lynne_pp_test_tablecombo2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/josh/Documents/Harvard/GLM\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "dir_path = '/'.join(os.path.realpath('.').split('/')[:-1])\n",
    "print(dir_path)\n",
    "sys.path.append(f'{dir_path}/sabatinilab-glm/backend')\n",
    "sys.path.append(f'{dir_path}/..')\n",
    "sys.path.append(f'{dir_path}/backend')\n",
    "sys.path.append(f'{dir_path}/../backend')\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "import sglm\n",
    "import sglm_cv\n",
    "import sglm_pp\n",
    "import sglm_ez\n",
    "import sglm_plt as splt\n",
    "import sglm_save as ssave\n",
    "from lynne_pp import *\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f80d8460190>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('/Users/josh/Documents/Harvard/GLM/GLM_SIGNALS_WT61_10062021.txt')\n",
    "df = df[[_ for _ in df.columns if 'Unnamed' not in _]]\n",
    "df = rename_columns(df)\n",
    "df = define_trial_starts_ends(df, trial_shift_bounds=1)\n",
    "\n",
    "lb = 2000\n",
    "ub = 3000\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "care = df.iloc[lb:ub]\n",
    "for col in ['trial_start_flag', 'trial_end_flag', 'cpn', 'lpn', 'rpn', 'lpx', 'rpx']:\n",
    "    if col == 'trial_start_flag':\n",
    "        marker = '^'\n",
    "        mult = 0.5\n",
    "    elif col == 'trial_end_flag':\n",
    "        marker = 'v'\n",
    "        mult = 0.5\n",
    "    else:\n",
    "        marker = '.'\n",
    "        if col[-1] == 'n':\n",
    "            mult = 0.6\n",
    "        else:\n",
    "            mult = 0.4\n",
    "    care_filt = care#[care[col] > 0]\n",
    "    plt.scatter(care_filt.index, care_filt[col]*mult, label=col, alpha=0.5, marker=marker)\n",
    "plt.ylim(0,1)\n",
    "plt.grid(True, which='both')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>hasAllPhotometryData</th>\n",
       "      <th>photometryCenterInIndex</th>\n",
       "      <th>photometryCenterOutIndex</th>\n",
       "      <th>photometrySideInIndex</th>\n",
       "      <th>photometrySideOutIndex</th>\n",
       "      <th>photometryFirstLickIndex</th>\n",
       "      <th>choseLeft</th>\n",
       "      <th>choseRight</th>\n",
       "      <th>leftRewardProb</th>\n",
       "      <th>rightRewardProb</th>\n",
       "      <th>wasRewarded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>1</td>\n",
       "      <td>28386</td>\n",
       "      <td>28387</td>\n",
       "      <td>28404</td>\n",
       "      <td>28444</td>\n",
       "      <td>28408</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>1</td>\n",
       "      <td>28544</td>\n",
       "      <td>28545</td>\n",
       "      <td>28561</td>\n",
       "      <td>28588</td>\n",
       "      <td>28564</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>1</td>\n",
       "      <td>28714</td>\n",
       "      <td>28715</td>\n",
       "      <td>28729</td>\n",
       "      <td>28758</td>\n",
       "      <td>28736</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>1</td>\n",
       "      <td>28810</td>\n",
       "      <td>28811</td>\n",
       "      <td>28828</td>\n",
       "      <td>28864</td>\n",
       "      <td>28832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>1</td>\n",
       "      <td>28946</td>\n",
       "      <td>28948</td>\n",
       "      <td>28965</td>\n",
       "      <td>29002</td>\n",
       "      <td>28969</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>265 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     hasAllPhotometryData  photometryCenterInIndex  photometryCenterOutIndex  \\\n",
       "0                       0                        0                         0   \n",
       "1                       0                        0                         0   \n",
       "2                       0                        0                         0   \n",
       "3                       0                        0                         0   \n",
       "4                       0                        0                         0   \n",
       "..                    ...                      ...                       ...   \n",
       "260                     1                    28386                     28387   \n",
       "261                     1                    28544                     28545   \n",
       "262                     1                    28714                     28715   \n",
       "263                     1                    28810                     28811   \n",
       "264                     1                    28946                     28948   \n",
       "\n",
       "     photometrySideInIndex  photometrySideOutIndex  photometryFirstLickIndex  \\\n",
       "0                        0                       0                         0   \n",
       "1                        0                       0                         0   \n",
       "2                        0                       0                         0   \n",
       "3                        0                       0                         0   \n",
       "4                        0                       0                         0   \n",
       "..                     ...                     ...                       ...   \n",
       "260                  28404                   28444                     28408   \n",
       "261                  28561                   28588                     28564   \n",
       "262                  28729                   28758                     28736   \n",
       "263                  28828                   28864                     28832   \n",
       "264                  28965                   29002                     28969   \n",
       "\n",
       "     choseLeft  choseRight  leftRewardProb  rightRewardProb  wasRewarded  \n",
       "0            1           0             0.1              0.9            0  \n",
       "1            1           0             0.1              0.9            0  \n",
       "2            1           0             0.1              0.9            0  \n",
       "3            1           0             0.1              0.9            0  \n",
       "4            1           0             0.1              0.9            0  \n",
       "..         ...         ...             ...              ...          ...  \n",
       "260          0           1             0.1              0.9            1  \n",
       "261          0           1             0.1              0.9            1  \n",
       "262          0           1             0.1              0.9            1  \n",
       "263          0           1             0.1              0.9            1  \n",
       "264          0           1             0.1              0.9            1  \n",
       "\n",
       "[265 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t = pd.read_csv('/Users/josh/Documents/Harvard/GLM/GLM_TABLE_WT61_10062021.txt')\n",
    "df_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dftmp = df.copy()\n",
    "# dftmp.loc[df_t['photometrySideOutIndex'].values].drop(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# idx = 1354\n",
    "# dftmp.loc[idx-10:idx+10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_lst = [\n",
    "    'photometryCenterInIndex',\n",
    "    'photometryCenterOutIndex',\n",
    "    'photometrySideInIndex',\n",
    "    'photometrySideOutIndex',\n",
    "    'photometryFirstLickIndex'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "df2 = pd.DataFrame()\n",
    "\n",
    "data_folder = '/Users/josh/Documents/Harvard/GLM'\n",
    "\n",
    "# GLM_SIGNALS_WT61_10062021.txt\n",
    "\n",
    "# for fn in glob.glob(f'{data_folder}/GLM_SIGNALS_WT61_*.txt'):\n",
    "#     table_fn = fn.replace('GLM_SIGNALS', 'GLM_TABLE')\n",
    "#     print(fn, '--', table_fn)\n",
    "\n",
    "#     df_t = pd.read_csv(table_fn)\n",
    "\n",
    "#     df = pd.read_csv(fn)    \n",
    "#     df = df[[_ for _ in df.columns if 'Unnamed' not in _]]\n",
    "#     # df = rename_columns(df)\n",
    "#     # df = define_trial_starts_ends(df, trial_shift_bounds=1)\n",
    "#     # df = preprocess_lynne(df, trial_shift_bounds=1)\n",
    "\n",
    "#     for col_val in col_lst:\n",
    "#         new_name = col_val.replace('Index', '')\n",
    "\n",
    "#         df[new_name] = 0\n",
    "\n",
    "#         inx = df_t[col_val].values - 1\n",
    "#         inx = inx[inx >= 0]\n",
    "#         df.loc[inx, new_name] = 1\n",
    "\n",
    "#         df[f'{new_name}_r'] = 0\n",
    "#         df[f'{new_name}_nr'] = 0\n",
    "#         df.loc[inx, f'{new_name}_r'] = df_t[df_t[col_val] - 1 >= 0]['wasRewarded'].values\n",
    "#         df.loc[inx, f'{new_name}_nr'] = df_t[df_t[col_val] - 1 >= 0]['wasRewarded'].values\n",
    "\n",
    "    \n",
    "#     df['nTrial'] = df['photometryCenterIn'].cumsum()\n",
    "#     df['nEndsTrial'] = df['photometrySideOut'].cumsum()\n",
    "\n",
    "#     print(df2.isna().sum().sum())\n",
    "\n",
    "#     if len(df2) == 0:\n",
    "#         df2 = df.copy()\n",
    "#     else:\n",
    "#         df2 = df2.append(df)\n",
    "\n",
    "#     # display(df2)\n",
    "#     # break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# glob.glob(f'{data_folder}/GLM_SIGNALS_WT61_*.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/josh/Documents/Harvard/GLM/GLM_SIGNALS_WT61_10152021.txt -- /Users/josh/Documents/Harvard/GLM/GLM_TABLE_WT61_10152021.txt\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "from collections import defaultdict\n",
    "\n",
    "# alignment_index_list = defaultdict(list)\n",
    "\n",
    "# for fn in glob.glob(f'{data_folder}/GLM_SIGNALS_WT61_*.txt'):\n",
    "for fn in glob.glob(f'{data_folder}/GLM_SIGNALS_WT61_10152021.txt'):\n",
    "    table_fn = fn.replace('GLM_SIGNALS', 'GLM_TABLE')\n",
    "    print(fn, '--', table_fn)\n",
    "\n",
    "    df_t = pd.read_csv(table_fn)\n",
    "    \n",
    "\n",
    "    df = pd.read_csv(fn)    \n",
    "    df = df[[_ for _ in df.columns if 'Unnamed' not in _]]\n",
    "    # df = rename_columns(df)\n",
    "    # df = define_trial_starts_ends(df, trial_shift_bounds=1)\n",
    "    # df = preprocess_lynne(df, trial_shift_bounds=1)\n",
    "\n",
    "    all_inx = (df_t[col_lst].values.reshape(-1) - 1).astype(int)\n",
    "    assoc_rew = np.tile(df_t['wasRewarded'].values, (len(col_lst))).astype(int)\n",
    "\n",
    "    assoc_rew = assoc_rew[all_inx >= 0]\n",
    "    all_inx = all_inx[all_inx >= 0]\n",
    "\n",
    "    df.loc[all_inx, 'wasRewarded'] = assoc_rew\n",
    "    # df['wasRewarded'] = df['wasRewarded']#.fillna(0)\n",
    "\n",
    "\n",
    "    \n",
    "    for col_val in col_lst:\n",
    "        new_name = col_val.replace('Index', '')\n",
    "\n",
    "        df[new_name] = 0\n",
    "\n",
    "        inx = df_t[col_val].values - 1\n",
    "        inx = inx[inx >= 0]\n",
    "        df.loc[inx, new_name] = 1\n",
    "\n",
    "        # df[f'{new_name}_r'] = df[new_name]*df['wasRewarded']\n",
    "        # df[f'{new_name}_nr'] = df[new_name]*(1-df['wasRewarded'])\n",
    "\n",
    "        # alignment_index_list[new_name].append(len(df2) + inx)\n",
    "\n",
    "        # print(col_val)\n",
    "        # display(df.loc[inx])    \n",
    "\n",
    "    df['nTrial'] = df['photometryCenterIn'].cumsum()\n",
    "    df['nEndTrial'] = df['photometrySideOut'].cumsum()\n",
    "\n",
    "    # with pd.option_context('max_columns', 1000, 'max_rows', 100):\n",
    "    #     display(df[~df['wasRewarded'].isna()])\n",
    "    #     # display(df.iloc[1183:1210])\n",
    "\n",
    "    # break\n",
    "\n",
    "    # print(df2.isna().sum().sum())\n",
    "\n",
    "    if len(df2) == 0:\n",
    "        df2 = df.reset_index().copy()\n",
    "    else:\n",
    "        df2 = df2.append(df.reset_index().copy())\n",
    "\n",
    "use_list = df[~df['wasRewarded'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2['combor'] = ((df2['reward'] + df2['wasRewarded']) > 0)*1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col_val in col_lst:\n",
    "    new_name = col_val.replace('Index', '')\n",
    "    # df2[f'{new_name}_r'] = df2[new_name]*df2['combor']\n",
    "    # df2[f'{new_name}_nr'] = df2[new_name]*(1-df2['combor'])\n",
    "\n",
    "    # df2[f'{new_name}_r'] = df2[new_name]*df2['reward']\n",
    "    # df2[f'{new_name}_nr'] = df2[new_name]*(1-df2['reward'])\n",
    "\n",
    "    df2[f'{new_name}_r'] = df2[new_name]*df2['wasRewarded']\n",
    "    df2[f'{new_name}_nr'] = df2[new_name]*(1-df2['wasRewarded'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([10532, 10535, 10570, 10571, 10592], dtype='int64')\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": [
    "lb = 10530\n",
    "ub = 10600\n",
    "\n",
    "lb, ub = df2['wasRewarded'].iloc[lb:ub].dropna().index.min(), df2['wasRewarded'].iloc[lb:ub].dropna().index.max()\n",
    "\n",
