diff --git a/developements/Developements_december.ipynb b/developements/Developements_december.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2fa530c6b5ba9f8b9485315061157715f60c0f81
--- /dev/null
+++ b/developements/Developements_december.ipynb
@@ -0,0 +1,436 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "51991acb-b6c6-4e4d-8bb3-ce409676ef44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import Inflow\n",
+    "from ResearchProjects import adaptation\n",
+    "from ResearchProjects.adaptation.new_pipelines_blocks import new_pipelines\n",
+    "import pandas as pd, one"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fbaf4c47-5947-4660-8374-6935e902c737",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Pipeline preproc_data requirement graph'}>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pipelines.graph.draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b75d24c5-19a1-462f-8de8-2e5c49723e4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading session details: 100%|███████████████████████████████████████████████████████████| 1/1 [00:04<00:00,  4.38s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "subject                                                                      wm30\n",
+       "users                                                                [wilsonmena]\n",
+       "location                                                 Fernbach - 2p stim setup\n",
+       "procedures                                                   [Two photon Imaging]\n",
+       "lab                                                                      HaissLab\n",
+       "projects                                                             [Adaptation]\n",
+       "type                                                                         None\n",
+       "task_protocol                                                                    \n",
+       "number                                                                          1\n",
+       "start_time                                                    2023-09-19T10:45:30\n",
+       "end_time                                                                     None\n",
+       "narrative                       - Vgat#27 / wm#30_20230919\\r\\n- Folder/Session...\n",
+       "parent_session                                                               None\n",
+       "n_correct_trials                                                             None\n",
+       "n_trials                                                                     None\n",
+       "url                             http://157.99.138.172/sessions/cd87bd90-03c1-4...\n",
+       "extended_qc                                                                    {}\n",
+       "qc                                                                           PASS\n",
+       "wateradmin_session_related                                                     []\n",
+       "data_dataset_session_related    [{'id': '0a34fcdc-5532-4cb1-a1cd-4a59b79d50e5'...\n",
+       "auto_datetime                                          2023-11-03T10:24:23.483558\n",
+       "alias                                                         wm30/2023-09-19/001\n",
+       "u_alias                                                       wm30_2023-09-19_001\n",
+       "path                            \\\\cajal\\cajal_data2\\ONE\\Adaptation\\wm30\\2023-0...\n",
+       "json                            {'whisker_stims': {'Stimulus right': {'0': 'B1...\n",
+       "probe_insertion                                                                []\n",
+       "notes                                                                          []\n",
+       "default_data_repository_path                   //cajal/cajal_data2/ONE/Adaptation\n",
+       "default_data_repository_name                                     Cajal2Adaptation\n",
+       "default_data_repository_pk                   05baa8e4-5eb5-4214-a008-c9e5331004b0\n",
+       "rel_path                                                      wm30\\2023-09-19\\001\n",
+       "admin_url                       http://157.99.138.172:80/admin/actions/session...\n",
+       "date                                                                   2023-09-19\n",
+       "local_path                                      D:\\LOCAL_DATA\\wm30\\2023-09-19\\001\n",
+       "remote_path                     \\\\cajal\\cajal_data2\\ONE\\Adaptation\\wm30\\2023-0...\n",
+       "Name: cd87bd90-03c1-465e-945c-95e9d9318229, dtype: object"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "connector = one.ONE(data_access_mode = \"remote\")\n",
+    "session = connector.search(id = r\"wm30\\2023-09-19\\001\", details = True, no_cache = True).iloc[0]\n",
+    "session"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08812718-deea-4f40-9c2d-9a971f975f83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resp_datas = new_pipelines.responsiveness_df.initial_calculation.generate(session, refresh = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4628a6db-e877-4c9a-82e7-501d0ec267dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['in_target_barrel', 'nontarget_amplitude', 'target_whisker', 'method',\n",
+       "       'fit_time', 'score_time', 'test_true_neg', 'test_false_pos',\n",
+       "       'test_false_neg', 'test_true_pos', 'test_score_acc',\n",
+       "       'test_score_acc_balanced', 'test_score_f1', 'shuffled_fit_time',\n",
+       "       'shuffled_score_time', 'shuffled_test_true_neg',\n",
+       "       'shuffled_test_false_pos', 'shuffled_test_false_neg',\n",
+       "       'shuffled_test_true_pos', 'shuffled_test_score_acc',\n",
+       "       'shuffled_test_score_acc_balanced', 'shuffled_test_score_f1',\n",
+       "       'average_score_acc', 'average_score_acc_balanced', 'average_score_f1',\n",
+       "       'shuffled_average_score_acc', 'shuffled_average_score_acc_balanced',\n",
+       "       'shuffled_average_score_f1', 'sum_true_neg', 'sum_false_pos',\n",
+       "       'sum_false_neg', 'sum_true_pos', 'shuffled_sum_true_neg',\n",
+       "       'shuffled_sum_false_pos', 'shuffled_sum_false_neg',\n",
+       "       'shuffled_sum_true_pos', 'uncorrected_score_acc', 'corrected_score_acc',\n",
+       "       'uncorrected_score_acc_balanced', 'corrected_score_acc_balanced',\n",
+       "       'uncorrected_score_f1', 'corrected_score_f1', 'balance_ratio',\n",
+       "       'pos_label', 'neg_label', 'pos_label_count', 'neg_label_count',\n",
+       "       'number_of_classes', 'default_random_state', 'cross_validator', 'model',\n",
+       "       'params', 'frequency_change'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resp_datas.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5909e88-2c6e-42c0-9860-d24190ee3868",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot( resp_datas.balance_ratio , resp_datas.shuffled_average_score_acc, 'o')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "6503547b-08a3-4aa7-89b7-b3d2fd5ae3d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1012., 1012.,  506., 1012., 1012.,    0., 1012.,  506.,    0.,\n",
+       "        1012.]),\n",
+       " array([0.5       , 0.50918367, 0.51836735, 0.52755102, 0.53673469,\n",
+       "        0.54591837, 0.55510204, 0.56428571, 0.57346939, 0.58265306,\n",
+       "        0.59183673]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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4rVu3LhjTAwAAhuOzkAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYJzwjp4AcD79Hnyzo6cAXNJM/Bn8v8cmdvQUcBFwBwYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgnKAHzGOPPaaQkBDNnj3b3nfq1CllZWWpZ8+euvzyyzVlyhQ1NDT4Pe7IkSOaOHGiLrvsMiUkJGju3Lk6ffp0sKcLAAAMENSA2bVrl5555hldffXVfvvnzJmjN954Q6+++qq2bt2qo0eP6vbbb7ePt7e3a+LEiWptbdX27du1Zs0arV69Wnl5ecGcLgAAMETQAubEiRPKzMzUs88+qyuuuMLe39zcrOeee07Lly/XzTffrGHDhmnVqlXavn27duzYIUnavHmz9u3bpxdffFFDhw7V+PHjtXjxYpWUlKi1tTVYUwYAAIYIWsBkZWVp4sSJSktL89tfXV2ttrY2v/0DBgxQ3759VVlZKUmqrKzU4MGD5XA47DHp6enyer2qra096/O1tLTI6/X6bQAAoGsKD8ZJX375ZdXU1GjXrl3fOObxeBQREaG4uDi//Q6HQx6Pxx7z3/Fy5viZY2dTUFCgRYsWBWD2AACgswv4HZj6+nr97ne/09q1axUVFRXo059Tbm6umpub7a2+vv6iPTcAALi4Ah4w1dXVamxs1LXXXqvw8HCFh4dr69atKioqUnh4uBwOh1pbW9XU1OT3uIaGBjmdTkmS0+n8xruSznx9ZszXRUZGKiYmxm8DAABdU8ADZsyYMdq7d692795tb8OHD1dmZqb9627duqmiosJ+TF1dnY4cOSK32y1Jcrvd2rt3rxobG+0x5eXliomJUUpKSqCnDAAADBPw18D06NFDV111ld++6Oho9ezZ094/ffp05eTkKD4+XjExMZo1a5bcbreuv/56SdLYsWOVkpKiqVOnaunSpfJ4PJo/f76ysrIUGRkZ6CkDAADDBOVFvN/mySefVGhoqKZMmaKWlhalp6fr6aefto+HhYVp06ZNuu++++R2uxUdHa1p06YpPz+/I6YLAAA6mYsSMO+9957f11FRUSopKVFJSck5H5OUlKS33noryDMDAAAm4rOQAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGCfgAVNQUKARI0aoR48eSkhI0OTJk1VXV+c35tSpU8rKylLPnj11+eWXa8qUKWpoaPAbc+TIEU2cOFGXXXaZEhISNHfuXJ0+fTrQ0wUAAAYKeMBs3bpVWVlZ2rFjh8rLy9XW1qaxY8fq5MmT9pg5c+bojTfe0KuvvqqtW7fq6NGjuv322+3j7e3tmjhxolpbW7V9+3atWbNGq1evVl5eXqCnCwAADBQe6BOWlZX5fb169WolJCSourpaN9xwg5qbm/Xcc89p3bp1uvnmmyVJq1at0sCBA7Vjxw5df/312rx5s/bt26d33nlHDodDQ4cO1eLFi/XAAw/ooYceUkRERKCnDQAADBL018A0NzdLkuLj4yVJ1dXVamtrU1pamj1mwIAB6tu3ryorKyVJlZWVGjx4sBwOhz0mPT1dXq9XtbW1Z32elpYWeb1evw0AAHRNQQ0Yn8+n2bNna9SoUbrqqqskSR6PRxEREYqLi/Mb63A45PF47DH/HS9njp85djYFBQWKjY21t8TExABfDQAA6CyCGjBZWVn68MMP9fLLLwfzaSRJubm5am5utrf6+vqgPycAAOgYAX8NzBnZ2dnatGmTtm3bpj59+tj7nU6nWltb1dTU5HcXpqGhQU6n0x6zc+dOv/OdeZfSmTFfFxkZqcjIyABfBQAA6IwCfgfGsixlZ2dr/fr12rJli5KTk/2ODxs2TN26dVNFRYW9r66uTkeOHJHb7ZYkud1u7d27V42NjfaY8vJyxcTEKCUlJdBTBgAAhgn4HZisrCytW7dOr7/+unr06GG/ZiU2Nlbdu3dXbGyspk+frpycHMXHxysmJkazZs2S2+3W9ddfL0kaO3asUlJSNHXqVC1dulQej0fz589XVlYWd1kAAEDgA2bFihWSpJtuuslv/6pVq3TPPfdIkp588kmFhoZqypQpamlpUXp6up5++ml7bFhYmDZt2qT77rtPbrdb0dHRmjZtmvLz8wM9XQAAYKCAB4xlWd86JioqSiUlJSopKTnnmKSkJL311luBnBoAAOgi+CwkAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGKdTB0xJSYn69eunqKgopaamaufOnR09JQAA0Al02oB55ZVXlJOTo4ULF6qmpkZDhgxRenq6GhsbO3pqAACgg3XagFm+fLlmzJihe++9VykpKSotLdVll12m559/vqOnBgAAOlh4R0/gbFpbW1VdXa3c3Fx7X2hoqNLS0lRZWXnWx7S0tKilpcX+urm5WZLk9XoDPj9fy5cBPyeACxOMn+1g48+Oi4PvjYsjWL/PZ85rWdZ5x3XKgPnss8/U3t4uh8Pht9/hcOif//znWR9TUFCgRYsWfWN/YmJiUOYIoGPFFnb0DNBZ8b1xcQT79/mLL75QbGzsOY93yoD5X+Tm5ionJ8f+2ufz6fjx4+rZs6dCQkIC9jxer1eJiYmqr69XTExMwM6LC8dadA6sQ+fAOnQOrMP3Z1mWvvjiC7lcrvOO65QB06tXL4WFhamhocFvf0NDg5xO51kfExkZqcjISL99cXFxwZqiYmJi+ObsJFiLzoF16BxYh86Bdfh+znfn5YxO+SLeiIgIDRs2TBUVFfY+n8+niooKud3uDpwZAADoDDrlHRhJysnJ0bRp0zR8+HBdd911Kiws1MmTJ3Xvvfd29NQAAEAH67QBc+edd+rf//638vLy5PF4NHToUJWVlX3jhb0XW2RkpBYuXPiNf67CxcdadA6sQ+fAOnQOrMPFE2J92/uUAAAAOplO+RoYAACA8yFgAACAcQgYAABgHAIGAAAYh4CRVFJSon79+ikqKkqpqanauXPnOceuXr1aISEhfltUVJTfGMuylJeXp969e6t79+5KS0vTwYMHg30ZxgvkOrS1temBBx7Q4MGDFR0dLZfLpbvvvltHjx69GJditED/PPy33/zmNwoJCVFhYWEQZt61BGMd9u/fr9tuu02xsbGKjo7WiBEjdOTIkWBeRpcQ6LU4ceKEsrOz1adPH3Xv3t3+wGJcmEs+YF555RXl5ORo4cKFqqmp0ZAhQ5Senq7GxsZzPiYmJkbHjh2zt8OHD/sdX7p0qYqKilRaWqqqqipFR0crPT1dp06dCvblGCvQ6/Dll1+qpqZGCxYsUE1NjV577TXV1dXptttuuxiXY6xg/DycsX79eu3YseNb/3twBGcdPvroI40ePVoDBgzQe++9pz179mjBggXnDU4EZy1ycnJUVlamF198Ufv379fs2bOVnZ2tjRs3BvtyuhbrEnfddddZWVlZ9tft7e2Wy+WyCgoKzjp+1apVVmxs7DnP5/P5LKfTaT3++OP2vqamJisyMtJ66aWXAjbvribQ63A2O3futCRZhw8f/j5T7dKCtQ6ffPKJ9cMf/tD68MMPraSkJOvJJ58M0Iy7pmCsw5133mndddddgZzmJSEYazFo0CArPz/fb9+1115r/fGPf/ze872UXNJ3YFpbW1VdXa20tDR7X2hoqNLS0lRZWXnOx504cUJJSUlKTEzUpEmTVFtbax87dOiQPB6P3zljY2OVmpp63nNeyoKxDmfT3NyskJCQoH5GlsmCtQ4+n09Tp07V3LlzNWjQoKDNv6sIxjr4fD69+eab+slPfqL09HQlJCQoNTVVGzZsCOalGC9YPxMjR47Uxo0b9emnn8qyLL377rs6cOCAxo4dG7Rr6You6YD57LPP1N7e/o3/3dfhcMjj8Zz1MVdeeaWef/55vf7663rxxRfl8/k0cuRIffLJJ5JkP+5CznmpC8Y6fN2pU6f0wAMP6Be/+AUfsHYOwVqHJUuWKDw8XPfff39Q599VBGMdGhsbdeLECT322GMaN26cNm/erJ/97Ge6/fbbtXXr1qBfk6mC9TNRXFyslJQU9enTRxERERo3bpxKSkp0ww03BPV6uppO+1ECnZXb7fb7QMmRI0dq4MCBeuaZZ7R48eIOnNml5ULWoa2tTXfccYcsy9KKFSsu9lS7tG9bh+rqaj311FOqqalRSEhIB860a/u2dfD5fJKkSZMmac6cOZKkoUOHavv27SotLdWNN97YIfPuir7Ln03FxcXasWOHNm7cqKSkJG3btk1ZWVlyuVx+d3twfpf0HZhevXopLCxMDQ0NfvsbGhrkdDq/0zm6deuma665Rv/6178kyX7c9znnpSYY63DGmXg5fPiwysvLuftyHsFYh/fff1+NjY3q27evwsPDFR4ersOHD+v3v/+9+vXrF+hL6BKCsQ69evVSeHi4UlJS/MYNHDiQdyGdRzDW4quvvtIf/vAHLV++XLfeequuvvpqZWdn684779SyZcsCfg1d2SUdMBERERo2bJgqKirsfT6fTxUVFX4FfT7t7e3au3evevfuLUlKTk6W0+n0O6fX61VVVdV3PuelJhjrIP3/eDl48KDeeecd9ezZM+Bz70qCsQ5Tp07Vnj17tHv3bntzuVyaO3eu3n777aBch+mCsQ4REREaMWKE6urq/MYdOHBASUlJgZt8FxOMtWhra1NbW5tCQ/3/+g0LC7PvlOE76uhXEXe0l19+2YqMjLRWr15t7du3z5o5c6YVFxdneTwey7Isa+rUqdaDDz5oj1+0aJH19ttvWx999JFVXV1tZWRkWFFRUVZtba095rHHHrPi4uKs119/3dqzZ481adIkKzk52frqq68u+vWZItDr0Nraat12221Wnz59rN27d1vHjh2zt5aWlg65RhME4+fh63gX0rcLxjq89tprVrdu3ayVK1daBw8etIqLi62wsDDr/fffv+jXZ5JgrMWNN95oDRo0yHr33Xetjz/+2Fq1apUVFRVlPf300xf9+kx2yQeMZVlWcXGx1bdvXysiIsK67rrrrB07dtjHbrzxRmvatGn217Nnz7bHOhwOa8KECVZNTY3f+Xw+n7VgwQLL4XBYkZGR1pgxY6y6urqLdTnGCuQ6HDp0yJJ01u3dd9+9iFdlnkD/PHwdAfPdBGMdnnvuOevHP/6xFRUVZQ0ZMsTasGHDxbgU4wV6LY4dO2bdc889lsvlsqKioqwrr7zSeuKJJyyfz3exLqlLCLEsy+rIO0AAAAAX6pJ+DQwAADATAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4/w9NMgKtYR4h3QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(resp_datas.balance_ratio)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "01e7ac97-7b01-4e91-a4e0-c10d929e4b65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x22809383c50>]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot( resp_datas.balance_ratio , resp_datas.shuffled_average_score_acc, 'o')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "43e0484f-ca48-4917-b425-5e396ab0a050",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  38.,  235., 4094., 1660.,  619.,  252.,  106.,   43.,   24.,\n",
+       "          13.]),\n",
+       " array([-0.34696235, -0.22830794, -0.10965353,  0.00900088,  0.12765529,\n",
+       "         0.2463097 ,  0.3649641 ,  0.48361851,  0.60227292,  0.72092733,\n",
+       "         0.83958174]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(resp_datas.corrected_score_acc, alpha = 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2b9ddd54-8704-4119-8e4b-5d9b313c8c73",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  20.,  151.,  646., 3461., 2280.,  317.,  124.,   45.,   26.,\n",
+       "          14.]),\n",
+       " array([0.31636364, 0.37727273, 0.43818182, 0.49909091, 0.56      ,\n",
+       "        0.62090909, 0.68181818, 0.74272727, 0.80363636, 0.86454545,\n",
+       "        0.92545455]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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lRQcOHFDfvn31s5/9TA899FCjD4yPUSNk+Bg1ADRZY9+/L+s5MG0ZAQYhQ4ABgCZrlefAAAAAhAIBBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4HUPdAIA2YGtmqDsI3vj0UHcAIIQ4AwMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABgnqACTmZmpm266ST169FBkZKTuvfdelZaWBtScO3dOycnJ6tWrl7p3766pU6eqoqIioKa8vFxJSUnq2rWrIiMjtXjxYl24cCGgpqCgQDfeeKOcTqcGDRqkrKysph0hAABod4IKMNu2bVNycrJ27dqlvLw8nT9/XhMmTFB1dbVdk5KSoj/84Q965513tG3bNh07dkz33Xefvb6urk5JSUmqra3Vzp079eabbyorK0tLly61a8rKypSUlKTx48eruLhYixYt0iOPPKLc3NxmOGQAAGC6MMuyrKZufPLkSUVGRmrbtm0aO3asqqqq1KdPH61bt07333+/JOngwYMaOnSoCgsLNWbMGG3atEl33323jh07pqioKEnSmjVrlJaWppMnT8rhcCgtLU3Z2dnav3+//VrTp09XZWWlcnJyGtWb3++X2+1WVVWVXC5XUw8RCN7WzFB3cGUYnx7qDgC0gMa+f1/WPTBVVVWSpJ49e0qSioqKdP78eSUkJNg1Q4YM0TXXXKPCwkJJUmFhoYYNG2aHF0lKTEyU3+9XSUmJXfPNfTTUNOzjYmpqauT3+wMmAADQPjU5wNTX12vRokW69dZbdf3110uSfD6fHA6HIiIiAmqjoqLk8/nsmm+Gl4b1Deu+q8bv9+vs2bMX7SczM1Nut9ueYmNjm3poAACgjWtygElOTtb+/fu1fv365uynydLT01VVVWVPR48eDXVLAACghXRsykYLFy7Uxo0btX37dvXt29de7vF4VFtbq8rKyoCzMBUVFfJ4PHbNnj17AvbX8Cmlb9b89SeXKioq5HK51KVLl4v25HQ65XQ6m3I4AADAMEGdgbEsSwsXLtS7776rLVu2qH///gHr4+Pj1alTJ+Xn59vLSktLVV5eLq/XK0nyer3at2+fTpw4Ydfk5eXJ5XIpLi7OrvnmPhpqGvYBAACubEGdgUlOTta6dev0H//xH+rRo4d9z4rb7VaXLl3kdrs1Z84cpaamqmfPnnK5XHr00Ufl9Xo1ZswYSdKECRMUFxenmTNnavny5fL5fMrIyFBycrJ9BmX+/Pl66aWXtGTJEj388MPasmWL3n77bWVnZzfz4QMAABMFdQZm9erVqqqq0rhx4xQdHW1Pb731ll3zwgsv6O6779bUqVM1duxYeTwebdiwwV4fHh6ujRs3Kjw8XF6vVw8++KBmzZqlZcuW2TX9+/dXdna28vLyNGLECP3rv/6rXn31VSUmJjbDIQMAANNd1nNg2jKeA4OQ4TkwrYPnwADtUqs8BwYAACAUCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4wQdYLZv36577rlHMTExCgsL03vvvRew/qGHHlJYWFjANHHixICaL7/8UjNmzJDL5VJERITmzJmjM2fOBNTs3btXt99+uzp37qzY2FgtX748+KMDAADtUtABprq6WiNGjNDLL798yZqJEyfq+PHj9vS73/0uYP2MGTNUUlKivLw8bdy4Udu3b9e8efPs9X6/XxMmTFC/fv1UVFSkZ599Vk888YReeeWVYNsFAADtUMdgN5g0aZImTZr0nTVOp1Mej+ei6z799FPl5OTo448/1qhRoyRJL774oiZPnqznnntOMTExWrt2rWpra/X666/L4XDouuuuU3FxsZ5//vmAoAMAAK5MLXIPTEFBgSIjIzV48GAtWLBAp06dstcVFhYqIiLCDi+SlJCQoA4dOmj37t12zdixY+VwOOyaxMRElZaW6quvvrroa9bU1Mjv9wdMAACgfWr2ADNx4kT9+te/Vn5+vv7lX/5F27Zt06RJk1RXVydJ8vl8ioyMDNimY8eO6tmzp3w+n10TFRUVUNMw31Dz1zIzM+V2u+0pNja2uQ8NAAC0EUFfQvo+06dPt38eNmyYhg8froEDB6qgoEB33nlnc7+cLT09Xampqfa83+8nxAAA0E61+MeoBwwYoN69e+vQoUOSJI/HoxMnTgTUXLhwQV9++aV934zH41FFRUVATcP8pe6tcTqdcrlcARMAAGifWjzAfP755zp16pSio6MlSV6vV5WVlSoqKrJrtmzZovr6eo0ePdqu2b59u86fP2/X5OXlafDgwbrqqqtaumUAANDGBR1gzpw5o+LiYhUXF0uSysrKVFxcrPLycp05c0aLFy/Wrl279Nlnnyk/P19TpkzRoEGDlJiYKEkaOnSoJk6cqLlz52rPnj366KOPtHDhQk2fPl0xMTGSpAceeEAOh0Nz5sxRSUmJ3nrrLa1cuTLgEhEAALhyBR1gPvnkE91www264YYbJEmpqam64YYbtHTpUoWHh2vv3r364Q9/qGuvvVZz5sxRfHy8/vM//1NOp9Pex9q1azVkyBDdeeedmjx5sm677baAZ7y43W5t3rxZZWVlio+P1z/+4z9q6dKlfIQaAABIksIsy7JC3URL8Pv9crvdqqqq4n4YtK6tmaHu4MowPj3UHQBoAY19/+a7kAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwTsdQNwDg8hQeOdVi+/YO6NVi+waAy8EZGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAODzIDmhjWvLBdADQXnAGBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABgn6ACzfft23XPPPYqJiVFYWJjee++9gPWWZWnp0qWKjo5Wly5dlJCQoD//+c8BNV9++aVmzJghl8uliIgIzZkzR2fOnAmo2bt3r26//XZ17txZsbGxWr58efBHBwAA2qWgA0x1dbVGjBihl19++aLrly9frl/+8pdas2aNdu/erW7duikxMVHnzp2za2bMmKGSkhLl5eVp48aN2r59u+bNm2ev9/v9mjBhgvr166eioiI9++yzeuKJJ/TKK6804RABAEB70zHYDSZNmqRJkyZddJ1lWVqxYoUyMjI0ZcoUSdKvf/1rRUVF6b333tP06dP16aefKicnRx9//LFGjRolSXrxxRc1efJkPffcc4qJidHatWtVW1ur119/XQ6HQ9ddd52Ki4v1/PPPBwQdAABwZWrWe2DKysrk8/mUkJBgL3O73Ro9erQKCwslSYWFhYqIiLDDiyQlJCSoQ4cO2r17t10zduxYORwOuyYxMVGlpaX66quvmrNlAABgoKDPwHwXn88nSYqKigpYHhUVZa/z+XyKjIwMbKJjR/Xs2TOgpn///t/aR8O6q6666luvXVNTo5qaGnve7/df5tEAAIC2qt18CikzM1Nut9ueYmNjQ90SAABoIc16Bsbj8UiSKioqFB0dbS+vqKjQyJEj7ZoTJ04EbHfhwgV9+eWX9vYej0cVFRUBNQ3zDTV/LT09Xampqfa83+8nxACtqPDIqaDqvQN6tVAnAK4EzXoGpn///vJ4PMrPz7eX+f1+7d69W16vV5Lk9XpVWVmpoqIiu2bLli2qr6/X6NGj7Zrt27fr/Pnzdk1eXp4GDx580ctHkuR0OuVyuQImAADQPgUdYM6cOaPi4mIVFxdL+v8bd4uLi1VeXq6wsDAtWrRIP//5z/X+++9r3759mjVrlmJiYnTvvfdKkoYOHaqJEydq7ty52rNnjz766CMtXLhQ06dPV0xMjCTpgQcekMPh0Jw5c1RSUqK33npLK1euDDjDAgAArlxBX0L65JNPNH78eHu+IVTMnj1bWVlZWrJkiaqrqzVv3jxVVlbqtttuU05Ojjp37mxvs3btWi1cuFB33nmnOnTooKlTp+qXv/ylvd7tdmvz5s1KTk5WfHy8evfuraVLl/IRagAAIEkKsyzLCnUTLcHv98vtdquqqorLSWhdWzMva/Ng7yVpScHcp9Lq98CMT7+87QG0SY19/243n0ICAABXDgIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABgn6C9zBBCctvTdRgDQXnAGBgAAGIcAAwAAjMMlJKAJXsj70yXXjSnnkhEAtDTOwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACM0zHUDQDfaWtmqDu4qDHlp0LdAgBc0TgDAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACM0+wB5oknnlBYWFjANGTIEHv9uXPnlJycrF69eql79+6aOnWqKioqAvZRXl6upKQkde3aVZGRkVq8eLEuXLjQ3K0CAABDtchXCVx33XX68MMP//IiHf/yMikpKcrOztY777wjt9uthQsX6r777tNHH30kSaqrq1NSUpI8Ho927typ48ePa9asWerUqZOefvrplmgXAAAYpkUCTMeOHeXxeL61vKqqSq+99prWrVunO+64Q5L0xhtvaOjQodq1a5fGjBmjzZs368CBA/rwww8VFRWlkSNH6qmnnlJaWpqeeOIJORyOlmgZAAAYpEXugfnzn/+smJgYDRgwQDNmzFB5ebkkqaioSOfPn1dCQoJdO2TIEF1zzTUqLCyUJBUWFmrYsGGKioqyaxITE+X3+1VSUnLJ16ypqZHf7w+YAABA+9TsAWb06NHKyspSTk6OVq9erbKyMt1+++06ffq0fD6fHA6HIiIiAraJioqSz+eTJPl8voDw0rC+Yd2lZGZmyu1221NsbGzzHhgAAGgzmv0S0qRJk+yfhw8frtGjR6tfv356++231aVLl+Z+OVt6erpSU1Pteb/fT4gBAKCdavGPUUdEROjaa6/VoUOH5PF4VFtbq8rKyoCaiooK+54Zj8fzrU8lNcxf7L6aBk6nUy6XK2ACAADtU4sHmDNnzujw4cOKjo5WfHy8OnXqpPz8fHt9aWmpysvL5fV6JUler1f79u3TiRMn7Jq8vDy5XC7FxcW1dLsAAMAAzX4J6Z/+6Z90zz33qF+/fjp27Jgef/xxhYeH60c/+pHcbrfmzJmj1NRU9ezZUy6XS48++qi8Xq/GjBkjSZowYYLi4uI0c+ZMLV++XD6fTxkZGUpOTpbT6WzudgEAgIGaPcB8/vnn+tGPfqRTp06pT58+uu2227Rr1y716dNHkvTCCy+oQ4cOmjp1qmpqapSYmKhVq1bZ24eHh2vjxo1asGCBvF6vunXrptmzZ2vZsmXN3SoAADBUmGVZVqibaAl+v19ut1tVVVXcD2OyrZmh7uCiCo+cCnULrcI7oFeja4Mdk2D2fVHj0y9vewBtUmPfv1vkQXaAaa6UQAIA7QVf5ggAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4/BdSADM1Ea/6PM78QWUQLPhDAwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgnI6hbgCtZGtmqDsAAKDZEGDQbhUeORXqFgAALYRLSAAAwDicgQGA1mLipdzx6aHuALgozsAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIzTpp8D8/LLL+vZZ5+Vz+fTiBEj9OKLL+rmm28OdVsAcOXg2TVoo9psgHnrrbeUmpqqNWvWaPTo0VqxYoUSExNVWlqqyMjIULcHXBH4OgYAbVWYZVlWqJu4mNGjR+umm27SSy+9JEmqr69XbGysHn30UT322GPfu73f75fb7VZVVZVcLlfzNmfi/0jaAd5M2xfvgF6hbgFoOzhrZGvs+3ebPANTW1uroqIipaf/5Q+0Q4cOSkhIUGFh4UW3qampUU1NjT1fVVUl6f8HotlVn2v+feJ7VZ+t+f4iGMPP3yPgLzY+HuoOgjf2H1tktw3v2993fqVNBpgvvvhCdXV1ioqKClgeFRWlgwcPXnSbzMxMPfnkk99aHhsb2yI9AgBwZVvWons/ffq03G73Jde3yQDTFOnp6UpNTbXn6+vr9eWXX6pXr14KCwu75HZ+v1+xsbE6evRo819qakcYp8ZhnBqHcWocxqlxGKfGMWWcLMvS6dOnFRMT8511bTLA9O7dW+Hh4aqoqAhYXlFRIY/Hc9FtnE6nnE5nwLKIiIhGv6bL5WrTf6BtBePUOIxT4zBOjcM4NQ7j1DgmjNN3nXlp0CafA+NwOBQfH6/8/Hx7WX19vfLz8+X1ekPYGQAAaAva5BkYSUpNTdXs2bM1atQo3XzzzVqxYoWqq6v14x//ONStAQCAEGuzAWbatGk6efKkli5dKp/Pp5EjRyonJ+dbN/ZeLqfTqccff/xbl58QiHFqHMapcRinxmGcGodxapz2Nk5t9jkwAAAAl9Im74EBAAD4LgQYAABgHAIMAAAwDgEGAAAY54oIMC+//LL+5m/+Rp07d9bo0aO1Z8+eS9Zu2LBBo0aNUkREhLp166aRI0fqN7/5TSt2GzrBjNM3rV+/XmFhYbr33ntbtsE2IphxysrKUlhYWMDUuXPnVuw2dIL9faqsrFRycrKio6PldDp17bXX6oMPPmilbkMnmHEaN27ct36fwsLClJSU1Iodh0awv08rVqzQ4MGD1aVLF8XGxiolJUXnzrX/798KZpzOnz+vZcuWaeDAgercubNGjBihnJycVuz2Mlnt3Pr16y2Hw2G9/vrrVklJiTV37lwrIiLCqqiouGj91q1brQ0bNlgHDhywDh06ZK1YscIKDw+3cnJyWrnz1hXsODUoKyuzrr76auv222+3pkyZ0jrNhlCw4/TGG29YLpfLOn78uD35fL5W7rr1BTtONTU11qhRo6zJkydbO3bssMrKyqyCggKruLi4lTtvXcGO06lTpwJ+l/bv32+Fh4dbb7zxRus23sqCHae1a9daTqfTWrt2rVVWVmbl5uZa0dHRVkpKSit33rqCHaclS5ZYMTExVnZ2tnX48GFr1apVVufOna0//vGPrdx507T7AHPzzTdbycnJ9nxdXZ0VExNjZWZmNnofN9xwg5WRkdES7bUZTRmnCxcuWLfccov16quvWrNnz74iAkyw4/TGG29Ybre7lbprO4Idp9WrV1sDBgywamtrW6vFNuFy/3164YUXrB49elhnzpxpqRbbhGDHKTk52brjjjsClqWmplq33npri/YZasGOU3R0tPXSSy8FLLvvvvusGTNmtGifzaVdX0Kqra1VUVGREhIS7GUdOnRQQkKCCgsLv3d7y7KUn5+v0tJSjR07tiVbDammjtOyZcsUGRmpOXPmtEabIdfUcTpz5oz69eun2NhYTZkyRSUlJa3Rbsg0ZZzef/99eb1eJScnKyoqStdff72efvpp1dXVtVbbre5y/32SpNdee03Tp09Xt27dWqrNkGvKON1yyy0qKiqyL58cOXJEH3zwgSZPntwqPYdCU8appqbmW5e0u3Tpoh07drRor82lzT6Jtzl88cUXqqur+9bTe6OionTw4MFLbldVVaWrr75aNTU1Cg8P16pVq3TXXXe1dLsh05Rx2rFjh1577TUVFxe3QodtQ1PGafDgwXr99dc1fPhwVVVV6bnnntMtt9yikpIS9e3btzXabnVNGacjR45oy5YtmjFjhj744AMdOnRIP/nJT3T+/Hk9/vjjrdF2q2vqv08N9uzZo/379+u1115rqRbbhKaM0wMPPKAvvvhCt912myzL0oULFzR//nz98z//c2u0HBJNGafExEQ9//zzGjt2rAYOHKj8/Hxt2LDBmP84tOszME3Vo0cPFRcX6+OPP9YvfvELpaamqqCgINRttRmnT5/WzJkz9atf/Uq9e/cOdTttmtfr1axZszRy5Ej97d/+rTZs2KA+ffro3/7t30LdWptSX1+vyMhIvfLKK4qPj9e0adP005/+VGvWrAl1a23Wa6+9pmHDhunmm28OdSttTkFBgZ5++mmtWrVKf/zjH7VhwwZlZ2frqaeeCnVrbcrKlSv1gx/8QEOGDJHD4dDChQv14x//WB06mBEN2vUZmN69eys8PFwVFRUByysqKuTxeC65XYcOHTRo0CBJ0siRI/Xpp58qMzNT48aNa8l2QybYcTp8+LA+++wz3XPPPfay+vp6SVLHjh1VWlqqgQMHtmzTIdDU36dv6tSpk2644QYdOnSoJVpsE5oyTtHR0erUqZPCw8PtZUOHDpXP51Ntba0cDkeL9hwKl/P7VF1drfXr12vZsmUt2WKb0JRx+tnPfqaZM2fqkUcekSQNGzZM1dXVmjdvnn76058a8wYdjKaMU58+ffTee+/p3LlzOnXqlGJiYvTYY49pwIABrdHyZWt/f4rf4HA4FB8fr/z8fHtZfX298vPz5fV6G72f+vp61dTUtESLbUKw4zRkyBDt27dPxcXF9vTDH/5Q48ePV3FxsWJjY1uz/VbTHL9PdXV12rdvn6Kjo1uqzZBryjjdeuutOnTokB2EJelPf/qToqOj22V4kS7v9+mdd95RTU2NHnzwwZZuM+SaMk5ff/31t0JKQzi22unX/13O71Pnzp119dVX68KFC/r973+vKVOmtHS7zSPENxG3uPXr11tOp9PKysqyDhw4YM2bN8+KiIiwP8o6c+ZM67HHHrPrn376aWvz5s3W4cOHrQMHDljPPfec1bFjR+tXv/pVqA6hVQQ7Tn/tSvkUUrDj9OSTT1q5ubnW4cOHraKiImv69OlW586drZKSklAdQqsIdpzKy8utHj16WAsXLrRKS0utjRs3WpGRkdbPf/7zUB1Cq2jq37vbbrvNmjZtWmu3GzLBjtPjjz9u9ejRw/rd735nHTlyxNq8ebM1cOBA6+///u9DdQitIthx2rVrl/X73//eOnz4sLV9+3brjjvusPr372999dVXITqC4LTrS0iSNG3aNJ08eVJLly6Vz+fTyJEjlZOTY9/oVF5eHpDUq6ur9ZOf/ESff/65unTpoiFDhui3v/2tpk2bFqpDaBXBjtOVKthx+uqrrzR37lz5fD5dddVVio+P186dOxUXFxeqQ2gVwY5TbGyscnNzlZKSouHDh+vqq6/WP/zDPygtLS1Uh9AqmvL3rrS0VDt27NDmzZtD0XJIBDtOGRkZCgsLU0ZGhv73f/9Xffr00T333KNf/OIXoTqEVhHsOJ07d04ZGRk6cuSIunfvrsmTJ+s3v/mNIiIiQnQEwQmzrHZ6Pg0AALRb/JcaAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOP8Hzam6fNP96TNAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.hist(resp_datas.shuffled_average_score_acc, alpha = 0.5)\n",
+    "plt.hist(resp_datas.average_score_acc, alpha = 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b15d054b-afba-486f-a1cf-3a399c9c82b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'sessions' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _, session \u001b[38;5;129;01min\u001b[39;00m \u001b[43msessions\u001b[49m\u001b[38;5;241m.\u001b[39miterrows():\n\u001b[0;32m      2\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m : \n\u001b[0;32m      3\u001b[0m         new_pipelines\u001b[38;5;241m.\u001b[39mvideo_treatment\u001b[38;5;241m.\u001b[39mtdms_export\u001b[38;5;241m.\u001b[39mgenerate(session, extra \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpupil\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'sessions' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "for _, session in sessions.iterrows():\n",
+    "    try :\n",
+    "        new_pipelines.video_treatment.tdms_export.generate(session, extra = \"pupil\")\n",
+    "        new_pipelines.video_treatment.tdms_export.generate(session, extra = \"whiskers\")\n",
+    "    except Exception as e :\n",
+    "        print(f\"A problem occured for session : {session.alias}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "84cd35b5-40d6-4c35-a01e-2e5c1c0f5224",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[38;5;27mINFO    \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;27mgen.video_treatment.tdms_export  \u001b[0m\u001b[0m : \u001b[38;5;27mCould not find or load video_treatment.tdms_export.pupil saved file.\u001b[0m - \u001b[32m2023-12-15 14:59:57\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;27mINFO    \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;27mgen.video_treatment.tdms_export  \u001b[0m\u001b[0m : \u001b[38;5;27mPerforming the computation to generate video_treatment.tdms_export.pupil. Hold tight.\u001b[0m - \u001b[32m2023-12-15 14:59:57\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;27mINFO    \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;27mvideo_export                     \u001b[0m\u001b[0m : \u001b[38;5;27m1 videos out of 300 will be compressed\u001b[0m - \u001b[32m2023-12-15 14:59:57\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;9mERROR   \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;9mvideo_export                     \u001b[0m\u001b[0m : \u001b[38;5;9mError while reading \\\\cajal\\cajal_data2\\ONE\\Adaptation\\wm30\\2023-09-19\\001\\behaviour_imaging\\pupil\\behaviour.video.pupil.00299.tdms and writing to \\\\cajal\\cajal_data2\\ONE\\Adaptation\\wm30\\2023-09-19\\001\\behaviour_imaging\\pupil\\behaviour.video_compressed.pupil.00299.avi.\u001b[0m - \u001b[32m2023-12-15 14:59:57\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;9mERROR   \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;9mvideo_export                     \u001b[0m\u001b[0m : \u001b[38;5;9mFull traceback below :\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\users\\tjostmou\\documents\\python\\__packages__\\researchprojects\\ResearchProjects\\adaptation\\new_pipelines_blocks.py\", line 1454, in tdms_export\n",
+      "    for frame in reader.data_as_frames():\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\users\\tjostmou\\documents\\python\\__packages__\\inflow\\Inflow\\tdms\\content.py\", line 52, in data_as_frames\n",
+      "    framenb = tdms_properties[\"Pre_Trigger_Frames\"] + tdms_properties[\"Post_Trigger_Frames\"]\n",
+      "              ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyError: 'Pre_Trigger_Frames'\n",
+      "\u001b[0m - \u001b[32m2023-12-15 14:59:57\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;27mINFO    \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;27mvideo_export                     \u001b[0m\u001b[0m : \u001b[38;5;27mRegistering new compressed data to alyx\u001b[0m - \u001b[32m2023-12-15 14:59:57\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;27mINFO    \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;27mvideo_export                     \u001b[0m\u001b[0m : \u001b[38;5;27mFinished video export from tdms\u001b[0m - \u001b[32m2023-12-15 14:59:57\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;27mINFO    \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;27mgen.video_treatment.tdms_export  \u001b[0m\u001b[0m : \u001b[38;5;27mSaving the generated video_treatment.tdms_export.pupil output.\u001b[0m - \u001b[32m2023-12-15 14:59:57\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;27mINFO    \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;27mgen.video_treatment.tdms_export  \u001b[0m\u001b[0m : \u001b[38;5;27mCould not find or load video_treatment.tdms_export.whiskers saved file.\u001b[0m - \u001b[32m2023-12-15 14:59:57\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;27mINFO    \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;27mgen.video_treatment.tdms_export  \u001b[0m\u001b[0m : \u001b[38;5;27mPerforming the computation to generate video_treatment.tdms_export.whiskers. Hold tight.\u001b[0m - \u001b[32m2023-12-15 14:59:57\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tjostmou\\documents\\python\\__packages__\\pimage\\pImage\\writers.py:183: UserWarning: No data has been given, video was not created\n",
+      "  warnings.warn(\"No data has been given, video was not created\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[38;5;27mINFO    \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;27mvideo_export                     \u001b[0m\u001b[0m : \u001b[38;5;27m1 videos out of 300 will be compressed\u001b[0m - \u001b[32m2023-12-15 14:59:58\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;9mERROR   \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;9mvideo_export                     \u001b[0m\u001b[0m : \u001b[38;5;9mError while reading \\\\cajal\\cajal_data2\\ONE\\Adaptation\\wm30\\2023-09-19\\001\\behaviour_imaging\\whiskers\\behaviour.video.whiskers.00299.tdms and writing to \\\\cajal\\cajal_data2\\ONE\\Adaptation\\wm30\\2023-09-19\\001\\behaviour_imaging\\whiskers\\behaviour.video_compressed.whiskers.00299.avi.\u001b[0m - \u001b[32m2023-12-15 14:59:58\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;9mERROR   \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;9mvideo_export                     \u001b[0m\u001b[0m : \u001b[38;5;9mFull traceback below :\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\users\\tjostmou\\documents\\python\\__packages__\\researchprojects\\ResearchProjects\\adaptation\\new_pipelines_blocks.py\", line 1454, in tdms_export\n",
+      "    for frame in reader.data_as_frames():\n",
+      "                 ^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"c:\\users\\tjostmou\\documents\\python\\__packages__\\inflow\\Inflow\\tdms\\content.py\", line 52, in data_as_frames\n",
+      "    framenb = tdms_properties[\"Pre_Trigger_Frames\"] + tdms_properties[\"Post_Trigger_Frames\"]\n",
+      "              ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyError: 'Pre_Trigger_Frames'\n",
+      "\u001b[0m - \u001b[32m2023-12-15 14:59:58\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;27mINFO    \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;27mvideo_export                     \u001b[0m\u001b[0m : \u001b[38;5;27mRegistering new compressed data to alyx\u001b[0m - \u001b[32m2023-12-15 14:59:58\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;27mINFO    \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;27mvideo_export                     \u001b[0m\u001b[0m : \u001b[38;5;27mFinished video export from tdms\u001b[0m - \u001b[32m2023-12-15 14:59:58\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;27mINFO    \u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[38;5;27mgen.video_treatment.tdms_export  \u001b[0m\u001b[0m : \u001b[38;5;27mSaving the generated video_treatment.tdms_export.whiskers output.\u001b[0m - \u001b[32m2023-12-15 14:59:58\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tjostmou\\documents\\python\\__packages__\\pimage\\pImage\\writers.py:183: UserWarning: No data has been given, video was not created\n",
+      "  warnings.warn(\"No data has been given, video was not created\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_pipelines.video_treatment.tdms_export.generate(session, extra = \"pupil\")\n",
+    "new_pipelines.video_treatment.tdms_export.generate(session, extra = \"whiskers\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/developements/better_colored_logs.ipynb b/developements/better_colored_logs.ipynb
index 277b29e847abb824cbbdefa3a46d0f20500311ef..d3964423f3c6c4c07438545a1c9f16e995d86928 100644
--- a/developements/better_colored_logs.ipynb
+++ b/developements/better_colored_logs.ipynb
@@ -2,196 +2,63 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "1283655a-9d0d-46c1-909e-48641fb75eb2",
+   "execution_count": 1,
+   "id": "7dd90b33-d4cb-4b08-af94-4ae000dccf28",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import coloredlogs, logging"
+    "import logging, pypelines"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "ecc2377b-fadd-43e5-8830-2bb2995efc27",
+   "execution_count": 2,
+   "id": "3a200d67-aaa7-4964-b5dc-0718089923a0",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "logger = logging.getLogger()\n",
-    "\n",
-    "import sys\n",
-    "ch = logging.StreamHandler(sys.stdout)\n",
-    "#logger.addHandler(ch)\n",
-    "coloredlogs.install( level = \"debug\", logger = logger, stream = sys.stdout)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a0885063-af37-43a8-9a80-54fa8119172c",
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "\u001b[1;31mSignature:\u001b[0m \u001b[0mcoloredlogs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minstall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-       "\u001b[1;31mDocstring:\u001b[0m\n",
-       "Enable colored terminal output for Python's :mod:`logging` module.\n",
-       "\n",
-       ":param level: The default logging level (an integer or a string with a\n",
-       "              level name, defaults to :data:`DEFAULT_LOG_LEVEL`).\n",
-       ":param logger: The logger to which the stream handler should be attached (a\n",
-       "               :class:`~logging.Logger` object, defaults to the root logger).\n",
-       ":param fmt: Set the logging format (a string like those accepted by\n",
-       "            :class:`~logging.Formatter`, defaults to\n",
-       "            :data:`DEFAULT_LOG_FORMAT`).\n",
-       ":param datefmt: Set the date/time format (a string, defaults to\n",
-       "                :data:`DEFAULT_DATE_FORMAT`).\n",
-       ":param style: One of the characters ``%``, ``{`` or ``$`` (defaults to\n",
-       "              :data:`DEFAULT_FORMAT_STYLE`). See the documentation of the\n",
-       "              :class:`python3:logging.Formatter` class in Python 3.2+. On\n",
-       "              older Python versions only ``%`` is supported.\n",
-       ":param milliseconds: :data:`True` to show milliseconds like :mod:`logging`\n",
-       "                     does by default, :data:`False` to hide milliseconds\n",
-       "                     (the default is :data:`False`, see `#16`_).\n",
-       ":param level_styles: A dictionary with custom level styles (defaults to\n",
-       "                     :data:`DEFAULT_LEVEL_STYLES`).\n",
-       ":param field_styles: A dictionary with custom field styles (defaults to\n",
-       "                     :data:`DEFAULT_FIELD_STYLES`).\n",
-       ":param stream: The stream where log messages should be written to (a\n",
-       "               file-like object). This defaults to :data:`None` which\n",
-       "               means :class:`StandardErrorHandler` is used.\n",
-       ":param isatty: :data:`True` to use a :class:`ColoredFormatter`,\n",
-       "               :data:`False` to use a normal :class:`~logging.Formatter`\n",
-       "               (defaults to auto-detection using\n",
-       "               :func:`~humanfriendly.terminal.terminal_supports_colors()`).\n",
-       ":param reconfigure: If :data:`True` (the default) multiple calls to\n",
-       "                    :func:`coloredlogs.install()` will each override\n",
-       "                    the previous configuration.\n",
-       ":param use_chroot: Refer to :class:`HostNameFilter`.\n",
-       ":param programname: Refer to :class:`ProgramNameFilter`.\n",
-       ":param username: Refer to :class:`UserNameFilter`.\n",
-       ":param syslog: If :data:`True` then :func:`.enable_system_logging()` will\n",
-       "               be called without arguments (defaults to :data:`False`). The\n",
-       "               `syslog` argument may also be a number or string, in this\n",
-       "               case it is assumed to be a logging level which is passed on\n",
-       "               to :func:`.enable_system_logging()`.\n",
-       "\n",
-       "The :func:`coloredlogs.install()` function is similar to\n",
-       ":func:`logging.basicConfig()`, both functions take a lot of optional\n",
-       "keyword arguments but try to do the right thing by default:\n",
-       "\n",
-       "1. If `reconfigure` is :data:`True` (it is by default) and an existing\n",
-       "   :class:`~logging.StreamHandler` is found that is connected to either\n",
-       "   :data:`~sys.stdout` or :data:`~sys.stderr` the handler will be removed.\n",
-       "   This means that first calling :func:`logging.basicConfig()` and then\n",
-       "   calling :func:`coloredlogs.install()` will replace the stream handler\n",
-       "   instead of adding a duplicate stream handler. If `reconfigure` is\n",
-       "   :data:`False` and an existing handler is found no further steps are\n",
-       "   taken (to avoid installing a duplicate stream handler).\n",
-       "\n",
-       "2. A :class:`~logging.StreamHandler` is created and connected to the stream\n",
-       "   given by the `stream` keyword argument (:data:`sys.stderr` by\n",
-       "   default). The stream handler's level is set to the value of the `level`\n",
-       "   keyword argument.\n",
-       "\n",
-       "3. A :class:`ColoredFormatter` is created if the `isatty` keyword argument\n",
-       "   allows it (or auto-detection allows it), otherwise a normal\n",
-       "   :class:`~logging.Formatter` is created. The formatter is initialized\n",
-       "   with the `fmt` and `datefmt` keyword arguments (or their computed\n",
-       "   defaults).\n",
-       "\n",
-       "   The environment variable ``$NO_COLOR`` is taken as a hint by\n",
-       "   auto-detection that colors should not be used.\n",
-       "\n",
-       "4. :func:`HostNameFilter.install()`, :func:`ProgramNameFilter.install()`\n",
-       "   and :func:`UserNameFilter.install()` are called to enable the use of\n",
-       "   additional fields in the log format.\n",
-       "\n",
-       "5. If the logger's level is too restrictive it is relaxed (refer to `notes\n",
-       "   about log levels`_ for details).\n",
-       "\n",
-       "6. The formatter is added to the handler and the handler is added to the\n",
-       "   logger.\n",
-       "\n",
-       ".. _#16: https://github.com/xolox/python-coloredlogs/issues/16\n",
-       "\u001b[1;31mFile:\u001b[0m      c:\\users\\tjostmou\\anaconda3\\envs\\inflow\\lib\\site-packages\\coloredlogs\\__init__.py\n",
-       "\u001b[1;31mType:\u001b[0m      function"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[1;38;5;124mCRITICAL\u001b[0m\u001b[0m : \u001b[38;5;19m\u001b[1;38;5;124mmy test logger\u001b[0m                   \u001b[0m : \u001b[1;38;5;124mA test shiet\u001b[0m - \u001b[32m2023-12-15 17:55:45\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;9mERROR\u001b[0m   \u001b[0m : \u001b[38;5;19m\u001b[38;5;9mmy test logger\u001b[0m                   \u001b[0m : \u001b[38;5;9mA test shiet\u001b[0m - \u001b[32m2023-12-15 17:55:45\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;214mWARNING\u001b[0m \u001b[0m : \u001b[38;5;19m\u001b[38;5;214mmy test logger\u001b[0m                   \u001b[0m : \u001b[38;5;214mA test shiet\u001b[0m - \u001b[32m2023-12-15 17:55:45\u001b[0m\n",
+      "\u001b[1m\u001b[1;38;5;195;48;5;57mSTART\u001b[0m   \u001b[0m : \u001b[38;5;19m\u001b[1;38;5;195;48;5;57mmy test logger\u001b[0m                   \u001b[0m : \u001b[1;38;5;195;48;5;57mA test shiet\u001b[0m - \u001b[32m2023-12-15 17:55:45\u001b[0m\n",
+      "\u001b[1m\u001b[1;38;5;195;48;5;57mEND\u001b[0m     \u001b[0m : \u001b[38;5;19m\u001b[1;38;5;195;48;5;57mmy test logger\u001b[0m                   \u001b[0m : \u001b[1;38;5;195;48;5;57mA test shiet\u001b[0m - \u001b[32m2023-12-15 17:55:45\u001b[0m\n",
+      "\u001b[1m\u001b[4;1;38;5;27mHEADER\u001b[0m  \u001b[0m : \u001b[38;5;19m\u001b[4;1;38;5;27mmy test logger\u001b[0m                   \u001b[0m : \u001b[1;38;5;27mA test shiet\u001b[0m - \u001b[32m2023-12-15 17:55:45\u001b[0m\n",
+      "\u001b[1m\u001b[38;5;27mINFO\u001b[0m    \u001b[0m : \u001b[38;5;19m\u001b[38;5;27mmy test logger\u001b[0m                   \u001b[0m : \u001b[38;5;27mThe requirement for this logs text data to work with this javascript and css code, is that each line must be starting with an ansi color/style code.Each line must also finish with a line return and the next line be directly following with an ansi style character. any character in between will mess up the line by line formating and merge lines together.\u001b[0m - \u001b[32m2023-12-15 17:55:45\u001b[0m\n"
+     ]
     }
    ],
    "source": [
-    "coloredlogs.install?"
