DictBased-Type2_new_mean-all_mice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2caca142",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "import json\n",
    "\n",
    "from utils import create_dict, create_list, get_data, find_type2_locations, find_type3_locations, find_type4_locations\n",
    "from utils import applyArtifacts, removeCells, avg_firing, NumpyEncoder, avg_amplitude, nrActiveCells, div_avg_firing, div_avg_amplitude, top_least\n",
    "from utils import cellID, spindle_locs, search_spindle, spi_inOut_diff, zscore_calculator\n",
    "from Analyses_ import slo_spi\n",
    "%config Completer.use_jedi = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5238a7d3",
   "metadata": {},
   "source": [
    "### Input informations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "42bd0411",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "all_address =['L:/analysis_nima/250520_m18a/Batch_4', 'L:/analysis_nima/290520_m17a/Batch/extracells/Batch_2',\n",
    "                'L:/analysis_nima/110620_m18b/new/Batch_3', 'L:/analysis_nima/050820_m21a/Batch_4',\n",
    "                'L:/analysis_nima/030720_m20a/Batch_3', 'L:/analysis_nima/291020_m23a/Batch_2', \n",
    "               'L:/analysis_nima/210121_m24a/Batch_1']\n",
    "\n",
    "# sampling rate for different mice\n",
    "sfs = [30.995, 30.995, 30.955, 30.995, 30.995, 30.9576, 30.995]\n",
    "\n",
    "# defining artifacts\n",
    "custom_artifacts_1 = [[11007,11008,4]]\n",
    "custom_artifacts_2 = [[11630,11947,0],[12000,12700,0],[4533,5154,14]]\n",
    "custom_artifacts_3 = []\n",
    "custom_artifacts_4 = [[1595,1845,0],[4609,4907,0],[7740,7983,0],[13795,14101,0],[16706,16973,0],\n",
    "             [7266,7501,1],[11912,12219,1],[12703,13203,1],[13730,13909,1],[15097,15559,1],[17277,17519,1],\n",
    "             [1416,1793,2],[3816,4123,2],[13254,13593,2],[15224,15703,2],[17668,18067,2],\n",
    "             [7358,7521,3],[10240,10253,3],[13672,13748,3],[17306,17593,3],\n",
    "             [5505,5939,4],[6222,6231,4],[16760,16820,4],[18450,18573,4],                           \n",
    "             [1132,1357,7],[8653,8776,7],[9946,10391,7],\n",
    "             [9068,9323,7],[13589,13623,7],[17394,17547,7],\n",
    "             [1,140,8],[908,965,8],[7638,7665,8],[8014,8027,8],[14726,14922,8],[18022,18187,8],\n",
    "             [1912,2381,9],[2920,2923,9],[7767,7819,9],[7864,7997,9],[9876,10197,9],[15206,15545,9],\n",
    "             [6734,7220,10],[9668,9939,10],\n",
    "             [8914,9290,11],[9562,9571,11],[12216,12339,11],\n",
    "             [11492,11553,12],[13898,13919,12],\n",
    "             [6298,6619,13],[12282,12795,13],[14406,14507,13],[14798,14875,13],\n",
    "             [1,220,14],[1878,2108,14],[9872,9961,14],\n",
    "             [4764,4813,15],[5564,5571,15],[15419,15595,15],[16003,16089,15],[16420,16482,15],\n",
    "             [4240,4403,16],[9975,10077,16],[10274,10275,16],[13228,13451,16],[13805,13939,16],[14620,14664,16],[15764,15857,16],[18430,18537,16],\n",
    "             [10425,10591,17],[11058,11709,17],[12232,12311,17],[12784,12804,17],[17000,17115,17],\n",
    "             [10655,10825,18],[11993,12075,18],[12648,12685,18],[16013,16134,18],\n",
    "             [14466,15015,19],[16876,17009,19],[18398,18573,19],\n",
    "            [628,958,23],[7822,7976,23],[10298,10435,23],[13899,14241,23],[14466,14537,23],[15773,15848,23],[17664,17925,23],[18291,18426,23],\n",
    "            [4309,4512,24],[5271,5467,24],[6450,6700,24],[7186,7593,24],\n",
    "            [460,700,25],[1714,1725,25],[4045,4153,25],[13600,13699,25],\n",
    "            [4434,4568,26],[5984,6167,26],\n",
    "            [1770,1831,27],[4060,4064,27],\n",
    "            [2000,2193,28],[11098,11334,28],\n",
    "            [5921,5997,30],[8200,8210,30],[9189,9191,30],\n",
    "            [686,852,31],[13375,13543,31],[14059,14334,31],[15808,15883,31],\n",
    "            [10760,10800,33],[13942,14061,33],[17672,17738,33],\n",
    "            [10576,10604,41]]\n",
    "custom_artifacts_5 = [[3928,3971,0],[4584,4656,0],[7001,7119,0],[14144,14230,0],[14466,14501,0],[15591,15593,0],\n",
    "            [559,603,1],[2187,2214,1],[3999,4054,1],[11749,11790,1],[17785,17839,1],\n",
    "            [3403,3491,2],[11936,11980,2],\n",
    "            [1397,1465,3],[13881,13889,3],[14510,14526,3],[18377,18441,3],                                                                                           \n",
    "            [2585,2635,4], [6722,6771,4],[10867,10897,4],[16543,16615,4],\n",
    "            [740,825,5],[2026,2113,5],[13025,13051,5],\n",
    "            [798,863,6],[1795,1857,6],[3949,3980,6],[5190,5244,6],[10480,10547,6],[11219,11301,6],[12144,12191,6],[12690,12747,6],\n",
    "            [1343,1441,7],[2618,2684,7],[2709,2713,7],[3427,3496,7],[4138,4185,7],[4532,4567,7],[5694,8768,7],[9513,9562,7],[12510,12644,7],[15000,15073,7],\n",
    "            [96,140,8],[4175,4258,8],\n",
    "            [409,564,9],[13291,13497,9],[16520,18573,9],\n",
    "            [9968,10026,10],[14269,14335,10],[16569,16616,10],\n",
    "            [404,557,11],[12553,12649,11],\n",
    "            [503,598,13],[2832,2889,13],[13151,13218,13],[18276,18343,13],\n",
    "            [769,954,14],[980,1001,14],[2324,2389,14],[4286,4297,14],\n",
    "            [3349,3447,15],[6128,6221,15],\n",
    "            [8475,8543,16],\n",
    "            [9792,9840,17],\n",
    "            [2984,3007,20],[4131,4183,20],[5025,5042,20],[6330,6355,20],[11818,11893,20],\n",
    "            [6760,6875,21],\n",
    "            [12871,12895,28],[13645,13667,28],[14232,14319,28],[14837,14849,28],[15521,15542,28],[16421,16445,28],[17032,17040,28]]\n",
    "custom_artifacts_6 = [[8863,9191,4],\n",
    "           [5389,5393,7],[5575,5607,7],[5754,5755,7],[5798,6000,7],\n",
    "            [11050,11070,10],[12260,12280,10],\n",
    "            [15125,15290,12],[16420,18160,10],\n",
    "            [13280,13340,13],\n",
    "            [8,23,15],[92,94,15],[481,505,15],\n",
    "            [10035,10500,20],[11024,11044,20],\n",
    "            [5794,5800,21],\n",
    "            [3501,3507,22]]\n",
    "custom_artifacts_7 = [[0,3418,0],[13116,13507,0],[14502,14872,0],\n",
    "              [8000,9000,2],\n",
    "              [1828,2638,3],[8631,12630,3],\n",
    "              [1497,1988,4],[16470,17270,4],\n",
    "              [5113,8239,5],                                                                                         \n",
    "              [2322,2978,6],\n",
    "              [12460,14890,7],\n",
    "              [15600,16060,10],\n",
    "              [4231,4848,11],[6434,6909,11],[9110,11550,11],\n",
    "              [11890,12380,12],\n",
    "              [8410,8820,13],\n",
    "              [3026,3515,14],[13810,14170,14],\n",
    "              [10910,12010,15]]  \n",
    "artifacts = [custom_artifacts_1, custom_artifacts_2, custom_artifacts_3,\n",
    "                custom_artifacts_4, custom_artifacts_5, custom_artifacts_6, custom_artifacts_7]\n",
    "\n",
    "# cell removal \n",
    "remove_cell_1 = []\n",
    "remove_cell_2 = []\n",
    "remove_cell_3 = [62,63,64,65,66,67,68,69,70,80,81,160,161,162,163,164,167,194,195,202,203,204]\n",
    "remove_cell_4 = [13,53,55,57,79,84,85,86,95,99,112,141,144,147,153,177,178,183,184,188,189,191,196,197,198,199,202,219,226,234,235]\n",
    "remove_cell_5 = []\n",
    "remove_cell_6 = [66,84,85,86]\n",
    "remove_cell_7 = []\n",
    "remove_cells = [remove_cell_1, remove_cell_2, remove_cell_3,\n",
    "              remove_cell_4, remove_cell_5, remove_cell_6, remove_cell_7]\n",
    "# mouse name\n",
    "miceName = ['250520_m18a', '290520_m17a', '110620_m18b', '050820_m21a', '030720_m20a', '291020_m23a', '210121_m24a']\n",
    "\n",
    "\n",
    "# saving figure \n",
    "save_fig = True # or False\n",
    "fig_save_add = ''\n",
    "\n",
    "\n",
    "# saving dataframes\n",
    "save_file = True # or False\n",
    "file_save_add = ''"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea41e89f",
   "metadata": {},
   "source": [
    "### Creating base data structure and loading data and hypno to it\n",
    "####    If ncessary first apply artifact on data or remove cells then create data structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "27111cad",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your entered path is L:/analysis_nima/250520_m18a/Batch_4\n",
      "All files in the given path \n",
      "\n",
      "['unit01.mat', 'unit02.mat', 'unit03.mat', 'unit04.mat', 'unit05.mat', 'unit06.mat', 'unit07.mat', 'unit08.mat', 'unit09.mat', 'unit10.mat', 'unit11.mat', 'unit12.mat', 'unit13.mat', 'unit14.mat', 'unit15.mat', 'unit16.mat', 'unit17.mat', 'unit18.mat', 'unit19.mat', 'unit20.mat', 'unit21.mat', 'unit22.mat', 'unit23.mat', 'unit24.mat', 'unit25.mat', 'unit26.mat', 'unit27.mat', 'unit28.mat', 'unit29.mat', 'unit30.mat', 'unit31.mat', 'unit32.mat', 'unit33.mat', 'unit34.mat', 'unit35.mat', 'unit36.mat', 'unit37.mat', 'unit38.mat', 'unit39.mat', 'unit40.mat', 'unit41.mat', 'unit42.mat', 'unit43.mat', 'unit44.mat', 'unit45.mat']  Number of all files 45\n",
      "Data are loaded \n",
      "\n",
      "Start reading units \n",
      "\n",
      "unit length is 600 sec, data length is 18597 samples, number of cells are 236 \n",
      "\n",
      "Finished reading units \n",
      "\n",
      "Start reading hypno files \n",
      "\n",
      "Finished reading hypno \n",
      "\n",
      "Location-Time information\n",
      "data and hypno shapes in file L:/analysis_nima/250520_m18a/Batch_4 are: (18597, 236, 45), (18597, 45)\n",
      "Your entered path is L:/analysis_nima/290520_m17a/Batch/extracells/Batch_2\n",
      "All files in the given path \n",
      "\n",
      "['unit01.mat', 'unit02.mat', 'unit03.mat', 'unit04.mat', 'unit05.mat', 'unit06.mat', 'unit07.mat', 'unit08.mat', 'unit09.mat', 'unit10.mat', 'unit11.mat', 'unit12.mat', 'unit13.mat', 'unit14.mat', 'unit15.mat', 'unit16.mat', 'unit17.mat', 'unit18.mat', 'unit19.mat', 'unit20.mat', 'unit21.mat', 'unit22.mat', 'unit23.mat', 'unit24.mat', 'unit25.mat', 'unit26.mat', 'unit27.mat', 'unit28.mat', 'unit29.mat', 'unit30.mat', 'unit31.mat']  Number of all files 31\n",
      "Data are loaded \n",
      "\n",
      "Start reading units \n",
      "\n",
      "unit length is 600 sec, data length is 18597 samples, number of cells are 294 \n",
      "\n",
      "Finished reading units \n",
      "\n",
      "Start reading hypno files \n",
      "\n",
      "Finished reading hypno \n",
      "\n",
      "Location-Time information\n",
      "data and hypno shapes in file L:/analysis_nima/290520_m17a/Batch/extracells/Batch_2 are: (18597, 294, 31), (18597, 31)\n",
      "Your entered path is L:/analysis_nima/110620_m18b/new/Batch_3\n",
      "All files in the given path \n",
      "\n",
      "['unit01.mat', 'unit02.mat', 'unit03.mat', 'unit04.mat', 'unit05.mat', 'unit06.mat', 'unit07.mat', 'unit08.mat', 'unit09.mat', 'unit10.mat', 'unit11.mat', 'unit12.mat', 'unit13.mat', 'unit14.mat', 'unit15.mat', 'unit16.mat', 'unit17.mat', 'unit18.mat', 'unit19.mat', 'unit20.mat', 'unit21.mat', 'unit22.mat', 'unit23.mat', 'unit24.mat', 'unit25.mat', 'unit26.mat']  Number of all files 26\n",
      "Data are loaded \n",
      "\n",
      "Start reading units \n",
      "\n",
      "unit length is 600 sec, data length is 18573 samples, number of cells are 218 \n",
      "\n",
      "Finished reading units \n",
      "\n",
      "Start reading hypno files \n",
      "\n",
      "Finished reading hypno \n",
      "\n",
      "Location-Time information\n",
      "data and hypno shapes in file L:/analysis_nima/110620_m18b/new/Batch_3 are: (18573, 218, 26), (18573, 26)\n",
      "Your entered path is L:/analysis_nima/050820_m21a/Batch_4\n",
      "All files in the given path \n",
      "\n",
