PPR_scnn1a_data_from_Dec2023.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0ec00416",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import os\n",
    "import math\n",
    "import numpy as np\n",
    "import cv2\n",
    "import pandas as pd\n",
    "import pyautogui as pa\n",
    "from scipy.stats import binom_test\n",
    "from matplotlib.markers import TICKDOWN\n",
    "from matplotlib import rcParams\n",
    "import datetime\n",
    "from scipy.stats import norm, kstest, sem, ttest_ind\n",
    "from numpy.polynomial import polynomial\n",
    "\n",
    "from lib.auto_GUI.auto_GUI_base import AutoGUIBase\n",
    "from lib.auto_GUI.auto_PhotoZ import AutoPhotoZ\n",
    "from lib.analysis.laminar_dist import *\n",
    "from lib.file.ROI_reader import ROIFileReader\n",
    "from lib.analysis.cell_roi import OverlapCounterROI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "319d1034",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/scnn1a-ppr/\"\n",
    "#data_dir = \"P:/John/analyzed_archived/scnn1a-tg3-cre/PPR checkpoints/scnn1a-ppr/\"\n",
    "master_csv = data_dir + 'scnn1a-ppr.csv'\n",
    "###################################\n",
    "# Process paired-pulse ratio data\n",
    "#    - IPIs are shuffled, open <slice>_<loc>shuffle.txt for IPI order\n",
    "#    - extract Amplitude, half_width, latency for pulse 1 and pulse 2 for each ROI\n",
    "#    - ROIs are in order (in file \"<slice>_barrel_rois.dat\" for each date subdir):\n",
    "#          -  medial -> lateral if available\n",
    "#          - otherwise, in some order that preserves neighbors (1,2 are neighbors and 2,3 are neighbors)\n",
    "#    - which barrel is home barrel (stimulated) is recorded in master csv scnn1a-ppr.csv for each loc\n",
    "###################################\n",
    "enable_photoZ_interact = False\n",
    "initialize_photoZ = True\n",
    "disable_trace_export = True  # if True, don't export traces, only save arrays\n",
    "\n",
    "snr_cutoff_signal = 0\n",
    "amp_cutoff = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ce9aa91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up PhotoZ (open it manually)\n",
    "aPhz = AutoPhotoZ(data_dir=data_dir)\n",
    "if initialize_photoZ and enable_photoZ_interact:\n",
    "    aPhz.prepare_photoZ()\n",
    "    # normalization needs to be off since we are saving arrays\n",
    "    aPhz.click_normalize_2_array_max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "13be144e",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Slice_Loc</th>\n",
       "      <th>Medial Dir</th>\n",
       "      <th>Stim Layer</th>\n",
       "      <th>Caudal/Rostral</th>\n",
       "      <th>Home Barrel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>3_2</td>\n",
       "      <td>up</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Caudal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>4_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>Rostral</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>5_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>Rostral</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>5_2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>Rostral</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>6_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>6_2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12/13/2023</td>\n",
       "      <td>1_4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Rostral</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12/13/2023</td>\n",
       "      <td>2_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>Rostral</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12/13/2023</td>\n",
       "      <td>3_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>Caudal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12/13/2023</td>\n",
       "      <td>3_2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Caudal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12/13/2023</td>\n",
       "      <td>4_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12/14/2023</td>\n",
       "      <td>1_3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Caudal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12/14/2023</td>\n",
       "      <td>2_1</td>\n",
       "      <td>right</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Rostral</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12/14/2023</td>\n",
       "      <td>3_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Rostral</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12/14/2023</td>\n",
       "      <td>4_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Rostral</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12/18/2023</td>\n",
       "      <td>1_1</td>\n",
       "      <td>up/right</td>\n",
       "      <td>L4</td>\n",
       "      <td>Rostral</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>12/18/2023</td>\n",
       "      <td>1_2</td>\n",
       "      <td>up/right</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Rostral</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12/18/2023</td>\n",
       "      <td>2_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Rostral</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12/18/2023</td>\n",
       "      <td>3_1</td>\n",
       "      <td>up/right</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Caudal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12/18/2023</td>\n",
       "      <td>4_1</td>\n",
       "      <td>down</td>\n",
       "      <td>L4</td>\n",
       "      <td>Caudal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12/18/2023</td>\n",
       "      <td>4_2</td>\n",
       "      <td>down</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Caudal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12/19/2023</td>\n",
       "      <td>4_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>Rostral</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Slice_Loc Medial Dir Stim Layer Caudal/Rostral  Home Barrel\n",
       "0    12/8/2023       3_2         up       L2/3         Caudal            2\n",
       "1    12/8/2023       4_1        NaN         L4        Rostral            2\n",
       "2    12/8/2023       5_1        NaN         L4        Rostral            1\n",
       "3    12/8/2023       5_2        NaN         L4        Rostral            2\n",
       "4    12/8/2023       6_1        NaN         L4            NaN            1\n",
       "5    12/8/2023       6_2        NaN         L4            NaN            2\n",
       "6   12/13/2023       1_4        NaN       L2/3        Rostral            1\n",
       "7   12/13/2023       2_1        NaN         L4        Rostral            1\n",
       "8   12/13/2023       3_1        NaN         L4         Caudal            2\n",
       "9   12/13/2023       3_2        NaN       L2/3         Caudal            2\n",
       "10  12/13/2023       4_1        NaN       L2/3            NaN            2\n",
       "11  12/14/2023       1_3        NaN       L2/3         Caudal            2\n",
       "12  12/14/2023       2_1      right       L2/3        Rostral            3\n",
       "13  12/14/2023       3_1        NaN       L2/3        Rostral            2\n",
       "14  12/14/2023       4_1        NaN       L2/3        Rostral            2\n",
       "15  12/18/2023       1_1   up/right         L4        Rostral            2\n",
       "16  12/18/2023       1_2   up/right       L2/3        Rostral            3\n",
       "17  12/18/2023       2_1        NaN       L2/3        Rostral            2\n",
       "18  12/18/2023       3_1   up/right       L2/3         Caudal            2\n",
       "19  12/18/2023       4_1       down         L4         Caudal            2\n",
       "20  12/18/2023       4_2       down       L2/3         Caudal            3\n",
       "21  12/19/2023       4_1        NaN         L4        Rostral            2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load master csv file for metadata\n",
    "master_df = pd.read_csv(master_csv)\n",
    "master_df = pd.DataFrame(master_df)\n",
    "master_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cd790ac0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/scnn1a-ppr/12-08-23\n",
      "3 2 2\n",
      "3 2 3\n",
      "3 2 4\n",
      "3 2 5\n",
      "3 2 6\n",
      "3 2 7\n",
      "3 2 8\n",
      "3 2 9\n",
      "3 2 10\n",
      "3 2 11\n",
      "3 2 12\n",
      "3 2 13\n",
      "3 2 14\n",
      "3 2 15\n",
      "3 2 16\n",
      "3 2 17\n",
      "4 1 1\n",
      "4 1 2\n",
      "4 1 3\n",
      "4 1 4\n",
      "4 1 5\n",
      "4 1 6\n",
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      "5 1 2\n",
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      "5 1 13\n",
      "5 1 14\n",
      "5 1 15\n",
      "5 1 16\n",
      "5 1 17\n",
      "5 2 2\n",
      "5 2 3\n",
      "5 2 4\n",
      "5 2 5\n",
      "5 2 6\n",
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      "5 2 8\n",
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      "5 2 10\n",
      "5 2 11\n",
      "5 2 12\n",
      "5 2 13\n",
      "5 2 14\n",
      "5 2 15\n",
      "5 2 16\n",
      "5 2 17\n",
      "6 1 4\n",
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      "6 1 18\n",
      "6 1 19\n",
      "6 2 1\n",
      "6 2 2\n",
      "6 2 3\n",
      "6 2 4\n",
      "6 2 5\n",
      "6 2 6\n",
      "6 2 7\n",
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      "6 2 12\n",
      "6 2 13\n",
      "6 2 14\n",
      "6 2 15\n",
      "6 2 16\n"
     ]
    }
   ],
   "source": [
    "def get_shuffle_filename(slic, loc):\n",
    "    return str(slic) + \"_\" + str(loc) + \"shuffle.txt\"\n",
    "\n",
    "def get_barrel_roi_filename(slic):\n",
    "    return str(slic) + \"_barrel_rois.dat\"\n",
    "\n",
    "# locate all files\n",
    "data_map = {}\n",
    "for subdir, dirs, files in os.walk(data_dir):\n",
    "    if 'hidden' in subdir:\n",
    "        continue\n",
    "    if not ('dic' in dirs and 'selected_zda' in dirs):\n",
    "        continue\n",
    "    print(\"\\n\", subdir)\n",
    "    dic_dir = subdir + \"/dic/\"\n",
    "    selected_zda_dir = subdir + \"/selected_zda/\"\n",
    "    data_map[subdir] = {}\n",
    "    for zda_file in os.listdir(selected_zda_dir):\n",
    "        if zda_file.endswith('.zda'):\n",
    "            slr_id = zda_file.split('.')[0]\n",
    "            slic_id, loc_id, rec_id = [int(x) for x in slr_id.split(\"_\")]\n",
    "            print(slic_id, loc_id, rec_id)\n",
    "            if slic_id not in data_map[subdir]:\n",
    "                # find ROI file and index it\n",
    "                data_map[subdir][slic_id] = {'roi_file': subdir + \"/\" + get_barrel_roi_filename(slic_id)}\n",
    "            if loc_id not in data_map[subdir][slic_id]:\n",
    "                # find shuffle file and index it\n",
    "                data_map[subdir][slic_id][loc_id] = {'zda_files': [],\n",
    "                                                     'shuffle_file': subdir + \"/\" +  get_shuffle_filename(slic_id, loc_id)}\n",
    "            data_map[subdir][slic_id][loc_id]['zda_files'].append(selected_zda_dir + \"/\" + zda_file)  # appends in ascending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8981bf20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/scnn1a-ppr/12-08-23\n",
      "3 2 IPI: 10 ms for recording 03_02_02.zda\n",
      "3 2 IPI: 20 ms for recording 03_02_03.zda\n",
      "3 2 IPI: 30 ms for recording 03_02_04.zda\n",
      "3 2 IPI: 40 ms for recording 03_02_05.zda\n",
      "3 2 IPI: 50 ms for recording 03_02_06.zda\n",
      "3 2 IPI: 60 ms for recording 03_02_07.zda\n",
      "3 2 IPI: 70 ms for recording 03_02_08.zda\n",
      "3 2 IPI: 80 ms for recording 03_02_09.zda\n",
      "3 2 IPI: 90 ms for recording 03_02_10.zda\n",
      "3 2 IPI: 100 ms for recording 03_02_11.zda\n",
      "3 2 IPI: 110 ms for recording 03_02_12.zda\n",
      "3 2 IPI: 120 ms for recording 03_02_13.zda\n",
      "3 2 IPI: 130 ms for recording 03_02_14.zda\n",
      "3 2 IPI: 140 ms for recording 03_02_15.zda\n",
      "3 2 IPI: 150 ms for recording 03_02_16.zda\n",
      "3 2 IPI: 5 ms for recording 03_02_17.zda\n",
      "4 1 IPI: 5 ms for recording 04_01_01.zda\n",
      "4 1 IPI: 10 ms for recording 04_01_02.zda\n",
      "4 1 IPI: 20 ms for recording 04_01_03.zda\n",
      "4 1 IPI: 30 ms for recording 04_01_04.zda\n",
      "4 1 IPI: 40 ms for recording 04_01_05.zda\n",
      "4 1 IPI: 50 ms for recording 04_01_06.zda\n",
      "4 1 IPI: 60 ms for recording 04_01_07.zda\n",
      "4 1 IPI: 70 ms for recording 04_01_08.zda\n",
      "4 1 IPI: 80 ms for recording 04_01_09.zda\n",
      "4 1 IPI: 90 ms for recording 04_01_10.zda\n",
      "4 1 IPI: 100 ms for recording 04_01_11.zda\n",
      "4 1 IPI: 110 ms for recording 04_01_12.zda\n",
      "4 1 IPI: 120 ms for recording 04_01_13.zda\n",
      "4 1 IPI: 130 ms for recording 04_01_14.zda\n",
      "4 1 IPI: 140 ms for recording 04_01_15.zda\n",
      "4 1 IPI: 150 ms for recording 04_01_16.zda\n",
      "5 1 IPI: 5 ms for recording 05_01_02.zda\n",
      "5 1 IPI: 10 ms for recording 05_01_03.zda\n",
      "5 1 IPI: 20 ms for recording 05_01_04.zda\n",
      "5 1 IPI: 30 ms for recording 05_01_05.zda\n",
      "5 1 IPI: 40 ms for recording 05_01_06.zda\n",
      "5 1 IPI: 50 ms for recording 05_01_07.zda\n",
      "5 1 IPI: 60 ms for recording 05_01_08.zda\n",
      "5 1 IPI: 70 ms for recording 05_01_09.zda\n",
      "5 1 IPI: 80 ms for recording 05_01_10.zda\n",
      "5 1 IPI: 90 ms for recording 05_01_11.zda\n",
      "5 1 IPI: 100 ms for recording 05_01_12.zda\n",
      "5 1 IPI: 110 ms for recording 05_01_13.zda\n",
      "5 1 IPI: 120 ms for recording 05_01_14.zda\n",
      "5 1 IPI: 130 ms for recording 05_01_15.zda\n",
      "5 1 IPI: 140 ms for recording 05_01_16.zda\n",
      "5 1 IPI: 150 ms for recording 05_01_17.zda\n",
      "5 2 IPI: 5 ms for recording 05_02_02.zda\n",
      "5 2 IPI: 10 ms for recording 05_02_03.zda\n",
      "5 2 IPI: 20 ms for recording 05_02_04.zda\n",
      "5 2 IPI: 30 ms for recording 05_02_05.zda\n",
      "5 2 IPI: 40 ms for recording 05_02_06.zda\n",
      "5 2 IPI: 50 ms for recording 05_02_07.zda\n",
      "5 2 IPI: 60 ms for recording 05_02_08.zda\n",
      "5 2 IPI: 70 ms for recording 05_02_09.zda\n",
      "5 2 IPI: 80 ms for recording 05_02_10.zda\n",
      "5 2 IPI: 90 ms for recording 05_02_11.zda\n",
      "5 2 IPI: 100 ms for recording 05_02_12.zda\n",
      "5 2 IPI: 110 ms for recording 05_02_13.zda\n",
      "5 2 IPI: 120 ms for recording 05_02_14.zda\n",
      "5 2 IPI: 130 ms for recording 05_02_15.zda\n",
      "5 2 IPI: 140 ms for recording 05_02_16.zda\n",
      "5 2 IPI: 150 ms for recording 05_02_17.zda\n",
      "6 1 IPI: 5 ms for recording 06_01_04.zda\n",
      "6 1 IPI: 10 ms for recording 06_01_05.zda\n",
      "6 1 IPI: 20 ms for recording 06_01_06.zda\n",
      "6 1 IPI: 30 ms for recording 06_01_07.zda\n",