+    "logger = logging.getLogger(\"my test logger\")\n",
+    "logger.critical(\"A test shiet\")\n",
+    "logger.error(\"A test shiet\")\n",
+    "logger.warning(\"A test shiet\")\n",
+    "logger.start(\"A test shiet\")\n",
+    "logger.close(\"A test shiet\")\n",
+    "logger.header(\"A test shiet\")\n",
+    "logger.info(\"The requirement for this logs text data to work with this javascript and css code, is that each line must be starting with an ansi color/style code.\"\n",
+    "\"Each line must also finish with a line return and the next line be directly following with an ansi style character. any character in between will mess up the line by line formating and merge lines together.\")\n",
+    "logger.load(\"A test shiet\")\n",
+    "logger.save(\"A test shiet\")\n",
+    "logger.debug(\"\"\"Cool formating\n",
+    "Also, line  returns now work in the same line, until the logging line escapes correctly\"\"\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "86938b0e-8bfc-4b85-a140-af37fe503e49",
+   "execution_count": 1,
+   "id": "e23264f2-d62e-4481-944e-3fe9dc1d2524",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from functools import wraps\n",
-    "class ContextFilter(logging.Filter):\n",
-    "    \"\"\"\n",
-    "    This is a filter which injects contextual information into the log.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    def __init__(self, context_msg):\n",
-    "        self.context_msg = context_msg\n",
-    "\n",
-    "    def filter(self, record):\n",
-    "        record.msg = f\"{self.context_msg} {record.msg}\"\n",
-    "        return True\n",
-    "\n",
-    "\n",
-    "class LogContext:\n",
-    "    def __init__(self, context_msg):\n",
-    "        self.context_msg = context_msg\n",
-    "        self.filter_was_added = False\n",
-    "\n",
-    "    def __enter__(self):\n",
-    "        self.root_logger = logging.getLogger()\n",
-    "        for filter in self.root_logger.filters:\n",
-    "            if getattr(filter, \"context_msg\", \"\") == self.context_msg:\n",
-    "                return\n",
-    "\n",
-    "        self.filter_was_added = True\n",
-    "        self.context_filter = ContextFilter(self.context_msg)\n",
-    "        self.root_logger.addFilter(self.context_filter)\n",
-    "\n",
-    "    def __exit__(self, exc_type, exc_val, exc_tb):\n",
-    "        if self.filter_was_added:\n",
-    "            self.root_logger.removeFilter(self.context_filter)\n",
-    "\n",
-    "\n",
-    "class LogSession(LogContext):\n",
-    "    def __init__(self, session):\n",
-    "        context_msg = f\"<{session['alias']}>\"\n",
-    "        super().__init__(context_msg)\n",
-    "\n",
-    "def loggedmethod(func):\n",
-    "    @wraps(func)\n",
-    "    def wrapper(session, *args, **kwargs):\n",
-    "        if kwargs.get(\"no_session_log\", False):\n",
-    "            return func(session, *args, **kwargs)\n",
-    "        with LogSession(session):\n",
-    "            return func(session, *args, **kwargs)\n",
-    "\n",
-    "    return wrapper"
+    "from pypelines.loggs import loggedmethod"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "id": "f52b2027-0297-413e-86fb-c5da30219877",
    "metadata": {},
    "outputs": [],
@@ -210,311 +77,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "d3a7a75a-251a-4a63-a856-390d564d84aa",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2023-12-08 18:49:20\u001b[0m \u001b[35mPC35-HAISSLAB\u001b[0m \u001b[34mpapidou[28076]\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mchienasse\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "--- Logging error ---\n",
-      "Traceback (most recent call last):\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\logging\\__init__.py\", line 1113, in emit\n",
-      "    stream.write(msg + self.terminator)\n",
-      "    ^^^^^^^^^^^^\n",
-      "AttributeError: 'StreamHandler' object has no attribute 'write'\n",
-      "Call stack:\n",
-      "  File \"<frozen runpy>\", line 198, in _run_module_as_main\n",
-      "  File \"<frozen runpy>\", line 88, in _run_code\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n",
-      "    app.launch_new_instance()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1053, in launch_instance\n",
-      "    app.start()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 736, in start\n",
-      "    self.io_loop.start()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 195, in start\n",
-      "    self.asyncio_loop.run_forever()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\asyncio\\base_events.py\", line 607, in run_forever\n",
-      "    self._run_once()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\asyncio\\base_events.py\", line 1922, in _run_once\n",
-      "    handle._run()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\asyncio\\events.py\", line 80, in _run\n",
-      "    self._context.run(self._callback, *self._args)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 516, in dispatch_queue\n",
-      "    await self.process_one()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 505, in process_one\n",
-      "    await dispatch(*args)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 412, in dispatch_shell\n",
-      "    await result\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 740, in execute_request\n",
-      "    reply_content = await reply_content\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 422, in do_execute\n",
-      "    res = shell.run_cell(\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 546, in run_cell\n",
-      "    return super().run_cell(*args, **kwargs)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3024, in run_cell\n",
-      "    result = self._run_cell(\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3079, in _run_cell\n",
-      "    result = runner(coro)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n",
-      "    coro.send(None)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3284, in run_cell_async\n",
-      "    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3466, in run_ast_nodes\n",
-      "    if await self.run_code(code, result, async_=asy):\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3526, in run_code\n",
-      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"C:\\Users\\tjostmou\\AppData\\Local\\Temp\\ipykernel_28076\\143529251.py\", line 2, in <module>\n",
-      "    logger2.warning(\"chienasse\")\n",
-      "Message: 'chienasse'\n",
-      "Arguments: ()\n"
-     ]
-    }
-   ],
-   "source": [
-    "logger2 = logging.getLogger(\"papidou\")\n",
-    "logger2.warning(\"chienasse\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "98ee0e5c-82ae-45a0-bfd9-ad0244f3cb4d",
+   "execution_count": 5,
+   "id": "39193d41-d917-4b8b-b172-d22cd0011f66",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "--- Logging error ---\n",
-      "Traceback (most recent call last):\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\logging\\__init__.py\", line 1113, in emit\n",
-      "    stream.write(msg + self.terminator)\n",
-      "    ^^^^^^^^^^^^\n",
-      "AttributeError: 'StreamHandler' object has no attribute 'write'\n",
-      "Call stack:\n",
-      "  File \"<frozen runpy>\", line 198, in _run_module_as_main\n",
-      "  File \"<frozen runpy>\", line 88, in _run_code\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n",
-      "    app.launch_new_instance()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1053, in launch_instance\n",
-      "    app.start()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 736, in start\n",
-      "    self.io_loop.start()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 195, in start\n",
-      "    self.asyncio_loop.run_forever()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\asyncio\\base_events.py\", line 607, in run_forever\n",
-      "    self._run_once()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\asyncio\\base_events.py\", line 1922, in _run_once\n",
-      "    handle._run()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\asyncio\\events.py\", line 80, in _run\n",
-      "    self._context.run(self._callback, *self._args)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 516, in dispatch_queue\n",
-      "    await self.process_one()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 505, in process_one\n",
-      "    await dispatch(*args)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 412, in dispatch_shell\n",
-      "    await result\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 740, in execute_request\n",
-      "    reply_content = await reply_content\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 422, in do_execute\n",
-      "    res = shell.run_cell(\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 546, in run_cell\n",
-      "    return super().run_cell(*args, **kwargs)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3024, in run_cell\n",
-      "    result = self._run_cell(\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3079, in _run_cell\n",
-      "    result = runner(coro)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n",
-      "    coro.send(None)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3284, in run_cell_async\n",
-      "    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3466, in run_ast_nodes\n",
-      "    if await self.run_code(code, result, async_=asy):\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3526, in run_code\n",
-      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"C:\\Users\\tjostmou\\AppData\\Local\\Temp\\ipykernel_28076\\2338285467.py\", line 1, in <module>\n",
-      "    logger2.warning(\"chienasse\")\n",
-      "Message: 'chienasse'\n",
-      "Arguments: ()\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "logger2.warning(\"chienasse\")"
+    "activate_logging()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "3f307f29-de59-4c7a-91f3-ce36f37928cb",
+   "execution_count": 14,
+   "id": "f8c9439e-15a5-4b08-b804-9299275fa452",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "--- Logging error ---\n",
-      "Traceback (most recent call last):\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\logging\\__init__.py\", line 1113, in emit\n",
-      "    stream.write(msg + self.terminator)\n",
-      "    ^^^^^^^^^^^^\n",
-      "AttributeError: 'StreamHandler' object has no attribute 'write'\n",
-      "Call stack:\n",
-      "  File \"<frozen runpy>\", line 198, in _run_module_as_main\n",
-      "  File \"<frozen runpy>\", line 88, in _run_code\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n",
-      "    app.launch_new_instance()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1053, in launch_instance\n",
-      "    app.start()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 736, in start\n",
-      "    self.io_loop.start()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 195, in start\n",
-      "    self.asyncio_loop.run_forever()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\asyncio\\base_events.py\", line 607, in run_forever\n",
-      "    self._run_once()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\asyncio\\base_events.py\", line 1922, in _run_once\n",
-      "    handle._run()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\asyncio\\events.py\", line 80, in _run\n",
-      "    self._context.run(self._callback, *self._args)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 516, in dispatch_queue\n",
-      "    await self.process_one()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 505, in process_one\n",
-      "    await dispatch(*args)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 412, in dispatch_shell\n",
-      "    await result\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 740, in execute_request\n",
-      "    reply_content = await reply_content\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 422, in do_execute\n",
-      "    res = shell.run_cell(\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 546, in run_cell\n",
-      "    return super().run_cell(*args, **kwargs)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3024, in run_cell\n",
-      "    result = self._run_cell(\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3079, in _run_cell\n",
-      "    result = runner(coro)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n",
-      "    coro.send(None)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3284, in run_cell_async\n",
-      "    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3466, in run_ast_nodes\n",
-      "    if await self.run_code(code, result, async_=asy):\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3526, in run_code\n",
-      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"C:\\Users\\tjostmou\\AppData\\Local\\Temp\\ipykernel_28076\\2081830759.py\", line 1, in <module>\n",
-      "    test({\"alias\":\"wm30/1212-15-12/002\"},\"enculard\")\n",
-      "  File \"C:\\Users\\tjostmou\\AppData\\Local\\Temp\\ipykernel_28076\\3592227974.py\", line 46, in wrapper\n",
-      "    return func(session, *args, **kwargs)\n",
-      "  File \"C:\\Users\\tjostmou\\AppData\\Local\\Temp\\ipykernel_28076\\1664290396.py\", line 4, in test\n",
-      "    logger.warning(moncul)\n",
-      "Message: 'enculard'\n",
-      "Arguments: ()\n",
-      "--- Logging error ---\n",
-      "Traceback (most recent call last):\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\logging\\__init__.py\", line 1113, in emit\n",
-      "    stream.write(msg + self.terminator)\n",
-      "    ^^^^^^^^^^^^\n",
-      "AttributeError: 'StreamHandler' object has no attribute 'write'\n",
-      "Call stack:\n",
-      "  File \"<frozen runpy>\", line 198, in _run_module_as_main\n",
-      "  File \"<frozen runpy>\", line 88, in _run_code\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n",
-      "    app.launch_new_instance()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1053, in launch_instance\n",
-      "    app.start()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 736, in start\n",
-      "    self.io_loop.start()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 195, in start\n",
-      "    self.asyncio_loop.run_forever()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\asyncio\\base_events.py\", line 607, in run_forever\n",
-      "    self._run_once()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\asyncio\\base_events.py\", line 1922, in _run_once\n",
-      "    handle._run()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\asyncio\\events.py\", line 80, in _run\n",
-      "    self._context.run(self._callback, *self._args)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 516, in dispatch_queue\n",
-      "    await self.process_one()\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 505, in process_one\n",
-      "    await dispatch(*args)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 412, in dispatch_shell\n",
-      "    await result\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 740, in execute_request\n",
-      "    reply_content = await reply_content\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 422, in do_execute\n",
-      "    res = shell.run_cell(\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 546, in run_cell\n",
-      "    return super().run_cell(*args, **kwargs)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3024, in run_cell\n",
-      "    result = self._run_cell(\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3079, in _run_cell\n",
-      "    result = runner(coro)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n",
-      "    coro.send(None)\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3284, in run_cell_async\n",
-      "    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3466, in run_ast_nodes\n",
-      "    if await self.run_code(code, result, async_=asy):\n",
-      "  File \"C:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3526, in run_code\n",
-      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"C:\\Users\\tjostmou\\AppData\\Local\\Temp\\ipykernel_28076\\2081830759.py\", line 1, in <module>\n",
-      "    test({\"alias\":\"wm30/1212-15-12/002\"},\"enculard\")\n",
-      "  File \"C:\\Users\\tjostmou\\AppData\\Local\\Temp\\ipykernel_28076\\3592227974.py\", line 46, in wrapper\n",
-      "    return func(session, *args, **kwargs)\n",
-      "  File \"C:\\Users\\tjostmou\\AppData\\Local\\Temp\\ipykernel_28076\\1664290396.py\", line 5, in test\n",
-      "    tourte({\"alias\":\"enfoirax\"},\"jeanfraon\")\n",
-      "  File \"C:\\Users\\tjostmou\\AppData\\Local\\Temp\\ipykernel_28076\\3592227974.py\", line 46, in wrapper\n",
-      "    return func(session, *args, **kwargs)\n",
-      "  File \"C:\\Users\\tjostmou\\AppData\\Local\\Temp\\ipykernel_28076\\1664290396.py\", line 10, in tourte\n",
-      "    logger.warning(anus)\n",
-      "Message: 'jeanfraon'\n",
-      "Arguments: ()\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "test({\"alias\":\"wm30/1212-15-12/002\"},\"enculard\")"
+    "class LogToLocalFile:\n",
+    "    def __init__(self, logger_name, worker_pk):\n",
+    "        self.logger_name = logger_name\n",
+    "        self.worker_pk = worker_pk\n",
+    "\n",
+    "    def __enter__(self):\n",
+    "        self.logger = logging.getLogger(self.logger_name)\n",
+    "        set_logger_formatter(self.logger, self.worker_pk)\n",
+    "        return self.logger\n",
+    "    \n",
+    "    def __exit__(self, exc_type, exc_val, exc_tb):\n",
+    "        remove_logger_handler(self.logger)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "4321b88d-f901-48c0-bfd5-85c764fada3a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "ce07b9ac-8634-426d-a351-c0ae12102344",
+   "execution_count": 15,
+   "id": "30d4537b-ea8c-4606-81ac-94c56905ffe2",
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2023-12-08 18:46:20\u001b[0m \u001b[35mPC35-HAISSLAB\u001b[0m \u001b[34mroot[35428]\u001b[0m \u001b[1;30mCRITICAL\u001b[0m \u001b[1;31mPROUT\u001b[0m\n",
-      "\u001b[32m2023-12-08 18:46:20\u001b[0m \u001b[35mPC35-HAISSLAB\u001b[0m \u001b[34mroot[35428]\u001b[0m \u001b[1;30mDEBUG\u001b[0m \u001b[32mPROUT\u001b[0m\n",
-      "\u001b[32m2023-12-08 18:46:20\u001b[0m \u001b[35mPC35-HAISSLAB\u001b[0m \u001b[34mroot[35428]\u001b[0m \u001b[1;30mINFO\u001b[0m PROUT\n",
-      "\u001b[32m2023-12-08 18:46:20\u001b[0m \u001b[35mPC35-HAISSLAB\u001b[0m \u001b[34mroot[35428]\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mPROUT\u001b[0m\n",
-      "\u001b[32m2023-12-08 18:46:20\u001b[0m \u001b[35mPC35-HAISSLAB\u001b[0m \u001b[34mroot[35428]\u001b[0m \u001b[1;30mERROR\u001b[0m \u001b[31mPROUT\u001b[0m\n"
+      "\u001b[1;30mWARNING   \u001b[0m : \u001b[34mtesis                            \u001b[0m : \u001b[33manus\u001b[0m  -  \u001b[32m2023-12-13 20:56:20\u001b[0m\n"
      ]
     }
    ],
    "source": [
-    "logger.critical(\"PROUT\")\n",
-    "logger.debug(\"PROUT\")\n",
-    "logger.info(\"PROUT\")\n",
-    "logger.warning(\"PROUT\")\n",
-    "logger.error(\"PROUT\")"
+    "with LogToLocalFile(\"tesis\", pk1) as logger : \n",
+    "    logger.warning(\"anus\")"
    ]
   }
  ],
diff --git a/developements/shitafucker.log b/developements/shitafucker.log
deleted file mode 100644
index bfc03e32f3c46c9e8ba9c7786fee2bbc4876a9c3..0000000000000000000000000000000000000000
--- a/developements/shitafucker.log
+++ /dev/null
@@ -1 +0,0 @@
- MEs fesses c'est du poulé
\ No newline at end of file
diff --git a/developements/test.log b/developements/test.log
new file mode 100644
index 0000000000000000000000000000000000000000..87d01b3431c5569b33a1fb9a988d63fa6574962d
--- /dev/null
+++ b/developements/test.log
@@ -0,0 +1,147 @@
+[2023-12-15 14:55:27] PC35-HAISSLAB CRITICAL : my test logger                    : A test shiet
+[2023-12-15 14:55:27] PC35-HAISSLAB ERROR    : my test logger                    : A test shiet
+[2023-12-15 14:55:27] PC35-HAISSLAB WARNING  : my test logger                    : A test shiet
+[2023-12-15 14:55:27] PC35-HAISSLAB OPEN     : my test logger                    : A test shiet
+[2023-12-15 14:55:27] PC35-HAISSLAB CLOSE    : my test logger                    : A test shiet
+[2023-12-15 14:55:27] PC35-HAISSLAB HEADER   : my test logger                    : A test shiet
+[2023-12-15 14:55:27] PC35-HAISSLAB INFO     : my test logger                    : The requirement for this logs text data to work with this javascript and css code, is that each line must be starting with an ansi color/style code.Each line must also finish with a line return and the next line be directly following with an ansi style character. any character in between will mess up the line by line formating and merge lines together.
+[2023-12-15 14:55:27] PC35-HAISSLAB LOAD     : my test logger                    : A test shiet
+[2023-12-15 14:55:27] PC35-HAISSLAB SAVE     : my test logger                    : A test shiet
+[2023-12-15 14:56:56] PC35-HAISSLAB CRITICAL : root                              : Test
+[2023-12-15 14:56:57] PC35-HAISSLAB CRITICAL : my test logger                    : A test shiet
+[2023-12-15 14:56:57] PC35-HAISSLAB ERROR    : my test logger                    : A test shiet
+[2023-12-15 14:56:57] PC35-HAISSLAB WARNING  : my test logger                    : A test shiet
+[2023-12-15 14:56:57] PC35-HAISSLAB OPEN     : my test logger                    : A test shiet
+[2023-12-15 14:56:57] PC35-HAISSLAB CLOSE    : my test logger                    : A test shiet
+[2023-12-15 14:56:57] PC35-HAISSLAB HEADER   : my test logger                    : A test shiet
+[2023-12-15 14:56:57] PC35-HAISSLAB INFO     : my test logger                    : The requirement for this logs text data to work with this javascript and css code, is that each line must be starting with an ansi color/style code.Each line must also finish with a line return and the next line be directly following with an ansi style character. any character in between will mess up the line by line formating and merge lines together.
+[2023-12-15 14:56:57] PC35-HAISSLAB LOAD     : my test logger                    : A test shiet
+[2023-12-15 14:56:57] PC35-HAISSLAB SAVE     : my test logger                    : A test shiet
+[2023-12-15 14:59:57] PC35-HAISSLAB INFO     : gen.video_treatment.tdms_export   : Could not find or load video_treatment.tdms_export.pupil saved file.
+[2023-12-15 14:59:57] PC35-HAISSLAB INFO     : gen.video_treatment.tdms_export   : Performing the computation to generate video_treatment.tdms_export.pupil. Hold tight.
+[2023-12-15 14:59:57] PC35-HAISSLAB INFO     : video_export                      : 1 videos out of 300 will be compressed
+[2023-12-15 14:59:57] PC35-HAISSLAB ERROR    : video_export                      : Error while reading \\cajal\cajal_data2\ONE\Adaptation\wm30\2023-09-19\001\behaviour_imaging\pupil\behaviour.video.pupil.00299.tdms and writing to \\cajal\cajal_data2\ONE\Adaptation\wm30\2023-09-19\001\behaviour_imaging\pupil\behaviour.video_compressed.pupil.00299.avi.
+[2023-12-15 14:59:57] PC35-HAISSLAB ERROR    : video_export                      : Full traceback below :
+Traceback (most recent call last):
+  File "c:\users\tjostmou\documents\python\__packages__\researchprojects\ResearchProjects\adaptation\new_pipelines_blocks.py", line 1454, in tdms_export
+    for frame in reader.data_as_frames():
+                 ^^^^^^^^^^^^^^^^^^^^^^^
+  File "c:\users\tjostmou\documents\python\__packages__\inflow\Inflow\tdms\content.py", line 52, in data_as_frames
+    framenb = tdms_properties["Pre_Trigger_Frames"] + tdms_properties["Post_Trigger_Frames"]
+              ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
+KeyError: 'Pre_Trigger_Frames'
+
+[2023-12-15 14:59:57] PC35-HAISSLAB INFO     : video_export                      : Registering new compressed data to alyx
+[2023-12-15 14:59:57] PC35-HAISSLAB INFO     : video_export                      : Finished video export from tdms
+[2023-12-15 14:59:57] PC35-HAISSLAB INFO     : gen.video_treatment.tdms_export   : Saving the generated video_treatment.tdms_export.pupil output.
+[2023-12-15 14:59:57] PC35-HAISSLAB INFO     : gen.video_treatment.tdms_export   : Could not find or load video_treatment.tdms_export.whiskers saved file.
+[2023-12-15 14:59:57] PC35-HAISSLAB INFO     : gen.video_treatment.tdms_export   : Performing the computation to generate video_treatment.tdms_export.whiskers. Hold tight.
+[2023-12-15 14:59:58] PC35-HAISSLAB INFO     : video_export                      : 1 videos out of 300 will be compressed
+[2023-12-15 14:59:58] PC35-HAISSLAB ERROR    : video_export                      : Error while reading \\cajal\cajal_data2\ONE\Adaptation\wm30\2023-09-19\001\behaviour_imaging\whiskers\behaviour.video.whiskers.00299.tdms and writing to \\cajal\cajal_data2\ONE\Adaptation\wm30\2023-09-19\001\behaviour_imaging\whiskers\behaviour.video_compressed.whiskers.00299.avi.
+[2023-12-15 14:59:58] PC35-HAISSLAB ERROR    : video_export                      : Full traceback below :
+Traceback (most recent call last):
+  File "c:\users\tjostmou\documents\python\__packages__\researchprojects\ResearchProjects\adaptation\new_pipelines_blocks.py", line 1454, in tdms_export
+    for frame in reader.data_as_frames():
+                 ^^^^^^^^^^^^^^^^^^^^^^^
+  File "c:\users\tjostmou\documents\python\__packages__\inflow\Inflow\tdms\content.py", line 52, in data_as_frames
+    framenb = tdms_properties["Pre_Trigger_Frames"] + tdms_properties["Post_Trigger_Frames"]
+              ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
+KeyError: 'Pre_Trigger_Frames'
+
+[2023-12-15 14:59:58] PC35-HAISSLAB INFO     : video_export                      : Registering new compressed data to alyx
+[2023-12-15 14:59:58] PC35-HAISSLAB INFO     : video_export                      : Finished video export from tdms
+[2023-12-15 14:59:58] PC35-HAISSLAB INFO     : gen.video_treatment.tdms_export   : Saving the generated video_treatment.tdms_export.whiskers output.
+[2023-12-15 17:31:26] PC35-HAISSLAB CRITICAL : my test logger                    : A test shiet
+[2023-12-15 17:31:26] PC35-HAISSLAB ERROR    : my test logger                    : A test shiet
+[2023-12-15 17:31:26] PC35-HAISSLAB WARNING  : my test logger                    : A test shiet
+[2023-12-15 17:31:26] PC35-HAISSLAB OPEN     : my test logger                    : A test shiet
+[2023-12-15 17:31:26] PC35-HAISSLAB CLOSE    : my test logger                    : A test shiet
+[2023-12-15 17:31:26] PC35-HAISSLAB HEADER   : my test logger                    : A test shiet
+[2023-12-15 17:31:26] PC35-HAISSLAB INFO     : my test logger                    : The requirement for this logs text data to work with this javascript and css code, is that each line must be starting with an ansi color/style code.Each line must also finish with a line return and the next line be directly following with an ansi style character. any character in between will mess up the line by line formating and merge lines together.
+[2023-12-15 17:31:26] PC35-HAISSLAB LOAD     : my test logger                    : A test shiet
+[2023-12-15 17:31:26] PC35-HAISSLAB SAVE     : my test logger                    : A test shiet
+[2023-12-15 17:31:29] PC35-HAISSLAB WARNING  : papidou                           : chienasse
+[2023-12-15 17:39:28] PC35-HAISSLAB CRITICAL : my test logger                    : A test shiet
+[2023-12-15 17:39:28] PC35-HAISSLAB ERROR    : my test logger                    : A test shiet
+[2023-12-15 17:39:28] PC35-HAISSLAB WARNING  : my test logger                    : A test shiet
+[2023-12-15 17:39:28] PC35-HAISSLAB OPEN     : my test logger                    : A test shiet
+[2023-12-15 17:39:28] PC35-HAISSLAB CLOSE    : my test logger                    : A test shiet
+[2023-12-15 17:39:28] PC35-HAISSLAB HEADER   : my test logger                    : A test shiet
+[2023-12-15 17:39:28] PC35-HAISSLAB INFO     : my test logger                    : The requirement for this logs text data to work with this javascript and css code, is that each line must be starting with an ansi color/style code.Each line must also finish with a line return and the next line be directly following with an ansi style character. any character in between will mess up the line by line formating and merge lines together.
+[2023-12-15 17:39:28] PC35-HAISSLAB LOAD     : my test logger                    : A test shiet
+[2023-12-15 17:39:28] PC35-HAISSLAB SAVE     : my test logger                    : A test shiet
+[2023-12-15 17:40:30] PC35-HAISSLAB CRITICAL : my test logger                    : A test shiet
+[2023-12-15 17:40:30] PC35-HAISSLAB ERROR    : my test logger                    : A test shiet
+[2023-12-15 17:40:30] PC35-HAISSLAB WARNING  : my test logger                    : A test shiet
+[2023-12-15 17:40:30] PC35-HAISSLAB OPEN     : my test logger                    : A test shiet
+[2023-12-15 17:40:30] PC35-HAISSLAB CLOSE    : my test logger                    : A test shiet
+[2023-12-15 17:40:30] PC35-HAISSLAB HEADER   : my test logger                    : A test shiet
+[2023-12-15 17:40:30] PC35-HAISSLAB INFO     : my test logger                    : The requirement for this logs text data to work with this javascript and css code, is that each line must be starting with an ansi color/style code.Each line must also finish with a line return and the next line be directly following with an ansi style character. any character in between will mess up the line by line formating and merge lines together.
+[2023-12-15 17:40:30] PC35-HAISSLAB LOAD     : my test logger                    : A test shiet
+[2023-12-15 17:40:30] PC35-HAISSLAB SAVE     : my test logger                    : A test shiet
+[2023-12-15 17:41:15] PC35-HAISSLAB CRITICAL : my test logger                    : A test shiet
+[2023-12-15 17:41:15] PC35-HAISSLAB ERROR    : my test logger                    : A test shiet
+[2023-12-15 17:41:15] PC35-HAISSLAB WARNING  : my test logger                    : A test shiet
+[2023-12-15 17:41:15] PC35-HAISSLAB OPEN     : my test logger                    : A test shiet
+[2023-12-15 17:41:15] PC35-HAISSLAB CLOSE    : my test logger                    : A test shiet
+[2023-12-15 17:41:15] PC35-HAISSLAB HEADER   : my test logger                    : A test shiet
+[2023-12-15 17:41:15] PC35-HAISSLAB INFO     : my test logger                    : The requirement for this logs text data to work with this javascript and css code, is that each line must be starting with an ansi color/style code.Each line must also finish with a line return and the next line be directly following with an ansi style character. any character in between will mess up the line by line formating and merge lines together.
+[2023-12-15 17:41:15] PC35-HAISSLAB LOAD     : my test logger                    : A test shiet
+[2023-12-15 17:41:15] PC35-HAISSLAB SAVE     : my test logger                    : A test shiet
+[2023-12-15 17:41:41] PC35-HAISSLAB CRITICAL : my test logger                    : A test shiet
+[2023-12-15 17:41:41] PC35-HAISSLAB ERROR    : my test logger                    : A test shiet
+[2023-12-15 17:41:41] PC35-HAISSLAB WARNING  : my test logger                    : A test shiet
+[2023-12-15 17:41:41] PC35-HAISSLAB OPEN     : my test logger                    : A test shiet
+[2023-12-15 17:41:41] PC35-HAISSLAB CLOSE    : my test logger                    : A test shiet
+[2023-12-15 17:41:41] PC35-HAISSLAB HEADER   : my test logger                    : A test shiet
+[2023-12-15 17:41:41] PC35-HAISSLAB INFO     : my test logger                    : The requirement for this logs text data to work with this javascript and css code, is that each line must be starting with an ansi color/style code.Each line must also finish with a line return and the next line be directly following with an ansi style character. any character in between will mess up the line by line formating and merge lines together.
+[2023-12-15 17:41:41] PC35-HAISSLAB LOAD     : my test logger                    : A test shiet
+[2023-12-15 17:41:41] PC35-HAISSLAB SAVE     : my test logger                    : A test shiet
+[2023-12-15 17:42:44] PC35-HAISSLAB CRITICAL : my test logger                    : A test shiet
+[2023-12-15 17:42:44] PC35-HAISSLAB ERROR    : my test logger                    : A test shiet
+[2023-12-15 17:42:44] PC35-HAISSLAB WARNING  : my test logger                    : A test shiet
+[2023-12-15 17:42:44] PC35-HAISSLAB OPEN     : my test logger                    : A test shiet
+[2023-12-15 17:42:44] PC35-HAISSLAB CLOSE    : my test logger                    : A test shiet
+[2023-12-15 17:42:44] PC35-HAISSLAB HEADER   : my test logger                    : A test shiet
+[2023-12-15 17:42:44] PC35-HAISSLAB INFO     : my test logger                    : The requirement for this logs text data to work with this javascript and css code, is that each line must be starting with an ansi color/style code.Each line must also finish with a line return and the next line be directly following with an ansi style character. any character in between will mess up the line by line formating and merge lines together.
+[2023-12-15 17:42:44] PC35-HAISSLAB LOAD     : my test logger                    : A test shiet
+[2023-12-15 17:42:44] PC35-HAISSLAB SAVE     : my test logger                    : A test shiet
+[2023-12-15 17:44:04] PC35-HAISSLAB CRITICAL : my test logger                    : A test shiet
+[2023-12-15 17:44:04] PC35-HAISSLAB ERROR    : my test logger                    : A test shiet
+[2023-12-15 17:44:04] PC35-HAISSLAB WARNING  : my test logger                    : A test shiet
+[2023-12-15 17:44:04] PC35-HAISSLAB OPEN     : my test logger                    : A test shiet
+[2023-12-15 17:44:04] PC35-HAISSLAB CLOSE    : my test logger                    : A test shiet
+[2023-12-15 17:44:04] PC35-HAISSLAB HEADER   : my test logger                    : A test shiet
+[2023-12-15 17:44:04] PC35-HAISSLAB INFO     : my test logger                    : The requirement for this logs text data to work with this javascript and css code, is that each line must be starting with an ansi color/style code.Each line must also finish with a line return and the next line be directly following with an ansi style character. any character in between will mess up the line by line formating and merge lines together.
+[2023-12-15 17:44:04] PC35-HAISSLAB LOAD     : my test logger                    : A test shiet
+[2023-12-15 17:44:04] PC35-HAISSLAB SAVE     : my test logger                    : A test shiet
+[2023-12-15 17:47:20] PC35-HAISSLAB CRITICAL : my test logger                    : A test shiet
+[2023-12-15 17:47:20] PC35-HAISSLAB ERROR    : my test logger                    : A test shiet
+[2023-12-15 17:47:20] PC35-HAISSLAB WARNING  : my test logger                    : A test shiet
+[2023-12-15 17:47:28] PC35-HAISSLAB CRITICAL : my test logger                    : A test shiet
+[2023-12-15 17:47:28] PC35-HAISSLAB ERROR    : my test logger                    : A test shiet
+[2023-12-15 17:47:28] PC35-HAISSLAB WARNING  : my test logger                    : A test shiet
+[2023-12-15 17:47:28] PC35-HAISSLAB START    : my test logger                    : A test shiet
+[2023-12-15 17:47:28] PC35-HAISSLAB END      : my test logger                    : A test shiet
+[2023-12-15 17:47:28] PC35-HAISSLAB HEADER   : my test logger                    : A test shiet
+[2023-12-15 17:47:28] PC35-HAISSLAB INFO     : my test logger                    : The requirement for this logs text data to work with this javascript and css code, is that each line must be starting with an ansi color/style code.Each line must also finish with a line return and the next line be directly following with an ansi style character. any character in between will mess up the line by line formating and merge lines together.
+[2023-12-15 17:47:28] PC35-HAISSLAB LOAD     : my test logger                    : A test shiet
+[2023-12-15 17:47:28] PC35-HAISSLAB SAVE     : my test logger                    : A test shiet
+[2023-12-15 17:47:56] PC35-HAISSLAB CRITICAL : my test logger                    : A test shiet
+[2023-12-15 17:47:56] PC35-HAISSLAB ERROR    : my test logger                    : A test shiet
+[2023-12-15 17:47:56] PC35-HAISSLAB WARNING  : my test logger                    : A test shiet
+[2023-12-15 17:47:56] PC35-HAISSLAB START    : my test logger                    : A test shiet
+[2023-12-15 17:47:56] PC35-HAISSLAB END      : my test logger                    : A test shiet
+[2023-12-15 17:47:56] PC35-HAISSLAB HEADER   : my test logger                    : A test shiet
+[2023-12-15 17:47:56] PC35-HAISSLAB INFO     : my test logger                    : The requirement for this logs text data to work with this javascript and css code, is that each line must be starting with an ansi color/style code.Each line must also finish with a line return and the next line be directly following with an ansi style character. any character in between will mess up the line by line formating and merge lines together.
+[2023-12-15 17:47:56] PC35-HAISSLAB LOAD     : my test logger                    : A test shiet
+[2023-12-15 17:47:56] PC35-HAISSLAB SAVE     : my test logger                    : A test shiet
+[2023-12-15 17:55:45] PC35-HAISSLAB CRITICAL : my test logger                    : A test shiet
+[2023-12-15 17:55:45] PC35-HAISSLAB ERROR    : my test logger                    : A test shiet
+[2023-12-15 17:55:45] PC35-HAISSLAB WARNING  : my test logger                    : A test shiet
+[2023-12-15 17:55:45] PC35-HAISSLAB START    : my test logger                    : A test shiet