      "['unit01.mat', 'unit02.mat', 'unit03.mat', 'unit04.mat', 'unit05.mat', 'unit06.mat', 'unit07.mat', 'unit08.mat', 'unit09.mat', 'unit10.mat', 'unit11.mat', 'unit12.mat', 'unit13.mat', 'unit14.mat', 'unit15.mat', 'unit16.mat', 'unit17.mat', 'unit18.mat', 'unit19.mat', 'unit20.mat', 'unit21.mat', 'unit22.mat', 'unit23.mat', 'unit24.mat', 'unit25.mat', 'unit26.mat', 'unit27.mat', 'unit28.mat', 'unit29.mat', 'unit30.mat', 'unit31.mat', 'unit32.mat', 'unit33.mat', 'unit34.mat', 'unit35.mat', 'unit36.mat', 'unit37.mat', 'unit38.mat', 'unit39.mat', 'unit40.mat', 'unit41.mat', 'unit42.mat', 'unit43.mat', 'unit44.mat', 'unit45.mat', 'unit46.mat']  Number of all files 46\n",
      "Data are loaded \n",
      "\n",
      "Start reading units \n",
      "\n",
      "unit length is 600 sec, data length is 18597 samples, number of cells are 236 \n",
      "\n",
      "Finished reading units \n",
      "\n",
      "Start reading hypno files \n",
      "\n",
      "Finished reading hypno \n",
      "\n",
      "Location-Time information\n",
      "data and hypno shapes in file L:/analysis_nima/050820_m21a/Batch_4 are: (18597, 236, 46), (18597, 46)\n",
      "Your entered path is L:/analysis_nima/030720_m20a/Batch_3\n",
      "All files in the given path \n",
      "\n",
      "['unit01.mat', 'unit02.mat', 'unit03.mat', 'unit04.mat', 'unit05.mat', 'unit06.mat', 'unit07.mat', 'unit08.mat', 'unit09.mat', 'unit10.mat', 'unit11.mat', 'unit12.mat', 'unit13.mat', 'unit14.mat', 'unit15.mat', 'unit16.mat', 'unit17.mat', 'unit18.mat', 'unit19.mat', 'unit20.mat', 'unit21.mat', 'unit22.mat', 'unit23.mat', 'unit24.mat', 'unit25.mat', 'unit26.mat', 'unit27.mat', 'unit28.mat', 'unit29.mat']  Number of all files 29\n",
      "Data are loaded \n",
      "\n",
      "Start reading units \n",
      "\n",
      "unit length is 600 sec, data length is 18597 samples, number of cells are 185 \n",
      "\n",
      "Finished reading units \n",
      "\n",
      "Start reading hypno files \n",
      "\n",
      "Finished reading hypno \n",
      "\n",
      "Location-Time information\n",
      "data and hypno shapes in file L:/analysis_nima/030720_m20a/Batch_3 are: (18597, 185, 29), (18597, 29)\n",
      "Your entered path is L:/analysis_nima/291020_m23a/Batch_2\n",
      "All files in the given path \n",
      "\n",
      "['unit01.mat', 'unit02.mat', 'unit03.mat', 'unit04.mat', 'unit05.mat', 'unit06.mat', 'unit07.mat', 'unit08.mat', 'unit09.mat', 'unit10.mat', 'unit11.mat', 'unit12.mat', 'unit13.mat', 'unit14.mat', 'unit15.mat', 'unit16.mat', 'unit17.mat', 'unit18.mat', 'unit19.mat', 'unit20.mat', 'unit21.mat', 'unit22.mat', 'unit23.mat']  Number of all files 23\n",
      "Data are loaded \n",
      "\n",
      "Start reading units \n",
      "\n",
      "unit length is 600 sec, data length is 18574 samples, number of cells are 87 \n",
      "\n",
      "Finished reading units \n",
      "\n",
      "Start reading hypno files \n",
      "\n",
      "Finished reading hypno \n",
      "\n",
      "Location-Time information\n",
      "data and hypno shapes in file L:/analysis_nima/291020_m23a/Batch_2 are: (18574, 87, 23), (18574, 23)\n",
      "Your entered path is L:/analysis_nima/210121_m24a/Batch_1\n",
      "All files in the given path \n",
      "\n",
      "['unit01.mat', 'unit02.mat', 'unit03.mat', 'unit04.mat', 'unit05.mat', 'unit06.mat', 'unit07.mat', 'unit08.mat', 'unit09.mat', 'unit10.mat', 'unit11.mat', 'unit12.mat', 'unit13.mat', 'unit14.mat', 'unit15.mat', 'unit16.mat', 'unit17.mat', 'unit18.mat', 'unit19.mat', 'unit20.mat', 'unit21.mat', 'unit22.mat', 'unit23.mat', 'unit24.mat', 'unit25.mat', 'unit26.mat', 'unit27.mat']  Number of all files 27\n",
      "Data are loaded \n",
      "\n",
      "Start reading units \n",
      "\n",
      "unit length is 600 sec, data length is 18597 samples, number of cells are 143 \n",
      "\n",
      "Finished reading units \n",
      "\n",
      "Start reading hypno files \n",
      "\n",
      "Finished reading hypno \n",
      "\n",
      "Location-Time information\n",
      "data and hypno shapes in file L:/analysis_nima/210121_m24a/Batch_1 are: (18597, 143, 27), (18597, 27)\n"
     ]
    }
   ],
   "source": [
    "apply_artifact = True\n",
    "rmCell = True\n",
    "\n",
    "data_list = []\n",
    "for address, sf, mouse_name, artifact, remove_cell in zip(all_address, sfs, miceName, artifacts, remove_cells):\n",
    "    \n",
    "    #clear and read data\n",
    "    data = []; hypno = []\n",
    "    data, hypno = get_data(address = address, sf = sf)\n",
    "    \n",
    "    if apply_artifact:\n",
    "        data = applyArtifacts(data=data, artifacts=artifact)\n",
    "    \n",
    "    if rmCell:\n",
    "        data = removeCells(data=data, cells = remove_cell)\n",
    "    \n",
    "    # creat per mouse list\n",
    "    data_list.extend(create_list(data = data, hypno = hypno, mouseName = mouse_name, \n",
    "                                 sf = sf, apply_artifact=apply_artifact, removeCell=rmCell))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8e37c9e",
   "metadata": {},
   "source": [
    "### Adding analyses as dictionary key, value pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eaf39b2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add average firing rate to the dictionary\n",
    "[data_dict.update({'avg_firing_perSecond':avg_firing(data = data_dict['data'], \n",
    "                                                            sf=data_dict['samplingRate'])}) for data_dict in data_list];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91b92a6e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Sleep_Pipeline_Notebooks\\Analysis_all_mice\\SleepAnalyses\\utils.py:229: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(np.where(data>0 , data, np.nan), axis = 0)\n"
     ]
    }
   ],
   "source": [
    "# add average amplitude to the dictionary\n",
    "[data_dict.update({'avg_amplitude':avg_amplitude(data = data_dict['data'])}) for data_dict in data_list];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "47cb349f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                    | 0/7 [00:00<?, ?it/s]C:\\Users\\admin\\Sleep_Pipeline_Notebooks\\Analysis_all_mice\\SleepAnalyses\\utils.py:412: RuntimeWarning: Mean of empty slice\n",
      "  mouse_zscore_info.append({\"mouseName\":mouse, \"nanmean\": np.nanmean(np.stack(mouse_avg), axis = 0), \"nanstd\": np.nanstd(np.stack(mouse_avg), axis = 0)})\n",
      "C:\\Users\\admin\\Anaconda3\\envs\\sleep_project\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1879: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
      "  var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "100%|███████████████████████████████████████████| 7/7 [00:00<00:00, 199.99it/s]\n",
      "  0%|                                                 | 0/2018 [00:00<?, ?it/s]C:\\Users\\admin\\Sleep_Pipeline_Notebooks\\Analysis_all_mice\\SleepAnalyses\\utils.py:419: RuntimeWarning: invalid value encountered in true_divide\n",
      "  dict.update({\"avg_firing_perSecond_zscore\": (dict[\"avg_firing_perSecond\"] - info[\"nanmean\"]) / info[\"nanstd\"]})\n",
      "100%|██████████████████████████████████| 2018/2018 [00:00<00:00, 168158.81it/s]\n",
      "  0%|                                                    | 0/7 [00:00<?, ?it/s]C:\\Users\\admin\\Sleep_Pipeline_Notebooks\\Analysis_all_mice\\SleepAnalyses\\utils.py:450: RuntimeWarning: Mean of empty slice\n",
      "  mouse_zscore_info.append({\"mouseName\":mouse, \"nanmean\": np.nanmean(np.stack(mouse_avg), axis = 0), \"nanstd\": np.nanstd(np.stack(mouse_avg), axis = 0)})\n",
      "100%|███████████████████████████████████████████| 7/7 [00:00<00:00, 304.33it/s]\n",
      "  0%|                                                 | 0/2018 [00:00<?, ?it/s]C:\\Users\\admin\\Sleep_Pipeline_Notebooks\\Analysis_all_mice\\SleepAnalyses\\utils.py:457: RuntimeWarning: invalid value encountered in true_divide\n",
      "  dict.update({\"avg_amplitude_zscore\": (dict[\"avg_amplitude\"] - info[\"nanmean\"]) / info[\"nanstd\"]})\n",
      "100%|██████████████████████████████████| 2018/2018 [00:00<00:00, 168155.47it/s]\n"
     ]
    }
   ],
   "source": [
    "# zscore normalization of avg firing rate. Normalization is applied per cell for each individual mouse separately\n",
    "data_list = zscore_calculator(dataBase = data_list, mouse_name_list=miceName, variable = \"frequency\")\n",
    "\n",
    "# zscore normalization of avg amplitude. Normalization is applied per cell for each individual mouse separately\n",
    "data_list = zscore_calculator(dataBase = data_list, mouse_name_list=miceName, variable = \"amplitude\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "121950d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add top and least firing rate \n",
    "fraction = 20\n",
    "\n",
    "#top\n",
    "[data_dict.update({'top' + str(fraction) + '_avg_firing':top_least(data=data_dict['avg_firing_perSecond'], \n",
    "                                                                   prc = fraction, top = True)}) for data_dict in data_list];\n",
    "\n",
    "#least\n",
    "[data_dict.update({'least' + str(fraction) + '_avg_firing':top_least(data=data_dict['avg_firing_perSecond'], \n",
    "                                                                   prc = fraction, top = False)}) for data_dict in data_list];\n",
    "\n",
    "\n",
    "#top-zscore\n",
    "[data_dict.update({'top' + str(fraction) + '_avg_firing_zscore':top_least(data=data_dict['avg_firing_perSecond_zscore'], \n",
    "                                                                   prc = fraction, top = True)}) for data_dict in data_list];\n",
    "\n",
    "#least-zscore\n",
    "[data_dict.update({'least' + str(fraction) + '_avg_firing_zscore':top_least(data=data_dict['avg_firing_perSecond_zscore'], \n",
    "                                                                   prc = fraction, top = False)}) for data_dict in data_list];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e5f5788c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add top and least amplitude \n",
    "fraction = 20\n",
    "\n",
    "#top\n",
    "[data_dict.update({'top' + str(fraction) + '_amplitude':top_least(data=data_dict['avg_amplitude'], \n",
    "                                                                   prc = fraction, top = True)}) for data_dict in data_list];\n",
    "\n",
    "#least\n",
    "[data_dict.update({'least' + str(fraction) + '_amplitude':top_least(data=data_dict['avg_amplitude'], \n",
    "                                                                   prc = fraction, top = False)}) for data_dict in data_list];\n",
    "\n",
    "\n",
    "#top-zscore\n",
    "[data_dict.update({'top' + str(fraction) + '_amplitude_zscore':top_least(data=data_dict['avg_amplitude_zscore'], \n",
    "                                                                   prc = fraction, top = True)}) for data_dict in data_list];\n",
    "\n",
    "#least-zscore\n",
    "[data_dict.update({'least' + str(fraction) + '_amplitude_zscore':top_least(data=data_dict['avg_amplitude_zscore'], \n",
    "                                                                   prc = fraction, top = False)}) for data_dict in data_list];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3795e1b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                    | 0/7 [00:00<?, ?it/s]C:\\Users\\admin\\Sleep_Pipeline_Notebooks\\Analysis_all_mice\\SleepAnalyses\\utils.py:431: RuntimeWarning: Mean of empty slice\n",
      "  mouse_zscore_info.append({\"mouseName\":mouse, \"nanmean\": np.nanmean(np.vstack(mouse_avg), axis = 0), \"nanstd\": np.nanstd(np.vstack(mouse_avg), axis = 0)})\n",
      "100%|███████████████████████████████████████████| 7/7 [00:00<00:00, 102.94it/s]\n",
      "  0%|                                                 | 0/2018 [00:00<?, ?it/s]C:\\Users\\admin\\Sleep_Pipeline_Notebooks\\Analysis_all_mice\\SleepAnalyses\\utils.py:438: RuntimeWarning: invalid value encountered in true_divide\n",
      "  dict.update({\"div_avg_firing_perSecond_zscore\": [(el - info[\"nanmean\"]) / info[\"nanstd\"] for el in dict[\"div_avg_firing_perSecond\"]]})\n",
      "100%|███████████████████████████████████| 2018/2018 [00:00<00:00, 74736.26it/s]\n"
     ]
    }
   ],
   "source": [
    "# add divided average firing rate to the dictionary\n",