      "6 1 IPI: 40 ms for recording 06_01_08.zda\n",
      "6 1 IPI: 50 ms for recording 06_01_09.zda\n",
      "6 1 IPI: 60 ms for recording 06_01_10.zda\n",
      "6 1 IPI: 70 ms for recording 06_01_11.zda\n",
      "6 1 IPI: 80 ms for recording 06_01_12.zda\n",
      "6 1 IPI: 90 ms for recording 06_01_13.zda\n",
      "6 1 IPI: 100 ms for recording 06_01_14.zda\n",
      "6 1 IPI: 110 ms for recording 06_01_15.zda\n",
      "6 1 IPI: 120 ms for recording 06_01_16.zda\n",
      "6 1 IPI: 130 ms for recording 06_01_17.zda\n",
      "6 1 IPI: 140 ms for recording 06_01_18.zda\n",
      "6 1 IPI: 150 ms for recording 06_01_19.zda\n",
      "6 2 IPI: 5 ms for recording 06_02_01.zda\n",
      "6 2 IPI: 10 ms for recording 06_02_02.zda\n",
      "6 2 IPI: 20 ms for recording 06_02_03.zda\n",
      "6 2 IPI: 30 ms for recording 06_02_04.zda\n",
      "6 2 IPI: 40 ms for recording 06_02_05.zda\n",
      "6 2 IPI: 50 ms for recording 06_02_06.zda\n",
      "6 2 IPI: 60 ms for recording 06_02_07.zda\n",
      "6 2 IPI: 70 ms for recording 06_02_08.zda\n",
      "6 2 IPI: 80 ms for recording 06_02_09.zda\n",
      "6 2 IPI: 90 ms for recording 06_02_10.zda\n",
      "6 2 IPI: 100 ms for recording 06_02_11.zda\n",
      "6 2 IPI: 110 ms for recording 06_02_12.zda\n",
      "6 2 IPI: 120 ms for recording 06_02_13.zda\n",
      "6 2 IPI: 130 ms for recording 06_02_14.zda\n",
      "6 2 IPI: 140 ms for recording 06_02_15.zda\n",
      "6 2 IPI: 150 ms for recording 06_02_16.zda\n"
     ]
    }
   ],
   "source": [
    "# go through the IPIs in their shuffled order (ascending recordings) \n",
    "# and extract Amplitude, half_width, latency for pulse 1 and pulse 2 for each ROI\n",
    "copy_enable_photoZ_interact = enable_photoZ_interact\n",
    "enable_photoZ_interact = not disable_trace_export\n",
    "\n",
    "for subdir in data_map:\n",
    "    aPhz = AutoPhotoZ(data_dir=subdir)\n",
    "    print(subdir)\n",
    "    for slic_id in data_map[subdir]:\n",
    "\n",
    "        # open roi_file for whole slice\n",
    "        if enable_photoZ_interact:\n",
    "            roi_file = data_map[subdir][slic_id]['roi_file']\n",
    "            aPhz.select_roi_tab()\n",
    "            aPhz.open_roi_file(roi_file)\n",
    "\n",
    "        for loc_id in data_map[subdir][slic_id]:\n",
    "            if type(loc_id) == str:\n",
    "                continue\n",
    "            slic_loc_id = str(slic_id) + \"_\" + str(loc_id)\n",
    "\n",
    "            ipi_file = data_map[subdir][slic_id][loc_id]['shuffle_file']\n",
    "            with open(ipi_file, 'r') as f:\n",
    "                lines = f.readlines() \n",
    "            ipi_order = [int(x) for x in lines]\n",
    "            # open zda files in order\n",
    "            i_ipi = -1\n",
    "            for zda_file in data_map[subdir][slic_id][loc_id]['zda_files']:\n",
    "                if enable_photoZ_interact:\n",
    "                    aPhz.open_zda_file(zda_file)\n",
    "                i_ipi += 1\n",
    "                try:\n",
    "                    ipi = ipi_order[i_ipi]\n",
    "                except Exception as e:\n",
    "                    print(e)\n",
    "                    print(\"Not enough IPIs for how many recordings we have! Last IPI:\", ipi, \"Check\", subdir)\n",
    "                print(slic_id, loc_id, \"IPI:\", ipi, \"ms for recording\", zda_file.split(\"/\")[-1])\n",
    "                \n",
    "                for i_pulse in [1, 2]:\n",
    "                    if enable_photoZ_interact:\n",
    "                        measure_window_start = 96 + (i_pulse - 1) * ipi * 2\n",
    "                        aPhz.set_measure_window(measure_window_start, None)\n",
    "\n",
    "                        # change polynomial skip window too to match measure window\n",
    "                        aPhz.set_polynomial_skip_window(measure_window_start)\n",
    "\n",
    "                    # take Amplitude, half_width, latency and label which pulse\n",
    "                    # save SNR values from PhotoZ\n",
    "                    snr_filename = subdir + \"/\" + \"SNR_\" + slic_loc_id + \"_ipi\" + str(ipi) + \"_p\" + str(i_pulse) + \".dat\"\n",
    "                    if enable_photoZ_interact:\n",
    "                        aPhz.select_SNR_trace_value()\n",
    "                        aPhz.save_trace_values(snr_filename)\n",
    "\n",
    "                    # save half-width values from PhotoZ\n",
    "                    hw_filename = subdir + \"/\" + \"hw_\" + slic_loc_id + \"_ipi\" + str(ipi) + \"_p\" + str(i_pulse) + \".dat\" \n",
    "                    if enable_photoZ_interact:\n",
    "                        aPhz.select_half_width_trace_value()\n",
    "                        aPhz.save_trace_values(hw_filename)\n",
    "\n",
    "                    # save amp values from PhotoZ\n",
    "                    amp_filename = subdir + \"/\" + \"amp_\" + slic_loc_id + \"_ipi\" + str(ipi) + \"_p\" + str(i_pulse) + \".dat\"\n",
    "                    if enable_photoZ_interact:\n",
    "                        aPhz.select_maxamp_trace_value()\n",
    "                        aPhz.save_trace_values(amp_filename)\n",
    "                        \n",
    "                    # save latency values from PhotoZ\n",
    "                    lat_filename = subdir + \"/\" + \"lat_\" + slic_loc_id + \"_ipi\" + str(ipi) + \"_p\" + str(i_pulse) + \".dat\"\n",
    "                    if enable_photoZ_interact:\n",
    "                        aPhz.select_latency_trace_value()\n",
    "                        aPhz.save_trace_values(lat_filename)\n",
    "\n",
    "\n",
    "                    if ipi not in data_map[subdir][slic_id][loc_id]:\n",
    "                        data_map[subdir][slic_id][loc_id][ipi] = {}\n",
    "                    data_map[subdir][slic_id][loc_id][ipi][i_pulse] = {'amp': amp_filename,\n",
    "                                                                       'hw': hw_filename,\n",
    "                                                                       'snr': snr_filename,\n",
    "                                                                       'lat': lat_filename}\n",
    "            if i_ipi < len(ipi_order) - 1:\n",
    "                print(\"Not enough recordings for how many IPIs we have! Last IPI:\", ipi, \"Check\", subdir)\n",
    "enable_photoZ_interact = copy_enable_photoZ_interact  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a784815f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/scnn1a-ppr/12-08-23\n",
      "3 2 IPI: 10 ms for recording 03_02_02.zda\n",
      "3 2 IPI: 20 ms for recording 03_02_03.zda\n",
      "3 2 IPI: 30 ms for recording 03_02_04.zda\n",
      "3 2 IPI: 40 ms for recording 03_02_05.zda\n",
      "3 2 IPI: 50 ms for recording 03_02_06.zda\n",
      "3 2 IPI: 60 ms for recording 03_02_07.zda\n",
      "3 2 IPI: 70 ms for recording 03_02_08.zda\n",
      "3 2 IPI: 80 ms for recording 03_02_09.zda\n",
      "3 2 IPI: 90 ms for recording 03_02_10.zda\n",
      "3 2 IPI: 100 ms for recording 03_02_11.zda\n",
      "3 2 IPI: 110 ms for recording 03_02_12.zda\n",
      "3 2 IPI: 120 ms for recording 03_02_13.zda\n",
      "3 2 IPI: 130 ms for recording 03_02_14.zda\n",
      "3 2 IPI: 140 ms for recording 03_02_15.zda\n",
      "3 2 IPI: 150 ms for recording 03_02_16.zda\n",
      "3 2 IPI: 5 ms for recording 03_02_17.zda\n",
      "4 1 IPI: 5 ms for recording 04_01_01.zda\n",
      "4 1 IPI: 10 ms for recording 04_01_02.zda\n",
      "4 1 IPI: 20 ms for recording 04_01_03.zda\n",
      "4 1 IPI: 30 ms for recording 04_01_04.zda\n",
      "4 1 IPI: 40 ms for recording 04_01_05.zda\n",
      "4 1 IPI: 50 ms for recording 04_01_06.zda\n",
      "4 1 IPI: 60 ms for recording 04_01_07.zda\n",
      "4 1 IPI: 70 ms for recording 04_01_08.zda\n",
      "4 1 IPI: 80 ms for recording 04_01_09.zda\n",
      "4 1 IPI: 90 ms for recording 04_01_10.zda\n",
      "4 1 IPI: 100 ms for recording 04_01_11.zda\n",
      "4 1 IPI: 110 ms for recording 04_01_12.zda\n",
      "4 1 IPI: 120 ms for recording 04_01_13.zda\n",
      "4 1 IPI: 130 ms for recording 04_01_14.zda\n",
      "4 1 IPI: 140 ms for recording 04_01_15.zda\n",
      "4 1 IPI: 150 ms for recording 04_01_16.zda\n",
      "5 1 IPI: 5 ms for recording 05_01_02.zda\n",
      "5 1 IPI: 10 ms for recording 05_01_03.zda\n",
      "5 1 IPI: 20 ms for recording 05_01_04.zda\n",
      "5 1 IPI: 30 ms for recording 05_01_05.zda\n",
      "5 1 IPI: 40 ms for recording 05_01_06.zda\n",
      "5 1 IPI: 50 ms for recording 05_01_07.zda\n",
      "5 1 IPI: 60 ms for recording 05_01_08.zda\n",
      "5 1 IPI: 70 ms for recording 05_01_09.zda\n",
      "5 1 IPI: 80 ms for recording 05_01_10.zda\n",
      "5 1 IPI: 90 ms for recording 05_01_11.zda\n",
      "5 1 IPI: 100 ms for recording 05_01_12.zda\n",
      "5 1 IPI: 110 ms for recording 05_01_13.zda\n",
      "5 1 IPI: 120 ms for recording 05_01_14.zda\n",
      "5 1 IPI: 130 ms for recording 05_01_15.zda\n",
      "5 1 IPI: 140 ms for recording 05_01_16.zda\n",
      "5 1 IPI: 150 ms for recording 05_01_17.zda\n",
      "5 2 IPI: 5 ms for recording 05_02_02.zda\n",
      "5 2 IPI: 10 ms for recording 05_02_03.zda\n",
      "5 2 IPI: 20 ms for recording 05_02_04.zda\n",
      "5 2 IPI: 30 ms for recording 05_02_05.zda\n",
      "5 2 IPI: 40 ms for recording 05_02_06.zda\n",
      "5 2 IPI: 50 ms for recording 05_02_07.zda\n",
      "5 2 IPI: 60 ms for recording 05_02_08.zda\n",
      "5 2 IPI: 70 ms for recording 05_02_09.zda\n",
      "5 2 IPI: 80 ms for recording 05_02_10.zda\n",
      "5 2 IPI: 90 ms for recording 05_02_11.zda\n",
      "5 2 IPI: 100 ms for recording 05_02_12.zda\n",
      "5 2 IPI: 110 ms for recording 05_02_13.zda\n",
      "5 2 IPI: 120 ms for recording 05_02_14.zda\n",
      "5 2 IPI: 130 ms for recording 05_02_15.zda\n",
      "5 2 IPI: 140 ms for recording 05_02_16.zda\n",
      "5 2 IPI: 150 ms for recording 05_02_17.zda\n",
      "6 1 IPI: 5 ms for recording 06_01_04.zda\n",
      "6 1 IPI: 10 ms for recording 06_01_05.zda\n",
      "6 1 IPI: 20 ms for recording 06_01_06.zda\n",
      "6 1 IPI: 30 ms for recording 06_01_07.zda\n",
      "6 1 IPI: 40 ms for recording 06_01_08.zda\n",
      "6 1 IPI: 50 ms for recording 06_01_09.zda\n",
      "6 1 IPI: 60 ms for recording 06_01_10.zda\n",
      "6 1 IPI: 70 ms for recording 06_01_11.zda\n",
      "6 1 IPI: 80 ms for recording 06_01_12.zda\n",
      "6 1 IPI: 90 ms for recording 06_01_13.zda\n",
      "6 1 IPI: 100 ms for recording 06_01_14.zda\n",
      "6 1 IPI: 110 ms for recording 06_01_15.zda\n",
      "6 1 IPI: 120 ms for recording 06_01_16.zda\n",
      "6 1 IPI: 130 ms for recording 06_01_17.zda\n",
      "6 1 IPI: 140 ms for recording 06_01_18.zda\n",
      "6 1 IPI: 150 ms for recording 06_01_19.zda\n",
      "6 2 IPI: 5 ms for recording 06_02_01.zda\n",
      "6 2 IPI: 10 ms for recording 06_02_02.zda\n",
      "6 2 IPI: 20 ms for recording 06_02_03.zda\n",
      "6 2 IPI: 30 ms for recording 06_02_04.zda\n",
      "6 2 IPI: 40 ms for recording 06_02_05.zda\n",
      "6 2 IPI: 50 ms for recording 06_02_06.zda\n",
      "6 2 IPI: 60 ms for recording 06_02_07.zda\n",
      "6 2 IPI: 70 ms for recording 06_02_08.zda\n",
      "6 2 IPI: 80 ms for recording 06_02_09.zda\n",
      "6 2 IPI: 90 ms for recording 06_02_10.zda\n",
      "6 2 IPI: 100 ms for recording 06_02_11.zda\n",
      "6 2 IPI: 110 ms for recording 06_02_12.zda\n",
      "6 2 IPI: 120 ms for recording 06_02_13.zda\n",
      "6 2 IPI: 130 ms for recording 06_02_14.zda\n",
      "6 2 IPI: 140 ms for recording 06_02_15.zda\n",
      "6 2 IPI: 150 ms for recording 06_02_16.zda\n"
     ]
    }
   ],
   "source": [
    "# Also extract actual pulse admin times for pulse 1 and pulse 2\n",
    "if enable_photoZ_interact:\n",
    "    pa.alert(\"Make sure the only trace selected is analog input from stimulator\")\n",
    "latency_selected =  False\n",
    "for subdir in data_map:\n",
    "    aPhz = AutoPhotoZ(data_dir=subdir)\n",
    "    if not latency_selected and enable_photoZ_interact:\n",
    "        latency_selected = True\n",
    "        aPhz.select_latency_trace_value()\n",
    "    print(subdir)\n",
    "    for slic_id in data_map[subdir]:\n",
    "\n",
    "        for loc_id in data_map[subdir][slic_id]:\n",
    "            if type(loc_id) == str:\n",
    "                continue\n",
    "            slic_loc_id = str(slic_id) + \"_\" + str(loc_id)\n",
    "\n",
    "            ipi_file = data_map[subdir][slic_id][loc_id]['shuffle_file']\n",
    "            with open(ipi_file, 'r') as f:\n",
    "                lines = f.readlines() \n",
    "            ipi_order = [int(x) for x in lines]\n",
    "            # open zda files in order\n",
    "            i_ipi = -1\n",
    "            for zda_file in data_map[subdir][slic_id][loc_id]['zda_files']:\n",
    "                if enable_photoZ_interact:\n",
    "                    aPhz.open_zda_file(zda_file)\n",
    "                i_ipi += 1\n",
    "                try:\n",
    "                    ipi = ipi_order[i_ipi]\n",
    "                except Exception as e:\n",
    "                    print(e)\n",
    "                    print(\"Not enough IPIs for how many recordings we have! Last IPI:\", ipi, \"Check\", subdir)\n",
    "                print(slic_id, loc_id, \"IPI:\", ipi, \"ms for recording\", zda_file.split(\"/\")[-1])\n",
    "                \n",
    "                for i_pulse in [1, 2]:\n",
    "                    if enable_photoZ_interact:\n",
    "                        measure_window_start = 96 + (i_pulse - 1) * ipi * 2\n",
    "                        aPhz.set_measure_window(measure_window_start, None)\n",
    "\n",
    "                        # change polynomial skip window too to match measure window\n",
    "                        aPhz.set_polynomial_skip_window(measure_window_start)\n",
    "                    \n",
    "                    # save pulse admin times\n",
    "                    pa_filename = subdir + \"/\" + \"pa_\" + slic_loc_id + \"_ipi\" + str(ipi) + \"_p\" + str(i_pulse) + \".dat\"\n",
    "                    data_map[subdir][slic_id][loc_id][ipi][i_pulse]['pa'] = pa_filename\n",
    "\n",
    "                    if enable_photoZ_interact:\n",
    "                        aPhz.save_trace_values(pa_filename)\n",
    "                        print(\"\\t Saved file\", pa_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "45faeb82",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Slice_Loc</th>\n",
       "      <th>ROI</th>\n",
       "      <th>SNR</th>\n",
       "      <th>Amp</th>\n",
       "      <th>Halfwidth</th>\n",
       "      <th>Latency</th>\n",
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       "      <td>1</td>\n",
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       "      <td>L2/3</td>\n",
       "      <td>Caudal</td>\n",
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       "      <td>10</td>\n",