    "# inx = df2.iloc[lb:ub].query(\"(wasRewarded != reward)&(wasRewarded == wasRewarded)&(reward == reward)\")['wasRewarded'].dropna().index\n",
    "inx = df2.iloc[lb:ub].query(\"(wasRewarded == wasRewarded)&(reward == reward)\")['wasRewarded'].dropna().index\n",
    "print(inx)\n",
    "\n",
    "# plt.scatter(data=df2['photometrySideIn'].iloc[lb:ub].replace(0, np.nan).reset_index(), x='index', y='photometrySideIn', label='photometrySideIn')\n",
    "# plt.scatter(data=(df2['photometrySideIn_r']*2).iloc[lb:ub].replace(0, np.nan).reset_index(), x='index', y='photometrySideIn_r', label='photometrySideIn_r')\n",
    "# plt.scatter(data=(df2['photometrySideIn_nr']*0.5).iloc[lb:ub].replace(0, np.nan).reset_index(), x='index', y='photometrySideIn_nr', label='photometrySideIn_nr')\n",
    "# plt.scatter(data=(df2['wasRewarded']*3).iloc[lb:ub].reset_index(), x='index', y='wasRewarded', label='wasRewarded')\n",
    "# plt.scatter(data=(df2['reward']*4).iloc[lb:ub].replace(0, np.nan).reset_index(), x='index', y='reward', label='reward')\n",
    "# # plt.scatter(data=(df2['combor']*5).iloc[lb:ub].replace(0, np.nan).reset_index(), x='index', y='combor', label='combor')\n",
    "\n",
    "plt.scatter(data=df2.loc[inx,'photometrySideIn'].replace(0, np.nan).reset_index(), x='index', y='photometrySideIn', label='photometrySideIn')\n",
    "plt.scatter(data=(df2.loc[inx,'photometrySideIn_r']*2).replace(0, np.nan).reset_index(), x='index', y='photometrySideIn_r', label='photometrySideIn_r')\n",
    "plt.scatter(data=(df2.loc[inx,'photometrySideIn_nr']*0.5).replace(0, np.nan).reset_index(), x='index', y='photometrySideIn_nr', label='photometrySideIn_nr')\n",
    "plt.scatter(data=(df2.loc[inx,'wasRewarded']*3).reset_index(), x='index', y='wasRewarded', label='wasRewarded')\n",
    "plt.scatter(data=(df2.loc[inx,'reward']*4).reset_index(), x='index', y='reward', label='reward')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>hasAllPhotometryData</th>\n",
       "      <th>photometryCenterInIndex</th>\n",
       "      <th>photometryCenterOutIndex</th>\n",
       "      <th>photometrySideInIndex</th>\n",
       "      <th>photometrySideOutIndex</th>\n",
       "      <th>photometryFirstLickIndex</th>\n",
       "      <th>choseLeft</th>\n",
       "      <th>choseRight</th>\n",
       "      <th>leftRewardProb</th>\n",
       "      <th>rightRewardProb</th>\n",
       "      <th>wasRewarded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>1</td>\n",
       "      <td>10516</td>\n",
       "      <td>10572</td>\n",
       "      <td>10533</td>\n",
       "      <td>10536</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>1</td>\n",
       "      <td>10571</td>\n",
       "      <td>10572</td>\n",
       "      <td>10593</td>\n",
       "      <td>10624</td>\n",
       "      <td>10596</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     hasAllPhotometryData  photometryCenterInIndex  photometryCenterOutIndex  \\\n",
       "155                     1                    10516                     10572   \n",
       "156                     1                    10571                     10572   \n",
       "\n",
       "     photometrySideInIndex  photometrySideOutIndex  photometryFirstLickIndex  \\\n",
       "155                  10533                   10536                         0   \n",
       "156                  10593                   10624                     10596   \n",
       "\n",
       "     choseLeft  choseRight  leftRewardProb  rightRewardProb  wasRewarded  \n",
       "155          0           1            0.85             0.15            0  \n",
       "156          1           0            0.85             0.15            1  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t[df_t['photometrySideInIndex'].between(10530, 10600)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Ch1</th>\n",
       "      <th>Ch2</th>\n",
       "      <th>Ch5</th>\n",
       "      <th>Ch6</th>\n",
       "      <th>GP_1</th>\n",
       "      <th>GP_2</th>\n",
       "      <th>GP_5</th>\n",
       "      <th>GP_6</th>\n",
       "      <th>SGP_1</th>\n",
       "      <th>SGP_2</th>\n",
       "      <th>SGP_5</th>\n",
       "      <th>SGP_6</th>\n",
       "      <th>centerOcc</th>\n",
       "      <th>centerIn</th>\n",
       "      <th>centerOut</th>\n",
       "      <th>rightOcc</th>\n",
       "      <th>rightIn</th>\n",
       "      <th>rightOut</th>\n",
       "      <th>rightLick</th>\n",
       "      <th>leftOcc</th>\n",
       "      <th>leftIn</th>\n",
       "      <th>leftOut</th>\n",
       "      <th>leftLick</th>\n",
       "      <th>reward</th>\n",
       "      <th>noreward</th>\n",
       "      <th>wasRewarded</th>\n",
       "      <th>photometryCenterIn</th>\n",
       "      <th>photometryCenterOut</th>\n",
       "      <th>photometrySideIn</th>\n",
       "      <th>photometrySideOut</th>\n",
       "      <th>photometryFirstLick</th>\n",
       "      <th>nTrial</th>\n",
       "      <th>nEndTrial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10530</th>\n",
       "      <td>0.577638</td>\n",
       "      <td>2.092008</td>\n",
       "      <td>0.810387</td>\n",
       "      <td>0.342304</td>\n",
       "      <td>0.202569</td>\n",
       "      <td>0.666818</td>\n",
       "      <td>-0.056357</td>\n",
       "      <td>-0.165699</td>\n",
       "      <td>0.202569</td>\n",
       "      <td>0.666818</td>\n",
       "      <td>-0.056357</td>\n",
       "      <td>-0.165699</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10531</th>\n",
       "      <td>2.081151</td>\n",
       "      <td>0.593498</td>\n",
       "      <td>1.163298</td>\n",
       "      <td>-0.913881</td>\n",
       "      <td>-0.249807</td>\n",
       "      <td>-1.260880</td>\n",
       "      <td>-0.802093</td>\n",
       "      <td>0.866688</td>\n",
       "      <td>-0.249807</td>\n",
       "      <td>-1.260880</td>\n",
       "      <td>-0.802093</td>\n",
       "      <td>0.866688</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10532</th>\n",
       "      <td>2.715299</td>\n",
       "      <td>-0.958491</td>\n",
       "      <td>2.077475</td>\n",
       "      <td>-0.240998</td>\n",
       "      <td>0.008618</td>\n",
       "      <td>-0.180596</td>\n",
       "      <td>0.072047</td>\n",
       "      <td>-2.363312</td>\n",
       "      <td>0.008618</td>\n",
       "      <td>-0.180596</td>\n",
       "      <td>0.072047</td>\n",
       "      <td>-2.363312</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10533</th>\n",
       "      <td>1.768283</td>\n",
       "      <td>-0.050291</td>\n",
       "      <td>1.274917</td>\n",
       "      <td>-1.115855</td>\n",
       "      <td>0.465773</td>\n",
       "      <td>-0.547793</td>\n",
       "      <td>1.549573</td>\n",
       "      <td>0.970920</td>\n",
       "      <td>0.465773</td>\n",
       "      <td>-0.547793</td>\n",
       "      <td>1.549573</td>\n",
       "      <td>0.970920</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10534</th>\n",
       "      <td>0.599657</td>\n",
       "      <td>0.233410</td>\n",
       "      <td>0.730695</td>\n",
       "      <td>-0.630397</td>\n",
       "      <td>1.056659</td>\n",
       "      <td>0.793070</td>\n",
       "      <td>2.356037</td>\n",
       "      <td>1.909040</td>\n",
       "      <td>1.056659</td>\n",
       "      <td>0.793070</td>\n",
       "      <td>2.356037</td>\n",
       "      <td>1.909040</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10535</th>\n",
       "      <td>-0.380067</td>\n",
       "      <td>0.818205</td>\n",
       "      <td>-0.357238</td>\n",
       "      <td>1.050167</td>\n",
       "      <td>1.443007</td>\n",
       "      <td>0.577319</td>\n",
       "      <td>1.355929</td>\n",
       "      <td>1.781868</td>\n",
       "      <td>1.443007</td>\n",
       "      <td>0.577319</td>\n",
       "      <td>1.355929</td>\n",
       "      <td>1.781868</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10536</th>\n",
       "      <td>-1.138131</td>\n",
       "      <td>0.858352</td>\n",
       "      <td>-1.279146</td>\n",
       "      <td>1.627810</td>\n",
       "      <td>1.177682</td>\n",
       "      <td>2.946444</td>\n",
       "      <td>2.048580</td>\n",
       "      <td>-0.011725</td>\n",
       "      <td>1.177682</td>\n",
       "      <td>2.946444</td>\n",
       "      <td>2.048580</td>\n",
       "      <td>-0.011725</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10537</th>\n",
       "      <td>-1.229236</td>\n",
       "      <td>-0.120901</td>\n",
       "      <td>-1.369392</td>\n",
       "      <td>-0.234454</td>\n",
       "      <td>0.554688</td>\n",
       "      <td>0.994267</td>\n",
       "      <td>2.610200</td>\n",
       "      <td>-0.387338</td>\n",
       "      <td>0.554688</td>\n",
       "      <td>0.994267</td>\n",
       "      <td>2.610200</td>\n",
       "      <td>-0.387338</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10538</th>\n",
       "      <td>-0.599212</td>\n",
       "      <td>0.065614</td>\n",
       "      <td>-0.400114</td>\n",
       "      <td>0.384548</td>\n",
       "      <td>0.395955</td>\n",
       "      <td>0.711632</td>\n",
       "      <td>2.429250</td>\n",
       "      <td>-1.270167</td>\n",
       "      <td>0.395955</td>\n",
       "      <td>0.711632</td>\n",
       "      <td>2.429250</td>\n",
       "      <td>-1.270167</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10539</th>\n",
       "      <td>0.371020</td>\n",
       "      <td>0.385071</td>\n",
       "      <td>0.735276</td>\n",
       "      <td>1.291436</td>\n",
       "      <td>0.459792</td>\n",
       "      <td>0.223036</td>\n",
       "      <td>1.933694</td>\n",
       "      <td>-1.147131</td>\n",
       "      <td>0.459792</td>\n",
       "      <td>0.223036</td>\n",
       "      <td>1.933694</td>\n",
       "      <td>-1.147131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10540</th>\n",
       "      <td>-0.098692</td>\n",
       "      <td>1.515862</td>\n",
       "      <td>0.087765</td>\n",
       "      <td>-0.292024</td>\n",
       "      <td>0.425827</td>\n",
       "      <td>-0.222074</td>\n",
       "      <td>0.836550</td>\n",
       "      <td>1.405037</td>\n",
       "      <td>0.425827</td>\n",
       "      <td>-0.222074</td>\n",
       "      <td>0.836550</td>\n",
       "      <td>1.405037</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10541</th>\n",
       "      <td>-0.178786</td>\n",
       "      <td>1.017286</td>\n",
       "      <td>0.290773</td>\n",
       "      <td>-0.565424</td>\n",
       "      <td>0.801667</td>\n",
       "      <td>-1.014257</td>\n",
       "      <td>1.257198</td>\n",
       "      <td>0.548736</td>\n",
       "      <td>0.801667</td>\n",
       "      <td>-1.014257</td>\n",
       "      <td>1.257198</td>\n",
       "      <td>0.548736</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10542</th>\n",
       "      <td>0.109804</td>\n",
       "      <td>0.047331</td>\n",
       "      <td>0.209351</td>\n",
       "      <td>0.911531</td>\n",
       "      <td>0.768853</td>\n",
       "      <td>0.025429</td>\n",
       "      <td>1.062412</td>\n",
       "      <td>-0.362672</td>\n",
       "      <td>0.768853</td>\n",
       "      <td>0.025429</td>\n",
       "      <td>1.062412</td>\n",
       "      <td>-0.362672</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10543</th>\n",
       "      <td>0.067454</td>\n",
       "      <td>1.115703</td>\n",
       "      <td>0.712253</td>\n",
       "      <td>-0.312969</td>\n",
       "      <td>1.302056</td>\n",
       "      <td>-0.065378</td>\n",
       "      <td>0.341375</td>\n",
       "      <td>2.516913</td>\n",
       "      <td>1.302056</td>\n",
       "      <td>-0.065378</td>\n",
       "      <td>0.341375</td>\n",
       "      <td>2.516913</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10544</th>\n",
       "      <td>-0.258639</td>\n",
       "      <td>1.440980</td>\n",
       "      <td>0.209708</td>\n",
       "      <td>0.304224</td>\n",
       "      <td>0.610644</td>\n",
       "      <td>-1.781744</td>\n",
       "      <td>-0.090244</td>\n",
       "      <td>0.619543</td>\n",
       "      <td>0.610644</td>\n",
       "      <td>-1.781744</td>\n",
       "      <td>-0.090244</td>\n",
       "      <td>0.619543</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10545</th>\n",
       "      <td>-0.078407</td>\n",
       "      <td>1.271190</td>\n",
       "      <td>0.499801</td>\n",
       "      <td>0.119116</td>\n",
       "      <td>-0.275857</td>\n",
       "      <td>-1.557134</td>\n",
       "      <td>0.153473</td>\n",
       "      <td>0.453365</td>\n",
       "      <td>-0.275857</td>\n",
       "      <td>-1.557134</td>\n",
       "      <td>0.153473</td>\n",
       "      <td>0.453365</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10546</th>\n",
       "      <td>0.037957</td>\n",
       "      <td>-1.003705</td>\n",
       "      <td>1.135342</td>\n",