    "[data_dict.update({'div_avg_firing_perSecond':div_avg_firing(data = data_dict['data'], \n",
    "                                                             sf=data_dict['samplingRate'], \n",
    "                                                             n = 3)}) for data_dict in data_list];\n",
    "\n",
    "# zscore normalization of avg firing rate. Normalization is applied per cell for each individual mouse separately\n",
    "data_list = zscore_calculator(dataBase = data_list, mouse_name_list=miceName, variable = \"frequency\", divided_data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "476af675",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                    | 0/7 [00:00<?, ?it/s]C:\\Users\\admin\\Sleep_Pipeline_Notebooks\\Analysis_all_mice\\SleepAnalyses\\utils.py:468: RuntimeWarning: Mean of empty slice\n",
      "  mouse_zscore_info.append({\"mouseName\":mouse, \"nanmean\": np.nanmean(np.vstack(mouse_avg), axis = 0), \"nanstd\": np.nanstd(np.vstack(mouse_avg), axis = 0)})\n",
      "100%|███████████████████████████████████████████| 7/7 [00:00<00:00, 106.05it/s]\n",
      "  0%|                                                 | 0/2018 [00:00<?, ?it/s]C:\\Users\\admin\\Sleep_Pipeline_Notebooks\\Analysis_all_mice\\SleepAnalyses\\utils.py:475: RuntimeWarning: invalid value encountered in true_divide\n",
      "  dict.update({\"div_avg_amplitude_zscore\": [(el - info[\"nanmean\"]) / info[\"nanstd\"] for el in dict[\"div_avg_amplitude\"]]})\n",
      "100%|███████████████████████████████████| 2018/2018 [00:00<00:00, 74736.26it/s]\n"
     ]
    }
   ],
   "source": [
    "# add divided average firing rate to the dictionary (n subdata)\n",
    "[data_dict.update({'div_avg_amplitude':div_avg_amplitude(data = data_dict['data'], n=3)}) for data_dict in data_list];\n",
    "\n",
    "# zscore normalization of avg amplitude. Normalization is applied per cell for each individual mouse separately\n",
    "data_list = zscore_calculator(dataBase = data_list, mouse_name_list=miceName, variable = \"amplitude\", divided_data= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ae5d4e50",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# add epoch duration in sample\n",
    "[data_dict.update({'epochDuration': data_dict['data'].shape[0]}) for data_dict in data_list];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d3d1971",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add nr of active cell per epoch\n",
    "[data_dict.update({'nrActiveCells': nrActiveCells(data = data_dict['avg_amplitude'])}) for data_dict in data_list];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "38758cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add nr of total cells per epoch\n",
    "[data_dict.update({'nrTotalCells': data_dict['data'].shape[1]}) for data_dict in data_list];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "597bbebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding location of type2 pattern to the dictionaries\n",
    "\n",
    "\n",
    "# type2: 1 epoch is first element of the pattern\n",
    "#        2 epoch is second element of the pattern\n",
    "#        0 epoch is not involved in the pattern\n",
    "\n",
    "\n",
    "# finding locations (each location is the location of first element of the pattern)\n",
    "type2_locations = find_type2_locations(dictLists=data_list, pattern=[-2, -3], \n",
    "                                       cond1_min= 900, cond1_max=10000, \n",
    "                                       cond2_min=900, cond2_max=10000)\n",
    "\n",
    "\n",
    "\n",
    "[data_dict.update({'type2': 0}) for data_dict in data_list]\n",
    "\n",
    "for i, data_dict in enumerate(data_list):\n",
    "    if i in type2_locations:\n",
    "        data_dict.update({'type2':1})\n",
    "        data_list[i+1].update({'type2':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee23a634",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# adding location of type3 pattern to the dictionaries\n",
    "\n",
    "\n",
    "# type2: 1 epoch is first element of the pattern\n",
    "#        2 epoch is second element of the pattern\n",
    "#        3 epoch is third element of the pattern\n",
    "#        0 epoch is not involved in the pattern\n",
    "\n",
    "\n",
    "# finding locations (each location is the location of first element of the pattern)\n",
    "type3_locations = find_type3_locations(dictLists=data_list, pattern=[-2, -3, -2], \n",
    "                                       cond1_min= 900, cond1_max=10000, \n",
    "                                       cond2_min=900, cond2_max=10000, \n",
    "                                       cond3_min=900, cond3_max=10000)\n",
    "\n",
    "\n",
    "\n",
    "[data_dict.update({'type3': 0}) for data_dict in data_list]\n",
    "\n",
    "for i in range(type3_locations.shape[0]):\n",
    "    \n",
    "    data_list[type3_locations[i,0]].update({'type3':1})\n",
    "    data_list[type3_locations[i,1]].update({'type3':2})\n",
    "    data_list[type3_locations[i,2]].update({'type3':3})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1268d7da",
   "metadata": {},
   "source": [
    "# Plotting of analyses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7caef70f",
   "metadata": {},
   "source": [
    "### Type2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4739dc8c",
   "metadata": {},
   "source": [
    "#### Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "786dd24d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp/ipykernel_5828/451431331.py:7: RuntimeWarning: Mean of empty slice\n",
      "  df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'FiringRate':[np.nanmean(indDict['avg_firing_perSecond'])],\n"
     ]
    }
   ],
   "source": [
    "# plotting average firing rate \n",
    "\n",
    "df_typ2_firing = pd.DataFrame()\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'FiringRate':[np.nanmean(indDict['avg_firing_perSecond'])], \n",
    "                                                         'name':indDict['mouseName'], \n",
    "                                                         'Pattern': indDict['type2'], \n",
    "                                                         'epochID': i}), ignore_index = True)\n",
    "    \n",
    "df_typ2_firing = df_typ2_firing[df_typ2_firing.Pattern != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e8bbfd70",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp/ipykernel_5828/2371194263.py:2: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resulting Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.\n",
      "  df_temp = df_typ2_firing.melt(id_vars=['name', 'Pattern', 'epochID'], var_name='epochID', value_name='FiringRate')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 0.2)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 316.8x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df_temp = pd.DataFrame()\n",
    "df_temp = df_typ2_firing.melt(id_vars=['name', 'Pattern', 'epochID'], var_name='epochID', value_name='FiringRate')\n",
    "x = df_temp\n",
    "sns.set(rc={'figure.figsize':(4.4,8.27)})\n",
    "sns.set_style(\"ticks\")\n",
    "#g=sns.boxplot(x=\"Pattern\", y=\"FiringRate\",dodge=False,fliersize=3, palette=['darkorange','royalblue'],linewidth=4,data=x )\n",
    "g=sns.boxplot(x=\"Pattern\", y=\"FiringRate\",dodge=False,fliersize=3, palette=['limegreen','darkorange'],linewidth=4,data=df_typ2_firing )\n",
    "\n",
    "#g.set_xticklabels(['SWSn','Awake','SWSn.1'])\n",
    "plt.ylim(0,0.2)\n",
    "#plt.ylim(-2,2)\n",
    "#if save_file:\n",
    "   # df_typ2_firing.to_csv(path_or_buf=file_save_add + 'L:/analysis_nima/statistics_epoch_based/Triplet_type2_fr_z_score/awake_sws/Type2_fr_all_cells.csv')\n",
    "#if save_fig:\n",
    "  #  plt.savefig(fig_save_add + 'L:/analysis_nima/epoch_based_analysis/Triplet_type2_fr_z_score/awake_sws/Type2_fr_all_cells_awake_sws_boxplot.pdf', dpi=600, format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fddeba89",
   "metadata": {},
   "source": [
    "##### Correlation per epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "be3afcfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.283399</td>\n",
       "      <td>-0.041246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.521026</td>\n",
       "      <td>0.136991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.276223</td>\n",
       "      <td>-0.615120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "0  0.283399 -0.041246\n",
       "1  0.521026  0.136991\n",
       "2  0.276223 -0.615120"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_typ2_firing[df_typ2_firing.Pattern==1][\"FiringRate\"].values\n",
    "y = df_typ2_firing[df_typ2_firing.Pattern==2][\"FiringRate\"].values\n",
    "\n",
    "df_correlation_type2 = pd.DataFrame({\"x\":x, \"y\":y})\n",
    "df_correlation_type2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "af8b6b1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 316.8x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=x, y=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78219bfd",
   "metadata": {},
   "source": [
    "##### correlation per cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "06b2158f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.134847</td>\n",
       "      <td>0.658879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.805349</td>\n",
       "      <td>-0.272624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "0       NaN       NaN\n",
       "1  0.134847  0.658879\n",
       "2  0.805349 -0.272624"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plotting average firing rate \n",
    "\n",
    "df_typ2_firing = pd.DataFrame()\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'FiringRate':(indDict['avg_firing_perSecond_zscore']), \n",
    "                                                         'name':indDict['mouseName'], \n",
    "                                                         'Pattern': indDict['type2'], \n",
    "                                                         'epochID': i}), ignore_index = True)\n",
    "    \n",
    "df_typ2_firing = df_typ2_firing[df_typ2_firing.Pattern != 0]\n",
    "\n",
    "x = df_typ2_firing[df_typ2_firing.Pattern==1][\"FiringRate\"].values\n",
    "y = df_typ2_firing[df_typ2_firing.Pattern==2][\"FiringRate\"].values\n",
    "\n",
    "df_correlation_type2 = pd.DataFrame({\"x\":x, \"y\":y})\n",
    "df_correlation_type2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "be306afe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 316.8x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=x, y=y, marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "908967d0",
   "metadata": {},
   "source": [
    "#### Frequency based on top x%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9bcaf040",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp/ipykernel_5828/3994832376.py:7: RuntimeWarning: Mean of empty slice\n",
      "  df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'FiringRate':[np.nanmean(indDict['avg_firing_perSecond'][indDict['top20_avg_firing'][0]])],\n"
     ]
    }
   ],
   "source": [
    "# plotting average firing rate \n",
    "\n",
    "df_typ2_firing = pd.DataFrame()\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'FiringRate':[np.nanmean(indDict['avg_firing_perSecond'][indDict['top20_avg_firing'][0]])], \n",
    "                                                         'name':indDict['mouseName'], \n",
    "                                                         'Pattern': indDict['type2'], \n",
    "                                                         'epochID': i}), ignore_index = True)\n",
    "    \n",
    "df_typ2_firing = df_typ2_firing[df_typ2_firing.Pattern != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "85b4bc70",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp/ipykernel_5828/1493716743.py:2: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resulting Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.\n",
      "  df_temp = df_typ2_firing.melt(id_vars=['name', 'Pattern', 'epochID'], var_name='epochID', value_name='FiringRate')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 0.2)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 316.8x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df_temp = pd.DataFrame()\n",