       "      <td>up</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Caudal</td>\n",
       "      <td>2</td>\n",
       "      <td>59.8083</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>6_2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.69281</td>\n",
       "      <td>1.042970</td>\n",
       "      <td>-379.79400</td>\n",
       "      <td>0.6690</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>189.7730</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>6_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92106</td>\n",
       "      <td>0.773330</td>\n",
       "      <td>-105.54000</td>\n",
       "      <td>3.7073</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>48.5041</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>6_2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.75774</td>\n",
       "      <td>1.129620</td>\n",
       "      <td>-99.95180</td>\n",
       "      <td>0.9317</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>48.5041</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>6_2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.03441</td>\n",
       "      <td>0.830097</td>\n",
       "      <td>-457.84500</td>\n",
       "      <td>30.1280</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>199.7730</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>6_2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.84102</td>\n",
       "      <td>1.094720</td>\n",
       "      <td>-399.13800</td>\n",
       "      <td>0.3790</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>199.7730</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>448 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Slice_Loc  ROI      SNR       Amp  Halfwidth  Latency  Pulse  \\\n",
       "0   12/8/2023       3_2    1  1.79207  0.301058 -104.79500   3.9982      1   \n",
       "1   12/8/2023       3_2    2  4.90353  0.312345    5.67150   3.1406      1   \n",
       "2   12/8/2023       3_2    3  1.83552  0.306782 -104.42200   3.8114      1   \n",
       "0   12/8/2023       3_2    1  1.77536  0.926149    7.76580   0.2688      2   \n",
       "1   12/8/2023       3_2    2  4.05187  1.837380    4.46569   1.7628      2   \n",
       "..        ...       ...  ...      ...       ...        ...      ...    ...   \n",
       "1   12/8/2023       6_2    2  1.69281  1.042970 -379.79400   0.6690      2   \n",
       "0   12/8/2023       6_2    1  0.92106  0.773330 -105.54000   3.7073      1   \n",
       "1   12/8/2023       6_2    2  1.75774  1.129620  -99.95180   0.9317      1   \n",
       "0   12/8/2023       6_2    1  1.03441  0.830097 -457.84500  30.1280      2   \n",
       "1   12/8/2023       6_2    2  1.84102  1.094720 -399.13800   0.3790      2   \n",
       "\n",
       "    IPI Medial Dir Stim Layer Caudal/Rostral  Home Barrel  Actual Stim PPR  \n",
       "0    10         up       L2/3         Caudal            2      48.4505      \n",
       "1    10         up       L2/3         Caudal            2      48.4505      \n",
       "2    10         up       L2/3         Caudal            2      48.4505      \n",
       "0    10         up       L2/3         Caudal            2      59.8083      \n",
       "1    10         up       L2/3         Caudal            2      59.8083      \n",
       "..  ...        ...        ...            ...          ...          ...  ..  \n",
       "1   140        NaN         L4            NaN            2     189.7730      \n",
       "0   150        NaN         L4            NaN            2      48.5041      \n",
       "1   150        NaN         L4            NaN            2      48.5041      \n",
       "0   150        NaN         L4            NaN            2     199.7730      \n",
       "1   150        NaN         L4            NaN            2     199.7730      \n",
       "\n",
       "[448 rows x 15 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load all these amp, hw, snr, lat into a dataframe\n",
    "def read_dat_trace_file(filename):\n",
    "    data_arr = pd.read_csv(filename,\n",
    "                                 sep='\\t',\n",
    "                                 header=None,\n",
    "                                 names=['ROI',  'Value'])\n",
    "    return data_arr\n",
    "\n",
    "snr_dfs = []\n",
    "for subdir in data_map:\n",
    "    \n",
    "    # extract date from subdir so we can look up metadata from master_df\n",
    "    date = subdir.split(\"/\")[-1].split('-')\n",
    "    date = [int(x) for x in date]\n",
    "    date[-1] += 2000\n",
    "    date = \"/\".join([str(x) for x in date])\n",
    "    metadata_date = master_df[master_df['Date'] == date]\n",
    "    \n",
    "    for slic_id in data_map[subdir]:\n",
    "        for loc_id in data_map[subdir][slic_id]:\n",
    "            if type(loc_id) == str:\n",
    "                continue\n",
    "            slic_loc_id = str(slic_id) + \"_\" + str(loc_id)\n",
    "            metadata = metadata_date[metadata_date['Slice_Loc'] == slic_loc_id]\n",
    "            medial_dir = metadata['Medial Dir'].item()\n",
    "            stim_layer = metadata['Stim Layer'].item()\n",
    "            caudal_rostral = metadata['Caudal/Rostral'].item()\n",
    "            home_barrel = int(metadata['Home Barrel'].item())\n",
    "\n",
    "            for ipi in data_map[subdir][slic_id][loc_id]:\n",
    "                if type(ipi) == str:\n",
    "                    continue\n",
    "                for i_pulse in data_map[subdir][slic_id][loc_id][ipi]:\n",
    "                    dm = data_map[subdir][slic_id][loc_id][ipi][i_pulse]\n",
    "                    \n",
    "                    try:\n",
    "                        amp = read_dat_trace_file(dm['amp'])\n",
    "                        hw = read_dat_trace_file(dm['hw'])\n",
    "                        snr = read_dat_trace_file(dm['snr'])\n",
    "                        lat = read_dat_trace_file(dm['lat'])\n",
    "                        actual_stim = read_dat_trace_file(dm['pa'])  # will be 2 values per pulse per recording, broadcast to all ROIs\n",
    "                    except Exception as e:\n",
    "                        print(e)\n",
    "                        continue\n",
    "                    actual_stim = actual_stim['Value'].item()\n",
    "                    snr = snr.join(amp, rsuffix='_amp')\n",
    "                    snr = snr.join(hw, rsuffix='_hw')\n",
    "                    snr = snr.join(lat, rsuffix='_lat')\n",
    "                    snr.insert(4, 'Pulse', [i_pulse for _ in range(len(snr['ROI']))])\n",
    "                    snr.insert(5, 'IPI', [ipi for _ in range(len(snr['ROI']))])\n",
    "                    snr.insert(6, 'Medial Dir', [medial_dir for _ in range(len(snr['ROI']))])\n",
    "                    snr.insert(7, 'Stim Layer', [stim_layer for _ in range(len(snr['ROI']))])\n",
    "                    snr.insert(8, 'Caudal/Rostral', [caudal_rostral for _ in range(len(snr['ROI']))])\n",
    "                    snr.insert(9, 'Home Barrel', [home_barrel for _ in range(len(snr['ROI']))])\n",
    "                    snr.insert(10, 'Date', [date for _ in range(len(snr['ROI']))])\n",
    "                    snr.insert(11, 'Slice_Loc', [slic_loc_id for _ in range(len(snr['ROI']))])\n",
    "                    snr.insert(12, \"Actual Stim\", [actual_stim for _ in range(len(snr['ROI']))])\n",
    "                    snr = snr[pd.to_numeric(snr.Value, errors='coerce').notnull()]\n",
    "                    snr_dfs.append(snr)\n",
    "        \n",
    "pre_filter_snr = pd.concat(snr_dfs)\n",
    "pre_filter_snr = pre_filter_snr[['Date', 'Slice_Loc', 'ROI', 'Value', 'Value_amp', 'Value_hw', \n",
    "                                 'Value_lat', 'Pulse', 'IPI',\n",
    "                                 'Medial Dir', 'Stim Layer', 'Caudal/Rostral', 'Home Barrel', 'Actual Stim']]\n",
    "\n",
    "# correct Latency for actual stim time\n",
    "pre_filter_snr['Value_lat'] = pre_filter_snr['Value_lat'] - pre_filter_snr['Actual Stim']\n",
    "\n",
    "pre_filter_snr = pre_filter_snr.rename(columns={'Value': \"SNR\",\n",
    "                                                'Value_amp': \"Amp\", \n",
    "                                                'Value_hw' : \"Halfwidth\", \n",
    "                                                'Value_lat': \"Latency\"})\n",
    "pre_filter_snr[\"Amp\"] = pd.to_numeric(pre_filter_snr[\"Amp\"])\n",
    "pre_filter_snr['PPR'] = ''\n",
    "pre_filter_snr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b8af8220",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['3_2' '4_1' '5_1' '5_2' '6_1' '6_2']\n"
     ]
    }
   ],
   "source": [
    "for date in pre_filter_snr['Date'].unique():\n",
    "    print(pre_filter_snr[pre_filter_snr['Date'] == date]['Slice_Loc'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7e922230",
   "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>Date</th>\n",
       "      <th>Slice_Loc</th>\n",
       "      <th>ROI</th>\n",
       "      <th>SNR</th>\n",
       "      <th>Amp</th>\n",
       "      <th>Halfwidth</th>\n",
       "      <th>Latency</th>\n",
       "      <th>Pulse</th>\n",
       "      <th>IPI</th>\n",
       "      <th>Medial Dir</th>\n",
       "      <th>Stim Layer</th>\n",
       "      <th>Caudal/Rostral</th>\n",
       "      <th>Home Barrel</th>\n",
       "      <th>Actual Stim</th>\n",
       "      <th>PPR</th>\n",
       "      <th>PPL</th>\n",
       "      <th>PPHW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>3_2</td>\n",
       "      <td>2</td>\n",
       "      <td>5.27212</td>\n",
       "      <td>2.46748</td>\n",
       "      <td>4.47188</td>\n",
       "      <td>3.1563</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>up</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Caudal</td>\n",
       "      <td>2</td>\n",
       "      <td>48.4439</td>\n",
       "      <td>0.758644</td>\n",
       "      <td>0.815892</td>\n",
       "      <td>0.852114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>3_2</td>\n",
       "      <td>2</td>\n",
       "      <td>5.43864</td>\n",
       "      <td>2.66691</td>\n",
       "      <td>5.44523</td>\n",
       "      <td>3.1951</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>up</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Caudal</td>\n",
       "      <td>2</td>\n",
       "      <td>48.3476</td>\n",
       "      <td>0.616852</td>\n",
       "      <td>0.891021</td>\n",
       "      <td>0.653385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>3_2</td>\n",
       "      <td>2</td>\n",
       "      <td>5.60122</td>\n",
       "      <td>2.70548</td>\n",
       "      <td>5.09992</td>\n",
       "      <td>3.0578</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>up</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Caudal</td>\n",
       "      <td>2</td>\n",
       "      <td>48.5474</td>\n",
       "      <td>0.662600</td>\n",
       "      <td>0.949310</td>\n",
       "      <td>0.683699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>3_2</td>\n",
       "      <td>2</td>\n",
       "      <td>5.48550</td>\n",
       "      <td>2.87610</td>\n",
       "      <td>5.19859</td>\n",
       "      <td>3.2336</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>up</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Caudal</td>\n",
       "      <td>2</td>\n",
       "      <td>48.3428</td>\n",
       "      <td>0.629731</td>\n",
       "      <td>0.921110</td>\n",
       "      <td>0.644102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>3_2</td>\n",
       "      <td>2</td>\n",
       "      <td>5.47581</td>\n",
       "      <td>3.00279</td>\n",
       "      <td>5.67457</td>\n",
       "      <td>3.1891</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>up</td>\n",
       "      <td>L2/3</td>\n",
       "      <td>Caudal</td>\n",
       "      <td>2</td>\n",
       "      <td>48.4366</td>\n",
       "      <td>0.583807</td>\n",
       "      <td>0.962340</td>\n",
       "      <td>0.702125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>6_2</td>\n",
       "      <td>2</td>\n",
       "      <td>7.43174</td>\n",
       "      <td>3.46140</td>\n",
       "      <td>5.42646</td>\n",
       "      <td>2.0206</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>48.3878</td>\n",
       "      <td>0.574253</td>\n",
       "      <td>0.804662</td>\n",
       "      <td>0.668915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>6_2</td>\n",
       "      <td>2</td>\n",
       "      <td>6.73198</td>\n",
       "      <td>3.30079</td>\n",
       "      <td>5.88365</td>\n",
       "      <td>2.0286</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>48.5569</td>\n",
       "      <td>0.557206</td>\n",
       "      <td>0.904269</td>\n",
       "      <td>0.721125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>6_2</td>\n",
       "      <td>2</td>\n",
       "      <td>5.62968</td>\n",
       "      <td>3.08529</td>\n",
       "      <td>6.32905</td>\n",
       "      <td>2.2135</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>48.4140</td>\n",
       "      <td>0.550671</td>\n",
       "      <td>0.997018</td>\n",
       "      <td>0.555322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>6_2</td>\n",
       "      <td>2</td>\n",
       "      <td>4.81477</td>\n",
       "      <td>2.76664</td>\n",
       "      <td>5.33929</td>\n",
       "      <td>2.1834</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>48.4789</td>\n",
       "      <td>0.547845</td>\n",
       "      <td>1.155079</td>\n",
       "      <td>0.691395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/8/2023</td>\n",
       "      <td>6_2</td>\n",
       "      <td>2</td>\n",
       "      <td>4.08418</td>\n",
       "      <td>2.51885</td>\n",
       "      <td>6.58491</td>\n",
       "      <td>2.3080</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>48.5104</td>\n",
       "      <td>0.550422</td>\n",
       "      <td>1.179809</td>\n",
       "      <td>0.675306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Slice_Loc  ROI      SNR      Amp  Halfwidth  Latency  Pulse  \\\n",
       "1   12/8/2023       3_2    2  5.27212  2.46748    4.47188   3.1563      1   \n",
       "1   12/8/2023       3_2    2  5.43864  2.66691    5.44523   3.1951      1   \n",
       "1   12/8/2023       3_2    2  5.60122  2.70548    5.09992   3.0578      1   \n",
       "1   12/8/2023       3_2    2  5.48550  2.87610    5.19859   3.2336      1   \n",
       "1   12/8/2023       3_2    2  5.47581  3.00279    5.67457   3.1891      1   \n",
       "..        ...       ...  ...      ...      ...        ...      ...    ...   \n",
       "1   12/8/2023       6_2    2  7.43174  3.46140    5.42646   2.0206      1   \n",
       "1   12/8/2023       6_2    2  6.73198  3.30079    5.88365   2.0286      1   \n",
       "1   12/8/2023       6_2    2  5.62968  3.08529    6.32905   2.2135      1   \n",