+[2023-12-15 17:55:45] PC35-HAISSLAB END      : my test logger                    : A test shiet
+[2023-12-15 17:55:45] PC35-HAISSLAB HEADER   : my test logger                    : A test shiet
+[2023-12-15 17:55:45] PC35-HAISSLAB INFO     : my test logger                    : The requirement for this logs text data to work with this javascript and css code, is that each line must be starting with an ansi color/style code.Each line must also finish with a line return and the next line be directly following with an ansi style character. any character in between will mess up the line by line formating and merge lines together.
+[2023-12-15 17:55:45] PC35-HAISSLAB LOAD     : my test logger                    : A test shiet
+[2023-12-15 17:55:45] PC35-HAISSLAB SAVE     : my test logger                    : A test shiet
diff --git a/developements/wm33.ipynb b/developements/wm33.ipynb
index c2d79e5dceb11c2e6e521edff3c5ab501cd6ddc8..17f27f8c6758a1ea62510037c16fae7377fbeacf 100644
--- a/developements/wm33.ipynb
+++ b/developements/wm33.ipynb
@@ -10,7 +10,6 @@
     "import Inflow\n",
     "from ResearchProjects import adaptation\n",
     "from ResearchProjects.adaptation.new_pipelines_blocks import new_pipelines\n",
-    "\n",
     "import pandas as pd, one\n",
     "\n",
     "Inflow.logging.enable_logging(level = \"INFO\")"
@@ -20,2682 +19,609 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "13993221-486f-4878-9b6a-8d76b9a31b6f",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "connector = one.ONE(data_access_mode = \"remote\")\n",
-    "session = connector.search(id = r\"wm30\\2023-09-19\\001\", details = True, no_cache = True).iloc[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "fab1a4d0-41da-4309-96d3-298f0d3253f1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sessions = connector.search(object = \"behaviour\", details = True)"
+    "sessions = []\n",
+    "sessions.append( connector.search(id = r\"wm33/2023-12-13/002\", details = True, no_cache = True) )\n",
+    "sessions.append( connector.search(id = r\"wm33/2023-12-13/001\", details = True, no_cache = True) )\n",
+    "sessions = pd.concat(sessions)\n",
+    "\n",
+    "session = sessions.iloc[0]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b15d054b-afba-486f-a1cf-3a399c9c82b2",
+   "id": "eb0965b3-e398-4ec8-9ecb-0b499f954892",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33;20mWARNING    : autoload_arguments                : FileNotFoundError : Could not open the config file preproc_data_arguments.json for the session wm33/2023-12-05/001. Skipping (logging.py:42) - 2023-12-11 19:23:59,041\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.video_treatment.tdms_export   : Found data. Trying to load it\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.video_treatment.tdms_export   : Loaded video_treatment.tdms_export.pupil sucessfully.\u001b[0m\n",
-      "\u001b[33;20mWARNING    : autoload_arguments                : FileNotFoundError : Could not open the config file preproc_data_arguments.json for the session wm33/2023-12-05/001. Skipping (logging.py:42) - 2023-12-11 19:23:59,112\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.video_treatment.tdms_export   : Could not find or load video_treatment.tdms_export.whiskers saved file.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.video_treatment.tdms_export   : Performing the computation to generate video_treatment.tdms_export.whiskers. Hold tight.\u001b[0m\n",
-      "\u001b[94;20mINFO       : video_export                      : 300 videos out of 300 will be compressed\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [05:31<00:00,  1.11s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[94;20mINFO       : video_export                      : Registering new compressed data to alyx\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.files                : Dataset behaviour_imaging/whiskers/behaviour.video_compressed.avi\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.make_dataset         : Registered the new dataset : behaviour_imaging/whiskers/behaviour.video_compressed.avi for session wm33/2023-12-05/001\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.add_file_records     : Starting registration of 300 new files to the dataset behaviour_imaging/whiskers/behaviour.video_compressed.avi for the session wm33/2023-12-05/001.\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.files                : Dataset behaviour_imaging/whiskers/behaviour.video_compressed_meta.json\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.make_dataset         : Registered the new dataset : behaviour_imaging/whiskers/behaviour.video_compressed_meta.json for session wm33/2023-12-05/001\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.add_file_records     : Starting registration of 300 new files to the dataset behaviour_imaging/whiskers/behaviour.video_compressed_meta.json for the session wm33/2023-12-05/001.\u001b[0m\n",
-      "\u001b[94;20mINFO       : video_export                      : Finished video export from tdms\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.video_treatment.tdms_export   : Saving the generated video_treatment.tdms_export.whiskers output.\u001b[0m\n",
-      "\u001b[33;20mWARNING    : autoload_arguments                : FileNotFoundError : Could not open the config file preproc_data_arguments.json for the session wm33/2023-12-04/001. Skipping (logging.py:42) - 2023-12-11 19:30:32,953\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.video_treatment.tdms_export   : Found data. Trying to load it\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.video_treatment.tdms_export   : Loaded video_treatment.tdms_export.pupil sucessfully.\u001b[0m\n",
-      "\u001b[33;20mWARNING    : autoload_arguments                : FileNotFoundError : Could not open the config file preproc_data_arguments.json for the session wm33/2023-12-04/001. Skipping (logging.py:42) - 2023-12-11 19:30:33,029\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.video_treatment.tdms_export   : Could not find or load video_treatment.tdms_export.whiskers saved file.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.video_treatment.tdms_export   : Performing the computation to generate video_treatment.tdms_export.whiskers. Hold tight.\u001b[0m\n",
-      "\u001b[94;20mINFO       : video_export                      : 300 videos out of 300 will be compressed\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [05:34<00:00,  1.12s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[94;20mINFO       : video_export                      : Registering new compressed data to alyx\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.files                : Dataset behaviour_imaging/whiskers/behaviour.video_compressed.avi\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.make_dataset         : Registered the new dataset : behaviour_imaging/whiskers/behaviour.video_compressed.avi for session wm33/2023-12-04/001\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.add_file_records     : Starting registration of 300 new files to the dataset behaviour_imaging/whiskers/behaviour.video_compressed.avi for the session wm33/2023-12-04/001.\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.files                : Dataset behaviour_imaging/whiskers/behaviour.video_compressed_meta.json\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.make_dataset         : Registered the new dataset : behaviour_imaging/whiskers/behaviour.video_compressed_meta.json for session wm33/2023-12-04/001\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.add_file_records     : Starting registration of 300 new files to the dataset behaviour_imaging/whiskers/behaviour.video_compressed_meta.json for the session wm33/2023-12-04/001.\u001b[0m\n",
-      "\u001b[94;20mINFO       : video_export                      : Finished video export from tdms\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.video_treatment.tdms_export   : Saving the generated video_treatment.tdms_export.whiskers output.\u001b[0m\n",
-      "\u001b[33;20mWARNING    : autoload_arguments                : FileNotFoundError : Could not open the config file preproc_data_arguments.json for the session wm33/2023-11-20/001. Skipping (logging.py:42) - 2023-12-11 19:37:09,144\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.video_treatment.tdms_export   : Found data. Trying to load it\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.video_treatment.tdms_export   : Loaded video_treatment.tdms_export.pupil sucessfully.\u001b[0m\n",
-      "\u001b[33;20mWARNING    : autoload_arguments                : FileNotFoundError : Could not open the config file preproc_data_arguments.json for the session wm33/2023-11-20/001. Skipping (logging.py:42) - 2023-12-11 19:37:09,214\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.video_treatment.tdms_export   : Could not find or load video_treatment.tdms_export.whiskers saved file.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.video_treatment.tdms_export   : Performing the computation to generate video_treatment.tdms_export.whiskers. Hold tight.\u001b[0m\n",
-      "\u001b[94;20mINFO       : video_export                      : 300 videos out of 300 will be compressed\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 85%|███████████████████████████████████████████████████████████████████▋            | 254/300 [04:44<00:52,  1.13s/it]"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "for _, session in sessions.iterrows():\n",
-    "    try : \n",
-    "        new_pipelines.video_treatment.tdms_export.generate(session, extra = \"pupil\")\n",
-    "        new_pipelines.video_treatment.tdms_export.generate(session, extra = \"whiskers\")\n",
-    "    except Exception as e:\n",
-    "        print(f\"A problem occured for session : {session.alias}\")"
+    "new_pipelines.registration.do_registration.generate(session, check_requirements = True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "eb0965b3-e398-4ec8-9ecb-0b499f954892",
+   "id": "f65b81e3-f18c-4e9c-8bea-78be8eafea83",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[33;20mWARNING    : autoload_arguments                : FileNotFoundError : Could not open the config file preproc_data_arguments.json for the session wm33/2023-12-05/001. Skipping (logging.py:42) - 2023-12-08 17:35:00,123\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.registration.do_registration  : Could not find or load registration.do_registration saved file.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.registration.do_registration  : Running requirement registration.check\u001b[0m\n",
-      "\u001b[33;20mWARNING    : autoload_arguments                : FileNotFoundError : Could not open the config file preproc_data_arguments.json for the session wm33/2023-12-05/001. Skipping (logging.py:42) - 2023-12-08 17:35:00,127\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.registration.check            : Could not find or load registration.check saved file.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.registration.check            : Performing the computation to generate registration.check. Hold tight.\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.check                : 0 files will be renamed, 0 files will be deleted.\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.check                : 602 files will be registered to alyx\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.check                : There seem to be no problem to apply the renaming and alyx registration of the files found.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.registration.check            : Saving the generated registration.check output.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.registration.do_registration  : Running requirement registration.do_renaming\u001b[0m\n",
-      "\u001b[33;20mWARNING    : autoload_arguments                : FileNotFoundError : Could not open the config file preproc_data_arguments.json for the session wm33/2023-12-05/001. Skipping (logging.py:42) - 2023-12-08 17:35:02,046\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.registration.do_renaming      : File(s) have been found but with a step too low in the requirement stack. Reloading the generation tree\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.registration.do_renaming      : Running requirement registration.check\u001b[0m\n",
-      "\u001b[33;20mWARNING    : autoload_arguments                : FileNotFoundError : Could not open the config file preproc_data_arguments.json for the session wm33/2023-12-05/001. Skipping (logging.py:42) - 2023-12-08 17:35:02,050\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.registration.check            : File exists for registration.check. Loading and processing will be skipped\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.registration.do_renaming      : Performing the computation to generate registration.do_renaming. Hold tight.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.registration.do_renaming      : Saving the generated registration.do_renaming output.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.registration.do_registration  : Performing the computation to generate registration.do_registration. Hold tight.\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.files                : Dataset behaviour_imaging/whiskers/behaviour.video.tdms\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.make_dataset         : The dataset behaviour_imaging/whiskers/behaviour.video.tdms for session wm33/2023-12-05/001 was already existing. Using it to attach files instead of creating a new one.\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.add_file_records     : Found 300 files already registered for the dataset behaviour_imaging/whiskers/behaviour.video.tdms. Skipping them\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.files                : Dataset imaging_data/imaging.fieldOfView.png\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.make_dataset         : The dataset imaging_data/imaging.fieldOfView.png for session wm33/2023-12-05/001 was already existing. Using it to attach files instead of creating a new one.\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.add_file_records     : Found 1 files already registered for the dataset imaging_data/imaging.fieldOfView.png. Skipping them\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.files                : Dataset imaging_data/imaging.frames.tif\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.make_dataset         : The dataset imaging_data/imaging.frames.tif for session wm33/2023-12-05/001 was already existing. Using it to attach files instead of creating a new one.\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.add_file_records     : Found 300 files already registered for the dataset imaging_data/imaging.frames.tif. Skipping them\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.files                : Dataset trials.eventTimeline.tdms\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.make_dataset         : The dataset trials.eventTimeline.tdms for session wm33/2023-12-05/001 was already existing. Using it to attach files instead of creating a new one.\u001b[0m\n",
-      "\u001b[94;20mINFO       : registration.add_file_records     : Found 1 files already registered for the dataset trials.eventTimeline.tdms. Skipping them\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.registration.do_registration  : Saving the generated registration.do_registration output.\u001b[0m\n"
+      "\u001b[94;20mINFO       : gen.rois_df.mapping_labels        : Found data. Trying to load it\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.rois_df.mapping_labels        : Loaded rois_df.mapping_labels sucessfully.\u001b[0m\n",
+      "\u001b[33;20mWARNING    : pickle.check_disk                 : A single partial match was found for rois_df.mapping_labels. Please make sure it is consistant with expected behaviour. Expected : {'step_name': 'mapping_labels', 'version': 'a3ff5b9'}, Found : {'step_name': 'suite2p_export', 'version': 'a3ff5b9'} (logging.py:42) - 2023-12-14 03:48:50,039\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.rois_df.mapping_labels        : File(s) have been found but with a step too low in the requirement stack. Reloading the generation tree\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.rois_df.mapping_labels        : Running requirement trials_df.tdms_export\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.trials_df.tdms_export         : File exists for trials_df.tdms_export. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.rois_df.mapping_labels        : Running requirement suite2p.run\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.suite2p.run                   : File exists for suite2p.run.0. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.rois_df.mapping_labels        : Running requirement suite2p.treat2p\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.suite2p.treat2p               : File exists for suite2p.treat2p.0. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.rois_df.mapping_labels        : Running requirement rois_df.suite2p_export\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.rois_df.suite2p_export        : File exists for rois_df.suite2p_export. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.rois_df.mapping_labels        : Running requirement neuropil_mask.maps_calculation\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : Could not find or load neuropil_mask.maps_calculation.C1 saved file.\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : Performing the computation to generate neuropil_mask.maps_calculation.C1. Hold tight.\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_trials_arrays            : Processing trials 3D fluorescent signal (read tiff, zscore, gaussian filter)\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_trials_arrays            : Accessing tiff files data\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_trials_arrays            : Using only the last 20th trials\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_trials_arrays            : Performing delta_over_f calculation on 20 trials\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_trials_arrays            : Performing gaussian calculation\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_trials_arrays            : Packaging obtained signals into TimelinedArrays\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_trials_arrays_mean       : Generating neuropil mean of all provided trials in time synchronized with stimulation\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_mask                     : Fitting gaussian 2D function onto neuropil difference map (after - before stim)\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_plots                    : Finished calculating neuropil_mask. You may now call refine_neuropil_mask on this session\u001b[0m\n",
+      "\u001b[38;5;57;48;5;195;1mâ–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘ neuropil_plots                    : Finished for trials subset : C1 from session wm33/2023-12-13/001.  - 2023-12-14 03:50:14,114 \n",
+      "\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : Saving the generated neuropil_mask.maps_calculation.C1 output.\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : Could not find or load neuropil_mask.maps_calculation.Gamma saved file.\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : Performing the computation to generate neuropil_mask.maps_calculation.Gamma. Hold tight.\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_trials_arrays            : Processing trials 3D fluorescent signal (read tiff, zscore, gaussian filter)\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_trials_arrays            : Accessing tiff files data\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_trials_arrays            : Performing delta_over_f calculation on 17 trials\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_trials_arrays            : Performing gaussian calculation\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_trials_arrays            : Packaging obtained signals into TimelinedArrays\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_trials_arrays_mean       : Generating neuropil mean of all provided trials in time synchronized with stimulation\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_mask                     : Fitting gaussian 2D function onto neuropil difference map (after - before stim)\u001b[0m\n",
+      "\u001b[94;20mINFO       : neuropil_plots                    : Finished calculating neuropil_mask. You may now call refine_neuropil_mask on this session\u001b[0m\n",
+      "\u001b[38;5;57;48;5;195;1mâ–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘ neuropil_plots                    : Finished for trials subset : Gamma from session wm33/2023-12-13/001.  - 2023-12-14 03:51:17,538 \n",
+      "\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : Saving the generated neuropil_mask.maps_calculation.Gamma output.\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.rois_df.mapping_labels        : Running requirement neuropil_mask.refinement\u001b[0m\n",
+      "\u001b[33;20mWARNING    : pickle.check_disk                 : A single partial match was found for neuropil_mask.refinement.C1. Please make sure it is consistant with expected behaviour. Expected : {'step_name': 'refinement', 'version': 'fbb7d54'}, Found : {'step_name': 'maps_calculation', 'version': 'fbb7d54'} (logging.py:42) - 2023-12-14 03:51:17,816\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.refinement      : File(s) have been found but with a step too low in the requirement stack. Reloading the generation tree\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.refinement      : Running requirement trials_df.tdms_export\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.trials_df.tdms_export         : File exists for trials_df.tdms_export. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.refinement      : Running requirement neuropil_mask.maps_calculation\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : File exists for neuropil_mask.maps_calculation.C1. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.refinement      : Performing the computation to generate neuropil_mask.refinement.C1. Hold tight.\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.refinement      : Saving the generated neuropil_mask.refinement.C1 output.\u001b[0m\n",
+      "\u001b[33;20mWARNING    : pickle.check_disk                 : A single partial match was found for neuropil_mask.refinement.Gamma. Please make sure it is consistant with expected behaviour. Expected : {'step_name': 'refinement', 'version': 'fbb7d54'}, Found : {'step_name': 'maps_calculation', 'version': 'fbb7d54'} (logging.py:42) - 2023-12-14 03:51:18,494\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.refinement      : File(s) have been found but with a step too low in the requirement stack. Reloading the generation tree\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.refinement      : Running requirement trials_df.tdms_export\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.trials_df.tdms_export         : File exists for trials_df.tdms_export. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.refinement      : Running requirement neuropil_mask.maps_calculation\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : File exists for neuropil_mask.maps_calculation.Gamma. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.refinement      : Performing the computation to generate neuropil_mask.refinement.Gamma. Hold tight.\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.refinement      : Saving the generated neuropil_mask.refinement.Gamma output.\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.rois_df.mapping_labels        : Running requirement neuropil_mask.separation\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.separation      : Could not find or load neuropil_mask.separation.all saved file.\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.separation      : Performing the computation to generate neuropil_mask.separation.all. Hold tight.\u001b[0m\n",
+      "\u001b[94;20mINFO       : separate_masks                    : Starting mask separation\u001b[0m\n",
+      "\u001b[94;20mINFO       : separate_masks                    : Found an intersection. Processing to separation.\u001b[0m\n",
+      "\u001b[94;20mINFO       : separate_masks                    : Creating new masks from splitted polygons. This may take a few tens of seconds. Please wait.\u001b[0m\n",
+      "\u001b[33;20mWARNING    : separate_masks                    : Could not display P2 background, has suite2p been run and is pImage installed ? (logging.py:42) - 2023-12-14 03:51:21,020\u001b[0m\n",
+      "\u001b[94;20mINFO       : separate_masks                    : Mask separation complete\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.separation      : Saving the generated neuropil_mask.separation.all output.\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.rois_df.mapping_labels        : Performing the computation to generate rois_df.mapping_labels. Hold tight.\u001b[0m\n",
+      "\u001b[94;20mINFO       : mapping_labels                    : Generating in_C1 label based on the refined_mask variable inside neuropil_mask.\u001b[0m\n",
+      "\u001b[94;20mINFO       : mapping_labels                    : Generating in_Gamma label based on the refined_mask variable inside neuropil_mask.\u001b[0m\n",
+      "\u001b[94;20mINFO       : mapping_labels                    : Generating in_any_barrel label based on the refined_mask variables inside neuropil_masks of existing whiskers.\u001b[0m\n",
+      "\u001b[94;20mINFO       : mapping_labels                    : Generating is_neuron label based on iscell column values at 0 positionnal indices\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.rois_df.mapping_labels        : Saving the generated rois_df.mapping_labels output.\u001b[0m\n",
+      "\u001b[94;20mINFO       : show_roi_locations                : Making roi location plot for roi identities labels.\u001b[0m\n",
+      "\u001b[33;20mWARNING    : pickle.check_disk                 : A single partial match was found for rois_df.suite2p_export. Please make sure it is consistant with expected behaviour. Expected : {'step_name': 'suite2p_export', 'version': 'a3ff5b9'}, Found : {'step_name': 'mapping_labels', 'version': 'a3ff5b9'} (logging.py:42) - 2023-12-14 03:51:33,999\u001b[0m\n",
+      "\u001b[33;20mWARNING    : pickle.check_disk                 : A single partial match was found for neuropil_mask.maps_calculation.C1. Please make sure it is consistant with expected behaviour. Expected : {'step_name': 'maps_calculation', 'version': 'fbb7d54'}, Found : {'step_name': 'refinement', 'version': 'fbb7d54'} (logging.py:42) - 2023-12-14 03:51:47,189\u001b[0m\n",
+      "\u001b[33;20mWARNING    : pickle.check_disk                 : A single partial match was found for neuropil_mask.maps_calculation.Gamma. Please make sure it is consistant with expected behaviour. Expected : {'step_name': 'maps_calculation', 'version': 'fbb7d54'}, Found : {'step_name': 'refinement', 'version': 'fbb7d54'} (logging.py:42) - 2023-12-14 03:51:47,221\u001b[0m\n"
      ]
     },
     {
      "data": {
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ypix</th>\n",
+       "      <th>xpix</th>\n",
+       "      <th>lam</th>\n",
+       "      <th>med</th>\n",
+       "      <th>footprint</th>\n",
+       "      <th>mrs</th>\n",
+       "      <th>mrs0</th>\n",
+       "      <th>compact</th>\n",
+       "      <th>solidity</th>\n",
+       "      <th>npix</th>\n",
+       "      <th>...</th>\n",
+       "      <th>spks</th>\n",
+       "      <th>in_C1</th>\n",
+       "      <th>in_Delta</th>\n",
+       "      <th>in_any_barrel</th>\n",
+       "      <th>roi_location</th>\n",
+       "      <th>is_VGAT</th>\n",
+       "      <th>is_neuron</th>\n",
+       "      <th>roi</th>\n",
+       "      <th>session</th>\n",
+       "      <th>in_Gamma</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>roi#</th>\n",
+       "      <th></th>\n",
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+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0_wm33_2023-12-13_002</th>\n",
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+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1_wm33_2023-12-13_002</th>\n",
+       "      <td>[21, 21, 22, 22, 22, 22, 23, 23, 23, 23, 24, 2...</td>\n",
+       "      <td>[288, 289, 287, 288, 289, 290, 287, 288, 289, ...</td>\n",
+       "      <td>[0.18475324, 0.19342558, 0.19270252, 0.1931696...</td>\n",
+       "      <td>[25, 288]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.860854</td>\n",
+       "      <td>2.029624</td>\n",
+       "      <td>1.024389</td>\n",
+       "      <td>1.414634</td>\n",
+       "      <td>31</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[0.0, 4.552178859710693, 3.061532735824585, 0....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>outside</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
+       "      <td>wm33_2023-12-13_002</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2_wm33_2023-12-13_002</th>\n",
+       "      <td>[29, 29, 29, 30, 30, 31, 31, 31, 31, 32, 32, 3...</td>\n",
+       "      <td>[485, 486, 487, 485, 486, 485, 486, 487, 488, ...</td>\n",
+       "      <td>[0.25178888, 0.26499715, 0.25086245, 0.2587919...</td>\n",
+       "      <td>[33, 486]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.853344</td>\n",
+       "      <td>1.834612</td>\n",
+       "      <td>1.123392</td>\n",
+       "      <td>1.411765</td>\n",
+       "      <td>30</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[0.0, 8.512608528137207, 5.028446674346924, 0....</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>outside</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2</td>\n",
+       "      <td>wm33_2023-12-13_002</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3_wm33_2023-12-13_002</th>\n",
+       "      <td>[30, 30, 30, 31, 31, 31, 32, 32, 32, 32, 33, 3...</td>\n",
+       "      <td>[34, 35, 36, 34, 35, 36, 33, 34, 35, 36, 32, 3...</td>\n",
+       "      <td>[0.2638343, 0.29020253, 0.24311486, 0.3429862,...</td>\n",
+       "      <td>[32, 34]</td>\n",
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+       "      <td>1.564772</td>\n",
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+       "      <td>18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 24.866594314575195, 0.0, 0.0, ...</td>\n",
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+       "      <td>False</td>\n",
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+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>4_wm33_2023-12-13_002</th>\n",
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+       "      <td>[0.31842658, 0.34252906, 0.30469745, 0.2677773...</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>1.182667</td>\n",
+       "      <td>2.795279</td>\n",
+       "      <td>1.021854</td>\n",
+       "      <td>1.250000</td>\n",
+       "      <td>62</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 4.430496692657471, 0...</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>outside</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>4</td>\n",
+       "      <td>wm33_2023-12-13_002</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>[228, 229, 230, 231, 232, 228, 229, 230, 231, ...</td>\n",
+       "      <td>[0.74684805, 0.8357104, 0.8171027, 0.72122157,...</td>\n",
+       "      <td>[496, 231]</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>2.933724</td>\n",
+       "      <td>1.007821</td>\n",
+       "      <td>1.244898</td>\n",
+       "      <td>64</td>\n",
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+       "      <td>True</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>231</td>\n",
+       "      <td>wm33_2023-12-13_001</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>232_wm33_2023-12-13_001</th>\n",
+       "      <td>[494, 494, 494, 494, 494, 495, 495, 495, 495, ...</td>\n",
+       "      <td>[348, 349, 350, 351, 352, 348, 349, 350, 351, ...</td>\n",
+       "      <td>[0.61033565, 0.60549986, 0.634167, 0.6968652, ...</td>\n",
+       "      <td>[498, 351]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.027672</td>\n",
+       "      <td>2.511150</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.323529</td>\n",
+       "      <td>63</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 13.752457618713...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>232</td>\n",
+       "      <td>wm33_2023-12-13_001</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233_wm33_2023-12-13_001</th>\n",
+       "      <td>[494, 494, 495, 495, 495, 495, 495, 495, 496, ...</td>\n",
+       "      <td>[445, 446, 443, 444, 445, 446, 447, 448, 443, ...</td>\n",
+       "      <td>[0.29869604, 0.30142477, 0.32066616, 0.3251465...</td>\n",
+       "      <td>[501, 445]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.653975</td>\n",
+       "      <td>3.944389</td>\n",
+       "      <td>1.024631</td>\n",
+       "      <td>1.157895</td>\n",
+       "      <td>117</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[0.0, 19.724302291870117, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>outside</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>233</td>\n",
+       "      <td>wm33_2023-12-13_001</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234_wm33_2023-12-13_001</th>\n",
+       "      <td>[500, 500, 500, 501, 501, 501, 501, 502, 502, ...</td>\n",
+       "      <td>[317, 318, 319, 316, 317, 318, 319, 316, 317, ...</td>\n",
+       "      <td>[0.587601, 0.6209962, 0.66667587, 0.5530473, 0...</td>\n",
+       "      <td>[503, 318]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.750803</td>\n",
+       "      <td>1.834612</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.548387</td>\n",
+       "      <td>36</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 13.342823028564453, 0.0, 0.0, ...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>234</td>\n",
+       "      <td>wm33_2023-12-13_001</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235_wm33_2023-12-13_001</th>\n",
+       "      <td>[502, 502, 502, 502, 503, 503, 503, 503, 503, ...</td>\n",
+       "      <td>[421, 422, 423, 424, 420, 421, 422, 423, 424, ...</td>\n",
+       "      <td>[0.34779197, 0.38089228, 0.33844674, 0.2746388...</td>\n",
+       "      <td>[505, 422]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.988401</td>\n",
+       "      <td>2.404379</td>\n",
+       "      <td>1.004497</td>\n",
+       "      <td>1.301587</td>\n",
+       "      <td>45</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[0.0, 0.0, 26.47202491760254, 0.0, 0.0, 0.0, 0...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>outside</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>235</td>\n",
+       "      <td>wm33_2023-12-13_001</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>467 rows × 43 columns</p>\n",
+       "</div>"
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+       "1_wm33_2023-12-13_002    [288, 289, 287, 288, 289, 290, 287, 288, 289, ...   \n",
+       "2_wm33_2023-12-13_002    [485, 486, 487, 485, 486, 485, 486, 487, 488, ...   \n",
+       "3_wm33_2023-12-13_002    [34, 35, 36, 34, 35, 36, 33, 34, 35, 36, 32, 3...   \n",
+       "4_wm33_2023-12-13_002    [469, 470, 471, 468, 469, 470, 471, 472, 468, ...   \n",
+       "...                                                                    ...   \n",
+       "231_wm33_2023-12-13_001  [228, 229, 230, 231, 232, 228, 229, 230, 231, ...   \n",
+       "232_wm33_2023-12-13_001  [348, 349, 350, 351, 352, 348, 349, 350, 351, ...   \n",
+       "233_wm33_2023-12-13_001  [445, 446, 443, 444, 445, 446, 447, 448, 443, ...   \n",
+       "234_wm33_2023-12-13_001  [317, 318, 319, 316, 317, 318, 319, 316, 317, ...   \n",
+       "235_wm33_2023-12-13_001  [421, 422, 423, 424, 420, 421, 422, 423, 424, ...   \n",
+       "\n",
+       "                                                                       lam  \\\n",
+       "roi#                                                                         \n",
+       "0_wm33_2023-12-13_002    [0.23649898, 0.26192114, 0.27757052, 0.2595025...   \n",
+       "1_wm33_2023-12-13_002    [0.18475324, 0.19342558, 0.19270252, 0.1931696...   \n",
+       "2_wm33_2023-12-13_002    [0.25178888, 0.26499715, 0.25086245, 0.2587919...   \n",
+       "3_wm33_2023-12-13_002    [0.2638343, 0.29020253, 0.24311486, 0.3429862,...   \n",
+       "4_wm33_2023-12-13_002    [0.31842658, 0.34252906, 0.30469745, 0.2677773...   \n",
+       "...                                                                    ...   \n",
+       "231_wm33_2023-12-13_001  [0.74684805, 0.8357104, 0.8171027, 0.72122157,...   \n",
+       "232_wm33_2023-12-13_001  [0.61033565, 0.60549986, 0.634167, 0.6968652, ...   \n",
+       "233_wm33_2023-12-13_001  [0.29869604, 0.30142477, 0.32066616, 0.3251465...   \n",
+       "234_wm33_2023-12-13_001  [0.587601, 0.6209962, 0.66667587, 0.5530473, 0...   \n",
+       "235_wm33_2023-12-13_001  [0.34779197, 0.38089228, 0.33844674, 0.2746388...   \n",
+       "\n",
+       "                                med  footprint       mrs      mrs0   compact  \\\n",
+       "roi#                                                                           \n",