       "      <td>-1.983833</td>\n",
       "      <td>0.262225</td>\n",
       "      <td>0.399576</td>\n",
       "      <td>-1.530274</td>\n",
       "      <td>-0.922244</td>\n",
       "      <td>0.262225</td>\n",
       "      <td>0.399576</td>\n",
       "      <td>-1.530274</td>\n",
       "      <td>-0.922244</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10547</th>\n",
       "      <td>0.111719</td>\n",
       "      <td>-0.237736</td>\n",
       "      <td>-0.017185</td>\n",
       "      <td>-1.183449</td>\n",
       "      <td>0.630847</td>\n",
       "      <td>-0.566912</td>\n",
       "      <td>-1.319300</td>\n",
       "      <td>-1.092165</td>\n",
       "      <td>0.630847</td>\n",
       "      <td>-0.566912</td>\n",
       "      <td>-1.319300</td>\n",
       "      <td>-1.092165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10548</th>\n",
       "      <td>-0.116125</td>\n",
       "      <td>-1.260518</td>\n",
       "      <td>-0.090194</td>\n",
       "      <td>-0.520595</td>\n",
       "      <td>1.989481</td>\n",
       "      <td>0.794478</td>\n",
       "      <td>-1.037963</td>\n",
       "      <td>0.629138</td>\n",
       "      <td>1.989481</td>\n",
       "      <td>0.794478</td>\n",
       "      <td>-1.037963</td>\n",
       "      <td>0.629138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10549</th>\n",
       "      <td>0.260106</td>\n",
       "      <td>-0.361880</td>\n",
       "      <td>-0.581961</td>\n",
       "      <td>-1.951647</td>\n",
       "      <td>2.352428</td>\n",
       "      <td>-0.876099</td>\n",
       "      <td>0.082759</td>\n",
       "      <td>1.249049</td>\n",
       "      <td>2.352428</td>\n",
       "      <td>-0.876099</td>\n",
       "      <td>0.082759</td>\n",
       "      <td>1.249049</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10550</th>\n",
       "      <td>0.540584</td>\n",
       "      <td>0.133859</td>\n",
       "      <td>-0.153617</td>\n",
       "      <td>0.402394</td>\n",
       "      <td>2.242454</td>\n",
       "      <td>-1.086066</td>\n",
       "      <td>0.815397</td>\n",
       "      <td>-0.143157</td>\n",
       "      <td>2.242454</td>\n",
       "      <td>-1.086066</td>\n",
       "      <td>0.815397</td>\n",
       "      <td>-0.143157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10551</th>\n",
       "      <td>0.196092</td>\n",
       "      <td>-0.838494</td>\n",
       "      <td>-0.104214</td>\n",
       "      <td>2.082581</td>\n",
       "      <td>0.960365</td>\n",
       "      <td>-1.062898</td>\n",
       "      <td>0.411122</td>\n",
       "      <td>-1.660644</td>\n",
       "      <td>0.960365</td>\n",
       "      <td>-1.062898</td>\n",
       "      <td>0.411122</td>\n",
       "      <td>-1.660644</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10552</th>\n",
       "      <td>-0.288900</td>\n",
       "      <td>-0.158981</td>\n",
       "      <td>-0.193764</td>\n",
       "      <td>0.028747</td>\n",
       "      <td>-0.047456</td>\n",
       "      <td>-0.458399</td>\n",
       "      <td>-0.764412</td>\n",
       "      <td>0.636888</td>\n",
       "      <td>-0.047456</td>\n",
       "      <td>-0.458399</td>\n",
       "      <td>-0.764412</td>\n",
       "      <td>0.636888</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10553</th>\n",
       "      <td>-0.273312</td>\n",
       "      <td>0.948088</td>\n",
       "      <td>0.344550</td>\n",
       "      <td>0.278280</td>\n",
       "      <td>-0.868097</td>\n",
       "      <td>-0.270555</td>\n",
       "      <td>0.814049</td>\n",
       "      <td>-0.013100</td>\n",
       "      <td>-0.868097</td>\n",
       "      <td>-0.270555</td>\n",
       "      <td>0.814049</td>\n",
       "      <td>-0.013100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10554</th>\n",
       "      <td>-0.468596</td>\n",
       "      <td>0.037010</td>\n",
       "      <td>-0.329577</td>\n",
       "      <td>0.080221</td>\n",
       "      <td>0.190405</td>\n",
       "      <td>0.367812</td>\n",
       "      <td>1.246355</td>\n",
       "      <td>-1.274901</td>\n",
       "      <td>0.190405</td>\n",
       "      <td>0.367812</td>\n",
       "      <td>1.246355</td>\n",
       "      <td>-1.274901</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10555</th>\n",
       "      <td>0.212344</td>\n",
       "      <td>-0.065320</td>\n",
       "      <td>0.906049</td>\n",
       "      <td>-0.611310</td>\n",
       "      <td>0.223385</td>\n",
       "      <td>-0.871265</td>\n",
       "      <td>0.967159</td>\n",
       "      <td>-1.897753</td>\n",
       "      <td>0.223385</td>\n",
       "      <td>-0.871265</td>\n",
       "      <td>0.967159</td>\n",
       "      <td>-1.897753</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10556</th>\n",
       "      <td>0.159659</td>\n",
       "      <td>0.163164</td>\n",
       "      <td>-0.420224</td>\n",
       "      <td>-1.463635</td>\n",
       "      <td>0.980412</td>\n",
       "      <td>1.192114</td>\n",
       "      <td>0.574260</td>\n",
       "      <td>-0.303596</td>\n",
       "      <td>0.980412</td>\n",
       "      <td>1.192114</td>\n",
       "      <td>0.574260</td>\n",
       "      <td>-0.303596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10557</th>\n",
       "      <td>0.503501</td>\n",
       "      <td>1.524327</td>\n",
       "      <td>-0.177196</td>\n",
       "      <td>-0.332442</td>\n",
       "      <td>1.316807</td>\n",
       "      <td>1.053525</td>\n",
       "      <td>0.007964</td>\n",
       "      <td>-1.113171</td>\n",
       "      <td>1.316807</td>\n",
       "      <td>1.053525</td>\n",
       "      <td>0.007964</td>\n",
       "      <td>-1.113171</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10558</th>\n",
       "      <td>0.941624</td>\n",
       "      <td>-1.002622</td>\n",
       "      <td>0.119123</td>\n",
       "      <td>1.546684</td>\n",
       "      <td>1.622540</td>\n",
       "      <td>-0.788543</td>\n",
       "      <td>0.839575</td>\n",
       "      <td>-0.586603</td>\n",
       "      <td>1.622540</td>\n",
       "      <td>-0.788543</td>\n",
       "      <td>0.839575</td>\n",
       "      <td>-0.586603</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10559</th>\n",
       "      <td>0.844318</td>\n",
       "      <td>1.049626</td>\n",
       "      <td>0.115976</td>\n",
       "      <td>0.651937</td>\n",
       "      <td>1.130500</td>\n",
       "      <td>-0.238039</td>\n",
       "      <td>1.500056</td>\n",
       "      <td>0.333784</td>\n",
       "      <td>1.130500</td>\n",
       "      <td>-0.238039</td>\n",
       "      <td>1.500056</td>\n",
       "      <td>0.333784</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10560</th>\n",
       "      <td>0.632339</td>\n",
       "      <td>0.621627</td>\n",
       "      <td>0.076597</td>\n",
       "      <td>-0.874167</td>\n",
       "      <td>1.269413</td>\n",
       "      <td>0.315187</td>\n",
       "      <td>0.927096</td>\n",
       "      <td>0.980367</td>\n",
       "      <td>1.269413</td>\n",
       "      <td>0.315187</td>\n",
       "      <td>0.927096</td>\n",
       "      <td>0.980367</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10561</th>\n",
       "      <td>0.142785</td>\n",
       "      <td>0.818843</td>\n",
       "      <td>-0.529775</td>\n",
       "      <td>-0.888257</td>\n",
       "      <td>1.049418</td>\n",
       "      <td>-0.054583</td>\n",
       "      <td>0.927125</td>\n",
       "      <td>1.762252</td>\n",
       "      <td>1.049418</td>\n",
       "      <td>-0.054583</td>\n",
       "      <td>0.927125</td>\n",
       "      <td>1.762252</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10562</th>\n",
       "      <td>-0.264160</td>\n",
       "      <td>-0.347857</td>\n",
       "      <td>0.293572</td>\n",
       "      <td>-1.670190</td>\n",
       "      <td>0.925677</td>\n",
       "      <td>0.056813</td>\n",
       "      <td>-0.098979</td>\n",
       "      <td>-0.473746</td>\n",
       "      <td>0.925677</td>\n",
       "      <td>0.056813</td>\n",
       "      <td>-0.098979</td>\n",
       "      <td>-0.473746</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10563</th>\n",
       "      <td>0.121793</td>\n",
       "      <td>1.237577</td>\n",
       "      <td>-0.213743</td>\n",
       "      <td>-0.531324</td>\n",
       "      <td>0.429942</td>\n",
       "      <td>-0.151364</td>\n",
       "      <td>-0.831200</td>\n",
       "      <td>0.463586</td>\n",
       "      <td>0.429942</td>\n",
       "      <td>-0.151364</td>\n",
       "      <td>-0.831200</td>\n",
       "      <td>0.463586</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10564</th>\n",
       "      <td>0.530988</td>\n",
       "      <td>-1.062729</td>\n",
       "      <td>1.096037</td>\n",
       "      <td>0.413663</td>\n",
       "      <td>-0.088206</td>\n",
       "      <td>-0.386512</td>\n",
       "      <td>-1.795694</td>\n",
       "      <td>1.495673</td>\n",
       "      <td>-0.088206</td>\n",
       "      <td>-0.386512</td>\n",
       "      <td>-1.795694</td>\n",
       "      <td>1.495673</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10565</th>\n",
       "      <td>0.931248</td>\n",
       "      <td>-0.721829</td>\n",
       "      <td>0.103308</td>\n",
       "      <td>0.356026</td>\n",
       "      <td>-0.346860</td>\n",
       "      <td>-0.714284</td>\n",
       "      <td>-2.305849</td>\n",
       "      <td>0.561712</td>\n",
       "      <td>-0.346860</td>\n",
       "      <td>-0.714284</td>\n",
       "      <td>-2.305849</td>\n",
       "      <td>0.561712</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10566</th>\n",
       "      <td>1.089735</td>\n",
       "      <td>-0.820571</td>\n",
       "      <td>0.007411</td>\n",
       "      <td>-1.237551</td>\n",
       "      <td>-0.358799</td>\n",
       "      <td>-0.380119</td>\n",
       "      <td>-1.442821</td>\n",
       "      <td>0.671327</td>\n",
       "      <td>-0.358799</td>\n",
       "      <td>-0.380119</td>\n",
       "      <td>-1.442821</td>\n",
       "      <td>0.671327</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10567</th>\n",
       "      <td>0.837730</td>\n",
       "      <td>-1.447496</td>\n",
       "      <td>0.208965</td>\n",
       "      <td>-0.218333</td>\n",
       "      <td>-0.339295</td>\n",
       "      <td>-0.321877</td>\n",
       "      <td>-1.362828</td>\n",
       "      <td>0.282514</td>\n",
       "      <td>-0.339295</td>\n",
       "      <td>-0.321877</td>\n",
       "      <td>-1.362828</td>\n",
       "      <td>0.282514</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10568</th>\n",
       "      <td>1.177851</td>\n",
       "      <td>-0.905184</td>\n",
       "      <td>0.490216</td>\n",
       "      <td>1.911924</td>\n",
       "      <td>-0.933749</td>\n",
       "      <td>-0.401222</td>\n",
       "      <td>0.562436</td>\n",
       "      <td>2.241921</td>\n",
       "      <td>-0.933749</td>\n",
       "      <td>-0.401222</td>\n",
       "      <td>0.562436</td>\n",
       "      <td>2.241921</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10569</th>\n",
       "      <td>1.085517</td>\n",
       "      <td>-0.708308</td>\n",
       "      <td>1.033693</td>\n",
       "      <td>-0.007005</td>\n",
       "      <td>-1.688359</td>\n",
       "      <td>1.172298</td>\n",
       "      <td>1.342759</td>\n",
       "      <td>0.375403</td>\n",
       "      <td>-1.688359</td>\n",
       "      <td>1.172298</td>\n",
       "      <td>1.342759</td>\n",
       "      <td>0.375403</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10570</th>\n",
       "      <td>0.896292</td>\n",
       "      <td>-0.202975</td>\n",
       "      <td>0.300597</td>\n",
       "      <td>0.759117</td>\n",
       "      <td>-2.498788</td>\n",
       "      <td>0.251427</td>\n",
       "      <td>1.692981</td>\n",
       "      <td>0.883397</td>\n",
       "      <td>-2.498788</td>\n",
       "      <td>0.251427</td>\n",
       "      <td>1.692981</td>\n",
       "      <td>0.883397</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10571</th>\n",
       "      <td>0.990281</td>\n",
       "      <td>-2.257316</td>\n",
       "      <td>1.308361</td>\n",
       "      <td>-0.642651</td>\n",
       "      <td>-1.636972</td>\n",
       "      <td>0.849974</td>\n",
       "      <td>1.151035</td>\n",
       "      <td>0.136165</td>\n",
       "      <td>-1.636972</td>\n",
       "      <td>0.849974</td>\n",
       "      <td>1.151035</td>\n",
       "      <td>0.136165</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10572</th>\n",
       "      <td>0.523569</td>\n",
       "      <td>-0.162333</td>\n",
       "      <td>0.322114</td>\n",
       "      <td>0.510677</td>\n",
       "      <td>-0.967810</td>\n",
       "      <td>0.676064</td>\n",
       "      <td>0.275162</td>\n",