       "1   12/8/2023       6_2    2  4.81477  2.76664    5.33929   2.1834      1   \n",
       "1   12/8/2023       6_2    2  4.08418  2.51885    6.58491   2.3080      1   \n",
       "\n",
       "    IPI Medial Dir Stim Layer Caudal/Rostral  Home Barrel  Actual Stim  \\\n",
       "1    20         up       L2/3         Caudal            2      48.4439   \n",
       "1    30         up       L2/3         Caudal            2      48.3476   \n",
       "1    40         up       L2/3         Caudal            2      48.5474   \n",
       "1    50         up       L2/3         Caudal            2      48.3428   \n",
       "1    60         up       L2/3         Caudal            2      48.4366   \n",
       "..  ...        ...        ...            ...          ...          ...   \n",
       "1    30        NaN         L4            NaN            2      48.3878   \n",
       "1    40        NaN         L4            NaN            2      48.5569   \n",
       "1    50        NaN         L4            NaN            2      48.4140   \n",
       "1    60        NaN         L4            NaN            2      48.4789   \n",
       "1    70        NaN         L4            NaN            2      48.5104   \n",
       "\n",
       "         PPR       PPL      PPHW  \n",
       "1   0.758644  0.815892  0.852114  \n",
       "1   0.616852  0.891021  0.653385  \n",
       "1   0.662600  0.949310  0.683699  \n",
       "1   0.629731  0.921110  0.644102  \n",
       "1   0.583807  0.962340  0.702125  \n",
       "..       ...       ...       ...  \n",
       "1   0.574253  0.804662  0.668915  \n",
       "1   0.557206  0.904269  0.721125  \n",
       "1   0.550671  0.997018  0.555322  \n",
       "1   0.547845  1.155079  0.691395  \n",
       "1   0.550422  1.179809  0.675306  \n",
       "\n",
       "[118 rows x 17 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amps1 = []\n",
    "amps2 = []\n",
    "ipis = []\n",
    "# Now compute PPR = Amp(pulse 2) / Amp(pulse 1). \n",
    "for date in pre_filter_snr['Date'].unique():\n",
    "    pfs_date = pre_filter_snr[pre_filter_snr[\"Date\"] == date]\n",
    "    for slic_loc in pfs_date['Slice_Loc'].unique():\n",
    "        pfs_sl = pfs_date[pfs_date[\"Slice_Loc\"] == slic_loc]\n",
    "        for roi in pfs_sl['ROI'].unique():\n",
    "            pfs_roi = pfs_sl[pfs_sl['ROI'] == roi]\n",
    "            for ipi in pfs_roi['IPI'].unique():\n",
    "                pfs_ipi = pfs_roi[pfs_roi['IPI'] == ipi]\n",
    "                assert len(pfs_ipi) == 2  # should always be exactly 2 (otherwise can't compute PPR)\n",
    "\n",
    "                amp1 = pfs_ipi[pfs_ipi['Pulse'] == 1]['Amp'].item()\n",
    "                lat1 = pfs_ipi[pfs_ipi['Pulse'] == 1]['Latency'].item()\n",
    "                hw1 = pfs_ipi[pfs_ipi['Pulse'] == 1]['Halfwidth'].item()\n",
    "\n",
    "                # Compute PPR = Amp(pulse 2) / Amp(pulse 1)\n",
    "                ppr = None\n",
    "                if amp1 != 0:\n",
    "                    amp2 = pfs_ipi[pfs_ipi['Pulse'] == 2]['Amp'].item()\n",
    "                    ppr = amp2 / amp1\n",
    "                if ppr is not None and 0 < ppr < 2:\n",
    "                    pre_filter_snr.loc[(pre_filter_snr['Date'] == date) &\n",
    "                                   (pre_filter_snr['Slice_Loc'] == slic_loc) &\n",
    "                                   (pre_filter_snr['ROI'] == roi) & \n",
    "                                   (pre_filter_snr['IPI'] == ipi), \n",
    "                                       'PPR'] = ppr\n",
    "                    amps1.append(amp1)\n",
    "                    amps2.append(amp2)\n",
    "                    ipis.append(ipi)\n",
    "                \n",
    "                # Compute PPL = Latency(pulse 2) / Latency(pulse 1)\n",
    "                ppl = None\n",
    "                if lat1 != 0:\n",
    "                    lat2 = pfs_ipi[pfs_ipi['Pulse'] == 2]['Latency'].item()\n",
    "                    ppl = lat2 / lat1\n",
    "                if ppl is not None and 0 < ppl:\n",
    "                    pre_filter_snr.loc[(pre_filter_snr['Date'] == date) &\n",
    "                                   (pre_filter_snr['Slice_Loc'] == slic_loc) &\n",
    "                                   (pre_filter_snr['ROI'] == roi) & \n",
    "                                   (pre_filter_snr['IPI'] == ipi), \n",
    "                                       'PPL'] = ppl\n",
    "                    \n",
    "                # Compute PPHW = Halfwidth(pulse 2) / Halfwidth(pulse 1)\n",
    "                pphw = None\n",
    "                if hw1 != 0:\n",
    "                    hw2 = pfs_ipi[pfs_ipi['Pulse'] == 2]['Halfwidth'].item()\n",
    "                    pphw = hw2 / hw1\n",
    "                if pphw is not None and 0 < pphw:\n",
    "                    pre_filter_snr.loc[(pre_filter_snr['Date'] == date) &\n",
    "                                   (pre_filter_snr['Slice_Loc'] == slic_loc) &\n",
    "                                   (pre_filter_snr['ROI'] == roi) & \n",
    "                                   (pre_filter_snr['IPI'] == ipi), \n",
    "                                       'PPHW'] = pphw\n",
    "\n",
    "pre_filter_snr[\"PPR\"] = pd.to_numeric(pre_filter_snr[\"PPR\"])\n",
    "pre_filter_snr[\"PPL\"] = pd.to_numeric(pre_filter_snr[\"PPL\"])\n",
    "pre_filter_snr[\"PPHW\"] = pd.to_numeric(pre_filter_snr[\"PPHW\"])\n",
    "# write to csv\n",
    "csv_filename = 'scnn1a-ppr-output.csv'\n",
    "pre_filter_snr.to_csv(data_dir + csv_filename, index=False)\n",
    "\n",
    "# now some filtering\n",
    "all_snr = pre_filter_snr[pre_filter_snr['SNR'] > snr_cutoff_signal]\n",
    "all_snr = all_snr[all_snr['Halfwidth'] > 0.5]\n",
    "all_snr = all_snr[all_snr['Halfwidth'] < 13]\n",
    "all_snr = all_snr[all_snr['Latency'] < 15]\n",
    "all_snr = all_snr[all_snr['Latency'] > 0]\n",
    "all_snr = all_snr[all_snr['Amp'] > amp_cutoff]\n",
    "all_snr = all_snr[all_snr['IPI'] < 140]  # 140 ms IPI cannot be measured properly with this setup\n",
    "#all_snr = all_snr[all_snr['Date'] != '12/8/2023']  # exclude due to faulty baseline correction\n",
    "\n",
    "all_snr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2fc5138f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AhoionJYxM7MZpGblbqlUisHBwabs28ysXUnaExGlaut8paiZWSIc6GZmiXCgm5klwoFuZpYIB7qZWSIc6GZmiXCgm5klwoFuZpYIB7qZWSIc6GZmiXCgm5klwoFuZpaImndbNLPG6t87Su/AEGPjEyxa0Mnm1T2sX1ntIWBm9XGgm82i/r2jbO3bx8ThowCMjk+wtW8fgEPdTpinXMxmUe/A0FNhPmni8FF6B4aaVJGlxIFuNovGxifqajerhwPdbBYtWtBZV7tZPRzoZrNo8+oeOud1HNPWOa+Dzat7mlSRpcQnRc1m0eSJT3/LxWaCA91slq1f2e0AtxnhKRczs0Q40M3MElEo0CWtkTQkaVjSlmn6/byko5Le1rgSzcysiJqBLqkDuBZYC6wALpG0Yop+HwAGGl2kmZnVVuQIfRUwHBEHIuIQsANYV6Xf/wJuAh5sYH1mZlZQkUDvBu4vWx7J254iqRt4M7CtcaWZmVk9igS6qrRFxfJfAu+JiKNV+j69IWmjpEFJgwcPHixYopmZFVHke+gjwFlly4uBsYo+JWCHJICFwMWSjkREf3mniNgObAcolUqVvxTMzOwEFAn03cByScuAUWADcGl5h4hYNvmzpBuAz1WGuZmZzayagR4RRyRtIvv2SgdwfUTsl3RFvt7z5mZmLaDQpf8RsQvYVdFWNcgj4tdOvCwzM6uXrxQ1M0uEA93MLBEOdDOzRDjQzcwS4UA3M0uEA93MLBEOdDOzRPgRdFX07x2d8Wc+zsY+zGxucaBX6N87yta+fUwczu4zNjo+wda+fQANC9zZ2IeZzT2ecqnQOzD0VNBOmjh8lN6Bobbah5nNPQ70CmPjE3W1t+o+zGzucaBXWLSgs672Vt2Hmc09DvQKm1f30Dmv45i2znkdbF7d01b7MLO5xydFK0yelJzJb6DMxj7MbO5RRHMeHFQqlWJwcLAp+zYza1eS9kREqdo6T7mYmSXCgW5mlggHuplZIhzoZmaJcKCbmSXCgW5mlggHuplZInxhUeJ8m16zuaPQEbqkNZKGJA1L2lJl/TpJt0u6TdKgpFc1vlSr1+RtekfHJwievk1v/97RZpdmZjOgZqBL6gCuBdYCK4BLJK2o6PYl4NyIOA94F3Bdg+u04+Db9JrNLUWO0FcBwxFxICIOATuAdeUdIuLxePoeAqcCzbmfgB3Dt+k1m1uKzKF3A/eXLY8A51d2kvRm4P3AmcAbqm1I0kZgI8CSJUvqrdXqtGhBJ6NVwtu36Z2azzlYOytyhK4qbc84Ao+If4qIlwLrgWuqbSgitkdEKSJKXV1ddRVq9fNteuvjcw7W7ooE+ghwVtnyYmBsqs4R8VXgJyUtPMHa7AStX9nN+9/yM3Qv6ERA94JO3v+Wn/ER5xR8zsHaXZEpl93AcknLgFFgA3BpeQdJPwV8JyJC0s8C84GHG12s1W/9ym4HeEE+52DtrmagR8QRSZuAAaADuD4i9ku6Il+/DXgrcJmkw8AE8D+jWTdaNztOPudg7a7QhUURsQvYVdG2reznDwAfaGxpZrNr8+oetvbtO2baxeccrJ34SlGznB8NaO3OgW5WxuccrJ355lxmZolwoJuZJcKBbmaWCM+hW1vwJflmtTnQreVNXpI/+XXCyUvyAYe6WRlPuVjL8yX5ZsU40K3l+ZJ8s2Ic6Nbyprr03pfkmx3LgW4tz7cBNivGJ0Wt5fmSfLNiHOjWFnxJvlltnnIxM0uEA93MLBEOdDOzRDjQzcwS4UA3M0uEv+ViJ8w3zjJrDQ50OyG+cZZZ6/CUi50Q3zjLrHU40O2E+MZZZq2jUKBLWiNpSNKwpC1V1r9d0u3561ZJ5za+VGtFvnGWWeuoGeiSOoBrgbXACuASSSsqut0LvCYizgGuAbY3ulBrTb5xllnrKHJSdBUwHBEHACTtANYBd052iIhby/p/HVjcyCKtdfnGWWato0igdwP3ly2PAOdP0//XgVtOpChrL75xlllrKBLoqtIWVTtKv0AW6K+aYv1GYCPAkiVLCpZoZmZFFDkpOgKcVba8GBir7CTpHOA6YF1EPFxtQxGxPSJKEVHq6uo6nnrNzGwKRQJ9N7Bc0jJJ84ENwM7yDpKWAH3AOyLinsaXaWZmtdSccomII5I2AQNAB3B9ROyXdEW+fhvwXuD5wEckARyJiNLMlW1mZpUUUXU6fMaVSqUYHBxsyr7NzNqVpD1THTA3LdAlHQS+15SdF7cQeKjZRTRIKmNJZRzgsbSidhjHiyKi6knIpgV6O5A0mMrUUSpjSWUc4LG0onYfh+/lYmaWCAe6mVkiHOjTS+meNKmMJZVxgMfSitp6HJ5DNzNLhI/QzcwS4UA3M0uEAx2QdJakL0u6S9J+SVfm7WdI+qKkb+f/Pb3ZtRYlqUPSXkmfy5fbciySFkj6jKS78z+fC9pxLJL+d/536w5JN0p6druMQ9L1kh6UdEdZ25S1S9qaPwxnSNLq5lRd3RRj6c3/ft0u6Z8kLShb17JjqcaBnjkC/N+I+Gng5cDv5A/x2AJ8KSKWA1/Kl9vFlcBdZcvtOpYPAZ+PiJcC55KNqa3GIqkbeDdQioiXkd1CYwPtM44bgDUVbVVrz/+/2QCcnb/nI/lDclrFDTxzLF8EXpY/oOceYCu0xViewYEORMT3I+I/8p9/QBYa3WQP8vh43u3jwPqmFFgnSYuBN5Dd/XJS241F0nOBVwMfA4iIQxExThuOhey+SZ2STgJOIbtjaVuMIyK+CjxS0TxV7euAHRHxo4i4Fxgme0hOS6g2loj4QkQcyRfLH9DT0mOpxoFeQdJSYCXwDeAFEfF9yEIfOLOJpdXjL4GrgB+XtbXjWF4MHAT+Lp8+uk7SqbTZWCJiFPggcB/wfeDRiPgCbTaOClPVXu2BOO309JN38fQDetpuLA70MpJOA24CfjciHmt2PcdD0huBByNiT7NraYCTgJ8FPhoRK4EnaN1piSnl88vrgGXAIuBUSb/a3KpmTOEH4rQaSX9ANv36ycmmKt1aeiwO9JykeWRh/smI6MubH5D0wnz9C4EHm1VfHV4J/LKk7wI7gF+U9A+051hGgJGI+Ea+/BmygG+3sbwOuDciDkbEYbJnB7yC9htHualqL/RAnFYj6XLgjcDb4+mLc9puLA50QNlN3D8G3BURf1G2aidwef7z5cBnZ7u2ekXE1ohYHBFLyU7o/GtE/CrtOZb/BO6X1JM3XUT2cPJ2G8t9wMslnZL/XbuI7DxNu42j3FS17wQ2SDpZ0jJgOfDNJtRXmKQ1wHuAX46IJ8tWtd1YiIg5/yJ7BmoAtwO35a+LyR7a8SXg2/l/z2h2rXWO67XA5/Kf23IswHnAYP5n0w+c3o5jAd4H3A3cAXwCOLldxgHcSDb3f5jsqPXXp6sd+APgO8AQsLbZ9RcYyzDZXPnk//vb2mEs1V6+9N/MLBGecjEzS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NE/DcqmncRL6bQ0QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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32+d3ipqZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKB3szoKAwNgZR9HR3td0VmZh11/CyXRWd0FNav37U9O7tre2ysPzWZmZXgM/RG4+PdtZuZDQgHeqPZ2e7azcwGhAO9Ua3WXbuZ2YBwoDcaGemu3cxsQPiiaKO5C5/j49k0S62WhbkviJrZgHOgNzM25gA3s8rxlIuZWSIc6GZmiSgV6JJWS9omaVLS+S36PF/StZK2Svp6b8s0M7NOOs6hS6oBFwEnAVPAZkmbIuKmQp+DgDFgdUTcIenQearXzMxaKHOGvgqYjIjbIuJhYCOwtqHP6cDlEXEHQETs7G2ZZmbWSZlAXwpsL2xP5W1FTwYOlvQfkrZIOqPZgSSNSJqQNDE9Pb1nFZuZWVNlAl1N2qJhewh4BvAS4MXAX0h68m4PihiPiHpE1IeHh7su1szMWiuzDn0KWF7YXgbsaNLnroh4EHhQ0jeAY4Bbe1KlmZl1VOYMfTNwpKQjJO0NrAM2NfT5PHCCpCFJ+wLPBG7ubalmZtZOxzP0iJiRdA5wDVADLo6IrZLOzvdviIibJX0RuB74FfDRiLhxPgs3M7NHUkTjdPjCqNfrMTEx0ZfnNjOrKklbIqLebJ/fKWpmlggHuplZIhzoZmaJcKCbmSXCgW5mlggHuplZIhzoZmaJcKCbmSXCgW5mlggHuplZIhzoZmaJcKCbmSXCgW5mlggHuplZIhzoZmaJcKCbmSXCgW5mlggHuplZIhzoZmaJKBXoklZL2iZpUtL5TfY/X9K9kq7Nb+/sfalmZtbOUKcOkmrARcBJwBSwWdKmiLipoet/RsQp81CjmZmVUOYMfRUwGRG3RcTDwEZg7fyWZWZm3SoT6EuB7YXtqbyt0fGSrpN0taSnNTuQpBFJE5Impqen96BcMzNrpUygq0lbNGx/Fzg8Io4BPgxc0exAETEeEfWIqA8PD3dVqJmZtVcm0KeA5YXtZcCOYoeIuC8iHsjvXwXsJWlJz6o0M7OOygT6ZuBISUdI2htYB2wqdpD0eEnK76/Kj/vTXhdrZmatdVzlEhEzks4BrgFqwMURsVXS2fn+DcArgNdKmgF+BqyLiMZpGTMzm0fqV+7W6/WYmJjoy3ObmVWVpC0RUW+2z+8UNTNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0SUCnRJqyVtkzQp6fw2/X5P0qykV/SuRDMzK6NjoEuqARcBa4CVwGmSVrbo97fANb0u0szMOitzhr4KmIyI2yLiYWAjsLZJv9cDlwE7e1ifmZmVVCbQlwLbC9tTeduvSVoKvAzY0O5AkkYkTUiamJ6e7rZWMzNro0ygq0lbNGx/ADgvImbbHSgixiOiHhH14eHhkiWamVkZQyX6TAHLC9vLgB0NferARkkAS4CTJc1ExBW9KNLMzDorE+ibgSMlHQH8CFgHnF7sEBFHzN2XdAnwBYe5mdnC6hjoETEj6Ryy1Ss14OKI2Crp7Hx/23lzMzNbGGXO0ImIq4CrGtqaBnlE/MlvXpaZmXXL7xQ1M0uEA93MLBEOdDOzRDjQzcwS4UA3M0tEtQJ9dBSGhkDKvo6O9rsiM7OBUWrZ4kAYHYX163dtz87u2h4b609NZmYDpDpn6OPj3bWbmS0y1Qn02Raf+9Wq3cxskalOoNdq3bWbmS0y1Qn0kZHu2s3MFpnqXBSdu/A5Pp5Ns9RqWZj7gqiZGVClQIcsvB3gZmZNVWfKxczM2nKgm5klwoFuZpYIB7qZWSIc6GZmiVBE9OeJpWngh3158vKWAHf1u4geSWUsqYwDPJZBVIVxHB4Rw8129C3Qq0DSRETU+11HL6QyllTGAR7LIKr6ODzlYmaWCAe6mVkiHOjtpfTZvKmMJZVxgMcyiCo9Ds+hm5klwmfoZmaJcKCbmSXCgQ5IWi7p3yXdLGmrpHPz9kMkfVnS9/OvB/e71rIk1SR9T9IX8u1KjkXSQZI+K+mW/N/n+CqORdKf599bN0q6VNKjqzIOSRdL2inpxkJby9olXSBpUtI2SS/uT9XNtRjL+/Lvr+slfU7SQYV9AzuWZhzomRngzRHxVOA44HWSVgLnA1+NiCOBr+bbVXEucHNhu6pj+SDwxYh4CnAM2ZgqNRZJS4E3APWIOAqoAeuozjguAVY3tDWtPf9/sw54Wv6YMUmD9GfFLmH3sXwZOCoijgZuBS6ASoxlNw50ICJ+HBHfze/fTxYaS4G1wMfybh8DXtqXArskaRnwEuCjhebKjUXSAcBzgX8CiIiHI+IeKjgWsr898BhJQ8C+wA4qMo6I+AZwd0Nzq9rXAhsj4hcR8QNgEli1EHWW0WwsEfGliJjJN78FLMvvD/RYmnGgN5C0Avhd4NvAYRHxY8hCHzi0j6V14wPA24BfFdqqOJYnAdPAP+fTRx+VtB8VG0tE/Ah4P3AH8GPg3oj4EhUbR4NWtS8Fthf6TeVtVfGnwNX5/cqNxYFeIGl/4DLgjRFxX7/r2ROSTgF2RsSWftfSA0PA04H1EfG7wIMM7rRES/n88lrgCOCJwH6SXtXfquaNmrRVYm20pHeQTb9+aq6pSbeBHosDPSdpL7Iw/1REXJ433ynpCfn+JwA7+1VfF54N/L6k24GNwAskfZJqjmUKmIqIb+fbnyUL+KqN5YXADyJiOiJ+CVwOPIvqjaOoVe1TwPJCv2Vk00sDTdKZwCnAK2PXm3MqNxYHOiBJZPO0N0fE3xd2bQLOzO+fCXx+oWvrVkRcEBHLImIF2QWdr0XEq6jmWH4CbJf0O3nTicBNVG8sdwDHSdo3/147kew6TdXGUdSq9k3AOkn7SDoCOBL4Th/qK03SauA84Pcj4qHCrsqNhYhY9DfgOWS/Sl0PXJvfTgYeR3YF//v510P6XWuX43o+8IX8fiXHAhwLTOT/NlcAB1dxLMC7gVuAG4FPAPtUZRzApWRz/78kO2v9s3a1A+8A/hfYBqzpd/0lxjJJNlc+939/QxXG0uzmt/6bmSXCUy5mZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWiP8HJulsgEQ/8AgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zRDjQzcwS4UA3M0uEA93MLBEOdDOzRDjQzcwS4UA3M0uEA93MLBEOdDOzRDjQzcwS4UA3M0uEA93MLBEOdDOzRDjQzcwS4UA3M0uEA93MLBEOdDOzRDjQzcwSUSrQJa2W1C9pQNLmKvtfJ+lpSffmjw82vlQzM6tlXr0OklqA24DrgEFgt6SdEfFARde/jYg3TUONZmZWQpkz9FXAQEQciIjngO3Amukty8zMpqpMoLcDBwvbg3lbpask3SfpK5JeVu1AktZL6pXUOzIycgLlmpnZZMoEuqq0RcX2PwMXRcTlwJ8B3dUOFBHbIqIjIjra2tqmVKiZmdVWdw6d7Iz8gsL2EmC42CEivl94vkvS7ZIWRcShxpSZ6e4boqunn+HRMRYvbGVT53LWrqz2jwUzs7mnzBn6bmCZpKWSFgDrgJ3FDpJ+RpLy56vy4z7ZyEK7+4bYsmMvQ6NjBDA0OsaWHXvp7htq5NuYmc1adQM9Io4AG4Ee4EHg7ojYL2mDpA15t7cD+yTdB3wMWBcRldMyJ6Wrp5+xw+PHtI0dHqerp7+Rb2NmNmuVmXIhInYBuyrathaefxz4eGNLO9bw6NiU2s3M5ppZ80nRxQtbp9RuZjbXzJpA39S5nNb5Lce0tc5vYVPn8iZVZGZ2aik15XIqmFjN4lUuZmbVzZpAhyzUHeBmZtXNmikXMzOrzYFuZpYIB7qZWSIc6GZmiXCgm5klwoFuZpYIB7qZWSIc6GZmiXCgm5klwoFuZpYIB7qZWSIc6GZmiXCgm5klwoFuZpYIB7qZWSIc6GZmiXCgm5klolSgS1otqV/SgKTNNfr9vKRxSW9vXIlmZlZG3UCX1ALcBlwPrABukLRikn5/AvQ0ukgzM6uvzBn6KmAgIg5ExHPAdmBNlX6/A3weeKKB9ZmZWUllAr0dOFjYHszbjpLUDrwF2FrrQJLWS+qV1DsyMjLVWs3MrIYyga4qbVGx/afAByJivNaBImJbRHREREdbW1vJEs3MrIx5JfoMAhcUtpcAwxV9OoDtkgAWAW+QdCQiuhtRpJmZ1Vcm0HcDyyQtBYaAdcCNxQ4RsXTiuaQ7gS85zM3MZlbdQI+II5I2kq1eaQHuiIj9kjbk+2vOm5uZ2cwoc4ZOROwCdlW0VQ3yiPiNky/LzMymyp8UNTNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLRKlAl7RaUr+kAUmbq+xfI+l+SfdK6pX0C40v1czMaplXr4OkFuA24DpgENgtaWdEPFDo9g1gZ0SEpMuAu4FLpqNgMzOrrswZ+ipgICIORMRzwHZgTbFDRDwTEZFvngkEZmY2o8oEejtwsLA9mLcdQ9JbJD0EfBl4d7UDSVqfT8n0joyMnEi9ZmY2iTKBriptx52BR8RfR8QlwFrgw9UOFBHbIqIjIjra2tqmVKiZmdVWJtAHgQsK20uA4ck6R8S3gRdLWnSStZmZ2RSUCfTdwDJJSyUtANYBO4sdJL1EkvLnrwAWAE82ulgzM5tc3VUuEXFE0kagB2gB7oiI/ZI25Pu3Am8DbpJ0GBgDfrVwkdTMzGaAmpW7HR0d0dvb25T3NjObrSTtiYiOavv8SVEzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0TUvQWdZbr7hujq6Wd4dIzFC1vZ1LmctSvbm12WmdlRDvQSuvuG2LJjL2OHxwEYGh1jy469AA51MztleMqlhK6e/qNhPmHs8DhdPf1NqsjM7HgO9BKGR8em1G5m1gylAl3Sakn9kgYkba6y/x2S7s8f90i6vPGlNs/iha1Tajcza4a6gS6pBbgNuB5YAdwgaUVFt0eA10bEZcCHgW2NLrSZNnUup3V+yzFtrfNb2NS5vEkVmZkdr8xF0VXAQEQcAJC0HVgDPDDRISLuKfT/DrCkkUU228SFT69yMbNTWZlAbwcOFrYHgStr9H8P8JVqOyStB9YDXHjhhSVLPDWsXdnuADezU1qZOXRVaYuqHaVfJAv0D1TbHxHbIqIjIjra2trKV2lmZnWVOUMfBC4obC8Bhis7SboM+CRwfUQ82ZjyzMysrDJn6LuBZZKWSloArAN2FjtIuhDYAfx6RDzc+DLNzKyeumfoEXFE0kagB2gB7oiI/ZI25Pu3Ah8EXgjcLgngSER0TF/ZZmZWSRFVp8OnXUdHR/T29jblvc3MZitJeyY7YW5aoEsaAb7XlDc/OYuAQ80uYoZ5zOmba+OF2TvmiyKi6qqSpgX6bCWpd65NJ3nM6Ztr44U0x+zvcjEzS4QD3cwsEQ70qUvqe2pK8pjTN9fGCwmO2XPoZmaJ8Bm6mVkiHOhmZolwoNchqUVSn6Qv5dvnSvq6pO/mf57T7BobSdJCSZ+T9JCkByVdNQfG/LuS9kvaJ+kuSaenNmZJd0h6QtK+QtukY5S0Jb+hTb+kzuZUfXImGXNX/nf7fkl/LWlhYd+sH7MDvb73AQ8WtjcD34iIZcA38u2UfBT4akRcAlxONvZkxyypHXgv0BERl5J9vcU60hvzncDqiraqY8xvYLMOeFn+mtvzG93MNndy/Ji/Dlya34znYWALpDNmB3oNkpYAbyT7FskJa4BP5c8/Bayd4bKmjaSzgdcAfw4QEc9FxCgJjzk3D2iVNA84g+zbRJMac0R8G3iqonmyMa4BtkfEjyPiEWCA7EY3s0q1MUfE1yLiSL5ZvBlPEmN2oNf2p8D7gZ8U2s6PiH8FyP88rwl1TZcXASPAX+TTTJ+UdCYJjzkihoCPAI8B/wo8HRFfI+ExF0w2xmo3tUnx7i7v5qc340lizA70SUh6E/BEROxpdi0zaB7wCuATEbESeJbZP9VQUz5vvAZYCiwGzpT0a82tqulK39RmtpL0B8AR4K8mmqp0m3VjdqBP7mrglyU9CmwH/pOkzwCPS/pZgPzPJ5pXYsMNAoMR8Y/59ufIAj7lMb8eeCQiRiLiMNn3+r+atMc8YbIxlrqpzWwl6Z3Am4B3xE8/iJPEmB3ok4iILRGxJCIuJrtY8s2I+DWym3u8M+/2TuALTSqx4SLi34CDkpbnTdeS3Qw82TGTTbW8StIZyr7M/1qyC8Epj3nCZGPcCayTdJqkpcAy4J+aUF/DSVpNdovMX46IHxZ2pTHmiPCjzgN4HfCl/PkLyVYEfDf/89xm19fgsV4B9AL3A93AOXNgzB8CHgL2AZ8GTkttzMBdZNcIDpOdjb6n1hiBPwD+Begnu61k08fQoDEPkM2V35s/tqY0Zn/038wsEZ5yMTNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwRDnQzs0T8B+KYgHyE4nN2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for each date, slice/loc, and ROI, plot PPR vs IPI\n",