+       "0_wm33_2023-12-13_002     [13, 485]          1  1.213739  2.911908  1.006698   \n",
+       "1_wm33_2023-12-13_002     [25, 288]          1  0.860854  2.029624  1.024389   \n",
+       "2_wm33_2023-12-13_002     [33, 486]          1  0.853344  1.834612  1.123392   \n",
+       "3_wm33_2023-12-13_002      [32, 34]          1  0.702509  1.564772  1.084306   \n",
+       "4_wm33_2023-12-13_002     [36, 470]          1  1.182667  2.795279  1.021854   \n",
+       "...                             ...        ...       ...       ...       ...   \n",
+       "231_wm33_2023-12-13_001  [496, 231]          1  1.209998  2.933724  1.007821   \n",
+       "232_wm33_2023-12-13_001  [498, 351]          1  1.027672  2.511150  1.000000   \n",
+       "233_wm33_2023-12-13_001  [501, 445]          1  1.653975  3.944389  1.024631   \n",
+       "234_wm33_2023-12-13_001  [503, 318]          1  0.750803  1.834612  1.000000   \n",
+       "235_wm33_2023-12-13_001  [505, 422]          1  0.988401  2.404379  1.004497   \n",
+       "\n",
+       "                         solidity  npix  ...  \\\n",
+       "roi#                                     ...   \n",
+       "0_wm33_2023-12-13_002    1.237113    64  ...   \n",
+       "1_wm33_2023-12-13_002    1.414634    31  ...   \n",
+       "2_wm33_2023-12-13_002    1.411765    30  ...   \n",
+       "3_wm33_2023-12-13_002    1.545455    18  ...   \n",
+       "4_wm33_2023-12-13_002    1.250000    62  ...   \n",
+       "...                           ...   ...  ...   \n",
+       "231_wm33_2023-12-13_001  1.244898    64  ...   \n",
+       "232_wm33_2023-12-13_001  1.323529    63  ...   \n",
+       "233_wm33_2023-12-13_001  1.157895   117  ...   \n",
+       "234_wm33_2023-12-13_001  1.548387    36  ...   \n",
+       "235_wm33_2023-12-13_001  1.301587    45  ...   \n",
+       "\n",
+       "                                                                      spks  \\\n",
+       "roi#                                                                         \n",
+       "0_wm33_2023-12-13_002    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "1_wm33_2023-12-13_002    [0.0, 4.552178859710693, 3.061532735824585, 0....   \n",
+       "2_wm33_2023-12-13_002    [0.0, 8.512608528137207, 5.028446674346924, 0....   \n",
+       "3_wm33_2023-12-13_002    [0.0, 0.0, 0.0, 24.866594314575195, 0.0, 0.0, ...   \n",
+       "4_wm33_2023-12-13_002    [0.0, 0.0, 0.0, 0.0, 0.0, 4.430496692657471, 0...   \n",
+       "...                                                                    ...   \n",
+       "231_wm33_2023-12-13_001  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "232_wm33_2023-12-13_001  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 13.752457618713...   \n",
+       "233_wm33_2023-12-13_001  [0.0, 19.724302291870117, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "234_wm33_2023-12-13_001  [0.0, 0.0, 0.0, 13.342823028564453, 0.0, 0.0, ...   \n",
+       "235_wm33_2023-12-13_001  [0.0, 0.0, 26.47202491760254, 0.0, 0.0, 0.0, 0...   \n",
+       "\n",
+       "                         in_C1 in_Delta  in_any_barrel  roi_location  is_VGAT  \\\n",
+       "roi#                                                                            \n",
+       "0_wm33_2023-12-13_002    False    False          False       outside    False   \n",
+       "1_wm33_2023-12-13_002    False    False          False       outside     True   \n",
+       "2_wm33_2023-12-13_002    False    False          False       outside    False   \n",
+       "3_wm33_2023-12-13_002    False    False          False       outside    False   \n",
+       "4_wm33_2023-12-13_002    False    False          False       outside    False   \n",
+       "...                        ...      ...            ...           ...      ...   \n",
+       "231_wm33_2023-12-13_001   True      NaN           True            C1    False   \n",
+       "232_wm33_2023-12-13_001   True      NaN           True            C1    False   \n",
+       "233_wm33_2023-12-13_001  False      NaN          False       outside    False   \n",
+       "234_wm33_2023-12-13_001   True      NaN           True            C1    False   \n",
+       "235_wm33_2023-12-13_001  False      NaN          False       outside    False   \n",
+       "\n",
+       "                         is_neuron  roi              session in_Gamma  \n",
+       "roi#                                                                   \n",
+       "0_wm33_2023-12-13_002        False    0  wm33_2023-12-13_002      NaN  \n",
+       "1_wm33_2023-12-13_002         True    1  wm33_2023-12-13_002      NaN  \n",
+       "2_wm33_2023-12-13_002        False    2  wm33_2023-12-13_002      NaN  \n",
+       "3_wm33_2023-12-13_002        False    3  wm33_2023-12-13_002      NaN  \n",
+       "4_wm33_2023-12-13_002         True    4  wm33_2023-12-13_002      NaN  \n",
+       "...                            ...  ...                  ...      ...  \n",
+       "231_wm33_2023-12-13_001      False  231  wm33_2023-12-13_001    False  \n",
+       "232_wm33_2023-12-13_001      False  232  wm33_2023-12-13_001    False  \n",
+       "233_wm33_2023-12-13_001      False  233  wm33_2023-12-13_001    False  \n",
+       "234_wm33_2023-12-13_001      False  234  wm33_2023-12-13_001    False  \n",
+       "235_wm33_2023-12-13_001      False  235  wm33_2023-12-13_001    False  \n",
+       "\n",
+       "[467 rows x 43 columns]"
       ]
      },
      "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_pipelines.registration.do_registration.generate(session, check_requirements = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "493e48e6-6de1-4675-92c7-b51541117b12",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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Tou6k3bsNgHnpqkTvCCA16hpWlRtmu58tW7awceNGPvzwQyD9rsbt27cpXbo048ePN/4SNzc3N27Tp08f4/8/++wzAA4dOsTvv/8OwIABA2jfvr1x/R49emS5byHEo9WsWZMrV65w9+5dDh48SIMGDbCwsDAuf/Da69evH9u2bQOyv7b37NnDH3/8AUDPnj0ZM2ZMnuK5fz1v27aN/fv3G+u4hIQEatSoAcCcOXPYsGEDAKGhoYSGhmJqapqprHr16lG0aFEASpcuTdMWrVnzv9uYFXUnOew8hpREkq+dIfzX9GNAKbRm/x67VaV6AJiVqED8mR0AaLQm/6yqcGo7EgtXb+P6kRvnYEi8h6lOa6yj7zfbt2zZkosXLwLg7e1N3759iY2NZeHChbi5ueHs7MytW7eoUKEC165do3jx4vz1119MnDiRag1bEXNgLZYVamHr25aUiGCiNs0FIO3eLdLu3eZmwFgA9An30MdHAWDqVIa727/HukojzJxKk3wzff8anRkaE1N0JkUwsSuKPu4OyalJdBzUA61Wi5ubG6amppw/n97Zp02bNtjZ2QFgYvJvCqPT6dDr9RgMBoYNG8b06dMznP958+Zl+znXr1+frVu38sYbb2QoU+SNdMp5CVhZWWX5/rRp02jfvj2nTp3il19+ITk5OdM6mzZtYtSoUezbt48mTZpkuw+N5tE9Fd2drMl2rWwWaHT/Vswajdb4WqPRwj8PotvX64FTuzGolERuLX0Hs6ToLMsyGAz8/vvvBAUFERQUxLVr1yhRogSAsenk9u3bWR6XRqPJ8O/sZHeuhRC506VLF3777TfWrFmT6QdadtdeTtd2brbPzv3r+X6Scr/8CxcuMGbMGAIDA/n77785cOAAx48fp3LlylnWo0CGH6parZZyxR3Sqz2NJr0uUwqttQMuQ+an//faAkoM+PcZP43O5P7GKEPGTjiWZf2IPfoHyqAHICUiGKUUpk5l2L9tEwCJiYkZmnnvi4mJwcPDg6lTp1KuXDkMBgO2trbExsai1WrR6/U4OTkxadIkPD09mffx+6TcvoLOxgmA+JPbHojRFLMSFY3HYFWpPlYV62LiWIrUOzdAoyEt9g7xZ3ZiUcYzi7P07+dT1Cb9fNnZ2WFqasq+ffuA9CQyNTU1y3MM0KJFC1atWkVUVHoiGx4ezs2bN2nevDmrVq3i3r30xwQePBejRo2ievXqDBkyBCWzUT82SShfQPcrg0eJiYmhVKlSQPoD6Q8zGAxcu3aNFi1aMHv2bEJDQ9Hr9ZnWc3BwwNzcnKNHjwKwcuXKLPdnbW6Cq+M/CZdGa6z8zEtXJeHCfpQ+FX3cXZJDTuTmMI1S797ErHg57Ov3wq6EO7fDrmW5XuvWrTP8Sg0KCgJgz549HDp0iP379zNhwgSio6ON66xatcr4/4YNGwLpd1HWrl0LwIoVK2jcuHGW+8vt5yCE+FePHj1YuXIlW7du5ZVXXsmwrGbNmqxfvx6An376yfh+dtd2w4YNjeutWbOG2rVrZ7vfnK7X7JKUmJgYnJycsLCwICgoiOPHjz+yrPusHqwPAa25FTprBxIuHgBAGfSkRATnWAZaDSolEZtqbTCxL8bNJWMIW/wmd7ctRmtmQakq1QkNvkqDBg2Ij4+nWLFiWFpaGu/sApw5c4Y333wTHx8fihUrhp2dHc2aNePo0aPs3buXv//+m9jYWMzMzPD39+ftceOwdS7J3W2LuLlkDOj+vaNnU601ydfPELZ4JCm3r6DSUtAnxqLRatGaWqC1sCEpOAiFwvKfO65Kn5pe98dHkxYbgc7agdJV/PjvR/9h/fr1hISEYGtry4oVK3j//fdZvXo1CQnpnZnMzMwynWdPT0+mTp1KixYt8PHxoX379ty5cwcvLy/GjBlDgwYN8PX1ZdasWRm2mz59Og4ODowbNy7ncy6yJfd2X0BOTk5Ur14db2/vHJtgJ06ciL+/P++//z5t27bNtFyv19OvXz9iY2NRSjFt2rRsezouWrSIgQMHYmpqSoMGDbh161amddq1a0fNHhO4Hq3Bxrs5N78fhXkZT5zajsKyXA3Cvh+FzsYJs1KV83S8sYd+Iyn0JGg0aM0smTt3Lvfu3aNNmzYZmi/ef/99xowZg4+PD2lpaVSvXp1FixbxxhtvsGrVKsqVK8eYMWMYN24cS5YsAeD27dt4e3sbO+VAetPJ4MGDmTlzJm5ubvz4449ZxjVo0CAGDRqEra2tdMoRIpdq1qzJpUuXqFevXobmbkjvJNinTx+mT59OixYtjO9ndW0vX76cGTNmMGjQIJYuXWrslJOdnK7XB5MUg8GAubk5AQEBtG3blq+//pqqVavi6elpbAZ/uA7O7kdnM49inPv737tyzp0mcOfPhUTvWgYGPTa+bTEr6p7ltiYOxSne52Mi1v2X+LO7cW4/DhOH4gDY+rUj9cZZorZ/xVVHGwIDA9m4cSMjR46kZs2aNGjQAEdHRwDq1q1L9+7d6dChA6dOnWLUqFE4Ojpy6NAh2rZtS4MGDahcuTLjx4/nxx9/xNLSkt5vTubPmyb/Dh3UxB8Aqwq1cWr3FjEHfiVy4xdodKaYl6jAvb9/xrpqYxwaDyAtNorbK6dgSIwBwNqrObeWv4shKQ6nNiOx82xMj1pluOcYzdSpUzExMSEgIICWLVsSEBDAqVOnsLe3Z9CgQXTq1Ik2bdrg5+fHRx99ZOxw1a9fP/r165fpnA0dOpShQ4dmeG/Hjh3Gf+fULC4eTaPk/q54gi7ejqXV3F2FVv4Ax2DWL/uWkydPUrx4cQYMGIC/vz9eXnkbCw3A3d2dU6dOYWNjUwiRCiFedoVdH/46xIdhvbuQlpaGqakpX3/9NbVq1cp3uQUVd/TuFWit7LCr0THD+1vHNaZCMdt8ly+eLGnyFk9UxeK2NKrgTEFPDqHTQKMKzvxnwkiOHz/O0aNH6dWrF0uWLMHb25tatWqxcOHCLJ8hEkKIp+F+fVjQs+XotBoaVXCmesUyHDlyhOPHj3P48OECSSah8OOWZPL5JHcoRZ589NFHrF69OsN7Y8aMYfDgwbnaPiUlhbcmf8AfmhpoTc3ItjdOXiiF0qcwtmIc414fyObNm3n33Xf/WaSIiUlvWrl27Ro6nY5OnToxaNCgTE3iQogXX37rsIJ27U4CLefsJDkt82w3j8vcRMvWcU0o41h4nQT9atTi7I07GB5IIYr3+Qidpd1jl/kk4haFRxJK8cSEhITQq1cvjh49ysAPvmVrTMHNIFP0yhYO/zIPf39/Fi5ciLW1daZ1bt++zU8//cSSJUsyNIkPGjQIT8+sehwKIUTh+/lQKJN+PVlg5X3yqje9arkWWHnZeV7jFoVDmrzFE7FhwwZ8fX2NY8MtnjyY8a0rFUjZrneDOLpmAa+99hqrV6+mVq1anD59OtN6xYsXZ9y4cZmaxL28vKRJXAjx1PSu5Vpg9eGE1h5PLCl7XuMWhUMSSlGoUlJSeOedd+jcuTNNmzbl6NGjxqE7RjWryKxXvTE30eb5WRydVoO5iZZPXvUmcOEkXn/9db7//nveeustdDodtWrVyrZHp0ajwc/Pjy+//NI4lZeLiwtjxoyhZMmS9OjRg02bNpGWlpbfwxdCiFwpqPpwZLMKhRRh1p7XuEXBkyZvUWgebOL+7LPPeOutt7IcWPjanQSmrDvJ7kuR6LSaf4eiyML95Y0qOPNxV2/jszZKKaZMmcKsWbOYMmUKt27d4ocffsixCfxhDzeJlyhRgv79+0uTuBDiiSmI+vBpeF7jFgVHEkpRKNavX8+gQYNwcHBg1apVOQ4ofN/F27GsOBBK4IVwQqMSePCLqQFcnaxoVqkY/eu6ZtsLcNasWUyePJkxY8bg5+fHm2++iZubG6tXr851UqiUIigoiICAAFasWEFUVBQ1a9Zk0KBB9OnTxzh+mxBCFJaCqA+fhuc1bpF/klCKApWSksKkSZOYM2cOXbp04YcffshyjvBHiU9OIzgqnpQ0A2YmWtydrLE2z12P7G+++YY333wzfVaHt9+mb9++XL58ma+++opBgwbl+Xg2bdpEQEAAmzZtkl7iQognLj/14dMUn5xGxer16dajFyNeH/rcxC0ej3yyosAEBwfTq1cvjh07xty5c7Nt4s4Na3MTPF3sH2vbESNGYGdnx8CBA4mJiWHXrl1MmDCBwYMHs2PHjlw3gUP61F5du3ala9euGZrEO3ToIE3iQognIj/14dOglDJOj6iPDMFBH427vQkm6JG048UldyhFgbjfxH1/isLcNHEXtt9//9047dm6detYu3Ytb7zxRp6bwB8mTeJCCJG99957j48++ijT+2ZmZpw5c4by5cs/hahEYZNe3iJfUlJSePvtt+nSpQtNmjTh2LFjz0QyCdCxY0f+/PNP9u3bR+vWrenUqROHDx9Gq9Xm2Av8UaSXuBBCZM/HxyfL9x0cHChVqtQTjkY8KXKHUjy2B5u4P/30U8aMGfPYTdyF6dChQ7Rt25YyZcqwefNmbG1tGT16dJ57gT+K9BIXQggwGAxUqVKFS5cuYTCkzwCk0WiYN28eo0aNesrRicIiCaV4LM9iE3dOTp8+TatWrbCxsWHr1q24urqydOnSAmkCf5g0iQshXna//PILvXr1Mr4uVqwYISEhWFhYPMWoRGGSJm+RJ89yE3dOPD092bNnD2lpaTRs2JDz588zcODAAmkCf5g0iQshXnbdu3enUqV/Z9F5//33JZl8wckdSpGtpUuX4uHhQZ06dYDcD1T+LAsLC6NVq1ZERESwefNm/Pz8SEhI4K233uL777/P1AS+cuVKSpcuTaNGjfK9b2kSF0K8TO7fpbS2tiYyMlISyhecJJQiS+fPn6dKlSrY2dlx6tQpjh49ir+/f54GKn9WRUVF0bZtWy5evMimTZto0KABQKYm8Bs3btC2bVtKlChBcHAwZmZmBbJ/aRIXQrwMDAYD7u7u9O3bl1mzZj3tcEQhk4TyBfe4A+L269ePX375BaUUxYoV4+bNm/kaqPxZExMTQ6dOnTh48CDr1q2jTZs2AJw9e5YePXpw+fJldDodCQkJKKX45ptvGD58eK7Kzss5f5YGTn9eB08WQjybpE55uUhC+QIyTn11PpzQO1lMfeVoRTOPYvSr40rF4pmnvrp/d/LBr0aLFi3466+/nrsm7pwkJibSs2dPNm/ezE8//UT37t2B9GSzYsWKhIeHA+nPRD7qLmV+zzk8nSbxgohbCCHukzrl5SUJ5Qvk2p0Epqw7ye5Lkei0GvSG7D/a+8sbVXDm467elHG0Mi7r168fP//8s3G4h/v++OMPXnnllUKL/2lITU3F39+fVatWsWjRIoYMGcLUqVP573//y8OXRlZ3KQvqnD/ocZvEIyIicHBwwNTU9JHHXRhxCyFeXlKnCEkoXxA/Hwpl+obTpBlUjhfyw3RaDSZaDR908qR3LVdOnjyZaVBaU1NTUlNTadCgAXv27Cno0J86vV7PqFGj+Oabb5g9ezZTpkwhJSXFeNz32dvbEx4ebrxLWVDnPCcpKSn88ccfxiZxrVabZZN4UlISpUqVwtvbmz///DPHh9+fRNxCiJeH1CkCJKF8ISwIvMjsLRfyXc741pX49T/DCQwMBMDJyQk/Pz/8/PyoVq0azZo1w8XFJd/7eRYppZg8eTKffPIJb7zxBtWqVePEiRMcOXKEkydPGuelnT17Nu+8806BnvNRzSrmat3w8HBjk/iJEycyNImfPn2aXr16odVq6dChA2vXrs3y+cunEbcQ4sUldYq4TxLK59zPh0KZ9OvJAitvXP1iON07T+vWrSlWrFiBlfu8mDVrFpMnT2bMmDF88cUXaLValFIEBwezefNm+vXrx6Zzdwv0nH/yqje98vjr/MEm8cjISOzs7IiLi8NgMKDRaBg4cCA//PADWu2/Q80W9HflceIWQrw4pE4RD5KE8jl27U4CLefsJDnN8OiVc8ncRMvWcU1e6mdavvnmG9588038/f1ZtGhRhjt9z9o5T0lJYfny5bz22muZlo0ZM4Y5c+ag0WieubiFEM83qVPEw2SmnOfYlHUnScvD8yq5kWZQTFlXcL84n0cjRoxg+fLlLFu2jF69epGcnGxc9qydczMzMyIiIrLsff/ll18ydOhQ4NmLWwjxfJM6RTxMEsrn1MXbsey+FJmnB6BzQ29Q7L4UyaXw2AIt93nTt29f1q1bx6ZNm+jYsSPx8fHP7DnfuHFjhh7pOp0OZ2dnihcvjo2NzTMbtxDi+SR1isjKS5tQRkdH89133wFw+PBhJkyYUCDlOjs7F3iZWVlxIBSdNuNdqVsrJpESEUxabBQRGz7Lcft7+9dk+b4hKY7443+yfH8okPE4IiIiqFOnDn5+fpw8eZIJEybg6enJRx99VABHlD8Pfp4FYejQoVStWpU///yTnTt30rp1a37YfTHTOb8vLSaCWysmEbboDcK+H0X8ufTe8FF/zCP17k0Arn3ZN9v96bQa4znPq6+//prff/+do0ePcvv2bVJSUoiIiKBy5coMGzYsy+9KQclP3Hl1/9rKq0GDBrFx48Yc15k7dy4pKSnG182aNXusfQlRmB71dyUoKIgtW7bkudwdO3Zw8OBB4+tp06axe/fubNd/nDolKeQEEes+fuR6T7JOeZQNGzYwZ86cpx3Gc+OlHbL+fgLy+uuvU7NmTWrWrFmg5RdGmQ8KPB+e7a9DE1sninbKOZmNOfAr9nW7Z3rfkBRPzLE/CbzwKjPwzHAc27Zto1atWixYsACAgIAAbt++naHjx9NgMBgyfJ4FYfHixQCUL18eS0tLzp07R9iOEyjbollvoNXh2PJ1zIqXQx93l5sBY7AsVxOndm/lan96gyLwQjgz8ESv16PT6XIdq5eXF15eXtkuz+m7kl8Pxv08mzt3LkOHDjUOCXV/pAMhCpPBYMhT/fmovytBQUGcOnWK1q1bZ1qWU72yY8cOnJ2djVPqzpw5M8c4nladkte6Mb86der0xPb1Inhp71BOnTqVM2fO4Ovry8yZM42zpMyYMYMhQ4bQsGFDypYty59//skbb7xB1apV6d+/v3H7zZs3U69ePfz8/Ojfv3+GuxuQfoE+WObQoUNp3Lgx5cqV4+effzau98knn1CrVi18fHyYPXs2AGFhYTRo0IBq1arh4+PDiRMnMpQdl5yWPgOBMhD15wJufDeC8DUzUWnpMaRF3+ZmwFgAlEHPnb++Jez7kel3zs7u5u7OpRiS4gj7YTRRfy7IUPbdXUtJjbzGvs9e471pM4zHcfLkSSZOnMgvv/xCzZo16dq1K3fv3qV69er88ccfWZ7jw4cP06RJE2rUqEHHjh25c+cOV65coUqVKsTExJCYmIiXlxcnT55kx44dtGjRgrZt2+Lh4ZHhV/iyZcvw9vbGy8uLzz5Lv/MaHByMt7c3vXv3pmrVqrzzzjsZPs/g4GCqVauGv78/VapUoVevXsTGxlKvXj2sra2xtLSkbt26xkHC3d3d+fzzz5kxYwYbN26kRIkSnDp1Ch8fH2JiYoiPTyBs7cckXj3K9a8GEzr7Va592Zeorel3RW9+P4rY45sJW/wmUf/7Eo25DYakWG6tmMS1+QO5uyMAQ1Ict5ZPJC02KtO5urlsAns/6oOdnR1OTk40bNiQvn374uPjQ4sWLXBzcwOgc+fOODo6YmNjg4ODA59//jkAEyZMwMPDg2rVqvHhhx8ay01ISSMk4h7hv35E7LGMn1Na9G3Cvh9FxIbPuPHtMKI2LyThwj5u/vg2YYvfJPXOjfQyLuzj5o/jCPvhLcJXz8CQFAdA5MY5RG3+iv2zh+FRuQo7d+4E4OTJk1SvXh1fX198fX2NMw49LC0tjTFjxuDt7Y2Pjw+//PILAK+//jo1atTA09PTeE087KOPPjJud/8Ogru7O3Fx6bFt3LiRQYMGZdpu+vTp1KpVCy8vL8aNGwfAwoULCQsLo379+sY/IPfvhiqlGDt2LF5eXvj6+rJ161Yg/cdUz549adWqFRUqVDB+DkI8yoN1l4eHB++88w61atWiWrVqrFixAoD4+Hi6du1K1apVGTx4MG5ubsTFxWX4uxIYGIi3tzfVqlWjZs2a6PV6pk2bxtKlS/H19eWPP/5gxowZ+Pv7U79+fd566y32799PvXr1qF69Ok2aNCEkJIRr167xzTffMGvWLHx9fQkKCspwV3/Lli34+vri5eXF22+/TWxSKqF3Erj2ZV/ubv+esO9HcnvlFAwpSZmONTnsPGGL3yTsh9HEn//b+H5q9K30Fp3vRxG+Zib6xIxN3KFRCcQnpwHp1/WkSZPw8/Nj+/btLFu2zHi+3n77beM5fbi+v/8YUFbX/P1yp06dSrVq1WjQoAGHDx+mefPmlCtXjnXr1gHp1/n48eOB9JaOMWPGULduXSpWrGis77L7rF5GL21C+dFHH1G1alWCgoJo3LhxhmUhISHs3LmT5cuX0717dwYPHszp06e5cuUKx44dIzIyks8++4zt27dz7NgxypUrx6JFi3Lc3+XLl9m2bRt//fUX7733HpB+oV6/fp2DBw9y7Ngx/vjjD06dOsXKlStp2rQpx48f5+jRo1SoUCFjfFHxKCDh/F70cXdxGfY1Do0HknLrUqb9xgX9iSEpjpJD5uPy2gIsyvpRpMlAtBY2uAyZj1PbURnWL9J4IKbOZXAZMp8+I/69+Ly9vZk5cyYDBw7k8OHDrFu3DgcHB4KCgmjXrl2m/aampvLOO++wbt06jhw5QteuXfnvf/9LuXLlGDlyJO+88w7vvfcevXr1wtvbG4D9+/fz3Xffcfr0aQ4cOMCOHTu4ceMGM2bMYOfOnRw+fJiVK1dy5MgRIH3e7SlTpnDu3Dk+//xz4+c5bdo04/J3332XM2fOcPv2bebOncuVK1e4du0aCQkJDBgwgMTExBw/t+rVq2Nqasrhyzcp3m8WUZvmYupchtLjVmHuUonU21dIuHQIQ2IMFq4+uAz9Cq2VPYaEaEzs/r2bqbO0Q2thg7VnM6J3Ls1yX/qEGLYdPcfJkyc5cOAArq6unDhxAkdHR2OcrVq1Ms6t3rhxYz788ENu3rzJqlWrOHv2LMePH2f06NH/flci7hG+bhaWZatj65fF5xR1HYcGfXAZ9g1JoSdJvn6Wkv5fYFujI7FH0v+gmLt6U2LgF7gMmYdF2erEHt30b8yxUZQYNJfPvglg2LBhKKX47rvveOONNwgKCmLfvn04ODhkebzfffcdd+7c4fjx45w4cYJWrVoB6UM3HTlyhOPHj7N27VquXbuWYbs//viD7du3c/jwYU6cOIG/v3+On+GDxowZw6FDhzh58iShoaH8/fffjBw5EhcXF/bu3cuGDRsyrL927VouXbrEiRMn+O233xg6dChJSel/OE+cOMG6des4fPgwn376aaYflUJk537dNWHCBEqWLMmhQ4fYv38/n376KVFRUSxcuBA3NzfOnDlDv379CA3N3AT8xRdf8MUXX3D8+HG2bduGTqcz1tEP1suXLl1ix44dLFy4kKpVq7Jnzx6OHj3KO++8w4cffkiZMmUYMWIEkyZNIigoCF9fX+M+EhMTGTZsGL/99hsnTpzg/PnzfL/8ZxSk13nlauDy2kJ0Nk4kXNibKcao/83Dqf04XIbMx5AQY3z/7l/fYlu9PS6vLcC8dFXu7fkpw3YKCI6KN74uU6YMx44do3Tp0qxfv559+/Zx/PhxIiMj2bRpk/GcPljf35+EI6tr/r4KFSpw/Phxqlatyrhx4/jf//7H//73P2bMmJHl53bnzh3279/Pt99+a7yLm5vP6mXx0jZ556Rdu3bodDq8vb2xtbU1NgN4eXkRHBzMjRs3OHHiBPXq1QMgOTmZ9u3b51hmhw4dMDU1pXz58kRHRwPpCeWmTZuMz6rExsZy4cIFatWqxcCBAzExMaF79+7GhOu+lH+GaUi+fgbrKo3QaDSYFXPHrFjZTPtNDA7CrlYXNJr03w46C5tcn4eUfAwHcf78eY4fP07z5s2B9LtR9+ejHjlyJE2aNCE+Pp4DBw4Yt2nQoAGuruljkHXv3p09e/YQHR1NixYtjHcS77/fuXNnKlWqlGlWnwd5eHhQtWpVAPz8/IiMjDQ+W+jg4ICZmVmm6SWzYmZmRqpekXbnBhozK1IjQrj149sYkuIwJCeQdjcMdKZYedRHnxhL8rXTmNhnHMPTqmoT7u1fg3XVJsQc+DXL/ZiXqoKJmSVlyhRBq9VSp04dAKpWrcq2bduA9Erz7t27NGjQgPDwcGxtbUlKSsLe3p4hQ4bQpUsXOnToYCxzytg3sXBvhK1f1lNmmjqVwtSp9D//LoOFe7X0fxd1J/HyYQD098KJ3P49+oRoVGoKZi6VjNvf//6VKVcJGxsbbty4Qb169Zg5cyZRUVH07NmTcuXKZbnvrVu38vbbbxub/IoUKQLAypUrWbx4MXq9nuvXr3Pu3DnKlCmTYbvBgwdjbm4OkO1UlFnZtm0bn332GUlJSYSHh9O2bVsaNGiQ7fp79uyhb9++aLVa3N3dqVSpEufPnwfSk3sbm/TrycXFhdu3b2eIU4js3K+7Zs6cyalTp1i+fDkA9+7d48qVK+zdu5d3330XgJYtW2b5HW/QoAGTJk3i7Nmz9OjRA3t7+yz31blzZ+OjHHfv3mXAgAFcvnwZg8FgvOayc/78eTw8PHB3dwfSp+XdtH03FO2AxswSS3dfAMxKVCDt3u0M2xqS4lBpqZiXTB+s3NqzKfGn0uux5JsXKdp92j/vNyN89QeZ9v3g358ePXoA6dfv/v37jc3+CQkJxtaMh+v74OBgGjVqlOM1f79FwtvbG2dnZ8zNzfHw8CAsLCzL89GlSxcAatSoQXBwMECuPquXxUt7hzIn9/9QabVa47/vv9br9RgMBtq3b09QUBBBQUGcPXs226a5h8t8kMFgYPr06cZyLl++zKuvvkrjxo35+++/cXFxoU+fPpnumpiZPPCxZTFcTEHJsJ88MhgM+Pn5GY/t1KlTrFq1CkhPnCMjI0lISDDe7QEyDH2j0WiyHArnQVZWOY9V9uA51+l0lC9fnvr16/PZZ5/h5OTEiBEjsLOzA8DExMQYy4PDBN2P5d9zobCp1gaXIfMp0sQfG++W2NXqnL4kLZWItR9i7dMK7cOJ+4PHks1haUzMjPvRaDTGZ4WUUiilSEpK4qeffqJr166cOHHC+AiGRqPh8OHDdOvWjdWrV9O7d29jmX4165AUHIQy6LPep+7feb81Go3xtUajAZVeod/Z+h12dbvh8tpCirQcBvrUBwpIPxgzE63xM+vbty8bN27E3NycVq1acfTo0awPOAtXrlxh4cKF7NixgxMnTtC8efNMn0d2TExMjD8QstomKSmJsWPHsn79euP5y23ZWXn4+6XXZ32OhXjY/brLYDDw7bffGuvJ4OBgatWqRW6Gh540aRI//PADsbGx1K1blxs3buS4L0jvbNO+fXtOnTrFL7/88ljff90/1/yDdQdaLWT14zy7OjwXf7Ye/Pvz4PkaNmyY8XxduHCBMWPGAFlfj4+65rP7W5/d+b+/zoPXuwzl/a+XNqG0tbUlNvbxhiaoV68egYGBhISEABATE8PVq1fzXE7r1q1ZvHixcVq/4OBg7t27R0hICCVKlGD48OEMGDAg0zOU7k7WaADz0lWJP7sbpRQpESGkhGeOwdLdl7igP1H/JAf6f55/Q6PNMsnQmFtiSElE889+HlflypW5du2asXk6OTmZc+fOATB+/HhGjhzJ0KFDmThxonGbv//+m+vXr5OWlsbatWtp2LAhtWvXZtu2bdy9e5fk5GR+/fVXGjVqlGl/ufk8k5KSuHnzJl5eXowdO5YjR45gaWlJSEgIrq6urFmzBqUUv/32W4bttFotZRwsMHUshUpJJP5UIGkJ0cSf2YlZMXfS4u6APpXbq97Dws0HlZaCeemqGcpIOJt+Fzr+3G7MS2ffgeX+OdfpdAQFBQFw6tQpY/wajQZLS0vu3btnjDM+Pp579+7RsWNHvvjiC+N2AOPHvYV5ifJEbZr72BWfITkBnY0TSiniT23PsCz+7G5QiqSIUGJjY3FxceHKlSuUL1+ecePG0bp1a86cOZNluS1btuS7774zJoF3794lNjYWGxsb7OzsuH79uvGZxYe3W7JkifEPw507dwBwc3MjKCgoy88Q/j1/Tk5OGc4fZP/9adiwIT///DNKKUJCQrh48SIeHh65Om9CPErr1q356quvjMnJqVOn0Ov11K9fn9WrVwOwfft243f8QZcvX6ZatWpMnTqVqlWrcvXq1UfWgzExMZQqVQpIfz7wvuy28/Dw4MKFC4SEhGAwGFi5ciWvtGqem3wQrYUNGp0pyf88ihV/ZqdxmXmJiiT880xl/JkdWJTJWCdm9/enRYsWrFq1iqio9OfQw8PDuXnzZrYx5HTNF5TcfFYvi5c2oXRycqJ69ep4e3uza9euPG1btGhRFi1aRLdu3fDx8aFx48bG5DIv2rZtS9euXalbty5eXl7079+fpKQkduzYQbVq1fDz8+PPP/80Dk59n7W5Ca6OVlh51Edn7UDYojeI3rkUsxIVMu3DxrctWgsbbn4/irDvR5EUHJT+vndzbn4/ytgpJ+yH9OfudJZ2mBUvz60fRjLrw8zNELllZmbGqlWrGDNmDNWqVaNGjRocP36crVu3cv78ed58803GjRvH8ePH2bFjBwB16tRh6NCheHp6UqtWLZo0aYKLiwvTp0+ncePG1KhRg169elG9evVM+3vw88yuh+Lt27fRaDQ0btyYIUOGsGfPHrp27Urz5s0JDw/nypUrLF68ONMzfy1atKBuTT9S9wTg1H4sypDGjYWDSQ47T8yRTRgSY9GYW5Fy/Qz39q8h9vAG4s/9TUp4sLEMffzd9CGZTm7DofEAAGKP/ZGho4yVmQnW5ulPoZibm7Nx40Z8fX2Nj0g4ODjQoEEDAgIC6NixI3Xr1gXSE8oOHTpQrVo1WrduzSeffJKhTJ9OQ9Fa2HB3W/pzvvc/69xyaNiHiDUzufXjOHS2GYfu0dk4ErX8bfr37smiRYvQaDSsWrXK2IklLCyMrl27Zlnu66+/joODg7FjwdatW6lWrRpVqlShcuXKvPbaazRs2DDTdu3ataNp06bGjj/Lli0D4P3332fo0KHUqVMny+c2HRwc8Pf3p2rVqhnOH8CwYcNo1qxZpl6dr776KuXKlcPb25vOnTuzaNEiLCws8nT+hMjOsGHDcHd3x8/Pz9hpRCnFyJEjuXz5Mp6enixfvpxSpUphaWmZYds5c+bg6emJj48PLi4u1KtXj2bNmnH06FH8/Pyy7Cw5ceJExo0bR/Xq1Y3N4AAdO3Zk5cqVxk4591laWvLdd9/RuXNnfHx8qFixIr17dMM1h5lsonctJ+Fi+qNMTq+MJmrjF4T98BY6S1vjOkVaDSf2yEbCvh9Fcugp7BtmHFLN1cnKWBc+yNPTk6lTp9KiRQt8fHxo3759jglcTtd8QcnNZ/WykKkXn1MzNpxm2YGQwhm6QRmIObIRw6FV9O7dm0GDBlGnTp1HNkHnx44dO1iwYAFr1mQ9PuazIKdzfu3LvpQZ81MWW8H1r4bgMnQhWrPsKxmdVsOAOm7M6FTww+8U1nclcuMcbKo0ZET/HoUStxAvq7S0NPR6Pebm5hw8eJCRI0dy6NChpx2WUWH+/SnMurAwPOuf1ZP00t6hfN71q+NaaOOAodHyy8ejGT16NH/88Qf16tWjSpUqzJo1K9vndF4GhXnO9QZF/7quhVJ2YcZtUIUXtxAvq7i4OOrXr0+1atV48803+eqrr552SBk8r3VhYXjWP6snSe5QPgeioqJo0aJFhvfMzc2p9Po89l6JKtgLW+lJDD5OC216R6MSJUoQGBjIkiVL+PXXX0lOTqZVq1YMGjSIzp07Y2lpyciRIzMMxQDp42u2adOm4OJ6Rgz4/kC+znlKeDCRGzOOWWjqUIJXJ85h2Wt1CiLELOU37qzotBrql3N6ZNwnT55kwIABGd4rW7ascaw3IcTzp6DrlKgtX5Ny/YzxkS54cf+OvKgkoXyOXbuTQMs5O0nOx/A+DzM30fKGayQfT32HxMREZsyYwVtvvYWpqSn37t1j9erVBAQE8Pfff2Nvb//EmsSfFYV1zreOa0KZHJ5Lyq/nNW4hxLNJ6hTxMGnyfo6VcbTigwJ+zmRmJ0/GDhvAhQsXGDx4MBMnTsTX15cdO3Zgb2/P0KFD2bNnDxcuXHgpm8QL65wXdgX6vMYthHg2SZ0iHiYJ5XOudy1Xxreu9OgVc2FCaw961Up/dsXBwYF58+Zx5MgRHBwcaNasGX379jUO+FqxYkX+85//EBwczNatW6lVqxYzZ87E1dWVtm3b8vPPPz9yFprnVWGd88L2vMYthHg2SZ0iHiRN3i+Inw+FMn3DadIMKk/PtOi0Gky0GmZ28sz2YjYYDCxbtowJEyZkagZ/UExMjLFJfM+ePS98k3hhnvPC9LzGLYR4NkmdIkASyhfKtTsJTFl3kt2XItFpNTle2PeXN6rgzMddvXPVzBAdHc20adNYuHAhlStXZuHChTRt2jTLdS9evMjSpUv58ccfuXbtGh4eHgwaNIgBAwYYB9Z9EeTpnGtAr8jTOS8shf1dEUK8XKROEZJQvoAu3o5lxYFQAi+EExqVwIMfsIb0QWObVSpG/7quVChmm10x2QoKCmLkyJHs3buXPn36MHv2bFxcXLJc12AwEBgYSEBAAGvXriUpKcnYS7xLly4vzACwD57zkKiETMsN925hcfcKS6cNo24V9ycfYDYK+7sihHi5SJ3y8pKE8gUXn5xGcFQ8KWkGzEy0uDtZZzkDQV7lthn8QVk1iffq1YtBgwZRt27dF6ZJ/M+tgXTq9xpj3x6P/4B+uDtZE3zpPK1atcLGxoatW7fi6vrsNe/EJ6dRspIPI0ePoX/f3gX2XRFCvJzik9OoWL0+3Xr0YsTrQ6VOecFJQinyJS/N4A96uEm8UqVKxibx0qVLF37ghWDBggX8/fff3L59m8DAQHx9falcuTJVqlRh2rRpXLlyhZYtW5KWlsZff/31zMwJfebMGXbuTJ9nd8yYMXTp0oVmzZrh4OBAjx49MDGRPwBCiNy7dOkSf/31FwCTJk2icePGtGvXDltbW3r27Jlh2kfx4pCEUhSIvDSDP+hFahK3srLKsme7RqPBYEgfqy0sLIxWrVoRERHB5s2b8fPze9JhZjJ48GACAgLQaDQ8XB3cvHmTEiVKPKXIhBDPo3HjxjF37tws65QLFy5QsWLFpxSZKEwybJAoEL6+vuzevZuAgAC2bt2Kh4cHn3/+OampqTlup9VqadGiBcuWLePWrVssWrSIhIQE+vbtS8mSJRk+fDj79u3LVCk9i3r27Jnl+40aNTL+28XFhV27duHm5kazZs0yzTD0NIwYMQIgwzk2MTGhc+fOkkwKIfJs2LBhmZJJnU5HkyZNJJl8gckdSlHgHrcZ/EEXL17kxx9/ZOnSpc9Nk3hCQgJ2dnbo9foM71+5coWyZctmeC8mJoZOnTpx8OBB1q1b99SnF2vTpg3bt28nLS3N+N6xY8fw9fV9ekEJIZ5bvXr14tdff81Qp+zcuZPGjRs/xahEoVJCFJJjx46p+vXrK0D16dNH3bhxI89l6PV6tXXrVtW/f39laWmpNBqNat26tfrpp59UQkJCIUSdP/7+/gow/te4ceNs101ISFAdOnRQpqamavXq1U8wysz2799vjFmr1arOnTs/1XiEEM+306dPK41GowCl0WhUkyZNnnZIopBJQikKlV6vVwEBAapo0aLKxsZGzZ49W6WkpDxWWffu3VOLFy9WDRs2VICys7NTr7/+utq7d68yGAwFHPnjiY+PV1qt1picXblyJcf1U1JSVJ8+fZRWq1Xff//9E4oya61btzbGfezYsacaixDi+dezZ09jnbJz586nHY4oZJJQiifi7t27avTo0Uqr1aqqVauqwMDAfJV34cIF9d5776kyZcooQFWqVEl9/PHH6tq1awUTcD60adNGAapcuXK5Wj8tLU2NGDFCAWrOnDmFG1wO7t+l9Pb2fmoxCCFeHKdPn1aAcnd3f9qhiCdAnqEUT9Tj9gbPzrPYSzz41h2a9X6ddyZOwbVUceqWdaKYnUWO2yilmDx5Mp988gnTp09n+vTpT3xczvjkNEZN/oBXOnTEs7KHjBknhMiX+OQ03pkxi3oNGlGzuq/UKS84SSjFE/c4g6LnxsMDp9vZ2RnnEi/sgdO3nrnF539d4GJ4HGlZTDlmotVQsZgN77SqRMuq2fecnjVrFpMnT2bMmDF88cUXaLWFOxCDcVaL8+GE3sliVgtHK5p5FKNfHVcqFpdZLYQQOZM65eUlCaV4agqiN3h2ntTA6QeuRDF8+RGiE3MeHulBDpamfNu/BnXKOWW5/Ouvv2bkyJH4+/uzaNGiQhlYXObdFUIUJKlThCSU4qkr6GbwB+W1SVyv1zNv3jy6d+9OmTJlciz77V+C+PXYjceO7VW/UnzR0zfLZStWrMDf35/OnTvz008/YW5u/tj7edjPh0KZvuE0aQaVY6X/MJ1Wg4lWwwedPOld69mbOlII8XRInSJAEkrxjCisZvAH5aZJfMuWLbRp04ayZcuyf/9+ihUrlmVZ/b/fz55LUfmOqWEFJ5a/VjfLZb///js9evSgcePGrFu3Dmtr63zvb0HgRWZvuZDvcsa3rsSoZjJAsRAvO6lTxH2SUIpnSmE2gz8ouybxffv28ccff6DRaKhSpQq7d+/G3t4+w7b5vTP5sG7VS/F5D98slwUGBtKpUyd8fHzYtGkTDg4Oj72fnw+FMunXk4+9/cM+edWbXnJXQYiXltQp4kGSUIpnUmE2gz/owSbxNWvWkJSUZFym0+moU6cOW7duNTaNH7gSRa9F+ws8jlXD6mb7TOXBgwd55ZVXKFOmDJs3b6Z48eJ5Lv/anQRaztlJcpohv6EamZto2TquiTz/JMRLSOoU8TCZy1s8kx53bvC8enAu8f/+978Zlun1evbt20fz5s1JSUkBYPjyIwW6//tyKrd27drs2rWL8PBwGjVqREhISJ7Ln7LuZJa9z/MjzaCYsq7g7k4IIZ4fUqeIh0lCKZ5ZWq0Wf39/Lly4wODBg5k4cSK+vr7s2LGjUPa3YsWKTPtXSrF//36GDx/O1jO38tSbOy+iE1PZdvZ2tss9PT3Zs2cPaWlpNGrUiPPnz+e67Iu3Y9l9KTJPD8vnht6g2H0pkkvhsQVarhDi2SZ1isiKJJTimefg4MC8efM4cuQIDg4ONGvWjL59+xIWFpbnsoYOHcrly5cB+PTTTzMsi42NpUiRInh6etKuXTuGDh3KuHHjcHV1Zc+ePXRt2YD4c3vytL+EC/tIvZO75y1nb8k5SSxXrhx9+/bF1taWRo0a8b///Y+mTZtStWpVfHx8WL16dZbbrTgQik5bOGNw6rQalu8PLZSyhRDPJqlTRFYkoRTPjdw2g+eUNC5evJjy5ctnWHbt2jWaNm2KVquldOnSTJ8+nU2bNvHtt98yYcIE1q9fz8WLF3Hq8QF3t36HISWJR0kKOUFy2HkSLuwn9c4NonctJ+naqQzrKIM+w+uL4XGZynnwWOzt7VmwYAG7du3Czc2NXr16MWjQIM6cOcOWLVsYO3Ys8fHxmcoIPB/+WHcSbq2YREpEcI7r6A2KwAvhOa6TnJxMy5Yt8fX1ZfPmzTRr1izPsRSm4OBgfvnll6cdhhAZHD58mAkTJmS7PCgoiC1btuS53B07dnDw4EHj62nTprF79+48lfGoOiUp5AQR6z7Oc2yQuzolOznVLdHR0Xz33Xd5LvPh+mHDhg3MmTPnseJ70ckcSOK5cr8ZvHPnzkybNo2JEyeyePFibG1tSUxMBGD27NnUqVOHyMhIPv30U6ysrIiMjGTGjBk0bdqUBQsWsHLlSqKjo/H19cXHx4e5c+dy8uRJPv/8c/r27cuuXbuYP38+JUuWpGTJktyOSQKrImgt7TAkxaI1s+D6V0OwrtqYxMuH0Jpb49z5XUxs0zvWJIWexJCaSOKlAyRfO4XG3Aobv3bcWjEJs+LlSL5+BtvqHdDZFCF6z0+otFTMirpyfVxDSjvb8frrr3PkyBGSkpJYt24d48ePJyEhAYPBQIsWLahevTrW1ta8+eablCxZkpYtW+Ls7MydO3ewtrbG3d2d3r178/vGjVy+pzLEVtBCoxKITUzB1tIsy+XHjh3DwsKCoKAgANq0aVMocTyu+38wevbs+bRDES8wg8GQp5mvatasSc2aNbNdHhQUxKlTp2jdunWmZXq9Hp1Ol+V2O3bswNnZmdq1awMwc+bMXMcEEJecRuidhDxtk1ehUQnEJCRjZ5W38XcDAwOzXXY/oXz99dczLcvpfD1cP3Tq1ClPMb1Unvz04ULkXWxsrGrTpo3y8vJSXl5e6s8//1ROTk7q2LFjysPDQwHKy8tLXb9+XTVo0EA5ODioKVOmKJ1Op0qVKqVq1KihlFKqcuXKytPTU/n4+CgLCwtj+WfOnFHdunVTqampysfHR7Vp00a5u7sra2tr5ejoqKxsbJVJUXelsyuqzMt4KbPi5RUmZsq+YT/l3GmCQqNRmFkpywp1lGO7MQoTU6WzdVJoTZRZqSpKa2mnrDybKdOibgqNRmkt7ZTGwkZhZqmK9/tEodUptCbK2tZetWvXTl29elUppVTjxo2Vj4+P2r9/v9JoNEqr1apq1aqpY8eOqf79+ys3Nzel0WiUp6ensrS0VLa2tsrU1FSZmpqqZs2aqYpVqiqTIi5Ka+OkTIu6K62VgzIt6qZMnV2VeemqyqxERWVa1F2Zl/FSJo6llGlRd2XfqL9ym7RRmZfxUiVfW6BcJ6xTlpXqKcc2byq3SRuN/5Ua8b0yLeqmrKo0UmXLV1RxcXHq7bffVjVr1lQ+Pj5q+fLlKjIyUpUvX17Z29uratWqqdu3bysnJyellFKBgYGqZcuWqnPnzqpixYpq3Lhxxs/jzz//VHXr1lW+vr6qX79+Kjk5WSmllJOTk3rrrbdU5cqVVadOndSuXbtUgwYNVPny5dXevXuVUkrFxcUpf39/VbNmTVW9enW1ZcsWpZRS06dPV6+99ppq1KiRKlu2rFq5cqVSSqn69esb41u0aFHhfpHFS+Xq1avKy8tL9erVS1WoUCHT9aFU+ve1S5cuqkqVKmrQoEHK1dVVxcbGqsDAQNWtWzellFLbt29XXl5eysfHR9WoUUOlpaWpMmXKqKJFi6pq1aqpTZs2qenTp6uBAweqevXqqTfffFPt27dP1a1bV/n5+anGjRur4OBgFRoaqooXL65KlSplrEf8/f3V77//rpRSavPmzapatWrK09NTjRs3ThkMBqVU+nX3zjvvKC8vL1WnQWNV5u01GeoCt0kbVYmBnytTZ1dlWqyssqneXll51FdukzYqlxGLlXkZL2Va1F1ZVqitSo9ZmWnb+//p7Iopu7rdlWmxcuq7levU0qVLjefrwfrhvffeU5UqVVLNmjVTbdu2NcZ/v265ceOGql+/vvLx8VHe3t7q+PHjqm/fvsrS0lJVq1ZNffDBByowMFA1a9ZMvfLKK6pOnTrq3r17qlmzZsrPz09Vq1ZN/fXXX0qpzPXDkiVL1DvvvKOUUury5cuqSZMmytvbW3Xs2FFFRUUppZRq0qSJmjhxoqpRo4by9PRUp06dKuyv2jNBEkrxXFizZo3q27evUkopg8Gg7t27Z6w8zp49q6ysrJSJiYmytLRU5cqVUw4ODkqp9Apm/vz5avr06erMmTPK2dlZHTt2TCmllLm5udq4caNSSqn58+erUqVKqUqVKikLCwvl7u6udDqdKlmypPrss89UJZ+aSmthozQWNsYKUWNhq2x8X1GmRd2U1rqI0tkXV2XGrlJFu09TpsXKqiKthisTh5LKvHRVZeXZXDl3nazQmSqNpZ1yfOUtZVaigkKrUxpTcwUorZWDsnVwVJMmTVKvvPKK8vX1VdbW1srOzk5t2bJFWVpaKmtra+M58ff3V926dVNdu3ZVgHJ0dFRxcXEqNDRUAapTp07qaMgdpbNxUhozq/QkcORSVebtNapozw+UiUMJVbzfJ6rU6GUKnakqMWSecpu0UZUZu8qYUJYYNDc9SW4zMlPlX2rE9wqNVpUcMl8dDbmjvv32W/XZZ58ppZRKSEhQPj4+KjIyMsMfxvufiVLpCaWjo6O6deuWSk5OVhUqVFAhISEqIiJCtWjRQiUkJCillHr//ffVggULlFJKAWr79u1KKaVatmypevbsqfR6vfrzzz9Vp06dlFJKTZ48Wa1evVoppVRERITy8PBQBoNBTZ8+XTVt2lSlpKSoS5cuqfLlyxvjeDA+IQrK1atXlU6nU8ePH8/2+vjkk0/UmDFjlFJK/fXXXwrIlFB26NDB+MMoOjpaKaUyJDZKpf9gql+/vvHH171791RaWppSSqn169eroUOHGtebP3++cbv7CWVCQoJydXVVV69eVXq9XrVr106tXbtWKZV+3d1PsNq/2ks5dXg7U31gWtRNlfCfo9wmbVRWlRsZE0rL8rWUc+d3ldukjcqh6SBlW6NjjgmlY6sRym3SRrVm6z7jj3yllBowYIDauHGjOnDggKpVq5ZKTk5Wt2/fVkWKFMmUUM6ePVtNmTJFKaVUamqqio+PV1evXjXeWFAq/bq3s7NTN27cUEoplZKSomJiYpRSSt28eVP5+PgY13uwfnjwvLdv316tWrVKKaXUrFmz1OjRo5VS6Qnl1KlTlVJKff/992rIkCG5+8I85+QZSvFc8Pb2ZteuXUycOJH9+/djZ2dnXFa5cmWmTZtG69atsbe358qVK8TExBh7gycnJwOwbds2YmJi6NWrF76+vqSmpnLp0iUgvTmqf//+mJiYsG3bNgIDA6lSpQpmZmZ069aNyFvXMS/tiUqK5/bKKYT9MBqVkoA+LgrzUlUwJMVhSEkEMj9XZFaqChqNBn1sFBoTMzRaE6wq1cW8jBdaS1tsa3QEnSmlRy9Do4FGjRqxc+dOduzYQc2aNaldu7bxGB7WtWtXIiIiqFKlCnfu3MHPz4927dr9u28TLYbkeDT/NOck3zjD7ZWTifr9c9LuhRO54VNu//w+KMXdrYtIuLAPjZmFcfuoTXOxcPfF1u+VLPdv6lgKs2JlMTPRsmXLFhYvXoyvry/16tXj3r17XLlyJcfPtX79+hQvXhwzMzO8vLwICQlh//79nDhxgnr16uHr68vq1au5evUqADY2NsbnpLy9vWnevDlarRZvb2+Cg4MB2LJlCzNnzsTX15eWLVsSHx/P7dvpPeg7dOiAqakp5cuXJzo6OsfYhCgIlSpVwsfHJ9vrY+/evfTq1QuAli1b4ujomKmMBg0aMGnSJObNm0dCQvbNzZ07d8bMLP3Rk7t37/Lqq6/i5eXFpEmTOHPmTI5xnj9/Hg8PD9zd3dFqtfTr18/4bKWNjQ0tW7YEwKuaL2n3Mo5IYUiKQ6WlYl4yfaYba8+mxmXJNy9iVbnhP+83I+na6RzjuL/u0f272b9/PzVr1sTX15f9+/dz6dIl9u7dS9euXTEzM6NYsWJZPjdZq1YtVqxYwfTp0zl79ixWVlmPa9mgQQPj+MZKKSZOnIi3tzdt27bl/PnzxuHisnPo0CF69OgBwIABAzI8i9q1a1cAatSoYaybXnSSUIrnQqVKlQgKCsLT05O3336bBQsWoNGk9zIMCwtDKUWtWrWYO3cuTk5OmJiY0KxZM2JiYli7di2QnjSWLFmStWvXEhQUhLOzM6NGjQLSk7h58+YxcuRI6tevT2RkpHHYoHHjxlHO3Q3z0lXQmJjiMmQ+LkPmo7NxwsKtGo5tRqIxMUNn5cDNgHEYkh+o8LU6NFnMHaDRmWLq6IIhKR6Vml5pGZIT0BgMJCQkYGJigp2dHcnJyezf/+9A6lqtFr0+vTOPUorvvvuOFi1aMGHCBGxtbbl48SLdunVDo9EQHByMu5M1yqDHxL44Ki2Fu1u/o2i397H2aoaFux92dbtT6rUFuL69GrtaXYg/t4fI9Z8Y92deqjJJwUGZOhAZj8PUHA3g7mSNwWDg22+/JSgoiKCgIIKDg6lVq1aOn+uDc5TrdDr0ej0Gg4H27dsbyzl79iyzZ8/OtL5WqzW+fvC8GAwGfv/9d+P2165do0SJEpm2F+JJuJ/MZHd9qFzMLTJp0iR++OEHYmNjqVu3LjduZD1yxIOJ07Rp02jfvj2nTp3il19+yfZHaW48eN0UtbMEQxaDmWuy6fWdx87g9+sUB0sThg0bZjxfFy5cYMyYMbk6X40bN+bvv//GxcWFPn36sGHDhizXe/B8rVixgvj4eI4dO0ZQUBA2NjaPTCg12R0z/56z+/Xay0ASSvFcCAsLw9raGn9/f8aOHUtQUBD29vaEhIRw7NgxPvzwQ7755hvmzp1L2bJlmTlzJkWLFiU1NZVDhw6xd+9eGjduTEREhPHOVI8ePahSpQrDhw/n008/pW3btnz33Xf4+PgwZMgQ0tLSSE5OZsOGDYRcvcK9fWtQ+lTiz+wC0hO6lMgQwhaNABT2jfqBVkfMgV/Tf7GnJGLiUJz4s7tIvHQAjakFKi0FZdCjlIHES4ewcPMh4dwe0Kdya8lolDIQFBRE2bJlqVy5MufPn6d69epAeuXl6+uLt7c3I0aMIDw8nN27d/Pbb78xffp0EhIS6N+/Px988AFKKZKSkqhb0w+tVovWygGVlgJo0OhMMS9VlaTQEyh9GoaURFIigrEo44Vji6Gk3L5qPO+2NTpgVqI8UZvmZluRuzpZYW1uQuvWrfnqq6+MleepU6ceqyKtV68egYGBxgHcY2JijHcoc6N169bMmzfP+Pp+Z6Ds2NraEhsr496JwpXd9VG/fn3jkF/bt2/nzp07mba9fPky1apVY+rUqVStWpWrV68+8nsbExNDqVKlAAgICDC+n912Hh4eXLhwgZCQEAwGAytXrqRx48aZ1jM30WFvaZrhPa2FDRqdKcm30lt84s/s/Hf9EhVJOP/3P+/vwKKMZ7Yx3+fqZEW7Nq1ZtWoVUVFRAISHh3Pz5k3q16/P+vXrSU1NJSIiIstxiUNCQihRogTDhw9nwIABnDhxIlfnq3jx4piYmLBx40bjfnParmbNmsYbFitWrMjyfL1MJKEUz4WTJ09Sq1YtfH19mTt3Lm+//TYffvghzZs35+OPP6ZXr16MGDGCffv2YW1tTfv27QkPD2f06NHY2try119/0a9fP8aOHcuoUaPw8fHhwIEDrFu3jgEDBrBq1Spj86xWq+XTTz/F0tISCwsL3n33XQwGAxa2DhTtPp3Yoxu5vsAfQ1wUFqWrYupUBq2FLVEbPsOQnIBF6aqYlahA/JldJIedx67Oq1hWqIPO2oFi3d9HJcdza+l4TBxdsChXAxvfNmgtbXEo70dcXBzHjh1j165dnD9/Hh8fHxYuXEiHDh0wMzMjOjoaMzMzRowYQfHixdmwYQNBQUHMnDmTDh068Pfff+Pqmj4XbmJiIseOHaN+54Gk3bvFrRWTUErPzSVvEXt4A2bOrsQcXMfNgLHc/mkyt5aN5/bP7+PQdFCGc+/QsC9aCxvublsEQNgPozMsb1apGADDhg3D3d0dPz8/vLy8GDduXK7uJjysaNGiLFq0iG7duuHj40Pjxo3zNDvQ+++/z7179/Dx8aFq1arGu5vZ8fHxITU1FV9fXxYvXpzneIXIjeyuj5EjR3L58mU8PT1Zvnw5pUqVMk71et+cOXPw9PTEx8cHFxcX6tWrR7NmzTh69Ch+fn788ccfmfY3ceJExo0bR/Xq1Y3N4AAdO3Zk5cqV+Pr6ZvixZWlpyXfffUfnzp3x8fGhYsWKdOnSJctjKetsjU6rIXrXchIuHgDA6ZXRRG38grAf3kJnaWtct0ir4cQe2UjY96NIDj2FfcO+OZ4nnVZDs0rF8PT0ZOrUqbRo0QIfHx/at2/PnTt3qFOnDi1atMDT05PevXtTrVq1DI9AQXpP9mrVquHn58eff/7J0KFDcXJyonr16nh7e2fZs/1+E7+3tzebNm0y1qM51Q/z5s1j/vz5+Pj4sGvXLqZPn57jsb3oZC5v8VJ43LnB3d3dOXXqFDY2Nmw9c4uhy/6dIvH6V0NwGboQrZllDiXkzrUv+2Lr7k3Mub20atWKQYMG0aVLl0x/WHKraNGi3Llzhy5dujBz7ne0/6rg5x+/b+u4xlQoZvvoFYUQmaSlpaHX6zE3N+fgwYOMHDmSQ4cOPe2wcnTxdiyt5u4qtPIfVafExcVhY2PD3bt3qV27Nvv27cPZ2bnQ4hG5I3coxUuhIOYGb1m1BA4PNPWUfvOHAkkmIf3xo9D9f7Bo0SISEhLo27evsclm3759eb7TFxERwW+//camTZsY91of6pctUuAzW+i0GhpVcJZkUoh8iIuLo379+lSrVo0333yTr7766mmH9EgVi9vSqILzU6tTXnvtNXx9fWnQoAFTpkyRZPIZIXcoxUsnOjqaadOmsXDhQipXrsyCBQtyPXvLgStR9FpU8Hf7Vg2rS51y6QOPb968mbFjx3L37l3u3LlDamoq9vb2vPvuu9SsWTPT7Blly5Zl3bp1WZYbGBhIp06dqFq7EdH1R5OclsXD9I/J3ETL1nFNKOOYdQ9KIcSL69qdBFrO2flYdUrUlq9Jvp6x13mRpoNwqFRL6pTnmCSU4qX1uM3gb/8SxK/Hcjc/d250q16Kz3v4ZrnMYDAQGBhIQEAAa9euJSkpKc9N4gcPHuSVV16hWN0uJPq8WmBxf/KqN71quRZYeUKI58vPh0KZ9OvJAitP6pTnmzR5i5fW4zaDf9HTl4YVCmYaw0YVnLNNJiG9g1CLFi1YtmwZt27deqwm8dq1a7Nz507uHfsfulObCiTuCa09pOIX4iXXu5Yr41tXKpCypE55/skdSiF4vGbw/N6pzOnO5KNcvHiRpUuX8uOPP3Lt2jUqVarEoEGDGDBgAKVLl85ym8uXL9OqVSvS3Opg0WAAegV6Q+4vf51Wg4lWw8xOnlLxCyGMfj4UyvQNp0kzKKlTXmKSUArxgLw2gx+4EsXw5UeITsx95x4HS1O+7V/D+MxkfuS1SfzGjRu0bt2ayERF3be+5PjtFHRaTY5/BO4vb1TBmY+7esvzTUKITK7dSWDKupPsvhQpdcpLShJKIR5iMBhYtmwZEyZMIDExkRkzZvDWW29hamqa7TZbz9zi878ucDE8jrQsKlITrYaKxWwY39qDFlWKF0rcMTExrF69moCAAPbs2YO9vT29evVi0KBB1K1b1zirQ1RUFG3btuXixYt8+/MGzqY4EXghnNCohAwTR2pIH2C4WaVi9K/rKr25hRCPdPF2LCsOhEqd8hKShFKIbDxub/DDpy7QuMdrvDvlffx8PKlb1olidhaP3K4gPapJPCYmhk6dOnHw4EHWrVtHmzZtiE9Oo2QlH0aOHkP/vr1xd7LG2tzkicYthHhxxCenERwVT0qaATMTrdQpLzjplCNENhwcHJg3bx5HjhzBwcGB5s2b07dvX8LCwnLcztHKhMRze2hc2pRO1Uo98WQSoGLFivznP/8hODiYrVu3Urt2bf7zn//g6upKmzZt2LRpE7/++istWrSgY8eOrFmzBmtzE/SRIZQ0S8bTxV4qfiFEvlibm+DpYo+faxGpU14CcodSiFzITTP4O++8w86dO0lOTubUqVNUqlQJW1tbKleuzPLly59i9OmyahLv0aMHe/bs4dy5c7i5uREaGkrp0qUpVqwYjo6OrF69Gnt7+6cduhBCiGecJJRC5EFOzeAdOnRg06bMw/J4eXlx8mTBjdVWEB5uEs+KjY0NYWFh2NrKc05CCCFyJk3eQuRBTs3g06dPz3KbDz744AlH+WgPNomvXbsWnU6XYblGo2HMmDGSTAohhMgVSSiFeAxZDYq+a9cu2rRpY0zONBoNVatWpUuXLk832BxotVpeffVVxo8fj1b7b3WglGL+/PmPPZe4EEKIl4s0eQuRTw82g7u5uXH16lXjsrVr1/LqqwU33WFhiYyMpEyZMiQlJRnf8/Hx4e7du7keOF0IIcTLS+5QCpFPDzaDlyxZ0vh+6dKln+m7kw9ydnZmzJgxAJiZmfHtt99y+vRpatasyR9//JGpl/jKlStJTEx8ylELIYR4VkhCKUQBud8MPmHCBADCw8OZM2fOI+cGf1aMHz8ejUZDly5deP3111m3bh1//PEHc+bM4Ztvvsk0l3jJkiWlSVwIIQQgTd5CFLj45DQCD59i1eq1rF61knJFbVn45Re5GhT9aYpPTuPIhVBs7BwwN9Xh7mTNwb276dSpE97e3mzatIkiRYoAjzeXeGHGLYMnCyHE0yUJpRAFwDjd2PlwQu9knG4MpUiNvkkZXQyfv9GFBl7ln1aYmeQUtwZwdbSiioNi7awxuNho2bJlC8WL/zt1ZF7nEn+ScTfzKEa/Oq5ULC491YUQorBJQilEPly7k8CUdSfZfSkSnVaDPot5vO9TBj0arY4ypnH8OKot5Yo9vQHD8xL3/eWGG6cxP/ErW9evws3NLdN6uZ1L/EnH3aiCMx939aaMo1W+9y+EECJrklAK8Zh+PhTK9A2nSTOoHBObhyl9GhoUg7ytmTGgVSFGmLXHjVunAX1qCobDv/DXNzPw8PDIdt28NImPGDGCmJgYli5diolJ9k3Vjx23VoOJVsMHnTzpXcs119sJIYTIPUkohXgMCwIvMnvLhccvQCnQaHC9G8TP0wbj4uJScMHlIN9x/yPl8Fo2fToaPz+/HNd7VJN4bGwsLi4u6PV6BgwYQEBAQIbxMAs67vGtKzGqWcV8lyOEECIjSSiFyKOfD4Uy6deCm0oxbtu3TO7VJNPc4AWtoONOCPyOdZ9PpEGDBrlaP6sm8apVq7J//35jL/G33nqLuXPnZmgeL+i4P3nVm15yp1IIIQqUJJRC5MG1Owm0nLOT5DRDgZWpVXqufzeCiiWLZJgbvCAVRtwafSoRS8ey9sdvadOmTZ62vd8kPmvWLNLS0jIsmzlzJu+//36hxW1uomXruCbyTKUQQhQgGYdSiDyYsu4kaXl4fi83NDoT2k1bmmlu8IJUGHFrTU1x7z6Jjh07smbNmjxtW7FiRbp165YpmQSYNm0a48aNAwon7jSDYsq6grvjKYQQQhJKIXLt4u1Ydl+KzFOHkNzQGxRBt5NZsvZ/GeYG//zzzwtkUPTCixvibMvQrvcQevXqxQ8//JCn7X/66ScA49znD/rll18K9XzvvhTJpfDYAi1XCCFeZpJQipfa0KFDuXz5MgCffvppjuuuOBCKiovk1opJhC16g7DvRxF/bk+BxKHTavjp4DX8/f25cOECDRs2ZPz48Tg5OTFu3DhWrVr12GWvOBCKTpv/IXuyotNq8Ok2ktdff53XXnuNuXPn5nrbNm3a8OabbzJz5kwCAgL466+/OHPmDPfu3ePGjRuFHvfy/aGFUrYQQryMJKEUL7XFixdTvnz6QOOPSigDz4ejR4tjy9dxGfY1xXv9h7tbv8OQkvRY+1YGvfHfeoMi8EI4kD43uFKK5cuX4+3tzdy5c1m+fDlhYWE4OzvneT+B58Mf6y5f7LE/uLHoDVIigrNdR29Q7LwYyVdffcW7777LuHHjmDFjBkopduzYQffu3QE4d+4cvr6++Pn5ERUVBUCLFi1YuHAhU6ZMwd/fn5YtW1KlShXs7OyyjDt613KSrp0CIObQepT+37u3cSe2oo+PNr6+/ct00hJiuP3LdG58N4KwxW8Sc/j3DHHfP9/Pmrlz55KSkvK0wxBCiDyR+cnECycuLo7u3btz48YNAGbPnk2/fv2IjIwEYMGCBURGRjJjxgyaNm3KggULWLlyJdHR0fj6+lK3bl2++eYbli1bxrx580hJSaFR02aEWrXCxMYRbBwB0NkUQWtphyEpFq2ZBde/GoJ11cYkXj6E1twa587vYmLrlCG2iN+/QGtmTsqtS1hVrItF2erc3f49KiWRcBtHrvXbyNIfFrFnzx6Cg4Pp06cPRYsWZdu2bXh4eJCQkMD48ePZsWMHSUlJrFq1Ck9PT+Lj4xk5ciSnT5/GYDAwa9YsWrVqRVxyGqF3EnI8X0oZ0Ggy/7a09WtH/JldjzzfoVEJJKTomTVrFg4ODkyePJno6Gg6depkXOe3336jf//+jB8//pHlAVnGbd+wDxptevN4zKH12FRrjUaX3is+7uRWzEpWQGftAEDxnh9gSE3Cvm53LFy9MaQkcjNgHJbla2BaxMUYd3xy2jM1TaNer2fu3LkMHToUMzOzpx2OEELk2rNTkwpRQDZv3oyTkxN//vknSiliYx/9rNxHH33Et99+S1BQEABnz57lp59+Ijk5GT8/P35c8gOp9lsp3m8WKbcucXf79+gTotHHRKIxtQBAHxuJ1tQSl9cWErX5a26vmESpEYuI3DgHjakZKbcuofR6tOaWKH0a8Wd2Env0D0oMmY+JlR2hn3endauWaJUec3NzAgICqFu3Lu7u7gwbNoy0tDTmz5/Pjz/+yC+//MLVq1f54osv+P777/noo4+wtrbGzc2N8PBw2rVrx6JFizh48hw3lq1CZ12EYt2noTExJXr3ChLO7yP17g1M7IqBBsyKupMUehJDSiImds5Ye7UAgx59bPrdxHsHfuXenp/QmltjWbE2Tm1GAmBISSRi3X/x+20i5iZahg0bRsmSJfnyyy/59ttvMTc3p3nz5pw5cwYTExOmTJnCsGHD2LFjB25ubtja2nLy5Enu3buHmZkZdnZ2XL58mS0HT6GAG4vfBKXQmpqj0ZliWqwsiZcOoY+N4PrCQViUroqNTyuSw85zM+BtNBqw8qhPYuhpSg37CgtXb+7tX0P8qe3o46KIO7mNIo0HEPbDaAxJcZRYkERqago9e/Zk6dKlWX43IiIiGD58OKGhoZiamvLVV1/h5eVF3bp1+fbbb6lZsyb9+/enUaNGDB8+HGdnZ3r16sWOHTtwd3dn5cqV2NnZcfToUUaMGEFiYiJ+fn589913WFhY4O7uTu/evdm8eTODBg0iLCyM+vXr4+7uzoYNG/J/QQghxBMgTd7ihePt7c2uXbuYOHEi+/fvNzah5sW2bds4duwYp06d4sCBAxRxKoo+7g7J109zd/v3OLUbg0ajxbZGR2L2rTZuZ1m5IQAWrl4ZmmANibGUGPgFps6upEZeo9ir7+HUaTz6hGhuBYwl7IfRqNQkzCwsOH36NNbW1hkSHAsLC+bNm4dOp8PFxYXmzZuzevVqzp8/D8CWLVvYsGEDGzduJDo6GmdnZ0aOHIl7BQ9cXluA1tKWxMuHALCt2Yli3d8Hgx4TO2cs3H3RmFmiMTWneJ+P0JiYY1ejo3HfSp9G9M4fcWg8kNKjfqRIk0HGZYlXjqK1tGXV5j2cOHGCb7/9lqVLl+Lp6UlSUhLW1tbUrVuX6tWrM2nSJFJTU2nevDmnT5+mePHi7Nu3jzNnznDy5EmCgoI4fvw4Op2OPzf+m0hpNBpKDpqLSREXzEt7UnrkEnR2xbD2bIpV1caYl/FEY2JGif6zcB3/Kyb2JVCpiQAk37xAwtndFH31PbQWNsSfDiTtnyRZHx/Nuh2HWLNmDatXryY0NOtnKseOHcvkyZM5fPgwS5cuZcSIEZiamrJ48WKGDRvG2rVruXXrFsOHDwcgKirKeIw+Pj588cUXAPj7+zN//nxOnjyJtbU1X331lXEfZcqU4dixY4wZMwYXFxf27t0ryaQQ4rkiCaV44VSqVImgoCA8PT15++23WbBgQYaBspOTkx9ZhsFgoHfv3nh6enLu3Dn+9/dRLCvUJin0JCnhV7j5w2gMKYkkXjpAWkzEvxs+OF/1A/+08miARqPBkJyAztYZE4fiaNBgUsQFq0r1cBkyH3SmrFi/BYBixYpx9OjRTHFpNBqWLVtGQEAABw4cYN++fXz++efo9XrGjBnDsGHDOHHiBDdv3sTc3JxWbdsD6Xcg0+6lPzOYFHKc8LX/Aa2OlMgQkq+fwcanJVpzK+KCNmNIjEGpf5/vjNo0F5MiJUkKPUH86R3wQK9ss2LuJF87zfxZM/jrr79ITU0lKSmJ8PBwihUrRlhYGPPmzTM+N2lubs6rr74KwGuvvUZERAQjR45k8eLFtG/fHm9vb+Lj47l25ZJxHzr7YsZ/6+/d5tbyiejjoki4eIDUyGskh51HpSYR+fvnhP0wmvjze+Cf51OTr5/FsmJdojZ9QZHmr2Hp7kfKzYvpsZcoT8mSJejQoQNKKa5evZrld2Hr1q0MGzYMX19fevTowa1btwDw8/Ojffv2DBkyhMWLFxvXf/AY+/Tpw549e4iOjiY5OZk6deoAMGDAAHbv3m3cpkePHlnuWwghnheSUIoXTlhYGNbW1vj7+zN27FiCgoKwt7cnJCSE1NRUNm7cmOV2Op0OvT49EWnRogUbN240DmljpY9HpaWgNbNCozPFrnZXSr+5BJehX1G0y7v/lKAh8fzfACSFnkBn5WAsW2Nqnml/pk6lMSTGok+INr6XHHnt3200Wfdw1mq1+Pv7s3HjRkqWLMnEiRO5du0av/zyC+bm6fsJCgpCq9VSycUxPa/VaFDKgEpL4e7W73B8ZTRmRd2w8Wz2zzSQOkr6z8XKoz6GlESiNs017s+8VGVMHFywrd6O5BvnuL1i8r/H4FgKlyHzqFu9GlOnTuXevXsYDAbat2/PokWLaNy4MRqNhrCwMBISHnom0t6eunXr0qRJE2bMmEHFihU5efIkRYoUwVyrT49bKeNzkwAxhzfg1HY0Ohsn7Gp2An1qepO4hS1Fu07GZch8Sg37Bq2FLembKxLO78GiXE2s/7l7bDy/OlPcnayN5/r+Z5+Vw4cPExQURFBQECEhIcb3T506hY2NDXfv3s1yO41Gk+3n+CArKxlkXQjxfJOEUrxwTp48Sa1atfD19WXu3Lm8/fbbfPjhhzRv3pymTZtSrly5LLfz9/fH29ubESNG4OnpyahRo7hw4QI+Pj70fLUz1jo9abFRGBLuEX9mJ2E/jObG96NIuLA/vQCtlpSIYG4sfpOE83sxK5Heezz1znUSLx9OX8XcCn1cVPrdQq0OU8dSpNy+TNj3o0CfysplPwLpz+1Vr149x+O0s7OjQoUKHDlyhAoVKnDo0CHmz59PpUqVmD17NgDW5ia4PjAjjEpLAY0GnYUNymAg4cJ+TBxKEH9yK4bkeHTWDpjYFSX1gcTWpno7TIuUIP5UIA7NhpAWE2HsoZ4WG4VbcUdeHzqE8ePHk5qairW1NYGBgSxevJiiRYuyfv16IiIi+PLLL0lOTmb9+vUALFmyhLp169KzZ08sLCy4cuUKKSkppKamEn0nijJFLDEkxmQ4ZqVPRWvtgNbMgoSz6R2GzEtVRqUkkHYnfTB4Q3KCMb7U8Cvo4+5gX6cb+qQ4kkJOYOZSCQBN/B2szU3YunUrtra2Wc4hDtCsWTO+/vpr4+vjx48D6eNompqasm7dOoYOHWocM/TBY1y1ahUNGzbEwcEBc3NzDh1Kf+xgxYoVNG7cOMv92dra5uq5XyGEeJbI1ItCZCM4OJju3btz+HB6Mli/6yAupRVB5+yePlxQcgIoA/YNemNdpTHX5vVHa2aJ1tIG85IeGFISce4wjsiNc7Cq3ACrCrUBSLx6lLuBS8Cgx6KsH0WaD8VEp+Xm/H4MHtA3U2eO+z3Rvby8cHZ2NvZWf5DBYOD111/np59+QqfTMWPGDP773/+m92bfcJr5cz7DoNFhX+dV7u5cSvypQAxJsVhVqotp8fIkXz9L0pX049TZOOHQdBCpEcHEn96Bc9dJ3N3yDal3rgPg0LAfscc34zJkPsnBRzHsW0ZJByssLS0ZM2YMH374IUlJScTExJCcnEzZsmVxc3Njx