       "      <td>2.233939</td>\n",
       "      <td>-0.967810</td>\n",
       "      <td>0.676064</td>\n",
       "      <td>0.275162</td>\n",
       "      <td>2.233939</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10573</th>\n",
       "      <td>0.268149</td>\n",
       "      <td>-1.131684</td>\n",
       "      <td>0.933801</td>\n",
       "      <td>-0.550170</td>\n",
       "      <td>0.035852</td>\n",
       "      <td>-0.137770</td>\n",
       "      <td>-0.876395</td>\n",
       "      <td>-0.898379</td>\n",
       "      <td>0.035852</td>\n",
       "      <td>-0.137770</td>\n",
       "      <td>-0.876395</td>\n",
       "      <td>-0.898379</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10574</th>\n",
       "      <td>0.217691</td>\n",
       "      <td>-1.292111</td>\n",
       "      <td>-0.490370</td>\n",
       "      <td>0.777760</td>\n",
       "      <td>0.770416</td>\n",
       "      <td>-1.095856</td>\n",
       "      <td>-0.777441</td>\n",
       "      <td>-0.934596</td>\n",
       "      <td>0.770416</td>\n",
       "      <td>-1.095856</td>\n",
       "      <td>-0.777441</td>\n",
       "      <td>-0.934596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10575</th>\n",
       "      <td>-0.202579</td>\n",
       "      <td>-2.718972</td>\n",
       "      <td>0.417264</td>\n",
       "      <td>0.580956</td>\n",
       "      <td>1.879051</td>\n",
       "      <td>-0.464932</td>\n",
       "      <td>0.155553</td>\n",
       "      <td>0.011744</td>\n",
       "      <td>1.879051</td>\n",
       "      <td>-0.464932</td>\n",
       "      <td>0.155553</td>\n",
       "      <td>0.011744</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10576</th>\n",
       "      <td>0.062175</td>\n",
       "      <td>-1.078387</td>\n",
       "      <td>-0.323793</td>\n",
       "      <td>0.215757</td>\n",
       "      <td>1.710266</td>\n",
       "      <td>0.920667</td>\n",
       "      <td>0.466217</td>\n",
       "      <td>1.325743</td>\n",
       "      <td>1.710266</td>\n",
       "      <td>0.920667</td>\n",
       "      <td>0.466217</td>\n",
       "      <td>1.325743</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10577</th>\n",
       "      <td>1.035802</td>\n",
       "      <td>0.059565</td>\n",
       "      <td>0.612262</td>\n",
       "      <td>2.003367</td>\n",
       "      <td>2.281256</td>\n",
       "      <td>-1.370215</td>\n",
       "      <td>0.627851</td>\n",
       "      <td>-0.063546</td>\n",
       "      <td>2.281256</td>\n",
       "      <td>-1.370215</td>\n",
       "      <td>0.627851</td>\n",
       "      <td>-0.063546</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10578</th>\n",
       "      <td>1.574733</td>\n",
       "      <td>-0.189101</td>\n",
       "      <td>1.552505</td>\n",
       "      <td>0.148881</td>\n",
       "      <td>1.232779</td>\n",
       "      <td>-2.158779</td>\n",
       "      <td>0.366533</td>\n",
       "      <td>0.153899</td>\n",
       "      <td>1.232779</td>\n",
       "      <td>-2.158779</td>\n",
       "      <td>0.366533</td>\n",
       "      <td>0.153899</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10579</th>\n",
       "      <td>1.406649</td>\n",
       "      <td>0.646652</td>\n",
       "      <td>0.860878</td>\n",
       "      <td>-1.839446</td>\n",
       "      <td>0.572221</td>\n",
       "      <td>-0.414179</td>\n",
       "      <td>1.226555</td>\n",
       "      <td>0.759978</td>\n",
       "      <td>0.572221</td>\n",
       "      <td>-0.414179</td>\n",
       "      <td>1.226555</td>\n",
       "      <td>0.759978</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10580</th>\n",
       "      <td>0.235404</td>\n",
       "      <td>0.412013</td>\n",
       "      <td>0.596428</td>\n",
       "      <td>1.352355</td>\n",
       "      <td>0.462185</td>\n",
       "      <td>0.123730</td>\n",
       "      <td>1.288764</td>\n",
       "      <td>0.339655</td>\n",
       "      <td>0.462185</td>\n",
       "      <td>0.123730</td>\n",
       "      <td>1.288764</td>\n",
       "      <td>0.339655</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10581</th>\n",
       "      <td>0.108759</td>\n",
       "      <td>0.423104</td>\n",
       "      <td>-0.055013</td>\n",
       "      <td>-0.278512</td>\n",
       "      <td>0.208452</td>\n",
       "      <td>-0.181976</td>\n",
       "      <td>0.343840</td>\n",
       "      <td>2.285963</td>\n",
       "      <td>0.208452</td>\n",
       "      <td>-0.181976</td>\n",
       "      <td>0.343840</td>\n",
       "      <td>2.285963</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10582</th>\n",
       "      <td>0.057135</td>\n",
       "      <td>0.893734</td>\n",
       "      <td>-0.492967</td>\n",
       "      <td>1.031343</td>\n",
       "      <td>0.333367</td>\n",
       "      <td>-1.596251</td>\n",
       "      <td>-0.538626</td>\n",
       "      <td>0.809408</td>\n",
       "      <td>0.333367</td>\n",
       "      <td>-1.596251</td>\n",
       "      <td>-0.538626</td>\n",
       "      <td>0.809408</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10583</th>\n",
       "      <td>-0.685668</td>\n",
       "      <td>0.862533</td>\n",
       "      <td>-0.062442</td>\n",
       "      <td>-1.184432</td>\n",
       "      <td>0.605015</td>\n",
       "      <td>0.334276</td>\n",
       "      <td>-0.736689</td>\n",
       "      <td>-0.246119</td>\n",
       "      <td>0.605015</td>\n",
       "      <td>0.334276</td>\n",
       "      <td>-0.736689</td>\n",
       "      <td>-0.246119</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10584</th>\n",
       "      <td>-0.957434</td>\n",
       "      <td>1.400223</td>\n",
       "      <td>-0.813942</td>\n",
       "      <td>0.825295</td>\n",
       "      <td>0.290957</td>\n",
       "      <td>-2.026923</td>\n",
       "      <td>1.359492</td>\n",
       "      <td>0.860790</td>\n",
       "      <td>0.290957</td>\n",
       "      <td>-2.026923</td>\n",
       "      <td>1.359492</td>\n",
       "      <td>0.860790</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10585</th>\n",
       "      <td>-0.696885</td>\n",
       "      <td>1.231144</td>\n",
       "      <td>-0.669356</td>\n",
       "      <td>-1.984311</td>\n",
       "      <td>0.975614</td>\n",
       "      <td>-0.461231</td>\n",
       "      <td>0.917940</td>\n",
       "      <td>1.567622</td>\n",
       "      <td>0.975614</td>\n",
       "      <td>-0.461231</td>\n",
       "      <td>0.917940</td>\n",
       "      <td>1.567622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10586</th>\n",
       "      <td>-0.843024</td>\n",
       "      <td>0.578655</td>\n",
       "      <td>-0.269846</td>\n",
       "      <td>0.523897</td>\n",
       "      <td>2.280839</td>\n",
       "      <td>-0.678616</td>\n",
       "      <td>0.880971</td>\n",
       "      <td>0.579330</td>\n",
       "      <td>2.280839</td>\n",
       "      <td>-0.678616</td>\n",
       "      <td>0.880971</td>\n",
       "      <td>0.579330</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10587</th>\n",
       "      <td>-1.081764</td>\n",
       "      <td>-0.923624</td>\n",
       "      <td>-0.375070</td>\n",
       "      <td>-0.783749</td>\n",
       "      <td>2.271242</td>\n",
       "      <td>0.306433</td>\n",
       "      <td>0.010365</td>\n",
       "      <td>1.106281</td>\n",
       "      <td>2.271242</td>\n",
       "      <td>0.306433</td>\n",
       "      <td>0.010365</td>\n",
       "      <td>1.106281</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10588</th>\n",
       "      <td>-0.898302</td>\n",
       "      <td>-0.761668</td>\n",
       "      <td>-0.287199</td>\n",
       "      <td>-2.075725</td>\n",
       "      <td>1.608068</td>\n",
       "      <td>0.241192</td>\n",
       "      <td>-0.237623</td>\n",
       "      <td>0.183246</td>\n",
       "      <td>1.608068</td>\n",
       "      <td>0.241192</td>\n",
       "      <td>-0.237623</td>\n",
       "      <td>0.183246</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10589</th>\n",
       "      <td>-0.712682</td>\n",
       "      <td>-0.598071</td>\n",
       "      <td>0.169181</td>\n",
       "      <td>-1.021346</td>\n",
       "      <td>1.323708</td>\n",
       "      <td>0.493434</td>\n",
       "      <td>0.545054</td>\n",
       "      <td>0.846292</td>\n",
       "      <td>1.323708</td>\n",
       "      <td>0.493434</td>\n",
       "      <td>0.545054</td>\n",
       "      <td>0.846292</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10590</th>\n",
       "      <td>-0.759611</td>\n",
       "      <td>0.041961</td>\n",
       "      <td>0.000904</td>\n",
       "      <td>0.311549</td>\n",
       "      <td>-0.748137</td>\n",
       "      <td>0.984547</td>\n",
       "      <td>0.926501</td>\n",
       "      <td>0.030012</td>\n",
       "      <td>-0.748137</td>\n",
       "      <td>0.984547</td>\n",
       "      <td>0.926501</td>\n",
       "      <td>0.030012</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10591</th>\n",
       "      <td>0.942255</td>\n",
       "      <td>0.115393</td>\n",
       "      <td>1.454640</td>\n",
       "      <td>-0.615808</td>\n",
       "      <td>-2.477060</td>\n",
       "      <td>-0.196548</td>\n",
       "      <td>1.634959</td>\n",
       "      <td>0.667079</td>\n",
       "      <td>-2.477060</td>\n",
       "      <td>-0.196548</td>\n",
       "      <td>1.634959</td>\n",
       "      <td>0.667079</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10592</th>\n",
       "      <td>2.209648</td>\n",
       "      <td>-0.686914</td>\n",
       "      <td>1.950430</td>\n",
       "      <td>-1.030000</td>\n",
       "      <td>-3.098565</td>\n",
       "      <td>0.420078</td>\n",
       "      <td>1.556779</td>\n",
       "      <td>1.215539</td>\n",
       "      <td>-3.098565</td>\n",
       "      <td>0.420078</td>\n",
       "      <td>1.556779</td>\n",
       "      <td>1.215539</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10593</th>\n",
       "      <td>1.492385</td>\n",
       "      <td>0.355438</td>\n",
       "      <td>1.181014</td>\n",
       "      <td>0.453986</td>\n",
       "      <td>-2.253038</td>\n",
       "      <td>0.059641</td>\n",
       "      <td>1.396897</td>\n",
       "      <td>0.249884</td>\n",
       "      <td>-2.253038</td>\n",
       "      <td>0.059641</td>\n",
       "      <td>1.396897</td>\n",
       "      <td>0.249884</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10594</th>\n",
       "      <td>2.097311</td>\n",
       "      <td>-0.028355</td>\n",
       "      <td>1.989851</td>\n",
       "      <td>1.107100</td>\n",
       "      <td>-1.789339</td>\n",
       "      <td>-0.597609</td>\n",
       "      <td>-0.038952</td>\n",
       "      <td>-0.024967</td>\n",
       "      <td>-1.789339</td>\n",
       "      <td>-0.597609</td>\n",
       "      <td>-0.038952</td>\n",
       "      <td>-0.024967</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10595</th>\n",
       "      <td>1.581694</td>\n",
       "      <td>0.026782</td>\n",
       "      <td>1.474495</td>\n",
       "      <td>0.273709</td>\n",
       "      <td>-1.681179</td>\n",
       "      <td>0.232973</td>\n",
       "      <td>-0.681833</td>\n",
       "      <td>-0.254138</td>\n",
       "      <td>-1.681179</td>\n",
       "      <td>0.232973</td>\n",
       "      <td>-0.681833</td>\n",
       "      <td>-0.254138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10596</th>\n",
       "      <td>0.408734</td>\n",
       "      <td>-0.092546</td>\n",
       "      <td>0.369469</td>\n",
       "      <td>1.864172</td>\n",
       "      <td>-0.708924</td>\n",
       "      <td>0.411761</td>\n",
       "      <td>0.058957</td>\n",
       "      <td>0.316539</td>\n",
       "      <td>-0.708924</td>\n",
       "      <td>0.411761</td>\n",
       "      <td>0.058957</td>\n",
       "      <td>0.316539</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10597</th>\n",
       "      <td>0.213750</td>\n",
       "      <td>-0.153925</td>\n",
       "      <td>-0.350158</td>\n",
       "      <td>1.574722</td>\n",
       "      <td>-1.153712</td>\n",
       "      <td>-0.782453</td>\n",
       "      <td>-0.997355</td>\n",
       "      <td>1.475298</td>\n",
       "      <td>-1.153712</td>\n",
       "      <td>-0.782453</td>\n",
       "      <td>-0.997355</td>\n",
       "      <td>1.475298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10598</th>\n",
       "      <td>0.023977</td>\n",
       "      <td>-1.752334</td>\n",
       "      <td>0.114436</td>\n",
       "      <td>1.937385</td>\n",
       "      <td>-1.609917</td>\n",
       "      <td>-0.421988</td>\n",
       "      <td>-0.940904</td>\n",
       "      <td>0.034817</td>\n",
       "      <td>-1.609917</td>\n",
       "      <td>-0.421988</td>\n",
       "      <td>-0.940904</td>\n",