    "df_temp = df_typ2_firing.melt(id_vars=['name', 'Pattern', 'epochID'], var_name='epochID', value_name='FiringRate')\n",
    "x = df_temp\n",
    "sns.set(rc={'figure.figsize':(4.4,8.27)})\n",
    "sns.set_style(\"ticks\")\n",
    "g=sns.boxplot(x=\"Pattern\", y=\"FiringRate\",dodge=False,fliersize=3, palette=['darkorange','royalblue'],linewidth=4,data=x )\n",
    "#g=sns.boxplot(x=\"Pattern\", y=\"FiringRate\",dodge=False,fliersize=3, palette=['limegreen','darkorange'],linewidth=4,data=df_typ2_firing )\n",
    "\n",
    "#g.set_xticklabels(['SWSn','Awake','SWSn.1'])\n",
    "plt.ylim(0,0.2)\n",
    "#plt.ylim(-2,2)\n",
    "### change address\n",
    "#if save_file:\n",
    " #   df_typ2_firing.to_csv(path_or_buf=file_save_add + 'L:/analysis_nima/statistics_epoch_based/Triplet_type2_fr_z_score/awake_sws/Type2_fr_top20_cells_total.csv')\n",
    "#if save_fig:\n",
    " #   plt.savefig(fig_save_add + 'L:/analysis_nima/epoch_based_analysis/Triplet_type2_fr_z_score/awake_sws/Type2_fr_top20_cells_total_awake_sws_boxplot.pdf', dpi=600, format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5429410",
   "metadata": {},
   "source": [
    "#### Frequency based on least x%\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3a9529dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp/ipykernel_5828/2916663983.py:7: RuntimeWarning: Mean of empty slice\n",
      "  df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'FiringRate':[np.nanmean(indDict['avg_firing_perSecond'][indDict['least20_avg_firing'][0]])],\n"
     ]
    }
   ],
   "source": [
    "# plotting average firing rate \n",
    "\n",
    "df_typ2_firing = pd.DataFrame()\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'FiringRate':[np.nanmean(indDict['avg_firing_perSecond'][indDict['least20_avg_firing'][0]])], \n",
    "                                                         'name':indDict['mouseName'], \n",
    "                                                         'Pattern': indDict['type2'], \n",
    "                                                         'epochID': i}), ignore_index = True)\n",
    "    \n",
    "df_typ2_firing = df_typ2_firing[df_typ2_firing.Pattern != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "15846830",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp/ipykernel_5828/4016194698.py:2: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resulting Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.\n",
      "  df_temp = df_typ2_firing.melt(id_vars=['name', 'Pattern', 'epochID'], var_name='epochID', value_name='FiringRate')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 0.2)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 316.8x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df_temp = pd.DataFrame()\n",
    "df_temp = df_typ2_firing.melt(id_vars=['name', 'Pattern', 'epochID'], var_name='epochID', value_name='FiringRate')\n",
    "x = df_temp\n",
    "sns.set(rc={'figure.figsize':(4.4,8.27)})\n",
    "sns.set_style(\"ticks\")\n",
    "g=sns.boxplot(x=\"Pattern\", y=\"FiringRate\",dodge=False,fliersize=3, palette=['darkorange','royalblue'],linewidth=4,data=x )\n",
    "#g=sns.boxplot(x=\"Pattern\", y=\"FiringRate\",dodge=False,fliersize=3, palette=['limegreen','darkorange'],linewidth=4,data=df_typ2_firing )\n",
    "\n",
    "#g.set_xticklabels(['SWSn','Awake','SWSn.1'])\n",
    "plt.ylim(0,0.2)\n",
    "#plt.ylim(-2,2)\n",
    "\n",
    "### change address\n",
    "#if save_file:\n",
    "   # df_typ2_firing.to_csv(path_or_buf=file_save_add + 'L:/analysis_nima/statistics_epoch_based/Triplet_type2_fr_z_score/awake_sws/Type2_fr_least20_cells_total.csv')\n",
    "#if save_fig:\n",
    "   # plt.savefig(fig_save_add + 'L:/analysis_nima/epoch_based_analysis/Triplet_type2_fr_z_Score/awake_sws/Type2_fr_least20_cells_total_awake_sws_boxplot.pdf', dpi=600, format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16d8355b",
   "metadata": {},
   "source": [
    "#### Frequency based on top x% of first element of pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bf020584",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp/ipykernel_19120/1255863660.py:9: RuntimeWarning: Mean of empty slice\n",
      "  'Second':[np.nanmean(data_list[i+1]['avg_firing_perSecond'][indDict['top20_avg_firing'][0]])],\n"
     ]
    }
   ],
   "source": [
    "# plotting average firing rate \n",
    "\n",
    "df_typ2_firing = pd.DataFrame()\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    if indDict['type2'] == 1:\n",
    "        df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'First':[np.nanmean(indDict['avg_firing_perSecond'][indDict['top20_avg_firing'][0]])],\n",
    "                                                             'Second':[np.nanmean(data_list[i+1]['avg_firing_perSecond'][indDict['top20_avg_firing'][0]])],\n",
    "                                                             'name':indDict['mouseName'], \n",
    "                                                             'epochID': i}), ignore_index = True)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "98e05d14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 316.8x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df_temp = pd.DataFrame()\n",
    "df_temp = df_typ2_firing.melt(id_vars=['name','epochID'], var_name='Pattern', value_name='FiringRate')\n",
    "\n",
    "sns.set(rc={'figure.figsize':(4.4,8.27)})\n",
    "sns.set_style(\"ticks\")\n",
    "g=sns.boxplot(x=\"Pattern\", y=\"FiringRate\",dodge=False,fliersize=3, palette=['darkorange','royalblue'],linewidth=4,data=df_temp )\n",
    "#g=sns.boxplot(x=\"Pattern\", y=\"FiringRate\",dodge=False,fliersize=3, palette=['limegreen','darkorange'],linewidth=4,data=df_temp )\n",
    "\n",
    "#g.set_xticklabels(['SWSn','Awake','SWSn.1'])\n",
    "plt.ylim(0,0.2)\n",
    "#plt.ylim(-2,2)\n",
    "\n",
    "##if save_file:\n",
    "    #df_typ2_firing.to_csv(path_or_buf=file_save_add + 'L:/analysis_nima/statistics_epoch_based/Triplet_type2_fr_z_score/awake_sws/Type2_fr_all_cells_top20%_awake.csv')\n",
    "#if save_fig:\n",
    "   # plt.savefig(fig_save_add + 'L:/analysis_nima/epoch_based_analysis/Triplet_type2_fr_z_score/awake_sws/Type2_fr_all_cells_top20%_awake_boxplot.pdf', dpi=600, format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0879646e",
   "metadata": {},
   "source": [
    "### correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c586883d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZgAiCIHJBVpbd4sWLcccdd6Cqqgocx+F3v/tdr52KCYIgRhJZid3WrVuxd+9e1NfXQ6fT4eqrr8bq1atzPTaC0KAOJsRAyUrsBEHAzTffjMsvvxwXX3wxQqEQeD6rGTBBDBjqYEIMBlkp1l/+8hdcddVV2LBhA1pbW7F48WK89957uR4bQQDIbVkZkT9kJXZPPvkknn/+eTgcDpSVleHJJ5/Eo48+muuxEQSA3JaVEflDVmIXDAYxfXpPVvsVV1wBSZIynKFw6NAhXHfddVi6dCn27t2b9PqpU6dQXV2NyspKbN++XYvyNjY2Ys2aNaiqqsLtt9+OpqambO+HGINQBxNiMMhK7HQ6Hbq6urTFqs+ePdvrOS0tLdi1axf27duHAwcOYP/+/ThzJn7asWXLFvzzP/8zXnvtNTDG8NJLLwFQVjO7/vrrcfDgQSxduhS7du3q630RY4i+lpU1nGrBg7Vv4Y5H/4AHa98alJZRxOgnqwDFxo0bcdttt8HtduP73/8+3nrrLfzoRz/KeM6xY8ewYMECOBwOAEBlZSUOHz6MTZs2AQCampoQDAYxb948AEB1dTWeeuop3HrrrZBlGV6vFwAQCATi1r1Q8Xg88Hg8cfuam5uzuZ28YaxEMPtSVpYpmAFgTPw8VMbK73eo6FXsGGNYtGgRpk2bhjfeeAM+nw8bNmzAjBkzMp7X2toKp9OpbbtcrrgeeImvO51OtLQon8D33HMPVq9ejRdffBGRSAT79+9Puv6ePXuwe/fu3u8wTxlrEcxsy8rStWPf8+pJBEPimPl5jLXf71CQcRp75swZVFRU4E9/+hNKS0uxb98+/Pa3v8W6devw1ltvZbywLMvatBdQRDN2O9Pr999/P370ox/hT3/6Ex5++GFs2rQJicvb1tTUoL6+Pu5fKr9gvpKvEcx0wYwmt3dM/Tzy9fc7EDKK3ZNPPol7770XS5YswauvvgqO4/Dqq6/ipZdewtNPP53xwmVlZXC73dq22+2Gy+VK+3pbWxtcLhfa29tx9uxZXHXVVQCU6a/b7UZHR0fc9e12OyZNmhT3r6ysLPs7H+PkawQzXTADwJj6eeTr73cgZBS7L7/8EjfccAMA4J133kFFRQV4nsf48eM1n1o6Fi5ciLfffhvt7e0IBAI4cuQIFi1apL0+ceJEGI1GHD9+HABw8OBBLFq0CEVFRTAajWhoaAAAHD9+HFarFcXFxQO60XwjXyKYicGIudNKUgYzJjitY+rnkS+/38Eko88utkri/fffj2vpFAqFMl64tLQUmzdvxtq1axGJRLBy5UqUl5dj/fr1uPvuuzF37lzs3LkTDz30ELxeL2bPno21a9eC4zjs3r0bjzzyCILBIKxWa69WJJFM9eLp+EXdCQQhwqgXEIpIo7IxZionPKBM4841d8MfjKDAqofDZkSHJ4DX3z2HKy+bjA8+vRAXzAAwJn4eKmPl9zuUZBS7wsJCnD59Gl6vF263G5dddhkA4L333kNpae9O0OXLl2P58uVx+5599lnt+5kzZ+KVV15JOq+8vBwvv/xyVjdApGYsNMZM5YT/6a/fA8dxsJp1CIZFSIyhyxuGQSfAatYjCBEffHoBj931reQLjvKfRyxj4fc71GQUu+9///v47ne/C6/Xi/vuuw8WiwX/8R//gZ///Of42c9+NlRjJPrJaG+MmSqy6u4IAOBQUmiCKDHoeA6yDHR6Q7Ca9Rn9VqP955HIWLufXJNR7ObNm4c333wTwWAQdrsdAPC1r30NL7/8Mr7yla8MxfiIPCbVQteSzLTIvF7HQ5RkcBwQEZXV78hvRaSj1zw7g8EAg8GgbX/961/P6YAIQiXV6mMCzwFQUpQcBUa4OwKQwaAXeFqwh8hI1gvuEMRQk8oJbzYqeWXBsAiLUUChzQCPLwyjQUCR3Tyq/FZUATG0kNgRI5ZUTvg7bpgLANq+CU4bNq0afSJBFRBDD4kd0SuqBXKuuRsRSYZO4DClzD4klkg6J/xoF4R0ZW11R8+M+nsbqVC7YSIjqgVy3u1Ftz+MYFiELxDBeXc3flF3gjqK9BOqgBh6SOyIjKgWiD8oguc56HgeHJRtqsXsP1QBMfTQNJbIiJr+ERFlCNGPRjXVYygtkcF05o+EwABVQAw9JHZERtT0DzWnjecAxpQct6GyRFI585/a/z7sNgMCIalPgjVSAgNUATH0kNgRGZk7rQQv138CSZIhM4DnAZ7jYDHpIYoMc6eV4MHat3JqJSU68yUZ8PjC8AcjmOSy9UmwRlJgIFXwZSRYnWMV8tkRaWk41YLX3z0Hu1UPg17QrDqDjscEZwGuvGwyXn/3HDo8gTgrabCDFonO/M7uEDhOqaboay+3kRwYUK3OXP888xWy7Ii0qFaQzWJCUYGyLxgWUWQ347G7voUHa9/qt5XUFwsmsZIiIsrgAOh0PZ/V2QpWqqqMkRIYGElW51iExI5IS6ra1FhR6e31dPTmN0sUwrnTSvD6u+c0Z77AcxAlBofNqF0zW8EayYGB/v48ieygaSyRllTpEZ3eELq8IVTffwjuDj/+1twNX1DUXs9GdDK1FE81lVN71BXZzfAGRIwfZ0WBRQ9B4LJabSyW+bNKsaG6XLtWkd2MDSOkaoHSUXILWXZ5TqbpZKIV1OkNocOjNG3leQ4cAFFiaLngg6vYAp3AZSU6XzR7EApLECUZeh0Ph80Ii0mH1na/JoSSxHC+yxdNeeHw1onz2H3flUnj7k8kc6S2RhrJVudYgMQuj+ltOpmYHhEMy4rIcRx4joPAAxFRAgNwoSuImV8pzrjEoVpy5vGFAQB6gYcoMrg7Ayi0GVBgMeL05+1a5FfgOQg8B1lmaGzx4ldHTuODTy+M2UglpaPkFhK7PCYbh3isFXTHo39AMCRqycUAoBN4yIyh2G5K3R0Y8aIaDCtTXsaAsKj0ouM4oMsbhiQx8BwHMbqQnCQr2+A4cGB4pf4TuIrNcV2LjQYB7VFrc4LTiu9eP3tUi8NItTrHAjn12R06dAjXXXcdli5dmnKZw1OnTqG6uhqVlZXYvn07RFF5EFpbW3HnnXfixhtvxOrVq9HY2JjLYeYtfU3DKC22RMWtZx+LWmCZ/EqxohoRZbCE82UZkBmDzaJHcaEJ6sscAFFWjldTTVQ/nyQxeHxhtLQHlGaeTLH+fvrr9yhVg0hJzsSupaUFu3btwr59+3DgwAHs378fZ87E50Ft2bIF//zP/4zXXnsNjDG89NJLAICtW7diyZIlOHDgAKqqqrBz585cDXPUkriqVn8e8L44xBtOtaDbF0ZElBARZURECbIsQ2YMZqMuo19JFVVfUIQkx6//a9QL0UCD8r3VpINBr/xZMihiyPOAJDHoY1JNOr0hTXQFngfP8+A5DoGQOGz1uoPxOyFyR87E7tixY1iwYAEcDgcsFgsqKytx+PBh7fWmpiYEg0HMmzcPAFBdXY3Dhw+jvb0dp0+fxurVqwEAN910E+69995cDXNU0pfk00wPYPXi6SmXHUwULvX9REnSAhEyAyTGMKnUhntWfz3j1Ku02IJObzi6fkQ8oqzkzHFcz/quVpM+7hhJZpBZfF6d2oY9Zp11zfobjlQNSgge+eTMZ9fa2gqn06ltu1wunDhxIu3rTqcTLS0tOHfuHCZMmIAnnngCDQ0NcDqd+OEPf5h0fY/HA4/HE7evubk5B3cy8sg2+bSvAYh0DvHE97OZ9XHJxemIDUp0+VIvvSlJyvR1XKEZwZCIIEQEQqIiYkyJ+hp0PAQjh1BYQjAcn2vH8z1ql82UOldQQvDIJ2diJ8syOC72D5HFbad7XRRFnDx5Et/73vfwwAMP4OWXX8a2bdvw4osvxl1/z5492L17d66GP6LJNvm0rwEIoMcSjI14pno/UWI4/Xk77nj0Dykjo7FCW1JohMcXQsIMViMQFLHw2+Nx8eQi1B09g/NtyrS3qMAIq1mx8hhjaOsKoshuRmu7H2XjrGjrDCIYEhGRJMiKoQeBKfW8qUiVrDxY0V1KCB755EzsysrK0NDQoG273W64XK64191ut7bd1tYGl8sFp9MJq9WKJUuWAACWLVuGHTt2JF2/pqYGK1asiNvX3NyMNWvWDPatjDiyLXnq6wOYzhI0G5WcL5NBB19QxIWuAMIRJZLKc0hZiF939AwikoQunxidcnLgOAb1403zx3EcHAUGfPDpBXxn6UzMn1WKB2vfSnl/U8rscZZkw6kW/Lzur2hpV6bHeoGD3WbE6++ew8WTizJauefdXpw8ewGOAoO2wPZAup+M5DI0QiFnYrdw4UI8/fTTaG9vh9lsxpEjR/DII49or0+cOBFGoxHHjx/HpZdeioMHD2LRokWYMmUKysrK8MYbb+CKK67AH//4R8yePTvp+na7XVvecSzQl1rRbJNP+/oAprMEOSjJwh2hEDq7Q1qQgeOAts4gnEVmRCQJO/ceh9WsR2mxBZ+e61BSS6LyJkdDsAyAwCtBBcYAZ5EZFqOAc83dmkVpMerg9UcAIOP9zZ9VClexFXodH3ePwXDy9DHx3vxBEeCUr0UFpgFPOykheOSTswBFaWkpNm/ejLVr1+LGG2/EsmXLUF5ejvXr1+ODDz4AAOzcuROPP/44rrnmGvj9fqxduxYA8PTTT+OXv/wlli1bhhdeeAGPPfZYroY5IuirczvbkqdsAxAq6VJRAiERG6rLEQwrAQQOimDpBSEqeAF0ecMIhkTNavKHJIgSQ0SSIUrx81dJVgIMfPSvr6UjgC5fCCfPXoDHG0K3PwSO4yAIfK8lXdmmzyQeFxFlCDHrzaY7LxWpgj7zZ5Xiyssmo6M7jM++7EZHdxhXXjaZ/HUjiJwmFS9fvhzLly+P2/fss89q38+cOROvvPJK0nkXXXRRko9uLNMf53bsdLElWmYVu1/7vg8Z+ZkswfmzSmE161FWbMb5Nh/CooywKGk5czwPGPU6+EMSurzhXu+ZAyDLDM1tPrDo+QKv5M91ecMotBlgtxqx+770ARAAMBsFNLZ6IclMKz0ThOQgReK96XU8wqIEQ0yEN5tpZ7qp/ifRdldFBQaUFZsRikgpp9PE8EEVFCOA/ji3s+24m21GfsOpFnh8IZxv80Mv8CiyG5NqXWMFIxCKj67KsiI8aq85Ltr7rleiUVe9wEPx6ynX8gVE7f7VKf4XzR6IkrIg9uSyAsydVgKPNwxRYuA4QBRltHYEUGDRa0suqiROMy0mHcLdEiwmHRhjWU87030wHXzzLIoKDMMWjR1pTT8ZY5BlBkEYOb1GSOxGAP1xbg801SH24TAbBXi8YVjNOrgcJnR0h9DaEcCU0gLcccPfaW2Xun1hnG/zKxUL0HRKE7dASNLWqlBfS4cgcPjKeDs+/1IRMJkpwQ6gZ40LV7FFE/WIJMEXiIABCDLgvNuLjz9vR4HVAFexBZ3dIUREZZlHR4EpKTix59WTcHcFIMuKME4ptaNywVR88OmFPtWhpvtgCoRElBWbk/YPRjS2NyEbrlbzjDGIEoMkyZAYgxx1WYiSDEmSIegEOB3m3i80RJDYjQD649zuzRrM9IDEC4iI1vaeKoqSQhNsFgOCYREFVoMmdOrD5Coyo/mC8h46HYdxhcofs7szgHBEgl7HIyIpDQPAmJYSkogoMZxt6lKmsFErUAYDzynJygLPoXrxdE3Uu3xKoETgOUQkCR2eEGTG0N4VRNk4KyY6rQCUB9Ab6Gk51XCqBU/tfx8eXxgcB+h4DpLM8EWLBx1vBTGlzI6NfRCFdB9MZqNOi1jH7h9oNDYbIevLB19/LEBJ7hEwKSpoEVGGJMtaVD0VQlam/dAxcmzMPKY/PdYylXr1FvDQ0kKixfcqHd0hnG3qQpPbB1HqqUSIfZhsZj3MRh0EgYNO4GE162E161FoM0AX9YNJkvKJn9G0AyCzqAXIlLzLiCgjFFHUcWXFxZg/q1QLLESiTQMkSdZqaTlOeQt3R0DrqZcoMHVHz2jLQAo8D3BK9YcsA92+ME6evYDHn/8zfnXkdFa/q3RBn6pFF/UpGJQtmXr/qWQbpEn8u+jyBvGfhz7E+x+3QpYZIqKEQCgCbyCMru4QLnQG0HLBh5Z2Hy50BtDRHYLHH4E/JCIS7UwzwvQsI2TZjRD62u0ikzXY2yd9S7sfvoAIBkBKML0YAFGS0druh14v4I5H/4AOTxAlhSYAgC8QgSTLkCSGgCThbFMX9DoeAs9Bx3MQwYHnlGlpuiTiWBwFRnR2h6JBCqUZpyQxvHXiPC6eXBS/upnIIKlT6Bi/IANDhycIgTclCUxLux+ipExvEb03FZkBBoGDxBherv8kq2BCpqCPmhQ9mO2ZsvHnZusGOfy/n6Ok0AizUa9Z0P5gBAfe/BSlJRag98+nUQ2J3Sgl00NXW3ci4wNSWmxBW4dfs6xiYUyxmmQGSKKkWADdivUUCkvo9ofjHgiZKSVfSu6d0ueOoSevrjfU9kzam0cH9Lcvu/H483/GwvLxcLf7YTHp0OUNx0R/OXDgUFigRyAkIizKKLKbkwSmtNiCrm5lyssnBE0UweSg45RpdW/+zsQpYOL0NxftmbIRspuWTMd//vYj8JwSJBJlBoPeiBuvmIbO7pA2BW1ye2GMLniu5koyxtDaERxUC+3DT9tw5J0vIEoSiuxmLPvWV0dERJrEbhST7uHq7QGpXjwdH37alva66tRWkpWk22K7Ca0dAXR6Q5rPC1AjqIrvLhxRfDgGHQ8xG5MuBUz7LzoOxvDmX5rgdJjh8UW0qSsHwKAT4ChQ1qDwB0UIfKJsQ7tX1WfHuPhx6aKJfnJ0HdxU075UQZyhDAKoFnyYUyLHksxg0utw0+LpCIQiEEWGaRMduOXqGfjDu1+grcOPQpsRV142CROdNviCEe1aFqMeXd5g3JQ3LMooKRy8IMKHn7bh10c+hiBwcDrM6OwennV5U0E+uzFIb8nE82eVwmxQ/uAzyRKDEngAAKfDFLX6lBd0AhftWqxETtX2S2yQ/DgcB3DRAEdndxhTSm2YMM6KQqsBZpMesqy0g/+yzYeIKKHEbkyZjD1/VinuvuVrmFxaoFhxQo8oRiQ56mOUYTXr46ylRP9Wc5sPHl8Ykow+L9/YXyRJxpxpJbhzxVxMLrVDloECqxHLvn0Rxo+zot0Tgscfhi8YwUWTCrFhxVxs/8fLsenmeZh90bik6y29fAokSUmzUdNtJIlh6eVTBm3MR975AjzfEwgy6XP/c8oWsuzGINkkE0+bXITz7m74gyJCYSmlf42PTu+aL/hh0PMwGgQ4HSZc6Appvi8WtYosJh3CohTnExsIHAPU8Evs+rCBkAiPPwx9QhPRbl8Ygeh9PPLcO1h99SX4ztKZ2s8jNhK9c+9x+AIRbfxq4CPW15e8MLdiVXZ2h2A1KfsGI7VEkqOpG7KsfC8qkU8xms7BGDDBacPt182KO68/nydzpo3D6qWKIF3oCqCk0Iyll0/BnGnJwhhLOCLB61cCF93+CLz+6NfEbX8Y7s6A9mHX1ObH1NKCEdMQgcRuBJCLhNDe/Edzp5Xg48/bk5ppAj2Of/UlBiAckQEOaGz1JR1vNOogSQwFFj26vJG419TZZV9ntnL0jXkecU07fQElBaW40KxVX8gM8IV6ItOyzPCr1z4GAE3wVOqOnkFRgQF2q0HLzRP45Nw8NTDgC4pKPbDEomkWPe+TbWpJXD6aHM1FE+VoRJMNaWBg9kUlmDbJESdQb/31fErh8gYi8PojSVH/bOE4gONHTkMEErthZjATQrMVzYZTLXj93XMosBrgC0S0mledoDj9eZ5DOCL1tEeP+snSCZY/KMJs1EMUJegFJUyqJu+q1Q0OmwHd/khKcU0HB4ADB0eBURMddayt7f6MmcsMwG9e/yRJ7FQR4zhOs9ASc/MAxe/52fku+PxKIrM6+ZVkBm8gAp3AgcnAyiXTtWukS7AVpcz5aANBZgz+QATeQATd/jC8/oSvgQi6/RH4/GF0BxQRS6xV7gs8z6HAoofNbIDNote+L7DoYbUY0OkJ4tgH56EXeLiKzQiLMoKhkdEQgcRumBmspo89icIyfIEILnQGcPrzdqyquDildaPTcbBZjCgqMKLJ7UNYlKIWneInUx8HnaD65VI/IHxU2NRpobJPKROSJKb5yMY5zDCb9HB3+NM+bAIPbX2JqGsQHAe0XPApkWOuR98kWUk8llIoiHqMmrMX+yHgC0QgSRKKCkza8aksj7nTSvDhp20QBL4ntUbgFV8lx+GrEwqxaN5EfGV8IVrb/RDlnmn9QJAkuRfhCkenlD3W10DeU6/jUWBRhcsAm1mPUETC//2tA4LAwajnlQ8oBqysuARfn+mK60OZiumTHTjyzhcIRyQ47Gbcdg1FYwkMXtNHJVFYRpc3WikgcBBlZUWuxPyxxPd0FBjR2q6kojij5WKQlGs4HWYtSJGKVCkmsqJGSq5ddAZ0tskDjlMeLoNOKdRvcnuVLPzoJeTo3DUutSXakh1QhMRqFLQpqyQzCAIXlxjNqf9FxTHRcpYkGZ3dSrqLw2ZEOCIpD/KSi5VrSjJEmeGL5m6ML7EqXVtExZ8mihKCYQbJF4LNYkCXL4xAON4iTET1d3UHUohXgt/LG03YHQhmoy5OuOK/jxe1AosBRoOQdI0f73sPBVZ9XNQ2FJHwxvtNuDQL0ZozbRzmTBsHg46Hs2j4p68qJHbDTGKaiC8QQbsnCJkBD9a+1es6rOqU9VxzN4JhMdpQU/nkFaJrMiRaiYnvaTXp4CgwIhiWwMDhkqnFKLEbcezEl2jtCGi1sH0hcbaq+qak6DSUgWFVxcX472OfozvD1CrxOmFJhl6nNChgAAw6HiE5PsCiDtdVZEbd0TMwGHhYTXoIPAerSQ+71QBRkmE06DDBacO3503ElNICNLd5NV/hx190oMsbik7HOTCZaSk1eh0Hd4cPe149icv+rhR2qzGtAz8c6X/AhgNgNeuh1/EIRhcVt5h0uHiyA2ajDn/52A1Bx8Go4yExgMkM36mc2WvAoTfaugKwGuNF0KDjcaEr/YfeaIDEbgAMRmAhthJCFGW4O4MAlKaW6fx3qfx8/mAEoiTHOfPVSGmilajmnrk7gtHqAiWaet+aS7Va2J/++j0lYbifOXOZ4KAkDf/ty4/7fG5EZBhfYkFrRwA6gceEcVZ0+ULweCMQhJ7pps2ixx03zMG+106DMaC9KwhwHMwmHXQ8EAjK2LRqHvxBxaf14dk2TaA+bexCuycYYzGypDF0dCttrF5vyH6ZT4HnYIvxcdks0a+JVlf0q9Wkx8nPLmDPqychShJkWal4+PhvHbBbDbCYdUnW15F3vhiw2I0rNOc8H284ILHrJ4MRWFDFMhAWIQYYQmEROoFHcaFJc56n8t+l8vMVWPW40BXS/FSA8nDZE/LHVBhTqh6UfxJCEQk/r/srGDh0dQejUUpeSRKOTkkHi75ciuOUrsaCoJSjCQIPR4EBJoMAR4EJYVFGWYkNX5lQgFOfd6Dlgg8Wkx5zLirB/354Hp82dmodVaLGJfho5cQ/Pfn6gKKg6qpok0sLFLEyx4tV3FezASaj0Ku/K5G6o2fgC0TAc9HqFFmx/n3BCCaOs8YdO1jW19LLp+DXRz5GCEq/v7AoD3o+3nBAYtdPBqPFkiqW4wpNCEUknG8TMc5u1IQOSN91N9HPp9fFTzvU5piyjKRIWN3RM1qfMb2OB5NliDLQ0h4Az/VMHdVpcA6MO226rRToK191Aq9tC7zycKsdNyKiDFlmCIZEfHa+G4wxuDuD0Ot4/N8XHTh24nyccH1yrjPu/WLvQU4TGuUA8NEAhC66lq2yPq4iklaTAJvFoI03IsootJnw/Vu/nvV9q6VUbV0BjMsiz6213a+IczSHh+MAXmYQZcXayoX11d98vGT6Juy5hsSunww0sJBKLPUChw5PCDZzz7qpqSKFqcrBOjwhGPU8iu0mdHqV/DEwBn9IRG3dibhpthqVVAUnHOvvinmf2Jbliah1pbz2levZx8fvVyotGDhw2vVl1aqUFOd/RJQRCiuZ/dmKayiDP0w1oAReEa1M03GeA4oLTRB4Dhe6gigrNietfNfpDWvCIvBcv6yd2FIqq1FAlzeIXx/5GKuXIq2Q8BzAOEX8uWj0ReYAXohGwhmDQccjIjEIPI9rvzkVqYxHDjHSw0FbG0S9pvq9uv/SmaWYP7NU+zmov1tOOz+6zfVsI/q6+v48id3YYKCrSaUSy6ICI9ydQW1t1HR97aoXT8dPf/0e3B0BLQUjFJExvsSMAqsBdpsB/oCIC54gZJkhHBbxWVMXfvrr91B1xTRMLSvA//1NjKaVcBAlnfYkqH+eimj1CJayT3kYlG7C6hRY1qbDcvSfYoVF01iiVQC5RMcruXg8z0GSZThsJvxgzaX4ya/fR2u7L22beC7mIe3qDqHEYVIsXSCuXXtYlPGV8YW48rJJ+GNDIzo8QZSV2LDk0kmY9dVi5VrqNWMuzicIyTsfNWOcwwSjvuf3HpEkNJxuweWzx2urtXHRgXEAZk9zornNGxUWprW3Kiu2YsWS6Tjyzt9woTOA0hIzKi+fivKLx0FpyIA48enr9HksklOxO3ToEGprayGKImpqapKWOTx16hS2b98On8+H+fPn4+GHH4ZO1zOkkydP4uabb8aHH36Yy2H2i9jAgsWoUxpWgtOSTHtjalkBurqDMBl1WpqETuAwrsgMm8WAC50BTCq145rLp2Lu9HFaPaNaLe8sMiMYkrS1F0RZhs2kpBrIDPD5I9GW48oDZNABEZnhd386iyvnT8aZc52ISDI4ABFJESmgp2WSLDOtA8pwt/5RH1ie57UppGY5Rqe7ZeOs4BHNrwtLsJn1+MbfleJ//vw3GGM+gPgYIVFmsz1iLHAcLplchHBEAi8ozQbCogSdwGPlldNRfrETS74+WTNdej4YshOT5gt+2EwCImJPwjZjDOeau7WmBonc8O2L8NT+9+EPinHBpBsXT8elM0tx6czhz18bLeRM7FpaWrBr1y7U1dXBYDBg9erVuPzyyzF9eo8YbNmyBTt27MC8efPw4IMP4qWXXsKtt94KAAgEAnjkkUcQiUTSvcWwElt/yhhDsVmPxV+fhKllhbjQGYQqD5pIREWERcVq0byJqPvjJxDlCPSCMi0SRYbqK6fj775agtiT26J5buq1XvqfT+D1R2DQ84rTmjH4/BG0XvCjuNAEg45Hk9unJeWqlpkiYsBr73wBs1GH1vZQzkUsschBFS41cqpMcXt8dDyvWJR8jJD1tJ3qsR61fTKDxGSlgoEBwXAEjgIziuwmfHj2AmTGepZ/5JT0jHQzWp4DdHo+2hapZ62L1Uun4+spRKWv0XhHgTFpNhAMiyjO4GdTGxkMdp+8fCRnYnfs2DEsWLAADocDAFBZWYnDhw9j06ZNAICmpiYEg0HMmzcPAFBdXY2nnnpKE7snnngCNTU1eO+993I1xAGj1p92+8Lw+JWpUjCSXVLoxVOKcP0/XKQ5q0sKzbj+W0pXiv+39704B/bsi0rgD4lKLpc/jHOt3dALHPyhnumk8tAjrkgfUCy1xCoDjy8Mj6/3FcCyhYOypoTAxwQYBE5LtQiGJE24BF5J3dCmvjECJjG5Zzqs7k8jTIkiGgyJ2rT/9msvAgCcb/OhwGKAIAhwdwSUSpAMc2q1IYAaMFJdCOnyHPsaje/v2rK56JOXj+RM7FpbW+F0OrVtl8uFEydOpH3d6XSipUVpzVNfX49gMIhrrrkm7fU9Hg88Hk/cvubm5sEafk6Ro/WVxXYTlv3DV7Vs+j+fbMaJT9qiM1UGjzeEn73SkbEuNenaWdhqPAcYDQI4cPjG7DLMmFoEd4cfB9/8FLKsRGh1Oh7+oBiX+qFGSeO3Fd9Wou9OFGWEmVI3KzNlm+MVH1KhzQC9IOD/W/n32PPqSTS2dms5bao1yoHLeM+JL50979EW0lFR/apWkw4oMmuF/6ngOMXy0rqcSAwd3UE89vyfMfMrxUmip7W294laiyuLSdf78pd9WNqSGFxyJnayLCdFtGK3073udrtRW1uL559/PuP19+zZg927dw/6uPtDRJTQ4QmmrF1M1RLHH10lK1sSj9ULSr0izwO6aAoJB+BrM1yYPtmhJav++aNmvPl+o5Y6oTrAi+0mbYGY820+rF46AwDwSWMXfIEwOI6D1x+B3WoABw5itA27am2FRQlSuCeSKvB82tZOHKeMV52qhiMyLnSFwPMcHnv+z0qRPFPuY1yhER3RKaSr2IJQWNQ6GaudWBBzH+pbclA7FwPd/pBmYcX7VQUIvBGiaMDMrxTh7Q+bEQor9cBOh7LOq8NmVOqKu4IIi7Lmk0tltX3R7FEi2tHuzKKorHcrSvEfwIkM1EobaUsmjiZyJnZlZWVoaGjQtt1uN1wuV9zrbrdb225ra4PL5cLRo0fR2dkZF8yoqqrC3r17YbPZtH01NTVYsWJF3Hs2NzcnBUFyiSwzPFj7Fj46e2FA17FE6xnbPUHoBR68wEHQ/FdKPtf3bp6nJa4a9IKWr5UpD+qrEwox+6IS/OGdL3D2fBdMBgFFBUbYzHptYRyvP4y9h0/hyvlTsPLK6Xj2wAdoj06FRbkncJGJxHUsOE5x9otR3xpjyjQ3tnRKTri2KMlobg9AxwMmox4Cr0SnRUlGt08pdrea9bhoQgHOnu+OCo1yXTVirFQYiCgpVCysx+76VtaW1IO1b+G8uzsqWPH+VkliWgNK9Vwx2qxA7ZDMcYAoswF1FOmN4VoycayQM7FbuHAhnn76abS3t8NsNuPIkSN45JFHtNcnTpwIo9GI48eP49JLL8XBgwexaNEirFq1CqtWrdKOmzFjBg4ePJh0fbvdDrvdnrR/KAlFlAVnYuE4xGXS95QE9ZQIxb1u1msJvj/e915SmU4oImGcw4IpZfH3qhZbJ6KmUwi80q3jm3PG4x/+fgKefvkv6PQEoRN4eAMRtES7j/AAPjsP/KLuBK68bHK/itET3WCMAVKMPSrJstYQoDdEGRADEa2LitEg4IqvT8QFTwjnmrtx9nw3dAIHvY6HzaxDZ3cYDCw63eYQEVlcvmO2llT14ul4/Pk/J1nRgsCh0xvChHHWuBxKvcAjGA2SaInYrKdVfS4YrA45+UrOxK60tBSbN2/G2rVrEYlEsHLlSpSXl2P9+vW4++67MXfuXOzcuRMPPfQQvF4vZs+ejbVr1+ZqODnBbNTh59sq8GljJ3ieg81igMWk06KffSXbMh11GqfjBeii7YdUa1CItiFSXQTqtOdcczf8wQgKrHr4AmI07YSDs8ictKq96jhX+7BlQ2zlBTA4vdt4DgiHJbz5fhNsFmXcatqILPfUp3IAwBRry6Dnk/IdU039ACTts5j0CIZFyJIEcMrUmgOiSzzGX3NyWQHOu73wB2N8dlalsUCuGKwOOflKTvPsli9fjuXLl8fte/bZZ7XvZ86ciVdeeSXjNT7+uO/F4kNJsd0E/dRiLRo7ENQyndcbzqHLG8KEcTZc9Y0pmDttnNJHLZquoRd4zRrMROy0p6TQCEHg4PGFlY4fegFFdlNci/FASESBWaeUZ/VhOsZxyX5Fs1GAxaSDxxfJWImRCa3aggEeX0R7L3UFs8TjAECWWFyEM1Wfv1OfXYBBp9Qgx04HHQVGiJIOkqyspgYovmSB55Kipqo/sKTQ2KfI6kAYaCJ7vkMVFMOIagDqeF5rDPkP8ybiiq9Ngk7Ha8GHdPTmrE6c9hQVGGE2CujoDqOowJD00Oh1PNydwWiKCIsLbBRajWCQk9quA8lWnEHHY6LTBm8ggogYSjo+W1JZh7GpNAKv9MBT15sFlBpSdYFxdb2J2IAQxwFMBkRJQqTdH7eGhhECRFHxz41zmNDhCUGUGSaMs+K7189OWjZxqCOr/U1dIRRI7HJMrA9NJ0S7dwg96Ru6aKSyr2TjrE437dEJiqWS+NAUWg240BVUpnA8D5lXpouTSwvw9H1LsPqh34Pne19BLCzKONPYlf6AwYLjYDIqwgooCbpFdrMmdL+oO4FAMD7yHTtuUWTa4ttqYOLe1V/XBOySqUUZBawvkdXBiKJS6srAILEbJDhOqdHUCUKCoMX70PpKuockG2d1psagRQUm+IJhtLQHIDMGncAjFJag1ym1r1I0366w0IBANGARCInQ8Rw4ju/3IiyJJPr6+gRDtCSOJVk56s8n00IVitXKgUGZ+np8YdQdPTPoAjKYUVRKMO4/tG7sIGAx61FWbIWr2IriQhMKbUZYzQaYjDrodf2z3IDktUtj10VtaffHRW2BZGd19eLp8PojONfSjbNNXfjygh8RUUZJoQnd/jDau0LQCUpqRSjaQTgiKtNXZ5EZDpsBHZ4g2j1BPFj7lrYAdlgcHKEDshO62E4aKlazHrdcfQkmOAvgDSgW3YYEq1b5+fRSMRFtXKBscynXnh0osR9MQ7XmLJEMWXaDQLrV6AdKJustW2c1F+3Bo4qK0j2Dgz8ogoEhEIr6utAjC6Ika8sUAoiKXgCyLEPuX6whLb1ZdjyUHnNqtxWJMfAcp3VV/k6a89SfjzG61my695YkWQt6GPR8r+kc/ZmOUhR1ZECW3Qgmk/VWvXi64ncLi2BM+ZrorK47egZWsw6TS23QCRwMOqV2VS2bUoVL7UkWi6o/gsDBG4hAklhM/7rBvc90l+OgCF2hzaAsrCMzCByHVRUX9yow6s/HYkr/ea4TBKVpaLRXnMOmdB5JJ0SZLO1UNJxqwYO1b6HDE0RjqzduBTaKog49JHYjmNJiS5JvLBSRYDYqFQLBsISO7jDauoJJ07iGUy04/Xk7vmzzo8nti0YsFWtGzQvTSGFZcQCMeh46ngcHJbFWii4+YzLo4s8fAOmsOp3AobTEAqNBh2BYRliUYTLqsDLF0pCpmD+rFBuqy2Gz6OP263U8xo+zoqzYrCQER6tUnA4zrNGmqemEqC/T0VhhLCk0QZQYWjsC8PrDKT+YiNxD09gRTKpUA19ABGMRiJKEkkJjyu4c6oPGRy02UZKj0zVOKfSP9kRTp3dM+68HvZ7X1m7gogIpRC+ofB9fr9obAs/FdQtWrblo4QFUT4DaMV2WAX8wgkhEgqvYjLJipX719XfP4eLJRQCSk4JTWXuhkASjXoAkK0nULBpK1ul4rcD/F3UnIAhcykBHLH2Zjia6IDiOQ3tXEBc8oZSNBYjcQ2I3zGTyASWmGpiNOviDAYgSQygiw1GgrFeR6GNSH7RiuwnuzgA4puSigSm+KVN0CcG500pw7MSXWhE/YwA4KKu7mw1wdwSULirRxFqdwCMQFiHLQGyMIhvRs1v06IjpGJx4eKqlFz2+CGwWfZLPcs+rJxEMiVp087zbi8ee/zMsJh2mlNmTItbFhSYlSZgDOAa0e4IoKjD1/KyzTOfoS1JvojBaTTpYjFZ4A6JSs5sCKvLPLTSNHUay8QHNn1WKx+76FjZWlyMYEiFKDAKvWGvujgB8QTHJutB8fZzadl1WCtQ5YGXFxZhcVoCWdj8ueEJYWXExZk8bB2eRBXOmj8N3ls6AJVqIP85hiq5zABRG/VksRYBCFTqeVxJ9VVRXoF7g0NXP/nmhhOCCUS+gye3VrCZ/SEKXNwyZKRHlDk8AT+1/H5t2vo6Pzl5AW8zSlDpBsVZlhrjEY1Vgestby8ZPqpLOBZHOT9dXfyDRd8iyG0b6UtitHmvQK0mwgFJg39zmg0EvYHzMsnqlxRacd3vR5Q2D46CcIzMwxvDfxz6H1azTHqjX3z0X5+sDgIsnF8Ul1s6dVoKDb56FL9rfTheNUEgy0yw0nudg1PNw2Izo9IYgijJYNIKqtnHqD4lla6qAqIGbzu6QEk2FIuqSrOzr9IYABgTDkmaxTXRaUyYeZ5v/1hcrsK/VDlTkn3tI7IaRvviA1GMdNiNaowvtqIiSjM7uIBpOtWD+rFJUL56Ox57/MxgYeCh+Og4AoiknJYUmAMkPVOI0amN1OQClI0pQ9e8xQGQMuoR0G1lmCIQkBELK2BUrj9NaqA+ExAWIJjitCEUkmAw6zX+oNh1t6wxoPkiBV5KjJZmhrdMPodiSMvG4LwKTbVJvX6sdKD0l95DYDSN98QFpXXfNevBdAa1ulec4uIrMEISefmvzZ5XCYtIhFJYgSkrk1WFT/HeJTTbVByqdlWM0ClGLUkAwLGqL78RadamQZYDnlJw4XsfFJO4qolRSaEZruz/jEoeAIliCwMMbEDXBAKBZTTqB0xYOcthMaL7g195HJwjgoC7VyFBkN8cJTjYCMxA/Wl+qHUZqkf9Y8iOSz24Y6YsPKP5YaHW1pSUWWM36pId0Spkd4xwmfGW8HROdNljNei3IEIv6QKVLqzjv9sGoF5SlCmO6jcQtopMiUY7nlJZLHMe0bSGa0yZF/Y52qyFjzh7PcXAUmGC3GvHL7Vfjsbu+pQnIhupyFNmV9lQCp+TiWUy6mFW7oFV6CNH+d+r5Kr351YbSj9aXv4WhYqz5EUnshpHYhzZVyVO6Y5UW5zycRWatRVOiFZDq4TEbdbCYdOjoVpJcPzvvQWt7AHOnlaRNYFavbTXp4Cq2wKDv+ZNRVglT8ve0NVihtHdyFZm1GltFlK1wFZm1vLYiuxl33/I1fGfpjOgqY/H3y3NAaYkFDpsh5VRODdy8+PA1eOC738AEZwHauoJxxzCmdA+WJKY1C4ilN4EZSJmXmlB8x6N/wIO1b/UqEH35WxgqxlqZG01jh5m+THXUY9VPXIGHlhvm9UegEwTc8egftOnGhgSf0R03zMUn5zrwcv0nkKPrzVrNerz+7jlYomtSJE6jJjptCIbEnnUciswQRQajUYDXH9YWoI71yzlsSu+8KWUFCIUk6HSc5nMrKjDFPcTq14NvnlVarUcrGVS/YjAs9jqVU38uSmt1Lzq7Q3GLcwsCh4Xl4/Fg7VvJ07EMfrX++tH6W/g/0or8x5ofkcRuFDJ/Vik+uWwyDr55FoGQqKwGxnMQJSnu4dpQXZ6U06XU1ZqT1i5VO38kRg/vuOHvtPNiBQFQ/GaFNgO6fRGEoz45h00PIdpC6o4bZqc8N/GB/s7SmfjO0plxItFbgm8qWtr9cNgMMOgFrSROJyjrU7z+7rm04pNOYPrrRxsrkdWR6kfsLyR2o5CGUy3472OfK8sTQmldHuEAu2QAZ+AyPlzpPq29AREbM1g5KR9S9XjBD8YYunxhePwiIhJQteiizOemYKD92mKXTlSn98GwiI7ucL/Ep7/NMseKRTTWmoWS2I1C9rx6Eh5fWCliFziEoukdFzxBrb4z3cOV6dO6r9OoxGm102HSHgq1rGso+7Wlezj1At9rO6x0Y+mP+I4Vi2isNQvNqdgdOnQItbW1EEURNTU1Scscnjp1Ctu3b4fP58P8+fPx8MMPQ6fT4fjx43j88ccRiUTgcDjw2GOPYeLEibkc6qjiXGu3tiC1GhhgACIxSxWme7hy8Wk92NO2/qY7pHs4646e6bf49Ed8x5JFNNL8iAMhZ2LX0tKCXbt2oa6uDgaDAatXr8bll1+O6dN7fuFbtmzBjh07MG/ePDz44IN46aWXcOutt2LLli145plntAV5duzYgdra2lwNdVTxqyOnIcVUFcQGBpQi+sy+rlx8Wg/mtG2gXX3TPZxDKT5jzSIaK+RM7I4dO4YFCxbA4XAAACorK3H48GFs2rQJANDU1IRgMIh58+YBAKqrq/HUU09h5cqVuOeeezBzptLGZ8aMGfiv//qvXA1zVNFwqgUv13+S9nWOAz77shtmoy7OZ5ZIpk/r/lhVgzlty4VzP534AEgdoR0ExpJFNFbImdi1trbC6XRq2y6XCydOnEj7utPpREtLCwwGA6qqqgAAsixj9+7duOqqq5Ku