44dxMXFUaJECaytrSlevDj37t1Do9Fw9+5d0tLScHV1pVixYuzZswdL51LcvnkTs5IVKNbt/X86N5mTdOUISqn0BNjSllIjFnN313JiDqxFo9Fg4lgKfdxdivebxc1FI9BaOWBIjgfSnx91bDaYmz+Mxkanp0yxImg0GooWLcqUKVNo2rRppnMbERHBiBEjuHTpEikpKXTq1Il33nmHRo0asXv3booVK8a7776LlZUV06dPz7ZTzpEjR3jjjTdISkrC19c3Q6ec+3c6AebNm8dXX31FpUqV5DlKIcRzQxJKIXLp4u1YWs3Nfhid618NwWXoQrRmlo9Vfsx3A7nzz7OGjys6Oppp06axcOFCKleuzIIFCyhdtWaOcefX1nGNqVDM9pHrXb58mUqVKlGqVCn++usvPDw8clz/Uec7P26tmMSvyxbTrkmdAi87u6RfCCFeZNLkLUQuVSxuS6MKztnO3lL6zR8eK5nUaTU0quCMNhfP2j2Kg4MD8+bN48iRI8a5waePG07tMrYFPuvM/bhzk0wClC9fHnt7e2xtbWnUqBHHjh3Lcf1Hne/HpdNqsLc0xfWf5yeFEELkn9yhFCIPrt1JoOWcnSSnGQqsTHMTLVvHNaGMY+46Zpw8eZIBAwZkeK9s2bKsW7cuw3sGg4Fly5YxYcIEkk1scPb/En0B/YaM/vtnEs7voayjFZbm6YOLjxkzhsGDBz9y28jISF555RUuXrzIpk2baNCgQbbrPsnzvWTJEr788ssM7/Xo0YOpU6cW2L6FEOJFJQmlEHn086FQJv16ssDK++RVb3rVci2w8h52vxk8YPcFHNuOLrByU/8OwHBxDytWrKB58+Z52jYmJoZOnTpx8OBB1q1bR5s2bbJd93k730II8TKSJm8h8qh3LVfGt65UIGVNaO1R6MnN/WbwXUtmYXd1R/qb+fwdOaG1B0dXL8DT05OWLVvywQcf5DjszsPs7Oz43//+R/PmzenYsSNr1qzJdt3n7XwLIcTLSO5QCvGYfj4UyvQNp0kzKPSG3F9GOq0GE62GmZ08n3hyYzAYGDv/F9ZfNwetLsMYj4+SVdx6vZ6PP/6YGTNm0KxZM1asWEHx4sVzXWZqair+/v6sWrWKRYsWMWTIkGzXfR7PtxBCvCwkoRQiH67dSWDKupPsvhSJTqvJMdG5v7xRBWc+7uqd62cmC8Pp4FsMWriZCBPn9FllckgscxN3YGAgffv2BchzE7her2fUqFF88803zJkzh7Fjx2a77vN6voUQ4kUnCaUQBeDi7VhWHAgl8EI4oVEJPHhRaQBXJyuaVSpG/7quue4V/ST8vvMQk77fRLRlKUyLlMwwdWRe4759+zb9+vVj+/btTJ8+nffee88409CjKKWYPHkyn3zyCdOnT2f69Ok5zjDzvJ5vIYR4UUlCKUQBi09OIzgqnpQ0A2YmWtydrLE2N3naYWXL2Bt88nukmNszbPgb9O/bmwrF7fMcd36bwGfNmsXkyZMZM2YMX3zxRbaz1zwoPjkNF49qFC9ZinVrVz/z51sIIV5EklAKIYCsB0Vv1qzZY5WVnybwr7/+mpEjR+Lv78+iRYswMck6OUxLSyMwMBCDwUCXLl1wdnZm8eLFWFtb07Bhwyy3EUIIUTikl7cQAsh6UPS+ffsSFhaW57KaNWtGUFDQY/UCf+ONN1i2bBnLli2jZ8+eJCcnZ7lejx49aN26NW3btiUpKYnr16/Ttm1bGjVqxKeffprnmIUQQjw+SSiFEBn4+vqye/duAgIC2Lp1Kx4eHnz++eekpqbmqZzixYuzefNmPvjgA2bOnEmbNm24fft2rrbt168f69at448//qBjx47Ex8dnWqd///7Zbt+7d+88xSqEECJ/pMlbCJGtgmoGf9wm8MDAQDp16oS3tzebNm2iSJEiGZaXKlUq0x3U5s2bs23btjzHKIQQ4vHJHUohRLYKqhn8cZvAmzVrxrZt2zh//jxNmzbNdIdz3rx5mbZZsmRJnmITQgiRf5JQCiEeqSCawR+3Cbx27drs3LmTiIgIGjVqREhIiHFZt27dcHFxMb5u3rw5rq4yeLkQQjxpklAKIXJFq9Xi7+/PhQsXGDx4MBMnTsTX15fAwMBcl6HT6Xj//ffZunUrp0+fxtfXl+3btxuX79+/n3fffZeHn8Tx8vJi9+7dpKWl0ahRI86fP29c9uBdSrk7KYQQT4c8QymEeCxBQUGMHDmSvXv30qdPH2bPnp3hbuGjPDwQ+rBhw6hWrRqRkZGsX7+eTp06Zdrmxo0btG7dmoiICDZv3oyfnx8ATk5OlClThqCgoII6PCGEEHkgCaUQ4rEZB0WfMIHExERmzJjBW2+9hampaa62vz8Q+vTp03FwcCAmJgalFJ6enhw/fjzL2XIiIyN55ZVXuHjxIps2baKidw32X40iMUWPpZmOumWdKGZnUdCHKoQQIgeSUAoh8i2/vcGHDBmSqbk6u7uUABuOXOGdH3eSYuGIRpd54HMTrYaKxWx4p1UlWlYtkbeDEUIIkWeSUAohCszjNIMHBgbSokWLDM9NajQavLy8Mt2lPHAliuHLjxCdmPvOQA6WpnzbvwZ1yjnl/YCEEELkinTKEUIUmMfpDb506VKUUmg0GuM0i0opTp48ydKlS43rvf1LEL0W7c9TMgkQnZhKr0X7efuXoMc6JiGEEI8mdyiFEIUit83gSUlJHD58mBMnTnD8+HGOHj3KiRMnSElJoWLFily4cIH+3+9nz6WofMfUsIITy1+rm+9yhBBCZCQJpRCiUD1OM7jBYODkyZM4OTnxxd5Ifj12o8Di6Va9FJ/38C2w8oQQQkiTtxCikD1OM7hWq6VatWrcSLEs0GQSYO3RGxy4kv+7nUIIIf4lCaUQotA97qDow5cfKZR4CqtcIYR4WUlCKYR4YvIyN/jWM7fy3AEnt6ITU9l29tHTPgohhMgdSSiFEBl88803rFq1KtvXj7Jjxw66d+8OwLlz56hQoQIODg5ERUVx7tw5fH19GTx4ML/99luOzeB9unfBkJpcIMd066fJ6f9fMYmUiGAAZm85n8MWOXN2ds5x+YPn4OH3Dx48aHw9bdo0du/eDYCtrS1VqlSha9eujx2XEEI8LZJQCvGSMhgMWb4eMWIEvXr1Mr7/8Ou8+O2332jVqhUtW7bEycmJ3377jf79+3Ps2DGKFi1qbAYfNGhQpmZw+67T0JqaP+bRZVSi738zvXcxPK5Ays6LhxPKmTNn0qhRIwCSk5NZtmwZ69ate+JxCSFEfkkvbyFeIsHBwXTs2BFPT0+OHTtG+fLl2b59O9bW1tja2lKrVi127dqFhYUF69evZ8eOHfznP//BwsKCTp064eHhwddff42FhQX169fn66+/BuDgwYMMGTIEExMTGjRowNmzZ7ly5Qo3btzA3NwcOzs7lixZgr+/PyYmJoSHh/PGG2+wZcsWKlasSFJSErt27SIlJQWlFN36DWbdb7+hs3VGowzoE2PQaLTo7Iujj4lAZ1cMlZpIyq1LWJarQVpMOBqtDo2ZFRoTM1JuXwEN6KwcMC3qRuKVo7iOXUnYD6PRmJihsy5CamQor/XtxjcL5gHpd2KnTp1KQkICdnZ2ODk58emnn9KhQwecnZ2JjIwEYMGCBUycOJGEhATmzJnD9OnTSU1NxcLCgpUrV9K2bVt8fX25e/cu0dHRJCUl0bdvX2bOnEmtWrXQ6/UkJCRQunRp0tLSmD17NmfOnOG9997Dw8ODjh07snfvXuLi4lBKsXz5cnx8fJ7ad0YIIXJD7lAK8ZI5e/YsU6ZMYdiwYTg6OpKWlsaXX35JSkoKRYsW5Y033qB79+74+/szc+ZMhg8fzuTJk5k1axYzZ87k6NGjHD9+nFmzZhnLfO211wgICCAoKIjIyEiOHDnCmjVrmDp1KpUqVaJ+/fq0adOGESNGMGnSJFJTU+nSpQtnz57l9u3bHD16lISEBDZt2oSPjw97Lt5GozOheO//4NjmTTQ6U8xdvSnaeSL6uCj0MeGU6PcJKAPJNy9Qov9naC3tSQ47h1PbUZR8bQGmRUrh2PJ1dNYOoE8zxpoaeQ2ntiNxGbqQDRt+JzQ0lBs3bvDhhx/i5ubGjRs3sLGxITg4+JHnsm/fvty4cYPExER69uzJ66+/blwWFhbGyZMnWbBgAf/73/+4fPkyAwcOxNHRkcjISM6fP4+NjQ2bN29m6tSpmJiYsGbNGkqUKEHTpk2NY3JWqFChID9+IYQoFJJQCvGSqVSpUnrStmcPXbt2pVKlSvTv35+YmBi6dOkCQNmyZUlMTMTPz49ff/2Vw4cPY2pqSu3atenfvz8//fQTpqamQPoA5snJydSsWROAzp07YzAYjK9r1aqVKQYbGxvjIOc1atQAYPTo0URHRwPw30+/AGUgcsNswtf+B0NyPGl3bqCzdkBnXwzzUlXQmJiiMbPEvHRVUsKvYurogoltUUzsi5EWGUpabCQR6z8l4fxelNIb921WrCw66yJodKaULudBSEgIhw4dwtXVle7du+Po6EiXLl2oWLHiI8/l8ePHqVSpEhYWFixZsoSwsDBSUlKMx+Xq6krt2rWxs7Njz549XL9+nbCwMOrVq4evry/BwcHcvp2xc1CtWrVYsWIF06dP5+zZs1hZWeX2oxVCiKdGEkohXjIPJyg5JSyrV6+mdu3aXL16lSZNmrBp0yZGjRrFvn37aNKkiXG9B+fbzg1z83+fjbSysuLDDz+kSZMmLFq0iBs3buDkYIMhKR7LctWxr9Md66qNUfoHenz/szuNzhQNGtBoQKNFkf4ET9QfX2JdtQl2NTtiV+fVDPvW/DO9I4CJqQ69Xm+cRzy743jw/eTkfzsK9evXj9q1axMXF8cXX3yBqampMaHUatOrV51OZ5xaUimFp6cnQUFBBAUF0bVrV1577bUM+2rcuDF///03Li4u9OnThw0bNuTqnAohxNMkCaUQL6mGDRuyfv16IP0ZSDs7O2PyEhwcjJWVFTExMZQrV46uXbsSEhJCcHAwLVq0YPbs2YSGhqLX63FwcMDc3JyjR48C8Pvvv6PVao2vDx8+nGMcCQkJKKXo2bMn48ePJykpibplnUAZ0Nk4YV66Cgln96CUQp9wD/29cJJvnE1PMJWB5FuXMCvqDoA+JoK0mEgMqUkkhZzArGQlEs7synK/Sik0yfFA+l3Ba9eusXbtWqKjo1m/fj0XL140rmtvb09ISAipqals3LjR+H5SUhJlypTBYDDw1VdfkZSUZFwWFBTE9evXSUtLIyIigoYNG1K5cmXOnz9PSEgIAKmpqdy6dStDXCEhIZQoUYLhw4czYMAATpw4kfMHKYQQzwCTR68ihHgRjRw5kj59+nDmzBlGjRrF2rVrWbhwIatXr8bCwoLVq1fTr18/Ll26hE6n47333mPgwIHExsailGLatGnodDoAFi1axMCBAzE1NaVBgwbUqFGDgQMHEhUVZWw6bteuHVWqVDEOudOuXTsWL15MXFwcs2bN4ptvvkEpRdGiRSlmZ4HW3Jq7O5agM7dG51CclFuXiVz/CVprB0yKuHBr+bsYkhNwbjMSrXn6XVadrRNR/0vvZJMaFUr07uWYFS9H8u3LmY5fowzs/3s3Bw8epGnTpkyaNIlp06ZRsmRJbGxsMjy7+OGHH9K8eXNKlChB5cqVOXDgAJA+7M/777/PDz/8QNmyZbG1tTVu4+3tzdChQzl37hy2trY0adKEkiVL8uOPP1KlShVKlSpFVFQUXl5eGeLasWMHn332Gaampjg4OLBy5cqC+siFEKLQSC9vIcQz6ZUvd3H2ViwAhpREtGaW6JPiCPtmKLZ1XsWhXs8M6yeFnCD26EaKdp2Sq/I9ittguvUztmzZwk8//UTbtm2xsbEhMTGRxo0b4+LiwrBhw+jQoUOBH5sQQrxo5A6lEOKZ9E6rSgxdlj5FYtQf80i9cx30aZiX9kRrZpnv8ie2qUzjkesYOHAgvXr1okWLFoSHh5OUlMTAgQO5cOFCvvchhBAvC7lDKYTItY8++ojVq1cbX9++fRtTU1McHR2N740ZM4bBgwcXyP58Z24plOkXI5a9Q0VnC+Pr69evExUVxZw5cxg7dqzx/a5du3L16tUM2y5btgxvb+8Cj0kIIZ5nklAKIZ5ZB65E0WvR/oIrUKX3A29vfp7508YanwFVSjFp0iQ+/fRTpk+fzvTp0/Pcc10IIV5m0stbCPHMqlPOiVf9ShVcgRoNFU2i+OY/E2jTpo1xDEiNRsMnn3zCf//7Xz744APGjh2baWpKIYQQ2ZOEUgjxTPuipy8NKzgVSFmNKjiz9UN/tm7dyqlTp/D19WX79u3G5ZMmTeKrr75i/vz5DBkyhLS0tBxKE0IIcZ8klEKIZ97y1+o+9p1KpRQoRbvKjix7rQ4AzZs3JygoCE9PT1q2bMkHH3yAXp8+m84bb7zBsmXLWL58OT179swwkLkQQoisyTOUQojnxoErUQxffiRPHXUstQbubphFfPAJZsyYwVtvvWWcNlKv1/PRRx8xY8YMmjVrxooVKyhRogSQPkB7jx49aNy4MevWrcPa2rpQjkkIIV4EklAKIZ47W8/c4vO/LnAxPI40Q+YqzESroWIxG8a39qBFleJER0fz/vvv89VXX1G5cmUWLFhgnEscYPv27fTt2xeAn376iebNmwMQGBhIp06d8Pb2ZtOmTRQpUuTJHKAQQjxnJKEUQjzXwmOS2H81isQUPZZmOuqWdaKYnUWW6wYFBTFy5Ej27t1Lnz59mD17Ni4uLgDcunWL/v37s337dqZPn857772HTqfj4MGDvPLKK5QuXZotW7ZQvHjxJ3l4QgjxXJCEUgjxUjEYDCxbtowJEyaQmJiYoRk8uybwU6dO0bp1a2xsbPjrr79wc3N72ochhBDPFEkohRAvpZyawbNqAr98+TKtWrUiLS2Nv/76Cw8Pj6cZvhBCPFOkl7cQ4qXk4ODA/PnzOXLkCA4ODjRv3py+ffsSFhZm7AXu5eVl7AXu7u7O7t27sbW1pVGjRhw7duxpH4IQQjwzJKEUQrzUfH192b17NwEBAWzduhUPDw8+//xznJyc2Lx5M9OnT+eDDz6gdevW6HQ6du7ciZubG82aNePvv/9+2uELIcQzQZq8hRDiH9k1gz/cBF6zZk06derEwYMHWbduHW3atAHg0KFDKKWoXbv20zwMIYR44uQOpRBC/CO7ZvDKlStnaAKfM2cOGzdupHnz5nTs2JE1a9awfft2GjZsyKuvvirTNgohXjpyh1IIIbKQVW/wkSNH8umnnzJjxgyaN29OQEAAEyZM4Oeff86w7Y4dO2jSpMlTilwIIZ48uUMphBBZ0Gq1+Pv7c+HCBQYNGsTEiROpUaMGjRo1Ms4FXqtWLezs7DJsZ2JiQkBAwCPLj09O43TYPY6F3uV02D3ik2XecCHE80vuUIr/t3fvYVFVCxvA37nAcBFRQFFUMEVI7ldFUVFQMy1LE7UkNa2+MjtmanmwwDjaKfNW2emkZuSdLDUMO6ImCSUIGgJiclEuinIXEGRwZvb3hzGJ3J1BQd7f8/QUe6+91tqbaXxda++1iagF7l0U/Z133sH//d//4fTp0/XK6uvro7CwsN7rGtPzK7ArLgcnLhYgp6QKd3/5igBYmhhgjG1PzBpqiUHmRm17QkREWsRASUTUQvdOg+vo6KC0tLTBstu3b8eLL74IAMgtqULggWREZxRBIhZB2cDrImvV7h9pbYYPpziin4lBm5wLEZE2MVASEbXSjRs38Mwzz+DkyZONlrGyskJWVhb2xucgOPw8FCqhySB5L4lYBKlYhA8m22Omp6U2uk1E1GakD7sDREQdjbGxMYqKihrd37VrVzg7O2PTiXSsjUy7rzaUfwXQ5fuTUXRTjoVjBt1vd4mI2hwfyiEiaqUjR44gNTUVIpGo3j6pVAoLCws8/97n9x0m77U2Mg1h8TlaqYuIqC1wypuIqJWuXLmC9evXo6CgAAUFBcjPz0dhYSEKCwuhUqmQdCkPU7eehVyhvfUoZVIxji324T2VRNQuMVASEWmRIAiYve00fr9U3Kp7JpsjEYswfIApdswfqrU6iYi0hVPeRERalFFwE9EZRVoNk8CdeyqjM4qQUVCh1XqJiLSBgZKISIt2xeVAIq5/b6U2SMQi7IzlvZRE1P4wUBJRu3Hjxg1s3ry5wX0JCQlYtmxZk8evXLkSmzZt0nq/8vLyMGvWrBaVPXGxAFkbngcACIrbyN+zAnnb3sStS2fUZYoPf4bbpdearOfuMmWx3wO4M0r5v/jUZvsSGhqKpUuXtqi/RETawEBJRO1GY4FSqVTCw8MDn3zySZu2r1QqG9xuYWGBXbt2NXv8TbkCOSVV6p9r8jMhkurCYt7n0B/grt5uOvEf0Oneu8m67i5THrdfvT1fYYDN275tti8PQmPXi4g6HwZKImo3VqxYgdTUVLi4uCAkJAS+vr6YOHEivL29ERUVhWnTpgEAYmNjMWzYMLi5ucHHxwfZ2dn16tq4cSNsbW3h7OyM119/vdE2R48ejbfeegseHh7YsWMHduzYAUdHRzg4OKgDbFZWFjw8PBqto7CwEL6+vnB1dkJJVCgAQHmrHEWH1qH6Sirytr0JZeUNdfnru5ajpjALAJD76Qso/eVr5H39BvL3BEJVU12nTOmv26Gqvom8bW+i+H+bcPtGPoYM8QQAZGZmYuTIkXBzc8OQIUOQmJjYous8evRoLFmyBO7u7nBycsLZs2cxadIkWFtb1xnh/fjjj+Hp6QknJyesXbsWABAVFVXn91JZWYkpU6bAzs4OL730EqysrHDz5s1GjyeiRxMXNieidmP16tW4ePEiEhISEBUVhXXr1uHChQuwsLBAVFSUupydnR1iYmIgkUgQHh6OVatWYcuWLXXqCgkJQW5uLgwNDVFWVtZkuzo6OkhISMDVq1cxatQoxMfHw8DAAMOHD4evry9MTU2bPP6DDz7AU089hTHTXsLYV94DAEj0u8L0yTdRcfYn9JgS2Oixqlvl0Bvgju6+81F0aB2q0n5HFwdf9f7uPrNxM/F/sJj3OQBAcSMftWtz9O7dG8eOHYNMJkNSUhKWLFmCo0ePNtnXWl26dMGZM2ewevVqzJgxA/Hx8QCAwYMHY+HChYiMjMSVK1dw+vRpqFQqjBs3DhMmTAAAnDlzRv17WbNmDaysrHDgwAEcO3YMoaGhANDo8Q4ODi3qHxF1LAyURNRueXt7w8LCot720tJSvPjii8jMzIRKpUL37t3rlRkyZAgCAgLg7++PZ599tsl2/P39AQDx8fHw8/ODiYkJAGDatGmIiYnBM8880+TxMTExCAwMRCnEMLQfjRvRO1t4hoBIVx/6/V0AALq9rKEoy2/+mL+e+ZHL5Vi4cCGSkpIgkUhQWFjY4nYnT54MAHB0dISHhwe6desGADAyMkJpaSkiIyMRERGB6OhoAEBFRQXS0tJgYmJS5/fy+++/49133wUAjB07Vn3tGjuegZLo0cQpbyJqtwwMGl7EOygoCJMmTUJKSgq+++47yOXyemUiIiKwcOFCnDp1Cj4+PvfVTmuIRCL0NzVEa5/vFkl0/v5BLAZUzS+GLpPe+ereuHEj+vfvj6SkJMTExDR4HRqtQyb7q0mx+r9rf1YqlVCpVAgODkZiYiISExORmZmJqVOnAqh7vRpbyrip44no0cNASUTthpGRESoqml9nsby8HH369AEA9RTr3VQqFXJzc+Hn54e1a9ciJyenRQ+QDBkyBMePH0dpaSnkcjn279+PkSNHNnvciBEjEBYWBkOZFLpZvzdbvtVEYgiqO/3v010f4r+GKMvLy2FhYQGRSNTgddDE+PHjsXXrVlRV3XnIKCsrq8FbB4YPH459+/YBAH755ReUlJS06ngiejQwUBJRu2Fqago3Nzc4Ojri5MmTjZZ75513sHjxYri5uUFXV7fefqVSiVmzZsHJyQkeHh4ICgqCRCJptn0LCwsEBwdj1KhRcHd3x4wZM+Dm5tbsccHBwQgPD4eDgwPMxQ0H4hsnd6IqPa7ZuhrSxdEX175eiJIjX2DYY3/fz7lgwQJ89dVXcHFxQXFx8X3V3ZgJEyZgypQp8PLygoODAwICAlBdXV2v3BtvvIHMzEzY29tj586d6NOnD/T19Vt8PBE9GvjqRSIiLUrPr8C4jY2HYU0dWzwK1j2N2qz+1lIoFFAqlZDJZDh9+jTeeOMN9QM+RNR58KEcIiItGmRuhJHWZm32Lu/2FCYB4ObNm/Dz84NCoYCOjg6+/PLLh90lInoIOEJJRJ3C6tWr1ff61Vq0aBFeeumlFtdx5MgR9RPNtby9vfHFF1/U2ZZbUoWxG36FXNH8AzYtJZOKcWyxD/qZtPwBIm2cMxFRSzBQEhG1gb3xOVi+P1lr9X081REzPC21Vh8RkTbxoRwiojYw09MSS8fbaKWuZeNtGSaJqF3jCCURURvaG5+D4PDzUKiEVt1TKRGLIBWLEDLZnmGSiNo9BkoiojaWW1KFwAPJiM4ogkQsajJY1u4faW2GD6c4tuqeSSKih4WBkojoAUnPr8CuuBycSCtATnEV7v7yFQGwNDXAGJueCPCybHdPcxMRNYWBkojoIaiUKzB/0XKkXbqEHaHfoL+pIQxlXMmNiDomfnsRET0EhjIpjJRl0C3Pg72F8cPuDhGRRjhCSUT0ACUmJuLZZ5+FXC5HWVkZFAoFTE1NIRKJsGnTJkydOvVhd5GIqNU4QklE9AAZGRkhNzcXKtXfi55fv34dABp8LzkRUUfAdSiJiB6ggQMHIiAgAFLp33+fF4vFcHZ2xqRJkx5iz4iI7h+nvImIHrCMjAzY2trWGaU8dOgQnnrqqYfYKyKi+8cRSiKiB8za2hoBAQEQiUQAwNFJIurwOEJJRPQQZGRkwMbGBoIgcHSSiDo8BkoioofEy8sLly5dQn5+vnq0koioI2KgJCJ6CCrlCmQVVUKuUEKmI+HC5kTUofHbi4joAVG/evFiAXJKGnj1ookBxtj2xKyhlhhkzlcvElHHwRFKIqI2lltShcADyYjOKIJELIJS1fjXbu3+kdZm+HCKI/qZGDzAnhIR3R8GSiKiNrQ3PgfB4eehUAlNBsl7ScQiSMUifDDZHjM9Lduwh0REmmOgJCJqI5tOpGNtZJrG9Swdb4OFYwZpoUdERG2D61ASEbWBvfE5WgmTALA2Mg1h8TlaqYuIqC0wUBIRaVluSRWCw89rtc6g8PPILanSap1ERNrCQElEpGWBB5KhaMX9ki2hUAkIPJCs1TqJiLSFgZKISIvS8ysQnVHUqgdwWkKpEhCdUYSMggqt1ktEpA0MlET0SLtx4wY2b97c4L6EhAQsW7asyeNXrlyJTZs2tbi9XXE5kIibf+uNoqIYheGftLje3E9fgEQsQmh0BsaOHQsXFxccOXKkxccTEbUlLmxORI+02kD56quv1tmuVCrh4eEBDw8PrbZ34mJBndFJQaWESCypV05qZIoek5sOs/dSqgRERP2OPnp6SExM1LSrRERawxFKInqkrVixAqmpqXBxcUFISAh8fX0xceJEeHt7IyoqCtOmTQMAxMbGYtiwYXBzc4OPjw+ys7Pr1bVx40bY2trC2dkZr7/+er39N+UK5JRU4fqu5Sg5thnXQt9CZcoJ3Ez5BXlfv4G8rQtQFvcDAEBxIx/XQt9qtN/KqjJc3x2IvK0LUBoVemfbrXKk7FyNmJgYuLi4oKCgoNG+lZeXw9bWFiqVCgCQmZkJT09PAED//v2xcuVKuLi4wNPTE9euXQMAXL9+HZMnT4azszNcXV2Rnp7e+gtORJ0SRyiJ6JG2evVqXLx4EQkJCYiKisK6detw4cIFWFhYICoqSl3Ozs4OMTExkEgkCA8Px6pVq7Bly5Y6dYWEhCA3NxeGhoYoKyur11Z2caX6dYoisRS9526EoqII+buWo9ecDRDryHB9xzLoWTlDotf0qxXLYnbDwNoTXYdMQcXZwwAAiX5XmD75JpzKYhEZ8WOTfevatSuGDh2K48ePY9y4cdi+fTtmz56tLt+3b18kJiYiKCgIW7duxfvvv49//OMfePrpp/HKK69ALpdDoVC04koTUWfGEUoi6lS8vb1hYWFRb3tpaSmmTp0KBwcHLF++HKmpqfXKDBkyBAEBAdi9ezd0dHTq7a9RqNT/bfC4951t19LvBEh9I4ikujCw9YY8t/klhaqvpMJg8CgAgKH96Dr7VA28j6Khvr300kv49ttvIQgCwsLC8Pzzz6vLT5kyBQDg7u6OrKwsAEB0dDTmz58PAJDJZDA0NGy2n0REAAMlEXUyBgYNvxs7KCgIkyZNQkpKCr777jvI5fJ6ZSIiIrBw4UKcOnUKPj4+9fbrSv/+ShXpyDTvrKjhh3vEDWxvqG+jR49GYmIifv75ZwwePBhmZmbq8jLZnf5JJBIolUrN+0pEnRoDJRE90oyMjFBR0fxSO+Xl5ejTpw8AIDQ0tN5+lUqF3Nxc+Pn5Ye3atcjJyakXxPqbGuLeqKfb2wbV2eegrL4JQXEbVWm/Q9bPvtn+6PW1Q9WFaABAZWpUnX0Gsrp3KzXWN5FIBH9/f7z88suYM2dOs22OHDkSX3/9NQCgpqYGlZWVzR5DRAQwUBLRI87U1BRubm5wdHTEyZMnGy33zjvvYPHixXBzc4Ourm69/UqlErNmzYKTkxM8PDwQFBQEiaTu09uGMiksTeqOgEqNTGHs/Tzyd72La6GLYDB4JGS9rJvtt/GIF1CVHoe8rQugKPv74RtzYz1I/1qWKCgoCOHh4U32bebMmaipqcGkSZOabfPTTz/FwYMH4eTkhGHDhiEvL6/ZY4iIAEAkCA3cjENERPdlZfh57IjL1vrC5gAgEYvw4lArrJzc/AhnrdDQUJw7dw4bNmzQen+IiGrxKW8iIi2aNdQSoaey2qRupUpAgJdli8u/+uqrOHnyJI4fP94m/SEiqsURSiKi+7R69Wrs27evzrZFixbhF5Udfr9U3KJRyluXzqjXmawl62sH0/F117mUiEUYPsAUO+YP1bjfRETaxkBJRKRluSVVGLvhV8jvWkZIUzKpGMcW+6CfScNPqRMRPUx8KIeISMv6mRjgg1bc59gSIZPtGSaJqN1ioCQiagMzPS2xdLyNVupaNt4WMzxbfu8kEdGDxilvIqI2tDc+B8Hh56FQCa168lsiFkEqFiFksj3DJBG1ewyURERtLLekCoEHkhGdUQSJWNRksKzdP9LaDB9OceQ0NxF1CAyUREQPSHp+BXbF5eBEWgFyiqtw95evCIClqQHG2PREgJclrHsaPaxuEhG1GgMlEdFDUClXYP6i5Ui7dAk7Qr9Bf1NDGMq4NDARdUz89iIieoBUKhViY2NRU1ODitwLqL5yGYVpf6BIJMLQoUOhp6f3sLtIRNRqHKEkInqA/ve//+HJJ59scN+aNWuwbNmyB9wjIiLNMVASET1AVVVVsLS0RHFxcZ3tYrEY6enpGDBgwEPqGRHR/eM6lERED5CBgQFWrFgBkUik3iaRSDBnzhyGSSLqsDhCSUT0gN07SsnRSSLq6DhCSUT0gBkYGOC9995T/8zRSSLq6DhCSUT0ENy6dQvdu3eHXC5HZmYmAyURdWhcNoiI6CHQ19fHypUrcfHiRYZJIurwOEJJRPQQVMoVyCquRI1CBV2pmAubE1GHxm8vIqIHRP3qxYsFyClp4NWLJgYYY9sTs4ZaYpA5X71IRB0HRyiJiNpYbkkVAg8kIzqjCBKxCEpV41+7tftHWpvhwymO6Gdi8AB7SkR0fxgoiYja0N74HASHn4dCJTQZJO8lEYsgFYvwwWR7zPS0bMMeEhFpjoGSiKiNbDqRjrWRaRrXs3S8DRaOGaSFHhERtQ2uQ0lE1Ab2xudoJUwCwNrINITF52ilLiKitsBASUSkZbklVQgOP6/VOoPCzyO3pEqrdRIRaQsDJRGRlgUeSIaiFfdLtoRCJSDwQLJW6yQi0hYGSiIiLUrPr0B0RlGrHsBpCaVKQHRGETIKKrRaLxGRNjBQElGHdePGDWzevLnBfQkJCVi2bFmTx69cuRKbNm3Sap92xeVAqCxBYfgnLT4m99MXAACC4jby96xA3rY3cevSGfX+6twU5G1dgOvb38bO2NbdS5mYmIjIyMhWHUNE1FoMlETUYTUWKJVKJTw8PPDJJy0PdfdDqVTW23biYgFEhiboMbnpMNuQmvxMiKS6sJj3OfQHuKu3V56PgvGIF9Br9nqcSCtoVZ33EygbOi8ioqYwUBJRh7VixQqkpqbCxcUFISEh8PX1xcSJE+Ht7Y2oqChMmzYNABAbG4thw4bBzc0NPj4+yM7OrlfXxo0bYWtrC2dnZ7z++uuNtjl69Gi89dZb8PDwwI4dO7Bjxw44OjrCwcEBq/79MXJKqqC4kY9roW81WoeyqgzXdwcib+sClEaF3tl2qxxFh9ah+koq8ra9CWXlDQDAzaSjqPozBjdOfIPi/21CdmEF/vHWYnh6esLZ2Rm7du0CAGRmZmLkyJFwc3PDkCFDkJiYCKVSiaCgIGzfvh0uLi44fPhwvVFZMzMzAEBUVFSd61dZWYm5c+fC09MT7u7uOHr0KADgxIkTcHR0hLOzMzw8PFr8uyKiRxtfvUhEHdbq1atx8eJFJCQkICoqCuvWrcOFCxdgYWGBqKgodTk7OzvExMRAIpEgPDwcq1atwpYtW+rUFRISgtzcXBgaGqKsrKzJdnV0dJCQkICrV69i1KhRiI+Ph4GBAdw8h6LaYx4kek2/NrEsZjcMrD3RdcgUVJw9DACQ6HeF6ZNvouLsT+gxJVBdtovTOFTnpMDgcW8YWA9BReL/oOtpjvj4eNy6dQteXl6YMGECevfujWPHjkEmkyEpKQlLlizB0aNHERISgpSUFKxduxYAcPr06Ub7debMGfX1CwwMxFNPPYXQ0FAUFRVhxIgRuHDhAtavX4/169dj3LhxzV4nIuo8GCiJ6JHh7e0NCwuLettLS0vx4osvIjMzEyqVCt27d69XZsiQIQgICIC/vz+effbZJtvx9/cHAMTHx8PPzw8mJiYAAN8nJ2P32fMwGOTV5PHVV1LRc9h0AICh/WjciN7ZktO7c+zls/ghvRDHwr8HAJSVleHSpUuwtrbGwoULkZSUBIlEgsLCwhbXWevu6xcZGYmffvoJq1atAgBUVlYiPz8f3t7eWL58OS5cuAB/f38YGxu3uh0ievRwypuIHhkGBg2/9zooKAiTJk1CSkoKvvvuO8jl8nplIiIisHDhQpw6dQo+Pj731Y5ELGp5Z0WtKHsXQRAQ/PEGJCYmIjExEVlZWfD09MTGjRvRv39/JCUlISYmpsFzBACpVAqVSqX++e5yd5+XSqXCoUOH1O3k5uaiV69eWL58ObZt24aKigp4eXnh6tWr93UeRPRoYaAkog7LyMgIFRXNL6NTXl6OPn36AABCQ0Pr7VepVMjNzYWfnx/Wrl2LnJycFj2YMmTIEBw/fhylpaWQy+WIPhoBvX72zR6n19cOVReiAQCVqVHNlr+b/mOuOLT3W3X/UlJSoFQqUV5eDgsLC4hEojrneO81srKyQmJiIgDg6NGjuHnzZoPtjB8/Hp999pn659pjMjMz4ezsjBUrVsDOzg6XL19uVf+J6NHEQElEHZapqSnc3Nzg6OiIkydPNlrunXfeweLFi+Hm5gZdXd16+5VKJWbNmgUnJyd4eHggKCgIEomk2fYtLCwQHByMUaNGwd3dHc/PnIlBdk7NHmc84gVUpcchb+sCKMoafmr7xsmdqEqPq7e9t/MomHbvBldXVzg4OGDx4sUQBAELFizAV199BRcXFxQXF6vLjxkzBmfPnoWrqysOHz6MqVOnIjs7G46OjoiIiICpqWmD7b///vsoKyuDk5MT7Ozs1PdgbtiwAfb29nBycoKFhQWGDRvW7PkS0aNPJAiCdlffJSLqxFaGn8eOuGytL2wOACJBhVvJkcg/vAkjRozA3Llz4e/vj65du2q9LSKi1uAIJRGRFs0aatkmYRIABJEYx74Mxu7du2FgYIBXXnkFvXr1wosvvojjx4/XuTeSiOhB4gglEVEDVq9ejX379tXZtmjRIrz00kvNHvvi13H4/VIxbmYkqNeZrCXrawfT8Y2vc9kYEQR4W5th5/y/nyDPzc3Fzp07ERoairS0NFhaWmL27NmYM2cOrK2tW90GEdH9YqAkItKy3JIqjN3wK+QKbY0YClDdrsHAi3sQ9vUX6NmzZ929goDY2FiEhoZi7969KC8vV0+JT58+HUZGTa+LSUSkKU55ExFpWT8TA3wwufmnvVtOhLmOBjgf9ytcXFzw66+/1t0rEmHYsGH46quvcP369XpT4rNnz8Yvv/zCKXEiajMMlEREbWCmpyWWjrfRSl3LxtsiZM4EJCYmwsbGBr6+vli9enWDAVFfXx/PP/88jhw5guzsbLz33nuIi4uDn58fHnvsMQQFBSEzM1Mr/SIiqsUpbyKiNrQ3PgfB4eehUAmtelhHIhZBKhYhZLI9ZnhaqrcrFAqEhIRg1apVGDduHHbs2FFvCvxeDU2Jjxw5Uv2UOKfEiUhTDJRERG0st6QKgQeSEZ1RBIlY1GSwrN0/0toMH05xRD+Tht/Kc/ToUcyaNQtSqRR79uxp9u0+tW7duoWDBw8iNDQUR48ehb6+Pp577jnMnTsXo0ePhljMiSsiaj0GSiKiByQ9vwK74nJwIq0AOcVVuPvLVwTA0tQAY2x6IsDLEtY9mx81zMvLwwsvvIDo6GiEhITgn//8Z6sCYUNPic+ZMwdz5szBwIEDW3+CRNRpMVASET0ElXIFsoorUaNQQVcqRn9TQxjKpK2u536mwO/FKXEi0hQDJRHRI+B+p8DvxSlxIrofDJRERI8ITafA78UpcSJqKQZKIqJHiDamwO/FKXEiag4DJRHRI0hbU+D3unXrFg4cOIBvv/2WU+JEpMZASUT0iNL2FPi9OCVORLUYKImIHmHNTYGXlpaie/fuGrXBKXEi4twEEdEjTCqVIiQkBEeOHMEff/xR513gv/zyC3r06IHQ0FCN2mjoXeL6+vp4+eWX+S5xok6CI5RERJ3E3VPgy5Ytw9atW1FcXIy+ffsiMzMTurq6zdbRmvUz29OUuLbW/SSihjFQEhF1IgqFAitXrsTq1ashEolQ+0fAli1b8PLLLzd4jPoNPxcLkFPSwBt+TAwwxrYnZg21xCDz+tPbD2tKXNN+E1HLMVASEXUyISEhCA4OrrOtoVHKtngH+YNYOL0t+k1ETWOgJCLqRBITE+Hm5oaGvvq/+uorvPrqqwCAvfE5CA4/D4VKaDKQ3UsiFkEqFuGDyfaY6WnZZNnc3Fzs2LEDoaGhSE9Pb3JKPD8/HwsWLMC//vUv2NnZNVrng+g3EdXHQElE1IlkZWXhzTffxJkzZ3Dt2rU6+/T09FBZWYn//JqJtZFpGre1dLwNFo4Z1Gw5QRBw6tQphIaGIiwsrMEp8X//+98IDAyEubk54uLiYGVlVa+eTSfSH2i/iehvDJRERJ3UjRs3kJSUhKSkJBw+fBjp6elYueMoVvx4XmttfDzVETNaMeLX0JT41KlTcfz4cVy7dg1SqRT9+vXDqVOnYG5urj5ub3wOlu9Pfmj9JursGCiJiAjAnXsPx274FXKF9pb3kUnFOLbY577uTaydEv/yyy9x5coV9XaJRILBgwcjJiYGxsbG7a7fRJ0R16EkIiIAQOCBZChacd9hSyhUAgIP3N/IYb9+/RAYGIgnn3wSEolEvV2pVCIlJQXu7u4oLy9vd/0m6owYKImICOn5FYjOKGrVgywtoVQJiM4oQkZBxX0dX11djd27d0OpVEJHRwdS6d9rR2ZmZmLVZ1vbZb+JOhuu6kpERNgVl9PsEjv3SyIWYWdsDlZOtm/1sWKxWL2cUN++fdGnTx/1Pz169MCBHCkkcTntrt9EnQ1HKImIHrCXX34ZmZmZD7sbdZy4WNBkKLuZdAylv3wNAKjOTUHe1gW49u3b6v2KimIUhn/S4LFKlYATaQXIy8vDrFmzkJWVBTs7O0RGRqrL/Pe//0VYWBgAYOXKldi0aRMAQEdHB+7u7khNTUVoaCiOHDkCX19fjBs3Di4uLvjwee96/ZZfS0fpL9uaPN+8bxahKGLDnfPJToI876J6342TO1Gdm6Lu991CQ0OxdOlS9O/fHzdv3myw7oMHDyIt7e+nzZv7fTdVV1Puvk6NCQ0NRUHB3+cwceJE3Lp1q9VtETWHI5RE1CmoVCqtLJqtDVu3bn1gbSmVyjr3HzbkplyBnJKqFtdZeT4KxiNegOHjI9TbpEam6DF5WaPH5BRXwdi0J3bt2oWsrCzcunULkZGRGD9+PADgtddea/C4zz77DGfPnkVKSgomTJiACRMmYMqUKUhISEBljRINZWBZ70GQ9W562R+xrj66DpkCAKjOSYbYoCtkFrYAgG6jAur0u1KuaNVrGg8ePAipVAobGxsAD/b3fa/Q0FB4eHigZ8+eAIDDhw8/tL7QI04gInpEXb58WXBwcBBmzJghWFtbC2+//bbg4eEhODk5CTt37hQEQRCSkpIEV1dXwdnZWXB2dhby8/OFEydOCL6+vsITTzwh2NjYCEuXLlXXuX37dsHBwUGwt7cX1qxZo27HyclJmD17tvD4448L06dPF1QqlSAIgrB06VLBxsZGcHJyEv71r38JgiAIPj4+QnJysvCf//xHeP/999V1f/DBB8L69esFQRCEjz76SPDw8BAcHR2FTz75RBAEQThx4oQwduxY4ZlnnhEGDRokzJs3T3B0dBT8/f2Ffv36CWZmZoKzs7NgaGgoLF26VHBxcRF8fX2FHj16CDKZTOjTp4+wc+dOdZ/Mzc2Fnj17CoMG2wndfV8WrJb/JPSas0GQ9XMQdM0HCrq9BwkS416Cbi9rQcfMShDrdREkRmaCSKorSI3NBV2LxwWpSR9Bp0d/wdButKDby1ro+49dgq75QEGs10UQyQwESRczofuYeYLF/20WbAbbC/r6+sKiRYsEHR0doUePHoKzs7MQEREhBAcHC59//rkgCIIwadIkwdLSUnBychL09fWFixcv1rluo0aNEo4ePSqkXL0hiPW7ClbLfxKslv8kmD75D0GnR39BYmwuSLtbCFbLfxL0BnoIYv2ugkhHJoh09ATTp5YIVst/EnR69Bf0+rsIfRZ8I4hkhgLEEgESHUFvgLtg6OAnyCydhR7TggRj7+eFwQ7Ogo6OjvDWW28J33zzjbBkyRLByspK2L17t+Dp6Sm4uLgIEydOFEpLS4XY2Fihe/fuQvfu3QU9PT1h8ODBwsCBA4Xk5GQhKChIeOyxxwSZTCaYmJioPz99+vQR7O3thdmzZwu9evUSjI2NBScnJ2HJkiV1zlsQBCE5OVnw8fERBEGoc82+/PJL9Wf7+eefF2pqaoT9+/cLhoaGgq2treDu7i4IgiBYWVkJFRUV6s+Yvb294ODgoP7/4d7P2OLFi7XwfyJ1Bu3jr+tERG3kwoULCAwMxLJly9C7d2/Ex8cjNjYWa9asQXFxMTZv3ozXX38diYmJOHXqFLp16wYAiI2NxebNm3H+/HnExcUhKioKV69excqVK/Hrr78iISEBe/bswZkzZ9TtvPvuu0hNTUV+fj5iYmJQXFyMsLAwXLhwAefOncObb75Zp29Tp07F/v371T//8MMP8Pf3R2RkJK5cuYLTp0/jjz/+wOHDh5GSkgIAOHv2LL766iukpKTg+PHjSElJwauvvgobGxv4+vrirbfeglQqRXZ2NrZu3YrCwkL069cPN27cQExMDN577z3s3r0b69atg729PZKTkxH2vxgYOvpBUCpQ+svX6DF1BXq9+Alul16H/mMu6DEtCLdLr8LQcSz6vhEK/UHD0H3c/0FRcgW9534Ki/mb0HXocwCAkmOboT/QE2KDbjB/4SNIDI1RFrcfJUe/wrSAebCzs4NMJkOfPn0we/ZsJCYmYuLEiXWui729PZYtW4bo6GioVCrEx8fX2e/m5obU1FTU3LVMUE1BFsoTfkSvWR/BbOIi6Jj2BQDomFlBp4cV+r0VBoPHR6in7WuJJDoAAGPv52G17ADMnl5aZ7+Rx2Rs+e4QevfujZycHKSnp6v3jRgxAnFxcfjjjz/wxBNP4IsvvsDQoUPh5uamvuapqanq9TL9/f0hEolw9epVjBw5Elu2bFF/fi5evIgpU6bAzMwMDg4O2LRpEwIDA5v8bN9t+vTpiI+Px7lz59CrVy989913mDJlCjw8PPD9998jISGhTvn4+Hh89913SEhIwK+//oqgoCDk5eXV+4wdOnQIOTk5Le4HdV4MlET0SLOxsYGTkxMiIyOxdetWuLi4YNiwYSgrK8OlS5cwbNgwrFu3Dh999BGuXbumfpe1t7c3LC0tIZVKMW3aNMTExCA+Ph5+fn4wMTGBnp6eejsA2Nraws7ODiKRCK6ursjKyoKxsTGMjY0xb948HDx4EIaGhnX6Zm5ujh49eiAlJQVpaWkwNDRE3759ERkZiYiICLi6usLd3R3Z2dnqe/KGDx8Oc3Nz6OrqwsbGBn369EF1dTWSkpJw+vRpLFmyBJWVlTAzM8OAAQNw5coV6Ojo4OTJk+jfvz/8/PwglUrx7rvvwtHRESYmJtCViiHRN8LtkiuoKbiM/D2ByNv2JqC8DVV1JSQGxhDrGaH68llUpZ0C/rp1QLe3DYoOrUXl+ShAfGdavTrrHG4mH4PqVjmKIzZAeascki4mqLmWjlG+fgCAWbNmNfk7u379OjZs2AAvLy/cvn0bqampDZbTlf79R1h1ThIMHh8JsV4XAIBIcmeKWlVZCkVZAa6FLkJ19jkINXWn9uV5FyE1NodY787vRqJvVGd/dfY5vDrjaVy7dg2//fabOnQBwJUrVzBu3Dg4Ojris88+U/fz2rVrGDt2LGQyGYA794ECwLfffouKigqMGTMGsbGxsLe3V39+Bg0ahJycHMyYMQOenp7IysqCiYlJk9fpbufOncOIESPg6OiI77//vtFrVuu3337Dc889Bz09PZiYmMDPz08d3O/+jDk4OCA7O7vF/aDOi4GSiB5pBgZ3FqZWqVT46quvkJiYiMTERGRlZcHT0xMvvPACfvrpJ8hkMowbNw5nz54FAIhEInUdIpGozs8NqQ0PwJ2Ft5VKJaRSKRISEvDcc89h3759mDlzZr3jpk+fjn379mHfvn3w9/dX9zU4OFjd18zMTEydOrXBdkQiEVQqFSZNmoTPPvsMU6dORZ8+fbBmzRp0794d7777Lh577DFs2LABS5cuhVgsxrp16zBo0CCcPn0aM2fORH9TQ4gAQBCgaz4AFvM+R49n3oWs72D0ePZdiMQSdB06FdLuFqj8Mwby3DujpT39g2Hk/hTkV/9EUfgadb+MvV9AF0c/WMz7HH0XfAPRXwG0b7eWLRIeHh6O559/HqmpqejatWudh0qAOyNodnZ26G9q2EgNf6tKj0MXu9GwmP8Fug6d2qL2AUAkFkNQ3Ebpsc0I/XY7LCwsEBAQgNu3b6vLLFu2DO+88w6Sk5OxceNGyOXyRuuTy+XYtm0bxo8fj6SkJAQEBEChUKj3N/T5qSWVSqFSqdT1NGT+/PnYsmULkpOT8c477zTZl+Y01ReixjBQElGnMH78ePznP/9R/+GYkpICpVKJS5cuYeDAgVi8eDHGjx+vHtn57bffcOXKFSgUCvzwww8YMWIEhgwZguPHj6O0tBRyuRz79+/HyJEjG23z5s2bKCsrw9NPP43169cjMTGxXpnaae/a6e7avm7duhVVVXdG07KyslBWVtZgG1euXIGhoSFOnDiBbdu2YcSIERAEAVlZWSgqKsLQoUORnp6OwMBAxMfH4/jx47C1tcWrr74KiUSCP/74A4YyKcyk1dAx7QtleRHk1zOgY9oXNcVXcSs7Ccrqm6i6EA2d7r1h4vcyVNU3IQgClOVF0O/vgu6+86G8WQJBEKBn5YjqnCR1/2ryLwEAjCwG4reTJwAAu3fvhlgsRkVFw2s81tTUwMjICDU1NejWrRtOnDihDkiHDx9GRUUF/Pz8YCiTQvxXztezckLVn9FQVVcCAASlQv1vkUwfgvI2qlJP1mtLZmELZXkBlBXFAADlrTt9Eht0Rc31DEgkYiTE/gaVSoWDBw/WOba8vBx9+vSBIAjYvn27evuAAQNw5MgRdZ9v374NuVwOXV1d/P7778jOzsaBAwdw7ty5Op8fX19fhIWFqY8rKSkBAFhZWak/O3ffInG3yspKmJubo6amBnv27FFvNzIyavA6jxgxAvv374dcLkdpaSl++eUXDBkypMG6iVqCT3kTUafwyiuv4PLly3B1dYVKpULv3r3x888/IywsDDt37oSOjg6srKwwZcoUxMfHY+jQoXj55Zdx+fJlTJ48GT4+PgCA4OBgjBo1CoIgYM6cOXBzc0NWVlaDbVZUVOCZZ55RB4SPP/64Xhlzc3OYmZlBLpejb9879/1NmDABqamp8PLygkqlQrdu3fDDDz802MZjjz2GzZs3Q6VS4eTJk8jIyMC1a9eQm5sLpVKJxYsX4/r16xg/fjx69+6NJUuWYP78+ZDL5bh+/Tpu376NLl26QNXHCeZT/gmzZ99F6bHNUMmrIIKAovBPIDHoBuXNYty8cR23Lv8BXXNrQFCh6NDaO+HTxAJG7k/jVkYcTMa9hoKw4DvT45nxMLAeCgB49qU38cUXG5Geno5Ro0bByMgIZ8+ehaurK1avXl3nnEaPHo1PPvkEBw4cwBNPPIFz587B3t4eeXl5UCgU2L9/v3rEWEcihkQsgljvzlT19Z3LoFLUQCS6M16iZ+mI8tgfUJV2CrrmA1CTX3f5HolhNxj7zEHpsc0oj/8RelZOkBh0g95jbqg8GwEdqLBixQpUVVVh2rRpdY795z//iaeffhomJibw8fFRTw0vX74czz77LIyNjWFtbY3q6mp07doV8+bNw9atW2FnZwcdHR2MHj0abm5u6vocHBywaNEiLF++HD/++CNSU1OxZs0avP3225gxYwY+/fRT+Pr6Nvg5WLlypfppbldXV/X2uXPnYu7cuTAyMqpzH6WHhwf8/f3h7u4OkUiEDz74AL1798bFixcbqp6oWXyXNxHRPaKiorBp0yZ8//33D7srTcrKysK0adPqPXDRFJVKhejoaISGhmLfvn2orKyEr68vJj4/D59ndmuzvh5bPArWPY2aL9hK6fkVGLex/sijtrRVv4keNRyhJCLqBC5fvozt27fj22+/xeXLlzFgwAC8++67mD17NqysrAAAiV/H4fdLxVp964xELMLwAaZtFsoGmRthpLVZh+s30aOGI5RERI+omzdv4ocffkBoaCiioqLQpUsXTJ8+HXPnzsWIESPU08ZvvPEGfvvtN9xWqpBZWAmVIKD76LnQH+CucR9kUjGOLfZBP5P6D+R88803+PTTT+ts8/f3x4oVK5qsc+jQoXUeOrmtVEE+fgUUul007m+tpvpdq7i4GH5+fnWPk8kQFxentX4QdRQMlEREj5DGprTnzp2LqVOn1lu66F5743OwfH+y1vrz8VRHzPC01Fp9jemo/SZ6VHDKm4joEdCSKe2WmOlpiaKbcqyNTGu+cDOWjbd9YKGso/ab6FHBEUoiog6qpVPa92NvfA6Cw89DoRJadW+iRCyCVCxCyGT7hxLKOmq/iTo6Bkoiog5E0ynt1sgtqULggWREZxRBIhY1GdBq94+0NsOHUxybvPewrXXUfhN1ZAyUREQdQENT2nPnzm31lPb9SM+vwK64HJxIK0BOcRXu/kNDBMDS1ABjbHoiwMuyXT0V3VH7TdQRMVASEbVTbTmlfb8q5QpkFVeiRqGCrlSM/qaGMJS1/9vxO2q/iToKBkoionbkQU5pExFpCwMlEVE70NiU9osvvoj+/fs/7O4RETWJ4/1ERA9JY1Pa33777UOb0iYiuh8MlERED1BjU9rbt2/nlDYRdVic8iYiegA4pU1EjzKOUBIRtRFOaRNRZ8FASUSkRZzSJqLOiFPeRERawCltIurMOEJJRB3aw1ywmlPaRER3MFASUYejfqXexQLklDTwSj0TA4yx7YlZQy0xyFy7r9TjlDYRUX2c8iaiDiO3pAqBB5IRnVEEiVgEparxr6/a/SOtzfDhFEf0MzFosu7KykrIZDJIpQ3/PZtT2kREjWOgJKIOYW98DoLDz0OhEpoMkveSiEWQikX4YLI9ZnpaNljmwoUL8Pb2xpw5c7Bhwwb19vb4Lm0iovaIgZKI2r1NJ9KxNjJN43qWjrfBwjGD6mzLzs6Gl5cXrl+/DiMjI1y/fh3x8fF8lzYRUSswUBJRu7Y3PgfL9ydrrb6Ppzpixl8jlQUFBfDy8kJOTg6USiUAoEePHigsLOSUNhFRK/ChHCJqt3JLqhAcfl6rdQaFn8fwgWboKrmNMWPGIDs7GyqVSr1fV1cXJ0+e5JQ2EVEriB92B4iIGhN4IBmKVtwv2RIKlYB/HkiCg4MDUlNT64RJAMjLy8PAgQMZJomIWoGBkojapfT8CkRnFLXqAZyWUKoExGQUQ9LdAl27doWOjk6d/YIgICIiQqttEhE96hgoiaie//73vwgLC7vv46OiojBt2jQAwJ9//gkXFxe4urqiuLi4xXXsisuBRNw2o4QSsQhzV21DWVkZ5HI5CgsLkZiYiIiICEyePBm9evVqk3aJiB5VvIeSqBNTqVQQi+v/vfK1117TWhsHDx5EQEAAli5d2qp+nLhYoPXRyVpKlYATaQVYCXuIRCKYmZnBzMwMzs7OmDhxYpu0SUT0KOMIJVEnk5WVBUdHR8ycORODBw+Gv78/nJycMGTIECQmJgIAVq5ciU2bNgEANm7cCFtbWzg7O+P1119vtN7Tp0/DwcEBLi4u2LdvHwDgyJEj2LhxIzZu3IjJkyfXKW9qaoqFCxfC0dERaWlp+Pjjj+Hp6QknJyes/mgNckqqoKgoxvUdy5D39ULkfb0QNQWXobiRj7yvF6Lw4Ee4uuU1FP20HoLqzhPaty6fRd62N5G3dQFKjm9B7SIWuZ++gNJfvkbe128gf08gVDXVyCmuwpq16+qd29y5c/HTTz/h559/xuzZs9X93b59OxYtWgQA2LFjBzw9PeHs7Iy3335bfV2dnZ0xZ84cDB48GDNmzFC3n5CQAB8fH7i7u+Ppp59GSUkJAGDZsmXq9letWtWq601E1J5whJKoE7pw4QJ27dqFyMhIXLp0Cfv27UNsbCzmzJmDc+fO1SkbEhKC3NxcGBoaoqysrNE658+fj9DQUHh4eGDGjBkAgCeeeAKvvfYazMzMsHDhwjrlS0pK8OSTT2LTpk2IjIzElStXcPr0aahUKgwfNQbywYaovnQWMktHdPeZDUGlhKC8DVVlGW4XZsN04iLIeg9CYfgnqEw5AYPBI1H88+fo9cJHkBj3QOH3IbiVdgoGtsOhulUOvQHu6O47H0WH1qEq7Xd0cfDF6tWrkHflSoPnNnbsWCxYsAA1NTXQ1dXFvn37sHz5cly4cAE//vgjTp06BalUitmzZyMiIgL29va4cOEC9uzZg8GDB2PMmDGIiYmBl5cXlixZggMHDsDExATbtm3Dv//9byxfvhxhYWHIysqCWCxWt9/S601E1J5whJKoE7KxsYGTkxNiYmIQEBAAAPDy8sKtW7fqhZghQ4YgICAAu3fvrvcAS60bN25ALpfDw8MDADBr1qxm+6Cvr49JkyYBACIjIxEREQFXV1e4u7sj70ouFCV50O1tg8rzUbgRvQu3i3Ih1tEDAEi794as950Fyg0Hj4L8SioUJVehY9IX0m7mEInEMLQbjercO0sOiXT1od/fBQCg28sairJ8AIC9s1uj56ajo4PRo0cjMjIS5eXlSE1NxfDhw3H8+HHExsbCw8MDLi4uiI2NRUZGBgDA1tYWdnZ2EIlEcHV1RVZWFi5evIhz587B19cXLi4uWL9+PXJycmBsbAxjY2PMmzcPBw8eVC+Y3pLrTUTU3jBQEnVCBgZNv9f6bhEREVi4cCFOnToFHx+fRsu1dpmdu/ugUqkQHByMxMREJCYm4n+nEmFgOxx6lg7o9eIaSLqYoCh8DarS42obu6fxptsSSe4KZmIx8NdSQf/dsa/Jc5s+fTr27duHQ4cO4amnnoJIJIJKpcIrr7yi7mtaWpp6Klwmk6mPlUgkUCqVUKlUcHV1VZdPSUlBWFgYpFIpEhIS8Nxzz2Hfvn2YOXMmgJZfbyKi9oSBkqgTGzFiBHbv3g3gzj2QBgYGMDY2Vu9XqVTIzc2Fn58f1q5dW+eNMnfr1q0bZDIZzp49CwDYs2dPq/oxfvx4bN26FVVVVQAAUUUhhOpKKMoKIDHsDiPXJ2Ho4IuagssAAEVJHuTX74wKVv0ZDVlfe0hN+uB2yVUoygogCCpUpv4KvX72jTcqqCCtKmny3MaOHYvo6Gjs2rUL06dPBwD4+fkhLCxM/cR6QUEBrl271mgzjz/+OHJzc3HmzBkAgFwux59//ombN2+irKwMTz/9NNavX4/ExMQWX28iovaG91ASdWJvvPEGXnnlFTg5OUFPTw/ffPNNnf1KpRKzZs1CRUUFBEFAUFAQJBJJg3Vt2bIFs2fPho6ODry9vXH9+vV6ZSZOnIitW7fCwsKizvYJEyYgNTUVXl5eUKlU6NatGyx8FyM9PRnlcT8AYinEeoboMfkdCIoa6PSwQnnsD6gpvAxZr0EwtPOBSCKF6ZMLUfDDvwCVEnqPuULfZlij596vux5emTenyXPT0dGBj48Pjh49iuHDhwMA7O3tsWLFCvj5+UGlUkEmkyE0NLTRd3zr6uoiLCwMixYtQkVFBZRKJd5//30YGxvjmWeegVwuBwB8/PHHrbreRETtCd/lTUTt0srw89gRl11v6SDFjXwUHvw3es/deN91S8QivDjUCisnNzGCSURELcYpbyJql2YNtWzTdSgDvCzbpG4ios6IgZKIWmX16tVwcXGp88+9U+XaMMjcCCOtzeq9LUfazVzj0cmR1maw7mmkYQ+JiKgWp7yJqN3KLanC2A2/Qq5Qaa1OmVSMY4t90M+k5U+6ExFR0zhCSUTtVj8TA3yg5fscQybbM0wSEWkZAyURtWszPS2xdLyNVupaNt4WMzx57yQRkbZxypuIOoS98TkIDj8PhUpo1cM6ErEIUrEIIZPtGSaJiNoIAyURdRi5JVUIPJCM6IwiSMSiJoNl7f6R1mb4cIojp7mJiNoQAyURdTjp+RXYFZeDE2kFyCmuwt1fYiIAlqYGGGPTEwFelnyam4joAWCgJKIOrVKuQFZxJWoUKuhKxehvaghDGV8CRkT0IDFQEhEREZFG+JQ3EREREWmEgZKIiIiINMJASUREREQaYaAkIiIiIo0wUBIRERGRRhgoiYiIiEgjDJREREREpBEGSiIiIiLSCAMlEREREWmEgZKIiIiINMJASUREREQaYaAkIiIiIo0wUBIRERGRRhgoiYiIiEgjDJREREREpBEGSiIiIiLSCAMlEREREWmEgZKIiIiINMJASUREREQaYaAkIiIiIo0wUBIRERGRRhgoiYiIiEgjDJREREREpBEGSiIiIiLSCAMlEREREWmEgZKIiIiINMJASUREREQaYaAkIiIiIo0wUBIRERGRRhgoiYiIiEgjDJREREREpBEGSiIiIiLSCAMlEREREWmEgZKIiIiINMJASUREREQaYaAkIiIiIo0wUBIRERGRRhgoiYiIiEgjDJREREREpBEGSiIiIiLSCAMlEREREWmEgZKIiIiINMJASUREREQaYaAkIiIiIo0wUBIRERGRRhgoiYiIiEgjDJREREREpBEGSiIiIiLSCAMlEREREWmEgZKIiIiINMJASUREREQaYaAkIiIiIo0wUBIRERGRRhgoiYiIiEgjDJREREREpBEGSiIiIiLSCAMlEREREWmEgZKIiIiINMJASUREREQaYaAkIiIiIo0wUBIRERGRRhgoiYiIiEgjDJREREREpBE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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 500x500 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "new_pipelines.draw_graph(x_spacing = 20, layout = \"aligned\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "db1479c2-01ee-4575-bdc1-02464beb712b",
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33;20mWARNING    : autoload_arguments           : FileNotFoundError : Could not open the config file preproc_data_arguments.json for the session wm33/2023-12-05/001. Skipping (logging.py:40) - 2023-12-06 16:54:22,799\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : Could not find or load neuropil_mask.maps_calculation.C1 saved file.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : Running requirement trials_df.tdms_export\u001b[0m\n",
-      "\u001b[33;20mWARNING    : autoload_arguments           : FileNotFoundError : Could not open the config file preproc_data_arguments.json for the session wm33/2023-12-05/001. Skipping (logging.py:40) - 2023-12-06 16:54:22,808\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.trials_df.tdms_export    : Could not find or load trials_df.tdms_export saved file.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.trials_df.tdms_export    : Performing the computation to generate trials_df.tdms_export. Hold tight.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.trials_df.tdms_export    : Saving the generated trials_df.tdms_export output.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : Performing the computation to generate neuropil_mask.maps_calculation.C1. Hold tight.\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_trials_arrays       : Processing trials 3D fluorescent signal (read tiff, zscore, gaussian filter)\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_trials_arrays       : Accessing tiff files data\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_trials_arrays       : Using only the last 20th trials\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_trials_arrays       : Performing delta_over_f calculation on 20 trials\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_trials_arrays       : Performing gaussian calculation\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_trials_arrays       : Packaging obtained signals into TimelinedArrays\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_trials_arrays_mean  : Generating neuropil mean of all provided trials in time synchronized with stimulation\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_mask                : Fitting gaussian 2D function onto neuropil difference map (after - before stim)\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_plots               : Finished calculating neuropil_mask. You may now call refine_neuropil_mask on this session\u001b[0m\n",
-      "\u001b[38;5;57;48;5;195;1mâ–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘ neuropil_plots               : Finished for trials subset : C1 from session wm33/2023-12-05/001.  - 2023-12-06 16:56:42,738 \n",
-      "\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : Saving the generated neuropil_mask.maps_calculation.C1 output.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : Could not find or load neuropil_mask.maps_calculation.D1 saved file.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : Running requirement trials_df.tdms_export\u001b[0m\n",
-      "\u001b[33;20mWARNING    : autoload_arguments           : FileNotFoundError : Could not open the config file preproc_data_arguments.json for the session wm33/2023-12-05/001. Skipping (logging.py:40) - 2023-12-06 16:56:43,203\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.trials_df.tdms_export    : File exists for trials_df.tdms_export. Loading and processing will be skipped\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : Performing the computation to generate neuropil_mask.maps_calculation.D1. Hold tight.\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_trials_arrays       : Processing trials 3D fluorescent signal (read tiff, zscore, gaussian filter)\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_trials_arrays       : Accessing tiff files data\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_trials_arrays       : Performing delta_over_f calculation on 14 trials\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_trials_arrays       : Performing gaussian calculation\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_trials_arrays       : Packaging obtained signals into TimelinedArrays\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_trials_arrays_mean  : Generating neuropil mean of all provided trials in time synchronized with stimulation\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_mask                : Fitting gaussian 2D function onto neuropil difference map (after - before stim)\u001b[0m\n",
-      "\u001b[33;20mWARNING    : neuropil_mask                : No map could be fitted with maxfev 2000 (logging.py:40) - 2023-12-06 17:00:08,296\u001b[0m\n",
-      "\u001b[94;20mINFO       : neuropil_plots               : Finished calculating neuropil_mask. You may now call refine_neuropil_mask on this session\u001b[0m\n",
-      "\u001b[38;5;57;48;5;195;1mâ–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘â–‘ neuropil_plots               : Finished for trials subset : D1 from session wm33/2023-12-05/001.  - 2023-12-06 17:00:11,216 \n",
-      "\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : Saving the generated neuropil_mask.maps_calculation.D1 output.\u001b[0m\n"
-     ]
     },
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "{'C1': {'masked_fit': masked_array(\n",
-       "    data=[[--, --, --, ..., --, --, --],\n",
-       "          [--, --, --, ..., --, --, --],\n",
-       "          [--, --, --, ..., --, --, --],\n",
-       "          ...,\n",
-       "          [--, --, --, ..., --, --, --],\n",
-       "          [--, --, --, ..., --, --, --],\n",
-       "          [--, --, --, ..., --, --, --]],\n",
-       "    mask=[[ True,  True,  True, ...,  True,  True,  True],\n",
-       "          [ True,  True,  True, ...,  True,  True,  True],\n",
-       "          [ True,  True,  True, ...,  True,  True,  True],\n",
-       "          ...,\n",
-       "          [ True,  True,  True, ...,  True,  True,  True],\n",
-       "          [ True,  True,  True, ...,  True,  True,  True],\n",
-       "          [ True,  True,  True, ...,  True,  True,  True]],\n",
-       "    fill_value=1e+20),\n",
-       "  'averaging_means': [TimelinedArray([[ 0.00162015, -0.00723976, -0.01400323, ..., -0.01450261,\n",
-       "           -0.01484378, -0.01447442],\n",
-       "          [ 0.00278228, -0.00470135, -0.01038949, ..., -0.01309108,\n",
-       "           -0.01349408, -0.01318465],\n",
-       "          [ 0.00243541, -0.00344246, -0.00781077, ..., -0.01146216,\n",
-       "           -0.01211615, -0.01208877],\n",
-       "          ...,\n",
-       "          [ 0.00769943,  0.00803701,  0.00884836, ...,  0.00345261,\n",
-       "            0.00188984, -0.00047476],\n",
-       "          [ 0.00993363,  0.0098899 ,  0.01035699, ...,  0.00163058,\n",
-       "           -0.00132428, -0.00496632],\n",
-       "          [ 0.01111848,  0.01072439,  0.01093394, ...,  0.00034089,\n",
-       "           -0.00417582, -0.00924503]]),\n",
-       "   TimelinedArray([[ 0.00971796,  0.01116242,  0.01293427, ...,  0.00187818,\n",
-       "           -0.0008868 , -0.00461066],\n",
-       "          [ 0.01336081,  0.01342854,  0.01396598, ...,  0.00179105,\n",
-       "            0.00044334, -0.00161218],\n",
-       "          [ 0.01689716,  0.0156127 ,  0.01492602, ...,  0.00183898,\n",
-       "            0.00148361,  0.00065886],\n",
-       "          ...,\n",
-       "          [ 0.0021099 ,  0.0029613 ,  0.00375736, ..., -0.00507865,\n",
-       "           -0.01142588, -0.01741427],\n",
-       "          [ 0.00384943,  0.00442754,  0.00491136, ..., -0.00820311,\n",
-       "           -0.01587594, -0.02335274],\n",
-       "          [ 0.00485796,  0.00533549,  0.0056842 , ..., -0.01111415,\n",
-       "           -0.02001889, -0.02892035]])],\n",
-       "  'map_difference': array([[ 0.00809781,  0.01840219,  0.0269375 , ...,  0.0163808 ,\n",
-       "           0.01395698,  0.00986375],\n",
-       "         [ 0.01057854,  0.01812988,  0.02435548, ...,  0.01488213,\n",
-       "           0.01393742,  0.01157246],\n",
-       "         [ 0.01446175,  0.01905516,  0.02273679, ...,  0.01330114,\n",
-       "           0.01359976,  0.01274762],\n",
-       "         ...,\n",
-       "         [-0.00558954, -0.00507571, -0.00509101, ..., -0.00853127,\n",
-       "          -0.01331572, -0.01693952],\n",
-       "         [-0.0060842 , -0.00546236, -0.00544563, ..., -0.00983368,\n",
-       "          -0.01455166, -0.01838642],\n",
-       "         [-0.00626052, -0.00538889, -0.00524973, ..., -0.01145504,\n",
-       "          -0.01584308, -0.01967532]]),\n",
-       "  'threshold_level': 0.033749195325188774,\n",
-       "  'percentile_level': 85,\n",
-       "  'optimal_params': {'amplitude': 0.043558106556742075,\n",
-       "   'x0': 88.57406904508541,\n",
-       "   'y0': 179.29556092427788,\n",
-       "   'x_sigma': 340.68834653867634,\n",
-       "   'y_sigma': -118.90364302650009,\n",
-       "   'theta': -79.79914738757965,\n",
-       "   'offset': -0.0035478018154338634},\n",
-       "  'initial_params': {'amplitude': 0.06067338823657639,\n",
-       "   'x0': 256.0,\n",
-       "   'y0': 256.0,\n",
-       "   'x_sigma': 256.0,\n",
-       "   'y_sigma': 256.0,\n",
-       "   'theta': 0,\n",
-       "   'offset': 0.014985199446392164},\n",