       "      <td>0.034817</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10599</th>\n",
       "      <td>0.191080</td>\n",
       "      <td>-1.826435</td>\n",
       "      <td>0.147392</td>\n",
       "      <td>0.183074</td>\n",
       "      <td>-1.520036</td>\n",
       "      <td>-1.205816</td>\n",
       "      <td>-1.478341</td>\n",
       "      <td>0.161291</td>\n",
       "      <td>-1.520036</td>\n",
       "      <td>-1.205816</td>\n",
       "      <td>-1.478341</td>\n",
       "      <td>0.161291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10600</th>\n",
       "      <td>0.287709</td>\n",
       "      <td>0.931521</td>\n",
       "      <td>-0.189909</td>\n",
       "      <td>0.497879</td>\n",
       "      <td>-1.118170</td>\n",
       "      <td>-0.190531</td>\n",
       "      <td>-0.710585</td>\n",
       "      <td>1.033781</td>\n",
       "      <td>-1.118170</td>\n",
       "      <td>-0.190531</td>\n",
       "      <td>-0.710585</td>\n",
       "      <td>1.033781</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Ch1       Ch2       Ch5       Ch6      GP_1      GP_2      GP_5  \\\n",
       "10530  0.577638  2.092008  0.810387  0.342304  0.202569  0.666818 -0.056357   \n",
       "10531  2.081151  0.593498  1.163298 -0.913881 -0.249807 -1.260880 -0.802093   \n",
       "10532  2.715299 -0.958491  2.077475 -0.240998  0.008618 -0.180596  0.072047   \n",
       "10533  1.768283 -0.050291  1.274917 -1.115855  0.465773 -0.547793  1.549573   \n",
       "10534  0.599657  0.233410  0.730695 -0.630397  1.056659  0.793070  2.356037   \n",
       "10535 -0.380067  0.818205 -0.357238  1.050167  1.443007  0.577319  1.355929   \n",
       "10536 -1.138131  0.858352 -1.279146  1.627810  1.177682  2.946444  2.048580   \n",
       "10537 -1.229236 -0.120901 -1.369392 -0.234454  0.554688  0.994267  2.610200   \n",
       "10538 -0.599212  0.065614 -0.400114  0.384548  0.395955  0.711632  2.429250   \n",
       "10539  0.371020  0.385071  0.735276  1.291436  0.459792  0.223036  1.933694   \n",
       "10540 -0.098692  1.515862  0.087765 -0.292024  0.425827 -0.222074  0.836550   \n",
       "10541 -0.178786  1.017286  0.290773 -0.565424  0.801667 -1.014257  1.257198   \n",
       "10542  0.109804  0.047331  0.209351  0.911531  0.768853  0.025429  1.062412   \n",
       "10543  0.067454  1.115703  0.712253 -0.312969  1.302056 -0.065378  0.341375   \n",
       "10544 -0.258639  1.440980  0.209708  0.304224  0.610644 -1.781744 -0.090244   \n",
       "10545 -0.078407  1.271190  0.499801  0.119116 -0.275857 -1.557134  0.153473   \n",
       "10546  0.037957 -1.003705  1.135342 -1.983833  0.262225  0.399576 -1.530274   \n",
       "10547  0.111719 -0.237736 -0.017185 -1.183449  0.630847 -0.566912 -1.319300   \n",
       "10548 -0.116125 -1.260518 -0.090194 -0.520595  1.989481  0.794478 -1.037963   \n",
       "10549  0.260106 -0.361880 -0.581961 -1.951647  2.352428 -0.876099  0.082759   \n",
       "10550  0.540584  0.133859 -0.153617  0.402394  2.242454 -1.086066  0.815397   \n",
       "10551  0.196092 -0.838494 -0.104214  2.082581  0.960365 -1.062898  0.411122   \n",
       "10552 -0.288900 -0.158981 -0.193764  0.028747 -0.047456 -0.458399 -0.764412   \n",
       "10553 -0.273312  0.948088  0.344550  0.278280 -0.868097 -0.270555  0.814049   \n",
       "10554 -0.468596  0.037010 -0.329577  0.080221  0.190405  0.367812  1.246355   \n",
       "10555  0.212344 -0.065320  0.906049 -0.611310  0.223385 -0.871265  0.967159   \n",
       "10556  0.159659  0.163164 -0.420224 -1.463635  0.980412  1.192114  0.574260   \n",
       "10557  0.503501  1.524327 -0.177196 -0.332442  1.316807  1.053525  0.007964   \n",
       "10558  0.941624 -1.002622  0.119123  1.546684  1.622540 -0.788543  0.839575   \n",
       "10559  0.844318  1.049626  0.115976  0.651937  1.130500 -0.238039  1.500056   \n",
       "10560  0.632339  0.621627  0.076597 -0.874167  1.269413  0.315187  0.927096   \n",
       "10561  0.142785  0.818843 -0.529775 -0.888257  1.049418 -0.054583  0.927125   \n",
       "10562 -0.264160 -0.347857  0.293572 -1.670190  0.925677  0.056813 -0.098979   \n",
       "10563  0.121793  1.237577 -0.213743 -0.531324  0.429942 -0.151364 -0.831200   \n",
       "10564  0.530988 -1.062729  1.096037  0.413663 -0.088206 -0.386512 -1.795694   \n",
       "10565  0.931248 -0.721829  0.103308  0.356026 -0.346860 -0.714284 -2.305849   \n",
       "10566  1.089735 -0.820571  0.007411 -1.237551 -0.358799 -0.380119 -1.442821   \n",
       "10567  0.837730 -1.447496  0.208965 -0.218333 -0.339295 -0.321877 -1.362828   \n",
       "10568  1.177851 -0.905184  0.490216  1.911924 -0.933749 -0.401222  0.562436   \n",
       "10569  1.085517 -0.708308  1.033693 -0.007005 -1.688359  1.172298  1.342759   \n",
       "10570  0.896292 -0.202975  0.300597  0.759117 -2.498788  0.251427  1.692981   \n",
       "10571  0.990281 -2.257316  1.308361 -0.642651 -1.636972  0.849974  1.151035   \n",
       "10572  0.523569 -0.162333  0.322114  0.510677 -0.967810  0.676064  0.275162   \n",
       "10573  0.268149 -1.131684  0.933801 -0.550170  0.035852 -0.137770 -0.876395   \n",
       "10574  0.217691 -1.292111 -0.490370  0.777760  0.770416 -1.095856 -0.777441   \n",
       "10575 -0.202579 -2.718972  0.417264  0.580956  1.879051 -0.464932  0.155553   \n",
       "10576  0.062175 -1.078387 -0.323793  0.215757  1.710266  0.920667  0.466217   \n",
       "10577  1.035802  0.059565  0.612262  2.003367  2.281256 -1.370215  0.627851   \n",
       "10578  1.574733 -0.189101  1.552505  0.148881  1.232779 -2.158779  0.366533   \n",
       "10579  1.406649  0.646652  0.860878 -1.839446  0.572221 -0.414179  1.226555   \n",
       "10580  0.235404  0.412013  0.596428  1.352355  0.462185  0.123730  1.288764   \n",
       "10581  0.108759  0.423104 -0.055013 -0.278512  0.208452 -0.181976  0.343840   \n",
       "10582  0.057135  0.893734 -0.492967  1.031343  0.333367 -1.596251 -0.538626   \n",
       "10583 -0.685668  0.862533 -0.062442 -1.184432  0.605015  0.334276 -0.736689   \n",
       "10584 -0.957434  1.400223 -0.813942  0.825295  0.290957 -2.026923  1.359492   \n",
       "10585 -0.696885  1.231144 -0.669356 -1.984311  0.975614 -0.461231  0.917940   \n",
       "10586 -0.843024  0.578655 -0.269846  0.523897  2.280839 -0.678616  0.880971   \n",
       "10587 -1.081764 -0.923624 -0.375070 -0.783749  2.271242  0.306433  0.010365   \n",
       "10588 -0.898302 -0.761668 -0.287199 -2.075725  1.608068  0.241192 -0.237623   \n",
       "10589 -0.712682 -0.598071  0.169181 -1.021346  1.323708  0.493434  0.545054   \n",
       "10590 -0.759611  0.041961  0.000904  0.311549 -0.748137  0.984547  0.926501   \n",
       "10591  0.942255  0.115393  1.454640 -0.615808 -2.477060 -0.196548  1.634959   \n",
       "10592  2.209648 -0.686914  1.950430 -1.030000 -3.098565  0.420078  1.556779   \n",
       "10593  1.492385  0.355438  1.181014  0.453986 -2.253038  0.059641  1.396897   \n",
       "10594  2.097311 -0.028355  1.989851  1.107100 -1.789339 -0.597609 -0.038952   \n",
       "10595  1.581694  0.026782  1.474495  0.273709 -1.681179  0.232973 -0.681833   \n",
       "10596  0.408734 -0.092546  0.369469  1.864172 -0.708924  0.411761  0.058957   \n",
       "10597  0.213750 -0.153925 -0.350158  1.574722 -1.153712 -0.782453 -0.997355   \n",
       "10598  0.023977 -1.752334  0.114436  1.937385 -1.609917 -0.421988 -0.940904   \n",
       "10599  0.191080 -1.826435  0.147392  0.183074 -1.520036 -1.205816 -1.478341   \n",
       "10600  0.287709  0.931521 -0.189909  0.497879 -1.118170 -0.190531 -0.710585   \n",
       "\n",
       "           GP_6     SGP_1     SGP_2     SGP_5     SGP_6  centerOcc  centerIn  \\\n",
       "10530 -0.165699  0.202569  0.666818 -0.056357 -0.165699          0         0   \n",
       "10531  0.866688 -0.249807 -1.260880 -0.802093  0.866688          0         0   \n",
       "10532 -2.363312  0.008618 -0.180596  0.072047 -2.363312          0         0   \n",
       "10533  0.970920  0.465773 -0.547793  1.549573  0.970920          0         0   \n",
       "10534  1.909040  1.056659  0.793070  2.356037  1.909040          0         0   \n",
       "10535  1.781868  1.443007  0.577319  1.355929  1.781868          0         0   \n",
       "10536 -0.011725  1.177682  2.946444  2.048580 -0.011725          0         0   \n",
       "10537 -0.387338  0.554688  0.994267  2.610200 -0.387338          0         0   \n",
       "10538 -1.270167  0.395955  0.711632  2.429250 -1.270167          0         0   \n",
       "10539 -1.147131  0.459792  0.223036  1.933694 -1.147131          0         0   \n",
       "10540  1.405037  0.425827 -0.222074  0.836550  1.405037          0         0   \n",
       "10541  0.548736  0.801667 -1.014257  1.257198  0.548736          0         0   \n",
       "10542 -0.362672  0.768853  0.025429  1.062412 -0.362672          0         0   \n",
       "10543  2.516913  1.302056 -0.065378  0.341375  2.516913          0         0   \n",
       "10544  0.619543  0.610644 -1.781744 -0.090244  0.619543          0         0   \n",
       "10545  0.453365 -0.275857 -1.557134  0.153473  0.453365          0         0   \n",
       "10546 -0.922244  0.262225  0.399576 -1.530274 -0.922244          0         0   \n",
       "10547 -1.092165  0.630847 -0.566912 -1.319300 -1.092165          0         0   \n",
       "10548  0.629138  1.989481  0.794478 -1.037963  0.629138          0         0   \n",
       "10549  1.249049  2.352428 -0.876099  0.082759  1.249049          0         0   \n",
       "10550 -0.143157  2.242454 -1.086066  0.815397 -0.143157          0         0   \n",
       "10551 -1.660644  0.960365 -1.062898  0.411122 -1.660644          0         0   \n",
       "10552  0.636888 -0.047456 -0.458399 -0.764412  0.636888          0         0   \n",
       "10553 -0.013100 -0.868097 -0.270555  0.814049 -0.013100          0         0   \n",
       "10554 -1.274901  0.190405  0.367812  1.246355 -1.274901          0         0   \n",
       "10555 -1.897753  0.223385 -0.871265  0.967159 -1.897753          0         0   \n",
       "10556 -0.303596  0.980412  1.192114  0.574260 -0.303596          0         0   \n",
       "10557 -1.113171  1.316807  1.053525  0.007964 -1.113171          0         0   \n",
       "10558 -0.586603  1.622540 -0.788543  0.839575 -0.586603          0         0   \n",
       "10559  0.333784  1.130500 -0.238039  1.500056  0.333784          0         0   \n",
       "10560  0.980367  1.269413  0.315187  0.927096  0.980367          0         0   \n",
       "10561  1.762252  1.049418 -0.054583  0.927125  1.762252          0         0   \n",
       "10562 -0.473746  0.925677  0.056813 -0.098979 -0.473746          0         0   \n",
       "10563  0.463586  0.429942 -0.151364 -0.831200  0.463586          0         0   \n",
       "10564  1.495673 -0.088206 -0.386512 -1.795694  1.495673          0         0   \n",
       "10565  0.561712 -0.346860 -0.714284 -2.305849  0.561712          0         0   \n",
       "10566  0.671327 -0.358799 -0.380119 -1.442821  0.671327          0         0   \n",
       "10567  0.282514 -0.339295 -0.321877 -1.362828  0.282514          0         0   \n",
       "10568  2.241921 -0.933749 -0.401222  0.562436  2.241921          0         0   \n",
       "10569  0.375403 -1.688359  1.172298  1.342759  0.375403          0         0   \n",
       "10570  0.883397 -2.498788  0.251427  1.692981  0.883397          1         1   \n",
       "10571  0.136165 -1.636972  0.849974  1.151035  0.136165          1         0   \n",
       "10572  2.233939 -0.967810  0.676064  0.275162  2.233939          0         0   \n",
       "10573 -0.898379  0.035852 -0.137770 -0.876395 -0.898379          0         0   \n",
       "10574 -0.934596  0.770416 -1.095856 -0.777441 -0.934596          0         0   \n",
       "10575  0.011744  1.879051 -0.464932  0.155553  0.011744          0         0   \n",
       "10576  1.325743  1.710266  0.920667  0.466217  1.325743          0         0   \n",
       "10577 -0.063546  2.281256 -1.370215  0.627851 -0.063546          0         0   \n",
       "10578  0.153899  1.232779 -2.158779  0.366533  0.153899          0         0   \n",
       "10579  0.759978  0.572221 -0.414179  1.226555  0.759978          0         0   \n",
       "10580  0.339655  0.462185  0.123730  1.288764  0.339655          0         0   \n",
       "10581  2.285963  0.208452 -0.181976  0.343840  2.285963          0         0   \n",
       "10582  0.809408  0.333367 -1.596251 -0.538626  0.809408          0         0   \n",
       "10583 -0.246119  0.605015  0.334276 -0.736689 -0.246119          0         0   \n",
       "10584  0.860790  0.290957 -2.026923  1.359492  0.860790          0         0   \n",
       "10585  1.567622  0.975614 -0.461231  0.917940  1.567622          0         0   \n",
       "10586  0.579330  2.280839 -0.678616  0.880971  0.579330          0         0   \n",
       "10587  1.106281  2.271242  0.306433  0.010365  1.106281          0         0   \n",
       "10588  0.183246  1.608068  0.241192 -0.237623  0.183246          0         0   \n",
