diff --git a/notebooks/220922_figure4_familiar_control.ipynb b/notebooks/220922_figure4_familiar_control.ipynb deleted file mode 100644 index 3e7785a26..000000000 --- a/notebooks/220922_figure4_familiar_control.ipynb +++ /dev/null @@ -1,1262 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d7613928-f970-469a-a53a-f9f79aeca193", - "metadata": {}, - "source": [ - "This notebook is a summary of Familiar session clustering control. I use the same steps as in original clustering analysis to examine clusters in the data from 3 Familiar sessions only in cells that were matched across at least 3 matched sessions. \n", - "\n", - "Two interesting questions that we can answer by looking at these clusters:\n", - "* Are the clusters from Familiar sessions only different from F/N/N+ dataset?\n", - "* What are the rates of represetional drift across Familiar sessions in three cre lines?" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "532eb2da-1cd0-4cd2-a9b6-59ed70949d53", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "sns.set_context('notebook', font_scale=1.5, rc={'lines.markeredgewidth': 2})" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7e799adc-7f56-4c67-a6be-c040ac8417a5", - "metadata": {}, - "outputs": [], - "source": [ - "import visual_behavior.data_access.utilities as utilities\n", - "from visual_behavior.data_access import loading as loading\n", - "import visual_behavior.dimensionality_reduction.clustering as vba_clust" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "20a8c91a-08b1-468f-a4fc-7ffbcf81b47a", - "metadata": {}, - "outputs": [], - "source": [ - "import visual_behavior_glm.GLM_params as glm_params\n", - "import visual_behavior_glm.GLM_analysis_tools as gat\n", - "import visual_behavior_glm.GLM_fit_dev as glm_fit" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6a24680f-f3b5-44bd-91b6-ea0d8217b086", - "metadata": {}, - "outputs": [], - "source": [ - "import visual_behavior.visualization.utils as utils\n", - "from visual_behavior.dimensionality_reduction.clustering import plotting\n", - "from visual_behavior.dimensionality_reduction.clustering import processing" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9c74732a-38c6-408a-8e66-9b666c32b51d", - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.cluster.hierarchy import dendrogram, linkage\n", - "from sklearn.cluster import AgglomerativeClustering as ac" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "532d01d9-77cd-4c59-b852-535388a739e4", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "%matplotlib inline\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "88a3b380-b198-49b9-aed1-777f0bdea5db", - "metadata": {}, - "outputs": [], - "source": [ - "glm_version = '24_events_all_L2_optimize_by_session'\n", - "model_output_type = 'adj_fraction_change_from_full'\n", - "\n", - "base_dir = r'\\\\allen\\programs\\braintv\\workgroups\\nc-ophys\\visual_behavior\\platform_paper_plots\\figure_4'\n", - "base_dir = os.path.join(base_dir, glm_version)\n", - "if not os.path.exists(base_dir):\n", - " os.mkdir(base_dir)\n", - " \n", - "folder = '220922_familiar_control'\n", - "save_dir = os.path.join(base_dir, folder)\n", - "if not os.path.exists(save_dir):\n", - " os.mkdir(save_dir)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "909924f7-7fad-431f-a5f9-60542ac0bcba", - "metadata": {}, - "outputs": [], - "source": [ - "dropout_features = ['all-images', 'omissions', 'behavioral', 'task']" - ] - }, - { - "cell_type": "markdown", - "id": "c86f86ae-b6cb-4b8b-8413-6546d511a48e", - "metadata": {}, - "source": [ - "## Load GLM results and select familiar sessions only" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d7a6f09c-09f2-47e0-8687-f6aa037c425b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1248 expts in cells table\n", - "28806 unique cell_specimen_ids in cells table\n" - ] - } - ], - "source": [ - "cells_table = loading.get_cell_table(platform_paper_only=True, limit_to_closest_active=False, limit_to_matched_cells=False, add_extra_columns=True)\n", - "print(len(cells_table.ophys_experiment_id.unique()), 'expts in cells table')\n", - "print(len(cells_table.cell_specimen_id.unique()), 'unique cell_specimen_ids in cells table')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "6be2d55b-d7f8-4e28-8df2-807920a5eea7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loaded feature matrix.\n" - ] - } - ], - "source": [ - "filename = os.path.join(save_dir, 'feature_matrix_familiar_only.h5')\n", - "if os.path.exists(filename):\n", - " feature_matrix = pd.read_hdf(filename, key = 'df')\n", - " print('loaded feature matrix.')\n", - "# load and reshape file\n", - "else:\n", - " run_params, results, results_pivoted, weights_df = glm_fit.get_analysis_dfs(glm_version)\n", - " # select correct sessions\n", - " active_only = results_pivoted[results_pivoted.session_type.isin(['OPHYS_1_images_B','OPHYS_3_images_B','OPHYS_1_images_A','OPHYS_3_images_A'])]\n", - " # count how many cells have >= 3 sessions\n", - " tmp = active_only.groupby('cell_specimen_id').count()[['session_type']]\n", - " matched_cell_ids = tmp[tmp['session_type']>=3].index.values\n", - " print('found {} cells...'.format(len(matched_cell_ids)))\n", - " \n", - " # drop cells that do not have at least 3 imaging sessions\n", - " selected_sessions = active_only[active_only.cell_specimen_id.isin(matched_cell_ids)]\n", - " # sort by date of imaging\n", - " selected_sessions = selected_sessions.sort_values('date_of_acquisition')\n", - " \n", - " # rename session numbers to be sequential. Since there shouldnt be any difference among the sessions, we can just enumerate them.\n", - " # I'm not going to create another column for this, since we already have \"session_number\" column. I will just replace those values\n", - " for cid in matched_cell_ids:\n", - " tmp = selected_sessions[selected_sessions.cell_specimen_id == cid]\n", - " count = 1\n", - " for index in tmp.index.values:\n", - " selected_sessions.at[index, 'session_number'] = count\n", - " count =count+1\n", - " \n", - " # group by cell id and session number\n", - " feature_matrix = selected_sessions.groupby(['cell_specimen_id', 'session_number']).sum()[dropout_features].unstack()\n", - " # drop cells with nan\n", - " feature_matrix = feature_matrix.dropna(axis=1)\n", - " \n", - " # save\n", - " feature_matrix.to_hdf(filename, key = 'df')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "5ab66de2-80c1-440d-a057-6e59f558bfbf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1450" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(np.unique(feature_matrix.index.values))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "d86644f2-0c79-402f-acbe-7c2a6997402f", - "metadata": {}, - "outputs": [], - "source": [ - "cre_lines = np.sort(cells_table.cre_line.unique())\n", - "cells_table_sel = cells_table[cells_table.cell_specimen_id.isin(feature_matrix.index.values)]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "98bcf7e9-a4b1-495e-8a9b-a81b0b263e8a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Slc17a7-IRES2-Cre 1163\n", - "Sst-IRES-Cre 149\n", - "Vip-IRES-Cre 138\n" - ] - } - ], - "source": [ - "# separate feature matrix by cre line\n", - "cre_line_dfs = {}\n", - "for cre_line in cre_lines:\n", - " cids = np.unique(cells_table_sel[cells_table_sel['cre_line']==cre_line]['cell_specimen_id'].values)\n", - " print(cre_line,len(cids))\n", - " df_cre = feature_matrix.loc[cids].copy()\n", - " cre_line_dfs[cre_line] = df_cre" - ] - }, - { - "cell_type": "markdown", - "id": "fcacf668-4294-4931-8451-f9469c45595c", - "metadata": {}, - "source": [ - "## Find optimal number of clusters" - ] - }, - { - "cell_type": "markdown", - "id": "fa451da0-b860-4962-ac76-b9959a7cac15", - "metadata": {}, - "source": [ - "### Compute eigen gap" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "e689ebb0-0812-4c45-93af-8a27214b8fa6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal number of clusters [ 2 1 4 6 5 3 9 10 8 7 1160 13 11 1162\n", - " 15 12 1161 1158 16 1159 20 14 21 18 17]\n", - "Optimal number of clusters [ 1 2 3 5 148 4 8 6 7 145 144 9 141 138 137 12 140 15\n", - " 19 16 