-       "  'principal_whisker_stim_params': [{'pulse_freq': '10',\n",
-       "    'peak_voltage': '10',\n",
-       "    'onset': 0,\n",
-       "    'corrected_onset': 0,\n",
-       "    'offset': 0.6,\n",
-       "    'sin_freq': '120',\n",
-       "    'tn': '0',\n",
-       "    'an': '0',\n",
-       "    'pulse_nr': '0'},\n",
-       "   {'pulse_freq': '90',\n",
-       "    'peak_voltage': '10',\n",
-       "    'onset': 0.6,\n",
-       "    'corrected_onset': 0.611,\n",
-       "    'offset': 1.0,\n",
-       "    'sin_freq': '120',\n",
-       "    'tn': '0',\n",
-       "    'an': '0',\n",
-       "    'pulse_nr': '0'}],\n",
-       "  'averaging_time_duration': [0.2, 0.2],\n",
-       "  'averaging_time_offset': [-0.4, 0.2],\n",
-       "  'whisker': 'C1',\n",
-       "  'target_stim': 'pw10_90',\n",
-       "  'F0_index': 0,\n",
-       "  'F0_span': 25,\n",
-       "  'channel': 'red',\n",
-       "  'gaussian_filter_values': (1, 2.5, 2.5),\n",
-       "  'trigger_id': 0,\n",
-       "  'barrel_center': {'x': 88.57406904508541, 'y': 179.29556092427788},\n",
-       "  'barrel_mask_method': 'apparent_values'},\n",
-       " 'D1': {'masked_fit': None,\n",
-       "  'averaging_means': [TimelinedArray([[ 0.00897769,  0.01289113,  0.01577843, ..., -0.00543787,\n",
-       "           -0.00887378, -0.01317623],\n",
-       "          [ 0.01351456,  0.01570544,  0.01731593, ..., -0.00411038,\n",
-       "           -0.00726397, -0.01083771],\n",
-       "          [ 0.0167472 ,  0.01767605,  0.01842694, ..., -0.00172795,\n",
-       "           -0.00433854, -0.00694022],\n",
-       "          ...,\n",
-       "          [ 0.00921099,  0.00776077,  0.00617914, ...,  0.0062304 ,\n",
-       "            0.00620742,  0.00493043],\n",
-       "          [ 0.01237792,  0.01141929,  0.01035727, ...,  0.00183282,\n",
-       "            0.00314028,  0.00301618],\n",
-       "          [ 0.0154237 ,  0.01544331,  0.01534313, ..., -0.00165088,\n",
-       "            0.00154944,  0.00313386]]),\n",
-       "   TimelinedArray([[ 1.93263216e-02,  1.44724917e-02,  1.15695983e-02, ...,\n",
-       "           -1.63999308e-02, -1.89624040e-02, -2.16850613e-02],\n",
-       "          [ 2.34808027e-02,  1.90704125e-02,  1.65098316e-02, ...,\n",
-       "           -1.30577470e-02, -1.51288667e-02, -1.72935610e-02],\n",
-       "          [ 2.57689041e-02,  2.18508676e-02,  1.96087337e-02, ...,\n",
-       "           -9.93315704e-03, -1.12791930e-02, -1.26923951e-02],\n",
-       "          ...,\n",
-       "          [-5.00240685e-03, -1.81276460e-03,  1.22725948e-03, ...,\n",
-       "            3.80037148e-03,  4.13701375e-03,  4.03663935e-03],\n",
-       "          [-4.32542837e-03, -2.00744791e-03,  5.35338938e-04, ...,\n",
-       "            1.18478940e-03,  1.91246800e-03,  2.21520017e-03],\n",
-       "          [-4.41346069e-03, -2.59775545e-03, -1.07392366e-04, ...,\n",
-       "           -1.35108311e-03, -4.06159809e-04,  5.76622394e-05]])],\n",
-       "  'map_difference': array([[ 0.01034863,  0.00158136, -0.00420884, ..., -0.01096206,\n",
-       "          -0.01008862, -0.00850883],\n",
-       "         [ 0.00996625,  0.00336497, -0.0008061 , ..., -0.00894737,\n",
-       "          -0.0078649 , -0.00645585],\n",
-       "         [ 0.00902171,  0.00417482,  0.00118179, ..., -0.00820521,\n",
-       "          -0.00694065, -0.00575217],\n",
-       "         ...,\n",
-       "         [-0.0142134 , -0.00957353, -0.00495188, ..., -0.00243003,\n",
-       "          -0.00207041, -0.00089379],\n",
-       "         [-0.01670335, -0.01342674, -0.00982193, ..., -0.00064804,\n",
-       "          -0.00122781, -0.00080098],\n",
-       "         [-0.01983716, -0.01804107, -0.01545053, ...,  0.0002998 ,\n",
-       "          -0.0019556 , -0.0030762 ]]),\n",
-       "  'threshold_level': None,\n",
-       "  'percentile_level': 85,\n",
-       "  'optimal_params': None,\n",
-       "  'initial_params': None,\n",
-       "  'principal_whisker_stim_params': [{'pulse_freq': '10',\n",
-       "    'peak_voltage': '10',\n",
-       "    'onset': 0,\n",
-       "    'corrected_onset': 0,\n",
-       "    'offset': 0.6,\n",
-       "    'sin_freq': '120',\n",
-       "    'tn': '0',\n",
-       "    'an': '0',\n",
-       "    'pulse_nr': '0'},\n",
-       "   {'pulse_freq': '90',\n",
-       "    'peak_voltage': '10',\n",
-       "    'onset': 0.6,\n",
-       "    'corrected_onset': 0.611,\n",
-       "    'offset': 1.0,\n",
-       "    'sin_freq': '120',\n",
-       "    'tn': '0',\n",
-       "    'an': '0',\n",
-       "    'pulse_nr': '0'}],\n",
-       "  'averaging_time_duration': [0.2, 0.2],\n",
-       "  'averaging_time_offset': [-0.4, 0.2],\n",
-       "  'whisker': 'D1',\n",
-       "  'target_stim': 'pw10_90',\n",
-       "  'F0_index': 0,\n",
-       "  'F0_span': 25,\n",
-       "  'channel': 'red',\n",
-       "  'gaussian_filter_values': (1, 2.5, 2.5),\n",
-       "  'trigger_id': 0}}"
+       "<Figure size 500x500 with 1 Axes>"
       ]
      },
-     "execution_count": 11,
      "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_pipelines.neuropil_mask.maps_calculation.generate(session, extra = \"all\", check_requirements = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "f65b81e3-f18c-4e9c-8bea-78be8eafea83",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33;20mWARNING    : autoload_arguments           : FileNotFoundError : Could not open the config file preproc_data_arguments.json for the session wm33/2023-12-05/001. Skipping (logging.py:40) - 2023-12-06 17:10:58,541\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.suite2p.run              : Could not find or load suite2p.run.0 saved file.\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.suite2p.run              : Running requirement trials_df.tdms_export\u001b[0m\n",
-      "\u001b[33;20mWARNING    : autoload_arguments           : FileNotFoundError : Could not open the config file preproc_data_arguments.json for the session wm33/2023-12-05/001. Skipping (logging.py:40) - 2023-12-06 17:10:58,546\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.trials_df.tdms_export    : File exists for trials_df.tdms_export. Loading and processing will be skipped\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.suite2p.run              : Performing the computation to generate suite2p.run.0. Hold tight.\u001b[0m\n",
-      "\u001b[94;20mINFO       : suite2p                      : Running Suite2p\u001b[0m\n",
-      "tif\n",
-      "** Found 300 tifs - converting to binary **\n",
-      "116000 frames of binary, time 893.97 sec.\n",
-      "\u001b[94;20mINFO       : suite2p                      : time 1067.52 sec. Wrote 70833 frames per binary for 1 planes\u001b[0m\n",
-      "\u001b[94;20mINFO       : suite2p                      : Starting processing plane : 0\u001b[0m\n",
-      "\u001b[94;20mINFO       : suite2p.plane                : NOTE: not registered / registration forced with ops['do_registration']>1\u001b[0m\n",
-      "\u001b[31;20mERROR      : suite2p.plane                :       (no previous offsets to delete) (logging.py:40) - 2023-12-06 17:28:46,107\u001b[0m\n",
-      "\u001b[94;20mINFO       : suite2p                      : NOTE: applying default C:\\Users\\tjostmou\\.suite2p\\classifiers\\classifier_user.npy\u001b[0m\n",
-      "\u001b[94;20mINFO       : suite2p.registration         : Starting\u001b[0m\n",
-      "registering two channels\n",
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-      "Second channel, Registered 22000/70833 in 175.07s\n",
-      "Second channel, Registered 22500/70833 in 178.88s\n",
-      "Second channel, Registered 23000/70833 in 182.65s\n",
-      "Second channel, Registered 23500/70833 in 186.54s\n",
-      "Second channel, Registered 24000/70833 in 190.55s\n",
-      "Second channel, Registered 24500/70833 in 196.11s\n",
-      "Second channel, Registered 25000/70833 in 199.79s\n",
-      "Second channel, Registered 25500/70833 in 203.57s\n",
-      "Second channel, Registered 26000/70833 in 207.32s\n",
-      "Second channel, Registered 26500/70833 in 211.10s\n",
-      "Second channel, Registered 27000/70833 in 214.82s\n",
-      "Second channel, Registered 27500/70833 in 218.77s\n",
-      "Second channel, Registered 28000/70833 in 224.74s\n",
-      "Second channel, Registered 28500/70833 in 228.45s\n",
-      "Second channel, Registered 29000/70833 in 232.18s\n",
-      "Second channel, Registered 29500/70833 in 235.93s\n",
-      "Second channel, Registered 30000/70833 in 239.62s\n",
-      "Second channel, Registered 30500/70833 in 243.24s\n",
-      "Second channel, Registered 31000/70833 in 246.97s\n",
-      "Second channel, Registered 31500/70833 in 250.83s\n",
-      "Second channel, Registered 32000/70833 in 254.72s\n",
-      "Second channel, Registered 32500/70833 in 260.36s\n",
-      "Second channel, Registered 33000/70833 in 264.04s\n",
-      "Second channel, Registered 33500/70833 in 267.75s\n",
-      "Second channel, Registered 34000/70833 in 271.64s\n",
-      "Second channel, Registered 34500/70833 in 275.32s\n",
-      "Second channel, Registered 35000/70833 in 279.05s\n",
-      "Second channel, Registered 35500/70833 in 282.99s\n",
-      "Second channel, Registered 36000/70833 in 289.01s\n",
-      "Second channel, Registered 36500/70833 in 292.75s\n",
-      "Second channel, Registered 37000/70833 in 296.46s\n",
-      "Second channel, Registered 37500/70833 in 300.26s\n",
-      "Second channel, Registered 38000/70833 in 304.03s\n",
-      "Second channel, Registered 38500/70833 in 307.83s\n",
-      "Second channel, Registered 39000/70833 in 311.69s\n",
-      "Second channel, Registered 39500/70833 in 315.61s\n",
-      "Second channel, Registered 40000/70833 in 319.68s\n",
-      "Second channel, Registered 40500/70833 in 325.28s\n",
-      "Second channel, Registered 41000/70833 in 329.03s\n",
-      "Second channel, Registered 41500/70833 in 332.67s\n",
-      "Second channel, Registered 42000/70833 in 336.36s\n",
-      "Second channel, Registered 42500/70833 in 340.10s\n",
-      "Second channel, Registered 43000/70833 in 343.89s\n",
-      "Second channel, Registered 43500/70833 in 347.77s\n",
-      "Second channel, Registered 44000/70833 in 353.59s\n",
-      "Second channel, Registered 44500/70833 in 357.34s\n",
-      "Second channel, Registered 45000/70833 in 361.19s\n",
-      "Second channel, Registered 45500/70833 in 364.98s\n",
-      "Second channel, Registered 46000/70833 in 368.67s\n",
-      "Second channel, Registered 46500/70833 in 372.42s\n",
-      "Second channel, Registered 47000/70833 in 376.33s\n",
-      "Second channel, Registered 47500/70833 in 380.32s\n",
-      "Second channel, Registered 48000/70833 in 384.30s\n",
-      "Second channel, Registered 48500/70833 in 389.88s\n",
-      "Second channel, Registered 49000/70833 in 393.65s\n",
-      "Second channel, Registered 49500/70833 in 397.44s\n",
-      "Second channel, Registered 50000/70833 in 401.16s\n",
-      "Second channel, Registered 50500/70833 in 405.11s\n",
-      "Second channel, Registered 51000/70833 in 408.89s\n",
-      "Second channel, Registered 51500/70833 in 412.95s\n",
-      "Second channel, Registered 52000/70833 in 418.81s\n",
-      "Second channel, Registered 52500/70833 in 422.58s\n",
-      "Second channel, Registered 53000/70833 in 426.43s\n",
-      "Second channel, Registered 53500/70833 in 430.18s\n",
-      "Second channel, Registered 54000/70833 in 433.96s\n",
-      "Second channel, Registered 54500/70833 in 437.69s\n",
-      "Second channel, Registered 55000/70833 in 441.49s\n",
-      "Second channel, Registered 55500/70833 in 445.42s\n",
-      "Second channel, Registered 56000/70833 in 449.26s\n",
-      "Second channel, Registered 56500/70833 in 454.86s\n",
-      "Second channel, Registered 57000/70833 in 458.67s\n",
-      "Second channel, Registered 57500/70833 in 462.36s\n",
-      "Second channel, Registered 58000/70833 in 466.21s\n",
-      "Second channel, Registered 58500/70833 in 469.98s\n",
-      "Second channel, Registered 59000/70833 in 473.71s\n",
-      "Second channel, Registered 59500/70833 in 477.63s\n",
-      "Second channel, Registered 60000/70833 in 483.65s\n",
-      "Second channel, Registered 60500/70833 in 487.58s\n",
-      "Second channel, Registered 61000/70833 in 491.63s\n",
-      "Second channel, Registered 61500/70833 in 495.58s\n",
-      "Second channel, Registered 62000/70833 in 499.62s\n",
-      "Second channel, Registered 62500/70833 in 503.42s\n",
-      "Second channel, Registered 63000/70833 in 507.13s\n",
-      "Second channel, Registered 63500/70833 in 511.10s\n",
-      "Second channel, Registered 64000/70833 in 517.03s\n",
-      "Second channel, Registered 64500/70833 in 520.73s\n",
-      "Second channel, Registered 65000/70833 in 524.55s\n",
-      "Second channel, Registered 65500/70833 in 528.26s\n",
-      "Second channel, Registered 66000/70833 in 532.08s\n",
-      "Second channel, Registered 66500/70833 in 535.95s\n",
-      "Second channel, Registered 67000/70833 in 539.82s\n",
-      "Second channel, Registered 67500/70833 in 543.77s\n",
-      "Second channel, Registered 68000/70833 in 547.61s\n",
-      "Second channel, Registered 68500/70833 in 553.19s\n",
-      "Second channel, Registered 69000/70833 in 556.84s\n",
-      "Second channel, Registered 69500/70833 in 560.54s\n",
-      "Second channel, Registered 70000/70833 in 564.37s\n",
-      "Second channel, Registered 70500/70833 in 568.19s\n",
-      "Second channel, Registered 70833/70833 in 570.76s\n",
-      "\u001b[94;20mINFO       : suite2p.registration         : Finished in 1997.98 sec\u001b[0m\n",
-      "\u001b[94;20mINFO       : suite2p.registration_metrics : Starting\u001b[0m\n",
-      "\u001b[94;20mINFO       : suite2p.registration_metrics : Finished in, 22.90 sec.\u001b[0m\n",
-      "\u001b[94;20mINFO       : suite2p.roi_detection        : ----------- Starting\u001b[0m\n",
-      "Binning movie in chunks of length 30\n",
-      "Binned movie of size [2361,508,508] created in 29.86 sec.\n",
-      ">>>> CELLPOSE finding masks in mean_img\n",
-      "!NOTE! diameter set to 10.00 for cell detection with cellpose\n",
-      "\u001b[94;20mINFO       : cellpose.models              : >> cyto << model set to be used\u001b[0m\n",
-      "\u001b[94;20mINFO       : cellpose.core                : >>>> using CPU\u001b[0m\n",
-      "\u001b[94;20mINFO       : cellpose.models              : >>>> model diam_mean =  30.000 (ROIs rescaled to this size during training)\u001b[0m\n",
-      ">>>> 192 masks detected, median diameter = 7.82 \n",
-      "Detected 192 ROIs, 7.99 sec\n",
-      "After removing overlaps, 192 ROIs remain\n",
-      ">>>> CELLPOSE estimating masks in anatomical channel\n",
-      "ERROR importing or running cellpose, continuing without anatomical estimates\n",
-      "\u001b[94;20mINFO       : suite2p.roi_detection        : ----------- Finished in 42.67 sec.\u001b[0m\n",
-      "\u001b[94;20mINFO       : suite2p.extraction           : ----------- Starting\u001b[0m\n",
-      "Masks created, 0.36 sec.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\users\\tjostmou\\documents\\python\\__packages__\\suite2p\\suite2p\\extraction\\extract.py:139: NumbaTypeSafetyWarning: \u001b[1m\u001b[1m\u001b[1munsafe cast from uint64 to int64. Precision may be lost.\u001b[0m\u001b[0m\u001b[0m\n",
-      "  Fi[n] = np.dot(data[:, cell_ipix[n]], cell_lam[n])\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracted fluorescence from 192 ROIs in 70833 frames, 43.64 sec.\n",
-      "Extracted fluorescence from 192 ROIs in 70833 frames, 40.48 sec.\n",
-      "\u001b[94;20mINFO       : suite2p.extraction           : ----------- Finished in 85.70 sec.\u001b[0m\n",
-      "\u001b[94;20mINFO       : suite2p.classification       : ----------- Starting\u001b[0m\n",
-      "['npix_norm', 'skew', 'compact']\n",
-      "\u001b[94;20mINFO       : suite2p.classification       : ----------- Finished in 0.03 sec.\u001b[0m\n",
-      "\u001b[94;20mINFO       : suite2p.spike_deconvolution  : ----------- Starting\u001b[0m\n",
-      "\u001b[94;20mINFO       : suite2p.spike_deconvolution  : ----------- Total 0.53 sec.\u001b[0m\n",
-      "\u001b[94;20mINFO       : suite2p                      : Plane 0 processed in 2150.85 sec (can open in GUI).\u001b[0m\n",
-      "\u001b[94;20mINFO       : suite2p                      : total = 3329.09 sec.\u001b[0m\n",
-      "\u001b[94;20mINFO       : suite2p                      : TOTAL RUNTIME 3329.09 sec\u001b[0m\n",
-      "\u001b[94;20mINFO       : gen.suite2p.run              : Saving the generated suite2p.run.0 output.\u001b[0m\n"
-     ]
+     "output_type": "display_data"
     },
     {
      "data": {
+      "image/png": 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16tX5I43BwcH4+dm/RnJzc0lOTiYiIoLGjRsXOKx90vXXX3/GiOO5PkdJ/PLLL/j7++ePIJ80btw4LMti+vTpBW7v168f9evXz3+7ZcuWlCtXrsDXWdyHDhW5qZdffpnrr7+emjVr0q5dOwYNGsSYMWOoV69e/n0OHz4MQGpqKmvXruWxxx7j8OHD/PzzzwQGBtKgQQMOHz58xiGDHj165A/LL1iwgPbt29O+fXsqVqzIggULiI2NZe3atfm/bE916twEgPLlywOccTilfPnyOJ1Ojh49WmCoNSgoiH79+gEwZMgQ+vbtS7du3YiJiTlj/YratWvnz1UZNWoUt956K/369WPLli35P/xWrFjBoUOHii0u99xzDzNmzGDChAm0atWqwPtCQ0MLnYuTkZGR//7CvPLKK3z00Uc8++yzDBo0qND7nC3/sWPHCvzC9/f3p3LlykV+HGeTm5vLyJEjiY+PZ/r06QWG6U+cOMHRo0cL3L9KlSrn9TrlypXL/xpedtllfPnll1x22WWsWrUq//ObnZ3NrFmzeOGFF/Ifd/JzWZLP9/l+Xc7XuXxfAwXKx+7du3niiSf44YcfziglJz/nwcHBvPTSS4wbN47Y2Fg6d+7MkCFDGDNmzBlfh3nz5vHzzz/z8MMPn3Vey0m9evVi+PDhPP3007z22mv07t2byy+/nGuuuSa/AG7bto2jR48SExNT6HMcPHiwwNsNGjQ44w+XRo0aAZCQkECVKlVwOp288cYbvPvuu+zcubPA0gCFHWI5/dA2cM7PURK7du2iWrVqZ8zdOnnocdeuXQVuP/3rD1ChQoUSlUxxPY24uKmrrrqKHTt28NZbb1GtWjVeeeUV4uLiCvylULlyZSpXrkz9+vVxOp1ceeWVVK5cmbFjx5KVlUWNGjXOWEMDoHv37iQlJbFjxw4WLFhAjx49cDgcdO/enQULFrB48WKcTmf+ceRT+fv7F5q3qNuts6xb0rVrV6pWrcrEiRPP9ilhxIgRJCYm5o8Wgf2XVZ06dWjWrFmhj3n66ad59913efHFFxk9evQZ769atSr79u074/aTt51+jB7g008/5eGHH+b2228v0V/DReV/9dVXqVq1av7/Jycfnq9bbrmFn376iU8//ZSLLrqowPu+/vrrAq916no/F2rYsGEATJo0Kf+2hQsXkpqaWqDUnXzNoj7fFStWzP8lW7VqVfbv33/G909xX5cLcb7f17m5ufTv3z+/aHz33XfMmjUrf9Ko0+nMf8x9993H1q1beeGFFwgJCeHxxx+nadOm+aMyJ8XFxdG4cWM+//xzdu7cWaL8DoeDqVOnsmTJEu6++26SkpK48cYbadeuXX45djqdxMTEMGvWrEL/PzkX6Vw8//zz/POf/6Rnz5588cUXzJw5k1mzZhEXF1fgYz+psMJ5rs9RFs7355eYoREXN1a1alXuvPNO7rzzTg4ePEjbtm157rnnuOSSSwDyF0l7//332bp1K//3f/8HwM0330zfvn0ZNWoUQIFRGiC/kMyaNYsVK1bwyCOPAPZE3Pfee49q1aoRHh5Ou3btXPJxZmRknDEaUJiThwdOve/PP/9c5IjHO++8w1NPPcV9993Hww8/XOh9WrduzZw5c0hNTS0wQXfZsmX57z/V999/z80338ywYcN45513zpq5uPxjxowpMMH0QkYRHnzwQcaPH8/rr7+e/3U/1YABA85YVK+0ZGZm5o+unfTzzz/TrFmzAodfqlevTuXKlVm5cuUZz7F8+fICn+vWrVvz8ccfs2nTpgKltKiviynr169n69atfPbZZ4wZMyb/9qI+1/Xr12fcuHGMGzeObdu20bp1a/773//yxRdf5N8nOjqaqVOn0r17d/r27cvChQtLXNQ6d+5M586dee655/jyyy+59tprmTRpEjfffDP169dn9uzZdOvWrUTfa9u3b8eyrAKjLlu3bgX+PpNs6tSp9OnTh//9738FHpuSklLohPXClPQ5zmVhwdq1azN79mzS0tIKjLps3rw5//3iuTTi4oZyc3PP+EUeExNDtWrVCgyfn5y/cujQIS666CL69etHly5d2LNnD1deeWX++08vLnXr1qV69eq89tprZGdn061bN8AuNH/++SdTp06lc+fOhc7bOF/Hjx8nPT39jNunTZvGkSNHCpxpUtgqqgD/+9//cDgc+WdVHThwgFWrVhV6mOjrr7/m3nvv5dprr80vdIUZMWIEubm5fPjhh/m3ZWZmMn78eDp16lTgUMH8+fMZOXIkPXv2ZOLEifnH5U9X0vz16tXL/xr169cv/+twrl555RVeffVVHnvsMf7xj38Uep+qVasWeK2Th3rORUpKSqHzJz7++GOAAl/DX375pdCvy/Dhw/npp5/yTzMH+O2339i6dStXXnll/m2XXXYZgYGBvPvuu/m3WZbF+++/T/Xq1Ys8m8TVTv6lfupf5pZl8cYbbxS4X3p6ev5hrpPq169PZGRkoYfEatSowezZszlx4gT9+/cnOTm52BxHjhw5Y3TgZLk7+fxXXXUVubm5PPvss2c8/uSZYqfau3cv3377bf7bqampTJgwgdatW+cf3vL39z/jdadMmUJSUlKxeU9V0ucIDw8HSrZ0wqBBg8jNzeXtt98ucPtrr72Gw+HI/+NPPJNGXNxQWloaNWrUYMSIEbRq1YqIiAhmz57NihUr+O9//1vgvtnZ2axYsSL/NM5ly5bhdDrp0qVLsa/Ro0cPJk2aRIsWLahQoQIAbdu2JTw8nK1btxY6v+VCbNu2jX79+nH11VfTpEkT/Pz8WLlyJV988QV16tQp8Av3ueeeY9GiRQwcOJBatWrx119/MW3aNFasWME999xDgwYNAPuXY0hICH369CnwWsuXL2fMmDFUqlSJvn37nnEYqmvXrvllrlOnTlx55ZU8+uijHDx4kAYNGvDZZ5+RkJBQ4C/AXbt2MXToUBwOByNGjChwmivYk/latmx5TvmLs2vXLj7//HOA/BGKk6fD1q5dO/+w17fffstDDz1Ew4YNadq0aYG/3AH69+9PbGxssa919OjR/EnLixYtAuDtt98mKiqKqKgo7r77bsBeS+Pee+9lxIgRNGzYkKysLBYsWMA333xD+/btue666wDYuXMnmzZtyl8D6FSPPfYYU6ZMoU+fPvzjH//g2LFjvPLKK7Ro0YKxY8fm369GjRrcd999vPLKK2RnZ9OhQwe+++47FixYwMSJEwsM7Zf0c1UWmjRpQv369XnggQdISkqiXLly+WX8VFu3bqVv375cddVVNGvWjICAAL799lsOHDjAyJEjC33uBg0a8Ouvv9K7d28GDBjA77//fsZp+yd99tlnvPvuu1xxxRXUr1+ftLQ0PvroI8qVK5c/ItmrVy9uu+02XnjhBdasWcPFF19MYGAg27ZtY8qUKbzxxhuMGDEi/zkbNWrETTfdxIoVK4iNjeWTTz7hwIEDjB8/Pv8+Q4YM4ZlnnmHs2LF07dqV9evXM3HixDP+WCpOSZ+jfv36REVF8f777xMZGUl4eDidOnUqdN7MpZdeSp8+ffjXv/5FQkICrVq14tdff+X777/nvvvuKzARVzyQiVOZpHiZmZnWgw8+aLVq1cqKjIy0wsPDrVatWuWfwnyqpUuXWoCVmJhoWZZl/ec//7Hi4uLO+hrvvPOOBVh33HFHgdv79etnAdZvv/1W4PaTpzNOmTKlwO3jx48v9DTMJ5980gLyV3U9dOiQdeutt1pNmjSxwsPDraCgIKthw4bWfffdd8bKr7/++qs1ZMgQq1q1alZgYKAVGRlpdevWzRo/fnz+6dmWZZ/aOGjQoDM+tpOZivr/9NMpT5w4YT3wwANWlSpVrODgYKtDhw7WjBkzCv34i/r/ySefPOf8xSnu9U499fvk57mo/ws7Tfl0O3fuLPLxp55iun37dmvMmDFWvXr1rNDQUCskJMSKi4uznnzyyQKrEb/99ttW+fLlCz1F3LLs1X4vvvhiKywszIqKirKuvfZaa//+/WfcLzc313r++eet2rVrW0FBQVZcXJz1xRdfnPfnqijAGUsPnPycnH76cmH/DuLj461+/fpZERERVnR0tHXLLbfkn0578nvt8OHD1l133ZX//V++fHmrU6dO1uTJkws8f2Er5y5btsyKjIy0evbsWeip15ZlWatWrbJGjRpl1apVywoODrZiYmKsIUOGWCtXrjzjvh9++KHVrl07KzQ01IqMjLRatGhhPfTQQ/lLJpyaY+bMmVbLli2t4OBgq0mTJmf8+8/IyLDGjRtnVa1a1QoNDbW6detmLVmyxOrVq1eBz31RPz/O5Tksy7K+//57q1mzZlZAQECBz+/pp0NblmWlpaVZ999/f/6/w4YNG1qvvPLKGf8GC/v6n/wcnFyNW9yLw7I0+0g8T05ODpUqVeKFF14osLqqmDdo0CAiIiIKrIQrnqVOnTo0b96cn376yXQUkTPoUJF4pL/++ov777//jJWCxbzevXsXekaaiEhp0IiLiIgUoBEXcWc6q0hEREQ8hkZcRERExGNoxEVEREQ8hoqLiIiIeAwVFxEREfEYKi4iIiLiMVRcRERExGOouIiIiIjHUHERERERj6HiIiIiIh5DxUVEREQ8hoqLiIiIeAwVFxEREfEYKi4iIiLiMVRcRERExGOouIiIiIjHUHERERERj6HiIiIiIh5DxUVEREQ8hoqLiIiIeAwVFxEREfEYKi4iIiLiMVRcRERExGOouIiIiIjHUHERERERj6HiIiIiIh5DxUVEREQ8hoqLiIiIeAwVFxEREfEYKi4iIiLiMVRcRERExGOouIiIiIjHUHERERERj6HiIiIiIh5DxUVEREQ8hoqLiIiIeAwVFxEREfEYKi4iIiLiMVRcRERExGOouIiIiIjHUHERERERj6HiIiIiIh5DxUVEREQ8hoqLiIiIeAwVFxEREfEYKi4iIiLiMVRcRERExGOouIiIiIjHUHERERERj6HiIiIiIh5DxUVEREQ8hoqLiIiIeAwVFxEREfEYKi4iIiLiMVRcRERExGOouIiIiIjHUHERERERj6HiIiIiIh5DxUVEREQ8hoqLiIiIeAwVFxEREfEYKi4iIiLiMVRcRERExGOouIiIiIjHUHERERERj6HiIiIiIh5DxUVEREQ8hoqLiIiIeAwVFxEREfEYKi4iIiLiMVRcRERExGOouIiIiIjHUHERERERj6HiIiIiIh5DxUVEREQ8hoqLiIiIeAwVFxEREfEYKi4iIiLiMVRcRERExGOouIiIiIjHUHERERERj6HiIiIiIh5DxUVEREQ8RoDpACLim7Jys8h15pqOISIlEOQfhL+fv+kYgIqLiBjwyOxHeHXxq+RaKi4inqBqRFW+ufobOtfobDoKDsuyLNMhRMR37D66m9qv1zYdQ0TOUf96/fl19K+mY2jERURcx7IsXlz4IgChhHIf95kNJCJntZWtTGMas3bMYtW+VbSt2tZoHhUXEXGZf//+b95b+R4AfelLMMGGE4nI2TSlKTWowR720G9CP2aPmW20vOhQkYi4xLoD62j1fisABjKQzpg/Vi4iJZNBBhOYwF720rF6R5bdvMxYFp0OLSIu8cHKDwCoRCWVFhEPE0IIAxgAwPKk5cQfijeWRcVFRMrcyr0reXfluwB0opPhNCJyPmpQgypUAWD0t6ON5VBxEZEy9/nazwGIJpqOdDScRkTOhz/+DGEIAKv2reLPv/40kkPFRUTK1PKk5by5/E0A2tHOcBoRuRDVqEYMMQAMnzzcSAYVFxEpU1+u/xKAGGI0t0XEw/nhxxVcAcDaA2vZk7rHQAYRkTLy2ZrPeHOZPdrSmtY4cBhOJCIXKpZYookGoM9nfVxeXlRcRKRMLNi1gLHfj8XCoj3t6UIX05FEpBT44cd1XEcUUWz/azsDvxiI03K68PVFRMrAhLUTsLCoTnUGMUijLSJeJIoobuAGADYe2sjGgxtd9toqLiJS6hbuXsj4NeMBaElL/PSjRsTrRBGVP1F39LejXTbqop8mIlLqPln9CblWLjWoQQc6mI4jImXkKq4C7Im6q/etdslrqriISKmavm06n6+z123RaIuId4smmlhiAbj5x5s5cuJImb+mfqKISKlZsGsBl399OTnOHJrSVOu2iPiAYQwjjDDW7F/DxZ9fTHZudpm+noqLiJSaN5e/SVZuFjWpyQhG4I+/6UgiUsZiieV6rgdg5b6VzNs1r0xfT8VFRErF0j1L+WbTNwC0p71Ki4gPiSWWWtQC4LHfHivTURcVFxEpFS8tegmn5aQ61YkjznQcEXGxk1ePXrF3BbN3zC6z11FxEZEL9sfeP/hhyw8AdKELAQQYTiQirlad6tSlLgD/WfAfsnKzyuR1VFxE5II9OfdJnJaTqlSlMY1NxxERQ/rQB4DFiYuZsX1GmbyGiouIXJB1B9bxy7ZfAOhBDwIJNJxIREypRS0a0ACA/y75b5mMuqi4iMh5W3dgHRd9dhEWFtWoRkMamo4kIoZ1oxsBBDB/13xGTh1Z6hN1VVxE5Lzd9ctdJJ9IpgpVGM1ojbaICHWpy9VcjQMH327+lkkbJpXq86u4iMh52Zq8lYW7FwLQn/6EEmo4kYi4i4Y0pClNAfh49cfkOnNL7blVXETkvFz/nb3gVAwx1KGO2TAi4nY60AEHDubvms9naz8rtedVcRGRc5aQksDSPUsBGMQgLTYnImeoS938NZ0+X/d5qV09WsVFRM7ZVVPsK8JGE01NahpOIyLuqg1tcOBgbsJcPln9Sak8p4qLiJyTfWn7WLF3BQCXcZlGW0SkSPWpTwtaADBx/UQsy7rg51RxEZES25e2jz6f2QtMVaQi1ahmOJGIuLtWtAJgbsJcHpr10AWXFxUXESkRy7IY/OVgtiRvoTzluY7rNNoiImdVn/oMYhAAry55lfdXvn9Bz6fiIiIlsv2v7azevxqAMYyhIhUNJxIRT9GRjrSlLQCfrv30gp5LxUVEzsqyLK755hoAKlFJpUVEzllLWgKwPGk5H/3x0Xk/j4qLiJzVxkMbWbl3JQAjGYkDh+FEIuJp6lAnf9Tl/T/O/3CRiouIFMtpObnlh1sA+/TnylQ2nEhEPNXJibqr9q3i0zWfntdzqLiISLFW71vN0iR7sbnhDDecRkQ8WS1q0ZrWALy+9PXzeg4VFxEp0vGs49w34z4AYomlKlXNBhIRj+bAQXvaA7D2wFo+XvXxOT+HiouIFCrHmcOlX13KwsSFBBHEEIaYjiQiXqA61elEJwBu+fEWPl/7+Tk9XsVFRAo1L2EecxLmAHAd12lpfxEpFQ4cDGRg/llGT8176pwer+IiImfIcebw7PxnAfuYdC1qGU4kIt7EgYOe9ARgx5EdTN44ucSPVXERkTPM3jGbebvmAdCHPobTiIg3iiY6/zpG434dV+LHqbiISAE5zhxeXfwqYF+Wvi51DScSEW/Vl74A7Endww9bfijRY1RcRCRfjjOHa7+5lt92/oY//nSnu+lIIuLFoojKH3W5+YebS/SYgLIMJJJv7164807YscN0EinGtBopTO6UCMBVXEV96htOJCLe7nIuJ5BAuuZ2LdH9VVzENcaNg++/N51CzmJfuL1ttt2P6T9NZzrTzQYSo+644w6Cg4NNxxAv548/QxkKmSW7v4qLlL3Dh+HHHwH4o00bNsTFGQ4kRdmz/3sgjaPRTjKOp5CRbTqRmGRZlukIImdQcZGydfgwXHQRHD/OsYgIZvXrx4nQUNOppAgJBxsTnrWSpCjo2Qfm/2o6kZg0ceJERo0aRVhYmOkoIvk0OVfK1o03wvr1pEVEMH7MGJUWN7ZhwwYWrNlN+4X22382g0B/s5nErMTERL755htSU1NNRxHJp+IiZefoUfjpJwB+GTiQw9HRhgNJcQ4dOsTBgwfZuQFCsiEpCrr0NZ1KTNu+fTtZWVmmY4jkU3GRsnP99WBZHA8LY3uDBqbTSDG2bNlCQkICALv/gk7z825vDn76KeHzlixZolEXcRv6kSRl4/jx/LOIfhgyhKygIMOBpChbt25l0aJFJCYm5t+2cyME5sKBctBDoy4+748//iAtLc10DBFAxUXKyujRAJwIDeXPevUMh5HirFu3jj179hS4bfdf0NW+viL79eUT7IKr8iLuQMVFSl9GBnz7LQDfDB1KtkZb3FZSUlKRv4zS9tvblAgXBhK3NW/ePA4cOGA6hoiKi5SyjAwYNgyAEyEh7NBoi1v7/fffCxwiOtXhv+ztgUjo1dOFocRtHTp0iPT0dNMxxMepuEjpuuYamD6drIAAJl15JTmBgaYTSRFSUlKKPVtk91/Qa669v7GjazKJe5s5cyarV6/mxIkTpqOID1NxkdJz8GD+IaIvR40ioa6uKuzOJk+eTFJSUrH32b7G3h6OgF7dyj6TuL9Zs2axdu1a0zHEh6m4SOm56y4AjoWHs7NOHbNZpFiZmZklWs49KQV65Z0avU7FRfJkZ2eTk5NjOob4KBUXKR2JiTB1KgDfXXopOByGA0lRcnNz+eCDD0o80XLzCnt7JAx6dirDYOIxfvvtN+bPn286hvgoFRe5cLm58MADAByNjGRbo0aGA0lxXnvtNVJSUkp8/wNp0CvvMgAbepVNJvE8lmXpIoxihIqLXBjLgptvhsmTyXU4mDFggOlEUga25Y26HNOZ7ZJnwYIF/Pzzz6ZjiA9ScZELs24dfPopANOGDSO+WTOzeaRYL7744nmdzpqZN50hKwC6tizlUOKxVq5cyZQpU0zHEB+j4iLnz7LgpZcAOFi5Mhvj4gwHkrM536H95OPQY6W9v2EQoClMImKIioucvxUr4KuvAJjXo4fhMHI2L730EtnZ2ef9+DWz7G1qCHRuWkqhxOPFx8czceJE0zHEh6i4yPmxLHj3XQD2x8SwoXlzw4HkbC6ktACkZUL3VfZ+/FAI9C+FUOLxLMvSqdHiUioucn4WL4bPPrN3u3QxHEbO5s0338TpdF7w8yzNm4uZGgJtG1/w04mXSExM5MsvvzQdQ3yEioucn/HjAdhbpQprW7UyHEaK88EHH5zT6c/FycmFrnmLpsZfBqG6ooMAOTk5unK0uIyKi5y755+H//0PgBXt2xsOI8WxLKvUr+i7/Ad7mxYMFcJK9anFgyUnJzNt2jTTMcQHqLjIufn1V/jXvwCY3acPq9q2NRxIiuJ0OsvkVNWcXAjJmy5TPbbUn148VFZWFps3b+a7774zHUW8nIqLnJu8swcSatVigc4kcmuWZbF58+Yyee6Wf9rb7ZfrcJH8LTs7m23btpmOIV5OxUVK7tdfYcIEANZoXotby83NZc6cOWX2/Bu+sbdHwqBF/TJ7GfFAWVlZzJ0713QM8WIqLlJyn38OwM7atVndurXZLFKkrKwslixZwuLFi8vsNdKzoGPeYM6fl0K4LgUgebKzs8v0e09ExUVKZvZsrLzDROtbtNDVn91YZmYmv//+e5m/zubvwM8JyeHQokGZv5x4kNzcXNauXWs6hngpFRcpmU8+wWFZ7Kxdmz/atDGdRoqQlZXFzp07XfJaqRnQLm86w86BGnWRv+Xm5vL999+bjiFeSsVFzm7yZKxJkwBY17KlRlvcWFpamkvP6tjxAwTlwIFy0Limy15WPERiYqLpCOKFVFykeLNmYV1zDQ7LYnWrVqzWpFy3lZOTQ3JysktfM/k41D1k7weFuPSlxc05nU7+97//cejQIdNRxMuouEjx3noLR24uf9aty/eXXorlp28Zd3XgwAEm5Y2MuVK5VHu7ty+E6XCRnOadd94576uSixRGv4WkaL//Dj/+CMDKdu1UWtyY0+kkMzPTyGvvnwGBubC7IrRqZCSCuLmMjAzTEcSL6DeRFO2VVwD79OfNTZoYDiNFsSyLbdu28cUXXxh5/cQj0HarvX+gl0Zd5EwvvfSSriAtpUbFRQq3cCHMmAHA0k6dcGq0xW1t3LiRr7/+2miGg7/Zk3R3VIbW6rhSCB0uktKi30ZSuKefBmBXzZpsa9jQcBhxdzsPQ7u8BekOdtNlAORMzz33nA4ZSalQcZEzLV0Ks2cDsKB7d3L9/Q0HkqIsX76cb775xnQMAA4tsEddtsdC2zjTaUTEW6m4SEHLlsGAAYA9t+XPevUMBxJPsf0AtMmb6+KvU6OlEC+//DJpaWmmY4iHU3GRgu65B1JTSaxRg4mjRuHUaIvbmj9/Pr/++qvpGAUEZNnbw80hUN86chqn08mbb77p8vWGxLuouMjf1q6FFSsAmNmvH9lBOj3EneXm5uJ0Ok3HKCBlJfg7Ib4GdGppOo24o+zsbE3UlQui4iJ/u+EGAJKqVWNPjRpms0ix5syZw/Lly03HOMPGPdBpg72f0hZ0MpoUZsKECezfv990DPFQ+rEiti1bYM0aAH6+5BItNufGZs2axcqVK40tOHc2aXmjLhtqQre2ptOIO0pNTdW6LnLe9NtJ7NLSuzcA+6pUYW+1ambzSLFSUlI4ceKE6RhFWr8bOq+z91NaArompxTi559/JikpyXQM8UAqLr7O6YShQ2H/fg7ExDDh2muxdPVntzVv3jz27NljOsZZpa0GPyesrwU92ptOI+5o3759zJgxg127dpmOIh5GxcXXbd1q/w98dfXVpIeHGw4kxdm1a5dHnE66bhd0XWPvp7QwGkXcWGJiIikpKaZjiIdRcfFlTifWFVcAsLdKFY5ERZnNI8VauXIlR44cMR2jxNLyDhetrwU9OpjNIu4rPj6exMRE0zHEg6i4+LING3Bsttdp/3LUKNAhIre1Zs0aFixYwNGjR01HKbG1CdD9D3s/p5bRKOLGtmzZorkuck5UXHxVbi7WNdcAsC82lrTISMOBpDgLFizwiENEp/M7ZG+PlzObQ9xbUlKSTo+WElNx8VWrVuHYuBGASVdfbTiMFGfHjh1kZWWZjnFejuUVl3W1oWsrs1nEfa1fv574+HjTMcRDqLj4osOHsW68EbBPf07R3Ba39s0333D8+HHTMc7Lqj+h61p7P62d2Szi3lJTUz1qDpeYo+Lia7KysPr2xbFhA2kREUwdNsx0IilGcnKy2y3rf67S19jb9bWgi84wkiKsWbOG2bNnk5qaajqKuDkVF18zdy6OdfbpHp+OGcPh6GjDgaQ4H374IRkZGaZjXJA1O6HLenv/aGezWcS9bdy4kRkzZpiOIW5OxcWXZGVhPfwwAAm1aqm0uDlPLyynOr7S3sZXh07NzGYR95abm+uxc7rENVRcfMmsWTjyrkc0feBAs1mkWLm5ubzyyitkZ2ebjlIq1u2CTnlzL491MZtF3NuWLVv48ssvTccQN6bi4isyM+HppwH4s25d9lepYjiQFOe5557DsizTMUpVzip7mxlkNod4Bm/7/pfSo+LiK2bMgBUrAPjtoosMhxFfdPJi1ttjoU09s1nEvSUkJPDBBx+YjiFuSsXFF2RkwMsvA7C1QQOSqlc3HEiKYlkWzzzzjOkYZWLDHmi73d5P7282i4h4LhUXb5eZCcOHw+LFZAcEsKB7d9OJxFdZkDHH3t1SFVroMgBSjP379/PWW2+ZjiFuSMXF202aBL/8gtPPjy9GjWJ3Lf22cFe5ubm8+OKLpmOUqfi90HqnvX9isNks4v6Sk5N5/fXXTccQN6Pi4s2ys+HDDwGIb9KEhLp1DQeSs/GWs4iKZEH6THt3eyw0rWY2jrg/r/83IedMxcWbTZgAixfjdDhY1qGD6TRSjKysLN5++23TMVxi635oucveP6GFm+Us0tPTeffdd03HEDei4uKtcnPhs88A2BAXx+7atQ0HkqKkp6fzySefcPToUdNRXOb4z/Z2f3mzOcT9WZZFSkqK6RjiRlRcvNXHH8OCBTgdDv5o29Z0GilGTk4OBw8eNB3DpU7knRqdEQi1KprNIu4vOzubzz//3HQMcRMqLt7IsmDiRADWtWhBQp06ZvNIkdLS0vjpp59Mx3C5vanQeJ+9HzDKbBZxf5ZlsXPnTtMxxE2ouHgby4J774UFC7AcDta0amU6kRQjKyuL7du3m47hehZkTrV3d1SGGhXMxhH3Z1kWP/74o+kY4gZUXLzNW2/B229jORx8P2QIO3UmkdtKTU1l6dKlpmMYk5AMDQ/Y+yGjAIfROOLmLMvijz/+4LffftPlAHycios3sSzIOw68sm1bVrdpYziQFCctLY0//vjDdAyj0r+yt9tjoJ4uVi4lsGDBAtMRxDAVF2/y3nuwciUA65o3NxxGipOWluabh4hOk5QCDfLmJQcMR6MuUiLr1q3TqIsPU3HxJh9/DMCKdu10+rMbS0tLY/Xq1cybN890FLeQmTfqsrUKVCtnNot4hm+//Zbc3FzTMcQQFRdv8fzzsHo1AGtbtjQcRoqTkJDA3LlzTcdwG4lHIDRvcdSwYLNZxHMkJSXhdDpNxxADVFy8wXvvwb/+BcBvffqQWLOm4UBSlIyMDNLS0kzHcDtVU+xtyKXocJGUyPjx49m3b5/Kiw9ScfEGr70GwPL27Znfo4fhMFKctWvXMnv2bNMx3E7gN/Z2Q01oXMVsFvEcH330EceOHTMdQ1xMxcXT/e9/sG0bACvatzccRoqTk5OjC8YVYcu+U06NHoBGXaTEMjMzNVHXx6i4eLr//AeAP9q04WBMjOEwUhSn08m8efP4/fffTUdxW8F5CwivrQPNqhuNIh7knXfeITk52XQMcSEVF0/2xReQkADAoq5dzWaRYk2fPp1FixaZjuHWNiRC0732fvBFaNRFSkwjLr5FxcWTPfooAKtbtSK5UiXDYUQuXPAse7u6HjSvYTaLeI533nmHPXv2mI4hLqLi4qmmTIG8f6hzevUyHEaKM3XqVJ9fIbek1uz8e65LcIjZLCLinlRcPNHUqTDKvqTuH23acDQqymweKZaGsc9NcJa99W+LDhdJif3vf//TatQ+QsXF0+TkwC23QG4u65o358fBg00nkmJ89dVXbN682XQMjxK22N4ubwota5nNIp7DsiwmTZrEhg0bTEeRMqbi4ml++glSUgD4cfBgLD99Cd1Zdna2RlzO0fJNEJdo74d1NJtFPEtOTo4uBeAD9FvPk2RkYF1/PQBrWrYkK1jro7uzr776isTERNMxPFLkUnu7NA7a1DObRTzLzJkzWZ13+RPxTiounmTGDBypqQB8f+mlhsNIcb766it27typv/7O09KN0GK3vR/SxmwW8Szp6elkZGSYjiFlSMXFUxw/jnXDDQCsj4vD6e9vNo8U69ChQ+Tk5JiO4dEiV9rbJS2gbQOzWcSzrFixglWrVpmOIWVExcVTzJyJ4+hRAL677DLDYaQ4P/74I8ePHzcdw+MtXgetEuz9iGijUcTD/PXXXxw5csR0DCkjKi6eYNkyrLFjAdjYtCk5AQGGA0lxtmzZomsSlZJw+8goTl3NQs7R9u3bWbdunekYUgZUXNzdkSNYAwbgSE0loVYtvtVoi1tbsGABWVlZpmN4Db9d9nZhW2hd12wW8Sz79u1jyZIlOj3aC6m4uLsffsBx9CjZ/v58cc01ZAcFmU4kxVi4cKHmtpSihX9Ay7zyEt7SbBbxPPv27SM+Pt50DCllKi7uLDkZa9w4ADY3aaLS4uY2bNiA0+k0HcPrlF9vbxe3hlZ1TCYRT3T06FF27NhhOoaUIhUXd/b99ziSk8kMDOQnrZDr1rZv3863336r05/LwIK8URfLAeE6NVrOUVJSEvPnzzcdQ0qRiou72rIF67HH7N3GjckI0RXn3NmXX36pFXLLigXl8uZYLm0BLWubjSOeJzMzk4MHD5qOIaVExcUd7d+P1bs3jgMH2B8Tw/QBA0wnkmIkJyebjuD1Fq2yR12cfhDe3nQa8TT79u1j8uTJOkXaS6i4uKOvv8axfz/poaF8Nno06eHhphNJESzL4p133jEdw+tZFpRbY+8vj4PmNY3GEQ90+PBhJkyYYDqGlAIVF3dz4ADWSy8BsLFZM5UWN6elxV1n6Tpongi5flC+nek04oksyyIzM9N0DLlAKi7u5osvcOzbR3pICPN69jSdRoqRm5vLK6+8YjqGz8jJhYob8/Z1gp2ch5SUFN544w3TMeQCqbi4k4MH4fXXAVjXsiVpkZFm80ixNBnX9XJP2NtVjaFRFbNZxHPp365nU3FxJ598Anv2cCIkhMVduphOI8XIzMzk+eefNx3D5/wRD432Q7Y/VOxhOo14ovT0dF544QXTMeQCqLi4i8OH4b33AFjdqhVHy5c3HEiKkpaWxkt585DEtTKyIXqxvb+yCdSrbDaPiLieios7OHwY+vaF3btJi4hgeceOphNJMTTMbNbqTVD/IOT4Q/RFptOIJ8rKyuK5554zHUPOk4qLO/jvf2HdOk6EhvLpmDEcqVDBdCIpQnJysk5/NuxENlSeZ++vagS1KprNI54pOztbh3s9lIqLaUePwsSJACzt0IHD0dGGA0lxLMsiOzvbdAyft3Yr1Dlsj7rEDDSdRjyVruTumVRcTHv2WUhMJCMkhDWtWplOI8XYv38/X3zxhekYgj3qUmWOvZ+mk+/kArzxxhs6/OthVFxMOn4cJk8GYFHnzqToEJHb2r17N9OmTSM1NdV0FMmTlbf2344YiI4wm0U8ly4D4HlUXEz6178gMZGsoCDWt2hhOo0UIzMzU9ckcjNbEqFKqn1qdJ2hptOIJ5swYYKu7O5BVFxMycyEqVMBmN+9uybkurHdu3ezcOFC0zHkNMezoPZ0e39VAygfajaPeK6dO3fqcJEHUXExISMDhg2DpCSyAwPZEBdnOpEU4+jRoyQmJpqOIYVY/ydUPmZfNbrhFabTiCf75ZdfNPHeQ6i4mPDPf8Ivv5AdGMjEkSM12uLG9uzZQ3x8vOkYUoT0LKjzo72/shFEBJvNI55r1apVzJkzRxdh9AAqLq6WnQ1ffgnArIsuYmfduoYDSXGSkpLYsmWL6RhSjA07oNJxe7/pMLNZxLMtXrxYxcUDqLi42j//CUePkuPvT3zTpqbTSDEOHDjA3r17TceQsziRDXW+s/fX1zMaRbzApk2bVF7cnIqLK+Xm2hdSBH7t35+0cuUMB5KiHDp0iIULF7J+/XrTUaQE9h60txmBEOhvNot4tunTp5OWlmY6hhRDxcWVHngA0tPJ9fNjQ7NmptNIMZYvX87GjRtNx5ASOpIO5fLWdelwmdks4vn279+vVXXdmIqLq1gW5F3jZmb//hyP0IpZ7iotLY2MjAzTMeQcZGRDk+/s/cUtISjAaBzxcFOnTuXgwYOmY0gRVFxcwbLg3nshO5tcf3/WabE5tzZjxgyNtnig1dtOGXUZYjaLeL7jx4+Tk5NjOoYUQsXFFR56CN5+Gwv4cdAgToSFmU4kRcjKytIKmh4qO/fvUZdFrSFSp0bLBfjqq6/YsmWLfh64IRWXspaVBa++CsDPgwaxuk0bw4GkOJMmTWLr1q2mY8h5Wr4Fyp+w91sNMJtFPN+UKVPYsGGD6RhyGhWXsvb00wBk+/uzqnVrs1lEvJ0FzX62dxe2hehws3HE8+lSAO5HxaUspabC888D9unPuQGaMejOPvjgAxISEkzHkAu0ZAME5U1NCA40m0U833fffceSJUtMx5BTqLiUpbxDRDn+/izv2NFwGCnOW2+9xYEDB0zHkFISkXcma339sxPxOiouZeXFF+HZZwH4rU8fw2HkbDQc7F3i5trb+V2hitZ5lAv066+/MmfOHNMxJI+KS1lITIRHHwXs0rK4a1fDgaQ4b7zxBikpKaZjSClasPzv6xc11qiLXCDLsli4cCHTp083HUVQcSkbH3wAwPGwMOb36GE4jJyNLmXvneIW2tt53aGGLsAuFyg3N1frurgJFZfStmMHPPccAEs6dTIcRs7mvffeIz093XQMKQPzl0BM3iVn6msVAikFGzZs4OeffzYdw+epuJS2CRMASAsPZ0H37obDSHE+/vhjDh06ZDqGlKGmy+3tvJ5Qu5LZLOL5MjMzOXbsmOkYPk/FpTRt3Yr1zDMArGzXDhwOw4GkOPv27TMdQcrYvAVQJdXer6TDRVIKdu/ezcyZM03H8GkqLqXpq69wWBZHIyOZ26uX6TRSjGnTpulMIh8RlfcHcmRVsznEOxw/flx/9Bim4lJapk7Fyjv9eU3r1hptcXO6iKLviP3T3i7qrcNFUjqSk5NZsGCB6Rg+S8WlNKxdizVyJI7cXNa0bMkcjba4tblz55qOIC40/3eoehRy/KFOnOk04g3S0tJYtmyZVtQ1RMWlNHz1FY7cXA5VqsR3Q4di+enT6q4sy2L+/PmmY4gLWRY0yrtO3lJN0pVScuzYMZYvX246hk/Sb9gLtXYt1muvAbCheXOVFjdmWZau9OqjFv0ONY5AZgDUaW46jXiLrKws4uPjTcfwOfote6G++AJHVhYHo6OZr9Of3ZbT6WTbtm18++23pqOIATm5UD9vWtOKbhp1kdJx/PhxfvrpJ9MxfI6Ky4VYvx7r7bcB2BAXh9Pf33AgKUpWVhaTJk0yHUMMWjoXah6B9CCo3dB0GvEWTqeTvXv3mo7hU1RcLsQnn+DIyOBgdDSLunUznUaK4HQ6+euvv0zHEMMyc6Bmor3vCDGbRbxHRkYG48ePNx3Dp6i4nK9ly7A+/BCAtS1bkhMQYDiQFOXYsWN8/PHHpmOIGwjIuwTAmk5QPcpoFPEyaWlppiP4DBWX87FuHdbFF+NITyehVi2WddTlZ92VZVlkZWWZjiFuYs0C+9Too6HQoLXpNOItsrOzee2118jIyDAdxSeouJyPN9/EkZrKwcqVmXjNNWQHBZlOJEVITk7m3XffNR1D3ERqBjRaa++v6whVy5vNI97D6XTy4osvmo7hE1RcztXGjVgTJwKwvEMHslRa3JqW9ZfTrV8KsalwJAwatTedRryNfuaUPRWXc/XyyzgyMjhUqRJrW7QwnUaKsWfPHt577z3TMcTN/JUOTVbZ+xvaQmyk2TziXZ5++mmVlzKm4nIutm6FKVMAWNKlC1nBwYYDSVG2bdvGJ598YjqGuKn4lRCTBsnh0Liz6TQici5UXM7FU0/BiRMkV6zIhmbNTKcRkfN06Bg0zVutfWNriA43Gke8zLPPPktOTo7pGF5LxaWkEhIgb9XV+d27kxmihSDc1YYNG5iSNzImUpTNayD6mD3q0qyH6TTiTZxOp+kIXk3FpSQSEqB3b8jIILlSJTY1aWI6kRTD6XTqrx05qwNpELfM3s/WiIuUMp0eXXZUXErirrtg1y7+qlCB8aNHa7TFja1Zs4ZZs2aZjiEewplpb3fWhghNWZNSdPz4cT744AOOHTtmOorXUXE5m337YOZMAH7t14+0cuUMB5LiZGRkcPz4cdMxxEPs2ArlMmB/OWjbx3Qa8TZHjhzRYaMyoOJyNmPHQm4uR8uXZ3uDBqbTSDHWrl3LypUrTccQD5KUAm0W2PtbmkFIoNE44oWmTp3K0aNHTcfwKiouxTl8OH+05eeBA8kO1E81d7Vq1SoWLlyoiynKOdu+EcKz4EA56NjPdBrxNrt379ZlR0qZiktRDh+Gvn0BSIuMZEe9eoYDSXH27dtHcnKy6RjigZJSoN1ce39THAT4GwwjXmnevHkcOXLEdAyvoUsaF+XKK2HdOo6Fh/Pp6NEabXFjGzduJCkpyXQM8WB/xkNIHzgUAT37w/wZphN5oUCgLdAIiAFCgVwgFUgCNgFbAS9cdHbDhg0EBATQo0cPKlWqZDqOx9OIS2GOHoW5cwGYPHw4h6OjzeaRYsXHx7N//37TMcSDJaVAx9/t/QO1jUbxTjWBe4FLgFrAX8BmYAfgBFoDo4A7DOVzgTVr1uhQdinRiEthRowAIC0igj01axoOI8XZuXOnJr5JqTh20N4eLgc48Mq//I2oClyP/dtmETAfyDztPuWALoCXX/QyISGBypUrExUVZTqKR9OIy+mSk2H2bAC+vvJKcv11wNtdJSYmMnPmTPbu3Ws6iniBA8ng57RX0u11kek0XsIBDMMuLb8DsziztIB9uGgm4OWXF1u0aBG7du0yHcPjacTldNdeC0BqRAR7qlc3HEaK88svv3Dw4EHTMcRLJKVAr99hXj9Y2x74zXQiL9AQqAykAAtKcP99p71dC2gO1MYelQkAjmIfZloInL4wbR3gBmANdknqiz2nJgjYn3dbYt592wMdgIpAOrAamMeZI21P5eV/E+iOfVgrMu+2RXmvBVAX6AlUwz78tRWYAZwo+HRJJ5LYn7WfxKBEjnCEE5wgnHDqUpee9CQaTU04G424nCopKf/056+vvBLLT58ed5WSkqJl/aXUbVkHDgtSQqFXb9NpvMDJpa/iOb9DbxcDbYBsYGfe/8HYBeJG7EJSmBDgJqAekAAcxC5Bo7GL1EBgAHYJ2pF3/95AcYsQXgl0BQ4Au4AKwOXYRaYZcB32b9TteXlbASPPfJrlOctZEriELLKoTnUa05hgglnHOj7iI/aj+XpnoxGXkzIz4cYbATgaGam5LW7us88+09wWKXX7U6HXHJh3EazqAsw1ncjDVcnbnj6SUlJzsUdITj285I89ybc99ryYeYU8rgmwFvgee/QD7GLSG7uAhADvAifPUK4M3AZ0xh7JOX3Zlai8DG9ij87A36M7ffMyTQK25b0vGLs41c67X8Ipz7UZOgV04qI2FxEc/Pd1Jlazmu/5nhnM4AZuKOSDkpM0pABgWVgjRsCvv5IVEMC3l19uOpEUQ4s5SVnauMqe65IWDL26m07j4ULztulFvH8o9qjFqf/XOuX92zlzTkwu9iGYXKBxEc+bAUzn79ICsAR71CcGmMPfpQXgEHbpCMI+1FOYGad9HAnYhSwy77HbTnlfJvBH3n6d055nDyybsYw5c+YUGDVuQxtqUpMEEsg44xiYnEojLgDr1+P46ScAvhw1ip116xoOJMV58803SU8v6iehyIU5fAx6zYN5fWB1d+y/wKVstObMP58TgN2nvB2JXVCisUcyHHm35wJFLYmylzPnv2RizzcJA/4s5DEni0xEIe/LpeCoyamPqXoezxcES48tZWfCTqo3qE4uuQAcw74g41/8RbUiG5SouFgWPPYYAAejo1VaRISta4A+cKyoORRSMicnpoYV8f5nTtkfwpmnQ3cB+mEfijkXaUXcnpWXpbD3nxzILey34jEKn6Nz8jHn8nx1gRFAOBzI++/Mh2pUuTgqLn/8AT//DMCv/fsbDiNn88ILL5CdnW06hni5jGx7kq7TD7q3hYWrTCfyUPuxD/1UBdaf42NrYE+gzQB+wB7xOAZ5gxMwDns0pjBnmwh8rhOFS+v5grDn2IRiz9/ZAI2rNGbkiJE4cDCVqWxgA5YWESqWb89xsSx47jkA9sXGsq1hQ8OBpDj/+c9/VFrEJY6kQ4+l9v7qgeBwFH9/KcL2vG0z/j7EU1JN8ra/YU+0PcrfpSWAwg/BuLta2CM+8djF5TD45frhyPvkHEHXMyoJ3y4uixfDd98BMLdXL7NZ5KwsS3+FiOusnGNvjwdB15Zms3isbdgTX6OAHuf42JMTe1MLeV8c516E3EEhH9OmTZuYMGECySSz77xPv/ItvltcLAvefBOApKpV2dykyVkeICa9+OKLKi7iUulZ0HOZvb9mMATrwPq5s4BvgBzgIqA/9gTb04Vy5kTbkxd7b0vB31SVsee9eKKTH1NTCsz7yQ7I5gd+wFngNCgpim/+U7QsuPdemDwZy+FgUdeuphPJWegUaDFh4UygU96oSzNYvM50Ig+0D5iAPbejG9AJ2IM9oTUAe0XcKtgTcA9hnxEE9kq2XbDPKLoH+wrSodinF28GqmOP5HiSvdhnINXH/pgS7JsT6yQSkBpA43KN2cIWY/E8hW+OuMyYAW+/DcD3Q4awMS7OcCA5m7vvvhuHJhqIizmd0GOlvb9mCITrLKPzsxt78bbpefvR2PNe6mGPwGzEXsDtPexVbsE+I+kjYB12qWmMXXJ+B6a6MHtp+wr7QpPp2JdEqAZsAOeHTvZs32M0mqdwWL42/m5ZMHIkTJ7Mztq1+fT6600nkhJ69tlndbhIXM7PD5xP2PvVX7evaSRSFqKiorjvvvtMxzDqySefPOt9fG/E5eefYfJkAFa2a2c4jJyL6667Dn9drVtczOmEsLwjlTW0JpiUoWPHjjE57/eTFM33issXXwDwZ926bGje3HAYORd169Zl2LBhBAVpvF5cq1XetINNQyGisMmlIqUgJyeHhIQE0zHcnm8VlxdfhK+/BmB169Zms8h5adq0KQEBvjmnXMxZ9b29TQ2B5g2Kv6/IhcjKymLGjBmmY7g13yku06bBo48C8Fvv3qxv0cJwIDlf3bp1K3BVVZGylpkDnTfa+5uHQKS+/aSM5OTksHz5cubOnWs6itvyneIyYQIAmxs1Yn7PnobDyIXo0qULISEhpmOIj1n/PQTmQkooNG9kOo14M6fTyfz5803HcFu+UVy++w5++AGAdRpp8QqNGzfWqIu41PEsaLfZ3t86EMqpO0sZsiyL9evP9QJPvsE3isvHHwP2aMvGZs0Mh5HSMHDgQKKiokzHEB8T/yOEZkNyODSsYTqNeDPLspg2bZrpGG7J+4vLjz/mX/15bcuWulqaF4mJidEZRuJSqRlQ+5C9HxxW/H1FSkNSUpLpCG7H+4vLu+8CsKVhQ+KbNjUcRkrTFVdcQePGjQkMDDQdRXxIuaP2dtdFmqQrZe+jjz7Swpun8e7iMnWqvbw/sKpNG422eKErrriCBg10fqq4zu7pEJINSVHQorHpNOIL0tLSTEdwK95bXH76CWvkSADWtGzJlkY6DcBbBQYG4ufnvd