       "10589  0.846292  1.323708  0.493434  0.545054  0.846292          0         0   \n",
       "10590  0.030012 -0.748137  0.984547  0.926501  0.030012          0         0   \n",
       "10591  0.667079 -2.477060 -0.196548  1.634959  0.667079          0         0   \n",
       "10592  1.215539 -3.098565  0.420078  1.556779  1.215539          0         0   \n",
       "10593  0.249884 -2.253038  0.059641  1.396897  0.249884          0         0   \n",
       "10594 -0.024967 -1.789339 -0.597609 -0.038952 -0.024967          0         0   \n",
       "10595 -0.254138 -1.681179  0.232973 -0.681833 -0.254138          0         0   \n",
       "10596  0.316539 -0.708924  0.411761  0.058957  0.316539          0         0   \n",
       "10597  1.475298 -1.153712 -0.782453 -0.997355  1.475298          0         0   \n",
       "10598  0.034817 -1.609917 -0.421988 -0.940904  0.034817          0         0   \n",
       "10599  0.161291 -1.520036 -1.205816 -1.478341  0.161291          0         0   \n",
       "10600  1.033781 -1.118170 -0.190531 -0.710585  1.033781          0         0   \n",
       "\n",
       "       centerOut  rightOcc  rightIn  rightOut  rightLick  leftOcc  leftIn  \\\n",
       "10530          0         0        0         0          0        0       0   \n",
       "10531          0         0        0         0          0        0       0   \n",
       "10532          0         1        1         0          0        0       0   \n",
       "10533          0         1        0         0          0        0       0   \n",
       "10534          0         1        0         0          0        0       0   \n",
       "10535          0         1        0         1          0        0       0   \n",
       "10536          0         0        0         0          0        0       0   \n",
       "10537          0         0        0         0          0        0       0   \n",
       "10538          0         0        0         0          0        0       0   \n",
       "10539          0         0        0         0          0        0       0   \n",
       "10540          0         0        0         0          0        0       0   \n",
       "10541          0         0        0         0          0        0       0   \n",
       "10542          0         0        0         0          0        0       0   \n",
       "10543          0         0        0         0          0        0       0   \n",
       "10544          0         0        0         0          0        0       0   \n",
       "10545          0         0        0         0          0        0       0   \n",
       "10546          0         0        0         0          0        0       0   \n",
       "10547          0         0        0         0          0        0       0   \n",
       "10548          0         0        0         0          0        0       0   \n",
       "10549          0         0        0         0          0        0       0   \n",
       "10550          0         0        0         0          0        0       0   \n",
       "10551          0         0        0         0          0        0       0   \n",
       "10552          0         0        0         0          0        0       0   \n",
       "10553          0         0        0         0          0        0       0   \n",
       "10554          0         0        0         0          0        0       0   \n",
       "10555          0         0        0         0          0        0       0   \n",
       "10556          0         0        0         0          0        0       0   \n",
       "10557          0         0        0         0          0        0       0   \n",
       "10558          0         0        0         0          0        0       0   \n",
       "10559          0         0        0         0          0        0       0   \n",
       "10560          0         0        0         0          0        0       0   \n",
       "10561          0         0        0         0          0        0       0   \n",
       "10562          0         0        0         0          0        0       0   \n",
       "10563          0         0        0         0          0        0       0   \n",
       "10564          0         0        0         0          0        0       0   \n",
       "10565          0         0        0         0          0        0       0   \n",
       "10566          0         0        0         0          0        0       0   \n",
       "10567          0         0        0         0          0        0       0   \n",
       "10568          0         0        0         0          0        0       0   \n",
       "10569          0         0        0         0          0        0       0   \n",
       "10570          0         0        0         0          0        0       0   \n",
       "10571          1         0        0         0          0        0       0   \n",
       "10572          0         0        0         0          0        0       0   \n",
       "10573          0         0        0         0          0        0       0   \n",
       "10574          0         0        0         0          0        0       0   \n",
       "10575          0         0        0         0          0        0       0   \n",
       "10576          0         0        0         0          0        0       0   \n",
       "10577          0         0        0         0          0        0       0   \n",
       "10578          0         0        0         0          0        0       0   \n",
       "10579          0         0        0         0          0        0       0   \n",
       "10580          0         0        0         0          0        0       0   \n",
       "10581          0         0        0         0          0        0       0   \n",
       "10582          0         0        0         0          0        0       0   \n",
       "10583          0         0        0         0          0        0       0   \n",
       "10584          0         0        0         0          0        0       0   \n",
       "10585          0         0        0         0          0        0       0   \n",
       "10586          0         0        0         0          0        0       0   \n",
       "10587          0         0        0         0          0        0       0   \n",
       "10588          0         0        0         0          0        0       0   \n",
       "10589          0         0        0         0          0        0       0   \n",
       "10590          0         0        0         0          0        0       0   \n",
       "10591          0         0        0         0          0        0       0   \n",
       "10592          0         0        0         0          0        1       1   \n",
       "10593          0         0        0         0          0        1       0   \n",
       "10594          0         0        0         0          0        1       0   \n",
       "10595          0         0        0         0          0        1       0   \n",
       "10596          0         0        0         0          0        1       0   \n",
       "10597          0         0        0         0          0        1       0   \n",
       "10598          0         0        0         0          0        1       0   \n",
       "10599          0         0        0         0          0        1       0   \n",
       "10600          0         0        0         0          0        1       0   \n",
       "\n",
       "       leftOut  leftLick  reward  noreward  wasRewarded  photometryCenterIn  \\\n",
       "10530        0         0       0         0          NaN                   0   \n",
       "10531        0         0       0         0          NaN                   0   \n",
       "10532        0         0       0         1          1.0                   0   \n",
       "10533        0         0       0         1          NaN                   0   \n",
       "10534        0         0       0         1          NaN                   0   \n",
       "10535        0         0       0         1          1.0                   0   \n",
       "10536        0         0       0         0          NaN                   0   \n",
       "10537        0         0       0         0          NaN                   0   \n",
       "10538        0         0       0         0          NaN                   0   \n",
       "10539        0         0       0         0          NaN                   0   \n",
       "10540        0         0       0         0          NaN                   0   \n",
       "10541        0         0       0         0          NaN                   0   \n",
       "10542        0         0       0         0          NaN                   0   \n",
       "10543        0         0       0         0          NaN                   0   \n",
       "10544        0         0       0         0          NaN                   0   \n",
       "10545        0         0       0         0          NaN                   0   \n",
       "10546        0         0       0         0          NaN                   0   \n",
       "10547        0         0       0         0          NaN                   0   \n",
       "10548        0         0       0         0          NaN                   0   \n",
       "10549        0         0       0         0          NaN                   0   \n",
       "10550        0         0       0         0          NaN                   0   \n",
       "10551        0         0       0         0          NaN                   0   \n",
       "10552        0         0       0         0          NaN                   0   \n",
       "10553        0         0       0         0          NaN                   0   \n",
       "10554        0         0       0         0          NaN                   0   \n",
       "10555        0         0       0         0          NaN                   0   \n",
       "10556        0         0       0         0          NaN                   0   \n",
       "10557        0         0       0         0          NaN                   0   \n",
       "10558        0         0       0         0          NaN                   0   \n",
       "10559        0         0       0         0          NaN                   0   \n",
       "10560        0         0       0         0          NaN                   0   \n",
       "10561        0         0       0         0          NaN                   0   \n",
       "10562        0         0       0         0          NaN                   0   \n",
       "10563        0         0       0         0          NaN                   0   \n",
       "10564        0         0       0         0          NaN                   0   \n",
       "10565        0         0       0         0          NaN                   0   \n",
       "10566        0         0       0         0          NaN                   0   \n",
       "10567        0         0       0         0          NaN                   0   \n",
       "10568        0         0       0         0          NaN                   0   \n",
       "10569        0         0       0         0          NaN                   0   \n",
       "10570        0         0       0         0          1.0                   1   \n",
       "10571        0         0       0         0          0.0                   0   \n",
       "10572        0         0       0         0          NaN                   0   \n",
       "10573        0         0       0         0          NaN                   0   \n",
       "10574        0         0       0         0          NaN                   0   \n",
       "10575        0         0       0         0          NaN                   0   \n",
       "10576        0         0       0         0          NaN                   0   \n",
       "10577        0         0       0         0          NaN                   0   \n",
       "10578        0         0       0         0          NaN                   0   \n",
       "10579        0         0       0         0          NaN                   0   \n",
       "10580        0         0       0         0          NaN                   0   \n",
       "10581        0         0       0         0          NaN                   0   \n",
       "10582        0         0       0         0          NaN                   0   \n",
       "10583        0         0       0         0          NaN                   0   \n",
       "10584        0         0       0         0          NaN                   0   \n",
       "10585        0         0       0         0          NaN                   0   \n",
       "10586        0         0       0         0          NaN                   0   \n",
       "10587        0         0       0         0          NaN                   0   \n",
       "10588        0         0       0         0          NaN                   0   \n",