7/F44PF44vY1NzcP9m30m1xkntcdPQNZVtaLSKyEAJRlHR02Q799Zv21qgZz2pYr536i+AzmuhDE6CBnYifLctzaC4yxuO3eXg+Hw9i2bRtEUcSGDRuSrr9nzx7s3r07R6MfGLl6kL5o9oCxHl9d7BRWJ3AoKlA6k/TXGuqvVTWY07ahcu6PlfQQIntyJnZlZWVoaGjQtt1uN1wuV9zrbrdb225ra9Ne9/l8uOuuu+BwOFBbWwu9Pr7bLADU1NRgxYoVcfuam5uTgiDDQS4epIZTLfAHRS0aoQqdKnrjHOa44/tjDQ3EqhqsadtQOffHSnoIkT05KxdbuHAh3n77bbS3tyMQCODIkSNYtGiR9vrEiRNhNBpx/PhxAMDBgwe117ds2YKpU6fiJz/5CQwGQ8rr2+12TJo0Ke5fWVlZrm6nT2Sz8ldfqTt6BnarATynrDcbW1NaWmyGTogvMu2PNdTXHmy5YKjKpkbCvRJDS84su9LSUmzevBlr165FJBLBypUrUV5ejvXr1+Puu+/G3LlzsXPnTjz00EPwer2YPXs21q5di5MnT6K+vh7Tp0/XLDeXy4Vnn302V0MddHIxFUtXHWAy6LCx+u8HxRoaKSkTQ+HcHyn3SgwdHGMD7TY2cmhsbERFRQXq6+sxadKkYRtHrM8u9kEaiIWirFIVSCrzKrKb8dhd39ICIgP1mQ3WdUYD+XSvo5nBeq6pgiIH5CLPqjdLZLCtoTHzCZgBSg/JL0jscsRgP0hDkahK6RjEWIbEbhSRa0uE0jGIsQw17yQ0chFFJoiRAokdoUHpGMRYhsSO0BiJ6yAQxGBBYkdojMR1EAhisKAABREHpWMQYxWy7AiCyAtI7AiCyAtI7AiCyAtI7AiCyAvGVIBCkpQcsZHUsZggiIGhPs/q891fxpTYff755wAwIhp4EgQxuHz++eeYOnVqv88fU2I3efJkAMALL7wwapdeVLst7927d8Q0I+0rY+EegLFxH2PhHpqamrB27Vrt+e4vY0rs1K7GEydOHNZ+doNBWVkZ3cMIYSzcx1i4h3Rdy7OFAhQEQeQFJHYEQeQFJHYEQeQFY0rs7HY7Nm3aBLvdPtxD6Td0DyOHsXAfdA89jKkFdwiCINIxpiw7giCIdJDYEQSRF4wqsTt06BCuu+46LF26FHv37k16/dSpU6iurkZlZSW2b98OURQBAOfPn8eaNWtwzTXX4K677oLP5xvqoWv09x6OHz+OlStXoqqqCjU1NWhqahrqoWv09x5UTp48iTlz5gzVcFPS33tobW3FnXfeiRtvvBGrV69GY2PjUA9do7/30NjYiDVr1qCqqgq33377iP5bUtm6dSvq6uq07X4902yU0NzczJYsWcI6OjqYz+djy5cvZ5988kncMddffz17//33GWOMPfDAA2zv3r2MMcbuvPNO9rvf/Y4xxtju3bvZk08+OaRjVxnIPSxZsoSdOnWKMcbYyy+/zDZu3DikY1cZyD0wxpjf72erV69ml1xyyVAOO46B3ENNTQ3bt28fY4yxffv2sXvuuWcoh64xkHu47777tO9feOEF9oMf/GBIx66SzT00NzezDRs2sPLycvab3/xG29+fZ3rUWHbHjh3DggUL4HA4YLFYUFlZicOHD2uvNzU1IRgMYt68eQCA6upqHD58GJFIBO+++y4qKyvj9g8H/b2HcDiMe+65BzNnzgQAzJgxA19++eVw3EK/70HliSeeQE1NzVAPO47+3kN7eztOnz6N1atXAwBuuukm3HvvvcNwBwP7PciyDK/XCwAIBAIwmUxDPn6g93sAFMuvoqIC1157rbavv8/0qBG71tZWOJ1ObdvlcqGlpSXt606nEy0tLejo6IDNZoNOp4vbPxz09x4MBgOqqqoAKH+ou3fvxlVXXTV0A4+hv/cAAPX19QgGg7jmmmuGbsAp6O89nDt3DhMmTMATTzyBm266CXfffTf0ev2Qjj3dGPvye7jnnnvw/PPP49vf/jaee+45rF+/fugGHkNv9wAA69atw6pVq+L29feZHjViJ8syOI7TthljcdvpXk88DkDS9lDR33tQCYfDuO+++yCKIjZs2DA0g06gv/fgdrtRW1uLH/7wh0M63lT09x5EUcTJkyexYMEC/OY3v0FFRQW2bds2pGPvbYzZvH7//ffjRz/6Ef70pz/h4YcfxqZNm8CGIQOtt3tIR3+f6VEjdmVlZXC73dq22+2Gy+VK+3pbWxtcLheKi4vR3d2t9cJKPG8o6e89AIDP58O6desgiiJqa2uHzaLo7z0cPXoUnZ2dmmMcAKqqqrTp1FDS33twOp2wWq1YsmQJAGDZsmU4ceLE0A08hv7eQ3t7O86ePavNDCorK+F2u9HR0TF0g08zxmyfzf4+06NG7BYuXIi3334b7e3tCAQCOHLkCBYtWqS9PnHiRBiNRhw/fhwAcPDgQSxatAh6vR7z58/H73//ewDAgQMH4s4bDfcAAFu2bMHUqVPxk5/8ZMDdHwZCf+9h1apV+J//+R8cPHgQBw8e1F6z2Wyj5h6mTJmCsrIyvPHGGwCAP/7xj5g9e/aQj38g91BUVASj0YiGhgYASpTfarWiuLh4xN1DOvr9TA80ojKU/Pa3v2XXX389W7p0Kfv3f/93xhhj69atYydOnGCMMXbq1Cl20003scrKSvb973+fhUIhxhhjjY2N7LbbbmPXXnst+8d//EfW2dk5qu7ho48+Ypdccgm77rrr2A033MBuuOEGtm7dulF1D4kMZzSWsf7fw6effspuu+02dv3117NbbrmFffbZZ8N1C/2+h7/+9a9s5cqVbNmyZeyWW25hH3300Yi9B5X7778/Lhrbn2eaysUIgsgLRs00liAIYiCQ2BEEkReQ2BEEkReQ2BEEkReQ2BEEkReMqdXFiNHPjBkzcMkll4Dnez6H58yZg0cffRRVVVV48cUXs+5Y293djX/6p3/CCy+8kKvhEqMIEjtixLFnz56USa5qMnK2dHV14YMPPhisYRGjHBI7YtQwY8YMvP322zh69CheeeUVBAIB2Gw2/PjHP8b999+vlTxdccUVuPfee/HAAw8gGAyiqqoKdXV1EARhmO+AGE5I7IgRR01NTdw09rnnnkNJSUncMWfOnMHrr78Om82Gn/3sZ5g0aRKee+45+P1+bN++Hd3d3Xj88cexfPnyPluExNiExI4YcaSbxsYyY8YMra7229/+Nu688058+eWXWLhwIX7wgx+goKAAXV1dQzFcYpRA0VhiVGKxWLTvy8vLUV9fj1tuuQVNTU1YtWoVPvzww2EcHTESIcuOGPXs3LkTjDFs2bIFFRUV+Pjjj/HJJ5/gm9/8JiRJyrpPGjG2IcuOGPXU1NTg9OnTWLZsGW666SZMmjQJ119/PZxOJ8rLy3H99dcPS782YmRBXU8IgsgLyLIjCCIvILEjCCIvILEjCCIvILEjCCIvILEjCCIvILEjCCIvILEjCCIvILEjCCIv+P8ByQrtdPhcMIcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 316.8x307.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(4.4,4.27)})\n",
    "sns.set_style(\"ticks\")\n",
    "g=sns.regplot(x=\"First\",\n",
    "           y=\"Second\", robust=True,\n",
    "           data=df_typ2_firing)\n",
    "plt.xlim(0,0.1)\n",
    "#plt.ylim(0,0.14)\n",
    "if save_file:\n",
    "    plt.savefig(fig_save_add + 'L:/analysis_nima/epoch_based_analysis/Triplet_type2_fr_correlation_analysis/awake_sws/Type2_fr_awake_sws_top20%_awake.pdf', dpi=600, format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d261d4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(94, 4)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_typ2_firing.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fed6c89",
   "metadata": {},
   "source": [
    "#### Frequency based on least x% of first element of pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "90235458",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp/ipykernel_19120/1393239596.py:9: RuntimeWarning: Mean of empty slice\n",
      "  'Second':[np.nanmean(data_list[i+1]['avg_firing_perSecond'][indDict['least20_avg_firing'][0]])],\n"
     ]
    }
   ],
   "source": [
    "# plotting average firing rate \n",
    "\n",
    "df_typ2_firing = pd.DataFrame()\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    if indDict['type2'] == 1:\n",
    "        df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'First':[np.nanmean(indDict['avg_firing_perSecond'][indDict['least20_avg_firing'][0]])],\n",
    "                                                             'Second':[np.nanmean(data_list[i+1]['avg_firing_perSecond'][indDict['least20_avg_firing'][0]])],\n",
    "                                                             'name':indDict['mouseName'], \n",
    "                                                             'epochID': i}), ignore_index = True)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "920c106a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.2)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 316.8x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df_temp = pd.DataFrame()\n",
    "df_temp = df_typ2_firing.melt(id_vars=['name', 'epochID'], var_name='Pattern', value_name='FiringRate')\n",
    "\n",
    "sns.set(rc={'figure.figsize':(4.4,8.27)})\n",
    "sns.set_style(\"ticks\")\n",
    "g=sns.boxplot(x=\"Pattern\", y=\"FiringRate\",dodge=False,fliersize=3, palette=['darkorange','royalblue'],linewidth=4,data=df_temp )\n",
    "#g=sns.boxplot(x=\"Pattern\", y=\"FiringRate\",dodge=False,fliersize=3, palette=['limegreen','darkorange'],linewidth=4,data=df_temp )\n",
    "\n",
    "#g.set_xticklabels(['SWSn','Awake','SWSn.1'])\n",
    "plt.ylim(0,0.2)\n",
    "#plt.ylim(-2,2)\n",
    "\n",
    "#if save_file:\n",
    "  #  df_typ2_firing.to_csv(path_or_buf=file_save_add + 'L:/analysis_nima/statistics_epoch_based/Triplet_type2_fr_z_score/awake_sws/Type2_fr_all_cells_least20%_awake.csv')\n",
    "#if save_fig:\n",
    "  #  plt.savefig(fig_save_add + 'L:/analysis_nima/epoch_based_analysis/Triplet_type2_fr_z_score/awake_sws/Type2_fr_all_cells_least20%_awake_boxplot.pdf', dpi=600, format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "739df233",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 316.8x307.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(4.4,4.27)})\n",
    "sns.set_style(\"ticks\")\n",
    "g=sns.regplot(x=\"First\",\n",
    "           y=\"Second\", robust=True,\n",
    "           data=df_typ2_firing)\n",
    "plt.xlim(0,0.1)\n",
    "#plt.ylim(0,0.14)\n",
    "if save_file:\n",
    "    plt.savefig(fig_save_add + 'L:/analysis_nima/epoch_based_analysis/Triplet_type2_fr_correlation_analysis/awake_sws/Type2_fr_awake_sws_least20%_awake.pdf', dpi=600, format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44f10c42",
   "metadata": {},
   "source": [
    "#### Frequency divided to n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebc180c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: RuntimeWarning: Mean of empty slice\n",
      "  import sys\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: RuntimeWarning: Mean of empty slice\n",
      "  \n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: RuntimeWarning: Mean of empty slice\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "# plotting average firing rate \n",
    "\n",
    "df_typ2_firing = pd.DataFrame()\n",
    "\n",
    "\n",
    "for i, indDict in enumerate (data_list):\n",
    "    df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'1th':[np.nanmean(indDict['div_avg_firing_perSecond'][0])], \n",
    "                                                         '2th':[np.nanmean(indDict['div_avg_firing_perSecond'][1])], \n",
    "                                                         '3th':[np.nanmean(indDict['div_avg_firing_perSecond'][2])], \n",
    "                                                         'name':indDict['mouseName'], \n",
    "                                                         'Pattern': indDict['type2'], \n",
    "                                                         'epochID': i}), ignore_index = True)\n",
    "    \n",
    "df_typ2_firing = df_typ2_firing[df_typ2_firing.Pattern != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "998a0489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'subEpoch'), Text(0, 0.5, 'Mean Frequency Rate')]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 316.8x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###choose pattern to plot####\n",
    "df_temp = pd.DataFrame()\n",
    "df_temp = df_typ2_firing.melt(id_vars=['name', 'Pattern', 'epochID'], var_name='subEpoch', value_name='firing_rate')\n",
    "x = df_temp[df_temp.Pattern == 2]\n",
    "#ax.set_facecolor('xkcd:salmon')\n",
    "#ax.set_facecolor((1, 0.47, 0.42)) ####REM####\n",
    "#ax.set_facecolor((0.4,0.6,0.7)) ####SWS####\n",
    "#sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "#sns.set()\n",
    "sns.set(rc={'figure.figsize':(4.4,8.27)})\n",
    "sns.set_style(\"ticks\")\n",
    "\n",
    "g=sns.boxplot(x=\"subEpoch\", y=\"firing_rate\",color='blue', data=x)\n",
    "g.set_xticklabels(['1st','2nd','3rd'])\n",
    "g.set_title('REM')\n",
    "g.set(xlabel='subEpoch', ylabel='Mean Frequency Rate')\n",
    "#g.sns.set_style(\"whitegrid\", {'axes.grid' : False})\n",
    "#plt.ylim(0.05,0.07)\n",
    "#plt.ylim(0.011,0.12)\n",
    "\n",
    "#if save_fig:\n",
    " #   plt.savefig(fig_save_add + 'L:/analysis_nima/epoch_based_analysis/Triplet_type2_fr/Type2_fr_divided_thirds_rem_all_cells.pdf', dpi=300, format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "110a65f8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_temp = pd.DataFrame()\n",
    "df_temp = df_typ2_firing.melt(id_vars=['name', 'Pattern', 'epochID'], var_name='subEpoch', value_name='firing_rate')\n",
    "\n",
    "myGrid = sns.FacetGrid(data=df_temp, col='name', col_wrap=2, aspect=2, ylim=[-.05, 0.6])\n",
    "myplot = myGrid.map(sns.violinplot, 'Pattern', 'firing_rate', 'subEpoch')\n",
    "myplot.add_legend()\n",
    "plt.ylim([-.025, 0.3])\n",
    "if save_file:\n",
    "    df_temp.to_csv(path_or_buf=file_save_add + 'L:/analysis_nima/statistics_epoch_based/Triplet_type2/Type2_fr_divided_thirds.csv')\n",
    "if save_fig:\n",
    "    plt.savefig(fig_save_add + 'L:/analysis_nima/epoch_based_analysis/Triplet_type2_fr/Type2_fr_divided_thirds.pdf', dpi=300, format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75264bbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2645217",
   "metadata": {},
   "source": [
    "#### Frequency based on top x% divided to n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbb263d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting average firing rate \n",
    "\n",
    "df_typ2_firing = pd.DataFrame()\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'1th':[np.nanmean(indDict['div_avg_firing_perSecond'][0][indDict['top20_avg_firing'][0]])], \n",
    "                                                         '2th':[np.nanmean(indDict['div_avg_firing_perSecond'][1][indDict['top20_avg_firing'][0]])], \n",
    "                                                         '3th':[np.nanmean(indDict['div_avg_firing_perSecond'][2][indDict['top20_avg_firing'][0]])], \n",
    "                                                         'name':indDict['mouseName'], \n",
    "                                                         'Pattern': indDict['type2'], \n",
    "                                                         'epochID': i}), ignore_index = True)\n",
    "    \n",