13 130 18 127 11]\n", - "Optimal number of clusters [ 1 3 4 2 5 7 137 10 13 136 12 11 16 135 8 18 15 23\n", - " 6 14 128 22 21 20 134]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.cluster import SpectralClustering\n", - "\n", - "eigenGap_df = {}\n", - "for i, cre_line in enumerate(cre_lines):\n", - " X = cre_line_dfs[cre_line].values\n", - " sc = SpectralClustering()\n", - " sc.fit(X)\n", - " A = sc.affinity_matrix_\n", - " \n", - " eigenvalues, eigenvectors, nb_clusters = vba_clust.get_eigenDecomposition(A)\n", - " \n", - " fig, ax = plt.subplots(2,1, figsize = (5,5), sharex=True)\n", - " ax[0].plot(np.arange(1,len(eigenvalues)+1), eigenvalues, '-o')\n", - " ax[0].grid()\n", - " ax[0].set_title(cre_line)\n", - " ax[0].set_ylabel('eigen value \\n(sorted)')\n", - " ax[0].set_xlabel('eigen number')\n", - " ax[0].set_xlim([0, 20])\n", - " #ax[0].set_xticks([np.arange(2,20,step = 2)])\n", - " \n", - " ax[1].plot(np.arange(1,len(eigenvalues)), np.diff(eigenvalues), '-o')\n", - " ax[1].set_ylabel('gap value \\n(difference)')\n", - " ax[1].set_xlabel('eigen number')\n", - " ax[1].set_xlim([0, 20])\n", - " ax[1].set_ylim([0, 0.20])\n", - " ax[1].grid()\n", - " plt.tight_layout()\n", - " fig.savefig(os.path.join(save_dir, f'eigenGap_{cre_line}.pdf'))\n", - " eigenGap_df[cre_line] = [ eigenvalues, eigenvectors, nb_clusters]\n", - " print(f'Optimal number of clusters {nb_clusters}')\n", - " \n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "5ca6ca90-ba59-4721-a362-7e5574d27515", - "metadata": { - "tags": [] - }, - "source": [ - "### Compute Gap statistic" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "89f8e499-1254-418b-888a-40b386f2b256", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loaded file\n" - ] - } - ], - "source": [ - "import pickle\n", - "\n", - "metric = 'euclidean' # default distance metric\n", - "shuffle_type ='all' # default shuffle type is all shuffle (cell id and regressors\n", - "\n", - "gap_filename = os.path.join(save_dir, 'gap_scores_{}_{}_nb20_unshuffled_to_{}.pkl'.format(metric, glm_version, shuffle_type))\n", - "if os.path.exists(gap_filename):\n", - " with open(gap_filename, 'rb') as f:\n", - " gap_df = pickle.load(f)\n", - " print('loaded file')\n", - "else:\n", - " gap_df = {}\n", - " for i, cre_line in enumerate(cre_lines):\n", - " X = cre_line_dfs[cre_line]\n", - " sc = SpectralClustering()\n", - " gap_statistics = vba_clust.compute_gap(sc, X, k_max = 25)\n", - " gap_df[cre_line]= gap_statistics\n", - " vba_clust.save_clustering_results(gap_df, gap_filename)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "ff6d618c-0794-4bad-b476-9d954bf95db8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['gap', 'reference_inertia', 'ondata_inertia', 'reference_sem', 'ondata_sem', 'gap_mean', 'gap_sem'])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gap_df[cre_line].keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "bda01b2b-a76b-410b-b649-965d3578ba65", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot gap statistic\n", - "\n", - "for cre_line in cre_lines:\n", - " fig, ax = plt.subplots(1,2, figsize = (10,3))\n", - " x = len(gap_df[cre_line]['gap'])\n", - " ax[0].plot(np.arange(2,x+2), gap_df[cre_line]['gap'], 'o-')\n", - " ax[0].set_ylabel('gap value')\n", - " ax[0].grid()\n", - " ax[1].plot(np.arange(2,x+2),gap_df[cre_line]['reference_inertia'], 'o-')\n", - " ax[1].plot(np.arange(2,x+2),gap_df[cre_line]['ondata_inertia'], 'o-')\n", - " ax[1].legend(['shuffled/reference', 'data'])\n", - " ax[1].grid()\n", - " plt.suptitle(cre_line + ' ' + metric + ' ' + shuffle_type)\n", - " fig.savefig(os.path.join(save_dir, 'Gap_nb20_{}_{}_unshuffled_to_{}.png'.format(cre_line, metric, shuffle_type)))\n" - ] - }, - { - "cell_type": "markdown", - "id": "40abe702-b0b9-43b1-af7a-1a80090c0762", - "metadata": {}, - "source": [ - "**Discussion**: From examining the plots above, it seems like the original number of clusters: 10, 5, 10 for threee cre lines may not the the most optimal. We can first cluster familiar sessions with ogiinal number of N for easy comparison and then cluster with a different more optimal number." - ] - }, - { - "cell_type": "markdown", - "id": "8fd72858-acf2-484b-af15-fdb2af7aa477", - "metadata": {}, - "source": [ - "## Cluster the data, 10 - 5 - 10" - ] - }, - { - "cell_type": "markdown", - "id": "14cd6d8a-52f2-4c6c-8857-7ce5cd4ed78f", - "metadata": {}, - "source": [ - "### Compute coclustering matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "832ed6db-4d02-42f5-99f5-1709ad74e282", - "metadata": {}, - "outputs": [], - "source": [ - "# n_clusters_cre = [10,5,10]\n", - "n_clusters_cre = [6,5,7]" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "e642f142-3cf9-421f-83b1-104770cad5bd", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:12<00:00, 4.13it/s]\n", - "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 22.02it/s]\n", - "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 18.27it/s]\n" - ] - } - ], - "source": [ - "import pickle\n", - "filename = os.path.join(save_dir, 'coClustering_matrix_6_5_7.pkl')\n", - "if os.path.exists(filename):\n", - " print('loading file...')\n", - " with open(filename, 'rb') as f:\n", - " coclustering_matrices = pickle.load(f)\n", - " f.close()\n", - " print('done.')\n", - "else:\n", - " coclustering_matrices = {}\n", - " for i, cre_line in enumerate(cre_lines):\n", - " X = cre_line_dfs[cre_line].values\n", - " m = vba_clust.get_coClust_matrix(X=X,n_clusters=n_clusters_cre[i], nboot=np.arange(50))\n", - " coclustering_matrices[cre_line]=m\n", - " vba_clust.save_clustering_results(coclustering_matrices, filename)" - ] - }, - { - "cell_type": "markdown", - "id": "cc1e3e93-e7fa-4ba9-924b-311e8f3a5e5a", - "metadata": {}, - "source": [ - "#### Assign labels based on agglomerative clustering" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "b8c53087-753a-4442-83cf-da261ff24aff", - "metadata": {}, - "outputs": [], - "source": [ - "labels_cre={}\n", - "for i,cre_line in enumerate(cre_lines):\n", - " X = coclustering_matrices[cre_line]\n", - " cluster = ac(n_clusters=n_clusters_cre[i], affinity='euclidean', linkage='average')\n", - " labels_cre[cre_line] = cluster.fit_predict(X)" - ] - }, - { - "cell_type": "markdown", - "id": "888a542b-7e75-4fda-a19a-2cfa2153bf1a", - "metadata": {}, - "source": [ - "#### Plot Coclustering matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "f115f709-a3f7-4f81-be7b-47b73593b20e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\iryna.yavorska\\Anaconda3\\envs\\vba\\lib\\site-packages\\seaborn\\matrix.py:654: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n", - " warnings.warn(msg)\n" - ] - }, - { - "data": { - "image/png": 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q83rgedXx6cBz6877XmYuqr7+NfBU4LPA9lU7Nddn5h/rrrs78GQevadnh7o2v5KZ9wwVt4msJEmSjmh4vHe1XwQcV329C7BnXZ2tqw3gwqouPJrsHtjkOt8F/lj3eB/KimE1L6s2gJMBE1lJkiQNLjNbTnuZmXsBe3Wirbq6O7ZbtxnHyEqSJKkrmchKkiSpK5nISpIkqSuZyEqSJKkrmchKkiSpK5nISpIkqSuZyEqSJKkrmchKkiT1sIiYHRFHR8T5EXFfRGRE7DhI3TdExKURsTQiboyIwyJijYY6O0XEryLilqrezRFxWkT8R0O9HatrDbZ9olXsLoggSZLU254JHARcB1wBvLhZpYh4DXAG8FvgQ8BzgE8BG1SPa7YElgBfAf4JbERZKeySiNguM/9S1bsK2L3JpXYHXg38plXgJrKSJEm9bQGwQWbeFRE7A6cPUu9zwJ+BnTJzOUBE3AscEhFfzsxrATLzi8AX60+MiG8BtwD7AR+s6t1OWYaWhrqHAddm5v+1CtyhBZIkST0sM+/LzLuGqhMRW1J6Wk+oJbGVr1LyyV1aXOYO4AFgVovrbAtsDny/RXuAPbKSJElTTkTMonnSOJCZAyNocutq319fmJm3RsTNdcfrY1gXWJMytGAOsA5wXovr7Fbt20pk7ZGVJEmaeuYAC5tsc0bY3uxqv7jJscXAxk3Kz6OMkb0CeCtwBDB/sAtExOrA24BLMvO6doKyR1aSJGnqmUfzpHFghO3NrPYPNTm2FHhMk/L3U3qFnwrsVdVZA3h4kGu8AtgQOKrdoExkJUmSpphq+MBAB5t8sNpPb3JsRt3x+hguqX0dET8C/lY9/Pgg19gNWA6c0m5QDi2QJElSK7UhBbObHJsN3DrUyVVifS6PjoFdSUTMBN4EnFvNZtAWE1lJkiS1clm176svjIiNgU3qjg9lJrDuIMfeAKxNmzd51ZjISpIkaUiZ+VfgauA91U1ZNe8DVgA/qRVExOMbz4+IJwGvosxZ28w7KNNzDTaHbVOOkZUkSepxEXFo9eUW1X73iNieMl3XcVXZAcBZwNkRcQrwbMriBidk5jV1zV0UEZdRkta7KPPC7kMZS3tIk2s/DngN8JPMXDKcuE1kJUmSdETD472r/SLgOIDM/HlEvBk4jLL87B3AkU3O/SbwZuDllLlj76RMxXVUZl7e5Nq7AtOAHww3aBNZSZKkHpeZ0Wa9M4AzWtT5LPDZYVz7BOCEduvXc4ysJEmSupKJrCRJkrqSiawkSZK6komsJEmSupKJrCRJkrqSiawkSZK6komsJEmSupKJrCRJUg+LiBdGxHkRsSQi7omIMyLiaS3OeXJEPBARGRFbNRzbKSJ+FRG3RMTSiLg5Ik6