    "for date in all_snr['Date'].unique():\n",
    "    pfs_date = all_snr[all_snr[\"Date\"] == date]\n",
    "    for slic_loc in pfs_date['Slice_Loc'].unique():\n",
    "        pfs_sl = pfs_date[pfs_date[\"Slice_Loc\"] == slic_loc]\n",
    "        for roi in pfs_sl['ROI'].unique():\n",
    "            pfs_roi = pfs_sl[pfs_sl['ROI'] == roi]\n",
    "            plt.scatter(pfs_roi['IPI'], pfs_roi['PPR'])\n",
    "            plt.title(date + \" \" + slic_loc + \" ROI \" + str(roi))\n",
    "            plt.show()\n",
    "            plt.scatter(pfs_roi['IPI'], pfs_roi['PPL'], c='r')\n",
    "            plt.title(date + \" \" + slic_loc + \" ROI \" + str(roi))\n",
    "            plt.show()\n",
    "            plt.scatter(pfs_roi['IPI'], pfs_roi['PPHW'], c='g')\n",
    "            plt.title(date + \" \" + slic_loc + \" ROI \" + str(roi))\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bb79f30c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# average grouping by IPI\n",
    "for pp_type, color in [['PPR', 'b'], ['PPL', 'r'], ['PPHW', 'g']]:\n",
    "    group_by_ipi = all_snr[['IPI', pp_type]].groupby(by=['IPI']).mean()\n",
    "    group_by_ipi_std = all_snr[['IPI', pp_type]].groupby(by=['IPI']).agg(np.std)\n",
    "    group_by_ipi.reset_index(inplace=True)\n",
    "    \n",
    "    \n",
    "\n",
    "    # plot average PPR vs IPI\n",
    "    plt.scatter(group_by_ipi['IPI'], group_by_ipi[pp_type], c=color)\n",
    "    plt.title(\"Paired Pulse Depression: Home and Neighbor\")\n",
    "    plt.xlabel(\"IPI (ms)\")\n",
    "    plt.ylabel(pp_type)\n",
    "    plt.show()\n",
    "    plt.scatter(all_snr['IPI'], all_snr[pp_type], alpha=0.2, c=color)\n",
    "    plt.xlabel(\"IPI (ms)\")\n",
    "    plt.ylabel(pp_type)\n",
    "    plt.title(\"All data (Home and Neighbor)\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7943d448",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PPR\n",
      "IPI     \n",
      "5      7\n",
      "10     8\n",
      "20     8\n",
      "30     7\n",
      "40     6\n",
      "50     6\n",
      "60     6\n",
      "70     7\n",
      "80     7\n",
      "90     8\n",
      "100    8\n",
      "110    7\n",
      "120    8\n",
      "130    6\n",
      "i= 0\n",
      "5\n",
      "p_val for IPI 10 : 3.499262852809432e-07\n",
      "p_val for IPI 20 : 4.881072372111939e-06\n",
      "p_val for IPI 30 : 1.2126658948097647e-09\n",
      "p_val for IPI 40 : 5.9914524625066665e-05\n",
      "p_val for IPI 50 : 0.00012144075252369524\n",
      "p_val for IPI 60 : 0.0003581820324181082\n",
      "p_val for IPI 70 : 3.5111662214529505e-08\n",
      "p_val for IPI 80 : 0.00011236085528281331\n",
      "p_val for IPI 90 : 5.770674321897844e-10\n",
      "p_val for IPI 100 : 4.459591231102506e-05\n",
      "p_val for IPI 110 : 0.000314412218509768\n",
      "p_val for IPI 120 : 2.7655268436851053e-06\n",
      "p_val for IPI 130 : 0.001173396804249497\n",
      "[None, '***', '***', '***', '***', '***', '***', '***', '***', '***', '***', '***', '***', '**']\n"
     ]
    },
    {
     "data": {
      "image/png": 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3MuTBzMzqoJqZ6EcBdwMfB94KnATcLOnDGe63BJgk6ZUZ65J6gKOpci6HpJ2Bk4EfRMSGDHkwM7M6qGYY7+eAB4EJEbEvsBewlDQBsFYLgLXAYklTJU0hjcp6BJhXPEhSt6Stkso76QFOIDWDufPczKyJqgkgbwIuKvaFRMRm4J+AvSXtX8vNCjPNjwPuB64F+oGHgeMi4tmSQwXsNEj+ziCN2vpeLfc2M7P6qqYPZD9SraHUWtIv+X2B39Vyw4hYD5w4zDHF61f6LMuEQzMzq7Nq9wPx6CUzM9tGtRMJ50uqtNfHtySVNj1FRBxTh3yZmVmLqyaA/JjKNZCVdc6LmZm1kWomEk4ufi9pL+DZiPD+G2ZmI1w180BGFTZzeoq08u3TkhZJem2jM2dmZq2rmiasj5IWTVwB/IK0n8c04GngQw3LmZmZtbRqAshHgAURMbOYIGkm8L8lzYyI8r1CzMwAmDw5va5Y0cxcWKNUM4z3YLbfefAG0kS/7rrnyMzM2kI1AWR3UnNVqeKQ3j3qmx0zM2sX1c4DOaB0AURS7aOY/lTpgRHx23pkzMzaW38/rFoFW7ZATw/MnQt9fc3OldVTtQHkxkHSb66QtlOFNDMbQfr7YcaMFDwA1q1L78FBpJNUE0A80qrFuGPSWt3s2TAwsG3awEBKdwDpHNVMJPSy6WZWk/Xra0u39lTtYopmZlXr6qot3dqTA4iZ1d3cuTB27LZpY8emdOscDiBmVnd9fTB/PuyyS3rf3Z3eu/+js1Q7CsvMrCZ9fbBgQfreAz46kwOImTWMA0dncxOWmZll4gBiZmaZOIC0meLyECtXpuUh+vubnSMzG6kcQNrIYMtDOIiYWTM4gLSRoZaHMDPLmwNIG/HyEFZPkyf/eV01sywcQNqIl4cws1biANJGvDyEmbUSB5A24uUhzKyVeCZ6m/HyEGbWKlwDMTOzTBxAzMwsEwcQMzPLxAHEzMwycQAxM7NMHEDMzCwTBxAzM8vE80DakOd/mFkrcA3EzMwycQAxM7NMHEDMzCyT3AOIpIMk3Shps6SnJd0kqeoFySW9QdJ3JW2U9CdJ90k6t5F5Nus03hrZ6iHXTnRJY4FlwBbgDCCAi4Hlko6IiOeGOb+3cP4K4MPAZuD1wO4NzLZZRxlsa2Twys5WG0VEfjdLNYXLgEMj4sFC2gTgAeBTEXHZEOeOAn4N3B8R07Lcv7e3N1avXp3lVLOO0dOTgka57m5Yuzbv3Firk3RXRPRW+izvJqwpwKpi8ACIiIeBnwJThzl3MjCRFIDMOl6jtpz11shWL3kHkMOB31RIX0MKDkN5Z+F1V0mrJL0o6Y+SvippTF1zadbBvDWy1UveAWQ8sKlC+pPAuGHO3b/wegNwK/Ae4H+R+kK+M9hJkmZIWi1p9YYNG2rPsVmH8dbIVi/NmIleqdNFVZxXDHbXRcSFhe9XSNoJ+KKkiRFx73Y3i5gPzIfUB5Ilw9Z+ik0/nrW/vWJH+fTpqSO9uzsFD3egW63yroFsItVCyo2jcs2k1BOF138vS7+18Hpk9myZjSx9fTBpEhxzTOo4d/CwLPIOIGtI/SDlJgLb1R4qnAvb12CKtZeXdyBflrNGdRDnqZFl8DwNawd5B5AlwCRJBxcTJPUARxc+G8oPSfNH/ros/X2FV4/PtY4w2DwNBxFrNXkHkAXAWmCxpKmSpgCLgUeAecWDJHVL2iqp2NdBRDwBfAE4W9Ilkt4t6Z+BC4Fvlw4NNmtns2fDwMC2aQMDKd2sleTaiR4Rz0k6DrgcuJbU/HQ7MCsini05VMBObB/gPg88A3wMOA/4PfBl4KIGZ90sN56nYe0i91FYEbEeOHGYY9ZSYWRWpGnzl+HJhNbBuroqzxT3PA1rNV6N1yrqhE7uduV5GtYuHEDMWkxfH8yfD7vskt53d6f3HmprrcZb2pq1oL4+WLAgfe/JkNaqXAOx7XgOgplVwwHEtuE5CGZWLQcQ24bnILSOFSvcfGWtzQHEtuE5CGZWLQcQ24b3ijCzajmA2DbymIPQ6E56DwIwy4eH8do2Gr1XxGCd9KX3buXrdxL3r9iOUlodZGTo7e2N1au9aG81GrUhU09P5WU6urvTvhStfv1S3rTKRgJJd0VEb6XP3IRluWp0J70HAZjlx01YVlGj/qpu9EKBXojQLD+ugViuGt1J74UIzfLjAGK5avRCgV6I0Cw/7kS3pmh0B3Sjr9/f37iRamatxJ3oZnXk9cLMEgcQsxp5vTCzxAHErEYeKmyWOICY1cjrhZklDiBmNfJQYbPEAcSsRh4qbJZ4JrpZBt6z3Mw1EDMzy8gBxMzMMnEAMTOzTBxAzMwsE3eiW1O449ms/bkGYmZmmTiAmJlZJm7Cso7kJjKzxnMNxMzMMnEAMTOzTBxAzMwsEwcQMzPLxAHEzMwycQAxM7NMHEDMzCyT3AOIpIMk3Shps6SnJd0kqarNQCXFIF9HNjjbZmZWJteJhJLGAsuALcAZQAAXA8slHRERz1VxmYXAvLK0++uZTzMzG17eM9E/AhwMHBoRDwJIugd4AJgJXFbFNR6LiFWNy6KZmVUj7yasKcCqYvAAiIiHgZ8CU3POi5mZ7YC8A8jhwG8qpK8BJlZ5jY9K2iJpQNIySX9Vv+yZVW/FCq+5ZSNb3gFkPLCpQvqTwLgqzr8O+BjwbmAGsCewTNLkOuXPzMyq1IzVeKNCmqo6MeK0krd3SFpMqtFcDLyz0jmSZpCCDV1dVQ32MjOzKuRdA9lEqoWUG0flmsmQIuIZ4PvAW4c4Zn5E9EZE7957713rLczMbBB5B5A1pH6QchOBezNeU1Su1ZiZWQPlHUCWAJMkHVxMkNQDHF34rCaSXg38N+Bn9cqgmZlVJ+8AsgBYCyyWNFXSFGAx8AglkwMldUvaKunCkrTzJC2Q9PeSJks6gzT8dz/gglxLYWZm+XaiR8Rzko4DLgeuJTU/3Q7MiohnSw4VsBPbBrj7gGmFr9cAT5MCyPSI+HkO2TczsxK5j8KKiPXAicMcs5aykVkRsRRY2ricmZlZLbwar5mZZaKIkTOASdIGYF3h7V7AxiZmJ28jrbzgMo8ULnNjdUdExTkQIyqAlJK0OiJ6m52PvIy08oLLPFK4zM3jJiwzM8vEAcTMzDIZyQFkfrMzkLORVl5wmUcKl7lJRmwfiJmZ7ZiRXAMxM7MdMKICiKSDJN0oabOkpyXdJKnt13iX9HeSFklaJ+lPku6T9AVJe5Qc0yMpBvl6bROzn0lhOZtKZXmq7Lhxkr4paaOk5yTdJukvm5TtHSJpxRDP8JbCMW39nCUdKOlfJN1Z2DQuCuvllR9X1XOVtKukL0v6feFn405J78qlMFWqpsySjpd0naSHCuV4SNJVkvapcL3Bnv+R9c57M/YDaQpJY4FlwBbgDNIKvhcDyyUdERHPNTN/O+g8YD1wPvAo8GZgDnCspHdExMslx36B7ReufCaPTDbIx4FflLzfWvxGkkhlnQD8I2nLgM+QnvmREfFonhmtg48Bry5LeztwGds/03Z9zocAHwTuAu4A3lt+QI3P9VukBVc/CfwWOAf4kaS3R8R/NLActRi2zMDZwO6k31m/BV4PfA54X+H317Nlxy+kZH3BgvvrmOckIkbEF3Au8BJwSEnaBNIvnP/R7PztYNn2rpB2OilIHld431N4/+Fm57dOZZ5cKM+7hzhmauGYY0vSXkPaAfOrzS5Dnf4dvkX6o2h8JzxnYFTJ9x8ulKUny3MF3lQ47kMlaaNJ6+otaXZZayxzpZ/xdxWOPassPYCL88j7SGrCmgKsiogHiwkR8TBpQcapTctVHUTEhgrJxb/KD8gzLy1mCvC7iFheTIiIzaQ11dr6mQNIGgN8AFgaEU82Oz/1ENvWlgdT7XOdArwI3FBy3FbgetJf7rvUJdM7qJoyt+rP+EgKIIeTtr8tt4a0oVWnOabw+p9l6V8oLJW/WdKSdu0PKNEv6SVJT0j6Tlmf1lDPvEvS7vlksWH+O7AH8O0Kn3Xacy5V7XM9HHg4IgYqHLczqemonQ32Mw7wUUlbCn0qyyT9VSMyMGL6QEhb6VbaNvdJ0pa6HUPSAcDngdsiYnUheQupTfRWYANwGKnP5P9KOioiKv0nbGWbgUuBlaSl/d9MKs+dkt4cEX8kPfO1Fc4t/rU+DihvO24npwN/BH5YktZpz7mSap/rUD/zxeu0pcIAmStIwePmso+vA74H/A7oJvX/LJP0nohYUc98jKQAApW3vlWFtLZV+OtrMalv50PF9Ij4PakjruiOwsidNcBs4NQ887mjIuJu4O6SpJWSfgz8nNSxfgGDb3fc9s9c0v7Au4ErC80yQOc950FU+1w78vlLGg38H1LT1dGlzx8gIk4reXuHpMWkGtvFwDvrmZeR1IS1icp/cYyj8l8pbUfSrqTRKQcD74thRhlFxCPAT4C35pC9houIX5JGmhTL8ySDP3No7+d+Kunnt1Lz1TY67TlT/XMd7ri26zeSVHzm7wb+NiLuGe6ciHgG+D4NeP4jKYCsIbWJlpsI3JtzXupO0quARcBRwN9ExK+rPZXKf6W1q9LyDPXM18f2Qx/byenAryLiV1Ue30nPudrnugaYUBjCX37cC8CDtJ9vACcBJ0fE7TWc15DnP5ICyBJgkqSDiwmFyTpHs/14+bZS+KukHzgemBoRq6o8r4tU/p81MHu5kdQL/Bf+XJ4lwAGSjik55tXA+2njZ14o5+FUUfsoHN9Rz5nqn+sS4FWkkWrF40aTfgHfGhFb8slufUi6lDTM90MRcXMN572aNBem7s9/xKyFJWk34FfAn0jt4wFcRBrFUmkiTtuQdBWp3XsuqfOs1KMR8WjhP98o4E5S5+qhpMlXrwHeFhH35ZjlHSapH3gY+CXwFKkT/TPAAPBfI2JjIbD+BDiI1JFYnHB2BPCmQtNO25H0VeCjwIER8XjZZ23/nCX9XeHb40n/rz9GKsuGiFhZy3OVdD3wvsJxD5P+3U4A3lFo8mwJVZT508AXgauBBWWnb4iIhwrXOY/0zJfz5070YtrxEXFHXTPe7Ek0eX4BXaRmnqdJs3JvpmzCTjt+kUakxCBfcwrHnEUaN76J1MH+B+A7wKHNzn/GMn8GuIc0GutF4BHSCqWvKztuPOmH7klScLmd9Eum6WXIWO5XkX6xLB3k87Z/zkP8X15R63MFxpBm6v8BeJ70V/jkZpex1jIDK4Y4ZmHJdd5Pmtu2sfBz8QSpJnZUI/I9YmogZmZWXyOpD8TMzOrIAcTMzDJxADEzs0wcQMzMLBMHEDMzy8QBxMzMMnEAMTOzTBxAzMpIOrOwh/QhJWkLy/aX3iDpx5L+uuzckHRxlfc5UdLjFdZqaghJryvsD3FUHvezzucAYla9DaQ9yN8OfIS0QN0PJB1f64UKazJdAnw5tt/wqCEiLfW+APhyHvezzjfS9gMx2xEvRMlClZKWAeuBc0lLadRiKmn/8qvrlrvqzAPWFDaX+nnO97YO4xqIWUYR8TRp/5EsW6N+GLglyvYyLzaBSfonSeskPSfp+5L2KXz9a2Gb2kcKC+yVnrufpG9L+l1hO9PfS/qepH1K8nwv8OvC/c12iGsgZhkVmqEOIq3yWst5uwCTgc8OcshppB3kPgbsS9q69BrSytE/JC0a+QHgi5J+HRE/KJx3LX/ewvSRwrnHA+V9LD8GptSSZ7NKHEDMalAIGgD7kQLAfsCXarzMkcCupO0FKtlC2tdla+GebwQ+AXw2Ii4upK0AppECSTGAvB04PyL6S6713QrXvxs4R9L+EfG7GvNu9goHELPqHUBaIrvoWeBC4Ks1Xmf/wuuGQT7/99h2n+v/V3j9UTEhIrZKepBUAyr6BfBJSQKWAb+JysttF++7P2nPCLNM3AdiVr0/kvaV7gUmAK+NiIsi4uUar7Nr4XWwHfHK92p/YYj0XUven0Ta++FTpL1SHpN0YWEDplJ/KryOqTrHZhU4gJhV78WIWB0Rd0XE2oh4KeN1nii8jqtXxgAi4o8RcU5EHAAcBiwEPgfMLDt0fOF1Yz3vbyOPA4hZ/opNUgc36gYRcV9EnE+qtbyx7OMJpNpLTZ3/ZuXcB2KWs4hYL2kdcBRwXT2uKek1wG1APylAvUiaazIOuLXs8LcBv4iI5+txbxu5XAMxa44bgBPqeL3ngV+SZsjfCPwbaVRWX0QsLh4kaQxpaO/1dby3jVDeE92sCST9BXAfMDkifpLjfU8CvgkcFBFP5XVf60wOIGZNImkB8LqIqGdNZLh7/hK4OSI+n9c9rXO5CcuseT4L/CLH1Xj3AxYDX8njftb5XAMxM7NMXAMxM7NMHEDMzCwTBxAzM8vEAcTMzDJxADEzs0z+P1kGi0f08qlYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PPR\n",
      "IPI     \n",
      "10     1\n",
      "20     1\n",
      "30     2\n",
      "40     2\n",
      "50     1\n",
      "60     1\n",
      "70     1\n",
      "80     1\n",
      "90     1\n",
      "100    2\n",
      "110    1\n",
      "120    2\n",
      "130    3\n",
      "i= 1\n",
      "110\n",
      "p_val for IPI 10 : nan\n",
      "p_val for IPI 20 : nan\n",
      "p_val for IPI 30 : nan\n",
      "p_val for IPI 40 : nan\n",
      "p_val for IPI 50 : nan\n",
      "p_val for IPI 60 : nan\n",
      "p_val for IPI 70 : nan\n",
      "p_val for IPI 80 : nan\n",
      "p_val for IPI 90 : nan\n",
      "p_val for IPI 100 : nan\n",
      "p_val for IPI 120 : nan\n",
      "p_val for IPI 130 : nan\n",
      "[None, None, None, None, None, None, None, None, None, None, None, None, None]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
      "image/png": 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I1Sg1y+yBSJJaMUAkSa0YIBqrxUXYvx+uvXb276z2LnFpZY6BaGz6d1YfP968nuU7q71LXFpdqmradejMjh076sCBA9Ouxtzatq35RbvU1q1w6FDXtTkx89QW6UQkua6qdgx7z1NYGpt5urN6ntoiTYoBorGZpzur56kt0qQYIBqbebqzep7aIk2KAaKx6d9ZfdppzetZvrPau8Sl1TmIrrHrr0a4b980ayFpHBxElySNnQEiSWrFAJEktWKASJJaMUAkSa0YIJo5TnIorQ9OpqiZ4iSH0vrReQ8kyUOTXJ7k1iS3JXlHklUniEiyI8meJH+T5FiSI0kWk5zZRb21PuzefXd49B071pRL6lanPZAkG4GrgePAs4ECLgauSfJNVXXHCrv/MHA28HrgIPAQ4ELgQJJHV9WNE628RjbJGwid5FBaP7o+hXU+sB14RFVdD5Dkw8DfAhcAr11h31+qqqODBUneB9zQ+9yLJlJjrStbtgyfZt1JDqXudX0K61xgfz88AKrqBuB9wHkr7bg0PHplh4GjNL0RnQSc5FBaP7oOkLOBjwwpPwictdYPS/Io4IHAX59gvTQjnORQWj+6PoV1BnDzkPKbgE1r+aAkpwJvpOmBvHmF7XYBuwC2eJ5jLiwsGBjSejCN+0CGTf+bFp/zq8C3Az9SVcNCqfmyqj1VtaOqdmzevLnF10iShum6B3IzTS9kqU0M75kMleSVNL2KZ1fVVWOqmyRpDboOkIM04yBLnQV8dJQPSLIbeBHwU1X1tjHWTZK0Bl2fwroSeFyS7f2CJNuAJ/TeW1GSn6K5b2R3Vb1hUpWUJK2u6wC5FDgE7E1yXpJzgb3AjcCb+hsl2ZrkziQXDZT9MPA64I+Aq5M8buCx5iu4JEknptNTWFV1R5JzgF8B3kYzeP4e4L9U1e0DmwY4hXsG3NN65U/rPQZdC+xc7fuvu+66zyYZchvaTHsA8NlpV2JKTua2w8ndftvena3LvXFSrYk+j5IcWG694nl3MrcdTu722/b10Xanc5cktWKASJJaMUBm355pV2CKTua2w8ndftu+DjgGIklqxR6IJKkVA0SS1IoBso4l+YEkv5vkcJIvJPlYklcm+aqBbbYlqWUe959i9U9Ikp3LtOmWJdttSvIbST6b5I4k707yjVOq9tgk2bfCcf2j3jZzceyTfH2SNyR5f2+56urNULF0u5GOdZLTk7wqyd/3/r95f5Lv6qQxazRK25M8Ocnbk3yi155PJPn1JA8c8nnL/Tw8ehL173ouLK3NC4AjwEuATwGPAV4KPCnJt1fVXQPbvpJ7Twfz+S4qOWE/BXxg4PWd/X8kCU2bzwR+kmZCzhfTLJH86Kr6VJcVHbMfA756SdnjaVbtXHqcZ/3YPwz4IeA64E+Bf710gzUe6zcD/xZ4IfBJ4MeBP07y+Kr6iwm2o41V2w48D7gfzTROnwQeDrwMeGpvKfDbl2z/FgZm9uj5+BjrfLeq8rFOH8DmIWXPopkS/5ze622918+ddn3H3PadvXY9ZYVtzutt86SBsq+hWV/m9dNuwwT+m7wZOA6cMU/HHtgw8O/n9tq0rc2xBr65t91zBspOBT4GXDnttrZs+7DfA9/V2/ZHl5QXcHFX9fcU1jpWQ5bx5e6/xl3Gt1ki+dNVdU2/oKpuBd7JKkskz5ok9wV+EHhnVd007fqMU92zJ72cUY/1ucCXgcsGtrsT+G2av9hPG0ulx2SUtq/n3wMGyOx5Yu956TK+r+xNQHlrkivnYRygZzHJPyX5XJLfSjK4rORKSyRvSXK/bqrYiX8PfBXw1iHvzeuxHzTqsT4buKGqjg3Z7itoThnNg+V+DwA8P8nx3pjK1Um+c1KVcAxkhiR5CPBy4N1VdaBXfJzmfOdVNMv7PpJmzOT/JnlsVc3qevG3Aq+hmSjzNprxn5cA70/ymKr6DM3iZIeG7Nv/C30TsPT88Kx6FvAZ4A8Hyub12A8z6rFeadns/ufMtN5FNK+jCY8rlrz9duD3gU/TTIL4QprZy7+7qvaNuy4GyIzo/YW1l2YQ+Tn98qr6e5pBtr4/7V2lcxDYDfxIl/Ucl6r6EPChgaJrk7wX+HOagfWfp5mdeVxLJK9bSR4MPAX4H73TMcD8HvtljHqs5/pnIsmpwP+mOXX1hMGfB4CqeubAyz9Nspem53Yx8B3jro+nsGZAktNprkDZDjy1Vrm6qKpuBP4M+NYOqteZqvogzdUk/XbdxPJLJMMalkle536E5v/VYaev7mFejz2jH+vVtpvZ8aMk/Z+BpwBPr6oPr7ZPVX0e+AMm9PNggKxzSe4D/C7wWOB7quqvRt2V4X+JzbrBdq20RPKRuvfljbPqWcBfVtVfjrj9PB77UY/1QeDMJBuHbPcl4PrJVXHi3gg8A/jhqnrPGvab2M+DAbKO9f7iWASeDJxXVftH3G8LzTLB/2+C1etckh3Av+Tudl0JPCTJEwe2+WrgexlhieRZ0Gvz2YzQ++htP5fHntGP9ZXAfWiuWOtvdyrNL96rqup4N9UdrySvobnM9zlVdcUa9vtqmntiJvLz4BjI+vZrNP8jXALckeRxA+99qqo+1fvB2gC8n2Yg9RE0N1jdBfxCx/UdmySLwA3AB4FbaAbRXwz8HfCG3mZX0rT77UleyN03lwX45Y6rPCnPohn3+q2lb8zTsU/yA71/fkvv+d8kOQocraprGfFYV9VfJLkMeF2v934D8HyaGxAXOmnMGq3W9iT/DfivwG8Cf7vk98DRqvpE73NeQPMzcA13D6K/AHgQk2r7tG+k8bH8g+aqk1rm8dLeNj9Kc034zTS/aP6B5pfNI6Zd/xNs+4uBD9NcjfVl4Eaaaay/bsl2Z9D8j3UTcIxmieRvnnb9x/Tf4D40wfDOZd6fm2O/ws/5vrUea+C+NHfs/wPwRZq/vndOu41t2w7sW2Gbtwx8zvcC76NZ7vbLwOdogvexk6q707lLklpxDESS1IoBIklqxQCRJLVigEiSWjFAJEmtGCCSpFYMEGmJJP+ptwzowwbK3rJkidCjSd6b5GlL9q0kF4/4Pd+f5B+HTLsxEUm+rjfF92O7+D7NPwNEGt1RmmVlHw+cT3MX9LuSPHmtH9SbXuMXgFfVvdeumIhqZu+9FHhVF9+n+edUJtLovlQD85EluZpmzfqfprkrei3Oo1mS9jfHVrvRvAk42Fsv5M87/m7NGXsgUktVdRvN9PJtVrl7LvBHtWR52v4psCQ/m+RwkjuS/EGSB/Ye/6e38uCNvTmSBvd9UJK3Jvl0b0W6v0/y+0keOFDnjwJ/1ft+6YTYA5Fa6p2GeijNhH1r2e80YCdw4TKbPJNmEaAfA76WZvW5/0WzpO0f0swJ9oPALyb5q6p6V2+/t3H3KnQ39vZ9MrB0jOW9NGuHSyfEAJHWoBca0MxwemHv+ZfW+DGPBk4Hllvf4zjN9P139r7zG4CfAS6sqot7ZfuA76MJkn6APB54SVUtDnzW7wz5/A8BP57kwVX16TXWXfpnBog0uofQzHLadztwEfD6NX7Og3vPR5d5/0/qnkuV/k3v+Y/7BVV1Z5LraXpAfR8AXpgkwNXAR2r4bKn9730wzbTfUiuOgUij+wzN0qA7aNaXuH9VvaKq7lrj55zee15ucaOlS/F+aYXy0wdeP4Nm+u6fo5kK/++SXNRbmGzQF3rP9x25xtIQBog0ui9X1YGquq6qDlXVP7X8nM/1njetuNUaVdVnqurHq+ohwCOBtwAvAy5Ysml/zfDPjvP7dfIxQKTu9U9JbZ/UF1TVx6rqJTS9lm9Y8vaZNL2XNQ3+S0s5BiJ1rKqOJDkMPBZ4+zg+M8nXAO8GFmkC6ss095psAq5asvm3AR+oqi+O47t18rIHIk3HZcC/G+PnfZFm/fjzgcuB36O5Kmuhqvb2N0pyX5pLe397jN+tk5RL2kpTkORfAB+jWav7zzr83mcAvwE8tKpu6ep7NZ8MEGlKklwKfF1VjbMnstp3fhC4oqpe3tV3an55CkuanguBD3Q4G++DgL3Aq7v4Ps0/eyCSpFbsgUiSWjFAJEmtGCCSpFYMEElSKwaIJKmV/w+F/IwtNYLP6gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PPL\n",