       "10589        0         0       0         0          NaN                   0   \n",
       "10590        0         0       0         0          NaN                   0   \n",
       "10591        0         0       0         0          NaN                   0   \n",
       "10592        0         0       1         0          0.0                   0   \n",
       "10593        0         0       1         0          NaN                   0   \n",
       "10594        0         0       1         0          NaN                   0   \n",
       "10595        0         1       1         0          0.0                   0   \n",
       "10596        0         1       1         0          NaN                   0   \n",
       "10597        0         1       1         0          NaN                   0   \n",
       "10598        0         0       1         0          NaN                   0   \n",
       "10599        0         1       1         0          NaN                   0   \n",
       "10600        0         0       1         0          NaN                   0   \n",
       "\n",
       "       photometryCenterOut  photometrySideIn  photometrySideOut  \\\n",
       "10530                    0                 0                  0   \n",
       "10531                    0                 0                  0   \n",
       "10532                    0                 1                  0   \n",
       "10533                    0                 0                  0   \n",
       "10534                    0                 0                  0   \n",
       "10535                    0                 0                  1   \n",
       "10536                    0                 0                  0   \n",
       "10537                    0                 0                  0   \n",
       "10538                    0                 0                  0   \n",
       "10539                    0                 0                  0   \n",
       "10540                    0                 0                  0   \n",
       "10541                    0                 0                  0   \n",
       "10542                    0                 0                  0   \n",
       "10543                    0                 0                  0   \n",
       "10544                    0                 0                  0   \n",
       "10545                    0                 0                  0   \n",
       "10546                    0                 0                  0   \n",
       "10547                    0                 0                  0   \n",
       "10548                    0                 0                  0   \n",
       "10549                    0                 0                  0   \n",
       "10550                    0                 0                  0   \n",
       "10551                    0                 0                  0   \n",
       "10552                    0                 0                  0   \n",
       "10553                    0                 0                  0   \n",
       "10554                    0                 0                  0   \n",
       "10555                    0                 0                  0   \n",
       "10556                    0                 0                  0   \n",
       "10557                    0                 0                  0   \n",
       "10558                    0                 0                  0   \n",
       "10559                    0                 0                  0   \n",
       "10560                    0                 0                  0   \n",
       "10561                    0                 0                  0   \n",
       "10562                    0                 0                  0   \n",
       "10563                    0                 0                  0   \n",
       "10564                    0                 0                  0   \n",
       "10565                    0                 0                  0   \n",
       "10566                    0                 0                  0   \n",
       "10567                    0                 0                  0   \n",
       "10568                    0                 0                  0   \n",
       "10569                    0                 0                  0   \n",
       "10570                    0                 0                  0   \n",
       "10571                    1                 0                  0   \n",
       "10572                    0                 0                  0   \n",
       "10573                    0                 0                  0   \n",
       "10574                    0                 0                  0   \n",
       "10575                    0                 0                  0   \n",
       "10576                    0                 0                  0   \n",
       "10577                    0                 0                  0   \n",
       "10578                    0                 0                  0   \n",
       "10579                    0                 0                  0   \n",
       "10580                    0                 0                  0   \n",
       "10581                    0                 0                  0   \n",
       "10582                    0                 0                  0   \n",
       "10583                    0                 0                  0   \n",
       "10584                    0                 0                  0   \n",
       "10585                    0                 0                  0   \n",
       "10586                    0                 0                  0   \n",
       "10587                    0                 0                  0   \n",
       "10588                    0                 0                  0   \n",
       "10589                    0                 0                  0   \n",
       "10590                    0                 0                  0   \n",
       "10591                    0                 0                  0   \n",
       "10592                    0                 1                  0   \n",
       "10593                    0                 0                  0   \n",
       "10594                    0                 0                  0   \n",
       "10595                    0                 0                  0   \n",
       "10596                    0                 0                  0   \n",
       "10597                    0                 0                  0   \n",
       "10598                    0                 0                  0   \n",
       "10599                    0                 0                  0   \n",
       "10600                    0                 0                  0   \n",
       "\n",
       "       photometryFirstLick  nTrial  nEndTrial  \n",
       "10530                    0      91         90  \n",
       "10531                    0      91         90  \n",
       "10532                    0      91         90  \n",
       "10533                    0      91         90  \n",
       "10534                    0      91         90  \n",
       "10535                    0      91         91  \n",
       "10536                    0      91         91  \n",
       "10537                    0      91         91  \n",
       "10538                    0      91         91  \n",
       "10539                    0      91         91  \n",
       "10540                    0      91         91  \n",
       "10541                    0      91         91  \n",
       "10542                    0      91         91  \n",
       "10543                    0      91         91  \n",
       "10544                    0      91         91  \n",
       "10545                    0      91         91  \n",
       "10546                    0      91         91  \n",
       "10547                    0      91         91  \n",
       "10548                    0      91         91  \n",
       "10549                    0      91         91  \n",
       "10550                    0      91         91  \n",
       "10551                    0      91         91  \n",
       "10552                    0      91         91  \n",
       "10553                    0      91         91  \n",
       "10554                    0      91         91  \n",
       "10555                    0      91         91  \n",
       "10556                    0      91         91  \n",
       "10557                    0      91         91  \n",
       "10558                    0      91         91  \n",
       "10559                    0      91         91  \n",
       "10560                    0      91         91  \n",
       "10561                    0      91         91  \n",
       "10562                    0      91         91  \n",
       "10563                    0      91         91  \n",
       "10564                    0      91         91  \n",
       "10565                    0      91         91  \n",
       "10566                    0      91         91  \n",
       "10567                    0      91         91  \n",
       "10568                    0      91         91  \n",
       "10569                    0      91         91  \n",
       "10570                    0      92         91  \n",
       "10571                    0      92         91  \n",
       "10572                    0      92         91  \n",
       "10573                    0      92         91  \n",
       "10574                    0      92         91  \n",
       "10575                    0      92         91  \n",
       "10576                    0      92         91  \n",
       "10577                    0      92         91  \n",
       "10578                    0      92         91  \n",
       "10579                    0      92         91  \n",
       "10580                    0      92         91  \n",
       "10581                    0      92         91  \n",
       "10582                    0      92         91  \n",
       "10583                    0      92         91  \n",
       "10584                    0      92         91  \n",
       "10585                    0      92         91  \n",
       "10586                    0      92         91  \n",
       "10587                    0      92         91  \n",
       "10588                    0      92         91  \n",
       "10589                    0      92         91  \n",
       "10590                    0      92         91  \n",
       "10591                    0      92         91  \n",
       "10592                    0      92         91  \n",
       "10593                    0      92         91  \n",
       "10594                    0      92         91  \n",
       "10595                    1      92         91  \n",
       "10596                    0      92         91  \n",
       "10597                    0      92         91  \n",
       "10598                    0      92         91  \n",
       "10599                    0      92         91  \n",
       "10600                    0      92         91  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context('max_rows', 1000, 'max_columns', 1000):\n",
    "    display(df[df.reset_index()['index'].between(10530, 10600)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # df2.columns\n",
    "\n",
    "# for col in ['photometryCenterInIndex',\n",
    "#             'photometryCenterOutIndex',\n",
    "#             'photometrySideInIndex',\n",
    "#             'photometrySideOutIndex',\n",
    "#             'photometryFirstLickIndex',\n",
    "#             'wasRewarded']:\n",
    "#     df2[col] = 0\n",
    "#     inx = df_t[col].values - 1\n",
    "#     inx = inx[inx >= 0]\n",
    "#     df2.loc[inx, col] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01\n",
      "02\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54a1011e6f37462cbeaf24b12b14fae2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=270.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "06\n",
      "07\n",
      "01\n",
      "02\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "028569b574d0414b9b82acb425598dfd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=114.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "06\n",
      "07\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01\n",
      "02\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49ed6708b62b48f196104b9654e43596",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=270.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "06\n",
      "07\n",
      "01\n",
      "02\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee2ca70787ff43fcac1666b72121d653",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=114.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "06\n",
      "07\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01\n",
      "02\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bab51dc4d63245acac0545988050866c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=270.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "06\n",