    "df_typ2_firing = df_typ2_firing[df_typ2_firing.Pattern != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccf61c98",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_temp = pd.DataFrame()\n",
    "df_temp = df_typ2_firing.melt(id_vars=['name', 'Pattern', 'epochID'], var_name='subEpoch', value_name='firing_rate')\n",
    "\n",
    "myGrid = sns.FacetGrid(data=df_temp, col='name', col_wrap=2, aspect=2, ylim=[-.05, 0.6])\n",
    "myplot = myGrid.map(sns.violinplot, 'Pattern', 'firing_rate', 'subEpoch')\n",
    "myplot.add_legend()\n",
    "plt.ylim([-.025, 0.3])\n",
    "if save_file:\n",
    "    df_temp.to_csv(path_or_buf=file_save_add + 'L:/analysis_nima/statistics_epoch_based/Triplet_type2/Type2_fr_divided_thirds_top20%.csv')\n",
    "if save_fig:\n",
    "    plt.savefig(fig_save_add + 'L:/analysis_nima/epoch_based_analysis/Triplet_type2_fr/Type2_fr_divided_thirds_top20%.pdf', dpi=300, format='pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f50d15c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd5cce2",
   "metadata": {},
   "source": [
    "#### Frequency based on least x% divided to n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1897a0bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting average firing rate \n",
    "\n",
    "df_typ2_firing = pd.DataFrame()\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'1th':[np.nanmean(indDict['div_avg_firing_perSecond'][0][indDict['least20_avg_firing'][0]])], \n",
    "                                                         '2th':[np.nanmean(indDict['div_avg_firing_perSecond'][1][indDict['least20_avg_firing'][0]])], \n",
    "                                                         '3th':[np.nanmean(indDict['div_avg_firing_perSecond'][2][indDict['least20_avg_firing'][0]])], \n",
    "                                                         'name':indDict['mouseName'], \n",
    "                                                         'Pattern': indDict['type2'],\n",
    "                                                         'epochID': i}), ignore_index = True)\n",
    "    \n",
    "df_typ2_firing = df_typ2_firing[df_typ2_firing.Pattern != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e905c8d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_temp = pd.DataFrame()\n",
    "df_temp = df_typ2_firing.melt(id_vars=['name', 'Pattern', 'epochID'], var_name='subEpoch', value_name='firing_rate')\n",
    "\n",
    "myGrid = sns.FacetGrid(data=df_temp, col='name', col_wrap=2, aspect=2, ylim=[-.05, 0.6])\n",
    "myplot = myGrid.map(sns.violinplot, 'Pattern', 'firing_rate', 'subEpoch')\n",
    "myplot.add_legend()\n",
    "plt.ylim([-.025, 0.3])\n",
    "if save_file:\n",
    "    df_temp.to_csv(path_or_buf=file_save_add + 'L:/analysis_nima/statistics_epoch_based/Triplet_type2/Type2_fr_divided_thirds_least20%.csv')\n",
    "if save_fig:\n",
    "    plt.savefig(fig_save_add + 'L:/analysis_nima/epoch_based_analysis/Triplet_type2_fr/Type2_fr_divided_thirds_least20%.pdf', dpi=300, format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "432b3681",
   "metadata": {},
   "source": [
    "#### Frequency based on top x% of first element divided to n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12955d28",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: RuntimeWarning: Mean of empty slice\n",
      "  if sys.path[0] == '':\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: RuntimeWarning: Mean of empty slice\n",
      "  app.launch_new_instance()\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: RuntimeWarning: Mean of empty slice\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: RuntimeWarning: Mean of empty slice\n"
     ]
    }
   ],
   "source": [
    "# plotting average firing rate \n",
    "\n",
    "df_typ2_firing = pd.DataFrame()\n",
    "df_typ2_firing1 = pd.DataFrame()\n",
    "df_typ2_firing2 = pd.DataFrame()\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    if indDict['type2'] == 1:\n",
    "        df_typ2_firing1 = df_typ2_firing1.append(pd.DataFrame({'1th':[np.nanmean(indDict['div_avg_firing_perSecond'][0][indDict['top20_avg_firing'][0]])], \n",
    "                                                               '2th':[np.nanmean(indDict['div_avg_firing_perSecond'][1][indDict['top20_avg_firing'][0]])], \n",
    "                                                               '3th':[np.nanmean(indDict['div_avg_firing_perSecond'][2][indDict['top20_avg_firing'][0]])],\n",
    "                                                               'name':indDict['mouseName'], \n",
    "                                                               'epochID': i}), ignore_index = True)\n",
    "        \n",
    "        df_typ2_firing2 = df_typ2_firing2.append(pd.DataFrame({'1th':[np.nanmean(data_list[i+1]['div_avg_firing_perSecond'][0][indDict['top20_avg_firing'][0]])], \n",
    "                                                               '2th':[np.nanmean(data_list[i+1]['div_avg_firing_perSecond'][1][indDict['top20_avg_firing'][0]])], \n",
    "                                                               '3th':[np.nanmean(data_list[i+1]['div_avg_firing_perSecond'][2][indDict['top20_avg_firing'][0]])], \n",
    "                                                               'name':indDict['mouseName'], \n",
    "                                                               'epochID': i}), ignore_index = True)\n",
    "\n",
    "\n",
    "df_typ2_firing1 = df_typ2_firing1.melt(id_vars=['name', 'epochID'], var_name='levels', value_name='First')\n",
    "df_typ2_firing2 = df_typ2_firing2.melt(id_vars=['name', 'epochID'], var_name='levels', value_name='Second')\n",
    "\n",
    "df_typ2_firing = df_typ2_firing1.merge(df_typ2_firing2, on=['name', 'levels', 'epochID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc0b3a0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 316.8x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###choose pattern to plot####\n",
    "df_temp = pd.DataFrame()\n",
    "df_temp = df_typ2_firing.melt(id_vars=['name', 'levels', 'epochID'], var_name='Pattern', value_name='firing_rate')\n",
    "x = df_temp[df_temp.Pattern == 'First'].groupby(by = [\"levels\", \"Pattern\"], as_index=False).agg(np.median)\n",
    "ax = plt.gca()\n",
    "#ax.set_facecolor('xkcd:salmon')\n",
    "#ax.set_facecolor((1, 0.47, 0.42)) ####REM####\n",
    "#ax.set_facecolor((0.4,0.6,0.7)) ####SWS####\n",
    "#sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "#sns.set()\n",
    "sns.set(rc={'figure.figsize':(4.4,8.27)})\n",
    "sns.set_style(\"ticks\")\n",
    "\n",
    "g=sns.lineplot(x=\"levels\", y=\"firing_rate\", style=\"Pattern\",color='blue',markers=True,err_style='band',legend = False, data=x)\n",
    "g.set_xticklabels(['1st','2nd','3rd'])\n",
    "g.set_title('SWS')\n",
    "g.set(xlabel='subEpoch', ylabel='Mean Frequency Rate')\n",
    "#g.sns.set_style(\"whitegrid\", {'axes.grid' : False})\n",
    "#plt.ylim(0.05,0.07)\n",
    "#plt.ylim(0.011,0.12)\n",
    "\n",
    "if save_fig:\n",
    "    plt.savefig(fig_save_add + 'L:/analysis_nima/epoch_based_analysis/Triplet_type2_fr/Type2_fr_divided_thirds_sws_top20_basedSWS_all_cells.pdf', dpi=300, format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc1a9548",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_temp = pd.DataFrame()\n",
    "df_temp = df_typ2_firing.melt(id_vars=['name', 'levels', 'epochID'], var_name='Pattern', value_name='firing_rate')\n",
    "\n",
    "myGrid = sns.FacetGrid(data=df_temp, col='name', col_wrap=2, aspect=2, ylim=[-.05, 0.6])\n",
    "myplot = myGrid.map(sns.violinplot, 'Pattern', 'firing_rate', 'levels')\n",
    "myplot.add_legend()\n",
    "plt.ylim([-.025, 0.3])\n",
    "if save_file:\n",
    "    df_temp.to_csv(path_or_buf=file_save_add + 'L:/analysis_nima/statistics_epoch_based/Triplet_type2/Type2_fr_divided_thirds_based_on_top_20%_first_element.csv')\n",
    "if save_fig:\n",
    "    plt.savefig(fig_save_add + 'L:/analysis_nima/epoch_based_analysis/Triplet_type2_fr/Type2_fr_divided_thirds_based_on_top_20%_first_element.pdf', dpi=300, format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f650252e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7610186",
   "metadata": {},
   "source": [
    "#### Frequency based on least x% of first element divided to n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c60edfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: RuntimeWarning: Mean of empty slice\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: RuntimeWarning: Mean of empty slice\n",
      "  if sys.path[0] == '':\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: RuntimeWarning: Mean of empty slice\n",
      "  app.launch_new_instance()\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: RuntimeWarning: Mean of empty slice\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: RuntimeWarning: Mean of empty slice\n"
     ]
    }
   ],
   "source": [
    "# plotting average firing rate \n",
    "\n",
    "df_typ2_firing = pd.DataFrame()\n",
    "df_typ2_firing1 = pd.DataFrame()\n",
    "df_typ2_firing2 = pd.DataFrame()\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    if indDict['type2'] == 1:\n",
    "        df_typ2_firing1 = df_typ2_firing1.append(pd.DataFrame({'1th':[np.nanmean(indDict['div_avg_firing_perSecond'][0][indDict['least20_avg_firing'][0]])], \n",
    "                                                               '2th':[np.nanmean(indDict['div_avg_firing_perSecond'][1][indDict['least20_avg_firing'][0]])], \n",
    "                                                               '3th':[np.nanmean(indDict['div_avg_firing_perSecond'][2][indDict['least20_avg_firing'][0]])],\n",
    "                                                               'name':indDict['mouseName'], \n",
    "                                                               'epochID': i}), ignore_index = True)\n",
    "        \n",
    "        df_typ2_firing2 = df_typ2_firing2.append(pd.DataFrame({'1th':[np.nanmean(data_list[i+1]['div_avg_firing_perSecond'][0][indDict['least20_avg_firing'][0]])], \n",
    "                                                               '2th':[np.nanmean(data_list[i+1]['div_avg_firing_perSecond'][1][indDict['least20_avg_firing'][0]])], \n",
    "                                                               '3th':[np.nanmean(data_list[i+1]['div_avg_firing_perSecond'][2][indDict['least20_avg_firing'][0]])], \n",
    "                                                               'name':indDict['mouseName'], \n",
    "                                                               'epochID': i}), ignore_index = True)\n",
    "\n",
    "\n",
    "df_typ2_firing1 = df_typ2_firing1.melt(id_vars=['name', 'epochID'], var_name='levels', value_name='First')\n",
    "df_typ2_firing2 = df_typ2_firing2.melt(id_vars=['name', 'epochID'], var_name='levels', value_name='Second')\n",
    "\n",
    "df_typ2_firing = df_typ2_firing1.merge(df_typ2_firing2, on=['name', 'levels', 'epochID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4e66ccd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 316.8x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###choose pattern to plot####\n",
    "df_temp = pd.DataFrame()\n",
    "df_temp = df_typ2_firing.melt(id_vars=['name', 'levels', 'epochID'], var_name='Pattern', value_name='firing_rate')\n",
    "x = df_temp[df_temp.Pattern == 'Second'].groupby(by = [\"levels\", \"Pattern\"], as_index=False).agg(np.median)\n",
    "ax = plt.gca()\n",
    "#ax.set_facecolor('xkcd:salmon')\n",
    "#ax.set_facecolor((1, 0.47, 0.42)) ####REM####\n",
    "#ax.set_facecolor((0.4,0.6,0.7)) ####SWS####\n",
    "#sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "#sns.set()\n",
    "sns.set(rc={'figure.figsize':(4.4,8.27)})\n",
    "sns.set_style(\"ticks\")\n",
    "\n",
    "g=sns.lineplot(x=\"levels\", y=\"firing_rate\", style=\"Pattern\",color='blue',markers=True,err_style='band',legend = False, data=x)\n",
    "g.set_xticklabels(['1st','2nd','3rd'])\n",
    "g.set_title('REM')\n",
    "g.set(xlabel='subEpoch', ylabel='Mean Frequency Rate')\n",
    "#g.sns.set_style(\"whitegrid\", {'axes.grid' : False})\n",
    "#plt.ylim(0.05,0.07)\n",
    "#plt.ylim(0.011,0.12)\n",
    "\n",