      "IPI     \n",
      "30     7\n",
      "40     6\n",
      "50     6\n",
      "60     6\n",
      "70     7\n",
      "80     7\n",
      "90     8\n",
      "100    8\n",
      "110    7\n",
      "120    8\n",
      "130    6\n",
      "i= 0\n",
      "30\n",
      "p_val for IPI 40 : 0.9469428846460773\n",
      "p_val for IPI 50 : 0.9871595008785773\n",
      "p_val for IPI 60 : 0.994574736699532\n",
      "p_val for IPI 70 : 0.9747372838754674\n",
      "p_val for IPI 80 : 0.999113680029144\n",
      "p_val for IPI 90 : 0.9979621567482149\n",
      "p_val for IPI 100 : 0.9465627967324213\n",
      "p_val for IPI 110 : 0.9933653615033824\n",
      "p_val for IPI 120 : 0.5235816368615828\n",
      "p_val for IPI 130 : 0.9096481799539311\n",
      "[None, None, None, None, None, None, None, None, None, None, None]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PPL\n",
      "IPI     \n",
      "30     2\n",
      "40     2\n",
      "50     1\n",
      "60     1\n",
      "70     1\n",
      "80     1\n",
      "90     1\n",
      "100    2\n",
      "110    1\n",
      "120    2\n",
      "130    3\n",
      "i= 1\n",
      "30\n",
      "p_val for IPI 40 : 0.7394508588303261\n",
      "p_val for IPI 50 : nan\n",
      "p_val for IPI 60 : nan\n",
      "p_val for IPI 70 : nan\n",
      "p_val for IPI 80 : nan\n",
      "p_val for IPI 90 : nan\n",
      "p_val for IPI 100 : 0.37172262230825404\n",
      "p_val for IPI 110 : nan\n",
      "p_val for IPI 120 : 0.21422000464406213\n",
      "p_val for IPI 130 : 0.7071500911328281\n",
      "[None, None, None, None, None, None, None, None, None, None, None]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PPHW\n",
      "IPI      \n",
      "30      7\n",
      "40      6\n",
      "50      6\n",
      "60      6\n",
      "70      7\n",
      "80      7\n",
      "90      8\n",
      "100     8\n",
      "110     6\n",
      "120     8\n",
      "130     4\n",
      "i= 0\n",
      "30\n",
      "p_val for IPI 40 : 0.40391168808305955\n",
      "p_val for IPI 50 : 0.025232281570557985\n",
      "p_val for IPI 60 : 0.48239794226631594\n",
      "p_val for IPI 70 : 0.046379594152108385\n",
      "p_val for IPI 80 : 0.7637390166028609\n",
      "p_val for IPI 90 : 0.8890783803288781\n",
      "p_val for IPI 100 : 0.9906225097965882\n",
      "p_val for IPI 110 : nan\n",
      "p_val for IPI 120 : 0.9999472370148175\n",
      "p_val for IPI 130 : nan\n",
      "[None, None, '*', None, '*', None, None, None, None, None, None]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PPHW\n",
      "IPI      \n",
      "30      2\n",
      "40      2\n",
      "50      1\n",
      "60      1\n",
      "70      1\n",
      "80      1\n",
      "90      1\n",
      "100     1\n",
      "110     0\n",
      "120     0\n",
      "130     0\n",
      "i= 1\n",
      "30\n",
      "p_val for IPI 40 : 0.1718030292482862\n",
      "p_val for IPI 50 : nan\n",
      "p_val for IPI 60 : nan\n",
      "p_val for IPI 70 : nan\n",
      "p_val for IPI 80 : nan\n",
      "p_val for IPI 90 : nan\n",
      "p_val for IPI 100 : nan\n",
      "p_val for IPI 110 : nan\n",
      "p_val for IPI 120 : nan\n",
      "p_val for IPI 130 : nan\n",
      "[None, None, None, None, None, None, None, None, None, None, None]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n"
     ]
    },
    {
     "data": {
      "image/png": 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gzX6wE/BX4NfAvIh4vBHBmZlZe8s1d5ykl5MeOv0cMJ60nPd44Hjgj5Km11qxpBdKmlL6qvU8ZmbWuvL2hM4GHgV6I2J5oVBSD2m11K+RpuSpStIY4DTgaGC7CruNzRmTmZm1uLyzaL8SOKE4AQFExDLS8OxX5TzPx4GPAF8irSf0BVJSWgrcBxyV8zxmZtYG8iahR4FnKmx7Otuex/tJi+Wdkf3844g4CXgp6R6TL8eZmXWQvEnom8CnJY0vLpQ0AfgUae64PKYCC7MZFNYBEwAiYi3peaMP5DyPmZm1gbxJqAvoBlZIulDSGZIuJD2sOgXYWtLc7FVtVoTVpAENAPcDexRt2wKYXFP0ZmaZwcWD9MzrYcwpY+iZ18Pg4sFmh2Q55B2YcHzR5/eW2d5X9DmoPI3P74FpwC+y1ymSniL1ivqBW3LGY2a2weDiQeZcOYc1a9cAsHz1cuZcOQeA2dNnNzM0G0KunlBEjKnhVW102zxgTfb5JOBBYBC4hLTw3UdH8LuYbfb8bb0x+ub3bUhABWvWrqFvfl+FI2xz0fAZE4pFxDVFnx+U9CpgV9Llvj9k94bM2pK/rTfOitUraiq3zUfee0IbSBoj6TpJu4+08kjujYjbnYCs3fnbeuNMmVh+YG2lctt8DKcnJNKDqdvm2rnGWRAiwl9drC3523rj9M/s36iXCdA1rov+mf1NjMryGI3LccuobXZsz5hgbWnKxCksX728bLmNTOFyZt/8PlasXsGUiVPon9nvy5wtYDSS0Bw2TkJjgXNIMyVs+n+kWZvyt/XGmj19tpNOCxpOEloPnEJ6zmdIEXFe8c+SCknoJxHhIdnWMfxt3QoGFw/67yBTcxKKiCAlITOrkb+tm0dJbqxiEpK0by0niogbRx6OmVl7qzZK0kloYwv4x70cMfTgAg8oMDMbgkdJbqxaEtq/6PN2pDWD7gAuBh4CXgS8C9iLtDxDrWoZMWdm1hY8SnJjFZNQRNxQ+JxNVnp1RBxZsttFks4H3gpcWe48kipdpjtf0t82rTb2GzJqM7MW5VGSG8s7Y8IhpPndyrkk217JeuC5ktcNpBm1S8vX54ynpQwuHuSmv9zEDctv8HxhZh1u9vTZDMwaoHtiN0J0T+xmYNZAR94Pgvyj48YAuwHXlNm2O1XuB0XEjNrDah+FkTDPPJfWBOz0kTBm5lGSxfL2hP4LOF3S27PnfJA0VtLhpIdOf9qoAFud5wszM6ssb0/o34GdSZfe1kl6DJiUHf/LbLuV4ZEwZmaV5UpCEfEI8C+SXg+8BtgBeAD4TURc28D4Wp5HwpiZVVbTUg4RcU1EzI2IYyLi1GYmIEk7S7pM0mpJT0j6US0zdkt6qaRLJT0i6SlJd0v6WL3j7J/ZT9e4ro3KOnkkjJlZsdxJSMnBks6S9G1J3Vn5fpJ2bFyIZWPpAq4D9gTeB7yHNEDieklb5zi+F/gtsBVwJPBm4Es04IHbwkiYrcZuBdDxI2HMzIrluhwnaRJwFfBq4AnSWkJfI82CfRSwigr3hSTdArwnIpZIOhE4LyJyTX5axVHAVGCPiLg3q+d24B7gaODLVX6XMcB3gPkRcWjRputHGFNFs6fP5txF5wKw4IgFjarGzKzl5O0JnUkamPBaYHvSND4F1wIzqxw7Hdgm+3wS8JIaYyznYOCmQgICiIilwK+o/swSpAX5plElUZmZ2eio5WHVvoj4DZtOt7OClKAquR/4V0k9pOS1g6QplV4549mLNIVQqSWkBFPN67L38ZJukrRW0sOSzpY0IWf9ZmZWB3mHaG8D/LXCtvFs3DMq9S3Ss0SfISWwHw9RV577MpOBx8qUryINHa+mcP/qEuDrwGeBXmAuKZkeWnqApDmkxfmYMsWj2szM6iVvErobeAPp0lup/YDFlQ6MiC9IuobUQ/k2cDrwpxrjLHvqMmXVkmFBoff3vYg4Mfu8IHsI94uSpkXEnRtVFDEADAD09vZ64lUzszrJm4S+AXxD0mrgP7Oy7SS9H/goWS+hkoi4GbhZ0hHAdyPirmHGW/AYqTdUahLle0jFHs3eS6cguhr4IrA3cCdmZtZweR9WPVfSrqQVVedmxdeQJhz9j4jINSNnROw/9F65LCHdFyo1jaETyJJCOCXlhV5UW06iama2Ocr9nFBEfBbYlTQE+vPAMaQh0jVNgiZpevaQ6UpJ67JBAT+QNL2G01wB7CNpatF5e0ij964Y4tifAc8AbywpPzB7X1hDHGZmNgJD9oQkbQk8CBwREVcA5w23MkmvJC3j8BQpWTxImgJoFvAWSftGxKIcpzqXdBnwckmfJ/VqTgX+TBoIUaivG7gPmBsRcwEi4lFJpwMnSHqC9NBrL3Ai8J3iYd9mZtZYQyahiHhW0jrg6TrUdzppaPXMiHiyUChpW9Kgh9NJAyCGiunvkg4AvgJ8l3QpbT7w8YgoXihPpNF2pT2+ucCTpN7cp0jz4J1JSmRmZjZK8g5M+AnwNtLN+5HYhzR7wpPFhRHxpKQzSDMZ5BIRK4DDhthnGWVGzEVEkB5W9QOrZmZNlDcJ/Qw4W9JlpIT0ACU39iPiuhznGWp4s4c/m5l1kLxJ6IfZ+1uzV0GQehpBvodMfwscL+nakstxWwPHATfljMfMzNpA3iRUr6HVxwMLgOWSfkrqUe0AvAWYQJrXzczMOkTe54RuqEdlEfE7SfuQRqIdSHrgdBVphNqpEVFx5gUzM2s/eXtCdRMRt5MGOZiZWYermIQkXQccExF3ZZ+riYiotpyDmZnZJqr1hIqHNo+h+si1PBOHmpmZbaRiEiqe5y0iZoxKNGZm1lFyzx1nZmZWbzUNTJA0CdidtJDdRiLixnoFZWZmnSFXEpI0HrgAOJzK93/yPKxqZma2Qd7LcSeQHiR9HykJfRQ4EvglaZbqgxoRnJmZtbe8Segw0szTF2c//zYivh0R+wG3senaPGZmZkPKm4SmAEsi4jlgLbB10bYLgHfUOzAzM2t/eZPQo8A22ec/Ay8v2rY9ad43MzOzmuQdHXcT8L9JSzr8EDg1W4huHXAs6d6QmZlZTfImoTNIl+QATgN2I90jGktKUB+uf2hmZtbu8iahZaRlucnWATpM0lbAVhHxRINiMzOzNlfxnpCksZJOlvQ48BDwhKQfStoOICKecQIyM7ORqNYT+hBp3Z8FwM3AVOBQ4Ang/Q2PzMzM2l61JHQUcG5EHF0okHQ08HVJR0fEsw2PzszM2lq1IdpTgUtLyi4hDUboblhEZmbWMaoloW1Il96KPZm9b9uYcMzMrJMMNTpuJ0lTi34eW1T+ePGOEfGnegZmZmbtb6gkdFmF8p+UKfMs2mZmVpNqScgj4MzMrKGqLe/9ndEMpN0tOGJBs0MwM9vseHlvMzNrGicha2mDiwfpmdfDmFPG0DOvh8HFg80OycxqkHfuOLPNzuDiQeZcOYc1a9cAsHz1cuZcOQeA2dNnNzM0M8vJPSFrWX3z+zYkoII1a9fQN7+vSRGZWa2chKxlrVi9oqZyM9v8OAlZy5oycUpN5Wa2+XESspbVP7OfrnFdG5V1jeuif2Z/kyIys1o5CVnLmj19NgOzBuie2I0Q3RO7GZg14EEJZi1EEdHsGFpKb29vLFy4sNlhmJm1FEmLIqK3tNw9ITMzaxonITMzaxonITMzaxonITMzaxonITMzaxonITMzaxonITMzaxonITMzaxonITMzaxonITMzaxonITMzaxonITMzaxonITMzaxonITMzaxonIdvE4OJBeub1MOaUMfTM62Fw8WCzQ7Im8N+BjYaWTUKSdpZ0maTVkp6Q9CNJNa/rLOlzkkLSLxsRZ6sZXDzInCvnsHz1coJg+erlzLlyjv8B6jD+O7DR0pJJSFIXcB2wJ/A+4D3A7sD1krau4TxTgT7g4UbE2Yr65vexZu2ajcrWrF1D3/y+JkVkzeC/AxstWzQ7gGE6CpgK7BER9wJIuh24Bzga+HLO83wTGAT2oHXboq5WrF5RU7m1J/8d2GhpyZ4QcDBwUyEBAUTEUuBXwCF5TiDp3cArgM81JMIWNWVi+SualcqtPfnvwEZLqyahvYA7ypQvAaYNdbCkScBXgM9ExKo6x9bS+mf20zWua6OyrnFd9M/sb1JE1gz+O7DR0qpJaDLwWJnyVcCkHMefCfwRuDBPZZLmSFooaeHKlStzB9mKZk+fzcCsAbondiNE98RuBmYNMHv67GaHZqPIfwc2WhQRzY6hZpKeBb4UEZ8rKe8HjouIivd3JP0LMB94RUTckZUtALaIiNcNVXdvb28sXLhwJOGbmXUcSYsiore0vFVvxj9G6g2VmkT5HlKxbwHnA3+RtF1WtgUwNvv5qYh4pk5xmplZFa2ahJaQ7guVmgbcOcSxL81eHyqz7THgE8C8kQRnZmb5tGoSugI4S9LUiPgTgKQe4LXAZ4c4dv8yZfOAscC/AfeW2W5mZg3QqknoXOCjwOWSPg8EcCrwZ9LlNgAkdQP3AXMjYi5ARCwoPZmkx0n3hDbZZmZmjdOSo+Mi4u/AAaQRbt8lPXC6FDggIv5WtKtIPZyW/D3NzNpdq/aEiIgVwGFD7LOMlIiGOteM+kRlZma1aMkh2s0kaSWwvNlxjJLtgUeaHUSbcts2jtu2cUbStt0R8YLSQichq0jSwnLj+m3k3LaN47ZtnEa0re+VmJlZ0zgJmZlZ0zgJWTUDzQ6gjbltG8dt2zh1b1vfEzIzs6ZxT8jMzJrGScjMzJrGScgAkPRzSSHptJLySZLOk/SIpL9LulbS9GbF2UokvVnSjZL+JumJbE2qA4q2u22HQdJrJV0t6eGsXW+R9IGSfdy2VUh6iaSvSfqNpDXZ//s9ZfbL1Y6Sxks6U9IDkp7KzrtvnlichAxJ7wJeXqZcpMli30ia3PUwYBxwvaSXjGqQLUbS0cDlwCLgUODtwKVAV7bdbTsMkv4JuJbUVkeR2u1m4HxJH872cdsObTfgcNLKAf9dboca2/F80n+PE4GDgAeAX0jae8hIIsKvDn4B2wEPAu8iTQR7WtG2Q7Ky/YvKJpJWsD272bFvri+gB3gK+HiVfdy2w2vbLwDPAtuUlN8E/MZtm7sdxxR9PjJrr56SfXK1I+kLbADvLyrbArgbuGKoWNwTsv8AlkTE98tsOxi4PyKuLxRExGrgStIfqJX3AWA9cE6Vfdy2w7MlsJaU5Is9zj+u7LhthxAR63PslrcdDyb9N7mkaL91wMXAgZK2qlaJk1AHk/Q64L3AMRV22Qu4o0z5EmCKpG0aFVuLex1wF/BOSfdJWifpXkkfKdrHbTs8F2bvZ0vaUdJ2ko4CZgJfyba5besjbzvuBSyNiDVl9tuSdOmvIiehDiVpHGntpbMi4u4Ku02m/HLpq7L3SY2IrQ3sCOwOnAl8EXgDcA3wdUkfy/Zx2w5DRNwBzCB9E/8rqQ2/AXwoIi7OdnPb1kfedhxqv8nVKmnZpRxsxI4DJgD9VfYR6VpvuXKrbAywLXBERPwoK7suG330OUln47YdFkm7Az8kfcv+EOmy3CHAOZKejohB3Lb1krcdR9TeTkIdSNIUoI90Q3Krkmu2W0naDniS9E2m3LeYwjegct9+DB4l9YSuKSm/mjTS6MW4bYfrC6T7DwdFxNqsbL6k5wNflfR93Lb1krcdVwFTquy3qsy2DXw5rjNNBcYD3yP9IRVeAJ/KPk8nfdvcq8zx04AVsfEqtvYPSyqUF74ZrsdtO1zTgduKElDB74DnAy/EbVsvedtxCbCLpK4y+z0L3FutEiehznQrsH+ZF6TEtD/pD+cKYCdJ+xUOlPQ8YFa2zcr7cfZ+YEn5gcBfIuJB3LbD9SCwt6QtS8pfDTxN+tbttq2PvO14Ben5obcX7bcF8A7g6oh4pmotzR6v7tfm82LT54TGAL8G/gy8k/SP6ALS/+g7NzvezfVF6vFcR7os9yHSwISBrH2PcNuOqG3flrXjL0j3gt4AfD0r+7Lbtua2fBvwzaz9Ppz9vF+t7Ugajv0Y6RL/TOAy0peCVwwZR7Mbwq/N51WahLKyycAF2R/eGmA+8PJmx7q5v4DnkUZtPUS6JHE78G63bV3a9k3ZP4YrSfcubyU9ZjDWbVtTO0aF14Ja25E0yOnLpJ7q08BvgRl54vBSDmZm1jS+J2RmZk3jJGRmZk3jJGRmZk3jJGRmZk3jJGRmZk3jJGRmZk3jJGTWAJKOyJZM3q2o7MKsrPBamS3//caSYzdZZr1KPYdJeqjMlCkNIenF2XLQrxqN+qz9OQmZja6VwGuy11Gk2RWukjSz1hNlU6N8ATgzNl3LpSEi4gHgXNIyFWYj5lm0zUbXsxFxU+EHSdcBK4CPkZ5Gr8UhpKXEL6hbdPl8C1gi6VUR8btRrtvajHtCZk0UEU8Af2SI1ScrOBL4eURsNFV+4XKepGMlLZf0d0n/JemF2esHklZL+rOk40qO3UHSdyTdL+kZSQ9I+qmkFxbFfCewOKvfbETcEzJrouyS2s7A0hqP24q0wugJFXZ5D2lp5mOAFwHzgItIi+39jDSh6tuBL0paHBFXZcd9F+gGPk2auPJFpAkpS+853QgcXEvMZuU4CZmNsizxAOxASiI7AGfUeJq9SWtC3VZh+zPAIRGxLqvzZcAngBMi4rSsbAFwKCkZFZLQa4DjI61QWnBpmfP/HviIpB0j4v4aYzfbwEnIbHTtRFoZtOBvwInA2TWeZ8fsfWWF7dcUElDmruz9F4WCiFgn6V5ST6zgZuDTkgrLUdwR5Wc5LtS7I+AkZMPme0Jmo+th4JVAL7ALsF1EnBoR62s8z/jsvdKCYaVLWD9bpXx80c/vIC1S9hnS8hN/lXSipNJ/K57K3ifkjtisDCchs9G1NiIWRsSiiFgWEc8N8zyPZu+T6hUYQEQ8HBEfiYidgD2BC4FTgKNLdp2cvT9Sz/qt8zgJmbWmwuW1qY2qICLujojjSb2nl5Vs3oXUi6ppQIVZKd8TMmtBEbFC0nLgVcD36nFOSROBa4FBUpJbS3oWaRJwdcnurwZujoin61G3dS73hMxa1yXAQXU839PALaSZHC4DfkwaLTc7Ii4v7CRpAmnY9sV1rNs6lJf3NmtRknYF7gZmRMQvR7HedwDnATtHxOOjVa+1JychsxYm6VzgxRFRzx7RUHXeAvwkIuaOVp3Wvnw5zqy1nQDcPIqzaO8AXA6cNRr1WftzT8jMzJrGPSEzM2saJyEzM2saJyEzM2saJyEzM2saJyEzM2ua/wEvAI5V5BC3NQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# home barrel or neighboring barrel\n",
    "plt.rcParams.update({'font.size': 16})\n",
    "\n",
    "home_barrel_df = all_snr.query('`Home Barrel` == ROI')\n",
    "neighbor_barrel_df = all_snr.query('`Home Barrel` != ROI')\n",
    "titles = ['Home Barrel', 'Neighbor Barrel']\n",
    "\n",
    "for pp_type, color in [['PPR', 'b'], ['PPL', 'r'], ['PPHW', 'g']]:\n",
    "    i = 0\n",
    "    for barrel_df in [home_barrel_df, neighbor_barrel_df]:\n",
    "        \n",
    "        if pp_type in ['PPL', 'PPHW']:\n",
    "            barrel_df = barrel_df[barrel_df['IPI'] > 20]\n",
    "        group_by_ipi = barrel_df[['IPI', pp_type]].groupby(by=['IPI']).mean()\n",
    "        group_by_ipi_std = barrel_df[['IPI', pp_type]].groupby(by=['IPI']).agg(np.std) / np.sqrt(barrel_df[['IPI', pp_type]].groupby(by=['IPI']).count())\n",
    "        group_by_ipi.reset_index(inplace=True)\n",
    "\n",
    "        count_by_ipi = barrel_df[['IPI', pp_type]].groupby(by=['IPI']).count()\n",
    "        print(count_by_ipi)\n",
    "\n",
    "        # significance tests against the first IPI\n",
    "        first_ipi = np.min(group_by_ipi['IPI'])\n",
    "        pvals = []\n",
    "        print(\"i=\", i)\n",
    "        if pp_type == 'PPR' and titles[i] == 'Neighbor Barrel':\n",
    "            first_ipi = 110\n",
    "        print(first_ipi)\n",
    "        \n",
    "        first_ipi_data = barrel_df[barrel_df['IPI'] == first_ipi][pp_type]\n",
    "        for ipi in group_by_ipi['IPI']:\n",
    "            if ipi == first_ipi:\n",
    "                pvals.append(1)\n",
    "                continue\n",
    "            ipi_data = barrel_df[barrel_df['IPI'] == ipi][pp_type]\n",
    "            \n",
    "            # Welch's (one-sided) t-test if ipi_data is less than first_ipi_data\n",
    "            t_stat, p_val = ttest_ind(ipi_data, first_ipi_data, alternative='less', equal_var=False)\n",
    "            print(\"p_val for IPI\", ipi, \":\", p_val)\n",
    "            pvals.append(p_val)\n",
    "\n",
    "        def get_sig_level(x):\n",
    "            if x < 0.001:\n",
    "                return '***'\n",
    "            if x < 0.01:\n",
    "                return '**'\n",
    "            if x < 0.05:\n",
    "                return '*'\n",
    "        significance_levels = [get_sig_level(x) for x in pvals]\n",
    "        print(significance_levels)\n",
    "            \n",
    "\n",
    "        plt.scatter(group_by_ipi['IPI'], group_by_ipi[pp_type], c=color)\n",
    "        plt.errorbar(group_by_ipi['IPI'], group_by_ipi[pp_type], \n",
    "                     group_by_ipi_std[pp_type], ls='none', c=color)\n",
    "        y_label = pp_type\n",
    "        if pp_type == 'PPL':\n",
    "            y_label = \"Paired-pulse ratio\\n of Latency\"\n",
    "        if pp_type == 'PPHW':\n",
    "            y_label = \"Paired-pulse ratio\\n of Half-width\"\n",
    "        # plt.title(\"Paired Pulse Depression in \" + titles[i])\n",
    "        \n",