/K4l72p0LrvAXpEntr1EXK3v/93/9x/Phx0zHchvf+tH/xRRy5uWxt0IDvhg7F0i82r3X55ZfTpk0b0zHEhyT9Zk/STawILTXfX1zglVdeISMjw3QMt+Cdv81//RUWLQJgUZcuKi0iUqoSj0CbTfZ+UjddBkBcQ3NdbN75G/3xxwHYXq8eu+rUMZtFXGLw4MH06NHDdAzxIfvm23NdEqKhdZzpNOILXnrpJVJTU03HMM77isu8ebB8ub3bsyeWJuT6jD59+nDxxRebjiE+Yufhv+e6+GnERcRlvK+43H8/ADtr1yaxZk3DYUTEmwVk2dsDLSFY1/4UF3j99dc5dOiQ6RhGeVdxWbYMVq8G4Nd+/TTa4oM6dOjAkCFDTMcQH3F4BQTlwJaq0LG16TTiC5xOp+kIxnlPcVm+HPIOE+ysU4d91aoZDiQm+Pv7ExCgP33FNTbvgw55l5M53BYC/M3mEd8wfvx49u7dazqGMd5TXEaNgtRUdtesyZcjR2q0xYc1adJEoy7iMn+ttK8avakadGlnOo34gvT0dCZNmkRCQoLpKEZ4R3FZtw527ADgm8suI0vXr/FpQUFBREZGmo4hPmJTEnRaY+8ntwKtviCukJqaSlZWlukYRnj+PzGnEy67DIBdNWtypEIFw4HEHVSvXp2BAweajiE+4q/VEJAL8dWhe0fTacRX/P777+zI+6Pdl3h+cVm/HvKGy6YOH67rEQkAYWFhtGjRQqdHi0vE74Eu9nkBHG4B6MeQuMD+/ft98jpGnl1ccnKwhg0DILF6dVJ1eEBOERoaqgswisscWQt+TnvUpWcn02nEV6xdu5adO3eajuFSnl1cVq/GkTdM9uXIkRptkTOEhYXRuXNn0zHEB2xIhG4r7f3s6maziO/YsWMH+/fvNx3DpTy3uCQkYF15JQB7qlUjPTzccCBxRyou4kqOZHubqql24kK7du1iz549pmO4jGcWl8xMrL59cezaRXLFiky66irTicSNBQYG0qRJE9MxxAccPWBvN9aA7jo1Wlxk8+bNLFiwgKSkJNNRXMIzi8u8efmHiMaPGUNauXKGA4k7Cw0NZejQoaZjiA9YmwDd/7D3k9sajSI+ZsuWLfzxxx+mY7iE5xWXjAysu+8G7OsRqbRISfj5+VG1alXTMXxKGHA/8DuwH8gE/gIWA08DNU+773XAm8BSIAOwgCddmLe0pK6zt5uqQ7fWRqOIj0lPT+evv/4yHaPMeV5x+e03HNu2AfC9/oqWEgoKCuLGG2+kgtb5cYkuwHbg/4COwAZgKnZpqQ88AWwF+ubdvyHwOXAP0Anw5Istr9sF3U6eGq2zi8SFNm/ezK+//mo6RpnzrOJy+DDWQw8B8GfdulpsTs6Jv78/d+eN1knZaQX8BlQFXgRigH7AtcAQoAowDNgD1Mh7TBrwMXAb0BZ43LWRS11K3oj9lqrQtaXZLOJbcnJyOHHihOkYZcpzisvx41j9+uGIjyctPJxftCqqnKcgXRKiTH0OhGIf5nkUSD/t/RbwLdAOyDt7mB3ALcCHwGog2yVJy87GPdA175BRikZdxIW2b9/OV1995dWXA/Cc4vLDDzjWriXXz49Pr7+ew5Urm04kHsjhcPDII4+YjuG1BgItgETgubPcNxXYWOaJzMlYm7dVTxYX2717N5988onpGGXGM4rL8ePwwgsAbGnUiMPR0YYDiUhhBudtpwC5JoO4gYwMe7ujMrRvZDaL+B7LsrAsy3SMMuEZxWXaNFi/nlw/P+b26mU6jXiBJ554AodWWi51rfO2q0yGcBPxSdBhi71/tG/x9xUpbQcOHOCNN94wHaNMuH9xOXECXnsNgPimTTkQG2s4kIgUpVLe9pDRFO7j6Bx7uy0W2tQzm0XEW7h/cfnyS1izhlw/PxZ17Wo6jXiRxx57jMDAQNMxxItt3Q/t7NUbSBlkNov4npSUFP773/+ajlHq3Lu4ZGbC++8DsCEujn1aQExKkb+/P+PGjSM0NNR0FK+Rd6keNHX+b0fyltXYGQ0tapnNIr4nN9f7Zpu5b3HJzIRhw2DlSrIDA1nWoYPpROKFgoKCNNelFK3J22q1+7/tOARt7CuUkHK50Sjig9LT071urov7Fpd33oFffiHH35+JV19NUo0aZ3+MyHm46aabKKdLR5SKn/O2VwL+JoO4mb/yPjEH9W0mBhw5coS3337bdIxS457FJScHvvgCgNWtW7Oznma1SdmpUKECfn7u+U/B08zAXt6/JvCvs9w3EmhW5oncQ2beinqZAVA/xmwW8U3edA0j9/xp/dprsHo1uX5+rG7d2nQa8QGXX365rmNUSq4DTmBfSPF57Asonu5S7FVzfeUA8P5UiNtj72ePNJtFfJPT6eTzzz83HaNUuGdx2bQJgKRq1UiqXt1wGPEFtWrV4pJLLqGyVmS+YGuxr020H3vJ/4PALOAL4EdgH/AD9qhM4imP+wZYkvf/nXm33XzKbd+4IHtZSplqb3dXhLpaQ1MM+PPPP01HKBXuWVz69QOg1p49NN6yxXAY8RUNGjQgPDzcdAyvsBhoAIwDVgAtgauAbkAC8BT2FaF/P+UxbYDOef+fPPmmxim3tSn72GUqKQWaJtn71ijQnHBxJYfDwSWXXGI6Rqlwz+JyzTUwYAAArdatMxxGfEnr1q2pVKnS2e8oZ3Uc+D+gDxALBAEVgS7Yh5GSTrt/XcBRzP91XZK6bB3+yt4mVIIGmusiLuRwOOjUyTuu+OmexQXguusAiNu0iY7LlxsOI76iZcuWxMToN4qUjUPHoMleez/nSo26iGv4+fnRwYuWFHHf4nLttfDwwwAMnjGDJps3Gw4kvqJmzZqaqCtlJmWSvd0ZDdXKm80i3s/f358WLVp4zWEicOfi4nDYV4S+7DIA2q3SZdvENTp37kw9nYIvZWR/KoRl2fsRWrRZylhwcDBXXHGF6Rilyn2LC9jlZfRoABpt366JuuIy5cuX10RdKTPVj9hbv0uxJ/CIlAF/f3+qVKliOkapc+/iAvay/0OGANBxxQrDYcRXdO/enW7duuk6RlImcvNOjd5UDZpVM5tFvFdkZCRjxowxHaPUuX9xcTjgllsAaLBjBw23bTMcSHxF586d6dmzp+kY4oV2HPp7ki6XaJKulD4/Pz+vHTV2/+ICMHgwDBwIQNclSwyHEV/i7++Pv7+uuiNl4Ed7E19DV42W0hcTE8MteX/0exvPKC7+/nDvvQDUS0jQqIu4TPv27Rk6dKjpGOKFNu/7+zIAXKRRFyld3nz9Nc/5yPr3h+HDARg5eTL1t283HEhE5MI4ZtrbdbU16iKlp06dOtx6662mY5QZzykuAQHw1Vdw8cUE5ObSd+5c04nER7Ro0YJRo0aZjiFeaEMiNN5n7wcFm80i3qFJkybccMMNpmOUKc8pLgCBgfDkkwBU37uX2gkJZvOIiFyg4Lw1XawOOlwkUhKeVVwAOneGPn0AuGTmTMNhxFc0bNjQ6/+KETMCF9rbPxpCa2+4IJMY07p1a6666irTMcqc5xUXPz94+WUAqh44QI09e87yAJHSUbNmTW6++WbTMcTL/LENWu6y9wM6oQXp5Lz5+fl59aTckzzzI2zbFitv1GX0xIlUV3kRF3A4HAQFBZmOIV4oJG+VhxWNoV19s1nEM3Xs2JGLL77YdAyX8Mzi4ueH48cfsbp3JyQzkyu/+cZ0IvERFSpU0CEjKXXLN0PrnfZ+QFuzWcTzdO/end69exMSEmI6ikt4ZnEBCA/HMWECABVSUojdv99wIPEF/v7+REVFmY4hXig474omy5pBh8Zms4hnCQ8PJywszHQMl/Hc4gJQpw706AHAqK+/xuF0ms0jPiE8PNwnJsCJay2LhzY77P3QimaziOfo2rUrTZs2NR3DpTy7uDgc8PXXAFQ4epTYAwcMBxJf4O/vT6NGjbj88stNRxEvE3rM3uZ43wV9pYzExsb63CiwZxcXgCpVoEsXAEZOmYKfRl3EBfz8/IiLizMdQ7yMI2/EZXEraNvAbBZxf+3ataN69eqmY7ic5xcXhwO+/x6w57pU3bv3LA8QKR1+fn66erSUqkVr/j5cFNLSaBTxAI0bNyY6Otp0DJfz/OICULmyvTAdcNW0aUSlpJjNIz7B4XDQq1cv0zHEy4Sts7eLW0JbnRotRWjSpInPHSI6yTuKC8C0adCwIVFHj3LD55/jn5NjOpH4AIfDQbNmzUzHEC9y6qhLcBujUcRNNWjQgP79+xMTE2M6ihHeU1yqVYM5cwCocOQIdXUdI3GRESNGmI4gXubkqMvSOGhTz2wWcT8DBgygUqVKpmMY4z3FBaB6dejdG4BBM2YQoFEXcZGqVauajiBeZNFae9TFckBwO9NpxJ3ExsYSEBBgOoZR3lVcAD75BIBKf/1FvT//NBxGfMUtt9zi038BSSmzIHSNvbusmS6+KH+79tprqVChgukYRnlfcalbF/r3B+Di334jIDvbcCDxFXfddReBgYGmY4iXWLoBWiXYoy5hmusiQFhYGA6HrsLpfcUF4M03weGg8uHDNNCoi7iQLsIopcXphPKb7P0cfVsJcO+99xIZGWk6hnHeWVyaNIFBgwDoM28ewRkZhgOJrxg3bpxPXTNEylbOCXv7RyNo6nvrjMkp/Py889f1+fDez8Rzz0HFilQ5cIDREycSmJVlOpH4CA3lSmlZGQ9NkyDXD8p1N51GTHrsscd85urPZ+O9xaVVK/jtNwgMpGZSEs02bTKdSHzEuHHjfHZhKCldWTlQfrG9v6IxNNI1jHzSk08+6fNnEp3Ke4sLQOvWkLfGRpelSwnURF0R8TCrt0DjfeD0g3IXmU4jYp53FxeABx7ACgyk6oEDtFy/3nQa8RF33XUXsbGxpmOIF8jMgfLz7f0/GkK9ymbziGv9+9//1uHn03h/cWnbFsfIkQC0++MPXQpAXMLf35+xY8dSq1Yt01HEC6zdBg0O2qdGRw0wnUZcSYeIzuT9xQXg7ruxAgOpvm8fbVevNp1GfERQUJDOBJBSkZkDFe0rmnAswmwWcQ0/Pz/GjRtnOoZb8o2fqh074hg9GoDWa9fil5trOJD4issvv5z69XWJX7lwWXmrOmyPgSrlzGYR19CaLYXzjeICcMstWEFB1Ni7l+HffqvyIi5Rrlw5LUonpWLzHqh5xJ6kW+0y02mkLAUEBHDTTTeZjuG2fKe4dO6MY9o0rMBAmsfH02XpUtOJxEf06NGDhg0bmo4hHi4jG6rMsPdX1YfocLN5pGwEBwdzzTXXUL26Vhwsiu8UF4AhQ3DcfjsALTZswOF0Gg4kvqBKlSqUK6exfblw6/6Eqkft/ZrDzGaRsuHv70+9evVMx3BrvlVcAEaPxvL3p+qBA3RbssR0GvERcXFx+mEkFywzB6r+aO+vrg/lQ83mkdIVGhpK3759Tcdwe75XXDp0wHHPPQD2ui6WZTiQ+II6derQrVs3lRe5YBsSIDbN3q8/wmgUKWXBwcG0a9fOdAy353vFBeCaa7AcDmIPHmTQjBkqL+ISdevW1VwXuWBZOVD1G3t/Q22zWaT0hIaG0rJlS9MxPIJvFpcOHXB8/DGWw0GnFSvosWiR6UTiIypWrKhJd3LB9ifb26wACNb6ZB4vLCyMtm3bctFFuqZDSfhmcQG48UYcDz0EQLtVqzTqIi7RsGFDOnbsaDqGeLgj6VAh3d5vrUm6Hi8mJob+/fubjuExfLe4AOQtSlchJYWuOj1aXCQkJIQKFSqYjiEeLDMH6uYdLlrWDEIDzeaR8xcSEkKlSpVMx/Aovl1c4uLggQcA6LRsmeEw4isaNmzI0KFDtSqmXJC1O/4edWmlBek8Vt26dbn00ktNx/Aovl1cAG65BYCo1FSVF3GZ2rVrc9VVV5mOIR4s1/n3qMvS5lAhzGweOXcBAQGEhISYjuFxVFwaNYL77gOguybpigs5HA5d+VUuyKrtUDFv1KXpQLNZ5Ny1atWKyy7TcNm5UnEBuPdeAModO0bHFSsMhxFfUa1aNW677TbTMcTDNfjB3i5uCZV15WiP4XA4cDgcpmN4JBUXgLp14ZFHABg8fTrtV640HEhEpGSWb4bQbHs/WJN0PUa3bt0YMmSI6RgeScXlpOefzx95uXjWLMNhxFdUqlSJ+/IOVYqcr4hMe1u3q9kcUjIDBgygX79+pmN4LBWXkxwOePppAIKzs2m1dq3hQCIiJdPoN3u7oANUK282i0hZU3E5VVQU3H03YB8y8s/NNZtHfEK5cuUYN26c6RjiwRathpi86xc10KiLWxs6dCidOnUyHcOjqbic7pVXAAjOyiJu40bDYcRXhIeHq7zIBWk8397O7wS1KprNIkXz9/fHz0+/ei+EPnunCwmB228H4NKffyYwK8twIPEVWs9BLsSCFVDtqL1fp73ZLFK4K664gmbNmpmO4fFUXArz9tsABGVn02TLFsNhxFf4+/tz5513mo4hHqzBEns7vyvUq2w2i5wpLCyMwECd+nWhVFwK4+8PY8cCcPkPP9Bw2zbDgcRX6JolciHmL4XqKfZ+hSiTSeR0l156KTVr1jQdwyuouBTlgw9g+HACcnO5esoUwtLTTScSH+BwOLj22mtNxxAPVi7vR1VEDbM55G9DhgwhLi5Oh4NLiYpLUQID4auvICiIwJwcmmzebDqR+Ij69eubjiAeLGarvV3QQ4eL3EXt2rVVWkqRiktxAgPhuusAuGTmTMKPHzccSHzFwIG68Iycn3nzoMYRcPpBzeam00jv3r2JiNC1GEqTisvZvPMOREQQlJ1NU426iIt07NiRXr166bRJOXcW1F9n7y7qrlEX09q2bUtoaKjpGF5FPxXPJiQERowAoP/s2VTfs8dwIPEVvXr1wt/f33QM8UAL5kHtZMjxh5otTKfxXa1btyYoKMh0DK+j4lISb78NPXsSkpnJ9RMnUi411XQi8RFNmzZVeZFz5nRCnbz1M5fq1GgjmjZtyqBBgzS3pQyouJREeDj8/DPExBCcmakVdcVlLr/8coKDg03HEA+0aB7UOQyZAVBD871dbvjw4RptKSMqLiUVEQHDhwPQa+FCyh09ajiQ+IrY2FjNdZFzlpML1ZLsfUeY2Sy+pmrVqjgcDtMxvJZ+Gp6L55+H2FhCT5ygxYYNptOIjxg9ejTly+uSv3LuAvOOaq/S9Ytc6tZbb9Uh3jKk4nIuoqLgqqsA6LZkiea6iMuEhobqLzg5ZysXQM0jkBYMdduYTuMbwsPDTUfweiou5+rxxyEmhvD0dFqtXWs6jfiIm2++mRo1aqi8yDk5ngX18n5Mre4ANSuYzeMLHnzwQf07LWMqLueqcmUYPRqAzsuXE5WSYjaP+IyxY8dSu3Zt0zHEw6xZZl+/KDUE6umq0WVKh4dcQ8XlfIwbBw0bEnH8ODdMmKBDRuIy+ktOztXRE9DgD3t/TVuoHmU0jtfy8/Pj8ccfNx3DJ6i4nI+qVWHOHKhShQopKXRcvtx0IvERo0ePpmnTpqZjiIdZ9wdUOwpHQ6F+Z9NpvE9gYCBPPPGE6Rg+Q8XlfFWvDnfeCUCbNWuIOHbMcCARkcIdSYcGeX9frW8JVcqZzSNyIVRcLsRNN2HFxBCRnk63xYtNpxEfMXz4cNq2bWs6hniY+DUQmwpHwqBhd9NpvEdYWBgPP/yw6Rg+RcXlQlSrhuO++wBosX49YenpZvOIT/Dz82PgwIF07drVdBTxIIePQ5Nl9n62FqQrVQEBAaYj+BQVlws1ejRWpUpEHj9O73nzTKcRHxEQEKAzGOScOTPt7fY6UF4XLL5g5cuX5868KQPiOiouF6pGDRx5w4TN4uMJzsgwHEh8RadOnejeXWP+UnLbtkKFdDgcAS0vMp3G8/n5+REREWE6hs9RcSkNI0diVaxI5PHjjJ44UeVFXCIsLEw/NOWc7E+FFgvs/U1NIVzXADxvlSpV4uqrrzYdwyepuJSGmjVx/PYbVoUK1ExK4pKZM00nEh/RuHFjzXWRc7J1I5TLsEdd2l5sOo1nio2N5bLLLqNKlSqmo/gkFZfS0ro1jueeA6DJ5s0EZmUZDiS+oHz58vrhKedkfyq0nmPvb2wGwZpXes5CQ0OpVauW6Rg+S8WlNA0bhhUZSWhmJoOnTzedRnxE1apVdXq0nJMt8RCeBX+FQceBptN4ltjYWDp27Gg6hk9TcSlNsbE4Xn0VgLj4ePxzcgwHEl9QqVIlunbtSuvWrU1HEQ9xIA3azs7br2E2i6epWLEizZo1Mx3Dp6m4lLbLL8cKDSUoO1ujLuIyFStWpF27dqZjiAc5dtjeHiwPugRWyVSuXJn69eubjuHzVFxKW0wMjjffBKD12rX45eYaDiS+IjQ0VD9UpcT2HYaAXEgJhR79TafxDLVq1aJ9e11i2zQVl7JwxRVYISH4O50M//ZblRdxiYoVKzJwoCYsSMnsT4WueYeLVrcDNOpSrPLlyxMdHW06hqDiUjYqVcIxZQpWYCDN4+MZ8ssvphOJj/D396dy5cqmY4iH2LwB/J2QFgy9+ppO474iIyPp2rUrXbp0MR1FUHEpO0OG4Bg/HoB2q1fjsCzDgcQXREVFcd1115mOIR7iYBp0m2XvL+tkNos769q1K5066RPkLlRcytKwYVjh4QAM0kRdcRE/Pz/CwnQVPSmZjevsUZeMQOjVx3Qa9xMcHExgYKDpGHIKFZeyFBqK4/33Aei4ciWB2dmGA4kvCA8P55577tEPWymR5OPQ7Td7/4/OZrO4o0GDBmlCrptRcSlrV18NeaMuF8+aBTpkJC4QHBzMQw89ZDqGeIit6+3tsWCzOdyNQ+eJuyUVl7IWGAhvvQUOBx1XrmTgr7+aTiQiUkB6Fvg57f2emsqR75prrqFVq1amY8hpVFxcYexY+PhjALosW0ZkaqrhQOIL/P39efzxx03HEA+QmgE9Ftr7y/qDn34zcPPNN9OwYUPTMaQQ+vZ0lRtvhJgYAHosWmQ4jIhIQcsW2NvMAOimS1+JG1NxcaWnnwag04oVVDhyxHAY8QUOh4N//etfpmOIB8jIhl55oy4rBkCID8/tvv3226levbrpGFIEFRdXuv12qFYNgE7LlxsOI77C39+fhx9+GD+N/8tZzMs7uygjENq3NJvFpICAAE3MdWP6SeZqeWd6dFm2jOjDhw2HEV8RHKzTRaQELOi51N5dcQlE+uC3za233krFihVNx5BiqLi42j/+AbVqAdBm9WrDYcSX3H777QQEBJiOIW5u/kx7mxkA5X1wHcOIiAiNTro5fXVMuOsuALovWULHFSsMhxFfER0dreFvOTsLIjLt3Vo1zUZxtdGjRxOet+6WuC8VFxMefDD/kNHg6dOptnev4UDiK66++modNpKzarXJ3q4ZAuVCzGZxlVGjRlG3bl38/f1NR5GzUHExweGAF1+EZs0AaLF+veFA4ivq1aunH8xyVst+tBekSw+C5o1Mp3GN+vXr6xCRh9BXyRSHA264AYAuy5dTLSnJbB7xGRdddBGhoaGmY4gby8mFznl/T60bDFFe/u0yYMAAlRYPoq+USQ88AC1b4rAsWm7YYDqN+Ii2bdvSo0cPHcuXYq36CYJz7OsXxTU2naZsde7cWcXFg+grZZLDAddeC9iL0mnURVylc+fOREZGmo4hbiwjG9rF2/sbBkAFLz3DqF27dqYjyDlScTFt3Dho3Ro/p5PW69aZTiM+pF69eoSFeelvIykV6362zzA6Ggr1vHQh2UsvvVRn23kYFRfT/P1h1CgA2q9aRcNt2wwHEl/Rr18/YmNjTccQN3YsE2rmrZMZ4oVHFuvWrWs6gpwHFRd3cP/9MGwY/rm5jJw8mRp79phOJD6iYsWKOj1ailU+77Jq2/tCeS+ZpOtwOKhWrRrXX3+96ShyHlRc3EFgIEyaBB06EJCbS/s//jCdSHzE4MGDqV+/vukY4sa2z4DwLDgQCS2amE5TOgIDA7n11ltNx5DzpOLiLgIDYexYAFpu2KCJuuIywcHBWttFinT4GLTabO/v7OUdoy6a2+XZVFzcydix0KYN/rm5dNbVo8VFLr30Utq3b6/TQaVIO3+zR12SoqBFM9NpLkxYWBj33Xef6RhyAfSTyp2EhORfx6j5xo1U2bfPcCDxFQMGDKBbt26mY4ib2ncUWm+093d19+zLAGh00fOpuLibUaOgVSv8nU66L15sOo34EJ0SKsXZPd8+NTqxArRqbjrN+alQoQLjxo0zHUMukIqLuwkLg3/+E4CmmzYRe+CA4UDiK3r37s3AgQNNxxA3lXgEWmy19x06EU0MUnFxR8OHQ/v2BDidXD9hAjEqLyLiBvyz7G1SKwgLMpvlXFWpUoV7773XdAwpBSou7ig8HGbNwmrRgvATJ+j/22+mE4mP6NChA5dddpnpGOKm9i+HkGz4MwbatTadpuTq1KnDLbfcosOhXkLFxV1FReF4+mkAGmzfTqXkZMOBxBc4HA5NXpQibT8A7fOuTLK/HQQHmM1zLvR97d52spMsskp0XxUXdzZwIFaTJvgBg3/5xXQa8RFNmjThiiuuMB1D3NTBFfZVo7fFQqcOptOcXcOGDbnqqqtMx5BixBPP53zOwpoLS3R/FRd3FhqK49VXAai/cydRKSlm84hPCAgIoHHjxowYMcJ0FHFDW/dDx9X2/v5WEODmAxmBgYFacM6NneAE3/ANTpzUjKpZoseouLi7vn2xGjUC4IYJE1RexCWCgoIoX7686Rjipg6tgqAc2FoFunY2naZoTZo0oW/fvqZjSDE2s5kccgD47PLPSvQYFRd3FxKC4/ffsRo0oEJKCiOmTTOdSHxEdHQ0Q4YMMR1D3NDmfdA575Jq+1uAO855jYuL46KLLqJSpUqmo0gR0knnF+xpEDe1uQl/v5IN36m4eILq1XF8+ikANZOSiEhLM5tHfEJwcDC1a9c2HUPc1MG1EJBrj7r0dMNFlytWrEhMTIzpGFKMzWwmm2wC/QJ5d/C7JX6cioun6NQJK+8qviMnTzYcRnxFeHg4vXv3Nh1D3NDmvdA175JqmVXMZjldkyZNaJR3iF3cUwIJTGc6ANe2vJYg/5IvDKTi4ikCAnB89RVgj7qEpacbDiS+ICQkhM6dO9OlSxfTUcQNWUfsbUpFszlOV7t2bWrWLNlET3G9VFL5ki/JJpuL61/Mu4NKPtoCKi6epU0brDp1ABj59ddgWWbziE8ICgqiZ8+epmOIGzqSdx3YzdWgR0ezWU6qU6cOsbGxpmNIMTaxiSyyqBhake+u/o7QwNBzeryKiycJCMDx7bcA1E5MJFJzXcRF/Pz8aNKkiekY4mY2JEKPvMNFB9qazXJSmzZtqFevnukYUoSjHGUOcwAY3nT4OZcWUHHxPC1bQt6EyZFTphCckWE4kPiCwMBAhg8fbjqGuKG/8lbS3VoFehhekC42NpbIyEizIaRY8cSTQQaVwyrzfwP+77yeQ8XF0/j5wbffQsWK1EhK4tpJk3TISFzC4XBoCF7OsHEP9Fhh7+8zWFwqVqzI4MGDNdrixlJJZT7zARjWdBgRQRHn9TwqLp6oTRuYPRuA2rt3E6urR4sL+Pn5cdttt5mOIW7ocN5KuttjoLuhQ0bDhg2jVq1aZl5cSmQ96znBCWLCY3ih7wvn/TwqLp6qTRuIiwNg8IwZODTqIi6i5dPldJv2QvdV9n5ye9e/fmhoqC6i6OaSSWYxiwG4qtlVVAitcN7PpeLiyT76CLBHXart3Ws4jPiKBx54gMDAQNMxxM0c32BvT5R8OY5Sc9NNN1G1alXXv7CUyFGO8imfcpzjxFWO4/Fej1/Q86m4eLIuXaB1awD6/fabRl3EZR599FH8/PTjQ/6WfsLeJkRDp2aue119H7q/VawijTSqRFTh9+t/Jyb8wlY01lfc0/2fPSu7XkICNRITDYcREV+1ZR903mjvH+rjute97777iI6Odt0LyjlJJZUV2LO3b2pz0wWXFlBx8Xx9+kBHe+WnXgsW4Od0Gg4kvuLf//63DhlJAYfn2dsdlaGDVtwXYDnLSSed2PBY7upwV6k8p4qLN3jmGazAQBr++SfDv/kGh8qLiBiw/SB03GTvHxhY9q/38MMPU65cubJ/ITkvxzjGauxTzm5vfztVI0tnHpKKizcYMADHtGkANI+Pp8GffxoOJL7ioYce0llGUsABe6UGdleENmW4pMojjzxCaOi5r7oqrrOYxRznOJXDKnNz25tL7XlVXLzFpZdC374AdFq+XBN1xSX8/f1xOBymY4gb2ZUMHbbY+wcuK7vXCQgIKLsnlwuWTjrrsJdV/kenf1CjXI1Se24VF29y//0ANPzzTxpv2WI4jPiK2267jfLly5uOIW5k/wx7e+j8FkY9q/vvv1/FxY2lk84EJnCMY8SExTCm1ZhSfX4VF28yeDAMGABAmzVrdCkAcYmIiAhGjx5N5cqVTUcRN5GZY2+z/aFxGSyvonkt7m02s9nPfqJDo5lzwxxqlq9Zqs+v4uJt7rgDgCZbt3LJzJkqL+ISFStW1F/Aku9gGrRKsPePXQ2U4tHEm2++WYcn3VgGGWzCnqH9757/plnl0l/UR8XF21x2GXz8MQCdly+n5fr1hgOJrxgwYIAuwij59tvnC5AUBY1K4dvC4XBw7bXXUqNG6c2VkNI3k5mc4ARRwVFcFXdVmbyGios3uukmGD4cwC4uGnURF6hVq5bOMJJ8B9Kg5S57//hI+8L2F8LhcNCwYcMLDyZlJous/NGW/1z0n1I7/fl0Ki7e6vrrAXuibosNGwyHEV/Rvn17jbpIvt2T7G1S1IXNdfH39+fiiy8ulUxSdqYznQwyiAiMYHiz4WX2Oiou3urSS+Eqe5iu1bp1hsOIr2jatCmVKlUyHUPcRMoJiMu7EsmxEeB/nr9x/P396dy5c+kFk1KXQw4bsa/58MrFr1AlokqZvZaKize79lrAHnVprlEXcZF69erp2jGS78DX9jaxAlQ9j7PmAwICaJ13MVlxTznkMJnJZJFFaEAoVzS5okxfT8XFmw0dmj/q0nb1asNhxFe0bduWzp07a+RFADh8DCIy7f1y5zEFKjg4mEGDBpVuKClVP/ETW9lKSEAIP4z6gdiIsj1crOLi7a67DoD6O3fSccUKw2HEV7Rt25a4uDjTMcRN1Ey2t1lDz22SbkBAADVrlu4aIFK6csllDWsA+GDIB/Sr16/MX1PFxdsNGQIPPQTA4OnTaaHTo8VFIiIiiIyMNB1D3MDxqfZ2eyzElfBs5oCAAOrVq8fIkSPLLphcsJ/5GYDQgFAub3K5S15TxcXbORzw4ouQ94+/0/LlhgOJr2jfvj3dunUzHUPcwO6/oNkeez/rkpKNukRHR3PNNdeUbTC5IE6crGIVAO8PeZ9ywa5Z0VjFxRc4HHDbbQDUTEqiWXy84UDiKwIDAwkKCjIdQ9xA5g/2dktVaFm7+Pv6+fkREhJS9qHkvDlx8iM/AhAWGMbwpmV3+vPpVFx8Ra9e+YvSdV+82HAY8RVt2rRh6NCh+Pv7m44ihv158O8F6bL6FX9qdK1atbjhhhtckkvOzy/8wmpW4+fw46NLPyI8KNxlr63i4iscDvjnPwGovnevrh4tLtOsWTPNUxAAsmba2/jq0KKYURe/C11mV8pUOumsZCUAX1zxBde0cO0hPX13+JIuXexTpIE+c+eazSIiPmfzXmiy194PLOIIYrNmzRgzZozrQsk5m8McAMoHl2dUi1Euf30VF1/icMC//w1A1QMHaLBtm+FA4ivq16/PTTfdZDqGuIGQvDVdsjtf+PWLxPVSSWUF9tIa/zfg/4xk0LeNr2nfHoYNA2DU5Mk02L7dcCAR8Snz7c2autC6XsF3tWvXjiuvvNL1maTEFrEIgOiwaG5sc6ORDCouvsbhgEmTYPBgAnJzGfLLL6YTiY+oXr06t99+u+kYYtiandBmR94bne0fSQDdu3dn0KBBOE7eIG7nCEdYxjIAnu79tLEcKi6+KDAQ3ngDgAopKdROSDCbR3yGzi4SAEfeiY2rGkD7Rva+n5+fvj/c3MlDRFUiqnBnhzuN5VBx8VX16sEllwAw/NtvDYcRX1GhQgVuueUW0zHEsFXboe2f9r7VDnr36qnFCt3cQhayGLtxPtT1IaNZVFx8lcMBH30EQPm0NKonJRkOJL7Az8+P2NhYTdQV/O0jDqxsBFXCdxMcHGw2kBRpP/uZzWwAnur1FPd3ud9oHodlWZbRBGLWxRfDrFmkRkby2j/+gVPT/MUFMjMzeemll0zHEMM6XAMrGkFQrh/1/BuZjiNFSCSR4xynYcWGbL1nq+k4BJgOIIZ99RVER1MuLY1qSUns0ZVYxQUCAwO1KJ2P83M66bPpR1Y0OkGWv5PNbDYdSc7i1na3mo4AaMRFAPr2hd9/53hYGJ+NHs2B2FjTiUTEi/k5nQz/5huax8ezpLY/a19/BGqU8LLRYkTdqLpcXP9itzjrS8VFICUF+veHlSs5FhHBa/fcQ05goOlUIuKlLvr9d3otXIgVGIhj2jS49FLTkcSDaEKDQFQUzJoFQMSxY9TR6dEiUkYclkXLDRvs/UcfVWmRc6biIraoKHvUBbjs558JyM42m0dEvFLvuXOpkJKC5ecHmuck50HFRf72xRcAlEtNpf6OHWe5s4jIObIs2qxZA4Dj8cehaVOzecQjqbjI32Ji7NOjgUEzZlA+JcVsHhHxHpbFJTNnUj4tzX77mmvM5hGPpeIiBX3yCTRoQNTRo4ydMIHgzEzTiUTEC/SZO5fOy5djORzw8cfQSOu2yPlRcZGCqleHuXMhOJgKKSk03rLFdCIR8XSWReflywFwPPssaOVkuQAqLnKm6tXzZ/r3mTeP4IwMw4FExJP1WLiQkMxMe0Lu2LGm44iHU3GRwr38MoSEUPHIEZpu1oqWInL+eixcCIDjiSegWjXDacTTqbhI4erWhcsvB6DHokWa6yIi56XLkiUEZ2fbc1vuust0HPECKi5StKeegpAQopOTidu40XQaEfE0lkW/338HwPGvf0F0tOFA4g1UXKRojRvDVVcB0GXpUs11EZFz0nHlSgJyc+03HnzQbBjxGiouUrwHH8SqWJGYw4cZPXGiDhmJSIl0XLGCwdOn22/8619QrpzZQOI1VFykeM2b45g9GyssjJpJSbRbtcp0IhFxc8EZGQz89Vf7jYcegmefNRtIvIqKi5xdmzY4brwRgLarVxOYlWU4kIi4s9Zr1+J/8hDRiy+Cw2E2kHgVFRcpmVtvxQoNpfLhw/kLSYmInC40PZ0Bs2fbbzz4oEqLlDoVFymZFi1w3HorAC3Xr8c/J8dwIBFxRy02bLBHWwID4aWXTMcRL6TiIiU3dixWcDAxhw7RbfFi02lExM1EHDvGwFmz7DfuvFOjLVImVFyk5Fq1wnH33QC02LgRP6fTcCARcSfN4uPt0ZawMPjvf03HES+l4iLn5rrrsAIDiTl0iOHffKPyIiIANNi2jQEnR1tuuAH8/Y3mEe+l4iLnpnVrHNOmYQUG0jw+nt5z55pOJCKGRaWkMGryZHuxuWHD4PXXTUcSL6biIufu0ktxPPooAC03bMBhWYYDiYhJcfHxdmmJjYVJk+yJuSJlRMVFzs/IkVh+flRISaH3vHmm04iIIVFHjtDn5M+Aq69WaZEyp+Ii56dpUxyPPw5Am9WrQaMuIj6p+caNBGZnQ/XqOv1ZXELFRc7fqFEAlE9Lo9eCBYbDiIirVThyhJ6LFtlvjBgBISFmA4lPUHGR89e4MeSNunRYuVKjLiI+psX69faFV2vUgP/8x3Qc8REqLnJhbrgBgMhjx7hozhyVFxEfEXvgAF2XLrXfuOoqiIgwG0h8hoqLXJh69fJPfey1cCE9Fi40m0dEylzFv/7ihs8/JzQjA9q3zx95FXEFFRe5cP/4Bzz5JICKi4gPaLdqFWHp6VC7NsyaBVFRpiOJD1FxkdJx991YDgfB2dl0WbLEdBoRKSNRR47QbtUq+42bblJpEZdTcZHSER2N47HHAOj3+++a6yLipTqtWGEfIqpZE+64w3Qc8UEqLlJ6HnoIgIDcXDquXGk4jIiUtqiUFFqvWWO/ceedEB1tNI/4JhUXKT3lykHeqMvg6dPpsGKF4UAiUlrKHz3KDRMmEJaRgdWgAdx4o+lI4qNUXKR0/ec/+SMvl8ycSXBGhuFAIlIaes2fT4WUFKyaNXHMnQsxMaYjiY9ScZHS5XDAiy8C4O900nrtWsOBRORCRaSlERcfD4DjkUfs5f1FDFFxkdLncMADDwAwYPZsQtPTDQcSkQvR/7ffCMnMxKpWzV5sTsQgFRcpGy+9BIGB+Ofm0mLDBtNpROQ8haan02TLFgAcTzyhCblinIqLlA0/P/usA2DgrFlEHDtmOJCInI9BM2bYoy0xMTB8uOk4IiouUob++18IC8M/N5dmecfHRcRzBGdk0HjrVgAczz+v0RZxCyouUnb8/fMvwjhg1iwabNtmNo+IlFhwRgajJ04kOCsLq2JFuPxy05FEABUXKWuvvw5XXEFAbi6jJk8mKiXFdCIRKYHLf/iBmklJWBUq4Jg9GypVMh1JBACHZWltdilj2dn28uAHDrCrZk22NmpkOpGIFMNhWfalOwC+/lpnEolbUXER17j3XnjrLdMpROQcWOXL40hMhMhI01FE8gWYDiA+4uWXoXJl2LHDdBIRKYngYBy3367SIm5HIy4iIiLiMTQ5V0RERDyGiouIiIh4DBUXERER8RgqLiIiIuIxVFxERETEY6i4iIiIiMdQcRERERGPoeIiIiIiHkPFRURERDyGiouIiIh4DBUXERER8RgqLiIiIuIxVFxERETEY6i4iIiIiMdQcRERERGPoeIiIiIiHuP/AbT3dnnjZA2fAAAAAElFTkSuQmCC",
       "text/plain": [
-       "{'data':                                                    ypix  \\\n",
-       " roi#                                                      \n",
-       " 0     [15, 15, 15, 15, 16, 16, 16, 16, 17, 17, 17, 1...   \n",
-       " 1     [37, 37, 37, 38, 38, 38, 38, 38, 39, 39, 39, 3...   \n",
-       " 2     [42, 42, 42, 42, 43, 43, 43, 43, 43, 44, 44, 4...   \n",
-       " 3     [51, 51, 52, 52, 52, 52, 52, 53, 53, 53, 53, 5...   \n",
-       " 4     [52, 52, 53, 53, 53, 53, 54, 54, 54, 54, 55, 5...   \n",
-       " ...                                                 ...   \n",
-       " 187   [496, 496, 496, 497, 497, 497, 497, 498, 498, ...   \n",
-       " 188   [497, 497, 497, 497, 498, 498, 498, 498, 498, ...   \n",
-       " 189   [498, 498, 498, 498, 498, 499, 499, 499, 499, ...   \n",
-       " 190   [504, 504, 505, 505, 505, 505, 505, 506, 506, ...   \n",
-       " 191   [506, 506, 506, 507, 507, 507, 507, 508, 508, ...   \n",
-       " \n",
-       "                                                    xpix  \\\n",
-       " roi#                                                      \n",
-       " 0     [166, 167, 168, 169, 166, 167, 168, 169, 165, ...   \n",
-       " 1     [12, 13, 14, 12, 13, 14, 15, 16, 11, 12, 13, 1...   \n",
-       " 2     [45, 48, 50, 51, 46, 47, 48, 49, 50, 46, 47, 4...   \n",
-       " 3     [98, 99, 96, 97, 98, 99, 100, 95, 96, 97, 98, ...   \n",
-       " 4     [248, 249, 247, 248, 249, 250, 247, 248, 249, ...   \n",
-       " ...                                                 ...   \n",
-       " 187   [22, 23, 24, 21, 22, 23, 24, 21, 22, 23, 24, 2...   \n",
-       " 188   [296, 297, 298, 299, 295, 296, 297, 298, 299, ...   \n",
-       " 189   [316, 317, 318, 319, 320, 314, 315, 316, 317, ...   \n",
-       " 190   [228, 229, 226, 227, 228, 229, 230, 225, 226, ...   \n",
-       " 191   [209, 210, 211, 209, 210, 211, 212, 208, 209, ...   \n",
-       " \n",
-       "                                                     lam         med  \\\n",
-       " roi#                                                                  \n",
-       " 0     [0.25048703, 0.2825266, 0.26209846, 0.22899982...   [20, 167]   \n",
-       " 1     [0.427138, 0.47049677, 0.448507, 0.5278685, 0....    [43, 14]   \n",
-       " 2     [0.4339046, 0.4371859, 0.47181413, 0.5302998, ...    [44, 48]   \n",
-       " 3     [0.5093366, 0.5437041, 0.48653182, 0.50700283,...    [55, 98]   \n",
-       " 4     [0.29105517, 0.29707682, 0.29351893, 0.3146623...   [54, 249]   \n",
-       " ...                                                 ...         ...   \n",
-       " 187   [0.92245674, 1.0195774, 0.79342514, 1.0614704,...   [498, 23]   \n",
-       " 188   [0.15871558, 0.22151276, 0.30354294, 0.2215778...  [500, 297]   \n",
-       " 189   [0.26200435, 0.36405584, 0.5717866, 0.5238269,...  [504, 317]   \n",
-       " 190   [0.47760373, 0.5492389, 0.501179, 0.6533608, 0...  [507, 228]   \n",
-       " 191   [0.70670706, 0.9590761, 0.84982294, 1.1, 1.1, ...  [508, 210]   \n",
-       " \n",
-       "       footprint       mrs      mrs0   compact  solidity  npix  ...      skew  \\\n",
-       " roi#                                                           ...             \n",
-       " 0             1  1.283306  2.911908  1.072237  1.276596    62  ...  0.804174   \n",
-       " 1             1  1.368664  3.165271  1.052020  1.193277    77  ...  2.388659   \n",
-       " 2             1  0.708885  1.665467  1.035567  1.379310    25  ...  0.403693   \n",
-       " 3             1  0.998760  2.404379  1.010639  1.301587    49  ...  0.308693   \n",
-       " 4             1  0.763525  1.635435  1.135869  1.357143    20  ...  0.835810   \n",
-       " ...         ...       ...       ...       ...       ...   ...  ...       ...   \n",
-       " 187           1  0.441017  1.072984  1.000000  0.900000    16  ...  0.250935   \n",
-       " 188           1  0.754060  1.834612  1.000000  1.548387    37  ...  0.609746   \n",
-       " 189           1  1.598828  3.889914  1.000000  1.175824   117  ...  0.262649   \n",
-       " 190           1  0.926338  2.223794  1.013475  1.372549    37  ...  1.257543   \n",
-       " 191           1  0.648858  1.564772  1.008874  1.619048    19  ...  0.415241   \n",
-       " \n",
-       "             std                                      neuropil_mask  \\\n",
-       " roi#                                                                 \n",
-       " 0     23.348751  [4254, 4255, 4256, 4257, 4258, 4259, 4260, 426...   \n",
-       " 1     46.901821  [15364, 15365, 15366, 15367, 15368, 15369, 153...   \n",
-       " 2     29.339140  [15393, 15394, 15395, 15396, 15397, 15398, 153...   \n",
-       " 3     27.109699  [22615, 22616, 22617, 22618, 22619, 22620, 226...   \n",
-       " 4     31.203712  [20715, 20716, 20717, 20718, 20719, 20720, 207...   \n",
-       " ...         ...                                                ...   \n",
-       " 187   73.159027  [247817, 247818, 247819, 247820, 247821, 24782...   \n",
-       " 188   34.991699  [248602, 248603, 248604, 248605, 248606, 24860...   \n",
-       " 189   40.402195  [249131, 249132, 249133, 249134, 249135, 24914...   \n",
-       " 190   59.259262  [252116, 252117, 252118, 252119, 252120, 25212...   \n",
-       " 191   89.730827  [253124, 253125, 253126, 253127, 253128, 25312...   \n",
-       " \n",
-       "                                                       F  \\\n",
-       " roi#                                                      \n",
-       " 0     [104.9813232421875, 108.55770111083984, 55.846...   \n",
-       " 1     [131.17893981933594, 113.12724304199219, 103.3...   \n",
-       " 2     [70.86315155029297, 116.17134857177734, 79.594...   \n",
-       " 3     [52.76205062866211, 117.1761245727539, 150.299...   \n",
-       " 4     [81.58949279785156, 20.841588973999023, 68.408...   \n",
-       " ...                                                 ...   \n",
-       " 187   [377.6455078125, 403.3608703613281, 336.951904...   \n",
-       " 188   [90.83379364013672, 70.42933654785156, 100.175...   \n",
-       " 189   [313.332275390625, 290.0640563964844, 260.5965...   \n",
-       " 190   [213.46859741210938, 147.08824157714844, 209.9...   \n",
-       " 191   [381.1005554199219, 425.28369140625, 534.44708...   \n",
-       " \n",
-       "                                                 F_chan2  \\\n",
-       " roi#                                                      \n",
-       " 0     [28.803903579711914, 22.713159561157227, 14.08...   \n",
-       " 1     [33.476985931396484, 44.256221771240234, 39.97...   \n",
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-       " 3     [39.06512451171875, 47.88350296020508, 50.3446...   \n",
-       " 4     [26.94829750061035, 40.880123138427734, 59.214...   \n",
-       " ...                                                 ...   \n",
-       " 187   [141.80201721191406, 119.89483642578125, 149.8...   \n",
-       " 188   [75.4814453125, 61.588951110839844, 31.8861503...   \n",
-       " 189   [161.69003295898438, 109.21076202392578, 93.82...   \n",
-       " 190   [84.28337860107422, 88.76708984375, 63.6304206...   \n",
-       " 191   [51.541831970214844, 39.534881591796875, 67.72...   \n",
-       " \n",
-       "                                                    Fneu  \\\n",
-       " roi#                                                      \n",
-       " 0     [32.6827392578125, 28.725889205932617, 49.8071...   \n",
-       " 1     [91.29977416992188, 96.39373779296875, 87.9306...   \n",
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-       " 3     [110.7890853881836, 105.07691955566406, 125.59...   \n",
-       " 4     [47.92441177368164, 41.104087829589844, 42.929...   \n",
-       " ...                                                 ...   \n",
-       " 187   [56.3651237487793, 67.81062316894531, 61.50817...   \n",
-       " 188   [32.03458023071289, 25.73198890686035, 23.3170...   \n",
-       " 189   [30.01430892944336, 25.232114791870117, 21.082...   \n",
-       " 190   [104.87043762207031, 104.0364990234375, 90.142...   \n",
-       " 191   [141.45492553710938, 139.33543395996094, 124.7...   \n",
-       " \n",
-       "                                              Fneu_chan2  \\\n",
-       " roi#                                                      \n",
-       " 0     [27.786802291870117, 26.446701049804688, 25.60...   \n",
-       " 1     [44.38702392578125, 49.67561340332031, 48.3534...   \n",
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-       " 4     [53.68153762817383, 51.37298583984375, 57.4931...   \n",
-       " ...                                                 ...   \n",
-       " 187   [49.02043533325195, 48.44550323486328, 52.6485...   \n",
-       " 188   [36.132564544677734, 32.047550201416016, 29.03...   \n",
-       " 189   [24.050874710083008, 22.421302795410156, 17.03...   \n",
-       " 190   [62.02554702758789, 55.33759307861328, 54.7335...   \n",
-       " 191   [69.39412689208984, 64.65618133544922, 61.7358...   \n",
-       " \n",
-       "                             iscell                     redcell  \\\n",
-       " roi#                                                             \n",
-       " 0       [0.0, 0.47836308612841727]  [0.0, 0.45756494998931885]   \n",
-       " 1        [1.0, 0.6619458938025822]  [0.0, 0.49605751037597656]   \n",
-       " 2       [0.0, 0.39341260184431276]   [0.0, 0.5276590585708618]   \n",
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-       " 4       [0.0, 0.10103840581381524]   [0.0, 0.4228624105453491]   \n",
-       " ...                            ...                         ...   \n",
-       " 187    [0.0, 0.004782603998625507]   [1.0, 0.7535504102706909]   \n",
-       " 188     [0.0, 0.13019092181972408]   [0.0, 0.5898925065994263]   \n",
-       " 189   [0.0, 0.0023153990647640176]   [1.0, 0.8289082050323486]   \n",
-       " 190      [1.0, 0.9048422613825486]    [0.0, 0.519374430179596]   \n",
-       " 191     [0.0, 0.08028882619769626]  [0.0, 0.45886659622192383]   \n",
-       " \n",
-       "                                                    spks  \n",
-       " roi#                                                     \n",
-       " 0     [0.0, 0.0, 0.0, 0.0, 29.65685272216797, 0.0, 0...  \n",
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-       " ...                                                 ...  \n",
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-       " 190   [0.0, 0.0, 9.66218376159668, 0.0, 0.0, 0.0, 0....  \n",
-       " 191   [0.0, 38.713497161865234, 0.0, 0.0, 69.5405883...  \n",
-       " \n",
-       " [192 rows x 27 columns],\n",
-       " 'metadata': {'suite2p_version': '0.14.2.dev11+ga0fec11.d20231009',\n",
-       "  'look_one_level_down': False,\n",
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-       "  'xrange': [2, 510],\n",
-       "  'date_proc': datetime.datetime(2023, 12, 6, 17, 28, 46, 105061, tzinfo=datetime.timezone(datetime.timedelta(seconds=3600), 'Romance Standard Time')),\n",
-       "  'refImg': array([[36, 27, 38, ..., 32, 29, 25],\n",
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-       "          -0.01894706,  0.01254945],\n",
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-       "         ...,\n",
-       "         [-0.01382381,  0.00068278, -0.03157832, ..., -0.00227152,\n",
-       "          -0.00809634,  0.00961604],\n",
-       "         [-0.0130718 , -0.00116871, -0.03590043, ...,  0.02013807,\n",
-       "           0.00286977,  0.01734307],\n",
-       "         [-0.0138381 , -0.00520258, -0.01293696, ..., -0.00563635,\n",
-       "          -0.00376283,  0.02015186]], dtype=float32),\n",
-       "  'regPC': array([[[[38.416668, 43.01    , 44.77667 , ..., 19.51    , 17.116667,\n",
-       "            16.703333],\n",
-       "           [40.506668, 42.513332, 45.406666, ..., 21.46    , 17.503334,\n",
-       "            17.09    ],\n",
-       "           [36.943333, 40.196667, 48.836666, ..., 20.506666, 17.83    ,\n",
-       "            18.083334],\n",
-       "           ...,\n",
-       "           [21.313334, 22.393333, 31.12    , ..., 28.443333, 30.      ,\n",
-       "            31.213333],\n",
-       "           [23.35    , 26.386667, 32.506668, ..., 32.253334, 28.763334,\n",
-       "            28.57    ],\n",
-       "           [18.986666, 25.483334, 28.893333, ..., 33.463333, 34.666668,\n",
-       "            32.433334]],\n",
-       "  \n",
-       "          [[40.81333 , 37.103333, 41.993332, ..., 18.643333, 15.696667,\n",
-       "            15.423333],\n",
-       "           [32.97    , 35.24    , 38.24    , ..., 18.53    , 19.23    ,\n",
-       "            13.85    ],\n",
-       "           [31.37    , 37.52667 , 41.643333, ..., 14.48    , 14.926666,\n",
-       "            13.246667],\n",
-       "           ...,\n",
-       "           [22.616667, 22.263334, 27.59    , ..., 35.04    , 31.176666,\n",
-       "            30.856667],\n",
-       "           [21.343334, 22.203333, 30.99    , ..., 35.126667, 32.04    ,\n",
-       "            32.183334],\n",
-       "           [20.846666, 24.976667, 31.81    , ..., 37.756668, 34.84667 ,\n",
-       "            34.14    ]],\n",
-       "  \n",
-       "          [[40.15333 , 44.26    , 49.31333 , ..., 22.88    , 18.806667,\n",
-       "            14.746667],\n",
-       "           [37.71    , 41.31333 , 47.34667 , ..., 21.68    , 18.576666,\n",
-       "            16.833334],\n",
-       "           [36.286667, 41.363335, 41.543335, ..., 16.133333, 14.786667,\n",
-       "            18.79    ],\n",
-       "           ...,\n",
-       "           [27.833334, 26.016666, 33.1     , ..., 48.476665, 38.59    ,\n",
-       "            42.986668],\n",
-       "           [28.05    , 29.486666, 34.236668, ..., 47.99    , 47.546665,\n",
-       "            42.463333],\n",
-       "           [23.136667, 25.933332, 28.38    , ..., 45.52    , 40.743332,\n",
-       "            43.633335]],\n",
-       "  \n",
-       "          ...,\n",
-       "  \n",
-       "          [[40.19    , 38.03    , 44.383335, ..., 20.21    , 18.476667,\n",
-       "            15.65    ],\n",
-       "           [39.336666, 33.726665, 43.183334, ..., 18.906666, 20.533333,\n",
-       "            19.013334],\n",
-       "           [34.02667 , 37.886665, 47.053333, ..., 15.393333, 17.713333,\n",
-       "            16.633333],\n",
-       "           ...,\n",
-       "           [24.213333, 26.19    , 28.153334, ..., 37.416668, 32.543335,\n",
-       "            32.76    ],\n",
-       "           [24.553333, 25.433332, 27.443333, ..., 40.88    , 36.593334,\n",
-       "            35.093334],\n",
-       "           [22.493334, 28.676666, 26.04    , ..., 31.336666, 29.976667,\n",
-       "            32.58    ]],\n",
-       "  \n",
-       "          [[39.14    , 36.603333, 45.856667, ..., 20.76    , 17.083334,\n",
-       "            16.736666],\n",
-       "           [36.106667, 38.09    , 42.97    , ..., 18.866667, 18.626667,\n",
-       "            20.096666],\n",
-       "           [29.4     , 36.713333, 36.373333, ..., 20.04    , 19.573334,\n",
-       "            17.886667],\n",
-       "           ...,\n",
-       "           [23.75    , 28.55    , 29.493334, ..., 36.263332, 31.393333,\n",
-       "            37.33    ],\n",
-       "           [26.103333, 25.22    , 29.61    , ..., 39.68667 , 36.733334,\n",
-       "            33.336666],\n",
-       "           [21.293333, 28.88    , 28.85    , ..., 39.136665, 38.47    ,\n",
-       "            34.126667]],\n",
-       "  \n",
-       "          [[43.943333, 39.896667, 39.056667, ..., 19.41    , 17.87    ,\n",
-       "            15.123333],\n",
-       "           [37.93    , 39.543335, 41.94    , ..., 22.77    , 20.063334,\n",
-       "            18.466667],\n",
-       "           [28.873333, 39.863335, 39.84667 , ..., 19.92    , 20.526667,\n",
-       "            17.093334],\n",
-       "           ...,\n",
-       "           [22.19    , 23.353333, 24.48    , ..., 36.91    , 33.443333,\n",
-       "            36.093334],\n",
-       "           [23.04    , 26.693333, 28.403334, ..., 40.416668, 37.58    ,\n",
-       "            31.933332],\n",
-       "           [23.333334, 26.176666, 24.89    , ..., 38.43667 , 37.566666,\n",
-       "            33.863335]]],\n",
-       "  \n",
-       "  \n",
-       "         [[[34.046665, 35.803333, 39.966667, ..., 19.496666, 22.5     ,\n",
-       "            17.396667],\n",
-       "           [34.7     , 35.64    , 38.946667, ..., 17.2     , 19.22    ,\n",
-       "            19.136667],\n",
-       "           [33.516666, 40.52    , 38.293335, ..., 18.9     , 20.863333,\n",
-       "            18.      ],\n",
-       "           ...,\n",
-       "           [23.14    , 20.026667, 32.01    , ..., 39.9     , 39.27667 ,\n",
-       "            37.493332],\n",
-       "           [22.72    , 28.746666, 26.913334, ..., 44.06333 , 47.73    ,\n",
-       "            37.113335],\n",
-       "           [19.896667, 23.29    , 29.313334, ..., 46.69    , 43.09667 ,\n",
-       "            37.43667 ]],\n",
-       "  \n",
-       "          [[39.243332, 39.65    , 43.386665, ..., 18.15    , 17.24    ,\n",
-       "            13.05    ],\n",
-       "           [38.12    , 39.246666, 43.103333, ..., 20.02    , 16.033333,\n",
-       "            12.903334],\n",
-       "           [34.6     , 36.093334, 43.486668, ..., 23.063334, 19.23    ,\n",
-       "            17.606667],\n",
-       "           ...,\n",
-       "           [23.576666, 22.4     , 25.5     , ..., 32.663334, 32.986668,\n",
-       "            36.1     ],\n",
-       "           [21.55    , 23.416666, 26.376667, ..., 33.88    , 30.016666,\n",
-       "            35.25    ],\n",
-       "           [25.246666, 27.47    , 31.37    , ..., 34.933334, 35.47    ,\n",
-       "            33.803333]],\n",
-       "  \n",
-       "          [[32.77667 , 33.736668, 35.696667, ..., 16.496666, 18.306667,\n",
-       "            15.736667],\n",
-       "           [33.326668, 31.7     , 33.91    , ..., 19.756666, 19.086666,\n",
-       "            16.04    ],\n",
-       "           [32.763332, 34.613335, 40.213333, ..., 20.3     , 18.98    ,\n",
-       "            16.46    ],\n",
-       "           ...,\n",
-       "           [19.41    , 18.98    , 25.193333, ..., 32.13    , 30.696667,\n",
-       "            30.486666],\n",
-       "           [18.706667, 17.883333, 26.12    , ..., 35.54    , 29.476667,\n",
-       "            35.823334],\n",
-       "           [22.256666, 24.603333, 27.876667, ..., 40.88    , 36.79    ,\n",
-       "            34.246666]],\n",
-       "  \n",
-       "          ...,\n",
-       "  \n",
-       "          [[33.32    , 39.113335, 42.753334, ..., 23.6     , 18.26    ,\n",
-       "            17.056667],\n",
-       "           [34.826668, 36.9     , 47.283333, ..., 19.82    , 18.573334,\n",
-       "            18.303333],\n",
-       "           [34.69    , 39.173332, 46.383335, ..., 17.53    , 21.786667,\n",
-       "            17.926666],\n",
-       "           ...,\n",
-       "           [24.66    , 23.683332, 29.99    , ..., 35.37    , 32.166668,\n",
-       "            38.25    ],\n",
-       "           [23.696667, 25.966667, 29.123333, ..., 39.906666, 34.606667,\n",
-       "            32.566666],\n",
-       "           [23.02    , 24.036667, 29.223333, ..., 40.323334, 34.803333,\n",
-       "            32.593334]],\n",
-       "  \n",
-       "          [[32.4     , 41.393333, 45.503334, ..., 18.46    , 13.443334,\n",
-       "            13.453333],\n",
-       "           [39.733334, 41.683334, 40.906666, ..., 19.513334, 16.433332,\n",
-       "            14.996667],\n",
-       "           [35.793335, 44.076668, 45.496666, ..., 21.636667, 18.776667,\n",
-       "            19.393333],\n",
-       "           ...,\n",
-       "           [21.896667, 23.673334, 30.13    , ..., 43.326668, 34.633335,\n",
-       "            35.216667],\n",
-       "           [21.58    , 24.626667, 32.18667 , ..., 40.27    , 38.786667,\n",
-       "            38.986668],\n",
-       "           [24.776667, 25.053333, 24.466667, ..., 38.083332, 38.406666,\n",
-       "            36.483334]],\n",
-       "  \n",
-       "          [[36.29    , 44.48    , 47.923332, ..., 22.44    , 17.443333,\n",
-       "            16.393333],\n",
-       "           [33.43    , 36.59667 , 46.463333, ..., 17.95    , 20.13    ,\n",
-       "            18.98    ],\n",
-       "           [33.89    , 35.64    , 43.16    , ..., 16.256666, 16.03    ,\n",
-       "            17.386667],\n",
-       "           ...,\n",