    "if save_fig:\n",
    "    plt.savefig(fig_save_add + 'L:/analysis_nima/epoch_based_analysis/Triplet_type2_fr/Type2_fr_divided_thirds_rem_least20_basedSWS_all_cells.pdf', dpi=300, format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf90220b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_temp = pd.DataFrame()\n",
    "df_temp = df_typ2_firing.melt(id_vars=['name', 'levels', 'epochID'], var_name='Pattern', value_name='firing_rate')\n",
    "\n",
    "myGrid = sns.FacetGrid(data=df_temp, col='name', col_wrap=2, aspect=2, ylim=[-.05, 0.6])\n",
    "myplot = myGrid.map(sns.violinplot, 'Pattern', 'firing_rate', 'levels')\n",
    "myplot.add_legend()\n",
    "plt.ylim([-.025, 0.3])\n",
    "if save_file:\n",
    "    df_temp.to_csv(path_or_buf=file_save_add + 'L:/analysis_nima/statistics_epoch_based/Triplet_type2/Type2_fr_divided_thirds_based_on_least_20%_first_element.csv')\n",
    "if save_fig:\n",
    "    plt.savefig(fig_save_add + 'L:/analysis_nima/epoch_based_analysis/Triplet_type2_fr/Type2_fr_divided_thirds_based_on_least_20%_first_element.pdf', dpi=300, format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eac55c4d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "463f543e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fe579f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b005947",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e13602fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36df21f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d148582",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32610778",
   "metadata": {},
   "outputs": [],
   "source": [
    "# THIPLLET WITH n DIVISION \n",
    "# SWS REM SWSn+1\n",
    "# SWS AWAKE SWSn+1\n",
    "# AWAKE SWS REM SWSn+1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40c83c47",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a873cb6f",
   "metadata": {},
   "source": [
    "### Read and write data as JSON file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e91ae1ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('I:/Marco/analysis_nima/allData_JSON/fullData_with_analyses.json', 'w') as wr:\n",
    "    json.dump(data_list , wr, cls=NumpyEncoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "207e4d28",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(r\"I:/Marco/analysis_nima/allData_JSON/fullData_with_analyses.json\", \"r\") as read_file:\n",
    "    data_list = json.load(read_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fad70180",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change lists to array\n",
    "[data_dict.update({key: np.asarray(data_dict[key])}) for data_dict in data_list for key in data_dict.keys() if isinstance(data_dict[key], list)];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e3422c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a3ba1f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "41269315",
   "metadata": {},
   "source": [
    "### Correlation analysis Frequency 3rd-1st"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6723d2c6",
   "metadata": {},
   "source": [
    "#### Per epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bffbeb62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting average firing rate \n",
    "\n",
    "df_typ2_firing = pd.DataFrame()\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'FiringRate':[np.nanmean(np.where(np.isnan(indDict['div_avg_firing_perSecond'][2]), 0, indDict['div_avg_firing_perSecond'][2]) - np.where(np.isnan(indDict['div_avg_firing_perSecond'][0]), 0, indDict['div_avg_firing_perSecond'][0]))], \n",
    "                                                         'name':indDict['mouseName'], \n",
    "                                                         'Pattern': indDict['type2'], \n",
    "                                                         'epochID': i}), ignore_index = True)\n",
    "    \n",
    "df_typ2_firing = df_typ2_firing[df_typ2_firing.Pattern != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6f98b3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.005680</td>\n",
       "      <td>0.002508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002472</td>\n",
       "      <td>0.005710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.006880</td>\n",
       "      <td>0.003366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "0  0.005680  0.002508\n",
       "1  0.002472  0.005710\n",
       "2  0.006880  0.003366"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_typ2_firing[df_typ2_firing.Pattern==1][\"FiringRate\"].values\n",
    "y = df_typ2_firing[df_typ2_firing.Pattern==2][\"FiringRate\"].values\n",
    "\n",
    "df_correlation_type2 = pd.DataFrame({\"x\":x, \"y\":y})\n",
    "df_correlation_type2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efcfd386",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=x, y=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47d2b088",
   "metadata": {},
   "source": [
    "#### per cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02dcaaf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting average firing rate \n",
    "\n",
    "df_typ2_firing = pd.DataFrame()\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'FiringRate':np.where(np.isnan(indDict['div_avg_firing_perSecond'][2]), 0, indDict['div_avg_firing_perSecond'][2]) - np.where(np.isnan(indDict['div_avg_firing_perSecond'][0]), 0, indDict['div_avg_firing_perSecond'][0]), \n",
    "                                                         'name':indDict['mouseName'], \n",
    "                                                         'Pattern': indDict['type2'], \n",
    "                                                         'epochID': i}), ignore_index = True)\n",
    "    \n",
    "df_typ2_firing = df_typ2_firing[df_typ2_firing.Pattern != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff3e3c9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.011862</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.119672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "0  0.000000  0.000000\n",
       "1  0.011862  0.000000\n",
       "2  0.000000  0.119672"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_typ2_firing[df_typ2_firing.Pattern==1][\"FiringRate\"].values\n",
    "y = df_typ2_firing[df_typ2_firing.Pattern==2][\"FiringRate\"].values\n",
    "\n",
    "df_correlation_type2 = pd.DataFrame({\"x\":x, \"y\":y})\n",
    "df_correlation_type2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "485a0eaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=x, y=y, marker = '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "543396b2",
   "metadata": {},
   "source": [
    "### Correlation analysis Amplitude 3rd-1st"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b50876b2",
   "metadata": {},
   "source": [
    "### Per epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b609a056",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting average firing rate \n",
    "\n",
    "df_typ2_firing = pd.DataFrame()\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'FiringRate':[np.nanmean(np.where(np.isnan(indDict['avg_amplitude'][2]), 0, indDict['avg_amplitude'][2]) - np.where(np.isnan(indDict['avg_amplitude'][0]), 0, indDict['avg_amplitude'][0]))], \n",
    "                                                         'name':indDict['mouseName'], \n",
    "                                                         'Pattern': indDict['type2'], \n",
    "                                                         'epochID': i}), ignore_index = True)\n",
    "    \n",
    "df_typ2_firing = df_typ2_firing[df_typ2_firing.Pattern != 0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc594ae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_typ2_firing[df_typ2_firing.Pattern==1][\"FiringRate\"].values\n",
    "y = df_typ2_firing[df_typ2_firing.Pattern==2][\"FiringRate\"].values\n",
    "\n",
    "df_correlation_type2 = pd.DataFrame({\"x\":x, \"y\":y})\n",
    "df_correlation_type2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bd5175e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.regplot(x=x, y=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708f7eb9",
   "metadata": {},
   "source": [
    "### Per cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63d301aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting average firing rate \n",
    "\n",
    "df_typ2_firing = pd.DataFrame()\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    df_typ2_firing = df_typ2_firing.append(pd.DataFrame({'FiringRate':(np.where(np.isnan(indDict['avg_amplitude'][2]), 0, indDict['avg_amplitude'][2]) - np.where(np.isnan(indDict['avg_amplitude'][0]), 0, indDict['avg_amplitude'][0])), \n",
    "                                                         'name':indDict['mouseName'], \n",
    "                                                         'Pattern': indDict['type2'], \n",
    "                                                         'epochID': i}), ignore_index = True)\n",
    "    \n",
    "df_typ2_firing = df_typ2_firing[df_typ2_firing.Pattern != 0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3041dae",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_typ2_firing[df_typ2_firing.Pattern==1][\"FiringRate\"].values\n",
    "y = df_typ2_firing[df_typ2_firing.Pattern==2][\"FiringRate\"].values\n",
    "\n",
    "df_correlation_type2 = pd.DataFrame({\"x\":x, \"y\":y})\n",
    "df_correlation_type2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b0f1009",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.regplot(x=x, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0702134",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "85ebeff5",
   "metadata": {},
   "source": [
    "## Regression to Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "30f08c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# all cells\n",
    "test = []\n",
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    if (indDict[\"type2\"]) and (indDict[\"mouseName\"] == data_list[i+1][\"mouseName\"]):\n",
    "        test.append(1- np.abs(ma.corrcoef(ma.masked_invalid(indDict[\"avg_firing_perSecond_zscore\"]), ma.masked_invalid(data_list[i+1][\"avg_firing_perSecond_zscore\"]))[1,0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8c0cb2ca",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp/ipykernel_16640/2898948470.py:1: UserWarning: Warning: converting a masked element to nan.\n",
      "  plt.hist(np.array(test)*100, bins=50);\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 316.8x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.array(test)*100, bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e0df015",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To 20\n",
    "test = []\n",
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    if (indDict[\"type2\"]) and (indDict[\"mouseName\"] == data_list[i+1][\"mouseName\"]):\n",
    "        test.append(1- np.abs(ma.corrcoef(ma.masked_invalid(indDict[\"top20_avg_firing\"][1]), ma.masked_invalid(data_list[i+1][\"avg_firing_perSecond\"][indDict[\"top20_avg_firing\"][0]]))[1,0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a42b667",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Warning: converting a masked element to nan.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.array(test)*100, bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdcef1fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To 20\n",
    "test = []\n",
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "\n",
    "\n",
    "for i, indDict in enumerate(data_list):\n",
    "    if (indDict[\"type2\"]) and (indDict[\"mouseName\"] == data_list[i+1][\"mouseName\"]):\n",
    "        test.append(1- np.abs(ma.corrcoef(ma.masked_invalid(indDict[\"least20_avg_firing\"][1]), ma.masked_invalid(data_list[i+1][\"avg_firing_perSecond\"][indDict[\"least20_avg_firing\"][0]]))[1,0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1e22d2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Warning: converting a masked element to nan.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.array(test)*100, bins=50);"
   ]
  }
 ],
 "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}