAkAL5r+R0tS7dUOJEQL8dgc27c3d1laaHI17rXc+e3oQ1wttVv+Nf5xyMcvyMb/okccq4APX4GtRlc2QT+M5RO9Ezty7Tn3SrvAjDl8BTux91XOJEQhpej4mb+/Pl89NFHBAUFGSiOEIbn7eHI1vcbU62kPZEJqfRefoJfTsiGfyKHPJtCpwX6xwe/hfPrlM2TjQneE6jpUpO4tDgm+E8gRZOidCQhDCpHw1JFixYlMTGR9PR0rK2tMTMzy/R6ZGRkrgfMbTIsJf6RlKrhg40X2XZBv2dT7wal+axTVcxMjHN4QRipfdPg0Bx9L07fP6FMY6UTPVVoQijdt3UnKiWKNyu8yeeNPlc6khA5YrA5N2vWrMn29X79+r3oVylGihvxXzqdjoX+t5m9JxCdDhqUdeTHXrLhn8gBrRb+GABXt4BVURi8D5zKKZ3qqY49OsYwv2Ho0DGt0TS6VuiqdCQhXliebOKXX0lxI55m79XHjPntHAmpGhxtzPm4YxXerFMyX+y6LYxAWhKsfh0engbHcjB4L1g7Kp3qqZZcWMIP53/AwsSCnzv+TGXHykpHEuKFGLS40Wq13Lp1i7CwMLTazGf2NGvWLOdp85gUN+JZbjyOY9Sv5wh8rN9928fTkeldq1HeVTarFC8gPgyWtYaYYPBoDH22gKm50qmy0Oq0vL/vfQ49PIS7nTu/vf4b9ubyd6EwfgYrbo4fP06vXr24d+9ellVTKpUKjcZ4T8z9hxQ3IjtpGi0rDt9l/t4bJKdpMTNRMbRZWd5vWQErc9moUjzH46uwsp1+k7+avaDLQv3mf0YmJiWG7tu68yjhES3dW7Kg5QLppRRGz2BLwYcPH07dunW5fPkykZGRREVFZVz5YTKxEM9jZqJmePNy+I1rTuvKrqRpdPx44DZt5wfgHximdDxh7Ny84O1V+vOnLvyqn2hshBwsHJjbYi5majMO3D/AqiurlI4kRK7KUc+NjY0NFy5coHz58obMZFDScyNelE6nY/eVx3yx7QohMckAvFa9OJ928sLN3lLhdMKonVoO2yfoH7+1Cqp1UzbPM/we+DtfHv8StUrN8rbLqVesntKRhHgmg/Xc+Pj4cOvWrVcK9/8WLlyIp6cnlpaWeHt7c+jQoWe+9/DhwzRu3BgnJyesrKyoXLky8+bNy9U8QvxDpVLRvlox/MY3Z3ATT0zUKrZfCqH1nABWH7mLRs6nEs9SbzA0GKl/vHk43D+lbJ5neLvi23Qq2wmtTsukgEmEJxrnWVlC5FSOem42b97M1KlTmTRpEtWrV8+yz02NGjVy1Pj69evp06cPCxcupHHjxixZsoTly5dz9epVSpcuneX9586d4/r169SoUQMbGxsOHz7MsGHDmDdvHkOHDn2hNqXnRrysK49i+HjzZS7cjwagekkHpnetRo1SRRTNJYyUVgO/vQs3doK1MwzZB0XLKJ0qi6T0JHpt78Wt6FvUca3D8nbLMVObPf+DQuQxg00oVj/l7BSVSoVOp3upCcU+Pj7UqVOHRYsWZdyrUqUKXbp0YebMmS/0Hd26dcPGxoa1a9e+0PuluBGvQqPVse5kMF/vuk5ccjoqFfRt4MGEdpWwt5QfCOL/pMTDqvYQeglcKsOgPWDpoHSqLIJigui5vScJaQn0r9qfCXUnKB1JiCwMNix19+7dLNedO3cy/jcnUlNTOXPmDG3bts10v23bthw9evSFvuPcuXMcPXqU5s2bP/M9KSkpxMbGZrqEeFkmahW9G3iwb0JzOtcqgU4Ha47do82cAP66+EjOXhOZWdjCO+vBrjiEX4ff+4EmTelUWZRxKMNXjb8CYPWV1ey7t0/hREK8mhwVNx4eHtleOREREYFGo8HNzS3TfTc3N0JDQ7P9bKlSpbCwsKBu3bqMHDmSwYMHP/O9M2fOxMHBIeNyd3fPUU4hnsbVzpIFPWvz8yAfPJ1tCItL4f1fz9F/1SnuPUlQOp4wJg4lodd6MLOGOwdgxyQwwiK4jUcb+nr1BWDqkanci5Xz1kT+9VKH6Fy9epVdu3axdevWTNfL+P+9Ff4Z4srOoUOHOH36NIsXL2b+/PmsW/fsA+smT55MTExMxnX/vpyIK3JPkwrO7BzTlLFtKmBuoibgRjht5x3kh/03SUk3/n2fRB4pXhPeXAGo4MwqOPaj0omeaqz3WOq41iE+LZ7x/uNJSk9SOpIQLyVHc27u3LlD165duXTpUsZcG/i3QMnJnJvU1FSsra3ZsGEDXbv+e77JmDFjOH/+PAEBAS/0PV999RVr164lMDDwhd4vc26EodwJj+eTPy9z5NYTAMq52DC9a3UalHVSOJkwGsd+hN0fAyro+QtUfk3pRFmEJYbx9ra3iUyO5I1yb/BV469kgz9hFAw252bMmDF4enry+PFjrK2tuXLlCgcPHqRu3br4+/vnKKS5uTne3t74+flluu/n50ejRo1e+Ht0Oh0pKSk5alsIQyjrYsvPg3xY0LMWzrbm3A5PoOfS40z4/QJP4uX3qAAavAd1BwI62DgYHp1XOlEWrtauzG4+G7VKzdbbW9l4c6PSkYTIsRwVN8eOHWPatGm4uLigVqtRq9U0adKEmTNnMnr06Bw3Pn78eJYvX87KlSu5du0a48aNIzg4mOHDhwP6IaW+fftmvP/HH39k27Zt3Lx5k5s3b7Jq1Spmz55N7969c9y2EIagUqnoXKsk+8a34F2f0qhUsPHsA1rNCeC3k8FoZW+cwk2lgg7fQLlWkJYI63pCzEOlU2VRr1g9RtUeBcDMEzO5+uSqwomEyJkcFTcajQZbW1sAnJ2defToEaCfaPyiw0L/1aNHD+bPn8+0adOoVasWBw8eZMeOHRmTk0NCQggODs54v1arZfLkydSqVYu6devy/fffM2vWLKZNm5bjtoUwJAdrM6Z3rc7GEY2oUtyemKQ0Ptp0ibeXHON6qKzYK9RMzODt1eBSBeJCYF0P/ZJxIzOw2kBalGpBqjaV8f7jiUmJUTqSEC8sR3NumjZtyoQJE+jSpQu9evUiKiqKqVOnsnTpUs6cOcPly5cNmTVXyJwbkdfSNVpWHw1irt8NElM1mKpVDGrqyZjWFbA2N1U6nlBK1D1Y3hoSwqFiB/0cHLVxHc4akxJDj7968DD+Ic1KNeP7Vt+jVr3UOhQhXpnB5txMnToVrVYL6Cfy3rt3j6ZNm7Jjxw6+++67l08sRAFmaqJmcNOy7B3fnHZV3UjX6lgScAffuQfZe/Wx0vGEUop6QM91YGqp38V4z1SlE2XhYOHAvBbzMFebc/DBQVZcWqF0JCFeSI56bp4mMjKSokWL5pvZ9NJzI5S29+pjPtt6hYfR+mW27aq68VmnqpQoYqVwMqGIy5vgjwH6xx1nQ/0hyuZ5ik03N/HZ0c9Qq9Qs8V1Cg+INlI4kCiGD9dw8jaOjY74pbIQwBm283PAb34zhzcthqlax+8pj2swNYPmhO6RrtErHE3mtWjdo9Yn+8c4P4eZeZfM8RbcK3ehSvgtanZYPD37I4wTpcRTGLUc9NwkJCcyaNYt9+/YRFhaWMUT1j5wewaAE6bkRxuR6aCxTN1/m9L0oAKoUt2dG12rULl1U4WQiT+l0sOU9uPArmNvpz6By81I6VSbJ6cn03tGbwKhAarnUYmX7lXLApshTBjs485133iEgIIA+ffpQvHjxLD02Y8aMebnEeUiKG2FstFodG87cZ+bO60QnpqFSQa/6pfmgXWUcrOWHR6GRngpru8K9w+DgDoP3gZ3b8z+Xh4Jjg+n5V0/i0uLoXaU3H9b/UOlIohAxWHFTpEgRtm/fTuPGjV85pFKkuBHG6kl8CjN2XGfj2QcAONua88nrXrxRs4QM/RYWiZGwvA1E3oaS3tDvLzC3VjpVJvuD9zPmgP4fsrObz6ZdmXYKJxKFhcHm3BQtWhRHR8dXCieEeDonWwvmdK/JuiENKOdiQ0R8KmN+O0/vFSe4E258+6AIA7B2hHc3gFVReHgGtgwHrXHNw2pVuhUDquknQH965FPuxtxVOJEQWeWouPnyyy/59NNPSUxMNFQeIQq9huWc2DmmGRPbVsTCVM2RW09oP/8Q8/feIDlNDuMs8JzKQY9fQG0GV/+E/V8qnSiL0bVHU9etLonpiYz3H09imvxMEMblucNStWvXztQlfuvWLXQ6HWXKlMHMLPN8gLNnzxomZS6SYSmRn9x7ksAnf17h4I1wADydbfiyczWaVHBWOJkwuPPr9D03AJ1/hNrGdcxMRFIEb297m4ikCF4r+xozm8yU4VNhUDn5+f3c7VG7dOmSW7mEEDnk4WTDmgH12H4phGnbrnI3IoHeK07QuVYJprxWBVc7S6UjCkOp9Y5+7s3Bb2HbGChSGjybKZ0qg7OVM7Obz2bQ7kFsv7Od2i616VG5h9KxhAByYRO//EZ6bkR+FZucxtw9N1hzLAidDuwsTfmgfWXerV8atVr+xVwg6XSwcRBc3giWRWDwXnCuoHSqTFZfXs2cM3MwU5uxpv0aqrtUVzqSKKAMtlrqv5KTk1m/fj0JCQn4+vpSoYJx/YF7FiluRH538UE0UzZf5tJD/UGGtdyLML1rNaqWcFA4mTCItGRY0wkenISinvol4jZOSqfKoNPpGOc/jn3B+yhuU5zfX/+dIpZFlI4lCqBcL24mTZpEamoqCxYsACA1NZX69etz9epVrK2tSU9PZ8+ePTRq1Ch3/gsMSIobURBotDp+Pn6Pb3cHEp+SjloFAxp7Ms63IrYWchhngRMfDstbQXQwlG4Iff8EUwulU2WIS42j5189CY4LpnHJxixsvVAO2BS5LteXgu/cuZPWrVtnPP/ll18IDg7m5s2bREVF8fbbbzN9+vRXSy2EeGEmahX9GpVh34TmvFajOFodrDh8F9+5Aey6HEohG20u+GxdoNcGsLCH4GOwZQRojWflnJ25HXNbzMXSxJIjD4+w5OISpSOJQu6Fipvg4GC8vP7dCnzPnj289dZbeHh4oFKpGDNmDOfOnTNYSCHE07nZW/JjrzqsHlCP0o7WhMQkM/znMwxec5oHUbI8t0BxrQzd14DaVD8H58/3jWoPnEqOlZjaQH+y+aLzizj68KjCiURh9kLFjVqtzvQvwePHj9Ogwb+nwhYpUoSoqKjcTyeEeCEtKrmyZ1wz3m9ZHjMTFfuuh+E79yCLA26TJodxFhzlWsFbK0Floj+HattooypwOpfvzJsV3kSHjg8PfUhIfIjSkUQh9ULFTeXKldm2bRsAV65cITg4mJYtW2a8fu/ePdzcjOsMFCEKG0szEya2q8TOMU3x8XQkKU3DrJ3Xef27w5wOilQ6nsgtXp3hzWWgUsO5tbBjgn5VlZGY7DOZKo5ViE6JZmLARNI0aUpHEoXQCxU3kyZN4qOPPqJ169a0bt2ajh074unpmfH6jh07qF+/vsFCCiFeXHlXO34b2oDZb9fE0cacwMdxvLX4GB9tvEhUQqrS8URuqPYmdFkMqOD0Stj5odEUOBYmFsxtMRc7czsuRlzk29PfKh1JFEIvVNy8+eab7Nixgxo1ajBu3DjWr1+f6XVra2vee+89gwQUQuScSqXiLe9S7BvfnJ713AH47dR9Ws8N4PfT99FqjeMHoXgFNXvody4GOLkE9kw1mgKnlF0pZjWdBcC66+vYcWeHwolEYSOb+AlRCJwKimTK5kvceKw/gLNqCXumvFaFRuXkGId878xq/Q7GAI3HQpvPwUiOQfju7Hcsu7QMK1Mr1r22jnJFyikdSeRjBjsV/L+qV6/O/fv3X/bjQog8VK+MI9tHN2Vyh8rYWZhy5VEsvZadYPCaU9wKkxPH8zXv/tBxtv7xkflwwHi25RhZayQ+xXxISk9inP84EtISlI4kComXLm6CgoJIS5OJYkLkF2YmaoY1L4f/pBb0a+iBiVrF3mthtJt/kE+2XCYiPkXpiOJl1R8C7b/WPz74LQR8o2yev5moTfi62de4WrlyN+Yunx/9XPZgEnlCtpAUopBxsrXgi87V2DOuGb5ebmi0OtYev0eLb/1Z6H+L5DTj2RxO5ECD4dD2K/3jA9Ph0Fxl8/zNycqJOS3mYKoyZVfQLn69/qvSkUQh8NLFTdOmTbGyssrNLEKIPFTOxZZlfeuybkgDqpd0ID4lnW92BdJ6TgBbzj2UScf5UaNR0Poz/eN9X8DR75XN87darrUYX3c8ALNPzeZ82HllA4kCTyYUCyHQanX8eeEh3+4K5FFMMgDVSzow5bUqNChrPIc0ihfk/zX4z9A/bj8LGoxQNg/6AzYnBkxkz709uFm78Xun33G0dFQ6lshHDHoq+I0bN/D39ycsLAzt/+2M+emnn+Y8bR6T4kaIZ0tO07Di8F0W+d8mPiUdAF8vNz7qUJlyLrYKpxM5sv8r/fwb0E84rj9E2TxAfGo872x/h6DYIBoUb8DiNosxUZsoHUvkEwYrbpYtW8aIESNwdnamWLFiqP6z3FClUnH27NmXT51HpLgR4vki4lOYv/cG607eR6PVYapW8a5PaUa3roCTrfGcRi2yodPB3s/1K6gAOi3Qr6xS2M2om7y7412S0pMYVmMY79d+X+lIIp8wWHHj4eHBe++9x4cffvjKIZUixY0QL+5WWBwzd1xn3/UwAOwsTBnZqjz9G5XB0kz+xW30dDr95n7HfgBU+k3/ar+rdCr+uvMXkw9NBuDH1j/SrFQzhROJ/MBgxY29vT3nz5+nbNmyrxxSKVLcCJFzR29FMH3HNa48igWgZBErPmhfiU41SqBWG8eGceIZdDr98QwnlwAq6LpEv7uxwr46/hXrA9djb27P751+p6RtSaUjCSNnsE383n77bfbs2fNK4YQQ+U+j8s5se78Jc96uSTF7Sx5GJzHmt/N0XXiEk3flUE6jplJBh6+h7kBAB1uGw6U/lE7FB/U+oJpTNWJTY5ngP4FUjZx7JnJPjnpuZs6cydy5c3nttdeoXr06ZmZmmV4fPXp0rgfMbdJzI8SrSUrVsOLwHRb53yYhVb8nTruqbnzUoQqezjYKpxPPpNXCX2Pg7E+gMoG3V+lPGFfQo/hHdP+rOzEpMXSv2J1PGn6iaB5h3Aw2LPXfk8CzfJFKxZ07d148pUKkuBEid4TFJTN/701+OxmMVgemahW9G3gwpnUFitqYKx1PPI1WC3+OhAu/gtoUuv8ElV9TNNLhh4d5b+976NAxo8kMOpXrpGgeYbwMuhQ8v5PiRojcdeNxHDN3XONAYDgAdpamjGpVnn6NymBhKpOOjY5WA5uHwaUNoDaDnr9AxXaKRvrx/I8svrAYSxNLfnntFyoWrahoHmGcDH5wZmpqKoGBgaSnp79UQCFEwVHRzY5VA+rz8yAfKhezIy45nRk7rtN6TgDbLjySs4SMjdoEuiyGql1Bmwbre8OtvYpGGl5jOI1KNCJZk8x4//HEp8phruLV5Ki4SUxMZNCgQVhbW1O1alWCg4MB/VybWbNmGSSgECJ/aFLBme2jm/LNWzVws7fgQVQSo9ado+vCo5wOkknHRsXEFLotgyqdQJMKv70Ld/yVi6M2YVbTWRSzKca92Ht8cuQTtDrt8z8oxDPkqLiZPHkyFy5cwN/fH0tLy4z7bdq0Yf369bkeTgiRv5ioVXSv686BiS0Y16Yi1uYmnL8fzVuLjzHi5zPce5KgdETxDxMzeHMlVOwA6cnwa08IOqxYnKKWRZnTfA6malP2Bu/lo0MfkaZJUyyPyN9yVNxs2bKFH374gSZNmmTandjLy4vbt2/nejghRP5kbW7KmDYV8J/Ygnfqu6NWwc7LobSZG8C0bVeJTpRlv0bB1By6r4HyvpCeBL90h3vHFItTw6UGM5vMxFRlys67Oxm1fxSJaYmK5RH5V46Km/DwcFxdXbPcT0hIyFTsCCEEgKu9JTO71WDHmKY0r+hCmkbHyiN3afbNAZYfukNKukbpiMLUAnr8DGVbQloC/PIW3D+lWJz2nu35vvX3WJlaceTREYb4DSEmJUaxPCJ/ylFxU69ePbZv357x/J+CZtmyZTRs2PClAixcuBBPT08sLS3x9vbm0KFDz3zvpk2b8PX1xcXFBXt7exo2bMju3btfql0hRN6pXMyeNQPr89PA+lQuZkdscjpfbb+G79yDbL8YIpOOlWZmCT1/hTJNITUefu4GD88oFqdJySYs9V2Kvbk9F8Mv0m9nPx4nPFYsj8h/clTczJw5kylTpjBixAjS09NZsGABvr6+rF69munTp+e48fXr1zN27FimTJnCuXPnaNq0KR06dMiYqPz/Dh48iK+vLzt27ODMmTO0bNmSTp06ce7cuRy3LYTIe80qurB9dFO+frM6LnYWBEcmMvLXs7y56Chn7kUpHa9wM7eGXuvBozGkxMLarhByQbE4tVxrsab9GlytXbkdc5s+O/sQFBOkWB6Rv+R4n5tLly4xe/Zszpw5g1arpU6dOnz44YdUr149x437+PhQp04dFi1alHGvSpUqdOnShZkzZ77Qd1StWpUePXrw6aefvtD7ZZ8bIYxDQko6Sw/eYenBOySl6YenXqtRnA/bVaa0k7XC6QqxlDj4+U24fwKsikK/v6BYNcXiPIp/xDC/YQTFBlHUoiiLfBdR1amqYnmEcvLFJn6pqalYW1uzYcMGunbtmnF/zJgxnD9/noCAgOd+h1arpUyZMnzwwQe8//77T31PSkoKKSkpGc9jY2Nxd3eX4kYIIxEak8xcv0A2nHmATgfmJmr6NfLg/ZYVcLA2e/4XiNyXHAtru+iHpqydoP92cK2iWJwnSU8YsXcE1yKvYW1qzXetvsOnuI9ieYQyDLaJ39mzZ7l06VLG8z///JMuXbrw8ccfk5qas9UPERERaDQa3NzcMt13c3MjNDT0hb5jzpw5JCQk0L1792e+Z+bMmTg4OGRc7u7uOcophDCsYg6WfPNWTbaPakqT8s6karQsO3SX5rMPsPLwXVLTZb+TPGdpD703QfFakPgE1rwB4TcUi+Nk5cTKdivxKeZDYnoiI/aOwO+en2J5hPHLUXEzbNgwbtzQ/wa/c+cOPXr0yOh9+eCDD14qwP+vstLpdC+08mrdunV8/vnnrF+//qkruP4xefJkYmJiMq779++/VE4hhGF5lbBn7aD6rBpQjwqutkQnpjHtr6v4zgtg5yWZdJznrIpAn81QrDokhMGaTvBEuS0/bM1t+bHNj7Qp3YY0bRoTAybyxw3lTzcXxilHxc2NGzeoVasWABs2bKB58+b8+uuvrF69mo0bN+aoYWdnZ0xMTLL00oSFhWXpzfl/69evZ9CgQfz++++0adMm2/daWFhgb2+f6RJCGCeVSkXLSq7sHNOUGV2r42xrwb0niYz45SxvLz7GuWCZdJynrB2hz5/gWhXiQ2H16xCp3AHJFiYWzG4+mzcrvIlWp+WLY1+w/NJyKXxFFjkqbnQ6HVqtvot47969dOzYEQB3d3ciIiJy1LC5uTne3t74+WXuWvTz86NRo0bP/Ny6devo378/v/76K6+9puxptkIIwzA1UdPLpzT+k1owqlV5LM3UnL4XRdeFRxm17hz3I2Vjtzxj4wR9/wTnShD3SD9EFXVPsTgmahM+a/gZQ6oPAWDB2QV8e/pbOa5BZJKj4qZu3bp89dVXrF27loCAgIzi4u7du8/tbXma8ePHs3z5clauXMm1a9cYN24cwcHBDB8+HNAPKfXt2zfj/evWraNv377MmTOHBg0aEBoaSmhoKDExssGTEAWRrYUpE9pW4sDEFrzlXQqVCrZdeETrOQHM3HGNmCTZnj9P2LpAv63gVB5i7uuHqGIeKBZHpVIxus5oJtWdBMDaq2uZengqaVr5/SD0crRa6uLFi7z77rsEBwczfvx4PvvsMwBGjRrFkydP+PXXX3McYOHChXzzzTeEhIRQrVo15s2bR7NmzQDo378/QUFB+Pv7A9CiRYunrqLq168fq1evfqH2ZCm4EPnX5YcxzNhxjaO3nwBQ1NqMMa0r8G4DD8xMcvRvNfEyYh/Bqo4QdReKesKAHWBfQtFIW29v5dMjn6LRaWhWqhmzm8/GytRK0UzCMPJ8KXhycjImJiaYmRn/sk0pboTI33Q6HQcCw5ix4zq3wuIB8HS24aMOlWnr5SZHwRhazAN9gRN9T9+T038H2OW85z43BdwPYELABFI0KdR2rc33rb7HwcJB0Uwi9xm8uElNTSUsLCxj/s0/SpcundOvynNS3AhRMKRrtPx26j7z/G7wJEG/FUV9T0emvlaFGqWKKBuuoIu6B6tf0w9ROVfS74Nj66JopLOPz/L+/veJS42jQtEKLG6zGFfrZ6+kFfmPwYqbGzduMGjQII4ePZrp/j/LtzUa4z8ET4obIQqWuOQ0FgfcZvmhu6T8vSdOtzol+bB9ZdzsLRVOV4BF3oFVr+knGbtWhX7b9JOPFRQYGciIvSMITwqnpG1JlvoupbS98f+jW7wYgxU3jRs3xtTUlI8++ojixYtn6f6tWbPmyyXOQ1LcCFEwPYpOYvbuQDadewiAtbkJ77cqz8DGnliamSicroB6cls/RBUfqt8Pp+9W/fJxBT2Ie8BQv6Hcj7uPk6UTi30XU9mxsqKZRO4wWHFjY2PDmTNnqFw5//5GkeJGiILt/P1oPt96hfP3owEo7WjN1Neq4CvzcQwj/IZ+iCohTL+jcd8/9RsAKigiKYLhfsMJjArE1syW71p9R71i9RTNJF6dwY5f8PLyyvF+NkIIkZdquRdh04hGzO1eE9e/Tx4fuvYMfVac5MbjOKXjFTwuFfXLxK2dIOQ8/NxNfzaVgpytnFnVfhXebt7Ep8Uz3G84+4P3K5pJ5K0c9dzs37+fqVOnMmPGDKpXr55ldVR+6AmRnhshCo+ElHQW+t9i2cG7pGq0mKhV9Gngwdg2FShiba50vIIl9JJ+/5ukKHD3gd4bwcJO0UjJ6clMOjgJ//v+qFVqPm/4OV0rdH3u54RxMtiwlFqt7+h51nlQMqFYCGGMgp8k8tX2q+y5+hjQ748zvm0l3qnnjqnsj5N7Hp2Hn96A5Bgo3Qh6/wHmNopGStem88WxL9hyawsA473HM6DaAEUziZdjsOLmaRvo/Vfz5s1f9KsUI8WNEIXX4ZsRTPvrCjce6/fHqVzMjk87edGonLPCyQqQh2fgpy6QEgtlmkKv38HcWtFIOp2OeWfmserKKgAGVB3AOO9xMgcrn8nzTfzyEyluhCjc0jVafj0ZzJw9NzKOb+hQrRgfd6yCu6OyP4QLjPunYG1XSI2Dsi3hnd/ATPll+asur2LumbkAdCnfhc8afoap2lThVOJFGWxCMcChQ4fo3bs3jRo14uFD/ZLLtWvXcvjw4ZdLK4QQecjURE3fhmXwn9iCvg09UKtg5+VQWs8NYM6eQBJT05WOmP+514N3N4CZDdw5AOt7Q3qK0qkYUG0A0xpNQ61Ss+XWFsb7jyc5PVnpWMIAclTcbNy4kXbt2mFlZcXZs2dJSdH/Zo2Li2PGjBkGCSiEEIZQ1MacaZ2rsWNMUxqWdSI1Xcv3+2/RanYAf55/SCHr1M59Hg3h3d/B1Apu+cHv/SA9VelUdK3QlXkt5mGuNufA/QMM3zucuFRZRVfQ5Ki4+eqrr1i8eDHLli3LtFKqUaNGnD17NtfDCSGEoVUuZs+vQ3xY3LsOpYpaERqbzJjfzvPW4mNcfBCtdLz8rUwT6PUbmFrCjZ3wxwDQKH9yd6vSrVjsuxhbM1vOPD7DwN0DiUiSbU4KkhwVN4GBgRkndv+Xvb090dHRuZVJCCHylEqlon214uwd35xJ7SphZWbCmXtRdP7xCJM2XCAsToYuXlrZFtDzFzAxh+t/wcbBoFF+6K9esXqsbLcSR0tHrkdep+/OvjyIe6B0LJFLclTcFC9enFu3bmW5f/jwYcqWLZtroYQQQgmWZiaMbFmeAxNb0LV2SXQ62HDmAa1mB7D04G1S07XP/xKRVfk20ONnUJvB1S2wZThold86pIpTFdZ2WEtJ25Lcj7tP3519uRF1Q+lYIhfkqLgZNmwYY8aM4cSJE6hUKh49esQvv/zCxIkTee+99wyVUQgh8lQxB0vm9ajFxhGNqFHKgfiUdGbsuE67+QfZd+2xzMd5GRXbQfc1oDaFSxvgz/dBq3yxWNq+NGs7rKVC0QqEJ4XTf1d/zj6WaRb5XY6Xgk+ZMoV58+aRnKzvprWwsGDixIl8+eWXBgmY22QpuBAiJ7RaHRvPPuDrXYFExOsXUTSr6MKnr3tR3tVW4XT50NU/YcMA0Gmgdh/o9B2old9IMSYlhlH7R3Eu7BwWJhbMbTGXZqWyTsMQyjH4PjeJiYlcvXoVrVaLl5cXtrb55w+4FDdCiJcRl5zGDwdusfLwXdI0OkzVKvo2LMOYNhVwsDJ7/heIf13eqJ97o9NC3YHw2lwwgg31ktKTmBgwkYMPDmKiMuHLxl/SqVwnpWOJv+V6cdOtW7fnNmpqakqxYsXw9fWlUyfj/c0gxY0Q4lXcjUhg+var7L0WBoCjjTkT21aiRz13TNTK/4DONy6sh83DAB3UHwYdvjaKAidNm8ZnRz5j251tAEyqO4m+VfsqnEqAAYqbAQOefw6HVqslLCyMgIAAJk6cyLRp0148cR6S4kYIkRsCboTz5V9XuRWmP8rBq7g9n3Xywqesk8LJ8pFzP8OfI/WPG74Pbb8yigJHq9My+/Rs1l5dC8CQ6kMYVXuUHNegMEWPX9i+fTsjRowgODg4N78210hxI4TILWkaLWuP3WPe3hvEJeuXN79Wozgfd6xCySJWCqfLJ86shm1j9I8bj4U2nxtFgaPT6VhxeQULzi4A4K2KbzHVZyomahOFkxVeBj1+4XkaN25M3bp1c/trhRDC6JiZqBnYxBP/iS3o5VMalQq2Xwyh1Wx/5vndIClV+eXORs+7P3ScrX98ZD4cmK5kmgwqlYrB1QfzWcPPUKvU/HHjDyYdnESqRvldlsXzycGZQgiRS648iuGLbVc5eTcSgBIOlkzuWIXXaxSXIY3nOb4Ydn2of9z8I2jxkVH04AD43fPjw4MfkqZNw6eYDwtaLcDGzEbpWIWOnAqeDSluhBCGpNPp2HEplBk7rvEwOgmA+mUc+bSTF9VKOiiczsgd/R72TNU/LtsSOi2Aoh7KZvrbiZATjN4/msT0RLycvFjYeiFOVjK/Ki9JcZMNKW6EEHkhKVXD0oN3WBRwi+Q0LSoV9KznzsS2lXCytVA6nvE6uQx2TwFNiv5Ucd8voO4go9gL50rEFUbsHUFUShRl7MuwxHcJJWxLKB2r0JDiJhtS3Agh8tKj6CRm7bzO1guPALCzNGVM6wr0bVgGc1Plf2AbpYib+h2M7x/XPy/dCDr/AE7llM0FBMUEMdRvKCEJIbhau7KkzRLKFy2vdKxCQYqbbEhxI4RQwqmgSD7feoUrj2IBKOtiw6eve9GikqvCyYyUVgunlsPezyEtQX+yeMsp0HAkKLxiKTQhlOF+w7kdcxt7c3sWtllITZeaimYqDKS4yYYUN0IIpWi0Ojacvs+3uwN5kqBfddOqsitTX6tCWZf8s9N7nooKgq2j4W6A/nlJb+j8I7hWUTRWdHI0I/eP5GL4RaxMrZjbYi5NSjZRNFNBJ8VNNqS4EUIoLTY5je/33WTVkSDStTrMTFQMaOzJqFblsbOUoxyy0Ong3Fr9XJyUWP3p4s0/gCbjwES5X6/EtETG+4/nyKMjmKpMmd5kOh3LdlQsT0EnxU02pLgRQhiL2+HxfPnXVfwDwwFwtjXng3aVecu7FGo5yiGr2Efw1zi4sUv/3K26fi5OiVqKRUrTpDHlyBR23t2JChUf1f+IXlV6KZanIJPiJhtS3AghjM2B62F8+ddV7kQkAFC9pAOfv+GFt4ejwsmMkE4Hl/6AnR9AUiSoTKDxGGj+IZhZKhJJq9My6+Qs1l1fB8DwmsN5r+Z7srdRLpPiJhtS3AghjFFqupafjgWxYO9N4lL0Rzl0rlWCjzpUpriDHOWQRXw47JwEVzbrnztX1M/Fca+vSBydTsfii4tZeH4hAD0q9WBy/clyXEMukuImG1LcCCGMWXhcCnP2BLL+9H10OrAyM+G9FuUY0qwslmbygzKLa9vgr/GQEAaooMEIaDUVzJXZQXj99fVMPzEdHTral2nPjCYzMFNwXlBBIsVNNqS4EULkB5cexPDFtiucvhcFQKmiVkzpWIX21YrJcMf/S4zUTza+8Kv+edEy8Mb34NlMkTi77u5i8uHJpGvTaVi8IfNbzsfazFqRLAWJFDfZkOJGCJFf6HQ6tl54xKyd1wmJSQagQVlHPn29Kl4l5O+vLG766U8Yj32of+49AHyngWXe/1odfXiUsf5jSUpPorpzdX5s/SNFLYvmeY6CRIqbbEhxI4TIbxJT01kccIclAbdJSdcf5dCjrjvj21bE1U6ZSbRGKzkW/D6FM6v0z+1L6s+oquCb51Euhl/kvX3vEZMSg6eDJ0t9l1LMplie5ygopLjJhhQ3Qoj86kFUIjN3Xmf7xRAAbMxNeK9leQY18ZT5OP/v7kHYOkq/CSBAzXeg3QywztsVaHei7zDUbyiPEx9TzKYYS3yXUNahbJ5mKCikuMmGFDdCiPzuzL1Ipv11jQv3owEo4WDJhx0q06lGCdkf579SE2D/dDi+ENCBjSu8Nge83sjTGCHxIQz1G0pQbBBFLIqwqM0iqjlXy9MMBYEUN9mQ4kYIURBotTq2XXzE1zuv8+jv+Tg13YvwyWtVqFtG9sfJ5P5J+HMkRNzQP/fqAh1ng61LnkWITI7kvb3vceXJFaxMrVjQcgENSzTMs/YLgpz8/Fb8SNqFCxfi6emJpaUl3t7eHDp06JnvDQkJoVevXlSqVAm1Ws3YsWPzLqgQQhgRtVpF51ol2T+xBZPaVcLG3IQL96N5a/ExRv56lvuRiUpHNB7u9WHYIWg6Qb/p39Ut8GN9uLhBvylgHnC0dGRFuxX4FPchKT2JkftGsu/evjxpuzBStLhZv349Y8eOZcqUKZw7d46mTZvSoUMHgoODn/r+lJQUXFxcmDJlCjVrygmsQghhaWbCyJblOTCpBT3ruaNSwfaLIbSeE8DMndeITU5TOqJxMLOE1p/CkP36YxuSImHTYFjXU3+sQx6wMbNhYeuF+Hr4kqZNY3zAeLbc2pInbRc2ig5L+fj4UKdOHRYtWpRxr0qVKnTp0oWZM2dm+9kWLVpQq1Yt5s+fn6M2ZVhKCFGQXX0Uy/QdVzly6wkATjbmjPOtSM967piaKN5Zbxw0aXB4PgR8Ddo0sLCHtl9Bnb6QB3sIpWvT+eLYFxmFzYf1PqS3V2+Dt5vf5YthqdTUVM6cOUPbtm0z3W/bti1Hjx7NtXZSUlKIjY3NdAkhREHlVcKenwf5sLJ/Xcq62PAkIZWpWy7TYcEh/APDlI5nHEzMoPkkGH4ISnrrTxrfNhrWdvl3dZUBmapN+aLRF/Tx6gPA16e+ZtH5RRSyKbAGpVhxExERgUajwc3NLdN9Nzc3QkNDc62dmTNn4uDgkHG5u7vn2ncLIYQxUqlUtKrsxu6xzfjijaoUsTbjZlg8/Vedot/Kk9x4HKd0ROPgWgUG+el7bUwt4Y4/LGwEJ5aAVmvQptUqNZPqTmJkrZEALLywkG9OfYNWZ9h2CwvF+yj/fxtxnU6Xq1uLT548mZiYmIzr/v37ufbdQghhzMxM1PRrVIaAiS0Z3MQTMxMVATfCaT//IFM2XyIiPkXpiMpTm0CjUTDiKJRuBGkJ+hPHV3eEiFsGbVqlUjG85nA+qv8RAD9f+5lPj3xKujbdoO0WBooVN87OzpiYmGTppQkLC8vSm/MqLCwssLe3z3QJIURh4mBtxtTXvfAb15z2VYuh1cEvJ4Jp8a0/i/xvk5ymUTqi8pzKQf/t+iXiZjYQfAwWN4YjC0Bj2GLj3SrvMr3JdExUJvx5+08mBkwkVZNq0DYLOsWKG3Nzc7y9vfHz88t038/Pj0aNGimUSgghCq4yzjYs7uPN+qENqF7SgfiUdL7edZ02cwP46+IjmfOhVkP9IfDeMSjbEtKT9Uc5rGgDj68YtOk3yr3B3BZzMVObsS94HyP3jSQxTZbzvyxFh6XGjx/P8uXLWblyJdeuXWPcuHEEBwczfPhwQD+k1Ldv30yfOX/+POfPnyc+Pp7w8HDOnz/P1atXlYgvhBD5kk9ZJ/4c2Zg5b9fEzd6CB1FJvP/rOd5cdJRzwVFKx1NeUQ/osxk6/wgWDvDoHCxpDv5fQ7rhelRalW7FojaLsDK14njIcYb4DSEmJcZg7RVkiu9QvHDhQr755htCQkKoVq0a8+bNo1kz/TH1/fv3JygoCH9//4z3P20+joeHB0FBQS/UniwFF0KIfyWmprPs4F0WB9wm6e/hqc61SvBB+8qULGKlcDojEBsC28dD4A79c9eq0OVHKFHbYE1eCr/EiH0jiEmJoXyR8iz1XYqLdd7tpmys5PiFbEhxI4QQWT2OTebb3YFsPPsAnQ4sTNUMburJiBblsbUwVTqesnQ6uLxRP9E48Yl+l+NGo6DFZP3mgAZwM+omw/yGEZ4UjrudO0t9l1LKrpRB2sovpLjJhhQ3QgjxbJcfxvDlX1c5cTcSAGdbCya2rcjbdd0xKeyHciZE6Aucyxv1z50qQOcfoHQDgzR3P+4+Q/YM4WH8Q1ytXFnadinlipQzSFv5gRQ32ZDiRgghsqfT6fC7+pgZO64R9EQ/qbVyMTumvuZFkwrOCqczAtf+0g9VxT8GVOAzTH+0g7lNrjcVlhjGML9h3Iq+VehPFJfiJhtS3AghxItJTdey9vg9Fuy9QWyyfjl068quTO5YhfKutgqnU1hSFOyeCud/1j8v4gFvfA9lm+d6U9HJ0by37z0uRVzC2tSaH1r/QL1i9XK9HWMnxU02pLgRQoiciUpIZcG+m/x8/B7pWh0mahW9fUozpk1FHG3MlY6nrFt7YesYiH2gf+7dH3yngaVDrjaTkJbAmP1jOBF6AnO1OXNazKGFe4tcbcPYSXGTDSluhBDi5dwOj2fmjuvsvfYYADtLU0a3qkDfRh5YmJoonE5BybGw93M4vUL/3K4EdJoPFdvlajMpmhQmBUziwP0DmKhM+LLxl3Qq1ylX2zBmUtxkQ4obIYR4NUdvRfDl9mtcC9EfROzhZM3kDpVpV7VYrh6fk+8EHYY/34eou/rnNXpC+5lg7ZhrTaRr0/n0yKdsu7MNgI99Puadyu/k2vcbMylusiHFjRBCvDqNVsfGMw/4dk8g4XH6M6rql3Fk6utVqFGqiLLhlJSaCAemw/GFoNOCjQu8Nge8OudaE1qdllknZ7Hu+joARtUexZDqQwp8YSnFTTakuBFCiNyTkJLOkoDbLD10h+Q0/YnW3eqUZFK7ShR3KMSbAN4/BX+OhIhA/fMqb+iLHFvXXPl6nU7Hj+d/ZMnFJQD08+rHhLoTCnSBI8VNNqS4EUKI3PcoOolvdwey+dxDACzN1AxtVo5hzcpiU1g3AUxPgYPfwqG5oNOAVVFo/zXU6A65VIT8dOUnvj39LQDdKnTj0wafYqIumPOfpLjJhhQ3QghhOBfuR/PV9qucCtKfUeVqZ8HEdpV4q04p1IV1E8CQC/penNBL+ucV2upPHy/qkStfv/nmZj4/9jlanRZfD19mNZ2FuUnBW8UmxU02pLgRQgjD0ul07Lwcysyd17gfmQRA1RL2TH3Ni4blnBROpxBNGhxZAAFfgyYVTMzBZzg0nQBWRV756/3u+fHBwQ9I16bTuERj5raYi7WZ9avnNiJS3GRDihshhMgbKeka1hwN4vt9t4hL0W8C2NbLjckdq+DpnPu7+eYLYdf1RzjcDdA/t3KEFh9B3YFgYvZKX3304VHG+o8lKT2J2q61+aH1D9ibF5yfc1LcZEOKGyGEyFtP4lOYv/cmv54MRqPVYapW0bdhGUa3Lk8R64I3fPJcOh3c3AN7Pvl3wrFjOf3mf5Vfe6X5OOfDzvPevveIS42jsmNlFrVZhLNVwTgyQ4qbbEhxI4QQyrj5OI4ZO65xIDAcAAcrM8a0rkCfhh6YmagVTqcATTqcXQMHZkBihP6eR2No+xWUrPPSXxsYGcgwv2E8SX6Ch70Hy3yXUdy2eC6FVo4UN9mQ4kYIIZR18EY407dfI/BxHABlnW2Y3LEKbaq4FuilzM+UHAtH5sOxHyE9WX+vendo/QkUKf1SX3kv9h5