lynne_pp_test_tablecombo.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 0x7f7f11c4da60>"
      ]
     },
     "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": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hasAllPhotometryData</th>\n",
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       "      <th>260</th>\n",
       "      <td>1</td>\n",
       "      <td>28386</td>\n",
       "      <td>28387</td>\n",
       "      <td>28404</td>\n",
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       "<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",
    "    'wasRewarded'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
      "Percent of Data in ITI: 0.500351881392512\n",
      "0.0\n",
      "/Users/josh/Documents/Harvard/GLM/GLM_SIGNALS_WT61_10042021.txt -- /Users/josh/Documents/Harvard/GLM/GLM_TABLE_WT61_10042021.txt\n",
      "Percent of Data in ITI: 0.5158371040723982\n",
      "235\n",
      "/Users/josh/Documents/Harvard/GLM/GLM_SIGNALS_WT61_10062021.txt -- /Users/josh/Documents/Harvard/GLM/GLM_TABLE_WT61_10062021.txt\n",
      "Percent of Data in ITI: 0.5177656244625598\n",
      "312\n",
      "/Users/josh/Documents/Harvard/GLM/GLM_SIGNALS_WT61_10132021.txt -- /Users/josh/Documents/Harvard/GLM/GLM_TABLE_WT61_10132021.txt\n",
      "Percent of Data in ITI: 0.5100021248823726\n",
      "385\n",
      "/Users/josh/Documents/Harvard/GLM/GLM_SIGNALS_WT61_10082021.txt -- /Users/josh/Documents/Harvard/GLM/GLM_TABLE_WT61_10082021.txt\n",
      "Percent of Data in ITI: 0.474248460702644\n",
      "549\n",
      "/Users/josh/Documents/Harvard/GLM/GLM_SIGNALS_WT61_10182021.txt -- /Users/josh/Documents/Harvard/GLM/GLM_TABLE_WT61_10182021.txt\n",
      "Percent of Data in ITI: 0.5737461467497693\n",
      "659\n",
      "/Users/josh/Documents/Harvard/GLM/GLM_SIGNALS_WT61_10112021.txt -- /Users/josh/Documents/Harvard/GLM/GLM_TABLE_WT61_10112021.txt\n",
      "Percent of Data in ITI: 0.45576356809033686\n",
      "674\n"
     ]
    }
   ],
   "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",
    "    #     df[col_val] = 0\n",
    "    #     df.loc[df_t[col_val].values, col_val] = 1\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": 16,
   "metadata": {},
   "outputs": [
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37014</th>\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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37015</th>\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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37016</th>\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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>247527 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ft_xx  ft_lpn  ft_rpn  ft_spn  ft_lpx  ft_rpx  ft_spx  ft_cpn  \\\n",
       "0        NaN     0.0     0.0     0.0     0.0     0.0     0.0     NaN   \n",
       "1        0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2        0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "3        0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "4        0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "...      ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "37012    0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "37013    0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "37014    0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "37015    0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "37016    0.0     0.0     0.0     0.0     0.0     0.0     0.0    -0.0   \n",
       "\n",
       "       ft_r_rpn  ft_r_lpn  ft_r_spn  ft_nr_rpn  ft_nr_lpn  ft_nr_spn  \n",
       "0           0.0       0.0       0.0        0.0        0.0        0.0  \n",
       "1           0.0       0.0       0.0        0.0        0.0        0.0  \n",
       "2           0.0       0.0       0.0        0.0        0.0        0.0  \n",
       "3           0.0       0.0       0.0        0.0        0.0        0.0  \n",
       "4           0.0       0.0       0.0        0.0        0.0        0.0  \n",
       "...         ...       ...       ...        ...        ...        ...  \n",
       "37012       0.0       0.0       0.0        0.0        0.0        0.0  \n",
       "37013       0.0       0.0       0.0        0.0        0.0        0.0  \n",
       "37014       0.0       0.0       0.0        0.0        0.0        0.0  \n",
       "37015       0.0       0.0       0.0        0.0        0.0        0.0  \n",
       "37016       0.0       0.0       0.0        0.0        0.0        0.0  \n",
       "\n",
       "[247527 rows x 14 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.iloc[:,-14:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01\n",
      "02\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c8dffea37db48ac9f6d55599b286504",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=1839.0), 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": "2209c436098c44c5a4fd5e588d5dc9ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=3004.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "06\n",
      "07\n"
     ]
    },
    {
     "data": {
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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": "d88a96f6c4e14b4a8935f73d42939efe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=1839.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-5a5bb9ad1ed4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m                 \u001b[0mrelative_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextended_trial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m                 \u001b[0mrelative_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrelative_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextended_trial\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m             \u001b[0;31m# print('05', idx)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    285\u001b[0m     )\n\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    500\u001b[0m                 \u001b[0mmgrs_indexers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    501\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m             new_data = concatenate_block_managers(\n\u001b[0m\u001b[1;32m    503\u001b[0m                 \u001b[0mmgrs_indexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_axes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconcat_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbm_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m             )\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/internals/concat.py\u001b[0m in \u001b[0;36mconcatenate_block_managers\u001b[0;34m(mgrs_indexers, axes, concat_axis, copy)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_extension\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconcat_compat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m                 \u001b[0;31m# TODO(EA2D): special-casing not needed with 2D EAs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/dtypes/concat.py\u001b[0m in \u001b[0;36mconcat_compat\u001b[0;34m(to_concat, axis)\u001b[0m\n\u001b[1;32m    178\u001b[0m                 \u001b[0mto_concat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"object\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mto_concat\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_concat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "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",
    "        relative_df = pd.DataFrame()\n",
    "        rel_points = df2[df2[alignment_col] > 0].reset_index()\n",
    "        print('01')\n",
    "        identifiers = rel_points[['index', 'nTrial', 'nEndTrial']].dropna().values.astype(int)\n",
    "        print('02')\n",
    "\n",
    "\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",
    "            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",
    "        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(-1.5,1.5)\n",
    "        plt.grid()\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "display(relative_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(plot_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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