    "        # place significance levels on plot\n",
    "        for j, txt in enumerate(significance_levels):\n",
    "            plt.annotate(txt, (group_by_ipi['IPI'].iloc[j], min(0.95, group_by_ipi[pp_type].iloc[j] + 0.25)), fontsize=12)\n",
    "\n",
    "        plt.xlabel(\"IPI (ms)\")\n",
    "        #plt.xticks([])\n",
    "        plt.ylabel(y_label)\n",
    "        plt.show()\n",
    "        i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4d1eaa7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p_val for IPI 30 : 0.12107702954125943\n",
      "p_val for IPI 40 : 0.012332255973676352\n",
      "p_val for IPI 50 : nan\n",
      "p_val for IPI 60 : nan\n",
      "p_val for IPI 70 : nan\n",
      "p_val for IPI 80 : nan\n",
      "p_val for IPI 90 : nan\n",
      "p_val for IPI 100 : 0.5821841606856758\n",
      "p_val for IPI 110 : nan\n",
      "p_val for IPI 120 : 0.05075532076557369\n",
      "p_val for IPI 130 : 0.276290636070835\n",
      "[None, '*', None, None, None, None, None, None, None, None, None]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    }
   ],
   "source": [
    "# home barrel or neighboring barrel\n",
    "plt.rcParams.update({'font.size': 16})\n",
    "\n",
    "home_barrel_df = all_snr.query('`Home Barrel` == ROI')\n",
    "neighbor_barrel_df = all_snr.query('`Home Barrel` != ROI')\n",
    "pvals = []\n",
    "# significance tests: home versus neighbor\n",
    "for ipi in group_by_ipi['IPI']:\n",
    "    \n",
    "    # one-sided t-test if ipi_data is less than first_ipi_data\n",
    "    t_stat, p_val = ttest_ind(neighbor_barrel_df[neighbor_barrel_df['IPI'] == ipi][\"PPR\"],\n",
    "                              home_barrel_df[home_barrel_df['IPI'] == ipi][\"PPR\"],  \n",
    "                                alternative='less', \n",
    "                                equal_var=False)\n",
    "    print(\"p_val for IPI\", ipi, \":\", p_val)\n",
    "    pvals.append(p_val)\n",
    "\n",
    "def get_sig_level(x):\n",
    "    if x < 0.001:\n",
    "        return '***'\n",
    "    if x < 0.01:\n",
    "        return '**'\n",
    "    if x < 0.05:\n",
    "        return '*'\n",
    "significance_levels = [get_sig_level(x) for x in pvals]\n",
    "print(significance_levels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cab05f07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58\n",
      "34\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13,) (13,) (13,) (13,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58\n",
      "34\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13,) (13,) (13,) (13,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58\n",
      "34\n"
     ]
    },
    {
     "data": {
      "image/png": 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jPPHEE5x77rktnmlu/vz5TJo0iTFjxtSvGz16dP3fjzzyCPPmzWPu3LmcdNJJAAwfPhyAm266iXHjxtGzZ8+0j3fVVVfh7lxzzTUtynehUjGUSB6aXjm9PlAA1HkdNbU1TK+cHnleYtOedu/enSuuuIIxY8YwfXrL8zF06FCmTJnCnXfeSWVlJYkTtT377LP079+fr33ta9TV1dUvI0aMoLa2lgULFqR9rMmTJ/Pwww9z11131de/yN4ULETyUOXGyvpAEVPndVRuqow8L08++SSvv/46y5YtY/v27Tz44IN07969xek++uijjBw5kttuu42jjjqKvn37MnHixPr6iOrqalavXk1xcfFey7BhwwDYtGlTWse55557GD9+PLfccgvl5TmdubRVUzGUSB4qKylj2eZlewWMIiuirEdZ5Hk58sgjG/w13qlTJwB27dq11/p0LuQ9e/Zk6tSpTJ06lXfffZcHHniAn/70pxxwwAGMHTuWHj16MGDAAB577LGkr09nDu0//vGPXHHFFVx77bX85Cc/Sbl/W6Y7C5E8VF5WTufizhRZ8HuvyIroXNyZ8rLW9cu4f//+ACxevHiv9c8880yT0hk4cCCTJk1i//33r0/rzDPP5IMPPqBr164MGTLkc0tJSUmjaT755JNceumlXHbZZdx+++1Nyk9bpDsLkTzUq0svZo6cGbSG2lRJWY/W0xoqXu/evTn55JOZPHkyJSUl9OzZkxkzZrBixYpGX7d161ZOP/10xowZU18n8vTTT7NlyxZGjBgBwJgxY7jvvvsYPnw41157LYMHD2bXrl2sWLGCWbNm8dRTTzVYyf7KK6/wve99j6OOOopLLrlkr/qNjh078pWvfCVzb0KBULAQyVO9uvRi/FfH5zobKc2YMYOxY8dy9dVX06lTJ8rLy7nhhhu4/PLLG3xNp06dOOaYY7j33ntZvXo17dq1Y+DAgTz00EOcd955ABQXF/Pcc8/xs5/9jGnTprFy5Uq6dOnCIYccwtlnn02HDh0aTP+ll17i008/5c033+SEE07Ya1v//v0b7f/RVlliC4N4Q4YM8YULF0aYHcm4dW/CurdgfVgMcOCRwWOfo6GPfj1F6Z133uGII47IdTYkA1J9lma2yN2HRJilrNOdRaHr85VgefevwfOB38xtfkQkL6mCW0REUlKwEBGRlBQsRERywMxOMLPnzazazD42szfMrHW1fY6jYCESocYalEh+yMRnaGZHAS8AxcDlwCjgdeAPZja2xQfIAlVwi0SkuLiYHTt2tHiAPcme2t21bNyxkR11O9inaB9K9imhuH3xXvvs2LGD4uLiBlJI2wVAe+Bcd/8kXPc3MxsMXATc3dIDZJruLEQi0rNnT9auXUtNTY3uMFqh2t21rPhoBVt2bmFH3Q627NzCio9WULu7FgjuKGpqali7dm2TRrNtQAegFtiRsP4jWul1WXcWIhHZb7/9AFi3bh21tbU5zo0k2vrpVmpqa3A+C+SG8VHxR3Tr2A0I7g4PPPDA+s+yBe4HxgK/NrNbgRpgNDAcuLCliWeDgoVIhPbbb79MXGgkC773l++xeNPiz63v1rEb44aO49xDzm1KciVmFt+jeZq7T4s9cffFZnYK8CRwRbi6FviBu/+pqXmPgoKFiAgNj+R7VulZTQ0UABsb68FtZl8CZgJLgB8QFEedB9xjZjvd/aEmn0CWtcqyMRGRqEU8ku8kgjuJc9z9L+7+ortfDTwG3Glmre7a3OoyJCKSC7GRfEcfNpojS45k9GGjszlNbRnwtrsnVl69BvQAWlyDnmkqhhIRCUU4km8VcLSZdXD3+JmhjgN2ApujyERTKFiIiETvLqAC+LOZ/ZagzmIk8D3gVwkBpFVQMZSISMTc/XHgLKAj8HuCyu4TgSuBH+cwaw3SnYWI5IUlm5awdNNSlm9eDsBh3Q8D4Ms9vsygHoNymbVmcfe/An/NdT7SpWAhInlhUI9BDOoxiDkfzAHglINOyWV22hwVQ4mISEq6sxCR5tO0vW2GgoWINJ+m7W0zVAwlIiIpKViIiEhKChYiIpKSgoWIiKSkCm4RkQJnZhcTDCVyMNApYbO7+yGp0lCwEJEWWbJpCUvXL2B5zYew5Y2871ldaMxsAnATsBh4C/i0OekoWIhIiwzqMYhBXQ9lzur/hjWLOaX3FjjxGujWLzMHSOjL0btDh2B9+27qy5GefwXudPcftSQRBQsRaZmta+Cv46AY8D2wbjlUVsDYVzMTMBL6cnzYeR8ABipQpKsH8OeWJqJgIRK1Quv1PO8OqNsJReEv/j21sGt7sP7s23OZMwnMBQYDL7UkEQULkXhRXMgLrdfz2kXgu/det6cW1r6Rm/wICdOyXgM8YWabgGdIMrGSu+9JlaaChUi8QruQR6HvsfDh23uva1cMfY/JTX4EoA7wuOcG3NfAvk4asUDBQkRa5sRr4K2Hg8uR7wkCRYcuwXrJlYnsHSxaTMFCRFqmWz/45s9hyUPw6TbofXxmW0OFtuzcwt9Wv8ADdesp3a+Uw7sfTq8uvTJ6jELh7jdmOk314G4rtm+EhdNh2qkw+7qgBYtIpnQpgS99A864NajUznCgqNpexeT/nsSra1/h/W3vM2/tK4x6+ltUba/K6HEKkZlNN7MBDWzrb2bT00lHwaItiDVtfO9FWPcGLLof7j5BAUPyxvRFv+bTuh3sDktW9gA1u7YxfdGvc5ux/HAJcEAD20qAi9NJRMGiLYg1bYy1WIlv2iiSByo/mMtu23tdnRmVH8zNTYbyT0P1F72AHekkoDqLtkBNGyXPle3cxXpnr4BR5E7Zzl25y1QrZmbfAr4Vt+omM9uYsNs+wNeBRemkqWDRFsSaNsYHjHxs2lhondkkbeUHDOOX7/+VT9sF0aKdO533OOUHDMtxzlqtgwkCAQR3FUfz+TGhPgX+AVyfToIKFm1BrGljrCgqX5s2qg9Em9Xr5Ou5fuqT/K1jO94rLqa0bjcTttXSa1Ra17k2x93vBO4EMLOVwPnu/nbjr2qcgkVbEGva+M4s2L4puKPIQtNGaaLtG4PPZO5twd1fPn4msbs9oPe2jbDsOVi/MvN3e936sf+ZP+c778yi5451cOAh9PrnSfn3fuWAuydtCdVUChZtRZcSGFKuX+OtRayFWuxur6oys4PvRSV2twd8+MEcAAYedEp2jhX7DocDCebV+xQxMzupKfu7+yup9lGwkPxTCL/IG2uhlqnB91TH05bN4bMWUEbq3tztUyWY2WChL2fbFdVnXyi/yKNooaY6nlbPzM4C/hM4hqD7yHLgP9y9RSPEAqfG/f0F4DcEkx/9CVgPHEgwc94g4Mp0EsxssNCXs+2K6rOP4hc5ZP/uJaoWalvXBD33N70XdMrM0l1Y1fYqKt6tYNXHq/jH2n9QXlaef0NxRPxj18y+D9wVLjcT9Hs7Gujc0rTdvb4DipndDzzv7pcl7Pagmf0B+CfSmO8iP4uhdAfTdkXxizyKu5coWqhtXRP01P90W3CMjz7Iyl1Y1fYqRs0axSe7PmEPe1i3fB2zV85m5siZ+RUwIvyxa2alwB3Aj939jrhNz2XhcOcB32lg26MEdxsp5WcP7j5fgSGXwqGnB8uQS4NFgaLw9T0WLKF4NdO/yKPo8R5roXbocOhzLBx7SeaL0ubdEeQ7yz33p1dOp6a2hj0EUyLUeR01tTVMr0xryKG2qpyg2OmeCI7VDji0gW1fIo36CsjXOwtpu6L4RR5Vj/dst1BbuyjId7wsnEflxkrqvG6vdXVeR+WmyswcIK55LkDv95cHf+T3HNwnAsuAC8xsAtAfWAX8yt2nZvhYs4HJYQ/uJ9x9t5m1B0YBtwB/SScRBQvJL1H0GSmUHu99jw2K0OIDRhbOo6ykjGWbl+0VMIqsiLIeZZk5QFzzXIhropuNQJG5Op4SM1sY93yau0+Le94nXKYA44EVwGjgLjMrCjvVZcrVwEEERU51ZrYF2J/g+j8v3J6SgoXkn2z/Ii+UHu8nXhPUUcTqLLJ0HuVl5cxeObu+zqLIiuhc3JnysvKMHifrMlvHs9HdhzSyvR2wL3CJuz8RrnsprMu43sx+7e4ZmbzI3TcCXzezbwBfBXoDHwLz3f2FdNNRsBBJVCg93rv1Cy50f/kRbFoBh5yWlfPo1aUXM0fO5Ob5N7Pq41Wc0OeE/GwN1VgdTyZb2gU2EdQX/C1h/fPAmQQX9HWZPKC7/y3J8dKmYCGSTKH0eO/WLzgPyOq59OrSi9EDRwNwSrZ6cGdbRHU8oSUEv/ITxcbV3ZONg7aEgkUuFVoT4Gy36U+o6GRhOP98vr5fEViyaQlL1y9gec2HsOUNDut+GABf7vFlBvUYlOPctTIR1fGEngT+FTgDeDxu/RnAGndv0RSAZrYbON7dXzOzPTTeg9vdPWUsULBoSBQX8kLqxBhFm/6Eik5JYd2bDFr3FoM6l/Lup7tg2ycMLPok+A4rUHxeRHU8oWeAl4HfmVkJ8H/At4ERwKUZSH8isCbu7xbXfyhYNKSQLuRRiLa8V9IR5SB/hSCiOh4Ifsqb2fnAZOAmgtZJy4Ax7v5wBtK/Ke7vG1uaHihYSKZEW94rkh0R1fEAuPvHBOMypTU2U3OZ2eHuvqyl6eRnD+4obd8YlMNPOxVmXxcUt8jn9T02uG2Pl499E0QKz1Iz+9DMHjazy8zskOYkkp07i0IYQhqiG+G0EN6vaMt785sq6ptlyaYlLN20tP55xfIKQJX1aTiDYBTaU4HfAu3NbC3wEkG9ycvu/n6qRDIfLAplCGmIZoTTQnm/IizvzXuqqG+WQT0GZTcoFGgQj+9fYWZdgZOA04DhwIUEld85aA0V1RDSUYhijKBCer8iLO8Vybi2EcT7AgcTjEUV+yW3OJ0XZj5YRDUIWxTj9EcxRlBU75eItElmVk5wJ3EqQc/wdwmKoH4AzHH3Temkk/lgEcUFNqJx+iMZI6hQBq0Tkdbq90ANcDfwS3f/sDmJZL411InXQFGnz+YcyMYFNqJx+iOZcyCK90tE2rJfEUzX+iOg0sxmmtmVZnZEUxLJ/J1FFIOwRdmmP9tjBBXKoHUi0iq5+7UAZrY/QVHUKcBY4NdmVg285O5jUqWTnaaz2b7ARjuGS/YVyqB1haBAW8SIuPsW4AkzewN4G7iAoEXUBUCOgkW2qU2/ZEuBtYip2l7F9MrpvLruVUr3K+Xw7ofn39Dh0iJm1ofPKrhPI2gNtQd4i2DypZfTSSc/g0UUbfr1C1OyLNbJbPnmYJrQTI8IW7W9ilGzRlFTW0Od17Fm2xpGzRrFzJEzFTDaljUEfSkqgacIWkLNDYcbSVtWgsWST9awdPsalm8J6hCyMixyttv0F9gvTGl9Yp3M5nwwB8j8PBDTK6fXBwqAPeyhpraG6ZXTGf/V8Rk7jnpWtz5mdjXwJ3evBn4M/DH8u9myEiwGde3HoK79mNN5HyCPJ0OR9OlOrNWp3Fi517zYAHVeR+WmyoweJ+s9q6U5fgUsAKqB24C/h383W34WQxWKQrrA6k6s1SkrKWPZ5mV7BYwiK6KsR1kOcyUR+QiIlTUams8iz+kCK1lUXlbO7JWz64ui2tGOzsWdKS8rz3XWJPteBR4ws7fD53ebWUN1FO7uw1MlqGAhkiPZbqnUq0svZo6cudcxJhw/QZXbbcPlwE+Bw/lsoMDiRl+RgoKFSA5E1VKpV5dejP/q+PpKdAWKtsHd1wNXAIRzcP+bu7/WkjQ1+ZFIDjTWUkkkwwYQ9KloEd1ZFLpCqkQvIFG1VBJx99WZSEfBohHZ7jQVCVWit0pqqSTZFBY9pdsCyt09B5MfFZBsd5qStkstlSTLJpKB5rLxMhssEoo8er8f/CKnfTf9upW8EcUdpVoqSTa5+42ZTjOzwSKhyOPDD+YAMFCBQvJIVHeUaqkk+UTFUCJJVG2vouLdClZ9vIp/rP0H5WXluphL3jKzDsA3gYFAp4TN7u43p0ojK8FCwyJLPov1gfhk1yfsYQ/rlq9j9srZGq1V8lI4RPk8oJSgHsPCTfF1GimDRcb7WcT+0SqWV/D+tveZt3Yeo2aNomp7VeYOsu7NoAnotqpgWXhfsKx7M3PHkDYr1gdiD3uAoEmr+kBIHpsCbCCYx8KA44AvArcC74V/p5TxO4tIhkVWc1DJoqj6QGhob4nI14HrgHXh8z3uvgr4LzNrD/waOC9VIhkPFlH/o+V1HwhplaLqAxHF0N4KSPnDzJ4FzgBudfcbMph0D2Cdu+8xs+3A/nHbXgL+XzqJZDxYRP2Ppj4QkmmxPhCxOosiK8rbPhCaayI/mNn3gMFZSn4NUBL+vQIYAbwQPh8G7EwnkYzXWZSXldO5uDNFVhQeQJ2NJL/E+kCc2PdEDt73YEYfNlqV25I1ZvYFgsmK/j1Lh3gZODn8+3fAdWb2vJnNJqjYfjydRDJ+Z6HORlIIenXpxeiBowHdtUrW3QYscfdHzOzhLKR/A9AdwN3vNrMi4LtA5/DYE9NJJCtNZ6PobBRV81y1t09f7DOp3FhJWUlZ1t6rbB9H5fwSFTM7EbiI7BVB4e4bgY1xz38D/Kap6Zh7w8OHDBkyxBcuXNisDAJZq09InAugHe3o2qFrxosKEtvbx8quVSTxeYmfSbbeq6iOI9ISZraauAs0MM3dpyXsU0wwdPiTsQptM3MyUMFtZv/VhN1z1ykv2yJpnkvj7e0zeZxCkPiZZOu9iuo4Ii200d2HpNhnHLAPQX+HTLsxybr4DnmJ6wszWETVPFdzDqQvqvfqtarXkh7ntfUtmgRMJFJmdjDwE+AyoKOZdYzb3DGs9N7m7rubeYjEKVSLgB0EHfLeaE6CeTlTXllJWX1rq5hsNM+N6jiFIKr36tAvHEq7hK9tO9pxaLdDM3ockSz7IsEYTTOALXELBB3otgDN/udx993xCxD7hbU7cVu6ASkvg0VUzXNjx4ldnPK5vX22JX4m2Xqvrht6HV07dN3rOF07dOW6oddl9DgiWfYWcGqSBYIAcirBUBytRl5WcMPnW0Nlq3lu1fYqbp5/M6s+XsUJfU5Qa6hG1LdS2lRJWY8IWkNl+TgizWVmi9Kos0j2uoxUcCdJtz1QCwxx92YVQ+VlnQVENxdAFO3to2pymm2xz6RQjiMin8nbYFEoEpuCLtu8TMNhi7RR7p6stVKTmVniSLLtw8e+ZvZRkuP+X6o08zZYFErHKTUFFZEseI/kc3A/1cD+7RtYXy9vg0WhDJAWVZPTQinqEpG0XJrpBPM2WBSKKEbpVVGXSNvi7g9kOs2sNJ1dsmkJFcsr2LBjAxt2bKBieQUVyytYsmlJNg6X16JoctpYUZeISDqycmdRKEVEUYgfpTdbTUHVE11EWkrFUK1AtpuCRjUhlYgUrrzswS1NE1XvahEpXLqzaAOiKOoSkcKmYNFGqNeziLSEgkUjCqXjn4hISylYNEKtukREAqrgFhGRlBQsREQkJQULERFJScFCRERSUrAQEZGUFCxERCQlBQsREUlJwUJERFJSsBARkZQULEREJCUFCxERSUnBQkREUlKwEBGRlBQsREQkJQULERFJScFCRCRiZvZtM5tpZqvNbIeZvWtmk81s31znrSEKFiIi0bsO2A2MB84E7gbGAn8zs1Z5XdZMeSIi0TvX3TfEPZ9rZpuBB4BTgJdykqtGtMoIJiJSyBICRczr4WPfKPOSLgULEZHW4eTw8Z2c5qIBKoYSEcm8EjNbGPd8mrtPa2hnM+sLTARecPeFDe2XSwoWIiKZt9Hdh6Szo5l1BZ4G6oBLs5qrFlCwEBHJETPrBMwCvgic7O5rcpylBilYiIjkgJkVAzOBYcDp7l6Z4yw1SsFCRCRiYV+Kh4DhwNnuviDHWUpJwUJEJHpTgdHArcB2M/tq3LY1rbE4Sk1nRUSi983w8SfA/ITlslxlqjG6sxARiZi7l+Y6D02lOwsREUlJwUJERFJSsBARkZQULEREJCUFCxERSUnBQkREUlKwEBGRlBQsREQkJQULERFJScFCRERSUrAQEZGUFCxERCQlBQsREUlJwUJERFJSsBARkZQULEREJCUFCxERSUnBQkREUlKwEBGRlBQsREQkJQULERFJScFCRERSUrAQEZGUFCxERCQlBQsREUlJwUJERFJSsBARkZQULEREcsDMDjKzx81sq5l9bGZPmNnBuc5XQxQsREQiZmadgZeAw4GLgQuBLwEvm1mXXOatIUW5zoCISBt0OfBFYKC7vwdgZv8D/C/wfeCXOcxbUrqzEBGJ3khgQSxQALj7SuBV4Lyc5aoRChYiItEbBCxOsn4J8OWI85KWRouhFi1atNHMVkeVmQiVABtznYkc0bm3TW353CH68x9oZgvjnk9z92lxz7sDW5K8bjOwf1Zz1kyNBgt3PyCqjETJzBa6+5Bc5yMXdO4697aolZ6/J1lnkeciTSqGEhGJ3haCu4tE+5P8jiPnFCxERKK3hKDeItGXgaUR5yUtbTVYTEu9S8HSubdNbfncofWd/yzgq2b2xdgKMysFTgi3tTrmnqzYTEREsiXsePc2sAO4gaD+4mZgX+Aod/8kh9lLqq3eWYiI5Iy7bwdOA5YDfwQeAlYCp7XGQAG6sxARkTQU3J2FmX3bzGaa2Woz22Fm75rZZDPbN26fUjPzBpYv5DD7LWJmpzRwTh8l7Le/mf3ezDaa2XYze8HMynKU7YwwszmNfKbPhvsUxOduZv3M7DdmNt/MasL8lybZL63P2cw6mdkUM/sw/J+Zb2YnRXIyTZTOuZvZcDObYWYrwvNZYWZ3m1nPJOk19H04OqpzyheFODbUdcD7wHhgDfAV4EbgVDP7mrvvidt3Mp+vTNoWRSaz7Grg9bjndbE/zMwIznkAcBVBM73rCQYwO9rd10SZ0Qy6AtgvYd3xBGPsJH7G+f65Hwp8B1gE/B0YkbhDEz/nPwBnAz8G/g+4EnjOzI5397eyeB7NkfLcgR8AXYFbCM7nS8BNwBlmlqw+4H7gdwnrlmcwz4XB3QtqAQ5Isu4iggqk08LnpeHzy3Kd3wyf+ynheZ3eyD7nhfucGreuG0HP0V/n+hwy/H78AfgU6F5InzvQLu7vy8JzKm3O5wwMDve7NG5dEfAuMCvX59rMc092DTgp3Lc8Yb0Dt+T6vPJhKbhiKHffkGR17Fd23yjz0kqNBNa5+8uxFe6+FfgzrXQAs+Yws32A0cCf3X1zrvOTSb733XFD0v2cRwK1wKNx+9UBfyL4Jd4xI5nOkHTOXdeA7Ci4YNGAk8PHdxLWTzazunDykVn5Xm4f5yEz221mm8zs4YQJVRobwOxgM+saTRaz7p8ImiE+kGRboX7u8dL9nAcBK929Jsl+HQiKfQpBQ9cAgLFm9mlYB/KSmX09yozli0Kss9iLmfUFJgIvuHtsYK9PCcoonwc2EExAMh74h5kNc/dkX6h8sBX4BTAX+JigvmY8MN/MvuLu1QRDDKxK8trYr+/9gVbZdK+JLgKqgb/GrSvUzz2ZdD/nxga0i6WT18LGLXcQBIqnEjbPAP4CrAP6E9TbvGRm33D3OdHlsvUr6GAR/np6mqCC99LYenf/kKASLObvYYuZJcBPgH+JMp+Z4u5vAm/GrZprZq8ArxFUet9AMFBZXg1g1lRm1gc4HbgzLFIBCvdzb0C6n3NBfx/MrAh4hKD46YT47wOAu18Y9/TvZvY0wR3ZLcCJkWU0DxRsMZSZdSJoDfJF4AxP0crH3T8A5gFDI8heZNz9DYKWHbHz2kzDA5hBKx3ErIn+heC7nawIai+F+rmT/uecar+8re8xs9h34HTgfHf/n1SvcfdtwGwK7/vQYgUZLMysGJgJDAPOcvfKdF9K8l9Z+S7+vBobwOx9b6W9R5voIuBtd387zf0L8XNP93NeAgywYE7oxP12Ae+Rv+4Bvgtc4O4vNuF1hfh9aLGCCxbhr4mHgOHAee6+IM3XHUwwiNd/ZzF7kTOzIcBhfHZes4C+ZnZy3D77AefSSgcwa4rwfAeRxl1FuH9Bfu6k/znPAooJWo7F9isiuMg+7+6fRpPdzDKzXxA0rb3U3Z9qwuv2I+hzUmjfhxYrxDqLqQRf/FuB7Wb21bhta9x9TfhFagfMJ6joHEjQYWkPMCni/GaMmcXGl3kD+Iiggvt6YC3wm3C3WQTnPcPMfsxnnbUMuC3iLGfDRQR1VA8nbiikz93Mvh3+eWz4+E0z2wBscPe5pPk5u/tbZvYocEd4R74SGEvQmW9MJCfTRKnO3czGAf8OTAf+N+EasMHdV4TpXEfwHXiZzyq4rwN60UrPPady3dEj0wtBCxBvYLkx3KecoN31FoILSxXBxWVgrvPfwnO/HvgfglZRtcAHBEMz907YrzvBP9JmoAZ4ERic6/xn4PyLCYLAnxvYXjCfeyPf8TlN/ZyBfQh6ulcBOwl+VZ+S63Ns7rkDcxrZ5/64dM4FXiWYbrUW2EQQZIfl+hxb46KBBEVEJKWCq7MQEZHMU7AQEZGUFCxERCQlBQsREUlJwUJERFJSsBARkZQULKTFzOyScCrKQ+PW3Z8wTeUGM3vFzM5MeK2b2S1pHmeUma1PMjRFVphZ73DY6mFRHE+kNVOwkGzaQDC16fHA5QS9h58xs+FNTSgcgmISMMU/P/dCVngwSu29wJQojifSmhXicB/SeuzyuLG5zOwlgvnRf0jQm7gpziOYFnV6xnKXnt8BS8L5Ll6L+NgirYbuLCQy7v4xwXDpzZl97TLgWU+YIjVWjGVm15rZajPbbmazzaxnuDwWzoj3QThmUPxre5nZA2a2Lpwp7UMz+4uZ9YzL81KgMjy+SJulOwuJTFiUdBDBYHVNeV1H4BRgQgO7XEgwYc0VwIEEs6I9SDCt6l8JxscaDfzMzCrd/ZnwdX/ks9nRPghfOxxIrBN5hWCuapE2S8FCsioMEBCM5DkhfPx5E5M5GugENDQ/xacEw9HXhcc8EvgRMMHdbwnXzQG+RRA0YsHieGC8uz8Ul1ZFkvTfBK40sz7uvq6JeRcpCAoWkk19CUbzjPkE+C/g101Mp0/4uKGB7X/zvafLXBY+Phdb4e51ZvYewZ1NzOvAj83MgJeAxZ58ZM3YcfsQDGUt0uaozkKyqZpgesohBPMjfMHdb3b3PU1Mp1P42NBEPIlTwe5qZH2nuOffJRiS+j8IhnZfa2b/FU6gFW9H+LhP2jkWKTAKFpJNte6+0N0Xufsqd9/dzHQ2hY/7N7pXE7l7tbtf6e59gcOB+4GbgO8n7Bqbo3pjJo8vkk8ULCQfxIqVvpitA7j7u+4+nuBu5MiEzQMI7kqaVDEvUkhUZyGtnru/b2argWHAjEykaWbdgBcI5mtfRlC3ch7B3cvzCbsfB7zu7jszcWyRfKQ7C8kXjwLnZDC9nQRzlV8OPA48SdA6aoy7Px3bycz2IWhO+6cMHlsk72haVckLZnYI8C7B3NDzIjzud4HfAwe5+0dRHVektVGwkLxhZvcCvd09k3cYqY75BvCUu0+M6pgirZGKoSSfTABej3DU2V7A08DtURxPpDXTnYWIiKSkOwsREUlJwUJERFJSsBARkZQULEREJCUFCxERSen/A3nFxQTgDoqIAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13,) (13,) (13,) (13,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17\n",
      "2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_asarray.py:102: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12,) (12,) (2,) (12,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17\n",
      "2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_asarray.py:102: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12,) (12,) (2,) (12,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17\n",