-       "           [21.73    , 22.913334, 27.023333, ..., 30.693333, 34.793335,\n",
-       "            35.46    ],\n",
-       "           [24.543333, 22.943333, 29.68    , ..., 34.83    , 36.793335,\n",
-       "            35.13    ],\n",
-       "           [25.583334, 30.17    , 29.886667, ..., 34.066666, 32.416668,\n",
-       "            34.31    ]]]], dtype=float32),\n",
-       "  'regDX': array([[0.        , 0.10930331, 0.31622776],\n",
-       "         [0.        , 0.01503948, 0.14142136],\n",
-       "         [0.        , 0.03236189, 0.22360681],\n",
-       "         [0.        , 0.04396786, 0.14142136],\n",
-       "         [0.        , 0.01896785, 0.14142136],\n",
-       "         [0.        , 0.02010019, 0.22360681],\n",
-       "         [0.        , 0.01944445, 0.1       ],\n",
-       "         [0.        , 0.01503948, 0.14142136],\n",
-       "         [0.        , 0.02059504, 0.14142136],\n",
-       "         [0.        , 0.02452341, 0.14142136],\n",
-       "         [0.        , 0.02452341, 0.14142136],\n",
-       "         [0.        , 0.02337282, 0.14142136],\n",
-       "         [0.        , 0.01291745, 0.22360681],\n",
-       "         [0.        , 0.02222222, 0.1       ],\n",
-       "         [0.        , 0.03630853, 0.14142136],\n",
-       "         [0.        , 0.02822855, 0.31622776],\n",
-       "         [0.        , 0.02892837, 0.14142136],\n",
-       "         [0.        , 0.0122617 , 0.14142136],\n",
-       "         [0.        , 0.025     , 0.1       ],\n",
-       "         [0.        , 0.01111111, 0.1       ],\n",
-       "         [0.        , 0.03170615, 0.2       ],\n",
-       "         [0.        , 0.02845178, 0.14142136],\n",
-       "         [0.        , 0.01063452, 0.14142136],\n",
-       "         [0.        , 0.01944445, 0.1       ],\n",
-       "         [0.        , 0.03170615, 0.14142136],\n",
-       "         [0.        , 0.025     , 0.1       ],\n",
-       "         [0.        , 0.01503948, 0.14142136],\n",
-       "         [0.        , 0.02059504, 0.14142136],\n",
-       "         [0.        , 0.02059504, 0.2       ],\n",
-       "         [0.        , 0.01944445, 0.1       ]]),\n",
-       "  'max_proj': array([[38.52501 , 38.829338, 48.636665, ..., 27.275005, 25.467007,\n",
-       "          34.484333],\n",
-       "         [34.559654, 35.472008, 46.447334, ..., 38.648   , 35.05033 ,\n",
-       "          25.364   ],\n",
-       "         [41.673004, 41.677666, 50.553665, ..., 29.676   , 31.617998,\n",
-       "          28.398333],\n",
-       "         ...,\n",
-       "         [30.413998, 34.583008, 59.589664, ..., 42.942   , 42.81166 ,\n",
-       "          37.12599 ],\n",
-       "         [38.564003, 47.197   , 38.232   , ..., 41.93667 , 55.564995,\n",
-       "          42.000668],\n",
-       "         [36.065666, 41.536335, 58.282333, ..., 53.403343, 44.52533 ,\n",
-       "          37.264328]], dtype=float32),\n",
-       "  'Vmax': 0,\n",
-       "  'ihop': 0,\n",
-       "  'Vsplit': 0,\n",
-       "  'Vcorr': array([[34.196426, 36.622467, 39.157295, ..., 17.777582, 17.713242,\n",
-       "          16.801374],\n",
-       "         [34.513317, 36.16989 , 39.15107 , ..., 18.199572, 17.87976 ,\n",
-       "          17.335924],\n",
-       "         [34.56729 , 36.41707 , 39.38435 , ..., 18.334616, 17.996906,\n",
-       "          17.671822],\n",
-       "         ...,\n",
-       "         [22.469698, 23.99036 , 26.140385, ..., 35.576973, 33.813595,\n",
-       "          32.325447],\n",
-       "         [22.146204, 24.302187, 26.515179, ..., 36.447445, 34.568283,\n",
-       "          32.968037],\n",
-       "         [21.550625, 24.063747, 26.79162 , ..., 36.534554, 35.02619 ,\n",
-       "          33.981953]], dtype=float32),\n",
-       "  'Vmap': 0,\n",
-       "  'meanImg_chan2_corrected': array([[21.426262 , 21.057602 , 23.156729 , ..., 23.84821  , 23.292223 ,\n",
-       "          22.019958 ],\n",
-       "         [20.963943 , 21.613068 , 23.415472 , ..., 24.272278 , 23.472767 ,\n",
-       "          22.058514 ],\n",
-       "         [19.657166 , 21.352703 , 24.795181 , ..., 25.532999 , 24.594463 ,\n",
-       "          23.30623  ],\n",
-       "         ...,\n",
-       "         [19.450539 , 24.402084 , 27.443703 , ...,  1.2910261,  1.8725271,\n",
-       "           2.6542025],\n",
-       "         [23.392796 , 26.06793  , 24.851593 , ...,  3.2817297,  3.7777643,\n",
-       "           4.6232214],\n",
-       "         [25.843311 , 24.655828 , 21.127558 , ..., 10.165245 , 10.048186 ,\n",
-       "          10.617927 ]], dtype=float32),\n",
-       "  'timing': {'registration': 1997.983285188675,\n",
-       "   'registration_metrics': 22.896734476089478,\n",
-       "   'detection': 42.67090892791748,\n",
-       "   'extraction': 85.70417094230652,\n",
-       "   'classification': 0.028999805450439453,\n",
-       "   'deconvolution': 0.5291159152984619,\n",
-       "   'total_plane_runtime': 2150.8474967479706}}}"
+       "<Figure size 700x700 with 1 Axes>"
       ]
      },
-     "execution_count": 12,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "new_pipelines.suite2p.run.generate(session, check_requirements = True)"
+    "new_pipelines.rois_df.mapping_labels.multisession.generate(sessions, check_requirements = True)"
    ]
   }
  ],
diff --git a/developements/wm36.ipynb b/developements/wm36.ipynb
index 01f04bd147f4084ddfd8bae7b7d538e6320824d1..608859c24bb72ed2b4c38c1b51b788b87711f80d 100644
--- a/developements/wm36.ipynb
+++ b/developements/wm36.ipynb
@@ -20,12 +20,664 @@
    "execution_count": 2,
    "id": "ea5a8260-d905-4ccd-ba02-a363b58306d4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading session details: 100%|███████████████████████████████████████████████████████████| 1/1 [00:04<00:00,  4.40s/it]\n"
+     ]
+    }
+   ],
    "source": [
     "connector = one.ONE(data_access_mode = \"remote\")\n",
-    "sessions = connector.search(subject = \"wm36\", date_range=[\"2023-09-13\",\"2023-11-02\"],procedures = \"Two photon Imaging\", qc = \"NOT_SET\", details = True, no_cache = True)\n",
+    "sessions = connector.search(subject = \"wm36\", date_range=[\"2023-09-13\",\"2023-11-02\"],procedures = \"Two photon Imaging\", qc = \"PASS\", details = True, no_cache = True)\n",
     "\n",
-    "multisession = one.api.MultiSessionPlaceholder(project = \"Adaptation\", analysis_group = \"wm36\", data_repository = \"Cajal2Adaptation\")"
+    "multisession = connector.multisession(project = \"Adaptation\", analysis_group = \"wm36\", data_repository = \"Cajal2Adaptation\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7eca258c-95b8-4f4c-8f82-25f0d100d8f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94;20mINFO       : gen.responsiveness_df.initial_calculation : `refresh` was set to True, ignoring the state of disk files and running the function.\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.responsiveness_df.initial_calculation : Running requirement trials_df.tdms_export\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.trials_df.tdms_export         : File exists for trials_df.tdms_export. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.responsiveness_df.initial_calculation : Running requirement suite2p.run\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.suite2p.run                   : File exists for suite2p.run.0. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.responsiveness_df.initial_calculation : Running requirement suite2p.treat2p\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.suite2p.treat2p               : File exists for suite2p.treat2p.0. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.responsiveness_df.initial_calculation : Running requirement rois_df.suite2p_export\u001b[0m\n",
+      "\u001b[33;20mWARNING    : pickle.check_disk                 : A single partial match was found for rois_df.suite2p_export. Please make sure it is consistant with expected behaviour. Expected : {'step_name': 'suite2p_export', 'version': 'a3ff5b9'}, Found : {'step_name': 'responsiveness', 'version': 'a3ff5b9'} (logging.py:42) - 2023-12-13 17:12:46,612\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.rois_df.suite2p_export        : File exists for rois_df.suite2p_export. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.responsiveness_df.initial_calculation : Running requirement neuropil_mask.maps_calculation\u001b[0m\n",
+      "\u001b[33;20mWARNING    : pickle.check_disk                 : A single partial match was found for neuropil_mask.maps_calculation.B1. Please make sure it is consistant with expected behaviour. Expected : {'step_name': 'maps_calculation', 'version': 'fbb7d54'}, Found : {'step_name': 'refinement', 'version': 'fbb7d54'} (logging.py:42) - 2023-12-13 17:12:46,625\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : File exists for neuropil_mask.maps_calculation.B1. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[33;20mWARNING    : pickle.check_disk                 : A single partial match was found for neuropil_mask.maps_calculation.Beta. Please make sure it is consistant with expected behaviour. Expected : {'step_name': 'maps_calculation', 'version': 'fbb7d54'}, Found : {'step_name': 'refinement', 'version': 'fbb7d54'} (logging.py:42) - 2023-12-13 17:12:46,633\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.maps_calculation : File exists for neuropil_mask.maps_calculation.Beta. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.responsiveness_df.initial_calculation : Running requirement neuropil_mask.refinement\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.refinement      : File exists for neuropil_mask.refinement.B1. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.refinement      : File exists for neuropil_mask.refinement.Beta. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.responsiveness_df.initial_calculation : Running requirement neuropil_mask.separation\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.neuropil_mask.separation      : File exists for neuropil_mask.separation.all. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.responsiveness_df.initial_calculation : Running requirement rois_df.mapping_labels\u001b[0m\n",
+      "\u001b[33;20mWARNING    : pickle.check_disk                 : A single partial match was found for rois_df.mapping_labels. Please make sure it is consistant with expected behaviour. Expected : {'step_name': 'mapping_labels', 'version': 'a3ff5b9'}, Found : {'step_name': 'responsiveness', 'version': 'a3ff5b9'} (logging.py:42) - 2023-12-13 17:12:46,663\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.rois_df.mapping_labels        : File exists for rois_df.mapping_labels. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.responsiveness_df.initial_calculation : Running requirement timelines_dict.tiff_export\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.timelines_dict.tiff_export    : File exists for timelines_dict.tiff_export. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.responsiveness_df.initial_calculation : Running requirement trials_roi_df.initial_merge\u001b[0m\n",
+      "\u001b[33;20mWARNING    : pickle.check_disk                 : A single partial match was found for trials_roi_df.initial_merge. Please make sure it is consistant with expected behaviour. Expected : {'step_name': 'initial_merge', 'version': '8638c39'}, Found : {'step_name': 'features', 'version': '8638c39'} (logging.py:42) - 2023-12-13 17:12:46,680\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.trials_roi_df.initial_merge   : File exists for trials_roi_df.initial_merge. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.responsiveness_df.initial_calculation : Running requirement trials_roi_df.sync\u001b[0m\n",
+      "\u001b[33;20mWARNING    : pickle.check_disk                 : A single partial match was found for trials_roi_df.sync. Please make sure it is consistant with expected behaviour. Expected : {'step_name': 'sync', 'version': '8638c39'}, Found : {'step_name': 'features', 'version': '8638c39'} (logging.py:42) - 2023-12-13 17:12:46,688\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.trials_roi_df.sync            : File exists for trials_roi_df.sync. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.responsiveness_df.initial_calculation : Running requirement trials_roi_df.features\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.trials_roi_df.features        : File exists for trials_roi_df.features. Loading and processing will be skipped\u001b[0m\n",
+      "\u001b[94;20mINFO       : gen.responsiveness_df.initial_calculation : Performing the computation to generate responsiveness_df.initial_calculation. Hold tight.\u001b[0m\n",
+      "Computing ROIs responsiveness: 100%|█████████████████████████████████████████████████| 112/112 [04:14<00:00,  2.28s/it]\n",
+      "\u001b[94;20mINFO       : gen.responsiveness_df.initial_calculation : Saving the generated responsiveness_df.initial_calculation output.\u001b[0m\n",
+      "\u001b[33;20mWARNING    : pickle.check_disk                 : A single partial match was found for trials_roi_df.sync. Please make sure it is consistant with expected behaviour. Expected : {'step_name': 'sync', 'version': '8638c39'}, Found : {'step_name': 'features', 'version': '8638c39'} (logging.py:42) - 2023-12-13 17:17:08,013\u001b[0m\n",
+      "\u001b[31;20mERROR      : gen.responsiveness_df.initial_calculation : The callback <function classifier_temporal_fluo_plot at 0x000001F4276A6A20> failed with error : 'corrected_accuracy' (logging.py:42) - 2023-12-13 17:17:14,530\u001b[0m\n"
+     ]
+    },
+    {
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+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>in_target_barrel</th>\n",
+       "      <th>nontarget_amplitude</th>\n",
+       "      <th>target_whisker</th>\n",
+       "      <th>method</th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_true_neg</th>\n",
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+       "      <th>roi</th>\n",
+       "      <th>session</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>roi#</th>\n",
+       "      <th>responsiveness_to</th>\n",
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+       "      <th rowspan=\"5\" valign=\"top\">0_wm36_2023-11-02_001</th>\n",
+       "      <th>onset</th>\n",
+       "      <td>False</td>\n",
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+       "      <td>0.502348</td>\n",
+       "      <td>0.501472</td>\n",
+       "      <td>0</td>\n",
+       "      <td>wm36_2023-11-02_001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <th rowspan=\"5\" valign=\"top\">111_wm36_2023-11-02_001</th>\n",
+       "      <th>amplitude_late</th>\n",
+       "      <td>True</td>\n",
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+       "      <td>wm36_2023-11-02_001</td>\n",
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+       "    <tr>\n",
+       "      <th>amplitude_late</th>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>fluorescence</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>&lt;all&gt;</td>\n",
+       "      <td>[0.022777303765361356, -0.008119865028178685, ...</td>\n",
+       "      <td>0.006997</td>\n",
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+       "      <td>111</td>\n",
+       "      <td>wm36_2023-11-02_001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>amplitude_late</th>\n",
+       "      <td>False</td>\n",
+       "      <td>10</td>\n",
+       "      <td>B1</td>\n",
+       "      <td>fluorescence</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>&lt;all&gt;</td>\n",
+       "      <td>[0.002576530954720213, 0.003780074252022647, -...</td>\n",
+       "      <td>0.009307</td>\n",
+       "      <td>[0.5035613084832826, 0.4995743989944458, 0.501...</td>\n",
+       "      <td>[0.5061378394380028, 0.5033544732464684, 0.501...</td>\n",
+       "      <td>0.489148</td>\n",
+       "      <td>0.498455</td>\n",
+       "      <td>111</td>\n",
+       "      <td>wm36_2023-11-02_001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>amplitude_late</th>\n",
+       "      <td>True</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Beta</td>\n",
+       "      <td>fluorescence</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>&lt;all&gt;</td>\n",
+       "      <td>[0.016496246075732413, 0.004642879185469129, 0...</td>\n",
+       "      <td>0.009632</td>\n",
+       "      <td>[0.5019831981187987, 0.5051954686641694, 0.495...</td>\n",
+       "      <td>[0.5184794441945311, 0.5098383478496386, 0.506...</td>\n",
+       "      <td>0.489005</td>\n",
+       "      <td>0.498638</td>\n",
+       "      <td>111</td>\n",
+       "      <td>wm36_2023-11-02_001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>amplitude_late</th>\n",
+       "      <td>True</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Beta</td>\n",
+       "      <td>fluorescence</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>&lt;all&gt;</td>\n",
+       "      <td>[-0.007294281050202911, -0.01048480052015055, ...</td>\n",
+       "      <td>-0.011556</td>\n",
+       "      <td>[0.5122882439030542, 0.5106968591610591, 0.511...</td>\n",
+       "      <td>[0.5049939628528513, 0.5002120586409086, 0.494...</td>\n",
+       "      <td>0.502907</td>\n",
+       "      <td>0.491351</td>\n",
+       "      <td>111</td>\n",
+       "      <td>wm36_2023-11-02_001</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6272 rows × 61 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           in_target_barrel  \\\n",
+       "roi#                    responsiveness_to                     \n",
+       "0_wm36_2023-11-02_001   onset                         False   \n",
+       "                        onset                         False   \n",
+       "                        onset                         False   \n",
+       "                        onset                         False   \n",
+       "                        onset                         False   \n",
+       "...                                                     ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late                 True   \n",
+       "                        amplitude_late                False   \n",
+       "                        amplitude_late                False   \n",
+       "                        amplitude_late                 True   \n",
+       "                        amplitude_late                 True   \n",
+       "\n",
+       "                                          nontarget_amplitude target_whisker  \\\n",
+       "roi#                    responsiveness_to                                      \n",
+       "0_wm36_2023-11-02_001   onset                               0             B1   \n",
+       "                        onset                               0           Beta   \n",
+       "                        onset                              10             B1   \n",
+       "                        onset                              10           Beta   \n",
+       "                        onset                               0             B1   \n",
+       "...                                                       ...            ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late                     10           Beta   \n",
+       "                        amplitude_late                      0             B1   \n",
+       "                        amplitude_late                     10             B1   \n",
+       "                        amplitude_late                      0           Beta   \n",
+       "                        amplitude_late                     10           Beta   \n",
+       "\n",
+       "                                                 method  \\\n",
+       "roi#                    responsiveness_to                 \n",
+       "0_wm36_2023-11-02_001   onset                classifier   \n",
+       "                        onset                classifier   \n",
+       "                        onset                classifier   \n",
+       "                        onset                classifier   \n",
+       "                        onset              fluorescence   \n",
+       "...                                                 ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late       classifier   \n",
+       "                        amplitude_late     fluorescence   \n",
+       "                        amplitude_late     fluorescence   \n",
+       "                        amplitude_late     fluorescence   \n",
+       "                        amplitude_late     fluorescence   \n",
+       "\n",
+       "                                                                                    fit_time  \\\n",
+       "roi#                    responsiveness_to                                                      \n",
+       "0_wm36_2023-11-02_001   onset              [0.0010006427764892578, 0.0, 0.001002311706542...   \n",
+       "                        onset              [0.0009999275207519531, 0.0009989738464355469,...   \n",
+       "                        onset              [0.0009992122650146484, 0.0, 0.001001358032226...   \n",
+       "                        onset              [0.0009996891021728516, 0.0, 0.001001596450805...   \n",
+       "                        onset                                                            NaN   \n",
+       "...                                                                                      ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late     [0.0009999275207519531, 0.0, 0.001001596450805...   \n",
+       "                        amplitude_late                                                   NaN   \n",
+       "                        amplitude_late                                                   NaN   \n",
+       "                        amplitude_late                                                   NaN   \n",
+       "                        amplitude_late                                                   NaN   \n",
+       "\n",
+       "                                                                                  score_time  \\\n",
+       "roi#                    responsiveness_to                                                      \n",
+       "0_wm36_2023-11-02_001   onset              [0.0030002593994140625, 0.003000020980834961, ...   \n",
+       "                        onset              [0.003001689910888672, 0.0029993057250976562, ...   \n",
+       "                        onset              [0.002999544143676758, 0.0029990673065185547, ...   \n",
+       "                        onset              [0.003000020980834961, 0.002997875213623047, 0...   \n",
+       "                        onset                                                            NaN   \n",
+       "...                                                                                      ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late     [0.003000020980834961, 0.0029985904693603516, ...   \n",
+       "                        amplitude_late                                                   NaN   \n",
+       "                        amplitude_late                                                   NaN   \n",
+       "                        amplitude_late                                                   NaN   \n",
+       "                        amplitude_late                                                   NaN   \n",
+       "\n",
+       "                                                test_true_neg  \\\n",
+       "roi#                    responsiveness_to                       \n",
+       "0_wm36_2023-11-02_001   onset                [13, 9, 5, 2, 6]   \n",
+       "                        onset              [10, 14, 10, 2, 0]   \n",
+       "                        onset              [14, 11, 13, 3, 0]   \n",
+       "                        onset               [13, 11, 3, 4, 7]   \n",
+       "                        onset                             NaN   \n",
+       "...                                                       ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late        [6, 6, 6, 6, 6]   \n",
+       "                        amplitude_late                    NaN   \n",
+       "                        amplitude_late                    NaN   \n",
+       "                        amplitude_late                    NaN   \n",
+       "                        amplitude_late                    NaN   \n",
+       "\n",
+       "                                              test_false_pos  \\\n",
+       "roi#                    responsiveness_to                      \n",
+       "0_wm36_2023-11-02_001   onset              [2, 6, 11, 14, 9]   \n",
+       "                        onset              [5, 0, 4, 13, 15]   \n",
+       "                        onset              [2, 4, 2, 13, 16]   \n",
+       "                        onset              [1, 3, 12, 11, 7]   \n",
+       "                        onset                            NaN   \n",
+       "...                                                      ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late       [0, 0, 0, 0, 0]   \n",
+       "                        amplitude_late                   NaN   \n",
+       "                        amplitude_late                   NaN   \n",
+       "                        amplitude_late                   NaN   \n",
+       "                        amplitude_late                   NaN   \n",
+       "\n",
+       "                                              test_false_neg  \\\n",
+       "roi#                    responsiveness_to                      \n",
+       "0_wm36_2023-11-02_001   onset              [16, 12, 8, 2, 9]   \n",
+       "                        onset              [8, 15, 12, 4, 1]   \n",
+       "                        onset              [14, 8, 13, 5, 1]   \n",
+       "                        onset              [15, 14, 3, 3, 9]   \n",
+       "                        onset                            NaN   \n",
+       "...                                                      ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late       [5, 5, 5, 4, 4]   \n",
+       "                        amplitude_late                   NaN   \n",
+       "                        amplitude_late                   NaN   \n",
+       "                        amplitude_late                   NaN   \n",
+       "                        amplitude_late                   NaN   \n",
+       "\n",
+       "                                               test_true_pos  ...  \\\n",
+       "roi#                    responsiveness_to                     ...   \n",
+       "0_wm36_2023-11-02_001   onset               [0, 4, 7, 13, 6]  ...   \n",
+       "                        onset              [7, 0, 3, 10, 13]  ...   \n",
+       "                        onset              [2, 8, 3, 10, 14]  ...   \n",
+       "                        onset              [0, 1, 11, 11, 5]  ...   \n",
+       "                        onset                            NaN  ...   \n",
+       "...                                                      ...  ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late       [0, 0, 0, 0, 0]  ...   \n",
+       "                        amplitude_late                   NaN  ...   \n",
+       "                        amplitude_late                   NaN  ...   \n",
+       "                        amplitude_late                   NaN  ...   \n",
+       "                        amplitude_late                   NaN  ...   \n",
+       "\n",
+       "                                                                                      params  \\\n",
+       "roi#                    responsiveness_to                                                      \n",
+       "0_wm36_2023-11-02_001   onset              {'C': 1.0, 'class_weight': None, 'dual': 'auto...   \n",
+       "                        onset              {'C': 1.0, 'class_weight': None, 'dual': 'auto...   \n",
+       "                        onset              {'C': 1.0, 'class_weight': None, 'dual': 'auto...   \n",
+       "                        onset              {'C': 1.0, 'class_weight': None, 'dual': 'auto...   \n",
+       "                        onset                                                            NaN   \n",
+       "...                                                                                      ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late     {'C': 1.0, 'class_weight': None, 'dual': 'auto...   \n",
+       "                        amplitude_late                                                   NaN   \n",
+       "                        amplitude_late                                                   NaN   \n",
+       "                        amplitude_late                                                   NaN   \n",
+       "                        amplitude_late                                                   NaN   \n",
+       "\n",
+       "                                          frequency_change  \\\n",
+       "roi#                    responsiveness_to                    \n",
+       "0_wm36_2023-11-02_001   onset                        <all>   \n",
+       "                        onset                        <all>   \n",
+       "                        onset                        <all>   \n",
+       "                        onset                        <all>   \n",
+       "                        onset                        <all>   \n",
+       "...                                                    ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late               <all>   \n",
+       "                        amplitude_late               <all>   \n",
+       "                        amplitude_late               <all>   \n",
+       "                        amplitude_late               <all>   \n",
+       "                        amplitude_late               <all>   \n",
+       "\n",
+       "                                                                                        diff  \\\n",
+       "roi#                    responsiveness_to                                                      \n",
+       "0_wm36_2023-11-02_001   onset                                                            NaN   \n",
+       "                        onset                                                            NaN   \n",
+       "                        onset                                                            NaN   \n",
+       "                        onset                                                            NaN   \n",
+       "                        onset              [-0.006021372941665271, -0.0012420212680643306...   \n",
+       "...                                                                                      ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late                                                   NaN   \n",
+       "                        amplitude_late     [0.022777303765361356, -0.008119865028178685, ...   \n",
+       "                        amplitude_late     [0.002576530954720213, 0.003780074252022647, -...   \n",
+       "                        amplitude_late     [0.016496246075732413, 0.004642879185469129, 0...   \n",
+       "                        amplitude_late     [-0.007294281050202911, -0.01048480052015055, ...   \n",
+       "\n",
+       "                                          grand_diff  \\\n",
+       "roi#                    responsiveness_to              \n",
+       "0_wm36_2023-11-02_001   onset                    NaN   \n",
+       "                        onset                    NaN   \n",
+       "                        onset                    NaN   \n",
+       "                        onset                    NaN   \n",
+       "                        onset              -0.000877   \n",
+       "...                                              ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late           NaN   \n",
+       "                        amplitude_late      0.006997   \n",
+       "                        amplitude_late      0.009307   \n",
+       "                        amplitude_late      0.009632   \n",
+       "                        amplitude_late     -0.011556   \n",
+       "\n",
+       "                                                                                      mean_0  \\\n",
+       "roi#                    responsiveness_to                                                      \n",
+       "0_wm36_2023-11-02_001   onset                                                            NaN   \n",
+       "                        onset                                                            NaN   \n",
+       "                        onset                                                            NaN   \n",
+       "                        onset                                                            NaN   \n",
+       "                        onset              [0.5029076509403461, 0.5011911972776636, 0.501...   \n",
+       "...                                                                                      ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late                                                   NaN   \n",
+       "                        amplitude_late     [0.48424833710642845, 0.5056125525979029, 0.49...   \n",
+       "                        amplitude_late     [0.5035613084832826, 0.4995743989944458, 0.501...   \n",
+       "                        amplitude_late     [0.5019831981187987, 0.5051954686641694, 0.495...   \n",
+       "                        amplitude_late     [0.5122882439030542, 0.5106968591610591, 0.511...   \n",
+       "\n",
+       "                                                                                      mean_1  \\\n",
+       "roi#                    responsiveness_to                                                      \n",
+       "0_wm36_2023-11-02_001   onset                                                            NaN   \n",
+       "                        onset                                                            NaN   \n",
+       "                        onset                                                            NaN   \n",
+       "                        onset                                                            NaN   \n",
+       "                        onset              [0.4968862779986808, 0.49994917600959926, 0.50...   \n",
+       "...                                                                                      ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late                                                   NaN   \n",
+       "                        amplitude_late     [0.5070256408717898, 0.49749268756972426, 0.49...   \n",
+       "                        amplitude_late     [0.5061378394380028, 0.5033544732464684, 0.501...   \n",
+       "                        amplitude_late     [0.5184794441945311, 0.5098383478496386, 0.506...   \n",
+       "                        amplitude_late     [0.5049939628528513, 0.5002120586409086, 0.494...   \n",
+       "\n",
+       "                                          grand_mean_0 grand_mean_1  roi  \\\n",
+       "roi#                    responsiveness_to                                  \n",
+       "0_wm36_2023-11-02_001   onset                      NaN          NaN    0   \n",
+       "                        onset                      NaN          NaN    0   \n",
+       "                        onset                      NaN          NaN    0   \n",
+       "                        onset                      NaN          NaN    0   \n",
+       "                        onset                 0.502348     0.501472    0   \n",
+       "...                                                ...          ...  ...   \n",
+       "111_wm36_2023-11-02_001 amplitude_late             NaN          NaN  111   \n",
+       "                        amplitude_late        0.492645     0.499642  111   \n",
+       "                        amplitude_late        0.489148     0.498455  111   \n",
+       "                        amplitude_late        0.489005     0.498638  111   \n",
+       "                        amplitude_late        0.502907     0.491351  111   \n",
+       "\n",
+       "                                                       session  \n",
+       "roi#                    responsiveness_to                       \n",
+       "0_wm36_2023-11-02_001   onset              wm36_2023-11-02_001  \n",
+       "                        onset              wm36_2023-11-02_001  \n",
+       "                        onset              wm36_2023-11-02_001  \n",
+       "                        onset              wm36_2023-11-02_001  \n",
+       "                        onset              wm36_2023-11-02_001  \n",
+       "...                                                        ...  \n",
+       "111_wm36_2023-11-02_001 amplitude_late     wm36_2023-11-02_001  \n",
+       "                        amplitude_late     wm36_2023-11-02_001  \n",
+       "                        amplitude_late     wm36_2023-11-02_001  \n",
+       "                        amplitude_late     wm36_2023-11-02_001  \n",
+       "                        amplitude_late     wm36_2023-11-02_001  \n",
+       "\n",
+       "[6272 rows x 61 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_pipelines.responsiveness_df.initial_calculation.multisession.generate(sessions, refresh = True, check_requirements = True, n_jobs = 6)"
    ]
   },
   {