D9wzlUcIj3KzdWNp2KWUdyuZeZgVIcZMNKW6EEEJ56Rotv59+wFy/QCLiUwFoWNaJqa9XoWqJ3D2XKd+IeQD7voSLv+mfm1hAw/egybiXOqsqNCGUoX5DuRtzl6IWRVnsuxgvJ69cDp13pLjJhhQ3QghhPOKS01jkf5vlh++Smq5FpYI3apagQ7XiNKngjG1h3CPn0XnYMxWCDumfWzvrJx1798/xpOPI5EhG7B3B1SdXsTWz5YfWP+Dt5p3rkfOCFDfZkOJGCCGMz/3IRL7ZHci2C48y7pmZqKhXxpGWlVxpWdmFci62hWfYSqeDG7v0k46f3NTfc66on3RcsX2OJh3Hp8bz/v73OfP4DBYmFsxtMZdmpZoZKLjhSHGTDSluhBDCeF24H82W8w85cD2MoCeJmV4rVdQqo9BpWNYZK/OCuRNvJpo0OLMa/GdC4hP9vTJN9ZOOS9R64a9JTk9mQsAEDj44iKnKlJlNZ9Les71BIhuKFDfZkOJGCCHyh7sRCRy4HsaBwDBO3IkkVaPNeM3cVE3Dsk60rORCy8queDgV8H1zkmP0xzgcXwQa/UGl1Oipn3TsUOqFviJNm8bUw1PZcXcHKlR80vAT3q74tgFD5y4pbrIhxY0QQuQ/ianpHL31hAOBYfgHhvMwOinT62WdbWjxd69OfU9HLEwLaK9OdLB+0vGl3/XPTS2h4fvQZCxY2D3341qdlhknZrA+cD0AY+uMZVD1QQYMnHukuMmGFDdCCJG/6XQ6bobFZ/TqnA6KIl37748ya3MTGpd3pmUlV1pUcqFEkQJ4OvnDM7B7KgQf1T+3cYGWH0PtvmCS/SRsnU7Hd+e+Y/ml5QAMqjaIMXXGGP18JilusiHFjRBCFCyxyWkcuRnBgcAwDgSGEx6Xkun1ysXs9L06lVyo41G04GwYqNPB9e3g9ylE3tbfc6kMvl9CBd/nTjpeeXkl887MA6B7xe587POxUZ8oLsVNNqS4EUKIgkur1XE1JBb/vwudc8FR/KdTBztLU5pVcKFFJReaV3LB1c5SubC5JT0VzqwC/1mQFKm/V7aFftJxserZfnTDjQ18eexLdOjoUKYD05tOx0z9amdcGYoUN9mQ4kYIIQqPqIRUDt4Mxz8wnIAb4UQmpGZ6vXpJB1pWcqFFZVdqliqCidq4h2aylRQNh+bAicX6k8dRQa13odUUsC/xzI/turuLyYcmk65Lp2nJpsxpMQcrU+MbypPiJhtS3AghROGk0eq4+CCaA4Hh+AeGcfFBTKbXi1qb0byifvVVswouFLXJp4d6RgXB3i/gyib9czNraDQKGo0GC9unfuTQg0OM9x9PsiaZOq51+KH1D9iZP3+Ccl6S4iYbUtwIIYQACI9LIeBGOAcCwzh4I5y45PSM19QqqOVe5O99dVzxKm6POr/16tw/BXumwP0T+ue2btByCtTuDU+ZW3Pm8Rne3/c+8WnxVHGswmLfxThaOuZx6GeT4iYbUtwIIYT4f+kaLWeDo/WTkq+HcT00LtPrLnYWtPi7V6dJBWfsLY1zXkoWOh1c2wp+n0HUXf09Vy9o+yWUb5Pl7VefXGXE3hFEJkfi6eDJUt+lFLMplsehn06Km2xIcSOEEOJ5QmKS8A8M58D1MA7fiiAxVZPxmqlahbdHUVpWdqVlJVcquuWDYyHSU+HUcgj4GpKj9ffKtdYXOW5VM731bsxdhvoNJTQhlOI2xVnWdhke9h55n/n/SHGTDSluhBBC5ERKuobTQVEZ++rcDk/I9HoJh/+1d+9BTd2JHsC/AUIQRBCQRxQ0QgUEn2T1AmJAd3FZ16ltp6/ZKg5722UrLZpaxdoZW68WW3Wvba0oirZb6+of1tbOrQ+0hIfoFVFaB/HRiuJtYymiPIIEkpz7BxJFlLX1hCPH72cm055fTsL3oDP5es4v5+eGxAh/TAn3R1yYL9xdH+LFPlvqgaLVwLFcwNYOKJw6LlMlLQE8b52hMTYb8VL+S7jYeBE+bj7I/UMuwn3CJQzOctMjlhsiInoQNVdbYDjXcfmq9IerMFtuWxbC2QkTh/vY5+po/B7SZSHqLwAH3wJOf9mxrfQA4jOBuAzAtSPz1RtXkX4wHWfqz8DT1RPrp67HWP+xkkVmuekByw0REYmltd2KIxeuwnCmFt+crcXl+q7LQgzzdb+5LIQ/Jmp84KZ8yG6SV/O/HZOO/6+sY9szCJjyJjDmecDJGY1tjcg4lIGTtSfRz6Uf1iauRdzgOEmistz0gOWGiIgcQRAEXLhtsc9j1fVot976iHVTOiE+1A+68EGIDBqAYb4e8OvvKv18HUEAKnd3nMm5fqljLGBUx3yc0CTcsNzAfMN8HP7xMFycXPBuwrtIHpbc6zFZbnrAckNERL2h2WzB4e/rYLi52KexobXbPp4qFwzz84DGz+Pmf92h8esPja8HvNx7+RtZFnPHXJzCVYD55j2Awv4AJP8X2n3DkFWchQOXDsBJ4YS3Yt/CE4890avxWG56wHJDRES9TRAEnP25CQVnfkHpD3WorjPhx+s30NMnsI+HK4b53iw7N0vPMD93aPw8HDtpuaUeKHwPKNsE2Cwdk47Hz4Y1MQv/dWojdp3fBQBYoF2A1KhUx+W4A8tND1huiIjoYdDabsXl+hZcqDPhYp0J1bc9au9Y/PNOAQNU0Nw846Px88AwXw8MH+SBYB93qFxEmtdz9Qfg4FKg6quObdf+EOIz8d9uArae2QYAeGn0S8gYm9Erl9ZYbnrAckNERA+7ZrMFF+tMuHi1o/jcXoCutbTf83VOCmDwwH4dZcd+qavjMdi7H1x+y4rol0qB/UuAn04AAIQBg5EXNRXvXzEAAJ6PeB5ZE7LgpHDsaussNz1guSEior7seksbqm8Wn+pfTKi+2oLqumZcrGtBs9lyz9cpnRUI9nGHxvfWHJ/OAhQ4wK3n5SVsto61qg6+DTTUAAB2Dg7HCtdWCBDw5+F/xrL4ZQ5dUbxPlZv169dj1apVMBqNiIqKwtq1a5GQkHDP/QsLC6HX61FZWQm1Wo2FCxciPT39vn8eyw0REcmRIAj4pdmMi3UdZae67lbpqb5qQttt9+O5k5vSCcN8b5/YfOvh63HbN7raWztWHS9eA5gb8T8e7lji7wcrgMTgRKzWrYbKWeWQ4+sz5Wbnzp2YNWsW1q9fj/j4eGzcuBGbN2/G6dOnERIS0m3/6upqREdH48UXX8Tf/vY3HD58GC+//DL+9a9/4amnnrqvn8lyQ0REjxqbTYCxsfXmmZ5bl7gu1plQU98Ci+3eVcBT5QLNoI55PZ1ne0I9WjHizHqoTm5FoZsSr/kPgtlJgQmDxuKDP2yAh1L8mxf2mXIzceJEjB8/Hjk5OfaxyMhIzJw5E9nZ2d32X7RoEfbs2YOqqir7WHp6Or799lscOXLkvn4myw0REdEt7VYbfrx2o8uE5otXTbjwiwk/NfT8ja5x7r9gsXIHFIoTeCVgEExOTgh38cGHKZ8hyGeIqDl/zee3ZAtgtLW1oby8HFlZWV3Gk5OTUVpaetfXHDlyBMnJXW8cNG3aNOTl5aG9vR1KZfdrfWazGWbzrVnnjY2NIqQnIiKSB6WzE4bdvByVdMdzre1W1NS33Co9t01urm0y42TLIDyDV/