      "07\n",
      "01\n",
      "02\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee6186f66f83449b87c7579e2f5385d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=114.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "06\n",
      "07\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01\n",
      "02\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c0daac68cd524baa991f2b338cede942",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=270.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "06\n",
      "07\n",
      "01\n",
      "02\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d3e8c56b6fc147d5a9533e95347008cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=114.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "06\n",
      "07\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>level_0</th>\n",
       "      <th>index</th>\n",
       "      <th>Ch1</th>\n",
       "      <th>Ch2</th>\n",
       "      <th>Ch5</th>\n",
       "      <th>Ch6</th>\n",
       "      <th>GP_1</th>\n",
       "      <th>GP_2</th>\n",
       "      <th>GP_5</th>\n",
       "      <th>GP_6</th>\n",
       "      <th>...</th>\n",
       "      <th>photometryCenterIn_r</th>\n",
       "      <th>photometryCenterIn_nr</th>\n",
       "      <th>photometryCenterOut_r</th>\n",
       "      <th>photometryCenterOut_nr</th>\n",
       "      <th>photometrySideIn_r</th>\n",
       "      <th>photometrySideIn_nr</th>\n",
       "      <th>photometrySideOut_r</th>\n",
       "      <th>photometrySideOut_nr</th>\n",
       "      <th>photometryFirstLick_r</th>\n",
       "      <th>photometryFirstLick_nr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1241</td>\n",
       "      <td>-105</td>\n",
       "      <td>-0.305496</td>\n",
       "      <td>-0.372916</td>\n",
       "      <td>-0.538906</td>\n",
       "      <td>0.502469</td>\n",
       "      <td>-0.272448</td>\n",
       "      <td>0.115847</td>\n",
       "      <td>0.259745</td>\n",
       "      <td>0.576892</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1242</td>\n",
       "      <td>-104</td>\n",
       "      <td>-0.040803</td>\n",
       "      <td>-0.782688</td>\n",
       "      <td>1.075134</td>\n",
       "      <td>1.979788</td>\n",
       "      <td>0.229221</td>\n",
       "      <td>0.361420</td>\n",
       "      <td>0.654071</td>\n",
       "      <td>0.708443</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1243</td>\n",
       "      <td>-103</td>\n",
       "      <td>0.812755</td>\n",
       "      <td>0.188967</td>\n",
       "      <td>1.661868</td>\n",
       "      <td>-0.759583</td>\n",
       "      <td>0.012197</td>\n",
       "      <td>-0.618724</td>\n",
       "      <td>0.602691</td>\n",
       "      <td>-1.354157</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1244</td>\n",
       "      <td>-102</td>\n",
       "      <td>1.084521</td>\n",
       "      <td>0.835261</td>\n",
       "      <td>1.632686</td>\n",
       "      <td>-1.526374</td>\n",
       "      <td>-0.421164</td>\n",
       "      <td>-0.307188</td>\n",
       "      <td>1.659030</td>\n",
       "      <td>0.669592</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1245</td>\n",
       "      <td>-101</td>\n",
       "      <td>0.438955</td>\n",
       "      <td>-1.504360</td>\n",
       "      <td>0.645659</td>\n",
       "      <td>0.013484</td>\n",
       "      <td>-1.148756</td>\n",
       "      <td>-0.314019</td>\n",
       "      <td>3.064368</td>\n",
       "      <td>0.515759</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>42252</td>\n",
       "      <td>78</td>\n",
       "      <td>-0.359994</td>\n",
       "      <td>1.961379</td>\n",
       "      <td>-0.028995</td>\n",
       "      <td>2.618855</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>42253</td>\n",
       "      <td>79</td>\n",
       "      <td>-1.198795</td>\n",
       "      <td>1.602565</td>\n",
       "      <td>-0.630819</td>\n",
       "      <td>0.245484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>42254</td>\n",
       "      <td>80</td>\n",
       "      <td>-1.517797</td>\n",
       "      <td>-0.427352</td>\n",
       "      <td>-1.655700</td>\n",
       "      <td>0.057226</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>42255</td>\n",
       "      <td>81</td>\n",
       "      <td>-1.438277</td>\n",
       "      <td>-1.952560</td>\n",
       "      <td>-0.655539</td>\n",
       "      <td>-0.040746</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>42256</td>\n",
       "      <td>82</td>\n",
       "      <td>-1.197922</td>\n",
       "      <td>-0.695736</td>\n",
       "      <td>-0.728797</td>\n",
       "      <td>0.505452</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20452 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     level_0  index       Ch1       Ch2       Ch5       Ch6      GP_1  \\\n",
       "0       1241   -105 -0.305496 -0.372916 -0.538906  0.502469 -0.272448   \n",
       "1       1242   -104 -0.040803 -0.782688  1.075134  1.979788  0.229221   \n",
       "2       1243   -103  0.812755  0.188967  1.661868 -0.759583  0.012197   \n",
       "3       1244   -102  1.084521  0.835261  1.632686 -1.526374 -0.421164   \n",
       "4       1245   -101  0.438955 -1.504360  0.645659  0.013484 -1.148756   \n",
       "..       ...    ...       ...       ...       ...       ...       ...   \n",
       "139    42252     78 -0.359994  1.961379 -0.028995  2.618855  0.000000   \n",
       "140    42253     79 -1.198795  1.602565 -0.630819  0.245484  0.000000   \n",
       "141    42254     80 -1.517797 -0.427352 -1.655700  0.057226  0.000000   \n",
       "142    42255     81 -1.438277 -1.952560 -0.655539 -0.040746  0.000000   \n",
       "143    42256     82 -1.197922 -0.695736 -0.728797  0.505452  0.000000   \n",
       "\n",
       "         GP_2      GP_5      GP_6  ...  photometryCenterIn_r  \\\n",
       "0    0.115847  0.259745  0.576892  ...                   0.0   \n",
       "1    0.361420  0.654071  0.708443  ...                   NaN   \n",
       "2   -0.618724  0.602691 -1.354157  ...                   NaN   \n",
       "3   -0.307188  1.659030  0.669592  ...                   NaN   \n",
       "4   -0.314019  3.064368  0.515759  ...                   NaN   \n",
       "..        ...       ...       ...  ...                   ...   \n",
       "139  0.000000  0.000000  0.000000  ...                   NaN   \n",
       "140  0.000000  0.000000  0.000000  ...                   NaN   \n",
       "141  0.000000  0.000000  0.000000  ...                   NaN   \n",
       "142  0.000000  0.000000  0.000000  ...                   NaN   \n",
       "143  0.000000  0.000000  0.000000  ...                   NaN   \n",
       "\n",
       "     photometryCenterIn_nr  photometryCenterOut_r  photometryCenterOut_nr  \\\n",
       "0                      0.0                    0.0                     0.0   \n",
       "1                      NaN                    NaN                     NaN   \n",
       "2                      NaN                    NaN                     NaN   \n",
       "3                      NaN                    NaN                     NaN   \n",
       "4                      NaN                    NaN                     NaN   \n",
       "..                     ...                    ...                     ...   \n",
       "139                    NaN                    NaN                     NaN   \n",
       "140                    NaN                    NaN                     NaN   \n",
       "141                    NaN                    NaN                     NaN   \n",
       "142                    NaN                    NaN                     NaN   \n",
       "143                    NaN                    NaN                     NaN   \n",
       "\n",
       "     photometrySideIn_r  photometrySideIn_nr  photometrySideOut_r  \\\n",
       "0                   0.0                  0.0                  1.0   \n",
       "1                   NaN                  NaN                  NaN   \n",
       "2                   NaN                  NaN                  NaN   \n",
       "3                   NaN                  NaN                  NaN   \n",
       "4                   NaN                  NaN                  NaN   \n",
       "..                  ...                  ...                  ...   \n",
       "139                 NaN                  NaN                  NaN   \n",
       "140                 NaN                  NaN                  NaN   \n",
       "141                 NaN                  NaN                  NaN   \n",
       "142                 NaN                  NaN                  NaN   \n",
       "143                 NaN                  NaN                  NaN   \n",
       "\n",
       "     photometrySideOut_nr  photometryFirstLick_r  photometryFirstLick_nr  \n",
       "0                     0.0                    0.0                     0.0  \n",
       "1                     NaN                    NaN                     NaN  \n",
       "2                     NaN                    NaN                     NaN  \n",
       "3                     NaN                    NaN                     NaN  \n",
       "4                     NaN                    NaN                     NaN  \n",
       "..                    ...                    ...                     ...  \n",
       "139                   NaN                    NaN                     NaN  \n",
       "140                   NaN                    NaN                     NaN  \n",
       "141                   NaN                    NaN                     NaN  \n",
       "142                   NaN                    NaN                     NaN  \n",
       "143                   NaN                    NaN                     NaN  \n",
       "\n",
       "[20452 rows x 45 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "for channel in ['Ch1', 'Ch2', 'Ch5', 'Ch6']:\n",
    "    plt.figure(figsize=(10,5))\n",
    "    # for alignment_col in ['ft_r_spn', 'ft_nr_spn']:\n",
    "    for alignment_col in ['photometrySideIn_r', 'photometrySideIn_nr']:\n",
    "    # for alignment_col in ['photometrySideIn_nr']:\n",
    "        relative_df = pd.DataFrame()\n",
    "        rel_points = df2[df2[alignment_col] > 0].reset_index()\n",
    "        # display(rel_points.iloc[:, -15:])\n",
    "    #     break\n",
    "    # break\n",
    "\n",
    "        print('01')\n",
    "        identifiers = rel_points[['index', 'nTrial', 'nEndTrial']].dropna().values.astype(int)\n",
    "        print('02')\n",
    "\n",
    "        lst_extendeds = []\n",
    "        \n",
    "        for idx, nTrial, nEndTrial in tqdm(identifiers):\n",
    "            extended_trial = df2[(df2['nTrial'] == nTrial) | (df2['nEndTrial'] == nEndTrial)].reset_index().copy()\n",
    "            # print('03', idx)\n",
    "            extended_trial['index'] -= idx\n",
    "            # print('04', idx)\n",
    "\n",
    "            lst_extendeds.append(extended_trial.copy())\n",
    "\n",
    "\n",
    "    #         # display(rel_points.iloc[:, -15:])\n",
    "    #         with pd.option_context('max_columns', 1000, 'max_rows', 2000):\n",
    "    #             display(extended_trial[extended_trial['index'].between(-3, 3)])\n",
    "    # #         break\n",
    "    # #     break\n",
    "    # # break\n",
    "\n",
    "            # if len(relative_df) == 0:\n",
    "            #     relative_df = extended_trial.copy()\n",
    "            # else:\n",
    "            #     relative_df = pd.concat([relative_df, extended_trial])\n",
    "            # # print('05', idx)\n",
    "\n",
    "        relative_df = pd.concat(lst_extendeds)\n",
    "\n",
    "        \n",
    "        alignment_name = alignment_col.split('_')[-1]\n",
    "        sns.lineplot(x='index', y=channel, data=relative_df[relative_df['index'].between(-40, 60)], label=f'{alignment_col} — {channel}')\n",
    "\n",
    "        print('06')\n",
    "        # plot_df = relative_df[relative_df['index'].between(-40, 60)].groupby('index')[channel].mean().reset_index()\n",
    "        # plt.plot(plot_df['index'], plot_df[channel], label=f'{alignment_col} — {channel}')\n",
    "\n",
    "        print('07')\n",
    "\n",
    "        plt.title(f'{alignment_name} — {channel}')\n",
    "        plt.ylim(-1,2.5)\n",
    "        plt.grid()\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "display(relative_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plot_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-7a640813f3aa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'plot_df' is not defined"
     ]
    }
   ],
   "source": [
    "print(plot_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "fa0fc083a9a7b25dab36cbe71fb89b2f1907d4eced1698b208dea6977346b521"
  },
  "kernelspec": {
   "display_name": "Python 3.8.5 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}