LiP/odOwuiCBJktSjImIb4HfADZQVu1YD3g/8b0RsnZm3D3Lq54AVgxzbElhCWf3rn8BGlJXCLomI7TLzL52K30RWkiSpd30auA94YWbeDRARJwPXAIcAcxpPiIgdgTcAxwKfaDyemV8EvthwzreAW4D9gA92KniHFkiSJPWulwC/qSWxAJm5mNJL+9bGyhGxOvAl4DjgumFc5w7gAWDWaIJtZI+sJEnSFBMRs2ieNA5k5kDd4+nAg03qPQDMjojZVWJb817gicARwM4tYlgXWJMytGAOsA5wXjvxt8seWUmSpKlnDrCwyTanod7fgRdFxL9zwohYE9iuerhxXfnjKAns3IZkeDDnUcbIXkHp3T0CmD/cb2Qo9shKkiRNPfNonjQONDz+KvA14JsR8XlKJ+ehwOzq+My6up+mJKZfbzOG91N6hZ8K7AU8hpJ7Ptzm+S2ZyEqSJE0xVY/pQBv1vh4RmwIHUGYWAOgHPku5kWsJQEQ8m3Kj1hsy85E2Y7ik9nVE/Aj4W/Xw4219E21waIEkSVIPy8xPABsC/wk8NzO3oeSICVxfVTsKuBT4W0RsFhGbARtUxzaukuGhrjEAnAvs1snY7ZGVJEnqcdWsBRfWFb0SuCQz76sePwl4HmWcbaNfALdTbuoaykxg3VGGuhITWUmSJP1bRLwN2Ab4/+qKP8KqSejLgQ8BHwWuqjv/8Zl5R0ObTwJeBSzoZKwmspIkST0qIl4O/DfwG+Au4EWUG7O+n5k/qtXLzPObnDur+vL8zLys7tBFEXEZJWm9C9gc2AeYQVlkoWNMZCVJknrXTZSlZg8A1gaupfSwHjeKNr8JvJnSY7sOcCdlKq6jMvPyUUXbwERWkiSpR2XmtcCrR3jufJpM8ZWZn6XMejDmnLVAkiRJXclEVpIkSV3JRFaSJEldyURWkiRJXclEVpIkSV3JRFaSJEldyURWkiRJXclEVpIkqcdFxNMj4pSIuDki7o+Iv0XEwRExvTr+mIj4QEScExGLI+K+iLg0IvaLiNUb2tomIo6v2rg/Im6MiB9FxOadjtsFESRJknpYRDwRuAS4h7Ki17+A/wT+B/gPYHfgqcBXKCt0fQG4F9gJ+BqwDWUJ2pqDgJcAPwauADYCPgj8OSK2zcyrOhW7iawkSVJveycwC9g+M/9alX0jImYCb4+IvYHbgOfUHQc4ISJOBPaOiCMzc2FV/gXgHZm5rFYxIk4BrqQkuXt1KnCHFkiSJPW2dar97Q3ltwEPA8sz886GJLbm9Gr/rFpBZl5Un8RWZdcCfwW26EzIhYmsJEnSFBMRsyJisybbrCbVf1ftvx0Rz4uITSNiN0rP6TGZuWKIS21U7e9sEU8AG7aqN1wmspIkSVPPHGBhk21OY8XM/A3wSeBVwGXAjcDJlCT28MEuEBFrVu1dDyxoEc9uwBOBU4fzTbTiGFlJkqSpZx4wv0n5wCD1FwIXUIYK3AW8Djg8Iu7IzK8Pcs5xlKECrxmq1zYingUcD1wIfK916O0zkZUkSZpiMnOAwZPWlUTE24ETgGdk5q1V8U8jYjXgcxFxSmbe3XDOAcC7gUMy8+wh2t4I+AVwN7Bri2EKw+bQAkmSpN72fmBBXRJbcxbwWOB59YURsRdwDHB8Zh49WKMRsS7wK2BdYKfMvK2TQYOJrCRJUq/bEFi9Sfm0av/v/+BHxBuBbwE/AT48WIMRMQP4GfAM4L8y8+8di7aOiawkSVJvuwboi4inNZT/f8ByyqIGRMQOwI+A3wPvHGyYQLXS1ynAiyjDCS4eq8AdIytJktTbjgVeA/whImore/1XVfb1zPxnRDyZMtQggdOAXcuMWv92UWb+o/r688AbKD2yj4uId9bVW5KZZ3QqcBNZSZKkHpaZv4+IFwNzgQ8A61NmMTiEkuQCPIUy1hXKDASN3gXUEtmtqv3rq63eIuCMDoQNmMhKkiT1vMy8BHjtEMcvAGKw4w11d+xMVK05RlaSJEldyURWkiRJXclEVpIkSV3JRFaSJEldyURWkiRJXclEVpIkSV3JRFaSJKmHRcT8iMghtidGxGMi4gMRcU5ELI6I+yLi0ojYr1rJq769vog4PSIWRcSDEXFbRPy6mqu2o5xHVpIkqbedAJzbUBbA14EbMvOWiHg28BXgPOALwL3ATsDXgG2AferOfRolx/wmsBiYBewG/D4iXpOZ53QqcBNZSZKkHpaZfwT+WF8WEdsDjwG+XxXdBjwnM/9aV+2EiDgR2DsijszMhVV7pwCnNLT3NcrKX/sDHUtkHVogSZKkRu8AEvgBQGbe2ZDE1pxe7Z81VGOZ+QBwB6V3tmPskZUkSdK/RcQ04K3ARZl5Q4vqG1X7O5u0szYwHVgf2BN4NvDpzkVqIitJkjTlRMQsmvd+DmTmQIvTd6Ikn98fqlJErAnMAa4HFjSp8h1gl+rrZZQxt0e1uPawOLRAkiRp6pkDLGyyzWnj3HcADwOntqh3HLAF8IHMXNHk+OHAq4G9gT9QementXH9ttkjK0mSNPXMA+Y3KR8Y6qSIWAt4I3B2Zt41RL0DgHcDh2Tm2c3qZOaVwJVV/ZOB/iqmt7QKvl0mspIkSVNMNXxgYASn7szKsxWsIiL2Ao4Bjs/Mo9uM5+GIOBM4NCJmZuaDI4htFQ4tkCRJUs1uwBLgrGYHI+KNwLeAnwAfHmbbMynz0649mgDrmchKkiSJiHg88Erg9Gq6rMbjOwA/An4PvHOQcbG1dhrL1gF2BW7KzH92KmaHFkiSJAngbZTccJVhBRHxZEovbQKnAbtGRH2VizLzH9XXp0TEUuAiykIKmwLvAjYB3t7JgE1kJUmSBGVYwT9ZdblagKcA61ZfH9/k+LsoK3cBnAzsQRl6sB5lrO7FwO6Z+bsOxmsiK0mSJMjMFw1x7ALK+NZ22jkROLFDYQ3JMbKSJEnqSiaykiRJ6komspIkSepKJrKSJEnqSiaykiRJ6komspIkSepKJrKSJEnqSiaykiRJIiK2iYhfRMTdEbEkIi6PiL3qju8XEadGxKKIyIiYP0g720bEVyNiQUQsi4gcq5hNZCVJknpcRLwG+AMwDfgk8DHKCl+b1lU7GHglcBWwbIjmXgu8u/r6+o4HW8eVvSRJknpYRKwLzAe+lpn7D1H1pcCNmZkRMTBEva8Bx2TmgxExD3hWp2JtZCIrSZLU294BzAI+BRARawNLMnOlIQGZuaidxjLz9k4HOBiHFkiSJE0xETErIjZrss1qUv2VwNXAayPiJuBe4F8RcXRErD6ecQ+XiawkSdLUMwdY2GSb06Tu5pSxsPOrbRfgdOAg4PNjHehoOLRAkiRp6plHSUobDTQpWwtYDzg4M4+pyn4aEWsB74+IIzPzzrEIcrRMZCVJkqaYzBygedLazIPV/ocN5d8HdgW2BX7ZkcA6zKEFkiRJvW1xtW+8Sav2eL1xjGVYTGQlSZJ624Jq/8SG8k2q/R3jGMuwmMhKkiT1th9X+31qBRERwL7A/cDFExFUOxwjK0mS1MMyc0FEnAQcEhFPAC4FXgfsBByYmfcCRMTrgedVp00HnhsRh1aPv1ebZzYingzsXpVvW5XV6l2emT/rVOwmspIkSXo3cCOwZ7X9A9gvM0+oq7NLdaxm62oDuBCoLZjwFOCIhvZrj78LmMhKkiSpMzJzGfDJahuszl7AXm20dQEQHQptSI6RlSRJUlcykZUkSVJXMpGVJElSVzKRlSRJUlcykZUkSVJXMpGVJElSVxrR9Ft9fX0HAzM6HMtkttlEByBJkqSVjXQe2Rn9/f1zOxnIZNbX1zd3omOQJEkaCxGxI3D+IIe3yMyrI+IxwLuAnYFnA2sB1wLfAL6Zmcvr2psN7A9sB/RVdV9WzS/bUS6IIEmSJIB5wIKGslur/VOBrwDnAV8A7qUsYfs1YBtgn7pzngkcBFwHXAG8eKwCNpGVJEkSwO8y84xBjt0GPCcz/1pXdkJEnAjsHRFHZubCqnwBsEFm3hUROwOnj1XA3uwlSZIkACJi7YhYpaMzM+9sSGJraknqs+rq3peZd41VjPXskZUkSZpiImIWMKvJoYHMHBjktO9RxrM+EhHnAx/LzCtbXGqjan/nCMIcNXtkJUmSpp45wMIm25wmdZcBp1Fu0HojcDiwLXBhRDxjsAtExJpVe9ez6tjacWGPrCRJ0tQzD5jfpHygsSAzLwIuqis6KyJ+BvQDhwG7DXKN44AtgNdk5opRxDpiJrKSJElTTDV8YGAU518eEecCr2h2PCIOAN4NHJKZZ4/0OqPl0AJJkiQ1cxPwuMbCiNgLOAY4PjOPHu+g6pnISpIkqZmnAnfUF0TEG4FvAT8BPjwRQdUzkZUkSephEfH4JmXbAy8Dzq4r2wH4EfB74J0TNS62nmNkJUmSetspEfEA5YavOylL0L6n+nouQEQ8GTgLSMoMB7tGRH0bF2XmP2oPIuLQ6sstqv3uVXI8kJnHdSpwE1lJkqTedgZlZoKPAesA/wR+AMzNzBurOk8B1q2+Pr5JG+8C/lH3+IiG43tX+0WU2Q46wkRWkiSph2Xml4Evt6hzARBD1Wmo33bd0XCMrCRJkrqSiawkSZK6komsJEmSupKJrCRJkrqSiawkSZK6komsJEmSupKJrCRJkv4tIg6MiIyIyxrKPxMR/xcRd0XEgxFxVUQcFhGPbdLG9Ig4JiJurepeHBGv6HSsziMrSZIkACJiI+BQ4P4mh18AXAx8D3gQeB5wCPCyiHhZZmZd3fnALsA84DpgL+BXEfHSzPxjp+I1kZUkSVLN0UA/5b/2s+oPZOb/a6wcEf8APk9Jcvursm2BtwMfycx5VdlJwF+AY4AdOhWsQwskSZJUS0DfCXx0GKctqvaz6sreAjwMfKtWkJlLgW8D20fE7NFF+ih7ZCVJknpcRATwFeC7mXlZedi03urAesCawLOBI4F7qHpjK1sDV2fmkobTL6Esc7sVsLgTcZvISpIkTTERMYuGoQGVgcwcaFK+B7AlsHOLprcArqx7/HfgjQ1tzgZuaXJuLXnduMU12ubQAkmSpKlnDrCwyTansWJErE0ZG3t0ZrbqKV0IvIqS8B4NLAHWbqgzE3ioyblL6453hD2ykiRJU888yswBjQaalB0KLAO+0KrRzLwfOLd6eGZE/LnaPz8zL6/KHwSmNzl9Rt3xjjCRlSRJmmKqf/UPtKpX3Xg1B/gksGHd2NgZwJoRsRlwT2bePUgTZwArKLMU1BLZxZThBY1qZbe2iqtdDi2QJEnqXRtSbtw6hpWHIGxHGQ+7EDhoiPPXBFYH1q0ruwx4VkSs1VB3u2p/OR1ij6wkSVLvWgi8qUn5kcBjgY8A10TEOsBDmdk49nUfykwEC+rKTgM+DuxLGeJAREwH3gX8ITM71iNrIitJktSjMvMeyvCAlUTEHOCRzDyjerwj8MOIOAW4hpJDbk+ZM/ZS4OS6Nv8UET8GPlsNXbge2BN4MmWFr44xkZUkSVIr1wG/BF4HvJsynOB64DPAMU16avcAjqj26wFXAK/NzD90MigTWUmSJK0kM3dseHwzZRhBu+cvBQ6otjHjzV6SJEnqSiaykiRJ6komspIkSepKJrKSJEnqSiaykiRJ6komspIkSepKJrKSJEnqSiaykiRJPSwi+iLi9IhYFBEPRsRtEfHriHhxQ723RcTJEXFNRGREXNBm+1+t6p/R6dhdEEGSJKm3PY2SE34TWAzMAnYDfh8Rr8nMc6p67wNeAPQD67fTcEQ8F9gXWNrhmAETWUmSpJ6WmacAp9SXRcTXgH8A+wO1RHZ34NbMXB4Rl7XZ/JeAk4GXdybalTm0QJIkSSvJzAeAOyi9s7WymzJzebttRMSuwDbAJzoeYMUeWUmSpCkmImZRl4TWGcjMgUHOWRuYThk2sCfwbODTI7z+TOBzwDGZuTgiRtJMS/bISpIkTT1zgIVNtjlDnPMdSi/s1cDHgK8DR43w+gcCQUlmx4w9spIkSVPPPGB+k/KBIc45HDgB2IQyHnY6MA14aDgXjognAQcB787MB4dz7nCZyEqSJE0x1fCBgWGecyVwJUBEnEyZnWA+8JZhXv7Yqp0fDPO8YTORlSRJ0koy8+GIOBM4NCJmttuzGhEvAN5Kmb7ryXVjY9cAHhMRmwF3ZeZ9nYjTRFaSJEnNzKSMc10baHeIwKbV/vtNjj2RMk73fZTxt6NmIitJktTDIuLxmXlHQ9k6wK7ATZn5z2E09yfgTU3Kv0FJYv8HuGyEoa7CRFaSJKm3nRIRS4GLgNsovarvotz09fZapYjYAdiherghsG5EHFo9Piszr8jMxcAZjReIiHnA4sxc5dhomMhKkiT1tpOBPYAPA+tRbhK7GNg9M39XV+/lwGEN5x5R7W8GrhjbMFdlIitJktTDMvNE4MQ26s0F5o7wGpuN5LxWXBBBkiRJXclEVpIkSV3JRFaSJEldyURWkiRJXclEVpIkSV3JRFaSJEldyURWkiRJXclEVpIkqYdFxDYRcXxE/C0i7o+IGyPiRxGxeUO9t0XEyRFxTURkRFwwRJuvjYiLI+LBiLgrIk6KiCd0OnYXRJAkSeptBwEvAX5MWZ1rI+CDwJ8jYtvMvKqq9z7gBUA/sP5gjUXEzsBPq3oHAesA+wPPj4htMvPBTgVuIitJktTbvgC8IzOX1Qoi4hTgSkoiuldVvDtwa2Yuj4jLhmjvf4BrgJdk5sNVez8DLgXeC8zrVOAOLZAkSephmXlRfRJblV0L/BXYoq7spsxcPlRbEfE44FnAT2tJbHXu5cBVwNs6Gbs9spIkSVNMRMwCZjU5NJCZA22cH8CGwOXDvPT0at9s+MADwPMiYrXMXDHMdpuyR1aSJGnqmQMsbLLNafP83YAnAqcO87q3AwOUMbf/FhHrA1sCM4H1htnmoOyRlSRJmnrmAfOblA+0OjEingUcD1wIfG84F83MFRFxAnBQRHymimEd4LPAmlW1mcNpcygmspIkSVNMNXxgYLjnRcRGwC+Au4FdRzgE4FPABsDBwH9XZb8Bvg3sBywZQZtNObRAkiRJRMS6wK+AdYGdMvO2kbSTmcsyc19gY2AH4JmZuVPV7p3tjNFtlz2ykiRJPS4iZgA/A54BvCIz/z7aNjPzdsqYWSJidWBH4LzRtlvPRFaSJKmHVUnmKcCLgDdm5sVjcJmPUxZa+FInGzWRlSRJ6m2fB95A6ZF9XES8s+7Yksw8AyAidqAMFYAyNde6EXFo9fiszLyiqrcnsDPwe+B+4JXArsBRmfnHTgZuIitJktTbtqr2r6+2eouAM6qvXw4c1nD8iGp/M2V5Wyireq1PuelrOmVhhd0z8+SORVwxkZUkSephmbljm/XmAnPbqPdHHu25HVPOWiBJkqSuZCIrSZKkrmQiK0mSpK5kIitJkqSuZCIrSZKkrmQiK0mSpK5kIitJktTDImJ2RBwdEedHxH0RkRGxY0OdiIgTIuKKiBiIiCURcXlEfDgipjVpc1ZEfCMi7oiI+yPitxGxVadjdx5ZSZKk3vZM4CDgOsqiBi9uUmc14PnAb4CFwCOUJW3nAX3AHrWKEbEa8AvgOcDngLuA9wMXRMQLMvP6TgVuIitJktTbFgAbZOZdEbEzcHpjhcxcDmzTUHxCRNwLfDAiPpaZd1Tlb6Ekw2+qW972VMqKX4dRl/SOlkMLJEmSelhm3peZd43w9EVAAOvWlb0FuBU4s+4adwCnAjs3G4owUiaykiRJaktETIuIDSJi04h4E/Bx4B+U4QY1WwMLMjMbTr8EWBvYvFPxOLRAkiRpiomIWcCsJocGMnNgFE3vBPys7nE/8K5q6EHNbOC3Tc5dXO03Bq4aRQz/Zo+sJEnS1DOH0kvauM0ZZbsXA6+iDB/4KrAMWKuhzkzgoSbnLq073hH2yEqSJE0984D5TcoHRtNoZt4JnFs9/ElEHAicExFPz8zbqvIHgelNTp9Rd7wj7JGVJEmaYjJzIDNvaLINdPhSp1F6ZN9YV7aYMrygUa3s1k5d3ERWkiRJI1UbJlA/a8FlwAsiIhrqbgcsocxX2xEmspIkSRpSRDwuIlZvcmjfar+gruw0yg1d/+6ljYgNgF2BMzPz4U7F5RhZSZKkHhcRh1ZfblHtd4+I7SmzHBwHvAE4NCJ+ClwPPBZ4NWUWg19k5nl1zZ1GuSnspIj4HHAnZWWv1YC5nYzbRFaSJElHNDzeu9ovAo6jTLN1CaVXdSNgBfB3yjyyX64/MTOXR8RrgWOBD1OGH1wC7JGZHRtWACaykiRJPS8zG8ezNh7/C/COYbR3N2XYwb6t6o6GY2QlSZLUlUxkJUmS1JVMZCVJktSVTGQlSZLUlUxkJUmS1JVMZCVJktSVTGQlSZLUlUxkJUmSelhE9EXE6RGxKCIejIjbIuLXEfHihnpvi4iTI+KaiMiIuGCCQv43F0SQJEnqbU+j5ITfBBYDs4DdgN9HxGsy85yq3vuAF1BW+Vp/AuJchYmsJElSD8vMU4BT6ssi4mvAP4D9gVoiuztwa7UE7WXjGuQgTGQlSZK0ksx8ICLuoPTO1spumriImjORlSRJmmIiYhZ1SWidgcwcGOSctYHplGEDewLPBj49NhF2hjd7SZIkTT1zgIVNtjlDnPMd4A7gauBjwNeBo8YyyNGyR1aSJGnqmQfMb1I+MMQ5hwMnAJtQxsNOB6YBD3U2tM4xkZUkSZpiquEDA8M850rgSoCIOJkyO8F84C2dja5zHFogSZKklWTmw8CZwJsjYuZExzMYE1lJkiQ1MxMIYO2JDmQwJrKSJEk9LCIe36RsHWBX4KbM/Of4R9Uex8hKkiT1tlMiYilwEXAbsCnwLspNX2+vVYqIHYAdqocbAutGxKHV47My84rxC7kwkZUkSeptJwN7AB8G1qPcJHYxsHtm/q6u3suBwxrOPaLa3wyYyKr7ve51Hxx9I+P8Unj5Pm9l2cMrxveiUg9bf+3pIz73+5fe2MFIBLDb85800SFoAmXmicCJbdSbC8wd63iGw0S2PUv7+vrmjuL8jYFbq6+3Z/x+7msD9/X39+84TtfrWmvPmMaaa68+0WFIU96P550EDy9l1wPeM+I2TLo01h5ZnsOqv8bqAcBnzr12LMIZthnTgk+8YvOJDmNcmMi2ob+//+jRnN/X13cwMKN6eHN/f/9eow5q+NeVJEmaUkxkx0F9ItzX13fwKHt3B1Pf61sznr2/XW3NafbGSuNh1zl7APDj+WfDwO3DPv+ZG+7He7fbjLVm+NbWSZ//3XUsfXh4vZDj7ROvfPq4XavWwzpc4xnjUIbbo9zNfCcYZ6Pt3R3MIL2v/2DV5FaDWPbw8okOQeoJa05bHR5eWjZNGjOmjSx50+Qz0kS8G5nIThFjlSBLkiRNVi6IIEmSpK5kIitJkqSuZCIrSZLUwyKiLyJOj4hFEfFgRNwWEb+OiBfX1XlMRHwgIs6JiMURcV9EXBoR+0XEkHdMR8SBEZERcVmnYzeRlSRJ6m1Po9w39U3gg8CxwBOA30fEq6o6TwW+Un39BeDjwA3A14BvDNZwRGwEHArcPxaBe7OXJElSD8vMU4BT6ssi4muU2Y/2B84BbgOek5l/rat2QkScCOwdEUdm5sImzR8N9FM6T2d1OnZ7ZCVJkrSSzHwAuIMq+czMOxuS2JrTq/2zGg9ExLbAO4GPjlGY9shKkiRNNRExi+Y9oAOZOTDIOWsD04H1gT2BZwOfbnGpjar9nQ1tBWUowncz87LysPNMZCVJkqaeOcBhTcoPB+YOcs53gF2qr5cBXweOGuwCEbFmdZ3rgQUNh/cAtgR2bi/ckTGRlSRJmnrmAfOblA8Mcc7hwAnAJsDulN7ZacBDg9Q/DtgCeE1mrqgVVj27RwNHZ+biYcY9LCaykiRJU0w1fGBgmOdcCVwJEBEnU27Smg+8pbFuRBwAvBs4JDPPbjh8KKVH9wvDDHvYvNlLkiRJK8nMh4EzgTdHxMz6YxGxF3AMcHxmHt1wbDZluMHxwIYRsVlEbAbMANasHq/XqTjtkVXHPXPr/TvQym870Eb7vvHW547r9aRe99LNZ3HbfcuGfd6RH5/HBfu8k/XXnj4GUfWu52yy9kSH0NIjy5M1Vh+bG4Y0qJlAAGsDDwJExBuBbwE/AT7c5JwNgTUpie4xTY4vrMoP7kSAJrJjpK+v72DKXx8Trr+/f+5ExyBJkianiHh8Zt7RULYOsCtwU2b+syrbAfgR8HvgnfXjYussBN7UpPxI4LHAR4BrOhV71yeykylhbLBZf3//XuN90Un885jU/EtfGj+PLE92e/6TRnTu3w94Dz+efzYM3N7hqHrb/Is+N9EhTCqPLM9h1a99fgz3vLEygs+zUyJiKXARZeGDTYF3UW76ejtARDwZOAtI4DRg14YptS7KzH9k5j3AGY0XiIg5wCOZucqx0ej6RBaYMRl7HPv6+g7u6+ubOwGX3hi4tcnXGoJJrDR+RvN6W/bwcnh4adnUU8azw2Gk1+niz5KTKdNlfRhYj3KT2MXA7pn5u6rOU4B1q6+Pb9LGuygrgY2rqZDITkr9/f1Ht641tuydlSRJrWTmicCJLepcQBkvO9Jr7DjSc4diIjuFTYZkWpIkaaw4/ZYkSZK6komsJEmSupKJrCRJkrqSiawkSZK6komsJEmSupKJrCRJUg+LiNkRcXREnB8R90VERsSOg9R9Q0RcGhFLI+LGiDgsItZoqPOKiPhORFwTEQ9ExPUR8Y2I2KhJe/tFxKkRsai67vzhxG4iK0mS1NueCRxEWcnrisEqRcRrKKt2/Qv4UPX1p4AvNlQ9BngpcDplkYVTKCuEXRoRT2ioezDwSuAqYNlwA3ceWUmSpN62ANggM++KiJ0pCWgznwP+DOyUmcsBIuJe4JCI+HJmXlvV+yhwYWauqJ0YEb8Gfge8H5hb1+ZLgRszMyNiYLiB2yMrSZLUwzLzvsy8a6g6EbElsCVwQi2JrXyVkk/uUtfe7+uT2FoZpSd3i4byRZmZI43dRFaSJEmtbF3t++sLM/NW4Oa6401FxFrAWsCdnQzKoQWSJElTTETMAmY1OTSQmQMjaHJ2tV/c5NhiYOMW588B1gROHcG1B2WPrCRJ0tQzB1jYZJszwvZmVvuHmhxbWnd8FRGxA3AY8MPM/N0Ir9+UPbKSJElTzzxgfpPygRG292C1n97k2Iy64yuJiGdRbh67HHj3CK89KBNZSZKkKaYaPjDQwSZrQwpms+rwgtnARY0nRMSmwG+qOF6Xmfd3MB7AoQWSJElq7bJq31dfGBEbU+afvayhfH1KEjudMl3X7WMRlImsJEmShpSZfwWuBt4TEavXHXofsAL4Sa0gIh4L/BJ4IvDazLxurOJyaIEkSVKPi4hDqy9r87zuHhHbU2Y5OK4qOwA4Czg7Ik4Bng18kDK37DV1zX0f2BY4EdgiIurnjr09M8+pu+7rgedVD6cDz62L5XuZuWiouE1kJUmSdETD472r/SLgOIDM/HlEvJkyA8FXgDuAI5ucu1VdG3s3HPsdcE7d412APeseb82jc9JeWF1/UCaykiRJPS4zo816ZwBntKiz2TCuuxewV7v1GzlGVpIkSV3JRFaSJEldyURWkiRJXclEVpIkSV3JRFaSJEldyURWkiRJXclEVpIkSV3JRFaSJElExDYR8YuIuDsilkTE5RGxV93xdSPi+IhYHBFLq+PvGKStJ0bEqRExEBH3RsQZEfGUTsfsggiSJEk9LiJeA5wJXAB8EngYeAawaXV8DcqKXM+jrPR1HbAT8P2IWCMzT6pray3gfGBt4DPAI8BHgAsiYqvMvLtTcZvISpIk9bCIWBeYD3wtM/cfpNouwDbAnnVJ69ci4jTg2Ij4UWYuq8rfD2wOvCAz/1xd41fAXygJ7ac6FbtDCyRJknrbO4BZVAlmRKwdEY1L1r4ESODUhvIfAU8AXlZX9hbg4loSC5CZVwPnAW/tZOAmspIkSVNMRMyKiM2abLOaVH8lcDXw2oi4CbgX+FdEHB0Rq1d1plOGCCxrOPeBav/86rqrAc8F+ptc5xLgGRHxmFF9c3VMZCVJkqaeOcDCJtucJnU3p4yFnV9tuwCnAwcBn6/q/B2YBmzbcO5/VvuNq/3jKEnv4ibXWQwEMHtY38kQHCMrSZI09cyjJKWNBpqUrQWsBxycmcdUZT+tbtp6f0QcCfyAMvRgfkR8kHKz16sp42EBZjbsH2pynaUNdUbNRFaSJGmKycwBmietzTxY7X/YUP59YFdg28z8ZUS8AfgeZfYCKEMQPgR8F1jS0Nb0JteZ0VBn1ExkJUmSetti4D+A2xvKa4/XA8jM30fEU4HnAI8FLufRIQXXVvt/UXpjmw0fmE25YazZsIMRcYysJElSb1tQ7Z/YUL5Jtb+jVpCZyzPzssz8Q2YuodwoBvDb6vgK4Eqgr8l1tgOuzcwHmhwbERNZSZKk3vbjar9PraCafmtf4H7g4mYnRcTjKTeEnZ2ZV9UdOg14YURsXVf3mcDL667VEQ4tkCRJ6mGZuSAiTgIOiYgnAJcCr6Os3HVgZt4LEBEXAhdSbvTaCHgvpVP0vQ1NfhV4N/DLiPg8Zdquj1KGFHyxk7GbyEqSJOndwI3AntX2D2C/zDyhrs4CyoIGTwTuBn4BfDIzb61vKDPvi4gdKUnrJynJ7vnAnMy8q5NBm8hKkiT1uGp52U9W22B19gcGW8K2se7NlBkPxpRjZCVJktSVTGQlSZLUlUxkJUmS1JVMZCVJktSVTGQlSZLUlUxkJUmS1JVMZCVJktSVTGQlSZL0bxFxYERkRFzWUP62iDg5Iq6pjl8wRBuzIuIbEXFHRNwfEb+NiK06HauJrCRJkgCIiI2AQ4H7mxx+H/BG4BbgX0O0sRpl1a+3A18BDgQ2BC6IiKd1Ml5X9pIkSVLN0UA/pbNzVsOx3YFbM3N5Y29tg7cALwbelJlnAETEqcA1wGHAHp0K1h5ZSZIkERHbAu8EPtrseGbelJnL22jqLcCtwJl1594BnArsHBHTOhAuYI+sJEnSlBMRs1i1RxVgIDMHmtQPyjCA72bmZeXhiG0NLMjMbCi/BHgPsDlw1WguUGOPrCRJ0tQzB1jYZJszSP09gC0p42NHazawuEl5rWzjDlwDsEdWkiRpKpoHzG9SPtBYEBFrU8bGHp2ZzRLQ4ZoJPNSkfGnd8Y4wkZUkSZpiquEDA21WPxRYBnyhQ5d/EJjepHxG3fGOMJGVJEnqURExmzLc4JPAhnVjY2cAa0bEZsA9mXn3MJpdTBle0KhWduuIgm3CMbKSJEm9a0NgTeAYVh5Lux2wRfX1QcNs8zLgBbHqHWPbAUuA60YR70rskZUkSepdC4E3NSk/Engs8BHK/K/DcRplCq43AmcARMQGwK7AmZn58EiDbWQiK0mS1KMy8x6qZLNeRMwBHqktaFCV7QDsUD3cEFg3ImqzHJyVmVdUX58GXAycFBGfA+4E3k8ZCTC3k/GPNJFd2tfX19FARmGziQ5AkiSpB7ycsjJXvSOq/c3AFQDVyl+vBY4FPkyZpeASYI/M7NiwAhhhItvf3390J4MYjUmUUEuSJE0Jmbljk7K5tNmjWt0ctm+1jRlv9pIkSVJXMpGVJElSVzKRlSRJUlcykZUkSVJXMpGVJElSVzKRlSRJUlcykZUkSVJXMpGVJEnqYRGxTUQcHxF/i4j7I+LGiPhRRGzepO6LI+LCiHggIm6LiC9FxGMa6syOiKMj4vyIuC8iMiJ2HIvYTWQlSZJ620HAm4Fzgf2BbwA7An+OiC1qlSJiK+A8YAbwUeBbwHuBUxrae2bV5iZUq32NlZEuUStJkqSp4QvAOzJzWa0gIk4BrqQkpHtVxUcBdwE7ZuaSqt4NwDcj4uWZ+duq3gJgg8y8KyJ2Bk4fq8DtkZUkSephmXlRfRJblV0L/BXYAiAi1gFeBZxUS2IrJwFLgLfWnXtfZt415oFjj6wkSdKUExGzgFlNDg1k5kAb5wewIXB5VfQcSt7YX18vM5dFxGXA1iOPduTskZUkSZp65gALm2xz2jx/N+CJwKnV49nVfnGTuouBjUcY56jYIytJkjT1zAPmNykfaHViRDwLOB64EPheVTyz2j/U5JSldcfHlYmsJEnSFFMNHxgY7nkRsRHwC+BuYNfMXFEderDaT29y2oy64+PKRFaSJElExLrAr4B1gZdk5m11h2tDCmavcmIpu3WMw2vKMbKSJEk9LiJmAD8DngH8V2b+vaHKX4BHgL6G89YEtgIuG/soV2UiK0mS1MMiYnXKogYvogwnuLixTmbeQ1kwYfeIWKvu0O7AWsCPxyPWRg4tkCRJ6m2fB95A6ZF9XES8s+7Yksw8o/r6E8BFwAUR8S3Kyl0fA36VmefWNxgRh1Zf1lYG2z0itqdM/3VcpwI3kZUkSeptW1X711dbvUXAGQCZeWlEvBI4BvgicC/wTeCQJm0e0fB477r2TGQlSZI0epm54zDqXgi8pI16MZqY2uUYWUmSJHUlE1lJkiR1JRNZSZIkdSUTWUmSJHUlE1lJkiR1JWctmAB9fX0HU9YlHhf9/f1zx+taAM//zZdG3cbCDsQhafL6/qU3ctt9y4Z93nM2WZvjfnYIa83w46uT1tvmgzBt3D6WRuTuiz7HI8tzXK61xurjcsO9OqBn3wnGO5lssDHjtybxxuN0HUlSt5o2Ax5eOtFRtGSC2Z5Hlies0f7PKiJmA/sD21GWoF0LeFlmXtCk7huAucCWwD+BbwOfycxHGuq9FvgU8DzgAeAXwMcz858N9T4BbFtde0Pg8Myc227sPZvIAjPGu6dyIlQJuyRJ0mCeCRwEXAdcAby4WaWIeA1lcYTfAh8CnkNJVjeoHtfq7Qz8FOiv2l2Hkig/PyK2ycwH65o9Ergd+DPw/4YbeC8nsj2hv7//6ImOQZIkTWoLgA0y864qCT19kHqfoyScO2XmcoCIuBc4JCK+nJnXVvX+B7gGeElmPlzV+xlwKfBeYF5dm0/JzBsiYhZw93AD92YvSZKkHpaZ92XmXUPViYgtKcMJTqglsZWvUvLJXap6jwOeBfy0lsRW17gcuAp4W8O1bxhN7CaykiRJamXrat9fX5iZtwI31x2fXu3rhw/UPAA8LyI6ln86tECSJGmKqf5VP6vJoYHMHBhBk7Or/eImxxbz6M3ltwMDwEsa4lmf0qM7E1gPGLIHuF32yEqSJE09cyizWTZuc0bY3sxq/1CTY0trxzNzBXACsFNEfCYinh4RLwBOBdZsaGvU7JGVJEmaeuYB85uUD4ywvdpQgelNjs1g5aEEtZkMDgb+uyr7DWWqrv2AJSOMYRUmspIkSVNMNXxgoINN1oYUzGbV4QWzgYvqrr0M2LeaI/YZwO2ZeU1E/AC4c4RDG5oykZUkSVIrl1X7Pso0WgBExMbAJnXH/y0zb6eMmSUiVgd2BM7rZFCOkZUkSdKQMvOvwNXAe6qktOZ9wArgJy2a+DiwETD6dezr2CMrSZLU4yLi0OrLLar97hGxPWWWg+OqsgOAs4CzI+IU4NnABylzy15T19aewM7A74H7gVcCuwJHZeYfG667O/BkyjhbgB3qYvlKZt4zVNwmspIkSTqi4fHe1X4RcBxAZv48It4MHAZ8BbiDssRs47nXAOtTbvqaDvwV2D0zT25y3X2Al9Y9flm1AZwMmMhKkiRpcJkZbdY7AzijRZ0/Aju02d6O7dQbjGNkJUmS1JVMZCVJktSVTGQlSZLUlUxkJUmS1JVMZCVJktSVTGQlSZLUlUxkJUmS1JVMZCVJknpYROwYETnI9qy6eqtFxH4RcXlELImIxRFxVkT0tWj/wKqtyzoduwsiSJIkCWAesKCh7Na6r48BPk5Zcet44HHAfsCFEfGCzPxrY4MRsRFwKGWp2o4zkZUkSRLA76qVu1YREasB7wNOy8zd68p/DlwJvJWydG2jo4F+yiiAWR2O16EFkiRJKiJi7Yho1tG5BvAY4PaG8tuq/YNN2toWeCfw0Y4G2RCUJEmSppCImEXzHtCBzBwY5LTvAWsBj0TE+cDHMvNKgMxcFhEXA3tFxB+B31OGFnwaWAx8t+H6AXwF+G5mXlYedp6JrCRJ0tQzh+b/6j8cmNtQtgw4DfgVcCfwXMpY2AsjYpvMvKaqtwdwCmWMbM01wPaZubihzT2ALYGdR/wdtMFEVpIkaeqZB8xvUj7QWJCZFwEX1RWdFRE/o4xtPQzYrSq/F/gL8AfgfGAj4GDgZxHxn5n5LyjDEyhjY49ukuB2lImsJEnSFFMNHxgYxfmXR8S5wCsAqnGz5wHnZuZHavWqOn8FPgZ8oio+lNLL+4WRXr9d3uwlSZKkZm6ijIMF2AF4NnBWfYXMvBa4CngJQETMpgxrOB7YMCI2i4jNgBnAmtXj9ToVoImsJEmSmnkqcEf19YbVfvUm9abx6H/5NwTWpMw5u7Bu2w7Yovr6oE4F6NACSZKkHhYRj8/MOxrKtgdexqOzEdRu+Ho7cG5dvecDzwS+WhUtBN7U5DJHAo8FPlLX1qiZyEqSJPW2UyLiAcoNX3dShhC8p/p6LkBmLoiIc4B9qqm9zgVmAx+irNr1parePcAZjReIiDnAI4MtuDBSJrKSJEm97QzKzAQfA9YB/gn8AJibmTfW1XsjZVqutwOvBR4C/hc4NDOvG8+Aa6ZCIru0r69v7gjO26zDcUiSJHWdzPwy8OU26j0IHFFtw73GjsOPrLWuT2T7+/uPHsl5I0x+JUmSNEk4a4EkSZK6komsJEmSupKJrCRJkrqSiawkSZK6komsJEmSupKJrCRJkrqSiawkSVIPi4jZEXF0RJwfEfdFREbEjoPUfUNEXBoRSyPixog4LCLWaKizU0T8KiJuqerdHBGnRcR/NNRbPyIOiIj/jYg7ImIgIv4YEbu2G3vXzyPbjfr6+g4GZozX9fr7++eO17UA/vf/G7dvraMeWZ4THYLUE9ZYPXjb8zYd8bnqvLsv+txEh9DSei/++PhdbNp0ePihtqtPtp/fCF4nzwQOAq4DrgBe3KxSRLyGsgrYbylL0z4H+BSwQfW4ZktgCfAVyiphGwF7A5dExHaZ+Zeq3ouAzwC/BI4EHgF2AU6NiE9lZsuFF0xkJ8aM8Ugu+/r6fgXMHOvrTBV+QErjx9fb5PLI8uyO38nDS8fnOtOmj891Jo8FwAaZeVdE7AycPki9zwF/BnbKzOUAEXEvcEhEfDkzrwXIzC8CX6w/MSK+BdwC7Ad8sCr+K/D0zFxUV++rwLlVm5+rVhMblIks499DyvgtjzsT+PU4XUuSJHWhzLyvVZ2I2JLS0/reWhJb+SrwCUpP6lCrrd4BPADMqrvuwiaxZEScAbycki9dNVRcJrLFuPSQ1ozz8rjd+X9+SZI0YhExi7qksc5AZg6MoMmtq31/fWFm3hoRN9cdr49hXWBNytCCOcA6wHltXGujan9nq4re7CVJkjT1zAEWNtnmjLC92dV+cZNji4GNm5SfRxkjewXwVuAIYP5QF4mIxwH7Ahdk5h2tgrJHdmIsHade2VnjcA1JkjT5zKN50jgwwvZq99w0uwtuKfCYJuXvp+QiTwX2quqsATzc7AIRsRrwfWBd4MPtBGUiOwH6+/uHGkPSMX19fTuOx3UkSdLkUg0fGOhgk7WbrprdCTej7nh9DJfUvo6IHwF/qx4ONgXFV4CdgN0y88p2gnJogSRJklqpDSmY3eTYbODWoU6uEutzgd2aHY+Iwyg9uAdm5g/bDcpEVpIkSa1cVu376gsjYmNgk7rjQ5lJGTawkoj4ADAX+GJmDmtSXhPZqe2GiQ5AkiR1v8z8K3A18J6IWL3u0PuAFcBPagUR8fjG8yPiScCrKHPW1pe/DfgyZWzsx4Ybl2Nkp7YbJjoASZI0+UXEodWXW1T73SNie8p0XcdVZQcAZwFnR8QpwLMpixuckJnX1DV3UURcRkla7wI2B/ahjKU9pO6a2wInVXXOA3aLWGlhjnMy8/ah4jaRlSRJUuNysHtX+0XAcQCZ+fOIeDNwGOXGrDsoS8s2nvtN4M2URQ3WocwHex5wVGZeXldvS8o8s48HTmwS08sAE1lJkiQNLjPbWqM4M88AzmhR57PAZ9toaz4t5pVtxTGykiRJ6komspIkSepKJrKSJEnqSiayU9tSYLOJDkKSJGksmMhOYdVSuDdMdBySJEljwURWkiRJXclEVpIkqYdFxOyIODoizo+I+yIiI2LHJvXWjYjjI2JxRCyNiMsj4h1N6m0bEV+NiAURsSwicqxiN5GVJEnqbc8EDgI2Aa5oViEi1gDOAfYFfgB8BFgIfD8i9mio/lrg3dXX149FwDUmspIkSb1tAbBBZj4dOHaQOrsA2wDvzsyPZebXMnNn4CfAsRGxZl3drwHrZOYLgLPHMG4TWUmSpF6Wmfdl5l0tqr0ESODUhvIfAU+gLCdba+/2zHyws1E2ZyIrSZKkVqYDjwDLGsofqPbPH99wijUm4qKSJEkaOxExC5jV5NBAZg6MoMm/A9OAbYGL68r/s9pvPII2R80eWUmSpKlnDuVmrMZtzgjb+wFwDzA/Il4ZEZtFxHuA91fHZ44q2hHq5R7ZpX19fXOrrzebwDgkSZI6bR4wv0n5wEgay8zbIuINwPcosxcA3At8CPgusGQk7Y5Wzyay1apXANQltJIkSV2vGj4w0OE2fx8RTwWeAzwWuJxHhxRc28lrtatnE1lJkiQNT2YuBy6rPY6IV1Zf/nYi4nGMrCRJkoYtIh5PWUjh7My8aiJisEd26tt+ogPoBo8sT9ZYPSY6DKkn+HqbfLri9zFtetnGwwP3wLQZwz5t6cPLxyCY4VtjtdVgjeH9TiPi0OrLLar97hGxPWWWg+OqOhcCFwLXARsB76V0ir63oa0nA7tXD7dtaP/yzPzZsIIbgons1HfzRAcgSdKoPfzQ+F1r2gx4eOmwT1tjtcnxj+4R/mFyRMPjvav9IuC46usFwFuBJwJ3A78APpmZtzac+5Qm7dUefxcwkVXbrp7oACRJ0uSWmS2z38zcH9i/jXoXAOPSzT85/nTQmKmfnUGSJGkqMZGVJElSVzKRlSRJUlcykZUkSVJXMpGVJElSVzKRlSRJUlcykZUkSVJXMpGVJEnqYRExOyKOjojzI+K+iMiI2LFJvf0i4tSIWFTVmd+i3XdExCURcX9E/CsifhcR29YdXysiDo+IX1fHMyL2Gk7sJrKSJEm97ZnAQcAmwBVD1DsYeCVwFbBsqAYj4kjKKl5/oSyicDhwPWVp25oNgE8BWwKXjSRwV/aSJEnqbQuADTLzrojYGTh9kHovBW7MzIyIgcEai4gXA/8N7JKZg7UFsBjYODMXR8RWwJ+HG7iJbAf09fUdDMyY6DgG09/fP3c8r3fYLq8bdRvf5poORNK+Ea5LLWkEfL1pJO6+6HMTHcKg1tvmgwDc/X/HTXAkI5OZ97VZb1GbTe4P/F9mnh4RqwGPycwlTdp7iJLMjpiJbGfMGO9kUZIkaTARMQuY1eTQQGYOjPHlXwH8KCKOAj4ErBURi4BPZOb3O3khE9ku1m5PsEl2a48sT3uJpHHw+d9dx9KHk0+88ukTHYo0qKUPL2eN1dq/jajWE1vrmZ1w02YAzAEOa3L0cGDuWF06ItYD1gfeDiynjL39F/AB4OSIeKDFcINhMZHtbkP2BE/2IQ+SJGnMzAPmNykfGOPrrlXt1wdemJl/AoiI04HrKDd3mciqLQ55kCSpB1XDBwYm4NIPVvuFtSS2iuehiDgN2D8i1mo2ZnYknH5LkiRJnfIv4CHg9ibHbgcCWLdTF7NHtlja19c3dxTnb9ahOCRJkrpWZq6IiMuAJzY5vAll3Oy/OnU9E1mgv7//6NGcP8okWJIkaSr5MfC5iHhVZp4DEBHrAG8FLsrMB4c8exhMZCVJknpcRBxafblFtd89IranTNd1XFXn9cDzquPTgefWnfe9unlmvwbsC/wkIr4I3A3sQ5kO7JCG636wKq+t+PX6iNgEIDOPbBW3iawkSZKOaHi8d7VfBNRWetgF2LOuztbVBnBhVZfMfCAiXgYcS5lHdiZl9bBXZuYfGq7zceDJdY/fXG0AJrKSJEkaWma2nEw9M/cC9mqzvduA3duot1k77Q3GRLYzRnuz2EhtNgHXlCRJmhRMZDtgtDeLjZQ3mUmSpF7mPLKSJEnqSiaykiRJ6komspIkSepKJrKSJEnqSiaykiRJ6komspIkSSIitomIX0TE3RGxJCIuj4i96o7vFxGnRsSiiMiImD9IO7tFxG8j4raIeCgiboiI70TEk5vVHw2n35IkSepxEfEa4EzgAuCTwMPAM4BN66odDKwDXMKjS8o28zzgFuCXwL8oK3e9B3hdRDy3WiyhI0xkJUmSelhErAvMB76WmfsPUfWlwI2ZmRExMFilzDywyTXOpCxT+07gc6MKuI6JbHdrtaLYZuMUhyRJ6l7vAGYBnwKIiLWBJZmZ9ZUyc9EorlE7d9Yo2liFiWwXa7WimCt/SZLUmyJiFs2TxoHMHGgoeyVwNfDaiPgssAkwEBEnAJ/IzOUjjOFxlFzzSVRJMnDeSNoajImsJEnS1DMHOKxJ+eHA3IayzSljYecDnwX+DPwXcBAwo2prJK4B1q++vgv4YGaeP8K2mjKRlSRJmnrmURLTRgNNytYC1gMOzsxjqrKfRsRawPsj4sjMvHMEMbwZeCzwLMrY2LVH0MaQTGQlSZKmmGr4wECb1R+s9j9sKP8+sCuwLWUGguHG8Pvqy19FxBnAXyJiSWYeN9y2BuM8spIkSb1tcbW/vaG89ni90V4gMxdSZi3YbbRt1TORlSRJ6m0Lqv0TG8o3qfZ3dOg6M4F1O9QWYCIrSZLU635c7fepFUREAPsC9wMXD6exiHh8k7IXAFvxaNLcEY6RlSRJ6mGZuSAiTgIOiYgnAJcCrwN2Ag7MzHsBIuL1lFW7AKYDz42IQ6vH36ubZ3ZRRJwKXAksAf4D2Bu4Dziik7GbyEqSJOndwI3AntX2D2C/zDyhrs4u1bGarasN4EIeXfTgeMrctDsDj6GMwT0VOKIaK9sxJrKSJEk9LjOXAZ+stsHq7AXs1UZbB3QssBYcIytJkqSuZCIrSZKkrmQiK0mSpK5kIitJkqSuZCIrSZKkrmQiK0mSpK5kIitJkqTulJlubj2/AbOAucCsydSW7dme7dler7Y3mWPrhvZ6ZYvqhyf1tIjYDFgIPCUzb5gsbdme7dme7fVqe5M5tm5or1c4tECSJEldyURWkiRJXclEVpIkSV3JRFaSJEldyURWKgaAw6v9ZGrL9mzP9myvV9vrZFu92F5PcNYCSZIkdSV7ZCVJktSVTGQlSZLUlUxkJUmS1JVMZCVJktSVTGQlSZLUldaY6AAkSZLGUkQ8DtgWWA+4A7g4M5dMbFTqBKffkpqIiLWB9TLzxg61tynwlMz8/RB1tgT2A2YB52bmSRGxBnAUsBuwLtAPHJKZfxzGtVcHNgc2BmYCDwK3Atdl5vKRfUdjJyICeCbVB05mXteBNqcDa2fmnRMdW0RsCGzFqr+PyzPzttHEN1oR8TRgF2BrVo3vz8BPO/H7GK2ImJ6ZDw1xfCbw+OG+fsfiuVe1Ows4EDgpM68e5rmT+vU72Z7PEbEHsGlmfqZ6vBrwOeD9wDQggATuB+Zm5hfGO8ahVM/BTSkdjQvTJK21zHRzc2vYgE8Ay8erPWBLYAmwjDIZ9nJgLvBZ4Bbgu8CpwN3AA8Bz2rjmU4D5de0tB1bUfX1P1e5T2/wengGs1VC2OfA9ygfXUuAm4JvAJm209/+AfRvKPgzcXhfjcuAfwC5ttPcK4Czg98CnKB9aj61+bo9UbS1ss62Oxladvx1wQV0sKxq25cDvgBe22d6aTcoeBxwBXARcDfxv9dxbu0VbqwNfAh6uYrkR+FMVz5+qxyuq418BVmszxg2APYG3AtOrsmnAPsCJwEnAHGBWm+3tVv3Ma8/fbwEbDlJvqNdbx3+/LeJ+ctXe64dxTsdev8AhwPNH+32M5fN5kGusDbwaeDvl9f2YNs65DDi27vH/VPGcAryhivstwK+rGPdto81Ov/cFZeGDhcDfgD2q8tcBN9T9ju8CPt7J39tU3CY8ADe3ybgx/onsj4FrgMdXb3Lfrj6oLgLWrav3JMq/xX7Q4nrPoyS9/wK+DuwNvAZ4WbXfG/hGdfxu4HltfA/LgXfUPX52de4y4JfACcA51Qfbra3e0IGLga/UPf5I9YFzMSWpeBvwceDKqs03DNHWiyhJ1mLgiirW4ymJzmXVh8ax1YfEI8D24xVbdf7LgYeAa4GDq8dbUJKVLarH/109B5YCLxvB72NTYFEV59+As6vr1R7PGqKtT1XfxxHA7EHqzK6OPwJ8qo34nl49V2uJzZWURPv86vEAcF/19SJKL1qrn2Hte/kc8H3KH3X/bPx90jqR7fTv99IW21+q9q+vHi8Yz9cvjyaWVwOH0uYfr+P1fKb81+m5DWUHVs+P+gR+AHh3i7buq69T/Yy+NUjdnwN/G8FrbbTvfe+tvqdLgJ9VP8s9q/1vKX/cHUD5D9xyYO/R/L6m+jbhAbi5jddG+bBudztvqA/Cqr0Th7EtGKo9yl/zB9c9fnb1RveeJnWPAW5pEdt5lA/hDVrU26Cqd24bP78VDW/mZ1cfEo0fQNtT/r34zRbt3QO8r+7xYuD0JvXWAC4E+odo61fVm/7M6vFR1QfoucAadfXWoSRNZ4xXbFW9P1b1preoNx34A2X83nB/Hz+ufu6vaaj3juoD9gtDtLUImNfm6+hLwKI26n2fkmS+Duirvv8/VGU71NV7IyUh/V6L9n4L/B91PdGUXrE/V+fvUlfeKpHt9O93BXAvJUlvtv2RR5P584HzW7TX0ddvde1fUxLLWlL4R+ADlCEYLX/vY/l8bvJcfndVdhGlN/9FwDuBy6vYXzNEW/8C3l99vVbVzs6D1N0PWNrG99vp975LgbPqHn+I8n71k4Z6q1U/gz8P93fUS9uEB+DmNl5b3Rt447/ABttaJbIrqjetu9vYHmjxwfogsFfd4w2q9l/ZpO6+wEMtYltC3Qd1i7rvA+5r8+f3jurr1Sm9Ef89SN0vAze1aO8eqp6Tug+cNw9S9wPAg0O0tRj4SN3jZ1Tt7dmk7uHA7eMVW1XnAdr4F2ZV993AA8P8fUR1jaMGqXsicH2L598+bca3b5vx3Uhdzy3wwirmjzSpe2wbv5M7gA81KZ9JGVLyMI8mMO0ksp38/R5YtXkuDclNdXyz6hpD9uzW1e/o67fhubIt5Y+RxVX5MuAXlD94Wv7rfiyez6yaKF5HSZajod4MSq/yb4do6+fArxueh3MHqfsN4IY2vodOv/fdS10nBWXoyQrgLU3qfrDV86/XN6ffUi+5ndIrsV4b21FttHcjpWdlvVZbG+3dBTyh7vEySi/uQJO661PeCIfyAOXfuO3YgJLIDMdMSm/VFYMcv5KVv59mLgVeBZDl7uG7KB/4zTyF5j+LmnUbjt9V7W9uUvdGyg114xUblD9mntaiTs3TqvrDsRblQ/5Pgxz/E/DEIc7/G/DW6kaTQVXH3wZc1UZM67Pyz/+mat/sBqprKOMhh7I6pWd5JZn5ILAzZbziVyLi023E1tHfb2Z+lvLH081Af0R8IyLqn//ZRkz1xuz1m5mXZOb+lOfD/wN+SOlJPBm4PSJOjojXtmhmzJ7P1Y16T6X0aq70c8vMpZRx1c8foonDgZdFxBeqm3YPBA6KiI9HxCYRMS0iNouIIyhDNL7bbmyVTrz3PVC1UzOj2k9vUncGrd/ve9tEZ9JubuO1AWcAt7ZZt+UYWcrNA3d2oj3KOKtT2mzrVOCiFnW+QfnwHfLmEsrNDwPAN9q47grK2MQ3VNs91PUiN9Q9hHLX91Dt7UTpIf9w9Xh/yhv2W3h0RpU1KDcGLQW+PERbN1DXQ0JJ7H4M/Mcgv4vbxiu2qu4xVb0PMUivF/AYyvjMpcAxbf4+DgKeW233Au8cpO4BwL9aPA+WU8bs7UO5C31DSsJfuyt93+p4yzGjVZt/q/+5AP9VxXxEk7rfAq5q0d6fgJNb1Dm2usbVLV5vHf39NrS9LWWs7T2U8aPTebTHrd0e2Y6+fmno8WxyfAblD5QzKeM0W733dfT5zMo9nmtSetebfu/Ae2jdQ/4a4DZKgv9nyh8qyxu2FZSb6dYYqq26+Dr53vcLyjCJdet+3w9RPqNm1NVbl/JH3qA90G4OLXDroY2SwKwAntRG3XfSehzbfpS7Toe8SaWq+1/Ad4Y4/mpgThvtbFC9sQ15J2v1BviH6g37puoN8mvAvGp/RlW+nIYbyoZos9nwi6bJN+UGhj+20ea7Kb0TNwOnU/59vLz68LuF0jO9gjKucK0h2vkpcGabz4NftPPB0KnYqrbWpPR8rajOv4wyrveMan9ZVb6C8gfSKjMSDPL7aPxgHuymlh8Cl7Zo7zXAX5u0W9/+34DXtflz/m/Kh/PRwEern9m1lD/EPkRJ7p5a1XsY+FyL9uZS/uX+uBb1Dqh9D+P1+x2k/T0pN/4spNw4tpz2E9mOvn5pkcg21F2PJmPzx/L5zKM3Pv202pYAHxuk7lHAjW18H+tQhl2cVsVzHaW39DeUmQzansWBDr/3AS+ofj61oWnLKUMS9qf8x+g7lJ7n2iwarxru86+XNueRlaao6t/Au/LovKCzeXSex8WUN/fTgNMyc0Ub7b20SfGybJjTNiI2oHx4nZ6Zx7XR7maUD5xXUG7eWasuxgXAjzPzpy3aeAHwtMw8tUW9Wmzfy8z54xFbQ3vbUnr9tmKQ30dmXtJmW3s2Kb4/M09rqLcBJdn5UWZ+qo12t2CQ50tm/q2d2Kp2plP+Xb1LVXRT9fUAZVqw2r9fg5JAb5+Z9wzR3iaUG8N+m5lDDm2IiDdRxqoe3qLeZnTw99uk/bWAT1ISlDUpNx2d1ea5HXv9RsQKSm/9D0b2nQzabkeezxFxA6sOv7g8M3duqBfA3ym9928cXfTtG4v3vojYmtK7vDZlmrJvZ+aKiDiIMufthpROi6M7/XubakxkJUljphorug5lcvflVdk6lAToCZQP659l5sMTF+XYioiNKInJwswc9/GOVSJ2VWb+c7yv3UkRsR7lX/tXZualEx2PJgcTWWkSiYj1Kf9WXJjVi7NKBN5YlS/IzPMnMET1gGq1ptpKTUsoMx7cP7FRDS0iZgA7UIZQjGoFN00OI11WtvqvwlspQ0dOyszbq9XVPga8hDL++VLK2Od/jFH4GicmsuopEbEmZZWYLYA7KUtuLmxS74WUcWJ7t2ivI8vKVud8izI2NygTp+9M+bfTOZR/eUL599vPKVMFdWR5yoh4NmW82Elt1t+R8iHRbBnTy4BTh5Ns1yXqz2fl5Olaynya52Sbb1STPLaOf7hW/8p8HeV7PDMzH4qIacAeDW3Oz8yBFm09nnKjylsp/yaul5QxjF/MzB+3E9t4i4gnU1bi2jkzf9ai7rqUm5QW15VtSpmI/iU8mjidA3yp1c+uro0dmaTPv2Fc8wOU8alPbaPu8ynvff21nuaIeA7lPbb23vf9Vr3tnVxWthpm9L88OhPALcB/Ut43n06ZcWMaZSni+4D/zMy/tPpeq7afDDyLcpPvgkHqPKVqs6330zau+Rxg6061NyVN9CBdN7fx2ij/3ryClQfsL6OsVtQ4X+GQ81BWdTq2rCzl5pfllPkdP0b54PsT5Q35AMoUQFsCX6zintPBn0tbq5hRPkTPrOK8l7IU7CnV93lK9fje6vjPqBYnaNHmodXPpv538hDljuPa0pdX0HrlokkbW9XWCxrauoky3dNfqjYvo4wTfaR6vjy7jTY7tnIW5aarm6uf0c+qn9miKrZPA5+pno/Lge8O47m1JeUmlpN4dBnONepeI0soy5y+qI22vtBi+1b1vZ5ZPf78EG39lLoFECiJYu2mm0spNyxdWbXX8obOyf786/T7AWVGgt+y8lKq21P+qHqE8p5YO3YJLeanpYPLyla/h4XAf1Bujj2TkrwuBDZv+J3fRZOFMJq0uRpl+dn6mx//TvN5vlt+dnT699Hr24QH4OY2Xlv14bkU2J3Sw/n0hg+/mXV120lkO7asbPVGfmLd4zdVcX2lSd1f0WKloWH+XNpNZOdRTZwPTBukzrTq+AOU3ruh2vtQ9T0eS0n0tqAs3XgHZRLwGcBreXQ+3ad3Y2xVe2Px4dqxlbMod+1fD2zU8PP6EfCnurK3UWYY+EAb8XXsD72qvdpsCs3uIF/R5PhQ02/dSt3MH5TJ92+iYbo2yopSd1FulOvK10bV3g7D2L411M+uau9T1ff7EcrNaH+rtksoN6hNAx5LucltOYMsSFDXXseWla1+twfWPX5e9bN8b5O6hwN3tfFcri0p+23KDDTv5tE/PA9oqGsiO87bhAfg5jZeGyVx+GKT8ndUHy4XAetVZe0ksh1bVrbJG/kmVVtvalL3I7Reyee3w9iub+eNknIn8qfb/FkfCSxuUefv1CXvdeW1f7/Pqh7PrD40ftiNsVX1xuLDtWMrZ1GSoVWmO6qe08upS/Aoic7lbcTXsT/0qnq/rF4nh1I312bd8c1oc65Wyh+0e1VfT6/Oa9rLR5l14O4W7U32599gU6oNNs1aq/e+xjmCX12d95kmdU+l3Jw1VHv/okPLylbPkX3rHtfeS9/YpO57gSVt/M4WUGZgqC+bRpn/dQVl+EmtvJ3Pjo6/P/fy5spe6iVPovy7cCVZpjbZiTL26Q8R8aQ229uA8m++mtrXzcY3XlvVH8wjlH+51tRW6mk2HdH90PK1uyPwHNpbxWxm8yZWsQ7NV8pq5qaq/lCeTJk4vtHFlB6nLeHfKzd9B3hll8YGZazzv+oe11Yeu61J3VtpvsJPo06unLU6JYlptJyShK5bV3YR5b8ZrbyQkozdkeXT+4tVDPOzbpqtzLyRsoTuS4dqLDNfSxl7uTvw94h4R2OVNmKqWcSj30MtyRvsJqIlrPzabGayP/+WUH5vb25ja2eqp80oE/rX1N5Xm8X8v7ReBewiyhACstzMdTNlSq9mnk/z103N9ZRhDjU7VPuXNan7Ssp/RVp5OnB2fUFmPpyZ76EsSvLBiPhRNT69HTvS+ffnntXqxSlNJbdTktlVZOb/RsTLKGOw/kCZ/7KVTi4reyNlHGzNPcDrKUMOGj2Nod/IoSTON2fmK1rUIyIOpfQCtvJnYJ+I+F71ATpYe4+hrAJ1aYv2FlNuimm0NSUpubuu7B7KuLxujA0e/XD9VvW4/sP1zIa67X64LmLlD/tavNtSxmHW266qP5hLgPdGxIm58vRQH6M8r/9aV7Y+pderlU7+oQdAZv4iIn5DuSnraxGxP2W8+KA3Ug7iZODDEfGNzFwUEWcCcyLizPrnT3XX/H6Un89QJvvzrx94YmY2PteaxfjsVnVYdYnVZdW+2cwWtfG8QzkcuDAivgAcRllW9jsRsYQyvOV2ypK6+1CWlf3MEG19HfhqNYvFP4G9KD/vWRHxeeAsyh9ub6ck7p9qERuUPwSa/vGRmcdGxB2UMbTrU8ZftzIW7889y0RWveQSykwAc5sdzMzLI2J7ysovB7bR3hWU8Wu18+8Fthmk7gsob16DuZi6HqnMfISyAtVKqrt530wZ0zeUS4A3RERUvWFDabcn60DgXEpv2HcpSftiyg0o0yl3uvdResweT+teoh8AB0bEzZSbOh6k3DH+RcoYuPpJ77dk6ERsMscGY/PhejJwWEQ8ULX5MUrC/MyI+FBDm3tSbiQczCcoN11dVyWKD1LGh24BHJUrL1Twakri1kon/9D7tyx3wB8bEd+j3BT0vxHxY8rPuF3HUP6IuDQiTqCMOz8auCEifsajidMbKZ+Tjb2/jSb78+8S4ICIWC8z725RN6ptKP9g5V75uyk9jM3+AHsG5b8Mg8rM/4uInSm9y++jLDH8AOX3dExDbCdRbtAdzAmUP/b3oyT4F1N+7vdRZnqYU9fWOZTZEVr5C+V5//lB4p8fEXdTVjt7URvtjcX7c++a6LENbm7jtVEShDuouxFmkHobUnpCW41z6vSysqu10dZ6lKTkP1rUeyvl7vUnttHmfwKHtfkz3IoyVrG2fGfj2Lpl1fGt22hrTcpNRisa2lgIbNFQ9zTgoC6OLSjjVO+r2voDZaaAx1P+IKq/xtnA9DZinE4Zh1q7uWkRJVnanJJE1bd5Ja2XMd2GktDdQxlDejnNx/C+jLob1IZo75cMsoxnk7qnAhe1U7fJudtS/jVd6/lrdxnYaZTpxm6p+xk2zhBwVqvXWpc8/zai/KH82JH8jJu09xngl21+H7cC32yz3XXo3LKyQcONd5Q/7F5G+eNuOG19oPodbNGi3g5UNza2qDcm78+9ujmPrKRhi4i1KT0wjctS/iWHuXJRtczlSyiJ2d8pH5APTZLYtqtiW7NDsQWwRtbNqxkRq1M+ADcErslhrlg0WVfOiohXA1tm5rwW9WpL6H4jM9vpHRusnTcDmwJnZZO5oYc4LyizSTQuUXtlZrYzhKKxvUn7/Btv1c/i+cB1mXnLRMcDUM3dfCBlLuer2zxnJuUPz9sy864WdZ8EPCUzfzfaWNUeE1lpEurUykoRsXo2LJxQTQb/PMq/TK/INldBiojZWTeBfC+LiOnA2u3+7DQ5dfL10Yu68ec3nIUzhtHmsN4PfC/tLGctkJqIiE0jYofWNSEinhYRB0bEDyPidxFxSbX/YVW+eZvtPD4ivlCNi7uVcoPG/1LGI94TERdFxK5ttrVmNVbvgYhYUt0wQES8h3LX9PmU8WG3RsRxVa9UKzdHxBURcdAwZnYYKsZnRMRaDWWbR8T3IuLWiFgaETdFxDcjYpPxaqvu/FdExFkR8fuI+FRETIuIx0bEqZSbWm6PiIURsUub7T0zIk6MiD9GxM8i4p2D1HtjRDS7IWrEIuIDnW5zMmv1+u3062OMnn8T9nxpI7axeH/pVGyXDrVR7j0IYF5V1nSFribtdvL94JZOvpf2vIke2+DmNhk32lvdZnXKDTQPU8av3UhZ/eh31f7Gqvxh4CsMMQaWDq+sRJkSZgVlDOVxVbufqM4/kXLT266UcXjLaZjUe5A2V1B6h1dQxiP+DngP1dy7I/gZLwfeUff42ZSbRmpjCU+gfBg+QknsNxmPtqrzX1T93hbz6BjW4ymzDlxGuYv4WOCGqs3tW7T3tOp38CDlD5Sbqp/jBcATGup2dEL1dp/PY/haehrlX7k/rJ4zl1T7H1blLcfbdvr77fTrYwyefxP6fGnj59vx95cOxraiiuf8QbY/8ui48fOB89tos9PvBx19L+31bcIDcHObjFs7H/yUO8sfodxBO3uQOrOr449QN3l9k3qn08GVlSjTJX237vFu1ZvvKjddVB+0V7XxM1lRtfMS4Ks8ujzqUuCM6oNrlYnqW7RX/+F/NmWu1ec21Nu++kAf9IaRTrZV1fsVJYGYWT0+qvo+z6WMca3VW4fyB8cZLdr7YfUhWL+K1zspN4Zc31DeVmJCh1drGoPXUMf+0BvBtVslsh19fYzB86/jz5cO/247/v7SwdgOpNyweG7jz786vhltLpxRd06n3w86+l7a69uEB+DmNl4bpaeg3W1Bqw+H6g1rXpvX/hKwaIjjA3RwZSXKX/vNVrfZuUnd9zHESjl19Ro/rNegLNf4g+p6y6sPkO9QpheKdtujJD3LgP8epO6XgZvGo62qzmLqVsiiTCG0AtizSd3DGWLVrLrnyirxUBbhuJ4yfdY2VVm7iWzjnfGjWq2p0xsd/EOvqtux12+nXx9j8Pzr+POlw7/bjr+/dDi+DYH51e/hG9T1YlMWmxhuItvp94OOvpf2+uY8suole1H+Vb+0jbrtrKz0BJqsFDaIKynrcw9muCsrtZrX8m7gcXWPH9ewp+HYkHfiNpNlrtufAz+PiMcCb6J8qO4G7EGZi3PjNpubSXVzyCDHr6QsJzleba3LyvOd1n4+zVZvuhGY1aK99WmyiEVmXh0RL6YsxPHbiHhLi3bqLaFMkdXOnf67Av/fMNruhH2A4zLzk4NVyHLDyyejzLKwD2UYzWD2onOv37F8fXTi+TcWz5dOGvP3l9HIzNuBvSLiq5Q/HK6NiP+hzMM7Ep1+P1hJh99Le46JrHrJjZTJxF/bqmK0t5rK34C3RlkNKYdoKyhDAq4arA6dX1npImC/iDiL0ntzOOVfmm+OiF9Ub/RExFOAD1J6sEYsy4wKJwMnR5lO6e20TrYBnh9l9R4o39NgqzttQOsJ8zvZ1j8pvYU1DwE/ofmKahux8kpLzSwCntvsQGbeHhEvpXyQnUX5N2Y7Or1aU6d18g896OzrdyxeH518/o3F86WTxvX9ZaQy8xLghRGxJ2Ue2vdSxrYO+n49iE6/HwxqFO+lvWuiu4Td3MZro9xAdWebddsZI/sGSo/pJZTepK0o/9KaVe23oixHeQnlX6eD/iuLsi79Usob5smU5Q7/UrV/REPdc4Bft4htc8qbaf2/lj9BeUO8j3KTw+8pq+csA/ra+Jms9O+wDvw+mk1C33QCfcoNcH8cj7aqOj8Fzmzz+/gF8NsWdY6nTLy/xhB1plOWq21rGABlJarltHGDCHAosKJTv7s2fy4LKGNFWw0xqa2wtKBFvY69fjv9+hiD51/Hny8d/t12/P1lHGJei7JK2FKGP7Sg0+8HHX0v7fXNHln1kvOBbSNi08y8qUXdyylLIQ4qM8+KiP+i/Gv3mzT/Kz8oyy2+MTNXWXK2rq2LI+I/Kf9afT2PLg7w/sw8oaH6UZS7mIeK7bqI+A/K0oxrA7/LzHMAImIFZdzaEyg3Kxybmf1DtVf5LmV8Xqe8rEnZssaCqlfiMcD3x6ktKLNEPK1FnVp7M2jxXKGMdXsCZeWti5tVyMyHIuJNwBco83C2Mo/SG7fK99mk7SOBI9tos5MOp9zE+Kcoy8AOtmzreyiT5r+5RXsde/2Oweuj08+/sXi+dMwYvb+MqcxcAhwUEV+kdDQsHMbpnX4/6PR7aU9zQQSpAyJiC2BrVl3N57LM/NtExiZNlIh4DeUPvS0Y+g+9A4b6Q0+SBmMiK0kaU/6hJ2msmMhKTUTEByjTYT11MrYnaXC+3qTe4RK1UnOzKPMNTtb2pCljDJbQnYWvN6kneLOXesZQa6838ZTxbk/qYbNokXj6epPUjImseskFtD9/YLRRt9PtSVPGGCSeF+DrTVIDx8iqZ0TEvQxzJaTMXH282pOmkmoapmElnr7eJA2XPbLqJZ1eCWmyr6wkTaROL6Hr603SKkxk1UsuAQ6IiPUys9USglFt49meNJV0OvH09SZpFQ4tUM+IiI2AZwL9WdaznlTtSVNJRBwNHABs0CrxjIhDgU9n5qAz6fh6k9SMiawkqeNMPCWNBxNZSZIkdSUXRJAkSVJXMpGVJElSVzKRlSRJUlcykZUkSVJX+v8Bn4NO9zX0T/AAAAAASUVORK5CYII=\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for i, cre_line in enumerate(cre_lines):\n", - " labels = labels_cre[cre_line] \n", - " row_colors = vba_clust.get_cluster_colors(labels)\n", - " fig = sns.clustermap(coclustering_matrices[cre_line], cmap = 'Blues', row_colors = row_colors)\n", - " plt.tight_layout()\n", - " fig.savefig(os.path.join(save_dir, 'Clustermap_{}_{}_nc{}.png'.format(cre_line, \n", - " glm_version,\n", - " n_clusters_cre[i])))\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "57a495b6-68ea-480a-8780-70c8912ad73a", - "metadata": {}, - "source": [ - "### Plot cluster dropout scores" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "057e9b13-0a96-465a-b508-a00dfe7302f7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "sort_order = {}\n", - "for i, cre_line in enumerate(cre_lines):\n", - " tmp = np.abs(cre_line_dfs[cre_line])\n", - " labels = labels_cre[cre_line] \n", - " cluster_df = pd.DataFrame(index = tmp.index.values, columns = ['cluster_id', 'cre_line'], \n", - " data = np.transpose([labels+1, [cre_line]* len(labels)]))\n", - " sort_order[cre_line] = vba_clust.get_sorted_cluster_ids(cluster_df)\n", - " #rename cluster after sorting by their size\n", - " cl_dict = {}\n", - " for i, cl in enumerate(sort_order[cre_line]):\n", - " cl_dict[cl]=i+1\n", - " cluster_df = cluster_df.replace(cl_dict)\n", - " \n", - " vba_clust.plot_clusters_row(cluster_df, tmp, cre_line,\n", - " save_dir=save_dir, folder='', suffix=f'_familiar_only_{n_clusters_cre}',\n", - " abbreviate_experience=False)\n" - ] - }, - { - "cell_type": "markdown", - "id": "b73ec873-2e32-4df3-83a8-3a524ae70955", - "metadata": { - "tags": [] - }, - "source": [ - "### Plot area and depth analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "ad8c4715-2c48-493f-b91a-14b57a5a8aa5", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "## add cluster id\n", - "cells_table_sel = cells_table_sel.drop_duplicates('cell_specimen_id')\n", - "cells_table_sel = cells_table_sel.set_index('cell_specimen_id')\n", - "df_meta = cells_table_sel.copy()\n", - "for cre_line in cre_lines:\n", - " labels = labels_cre[cre_line] \n", - " index = cre_line_dfs[cre_line].index.values\n", - " df_meta.at[index, 'cluster_id'] = labels+1\n", - " \n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "abd72741-9047-4d7c-b250-38ab0c6953d1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'NoneType' object has no attribute 'merge'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_16224\\2308278511.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m vba_clust.plot_clusters_stats_pop_avg_rows(df_meta, feature_matrix, multi_session_df=None, cre_line = cre_line,\n\u001b[0;32m 5\u001b[0m \u001b[0mcolumns_to_groupby\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'targeted_structure'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchange\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0momitted\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m sort_order=None, save_dir=save_dir, folder='areas', suffix='', alpha=None)\n\u001b[0m", - "\u001b[1;32mc:\\users\\iryna.yavorska\\documents\\github\\visual_behavior_analysis\\visual_behavior\\dimensionality_reduction\\clustering\\plotting.py\u001b[0m in \u001b[0;36mplot_clusters_stats_pop_avg_rows\u001b[1;34m(cluster_meta, feature_matrix, multi_session_df, cre_line, columns_to_groupby, change, omitted, sort_order, save_dir, folder, suffix, alpha)\u001b[0m\n\u001b[0;32m 1742\u001b[0m \"\"\"\n\u001b[0;32m 1743\u001b[0m \u001b[1;31m# add cluster_id to multi_session_df\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1744\u001b[1;33m \u001b[0mcluster_mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmulti_session_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcluster_meta\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'cluster_id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'cell_specimen_id'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'inner'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1745\u001b[0m \u001b[0mcluster_ids\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcluster_meta\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcluster_meta\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcre_line\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mcre_line\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcluster_id\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1746\u001b[0m \u001b[1;31m# if order to sort clusters is provided, use it\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'merge'" - ] - } - ], - "source": [ - "# this plot doesnt work without multi_session_df\n", - "\n", - "for cre_line in cre_lines:\n", - " vba_clust.plot_clusters_stats_pop_avg_rows(df_meta, feature_matrix, multi_session_df=None, cre_line = cre_line,\n", - " columns_to_groupby=['targeted_structure'], change=False, omitted=True,\n", - " sort_order=None, save_dir=save_dir, folder='areas', suffix='', alpha=None)\n" - ] - }, - { - "cell_type": "markdown", - "id": "82aafe6d-0095-40be-8dda-ef6482d6874a", - "metadata": { - "tags": [] - }, - "source": [ - "## Cluster the data, 6 - 5 - 7" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1ddfb84-aea9-48be-9ee4-5771e7f87ed2", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "e6a5ae15-c0ca-4ec0-92a1-65dfc77ac1d6", - "metadata": {}, - "source": [ - "### Compute coclustering matrix" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "834bfe79-8220-452d-857b-49ff67c799c8", - "metadata": {}, - "outputs": [], - "source": [ - "n_clusters_cre = [6,5,7]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee03e6a7-f473-494f-af0f-12022e8c599d", - "metadata": {}, - "outputs": [], - "source": [ - "filename = os.path.join(save_dir, f'coClustering_matrix_{n_clusters_cre}.pkl')\n", - "if os.path.exists(filename):\n", - " print('loading file...')\n", - " with open(filename, 'rb') as f:\n", - " coclustering_matrices = pickle.load(f)\n", - " f.close()\n", - " print('done.')\n", - "else:\n", - " coclustering_matrices = {}\n", - " for i, cre_line in enumerate(cre_lines):\n", - " X = cre_line_dfs[cre_line].values\n", - " print(n_clusters_cre[i])\n", - " m = vba_clust.get_coClust_matrix(X=X,n_clusters=n_clusters_cre[i], nboot=np.arange(50))\n", - " coclustering_matrices[cre_line]=m\n", - " vba_clust.save_clustering_results(coclustering_matrices, filename)" - ] - }, - { - "cell_type": "markdown", - "id": "8aba909e-1fb1-4a64-ae20-8bb7f30b65a5", - "metadata": {}, - "source": [ - "#### Assign labels based on agglomerative clustering" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc508f2b-e2df-4318-ab00-00da011b0050", - "metadata": {}, - "outputs": [], - "source": [ - "labels_cre={}\n", - "for i,cre_line in enumerate(cre_lines):\n", - " X = coclustering_matrices[cre_line]\n", - " print(n_clusters_cre[i])\n", - " cluster = ac(n_clusters=n_clusters_cre[i], affinity='euclidean', linkage='average')\n", - " labels_cre[cre_line] = cluster.fit_predict(X)" - ] - }, - { - "cell_type": "markdown", - "id": "4af1ad36-2a4a-452b-b240-22e3293973ef", - "metadata": {}, - "source": [ - "#### Plot Coclustering matrix" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d2b129ab-b7af-4260-9456-459abdacd6ed", - "metadata": {}, - "outputs": [], - "source": [ - "for i, cre_line in enumerate(cre_lines):\n", - " labels = labels_cre[cre_line] \n", - " row_colors = vba_clust.get_cluster_colors(labels)\n", - " fig = sns.clustermap(coclustering_matrices[cre_line], cmap = 'Blues', row_colors = row_colors)\n", - " plt.tight_layout()\n", - " fig.savefig(os.path.join(save_dir, 'Clustermap_{}_{}_nc{}.png'.format(cre_line, \n", - " glm_version,\n", - " n_clusters_cre[i])))\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "b21b5d9e-1fe7-456a-b8e1-ac1ec4c66e93", - "metadata": {}, - "source": [ - "### Plot cluster dropout scores" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83343eed-44cd-4bec-86cf-ace008173592", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "sort_order = {}\n", - "for c, cre_line in enumerate(cre_lines):\n", - " feature_matrix = cre_line_dfs[cre_line]\n", - " labels = labels_cre[cre_line] \n", - " cluster_df = pd.DataFrame(index = feature_matrix.index.values, columns = ['cluster_id', 'cre_line'], \n", - " data = np.transpose([labels+1, [cre_line]* len(labels)]))\n", - " #cluster_df = original_cluster_labels[original_cluster_labels.cre_line == cre_line].set_index('cell_specimen_id')\n", - " sort_order[cre_line] = vba_clust.get_sorted_cluster_ids(cluster_df)\n", - " vba_clust.plot_clusters_row(cluster_df, feature_matrix, cre_line,\n", - " sort_order=sort_order, save_dir=save_dir, folder='test', suffix=f'_familiar_only_{n_clusters_cre[c]}',\n", - " abbreviate_experience=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5339e649-5899-4225-a670-2eb85b3b6d9c", - "metadata": {}, - "outputs": [], - "source": [ - "### pot mean of shuffled dropout scores regardless of clusters.