AfTqex2PopVgdZcBb1+M9d0/DxzF0YNChCkmOSrNzU1dXBarUiICCgy3hAQACuXLly19dcuXLlrvtbLBbU1dUhKCio22uys7Px9ttvixeciIjoEeGmdMaIAE+MCPDs9lznN7o6Ss8IFP0yFVN+3o7C/nsxwKqC98DhEiTuIPnSpXd+N14QhB6/L3+3/e823mnx4sXQ6/X27cbGRgQHB//WuERERASgv8oF0YO9ED3Y67bR8TBenwuV2Qqli6tk2SQrN35+fnB2du52lqa2trbb2ZlOgYGBd93fxcUFvr6+d32NSqWCSuWYmdtERETUVZC3RuoIcOwdd3rg6uqKmJgY5OfndxnPz89HXNzdVxyNjY3ttv+BAweg1WrvOt+GiIiIHj2SlRsA0Ov12Lx5M7Zs2YKqqirMnz8fNTU19vvWLF68GLNnz7bvn56ejkuXLkGv16OqqgpbtmxBXl4eFixYINUhEBER0UNG0jk3zz77LK5evYply5bBaDQiOjoaX3/9NYYOHQoAMBqNqKmpse+v0Wjw9ddfY/78+fjoo4+gVqvxwQcf3Pc9boiIiEj+JL9DcW/jfW6IiIj6nl/z+S3pZSkiIiIisbHcEBERkayw3BAREZGssNwQERGRrLDcEBERkayw3BAREZGssNwQERGRrLDcEBERkayw3BAREZGsSLr8ghQ6b8jc2NgocRIiIiK6X52f2/ezsMIjV26ampoAAMHBwRInISIiol+rqakJXl5ePe7zyK0tZbPZ8NNPP8HT0xMKhULU925sbERwcDAuX74sy3Wr5H58gPyPkcfX98n9GHl8fZ+jjlEQBDQ1NUGtVsPJqedZNY/cmRsnJycMGTLEoT9jwIABsv1LC8j/+AD5HyOPr++T+zHy+Po+Rxzjvztj04kTiomIiEhWWG6IiIhIVlhuRKRSqbB06VKoVCqpoziE3I8PkP8x8vj6PrkfI4+v73sYjvGRm1BMRERE8sYzN0RERCQrLDdEREQkKyw3REREJCssN0RERCQrLDciWb9+PTQaDdzc3BATE4Pi4mKpI4mmqKgIM2bMgFqthkKhwBdffCF1JFFlZ2fjd7/7HTw9PeHv74+ZM2fi7NmzUscSVU5ODkaPHm2/qVZsbCz27t0rdSyHyc7OhkKhwLx586SOIoq33noLCoWiyyMwMFDqWKL78ccf8cILL8DX1xfu7u4YO3YsysvLpY4limHDhnX7M1QoFJg7d67U0URhsVjw5ptvQqPRoF+/fhg+fDiWLVsGm80mSR6WGxHs3LkT8+bNw5IlS3Dy5EkkJCQgJSUFNTU1UkcThclkwpgxY7Bu3TqpozhEYWEh5s6di6NHjyI/Px8WiwXJyckwmUxSRxPNkCFDsHLlShw/fhzHjx/HlClT8Pjjj6OyslLqaKIrKytDbm4uRo8eLXUUUUVFRcFoNNofp06dkjqSqK5du4b4+HgolUrs3bsXp0+fxpo1a+Dt7S11NFGUlZV1+fPLz88HADz99NMSJxPHu+++iw0bNmDdunWoqqrCe++9h1WrVuHDDz+UJpBAD2zChAlCenp6l7GIiAghKytLokSOA0DYvXu31DEcqra2VgAgFBYWSh3FoQYOHChs3rxZ6hiiampqEh577DEhPz9f0Ol0QmZmptSRRLF06VJhzJgxUsdwqEWLFgmTJk2SOkavyczMFEJDQwWbzSZ1FFFMnz5dSEtL6zL25JNPCi+88IIkeXjm5gG1tbWhvLwcycnJXcaTk5NRWloqUSp6EA0NDQAAHx8fiZM4htVqxY4dO2AymRAbGyt1HFHNnTsX06dPx+9//3upo4ju/PnzUKvV0Gg0eO6553DhwgWpI4lqz5490Gq1ePrpp+Hv749x48Zh06ZNUsdyiLa2Nmzbtg1paWmiL+AslUmTJuHQoUM4d+4cAODbb79FSUkJ/vSnP0mS55FbOFNsdXV1sFqtCAgI6DIeEBCAK1euSJSKfitBEKDX6zFp0iRER0dLHUdUp06dQmxsLFpbW9G/f3/s3r0bI0eOlDqWaHbs2IETJ06grKxM6iiimzhxIv75z39ixIgR+Pnnn7F8+XLExcWhsrISvr6+UscTxYULF5CTkwO9Xo833ngDx44dw6uvvgqVSoXZs2dLHU9UX3zxBa5fv445c+ZIHUU0ixYtQkNDAyIiIuDs7Ayr1YoVK1bg+eeflyQPy41I7mzfgiDIppE/SjIyMvDdd9+hpKRE6iiiCw8PR0VFBa5fv45du3YhNTUVhYWFsig4ly9fRmZmJg4cOAA3Nzep44guJSXF/v+jRo1CbGwsQkND8cknn0Cv10uYTDw2mw1arRbvvPMOAGDcuHGorKxETk6O7MpNXl4eUlJSoFarpY4imp07d2Lbtm3Yvn07oqKiUFFRgXnz5kGtViM1NbXX87DcPCA/Pz84Ozt3O0tTW1vb7WwOPdxeeeUV7NmzB0VFRRgyZIjUcUTn6uqKsLAwAIBWq0VZWRnef/99bNy4UeJkD668vBy1tbWIiYmxj1mtVhQVFWHdunUwm81wdnaWMKG4PDw8MGrUKJw/f17qKKIJCgrqVrQjIyOxa9cuiRI5xqVLl3Dw4EF8/vnnUkcR1euvv46srCw899xzADpK+KVLl5CdnS1JueGcmwfk6uqKmJgY+8z3Tvn5+YiLi5MoFf0agiAgIyMDn3/+Ob755htoNBqpI/UKQRBgNpuljiGKqVOn4tSpU6ioqLA/tFot/vKXv6CiokJWxQYAzGYzqqqqEBQUJHUU0cTHx3e7BcO5c+cwdOhQiRI5xtatW+Hv74/p06dLHUVULS0tcHLqWimcnZ0l+yo4z9yIQK/XY9asWdBqtYiNjUVubi5qamqQnp4udTRRNDc34/vvv7dvV1dXo6KiAj4+PggJCZEwmTjmzp2L7du348svv4Snp6f9LJyXlxf69esncTpxvPHGG0hJSUFwcDCampqwY8cOGAwG7Nu3T+poovD09Ow2R8rDwwO+vr6ymDu1YMECzJgxAyEhIaitrcXy5cvR2Ngoyb+IHWX+/PmIi4vDO++8g2eeeQbHjh1Dbm4ucnNzpY4mGpvNhq1btyI1NRUuLvL6+J0xYwZWrFiBkJAQREVF4eTJk/jHP/6BtLQ0aQJJ8h0tGfroo4+EoUOHCq6ursL48eNl9TXigoICAUC3R2pqqtTRRHG3YwMgbN26VepooklLS7P//Rw0aJAwdepU4cCBA1LHcig5fRX82WefFYKCggSlUimo1WrhySefFCorK6WOJbqvvvpKiI6OFlQqlRARESHk5uZKHUlU+/fvFwAIZ8+elTqK6BobG4XMzEwhJCREcHNzE4YPHy4sWbJEMJvNkuRRCIIgSFOriIiIiMTHOTdEREQkKyw3REREJCssN0RERCQrLDdEREQkKyw3REREJCssN0RERCQrLDdEREQkKyw3REREJCssN0T0UDIYDFAoFLh+/foDvc+cOXMwc+ZMUTIRUd/AckNEDrdhwwZ4enrCYrHYx5qbm6FUKpGQkNBl3+LiYigUCqjVahiNRnh5efV2XCLq41huiMjhkpKS0NzcjOPHj9vHiouLERgYiLKyMrS0tNjHDQYD1Go1RowYgcDAQCgUCikiE1EfxnJDRA4XHh4OtVoNg8FgHzMYDHj88ccRGhqK0tLSLuNJSUndLkt9/PHH8Pb2xv79+xEZGYn+/fvjj3/8I4xGo/21VqsVer0e3t7e8PX1xcKFC3Hn8nlmsxmvvvoq/P394ebmhkmTJqGsrMz+fExMDNasWWPfnjlzJlxcXNDY2AgAuHLlChQKBc6ePSvmr4iIRMRyQ0S9IjExEQUFBfbtgoICJCYmQqfT2cfb2tpw5MgRJCUl3fU9WlpasHr1anz66acoKipCTU0NFixYYH9+zZo12LJlC/Ly8lBSUoL6+nrs3r27y3ssXLgQu3btwieffIITJ04gLCwM06ZNQ319vT1nZwkTBAHFxcUYOHAgSkpK7LkDAwMRHh4u2u+GiMTFckNEvSIxMRGHDx+GxWJBU1MTTp48icmTJ0On09nLxNGjR3Hjxo17lpv29nZs2LABWq0W48ePR0ZGBg4dOmR/fu3atVi8eDGeeuopREZGYsOGDV3m7JhMJuTk5GDVqlVISUnByJEjsWnTJvTr1w95eXn2nMXFxbDZbPjuu+/g7OyMWbNm2TMaDAbodDrH/JKISBQsN0TUK5KSkmAymVBWVobi4mKMGDEC/v7+0Ol0KCsrg8lkgsFgQEhICIYPH37X93B3d0doaKh9OygoCLW1tQCAhoYGGI1GxMbG2p93cXGBVqu1b//www9ob29HfHy8fUypVGLChAmoqqoCAEyePNlevgoLC6HT6ZCUlITCwkIALDdEfYGL1AGI6NEQFhaGIUOGoKCgANeuXbMXhMDAQGg0Ghw+fBgFBQWYMmXKPd9DqVR22VYoFN3m1PSkc987JykLgmAf8/LywtixY2EwGFBaWoopU6YgISEBFRUVOH/+PM6dO4fExMT7/plE1Pt45oaIek3nRGGDwdClIOh0Ouzfvx9Hjx695yWpf8fLywtBQUE4evSofcxisaC8vNy+HRYWBldXV/v8GaDjUtfx48cRGRlpH+ucH1RUVITExER4e3tj5MiRWL58Ofz9/bvsS0QPH5YbIuo1SUlJKCkpQUVFRZdLOzqdDps2bUJra+tvLjcAkJmZiZUrV2L37t04c+YMXn755S43AfTw8MDf//53vP7669i3bx9Onz6NF198ES0tLfjrX/9q3y8xMRH79u2DQqHAyJEj7WOfffYZL0kR9QG8LEVEvSYpKQk3btxAREQEAgIC7OM6nQ5NTU0IDQ1FcHDwb37/1157DUajEXPmzIGTkxPS0tLwxBNPoKGhwb7PypUrYbPZMGvWLDQ1NUGr1WL//v0YOHCgfZ/Jkyfbc3VertLpdFi7di3LDVEfoBB+zQVrIiIiooccL0sRERGRrLDcEBERkayw3BAREZGssNwQERGRrLDcEBERkayw3BAREZGssNwQERGRrLDcEBERkayw3BAREZGssNwQERGRrLDcEBERkaz8P9n2Bd8XgF6gAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels = ['1D', '2D', '3D']\n", "\n", "dres_fig2, dres_ax2 = plt.subplots()\n", "for data, label in zip(dres_conv2.T, labels):\n", " plt.plot(data, label=label)\n", "dres_ax2.set_xlabel('Window')\n", "dres_ax2.set_ylabel('Jensen-Shannon divergence')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "[1] Oliver Beckstein, Elizabeth J. Denning, Juan R. Perilla, and Thomas B. Woolf.\n", "Zipping and Unzipping of Adenylate Kinase: Atomistic Insights into the Ensemble of OpenClosed Transitions.\n", "Journal of Molecular Biology, 394(1):160–176, November 2009.\n", "00107.\n", "URL: https://linkinghub.elsevier.com/retrieve/pii/S0022283609011164, doi:10.1016/j.jmb.2009.09.009.\n", "\n", "[2] Richard J. Gowers, Max Linke, Jonathan Barnoud, Tyler J. E. Reddy, Manuel N. Melo, Sean L. Seyler, Jan Domański, David L. Dotson, Sébastien Buchoux, Ian M. Kenney, and Oliver Beckstein.\n", "MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations.\n", "Proceedings of the 15th Python in Science Conference, pages 98–105, 2016.\n", "00152.\n", "URL: https://conference.scipy.org/proceedings/scipy2016/oliver_beckstein.html, doi:10.25080/Majora-629e541a-00e.\n", "\n", "[3] Naveen Michaud-Agrawal, Elizabeth J. Denning, Thomas B. Woolf, and Oliver Beckstein.\n", "MDAnalysis: A toolkit for the analysis of molecular dynamics simulations.\n", "Journal of Computational Chemistry, 32(10):2319–2327, July 2011.\n", "00778.\n", "URL: http://doi.wiley.com/10.1002/jcc.21787, doi:10.1002/jcc.21787.\n", "\n", "[4] Matteo Tiberti, Elena Papaleo, Tone Bengtsen, Wouter Boomsma, and Kresten Lindorff-Larsen.\n", "ENCORE: Software for Quantitative Ensemble Comparison.\n", "PLOS Computational Biology, 11(10):e1004415, October 2015.\n", "00031.\n", "URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004415, doi:10.1371/journal.pcbi.1004415." ] } ], "metadata": { "kernelspec": { "display_name": "guide", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.15" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "vscode": { "interpreter": { "hash": "5edc5d8d8cbc0935a054a8e44024f729bc376180aae27775d15f2ff38c68f892" } } }, "nbformat": 4, "nbformat_minor": 2 }