      "2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jjudge3\\AppData\\Local\\anaconda3\\envs\\TSM-to-ZDA\\lib\\site-packages\\numpy\\core\\_asarray.py:102: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12,) (12,) (2,) (12,)\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": [
    "# check for pulse 1 vs pulse 2 changes in latency or halfwidth\n",
    "alpha = 0.5\n",
    "should_group_by_ipi = True\n",
    "if should_group_by_ipi:\n",
    "    alpha = 1\n",
    "ppr_snr = all_snr.query('PPR > 0')\n",
    "ppr_snr = ppr_snr.query('PPR < 2')\n",
    "pulse1_df = ppr_snr.query('Pulse == 1')\n",
    "pulse2_df = ppr_snr.query('Pulse == 2')\n",
    "titles = ['Pulse 1', 'Pulse 2']\n",
    "pulse_colors = ['tab:orange', 'tab:green']\n",
    "\n",
    "for barrel in ['Home Barrel', 'Neighbor Barrel']:\n",
    "    for met_type, color in [['Amp', 'b'], ['Latency', 'r'], ['Halfwidth', 'g']]:\n",
    "        i = 0\n",
    "        end1, end2 = None, None\n",
    "        for pulse_df in [pulse1_df, pulse2_df]:\n",
    "            total_n = len(pulse_df['IPI'])\n",
    "            if barrel == 'Home Barrel':\n",
    "                pulse_df = pulse_df.query('`Home Barrel` == ROI')\n",
    "            else:\n",
    "                pulse_df = pulse_df.query('`Home Barrel` != ROI')\n",
    "            pulse_df = pulse_df[pulse_df['IPI'] > 5]\n",
    "            subset_n = len(pulse_df['IPI'])\n",
    "            print(len(pulse_df['IPI']))\n",
    "            if should_group_by_ipi:\n",
    "                pulse_df_std = pulse_df[['IPI', met_type]].groupby(by=['IPI']).agg(np.std)\n",
    "                pulse_df = pulse_df[['IPI', met_type]].groupby(by=['IPI']).mean()\n",
    "                pulse_df.reset_index(inplace=True)\n",
    "                plt.errorbar(pulse_df['IPI'] + i * 0.5 - 0.25, pulse_df[met_type], \n",
    "                            pulse_df_std[met_type], ls='none', c=pulse_colors[i], capsize=3, alpha=0.4)\n",
    "            if end1 is None:\n",
    "                end1 = pulse_df\n",
    "            else:\n",
    "                end2 = pulse_df\n",
    "            plt.scatter(pulse_df['IPI'], pulse_df[met_type], alpha=alpha,\n",
    "                        s=30,\n",
    "                        color=pulse_colors[i],\n",
    "                         label=titles[i] )\n",
    "            i += 1\n",
    "        plt.title(barrel)\n",
    "        plt.xlabel(\"IPI (ms)\")\n",
    "        plt.ylabel(met_type)\n",
    "        plt.ylim(0, 11)\n",
    "        plt.legend()\n",
    "        plt.gca().yaxis.set_label_position(\"right\")\n",
    "        plt.gca().yaxis.tick_right()\n",
    "        \n",
    "        plt.show()\n",
    "        ppr = end2[met_type] / end1[met_type]\n",
    "        print(ppr.shape, end1[met_type].shape, end2['IPI'].shape, end1['IPI'].shape)\n",
    "        plt.scatter(end1['IPI'], ppr)\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "901cc292",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     IPI      Amp\n",
      "0     20  1.04767\n",
      "1     30  1.15874\n",
      "2     40  1.12238\n",
      "3     50  1.25799\n",
      "4     60  1.32523\n",
      "..   ...      ...\n",
      "206  100  1.50981\n",
      "207  110  1.34750\n",
      "208  120  1.26173\n",
      "209  130  1.18438\n",
      "210  140  1.20338\n",
      "\n",
      "[198 rows x 2 columns]\n",
      "     IPI       Amp\n",
      "0     20  0.747929\n",
      "1     30  0.530139\n",
      "2     40  0.553891\n",
      "3     50  0.463109\n",
      "4     60  0.404570\n",
      "..   ...       ...\n",
      "206  100  0.958694\n",
      "207  110  0.918727\n",
      "208  120  1.052120\n",
      "209  130  1.101890\n",
      "210  140  1.042970\n",
      "\n",
      "[198 rows x 2 columns]\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": [
    "# check for pulse 1 vs pulse 2 changes in latency or halfwidth\n",
    "alpha = 0.2\n",
    "titles = ['Pulse 1', 'Pulse 2']\n",
    "pulse_colors = ['tab:orange', 'tab:green']\n",
    "should_group_by_ipi = True\n",
    "i = 0\n",
    "if should_group_by_ipi:\n",
    "    alpha = 1\n",
    "for amp in [amps1, amps2]:\n",
    "\n",
    "    if should_group_by_ipi:\n",
    "        pulse_df = pd.DataFrame({'IPI': ipis, 'Amp': amp})\n",
    "        pulse_df = pulse_df[pulse_df['IPI'] <150]\n",
    "        print(pulse_df)\n",
    "        pulse_df_std = pulse_df[['IPI', 'Amp']].groupby(by=['IPI']).agg(np.std)\n",
    "        pulse_df = pulse_df[['IPI', 'Amp']].groupby(by=['IPI']).mean()\n",
    "        pulse_df.reset_index(inplace=True)\n",
    "        plt.errorbar(pulse_df['IPI'] + i * 0.5 - 0.25, pulse_df['Amp'], \n",
    "                    pulse_df_std['Amp'], ls='none', c=pulse_colors[i], \n",
    "                    capsize=3, alpha=0.4)\n",
    "\n",
    "    plt.scatter(pulse_df['IPI'], pulse_df['Amp'], alpha=alpha,\n",
    "                s=30,\n",
    "                color=pulse_colors[i],\n",
    "                    label=titles[i] )\n",
    "    i += 1\n",
    "plt.xlabel(\"IPI (ms)\")\n",
    "plt.ylabel('Amplitude')\n",
    "plt.legend()\n",
    "plt.gca().yaxis.set_label_position(\"right\")\n",
    "plt.gca().yaxis.tick_right()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c57f8e59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.870824556947021 1.4458107688717046\n"
     ]
    }
   ],
   "source": [
    "print(np.std(amps1), np.std(amps2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5a6f3d2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:/Users/jjudge3/Desktop/Data/mm_full_pipeline_targets/scnn1a-ppr/12-08-23\n",
      "3 2 IPI: 10 ms for recording 03_02_02.zda\n",
      "3 2 IPI: 20 ms for recording 03_02_03.zda\n",
      "3 2 IPI: 30 ms for recording 03_02_04.zda\n",
      "3 2 IPI: 40 ms for recording 03_02_05.zda\n",
      "3 2 IPI: 50 ms for recording 03_02_06.zda\n",
      "3 2 IPI: 60 ms for recording 03_02_07.zda\n",
      "3 2 IPI: 70 ms for recording 03_02_08.zda\n",
      "3 2 IPI: 80 ms for recording 03_02_09.zda\n",
      "3 2 IPI: 90 ms for recording 03_02_10.zda\n",
      "3 2 IPI: 100 ms for recording 03_02_11.zda\n",
      "3 2 IPI: 110 ms for recording 03_02_12.zda\n",
      "3 2 IPI: 120 ms for recording 03_02_13.zda\n",
      "3 2 IPI: 130 ms for recording 03_02_14.zda\n",
      "3 2 IPI: 140 ms for recording 03_02_15.zda\n",
      "3 2 IPI: 150 ms for recording 03_02_16.zda\n",
      "3 2 IPI: 5 ms for recording 03_02_17.zda\n",
      "4 1 IPI: 5 ms for recording 04_01_01.zda\n",
      "4 1 IPI: 10 ms for recording 04_01_02.zda\n",
      "4 1 IPI: 20 ms for recording 04_01_03.zda\n",
      "4 1 IPI: 30 ms for recording 04_01_04.zda\n",
      "4 1 IPI: 40 ms for recording 04_01_05.zda\n",
      "4 1 IPI: 50 ms for recording 04_01_06.zda\n",
      "4 1 IPI: 60 ms for recording 04_01_07.zda\n",
      "4 1 IPI: 70 ms for recording 04_01_08.zda\n",
      "4 1 IPI: 80 ms for recording 04_01_09.zda\n",
      "4 1 IPI: 90 ms for recording 04_01_10.zda\n",
      "4 1 IPI: 100 ms for recording 04_01_11.zda\n",
      "4 1 IPI: 110 ms for recording 04_01_12.zda\n",
      "4 1 IPI: 120 ms for recording 04_01_13.zda\n",
      "4 1 IPI: 130 ms for recording 04_01_14.zda\n",
      "4 1 IPI: 140 ms for recording 04_01_15.zda\n",
      "4 1 IPI: 150 ms for recording 04_01_16.zda\n",
      "5 1 IPI: 5 ms for recording 05_01_02.zda\n",
      "5 1 IPI: 10 ms for recording 05_01_03.zda\n",
      "5 1 IPI: 20 ms for recording 05_01_04.zda\n",
      "5 1 IPI: 30 ms for recording 05_01_05.zda\n",
      "5 1 IPI: 40 ms for recording 05_01_06.zda\n",
      "5 1 IPI: 50 ms for recording 05_01_07.zda\n",
      "5 1 IPI: 60 ms for recording 05_01_08.zda\n",
      "5 1 IPI: 70 ms for recording 05_01_09.zda\n",
      "5 1 IPI: 80 ms for recording 05_01_10.zda\n",
      "5 1 IPI: 90 ms for recording 05_01_11.zda\n",
      "5 1 IPI: 100 ms for recording 05_01_12.zda\n",
      "5 1 IPI: 110 ms for recording 05_01_13.zda\n",
      "5 1 IPI: 120 ms for recording 05_01_14.zda\n",
      "5 1 IPI: 130 ms for recording 05_01_15.zda\n",
      "5 1 IPI: 140 ms for recording 05_01_16.zda\n",
      "5 1 IPI: 150 ms for recording 05_01_17.zda\n",
      "5 2 IPI: 5 ms for recording 05_02_02.zda\n",
      "5 2 IPI: 10 ms for recording 05_02_03.zda\n",
      "5 2 IPI: 20 ms for recording 05_02_04.zda\n",
      "5 2 IPI: 30 ms for recording 05_02_05.zda\n",
      "5 2 IPI: 40 ms for recording 05_02_06.zda\n",
      "5 2 IPI: 50 ms for recording 05_02_07.zda\n",
      "5 2 IPI: 60 ms for recording 05_02_08.zda\n",
      "5 2 IPI: 70 ms for recording 05_02_09.zda\n",
      "5 2 IPI: 80 ms for recording 05_02_10.zda\n",
      "5 2 IPI: 90 ms for recording 05_02_11.zda\n",
      "5 2 IPI: 100 ms for recording 05_02_12.zda\n",
      "5 2 IPI: 110 ms for recording 05_02_13.zda\n",
      "5 2 IPI: 120 ms for recording 05_02_14.zda\n",
      "5 2 IPI: 130 ms for recording 05_02_15.zda\n",
      "5 2 IPI: 140 ms for recording 05_02_16.zda\n",
      "5 2 IPI: 150 ms for recording 05_02_17.zda\n",
      "6 1 IPI: 5 ms for recording 06_01_04.zda\n",
      "6 1 IPI: 10 ms for recording 06_01_05.zda\n",
      "6 1 IPI: 20 ms for recording 06_01_06.zda\n",
      "6 1 IPI: 30 ms for recording 06_01_07.zda\n",
      "6 1 IPI: 40 ms for recording 06_01_08.zda\n",
      "6 1 IPI: 50 ms for recording 06_01_09.zda\n",
      "6 1 IPI: 60 ms for recording 06_01_10.zda\n",
      "6 1 IPI: 70 ms for recording 06_01_11.zda\n",
      "6 1 IPI: 80 ms for recording 06_01_12.zda\n",
      "6 1 IPI: 90 ms for recording 06_01_13.zda\n",
      "6 1 IPI: 100 ms for recording 06_01_14.zda\n",
      "6 1 IPI: 110 ms for recording 06_01_15.zda\n",
      "6 1 IPI: 120 ms for recording 06_01_16.zda\n",
      "6 1 IPI: 130 ms for recording 06_01_17.zda\n",
      "6 1 IPI: 140 ms for recording 06_01_18.zda\n",
      "6 1 IPI: 150 ms for recording 06_01_19.zda\n",
      "6 2 IPI: 5 ms for recording 06_02_01.zda\n",
      "6 2 IPI: 10 ms for recording 06_02_02.zda\n",
      "6 2 IPI: 20 ms for recording 06_02_03.zda\n",
      "6 2 IPI: 30 ms for recording 06_02_04.zda\n",
      "6 2 IPI: 40 ms for recording 06_02_05.zda\n",
      "6 2 IPI: 50 ms for recording 06_02_06.zda\n",
      "6 2 IPI: 60 ms for recording 06_02_07.zda\n",
      "6 2 IPI: 70 ms for recording 06_02_08.zda\n",
      "6 2 IPI: 80 ms for recording 06_02_09.zda\n",
      "6 2 IPI: 90 ms for recording 06_02_10.zda\n",
      "6 2 IPI: 100 ms for recording 06_02_11.zda\n",
      "6 2 IPI: 110 ms for recording 06_02_12.zda\n",
      "6 2 IPI: 120 ms for recording 06_02_13.zda\n",
      "6 2 IPI: 130 ms for recording 06_02_14.zda\n",
      "6 2 IPI: 140 ms for recording 06_02_15.zda\n",
      "6 2 IPI: 150 ms for recording 06_02_16.zda\n"
     ]
    }
   ],
   "source": [
    "# autoPhotoZ to save amplitude arrays for each measure window and \n",
    "# baseline correction to create PPR maps\n",
    "def get_amp_array_filename(subdir, slic_loc_id, ipi, pulse):\n",
    "    return subdir + \"/\" + \"ampArray_\" + slic_loc_id + \"_ipi\" + str(ipi) + \"_p\" + str(pulse) + \".dat\"\n",
    "\n",
    "overwrite_amp_arrays = False\n",
    "select_amp_bkg = False\n",
    "for subdir in data_map:\n",
    "    aPhz = AutoPhotoZ(data_dir=subdir)\n",
    "    if not select_amp_bkg and enable_photoZ_interact:\n",
    "        aPhz.select_MaxAmp_array()\n",
    "    print(subdir)\n",
    "    for slic_id in data_map[subdir]:\n",
    "\n",
    "        for loc_id in data_map[subdir][slic_id]:\n",
    "            if type(loc_id) == str:\n",
    "                continue\n",
    "            slic_loc_id = str(slic_id) + \"_\" + str(loc_id)\n",
    "\n",
    "            ipi_file = data_map[subdir][slic_id][loc_id]['shuffle_file']\n",
    "            with open(ipi_file, 'r') as f:\n",
    "                lines = f.readlines() \n",
    "            ipi_order = [int(x) for x in lines]\n",
    "\n",
    "            # open zda files in order\n",
    "            i_ipi = -1\n",
    "            for zda_file in data_map[subdir][slic_id][loc_id]['zda_files']:\n",
    "\n",
    "                i_ipi += 1\n",
    "                try:\n",
    "                    ipi = ipi_order[i_ipi]\n",
    "                except Exception as e:\n",
    "                    print(e)\n",
    "                    print(\"Not enough IPIs for how many recordings we have! Last IPI:\", ipi, \"Check\", subdir)\n",
    "                print(slic_id, loc_id, \"IPI:\", ipi, \"ms for recording\", zda_file.split(\"/\")[-1])\n",
    "                \n",
    "                # check if all amp arrays are already saved for this recording/IPI\n",
    "                all_saved = True\n",
    "                for i_pulse in [1, 2]:\n",
    "                    amp_array_filename = get_amp_array_filename(subdir, slic_loc_id, ipi, i_pulse)\n",
    "                    if not os.path.exists(amp_array_filename):\n",
    "                        print(\"Missing\", amp_array_filename)\n",
    "                        all_saved = False\n",
    "                        break\n",
    "                if overwrite_amp_arrays:\n",
    "                    all_saved = False\n",
    "\n",
    "                if enable_photoZ_interact and not all_saved:\n",
    "                    aPhz.open_zda_file(zda_file)\n",
    "\n",
    "                for i_pulse in [1, 2]:\n",
    "                    amp_array_filename = get_amp_array_filename(subdir, slic_loc_id, ipi, i_pulse)\n",
    "                    if not os.path.exists(amp_array_filename) or overwrite_amp_arrays:\n",
    "                        if enable_photoZ_interact and not all_saved:\n",
    "                            measure_window_start = 96 + (i_pulse - 1) * ipi * 2\n",
    "                            aPhz.set_measure_window(measure_window_start, None)\n",
    "\n",
    "                            # change polynomial skip window too to match measure window\n",
    "                            aPhz.set_polynomial_skip_window(measure_window_start)\n",
    "\n",
    "                        # take Amplitude array from PhotoZ\n",
    "                        if enable_photoZ_interact and not all_saved:\n",
    "                            aPhz.save_background(filename=amp_array_filename)\n",
    "                    else:\n",
    "                        if not all_saved:\n",
    "                            time.sleep(5)\n",
    "\n",
    "                    if ipi not in data_map[subdir][slic_id][loc_id]:\n",
    "                        data_map[subdir][slic_id][loc_id][ipi] = {}\n",
    "                    if i_pulse not in data_map[subdir][slic_id][loc_id][ipi]:\n",
    "                        data_map[subdir][slic_id][loc_id][ipi][i_pulse] = {}\n",
    "                    data_map[subdir][slic_id][loc_id][ipi][i_pulse]['amp_array'] = amp_array_filename\n",
    "                    \n",
    "            if i_ipi < len(ipi_order) - 1:\n",
    "                print(\"Not enough recordings for how many IPIs we have! Last IPI:\", ipi, \"Check\", subdir)\n",
    "                        "
   ]
  }
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