\n", - "import visual_behavior_glm.GLM_clustering as glm_clust\n", - "figsize = (10,3.5)\n", - "\n", - "fig, ax = plt.subplots(1,3, figsize = figsize, sharey='row')\n", - "for c, cre_line in enumerate(cre_lines):\n", - " feature_matrix = cre_line_dfs[cre_line]\n", - " mean_df = feature_matrix.mean().unstack()\n", - " features = vba_clust.get_features_for_clustering()\n", - " mean_df = mean_df.loc[features]\n", - " ax[c] = sns.heatmap(mean_df.abs(), cmap='Blues', ax=ax[c], vmin=0, vmax=0.5)\n", - " ax[c].set_title(glm_clust.mapper(cre_line))\n", - "plt.tight_layout()\n", - "utils.save_figure(fig, figsize = figsize, save_dir=save_dir, folder='', fig_title=f'mean_dropout_scores_familiar_only')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b3f6ff5a-9390-4bf6-b971-42e7cd7a9b57", - "metadata": {}, - "outputs": [], - "source": [ - "feature_matrix.mean()" - ] - }, - { - "cell_type": "markdown", - "id": "e348809e-70a0-45ea-88a3-2818bc6dfb7d", - "metadata": {}, - "source": [ - "## Comapring clusters in Familiar and Original data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f57fa1b5-f571-40d3-8bfc-ffb6bcd75b2c", - "metadata": {}, - "outputs": [], - "source": [ - "Compare size of matched clusters\n", - "Find the same cells and see where they move to (which cluster)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aaa155a4-ccdb-4a88-bf8b-0cc099ae9f6f", - "metadata": {}, - "outputs": [], - "source": [ - "familiar_cluster_labels = df_meta.reset_index()[['cell_specimen_id', 'cre_line', 'cluster_id']].copy()\n", - "familiar_cluster_labels" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c283696c-8f54-4c69-b1a7-3cb7368b1f28", - "metadata": {}, - "outputs": [], - "source": [ - "### load original data clusters\n", - "file_dir = '220627_shuffle_test/files'\n", - "filename = 'cluster_labels_Vip_10_Sst_5_Slc17a7_10.h5'\n", - "base_dir = r'//allen/programs/braintv/workgroups/nc-ophys/visual_behavior/platform_paper_plots/figure_4'\n", - "base_dir = os.path.join(base_dir, glm_version)\n", - "original_cluster_labels = pd.read_hdf(os.path.join(base_dir, file_dir, filename))\n", - "original_cluster_labels = original_cluster_labels.reset_index(drop=True)\n", - "original_cluster_labels" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "baf8957e-c757-42fa-8fb3-bd173f810d26", - "metadata": {}, - "outputs": [], - "source": [ - "familiar_cids = familiar_cluster_labels['cell_specimen_id'].values\n", - "original_cids = original_cluster_labels['cell_specimen_id'].values\n", - "print(len(np.intersect1d(familiar_cids, original_cids)))\n", - "overlapping_cids = np.intersect1d(familiar_cids, original_cids)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5aeb74a7-0152-4022-9688-631b1b73e18d", - "metadata": {}, - "outputs": [], - "source": [ - "familiar_selected = familiar_cluster_labels[familiar_cluster_labels['cell_specimen_id'].isin(overlapping_cids)]\n", - "original_selected = original_cluster_labels[original_cluster_labels['cell_specimen_id'].isin(overlapping_cids)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3430d02e-8356-4c1c-abf4-f526a8d711ae", - "metadata": {}, - "outputs": [], - "source": [ - "# save dataframes\n", - "familiar_selected.to_hdf(os.path.join(save_dir, 'fa_matched_labels.h5'), key = 'df')\n", - "original_selected.to_hdf(os.path.join(save_dir, 'og_matched_labels.h5'), key = 'df')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "549e3abd-c988-4189-bb4e-2d9cdcbf3295", - "metadata": {}, - "outputs": [], - "source": [ - "for cre_line in [cre_lines[2]]:\n", - " tmp1 = original_selected[original_selected.cre_line==cre_line]\n", - " tmp2 = familiar_selected[familiar_selected.cre_line==cre_line]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3765dd5a-b54a-4efe-aad5-0ae30c87cb97", - "metadata": {}, - "outputs": [], - "source": [ - "# create one df\n", - "cluster_df = tmp2.join(tmp1.set_index('cell_specimen_id'), on='cell_specimen_id', lsuffix='_familiar')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40a765bb-02dd-4664-9a89-03b81d4d9ed5", - "metadata": {}, - "outputs": [], - "source": [ - "# convert cluster ids to str\n", - "cluster_df['cluster_id'] = cluster_df['cluster_id'].apply(str)\n", - "cluster_df['cluster_id_familiar'] = cluster_df['cluster_id_familiar'].apply(str)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1d73cb6-b3f1-4d5c-b5be-ebb186d75578", - "metadata": {}, - "outputs": [], - "source": [ - "# create variable for between cluster links\n", - "cluster_df['cluster_combos']=cluster_df['cluster_id']+ '_' + cluster_df['cluster_id_familiar']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "503b9b42-6ea8-477e-bf25-45d50ef2c0ad", - "metadata": {}, - "outputs": [], - "source": [ - "# convert df to unique cluster combo count\n", - "cluster_df_unique = cluster_df.groupby('cluster_combos').count()[['labels']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f23d77d0-cdf8-45f6-8704-256a98f8f852", - "metadata": {}, - "outputs": [], - "source": [ - "# create new columns\n", - "cluster_df_unique['familiar_cluster_id']=np.nan\n", - "cluster_df_unique['novel_cluster_id']=np.nan" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4fac43a6-0b9d-4519-bacd-8d1aa1dc5483", - "metadata": {}, - "outputs": [], - "source": [ - "# new cluster id labels\n", - "\n", - "for index in cluster_df_unique.index:\n", - " x = cluster_df[cluster_df['cluster_combos']==index]['cluster_id_familiar'].values[0]\n", - " cluster_df_unique.loc[index, 'familiar_cluster_id'] = x + '_F'\n", - " x = cluster_df[cluster_df['cluster_combos']==index]['cluster_id'].values[0]\n", - " cluster_df_unique.loc[index, 'novel_cluster_id'] = x + '_N'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58e4a5dc-b026-45db-8e3b-211c08c1302b", - "metadata": {}, - "outputs": [], - "source": [ - "cluster_df_unique['fam_index'] = np.nan\n", - "cluster_df_unique['nov_index'] = np.nan\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5721b4b4-2a08-49d9-8b6b-75888ce77c1d", - "metadata": {}, - "outputs": [], - "source": [ - "labels = []\n", - "i = 0\n", - "for index in cluster_df_unique.index.values:\n", - " labels.append(cluster_df_unique.loc[index, 'familiar_cluster_id'])\n", - "for index in cluster_df_unique.index.values:\n", - " labels.append(cluster_df_unique.loc[index, 'novel_cluster_id'])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0cb24f48-0699-4982-980f-d0f68c2a6353", - "metadata": {}, - "outputs": [], - "source": [ - "unique_labels = np.unique(labels)\n", - "label_dict = {}\n", - "i = 0\n", - "for label in unique_labels:\n", - " label_dict[label] = i\n", - " i+=1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "16fb1997-3edb-469f-8c13-eb284a15e185", - "metadata": {}, - "outputs": [], - "source": [ - "for index in cluster_df_unique.index.values:\n", - " cluster_df_unique.loc[index, 'fam_index'] = label_dict[cluster_df_unique.loc[index, 'familiar_cluster_id']]\n", - " cluster_df_unique.loc[index, 'nov_index'] = label_dict[cluster_df_unique.loc[index, 'novel_cluster_id']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "544fb6fb-fd49-499b-9287-334cec24b2ac", - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.graph_objects as go" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "958b1685-96a4-435f-a240-760e56f67fdc", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "fig = go.Figure(data=[go.Sankey(\n", - " node = dict(\n", - " pad = 1,\n", - " thickness = 20,\n", - " line = dict(color = \"black\", width = 0.5),\n", - " label = unique_labels,\n", - " color = \"blue\"\n", - " ),\n", - " link = dict(\n", - " source = cluster_df_unique['fam_index'].values, # \n", - " target = cluster_df_unique['nov_index'].values,\n", - " value = cluster_df_unique['labels'].values\n", - " ))])\n", - "\n", - "fig.update_layout(title_text=\"Familiar to Novel\", font_size=10, width = 500, height = 1000)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d653eec8-6163-4c9c-ad8d-f2e2b260de75", - "metadata": {}, - "outputs": [], - "source": [ - "len(cluster_df_unique['labels'].values)" - ] - }, - { - "cell_type": "markdown", - "id": "ecdb0723-7a12-4022-9d1e-025a22d66644", - "metadata": {}, - "source": [ - "## Summary" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec6d43be-33a8-4fe1-ad1a-d8ea74e6b530", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vba", - "language": "python", - "name": "vba" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}